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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "🚨 Don't worry if the code block below doesn't make sense, please scroll down to the instructions below  🚨"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setting up the notebook. \n",
+    "\n",
+    "import random\n",
+    "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 load_breast_cancer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
+    "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix\n",
+    "\n",
+    "# Dictionary mapping feature names to their descriptions\n",
+    "feature_descriptions = {\n",
+    "    'mean_radius': \"Average distance from the center to the outer edge of the tumor.\",\n",
+    "    'mean_texture': \"How rough or smooth the surface of the tumor feels, on average.\",\n",
+    "    'mean_perimeter': \"The average length of the outline of the tumor.\",\n",
+    "    'mean_area': \"The average size of the surface of the tumor measured in square units.\",\n",
+    "    'mean_smoothness': \"How smooth or bumpy the tumor surface feels, averaged over several observations.\",\n",
+    "    'mean_compactness': \"A measure of how tightly the tumor cells are packed together, averaged over several observations.\",\n",
+    "    'mean_concavity': \"Average number of indentations or hollow areas on the tumor's surface.\",\n",
+    "    'mean_concave_points': \"Average number of sharp dips or points found along the contour of the tumor.\",\n",
+    "    'mean_symmetry': \"How evenly shaped the tumor is. A perfectly symmetrical tumor looks the same on both sides.\",\n",
+    "    'mean_fractal_dimension': \"A measure that describes the complexity of the tumor shape, showing how jagged the border is on average.\",\n",
+    "    'radius_error': \"The change in the tumor's radius across different measurements, indicating how much the size of the tumor varies.\",\n",
+    "    'texture_error': \"The change in the tumor's texture across different measurements, showing how much the roughness or smoothness varies.\",\n",
+    "    'perimeter_error': \"The change in the outline length of the tumor across different measurements, showing how much the outline varies.\",\n",
+    "    'area_error': \"The change in the surface size of the tumor across different measurements, showing how much the area varies.\",\n",
+    "    'smoothness_error': \"The change in how smooth or bumpy the tumor surface feels across different measurements.\",\n",
+    "    'compactness_error': \"The change in how tightly the tumor cells are packed together across different measurements.\",\n",
+    "    'concavity_error': \"The change in the number of indentations or hollow areas on the tumor's surface across different measurements.\",\n",
+    "    'concave_points_error': \"The change in the number of sharp dips or points found along the contour of the tumor across different measurements.\",\n",
+    "    'symmetry_error': \"The change in how evenly shaped the tumor is across different measurements.\",\n",
+    "    'fractal_dimension_error': \"The change in the complexity of the tumor shape across different measurements, showing how much the jaggedness of the border varies.\",\n",
+    "    'worst_radius': \"The largest distance from the center to the outer edge of the tumor observed among all measurements.\",\n",
+    "    'worst_texture': \"The roughest texture observed on the surface of the tumor.\",\n",
+    "    'worst_perimeter': \"The longest outline of the tumor measured among all observations.\",\n",
+    "    'worst_area': \"The largest surface area of the tumor measured among all observations.\",\n",
+    "    'worst_smoothness': \"The least smooth texture observed on the tumor's surface, indicating the roughest feel.\",\n",
+    "    'worst_compactness': \"The highest degree of how tightly the tumor cells are packed together, observed among all measurements.\",\n",
+    "    'worst_concavity': \"The deepest indentations observed on the tumor's surface.\",\n",
+    "    'worst_concave_points': \"The highest number of sharp dips or points observed along the contour of the tumor.\",\n",
+    "    'worst_symmetry': \"The most uneven shape observed in the tumor, where one side differs the most from the other.\",\n",
+    "    'worst_fractal_dimension': \"The highest complexity of the tumor shape observed, showing the most jagged border among all measurements.\"\n",
+    "}\n",
+    "\n",
+    "def scatter_plot(X, y=None, line_plot=None, title='', show_legend=True, xlabel='', ylabel=''):\n",
+    "    \"\"\"\n",
+    "    Create a scatter plot with optional labels, decision boundary, and filled areas.\n",
+    "\n",
+    "    Parameters:\n",
+    "    X (dict): The data for the first class with keys 'data', 'color', and 'label'.\n",
+    "    y (dict, optional): The data for the second class with keys 'data', 'color', and 'label'.\n",
+    "    line_plot (dict, optional): The line plot details with keys 'x', 'y', 'color', 'linestyle', 'fill_colors', and 'model'.\n",
+    "    title (str): The title of the plot.\n",
+    "    xlabel (str): The label for the x-axis.\n",
+    "    ylabel (str): The label for the y-axis.\n",
+    "    ylim (tuple, optional): The limits for the y-axis.\n",
+    "    show_legend (bool, optional): Whether to show the legend.\n",
+    "    \"\"\"\n",
+    "    plt.figure(figsize=(8, 6))\n",
+    "\n",
+    "    # Plotting the data points for X\n",
+    "    plt.scatter(X['data'][0], X['data'][1], color=X['color'], label=X[\"label\"], edgecolors='k')\n",
+    "    \n",
+    "    # Plotting the data points for y, if provided\n",
+    "    if y is not None:\n",
+    "        plt.scatter(y['data'][0], y['data'][1], color=y['color'], label=y[\"label\"], edgecolors='k')\n",
+    "\n",
+    "    # Determining the ylim\n",
+    "    all_y_values = X['data'][1]\n",
+    "    if y is not None:\n",
+    "        all_y_values = np.concatenate([all_y_values, y['data'][1]])\n",
+    "    # if line_plot is not None:\n",
+    "    #     all_y_values = np.concatenate([all_y_values, line_plot[\"y\"]])\n",
+    "\n",
+    "    y_min, y_max = all_y_values.min(), all_y_values.max()\n",
+    "    y_range = y_max - y_min\n",
+    "    y_extension = y_range * 0.1  # 10% extension\n",
+    "    ylim = y_min - y_extension, y_max + y_extension\n",
+    "    plt.ylim(ylim)\n",
+    "\n",
+    "    # Plotting the line plot\n",
+    "    if line_plot is not None:\n",
+    "        plt.plot(line_plot['x'], line_plot['y'], color=line_plot['color'], linestyle=line_plot['linestyle'], label='Decision Boundary')\n",
+    "\n",
+    "        if 'fill_colors' in line_plot and ylim is not None:\n",
+    "            point_above = np.array([[line_plot['x'][0], line_plot['y'][0] + 1]])\n",
+    "            prediction_above = line_plot['model'].predict(point_above) if 'model' in line_plot else None\n",
+    "\n",
+    "            if prediction_above == 1:\n",
+    "                plt.fill_between(line_plot['x'], line_plot['y'], ylim[1], color=line_plot['fill_colors'][1], alpha=0.2)\n",
+    "                plt.fill_between(line_plot['x'], ylim[0], line_plot['y'], color=line_plot['fill_colors'][0], alpha=0.2)\n",
+    "            else:\n",
+    "                plt.fill_between(line_plot['x'], line_plot['y'], ylim[1], color=line_plot['fill_colors'][0], alpha=0.2)\n",
+    "                plt.fill_between(line_plot['x'], ylim[0], line_plot['y'], color=line_plot['fill_colors'][1], alpha=0.2)\n",
+    "\n",
+    "    # Setting the plot title and axis labels\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel(xlabel)\n",
+    "    plt.ylabel(ylabel)\n",
+    "\n",
+    "    if show_legend and (X['label'] or (y and y['label'])):\n",
+    "        plt.legend()\n",
+    "    \n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Notebook Title: Exploring a Dataset with Regression\n",
+    "\n",
+    "## Introduction\n",
+    "Welcome to Exercise 1! In this notebook, we will go through the importance of applying statistical concepts to our data problems and how to get meaningful answers to our questions using the power of programming. This module will help you understand many things about your data.\n",
+    "\n",
+    "In the world of statistics, we use a tool called **regression** to help us answer such questions. Regression is a method used to find how one thing (like temperature) can predict another thing (like ice cream sales)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Generate some made-up data\n",
+    "np.random.seed(0)\n",
+    "temperatures = np.random.uniform(60, 100, 500)  # Randomly choose temperatures between 60 and 100 degrees\n",
+    "ice_cream_sales = temperatures * 1.5 + np.random.normal(0, 10, 500)  # Randomly (kinda) choose sales made\n",
+    "\n",
+    "# Reshape the temperatures for sklearn\n",
+    "temperatures_reshaped = temperatures.reshape(-1, 1)\n",
+    "\n",
+    "# Create a linear regression model\n",
+    "ice_cream_model = LinearRegression()\n",
+    "ice_cream_model.fit(temperatures_reshaped, ice_cream_sales)\n",
+    "predictions = ice_cream_model.predict(temperatures_reshaped)\n",
+    "\n",
+    "# Plot the graph\n",
+    "scatter_plot(\n",
+    "    X={'data': [temperatures, ice_cream_sales], 'color': 'blue', 'label': 'Ice Cream Sales'}, \n",
+    "    line_plot={'x': temperatures, 'y': predictions, 'color': 'red', 'linestyle': '--'},\n",
+    "    title='Temperature vs Ice Cream Sales',\n",
+    "    show_legend=True,\n",
+    "    xlabel='Temperature (°F)',\n",
+    "    ylabel='Ice Cream Sales'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also use regression to classify things, like determining if a student will pass or fail an exam based on their study hours and sleep hours. If you look at the graph below, you can see that the blue line separates the data points. If you fall on the left side of the line, there is a good chance you will fail, and if you land on the right side of the line, there is a good chance you will pass!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Generate sample data: Hours studied, hours of sleep, and pass/fail outcomes\n",
+    "np.random.seed(42)\n",
+    "hours_studied = np.random.uniform(1, 10, 500)\n",
+    "hours_sleep = np.random.uniform(4, 10, 500)\n",
+    "pass_fail = (hours_studied + hours_sleep > 12).astype(int)\n",
+    "\n",
+    "# Fit the regression model\n",
+    "study_sleep_data = np.column_stack((hours_studied, hours_sleep))\n",
+    "study_sleep_model = LogisticRegression()\n",
+    "study_sleep_model.fit(study_sleep_data, pass_fail)\n",
+    "\n",
+    "# Line Plot Coordinates\n",
+    "study_sleep_x_values = np.linspace(0, 12, 300)\n",
+    "study_sleep_y_values = -(study_sleep_model.intercept_ + study_sleep_model.coef_[0][0] * study_sleep_x_values) / study_sleep_model.coef_[0][1]\n",
+    "\n",
+    "# Defining benign data and malignant data\n",
+    "benign_data = [hours_studied[pass_fail == 0], hours_sleep[pass_fail == 0], \"Fail\"]\n",
+    "malignant_data = [hours_studied[pass_fail == 1], hours_sleep[pass_fail == 1], \"Pass\"]\n",
+    "\n",
+    "# Plot the graph\n",
+    "scatter_plot(\n",
+    "    X={'data': [benign_data[0], benign_data[1]], 'color': 'red', 'label': 'Fail'}, \n",
+    "    y={'data': [malignant_data[0], malignant_data[1]], 'color': 'green', 'label': 'Pass'}, \n",
+    "    line_plot={'x': study_sleep_x_values, 'y': study_sleep_y_values, 'color': 'blue', 'linestyle': '--'},\n",
+    "    title='Decision Boundary', \n",
+    "    show_legend=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Being able to find patterns in your data and learning from it is powerful. Yes, ice cream sales and exam results may be low stakes, but regression can be used in more critical environments.\n",
+    "\n",
+    "Imagine you have a big source of cancer data at your disposal. You want to see if there is a pattern among them to better aid future patients. This is a big deal, as it allows you to better take care of patients and essentially buy more time by intervening sooner.\n",
+    "\n",
+    "Wouldn't it be nice to answer some key questions? For example, how big does a tumor have to be, to be considered cancerous? How smooth or rough must the texture be for it to be cancerous? \n",
+    "\n",
+    "In this exercise, we will go through breast cancer data to answer our questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: Loading the Dataset\n",
+    "In this notebook, we will explore the Breast Cancer dataset, use a Regression method to classify data, and evaluate the model's performance. \n",
+    "\n",
+    "First, we load the Breast Cancer dataset using the `load_breast_cancer()` function from the scikit-learn library. This dataset is a well-known collection of breast cancer data that includes various measurements and features related to tumor characteristics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the Breast Cancer dataset\n",
+    "breast_cancer = load_breast_cancer()\n",
+    "X = breast_cancer.data\n",
+    "y = breast_cancer.target\n",
+    "\n",
+    "# Convert the dataset into a DataFrame for visualization\n",
+    "df = pd.DataFrame(data=np.c_[X, y], columns=np.append(breast_cancer.feature_names, 'target'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data is loaded into variables `X` for the measurements and `y` for the target labels (cancerous or not cancerous). For easier visualization and manipulation, we convert the dataset into a Pandas DataFrame, combining the measurements and target labels into one structured table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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>mean radius</th>\n",
+       "      <th>mean texture</th>\n",
+       "      <th>mean perimeter</th>\n",
+       "      <th>mean area</th>\n",
+       "      <th>mean smoothness</th>\n",
+       "      <th>mean compactness</th>\n",
+       "      <th>mean concavity</th>\n",
+       "      <th>mean concave points</th>\n",
+       "      <th>mean symmetry</th>\n",
+       "      <th>mean fractal dimension</th>\n",
+       "      <th>...</th>\n",
+       "      <th>worst texture</th>\n",
+       "      <th>worst perimeter</th>\n",
+       "      <th>worst area</th>\n",
+       "      <th>worst smoothness</th>\n",
+       "      <th>worst compactness</th>\n",
+       "      <th>worst concavity</th>\n",
+       "      <th>worst concave points</th>\n",
+       "      <th>worst symmetry</th>\n",
+       "      <th>worst fractal dimension</th>\n",
+       "      <th>target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.30010</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>0.2419</td>\n",
+       "      <td>0.07871</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.16220</td>\n",
+       "      <td>0.66560</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.08690</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>0.1812</td>\n",
+       "      <td>0.05667</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.12380</td>\n",
+       "      <td>0.18660</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.19740</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>0.2069</td>\n",
+       "      <td>0.05999</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.14440</td>\n",
+       "      <td>0.42450</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.24140</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>0.2597</td>\n",
+       "      <td>0.09744</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.20980</td>\n",
+       "      <td>0.86630</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.19800</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>0.1809</td>\n",
+       "      <td>0.05883</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.13740</td>\n",
+       "      <td>0.20500</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "      <td>0.0</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",
+       "      <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",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>21.56</td>\n",
+       "      <td>22.39</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>1479.0</td>\n",
+       "      <td>0.11100</td>\n",
+       "      <td>0.11590</td>\n",
+       "      <td>0.24390</td>\n",
+       "      <td>0.13890</td>\n",
+       "      <td>0.1726</td>\n",
+       "      <td>0.05623</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26.40</td>\n",
+       "      <td>166.10</td>\n",
+       "      <td>2027.0</td>\n",
+       "      <td>0.14100</td>\n",
+       "      <td>0.21130</td>\n",
+       "      <td>0.4107</td>\n",
+       "      <td>0.2216</td>\n",
+       "      <td>0.2060</td>\n",
+       "      <td>0.07115</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>20.13</td>\n",
+       "      <td>28.25</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>1261.0</td>\n",
+       "      <td>0.09780</td>\n",
+       "      <td>0.10340</td>\n",
+       "      <td>0.14400</td>\n",
+       "      <td>0.09791</td>\n",
+       "      <td>0.1752</td>\n",
+       "      <td>0.05533</td>\n",
+       "      <td>...</td>\n",
+       "      <td>38.25</td>\n",
+       "      <td>155.00</td>\n",
+       "      <td>1731.0</td>\n",
+       "      <td>0.11660</td>\n",
+       "      <td>0.19220</td>\n",
+       "      <td>0.3215</td>\n",
+       "      <td>0.1628</td>\n",
+       "      <td>0.2572</td>\n",
+       "      <td>0.06637</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>16.60</td>\n",
+       "      <td>28.08</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>858.1</td>\n",
+       "      <td>0.08455</td>\n",
+       "      <td>0.10230</td>\n",
+       "      <td>0.09251</td>\n",
+       "      <td>0.05302</td>\n",
+       "      <td>0.1590</td>\n",
+       "      <td>0.05648</td>\n",
+       "      <td>...</td>\n",
+       "      <td>34.12</td>\n",
+       "      <td>126.70</td>\n",
+       "      <td>1124.0</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.30940</td>\n",
+       "      <td>0.3403</td>\n",
+       "      <td>0.1418</td>\n",
+       "      <td>0.2218</td>\n",
+       "      <td>0.07820</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>20.60</td>\n",
+       "      <td>29.33</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>1265.0</td>\n",
+       "      <td>0.11780</td>\n",
+       "      <td>0.27700</td>\n",
+       "      <td>0.35140</td>\n",
+       "      <td>0.15200</td>\n",
+       "      <td>0.2397</td>\n",
+       "      <td>0.07016</td>\n",
+       "      <td>...</td>\n",
+       "      <td>39.42</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>1821.0</td>\n",
+       "      <td>0.16500</td>\n",
+       "      <td>0.86810</td>\n",
+       "      <td>0.9387</td>\n",
+       "      <td>0.2650</td>\n",
+       "      <td>0.4087</td>\n",
+       "      <td>0.12400</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>7.76</td>\n",
+       "      <td>24.54</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.05263</td>\n",
+       "      <td>0.04362</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.1587</td>\n",
+       "      <td>0.05884</td>\n",
+       "      <td>...</td>\n",
+       "      <td>30.37</td>\n",
+       "      <td>59.16</td>\n",
+       "      <td>268.6</td>\n",
+       "      <td>0.08996</td>\n",
+       "      <td>0.06444</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.2871</td>\n",
+       "      <td>0.07039</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 31 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     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",
+       "564        21.56         22.39          142.00     1479.0          0.11100   \n",
+       "565        20.13         28.25          131.20     1261.0          0.09780   \n",
+       "566        16.60         28.08          108.30      858.1          0.08455   \n",
+       "567        20.60         29.33          140.10     1265.0          0.11780   \n",
+       "568         7.76         24.54           47.92      181.0          0.05263   \n",
+       "\n",
+       "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
+       "0             0.27760         0.30010              0.14710         0.2419   \n",
+       "1             0.07864         0.08690              0.07017         0.1812   \n",
+       "2             0.15990         0.19740              0.12790         0.2069   \n",
+       "3             0.28390         0.24140              0.10520         0.2597   \n",
+       "4             0.13280         0.19800              0.10430         0.1809   \n",
+       "..                ...             ...                  ...            ...   \n",
+       "564           0.11590         0.24390              0.13890         0.1726   \n",
+       "565           0.10340         0.14400              0.09791         0.1752   \n",
+       "566           0.10230         0.09251              0.05302         0.1590   \n",
+       "567           0.27700         0.35140              0.15200         0.2397   \n",
+       "568           0.04362         0.00000              0.00000         0.1587   \n",
+       "\n",
+       "     mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
+       "0                   0.07871  ...          17.33           184.60      2019.0   \n",
+       "1                   0.05667  ...          23.41           158.80      1956.0   \n",
+       "2                   0.05999  ...          25.53           152.50      1709.0   \n",
+       "3                   0.09744  ...          26.50            98.87       567.7   \n",
+       "4                   0.05883  ...          16.67           152.20      1575.0   \n",
+       "..                      ...  ...            ...              ...         ...   \n",
+       "564                 0.05623  ...          26.40           166.10      2027.0   \n",
+       "565                 0.05533  ...          38.25           155.00      1731.0   \n",
+       "566                 0.05648  ...          34.12           126.70      1124.0   \n",
+       "567                 0.07016  ...          39.42           184.60      1821.0   \n",
+       "568                 0.05884  ...          30.37            59.16       268.6   \n",
+       "\n",
+       "     worst smoothness  worst compactness  worst concavity  \\\n",
+       "0             0.16220            0.66560           0.7119   \n",
+       "1             0.12380            0.18660           0.2416   \n",
+       "2             0.14440            0.42450           0.4504   \n",
+       "3             0.20980            0.86630           0.6869   \n",
+       "4             0.13740            0.20500           0.4000   \n",
+       "..                ...                ...              ...   \n",
+       "564           0.14100            0.21130           0.4107   \n",
+       "565           0.11660            0.19220           0.3215   \n",
+       "566           0.11390            0.30940           0.3403   \n",
+       "567           0.16500            0.86810           0.9387   \n",
+       "568           0.08996            0.06444           0.0000   \n",
+       "\n",
+       "     worst concave points  worst symmetry  worst fractal dimension  target  \n",
+       "0                  0.2654          0.4601                  0.11890     0.0  \n",
+       "1                  0.1860          0.2750                  0.08902     0.0  \n",
+       "2                  0.2430          0.3613                  0.08758     0.0  \n",
+       "3                  0.2575          0.6638                  0.17300     0.0  \n",
+       "4                  0.1625          0.2364                  0.07678     0.0  \n",
+       "..                    ...             ...                      ...     ...  \n",
+       "564                0.2216          0.2060                  0.07115     0.0  \n",
+       "565                0.1628          0.2572                  0.06637     0.0  \n",
+       "566                0.1418          0.2218                  0.07820     0.0  \n",
+       "567                0.2650          0.4087                  0.12400     0.0  \n",
+       "568                0.0000          0.2871                  0.07039     1.0  \n",
+       "\n",
+       "[569 rows x 31 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Exploring the Dataset\n",
+    "Next, let's take a quick look at the dataset to understand its structure and contents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\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>mean radius</th>\n",
+       "      <th>mean texture</th>\n",
+       "      <th>mean perimeter</th>\n",
+       "      <th>mean area</th>\n",
+       "      <th>mean smoothness</th>\n",
+       "      <th>mean compactness</th>\n",
+       "      <th>mean concavity</th>\n",
+       "      <th>mean concave points</th>\n",
+       "      <th>mean symmetry</th>\n",
+       "      <th>mean fractal dimension</th>\n",
+       "      <th>...</th>\n",
+       "      <th>worst texture</th>\n",
+       "      <th>worst perimeter</th>\n",
+       "      <th>worst area</th>\n",
+       "      <th>worst smoothness</th>\n",
+       "      <th>worst compactness</th>\n",
+       "      <th>worst concavity</th>\n",
+       "      <th>worst concave points</th>\n",
+       "      <th>worst symmetry</th>\n",
+       "      <th>worst fractal dimension</th>\n",
+       "      <th>target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.30010</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>0.2419</td>\n",
+       "      <td>0.07871</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.16220</td>\n",
+       "      <td>0.66560</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.08690</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>0.1812</td>\n",
+       "      <td>0.05667</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.12380</td>\n",
+       "      <td>0.18660</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.19740</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>0.2069</td>\n",
+       "      <td>0.05999</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.14440</td>\n",
+       "      <td>0.42450</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.24140</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>0.2597</td>\n",
+       "      <td>0.09744</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.20980</td>\n",
+       "      <td>0.86630</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.19800</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>0.1809</td>\n",
+       "      <td>0.05883</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.13740</td>\n",
+       "      <td>0.20500</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "      <td>0.0</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",
+       "      <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",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>21.56</td>\n",
+       "      <td>22.39</td>\n",
+       "      <td>142.00</td>\n",
+       "      <td>1479.0</td>\n",
+       "      <td>0.11100</td>\n",
+       "      <td>0.11590</td>\n",
+       "      <td>0.24390</td>\n",
+       "      <td>0.13890</td>\n",
+       "      <td>0.1726</td>\n",
+       "      <td>0.05623</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26.40</td>\n",
+       "      <td>166.10</td>\n",
+       "      <td>2027.0</td>\n",
+       "      <td>0.14100</td>\n",
+       "      <td>0.21130</td>\n",
+       "      <td>0.4107</td>\n",
+       "      <td>0.2216</td>\n",
+       "      <td>0.2060</td>\n",
+       "      <td>0.07115</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>20.13</td>\n",
+       "      <td>28.25</td>\n",
+       "      <td>131.20</td>\n",
+       "      <td>1261.0</td>\n",
+       "      <td>0.09780</td>\n",
+       "      <td>0.10340</td>\n",
+       "      <td>0.14400</td>\n",
+       "      <td>0.09791</td>\n",
+       "      <td>0.1752</td>\n",
+       "      <td>0.05533</td>\n",
+       "      <td>...</td>\n",
+       "      <td>38.25</td>\n",
+       "      <td>155.00</td>\n",
+       "      <td>1731.0</td>\n",
+       "      <td>0.11660</td>\n",
+       "      <td>0.19220</td>\n",
+       "      <td>0.3215</td>\n",
+       "      <td>0.1628</td>\n",
+       "      <td>0.2572</td>\n",
+       "      <td>0.06637</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>566</th>\n",
+       "      <td>16.60</td>\n",
+       "      <td>28.08</td>\n",
+       "      <td>108.30</td>\n",
+       "      <td>858.1</td>\n",
+       "      <td>0.08455</td>\n",
+       "      <td>0.10230</td>\n",
+       "      <td>0.09251</td>\n",
+       "      <td>0.05302</td>\n",
+       "      <td>0.1590</td>\n",
+       "      <td>0.05648</td>\n",
+       "      <td>...</td>\n",
+       "      <td>34.12</td>\n",
+       "      <td>126.70</td>\n",
+       "      <td>1124.0</td>\n",
+       "      <td>0.11390</td>\n",
+       "      <td>0.30940</td>\n",
+       "      <td>0.3403</td>\n",
+       "      <td>0.1418</td>\n",
+       "      <td>0.2218</td>\n",
+       "      <td>0.07820</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>20.60</td>\n",
+       "      <td>29.33</td>\n",
+       "      <td>140.10</td>\n",
+       "      <td>1265.0</td>\n",
+       "      <td>0.11780</td>\n",
+       "      <td>0.27700</td>\n",
+       "      <td>0.35140</td>\n",
+       "      <td>0.15200</td>\n",
+       "      <td>0.2397</td>\n",
+       "      <td>0.07016</td>\n",
+       "      <td>...</td>\n",
+       "      <td>39.42</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>1821.0</td>\n",
+       "      <td>0.16500</td>\n",
+       "      <td>0.86810</td>\n",
+       "      <td>0.9387</td>\n",
+       "      <td>0.2650</td>\n",
+       "      <td>0.4087</td>\n",
+       "      <td>0.12400</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>7.76</td>\n",
+       "      <td>24.54</td>\n",
+       "      <td>47.92</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.05263</td>\n",
+       "      <td>0.04362</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.1587</td>\n",
+       "      <td>0.05884</td>\n",
+       "      <td>...</td>\n",
+       "      <td>30.37</td>\n",
+       "      <td>59.16</td>\n",
+       "      <td>268.6</td>\n",
+       "      <td>0.08996</td>\n",
+       "      <td>0.06444</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.2871</td>\n",
+       "      <td>0.07039</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>569 rows × 31 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     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",
+       "564        21.56         22.39          142.00     1479.0          0.11100   \n",
+       "565        20.13         28.25          131.20     1261.0          0.09780   \n",
+       "566        16.60         28.08          108.30      858.1          0.08455   \n",
+       "567        20.60         29.33          140.10     1265.0          0.11780   \n",
+       "568         7.76         24.54           47.92      181.0          0.05263   \n",
+       "\n",
+       "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
+       "0             0.27760         0.30010              0.14710         0.2419   \n",
+       "1             0.07864         0.08690              0.07017         0.1812   \n",
+       "2             0.15990         0.19740              0.12790         0.2069   \n",
+       "3             0.28390         0.24140              0.10520         0.2597   \n",
+       "4             0.13280         0.19800              0.10430         0.1809   \n",
+       "..                ...             ...                  ...            ...   \n",
+       "564           0.11590         0.24390              0.13890         0.1726   \n",
+       "565           0.10340         0.14400              0.09791         0.1752   \n",
+       "566           0.10230         0.09251              0.05302         0.1590   \n",
+       "567           0.27700         0.35140              0.15200         0.2397   \n",
+       "568           0.04362         0.00000              0.00000         0.1587   \n",
+       "\n",
+       "     mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
+       "0                   0.07871  ...          17.33           184.60      2019.0   \n",
+       "1                   0.05667  ...          23.41           158.80      1956.0   \n",
+       "2                   0.05999  ...          25.53           152.50      1709.0   \n",
+       "3                   0.09744  ...          26.50            98.87       567.7   \n",
+       "4                   0.05883  ...          16.67           152.20      1575.0   \n",
+       "..                      ...  ...            ...              ...         ...   \n",
+       "564                 0.05623  ...          26.40           166.10      2027.0   \n",
+       "565                 0.05533  ...          38.25           155.00      1731.0   \n",
+       "566                 0.05648  ...          34.12           126.70      1124.0   \n",
+       "567                 0.07016  ...          39.42           184.60      1821.0   \n",
+       "568                 0.05884  ...          30.37            59.16       268.6   \n",
+       "\n",
+       "     worst smoothness  worst compactness  worst concavity  \\\n",
+       "0             0.16220            0.66560           0.7119   \n",
+       "1             0.12380            0.18660           0.2416   \n",
+       "2             0.14440            0.42450           0.4504   \n",
+       "3             0.20980            0.86630           0.6869   \n",
+       "4             0.13740            0.20500           0.4000   \n",
+       "..                ...                ...              ...   \n",
+       "564           0.14100            0.21130           0.4107   \n",
+       "565           0.11660            0.19220           0.3215   \n",
+       "566           0.11390            0.30940           0.3403   \n",
+       "567           0.16500            0.86810           0.9387   \n",
+       "568           0.08996            0.06444           0.0000   \n",
+       "\n",
+       "     worst concave points  worst symmetry  worst fractal dimension  target  \n",
+       "0                  0.2654          0.4601                  0.11890     0.0  \n",
+       "1                  0.1860          0.2750                  0.08902     0.0  \n",
+       "2                  0.2430          0.3613                  0.08758     0.0  \n",
+       "3                  0.2575          0.6638                  0.17300     0.0  \n",
+       "4                  0.1625          0.2364                  0.07678     0.0  \n",
+       "..                    ...             ...                      ...     ...  \n",
+       "564                0.2216          0.2060                  0.07115     0.0  \n",
+       "565                0.1628          0.2572                  0.06637     0.0  \n",
+       "566                0.1418          0.2218                  0.07820     0.0  \n",
+       "567                0.2650          0.4087                  0.12400     0.0  \n",
+       "568                0.0000          0.2871                  0.07039     1.0  \n",
+       "\n",
+       "[569 rows x 31 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's look at the data\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each row in the table you created represents an individual sample (or patient) from the breast cancer dataset. The columns correspond to various features measured or calculated for that sample, except for the last column labeled “target,” which indicates the classification of the sample (0 indicates a benign tumor, which is not cancerous, and 1 indicates a malignant tumor, which is cancerous.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       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 texture  \\\n",
+      "count     569.000000              569.000000  ...     569.000000   \n",
+      "mean        0.181162                0.062798  ...      25.677223   \n",
+      "std         0.027414                0.007060  ...       6.146258   \n",
+      "min         0.106000                0.049960  ...      12.020000   \n",
+      "25%         0.161900                0.057700  ...      21.080000   \n",
+      "50%         0.179200                0.061540  ...      25.410000   \n",
+      "75%         0.195700                0.066120  ...      29.720000   \n",
+      "max         0.304000                0.097440  ...      49.540000   \n",
+      "\n",
+      "       worst perimeter   worst area  worst smoothness  worst compactness  \\\n",
+      "count       569.000000   569.000000        569.000000         569.000000   \n",
+      "mean        107.261213   880.583128          0.132369           0.254265   \n",
+      "std          33.602542   569.356993          0.022832           0.157336   \n",
+      "min          50.410000   185.200000          0.071170           0.027290   \n",
+      "25%          84.110000   515.300000          0.116600           0.147200   \n",
+      "50%          97.660000   686.500000          0.131300           0.211900   \n",
+      "75%         125.400000  1084.000000          0.146000           0.339100   \n",
+      "max         251.200000  4254.000000          0.222600           1.058000   \n",
+      "\n",
+      "       worst concavity  worst concave points  worst symmetry  \\\n",
+      "count       569.000000            569.000000      569.000000   \n",
+      "mean          0.272188              0.114606        0.290076   \n",
+      "std           0.208624              0.065732        0.061867   \n",
+      "min           0.000000              0.000000        0.156500   \n",
+      "25%           0.114500              0.064930        0.250400   \n",
+      "50%           0.226700              0.099930        0.282200   \n",
+      "75%           0.382900              0.161400        0.317900   \n",
+      "max           1.252000              0.291000        0.663800   \n",
+      "\n",
+      "       worst fractal dimension      target  \n",
+      "count               569.000000  569.000000  \n",
+      "mean                  0.083946    0.627417  \n",
+      "std                   0.018061    0.483918  \n",
+      "min                   0.055040    0.000000  \n",
+      "25%                   0.071460    0.000000  \n",
+      "50%                   0.080040    1.000000  \n",
+      "75%                   0.092080    1.000000  \n",
+      "max                   0.207500    1.000000  \n",
+      "\n",
+      "[8 rows x 31 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Display the summary statistics of the dataset\n",
+    "print(df.describe())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize the distribution of target classes\n",
+    "sns.countplot(x='target', data=df)\n",
+    "plt.title('Distribution of Target Classes')\n",
+    "plt.xlabel('Target')\n",
+    "plt.ylabel('Count')\n",
+    "plt.xticks(ticks=[1, 0], labels=breast_cancer.target_names)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Breast Cancer dataset includes a large number of measurements related to tumor characteristics, making it challenging to visualize all of them at once. \n",
+    "\n",
+    "To simplify our task and create a clear visualization, we decided to use the first two columns in the dataset: _\"average radius\"_ and _\"average texture\"_. This choice is made for convenience, allowing us to quickly build and visualize our model. Focusing on just these two measurements makes the visualization clearer and more interpretable while still providing meaningful insights."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So with that in mind lets draw a scatter plot of our data to see things better. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scatter_plot(\n",
+    "    X={'data': [X[:, 0], X[:, 1]], 'color': 'blue', 'label': \"mean_radius\"}, \n",
+    "    title=\"mean_texture vs mean_radius\", \n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By using the benign and malignant labels to color each data point, we can see how average radius and average texture (or mean radius and mean texture) affect (or don't affect) the classification."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the color-coded data points (with colors indicating benign and malignant tumors)\n",
+    "\n",
+    "feature1_index = np.argwhere(breast_cancer.feature_names == \"mean radius\").flatten()[0]\n",
+    "feature2_index = np.argwhere(breast_cancer.feature_names == \"mean texture\").flatten()[0]\n",
+    "\n",
+    "# Defining benign data and malignant data\n",
+    "benign_data = [X[y == 0][:, feature1_index], X[y == 0][:, feature2_index], \"Benign\"]\n",
+    "malignant_data = [X[y == 1][:, feature1_index], X[y == 1][:, feature2_index], \"Malignant\"]\n",
+    "\n",
+    "# Plot the graph\n",
+    "scatter_plot(\n",
+    "    X={'data': [benign_data[0], benign_data[1]], 'color': 'green', 'label': 'Benign'}, \n",
+    "    y={'data': [malignant_data[0], malignant_data[1]], 'color': 'red', 'label': 'Malignant'}, \n",
+    "    title=\"mean texture vs mean radius\", \n",
+    "    show_legend=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we can see our data visually, we notice that there may be a way to classify the benign and malignant cases based on mean radius and mean texture. As humans, we can visually observe potential patterns in the data points. To formalize this observation, we will use regression as a tool to help us classify these cases. The next step is to split the dataset into training and testing sets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Splitting the Dataset\n",
+    "Now, we will split the dataset into **training** and **testing** sets for model training and evaluation. This is important because it allows us to train the model on one part of the data and test it on another part to see how well it performs on new data. This helps us understand the model's ability to make accurate predictions on data it hasn't seen before. We will learn more about this process and its significance later on in the module.\n",
+    "\n",
+    "In our example today, we will use 80% of the data for training and 20% for testing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset split was successful\n"
+     ]
+    }
+   ],
+   "source": [
+    "# y = y_train + y_test\n",
+    "try:\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",
+    "    # random_state=42 ensures the split is reproducible, meaning the data is split the same way each time the code is run\n",
+    "    \n",
+    "    print(\"Dataset split was successful\")\n",
+    "except Exception as e:\n",
+    "    print(f\"An error occurred: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4: Building and Training a Classification Model\n",
+    "Next, we will build and train a regression model for breast cancer classification. Specifically, we will use a **logistic regression** model that classifies the data.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model training was successful\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    # Initialize the model\n",
+    "    model = LogisticRegression(max_iter=10000)  # Try up to 10,000 times to find the best fit\n",
+    "\n",
+    "    # Select specific variables (mean radius and mean texture) for training\n",
+    "    X_train_selected = X_train[:, [0, 1]]  # Columns 0 and 1 correspond to mean radius and mean texture\n",
+    "\n",
+    "    # Train the model on the selected variables of the training data\n",
+    "    model.fit(X_train_selected, y_train)\n",
+    "    \n",
+    "    print(\"Model training was successful\")\n",
+    "except Exception as e:\n",
+    "    print(f\"An error occurred: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, if everything went well, we should have successfully initialized and trained our regression model using the selected variables (mean radius and mean texture). The model has learned from the training data and is now ready to make predictions. In the next step, we will evaluate the performance of our model to see how well it can classify new data points."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5: Evaluating the Model\n",
+    "Now, let's evaluate the performance of the trained model on the testing data.\n",
+    "\n",
+    "We will use the `model.predict` method to make predictions on the testing data. This method takes the testing data as input and outputs the predicted classifications for each data point."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prediction made successfully, results are in y_pred\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    # Make a prediction\n",
+    "    y_pred = model.predict(X_train_selected)\n",
+    "    \n",
+    "    print(\"Prediction made successfully, results are in y_pred\")\n",
+    "except Exception as e:\n",
+    "    print(f\"An error occurred: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will evaluate it using something called a **confusion matrix**. A confusion matrix allows us to see how well our model is performing by comparing the predicted classifications to the actual classifications. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Calculate the confusion matrix\n",
+    "cm = confusion_matrix(y_train, y_pred)\n",
+    "\n",
+    "# Plot the heatmap\n",
+    "sns.heatmap(cm, annot=True, fmt='d', \n",
+    "            cmap='Greens', \n",
+    "            cbar=False, \n",
+    "            xticklabels=['benign', 'malignant'], \n",
+    "            yticklabels=['benign', 'malignant'])\n",
+    "plt.title('Confusion Matrix')\n",
+    "plt.xlabel('Predicted')\n",
+    "plt.ylabel('Actual')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['malignant', 'benign'], dtype='<U9')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "breast_cancer.target_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6285714285714286"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6228070175438597"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_test.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will also calculate its **accuracy** and generate a **classification report**.\n",
+    "\n",
+    "First, we calculate the accuracy of the model using the `accuracy_score` function. This function compares the true labels (`y_train`) with the predicted labels (`y_pred`) and returns the percentage of correct predictions. The accuracy score provides a simple metric to understand how often the model makes correct predictions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.8923076923076924\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate model Accuracy\n",
+    "accuracy = accuracy_score(y_train, y_pred)\n",
+    "print(\"Accuracy:\", accuracy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we generate a classification report using the `print_classification_report` function. This report provides a more detailed evaluation of the model, including precision, recall, and F1-score for each class. These metrics help us understand the performance of the model beyond just accuracy, giving insight into how well the model identifies each class. You don’t need to know the details of these metrics right now, but it’s good to be aware of them. They may come in handy down the road, so consider reading more about them when you have time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "   malignant       0.88      0.82      0.85       169\n",
+      "      benign       0.90      0.94      0.92       286\n",
+      "\n",
+      "    accuracy                           0.89       455\n",
+      "   macro avg       0.89      0.88      0.88       455\n",
+      "weighted avg       0.89      0.89      0.89       455\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Generate a classification report\n",
+    "the_report = classification_report(y_train, y_pred, target_names=breast_cancer.target_names)\n",
+    "print(the_report)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6: Visualizing the Decision Boundary\n",
+    "\n",
+    "Now that we see the accuracy is satisfactory, we will take the next step to visualize our model on the graph. By plotting the decision boundary of the regression model, we can better understand how the model classifies the data points and observe the separation between benign and malignant cases. This visualization will help us **see** the effectiveness of our model in distinguishing between the two classes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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gCIIgCIIQNERsCoIgCIIgCEFDxKYgCIIgCIIQNERsCoIgCIIgCEEj7Ezdq6ur+eWXX2jdurXLtmWCIAiCIAhCaFFKceTIETp06EBEhOfYZdiJzV9++YX09PRQD0MQBEEQBEHwQnl5OSaTyeM6YSc2W7duDeiD96W9mBAcfvoJ+vTRf/7sM+jRI7TjEQRBEAQhfDh8+DDp6elW3eaJsBObxtR5XFyciM0Qct558Oc/w2uvwbPPwoIFoR6RIAiCIAjhhi8pj1IgJLjloYf074sXQ3l5aMciCIIgCELDRMSm4Jbzz4esLKiqgnnzQj0aQRAEQRAaIiI2BY/89a/69y++AKVCOxZBEARBEBoeYZezKYQXV1wBH34I/fqBOFEJgiD4jtls5o8//gj1MASh1jRr1ozIyMg6b0fEpuARTYPMzFCPQhAEoeGglGL37t389ttvoR6KINSZNm3akJKSUifvcxGbgs8cPgw//wznnBPqkQiCIIQvhtBMTk6mZcuW0qBEaJAopfj999/Zu3cvAKmpqbXelohNwSc++0yfUo+P1wVns2ahHpEgCEL4YTabrUIzMTEx1MMRhDrRokULAPbu3UtycnKtp9SlQEjwiZ49ISYGduyAZctCPRpBEITwxMjRbNmyZYhHIgiBwbiX65J/LGJT8IkWLSAvT//5iSegujqkwxEEQQhrZOpcaCwE4l4WsSn4zD33QOvW8N138NZboR6NIAiCIAgNARGbgs+0aaMLToDHHw/pUARBEIQGSkZGBvn5+aEehlCPiNgU/CIvD6Kj4aOPYN26UI9GEARBCBSjRo1C0zTrV2JiIldddRXffPNNQPezYcMG7rzzzoBuUwhvRGwKfpGaCiNHQkQEfPppqEcjCILQODGbzZSUlLB06VJKSkowm831st+rrrqKXbt2sWvXLtauXUtUVBQDBgwI6D6SkpKkgKqJIWJT8JupU2HLFhg/PtQjEQRBaHwUFhaS0SWD7OxsRowYQXZ2NhldMigsLAz6vmNiYkhJSSElJYWzzz6bhx56iPLycvbt2wfAzp07GTZsGG3btiUxMZGBAweybds26/tHjRrFoEGDmD17NqmpqSQmJnLvvffaVTI7TqP/+OOP9OvXj+bNm3P66adTVFSEpmmsXr0agG3btqFpGoWFhWRnZ9OyZUvOOuss1q9fH/TzIQQGEZuC35hM0KVLqEchCILQ+CgsLGTIkCFUtKiA0cAkYDTsbLGTIUOG1IvgNDh69CivvvoqXbt2JTExkd9//53s7GxiY2P58MMPWbduHbGxsVx11VWcPHnS+r7i4mK2bt1KcXExixYtYuHChSxcuNDlPqqrqxk0aBAtW7bk008/5V//+heTJ092ue7kyZOZMGECGzdupHv37gwfPpyqqqpgHLoQYMTUXagTmzdDbCx06BDqkQiCIDRszGYzuWNzUd0VDKMmHJQOaphCW66RNy6PgQMHBqRftSvWrFlDbGwsAMeOHSM1NZU1a9YQERHBsmXLiIiI4MUXX7Ta4SxYsIA2bdpQUlLCFVdcAUDbtm155plniIyM5NRTT+Xaa69l7dq13HHHHU77e/fdd9m6dSslJSWkpKQA8Oijj3L55Zc7rTthwgSuvfZaAGbMmMEZZ5zBzz//zKmnnhqUcyEEDolsCrXmscfg1FOlMl0QBCEQlJaWUrGjAvrh/HSOANVPUb69nNLS0qCNITs7m40bN7Jx40Y+/fRTrrjiCq6++mq2b9/OF198wc8//0zr1q2JjY0lNjaWhIQETpw4wdatW63bOOOMM+zEcGpqqrXloSM//fQT6enpVqEJ0KdPH5fr9urVy26bgNvtCuGFRDaFWnP++aAUvPgi/O1vkJQU6hEJgiA0XHbt2qX/kOxmhWSH9YJAq1at6Nq1q/X3c889l/j4eF544QWqq6s599xzefXVV53el2TzAGjm0M9Y0zSq3XQCUUr5bBpuu13jPe62K4QXEtkUas2ll0Lv3nD8ODz9dKhHIwiC0LAxonW4C9btdVivHtA0jYiICI4fP84555zDli1bSE5OpmvXrnZf8fHxtdr+qaeeyo4dO9izZ4912YYNGwI1fCFMELEp1BpNg4ce0n9+5hk4ejS04xEEQWjIZGZmYupoQlungWPArhq0dRrpndLJzMwM2hgqKyvZvXs3u3fv5ocffuD+++/n6NGjXHfdddx00020a9eOgQMHUlpaSllZGR988AG5ublUVFTUan+XX345p5xyCiNHjuSbb77ho48+shYIScvPxoOITaFO/PnP0K0b/PorvPBCqEcjCILQcImMjGT+vPmwGbTlGpQDlUC55ffNkD83P2jFQQDvvPMOqamppKamcv7557NhwwZWrlxJVlYWLVu25MMPP6Rjx47k5ORw2mmncfvtt3P8+HHi4uJqtb/IyEhWr17N0aNHOe+88/jLX/7ClClTAGjevHkgD00IIZpSSoV6ELYcPnyY+Ph4Dh06VOubV6hfXnwR7rgD0tLgf//TOwwJgiA0RU6cOEFZWRmdO3eutVgqLCwkd2yuXixkIb1TOvlz88nJyQnUUMOWjz76iH79+vHzzz9zyimnhHo4TR5397Q/eq1Okc2ZM2eiaRp5eXnWZUoppk+fTocOHWjRogVZWVl89913ddmNEObccoveWej330EutSAIQt3Iyclh2/+2UVxczJIlSyguLqZsa1mjFZqvvfYa7733Htu2baOoqIg777yTiy66SIRmI6LW1egbNmzgX//6l50VAcCsWbOYO3cuCxcupHv37jzyyCNcfvnl/PTTT7Ru3brOAxbCj5gY+M9/oEcP3XNTEARBqBuRkZFkZWWFehj1wpEjR3jwwQcpLy+nXbt2XHbZZcyZMyfUwxICSK0im0ePHuWmm27ihRdeoG3bttblSiny8/OZPHkyOTk5nHnmmSxatIjff/+dJUuWBGzQQvhx7rkiNAVBEAT/ufXWW9myZQsnTpygoqKChQsXkpiYGOphCQGkVmLz3nvv5dprr+Wyyy6zW15WVsbu3butXQRA77N6ySWX8PHHH7vcVmVlJYcPH7b7EhouSsGHH+rfBUEQBEEQ/Baby5Yt48svv2TmzJlOr+3evRuA9u3b2y1v37699TVHZs6cSXx8vPUrPT3d3yEJYUJ1NVx4IVxyiS44BUEQBEEQ/BKb5eXl5ObmsnjxYo9Vdo7eWJ46BEyaNIlDhw5Zv8rLy/0ZkhBGRETAOefoP0sLS0EQBEEQwE+x+cUXX7B3717OPfdcoqKiiIqK4oMPPuCpp54iKirKGtF0jGLu3bvXKdppEBMTQ1xcnN2X0HCZMEEXne+8Axs3hno0giAIgiCEGr/EZv/+/dm0aRMbN260fvXu3ZubbrqJjRs30qVLF1JSUnjvvfes7zl58iQffPABffv2DfjghfCjSxcYOlT/WaKbgiAIgiD4ZX3UunVrzjzzTLtlrVq1IjEx0bo8Ly+Pxx57jG7dutGtWzcee+wxWrZsyYgRIwI3aiGseeghWLYMVq6ERx8FsUoTBEEQhKZLwNtVPvjgg+Tl5TFmzBh69+7Nzp07effdd8Vjswlx9tlw1VV6wZBYpQlNFbPZTElJCUuXLqWkpASz2RzqIQlCvbFt2zY0TWOjJZ+qpKQETdP47bffQjouITTUWWyWlJSQn59v/V3TNKZPn86uXbs4ceIEH3zwgVM0VGj8/PWv+vcvvgB5xgpNjcLCQjK6ZJCdnc2IESPIzs4mo0sGhYWFoR6aILhl1KhRaJrG3Xff7fTamDFj0DSNUaNG1Wrbffv2ZdeuXcTHx9dxlIFn4cKFtGnTJtTDaNQEPLIpCAAXXwwffACffAKRkaEejSDUH4WFhQwZMoSKFhUwGpgEjIadLXYyZMgQEZyCT4QqMp6ens6yZcs4fvy4ddmJEydYunQpHTt2rPV2o6OjSUlJcetMIzRuRGwKQUHTdMEp/1eEpoTZbCZ3bC6qu4JhQDoQo39XwxR0h7xxeTKlLniksLCQrhn2kfGuGfUTGT/nnHPo2LGj3b4KCwtJT0/nT3/6k3XZO++8Q79+/WjTpg2JiYkMGDCArVu3ut2uq2n0F154gfT0dFq2bMmf//xn5s6daxdhnD59OmeffTavvPIKGRkZxMfHc+ONN3LkyBGfx2FM5xcWFpKdnU3Lli0566yzWL9+vXVct912G4cOHULTNOvsrBBYRGwKQefoUX06XRAaO6WlpVTsqIB+OP93jQDVT1G+vZzS0tJQDE9oABiR8Z4VFawHjgDrgZ476y8yftttt7FgwQLr7y+//DK333673TrHjh1j3LhxbNiwgbVr1xIREcGf//xnqqurfdrHRx99xN13301ubi4bN27k8ssv59FHH3Vab+vWraxevZo1a9awZs0aPvjgAx63sTrxdRyTJ09mwoQJbNy4ke7duzN8+HCqqqro27cv+fn5xMXFsWvXLnbt2sWECRP8OV2CD/hVjS4I/vL113DppRAdDWVl4KEXgCA0eHbt2qX/kOxmhWSH9QTBBrPZzPjcXAYoxWpqPq9cAKxWikGaxoS8PAYOHEhkEPOTbrnlFiZNmmSNCn700UcsW7aMkpIS6zqDBw+2e89LL71EcnIy33//vU91Gk8//TRXX321Vdh1796djz/+mDVr1titV11dzcKFC61Fxrfccgtr1661ClNfxzFhwgSuvfZaAGbMmMEZZ5zBzz//zKmnnkp8fDyappGSkuLjGRL8RSKbQlA57TRo2RJ274ZXXgn1aAQhuKSmpuo/7HWzwl6H9QTBhtLSUrZVVPAwLgPjTFKKsvLgR8bbtWvHtddey6JFi1iwYAHXXnst7dq1s1tn69atjBgxgi5duhAXF0fnzp0B2LFjh0/7+Omnn+jTp4/dMsffATIyMuzcbFJTU9m7t+YPzNdx9OrVy24bgN12hOAiYlMIKtHRMG6c/vOTT0plutC4yczMxNTRhLZOA8fZxGrQ1mmkd0onMzMzJOMTwhsj4u0uLnimw3rB5Pbbb2fhwoUsWrTIaQod4LrrruPAgQO88MILfPrpp3z66aeA3sjFF1y1sVZKOa3XrFkzu981TbObIvd1HLbbMfbr65S/UHdEbApB5447oG1b2LIFXnst1KMRhOARGRnJ/HnzYTNoyzUoByqBcsvvmyF/bn5Qp0CFhosRcfvWzevfOqwXTK666ipOnjzJyZMnufLKK+1eO3DgAD/88ANTpkyhf//+nHbaafz6669+bf/UU0/ls88+s1v2+eef+7WNQIwD9Ep5KdoLLiI2haATGwv336///Pjj4OLDqyA0GnJycli1ahVpx9PgJWAm8BKYTphYtWoVOTk5oR6iEKZkZmaSYTLxmKa5CowzU9PonF4/kfHIyEh++OEHfvjhB6cPR23btiUxMZF//etf/Pzzz7z//vuMM6awfOT+++/nrbfeYu7cuWzZsoV//vOfvP32235ZIwViHKBP1R89epS1a9eyf/9+fv/9d7+3IXhGxKZQL9x/P7RooVelr10b6tEIQnDJyclh2/+2UVxczJIlSyguLqZsa5kITcEjkZGRzJk/nzXAIE2zq0YfpGmsAWbn119kPC4ujri4OKflERERLFu2jC+++IIzzzyTsWPH8uSTT/q17Ysuuojnn3+euXPnctZZZ/HOO+8wduxYmvtRRRqIcYBuOH/33XczbNgwkpKSmDVrlt/bEDyjKVdJEiHk8OHDxMfHc+jQIZc3udBweeABeOYZ+PvfYcqUUI9GEAQh8Jw4cYKysjI6d+7sl3CypbCwkPG5uWyrqLAu65yezuz8/Eb9geWOO+7gxx9/FGuwMMPdPe2PXhPrI6He+Otf4d57oUePUI9EEAQhfMnJyWHgwIGUlpaya9cuUlNTyczMbHS5vrNnz+byyy+nVatWvP322yxatIhnn3021MMSgoCITaHe6NAh1CMQBEFoGERGRpKVlRXqYQSVzz77jFmzZnHkyBG6dOnCU089xV/+8pdQD0sIAiI2hZDwv//ptkgmU6hHIgiCIISCFStWhHoIQj0hBUJCvTN3LnTrpuduCoIgCILQuBGxKdQ7ffpAdTUsWgTStU8QBEEQGjciNoV6p18/uOgiOHkS8vNDPRpBEARBEIKJiE0hJPz1r/r3556D334L6VAEQRAEQQgiIjaFkHDNNXDmmXDkiC44BUEQBEFonIjYFEJCRAQ8+KD+c34+HD8e0uEIgiAIghAkRGwKIePGG6FjR6ishG++CfVoBEEQhPogIyODfB8T9v1ZtzGTlZVFXl5eqIdRa0RsCiGjWTN47TXYsQPOPz/UoxEEQWi6jBo1Ck3T0DSNZs2a0b59ey6//HJefvllqqurA7qvDRs2cOeddwZ83dpSUlJiPXZN02jRogVnnHEG//rXv4K636aEiE0hpJxzDnhpqSoIgiDUA1dddRW7du1i27ZtvP3222RnZ5Obm8uAAQOoqqoK2H6SkpJo2bJlwNetKz/99BO7du3i+++/56677uKee+5h7dq19bLvYKOUCug19BcRm0JYoBSsW6d/FwRBEOqfmJgYUlJSSEtL45xzzuHhhx/m9ddf5+2332bhwoXW9Q4dOsSdd95JcnIycXFxXHrppXz99dd223rjjTfo3bs3zZs3p127duTk5Fhfc5wanz59Oh07diQmJoYOHTrwwAMPuF13x44dDBw4kNjYWOLi4hg6dCh79uyx29bZZ5/NK6+8QkZGBvHx8dx4440cOXLE6/EnJyeTkpJC586deeCBB8jIyODLL7+0vl5ZWckDDzxAcnIyzZs3p1+/fmzYsMH6+sKFC2nTpo3dNlevXo2maX6N79ixY9x6663ExsaSmprKnDlznMa6ePFievfuTevWrUlJSWHEiBHs3bvX+roRrf3vf/9L7969iYmJ4ZVXXiEiIoLPP//cbltPP/00nTp1QgXxASxiUwg5SkF2NmRmwrvvhno0giAIweHYMfdfJ074vq5jQaWrdQLFpZdeyllnnUVhYSGgR8iuvfZadu/ezVtvvcUXX3zBOeecQ//+/Tl48CAAb775Jjk5OVx77bV89dVXrF27lt69e7vc/qpVq5g3bx7//Oc/2bJlC6tXr6Znz54u11VKMWjQIA4ePMgHH3zAe++9x9atWxk2bJjdelu3bmX16tWsWbOGNWvW8MEHH/D444/7fMxKKd555x3Ky8s53ybH68EHH6SgoIBFixbx5Zdf0rVrV6688krrcfuKt/FNnDiR4uJiXnvtNd59911KSkr44osv7LZx8uRJ/u///o+vv/6a1atXU1ZWxqhRo5z29eCDDzJz5kx++OEHrr/+ei677DIWLFhgt86CBQusaRRBQ4UZhw4dUoA6dOhQqIci1CNjxyoFSmVlhXokgiAItef48ePq+++/V8ePH3d6Tf9o7frrmmvs123Z0v26l1xiv267ds7r+MvIkSPVwIEDXb42bNgwddpppymllFq7dq2Ki4tTJ06csFvnlFNOUf/85z+VUkpdeOGF6qabbnK7r06dOql58+YppZSaM2eO6t69uzp58qTXdd99910VGRmpduzYYX39u+++U4D67LPPlFJKTZs2TbVs2VIdPnzYus7EiRPV+eef73Y8xcXFClCtWrVSrVq1UlFRUSoiIkI98sgj1nWOHj2qmjVrpl599VXrspMnT6oOHTqoWbNmKaWUWrBggYqPj7fb9muvvaZspZa38R05ckRFR0erZcuWWV8/cOCAatGihcrNzXV7DJ999pkC1JEjR+yOafXq1XbrLV++XLVt29Z6/TZu3Kg0TVNlZWVut+3unvZHr0lkUwgLxo3TC4ZKSuCTT0I9GkEQBMFAKWWNen3xxRccPXqUxMREYmNjrV9lZWVs3boVgI0bN9K/f3+ftn3DDTdw/PhxunTpwh133MFrr73mNrfwhx9+ID09nfT0dOuy008/nTZt2vDDDz9Yl2VkZNC6dWvr76mpqXZTzO4oLS1l48aNbNy4kRdffJHHHnuM5yxG0Fu3buWPP/7goosusq7frFkz+vTpY7dvX/A0vq1bt3Ly5EkuvPBC6+sJCQn06NHDbhtfffUVAwcOpFOnTrRu3ZqsrCxATzOwxTGiPGjQIKKionjttdcAePnll8nOziYjI8OvY/CXqKBuXRB8xGSCm2+GBQvgiSf0KnVBEITGxNGj7l+LjLT/3ZM2inAIE23bVush+cQPP/xA586dAaiuriY1NZWSkhKn9Yx8xRYtWvi87fT0dH766Sfee+89ioqKGDNmDE8++SQffPABzZo1s1vXVvR6Wu74Pk3TfKqo79y5s/UYzjjjDD799FMeffRR7rnnHms+o+P+bfcdERHhlPf4xx9/OO3H0/gc3++KY8eOccUVV3DFFVewePFikpKS2LFjB1deeSUnT560W7dVq1Z2v0dHR3PLLbewYMECcnJyWLJkSb1YS0lkUwgbJk7Uv69eDX5+UBQEQQh7WrVy/9W8ue/rOmo5V+sEivfff59NmzYxePBgAM455xx2795NVFQUXbt2tftq164dAL169fKrirtFixZcf/31PPXUU5SUlLB+/Xo2bdrktN7pp5/Ojh07KC8vty77/vvvOXToEKeddlodj9SZyMhIjlsSZLt27Up0dDTr1q2zvv7HH3/w+eefW/edlJTEkSNHOGaTNLtx40a/9tm1a1eaNWvGJzZTfL/++iubN2+2/v7jjz+yf/9+Hn/8cTIzMzn11FN9itwa/OUvf6GoqIhnn32WP/74w654K1hIZFMIG047DQYN0sXmk0/Cyy+HekSCIAhNh8rKSnbv3o3ZbGbPnj288847zJw5kwEDBnDrrbcCcNlll3HhhRcyaNAgnnjiCXr06MEvv/zCW2+9xaBBg+jduzfTpk2jf//+nHLKKdx4441UVVXx9ttv86DRNs6GhQsXYjabOf/882nZsiWvvPIKLVq0oFOnTk7rXnbZZfTq1YubbrqJ/Px8qqqqGDNmDJdcconbAiR/2Lt3LydOnKCyspLPPvuMV155hSFDhgB6hPCee+5h4sSJJCQk0LFjR2bNmsXvv//O6NGjAazH8PDDD3P//ffz2Wef2VXx+0JsbCyjR49m4sSJJCYm0r59eyZPnkyETTi7Y8eOREdH8/TTT3P33Xfz7bff8n//938+7+O0007jggsu4KGHHuL222/3KxJdWySyKYQVDz2kf//iC3Ax+yAIgiAEiXfeeYfU1FQyMjK46qqrKC4u5qmnnuL1118n0jLPr2kab731FhdffDG333473bt358Ybb2Tbtm20b98e0LvdrFy5kjfeeIOzzz6bSy+9lE8//dTlPtu0acMLL7zARRddZI2I/uc//yExMdFpXU3TWL16NW3btuXiiy/msssuo0uXLixfvjwgx9+jRw9SU1Pp2rUrDz30EHfddRdPP/209fXHH3+cwYMHc8stt3DOOefw888/89///pe2bdsCem7l4sWLeeutt+jZsydLly5l+vTpfo/jySef5OKLL7ZWj/fr149zzz3X+npSUhILFy5k5cqVnH766Tz++OPMnj3br32MHj2akydPcvvtt/s9vtqgKV8SBOqRw4cPEx8fz6FDh4gTt+8mSUkJXHyxc16SIAhCuHPixAnKysro3LkzzR3nxgUhTHj00UdZtmyZy3QFR9zd0/7oNXmcC2FHVpYITUEQBEEINEePHmXDhg08/fTTdub5wUYe6ULYcvw4ODQ6EARBEAShltx3333069ePSy65pN6m0EEKhIQw5Ycf4JJLdHvi7duhnlrjCoIgCEKjZeHChX4XLQUCiWwKYUm3bhAbC/v3S1W6IAiCIDRkRGwKYUlUVI3v5uzZUpkuCELDIsxqbwWh1gTiXhaxKYQto0ZBcrI+jR4gZwtBEISgYnSH+f3330M8EkEIDMa97Nj5yB8kZ1MIW1q0gNxcmDwZHn8cRoyQKnVBEMKbyMhI2rRpY+3o0rJlS5ctFgUh3FFK8fvvv7N3717atGlj9VqtDeKzKYQ1v/0GHTvCkSPwn//AgAGhHpEgCIJnlFLs3r2b3377LdRDEYQ606ZNG1JSUpw+NPmj1ySyKYQ1bdrA3Xfr7Ss/+UTEpiAI4Y+maaSmppKcnMwfknAuNGCaNWtWp4imgUQ2hbBnzx69Kv2MM0I9EkEQBEEQQCKbQiOjfXv9SxAEQRCEhoeUWwgNih079Op0QRAEQRAaBiI2hQbDs8/CKafA3/4W6pEIgiAIguArIjaFBkOfPlBVBUuWSHRTEARBEBoKIjaFBkPv3tC/P5jNMGdOqEcjCIIgCIIviNgUGhR//av+/cUX9Qp1QRAEQRDCGxGbQoOif38491w4fhyefjrUoxEEQRAEwRsiNoUGhabVRDefeQaOHg3teARBEARB8IyITaHB8ec/Q7ducPIkfPllqEcjCIIgCIInxNRdaHBERsLy5ZCRAW3bhno0giAIgiB4QsSm0CD5059CPQJBEARBEHxBptGFBo1S8PHHUF0d6pEIgiAIguAKEZtCg0UpuPZauOgieOONUI9GEARBEARX+CU2n3vuOXr16kVcXBxxcXFceOGFvP3229bXR40ahaZpdl8XXHBBwActCKBXphvT6Y8/rotPQRAEQRDCC7/Epslk4vHHH+fzzz/n888/59JLL2XgwIF899131nWuuuoqdu3aZf166623Aj5oQTB44AGIiYFPP4UPPwz1aARBEARBcMQvsXnddddxzTXX0L17d7p3786jjz5KbGwsn3zyiXWdmJgYUlJSrF8JCQkBH7QgGLRvD7ffrv/8+OOhHYsgCIIgCM7UOmfTbDazbNkyjh07xoUXXmhdXlJSQnJyMt27d+eOO+5g7969ARmoILhjwgSIiIB33oGNG0M9GkEQBEEQbPFbbG7atInY2FhiYmK4++67ee211zj99NMBuPrqq3n11Vd5//33mTNnDhs2bODSSy+lsrLS7fYqKys5fPiw3Zcg+EOXLjB0qP7zE0+EdiyCIAiCINijKeVfWcXJkyfZsWMHv/32GwUFBbz44ot88MEHVsFpy65du+jUqRPLli0jJyfH5famT5/OjBkznJYfOnSIuLg4f4YmNGE2btSLhU49Fb76Cpo3D/WIBEEQBKHxcvjwYeLj433Sa36LTUcuu+wyTjnlFP75z3+6fL1bt2785S9/4aGHHnL5emVlpV3k8/Dhw6Snp4vYFPympAQyM/UOQ4IgCIIgBA9/xGadOwgppdxOkx84cIDy8nJSU1Pdvj8mJoaYmJi6DkMQyMoK9QgEQRAEQXDEL7H58MMPc/XVV5Oens6RI0dYtmwZJSUlvPPOOxw9epTp06czePBgUlNT2bZtGw8//DDt2rXjz3/+c7DGLwhOVFbCpk3Qu3eoRyIIgiAIgl9ic8+ePdxyyy3s2rWL+Ph4evXqxTvvvMPll1/O8ePH2bRpE//+97/57bffSE1NJTs7m+XLl9O6detgjV8Q7Ni6VZ9K//132LEDJBNDEARBEEJLnXM2A40/OQCC4Eh1NZxxBvz4Izz5pG6LJAiCIAhCYPFHr0lvdKFREREBDz6o/zx3rj6lLgiCIAhC6BCxKTQ6broJ0tJg1y545ZVQj0YQBEEQmjYiNoVGR3Q0jB+v/zxrFpjNoR2PIAiCIDRlRGwKjZI77oC2bWHLFli9OtSjEQRBEISmi4hNocFjNpspKSlh6dKllJSUYDabiY2F++7TX1+/PrTjEwRBEISmTJ1N3QUhlBQWFjI+N5dtFRXWZRkmE3Pmzyc3N4fBg+Gss0I4QEEQBEFo4khkU2iwFBYWMmTIEHpWVLAeOAKsB3ru3MmQIUP44INCEZqCIAiCEGLEZ1NokJjNZrpmZNCzooLV2H9qqgYGaRrfmkxsKSsjMjKSX37RbZA6dw7NeAXfMJvNlJaWsmvXLlJTU8nMzCRSmt0LgiCEHeKzKTR6SktL2VZRwcM438QRwCSlKCsvp7S0lAULdJFp+G8K4UlhYSEZXTLIzs5mxIgRZGdnk9Elg8LCwlAPTRAEQagDIjaFBsmuXbsAONPN62farHfeeXDyJBQU6NXpQvhhpERUtKiA0cAkYDTsbKGnRIjgFARBaLiI2BQaJKmpqQB86+b1b23WO/NMGDAAlNJbWArhhdlsJndsLqq7gmFAOhCjf1fDFHSHvHF5mMUwVRAEoUEiYlNokGRmZpJhMvGYplHt8Fo1MFPT6JyeTmZmJgB//av+2qJFemchIXwoLS2lYkcF9MNlToTqpyjfrqdECIIgCA0PEZtCgyQyMpI58+ezBr0YyLYafZCmsQaYnZ9vLS656CL96+RJyM8P2bAFFxgpESS7WSHZYT1BEAShQSFiU2iw5OTksGrVKjalpdEXiAP6At+aTKxatYqcnBy79Y3o5nPPwW+/1fNgBbcYKRHsdbPCXof1BEEQhAaFWB8JDR5f7XKqq3WD961b4fXX4fLLQzBYwQmz2UxGlwx2ttip52g6+FhpyzVMJ0yUbS0TGyRBEIQwwR+9Jh2EhAZPZGQkWVlZXteLiIBXXgGTCdq1C/64BN+IjIxk/rz5DBkyBG25huqn9KnzvaCt02Az5K/KF6EpCILQQJFpdKFJcfbZIjTDESMlIu14GrwEzAReAtMJ1ykRgiAIQsNBptGFJsunn8K550KUxPfDBukgJAiC0DCQaXRB8MLgwVBYCMuWwbBhoR6NYOBrSoQgCILQcJBpdKFJctZZ+vfHH9fN3gVBEARBCA4iNoUmyb33QqtWsHEjvPtuqEcjCIIgCI0XEZtCkyQxEe64Q//5iSdCOxZBaGiYzWZKSkpYunQpJSUl0kpUEASPiNgUmizjxunFQcXFerGQIAjeKSwsJKNLBtnZ2YwYMYLs7GwyumRQWFgY6qEJghCmiNgUmizp6XDzzfrPEt0UBO8UFhYyZMgQKlpUwGhgEjAadrbYyZAhQ0RwCoLgErE+Epo0P/wAp58OXbrAN9/oeZyCIDhjdHqqaFEBw5BOT4LQxPFHr0lkU2jSnHaaPo3+008iNAXBE6WlpVTsqIB+OD85IkD1U5RvL6e0tDQUwxMEIYwRn02hySO2joLgnV27duk/JLtZIdlhPUEQBAsS2RQECydPwuefh3oUghCepKam6j/sdbPCXof1BEEQLIjYFASgvBxOOUWPch48GOrRCEL4kZmZiamjCW2dBtUOL1aDtk4jvVM6mZmZIRmfIAjhi4hNQQBMJt1789gxePbZUI9GEMKPyMhI5s+bD5v1YiDKgUqg3PL7Zsifmy/FQYIgOCFiUxAATYOHHtJ/nj8ffv89tOMRhHAkJyeHVatWkXY8DV4CZgIvgemEiVWrVpGTkxPqIQqCEIaI9ZEgWKiqgu7doawMnn4a7rsv1CMShPDEbDZTWlrKrl27SE1NJTMzUyKagtDE8EevidgUBBueew7GjIFOnWDLFmjWLNQjEgRBEITwQ3w2BaGWjBoFycmwfTusWBHq0QiCIAhCw0fEpiDY0KIF5ObqP3/0UWjHIgiCIAiNATF1FwQHxoyByy+H884L9UgEQRAEoeEjkU1BcKBNGxGagiAIghAoRGwKggf27IGtW0M9CkEQBEFouIjYFAQ3LFmiV6WPHRvqkQiCIAhCw0VyNgXBDb176/3S//Mf+O47OOOMUI9IEAQQn09BaGhIZFMQ3NC9OxgNUWbNCu1YBEHQKSwsJKNLBtnZ2YwYMYLs7GwyumRQWFgY6qEJguAGEZuC4AGjheWSJbr3piAIoaOwsJAhQ4ZQ0aICRgOTgNGws8VOhgwZIoJTEMIUEZuC4IHzzoP+/fVWlnPmhHo0gtB0MZvN5I7NRXVXMAxIB2L072qYgu6QNy4Ps9kc4pEKguCIiE1B8MJf/6p/f/FF2L8/tGMRhKZKaWkpFTsqoB/OT64IUP0U5dvLKS0tDcXwBEHwgIhNQfBC//5wzjmgFHzySWjGYDabKSkpYenSpZSUlEj0Rmhy7Nq1S/8h2c0KyQ7rCYIQNkg1uiB4QdNgwQJo317/qm8KCwsZn5vLtooK67IMk4k58+eTY1QwCUIjJzU1Vf9hL/oUuiN7HdYTBCFskMimIPhAr16hE5pDhgyhZ0UF64EjwHqg586GVxAh0VmhLmRmZmLqaEJbp0G1w4vVoK3TSO+UTmZmZkjGJwiCe0RsCoKffPGF7r8ZbMxmM+NzcxmgFKuBC4BYy/fVSjEAmJDXMAoixK5GqCuRkZHMnzcfNoO2XINyoBIot/y+GfLn5ovfpiCEISI2BcEPbrlFN3tfsiT4+yotLWVbRQUP47IegklKUVYe/gURYlcjBIqcnBxWrVpF2vE0eAmYCbwEphMmVq1aJWklghCmiNgUBD/o2VP/PmsWVDtO5QUYo9DhTDevn+mwXjgidjVCoMnJyWHb/7ZRXFzMkiVLKC4upmxrmQhNQQhjRGwKgh/cfTfEx8MPP+htLIOJUejwrZvXv3VYLxwRuxohGERGRpKVlcXw4cPJysqSqXNBCHNEbAqCH8TFwZgx+s+PP67bIQWLzMxMMkwmHtM0V/UQzNQ0OqeHd0GE2NUIgiAIIjYFwU9ycyEmRvfcDGZALjIykjnz57MGGKRpdtXogzSNNcDs/PAuiLCzq3GF2NUIgiA0ekRsCoKftG8Pt92m/zxzZnD3ZRREbEpLoy8QB/QFvjU1jIIIsasRBEEQ/BKbzz33HL169SIuLo64uDguvPBC3n77bevrSimmT59Ohw4daNGiBVlZWXz33XcBH7QghBKz2Uy/fp+gadV88cUJDhwIbnFLTk4OP2+zL4jYUtYwCiLErkYQBEHwS2yaTCYef/xxPv/8cz7//HMuvfRSBg4caBWUs2bNYu7cuTzzzDNs2LCBlJQULr/8co4cORKUwQtCfVNYWEjXjAxuvvlClOrPvn1x9D47+H6RDbkgQuxqBEEQmjaaUnUrcUhISODJJ5/k9ttvp0OHDuTl5fHQQw8BUFlZSfv27XniiSe46667fNre4cOHiY+P59ChQ8TFxdVlaIIQUAy/yAFK8TC69dC3wGOW/EkRTq4xm82Ulpayc+dO9u3bR1JSEmlpaWRmZjYo0SwIgiDU4I9eq7XYNJvNrFy5kpEjR/LVV1/RvHlzTjnlFL788kv+9Kc/WdcbOHAgbdq0YdGiRQEfvCDUF2azma4ZGfSsqGA19lMCJ4nkMv5ERfoetpSViYCyobCwkNyxubr9kQVTRxPz50lfd0EQhIaMP3rN7wKhTZs2ERsbS0xMDHfffTevvfYap59+Ort37wagvUMD6fbt21tfc0VlZSWHDx+2+xKEcMNdN5/dtKcHW/iUDykrPyF+kTZI5yBBEAQBaiE2e/TowcaNG/nkk0+45557GDlyJN9//731dU3T7NZXSjkts2XmzJnEx8dbv9LT0/0dkiAEHXfdfNqzh2T2cpIWwAPiF2lBOgcJgiAIBn6LzejoaLp27Urv3r2ZOXMmZ511FvPnzyclJQXAKYq5d+9ep2inLZMmTeLQoUPWr/Lycn+HJAhBx103Hw34K49bfruXuDhTfQ4rbJHOQYIgCIJBnX02lVJUVlbSuXNnUlJSeO+996yvnTx5kg8++IC+ffu6fX9MTIzVSsn4EoRww1M3n+t4nVb8CLTlu+8ucvl+s9lMSUkJS5cupaSkpNFH9KRzkCAIgmDgl9h8+OGH9dy1bdvYtGkTkydPpqSkhJtuuglN08jLy+Oxxx7jtdde49tvv2XUqFG0bNmSESNGBGv8QhOnvkScp24+ORoc40kA8vMjqKy0f69hl5Sdnc2IESPIzs6ma0bw7ZJCiXQOEgJNU/vAJgiNCuUHt99+u+rUqZOKjo5WSUlJqn///urdd9+1vl5dXa2mTZumUlJSVExMjLr44ovVpk2b/NmFOnTokALUoUOH/Hqf0PQoKChQGSaTAqxfGSaTKigocPueqqoqVVxcrJYsWaKKi4tVVVVVnffZOT1dLVv2mkpLUwqUeuEF+/U1TVPXgVoP6ojl+3WapjRN8zjWhkxVVZUydTQprYemmIpius3XVJTWQ1PpndL9Pv9C06SgoECZOtr/3Zk6ev5bFwQhuPij1/wSm/WBiE3BF2oj4mojTl3hTrDOmaOLzZEja9bLMJnUdaDMoL9o+TJbxto5vfEKLuMaaT00xWgUk1CM1oVmYxbaQmAx7iN6IPeRIIQR/ui1Opu6Bxrx2RS84cnzshp9mvtbk8nO87I+DNmPHIFNm8BIUS4pKSE7O5v1wAUu1l+P3ue8uLiYrKysOu07XHHls5neKZ38ufnisyl4xWw2k9ElQ7fPGobTH7u2XMN0wkTZVvG3FYT6Jqg+m4IQatx5XmL5fZJSlJXXVDqbzWbG5+YyQClWowu/WMv31UoxAJiQV3cbntata4QmuLdLMjjTYb3GSE5ODtv+Z9/XvWxrw+jrLoQecTUQhMZBVKgHIAj+4q+IM8TpUtyL074WcRqoCOP+/VBd3QXQI6iuIpuGjVJjL5Ix+roLgr+Iq4EgNA4ksik0ONx5Xho4irj6jjAWFECnTrBwYR+Xdklm4H3gPiAlKcmjNZggNGXE1UAQGgciNoUGhyfPy2pgpqbROT2dzMxMwH9xWlfOPRcqK6GoSGPMA4vs7JJeBToC/YEvgd379tHjlFMatQ2SINSWzMxMTB1NaOs0XP2xa+s00jvV/K0LghCeiNgUGhyePC8HWQp+ZufnWwsG/BWndSUjA4YP13/esOFSVq1axaa0NPoCNwPnWMZqjLnnTukVLjQ86sP3MjIykvnz5sNmvRiIcqASKLf8vhny5+ZLcZAghDtBr433E7E+EnzFneelO9sjTdPUdZqmPgZ1GNTHQfS7/OYb3eUoIkKpzZuVqqysVKlJSWpAE7VBEhoX9e176Wp/6Z1c/60LglA/iPWR0GQwm82Ulpaya9cuUlNTyczMdBvlKCwsZHxuLtsqamx4OqenMzs/ODY8AwbAm2/CnXfC8OFigyQ0DgwbMdVd6VXiycBefUqbzYGxEXOFP3/rgiAEH3/0mohNoUlRnw+sdesgMxOio2HevNe4994cjqDbLjlyBIgDlixZwnBjDl4QwgzxvRQEwcAfvSbWR0KToj5tePr10303N2yAffu6A2KDJDRsrL6Xo3Hve/lSYG3EBEFo+EiBkCAEkeefh7IymDLl1HotUhKEYCC+l4Ig1AYRm4IQRHr2hLQ0/yvoBSEcCZTvZX1UsguCED5IzqbgE00tOT8Yx/v11/D996t5+MH7a12k1NSugxBeGDmbO1vsRA1TtcrZLCwsJHdsrj4db8HU0cT8efOljakgNCD80mtBrYuvBWJ9FH64shjKMAXP5iTUuDre9u3aqRUrVtR6m3fcoTsdPfecUlVVVaq4uFgtWbJEFRcX+2x31NSugxCeGDZiWg9NMRrFJBSjUVoP7zZixnvpgd/vFQQhvBDrIyFgGDYnA5TiYfTWjt8Cj1mmfoNlcxIqjOM9Vyl+AX6xeS0KGDtxIrNmzfJ7u08/DQ88AF26wE8/QZSX0jzHCOa+ffsYNmxYk7kO9UUoI8UNOUrtKjqZ3imd/LnuI/RSyS4IjQuJbAoBoaqqSmWYTOq6JmJEbhxvb1AaqOtArQd1xPJ9gCWauHLlSr+3ffSoUomJ+ulbtszzuq4imDGRkap3E7kO9UV9G5OHy74Dhb8R+uLiYv1YR6OY7uJrtH4eiouL6+cABEGoE/7oNSkQEtwm65eWlrKtooKHcelywiSlKCvXbU4aA8bx/gIMAFaj2xTFWr6/DlwL5I4Z43dBQ6tWemQT4PHHdaXoCiOy2rOiwq6I6DKzmS8sY7KlMV6H+sA4zxUtLDY+k4DRsLNF8FuHhnLfgcSwERs+fDhZWVleo5FSyS4ITRcRm02cwsJCumZkkJ2dzYgRI8jOzqZrRgaFhYXWf/pnunmvsbyxPByM4/gF3ArsycAv+/bVStjdey+0bAkbN8K77zq/bjabGZ+bywClnITuG+gCeALgKHPreh2aWmWw2Wwmd2yu3gFnGJAOxOjf1TAF3SFvXF5QzkMo9x1qalvJ3tTuT0FojIjYbMK4i6L13KlHWLZs2QLUGI470tiMyG2PIxgCOzFRb10J8MQTzq97jSQDZYCjzK3LdfD0YaOxYjUm74d7Y/LtwYkUh3LfoSYzMxNTR5Pe1tKF2ay2TiO9k73XbGFhIRld7O/PjC6N+/4UhMaIiM0miqco2mqlGAAsfOEFOqWlNRkj8szMTNq3awcET2CPG6cXB/34I+zbZ/+az5Fkm2V1uQ7ePmw01gd6KKdzm/JUcmRkJPPnzYfNejEQ5UAlUG75fTPkz63xmm0s6QaCIIjYbLL4lI9ZUcHtd95ZJyPyhjQFFhkZydPPPksU8Cgugy88BnUS2Onp8N578L//QVKS/WuGgPUmdA9Sd0N4Xz5sTMiT6dzGtO9wICcnh1WrVpF2PA1eAmYCL4HphMnOUaEppxsIQmNExGYTxdcoWrdu3Vi1ahWb0tLoC8QBfYFvTSavdjsNcYr2hhtuYOzEiawBrgc7gT0QeFPT6tzpJysLmjd3Xp6ZmemxpeVjQExkJPfh33VwhT/FXw3pA4Mv1GY6tzHsO1zIyclh2/+2UVxczJIlSyguLqZsa5ndPdyU0w0EoTHixe1PaKzYRtEucPG67XRxVlYWAwcO9MsT0Nafcyk2vpCWKdpw9oWcNWsWffr04YExY+hrM9fdOT2dVT52+vEFsxm++gp699Z/N1paDhkyhEGaxiSlrOdtpqbxJrBi2TLatWtXZ29GXz9svP7669wy8pZG1e3FmM4dMmQI2nIN1U/p09d7dbHHZshfFZzWoaHcdzhhVLK7oymnGwhCo6QerJj8Qnw26werh6amBdy7sbH4c9a2048vHDigVNeuSjVrplR5uf1rrnw2O6enB9SD0fA8XO9wfYyvj2323Vi7vbjyukzvFNjzHI77bgiIJ6cghD/SQUjwCWv0EZyiaHXpSlNSUkJ2djbrcR01XY8+BVxcXOwxutHYycqCDz6AsWNh7lz714LdXcZsNtM1I4OeO3eyWinHZi4M0jTejYig8hQz3Eij7fYiHYTCk0D0YBcEIbj4o9dEbDZxCgsLGZ+by7aKmmnSzunpzK7DdPHSpUsZMWIER9CLThw5gp5zuGTJEoYPH16rfTQG3n4brrlGN3zfsQMSEup3/x4/bCiFAr0KON3Fm8uBl+QDgxA8jPuT7rhMNwjnVBxBaAr4o9ekQKiJk5OTw8/b7JP1t5SV1emfuK9V1Y214tZXrroKevWCY8fg2WfrZ5+2xT4JCQksX77cZfFXbl6e/gbJmXOisRVMhSu+Vq4LghD+SGRTCDi+TNF+azKxpSywU2ANcVpy6VIYMQLatYPt2/UOQ8HCVRQ7w2TiyXnznIqOSktLyc7OlsimA4WFheSOzW1UBVPhgru/34b4dy0ITQG/9FpQs0drgRQINQ4KCgqUpmnqOk1TH4M6bCk6uU4LTnGJq6KaDJMp7Asu/vhDqc6d9bqcZ54J3n6s18NSFHTE8t3d9aisrFTtktspUlHcimKqTXHGVL1IKL1T+Bd5BRLjHDbWgqlQ4qpgytQx/P9+w4FgFjIKgif80WsiNoWgUR9V1cZ+/BFSgSJQ/+T/8Q9dbA4dGuABWvDXHcDVdYtqhSKn6YqrqqoqXQz1cBDeTVh8BwoR8bVHRLoQSqQaXQgb6q2quqKC1TgVrQZtyj6Q06nHj8Nnn8HFF4OmBWyIVvxxBzh48KDVH/VhavxRHwHetKyf3imd/LmB8xttCBjnUNIKAsvJkydJS09jf+R+uBzIoOaPWKrOPWIUUKnuSje/lwIqoZ6RAiEhbDDMm4cPH05WVlbAHxj+dMIJFIHu2dyiBVxySXCEJvhu4L5z5063LSzfAAYAHZKS+Hnzz03uISYm44GnsLAQU0cT+/fuh13Av4GngO8tK0inILdIO0+hoSFiU2jQ+CqkAiUCgv1P/tdfYfPmgAzViq/uAPv27fMo3B8Gftm3j48//jiwA2wANPWe5oHG+MC2L36f3Qc2koEV1AhOEfEukXaeQkNDxKbQoKlvm6Vg/pN/803o2BFGjw7IUK1467k+U9PonJ5OUlISUH/CvSHRlHqaB9vaye4D243YfWDjRqA78C76ea6jiG+sNlW+Rtpff/31ehmPIHhDxKbQoPFVSAVKBARzOvVPf4KTJ2HdOvjoo9qNzxVGz/U16Dms69GN9ddbfl8DzM7PJy0tDRB/VFcYPc3ZrOcRUg5UAuWW3zdD/tyG39O8sLCQjC4ZZGdnM2LECLKzs8nokuF3aognvH1gIxP4DdhWNxFfH8cSKnyNtOfn5zeK4xUaPiI2hQaNr0IqUCIgmNOpHTrArbfqPz/xhP9j84RhkO3KwN0oJKhv4d7QaOwm4+5ykSuaVzB4yGBWrVoVkP34+oGN96i1iA90XnW44S3STinQBsndFMIGqUYXGgXBaLvpimD3bN68GU49Vfcl+vZbOOOMgA0d8O4O4LGFJVLhCg2zeYA3jPu6okWFnovsaOuwFCK3RbL01aXccMMNddqXr5X9Se2TeP7Z5/2+37wdS2OpcC8sLGTwkMHQFbgYazU6pcBmYCjQGnFJEIKG9EYXmiT1JQKC3bN5yBAoKNCjnIsWBW7cvlJfwl0IH3wVgAAFBQV1ug+8fWBjGSQdTqJiewXR0dF+b78p2VSNHTuW/Kfy7aObbYArgNPRUz1mwpIlSxg+fHgIRih4oyF/eBXrI6FJEmybJYNgT6c+9JD+fckSvYVlfZOTk8PP27ZRXFzMkiVLKC4uZktZmQjNRoy1kMTb1HZq3adlveW/als0nv/H87USmtC0bKoGDhyoC81rgMHASOABdKEJ4pIQ5jTmvGJHJLIpCLUkmJ9I+/eHDz+EBQvg5psDsklBcElhYSGDBw/Wf/EW2bwGeCswUUFXjREC0TCgKUU2g53WIwSPxmDKL9PogtDA+f57aNUKOnUK9UiExow1v7F5hR4FS0a3H3Ixtc1e4C7gicBNywbjA1tTE2DBTusRAk9jySuWaXRBaOCcfroITSH4WG2IMtHz/DajC0ubqW2WWpZfAezX3xeoadlgpL40FZsqg8buktAYaYqm/FGhHoAgCJ757jvIyNAjnYIQSOzyG9PRK5jfxVoMBOgPwwuAUy0Rl06msLe/MgRY7thcKl6qmaY3dTKRv6rxFbrl5OQwcODABlto0tRoSnnFBiI2BSGMyc2Fp56C/Hz9Z0EIJHa+senohSWnAtuBo8Bx4C0gwSYquKphRAWbmgAzosRC+OP0d+dIIyzskpxNQQhj/vUvuOsuSE+Hn3+GWhbohoyGbOvRFPBqQ7QU2Kr/HIjiHUEQGk9eseRsCkIj4dZbISUFysth6dJQj8Y/mpKtR0PFa37jz5D3QB7FxcWUbRX7K0EIBE0trxgksikIYc+sWbr35mmn6V2FIhrAR8TGYOtRFxpaRDdYNkSCILinof/difVRCGloDxkh/Dl0CDp2hMOH4fXX4frrQz0izzQWW4/a4uoBYupoYv68+WH9AJH/XYJQ/zTkvzsRmyFi5cqV3D9mDHv277cuyzCZmDM/vB8yQvgzaRI8/jhccAF8/DFoWqhH5P6fZFMy1TYwzsXrr79Ofn4+dEe3E2piEV1BEJoOkrMZAh588EFGDB1qJzQ7AO0qKhgyZIjkqQl1IjcXYmJg61b45ZdQj0aP3nXNsM/H7Jqh52M2NVsP29zU/Px8feFe4AgQA6SjFwF0r3urR0EQhIaIiM0AsGrVKp588kmuAtajP2PWA+cCXwDnKsWEPHnICL5jNpspKSlh6dKllJSUkJRk5r//1Xulp6WFdmxGPmbPigq7+73nzp0MGTKELVu26CvudbMBy/I9e/ZYj6+h/m0Y56KiRYUeyZ2E/j0ZWAF8b1mxkRo1C4Ig+IJMo9cRs9lMemoq5+7bx+s4O4cMAr4EdtK4pg0DTUPOWwk0hYWFjM/NZVtFTc5fuKRjmM1mumZk0LOigtW4uN81jU1paRw9eYL9bfa7bX0YWRaJ+Y8agdkQchod8Zabam3x+IDltUpgZuBaPQqCIIQSf/SamLrXkdLSUnbt28dkXHadYhLQ1/J7Y5k2DDThLK6CjaPI3rdvH8OGDWOAUiwFzgS+BR6zRA1XrVrFoEE5fPkl9O5d/+MtLS1lW0UFS3FzvytFX+M67kX3abwYa+4ipcBmMHcww9U1y3euqzm+hnLNrS3nRuP6ZGSid+LZDnSmXo2aQ/nhTT44CoLgiIjNOmIIyDPdvG67vDF1AwgUxjSkJ3HVUMSHv7gS2TGRkZyrlF3U8AJgtVIM0jTGPTCZv/3tz/z4o8aWLdClS/2O2ef7PQtIAN7DrvVhZLNIXWj+hZoDtOQ0ass18sblMXDgwAYhTnzNTeUoehX+uvpp9RjKavhQ7ltEriCEL37lbM6cOZPzzjuP1q1bk5yczKBBg/jpp5/s1hk1ahSaptl9XXDBBQEddDhhCMhv3bxuLE9JSgr7fsL1jdlsZnxuLgMs4uoCIJYacTUAGm2uq7u8x8vMZr4AVjusb0QNt+/8kVatfqW6GubMcc7tDOS5crVtX+93OgG9gLHArUAqxLeN16fOr8ZlJLCh5TTatZxzhbH8eP0ZNbvLId3ZYmdQChVt75G///3vDB48uN72bYs0EBCE8MavnM2rrrqKG2+8kfPOO4+qqiomT57Mpk2b+P7772nVqhWgi809e/awYMEC6/uio6NJSEjwaR8NMWeza0YGZ1ZUuMzZHAi8AyxZsYIbbrghJGMMVwyLnPXoAtOR9egpCPWd6xrsCInXvEd00bYFsN3rESAOmDKliEce6U90tJnEpHPZtfNr6zqBiiK5S22YNXcuD44bR8+dO1mtlNPYrwf+GwdVeQ4HZrE7AnQREuNipw0spzHcWj3Wt7+pqygm0ej/9M4I7r4dx9GUGwg0RiRK3TAImvXRO++8w6hRozjjjDM466yzWLBgATt27OCLL76wWy8mJoaUlBTrl69CsyESGRnJnPnzeVPTGIh9Nfr1wBpg7MSJIjRd4OuUbH3munqy9AkURt7jw7jP8y1DT2+0xYgaXnppJN26HeTkyUh2HR8S8CiSp2rzYcOGccPw4axBLwZyvN/fBKqucnFgtlPNXiKBDSXdJNxaPVpzSPvhd+TYNkK5du1a1q5d6zFa7rYKPwNYSU0Vvg/7rgtms5ncsbm60ByG7usqdlMNGolSN07qZH106NAhACcxWVJSQnJyMt27d+eOO+5g7153T5fGQU5ODqtWreJbk4m+6NGnvsBXSUmsXLmSWbNmhXiE4YmvU7L1JT68WfoE6p+dzyLbZlk1MFPT6JyezkUX9eXgrw/pL/x+LyS1DtgD1pfUhlXLlrFixQo2paXZ3e//BegPnO5iw5Z/Ae2S2+kRp2qH1y05jemd0htUuonxt592PE2P3M4EXgLTCRMFqwqYN28eWVlZ9RKVqa2/qePD/bLLLuOyKy5z+6D3JPC4Ed3Q/l3sr3GQvFXrIrCF8KO+00CE+qPWYlMpxbhx4+jXrx9nnlnz2Lz66qt59dVXef/995kzZw4bNmzg0ksvpbKy0uV2KisrOXz4sN1XQyQnJ4eft22juLiYJUuWUFxczI5duxgyZEiohxa2ZGZmkmEy8ZimudIeVnFVH+KjPvNHfRXZB6kRvIM0jTXA7Px8Pv74Yw7sfwnif4QTbeG9O2ETejiUuj1gvUZdlaKsvJx27drZ3e9FRUW0T09Dq/AsJJ995ln3kcB6yGkMBjk5OWz7n/3fftnWMgYOHBi0fFpX+JpDavvhzW2Esqtlhf7OD3pvAo9M4Df0KnwP+w4ETa2BQGNGotSNHFVLxowZozp16qTKy8s9rvfLL7+oZs2aqYKCApevT5s2TQFOX4cOHart0IQGREFBgdI0TV2naepjUIdBfQzqOk1Tmqa5vW8CTXFxsQLUelDKxdfHlvuyuLi4zvuqqqpSGSaTuk7TlNlhP2ZQA0DFREba/T10Tk+3noslS5boy8+5zfK2N2rWbYMiR/95yZIlfo/N2PYRN+fhMO63bVxLrYemGI1iEorRKK2H/bUsKChQpo4mu+NL75Reb9e6PnB1jKaOpqAeY1VVlTJ1NOnnfyqK6TZfU/XrkN4pXVVVVdmtT3cUt6IYjGKkvi5T0Ze3QTHF/r3W+2+Swz6Mr0mWYx7sft+Bwvi7ZbSbsYwO3N+tEFzkWjY8Dh065LNeq5X10f33388bb7zBhx9+iMlk8rhuamoqnTp1qukq4sCkSZMYN26c9ffDhw+Tnu6qoXLDQBKb/cOYhhyfm1vjzwh0NplYlR/YggpP16Y+80eNPN8hQ4YwSNOYpJTV8mmmpvEmsGLZMtq1a+dyrNbo0JeLwbQdrngf2lPjY2mZaTI69PhzH9pGXV0VbXlKbTCuZe7YXCpesrG+6WQif1XNtczJyWHgwIGN4u/E1T31+uuv1xSsGN2EfPQSrcv/DyOHdMiQIWjLNVQ/5VQsk7+qJnJsjVC2Bv5ts6E2wBXU+ISWW6LlL+nRcrsIqqt/1UZkNUZ/r6t9B4rMzExMHU3sXOe6SKu+7KaEuiNR6kaOPyq2urpa3XvvvapDhw5q8+bNPr1n//79KiYmRi1atMin9f1RyuFGQUGByjDZRzMyTMGNZjQWqqqqVHFxsVqyZIkqLi4OeATE1bVpl5CgZsyYYd039RTZ9DQm2wimOyorK1Vks0g98uQigkUqighqFVXzFnW9TtNU53TPEapgX8twwVX0Mi09TSUmJSp6uL42niJ8gYqG+ho5zsvL01/vjl0kmu6W9+XYRCgn1US0vUVQ6W5//wU7au1rRF0IbySy2fDwR6/5JTbvueceFR8fr0pKStSuXbusX7///rtSSqkjR46o8ePHq48//liVlZWp4uJideGFF6q0tDR1+PDhgA8+nLBOB1sEyxHL9/qeDhacsb02M0F1snkIA6p9YqJauXJlnUVWbaiNMHP5T/mvrRX3dVcMxaWA8OfBGy6pDeGMcY7o4SDU0qnVA9Pd9mormLzdV1VVVSqpfZL7DyzdUbS2HMtI53F7E3gzZsyo1w8bTSE1o7HjbxqIEHr80Wt++WxqmuZy+YIFCxg1ahTHjx9n0KBBfPXVV/z222+kpqaSnZ3N//3f//k8Nd7QfDbBt37R35pMbCkLvMec4Bnba3MrMBQYADxMTbeiR4C3NI0JEyYwe/ZsBoDT1PYawsevb+nSpYwYMaLGr3Jrf1ixChI3w7Hz9Sl1Fz3J/fE6dOWz2Tk9ndkBTm1oiHj0s/wGPY3BDy/R+vbHhBqPW0bjeirc8EVtBYwFbaXzGFz5bNaHn6g7JIXJNQ3pvBgFa3THZRpIuPwPFnSC1hvdmy5t0aIF//3vf/3ZZKPAp37R5Xq+U32akzdkAvUP0rg2i4Gb0YXmauxbQb4BDFSKVcuWsXz5ch4cNy7o+aN1wSlnLuVrMEfDL32AbBhS7N4G5iXf7sPGlFcZaDz2RG9t+e4ln9E259Vbj3V/rpuv+NxqM0MXmq5yLsPtHomMjJT/rw6Esn1obfA171toeEhv9AAQjubkDRl33WvmzPf/H6Rxzg8D28DtB4KHgb7l5SQlJfHztm1h8wB1hVNRRKv98KeXYcN9wEOQXOz6jX4m2MvD2zUehVonIB74EBiOTwUroSiM8LnI5zvPD3q5R8IXu85KfhaqhZJw+xAjBIY6mboLOuFmTt6QCbSxunHON1h+9+UDgfEAHT58uM+G3MHsUe6Iy841veeAVgVcCd//yfUb6+h1WJ/HGM549LOMAHqj9xpdjk9eorXxx6wrxgcWdwb7rIPEpESKiorqpfuREFgaumdlbf4HC+GNXzmb9UGDztl00y9acjZ9Ixi5r8Y2Uy3iNRh92F1NVaWlp3HnX+6kW7duQftk7rzfV4CbIXY5jLsxoLl/gYw2u8LXtIlQ5p8Z+965cydjx49lf5v9ru12lmskHEygRYsWPuUzeuuxHqy+4uGQH9eQ8gkbEr7m5Nbm/50gGPil14JaqlQLGnw1ulTw1ppg2A9VVVWpGTNmKEA1B3Wtpbo8UNXmLquI+6Oiouwr3oNlgWVbdfzSS59ZDqlK0blrwGxgvDkt1LXy2FfLn1AYpXvaN16q/v1xGgiVfU8oq7hDeT0bO74a79em8YMgGATN+qg+aKhiU6na+yYKOnXpXuMKV9cjCtQ5FuFa1w8E1g4stp6KFuuhAQ7CbAAoDdTKlStrc2p85pprlIqIqFZtE8b7JCB8scjJMJnUdW5E+rWWc1pbseCr5U+grYH8wa3NUQec/EzrItRCJfzq2xfV9gMg6ShuE2/MQCOelUJ9EDTro/qgIU6j2yLTQrXHmPoJxFS3MUU4QCk7m6NHgTUO69bW0sdpqqoaovLhqsPwOs5pANcDRZGRvLJ0KTfccINf+/KV774z8/XXGzCbt/Dpp5+ilKJbt26MGTOG6Ohou3V9qVT19ZowAmjh3xSsr5Y/P2/+mVO6nVKv1kC+jpFlkHQoiXlz5pGWllbnv/fG/v/D1T1n7Vh0OkG/nk2FUKVmCE0LmUYXGiSB6F5jtx0PU+YdkpLU4sWL6xTJcZqqGolPaQBAUCI37iK5uIg4+hop9DXaXJs+2L5GX+bNmxeyKI1EiAKH9Z5z17FoqJzTQCKdlYRg449ek2p0IWwweoavQS8Gsq1GH2QxVp+d772/suGt+TDufU9/2bePtLS0OlU6OlURH9W/eat4JzXwlaBGJPdMmyr+5XTjUvRPmxVVFdZqfn8qVX11WiDW8t3whdyu+0J6wlfLn61bt/q0XjCsxaRfc2Cwu+duxO6e40agO/AuerS4DudUHBNqMDwr046n6Qb9M4GXwHTCFLa2R0LjRcSmEFYY/yA3paXRF4hDn6b91uT7P8j68j11so+xCC6vwuxP+CTGfMVsNjM+N5cBSvE6+nT3//E4N/IjWdzDtUDUUVDdFHnj8igpKdGnMfvh3kTcMr7MzEwyTCYe0zSXDjmPAlFx6P6SBj6KBV8tf0455RSf1guGtVgobInClboIOatxvZt7jkzgN2A7tT6nhYWFZHTJIDs7mxEjRpCdnU1Glwy/7dIaEzk5OWz73zaKi4tZsmQJxcXFYmUlhAQRm0LYkZOTw8/b7P9Bbinz/R9kffmeOvldRkBUa739pUdh1lNfFqhomKtI7hl8hyKC+eQxgRiqDgNddZFbUlKir+RDtM5TtPl64E2g6irs/5P4KBa8eT1q6zTSO6UzZswYn9azNUoPFL6OMRj7DifqKuR87lh0pHbn1IjsV7SwdGKaBIyGnS1q58/bmBDPSiEcELEphCV1+QfpLRo3U9PonB4YgWA3VbUAqo7oAux6cC/M9uvvDVQ0zFUkdzhLSWcHe0jha0bqCx37hfkYrXMXbf5vFHADemGHgR8CzKU5vQsD9OjoaJ/WC8ZD1NcxhpvxfyAJhJDzNULMBvy+ng3dwFwQmgT1kEPqF1IgJASC+vY9tbWPmTZtmoqJjLQv1ImzFED4UUDjK+78SfN5QIFSHfhZQaTiGn0sRUVFytTRpBcOTHUoePEwvsrKSjVv3jx13333qVGjRikgIMUHvlr+1MUaqK72PnW1JWqonpIu7b18uFfcbcfdPUc33UaqNudEirgEITSIz6YgqND6nq5YsULfZyq6yHsoeJWglZWVqn27duocUGtBVVnE5lFaqgT2K1AqosVQRXeswsDfSlVXYikxKVEltku0W5bQLkHNmDHDbzHnSgzWZZm3sddG1NRWsIbSI7SueBRyU7F+gJk3b57X8+HunqMHCo1a3TdKiYG5IIQKEZuCYKG+DattqQ+TbleCugOoxZZIbjem6oHOZl8o0JzsjxzHl5SS5GQ8700sDRs2TCW0Swho1C5QXYVCLfQCFRkMFW6F3FAUbfD7mgfjb0Iim4IQGsTUXQh7Grt5tUEwj9Odcf0j6PmhAJEkYGYHEREneeaZT7nnnqvstrFy5UrG3DeG/Xv3W5fZGrv7YmrOz0BX9IriAPTXNo5LdVd69bKbbXpbb/ny5YybMK5ezeAdr7fZbOayyy5rsD2qXfbY/h5YgW5XVItr7upvAqj134kvBuZpJ9JY+NJC9u7d26j/3whCfeKPXhOxKdQ7hYWFjM/NZVtFTReRDJOJOfPniyWHj5jNZrpmZNCzooLVOGvAgcAnbdow96mnOHDgTG67rRfx8fYPV19EXUJCgrPYsMUilrgV6GI/iNqIuUB2FWr3Wzv27dlXb0LPVXechMQEDh44qBfVxLh4UyUwE5YsWcLw4cPrPIZA4yTkAJ5Cv1duJCAC3pdOVr5sY8iQIdBdt+6yvZfVZkViYiIH9h+o9fYFQXDGH70m1ehCvWI8FHramI+vB3ruFIsSf/BmXP8wsP+330hPTycv709OQtPXCt6dO3fqb/BmWXPMeRC+mrs7HpcvHqBPP/201/X27dnn09gDYUHlrmL7YMuD+goN1KfTqRr/c3Q/zEy8erT6QqAsi9wZmCf8mgAKDiQeEEskQQghIjaFesPWfHw1uvl4rOX7aqUYAEzIE4sSX6iNcb1S8MUX+s++irp9+yyCzZtlTayL12oh5nz1Y5w2Y5pP69mN0ZEACT1Pwp2RQDRQSoP16bQTcm9ZFgZAwAfassjRwLyoqIjmMc2hB2KJJAghRsSmUG/40kayrDxwnXXqSjj7IvprXH/8OPzpT3DeefDTT76LuqSkJI+m5nwItMa+g5CBFzHn6vz66sd4rI0llPqZ5/WS2icF3ZDdo3CPQo8CbgaWU68eoYHEEHLz5s3TFwRAwPv6gcef/we2/ryRkZHsLN8Z0O0LglA7RGwK9UZ9tZEMBIWFhXTNsO+Y0jUjfFrf+Wtc36IFdOqkRzeffNJ3k+20tDTrNCrL0MXdl5bvy4At6GLTES9izl1Hmn379nkWt6VAG/SIYXfL71Xu9/3sM88G3Qzeq3Dvo39LOJDQoHtUR0ZGcv/99weso1Kw+85LX3tBCB9EbAr1Rn21kawrDSGv1FMbyYHAGqUY9Ze/2L3nr3/Vv//739Cli+9tGHNycpgwYQIxWyP1adQ3gLcgZmskAwcORNul+SXmPOXpDRs2jOHDhruMBLIMffkV1EQMTwKLcLvvIUOGuMzlC6TQ81W4r1i2osH3qA5kR6Vg952XvvaCED5INbpQb1grqHfuZLVSThXUgzSNb00mtpQFzoqm1mN0U+UdzDHWxibJVWV/FDXBPseq20sugQ8/hPHjoW9fmwrevkoXDb8AW4FyKCgosLMYcrRYekzTWANMmDCBpcuX2lUTJ7VP4h9P/4MbbrjB6Rh9qTafO3sud91zFwf3H6x5vQ260DTaY1oqua0V3xbSO6WTPzffTsgF04LKF+udQFssBRNfzpWrCnJX593bfoJ53hrbdRGEcMMvvRZUx89aIKbujZtAtZEMllm7u9aPxlepxYh6ypQpfu3Xly43jubsGSbfjNGrqqrUjBkz9Pelo7jNvXn5W2/phxIbq9TBg/p+E9slKiLsDboTkxJVQUGBqqqqUhkmk7oOlNnhXJgt161zerpatmyZSmqf5NXk22rAfQ2KwShGOpid2xhwFxUV6etmuVjPZt2ioqKQGfcb+NuRKVzxp9tSIP4Gg33eGst1EYRwRDoICWFNXdtI1kWYecPomHLEhdAsANXJZp++7tfVAzwhsaato1WAW0TuEct3XwW4P11qqquV6tVLP6RHHrHpsNPddYcdQ8S6E98f254PH7r05OXlOQlb2lg60ky3by3orZ92uHXfqY+OUcEkVN2Wgn3e/N1+KLuOCUJDQjoICWFPbac1vU3p1jUPz+iYsh7dksm6X2AIMAD82q+dcboJ3afwUM3rCe0SaKY0+hw44PO0vdcuNdXAduAouiVRBLCgxrx8yRK46Sbo31/x05aOHqe02x5oy8H9BzmCa3ejI0AcQCpwh+ttGFOVr7/+OoOHDIZu2HWeoRQ9F3MoerGRjdG6J7Pu2nYoCiYNtTOWr+kNwZpyDvZ583X7rtIDkton8ewzz+r3oSAIVqSDkNAoqY98Sld5pWb0bow9wW23ni+TkiirqCA6OtpuW9YHeC9gJU4t/ngH2ImTuDVYD/TFXnw55mgmJySw96ClS81W4F10422DeOBQTZeaqip4/31o1qyESy/1oTsQ3sfHNVirrl1to6ioiFG3j/Lc9nKvfl7SK9PtRE0g8gMFz7hsS2lLmLfVDATWD4bdlP3f6IfAFpg4cSKzZs0K7SAFIYyQDkJCo6Q+fDpdVXn/F9gGHrv1/LJvHx3T0uwq1a0+gn2B99CF5o3UmEsfgQhLXYsvdlDuquT7HLRsZA16z+pk7Kq8aa+/vGXLFgCiouCKK2D3bt+sYZITEtxaLD2GXpBET4cXyoBNwHF9UUlJiUdPRTLRBbKLamZHs+5wreQOZ19WbwTaJijQ5yLY59ZqMN9N2f+NpgPDge7w5JwnWblyZUD3KwhNBRGbQoOhvnw6jY4pm9LS6Atc67B9d/tN27/fzhrJOo5KnFv8fQ+sgD4WMebNDio5Odlt96XXLWOM2oQ+Re34sLwR6A4vvPyC3UNat3xpBZt7uN65xRrm3txclxZLgzSNN7FUvu+vOa6ofHQ7ogJgiS5Gf/zxR8uBuDlQy/K8vDyXItLWrDsrKytspqYNETR27FhSOqQ4+YaGg02WLwTSJsidh2ptz0Wgt+cK6wdDN204yQSq4d777w3LDxEN+YOO0DQQsSmEJZ66y9SHT2dOTg4/b9OjaVOmTPFpv0+CXctN6zh+saxgCK1qiHpHX/dDIAM9QujJnB3wGNWdjEX0dXOzQiZUbK+wi/oqlYmm7YD/rNBzBRx3/iEkJicyefJkO/Edhx6s/dZkYsXKlTV+nd8BK+Cqw9iJ0iuBglWr9O16ETMDBw50s0L4YSuC8vPz2b93v55zmkNY9t/2JEgyM333XfVEoHqdB2t77vA1srtvz76w6zhUH2JcEOqK5Gw2UcK5kMFVXmKGycSsuXN5cNy4evfp9OoPii44t6A31jFyLDMzM/WcTVWh57wZ+XBlwKKaPEjb4qNJ2BQfAW9qGqtWraKyspIRI0Z4L9S5HjjHxQoWT0ojbxNg/34zScnHQcVCh2vg6redinYSkxLZs2sPkZGRbu+ZwsJCBg8eTFQUXFWlR1odz9H16OkIVd3QpyXroQAlmPe4XeFXP1wXO51af16O3o7VVd6rowdrXYuxAl1kVJ9FS77mrIL935AxzlD9L3V3H4ZrAV1TJ5yfu7VBfDYFjwTTOigQY/NkAzRx4sSA+HTWdlwDLPuz7heUZrFFUpblWKx7jPcBimh0e6GpFn9JB3ulAlAZDrZKbWJjrfZI7vw/q0AVg5puvO9qB4sgF/6VBvo2Z+mbiixxtiPq7/wed/hlkdQt+NY6/vhF+os3qym6W87fVNfn3d99ebPh8Xas/lga1cWGyOqhOtr3e7A+t+eJqqoq3Se2m4dr2tp5f8G8z3wZs6+WZ0LoCeW9EizEZ1NwS109HYOJrwbiK1eurJNPZ20pKChQqUn2xuWdbYSmrahyfCAltkusEVrX4JtwtPmHtGLFCv3caJr13LgSqFFRKG7w7cGje4p2UERU6kO4+kJ7o3Ubz0tvePIntRXhnG8R3rUQM/5cp2D6Rfoqghjp3zl0dRzeHk7ejnXFihV+C5La+kwa9wCT3JwXP8/F4sWL9e1dj2tT/zqcW1esXLlS35+D5yzdLee/A3bnKlS+pAb1KcaFuhHqeyVY+KPXJGezCWE2m90WmaxWimuVYnxubsiSy32tNm/Xrp01n9KoTt5SFvzq5JycHLZVVNA2Lo6zgbXoU+fGXquBR4GUxES73LacnBz27N7DjBkzSPg1Ad7Si2YewT49LhK4GNgARLUCHsKuZ/gNw4dbC3Vmok+998QhP7IK3WKpFK89q/Wc0l+g67/1Bf97SN9gZ/QT7kdRiK/5tJwK3KT/OGXKlIBXllurirsrferVplBKDVPQHfLG5dXpHvc1v4+j1Lr/ti+5ir4c67333+vRBUD1U5Rvt3dwqE0xltlsZs+ePfovX+Oc94l/56KwsJCx48fqv7yBXnD2FHphXS225wtDhgxh4sSJ8DP6lPlMy/ddQAfQdmnWv6H6uM+8EWgHASE4hMO9Eg6I2GxCeBNzD6MXodx00031Pzj8qzYPRHVybSo4IyMjiWrWjK+BfPQcTWt1NvAWrp+zkZGRTJ06lb2791JUVETz2FjeRM9ltBWL14Ne4X0t0AJIA3W+QqUoFv57IUuWLGFDYiJT0SvQV2P/oeENy/KotVgflqYTJpe5W5mZmSQmJULlk/qofxoIe0/TX7QpEHJVFOJ47vr27UuGyeTWIulRICoO6ASk6MtPP/30gFeWW6uK/RBXno7L1T3ha+U2rXwvrHEcgy8PJ292UqqfYt+effrvQRQkRoHK2LEWcfgWMB8oQbe/KgOq/C8y2tdmn72FVzK6tdf3+FW05A+zZs1ixbIVtEtuV7PwCKQ3S7f7G6rrfRYIAukgEGikOr6GcLhXwoGoUA9AqD98FXPLly+nY8eO9W5gbBsdc2UgHshqc1dFSO3btWPYiBF07tyZpKQk0tLSnBK4S0tL2XfgADOBf2IxNLfQGV1UPXzgAKWlpS7NryMjI4mMjOTo0aPQH/67Ad48XPN6VCt0tXg6uo3QO1BleX0f+xh5002crNbl3GTcV6a/iR457N+/v+ckdAVs3wyxhXA0B7ZcCpU/6JHRLUCSvpptYvuWLVt44aUXnIpNhg8fzuzZsxmIfZelRy3j4Sr8jpj6S12iPb4U0UBN5fbOdTt18edYDVWKXpX+CbAF8lfl+yWorQ+n0bh/OL1UTklJiU/HCujn3FXhSx2vhV2BiiEIDSP0EpsVo0GdVOQXeD4XJ0+e5O4xd+t+l7ZFQYaF1zLgbWAjtTq3vnDDDTeQk5PjsZAjHKKK3u5DbZ2GqZMpoGLcF3z9O2oqhMO9Eg6I2GxC+CrmLgCenjuXRx55xK4jTrDJzMzUo2OWqm+F/tzehe5LPg/onF4TyQhEy8ul6E13HgR+2b+fp556CtD/MKrQq+DnzK/5J2n8Q7gPmGgzvlR0K77f0YWW4z8O27F+/71lLrAPVF2E3lryB+AzqLoPPaJp8eG8Cl08GsLtkepqXbjh/UODETl0R2lpKQf2H4D+wGeT9D29t1l/sQ3QHw6sPcBf/vIX3vjPGxw8cLDmzd2xExc71+1k9uzZTJgwgZVLl9LXRsRHxaEfyOkE/SFoGNf7K67ciaad6/Rpa9uoVmRkJPPnzWfIkCFoyzW7ym2j2wxAekI6+av873Lk8zS97TF5ONak9knsX7c/4ILEKQJrKwyHU9MV6s/AR1jPizsKCwu56567dAupAbi18OIlSGqZxPOrng+aeDFmTtxhF1UMgoj3BU/3oVGNHgwx7gl//o6aCuFwr4QDMo3ehDDE3KO48XREj849CZwwm3n22WfrdXy23XvOBzoC2cAIdD30DnDGWWdRWlrKqlWr6Jph7y3XNcO7t5xj3uovwC3obkG209lXAxrQrqLCzs/PVrBHAlnoz9Usy++uoq+FhYV2Y33kkUf0P7z/WlboDFhmr9mPnQ/n67iZJkdPjXOFrxFgq6jpA4z9GUZuhsHASOAB9DaXEbBw4cIaoRmJrqwdjOON6d1lK5bx09atzJgxQ18/HaoGA6fgMX80EBQWFjJt2jSIRv8U4KNfpF33GB9zqgzj/7TjaXb5fUlHksjLy6tTLqqv06NZWVk+eWOOvGUk6icFS9EtfCy5vCyjTtfC2/SgtStUNXAjaN01t7lphkjZH2npDuBFaM+bMy+koiVQvqR1xd196C51JphIbqJrwuVeCTUiNpsQhphzlSs4CL3b4WzgLMv6W7du9Wm7gczPycnJYcKECXyOswC8ElizZg3Z2dnccMMNnOnQtrHnTv3T89///ne3Y7HNW1XAeFyLutWW5QeAa5WyGrVbo69uchMNE3bjH4dti8mZ6CmLxrp8CVFPokcxO6FHE0uBbfrUuadp8ipgAm4+NDiMwR12oiYCXfT2BOI7wzdtdAPQrtjnzXVBD+X+6DwwI/fo448/ZurUqRQUFGDSTLCAoD8EjQcdPdCb1W9GF1M+iKtHH33UY/cYdzlVrtpo7qrYxbx58+qUi+rrwykrK4v58+bDZouItzlWQ9TfOPRG5syZAx2A3dgXvvwMQ4cOpbKyslZ/t34VSnk4j3Yi5XLLQi9C29f/TcHCiCp6OvfB+EDlinBp5yq5ia4Jp3slpNRDdbxfiPVR8LnttttUlINljq2Fj2HfM2/ePK/bCrRnpzf7o2tBRWn6d7evexiLrUVPMa4tiBxtjJ7B3kLEah/lxevT9lhWovtxOlpOXWuMNQerryUp+GYjZHm/P36jtrY2RUVFKi09TWk9tBpbmUumKbQqRfOpNb6gDnY5dj6SXqxoamuj4y9ONjBDLWO0vc8jUDNmzLB7n9UHlcBZ9gQC4x7Temi19sZ0sj2aarEQGoziVnQbrgj3tkre8MsCysN5tNvOVMt1c3XvTUFhQhGJapfcTlVWVgbsfNeWuviSNjYCbX3V2GiM94pYHwkeef7554mIiOACYDFQTI2FTzV655rmkZGMGTPG43Zso3auIoy1aZfmrWJ+MlCl4BpPr6MXxLoai+00uJFV6S33sYXluxHJceydbtu+0bFidVtFBQ+h53cOwEP1eCGwVu/YE3c8zjpGV1iXZ8F/4/A4Blscp/Mvu+wyqn4/gfpJ1Xzibvs9qEg48QBc0NLz9Oh2h9dc5B7VV09zpyjb6eipACPRUwNGANXQrVs363us0VAjjyqMqnr9mR51F9lKSkqyjzTZRq+7oPtsVaOfm1q0gPQWgaUUPVpvhPPdnEe7axcBXIFzZLoUmAVUAGbYv3c/po6mkLdkDJeoYjgQztXx4UBTv1dEbDZBoqOjyR0/nk/Q/5/HoBe2rEefgVwD3D9unMfiIG+enbY9wv3B14r5Fl5e/83NWGynwdtb1vUm6o5bvtv+k7Ttne7O69M4lsPANvDa13zevHns2bWHfXv3kZqU5Da31mojdDFUPQCkQ0K7BIqKitz6jbr7YNDn4EE0IOFAgi5qVhegGw0mwi9/cX1ibKdHbQZW19yjuqRjuHzQ2YqrFg7rYTPtdxk1KQyuTvg6QpJT5c/DyZWo93mau5Ja5dUZ04Nqs/t8UK5Avw4e7g+na3c6ervPvdQI7bVABnYpHfvi94VF7/n6+kAV7khuonea8r0i1egNiED2VTVsjZ6aO5c1Ng+WmIgIht1wAzNnzvT4fiNqtxT3Bux9y8vdWgC5w9eK+eMuXrN93ZAUtmMpKSmhf//+zJmvV3Aq9FS2R3Hdz/sx9D+QBeAyB9LXitUNlt+9Cej27dtbrZGeef55hgwZwiDL+J1shC4DdlqqTivghVUv0L9/f5fbd/xgYBynIcYHaRrfNm/B8qIi9u7dy2uvbWflyq6wfjxc+CxEVtlv0BAFx9HFRQCqX+tql1IbGxirGEtBF0Ur0EVSJvbV5T/bH1ew+hu7264/fz+2+FoFS6zlu42tkq9/twMHDiQxMZED2w9Ye4cDepFWf2oKwzzcHy6v3enoDQD+h35NuqAXpTlaIS3XxfHAgQND2nteCM/qeCGMqIdpfb+QnE3XBKufeWVlpRo1apRq3aqVX9v2tT2hv/k51jxHm7aMrnI2r8FNS0v0/NMqF2Npl5Bg1y/a9ny663l+LnoO6IoVK/w+t8axXIhvuaGObeVcXfOYyEi/c37c9VV3t/+jR6tURMQe/eWBt7jO2bTJ9fN1HO4IVCs3f/Icbc+LP3mewepvHIztGr2z7XJyHa9jG4fcSD/z6qzn8DabfNAsFPH25zEpJcnjsbi7dpjwKS80mC0ZG2NP62DSGHMTBddIb/RGRjD7mdd22/4KmFqNyaEAx1pM0xeXxTEDLAKxwM1YLgSnAp558+YpQHXA/uFoFEzV5TiMYwFUrGV8LgWypee7q+IZxwKbyspKVVRUpKZMmaKmTJmiioqKrO9zV4xTmw8GN9+8SX85+lvF7VrNw7+bvm5iu0Q1Y8aMOhf+GILIn97d3s63rw86l2LMKKLJQZGui4pg98IOZt9ktyLO6Pc9tG7izW1RiHEer9e3t3jxYp/G6njtiLR8f8hGzNr2SQ9y0Ulj7WkdbOqrMFAILf7oNU0ppWoVEg0Shw8fJj4+nkOHDhEXFxfq4YQcs9lM14wMelZU2E2Bgj7VO0jT+NZkYktZmd/TE3XZtvW9FgP2QI4LXHf4iWoFVVcCbYG3IeoXPdfRoDXwMnrPcLuxoE9B/wQMdhjX0qVLGTFiBL8BX2Fv0B6JntsYByxZsoThw4f7PH7HjjvPPPUU+w4c4FrsTdpnahprlCI3T58K7Nu3Lx9//LHb6Tp3083Dhw1n6fKlLqehExISyM7OZj2uUxPWoxcXFRcXW6dODx2CDh3+4Pjx31HqfMvZ0wuYHrjvASZPnhyQ6bCSkhKys7P1XDxXU73lwEv2Y/OGP1OeRi4r3XE57WcU45jNZjK6ZOi9ym0NzEGfpl+uYTphomyr9/vddnzJycmMun1UQLbrDlf3DNHoCdpn1G1/gb5+jn83Tz/zNPv37df/uI/YrNgGPfWhtX/b94dAXnNBaIz4pdeCLn39pClGNj19CgxmBLGu2/bVAqi2GOclLy9PtUtu5xStWrFihfW8zZgxw+N0uKOtk3FMwTi/rqa/O6WlqWHDhqnkhASP0+KOv9umM7iLslijVB1cL3/ggQdU+3bt/I6slpQodfBgcCMUgbZLcbR2Kioq8jp2X6Khvtr8eLtPXEbvHLdra1F0TWCmiW3Pi/G34mu6gbftepqq9zcy7bjtqVOn6uenu/t7vrbb90agrrkgNFZkGr0B4S0XM1i5kYHatqvxd04PfH6OL9MyBQUFTmLO1j/U1TEZeZXuhNgA/MvZ9JaWYAjkvLw8qzheD2qxRRQPcPO+lStXepxudsq/G4qKirMXNFGgeuOfL2ewCeQD3UnIOeSVesqz83Z/BUIUu/ywkIX9dt3kjebl5fk1Xr/PlQuB7c+2/MmV9XWbaelp+jX05PcaUbt8al8Q30hB8IyIzQaCL/mS4RzZNAin/JyioiIFqBnopu1VPhzTypUrPUZFe4PqbDJ5jZJ5M6Q3IoiVlZV261WByrDs76Rl3Ess309a3tchKUl/8F2Dc96ajShjpEWwuBCuA2wFDDUfbLzlXlZXK/XFF8G5XoGKjNkJOcMc3yEaVhfxU1dR7DY3dSQ12x1qM+7bUIywiNE0fblthDsQBSuB/LsNhnglnTqd87oeo0Q2BcEzIjYbAH4LEzfV2Z6KS3weQxC2HSpqc0zGQ8VbkZC76LPjdryJd6MoyViv2LJ8pkV02u0H1GPG75rDONpQU+BhibKQo0c0PU2Zd0hKUosXL1YzZszw6nBQWalUnz76Jr7+OjjXrK6RMTshNwX3HWjqMK1bV1HsVrgYHXO62Yz7Bpyjm9GoxKREtXLlyrAtWAmEeLW7ljnUKbJYV1EezBQBQWgMSAehBoC3TjmTlKKsXO8zPWf+fNagF93Y9TPXNL2feX7tvMuMXunB2HZtCESPddtjGgg+HZPht/g9ejelJdh3VTJ8MGfguUuSr4b0Rl9n43ejk9HD6P7jdt2Y0AuKAGiPfZ/yZHRvyO+p8Uw85rmv+iSl+GXfPrZu3cr06dO9dn+KjobOnfX3W6xZA44/3XJcYdeTuRzd0d/PPufeqGt/Y7cG6xHohS5bLONOB1Za1rO91hlwYN8BRt8xWu8hPsyybgy1MmQPBoEwrLa7lq0tC2vRkcYo/KpoUWF3Hv3pkiQ9rQUhcIjYDBG+CpNdu3b53B6xNgRz2/7g2EoxOzubrhkZte4OkpCYwDvYt3LckJDg8piMh9UPQBYw3PLdeIQYRvEX47lLkq0hvSuM5aeccord78no5vGu2lmuxtLOEnQjdxuBwY1Ad+BddPPxNkArfZve7qun58/3ufvTQw/p71m2DMrK3Gy4jrjrljNw4ECvH0DshJzR1chL1xzre/xg4MCBTJ8+nbYH2votij228judmqrwz9Gv6Y04X+s0OPzb4Zr2k7bUQUgHE38/QNpdy0547OzkriON0YY0EKK8rh+EBEHQkQ5CIcLXTjnGejk5OQwcODAoXSyCuW13GA+hkpISfvzxR1atWsUA9K53hi3QY5Yomz//1I2Ihuqu4Dr0SMQvwFbYXX7AaQylpaXs3LmT1KQkHtu/36WN02PoXQ9tH2lGlNC2S5K1FaYbO6iZmkZnk4kxY8Ywf84c63qgWzi5i3I/jN5C1G2f8pfQo2IdgBP6S97uq/0HD3qMqhsdl4y2h717X8nnnycwZw4884zzdgPRYcWxW44ry56ExARyH8i1s16yE3JGNxwvXXNcRcM8HYOvY3GHtw5HHLb8fAjdu8vVhekG7MQnIR0OHW9q0xXKqeuRu85O63DbkcYaHR2Ne1HuR5ekUPx/DFfC4b4SGij1MK3vF00uZ7MR5Uv6SkFBgUpKTFRRLvITC+pwHvwxCHdVRQ/ORULX4tooXuG6Wt9XOyjb9aZbtuPNFYAc93lrAwYMsOanRVnG7e6+ap+Y6NP+EhJtK/uzFCgVHV2lfvnFPjdv5cqVAe9u5VTw49CRJjEp0c6c35pb50POpqmTc7GXp/y++uhwBKjY1rGecxRH4FPByowZM0Le8aa258xlnqSLCn1TJ/fHI1XkwUE6KQmOSIFQAyHYPpXhiNFRRwPnKnwXws6fantfq0dnzJjh0gWgt0Wo2f4zjUIv3FHoVePF1FSKl7oZm692UI7reSssimiJx44vtr6kmuU+cnVfGT6L3vZHuo1QuB1F808VKNWixZMuRXqgulvZfWi4wbIPFz6LrsS71kPzWI2Opnc/shOu7RL14is3wiixXWK9dDgyrovb+/c2dCsgD2NJTLYcWwgLiOraFcqlKL8Na3X6jBkzPJ5vqSIPPNJJSXBF0DoIzZw5k8LCQn788UdatGhB3759eeKJJ+jRo4dtpJQZM2bwr3/9i19//ZXzzz+ff/zjH5xxxhketlxDU+sg5KpTTuf0dGbn5ze6fCCz2cwpnTpxYOdOssF11yL0qd4t+NfBx2w2M336dB555BEYAXTFeQqtEpgJSW3b0vXXX7kXSKOmW1A1elHR50lJzJ43j5SUFEaPHEmvX37hVqWYCGyz2Vws0DIxkV/27HHZYcmX6SYjnWDE0KH0OXiQ192cky+Bs4C3AIai5/l56GLi6b4aOHCgx+5P1wP/jYKqv2KfaPPdn2FlIWgfwch+0B6inoGrjuF63H52kTLO2dq1a/XrOBJ9w8noOYuOO1gO6SfSrcfuNG0bgV2uX2JyIgf2HoAe6HmPycBu4FUgw/0+2II+lk4uBh3ADkdms5n2qe050OaAnjjsMBZtuUbCwQQO7D+gT6nbTCsbHY8SEhM4kHjAZccblkHSoSTmzZlHWlpa0KZAA9FVyNUUfHqndPLnev+/aHT+2dnCdcqCdP7xD+mkJLjDH73mV4HQBx98wL333ssnn3zCe++9R1VVFVdccQXHjh2zrjNr1izmzp3LM888w4YNG0hJSeHyyy/nyJEjHrbcdMnJyeHnbfaFEVvKysJSaNa1Wry0tJTtO3dyFPf5iZOAMvSaAHDOXXWFUVz0yCOP6AuWQFQ+epW2LZZ8vV9//ZX1wM1ANrouLaQmP3L3vn2kpaXRv39/5j71FP9RiqE4V4pfAuw7cIDXX3/d6dwAPlXmRkZG0r9/f5574QXe1DRnVwD0fM2ngP9gKRZ6G9iOnsfmpiLW033lyYVgIPAmUHUJzhndp62GK64GlanH5HZD1THPle9l5b4VrBQWFpLRRS8Qs17HAjxWltMPu4IYxyKjoneLKCoq0n8uKqJ5THNdaNoWjVQDJz3vg2r0DyquqEXBkbuq7ddff10/r1vQk5cdqp/VZlUTD/0Zp4KV6dOn60LUVQHRj8Au2LdnHzfffDPZ2dlkdKl9AZ4n3FbeG/hwztwVjPnyf1GqyAOLnUNAAylME8IPvwqE3nnnHbvfFyxYQHJyMl988QUXX3wxSiny8/OZPHmy9Z/CokWLaN++PUuWLOGuu+4K3MgbEY6FEeGIq0hZhsnEnPnuk/0dsX24eK3Cx76oxrHi1HZcgwcP5gL0CvJkwAS8cxjeWoFdFJC39fdchX1/8sfQazJWAZc7jHXgwIG0T0zkvAMH7CKxFwBvoIu1e++8k3EPPMD2nTut4/L33BhVr/fdfTd99+2zLu9sGZexlcnAm0eABZCUksTzq553uw9P95Wxv/G5ufS1uabtExPhwAHo4+JNEQrOfUevfj9as9jbtSwoKABwG0mzK+oyLJ32on/i2Az8iusImQvR4u6YS0pK2Fm+07loxMfqdX5BrxJ3xEPBkT/YnYOz0KvSX6p5PTY+liPqiB61HAi0AzYBXwG7YM6Tc6iqqnJ9LN+jF9l0R/97sJzfnev8L8DzhtlsZs+ePfovtSjSsqUu/xeN+zt3bC4VL9kUKHUykb+qYc8a1XeRTiA+PAiCX5FNRw4dOgRAQkICAGVlZezevZsrrrjCuk5MTAyXXHIJH3/8cV12JYQQ40HozZPRG7YPF2/2QAfx7vVpNpu5+847aY7+bF4IzAIeAL4AzsE+Chj1i24v9DrO9kIDgAnA1w5jLS0tZc+BAx6jd7sPHKDDzp11OjegPyCfnDcPgJex9/o0MARc67jWVGyvqLPtlWP089Xly/UXd6OHmDdZvhvT0YZ1T/PmUHUq4P1aPvPMM24jaZ5sargRfbr4PZytb2zG4ovQc/vAtK1ed4WxfKuLMXiw3/EHp3OQCeSiT93nAOnos0fdqTlHLdA/ENwBWg+N8RPHk5yc7Hws1egfDlzYKQXam9OITo8dO1b/4/iQoJ0zX6hLdDRcsZ0BMCzighWhNvBo22WzvK4fuITGTa3FplKKcePG0a9fP848U38E7t69G4D27dvbrdu+fXvra45UVlZy+PBhuy8hfDCbzYzPzfXZk9ET+/fvB8v7H8Xlc4hH0cPt9+Hd6/PRRx9l34EDVAJXYz/F3Rs96FNlRAEPJVGFhylfdE31IHpuo/Eg9NUP9V7qdm4M0tLSADgNe69PA0PATRg/gejoaJ+36w4jejR06FBA/xtu0bIFUa8Ai9CnshdZ0hK+Q482xvaB17bDuteIbB3BI3i+lpGxQH/Xhtrepui4GP2ibnPega1o8Zbi4faB2QmvXo6JyYk1U7BBmJJ1eQ4i0MPavYAroLqq2nUess00JujWQto6reZYthMUk3tHnEzUB6F/UlqG23MG1LmJgzcCYTRfFwLRqMIgEEb1tcGw7bK7rwzq8cOD0LCptdi87777+Oabb1i6dKnTa5qm2f2ulHJaZjBz5kzi4+OtX+npruZdhFDha6cj42Hl7p+r2Wxm4tixDAAWoOcFDsJeIF6PXgBzX16e19xVs9nMM/n5xOLaDP0N4Bp0sfPwww8zb44eMfQmGtdjH0n11ag9zYdz4wtWr05NcyvgEuPiOOWUU7w+vGyvxdq1a1m7dq3Lh56tof7NN9/M8d+Pc1WV/bW58jB6Z5vNwGXfQ3UzOHAq5p4DeRP92jnmfr4F/B9w1VFgLahezpE0X6foeBe3ouX111/3Gu1x+8CMQM+d2IxbYfSv5/5FQUFB0Iy9fT4H7pKejIDm3r3OuYq/OmzDzXvrMgXqMjrdC33Kfi8uzxlQ7xG6+iaQUchAGtX7i+TACgGhNuXu9913nzKZTOp///uf3fKtW7cqQH355Zd2y6+//np16623utzWiRMn1KFDh6xf5eXlTcb6qCFgeNZ582RcsmSJS8sfw2/RsW94Ac59wKMstia+YLU38cHCZ968eT73LXfcvzc/1Gstx1Hl5dw4btNTD2l3llgDHM4XuPe5c3Utoly8z7ovUOtAdUK3oHJ3rFGg9/E+6//0l9p9qkh1towy+srbvTfOYmFjYzvjq01N67jWLu2CPFmyGNfTOM8rVqzw6HOZmJToch++Xrfa4us54BrfrHxc2SsF0wbI4/inWsZt+Ts0PE2t1+w2dP/QrBpro8ZgoxNoq6BwsHPyZNslNE2C5rNZXV2t7r33XtWhQwe1efNml6+npKSoJ554wrqssrJSxcfHq+eff96nfTQln82GgD8izZV3peG3mJeX5yRabX0r33QjzNxhNW7GuxBevHixV9E4AFRnk8mlgDAeHK4M36HGh9PdubF9AHgS5I77dCkWO3h/eNkKSNtrYYyX/jUCq31iolVcFuObeI9rE6cgScHvCpRKaj9UTZs2Tb8PLNtxFN9W784R9tfZpYm3jVAxPBkrKyudhJ5HP8cbUEQ7C/OJEye6fWA6iklX+wwGvpyDyGaRHo3qHX0rjWNZvHixSmqf5PX81uXY/DFRd/JQbWN/jYjWRX9DbmZRV59RV4SLUX2wPnAJDZOgic177rlHxcfHq5KSErVr1y7r1++//25d5/HHH1fx8fGqsLBQbdq0SQ0fPlylpqaqw4cPB3zwQvCprKxU7du1U+eAWmsREYZIXAzqQlCd0tJ0IYf7rjUdkpJ8EjK+fjL3J7JpG/GprYn+xIkTnaJ3HUA1x3O3HtvuR+5E4AB0M3tHs2pbwdAuuZ1PYsMqqt2MyRphnFITSTLO3xJci3fjer9kef3uu+9WxcXF6vLLf1Kg1OWXV/scASfL+TpPnDhRf60bTsbtaO5NvN1Ge4Za9uXC1F3TNLVy5UqvD0xfPxQECk/dhTRNUxMnTvT4uqdxedt2XY/Jn6ibdV03xvt0x/rhtaESjChkOEQ2BcGRoIlNxyk842vBggXWdaqrq9W0adNUSkqKiomJURdffLHatGlTUAYvBBdXD9xEUEkO179tXJxPoq99u3YBa89ZVVWlOqWlqVg8T/s6Rit97e7juK8Mk0kNQBfcRgehKlArLULxWvAoYH0SgaCS2rZ1Ele1eZh77Q40skb4GQKxGGfx7irdoV18vCooKFD/+59SkZH6qv/85wbf9puOXVTHOt3YAUVrh/8tETU/u0oXcBntmYrXdpXeokruPhTUtiuSr9Egb9OUdZnGDNQUqKtj8TU6XVVVVXPN4t1fI7o17OhmMKKQ/pxjQagvpF2lUGc8TcVq6FPHxrJzsBct7qJaRhtFR2Fm7T/u54PPtvWl257mLrbp71SQNwH3GC5yFh0ErK8i8ALL96TEmt7f/jy8HCOMji02fzXGOBjrlPZ6m3UzqBHvBbhpK2ojukaM0A/h8cfNXnNbo6L0/Tn2NLdON06tEcCY0PP5HCJeeXl51mvmUoSPpE4RIG8fCvz9UORvP2lv92ZdpjHrOgXqS/94b9FT2xmJxhqlC1YUMtgRakHwFxGbQp3w+sBFLwAx8vLW4hwRcyWkjN7PsQ7CrDX2IsQfCgoKVFJiopPYS7ERa+6O0dcHr69TxFOmTHG7PV+3sRhdONueE18fXvPmzVNTpkyxXgtXUckOxs8jdTEXZdmfcZ0NgTnAsq430fXTT1Xq229rroWrNAUjVzQxKdGlALcel7uo5FCccvtMHU1q5cqVztGewdQpquTrhwJfhEJj6ifty7H4Ej2tqqpSCYkJAY/8hRPBjEJKkY4QTgStN3p90NR6o4cjRm/j9eg2Qo6sB/qim45nAWagI/AndMuhCJt1rT2y09KoVopeO3dSAHyE3iUoFbgIGOxnH21bDIuf999/n/LyctLT07n00kvtfPVsu25s2bKFl//1L587/vh8PoqLyczMdNndw59zGmP5+UJgd3o6P/78M6d0O8Vtr2dehJg9kVRabE+i0J1nvkK3hXqYmm5Jj6K3v2QIaJv0XtsH9x9kALpV05nAM8DfgROWcXk7ZtsuL4WFhU7dlOJbtSJn6FCef/55O2/QpUuXMmLECN0vMAbd6HQR9j21bbvfuOgFPmHCBGbPng3dQfVTcBxYQq37chtjOkKN37stR4A4dOu3wYMHe+x531j6SftzLIDX7jZ///vfmTZtWp16p4c7hiem9b50uG/rYplV3x2EBMEdfum1oEtfP5HIZujxNQqXZ7NssWWZ43S2MeVqRDUdI0bGNO90y/uLiopqNWZPBR2uXovFPhXAUz6eq0p2xyKpDJMeaXM3Bm/V8LbRYuP8GuekuLjY7RQaHWrOuzHV/SjOEUvbfTlOabs6P+3atvXpHrCNPm3frtSCBf9xa7nkOH3sFNl0jEr6mH+5YsUK+2hPhPf3uIsq+Zzz6uaY3B6b41cDmioO9LFUVVXpNlN1yKttCEgUUmjsyDS6oJSqfY6Wrw/cFGqm0o1lqZaqc+PLyF10JWBdTfMmJyTUKnfTNr/0N1D/oCaX1GXeoWW5rRfkAFAdkpJUZWWl0/kzxPJ1mqYeQ/ejtB13fGys1/xGdxZKjmMxzqWjHZSrh1d0hOYkKostr/njKep4rxQVFfm0DUNgzJypVGSkWcFMO+FrZ7nUAaeiKbvpRsd8Sz/yLx2vVW1z23zxVY1qheIhz9sMF6sab/jyP8JtIdZI7HJ//TmWxpRi4AmxChIaMyI2hTpZt1RVVanUpCT3lj6g0gyxgX0OnztvQkcB6674ZAD4JAiMfRQVFdnllzoKWE8RPsfcU9uq+YkTJzqdv/aJiSouNtYpkrgOPVLqdj+apjJMJlVUVKTy8vJUart29oIce9FrjKvUQdAZxz5jxgyVnJBgfb+jIHRnYWR8uYpKujrHHiOxDoUyr71WpUCpKH5TB4lzLdLi9Iijq2p0rYemFwTZVinXIf+yLlGllStXui06A/QcUhvR5SoSF4zIZqCFi6/FS3bHYhRxtbT/sEWE/3ZFEvkThIaNiM0mTiCsWwwT9utwHYUzps2n+7hdW/FyEvuqZ29CxvHYnLqjUFMQYytg33Ijxpyicw4irLPlu2OE7jpNN0LvjX+RRFfV6oZd1AXootLx/K50cx5WrFihQI/ajsK1qPQ2Hl+LXPzxJV27tljBtwqUepwH3U8/X+O8b5fXtHvNurUVbJWVlWrevHnqvvvuU/PmzbNGrL0dszEWx2sW1cpBaHoYR6CLRPytavdle75GFq2uAR1QtHIQmfHonpndvX9IdIVE/gSh4SJiswkTKOsWI5rRweGBa0ThbHPXvPlUGhgPuAtrKYZcPiCz9HV/w1nA+hzhc9hvKp7zHdOw75DjaT+21d1Owh99+t0u+owuTl0Zva9cuVLFREbaCyBqOhjZ5pG2x3ezeW/XzBdfUn2q9VYFSqXwizpOjMtzzfWuo5GO0+BWYRWBbvbup2CrjThzur8esgjeNpZtPORG9LqJsAbKqibQU8616XBjNd53Y8LODY0n11IQBN8QsdlI8SUKECjrFk9G5kZ+Y/vERFVUVOTXw6WgoEAlWopPXrbZpksRaNPSsKioSLdMSUfvfmM8HC15ff+wvGcdNb6S8/A9smlMX6f48R5jWbGb9zj6VroUfSaTmjZtmvWcGF+RDiJp4sSJLkWr4Sc6Ade95s/Bs9l8oO47Pae1mWrPdgVK/ZM7fI5setpnXl6eQsMvwVYbceZRgN1qGXstIqx1nSq2jsudAXp3VFJKkk9RWwN/rLSMVBVvY6ANTn3vBUFo3IjYbIT4moPpayW5L8n8tlO2z6AbgtdWsNgeh2POYgY1OYuOgthlj/A4aqYzp+q/n215rZOD4IrBedrbEHsDLPsupWb6Os/yPl+jobaisi6FOk7H2BJFTk30yGt1Oa6FKA5fvkah/cG2m9MZPKBAqa5sVlVEeM3Z9PWe8VWw1bYvtUcBNhW9u1EtIqzGmGo7VeyrMGyX3E7NmDHDL99Yb7mwTl9exlCbQiFBEBou/ui1CISwx/Bs61lRwXp0r7/1QM+dOxkyZAiFhYXWdVNTUwHdU9EV3zqs52mfD44bB8CXwH1AErq34rcmU6184ozj6L1/v/1xAEOAQnTbyJmaRuf0dPbv3+/yuK88jO69+D0QAVVXwUbLPnpZ1jHWvRz4HOjjsPx6dL/Jbej2jd8Cq4CBDufJEWN5e5tlkcCTwJuW7Rr7+cDy+plutmUsnzZtGmc6HuPvlhNyBLgAqoDJ2FscYvl9MvrrY/VVibV8fwO4TtPokJTE4sWLKS4uZktZWa39/dxRWlrK9p07mQx8x4s04wC/kMJnnMF69HP6JlDVDLQtGvlz8/3yBczJyWHb/7ZRXFzMkiVLKC4upmyr6+MoLS2lYkcF9MPlyVL9FOXbyyktLbV7adeuXfoPyS4GEIF+I20BlqH7QFbq37Xlum+ip2OKjIwkKyuL4cOH23m/+sJOw6/U1bhslu+P2M+0adMYMWIE2dnZZHTJsPu/YIv1b3+vm20ay69B90DNst+XuzHwi8P2BUGoNwy/6aVLl1JSUoLZ4rscNtSD+PULiWza428Opr9VxK5wV2Bk5BGuXLky8MeBngtpVKOvWLHCey/xOKxtDqNi3Ef+BoBq5hClaW/5fjZ6mkCV5asIVLKHKOIASxTxGpwLp2y3a/vlS4tKj8eY43+01VWUOFjYRtMLQKXQT0Ebu8g1oBLa+W9rVdux+FvB7msEMam9vbVXsKun582b51tU8VbsprN9SRlwV7xk3Y7xmo8WVI597wVBqB8CXUDoKxLZbESUlpayraKCh3Ed1ZqkFGXlNZGayMhI5syfzxr0zj220bJBmsYaYHa+fRTG9hPR2rVrGZ+bywClWI19pOx1YICm8eC4cX5/anJ1HGagBFgOXAnsBD5PSmLVqlUkJSV5PO7JQNVhYDuwDaoq3Uf+Hgb+AFrZLN+D3mlnIzAXmAWcAlyGHthZg32U0jh/b2oaLWNj+RA9yhtn+f4tUAA8jX3UMwp4BD1ia0s1ejefCMuxf2g5Hy6P8Zi+zGu02sVrRvTUGrkLArbR9ByggnUU8xtL0DsivWJZb8WyFQGPqrobi7eoXXJysl0UoG/fvpg6mvQOLy4ulrZOI71TOhU7KnyKsAaKpKQk/WYoxfVNVAq0ATLQQ/S/6cvVMAXdIW9cntPfamRkJPPnzYfNlsisTaSWpcBm4Apq/pg6WfbhaQzR+vv9jVoLglA3jBnDihYVeleuScBo2NnCeeYzpARV9tYCiWzaU9scTF+riF2thw/ROH8jZY7H4crQPQpUbm6uX8dNFopU3yJ/f0IvJHrZ8v0ay3LN0k3HNudxJs493Dunp1vN3deBmmJZ/hZ6VNSVd+hMy7JrqYmEloLqDirCYfsZlm0YVeUvGa/9WbfdcVdd7qpCvj4jm+6i6dWgPudsv6rfAzEWb5ZDiUmJLvOfJ06cGJDq8UBijbiC+0pwI3/ZyLUcbB9xdHftXVpORVgi6dMdvoZaXu/mMIZu+nLHvveCIASf2uaoBwopEGpE1KW63Fthgqvp8hn4Jtzy8vJqfRzuDN2NyuqCggKfjxtqpja9res4xZ0B6lx0ketKyJ1En+KOj421Vt3biuBiaoS5q8pzQzTmgmpus19H/8ZO6KLU3VR8VEus/obujMbPdTF+f2yO6up36OjJ+SsR6iw+sAylT70KEU+WQ7i594yit4kTJ4aV0bidx2Ws/X1BG+x9P43p7JH24tObeb9x3T1O2Q9F0dwiRm3GEBsXq2677Ta1ePFi8ckUhHomGM0j/EHEZiMiEDmYHrdL7SqoAZ+rX233Z1SAe8tBrays9HjcttZLy5Ytq1W1tlGBDnqk09PxTps2TRUXF6spU6YoI7JpKzDXWtYzrJfy0P06bR/MEbjx3KTGvsidvRGguuAcDe4M6hTLz76Yr7uiLt2mPG9ngQKlzj+/wq/t1Ba3Xp2WL1NHk2qfmOjTvRdORuNWK6duKFqgSEHP0bSNZLjKtaxFz3KXUWEjqtkd3d5oBPqMgslB/FI/eWKCIOiEui2uiM1Ghj+dXHzFXeTQnY2PrcjrjD4FbRuh80WcFBQUWNf3JVLry3EbIrY3NREr23UHWLbnzv7oOstxvOhmPEYkN9rSPcj4aglqOTVR2nOoiVIa6/jV0hLdpsmbmXwlNT6ixZbr5a0vvU/3Fq4jfXXpCLNgwacKlNI0pX780a/N+I2rKeG09DS7D0T+9nsPJ5yOz3E623FKvZZTaE5R4YfQbZ88eWy2xmuveEEQAo9ENuuAiE3X+JqD6SueciIn2oglV8Jtos2D+S0/xYnRBtPXHFRvx+04Pe8Y+WuHb+I212G5MQU+1IVwtI02Xgrq39Tkdw5A77pU24ixtwhrsYdztnjxYlVUVKSmTJmipkyZ4tVwP1Ddpjxx/fX6JkePrvUmrGN1F210Z+JOD/sWioH0oA0Ftkb3jlXxRKO3jQxAnqnLXE5vlejG1H095IkJgqAT6La4/iJis5ESyD7C3iKbvV0It86W5Z3RDd6hxm7HV3FSmxxUT8ftKCCKLO+fYRFm/7b87k1gnG0juGxFqy/T85HoU+RGtyVXx+dr68yXvQkhF9frGctr/9/euUe3UZ75/yMrqQmNHfAlvsmxA4H+aMNSKC2ExdRpSxYap0kdl0va3YRl6e4plLiAC4T8mvgUEsoGkrSh28OvtPSsGxI7NuGWLaBip05hW9qFltBuoeCAHUIJJOTCxUHy+/tjNPJoNCONpNHVz+ccH8vS6J1n3hlrvnre57Js2TLVUFcXcc5ieZzd6jYVi1//Whtu8mSlRpJcTY9V1iNegDynosqnl4evoXz1bJoxhwzU1Uee91TjTPXxr7nmGuVkmS6clLQ6/d4UQRDGcastbjKI2BTiYhcLavTA6d49qyVbXeAYPW1ObtZux6CaBYRZ1BmPJ5bA0L2Saxhfjr/b4XvPDf1eG0NUOrUjnmfzl4bnrDy5U0N2OFkOz5Snr6lJG/L66xN/b7zWk3p1gHieN73HfDrin3MBN7+IGnG6TBf2bK4mLXFi6To+QSgEUm2LmywiNgVHWMVErrYRS2YBchaahzNg8Vq8m4ybMahmAWEWdU5iUCeBYjLqo0R6MtsdzsUtaJ7RKlCP24hKJ3ZMRouFjWWnHtrQRexko17iiyg3PH1ORMCjj2pDnnmmUsGg41PrqKxHWUWZI89bWUWZCgQCaYl/LmQSLgC/2n3PZrYKVgtCPpGNL2QiNgXHJFtnEyL7mTsVJ7H2GysG1UnM3gKPRw2ERN+ZjHcGMpZasiobxHmMZ9wyHv+ZyFzoP9Wh/VvFQfYQXXPTaMfxxx2n2WkjhDo6OsJzZrW8Hwgd81khO0bjnJdUPX12WezmKgVjY0o98ohSiX72OfaqOdxGP363459zFbduPnbLdFFJSatxPU4snme70M6ZIOQTIjaFhDDelPx+f0wBMh/NA7fF9NoxUHNAVZaVqccff1z5/f64NzmnN0MnpXl6e3tVZXl5VA3LWjQv4Bqii7R7i1B8EsWXUHwRxTTt+XcY90I22ghHfS5K0DLNjS09YdzraNXS8niTHeWh336/P64QCgQC4XqIRhFstaReE3o+lsc5WU+fXRb7fJMNqXignJb1mFo6VcvOjuV5Mx1/IS7LxovjTOVc2BaAr02fCMx2wWpBEGIjYlNICTsBovdGh0jvm5WQM4q+msrKpPqpR9hC7NI8TsSP0et26aWXqmJvUYTNk0PljYyxmrpX1KqYugfNW2kWoS2gKkF9xDQnM0Pj6cXiK0E9Edre3N8+Vla5VTcmq0Ll+vlaQ2yPczJe5rh960uw7NH9wQdKvfCCs3Pv1LN5xRVXaNvZddj5fOzjLwQsxaCLGepKRQv07u7utMaJZbusi5B/FOKXyFxGxKaQMrEEiNVr5thBXeisNQi+jo6OhGxwWponXADeZrsWULWVlWp0dDR8bLGEqe4hjNdac61pX/qPcXn9biITrMzbnApRAiCeJ9cYa2nVuShijtC8r40+X9wySE4/pJ3GerI00gP1zDMBVVOjVGOjUh9+6Oz8OynrMTo6qsoryjVxZTxPJ6D4SuF7wGxLP7lUezMW6by5W3q2vxO6rhajFZcnd0tVCZlFYnszj4hNwZWbgN0YuudtxYoV6sSSkpiFymeiefL0BJfu7m7H+3cqaqyWla22048hnjDVvbLG8QKMt/KcZRKj+uv9IWH5qOHDLm5vdyJFuBNPrjHW0q7UkvnYOzs7Ez7/djjuW2/q0f1f/7VTVVZqm/3nfwYdh1A4KesRbhhQj9bdZom1Z7XQcFL6KZWuQtkkyrN5CeGQCONSvpvXtpCfSGxvdhCxOcFxq/2g07HjijzD4+rKSsfC16mo0WsBOunn7lTATsa6X3oP433O9THsPJ9O9jOH8SX0RIqs6x+uZ+FM1K5cudL1cjiOPJuriSiF893vaptNnvxCxHzF8kA4LeuRrfIf2STh0kRpbl/nJhGe7a+EjsMiVCLdYkKWZnMbie3NHonotSKEgqKvr4+2tjZOHxnhaeAI8DRw+t69tLW10dfX59rYPwk9P9tme/35fYbHb+zfz+DgoKP91dTUALDb5nX9+ZNPPtnRdhs2bODBBx90ZPM8YAfwJYiYx/uAD4BJwG3ANqANON203T+ExlkCPAkEDfsYA9YCM4E7gKHhYQYHBxkcHGTPyAgrAPM/ZhFws1LhbVtbW9m2bRt7KyocHfutt97KrMZGenp6GBgY4P7772dgYIBgMGjzTnuamppo9PlY4/EwZnptDG1eJpUCDaEn39R+1dTUMGPGw8ARPvzw4zDvIrgZuBL2TrG/PltbW9nzyh76+/vZvHkz/f39DL08RGtra1LbFRL79u3THky32UB//hW0k2M4F7mO1+tl4/qNqL8oeBA4FbgMqAeKQ78v055vv649qWs5Hn19fTSe1MjcuXNZsmQJc+fOpfGkxpQ+RwV3GRwcZOS1ETgfyw9Odb5i+NVhx/cdIU1kQPwmhHg2kyed7Qetxu536L0zejZJwKsSCARUVXm5pYdRT0QpLipSW7ZsiV3CB205vwXUtJISRzZXo3UfMme3V5WXq7a2tvDfU4mdra6/X8+K17PS9VqYxkzxZIqsj46OqprKypj1OxvRMuY/ZXE8yXq87ZLIwglZFnGCo6OjmgfixH/XTGwYKCgPRDY8YAmVhzoBRS15N8dOC/e7HRogS7P5gdOqFfngzc83xLM5QUnEM5YoAwMDUWM3AY3AGrD0cOneu78PPa4LvZaIV2UMzcO4kEjP4aLQ858YG+Pyyy/nK5dfziNKRXkiFwGPAOuAc4GjR44wCbg1hs11wBvAYSAQeu2aa66hv7+fvX/7G1dffTUAVwFHwXa+bwm9/27gTOBrwHlo3sZtQCvjnseamhrHnlzj/H3kIx9h049+xKMeD4s8nqhjfxS4E/gb8D/ARab5SdbjrXtWn6+r4zygNHRsj00CPg+cDAyDZ6sHXoQNd23gqaee0jwQ/7Aeio7Bq5+F1+aEJyyfPRDZ8oA1NTXhm+HDs8tjfUEPAicAV6B5OV+Hyy65DK/Xm1a73OSUU07RHsTx3oa9vC4QDAZZ/q3lqFMVXEqEN1VdqtLqTRUSI/x5+KbNBnnkzS9kRGwWEPqHbdxl7QQ/lPv6+rjskkuixvaiCZlH0ISNUcQsDD1/FbA49LgGmFlfT1NTk6P9Dg4Osv/tt7kVeAIiRI0u2J4B5ivFti1b+Oa11/KYzXYA/xe4OGTPoxY2LwrZ+b3Q9j9hXCAvXryY5uZmvF5veBn596HX4s33icBDQAtQC/wvmtAcA9Z6POE5ibc8fStwYmkpe/fujVgCtxN++rEvBK4P7f9BNNE9NfR7u1K0ADe0J37jbG1t5a97tGXrrq4uli1bRknpifBLNNV+L1S8U8HWrVtpbW0dv+5mvg5n/Kf2eOjz4wOmQTRkAj28ZGTKCFyJo9AAt9CXmnkxJOyHgVG031uAF9FiQhoILzlv6d6SVyIpG2JClmbzh3hfuDy7PNQ3OL/vCOlBxGaWCQaDScXQWb0vGc9YvPG/9a1vsXjxYmYdOGA5diuaoPk9kSLvF2hrdyvQPGqfAn7v8bBuwwbHXhVddJyJdv+8G9gM9AMvhfZdFNrH0PAwM2fODHsSjdvpYms+mtj6t9D4Zpt1cXZS6PX/wVoge71e7ty4kf8J/R13vg12vg74CYlbj0fzuobmRB/XykP7GeBx4ODhw3zta19j7ty5zGoc95zpwm/lypWA5vXV52gQ2IO9B1b3eA8MDMS9Fs3XHcCBAwdYedNN3HfffRw8cBDQYloB9v9tP9++7jr6+voiRcMF34V/OwM+e+v44HnogUiXByyRzwX9y0bd+3VwL2Ghz5vAJcDHQxsWAU3knUjKhphwGgubb1+MCpFYX7iMKyv55M0vSDKwrJ8QEylmM9mscbv39fT0pNR+MOb4aEXIY5UMqq6sVF1dXWrVqlVqenl5xPuTaQeox6PpJYfixTJ2dXVZHr85ttTYp/yXRNbBDMeChmIc7WKzAoGAqigrs81aN8aKBkx2xpqTQCCgKsvLo4rkg0UfdIsuP1aZ4psdzl9FWVnMa9HquqgKnWergvKAugrCGfNbtmxxVDczn+IJ01F4PNl6gYFAQK1cuVJ7zxKLzNzV+Ru/5rQElltIQfn8YyJWo8g2UvooD3DaGSfR93V0dCTVfjDu+Bi6B0F0ZyHT2G7V+Wz0+dQck1g0/xjraFolrqy2EFtx+6XbiEEd/WZ0G/atKfUkIF3gbgqNe8EFF0R1BTKPuwtNAHehtZ10mvQ1Ojqqir3eCAFsFtt28zcnxrUYqxC+VSelHqK7Sn2kqEhdcskl0aLhslrFydPzMunC7eSEVJNSClkkZVJMOG0okE9fjCYCUqYqs4jYzHGSzRp3+r7u7u6E2g86Hh8tS7vBJCKqysttvX+p/uPrxbqnhgSdnVe12OuN6BBkPn4rsWVVH7OstDRcjzOWvcbMcatx9NaUdvux82CbM9KdCkVdPITFRkgIDoJ6GNQJaC0yj1nMn97j3eq1BR6PavT54hbCN3pwjULeqkPT1JKpqrxS93wvVzCqppb8OO+EplLuijs36gUWukjKpJjItDdVEPINEZs5jtOi2OYbVCLvS+ZD2en4/pAI0j2Gfr8/aiw3C8t3dnaqIoOAsvJEFhHZScR4/H6/3za8QO9T7g2NU1Ze5shGv98fMVd6B6F2k51djHs+nXiwzefA6RK47jkLe9paUUVToksdTUXrlW6eP3PrTf14Vhve66TEVbz2mfNBTZqkbd/Z2aluvvlJBUodf/yYeuuthC+NrOOmuHNLuIpIcg9ZmhUEe0Rs5jjJ1FNM5X2u20VsD2yyIQLx7LoFzUMXIWAZ77/ugbjhB2axqvdw32oSQrFs7O3tVQ11dWoS9l2G9CXkSdh7ZK3mz9iKMkgKns3Pa8+bRa4xTABQJ06dGnXOrTyxTq8Lp/ZSr920P/wwoM48U3t59eqELoucwS1x5+aSvIgk95ClWUGwRsRmjpMJz2Y67doUQzg66T0+0+dL6APbGMfYgBZb2MW4Jy1i7BgJUJ2dnVGePuNyt1kIWY1jFNJr0YSqvlz9KJonUI8xXZagUIzah8ejBkPHHFOw+nzK7/eHvbi1vlo1aZL9e+aD+kiRR61atSrKQ2u1BH630+PAuSeW5vFj37pVe7msTKmjRx1fFlnHKEI6OztTFndux1uKSBIEIZ2I2MxxzN4rJx6vVN7nll3Gjjh2MaBOBatxydupXYkkClmhe452EJmBHksIWc6PQcT1hObELGJLQS12KLys+pZbhSHYeWUrTZn/U48/3vE8Gc/5MayXwI3Z+3bXxVTGBbcjz+aScS9dIKDUySdrm2zY4PiyyCpWnsO6+jrV2dmZtLgr9HhLQRAKCxGbeYBduz/H2ehJZpunZBfETZ5xuhQfb6nayi7Hy7k2y4xOhbBRCMV7v+4JbEHzdDaYRKej/WEdzxoIBMKt+k5Fa3lpHFcXuGfbvNblcJ70cx5LzOvHaRszOyVy33bloFp0uz8XKeh/9CNts/p6pY4dc3xZZIV0tjGUeEtBEPIFEZt5gpX3yknWeLLvS7ddSjkXdHOIveRthS68kvVs6l68+djX15xUiuIK63HMQtqYDNND9PLzLjSPX6ze7pNATQuJSbMAN3tS9aSdzSH7vwjqOIv9GksS9eJsnnp7e8N1Nu3EfBfRHtxir1dRi2IliqUoFqO4wNoTq5eDOgstNtY3Yzyc4v33laqqUmrKFKV+8xvHl0TGcSNjPB5WXtPK6krV09Pj4pEIgiCkhojNPCLZuKp0x2OlYlejz2cfY4gWJzkYRxgmNbYpjMDqGDo6OqIEUy3jhcf5ir1gMAvp/tB7dmGfga2LUKslcLMH9HhQ08vLw/uNJ9wHQ+LPaUmiiOcthL45ftNOpK5fvz48p93d3dGeuC+Oz2vEF5aQ+LULpRgcVGr/fseXQ8zrJF3/G27HVdrR3d2tKqZXRMyfk8LugiAImULEppBVOjo6Ynq2ekk+c95pGIGVd7ayrMyyDFE4Q/v02MuV5phWPRlmB7GXy9cQ7RGchLb8bWWHLsLihSQ4jY/cZCFy58+fHyXEko0JtvLEAeodxj2xugdZpXDunV4fbpXcssLtIu52x5CuZXpBEAS3ELEppEQqniFdsJxqIbCsMr+T8QDFW+a3Kr10G6jJxE5ymeTAe6SP3QLqWl0c4iyWFFCrQFURe2m9KuTdjOfZXJ3AfsPL3qa/rVpSJhMTbLxm1q9f70gExzr3zz2X8GXhesktK9Lt2czEMr0TGySLXRCEeIjYnACk64aQqmfIrTJF8bA7fquM8V7DscQtWG9RoN5MR0eHFqsYEqjnOhzbiRfUaIff71cVZWVqDtbdfZzudz3jxeadFJdPNSY4laoJwaBSX/iCtvngoMOLwea8O91nIqQ7YzxTy/R2JNuXXRCEiYeIzQInliBMRYS64Rkyt3G06z8eqwB7Kpi9gXoSjx6TmWpBfPMcdYXeF7edZlGRAtT/DW3/E6JLLxnt0JN1MIxv7PyjL4fHy/yuBfUesbv62BWXT+XLTCpVE77+dc28+fOd7y/dNWitji0dGeOZWKa3Q5bvBUFIBBGbBUwsQQjaEmwyXslYnqFjaB7KyrIy9dhjj4ULiFuJEPNN36obzSQSq7OZCHa9xR0XJg+JESuxZTdHvaAqQ++PilMNiautW7eqE0pK1HGmuWgkMmNct+NcbOJKDXPY0dGhWlpaFFgLen3bmtDvXVjHUDoVYokK0GQ9pC+9pFRRkTYlf/hDcudd/9Ez+O8N7b+rq8vZgHFIV4eebHk2c2H5XhCE/ELEZoESSxDqWc9Oe3CbsfMMmcWiOQ7TLGatllD1G34XmmhtTKCDUKICx3wcehLPO8T27hmX9e08x7FKLwVA/YvF/Ojiqre31/r8MJ40pcdslmC9bN6CFu/5uMFePYPcnPldF5rvpxn3glaattGFrhOvbrLhFcl6SC+5RDv0r37V0ea2NVDNX3RqKitd89ClI5QlW4Xds718LwhC/iFis0CxE4TGeo/JxqtZeYaMy+Brsa7naBfz50bh+UQEjn7j7+rqUlUVFeElbd2z+TTOlvXjeY6dLMVzBooa7XF3d7ejclB1jHsj19qMb2wLqT9etWqVKvZ61cUhIXoWWg1Oc7kjXcTuIlroriG2kEgmvCJVIfb732vme71KDQ3F3jYQCCi/36+ml5WF59iq7abbyULpIhuF3bO5fC8IQn4iYrNAsVsq1AXV3Vi3YXSyTGoX67gAbNsYxhKzdkuoPT09jkRIIgLHal96aSFzb3Erb1dxUZHaunVr3CQTp+0yWRrphXJav/JEnInZzYbHntBx6sfipH+5WeiWYO9tTibxxq3yQxdeqO3qmmvst7Fazv4imqc33clC6SRdy/R2iGdTEIREEbFZoNh5NtuxWN5mPBbQyTKpefnb6BE0Po4pZCx6iRuFZXd3tyMRkojAsROlVkXT9XjKg2i1Jz9psiNehyK9iHrM8kmlhpi30A165cqVjkTkyU7nmeiC7u04F6pWY9rF0CaaeONm+aFf/lLbzcc/rpSVLrRMaPk8qqgoues118hkCSLpyy4IQqKI2CxQrOIh9eXCWLGATm+uxuXv1QbxshmHQiZOzJ9TEeJU4Pj9/piiVK+dCahpJSVRyVPGuEa997uT4/SEtrdMyLkkeulRF5vxjqcIrRaoXXb5ArRapXrClnFMx18IEjx3Tnvdb9682fXyQ2NjSj38sFIffmj9v2Cb0LIo9et1IiJ92QVBSAQRmwWMURDqS8S2N/eQOEmkpqV5CTQVz6ZOoiLEqcBxKuLuZjzm8sTSUtu4xhY0cbrLZjy9xWZbW5uqqohsJTip1CQ0V497Nv1+v6qprIxZokhP3jkV67jSFsbjK63iR+PF7Vq1rnRy7hLxbGay/FDMZd+lheHZzAaZXr4XBCF/SUSvFSHkFa2trWzbto3n6+poAl4FVgDmE1kE3AwMAY8A6zZswOv1Ohr/r3v28Nhjj/ERj4fbgL8HGoE1wJhp+zFgrcfDTJ+PYDDI/fffz8DAAMFgMLzN4OAge0ZG7O1UiqHhYX7wgx9w//3387e//Q2A3TY2mp+fbbOd/vyJwHalmAMcPHyYu4HPAcbZKEKbxwDQYXGc24CL9cfbtvG3t96ipqKCqVOnQj0ErgU+bnjDGHh2eahvqKe5uZnv//CHPAosBJ4GjoR+LwIeBeqBktCxbQOeB84DSkO/f4F2518B7Pb56OzsjJqLK9DOtXkfXwrt4w7TMYfPXX09TU1NUfMXDAYJBoNMLyvjNos5GQNuA6rLy2lqamLfvn1A/POhb5cIx47Bn/40/nd4jOkWGzfApBK41cbmWMc80WltbWXPK3vo7+9n8+bN9Pf3M/TyEK2trdk2TRCEfCYD4jchxLPpjEAg4DgWsL29PeHxdc+R7mVbw/hyvZXHrTJGfU+nnkrjT7HXa5+9HfKEOk286Q/9vdrkDYxlh3GZ3HjsUSEA+nvqUTSjWILiCuulx46OjqjY2jrGC84bs9D1clGb0eJLQfPkRtX89HhUD7HLU+l/29UAtfJaWSX5mN+vn3vQMvnT5dncvVspn0+p2lqlPvgg8vq0TWj5vLYv8/WarWx0aQGZe8g5EYTUSOsy+s6dO1VLS4uqqalRgHrggQciXl+6dGmUcDjnnHPSYvxEJ53LlrpA7IojZE6cOlUTZ1ZCLHRTt8p014VUP+PL03cb3v8pYogFNAEdjtm0a4tI5NLxozhbXl22bFlEB59YSUHG7kDG7csryy0FTXd3d9QS/OTQsniiMYa9vb0RXwj0+d/FeCvLq9BqjK5F60Jk3K9dgXWr+FrL96PVd9XF/+joaNJtKmPxwQea0ASl7r1Xe85JQkt5ZXlKbTfdQlpA5h5yTgQhddIqNnfs2KFuueWW8I3OSmxedNFFat++feGft99+Oy3GT3RS6UEdD6NANIpDf+hH9xJWV1TEjBOsraxU7733nq0XjpCImU50DOVZaAkzZo+n8e/KsjLLhB09QardYPN34gjHsy3GP7GkxFagBhj3Oi4PiTo9Ez6W98zsUVm1alVSXxoCgYCqKi+PWb/TKLaNnaD8fn/CpY7C7w/Np7kDkd/vD2f0z0H7EuGWR3HdOs2Mj31M65+ulLOElmx7r6QFZO4h50QQ3CFjCUJ2YnPhwoVJjyliMzHcKqBuxomQra6sdCSSqioqVEdHh6UXTm/FqGfOW70fCLdlPN4kPj+q78O0jF9seGz2xlp5TM82PG9VQqnLZJtVvc5GxjsBJSL0ddFom4VuM5Zjz7YD4ZrKmLrndbqpn7tx3lP1KB4+rNQJJ2i77Osbf97KQ1VZVana29uzvjQqLSBzDzknguAeWU8QGhgYYPr06Zx66qlcddVVvPnmm+nYjUBkwpAxoWS3z8e2bduSDuz3er3cuXEjjwCLPJ7IpBaPh0eAy776VSB+QkjdW2/x7//+75w4dSrzge3AucDU0O+HgBbgBiBo8f4zgV/s2AFEJxh5Qr/HlGLdunWceeaZAHweWBt6/WIiE2bORku4Mc7X814vLcCDJtseBOYDNxps6wPagNNN454een4740lPg4ODNrMzjtfr5Yf33MMOjycquWch8IhSLPuXf4l6n+OEHKvnbJJ0EhkzCAwAumWfOXAgwvaL0Oa/s7OTl4ZSSzIpKYGrr9Ye3367pnIhMqGlvb2dmspK9v9tPxs2bGDu3LnMamykr68v6f2mwuDgICOvjcD5WGbGqfMVw686u0YEd5BzIgjZwXWxefHFF/Pzn/+cJ598kjvvvJNnnnmGz33uc4yOjlpuPzo6yuHDhyN+hMTQM8iNGaSp3tz1cWMJ2YULFwL2WeN/CP2+GC1R++DRo9xC7Mx540e8Pu7fA2NjY3iAuUSKsblogubtAwe44YYbePbZZwH4E3AXmojdTqSA/A2aEKqqqGDFihX84z/+I6PBIDfZ2HYLsBfYiSawrrcZdzvjovm00PudZl7rc73b57PMQl+1alWUcKqpqYmYJzP68zVWz9XUYIXTMV8CZqHNfy/a8V8GfABMYVyot3g83PfjH9uMlhjXXgvHHQe//S0MDIw/7/V6OXDgABs3buTs/fsjvwDs3UtbW1tWBGfMjHnD88lk5wvJIedEELJEKi5UiF5GN/P666+ryZMn2y6h6fFq5h9ZRk+cdMWn2Y0ba6m9h+iEEnDe4cYYc3gdkd1yzMvMen3Mf7JY/r6NyGQkPc5wDdHL6w1EL+UbbTuL8RjNeMvM+naJJmcFAoGIuMeIXuam0Ii4oQ5Exmw6Wd6PN2YLqDLGwyGuspjHRsM8ul3T8uqrNXNuvtnCZpvrI1vtKaUFZO4h50QQ3COrMZtWzJo1S91+++2Wr33wwQfq0KFD4Z/h4WERm0ngVj/qZPbr8XgiYiCtSgXd7VCkPUpkgk8vqH9w+N6vmUTGWUTGbupCqMPCvnBiD/axo5MNWefxRPNZOC+kbyRR4WQXs6vHwa4h8TheuzF1Aa8nWfXYzKPx3LndrWd4WKnf/jbyuUwWk08EaQGZe8g5EQT3yCmx+dZbb6ni4mL1s5/9zNGYkiAUH6ue4271o06G3t5eVRNKFjKKEaNY0jvcxOwrbhCFMxlPtpnlUOCtMDxnbOO5C9QOUJ1oJYG8Meyw8gbOB1WLoSWlA2EDJDXvyQgnqy8a1eXlUbVPE0nSsRpzZn29Wr16tSI0p43E7141aGGv2yTSUjPTSAvI3EPOiSC4Q1rF5pEjR9Szzz6rnn32WQWou+66Sz377LPq1VdfVUeOHFHXX3+9euqpp9TQ0JDq7+9Xc+bMUXV1derw4cOuGz8RsRIBxV6vOttC3P0SzcNWXVmpRkdH02rX6OioqqqoUKfEEGO6ALQsLk7kUvVBw2tOBZ7fcOy6ELIqtTSJyALqVmPpHlbdm9eJoa6m12u7zDwf1HFer+ru7k5qHhPtRa5/6fD7/crv90eEOqQaVmH1ft2+HQ7PyRyS8/A64Y03lNq3L3c9mzrSAtIZmSxTJedEEFInrWIzHPNi+lm6dKl677331Lx581RlZaWaPHmymjFjhlq6dKl67bXX0mL8RMOq2LZV6SCrsjw1lZUZ8XDGW2buIjrGT/e42XnTuru71QlTp9qWBpoPqppxb2R/6L1rSazUklHMGZfdjbGkunCxqu2pL8P39PQkPYdOhVNnZ2dWwiZ0+zrjnGfjPKbDpv/4D6WKi5X6t39Lb71Zt8h2vc9cJxtF1uWcCEJqZGwZPR2I2LQmbiwf4x1drARWvELjbqEnt8QTS+vXr7f8kLe7Aejjmr2i+tJ2j2EfmxlP+ImVVGRcLjfbt5LI7kb9JhHV3t7uSnca8/E66cJTHVoez0bYhH4dzsGZZ7Ozs9PxsSdys9+5U9tVcbHm3UxXvVkh/UiRdUHIT0RsFiBOPV41MQRWJjw8uhiJ19c8mSXdyvLyqAz3qtBvY4LSJsPriRQnN8dsWsVwGpdkkxVL+vva29sjYl1172RHR0dM4VRZXp7Vc6x7sKcSu43nTJ/P1o5UPVljY0rNmaPt8sYbx8fMhfaUgnOkyLog5C8iNgsQp7F8uRC7li4vky5y5qC1y3wY1PdDYrDIJEL1v+PN12qil8G7dHsZX24PgHoc1CmgppWUqMcffzypG6BZEEVlcofmqKOjw1I46R7eu4ku55Tpc6wnIFnG4MY4z255srZv1w67tFSpd97RnpOl0fxCShEJQv6S9Q5Cgvs4LbYNDrq/pLlgcbq6GrW2ttLb28s+n4/VwJeBa9GKwY8Bk4CZwN1oHX8g/nytNtj3hNeLAr4W+nsAuA14H6gEvohWzPzQkSPMmzePuqqqhIqF9/X10dbWxuyREWqBBUR3LNquFC3Ati1b+MvLL0cV6j948CCTgKuBJWhF1WehdTWC9JzjYDDIwMAA999/PwMDAxw7doyysjLWb9zIFVdcwTNlZY7PczAYZPm3lqNOVXApUA8Ua7/VpQpOhfbr2gkGg1HvNbNgAZx2Ghw+DD/6kfac1+ulubmZyy+/nObmZrxer2vzILiPFFkXhImBRymlsm2EkcOHDzNt2jQOHTpEaWlpts3JGYLBILMaGzl97162KxXR6WYMra3hY0VFfDg2xtNowsXM02hiwO/34/V62bdvHzU1NTQ1NaXlphwMBhkcHHR9P9u2beOSr3yF+WjdfWajicfbgEeBbWjzUYfWmvIhsJyvJ7xeRg2ipqGujn/++tc55ZRTeOmll/jJPffw6t69gNalqAVYYdjfrcAOYFtvb1wBHT5/IyO0o7XTjHee+vv7aW5uDj/f19dH2+LFtsfdHTpmq/cmS19fH9cvX86ekZHwc8Ux5i3eeR4YGGDu3LlwJZrQNDMM3Btpf6zr6Gc/g2XLoLoahoa0DkNC/pDM9SAIQm6QkF5Lu581QWQZ3Z54y9NbtmxRNZWVMePopk2dqmoqKqLiBPMlrs1polSAyFqbVsXJz7ZZwtbnYnR0VFVXVKip2MfBzid2bKKOMeZWT2BKpC5kvOOej1bA/lO4V2rIrvqBMdwg0cQkPRyEm22WTW+OPPZ4zQpGR5Wqr1fquOOUGhxM+ZCFDCNF1gUhf5GYzQImXhJErI4y+vZ2cYL5IDgd11QM/d2LlpVunC+ruqRWCTbGMl+pxMEGAgG1cuVKBVp9Sr/DMY2Z3E6PG1AdHR0pz3Mioj6RxKREYvTsxK75et21S6u5KeQnUmRdEPITEZsFTrwkCCtBOhVUOdnNVHcDx0XPDc/p5YtWrlyp1q9f71g8hr1wTvZn053G6lw0oGXRxyrLVBLatru7W/X396trrrnGkR1n4o5n0yxuA0T2mDeXhHKamOTUkxUuAZXn16vgDCmyLgj5h4hNQQUCAeX3+1VFWZmaA+oJciNTPVUS9WyahUkiHXpS9WzaeuZC77HsphR6vgdtmb/Y6424AcezY1MMexLBOE9WTQIaiBT1ibSEdOLJSrYr0B/+oJVFEvIPqSQgCPmFZKMLeL1evF4vbx04wF3A/tDz2c5UT5WmpiYafT7WeDyMmV4bA9agJcl8Ei3RZpHHwyPAug0b8Hq9jrP69USUhro6pobGtdrfbcBMn4+mpqaI14LBINcvX06LUmzHlHGOlmxUCjwGkZncaAlORcDvgQuDQZ4G3gFqQ/uzsmMtWib+V0PPpXoe9XnaBLQBp6PN55HQ79ND270U+r3b9L5Y6NUK6t6vg3tDxt8Lvg/Gs9h1+51er0rBl74EZ5wBfr/jwxRyCKkkIAiFy4QXm+ayLk5KruQLxhu2LgGciKxcxuv1cufGjTyCJiSNAmgh8AiwFzgR6zI88cTqWo+HmfX1YfH4z1//OkeBh4EvESm4voSWjb5u48aoG+Pg4CB7RkZYQfQ/WRFaVvshIBAaYzPQjybeFgLXowlSvTTSNOAHaFnnC012LAod9zrgz6F9JHse9f+HvXv3Ul1RwW0hO7YTKZgfBOYD9wEfWsxbPFpbW9nzyp6I0k5DLw+Fz1MiXwoAPB446STtudtvT+SIBUEQhLSTAU9rQmRyGT1epmu+Y1yKDISWQgslBs7q3NVWVqrly5er9evXq66uLtulOCdF563GN/d0ry4vt71WzMv15pjHgzGWxvtjLJn3gqo12TEz9Hyq59HqmO3sMC5lz8H9VqjJ9Dt/9VWlJk3SNvvtb10zRRAEQbBAYjYd4DTTNZ8x37D1UkALSKzjS65ibPtYlWA5p1hZ/fFK/nzmM59R69atU6Ojo7bjG4W+VczjCbpgraiIElTxSiPpQrURLUbzoOk86klFicS+WR1zZxw79DjNyrKytFw7yXSi+qd/0sxbvNh1cwRBEAQDIjbjELesS555+WJhvmF3WXnG8rh/dCpfGqwSEpzUs9Q9nLEErT7O2QaBv5boMkwnTp2qCNlr7u0ez6NoFtgz6+st21zGE952xxzLw2q0w+/3OzpXySSAJNrvfPduzTyPR6n//V9HZuUEkhwjCEK+IWIzDslmuuYrVjfs6spK1d7eHvfGlss3wXR8aXB6bdztQNB2d3erSWge0R6D6DSK4vmh5/U+4/pPsddrW5xfP67R0dHwufH7/WrVqlWW+9CL2Nudb7tj1kMv4tmRrGh0GrKS6DX4pS9pZl55Zdyhc4JCD+cRBKEwEbEZh0TK3+QKqYo+t7xKuXQT9Pv9CrTl3n5Qo9jUgkzgS0MidTzjCS5dxO0idrzsfFBV5eXK7/eHz09PT4/jJeTe3l7VUFcXFrbGfVgt4ZvPYaxj1kMvoko0JRB6kemQlV//WjP/5JO1DkO5zEQI5xEEoTARsRmHfPNsZkP05cpN0E4k9/b2qullZRFzMtkkqhqS+NKQaB3PWNeKLuJ24Gw52tgxSD/GeEvI+nmaY7EPY4xurHMY75jXEJ0c5TT0IlshKw89lPtCcyKF8wiCUHiI2IxDMpmu2SIboi9XboJ2IrujoyNiTpZbiKFatD7hViIuFnGvDcbbNMbzgusiLpFEG/OcxvJIG89Tl2kfiVQfcPL/0OjzRXhenZ77fPtil0lkbgRByGdEbDogmUzXTJMu0RdvST0XboKxRDYQ7m3eE7Ilqt87mlfvLFAzfb6wqHISSmB7bYTG7LWYC7/fb5tsdC7OPJuJzqnxPPWb9mH+226fK1euVP39/aq7uzst/w9OwxLa29uTGj8eH36o1AsvpGXolMnHcB5BEAQdEZsOSTTTNdOkQ/Q5WZLP9k3QSUZ4HVqMZg0xElhC2+nezURCESyvDZPQ1AX/9PJy27E7OzsVaL3pY9mpx1UmMqfG82T2ZMYrn3TYYKvRY+z2/4PTaxhw/f/uxReVamxUqrJSqffec3VoV8iFL3WCIAjJImIzAXI529pt0ed0ST7bN0Gn+1+Pc48hxI9dNM9VQ11dxPs/iha/aPT6Qey4yPb2dgXjdTXteqGvSXBOA4GAWr9+vQItMz5AZIym0/JJO0z29vT0uPr/4DQsoSUkbN38//vwQ6UaGrTd/fCHrg3rGvkUziMIgmBGxGaB4KboS2RJPts3Qaci+xqce+/mODhuHTtRPp9ob2BVeXnMOa2trFSA2glqGqgppjFmooUCJCK2LL3TIbFpzD6fFLLZSexpOs9rb2+vdUY742EJ6foC84MfaIc9c6YmPnONfAjnEQRBsELEZoHgpuhLVLhm8ybo1NY7cea9K0IrP+TkuOOJ8hbGyxTppZecFF9f4PGE40vngFoN6lG08kzzcd7uMV53o67QmHp2ugeizqG+ba+NvenwWLe3t0dntBtsSFdoxrvvKlVRoR1iroY+5no4jyAIghUiNgsIt0SfU2/hypUrI2o9ZuMmGE9ktzDutavFPhZS3y6RUIRERHkiyS/6OVxDdBehWD3WLefF5niN3Y2MrTftvKDx5sJN9Hm9m/E6qAGs59VtvvtdbTd/93dKjY25Prwr5HI4jyAIghUiNgsMNzwfiSRqGJeJk+mz7Qa6yG5Biz+8N/S7Bc0LaE5mMS/R6kvel156qWPxqJRzUd7V1RWOmXQyttU5rCwrU52dna6XEVq/fn1UiaT+/n61cuVKBc69vG6SzdCMt99W6qMf1Xa3Y4frwwuCIExIRGwWIG50EIp1s58PqiQkRHKli0lHR4cq9nojBFqx16s6OjrCx9Tf36/a29tVTSg2Uv+praxUPT09CYscp4JO318RqE+C+qXJU2c1dqrnMNWEsWzH4mYzNOO667RDXb48bbsQBEGYUIjYFCyxu9nr/bl7XBYgqYirRIvZx9pXIiInniA7OyTozkZbwo8QuGgxk+kSUG4kjGU7ISVb8Ymvv661sRQEQRDcQcSmYIvVzX4SqLUuL62m0mIzHcXsExE5doJMjwH9FNbljuZbjO1mLJ5bnslsJ6RIfKIgCEL+I2JTiInxZq/H8blZwD3VFpvpqvOZiMixEmTVoaXzWuzbQM4HVXHCCWp0dDQtPe3d8kxOZMG3f79Se/dm2wpBEIT8RsSm4JhEkk6cCBM3vJLZ7mBkPBajIOvq6gqLxnjz1dnZmbae9tn2TOYzP/mJUscfr9SyZdm2RBAEIb9JRK95lFKKHOLw4cNMmzaNQ4cOUVpamm1zCp5gMMisxkZO37uX7UpRZHhtDDgHeN7rZTQYDD/f6PNx58aNtLa2Ro03MDDA3LlzeRo412J/TwPnAf39/TQ3N1va5MYY6UC3C+AIMNVimyNAKVBRVsacAwfYDlFzusjjYbfPx0tDQ3i93qRsCQaDDA4Osm/fPmpqamhqakp6rFwgU8fzm9/AuefCpEnwyitQX+/6LgRBECYEiei1opivCgWP1+vlzo0beQRNBD2NJpieRhOavwMuDAYjnj99717a2tro6+uLGm/fvn0AzLbZ32zTdlY0NTXR6POxxuNhzPTaGLDW42FmfT1NTU1OD9MVmpqaqKqoAGC3zTb6828dOMAKov/BioCblWJoeJjBwcGkbfF6vTQ3N3P55ZfT3Nyc10Kzr6+PxpMamTt3LkuWLGHu3Lk0ntRoeX2lyjnnwNy5EAjAXXe5PrwgCIJggYjNPCIYDDIwMMD999/PwMAAQYO3MRVaW1vZtm0bz9fVcR6aZ+48NI9mC/Agmodxauj3dqVoAW5ob4+yoaamBogvxvTtrIglgBd5PDwCrNuwIeMCy+v18oMf/pBJwG1gKYTXAFXl5UBqgnui0NfXR1tbGyNTRuBK4GbgStg7xf4LTarcdJP2+5574O23XR9eEARBMJP2Rf0EkZhNa9KRbGLGGKOYSMFy8xhu1XLM1djEjo4OBdGF5PWC852dnWlJcCo0AoGA8s3wKT6G4jsoVht+voPyfMyj6hvcr/s5NqbUmWdqp6Oz09WhBUEQJgyJ6DXxbOYBuvfn9JERx8vZyWBcmq2qqgIS98656ZVsbW3lr3v20N/fz+bNm+nv7+eloSHLWNFMcscdd9DT08P/VFZGeIJfqK9n27Zt3HLLLTkZBpBrDA4OMvLaCJyPZbyBOl8x/Gpq4QZWeDxw443a4+9/H95919XhBUEQBBMiNnOcYDDI9cuX06IU23G+nJ0qqSyH2y3L7/b52LZtW0JiMVdjE9va2hjet89SCOdqGECuEf6iMt1mg+mm7Vxk8WI4+WQ4fBieftr14QVBEAQDko2e42QrMztelrqTjOpCy5hOlL6+Pq5fvpw9IyPh52bW17Nuw4ase2dzgXB2/5WAVVb4MHBv+qoOPP00zJgBdXWuDy0IglDwJKLXJmXIJiFJ3MjuTgbdO9fW1sYij4eblWI2mkdzbcg7ty2Od073Sk5UWltbWbhw4YQW3LFoamrCN8PH3l17UZeqqBpRnl0efA2+tIUbzJmTlmEFQRAEE7KMnuO4kd2dLG4uh09UcjUMIBfwer1sXL8RXgTPVo/myRwFhkN/vwgb7toAkJYqDEZeeAHGzAG2giAIgivIMnqO48Zyths2iHdOSBd9fX0s/9ZyLVkoRH1DfVhomkMRYjUVSIbLLoOtW+Ghh2DBAleGFARBKHikqHsBkQvJJuKdE9JJa2sre16JrDow9PIQQEaqMDQ0aL/XrtVqUwmCIAjuIp7NPEGSTYSJRNijPzKStpafOvv2QWMjHDsGv/oVTPCKVIIgCI5IRK+J2MwjZDlbmChkugrD178O/+//wfz58MgjKQ8nCIJQ8Eg2eoEy0bO7M0G+CPpM2Zmt+ch0FYaODvjxj+HRR+GPf4S/+ztXhhUEQRCQmE2hQEmmj3xfXx+zGhuZO3cuS5YsYe7cucxqbExLf+5UyJSd2ZyPTFdhOOUUaGvTHt9xhytDCoIgCDppbJuZFNIbXUiVZPrI9/b2Ko/HoxaEepofCf1e4PEoj8eT9X7sOpmyM9vzEQgEVKPPpxZ4PCpo6i0fDNkxs97dvum/+522i/p6pd57z7VhBUEQCpJE9JrEbAoFhd5HvkUpVqAtt/4B6ECL8+vs7OSWW26JWArOZDJKKmTKzlyZj/C5BOumAmmo9frww3DhhXDcca4OKwiCUHBI6SNhQmLVR/5x4GtoQhNg1apVUUvBg4OD7BkZYQXR/xBFaEJnaHiYwcHB9B9EDDJlZ67MRzaaCixYIEJTEATBbURsCgWDWST1AW3A6RBRp3H2yEhEncZstQRNlEzZmUvz0drayl/3RNbgfGloKO3lvoJB+NOf0roLQRCECYNkowsFg1EkBYHrgRaIWAo+F3gQWATc0N7OwoULI5JRrMrspLMlaCJkys5cm49MV2F49VX4whfg7be1xyUlGdu1IAhCQSKeTaFgMIqkQWAPOFoKbmpqotHnY43Hg7k99hhajODM+nqaslztO1N25st8pAufD4qK4OBBuOeezO8/mUoKgiAIuYyITaFgMIqkvaHnnCwF50JLUCdkys58mY904fXCt7+tPV6/XusslCn6+vpoPCmy3FTjSblXfksQBCEh0p4bnyBS+khIBb1kz5xQyaOnTWVz9J+nQq/39/dHvNdcMmlmfX3OlD3SyZSd+TIf6eCDD5SqrdUul5/8JDP71K9dPobiShQ3a789H8ut8luCIAhKSekjYYLT19fHdddey969e/kH4CGcl++RDkLZ2U8usm6d1lnoYx/TkoWK0rgOFAwGaTypkZEpI3ApUResZ6sH3wc+hl7ObvktQRAEHemNLkx4gsEgt912G6tXrWI+hGtuprtOo1A4HD4MDQ3wzjvQ1wdf/nL69qX3gudKoN5ig2HgXvd6wQuCIKSK1NkUJjxer5fvfOc7bOvtZbfPl7E6jULhUFoKV1+tPf7FL9K7r3AZqek2G0w3bScIgpBHJCw2f/WrX7FgwQJqa2vxeDxs37494nWlFKtXr6a2tpYpU6bQ3NzMCy+84Ja9gpAQ2arTKBQGy5fDzp3wox+ldz/hMlJv2mzwpmk7QRCEPCJhsfnuu+9yxhlnsGnTJsvX77jjDu666y42bdrEM888Q3V1NRdeeCFHjhxJ2VhBSAa9TuPll19Oc3OzxLwJjqmshAsuAI8nvftpamrCN8OHZ5cHq3pTnl0e6hsKt9yUIAiFTcJi8+KLL+bWW2+19AwppdiwYQO33HILra2tzJ49m5/97Ge89957bN682RWDBUEQssHBgzAykp6xvV4vG9dvhBe1ZCCGgVFgOPT3i7DhrsItNyUIQmHjaszm0NAQb7zxBvPmzQs/V1xczGc/+1meeuopy/eMjo5y+PDhiB9BkMLWQi5x//0wYwbccEP69qH3gq97vw7uBdYC94LvA4kxFgQhv3FVbL7xxhsAVFVVRTxfVVUVfs3M2rVrmTZtWvinvt4qFVOYSPT19TGrMbKw9axGKWwtZI+PfxyOHoWeHnj55fTtp7W1lT2vRMYYD70sMcaCIOQ3aclG95gCnJRSUc/p3HzzzRw6dCj8Mzw8nA6ThDyhr6+PtrY2Th8Ziehcc/revbS1tYngFLLCGWfAxRfD2JhWfzOdSIyxIAiFhqtis7q6GiDKi/nmm29GeTt1iouLKS0tjfgRJibBYJDrly+nRSm2A+cCU0O/tytFC3BDe7ssqQtZ4cYbtd8//SnYLNQIgiAIFrgqNmfOnEl1dTVPPPFE+Lljx46xc+dOzjvvPDd3JRQgg4OD7BkZYQXRF2YRcLNSDA0PMzg4mAXrhInOBRfAuefC6Ch8//vZtkYQBCF/SFhsHj16lOeee47nnnsO0JKCnnvuOV577TU8Hg/t7e2sWbOGBx54gN27d7Ns2TKOP/54lixZ4rbtQoGhF6yebfP6bNN2gpBJPB646Sbt8d13w6FD2bVHEAQhX5iU6Bt+97vfaW3VQlx33XUALF26lPvuu49vf/vbvP/++3zjG9/g4MGDnHPOOTz++OOUlJS4Z7VQkOgFq3ejLZ2b2W3aThAyzYIFcNpp8NJLMDgILS3ZtkgQBCH3kd7oQs4QDAaZ1djI6Xv3sl2pCLf7GLDI42G3z8dLQ0OSNCFkjd/8BmpqtFJIgiAIExXpjS7kJV6vlzs3buQRNGFpzEZf5PHwCLBugxS2FrLLOeeI0BQEQUgEEZtCTqEXtn6+ro7zgFLgPGC3TwpbC7nHn/8MUhxBEAQhNrKMLuQkwWCQwcFB9u3bR01NDU1NTeLRFHKKK66A++6D7m74yleybY0gCEJmSUSvJZwgJAiZQC9sLQi5ir6U/r3vQVublq0uCIIgRCPL6IIgCEnwzW/ClCnw+9/DL3+ZbWsEQRByFxGbgiAISVBRAVddpT2+/fbs2iIIgpDLiNgUBEFIkuuug0mTNM/m736XbWsEQRByExGbgiAISdLQAHpzNPFuCoIgWCNiUxAEIQW+/W3t969/DUeOZNcWQRCEXETEpiAIQgp84hPw8MPwyisgXXkFQRCikdJHgiAIKSI90gVBEOwRz6YgCIJLjI3Bn/6UbSsEQRByCxGbgiAILrBvn7akfs45cPBgtq0RBEHIHURsCoIguEB1NUyeDEePwn/8R7atEQRByB1EbAqCILiAxwM33qg93rAB3n8/q+YIgiDkDCI2BUEQXOLSS6GxEfbvh5/+NNvWCIIg5AYiNgVBEFxi0iS4/nrt8bp1EAhk1x5BEIRcQMSmIAiCi/zzP2t904eGoKcn29YIgiBkHxGbgiAILnL88bB8ufb4v/4ru7YIgiDkAlLUXRAEwWWuvhrOPRc+//lsWyIIgpB9RGwKgiC4zIknwhe+kG0rBEEQcgNZRhcEQUgjhw/D8HC2rRAEQcgeIjYFQRDSRF8fzJgB116bbUsEQRCyh4hNQRCENHHaaZpnc/t2+POfs22NIAhCdhCxKQiCkCZOOw0WLdIe33FHVk0RBEHIGiI2BUEQ0ojewvLnP5fYTUEQJiYiNgVBENLIOedAczN8+CGsX59tawRBEDKPiE1BEIQ0c9NN2u977oG3386uLYIgCJlGxKYgCEKamTcPPvlJeP99GBjItjWCIAiZRYq6C4IgpBmPR/NqlpfDSSdl2xpBEITMImJTEAQhA3z609m2QBAEITvIMrogCEKGefFFLWFIEARhIiBiUxAEIYN84xvwf/4PbNmSbUsEQRAyg4hNQRCEDDJjBigF3/sejI1l2xpBEIT041FKqWwbYeTQoUOccMIJDA8PU1pamm1zBEEQXOXQIfjEJ+DIEdi6FS66KNsWCYIgJM7hw4epr6/nnXfeYdq0aTG3zTmxOTIyQn19fbbNEARBEARBEOIwPDyMz+eLuU3Oic2xsTFef/11SkpK8Hg82TZHcID+7Ua80YIRuS4EM3JNCGbkmshflFIcOXKE2tpaiopiR2XmXOmjoqKiuApZyE1KS0vlw0KIQq4LwYxcE4IZuSbyk3jL5zqSICQIgiAIgiCkDRGbgiAIgiAIQtoQsSmkTHFxMatWraK4uDjbpgg5hFwXghm5JgQzck1MDHIuQUgQBEEQBEEoHMSzKQiCIAiCIKQNEZuCIAiCIAhC2hCxKQiCIAiCIKQNEZuCIAiCIAhC2hCxKTjmV7/6FQsWLKC2thaPx8P27dsjXldKsXr1ampra5kyZQrNzc288MIL2TFWyAjxrolly5bh8Xgifs4999zsGCtkhLVr1/LpT3+akpISpk+fzqJFi/jLX/4SsY18VkwsnFwT8llR2IjYFBzz7rvvcsYZZ7Bp0ybL1++44w7uuusuNm3axDPPPEN1dTUXXnghR44cybClQqaId00AXHTRRezbty/8s2PHjgxaKGSanTt3cvXVV/Pf//3fPPHEEwQCAebNm8e7774b3kY+KyYWTq4JkM+KQkZKHwlJ4fF4eOCBB1i0aBGgeSpqa2tpb2/nxhtvBGB0dJSqqiq+973v8a//+q9ZtFbIBOZrAjRvxTvvvBPl8RQmDvv372f69Ons3LmTCy64QD4rhKhrAuSzotARz6bgCkNDQ7zxxhvMmzcv/FxxcTGf/exneeqpp7JomZBtBgYGmD59OqeeeipXXXUVb775ZrZNEjLIoUOHACgrKwPks0KIviZ05LOicBGxKbjCG2+8AUBVVVXE81VVVeHXhInHxRdfzM9//nOefPJJ7rzzTp555hk+97nPMTo6mm3ThAyglOK6667j/PPPZ/bs2YB8Vkx0rK4JkM+KQmdStg0QCguPxxPxt1Iq6jlh4nDppZeGH8+ePZuzzz6bhoYGHn30UVpbW7NomZAJrrnmGv74xz+ya9euqNfks2JiYndNyGdFYSOeTcEVqqurAaI8E2+++WaUB0OYuNTU1NDQ0MBLL72UbVOENPPNb36Thx56iP7+fnw+X/h5+ayYuNhdE1bIZ0VhIWJTcIWZM2dSXV3NE088EX7u2LFj7Ny5k/POOy+Llgm5xNtvv83w8DA1NTXZNkVIE0oprrnmGvr6+njyySeZOXNmxOvyWTHxiHdNWCGfFYWFLKMLjjl69Ch//etfw38PDQ3x3HPPUVZWxowZM2hvb2fNmjWccsopnHLKKaxZs4bjjz+eJUuWZNFqIZ3EuibKyspYvXo1ixcvpqamhj179rBixQoqKir48pe/nEWrhXRy9dVXs3nzZh588EFKSkrCHsxp06YxZcoUPB6PfFZMMOJdE0ePHpXPikJHCYJD+vv7FRD1s3TpUqWUUmNjY2rVqlWqurpaFRcXqwsuuEA9//zz2TVaSCuxron33ntPzZs3T1VWVqrJkyerGTNmqKVLl6rXXnst22YLacTqegDUT3/60/A28lkxsYh3TchnReEjdTYFQRAEQRCEtCExm4IgCIIgCELaELEpCIIgCIIgpA0Rm4IgCIIgCELaELEpCIIgCIIgpA0Rm4IgCIIgCELaELEpCIIgCIIgpA0Rm4IgCIIgCELaELEpCIIgCIIgpA0Rm4IgCIIgCELaELEpCIIgCIIgpA0Rm4IgCIIgCELaELEpCIIgCIIgpI3/D02U0MQWKRaAAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Line Plot Coordinates\n",
+    "x_values = np.linspace(X[:, feature1_index].min(), X[:, feature1_index].max(), 100)\n",
+    "y_values = -(model.intercept_[0] + model.coef_[0][0] * x_values) / model.coef_[0][1]  # Decision boundary equation\n",
+    "\n",
+    "# Plot the graph\n",
+    "scatter_plot(\n",
+    "    X={'data': [benign_data[0], benign_data[1]], 'color': 'green', 'label': 'Benign'}, \n",
+    "    y={'data': [malignant_data[0], malignant_data[1]], 'color': 'red', 'label': 'Malignant'}, \n",
+    "    line_plot={'x': x_values, 'y': y_values, 'color': 'blue', 'linestyle': '--'},\n",
+    "    title=\"mean texture vs mean radius\", \n",
+    "    show_legend=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see why the accuracy wasn't 100% by looking at the graph. The data points are not perfectly separable by a linear line, indicating that there is some overlap between the benign and malignant cases. This overlap means that a simple linear model will never be able to achieve 100% accuracy with this dataset. The complexity and nature of the data make it challenging for a linear boundary to perfectly classify all instances."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What if we repeat all of the steps above **using different columns**? The following Python cell will randomly select two columns and run regression on them to classify the data. Each time you run the cell, it will select different columns for the classification, and you will observe that the accuracy changes accordingly. This is because different variables have varying degrees of correlation with the target variable, impacting the model's performance.\n",
+    "\n",
+    "run the cell below many times..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on test set: 0.7280701754385965\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The graph plots \"worst texture\" on the x-axis and \"mean texture\" on the y-axis.\n",
+      "\t\"worst texture\": The roughest texture observed on the surface of the tumor. (x-axis)\n",
+      "\t\"texture error\": The change in the tumor's texture across different measurements, showing how much the roughness or smoothness varies. (y-axis).\n",
+      "\n",
+      "The green points 🟢 represent \"benign tumors\", and the red points 🔴 represent \"malignant tumors\".\n",
+      "The blue dashed line represents the decision boundary determined by the regression model.\n",
+      "\n",
+      "Each run of this script might result in different features being selected, hence different visualizations and boundaries.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  running all of it again, but this time with different columns\n",
+    "\n",
+    "# Randomly select two features to visualize\n",
+    "new_feature_indices = random.sample(range(X.shape[1]), 2)\n",
+    "new_feature1_index = new_feature_indices[0]\n",
+    "new_feature2_index = new_feature_indices[1]\n",
+    "\n",
+    "# Extract the feature base names correctly\n",
+    "new_feature1_base = breast_cancer.feature_names[new_feature1_index].replace(' ', '_').lower()\n",
+    "new_feature2_base = breast_cancer.feature_names[new_feature2_index].replace(' ', '_').lower()\n",
+    "\n",
+    "# Split the data into training and testing sets\n",
+    "new_X_train, new_X_test, new_y_train, new_y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Initialize and train the regression model\n",
+    "new_model = LogisticRegression(max_iter=10000)\n",
+    "new_model.fit(new_X_train[:, new_feature_indices], new_y_train)  # Fit model only on selected features\n",
+    "\n",
+    "# Line Plot Coordinates\n",
+    "new_x_values = np.linspace(new_X_test[:, new_feature1_index].min(), new_X_test[:, new_feature1_index].max(), 100)\n",
+    "new_y_values = -(new_model.intercept_ + new_model.coef_[0][0] * new_x_values) / new_model.coef_[0][1]\n",
+    "\n",
+    "# Predict on the test set using the same features\n",
+    "new_y_pred = new_model.predict(new_X_test[:, new_feature_indices])\n",
+    "\n",
+    "# Calculate the accuracy\n",
+    "accuracy = accuracy_score(new_y_test, new_y_pred)\n",
+    "print(\"Accuracy on test set:\", accuracy)\n",
+    "\n",
+    "# Plot the graph\n",
+    "scatter_plot(\n",
+    "    X={'data': [new_X_test[new_y_test == 0][:, new_feature1_index], new_X_test[new_y_test == 0][:, new_feature2_index]], 'color': 'green', 'label': breast_cancer.target_names[0]}, \n",
+    "    y={'data': [new_X_test[new_y_test == 1][:, new_feature1_index], new_X_test[new_y_test == 1][:, new_feature2_index]], 'color': 'red', 'label': breast_cancer.target_names[1]},\n",
+    "    line_plot={'x': new_x_values, 'y': new_y_values, 'color': 'blue', 'linestyle': '--'},\n",
+    "    title=f'{breast_cancer.feature_names[new_feature2_index]} vs {breast_cancer.feature_names[new_feature1_index]}', \n",
+    "    show_legend=True,\n",
+    "    xlabel=breast_cancer.feature_names[new_feature1_index],\n",
+    "    ylabel=breast_cancer.feature_names[new_feature2_index]\n",
+    ")\n",
+    "# Print statements to describe the plotted graph and its components\n",
+    "print(f\"The graph plots \\\"{breast_cancer.feature_names[new_feature1_index]}\\\" on the x-axis and \\\"{breast_cancer.feature_names[feature2_index]}\\\" on the y-axis.\\n\"\n",
+    "    f\"\\t\\\"{breast_cancer.feature_names[new_feature1_index]}\\\": {feature_descriptions[new_feature1_base]} (x-axis)\\n\"\n",
+    "    f\"\\t\\\"{breast_cancer.feature_names[new_feature2_index]}\\\": {feature_descriptions[new_feature2_base]} (y-axis).\\n\\n\"\n",
+    "    \"The green points 🟢 represent \\\"benign tumors\\\", and the red points 🔴 represent \\\"malignant tumors\\\".\\n\"\n",
+    "    \"The blue dashed line represents the decision boundary determined by the regression model.\\n\\n\" # explain what a decision boundary is better\n",
+    "    \"Each run of this script might result in different features being selected, hence different visualizations and boundaries.\\n\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Circling back to the real-world application of our work, this model has the potential to save lives. By using past data to train the model, we can now quickly and accurately classify new patients as having benign or malignant tumors based on their data. This means that if a new patient's data falls on one side of the decision boundary, with the accuracy of the model in mind, we can quickly determine their diagnosis and take appropriate action. This ability to rapidly and accurately diagnose breast cancer can lead to earlier interventions, better treatment plans, and ultimately, improved patient outcomes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "Regression is a method for modeling the relationship between a dependent variable and one or more independent variables. By leveraging past data, regression helps us find patterns and make predictions about future outcomes.\n",
+    "\n",
+    "With the power of programming, regression becomes an even more potent tool in the hands of data scientists. It allows us to automate the analysis of large datasets, quickly build models, and visualize complex relationships. This enables us to gain insights and make data-driven decisions more efficiently.\n",
+    "\n",
+    "However, there is always room for improvement. While our current model uses a simple regression approach, we can explore more advanced techniques. For instance, we can use polynomial regression to fit curves rather than straight lines, or employ regularization methods to enhance model performance, you don't need to know what these methods are now, but they open doors for future exploration. Additionally, there are many ways to refine the models we've created, such as incorporating more variables, and using cross-validation to ensure robustness.\n",
+    "\n",
+    "As we continue to learn and apply these advanced techniques, we can build more accurate and reliable models, ultimately leading to better predictions and more informed decisions in various fields, including healthcare, finance, and beyond.\n"
+   ]
+  }
+ ],
+ "metadata": {
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+   "language": "python",
+   "name": "python3"
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+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+   "mimetype": "text/x-python",
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+   "pygments_lexer": "ipython3",
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