Logistic-Regression-Adaboost/eda-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports and Pre-requisites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7043, 21)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</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>7038</th>\n",
       "      <td>6840-RESVB</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>24</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>84.80</td>\n",
       "      <td>1990.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>2234-XADUH</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>72</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>103.20</td>\n",
       "      <td>7362.9</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>4801-JZAZL</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.60</td>\n",
       "      <td>346.45</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>8361-LTMKD</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>74.40</td>\n",
       "      <td>306.6</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>3186-AJIEK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>66</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>105.65</td>\n",
       "      <td>6844.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7043 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
       "0     7590-VHVEG  Female              0     Yes         No       1   \n",
       "1     5575-GNVDE    Male              0      No         No      34   \n",
       "2     3668-QPYBK    Male              0      No         No       2   \n",
       "3     7795-CFOCW    Male              0      No         No      45   \n",
       "4     9237-HQITU  Female              0      No         No       2   \n",
       "...          ...     ...            ...     ...        ...     ...   \n",
       "7038  6840-RESVB    Male              0     Yes        Yes      24   \n",
       "7039  2234-XADUH  Female              0     Yes        Yes      72   \n",
       "7040  4801-JZAZL  Female              0     Yes        Yes      11   \n",
       "7041  8361-LTMKD    Male              1     Yes         No       4   \n",
       "7042  3186-AJIEK    Male              0      No         No      66   \n",
       "\n",
       "     PhoneService     MultipleLines InternetService OnlineSecurity  ...  \\\n",
       "0              No  No phone service             DSL             No  ...   \n",
       "1             Yes                No             DSL            Yes  ...   \n",
       "2             Yes                No             DSL            Yes  ...   \n",
       "3              No  No phone service             DSL            Yes  ...   \n",
       "4             Yes                No     Fiber optic             No  ...   \n",
       "...           ...               ...             ...            ...  ...   \n",
       "7038          Yes               Yes             DSL            Yes  ...   \n",
       "7039          Yes               Yes     Fiber optic             No  ...   \n",
       "7040           No  No phone service             DSL            Yes  ...   \n",
       "7041          Yes               Yes     Fiber optic             No  ...   \n",
       "7042          Yes                No     Fiber optic            Yes  ...   \n",
       "\n",
       "     DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "0                  No          No          No              No  Month-to-month   \n",
       "1                 Yes          No          No              No        One year   \n",
       "2                  No          No          No              No  Month-to-month   \n",
       "3                 Yes         Yes          No              No        One year   \n",
       "4                  No          No          No              No  Month-to-month   \n",
       "...               ...         ...         ...             ...             ...   \n",
       "7038              Yes         Yes         Yes             Yes        One year   \n",
       "7039              Yes          No         Yes             Yes        One year   \n",
       "7040               No          No          No              No  Month-to-month   \n",
       "7041               No          No          No              No  Month-to-month   \n",
       "7042              Yes         Yes         Yes             Yes        Two year   \n",
       "\n",
       "     PaperlessBilling              PaymentMethod MonthlyCharges  TotalCharges  \\\n",
       "0                 Yes           Electronic check          29.85         29.85   \n",
       "1                  No               Mailed check          56.95        1889.5   \n",
       "2                 Yes               Mailed check          53.85        108.15   \n",
       "3                  No  Bank transfer (automatic)          42.30       1840.75   \n",
       "4                 Yes           Electronic check          70.70        151.65   \n",
       "...               ...                        ...            ...           ...   \n",
       "7038              Yes               Mailed check          84.80        1990.5   \n",
       "7039              Yes    Credit card (automatic)         103.20        7362.9   \n",
       "7040              Yes           Electronic check          29.60        346.45   \n",
       "7041              Yes               Mailed check          74.40         306.6   \n",
       "7042              Yes  Bank transfer (automatic)         105.65        6844.5   \n",
       "\n",
       "     Churn  \n",
       "0       No  \n",
       "1       No  \n",
       "2      Yes  \n",
       "3       No  \n",
       "4      Yes  \n",
       "...    ...  \n",
       "7038    No  \n",
       "7039    No  \n",
       "7040    No  \n",
       "7041   Yes  \n",
       "7042    No  \n",
       "\n",
       "[7043 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the data into a dataframe\n",
    "df = pd.read_csv('Dataset-1/Telco-Customer-Churn.csv')\n",
    "print(df.shape)\n",
    "df\n",
    "# 21 columns (features) and 7043 rows (instances)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical vs Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SeniorCitizen', 'tenure', 'MonthlyCharges'], dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding out the numerical features\n",
    "numerical_features = df.select_dtypes(include=[np.number])\n",
    "numerical_features.columns\n",
    "\n",
    "# 3 apparent numerical features: SeniorCitizen, tenure, MonthlyCharges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customerID', 'gender', 'Partner', 'Dependents', 'PhoneService',\n",
       "       'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup',\n",
       "       'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies',\n",
       "       'Contract', 'PaperlessBilling', 'PaymentMethod', 'TotalCharges',\n",
       "       'Churn'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding out the categorical features\n",
    "categorical_features = df.select_dtypes(include=[object])\n",
    "categorical_features.columns\n",
    "\n",
    "# 18 apparent categorical features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution Visualization: Missing Values and Outliers Identification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID          0\n",
       "gender              0\n",
       "SeniorCitizen       0\n",
       "Partner             0\n",
       "Dependents          0\n",
       "tenure              0\n",
       "PhoneService        0\n",
       "MultipleLines       0\n",
       "InternetService     0\n",
       "OnlineSecurity      0\n",
       "OnlineBackup        0\n",
       "DeviceProtection    0\n",
       "TechSupport         0\n",
       "StreamingTV         0\n",
       "StreamingMovies     0\n",
       "Contract            0\n",
       "PaperlessBilling    0\n",
       "PaymentMethod       0\n",
       "MonthlyCharges      0\n",
       "TotalCharges        0\n",
       "Churn               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look for missing values in all the columns\n",
    "df.isnull().sum()\n",
    "\n",
    "# look for NaN values in all the columns\n",
    "df.isna().sum()\n",
    "\n",
    "# no missing values apparently"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.162147</td>\n",
       "      <td>32.371149</td>\n",
       "      <td>64.761692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.368612</td>\n",
       "      <td>24.559481</td>\n",
       "      <td>30.090047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>35.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>70.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>89.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>118.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SeniorCitizen       tenure  MonthlyCharges\n",
       "count    7043.000000  7043.000000     7043.000000\n",
       "mean        0.162147    32.371149       64.761692\n",
       "std         0.368612    24.559481       30.090047\n",
       "min         0.000000     0.000000       18.250000\n",
       "25%         0.000000     9.000000       35.500000\n",
       "50%         0.000000    29.000000       70.350000\n",
       "75%         0.000000    55.000000       89.850000\n",
       "max         1.000000    72.000000      118.750000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look for outliers in all the numerical columns\n",
    "df.boxplot(numerical_features.columns.to_list())\n",
    "df.describe()\n",
    "\n",
    "# SeniorCitizen is not a numerical feature, it is a categorical feature\n",
    "# no outliers\n",
    "# no boxplot for categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'SeniorCitizen'}>,\n",
       "        <Axes: title={'center': 'tenure'}>],\n",
       "       [<Axes: title={'center': 'MonthlyCharges'}>, <Axes: >]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rr+LIkSN4/vnnoVarERYWhrS0NACAi4uLbD4XFxdpWlpaGpydneWDsLKCk5OTrM/de3buXmZaWlqZAWbJkiVYsGBBqfa4uDjY2dndt7b4+Pj79jF3rME81PYa8vPza3AkRERlMyjA6PV6dO3aFW+++SYAoEuXLjhx4gRWrlyJsLCwahlgZc2dOxcRERHS65KTgAICAu57jDo+Ph79+/dXzHHBe7EG81BXaijZu0lEZEoGBRhXV1e0bdtW1tamTRv873//AwDodDoAQHp6OlxdXaU+6enp6Ny5s9QnIyNDtozbt28jKytLml+n0yE9PV3Wp+R1SZ97aTQaaDSaUu3W1taV+mXS5Y3dKCh+8JMSL74V8sDLqKrK1mrOWIN5qKgGpddmCu2jdhpl+wKYdhtDZE4MugtVz549cfbsWVnbH3/8AQ8PDwB3TujV6XRISEiQpufk5ODQoUPw9fUFAPj6+iI7OxvJyclSn927d0Ov18PHx0fqk5iYKDvWHh8fj9atW5d5+IiIiIjqFoMCzKxZs3Dw4EG8+eabOH/+PNasWYNPP/0U4eHhAO5c8jRz5kwsXrwYmzdvxvHjxzF27Fi4ublh8ODBAO7ssQkKCsKkSZNw+PBh7N+/H9OnT0doaCjc3NwAAKNGjYJarcbEiRNx8uRJfP/993j//fdlh4iIiIio7jLoEFK3bt2wceNGzJ07FwsXLoSnpyfee+89jB49Wurz8ssv48aNG5g8eTKys7PRq1cv7NixQ7oHDACsXr0a06dPh5+fn3Qjuw8++ECabm9vj7i4OISHh8Pb2xuNGjXC/PnzeQk1ERERAajCnXifeOIJPPHEE+VOV6lUWLhwIRYuXFhuHycnJ+mmdeXp2LEjfv75Z0OHR0RERHVA3XwSGxERESkaAwwREREpDgMMEZmNxMREDBw4EG5ublCpVNi0aZNs+rhx46BSqWRfQUFBsj5ZWVkYPXo0tFotHBwcMHHiROTl5cn6HDt2DL1794aNjQ3c3d0RHR1d3aURkZExwBCR2bhx4wY6deqEmJiYcvsEBQXh2rVr0td3330nmz569GicPHkS8fHx2LJlCxITE2UXAJQ8LNbDwwPJyclYunQpoqKi8Omnn1ZbXURkfAafxEtEVF2Cg4MRHBxcYR+NRlPuDS35sFiiuoMBhogUZe/evXB2doajoyP69euHxYsXo2HDhgBgtg+L1ViIByu6jGWaUm14cGl5amttSqqrsmNkgCEixQgKCsLQoUPh6emJCxcu4NVXX0VwcDCSkpJgaWlptg+LXdRVX+ka72fbtm1GW9aDqg0PLi1Pba1NCXVV9oGxDDBEpBihoaHS/zt06ICOHTuiRYsW2Lt3L/z8/KptvQ/6sNh5Ry1QoDfOs5BORAUaZTkPojY8uLQ8tbU2JdVV2QfGMsAQkWI99NBDaNSoEc6fPw8/Pz+zfVhsgV5ltIc5mtMvn9rw4NLy1NbalFBXZcfHq5CISLGuXLmCzMxMuLq6AuDDYonqEgYYIjIbeXl5SElJQUpKCgAgNTUVKSkpuHTpEvLy8jB79mwcPHgQFy9eREJCAp588km0bNkSgYF3DqvwYbFEdQcDDBGZjaNHj6JLly7o0qULACAiIgJdunTB/PnzYWlpiWPHjmHQoEF4+OGHMXHiRHh7e+Pnn3+WHd5ZvXo1vLy84OfnhwEDBqBXr16ye7yUPCw2NTUV3t7eePHFF/mwWCIF4jkwRGQ2+vbtCyHKv+R4586d910GHxZLVDdwDwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpzgMFmLfeegsqlQozZ86U2m7duoXw8HA0bNgQ9evXx7Bhw5Ceni6b79KlSwgJCYGdnR2cnZ0xe/Zs3L59W9Zn7969eOSRR6DRaNCyZUusWrXqQYZKREREtUiVA8yRI0fwySefoGPHjrL2WbNm4aeffsL69euxb98+XL16FUOHDpWmFxcXIyQkBIWFhThw4AC++uorrFq1CvPnz5f6pKamIiQkBI8//jhSUlIwc+ZMPPvss9i5c2dVh0tERES1iFVVZsrLy8Po0aPx2WefYfHixVL79evX8cUXX2DNmjXo168fACA2NhZt2rTBwYMH0aNHD8TFxeHUqVPYtWsXXFxc0LlzZyxatAhz5sxBVFQU1Go1Vq5cCU9PT7z77rsAgDZt2uCXX37B8uXLERgYaISyiYiI6H6av7LVqMu7+FaI0ZZVpQATHh6OkJAQ+Pv7ywJMcnIyioqK4O/vL7V5eXmhWbNmSEpKQo8ePZCUlIQOHTrAxcVF6hMYGIipU6fi5MmT6NKlC5KSkmTLKOlz96GqexUUFKCgoEB6nZOTAwAoKipCUVFRufOVTNNYiMoVfx8Vrau6lKzTFOs2FtZgHipTg5LrI6Law+AAs3btWvz66684cuRIqWlpaWlQq9VwcHCQtbu4uCAtLU3qc3d4KZleMq2iPjk5Obh58yZsbW1LrXvJkiVYsGBBqfa4uDjY2dndt65FXfX37VMZ27ZtM8pyqiI+Pt5k6zYW1mAeKqohPz+/BkdCRFQ2gwLM5cuX8cILLyA+Ph42NjbVNaYqmTt3LiIiIqTXOTk5cHd3R0BAALRabbnzFRUVIT4+HvOOWqBAr3rgcZyIqvlDXCU19O/fH9bW1jW+fmNgDeahMjWU7N0kIjIlgwJMcnIyMjIy8Mgjj0htxcXFSExMxIcffoidO3eisLAQ2dnZsr0w6enp0Ol0AACdTofDhw/LlltyldLdfe69cik9PR1arbbMvS8AoNFooNFoSrVbW1tX6pdJgV6FguIHDzCm/MVV2VrNGWswDxXVoPTaiKh2MOgqJD8/Pxw/fhwpKSnSV9euXTF69Gjp/9bW1khISJDmOXv2LC5dugRfX18AgK+vL44fP46MjAypT3x8PLRaLdq2bSv1uXsZJX1KlkFERER1m0F7YBo0aID27dvL2urVq4eGDRtK7RMnTkRERAScnJyg1WoxY8YM+Pr6okePHgCAgIAAtG3bFmPGjEF0dDTS0tLw+uuvIzw8XNqDMmXKFHz44Yd4+eWXMWHCBOzevRvr1q3D1q3GPRuaiIiIlMnod+Jdvnw5nnjiCQwbNgx9+vSBTqfDhg0bpOmWlpbYsmULLC0t4evri2eeeQZjx47FwoULpT6enp7YunUr4uPj0alTJ7z77rv4/PPPeQk1US2XmJiIgQMHws3NDSqVCps2bZJNF0Jg/vz5cHV1ha2tLfz9/XHu3DlZn6ysLIwePRparRYODg6YOHEi8vLyZH2OHTuG3r17w8bGBu7u7oiOjq7u0ojIyKp0GfXd9u7dK3ttY2ODmJgYxMTElDuPh4fHfa/W6du3L3777bcHHR4RKciNGzfQqVMnTJgwQXYDzBLR0dH44IMP8NVXX8HT0xPz5s1DYGAgTp06JV1YMHr0aFy7dg3x8fEoKirC+PHjMXnyZKxZswbAnZOQAwIC4O/vj5UrV+L48eOYMGECHBwcMHny5Bqtl4iq7oEDDBGRsQQHByM4OLjMaUIIvPfee3j99dfx5JNPAgC+/vpruLi4YNOmTQgNDcXp06exY8cOHDlyBF27dgUArFixAgMGDMA777wDNzc3rF69GoWFhfjyyy+hVqvRrl07pKSkYNmyZeUGGHO5z9TdyzSl2nDPo/LU1tqqWpfG0njfu5Vdf2XHyABDRIqQmpqKtLQ02U0u7e3t4ePjg6SkJISGhiIpKQkODg5SeAEAf39/WFhY4NChQxgyZAiSkpLQp08fqNVqqU9gYCDefvtt/Pfff3B0dCy1bnO5zxRg2ntN3as23POoPLW1NkPriu5u3PVX5vu3sveaYoAhIkUoudFlWTe5vPsmmM7OzrLpVlZWcHJykvXx9PQstYySaWUFGHO5zxRgmntN3as23POoPLW1tqrW1T7KuM8grMz3b2XvNcUAQ0R0H+Zyn6mSdZqL2nDPo/LU1toMrctY37d3r98YfYBquAqJiKg6lNzosqybXN59E8y77zEFALdv30ZWVtZ9b5R59zqIyPwxwBCRInh6ekKn08lucpmTk4NDhw7JbpSZnZ2N5ORkqc/u3buh1+vh4+Mj9UlMTJSdKBgfH4/WrVuXefiIiMwTAwwRmY28vDzpLt/AnRN3U1JScOnSJahUKsycOROLFy/G5s2bcfz4cYwdOxZubm4YPHgwAKBNmzYICgrCpEmTcPjwYezfvx/Tp09HaGgo3NzcAACjRo2CWq3GxIkTcfLkSXz//fd4//33Zee4EJH54zkwRGQ2jh49iscff1x6XRIqwsLCsGrVKrz88su4ceMGJk+ejOzsbPTq1Qs7duyQPVx29erVmD59Ovz8/GBhYYFhw4bhgw8+kKbb29sjLi4O4eHh8Pb2RqNGjTB//nzeA4ZIYRhgiMhs9O3bF0KUf98JlUqFhQsXyu7cfS8nJyfppnXl6dixI37++ecqj5OITI+HkIiIiEhxGGCIiIhIcRhgiIiISHEYYIiIiEhxGGCIiIhIcRhgiIiISHEYYIiIiEhxGGCIiIhIcRhgiIiISHEYYIiIiEhxGGCIiIhIcRhgiIiISHEYYIiIiEhxGGCIiIhIcRhgiIiISHGsTD0AIiKqvOavbDXasi6+FWK0ZRHVNO6BISIiIsVhgCEiIiLFYYAhIiIixWGAISIiIsVhgCEiIiLFYYAhIiIixWGAISIiIsVhgCEiIiLFYYAhIiIixWGAISIiIsVhgCEiIiLFYYAhIiIixWGAISIiIsVhgCEiIiLFYYAhIiIixWGAISIiIsUxKMAsWbIE3bp1Q4MGDeDs7IzBgwfj7Nmzsj63bt1CeHg4GjZsiPr162PYsGFIT0+X9bl06RJCQkJgZ2cHZ2dnzJ49G7dv35b12bt3Lx555BFoNBq0bNkSq1atqlqFREREVOtYGdJ53759CA8PR7du3XD79m28+uqrCAgIwKlTp1CvXj0AwKxZs7B161asX78e9vb2mD59OoYOHYr9+/cDAIqLixESEgKdTocDBw7g2rVrGDt2LKytrfHmm28CAFJTUxESEoIpU6Zg9erVSEhIwLPPPgtXV1cEBgYa+S0gIqqbmr+ytUrzaSwForsD7aN2oqBYJbVffCvEWEMjui+DAsyOHTtkr1etWgVnZ2ckJyejT58+uH79Or744gusWbMG/fr1AwDExsaiTZs2OHjwIHr06IG4uDicOnUKu3btgouLCzp37oxFixZhzpw5iIqKglqtxsqVK+Hp6Yl3330XANCmTRv88ssvWL58OQMMERERGRZg7nX9+nUAgJOTEwAgOTkZRUVF8Pf3l/p4eXmhWbNmSEpKQo8ePZCUlIQOHTrAxcVF6hMYGIipU6fi5MmT6NKlC5KSkmTLKOkzc+bMcsdSUFCAgoIC6XVOTg4AoKioCEVFReXOVzJNYyEqWXXFKlpXdSlZpynWbSyswTxUpgYl10fVq6p7dMrCvTl0P1UOMHq9HjNnzkTPnj3Rvn17AEBaWhrUajUcHBxkfV1cXJCWlib1uTu8lEwvmVZRn5ycHNy8eRO2tralxrNkyRIsWLCgVHtcXBzs7OzuW8+irvr79qmMbdu2GWU5VREfH2+ydRsLazAPFdWQn59fgyORi4qKKvVz3rp1a5w5cwbAnXPwXnzxRaxduxYFBQUIDAzERx99JNueXLp0CVOnTsWePXtQv359hIWFYcmSJbCyeqC/54iohlX5JzY8PBwnTpzAL7/8YszxVNncuXMREREhvc7JyYG7uzsCAgKg1WrLna+oqAjx8fGYd9QCBXpVuf0q60RUzR/iKqmhf//+sLa2rvH1GwNrMA+VqaFk76aptGvXDrt27ZJe3x08jHEOHhEpQ5UCzPTp07FlyxYkJiaiadOmUrtOp0NhYSGys7Nle2HS09Oh0+mkPocPH5Ytr+Qqpbv73HvlUnp6OrRabZl7XwBAo9FAo9GUare2tq7UL5MCvUp2MlpVmfIXV2VrNWeswTxUVIOpa7OyspK2FXcz1jl4RKQMBgUYIQRmzJiBjRs3Yu/evfD09JRN9/b2hrW1NRISEjBs2DAAwNmzZ3Hp0iX4+voCAHx9ffHGG28gIyMDzs7OAO7srtZqtWjbtq3U595DMfHx8dIyiKjuOnfuHNzc3GBjYwNfX18sWbIEzZo1M9o5eGUxl3PszEVJPdVZl6nOtaoN57KVpap1aSyN+xlXZv2VHaNBASY8PBxr1qzBjz/+iAYNGkjnrNjb28PW1hb29vaYOHEiIiIi4OTkBK1WixkzZsDX1xc9evQAAAQEBKBt27YYM2YMoqOjkZaWhtdffx3h4eHSHpQpU6bgww8/xMsvv4wJEyZg9+7dWLduHbZuNd4JYkSkPD4+Pli1ahVat26Na9euYcGCBejduzdOnDhhtHPwymIu59iZm+qsy5TnEwK141y2shhaV3R3466/Mp9rZc+zMyjAfPzxxwCAvn37ytpjY2Mxbtw4AMDy5cthYWGBYcOGyU6iK2FpaYktW7Zg6tSp8PX1Rb169RAWFoaFCxdKfTw9PbF161bMmjUL77//Ppo2bYrPP/+cl1AT1XHBwcHS/zt27AgfHx94eHhg3bp15R5eNgZzOcfOXGgsBBZ11de6uoDK12bM8x3bR+002rLKYy6fWWXet8qeZ2fwIaT7sbGxQUxMDGJiYsrt4+Hhcd8U1rdvX/z222+GDI+I6hgHBwc8/PDDOH/+PPr372+Uc/DKYi7n2Jmb2loXcP/ajHkuWE2+h6b+zCrzvlX2veWzkIhIsfLy8nDhwgW4urrKzsErUdY5eMePH0dGRobU595z8IhIGXjjAyJSjJdeegkDBw6Eh4cHrl69isjISFhaWmLkyJFGOwePqDKMedM+qhoGGCJSjCtXrmDkyJHIzMxE48aN0atXLxw8eBCNGzcGYJxz8IhIGRhgiEgx1q5dW+F0Y52DR0Tmj+fAEBERkeIwwBAREZHiMMAQERGR4jDAEBERkeIwwBAREZHiMMAQERGR4jDAEBERkeIwwBAREZHiMMAQERGR4jDAEBERkeIwwBAREZHi8FlIRApnzKfiaiwForsbbXFERNWGe2CIiIhIcRhgiIiISHEYYIiIiEhxGGCIiIhIcRhgiIiISHEYYIiIiEhxGGCIiIhIcRhgiIiISHEYYIiIiEhxGGCIiIhIcRhgiIiISHEYYIiIiEhxGGCIiIhIcRhgiIiISHEYYIiIiEhxGGCIiIhIcRhgiIiISHEYYIiIiEhxGGCIiIhIcRhgiIiISHEYYIiIiEhxGGCIiIhIcRhgiIiISHEYYIiIiEhxGGCIiIhIccw6wMTExKB58+awsbGBj48PDh8+bOohEVEtwm0MkXKZbYD5/vvvERERgcjISPz666/o1KkTAgMDkZGRYeqhEVEtwG0MkbJZmXoA5Vm2bBkmTZqE8ePHAwBWrlyJrVu34ssvv8Qrr7xSqn9BQQEKCgqk19evXwcAZGVloaioqNz1FBUVIT8/H1ZFFijWqx543JmZmQ+8DEOV1JCZmQlra+saX78xsIaqs7p9w3jL0gvk5+srrCE3NxcAIIQw2npNwZBtjLlsX8xFyfdJbasLqL21mUtdlfkdWeltjDBDBQUFwtLSUmzcuFHWPnbsWDFo0KAy54mMjBQA+MUvftXQ1+XLl2tga1A9DN3GcPvCL37V/Nf9tjFmuQfm33//RXFxMVxcXGTtLi4uOHPmTJnzzJ07FxEREdJrvV6PrKwsNGzYECpV+WkzJycH7u7uuHz5MrRarXEKqGGswTzUlRqEEMjNzYWbm1sNj854DN3G1OXtS1lqa11A7a1NSXVVdhtjlgGmKjQaDTQajazNwcGh0vNrtVqz/1DvhzWYh7pQg729fQ2OxvS4fSlbba0LqL21KaWuymxjzPIk3kaNGsHS0hLp6emy9vT0dOh0OhONiohqC25jiJTPLAOMWq2Gt7c3EhISpDa9Xo+EhAT4+vqacGREVBtwG0OkfGZ7CCkiIgJhYWHo2rUrunfvjvfeew83btyQrhgwFo1Gg8jIyFK7h5WENZgH1qAsNbGNqa3vZ22tC6i9tdXGulRCmO+1kB9++CGWLl2KtLQ0dO7cGR988AF8fHxMPSwiqiW4jSFSLrMOMERERERlMctzYIiIiIgqwgBDREREisMAQ0RERIrDAENERESKU+sCTExMDJo3bw4bGxv4+Pjg8OHDFfZfv349vLy8YGNjgw4dOmDbtm2y6UIIzJ8/H66urrC1tYW/vz/OnTtXnSUYVMNnn32G3r17w9HREY6OjvD39y/Vf9y4cVCpVLKvoKAgs6lh1apVpcZnY2Mj62Pun0Pfvn1L1aBSqRASEiL1qenPITExEQMHDoSbmxtUKhU2bdp033n27t2LRx55BBqNBi1btsSqVatK9TH0Z6yuqg3v05IlS9CtWzc0aNAAzs7OGDx4MM6ePSvrc+vWLYSHh6Nhw4aoX78+hg0bVuoGgeburbfegkqlwsyZM6U2Jdf1999/45lnnkHDhg1ha2uLDh064OjRo9J0U2xPq4URnotmNtauXSvUarX48ssvxcmTJ8WkSZOEg4ODSE9PL7P//v37haWlpYiOjhanTp0Sr7/+urC2thbHjx+X+rz11lvC3t5ebNq0Sfz+++9i0KBBwtPTU9y8edMsahg1apSIiYkRv/32mzh9+rQYN26csLe3F1euXJH6hIWFiaCgIHHt2jXpKysrq1rGX5UaYmNjhVarlY0vLS1N1sfcP4fMzEzZ+E+cOCEsLS1FbGys1KemP4dt27aJ1157TWzYsEEAKPXgwnv9+eefws7OTkRERIhTp06JFStWCEtLS7Fjxw6pj6HvS11VW96nwMBAERsbK06cOCFSUlLEgAEDRLNmzUReXp7UZ8qUKcLd3V0kJCSIo0ePih49eohHH33UhKM2zOHDh0Xz5s1Fx44dxQsvvCC1K7WurKws4eHhIcaNGycOHTok/vzzT7Fz505x/vx5qU9Nb0+rS60KMN27dxfh4eHS6+LiYuHm5iaWLFlSZv/hw4eLkJAQWZuPj4947rnnhBBC6PV6odPpxNKlS6Xp2dnZQqPRiO+++64aKjC8hnvdvn1bNGjQQHz11VdSW1hYmHjyySeNPdRyGVpDbGyssLe3L3d5Svwcli9fLho0aCDb0Nf053C3ygSYl19+WbRr107WNmLECBEYGCi9ftD3pa6ore9TRkaGACD27dsnhLjzc2htbS3Wr18v9Tl9+rQAIJKSkkw1zErLzc0VrVq1EvHx8eKxxx6TAoyS65ozZ47o1atXudNNsT2tLrXmEFJhYSGSk5Ph7+8vtVlYWMDf3x9JSUllzpOUlCTrDwCBgYFS/9TUVKSlpcn62Nvbw8fHp9xl1nQN98rPz0dRURGcnJxk7Xv37oWzszNat26NqVOnIjMz06hjL1HVGvLy8uDh4QF3d3c8+eSTOHnypDRNiZ/DF198gdDQUNSrV0/WXlOfQ1Xc7+fBGO9LXVCb36fr168DgLR9SU5ORlFRkaxWLy8vNGvWTBG1hoeHIyQkpNT3vZLr2rx5M7p27Yqnn34azs7O6NKlCz777DNpek1vT6tTrQkw//77L4qLi+Hi4iJrd3FxQVpaWpnzpKWlVdi/5F9DlvkgqlLDvebMmQM3NzfZN2dQUBC+/vprJCQk4O2338a+ffsQHByM4uJio44fqFoNrVu3xpdffokff/wR3377LfR6PR599FFcuXIFgPI+h8OHD+PEiRN49tlnZe01+TlURXk/Dzk5Obh586ZRvj/rgtr6Pun1esycORM9e/ZE+/btAdz5nlGr1aWezK2EWteuXYtff/0VS5YsKTVNyXX9+eef+Pjjj9GqVSvs3LkTU6dOxfPPP4+vvvoKQM1vT6uT2T4LiQz31ltvYe3atdi7d6/sJNjQ0FDp/x06dEDHjh3RokUL7N27F35+fqYYqoyvr6/sAXqPPvoo2rRpg08++QSLFi0y4ciq5osvvkCHDh3QvXt3Wbu5fw5EFQkPD8eJEyfwyy+/mHooD+zy5ct44YUXEB8fX+qCAaXT6/Xo2rUr3nzzTQBAly5dcOLECaxcuRJhYWEmHp1x1Zo9MI0aNYKlpWWps8TT09Oh0+nKnEen01XYv+RfQ5b5IKpSQ4l33nkHb731FuLi4tCxY8cK+z700ENo1KgRzp8//8BjvteD1FDC2toaXbp0kcanpM/hxo0bWLt2LSZOnHjf9VTn51AV5f08aLVa2NraGuWzrQtq4/s0ffp0bNmyBXv27EHTpk2ldp1Oh8LCQmRnZ8v6m3utycnJyMjIwCOPPAIrKytYWVlh3759+OCDD2BlZQUXFxdF1gUArq6uaNu2raytTZs2uHTpEoCa355Wp1oTYNRqNby9vZGQkCC16fV6JCQkyP66v5uvr6+sPwDEx8dL/T09PaHT6WR9cnJycOjQoXKXWdM1AEB0dDQWLVqEHTt2oGvXrvddz5UrV5CZmQlXV1ejjPtuVa3hbsXFxTh+/Lg0PqV8DsCdy/ILCgrwzDPP3Hc91fk5VMX9fh6M8dnWBbXpfRJCYPr06di4cSN2794NT09P2XRvb29YW1vLaj179iwuXbpk1rX6+fnh+PHjSElJkb66du2K0aNHS/9XYl0A0LNnz1KXuv/xxx/w8PAAUPPb02pl6rOIjWnt2rVCo9GIVatWiVOnTonJkycLBwcH6ZLcMWPGiFdeeUXqv3//fmFlZSXeeecdcfr0aREZGVnmZdQODg7ixx9/FMeOHRNPPvlktV++a0gNb731llCr1eKHH36QXZ6bm5srhLhzlv1LL70kkpKSRGpqqti1a5d45JFHRKtWrcStW7fMooYFCxaInTt3igsXLojk5GQRGhoqbGxsxMmTJ2V1mvPnUKJXr15ixIgRpdpN8Tnk5uaK3377Tfz2228CgFi2bJn47bffxF9//SWEEOKVV14RY8aMkfqXXEY9e/Zscfr0aRETE1PmZdQVvS90R215n6ZOnSrs7e3F3r17ZduX/Px8qc+UKVNEs2bNxO7du8XRo0eFr6+v8PX1NeGoq+buq5CEUG5dhw8fFlZWVuKNN94Q586dE6tXrxZ2dnbi22+/lfrU9Pa0utSqACOEECtWrBDNmjUTarVadO/eXRw8eFCa9thjj4mwsDBZ/3Xr1omHH35YqNVq0a5dO7F161bZdL1eL+bNmydcXFyERqMRfn5+4uzZs2ZTg4eHhwBQ6isyMlIIIUR+fr4ICAgQjRs3FtbW1sLDw0NMmjSp2jekhtQwc+ZMqa+Li4sYMGCA+PXXX2XLM/fPQQghzpw5IwCIuLi4UssyxeewZ8+eMr83SsYdFhYmHnvssVLzdO7cWajVavHQQw/J7mNToqL3hf5PbXifyvr+ASD7vrh586aYNm2acHR0FHZ2dmLIkCHi2rVrpht0Fd0bYJRc108//STat28vNBqN8PLyEp9++qlsuim2p9VBJYQQNb3Xh4iIiOhB1JpzYIiIiKjuYIAhIiIixWGAISIiIsVhgCEiIiLFYYChSlGpVJg+ffp9+61atQoqlQoXL16s1vFERUVBpVLh33//rdb1EBGReWKAMQMlv/RVKlWZt+kWQsDd3R0qlQpPPPFEtY3jwIEDiIqKKnX3yZpUXFyM2NhY9O3bF05OTtBoNGjevDnGjx+Po0ePmmxcRERkXhhgzIiNjQ3WrFlTqn3fvn24cuUKNBpNta7/wIEDWLBggckCzM2bN/HEE09gwoQJEELg1Vdfxccff4yxY8ciKSkJ3bt3lx7wSEREdRsf5mhGBgwYgPXr10vP4yixZs0aeHt71/rDJbNnz8aOHTuwfPlyzJw5UzYtMjISy5cvr9HxCCFw69Yt2Nra1uh6iYjo/rgHxoyMHDkSmZmZiI+Pl9oKCwvxww8/YNSoUaX637hxAy+++CLc3d2h0WjQunVrvPPOO7j33oQl569s2rQJ7du3h0ajQbt27bBjxw6pT1RUFGbPng3gzrMySg5p3XsuS0XLKEtYWBgaNWqEoqKiUtMCAgLQunVrAHeeC/TJJ5+gf//+pcILAFhaWuKll16SPUgOALKzszFu3Dg4ODjA3t4e48ePR35+vqxPbGws+vXrB2dnZ2g0GrRt2xYff/xxqXU0b94cTzzxBHbu3ImuXbvC1tYWn3zyCQDgr7/+wqBBg1CvXj04Oztj1qxZ2LlzJ1QqFfbu3StbzqFDhxAUFAR7e3vY2dnhsccew/79+2V9cnNzMXPmTDRv3hwajQbOzs7o378/fv311wrfTyIiuoMBxow0b94cvr6++O6776S27du34/r16wgNDZX1FUJg0KBBWL58OYKCgrBs2TK0bt0as2fPRkRERKll//LLL5g2bRpCQ0MRHR2NW7duYdiwYcjMzAQADB06FCNHjgQALF++HN988w2++eYbNG7cuNLLKMuYMWOQmZmJnTt3ytrT0tKwe/du6aGH27dvx+3btzFmzBiD3rPhw4cjNzcXS5YswfDhw7Fq1SosWLBA1ufjjz+Gh4cHXn31Vbz77rtwd3fHtGnTEBMTU2p5Z8+exciRI9G/f3+8//776Ny5M27cuIF+/fph165deP755/Haa6/hwIEDmDNnTqn5d+/ejT59+iAnJweRkZF48803kZ2djX79+uHw4cNSvylTpuDjjz/GsGHD8NFHH+Gll16Cra0tTp8+bVD9RER1lgkfY0D/X2xsrAAgjhw5Ij788EPRoEED6WFpTz/9tHj88ceFEHeeexQSEiKEEGLTpk0CgFi8eLFsWU899ZRQqVTi/PnzUhsAoVarZW2///67ACBWrFghtS1dulQAEKmpqaXGWNlllNRSsozi4mLRtGnTUg84XLZsmVCpVOLPP/8UQggxa9YsAUD89ttvlXrPIiMjBQAxYcIEWfuQIUNEw4YNZW13P3iuRGBgoHjooYdkbSXPlbr74YVCCPHuu+8KAGLTpk1S282bN4WXl5cAIPbs2SOEuPN8kVatWonAwECh1+tl6/f09BT9+/eX2uzt7UV4eHilaiUiotK4B8bMDB8+HDdv3sSWLVuQm5uLLVu2lHn4aNu2bbC0tMTzzz8va3/xxRchhMD27dtl7f7+/mjRooX0umPHjtBqtfjzzz8rPbaqLMPCwgKjR4/G5s2bkZubK7WvXr0ajz76KDw9PQHceZw7ADRo0KDS4wHu7Mm4W+/evZGZmSktD4DsHJbr16/j33//xWOPPYY///wT169fl83v6emJwMBAWduOHTvQpEkTDBo0SGqzsbHBpEmTZP1SUlJw7tw5jBo1CpmZmfj333/x77//4saNG/Dz80NiYiL0ej0AwMHBAYcOHcLVq1cNqpeIiO5ggDEzjRs3hr+/P9asWYMNGzaguLgYTz31VKl+f/31F9zc3Er9wm/Tpo00/W7NmjUrtQxHR0f8999/lR5bVZcxduxY3Lx5Exs3bgRw5zBNcnKy7HCRVqsFAFnIqcqYHB0dAUA2pv3798Pf3x/16tWDg4MDGjdujFdffRUAygww9/rrr7/QokULqFQqWXvLli1lr8+dOwfgznk/jRs3ln19/vnnKCgokNYXHR2NEydOwN3dHd27d0dUVJRBYZKIqK7jVUhmaNSoUZg0aRLS0tIQHBwMBweHB16mpaVlme3CgIeRV3UZbdu2hbe3N7799luMHTsW3377LdRqNYYPHy718fLyAgAcP34cnTt3NtqYLly4AD8/P3h5eWHZsmVwd3eHWq3Gtm3bsHz5cmmPSIkHueKoZFlLly4tt4b69esDuLOnrXfv3ti4cSPi4uKwdOlSvP3229iwYQOCg4OrPAYiorqCAcYMDRkyBM899xwOHjyI77//vsw+Hh4e2LVrF3Jzc2V7Yc6cOSNNN9S9exiMaezYsYiIiMC1a9ewZs0ahISESHtLACA4OBiWlpb49ttvDT6RtyI//fQTCgoKsHnzZtnemj179lR6GR4eHjh16hSEELL36Pz587J+JYfXtFot/P3977tcV1dXTJs2DdOmTUNGRgYeeeQRvPHGGwwwRESVwENIZqh+/fr4+OOPERUVhYEDB5bZZ8CAASguLsaHH34oa1++fDlUKlWVfgnWq1cPAKrlRnYjR46ESqXCCy+8gD///FO6+qiEu7s7Jk2ahLi4OKxYsaLU/Hq9Hu+++67BN7Ir2UNz916i69evIzY2ttLLCAwMxN9//43NmzdLbbdu3cJnn30m6+ft7Y0WLVrgnXfeQV5eXqnl/PPPPwDu3G343kNXzs7OcHNzQ0FBQaXHRURUl3EPjJkKCwurcPrAgQPx+OOP47XXXsPFixfRqVMnxMXF4ccff8TMmTNlJ9tWlre3NwDgtddeQ2hoKKytrTFw4EAp2DyIxo0bIygoCOvXr4eDgwNCQkJK9Xn33Xdx4cIFPP/889iwYQOeeOIJODo64tKlS1i/fj3OnDlT6nLy+wkICIBarcbAgQPx3HPPIS8vD5999hmcnZ1x7dq1Si3jueeew4cffoiRI0fihRdegKurK1avXg0bGxsA/7fnysLCAp9//jmCg4PRrl07jB8/Hk2aNMHff/+NPXv2QKvV4qeffkJubi6aNm2Kp556Cp06dUL9+vWxa9cuHDlyBO+++65B9RER1VUMMAplYWGBzZs3Y/78+fj+++8RGxuL5s2bY+nSpXjxxRertMxu3bph0aJFWLlyJXbs2AG9Xo/U1FSjBBjgzmGkLVu2YPjw4WU+FsHOzg7bt2/HqlWr8NVXX2HRokXIz8+Hm5sb+vXrh9WrV6NJkyYGrbN169b44Ycf8Prrr+Oll16CTqfD1KlT0bhxY0yYMKFSy6hfvz52796NGTNm4P3330f9+vUxduxYPProoxg2bJgUZACgb9++SEpKwqJFi/Dhhx8iLy8POp0OPj4+eO6556Q6p02bhri4OGzYsAF6vR4tW7bERx99hKlTpxpUHxFRXaUShpzFSfQAfvzxRwwePBiJiYno3bu3qYfzwN577z3MmjULV65cMThYERHRg2GAoRrzxBNP4PTp0zh//ny1njBcHW7evCm7QunWrVvo0qULiouL8ccff5hwZEREdRMPIVG1W7t2LY4dO4atW7fi/fffV1x4Ae48aqFZs2bo3Lkzrl+/jm+//RZnzpzB6tWrTT00IqI6iXtgqNqpVCrUr18fI0aMwMqVK2VP2laK9957D59//jkuXryI4uJitG3bFi+//DJGjBhh6qEREdVJDDBERESkOLwPDBERESkOAwwREREpjvJORqgkvV6Pq1evokGDBoo8aZTIXAkhkJubCzc3N1hY8G8gIjKNWhtgrl69Cnd3d1MPg6jWunz5Mpo2bWrqYRBRHVVrA0zJAw4vX74MrVZr1GUXFRUhLi4OAQEBsLa2Nuqya4rSa1D6+AHl1pCTkwN3d3fZQ0SJiGparQ0wJYeNtFpttQQYOzs7aLVaRf3iuZvSa1D6+AHl18BDs0RkSjyATURERIrDAENERESKwwBDREREisMAQ0RERIpTa0/iNUTzV7Ya1F9jKRDdHWgftRMFxfITGS++FWLMoREREVEZuAeGiIiIFIcBhoiIiBSHAYaIiIgUhwGGiIiIFIcBhoiIiBTH4ACTmJiIgQMHws3NDSqVCps2bZJNHzduHFQqlewrKChI1icrKwujR4+GVquFg4MDJk6ciLy8PFmfY8eOoXfv3rCxsYG7uzuio6MNr46IiIhqJYMDzI0bN9CpUyfExMSU2ycoKAjXrl2Tvr777jvZ9NGjR+PkyZOIj4/Hli1bkJiYiMmTJ0vTc3JyEBAQAA8PDyQnJ2Pp0qWIiorCp59+auhwiYiIqBYy+D4wwcHBCA4OrrCPRqOBTqcrc9rp06exY8cOHDlyBF27dgUArFixAgMGDMA777wDNzc3rF69GoWFhfjyyy+hVqvRrl07pKSkYNmyZbKgQ0RERHVTtdzIbu/evXB2doajoyP69euHxYsXo2HDhgCApKQkODg4SOEFAPz9/WFhYYFDhw5hyJAhSEpKQp8+faBWq6U+gYGBePvtt/Hff//B0dGx1DoLCgpQUFAgvc7JyQFw54m/RUVFFY5XYykMqk9jIWT/3u1+6zIXJeNUynjvpfTxA8qtQWnjJaLayegBJigoCEOHDoWnpycuXLiAV199FcHBwUhKSoKlpSXS0tLg7OwsH4SVFZycnJCWlgYASEtLg6enp6yPi4uLNK2sALNkyRIsWLCgVHtcXBzs7OwqHHN0d4NKlCzqqi/Vtm3btqotzETi4+NNPYQHovTxA8qrIT8/39RDICIyfoAJDQ2V/t+hQwd07NgRLVq0wN69e+Hn52fs1Unmzp2LiIgI6XVOTg7c3d0REBAArVZb4bzto3YatC6NhcCirnrMO2qBAr38UQInogINWpapFBUVIT4+Hv3794e1tbWph2MwpY8fUG4NJXs3iYhMqdqfhfTQQw+hUaNGOH/+PPz8/KDT6ZCRkSHrc/v2bWRlZUnnzeh0OqSnp8v6lLwu79wajUYDjUZTqt3a2vq+vxzufZ5RZRXoVaXmVdIvIqBy7485U/r4AeXVoKSxElHtVe33gbly5QoyMzPh6uoKAPD19UV2djaSk5OlPrt374Zer4ePj4/UJzExUXasPT4+Hq1bty7z8BERERHVLQYHmLy8PKSkpCAlJQUAkJqaipSUFFy6dAl5eXmYPXs2Dh48iIsXLyIhIQFPPvkkWrZsicDAO4dW2rRpg6CgIEyaNAmHDx/G/v37MX36dISGhsLNzQ0AMGrUKKjVakycOBEnT57E999/j/fff192iIiIiIjqLoMDzNGjR9GlSxd06dIFABAREYEuXbpg/vz5sLS0xLFjxzBo0CA8/PDDmDhxIry9vfHzzz/LDu+sXr0aXl5e8PPzw4ABA9CrVy/ZPV7s7e0RFxeH1NRUeHt748UXX8T8+fN5CTUREREBqMI5MH379oUQ5V92vHPn/U+IdXJywpo1ayrs07FjR/z888+GDo+IiIjqAD4LiYiIiBSHAYaIiIgUhwGGiIiIFIcBhoiIiBSHAYaIiIgUhwGGiIiIFIcBhoiIiBSHAYaIiIgUhwGGiIiIFIcBhoiIiBSHAYaIiIgUhwGGiIiIFIcBhoiIiBSHAYaIiIgUhwGGiIiIFIcBhoiIiBSHAYaIiIgUhwGGiIiIFIcBhoiIiBSHAYaIiIgUhwGGiIiIFIcBhoiIiBSHAYaIiIgUhwGGiIiIFIcBhoiIiBSHAYaIiIgUhwGGiIiIFIcBhoiIiBSHAYaIiIgUhwGGiIiIFIcBhoiIiBSHAYaIiIgUhwGGiIiIFIcBhoiIiBSHAYaIiIgUhwGGiIiIFIcBhoiIiBTH4ACTmJiIgQMHws3NDSqVCps2bZJNF0Jg/vz5cHV1ha2tLfz9/XHu3DlZn6ysLIwePRparRYODg6YOHEi8vLyZH2OHTuG3r17w8bGBu7u7oiOjja8OiIiIqqVDA4wN27cQKdOnRATE1Pm9OjoaHzwwQdYuXIlDh06hHr16iEwMBC3bt2S+owePRonT55EfHw8tmzZgsTEREyePFmanpOTg4CAAHh4eCA5ORlLly5FVFQUPv300yqUSERERLWNlaEzBAcHIzg4uMxpQgi89957eP311/Hkk08CAL7++mu4uLhg06ZNCA0NxenTp7Fjxw4cOXIEXbt2BQCsWLECAwYMwDvvvAM3NzesXr0ahYWF+PLLL6FWq9GuXTukpKRg2bJlsqBDREREdZPBAaYiqampSEtLg7+/v9Rmb28PHx8fJCUlITQ0FElJSXBwcJDCCwD4+/vDwsIChw4dwpAhQ5CUlIQ+ffpArVZLfQIDA/H222/jv//+g6OjY6l1FxQUoKCgQHqdk5MDACgqKkJRUVGF49ZYCoPq1FgI2b93u9+6zEXJOJUy3nspffyAcmtQ2niJqHYyaoBJS0sDALi4uMjaXVxcpGlpaWlwdnaWD8LKCk5OTrI+np6epZZRMq2sALNkyRIsWLCgVHtcXBzs7OwqHHd09wonl2tRV32ptm3btlVtYSYSHx9v6iE8EKWPH1BeDfn5+aYeAhGRcQOMKc2dOxcRERHS65ycHLi7uyMgIABarbbCedtH7TRoXRoLgUVd9Zh31AIFepVs2omoQIOWZSpFRUWIj49H//79YW1tberhGEzp4weUW0PJ3k0iIlMyaoDR6XQAgPT0dLi6ukrt6enp6Ny5s9QnIyNDNt/t27eRlZUlza/T6ZCeni7rU/K6pM+9NBoNNBpNqXZra+v7/nIoKFZVOL3c+fSqUvMq6RcRULn3x5wpffyA8mpQ0liJqPYy6n1gPD09odPpkJCQILXl5OTg0KFD8PX1BQD4+voiOzsbycnJUp/du3dDr9fDx8dH6pOYmCg71h4fH4/WrVuXefiIiIiI6haD98Dk5eXh/Pnz0uvU1FSkpKTAyckJzZo1w8yZM7F48WK0atUKnp6emDdvHtzc3DB48GAAQJs2bRAUFIRJkyZh5cqVKCoqwvTp0xEaGgo3NzcAwKhRo7BgwQJMnDgRc+bMwYkTJ/D+++9j+fLlxqmaar3mr2w16vIuvhVi1OUREdGDMTjAHD16FI8//rj0uuS8k7CwMKxatQovv/wybty4gcmTJyM7Oxu9evXCjh07YGNjI82zevVqTJ8+HX5+frCwsMCwYcPwwQcfSNPt7e0RFxeH8PBweHt7o1GjRpg/fz4voSYiIiIAVQgwffv2hRDlX3asUqmwcOFCLFy4sNw+Tk5OWLNmTYXr6dixI37++WdDh0dERER1AJ+FRERERIrDAENERESKwwBDREREilNrbmRHymfIlUMaS4Ho7nduQljV+/gQEZFycQ8MERERKQ73wBBVgjHvK8N7yhARPTjugSEiIiLFYYAhIiIixWGAISIiIsVhgCEiIiLFYYAhIiIixWGAISIiIsVhgCEiIiLFYYAhIiIixWGAISIiIsVhgCEiIiLF4aMEiGpYyWMJjPVASj6agIjqIu6BISIiIsVhgCEiIiLFYYAhIiIixWGAISIiIsVhgCEiIiLFYYAhIiIixWGAISIiIsVhgCEiIiLFYYAhIiIixWGAISIiIsXhowTqkJJb2APGuY09b2FPRESmwj0wREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOEYPMFFRUVCpVLIvLy8vafqtW7cQHh6Ohg0bon79+hg2bBjS09Nly7h06RJCQkJgZ2cHZ2dnzJ49G7dv3zb2UImIiEihquVRAu3atcOuXbv+byVW/7eaWbNmYevWrVi/fj3s7e0xffp0DB06FPv37wcAFBcXIyQkBDqdDgcOHMC1a9cwduxYWFtb480336yO4RIREZHCVEuAsbKygk6nK9V+/fp1fPHFF1izZg369esHAIiNjUWbNm1w8OBB9OjRA3FxcTh16hR27doFFxcXdO7cGYsWLcKcOXMQFRUFtVpdHUMmIiIiBamWAHPu3Dm4ubnBxsYGvr6+WLJkCZo1a4bk5GQUFRXB399f6uvl5YVmzZohKSkJPXr0QFJSEjp06AAXFxepT2BgIKZOnYqTJ0+iS5cuZa6zoKAABQUF0uucnBwAQFFREYqKiiocr8ZSGFSfxkLI/r3b/dZlSnfXWVENlWXsWg35HIwxflMzVg01/T1nzt/jRFR3GD3A+Pj4YNWqVWjdujWuXbuGBQsWoHfv3jhx4gTS0tKgVqvh4OAgm8fFxQVpaWkAgLS0NFl4KZleMq08S5YswYIFC0q1x8XFwc7OrsIxR3evTGWlLeqqL9W2bdu2qi2sBpRVZ1k1VJaxa63K5/Ag4zcXD1pDTX/P5efn1+j6iIjKYvQAExwcLP2/Y8eO8PHxgYeHB9atWwdbW1tjr04yd+5cRERESK9zcnLg7u6OgIAAaLXaCudtH7XToHVpLAQWddVj3lELFOhVsmknogINWlZNurvOimqoLGPXasjnYIzxm5qxaqjp77mSvZtERKZULYeQ7ubg4ICHH34Y58+fR//+/VFYWIjs7GzZXpj09HTpnBmdTofDhw/LllFylVJZ59WU0Gg00Gg0pdqtra1hbW1d4RgLiqv2y6NAryo17/3WZUpl1VlWDZXVal7cgw7pHoaP40HGby4etIaa/p4z5+9xIqo7qv0+MHl5ebhw4QJcXV3h7e0Na2trJCQkSNPPnj2LS5cuwdfXFwDg6+uL48ePIyMjQ+oTHx8PrVaLtm3bVvdwiYiISAGMvgfmpZdewsCBA+Hh4YGrV68iMjISlpaWGDlyJOzt7TFx4kRERETAyckJWq0WM2bMgK+vL3r06AEACAgIQNu2bTFmzBhER0cjLS0Nr7/+OsLDw8vcw0JERER1j9EDzJUrVzBy5EhkZmaicePG6NWrFw4ePIjGjRsDAJYvXw4LCwsMGzYMBQUFCAwMxEcffSTNb2lpiS1btmDq1Knw9fVFvXr1EBYWhoULFxp7qERERKRQRg8wa9eurXC6jY0NYmJiEBMTU24fDw8Ps76ah4iIiEyLz0IiIiIixWGAISIiIsVhgCEiIiLFYYAhIiIixWGAISIiIsWp9jvx0oNp/spWUw+BiIjI7HAPDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4voyZSOGNfan/xrRCjLo+IqDpwDwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkO78RrZMa+KyoRERGVxj0wREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDhmHWBiYmLQvHlz2NjYwMfHB4cPHzb1kIiIiMgMmG2A+f777xEREYHIyEj8+uuv6NSpEwIDA5GRkWHqoREREZGJmW2AWbZsGSZNmoTx48ejbdu2WLlyJezs7PDll1+aemhERERkYlamHkBZCgsLkZycjLlz50ptFhYW8Pf3R1JSUpnzFBQUoKCgQHp9/fp1AEBWVhaKiooqXJ/V7RsGjc9KL5Cfr4dVkQWK9SqD5jUXSq9B6eMHzLeGzMzMCqfn5uYCAIQQNTEcIqIymWWA+ffff1FcXAwXFxdZu4uLC86cOVPmPEuWLMGCBQtKtXt6elbLGEdVy1JrltJrUPr4AfOsodG7leuXm5sLe3v76h0MEVE5zDLAVMXcuXMREREhvdbr9cjKykLDhg2hUhn3r9ucnBy4u7vj8uXL0Gq1Rl12TVF6DUofP6DcGoQQyM3NhZubm6mHQkR1mFkGmEaNGsHS0hLp6emy9vT0dOh0ujLn0Wg00Gg0sjYHB4fqGiIAQKvVKuoXT1mUXoPSxw8oswbueSEiUzPLk3jVajW8vb2RkJAgten1eiQkJMDX19eEIyMiIiJzYJZ7YAAgIiICYWFh6Nq1K7p374733nsPN27cwPjx4009NCIiIjIxsw0wI0aMwD///IP58+cjLS0NnTt3xo4dO0qd2GsKGo0GkZGRpQ5ZKYnSa1D6+IHaUQMRkamoBK+FJCIiIoUxy3NgiIiIiCrCAENERESKwwBDREREisMAQ0RERIrDAENERESKwwBTjiVLlqBbt25o0KABnJ2dMXjwYJw9e1bWp2/fvlCpVLKvKVOmmGjEpUVFRZUan5eXlzT91q1bCA8PR8OGDVG/fn0MGzas1N2PTa158+alalCpVAgPDwdgfp9BYmIiBg4cCDc3N6hUKmzatEk2XQiB+fPnw9XVFba2tvD398e5c+dkfbKysjB69GhotVo4ODhg4sSJyMvLq8EqiIjMHwNMOfbt24fw8HAcPHgQ8fHxKCoqQkBAAG7ckD+5etKkSbh27Zr0FR0dbaIRl61du3ay8f3yyy/StFmzZuGnn37C+vXrsW/fPly9ehVDhw414WhLO3LkiGz88fHxAICnn35a6mNOn8GNGzfQqVMnxMTElDk9OjoaH3zwAVauXIlDhw6hXr16CAwMxK1bt6Q+o0ePxsmTJxEfH48tW7YgMTERkydPrqkSiIiUQVClZGRkCABi3759Uttjjz0mXnjhBdMN6j4iIyNFp06dypyWnZ0trK2txfr166W206dPCwAiKSmphkZouBdeeEG0aNFC6PV6IYR5fwYAxMaNG6XXer1e6HQ6sXTpUqktOztbaDQa8d133wkhhDh16pQAII4cOSL12b59u1CpVOLvv/+usbETEZk77oGppOvXrwMAnJycZO2rV69Go0aN0L59e8ydOxf5+fmmGF65zp07Bzc3Nzz00EMYPXo0Ll26BABITk5GUVER/P39pb5eXl5o1qwZkpKSTDXcChUWFuLbb7/FhAkTZE8YN/fPoERqairS0tJk77m9vT18fHyk9zwpKQkODg7o2rWr1Mff3x8WFhY4dOhQjY+ZiMhcme2jBMyJXq/HzJkz0bNnT7Rv315qHzVqFDw8PODm5oZjx45hzpw5OHv2LDZs2GDC0f4fHx8frFq1Cq1bt8a1a9ewYMEC9O7dGydOnEBaWhrUanWpJ3a7uLggLS3NNAO+j02bNiE7Oxvjxo2T2sz9M7hbyft67+Mw7n7P09LS4OzsLJtuZWUFJycns/1ciIhMgQGmEsLDw3HixAnZ+SMAZOcldOjQAa6urvDz88OFCxfQokWLmh5mKcHBwdL/O3bsCB8fH3h4eGDdunWwtbU14ciq5osvvkBwcDDc3NykNnP/DIiIqHrwENJ9TJ8+HVu2bMGePXvQtGnTCvv6+PgAAM6fP18TQzOYg4MDHn74YZw/fx46nQ6FhYXIzs6W9UlPT4dOpzPNACvw119/YdeuXXj22Wcr7GfOn0HJ+3rvlV53v+c6nQ4ZGRmy6bdv30ZWVpZZfi5ERKbCAFMOIQSmT5+OjRs3Yvfu3fD09LzvPCkpKQAAV1fXah5d1eTl5eHChQtwdXWFt7c3rK2tkZCQIE0/e/YsLl26BF9fXxOOsmyxsbFwdnZGSEhIhf3M+TPw9PSETqeTvec5OTk4dOiQ9J77+voiOzsbycnJUp/du3dDr9dL4YyIiHgIqVzh4eFYs2YNfvzxRzRo0EA6/8De3h62tra4cOEC1qxZgwEDBqBhw4Y4duwYZs2ahT59+qBjx44mHv0dL730EgYOHAgPDw9cvXoVkZGRsLS0xMiRI2Fvb4+JEyciIiICTk5O0Gq1mDFjBnx9fdGjRw9TD11Gr9cjNjYWYWFhsLL6v29Zc/wM8vLyZHt/UlNTkZKSAicnJzRr1gwzZ87E4sWL0apVK3h6emLevHlwc3PD4MGDAQBt2rRBUFAQJk2ahJUrV6KoqAjTp09HaGio7NAZEVGdZ+rLoMwVgDK/YmNjhRBCXLp0SfTp00c4OTkJjUYjWrZsKWbPni2uX79u2oHfZcSIEcLV1VWo1WrRpEkTMWLECHH+/Hlp+s2bN8W0adOEo6OjsLOzE0OGDBHXrl0z4YjLtnPnTgFAnD17VtZujp/Bnj17yvy+CQsLE0LcuZR63rx5wsXFRWg0GuHn51eqrszMTDFy5EhRv359odVqxfjx40Vubq4JqiEiMl8qIYQwTXQiIiIiqhqeA0NERESKwwBDREREisMAQ0RERIrDAENERESKwwBDREREisMAQ0RERIrDAENERESKwwBDREREisMAQ0RERIrDAENERESKwwBDREREivP/AMkHx6D9CmafAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look for the distribution of the numerical columns\n",
    "df.hist(numerical_features.columns.to_list())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # look for the distribution of the categorical columns\n",
    "# # combine all the plots into one\n",
    "# fig, axes = plt.subplots(6, 3, figsize=(25, 12))\n",
    "# for idx, cat_col in enumerate(categorical_features):\n",
    "#     row, col = idx//3, idx%3\n",
    "#     sns.countplot(x=cat_col, data=df, ax=axes[row, col])\n",
    "# plt.subplots_adjust(hspace=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "object\n",
      "TotalCharges\n",
      "True     7032\n",
      "False      11\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "488      \n",
       "753      \n",
       "936      \n",
       "1082     \n",
       "1340     \n",
       "3331     \n",
       "3826     \n",
       "4380     \n",
       "5218     \n",
       "6670     \n",
       "6754     \n",
       "Name: TotalCharges, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Further inspect TotalCharges column\n",
    "# Why?\n",
    "# Because it is a numerical column (clear from the distribution) but it is not included in the numerical_features list\n",
    "# And the graph above shows that it is a categorical column\n",
    "# So, there must be some non-numeric values in the column\n",
    "\n",
    "print(df['TotalCharges'].dtypes)\n",
    "\n",
    "# look for values that are not numeric in TotalCharges column\n",
    "print(df['TotalCharges'].str.contains(r'[0-9]').value_counts())\n",
    "\n",
    "# extract the index of the rows that have non-numeric values in TotalCharges column\n",
    "idx = df[df['TotalCharges'].str.contains(r'[0-9]') == False].index\n",
    "\n",
    "df['TotalCharges'].loc[idx]\n",
    "\n",
    "# replace the empty ' ' values with NaN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observations\n",
    "\n",
    "1. `SeniorCitizen` is a categorical feature but is represented as a numerical feature.\n",
    "2. `TotalCharges` is a numerical feature but has missing values which are represented as blank spaces in the data. We will need to impute these values.\n",
    "3. There are no outliers in the numerical features.\n",
    "4. We will need to drop the customerID column as it is not useful for our analysis.\n",
    "5. `No internet service` and `No phone service` are not different from `No` and can be replaced with `No`.\n",
    "\n",
    "## Next Steps\n",
    "\n",
    "1. Handle missing values and outliers. **(Cleaning)**\n",
    "    - Impute missing values in TotalCharges column. **(Imputation)**\n",
    "    - Drop customerID column. **(Dropping)**\n",
    "    - Replace `No internet service` and `No phone service` with `No`. **(Modifying)**\n",
    "2. Convert categorical features to numerical features. **(Encoding)**\n",
    "3. Train-test split. **(Splitting)**\n",
    "4. Scale the numerical features. **(Scaling/Normalization/Standardization)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning\n",
    "\n",
    "[Imputation Reference](https://scikit-learn.org/stable/modules/impute.html#impute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "488     2283.300441\n",
       "753     2283.300441\n",
       "936     2283.300441\n",
       "1082    2283.300441\n",
       "1340    2283.300441\n",
       "3331    2283.300441\n",
       "3826    2283.300441\n",
       "4380    2283.300441\n",
       "5218    2283.300441\n",
       "6670    2283.300441\n",
       "6754    2283.300441\n",
       "Name: TotalCharges, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Imputation\n",
    "\n",
    "# Declare a simple imputer object with strategy as 'mean'\n",
    "from sklearn.impute import SimpleImputer\n",
    "mean_imputer = SimpleImputer(strategy='mean', missing_values=np.nan)\n",
    "\n",
    "# replace ' ' values with NaN\n",
    "df['TotalCharges'].replace(' ', np.nan, inplace=True)\n",
    "\n",
    "# Fit transform the imputer object on the columns with missing values\n",
    "df['TotalCharges'] = mean_imputer.fit_transform(df['TotalCharges'].values.reshape(-1,1))\n",
    "\n",
    "df['TotalCharges'][idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7038</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>24</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>84.80</td>\n",
       "      <td>1990.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>72</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>103.20</td>\n",
       "      <td>7362.90</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.60</td>\n",
       "      <td>346.45</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>74.40</td>\n",
       "      <td>306.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>66</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>105.65</td>\n",
       "      <td>6844.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7043 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0     Female              0     Yes         No       1           No   \n",
       "1       Male              0      No         No      34          Yes   \n",
       "2       Male              0      No         No       2          Yes   \n",
       "3       Male              0      No         No      45           No   \n",
       "4     Female              0      No         No       2          Yes   \n",
       "...      ...            ...     ...        ...     ...          ...   \n",
       "7038    Male              0     Yes        Yes      24          Yes   \n",
       "7039  Female              0     Yes        Yes      72          Yes   \n",
       "7040  Female              0     Yes        Yes      11           No   \n",
       "7041    Male              1     Yes         No       4          Yes   \n",
       "7042    Male              0      No         No      66          Yes   \n",
       "\n",
       "         MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
       "0     No phone service             DSL             No          Yes   \n",
       "1                   No             DSL            Yes           No   \n",
       "2                   No             DSL            Yes          Yes   \n",
       "3     No phone service             DSL            Yes           No   \n",
       "4                   No     Fiber optic             No           No   \n",
       "...                ...             ...            ...          ...   \n",
       "7038               Yes             DSL            Yes           No   \n",
       "7039               Yes     Fiber optic             No          Yes   \n",
       "7040  No phone service             DSL            Yes           No   \n",
       "7041               Yes     Fiber optic             No           No   \n",
       "7042                No     Fiber optic            Yes           No   \n",
       "\n",
       "     DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "0                  No          No          No              No  Month-to-month   \n",
       "1                 Yes          No          No              No        One year   \n",
       "2                  No          No          No              No  Month-to-month   \n",
       "3                 Yes         Yes          No              No        One year   \n",
       "4                  No          No          No              No  Month-to-month   \n",
       "...               ...         ...         ...             ...             ...   \n",
       "7038              Yes         Yes         Yes             Yes        One year   \n",
       "7039              Yes          No         Yes             Yes        One year   \n",
       "7040               No          No          No              No  Month-to-month   \n",
       "7041               No          No          No              No  Month-to-month   \n",
       "7042              Yes         Yes         Yes             Yes        Two year   \n",
       "\n",
       "     PaperlessBilling              PaymentMethod  MonthlyCharges  \\\n",
       "0                 Yes           Electronic check           29.85   \n",
       "1                  No               Mailed check           56.95   \n",
       "2                 Yes               Mailed check           53.85   \n",
       "3                  No  Bank transfer (automatic)           42.30   \n",
       "4                 Yes           Electronic check           70.70   \n",
       "...               ...                        ...             ...   \n",
       "7038              Yes               Mailed check           84.80   \n",
       "7039              Yes    Credit card (automatic)          103.20   \n",
       "7040              Yes           Electronic check           29.60   \n",
       "7041              Yes               Mailed check           74.40   \n",
       "7042              Yes  Bank transfer (automatic)          105.65   \n",
       "\n",
       "      TotalCharges Churn  \n",
       "0            29.85    No  \n",
       "1          1889.50    No  \n",
       "2           108.15   Yes  \n",
       "3          1840.75    No  \n",
       "4           151.65   Yes  \n",
       "...            ...   ...  \n",
       "7038       1990.50    No  \n",
       "7039       7362.90    No  \n",
       "7040        346.45    No  \n",
       "7041        306.60   Yes  \n",
       "7042       6844.50    No  \n",
       "\n",
       "[7043 rows x 20 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping\n",
    "\n",
    "# drop the customerID column\n",
    "df.drop('customerID', axis=1, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
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       "      <th>0</th>\n",
       "      <td>Female</td>\n",
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       "      <td>No</td>\n",
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       "      <td>No</td>\n",
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       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
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       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
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       "    <tr>\n",
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       "      <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>7038</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>24</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>84.80</td>\n",
       "      <td>1990.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>72</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>103.20</td>\n",
       "      <td>7362.90</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.60</td>\n",
       "      <td>346.45</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>74.40</td>\n",
       "      <td>306.60</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>66</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>105.65</td>\n",
       "      <td>6844.50</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7043 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0     Female              0     Yes         No       1           No   \n",
       "1       Male              0      No         No      34          Yes   \n",
       "2       Male              0      No         No       2          Yes   \n",
       "3       Male              0      No         No      45           No   \n",
       "4     Female              0      No         No       2          Yes   \n",
       "...      ...            ...     ...        ...     ...          ...   \n",
       "7038    Male              0     Yes        Yes      24          Yes   \n",
       "7039  Female              0     Yes        Yes      72          Yes   \n",
       "7040  Female              0     Yes        Yes      11           No   \n",
       "7041    Male              1     Yes         No       4          Yes   \n",
       "7042    Male              0      No         No      66          Yes   \n",
       "\n",
       "     MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
       "0               No             DSL             No          Yes   \n",
       "1               No             DSL            Yes           No   \n",
       "2               No             DSL            Yes          Yes   \n",
       "3               No             DSL            Yes           No   \n",
       "4               No     Fiber optic             No           No   \n",
       "...            ...             ...            ...          ...   \n",
       "7038           Yes             DSL            Yes           No   \n",
       "7039           Yes     Fiber optic             No          Yes   \n",
       "7040            No             DSL            Yes           No   \n",
       "7041           Yes     Fiber optic             No           No   \n",
       "7042            No     Fiber optic            Yes           No   \n",
       "\n",
       "     DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "0                  No          No          No              No  Month-to-month   \n",
       "1                 Yes          No          No              No        One year   \n",
       "2                  No          No          No              No  Month-to-month   \n",
       "3                 Yes         Yes          No              No        One year   \n",
       "4                  No          No          No              No  Month-to-month   \n",
       "...               ...         ...         ...             ...             ...   \n",
       "7038              Yes         Yes         Yes             Yes        One year   \n",
       "7039              Yes          No         Yes             Yes        One year   \n",
       "7040               No          No          No              No  Month-to-month   \n",
       "7041               No          No          No              No  Month-to-month   \n",
       "7042              Yes         Yes         Yes             Yes        Two year   \n",
       "\n",
       "     PaperlessBilling              PaymentMethod  MonthlyCharges  \\\n",
       "0                 Yes           Electronic check           29.85   \n",
       "1                  No               Mailed check           56.95   \n",
       "2                 Yes               Mailed check           53.85   \n",
       "3                  No  Bank transfer (automatic)           42.30   \n",
       "4                 Yes           Electronic check           70.70   \n",
       "...               ...                        ...             ...   \n",
       "7038              Yes               Mailed check           84.80   \n",
       "7039              Yes    Credit card (automatic)          103.20   \n",
       "7040              Yes           Electronic check           29.60   \n",
       "7041              Yes               Mailed check           74.40   \n",
       "7042              Yes  Bank transfer (automatic)          105.65   \n",
       "\n",
       "      TotalCharges Churn  \n",
       "0            29.85    No  \n",
       "1          1889.50    No  \n",
       "2           108.15   Yes  \n",
       "3          1840.75    No  \n",
       "4           151.65   Yes  \n",
       "...            ...   ...  \n",
       "7038       1990.50    No  \n",
       "7039       7362.90    No  \n",
       "7040        346.45    No  \n",
       "7041        306.60   Yes  \n",
       "7042       6844.50    No  \n",
       "\n",
       "[7043 rows x 20 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Modifying\n",
    "\n",
    "df['MultipleLines'].replace('No phone service', 'No', inplace=True)\n",
    "for i in ['OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies']:\n",
    "    df[i].replace('No internet service', 'No', inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7043, 19)\n",
      "(7043,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0       0\n",
       "1       0\n",
       "2       1\n",
       "3       0\n",
       "4       1\n",
       "       ..\n",
       "7038    0\n",
       "7039    0\n",
       "7040    0\n",
       "7041    1\n",
       "7042    0\n",
       "Name: Churn, Length: 7043, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Modify the target column values\n",
    "df['Churn'].replace({'Yes':1, 'No':0}, inplace=True)\n",
    "\n",
    "# Separate the features and target\n",
    "y = df['Churn']\n",
    "X = df.drop('Churn', axis=1)\n",
    "\n",
    "print(X.shape)\n",
    "print(y.shape)\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical Feature Encoding\n",
    "\n",
    "[Encoding Reference](https://scikit-learn.org/stable/modules/preprocessing.html#encoding-categorical-features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>InternetService_DSL</th>\n",
       "      <th>InternetService_Fiber optic</th>\n",
       "      <th>InternetService_No</th>\n",
       "      <th>Contract_Month-to-month</th>\n",
       "      <th>Contract_One year</th>\n",
       "      <th>Contract_Two year</th>\n",
       "      <th>...</th>\n",
       "      <th>Dependents_Yes</th>\n",
       "      <th>PhoneService_Yes</th>\n",
       "      <th>MultipleLines_Yes</th>\n",
       "      <th>OnlineSecurity_Yes</th>\n",
       "      <th>OnlineBackup_Yes</th>\n",
       "      <th>DeviceProtection_Yes</th>\n",
       "      <th>TechSupport_Yes</th>\n",
       "      <th>StreamingTV_Yes</th>\n",
       "      <th>StreamingMovies_Yes</th>\n",
       "      <th>PaperlessBilling_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <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>7038</th>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>84.80</td>\n",
       "      <td>1990.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>103.20</td>\n",
       "      <td>7362.90</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>29.60</td>\n",
       "      <td>346.45</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>74.40</td>\n",
       "      <td>306.60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>105.65</td>\n",
       "      <td>6844.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7043 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SeniorCitizen  tenure  MonthlyCharges  TotalCharges  \\\n",
       "0               0.0     1.0           29.85         29.85   \n",
       "1               0.0    34.0           56.95       1889.50   \n",
       "2               0.0     2.0           53.85        108.15   \n",
       "3               0.0    45.0           42.30       1840.75   \n",
       "4               0.0     2.0           70.70        151.65   \n",
       "...             ...     ...             ...           ...   \n",
       "7038            0.0    24.0           84.80       1990.50   \n",
       "7039            0.0    72.0          103.20       7362.90   \n",
       "7040            0.0    11.0           29.60        346.45   \n",
       "7041            1.0     4.0           74.40        306.60   \n",
       "7042            0.0    66.0          105.65       6844.50   \n",
       "\n",
       "      InternetService_DSL  InternetService_Fiber optic  InternetService_No  \\\n",
       "0                     1.0                          0.0                 0.0   \n",
       "1                     1.0                          0.0                 0.0   \n",
       "2                     1.0                          0.0                 0.0   \n",
       "3                     1.0                          0.0                 0.0   \n",
       "4                     0.0                          1.0                 0.0   \n",
       "...                   ...                          ...                 ...   \n",
       "7038                  1.0                          0.0                 0.0   \n",
       "7039                  0.0                          1.0                 0.0   \n",
       "7040                  1.0                          0.0                 0.0   \n",
       "7041                  0.0                          1.0                 0.0   \n",
       "7042                  0.0                          1.0                 0.0   \n",
       "\n",
       "      Contract_Month-to-month  Contract_One year  Contract_Two year  ...  \\\n",
       "0                         1.0                0.0                0.0  ...   \n",
       "1                         0.0                1.0                0.0  ...   \n",
       "2                         1.0                0.0                0.0  ...   \n",
       "3                         0.0                1.0                0.0  ...   \n",
       "4                         1.0                0.0                0.0  ...   \n",
       "...                       ...                ...                ...  ...   \n",
       "7038                      0.0                1.0                0.0  ...   \n",
       "7039                      0.0                1.0                0.0  ...   \n",
       "7040                      1.0                0.0                0.0  ...   \n",
       "7041                      1.0                0.0                0.0  ...   \n",
       "7042                      0.0                0.0                1.0  ...   \n",
       "\n",
       "      Dependents_Yes  PhoneService_Yes  MultipleLines_Yes  OnlineSecurity_Yes  \\\n",
       "0                0.0               0.0                0.0                 0.0   \n",
       "1                0.0               1.0                0.0                 1.0   \n",
       "2                0.0               1.0                0.0                 1.0   \n",
       "3                0.0               0.0                0.0                 1.0   \n",
       "4                0.0               1.0                0.0                 0.0   \n",
       "...              ...               ...                ...                 ...   \n",
       "7038             1.0               1.0                1.0                 1.0   \n",
       "7039             1.0               1.0                1.0                 0.0   \n",
       "7040             1.0               0.0                0.0                 1.0   \n",
       "7041             0.0               1.0                1.0                 0.0   \n",
       "7042             0.0               1.0                0.0                 1.0   \n",
       "\n",
       "      OnlineBackup_Yes  DeviceProtection_Yes  TechSupport_Yes  \\\n",
       "0                  1.0                   0.0              0.0   \n",
       "1                  0.0                   1.0              0.0   \n",
       "2                  1.0                   0.0              0.0   \n",
       "3                  0.0                   1.0              1.0   \n",
       "4                  0.0                   0.0              0.0   \n",
       "...                ...                   ...              ...   \n",
       "7038               0.0                   1.0              1.0   \n",
       "7039               1.0                   1.0              0.0   \n",
       "7040               0.0                   0.0              0.0   \n",
       "7041               0.0                   0.0              0.0   \n",
       "7042               0.0                   1.0              1.0   \n",
       "\n",
       "      StreamingTV_Yes  StreamingMovies_Yes  PaperlessBilling_Yes  \n",
       "0                 0.0                  0.0                   1.0  \n",
       "1                 0.0                  0.0                   0.0  \n",
       "2                 0.0                  0.0                   1.0  \n",
       "3                 0.0                  0.0                   0.0  \n",
       "4                 0.0                  0.0                   1.0  \n",
       "...               ...                  ...                   ...  \n",
       "7038              1.0                  1.0                   1.0  \n",
       "7039              1.0                  1.0                   1.0  \n",
       "7040              0.0                  0.0                   1.0  \n",
       "7041              0.0                  0.0                   1.0  \n",
       "7042              1.0                  1.0                   1.0  \n",
       "\n",
       "[7043 rows x 26 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One-hot encoding using pandas\n",
    "X = pd.get_dummies(X, columns=['InternetService', 'Contract', 'PaymentMethod']) # these columns have more than 2 categories\n",
    "X = pd.get_dummies(X, drop_first=True).astype('float64') # these columns have only 2 categories\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train-Test Split \n",
    "\n",
    "#### Before Scaling to prevent data leakage from test set and to avoid overfitting\n",
    "\n",
    "[Train-Test Split Reference](https://developers.google.com/machine-learning/crash-course/training-and-test-sets/splitting-data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5634, 26)\n",
      "(1409, 26)\n",
      "(5634,)\n",
      "(1409,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train and test sets in 80-20 ratio\n",
    "\n",
    "# Import train_test_split function\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=12345, stratify=y)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling/Standardization\n",
    "\n",
    "[Standardization Reference](https://scikit-learn.org/stable/modules/preprocessing.html#standardization-or-mean-removal-and-variance-scaling), [Splitting Scaling Order Reference](https://medium.com/@megha.natarajan/scaling-data-before-or-after-train-test-split-35e9a9a7453f#:~:text=By%20splitting%20first%2C%20you%20ensure,information%20from%20the%20test%20set.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'> <class 'pandas.core.frame.DataFrame'> <class 'pandas.core.series.Series'> <class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "# Scale the numerical features\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "columns = ['tenure', 'MonthlyCharges', 'TotalCharges']\n",
    "\n",
    "# Fit and transform the training data, save the scaling parameters for future use in test data\n",
    "X_train[columns] = scaler.fit_transform(X_train[columns])\n",
    "X_test[columns] = scaler.transform(X_test[columns])\n",
    "\n",
    "# return as numpy array\n",
    "print(type(X_train), type(X_test), type(y_train), type(y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection: Information Gain Calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Contract_Month-to-month', 'tenure', 'Contract_Two year',\n",
       "       'InternetService_Fiber optic', 'TotalCharges', 'MonthlyCharges',\n",
       "       'PaymentMethod_Electronic check', 'InternetService_No', 'Partner_Yes',\n",
       "       'PaperlessBilling_Yes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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B0axZs5yqorwV4nc5NYceemhxv2nRokXRtm3buOKKK+LQQw+NG264Iefqyp7AvjCl1Hf8TxMmTIhzzjkn+vXrF3369CmxFbIlS5bE2LFj44033si7lDL197//PXr16hWNGzeOypUrR40aNaJOnTpx4IEHxp/+9Kf48MMP8y5xg1q0aFHcfffdeZcBP1ihnqtScdddd0WbNm1ixx13jLPOOivGjh0bzz33XNxyyy3RuXPnqFevXvzud7+LOXPm5F1qmdpnn33i4IMPjs8//7x43z/+8Y/o0qVLDBkyJMfKyse8efNKjKJ54YUX4vTTT4+bbropx6rKz6JFi+KWW26Js88+uzgQmzZtWvF95p+Somx9j+LyjR599NHo2bNnLF26NOrUqRNFRUXFrxUVFa03Ad1YPfjgg3HsscdGz549484774zXX389GjduHNdee22MGzcuxo0bl3eJZeriiy+OK664Ijp06BB77rln1KxZs8Trp512Wk6Vlb3NNtssnn/++WjevHmJ/TNnzox99903Pv3005wqKx/Vq1ePt956Kxo2bBgDBgyIL7/8Mm688cZ46623om3btvHvf/877xLL3KWXXhqNGjWKHj16RMTXN+UefPDBqF+/fowbNy522223nCssG5988kkccMABccghh8SIESPiww8/jJ/97Gex2267xZgxY6JChcJ9HuCTTz6JN998MyIimjZtGltuuWXOFZWPVL7L67NkyZJ4+umno2nTpuucrzd2W2yxRUycODFatmwZt9xyS1xzzTXx8ssvx4MPPhjnn39+wV3c77DDDnHXXXdF+/btY/z48XHkkUfGvffeG/fdd1/MnTu34EbSTJs2LSpXrhytWrWKiIj/9//+X9x2223RokWLuOCCC6JKlSo5V1g2Uuo7rjV06NC48MILY6+99oqtt966xLVQRMTDDz+cU2Vl78gjj4wDDjggTj311Pjiiy9it912i9mzZ0eWZTFmzJj4zW9+k3eJP8rDDz8cZ511Vnz++efRpUuXaNOmTTRo0CCqV68en332WcyYMSOee+65mDJlShx//PFx0UUXFWxf4z9Nnz499thjj1i9enXepZSpefPmRVFRUWy77bYR8fVNubvvvjtatGgRJ554Ys7Vla2U2hpR+Oeq71JI/eXdd989qlSpEr169Ypf/epXxQ97rrVixYqYMmVKjBkzJh588MG4/vrr44gjjsip2rK1Zs2aOPzww+Ozzz6LJ554IiZPnhzdunWLiy++OAYMGJB3eWVu//33jxNPPDGOPfbYmD9/fjRt2jRatmwZb7/9dvTv3z/OP//8vEssM6+++mp07Ngx6tatG7Nnz44333wzGjduHOeee27MnTv3pzfoIKNUmjRpkg0YMCBbtmxZ3qWUu9atW2d33HFHlmVZVqtWrWzWrFlZlmXZtGnTsnr16uVZWrlo1KjRN2477LBD3uWVqRo1amSvvvrqOvtfffXVrHr16jlUVL623nrr7Pnnn8+yLMt23nnn7L777suyLMtmzpyZ1a5dO8/Syk2jRo2K2/zkk09mm2yySfbEE09kffv2zX7xi1/kXF3Zmjt3brbddttlv//977MmTZpkPXr0yL766qu8yyo3S5cuzXr37p1VqlQpKyoqyoqKirJKlSplffr0KcjfTSl9l4844ojsmmuuybIsy5YvX541adIkq1y5clapUqXsgQceyLm6slW9evVszpw5WZZ93e4LLrggy7Kvf54L8fdQtWrVsrlz52ZZlmWnnXZaduKJJ2ZZlmVvvvlmtskmm+RZWrnYa6+9ir+zs2bNyqpVq5YdffTR2U477ZQNGDAg3+LKUEp9x7Xq16+fjR49Ou8yNoh69eplr7zySpZlWXbXXXdlO+20U7Zs2bLs+uuvz1q3bp1zdT/ePvvsk/3tb3/LVq9e/a3Hvf/++9lZZ52VjRgxYgNVlq9XXnklq1ChQt5llLn99tuv+Gf3o48+yurUqZO1a9cu22KLLbKhQ4fmXF3ZSqmtWVb456r/Vsj95ccff/x7H7tw4cJs6tSp5VjNhrdixYqsY8eOWfv27bNatWoV/zsXok022SSbOXNmlmVZdtVVV2Xt27fPsizLnnjiiYLrQ3bo0CEbNGhQlmUl7y0///zz2fbbb59jZesnKCmlGjVqFP+jFrrq1atn7733XpZlJb/Ms2bNyqpWrZpjZfxYBx10UHbqqaeus/93v/tdtt9+++VQUfk65ZRTsu233z7r2LFjtvnmm2eff/55lmVZds8992S77757ztWVj9Ruyr355pvZVlttlfXs2TNbs2ZN3uWUqxNPPDFr3LhxNm7cuGzx4sXZ4sWLs8ceeyzbcccds5NPPjnv8spcSt/llC50W7VqlV111VXZ3Llzszp16mSTJ0/OsizLpk6dWpAPY6QW2NepUyd75513sizLsuHDh2cHH3xwlmVZNmnSpGzbbbfNszR+pM0226z437bQ/efvn2OPPTY766yzsizLsjlz5mQ1a9bMszTKUaEGJSndlEuprVmW3rkqpf5yoZs+ffo626RJk7KGDRtmJ598con9haZmzZrF91t/9atfZcOHD8+y7Ouf22rVquVYWdn7z+uC/7y3PHv27J/kveVKeY9o2dh06tQppk6dGo0bN867lHJXv379eOedd6JRo0Yl9k+aNCmJ9heyiy++ODp27BjTp0+PDh06RMTX802/+OKLBTf9R0TElVdeGY0aNYp58+bFZZddFrVq1YqIiI8++ih+97vf5Vxd+dh0001j3rx50bBhw3j88cfj4osvjoiILMs2+qkENt1003Wm+oiIWL58eTz66KOx+eabF+8rpOkQ13rwwQfjgQceKLFYfZcuXaJ69epx5JFHFtz6DoX8Xf5vixcvjs022ywiIh5//PH4zW9+EzVq1IiuXbsW3HpK559/fhxzzDHx+9//Pn7+859Hu3btIiLiySefjN133z3n6srer3/96zjmmGOiSZMm8emnn8YhhxwSEREvv/xy7LTTTjlXV/ayLIs1a9ZERMRTTz0Vv/zlLyMiomHDhrFw4cI8S+NH6tevX9x9991x3nnn5V1KuWvYsGFMmTIlNttss3j88ceL1/f797//HdWqVcu5OiidVatWRdWqVSPi6/Nyt27dIiKiWbNm8dFHH+VZWplLqa0R6Z2rUukvjxs3LipWrBidOnUqsf+JJ56INWvWFPclN2atW7eOoqKiyP5jRYi1/33jjTfGTTfdFFmWRVFRUcFd97Vs2TJGjRoVXbt2jfHjx8dFF10UEREffvhhifsZhaBq1aqxZMmSdfa/9dZbP8kpPQUl38MjjzxS/Oe1J9/XX389WrVqFZUrVy5x7NpfwoXghBNOiAEDBsStt94aRUVF8eGHH8aUKVPizDPPLNiLo/fffz8eeeSRmDt3bqxcubLEayNGjMipqrK37777xpQpU+Lyyy+P++67L6pXrx677rpr/OUvf4kmTZrkXV6Zq1y5cpx55pnr7P/973+fQzUbRiHflBs5cmTeJeRq+fLlUa9evXX2b7XVVrF8+fIcKipfhfxd/m8pXegefvjhsd9++8VHH31UYp2ZDh06xGGHHZZjZeUjtcB+r732Kn4oY+LEicUB7nvvvbfe89fGLJW+41pffvll3HTTTfHUU0/Frrvuus61UCG1+fTTT4+ePXtGrVq1Yrvttit+QOEf//hH8fo7heI3v/lNtGnTJs4666wS+y+77LJ48cUX4/7778+psrJ39dVXf+vrP8WFZctCSjflUmprRFrnqoh0+suDBw+O4cOHr7M/y7IYPHhwQQQl7733Xt4l5ObSSy+Nww47LC6//PLo1atX8fXQI488Em3atMm5urLVrVu3uPDCC+O+++6LiK/DsLlz58ZZZ531k1xDyWLu38P3XQy40FLOLMvikksuiWHDhhXffKtatWqceeaZxZ2NQjJhwoTo1q1bNG7cOGbOnBm77LJL8SJoe+yxRzz99NN5l8iPcOedd8aNN94Y7777bkyZMiW23377GDlyZOywww5x6KGH5l1emVu1alVcddVVMW/evDj++OOLn9C+8soro3bt2tGvX7+cK+SH6tChQ2y++eYxevTo4ouBL774Inr16hWfffZZPPXUUzlXWLZS+i5ff/31MWDAgOIL3ZdffjkqVKgQ11xzTTz00EPxzDPP5F1imXvnnXdi1qxZccABB0T16tWLnxpj4/bqq69Gz549Y+7cuTFw4MAYMmRIRET0798/Pv3007j77rtzrrBspNh3/NnPfvaNrxUVFRVcm6dOnRrz5s2LX/ziF8UB52OPPRabbLJJ7LvvvjlXV3a23HLLePrpp9e5qfraa69Fx44dY8GCBTlVVvZ22GGH73Vcod3Ae/bZZ+Owww6LJUuWRK9eveLWW2+NiIhzzjknZs6cGQ899FDOFZadlNq6Virnqoh0+svVq1ePN954Y50ZXmbPnh0tW7aMZcuW5VMYZWb16tWxZMmS2HTTTYv3zZ49O2rUqBFbbbVVjpWVrcWLF8fhhx8eU6dOjc8//zwaNGgQ8+fPj3bt2sW4ceOiZs2aeZdYgqCE9Vq9enU8//zzseuuu0aNGjXinXfeiaVLl0aLFi2Kf/EWmjZt2sQhhxwSQ4cOjdq1a8f06dNjq622ip49e0bnzp3jt7/9bd4l/ihLliyJOnXqFP/526w9rlDccMMNcf7558fpp58ef/rTn2LGjBnRuHHjuP322+OOO+4omM5Uir5pSPKTTz4Zq1evLognbf7bjBkzolOnTrFixYriJ0+mT58e1apViyeeeCJatmyZc4X8GKlc6H766adx5JFHxjPPPBNFRUXx9ttvR+PGjaNPnz6x6aabxhVXXJF3iWUutcB+fb788suoWLHiOqMQNlaF3nfkaytXroz33nsvdtxxx6hUqTAnZKhevXq88sor0bRp0xL7Z86cGbvvvnt88cUXOVVGWUrlplxEWm1dK4Vz1Vop9Jfr168fd999d/z85z8vsf+pp56KY445Jj7++OOcKisfw4YNi3r16kWfPn1K7L/11lvjk08+WWfEYyH46quv4tlnn41Zs2bFMcccE7Vr144PP/ww6tSpU5D3XSdNmhSvvvpqLF26NPbYY4/o2LFj3iWtXw7romzU7rjjjuzLL79cZ/+KFSuyO+64I4eKyk/VqlWzd999N+8yNphatWoVLzC0ySabZDNmzMiy7OsF/bbffvscKysbFSpUyBYsWJBlWZYVFRVlFSpUWGdbu7/QNG/ePHv44YezLCu5eNRrr72Wbb755jlWVr5Gjx6d7bvvvtnWW2+dzZ49O8uyLLvyyiuzsWPH5lxZ2WnVqlX22GOPrbP/73//e7brrrvmUNGGsWzZsuymm27KBg4cmA0cODC7+eabs+XLl+ddVrlJ4bv8n1asWJHNnDkzW7VqVd6llJtjjz0269SpUzZv3rwS5+XHH388a9GiRc7Vlb3rr78+22KLLbKLL744q169enF7b7vttuyggw7Kubry8e9//zu7+eabs8GDB2effvpplmVZ9tJLL2Xvv/9+zpWVnULvO36bt99+O3v88ceLf/esWbMm54rK3rJly7I+ffpkFStWzCpWrFj8c3vqqadmw4YNy7m6srX33ntnQ4cOXWf/kCFDsj322COHiigPq1atysaPH5+NGjUqW7JkSZZlWfbBBx9kn3/+ec6Vlb2U2prSueo/FXp/+cQTT8xatWpV3M/Isq9/9+66665Z3759c6ysfGy//fbZ888/v87+f/7zn1mjRo1yqKh8zZ49O2vWrFlWo0aNEj+3p512WnbSSSflXF3aCjtmLge9e/eOzp07r/MUwueffx69e/eO4447LqfKyt4uu+wS77777vcenryxq1mzZvHc0ltvvXXMmjWr+MnsQlh49Omnny5e9Cy1ERTvvffeehcHrlq1asEOWf3vUTRrpwXcZJNNYuTIkQXz9PLbb78dLVq0WGd/s2bN4p133smhog2jRo0accIJJ+RdxgaRync54uv1Z/r37x933HFHRHy9wF3jxo2jf//+sc0228TgwYNzrrDsPPnkk/HEE0/EtttuW2J/kyZNYs6cOTlVVX6uueaauPnmm6N79+4l5pvea6+91ruG1sbu1VdfjQ4dOsQmm2wSs2fPjhNOOCE222yzeOihh2Lu3LkxevTovEssE4Xed1yfbxoN1rdv34IbDXb22WfH9OnT49lnn43OnTsX7+/YsWNccMEFBXVOPu+88+LXv/51zJo1q/jp5QkTJsQ999xTUOuTRERMmTIlPv300/jlL39ZvG/06NExZMiQWLZsWXTv3j2uueaa4sXAC8WcOXOic+fOMXfu3FixYkX84he/iNq1a8ell14aK1asiFGjRuVdYplJqa0RaZ2rItLpL1922WXRuXPnaNasWXF/+f3334/9998//vznP+dcXdmbP39+bL311uvs33LLLeOjjz7KoaLyNWDAgNhrr71i+vTpJdZOOuywwwriOv+71gP7T6eddlo5VlJ6gpJSyr5h7uz3338/6tatm0NF5efiiy8uXo9kzz33XGfeuEKbnmmfffaJSZMmRfPmzaNLly5xxhlnxGuvvRYPPfRQ7LPPPnmX96MdeOCBxX/eYYcdomHDhut8l7Msi3nz5m3o0srdDjvsEK+88kpsv/32JfY//vjj0bx585yqKl+p3JSrW7duvPvuu+vM3frOO+/85Oa6/LH+8Y9/fK/jDjjggHKuZMNK5bsckdaF7rJly6JGjRrr7P/ss88K7uZURHqB/cCBA6N3795x2WWXRe3atYv3d+nSJY455pgcKytbhd53XJ/f//73Ubly5Zg7d26JPlSPHj1i4MCBBRWUjB07Nu69997YZ599SvSZW7ZsGbNmzcqxsrL3q1/9KsaOHRuXXHJJPPDAA1G9evXYdddd46mnnipxDVEILrzwwjjooIOKg5LXXnst+vbtG8cff3w0b948Lr/88mjQoEFccMEF+RZaxgr9ptx/SqmtEWmdqyLS6S/XrVs3Jk+eHOPHj4/p06cXn5cL7VpvrYYNG8bzzz+/zoPazz//fDRo0CCnqsrPc889F5MnT44qVaqU2N+oUaP44IMPcqqq7Fx55ZXf67iioiJBycZq9913j6KioigqKooOHTqUmPNx9erV8d5775U4SReCLl26REREt27dSvzCXRsWFdLC9RERI0aMiKVLl0ZExNChQ2Pp0qVx7733RpMmTWLEiBE5V1e2dthhh/joo4/WGRn12WefxQ477FBw/7YDBw6MU045Jb788svIsixeeOGFuOeee2LYsGFxyy235F1euUjlptyhhx4ap59+ejz88MOx4447RsTXIckZZ5wR3bp1y7m6snXQQQd942trz9FFRUXx1VdfbaCKNoxUvssRaV3o7r///jF69Oi46KKLIuLr7+6aNWvisssu+9bFojdWqQX2L774Ytx4443r7N9mm21i/vz5OVRUPlLqO66V0miwTz75ZL1rGSxbtmy9D85t7Lp27Rpdu3bNu4xy98orrxT/7omIGDNmTLRt2zZuvvnmiPj6Zt2QIUMKLigp9Jty/ymltkakd65Kqb9cVFQUBx98cBx88MF5l1LuTjjhhDj99NNj1apVJUY2/uEPf4gzzjgj5+rK3po1a9Z73+39998v8ZDRxuq9997Lu4QfTFDyPXXv3j0ivu5YderUqcTCOlWqVIlGjRrFb37zm5yqKx+pTc/UuHHj4j/XrFmz4Ibk/qdvGhm1dOnSqFatWg4Vla9+/fpF9erV49xzz43ly5fHMcccEw0aNIirrroqjjrqqLzLKxep3JRLaUjyv//97/XuX758eVx11VVx9dVXlziPFYpUvssRaVzofvjhh9GgQYO47LLLokOHDjF16tRYuXJl/OEPf4j//d//jc8++yyef/75vMssc6kF9lWrVo0lS5ass/+tt96KLbfcMoeKykdKfce1UhoNttdee8Vjjz0W/fv3j4j/eyjhlltuiXbt2uVZGj/Cv//976hXr17xf0+cODEOOeSQ4v/ee++9C3KEfaHflPtPKbU1Ir1zVSH3l6+++uo48cQTo1q1at85ddFP7Sn8H2vQoEHx6aefxu9+97viaU2rVasWZ511Vpx99tk5V1f2Dj744Bg5cmTcdNNNEfH1z+3SpUtjyJAhxQ+tkw9Byfc0ZMiQiPj6KYQePXoU5M3k/1Zow6y/j0WLFsUDDzwQs2bNikGDBsVmm20W06ZNi3r16sU222yTd3k/2sCBAyPi65PweeedV+JCd/Xq1fGvf/0rWrdunVN15atnz57Rs2fPWL58eSxdunS9natCkspNuZSGJP/39I5r1qyJW2+9NYYOHRoVKlSI6667Lnr16pVTdeUnle9yRBoXui1btozrrrsujjnmmHjzzTfjuuuui9q1a8fSpUvj17/+dZxyyinrnZ94Y3XkkUfGjTfemFxg361bt7jwwgvjvvvui4ivv8tz586Ns846q+AeLCr0vuN/S2E02LJly6JmzZpxySWXxCGHHBKvv/56fPXVV3HVVVfF66+/HpMnT46JEyfmXeaPttlmm8Vbb70VW2yxRWy66abfeoPxs88+24CVla969erFe++9Fw0bNoyVK1fGtGnTYujQocWvf/7551G5cuUcKywfKd2US6WtqZyr/lsh95evvPLK6NmzZ1SrVu1bpy76KU5X9GMVFRXFpZdeGuedd1688cYbUb169WjSpEnBPYTxyiuvROvWrePPf/5zdO7cOVq0aBFffvllHHPMMfH222/HFltsEffcc0/eZZa5999/Px555JGYO3ducRC21k9tFHZRlmVZ3kVsjFauXBkff/xxrFmzpsT+7bbbLqeKyt53zYdfaDciX3311ejYsWPUrVs3Zs+eHW+++WY0btw4zj333IJZeHTtBezEiROjXbt2JYYjrx0ZdeaZZ0aTJk3yKpEydNddd8UFF1xQPAS5QYMGMXTo0Ojbt2/OlZWPL7/8MqpWrbrRP0n0fTz00ENxzjnnxCeffBJnn3129O/fv+A6kWtvMG+66aYF/11ee6E7adKkOOSQQ+J//ud/4vbbb4+TTjqpxIXunnvumXepP9r1118fZ511VnTu3DluvPHG2GyzzfIuqVy1b98+Zs+eHTfffHPxlDaFHNiPGzcuunTpEosXL47DDz88pk6dGp9//nk0aNAg5s+fH+3atYtx48YVzBpSKfQd/9uMGTOiQ4cOsccee8TTTz8d3bp1KzEabO00mBuzHXfcMe64447Yb7/9YtasWTF8+PCYPn16LF26NPbYY48466yzolWrVnmX+aPdcccdcdRRR0XVqlXj9ttv/9b+UyE9iPHb3/42pk+fHpdeemmMHTs27rjjjvjwww+Lr4vuuuuuGDlyZLz44os5V1o21t6UmzdvXnTu3DmyLIu333479tprr+Kbcv/4xz8K4ndSSm2NSOdctVZK/eWUvfPOOzFr1qw44IADonr16t84G8rGqmrVqjFkyJAYPHhwrFmzJsaMGROvvvpq8c9tz549o3r16nmXWaYmTJgQ3bp1i8aNG8fMmTNjl112idmzZ0eWZcX9yZ+UjFJ56623sv322y+rUKFCia2oqCirUKFC3uWVqaKionW2/2xzoenQoUM2aNCgLMuyrFatWtmsWbOyLMuy559/Ptt+++1zrKzsHX/88dnixYvzLmODad26dbb77ruvs+2xxx5Z+/bts+OOOy57+umn8y6z3CxbtixbsGBB3mWUi9WrV2cXXnhh1qBBg6xixYrFP7fnnntudsstt+RcXdl79tlns7Zt22Y1atTIzj777GzRokV5l1Ru2rVrl2299dbZ3/72t+J9hfpdbty4cfbcc89lWZZl77zzTtavX79s7733zpo3b5717Nkze/XVV3OusGy9++672c9+9rOsXr162SOPPJJ3OeVqzZo12WWXXZZVr14969u3b7Z06dK8SypXVatWzU444YTs888/z7Isy5577rnsuuuuyy699NJs/PjxOVdX9lLqO/6nRYsWZRdffHF2xBFHZIccckj2xz/+Mfvwww/zLqvMDBo0KKtcuXJ25plnZitWrMi7HMrYJ598ku2///5ZUVFRVrt27ezBBx8s8frPf/7z7JxzzsmpurJXpUqV7E9/+lO2evXqbNWqVdmdd96ZDRo0KPvtb3+b3Xzzzdny5cvzLrHMpNTWLEvvXJVaf3no0KHZsmXL1tm/fPnybOjQoTlUVL4WLlyY/fznPy++77i2T9W7d+9s4MCBOVdXdh577LFsm222ydq2bZu99dZbeZezQey9997Z+eefn2XZ//WXP//886xbt27Z9ddfn3N16xKUlFL79u2zAw44IBs3blz28ssvZ6+88kqJrZAsWrSoxPbJJ59kTz75ZNa2bdvsqaeeyru8MlenTp3snXfeybKs5MXu7Nmzs6pVq+ZZWrmaO3duNnfu3LzLKFeDBw/O6tatm+23337ZwIEDs4EDB2b7779/Vrdu3WzAgAHZL37xi6xChQrZ2LFj8y6VUho6dGjWuHHj7K9//WtWvXr14p/bMWPGZPvss0/O1ZWtQw45JKtcuXJ20kknZR999FHe5ZS7lG4wp3ahu9Y111yTVapUKWvVqtU6QXaheeONN7J99tkna9SoUXbFFVdkV111VYmtULzyyitZ69atsx122CGbOHFi3uWUu1T7jimYMmVK1rx586xly5bZtGnT8i6n3FWoUGG9DyIsXLiwIB+Qy7Kvr3W/+uqrdfZ/+umnBfW7OKWbcim1da2UzlWp9ZdTOy8fe+yxWadOnbJ58+aV6FM9/vjjWYsWLXKurmwtWrQo69WrV1azZs3s6quvzrucclerVq3i/vImm2ySzZgxI8uyr68bfooPFpl6q5Rq1qwZL730UjRr1izvUnIzceLEGDhwYLz00kt5l1Kmttpqq3jiiSdi9913j9q1a8f06dOjcePGMX78+OjTp09BLer31VdfxdChQ+Pqq6+OpUuXRkRErVq1on///jFkyJCCm5f3hBNOiO222y7OO++8EvsvvvjimDNnTtx8880xZMiQeOyxx2Lq1Kk5VVm2dt999/UOUS0qKopq1arFTjvtFMcff/xGP5/4TjvtFDfeeGN06NChxM/tzJkzo127dt+4APrGqEKFClGpUqWoWbNmMvOIR0TMnDkzevfuHfPnz4/+/ftHpUoll1crlPl5//nPf0afPn2iQoUKceedd8buu++ed0nlas6cOdG7d++YMWNGnHTSSev8u65dG66Q3HLLLXHyySfH1ltvXaK9RUVF8e677+ZYWdn66quv4uKLL47hw4fHKaecEueee25UrFixxDF16tTJqbqylVLfca3GjRvHgQceGKNGjSox5ePChQujTZs2BfVdXrFiRZx77rlx7bXXxi9+8Yt1zlMPPfRQTpWVvQoVKsT8+fPXmZLoww8/jB133DG++OKLnCorX1mWxaeffhpFRUWx+eab511OuVm8eHEMGDAgHnjggRg2bFjx2g6FKKW2rpXSuSql/nKFChViwYIFseWWW5bY//TTT0ePHj3ik08+yamy8lG/fv144oknYrfddivRp3r33Xdj1113Lb5vVUgeeOCBOOqoo6JmzZrr9JUL6Zq+fv368cwzz0Tz5s2jRYsWMXz48OjWrVtMnz499t1335/cv63F3EupRYsWsXDhwrzLyFW9evXizTffzLuMMpfSwqP9+/ePhx56KC677LLiBc+mTJkSF1xwQXz66adxww035Fxh2brvvvvWG+wdddRRseeee8bNN98cRx999E9uEakfo3PnznHDDTdEq1atok2bNhER8eKLL8arr74axx9/fLz++uvRsWPHeOihh+LQQw/Nudof7oMPPoiddtppnf1r1qyJVatW5VBR+bntttvyLiEXzZo1i759+8bJJ58cV1555To3mAslKNlnn33i5ZdfjnPPPTfat29f0Be6N998c5xxxhnRsWPH+N///d91LgALzYIFC6Jfv34xadKk+Mtf/lJQc/2vT6VKleKCCy6I9u3bR5cuXWLkyJHFr2X//zzTq1evzq/AMpRS33Gt2bNnR6VKlWL//fePRx55JOrXrx8REatXr445c+bkXF3ZWrFiRXz88cdRVFQUdevWXeecXAiuvvrqiPj6u3vLLbdErVq1il9bvXp1/OMf/yjIBwTnz58ff/jDH+KRRx6Jzz//PCK+DnAPO+ywGDZsWNSrVy/nCstW3bp14/bbb49f/vKXcdRRR603wC6Um3IptXWtFM5Va6XQX950002jqKgoioqKYueddy7xgNzq1atj6dKlcfLJJ+dYYflYtmxZ1KhRY539n332WcGtxRnx9b2Z8847L5o0aRJnnnlmwf/cTpo0KZo3bx5dunSJM844I1577bV46KGHYp999sm7vHUU7r9EObn00kvjD3/4Q1xyySXRqlWrdZ68L5Qn5CK+XqDyP2VZFh999FEMHz48WrdunU9R5eiKK66Iww8/PLbaaqv44osv4sADDyxeePRPf/pT3uWVqbvvvjvGjBkThxxySPG+XXfdNRo2bBhHH310wQUl1apVi8mTJ69zQ33y5MlRrVq1iPj6xvraPxeChQsXxhlnnPGNo2iefPLJGDJkSFx00UUbdVDSokWLeO6552L77bcvsf+BBx4ouCeMSntz9Z577olu3bpt1Ismp3aDOYUL3c6dO8cLL7wQ1157bRx33HF5l1PuxowZE6eeemq0bt06pk+fHtttt13eJW0QDz30UPz2t7+NAw44IP74xz8W5Hc5Iq2+41pFRUXx+OOPx5lnnhl77rlnjB07Nvbee++8yypza0cFbb311vHSSy9F8+bN8y6pXFx55ZUR8fV13qhRo0rcUK5SpUo0atQoRo0alVd55WLJkiXRvn37WLp0afTu3TuaNWsWWZbF66+/Hvfcc09MmjQppk2bViI0KgQp3ZRLqa2pnKv+U6H3l0eOHBlZlkWfPn1i6NChUbdu3eLX1p6X1z7sWkj233//GD16dFx00UUR8XV/Y82aNXHZZZdt9LNg/KevvvoqhgwZEn/+85/jlFNOiUsuuaSg7kOtz4gRI4pHjQwdOjSWLl0a9957bzRp0uSn+bByfrN+bZzWt6h5IS/mvrZt/7m1a9cue+ONN/Iur9wU+sKjWZZlW265Zfb666+vs//111/PtthiixwqKl8XXXRRVr169ey0007L7rzzzuzOO+/MTjvttKxGjRrZxRdfnGVZlo0YMSLr2LFjzpWWnTp16mRvv/32OvvffvvtrE6dOlmWfT1nfq1atTZ0aWVq7NixWd26dbPhw4dnNWrUyC6//PKsX79+WZUqVbInn3wy7/JyVbt27eK5XTdG99xzT7b55ptnHTp0yObMmZN3OeXuySefzLbddtts7733Xu/5uVB07Ngxmzdv3vc+ft68ednq1avLsaLyVaNGjSTmHl7r3//+d3b00UdnNWrUyEaOHJl3ORtMCn3HtYqKiornTB88eHBWvXr17M4778zmz59fMNdCJ554Yla1atVs6NCh613DohAddNBB2WeffZZ3GRvEhRdemO20007Zxx9/vM5rCxYsyHbaaafsT3/6Uw6VlY9Vq1Zl55xzTlalSpXs97//ffbFF1/kXVK5SamtWZbmuSqV/nKWZdmzzz6brVy5Mu8yNpjXXnst22qrrbLOnTtnVapUyQ4//PCsefPmWb169YrXtygErVq1ynbYYYfsmWeeybsUvoE1Skpp4sSJ3/r6gQceuIEqKX//PXy+QoUKseWWWxZs2vnuu+9G48aN8y5jg7jwwgtj5syZcdtttxUPY1yxYkX07ds3mjRpUpBzw991111x7bXXFk8b17Rp0+jfv38cc8wxERHxxRdfFK/fUQjq1asXl19++TpPbI8ePToGDRoUCxYsiNdffz0OPPDAjX5+0+eeey4uvPDCmD59eixdujT22GOPOP/88+Pggw/Ou7Rc/efcrhujmjVrxvDhw5OYW/qkk06KO+64I84555z44x//uM4UESmrU6dOvPLKKxvt9/jtt9+OJk2afO/ju3btGrfccktsvfXW5VhV+WnQoEFst912cccdd0TTpk3zLqfcpdR3XKtixYrx0UcfFa9l8de//jVOOOGEOProo+OOO+4oiGnVdtlllxg9enTsscceeZdCOdhnn33ipJNOit69e6/39VtvvTVuvvnmmDJlygaurHysndv/1ltvjYMOOijvcspVSm2NSO9clXJ/+csvv4yVK1eW2FdIs9mstXjx4rj22mtLXNefcsopG22/eH369esXV155ZdSuXft7Hf/888/HXnvtVZDTj/1UCUrg/1ehQoU48MADo2/fvnH44YcXzA3z9TnssMNiwoQJUbVq1dhtt90iImL69OmxcuXK6NChQ4ljN/Y5Pr/66qu45JJLok+fPrHtttvmXc4Gc/HFF8cll1wSJ5xwQvGUGC+++GLccsstxZ3LK6+8MsaNGxfjx4/PuVrKw8YelKR0gzm1C93S2Ni/x6W1sbf34osvjrPPPvt737zY2KcITKnvuNb6Fv2eMmVKHHbYYfHJJ58URFCycuXKqFKlyvc+fmMPdNd6//3345FHHom5c+euc0PuJzk1xg+02WabxZQpU74xzJ05c2a0b9++YNaxSOmmXEptjUjvXJVaf3n58uXxhz/8Ie6777749NNP13m9EH7f8t025p/b71vzu+++W86VlI6g5AdYtGhR/OUvf4k33ngjIiJatmwZffr0KTF3YKGYMGFCTJgwIT7++ONYs2ZNidduvfXWnKoqH6+88krcdtttcc8998TKlSujR48e0adPn2jbtm3epZW5b3qCan0KYQHpWrVqxYwZM6JRo0Z5l7JBpTSKZuXKles9T6WyHsD6bOw3XEtrY25vahe6pbEx/7v+EKm1d2P/LqfUd/wuCxYsiJkzZxbU6PrvqxB+bidMmBDdunWLxo0bx8yZM2OXXXaJ2bNnR5Zlsccee8TTTz+dd4llplKlSvHBBx9844Lt8+fPj2233Ta++uqrDVzZT8PGfl4ujZTaGrHxn6tS6y+fcsop8cwzz8RFF10Uxx57bFx33XXxwQcfxI033hjDhw+Pnj175l1imVu0aFG88MIL672uT2Ftw/XZmH9uK1SoENtvv30cc8wxJR6w+W8DBgzYgFV9t8Ja9WgDmDp1anTq1CmqV68ebdq0iYivn7D505/+FE8++WRBpdtDhw6NCy+8MPbaa6/Yeuuto6ioKO+SylXr1q3jqquuiiuuuCIeeeSRuP3222P//fePnXfeOfr06RPHHntsbLnllnmXWSYKIfwojQ4dOsTEiROTCUr+cxTNt3WgqlevvgGrKh9vv/129OnTJyZPnlxif5ZlUVRU5EkbNgqlueiL+Pr7DYVgY/8up9R3/C716tX7xhvP/PSdffbZceaZZ8bQoUOjdu3a8eCDD8ZWW20VPXv2jM6dO+ddXpnKsiwqVKjwja8XFRVt9OemHyOltqfU1kKQWn/50UcfjdGjR8dBBx0UvXv3jv333z922mmn2H777eOuu+4quKDk0UcfjZ49e8bSpUujTp06Je4/FhUVJRuUbMzuvffeuPXWW2PEiBFxyCGHRJ8+faJLly7f+jv4p8CIklJae3K6+eabo1Klr3Omr776Kvr16xfvvvtu/OMf/8i5wrKz9dZbx2WXXRbHHnts3qXkYsWKFXH99dfH2WefXfz0wpFHHhmXXnrpRjm1y/p88sknJUYbFOrF/KhRo2Lo0KHRs2fP2HPPPdeZ4qNbt245VVZ+UhlFs++++0alSpVi8ODB6w10104tl6KN+emTHyKl9mpr4dLejVuh9h332GOPmDBhQmy66aax++67f+vDU9OmTduAlf00FML3uHbt2vHKK6/EjjvuGJtuumlMmjQpWrZsGdOnT49DDz00Zs+enXeJZaZChQpRt27db/weZ1kWS5YsSfZhm0L4Pn9fKbU1Qns3NrVq1YrXX389tttuu9h2223joYceijZt2sR7770XrVq1iqVLl+ZdYpnaeeedo0uXLnHJJZdEjRo18i7nJ2Nj/x5HRHzwwQdx++23x+233x7Lly+PY489tnh95J8iI0pKaerUqSVCkoivh+/+4Q9/iL322ivHysreypUro3379nmXscFNnTo1br311hgzZkzUrFkzzjzzzOjbt2+8//77MXTo0Dj00EPjhRdeyLvMH2XZsmXRv3//GD16dPGQxooVK8Zxxx0X11xzTcH9Yvrd734XEeufX7lQRx2kMormlVdeiZdeeimaNWuWdyk/Odtvv31Urlw57zLgRyn00awUhkLvOx566KHFc/gfeuihfi4LUM2aNYvXJdl6661j1qxZ0bJly4iIWLhwYZ6llbnURtYDG6fGjRvHe++9F9ttt100a9Ys7rvvvmjTpk08+uijsckmm+RdXpn74IMP4rTTTiu4e1FEbLPNNvHHP/4x/vjHP8bEiRPjggsuiMsvvzwWLlwYm266ad7lrUNQUkp16tSJuXPnrnNTbt68ed970bCNRb9+/eLuu++O8847L+9SNogRI0bEbbfdFm+++WZ06dIlRo8eXWJY2A477BC33357Qdx4HjhwYEycODEeffTR2HfffSMiYtKkSXHaaafFGWecETfccEPOFZat/57fMgWHHHJIDB48OF577bWCHkXTokWLgruA/z4WLVoUDzzwQMyaNSsGDRoUm222WUybNi3q1asX22yzTUREzJgxI+cq4ccz8JmfslT6jkOGDCn+8wUXXJBfIT9RhRAc7bPPPjFp0qRo3rx5dOnSJc4444x47bXX4qGHHop99tkn7/LKVK9evUp1/D333BPdunVbpy8NG5tCOFelpHfv3jF9+vQ48MADY/DgwfGrX/0qrr322li1atV6HwDd2HXq1CmmTp26UY+cKA+F8nP75ZdfxgMPPBC33npr/Otf/4ojjjjiJxuKCUpKqUePHtG3b9/485//XDza4vnnn49BgwbF0UcfnXN1ZevLL7+Mm266KZ566qnYdddd13kyudBOzjfccEP06dMnjj/++G+cHmGrrbaKv/zlLxu4srL34IMPxgMPPBAHHXRQ8b4uXbpE9erV48gjjyy4oCRFhTyKZsmSJcV/vvTSS+MPf/hDXHLJJdGqVat1zlN16tTZ0OWVu1dffTU6duwYdevWjdmzZ8cJJ5wQm222WTz00EMxd+7cGD16dN4lUs4KocPcp0+fuOqqq9Z5yGTtiMdbb701IiJef/31aNCgQR4llqlly5Z9r5ts55xzTmy22WYboCLKQkp9x7UaN24cL774Ymy++eYl9i9atCj22GOPePfdd3OqLD+FEOiOGDGieBqXoUOHxtKlS+Pee++NJk2aFNw1X2mddNJJ0bZt22Ru3hVCH+P7SqmtEYVxriqNjf3f9/e//33xnzt27BgzZ86Ml156KXbaaafYddddc6ys7DzyyCPFf+7atWsMGjQoXn/99fVe1xfKg56ltbH/3P7rX/+Kv/zlL3HfffdF48aNo0+fPvHggw/+JEeSrGWNklJauXJlDBo0KEaNGhVfffVVRERUrlw5fvvb38bw4cOLh6UXgp/97Gff+FpRUVE8/fTTG7AaylKNGjXipZdeiubNm5fY/7//+7/Rpk2bWLZsWU6VlZ9ly5bFxIkTY+7cucVTC6x12mmn5VQVP0SFChVKdHzXLtz+nwp5MfeOHTvGHnvsEZdddlmJOUsnT54cxxxzTEHNI75q1ao46aST4rzzzosddtjhW48dNmxY/Pa3vy3Ioej/rRDmqq1YsWJ89NFHsdVWW5XYv3Dhwqhfv35xH6tQ1KpVK4488sjo06dP7LfffnmX85Oxyy67xN///vdo2LBh3qXwPVWoUCHmz5+/zs/uggULomHDhuv0sQrF2kvm9d14mzRpUuy9994FdR3I/ymE37mlkVJ7N/a2nnnmmdGvX7/vPQVxaueqjf3fNwXfd1HvQryu/+KLLyLLsuJRFXPmzImHH344WrRoEQcffHDO1ZWNli1bxscffxzHHHNM9OnTZ6NZP1ZQ8gMtX748Zs2aFRERO+644092yBCsT4cOHWLzzTeP0aNHR7Vq1SLi6xN1r1694rPPPounnnoq5wrL1ssvvxxdunSJ5cuXx7Jly2KzzTaLhQsXRo0aNWKrrbZK8unHjdnEiRO/97EHHnhgOVaSj7p168a0adNixx13LHEBMGfOnGjatGl8+eWXeZdYpurWrRuvvPLKdwYlKdmYL3SXLFkSWZbFpptuGm+//XZsueWWxa+tXr06Hn300Rg8eHB8+OGHOVZZ9saOHRu33357jBs3Lho1ahR9+vSJ4447riBGy6zPvHnzoqioKLbddtuIiHjhhRfi7rvvjhYtWsSJJ56Yc3X8EGuf+uzevXvccccdUbdu3eLXVq9eHRMmTIjx48fHm2++mVeJ5eIvf/lLXHnllfH2229HRESTJk3i9NNPj379+uVcWflZunTpOtPWFuII3e+rkG62fvXVV/Hss8/GrFmz4phjjonatWvHhx9+GHXq1IlatWrlXV6ZSeEGZMTX56N333032rZtG/369YsePXqYIu4/bMz95bVefPHFeOaZZ+Ljjz9e57yc+mi/jd3BBx8cv/71r+Pkk0+ORYsWRbNmzaJy5cqxcOHCGDFiRPz2t7/Nu8QfrUKFClGzZs2oVKnSt47w+uyzzzZgVd9NUMJ3euedd2LWrFlxwAEHRPXq1df79DYblxkzZkSnTp1ixYoVxanu9OnTo1q1avHEE08UL95YKA466KDYeeedY9SoUVG3bt2YPn16VK5cOf7nf/4nBgwYEL/+9a/zLrFcGEVTmLbaaqt44oknYvfddy9x8T5+/Pjo06dPzJs3L+8Sy1SvXr2idevWJYafF6qBAweud39RUVFUq1Ytdtpppzj00EM36qmZ/ntE2H8rKiqKoUOHxh//+McNWNWG88knn8Sdd94Zt99+e7zxxhvRqVOn6NOnT3Tr1i0qVSqcGXH333//OPHEE+PYY4+N+fPnR9OmTaNly5bx9ttvR//+/eP888/Pu0RKae1Tn0VFRetMA1G5cuVo1KhRXHHFFfHLX/4yj/LKxfnnnx8jRoyI/v37R7t27SIiYsqUKXHttdfG73//+7jwwgtzrrDsvPfee3HqqafGs88+W+KBi0Ieoft9FUpQMmfOnOjcuXPMnTs3VqxYEW+99VY0btw4BgwYECtWrIhRo0blXWKZSeEG5Fr/+Mc/4tZbb40HH3wwIiKOOOKI6NevX/E08YVo9erVcfvtt8eECRPWGyAUyswnl1xySZx77rnRtGnTqFevXon+sxleNn5bbLFFTJw4MVq2bBm33HJLXHPNNfHyyy/Hgw8+GOeff3688cYbeZf4o91xxx3f67jSrh1W3gQlpfTll1/GNddc842p7rRp03KqrOx9+umnceSRR8YzzzwTRUVF8fbbbxfPKbfpppvGFVdckXeJ/AjLly+Pu+66K2bOnBkREc2bN4+ePXtG9erVc66s7G2yySbxr3/9K5o2bRqbbLJJTJkyJZo3bx7/+te/olevXsX/HxSSVEbR3HbbbVGrVq044ogjSuy///77Y/ny5T+5X7ploV+/fvHpp5/GfffdF5tttlm8+uqrUbFixejevXsccMABMXLkyLxLLFMXX3xxXHHFFdGhQ4fYc88913lSrpBCv5/97Gcxbdq0WL16dTRt2jQiIt56662oWLFiNGvWLN58880oKiqKSZMmRYsWLXKu9oeZOHFiZFkWP//5z+PBBx8sEfpUqVIltt9++4IdZfHfrrnmmhg0aFCsXLkytthiizj55JNj8ODBBTFKedNNN41//vOf0bRp07j66qvj3nvvjeeffz6efPLJOPnkkwvmd1CKdthhh3jxxRdjiy22yLuUcrflllvG1Vdfvc46lPfcc0/0798/Fi5cmFNlZW/fffeNLMtiwIAB69yQiyjMEbrfV6EEJd27d4/atWvHX/7yl9h8882L2/Tss8/GCSecUDxqqhCkcAPyvy1btizuvffeuO222+L555+Ppk2bRt++fePYY4+NevXq5V1emTr11FPj9ttvj65du8bWW2+9zvnqyiuvzKmyslWvXr249NJL4/jjj8+7lA3itNNOi5122mmda7trr7023nnnnYK7xq1Ro0bMnDkztttuuzjyyCOjZcuWMWTIkJg3b140bdo0li9fnneJG9w999wT3bp1y31knKCklHr27BlPPvlkHH744evtRA4ZMiSnysrecccdFx9//HHccsst0bx58+LO1BNPPBEDBw6M//3f/827RPhettxyy5g8eXI0adIkdt5557jmmmuiU6dOMXPmzNhzzz0Lck2WVEbR7LzzznHjjTeus6bSxIkT48QTTyy4KUAiIhYvXhyHH354TJ06NT7//PNo0KBBzJ8/P9q1axfjxo3LvWNR1r5tyq2ioqKCuuE6cuTIeO655+K2224rnuZk8eLF0a9fv9hvv/3ihBNOiGOOOSa++OKLeOKJJ3Ku9of76quv4oQTTogLL7wwubUpFixYEHfccUfcfvvtMWfOnDjssMOib9++8f7778ell14aDRo0iCeffDLvMn+0WrVqxYwZM6JRo0bRrVu32HfffeOss86KuXPnRtOmTeOLL77Iu0T4Tptsskm8+OKL0aRJkxL733rrrWjTpk0sWrQon8LKQa1ateKll14qDun5P4USlGy++eYxefLkaNq0aYk2zZ49O1r8f+ydeVyN6f//X6copV1C0Z6UQg3GXirZE8NIpchupJEaDEpFskSW+ShMVPYlg7GEJJI9LaRSWiyVRLTRdn5/9Ov+djpZGud0O9d9Px+Pz+PjXPf549XUuc99Xe/3+/UyNCTqUI7pB5CZmZnYt28fgoODUVZWhk+fPtEtSaAoKysjPDwcY8aMoVuKUOnSpQuuX7/O9x1EKmpqajhz5gx++uknnvWEhATY2NjgxYsXNCkTDr169cLs2bMxceJEGBkZ4eLFixg4cCAePHiAsWPHoqCggG6JrY6cnBwSExNp/74lZ8a/lfj3339x/vx5DB48mG4pQufSpUuIioqi/KUb0NPTQ25uLk2qhE9RURF1uKqvr8/jn84impiYmFAbXTMzM3h5eeHNmzeIiIiAkZER3fKEQmJiIkJCQiAmJgZxcXF8+vQJ2tra2LhxI5ydnYkplOTl5TV7kK6hoYG8vDwaFAkfeXl5XL58GTdv3kRSUhLKyspgamoKKysruqUJhezsbLoltBqbNm3C5cuXebzg5eXlsWbNGlhbW8PNzQ1eXl4i76/dpk0bnDhxgqjmkq8RGRmJffv2ISoqCoaGhli4cCEcHR2hoKBAvWfQoEEwMDCgT6QA6dmzJ4KDgzF27FhcvnwZfn5+AIBXr16hQ4cONKsTLEzw/N++fTvmzp2Ldu3aYfv27V98L0lTftOnT8euXbv4fOB3794NBwcHmlQJh379+lGHyCy8aGhooG3btnTL+G7q6uqatVB78eIFZGVlaVAkPHR1dfHPP/9g4sSJiIqKouxbX79+TXzeTnl5OW7cuIHY2Fi8e/eOyM+0hIQEdHV16ZYhdJYsWYK//vqLuEmKz1FcXMyTf9aAnJwcUROcDXh5ecHe3h5LliyBhYUFZfF56dIlmJiY0KyOHn6UOQ62UNJC1NTUiHuQ+Bzl5eXN2j+8fftWpAOxPkd5eTlcXV0RERFBPUSKi4vDyckJO3bsIMIKg6n4+/ujtLQUALBu3To4OTlhwYIF0NPTQ2hoKM3qhEPbtm0pP3EVFRXk5eXBwMAA8vLyRGVYqKioIDk5GZqamjzrSUlJxB3GNWXw4MGMKNo3UFVVhezsbOjo6BCV5dCY9+/f4/Xr13y2WkVFRfjw4QOA+g7npplDooiFhQViY2P5PrukMnPmTNjZ2eHmzZvo169fs+9RVVUlJptlw4YNmDhxIjZt2gRnZ2cqD+3MmTPo378/zeoER1PP/xEjRkBWVhYbNmwgyvN/69atcHBwQLt27b5oacLhcIgqlAD1Ye6XLl3CgAEDAAB37txBXl4enJyceHKlRD1Ud+/evZg/fz5evnwJIyMjvsJAr169aFImXEpKSnDixAlkZWXB09MTSkpKSEhIQKdOnaCmpgagPtuRBKytrREUFITdu3cDqP+8lpWVwdvbm7jO/MYHkJaWlow4gIyLi0NoaChOnDgBLpeLKVOmYMOGDUTuFZYuXYpt27Zh586dRGfnenh4YOzYsdDR0YGhoSHffTkyMpImZcJBV1cXFy9exKJFi3jWL1y4QPuEgTCYPHkyhgwZgvz8fOo5GQAsLS0xceJEGpWxkHnSIEQCAwOxbNkyBAcHQ0NDg245QmXo0KEIDw+nugA5HA7q6uqwceNGPpsbEnB3d0dsbCzOnDlDPVDExcVh8eLFWLp0KXbt2kWzQpb/St++fal/q6io4OLFizSqaR2YMkUzbdo0LF68GLKyshg2bBiAetstNzc32NnZ0axOODDNv7WiogKurq5UGFxD+KirqyvU1NSwfPlymhUKjgkTJsDFxQWBgYHUYfq9e/fg4eEBW1tbAMDdu3fRvXt3GlUKhtGjR2P58uVISUlpNnvGxsaGJmXCIT8//6sNF1JSUsRM2Zibm+PNmzf48OEDFBUVqfW5c+cS1Xji5uaGvn378hXnJ06ciDlz5tCoTLA0nuxj0pTfo0ePYGpqCgDIysoCUG/5oqyszHN4TsJBXVFREbKysjBz5kxqjcPhEB3mnpycDCsrK8jLyyMnJwdz5syBkpISIiMjkZeXh/DwcLolCpTAwECMHDkShoaG+PjxI+zt7fH06VMoKyvj8OHDdMsTKF86gCRlqh6of7ZosPPMyMjAgAEDsGXLFtjZ2REz0dgccXFxiImJwYULF9CzZ09iCwiLFy9GTEwMhg8fjg4dOhDxXfMl3N3dsWjRIhQVFcHCwgIAEB0djcDAQOL2tw107twZZWVluHz5MoYNGwYpKSn069eP+N/1jw6bUdJCioqK8Ouvv+L69euQlpbmuym/ffuWJmWC59GjR7C0tISpqSmuXr0KGxsbPH78GG/fvsXNmzeho6NDt0SBoqysjBMnTsDc3JxnPSYmBr/++iuKioroESZgamtrcfPmTfTq1YvH8oOFLBryK4YPH47Xr1/DycmJymkJDQ3l2TSIMlVVVZg+fTqOHz9OTRnU1dXByckJwcHBkJCQoFmh4GGaf6ubmxtu3ryJoKAgjBo1CsnJydDW1sbp06exZs0aPHz4kG6JAqOsrAxLlixBeHg4ampqANTbVDk7O2Pr1q1o3749EhMTAQB9+vShT6gAaJh4aw4SD+XExcWRn58PFRUVnvXi4mKoqKgQ9/MCzLCkYpLnPwB8+PABMjIyfJ/furo6lJWVEW9pQzKGhoYwMDDAH3/80WwOJ4kNglZWVjA1NcXGjRt5Pr/x8fGwt7dHTk4O3RIFTk1NDY4ePcpj3erg4AApKSm6pQkUFxcXbNu2jc8JpMFBghRHgTZt2kBZWRmOjo6YNWsWMfadX6NxQbc59u3b10pKhIusrCyOHDmCsWPH0i2l1di1axfWrVuHV69eAQA0NTWxZs0aODk50axM8BQXF+PXX39FTEwMOBwOnj59Cm1tbbi4uEBRURGBgYF0S2x1fpRMMLZQ0kKsrKyQl5eHWbNmNfsQ6ezsTJMywZOXlwcZGRns2rWL52Hqt99+Q3V1NdTV1emWKFCkpaXx4MEDvgeMx48fo3///kQFfrdr1w5Pnjz5YkgySRQWFsLDwwPR0dF4/fo1n/chiQdUTOPp06dITEyElJQUjI2NidzQN9CuXTs8evSIz5s3MzMTRkZG+PjxI03KhIOGhgaOHj2KAQMG8Dw8ZWZmwtTUlLKkIomysjIqpF5bW5uYQ2UmIyYmhoKCAr5CyatXr6Cjo0NcuHlTS6qGSTA3NzeiLKkUFRVx8+ZNGBoa8tyf4uLi8Msvv6CwsJBuiQLj1KlTWLZsGRITE/mmgsrLy2FqaorNmzdj/PjxNClk+R7at2+PpKQkRvj+NyAvL4+EhATo6OjwfH5zc3Ohr69P3PMUk/hcc8KbN2/QuXNnqhlF1NmyZQvmzp3LPicSioaGBqKiotCjRw+6pbQ6RUVFkJKSIvpv28nJCa9fv8bevXthYGBAfQdFRUXB3d0djx8/pltiq/OjFEpY660WEh8fj1u3bhHTjf0ltLS0kJ+fz+eXXVxcjK5duxJ3uDxw4EB4e3sjPDwc7dq1AwBUVlbCx8eH8jUlBSMjIzx79owxhZIZM2YgLy8Pq1evRpcuXdhRRgLR09ODnp4e3TJaBab5txYVFfFtdIH6gzlSP8syMjLE+sEzjYbgaw6Hg7179/Js+Gpra3H9+nUiN8BMsaRikuf/rl278McffzRrnda+fXssW7YMO3fuJKpQMnz48C9+z1y9erUV1QgXCwsLxhVKJCUlm222yMjIQMeOHWlQJFzWr1+PTp06wcXFhWc9NDQURUVFWLZsGU3KBMeHDx/A5XLB5XJRWlpK7emB+u/c8+fPN/tMKap4enrC0dGR6MPkL1FUVIT09HQAgL6+PnGf2zVr1sDb2xv79u0jyrb0WyDtd9kcly5dQlRUFLp27cqzrqenh9zcXJpUCZ6WONpoaGjwuTbRAVsoaSE9evQgruvvc3xu2KisrIznoYMUtm3bhpEjR6Jr165UISwpKQnt2rVDVFQUzeoEy9q1a+Hh4QE/P79mveFJs06Ii4vDjRs3RN6qpiWwUzTkwjT/1r59++LcuXNwdXUF8H9e8Hv37iWuiF1eXo6AgADqc1tXV8dzvWHKhBRiY2OxefNmPHnyBEC99YunpyeGDh1KszLB0RB8zeVyERwcDHFxceqahIQENDU1iZmuaMyNGzcQHx/PZ3+oqamJly9f0qRK8DDJ8//Ro0f43//+99nrw4YNw6pVq1pRkfBp+txYXV2NxMREPHr0iCgXAQAYP348lixZgpSUFBgbG/MdVJCWGwXU/0y+vr44duwYgPrni7y8PCxbtgy//PILzeoET0hICA4dOsS33rNnT9jZ2RFRKFFQUACHwwGHw2k2z43D4cDHx4cGZcKBqeYwDRZq4eHh1LOyuLg4nJycsGPHDmKKCtu3b0dWVhY6deoETU1NvvtyQkICTcpYBEF5eXmzf6tv376FpKQkDYqEg7i4OKytrfHkyZOvFkoa57/RCVsoaSEBAQFYunQp1q1b1+xDJAkHzO7u7gDqHyS8vLx4Pry1tbW4c+cOkQfORkZGePr0KQ4ePIi0tDQA9UHRJPq2NnQ52tjY8HTKkRrY2K1bN8Y9SLJTNOTi4uKCT58+Yd26dfDz8wNQf/i4a9cuIv1b/f39MXr0aKSmpqKmpgbbtm1Damoq4uPjERsbS7c8gTJ79mzExsZi+vTpxH9uDxw4gJkzZ2LSpElYvHgxAODmzZuwtLTE/v37YW9vT7NCwdAQfD18+HBERkbyBJuTTF1dXbPPEi9evODzjBdlunbtiqSkJBw5cgTJyckoKyvDrFmziHx2fPfu3Rftaqqrq/Hu3btWVCR8GgqdTVmzZg3KyspaWY1wmT9/PgDA19eX7xqJewOgvtA5efJkqKiooLKyEmZmZigoKMDAgQOxbt06uuUJnIKCAnTp0oVvvWPHjsjPz6dBkeCJiYkBl8uFhYUFTp48CSUlJeqahIQENDQ0oKqqSqNCwUPys+LncHd3R2xsLM6ePYvBgwcDqG+MXLx4MZYuXYpdu3bRrFAw2Nra0i2BRYgMHToU4eHh1H6ew+Ggrq4OGzduxPDhw2lWJ1hEzdGGzShpIQ3hhU2/kEg6YG74UMbGxmLgwIE83YAN3Y8eHh6Msbkhka8dLpqZmbWSktbh0qVLCAwMREhICDQ1NemW0yrIysoyboqGiTDBvxUAsrKyEBAQwJOXtWzZMhgbG9MtTaAoKCjg3Llz1KaPZAwMDDB37lwsWbKEZ33Lli3Ys2cPNWXCIppMnToV8vLy2L17N2RlZZGcnIyOHTtiwoQJUFdXJyZolUkYGBhg5cqVcHR0bPZ6REQE1q1bRzUbkUxmZib69++Pt2/f0i2FRQDcvHmT5/nCysqKbklCQU9PD97e3nyf4YiICHh7exM1tZqbm4tu3bpRZzekIiYmBiMjI7Rp8+X+Z9ImD5SVlXHixAmYm5vzrMfExODXX39FUVERPcJYWFrAo0ePYGlpCVNTU1y9ehU2NjZ4/Pgx3r59i5s3b0JHR4duiQLj4sWLWLFihcg42rATJS0kJiaGbglCp+FnnDlzJrZt2/bD/dEKC3V1dZibm8PMzAzDhw8n0uu/AdIKIc2hqKjIU9AsLy+Hjo4OpKWl+SbBSNzoMnGKhokwwb8VAHR0dLBnzx66ZQgdRUVFnu5Hknn27FmzWQY2Njb4888/aVAkeNzd3eHn54f27dtT07qfY8uWLa2kqnVgkiXV06dPERMT06xdnpeXF02qBM+kSZOwcuVKjBgxAp06deK5VlBQgFWrVn22iEIat27dItKGmKkMHjyYEQ0Kc+bMwe+//47q6moe69Y//vgDS5cupVmdYNHQ0EBJSQnu3r3b7L2ZpAnskSNHEt8w1ZSKigq+7yEAUFFRQUVFBQ2KhMuDBw+oBqKePXvCxMSEZkUsgsDIyAgZGRnYuXMnZGVlUVZWhkmTJuG3335rdvpPlBE1Rxt2ooSF5f9z4MABXL9+HdeuXUNmZibU1NRgZmYGMzMzmJubEzdBc+PGDYSEhODZs2c4fvw41NTUEBERAS0tLQwZMoRued9NWFjYN7+XNJ9pgDlTNPv27YOMjAymTJnCs378+HFUVFQQ87s1NTVFdHQ0FBUVYWJi8sUxe9K6xoB6G5/MzMxmN7vDhg2jSZXgOXDgAE6fPo2wsDBi/JU/h66uLjw9PTFv3jye9eDgYAQGBuLp06c0KRMcw4cPx6lTp6CgoABzc/PPfm45HA5RodAN1NTU8FhSmZqaEmdJtWfPHixYsADKysro3Lkzz++Yw+EQdT8uLS3FwIEDkZeXB0dHR+jr6wMA0tLScPDgQXTr1g23b98mylpt0qRJPK+5XC7y8/Nx//59rF69Gt7e3jQpEw5MyI1qzOLFi6Grq0vZPzawc+dOZGZmEpf7xuVysXz5cmzfvh1VVVUAgHbt2mHZsmVEFXUB4OzZs3BwcEBZWRnk5OT47s2kNMmJiYmhoKCAqID6b8HS0hIdOnRAeHg4VbSurKyEs7Mz3r59iytXrtCsUDC8fv0adnZ2uHbtGpXtUFJSguHDh+PIkSNENMxt3779m9/b9F7NIjqImqMNWyj5DoyNjXH+/Hl069aNbiksAiY/Px+xsbH4999/cfTo0c96bYsqJ0+exPTp0+Hg4ICIiAikpqZCW1sbO3fuxPnz53H+/Hm6JbL8B5qboqmpqSF6iqZ79+4ICQnh8/GMjY3F3LlzkZ6eTpMyweLj4wNPT09IS0tjzZo1XyyUkHZwc/v2bdjb2yM3N5dvSupH7ED5HkxMTJCVlQUul0t8aOOuXbvw+++/w8XFBYMGDQJQb3+yf/9+bNu2ja+AwsLyI6KhoYGFCxcSEYL8Lbx//x4rVqzA0aNHqTwSBQUF2NnZYd26dcRl8MycOZPntZiYGDp27AgLCwtYW1vTpEo4NM6NapiuuHnzJk6dOkVUblRj1NTUcObMGfz000886wkJCbCxscGLFy9oUiZcysrK8OTJE0hJSUFPT4+o0OAGunfvjjFjxsDf35/oxhNxcXHk5+czrlDy6NEjjBw5Ep8+fULv3r0BAElJSWjXrh2ioqLQs2dPmhUKhqlTp+LZs2cIDw+HgYEBACA1NRXOzs7Q1dUlYkK3aWZFUVERKioqeApD0tLSUFFRIcIeMDk5GUZGRhATE0NycvIX39urV69WUsXSFLZQ8h3IysoiKSmJaIsmplFRUYG4uDhcu3YNMTExePjwIQwMDGBubv7ZQEdRxMTEBEuWLIGTkxPP3/HDhw8xevRoFBQU0C1RILx69QpbtmyBl5cXn4Xc+/fvsXbtWnh4eDQ7uiuKMHGKpl27dkhLS+ObmsnJyYGBgQEqKyvpEcYiMPr06YPu3bvDx8en2YBzeXl5mpQJHh8fny9eJ60IdurUKQQGBlLdywYGBvD09MSECRNoViZYqqurISUlhcTERBgZGdEtp9VggiWVnJwcEhMTGbUXyMvLQ8eOHVFWVgYul4uOHTsSGyb84cOHz1oQZ2ZmQldXt5UVCQ8m5ka1a9cOjx494vs9ZmZmwsjICB8/fqRJGcv30r59e6SkpBB/b2bqRAlQf25z8OBBKhfLwMCAuKlVeXl5XLlyBf369eNZv3v3LqytrVFSUkKPMCFx6NAh/O9//8Pff/9NTa2mp6djzpw5mDdvHhwcHGhW+P00/syKiYmBw+E0a5dOWjMgIFqONmxGCQvL/2fQoEE8hZHly5dj2LBhxHXHAfVfOM3Z1cjLyxP1hbtly5bPbnLl5eVRWlqKLVu2YMOGDTSoEzykFD9agoqKCpKTk/kKJUlJSejQoQM9ooTM7Nmz4ejoyBdgSCpPnz7FiRMniDqQ+hykFUK+xsSJEzFx4kS6ZQidtm3bQl1dnbgNz5f4miUVKYWSKVOm4NKlS5g/fz7dUlqFuro66Orq4vHjx8RZ0jbH2LFjceXKFb6O+/T0dFhaWhI1ccCE3Kim6Orq4uLFi1i0aBHP+oULF4g5YJ80aRL2798POTk5Piu5pkRGRraSKuEzcuRI3L9/n5jf4+fIzs4mwn7pvyAtLY05c+bQLUOo1NXV8U2XA/XPlU0bUEhg9erVOHHiBFUkAQB9fX1s3boVkydPJqJQ0vgzm52dTbOa1qOxo01CQgI+ffoEoL552d/f/4dztGELJd/B0KFDiapYM520tDS0b98ePXr0QI8ePWBgYEBkkQQAOnfujMzMTL7D5bi4OKIeKC9evIjg4ODPXndycsKcOXOIKZQAzJuimTZtGhYvXgxZWVmq+BcbGws3NzfY2dnRrE44FBUVYdSoUejYsSPs7Ozg6OhIjZ2TyM8//0xc5y7L/3H//n0eP/ymFiiksHLlSvz555+IiIiAkpIS3XKEztq1a7Fu3TriLal0dXWxevVq3L59G8bGxnwHGqT5aYuJiUFPTw/FxcWMKJTIyMhg4sSJOHPmDNq0qd82P3nyBBYWFvj1119pVidYunXrhujoaL7v2itXrhBrM+3u7o5FixahqKiIJ9w8MDCQmHwSeXl5qlBN0gTu1xg7diw8PT2Rmpra7L3ZxsaGJmWCRUNDA0D933JzcDgctGvXDrq6upgwYYJIP3+cOXMGo0ePRtu2bXHmzJkvvpeU36+FhQXc3Nxw+PBhqKqqAgBevnyJJUuWwNLSkmZ1gic/Px81NTV867W1tSgsLKRBkeBp+MwCQKdOnaiMHdJZu3YtgoOD4eTkhCNHjlDrgwcPxtq1a2lU1jys9RYLy/+Hy+UiJSUF165dQ2xsLK5fvw4JCQmYmZlh+PDhRHUsrF+/HgcOHEBoaChGjBiB8+fPIzc3F0uWLMHq1avh6upKt0SB0L59ezx58gTq6urNXs/Ly4OBgQHKy8tbWZnw8PDwwIcPH7B79+5mr8+fPx/y8vLEFIeqqqowffp0HD9+nDrEqKurg5OTE4KDgyEhIUGzQuHw7t07HD9+HIcOHcKNGzfQo0cPODg4wN7enq8AKuqcOnUKq1atgqenZ7ObXVH3b1VSUkJGRgaUlZX5coaaQkq2EAC8ePEC06ZNw82bN3l8iAcNGoQjR46ga9eu9AoUMCYmJsjMzER1dTU0NDTQvn17nusk5c8AzLGkauqt3RgOh0OEn3ZTzp49i40bN2LXrl3EW8lVVlbCysoKXbt2xZEjR/D48WNYWlrCwcEBW7ZsoVueQGFqbtSuXbuwbt06vHr1CgCgqamJNWvWwMnJiWZlLN+DmJjYZ6+RaGkzfPhwJCQkoLa2lurGz8jIgLi4OHr06IH09HRwOBzExcXB0NCQZrX/jaaWRZ+DpN/v8+fPYWNjg8ePH1MF6+fPn8PIyAhnzpwh7ll5/PjxePnyJfbu3QtTU1MAwIMHDzB37lwqU4ok5OTkMHHiRDg6OsLS0vKLf9eijrS0NFJTU6Gpqclj/f/s2TMYGhr+cFaXbKHkP1BbW4t//vmH6oDs2bMnbGxsIC4uTrMyFkHB5XLx4MED7Ny5EwcPHiQuzJ3L5cLf3x/r169HRUUFAEBSUhIeHh7w8/OjWZ3gUFZWRmRkZLM2YwBw/fp1TJo0CW/evGllZcLDyMgIwcHBn/V5jI+Px5w5c/D48eNWViZcMjIykJSUBCkpKRgbG/N0a5DOixcvcPjwYYSGhuLp06fNduKIMs09NDb4uZKwGQoLC4OdnR0kJSW/mjNEkr3eqFGjUFJSgrCwMB4f4pkzZ0JOTg4XL16kWaFgYVr+zKxZs9CvXz/GWFIxCUVFRVRUVKCmpgYSEhJ80/UkFXSB+gKuubk59PT0cP36dTg5OWHTpk10yxIKTMmNao6ioiJISUlBRkaGbilCY+3atXBwcPhigZdFNAkKCsKNGzewb98+ylHg/fv3mD17NoYMGYI5c+bA3t4elZWViIqKolktS0vgcrm4cuUKTxaLlZUVzaqEQ1FREZydnXHx4kWqMa6mpgYjR47E/v37icviOXXqFA4dOoRz585BXl4eU6dOhaOjI/r27Uu3NIGjra2N3bt3w8rKiqdQEh4ejoCAAKSmptItkQe2UNJCMjMzMXbsWLx48YJnY9+tWzecO3cOOjo6NCtkaSm+vr7w8PBAWloarl27hmvXriEuLg6lpaUwNjaGubk5zMzMiNwkVFVVITMzE2VlZTA0NCRuczB27Fioqqpiz549zV6fPXs2Xr169cN5In4PTJyiYTLV1dU4d+4cDhw4gHPnzkFJSQkvX76kW5ZAyc3N/eJ1JhXFSEJKSgrx8fEwMTHhWX/w4AGGDh1KFfFZRJP169djy5YtGDt2LCMsqRoaLpSVlWlWInxIL+h++PCBby0/Px8jRozAuHHjEBAQQK1/LuidheVHpHfv3nj06BF+/vlnODo64tdff2XEPevjx4/E29uoqanh8uXLfNMijx8/hrW1NV6+fImEhARYW1sT0SAYHh6OqVOn8uVHVVVV4ciRI8RMhDHl52xKRkYGVRjq0aMHunfvTrMi4VJaWooTJ07g8OHDuHr1KrS1teHo6EhMnh8geo42bKGkhYwZMwZcLhcHDx6kPB6Li4vh6OgIMTExnDt3jmaFLC1FXFwc+fn5UFVVhYmJCczMzGBmZoZhw4Yxwsv1+fPnAECk/3BMTAxGjBiB33//HZ6enlQuR2FhITZu3Iht27bh0qVLlC8xCTBhisbd3R1+fn5o3779Zz15GyDNGqOBmJgYHDp0CCdPnkRdXR0mTZoEBwcHWFhYfNG6SRS5fv06Bg0aRFmrNVBTU4P4+PjP/q2LCs0dyn0Okg7lunfvjgMHDqB///4863fv3oW9vT0yMzNpUsYiCJhgSVVSUoKVK1fi6NGjePfuHYD6aQs7OzusXbuWspRjES3ExMSa/R5t2DKTNNHYGG1tbdy7dw8dOnTgWS8pKYGpqSkRn1kAMDU1RXR0NBQVFWFiYvLFZybSLBGB+oPzgwcP4siRI3jx4gVGjBgBBwcH2NraQlpamm55AqO2thb+/v4IDg5GYWEhMjIyoK2tjdWrV0NTUxOzZs2iW6JAkZGRwb///gtzc3Oe9WvXrmH8+PEoLS3Fs2fP0KdPnxY9d/6oNJzfNJ0wKC4uhoqKCjH3Zqb8nE2pqqpCdnY2dHR0+PZ/pJOamgoHBwckJycT9fsVNUcbZv3VCYDY2Fjcvn2bJwirQ4cOCAgIwODBg2lUxvJfadj4vH37lqhDqC9RU1MDHx8fbN++HWVlZQDqH7BcXV3h7e3N1/kpqgwfPhx//fUX3NzcsHXrVsjJyYHD4eD9+/do27YtduzYQVSRBKgPvo6IiPjs4XF4eDjfwaSo8fDhQ1RXV1P//hykFQwaUFNTw9u3bzFq1Cjs3r0b48eP5+s0Ionhw4c3u0l4//49hg8fLvIPkQoKCl/9WyXxUG7Tpk1wdXXFX3/9RY2Y379/H25ubti8eTPN6gTP5w5fGyDpdwsA2dnZdEsQKm/fvsXAgQPx8uVLODg4wMDAAED9Bnf//v2Ijo5GfHw8FBUVaVYqHEi2IY6JiaFbAi3k5OQ0ex/69OkTUZOqEyZMoJ6ZJkyYQOyz4ufo2bMn/P394e/vj5s3b+LQoUP4/fffMX/+fCIO0BtYt24dwsLCsHHjRp6cUSMjIwQFBRFXKJkwYQJcXFwQGBiIfv36AQDu3bsHDw8P2NraAqhvRCGlM7/hubgpL168IKrRlSk/ZwMVFRVwdXWlJlcbCpyurq5QU1PD8uXLaVYoHD5+/IgzZ87g0KFDuHjxIjp16gRPT0+6ZQkUDoeDlStXwtPTUyQcbdhCSQuRlJREaWkp33pZWRmxocFMgMPhMKZIAgCurq6IjIzExo0bMXDgQADArVu3sGbNGhQXF2PXrl00KxQc8+bNw7hx43Ds2DFkZmaCy+Wie/fumDx5MnEBaEB9mPuIESMgLy/f7BTN/v37cenSJZpVfh+NDzGYeKCxZs0aTJkyhTHdyp/bJBQXF/MFYosiTPwbBoAZM2agoqICP//8M9UtVlNTgzZt2sDFxQUuLi7Ue0nIPDh16hTP6+rqajx8+BBhYWFfzS9h+fHw9fWFhIQEsrKyqO/Zxtesra3h6+uLrVu30qRQeGRmZmLMmDF4+fIlZUO8fv16YmyIzczM6JbQqjQOx42KiuI5fKutrUV0dDQ0NTVpUCYcGudBrVmzhj4hPwDt27eHlJQUJCQkmj3fEGXCw8Oxe/duWFpa8mRl9e7dm7L0IYmQkBAsWbIEdnZ2VFZhmzZt4OzsTH0P9ejRA3v37qVT5nfTMAXG4XBgaWnJM21QW1uL7OxsjBo1ikaFgoEpP2dTVqxYgaSkJFy7do3n57OyssKaNWuIK5RERUXh0KFD+Oeff9CmTRtMnjwZly5dEnm3hC8hISHBZxH4I8Jab7UQJycnJCQk4O+//6a6su/cuYM5c+bgp59+wv79++kVyNJixMTEIC8v/9WOIhIOahqQl5fHkSNHMHr0aJ718+fPY9q0aXj//j1NylgEQUhICNzc3FBdXc03RbN161YsWLCAboks/5Hq6mpISUkhMTERRkZGdMsRKpMmTQIAnD59GqNGjeKZmqmtrUVycjL09fWJC/1mCl/LOWiMqGcefIlDhw7h6NGjOH36NN1Svhsm2SJqamoiJCQEI0eObPb6xYsXMX/+fOTk5LSusFaASTbE+/btg4yMDKZMmcKzfvz4cVRUVBBxbxITEwPwf5ZijWnbti00NTURGBiIcePG0SFPqMyePRuOjo58dkUkk52djUOHDuHQoUNIT0+HmZkZ7O3tMXnyZKI61KWkpJCWlgYNDQ2e4ODU1FT079+fclQgjbKyMsomT1tb+4ft1v6vNDSW+Pj4YOnSpTw/n4SEBDQ1NfHLL7+IfAMzU37OpmhoaODo0aMYMGAAz+c2MzMTpqamRE29AYC0tDTGjRsHBwcHjBkzhhhXlwYa9vLfQmRkpBCVtBx2oqSFbN++Hc7Ozhg4cCD1h1xTUwMbGxsEBQXRK47lP+Pj40PUw+HXkJSUbLY7TEtLi5gv3MYdcl/DxsZGiEpan3nz5mHs2LE4fvw48VM05eXlCAgIQHR0NF6/fo26ujqe66R4ajfQtm1bqKurE2fT0xwN92QulwtZWVlISUlR1yQkJDBgwAAeOwVRJTk5+Zvf26tXLyEqaV1IOGAUBAMGDMDcuXPpliEQvtUWkQTy8/PRs2fPz143MjJCQUFBKypqPZhkQ7x+/XqEhITwrauoqGDu3LlE3Mcanpu0tLRw7949RoR7N1BUVIRRo0ahY8eOsLOzg6OjI3r37k23LKExYMAA3Lt3D7169cLMmTMxbdo0qKmp0S1LKBgaGuLGjRvQ0NDgWT9x4gRMTExoUiV8ZGRkiHpWbErDRJimpibs7OyItR5u/HNOnToV7dq1o1lR61BUVMRntQzU7/dJtEksLCyErKws3TKERuPzVS6Xi1OnTkFeXp6yXH7w4AFKSkpaVFBpLdhCSQtRUFDA6dOnkZmZSfnyGhgYQFdXl2ZlLN+DnZ1dszdlUlm0aBH8/Pywb98+6gHj06dPWLduHRYtWkSzOsHQ4Mf6NUjz/W9AUVERS5YsoVuG0Jk9ezZiY2Mxffp0dOnShciHqKasXLkSf/75JyIiIngOqkhj3759VHfrjh07iOuKa6BPnz7NdvI2hcR7VW1tLU6dOkU9TxkaGmLChAmMCW6srKzE9u3biTmoYpItorKyMnJycj7bfJCdnU3s/ZlJNsR5eXnQ0tLiW9fQ0EBeXh4NioQH6blCzXH69Gm8e/cOx48fx6FDh7Blyxb06NEDDg4OsLe3J8pyDAAsLS0RGhoqErYn34uXlxecnZ3x8uVL1NXVITIyEunp6QgPD8e///5LtzyBw7TGMUNDQyQmJuLnn3/mWb9z5w7ExcWpg1hRh4RifEvo27cvzp07B1dXVwD/lze6d+9eyi6eJGRlZfky3xr2QiRkvu3bt4/697Jly/Drr78iODiY+tlqa2uxcOHCHzICgbXeaiG+vr7w8PCAtLQ0z3plZSU2bdoELy8vmpSx/FfExcWbDQomjaaV2itXrkBSUpLqnEpKSkJVVRUsLS1/uNE3lpYjIyODX3/9FS4uLhgyZAjdcoSGgoICzp07R1wX65cwMTFBZmYmqquroaGhwZfTkZCQQJMywVNXV4d27drh8ePH0NPTo1uOUMjNzf3m9zbtjBRlHj9+DBsbGxQUFFA5BxkZGejYsSPOnj1LnLWcoqIiTyGXy+WitLQU0tLSOHDgAHGTjZ8jLS0NNjY2yMjIoFvKd+Hi4oKsrCxcvnyZrzjw6dMnjBw5Etra2ggNDaVJofBgkg2xuro6du7cyff5PH36NH777Te8ePGCJmWCx9fX94vXmbDHffHiBQ4fPozQ0FA8ffqUynoggerqavTo0QP//vsvDAwM6JbTKty4cQO+vr5ISkpCWVkZTE1N4eXlBWtra7qlCZxp06Z9sXHMzc2NJmXCoX///vjjjz8wefJknvXIyEhs2LABd+7coUmZYBETE/tiEyBpDVRxcXEYPXo0HB0dsX//fsybNw+pqamIj49HbGwsfvrpJ7olCpTmMt/S09OJyXxrTMeOHREXF0f9nA2kp6dj0KBBKC4upklZ8zCjZU+A+Pj4YP78+XyFkoqKCvj4+DDiIZI0WlorfPHiBVRVVSlPX1GhqbXYL7/8wvO6W7durSmHRcgcOHAA+/fvh4WFBTQ1NeHi4gInJyeoqqrSLU2gKCoqEtu1+zm+dVqKBMTExKCnp4fi4mJiCyUkFT9awuzZs9GzZ0/cv38fioqKAIB3795hxowZmDt3LuLj42lWKFi2bt3Ks9kVExNDx44d8fPPP1M/PxP49OkTsrKy6Jbx3fj6+qJv377Q09PDb7/9hh49eoDL5eLJkyf43//+h0+fPiEiIoJumULhSzbE27Zto1mdYJk2bRoWL14MWVlZKlw1NjYWbm5usLOzo1mdYDl16hTP6+rqamRnZ6NNmzbQ0dEhfo9bXV2N+/fv486dO8jJyUGnTp3oliRQ2rZti48fP9Ito1UZOnQoLl++TLeMVuHChQuMahxLTU2Fqakp37qJiQlSU1NpUCQcIiMjeZ4dq6ur8fDhQ4SFhVE5JiQxZMgQJCYmIiAgAMbGxrh06RJMTU1x69YtGBsb0y1P4CxevBg6Ojo8dqYNmW+LFy8mKvOtpqYGaWlpfIWStLQ0vgm4HwF2oqSFiImJobCwEB07duRZv3r1KqZOnYqioiKalLG0FnJyckhMTIS2tjbdUlhaQHl5OWJjY5GXl4eqqiqea4sXL6ZJlfApKipCREQE9u/fjydPnmDkyJFwcXGBjY0NEfY2Bw4cwOnTpxEWFsZXwGYhg7Nnz2Ljxo3YtWsXcVMGQH2e0ujRo9G2bduvZiuRNHUgJSWF+/fv8+U8PHr0CP369UNlZSVNyliESVJSEkxNTYnogszOzsbChQtx6dIlqumGw+FgxIgR2LlzJ/G2vE+fPkVaWhoAcm2Iq6qqMH36dBw/fpx6Zqqrq4OTkxOCg4OJsxpryocPHzBjxgxMnDgR06dPp1uOUIiJicGhQ4dw8uRJ1NXVYdKkSXBwcICFhQVxdq7+/v7IyMjA3r17idgDsPwfWlpaOH/+PGOmhTp06IB///2Xz44pPj4eY8eOxbt372hS1jocOnQIR48exenTp+mWwvIdtG/fHrdv3+YrAiUlJWHw4MEoKyujSZngcXd3R3h4OP7880+eaeSAgABMnz4dW7ZsoVkhL2yh5BtpsEx4//495OTkeB6camtrUVZWhvnz5+Ovv/6iUSVLayArK4ukpCS2UCJCPHz4EGPGjEFFRQXKy8uhpKSEN2/eQFpaGioqKsT5tn6OHTt2wNPTE1VVVVBWVsb8+fOxfPlykS4wmJiYICsrC1wuF5qamlR3awMk2VA1pqSkBCdOnEBWVhY8PT2hpKSEhIQEdOrUiZi8gwYUFRVRUVGBmpoaSEhI8IS6A8Dbt29pUiYYxMTEUFBQABUVlS9OKpKWUdK7d29s3boVFhYWPOtXr16Fm5sbUlJSaFImHPbt2wcZGRlMmTKFZ/348eOoqKhgjA81SYWSBt69e4enT58CAHR1dZudchTVaWSWejIyMpCUlAQpKSkYGxszahIwJSUF48ePR05ODt1SBI6amhrevn2LUaNGwcHBAePHjyc2HBoAJk6ciOjoaMjIyMDY2JjPulXUrZebWlx+CVF/dmwK0xrHpk2bhvz8fJw+fZpyzSgpKYGtrS1UVFRw7NgxmhUKl2fPnqFXr15EHKR/+PDhm9/7I2ZZfA9KSkr4999/MWjQIJ71mzdvYvz48UTdp+rq6rB582Zs27YN+fn5AIAuXbrAzc0NS5cu/eEyWdhWgm8kKCgIXC4XLi4u8PHx4bExkpCQgKamJpEBQyzkYGpqiujoaCgqKsLExOSLD5KkHS4vWbIE48ePR3BwMOTl5XH79m20bdsWjo6OxHm2NqWwsBBhYWHYv38/cnNzMXnyZMyaNQsvXrzAhg0bcPv2bVy6dIlumf8ZJtlQNZCcnAwrKyvIy8sjJycHc+bMgZKSEiIjI5GXl4fw8HC6JQqUoKAguiUIlcbjxj/i6LGwWL9+PRYvXow1a9ZgwIABAIDbt2/D19cXGzZs4Nk4kbAxWr9+PUJCQvjWVVRUMHfuXMYUSkhEUVGR6o77HA3Bs6LaZOPu7g4/Pz+0b98e7u7uX3zvj9YVKAi6d++O7t270y2DFt6/f4/379/TLUMorFmzBlOmTIGCggLdUloFBQUFPutlkmj8vFhcXIy1a9di5MiR1BnNrVu3EBUVhdWrV9OkUHgEBgYiKysLnTp1YkTj2ObNmzFs2DBoaGjAxMQEAJCYmIhOnToRa3vZQGVlJbZv305MY5yCgsJXC5xcLpe4hjEAGDduHObOncuX+TZ//nyiXASA+sbAP/74A3/88Qe1x/uR93fsREkLiY2NxeDBg9lxVQYjqhMlPj4+8PT0hLS09Fc9Lb29vVtJVeugoKCAO3fuQF9fHwoKCrh16xYMDAxw584dODs7U9YRJBEZGYl9+/YhKioKhoaGmD17NhwdHXk2g1lZWTAwMOCzImP5sbGysoKpqSk2btzIcz+Kj4+Hvb09kV2fLOTRuLO+YYPU2L6o4TUpG6N27dohLS0NmpqaPOs5OTkwMDAgxmrsax29NTU1KC8vJ+J32hJE9dmxgeHDh+PUqVNQUFDA8OHDP/s+DoeDq1evtqIy4fPixQucOXOmWetWkopC27dv53nN5XKRn5+PiIgImJmZ4dChQzQpEw7V1dWQkpJCYmIikbaeTOeXX37B8OHDsWjRIp71nTt34sqVK/jnn3/oESYkmLa3B+pttQ8ePEhN+/Xq1QvTpk3jKxKJMk2fqbhcLkpLSyEtLY0DBw4QcZgeGxv7ze81MzMTopLWp6SkBM7Ozjh79ixf5tv+/fv5MoZZWg/2tL+FNP5wjh07Fnv37kWXLl1oVMTC8m14e3sjNDQUDg4ORD4sfYm2bdtSh3IqKirIy8uDgYEB5OXl8fz5c5rVCYeZM2fCzs4ON2/eRL9+/Zp9j6qqKlauXNnKyoTDgwcP8OTJEwBAz549qe4iErl3716znelqamooKCigQVHr8fHjR76Dqh+5G6UllJaWIiMjA/r6+pCRkUFCQgKCgoJQWVkJW1tbODg40C1RoMTExNAtoVVRUVFBcnIyX6EkKSkJHTp0oEeUECB9AoypNP68MumzGx0dDRsbG2hrayMtLQ1GRkbIyckBl8ttNkhYlNm6dSvPazExMXTs2BHOzs5YsWIFTaqER9u2baGurs64om1NTQ2uXbuGrKws2NvbQ1ZWFq9evYKcnBxkZGTolicwoqKisGHDBr71UaNGYfny5TQoEi5M29sD9fkOc+fOpVuGUGn6TNVwX/7555+hqKhIjygBQ1rxoyUoKCjg9OnTjMh8KywshIeHB6Kjo/H69Ws0ndf40b6L2ULJd3D9+nViOgBZvh1RDvabM2cOxo0bBxUVFQD1B+Xx8fF8BzekYWJignv37kFPTw9mZmbw8vLCmzdvEBERQWwXWX5+/lc9aqWkpET+wfr169ews7PDtWvXqGmZkpISDB8+HEeOHEHHjh3pFSgEJCUlm/VzzcjIIPLnLS8vx7Jly3Ds2DEUFxfzXf/RHqz+C9evX8e4ceNQVlYGRUVFHD58GJMnT4aamhrExcURGRmJiooKzJkzh26pAoNpG6Np06Zh8eLFkJWVxbBhwwDUd9G5ubnBzs6OZnWCg7UQYyGJFStWwMPDAz4+PpCVlcXJkyehoqICBwcHjBo1im55AiU7O5tuCa3OypUr8eeffyIiIqLZbCHSyM3NxahRo5CXl4dPnz5hxIgRkJWVxYYNG/Dp0ycEBwfTLVFgdOjQAadPn8bSpUt51k+fPk1UcwKTiYiIQEhICJ49e4Zbt25BQ0MDW7duhba2NiZMmEC3PIHAhGeq5ORkGBkZQUxMDMnJyV98b69evVpJVeuip6cHPT09umUIlRkzZiAvLw+rV69Gly5dfvgzVbZQwsLSQkTZra6p9tLSUkZ44vv7+6O0tBQAsG7dOjg5OWHBggXQ09PD33//TbM6wdH08PxL4WikdOG7urqitLQUjx8/hoGBAQAgNTUVzs7OWLx4MQ4fPkyzQsFjY2MDX19fKqiQw+EgLy8Py5YtI9J7+o8//kBMTAx27dqF6dOn46+//sLLly8REhKCgIAAuuUJhFWrVmHKlCnw9fVFaGgopk6dikWLFsHf3x8AsHbtWvz1119EFUquX7/+xesNxQRS8PPzQ05ODiwtLSn71rq6Ojg5OVG/Z9IoKSnBiRMnkJWVBU9PTygpKSEhIQGdOnUixlubiZSXlyMgIIDqCmz6HPns2TOalAmeJ0+eUM8Rbdq0QWVlJWRkZODr64sJEyZgwYIFNCtk+R527tyJzMxMqKqqQkNDgy/cnLRcBzc3N/Tt25dvknHixIlEPV8A9VZUs2fPxrVr1/Dzzz8DqPf+v3jxIvbs2UOzOsGgpKSEjIwMKCsrf9X2kqRQaADYtWsXvLy88Pvvv2Pt2rVU05SioiKCgoKIKZQ0UFFR0az9IwmFgz59+qCgoAAqKiro06cPOBxOs+dtpFjxAoCvr+83vc/Ly0vISlqPuLg43LhxA3369KFbyjfBZpR8B0ZGRrhw4QK6detGtxQWAeDi4oJt27ZBVlaWZ728vByurq4IDQ0FADx//hyqqqoQFxenQ+Z3ISYmRn0RAaLvmc3Ci5iYGOPC0OTl5XHlyhU+e7G7d+/C2toaJSUl9AgTIu/fv8fkyZNx//59lJaWQlVVFQUFBRg4cCDOnz/Pt9EXddTV1REeHg5zc3PIyckhISEBurq6iIiIwOHDh3H+/Hm6JX43CgoKuH37Nnr06IGqqipISUkhISEBvXv3BgBkZmbCxMSEKviSQOOMkgYa379IuUc1JSMjg/LTNjY2hoaGBt2ShEJycjKsrKwgLy+PnJwcpKenQ1tbG6tWrUJeXh7Cw8PpltiqyMnJiXSYe2OmTZuG2NhYTJ8+vdmuQDc3N5qUCZ7OnTsjJiYGBgYGMDQ0REBAAGxsbJCUlITBgwejrKyMbokC5f79+zh27FizB3KRkZE0qRIeTMt16NChA+Lj46Gvr8+zB8zJyYGhoSEqKirolihQ7ty5g+3bt1PWvAYGBli8eDFVOBF1wsLCYGdnB0lJSYSFhX3xvaRNJhgaGsLf3x+2trY8f8uPHj2Cubk53rx5Q7dEgVBUVIQZM2bg4sWLzV4n4Vk5NzcX6urq4HA4yM3N/eJ7SXlmFhMTg6qqKlRUVD7bhM3hcIgq1hsaGuLgwYMiY4/OTpR8B48ePaJbAosACQsLQ0BAAF+hpLKyEuHh4VShRJQLYxwOh2dD2/Q1qVhYWCAyMpInyByon7iwtbUlJniUSb7hDdTV1TUb2te2bVtip6Xk5eVx+fJl3Lx5E0lJSSgrK4OpqSmsrKzoliYU3r59Sx0uysnJUV1xQ4YMIaab98OHD5Tth4SEBKSlpXm+i2RlZYk7wHj37h3P6+rqajx8+BCrV6/GunXraFIlfDQ1NcHlcqGjo0NNlpCIu7s7ZsyYgY0bN/L8LY8ZMwb29vY0KqMHkvrSLly4gHPnzmHw4MF0SxE6AwYMQFxcHAwMDDBmzBgsXboUKSkpiIyMxIABA+iWJ1COHDkCJycnjBw5EpcuXYK1tTUyMjJQWFiIiRMn0i1PKJBWCPkadXV1zR6svnjxgm//SwI///wzDh48SLcModG4+EFaIeRrZGdnN3vgKikpifLychoUCYfff/8d79+/x507d2Bubo5Tp06hsLAQa9euRWBgIN3yBELj4gcphZCvMXr0aFy9ehV9+/aFi4sLxo0b12wDGUkEBQVh+fLlCAkJEQnbf3J3aEKkpKQEd+/ebXbc3MnJiSZVLP+VDx8+gMvlgsvlorS0FO3ataOu1dbW4vz589QEhqjD5XLRvXt3qjhSVlYGExMTvhszaeO5165d4+uMA+qDoW/cuEGDIuHANM9/oL4I5ubmhsOHD0NVVRUA8PLlSyxZsgSWlpY0qxMugwcPZsRBlba2NrKzs6Guro4ePXrg2LFj6N+/P86ePctX/BRVmFjElpeX51sbMWIEJCQk4O7ujgcPHtCgSnhUVFTA1dWV6vrMyMiAtrY2XF1doaamRly47L179xASEsK3rqamhoKCAhoUCYdvnUZOTU2lvqNEHUVFRUbkOQDAli1bqKkRHx8flJWV4ejRo9DT08OWLVtoVidY/P39sXXrVvz222+QlZXFtm3boKWlhXnz5qFLly50yxMaTLIItLa2RlBQEHbv3g2g/lmjrKwM3t7eGDNmDM3qBE9dXR0yMzObPbMhzd6zgdevXzf785Jg0dQYLS0tJCYm8h2sX7x4kbJiJoGrV6/i9OnT6Nu3L8TExKChoYERI0ZATk4O69evx9ixY+mWKBRSU1ObnWy0sbGhSZFgOXfuHF69eoWwsDB4enpi3rx5cHJygouLC/T19emWJxSmTp2KiooK6OjoQFpamq/R9Uc7f2QLJS3k7NmzcHBwQFlZGeTk5PgONthCieihoKBAHUp1796d7zqHw/nqaLaosG/fProltCqNA8FSU1N5Dmdqa2tx8eJF4jZBjWFCUXfnzp2wsbGBpqYmNe31/PlzGBkZ4cCBAzSrEyy3bt1CcXExxo0bR62Fh4fD29sb5eXlsLW1xY4dOyApKUmjSsEzc+ZMJCUlwczMDMuXL8f48eOxc+dOVFdXE3NQxeVyebIrKioqMH78eEhISAAAampq6JTXqnTq1Anp6el0yxA4K1asQFJSEq5du8YTAm1lZYU1a9YQVyiRlJRsNicrIyMDHTt2pEGRcGDCNHJT/Pz84OXlhbCwMEhLS9MtR6g0tkpr3749UWHXTcnKyqIO3SQkJFBeXg4Oh4MlS5bAwsKCmL1QY5paBM6ZMwdKSkqIjIwk0iIwMDAQI0eOhKGhIT5+/Ah7e3s8ffoUysrKxGX63b59G/b29sjNzeWb6CPJgriBBw8ewNnZGU+ePGHEz+vu7o7ffvsNHz9+BJfLxd27d3H48GGsX78ee/fupVuewCgvL6cadhUVFVFUVITu3bvD2NiYKFumBp49e4aJEyciJSWFJ6uk4cyVpL9jVVVVrFixAitWrMD169exb98+9OvXD8bGxrhy5QqkpKTolihQgoKC6JbQIthCSQtZunQpXFxc4O/vT/zmgCnExMSAy+XCwsICJ0+e5OmSk5CQgIaGBjFdgC0dyz18+DBsbGxENvOgIRCMw+HAwsKC77qUlBR27NhBgzLhw5Sibrdu3ZCQkIArV64gLS0NQL0HMYk2VL6+vjA3N6cKJSkpKZg1axZmzJgBAwMDbNq0CaqqqlizZg29QgVEXV0dNm3ahDNnzqCqqgqvXr2Ct7c30tLS8ODBA+jq6hLTIdfU/qO5EMpffvmlteS0Co0L2UB9sSg/Px8BAQEiE/TXEv755x8cPXoUAwYM4Lkf9+zZE1lZWTQqEw42Njbw9fXFsWPHANR/7+Tl5WHZsmVE/C0zaRq5KYGBgcjKykKnTp2gqanJ1xVI4uFNA8+ePUNlZSUMDAyIs8lQVFSkcrDU1NTw6NEjGBsbo6SkhDjrxwaYZhHYtWtXJCUl4ejRo5R166xZs+Dg4EDcodz8+fPRt29fnDt3rtksJdJwcXFB9+7d8ffff6NTp07E/7yzZ8+GlJQUVq1ahYqKCtjb20NVVRXbtm2DnZ0d3fIEhr6+PtLT06GpqYnevXtTtkXBwcFETvq5ublBS0sL0dHR0NLSwt27d1FcXIylS5di8+bNdMsTGv369UNOTg5SU1Px8OFDVFdXE3dPFjV7QDbMvYW0b98eKSkpRIQxsvDSOEiKpR5RDx9t6CLS1tbG3bt3ebpYJSQkoKKiAnFxcRoVCo/u3btjzJgxRBd1Gx4iEhMTYWRkRLccodOlSxecPXsWffv2BQCsXLkSsbGxiIuLAwAcP34c3t7eSE1NpVOmwPDz88OaNWtgZWUFKSkpREVFYdq0aVSHNotoIyYmxtMt1sCAAQMQGhqKHj160KRMOEhLS+PRo0fQ1tbmCR5NSkrCsGHD8P79e7olCpT3799j8uTJuH//PkpLS6GqqoqCggIMHDgQ58+fF9kGjAYa/n4/R8M08sqVK1tRVevAhADs6upqrF27FgkJCRgwYACWL18OR0dHqvCnr6+P8+fPi4TP9rdib2+Pvn37wt3dHX5+ftixYwcmTJiAy5cvw9TUlMgwd3l5eSQkJEBHR4fnvpybmwt9fX18/PiRboks/5H27dsjKSkJurq6dEtpFWRlZfHw4UPG/LyNqaioQFlZGZHNCQcOHEBNTQ1mzJiBBw8eYNSoUXj79i0kJCSwf/9+TJ06lW6JAkVZWRlXr15Fr169IC8vj7t370JfXx9Xr17F0qVL8fDhQ7olCpRbt24hNDQUx44dQ/fu3TFz5kzY29sTYy3dlKysLOzbtw9ZWVnYtm0bVFRUcOHCBairq6Nnz550y+OBnShpISNHjsT9+/dF9uCY5fNcvXoVMjIymDJlCs/68ePHUVFRIXJVUEEg6nXUBt9SUkO9v8TLly+xePFiYoskQH1gu7q6OlFjuF/i3bt36NSpE/U6NjYWo0ePpl7369cPz58/p0OaUAgPD8f//vc/zJs3DwBw5coVjB07Fnv37iWuk7eByspKcLlc6nObm5uLU6dOwdDQENbW1jSrEyzZ2dk8r8XExNCxY0eeznySaOhsdXV1BfB/NgJ79+7FwIED6ZQmFOTl5XH58mXExcUhOTkZZWVlMDU1JWbaj0nTyE0hoRDyNZYvX46IiAhMmDABoaGhuHv3LtLT03Ho0CGIiYnBz88PK1euJCooeufOnVRhYOXKlWjbti3i4+Pxyy+/YNWqVTSrEw5MsQjMyMhASUkJ+vfvT61FR0dj7dq1lHXrn3/+SaNCwfPzzz8jMzOTMYUDS0tLRhWGLCwsEBkZCQUFBUhLS1PPzR8+fICtrS2uXr1Ks0LB4OjoSP37p59+Qm5uLtLS0qCurg5lZWUalQmH2tpaarpPWVkZr169gr6+PjQ0NIiy5d24cSP279+PN2/ewMHBATdu3CDGJeFzNJxbDB48GNevX8e6deugoqKCpKQk/P333zhx4gTdEnlgJ0q+gTNnzlD/Lioqgq+vL2bOnAljY2O+cXNSAoaYSPfu3RESEoLhw4fzrMfGxmLu3LlE3Zy/lcbdVaJOVlYWgoKC8OTJEwCAoaEh3NzcoKOjQ7My4TBp0iTY2dnh119/pVuKUPn7778RGRmJiIgI4sNlNTQ0EBERgWHDhqGqqgoKCgo4e/YsFVqfkpICMzOzHy4M7b8iKSmJzMxMHm//du3aITMzE127dqVRmfCwtrbGpEmTMH/+fJSUlEBfXx8SEhJ48+YNtmzZggULFtAtkeU/EhcXh9GjR8PR0RH79+/HvHnzkJqaivj4eMTGxuKnn36iWyLLf4Cp08ikB2BraGhg165dGDNmDDIyMtCjRw+cO3eOak6IjY2Fg4MDXrx4QbNSlu9h9uzZKC4uxrFjx6CkpITk5GSIi4vD1tYWw4YNEzlP9c8xceJEGBsbw9fXF0B9o0LPnj0xdOhQ9OjRA6GhofDz88Pvv/9Or1ABcurUKaxatQqenp7NntmQdij55s0bODs7o3///jAyMiL+jEpMTAwFBQV8UySvX7+GmpoaqquraVLG8j0MHToUS5cuha2tLezt7fHu3TusWrUKu3fvxoMHD/Do0SO6JQoEMTExqKurY9y4cVQeZXOQksUJAAMHDsSUKVPg7u7Oc8Z49+5dTJo06Yd7nmInSr4BW1tbvrWGB43GkBiUxSTy8vKgpaXFt66hoYG8vDwaFLEIiqioKNjY2KBPnz4YPHgwAODmzZvo2bMnzp49ixEjRtCsUDA0LuqOHTsWnp6eSE1NJbqou3PnTmRmZkJVVRUaGhp8di4keaWPGTMGy5cvx4YNG/DPP/9AWloaQ4cOpa4nJycTVfirqanhmy5o27Yt0ZufhIQEbN26FQBw4sQJdO7cGQ8fPsTJkyfh5eVFRKHk1q1bKC4uprJ2gPrpIW9vb6qzdceOHZCUlKRRpeAZMmQIEhMTERAQAGNjY1y6dAmmpqa4desWjI2N6ZYnELZv3/7N7128eLEQlbQeTJxGZkIA9qtXr9C7d28A9Y1UkpKSPJ3a3bt3R0FBAV3yBEZ5eXmLbPBa+v4fncDAQEyePBkqKiqorKyEmZkZZRG4bt06uuUJjPv37+OPP/6gXh88eBDdu3dHVFQUgPqiwY4dO4gqlDRkYbm4uFBrDXafJJ7Z3Lp1Czdv3sSFCxf4rpH08zbOt0tNTeW5D9fW1uLixYsiX6wPCAiAm5vbN2VU3LlzB2/evMHYsWNbQZnwWbVqFcrLywHUn7eOGzcOQ4cORYcOHXD06FGa1QmOYcOGgcPh4PHjx599D2kNOCkpKTh06BDfuoqKCt68eUODoi/DFkq+ASba9jARFRUVJCcn8/kNJyUloUOHDvSIYhEIy5cvx5IlSxAQEMC3vmzZMmIKJUws6k6YMIG4B4nP4efnh0mTJsHMzAwyMjIICwvj6UIJDQ0lyp6Jy+VixowZPAfmHz9+xPz583kOakjyTa+oqKBGzi9duoRJkyZBTEwMAwYMQG5uLs3qBIOvry/Mzc2pQklKSgpmzZqFGTNmwMDAAJs2bYKqqirWrFlDr1AhoKOjgz179tAtQ2g0FPm+BofDIaZQsn79eoSEhPCtq6ioYO7cuUQWSpgQgF1bW8vTYNKmTRueTDsxMTGRt6cFAF1dXbi5ucHZ2fmzwcBcLhdXrlzBli1bMGzYMKxYsaKVVQqPBovAmzdvUuHmJFkENvDmzRueSdyYmBiMHz+eem1ubo6lS5fSIU1oNLX3JB1XV1c4Ojpi9erVPDa9pNGnTx9wOBxwOBxYWFjwXZeSksKOHTtoUCY4UlNToa6ujilTpmD8+PHo27cvZQVYU1OD1NRUxMXF4cCBA3j16hURzQkNjBw5kvq3rq4u0tLS8PbtWygqKhK137927RrdElodBQUF5Ofn8zWmP3z48IcsbrKFkhYSHh6OqVOn8nU6VlVV4ciRI3BycqJJGcv3Mm3aNCxevBiysrIYNmwYgPrRejc3N9jZ2dGsjuV7ePLkCRXA2RgXFxdixuoBZhZ1STxM/RzKysq4fv063r9/DxkZGZ5DG6C+g7nxoZWo09wBY2OvXhLR1dXFP//8g4kTJyIqKgpLliwBUG8lICcnR7M6wZCYmAg/Pz/q9ZEjR/Dzzz9TBYRu3brB29ubmM92TU0NamtreZ4bCwsLERwcjPLyctjY2GDIkCE0KhQcTDuYApg5jXzv3r1mi0NqampETFk0EBUVBXl5eQD1z1fR0dGU7UdJSQmNygTHtWvX8Oeff2LNmjXo3bs3+vbtC1VVVbRr1w7v3r1Damoqbt26hTZt2mDFihVUZhhpDB48mJo4JxElJSXk5+ejW7duqKurw/379+Hu7k5dr6qqIqLw15iGnEqmUFxcjCVLlhBdJAHqnzO4XC5l2dM4S0hCQgIqKip8+yNRIzw8HElJSdi5cyfs7e3x4cMHiIuLQ1JSEhUVFQAAExMTzJ49GzNmzCA2268B0q21mYKdnR2WLVuG48ePg8PhoK6uDjdv3oSHh8cPeYbOFkpayMyZMzFq1Cg+P8TS0lLMnDnzh/wls3wbfn5+yMnJgaWlJdq0qf9o1NXVwcnJCf7+/jSrowcNDQ0+yyZRpGPHjkhMTISenh7PemJiIt9nmRSYUtTV1tbGvXv3+Ka+SkpKYGpqimfPntGkTHgsWbIE27Zt4yuKSEpKYt68eQgNDaVJmWDZt28f3RJaHS8vL9jb22PJkiWwtLSkQr4vXboEExMTmtUJhnfv3vFs5BvC/Rro168fnj9/Toc0oTBnzhxISEhQB8ulpaXo168fPn78iC5dumDr1q04ffo0xowZQ7NS4dFwAEdSN2ADTJxGZkoAdtNifdMiAQl/z/r6+jh58iTy8vJw/Phx3LhxA/Hx8aisrISysjJMTEywZ88ejB49WuQPHxvDNAtIc3Nz+Pn54X//+x+OHz+Ouro6mJubU9dTU1P57mEkEBERgeDgYGRnZ+PWrVvQ0NBAUFAQtLS0MGHCBLrlCZRJkyYhJiaGKAve5mgogJHeHNi7d2/s2bMHISEhSE5ORm5uLnVf7tOnD3FB7o0t8r4EKXvcBhoXrBvD4XDQrl076OrqYsKECUQUi/z9/fHbb7+hW7duqK2thaGhIWpqauDg4IBVq1bRLY8PNsy9hYiJiaGwsJBvI5CUlIThw4cTE6LLZDIyMpCUlAQpKSkYGxsT25FCehAnUG/x4uHhgc2bN2Pr1q1Yvnw5Bg0aBKA+o2TDhg1wd3fH6tWraVYqeMTFxZGfn89XCCouLoaKigox1lufC/MrLCxEt27dUFVVRZMy4fG53+2bN2/QuXNn1NTU0KSMRRAUFBQgPz8fvXv3hpiYGADg7t27kJOTQ48ePWhW9/1oaGggIiICw4YNQ1VVFRQUFHD27FlYWloCqLfiMjMzI+Z5qnv37ti5cydli/fXX3/B398fqampkJeXx7Jly3D37l3ExMTQrFTw/P3339i6dSuePn0KANDT08Pvv/+O2bNn06xMcCxbtgxHjx7Fvn37eKaRXVxcMHnyZGzevJlmhYKHKQHYLOQyevRomJubY9myZQDqv3dMTU15LCDnzZtHzGRjTk4ORowYgaysLIiLi2P79u08mWe2trbQ0tL6ZvtEUWDXrl3w8vLC77//jnXr1uHRo0fQ1tbG/v37ERYWRtx37rp16xAUFISxY8c2m01Jit1lY7KyshAUFIQnT54AAAwNDeHm5kZ8sYhExMTEoKGhARMTky9Ot506daoVVQmf4cOHIyEhAbW1tdDX1wdQfxYpLi6OHj16ID09HRwOB3FxcTA0NKRZrWB4/vw5UlJSUFZWBhMTE75G5h8FtlDyjZiYmIDD4SApKQk9e/akJg6Aeh/b7OxsjBo1qll7HxaWH42mQZzp6enQ1tbGqlWriAniBP7vQLljx44ICgpCYGAgXr16BQBQVVWFp6cnFi9eTERXYFNIL+o2BNfb2toiLCyMsscA6u/J0dHRuHz5MtLT0+mSKHA+fPgALpcLRUVFPH36lOd3W1tbi7Nnz2L58uXU3ziL6PPhwwdcvXoV+vr6MDAwoFuOQFiwYAGSkpKwYcMG/PPPPwgLC8OrV6+ovJ2DBw8iKCgI9+7do1mpYGjfvj0ePXpE2TNNmjQJXbt2pYLPU1NTYW5ujtevX9MpU+B4eXlhy5YtcHV1pSajbt26hZ07d2LJkiXN5meJIlVVVZg+fTqOHz/ON40cHBzMkyNFCu/fv8fkyZNx//59lJaWQlVVFfn5+Rg4cCAuXLhAVNg3C5l06dIFZ8+eRd++fQEAK1euRGxsLOLi4gDUW5l6e3sjNTWVTpkCpaamBo8fP0bHjh2hqqrKcy0pKQldu3YlagrO0NAQ/v7+sLW1haysLJKSkqCtrY1Hjx7B3Nz8hwwP/h6as4BsgMPhEDdhHxUVBRsbG/Tp04eyzWvIGjp79iwx+aNM4bfffsPhw4ehoaGBmTNnwtHRkYgpiq8RFBSEGzduYN++fZTF8vv37zF79mwMGTIEc+bMgb29PSorKxEVFUWz2u9D1KZn2ELJN+Lj40P9/9KlSyEjI0Ndk5CQgKamJn755RciN0RM4sWLFzhz5gzy8vL4OtG3bNlCkyrBY2VlBVNTUyqIs+HhMT4+Hvb29sjJyaFbokBobtqgtLQUAIjKcmgMU4q6DZ32HA6Hr/Okbdu20NTURGBgII+tgqgjJib2xaIeh8OBj48PVq5c2YqqWATJr7/+imHDhmHRokWorKxE7969kZOTAy6XiyNHjuCXX36hW+J38+bNG0yaNAlxcXGQkZFBWFgYJk6cSF23tLTEgAEDsG7dOhpVCo4OHTrgxo0bVCeYqqoqNm3aBAcHBwDAs2fPYGRkRPlOk0LHjh2xfft2TJs2jWf98OHDcHV1Je6QiinTyI2Ji4tDcnIyysrK8NNPP1FTYaTRnH3P1q1boa2tTZx9D1No164dnj59im7dugEAhgwZgtGjR1PPTzk5OTA2Nqb2DKRRVVWF7Oxs6Ojo8OwTSEJKSgppaWnQ0NDg2es+ffoUvXr1QmVlJd0SWb4DExMTjBw5EgEBATzry5cvx6VLl5CQkECTMpb/yqdPnxAZGYnQ0FDEx8dj7NixmDVrFqytrYlsagXqs90uX77MNy3y+PFjWFtb4+XLl0hISIC1tbXIPzeL2vQMmd+MQsDb2xsAoKmpialTpxIfmsREoqOjYWNjA21tbaSlpcHIyIg6oDI1NaVbnkBhShAnwO8hTWqBpAFbW1sA9fkrI0eO/GxRV9Rp8KbV0tLCvXv3iPNqbY6YmBhwuVxYWFjg5MmTPB0XEhIS0NDQ4OsSZBEtrl+/Th3UnDp1ClwuFyUlJQgLC8PatWuJ+OwqKyvj+vXreP/+PWRkZPh8748fP85z3xJ1+vTpg4iICKxfvx43btxAYWEhLCwsqOtZWVlEfm6rq6upbu3G/PTTT0TaA3bv3h3du3enW4ZQaZrrMGTIEGRlZWHjxo2oqKggLtcB4LfvabAtVVRURFBQEFsoEVE6deqE7OxsyqI1ISGBaooE6puqSMhobEpFRQVcXV0RFhYGoP6QSltbG66urlBTU8Py5ctpVig4tLS0kJiYyFe0vnjxIjETukzmyZMnzTb9ubi4sPaPIoqkpCSmTZuGadOmITc3F/v378fChQupaTiS9gYNvH//Hq9fv+YrDBQVFVFZcAoKCkRYiTdMi3xtembJkiU/xPQMWyhpIQ3BflVVVXj9+jVfkJS6ujodslgEwIoVK+Dh4QEfHx/Iysri5MmTUFFRgYODA0aNGkW3PIHClCBOoP7w4mtdCKJuQ9UYphV1s7Oz6ZbQapiZmQGo/5nV1dWJ7a5hMu/fv6cKYBcvXsQvv/wCaWlpjB07Fp6enjSrEyyN7fIa86OMXAsKLy8vjB49GseOHUN+fj5mzJiBLl26UNdPnTpF2UaQxPTp07Fr1y6+adzdu3dT0zSkwJRpZF9fX5ibm1OFkpSUFMyZMwfOzs5UroOqqioxuQ4AsGPHDuzZswe2trY8nct9+/aFh4cHjcpYvocxY8Zg+fLllAWktLQ0hg4dSl1PTk4mMudgxYoVSEpKwrVr13j2tlZWVlizZg1RhRJ3d3f89ttv+PjxI7hcLu7evYvDhw9j/fr12Lt3L93yhAJTvouA+qnVxMREvnyDxMREvgxHFtGjwUWBy+USk6vaHBMmTICLiwsCAwPRr18/APUNzR4eHlTz6927d4loxNm0aRMuX75MFUmA+r3gmjVrYG1tDTc3N3h5eVGZjnTDFkpayNOnT+Hi4oL4+HiedS6XCw6HQ/QHmXSePHmCw4cPAwDatGmDyspKyMjIwNfXFxMmTOAJvRN1bGxs4OvrS3VicDgc5OXlYdmyZUR0LDfGx8fnswdyJMOUou7ixYuhq6vLF1K4c+dOZGZmEtlVpKGhgRs3biAkJATPnj3D8ePHoaamhoiICGhpaWHIkCF0S2T5j3Tr1g23bt2CkpISLl68iCNHjgAA3r17R1zRs7y8HAEBAYiOjm72HkWKn7aZmRkePHiAS5cuoXPnzpgyZQrP9T59+qB///40qRMuf//9Ny5duoQBAwYAAO7cuYO8vDw4OTnxeBWL8gEOk6aRExMT4efnR70+cuQI+vfvjz179gCov395e3sTVSjJzs6GiYkJ37qkpCTKy8tpUCQcampq4O/vDxcXF3Tt2pVuOULHz88PkyZNgpmZGWUB2dg+OzQ09Ic5rBEk//zzD44ePYoBAwbwNNv07NkTWVlZNCoTPLNnz4aUlBRWrVqFiooK2NvbQ1VVFdu2bYOdnR3d8gQOk76LAGDOnDmYO3cunj17hkGDBgGozyjZsGHDZ7MQWH5sGltvxcXFYdy4cdi5cydGjRpFWW6TRkhICJYsWQI7Oztq2rpNmzZwdnbG1q1bAQA9evQgorgratMzbKGkhcyYMQNt2rTBv//+iy5durAdvQTRvn176oPZpUsXZGVloWfPngAg8p6ATQkMDMTkyZOhoqKCyspKmJmZoaCgAAMHDiTGF74BOzs7RnaWMKWoe/LkSSrYvTGDBg1CQEAAkYWSkydPYvr06XBwcEBCQgI+ffoEoP4BxN/fH+fPn6dZIct/5ffff4eDgwNkZGSgrq4Oc3NzAPWWXMbGxvSKEzCzZ89GbGwspk+fTvzzlIGBwWetPubOndvKalqHR48eUYczDQdwysrKUFZWxqNHj6j3ifrvnUnTyO/evUOnTp2o17GxsRg9ejT1ul+/fnj+/Dkd0oQGU+x72rRpg02bNsHJyYluKa3Ct1hAkmjVW1RU1OyeqLy8XOTvxc3h4OAABwcHVFRUoKysjOj9IJO+iwBg9erVkJWVRWBgIFasWAEA1ERj0+Y5UWPSpEnf/N7IyEghKmk9Fi5ciCNHjqBbt25wcXHB4cOHGWGrLSMjgz179mDr1q1Ug5i2tjaPzVifPn1oUidYRG16hi2UtJDExEQ8ePAAPXr0oFsKi4AZMGAA4uLiYGBggDFjxmDp0qVISUlBZGQk1Q1JCvLy8rh8+TJu3ryJpKQklJWVwdTUFFZWVnRLEygkPvR/K0wp6hYXFzc7MSQnJ0dcgbOBtWvXIjg4GE5OTtTEAQAMHjwYa9eupVEZy/eycOFC9O/fH8+fP8eIESOoDiptbW3ifrcXLlzAuXPniLSd+hzNhUIHBQVBS0uLuKyDmJgYuiW0CkyaRmZirgOT7HssLCwQGxsLTU1NuqW0GkuWLMG2bdv4iiKSkpKYN28eQkNDaVImHPr27Ytz587B1dUVwP/tk/bu3YuBAwfSKU1ovH79Gunp6QDqf17SLKYbYNJ3UU1NDQ4dOkTlGZSWlgIgJ4e08b6Wy+Xi1KlTkJeXp3LfHjx4gJKSkhYVVH50goODoa6uDm1tbcTGxiI2NrbZ95FSGGqKjIwMevXqRbcMoSJq0zNsoaSFGBoaEnv4xnS2bNmCsrIyAPV2TWVlZTh69Cj09PRE2hbiSwwePJjoQyoul0u3BNpgSlFXV1cXFy9exKJFi3jWL1y4AG1tbZpUCZf09HQMGzaMb11eXh4lJSWtL4hFoPTt2xe9evVCdnY2dHR00KZNG4wdO5ZuWQJHUVGRuDySL/G5UGgFBQU2FFqEYdI0MhNzHZhk3zN69GgsX74cKSkp+Omnn9C+fXue6zY2NjQpEx5hYWEICAjgO2CtrKxEeHg4cYUSf39/jB49GqmpqaipqcG2bduQmpqK+Pj4zx5MiiqlpaVYuHAhDh8+TFl7iouLY+rUqfjrr7+Is2Vm0ndRmzZtMH/+fDx58gQAOQWSBvbt20f9e9myZfj1118RHBxMTb7V1tZi4cKFPFkPoo6TkxOxTZ1fgik2xIDoTc+whZIWsmHDBvzxxx/w9/eHsbExX+cUSTcsJlFbW4sXL15Qldz27dsjODiYZlXCgym5Dk2/bJgEU4q67u7uWLRoEYqKimBhYQGg3qc3MDCQmL/jpnTu3BmZmZl8XZ9xcXHEFoeYQkVFBVxdXREWFgYAyMjIgLa2NlxdXaGmpkZU0Kqfnx+8vLwQFhYGaWlpuuUIHSaEQk+aNAn79++HnJzcVzsdSekKZNI0MlNzHZhi37Nw4UIAzWcGkWTZCgAfPnwAl8sFl8tFaWkpTwZYbW0tzp8/T+TveciQIUhMTERAQACMjY1x6dIlmJqa4tatW0Taez58+BDnzp2jpmVu3boFNzc3zJs3j2cimwSY9F0EAP3798fDhw/5bBFJoyGvo7E9oLi4ONzd3TFo0CBs2rSJRnWCY//+/S16/4sXL6Cqqiry2SVMsiFuQFSmZ9hCSQtpsCaytLTkWSfN959piIuLw9raGk+ePIGCggLdcoQOE3MdmAZTirouLi749OkT1q1bR4XMampqYteuXcR6bc+ZMwdubm4IDQ0Fh8PBq1evcOvWLXh4eGD16tV0y2P5DlasWIGkpCRcu3aNx1PaysoKa9asEflCiYmJCc8mIDMzE506dYKmpibfPSohIaG15QkVJoRCy8vLU79fOTk5Rmz4mDSN/C25Do07A0lDWlqa6KIuk5qLFBQUwOFwwOFwmvVD53A4PLZyJKGjo4M9e/bQLUPo/Pvvv4iKisKQIUOotZEjR2LPnj1EZnYw6bsIqC/sLl26FC9evGh2Ak4UDmK/hZqaGqSlpUFfX59nPS0tjVH37KYYGhoiMTFR5BsEmWhDLCqwhZIWwhTPZSZiZGSEZ8+eQUtLi24pQoeJuQ5Mg0lF3QULFmDBggUoKiqClJQU0Qc1ALB8+XLU1dXB0tISFRUVGDZsGCQlJeHh4UH5TrOIJv/88w+OHj2KAQMG8Bwy9+zZkwrEFmUawvqYCBNCoRvbRbS0O1AUYdo0cgOfs6wh0UqvsLAQHh4elC1GU0tXkp6lGvPx40eeKQvSiImJAZfLhYWFBU6ePMnztyshIQENDQ2oqqrSqFB41NXVITMzs1mbl+ZsXUWVDh06NHuvkpeXh6KiIg2KhEvjA2MmfBc1WB82dsfgcDjE7XNnzpyJWbNmISsrC/379wcA3LlzBwEBAZg5cybN6uiDFHt1ptkQixJsoaSFmJmZ0S2BRUisXbsWHh4e8PPza7YzgZQOfICZuQ5Mg0lF3ZqaGly7dg1ZWVmwt7cHALx69QpycnJEFk04HA5WrlwJT09PZGZmoqysDIaGhkT+rEyjqKioWbuP8vJyIrrzvb296ZZAG0wKhQbqg6EjIyP5pnQ/fPgAW1tbXL16lR5hAoRp08hMZMaMGcjLy8Pq1auJt8Wora2Fv78/goODUVhYSFk/rl69Gpqampg1axbdEgVGw34+Ozsb6urqRP9eG3P79m3Y29sjNzeX76CRpMNlAFi1ahXc3d0RERGBzp07AwAKCgrg6elJ7PR1SUkJTpw4gaysLHh6ekJJSQkJCQno1KkT1NTU6JYnULKzs+mW0Cps3rwZnTt3RmBgIPLz8wHUZ9B4enpi6dKlNKtj+V6YZkMsSnC4pJTjWpGSkhL8/fffVIBUz5494eLiQlwoGNNo7HHY+IGZtM4EoN7vctGiRfD09Gw212HOnDk0K2Rh+TZyc3MxatQo5OXl4dOnT9TG3s3NDZ8+fSK+o4qFLIYNG4YpU6bA1dUVsrKySE5OhpaWFlxdXfH06VNcvHiRbokC4969e6irq8PPP//Ms37nzh2Ii4ujb9++NCkTHgcPHsSaNWuo6SBVVVX4+PgQdQDZgJiYGAoKCvgKf69fv4aamhqqq6tpUiZY+vbtiw0bNvBNb7KQgaysLG7cuPFDBYwKC19fX4SFhcHX1xdz5szBo0ePoK2tjaNHjyIoKAi3bt2iW6JQuHHjBkJCQvDs2TMcP34campqiIiIgJaWFo9tEwn06dMH3bt3h4+PT7OFP5LOMkxMTJCZmYlPnz5BXV0dAJCXlwdJSUno6enxvJcEq8/k5GRYWVlBXl4eOTk5SE9Ph7a2NlatWoW8vDyEh4fTLVFgfPjwAXfu3EFVVRX69++Pjh070i1JKNTU1ODQoUMYOXIkOnXqhA8fPgAgq3n3vyIrK4ukpCSRb/A1MTFBVlYWuFwuI2yIRQl2oqSF3L9/HyNHjoSUlBQ1/rZlyxasW7eOCkRjEU2Y1IHPxFwHJsKEoq6bmxv69u2LpKQkdOjQgVqfOHEisQW/8vJyBAQEUFYgTa0Tnj17RpMylu/F398fo0ePRmpqKmpqarBt2zakpqYiPj4esbGxdMsTKL/99hv++OMPvkLJy5cvsWHDBty5c4cmZcKDCaHQycnJ1L9TU1NRUFBAva6trcXFixeJ6mxl0jQyE+nWrRsxFh9fIzw8HLt374alpSXmz59Prffu3RtpaWk0KhMeJ0+exPTp0+Hg4ICEhAR8+vQJAPD+/Xv4+/vj/PnzNCsULE+fPsWJEyegq6tLtxShwzSrT3d3d8yYMQMbN26ErKwstT5mzBhq2p4EEhMTMWbMGBQWFoLL5UJWVhbHjh3DyJEj6ZYmcNq0aYP58+dT+3j2eYI8mHafEiXYiZIWMnToUOjq6mLPnj1o06a+zlRTU4PZs2fj2bNnuH79Os0KWf4reXl56NatG193DZfLxfPnz6luFNJgSq4D02iuqHvv3j1UVlYSVdTt0KED4uPjoa+vz9NdkpOTA0NDQ1RUVNAtUeBMmzYNsbGxmD59erMdgW5ubjQpYxEEWVlZCAgIQFJSEsrKymBqaoply5bB2NiYbmkCRUZGBsnJyXzdYNnZ2ejVqxdKS0tpUsbyPYiJiVH3pOa2GFJSUtixYwdcXFxaW5pQYNI0MhO5dOkSAgMDERISAk1NTbrlCBUpKSmkpaVBQ0OD53kqNTUV/fv3p4KiScLExARLliyBk5MTz8/88OFDjB49mqfQSwIWFhb4448/iAwzZzry8vJISEiAjo4Oz99ybm4u9PX18fHjR7olCoSRI0eirKwMmzdvRrt27eDn54eUlBQ8ffqUbmlCwdzcHL///jt7oN4EOTk5IsLcWX5c2ImSFnL//n2eIglQX+39448/iLSJYBJaWlrIz8/n6/B8+/YttLS0iN3skjquynSWLFkCGxubZou6v//+OzFF3bq6umY/my9evODpqCKJCxcu4Ny5cxg8eDDdUliEgI6ODvbs2UO3DKEjKSmJwsJCvk1Ofn4+zzMWKTAlFDo7OxtcLhfa2tq4e/cuzzOGhIQEVFRUIC4uTqNCwcKkaWQmMnXqVFRUVEBHRwfS0tJ8thhv376lSZngMTQ0xI0bN6ChocGzfuLECZiYmNCkSrikp6c3G2AuLy+PkpKS1hckZFxdXbF06VIUFBTA2NiY7++5V69eNCkTDkzK7JCUlKSsmRqTkZFB1F7/wYMHPA1/oaGhUFJSwocPH4icuFi4cCGWLl2KFy9eNDu1Stpn9lthe/1ZhA15O1EhIycnh7y8PPTo0YNn/fnz58QeyjGFhu6/ppSVlaFdu3Y0KBIspqamiI6OhqKiIkxMTL4YXMj6IYo+TCnqWltbIygoCLt37wZQ39FbVlYGb29vjBkzhmZ1wkFRURFKSkp0y2AREM1tbD8HSZtAa2trrFixAqdPn6bsAEtKSvDnn39ixIgRNKsTPEwJhW44ZG1qCUgqWlpaX5xGZhFtgoKC6JbQanh5ecHZ2RkvX75EXV0dIiMjkZ6ejvDwcPz77790yxMKnTt3RmZmJt+0UFxcHJGdyr/88gsA8Ez0cTgcIifgmmZ2zJkzB0pKSoiMjCQuswMAbGxs4Ovri2PHjgGo/73m5eVh2bJl1O+dBN6+fYuuXbtSrxUUFNC+fXsUFxcT9YzcgJ2dHQBg8eLF1Bqpn9mWkJqaClVVVbpl/CeUlJSQkZEBZWVlKCoqfnE/QFIzhqjBFkpayNSpUzFr1ixs3rwZgwYNAgDcvHkTnp6emDZtGs3qWP4L7u7uAOq/dFavXg1paWnqWm1tLe7cuUNEiOOECRMgKSlJ/ZvUQxqWephS1A0MDMTIkSNhaGiIjx8/wt7eHk+fPoWysjIOHz5Mtzyh4OfnBy8vL4SFhfHcr1hEEwUFha/ej0ncEG3evBnDhg2DhoYG1a2cmJiITp06ISIigmZ1gicuLo74UOgzZ85883ttbGyEqKT1YOo0MlNwdnamW0KrMWHCBJw9exa+vr5o3749vLy8YGpqirNnzxJZvAaAOXPmwM3NDaGhoeBwOHj16hVu3boFDw8PrF69mm55Aic7O5tuCa0GUzI7GggMDMTkyZOhoqKCyspKmJmZoaCgAAMHDsS6devolidQmuafcblcPHnyhMeylZRJCyZ8ZidNmvTN742MjARQnx8mqmzdupW6JzGpGUPUYDNKWkhVVRU8PT0RHByMmpoaAEDbtm2xYMECBAQEUAfRLKLD8OHDAQCxsbEYOHAgJCQkqGsSEhLQ1NSEh4cH9PT06JLIwtJiFi9ejFOnTjVb1P3ll1+I+mKuqanBkSNHkJycTGU6ODg4QEpKim5pQsHExARZWVngcrnQ1NTks05gJ8JEi5aEtJuZmQlRSetTXl6OgwcPIikpCVJSUujVqxemTZvG9zdNAoaGhjh48CCxFjYAb17HlyCp6CcmJobCwkI+a5Pc3FwYGhqivLycJmUs/5XGFi5fm/gT9Q7m7du3Y+7cuWjXrt1nsxpJhsvlwt/fH+vXr6cy7SQlJeHh4QE/Pz+a1bF8D0zJ7GjKzZs3eTLurKys6JYkUBpy0Jo7wmQnLUSTmTNnUv/mcrk4deoU5OXlKQeMBw8eoKSkBJMmTcK+ffvoksnCMNhCyX+koqICWVlZAED51rKINjNnzsS2bdtEftPzLcyePRuOjo4wNzenWwqLkGha1OVyuZCQkGCLugTg4+Pzxeve3t6tpIRFkNTU1MDf3x8uLi48tgIsZMCkUGgm0DCNvG3bNsyZM6fZaWRxcXHcvHmTLoks/xFxcXFqSqjhUK4ppBzGtWnTBq9evaKyg5qbjmICVVVVyMzMRFlZGQwNDSEjI0O3JKGRlZWFoKAgPHnyBEB9Ed/NzQ06Ojo0KxMsKioqiIqKgomJCU+h5PLly3BxcSHKGrG6uhpSUlJITEyEkZER3XKESm5u7je9r2nWkqiTmpqKvLw8VFVV8ayTMp3bwLJly/D27VsEBwdTeXa1tbVYuHAh5OTksGnTJpoVCofXr1/j9evXfLa1pExGiSJsoYSFhYFMmDABUVFR6NixI+zs7ODo6IjevXvTLYtFCJBY1GWivUsD7GE62cjKyiIlJYUxB+kREREICQnBs2fPcOvWLWhoaGDr1q3Q1tbGhAkT6JYnUBQVFVFRUYGamhriQ6GZADuNTC6xsbEYPHgw2rRp89WJP1Gf8lNXV8eKFSswZswYaGlp4f79+1BWVv7se1lEm6ioKNjY2KBPnz4YPHgwgP+bQCDNYm327NkoLi7GsWPHoKSkhOTkZIiLi8PW1hbDhg0jarIeALS1tXHq1Cl2P08Yz549w8SJE5GSksIzSdNQwBf1Yn1TOnbsiLi4OOjr6/Osp6enY9CgQSguLqZJmXB48OABnJ2d8eTJE74pKRKaMUQZtlDyjTQOPfsSoaGhQlbCIizKy8sREBCA6OjoZiu6z549o0mZcHj37h2OHz+OQ4cO4caNG+jRowccHBxgb2/PmEM6EmHCvYqJ9i6NYdphOpOYMGECJk2axAhf/F27dsHLywu///471q5di8ePH0NbWxv79+9HWFgYYmJi6JYoUMLCwr54nbTfua+v7xeve3l5tZIS4cKkaWQW8ti9ezdcXV0pO+nmIGV6pjmYtvczMTHByJEjERAQwLO+fPlyXLp0iSjr1vfv32Py5Mm4d+8eysrKoKqqSmV2nD9/Hu3bt6dbokD5+++/ERkZiYiICCgpKdEtp1W4ceMGQkJCkJWVhRMnTkBNTQ0RERHQ0tLCkCFD6JYnEMaPHw9xcXHs3bsXWlpauHv3LoqLi7F06VJs3rwZQ4cOpVuiQFFUVMT+/fv5mqVOnz6NGTNm4N27dzQpEw69e/eGjo4Oli1bhk6dOvFNsJI2GSVKsIWSb0RMTIwKHP3Sf7JTp061oioWQTJt2jTExsZi+vTp6NKlC9+Nys3NjSZlwufFixc4fPgwQkND8fTp0y9umFh+bNh7Ffkw6TCdaQQHB8PHxwcODg746aef+DbyJE1IGRoawt/fH7a2tjy2GI8ePYK5uTnevHlDt0SW76BpFkt1dTWys7PRpk0b6OjoEHUgx0IWycnJ3/xeEmwxSktLkZubi169euHKlSvo0KFDs+8jsVOdaXu/du3aISUlhW/SLSMjA7169SIyt4P0zI4GTExMkJmZierqamhoaPA9P5L2nXvy5ElMnz4dDg4OiIiIQGpqKrS1tbFz506cP38e58+fp1uiQFBWVsbVq1fRq1cvyMvL4+7du9DX18fVq1exdOlSPHz4kG6JAsXd3R3h4eH4888/0b9/fwDAnTt3EBAQgOnTp2PLli00KxQssrKyePjwIXR1demWwtKENnQLEBUWLFiAw4cPIzs7GzNnzoSjoyNjqvVM4cKFCzh37hw1iswUqqurcf/+fdy5cwc5OTno1KkT3ZJYvgP2XkU+o0ePxvLly5GSkkL8YTrTWLhwIQA0uxEgraM3Ozu72WBzSUlJYgKwmRQK3ZTmNu8fPnzAjBkzMHHiRBoUCQemdaQzgT59+nw2LLgxpNyTZWVlYWRkhH379mHw4MGMyrBj2t6vY8eOSExM5CuUJCYmEpVNU1dXh/379yMyMhI5OTngcDjQ0tJC586dqQkp0pgwYQKRP9fnWLt2LYKDg+Hk5IQjR45Q64MHD8batWtpVCZYamtrISsrC6C+aPLq1Svo6+tDQ0MD6enpNKsTPJs3b0bnzp0RGBiI/Px8AECXLl3g6emJpUuX0qxO8FhaWiIpKYktlPyAsBMlLeDTp0+IjIxEaGgo4uPjMXbsWMyaNQvW1taM+mIiFS0tLZw/fx4GBgZ0S2kVYmJicOjQIZw8eRJ1dXWYNGkSHBwcYGFhwf49izhMuFeNGTMGhw8fhry8PAAgICAA8+fPh4KCAgCguLgYQ4cORWpqKo0qhcOXrMdIObhhIR9DQ0OsX78eEyZM4Jko2bFjB/bt20dE9yOTQqG/lZSUFIwfPx45OTl0SxEITOtIZwLfGhYMkGWL8fz5c3A4HCr/7O7duzh06BAMDQ0xd+5cmtUJB6bt/Xx9fbF161YsX74cgwYNAlA/cbFhwwa4u7tj9erVNCv8frhcLsaPH4/z58+jd+/e6NGjB7hcLp48eYKUlBTY2Njgn3/+oVsmy3ciLS2N1NRUaGpq8jxDPnv2DIaGhsRMRw0dOhRLly6Fra0t7O3t8e7dO6xatQq7d+/GgwcP8OjRI7olCo2GBiPSGoka8+bNGzg7O6N///4wMjLiyy9kmx9phMvyn8jJyeGuWbOGq62tzVVXV+eWlpbSLYnlO4mIiOBOnjyZW15eTrcUoaOqqspt164d19bWlnv8+HHux48f6ZbEIiRIvVeJiYlxCwsLqdeysrLcrKws6nVBQQFXTEyMDmksLAKhsrKSbglCwcfHh1teXs7ds2cPV01NjXvkyBFu+/btuYcPH+auXbuW+jcJXLt2jVtdXU39+0v/Ywo3btzgKigo0C1DYMjLy3Pj4uLolsHC8t0MGTKEGx4ezuVyudz8/HyurKwsd+DAgVxlZWWuj48PzeqEA5P2flwul1tXV8fdsmULV01NjcvhcLgcDoerpqbGDQoK4tbV1dEtTyCEhoZyZWVluVevXuW7Fh0dzZWVleWGhYXRoEy4aGlpcd+8ecO3/u7dO66WlhYNioSLlpYW9/Lly1wul8uVkZGh9oBhYWFcAwMDOqUJlIsXL3JPnjzJ5XK53KdPn3L19fW5HA6Hq6yszI2OjqZZnfB4/fo198aNG9wbN25wi4qK6JYjNM6cOcOVl5en7seN/8eeY9ALO1HyH3n+/Dn27duH/fv3o6qqCmlpaZCRkaFbFst3YGJigqysLHC5XGhqavJVdEnobm1gz549mDJlCtV9z0IupN6rxMTEUFBQQFkFNO4mAoDCwkKoqqoS36X98eNHtGvXjm4ZLAKitrYW/v7+CA4ORmFhITIyMqCtrY3Vq1dDU1MTs2bNolvid9N4yuLgwYNYs2YNsrKyAACqqqrw8fEh4udkOtu3b+d5zeVykZ+fj4iICJiZmeHQoUM0KRMsTOtIZyJPnz5FTExMs9ZqXl5eNKkSPIqKirh9+zb09fWxfft2HD16FDdv3sSlS5cwf/58Im3kmLT3a0ppaSkAULY+pGBtbQ0LCwssX7682ev+/v6IjY1FVFRUKysTLk33RQ0UFhaiW7duqKqqokmZcFi/fj0OHDiA0NBQjBgxAufPn0dubi6WLFmC1atXw9XVlW6JQuPt27dQVFQkxiWiMeXl5XB1dUV4eDj1fSsuLg4nJyfs2LED0tLSNCsULJqamhg3bhxWr17N2t//YLAZJS2gsZ1NXFwcxo0bh507d2LUqFFftEJhEQ1sbW3pltAqVFdXY8GCBRg4cCBbKCEU9l5FNkw4TGcq69atQ1hYGDZu3Ig5c+ZQ60ZGRggKCiLid9u4P8fBwQEODg6oqKhAWVkZUR7pAPNCoRuzdetWntdiYmLo2LEjnJ2dsWLFCppUCR4/Pz94eXkhLCyMuA08S31j0YIFC6CsrIzOnTvzHExxOByiCiXV1dVUPsmVK1coy48ePXpQXvGkwZS9X3OQViBpIDk5GRs3bvzs9dGjR/MV8kWZM2fOUP+OioqiLImB+v1CdHQ0tLS06JAmVJYvX466ujpYWlqioqICw4YNg6SkJDw8PIgqkrx//x61tbU8eaNKSkp4+/Yt2rRpQ5wtlbu7O2JjY3H27FkqOyouLg6LFy/G0qVLsWvXLpoVCpbi4mIsWbKELZL8gLATJd/IwoULceTIEXTr1g0uLi5wcHCAsrIy3bJYWP4T2traOHXqFHr37k23FBYBw5R7lbi4OAoKCtCxY0cA9Ru+5ORkajNA8kSJr68vwsLC4Ovrizlz5uDRo0fQ1tbG0aNHERQUhFu3btEtkeU/oquri5CQEFhaWvJMSaWlpWHgwIF49+4d3RK/GzExMRQWFlKfXZJpyCX52qM2kzJKSIPJHelMQENDAwsXLsSyZcvoliJ0fv75ZwwfPhxjx46FtbU1bt++jd69e+P27duYPHkyXrx4QbdEgVJTUwN/f3+4uLhQuSykU1hYCA8PD0RHR+P169d8300kfA9JSEggNzcXXbp0afb6q1evoKWlhU+fPrWyMuHQ0ADX3LNG27ZtoampicDAQIwbN44OeUKnqqoKmZmZKCsrg6GhIRGuCY0ZPXo0xo8fj4ULF/KsBwcH48yZMzh//jxNyoSDsrIyTpw4AXNzc571mJgY/PrrrygqKqJHmJBwdnbG0KFDMXv2bLqlsDSBnSj5RoKDg6Gurg5tbW3ExsYiNja22fdFRka2sjIWQfPgwQM8efIEANCzZ0+YmJjQrEjwrFy5En/++SciIiJ4OhRYRB+m3Ku4XC5mzJhBdT9+/PgR8+fPR/v27QGAmA1Qc4SHh2P37t2wtLTE/PnzqfXevXsjLS2NRmUs38vLly+hq6vLt15XV4fq6moaFAmH7t27f9Uy4O3bt62kRnhkZ2fTLaHVcXFx+ab3hYaGCllJ68DkjnQm8O7dO0yZMoVuGa3Chg0bMHHiRGzatAnOzs5UM9WZM2fQv39/mtUJnjZt2mDTpk1wcnKiW0qrMWPGDOTl5WH16tXo0qULkdY9tbW1aNPm80dc4uLiqKmpaUVFwqXBnkhLSwv37t0jsjmuORpPWhgaGlLrpE1a3LlzB1u2bOFbNzc3x8qVK2lQJFwqKiqana5QUVFBRUUFDYqES/fu3bFixQrExcXB2NiYr9lm8eLFNCljYQsl34iTkxORDxMs/8fr169hZ2eHa9euUZZUJSUlGD58OI4cOUJU9+vOnTuRmZkJVVVVaGhoUIfLDbAdkKILU+5Vzs7OPK8dHR353kPq5pcph+lMxNDQEDdu3ICGhgbP+okTJ4gq2vv4+PDYQ5BK098jE9i/fz80NDRgYmLy1UkaEvD29qZbAosQmTJlCpXRQTrm5uZ48+YNPnz4AEVFRWp97ty5xNrKWVhYIDY2FpqamnRLaRXi4uJw48YN9OnTh24pQqNpI1VTSGukunXrFoqLi3kaM8LDw+Ht7Y3y8nLY2tpix44dn/3vIarY2dk1O2lx7NgxoiYtPn361Gxhr7q6GpWVlTQoEi4DBw6Et7c3wsPDqQzOyspK+Pj4YODAgTSrEzx79+6FjIxMs82tHA6HLZTQCFso+Ub279/fove/ePECqqqqbB6ACOHq6orS0lI8fvyYCuVMTU2Fs7MzFi9ejMOHD9OsUHCwHZDk0tJ7laiyb9++Fr2fpHsyUw7TmYiXlxecnZ3x8uVL1NXVITIyEunp6QgPD8e///5LtzyBYWdnR1weybfAhFDoBQsW4PDhw8jOzsbMmTPh6OjIiMlVJkwjMxFdXV2sXr0at27dQq9evYjv9hQXF+cpkgAguogwevRoLF++HCkpKfjpp5/4GscaclpIoVu3bsQXsJs2UjUHSY1UPj4+GD58OGWtlZKSglmzZmHGjBkwMDDApk2boKqqijVr1tArVMAwZdKif//+2L17N3bs2MGzHhwcjJ9++okmVcJj27ZtGDlyJLp27UpNNSYlJaFdu3aIioqiWZ3gYeLkuajAZpQICTk5OSQmJkJbW5tuKSzfiLy8PK5cuYJ+/frxrN+9exfW1tYoKSmhRxgLC8t3Q9I9+fTp01Qgsq+vL3x8fHgO00eMGEG3RJbv4MaNG/D19UVSUhLKyspgamoKLy8vWFtb0y1NIIiLiyM/P59xhZKvhUKTNMn56dMnREZGIjQ0FPHx8Rg7dixmzZoFa2tr4iYemTSNzES+FILM4XDw7NmzVlQjXJiQX9GULzXPkJgddenSJQQGBiIkJIToAhiT6NKlC86ePYu+ffsCqLfXjo2NRVxcHADg+PHj8Pb2RmpqKp0yBU779u1x+/ZtGBsb86ynpKTg559/Jsam6ebNm7CyskK/fv1gaWkJAIiOjsa9e/dw6dIlDB06lGaFgqeiogIHDx6k7KQNDAzg4OAAKSkpmpWxMAm2UCIkGoewsogGsrKyzY4jP3z4EGZmZvjw4QM9woRESUkJTpw4gaysLHh6ekJJSQkJCQno1KkT1NTU6JbHwiJQSLsnk36YzkIuYmJiKCgoYFyhhEmh0I3Jzc3F/v37ER4ejpqaGjx+/JiosNWpU6fi2bNnCA8P55tG1tXVJWoamcm8efMGAIj2/x89ejTy8vKwaNGiZvMrJkyYQJMylu9BUVGR53dZXl6OmpoaSEtL801IkZANxjTatWuHp0+folu3bgCAIUOGYPTo0dRURU5ODoyNjVFaWkqnTIEzfPhwGBkZ8U1a/Pbbb0hOTsaNGzdoUiZ4EhMTsWnTJiQmJkJKSgq9evXCihUroKenR7c0FgHw4sULnDlzBnl5eaiqquK51tzUFEvrwFpvsbD8fywsLODm5obDhw9DVVUVQH0WwJIlS6gKPikkJyfDysoK8vLyyMnJwZw5c6CkpITIyEjk5eUhPDycboksLCxfYOjQobh8+TLdMliExP379ykbH0NDQ6LG65taTjEFJoVCN0ZMTAwcDgdcLpe47mwAuHjxIq5cuUIVSYD6z+xff/3FFq5FnJKSEqxcuRJHjx7Fu3fvANQfOtvZ2WHdunXE5SwxIb/iS3z8+JHyxCeJoKAguiWwCJFOnTohOzsb3bp1Q1VVFRISEuDj40NdLy0t5SuIkcDatWthZWWFpKSkZictSKJPnz44ePAg3TJaDSbY1DYQHR0NGxsbaGtrIy0tDUZGRsjJyQGXy4WpqSnd8hgNWyhhYfn/7Ny5EzY2NtDU1KS6Mp4/fw4jIyMcOHCAZnWCxd3dHTNmzMDGjRshKytLrY8ZMwb29vY0KmNhYfka2trauHfvHjp06MCzXlJSAlNTU6KsQJjGixcvMG3aNNy8eZPHxmfQoEE4cuQIunbtSq9Alv8Mk0KhG1tvxcXFYdy4cdi5cydGjRpFRE5UY+rq6po9hGrbti1ji4Ik8PbtWwwcOBAvX76Eg4MDz7TQ/v37ER0djfj4eL48D1GGCfkVTamtrYW/vz+Cg4NRWFiIjIwMaGtrY/Xq1dDU1MSsWbPolvjdODs7o7a2Fps3b8aZM2dQVVUFS0tLeHt7s1Y2BDBmzBgsX74cGzZswD///ANpaWkeO6bk5GTo6OjQqFA4DB48GLdu3cKmTZtw7NgxatLi77//ZictRJiv2dSSVihZsWIFPDw84OPjA1lZWZw8eRIqKipwcHDAqFGj6JbHaFjrLSFBms0LU+Byubhy5QqPJ6KVlRXNqgSPvLw8EhISoKOjw/O3mpubC319fXz8+JFuiSwsAoWke/LnrIsKCwuhrq6OT58+0aSM5XsZNWoUSkpKEBYWBn19fQBAeno6Zs6cCTk5OVy8eJFmhSz/lfXr12PLli0YM2YM0aHQCxcuxJEjR9CtWze4uLjAwcGBaLuiCRMmoKSkhG8a2cHBAYqKijh16hTNCln+C7///juio6Nx5coVdOrUiedaQUEBrK2tYWlpia1bt9KkUPAwMb/C19cXYWFh8PX1xZw5c/Do0SNoa2vj6NGjCAoKwq1bt+iWKBD8/PywZs0aWFlZQUpKClFRUZg2bRpCQ0Pplsbynbx58waTJk1CXFwcZGRkEBYWhokTJ1LXLS0tMWDAAKxbt45GlSws3wbTbGplZWWRmJgIHR0dKCoqIi4uDj179kRSUhImTJiAnJwcuiUyFrZQIiRICg5mIQ8VFRVERUXBxMSE5wD58uXLcHFxwfPnz+mWyMIiUEi4J585cwYAYGtri7CwMB7bj9raWkRHR+Py5ctIT0+nSyLLdyIlJYX4+HiYmJjwrD948ABDhw4lJpySiTAlFFpMTAzq6uowMTH5YnB7ZGRkK6oSHs+fP4eNjQ0eP37MN4185swZdgpMRNHU1ERISAhGjhzZ7PWLFy9i/vz5RB1iKCoqoqKiglH5Fbq6uggJCYGlpSXPfigtLQ0DBw6kLNdEHT09PXh4eGDevHkAgCtXrmDs2LGorKwkbsqPqbx//x4yMjIQFxfnWX/79i1kZGQgISFBkzLB8eHDB8jJyVH//hIN72MRLUjYr7eEzp07IyYmBgYGBjA0NERAQABsbGyQlJSEwYMHo6ysjG6JjIW13hISbP1JdLh69SoWLVqE27dv832pvn//HoMGDUJwcDDPGKuoY2NjA19fXxw7dgxA/SFNXl4eli1bhl9++YVmdSwsgoeEe7KtrS2A+s+rs7Mzz7W2bdtCU1MTgYGBNChjERTdunVDdXU133ptbS3Vrc4immRnZwMgPxTaycnpiwUS0ujWrRsSEhIYMY3MJPLz89GzZ8/PXjcyMkJBQUErKhI+TMyyePnyJXR1dfnW6+rqmv0uFlXy8vIwZswY6rWVlRU4HA5evXrFFnMJ4XOZSUpKSq2sRHgoKioiPz8fKioqUFBQaPZZg8vlgsPhEJmJxgSYZFMLAAMGDEBcXBwMDAwwZswYLF26FCkpKYiMjMSAAQPolsdo2EJJC3FxccG2bdt4ch0AoLy8HK6urtQIa2pqKnuoISIEBQVhzpw5zXYeyMvLY968ediyZQtRhZLAwEBMnjwZKioqqKyshJmZGQoKCjBw4EB2NJeFSEi4Jzf43WtpaeHevXvEHrQymU2bNsHV1RV//fUX+vbtC6A+2N3NzQ2bN2+mWR3Lf4VJodD79++nW0Krw+FwMGLECIwYMYJuKSwCQllZGTk5OZ89RM7OzibqABIAXwMGEzA0NMSNGzegoaHBs37ixAm+yU5Rpqamhi+ovm3btkQVg1jI5+rVq9R99+rVq4xoyvjWs0dRZvv27dS/dXV1sXr1aty+fRvGxsbE2tQ2sGXLFmpqxMfHB2VlZTh69Cj09PSwZcsWmtUxG9Z6q4WIi4tTlezGvHnzBp07d0ZNTQ1Nylj+KxoaGrh48SIV1NiUtLQ0WFtbIy8vr5WVCZ+bN28iKSkJZWVlMDU1ZTsgWUSCSZMmffN7SbF3YWEGja1P2rSp72Vp+Hf79u153kuiDQqJfCkU+tChQ+jWrRtxodBMgInTyEzCxcUFWVlZuHz5Mp9lzadPnzBy5Ehoa2sTcUjVHB8/fkRVVRXPGolWNqdPn4azszNWrFgBX19f+Pj4ID09HeHh4fj333+JKX6KiYlh9OjRkJSUpNbOnj0LCwsLnmcL9pmZheXHgglnj1+ypm0MSTa1LD8+7ETJN/LhwwdwuVxwuVyUlpbydGXU1tbi/PnzfDcwFtGgsLCQr1rdmDZt2qCoqKgVFbUegwcPxuDBg+mWwcLSIhp3X3O5XJw6dQry8vJUB/6DBw9QUlLSooKKqBEdHY3o6Gi8fv2amjRpgNSDGybAROsT0vH19YWEhASysrL4QqF9fX1hbW0NX19fokKhmQATp5GZhK+vL/r27Qs9PT389ttv6NGjB7hcLp48eYL//e9/+PTpEyIiIuiWKVDKy8uxbNkyHDt2DMXFxXzXSbSymTBhAs6ePQtfX1+0b98eXl5eMDU1xdmzZ4kpkgDNTws5OjrSoISFRTDo6enBwcEBDg4O0NPTo1uOwGHS2WODNS1TKSkpwYkTJ5CVlQVPT08oKSkhISEBnTp1gpqaGt3yGAs7UfKNiImJfXG8j8PhwMfHBytXrmxFVSyCQEdHB4GBgZT/f1MiIyPh4eFBRAX71q1bKC4uxrhx46i18PBweHt7o7y8HLa2ttixYwdPxxELy4/MsmXL8PbtWwQHB1MBhrW1tVi4cCHk5OSwadMmmhUKHh8fH+oQp0uXLnzfTadOnaJJGQsLS1OYGArNBJg8jcwUsrOzsXDhQly6dInKOWuwWdu5c2ez2RaizG+//YaYmBj4+flh+vTp+Ouvv/Dy5UuEhIQgICAADg4OdEtkYWFhAQBs3boVhw4dQkJCAkxNTeHo6IipU6eic+fOdEsTCEw9e/T19YWHhwekpaV51isrK7Fp0yZ4eXnRpEw4JCcnw8rKCvLy8sjJyUF6ejq0tbWxatUq5OXlITw8nG6JjIUtlHwjsbGx4HK5sLCwwMmTJ3l8aSUkJKChoSHy/vdMxdXVFdeuXcO9e/f4/FsrKyvRv39/DB8+nMc/UVQZPXo0zM3NsWzZMgBASkoKTE1NMWPGDBgYGGDTpk2YN28e1qxZQ69QFpZvpGPHjoiLi4O+vj7Penp6OgYNGtRsV6So06VLF2zcuBHTp0+nWwqLEGGK9QnpSEpKIisr67NZBy9evICuri4+fvzYyspYvod27drh0aNHnz0sz8zMhLGxMSorK1tZGYugeffuHZ4+fQqg3j+dtGySBtTV1REeHg5zc3PIyckhISEBurq6iIiIwOHDh3H+/Hm6JQocbW1t3Lt3Dx06dOBZLykpgampKRFNciwsJJORkYGDBw/i8OHDyM7OxvDhw+Ho6AgnJye6pX0XTD17/JzVWHFxMVRUVIibbLSysoKpqSk2btwIWVlZJCUlQVtbG/Hx8bC3t2ebqGiEtd76RszMzADUdxepq6szIjyKKaxatQqRkZHo3r07Fi1aRB24pqWl4a+//kJtbS0x1frExET4+flRr48cOYKff/4Ze/bsAQB069YN3t7ebKGERWSoqalBWloaX6EkLS2Nz5KKFKqqqjBo0CC6ZbAIASZan5AOE0OhmYCamtoXCyXJycno0qVLK6tiEQaKioro378/3TKEztu3b6GtrQ2gvijfkIM1ZMgQLFiwgE5pQiMnJ6fZ79VPnz7h5cuXNChiYWFpCd27d4ePjw98fHxw+/ZtLFiwADNnzhT5QglTzx65XG6zP2tSUhKRz8r37t1DSEgI37qamhoKCgpoUMTSAFsoaSFXr16FjIwMpkyZwrN+/PhxVFRUNOsByvJj06lTJ8THx2PBggVYsWIFz3j9yJEj8ddff/H5iosq79694/lZYmNjMXr0aOp1v3798Pz5czqksbD8J2bOnIlZs2YhKyuLOsi4c+cOAgICMHPmTJrVCYfZs2fj0KFDWL16Nd1SWATMH3/8gZiYGOzatatZ6xMW0WPkyJFYuXLlZ0OhV69ejVGjRtGkjuW/MmbMGOp319w0sre3N4/NKQvLj462tjZ1KNejRw8cO3YM/fv3x9mzZ6GgoEC3PIFy5swZ6t9RUVE82Xe1tbWIjo6GpqYmDcpYWFhayt27d3Ho0CEcPXoUHz584DunEzWSk5NhZGQEMTExvH//HikpKZ99b69evVpRmfBQVFQEh8MBh8NB9+7deYoltbW1KCsrw/z582lUKBwkJSXx4cMHvvWMjAx07NiRBkUsDbDWWy2ke/fuCAkJwfDhw3nWY2NjMXfuXKSnp9OkjEUQvHv3DpmZmeByudDT04OioiLdkgSKhoYGIiIiMGzYMFRVVUFBQQFnz56FpaUlgHorLjMzM6qLjIXlR6eurg6bN2/Gtm3bkJ+fD6DemsrNzQ1Lly6lcktIws3NDeHh4ejVqxd69eqFtm3b8lzfsmULTcpYvhcmWp+QzosXL9C3b19ISkp+NhT6/v376NatG91SWVpAYWEhTE1NIS4u/tlp5IYwThYWUWDr1q0QFxfH4sWLceXKFYwfPx5cLhfV1dXYsmUL3Nzc6JYoMMTExADUN8U1PQpp27YtNDU1ERgYyBY7WVh+UJpabllYWMDBwQGTJk2CjIwM3fK+CzExMRQUFEBFRYXKKmnuyJbD4RAzaR4WFgYulwsXFxcEBQXxFK8lJCSgqamJgQMH0qhQOMyePRvFxcU4duwYlJSUkJycDHFxcdja2mLYsGEICgqiWyJjYQslLaRdu3ZIS0vj6zLJycmBgYEB60XM8kOzYMECJCUlYcOGDfjnn38QFhaGV69eUV2uBw8eRFBQEO7du0ezUhaWltPQkUF6jkPTQn1TYmJiWkkJi6CRkZFBamoq1NXV0bVrV0RGRqJ///7Izs6GsbExysrK6JbI8h9gWig0U8jNzcWCBQsQFRXV7DSylpYWzQpZWP47ubm5ePDgAXR1dYnpWm6KlpYW7t27B2VlZbqlsLCwtAAxMTH069cP9vb2sLOzI6opITc3l7Lbys3N/eJ7NTQ0WklV6xAbG4vBgwejTRtmGB+9f/8ekydPxv3791FaWgpVVVUUFBRg4MCBOH/+PNq3b0+3RMbCjL9AAaKiooLk5GS+QklSUhJfEBwLy4+Gn58fJk2aBDMzM8jIyCAsLIzHCiQ0NBTW1tY0KmRh+W8UFRVRE309evQgetPLFkLIhUnWJ0xCS0sLFy5cYEwoNFPQ0NDA+fPniZ9GZiGf6upqjBo1CsHBwdDT0wNQ//dN2iFcU7Kzs+mWwMLC8h9IT0+n7lWk0fi+S/o9uClmZmbIysrCvn37kJWVhW3btkFFRQUXLlyAuro6evbsSbdEgSIvL4/Lly/j5s2bSEpKQllZGUxNTWFlZUW3NMbDTpS0kGXLluHo0aPYt28fhg0bBqC+8uni4oLJkydj8+bNNCtkYfk679+/h4yMDJ8t0du3byErK8tn5cPC8qNSXl4OV1dXhIeHU+Ht4uLicHJywo4dOyAtLU2zQsExadKkr76Hw+Hg5MmTraCGRRgwyfqEhYWFheXHoWPHjoiPjyf28PFzREdHIzo6Gq9fv6aeIxsIDQ2lSRULC8vXKCkpwYkTJ5CVlQVPT08oKSlRlpdqamp0y/vPNM5Q+ho2NjZCVNL6NOTnDh48GNevX8eTJ0+gra2NgIAA3L9/HydOnKBbosCorq6GlJQUEhMTYWRkRLccliawEyUtxM/PDzk5ObC0tKRGwurq6uDk5AR/f3+a1bGwfBtLlizBtm3bICsry7MuKSmJefPmsRsDFpHB3d0dsbGxOHv2LAYPHgwAiIuLw+LFi7F06VLs2rWLZoWCo7FfKwtZ1NXVYdOmTThz5gyqqqrw6tUreHt7Iy0tjXjrExYWFhYW+nF0dMTff/+NgIAAuqW0Gj4+PvD19UXfvn3RpUsXngBhFhaWH5fk5GRYWlpCQUEBOTk5mDNnDpSUlBAZGYm8vDyEh4fTLfE/Y2try/O6aUZJ06Bzkli+fDnWrl0Ld3d3nnMqCwsL7Ny5k0Zlgqdt27ZQV1cn7ndICuxEyf9r716jqizz/49/NgcBUUQzVDROAamljuVhPIyaWkaamf+pMU0boCbPlGnamCg0muVkmpk2OnIws9QoI00LZUgdLfOA0qgJKmaepXI4GLrZ/wf+Yg2DNVDCxeZ+v9ZyLbzu/eDtjAZ7f+/7un6hr776SpmZmfLy8lKbNm0s91gcnJurq6tOnTolPz+/Muvnz59X06ZNdeXKFUNlQOU0btxYa9asUa9evcqsp6en66GHHtK5c+fMhAGV8Pzzz2vGjBnq27evvLy8tHHjRj388MMMrQEA1eLHp3PDwsJ0xx13lNsbfe7cuYbKqk6zZs300ksvafjw4aZTAFRCnz59dMcdd+ill15S/fr1lZmZqZCQEP3zn//U0KFDdezYMdOJ10VaWpomT56sWbNmlR5mvn37dj333HOaNWuW7rrrLsOF11e9evW0f/9+BQcHl/n/9dixY2rZsqUuXbpkOvG6+vvf/66UlBQtX76c7XhrGJ4o+YXCw8MVHh5uOgOolIsXL8rhcMjhcOjf//63PD09S6/Z7XatX7++3PAEqMkKCwuveYCfn5+fCgsLDRQBlZecnKzXX39dTzzxhKSrb4z69++vpUuXysXFxXAdAKC2y8rK0u233y7p6g2BVlBcXKyuXbuazgBQSV988YX+9re/lVtv3ry5Tp8+baCoajz55JNavHixunfvXrrWr18/1a1bV3/605904MABg3XXn6+vr06dOqXg4OAy63v27HHq7dR+ymuvvabs7Gz5+/srMDCw3A0Ku3fvNlQGBiW/wIkTJ/TBBx/o+PHjKi4uLnOtNt5tg9rD19dXNptNNpvtmoM+m82muLg4A2XAL9OlSxdNnz5dycnJpYO/oqIixcXFld55A9R0x48f17333lv6+759+8pms+nkyZNq0aKFwTIAgBWkp6ebTqh2jz32mN566y1NmzbNdAqASvDw8NDFixfLrX/11Ve68cYbDRRVjZycHPn6+pZbb9CgQa15auY/DRkyRJMnT9bq1atls9lUUlKibdu2aeLEiRoxYoTpvOvu/vvvZ8vHGoqttypp06ZNGjhwoEJCQnTw4EHddtttOnbsmBwOh26//XZt3rzZdCLwkzIyMuRwONS7d2+9++67ZR7xq1OnjgIDA+Xv72+wEKicrKws9evXTz/88IPatWsnScrMzJSnp6c2btyoW2+91XAh8L+5urrq9OnTZd7c1a9fX/v27St3VxUAANdbVFTUNc8vLCgo0Lhx42rlVpAxMTFKTk5W27Zt1bZtW7m7u5e5zg2QQM302GOP6cKFC1q1apUaNWqkffv2ydXVVYMGDVKPHj00b94804nXRY8ePeTp6anly5eX7qBw5swZjRgxQpcuXVJGRobhwuuruLhYY8aMUWJioux2u9zc3GS32zV06FAlJibK1dXVdCIsgkFJ/pYDxQAAOUJJREFUJXXq1EkRERGKi4sr3TfPz89Pw4YN0z333KNRo0aZTgT+p9zcXAUEBDDBRq1QWFioFStW6ODBg5KkVq1aadiwYfLy8jJcBlSMi4uLIiIi5OHhUbqWmpqq3r17l3kMOyUlxUQeAKCWs+L5hXfeeefPXrfiUzaAM/j+++/1+9//Xjt37lR+fr78/f11+vRpdenSRevXry+3hZGzys7O1gMPPKCvvvpKN910kyTp66+/VlhYmN5//32FhoYaLqwax48fV1ZWlvLz89W+fXuFhYWZTqoSISEh2rlzp2644YYy6999951uv/12HTlyxFAZGJRUUv369bV3717dfPPNatiwobZu3apbb71VmZmZuv/++2vlI3ConbZs2aI33nhDR44c0erVq9W8eXMtX75cwcHBZfbBBABUrcjIyAq9LiEhoYpLAABW8uP5hQ0bNtThw4fLPNlot9uVmpqqKVOm6OTJkwYrAaC8bdu2KTMzU/n5+br99tvVt29f00nXncPh0CeffFLmhsAft+iFc3NxcdHp06fL3aBw5swZ3XTTTeWOeUD14YySSvL29i79C9usWTPl5OSUbu1y/vx5k2lAhb377rsaPny4hg0bpt27d+uHH36QdPXujFmzZmn9+vWGC4GKO3z4sNLT03X27FmVlJSUuRYbG2uoCqg4BiAAABOseH7h4MGD/+drbDab3n333WqoAVAZJSUlSkxMVEpKio4dOyabzabg4GA1bdpUDoej1g0QbDab7r77bvXo0UMeHh617s8nSfHx8RV6XW15X//BBx+Ufr1x40Y1aNCg9Pd2u12bNm1i62XDeKKkkgYNGqT+/fvr8ccf18SJE7V27Vr98Y9/VEpKiho2bKi0tDTTicD/1L59ez311FMaMWJE6RZyISEh2rNnjyIiInT69GnTiUCFLFmyRKNGjVLjxo3VtGnTMj882mw27d6922AdAABAzWXF8wt5ihNwTg6HQ/fdd5/Wr1+vdu3aqWXLlnI4HDpw4ID279+vgQMH6v333zeded2UlJRo5syZWrx4sc6cOaOvvvpKISEhmjZtmoKCghQdHW068bpwcXGRv7+//Pz89FMfT9em9/UuLi6Srv6Z/vvP6+7urqCgIL388ssaMGCAiTyIQUmlHTlyRPn5+Wrbtq0KCgr09NNP65///KfCwsI0d+5cBQYGmk4E/qe6devqX//6l4KCgsoMSo4cOaLWrVvr0qVLphOBCgkMDNTo0aM1efJk0ykAAABOifMLAdR0CQkJiomJ0dq1a8udMbR582YNGjRIr732mkaMGGGo8PqKj49XUlKS4uPj9fjjjysrK0shISF65513NG/ePG3fvt104nXRv39/bd68Wf369VNUVJQGDBhQOkyozYKDg7Vz5041btzYdAr+S+3/23cd2e12nThxQgEBAZKubsO1ePFi7du3T++++y5DEjiNpk2bKjs7u9z61q1bFRISYqAI+GW+/fZbPfjgg6YzAAAAnFZgYCBDEgA12sqVK/XnP/+53JBEknr37q0pU6ZoxYoVBsqqRnJysv72t79p2LBhcnV1LV1v165d6ZkltcG6deuUk5Ojzp07a9KkSWrevLkmT56sQ4cOmU6rEtu3b9eHH36oo0ePlg5JkpOTFRwcLD8/P/3pT38q3RofZjAoqQRXV1fdfffd+vbbb02nAL/K448/rpiYGH322Wey2Ww6efKkVqxYoYkTJ2rUqFGm84AKe/DBB/Xxxx+bzgAAAAAAVJF9+/bpnnvu+cnrERERyszMrMaiqvXNN98oNDS03HpJSYkuX75soKjq+Pv769lnn9WhQ4f0zjvv6OzZs+rYsaO6deumoqIi03nXVVxcnL788svS3+/fv1/R0dHq27evpkyZotTUVL3wwgsGC8Fh7pV022236ciRIxyuA6c2ZcoUlZSUqE+fPiosLCw9HGzixIkaN26c6TzgZ7366qulX4eGhmratGnasWOH2rRpI3d39zKvHT9+fHXnAQAAAACuo7y8PDVp0uQnrzdp0qRW3dTcunVrbdmypdzONWvWrFH79u0NVVW9jh076tixY/rXv/6lPXv26PLly/Ly8jKddd1kZmbqL3/5S+nv3377bXXu3FlLliyRJN10002aPn26ZsyYYagQnFFSSRs2bNCzzz6r559/XnfccYe8vb3LXPfx8TFUBlRecXGxsrOzlZ+fr9atW6tevXqmk4D/qaKDapvNpiNHjlRxDQAAAACgKrm6uur06dO68cYbr3n9zJkz8vf3l91ur+ayqrF27Vo9+uijevbZZxUfH6+4uDgdOnRIycnJ+vDDD3XXXXeZTryutm/frmXLlmnVqlUKDw9XZGSkhg4dKl9fX9Np15Wnp6cOHz6sm266SZLUvXt3RUREaOrUqZKkY8eOqU2bNvr3v/9tMtPSGJRU0n8eKvSf+7g6HA7ZbLZa8x9lAAAAAEDtt3LlSj388MPXvDZp0iTNmTOnmosAoCwXFxdFRETIw8Pjmtd/+OEHbdiwoVZ9JrdlyxbFx8crMzNT+fn5uv322xUbG6u7777bdNp189JLLykxMVHnz5/XsGHDFBkZqbZt25rOqjKBgYFavny5evTooeLiYvn6+io1NVV9+vSRdHUrrp49eyovL89wqXUxKKmkjIyMn73es2fPaioBfrmCggLNnj1bmzZt0tmzZ1VSUlLmOnfhw1nEx8dr4sSJqlu3bpn1oqIizZkzR7GxsYbKAAAAnIOvr69WrlypiIiIMutPPfWU3n77bZ06dcpQGQBcFRkZWaHXJSQkVHFJ1bty5YpmzZqlqKgotWjRwnROlXJxcVFAQIAGDBigOnXq/OTr5s6dW41VVWfUqFHKzMzUiy++qPfff19JSUk6efJk6Z99xYoVmjdvnnbu3Gm41LoYlFTS8ePHddNNN5V5mkS6+kTJ119/rYCAAENlQMU9/PDDysjI0PDhw9WsWbNyf59jYmIMlQGV4+rqqlOnTsnPz6/M+oULF+Tn51er7igCAACoCuvWrdOwYcP04Ycfqnv37pKkcePGKSUlRZs2bVLLli0NFwKAtdSrV09ZWVkKCgoynVKlevXqVe7zqGtJT0+vhpqqd/78eQ0ePFhbt25VvXr1lJSUpAceeKD0ep8+ffTb3/5WM2fONFhpbQxKKokP5VAb+Pr6at26derWrZvpFOBXcXFx0ZkzZ8rtVbt582b94Q9/0Llz5wyVAQAAOI+33npLY8eO1SeffKK///3vWrt2rdLT0xUeHm46DQAs5/7779fgwYP16KOPmk5BFfj+++9Vr149ubq6llnPy8tTvXr1fvbpGlQtN9MBzubHs0j+W35+vjw9PQ0UAZXXsGFDNWrUyHQG8Is1bNhQNptNNptN4eHhZf67bLfblZ+fr5EjRxosBAAAcB5Dhw7Vd999p27duunGG29URkaGQkNDTWcBgCVFRERoypQp2r9/v+644w55e3uXuT5w4EBDZVXDaltqN2jQ4JrrfE5nHk+UVNCECRMkSfPnz9fjjz9e5h+v3W7XZ599JldXV23bts1UIlBhb775ptauXaukpKRy34gAZ5CUlCSHw6GoqCjNmzevzA8aderUUVBQkLp06WKwEAAAoOb68f3tf1u9erVuv/123XzzzaVrtWVveABwFi4uLj95zWaz1brdbNi9BzUFT5RU0J49eyRdfaJk//79ZR6DqlOnjtq1a6eJEyeaygMq5eWXX1ZOTo6aNGmioKAgubu7l7m+e/duQ2VAxfz4CHJwcLC6desmNze+nQEAAFTUj+9v/1toaKguXrxYer0ie8cDAK6vkpIS0wnV6qd278nMzOQpC1QrPlmqoB8PDoqMjNT8+fPl4+NjuAj45QYNGmQ6AbguevbsqZycHCUkJCgnJ0fz58+Xn5+fPvroIwUEBOjWW281nQgAAFDj1JaDcQEAzosttVHTsPUWYDFXrlzRrFmzFBUVpRYtWpjOAX6VjIwMRUREqFu3bvr000914MABhYSEaPbs2friiy+0Zs0a04kAAAAAAPxPmzdv1tixY7Vjx45yN2h///336tq1qxYtWqQePXoYKry+2FIbNQ2DkkoqKCjQ7NmztWnTJp09e7bc43BHjhwxVAZUXP369bV//34FBQWZTgF+lS5duujBBx/UhAkTVL9+fWVmZiokJESff/65Bg8erBMnTphOBAAAqNF4jwsANcPAgQN155136qmnnrrm9VdffVXp6el67733qrmsamVkZKhr167ltoUHqhtbb1XSY489poyMDA0fPlzNmjVjz1Y4pd69eysjI4NBCZze/v379dZbb5Vb9/Pz0/nz5w0UAQAAOBfe4wJAzZCZmakXX3zxJ6/ffffd+utf/1qNRdWjZ8+eKikp0VdffXXNgX1teYIGNR+Dkkr66KOPtG7dOnXr1s10CvCLRUREaMqUKdq/f7/uuOMOeXt7l7k+cOBAQ2VA5fj6+urUqVMKDg4us75nzx41b97cUBUAAIDz4D0uANQMZ86c+dmnKtzc3HTu3LlqLKoeO3bs0NChQ5Wbm6v/3vjIZrPJbrcbKoPVMCippIYNG6pRo0amM4BfZfTo0ZKkuXPnlrvGNyE4kyFDhmjy5MlavXq1bDabSkpKtG3bNk2cOFEjRowwnQcAAFDj8R4XAGqG5s2bKysrS6Ghode8vm/fPjVr1qyaq6reyJEj1aFDB61bt44nG2EUZ5RU0ptvvqm1a9cqKSlJdevWNZ0DAJZWXFysMWPGKDExUXa7XW5ubrLb7Ro6dKgSExPl6upqOhEAAKBG4z0uANQM48aN0z/+8Q/t3LlTnp6eZa4VFRWpU6dOuvPOO/Xqq68aKqwa3t7eyszM/MkBEVBdGJRUUvv27ZWTkyOHw6GgoKByj8Tt3r3bUBnwy1y6dKncN2DA2Rw/flxZWVnKz89X+/btFRYWZjoJAADAKfAeFwBqhjNnzuj222+Xq6urxo4dq1tuuUWSdPDgQS1cuFB2u127d+9WkyZNDJdeX71799Yzzzyje+65x3QKLI6ttypp0KBBphOAX81ut2vWrFlavHixzpw5o6+++kohISGaNm2agoKCFB0dbToRqJSAgAAFBASYzgAAAHA6vMcFgJqhSZMm+uc//6lRo0bp2WefLT2vw2azqV+/flq4cGGtG5JIV5+kefrpp3X69Gm1adOm3MC+bdu2hspgNTxRAlhQfHy8kpKSFB8fr8cff1xZWVkKCQnRO++8o3nz5mn79u2mE4GfFR8fX6HXxcbGVnEJAAAAAADX17fffqvs7Gw5HA6FhYWpYcOG5V5z4sQJ+fv7y8XFxUDh9XOtfpvNJofDwTm6qFYMSn6hXbt26cCBA5KkW2+9Ve3btzdcBFRcaGio3njjDfXp00f169dXZmamQkJCdPDgQXXp0kXffvut6UTgZ7m4uMjf319+fn76qW9jNpuNrSIAAAAAALWSj4+P9u7dq5CQENMpv0pubu7PXg8MDKymElgdW29V0tmzZzVkyBD94x//kK+vryTpu+++05133qm3335bN954o9lAoAK++eabax6SVVJSosuXLxsoAionIiJCmzdvVocOHRQVFaUBAwY4/V00AAAAJtjtdr3yyitatWqVjh8/ruLi4jLX8/LyDJUBAH5Obbn3nUEIago+VaqkcePG6d///re+/PJL5eXlKS8vT1lZWbp48aLGjx9vOg+okNatW2vLli3l1tesWcPTUXAK69atU05Ojjp37qxJkyapefPmmjx5sg4dOmQ6DQAAwKnExcVp7ty5+sMf/qDvv/9eEyZM0ODBg+Xi4qIZM2aYzgMAWMDy5cvVrVs3+fv7lz5hMm/ePK1du9ZwGayEQUklbdiwQa+//rpatWpVuta6dWstXLhQH330kcEyoOJiY2M1duxYvfjiiyopKVFKSooef/xxzZw5kzMd4DT8/f317LPP6tChQ3rnnXd09uxZdezYUd26dVNRUZHpPAAAAKewYsUKLVmyRE8//bTc3Nz08MMPa+nSpYqNjdWOHTtM5wEAarlFixZpwoQJuvfee/Xdd9+Vnkni6+urefPmmY2DpTAoqaSSkhK5u7uXW3d3d1dJSYmBIqDy7r//fqWmpiotLU3e3t6KjY3VgQMHlJqaqrvuust0HlBpHTt21J133qlWrVppz549bCEHAABQQadPn1abNm0kSfXq1dP3338vSRowYIDWrVtnMg0AYAELFizQkiVLNHXqVLm6upaud+jQQfv37zdYBqthUFJJvXv3VkxMjE6ePFm69s033+ipp55Snz59DJYBlfO73/1On3zyic6ePavCwkJt3bpVd999t+ksoFK2b9+uxx9/XE2bNtWCBQv06KOP6uTJk/Lx8TGdBgAA4BRatGihU6dOSZJuvvlmffzxx5KknTt3ysPDw2QaAOBn2Gw20wnXxdGjR6+5DbyHh4cKCgoMFMGqGJRU0muvvaaLFy8qKChIN998s26++WYFBwfr4sWLWrBggek8oEJCQkJ04cKFcuvfffedQkJCDBQBlfPSSy+pdevWuv/++1WvXj1t2bJFO3fu1OjRo+Xr62s6DwAAwGk88MAD2rRpk6SrZ3JOmzZNYWFhGjFihKKiogzXAQB+Sm05zD04OFh79+4tt75hw4YyRx8AVc3mqC3/qqqRw+FQWlqaDh48KElq1aqV+vbta7gKqDgXFxedPn1afn5+ZdbPnDmjgIAA/fDDD4bKgIpxcXFRQECABgwYoDp16vzk6+bOnVuNVQAAAM5v+/bt2r59u8LCwnTfffeZzgEA/ISvv/5a/v7+ZbarckZLly7VjBkz9PLLLys6OlpLly5VTk6OXnjhBS1dulRDhgwxnQiLYFBSQZs3b9bYsWO1Y8eOclu6fP/99+ratasWL16s3/3ud4YKgf/tgw8+kCQNGjRISUlJatCgQek1u92uTZs26ZNPPtGhQ4dMJQIV0qtXrwo9Zpyenl4NNQAAAAAA/HKDBw+u8GtTUlKqsMSMFStWaMaMGcrJyZEk+fv7Ky4uTtHR0YbLYCUMSipo4MCBuvPOO/XUU09d8/qrr76q9PR0vffee9VcBlSci8vV3fZsNlu5RzTd3d0VFBSkl19+WQMGDDCRBwAAAKCaXbhwQTfccIOkq3cnL1myREVFRRo4cCA3AgJANYmMjCz92uFw6L333lODBg3UoUMHSdKuXbv03XffafDgwUpISDCVWeUKCwuVn59fbgcUoDowKKmgwMDAn90b7+DBg7r77rt1/Pjxai4DKi84OFg7d+5U48aNTacAv0p8fLwmTpyounXrllkvKirSnDlzFBsba6gMAACgZtu/f7/uu+8+ff311woLC9Pbb7+te+65RwUFBXJxcVFBQYHWrFmjQYMGmU4FAEuZPHmy8vLytHjx4tJttex2u0aPHi0fHx/NmTPHcCFQOzEoqSBPT09lZWUpNDT0mtezs7PVpk0bFRUVVXMZAFiXq6urTp06Ve5ukwsXLsjPz092u91QGQAAQM0WEREhNzc3TZkyRcuXL9eHH36ofv36acmSJZKuHuy+a9cu7dixw3ApAFjLjTfeqK1bt+qWW24ps37o0CF17dpVFy5cMFRWNS5cuKDY2Filp6fr7NmzKikpKXM9Ly/PUBmsxs10gLNo3rz5zw5K9u3bp2bNmlVzFfDLbdq0SZs2bbrmN6Fly5YZqgIqx+FwXPOskszMTDVq1MhAEQAAgHPYuXOnNm/erLZt26pdu3b629/+ptGjR5du1ztu3Dj99re/NVwJANZz5coVHTx4sNyg5ODBg+U+v6kNhg8fruzsbEVHR6tJkyYVOo8UqAoMSiro3nvv1bRp03TPPffI09OzzLWioiJNnz6dcx3gNOLi4hQfH68OHTqoWbNmfBOC02nYsKFsNptsNpvCw8PL/B222+3Kz8/XyJEjDRYCAADUbHl5eWratKkkqV69evL29lbDhg1Lrzds2FD//ve/TeUBgGVFRkYqOjpaOTk56tSpkyTps88+0+zZs8ucZVJbbNmyRVu3blW7du1Mp8DiGJRU0HPPPaeUlBSFh4dr7NixpVPdgwcPauHChbLb7Zo6darhSqBiFi9erMTERA0fPtx0CvCLzJs3Tw6HQ1FRUYqLi1ODBg1Kr9WpU0dBQUHq0qWLwUIAAICa779vmOIGKgAw769//auaNm2ql19+WadOnZIkNWvWTJMmTdLTTz9tuO76a9myJUcZoEbgjJJKyM3N1ahRo7Rx40b9+D+bzWZTv379tHDhQgUHBxsuBCrmhhtu0Oeff66bb77ZdArwq2RkZKhr165yd3c3nQIAAOBUXFxcFBERIQ8PD0lSamqqevfuLW9vb0nSDz/8oA0bNnDmGwAYdPHiRUmSj4+P4ZKqs3PnTk2ZMkWxsbG67bbbyr2/r81/dtQsDEp+gW+//VbZ2dlyOBwKCwsr83gy4AwmT56sevXqadq0aaZTgF+tpKRE2dnZ1zxvp0ePHoaqAAAAaraKbt+SkJBQxSUAgGs5d+6cDh06JOnqUxeNGzc2XFQ1Dh8+rKFDh2r37t1l1n88k5SBPaoLgxLAgmJiYpScnKy2bduqbdu25ab1c+fONVQGVM6OHTs0dOhQ5ebm6r+/nfEDFQAAwPVz4sQJ+fv7lx72DgCoGgUFBRo3bpySk5NLbwZ0dXXViBEjtGDBAtWtW9dw4fXVqVMnubm5KSYm5pqHuffs2dNQGayGQQlgQXfeeedPXrPZbNq8eXM11gC/3G9+8xuFh4crLi5OzZo1K/cD1X+eXQIAAIBfzsfHR3v37lVISIjpFACo1Z544gmlpaXptddeU7du3SRJW7du1fjx43XXXXdp0aJFhguvr7p162rPnj2l50EDpjAoAQA4LW9vb2VmZio0NNR0CgAAQK1Wv359ZWZmMigBgCrWuHFjrVmzRr169Sqznp6eroceekjnzp0zE1ZFevToodjYWPXt29d0CizOzXQAAAC/VOfOnZWdnc2gBAAAAABQKxQWFqpJkybl1v38/FRYWGigqGqNGzdOMTExmjRpktq0aVNue/i2bdsaKoPV8EQJYCGDBw+u0OtSUlKquAS4Pt577z0999xz/EAFAABQxXiiBACqR58+fXTDDTcoOTlZnp6ekqSioiI9+uijysvLU1pamuHC6+taZ1/ZbDYOc0e144kSwEI4rwG1zf/7f/9PkhQVFVW6xg9UAAAAAABnNX/+fPXr108tWrRQu3btJEmZmZny9PTUxo0bDdddf0ePHjWdAEjiiRIAgBPLzc392euBgYHVVAIAAFC7cZg7AFSfwsJCrVixQgcPHpQktWrVSsOGDZOXl5fhsuuvoKBA3t7epjMABiUAAAAAAODnsfUWAKAq1KtXTw899JCioqLUvXt30zmwMAYlAACntnz5ci1evFhHjx7V9u3bFRgYqHnz5ik4OFj333+/6TwAAIBa4euvv5a/v79cXV1NpwBArXf48GGlp6fr7NmzKikpKXMtNjbWUFXVeP/995WYmKj169crKChIUVFRGjFihPz9/U2nwWIYlAAAnNaiRYsUGxurJ598UjNnzlRWVpZCQkKUmJiopKQkpaenm04EAACocQYPHlzh16akpFRhCQDgvy1ZskSjRo1S48aN1bRpU9lsttJrNptNu3fvNlhXdc6dO6fly5crMTFRBw4cUL9+/RQVFaWBAwfKzY1jtlH1GJQAAJxW69atNWvWLA0aNKjMdhBZWVnq1auXzp8/bzoRAACgxomMjCz92uFw6L333lODBg3UoUMHSdKuXbv03XffafDgwUpISDCVCQCWFBgYqNGjR2vy5MmmU4xZsGCBJk2apOLiYjVu3FgjR47UlClTVLduXdNpqMUYxwEAnNbRo0fVvn37cuseHh4qKCgwUAQAAFDz/efwY/LkyXrooYe0ePHi0m217Ha7Ro8eLR8fH1OJAGBZ3377rR588EHTGdXuzJkzSkpKUmJionJzc/X73/9e0dHROnHihF588UXt2LFDH3/8selM1GIMSgAATis4OFh79+5VYGBgmfUNGzaoVatWhqoAAACcx7Jly7R169YyZ4+4urpqwoQJ6tq1q+bMmWOwDgCs58EHH9THH3+skSNHmk6pFikpKUpISNDGjRvVunVrjR49Wo888oh8fX1LX9O1a1fe46PKMSgBADitCRMmaMyYMbp06ZIcDoc+//xzrVy5Ui+88IKWLl1qOg8AAKDGu3Llig4ePKhbbrmlzPrBgwfLHSAMAKgar776aunXoaGhmjZtmnbs2KE2bdrI3d29zGvHjx9f3XlVKjIyUkOGDNG2bdvUsWPHa77G399fU6dOreYyWA1nlAAAnNqKFSs0Y8YM5eTkSLr6A1RcXJyio6MNlwEAANR8EyZMUHJysv785z+rU6dOkqTPPvtMs2fP1vDhwzV37lzDhQBQ+wUHB1fodTabTUeOHKnimupVWFjI2SOoERiUAABqhcLCQuXn58vPz890CgAAgNMoKSnRX//6V82fP1+nTp2SJDVr1kwxMTF6+umny2zJBQBAVbp06ZKKi4vLrHFeFqoLgxIAAAAAAKCLFy9K4kMpADApPj5eEydOLPeURVFRkebMmaPY2FhDZVWjoKBAkydP1qpVq3ThwoVy1+12u4EqWBGDEgCA07pw4YJiY2OVnp6us2fPlttHOy8vz1AZAACAczl37pwOHTokSWrZsqUaN25suAgArMnV1VWnTp0qt1vChQsX5OfnV+sGB2PGjFF6erqef/55DR8+XAsXLtQ333yjN954Q7Nnz9awYcNMJ8IiOMwdAOC0hg8fruzsbEVHR6tJkyay2WymkwAAAJxKQUGBxo0bp+Tk5NKbTlxdXTVixAgtWLCAfeMBoJo5HI5rvrfNzMxUo0aNDBRVrdTUVCUnJ6tXr16KjIzU7373O4WGhiowMFArVqxgUIJqw6AEAOC0tmzZoq1bt6pdu3amUwAAAJzShAkTlJGRodTUVHXr1k2StHXrVo0fP15PP/20Fi1aZLgQAKyhYcOGstlsstlsCg8PLzMssdvtys/P18iRIw0WVo28vDyFhIRIurr14487Q3Tv3l2jRo0ymQaLYVACAHBaLVu2VFFRkekMAAAAp/Xuu+9qzZo16tWrV+navffeKy8vLz300EMMSgCgmsybN08Oh0NRUVGKi4tTgwYNSq/VqVNHQUFB6tKli8HCqhESEqKjR48qICBALVu21KpVq9SpUyelpqbK19fXdB4shDNKAABOa+fOnZoyZYpiY2N12223yd3dvcx1DiIFAAD4eXXr1tWuXbvUqlWrMutffvmlOnXqpIKCAkNlAGBNGRkZ6tatm9zcrHF/+yuvvCJXV1eNHz9eaWlpuu++++RwOHT58mXNnTtXMTExphNhEQxKAABO6/Dhwxo6dKh2795dZv3HPV1r2yF3AAAA11ufPn10ww03KDk5WZ6enpKkoqIiPfroo8rLy1NaWprhQgCwnpycHCUkJCgnJ0fz58+Xn5+fPvroIwUEBOjWW281nVelcnNztWvXLoWGhqpt27amc2AhDEoAAE6rU6dOcnNzU0xMzDUPc+/Zs6ehMgAAAOeQlZWlfv366Ycffig99y0zM1Oenp7auHFjrf9ADgBqmoyMDEVERKhbt2769NNPdeDAAYWEhGj27Nn64osvtGbNGtOJ10VJSYnmzJmjDz74QMXFxerTp4+mT58uLy8v02mwKAYlAACnVbduXe3Zs0e33HKL6RQAAACnVVhYqBUrVujgwYOSpFatWmnYsGF8WAUABnTp0kUPPvigJkyYoPr16yszM1MhISH6/PPPNXjwYJ04ccJ04nXx/PPPa8aMGerbt6+8vLy0ceNGPfzww1q2bJnpNFgUgxIAgNPq0aOHYmNj1bdvX9MpAAAAAAD8avXq1dP+/fsVHBxcZlBy7NgxtWzZUpcuXTKdeF2EhYVp4sSJeuKJJyRJaWlp6t+/v4qKiuTi4mK4DlZkjVOBAAC10rhx4xQTE6NJkyapTZs25Q5zZz9TAACA/+3w4cNKT0/X2bNnVVJSUuZabGysoSoAsCZfX1+dOnVKwcHBZdb37Nmj5s2bG6q6/o4fP65777239Pd9+/aVzWbTyZMn1aJFC4NlsCqeKAEAOK1r3WVis9k4zB0AAKCClixZolGjRqlx48Zq2rRpmTPfbDabdu/ebbAOAKxn4sSJ+uyzz7R69WqFh4dr9+7dOnPmjEaMGKERI0Zo+vTpphOvC1dXV50+fVo33nhj6Vr9+vW1b9++ckMioDowKAEAOK3c3NyfvR4YGFhNJQAAAM4pMDBQo0eP1uTJk02nAAAkFRcXa8yYMUpMTJTdbpebm5vsdruGDh2qxMREubq6mk68LlxcXBQRESEPD4/StdTUVPXu3Vve3t6laykpKSbyYEEMSgAATqugoKDMD1AAAACoHB8fH+3du1chISGmUwAA/+H48ePKyspSfn6+2rdvr7CwMNNJ11VkZGSFXpeQkFDFJcBVDEoAAE6rXr16euihhxQVFaXu3bubzgEAAHA60dHR6tixo0aOHGk6BQAAwBgGJQAAp/X+++8rMTFR69evV1BQkKKiojRixAj5+/ubTgMAAKixXn311dKvCwoKNHfuXPXv319t2rSRu7t7mdeOHz++uvMAwJLi4+Mr9LrY2NgqLgGsiUEJAMDpnTt3TsuXL1diYqIOHDigfv36KSoqSgMHDpSbm5vpPAAAgBqloofk2mw2HTlypIprAADS1TM7/P395efnp5/6uNZms2n37t3VXAZYA4MSAECtsmDBAk2aNEnFxcVq3LixRo4cqSlTpqhu3bqm0wAAAAAAuKb+/ftr8+bNpTf+DRgwQC4uLqazAMvgXxsAwOmdOXNGL730klq3bq0pU6bo97//vTZt2qSXX35ZKSkpGjRokOlEAACAGik+Pl6FhYXl1ouKiiq8DQwA4Ndbt26dcnJy1LlzZ02aNEnNmzfX5MmTdejQIdNpgCXwRAkAwGmlpKQoISFBGzduVOvWrfXYY4/pkUceka+vb+lrcnJy1KpVKxUXF5sLBQAAqKFcXV116tQp+fn5lVm/cOGC/Pz8ZLfbDZUBgLV9+umnSkhI0Lvvvqs2bdooLS1NXl5eprOAWouN2wEATisyMlJDhgzRtm3b1LFjx2u+xt/fX1OnTq3mMgAAAOfgcDhks9nKrWdmZqpRo0YGigAAktSxY0cdO3ZM//rXv7Rnzx5dvnyZQQlQhXiiBADgtAoLCzl7BAAA4Bdo2LChbDabvv/+e/n4+JQZltjtduXn52vkyJFauHChwUoAsJ7t27dr2bJlWrVqlcLDwxUZGamhQ4eW2TkBwPXHoAQAUCtcunSp3PZaPj4+hmoAAABqtqSkJDkcDkVFRWnevHlq0KBB6bU6deooKChIXbp0MVgIANby0ksvKTExUefPn9ewYcMUGRmptm3bms4CLINBCQDAaRUUFGjy5MlatWqVLly4UO46e2oDAAD8vIyMDHXr1k1ubuzMDQAmubi4KCAgQAMGDFCdOnV+8nVz586txirAOvhJCADgtJ555hmlp6dr0aJFGj58uBYuXKhvvvlGb7zxhmbPnm06DwAAoMbr2bOncnJylJCQoJycHM2fP19+fn766KOPFBAQoFtvvdV0IgBYQo8ePWSz2fTll1+aTgEsiSdKAABOKyAgQMnJyerVq5d8fHy0e/duhYaGavny5Vq5cqXWr19vOhEAAKBGy8jIUEREhLp166ZPP/1UBw4cUEhIiGbPnq0vvvhCa9asMZ0IAABQ5VxMBwAA8Evl5eUpJCRE0tXzSPLy8iRJ3bt316effmoyDQAAwClMmTJFf/nLX/TJJ5+U2eqld+/e2rFjh8EyALCm+Ph4FRYWllsvKipSfHy8gSLAGhiUAACcVkhIiI4ePSpJatmypVatWiVJSk1Nla+vr8EyAAAA57B//3498MAD5db9/Px0/vx5A0UAYG1xcXHKz88vt15YWKi4uDgDRYA1MCgBADityMhIZWZmSrp6N+TChQvl6empp556SpMmTTJcBwAAUPP5+vrq1KlT5db37Nmj5s2bGygCAGtzOByy2Wzl1jMzM9WoUSMDRYA1cJg7AMBpPfXUU6Vf9+3bVwcPHtSuXbsUGhqqtm3bGiwDAABwDkOGDNHkyZO1evVq2Ww2lZSUaNu2bZo4caJGjBhhOg8ALKNhw4ay2Wyy2WwKDw8vMyyx2+3Kz8/XyJEjDRYCtRuHuQMAnE5JSYnmzJmjDz74QMXFxerTp4+mT58uLy8v02kAAABOpbi4WGPGjFFiYqLsdrvc3Nxkt9s1dOhQJSYmytXV1XQiAFhCUlKSHA6HoqKiNG/ePDVo0KD0Wp06dRQUFKQuXboYLARqNwYlAACn8/zzz2vGjBnq27evvLy8tHHjRj388MNatmyZ6TQAAACndPz4cWVlZSk/P1/t27dXWFiY6SQAsKSMjAx17dpV7u7uplMAS2FQAgBwOmFhYZo4caKeeOIJSVJaWpr69++voqIiubhw/BYAAAAAwHmVlJQoOztbZ8+eVUlJSZlrPXr0MFQF1G4MSgAATsfDw0PZ2dm66aabStc8PT2VnZ2tFi1aGCwDAABwDvHx8RV6XWxsbBWXAAD+044dOzR06FDl5ubqvz+2tdlsstvthsqA2o1BCQDA6bi6uur06dO68cYbS9fq16+vffv2KTg42GAZAACAc3BxcZG/v7/8/PzKfRD3I5vNpt27d1dzGQBY229+8xuFh4crLi5OzZo1K3Oou6QyZ5cAuH4YlAAAnI6Li4siIiLk4eFRupaamqrevXvL29u7dC0lJcVEHgAAQI3Xv39/bd68Wf369VNUVJQGDBjAFqYAUAN4e3srMzNToaGhplMAS+GnIACA03n00Ufl5+enBg0alP565JFH5O/vX2YNAAAA17Zu3Trl5OSoc+fOmjRpkpo3b67Jkyfr0KFDptMAwNI6d+6s7Oxs0xmA5fBECQAAAAAAFvfpp58qISFB7777rtq0aaO0tDR5eXmZzgIAy3nvvff03HPPadKkSWrTpo3c3d3LXG/btq2hMqB2Y1ACAAAAAIDFFRUVafXq1Vq4cKH279+v06dPy8fHx3QWAFjOtbZBtNlscjgcHOYOVCE30wEAAAAAAMCM7du3a9myZVq1apXCw8MVGRmpoUOHMiQBAEOOHj1qOgGwJAYlAAAAAABYzEsvvaTExESdP39ew4YN05YtW9jOBQBqgMDAQNMJgCWx9RYAAAAAABbj4uKigIAADRgwQHXq1PnJ182dO7caqwAAkrR8+XItXrxYR48e1fbt2xUYGKh58+YpODhY999/v+k8oFbiiRIAAAAAACymR48estls+vLLL02nAAD+w6JFixQbG6snn3xSM2fOLD2TxNfXV/PmzWNQAlQRnigBAAAAAAAAgBqgdevWmjVrlgYNGqT69esrMzNTISEhysrKUq9evXT+/HnTiUCt5GI6AAAAAAAAmBEfH6/CwsJy60VFRYqPjzdQBADWdvToUbVv377cuoeHhwoKCgwUAdbAoAQAAAAAAIuKi4tTfn5+ufXCwkLFxcUZKAIAawsODtbevXvLrW/YsEGtWrWq/iDAIjijBAAAAAAAi3I4HLLZbOXWMzMz1ahRIwNFAGBtEyZM0JgxY3Tp0iU5HA59/vnnWrlypV544QUtXbrUdB5QazEoAQAAAADAYho2bCibzSabzabw8PAywxK73a78/HyNHDnSYCEAWNNjjz0mLy8vPffccyosLNTQoUPl7++v+fPna8iQIabzgFqLw9wBAAAAALCYpKQkORwORUVFad68eWrQoEHptTp16igoKEhdunQxWAgAKCwsVH5+vvz8/EynALUegxIAAAAAACwqIyNDXbt2lbu7u+kUAAAAYxiUAAAAAABgYSUlJcrOztbZs2dVUlJS5lqPHj0MVQGANV24cEGxsbFKT0+/5n+X8/LyDJUBtRtnlAAAAAAAYFE7duzQ0KFDlZubq/++j9Jms8lutxsqAwBrGj58uLKzsxUdHa0mTZqUOUMKQNXhiRIAAAAAACzqN7/5jcLDwxUXF6dmzZqV+0DuP88uAQBUvfr162vr1q1q166d6RTAUniiBAAAAAAAizp8+LDWrFmj0NBQ0ykAAEktW7ZUUVGR6QzAclxMBwAAAAAAADM6d+6s7Oxs0xkAgP/z+uuva+rUqcrIyNCFCxd08eLFMr8AVA2eKAEAAAAAwKLGjRunp59+WqdPn1abNm3k7u5e5nrbtm0NlQGANfn6+urixYvq3bt3mXWHw8HZUUAV4owSAAAAAAAsysWl/EYTNpuND+QAwJBOnTrJzc1NMTEx1zzMvWfPnobKgNqNQQkAAAAAABaVm5v7s9cDAwOrqQQAIEl169bVnj17dMstt5hOASyFrbcAAAAAALAoBiEAULN06NBBX3/9NYMSoJpxmDsAAAAAABa2fPlydevWTf7+/qVPmMybN09r1641XAYA1jNu3DjFxMQoMTFRu3bt0r59+8r8AlA12HoLAAAAAACLWrRokWJjY/Xkk09q5syZysrKUkhIiBITE5WUlKT09HTTiQBgKZwdBZjBoAQAAAAAAItq3bq1Zs2apUGDBql+/frKzMxUSEiIsrKy1KtXL50/f950IgBYCmdHAWZwRgkAAAAAABZ19OhRtW/fvty6h4eHCgoKDBQBgLU1btxY3t7epjMAy+GMEgAAAAAALCo4OFh79+4tt75hwwa1atWq+oMAwOKaNGmiqKgobd261XQKYCkMSgAAAAAAsKgJEyZozJgxeuedd+RwOPT5559r5syZevbZZ/XMM8+YzgMAy3nzzTeVl5en3r17Kzw8XLNnz9bJkydNZwG1HmeUAAAAAABgYStWrNCMGTOUk5MjSfL391dcXJyio6MNlwGAdZ07d07Lly9XYmKiDhw4oH79+ikqKkoDBw6UmxunKQDXG4MSAAAAAACgwsJC5efny8/Pz3QKAOA/LFiwQJMmTVJxcbEaN26skSNHasqUKapbt67pNKDWYFACAAAAAAAAADXImTNnlJSUpMTEROXm5uqBBx5QdHS0Tpw4oRdffFH+/v76+OOPTWcCtQaDEgAAAAAALOrChQuKjY1Venq6zp49q5KSkjLX8/LyDJUBgDWlpKQoISFBGzduVOvWrfXYY4/pkUceka+vb+lrcnJy1KpVKxUXF5sLBWoZNrQDAAAAAMCihg8fruzsbEVHR6tJkyay2WymkwDA0iIjIzVkyBBt27ZNHTt2vOZr/P39NXXq1GouA2o3nigBAAAAAMCi6tevr61bt6pdu3amUwAAunpeFGePANWPJ0oAAAAAALColi1bqqioyHQGAOD//OeQ5NKlS+W21/Lx8anuJMASXEwHAAAAAAAAM15//XVNnTpVGRkZunDhgi5evFjmFwCgehUUFGjs2LHy8/OTt7e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      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.feature_selection import mutual_info_classif\n",
    "\n",
    "# determine the mutual information\n",
    "mutual_info = mutual_info_classif(X_train, y_train)\n",
    "mutual_info\n",
    "\n",
    "# sort the mutual information values in descending order\n",
    "mutual_info = pd.Series(mutual_info)\n",
    "mutual_info.index = X_train.columns\n",
    "mutual_info.sort_values(ascending=False)\n",
    "\n",
    "# plot the mutual information values\n",
    "mutual_info.sort_values(ascending=False).plot.bar(figsize=(20,8))\n",
    "\n",
    "# select the top 10 features manually\n",
    "selected_top_columns = mutual_info.sort_values(ascending=False).head(10).index\n",
    "selected_top_columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch-learn",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}