churn_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/keyor/miniconda3/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import shap\n",
    "import joblib\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()\n",
    "\n",
    "from sklearn.preprocessing import normalize\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# from sklearn.metrics import plot_roc_curve, classification_report\n",
    "from sklearn.metrics import RocCurveDisplay, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['QT_QPA_PLATFORM']='offscreen'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>Unnamed: 0</th>\n",
       "      <th>CLIENTNUM</th>\n",
       "      <th>Attrition_Flag</th>\n",
       "      <th>Customer_Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Dependent_count</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income_Category</th>\n",
       "      <th>Card_Category</th>\n",
       "      <th>...</th>\n",
       "      <th>Months_Inactive_12_mon</th>\n",
       "      <th>Contacts_Count_12_mon</th>\n",
       "      <th>Credit_Limit</th>\n",
       "      <th>Total_Revolving_Bal</th>\n",
       "      <th>Avg_Open_To_Buy</th>\n",
       "      <th>Total_Amt_Chng_Q4_Q1</th>\n",
       "      <th>Total_Trans_Amt</th>\n",
       "      <th>Total_Trans_Ct</th>\n",
       "      <th>Total_Ct_Chng_Q4_Q1</th>\n",
       "      <th>Avg_Utilization_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>768805383</td>\n",
       "      <td>Existing Customer</td>\n",
       "      <td>45</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>High School</td>\n",
       "      <td>Married</td>\n",
       "      <td>$60K - $80K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>12691.0</td>\n",
       "      <td>777</td>\n",
       "      <td>11914.0</td>\n",
       "      <td>1.335</td>\n",
       "      <td>1144</td>\n",
       "      <td>42</td>\n",
       "      <td>1.625</td>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>818770008</td>\n",
       "      <td>Existing Customer</td>\n",
       "      <td>49</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Single</td>\n",
       "      <td>Less than $40K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8256.0</td>\n",
       "      <td>864</td>\n",
       "      <td>7392.0</td>\n",
       "      <td>1.541</td>\n",
       "      <td>1291</td>\n",
       "      <td>33</td>\n",
       "      <td>3.714</td>\n",
       "      <td>0.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>713982108</td>\n",
       "      <td>Existing Customer</td>\n",
       "      <td>51</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Married</td>\n",
       "      <td>$80K - $120K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3418.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3418.0</td>\n",
       "      <td>2.594</td>\n",
       "      <td>1887</td>\n",
       "      <td>20</td>\n",
       "      <td>2.333</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>769911858</td>\n",
       "      <td>Existing Customer</td>\n",
       "      <td>40</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>High School</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Less than $40K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3313.0</td>\n",
       "      <td>2517</td>\n",
       "      <td>796.0</td>\n",
       "      <td>1.405</td>\n",
       "      <td>1171</td>\n",
       "      <td>20</td>\n",
       "      <td>2.333</td>\n",
       "      <td>0.760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>709106358</td>\n",
       "      <td>Existing Customer</td>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>Uneducated</td>\n",
       "      <td>Married</td>\n",
       "      <td>$60K - $80K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4716.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4716.0</td>\n",
       "      <td>2.175</td>\n",
       "      <td>816</td>\n",
       "      <td>28</td>\n",
       "      <td>2.500</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  CLIENTNUM     Attrition_Flag  Customer_Age Gender  \\\n",
       "0           0  768805383  Existing Customer            45      M   \n",
       "1           1  818770008  Existing Customer            49      F   \n",
       "2           2  713982108  Existing Customer            51      M   \n",
       "3           3  769911858  Existing Customer            40      F   \n",
       "4           4  709106358  Existing Customer            40      M   \n",
       "\n",
       "   Dependent_count Education_Level Marital_Status Income_Category  \\\n",
       "0                3     High School        Married     $60K - $80K   \n",
       "1                5        Graduate         Single  Less than $40K   \n",
       "2                3        Graduate        Married    $80K - $120K   \n",
       "3                4     High School        Unknown  Less than $40K   \n",
       "4                3      Uneducated        Married     $60K - $80K   \n",
       "\n",
       "  Card_Category  ...  Months_Inactive_12_mon  Contacts_Count_12_mon  \\\n",
       "0          Blue  ...                       1                      3   \n",
       "1          Blue  ...                       1                      2   \n",
       "2          Blue  ...                       1                      0   \n",
       "3          Blue  ...                       4                      1   \n",
       "4          Blue  ...                       1                      0   \n",
       "\n",
       "   Credit_Limit  Total_Revolving_Bal  Avg_Open_To_Buy  Total_Amt_Chng_Q4_Q1  \\\n",
       "0       12691.0                  777          11914.0                 1.335   \n",
       "1        8256.0                  864           7392.0                 1.541   \n",
       "2        3418.0                    0           3418.0                 2.594   \n",
       "3        3313.0                 2517            796.0                 1.405   \n",
       "4        4716.0                    0           4716.0                 2.175   \n",
       "\n",
       "   Total_Trans_Amt  Total_Trans_Ct  Total_Ct_Chng_Q4_Q1  Avg_Utilization_Ratio  \n",
       "0             1144              42                1.625                  0.061  \n",
       "1             1291              33                3.714                  0.105  \n",
       "2             1887              20                2.333                  0.000  \n",
       "3             1171              20                2.333                  0.760  \n",
       "4              816              28                2.500                  0.000  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"./data/bank_data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10127, 22)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                  0\n",
       "CLIENTNUM                   0\n",
       "Attrition_Flag              0\n",
       "Customer_Age                0\n",
       "Gender                      0\n",
       "Dependent_count             0\n",
       "Education_Level             0\n",
       "Marital_Status              0\n",
       "Income_Category             0\n",
       "Card_Category               0\n",
       "Months_on_book              0\n",
       "Total_Relationship_Count    0\n",
       "Months_Inactive_12_mon      0\n",
       "Contacts_Count_12_mon       0\n",
       "Credit_Limit                0\n",
       "Total_Revolving_Bal         0\n",
       "Avg_Open_To_Buy             0\n",
       "Total_Amt_Chng_Q4_Q1        0\n",
       "Total_Trans_Amt             0\n",
       "Total_Trans_Ct              0\n",
       "Total_Ct_Chng_Q4_Q1         0\n",
       "Avg_Utilization_Ratio       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "editable": true
   },
   "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>Unnamed: 0</th>\n",
       "      <th>CLIENTNUM</th>\n",
       "      <th>Customer_Age</th>\n",
       "      <th>Dependent_count</th>\n",
       "      <th>Months_on_book</th>\n",
       "      <th>Total_Relationship_Count</th>\n",
       "      <th>Months_Inactive_12_mon</th>\n",
       "      <th>Contacts_Count_12_mon</th>\n",
       "      <th>Credit_Limit</th>\n",
       "      <th>Total_Revolving_Bal</th>\n",
       "      <th>Avg_Open_To_Buy</th>\n",
       "      <th>Total_Amt_Chng_Q4_Q1</th>\n",
       "      <th>Total_Trans_Amt</th>\n",
       "      <th>Total_Trans_Ct</th>\n",
       "      <th>Total_Ct_Chng_Q4_Q1</th>\n",
       "      <th>Avg_Utilization_Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10127.000000</td>\n",
       "      <td>1.012700e+04</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "      <td>10127.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5063.000000</td>\n",
       "      <td>7.391776e+08</td>\n",
       "      <td>46.325960</td>\n",
       "      <td>2.346203</td>\n",
       "      <td>35.928409</td>\n",
       "      <td>3.812580</td>\n",
       "      <td>2.341167</td>\n",
       "      <td>2.455317</td>\n",
       "      <td>8631.953698</td>\n",
       "      <td>1162.814061</td>\n",
       "      <td>7469.139637</td>\n",
       "      <td>0.759941</td>\n",
       "      <td>4404.086304</td>\n",
       "      <td>64.858695</td>\n",
       "      <td>0.712222</td>\n",
       "      <td>0.274894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2923.557422</td>\n",
       "      <td>3.690378e+07</td>\n",
       "      <td>8.016814</td>\n",
       "      <td>1.298908</td>\n",
       "      <td>7.986416</td>\n",
       "      <td>1.554408</td>\n",
       "      <td>1.010622</td>\n",
       "      <td>1.106225</td>\n",
       "      <td>9088.776650</td>\n",
       "      <td>814.987335</td>\n",
       "      <td>9090.685324</td>\n",
       "      <td>0.219207</td>\n",
       "      <td>3397.129254</td>\n",
       "      <td>23.472570</td>\n",
       "      <td>0.238086</td>\n",
       "      <td>0.275691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.080821e+08</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1438.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2531.500000</td>\n",
       "      <td>7.130368e+08</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2555.000000</td>\n",
       "      <td>359.000000</td>\n",
       "      <td>1324.500000</td>\n",
       "      <td>0.631000</td>\n",
       "      <td>2155.500000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>0.582000</td>\n",
       "      <td>0.023000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5063.000000</td>\n",
       "      <td>7.179264e+08</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4549.000000</td>\n",
       "      <td>1276.000000</td>\n",
       "      <td>3474.000000</td>\n",
       "      <td>0.736000</td>\n",
       "      <td>3899.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>0.702000</td>\n",
       "      <td>0.176000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7594.500000</td>\n",
       "      <td>7.731435e+08</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>11067.500000</td>\n",
       "      <td>1784.000000</td>\n",
       "      <td>9859.000000</td>\n",
       "      <td>0.859000</td>\n",
       "      <td>4741.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>0.818000</td>\n",
       "      <td>0.503000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10126.000000</td>\n",
       "      <td>8.283431e+08</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>34516.000000</td>\n",
       "      <td>2517.000000</td>\n",
       "      <td>34516.000000</td>\n",
       "      <td>3.397000</td>\n",
       "      <td>18484.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>3.714000</td>\n",
       "      <td>0.999000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0     CLIENTNUM  Customer_Age  Dependent_count  \\\n",
       "count  10127.000000  1.012700e+04  10127.000000     10127.000000   \n",
       "mean    5063.000000  7.391776e+08     46.325960         2.346203   \n",
       "std     2923.557422  3.690378e+07      8.016814         1.298908   \n",
       "min        0.000000  7.080821e+08     26.000000         0.000000   \n",
       "25%     2531.500000  7.130368e+08     41.000000         1.000000   \n",
       "50%     5063.000000  7.179264e+08     46.000000         2.000000   \n",
       "75%     7594.500000  7.731435e+08     52.000000         3.000000   \n",
       "max    10126.000000  8.283431e+08     73.000000         5.000000   \n",
       "\n",
       "       Months_on_book  Total_Relationship_Count  Months_Inactive_12_mon  \\\n",
       "count    10127.000000              10127.000000            10127.000000   \n",
       "mean        35.928409                  3.812580                2.341167   \n",
       "std          7.986416                  1.554408                1.010622   \n",
       "min         13.000000                  1.000000                0.000000   \n",
       "25%         31.000000                  3.000000                2.000000   \n",
       "50%         36.000000                  4.000000                2.000000   \n",
       "75%         40.000000                  5.000000                3.000000   \n",
       "max         56.000000                  6.000000                6.000000   \n",
       "\n",
       "       Contacts_Count_12_mon  Credit_Limit  Total_Revolving_Bal  \\\n",
       "count           10127.000000  10127.000000         10127.000000   \n",
       "mean                2.455317   8631.953698          1162.814061   \n",
       "std                 1.106225   9088.776650           814.987335   \n",
       "min                 0.000000   1438.300000             0.000000   \n",
       "25%                 2.000000   2555.000000           359.000000   \n",
       "50%                 2.000000   4549.000000          1276.000000   \n",
       "75%                 3.000000  11067.500000          1784.000000   \n",
       "max                 6.000000  34516.000000          2517.000000   \n",
       "\n",
       "       Avg_Open_To_Buy  Total_Amt_Chng_Q4_Q1  Total_Trans_Amt  Total_Trans_Ct  \\\n",
       "count     10127.000000          10127.000000     10127.000000    10127.000000   \n",
       "mean       7469.139637              0.759941      4404.086304       64.858695   \n",
       "std        9090.685324              0.219207      3397.129254       23.472570   \n",
       "min           3.000000              0.000000       510.000000       10.000000   \n",
       "25%        1324.500000              0.631000      2155.500000       45.000000   \n",
       "50%        3474.000000              0.736000      3899.000000       67.000000   \n",
       "75%        9859.000000              0.859000      4741.000000       81.000000   \n",
       "max       34516.000000              3.397000     18484.000000      139.000000   \n",
       "\n",
       "       Total_Ct_Chng_Q4_Q1  Avg_Utilization_Ratio  \n",
       "count         10127.000000           10127.000000  \n",
       "mean              0.712222               0.274894  \n",
       "std               0.238086               0.275691  \n",
       "min               0.000000               0.000000  \n",
       "25%               0.582000               0.023000  \n",
       "50%               0.702000               0.176000  \n",
       "75%               0.818000               0.503000  \n",
       "max               3.714000               0.999000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "cat_columns = [\n",
    "    'Gender',\n",
    "    'Education_Level',\n",
    "    'Marital_Status',\n",
    "    'Income_Category',\n",
    "    'Card_Category'                \n",
    "]\n",
    "\n",
    "quant_columns = [\n",
    "    'Customer_Age',\n",
    "    'Dependent_count', \n",
    "    'Months_on_book',\n",
    "    'Total_Relationship_Count', \n",
    "    'Months_Inactive_12_mon',\n",
    "    'Contacts_Count_12_mon', \n",
    "    'Credit_Limit', \n",
    "    'Total_Revolving_Bal',\n",
    "    'Avg_Open_To_Buy', \n",
    "    'Total_Amt_Chng_Q4_Q1', \n",
    "    'Total_Trans_Amt',\n",
    "    'Total_Trans_Ct', \n",
    "    'Total_Ct_Chng_Q4_Q1', \n",
    "    'Avg_Utilization_Ratio'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "df['Churn'] = df['Attrition_Flag'].apply(lambda val: 0 if val == \"Existing Customer\" else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10)) \n",
    "df['Churn'].hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10)) \n",
    "df['Customer_Age'].hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CuZaWlpashwAAAAAAACiUvG6rBQAAAAAAUOzEEQAAAAAAICniCAAAAAAAkBRxBAAAAAAASIo4AgAAAAAAJEUcAQAAAAAAkiKOAAAAAAAASRFHAAAAAACApIgjAAAAAABAUsqyHgAAgK1TV1cXf/rTn2Lx4sVx3HHHRe/eveN//ud/Ypdddomdd9456/EAAACg3RJHoAjU1tbmdfzRRx+9Q+YAoH1Ys2ZNTJ48OWbPnh0RES0tLXHooYdG79694wc/+EHssccecc4552Q8JQCFUFdXF3Pnzo3FixfHunXr2uzL5XJxySWXZDQZADvSiBEjIpfLbfHx99577w6cBoqTOAJF4Lzzzmvz/tuLX0tLywbbIsQRgHe77373u7FgwYK47rrrYvDgwXHAAQe07hs+fHjccMMN4ghAAmpra2PSpEnRoUOH2HXXXaO8vDzrkQAokMMOO6zNvwXdc8890dDQEAcffHD06tUrli5dGgsWLIhu3brFEUcckeGk0H6JI1AEHnrooda3Fy1aFGeddVaMGTMmRo4cGbvssku89dZbMWfOnJg1a1ZcccUVGU4KQCHcfffdcc4558SwYcOiqampzb7dd989XnvttYwmA6CQpk+fHkcccURMmzYtunTpkvU4ABTQRRdd1Pr2jBkzYtddd4077rgjKioqWrevWLEivvrVr0afPn2yGBHaPQ9khyLQo0eP1v9deeWV8bnPfS7OOuusGDhwYPTp0ycGDhwYZ599dnz2s5+Nyy+/POtxAdjBVq9eHb17997ovjVr1hR4GgCy8uabb8aJJ54ojAAk7j/+4z/ia1/7WpswEhHRrVu3+OpXvxo33nhjRpNB+yaOQJF5/PHHY5999tnovn322SeefPLJAk8EQKENGDAgfve732103+9///tNrhMAvLsMHjw4XnjhhazHACBjK1asiJUrV25038qVK6OhoaHAE0FxcFstKDI9e/aMu+66Kw455JAN9t15553Rs2fPDKYCoJAmTJgQEyZMiDVr1sSRRx4ZuVwunnrqqZg1a1b8+te/jp/85CdZjwhAAZx11llxzjnnRMeOHeOQQw6Jrl27bnBM9+7dCz8YAAV18MEHx2WXXRa77bZbHHTQQa3bH3744fjBD34QBx98cIbTQfuVa/m/T3QG2r3//M//jIsuuiiqqqriiCOOaH3myD333BOPPPJIXHzxxfHZz3426zEB2MHmzJkT3/ve9+L1119v3bbrrrvGeeedF0ceeWSGkwFQKHvvvXfr2//3obz/17PPPluocQDIyJtvvhmnnXZaPPPMM9G1a9fo0aNH1NfXx8qVK+NDH/pQTJ8+3XNHYCPEEShC999/f1x33XXx9NNPx/r166OsrCwGDhwYp556aowYMSLr8QAooIULF0Z9fX1069YtKisrsx4HgAK67bbbNhlF3nbMMccUaBoAsjZ//vx46qmnYsmSJdG7d+/Yd999Y9iwYVmPBe2WOAJFrLm5OZYtWxY9e/aMkhKPEAIAAAAA2BKeOQJFLJfLRVNTUzQ3N4sjAO9yP/vZz7b42FwuFyeffPKOGwaAduG6666Lqqqq2HfffaNDhw5ZjwNAxubPnx9/+tOfYvHixXHaaadF375945FHHol+/fq5rRZshCtHoAg98MADcc0118QzzzwTzc3N8atf/SoGDRoUF154YVRVVcWnPvWprEcEYDv7v/eV35xcLuce8wAJOOyww2Lx4sXRsWPH2HfffaOqqiqqqqpi//33j06dOmU9HgAFsmzZspgwYUI8+eST0bt371iyZEnceuutMWjQoDjvvPOic+fO8c1vfjPrMaHdceUIFJlZs2bFN77xjRg5cmQce+yx8a1vfat135577hm33XabOALwLvTcc89lPQIA7czvf//7eOWVV+KRRx6JRx99NGbNmhXXXnttlJWVxaBBg6KqqipqamqyHhOAHew73/lO1NfXxx133BHve9/7Yp999mndV11dHdOnT89wOmi/3IcHisy1114b48aNiyuvvDKOP/74Nvs+8IEPxIsvvpjRZAAAQKHtueeeceyxx8Yll1wSv/vd7+KGG26I/fffP5588smYMWNG1uMBUADz5s2LM888M97//vdHLpdrs2+33XaLN954I6PJoH1z5QgUmVdeeSWGDx++0X2dO3eOlStXFngiAArt9ddf3+S+kpKS2HnnnWPnnXcu4EQAZOXFF1+MP/7xj61Xj9TX18cHPvCB+MIXvhAHHXRQ1uMBUABNTU2x0047bXRfQ0OD51LBJogjUGR69+4dL7/8clRXV2+w7/nnn4++fftmMBUAhTRixIgNfiLsX+2+++4xbty4OOmkkwo0FQCFdvDBB8ff/va3GDhwYFRVVcWnP/3pGDx4cHTt2jXr0QAooH333Td+/etfb/SHae+888444IADMpgK2j9xBIrM6NGj45prrom99tqr9SfBcrlcvPDCC/Hv//7vceKJJ2Y8IQA72rRp0+Kqq66KPffcMw4//PDYZZdd4q233op77rknXn311Rg/fnw8+eSTMW3atIgIgQTgXaqlpSUi/vH3gVwuFyUlJZuN5wC8+5x55pnxxS9+McaOHRsjR46MXC4X99xzT/z4xz+OefPmxc0335z1iNAu5Vre/q8poCg0NjbGv/3bv8X9998f3bt3j+XLl8cuu+wSy5Yti8MOOyyuueaaKCvTPQHezSZPnhwREVOnTt1g3wUXXBAtLS1xySWXxLe//e1YsGBBzJ49u9AjAlAgzz//fDz66KNtbqs1YMCAGDx4cFRVVcXHP/7xrEcEoAAef/zx+MEPfhCPP/54NDU1RS6Xi/322y/OOeec2H///bMeD9olcQSK1IIFC+IPf/hD1NfXR7du3WLIkCExZMiQrMcCoAAGDx4cV111VRxyyCEb7HvwwQfjzDPPjEceeSTmzZsXp59+evzpT3/KYEoAsrBgwYL40Y9+FI888kjkcrl49tlnsx4JgAJau3ZtrFixIioqKqJz585ZjwPtmh8vhyJ18MEHx8EHH5z1GABkoLS0NJ577rmNxpFnn302SkpKWo/zFyKAd7fXXnstHnnkkfjjH/8Yjz76aLzyyitRWloaH/7wh6Oqqirr8QAosE6dOkWnTp2yHgOKgjgCRWD58uVRUVERJSUlsXz58s0e37179x0+EwDZGTNmTFx11VXx97//PT72sY9Fz549Y9myZXHvvffG9OnT43Of+1xERDz99NOx1157ZTwtADvKxz72sVi8eHF06NAhPvzhD8dRRx0VVVVVsf/++8dOO+2U9XgAFMikSZNi1apVcdVVV22w7+yzz44uXbrElClTMpgM2jdxBIpAdXV1/PKXv4x99903Dj744M0+ZNGl8wDvbueee26UlpbG9OnT2/wFqLy8PMaOHRs1NTUREXHAAQfE0KFDsxoTgB3s+OOPj6qqqvjIRz4S5eXlWY8DQEYefPDBOPfccze67+Mf/3h873vfK/BEUBzEESgCl1xySey5556tb28ujgDw7tahQ4c4//zzY8KECfHCCy/EkiVLonfv3vHBD34wunXr1nqc26kAvLtNmDAh6xEAaAeWLVsWPXr02Oi+7t27x9KlSws8ERQHcQSKwDHHHBMRES0tLfGxj30sOnfu7P6RAES3bt0EEIDELVy4MK6//vp47LHHYsWKFdGtW7cYPHhwnHLKKdG/f/+sxwOgAPr06RNPPfVUVFdXb7Dvqaeeit69e2cwFbR/4ggUkfXr18fQoUPjRz/6URx22GFZjwNAhlasWBHz58+PxYsXx7p169rsy+Vy8fWvfz2jyQAolD//+c9x0kknRXl5eYwYMSJ69eoVS5cujfvuuy9mz54dN910UwwaNCjrMQHYwUaNGhXXXXdd7LnnnnHUUUe1bp89e3Zcd9118cUvfjHD6aD9yrW0tLRkPQSw5UaMGBEXXHBBHH744VmPAkBG/uu//ivOOOOMWL16dXTo0CHKytr+vEsul4v//u//zmg6AApl3Lhx0djYGDNmzGjzAPbVq1fH+PHjo2PHjnHDDTdkNyAABdHY2BgTJ06MefPmRefOneM973lPvPnmm7F27doYNmxYXHPNNZ5NBRshjkCRmTFjRvz+97+PGTNmWNgAEjVmzJjo3r17fOc734l+/fplPQ4AGdlvv/3i8ssvjxEjRmyw7957742ampp4/PHHM5gMgCw8+OCDsWDBgli+fHl07949hgwZstFbbQH/4LZaUGRef/31WLhwYRx22GFx0EEHxS677LLBA9onT56c0XQAFMIrr7wS5513njACkLgOHTrEmjVrNrpvzZo1G1xZCMC7z7p16+Izn/lMnHPOOXHooYfGIYcckvVIUDT8lxIUmfvvv7/1ipGnnnpqg/25XE4cAXiXGzhwYPz1r3/NegwAMlZdXR2XX3557L333lFZWdm6va6uLq666ir/QAaQgI4dO8abb74ZpaWlWY8CRcdttQAAiszzzz8f55xzTtTU1ER1dbWfDAZI1F//+tcYO3ZsLF68ON7//vdH7969Y+nSpfHiiy/GbrvtFj//+c9j1113zXpMAHawCy+8MHK5XFx88cVZjwJFRRyBIrJu3bqorq6O73//+x7IDpCw/fffP9avXx/r16+PkpKS6NixY5v9uVwuHnvssYymA6CQVq1aFb/+9a/jsccei4aGhujWrVsceOCBceyxx0aXLl2yHg+AArj99tvj8ssvj0GDBsWwYcM2egv2T3ziExlNB+2XOAJFZujQoXHJJZfEsGHDsh4FgIxcc801G/xl51+dfvrpBZoGAADI0t577/2O+3O5XDz77LMFmgaKhzgCReayyy6LRYsWxdVXX531KAAAQDvx1ltvxbp16zbY3rdv3wymAaCQXnvttc0es/vuuxdgEigublANRaaioiIef/zx+NSnPhWHHnroBpdK5nK5OPnkk7MbEAAAKIj6+vqYOnVq/O53v4v169e32dfS0uInhQESIXzA1nHlCBQZl0oCpOnUU0+N8847L973vvfFqaee+o7H5nK5mD59eoEmAyArX//61+OPf/xjjB07Nt7//vdHhw4dNjhm5MiRGUwGQKE1NjbGXXfdFY8++misWLEiunXrFoMHD46jjjoqysvLsx4P2iVXjkCRee6557IeAYAMrFq1KpqamlrfBoCHH344Jk+eHEcffXTWowCQobfeeitOPvnkePHFF6Nv377Ru3fvePrpp+PWW2+NGTNmxA033BC77LJL1mNCuyOOAAAUgRtvvHGjbwOQroqKiujRo0fWYwCQse9+97tRX18ft9xyS+y3336t25988smYOHFifO9734vvfve72Q0I7ZQ4AkVq3bp18corr2z0oYuDBg3KYCIAAKCQxo8fHzfeeGMccsghUVbmr/cAqZo3b16cf/75bcJIRMRHPvKROPvss2PatGnZDAbtnP96giLT2NgY3/72t+M3v/lN6+1V/pVnjgC8+yxbtizefPPNDZ499dxzz8W1114bdXV10atXrxg3blyMGDEioykBKKSXX3456urq4uMf/3hUVVVFRUXFBsdMnjw5g8kAKKS1a9du8krC7t27x9q1aws8ERQHD2SHInPFFVdEbW1tfOMb34iampq46KKLYqeddorf/va3sWjRorjwwgtj+PDhWY8JwHY2efLkePrpp+P2229v3fbaa6/Fpz71qVi7dm0MGDAgFi9eHMuXL4+ZM2dGVVVVhtMCUAibi+G5XC7uvffeAk0DQFY+//nPR+fOneP666+P0tLS1u1NTU1xyimnxNq1a+Pmm2/OcEJon8QRKDIjR46Mr3zlK3HsscfGoEGD4tZbb4199tknIiLOO++86NixY3z729/OeEoAtrejjjoqPvOZz8SXv/zl1m3f+c534qabboqf/OQnMXTo0Fi7dm186Utfiq5du8b111+f4bQAAEChPProo/GlL30pevXqFYcffnj07t07li5dGvfee28sXbo0fvrTn8bgwYOzHhPanZKsBwDys3jx4ujfv3+UlpZGx44do6GhoXXfmDFjYs6cORlOB8CO8sYbb8QHPvCBNtvuv//++NCHPhRDhw6NiIhOnTrFSSedFM8//3wWIwJQYK+88so77n/wwQcLNAkAWRo8eHD84he/iEGDBsWdd94ZV199ddx5550xaNCg+MUvfiGMwCZ45ggUmd69e7cGkT322CMefvjhGDJkSERE/OUvf8lwMgB2pFwuF7lcrvX9pUuXxquvvhrjxo1rc9x73vOeqK+vL/R4AGTg5JNPjptvvjn69Omzwb777rsvzjrrrHjyySczmAyAQttnn33ihz/8YdZjQFFx5QgUmYMOOigeffTRiIg4/vjj4yc/+UlMnDgxzj777Pjud78bhx9+eMYTArAj9O/fP/7whz+0vn///fdHLpeLQw45pM1xS5YsiZ49exZ6PAAy8IEPfCBOPvnkDaL4nDlz4owzzoixY8dmNBkAhXTZZZfFc889l/UYUHQ8cwSKzJIlS6K+vj4++MEPRkTEDTfcEHPmzIl169bFkCFD4utf/3rstNNOGU8JwPb229/+Ns4999w47rjjolevXvGLX/wiunfvHnfeeWeUlf3zYuALLrgglixZ4pkjAAlobGyMU045JRoaGuLGG2+MnXfeOX7zm9/EpEmT4mtf+1qcccYZWY8IQAEMGTIk6uvro3///jFmzJgYNWpU9OvXL+uxoN0TRwAAisT1118fN910U6xcuTIGDRoU3/zmN9s8h+Stt96KMWPGxMSJE+PEE0/McFIACmXVqlXx5S9/OXK5XIwaNSqmTZsWZ555Znz1q1/NejQACqS5uTkeeuihmDVrVtx7772tf18YM2ZMfPKTn4z3vOc9WY8I7ZI4AkXg1FNP3eJjc7lcTJ8+fQdOAwAAtCcrV66Mk046KZ5//vk4//zz44tf/GLWIwGQkb///e8xb968uOuuu+L++++PdevWRVVVVcycOTPr0aDdEUegCOy9997RpUuXGDhw4BYdf+ONN+7giQAAgCxs6gen6uvr4y9/+Uvsv//+rdv84BRAupqbm+O+++6Liy++OJYsWRLPPvts1iNBu1O2+UOArB166KHx0EMPxWuvvRajRo2K0aNHx4ABA7IeCwAAKLBVq1ZtdHt5eXl88IMf3OR+ANLw2GOPxZ133hl33313LFu2LD70oQ/FuHHjsh4L2iVXjkCRqK+vj9mzZ8esWbPiiSeeiMrKyhgzZkyMHj06+vbtm/V4AAAAAGTgmWeeiVmzZsXs2bNj8eLF0a9fvxg9enSMGjUq9tprr6zHg3ZLHIEi9Nprr8WsWbNi1qxZ8dJLL8X+++8fJ598cnziE5/IejQAAAAACmjvvfeOPn36xFFHHRWjRo2KffbZJ+uRoCiII1DEVq9eHddee2389Kc/jREjRsQPf/jDrEcCAAAKqK6uLubOnRuLFy+OdevWtdmXy+XikksuyWgyAArlj3/8Y1RVVUUul8t6FCgqnjkCRWb9+vUxf/78mDVrVtx///3RpUuX+PznPx+f+cxnsh4NAAAooNra2pg0aVJ06NAhdt111ygvL896JAAycNBBB2U9AhQlV45AkXj44Ydj1qxZcffdd0dTU1McccQRMWbMmBgyZEiUlJRkPR4AAFBgI0eOjAEDBsS0adOiS5cuWY8DQAGdeuqpcd5558X73ve+OPXUU9/x2FwuF9OnTy/QZFA8XDkCRWD48OFRX18fw4YNi4svvjhGjBjhp8IAACBxb775ZnzrW98SRgAStGrVqmhqamp9G8ifK0egCOy9995RVlYWHTp02Oz9I3O5XDz22GMFmgwAAMjKKaecEkOHDo1x48ZlPQoAQNFx5QgUgdNPPz3rEQAAgHbmrLPOinPOOSc6duwYhxxySHTt2nWDY7p37174wQAomFdffTV+9atfxRNPPBFLly6NXC4XvXr1igMOOCCOP/742G233bIeEdotV44AAABAEdp7771b397UFebPPvtsocYBoMDuuOOOuOCCC6KxsTH69OkTu+22W7S0tMTixYvjjTfeiI4dO8a0adPiqKOOynpUaJfEEQAAAChCt91222Zvu3vMMccUaBoACqmuri6OPvroOPDAA+PCCy+MysrKNvtffPHFmDJlSjzxxBPxm9/8Jvr375/RpNB+iSMAAAAAAEVkypQp8dBDD0VtbW2Ul5dv9JjGxsY4+uijY8iQITF58uQCTwjtn2eOAAAAQJEYMWLEJq8WKSsri1122SWqqqripJNOil69ehV4OgAK5ZFHHonPfvazmwwjERHl5eXx2c9+Nm677bYCTgbFQxwBAACAInHYYYdtMo40NTXFkiVL4uabb45bb701fvGLX0S/fv0KPCEAhfD666/HgAEDNnvcgAED4vXXXy/ARFB8xBEAAAAoEhdddNFmj1m5cmWMHTs2rrzyyrj88ssLMBUAhbZq1aro0qXLZo/baaedYvXq1QWYCIpPSdYDAAAAANtP165dY/z48fHQQw9lPQoAO0g+j5H2yGnYOFeOAAAAwLtMnz59YuXKlVmPAcAONG7cuE3eavFtwghsmjgCAAAA7zIvvvhivOc978l6DAB2kNNPPz3rEaDoiSMAAADwLvLoo4/Gj370ozj66KOzHgWAHUQcgW0njgAAAECRGDNmzCb3NTc3x9KlS6OhoSEOPPDAOOOMMwo4GQBAcRFHAAAAoEgMGjRok/eXLy0tjZ49e0ZVVVUMHTp0s/ehBwBIWa7FU3kAAAAAAICElGQ9AAAAAAAAQCGJIwAAAAAAQFLEEQAAAAAAICniCAAAAAAAkBRxBAAAAAAASIo4AgAAAAAAJEUcAQAAAAAAkiKOAAAAAAAASfn/SmrvUBQPNWoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10)) \n",
    "df.Marital_Status.value_counts('normalize').plot(kind='bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAMAoR3kDAAAAYMjqWrr1m5e2SZIuPatEp03OtjkR7NDWEVBzW8/Av03TUEKCRz09QUUixy9vMlO8yvB51eIP6N31tTpjeo6MIbYvacneIR0HAAAAOA0TAgMAAAAYkkBfWP/73Cb1BsOaUpymqxeV2R0Jo5BhGDq1PFOGIdW39uhAc7fdkQAAAADbUd4AAAAAGJI/vL5TNY1d8iV5dOuVM+UyeXmBoUlJ9GhyUaokaVNli0LhiM2JAAAAAHvx6goAAADAoG2saNK7Hx+QIekrV8xguioM2+SiVCXGuxXoC6ui1m93HAAAAMBWlDcAAAAABqWrt0+/fXm7JOmi04s1fUKGzYkwFrhMUzNK0yVJu2vb1RsM2ZwIAAAAsA/lDQAAAIBB+f1ru9TWGVRuRiLr3CCq8jMTlZbsUThiace+drvjAAAAALahvAEAAABw0tbvbNSHW+pkGNItl0+XJ85ldySMIYZhaObBkVx76zrU0R20OREAAABgD8obAAAAACels6dPj766Q5K0+IwSlRem2pwIY1FmarzyMhJkSdpW3WZ3HAAAAMAWbrsDAAAAABgdnnhtp/xdQRVkJemqcyfaHWdcc7mc8Tm8WOWYXpquupYe1bV0q9nfq0xffExuBwAAAHAqyhsAAAAAJ7R2e4NWba2XaRi6+fLpinMzXZodErxuWZYlny/B7iiHMWRE9fpSEj0qzU1WdX2ntu5p1cJZeTKM6N4GAAAA4GSUNwAAAACOy98V1O8OTpd22YISTcz32Zxo/PLGuWQYht5eu1ct/l6746goN0VnzMxXLHqVqSVpqmnsUmtHQAdaulWQmRT9GwEAAAAcivIGAAAAwDFZlqXHlu9QZ0+firKTdMXZTJfmBG0dATW39dgdQ2nJ3phdd7zHrfICn3bWtGvbnlblpSfKNBl9AwAAgPHBGRMlAwAAAHCkNdsb9NGORrlMQzdfPkNxbl5CYORMKkyVJ85UV29I1fUddscBAAAARgyvvAAAAAAcVVdvn554back6fIFpSrNS7E5EcYbt9vU1OI0SdKOfW0KhSL2BgIAAABGCOUNAAAAgKN69t1KdXT3KT8zUUvOnmB3HIxTpbkpSop3K9gX0e7adrvjAAAAACOCNW8AAAAAHGFPnV9vr6uVJN148VS5XSPzuS/TNBy1rolrhL5vHJtpGpo+IV1rtzeqYr9fE/N98npcdscCAAAAYoryBgAAAMBhIpalx17dKUvSWTNyNa00fURu1zQNpacnOaq8OcSQ8zKNJ/kZiUpL9qitM6iK/X7NmDAyj0kAAADALpQ3AAAAAA7z/sYDqjrgV7zHpWs/NWnEbvfQqJs311SrrSMwYrd7PEW5KTpjZr4MuhtbGYahKUVpWr29QXvq/JpU5JPHzegbAAAAjF2UNwAAAAAGdPb06Zm3KyRJVy2cqPQU74hnaOsIqLmtZ8Rv92jSkkf++8fR5WYkyJcYJ393n6r2d2hqSZrdkQAAAICYYQJnAAAAAAP+9E6FOnv6VJSdpAvnF9kdBxhgGIYmF6VJkioP+BUKR+wNBAAAAMQQI28AAACAcc7l6v9MV0Vtu97dsF+SdNOl0+T1jOzLhUM5gGMpyErU9r1udfWGtKeuQ5MKU+2OBAAAAMQE5Q0AAAAwTiV43bIsSz5fgsIRS088ulaWpE/NK9JZp9o36sYQC8zg6PpH36Rqw+5mVdS2a2JeCqUfAAAAxiTKGwAAAGCc8sa5ZBiG3l67Vx9sOqDd+9oU5zaVm56gZ9/cOeJ5inJTdMbMfBl0NziOouxk7djXpp5AWHsbOjUx32d3JAAAACDqKG8AAACAce5AU5fWbm+QJE0tTlN3T5+6e/pGPEdasnfEbxOjj2kamlSYqk2VLdpd067S3BSZJo0fAAAAxhbGlwMAAADj3NrtDeoLReRL8mhCfordcYATKslNljfOpZ5gWDWNnXbHAQAAAKKOkTcAANjMNA2ZpjEwZ7/dc/dHIpYiEcvWDABGTtX+du3c1yZJml2WIZM5yzAKuExT5YU+bd3Tql017SrOSZbBYxcAAABjCOUNAAA2Mk1D6elJh0334vMl2Jiov7xpbe2iwAHGid88v0WSVJiVpAxfvM1pgJM3IS9Fu2ra1dUbUm1Tl4qyk+2OBAAAAEQN5Q0AADY6NOrmzTXV8ncG5fW6FQiEFLHsKU7SUry64PRSmaZBeQOMA9V1fq3f2SjTkKaVptkdBxgUt8tUeYFP2/e2aVdNuwqzkuyOBAAAAEQN5Q0AAA7Q1hFQq79XCQke9fQEKU4AxJxlWXrv4/2SpOkTMpQUH2dzImDwJuanaHdtuzq6+1TX0qNJxel2RwIAAACiwt5J9QEAAADYoqaxS41tvUqKd2vO5Cy74wBDEud2aWK+T5K0q6ZNlk0jVwEAAIBoo7wBAAAAxplwOKLt1a2SpM99eoriPQzIx+hVVuCTyzTU1hlUTUOn3XEAAACAqKC8AQAAAMaZygMd6gmGlZIYpyULy+yOAwyLN86l4pxkSdL6XY02pwEAAACig/IGAAAAGEcCfWHtqmmTJJ0zK1+eOJe9gYAomJifIkmqqPWrrrnL5jQAAADA8FHeAAAAAOPIrn1tCoUt+ZI8mlbK4u4YG1ISPcpOi5ckvfB+lc1pAAAAgOGjvAEAAADGia6ePlXVdUiSZk5Il2EYNicCoqeswCdJem11tYKhsM1pAAAAgOGhvAEAAADGiW3VrbIsKSctQdlpCXbHAaIqJy1B6SledfeGtLum3e44AAAAwLBQ3gAAAADjQGtHQPubuyVJ0ycwXRrGHsMwNGdyliRpa1WLLMuyOREAAAAwdJQ3AAAAwBhnWZa27mmRJBXnJCs1yWNzIiA2ZkzIUGK8W+1dQTW09dgdBwAAABgyyhsAAABgjGts61WzPyDTNDStJM3uOEDMeOJcuuiMUklS5X6/zWkAAACAoaO8AQAAAMYwy7K0Y2+rJGlCXooSvG6bEwGxtWThREn9pWVHd9DmNAAAAMDQUN4AAAAAY1hDa49aO4NymYYmFabaHQeIubzMJJXkpkiSqg502JwGAAAAGBrKGwAAAGCMsixL2/e2SZIm5Kco3uOyNxAwQmZOzJAk7WvoVDAUtjkNAAAAMHiUNwAAAMAYVd/So/YuRt1g/MnPTFRKYpzCEUt76zvtjgMAAAAMGuUNAAAAMAZZlqXt+/rXuikr8Mkbx6gbjB+GYags3ydJ2nOgQ5Zl2ZwIAAAAGBzKGwAAAGAMOtDSLX9Xn9wuQ+UFPrvjACOuKDtJHrep7kBIdS3ddscBAAAABoXyBgAAABhjLMvSjoNr3ZTl++Rh1A3GIZfLVGleiiSpcr/f5jQAAADA4FDeAAAAAGPMgeZudXT3j7opK2TUDcavCXkpMiQ1+wNq7wraHQcAAAA4aZQ3AAAAwBjyyVE35QWp8rgZdYPxK8HrVl5moiSpuq7D5jQAAADAyaO8AQAAAMaQ2qYudfT0Kc5tqoy1boCBqdNqGjsVCkdsTgMAAACcHMobAAAAYIyIHDbqxqc4N3/uA9mp8Ur0uhUKWzrQ3G13HAAAAOCk8GoOAAAAGCNqG7vU1RvqH3WTz6gbQJIMw1BJbrIkpk4DAADA6EF5AwAAAIwBEcvSzn1tkqRJhalyM+oGGFCSkyxDUktHQB3dQbvjAAAAACfEKzoAAABgDNh/cNSNx21qYn6K3XEAR4n3upWbkSBJqq7vtDkNAAAAcGKUNwAAAMAoZ1mWdtW2S5LKCnxyu/gzH/hbpbn9pWZNQ6fCkYjNaQAAAIDj41UdAAAAMMrVtXSro7tPbpehiax1AxxVTnqC4j0uBUMRHWjutjsOAAAAcFyUNwAAAMAoZlmWdtX0j7qZmO9THGvdAEdlGIZKcpMlSXuZOg0AAAAOxys7AAAAYBRrau9VW2dQLtNQGaNugOMqOTh1WlN7rzp7+mxOAwAAABwb5Q0AAAAwih0adVOSmyyvx2VzGsDZEr1u5aQnSJL21nfYnAYAAAA4NsobAAAAYJRq8feqqb1XhiFNKky1Ow4wKpQemjqtoVORiGVzGgAAAODoKG8AAACAUerQqJvi7GQleN02pwFGh9z0RHnjXAr2RVTX0m13HAAAAOCoKG8AAACAUai9K6j61h5J0qQiRt0AJ8s0DZUcHH1TzdRpAAAAcCjKGwAAAGAU2n1w1E1BZqKSE+JsTgOMLiU5/eVNY1uvunv7bE4DAAAAHInyBgAAABhlOnv6VNvUJUmaXJxmbxhgFEpKiFNWarwkaW99p81pAAAAgCNR3gAAAACjzO7a/lE3uekJSk3y2JwGGJ1K81IkSXsbOhWxLJvTAAAAAIejvAEAAABGkZ5ASPsa+kcKTGatG2DI8jIS5XGb6g2G1XBw/SgAAADAKShvAAAAgFGkorZdliVl+rzK8MXbHQcYtVymoaKDa9/UNDB1GgAAAJyF8gYAAAAYJQJ9YVXXHxp1k2ZvGGAMKM5OkiTVtXQrGArbnAYAAAD4K8obAAAAYJTYc6BD4Yil1CSPstMYdQMMly/Jo5TEOEUsaX9Tt91xAAAAgAGUNwAAAMAoEApHVHXAL0maVOiTYRg2JwJGP8MwVMzUaQAAAHAgR5Q3VVVVuvnmmzVnzhwtWLBA99xzj3p7e0/q2Oeee06LFy/WrFmztGTJEr388stH7PPTn/5Uf//3f6958+Zp6tSp2rRp0xH7rFq1SlOnTj3i64477hj29wcAAAAMV01Dp4KhiBK9buVnJdkdBxgzig6eTy0dAXX19NmcBgAAAOjntjuA3+/XTTfdpIKCAj344INqaWnRvffeq7a2Nt1///3HPfaVV17Rd7/7XX35y1/WOeeco9dff1133HGHUlJStHDhwoH9/vjHP6qkpETnnHOOXn311eNe57333quysrKBf6enpw/vGwQAAACGybIsVezvH3VTVuCTyagbIGrivW5lp8Wrsa1X+xo7Na2E14AAAACwn+3lzZNPPim/369ly5YpIyNDkuRyuXTnnXfqq1/9qsrLy4957AMPPKDFixfr29/+tiTprLPOUlVVlR588MHDypu3335bpmlq1apVJyxvJk+erFmzZkXhOwMAAACi40BLt7p6Q4pzmyrJTbY7DjDmFOckq7GtVzUNXZpanMa0hAAAALCd7dOmvfvuu1qwYMFAcSNJl1xyiTwej955551jHrdv3z5VVlZqyZIlh21fsmSJNm7cqJaWloFtpmn7twkAAAAMWUVt/6ibCXkpcrv42xaItryMRLldhroDIbV0BOyOAwAAANg/8qaiokLXXHPNYds8Ho9KSkpUUVFxzOMqKysl6bApziSpvLxclmWpsrLysELoZH35y19WW1ubsrOzdfnll+sb3/iG4uPjB309n+R28wIbo5vr4JtELt4sAqLu0HllGoZMs/9Tvof+a4dDUzFxvmOssvs57Wjn/Ik0t/eqtSMg05DKC31R/R1xaHSBadr7u+cQp+WRnJdptOSx6zltqPePx3SpICtJe+s7VdPYpey0hKjk4XkVsWT3cxowXnCuAbHHeXZ0tpc3fr9fPp/viO0+n0/t7e3HPO7QZX97bGpq6mGXn6yUlBTdcsstOv300+X1erVy5Ur9+te/VmVlpX7+858P6ro+yTQNpaezoCzGBp8vOi9iARzJ63XLG4w7+P9xtuaQON8x9tn9GPd63UpI8JzUvlU7GiVJEwpSlZ6aGNUcHo9LkhQXd/J5YslpeSTnZRpteUb6OW0498/k4nTtre/U/qYunTEzLyqj3HhexUjg8QWMDM41IPY4zw5ne3lzLJZlndQ8w3+7j2VZR91+IjNmzNCMGTMG/r1gwQLl5OToP//zP7Vx40bNnj17UNd3SCRiye/vHtKxgFO4XKZ8vgT5/T0KhyN2xwHGlEPnVyAQUiDQJ683ToFAnyIRy5Y8iQff9OJ8x1hl93PaJ8/5np7gCffv7O5TTUOnJGliXvJJHTMYwWBYktTXd3J5Ys1peSTnZRoteUzTsOU5bTj3T3K8Swlet3oCIVXVtqkoe/jrS/G8iliy+zkNGC8414DYG2/nmc+XcFKjjGwvb3w+n/x+/xHbOzo6VF5efszjPjnCJisra2D7oes62miewbr00kv1n//5n9q8efOQyxtJCoXG/gMO40M4HOHxDMRIxLIG3tyKRCzbypvIwQ9BcL5jrLP7Mf7Jc/54dtW0SZJy0xOUFB8X9d8Nhz74FInItt87n+S0PJLzMo22PCP9nDbc+6c4O0k7a9q1t75TBZnDn0GB51WMBB5fwMjgXANij/PscLZPIldeXn7E2jbBYFB79+49bnlzaK2bQ2vfHFJRUSHDMI5YCwcAAAAYTQLBsPYdHHUzqTDV5jTA+FCU0z/aprG1R73BkM1pAAAAMJ7ZXt4sWrRIK1euVGtr68C21157TcFgUOedd94xjysuLlZZWZleeumlw7a/8MILmj17tjIyMoad7cUXX5QkzZo1a9jXBQAAAAxG1QG/IpaUluxRhs9rdxxgXEhOiFN6ileWpNrGLrvjAAAAYByzfdq066+/Xo8//rhuu+023XbbbWpubtZ9992nK6644rCRN3fffbeWLVumrVu3Dmy7/fbbdccdd6ikpERnn3223njjDa1YsUK/+tWvDruN1atXq6WlRbt375YkrVy5UrW1tSosLBwoZu68806VlpZqxowZ8nq9WrlypX7729/qwgsvpLwBAADAiAqFI6qq65DUP+pmsOs5Ahi6ouwktXYEtK+xS+WMegMAAIBNbC9vfD6fHn30Ud1zzz36+te/rvj4eC1ZskR33nnnYftFIhGFw+HDtl166aXq7e3Vww8/rEceeUSlpaX60Y9+pIULFx62309+8hOtXr164N/333+/JGnp0qW67777JEmTJ0/W888/r1//+tfq6+tTYWGhbr31Vn35y1+OxbcNAAAAHNO+hk71hSJKjHcrPzPR7jjAuFKYlaTNVS3ydwXV3hVUapLH7kgAAAAYh2wvbyRp4sSJeuSRR467z3333TdQtHzS0qVLtXTp0uMe+9hjj50ww1e+8hV95StfOeF+AAAAQCxZlqWK/X5JUnmBj1E3wAjzxLmUl5GoA83dqmnoVOrE4U/JDQAAAAyW7WveAAAAAPirAy3d6u4NKc5tqvjg4ukARlZRdv+5V9PYpYhl2ZwGAAAA4xHlDQAAAOAglQdH3UzIS5HbxZ/rgB1y0xPkcZsK9IXV1NZrdxwAAACMQ7waBAAAAByirSOgFn9AhiFNzEuxOw4wbpmmoYKsJElSTWOnzWkAAAAwHlHeAAAAAA5ReaB/1E1hVpLivY5YnhIYtwqz+8ubupZuhcMRm9MAAABgvKG8AQAAABygNxBSbVOXJKmswGdzGgAZKV4leF0KhS3Vt/XYHQcAAADjDOUNAAAA4ABVdR2yLCnD51VastfuOMC4Zxh/nTqttrHL5jQAAAAYbyhvAAAAAJuFwxFV13VIYtQN4CRFB8ub+tYe9YWYOg0AAAAjh/IGAAAAsFlNY5eCoYgSvW7lZyTaHQfAQb4kj5IT3IpELNW1dNsdBwAAAOMI5Q0AAABgI8uyVLnfL0mamJ8iwzBsTgTgkMOmTmti6jQAAACMHMobAAAAwEaN7b3q6OmTyzRUkptidxwAf6MoK1mS1NjWo2Bf2OY0AAAAGC8obwAAAAAbHRp1U5KbrDg3f54DTpOcGKfUJI8sS9rfzNRpAAAAGBm8OgQAAABs0tEdVENrjySpLN9ncxoAxzIwdVojU6cBAABgZFDeAAAAADapOtAhScrLSFBSQpzNaQAcS2F2f3nT7O9VTyBkcxoAAACMB5Q3AAAAgA2CfWHta+iUJJUVMOoGcLJEr1sZKV5J0v5mRt8AAAAg9ihvAAAAABtU13cqHLHkS4pTpi/e7jgATuDQ6BumTgMAAMBIoLwBAAAARlgkYqnqgF+SVFaQKsMwbE4E4ETyM/vLm7bOoLp6+mxOAwAAgLGO8gYAAAAYYXvqOtQbDMsTZ6rw4ELoAJwt3uNSVmr/KLnaJkbfAAAAILYobwAAAIARtrWqWZI0Mc8nl8moG2C0KDo0dRrlDQAAAGKM8gYAAAAYQdurW9TY1ivTkCbkpdgdB8Ag5GcmyjCkju4++buCdscBAADAGEZ5AwAAAIygv7xbKUkqzE6W1+OyOQ2AwYhzu5SbniCJ0TcAAACILcobAAAAYIQ0tfdqxcb9kqSyAp/NaQAMxaF1qmqbumRZls1pAAAAMFZR3gAAAAAj5I21+xSJWMrPTFRqksfuOACGIDcjUS7TUHdvSG2dTJ0GAACA2KC8AQAAAEZAIBjWW+trJUkzJmbYnAbAULldpvIyEiUxdRoAAABih/IGAAAAGAErNh9Qd29I+VlJKs5JtjsOgGEoyOovbw40M3UaAAAAYoPyBgAAAIixiGXptbU1kqTPnFsmwzBsTgRgOHLSEuQyDfUEwkydBgAAgJigvAEAAABibHNls+pbupXodevC00vsjgNgmFyfmDptP1OnAQAAIAYobwAAAIAYW75mnyTpvNMKlOB125wGQDTkH5w6bT9TpwEAACAGKG8AAACAGKpp7NTWPa0yDOmi+cV2xwEQJblMnQYAAIAYorwBAAAAYuj1tf2jbuZNyVZWWoLNaQBEi8tlKje9/5ze38zUaQAAAIguyhsAAAAgRvzdQX2wuV6SdNHpjLoBxpqCrCRJ0oGmbqZOAwAAQFRR3gAAAAAx8s76WoXCEU3MT9GkwlS74wCIspz0/qnTugMhtXcxdRoAAACih/IGAAAAiIFQOKI319VK6l/rxjAMmxMBiDb3J6dOa2LqNAAAAEQP5Q0AAAAQA6u31au9K6i0ZI/mT8uxOw6AGDk0ddr+ZqZOAwAAQPRQ3gAAAABRZlmWlq/ZJ0m6YG6R3C7+7AbGqoGp03qZOg0AAADRw6tIAAAAIMp27mvT3vpOxblNnX9aod1xAMSQ22UqZ2DqtG6b0wAAAGCsoLwBAAAAouy1tTWSpLNPyVNyQpzNaQDEWkHmoanTupg6DQAAAFFBeQMAAABEUUNbj9bvbJQkfXp+sc1pAIyE3IwEmQenTvMzdRoAAACigPIGAAAAiKI31tbIknTKxAwVHlzIHMDY5naZykk7OHVaM1OnAQAAYPgobwAAAIAo6QmE9N7G/ZKki09n1A0wnhRkJUqS9jcxdRoAAACGj/IGAAAAiJL3Nh5QbzCs/MxEzZyYYXccACMoLz1RpiF19Ybk7+6zOw4AAABGOcobAAAAIAoiEUuvr90nSbro9GIZhmFzIgAjye02lZP+19E3AAAAwHBQ3gAAAABRsH5Xk5rae5UU79aCmXl2xwFgA6ZOAwAAQLRQ3gAAAABR8NqavZKk808rlDfOZXMaAHbI/cTUaR1MnQYAAIBhoLwBAAAAhmlPnV87a9rlMg1dMLfI7jgAbBLnNpWTniCJqdMAAAAwPJQ3AAAAwDC9tqZ/rZvTp+coPcVrcxoAdsrPTJIkHWjptjkJAAAARjPKGwAAAGAYWjsCWr2tQZJ00fxim9MAsFteRoIMQ+ro7lN7Z8DuOAAAABilKG8AAACAYXhrfY3CEUuTi1I1Md9ndxwANotzu5SVGi9Jqq7rsDkNAAAARivKGwAAAGCIgn1hvb1+vyRG3QD4q0NTp1HeAAAAYKgobwAAAIAh+nBLnTp7+pSVGq+5U7LtjgPAIfIyEiVJTe29amhl7RsAAAAMHuUNAAAAMASWZem1tTWSpAvnFck0DZsTAXCKeI9LGT6vJGnlpgM2pwEAAMBoRHkDAAAADMGWPS3a39Qlr8elc2cX2B0HgMMUHJw67QPKGwAAAAwB5Q0AAAAwBK+t6R91c+6sfCXGu21OA8Bp8jL7p07bWtWs9s6AzWkAAAAw2lDeAAAAAIO0v6lLmyqbZUj69Pwiu+MAcKBEr1tZqfGyLGndzia74wAAAGCUobwBAAAABun1j/pH3cyZnKWc9ESb0wBwqtK8FEnSRzsabE4CAACA0YbyBgAAABiEzp6+gTUsLj692OY0AJzsUHmzpapF3b19NqcBAADAaDLk8iYYDEYzBwAAADAqvLOhVsFQRCU5yZpSnGZ3HAAOlprsVXFuisIRSx/vbrY7DgAAAEaRIa+sumjRIl177bW64YYbVFBQEM1MAACMGZZlqa6lW7tq2tXWEVBHT5+6evrU2dM38P9dvSFJkjfOVHKiR3EuQ/Eel+I9bsV7XPIleeSNc9n8nQCQpFA4ojfX1UqSLjq9WIZh2JwIgNOdPTtff3ytQx/tbNSCU/LsjgMAAIBRYsjlzac+9Sk99thj+vWvf63zzz9fX/jCF7RgwYJoZgMAYFRq8fdqW3XrwFdrR+CkjusJSG2dRx/ZmpbsUU5agnLSE5Se4uUNY8Ama3c0qLUjIF+SR2dMz7U7DoBR4OxZBfrjazu1ubJZgWBYXg8fyAAAAMCJDbm8uffee/Xd735XTz31lJ588kn9wz/8gyZMmKDPf/7zuuqqq5ScnBzNnAAAONre+g69+/F+bdnTqvqW7sMuc7sMlRekKi8zUckJcQNfSQlxSk32qCDXp5dWVKqhpVvhiOTvCqgnEFJPIKyeQEhdvSG1dQbV1hnUzpp2xblNZafFKyc9UbnpCYzKAUaIZVl6bc0+SdIFcwsV52b5SAAnNrHAp+y0BDW29WhzVbPmTc2xOxIAAABGgSGXN5KUmpqqf/zHf9Qtt9yiN998U0888YR+8IMf6H/+53901VVX6fOf/7zKy8ujlRUAAEexLEubKlv06uq92lbdOrDdMKQJeT5NL03X9AnpmlyYKs8xCha321R6epKyUhPkMgwlJHjU0xNUJGIN7NMbDKmhtUcNrT1qbOtVXyii/U3d2t/ULdOQinOSVV6QquTEuJh/z8B4VlHrV9WBDrldps6fU2h3HACjhGEYmj8tWy+v3KuPdjZS3gAAAOCkDKu8OcQwDF144YXKz8/Xfffdp9WrV+v3v/+9/vCHP+jTn/60/v3f/12ZmZnRuCkAAGzXFwrrwy31Wr5mn/Y3dUmSTMPQvKnZOmtmrqYWpykxPnpFSrzHrZLcFJXkpihiWWrrCKihtUd1rd3yd/Wpur5T1fWdystIUHlhqjKYVg2IieVr9kqSFszMlS/JY3MaAKPJ/Kk5ennlXn28u0l9oQgj9wAAAHBCwy5vQqGQXnnlFT3xxBPasGGD8vPzdeedd+qyyy7TG2+8of/93//Vd77zHT3yyCPRyAsAgG16AiG9tnaf3vyoRv7uPklSvMelRacW6NPzipSVlhDzDKZhKMMXrwxfvKaWpKmlI6CK2nbVtfQMfKWneFVe6FN+RiIlDhAlTe09+mhnoyTpovnFNqcBMNqUF6UqNdmj9s6gtlW3anY5H24EAADA8Q25vKmvr9eTTz6pp59+Wk1NTZo/f75+/OMf66KLLpJp9n+K6MYbb1Rubq7uuuuuqAUGAMAOG3Y36fHlO9TiD0iS0lO8umh+sRadWqDE+KgMZB00wzCU6YtXpi9end19qtjfrn0NnWrtCGjt9kYlJ8RpdlnGiJRKwFj3xkc1sixpxoR0FeWwtiOAwTENQ3OnZOutdbVat7OB8gYAAAAnNOR3my644AK5XC5dfvnl+uIXv6jp06cfdb/i4mKmTAMAjFrtXUH9/rWdWrO9QZKUnRavpYvKNH9qjtwu50x5kpwYp1MnZWlqSbr2HPCrqq5DnT19+mBLvYqykzRzQoa8nqOvuwPg+HoCIb378QFJjLoBMHTzBsqbJn3xEkumyehYAAAAHNuQy5uvfe1ruv7665WRkXHc/aZPn64333xzqDcDAIAtLMvS+xsP6Km3dqurNyTTMHTxGcW6cuFEeeOcW4LEe1yaVpqu8kKftlW3aU9dh2oau1Tf2qMZpekqyU1mKjVgkFZsOqCeQEi5GYmaxaflAQzRlOI0JcW71dnTp101bZpakm53JAAAADjYkD8yXFBQcMw3f9ra2rRs2bKhXjUAALaqb+nWf/9hvX7z8nZ19YZUkpusf71pvj73qUmOLm4+Kc7t0uzyTJ07O1++JI/6QhF9XNGsFZvq5O8K2h0PGDXCkYiWr9knSbpofpFMyk8AQ+R2mZozOUuStHZHo81pAAAA4HRDLm++973vad++fUe9rKamRt/73veGHAoAALu8vaFW//fXq7V9b5s8blOf+9Qk/etN81Wal2J3tCFJT/Fq0an5mjkhXS7TUEtHQO98vF9b97QqErHsjgc43kc7GtXU3qvkhDidMyvf7jgARrl5U3IkSet3NcqyeB4GAADAsQ152rTj/aEZCATkco2OTyYDACBJoXBET76xS2+uq5XUvyj5FxdPU05ags3Jhs80DJUXpqogK0mbKltU19Kt3bXtavb3av7UbCV4h/znADCmWZall1ftlSRdMLdw1Iy8A+BcMyemyxvnUos/oD11HZqY77M7EgAAABxqUO/W7N+/X7W1tQP/3rp1qwKBwGH79Pb26qmnnlJ+Pp9MBACMDp09ffrZss3aVt0qSbp6UZkuX1A65taGSfC6dcb0HB1o7tL6XU1qPTgKZ96UbGWPgZIKiLbte9tUXdehOLepC+YV2R0HwBgQ53ZpVlmG1u5o1LqdjZQ3AAAAOKZBlTfPPvusHnroIRmGIcMw9B//8R9H7HNoRM7/+T//JzoJAQCIoQPNXXrgmY1qaO2RN86lf7xihuZOybY7VkzlZybJl+jRmh2N8ncF9eGWek0vTdOkwlS7owGO8srBUTcLZ+XLl+ixOQ2AseK0KdkD5c0155XbHQcAAAAONajy5tJLL9XkyZNlWZa++c1v6lvf+pZKS0sP28fj8Wjy5MkqKuLTiQAAZ9tY0ayf/2WzegJhZfridftnZ6s4J9nuWCMiKSFO587K08bKFu1r6NS26ja1+AO68PRiu6MBjlDT2KlNlc0yDOniMzgvAETPqeWZcpmGDjR360Bzl/Izk+yOBAAAAAcaVHlTXl6u8vL+Twbde++9Ov/885Wenh6TYAAAxIplWVq+Zp+eemu3LEuaXJSqry2dJV/S+Ppkvctl6rTJWcrwebWpoln1rT16/v0qnXlKgdITWQcH49urB0fdzJuSrdz0RJvTABhLEuPjNK00XVuqWrR+VxPlDQAAAI7KHOqBS5cupbgBAIw6lmXp96/t0h/f7C9uzp2dr7tuOG3cFTefVJqbooWz85Xodauju093PfiuPtrRYHcswDatHQGt3FovSVp8ZukJ9gaAwTs0Reu6nY02JwEAAIBTDepjtQ899JCuvfZa5ebm6qGHHjruvoZh6Gtf+9qwwgEAEE2WZen3r+/SG+tqZEi67sLJumh+kQzDsDua7dKSvVp0ar42VbWotrFLDz6zUV9aPE3nnlpgdzRgxL22dp/CEUtTitNUVsBi4gCi77TJWXr81R2q3O9Xa0dA6SleuyMBAADAYQZd3ixatIjyBgAw6liWpT+8sUtvfFQjSfrSpRQTf8sT59Kn5xertqlbr6/Zq9+8vF3+7qAuO6uUggvjRndvSG+vr5UkLT6zxOY0AMaqtGSvygp9qqj1a/2uRl0wlzVjAQAAcLhBlTfbt28/6v8DAOBklmXpj2/u1utrKW5OxDQN3X7dHHnjDL34QbX+9E6lOrr79LkLJsmkwME48M7HteoNhpWfmajZ5Zl2xwEwhs2dkq2KWr/W7aS8AQAAwJGGvOYNAACjgWVZevrtCi1fs0+S9MXFU7WI4ua4DMPQdRdM1nUXTJIkLV+zT4+8sFWhcMTmZEBshcKRgZJ38RklFJYAYurQujc79rapq7fP5jQAAABwmiGXN4FAQJ2dnYdte+mll3T//ffrgw8+GHYwAACGy7Is/emdSr2yaq8k6caLp+j8OYU2pxo9LjmjRLcsmS6XaejDLfV68E8bFQiG7Y4FxMyqrfVq7QgoNdmjs2bm2R0HwBiXm56owuwkhSOWPt7dZHccAAAAOMyQy5u77rpL99xzz8C/f/e73+lb3/qWfvWrX+nmm2/WO++8E5WAAAAMhWVZeu69Sr20slqS9PmLpuhTTEkyaGefkq+vXzNLHrepzZUt+u8n1/PpYIxJlmXpldX9Re9F84sV52aAOoDYmzu5f/TNup2UNwAAADjckF+Vbtq0Seeee+7Avx977DF95jOf0dq1a3XRRRfpkUceiUpAAACG4oUPq/XCB/3FzQ2fnqwL51HcDNXs8izdecNpSop3q3K/Xz966mP1BEJ2xwKialNli2obu+T1uHT+HKZWBDAyDk2dtrmyWYE+RrcCAADgr4Zc3rS0tCg3N1eStG/fPu3bt09f+MIXlJycrM9+9rPatWtX1EICADAYq7bW67l3KyVJ118wSRfNL7Y50eg3qTBV3/m7uQMFzo+f/pgp1DCmvHxwlN55pxYoMT7O5jQAxouS3GRl+uIVDEW0parF7jgAAABwkCGXN/Hx8ero6JAkffTRR0pMTNSsWbMkSV6vV93d3dFJCADAIFTUtuuRF7dJ6l9w/OIzSmxONHYU5yTr29fPUYLXrV017XrwTxsV5FPCGAN21bRpx742uUxDF59O2Qtg5BiGodOmZEmS1u9stDkNAAAAnGTI5c2UKVP0xBNPaMeOHfr973+vM888U4ZhSJIOHDigrKysqIUEAOBkNLf36ifPblIoHNGcSVn67PnlR93PNA253aYjvlyu0bWuxoQ8n+743KnyelzaVt2q/122WX2hiN2xgGE5NMXiObPyleGLtzkNgPFm3sGp0zbsblI4wnMqAAAA+rmHeuBtt92mW2+9VVdddZXi4uL0m9/8ZuCyt99+WzNnzoxKQAAATkZPIKQHntkof1dQxTnJ+vJnZsg0jSP2M01D6elJR73MToacled4JhWm6pufna0fPfWxNlY06+E/b9ZXrzpF7lFWRAGSVF3XoU2VzTIM6dKzGKkHYORNLkpTckKcOnv6tHNvm6ZPyLA7EgAAABxgyOXNggUL9NJLL2nLli2aPn26iov/OsXEWWedpenTp0clIABg8EzTcFQ5EYlYikSsmF7/L/6yRTWNnUpN8ugbn52teM/Rn+IO3TdvrqlWW0cgZplOVlFuis6YmS/DOT+ukzK1JF1f/+xsPfD0Rq3f1aRfvbBVX75ipqMed8DJePHDPZKkM6fnKjc90d4wAMYl0zQ0Z3KW3t94QOt2NlHeAAAAQNIwyhtJKiwsVGFh4RHbr7/++uFcLQBgGJw4siQSsdTa2hWzAufpt3fr44pmxblNff2a2Sc17VFbR0DNbT0xyTMYacleuyMM2cwJGfra0lP00LObtHpbg+Jcpv7+8ukyR1sThXFrf1OXPtrRv8bEZQtKbU4DYDybOyW7v7zZ1ai/u2jywJTkAAAAGL+GVd5IUnNzs2praxUIHPnp5dNPP324Vw8AGCSnjSxJS/HqgtNLZZpGTMqbdz/er1dX75Mk3Xz5dJUV+KJ+Gzi2Uydl6dYrZ+pny7ZoxeY6JcbH6YZPT7Y7FnBSXlpZLUvSaZOzVJSdbHccAOPYzAnp8npcau0IaE9dhybm8/cMAADAeDfk8qahoUHf+c53tGrVqiMusyxLhmFo27ZtwwoHABg6p4wsiaVt1a167NUdkqSrFk7UGdNzbU40Ps2bmqObl0T0y+e36rW1+5SZGq+LTy8+8YGAjRpbe7RyS70kacnZE+wNA2Dci3O7NKssU2u3N2jdzkbKGwAAAAy9vPn+97+vbdu26c4779TUqVPl8XiimQsAgONq7QjoZ8s2KxyxdOaMXF1xzgS7I41rC2bmqa0joKffrtAf39iljBSv5k/LsTsWcEwvrqxWxLI0c0I6b5ICcIS5U7IGyptrziu3Ow4AAABsNuTyZvXq1frOd76ja665Jpp5AAA4oUjE0i+f36LOnj4V5yTr7y+dxtzwDrD4zBI1+Xv11rpa/eL5rfIleTSlOM3uWMARWvy9em/DfkmMugHgHLPLsuQyDR1o7taB5i7lZybZHQkAAAA2Mod6oGEYys/Pj2YWAABOygsf7NH2vW3yxrn01atOkSfOZXckqP9vg89/eopOm5ylUDiin/xpow40d9kdCzjCc2/vVl84okmFqRSMABwjMd6t6RPSJUnrdjbanAYAAAB2G3J5s3jxYr311lvRzAIAwAnt2NuqP6+okiTdeMkU5WUk2pwIn2Sahr78mZkqK/CpqzekHz31sdo7A3bHAgZ0dAf1yod7JElLzi5l1B4AR5k7JVsS5Q0AAACGUd5ceumleuedd3TPPfdoxYoV2rJlyxFfAABEk787qJ//ZYssSzpnVp7OPoURoE7kjXPp9s/OVk56gprae/XjpzeqNxiyOxYgSXptzT71BsMqzUvRrLJMu+MAwGFOm5QlQ1LVgQ61+HvtjgMAAAAbDXnNm5tuukmS9Pjjj+uJJ5447DLLsmQYhrZt2za8dAAAHBSxLP36xW1q6wwqPzNRX7hoqt2RcBy+RI/u+Nyp+sHvPlJ1fYd+tmyLbv/sLLnMIX9uBBi2nkBIy9fskyRdcc4ERt0AcJzUZK/KC1O1u7Zd63c16cJ5RXZHAgAAgE2GXN7ce++90cwBAMBxLV+9TxsrmuV2mbr1ylPk9bDOjdPlpifqG9fO1n//fr02VTbr96/t0o2XULrBPm+tr1V3b0iF2cmaPzVHkYhldyQAOMLcKdnaXduudTsbKW8AAADGsSGXN0uXLo1mDgAAjqlyv19/eqdCknTDpyerOCfZ5kQ4WeUFqfrKZ2bqoWc36a31tSrKTtKn5vJGFEZeTyCkV1btlSRde+FkmaZBeQPAkeZOydJTb+3Wjr1t6uzpU3JCnN2RAAAAYIOozF1SWVmpjz76SN3d3dG4OgAABnT3hvTwnzcrHLE0f2q2zp9TYHckDNJpU7J19XllkqQnXtulbdWtNifCePTmuhp19vQpNz1B51MgAnCwnPREFWUnKWJZ+nh3k91xAAAAYJNhlTfLli3TokWLdPnll+sLX/iCqqqqJEnf+MY39NRTT0UlIABgfPvdq9vV1N6rrNR4fenSaaxRMUpddlapzpqRq4hl6X+f26SGVj7wgZHT3fvXUTdXLSqTy8XaSwCcbe6UbEnSup2NNicBAACAXYb8yvXll1/Wd7/7Xc2YMUP/+q//Ksv667QTM2fO1MsvvxyVgACA8Wv1tnqt3tYgl2no1itPUWI804aMVoZh6EuXTtPE/BR19Yb0wDMb1RMI2R0L48Rra/epqzek/MxELZiZZ3ccADihQ+XNlqoWBfrCNqcBAACAHYZc3vziF7/Q1VdfrYcffljXXXfdYZeVlZVp9+7dww4HABi//F1BPb58pyTp8gWlKivw2ZwIw+WJc+mfrp6ttGSPDjR36+d/2cKaI4i5rt4+LV/TP+rmyoUTZZqM3gPgfMU5ycpKjVcwFNHmyha74wAAAMAGQy5vKioqdPnllx/1srS0NLW1tQ31qgEA0OOv7VRnT5+KspO15OwJdsdBlKSnePX1a2Yrzm1qY0Wz/vROhd2RMMa9unqvegJhFWUnaf60HLvjAMBJMQyDqdMAAADGuSGXNwkJCero6DjqZfX19UpNTR1yKADA+LZme4PWbu+fLu3my6fLzfoUY8rEfJ/+/rJpkqSXV+3Vik0HbE6EsaqjO6jX1tZIkq5cWCaTNbMAjCKHypuPdzcpFI7YnAYAAAAjbcjvhp122ml64oknDlvr5pBnn31WZ5xxxrCCAQDGJ393UI+9ukNS/3RppXkpNidCLJw1I0+XLyiVJD36ynZV1LbbnAhj0Sur9ioQDKskN1lzp2TZHQcABmVSYapSEuPUHQhpx742u+MAAABghA25vPna176mDRs26LOf/awee+wxGYah5cuX69Zbb9XatWt16623RjMnAGCceHw506WNF0sXlem0yVkKhS399LlNau8K2h0JY0h7V1BvrOsfdXPVuWUyGHUDYJQxTUOnTe4vntczdRoAAMC4M+TyZtasWfrlL3+p7u5u3XfffbIsSz//+c9VVVWlX/ziF5oyZUo0cwIAxoFD06WZBtOljQemYeiWJTOUn5mots6gHl62mWlhEDUvfVitYF9EZQU+nVqeaXccABiS0yb3T522fleTIkeZ9QIAAABjl3s4B5911ll6+eWXtXfvXjU1NSk9PV0TJ06MVjYAwDji7w7q8eVMlzbeJHjd+qerZ+n7j67Vjn1teubtCl1/4WS7Y2GUa+0I6K31tZKkq86dyKgbAKPWjAnp8npcau0IaM+BDpUV+OyOBAAAgBEypPKmpaVFTz75pNauXauGhgZJUk5Ojs4880x97nOfU3p6elRDAgDGvieW71RHd5+KspN0xTkT7I6DEZSfmaSbL5+unz63WcvX7NPEfJ/OnJFrdyyMYi9+uEehcESTi1I1c0KG3XEAYMji3C7NLsvUmu0NWrezkfIGAABgHBn0fDQffvihLr74Yj344INatWqV2tra1NraqlWrVulHP/qRFi9erDVr1sQiKwBgjFq7vUFrBqZLm8F0aePQvKk5uvSsEknSb17epprGTpsTYbRqbu/Vux/vl8RaNwDGhrlT+qdOW8e6NwAAAOPKoN4da2lp0Te/+U2lpKToxz/+sdauXav3339fK1as0Nq1a/U///M/SkhI0O23367W1tZYZQYAjCFdvX0D06VdxnRp49rVi8o0Y0K6gn0R/fTZTeru7bM7Ekah5z/Yo1DY0rSSNE0vZTQ4gNFvdnmm3C5DdS3d2t/UZXccAAAAjJBBlTfPPPOMIpGI/vCHP2jx4sVKSEgYuCwhIUGXXXaZfv/73ysUCumZZ56JelgAwOjlcplyu4/8evbdSvm7+1SQlaSrzys76j7R/HIxqsexXKapr3xmpjJ9XtW39uhXL2xjcWYMyv6mLr2/8YAkaemiMpvTAEB0JHjdml7aPwUko28AAADGj0GtefP+++/rmmuuUV5e3jH3KSgo0NVXX6333ntP//iP/zjsgACA0S3B65ZlWfL5Eo64bEd1i94+uKj4P31ujrKzRm7UjSGmUnKilESPbls6S/c+vk4bdjfpxQ/26IpzJtodC6PEM29XKGJZOm1yliYXpdkdBwCiZu6ULG2qbNa6nY1acvYEu+MAAABgBAyqvKmsrNSNN954wv3mz5+vF198ccihAABjhzfOJcMw9PbavWrx9w5sj0QsPb+iSpYllRemald1i3ZVt8Q8T1Fuis6YmS+WwXCuifk+3XjxFP3m5e1a9l6VJuT7NKss0+5YcLjt1a3asLtJpmHos+eX2x0HAKJqzuRs/e6VHdpT16EWf68yfPF2RwIAAECMDaq88fv9ysjIOOF+GRkZ8vv9Qw4FABh72joCam7rGfh35X6/WvwBxblNTSrwHXZZLKUle0fkdjA8555aoMoDfr2zYb9+8Zct+r9fOl3ZaUeO3gIkKWJZ+uNbuyVJ551WoPzMJJsTAUB0pSZ5NKkoVbtq2rVuZ6M+Pb/Y7kgAAACIsUFN/B8MBhUXF3fC/dxut/r6WGQYAHB0vYGQtu9tlSRNL02X1+OyORGc6O8+PUUT833q6g3pp89uUqAvbHckONTqrfWqrutQvMelK5lmD8AYNXdKtiTWvQEAABgvBjXyRuqfOs3lOv6bbJWVlUMOBAAY+zZXtSgUtpSe7FFpbrLdceBQcW5TX1t6iv7jt2u0t6FTj726QzdfPl0Gc97hE/pCYf3pnf6/PS87q1S+JI/NiQAgNk6bkq0/vrlbO/e1q7OnT8kJJ/5gJQAAAEavQZc33/ve9064j2VZvLECADiqhtYe7W/uliTNLs/k+QLHleGL161XnqL7n1yvDzbXqazApwvmFtkdCw7y+kc1avb3Kj3Fq4tOZxohAGNXTlqCirKTVdPYqY93N+mcWfl2RwIAAEAMDaq8uffee2OVAwAwDoQjEW2qbJYkleWnKJX1Z3ASppem69rzJ+mpt3brD6/vUklOiiYVpdodCw7Q2dOnFz6oliQtPbdM3jimYAQwts2dkqWaxk6t29lIeQMAADDGDaq8Wbp0aaxyAADGgV017erqDSne49LUknS742AUueSMYlUe8Gvt9gb9dNkm/fuXTqf8g/6yoko9gZCKc5J19il5dscBgJibOyVbf1mxR5urWhQIhlk3EAAAYAwz7Q4AABgf2jsD2l3TLkmaOTFDcW6egnDyDMPQP1w2TQVZSWrvDOpnyzYrFI7YHQs2amjt1lvraiVJn/vUJJkmUzACGPuKc5KVlRqvvlBEm6ua7Y4DAACAGOKdMwBAzFmWpQ821yliSdlp8SrITLQ7EkaheI9bX1t6iuI9Lu2sadfTb1XYHQk2euadSoUjlk6ZmKGZEzPsjgMAI8IwDM2dki1JWrez0eY0AAAAiCXKGwBAzK3cXKf9TV0yDWlWWaYMg0/IY2jyM5N0y5IZkqTX1u7Tyq11NieCHXbXtmvt9gYZRv+oGwAYTw6VNx/vbmYUKgAAwBhGeQMAiKlQOKJfP79ZklRemKrkhDibE2G0mzslW5cvKJUk/fbl7app6LQ5EUZSxLL0xzd3SZLOmZWvopxkmxMBwMiaVJgqX2KcugMh7djbZnccAAAAxAjlDQAgptbvbFRdc7cSvW5NLkq1Ow7GiKXnlmnmxAwF+yJ66LlN6u7tszsSRsiKTQdUUeuXN86lpeeW2R0HAEacaRqaM5mp0wAAAMY6yhsAQMz0BkNatbVekjR/eo7cLp52EB2maegrn5mpTF+8Glp79KsXtiliWXbHQox19vQNrHV05cKJSk/xntRxpmnI7TbldptyHfw95HKZA9tG8svF70EAUTCw7s2uRp7/AAAAxii33QEAAGPXtuo29YUimlKSpkmFqWpq67E7EsaQ5IQ4fe3qU/Rfj63Tht1NevGDPbrinIl2x0IMPftOhTp7+lSYlaRPzy86qWNM01B6epJM8/C1tny+hFhEPGmGWPsLwNBNL01XvMel9s6gqvb7VV7I6GYAAICxhvIGABATbZ0B7Tu4Fsk/XjlL26uabU6EsWhCnk83XjxFv3l5u5a9V6WJ+T6dUpZpdyzEQOV+v97ZsF+S9IWLp5z0SD7TNGSaht5cU622joBMw5DX61YgELLl0+pFuSk6Y2a+DLobAMMQ5zY1uzxTq7c1aN3ORsobAACAMYjyBgAQdZZlaXNliyRpWmm6pk3IoLxBzJx7aoEqD/S/sf/zv2zRv33pdGWl2TuqAtEViVh67NUdsiQtmJmnqSXpg76Oto6Amtt6ZJqGEhI86ukJKhIZ+fImLfnkpnoDgBOZOyV7oLz57PnlMmiFAQAAxhQm3QYARN3+pi61dATkMg0tnJ1vdxyMA3/36SmamJ+irt6QHnpuk4J9YbsjIYre3lCr6voOJXjd+twFk+yOAwCOMKssU26XofrWHu1v7rY7DgAAAKKM8gYAEFWhcERb97RKkiYVpiol0WNzIowHcW5TX1s6S8kJcdpb36nHlu+QNY4WcDZNQ2636Zivv11fZjjau4L60zuVkqSrF5UpNYnfKQAgSQlet2ZMyJAkrdvZaHMaAAAARBvTpgEAoqqi1q+eYFgJXpfKC312x8E4kuGL161XztT/98cNWrGpTmUFqfrUaYV2x4o50zSUnp4U1cJkuCIRS62tXVGZluzpt3arJxBSaW7KuPh5AsBgzJ2SrY0VzVq3s1FXnD3B7jgAAACIIsobAEDU9ARC2l3bLkmaUZpx0guKw3lcDvvZRSLWSRUBMyZk6JrzyvXM2xX6/Ws7VZSdpMlFabEPaCPTNGSaht5cU622joDdcZSW4tUFp5fKNI1hlzc79rbqg811MiTdeMlURxVUAOAEcyZlyTCk6roONbf3KjM13u5IAAAAiBLKGwBA1Gzd06pwxFKGz6uCrES742AIErxuWZYlny/B7iiHGcxIjkvPLNGeug6t3d6gnz63Wf/3pvnK8I39N7PaOgJqbuuxO0bUhMIRPb58pyTpvDkFKitgJB8A/C1fkkeTC1O1s6Zd63Y16qL5xXZHAgAAQJRQ3gAAoqK1I6Dapi5J0ikTM2QYfEJ+NPLGuWQYht5eu1ct/l6740ga/EgOwzB082XTVdfcrZrGTj307CZ99/Nz5YlzjUBaRMvra2tU29Sl5IQ4XX1eud1xAMCx5k7J1s6adq3fSXkDAAAwllDeAACGzbIsbdnTIkkqzklSWrLX5kQYrtE+isPrcenr18zS9x9dqz11HXr0lR26Zcl0SsVRoqG1W8ver5QkXfupciUnxNmcCACca+6UbD355m7t2Nemju6gUhI9dkcCAABAFDhrQnsAwKhU39KjFn9ApmloWkm63XEASVJ2WoK+euVMmYahD7fU6bW1NXZHwkmIRCw98uI2BfsimlaSpnNm5dsdCQAcLSstQSU5ybIsacPuJrvjAAAAIEoobwAAwxKxLG2tbpUkleX7lOBlUCecY/qEDF13wSRJ0lNv7tbWgyPE4FzL1+zTrpp2xXtc+ofLpstktBQAnNDcKdmSpPU7KW8AAADGCsobAMCw7K3vVGdPnzxuU5OLUu2OAxzh0/OLdM4peYpYln62bLMaRvF0cGNdbWOnnn23f7q06y+crKy0BJsTAcDocKi82VzVot5gyOY0AAAAiAbKGwDAkIVCEe3Y2z/qZkpxmuLcPK3AeQzD0BcXT9XE/BR19Yb00J828saWA4XCEf3qxW0KhSOaXZ6pc2czXRoAnKzC7CTlpCUoFI5ocyWjTAEAAMYC3mUDAAzZ7v3tCvRFlBjv1oS8FLvjAMcU53bpa0tnyZfkUU1jlx55cZsilmV3LHzCix9Wq7quQ0nxbt20eJoMpksDgJNmGMbA6Jt1uxptTgMAAIBooLwBAAxJbzCkilq/JGlGabpMkzda4WwZvnj909JZcpmGPtrRqOcOTs8F++2p8+uFD/ZIkj5/8RSlp3jtDQQAo9BpU7IkSR/vblYoHLE5DQAAAIaL8gYAMCQ79rYpHLGUnuxRfmai3XGAkzKpKFVfunSapP6RHu9t3G9zIvSFwnrkhW0KRyzNn5ajM6fn2h0JAEal8sJU+ZI86gmEtP3gtLYAAAAYvShvAACD1tEdVHV9pyRpxoQMpjfCqHLOrHxdcfYESdLvXtmhbdW8wWWn596rUm1Tl3yJcbrx4in8PgEwJrlcptzu2H554lyad3DqtA27mo65H6OlAQAARge33QEAAKPP1j39b3bnZSQoMzXe5jTA4F117kTVt3Zr9bYG/fTZTfo/X5yn/Mwku2ONO7tq2vTqqr2SpJsunaaURI/NiQAguhK8blmWJZ8vYURu77z5xXprfa027G5SamriUYuaSMRSa2uXIhHWfgMAAHAyyhsAwKA0tfeqvrVHhqTppel2xwGGxDAM3Xz5dDX7e1VR69ePn/5Y//LF+ZQHI6i7N6RHXtgmS9I5p+TptMnZdkcCgKjzxrlkGIbeXrtXLf7emN9eOGIpzm2qxR/QL577WDnph09tm5bi1QWnl8o0DcobAAAAh6O8AQCcNMuytHVPiySpJC+FN7oxqsW5Xfr6NbN1z6Nr1djWq588u0l3XT9HcW6X3dHGPMuy9MiLW9XQ1qNMn1c3fHqK3ZEAIKbaOgJqbusZkdvKSUtQbVOXtle3ysVUlAAAAKMWa94AAE7ageZutXUG5TINTS1OszsOMGy+RI++ee2pSvC6tbumXb95abssi08ix9rLq/Zq/a4muV2GvnrVLCXG83kiAIiW/Mz+0TZ1zd08pwEAAIxijihvqqqqdPPNN2vOnDlasGCB7rnnHvX2ntyQ8ueee06LFy/WrFmztGTJEr388stH7PPTn/5Uf//3f6958+Zp6tSp2rRp01Gvq7GxUd/85jc1d+5czZ8/X9/5znfU1tY2nG8NAMaMSMQaWNi9vNCneA+jEzA2FGQl6WtLT5HLNLRya73+/H6V3ZHGtK17WvSndyokSX930RSVFfhsTgQAY0tOeoJMQ+rqDamju8/uOAAAABgi28sbv9+vm266SV1dXXrwwQf1z//8z3r++ef1L//yLyc89pVXXtF3v/tdXXTRRfrlL3+ps846S3fccYfef//9w/b74x//qL6+Pp1zzjnHvK5QKKRbbrlFO3fu1A9/+EPdc889+uijj3TbbbfxaSUAkLSvoVNdvSF53KbKC1LtjgNE1YwJGbrxkqmSpL+s2KM319XYnGhsam7v1cN/3iLLkhbOytd5pxbYHQkAxhy3y1R2WoIk6UBLt81pAAAAMFS2z1Hx5JNPyu/3a9myZcrIyJAkuVwu3XnnnfrqV7+q8vLyYx77wAMPaPHixfr2t78tSTrrrLNUVVWlBx98UAsXLhzY7+2335Zpmlq1apVeffXVo17X8uXLtX37dr3wwguaPHmyJCknJ0c33HCD3nvvPS1atCha3zIAjDrhcEQ79rVJkiYXpyrObXv3D0TdolML1OLv1V9W7NETy3cq0evWWTPz7I41ZvSFIvrfZZvU2dOnktxkfeHiKTJYiwEAYiI/M1H1rT2qa+5mqlsAAIBRyvZ33959910tWLBgoLiRpEsuuUQej0fvvPPOMY/bt2+fKisrtWTJksO2L1myRBs3blRLS8vANtM88bf5zjvvaOrUqQPFjSTNnTtXhYWFx80BAONBVV2HeoNhJXhcmpCXYnccIGauXDhRF84tkiXpVy9s04bdTXZHGjP+8PpOVR3oUFK8W19bOkueOKZeBIBYyc3oX/emvSuo7t6QzWkAAAAwFLaPvKmoqNA111xz2DaPx6OSkhJVVFQc87jKykpJUllZ2WHby8vLZVmWKisrDyuETibH0Ub5TJo06bg5ToabT6hjlHO5zMP+C2c79HMyDUOmOfxPtQdDYe2qaZckTStNV5x7cG+4HvpkvWkqKnmGazTksTOX0+4fqf+xLI3c76AbL52qnmBIH2yu08+WbdZdN5ymaaXpI3LbQxHtc364jvbzenfDfr29Yb8MSbdedYrys5JGLM/f3j92n2dOO8fIc2JOyzRa8th1ro2W+yfWErxuZfri1ezvVX1rt8oL+6e8HennVMQWr9OAkcG5BsQe59nR2V7e+P1++XxHLlTr8/nU3t5+zOMOXfa3x6amph52+WBypKQc+Wlyn883rPLGNA2lp4/cGxRALPl8CXZHwCB4vW4lJHiGfT27djWqLxSRL8mjKaUZg37jwePpL3vi4qKTZ7icnMfrjZOkgf/anccJ94/U/1iWRvZ30F1fPF33/naNVm+t04+f/lg/+Oo5mlSUNmK3PxTROuejkUP6689rd02bHn1luyTphkum6fzTS23L9cn7x67zzGnnGHlOzGmZRluekT7XRtv9E0sl+SkHy5senTIpW5I9z6mIPX6ewMjgXANij/PscLaXN8diWdZJzYP+t/tYlnXU7SfjaMecbI5jiUQs+f0sEonRzeUy5fMlyO/vUTgcsTsOTuDQzysQCKmnJzis6+oNhrS9ulWSNK0kTYFA36CvIxgMS5L6+oafJxqcnCcQ6JPXG6dAoE+RiGV7HifcP5KUePCNr5H+HfTlK6bL39mr7Xvb9H9//oH+5ab5ys903gcyonnOR8Mnf14t7T36wW/WqC8U0amTMnXx/EK1tnaNaJ6/vX9M07D1PHPaOUaeE3NaptGSx65zbbTcPyMhy+eVJDW09qitvUdej8u251TEBq/TgJHBuQbE3ng7z3y+hJMaZWR7eePz+eT3+4/Y3tHRcdRpzA755AibrKysge2Hrutoo3mGmmOw1/W3QqGx/4DD+BAOR3g8jyIRyxr2myU79rYpHLGUluxRbnrCkK7vUKkeici2QuKTRkOeSGT4P7to5rFb5GCmkf4dZBqGvn7NbP3w9+tVXd+h//fEOn3v8/OUmRo/YhkGIxrnfLRySJK/K6Af/n69mtp7lZ0Wr1uWzFAkbCkiezL+7f1j13nmtHOMPCfmtEyjLc9In2uj7f6JpQSPW6lJHrV3BXWguUsluSm2Pacitvh5AiODcw2IPc6zw9k+iVx5efkR05IFg0Ht3bv3uOXNobVuDq19c0hFRYUMwzhiLZyh5JCk3bt3HzcHAIxVXb192lPXIUmaXpo+rFGIwGiV4HXrjutOVV5Golr8Ad3/5Hq1+HvtjuV4gb6wfvzHj7WvoVO+xDh967o5Soq3bzpAABiv8jITJUkHmpkNAgAAYLSxvbxZtGiRVq5cqdbW1oFtr732moLBoM4777xjHldcXKyysjK99NJLh21/4YUXNHv2bGVkZAwqx3nnnaedO3ceVuBs2LBBtbW1x80BAGPVjr1tsiwpOy1e2WnMOYrxy5fo0Z3Xz1Gmz6v61h7d98Q6NbTyJtixRCKW/t/v1mjHvjYleF361nVzlJueaHcsABiX8jP6f/82tvWoj0+xAgAAjCq2lzfXX3+9UlJSdNttt+m9997TsmXL9P3vf19XXHHFYSNe7r77bs2YMeOwY2+//Xa9/PLL+tGPfqRVq1bpv/7rv7RixQrdfvvth+23evVqvfLKK1qzZo0kaeXKlXrllVe0adOmgX0uvvhiTZ06VbfffrveeOMNLV++XHfeeafmzZunc889N4b3AAA4T3tXUDWN/etSTC9JtzkNYL8MX7z++fNzlZOeoKb2Xt37xDrVNo3s2i2jgWVZen/jfq3ZWq84t6lvfPZUleSm2B0LAMatlMQ4JcW7FbGkej54AAAAMKo4Ys2bRx99VPfcc4++/vWvKz4+XkuWLNGdd9552H6RSEThcPiwbZdeeql6e3v18MMP65FHHlFpaal+9KMfaeHChYft95Of/ESrV68e+Pf9998vSVq6dKnuu+8+SZLb7dYvf/lL/eAHP9Bdd90lwzB0wQUX6O6772aqIADjzvbq/tGQBZmJSkvx2pwGcIas1AR99/Nz9f89uUG1TV36f0+s07evm6PSPMoJqb+42VLVosoDHTJNQ/90zSxNKU6zOxYAjGuGYaggK0m7atq1v6lbp062OxEAAABOlu3ljSRNnDhRjzzyyHH3ue+++waKlk9aunSpli5detxjH3vssZPKkZOTowceeOCk9gWAsarF36v61h4ZkqYx6gYO4nLZPmBYWWkJuvuL83T/H9ar6kCHfviH9brj2lM1qSjV7mi221XTrsoD/etkffP60zSnLIOFJgHAAQ6VNw1MnQYAADCqOKK8AQA4g2VZ2lbdJkkqzklWciILjMN+CV63LMuSz+eMtZfS05P0X7ct1L/9/APtrGnX/X9cr9uvma0ZEwa33t5YUnXAr+172yRJZ87I1afmFau1lWnlAMAJfAenTuvqDWlfQ4fdcQAAAHCSKG8AAAOa2nvV7O+VaUhTStLsjgNIkrxxLhmGobfX7lWLv9fuOEpL8eqC00t119/N1Y+f/lhbqlr046c36rarTtGcyVl2xxtxFfvbtaWqf6rFKcWpmjFx/JZYAOBEn5w6bc8ByhsAAIDRgvIGACDp0Kib/jdgS/NSlOjlKQLO0tYRUHNbj90xBng9Lt1+zWw9/OfNWr+rSQ89u0mf+1S5Ljq9eFysl/fJNW4kqSzfp6mscQMAjlSQmahdNe2qaehUbyBkdxwAAACcBPsnjwcAOEJ9S4/aOoNymYYms34HcFLi3Ka+etUpOmdWniKWpSff3K1fvrBVgb6w3dFiKhyOaM32xoHiZsaEdM2cmD4uSisAGI18SR4lxrsVjlhas63e7jgAAAA4CZQ3AABZlqXte/tH3UzMT1G8h1E3wMlyu0z9w2XTdcOnJ8s0DK3cUq97H/tITQ4aJRRNgb6wPthSr7qWbpmGNG9KtiYVplLcAICDGYahgsxESdKKj/fbnAYAAAAng/IGAKD9Td3yd/fJ7TI0qZBRN8BgGYahi+YX687r5yglMU57Gzr1n4+u1dY9LXZHi6rOnj69v/GAWjsCinObWjAzT4XZSXbHAgCchIKs/t/Xa7fXKxAc2yNEAQAAxgLKGwAY5yKfGHVTXpD6/7d33+FxlQf69+8zM5oZtVHv1ZJ7t8HdNJteQwiEVDYJIQkh5CVLEhJ2fxsIWdgkGxJIgwBLCQkBEtMNARtcANu49ybJVrW6NKqjKef9Q7aCcJNtSWckfT8XumSdMrpn8CN55p7nOXJG2C1OBAxd4/MS9P9umqW89Fi1dvj1v3/brLfWlco0TaujnbHGFp9Wb61SW2dAkS67Fk5JV1Kc2+pYAIA+iot2KiYyQr6uoLYU1VkdBwAAACdBeQMAI1x5TavaOgNyOmwqyPRYHQcY8pLi3PrRF2Zq/uR0mab0t+X79cgrO9TW6bc62mkxTVPFlV69v/2QugIhxUU7dc7UDMVGOa2OBgA4BYZhKD+j+99663bVWJwGAAAAJ0N5AwAjWDBkak9ZkyRpdFacIhz8WgD6gzPCrq9dMUFfuGis7DZD63bV6O5H12jNjkNDahZOa4df728/pO0lDQqFTKUlRGrBlHSuiwUAQ1R+Rqwkacu+Ovn8LJ0GAAAQzniVDgBGsNLqFnX4gnJF2HuezAPoH4ZhaPFZ2frh52cqIylK3na/Hn11p/73b5tV3dhudbwTMk1TRZXNWrG5Ug1en+w2Q1MKEjV7Qqocdv75CABDVXKcW6mJUfL5g9peXG91HAAAAJwAz74BYIQKBEPaW9YsSRqbE8cLssAAGZ0dp3u+OlvXnlsgh92mnQca9Z+PrdOr75coEAxZHe8oR2bb7ChpVDBkKjnOrQtmZGpUhkeGYVgdDwBwBgzD0IKpmZKkj3azdBoAAEA445U6ABihDhxqkc8fVKTLrrw0Zt0AA8lht+mq+fn66c2zNSk/QYFgSEtWlei/nlinPaWNVseTJIVCpooqmvXex2bbTC1M0rxJaYpyR1gdDwDQTxZMzZAkbdlfry6WTgMAAAhbLFgOACOQPxDS/vLuWTfjcuJls/FuemAwpCVE6Xufna61O6v13LJ9qqpv1//8ZZPGZMfpwrNzNHNssuy2wX1vTZc/qAOHWlRS1dJz/YPkOLemj05WlJt/KgLAcDM2N0FJHrfqvZ3aXtKgmWNTrI4EAACAY+AZOQCMQMVVXnUFQop2O5SdGmN1HGBEMQxDcyela3JBkv6+okirt1ZpX3mz9pU3KyHWpUUzs3TutEzFRjkHNEdrh1/FlV6V1bQqGDIlSW6nXeNy4pWbFsMSaQAwTBmGoVkTUvXm2lKt311DeQMAABCmKG8AYITxB4Iqqjg86yY3XjZeoAUsERMZoZsuHa+rF4zSe5sq9N7mCjW2+PT3FcV6efUBzZ2YpkVnZSk3LbbfxmnINFXf3KniSq+qGzt6tnuinSrM9CgrOZqZeAAwAsyekKY315Zq8/46+QNBRTjsVkcCAADAJ1DeAMAIU1ThVSBoKjYqQlnJ0VbHAUa8hFiXrj23QFfOz9e6XdV6Z0O5Dh5q0eptVVq9rUrRbodGZXpUmBmnwkyPRmV6FN3Ha9D4/EE1en1qaPGpsaVTTa1dPbNsJCktIVKFWR4ledzMtAGAEaQgy6OEWJcaW3zaXtKgGWOYfQMAABBuKG8AYATp8gdVXOWV1H2tG16sBcJHhMOmBVMyNH9yuooqvXpnfZk276tTW2dA24sbtL24oefYjKQo5aXHKsJuk81myOl06EBVs3y+7mvWBEOmGlt9au8MHPV9HHZDWSkxKszwKCaqbyUQAGB4sRmGzh6XqrfXl2n97hrKGwAAgDBEeQMAI8j+w7NuPNFOZSRFWR0HwDEYhqHRWXEanRWnQDCk8tpWFVV4VVzZrKJKr2oaO1RV366q+vY+3V5sZIQSYl1K8LiUGOtSTGQExS0AQLPGd5c3LJ0GAAAQnihvAGCE8HUFVXJ41s14Zt0AQ4LDblN+ukf56R4tPitbkuRt71JxpVeVdW0yTVM2m6HISKd2FNWpvdN/+ExDcdERio91ycmLcQCAY/j40mnbihs0cyyzbwAAAMIJ5Q0AjBD7KpoVDJmKj3EqLTHS6jgATpMnyqnpo5M1fXSyJMnhsCkhIVp2Q6pv6rA4HQBgqLAZhmZPSNVb68q0blc15Q0AAECYsVkdAAAw8Dp9AR041CJJGp/LrBsAAABIsyekSZI276+TrytocRoAAAB8HOUNAIwA+yqaFQqZSoh1KSWeWTcAAACQ8tNjlRofqS5/SJv211odBwAAAB9DeQMAw1yHL6CDzLoBAADAJxiGodkTu2ffrNtZY3EaAAAAfBzlDQAMc3vLmhQypSSPS8lxbqvjAAAAIIzMmZAqSdpWXK+2Tr/FaQAAAHAE5Q0ADGNtnX6V1rRKksbnJjDrBgAAAL1kpcQoOyVawZCpjXtYOg0AACBcUN4AwDC2r6xZpimlxLuVxKwbAAAAHMPsCYeXTttVbXESAAAAHEF5AwDDlLetS2WHZ92My423NgwAAADC1uzDS6ftPNio5rYui9MAAABAorwBgGFr875amZJSEyKVGMusGwAAABxbakKURmV4ZJrS+t01VscBAACAKG8AYFgqq25RcYVXkjSeWTcAAAA4iTmHZ9+wdBoAAEB4cFgdAADQ//76zz0yJaUnRik+xmV1HAAjkN0eHu8RCpccABDuZk1I09+W79e+8mbVN3dyvUQAAACLUd4AwDBTVtOqVZsrJHGtGwCDL9LlkGma8ngirY7SiyHD6ggAENYSYl0amxOvPWVN+mh3jS6dk2t1JAAAgBGN8gYAhpl/rCiSJOVnxCou2mlxGgAjjSvCLsMw9N76UjV4O62Oo+y0WM2elCGD7gYATmrOxDTtKWvS2p3VlDcAAAAWo7wBgGHk4KEWbdhTK8OQZoxJUTAYsjoSgBGqqcWn+qYOq2OwdCQAnIKzxqXoz//cq4PVLTrU0K70xCirIwEAAIxYLAIOAMPIS6uKJUnnzchWfCwvWAIAAKDvYqOcmjgqQZK0ble1xWkAAABGNsobABgmiiqbtaWoXoYh3XjxOKvjAAAAYAiaMyFNkrR2Z7VM07Q4DQAAwMhFeQMAw8TLq0okSQunZCgrJcbiNAAAABiKZo5NkcNuU1V9u8pr26yOAwAAMGJR3gDAMLCvvEnbSxpktxn61DkFVscBAADAEBXpcmhaYZIklk4DAACwEuUNAAwDS1Z2X+tm4dQMpSREWpwGAAAAQ9nsiSydBgAAYDXKGwAY4nYdbNTu0iY57IaunJdvdRwAAAAMcVMLk+Ry2lXX3KmiCq/VcQAAAEYkyhsAGMJM09RLq7pn3Zw7LVNJcW6LEwEAAGCoc0XYddbYFEnSBzsOWZwGAABgZKK8AYAhbMeBBu0rb5bDbtMVzLoBAABAP5k3OV2S9NGuavkDIYvTAAAAjDyUNwAwRJmmqSUrSyRJF8zIUkKsy+JEAAAAGC4m5CYoIdalts6AthbVWx0HAABgxKG8AYAhaktRvUqqvHJG2HT5vDyr4wAAAGAYsdkMzZ2YJkn6YHuVxWkAAABGHsobABiCPn6tm8UzsxUX7bQ4EQAAAIabI0unbS2qV2uH3+I0AAAAIwvlDQAMQRv31qm0ulUup12Xzsm1Og4AAACGoeyUGOWmxSgYMvXRrmqr4wAAAIwolDcAMMSETFMvre6edXPR2dmKjWLWDQAAAAbG/Ends28+2H7I4iQAAAAjC+UNAAwx63fXqKK2TZEuhy6ZzawbAAAADJw5E9NkGFJRpVfVDe1WxwEAABgxKG8AYAgJhUy9vLpEknTJrBxFuyMsTgQAAIDhLC7GpcmjkiQx+wYAAGAwUd4AwBCydme1qurbFe126KJZOVbHAQAAwAgwb3KaJOnDHYdkmqbFaQAAAEYGyhsAGCKCoZBefr971s2lc3IV6XJYnAgAAAAjwYwxKXI77apr7tS+8mar4wAAAIwIlDcAMER8sP2Qaho7FBsVocVnZVsdBwAAACOEK8Kus8elSmLpNAAAgMFCeQMAQ0AgGNKr7x+QJF02J09uJ7NuAAAAMHjmTU6XJH20u0b+QNDiNAAAAMMf5Q0ADAGrt1aprrlTcdFOXTAzy+o4AAAAGGHG5cYr0eNShy+gLfvrrY4DAAAw7FHeAECY8weCevWDA5Kky+flyRVhtzYQAAAARhybYWjepO7ZNyydBgAAMPAobwAgzK3cUqXGFp8SYl06f3qm1XEAAAAwQh0pb7YV18vb3mVxGgAAgOGN8gYAwliXP6jXPjwgSbpyfr4iHMy6AQAAgDUyk6OVlx6rYMjUup3VVscBAAAY1ihvACCMvbupQs2tXUryuHXO1Ayr4wAAAGCEmz+5e/bNhztYOg0AAGAgUd4AQJjydQX1xpqDkqSrFuTLYedHNgAAAKw1Z0KabIahkqoWVda1WR0HAABg2OKVQAAIU8s2lqul3a/U+MiedzgCAAAAVvJEOzW1MEmStHprlcVpAAAAhi/KGwAIQx2+gJYennVz9UJm3QAAACB8nDOteznf97dXKRAMWZwGAABgeOLVQAAIQ2+vL1NbZ0DpiVGaO5FZNwAAAAgfUwuTFBfjVEu7X1v211kdBwAAYFiivAGAMNPe6ddb68okSdcsHCWbzbA4EQAAAPAvdptNC6d0z75ZuYWl0wAAAAYC5Q0AhJm31pWpwxdQVnK0Zk1ItToOAAAAcJSFU7vLm+3F9WrwdlqcBgAAYPihvAGAMNLa4dfb6z8268Zg1g0AAADCT1pClMbnxsuUtHors28AAAD6G+UNAISRpWsPqrMrqNzUGM0cl2J1HAAAAOC4zpmWKUlatbVSoZBpcRoAAIDhhfIGAMKEt61LyzaUS5I+dU4Bs24AAAAQ1s4am6Iol0P1Xp92HmywOg4AAMCwQnkDAGHijTUH1eUPaVRGrKaNTrI6DgAAAHBCzgi75k1KlySt2sLSaQAAAP2J8gYAwkBji0/vbqqQ1D3rxmDWDQAAAIaAc6ZlSJI27q1VS3uXxWkAAACGD8obAAgDb6w5KH8gpNFZcZo8KtHqOAAAAECf5KbFKi89VsGQqQ+3H7I6DgAAwLBBeQMAFmvwdmrF5u5ZN9eeM4pZNwAAABhSzp2WKUlaubVKpmlanAYAAGB4oLwBAIu99sEBBYKmxufGa0I+s24AAAAwtMyZkCanw6bKujYVVXqtjgMAADAsUN4AgIVqmzq0amv3xV0/dU6BxWkAAACAUxfldujs8amSpFVbKi1OAwAAMDxQ3gCAhV794ICCIVOT8hM0Nife6jgAAADAaTmydNq6XTXq8AUsTgMAADD0Ud4AgEWqG9v1wbbui7oy6wYAAABD2ZjsOKUlRsnnD+qj3TVWxwEAABjyHFYHAICR6pXVJQqZpqYWJqkwK87qOABOkd0eHu+BCZccAICRzTAMnTstQy+8W6SVWyp7ZuIAAADg9FDeAIAFKuvatGZHtSTpU+eMsjgNgFMR6XLINE15PJFWR+nFkGF1BADACDd/cob+saJYxZVelde0Kjs1xupIAAAAQxblDQBY4JX3S2RKmjEmWfnpHqvjADgFrgi7DMPQe+tL1eDttDqOstNiNXtShgy6GwCAxeKinZo+Olkb9tbq3U0V+tIl46yOBAAAMGRR3gDAICuvadW6Xd3rgHOtG2Doamrxqb6pw+oYio9xWR0BAIAei87K1oa9tfpg+yFdd16hoty87AAAAHA6WCQdAAbZS6tLJElnj09VDktJAAAAYBgZnxuvzORo+fxBvb+9yuo4AAAAQxblDQAMooOHWrRxb60MSdcs5Fo3AAAAGF4Mw9CimVmSpOUbKxQyTYsTAQAADE2UNwAwiF5aVSxJmjMpTVnJ0RanAQAAAPrfvEnpcjvtqm5o164DjVbHAQAAGJIobwBgkBRVNmtLUb1shqFrFjDrBgAAAMNTpMuhBVMyJEnLNpRbnAYAAGBoorwBgEHy0qrua93Mm5ymtMQoi9MAAAAAA+fI0mlb9teprqnD4jQAAABDD+UNAAyCPaWN2lHSILvN0FXMugEAAMAwl5EUrUn5CTIlvbupwuo4AAAAQw7lDQAMMNM09Y+V3de6OWdaplLjIy1OBAAAAAy8RTOzJUkrt1Sqyx+0OA0AAMDQQnkDAANse0mD9pU3K8Jh01Xz862OAwAAAAyKaaOTleRxqa0zoHW7aqyOAwAAMKRQ3gDAAPr4rJsLZmQpIdZlcSIAAABgcNhshi44PPtm2YZymaZpcSIAAIChg/IGAAbQxr21OnioRS6nXZfPy7M6DgAAADCozpmaIYfdpoPVLSqu9FodBwAAYMigvAGAARIKmVqyqkSSdNHZOfJEOS1OBAAAAAyu2Cin5kxMlSQt21hucRoAAIChg/IGAAbI2p3VqqxrU5TLoUtn51gdBwAAALDE4rO6l077aFeNmtu6LE4DAAAwNDisDgAAw4HNZshmM3q+DgRDevn97lk3V8zPkydm8K51Y7fTywMAACB85Kd7VJDpUXGlVyu3VOqq+flWRwIAAAh7lDcAcIZsNkMJCdG9ypu31hxQTWOH4mNcuuGi8XK7Bv/HrSHj5AcBAAAAg2DxzGwVV+7Ue5sqdPncXNltvOEIAADgRChvAOAMHZl1s/yjg2pq8SkQDOnv7xVJksbmxuuN94sHNU92WqxmT8qQQXcDAACAMHH2+FQ9t3yfGlt82ri3TrPGp1odCQAAIKxR3gBAP2lq8am+qUPFlV61dwbkdtqVEudSfVPHoOaIH8Ql2gAAAIC+iHDYdP70LL36wQG9ufagzh6XIoN3GwEAABwX85QBoB8FgiHtLW+SJI3LiWc5CAAAAOCwxWdlK8JhU0lVi/aWNVkdBwAAIKzxqiIA9KOSKq+6/CFFuR3KSY2xOg4AAAAQNjzRTi2ckiFJWrq21OI0AAAA4Y3yBgD6ic8f1P5yr6TuWTc2G8tAAAAAAB938ewcGYa0tahe5TWtVscBAAAIW5Q3ANBPdhTXyx8MKTYyQtkp0VbHAQAAAMJOWkKUzhqbIkl6cx2zbwAAAI6H8gYA+kFzq087SholSePy4rn4KgAAAHAcl83NkySt3VmtBm+nxWkAAADCE+UNAPSDF5fvUyAYUly0UxmJUVbHAQAAAMLWqAyPxufGKxgy9fb6MqvjAAAAhCXKGwA4Qw3eTr3+fokkaQKzbgAAAICTunRO9+yb9zZXqr3Tb3EaAACA8EN5AwBn6JXVJfIHQkpLiFRKfKTVcQAAAICwN6UgUVkp0fJ1BfXupgqr4wAAAIQdyhsAOAM1TR1asblSkjRzXCqzbgAAAIA+MAxDl83JlSS9s75c/kDI4kQAAADhhfIGAM7AK6tLFAyZmjE2RelJXOsGAAAA6KvZE9KUEOtSc1uXPtxxyOo4AAAAYYXyBgBOU2VdW8+TzC9eNsHiNAAAAMDQ4rDbdPGsHEnSm2tLFTJNixMBAACED8obADhNL60ukWlKZ41L0djcBKvjAAAAAEPOudMyFely6FBDu7bsq7M6DgAAQNigvAGA03DwUIvW766RIem68wqtjgMAAAAMSZEuhxbNzJIkLV1banEaAACA8EF5AwCnYcmqYknSnIlpyk6NsTgNAAAAMHRdeFa2HHZD+yuatbesyeo4AAAAYYHyBgBO0f6KZm0tqpfNMHTNwlFWxwEAAABOid1uk8MRHh82m6G4GJfmT86QJL36wQFrHxwAAIAw4bA6AAAMJaZp6h8riiRJC6akKy0xyuJEAAAAQN9EuhwyTVMeT6TVUXqEQqYaG9t0xbw8vb+tSjtKGrS3rEljc+KtjgYAAGApyhsAOAU7DjRod2mTHHZDVy9g1g0AAACGDleEXYZh6L31pWrwdlodR/GxLi2alSebzVBKfKQWTs3Qis2Venl1ib7/uRlWxwMAALAU5Q0A9FHINPX397qvdbNoZraS4twWJwIAAABOXVOLT/VNHVbHOMqV8/K1emuVdh1s1O6DjRqfl2B1JAAAAMtwzRsA6KP1u2t0sLpFbqddl8/LszoOAAAAMKwkxbl17vRMSdJLq0tkmqbFiQAAAKxDeQMAfRAIhvSPld2zbi6dnStPlNPiRAAAAMDwc8XcPDnsNu0ta9Kug41WxwEAALAM5Q0A9MHqrVWqaexQbFSELp6dY3UcAAAAYFhK9Lh1/pHZN6uYfQMAAEYuyhsAOAmfP6iX3y+RJF01P19uJ5cLAwAAAAbK5fPyFOGwaX9Fs3YcaLA6DgAAgCUobwDgJN5ZX6bm1i4lx7l1/owsq+MAAAAAw1p8jEsXHP53N7NvAADASEV5AwAn0Nrh1xtrSiVJ155TIIedH5sAAADAQLtsbp6cDpuKK73aVlxvdRwAAIBBx6uQAHACS9ccVIcvoOyUaM2ZmGZ1HAAAAGBEiIt2atFZ2ZKkJcy+AQAAIxDlDQAcR2OLT+9sKJckXXdeoWw2w+JEAAAAwMhx6ZxcuSLsOnioRZv311kdBwAAYFBR3gDAcby8ukT+QEhjsuM0tTDJ6jgAAADAiOKJcurCs7tn37y0qkQhZt8AAIARhPIGAI6hqr5Nq7dWSZI+c36hDINZNwAAAMBgu2R2rtxOu8pqWrVxT63VcQAAAAYN5Q0AHMOSw+/smz46WWOy462OAwAAAIxIMZERuujsHEnSiyuKFAiGLE4EAAAwOChvAOATSqq8Wr+7RoakT59bYHUcAAAAYES7dE6uPNFO1TR2aPnGCqvjAAAADArKGwD4hL+vKJIkzZucruzUGIvTAAAAACNbpMvR86aqV1aXqLXDb3EiAACAgUd5AwAfs+NAg3YeaJTdZuhTC0dZHQcAAACApIVTMpSdEqN2X0CvrC6xOg4AAMCAo7wBgMNM09SL73XPurlgRpaS4yMtTgQAAABAkmw2Q59dPFqS9O6mClXVt1mcCAAAYGBR3gDAYRv21OrgoRa5nHZdOT/f6jgAAAAAPmZSfqKmFSYpGDL1wrtFVscBAAAYUJQ3ACApGArp7yuLJUmXzMqRJ9ppcSIAAAAAn3TDotGy2wxt3l+nnQcarI4DAAAwYChvAEDS6q1Vqm5oV0xkhC6ZnWt1HAAAAADHkJEUrfNnZEmSnlu2X6GQaXEiAACAgUF5A2DE8/mDevnwRU+vmp+vSJfD4kQAAAAAjueahaMU5XKovLZVq7dVWR0HAABgQFDeABjxlm8oV1Nrl5I87p538QEAAAAITzGREbp6Qb4k6R8ri9XhC1gbCAAAYABQ3gAY0do6/Xr9w4OSpE+dM0oRDn4sAgAAAOFu0VnZSk2IlLetS0vXHrQ6DgAAQL/jVUoAI9rSNaVq9wWUlRKteZPSrY4DAAAAoA8cdptuuGC0JOmtdWWqb+60OBEAAED/orwBMGI1tvj0zvoySdJ15xbKZjMsTgQAAACgr2aMSda4nHj5AyH9bfk+q+MAAAD0K8obACPWq++XqCsQ0uisOE0bnWR1HAAAAACnwDAMfe7CMbIZhtbvqdXmfXVWRwIAAOg3lDcARqRDDe1auaVKkvSZ8wtlGMy6AQAAAIaa3LRYXTw7R5L057f3qLMrYHEiAACA/kF5A2BEevG9IoVMU1MLkzQ2J97qOAAAAABO0zULRik5zq0Gr09LVpZYHQcAAKBfUN4AGHH2lTdp495aGYZ0/eGLnAIAAAAYmlxOu758yThJ0jsbylRS5bU4EQAAwJmjvAEwopimqeff3S9JOmdqprKSoy1OBAAAAOBMTS5I0txJaTJN6cmluxUIhqyOBAAAcEYobwCMKBv21KqowitnhE2fOmeU1XEAAAAA9JMbF41RtNuhsppWvb2+zOo4AAAAZ4TyBsCIEQiG9OKKIknSpbNzFR/jsjgRAAAAgP7iiXbqxsVjJEkvrypRTVOHxYkAAABOH+UNgBFjxeZK1TR2yBMVoUtm51odBwAAAEA/mz85XRPyEtQVCOmZt/bINE2rIwEAAJwWyhsAI0J7Z0Avry6RJF1zToEiXQ6LEwEAAADob4Zh6MuXjlOEw6YdJQ1as7Pa6kgAAACnhfIGwIiwdO1BtXb4lZ4YpXOmZlgdBwAAAMAASUuI0tUL8iVJf31nn1o7/NYGAgAAOA1hUd6UlJToa1/7mqZPn6558+bpvvvuU2dnZ5/OXbJkiS699FJNmTJFV155pZYuXXrUMX6/X//7v/+rhQsXatq0afrSl76k3bt39zpm7dq1Gjdu3FEfd9xxR7/cRwDWafB26p8fdV+w9PrzC+Wwh8WPPgAAAAAD5JLZucpKiVZrh19/eWev1XEAAABOmeXrBnm9Xt10003KzMzUQw89pIaGBt1///1qamrSL3/5yxOe++abb+quu+7SLbfcogULFuidd97RHXfcodjYWC1cuLDnuPvvv18vvfSS7rrrLmVlZemxxx7Tv/3bv+nVV19VSkpKr9u8//77VVBQ0PN1QkJC/95hAINuyapi+QMhjcmO0/QxyVbHAQAAADDAHHab/u2y8frvZzZozY5qTS1M0tyJ6VbHAgAA6DPLy5vnnntOXq9XL730khITEyVJdrtdd955p771rW+psLDwuOf+5je/0aWXXqp///d/lyTNnTtXJSUleuihh3rKm+rqaj333HO6++67dcMNN0iSpk2bpsWLF+upp57SnXfe2es2x4wZoylTpgzEXQVggbKaVn2w7ZAk6YZFo2UYhsWJAAAAAAyGwsw4XTU/X6+8f0DPvLVXo7PilBwXaXUsAACAPrF87aCVK1dq3rx5PcWNJF1yySVyOp1asWLFcc8rKytTcXGxrrzyyl7br7zySm3dulUNDQ2SpNWrVysYDOqKK67oOSYmJkaLFi064e0DGB5eeHe/TElnj09VYWac1XEAAAAADKKrFuSrMMujDl9Af3p1p0Ih0+pIAAAAfWL5zJuioiJdd911vbY5nU7l5uaqqKjouOcVFxdLUq8lziSpsLBQpmmquLhYiYmJKioqUnJysuLj44867tVXX1UoFJLN9q8O65ZbblFTU5NSUlJ0xRVX6Lvf/a7cbvcZ3UeHw/KODDgj9sPXiLEPsWvFbCuq1/aSBtlthj67aPSAjcUjj4vNMGSzWT+z58jsIptN5DmGoZDHylzh9vhI4ZeJPCcW7nmsHmfh/vhYLdzySOGXaajksWqsDZXHxyrhlsd2OM9APs9wyKZbPzVFd/9pjfaVN2vp2oO65pyCk584BAzV52nAUMNYAwYe4+zYLC9vvF6vPB7PUds9Ho+am5uPe96RfZ88Ny4urtd+r9er2NjYo86Pi4uT3+9Xe3u7YmJiFBsbq5tvvlmzZs2Sy+XSmjVr9MQTT6i4uFiPPPLIad8/m81QQkL0aZ8PhBOPZ+gsMRAMhvS3d/dLkq5YMErjC1NOcsaZc7kciox0Dvj3ORmn0y5Jioggz7GEcx6XK0KSej5bnSccHh8p/DKR58SGSh6rxtlQeXysEm55pPDLNNTyDPZYG2qPz2ALtzwuV/dLEgP9PCMhIVq3fmaafvWXjVqyqkTzpmVpXF7iyU8cIobS8zRgKGOsAQOPcdab5eXN8Zim2adrU3zyGNM0j9p+rNs5ctwREydO1MSJE3u+njdvnlJTU3Xvvfdq69atmjp16inlPyIUMuX1tp/WuUC4sNtt8ngi5fV2KBgMWR2nT5ZvKFfpoRZFux26dHaOGhvbBux7HXl8fL6AOjq6Buz79FVXV1CS5PeT51jCOY/P55fLFSGfz2/Zkh7h9vhI4ZeJPCcW7nlsNsPScRbuj4/Vwi2PFH6Zhkoeq8baUHl8rBJueaIOl0mD8Txj2qgEzZ2UpjU7qvXzp9frp1+fo0hX2L4k0idD8XkaMBQx1oCBN9LGmccT2adZRpb/S8Xj8cjr9R61vaWlRYWFhcc97+MzbJKTk3u2H7mtIzNyjnf7Xq9XERERioqKOu73uOyyy3Tvvfdq+/btp13eSFIgMPz/wmFkCAZDQ+Lvc4cvoL+v6F528eqFo+SOsA9K7pBphsUa2kfK6VBI5DmGoZAnFLLu71K4PT5S+GUiz4kNlTxWjbOh8vhYJdzySOGXaajlGeyxNtQen8EWbnlCh/MM1vOML140VvvKmlXT1KGnl+7W166cePKThoCh8jwNGOoYa8DAY5z1ZvkicoWFhUdd26arq0ulpaUnLG+OXOvmyLVvjigqKpJhGD37CwsLVV9fr6ampqOOGzVqVK/r3QAYHl778IBa2v1KS4zSBTOyrI4DAAAAIAxEuSP09asmyjCk97cf0rpd1VZHAgAAOC7Lm4tzzz1Xa9asUWNjY8+2t99+W11dXTrvvPOOe15OTo4KCgr0xhtv9Nr+2muvaerUqUpM7F6/duHChbLZbFq6dGnPMW1tbVq+fPkJb1+SXn/9dUnSlClTTvl+ARhYNpshh8N21Edji09vf1QmSfr8hWPkdjmOeVx/fnAxNQAAAGBoGJsTryvm5UuSnnpzj+qbO60NBAAAcByWL5t244036s9//rNuvfVW3Xrrraqvr9cDDzygq666qtfMmx//+Md66aWXtHPnzp5tt99+u+644w7l5uZq/vz5WrZsmd5//3099thjPcekpaXpxhtv1C9/+Us5HA5lZmbqiSeekCTddNNNPcfdeeedysvL08SJE+VyubRmzRo9+eSTWrx4MeUNEGZsNkMJCdGy2Y6+ntWfXtulQNDUtDHJumB2Xp+undVfDA3e9wIAAABweq5ekK+dBxpUXOnVI6/s0A8+P0MO3pAFAADCjOXljcfj0VNPPaX77rtP3/nOd+R2u3XllVfqzjvv7HVcKBRSMBjste2yyy5TZ2en/vjHP+rxxx9XXl6eHnzwQS1cuLDXcXfddZeioqL061//Wi0tLZo2bZqeeuoppaSk9BwzZswYvfrqq3riiSfk9/uVlZWlb37zm7rlllsG7s4DOC02myGbzdDyjw6qqcXXs726oV2rNldIkvLTY7Xk3X2Dkic7LVazJ2VoEHsiAAAAAKfJYbfplqsm6p4n12t/RbP+8vZeffnS8VbHAgAA6MXy8kaSRo0apccff/yExzzwwAN64IEHjtp+7bXX6tprrz3huU6nU3feeedRhdDHfeMb39A3vvGNvgUGEBaaWnyqb+qQ1H3x1Q+2VUmSctNiZIbMnn0DLT7GNSjfBwAAAED/SE2I0jeunqjfvLBV722uVG5arM7nepkAACCMMC8YwLBQUdemptYu2W2GxufGWx0HAAAAQJibWpisT59XIEl69u292lfeZG0gAACAj6G8ATDkBYMh7TrQKEkakx0ntzMsJhUCAAAACHOXz83T2eNTFQyZ+t2S7WrwdlodCQAAQBLlDYBhoKjSq46uoCJddhVmeqyOAwAAAGCIMAxDX7t8grJTYuRt69Jv/7FN/kDw5CcCAAAMMMobAENaZ1dA+8qbJUkT8hJkt/NjDQAAAEDfuZx2fee6KYp2O3TgUIuefmuv7HZDDoctLD5sNsPqhwgAAFiAtYUADGm7DjYqGDKVEOtSVnK01XEAAAAADEEp8ZH69rVT9Mu/bdb726o0oSBJ15xbaHUsSVIoZKqxsU2hkGl1FAAAMIgobwAMWTWNHSqraZMkTR6VKMPgHWkAAAAATs/kwiR99apJeuzl7Xr8le0qO+RVpsVvEIuPdWnRrDzZbAblDQAAIwzlDYAhKRQytXbHIUlSTmqMEmJdFicCAAAAMNRdfU6B3l1fpqKKZi1fX64FU9LliXZaHQsAAIxAXBwCwJC07KNS1TV3ymE3NCEvweo4AAAAAIYBwzA0f0q6EmJd8gdD+nBHtdo6/VbHAgAAIxDlDYAhp70zoKff2CVJGpcTL7fTbnEiAAAAAMOFw27TnImpio2KkM8f1Jod1fJ1Ba2OBQAARhjKGwBDzkuritXU6lNctFOjMjxWxwEAAAAwzDgdds2dmKZIl11tnQGt2VktfyBkdSwAADCCUN4AGFIq69r09kdlkqQ5k9JksxkWJwIAAAAwHEW6HJo3KV3OCJua27r00e4aBUMUOAAAYHBQ3gAYMkzT1F+X7VMwZGrOpHRlpcRYHQkAAADAMBYTGaG5E9PksBuqa+7Uxr11Mk3T6lgAAGAEoLwBMGRs3lenHSUNctgNfe3qyVbHAQAAANAP7HabHA7rP+z2Y79EEh/j0qzxqbIZUlV9u7YW1VPgAACAAeewOgAA9IU/ENRzy/dJki6bm6eM5GiLEwEAAAA4E5Euh0zTlMcTaXWUXgwdvTRzSnykZo5N0fo9tTpY3aoIh10T8uJlGCzjDAAABgblDYAh4a11Zapt6lR8jFNXLci3Og4AAACAM+SKsMswDL23vlQN3k6r4yg7LVazJ2XoeH1MZnK0pgZC2lpUr/0VzTJlamJeAgUOAAAYEJQ3AMJeg7dTr314QJJ0wwWj5XbyowsAAAAYLppafKpv6rA6huJjXCc9Jj89VqGQqe0lDSqq8CoYNDWlIJECBwAA9DuueQMg7D23bJ+6/CGNzo7TnIlpVscBAAAAMIIVZHo0tTBJknTgUIu27OcaOAAAoP9R3gAIa9uK67V+T61shqEvXTyOd7QBAAAAsFx+eqxmjEmWJJXWtGrj3jqFQhQ4AACg/1DeAAhbXf6gnv3nXknShWdnKyc1xuJEAAAAANAtJzVGZ49LkWFIFXVtWr+nVkEKHAAA0E8obwCErTfWHFRNU4fiY5y6ZuEoq+MAAAAAQC+ZydGaNT5VNkM61NCuj3bXKBgMWR0LAAAMA5Q3AMJSdUO73lhzUJL0uQvHKtLlsDgRAAAAABwtPTFKsyemyW4zVNPYoTU7a9QVCFodCwAADHGUNwDCjmma+vPbexUImpo8KlFnj0uxOhIAAAAAHFdqfKTmHi5w6r2dWr21Sm0dfqtjAQCAIYzyBkDY+Wh3jXaUNMhht+kLF4+VYRhWRwIAAACAE0qKc2vhlHS5nXa1dgS0amuV6ps7rY4FAACGKMobAGGlwxfQX5ftkyRdMS9PaQlRFicCAAAAgL6Ji3Hp3GkZio9xqisQ0gc7DqmsptXqWAAAYAiivAEQVl5aVaLm1i6lJkTq8rm5VscBAAAAgFPidjo0f3K6MpKiZJrSpn112nWwUaZpWh0NAAAMIZQ3AMJGaXWL3tlQJkn64sVjFeGwW5wIAAAAAE6dw27T2eNSNCY7TpK0r7xZ6/fUKhAMWZwMAAAMFZQ3AMJCyDT1zD/3yDSls8enavKoJKsjAQAAAMBpMwxDE/ISNH10kgxDqqpv1wfbD6m90291NAAAMARQ3gAICyu3VKqowiuX067PLR5jdRwAAAAA6Be5abGaPyldEQ6bmlq7tGJzlarq26yOBQAAwhzlDQDLNbb49MK7+yVJ155ToIRYl8WJAAAAAKD/JMW5dd60DMXHOOUPhvTR7lptL65XKMR1cAAAwLFR3gCwlGma+vM/96jDF1RBpkcXnpVtdSQAAAAA6HdR7ggtnJKhgkyPJKm4qkWrt1WpjWXUAADAMVDeALDU+j212rSvTnaboX+7bLxsNsPqSAAAAAAwIGw2Q5NHJWr2hNSPLaNWqUqWUQMAAJ9AeQPAMq0dfj37zz2SpCvm5Sk7JcbiRAAAAAAw8NITo3Te9EwlxLoUCJpav7tW24rrFQiGrI4GAADCBOUNAMv8bfk+edv9ykiK0hXz8q2OAwAAAACDJsrl0ILJ6Rqd1b2MWklVi1ZsrlS9t9PiZAAAIBxQ3gCwxI6SBr2/7ZAMSV+5fIIiHPw4AgAAADCy2GyGJuYnau7EVLmddrV1BvT+tkPaziwcAABGPF4tBTDoOrsCeurN3ZKkRWdla3RWnMWJAAAAAMA6qQlRumBGlnJTu5eSLq5q0XubK3WIa+EAADBiOawOACD8GYYhSbLb+6fvfXl5ieqaO5Xkceuzi0fLcYqzbvorBwAAAACEiwiHTdPHJCszOVqb99epvTOgpWtKZbPbdc2CPDlsPA8CAGAkobwBcEI2myGPJ1KSej6fiT0HG/TPj8okSd/57HRlpJ3+rBtDxhnnAQAAAIBwkpoQqQtmZGnngQYdrG7V6++XaO32Kn3+wrGaNjqp5811AABgeKO8AXBCNpshm83QB1srVVPfppBpnvZtBUOmXlldItOUCrPidLCyWQcrm0/5drLTYjV7UoZ4zgIAAABgOIpw2DRtdLLG5SVo07461TZ26KG/b9WUgiR9/sIxSkuMsjoiAAAYYJQ3APqkudWn+uYOhUKnX97sLm1UU4tPzgibxmR5VN/UcVq3Ex/jOu0MAAAAADBUZKXE6OZPTdXTr+3Q0jUHta24Xv/5eIMunpWrT507yup4AABgALFgKoBB0dji076y7lk2UwqS5IywW5wIAAAAAMJfpMuhGxaN1k9vnqPJBYkKBE29seagfviHD7VyU7nMM1gdAQAAhC/KGwADLhgMadO+OpmSspKjlZUcbXUkAAAAABhS0hOjdMf10/Sd66YoOc6txhaffvHnDbr/mQ0qqfJaHQ8AAPQzyhsAA27XwSa1dvjlirBrSkGi1XEAAAAAYEgyDEMzxqTovpvn6NPnFcjpsGl3aZN++tR6/f6l7TrU0G51RAAA0E8obwAMqLqmDhUffhfY9DEslwYAAAAAZ8oZYdenzinQH364WAumZMiQtH53jf7jT2v11Ju71djiszoiAAA4Q5Q3AAaMPxDSpv11kqS8tBilJURZnAgAAAAAho/UxCh945pJuuerszV9dLJCpqkVmyv1o0c+1Avv7Vdbp9/qiAAA4DQ5rA4AYPjaXtKgDl9QUW6HJo1iuTQAAAAAGAjZqTG6/TNTtbesSS+uKNL+8mYtXVOqFZsqdfGsHC0+O1vR7girYwIAgFPAzBsAA6Kqvl1lNa2SpBljkuWw8+MGAAAAAAbS2Jx4/egLM3X7Z6YqKyVa7b6AXlpdoh/84QP9fUWRvO1dVkcEAAB9xMwbAP3O1xXUlqLu5dJGZ3mU5HFbnAgAAAAARgbDMDR9dLKmFiRp/Z4avfbBAZXXtun1Dw/q7fVlOn96li6dk6v4GJfVUQEAwAlQ3gDoV6ZpaktRvbr8IcVGRWhcboLVkQAAAABgxLHZDM2ekKazx6dqy746vfLBAR081KJ/flSm5RsrdO60DF0yO1cp8ZFWRwUAAMdAeQOgX5XVtulQQ7sMQ5o5Jll2m2F1JAAAAAAYsWyGoRljUzR9TLK2lzTo1fcPaH9Fs5ZvrNC7myp01rhUXTIrR4VZcVZHBQAAH0N5A6DftLb7ta2oXpI0LidecUzDBwAAAICwYBiGphQkafKoRO0ubdIbaw5qR0mD1u+u0frdNRqdFadLZudoxpgU2XgTHgAAlqO8AdAvgqGQ1u+tUTBkKjnOrTHZvGsLAAAAAMKNYRiakJegCXkJKq9p1VsflWrNjmrtr2jW/iXNSol366Kzc7RwaobcTl42AgDAKvwWBtAvdh5olLfNL2eETTPHJMsweKcWAAAAAISz7NQYfe2KibruvEIt31iudzdWqLapU395Z5+WrCrW/MkZWjQzSxlJ0VZHBQBgxKG8AXDGqurbVFLVIkmaMSZZbhc/WgAAAABgqIiPcenT5xbqirn5+mB7lf65vlzVDe1atqFcyzaUa2J+ghbNzNa00Umy22xWxwUAYETgFVYAZ6S9M6DN+7qvc1OY6VFaQpTFiQAAAAAAp8PltOuCmdk6b0aWdh1o1PKN5dq8v047DzRq54FGJXlcOn9Gls6ZmilPtNPquAAADGuUNwBOWyhkasPeWvmDIcXHODUhL8HqSAAAAACAM2QzDE0alahJoxJV19yh9zZVauWWStV7ffr7imK9tKpEM8Yk67zpWZqQnyAby2YDANDvKG8AnLbdpU1qbPHJYTd01rgU2Wz8gx0AAAAAhpPkuEh95vxCXbMwX+t21Wj5xgqVVHm1fk+t1u+pVXKcW+dNz9SCKRmKj3FZHRcAgGGD8gbAaalp7ND+imZJ0vTRyYp2R1icCAAAAAAwUCIcdi2YkqEFUzJUVtOqlZsr9cGOQ6pr7tTfVxRrycoSTR+TrHOmZmhyQSLXxgEA4AxR3gA4ZZ1dAW3cVytJykuPVWZytMWJAAAAAACDJSc1Rl+4eKw+c0Gh1u+u0Yotldpf3qyNe2u1cW+t4qKdmjc5XQunZPB8EQCA00R5A+CUhEKmNuypU5c/pNioCE3O5zo3AAAAADASuSL+NRunorZVK7dU6cMdh9Tc1qU315bqzbWlKsj0aMGUDM2ZkKooVmwAAKDPKG8AnJKdBxpV7+2U3Wbo7HEpstuZCg8AAAAAI11WSow+d+EYXX9BobYW1Wv11iptLapXcaVXxZVePbdsn2aMSda8SemaNCpRDp5LAgBwQpQ3APqstLpFxVVeSdLMscmKjXJanAgAAAAAEE4cdptmjk3RzLEpam7r0podh7R6W5Uqatu0bleN1u2qUUxkhOZMSNPcyWkqyPDIMAyrYwMAEHYobwD0SW1juzbvr5ckjc2OU0YS6xYDAAAAwEhjsxmy2fpWtiTFuXXF/HxdPi9PBw616INtVVqzo1rNbV1atrFcyzaWKy0hUvOnZGje5HSlJ0adcp5QyFQoZJ7yeQAAhDvKGwAn1dzq09I1BxUKmUpNiNS43HirIwEAAADAiHGs5aqPbBvMpawNw5DHE9nn8ubjEhNjNHNihr4VDGnLvjq9u6FMH26vUnVjh5asLNaSlcUqzI7TOdOytHB6ltL6WOSEQqYaG9socAAAww7lDYATCoZC+uUz69Xa7le026GzxiYzpR0AAAAABkGkyyHTNOXxRB73mBPtGyjvflSqxpbOM7qNURmxyk6JVumhFhVVNquyrk1F5c0qKm/Wk6/vVEp8pEZlepSfEatod8QxbyM+1qVFs/JksxmUNwCAYYfyBsAJ/W3Zfm3dXyeH3aY5E9MU4bBbHQkAAAAARgRXhF2GYei99aVq8PYuS2yGIZfLIZ8voJA5OMVFdlqsZk/KUHOrT/VNHf1ym/ExTp01NkWTRyWqqr5dlXVtqmvuVG1Th2qbOrRuZ7USPS5lJEYpIylKUccpcjA0nMqye4OBZfcAhDPKGwDHtWbHIb25tlSStHhWjiIjbPyjBgAAAAAGWVPL0WWJzWYoMtKpjo6uQXueFh/jGrDbdkXYlZ8eq/z0WHV2BVRZ113kNLT41ODt/thxoFFx0U5lJEUpPSlKiXHuAcuD/mezGUpIiA678oZl9wCEK8obAMdUWt2iJ5fuliRdv3iMUuIjVVnTYnEqAAAAAMBw53Y6VJDpUUGmRx2+gKrq21VV3656b6ea27rU3Nal3aVN8uytU0tHQOOy4zQqI1Z22+Bd/wen7sism+UfHVRTi8/qOCy7ByDsUd4AOEpji0+/eXGrugIhTS1M0hcunaB/rjlgdSwAAAAAwAgT6fpXkePzB1Xd0F3k1DZ1yNvWpb+/u1+SFOV2aEpBkqaPTtaUwiR5op2W5GUZrpM71kwyAMDRKG8A9NLhC+g3L2xRY4tP6YlR+tanJsseRlOaAQAAAAAjkyvCrty0WOWmxcofCClgmuoKhLRxd61a2ru0dme11u6slmFIY3MSdNaENE0bk6yxuQly2AdnVg7LcAEA+gvlDYAewVBIf3h5u0prWuWJitAdN0xTdCQXgwQAAAAAhJcIh03js+O1aHaelq87qL1ljSqraVV5TasavD7tKW3UntJG/eUtyWE3lJYYpczkaGUkRSvR45Jh9P+bFFmGCwDQnyhvAEiSTNPUn/+5V9uLG+R02PTd66cpJT7S6lgAAAAAAJyQt61LdsNQflqs8tNi1eELqKaxQ7VNHapr7lRXIKSK2jZV1LZJ6i5+kuPcSoh1KTHWpbgYFytOAADCDuUNAEnS0rWlWrG5Uoakb1w9SaMyPFZHAgAAAADglEW6HMpLj1VeeqxM05S33a+6pg7VNneqvrlT/kBIVfXd186RJJshxcW4esqchFiX3E77gMzOAQCgryhvAGjtzmq9+F6RJOnGC8doxtgUixMBAAAAAHDmDMNQXLRTcdFOFWbFKRQy1dTqU723U40tPjW0+NTlD6mxxafGFp+KD5/ndNjkiXbKc/hcT7RTsZERsjFDBwAwSChvgBFub1mTHn99pyTporNzdNHZORYnAgAAAABgYNhshhI9biV63JK6lxBv6wz0lDcNLZ3ytvnVFQiprrlTdc2dPecahhQbGaHoyAjFREYo2u1QdGSEot0RckXYrLpLAIBhivIGGMGq6tv08N+3KhA0NXNsij67aLTVkQAAAAAAGDSGYSjmcBmTkxojSQoGQ2pp96u5vUvetu6P5rYuBYLdS7B52/1H3Y7DbsgT7dSOkkZFu+2Ki3Yq4fBSbPExLsXHuhTldsjGUmwAgD6ivAFGqAZvpx58fovaOgMqyPTo61dNZPo3AAAAAGDEs9ttio/tLlyOME1THb6AvO1+tXX41dYZUFunX20dAbX7AgoETTV4fVq389Bxb9eQFOV2KNodoehIh6Lch2fvuCPkctrldNjkjDj6s81myGZINsOQYRgyev7cXT7ZbP/a9vHtH++Jej3b/9iOT74KcLxu6cj1f2yG5LDbZLfb5LAbchz+fCQbAKD/UN4AI1BzW5d+8dxm1TV3Ki0hUrdfN1WuCLvVsQAAAAAACEuGYSjKHaEod8RR+4IhU+2dftntNo3LT1JFtVf1zZ1qau1SY4tPTa0+tXb4ZUqHS5+A1DTod2FAGZIiDpdNbqddrgi7XEc+R9jldtkVExmh5IRoFVc0ye8PKsJh6z7HYZPbaZfDbqMAAoCPobwBRpjWDr/+97lNqm5oV5LHre9/boY80U6rYwEAAAAAMCTZbYZio5xKio/UpfPy1djYpkAg1OuYQDDUXdx0+NXeGVBrp7/nz22dfnX5Q/IFguryB9XlD3V/DnR/DoZMmWb37J9Qz+ePb+u9P2SaMkNmz/c2P5bDNHVM5vF2fOKcoGkqGDQVCPa+f6bUnTcQUmvH0cvK9YXNkFxOu9xOR0/5446wK9LtUJTLriiXQ24XS88BGDkob4ARpMMX0IPPb1Z5bZviYpy683PTey7SCAAAAAAABobDblNctFNxw+TNk6ZpKhg6XOSEQgoEQvIHQvL5g+r0B9XV1f3Z5w/K1xVUhy+ozq6AAqa060CDWtu75D9c9nT5gwoEu4unDl/3sScS6bIr0uVQlMuh6MPXK4qJ7F6CzmG3DdIjAAADj/IGGCF8/qB+88IWlVS1KCYyQnfeOENpCVFWxwIAAAAAAEOMYRiHr3kjudS3ZdgdDpsSEqL1j+V7Vd/U0WtfMHi4+OkK/utzV/fndl9AHYc/Pl7wNMh31PeIPLw8W0xkhGKjnPJER8gT5aTUATAkUd4AI4A/ENLv/rFNe8ubFemy698/O11ZydFWxwIAAAAAAJDdblOU3XbMawodYZqmfIfLnHZfoGfJudaOgFo7/PIHQj3FTm1TZ69zo90OeaKd8hwudOJjXAN9lwDgjFHeAMNcIBjSH1/eru0lDXJG2HTH9dOVlx5rdSwAAAAAAIA+MwxD7sPXvUn8xD7TNHuut9Pa4Vdru1/e9i552/zy+YPd1xvqDKiqvr3nnCiXQ7sONionJVr5abHKz/Ao0sVLpQDCBz+RgGEsGArp8dd3adO+OjnsNt1+3VSNzo6zOhYAAAAAAEC/MQxDrgi7XBF2JX3i2r6+ruDhIqdL3vYuNbd1qaXNr3ZfQGu2H9KaI7chKT0pSmOy4zQ2J17jchKUFMd1ggFYh/IGGKYCwZAeeXmHNuytld1m6NZrJ2ti/iffmwIAAAAAAPqTneurHJNVj4vLaVeKM1Ip8ZE92wLBkGQYykiJ0faiOhWVN6uuuVNV9e2qqm/Xyi1VkqTkOLcm5Cdo+rg05afFKNnjkmEYA5o3FDIVCpkD+j0ADA2UN8Aw1OUP6ndLtmtbcb0cdkO3fmqKpo9OtjoWAAAAAADDVqTLIdM05fFEnvzgQRIyTdkGuGw4VYasz+Ow25SdFqtL54/Sp84bLUlqbOnU3oON2lnSoO3Fddp/uNBZtaVKq46UOfGRmjE2RTPHp2r6mBTFRDn7PVsoZKqxsY0CBwDlDTDcdHYF9NCLW7W7tElOh03fuW6qJo1ixg0AAAAAAAPJFWGXYRh6b32pGrydVsdRdlqsZk/KCLs84dIlHe//V1x0hBZMydDsCWmqaWxXdUNH9+fGDtU1dejtdaV6e12pDHWXOVkp0cpKiVZyXKRstjO7c/GxLi2alSebzaC8AUB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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10)) \n",
    "# distplot is deprecated. Use histplot instead\n",
    "# sns.distplot(df['Total_Trans_Ct']);\n",
    "# Show distributions of 'Total_Trans_Ct' and add a smooth curve obtained using a kernel density estimate\n",
    "sns.histplot(df['Total_Trans_Ct'], stat='density', kde=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10)) \n",
    "sns.heatmap(df.corr(), annot=False, cmap='Dark2_r', linewidths = 2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "y = df['Churn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "X = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n"
     ]
    }
   ],
   "source": [
    "# gender encoded column\n",
    "gender_lst = []\n",
    "gender_groups = df.groupby('Gender').mean()['Churn']\n",
    "\n",
    "for val in df['Gender']:\n",
    "    gender_lst.append(gender_groups.loc[val])\n",
    "\n",
    "df['Gender_Churn'] = gender_lst    \n",
    "#education encoded column\n",
    "edu_lst = []\n",
    "edu_groups = df.groupby('Education_Level').mean()['Churn']\n",
    "\n",
    "for val in df['Education_Level']:\n",
    "    edu_lst.append(edu_groups.loc[val])\n",
    "\n",
    "df['Education_Level_Churn'] = edu_lst\n",
    "\n",
    "#marital encoded column\n",
    "marital_lst = []\n",
    "marital_groups = df.groupby('Marital_Status').mean()['Churn']\n",
    "\n",
    "for val in df['Marital_Status']:\n",
    "    marital_lst.append(marital_groups.loc[val])\n",
    "\n",
    "df['Marital_Status_Churn'] = marital_lst\n",
    "\n",
    "#income encoded column\n",
    "income_lst = []\n",
    "income_groups = df.groupby('Income_Category').mean()['Churn']\n",
    "\n",
    "for val in df['Income_Category']:\n",
    "    income_lst.append(income_groups.loc[val])\n",
    "\n",
    "df['Income_Category_Churn'] = income_lst\n",
    "\n",
    "#card encoded column\n",
    "card_lst = []\n",
    "card_groups = df.groupby('Card_Category').mean()['Churn']\n",
    " \n",
    "for val in df['Card_Category']:\n",
    "    card_lst.append(card_groups.loc[val])\n",
    "\n",
    "df['Card_Category_Churn'] = card_lst\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "keep_cols = ['Customer_Age', 'Dependent_count', 'Months_on_book',\n",
    "             'Total_Relationship_Count', 'Months_Inactive_12_mon',\n",
    "             'Contacts_Count_12_mon', 'Credit_Limit', 'Total_Revolving_Bal',\n",
    "             'Avg_Open_To_Buy', 'Total_Amt_Chng_Q4_Q1', 'Total_Trans_Amt',\n",
    "             'Total_Trans_Ct', 'Total_Ct_Chng_Q4_Q1', 'Avg_Utilization_Ratio',\n",
    "             'Gender_Churn', 'Education_Level_Churn', 'Marital_Status_Churn', \n",
    "             'Income_Category_Churn', 'Card_Category_Churn']\n",
    "\n",
    "X[keep_cols] = df[keep_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>Customer_Age</th>\n",
       "      <th>Dependent_count</th>\n",
       "      <th>Months_on_book</th>\n",
       "      <th>Total_Relationship_Count</th>\n",
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      ],
      "text/plain": [
       "   Customer_Age  Dependent_count  Months_on_book  Total_Relationship_Count  \\\n",
       "0            45                3              39                         5   \n",
       "1            49                5              44                         6   \n",
       "2            51                3              36                         4   \n",
       "3            40                4              34                         3   \n",
       "4            40                3              21                         5   \n",
       "\n",
       "   Months_Inactive_12_mon  Contacts_Count_12_mon  Credit_Limit  \\\n",
       "0                       1                      3       12691.0   \n",
       "1                       1                      2        8256.0   \n",
       "2                       1                      0        3418.0   \n",
       "3                       4                      1        3313.0   \n",
       "4                       1                      0        4716.0   \n",
       "\n",
       "   Total_Revolving_Bal  Avg_Open_To_Buy  Total_Amt_Chng_Q4_Q1  \\\n",
       "0                  777          11914.0                 1.335   \n",
       "1                  864           7392.0                 1.541   \n",
       "2                    0           3418.0                 2.594   \n",
       "3                 2517            796.0                 1.405   \n",
       "4                    0           4716.0                 2.175   \n",
       "\n",
       "   Total_Trans_Amt  Total_Trans_Ct  Total_Ct_Chng_Q4_Q1  \\\n",
       "0             1144              42                1.625   \n",
       "1             1291              33                3.714   \n",
       "2             1887              20                2.333   \n",
       "3             1171              20                2.333   \n",
       "4              816              28                2.500   \n",
       "\n",
       "   Avg_Utilization_Ratio  Gender_Churn  Education_Level_Churn  \\\n",
       "0                  0.061      0.146152               0.152012   \n",
       "1                  0.105      0.173572               0.155691   \n",
       "2                  0.000      0.146152               0.155691   \n",
       "3                  0.760      0.173572               0.152012   \n",
       "4                  0.000      0.146152               0.159381   \n",
       "\n",
       "   Marital_Status_Churn  Income_Category_Churn  Card_Category_Churn  \n",
       "0              0.151269               0.134807             0.160979  \n",
       "1              0.169414               0.171862             0.160979  \n",
       "2              0.151269               0.157655             0.160979  \n",
       "3              0.172230               0.171862             0.160979  \n",
       "4              0.151269               0.134807             0.160979  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "`max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random forest results\n",
      "test results\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.99      0.98      2543\n",
      "           1       0.93      0.80      0.86       496\n",
      "\n",
      "    accuracy                           0.96      3039\n",
      "   macro avg       0.95      0.90      0.92      3039\n",
      "weighted avg       0.96      0.96      0.96      3039\n",
      "\n",
      "train results\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      5957\n",
      "           1       1.00      1.00      1.00      1131\n",
      "\n",
      "    accuracy                           1.00      7088\n",
      "   macro avg       1.00      1.00      1.00      7088\n",
      "weighted avg       1.00      1.00      1.00      7088\n",
      "\n",
      "logistic regression results\n",
      "test results\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.96      0.93      2543\n",
      "           1       0.71      0.45      0.55       496\n",
      "\n",
      "    accuracy                           0.88      3039\n",
      "   macro avg       0.81      0.71      0.74      3039\n",
      "weighted avg       0.87      0.88      0.87      3039\n",
      "\n",
      "train results\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.96      0.94      5957\n",
      "           1       0.72      0.50      0.59      1131\n",
      "\n",
      "    accuracy                           0.89      7088\n",
      "   macro avg       0.82      0.73      0.76      7088\n",
      "weighted avg       0.88      0.89      0.88      7088\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# This cell may take up to 15-20 minutes to run\n",
    "# train test split \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.3, random_state=42)\n",
    "\n",
    "# grid search\n",
    "rfc = RandomForestClassifier(random_state=42)\n",
    "# Use a different solver if the default 'lbfgs' fails to converge\n",
    "# Reference: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
    "lrc = LogisticRegression(solver='lbfgs', max_iter=3000)\n",
    "\n",
    "param_grid = { \n",
    "    'n_estimators': [200, 500],\n",
    "    'max_features': ['auto', 'sqrt'],\n",
    "    'max_depth' : [4,5,100],\n",
    "    'criterion' :['gini', 'entropy']\n",
    "}\n",
    "\n",
    "cv_rfc = GridSearchCV(estimator=rfc, param_grid=param_grid, cv=5)\n",
    "cv_rfc.fit(X_train, y_train)\n",
    "\n",
    "lrc.fit(X_train, y_train)\n",
    "\n",
    "y_train_preds_rf = cv_rfc.best_estimator_.predict(X_train)\n",
    "y_test_preds_rf = cv_rfc.best_estimator_.predict(X_test)\n",
    "\n",
    "y_train_preds_lr = lrc.predict(X_train)\n",
    "y_test_preds_lr = lrc.predict(X_test)\n",
    "\n",
    "# scores\n",
    "print('random forest results')\n",
    "print('test results')\n",
    "print(classification_report(y_test, y_test_preds_rf))\n",
    "print('train results')\n",
    "print(classification_report(y_train, y_train_preds_rf))\n",
    "\n",
    "print('logistic regression results')\n",
    "print('test results')\n",
    "print(classification_report(y_test, y_test_preds_lr))\n",
    "print('train results')\n",
    "print(classification_report(y_train, y_train_preds_lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fed70f9db38>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lrc_plot = plot_roc_curve(lrc, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fed70f9d630>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plots\n",
    "plt.figure(figsize=(15, 8))\n",
    "ax = plt.gca()\n",
    "rfc_disp = plot_roc_curve(cv_rfc.best_estimator_, X_test, y_test, ax=ax, alpha=0.8)\n",
    "lrc_plot.plot(ax=ax, alpha=0.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./models/logistic_model.pkl']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save best model\n",
    "joblib.dump(cv_rfc.best_estimator_, './models/rfc_model.pkl')\n",
    "joblib.dump(lrc, './models/logistic_model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "rfc_model = joblib.load('./models/rfc_model.pkl')\n",
    "lr_model = joblib.load('./models/logistic_model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fed70fda4e0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lrc_plot = plot_roc_curve(lr_model, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fed70fcee80>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 8))\n",
    "ax = plt.gca()\n",
    "rfc_disp = plot_roc_curve(rfc_model, X_test, y_test, ax=ax, alpha=0.8)\n",
    "lrc_plot.plot(ax=ax, alpha=0.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9yMjIKPdaZDIZnj17BkNDw5oMsdodP34c//zzD3788UcsXrwY0dHRNVo8EBEREdUlLB7qqMzMTISFhSEpKQmPHj2ChYUF3NzcMHXqVDRq1AgAEBsbiy+++AIAEBYWpnwD4eLigtDQUMhkMoSHhyMlJQU3btxAVlYWrK2t0atXL0ydOhUWFha1ci0lcQYFBeH8+fOIjY3FvXv3sGjRIvj6+iIlJQXR0dG4ePEiHj58CH19fTg7O2PChAno2rWryrGmTJmCu3fvYt26dfj+++9x4sQJFBUVoXPnzpg3bx5atGihbFtYWIiNGzfiwIEDuHfvHvT09NCoUSN0794d8+bNq9S1REdHw87ODq6urhgwYAC2bduG7OxsmJmZqbQr6dK1fft27N69GwcOHMDTp0/x6quv4pNPPkHLli1x5MgRrF+/HhkZGbC0tMS4cePw9ttvAwDu3LmDt956CwBw9+5dlbdKqamplYqdiIiIqKpYPNRBubm5mDRpEq5fvw6JRAJnZ2dcuXIFu3btQkpKCiIiImBtbY0uXbpg9uzZCAgIUOn2ZGVlBQAoKipCZGQk+vXrBw8PDxgaGuLPP/9EdHQ0zp07h8jISOjr69fadQUGBqK4uBhDhgyBsbGx8kY/NjYWOTk58PX1RaNGjfDgwQNER0dj2rRpCA4ORpcuXVSOk5+fjylTpqBTp06YPn06bt++ja1bt2LOnDnYtm0bRCIRAGD58uWIiYnBwIEDMXLkSMjlcty6dQsnTpyoVPyPHj3CL7/8ggkTJkAgEEAikSAiIgIJCQl45513Sv3M4sWLYWJigvHjxyMrKwuRkZGYMWMGpk6ditWrV8PPzw9mZmaIjo7Gt99+CwcHB7i4uMDS0hJLly5FQEAALCwsMGHChErFTERERFSdWDzUQREREcjIyMCcOXMwcuRI5fZOnTrhs88+Q3BwMBYuXAh7e3t4eHggICCg1G5PDRo0QEJCgkrXID8/P3Tq1AnLli1DUlIS+vfvX2vXVVhYiM2bN6t1VVq0aBGMjIxUtvn5+WH48OEIDw9XKx4yMzMxevRojB07VrnN0tISq1atwsmTJ9GjRw8AQFJSEtzc3NTGX1RWXFwcZDIZfHx8AAAODg7o0KEDYmJiXlg8NG7cGCtWrFAOqrawsMB3332H//u//8P27dtha2sLAHjzzTfh4+ODHTt2wMXFBUZGRhg4cCDWrl0LKyurl65LGxHRy0QoVPw3WiTS7UkoS65fl/PAHJSPxUMdlJSUBHNzcwwbNkxlu7e3N0JDQ5GYmIiFCxeWexyBQKC8UZdKpcjLy4NUKoWrqysA4MKFC7VaPLz99tuljnF4vnDIy8vDs2fPIBKJ0LFjR1y4cEGtvVAoxIgRI1S2lVzTjRs3lMWDqakprly5gsuXL8PR0bHK8cfExKBLly4qg799fX2xfPlyXLp0Ce3bt1f7zPDhw1VmY3rttdcAAO7u7srCAVAUPy1atMCtW7eqHCcREVWMiYni/01mZkbltNQNzANzUBYWD3XQ7du30bZtW+jpqX49AoEADg4OSE5ORm5uLkxMTMo91sGDBxEZGYn09HQUFxer7MvOzq7WuMvz35mZSty6dQtBQUFISUlBTk6Oyr7SpkG1sbGBgYGByjZzc3MAQFZWlnLbnDlz8Nlnn2HEiBFo1qwZunbtit69e6NPnz4QCiv2ROHcuXPIyMiAl5cXbt68qdzeoUMHCIVCREdHl1o8NGvWTOXnkrERpU3Ba2pqinv37lUoLiIiqrrc3AKYmBgiOzsfUqlM2+FojUgkhJmZkU7nQddzYGlpXG4bFg8vGblcrnHbw4cPY8GCBXB2dsbcuXNha2uLBg0aQCaTYebMmRU6VnUo7a3D06dPMWnSJBQUFGDkyJFwdHSEsbExBAIBNm7ciFOnTql9pqwb/+evyd3dHbGxsTh+/DhOnz6NU6dOISYmBh07dkRwcHCFZnqKjo4GoBgIHRISorZ/3759+Oijj9SKmhfF+qLttf2dEBERIJMp/tsrlcpQXKx7N4z/xTwwB2Vh8VAHNWvWDDdu3EBxcbHa24dr167BwsJC+dahrAXKEhISYGBggJCQEJUb5YyMjBqJuzJOnTqFhw8f4vPPP1fOLlRi7dq1VT6+mZkZvL294e3tDQAIDQ1FaGgoDhw4oHa+F3n69CkOHTqE119/HUOHDlXbn5GRgeDgYCQmJirPU11qcgE6IiIioopi8VAHeXh4YMOGDdi5c6fKQNz9+/fj5s2bGDJkiHJbyXiB/3b3Af73dFsm+1/lLJfLsX79+poKvcJKZkb67xP3lJSUUsc7aKpkjIepqanK9pKuRRXpsnXw4EHk5+dj6NCh6Nevn9r+klmtYmJiqr14MDIyKvW7JSIiItIGFg910JgxY3D48GGsWLEC6enp6NChg3KqVltbW/j7+yvbWlhYwN7eHgcOHIC9vT0sLS1hZWUFV1dX9O3bF0eOHIG/vz98fHxQXFyM5ORkFBQUaPHqVHXu3BnW1tb44YcfcPfuXTRu3Bh//fUX4uPj4ejoiMuXL1fquHl5efD29oa7uzvatm0LKysr3Lt3Dzt37kTDhg3VVvMuS3R0NAwMDODm5lbqfn19ffTu3Rv79u3D7du31cY5VEXHjh0RExODkJAQtGjRAgKBAF5eXtV2fCIiIqKKYPFQB5mYmGD9+vUIDQ1FcnIy4uPjYW5uDolEAn9/f1hbW6u0L1kPYPXq1SgsLISLiwtcXV3h5eWFvLw8REVFITAwEKampnB3d8eMGTPQt29fLV2dKlNTU6xZswarVq3Ctm3bIJVK0b59ewQGBiI6OrrSxYOhoSFGjhyJU6dO4eTJk8jLy4O1tTW6d++O8ePHa3yDf/XqVZw/fx6enp5q08k+74033kBCQgJiY2NViruqmjp1KjIzM7Flyxbk5uYCAIsHIiIi0hqBnCM0iXSGS0Qxzj7QdhSky0a2B6IkeoDLbODsVW2HQ7quiwNwJgDZ2fkwMzPCkydPdXqQrJ6eEJaWxjqdB13PgY2NabltuAIGERERERFphN2WqFRZWVkoKioqs42hoaFGa03UVbpwjURERETVicUDlWrevHk4c+ZMmW0kEgmWLFlSOwHVAF24RiIiIqLqxOKBSjVr1qxypzO1sbGppWhqhi5cIxEREVF1YvFApXJyctJ2CDVOF66RiIiIqDpxwDQREREREWmEbx6IdIiTtQAAZ2cm7WllLlD8xcleu4EQAfw9JKoEFg9EOkIqk2Ozj0jbYRBBLpVBsHm2tsMgAgDIi6WQyfhQhUhTLB6IdIRIKMCTJ7kABNoORWtEIiHMzIyQnZ0PqVT3Fv8BmIMSzIMC8wDIZHIIhbr730WiimLxQKRDFKtl8n+SUqlMJ1cOfR5zoMA8KOh6Hlg8EGmOA6aJiIiIiEgjLB6IiIiIiEgjLB6IiIiIiEgjLB6IiIiIiEgjLB6IiIiIiEgjLB6IiIiIiEgjLB6IiIiIiEgjLB6IiIiIiEgjXCSOSIfo6Qmhy4vEiURClX/qIuZAgXlQqCt5kMnkkMnkWo2BiDTD4oFIR0hlclhammg7jDrBzMxI2yFoXVVzIJfKIKgHN978XVDQdh7kxVI8zspnAUH0EmDxQKQjREIB3o2TIu0R/+dMVTOglQBf9RYB7wYAabe0HQ697JzsIdg8G0KhgMUD0UuAxQORDkl7JMfZB9qOgl527a3+vcFLuwWcvardYIiIqFa9/O+ciYiIiIioVrB4ICIiIiIijbB4ICIiIiIijbB4ICIiIiIijbB4ICIiIiIijbB4qEPEYjGWLFmi7TBq3J07dyAWixESElKpz8fGxkIsFiM1NbWaI6u7pkyZAl9fX22HQURERDpO56ZqFYvFGreNiYmBnZ1dmW1ycnIQFRWFrl27VujY1WnKlCk4c+aM8meRSARLS0t06dIFEydOhKOjo1bi0iWpqanw9/dX2dagQQPY2Niga9euGDt2LFq0aKGl6IiIiIiqh84VD0uXLlX5+dq1awgPD4enpyc8PT1V9llaWpZ7vJycHISFhQGoWGFS3fT09PD5558DAAoLC5GWlobY2FgcO3YMERERaNmypdZiq24DBw7Em2++CX19fW2HoqZ///7o3bs3AMX38PfffyM6OhqHDx/Gtm3b0KRJEy1HSERERFR5Olc8DBw4UOXn1NRUhIeHw9HRUW3fy0QoFKrEP2TIEDg4OGDFihXYvn07Pv74Yy1GV71EIhFEIpG2wyhVu3bt1H6PmjdvjhUrVuDIkSMYNWqUliIjIiIiqjqOeXiBzMxMfPfdd/Dx8UH37t3h7e2NL7/8Eg8fPlS2iY2NxVtvvQUACAsLg1gshlgsxpQpUwAAMpkM69evx+TJk+Hl5YXu3bvDx8cH33zzDTIzM2v8GlxdXQEAN2/eVNt34MABTJw4Ee7u7nBzc8PYsWNx6NAh5X6pVIoBAwZgxIgRpR57z549EIvFKp/RJGelycnJQc+ePTFr1qxS969duxZisRgXL14EUPqYh5Jtp06dwsaNGzFo0CD06NEDQ4cOxd69e9WOKZPJ1Npt3bq1RsZTNGrUCIDi7dDzDhw4gFmzZsHHxwc9evRA3759MWfOHPz999/Vdm4iIiKi6qRzbx40kZubi0mTJuH69euQSCRwdnbGlStXsGvXLqSkpCAiIgLW1tbo0qULZs+ejYCAAJVuT1ZWVgCAoqIiREZGol+/fvDw8IChoSH+/PNPREdH49y5c4iMjKzRrjclRYO5ubnK9h9//BEbNmxAz5494e/vD6FQiKSkJMyfPx8ff/wxhg8fDpFIBG9vb2zatAnp6elo166dyjHi4+NhZmYGd3f3CuWsNKampnB3d0dSUhKePHmi0l1MLpcjISEBDg4O6NChQ7nXvGbNGjx79gxDhw6Fvr4+du7ciSVLlsDe3h6dO3dWtit5I9O5c2eMGDECubm5ZcaoqYKCAmVhWFBQgCtXruDHH3+EhYUF3njjDZW2O3bsgIWFBd5++21YWlri1q1b2L17NyZOnIjIyEg0b968SrEQEb1MRCLtPc8sObc2Y6gLmAfmQBMsHkoRERGBjIwMzJkzByNHjlRu79SpEz777DMEBwdj4cKFsLe3h4eHBwICAkrt9tSgQQMkJCTA0NBQuc3Pzw+dOnXCsmXLkJSUhP79+1db3M/ftF66dAkrV64EAHh7eyvbpKWlYcOGDRg3bhxmzJih3D5ixAjMmTMHQUFB8PHxgbGxMSQSCTZt2oS4uDiV4uHOnTs4e/Ys/Pz80KBBgwrl7EUkEgkOHTqE/fv3q7ztOHPmDO7cuYOZM2dqlIOioiJEREQoi7J+/fph0KBBykIBUIxz2b59O8RiMYKCgpRdoAYNGoS3335bo/O8yLp167Bu3TqVba1atUJoaKjyDUSJVatWwcjISGWbj48PRo0ahaioKMyfP79KsRARvUzMzIzKb6QDMdQFzANzUBYWD6VISkqCubk5hg0bprLd29sboaGhSExMLPNGuIRAIFAWDlKpFHl5eZBKpcruRBcuXKi24uHZs2fo16+fyjZra2t8/vnn6NWrl3Lbvn37AChuUv/bdcrd3R3Jyck4f/48unfvjtatW8PJyQn79+/Hhx9+qLzJjo+Ph1wuh0QiUX62qjnr3r07rK2tERcXp1I8xMXFqY3nKMuwYcNU3uY0btwYzZs3V+m6lZycDAAYNWqUytiJxo0bY8CAAdi5c6dG5yrNoEGD8OabbwJQFDI3b97Eli1bMG3aNKxZs0Zl5quSwkEul+Pp06coLi6GpaUlWrRogQsXLlQ6BiKil1F2dj6kUplWzi0SCWFmZqTVGOoC5oE5sLQ0LrcNi4dS3L59G23btlXroy4QCODg4IDk5GTk5ubCxMSk3GMdPHgQkZGRSE9PR3Fxscq+7OzsaotZT08PgYGBABRjCOLj45GSkqI2sPjatWsAoHaT/7xHjx4p/+7j44MVK1bgt99+UxYh8fHxaNGiBTp27KhsV9Wc6enpwcvLC1FRUbh69SocHBxQUFCAw4cPo1u3brCxsdEoD82aNVPbZm5ujnv37il/vnPnDgCUOnVqVWeleuWVV9CtWzeVbX369MHbb7+Nb7/9VuWtxKVLlxAcHIzTp08jPz9f5TOlXQcRUX0mlcpQXKzdm7W6EENdwDwwB2Vh8VBBcrlc47aHDx/GggUL4OzsjLlz58LW1hYNGjSATCbDzJkzK3Ss8giFQpWb1r59++Kjjz7CsmXL0K5dO7W1HgIDA9Vu9Eu0bt1a+Xdvb2/88MMPiIuLQ69evfDHH3/gxo0bmDZtmsaxaXqdEokEUVFRiIuLw8yZM5GUlISnT5/Cx8dH43MJhaX3UXw+hrLiqc7vpISdnR1atmyJ33//HQUFBTA0NMS9e/cwefJkmJiYYOLEiWjZsiUMDQ0hEAiwcuVKtWKCiIiIqC5g8VCKZs2a4caNGyguLla7wb527RosLCyUT9AFAsELj5OQkAADAwOEhISojHvIyMiokbifJxAIMGfOHAwbNgyBgYFYvXo1AMW0ocePH4etra1Gi8dZWFjAzc0NR48eRW5u7gu7EVUkZy/Stm1btG3bFvv27cP06dMRHx8PY2NjeHh4VOziy1HyVD8jI0Pt7cP169er9VwliouLIZfLkZeXB0NDQyQmJiI/Px/ff/+92vogWVlZyrEkRERERHUJh5KXwsPDA1lZWWp93/fv34+bN2+qLCZX0m89JydH7TglT8Flsv+99pLL5Vi/fn1NhK2mefPm8Pb2xm+//YZz584BAAYMGAAACAoKUutGBQCPHz9W2yaRSFBYWIiEhAQcOnQIYrFYbbGziuSsLD4+Prh//z727duHEydOoF+/fiqFV3UomSFqy5YtkEqlyu0PHjxAQkJCtZ4LAC5fvozr16/D1tZWORNXye/Gf9907N69W6XbGBEREVFdwjcPpRgzZgwOHz6MFStWID09HR06dFBOO2prawt/f39lWwsLC9jb2+PAgQOwt7eHpaUlrKys4Orqir59++LIkSPw9/eHj48PiouLkZycjIKCglq7lvHjxyMhIQEhISFYu3YtnJ2d8f777yMkJASjRo1C//79YWNjg4cPHyItLQ3Hjh1DSkqKyjF69eoFc3NzrFmz5oXdiCqSs7IMGDAAq1atwvLlyyGVSlUGZVcXBwcHDBs2DDt27MD777+Pvn374unTp9i1axdatmyJixcvlvlGqSzp6emIj48H8L8B07t374ZMJsMHH3ygbOfm5obVq1fj888/x/Dhw2Fqaorff/8dx48fh729vUpRQ0RERFRXsHgohYmJCdavX4/Q0FAkJycjPj4e5ubmkEgk8Pf3V1sLYOnSpQgICMDq1atRWFgIFxcXuLq6wsvLC3l5eYiKikJgYKByPYMZM2agb9++tXItLVu2RL9+/XDgwAGcPn0aXbt2xeTJk+Hk5IStW7diy5YtyM/Ph5WVFVq3bo25c+eqHUNfXx9eXl7Yvn07GjZsqLZeAVDxnL2IlZUVevbsiV9++QXNmjVTWZuhOs2bNw82NjbYvXs3Vq1ahaZNm2LChAkoLi7GxYsXYWBgUKnjHjx4EAcPHgSg6DpmZmYGZ2dnjB49WjnLFgDY29tj1apVCAoKQnh4OIRCIV577TWEhITg//7v/3D37t1quU4iIiKi6iSQ18QIUaKX1PLly7Fjxw7s27dPbV2G+sAlohhnH2g7CnrZjWwPREn0AJfZwNmr2g6HXnZdHIAzAXjy5KnWZrfR0xPC0tJYqzHUBcwDc2BjY1puG455IJ1UWtex+/fvIz4+Ho6OjvWycCAiIiKqKnZbqqOysrJQVFRUZhtDQ0ON1pogdXv37kV8fDzc3NxgZWWFW7duYc+ePSgoKFCOTSgqKkJWVla5x7K0tFRbT4OIiIioPmLxUEfNmzcPZ86cKbONRCLBkiVLaiegeqZ9+/ZITk7G9u3bkZWVBUNDQ3Ts2BHjx49H165dAQC///67RgO9Y2JiYGdnV9MhExEREWkdi4c6atasWeWuQK3pqsukrmPHjsq1L16kbdu2CAoKKvdYmg4GJyIiInrZsXioo5ycnLQdgs4zMzNTWbWbiIiISNexeCDSIU7WAgCcYI2qppX5v+ugONlrNxCqH/h7RPRSYfFApCOkMjk2+3BgN1UPuVQGwebZ2g6D6gl5sRQyGR9sEL0MWDwQ6QiRUIAnT3IBVG717PpAJBLCzMwI2dn5kEp1b/5ugDkowTwo1JU8yGRyFg9ELwkWD0Q6RLHgje4WDyWkUplOLv7zPOZAgXlQYB6ISFNcJI6IiIiIiDTC4oGIiIiIiDTC4oGIiIiIiDTC4oGIiIiIiDTC4oGIiIiIiDTC4oGIiIiIiDTC4oGIiIiIiDTC4oGIiIiIiDTCReKIdIienhC6vEicSCRU+acu0qUccNViIqLqx+KBSEdIZXJYWppoO4w6wczMqFbPJ5fKIKhjN+u1nQNtkBdL8TgrnwUEEVE1YvFApCNEQgHejZMi7RFvpGrTgFYCfNVbBLwbAKTd0nY4usPJHoLNsyEUClg8EBFVIxYPRDok7ZEcZx9oOwrd0t7q3xvXtFvA2avaDYaIiKiK6tZ7dCIiIiIiqrNYPBARERERkUZYPBARERERkUZYPBARERERkUZYPBARERERkUZYPFSRWCzGkiVLtB1GjQgJCYFYLMadO3e0HQoRERER1QEvxVStYrFY47YxMTGws7Mrs01OTg6ioqLQtWvXCh27JqSnp2Pbtm04ffo0Hj58CACws7ODWCyGn58fHB0dAVRvzElJSYiNjcWff/6JzMxMGBgYoFWrVnBzc4Ofnx+srKyqfF3aIpPJEB8fj7179+Lvv/9GXl4erKys0LVrV4wePRpt2rQp9xiBgYHYtGkTGjRogOPHj1c6ltzcXGzduhWJiYm4efMmpFIp7Ozs0KtXL4wePbrUPO/cuRNnz55FWloabty4AblcjtTU1ErHQERERFSdXoriYenSpSo/X7t2DeHh4fD09ISnp6fKPktLy3KPl5OTg7CwMAAVK0yq28aNGxEUFARzc3N4e3ujVatWEAgEyMjIwJEjR7Bz507ExsbC1ta2WmIuLCzEokWLkJiYiFatWmHw4MFo0qQJCgsLcenSJWzatAkJCQnYtWtXdV5mrcnPz8fcuXNx4sQJdOzYEWPHjoWZmRlu3LiB2NhY7N+/H5988gmGDh36wmP89ddfiIqKQsOGDVFcXFzpWK5fv46ZM2fi7t278PT0xKBBg6Cnp4fz589jy5YtiI2NxQ8//ICOHTuqfG7jxo3IyspCu3btUFBQgPv371c6BiIiIqLq9lIUDwMHDlT5OTU1FeHh4XB0dFTb97KIj4/HmjVr4OLigpUrV8LU1FRl/wcffICNGzdCLq++lVGXL1+OxMREjBo1Ch999BGEQtVeax9++CF++umnajtfbfv6669x4sQJTJgwAdOmTVPZN2bMGEydOhXffvstXnnlFbi6uqp9XiaTYdmyZejZsyeePn2KCxcuVCqOgoICzJo1Cw8ePMD333+PXr16KfcNHToUw4YNw7Rp0zB79mxs3bpV5Q1ESEgImjRpAqFQiI8++ojFAxEREdUp9WrMQ2ZmJr777jv4+Pige/fu8Pb2xpdffqnsDgQAsbGxeOuttwAAYWFhEIvFEIvFmDJlCgDFDeT69esxefJkeHl5oXv37vDx8cE333yDzMzMaomzqKgIq1evhpGREb799lu1wgEA9PT0MGnSJDRp0qTcmDVx+fJlxMbGomPHjpg1a5Za4QAAFhYW+PDDD9W2P3v2DIGBgRgwYAB69OiBkSNH4tdff1Vpc+fOHYjFYoSEhCApKQnvvfceevbsCS8vLwQGBpb6FD8pKQnvvvuust13332HK1euKI9TEX///TcSEhLQsWNHTJ06tdRrW7ZsGeRyOVavXl3qMbZu3YqrV6/i448/rtC5/2vPnj24ceMGRo0apVI4lOjQoQOmT5+Ox48fY9OmTSr77OzsSv1uiIiIiOqCl+LNgyZyc3MxadIkXL9+HRKJBM7Ozrhy5Qp27dqFlJQUREREwNraGl26dMHs2bMREBCg0u2p5OlvUVERIiMj0a9fP3h4eMDQ0BB//vknoqOjce7cOURGRkJfX79Ksf7+++/4559/MGDAAI3GF5QXsyYOHz4MuVyOwYMHQyAQVCjexYsXo0GDBhg9ejSKioqwZcsWzJ07F7t27VIbX3Ls2DH8/PPP8PPzw+DBg5GcnIxNmzbB1NQUEyZMULY7dOgQFixYgKZNm2LixIkwNDTEgQMH8Mcff1QothJHjhwBgDKvr3Xr1nj11Vfxxx9/4N69e2jSpIly371797B27VpMmjQJTZs2rVQM/41lyJAhL2zj6+uLlStX4siRI6UWbERUPUSi0ovxku0v2q8rmAcF5kGBeWAONFFvioeIiAhkZGRgzpw5GDlypHJ7p06d8NlnnyE4OBgLFy6Evb09PDw8EBAQUGq3pwYNGiAhIQGGhobKbX5+fujUqROWLVuGpKQk9O/fv0qxXr58GQDQrl07jdqXF3NFztm+ffsKf9bS0hLff/+98qZcLBZj7Nix2LVrF2bMmKHS9urVq9i+fbuyqPDz88M777yDbdu2KYuH4uJiBAQEwMzMDD/99JNynMrw4cMxefLkCsdXketr3749/vjjD/z9998qxcO3336Lpk2b4r333qvU+Z935coVGBsb45VXXnlhG0NDQ7Ro0QJXrlxBXl4eGjZsWOXzEpE6MzOjKu3XFcyDAvOgwDwwB2WpN8VDUlISzM3NMWzYMJXt3t7eCA0NRWJiIhYuXFjucQQCgbJwkEqlyMvLg1QqVfaRv3DhQpWLh6dPnwIATExMqnScypzT2Ni4wp8dMWKEytN8Z2dnGBsb48aNG2ptPTw8VN5GCAQCiMVibN++XXmTfOnSJTx48ADvvvuuygB3fX19jBo1Cp9++mmFY9Q0pyX7S9oDwIEDB3Ds2DGEhYVBT6/q/0rk5uaiUaNG5bYriYXFA1HNyc7Oh1QqU9suEglhZmb0wv26gnlQYB4UmAfmwNKy/PvEelM83L59G23btlW7+RMIBHBwcEBycjJyc3M1umE/ePAgIiMjkZ6ertZXPzs7u8qxltzA5+bmVvlYFT3n8zfNmrK3t1fbZmZmhqysLLXtzZo1U9tmbm4OAMjKykLDhg1x+/ZtAECLFi3U2rZs2bLC8QGa57Rkv7W1NQDF97ly5Ur4+vqic+fOlTr3f5mYmGj03ebm5kIoFMLCwqJazktE6qRSGYqLX3wDUN5+XcE8KDAPCswDc1AWnejQVZEZiw4fPowFCxYAAObOnYvvv/8eQUFBykG21TH7UcnaDenp6VU+VkXPeenSpQp/9kUDeEvLRVmDfUval5XDyuZX0+sr2V/SpSgsLAx5eXkYNmwY7ty5o/zz7NkzyOVy3LlzBw8ePKhQLK1bt8bTp09x8+bNF7bJz8/H9evX0bRp02p520FERERUG+pN8dCsWTPcuHGj1Fl9rl27BgsLC+Vbh7IGDCckJMDAwAAhISF4++230bt3b3Tr1q3Kg2if99prr8HGxgbJycl48uSJRp+p6CDn/+rbty8EAgGio6OrdfrXyih5k5GRkaG27/r165U65htvvAEA2L179wuv7/Llyzh//jy6dOmiHO9w584d5OfnY/To0XjrrbeUfy5cuICioiK89dZbpc7epEksZa2XERsbi+LiYgwYMKBCxyYiIiLSpnpTPHh4eCArKws7d+5U2b5//37cvHlTZTE5IyPFIJicnBy145Q8OZfJ/veqSi6XY/369dUWq76+PqZPn468vDwsWLCg1C4uRUVFWLduHe7du1duzJpwdHSERCLB+fPnsWrVKpXrK5GZmYkffvihUseviPbt28PGxgZxcXEqxVNRURGioqIqdcw2bdpgwIAB+PPPP7F27Vq1/ZmZmfj8888hFApVprgdP348VqxYofbHwcEBenp6WLFiBT755JMKxTJ48GA0b94cW7ZsUZvSFgAuXryIH3/8EY0aNVIbo0NERERUl9Wb/hJjxozB4cOHsWLFCqSnp6NDhw7KqVptbW3h7++vbGthYQF7e3scOHAA9vb2sLS0hJWVFVxdXdG3b18cOXIE/v7+8PHxQXFxMZKTk1FQUFCt8UokEty/fx/BwcEYMmSIcoVpAMoVph88eKBc36GsmDX1ySefICsrC5s2bcKxY8fQt29f5QrTaWlpOHz4MGxsbPDRRx9V67X+l56eHmbNmoWFCxdi7NixGDx4MAwMDHDgwAHlW4PKvGlZsGABHj9+jA0bNuDUqVN44403lCtMx8TEIDc3F/Pnz1fJ2X9XeC4RFRWFW7duwcPDo8JxGBoaIiAgAB988AFmzZqFN954A2KxGCKRCBcuXEBCQgJMTU2xcuVK5diLEkePHsVff/0FAMpuT+vWrVPunzRpUoXjISIiIqou9aZ4MDExwfr16xEaGork5GTEx8fD3NwcEokE/v7+ajdpS5cuRUBAAFavXo3CwkK4uLjA1dUVXl5eyMvLQ1RUFAIDA2Fqagp3d3fMmDEDffv2rdaYJ06cCDc3N2zbtg1Hjx7Fzp07IRAIYGdnhz59+sDPzw+NGzcuN2ZNGRoaYuXKlUhMTERsbCx27dqFrKwsGBgYoFWrVhg7diz8/Pyq9Rpf5M0334S+vj7CwsIQFhYGMzMzvPnmm/Dy8sK4ceNgYGBQ4WM2bNgQq1atQnx8PPbu3Yvw8HDlAHcDAwNERESgTZs21X0ppWrZsiWioqKwdetWHDlyBMePH0d+fj4AwMHBAevXry91ccAjR45g7969KtuCg4OVf2fxQERERNokkGu7AzzRcw4dOoT58+fjq6++gpeXV7Ucc9OmTQgMDISnpye++eYbrQ1QLi4uxvz585GUlISPPvqoWtaUqCiXiGKcrdj4b6qike2BKIke4DIbOHtV2+Hoji4OwJkAPHnytNQZU/T0hLC0NH7hfl3BPCgwDwrMA3NgY6P+YPO/6s2YB3q5FBUVQSqVqm3bvHkz9PT0IBaLq+1co0ePhr+/PxITE7FkyZJSx3vUBj09PXzzzTdwc3PDDz/8gJ9//lkrcRARERFVVr3ptlQXZGVloaioqMw2hoaG1bo43MOHD8ttY2JiorJidl1w+/ZtfPDBB/Dy8oKdnR0ePXqEAwcO4OrVqxg/fryym1l1Xd+kSZOq1OWnqKio1HUt/svS0hIikeiF+/X19REYGFjpOIiIiIi0icVDNZo3bx7OnDlTZhuJRIIlS5ZU2zm9vb3LbbN48WL4+vpW2zmrg4WFBTp27IiEhATljEsODg5YuHAhhgwZomxXV67v999/Vxl0/yIxMTEqK2wTERER1ScsHqrRrFmzyl2B2sbGplrPGRQUVG6b1q1bV+s5q4OFhQW+/vrrctvVletr27atRrH8d2A+ERERUX3C4qEaOTk51fo5u3XrVuvnrE115frMzMzqTCxERERE2sLigUiHOFkLAHCCtdrUyvzfNUuc7LUbiK5hvomIagSLByIdIZXJsdnnxYO5qebIpTIINs/Wdhg6R14shUzGYpmIqDqxeCDSESKhAE+e5AKo+Ord9YVIJISZmRGys/Mhlere/N2AbuVAJpOzeCAiqmYsHoh0iGLBG90tHkpIpTKdXPznecwBERFVBheJIyIiIiIijbB4ICIiIiIijbB4ICIiIiIijbB4ICIiIiIijbB4ICIiIiIijbB4ICIiIiIijbB4ICIiIiIijbB4ICIiIiIijXCROCIdoqcnhC4vEicSCVX+qYtehhxwZWgiorqLxQORjpDK5LC0NNF2GHWCmZlRtRxHLpVBUIdvwstSXTmoCfJiKR5n5bOAICKqg1g8EOkIkVCAd+OkSHvEG7LqMKCVAF/1FgHvBgBpt7QdTv3hZA/B5tkQCgUsHoiI6iAWD0Q6JO2RHGcfaDuK+qG91b83tmm3gLNXtRsMERFRLXk537cTEREREVGtY/FAREREREQaYfFAREREREQaYfFAREREREQaYfFAREREREQaqVfFg1gsxpIlS7QdhgpfX19MmTKlRo69ZMkSiMXiGjl2ZU2ZMgW+vr4atU1NTYVYLEZsbGwNR0VERERE1aHCU7VW5GY1JiYGdnZ2ZbbJyclBVFQUunbtqrUb4SlTpuDMmTPKn0UiESwtLdGlSxdMnDgRjo6OWokLAJKSkpCeno73339fazHUFw8fPsTWrVtx/Phx3L59G8+ePYO1tTVeffVVSCQSuLm5aTtENSEhIWjXrh08PDy0HQoRERFRxYuHpUuXqvx87do1hIeHw9PTE56enir7LC0tyz1eTk4OwsLCAFSsMKluenp6+PzzzwEAhYWFSEtLQ2xsLI4dO4aIiAi0bNlSK3ElJSVh7969pRYPixYtwoIFC7QQVfVwcXHBsWPHoKdX88uNpKSkYP78+SgoKEC/fv3w1ltvwcjICPfu3cOxY8fw4YcfYtmyZfD29q7xWCoiLCwMEomExQMRERHVCRW+axs4cKDKz6mpqQgPD4ejo6PavpeJUChUiX/IkCFwcHDAihUrsH37dnz88cdajK50enp6tXLjXVOEQiEMDAxq/DwZGRmYN28eTExMsH79erRu3Vpl//vvv4/ExEQIhfWqFx8RERFRtauxu6XMzEx899138PHxQffu3eHt7Y0vv/wSDx8+VLaJjY3FW2+9BUDxhFUsFkMsFivHCMhkMqxfvx6TJ0+Gl5cXunfvDh8fH3zzzTfIzMysqdCVXF1dAQA3b95U23fgwAFMnDgR7u7ucHNzw9ixY3Ho0CGNjpuSkoIFCxZg0KBBcHNzg4eHB6ZPn47Tp0+rtPP19cXevXsBQJkbsViM1NRUAC8e83D58mXMmzcPffv2RY8ePTB06FCEhYXh2bNnKu1CQkIgFouRkZGBwMBADBgwAD169MDIkSPx66+/qh03Li4OY8aMgaenJ9zc3ODj44MFCxaofKcl7t+/j/nz58PT0xO9evXCjBkzcP36dZU2pY15eH7b1q1bMXToUPTs2RNDhgzBli1bNMrvf61duxb5+flYuHChWuFQwtPTE3369FHZFhMTg9GjR8PNzQ3u7u54//33kZKSovbZF421iY2NVfm+AM1zXpIHANi7d6/yu9d0PAkRERFRTaiRx9a5ubmYNGkSrl+/DolEAmdnZ1y5cgW7du1CSkoKIiIiYG1tjS5dumD27NkICAhQ6fZkZWUFACgqKkJkZCT69esHDw8PGBoa4s8//0R0dDTOnTuHyMhI6Ovr18QlAPhf0WBubq6y/ccff8SGDRvQs2dP+Pv7QygUIikpCfPnz8fHH3+M4cOHl3nc2NhY5OTkwNfXF40aNcKDBw8QHR2NadOmITg4GF26dAEAzJkzB5s3b8bZs2dVuou1atXqhce+dOkSJk+eDKFQiGHDhqFx48b47bffEBISgvPnz+OHH35Qe8K+ePFiNGjQAKNHj0ZRURG2bNmCuXPnYteuXcoxK/Hx8Vi8eDG6dOmC999/H4aGhrh//z5+++03/PPPP2jUqJHyePn5+ZgyZQo6deqE6dOn4/bt29i6dSvmzJmDbdu2QSQSlZv7bdu24dGjRxg6dCgaNmyI/fv3Y+XKlcjKyoK/v3+5ny9RWFiIX3/9Fba2tujVq5fGnwsKCkJ4eDicnJwwdepUFBYWIiYmBjNnzsTSpUsxYMAAjY9VmvJy3qpVKyxduhSff/45unTpgiFDhgAAGjZsWKXzEhEREVVFjRQPERERyMjIwJw5czBy5Ejl9k6dOuGzzz5DcHAwFi5cCHt7e3h4eCAgIKDUbk8NGjRAQkICDA0Nldv8/PzQqVMnLFu2DElJSejfv3+1xV3yNqOgoACXLl3CypUrAUClH3xaWho2bNiAcePGYcaMGcrtI0aMwJw5cxAUFAQfHx8YGxu/8DyLFi2CkZGRyjY/Pz8MHz4c4eHhyuLBw8MDSUlJOHv2rMZdwr777jsUFhYiIiIC7du3BwAMHz4cX331FXbv3o0DBw6o9eu3tLTE999/D4FAAEDxJH3s2LHYtWuX8hoTExNhbGyMtWvXqnSVKm0sRmZmJkaPHo2xY8eqnGPVqlU4efIkevToUe513LhxAzt27ICtra3yGiZOnIjw8HAMGjQITZs21SgfN2/eRGFhIdq2batRewC4fv06Nm7ciI4dOyI0NBQNGjQAoPiO3nnnHXz33Xfw8PBQ+w4rorycW1tbY+DAgfj888/RrFmzl7pLIFFliEQ1242w5Pg1fZ66jnlQYB4UmAfmQBM1UjwkJSXB3Nwcw4YNU9nu7e2N0NBQJCYmYuHCheUeRyAQKAsHqVSKvLw8SKVSZXeiCxcuVFvx8OzZM/Tr109lm7W1NT7//HOVJ9b79u0DAPj4+Kh1nXJ3d0dycjLOnz+P7t27v/Bcz9905uXl4dmzZxCJROjYsSMuXLhQ6Wt48uQJfv/9d/Tu3VtZOJSYOHEidu/ejSNHjqgVDyNGjFDexAKAs7MzjI2NcePGDeU2ExMTFBQU4Ndff0WfPn1U2v+XUCjEiBEjVLaVfGc3btzQqHjw9vZWFg4AoK+vj1GjRmHRokU4evQo3nnnnXKPASjegpXEr6nk5GTI5XKMGTNGWTgAgIWFBYYNG4bg4GCkpqaid+/eGh/zvzTJOZEuMzOrfHFeF89T1zEPCsyDAvPAHJSlRoqH27dvo23btmqDeQUCARwcHJCcnIzc3FyNbugOHjyIyMhIpKeno7i4WGVfdnZ2tcWsp6eHwMBAAIoZoOLj45GSkqLWxebatWsAoFYYPe/Ro0dlnuvWrVsICgpCSkoKcnJyVPaVdVNentu3bwNAqf36mzRpAhMTE2Wb59nb26ttMzMzQ1ZWlvLniRMn4ty5c5g7dy7Mzc3RpUsX9OzZE2+++aba92hjY6M2ELqk69fzxyxLaV2zHBwcACjyp6mS2EqKCE2U5KjkfM8rmba3tDxWhCY5J9Jl2dn5kEplNXZ8kUgIMzOjGj9PXcc8KDAPCswDc2Bp+eKeMyVqfaoeuVyucdvDhw9jwYIFcHZ2xty5c2Fra4sGDRpAJpNh5syZFTpWeYRCIbp166b8uW/fvvjoo4+wbNkytGvXTm2th8DAwBfOdPSiQbkA8PTpU0yaNAkFBQUYOXIkHB0dYWxsDIFAgI0bN+LUqVOVvobK5uNFsww9fzx7e3ts374dqampOHnyJE6fPo2vv/4aISEhWLt2rcqNdlmzFmkaY2lFVMlnK1JgvfLKKzAwMMBff/2l8WfKirEiOZZKpS/cp0nOiXSZVCpDcXHN/4+7ts5T1zEPCsyDAvPAHJSlRoqHZs2a4caNGyguLla7wb527RosLCyUT4TLuhFMSEiAgYEBQkJCVMY9ZGRk1ETYKgQCAebMmYNhw4YhMDAQq1evBgA0b94cx48fh62tbaUWjzt16hQePnyIzz//XDnTVIm1a9eWGoemSp5mX7lyRW3f/fv3kZubW+oTb03p6+ujR48eym5Hqamp8Pf3x08//YQvvvii0sctzdWrV9W2lbz1adasmcbHMTAwgJubG44cOYLjx4+jZ8+e5X6mJEdXr15VW9+jJLfP59Hc3LzUNwZVfTtBREREVNfUyGgQDw8PZGVlYefOnSrb9+/fj5s3b6osJlfS//+/3XeA/z2dlcn+V/nJ5XKsX7++JsJW07x5c3h7e+O3337DuXPnAEA5y05QUJBaNyoAePz4cZnHLOkG9d8nzCkpKaWOdyjJjyZdtCwtLfHaa6/h+PHjSE9PV9m3YcMGAFBbyE9TpU2N2759ewiFwmrtPlZi3759uH//vvLnoqIiREVFQSQSwd3dvULH8vf3h6GhIZYtW6YsQP7ryJEjSE5OBqD4/RUIBIiMjERRUZGyTVZWFn7++WeYmZmha9euyu3NmzfH+fPnUVBQoNyWnZ2NmJiYCsVZmoYNG9ZIfomIiIgqo0bePIwZMwaHDx/GihUrkJ6ejg4dOiinarW1tVWZatPCwgL29vY4cOAA7O3tYWlpCSsrK7i6uqJv3744cuQI/P394ePjg+LiYiQnJ6vcpNW08ePHIyEhQdk9x9nZGe+//z5CQkIwatQo9O/fHzY2Nnj48CHS0tJw7NixUtcCKNG5c2dYW1vjhx9+wN27d9G4cWP89ddfiI+Ph6OjIy5fvqzSvmPHjti+fTuWL1+Onj17Qk9PD66ursrpbP9r3rx5mDx5MqZMmYLhw4fDxsYGKSkpOHr0KHr06IE333yzUnmYPn06TExM4OLiAltbW+Tm5iIuLg4ymQw+Pj6VOmZZmjdvjnHjxsHPzw8NGzbEvn37cPHiRUyaNEnjmZZKODg44P/+7/+wYMECjBo1Cv369UPHjh2V083++uuvuHjxIr766iuVc4eHh2PixIl488038ezZM0RHR+PRo0f44osvVAa9Dx8+HJ999hn8/f0xcOBA5OTkYM+ePWjatGm541/K07FjR5w8eRIRERGwtbWFkZFRhYsnIiIioupSI8VDyUq+oaGhSE5ORnx8PMzNzSGRSODv7w9ra2uV9kuXLkVAQABWr16NwsJCuLi4wNXVFV5eXsjLy0NUVBQCAwNhamoKd3d3zJgxA3379q2J0NW0bNkS/fr1w4EDB3D69Gl07doVkydPhpOTE7Zu3YotW7YgPz8fVlZWaN26NebOnVvm8UxNTbFmzRqsWrUK27Ztg1QqRfv27REYGIjo6Gi14sHLywtpaWk4cOAADh48CJlMhuDg4BcWD+3bt0d4eDhCQkKwa9cuPH36FHZ2dpgyZQrGjRtX6VWUhw0bhoMHD2LXrl3Izs6GmZkZ2rRpgw8++ECj2ZMq6p133sHTp0+xbds23Lt3D02aNFGb+rcievbsiZ9//hlbtmzB8ePHkZycjGfPnqFRo0bo1KkTpk2bpjJD1vTp02Fvb48dO3Zg7dq1EAqFcHJywvz589Wud8CAAfjnn3+wfft2fP/992jWrBkmTZoEoVBYpdmzAODjjz/G8uXLsW7dOuTl5aFp06YsHoiIiEhrBHKO0KQ6pGQcxeLFi7macg1wiSjG2QfajqJ+GNkeiJLoAS6zgbPqY3Sokro4AGcC8OTJ0xodrKinJ4SlpXGNn6euYx4UmAcF5oE5sLExLbcNV8AgIiIiIiKN1PpUrbUlKytLZbBraQwNDSu0eBjVHXl5ecjLyyuzjUgkgqWlZS1FRERERFT/1dviYd68eThz5kyZbSQSCZYsWVI7AVG12rRpE8LCwsps07RpU8TGxtZSRERERET1X70tHmbNmlXuFJc2Nja1FA1pSiwWIzU1tdx2Pj4+6Ny5c5lt/rvKNRERERFVTb0tHpycnLQdAtUge3v7Ki14R0REREQVV2+LByJS52QtAMAJ1qpDK/N/V393YhFbrZhPIqI6jcUDkY6QyuTY7CPSdhj1ilwqg2DzbG2HUe/Ii6WQyVjkEhHVRSweiHSESCjAkye5AATaDkVrRCIhzMyMkJ2dD6lU9+bvBl6OHMhkchYPRER1FIsHIh2iWPBGd4uHElKpTCcX/3kec0BERJXBReKIiIiIiEgjLB6IiIiIiEgjLB6IiIiIiEgjLB6IiIiIiEgjLB6IiIiIiEgjLB6IiIiIiEgjLB6IiIiIiEgjLB6IiIiIiEgjXCSOSIfo6Qmhy4vEiURClX/WNq6cTERELzsWD0Q6QiqTw9LSRNthVJpcKoOgmm76zcyMquU4FSUvluJxVj4LCCIiemmxeCDSESKhAO/GSZH26OW7cR3QSoCveouAdwOAtFvaDqdynOwh2DwbQqGAxQMREb20WDwQ6ZC0R3KcfaDtKCquvdW/N9tpt4CzV7UbDBERkQ7jgGkiIiIiItIIiwciIiIiItIIiwciIiIiItIIiwciIiIiItIIiwciIiIiItIIi4eXXGxsLMRiMVJTU5XbUlNTIRaLERsbq9x2584diMVihISEaCNMAKXHqquYCyIiInoZ1cviITc3F25ubmo30HXBlClTIBaLX7g/JCRE7aYyPT0dISEhuHPnTm2EWCUvU6wAsGTJEojFYuWf119/Hf3798esWbNw7ty5Kh37ZcsFERERUXnq5ToP+/btw7Nnz2Bvb4/o6Gj4+vpqO6Qq+euvvxAWFoauXbvCzs5OZd/AgQPx5ptvQl9fv8xjNG3aFMeOHYNIJKrJUKslVm34+OOPYWJigqKiIly9ehW7d+/G8ePHsXbtWri4uFTqmC9rLoiIiIhepF4WD9HR0ejSpQvefPNNfPvtt8jIyEDLli21HVaNEIlEGhUEAoEABgYGtRDRi2kaqza88cYbaNSokfJnFxcXzJkzB5s2bap08VCWupwLIiIiohepd92W/v77b6SlpcHX1xdeXl5o0KABYmJiAABSqRQDBgzAiBEjSv3snj17IBaLcejQIeW2e/fuYcGCBfDw8EDv3r3h7++PS5cuYcqUKbXyRmPJkiX44osvAAD+/v7K7jUlYxc07Ttf2piHki5UL/pTIiMjA99++y2GDx8Od3d3uLm54b333sPu3burJdbMzEx899138PHxQffu3eHt7Y0vv/wSDx8+VGn3/FiOPXv2YNiwYejRowckEgl++umniqS1XK+//joA4MaNGyrb60ouiIiIiLSh3r152LNnD4yMjNC3b180bNgQ7u7uiIuLw7Rp06Cnpwdvb29s2rQJ6enpaNeuncpn4+PjYWZmBnd3dwBAdnY2Jk2ahH/++QdDhgyBo6MjLl26hKlTp8LCwqJWrmfo0KHQ19fH7t27MX78eLRq1QoA0KZNmyofe8KECRg8eLDKtidPnmDVqlUwNzdXbktNTcW5c+fQp08fNGnSBPn5+Th06BC++uorZGZmYvz48ZWONTc3F5MmTcL169chkUjg7OyMK1euYNeuXUhJSUFERASsra1VPvPzzz/jyZMnGDRoEExMTJCQkIDVq1fD1tYW3t7eVc4LANy8eRMAVPJQF3NBREREVJvqVfHw7Nkz7Nu3D2+88QYaNmwIAJBIJDh06BCOHTuGPn36QCKRYNOmTYiLi1MpHu7cuYOzZ8/Cz88PDRo0AAD89NNPuHfvHj777DMMGjRI2dbR0RErVqxA06ZNa/yaOnXqhOvXr2P37t3o1q1bmYOtK6p79+4qPxcUFMDf3x96enoICAhQbpdIJHj77bdV2o4aNQr+/v7YuHEjRo8eDT09vUrFGhERgYyMDMyZMwcjR45Ubu/UqRM+++wzBAcHY+HChSqfuX//Pnbs2AFTU1MAwKBBgyCRSLBt27ZKFw/Z2dnQ09NTjnn44YcfAAADBgxQaVfXckEvH5FIuy98S86v7Ti0jXlQYB4UmAcF5oE50ES9Kh4SExORlZWl0p2oR48eaNSoEaKjo9GnTx+0bt0aTk5O2L9/Pz788ENlv/P4+HjI5XJIJBLlZ5OTk2FpaamyDQD8/Pywdu3a2rmoWiKTyfDZZ5/h4sWLWL58OTp27KjcZ2hoqPx7YWEh8vPzASiKjzNnziAjIwOOjo6VOm9SUhLMzc0xbNgwle3e3t4IDQ1FYmKi2g2zr6+vsnAoie/VV1/FH3/8UakYAGD48OEqPxsbG2PGjBlqcdW1XNDLx8zMSNshAKg7cWgb86DAPCgwDwrMA3NQlnpVPERHR8PS0hKNGzdWdjsBgG7dumHfvn14+PAhGjVqBB8fH6xYsQK//fYbevXqBUBRPLRo0ULlpvnOnTto37692sBWfX19NGvWDDk5OTV2LQKBoMaOXZrvv/8eiYmJmDVrFjw9PVX25eXlITQ0FAcPHsT9+/fVPpudnV3p896+fRtt27aFnp7qr6JAIICDgwOSk5ORm5sLExMT5b5mzZqpHcfc3BxZWVmVjuPbb7+Fqakp8vLycPToUcTFxZXarq7lgl4+2dn5kEplWju/SCSEmZmR1uPQNuZBgXlQYB4UmAfmwNLSuNw29aZ4uHPnDk6dOgW5XI6hQ4eW2mbv3r0YN24cvL298cMPPyAuLg69evXCH3/8gRs3bmDatGk1HmfJk+uCggKVp9glCgoKAKBWZ0baunUrtmzZgmHDhuHdd99V279w4UL8+uuvGDJkCFxcXGBmZgaRSIRjx44hKioKMlnN/Msll8tL3V4TsxR17txZOduSp6cnDAwMsGbNGrRv316le1ddywW9fKRSGYqLtf8/pLoSh7YxDwrMgwLzoMA8MAdlqTfFQ2xsLORyOT799FOYmZmp7V+3bh1iYmIwbtw4WFhYwM3NDUePHkVubi7i4uIgFAoxcOBAlc/Y2dnh5s2bkEqlKjesRUVFuH37dqnnKU/JfP/Xrl2Dk5OT2v5r166ptANq9i1EUlISAgIC0Lt3b8ydO1dtf05ODn799VcMHDgQn376qcq+kydPqrWvaKzNmjXDjRs3UFxcrPbE/dq1a7CwsNDKk/Zp06Zh//79CAgIwNatWyEUCnU2F0REREQl6sVoEJlMhtjYWDg4OGDo0KHo16+f2h9vb2/cuHFDuWqwRCJBYWEhEhIScOjQIYjFYjRp0kTluO7u7njy5An27t2rsn3nzp14+vRppWLt06cPAJT6lPry5cs4ceIEOnXqBCsrK+V2IyNFv7vq7iZ14cIFLFq0CO3atcPXX39d6hN9oVDxK/LfJ98PHz7Enj171NpXNFYPDw9kZWVh586dKtv379+PmzdvqnWhqi1mZmYYMWIErl69iv379wPQ3VwQERERlagXbx5OnDiBe/fuYfLkyS9s07dvX6xZswbR0dHo3LkzevXqBXNzc6xZswZPnz6Fj4+P2mfGjBmD/fv34+uvv0ZaWppyqtbExES88sorkEqlFY61R48e8PLyQkJCAm7fvg13d3eYmJggIyMDe/bsgb6+Pj7++GOVz3To0AFCoRDh4eHIzs6GoaEhWrduXemBuSVmz56NZ8+eoX///khMTFTbP3DgQBgbG6N79+5ISEiAgYEBnJ2dcffuXezatQvNmjVTG2dQ0VjHjBmDw4cPY8WKFUhPT0eHDh2U05Pa2trC39+/StdYFSNHjkRUVBTCwsLw5ptv6nQuiIiIiIB6UjxER0cDUBQIL/LKK6+gTZs2OHToEObOnQtjY2N4eXlh+/btaNiwId544w21z1hYWGDdunUIDAxEQkICZDIZXn31VaxduxZLly5FYWFhpeL98ssv0aVLF+zduxcbNmxAYWEhGjVqhH79+mH8+PFo0aKFSvumTZti4cKF+Omnn/D1119DKpVi8uTJVS4eHj9+DABYtWpVqftLunF9+eWXWL16NX755RfExcXhlVdeUa6bUbIQWmVjNTExwfr16xEaGork5GTEx8fD3NwcEokE/v7+Wl3XwMzMDMOGDcPGjRuRkJAAiUSis7kgIiIiAgCBnCMxK6y4uBj9+/dHx44dsXr1am2HQ6Qxl4hinH2g7SgqbmR7IEqiB7jMBs5e1XY4ldPFATgTgCdPnmp1EJ6enhCWlsZaj0PbmAcF5kGBeVBgHpgDGxvTctvUizEPNalk9qPn/fzzz8jJyUG3bt20EBERERERkXbUi25LNenDDz9E06ZN0b59ewDA77//joMHD6J58+bKKWFzc3NLLTKep6+vD3Nz8xqPl/h9EBEREdUUFg/l6N27N+Lj45GUlISCggI0atQIw4YNw5QpU9CwYUMAwIoVK9RmZPovFxcXhIaG1kbIOo/fBxEREVHNYPFQjvfeew/vvfdemW3GjBmDAQMGlNmmMmtCUOXw+yAiIiKqGSweqoGDgwMcHBy0HQb9i98HERERUc1g8UCkQ5ysBQBevgnWWpn/u1q3k712A6mKlzl2IiKif7F4INIRUpkcm33UVxF/WcilMgg2z9Z2GFUiL5ZCJnv5ijciIqISLB6IdIRIKMCTJ7kABNoORWtEIiHMzIyQnZ0PqbT25++WyeQsHoiI6KXG4oFIhygWvNHd4qGEVCrTycV/iIiIqoqLxBERERERkUZYPBARERERkUZYPBARERERkUZYPBARERERkUZYPBARERERkUZYPBARERERkUZYPBARERERkUZYPBARERERkUa4SByRDtHTE0KXF4kTiYQq/9QEV4UmIiL6HxYPRDpCKpPD0tJE22GUSi6VQVCBG/qqMjMz0ritvFiKx1n5LCCIiIjA4oFIZ4iEArwbJ0Xao7p1EzyglQBf9RYB7wYAabe0HY4qJ3sINs+GUChg8UBERAQWD0Q6Je2RHGcfaDsKVe2t/r0pT7sFnL2q3WCIiIioTBwwTUREREREGmHxQEREREREGmHxQEREREREGmHxQEREREREGmHxQEREREREGtHp4kEsFmPJkiXaDqPOSk1NhVgsRmxsrLZDISIiIqI6oManahWLxRq3jYmJgZ2dXZltcnJyEBUVha5du1bo2DUlKCgI4eHhsLOzQ3R0NASCmlu9NyoqCqampvD19a3ScdLT07Ft2zacPn0aDx8+BADY2dlBLBbDz88Pjo6O1RGu1pw5cwbbtm3D77//jszMTJiamqJDhw4YOnQo+vTpU+7njx07hg8//BAAEB4ejldffbVScchkMsTHx2Pv3r34+++/kZeXBysrK3Tt2hWjR49GmzZt1D6TkpKCI0eO4NKlS/j7779RVFSE4ODgOvG7TkRERFTjxcPSpUtVfr527RrCw8Ph6ekJT09PlX2WlpblHi8nJwdhYWEAKlaY1ASpVIq9e/eiefPmuHHjBk6dOoXXX3+9xs63ZcsWNG3atErFw8aNGxEUFARzc3N4e3ujVatWEAgEyMjIwJEjR7Bz507ExsbC1ta2GiOvPSXFXNOmTTFo0CDY2dnh0aNH2LdvH+bMmQOJRILPP/8cQmHpL93y8/Px7bffomHDhsjLy6t0HPn5+Zg7dy5OnDiBjh07YuzYsTAzM8ONGzcQGxuL/fv345NPPsHQoUNVPrdv3z7s27cPrVu3RqtWrfDXX39VOgYiIiKi6lbjxcPAgQNVfk5NTUV4eDgcHR3V9r1sjh8/jn/++Qc//vgjFi9ejOjo6BotHqoqPj4ea9asgYuLC1auXAlTU1OV/R988AE2btwIufzlXEl3z549CA8Px+uvv46AgAAYGhoq940ZMwZffvkl9u7dCzs7O0yZMqXUY6xduxbFxcUYMmQINm/eXOlYvv76a5w4cQITJkzAtGnTVPaNGTMGU6dOxbfffotXXnkFrq6uyn3Tpk3Dp59+igYNGmDTpk0sHoiIiKhOqTNjHjIzM/Hdd9/Bx8cH3bt3h7e3N7788ktltxoAiI2NxVtvvQUACAsLg1gshlgsVt4IymQyrF+/HpMnT4aXlxe6d+8OHx8ffPPNN8jMzKz2mKOjo2FnZwdXV1cMGDAASUlJyM7OVmsXEhICsViMq1evYuXKlfDy8kKvXr0wdepUZGRkAACOHDmCd999F25ubpBIJPj555+Vn79z5w7EYjHu3r2LM2fOKK+7Im9eioqKsHr1ahgZGeHbb79VKxwAQE9PD5MmTUKTJk3U9u3ZswfDhg1Djx49IJFI8NNPP6m18fX1xZQpU3DlyhXMnDkT7u7u6NOnDz7++GOV77HElStX8MEHH6B3797w8PDA7NmzcevWLeVxKqKoqAhr165Fw4YN8dVXX6kUDiXX9umnn6JJkyb46aefSv19SEtLw7Zt2zBnzhw0bNiwQud/3t9//42EhAR07NgRU6dOVdtvYWGBZcuWQS6XY/Xq1Sr7GjdujAYNGlT63EREREQ1qcbfPGgiNzcXkyZNwvXr1yGRSODs7IwrV65g165dSElJQUREBKytrdGlSxfMnj0bAQEBKt2erKysAChuICMjI9GvXz94eHjA0NAQf/75J6Kjo3Hu3DlERkZCX1+/WmJ+9OgRfvnlF0yYMAECgQASiQQRERFISEjAO++8U+pnFi9eDBMTE4wfPx5ZWVmIjIzEjBkzMHXqVKxevRp+fn4wMzNDdHQ0vv32Wzg4OMDFxQWWlpZYunQpAgICYGFhgQkTJlQ43t9//x3//PMPBgwYoMyXpn7++Wc8efIEgwYNgomJCRISErB69WrY2trC29tbpe0///yDqVOnwtPTEx4eHkhPT8fu3bvx9OlTBAUFKdvdunULkyZNwrNnzzB8+HDY2dnh9OnT8Pf3R0FBQaWu79GjRxgwYMALu78ZGBhgwIABCA8Px6+//gqJRKLcJ5VKsWzZMnTr1g39+vXDlStXKhxDiSNHjgAABg8e/MIxMK1bt8arr76KP/74A/fu3Su1YCMiIiKqa+pE8RAREYGMjAzMmTMHI0eOVG7v1KkTPvvsMwQHB2PhwoWwt7eHh4cHAgICSu321KBBAyQkJKg8dfbz80OnTp2wbNkyJCUloX///tUSc1xcHGQyGXx8fAAADg4O6NChA2JiYl5YPDRu3BgrVqxQ3lBaWFjgu+++w//93/9h+/btynEGb775Jnx8fLBjxw64uLjAyMgIAwcOxNq1a2FlZVWp7l6XL18GALRr167Cn71//z527NihfFsxaNAgSCQSbNu2Ta14uHnzJr755huVPItEIuzYsQMZGRlo2bIlAMXYhJycHKxdu1bZbWfYsGEICAhAVFRUjV1f+/btVdqX2Lx5MzIyMrB8+fIKn/tFsZScq6xY/vjjD/z9998sHuo4kajOvKStspJrqU/XVBnMgwLzoMA8KDAPzIEm6kTxkJSUBHNzcwwbNkxlu7e3N0JDQ5GYmIiFCxeWexyBQKAsHKRSKfLy8iCVSpU3pxcuXKi24iEmJgZdunSBvb29cpuvry+WL1+OS5culXrjOHz4cJUn0a+99hoAwN3dXWWAsqWlJVq0aIFbt25VS6wA8PTpUwCAiYlJhT/r6+ur0s3J0NBQ+dT8v2xsbNRyLBaLsWPHDty8eRMtW7aEVCrFr7/+ivbt26v09weAsWPHVqp40PT6SvaXtAeA27dvIzQ0FBMmTFD5PiurKrFQ3WRmZqTtEKpdfbymymAeFJgHBeZBgXlgDspSJ4qH27dvo23bttDTUw1HIBDAwcEBycnJyM3N1ejG9+DBg4iMjER6ejqKi4tV9pU2HqEyzp07h4yMDHh5eeHmzZvK7R06dIBQKER0dHSpxUOzZs1UfjYzMwOAUqenNTU1xb1796olXgAwNjYGoOgiVlH/jRsAzM3NkZWVpXFbAMr2T548QX5+Plq0aKHW1trautTxGOXR9PpK9ltbWyu3ffPNN2jSpAnGjBlT4fNWdyxUN2Vn50MqlWk7jGohEglhZmZUr66pMpgHBeZBgXlQYB6YA0tL43Lb1InioSwVmfnn8OHDWLBgAZydnTF37lzY2tqiQYMGkMlkmDlzZrXNIhQdHQ1AMRA6JCREbf++ffvw0UcfwcDAQGX7i6YHfdH26pz1qGTthvT09Ap/ViQSadz2RdcCaH49lbluTa/v0qVLAKB8w5CUlISUlBQsWbIE//zzj7JdyY39w4cPcefOHdja2mqcB0dHRyQmJr7wDdR/Y3nllVc0Oi5pj1QqQ3Fx/fqfSH28pspgHhSYBwXmQYF5YA7KUieKh2bNmuHGjRsoLi5We/tw7do1WFhYKN86lLUIW0JCAgwMDBASEqIy7qFkRqPq8PTpUxw6dAivv/662hz9JecKDg5GYmKi2niAqqrKAnSvvfYabGxskJycjCdPnmi0pkZNsbS0hJGRUanfy6NHjyr1duS1116DtbU1kpKS8Pjx41IHhRcWFiI+Ph5GRkbw8PAAoJjJCsALVxqfN28eAEVB2KhRI41ieeONNxAWFobdu3dj0KBBpX5vly9fxvnz59GlSxeOdyAiIqKXRp0YDeLh4YGsrCzs3LlTZfv+/ftx8+ZNlcXkjIwUfdBycnLUjlPy1Fsm+1+lKJfLsX79+mqL9eDBg8jPz8fQoUPRr18/tT9jx46FiYkJYmJiqu2cJYyMjEq9bk3o6+tj+vTpyMvLw4IFC0q9QS8qKsK6deuqtbtUaUQiEXr16oX09HScOnVKZV9pU8BqQl9fH1OnTkV+fj4WLVqkNmOTVCrF119/jfv37yu/I0Ax3mTFihVqf0rGbcycORMrVqxQdjHTRJs2bTBgwAD8+eefWLt2rdr+zMxM5UJ1FZ2SloiIiEib6sSbhzFjxuDw4cNYsWIF0tPT0aFDB+VUrba2tvD391e2tbCwgL29PQ4cOAB7e3tYWlrCysoKrq6u6Nu3L44cOQJ/f3/4+PiguLgYycnJlZr680Wio6NhYGAANze3Uvfr6+ujd+/e2LdvH27fvl3qGIDK6tixI2JiYhASEoIWLVpAIBDAy8tL489LJBLcv38fwcHBGDJkiHKFaQDKFaYfPHigXEujJk2dOhW//fYbPvroI5WpWv/8809YWFhU6i3L4MGDcfv2bYSHh2PYsGGQSCRo2rSpcoXpK1eu4K233sLEiROVn7G3ty91kHRJ9ycXFxe8+uqrFY5lwYIFePz4MTZs2IBTp07hjTfeUK4wHRMTg9zcXMyfP19twPjff/+N5ORkAFAOSI+Pj8e5c+cAAD4+PmjatGmF4yEiIiKqDnWieDAxMcH69esRGhqK5ORkxMfHw9zcHBKJBP7+/moDSkvWPFi9ejUKCwvh4uICV1dXeHl5IS8vD1FRUQgMDISpqSnc3d0xY8YM9O3bt8pxXr16FefPn4enp6fyDUhp3njjDSQkJCA2Nlal8KmqqVOnIjMzE1u2bFG+OahI8QAAEydOhJubG7Zt24ajR49i586dEAgEsLOzQ58+feDn54fGjRtXW8wv0rx5c6xbtw6BgYHYsWMH9PT04OrqitDQULz33ntq40U0NX36dHTv3h3btm3Drl27kJmZCalUCgBYuHAhhgwZUp2X8UINGzbEqlWrEB8fj7179yI8PFw5YN/AwAARERFo06aN2ucuXbqE4OBglW3Pv8Xq3LkziwciIiLSGoG8OkflElVRZmYm+vXrh6FDh+LTTz+tlmOeOXMGH3zwARo3bozQ0FCNxy7UhE2bNiEwMBCenp745ptv1Mb41DSXiGKcfVCrpyzXyPZAlEQPcJkNnL2q7XBUdXEAzgTgyZOn9WbgnJ6eEJaWxvXqmiqDeVBgHhSYBwXmgTmwsSl/xss6MeaBdFNp3ck2bNgAAOjevXu1ncfFxQUrV67E/fv3MW3aNGRmZlbbsStq9OjR8Pf3R2JiIpYsWaIyPoeIiIiorqsT3ZbqgqysLBQVFZXZxtDQsFKLrNWklzVuABg1ahRef/11tG7dGoWFhTh58iSOHz8OFxcX9OnTB0D1XV+3bt1w7NixKsX78OHDctuYmJiozPRVmkmTJmHSpElVioWIiIhIG1g8/GvevHk4c+ZMmW0kEskLp/TUlpc1bgDo06cPfvnlF8THx6OoqAhNmjTBuHHjMHHiROWaCnXp+jSZenfx4sXw9fWt8ViIiIiItIHFw79mzZpV7grUNjY2tRSN5l7WuAHgww8/xIcfflhmm7p0fUFBQeW2ad26dS1EQkRERKQdLB7+5eTkpO0QKuVljVtTden6unXrpu0QiIiIiLSKxQORDnGyFgCoWxOstTL/d00PJ/X1NrSuLsZERESkRSweiHSEVCbHZh+RtsMolVwqg2DzbG2HUSp5sRQyWd0quIiIiLSFxQORjhAJBXjyJBdAxVfvri9EIiHMzIyQnZ0PqVSzaXJlMjmLByIion+xeCDSIYoFb3S3eCghlcp0cvEfIiKiquIicUREREREpBEWD0REREREpBEWD0REREREpBEWD0REREREpBEWD0REREREpBEWD0REREREpBEWD0REREREpBEWD0REREREpBEWD0REREREpBGuME2kQ/T0hNDlFaZFIqHKPzUhk8khk8lrKiQiIqKXCosHIh0hlclhaWmi7TBKJZfKIKjADX1VmZkZadxWXizF46x8FhBERERg8UCkM0RCAd6NkyLtUd26CR7QSoCveouAdwOAtFvaDkeVkz0Em2dDKBSweCAiIgKLByKdkvZIjrMPtB2FqvZW/96Up90Czl7VbjBERERUJg6YJiIiIiIijbB4ICIiIiIijbB4ICIiIiIijbB4ICIiIiIijdT74iE2NhZisRipqanaDqXOmzJlCnx9fbUdBhERERHVURUuHlJTUyEWiyEWi7F8+fJS2zx+/Bjdu3eHWCzGlClTqhxkee7cuYOQkBCkp6fX+Lk0VVK07N+/X9uhqIiNjUVUVJS2w9DIhQsX8N1332HChAno1asXxGIxYmNjS217/fp1BAcHY9y4cejXrx/c3d0xatQorF+/Hvn5+bUcOREREVH9VOk3DwYGBti/fz+ePXumti8+Ph5yuRwikahKwWnqzp07CAsLw19//VUr53uZxcbGYsuWLaXuCwoKws6dO2s5ohc7duwYduzYgdzcXLRt27bMtjExMdi8eTPs7OwwadIkfPDBB2jRogXWrl2LCRMmoKCgoJaiJiIiIqq/Kr3Og4eHB/bv34/k5GT0799fZV9MTAzc3Nxw6tSpKgdItUdfX1/bIah4++23MWbMGBgZGeHQoUP4448/Xti2b9++GDduHExNTVU+/8orr2DDhg2IiYnB8OHDayNsIiIionqr0sVDmzZtcP36dcTGxqoUDxcuXMDVq1cxbdq0UouHo0ePIiIiAn/99RdkMhkcHBwwatQoeHt7q7SbMmUK7t69i3Xr1uH777/HiRMnUFRUhM6dO2PevHlo0aIFACAkJARhYWEAgC+++AJffPEFAEAikWDJkiXK48nlcmzcuBG7d+/GgwcP0LRpU0yYMAESiUTlvL/++isiIiJw9epV5OXlwczMDE5OTpgxYwZat25d2XQBUHT58vf3x+LFiyGVSrF582bcunUL1tbWGDZsGMaOHavSPiUlBdHR0bh48SIePnwIfX19ODs7Y8KECejatava8W/evIkNGzbgxIkTePz4MSwsLNChQwdMnjwZTk5OEIvFyrbP/z0mJgZ2dnbKnJd0DVqwYAGOHDmChIQEWFlZqZzr1q1bGDx4MIYNG4ZPPvlEuf3AgQPYtm0b/v77b0ilUjg6OmL06NHo169fhfNlbW2tcdsOHTqUur1///7YsGEDLl++XOHzl+QjJCQEAQEBSE1NhUAgQJ8+ffDxxx/D0NAQGzduxJ49e/DPP/+gVatWmDdvHrp06aJynIKCAmzYsAEHDx7EvXv3YGxsDFdXV/j7+yt/jwHFG7S33noLkydPRrt27bBu3TpcvXoVpqamGDhwIKZPnw49Pa7rSERERNpTpTsRX19frFy5Evfv34etrS0AxY2olZUVevXqpdZ+165d+Prrr9G8eXOMGzcO+vr6SEhIwKJFi3Dnzh1MmDBBpX1+fj6mTJmCTp06Yfr06bh9+za2bt2KOXPmYNu2bRCJRHjjjTdQXFyM8PBwDBkyRHnjZm9vr3KsNWvW4NmzZxg6dCj09fWxc+dOLFmyBPb29ujcuTMA4PTp05g9ezYcHR0xbtw4mJiY4OHDhzh9+jRu3LhR5eKhxM8//4wnT55g0KBBMDExQUJCAlavXg1bW1uVIio2NhY5OTnw9fVFo0aN8ODBA0RHR2PatGkIDg5WuUm9ePEipk6diuLiYgwePBgODg7Izs7GmTNn8Pvvv8PJyQlLly7Fhg0bkJmZidmzZys/a2lpWWqcPj4+OHjwIPbt24dRo0ap7IuLiwMAleLrxx9/xIYNG9CzZ0/4+/tDKBQiKSkJ8+fPx8cff6yVJ/8PHiiWU/5v8aOp/Px8+Pv7o2vXrpgxYwYuXbqEPXv2oLCwEBYWFvjzzz8xfPhwFBcXIzIyErNnz0ZsbCxMTEwAAMXFxfjggw9w5swZeHp6YuTIkbh79y527NiB3377DeHh4WjVqpXKOY8dO4aff/4Zfn5+GDx4MJKTk7Fp0yaYmpqq/TtCREREVJuqVDx4e3sjMDAQcXFxyn7lBw4cwODBg9WekObk5OD777+HnZ0dIiIilDdXw4YNw/jx4xESEoKBAweiSZMmys9kZmZi9OjRKk/kLS0tsWrVKpw8eRI9evRAmzZtkJWVhfDwcHTq1AkDBw4sNdaioiJEREQou+b069cPgwYNwvbt25XFQ3JyMmQyGYKCglRuqCdNmlSVNKm5f/8+duzYoexiM2jQIEgkEmzbtk2leFi0aBGMjIxUPuvn54fhw4cjPDxcWTzI5XIsWbIERUVF2LRpk0qRM378eMhkMgDAwIEDlTe+L8rT83r06AFra2vExcWpFA9yuRzx8fFo1aoVnJ2dAQBpaWnYsGEDxo0bhxkzZijbjhgxAnPmzEFQUBB8fHxgbGxc0XRVmlQqxbp16yASidTebGkqMzMT48aNw3vvvafclp2djUOHDsHJyQkbNmxQ/q63atUKc+bMwb59+/D2228DAPbu3YszZ85g5MiRmDNnjvIYffr0waRJk7BixQoEBQWpnPPq1avYvn077OzsACi+83feeQfbtm1j8aAlIlH9mZiu5Frq0zVVBvOgwDwoMA8KzANzoIkqFQ/m5ubo06cP9u7diwkTJiAxMRG5ubl466231NqeOHEC+fn5eP/995WFAwAYGhrivffew+LFi5GcnIx33nlHuU8oFGLEiBEqx3F1dQUA3LhxAz169NA41mHDhqn06W/cuDGaN2+OmzdvKreV3MwfOnQIQ4YMqbEuIr6+vip98w0NDfHqq6+q9el/vnDIy8vDs2fPIBKJ0LFjR1y4cEG5Lz09HVevXsXQoUNLfTsiFFbuXwCRSIQBAwYgMjISly9fhqOjIwDg3LlzuH37tkqRsG/fPgCKtxWZmZkqx3F3d0dycjLOnz+P7t27VyqWylixYgXOnz+PqVOnomXLlpU6hkgkUntj8tprr+HIkSMYOnSoyu9ISTF369Yt5bbExEQIBAJMnDhR5RidO3eGq6srTp06hdzcXJV/Jzw8PJSFAwAIBAKIxWJs374deXl5aNiwYaWuhSrPzMyo/EYvmfp4TZXBPCgwDwrMgwLzwByUpcp3x76+vjh48CDOnTuHmJgYODs7w8HBQa1dyQ1VaTe3JTelt2/fVtluY2MDAwMDlW3m5uYAgKysrArF2axZM7Vt5ubmuHfvnvLn4cOH4+jRo1i+fDnWrFmD1157DT169MCbb75Zof73lY3lv9d069YtBAUFISUlBTk5OSr7BAKB8u8lBVB5MxJVhkQiQWRkJOLi4vDhhx8CUHRZEgqFKm8vrl27BkBRpL3Io0ePqj2+F/nxxx+xY8cODB48uEpP6xs1aoQGDRqobDMzMwMAlRv857c//z3evn0bVlZWsLCwUDu2o6MjTp06hbt376JNmzbK7S/6/Sg5NouH2pednQ+pVKbtMKqFSCSEmZlRvbqmymAeFJgHBeZBgXlgDiwty+8hUuXioXv37rC1tUVoaChSU1Mxf/78Ch9DLpeXur2sJ+Yv+syLvOhYzx/H3NwcP/30E86dO4cTJ07g7Nmz+OGHHxAcHIyVK1eqDDKuCk2msH369CkmTZqEgoICjBw5Eo6OjjA2NoZAIMDGjRtVBqNXNBcV4ejoiLZt22Lfvn2YOXMmioqKcOjQIbi6uqJx48Zq7QMDA1/4xqa6xoyUJyQkBBs2bICPjw8+/fRTlUKrosr6HdTkd6qs76Y2fu+pekilMhQX16//idTHa6oM5kGBeVBgHhSYB+agLFUuHkqeQIeHh8PAwABeXl6ltisZwHzlyhW17kZXr15VaVNRVbk5/C+hUAgXFxe4uLgAUDxRf++99xAaGlptxYMmTp06hYcPH+Lzzz9X6wa2du1alZ9LZuzRZJG8yuRKIpEgICAAJ0+eRHZ2NnJzc9VmqWrevDmOHz8OW1tb5ZskbQgNDUVYWBgGDBiAxYsXV7rLVnWxt7fH8ePHkZmZqfb24erVqxAKhWjatKl2giMiIiKqoGq5s/Lz88PkyZOxYMEClb7bz+vWrRuMjIyUi36VKCwsRGRkJEQiEdzd3St1/pJuHNnZ2ZX6fIn/9tUHFDfFxsbGFe4mVVUlbyf++6Q5JSVFZbwDoOiu5ODggLi4OFy5ckXtWM8fo2HDhsjJyanQE2xvb2+IRCLExcUhLi4OxsbG8PT0VGkzYMAAAIqF5oqLi9WO8fjxY43PV1lhYWEIDQ3FgAEDsGTJEq0XDgDg6empnCb4eX/88QdOnTqF119//YX/zhARERHVNdUyIrhJkyZ4//33y2xjamqKjz76CN988w3GjBmDt956C3p6eoiPj8dff/2FadOmqcy0VBGtWrVCw4YN8fPPP8PIyAjGxsZo1qwZOnbsWKHjLFu2DA8ePEC3bt3QtGlTPHv2DIcPH8bjx48xevToSsVWWZ07d4a1tTV++OEH3L17F40bN8Zff/2F+Ph4ODo6qqxbIBAIsHjxYkybNg1jx47FoEGD0Lp1a+Tk5ODMmTPo0aOHcuC5s7MzfvnlF3z33Xd49dVXIRQK4e7urjar0/OsrKzQs2dPJCYmoqioCD4+PjA0NFRp4+zsjPfffx8hISEYNWoU+vfvDxsbGzx8+BBpaWk4duwYUlJSKpSDu3fvKqeELXk7dfToUdy/fx+AYsaikrEC27dvR0hICJo0aYJu3bph//79atdQm4O1S0gkEsTHxyMyMhJ37tyBq6urcqpWY2NjlRmYiIiIiOq6Wl1xys/PD40aNUJERATWrVsHuVyO1q1bY9myZZWeShNQzFa0bNkyrF27Ft999x2KioogkUgqXDwMHDgQsbGxiIuLw5MnT2BsbIyWLVtWOb7KMDU1xZo1a7Bq1Sps27YNUqkU7du3R2BgIKKjo9UWPXN2dsZPP/2E9evX49ChQ9i5cycsLCzg7OysnIoWAEaNGoWbN29i//792LFjB+RyOWJiYsosHgDFTfAvv/wCQDGjUmlKFqPbunUrtmzZgvz8fFhZWaF169aYO3duhXNw+/ZtBAcHq2xLTExEYmIiAMDW1lZZPFy8eBEAcO/ePZXFAUu4uLhopXjQ09PDqlWrsH79ehw8eBBHjx6FsbExevXqhffff7/Ss0ARERERaYNAzhGYRDrDJaIYZx9oOwpVI9sDURI9wGU2cPaqtsNR1cUBOBOAJ0+e1puBc3p6QlhaGtera6oM5kGBeVBgHhSYB+bAxsa03Dba7xROREREREQvhVrttvSyKygoUBns/SKNGjWqhWheTllZWSgqKiqzjaGhYY0NIs7NzUVBQUGZbfT19ZXrKhARERHR/7B4qICDBw/iiy++KLddampqLUTzcpo3bx7OnDlTZhuJRFLquIXqsGLFCuzdu7fMNi4uLggNDa2R8xMRERG9zFg8VECPHj0QFBSk7TBearNmzSp3Sl0bG5saO/+YMWOU08q+SMlK0URERESkisVDBTRq1IhdkqrIyclJq+d3cHCAg4ODVmMgIiIielmxeCDSIU7WAgB1a4K1Vub/rnruVLkV5mtUXYyJiIhIi1g8EOkIqUyOzT4ibYdRKrlUBsHm2doOo1TyYilksrpVcBEREWkLiwciHSESCvDkSS4AgbZD0RqRSAgzMyNkZ+dDKtVs/m6ZTM7igYiI6F8sHoh0iGLBG90tHkpIpTKdXPyHiIioqrhIHBERERERaYTFAxERERERaYTFAxERERERaYTFAxERERERaYTFAxERERERaYTFAxERERERaYTFAxERERERaYTFAxERERERaYTFAxERERERaYQrTBPpED09IXR1hWmZTK7tEIiIiF56LB6IdIRUJoelpYnWzi+XyiAQae9lp7xYiqzcQq2dn4iIqD5g8UCkI0RCAd6NkyLtUe0/gR/QSoCveouAdwOAtFu1fn442UOweTaEQt1860JERFRdWDwQ6ZC0R3KcfVD7521v9W/BknYLOHu19gMgIiKiasEB00REREREpBEWD0REREREpBEWD0REREREpBEWD0REREREpBEWD0REREREpJFqmW2psLAQu3fvxpEjR3DlyhXk5ubCzMwM7dq1Q9++feHj44MGDRpUx6leKCcnB1FRUejatSvEYnGNnksb57t16xaioqJw8uRJ3L9/H1KpFI0bN0aXLl0waNAgdO7cucZjqIjqyM/Bgwdx/PhxpKWl4dq1a5BKpYiJiYGdnZ1a29OnT+PQoUM4c+YM7t69iwYNGqB58+YYPnw4vLy8IBBwik4iIiKiqqpy8XDnzh189NFHuHr1KlxdXTFmzBhYWloiKysLp0+fxjfffIOLFy9i4cKF1RHvC+Xk5CAsLAwAaq14qK3zxcXF4auvvoJIJIKXlxfatWsHfX193L59G4mJiYiNjcX69evx2muv1WgcFVEd+dmxYwf+/PNPtGnTBvb29rh+/foL265evRr37t2Dp6cn2rRpg/z8fBw8eBCLFi1CamoqFi1aVKkYiIiIiOh/qlQ8FBYW4qOPPsL169fx7bffol+/fir7R48ejcuXLyMlJaVKQeqy1NRULF26FC1atMDq1atha2ursn/q1KnYs2cPRCKRliKsOUuXLkWjRo2gp6eH5cuXl1k8zJgxA507d4ae3v9+pUeOHIn3338fe/bswciRI9G6devaCJuIiIio3qrSmIfo6GhcvXoVo0aNUiscSjg6OuK9995T2Xbu3DnMnDkTHh4ecHNzw8iRI7F161bI5aor3y5ZsgRisRjZ2dlYtmwZ+vfvj549e2LChAm4cOGCsl1sbCzeeustAEBYWBjEYjHEYjGmTJkCAJDJZFi/fj0mT54MLy8vdO/eHT4+Pvjmm2+QmZlZatyHDx/G+++/r4xx6NCh+O6771BUVFTu+eRyOaKiojBixAi4u7ujd+/eGDRoEJYsWYKCgoIK5XjVqlWQyWT4+uuv1QoHABAKhRg6dCg6duyo3CaVSrFp0yYMHz4cPXv2hKenJz766CP8+eefKp+9c+cOxGIxQkJC1I4bEhICsViMO3fuKLdV1/ehqSZNmqgUA2URi8VqbYVCIfr27QsAuHz5coXOXXLMJUuW4NSpUxg/fjzc3NwwcOBAbNy4EQCQnZ2NpUuXon///nBzc8OHH36I+/fvqx3n3r17WLJkifJ3z9fXFwEBAcjNzVVpFxsbC7FYjFOnTmHjxo0YNGgQevTogaFDh2Lv3r0Vjp+IiIioulXpzcOhQ4cAAH5+fhp/5tdff8WcOXNgYWGBkSNHwszMDEeOHMGKFStw5cqVUrs3zZw5E1ZWVpg8eTIyMzOxefNmfPjhh4iJiYGxsTG6dOmC2bNnIyAgAJ6envD09AQAWFlZAQCKiooQGRmJfv36wcPDA4aGhvjzzz8RHR2Nc+fOITIyEvr6+srzBQUFITw8HA4ODnj33XdhbW2NW7du4ciRI/D39y/3fOvXr0dwcDB69+4NPz8/CIVC3Lt3D0ePHkVBQQEMDQ01ytXdu3dx8eJFdO7cGY6OjhrnePHixdi3bx9cXV0xdOhQZGVlYceOHZg0aRJWr15d5W5WVf0+atODBw+qdO709HT88ssvGDp0KHx8fHD48GGsWbMGDRo0QFxcHJo1a4YpU6bg5s2b2LZtGxYvXozg4GDl5+/du4exY8ciKysLfn5+aNmyJf744w9ERUUhNTUVGzZsUPt9WLNmDZ49e4ahQ4dCX18fO3fuxJIlS2Bvb1/nxrYQERGRbqlS8XDlyhUYGxvD3t5eo/ZSqRTLly+HoaEhIiIilE/Shw8fjlmzZmH37t2QSCRqffednJwwf/585c8ODg6YP38+9u3bBz8/P9jb28PDwwMBAQFwdHTEwIEDVT7foEEDJCQkqNyk+fn5oVOnTli2bBmSkpLQv39/AMCFCxcQHh4OV1dXBAYGqgz0njlzJgDA1NS0zPMlJibCwcEB33//vcr26dOna5SnEn///TcAoF27dhp/5sSJE9i3bx88PT2xfPlyCIWKl0s+Pj5455138M033+Dnn3+u0gDiqn4fteXBgwfYtWsXmjVrhi5dulTqGFeuXMHGjRvRoUMHAMDgwYMhkUjw/fffY8SIEZgzZ45K+6ioKGRkZKBly5YAFIXoo0ePsGLFCnh4eAAAhg0bhpYtW2Lt2rWIiorChAkTVI5RVFSEiIgIZUHbr18/DBo0CNu3b2fxUEVCoeL3XiTS3YnmSq5dl3MAMA8lmAcF5kGBeWAONFGl4iE3NxfW1tYat7906RLu3r2Ld955R6ULjkgkwvjx43H8+HEkJiaqFQ+jRo1S+bnkyfnNmzc1Oq9AIFAWDlKpFHl5eZBKpXB1dQWgKBhKiod9+/YBAKZNm6Y2Q5SmN9ympqZIS0vDuXPnqnSz9/TpUwCAsbGxxp9JSkoCAEycOFFZOACAvb09vLy8EBMTgytXrlToTcZ/VfX7qA0FBQWYO3cu8vPzsXLlSo27P/3Xq6++qiwcAEBPTw8dOnTA0aNH8c4776i07dKlC6KionDz5k20bNkSMpkMR48ehaOjo7JwKPHuu+/ip59+QmJiolrxMGzYMJU3YY0bN0bz5s3rVH5fViYmiv8OmJkZaTkS7WMOFJgHBeZBgXlQYB6Yg7JUqXgwMTFR3uBq4vbt2wAUT6r/q+RmtqTN85o1a6bys4WFBQAgKytL43MfPHgQkZGRSE9PR3Fxscq+7Oxs5d9v3LgBAGjTpo3Gx/6vGTNmYM6cOZg0aRIaNWqErl27ws3NDf369avQlLUlRUNlctyqVSu1fc/nuCrFQ3V8HzWpsLAQs2fPRlpaGhYvXoyuXbtW+lilTQtrZmYGAGjatKnKdlNTUwD/y8OTJ0/w9OnTUn/fDQ0NYW9vr9HvOwCYm5vj3r17Fb8AUpGbWwATE0NkZ+dDKpVpOxytEImEMDMz0ukcAMxDCeZBgXlQYB6YA0vL8h9YV6l4aN26Nc6cOYNbt25p1HXpvwOiNfWimYQ0Pd7hw4exYMECODs7Y+7cubC1tUWDBg0gk8kwc+ZMteNUdU2Ajh07Ys+ePUhJSUFqaipSU1Oxf/9+rFu3DmFhYRq/rSm5wU9PT9f43HK5/IXxV+Q6pVLpC/dV9fuoSYWFhZg7dy5OnTqFTz/9FBKJpErHK2sWq/LyUF4+XrT/+TdGmrQnzclkihxKpTIUF+ve/xSexxwoMA8KzIMC86DAPDAHZalSh66SmWx2796tUfuSAuPq1atq+65cuaLSpqLKuhFOSEiAgYEBQkJC8Pbbb6N3797o1q2b2pNjAGjRogXkcjn++uuvSp8PAIyMjODp6Yl58+YpB9LeuHEDP//8s2YXBMVTbycnJ/z++++l5qw09vb2kMvluHbtmtq+kmOU5LjkCfrzb15KlPZEvCK0sSjbs2fPMG/ePKSkpGD+/PkYMmRIrcfwPCsrKxgbG5f63RUWFuL27duV/n0nIiIi0oYqFQ+DBg1Cq1atEBkZiSNHjpTa5vLly4iMjAQAtG/fHk2bNsXevXuVs+AAiqlUw8PDAUCtb7imjIwUfdNycnLU9pU8yZXJ/ldByuVyrF+/Xq2tl5cXAGDt2rV49uyZ2v6Sp79lna+06V+dnJwAVLxrzwcffACBQIAFCxbgn3/+Udsvk8mwc+dO5VSpJfkLDw9XeVJ9+/Zt7Nu3Dy1atFB2ozE2Noa1tTVOnTql0vbWrVvKsROVVVZ+asKzZ88wd+5c/Pbbb/jkk08qNANYTREKhXB3d8fly5fxyy+/qOzbsmUL8vLylDNREREREb0MqtRtydDQEN9//z0++ugjfPzxx3j99dfRvXt3WFhYICsrC2fOnMGxY8cwePBgAIpuHp988gnmzJmDMWPGYOjQocqpWs+cOYMhQ4ZUepVkCwsL2Nvb48CBA7C3t4elpSWsrKzg6uqKvn37KqdZ9fHxQXFxMZKTk0tdc6Fjx44YO3YsfvrpJ7z33nt48803YW1tjTt37uDw4cP46aefYGpqWub53n77bbz66qtwdnaGjY0NHj9+rFzIbcCAARW6LldXV3z22Wf4+uuv4efnp1xhWk9PT7nCdEZGBjZs2AAA6NatG7y8vLB//35Mnz4d7u7uyMrKws8//wyZTIYFCxaovBUYPnw41q5diw8++AB9+vTBw4cPsXPnTrRu3RoXL16s1HdR3vehqTNnzuDMmTMAgLS0NADA9u3bYWJiAgAYMWKE8u+LFi3C8ePH8frrr6Nhw4aIj49XOVabNm2qNI6lsqZPn46TJ0/i448/Vk7Vev78ecTFxaFt27YYOXJkrcdEREREVFlVKh4ARReYyMhI7N69G4cPH8bGjRvx9OlTmJmZoX379li4cKHKVJ29evVCSEgI1q1bh82bN6OoqAivvPIK5s6dqzZ7TUUtXboUAQEBWL16NQoLC+Hi4gJXV1d4eXkhLy8PUVFRCAwMhKmpKdzd3TFjxgxl16vnzZw5E23atMH27dsREREBmUwGW1tbuLm5qUz3+qLzvffeezh27Bi2bduGnJwcWFlZwdnZGcuWLcOrr75a4evy9fXFa6+9hi1btuDkyZNISEiATCZD48aN4eLigiVLlqgsErd06VK0b98esbGxCAwMhIGBAV577TVMnjxZpR0AjB07Frm5uYiPj8fp06fRqlUrfPbZZ0hLS6tS8VBWfjR16tQphIWFqWwreYsFAAMHDlQWDyXFxcmTJ3Hy5Em1Y02ePFkrxUOTJk2wceNGBAcH4+DBg8jKykKjRo0watQoTJkyReM1P4iIiIjqAoGcozCJdIZLRDHOPii/XXUb2R6IkugBLrOBs5qN36lWXRyAMwHIzs6HmZkRnjx5qrMD4fT0hLC0NNbpHADMQwnmQYF5UGAemAMbG9Ny23AFDCIiIiIi0kiVuy1RxeXm5pY63uJ5+vr6MDc3r6WIas/Dhw/LbWNiYlJj3XmePHlS5jS0ANCwYUM0bNiwRs5PRERE9DJj8aAFK1aswN69e8ts4+LigtDQ0FqKqPZ4e3uX22bx4sXw9fWtkfOPGTMGd+/eLbPN5MmT8f7779fI+YmIiIheZiwetGDMmDHlzrpUsgZDfRMUFFRum9atW9fY+b/88ksUFhaW2aa0FZ6J6P/Zu++oqK71b+DfmQHpXUUFUREL9jLYUERFUYqK2I0lRhEVry366jUxasw1icaoEQvGjr0CCtjFRGPBGhOs2AArAtLLzLx/TGZ+TgZw6O37Weuuy5yzzy4PxHWeOXvvQ0RExOShTNja2irftVDVdOzYsUzbb9OmTZm2T0RERFSRMXkgqkLsLQQASn+DtQYm/7xbxL6M3qhdVu0SERFVMkweiKoIiVSGXe6iMmtfJpFCsGtW2bWfI4FUyp2piYiIioLJA1EVIRIKkJCQAkDwybKVkVQqg1BYNcdORERUXJg8EFUh8hfeVN0baCYPRERERcOXxBERERERkUaYPBARERERkUaYPBARERERkUaYPBARERERkUaYPBARERERkUaYPBARERERkUaYPBARERERkUaYPBARERERkUaYPBARERERkUb4hmmiKkRLS4jy+oZpqVQGqVRW1t0gIiKifDB5IKoiJFIZzMwMi60+mUQKgaj4Hl7KciR4n5TOBIKIiKgcY/JAVEWIhAKMOi5BVHzRb877NRDgu24iYNRKICqm6J2zt4Zg1ywIhQImD0REROUYkweiKiQqXoabb4peT1Pzf27wo2KAm9FFr5CIiIgqBC6YJiIiIiIijTB5ICIiIiIijTB5ICIiIiIijTB5ICIiIiIijTB5ICIiIiIijXC3pQooMzMTR44cwdmzZ/H48WOkpKTA2NgYTZo0Qa9eveDu7o5q1aqVWPu7d++GkZERPD09S6yNiuDy5cvw8/ODQCDAkSNHYG1tXdZdIiIiIipRfPJQwcTFxWH06NFYsWIFRCIRxowZgwULFmDMmDHQ0tLCsmXLsHz58hLtw549exASElKibVQEQUFBsLS0RLVq1RgPIiIiqhL45KECyczMxIwZM/Ds2TN8//33cHFxUTk/evRoPHr0CJcvXy6jHlZM6enp0NPTK9A1iYmJiIiIwNixY/H06VMcO3YMkyZNglDIfJyIiIgqLyYPFUhQUBCio6MxevRotcRBwc7ODnZ2dsrPnp6eqF27NgICAlTKRUZGwtfXF998841y+lFmZia2bduGkydP4tWrV9DS0kL16tXRqVMnzJkzB3Fxcejfvz8A4OXLlxCLxSr1KQQHB+PAgQOIjo6GSCSCvb09Pv/8c3Tq1EmlD4q+zZ49G6tXr8aff/4JXV1duLm5Ydq0aZBIJFi/fj1OnDiBpKQkNGvWDPPnz0fDhg1V6snKykJgYCDCw8MRExODatWqoW3btpg0aRKaNm2a65jT09Nx4MABxMTEYNy4cZg0aVJBfhUICwtDdnY2PDw88PTpU5w6dQp//PEHHB0d1cp++PABv/zyC86dO4f09HQ0atQIvr6+CA8Px7Fjx1RiBwDPnz/Hpk2bcPXqVSQlJaFGjRpwcXGBj49PgZMcIiIiouLE5KECOX36NADA29u7ROr/4YcfEBwcDDc3N4wYMQIymQwxMTG4cuUKAMDMzAxLlizBypUrYWpqivHjx6vV4e/vj61bt8Le3h6TJ09GZmYmgoODMW3aNCxZsgT9+vVTKf/mzRv4+fnB1dUVPXv2xJUrV7Br1y4IhUI8ffoUmZmZGDt2LJKSkrBz5058+eWXOHjwIEQiEQAgJycH06ZNw507d+Dm5oahQ4ciJSUFR48exRdffIFNmzahWbNmKm3u2bMHSUlJ8PLygrm5OSwtLQscq+DgYLRt2xZWVlaoVasWqlevjqCgILXkITs7G1OnTkVUVBT69euH1q1b49mzZ5g7dy6srKzU6o2KioKvry+MjIwwaNAg1KxZEw8fPsTevXtx+/ZtBAQEQEuL/9kSERFR2eBdSAXy+PFjGBgYlNjC3PPnz8PR0RFLlizJ9byenh7c3Nywfv16mJubw83NTeX8s2fPsG3bNrRo0QIBAQHKRdve3t4YNmwYli9fDmdnZ5Vvz2NiYvDjjz+iZ8+eAIDBgwdj9OjRCAwMRPfu3eHv7w+BQAAAMDExwYoVK3DlyhV06dIFALB3715cv34da9asUR5T1DNs2DCsWrVK7anL69evcejQIZiamhYqTn/99RcePnyIhQsXAgBEIhH69euHvXv3IjExUaXeoKAgREVFYcKECfD19VUeF4vFmD17tlrdS5YsgYWFBXbu3AkDAwPlcQcHB8yZMwdhYWGVeqG6SFSy074U9Zd0O+UZYyDHOMgxDnKMgxzjwBhogslDBZKSkgILC4sSq9/IyAiPHz/Go0ePVKY+aSoiIgIymQxjxoxR2e3J1NQUQ4YMwYYNGxAZGYlu3bopz1laWioTB4XWrVsjKioKQ4cOVSYOANCmTRsAwIsXL5THwsPDYWNjg2bNmiExMVGlno4dO+L48ePIyMiArq6u8ribm1uhEwdAnhDo6uqqTB3z9PTEzp07cfz4cYwaNUp5/MKFCxAIBPjss89U6ujevTvq16+Pp0+fKo89evQIDx8+xMSJE5Gdna0ynjZt2kBPTw+XL1+u1MmDsXHpTMsqrXbKM8ZAjnGQYxzkGAc5xoExyA+ThwrE0NAQqampJVb/7Nmz8fXXX2P48OGwsrJC+/bt0a1bN3Tv3l2jhcCxsbEAAFtbW7VzimREUUahdu3aamWNjIwAAHXq1FE5bmxsDABISkpSHnvy5AkyMzPzXAMCyBc316pVS/nZxsYm33HkJyMjAydOnED79u0RHx+P+Ph4AIC2tjbq1auHoKAgleQhNjYWFhYWMDQ0VKvr38nDkydPAACbNm3Cpk2bcm3//fv3he57RfDhQzokEmmJ1S8SCWFsrFfi7ZRnjIEc4yDHOMgxDnKMA2NgZmbwyTJMHiqQhg0b4saNG4iJidF46tLH39x/TCKRqB1zcnJCSEgILl26hOvXr+PatWsIDg5GixYtsGHDBpVv73Mjk8kKfC6/pCSvc/+uy9bWNtcpQApmZmYqnz81jvycPn0aqampuHjxIi5evJhrmbt376JFixa59vVj/z6n+DxixAh07do112sUCVRlJZFIkZNT8v9Yl1Y75RljIMc4yDEOcoyDHOPAGOSHyUMF0qtXL9y4cQNHjhzBtGnTNLrG2NgYHz58UDv+7ycAH5fv27cv+vbtCwAICAhAQEAATp48qdxpKa+ERJHQREdHo379+irnHj9+rFKmuNjY2ODdu3dwcHAolW1Sg4ODYWFhgTlz5qidk0qlWLhwIYKCgpTJg7W1Nf744w8kJycrn6goPHv2TOWz4omIUChEx44dS2gERERERIXH1SAVyIABA9CgQQMEBgbi7NmzuZZ59OgRAgMDlZ9tbGzw9OlTvHnzRnksKysLBw4cULlOIpEgOTlZrT7FVqcfJyB6enq5lnV2doZAIEBgYCCys7OVx5OSknDw4EEYGxujffv2Go5WM25ubkhISMCOHTtyPa+YVlQcnj9/jhs3bqBnz55wcXFR+1+fPn3QoUMHnDx5EhkZGQDkT3NkMhl27dqlUldERITKlCUAaNKkCezs7HDkyBGVdR0KOTk5KlO2iIiIiEobnzxUILq6uvj5558xY8YMzJ07Fx06dECnTp1gamqKpKQk3LhxAxcvXsTAgQOV1wwdOhQnT57ElClT4O3tjezsbISGhqpN3UlLS0Pfvn3h5OSExo0bw9zcHK9evcKhQ4egr6+PHj16KMu2aNECwcHB2LhxI+rVqweBQABXV1fY2Nhg3Lhx2Lp1K7744gv06dMHWVlZCAoKQnx8PBYvXlzs7ykYMWIErly5grVr1+LGjRtwcHCAgYEBXr16hWvXrqFatWrYuHFjsbQVHBwMQP4EKC89e/bEpUuXcPr0aXh4eGDAgAE4fPgwfv31V8TGxiq3ag0KCkKjRo3w8OFD5bUCgQCLFy/G5MmTMXLkSPTv3x+2trbIyMhATEwMzp49Cz8/v0q9YJqIiIjKNyYPFYy1tTUCAwNx5MgRnDlzBtu2bUNqaiqMjY3RtGlTLFiwQGUL1TZt2mDRokXYsmULVq9ejZo1a8Lb2xvNmjXD5MmTleV0dXUxYsQIXLt2DVevXkVaWhosLCzQqVMnfP755yrvJJg8eTISExOxZ88epKSkAABcXV0BAFOnToW1tTUOHDiA9evXQygUwt7eHvPmzUPnzp2LPR5aWlpYtWoVDh48iNDQUGWiUKNGDTRv3hweHh7F0o5EIsGxY8dgZmaGtm3b5lnO2dkZy5YtQ1BQEDw8PKCtrY1169bhl19+wfnz53H27Fk0adIEK1euxL59+/D8+XOV65s0aYJdu3Zh69atuHDhAg4dOgQDAwPUrl0bnp6ecHBwKJbxEBERERWGQJbfik4iKjFDhw6FRCLBoUOHSq3NdjtycPPNp8t9yoimwG4PLaDdLOBmdNErbGsL3FiJhITUEl2gpqUlhJmZQYm3U54xBnKMgxzjIMc4yDEOjEGNGkafLMM1D0QlTLH+4WMRERGIjo5Gp06dyqBHRERERIXDaUtU5aWlpSEtLS3fMiKRSG3LV0199913yMrKQsuWLaGrq4t79+4hJCQEZmZmGDduXKHqJCIiIioLTB6oytu5c2eeL2VTqF27NkJCQgpVf8eOHXHgwAFcu3YNqampMDU1haurKyZNmoQaNWoUqk4iIiKissDkgao8d3d3tGnTJt8yOjo6ha7fw8Oj2BZuExEREZUlJg9U5VlbWxf7y+uIiIiIKiMumCYiIiIiIo3wyQNRFWJvIQBQ9N2ZG5gI/qmwmJ7YFFc9REREVKKYPBBVERKpDLvcRcVWn0wihWDXrOKrL0cCqZSvnSEiIirPmDwQVREioQAJCSkABGXdlVxJpTImD0REROUckweiKkT+tszymTwQERFR+ccF00REREREpBEmD0REREREpBEmD0REREREpBEmD0REREREpBEmD0REREREpBEmD0REREREpBEmD0REREREpBEmD0REREREpBG+JI6oCtHSEqK8vCSOb5QmIiKqeJg8EFUREqkMZmaGRa5HJpFCICr6Q0tZjgTvk9KZQBAREVUgTB6IqgiRUIBRxyWIii/8zXq/BgJ8100EjFoJRMUUvjP21hDsmgWhUMDkgYiIqAJh8kBUhUTFy3DzTeGvb2r+z41+VAxwM7p4OkVEREQVBhdMExERERGRRpg8EBERERGRRpg8EBERERGRRpg8EBERERGRRpg8EBERERGRRpg8UImKjIyEWCxGSEhIWXelQMRiMRYtWlTW3SAiIiIqV7hVawWWmZmJ4OBgnDlzBo8ePUJycjL09PRgY2OD9u3bY8CAAahfv35Zd7PcePfuHfbu3YtLly4hNjYWWVlZsLCwQMuWLeHh4QFHR8ey7iIRERFRucbkoYKKiYnBzJkz8eTJE7Rr1w4jR45E9erVkZaWhgcPHiAkJAS7du3CsWPHULNmzbLubpm7fPky5s2bh4yMDLi4uKB///7Q09PDq1evcPHiRUyfPh1Lly5F3759y7qrREREROUWk4cKKCMjAzNmzEBMTAyWL1+OHj16qJXJzMzE7t27IRAIyqCHJS89PR16enoalX369CnmzJkDQ0NDbN68GQ0bNlQ5P2nSJJw7dw5CYdnN4ivIeIiIiIjKCtc8VEBHjx7F06dPMXr06FwTBwDQ0dHB559/jho1aqgcz8rKwpYtWzB06FB06dIFzs7OmDlzJu7du6dS7uO1CkePHsWQIUPQuXNneHh4YPv27bm2GRwcjKFDhyrLbdy4ERKJJNeyhenH/v37lf3YsWOHpuHC+vXrkZ6ejgULFqglDgo9evRA9+7d1Y7funULEyZMgKOjI3r16oVvv/0WaWlpKmV8fHzg6empdm1cXBzEYjE2btyo0Xg+Ln/+/Hl89tln6NKlC1xdXbF69Wrk5ORoPGYiIiKiksAnDxXQ2bNnAQADBw4s0HU5OTmYNm0a7ty5Azc3NwwdOhQpKSk4evQovvjiC2zatAnNmjVTuebgwYNISEjAgAEDYGhoiLCwMPzyyy+wtLRUmeKzZ88e/PTTT7C1tcXkyZMhkUgQEhKC3377rVj6sWfPHiQlJcHLywvm5uawtLTUaMyZmZn4/fffYWlpia5duxYoXg8ePMDs2bPRv39/9OvXD9evX0dQUBCEQiEWLFhQoLr+Lb/xXLx4EQcPHoS3tzcGDhyIiIgI7Ny5E0ZGRhg/fnyR2i1vRKLS/f5C0V5pt1ueMAZyjIMc4yDHOMgxDoyBJpg8VECPHz+GgYEBrKysVI5LJBIkJyerHNPV1YWuri4AYO/evbh+/TrWrFmDLl26KMsMHjwYw4YNw6pVqxAQEKBy/evXr3HgwAEYGRkBAAYMGAAPDw/s27dPmTwkJyfD398fdevWxbZt26Cvr69S778Vth+HDh2CqalpQUKFFy9eIDMzE40bNy7QdQDw8OFDbNmyBS1btgQAeHt7IzU1FcHBwZg5c6ZynIWR23ji4uIAANHR0di/fz/q1KmjbHfYsGHYt29fpUsejI3LZqpWWbVbnjAGcoyDHOMgxzjIMQ6MQX6YPFRAKSkpqF69utrxJ0+eYPjw4SrH/Pz8MG7cOABAeHg4bGxs0KxZMyQmJqqU69ixI44fP46MjAxlsgEAnp6eysQBkCcjLVu2xJ07d5THrly5goyMDAwZMkTlhtrIyAiDBw/GunXrVNoqTD/c3NwKnDgA8lgBgKGhYYGvbdmypTJxUHBwcMDFixcRFxcHOzu7AtepkN94nJ2dlYkDAAgEAojFYuzfvx9paWlFSlrKmw8f0iGRSEutPZFICGNjvVJvtzxhDOQYBznGQY5xkGMcGAMzM4NPlmHyUAEZGhoqb4o/ZmVlBX9/fwDyb81XrVqlcv7JkyfIzMyEi4tLnnUnJiaiVq1aKnX+m4mJCZKSkpSfY2JiAAANGjRQK2tra6t2rDD9sLGxybNsfhRJQ27x+pS8xg5AZfyFkd94PtVuZUoeJBIpcnJK/x/nsmq3PGEM5BgHOcZBjnGQYxwYg/wweaiAbG1tcfPmTcTGxqrcaOrp6aFjx44AAJFIlOe1s2fPzrNuMzMzlc951ZObguzsVNB+fPwUoiDq1q0LHR0dPHjwoMDX5jd2mUym/Dmvcee1WBzIfzz57fr0cbtEREREpY3JQwXUs2dP3Lx5E0ePHsXUqVM1vs7Gxgbv3r2Dg4NDsW5Lam1tDUA+V79Tp04q56Kjo0utH7nR0dGBo6Mjzp49i0uXLqmssSguxsbGartEAUBsbGyxt0VERERUlriUvALy8vKCjY0Ndu7ciXPnzml8nZubGxISEvLc5jQ+Pr5Q/enYsSN0dXVx4MABlW1Mk5OTcfDgwVLrR158fX2hq6uLpUuX4smTJ7mWOXv2LCIiIgpVf7169ZCamoq7d+8qj0mlUuzevbtQ9RERERGVV3zyUAHp6upi9erVmDFjBubMmYP27dujU6dOsLCwQGpqKp4+fYpTp05BJBKprBsYMWIErly5grVr1+LGjRtwcHCAgYEBXr16hWvXrqFatWoq7yTQlJGREfz8/LBixQqMGzcOHh4ekEqlCA4Ohrm5Od68eaNSvqT6kRdbW1v8+OOPmD9/PkaOHAkXFxe0aNECurq6eP36NX7//Xf8/fff+O677wpVv5eXFwIDAzFnzhwMHz4c2traOHPmTL7TloiIiIgqIiYPFVTdunWxa9cuHD16FGfPnkVgYCBSUlKgp6eHunXrYsCAARgwYADq16+vvEZLSwurVq3CwYMHERoaqrxBr1GjBpo3bw4PD49C92f48OHQ19fHzp07sX79elhYWMDT0xNt2rRRm1pVkv3IS5cuXXDw4EHs2bMHly5dQkREBLKyslC9enW0atUKU6ZMUZtypSkrKyusWLEC69atw4YNG2BiYgI3Nzf0798fgwcPLuaREBEREZUdgYwrMImqjHY7cnDzzafL5WVEU2C3hxbQbhZwU309i8ba2gI3ViIhIbVUd7PQ0hLCzMyg1NstTxgDOcZBjnGQYxzkGAfGoEYNo0+W4ZoHIiIiIiLSCKctUYWUlpamsjg7NyKRSG3LVyIiIiIqPCYPVCHt3LkTmzZtyrdM7dq1ERISUko9IiIiIqr8mDxQheTu7o42bdrkW0ZHR6d0OkNERERURTB5oArJ2tpa+XI6IiIiIiodTB6IqhB7CwGAwm+w1sBE8E9FRUzcino9ERERlQkmD0RVhEQqwy53UZHrkUmkEOyaVfR6ciSQSrlTNBERUUXC5IGoihAJBUhISAEgKOuuAACkUhmTByIiogqGyQNRFSJ/4U35SB6IiIio4uFL4oiIiIiISCNMHoiIiIiISCNMHoiIiIiISCNMHoiIiIiISCNMHoiIiIiISCNMHoiIiIiISCNMHoiIiIiISCNMHoiIiIiISCN8SRxRFaKlJUR5eEkc3y5NRERUMTF5IKoiJFIZzMwMi1yPTCKFQFS0h5ayHAneJ6UzgSAiIqpgmDwQVREioQCjjksQFV/4G/Z+DQT4rpsIGLUSiIopXCX21hDsmgWhUMDkgYiIqIJh8kBUhUTFy3DzTeGvb2r+z81+VAxwM7p4OkVEREQVBhdMExERERGRRpg8EBERERGRRpg8EBERERGRRpg8EBERERGRRpg8EBERERGRRpg8ULnm6ekJHx+fTx4rLYsWLYJYLC6RusViMRYtWlQidRMREREVB27VSioyMzNx5MgRnD17Fo8fP0ZKSgqMjY3RpEkT9OrVC+7u7qhWrVpZd1NNSEgIkpOTMXLkyEJdv2jRIhw7dgzh4eGoXr16Mfeu8Hbv3g0jIyN4enqWdVeIiIiImDzQ/4mLi8OMGTMQHR0NBwcHjBkzBmZmZkhKSsL169exbNky/P3331iwYEGZ9vPQoUMQCAQqx0JCQvDy5ctCJw+a+uqrrzB//vwSqfvixYsQiUQqx/bs2YPatWszeSAiIqJygckDAZA/cZgxYwaePXuG77//Hi4uLirnR48ejUePHuHy5cv51pOWlgZ9ff2S7GqZPvnQ0tKCllbJ/Gejo6NTIvUSERERFRcmDwQACAoKQnR0NEaPHq2WOCjY2dnBzs5O+dnHxwcvX77E+vXrsWbNGkRGRuLDhw+IjIwEALx79w6bNm3C77//jvj4eJiamqJbt26YPHkyzM3NVep+8uQJVq1ahRs3bkAkEqFdu3aYNWtWrv3w9PRE7dq1ERAQAAAqaxA+/jk4OBh16tQpXEDyoJjepBjjx8dOnz6NVatW4bfffkN2djYcHBwwf/58VK9eHYcPH8bu3bsRFxeHWrVqYdq0aejRo4dK3WKxGB4eHli0aBHi4uLQv39/AMDLly9VxvVx20RERESlickDAQBOnz4NAPD29i7QdWlpaZg0aRJat26NKVOm4P379wCAV69e4fPPP0d2djYGDBgAa2trxMTE4ODBg4iMjMTOnTthaGgIAIiNjcWECROQkZGBwYMHw8rKCteuXYOvry8yMjI+2YclS5Zgy5YtSExMVEk4zMzMCjSWopo2bRpq1aoFX19fxMTEYO/evZg9ezZ69eqFo0ePon///qhWrRr27duHefPm4dChQ7C2ts61LjMzMyxZsgQrV66Eqakpxo8fX6pjKQ0iUenv16BosyzaLi8YAznGQY5xkGMc5BgHxkATTB4IAPD48WMYGBjkeTObl6SkJAwdOhSTJk1SOf7DDz8gOzsbu3btgqWlpfJ4r1698Pnnn2PXrl3Ka9atW4ekpCSsWbMGXbp0AQAMHToUP/zwAw4cOPDJPri5ueHo0aPIzMyEm5tbgfpfnFq1aoU5c+YoPwsEAgQGBiI+Ph779+9XTudycHDAiBEjcOTIEUybNi3XuvT09ODm5ob169fD3Ny8TMdVUoyN9apk2+UFYyDHOMgxDnKMgxzjwBjkh8kDAQBSUlJgYWFRqGtHjRql8jk5ORkXL16Eu7s7dHR0kJiYqDxXp04dWFtb48qVK5g0aRKkUil+++03NG7cWJk4KIwfP16j5KG8GD58uMrn1q1bIzAwEO7u7irrQBo1agQDAwO8ePGitLtYrnz4kA6JRFqqbYpEQhgb65VJ2+UFYyDHOMgxDnKMgxzjwBiYmRl8sgyTBwIAGBoaIjU1tcDXmZmZKacfKTx79gxSqRQhISEICQnJ9TorKysAwPv375GWlob69eurlalRo4Za3eXZv9dXGBkZAQBq166tVtbY2BhJSUml0q/ySiKRIienbP5hLsu2ywvGQI5xkGMc5BgHOcaBMcgPkwcCADRs2BA3btxATExMgaYu6erq5nnO1dVVuej33/69s9C/t16tiP69zeqnjstkspLsDhEREVGxY/JAAORrEW7cuJHvPHxNWVtbQyAQICsrCx07dsy3rLm5OfT19fHkyRO1c2/fvkVKSopGbVaG5CM3lXVcREREVDFxKTkBAAYMGIAGDRogMDAQZ8+ezbXMo0ePEBgY+Mm6TE1N4ejoiAsXLuDWrVtq52UyGRISEgAAQqEQTk5OePDgAS5duqRSbsuWLRr3X19fH8nJyZXu23w9PT0kJyeXdTeIiIiIAPDJA/1DV1cXP//8M2bMmIG5c+eiQ4cO6NSpE0xNTZGUlIQbN27g4sWLGDhwoEb1zZs3DxMmTICvry/c3NzQtGlTSKVSxMbG4sKFC3Bzc1PutjR58mT88ccfmDNnDoYMGQIrKytcvXoVUVFRMDU11ai95s2b47fffsPy5cvRsmVLZVKip1ew3RL27NmT6zXt2rVDu3btClRXcWjRogWCg4OxceNG1KtXDwKBAK6urqXeDyIiIiKAyQN9xNraGoGBgThy5AjOnDmDbdu2ITU1FcbGxmjatCkWLFig8ZahtWrVQmBgILZv346IiAiEh4ejWrVqsLS0RLdu3dC7d29lWSsrK/z6669YtWoVDh06BKFQiPbt22PDhg2YPHmyRu2NHDkSL168wIkTJ3DgwAHIZDIEBwcXOHnYvn17rscnTpxYJsnD5MmTkZiYiD179iincDF5ICIiorIikFW2eR5ElKd2O3Jw803hrx/RFNjtoQW0mwXcjC5cJW1tgRsrkZCQWuo7WWhpCWFmZlAmbZcXjIEc4yDHOMgxDnKMA2NQo4bRJ8twzQMREREREWmE05ao0kpJSUFGRka+ZbS1tWFiYlJKPSIiIiKq2Jg8UKW1YsUKHDt2LN8y7dq1Q0BAQCn1iIiIiKhiY/JAldaYMWPQr1+/fMsYGxuXUm+IiIiIKj4mD1Rp2drawtbWtqy7QURERFRpMHkgqkLsLQQACr/BWgOTf954bW9dhE4U4VoiIiIqU0weiKoIiVSGXe6iItcjk0gh2DWraHXkSCCVcpdoIiKiiobJA1EVIRIKkJCQAkBQ1l2BVCpj8kBERFQBMXkgqkLkL7wp++SBiIiIKia+JI6IiIiIiDTC5IGIiIiIiDTC5IGIiIiIiDTC5IGIiIiIiDTC5IGIiIiIiDTC5IGIiIiIiDTC5IGIiIiIiDTC5IGIiIiIiDTCl8QRVSFaWkKU5kvi+CZpIiKiyoXJA1EVIZHKYGZmWKhrZRIpBKKCP6iU5UjwPimdCQQREVElweSBqIoQCQUYdVyCqPiC3cj3ayDAd91EwKiVQFSM5hfaW0OwaxaEQgGTByIiokqCyQNRFRIVL8PNNwW7pqn5Pzf+UTHAzeji7xQRERFVGFwwTUREREREGmHyQEREREREGmHyQEREREREGmHyQEREREREGmHyQEREREREGmHyQEREREREGuFWrUWQkpICV1dXZGZm4ptvvoGnp2dZd0nNw4cPERgYiOvXryM+Ph56enpo1KgR3N3d4e7uDpFIVNZdLLC4uDj0799f4/KRkZEl1q6WlhaqV6+OFi1aYMyYMWjWrFmxtEVERERUHjF5KILw8HBkZWXB2toaQUFB5S55OHjwIJYvXw4jIyN4enqiXr16SE5OxtmzZ7FkyRKcOHECK1asgJ6eXll3tUDMzMywZMkSlWM3b97EkSNH4OXlhbZt25Zo+w4ODsrfdXZ2Np49e4YjR47g/Pnz2Lx5M5o3b16i7RMRERGVFSYPRRAUFIS2bduiT58++P777/H06VPUr1+/rLsFALh8+TJ++OEH2NnZYf369TA1NVWeGz16NNatW4ctW7Zg2bJlajfi5Z2enh7c3NxUjkkkEhw5cgStWrVSO1fc6tatq9ZG69atMXv2bISHhzN5ICIiokqLax4K6eHDh4iKioKnpydcXV1RrVo1BAcHA5DfyPbr1w/Dhw/P9dqjR49CLBbj9OnTymOvXr3C/Pnz4ezsjG7dusHX1xf37t2Dj49PoZ5orF27FgCwdOlSlcRBYfLkyWjRogVCQ0Px6NEj5fFFixZBLBYjISEBCxcuRK9eveDo6AhfX19ERUXl2tbJkyfxxRdfwMnJCY6Ojhg7dqzK2BTEYjEWLVqEW7duYcKECXB0dESvXr3w7bffIi0trcBj1MSrV6+waNEiuLq6olOnTvD09MTKlSuRkpJSrO3UqFEDgHwa08cUY/63kJAQiMVi5ZSqXbt2QSwW4/Lly2pls7Oz4eLigvHjxxdrn4mIiIgKik8eCuno0aPQ09NDr169oK+vDycnJxw/fhxTpkyBlpYW+vbti507d+L+/fto0qSJyrWhoaEwNjaGk5MTAODDhw+YMGEC3r59Cy8vL9jZ2eHevXuYPHlyrjf+nxIbG4t79+6hdevWaNiwYa5lBAIBBgwYgLt37+Ls2bOws7NTOT9t2jQYGxtj4sSJiI+Px/79++Hj44MtW7agUaNGynKKJxhdunSBr68vhEIhzp8/j3nz5mHu3LkYOnSoSr0PHjzA7Nmz0b9/f/Tr1w/Xr19HUFAQhEIhFixYUOCx5ufVq1cYO3YskpKS4O3tjfr16+POnTvYvXs3IiMjsWXLFujq6ha43qysLCQmJgKQ39g/f/4ca9euha6ubqGfenh4eMDf3x9BQUHo1KmTyrmIiAgkJibiP//5T6HqLmsiUfn5jkLRl/LUp9LGGMgxDnKMgxzjIMc4MAaaYPJQCFlZWQgPD0fPnj2hr68PQH7zd/r0aVy8eBHdu3eHh4cHdu7ciePHj6skD3Fxcbh58ya8vb1RrVo1AMD27dvx6tUrfP311xgwYICyrJ2dHVasWIHatWsXqH+PHz8GADRt2jTfcorzHz95UKhduzZ+/PFHCAQCAEDPnj0xZswY/Pzzz1i3bh0AICoqClu2bMG4cePg5+envHb48OGYPXs2/P394e7uDgMDA+W5hw8fYsuWLWjZsiUAwNvbG6mpqQgODsbMmTOV8SwO/v7+iI+Px4oVK+Ds7AwAGDJkCOrXr4/169dj9+7dhfo2/9ixYzh27JjKsZo1a2Lt2rUqiVVBmJiYoGfPnjh79iwSExNVksbg4GAYGBigd+/ehaq7rBkbl781NeWxT6WNMZBjHOQYBznGQY5xYAzyw+ShEM6dO4ekpCSV6USdO3dG9erVERQUhO7du6Nhw4awt7fHiRMnMH36dOWuRqGhoZDJZPDw8FBeGxERATMzM5VjgPzGev369QXuX2pqKgDA0NAw33KK87lN4RkzZowycQAAe3t7dOzYEVevXkVKSgoMDQ0RHh4OAHB3d1d+E6/g5OSEiIgI/PnnnyrfpLds2VKZOCg4ODjg4sWLiIuLU3sCUlhSqRQXLlyAnZ2dMnFQGDVqFLZv345z584VKnno1q2bckqaRCLBy5cvceDAAUyfPh0//fQTxGJxofrs5eWF8PBwhIWFYcSIEQCA169f4/LlyxgwYECFW9iu8OFDOiQSaVl3A4D8myRjY71y1afSxhjIMQ5yjIMc4yDHODAGZmYGnyzD5KEQgoKCYGZmhpo1a+LFixfK4x07dkR4eDjevXuH6tWrw93dHStWrMAff/yBrl27ApAnD/Xq1UOLFi2U18XFxaFp06Zq26Zqa2vDysoKycnJBeqf4pv+T83rV5zPLclo0KBBrscuX76MuLg4NG7cGE+ePAEg/zY/L/Hx8Sqfrays1MqYmJgAAJKSkvLtb0EkJCQgNTUVtra2aud0dXVhbW2N2NjYQtVdo0YNdOzYUeWYq6srvLy88O233+LQoUNqax800b59e9SvXx9BQUHK5CEkJARSqRQDBw4sVF/LA4lEipyc8vUPcHnsU2ljDOQYBznGQY5xkGMcGIP8MHkooLi4OFy7dg0ymQyDBg3KtcyxY8cwbtw49O3bF6tWrcLx48fRtWtX3LlzB8+fP8eUKVNKtI+KdQ737t3Lt5zifEG/7f/4iQQArF69Os+b5X+vucjvvRIymaxA/cjPp+oqzrYAeQLWqlUrREREICYm5pO7bkkkklyPDxw4EKtWrcLdu3fRvHlzhISEoFGjRtzBiYiIiMoFJg8FFBISAplMhv/+978wNjZWO//rr78iODgY48aNg6mpKRwdHXHhwgWkpKTg+PHjEAqFaotq69SpgxcvXkAikajcXGdnZyM2NjbXdvJjZWWFxo0b486dO3j8+HGui6ZlMhmOHj0KAOjRo4fa+SdPnqhNL3ry5AmEQqFyDYaNjQ0uXboES0vLYptuVFzMzc1hYGCA6OhotXOZmZmIjY0t9m11c3JyAKg+8TExMcn1iUpeTz08PDywbt06BAUFIS0tDbGxsfjyyy+LtZ9EREREhcWl5AUglUoREhICW1tbDBo0CC4uLmr/69u3L54/f45bt24BkN8MZmZmIiwsDKdPn4ZYLEatWrVU6nVyckJCQoLaItxDhw4p1y8UlGIB89dff622HgEA1q9fj7/++gtubm65LvLdsWOHyrfz9+7dw9WrVyEWi5XTnPr16wdAvjBZceP8sffv3xeq78VBKBTCyckJjx49wm+//aZybs+ePUhLS8s1aSqs+Ph43L59Gzo6OipTpWxsbPDnn38iIyNDeezDhw/KbX3/zdTUFD169MDJkyexd+9e6OjolPh7K4iIiIg0xScPBXDlyhW8evUKEydOzLNMr169sHbtWgQFBaFNmzbo2rUrTExMsHbtWqSmpsLd3V3tmjFjxuDEiRP43//+h6ioKOVWrefOnUPdunXznOKSny5duuDLL7/ETz/9hCFDhsDDwwP169dXvmH6zp076NixI+bPn5/r9S9fvoSfnx+cnJzw7t077N+/Hzo6Opg5c6ayTPPmzTFp0iRs3LgRI0eORO/evVGjRg28e/cOUVFRuHjxYq7vLSgtU6dOxdWrVzF37lzlVq1//vknjh8/jsaNGyvXFRTUixcvEBoaCuD/FkwHBQUhJSUFfn5+KjtGDR06FF9//TV8fX3h5uaG5ORkHD16FLVr11ZbD6IwaNAgnDhxAhcuXEDfvn0L/OSJiIiIqKQweSiAoKAgAPIEIS9169ZFo0aNcPr0aXz55ZcwMDCAq6sr9u/fD319ffTs2VPtGlNTU/z6669YvXo1wsLCIJVK0bJlS6xfvx5LlixBZmZmofo7bNgwtG7dGrt27cLJkycRHx8PPT09NGrUCAsXLoS7u3ueaxB++eUXrFy5EgEBAcjIyEDLli0xffp0tacUEydOhL29Pfbu3Ys9e/YgPT0d5ubmaNiwYZlPt6lVqxa2bduGDRs24NSpU0hKSkL16tUxcuRI+Pj4FOodDwBw7do1XLt2TfnZ0NAQTZo0wcyZM+Hi4qJStl+/fnj79i3279+Pn3/+GVZWVpgwYQKEQiHu3r2ba/3t27dHvXr18OzZswq9UJqIiIgqH4GsuFeOUrHJyclB79690aJFC/zyyy+l0uaiRYtw7Ngx5ZuPqWwMHToUWVlZOHLkiNoC9aJotyMHN98U7JoRTYHdHlpAu1nATfU1JHlqawvcWImEhNRys2OFlpYQZmYG5apPpY0xkGMc5BgHOcZBjnFgDGrUMPpkGa55KCc+nhOvcPDgQSQnJ6ttC0qV27Vr1xAdHY1BgwYVa+JAREREVFSctlROTJ8+HbVr11a+9fn27ds4deoUbGxslFvCpqSk5JpkfExbW1v53oSKKCMj45PvpwCA6tWrF0t7EokECQkJnyxnYmICbW3tYmkzL9euXUNMTAy2bdsGMzOzPLcCJiIiIiorTB7KiW7duiE0NBTnz59HRkYGqlevjiFDhsDHx0e5AHfFihVqOzL9W7t27RAQEFAaXS4Rp06dwuLFiz9ZrrimVb1+/Rr9+/f/ZLkNGzYU+s3Rmtq0aRNu376NBg0aYNGiRZ98QzgRERFRaeOahwokOjoab9++zbeMsbEx7O3tS6lHxe/du3d4/PjxJ8sV11SuzMxM5ba6+bG3t68Uux5xzUPVnssKMAYKjIMc4yDHOMgxDoyBJmse+OShArG1tVV5h0BlVL169WKbkqQJHR0drikhIiIi0hCTB6IqxN5CAKBgDxsbmPyzaNveuoCNFbA8ERERlXtMHoiqCIlUhl3uub/X41NkEikEu2YV/LocCaRSzowkIiKqLJg8EFURIqEACQkpAEpv+1epVMbkgYiIqBJh8kBUhcgXf/HdEURERFQ4fEkcERERERFphMkDERERERFphMkDERERERFphMkDERERERFphMkDERERERFphMkDERERERFphMkDERERERFphMkDERERERFphC+JI6pCtLSEKMhL4viGaCIiIvoYkweiKkIilcHMzDDXczKJFAKR+oNIWY4E75PSmUAQERERACYPRFWGSCjAqOMSRMWrJgL9GgjwXTcRMGolEBXzfyfsrSHYNQtCoYDJAxEREQFg8kBUpUTFy3Dzjeqxpub/JAZRMcDN6NLvFBEREVUYXDBNREREREQaYfJAREREREQaYfJAREREREQaYfJAREREREQaYfJAREREREQaYfJAakJCQiAWixEZGVnWXSlVnp6e8PHxKdM+xMXFQSwWY+PGjWXaDyIiIqLcMHkoBZGRkRCLxRCLxfjhhx9yLfP+/Xt06tQJYrG4VG5g4+LisHHjRty/f7/E2yIiIiKiyoHJQynS0dHBiRMnkJWVpXYuNDQUMpkMIpGoVPoSFxeHTZs24cGDB6XSHhERERFVfEweSpGzszM+fPiAiIgItXPBwcFwdHREtWrVyqBnRERERESfxuShFDVq1AhNmzZFSEiIyvG7d+8iOjoa/fv3z/W6CxcuYMKECXByckLXrl0xZswYhIeHq5Xz8fGBp6cnXr9+jXnz5qFHjx7o2rUr/Pz88OzZM2W5jRs3wtfXFwCwePFi5ZSqRYsWqdQnk8mwbds2DBgwAJ07d8agQYNw7NgxtXZ///13+Pj4wMXFBV26dEHfvn0xc+ZMPH78uKAhwqtXr7Bo0SK4urqiU6dO8PT0xMqVK5GSkqJSTrEu49q1axr1sSDu3bsHX19fdOvWDT179sTChQsRHx+vVi4xMRHLly+Hu7s7OnXqhL59++Lbb7/Fu3fv1MpmZGRg3bp18PLyQufOneHi4oL58+er/F7yc/nyZXTv3h3jx49HUlJSkcZHREREVFhaZd2BqsbT0xM//fQTXr9+DUtLSwDypw7m5ubo2rWrWvnDhw/jf//7H2xsbDBu3Dhoa2sjLCwMX331FeLi4jB+/HiV8unp6fDx8UGrVq0wdepUxMbGYu/evZg9ezb27dsHkUiEnj17IicnB1u3boWXlxfatm0LALC2tlapa+3atcjKysKgQYOgra2NQ4cOYdGiRbC2tkabNm0AANevX8esWbNgZ2eHcePGwdDQEO/evcP169fx/PlzNGzYUOPYvHr1CmPHjkVSUhK8vb1Rv3593LlzB7t370ZkZCS2bNkCXV3dAvexIN68eYPJkyejZ8+e6NWrF+7du4fg4GD8/fff2LlzJ/T09AAAKSkpmDBhAp49ewYPDw80b94cjx8/xuHDh3H58mXs2LEDFhYWAICcnBz85z//wY0bN9CjRw+MGDECL1++xIEDB/DHH39g69ataNCgQZ59Cg0NxZIlS+Do6IjvvvtOLQZEREREpYXJQynr27cvVq9ejePHj2P8+PHIyMjAyZMnMXDgQGhpqf46kpOT8fPPP6NOnTrYsWMHDA0NAQBDhgzB559/jo0bN8LNzQ21atVSXpOYmIjRo0dj7NixymNmZmZYs2YNrl69is6dO6NRo0ZISkrC1q1b0apVK7i5ueXa1+zsbOzYsQPa2toAABcXFwwYMAD79+9X3phHRERAKpXC398fZmZmymsnTJhQ4Nj4+/sjPj4eK1asgLOzs3Ks9evXx/r167F79261ZEmTPhZETEwMZs2ahZEjRyqP2dra4ueff8bu3bvxxRdfAAB27NiBp0+fYvbs2RgxYoSybKtWrfD1119jw4YNWLBgAQDg2LFjuHHjBkaMGIHZs2cry3bv3h0TJkzAihUr4O/vn2t/tm3bBn9/fwwaNAhz584ttTUxHxOJKs8DSsVYKtOYCooxkGMc5BgHOcZBjnFgDDTB5KGUmZiYoHv37jh27BjGjx+Pc+fOISUlJdcpS1euXEF6ejomTZqkTBwAQFdXF5999hm++eYbREREYNiwYcpzQqEQw4cPV6nHwcEBAPD8+XN07txZ474OGTJEeVMOADVr1oSNjQ1evHihPGZkZAQAOH36NLy8vNQSIE1JpVJcuHABdnZ2ysRBYdSoUdi+fTvOnTunljxo0seCMDAwwODBg9XaCAgIwPnz55XJw/nz52FiYoIhQ4aolO3bty8CAgJw7tw5ZfJw7tw5CAQC5bUKbdq0gYODA65du4aUlBSV37FMJsOPP/6I/fv3w9fXt1DJWHExNtYrs7ZLSmUcU0ExBnKMgxzjIMc4yDEOjEF+mDyUAU9PT5w6dQq3bt1CcHAwmjdvDltbW7VyMTExAJDr1B87OzsAQGxsrMrxGjVqQEdHR+WYiYkJABR4rryVlZXaMRMTE7x69Ur5eejQobhw4QJ++OEHrF27Fq1bt0bnzp3Rp08f5bQdTSQkJCA1NTXXOOjq6sLa2lptrJr2sSCsrKzUFq1Xq1YNVlZWyt8HII9748aN1ZIlgUAAW1tbREREKBOC2NhYmJubw9TUVK09Ozs7XLt2DS9fvkSjRo2Ux/fs2YPU1FRMmTJFLWEqbR8+pEMikZZpH4qLSCSEsbFepRpTQTEGcoyDHOMgxzjIMQ6MgZmZwSfLMHkoA506dYKlpSUCAgIQGRmJefPmFbgOmUyW63GhMO/HbHldk5e86vq4HhMTE2zfvh23bt3ClStXcPPmTaxatQobNmzATz/9BLFYrFFbn+pbQcdb0LEqCASCQp3Lr+38+pLXuQ4dOuDWrVs4fPgw+vTpo7YepTRJJFLk5FSuf0Ar45gKijGQYxzkGAc5xkGOcWAM8sMJXWVAKBTCzc0NV69eRbVq1eDq6pprOcUNY267FkVHR6uUKShNb4Q1IRQK0a5dO0yePBkBAQHYu3cvcnJyEBAQoHEd5ubmMDAwUI7rY5mZmYiNjS2VG+iYmBhkZ2erHMvKykJsbKzKUw4rKys8f/4cOTk5anU8efIEpqamymlI1tbWeP/+PRITE9XKRkdHQygUonbt2irH7ezssGHDBmRnZ8PHx0fjXZmIiIiIShKThzLi7e2NiRMnYv78+Spz3T/WsWNH6Onp4cCBAypblWZmZiIwMBAikQhOTk6Fal9fXx8A8OHDh0Jdr5DbDbGNjQ0MDAwKNE1KKBTCyckJjx49wm+//aZybs+ePUhLS0OPHj2K1FdNpKam4sCBAyrHDhw4gNTUVJW1GM7OzkhKSsKhQ4dUyp44cQIvXrxQ6WuPHj2U295+7M6dO7h27Ro6dOiQ699Aw4YNsXHjRshkMkyaNAlPnjwp+gCJiIiIioDTlspIrVq1MGnSpHzLGBkZYcaMGVi2bBnGjBmD/v37Q0tLC6GhoXjw4AGmTJmistNSQTRo0AD6+vo4ePAg9PT0YGBgACsrK7Ro0aJA9SxduhRv3rxBx44dUbt2bWRlZeHMmTN4//49Ro8eXaC6pk6diqtXr2Lu3LnKrVr//PNPHD9+HI0bN1bZ1aikWFtbY9OmTXj8+DHs7e0RFRWF4OBg1K9fX2UHpjFjxuDMmTNYsWIF7t+/j2bNmim3arW0tFS+RwMAPDw8EBoaisDAQMTFxcHBwUG5VauBgYHKDkz/Vr9+fQQEBMDX1xeTJk3CunXrlOtdiIiIiEobk4dyztvbG9WrV8eOHTvw66+/QiaToWHDhli6dCn69u1b6Hp1dXWxdOlSrF+/HsuXL0d2djY8PDwKnDy4ubkhJCQEx48fR0JCAgwMDFC/fv1C9a9WrVrYtm0bNmzYgFOnTiEpKQnVq1fHyJEj4ePjUyrvN6hZsya+//57rFq1CidOnIC2tjb69u2LGTNmKN/xAACGhobYvHkzAgICEBERgdDQUJiYmMDDwwO+vr4qi8W1tLSwZs0abN68GadOncKFCxdgYGCArl27YtKkSahfv36+fapbty42bdoEX19f+Pr6wt/fH02aNCmpEBARERHlSSAr7MpSIqpw2u3Iwc03qsdGNAV2e2gB7WYBNz9ac9LWFrixEgkJqZVm0ZiWlhBmZgaVakwFxRjIMQ5yjIMc4yDHODAGNWoYfbIM1zwQEREREZFGOG2JSlRGRobKYu+8VK9evUTaT0pKUts96d90dXXzXLRORERERP+HyQOVqFOnTmHx4sWfLBcZGVki7c+ZMwc3btzIt4yHhwcWLVpUIu0TERERVSZMHqhEde7cGf7+/mXW/syZMz+5HW2NGjVKqTdEREREFRuTBypR1atXL7EpSZqwt7cvs7aJiIiIKhsmD0RViL2FAIDqBmsNTP5527j9v97g/e/PREREVOUxeSCqIiRSGXa5i3I9J5NIIdg1S/14jgRSKXdzJiIiIjkmD0RVhEgoQEJCCgCBxtdIpTImD0RERKTE5IGoCpG/8Ebz5IGIiIjoY3xJHBERERERaYTJAxERERERaYTJAxERERERaYTJAxERERERaYTJAxERERERaYTJAxERERERaYTJAxERERERaYTJAxERERERaYQviSOqQrS0hNDkJXF8szQRERHlhskDURUhkcpgZmaockwmkUIgUn8AKcuR4H1SOhMIIiIiUsHkgaiKEAkFGHVcgqh4eULQr4EA33UTAaNWAlEx/1fQ3hqCXbMgFAqYPBAREZEKJg9EVUhUvAw338h/bmr+T2IQFQPcjC67ThEREVGFwQXTRERERESkESYPRERERESkESYPRERERESkESYPRERERESkESYPRERERESkESYPRRQXFwexWIyNGzeWSP2enp7w8fEpkbopd4sWLYJYLC7rbhARERGVOxV6q9bIyEj4+voCAIYMGYL/9//+n1qZ9+/fw83NDTk5OWjXrh0CAgJKpW8bN25EkyZN4OzsXCrt5ebWrVvYsWMHHj58iHfv3sHIyAiWlpZo3bo1hg8fDmtrawBAcnIydu/ejfbt2xf5prk8jDs3EokEYWFhOHHiBO7du4fk5GTo6+vDzs4OTk5O8PLygoGBQVl3k4iIiKhcq9DJg4KOjg5OnDiBmTNnolq1airnQkNDIZPJIBKJSqTt2rVr4+LFi2r1b9q0CR4eHmV2E3348GH873//Q926deHh4QFLS0skJCTg8ePHCAkJQZs2bVSSh02bNgFAkZOHsh53bpKSkjB79mzcunULzZo1w7Bhw1CzZk2kpKTgzz//hL+/P/744w/4+/uXdVeJiIiIyrVKkTw4OzvjxIkTiIiIQO/evVXOBQcHw9HREdeuXSvWNtPS0qCvrw+BQAAdHZ1irbuocnJysHbtWtSuXRuBgYFq36hnZGQgMzOzjHpX+ubPn49bt25h5syZGDVqlNr5V69eISgoqAx6Jqf4WyIiIiIq7yrFmodGjRqhadOmCAkJUTl+9+5dREdHo3///mrXXL58GfPnz8eAAQPg6OgIZ2dnTJ06FdevX1cr6+PjA09PT8TExGDu3Lno2bMnnJycAKiveYiMjFR+e3/s2DGIxWKIxWJ4enoq6ztw4ACmTp2Kfv36oVOnTnB1dcXXX3+NuLi4YolHYmIiPnz4AHt7+1yn4ujq6sLExAQAEBISoozPpk2blP1VrLOQSqXYvHkzJk6cCFdXV3Tq1Anu7u5YtmwZEhMTlXV+atyK8//+HQG5rzF4/Pgx5s2bBzc3N3Tq1AkuLi6YMGECzp8/X6BY/P7777h69SpcXFxyTRwAoFatWpg0aZLa8Q8fPmDp0qXo3bs3unTpgvHjx+Pu3bsqZUJCQiAWixEZGal2veLv5mOKNSz37t2Dn58funfvjuHDh6uUf/36NebNm4cePXqga9eu8PPzw7Nnzwo0biIiIqKSUCmePADym7KffvoJr1+/hqWlJQD5Uwdzc3N07dpVrXxISAiSk5Ph6emJ6tWr482bNwgKCsKUKVOwYcMGtG3bVqV8WloaJk2ahNatW2PKlCl4//59rv1o0KABlixZgoULF6Jt27bw8vICAJVvlgMDA9GqVSt07NgRRkZGePz4MY4ePYpr165h7969MDU1LVIszM3Noa+vj5s3b+Lp06eoX79+nmXbtm2LWbNmYeXKlejRowd69OihrAMAsrOzERgYCBcXFzg7O0NXVxd//fUXgoKCcOvWLQQGBkJbW1ujcWsqMTERkydPBgB4e3ujVq1aSEpKwr1793Dnzp0CTYk6ffo0AGDQoEEF7se0adNgbm6OiRMnIjExEbt27cL06dMRHBxcpPURr1+/xpQpU9CrVy/07NkTaWlpynPp6enw8fFBq1atMHXqVMTGxmLv3r2YPXs29u3bV2LT74iIiIg0UWmSh759+2L16tU4fvw4xo8fj4yMDJw8eRIDBw6Elpb6ML/66ivo6empHPP29sbQoUOxdetWteQhKSkJQ4cOzfUb6o9ZWFjAzc0NCxcuhJWVFdzc3NTK7N27V61tJycnTJkyBUFBQRg7dqymw86VUCiEj48PVq1ahWHDhqFJkyZo1aoVWrRoAbFYjOrVqyvLWltbw9nZGStXroSdnZ1af6tVq4awsDDo6uoqj3l7e6NVq1ZYunQpzp8/j969e2s0bk3dvn0b79+/x/fffw8XF5dC1wMAjx49AgA0adKkwNfa29tj3rx5ys+2traYN28ewsPD4e3tXeg+xcbGYuHChbk+EUtMTMTo0aNV/gbMzMywZs0aXL16FZ07dy50uwUlElWKB5MqFGOqjGPTFGMgxzjIMQ5yjIMc48AYaKLSJA8mJibo3r07jh07hvHjx+PcuXNISUnJ9QYNgMrNe1paGrKysiASidCiRQu1qSkKeU17KShF21KpFGlpacjJyUHjxo1haGiYZ9sF9dlnn8HGxgYHDhzAjRs38PfffwMARCIR3N3dMXfuXJWEIC8CgUBZTiKRIC0tDRKJBA4ODgDkU8P+vc6kqIyMjAAAFy9eRKdOnWBoaFjoulJTUwGgUE8KRo4cqfJZMbXqxYsXhe4PIP9b9fDwyPWcUChUTmNSUMT6+fPnpZo8GBvrfbpQBVWZx6YpxkCOcZBjHOQYBznGgTHIT6VJHgD51KVTp07h1q1bCA4ORvPmzWFra5tr2ZiYGPj7++Py5ctITk5WOScQCNTKm5mZFekm9mPXrl3Dpk2b8Ndff6ktXP53X4rCyckJTk5OyMnJwbNnz3Dt2jXs3r0bwcHBEIlEWLBggUb1nDp1CoGBgbh//z5ycnJUzn348KHY+qvQrl07eHp6IiQkBGFhYWjWrBk6dOgAFxcX2NnZFaguRdKQmpqqXOehKSsrK5XPiulkSUlJBaont3qFwty/0ahRo4baAnxFv4vabkF9+JAOiURaqm2WNJFICGNjvUo5Nk0xBnKMgxzjIMc4yDEOjIGZ2ae/bK1UyUOnTp1gaWmJgIAAREZGqkw5+VhqaiomTJiAjIwMjBgxAnZ2djAwMIBAIMC2bdty3ZlJk2/pNXH37l34+fnB2toafn5+qFOnDnR0dCAQCPDf//4XUmnx/6FqaWmhYcOGaNiwIVxdXeHl5YXjx49j3rx5n5xDf+bMGcyfPx/NmzfHl19+CUtLS1SrVg1SqRTTpk2DTCbTqA+5JWQKEolE7dg333yD0aNH4+LFi7h16xZ2796NLVu2YNq0aRg9erRGbQKAnZ0d7t27h/v376NDhw4aXwcgz9h8POaCjgvI/28pr6Ti3+2WBolEipycyvkPZ2Uem6YYAznGQY5xkGMc5BgHxiA/lSp5EAqFcHNzw9atW6GjowNXV9dcy127dg3v3r3Ldd75+vXrS7SPJ06cgEQiwZo1a1S+2U5PTy/Wpw55MTMzg7W1Ne7du4fExERYWFjkewMcFhYGHR0dbNy4UeWm9+nTpwVqN79vz2NjY3O9xtbWFra2thg9ejRSUlIwceJE+Pv7Y/jw4dDW1tao3V69euHYsWM4cuRIgZMHTRgbGwPI/QlMXFxcruttiIiIiCqqSrcaxNvbGxMnTsT8+fPznGak+Eb539/kXr58udjWHOjr6+d6Q5lX21u2bCm2pw4ZGRm5bh0KyOfNP3nyBKampjAzMwPwf2swckteFN+Ef9w3mUyGzZs351p/XuOuU6cORCIRrl69qnL89u3b+PPPP1WOJSUlqcXC0NAQ1tbWyMnJUa5j0ETXrl0hFotx6tQp7N27N9cyL1++VG61W1A2NjYAoDau8PBwvH37tlB1EhEREZVXle5r0bz27P9YmzZtYGFhgVWrVuHly5eoWbMmHjx4gNDQUNjZ2Sl36CmKFi1a4OrVq9ixYwcsLS2hp6cHJycnODs7Y/fu3Zg+fTq8vLygra2NK1eu4NGjR0XeolUhIyMDvr6+sLW1haOjI+rWrQuZTIanT58iNDQUmZmZmDt3rjIxMDU1hbW1NU6ePAlra2uYmZnB3NwcDg4O6NWrF86ePQtfX1+4u7sjJycHERERyMjIKNC49fX14enpiaNHj+K///0v2rdvjxcvXiAkJASNGjXCgwcPlHUcP34cu3fvRo8ePWBlZYVq1arh1q1bOHfuHLp27VqgOAkEAnz//feYNWsWVqxYgbCwMDg5OaFGjRpITU3FnTt3cP78+UK/Wbt+/fro0KEDDh8+DJlMhsaNG+PBgwc4f/486tatq7ZGhIiIiKgiq3TJgyaMjIywdu1arFmzBvv27YNEIkHTpk2xevVqBAUFFUvyMHfuXPzwww/49ddfkZaWhtq1a8PJyQlt2rTBjz/+iF9//RUbNmyAjo4OOnTogICAAEycOLEYRif/ln7hwoW4fPkyIiIiEB8fj8zMTJiZmaFdu3YYNmyY2s3ykiVLsHLlSvzyyy/IzMxEu3bt4ODgAFdXV6SlpWH37t1YvXo1jIyM4OTkBD8/P/Tq1UvjcQPArFmzAADnzp1DREQEmjZtipUrV+LIkSMqyUP79u3x4MED/P7773j79i1EIhFq1aoFPz8/tZ2INGFqaoqAgACEhYUhPDwce/bsQXJyMgwMDGBnZ4f//Oc/GDBgQIHrVViyZAmWL1+O8PBwhIaGom3bttiwYQOWLVuGly9fFrpeIiIiovJGICvtVZhEVGba7cjBzTfyn0c0BXZ7aAHtZgE3o/+vUFtb4MZKJCSkVrrFYlpaQpiZGVTKsWmKMZBjHOQYBznGQY5xYAxq1DD6ZJlKt+aBiIiIiIhKRpWctlRRpaSk5LnWQEFbW7vA7zOoaCQSCRISEj5ZzsTERONdmYiIiIjo05g8VCArVqzAsWPH8i3Trl07BAQElFKPysbr16/zfHP4xzZs2FDohdBEREREpI7JQwUyZswY9OvXL98yivcOVGYWFhbw9/f/ZLnGjRuXQm+IiIiIqg4mDxWI4qVpVZ2Ojg46duxY1t0gIiIiqnKYPBBVIfYWAgDyDdYamPzzZnF7638V+tdnIiIion8weSCqIiRSGXa5i1SOySRSCHbNUisry5FAKuUuzkRERKSKyQNRFSESCpCQkAJA8MmyUqmMyQMRERGpYfJAVIXIX3jz6eSBiIiIKDd8SRwREREREWmEyQMREREREWmEyQMREREREWmEyQMREREREWmEyQMREREREWmEyQMREREREWmEyQMREREREWmEyQMREREREWmEL4kjqkK0tITgG6aJiIiosJg8EFUREqkMZmaGKsdkEikEIvUHkLIcCd4npTOBICIiIhVMHoiqCJFQgFHHJYiKlycE/RoI8F03ETBqJRAV838F7a0h2DULQqGAyQMRERGpYPJAVIVExctw843856bm/yQGUTHAzeiy6xQRERFVGFwwTUREREREGmHyQEREREREGmHyQEREREREGmHyQEREREREGmHyQEREREREGqnQyUNISAjEYjEiIyPLuisaE4vFWLRoUVl3o0Ly8fGBp6dnqbTF3xMRERGRujLbqjUyMhK+vr6fLFMRbdy4EU2aNIGzs3NZd+WTFL8HPz8/jBs3rqy7U6LevXuHvXv34tKlS4iNjUVWVhYsLCzQsmVLeHh4wNHRsay7SERERFSulfl7Hnr37o1u3bqVdTeK1aZNm+Dh4ZFr8nDx4kWIRKLS71QVd/nyZcybNw8ZGRlwcXFB//79oaenh1evXuHixYuYPn06li5dir59+5Z1V4mIiIjKrTJPHpo0aQI3N7ey7kap0dHRKesuVDlPnz7FnDlzYGhoiM2bN6Nhw4Yq5ydNmoRz585BKCy7WXzp6enQ09Mrs/aJiIiINFEh1jzIZDLs2LEDAwYMQOfOnTFo0CDs3bs317KLFi2CWCzO9Vxe89jPnDmDSZMmwdnZGY6Ojhg0aBCWL1+O7OxsAIBUKsXmzZsxceJEuLq6olOnTnB3d8eyZcuQmJiorCcyMlLZ9rFjxyAWiyEWi1Xm6efVh+DgYIwePRqOjo5wcnLCpEmTcPnyZbVynp6e8PHxwePHjzFt2jQ4OTmhe/fumDt3Lt69e5dXCIvF8+fP8fXXXytj4OnpidWrVyM9PV1ZZs2aNRCLxbh3757a9ampqejatStmzJihcvzKlSuYOnUqnJ2d0aVLFwwfPhwHDx4stn6vX78e6enpWLBggVrioNCjRw90795d7fitW7cwYcIEODo6olevXvj222+RlpamUiavtRhxcXEQi8XYuHGj8pjibyQkJAT79+/HkCFD0LlzZ+zYsUOl/Pnz5/HZZ5+hS5cucHV1xerVq5GTk1PESBAREREVTZk/ecjIyFC5AVfQ0tKCoaEhAGDlypXYs2cPWrVqhWHDhiE5ORlbt25FjRo1ity+v78/tm7dCltbW4waNQoWFhaIiYnB2bNn4evrC21tbWRnZyMwMBAuLi5wdnaGrq4u/vrrLwQFBeHWrVsIDAyEtrY2GjRogCVLlmDhwoVo27YtvLy8AAD6+voa9cHe3h6TJ09GZmYmgoODMW3aNCxZsgT9+vVTKf/27VtMnjwZPXr0gLOzM+7fv48jR44gNTUV/v7+RY5JbqKiouDr6wsjIyMMGjQINWvWxMOHD7F3717cvn0bAQEB0NLSgoeHB3bs2IHjx4+jadOmKnWcOXMGGRkZ8PDwUB47fPgwli1bhpYtW2L8+PHQ19fHlStX8P333yM2NhbTp08vUr8zMzPx+++/w9LSEl27di3QtQ8ePMDs2bPRv39/9OvXD9evX0dQUBCEQiEWLFhQpH7t2bMHSUlJ8PLygrm5OSwtLZXnLl68iIMHD8Lb2xsDBw5EREQEdu7cCSMjI4wfP75I7RIREREVRZknD7/++it+/fVXteMdOnTAunXr8PTpU+zduxdt2rTBhg0boKUl73L//v0xZMiQIrV99+5dbN26FQ4ODli9ejWqVaumPDdt2jTlz9WqVUNYWBh0dXWVx7y9vdGqVSssXboU58+fR+/evWFhYQE3NzcsXLgQVlZWGk3HevbsGbZt24YWLVogICBA2Qdvb28MGzYMy5cvh7Ozs8qUlhcvXmDZsmXo3bu38phIJMKBAwfw9OlT1K9fvyhhydWSJUtgYWGBnTt3wsDAQHncwcEBc+bMQVhYGDw9PWFra4tmzZrhxIkTmD59uvL3BQDHjx+HkZERnJycAMgXMK9YsQK9e/fG//73P2W5wYMHY8WKFdi1axe8vb1hbW1d6H6/ePECmZmZaNy4cYGvffjwIbZs2YKWLVsCkP9OUlNTERwcjJkzZ34yKczP69evcejQIZiamiqPxcXFAQCio6Oxf/9+1KlTR9nusGHDsG/fvlJNHkSiCvFgskAUY6qMY9MUYyDHOMgxDnKMgxzjwBhoosyThwEDBqBPnz5qx01MTAAAFy5cgEwmw2effaZyI1q7dm3069cPR44cKXTb4eHhAIApU6aoJA4AIBAIVH5WJA4SiQRpaWmQSCRwcHAAIE9CPr6RL4iIiAjIZDKMGTNGpQ+mpqYYMmQINmzYgMjISJVF5TVq1FBrTywW48CBA3jx4kWxJw+PHj3Cw4cPMXHiRGRnZ6s8KWrTpg309PRw+fJl5dQdd3d3LF++HH/88Yey369evcKNGzcwaNAg5ThPnz6NrKws9O/fX+3pU7du3bB3715cvXq1SMlDSkoKACifYhVEy5YtlYmDgoODAy5evIi4uDjY2dkVul9ubm4qicPHnJ2dlYkDIP/7E4vF2L9/P9LS0oqUtBSEsXHlXYNRmcemKcZAjnGQYxzkGAc5xoExyE+ZJw9169ZFx44d8zwfExMDALneEDdo0KBIbT9//hwA0KhRo0+WPXXqFAIDA3H//n21uecfPnwodB9iY2MBALa2tmrnFDenijIKVlZWamUVyVZSUlKh+5KXJ0+eAJDvIrVp06Zcy7x//175s6urK37++WccP35cmTwcP34cMplMZcrS06dPAQB+fn55tv1xvYWhSBoUSURBlGScbWxsCt1uaSUPHz6kQyKRlkpbpUUkEsLYWK9Sjk1TjIEc4yDHOMgxDnKMA2NgZmbwyTJlnjxo6uMnAYUpl9diU03qPXPmDObPn4/mzZvjyy+/hKWlJapVqwapVIpp06ZBJpNp1Lfc5HdtXufy2xWoKH35VJ0jRozIc92AsbGx8mdTU1N07doVFy5cQHJyMoyMjBAaGgobGxuVb/IV9X7zzTeoWbNmrvXmdiNdEHXr1oWOjg4ePHhQ4Gvz21L34zjn9TckkUjyvP7jKXD/Vtq/37xIJFLk5FTOfzgr89g0xRjIMQ5yjIMc4yDHODAG+Sn3yYNiysqTJ09Qr149lXOKb8Q/priJTUpKUn5bC6h/ew8A9erVw6VLl/DgwQO0atUqzz6EhYVBR0cHGzduVLnpU3xzXhSK8UVHR6s9XXn8+LFKmbKi+JZcKBTm+5ToYx4eHjh//jxOnTqFxo0b49mzZ5g8eXKu9ZqYmGhcb0Hp6OjA0dERZ8+exaVLl9ClS5dib8PY2DjX3aVy+5sjIiIiqsjK/WoQJycnCAQCBAYGqjw9ePnyJcLCwtTKK25Ir169qnI8MDBQrayrqysA+VaeWVlZaucV3/IqvgmWSqUq5zZv3pxrn/X19TWeyuTs7Kwcn2JrWECe/Bw8eBDGxsZo3769RnWVlCZNmsDOzg5HjhzBixcv1M7n5OSoTePp2rUrTE1Ncfz4cRw/fhwCgQDu7u4qZVxcXFCtWjUEBAQgIyNDrd6UlJRcfy8F5evrC11dXSxdujTXhBMAzp49i4iIiELVX69ePaSmpuLu3bvKY1KpFLt37y5UfURERETlVZk/ebh//z5CQ0NzPefk5IT69etjxIgR2L17N3x8fNC7d2+kpKTg4MGDqF+/vto3vq6urli3bh2+++47PH36FCYmJrh06VKu28G2aNECY8eOxfbt2/HZZ5+hT58+sLCwQFxcHM6cOYPt27fDyMgIvXr1Um7d6u7ujpycHEREROR6w6uo9+rVq9ixYwcsLS2hp6en3GHo32xsbDBu3Dhs3boVX3zxBfr06YOsrCwEBQUhPj4eixcvLpWXh0VGRuY6tcvIyAjDhg3D4sWLMXnyZIwcORL9+/eHra0tMjIylNva+vn5qbzrQEtLC66urti3bx8ePXoEsViMWrVqqdRtaWmJefPmYenSpRg8eDDc3d1Ru3ZtJCQk4NGjRzh//jwOHDigsni4MGxtbfHjjz9i/vz5GDlyJFxcXNCiRQvo6uri9evX+P333/H333/ju+++K1T9Xl5eCAwMxJw5czB8+HBoa2vjzJkz+U5bIiIiIqqIyjx5OHXqFE6dOpXruYMHD8LQ0BAzZ85E9erVcejQIaxZswa1a9fG559/DgMDAyxevFjlGkNDQ6xevRorV67E1q1boaenh549e+Lbb79Fjx491NqYNm0aGjVqhP3792PHjh2QSqWwtLSEo6OjcoqSq6sr0tLSsHv3bqxevVq53aifnx969eqlVufcuXPxww8/4Ndff0VaWhpq166dZ/IAAFOnToW1tTUOHDiA9evXQygUwt7eHvPmzUPnzp0LEs5Cu3z5cq4vpatduzaGDRuGJk2aYNeuXdi6dSsuXLiAQ4cOwcDAALVr14anp6dy56mPeXh4YN++fUhNTVV76qDQv39/2NjYIDAwEIcPH0ZycjJMTU1Rr149TJ48GRYWFsUyvi5duuDgwYPYs2cPLl26hIiICGRlZaF69epo1aoVpkyZgk6dOhWqbisrK6xYsQLr1q3Dhg0bYGJiAjc3N/Tv3x+DBw8ulv4TERERlQcCWWmuwCSiMtVuRw5uvpH/PKIpsNtDC2g3C7gZ/X+F2toCN1YiISG10i0W09ISwszMoFKOTVOMgRzjIMc4yDEOcowDY1CjhtEny5T7NQ9ERERERFQ+lPm0JSp+2dnZGr2HwMzMLN/tSMuztLQ0pKWl5VtGJBLBzMyslHpEREREVPkxeaiEbt++DV9f30+WCw4OLvJi5LKyc+fOPF9Yp1C7dm2EhISUUo+IiIiIKj8mD5VQ48aN4e/v/8lyxbUYuSy4u7ujTZs2+ZbR0dEpnc4QERERVRFMHiohY2PjEnvpWnlhbW1d5i/PIyIiIqpqmDwQVSH2FgIA8g3WGpgI/jn4ryTs35+JiIiI/sHkgaiKkEhl2OWuukBeJpFCsGuWWllZjgRSKXdxJiIiIlVMHoiqCJFQgISEFACCT5aVSmVMHoiIiEgNkweiKkT+wptPJw9EREREueFL4oiIiIiISCNMHoiIiIiISCNMHoiIiIiISCNMHoiIiIiISCNMHoiIiIiISCNMHoiIiIiISCNMHoiIiIiISCNMHoiIiIiISCNMHoiIiIiISCN8wzRRFaKlJcSn3jAtlcoglcpKp0NERERUoTB5IKoiJFIZzMwMVY7JJFIIRKoPIGU5ErxPSmcCQURERGqYPBBVESKhAKOOSxAVL08K+jUQ4LtuImDUSiAqRl7I3hqCXbMgFAqYPBAREZEaJg9EVUhUvAw338h/bmr+T3IQFQPcjC67ThEREVGFwQXTRERERESkESYPRERERESkESYPRERERESkESYPRERERESkESYPVCSLFi2CWCwu624QERERUSngbktFEBkZCV9fX+VnoVAIfX19VK9eHU2aNEGvXr3QvXt3iESiMuxl5ZGcnIzdu3ejffv2VSJhiYuLQ0hICJydndGkSZOy7g4RERERk4fi0Lt3b3Tr1g0ymQzp6el4/vw5fv/9d5w4cQLNmjXD8uXLYWlpWdbdrPCSk5OxadMmAKgyycOmTZtQp04dJg9ERERULjB5KAZNmjSBm5ubyrGZM2di+/btWLt2LWbMmIGdO3dCS4vhJiIiIqKKi2seSohAIMC4cePQp08fPHz4EKdOnVKey8rKwpYtWzB06FB06dIFzs7OmDlzJu7du6dSR2RkJMRiMUJCQrB3714MGjQIXbp0gZeXF/bs2ZNru8+fP8fXX38NV1dXdOrUCZ6enli9ejXS09NVyinWKnz48AFLly5F79690aVLF4wfPx53795Vqzc5ORnLli1D79694ejoiHHjxuHKlSt5jr+4+xESEoL+/fsDADZt2gSxWAyxWAwfH588+5CXM2fOYNKkSXB2doajoyMGDRqE5cuXIzs7W1kmIyMD69atg5eXFzp37gwXFxfMnz8fz549U6nr49/Rv+W2HsTHxweenp54/fo15s2bhx49eqBr167w8/NTqXvjxo3KKXGLFy9WjnfRokUFHi8RERFRceFX4SVs0KBBOHnyJH777Tf069cPOTk5mDZtGu7cuQM3NzcMHToUKSkpOHr0KL744gts2rQJzZo1U6lj3759iI+Px6BBg6Cvr48TJ07gp59+QlJSksqai6ioKPj6+sLIyAiDBg1CzZo18fDhQ+zduxe3b99GQECA2tOPadOmwdzcHBMnTkRiYiJ27dqF6dOnIzg4GAYGBgCAnJwc+Pn54a+//kKfPn3Qtm1bPHv2DLNnz4a1tbXamEuiH23btsWsWbOwcuVK9OjRAz169AAAmJubF+j34e/vj61bt8LW1hajRo2ChYUFYmJicPbsWfj6+kJbWxs5OTn4z3/+gxs3bqBHjx4YMWIEXr58iQMHDuCPP/7A1q1b0aBBgwK1+7H09HT4+PigVatWmDp1KmJjY7F3717Mnj0b+/btg0gkQs+ePZGTk4OtW7fCy8sLbdu2BYBc401ERERUWpg8lLBGjRoBkH8TDwB79+7F9evXsWbNGnTp0kVZbvDgwRg2bBhWrVqFgIAAlTqeP3+OAwcOKNdNDB06FF988QW2bt2KAQMGoHbt2gCAJUuWwMLCAjt37lTe+AOAg4MD5syZg7CwMHh6eqrUbW9vj3nz5ik/29raYt68eQgPD4e3tzcAIDg4GH/99RfGjh2LadOmKcu2adNG5VqFkuiHtbU1nJ2dsXLlStjZ2alNE9PE3bt3sXXrVjg4OGD16tWoVq2a8tzH4zp27Bhu3LiBESNGYPbs2crj3bt3x4QJE7BixQr4+/sXuH2FxMREjB49GmPHjlUeMzMzw5o1a3D16lV07twZjRo1QlJSErZu3YpWrVoVarxFIRJVzoeSinFV1vFpgjGQYxzkGAc5xkGOcWAMNMHkoYQpbp5TU1MBAOHh4bCxsUGzZs2QmJioUrZjx444fvw4MjIyoKurqzzet29flQXX2traGDlyJL766itcuHABw4YNw6NHj/Dw4UNMnDgR2dnZKnW3adMGenp6uHz5stpN+8iRI1U+K6bZvHjxQnksIiICAoEAY8aMUSnr4uICGxsbZWIEoET7UVTh4eEAgClTpqgkDoB8mpnCuXPnIBAI8MUXX6iUadOmDRwcHHDt2jWkpKTA0NCwUP0QCoUYPny4yjEHBwcA8kSxc+fOhaq3OBkb65V1F0pUZR+fJhgDOcZBjnGQYxzkGAfGID9MHkqYImlQJBFPnjxBZmYmXFxc8rwmMTERtWrVUn7ObYqMra0tACAmJkZZLyBfD6DYkejf3r9/r3bMyspK5bOpqSkAICkpSXksJiYG5ubmMDExUbu+QYMGKslDSfajqBT9VDwNyktsbCzMzc2VffiYnZ0drl27hpcvX36ynrzUqFEDOjo6KscUsS3O8RbFhw/pkEikZd2NYicSCWFsrFdpx6cJxkCOcZBjHOQYBznGgTEwMzP4ZBkmDyXswYMHAID69esrj9na2qpMh/k3MzMzlc8ffyuuIJPJVM4pPo8YMQJdu3bNtV5jY2O1Y3m9g0JRX359yO+6kupHUWkyjvzaLEhcJBJJrseFwrwfhRb3eAtLIpEiJ6fy/qNZ2cenCcZAjnGQYxzkGAc5xoExyA+ThxJ2+PBhAFDeSNvY2ODdu3dwcHDI9ybyY9HR0WrHFN/wK76xt7GxASC/Me3YsWOR+/0xa2trXLp0CUlJSWpPHxT9UCjJfmiawOSlXr16uHTpEh48eIBWrVrlWU4x3sTERLWnD9HR0RAKhcp1Jvk9MYiNjS1Sf4s6XiIiIqLixtUgJUQmk2H79u04deoUGjdujN69ewMA3NzckJCQgB07duR6XXx8vNqx8PBwvH79Wvk5Ozsbu3fvhkgkgpOTEwD5uybs7Oxw5MiRXNcJ5OTkFHpKjLOzM2QymVqfT58+rTJlqaT7oacnn3+YnJxcqOtdXV0BAOvXr0dWVpbaecW3/j169IBMJsO2bdtUzt+5cwfXrl1Dhw4dlOsd6tSpA5FIhKtXr6qUvX37Nv78889C9VNBX18fAPDhw4ci1UNERERUXPjkoRjcv38foaGhAIC0tDTExMTgwoULeP78OZo3b47ly5crp+WMGDECV65cwdq1a3Hjxg04ODjAwMAAr169wrVr11CtWjVs3LhRpX4bGxuMGzcO3t7e0NfXR3h4OP7++29MmDBB+Q24QCDA4sWLMXnyZIwcORL9+/eHra0tMjIylFuR+vn5qS1U1oSnpyeOHj2K7du34+XLl2jXrh2ePn2Ko0ePws7ODo8ePVKWLcl+mJqawtraGidPnoS1tTXMzMxgbm6uXGz8KS1atMDYsWOxfft2fPbZZ+jTpw8sLCwQFxeHM2fOYPv27TAyMoKHhwdCQ0MRGBiIuLg4ODg4KLdqNTAwUJlypq+vr4zPf//7X7Rv3x4vXrxASEgIGjVqpJy2VhgNGjSAvr4+Dh48CD09PRgYGMDKygotWrQodJ1ERERERcHkoRicOnUKp06dglAohJ6eHqpXrw57e3v4+fmhe/fuKvP5tbS0sGrVKhw8eBChoaHKRKFGjRpo3rw5PDw81OofNmwYUlNTsW/fPrx69Qq1atXC7NmzMWLECJVyTZo0wa5du7B161ZcuHABhw4dgoGBAWrXrg1PT0+Nb7L/TUtLC2vXrsUvv/yCs2fPIiIiAo0aNcJPP/2EsLAwleShJPsByLeBXblyJX755RdkZmaiXbt2Bapv2rRpaNSoEfbv348dO3ZAKpXC0tISjo6Oyh2utLS0sGbNGmzevBmnTp3ChQsXYGBggK5du2LSpEkq61cAYNasWQDkuzRFRESgadOmWLlyJY4cOVKk5EFXVxdLly7F+vXrlS+x8/DwYPJAREREZUYgKy8rNElNZGQkfH198c033xTqm3qif2u3Iwc338h/HtEU2O2hBbSbBdz8Z11NW1vgxkokJKRWyoViWlpCmJkZVNrxaYIxkGMc5BgHOcZBjnFgDGrUMPpkGa55ICIiIiIijXDaElV47969+2QZQ0NDlRfvEREREVHBMXmgCq9v376fLMOpX0RERERFx+ShHBOLxYiMjCzrbpR7/v7+nyzTsGHDUugJERERUeXG5IEqvOJ+GR0RERER5Y7JA1EVYm8hACDfYK2ByT9vsLa3/qiAtfpFRERERP9g8kBURUikMuxyF6kck0mkEOyapXosRwKplDs4ExERkTomD0RVhEgoQEJCCgBBvuWkUhmTByIiIsoVkweiKkT+wpv8kwciIiKivPAlcUREREREpBEmD0REREREpBEmD0REREREpBEmD0REREREpBEmD0REREREpBEmD0REREREpBEmD0REREREpBEmD0REREREpBEmD0REREREpBG+YZqoCtHSEiK/N0xLpTJIpbLS6xARERFVKEweiKoIiVQGMzND5WeZRAqBSPXhoyxHgvdJ6UwgiIiIKFdMHoiqCJFQgFHHJYiKl6FfAwG+6yYCRq0EomLkBeytIdg1C0KhgMkDERER5YrJA1EVEhUvw803QFPzf5KDqBjgZnTZdoqIiIgqDC6YJiIiIiIijTB5ICIiIiIijTB5ICIiIiIijTB5ICIiIiIijTB5IPqXRYsWQSwWl3U3iIiIiMqdYtttKTIyEr6+vvDz88O4ceOKq9pK7fr16zh06BBu376NhIQEiEQi1K1bF506dYK3tzesrKwKXGdycjJ2796N9u3b8wb4IxKJBGFhYThx4gTu3buH5ORk6Ovrw87ODk5OTvDy8oKBgUFZd5OIiIioXONWrWVAJpPhxx9/xIEDB1CrVi306dMH9erVQ3Z2Nh49eoSQkBDs2bMHf/zxR4HrTk5OxqZNmwCAycM/kpKSMHv2bNy6dQvNmjXDsGHDULNmTaSkpODPP/+Ev78//vjjD/j7+5d1V4mIiIjKNSYPZWDz5s04cOAAXFxcsGTJElSrVk3l/IwZM/DLL7+UUe/KRk5ODqRSqVosisP8+fNx69YtzJw5E6NGjVI7/+rVKwQFBRV7u5pKS0uDvr5+mbVPREREpKkSW/MQFxcHsViMjRs34vz58/jss8/QpUsXuLq6YvXq1cjJyVG75sWLF1i8eDHc3NzQqVMn9O3bF7NmzUJUVJRKuQsXLmDChAlwcnJC165dMWbMGISHh6vV5+PjA09PT8TFxeHLL7+Es7MzevTogUWLFiEtLQ1SqRRbtmxB//790blzZ4wcORI3b95Uq0cmk+HgwYP47LPP4OjoCCcnJ0yaNAmRkZEFjktCQgK2bt2KWrVqYdGiRbneLOvp6WHu3LnKz6mpqVi3bh3Gjh2LXr16oXPnzhg4cCB++eUXZGRkKMuFhISgf//+AIBNmzZBLBZDLBbDx8enUGPJzMzE6tWr0a9fP3Tp0gUjRoxAeHg4Nm7cCLFYjLi4OJXyjx49wpw5c5R9HDRoEDZt2oSsrCyVcorrHz9+jJUrV8LNzQ1dunTBjRs30Lt3b4wfPz7X2AUGBkIsFuPy5csaRFru999/x9WrV+Hi4pJr4gAAtWrVwqRJk9SOf/jwAUuXLkXv3r3RpUsXjB8/Hnfv3lUpExISArFYnGv8FH9/H/P09ISPjw/u3bsHPz8/dO/eHcOHD1cp//r1a8ybNw89evRA165d4efnh2fPnmk8ZiIiIqKSUuJPHi5evIiDBw/C29sbAwcOREREBHbu3AkjIyOVm8S///4bkydPRk5ODgYOHAhbW1t8+PABN27cwO3bt2Fvbw8AOHz4MP73v//BxsYG48aNg7a2NsLCwvDVV18hLi5O7cYzPT0dvr6+aN++Pfz8/HDv3j0cPXoUmZmZMDU1xV9//YWhQ4ciJycHgYGBmDVrFkJCQmBoaKisY+HChThx4gR69eoFT09PZGdnIywsDFOnTsWPP/6I7t27axyP33//HZmZmXB3d4eurq5G17x9+xZBQUFwcXFBv379IBQKcePGDezYsQP379/H2rVrAQBt27bFrFmzsHLlSvTo0QM9evQAAJibmxdqLPPmzcNvv/2Grl27okuXLnj79i2+//57WFtbq/Xx3r17mDhxIoRCIYYMGYKaNWvijz/+wMaNG/Hnn39i1apVEApVc9Wvv/4aurq6GDVqFAQCAWrVqgUPDw/s3LkTT58+Rf369VXKBwcHo06dOujYsaNGcQOA06dPAwAGDRqk8TUK06ZNg7m5OSZOnIjExETs2rUL06dPR3BwcJHWR7x+/RpTpkxBr1690LNnT6SlpSnPpaenw8fHB61atcLUqVMRGxuLvXv3Yvbs2di3bx9EIlGh2yUiIiIqqhJPHqKjo7F//37UqVMHAODt7Y1hw4Zh3759yht9mUyGRYsWITs7Gzt37kTDhg2V13/++eeQSqUA5PP5f/75Z9SpUwc7duxQ3uAPGTIEn3/+OTZu3Ag3NzfUqlVLeX1iYiLGjRuHzz77THnsw4cPOH36NOzt7bFlyxZoacnD0KBBA8yePRvh4eEYPHgwAODs2bMICwvD/Pnz4e3traxj+PDh+Pzzz/HTTz/ByckJAoFAo3g8evQIANCkSRONY2hlZYXjx48r+wkAQ4cOxfr167F582bcvXsXLVq0gLW1NZydnbFy5UrY2dnBzc1NpZ6CjOXSpUv47bff4O7ujsWLFyvLuri4YPTo0Wp9XL58OTIzM7Fjxw40bdpU2cfvvvsOR44cwcmTJ9G3b1+Va4yNjeHv769yQ+zl5YWdO3fi6NGjmDFjhvL43bt3ER0dDV9fX41jDRQu3gr29vaYN2+e8rOtrS3mzZuH8PBwlfgVVGxsLBYuXKh8SvSxxMREjB49GmPHjlUeMzMzw5o1a3D16lV07ty50O1qSiSqvJuwKcZWmcf4KYyBHOMgxzjIMQ5yjANjoIkSTx6cnZ2ViQMACAQCiMVi7N+/XznX+/79+4iOjsagQYNUEgcFxTfWV65cQXp6OiZNmqTyZEBXVxefffYZvvnmG0RERGDYsGHKcyKRCEOHDlWpr3Xr1jh79iwGDRqkckPetm1bAEBMTIzyWFhYGPT09ODs7IzExESVerp164aAgAA8f/4c9erV0ygeqampAFCgb661tbWVP+fk5CinXHXo0EElefiUgowlIiICANQShSZNmqBTp064dOmS8lhCQgJu376Nbt26KRMHhS+++AJHjhzB2bNn1ZKH4cOHq32TbmNjg/bt2+P48ePw8/NT/n6CgoIgFArVpgF9SmHirTBy5EiVz4oF6C9evChwXR8zMTGBh4dHrueEQqFyGpOCg4MDAOD58+elkjwYG+uVeBtlrSqM8VMYAznGQY5xkGMc5BgHxiA/JZ485LbdqImJCQD5Ljj6+vrKm7HGjRvnW5fipj63BMPOzg6A/Fvdj1WvXl1tXYGxsTEAqCQ1Hx9PSkpSHnv69CnS09Ph6uqaZ7/ev3+vcfKguIlV3NRq6sCBAzh06BCio6OVT2IUkpOTNaqjIGOJi4uDQCCAjY2NWpl69eqpJA+KmOf2e6lVqxYMDQ3Vfi8Acq0bkE8xWrBgAS5cuICePXsiPT0dJ0+eROfOnWFpafnJcX7s43gr/u409e+/XVNTUwCqfx+FYWVlpTaFS6FGjRrQ0dFROfbxfy+l4cOHdEgk0k8XrIBEIiGMjfUq9Rg/hTGQYxzkGAc5xkGOcWAMzMw+/WVriScPed0kAfLpSh//f1HkVUd+7ed17uO6ZDIZTExM8L///S/PenK7ac6LIsm5f/8+evbsqdE1gYGBWLVqFTp16oThw4ejevXq0NbWxtu3b7Fo0SK1ZCIvBRlLQX4nhf395bXmo2fPnjA1NcXRo0fRs2dPnD59GqmpqRg4cGCB27Czs8O9e/dw//59dOjQoUDX5rW+4OPx5jeFSiKR5Ho8v7Uumvz3UtIkEilycir3P5hVYYyfwhjIMQ5yjIMc4yDHODAG+SkXW7UqvrW/f/9+vuUUC3UfP36sNn0jOjpapUxxsbGxwbNnz9C8eXOVqVKF1bVrV+jo6CA0NBTjx49X+5Y5N6GhoahTpw7WrFmjcnP58bf/CvndzBZkLFZWVpDJZHj27BkaNWqkcu7fO/98/Hv5t9evXyMlJaVAvxdtbW14eHhg9+7deP36NYKCgmBhYYFu3bppXIdCr169cOzYMRw5cqTAyYMmFE+rPnz4oHYuLi5OZVocERERUUVXLlaDNG7cGLa2tjh+/HiuN6CKb1w7duwIPT09HDhwACkpKcrzmZmZCAwMhEgkgpOTU7H2zc3NDTKZDGvXrs31m9/4+PgC1WdmZoaxY8fi5cuXWLJkCbKzs9XKpKWl4ccff1R+FolEEAgEKu3n5ORg27Ztatfq6cnn6OU2lakgY1HEcefOnSpl7t+/r7ZVqpmZGVq3bo1Lly6pJYBbtmwBAOXOT5ry8vKCVCrF2rVrcevWLbi7uxfqRrxr164Qi8U4deoU9u7dm2uZly9fYuPGjQWuG/i/qVdXr15VOR4eHo63b98Wqk4iIiKi8qpcfC0qEAjwzTffYMqUKRg7diwGDBiAhg0bIjk5GTdu3EDnzp0xfPhwGBkZYcaMGVi2bBnGjBmD/v37Q0tLC6GhoXjw4AGmTJmistNScXBxcYGnpycOHjyIBw8eoFu3bjA1NcWbN29w584dxMTEFPgFYxMmTMC7d+9w+PBh3LlzB3369EHdunWRk5ODR48e4fTp00hLS1O+66FXr15Yu3Yt/vOf/6BHjx5ITU3FiRMncr2ZNjU1hbW1NU6ePAlra2uYmZnB3NwcDg4OBRqLo6MjHB0dERoaig8fPii3aj148CCaNGmCqKgolaccc+bMwcSJE+Hj44OhQ4eiRo0auHz5Mi5cuIDOnTujT58+BYpRvXr10L59e4SFhQEABgwYUKDrFQQCAb7//nvMmjULK1asQFhYGJycnFCjRg2kpqbizp07OH/+fKHfxl2/fn106NABhw8fhkwmQ+PGjfHgwQOcP39e+TslIiIiqizKRfIAAM2bN8f27duxefNmnD59GocOHYKpqSmaN2+ONm3aKMt5e3ujevXq2LFjB3799VfIZDI0bNgQS5cuVdvNp7h88803EIvFOHLkCLZt24bs7GxYWFigadOmmDp1aoHrEwqF+O9//4vevXvj8OHDCA8Px/v376GlpYW6deti4MCByq1iAfmORzKZDEFBQfjpp59gYWGB3r17o3///hgyZIha/UuWLMHKlSvxyy+/IDMzE+3atVPu2FOQsfzwww/YsGEDwsPDcfXqVdSvXx8LFizAn3/+iaioKJUpV02bNsXWrVuxceNGHD58GKmpqahTpw58fHwwbty4fOfy58XLywvXr19Hu3btNF6QnhtTU1MEBAQgLCwM4eHh2LNnD5KTk2FgYAA7Ozv85z//KXRyAsjjvXz5coSHhyM0NBRt27bFhg0bsGzZMrx8+bLQ9RIRERGVNwJZaa3CpEpjxowZiIyMRERERIm+tOz06dOYN28eFi9eDHd39xJrpypptyMHN98AI5oCuz20gHazgJvy9UJoawvcWImEhNRKu0hMS0sIMzODSj3GT2EM5BgHOcZBjnGQYxwYgxo1jD5ZplyseaDyKSMjQ+3YvXv38Mcff8DBwaHE33a8f/9+mJiYoFevXiXaDhERERFpptxMW6roEhIS8tyaU0FfXx/6+vql1KOi+/XXX3H//n2IxWIYGRnhyZMnOHLkCLS1tTF58uQSafP9+/e4evUqbt26hRs3bmDq1KlqW5tKJBIkJCR8si4TExOVF+wRERERUdEweSgmY8aM+eT89okTJ2LSpEml1KOia9u2Le7cuYOdO3ciOTkZRkZG6Ny5MyZOnPjJF/oVVnR0NL766isYGRnB29tb7Q3XgHz71/79+3+yrg0bNhR6ITQRERERqWPyUEy+/fZbZGZm5lsmt7dtl2eKHZdKk1gsRmRkZL5lLCws4O/v/8m6SirBISIiIqqqmDwUk493hKKSpaOjg44dO5Z1N4iIiIiqHCYPRFWIvYUAgAwNTP55R4f9R2/+ti/et7MTERFR5cPkgaiKkEhl2OX+fztkySRSCHbNUikjy5FAKuXuzURERJQ7Jg9EVYRIKEBCQgoAQZ5lpFIZkwciIiLKE5MHoipE/sKbvJMHIiIiovzwJXFERERERKQRJg9ERERERKQRJg9ERERERKQRJg9ERERERKQRJg9ERERERKQRJg9ERERERKQRJg9ERERERKQRJg9ERERERKQRJg9ERERERKQRJg9EVYiWlhBCId8wTURERIXD5IGoipBIZTA11oe5iR4TCCIiIioUrbLuABGVDpFQAECeNAiFAkilMo2vFQoFZZ5wSKWyAvWZiIiIih+TByLKl1AogImZAbTKOHnIkcqQlJBa6ATi0aOHOHBgN27duoG3b99CJBKhbl0b9OrVB56eA2FsbAIA8PPzAQCsXRtQbH0vDmFhx3D58kU8eHAfMTEvYGlZCwcPhpR1t4iIqIph8kBE+RIKBdASCjDquARR8WXzzb+9hQC73EUFfmKiEBx8BD/99D3q1auPL774ApaW1sjKysK9e1E4evQw7t79E8uWrSiBnhefEydC8f59PJo1aw6ZTIacnJyy7hIREVVBTB6ISCNR8TLcfFNWrRc+abl79w5++ul7iMUdsXz5SlhamiEhIRU5OVI4OHTC8OGf4cqVS8XY15KxcuVabimGPgAARyFJREFUCIXyZWpz585AdPTjMu4RERFVRUweiKhS27FjKwBg7tz/olq1amrntbW10bVr93zr2LIlAH/8cRExMS8gkUhgbW2NQYOGwN19AASC/5vOdf36NWzdugnR0Y+RkZEBU1NT2Ns3w9dffwtdXV0AwJEjB3H06CHExcUAEKBGjRro3r0nJk2amm8fFIkDERFRWWLyQESVlkQiwY0b19CkiT0sLWsVup5Xr15iwIBByjr++utP/Pzzcrx9+xaffz4RAPDyZRzmzJmB1q3bYP78r2FoaIS3b9/iypVLyM7Ohq6uLk6fPoGffvoegwcPg5/fdAgEQsTEvMDTp0+KZbxEREQljclDEXl6eqJ27doICChfiyupaDZu3IhNmzYhODgYderUKevuUCElJSUiIyMDtWsX7Xf43/9+o/xZKpWibdv2AIADB/Zg3LgJEAgEuH8/CllZmZgyZToaNWqsLN+nT1/lz3/+eRuGhkaYMWOO8phY3KFIfSMiIipNFS55yMzMxJEjR3D27Fk8fvwYKSkpMDY2RpMmTdCrVy+4u7vnOjWhvLp//z727duH69ev4927dwCAOnXqQCwWw9vbG3Z2doWqd+PGjWjSpAmcnZ2LsbcV3/nz5xESEoK//voLiYmJ0NHRQYMGDeDo6Ahvb2+Ym5uXdRepHLp+/Rp27NiKe/f+Qmpqqsq5hIT3MDe3QKNGTaCtrY0ff/wOgwYNQatWbWBlZa1S1t6+OQ4d2o9vvvkvXFz6oGXLNjA1NS3FkRARERVNhUoe4uLiMGPGDERHR8PBwQFjxoyBmZkZkpKScP36dSxbtgx///03FixYUNZd1ci2bdvg7+8PExMT9O3bFw0aNIBAIMDTp09x9uxZHDp0CCEhIbC0tCxw3Zs2bYKHhweTh39kZmbiq6++wrlz59CgQQMMHDgQtWrVQmZmJu7du4edO3ciLCwMhw8fLuuuUjEyMTGFrq4uXr6MK3Qdf/99F7Nm+aFNm/aYO/cr1KxZE9ra2rhw4Tx27NiCzMxMAICVlTVWrVqHXbt2YOXKH5Ceno46dawwePBwDB06AgDQt687JBIJQkKO4quv/h+kUimaNm0GH5/JcHDoVCxjJiIiKkkVJnnIzMzEjBkz8OzZM3z//fdwcXFROT969Gg8evQIly9fLrY2s7KyIBQKoaVV/GEKDQ3F2rVr0a5dO/z0008wMjJSOf+f//wH27Ztg0xWdV6KJZVKkZWVpVxYWpx++OEHnDt3DiNHjsSMGTPUFp9Onz4d27dvL/Z2NZWWlgZ9ff0ya7+yEolEaN/eAZcvX8KbN69Rp07tAtdx5sxJiERa+PHHn6Gjo6M8fuHCebWyrVu3RevWbSGRSHDvXhQOHdqHNWt+grm5OVxcXAEA7u794e7eH+np6bh16wa2bNmIuXNnYs+ew6hVq+D9IyIiKk0VZvuOoKAgREdHY+TIkWqJg4KdnR0+++wz5ee7d+9i0aJFGDRoEBwdHeHk5ITx48fj3LlzatcuWrQIYrEYCQkJWLx4Mfr06QNHR0e8eSPfm/LJkyeYPn06unXrBmdnZ8yaNQsxMTGFGkt2djZ++eUX6Onp4fvvv1dLHABAS0sLEyZMQK1a8gWaUqkUmzdvxsSJE+Hq6opOnTrB3d0dy5YtQ2JiovK6yMhIiMViAMCxY8cgFoshFovh6empUv/JkyfxxRdfwMnJCY6Ojhg7dixOnz6t1g+pVIpt27ZhwIAB6Ny5MwYNGoS9e/ciJCQEYrEYkZGRKuVfvXqFRYsWKfvo6emJlStXIiUlRaWc4vorV67g119/VdZ/6tQpjBgxAu7u7pBKpWr9OXv2LMRiMY4cOaJZsAE8evQIISEhaNGiBWbOnJnrrjWmpqaYPn262vGsrCysXr0a/fr1Q+fOnTFixAj8/vvvKmUUMQ8JUX9hl+Lv6mM+Pj7w9PRETEwM5s6di549e8LJyUml/IcPH7B06VL07t0bXbp0wfjx43H37l2Nx0z/Z/TozwEAP/zwHbKzs9XO5+Tk4PffL+RTgwAikQgikUh5JDMzAydOhOZ5hUgkQvPmLTBr1v8DADx4cE+tjJ6eHjp3dsSYMeORnZ2NJ0+49SoREZV/FebJg+LG1tvbW+Nrzp8/j+fPn8PV1RU1a9ZEUlISjh07hjlz5mDp0qXo27ev2jVTp05F9erV8cUXXyA9PR36+vqIjY3FhAkTkJGRgcGDB8PKygrXrl2Dr68vMjIyCjyW27dv4+3bt+jXr5/Gc+yzs7MRGBgIFxcXODs7Q1dXF3/99ReCgoJw69YtBAYGQltbGw0aNMCSJUuwcOFCtG3bFl5eXgCg8q32unXrsGXLFnTp0gW+vr4QCoU4f/485s2bh7lz52Lo0KHKsitWrMD+/fvRpk0bDB8+HCkpKdixYwcsLCzU+vjq1SuMHTsWSUlJ8Pb2Rv369XHnzh3s3r0bkZGR2LJli9pThdWrVyMnJwdeXl4wMDBAvXr14OXlhR9//BGXL19Gly5dVMoHBwdDT08Pffr00TjeZ86cgUwmw8CBA1W21dTEN998g2rVqmH06NHIzs7Gnj178OWXX+Lw4cNFWkidlpaGSZMmoXXr1pgyZQrev3+vcn7atGkwNzfHxIkTkZiYiF27dmH69OkIDg6GgYFBodutilq0aIXZs+fhp5++x7hxozBq1EjUqlUXmZlZePjwPoKDj6BBg4bo2tUp1+u7dOmKfft2YdGiBRgwwAtJSUnYsycQ2tqqa6uOHj2I69cj0aVLV1hayqfEHT8eDAAQizsCAH74YSl0dHTQsmVrWFhUR3x8PAIDt8LQ0BBNmzbPdxxPnkTj6dNoAEB8fDwyMjJw7pz838X69W3RoIFtkeJERESkiQqTPDx+/BgGBgawtrb+dOF/fPHFF/Dz81M5Nnz4cIwcORKbN2/ONXlo1KgRFi9erHJs+fLlSEpKwpo1a5Q3s0OHDsUPP/yAAwcOFHgsjx49AgA0adJE42uqVauGsLAwlZtvb29vtGrVCkuXLsX58+fRu3dvWFhYwM3NDQsXLoSVlRXc3NxU6omKisKWLVswbtw4ldgMHz4cs2fPhr+/P9zd3WFgYIAnT55g//79EIvF8Pf3V37zOmDAAAwePFitj/7+/oiPj8eKFSuUay2GDBmC+vXrY/369di9ezfGjx+vck1mZiZ27dqlMi5bW1usWbMGR48eVUke3r59iz/++EPZP00p4t20aVONr1EwMzPDzz//rEw6xGIxxo4di8OHD6v9bRVEUlIShg4dikmTJuV63t7eHvPmzVN+trW1xbx58xAeHl6gBDovIpHmDx0VZe0tBCjKy9qKQt52wfr9sUGDvNGyZUvs3bsLv/76K96+fQstLS3UrVsPrq59MXjwcGhpyetW/K4Vnzt27IivvvoGO3dux//7f7NQo0ZNDBjgBTMzM3z33RKIREJoaQnRtGlTXLt2BZs3b8T79/HQ09OHrW1DLF/+s/LvuG3bdv+/vTuPqyn//wD+urdFJW1EiEp1Q6UoLUKjNBqhGA0qxlgaFMYyss0wY2aMfamfEZFJloaihZjKNsoyYw3ZSvYlldum/fz+6HHPt9O91W0P7+fj4fHQ537OOZ/zPud87v2c8/l8Do4di8KpU/HIzc2Bmpoa+vQxx4oVq6CpKd4gr+zMmXjs2sWd1e2HHyrOkalTvWFoOEOqWIhiWN9YfiwoDhUoDhUoDhUoDhQDaXwwjYe8vDyJd7troqioyP6/sLCQfUrQv39/hIeHIy8vD8rKypxlPD09OX+Xl5fjn3/+gUAgELsLPmXKlHo1HkSztVTddk14PB77A7usrAwFBQUoKytD//79AVR00XJycqp1PSdOnAAAuLi4cLo7AcDgwYNx9uxZJCcnw8bGBmfPngUAeHh4cLpsdOzYEV988QXCw8PZtPLycpw7dw4GBgZig7Q9PT3x559/4vTp02KNh7Fjx4o9jVBWVoaTkxNiY2ORnZ0NdXV1ABVdncrKyuDm5lbrflYmind97tiPHz+e87TC2NgYbdu2xZMnT+q8rqqqnmuVeXh4cP4WdX16+vRpg7cLACoqirVnqqSsnME+F5naMzahsnKmzuWuzMqqL6ys+taa7+DB/WJpEyd6YOJED7H0SZP+dwwHDbLFoEG2Na7b03McPD3HSVFacYsWLcCiRQvqtawkDYnlx4TiUIHiUIHiUIHiQDGoyQfTeFBWVhabIrE2WVlZ+OOPP3D27FmxbiEAJDYeunfvLraOgoIC6Orqii2vqalZpwaAiOhHbNVxALWJi4tDaGgo7t27h9LSUs5nOTk5Uq3j0aOKl1G5u7tXmyczMxNAxexWAKCjoyOWp2o8srOzkZ+fjx49xLtOKCgoQFtbG8+fPxf7rFu3bhLLMGbMGERHR+PYsWPw8vICwzCIiopCjx490KdPn2rLLoko3nU9fwBIfNKloqICoVBY53VVpq6uXuO507VrV87fouk8G7pdkZyc9ygrEx9TUh0ejwc+v25dvhpbeTnT4AkEZGT4UFFRrPP+f0woBhUoDhUoDhUoDhUoDhQDdfXab7R+MI0HfX19XL16Fc+ePZOq61J5eTl8fHyQnp6O8ePHo3fv3lBWVgafz0d0dDROnDghcUBudTP91LWvfE1E7264d++e1MskJCRgyZIlMDY2xsKFC9GpUyfIy8ujvLwcs2fPrvOPqi1btlQ7i5S+vj4A1LjOqp/Vtv3qPq8u3qampjA0NERUVBS8vLxw5coVPHv2DPPmzatxO5IYGBjg9OnTuHv3bp27LkkaXA1w96emc6OsrExiem0zSlV+0lPddhuirKwcpaWfXqUo8qnvP0AxEKE4VKA4VKA4VKA4UAxq8sE0HhwdHXH16lUcOXIEs2fPrjX/w4cP8eDBA0yfPl2sX/nRo0el3q6GhgaUlJTYO/aVZWRk1PnpAQCYmZlBU1MTZ8+e5XTLqUlsbCzatGmDwMBAzg/P9PT0Om27e/fuSEpKQqdOnWp9AZ3o7nd6errY04fHjx9z/tbQ0EDbtm2RlpYmtp6ioiI8f/5c4tObmri5uWHdunW4efMmIiMjIScnBxcXlzqtA6g4d4KCghAZGQlXV9dGbQgCgKqqKgDJTwUkPW0hhBBCCPlQfTCjQVxdXaGnp4fQ0FCcOnVKYp6HDx8iNDQUwP/uGFe9U/vw4UOcOXNG6u3y+XwMHjwY9+/fR1JSEuez3bt312EP/kdOTg4+Pj4oKCjAkiVLJDZASkpKEBQUhFevXrHlAMB5WsIwDHbt2iVxG0pKShK7Mn3xxRcAKgY3V+36BIDTvUs0feiBAwc4d9DfvHmD2NhYznKiOD18+BD//PMP57MDBw6goKAAQ4YMkVjW6ri4uEBBQYE95p999lm93sZrYGCAESNGIDk5GVu3bpX4xOndu3fYvHlzndcNVLwRXEZGBpcvX+ak37hxA8nJyfVaJyGEEEJIa/TBPHlQUFDApk2b8N1332HRokWwsrKCjY0N1NTUIBQKcfXqVSQmJrKDafX09NCjRw+EhISgsLAQOjo6ePLkCSIiIqCvr4+7d8XnXa/OzJkzceHCBXz//fdwd3dH165dcfnyZaSkpNTrxywAjBgxAq9fv8b27dsxevRo9g3TANg3TL958wajRo0CUHH3/NSpU5gxYwZcXFxQWlqKs2fPVjtVrImJCS5fvoyQkBB06tQJioqKGDx4MIyNjfHtt98iMDAQHh4ecHJygqamJt6+fYuUlBQkJiayL9rr0aMH3N3dcejQIXz77bdwdHREfn4+IiIioKurizt37nDu4vv4+ODy5ctYtGgRO1VrcnIyjh07BoFAgAkTJtQpRqKB06L3J7i6utY5ziJ+fn4QCoXYu3cvEhMT4ejoyL5hOiUlBQkJCdDU1MR3331X53UrKSlh5MiROHr0KJYuXQoLCws8ffoU0dHRMDQ0xP379+tdbkIIIYSQ1uSDaTwAFYNXQ0NDceTIESQkJGDPnj3Iz8+HiooKevbsiWXLlrFTk8rIyGDLli3YvHkzYmJi8P79e+jr62PlypW4f/9+nRoPXbt2RVBQEDZv3ozw8HDw+XxYWFhg+/btmDlzZr33Z+rUqbCzs0NYWBjOnTuH8PBw8Hg8dOnSBfb29vjyyy/RsWNHAMCwYcNQUFCA/fv3Y8uWLWjXrh0GDx4MX19fODo6iq170aJFWLNmDYKCglBQUIDOnTuzTxKmT5+OXr164eDBgzhw4ADev38PDQ0N6OvrY+HChZz1fP/999DU1MSRI0ewdetWdO7cGVOmTEFpaSnu3LnDeeOulpYW9uzZg+3btyMuLg5CoRAdOnSAh4cHvL296/Xm6NGjRyM6OhpdunSBtbV1nZcXUVBQwIYNG3D69GlER0cjIiICQqEQbdq0gZ6eHr7++usGTYE6f/58AMDp06dx9uxZ9OzZExs3bsSRI0eo8UAIIYSQjwaPaawRmOSTInrHxYkTJ9ChQ4cm286dO3cwadIkzJgxA9OmTWuy7XxqsrPzP8mBYLKyfKirt/1k9x+gGIhQHCpQHCpQHCpQHCgGmprtas3zwYx5IC1DUreo169f4/jx4zAwMGjShgMAHDx4EDIyMg3qskQIIYQQQhrHB9VtqbUTCoUoKSmpMY+CgkK93g3RUmJiYnD8+HHY2dlBQ0MDz549w9GjR1FYWIg5c+Y0yTbfv3+Pc+fOIS0tDbGxsXBzc4OmpqZYvrdv39a6LmVl5Xp1lyKEEEIIIeKo8dCIvv/+e1y9erXGPCNGjMDKlSubp0CNoGfPnjh79iz++usvCIVCKCgowMTEBN988w0sLCyaZJvZ2dlYtmwZlJSU4OjoiLlz50rM5+zsXOu6VqxYgZEjRzZ2EQkhhBBCPkk05qERpaSk1PqmZ01NTYlvYSZ1d+nSpVrz6OvrN3nXqg9RXfty8vmt4w3T5eX1r64ePnyAQ4f24/r1q8jIyICMjAy6desOR8fPMXKkG1RUKt7X4evrDQAICNjRKOVuTPHxJxEa+ieePElHu3YqcHBwwvTpM6GkpCT1Oj71/rwiFIcKFIcKFIcKFAeKgTRjHujJQyPq1atXSxfhk9KQ2ZeI9Ph8HjRUFcGTlfzW6+bClJYhS/i+Xg2IqKgj2LDhd+jo6GLq1Kno1EkbxcXFuHs3BUePRuDWrWSsXr2+CUrdeP7+OxY///wDRo50w5w58/HkyWNs3+6P9PQ0bNr0fy1dPEIIIZ8IajwQQmrE5/MqGg6eG4GUZy1TiF7a4O2bDz6fV+fGw61bN7Fhw++wtLTGunUb0amTOntHqX9/G4wf74VLl5JqX1ELKisrw//93xZYWdnAz285AKBfP0soKbXFzz8vx4ULibC1tWvhUhJCCPkUUOOBECKdlGfAtbSWLkWdhYQEAwAWLVoKeXl5sc/l5OQwcKB9jevYvXsHLlxIxLNnT1FWVgZtbW2MGeMOFxdXzosSr1z5F8HBO5GWlorCwkKoqamhV6/e+OGHVezA/SNHDuPo0XC8ePEMAA+ampqwt3fAt9/6VLv927eTkZn5FrNnz+OkOzgMxbp1v+HcuTPUeCCEENIsqPFAyCeirJwBn2EApmFjBz4kZWVluHr1XxgZ9UKnTlr1Xs+rVy/h6jqGXcft28nYtGkdMjIy8M030wEAL1++wPfffwczM3MsWfIDlJXbISMjA5cuJaGkpAQKCgqIjz+JDRt+x9ix4+DrOxc8Hh/Pnj1FevqjGreflpYKANDXN+Sky8rKQkdHF48epdZ73wghhJC6oMYDIZ8IGT4P2dn5KC/HJ9N4EArfobCwEJ07d2nQepYuXcH+v7y8HH37Vsw0dujQAUyePA08Hg/37qWguLgIs2bNhaGhgM3/+ef/mxUsOfkGlJXb4bvvvmfTLC2tat1+To4QAKCioiL2mYqKCl6+fFH3nSKEEELqgRoPhHxCKmaOaNlZkz5EV678i5CQYNy9exv5+fmcz7Kzs6Ch0R6GhkaQk5PD2rW/YswYd/TpY46uXbU5eXv1MkZ4+F9YsWIphg79HKam5lBTU5O6HJW7SEmTTgghhDQ2ajwQQj5aqqpqUFBQaNCd+Tt3bmH+fF+Ym1tg0aLl6NixI+Tk5HDu3BmEhOxGUVERAKBrV21s3rwN+/aFYOPGNXj//j26dOmKsWPH46uvJgAAnJ1dUFZWhujoo1i+3A/l5eXo2bM3vL1non9/m2rLIJpGVigUQkOjPeeznJwc9nNCCCGkqVHjgRDy0ZKRkYGFRX9cvJiEN29eo0uXznVeR0LC35CRkcXatZvQpk0bNv3cuTNiec3M+sLMrC/Kyspw924KwsPDsHXrBmhoaGDo0GEAABeXUXBxGYX379/j+vWr2L07EIsWzcOBAxHQ0pJcPn19AwBAWtpD6On97z0xpaWlePw4nV03IYQQ0tT4LV0AQghpShMnfgMAWLPmV5SUlIh9XlpaivPnz9WwBh5kZGQgI/O/91wUFRXi5Mnj1S4hIyMDY2MTzJ/vBwC4f/+uWB5FRUXY2tph0qQpKCkpqXHQc+/eJmjfvgOOH4/hpJ85k4D37wtgbz+khvITQgghjYeePBBCPmomJn2wYMFibNjwOyZP9oSnpwe0tLqhqKgYDx7cQ1TUEejp6WPgwMESlx8wYCDCwvZh5cplcHUdDaFQiAMHQiEnx5329ejRw7hy5T8MGDAQnTppoaioCMeORQEALC0rXmi4Zs0vaNOmDUxNzdC+fQdkZmYiNDQYysrK6NnTuNp9kJGRwaxZc7Bq1Y9Yu/ZXDB06DM+ePcW2bVvRv781bGwGNFK0CCGEkJpR44EQIp1e2rXnaaXbHjVqNHr1MsahQwcQFBSEjIwMyMrKolu37nBycsaYMV9Vu6yFRX8sWfIj9u37E35+89GhgyZGjhwNdXV1/P77KjafgYERLl++hF27ApGVlQlFRUXo6enj9983wsqqYjxDnz7miI2NwalT8cjNzYGqqhr69DHH8uU/Q11dvcZ9GDZsOPh8PkJD/0RsbAxUVFTg7OwCb+9ZDYoNIYQQUhc8hmE+jTkbCSHIyMhBXWdb4vN50FBVrHjLdAtiSsuQJXzfoGlmZWX5UFdvy75h+lNEMahAcahAcahAcahAcaAYaGq2qzUPPXkghNSovJxBlvA9+PyWnQ60vPzTebkdIYQQ0lpR44EQUiv64U4IIYQQgGZbIoQQQgghhEiJGg+EEEIIIYQQqVDjgRBCCCGEECIVajwQQgghhBBCpEKNB0IIIYQQQohUqPFACCGEEEIIkQo1HgghhBBCCCFSocYDIYQQQgghRCrUeCCEEEIIIYRIhRoPhBBCCCGEEKlQ44EQQgghhBAiFWo8EEIIIYQQQqRCjQdCCCGEEEKIVKjxQAghhBBCCJEKj2EYpqULQQhpHmVl5S1dhBYnI8P/5ONAMahAcahAcahAcahAcfi0YyAjU/tzBdlmKAchpIXdunULHTp0gJaWVksXpUW9evUKAD7pOFAMKlAcKlAcKlAcKlAcKAbSoCcPhHwCXF1dAQCRkZEtXJKWRXGgGIhQHCpQHCpQHCpQHCgG0qAxD4QQQgghhBCpUOOBEEIIIYQQIhVqPBBCCCGEEEKkQo0HQgghhBBCiFSo8UAIIYQQQgiRCjUeCCGEEEIIIVKhqVoJIYQQQgghUqEnD4QQQgghhBCpUOOBEEIIIYQQIhVqPBBCCCGEEEKkQo0HQgghhBBCiFRkW7oAhBDJHj9+jPXr1+PatWtQVFTEsGHD4OvrCwUFhVqXjYmJQXBwMF6+fAltbW14e3tj6NChnDylpaXYvn07oqOjkZeXBxMTEyxcuBCGhoacfG/fvsWGDRuQlJQEHo+HwYMHY8GCBVBVVW3U/a1OU8bh8ePHCAsLw7///ouXL19CTU0NVlZWmDVrFjp06MDm+++//zBjxgyx9Ts5OWH16tWNs6M1aOpzwdLSUmy59u3b4+TJk5y0j/lciI6Oxk8//SRxWVtbW/j7+wNo+XMBqH8c/v77b8TFxeHWrVvIyMjA3LlzMXHiRLF8H3vdIE0cPpS6QVTWpjwfPoT6oSlj8CHVDc2FGg+EtEK5ubmYOXMmtLS0sHbtWmRlZWHTpk0QCoVYtWpVjcvGx8dj5cqVmDx5MmxsbHDmzBksWbIEysrKsLGxYfNt2LABx48fx3fffYfOnTsjJCQEM2fOxMGDB9kvx9LSUsyZMwclJSX46aefUFpaCn9/fyxYsAA7d+4Ej8f7oONw8eJFXL16FaNHj4ZAIMCbN2+wY8cOTJkyBQcPHoSSkhJnnStWrICuri77t5qaWmPvspjmOBcAYNy4cXB2dmb/lpOT43z+sZ8LAwcORHBwMGe5J0+eYMWKFRgwYIDYOlviXAAaFoeEhAQ8f/4cgwYNQkRERLX5Pva6QZo4fAh1A9A85wPQuuuHpo7Bh1I3NCuGENLqBAcHM3Z2dkx2djabFhsby1hYWDBpaWk1Lvvll18yfn5+nDQfHx/m66+/Zv9+/fo1Y2Vlxfz1119sWl5eHuPg4MBs3bqVTTt58iRjYWHBPHz4kE27fv06Y2FhwSQmJtZz76TX1HHIzs5mysvLOXnu37/PWFhYMNHR0Wzav//+y1hYWDC3b9+u/87UU1PHgGEYxsLCggkJCalxXR/7uSDJ9u3bGSsrKyYjI4NNa8lzgWEaFoeysjL2/9Ud80+hbpAmDh9C3cAwTR+H2j4TacnzoTliUFVrrBuaE415IKQVSkpKgpWVFeeOhYODA+Tl5ZGYmFjtcs+fP0d6ejqGDRvGSXd2dsbt27fx7t07ABV31crKyvD555+zedq2bYvBgwfj/PnzbFpiYiIMDQ2hr6/PppmZmaFLly6cfE2lqeOgpqYmdkfMwMAAMjIyyMjIaLT9aIimjoG0PvZzQZKTJ0/C0tKS002lpdU3DgDA59f+lf+x1w2AdHH4EOoGoOnjIK2WPB9aIgatsW5oTtR4IKQVevToEfT09Dhp8vLy0NbWxqNHj2pcDoDYsnp6emAYBunp6Wy+9u3bi/VF1dPTw+PHj1FeXs7mq/z4tXI+0bqaUlPHQZKbN2+irKxMbFkAmDt3LqysrDB8+HBs2bIFhYWFddib+mmuGOzZswfW1tb47LPPsGTJErx69UpsfZ/SuXDnzh08efKE01WjspY4F4D6x6Eu6/+Y64aGaG11A9B8cWjN9UNznwuttW5oTjTmgZBWKCcnB+3atRNLb9euHXJycqpdLjc3FwCgrKzMSVdRUQEACIVCNl/VPKJ8paWlKCgogLKyMnJzcyWWQ0VFBWlpadLvUD01dRyqKi0txYYNG6Cjo4OBAwey6crKypg0aRL69euHNm3a4N9//0VoaCgePXqEzZs313W36qQ5YuDi4oJBgwZBQ0MDqampCAoKwtSpU3HgwAE2/6d2Lpw4cQJt2rTBkCFDOOkteS4A9Y+DtD72uqG+WmPdADRPHFp7/dDc50JrrRuaEzUeCPmAMAwjVb6qj9tFy1VOlzSArS75mnpAZE0aMw6VrVmzBqmpqdi5cydkZf9XPfbs2RM9e/Zk/+7fvz86dOiAtWvX4tatWzAxManrLjRYY8ag8kwi/fr1g7m5Oby8vHDkyBF8/fXX1a5LtL6P7VwoLy9HXFwc7OzsxH5It8ZzAZA+DtL4FOqGuvqQ6gagcePwodYPTXEufIh1Q1OgbkuEtEIqKirsHdPK8vLy2Ds9kojuvlRdVvS3aNl27dpJXH9ubi5kZWWhqKjI5pN05yY3N7fGcjSWpo5DZTt27EBUVBR+++039O7du9ayOTk5AQDu3r1ba96GaM4YiBgaGkJHR4ezb5/SufDff/8hIyMDX3zxhVRla65zAah/HKT1sdcN9dFa6wageeMg0trqh+aMQWuuG5oTNR4IaYX09PTE+moWFxfj2bNnEvvbVl4OgNiyjx49Ao/HY/uk6unpISsrS6zLxqNHj6Cjo8MOIquuv2p1/VsbW1PHQeTQoUPYsWMH/Pz8YG9v3ziFbyTNFYOqqt61+1TOBaCiW4KysjLs7OwaXvBGVt841GX9H3PdUFetuW4Ami8OVbWm+qE5Y9Ca64bmRI0HQlqhAQMG4N9//+XMBHP69GkUFxfXWGl17doVurq6+PvvvznpJ0+ehLGxMTsbhY2NDfh8PuLi4tg8BQUFOHfuHKc/r52dHR4+fMipmJOTk/HixQtOvqbS1HEQpa1btw4zZszAmDFjpC6b6AVJ0tyJbIjmiEFV9+7dw5MnTzj79imcC0DFj47Tp0+zs7VIo7nOBaD+cZDWx1431EVrrxuA5olDVa2tfmiuGLT2uqE50ZgHQlqhL7/8En/99RcWLFiAadOmsS+9+eKLLzh3Un7++WccO3YMly5dYtNmzJiBJUuWQFtbG9bW1jh79iwuXrzIvgUTADp27IgxY8bA398fsrKy0NLSQmhoKABgwoQJbD4HBwcYGhrCz88PPj4+KCsrw5YtW2Bubg5bW9sPPg5XrlzBihUrYG5uDmtrayQnJ7OfqaurQ1tbGwDwww8/QFtbGz179mQHwu3fvx/29vZN/qXQ1DHYu3cvnj9/jn79+kFDQwMPHz5EcHAwOnXqBDc3Nzbfx34uiCQmJiI3N7famVRa8lwAGhaHtLQ0zuDVhw8fIj4+HoqKiuyPrE+hbpAmDh9C3QA0fRw+hPqhqWMg0trrhubEY5pqdBEhpEEeP36MdevW4fr161BQUMCwYcMwe/ZsKCgosHlWrlyJmJgY/Pfff5xlY2JisHv3brx8+RLdunWDt7c3hg4dyslTUlKC7du3IyYmBnl5eTA2NsbChQshEAg4+d6+fYv169fjwoULAIDBgwdjwYIFzfbWzKaMQ2BgIHbu3ClxuyNGjMDKlSsBAMHBwYiNjcWrV69QXFyMLl26wNnZGd98843Ym1abQlPG4Ny5cwgODsbjx4+Rn58PdXV1DBgwALNmzRKbw/xjPhdE/Pz8cOPGDRw/flziHPAtfS4A9Y9Dded7586dER0dzf79sdcN0sThQ6kbgKaNw4dSPzT1NQF8GHVDc6HGAyGEEEIIIUQqNOaBEEIIIYQQIhVqPBBCCCGEEEKkQo0HQgghhBBCiFSo8UAIIYQQQgiRCjUeCCGEEEIIIVKhxgMhhBBCCCFEKtR4IIQQQgghhEiFGg+EEEIIIYQQqVDjgRBSq8zMTFhYWCAsLIyTvnjxYjg4OLRQqT4OERERMDIywqVLl5ple5cuXRLbHsMwGDlyJJYvX17n9RUVFcHBwQEbN25szGJ+0p49ewYjIyP4+/u3dFFIK+Dg4ICJEyfWe/mJEydSPf2JEtX3ERERjbpeajwQQmq1ZcsWqKqqYsyYMVLlz8vLw/bt2+Hm5gZLS0v07dsXDg4OmDVrFg4dOsTJu3jxYhgZGSEjI0Piuk6cOFFj5VdeXg57e/taf2xNnDgRRkZG7D9jY2MMGjQI8+fPx4MHD6Tar48Vj8fD7NmzER4ejrt379Zp2T179uDdu3eYMmVKE5WOfGz8/f0RHx/f0sUgzSglJQX+/v549uxZs2730qVL8Pf3R05OTrNutzXLycmBv79/g25YUeOBEFKj169fIzw8HJ6enpCTk6s1f15eHsaOHQt/f3/o6+tjzpw5WLRoEZydnfHixQuEhIQ0avn++ecfvHr1Crq6uoiIiEB5eXm1eeXk5LB27VqsXbsWK1aswODBg3Hy5EmMGzcOqampjVquD42TkxM6d+6Mbdu2Sb1MYWEhdu3aBTc3N6ipqTVd4T4xXbt2xc2bNzFz5syWLkqTCAgIoMbDJyYlJQUBAQF4/vx5s2738uXLCAgI+GQbD/3798fNmzfh6urKpuXk5CAgIACXL1+u93plG6NwhJCPV1hYGNutRRqHDh3Co0ePsHz5comP2l+9etWo5QsPD4e2tjaWLl0Kb29vJCUlYeDAgRLz8ng8TiX61VdfQV9fH2vWrMHevXuxcuXKRi3bh4TH42HUqFHYuXMn3rx5g44dO9a6zLFjxyAUCuHm5tb0BWwkBQUFUFJSauli1IjH46FNmzYtXQxCyAeOz+c3SV1CTx4IaWSiPuxJSUkICAjAkCFD0KdPH7i7u+PatWsAKu6GTJgwAebm5rCzs0NAQAAYhhFbV3JyMnx8fGBtbQ0TExMMGzYMf/zxB0pLSzn5bt68icWLF2PYsGEwMzND3759MX78eMTFxYmtU9RNSCgUYvny5bC1tYWpqSnGjx+PGzduiOWPjY1Fz549pfoxCQCPHj0CAFhbW0v8XEtLS6r1SCMrKwunTp2Cm5sbBg4cCE1NTRw+fLhO6xA1NJ4+fVptntTUVBgZGeGXX36R+Pn333+P3r17s12vUlNTsXLlSri4uKBv374wMzPDmDFj8Ndff0lVJn9/fxgZGUl8xF9d/+ekpCRMmTIFlpaWMDU1xciRI3HgwAGptify2WefobS0VOJ5I0lsbCzU1dXRp08fsc/27duHKVOmYNCgQTAxMcHAgQOxcOFCzj6VlZVh0KBB1TZMDx06BCMjI5w4cYJNKy4uxvbt2+Hi4gJTU1NYWlpixowZuHPnDmfZyn199+3bh+HDh8PExAS7du0CULdrBgD+++8/eHh4oE+fPrC2tsaiRYuQlZUFIyMjLF68WCz/8ePHMWHCBPb4u7u7c/ajJpLGPFROO378OFxdXdGnTx84OTkhPDwcAPDixQvMmTMHVlZW6Nu3LxYsWIDc3FzOukXXf1ZWFhYtWgRra2uYmZlh0qRJuHXrllhZpDmOlV28eBHe3t6wtraGqakpHB0dsXTpUmRlZbHHBACOHDnCdiGUpj9+dnY2fvnlF3z22WdsOZYtW4Y3b95w8lU+7ocOHWKP+5AhQ7Bz585at9OYsQaA+/fvY/bs2Zw6PCAgAMXFxWJ5U1NT4e3tjb59+8LS0hIzZ86ssV5qjGtekvDwcIwZM4a9LiZOnIjz58+L5avu3K86jmvx4sVYsmQJAGDSpEnscRed36L67sGDB/jll19gZ2cHU1NTjB07FomJiZx11zQeqGq9OXHiRAQEBAAAHB0d2e3W1v9fVMempKRg8uTJ6Nu3L2xtbfH777+jtLQURUVFWLNmDQYNGgRTU1N4eHiIdX3Ny8vDpk2b4O7uzh57JycnrF+/Hu/fvxfbplAoxA8//AAbGxuYmZnhq6++QmJiInu9ViYaw/Lq1SvMnTsX/fv3h7m5OaZOncp+/4pUHfMQEREBR0dHABVPAEUxEX2n1DQGr/LYGXryQEgTWb9+PQDg66+/RnFxMYKDgzF16lSsXbsWy5Ytw7hx4zBy5EjExsbC398f2tranDu4Z8+ehY+PD3R0dDBlyhSoqqri+vXr2Lp1K1JSUrB161Y2b1xcHNLT0+Hi4gItLS28e/cOR44cga+vL9avXy/xx9m0adPQvn17+Pr6Ijs7G8HBwfD29kZCQgKUlZUBVAyUTktLg4eHh9T73a1bNwAVldDChQshKytdNSMUCiEjIyOWnp+fX+0ykZGRKC0thZubG2RkZDBq1CiEhIQgOzsb6urqUm338ePHAFBjtxt9fX2YmpoiJiYGfn5+nO5b+fn5iI+PZxsvQEXj8MqVKxg6dCg6d+6MgoICnDhxAj/88AOys7Px7bffSlU2aYWFhWHFihUwNzfHjBkzoKSkhKSkJKxcuRJPnjyBn5+fVOvp3bs35OXlcenSJXh6etaYt6ysDFevXoWlpaXEz4ODg9G3b18MGDAAKioquH//Pg4fPoyLFy8iOjoa6urqkJGRwciRI7Fr1y6kpKSgV69enHVERkZCVVWV/cIqKSnB1KlTce3aNbi6usLT0xN5eXk4dOgQJkyYgNDQUJiamnLW8eeff0IoFMLd3R0dOnRgG691uWauXr2Kb775BkpKSpg6dSo0NDRw+vRpTJs2TeK+b9q0Cdu3b8egQYMwd+5cyMjIIC4uDnPnzsWPP/5Ya2xrcvr0aYSFhWH8+PFQU1NDeHg4li5dCllZWWzZsgU2NjaYN28ekpOTER4eDnl5eaxevVpsPdOmTYOqqip8fX3x9u1bhIaGYuLEiThw4AB69uzJ5pPmOIocPHgQK1euROfOnTFhwgR06dIFL168wOnTp/H69Wvo6+tj7dq1WLRoESwtLfHVV18BANq2bVvjPufl5cHDwwOPHj3C6NGjYWpqigcPHiAsLAyJiYk4fPgwOnTowFnmwIEDyMzMhLu7O9q1a4eoqCisX78eWlpaUj9FbWisb9++DS8vL/B4PHh6ekJLSwv//PMP/P39cf36dezYsQN8fsU93KdPn8LDwwPv37+Hh4cHunXrhgsXLmDSpEkSf2w21jVf1caNGxEYGAhjY2PMnTsXRUVFOHz4MKZNm4a1a9di1KhRdV7nuHHjIC8vj7CwMMyYMQM9evQAALEfxX5+fuDz+Zg+fTry8vIQFhaG6dOnY8eOHdU+Ta7JjBkzoKqqiri4OCxZsoQ9V/v161frsq9evcKUKVPg4uKCYcOGISkpCcHBweDz+UhLS0NhYSG8vb2RnZ2N3bt3w8fHB7Gxsex3mKi7r7OzM0aOHAkZGRlcvnwZQUFBSElJYW9iABU3RL755hvcvn0bI0eORL9+/ZCeng5fX1/2+7SqgoICeHl5wdzcHPPmzcOzZ88QEhKCWbNmISYmRuJ3KVDRjWnJkiVYvXo1nJyc4OTkBABi10+tGEJIowoPD2cEAgEzevRopri4mE0/deoUIxAImN69ezO3bt1i04uKihg7OzvG3d2dTSssLGRsbW0ZDw8PpqSkhLP+4OBgRiAQMBcvXmTT8vPzxcpRUFDAfP7558wXX3zBSffz82MEAgGzYsUKTvrx48cZgUDAHDhwgE27cOECIxAImN27d0vcVz8/P2bIkCGctHfv3jH29vaMQCBgbG1tmdmzZzM7duxg/vvvP6asrEziOgQCQa3/wsPDxZZ1cXFhPD092b8fPHjACAQC5s8//xTL6+XlxZiYmDCZmZlMZmYm8+LFCyY2NpYZPHgwIxAImDNnzkjcR5HQ0FBGIBAw8fHxnPSIiAhGIBAwx44dY9MKCgrEli8rK2O8vLyYfv36cc4L0flS+Xhu3bqVEQgEzNOnT8XWM2TIEMbLy4v9+/Xr14yJiQkzb948sbyrVq1ievbsyTx+/JhNu3jxotj2Khs6dCjj7Ows8bPKnjx5wggEAmbVqlUSP5d0TiYlJTECgYDZsWMHm3b//n1GIBAwv/32Gyfv06dPGSMjI855unv3bkYgEDBnz57l5M3NzWXs7e05cRHtp5WVFZOZmSlV+aq7Ztzd3RljY2MmNTWVTSsvL2d8fX0ZgUDA+Pn5senJycmMQCBg1q9fL7b+mTNnMn379mVyc3PFPqu67wKBgNm6datYmrm5OfPixQs2PTMzkzE1NWWMjIyYPXv2cNbj4+PD9O7dm8nLy2PTRNebj48PU15ezim3kZERM3nyZM46pD2OL1++ZIyNjZnhw4dL3L/K137VmNVm48aNjEAgENu/yMhIRiAQMMuXL2fTRMfdzs6OEQqFbHpBQQFjbW3NfPXVV7Vur7FiPX78eKZnz56c+p5hGGb58uWMQCBgoqKi2LT58+dLPLd/+uknRiAQNOia9/LyEqunJUlLS2OMjIwYd3d3pqioiE3Pyspi7OzsmP79+3POh+qOo6Q6TVKaiKi+Gzt2LGe7L1++ZMzNzZnPP/+cPVclXRtV11O53qypLq3OkCFDGIFAwJw8eZKTPnr0aMbIyIiZNWsW59r5888/xY5dUVGR2Hc3wzDMpk2bGIFAwNy4cYNN279/PyMQCJjNmzdz8sbHx7Pff5V5eXmJXX8MwzA7d+5kBAIBc+7cOTZNdD1U/v6sKYY1HafK5xF1WyKkiUyYMIFzh1p0t8PMzAzGxsZsury8PExNTdk74ACQmJiIzMxMuLm5IScnB1lZWey/wYMHs3lEKvfhfv/+PbKzs/H+/XvY2NggNTUVeXl5YuWbPHky528bGxsA4JQjKysLAKCqqir1fquqqiIiIgLTp09H27ZtcfLkSaxfvx4eHh5wcnKS+PgbADZv3ozg4GCxfz4+PhLzX79+HQ8ePMDo0aPZNAMDA5iamlbbdam4uBi2trawtbXFZ599hrlz56KkpASrVq2Cvb19jfvl4uICOTk5REZGctKPHj0KFRUV9lEwACgqKrL/LyoqQnZ2Nt69ewc7Ozvk5eUhLS2txm3VxcmTJ1FcXIwxY8ZwzpOsrCw4ODigvLwcFy5ckHp9ampq7HGvSXZ2NoDqzw3ROVleXo7c3Fy2i0+7du1w8+ZNNp+hoSGMjY0RExODsrIyNj0yMhIMw3COb3R0NHR1dWFiYsLZz+LiYgwYMABXrlxBYWEhpxyurq7Q0NCotnxAzdfM27dvcePGDQwZMoS9YwpUjEuQ9OQhJiYGAODm5ibxeOTn5+P69euSgyoFR0dHdO7cmf1bQ0MDurq64PP5mDBhAievpaUlSktLJQ5SnTZtGng8Hvu3iYkJ7OzscPHiRU59Ie1xPHHiBEpKSjBr1iz2yWVlojvs9REfHw81NTWxJ6AjR46Ejo6OxK5mX375JVRUVNi/FRUVYW5ujvT0dKm325BYZ2Vl4erVq7C3t+fU9wAwa9YsAGDLXV5ejlOnTqFnz55s/S4i6SllY1/zIgkJCWAYBtOmTYO8vDybrq6ujgkTJkAoFDbplNKTJ0/mbFf0lCg9Pb3ZZ8TT0tLC559/zknr168fGIZhnyaJiJ6+PnnyhE2Tl5dnn7qXlpZCKBQiKysLAwYMAABOF+GEhATweDyxGescHR05dU5lfD4fkyZN4qRJ+g5vKtRtiZAmoq2tzflb9COra9euYnlVVVXx7t079m/RzD/Lly+vdu79t2/fsv/PzMzE5s2bkZCQgMzMTLG8OTk5Yl/oVR+Hih7pVi6HqIJkJIzHqImGhgYWLlyIhQsXIisrC8nJyTh+/DgiIyPh6+uLyMhI6OjocJaxtLRku/1ULbskhw8fhpycHHr16sWpLO3s7LB9+3YkJyeLdWGRk5PDjh07AAAyMjJo37499PT0qn3EW5mamho+++wznD59GkKhEKqqqnj16hUuX74Md3d3zqC0/Px8BAQEIDY2Fi9fvpR6n+pDdK5MnTq12jyVz5Xa1PVYV5f/woUL2LZtG27cuIGioiLOZ0KhkPO3m5sbfv31V5w/f55txEVFRUFPTw9mZmZsvtTUVBQWFsLW1rba8mRnZ3N+8FU9z0SkvWZE/af19PTE8kj6Yhcdj+HDh1dbxrocj6qq1itARf2hqanJ+eEFgP3xXPmaFtHX15eYdv78eTx79oztuiTtcRT9KK/c5amxPH36FL169RKb7Y3H48HAwAAJCQnIy8vj1HGS4qSmpiYxFtVpSKxFYxUMDQ3F1tG5c2e0a9eOzZOZmYmCggKJx6RTp05o164dJ62xr3kRUXkMDAzEPhN1MappDEZDVXdOAhU/zAUCQZNtu6rqvqclfVbddbZv3z4cPHgQDx8+FJsFsPK18+zZM3To0EHsOAMVdYykm00dO3YUGwgt6npbl3O8vqjxQEgTqe5OmzQ/VEU/yBYsWAATExOJeUQDmMvLy/HNN98gLS0NEydOhKmpKdq1awcZGRmEh4cjJiZG4vSl1ZWj8o9B0R3bhvzY1dDQgL29Pezt7dGpUycEBgbi2LFj7N23+igoKMDx48dRUlLCuTNd2eHDh8UaDzwej73zUx9ubm6Ii4tjB8NGRkaivLxcbLahBQsW4MyZM/jqq6/Qv39/qKqqQlZWFmfPnsWePXtqnE5WVM7qVB0sLzpeq1evrnYwenX9ZiURCoUS79RXVdO5cfPmTUydOhXdu3fHggULoK2tDQUFBfB4PMybN0+swTFixAisXbsWR48ehb29Pa5du4b09HTMmzePk49hGBgYGGDZsmW1lkuk8lMgkbpcM/VtTO3YsaPaqY0l/TiTVnXXbU31irT7UDVfXY5jXePUWKrbrjT1bG0aEuv6xKOm617SNhrrmq+63rp+VlXlJ4gNJdquKDZ1qRsboqZjXN13e+UY7d69G2vWrMHAgQMxadIkdOzYEXJycnj9+jUWL14s9bVTn/O7IdeitPGlxgMhrZDoLqeiomKtP3bv37+Pe/fuwcfHB3PmzOF8VvWFbHVlaGgIHo/XaI9Bzc3NAVQMJmuI2NhY5Ofn47vvvpN4R/jAgQM4duwYlixZAgUFhQZtqzJ7e3toaGggMjKSbTzo6OhwBuDl5OTgzJkzcHV1xc8//8xZPikpSartiO5wCYVCzt3PoqIiZGRkcO6mi/ZfTU2tQQ0joKJb18uXL6Wa/aZz585QVlaWeG6IuiDt3LmT8yOmoKBAYmNDQ0MDgwcPRkJCAnJzc3H06FHw+XzOtLoAoKuri4yMDNjY2DSoG0xdrhlR+SXd/ZOUpquri3/++QdaWlpiA0Jbk9TUVPZ6FElLSwOfz2fPubocR9F5mJKSIvEOckN069YN6enpKCkpEWuQpaamQl1dXWJXqZbUvXt3AJDY3ebVq1fIzc1lY9q+fXsoKSnh4cOHYnlfv34tNotTY17zksr88OFDsadqov0Q5RFtX9JdbklPJ6RpGKWmpoo9uRJdY6JYVa4bq5I0A5i0DbLGFhUVha5du2Lnzp2cuurcuXNiebt164bz588jJyeH09UOgNjsSY2hppjUFl/R9UdjHghphQYOHIj27dsjKChIYv/zwsJCtl+yqGKqerfh/v37Uk+5WR0NDQ0YGBhw+jbX5tq1a9U+qUhISADQsDuvQMVTBRUVFUybNg3Ozs5i/8aNG4fc3FycPHmyQdupSk5ODi4uLrh27RpiYmKQmpoq9tShuuPx5s0bqRtzurq6AMQbG5KeWjg7O0NeXh4BAQESZ2XJzc2VOC2kJHfu3EFJSQmsrKxqzSsjIwNLS0skJydL/EySwMDAap+6jB49GkVFRYiKisKJEydgbW3N6X4EVIxfyMrKQlBQkMR1SNtVoy7XTIcOHdCnTx+cOXOG01hgGIYzY4qIaDaaTZs2SbwTKqmLVEsICgri7P/t27eRlJQEGxsb9od4XY6js7Mz5OTk8Mcff0gcY1V5W0pKShJ/nFRn6NChePfuHcLCwjjpx44dw+PHj8X6prcGGhoa6NevH86dO4eUlBTOZ3/88QcAsOXm8/lwcHDA3bt3xX5cBgYGiq27Ma/5yoYOHQoej4fdu3dzln/37h32798PVVVVTt2gq6uL69evc8ogFAolToUqGjtT01PsPXv2cLb76tUrdpyTqPuXsrIyNDU1cfHiRc459fTpU4kvHpRmu02Bz+eDx+NxylhaWipxumAHBwcwDIPg4GBOekJCQqOOjxMRxUTSNVjdd09MTAxnWmR68kBIK6SoqIg1a9bAx8cHX3zxBb788kvo6uoiJycHaWlpiIuLQ0BAAKytraGvrw9DQ0MEBQWhsLAQenp6ePToEcLCwiAQCHD79u0GlcXZ2Rnbtm2T+sVh0dHRiIiIwODBg2FmZsbenTp79iwuXboEAwMDfPnll/UuT1paGq5evQo3N7dqu4XY29ujTZs2OHz4sNjd64YaPXo09u7dixUrVoi9dA6o+HKzs7NDVFQUFBQUYGpqiufPnyMsLAza2tpS9UcdMGAAevToga1bt+Ldu3fQ1tbGlStXcOPGDbEpaLW0tLBy5UosX74cw4cPh6urK7p27YqsrCzcv38f8fHxOHbsmMT+21WdOXMGsrKy7PR9tXF2dsaZM2dw8+ZNzrsehg4dij179mD69OkYN24c5OTkkJiYiHv37lU7ha69vT3U1NSwceNG5OXlSXzx3KRJk5CUlIQNGzbg8uXLsLW1hbKyMl68eIGLFy9CXl4ee/furbXcdb1m/Pz8MHnyZEyYMAGenp7Q0NDAqVOn2C/fynfy+vTpg9mzZ8Pf3x9ubm5wdnZGp06d8ObNG9y+fRvnzp2T+D6F5vbixQtMnToVDg4OyMjIQGhoKBQUFDhTfNblOGppaWHp0qX4+eefMXLkSPY8fP36NRISEvDbb7+xU/GamZnhwoULCAoKQufOnaGoqFjj067p06fj5MmT+OWXX3Dnzh3OVK1aWlpiT49ai+XLl8PLywteXl7w9PREp06dcP78eZw6dQoDBw7kjIv57rvvcP78efj6+sLT0xPa2tq4ePEibt261aTXfGW6urrw9vZGYGAgJkyYgBEjRrBTtb59+xZr1qzhTDTg6emJ77//Hl9//TVcXV2Rk5ODQ4cOoUuXLuw7b0RMTEzA5/MRGBgIoVAIRUVFGBoacsYxlJWVwdPTEy4uLsjPz8fBgwdRVFSEH374gXONeXp6YvPmzZg2bRqGDh2KN2/e4ODBgzA0NBS7mSGqlzZs2IARI0ZAXl4effr0qVe3rrpwdnbGhg0bMH36dDg5OSEvLw8xMTESpy4fO3Ys/vrrL2zbtg1Pnz5lp2oVvefm3r17jVo2dXV1dO/eHcePH4eOjg40NDSgoaEBW1tb9OjRAwMGDGBfDturVy+kpKQgPj4eOjo67A0RajwQ0koNGjQIhw8fxo4dOxAdHY3s7GyoqKige/fumDx5MtslQkZGBoGBgVizZg2OHDmC9+/fw9DQEGvWrMHdu3cb3Hhwd3fHH3/8gZiYGLHZICQZP3482rVrh0uXLiE4OBjv3r2DnJwcdHR04Ovry86XX1+imZSGDRtWbZ62bdvCzs4Op0+fxpMnTziP2hvK2NgYAoEA9+/fh5WVlcSBdevWrcOGDRtw6tQpHDlyBLq6upg3bx5kZWXZlyXVREZGBtu2bcMvv/yC0NBQyMnJwc7ODqGhoWKzvABgG5e7d+9GWFgYcnNzoaamBj09PcydO1fiQPSqGIZBVFQUHB0d0alTJ6liMXz4cPz++++IjIzkNB4sLCzg7++Pbdu2YcuWLWjTpg0GDBiA0NBQeHl5SVyXvLw8RowYgdDQUCgpKUm8mywnJ4fAwEDs378fkZGR7IuiOnbsCFNT02rHv1RV12vG0tISwcHB2LhxI4KCgtgfuz///DMcHR3FBi76+vrCxMQEe/fuRUhICAoKCtC+fXsYGhpi6dKlUpWxqQUFBWH16tXw9/dHYWEhzMzMsGjRIk63kboeRw8PD3Tv3h27du3C3r17UVxcjI4dO8LW1pbTN//HH3/ETz/9hP/7v/9DQUEBunbtWmPjQVlZGQcOHEBAQADi4+MRFRUFNTU1uLm5Yc6cOXWfo76ZGBsbIywsDFu3bkVYWBjy8/PRtWtXzJ49G97e3pzuLN26dcP+/fuxZs0aHDx4EHw+H1ZWVggJCcHXX38ttu7GuOYlmT9/Prp37479+/dj06ZNkJGRgYmJCVasWIFBgwZx8o4aNQpv3rzBvn37sHr1anTr1g2zZs0Cn88Xe+Fo165dsWrVKuzcuRMrVqxAaWkpfH19OY0H0b7v3LkTOTk5MDIywu+//w47OzvOuqZPn47c3FxERUXh8uXLMDAwwK+//orbt2+LNR4sLS0xb948hIWFYdmyZSgrK2PL2pSmTp0KhmFw+PBh/Prrr9DU1GRvBFadTEFeXh7BwcFYv3494uPjERcXh169euGPP/7Avn37mmT2pLVr12L16tVYt24dioqKYGVlxU5EsXbtWqxatQrR0dGIioqChYUFQkJCsHLlSnY2MR7TUqOcCCEfjB9//BGJiYk4ceIE527/4sWLcfnyZZw6daoFS0fq4tKlS5g0aRJCQkI4bwGPi4vDnDlzEBERIfaytprs2LEDgYGBSEhIqPFFex+j5ORkjB07FgsWLIC3t3dLF0cqixcvxpEjRxr9biYh9eXv74+AgAAkJCTU+WnJx27EiBEoLS2V+u30zYXGPBBCajV37ly8e/dOYl9W8uFjGAb+/v4YM2ZMnRoOQMUb1FVVVbF79+4mKl3LYxhGbJpShmHY/stV74wSQkhdVH0/DVAx5uHBgwf1ert2U6NuS4SQWrVv3x5Xrlxp6WKQJsLj8RAVFVWvZdu0afPRP3kqLi7GkCFDMGrUKHbs0alTp3Dt2jWMGDFC7CVghBBSF8uXL0dxcTH69u2LNm3a4M6dOzhy5Ag0NDQwffr0li6eGGo8EEIIITWQlZWFvb09EhISkJGRgbKyMujo6GDBggVSjQMihJCaDBw4EPv27cPFixeRn58PdXV1uLi4YPbs2VKPQWtONOaBEEIIIYQQIhUa80AIIYQQQgiRCjUeCCGEEEIIIVKhxgMhhBBCCCFEKtR4IIQQQgghhEiFGg+EEEIIIYQQqVDjgRBCCCGEECIVajwQQgghhBBCpEKNB0IIIYQQQohUqPFACCGEEEIIkcr/A9+zhbHKDNGEAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x910 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explainer = shap.TreeExplainer(cv_rfc.best_estimator_)\n",
    "shap_values = explainer.shap_values(X_test)\n",
    "shap.summary_plot(shap_values, X_test, plot_type=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fed71523710>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate feature importances\n",
    "importances = cv_rfc.best_estimator_.feature_importances_\n",
    "# Sort feature importances in descending order\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "# Rearrange feature names so they match the sorted feature importances\n",
    "names = [X.columns[i] for i in indices]\n",
    "\n",
    "# Create plot\n",
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "# Create plot title\n",
    "plt.title(\"Feature Importance\")\n",
    "plt.ylabel('Importance')\n",
    "\n",
    "# Add bars\n",
    "plt.bar(range(X.shape[1]), importances[indices])\n",
    "\n",
    "# Add feature names as x-axis labels\n",
    "plt.xticks(range(X.shape[1]), names, rotation=90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rc('figure', figsize=(5, 5))\n",
    "#plt.text(0.01, 0.05, str(model.summary()), {'fontsize': 12}) old approach\n",
    "plt.text(0.01, 1.25, str('Random Forest Train'), {'fontsize': 10}, fontproperties = 'monospace')\n",
    "plt.text(0.01, 0.05, str(classification_report(y_test, y_test_preds_rf)), {'fontsize': 10}, fontproperties = 'monospace') # approach improved by OP -> monospace!\n",
    "plt.text(0.01, 0.6, str('Random Forest Test'), {'fontsize': 10}, fontproperties = 'monospace')\n",
    "plt.text(0.01, 0.7, str(classification_report(y_train, y_train_preds_rf)), {'fontsize': 10}, fontproperties = 'monospace') # approach improved by OP -> monospace!\n",
    "plt.axis('off');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fed710f8a20>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rc('figure', figsize=(5, 5))\n",
    "plt.text(0.01, 1.25, str('Logistic Regression Train'), {'fontsize': 10}, fontproperties = 'monospace')\n",
    "plt.text(0.01, 0.05, str(classification_report(y_train, y_train_preds_lr)), {'fontsize': 10}, fontproperties = 'monospace') # approach improved by OP -> monospace!\n",
    "plt.text(0.01, 0.6, str('Logistic Regression Test'), {'fontsize': 10}, fontproperties = 'monospace')\n",
    "plt.text(0.01, 0.7, str(classification_report(y_test, y_test_preds_lr)), {'fontsize': 10}, fontproperties = 'monospace') # approach improved by OP -> monospace!\n",
    "plt.axis('off');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "a98c09d3655d85938471d06b393b344a06329d6ca6d418e8958378d41e6481f6"
  },
  "kernelspec": {
   "display_name": "Python 3.10.8 ('base')",
   "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}