Project 2_Diabetes Patients Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29ab41b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from sklearn.model_selection import cross_val_score, KFold\n",
    "\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "\n",
    "import joblib as jb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "362f3bca",
   "metadata": {},
   "source": [
    "dehuiwe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c1a9fc11",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>116</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0.201</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>88</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.248</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.134</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>197</td>\n",
       "      <td>70</td>\n",
       "      <td>45</td>\n",
       "      <td>543</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.158</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>125</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.232</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "5            5      116             74              0        0  25.6   \n",
       "6            3       78             50             32       88  31.0   \n",
       "7           10      115              0              0        0  35.3   \n",
       "8            2      197             70             45      543  30.5   \n",
       "9            8      125             96              0        0   0.0   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  \n",
       "5                     0.201   30        0  \n",
       "6                     0.248   26        1  \n",
       "7                     0.134   29        0  \n",
       "8                     0.158   53        1  \n",
       "9                     0.232   54        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r\"C:\\Users\\amit9\\Downloads\\Project 2 - Diabetes Data-20230929T083420Z-001\\Project 2 - Diabetes Data\\Project 2 MeriSKILL\\diabetes.csv\")\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2888ff9",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1729b836",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dc9707aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of duplicate entries in the data : 0\n"
     ]
    }
   ],
   "source": [
    "print(f\"Number of duplicate entries in the data : {data.duplicated().sum()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2150d8e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               768 non-null    int64  \n",
      " 1   Glucose                   768 non-null    int64  \n",
      " 2   BloodPressure             768 non-null    int64  \n",
      " 3   SkinThickness             768 non-null    int64  \n",
      " 4   Insulin                   768 non-null    int64  \n",
      " 5   BMI                       768 non-null    float64\n",
      " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
      " 7   Age                       768 non-null    int64  \n",
      " 8   Outcome                   768 non-null    int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f25b535d",
   "metadata": {},
   "source": [
    "# Exploring the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "86f5a52a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "67876ef5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                  17\n",
       "Glucose                     136\n",
       "BloodPressure                47\n",
       "SkinThickness                51\n",
       "Insulin                     186\n",
       "BMI                         248\n",
       "DiabetesPedigreeFunction    517\n",
       "Age                          52\n",
       "Outcome                       2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "260e7b2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4601fcb3",
   "metadata": {},
   "source": [
    "Some of the features present in the data have an mimimum value of 0 such as blood pressure, insulin, skin thickness etc. that cannot be zero in a real case scenario. So the best approch is to remove them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cc79794d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in data.columns:\n",
    "    if i == \"Outcome\" or i == 'Pregnancies':\n",
    "        continue\n",
    "    else:\n",
    "        data[i].replace(0, np.nan, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3268ec7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                   0\n",
       "Glucose                       5\n",
       "BloodPressure                35\n",
       "SkinThickness               227\n",
       "Insulin                     374\n",
       "BMI                          11\n",
       "DiabetesPedigreeFunction      0\n",
       "Age                           0\n",
       "Outcome                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "287c4996",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of data before removing null values: \n",
      "0    500\n",
      "1    268\n",
      "Name: Outcome, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(f\"Distribution of data before removing null values: \\n{data['Outcome'].value_counts()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "02c123d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(figsize = (10, 7), grid = True)\n",
    "plt.title(\"Features Distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0f143d44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = data[['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
    "       'BMI', 'DiabetesPedigreeFunction', 'Age']]\n",
    "\n",
    "df.boxplot(figsize = (12, 7))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbd83660",
   "metadata": {},
   "source": [
    "Except one column \"Glucose\" all other columns contains outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "20115216",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                   3.0000\n",
       "Glucose                     117.0000\n",
       "BloodPressure                72.0000\n",
       "SkinThickness                29.0000\n",
       "Insulin                     125.0000\n",
       "BMI                          32.3000\n",
       "DiabetesPedigreeFunction      0.3725\n",
       "Age                          29.0000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fb35a541",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                   3.845052\n",
       "Glucose                     121.686763\n",
       "BloodPressure                72.405184\n",
       "SkinThickness                29.153420\n",
       "Insulin                     155.548223\n",
       "BMI                          32.457464\n",
       "DiabetesPedigreeFunction      0.471876\n",
       "Age                          33.240885\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "961e65b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.fillna(df.median(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b60de4dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "70a27eb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pregnancies</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.128213</td>\n",
       "      <td>0.208615</td>\n",
       "      <td>0.081770</td>\n",
       "      <td>0.025047</td>\n",
       "      <td>0.021559</td>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.221898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose</th>\n",
       "      <td>0.128213</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.218937</td>\n",
       "      <td>0.192615</td>\n",
       "      <td>0.419451</td>\n",
       "      <td>0.231049</td>\n",
       "      <td>0.137327</td>\n",
       "      <td>0.266909</td>\n",
       "      <td>0.492782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BloodPressure</th>\n",
       "      <td>0.208615</td>\n",
       "      <td>0.218937</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.191892</td>\n",
       "      <td>0.045363</td>\n",
       "      <td>0.281257</td>\n",
       "      <td>-0.002378</td>\n",
       "      <td>0.324915</td>\n",
       "      <td>0.165723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkinThickness</th>\n",
       "      <td>0.081770</td>\n",
       "      <td>0.192615</td>\n",
       "      <td>0.191892</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.155610</td>\n",
       "      <td>0.543205</td>\n",
       "      <td>0.102188</td>\n",
       "      <td>0.126107</td>\n",
       "      <td>0.214873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insulin</th>\n",
       "      <td>0.025047</td>\n",
       "      <td>0.419451</td>\n",
       "      <td>0.045363</td>\n",
       "      <td>0.155610</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.180241</td>\n",
       "      <td>0.126503</td>\n",
       "      <td>0.097101</td>\n",
       "      <td>0.203790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>0.021559</td>\n",
       "      <td>0.231049</td>\n",
       "      <td>0.281257</td>\n",
       "      <td>0.543205</td>\n",
       "      <td>0.180241</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.153438</td>\n",
       "      <td>0.025597</td>\n",
       "      <td>0.312038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.137327</td>\n",
       "      <td>-0.002378</td>\n",
       "      <td>0.102188</td>\n",
       "      <td>0.126503</td>\n",
       "      <td>0.153438</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>0.173844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.266909</td>\n",
       "      <td>0.324915</td>\n",
       "      <td>0.126107</td>\n",
       "      <td>0.097101</td>\n",
       "      <td>0.025597</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.238356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</th>\n",
       "      <td>0.221898</td>\n",
       "      <td>0.492782</td>\n",
       "      <td>0.165723</td>\n",
       "      <td>0.214873</td>\n",
       "      <td>0.203790</td>\n",
       "      <td>0.312038</td>\n",
       "      <td>0.173844</td>\n",
       "      <td>0.238356</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Pregnancies   Glucose  BloodPressure  SkinThickness  \\\n",
       "Pregnancies                  1.000000  0.128213       0.208615       0.081770   \n",
       "Glucose                      0.128213  1.000000       0.218937       0.192615   \n",
       "BloodPressure                0.208615  0.218937       1.000000       0.191892   \n",
       "SkinThickness                0.081770  0.192615       0.191892       1.000000   \n",
       "Insulin                      0.025047  0.419451       0.045363       0.155610   \n",
       "BMI                          0.021559  0.231049       0.281257       0.543205   \n",
       "DiabetesPedigreeFunction    -0.033523  0.137327      -0.002378       0.102188   \n",
       "Age                          0.544341  0.266909       0.324915       0.126107   \n",
       "Outcome                      0.221898  0.492782       0.165723       0.214873   \n",
       "\n",
       "                           Insulin       BMI  DiabetesPedigreeFunction  \\\n",
       "Pregnancies               0.025047  0.021559                 -0.033523   \n",
       "Glucose                   0.419451  0.231049                  0.137327   \n",
       "BloodPressure             0.045363  0.281257                 -0.002378   \n",
       "SkinThickness             0.155610  0.543205                  0.102188   \n",
       "Insulin                   1.000000  0.180241                  0.126503   \n",
       "BMI                       0.180241  1.000000                  0.153438   \n",
       "DiabetesPedigreeFunction  0.126503  0.153438                  1.000000   \n",
       "Age                       0.097101  0.025597                  0.033561   \n",
       "Outcome                   0.203790  0.312038                  0.173844   \n",
       "\n",
       "                               Age   Outcome  \n",
       "Pregnancies               0.544341  0.221898  \n",
       "Glucose                   0.266909  0.492782  \n",
       "BloodPressure             0.324915  0.165723  \n",
       "SkinThickness             0.126107  0.214873  \n",
       "Insulin                   0.097101  0.203790  \n",
       "BMI                       0.025597  0.312038  \n",
       "DiabetesPedigreeFunction  0.033561  0.173844  \n",
       "Age                       1.000000  0.238356  \n",
       "Outcome                   0.238356  1.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "18cb1930",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QnJzMxx9/XOnPkhFClINKHLAuhBD/alFRUcqwYcOUoKAgxcbGRnFwcFAaNmyovPPOO8q1a9eMcTqdTvnoo4+UGjVqKNbW1oqnp6fyn//8R7l8+bLJ8kq7c9CNu1TNmDHDbDmOHDmiDB48WPH29lasra0VX19fpVOnTsrXX39tjDF3l6orV64oAwcOVNzc3BQnJyelR48eyvHjx83eZWnmzJlKaGiootFoFECZP3++oigl71KlKIY7Tb3xxhtKcHCwYm1trfj5+SmjR49WUlNTTeKCg4OV3r17l6hP+/btlfbt25ut6834+y5Vt2PuTlMnT55Uunbtqjg5OSlubm7KoEGDlNjYWAVQJk2aZDL/hAkTFH9/f0WtVptsv9LKfuO9G9svKytLiYiIUGrVqqVkZ2ebxD3//POKtbW1sm/fvtvWITk5WXnuuecUPz8/xcrKSgkODlYmTJig5OXlmcTd7V2qAOPLwcFBqVq1qvLII48oy5YtM97d7GZ//vmnUr9+fUWr1SoBAQHKa6+9Zryb1c37VUpKivLII48orq6uikqlUm4+FZk3b54SHh6u2NraKlWrVlWmTZumzJ0797Z3EhNC/POpFEWepiOEEEIIIYSwDBnDIYQQQgghhLAYaXAIIYQQQgghLEYaHEIIIYQQQgiLkQaHEEIIIYQQ/wd27NhB37598ff3R6VSlek5N9u3b6dx48ZotVqqVq1qcoe6spIGhxBCCCGEEP8HsrOzqV+/PrNmzSpT/IULF+jVqxdt27bl8OHDTJw4kbFjx7J8+fK7Wq/cpUoIIYQQQoj/MyqVit9++43+/fuXGvPGG2/wxx9/EB0dbZz23HPPceTIEfbs2VPmdUmGQwghhBBCiAdUfn4+GRkZJq/8/PxyWfaePXtKPDC1e/fuHDx4kMLCwjIvR540LsQdrLYOr+wiWMy6GQcquwgW4+/vUNlFsJjoY9cquwgWlZJQ8unU/xaKoq/sIlhMemJyZRfBYlZOs6nsIljMwpgWlV0Ei3mlv6rS1l2R5w4H3nycKVOmmEybNGkSkydPvu9lJyQk4OPjYzLNx8eHoqIikpKS8PPzK9NypMEhhBBCCCHEA2rChAmMGzfOZJqtrW25LV+lMm243RiNcev025EGhxBCCCGEEOVIZV1x2RVbW9tybWDczNfXl4SEBJNp165dw8rKCg8PjzIvR8ZwCCGEEEIIIUpo2bIlGzduNJm2YcMGmjRpgrW1dZmXIxkOIYQQQgghypHaqvLGj9xOVlYWMTExxr8vXLhAVFQU7u7uBAUFMWHCBOLi4vjhhx8Awx2pZs2axbhx4xg1ahR79uxh7ty5/Pzzz3e1XmlwCCGEEEII8X/g4MGDdOzY0fj3jbEfw4YNY8GCBcTHxxMbG2t8PzQ0lDVr1vDyyy/z1Vdf4e/vzxdffMHAgQPvar3S4BBCCCGEEKIcqaz/maMWOnTowO0ewbdgwYIS09q3b8+hQ4fua73/zK0hhBBCCCGE+FeQDIcQQgghhBDl6J86hqOySIZDCCGEEEIIYTGS4RBCCCGEEKIcVeRzOB4EkuEQQgghhBBCWIw0OIQQQgghhBAWI12qhBBCCCGEKEcyaNyUZDiEEEIIIYQQFiMZDiGEEEIIIcqRDBo3JRkOIYQQQgghhMVIhkMIIYQQQohyJGM4TEmGQwghhBBCCGExkuEQQgghhBCiHKk0kuG4mWQ4hBBCCCGEEBYjGQ4hhBBCCCHKkVoyHCYkwyGEEEIIIYSwGGlwiAdKSEgIM2fOrOxiCCGEEEKUSqVWVdjrQSBdqh4ww4cPZ+HChQBYWVkRGBjIgAEDmDJlCg4ODpVcOss7cODAv7Ke7m2aUPWVEbg0qoPW35uDA8eQ+Mfmyi7WbbWtZ03nxrY4O6iIT9azYnse567qzMY626t4uJ2WQG81Xm5qtkcVsGJ7vklM/WpWdGtmi6erGo0arqfp2RJZwIFThRVRHRONqqloHq7C0Q6up8OmKD1XkszH1giARtXU+LiCRgNJ6bDzhJ4LicUxns7Qto4aXzdwdVCx6bCeA2eVCqlL52b29GrjgIujhrhrRSxem86ZS6Vv0/AQG4b0cCbA24q0TB2rd2Wz9UCOSUz3lvZ0auaAh4uGzBw9B07ksmxjJoVFhvf7tHOgSU0tfl5WFBYqnL1cyNINGSQkmd8/7lWvDm4M6O6Bu4sVsVfz+W5pIifO5pQaX6eGPSMH+xDkb0tKWhHL1yezdntqcb3autKppSvB/rYAxFzK5YffrnHmYp4xZkhfL4b08zJZbmp6EU+8eqZc69a7gzsDunvi7mqo27dL4u9Yt1GP+hnr9uu667fUzY1OLV0JCdAa67bwt0TOXMg1xgzq6UmrRs5U8bOloEAh+lwO839NIC6xoFzrdsPTjwfTr7sfTo5WnDyTyadfn+VCbOl1BGjfypORQ0MI8LMjLj6X7368wI69ycb3+/f0o39Pf/x8DPW8EJvDgiWX2BuZYnZ5rz1fnYd6+PP5dzEs+yOu/Cp3kxXrNvPT72tJTk0jNDCAsU8NoUGtcLOxh45H8+Kkj0pM/+nzqQRX8S8xfdOuvUz67GvaNm3Ih+P/W+5lv5MTe37i6Pa55GRex80njJZ9J+IX2sRs7IXjGzi5ZwnJ8dHoigpw8wmjcZcXCAxva4yJ3vcLZw/9TkriWQC8AmrTtMfLeAfWq5D6CMuRBscDqEePHsyfP5/CwkJ27tzJyJEjyc7OZs6cOSZxhYWFWFtbV1IpLcPLy+vOQQ8gjYM9GUdPc2XhChovm1XZxbmjRjWsGNBeyy9b8jh/VUfretaM7m/PBz9mkZpZ8kTaygqycvVsOFBEh4Y2ZpeZna+wfn8+iSl6dHqF2qHWDO2mJTNXz6lL5Xuiejs1A1V0aaBi/SGFK0kKDaupeLStmu/W68kwcy4U5KXiQqLC9mMKeYVQL1TFoDZqFm7Wk5hmiLHWQFqWwqnL0KVBhVWF5nW0DO3pzMJV6ZyNLaRjE3tefcKdCV9eJzldXyLe01XDq0+4se1gLt8sT6N6kDXD+riQma3n4EnDSXfLeloGdXVm7so0zsYW4uuhYdQAVwB+WpsJQESIDZv253AhrhC1GgZ1ceL1Ye6M/yKJgsLyaWi1beLMqEd9mbM4npMxOfRs78bksUGMmRTD9ZSiEvE+ntZMHhvE+p2pfPx9HLXC7Bk91I/0zCJ2HzKUu264A9v3pxN9LofCQoWB3T149+Vgnp90juS04mVeisvjzU8vGf/Wl9yU91e3ps6MesyX2YvjiY7JoUc7N6b8N5jR78RwPaVkY9HH05op/w1h3Y4UPv7+CjXD7Bkz1I/0TB27D2UY67ZjfzrfnIunoFDPwB5evPdyCGPeOWusW91wB1ZvTeHMxVw0ahVPPuzN++NCeO7ts+QXlG8DeejAQB7tX4UPZp7mclwOwx4N5rN36/H46APk5pr/vtcOd2bK67X4ftEFduxNol0LT959oxZj3oji5BnDZ3g9qYCvF14gLt7QkOrZ2Ydpb9bm6ZciSzRm2rbwoFYNZ64n55dYV3nZ9Nc+Pp//E6+MepJ6EdVZuWErr37wKYtmTsXXy6PU+X7+8kMc7LTGv12dnUvEJFxLYtbCpdSvWcMiZb+Tc0fWsOfPabTp/w4+wY2I3reUtfOeYfC4VTi6lWwcxZ8/SED1VjTt8TK2dk6cPriC9QvH0P/5pXgG1Po7Zj/VGvSmVXBDrKxsidr+PWu+H8GgcatwcPGp6CreF5VGOhHdTLbGA8jW1hZfX18CAwMZMmQIQ4cOZeXKlUyePJkGDRowb948qlatiq2tLYqikJ6ezjPPPIO3tzfOzs506tSJI0eOmCzz/fffx9vbGycnJ0aOHMn48eNp0KCB8f3hw4fTv39/Pv74Y/z8/PDw8OD555+nsLD4x2/RokU0adIEJycnfH19GTJkCNeuXTO+v23bNlQqFZs3b6ZJkybY29vTqlUrTp8+bVKWP/74gyZNmqDVavH09GTAgAHG927tUnWnuh05coSOHTvi5OSEs7MzjRs35uDBg/f7EZS76+t3cGbSTBJWbqzsopRJx0a27DlRyJ4ThSSm6lmxPZ/ULD1t6plvTKRkKCzfns/+6ELySjlxibmi4+i5IhJT9SSlK2yPKuBqkp5q/hV7XaRZDRVHLigcuaCQnAmbohQycqFhNfNp601RCvtOK8SnQmoWbD+mkJIFYf7F8fGpsPWoQvRlhaJyPjm9nR6tHNh+KIftkblcvV7E4rUZpGTo6dTMfJawUzN7ktP1LF6bwdXrRWyPzGXHoRx6tS6ODwu04WxsAXuO5pGUpuP4uQL2Hssl1L/4s//4h1R2Hc4l7loRlxOK+G5FOp6uVoT6l98FkP5dPdi4K5UNu9K4klDAd0sTSUotpFd7d7PxPdu7cT2lkO+WJnIloYANu9LY9FcqA7oVn/R9/H0ca7alcuFyPlcSCvjyh3jUKqhf03R76fSQlqEzvjKyyrdB/HBXTzbsSmXDzlQux+fz3dIEQ906mK9br/buXE8p4LulCVyOz2fDzlQ27kpjQHfPm+p2hdXbUjh/Oc9Qt4Vxf9fN0RjzzsxLbNqdRuzVfC5cyeOz+XF4e9gQFmxXrvUDGNQvgB9+iWXHniQuxObwwWensLXV0K29d6nzDH4ogINRqSz69TKxV3JZ9OtlIo+kMbhfFWPMXweS2RuZwuWruVy+msu3P14kN09HrXDTE3ZPdxtefrY6734STVGR5bKNS/9cT59O7ejXpT0hVfx56emheHu489v6Lbedz83FCQ83V+NLc8vJq06nZ8rn3zDi0f74+1TOhbijOxcQ3nQgEc0G4eZTjVb9JuLo4svJvT+bjW/VbyINOozEO7AuLp4hNOsxDhePYC5FbzXGdHr8Y2q3HIKnf01cvavSbuB7KIqeuJg9FVUtYSHS4PgXsLOzM574x8TE8Msvv7B8+XKioqIA6N27NwkJCaxZs4bIyEgaNWpE586dSUkxpJgXL17MBx98wEcffURkZCRBQUElsiUAW7du5dy5c2zdupWFCxeyYMECFixYYHy/oKCA9957jyNHjrBy5UouXLjA8OHDSyznzTff5JNPPuHgwYNYWVnx9NNPG99bvXo1AwYMoHfv3hw+fNjYODFHUZQ71m3o0KFUqVKFAwcOEBkZyfjx4/91WZ+KplFDoLeaU5dMryKfulREqJ+m3NZTI1CDt5uamLiSV6stRa0GXze4kGh6AnIhQaGKR9n7ydpYQZ5leqGUmUYDIf7WHI8xvXp7LCaf6oHmvwNhgdYcMxMfEmDNjfOdM7EFhPhbUzXAsAwvNw31a2g5cibv1sUZ2WkN2y4rt3xaW1YaCAvWcvhktsn0wyeyiKhm/uQ4oqodh09kmUw7dCKbsGA7NKXstrY2ajQaFZnZpg0Kf28bFs6ozvfTwnh9VAA+nuV3TLHSqAgLNlfWLGpWszc7T0Q1ew6ViM+k+j3U7WYO9oaZs24Tcy/8fbR4utuy/3Bxl6/CIoWo42nUiSh5Jf+GOhHO7D9s2jVq3+EU6tQ0P49aDZ3beqHVajhxKsM4XaWCt8dF8POKy3fswnU/CguLOH3uIs0a1DGZ3qx+HY6fjrntvE+9Ool+I/7L2MkfEXksusT785f9jquzE327tC/XMpeVrqiApLgTVKne2mR6lRqtSbx0uEzLUPR6CvKzsbV3KTWmqDAXva7otjH/VGqNqsJeDwLpUvWA279/Pz/99BOdO3cGDCf9P/74o7Hr0ZYtWzh27BjXrl3D1tbQL/njjz9m5cqV/PrrrzzzzDN8+eWXjBgxgqeeegqAd955hw0bNpCVZfoD5ubmxqxZs9BoNERERNC7d282b97MqFGjAEwaDlWrVuWLL76gWbNmZGVl4ehYfBXtgw8+oH17w0Fy/Pjx9O7dm7y8PLRaLR988AGPPfYYU6ZMMcbXr1/fbN23bt16x7rFxsby2muvERERAUD16tXvcUuLGxzsVGjUKjJzTE/KM3MUnO3v78CntYH3RzphpQG9Ar9syeN0bMV1p7K3AbVaRfYt587Z+eCgNT/PrZqHq7CxgujLFTNGozRO9oYTyvQs05P8jCwdLk62ZudxddRwLMu0wZGepcdKo8LRXk16lp59x/Jwtlfz1kgPUBlOkDfvy2bVzmyzywQY0tOZ0xcLiLtWPo1HZ0crNBoVqRmmy0vN1NHIxfzPmpuLFamZpvtSakYRVlYqnB2tSE0vWbZhA71JTisi6qaGzekLuXw6L464xAJcna14rLcnH48PZcykc7c9eS973TRoNCrSbqlbWoYOt9Lq5mxFWobulvjb1234QB+S0wqJOplV4r0bRg325fiZbC5dLd8uR+5uhmxYSpppqzw1rQAf79K/aO6uNqSmmXYpS00rNC7vhqrBDnw9oyE2Nmpyc3VM/OAEFy8XNyyGDgxEp1dY9qdlxmzckJaZiU6vx93FtEHk5upMclq62Xk83Fx547nhhFcLobCwiHXbd/PfKdOZNWU8DWobxn0cPXWWVZt3sOCTdy1a/tvJy0lF0euwczTtFmbn6EFOZikD3m5xdOd8igpzqFavZ6kx+9d+ioOLDwFhre6rvKLySYPjAbRq1SocHR0pKiqisLCQhx56iC+//JLZs2cTHBxsMs4hMjKSrKwsPDxMDwq5ubmcO3cOgNOnTzNmzBiT95s1a8aWLaYp39q1a6O56XKZn58fx44dM/59+PBhJk+eTFRUFCkpKej/7tgcGxtLrVq1jHH16tUzWQbAtWvXCAoKIioqytiAuZOy1G3cuHGMHDmSH3/8kS5dujBo0CCqVatW6jLz8/PJzzf9cS1U9FirJBl4K0ucTucXwIeLs7C1UREeaMXD7bUkZeiJuVJxjQ5zytqMqhWook1tFct36cmxXLfw+6NSodzmw7v1LdUtlY8IsaFve0cWrkrn3JVCfNw1/KeXC2lZen7fVvLk9ck+zgT6WPH+98kl3rtvtxRWBbet261vqoyTS840sLsH7Zu5MGHGRQpv6nITeby4jpfi8jl1Lofvp1ancysXVm40PzD5XtxaJJXq9nUrUYcbH5yZeQb28KR9cxfGz7hgUrebjR7iR0gVLa99dP4uSm1e1/bevPZ88TiD1989Zr5sKtUdDyzmPqtbJ8XG5fDUfw/i6GBFh1ZevPlyOC9OOMLFyzmEV3NkUL8qPP1S5D3U5N6obv0SKUqpx5TgAD+CA/yMf9cJD+NaUjI//bGWBrXDyc7N5d3Pv+GN0U/h6uxkuUKX0a11UwwT7zhfTNQqIjfOotuwr0o0Wm6I2vY956JW0+fZH7CyNn+R5J/sQbl7VEWRBscDqGPHjsyZMwdra2v8/f1NugjdegcnvV6Pn58f27ZtK7EcV1dX4/9LHDTMHNRv7YqkUqmMjYrs7Gy6detGt27dWLRoEV5eXsTGxtK9e3cKCgpKXc6N9d5Yjp1d2fsKl6VukydPZsiQIaxevZq1a9cyadIklixZwsMPP2x2mdOmTTPJrgA8rnJnqMbTbPz/o+xcBZ2+ZDbDyV5FRs79NUMUICldARTirhfg466mW1NbYq5YrtvDzXIKQK9XSmQz7G0pkfW4Vc1AFb2aqvhtj56L124fWxEyc/TodAoujqaNZWcHdaljDtKydLg4akrEF+kUsnIM39GBnZ3YfSSX7ZGGQblXEouwtcnkqX4u/LE9y+Tk74nezjSM0PLB98mkZpTf4JWMrCJ0OqXEFX9XJ02JzMANqelFuDnfEu9sRVGRUiIz8XA3Dwb18uStTy9xMe72Lcf8AoWLcXn4e5sfv3S3MrJ0Zuvmcru6ZRSZ3RZFRQoZ2abzDOjmweBeXrz5yQUuXjFft+ce96N5A2femH6e5NT7z0rt2p/MyTPFY+dsrA37pLubDcmpxb8Pbi7WJbIeN0tJKyiRzXBztSb1lnmKihTi4g1f2NMxWdSs7sSgfgHM+Oos9Wq74OZizfJ5LYzxVhoVLzxdjcH9qjBo5L57r+gtXJ2c0KjVJbIZqemZuLuWvYtQ7RrVWL/DMIYhLuEa8deSeGPaTOP7+r+/dO0GPc1PX35IFd/Sx8GUF629Gyq1pkQ2Iy8rGftSGhA3nDuyhu2/vkXXoTOpUt185uLI9rlEbf2G3qPm4eFn/o5e4sEiDY4HkIODA2FhYWWKbdSoEQkJCVhZWRESEmI2Jjw8nP379/PEE08Yp93twOpTp06RlJTEhx9+SGBg4D0tAwzZj82bNxu7d91OWeoGUKNGDWrUqMHLL7/M448/zvz580ttcEyYMIFx48aZTNvi3viu6vBvp9PD5Wt6IoKsOHqu+GQkPMiKY+fLd7yFCkN//Yqi10NCKoT6qDgTV3zmHOqj4szV0htTtf5ubPy+V8+5+Ioo6Z3pdHDxaiF1qtkSGV18Ylmnmg2HTpk/0Yy5XEjDcNMriXXCbLkYV4ju7/aCjbUK/S2bQq9XSlzUfKK3M41raZk2N5mktPLNUBXpIOZSHg1qOrDncKZxeoNajuyLyjQ7z6nzuTSr5wQU36+4YS0HYi7lorupeAO6efBob0/e+TyWmEt3aGUCVlYqAv1sb3vL2rtRpFOIuZRLw1qOJnVrWMuRvaXV7VwOzeqbXu1uWNuRs7fWrbsnj/X24u2ZF0ut23ND/GjZ0JkJMy6QmFQ+t6TOzdURd8udp5JS8mnawI2z5w0ZIysrFQ3quPL1wtIzKsdPZdC0gRu//F7cFapZQ3eOR2eUOg8AKrD+u5GzfmsiB6NSTd7+9N16rN+ayOpNCXdTrTuytrYivFoIB46coH3z4t+RA0dP0KZpwzIv58yFWDzcXAFDBuTHz943ef/bn5aTk5fHS08PxcfD/I0FypvGygbPgNrEnd1NaJ2uxulXzu4mpFanUueLiVrF9mVv0nnIJwTV7GA25sj2uRzaPIdeI77Hq0rd8i56hXlQxlZUFOkn8i/XpUsXWrZsSf/+/Vm/fj0XL15k9+7dvPXWW8YGwYsvvsjcuXNZuHAhZ8+e5f333+fo0aMl08C3ERQUhI2NDV9++SXnz5/njz/+4L333rvr8k6aNImff/6ZSZMmER0dzbFjx5g+ffo91S03N5cXXniBbdu2cenSJf766y8OHDhAzZo1S12/ra0tzs7OJq+K6E6lcbDHuX4EzvUNY03sQ6vgXD8CbaDfHeasHFsP5dOyjjUtalnj46ZmQDtb3J3U7DpquNLYt7UtT3QzTRMEeKkJ8FJja63C0c7wf1/34m3btakN4UEaPJxV+Lip6djQhmY1rTkQXbHP4dh/RqF+qIp6oSo8nKBzAxXO9nD4nOEsu31dFX2aFX83agWq6NNcxZYjCldTDGM9HLRge1NCUK0Gb1fDS6MGRzvD/90csah1u7Np39iedo3s8PeyYkhPJzxcNGzZbzg5HtTViWcGFl9p3bI/B09XDUN6OOHvZUW7Rna0b2TPmr+KxzBEnc6jc1N7mtfV4umqoXY1GwZ2duLwqTxjdmNYH2da1bdjzrI08goMWRYXRzXW5XiJa+XGZLq1daNra1eq+NowcrAPXu7WrPn72RPDHvZm3NPFt+Zcuz0Vbw9rRg72oYqvDV1bu9K1jRsrNhR39RrY3YMn+nvx+cKrJCYV4OqswdVZg9a2+PN++hEf6tSwx8fTmhqhdkx8rgr2WjWbd5vvk38vftuYZKxboJ8tox71NdRtm6HL1rABPox7OsAYv2Z7Ct4eNowc7Eugny1dW7vSrY0bK9YXX30e2MOTJ/t7M3NBHNeSCnFztsLN2QqtbfF3cMxQPzq2cGXGd5fJzdMbY2ysy//EadkfcTwxKIh2LTwIDbLnzZfCyc/XsWF7cXrwrZfDefbJUJN5mjZ0Z+jAQIKq2DF0YCBN6rvyyx9XjDHPPBFKvVou+HrbUjXYgWeeCKFhHVc2bDMsNyOziAuxOSavoiKF5NQCLscVP5OkvDzatzt/bt7Oqs07uHjlKp/P/4nEpGQe7tYRgDmLlvHeF98a45euWs+OfZFcvprA+dg45ixaxra9BxnY0zBO09bGhqpBVUxejg722Gu1VA2qgnV5fsnuoF7b4Zw68CunDiwnNfEcu/+cRlZaPDVbPAbA/rWfsHXpG8b4mKhVbF06nhZ93sA7qD45mdfJybxOQW5xQzpq2/ccWD+T9oM+wMk9wBhTmF/6GDHxYJAMx7+cSqVizZo1vPnmmzz99NNcv34dX19f2rVrh4+P4Z7WQ4cO5fz587z66qvk5eUxePBghg8fzv79+8u8Hi8vLxYsWMDEiRP54osvaNSoER9//DH9+vW7q/J26NCBZcuW8d577/Hhhx/i7OxMu3bt7qluGo2G5ORknnzySRITE4232L21y9Q/gUvjOrTc/KPx71ofTwTg8g8rODpiQmUVq1SHzhThoM2jRwtbnO0ND/6b83uO8RkcLg4q3JxNG2rjhxafXQf5aGgaYU1yhp7J8wxXOG2sVAzuqMXVSU1hESSm6PhhfS6HzlTcXarAMNjbzgZa11LhqFVxPR1+2Vn8DA5HLX93JzPUtUE1wyD67o1VdL8pGXb0gp7VBwwxTloY0a04VdMiQkWLCLh0TeGnbZa7T+6+43k42mfwUAdHXJ00XEks4pMfU0lON1xtdnVU4+FSXK6kNB0f/5jK0J7OdG7uQFqmjh/XZBifwQHw+/YsFOCRzk64OWvIzNZz+HQev24qPmno3NzQtfPNEaZdK75dkcauw+VzUrfzYAZOjhoe6+OJu4sVl67mM/mLWONzKtxcrfByL271JSYVMvmLWEYO9qF3BzeS04v4dkmC8RkcYHiQoLW1momjA03W9dMf1/npz+sAeLpZ8dqoAJwdrcjILOLU+VxemXbB7PMx7rluBzJwdkjg8b7exrpN+vyScR3uLlZ4eRR3LUpMKmTS5xcZ9agffTq6k5xWxDc/xxufwQGGBwlaW6t5c0yQyboW/3GNn/4wnIz37mj4vD56vapJzGfzrrBpd1q51Q9g8fLL2NqoGTe6Ok6O1pw8k8HL7xw1eQaHj5fWJJt2/FQGk6efZNQToYwcGkJcQi7vTI82PoMDwN3VmrfHReDhbkN2dhHnLmbzyuRjJbIaFaVL6+ZkZGYxf9nvJKemUzUogI8njsPX29BNNzk1jcSk4kZvUZGOWT8s5XpKKrY2NoQGBjBj4su0amz+5imVqVr9XuTlpHFo81fkZFzH3bc6PZ/6Bic3Q2M4J/M6WWlXjfHR+5ai6Iv4a+W7/LWyeMB7jcb96TD4QwBO7v0Jva6QTYtMH2LYqMvzNOn6YgXUSliKSjHXWV/83+vatSu+vr78+OOPdw7+l1tt/e/tP7puxoHKLoLF+Pv/+55If0P0sX/AIBELSkmonJPDiqAoFfgglgqWnmiBGwP8Q6ycVj5jdP6JFsa0uHPQA+qV/pXXrelg+5YVtq4m2//5zymRDIcgJyeHr7/+mu7du6PRaPj555/ZtGkTGzc+GA+hE0IIIYQQ/1zS4BDGrknvv/8++fn5hIeHs3z5crp06VLZRRNCCCGEeOCo1DJM+mbS4BDY2dmxadOmyi6GEEIIIYT4F5IGhxBCCCGEEOVIHvxnSvI9QgghhBBCCIuRDIcQQgghhBDlSB78Z0oyHEIIIYQQQgiLkQyHEEIIIYQQ5UjGcJiSDIcQQgghhBDCYiTDIYQQQgghRDmS53CYkq0hhBBCCCGEsBjJcAghhBBCCFGOZAyHKclwCCGEEEIIISxGMhxCCCGEEEKUI3kOhynJcAghhBBCCCEsRjIcQgghhBBClCMZw2FKMhxCCCGEEEIIi5EGhxBCCCGEEMJipEuVEEIIIYQQ5Uge/GdKtoYQQgghhBDCYiTDIYQQQgghRDmSQeOmJMMhhBBCCCGEsBjJcAghhBBCCFGOJMNhSjIcQgghhBBCCIuRDIcQQgghhBDlSDIcpqTBIcQdrJtxoLKLYDE9Xmta2UWwmOil0ZVdBIupXsursotgUb2esK3sIljMzlNulV0Ei7l4Kbuyi2AxKxMdK7sIFjOwzpnKLoIF1ajsAoi/SYNDCCGEEEKIciTP4TAlW0MIIYQQQghhMZLhEEIIIYQQohypNTKG42aS4RBCCCGEEEJYjGQ4hBBCCCGEKEdylypTkuEQQgghhBBCWIxkOIQQQgghhChHcpcqU7I1hBBCCCGEEBYjGQ4hhBBCCCHKkYzhMCUZDiGEEEIIIYTFSIZDCCGEEEKIciQZDlOS4RBCCCGEEEJYjDQ4hBBCCCGEEBYjXaqEEEIIIYQoR3JbXFOyNYQQQgghhBAWIxkOIYQQQgghypEMGjclGQ4hhBBCCCGExUiGQwghhBBCiHIkYzhMydYQQgghhBBCWIxkOIQQQgghhChPKhnDcTPJcAghhBBCCCEsRjIcQgghhBBClCO5S5UpyXCIcqVSqVi5cmVlF0MIIYQQQvxDSIZDlFlCQgLTpk1j9erVXLlyBRcXF6pXr85//vMfnnzySezt7Su7iP84betZ07mxLc4OKuKT9azYnse5qzqzsc72Kh5upyXQW42Xm5rtUQWs2J5vElO/mhXdmtni6apGo4braXq2RBZw4FRhRVTnnri3aULVV0bg0qgOWn9vDg4cQ+Ifmyu7WLd1cs9PHNk5j9zM67h5h9GizwT8QpuYjb1wfAPR+5aQHH8KXVEBbt5hNOryAoE12hhjTu3/hTOH/yA14SwAngG1aNr9ZbwD61VIfW7WOExFy5pqnOzgejqsP6Tj8nXzsY5a6NpQjZ+7Cncn2H9GYcMhvUmMWgWta6moF6rG2R6SM2DzET3n4pUKqI2pjauXs2rFYtJSkwkICuXJUS8RUbvBHec7ffII7014nsDgqkz74gfj9C3rf2fnlrVcvnQegNCwcB598jnCatS2VBVu6/jun4jaNpeczOu4+YTRut9E/Kua3y/PH9vAiT1LSLoaja6oAHefMJp0e4Gg8LYmMYe2fEN6Uix6XREunsHUb/8U4Y0fqqgqGbWua0WnhjY4O6hISNHz2858zl/Vm411tlfxUBsbAr01eLqq2HmkkN92FpS67IbVrRjWQ8ux80XMXZ1nqSqU6vD2xRzYNJes9Ot4+lWn06CJVAkz/7mdObyBqJ0/c+2K4XPz8KtO694vEFqr+HNb8tkTXD67v8S8VWu3Z+Dz31qsHub8uWo1y1asICUlleCgIJ57ZhR169z5+3Hi5ElefWMCIcHBzJn1hXF6UVERS35ZxqbNW0hKTqZKlQBGDB9O0yaNLVkNi5C7VJmSrSHK5Pz58zRs2JANGzYwdepUDh8+zKZNm3j55Zf5888/2bRpU2UX8R+nUQ0rBrTXsn5/Ph8tzubc1SJG97fHzcl8mtXKCrJy9Ww4UEDcdfM/tNn5Cuv35/Ppkmw+XJTF3hOFDO2mJSJYY8mq3BeNgz0ZR09z4r/vVnZRyuTc0TXsWf0hDTs+y8MvrsA3pDHrFjxLVtpVs/EJFw4SENaKHsO+4eEXfsW/WnM2/DCGpKsnjTFXzx8grF4v+oxawEOjf8bR1Z+180aSnZ5YUdUCoFaQiu6N1Ow6oee7dTpirysMaa/BuZRrBRoNZOfDrhN6EtPMx3Ssp6ZRmJr1kXrmrNYRGaNnUBs1vm4Wq4ZZe3Zu4ofvZ9J/8HCmfr6QiNr1+WjyOJKuJdx2vpzsLOZ89h6165c8ATx57BCt2nXlramzmDLjWzy9fPjwnZdISb5mqWqUKiZqDX/9MY1GnZ9j0Eu/4RfahNVznyEz1fx+efX8QapUb0XvEd/yyH+X4x/WnLXzx3A9rni/tLV3oVGn5xjwwhIGj/udiKYD2PrLRGJP76yoagGGBsHDbW3ZeLCAj5fkcP6qjmf72uHqWMqxUgNZuQobDxZwNcn8sfIGNydD4+RcnPkLPZZ26uAatvw6jRY9RjNswkqqhDXm169GkZFi/nO7EnOA4IhWDBzzLU+OX0FQjeasmDOaxMvFn9tDz3zJ6Gm7jK+n3lqFSq0hvFGPiqoWANt27OTr777n8UcHM/uLz6lTpzZvTZrMtWu3/35kZ2cz45PPaNigfon3FvywiDXr1jHmuWf5bs5sevfsybsfTCXm3DlLVUNUEGlwiDIZM2YMVlZWHDx4kMGDB1OzZk3q1q3LwIEDWb16NX379i0xz7Zt21CpVKSlpRmnRUVFoVKpuHjxonHaX3/9Rfv27bG3t8fNzY3u3buTmpoKQH5+PmPHjsXb2xutVkubNm04cOCAcd7U1FSGDh2Kl5cXdnZ2VK9enfnz5xvfj4uL49FHH8XNzQ0PDw8eeughk3VbUsdGtuw5UcieE4UkpupZsT2f1Cw9berZmI1PyVBYvj2f/dGF5BWYvzocc0XH0XNFJKbqSUpX2B5l+MGt5v/PTVZeX7+DM5NmkrByY2UXpUyO7VxIeJMBRDQdhJt3NVr2nYijiy8n9y4xG9+y70Tqtx+JV2BdXDxDaNr9ZZw9goiN3mqM6fTYDGq1HIKHf01cvavSdsC7KIqeuHN7KqpaALQIV3P4vELUeYWkDNhwSE9GDjSpbv6nID3bEHP0okJeKReQ64ao+Ouknph4hbRsiIxROJ+g0CKiYn9e1qz8mQ5d+9Kxez8CAkN4ctTLeHh6s2ntitvO9/1XH9GqfVeqR9Qp8d4Lr06ha++BhFStQUBgCKNemICi13P8yEFLVaNUR3YsIKLpQGo1H4SbTzXaPDQRR1dfTuz52Wx8m4cm0rDjSLwD6+LqFUKLnuNw8Qzm0sni/TKgWnOq1u2Km081XDyDqNf2STz8wkm4cKiiqgVAhwbW7DtZxN6TRSSmKvy2s4C0LIU2da3NxqdkGmIOnCoiL99sCGC4SdAT3bSs3VdAcsbtGyaWcnDLfOq2Gki91oPw8KtGp0Fv4uTqS9QO859bp0Fv0rzbKPxC6uHmHUK7h8bh5h3MuWNbjDF2Dq44ungZXxdP/YW1jZYaFdzgWPHbSrp360rP7t0JCgpk9DOj8PL0ZNWatbed7/NZX9GxQ3tqRkSUeG/z1q08NngwzZo2wc/Pl769e9G4UUOWr1hpoVpYjkqtqrDXg0AaHOKOkpOT2bBhA88//zwODg5mY1T3ePu3qKgoOnfuTO3atdmzZw+7du2ib9++6HSGq1Gvv/46y5cvZ+HChRw6dIiwsDC6d+9OSkoKAG+//TYnT55k7dq1REdHM2fOHDw9PQHIycmhY8eOODo6smPHDnbt2oWjoyM9evSgoKD09Ht50Kgh0FvNqUtFJtNPXSoi1K/8shE1AjV4u6mJiSu6c7C4I11RAUlXTxBQvbXJ9IDqrUmMPVymZSh6PYX5OdjauZYaU1SYh15XhK2dy/0U966o1eDnDucTTBuz5xIUqnje+w+WRgNFt1w8LtRB4H0s824VFRZyIeY09Ro2M5let2FzzkQfK3W+bZtWcS0+joGPjyjTevLz8yjSFeHo6Hxf5b1buqICrsedILCG6X4ZWKM1CZfuZr/Mxtbe/D6nKApXzu4h7doF/ErppmUJGjVU8VZzKvaWY2VsESH3eazs3syGrFyFfScr5/ioKyogIfYEITXbmEwPqdmauPNl/9wK8rLR2ruWGnNs93IiGvfGxrbiujUXFhZyNiaGxg0bmkxv3KghJ6OjS51v/cZNxMfH858hj5e6XBtr04amrY0tJ06eNBsvHhz/3Mui4h8jJiYGRVEIDw83me7p6UlenqE/7PPPP89HH31018uePn06TZo0Yfbs2cZptWsb+n9mZ2czZ84cFixYQM+ePQH47rvv2LhxI3PnzuW1114jNjaWhg0b0qSJ4QcyJCTEuJwlS5agVqv5/vvvjQ2i+fPn4+rqyrZt2+jWrdtdl7esHOxUaNQqMnNMT+4ycxSc7e/vRExrA++PdMJKA3oFftmSx+nYyuku8G+Tl5OGotdh7+hpMt3O0YPczKQyLePorvkUFeRQtV7pVxsPrPsEB2cfAsJa3Vd574a9LajVKrLzTPfJ7DwFR+2975Pn4w3ZjNhrOlKyINRXRXiAqkJvQZ+ZkYZer8PF1d1kuourG+lpKWbnib96mSULZzPpw6/RaMr2U7hk4WzcPbyo06DpfZf5buRlpxr2SycPk+l2jh7klHG/jNoxn8KCHKrV72kyPT83kx/eb4++qACVWk3bhyeVaNhYUqnHytz7O1aG+qlpUcuKGT/n3G8R71luluFzc7jlc3Nw9iQ7o5SBU7c4sHkehQW5hDfuafb9+ItHSbp6hh7/+eC+y3s3MjIy0Ov1uLq6mkx3dXUlNTXN7DxxcVeZt2Ahn0z/EI3GfGOycaOGLF+5krp16uDn58vhI0fYs28vel3lZKjuh4zhMCUNDlFmt2Yx9u/fj16vZ+jQoeTn3yavfRtRUVEMGjTI7Hvnzp2jsLCQ1q2Lf/ysra1p1qwZ0X9fQRk9ejQDBw7k0KFDdOvWjf79+9OqleEkLjIykpiYGJycnEyWm5eXx7lS+oPm5+eXqIuuKB+Nle091c8Sw2bzC+DDxVnY2qgID7Ti4fZakjL0xFyRRoflKGV6iFNM1GoObfqKbk/Ows7Rw2zMke3fc+7IGnqPWoiV9b3tV/dDuWWnVHF/++n6Q3r6NFMzurfhBCI1C6LOKzSoWglp/ls/o1Iqptfp+GrGJB4ZMhK/gKAyLfrP5YvYvWMjb0+djY1NxX9uBiW3qcrMtFudPbyKgxtm0XP4V9jfsl/a2Dow+OXfKMzP4UrMHnb/+SHOHlUIqNa83Ep9L+5nv7S1hv901bJ0Sz7ZFT9GvKRb9ktFUcrUKyD6wCp2r55F/+dml2i03HB09694+tfAL6Tib0ABJc8LFEUxt5ui0+n4cMYMnhg6hCoBAaUub/SzzzDziy8Z+dxoAPz9/OjWpQsbZJzoA08aHOKOwsLCUKlUnDp1ymR61apVAbCzszM7n/rv1r1y0xlOYaHp3ZRKm/fm+cwd0G5M69mzJ5cuXWL16tVs2rSJzp078/zzz/Pxxx+j1+tp3LgxixcvLrFsLy8vs+ucNm0aU6ZMMZnWtPt4mveYUGo5zcnOVdDpS16hc7JXkZFzf80QBUhKVwCFuOsF+Lir6dbUlpgrlXcl799Ca++KSq0hJ8v0qnFuVkqpDYgbzh1dw44Vb9FlyGelZi6O7phH1LZv6TViHh5+4WZjLCUnH/R6BUc701M5e63qvk7KcvLhl516NGpDFiUzFzrXV5OWff9lLisnZ1fUag3pqckm09PTU0tkPQByc3M4HxPNxfNnWPD1pwAoih5FUfjPQ22Y8O5Mk0Hkq1Ys5vdlC5n43hcEhYZZtjJmaB3cDPtl5q37ZTJ2pZyI3hATtYZty96i2xMzqVKj5H6pUqtx8QwGwDOgJqnXznN4y7cV1uC4cax0uuVY6WhXMutRVp4uajxc1IzsozVOu/Ez8snzDkz9MYfkDMvfRc3O0fC5ZWeYfm45mcnYO3mWMpfBqYNrWLfoTfqN/JyQCPPHk8KCXE4dXE2bPmPLrcxl5ezsjFqtNo63vCE9PR23W7IeALm5uZw5G0PMufN8NedrwPBbrigKPfs+xLT336VB/fq4urgw+e23KCgoICMjEw8Pd+bOX4iPj09FVKtcPShjKyqK5HvEHXl4eNC1a1dmzZpFdnbZzyJunNTHx8cbp0VFRZnE1KtXj82bzd8iNSwsDBsbG3bt2mWcVlhYyMGDB6lZs6bJeoYPH86iRYuYOXMm335ruC1go0aNOHv2LN7e3oSFhZm8XFzM92OeMGEC6enpJq8mXcaVuc436PRw+ZqeiCDTNn14kBUX4ss3E6HCcNcWcf80VjZ4+tcm7uxuk+lxMbvxCWpYylyGzMb2ZRPp9OgMgiI6mI05smMuh7bMocdT3+JVpeQAZUvT6yE+Bar6mv4IVvVVcSXp/k++dHpDY0OtgohAFaevVNxtca2srQkNC+fY4QMm049H7adGzbol4u3sHfho1iKmfbHQ+Orc42H8AoKY9sVCqoUX39bzzxWL+G3pfN6Y/BlVq9cssayKoLGywSugNldu2S+vnNmNb3Dp++XZw6vYsnQCXYZ8THDNDmVbmaKgK7LsGLeb6fRw5Zqe8MCSx8qL93isTEzV8+HiHGb8nGt8nbigI+aKjhk/55KWVTH7psbKBt+g2lyK/stk+qVTuwmoWvrnFn1gFWt/HE+fpz6hWt0OpcadjlyLrqiAWs36lVeRy8za2prqYWEcOmw6FuXQ4Shq1Sz5PbG3t+ebr2Yx58svjK/ePXtQpUoAc778gohbumzb2Njg6emBTqdj1+7dtGzRwqL1EZYnGQ5RJrNnz6Z169Y0adKEyZMnU69ePdRqNQcOHODUqVM0blzyHtlhYWEEBgYyefJk3n//fc6ePcsnn3xiEjNhwgTq1q3LmDFjeO6557CxsWHr1q0MGjQIT09PRo8ezWuvvYa7uztBQUFMnz6dnJwcRowwDPJ85513aNy4MbVr1yY/P59Vq1YZGyNDhw5lxowZPPTQQ7z77rtUqVKF2NhYVqxYwWuvvUaVKlVKlNnW1hZbW9PuEhqrjHvaZlsP5fNEdztiE3VciNfRuq417k5qdh01/Jj3bW2Lq4OKHzcUX14O8DJcA7C1VuFopybAS41OBwkphv6rXZvaEJuoIylNj5VGRa0QK5rVtGbpln9CvwHzNA72OIQVd1uxD62Cc/0IClLSybscf5s5K0fdtsPY9st4vKrUwTuoAaf2/0JWWjw1mz8KwP51n5KdkUjHwYYxSzFRq9m2bDyt+kzAO6g+OZmGvtlW1lpstIbufEe2f8/BjV/Q6bGPcXILMMZY29hjbWv+RgyWsPe0nv4t1FxNURGXpNCwmhoXe4g8a9i/OtU3PJ/j973F/aV9XA3/2lgZMhg+roaTxKS/vxb+HuBspyIhVcHJHtrXUaNSwe7oiu1z3av/48z+dApVq0dQPaIuW9atJOl6Ip17PgwYxl+kJF9nzLhJqNVqAoOrmczv7OqGjY2tyfQ/ly9i2aJveeHVKXj5+JH2dwZFq7VDa1exzx2q3244m5e8gVeVOvgGN+Dkvl/ITIundsvHANi75hOy06/R+XHDfnn28Cq2LBlP64cm4hNUn5y/xwxorLXY2hn2y0NbvsGrSh1cPILQ6QqJjd7OmcjfaTtgUoXWbVtUIUO72nL5mo6LCTpa1rbGzVHFX8cNGfE+LW1wcVSxeGNxd9cAT8Ox0sbaMA4kwFNNkU4hMVWh6KZj5g25+QqgKjHd0pp0eorVC1/HN7gO/qENOfLXUjJS46nf1vC57Vj5CZlpifQePh0wNDbWLHyDToMm4hdan6z0G8eK4s/thqO7f6V6/S7YOVbwPaj/NuDh/sz45FNqVK9OzYgI1qxbx7Xr1+ndyzDeZN6ChSQlJ/P6K+NQq9WEhASbzO/q6oqNtY3J9FOnTpOUnEy1qlVJSk5m0U8/oej1DB44oELrJsqfNDhEmVSrVo3Dhw8zdepUJkyYwJUrV7C1taVWrVq8+uqrjBkzpsQ81tbW/Pzzz4wePZr69evTtGlT3n//fZMxGzVq1GDDhg1MnDiRZs2aYWdnR/PmzXn8ccMdLD788EP0ej1PPPEEmZmZNGnShPXr1+PmZjjA2tjYMGHCBC5evIidnR1t27ZlyRLD7Uvt7e3ZsWMHb7zxBgMGDCAzM5OAgAA6d+6Ms7Pl7zJz6EwRDto8erSwxdne8OC/Ob/nkJppuLrm4qDCzdk0yTh+qKPx/0E+GppGWJOcoWfyvCxDfa1UDO6oxdVJTWERJKbo+GF9LofO/HPvUuXSuA4tN/9o/LvWxxMBuPzDCo6OuLuuahWhWr1e5GencWjzbHIyr+PuU50ew7/Gyc3Q7zgn8zrZacUNpVP7l6Loi/jrj/f464/3jNOrN+pPh0HTADi592f0ukI2Lf6vyboadX6exl1eqIBaGZyMVbCz0dOuthrHvx/89/N2Hel/98Zz1FKiG+AzPYt/Jvw9VNQNUZOWpfDln4arz1Zq6FBPjZsjFBRBzFWFlXt15Ffwsyhbtu1CVkY6K5bMIy0lmSrBVXl90id4efsBkJaSTPL1u3vuycY1yykqKmTmhxNNpg94fASPDBlZbmUvi7AGvcjLSSNy01dkZ1zH3bc6vUd8U7xfZlw3eVbMyb1L0euL2Pnbu+z8rfgZOOGN+9PpsQ8BQ5ecnb+9S1ZaAlbWWly9Q+n8+HTCGvSq0LodPluEvdZwV6kbD0n95s9c47HS2UGFm6PpsfK1x4sbfEE+GpqEW5OSoefdhf+srqURTXqRm53K7jWzyc64hqdfDQaO+RYXD8PnlpVxnczU4uPJkV2Gz23T0nfZtLT4c6vd4mF6Pfmh8e+UxAvEnYtk0IvzKq4yt+jQri2ZGRks/nkJKSkpBAcH8/6USfh4exvKmJLC9etlGxx/Q0FhAQt/XER8QgJ2dlqaNmnC66+Mw9HR8c4z/8NIlypTKkW5dQihEOJmL868twzHg6DHaxV7t52KFL209FszPuhy8x+8O7bcjV6N/73fuZ2nKudqdEW4eKkCB+5UsDq1HrwT3rLqEnymsotgMSFhNSpt3dcmPFlh6/Ke9kOFreteSYZDCCGEEEKI8iS3xTUhW0MIIYQQQghhMZLhEEIIIYQQohyV5Vkr/08kwyGEEEIIIYSwGMlwCCGEEEIIUY5UMobDhGwNIYQQQggh/o/Mnj2b0NBQtFotjRs3ZufOnbeNX7x4MfXr18fe3h4/Pz+eeuopkpOTy7w+aXAIIYQQQghRjlRqVYW97tbSpUt56aWXePPNNzl8+DBt27alZ8+exMbGmo3ftWsXTz75JCNGjODEiRMsW7aMAwcOMHJk2Z9HJA0OIYQQQggh/k98+umnjBgxgpEjR1KzZk1mzpxJYGAgc+bMMRu/d+9eQkJCGDt2LKGhobRp04Znn32WgwcPlnmd0uAQQgghhBCiPKnVFfbKz88nIyPD5JWfn2+2WAUFBURGRtKtWzeT6d26dWP37t1m52nVqhVXrlxhzZo1KIpCYmIiv/76K7179y775ij7lhNCCCGEEEL8k0ybNg0XFxeT17Rp08zGJiUlodPp8PHxMZnu4+NDQkKC2XlatWrF4sWLefTRR7GxscHX1xdXV1e+/PLLMpdRGhxCCCGEEEKUo4ocwzFhwgTS09NNXhMmTLh9+W55ToiiKKU+O+TkyZOMHTuWd955h8jISNatW8eFCxd47rnnyrw95La4QgghhBBCPKBsbW2xtbUtU6ynpycajaZENuPatWslsh43TJs2jdatW/Paa68BUK9ePRwcHGjbti3vv/8+fn5+d1yvZDiEEEIIIYQoRyqVusJed8PGxobGjRuzceNGk+kbN26kVatWZufJyclBfctzRTQaDWDIjJSFNDiEEEIIIYT4PzFu3Di+//575s2bR3R0NC+//DKxsbHGLlITJkzgySefNMb37duXFStWMGfOHM6fP89ff/3F2LFjadasGf7+/mVap3SpEkIIIYQQojzdw/MxKsqjjz5KcnIy7777LvHx8dSpU4c1a9YQHBwMQHx8vMkzOYYPH05mZiazZs3ilVdewdXVlU6dOvHRRx+VeZ3S4BBCCCGEEOL/yJgxYxgzZozZ9xYsWFBi2osvvsiLL754z+uTBocQQgghhBDlSKWWUQs3k60hhBBCCCGEsBhpcAghhBBCCCEsRrpUCSGEEEIIUY5U/+BB45VBMhxCCCGEEEIIi5EMhxBCCCGEEOXpLh/I928nW0MIIYQQQghhMZLhEEIIIYQQohzJGA5TkuEQQgghhBBCWIxkOIS4A39/h8ougsVEL42u7CJYTM1Ha1Z2ESwmcsGJyi6CRX0wK6Wyi2Ax/Qa4V3YRLOb47lOVXQSLad+8YWUXwWK+3R5c2UWwmKlhlbhyefCfCdkaQgghhBBCCIuRDIcQQgghhBDlSKWSMRw3kwyHEEIIIYQQwmIkwyGEEEIIIUR5kjEcJmRrCCGEEEIIISxGMhxCCCGEEEKUI3kOhynJcAghhBBCCCEsRjIcQgghhBBClCeVXNO/mWwNIYQQQgghhMVIhkMIIYQQQojyJGM4TEiGQwghhBBCCGEx0uAQQgghhBBCWIx0qRJCCCGEEKIcqWTQuAnZGkIIIYQQQgiLkQyHEEIIIYQQ5UkGjZuQDIcQQgghhBDCYiTDIYQQQgghRDlSqeWa/s1kawghhBBCCCEsRjIcQgghhBBClCeVjOG4mWQ4hBBCCCGEEBYjGQ4hhBBCCCHKk4zhMCFbQwghhBBCCGEx0uD4h7p48SIqlYqoqCiLrmfbtm2oVCrS0tIsuh4hhBBCiP8bKlXFvR4A0qWqkgwfPpyFCxca/3Z3d6dp06ZMnz6devXqVVq5tm3bRseOHY1/e3p60qRJEz788EPq169faeV6UDWqpqJ5uApHO7ieDpui9FxJMh9bIwAaVVPj4woaDSSlw84Tei4kFsd4OkPbOmp83cDVQcWmw3oOnFUqpC63OrnnJ47snEdu5nXcvMNo0WcCfqFNzMZeOL6B6H1LSI4/ha6oADfvMBp1eYHAGm2MMaf2/8KZw3+QmnAWAM+AWjTt/jLegZX3fbgT9zZNqPrKCFwa1UHr783BgWNI/GNzZRfrtppUV9GylhonO7iWBhsidcReNx/rqIWujdX4uavwcIJ9pxU2ROpNYtQqaFNbRb2qapztISkDNh/Wcy7e8vtljzbOPNTZDTdnDZcTCpi3PIno83mlxtcK0/LUw54E+tqQkq5j5eZUNvyVYTa2dSNHXhnuy76jWXz0fYJx+qM93Xm0p7tJbGpGESPeulgudbqdyG2L2bdhLlnp1/Hyr06XwRMJrG7+O3f60AYO7fiZxMvR6IoK8PSrTtu+L1C1dluTuP2bFnB4x89kpMRj5+hGRKPudHj4Faysbcu17E8O9KdXZ0+cHKw4FZPNF/MvcelK6Z8VQNtmrgwfFICfjy3xifnMWxrHXwfTTGL6dfViUB9fPFytuXgll9k/XOb46SwANBoVTw32p3kDF3y9bcnO1XH4WAbfL4kjObXQ7DqnvlGdZg1ceOeTGHbfsq57tWfTz+xYPY/M9Ov4BITR5z/jCQ03/7ldPB3J2qWfcj3+PAX5ebh5+tOs42Da9hxmjNm/dRmHdv1OwpUYAKqE1qL7oJcIrFbxx8rmNdW0rWv19/FEYfXeIi4mmv/uO9lBr+ZW+Huo8HBRseeEjtX7dCYxI3tZU9Wv5LXwU5d1/LChyCJ1EBVDMhyVqEePHsTHxxMfH8/mzZuxsrKiT58+lV0sAE6fPk18fDyrV68mNTWVHj16kJ6ebja2sND8gbsy/RPKVDNQRZcGKnZHK8zboOdKksKjbQ0nZeYEeam4kKjwy0498zfquXRdYVAbQwPkBmsNpGUpbDuqkJVbOQ0NgHNH17Bn9Yc07PgsD7+4At+Qxqxb8CxZaVfNxidcOEhAWCt6DPuGh1/4Ff9qzdnwwxiSrp40xlw9f4Cwer3oM2oBD43+GUdXf9bOG0l2eqLZZf4TaBzsyTh6mhP/fbeyi1ImtYJVdG+sZtdxPd+u0RF7XWFIR02p+6RGAzl5sOu4noRU8zEd66tpVF3NuoN6Zq/SEXlWz+B2hkaxJbVu6MhTA7xYviGVV6ZfJvpcHm+N9sfTzfx1NG93K9561p/oc3m8Mv0yKzamMmKgFy3qO5SI9XKzYnh/T07E5JpdVuzVfJ5+84Lx9fKHseVaN3NOHljDpl+m0arXaJ5+ayVVwhqz9MtRpKeY/87Fnj1AaM1WDH7xW56auILg8OYs+2o0CbHF37nj+/5g22+f0KbPC4yavIZeT35A9ME1bPvtk3It+6N9fRnYy4dZ82N5/s2TpKQV8tHEGthpSz8FqVndgbfGVmPTrmSeHX+STbuSefu/VYmoVvx5dWjhxugnA/lpZTzPTTjJsdNZTBtfHW8PGwC0Nmqqhzqw6Ld4Rk88yZRPz1HFT8u7r4aZXefAnj4o5XxYPbJ3LasWTaPjQ88y9r3lhIQ3Zv6MZ0lLMv+5Wdva0bLrEJ598wfGfbSKTg89y4Zfv2Dfll+MMeej91O/ZW+emTifMZN+wsXDj7nTR5GeUrHHyrqhano3t2JbVBGzVhZyMUHPsO7WuJT8SgGG40l2nsK2IzoSUsxv6MWbCpn6U77xNXN5ATq9wvELerPx/2QqtbrCXg+CB6OU/1K2trb4+vri6+tLgwYNeOONN7h8+TLXr5u/3Lh9+3aaNWuGra0tfn5+jB8/nqKi4hZ/fn4+Y8eOxdvbG61WS5s2bThw4IDJMtasWUONGjWws7OjY8eOXLx40ey6vL298fX1pVmzZnzyySckJCSwd+9eY1evX375hQ4dOqDValm0aBEA8+fPp2bNmmi1WiIiIpg9e7ZxeQUFBbzwwgv4+fmh1WoJCQlh2rRpxvcnT55MUFAQtra2+Pv7M3bsWON7KpWKlStXmpTP1dWVBQsWANxzmSytWQ0VRy4oHLmgkJwJm6IUMnKhYTXz6c9NUQr7TivEp0JqFmw/ppCSBWH+xfHxqbD1qEL0ZYWiSjz+Htu5kPAmA4hoOgg372q07DsRRxdfTu5dYja+Zd+J1G8/Eq/Aurh4htC0+8s4ewQRG73VGNPpsRnUajkED/+auHpXpe2Ad1EUPXHn9lRUte7a9fU7ODNpJgkrN1Z2UcqkZYSaw+cUDp9TSMqADZF60nOgSQ3zPwXp2bA+Us/RCwr5pbTh64Wq2HVCT8xVhbQsiDyrcC5eoUVNy/689O3oyua9GWzak0FcYiHzViSRnFpE9zYuZuO7t3EhKbWIeSuSiEssZNOeDLbszeChTqYtI7UKXnrShyVrkklMNl9pnR7SMnXGV0aW5b+M+zfNp37rgTRoMwhPv2p0ffRNnN18Obz9Z7PxXR99kxbdR+EfUg93nxA6PDwOd+9gYo5uMcbEnY+iSrVG1G7WF1fPKlSt1YZaTfsQf+l4uZZ9QE9vfloZz64DaVy8ksf0ORfQ2qjp1Nq91HkG9vQh8lgGP/+ewOWrefz8ewKHT2QyoJd3cUxvH9ZtTWLt1iRir+Yx54fLXEsuoG9XLwCyc3W8MfUM2/emciU+n+iYbGYtiCW8qoOxUXJD1SA7Bvb24eNvLpRr3XetXUCT9gNp1uERvAOq0fc/E3Dx8GPvZvPHyoCQWjRo2RufKtVx9wqgYet+1KjXmotnIo0xj42ZQcsuj+MfXBNv/6oMHPEuil5PzMm95Vr2O2lTR0PkGT0Hz+i5nq6wep+O9GyF5jU1ZuPTsmDVXh2HY/TkFZhfZm4BZOUWv8IC1BQWwbEHsMEhTEmD4x8iKyuLxYsXExYWhoeHR4n34+Li6NWrF02bNuXIkSPMmTOHuXPn8v777xtjXn/9dZYvX87ChQs5dOgQYWFhdO/enZSUFAAuX77MgAED6NWrF1FRUYwcOZLx48ffsWx2dnaAadbgjTfeYOzYsURHR9O9e3e+++473nzzTT744AOio6OZOnUqb7/9trHb2BdffMEff/zBL7/8wunTp1m0aBEhISEA/Prrr3z22Wd88803nD17lpUrV1K3bt273oZ3WyZLUqvB1w0u3JJavpCgUMWj7P0tbawo9cBcWXRFBSRdPUFA9dYm0wOqtyYx9nCZlqHo9RTm52Br51pqTFFhHnpdEbZ25k8gxd1Rq8HPnRJdnc7HKwR63nsfYI0Gikx7RVCkM2TsLMVKA9UCbTlyKsdketSpHCJCtWbnqRGiJcpMfLUgWzQ3/RIO6uFORpaOzXszS12/n5c1378XwpxJwYwb5oOPh2V7J+uKCkiIPUForTYm00NrtebKubJ/5wrystE6uBqnBYY1JiH2BFcvHAUg9fplzh3fTljdDuVVdPy8bfBwsyHyWHGGvLBI4Wh0JrVrOJY6X63qDkQeNe3udvBIBrWrG+ax0qioEerAwVtiIo9mUOs2y3Ww16DXK2TlFF+ss7VR8+aLVZk1P5bU9PLrtlNUVEDcxZNUr2t6rKxepxWXzkaVaRlxF09y6exhQiOalhpTmJ+HTleEvUPFHSs1avD3VHE2zrQhEBOnJ9i7/E4tm9RQc/S8nsIHsTeVSl1xrweAjOGoRKtWrcLR0XBgzM7Oxs/Pj1WrVqE2kx6bPXs2gYGBzJo1C5VKRUREBFevXuWNN97gnXfeITc3lzlz5rBgwQJ69uwJwHfffcfGjRuZO3cur732GnPmzKFq1ap89tlnqFQqwsPDOXbsGB999FGpZUxOTmbKlCk4OTnRrFkzcnIMP9gvvfQSAwYMMMa99957fPLJJ8ZpoaGhnDx5km+++YZhw4YRGxtL9erVadOmDSqViuDgYOO8sbGx+Pr60qVLF6ytrQkKCqJZs2Z3vT3vtkyWZG8DarWK7Fu6KGfng4P586ESmoersLGC6MuV13XKnLycNBS9DntHT5Ppdo4e5GaWMkDlFkd3zaeoIIeq9XqUGnNg3Sc4OPsQENbqvsorDOxtb+yTpvtTdp6Cg929Nw7OxSu0iFATe01HSiZU9VURXkVl0XGMTg4aNBoVaZmmLZ30TB2uTuavrro5a4i6JT4tU4eVRoWzo4bUDB0RoVq6tHRm3Eeld5E6czGPLxYlcvVaIa5OGh7p7s7Ul6vw36mxZOVY5ipsTlYqil6Hg7PpxSgHJ0+yM0oZgHOLfRvnUVCQS83GPY3TajXtTU5mCj/OGAKKgl5fRMP2j9OyxzPlVnY3F2uAEifyqelF+HjamJvFMJ+rNanpphmm1PRC3FwNy3NxtkKjUZmNcf97nbeytlYx4vEqbNmdQk5u8Wc1+olATpzJYndkWpnrVRY5mWno9TqcbvncnFw8OJN++2Pl1LEdyc5MQa/T0WXA8zTr8EipsWuXfoqLmzdhtVuWS7nLwl4LGrWqRNfezFyoblc+66jiqcLXXc2Knf+wq27inkiDoxJ17NiROXPmAJCSksLs2bPp2bMn+/fvLxEbHR1Ny5YtUd30K966dWuysrK4cuUKaWlpFBYW0rp18ZUUa2trmjVrRnR0tHEZLVq0MFlGy5bmD1BVqlQBDA2h6tWrs2zZMry9vY1dsJo0KR7wdv36dS5fvsyIESMYNWqUcXpRUREuLoYrLsOHD6dr166Eh4fTo0cP+vTpQ7du3QAYNGgQM2fOpGrVqvTo0YNevXrRt29frKzubve82zKZk5+fT35+vsm0okKrchtAWdZzsFqBKtrUVrF8l56c/DvH/zMoZbpbRkzUag5t+opuT87CzrFkNg/gyPbvOXdkDb1HLSz3wavCjPto064/qKdPczVj+hhO9FOyIOq8QoOqlr9zSon+9qrbV8VMuHE5WlsV/33Sh9k/XyMzu/SGw+Ho4ixJbDycvniV2e8E07G5M39uTbuL0t8L022qoJSYZs6J/avYtWoWA8fMNmm0XDq9j91rv6b7kEn4h9Yj9Vosm5Z+wC6Xr2jT+/l7KmGn1u68PLL4gtKb0w03gbj1s1Ld4bMCM++rSk4s8ZmqVChmBmJoNCreerEqahV8Me+ScXrLxi40qO3EcxNOlpin3NxyXFQUxeR32Jzn3vqRgvwcYmOOsO6XT/HwCaJBy94l4ravmsuRvat5ZuJCrG0q/lhZ2neqPDQJ15CQYhj/+EBSPxh3j6oo0uCoRA4ODoSFFQ9ea9y4MS4uLnz33XeMHDnSJNbcAerGQfXmA6y5mBvTzB2ES7Nz506cnZ3x8vLC2dnZbNlv0OsNP87fffcdzZs3N4nTaAwnIY0aNeLChQusXbuWTZs2MXjwYLp06cKvv/5KYGAgp0+fZuPGjWzatIkxY8YwY8YMtm/fjrW1tdkfEHODwu+2TOZMmzaNKVOmmEzr9MjbdBk0qdR5zMkpAL1eKZHNsLelRNbjVjUDVfRqquK3PXouXrur1VYIrb0rKrWGnCzTK3S5WSmlNiBuOHd0DTtWvEWXIZ+Vmrk4umMeUdu+pdeIeXj4hZdbuf/f5eTf2CdNz9octCUzcXe73F926NGoDft3Zi50bqAmNev+y1yazGwdOp2Cm7Ppd9nFUUP6LVmMG1IzSmY/XJw0FOkUMrN1BPrZ4ONhzcRn/Izv3zicLvusGi98cInEpJL9OvILFGKvFuDnZf6qenmwd3RDpdaQnWH6ncvJTMbB2bOUuQxOHljDmh/e5OFnPye0pul3bscfn1OneT8atBkEgHdAOIX5Oaxd9A6te46+p8GoeyLTOBWTbfzb2tqwEd1drUhJKz5uuzpblchO3Cw1rWSmws25OOuRnlGETqeUiHF1tiI1w/Rz0mhUvP3fqvh62/La+6dNshsNajvj72PL73Mbmswz6eVqHD+VxSvvnS5Ltc2yd3JFrdaQeUs2IysjBUfn2x8r3b0NF/18A2uQmZ7MphVflWhw7Fg9j61/fsvIN+biF1Sxx8qcPNDpFZzsTI8njnaGsRf3y1oD9aqq2XTI/PdZPHikwfEPolKpUKvV5OaW/LbWqlWL5cuXmzQgdu/ejZOTEwEBAbi7u2NjY8OuXbsYMmQIYDgpP3jwIC+99JJxGbcOvt671/wgs9DQUFxdXctUbh8fHwICAjh//jxDhw4tNc7Z2ZlHH32URx99lEceeYQePXqQkpKCu7s7dnZ29OvXj379+vH8888TERHBsWPHaNSoEV5eXsTHxxuXc/bsWWPXrvst060mTJjAuHHjTKZ9/ufdf030ekhIhVAfFWfiig/GoT4qzlwtveFX6+/Gxu979ZyLLzWsUmmsbPD0r03c2d2E1u5qnB4Xs5vgmp1KnS8majU7lr9Jp8c+Jiiig9mYIzvmcnjL1/R8+ju8qtQp76L/X9PrIT4FqvqpOH2leB+89e97pdMbGhtqFdQMUnHykuWuShbp4NzlfOqH27PvaPHJbf0Ie/YfyzY7z5mLeTSpY3r7nPoR9pyLzUenh7jEQl6aZtqV6vHe7tjZqo0D0s2xsoIqvjacPF8OZ1ml0FjZ4BtUmwvRfxHesPg7dyF6NzXqdy51vhP7V7Hmh4k8NPJTs+MyigryUN3S/1ul1mDInSj3dLU6N09Pbp5pWjY5tYBGdV2IuWjYRlYaFfVqOvHdz1dKXc7Js9k0quvM8rXFd15qXM+ZE2cNLdkincKZC9k0rudscqvcxnWdTbpG3WhsBPhqefW902RkmZ7ALvk9nrVbTLulfT+jDnN+uMzeQ2ncDysrGwJCahFzfDd1mnQxTo85vptajUo/VpakUFRk2q1o++q5bPn9G0a8/h1Vqlb8sVKnh6tJCmEBak5eKm7AhfmrORl7/10L61ZVo1HD4ZgHt8Fx63fr/500OCpRfn4+CQmG+7unpqYya9YssrKy6Nu3b4nYMWPGMHPmTF588UVeeOEFTp8+zaRJkxg3bhxqtRoHBwdGjx7Na6+9hru7O0FBQUyfPp2cnBxGjBgBwHPPPccnn3zCuHHjePbZZ4mMjDTe6el+TZ48mbFjx+Ls7EzPnj3Jz8/n4MGDpKamMm7cOD777DP8/Pxo0KABarWaZcuW4evra7zblE6no3nz5tjb2/Pjjz9iZ2dnHOfRqVMnZs2aRYsWLdDr9bzxxhtYW9/5auKdymSOra0ttramaWkr63s74O0/o9C3mYr4VIhLUmhQTYWzPRw+ZzgRa19XhZMdrNpv+LtWoIo+zVVsOqxwNaV4rEeRDuMdgtRqw7M4wDBoz9EOvF2hsAiLXlG+Vd22w9j2y3i8qtTBO6gBp/b/QlZaPDWbP2qo+7pPyc5IpONgw/igmKjVbFs2nlZ9JuAdVJ+cTMMPvJW1FhutE2DoRnVw4xd0euxjnNwCjDHWNvZY25Zyn8VKpnGwxyEsyPi3fWgVnOtHUJCSTt7lf16Lcc8pPQ+3VBOfrOJKkkKjMDUu9hB51nCC0KmB4fkcv+8pPmHw+fsmTjZW4GBr+FunMzxvAyDAA5zsVSSkKjjbQft6alTAXycte1eZP7emMfYJH2Iu53H6Qh7dWrng6WbFhl2GwclD+3rg4aLhi0WGNOH6Xen0bOvC8Ic92bg7nfBQLZ1bOPPZQsMxuLBIITbe9KQu++8r4TdPH/aQBwdOZJOUUoTL32M47LRqtu0rfZB5eWjW5Sn+nP86fsF1CKjakKidS8lIiadhu8cA2PbbJ2SmJdL3qemAobGxav4bdHl0Iv6h9clK//s7Z6NFa2f4zoXV68j+TfPxCapl7FK144/PqV6vE2p16Zngu7Vi7TWGPORLXHwecQl5DOnvR16Bni1/pRhj3hgdQlJqIXOXxP09TyKfTYrg0b6+7I5Mo1VjVxrVceKlycUZh+WrE3nj+VDOnM/m5Jlsenf2wtvThj83GeqqVsOkl6oSFurAW9PPolaDm4vhtCczS0eRTiE1vcjsQPFryQUkXL//sQNteg7nl6/fICC0NsFhDdi3dRlpyfE072w4Vq5b+inpqdd49LkPAdiz0XCbW2//UAAunjnEjjXzadW1+MLZ9lVz2bD8Cx4bMwM3T38y0wz1tdHaY6utuGPlruM6BrW3Iu66mthrCk0j1Lg4qth/yvCb2a2JBmd7Fb/uKN6+fu6GZqyNlSG76ueuQqc3PMPjZk1qaIiO1ZP7wHQpFnciDY5KtG7dOvz8DOl7JycnIiIiWLZsGR06dChxu9qAgADWrFnDa6+9Rv369XF3d2fEiBG89dZbxpgPP/wQvV7PE088QWZmJk2aNGH9+vW4uRnOGIKCgli+fDkvv/wys2fPplmzZkydOpWnn376vusycuRI7O3tmTFjBq+//joODg7UrVvXmF1xdHTko48+4uzZs2g0Gpo2bcqaNWtQq9W4urry4YcfMm7cOHQ6HXXr1uXPP/803q3rk08+4amnnqJdu3b4+/vz+eefExkZeZvSlK1MlhZ9WcHOBlrXUuGoVXE9HX7ZqSfj7+SMoxac7YvT0Q2qqdCoVXRvrKJ74+LlHL2gZ/UBQ4yTFkZ0Kz4RaBGhokUEXLqm8NO2irttYLV6vcjPTuPQ5tnkZF7H3ac6PYZ/jZNbAAA5mdfJTis+4T61fymKvoi//niPv/54zzi9eqP+dBhkuD3yyb0/o9cVsmnxf03W1ajz8zTu8kIF1OruuTSuQ8vNPxr/rvXxRAAu/7CCoyMmVFaxSnXykoK9jZ52ddU4/v3gv5+26Uj/OyngqAUXB9Pr2s/2Kv6Z8PdQUTdUTVqWwhe/G04qrDSGZ3G4OUJBIZy9qvDbbl2pt9EtL38dzsLJQc3g7u64uVgRG5/PB19f5frfmQg3Zw2ebsUXJq6lFPH+N1d5+mFPerZ1ISW9iLnLr7P3iPmMSGk8XK0YN8wXJwcNGVk6zlzMY/ynl43rtZRaTXuRm53KX6tnk5V+DS//Ggx+4VtcPAzfuaz062SkFH/nonYuRa8vYsPP77Lh5+LnxNRt+TB9hhtOblv3Gg2o2P77TLLSErF3dCesXkfa93+5XMu+9M8EbG3UjH06CCcHK6LPZTN+6hly84qPWd6etuhvOuc8eTab9784z1OD/Rk+2J+rifm8/8V5Tp0r/ry27U3F2cmK/wzwx93VmouXc5n40VmuJRkaCl7uNrRqYvj9+/aj2iZleuXd0xyJtmwjEaB+i57kZKWxeeUcMtOu41ulOsNf/QY3T8PnlpGWRFpy8eemV/Ss/+UzUq7HodZo8PAOpOfgcTTrNNgYs2fzz+iKCln8xUsm6+r88Bi6Dqi4Y+WxC3rstUV0amiFkz0kpios3FBI2t8Xv5zsVLg6mh5PXny4+EYBVbygQZiG1EyFGb8UN+48nFWE+KqZt1YGi/+bqJS76dgvxP+hab88uCndO7G2+vcOaqv5aM3KLoLFRC44UdlFsKiofRcruwgW02+A+YfO/Rss+uZgZRfBYp5/ueGdgx5QB449iPecLZupIyrvpiN5S6dX2Lq0j75eYeu6V9LBTAghhBBCCGEx0qVKCCGEEEKI8iSDxk3I1hBCCCGEEEJYjGQ4hBBCCCGEKE9leBDu/xPJcAghhBBCCCEsRjIcQgghhBBClCe1XNO/mWwNIYQQQgghhMVIhkMIIYQQQojyJHepMiFbQwghhBBCCGExkuEQQgghhBCiPKnlLlU3kwyHEEIIIYQQwmIkwyGEEEIIIUR5kjEcJmRrCCGEEEIIISxGMhxCCCGEEEKUJ3nSuAnJcAghhBBCCCEsRjIcQgghhBBClCd50rgJ2RpCCCGEEEIIi5EGhxBCCCGEEMJipEuVEEIIIYQQ5UkGjZuQDIcQQgghhBDCYiTDIYQQQgghRHmSB/+ZkK0hhBBCCCGEsBjJcAghhBBCCFGe5La4JmRrCCGEEEIIISxGMhxCCCGEEEKUJ7lLlQlpcAhxB9HHrlV2ESymei2vyi6CxUQuOFHZRbCYxsNrV3YRLCrry6jKLoLF9PbaV9lFsJhddapVdhEsZvOurMougsUEBTtVdhHE/wFpcAghhBBCCFGe5C5VJmRrCCGEEEIIISxGMhxCCCGEEEKUJxnDYUIyHEIIIYQQQgiLkQyHEEIIIYQQ5Umew2FCtoYQQgghhBDCYiTDIYQQQgghRDlSZAyHCclwCCGEEEIIISxGMhxCCCGEEEKUJ3kOhwnZGkIIIYQQQgiLkQaHEEIIIYQQwmKkS5UQQgghhBDlSbpUmZCtIYQQQgghhLAYyXAIIYQQQghRjuS2uKYkwyGEEEIIIYSwGMlwCCGEEEIIUZ5kDIcJ2RpCCCGEEEIIi5EMhxBCCCGEEOVJxnCYkAyHEEIIIYQQwmIkwyGEEEIIIUR5Uss1/ZvJ1hBCCCGEEEJYjGQ4hBBCCCGEKEfyHA5TkuH4B1OpVKxcubLU90NCQpg5c2a5rnP48OH079//tjF3s94FCxbg6up63+USQgghhBAPJslwVKJr167x9ttvs3btWhITE3Fzc6N+/fpMnjyZli1b3nH+AwcO4ODgUKZ1TZ48mSlTptw25sKFC2Va1t2s9/9N52b29GrjgIujhrhrRSxem86ZS4WlxoeH2DCkhzMB3lakZepYvSubrQdyTGK6t7SnUzMHPFw0ZOboOXAil2UbMyksMrzfp50DTWpq8fOyorBQ4ezlQpZuyCAhSWfJqtI4TEXLmmqc7OB6Oqw/pOPydfOxjlro2lCNn7sKdyfYf0ZhwyG9SYxaBa1rqagXqsbZHpIzYPMRPefiFYvWw5wm1VW0rGWo27U02BCpI/Z2dWtsqJuHE+w7rbAhsmTd2tRWUa+qoW5JGbD5cOXUrazc2zSh6isjcGlUB62/NwcHjiHxj82VXazballbQ/v6VjjZq0hMVfjjr0IuJujNxjrZQ5+W1lTxUuPhouKvYzr+3F3yu6q1gR7NrKkTqsHOFlIyFVbvKeRUrPnlWtKKtZv4+fc1JKemExIYwH+fHkr9WuFmYw8dj2bsO9NKTF/8xYcEV/EvMX3Trr1M/nQ2bZs1Ytr4l8q76HetQ2Mt3VvY4eqo5up1HUs2ZnH2cpHZWBdHFYM7OxDsZ4W3u4bNB/JYujG7gktcurb1bejcxBYXBxXxyXqWb8vlXJz547Ozg4oB7bUEemvwclOz/XABy7flmcTUD7OiezNbPF01aDRwPVXP5sh8DkSX/ltjKSf3/sSxnfPIzbyOq3cYLXpPwDe0idnYi8c3EL1/CSlXT6HTFeDqHUajzi9QpUYbY0xq4lkObfqSpLgTZKVdpXnv8dRpPayiqlO+/uHP4Zg9ezYzZswgPj6e2rVrM3PmTNq2bVtqfH5+Pu+++y6LFi0iISGBKlWq8Oabb/L000+XaX3S4KhEAwcOpLCwkIULF1K1alUSExPZvHkzKSkpZZrfy8urzOt69dVXee6554x/N23alGeeeYZRo0bd9fLuZr3/T5rX0TK0pzMLV6VzNraQjk3sefUJdyZ8eZ3k9JInJ56uGl59wo1tB3P5Znka1YOsGdbHhcxsPQdPGn5gWtbTMqirM3NXpnE2thBfDw2jBrgC8NPaTAAiQmzYtD+HC3GFqNUwqIsTrw9zZ/wXSRQUWuaEtlaQiu6N1Kw5qOdKkkKjMDVD2muYs0ZHRk7JeI0GsvNh1wk9zSPMH4Q71lNTJ0TF6v16kjIUqvmpGNRGzYJNOhJSLVINs2oFq+jeWM2aA3ouX1doVF3NkI4aZq8qvW45ebDr+G3qVl9N3VAVq/YV121wOzXzN1Rs3e6GxsGejKOnubJwBY2Xzars4txR/Woa+rayZuVOQyOjeS0rRvS24ZOl+aRllfweWGlUZOfB5kNFtK1n/qdQo4ZRfWzJylX4cWMB6VkKro4q8i30vbqdzbv28sX8xbwyahh1a1bn9/VbefX9j/nx82n4enmWOt9Psz7Cwc7O+Lers3OJmIRrSXy14OdSGy8VrWlNGx7r6sDidVnEXC6iXSMt/33MhXe+SSUlo+Sx1EqjIjNHYfVfuXRtZmdmiZWnUQ1rBnbQsnRzLuev6mhTz4YxDzvw/sJMUjPN7ZeQmaOwfl8+HRvbml1mTp7Cuv35JKbo0emgTlUr/tPdjqwchehL5htllnD+6Br2rf6QVv3exie4Eaf2L2X9wmcZ+NKfOLqWbNQmXDxIQFgrmnR7GRutE2cjf2Pjj2PoO3oJnv61ACgqzMPJPZCQOt3Zt+bDCqvL/5ulS5fy0ksvMXv2bFq3bs0333xDz549OXnyJEFBQWbnGTx4MImJicydO5ewsDCuXbtGUVHZ97d/dvPrXywtLY1du3bx0Ucf0bFjR4KDg2nWrBkTJkygd+/eZud599138fHxISoqCijZtUmlUvH999/z8MMPY29vT/Xq1fnjjz8AcHR0xNfX1/jSaDQ4OTmVmHbDxx9/jJ+fHx4eHjz//PMUFhZfObl1vWlpaTzzzDP4+Pig1WqpU6cOq1atMluH5ORkmjVrRr9+/cjLy2Pbtm2oVCo2b95MkyZNsLe3p1WrVpw+fdpkvj///JPGjRuj1WqpWrUqU6ZMMdnRJ0+eTFBQELa2tvj7+zN27Fjje7Nnz6Z69epotVp8fHx45JFHbv/h3KMerRzYfiiH7ZG5XL1exOK1GaRk6OnUzHw2qFMze5LT9Sxem8HV60Vsj8xlx6EcerUujg8LtOFsbAF7juaRlKbj+LkC9h7LJdTfxhjz8Q+p7DqcS9y1Ii4nFPHdinQ8Xa0I9be2SD0BWoSrOXxeIeq8QlIGbDikJyMHmlQ3f0hJzzbEHL2okFdgfpl1Q1T8dVJPTLxCWjZExiicT1BoUcpJvKW0jFBz+JzC4XN/1y1ST3oONKlRet3WR+o5ekEhv5QLjPVCVew6oSfmqkJaFkSeVTgXr9Ci5j/3EHx9/Q7OTJpJwsqNlV2UMmlbz4oDp3TsP6XjWprCn7sLSctSaFFLYzY+NdOQATl0RkdegfkGRNMIDfa2sHB9AZcS9KRlKVxM0BOfXPENjiV/rqNP5/b07dqBkCoB/HfEf/D2cGfl+i23nc/NxRkPN1fjS6Mx3ed0Oj1TZs5hxGMD8Pf5Z1xM6trcjl1ReeyMyic+WcfSjdmkZujo0EhrNj45Xc+SjdnsOZZPbv4/K2vYqbENe44XsOd4IYkpepZvyyM1U0/b+jZm41MyFJZvy2N/dCF5pdTl7BUdR2OKSEzRk5SuZ9vhAq5e11M1wPy+binHdy2kRuMBhDcdhKt3NVr0mYiDiy/R+5aYjW/RZyL12o3Eq0pdXDxDaNL9ZZw9grgcvdUY41WlLs16vka1+r3RaMxvoweFolJX2Otuffrpp4wYMYKRI0dSs2ZNZs6cSWBgIHPmzDEbv27dOrZv386aNWvo0qULISEhNGvWjFatWpV5nf/cX7t/OUdHRxwdHVm5ciX5+fm3jVUUhf/+97/MnTuXXbt20aBBg1Jjp0yZwuDBgzl69Ci9evVi6NChZc6Y3LB161bOnTvH1q1bWbhwIQsWLGDBggVmY/V6PT179mT37t0sWrSIkydP8uGHH5o0Xm64cuUKbdu2JSIighUrVqDVFv94vPnmm3zyySccPHgQKysrkxTd+vXr+c9//sPYsWM5efIk33zzDQsWLOCDDz4A4Ndff+Wzzz7jm2++4ezZs6xcuZK6desCcPDgQcaOHcu7777L6dOnWbduHe3atbur7VEWGg2E+FtzPMb0szwWk0/1QPMn/mGB1hwzEx8SYM2Nc4IzsQWE+FtTNcCwDC83DfVraDlyJu/WxRnZaQ0D1bJyLdPlQ60GP3c4n2D6Y3guQaGK570PktNooOiWXgaFOgi8j2XerRt1u7Wr0/l45b7KYa5uRToI8pJBheVBo4YALxVnLptu5LNX9IT43vvPXK0QDZcS9Tzcxpq3n9QybrAtHRtaVfjzvAoLizhz7iJN69cxmd60QV2Onzp723mffuVtHnr6Rf476UMOHTtZ4v0Fy1bi6uxMny7ty7XM90qjhmA/K05cMG29nzhfSLUqlruIYgkaNQT6aEpkHaIvFRHqX34dTGoEavB2V3PuimW70d5MV1RA0tUTBFRvbTI9IKw11y4dLtMyFL2ewvwcbO1dLVBCUZqCggIiIyPp1q2byfRu3bqxe/dus/P88ccfNGnShOnTpxMQEECNGjV49dVXyc3NLfN6pUtVJbGysmLBggWMGjWKr7/+mkaNGtG+fXsee+wx6tWrZ4wrKiriySef5ODBg/z1119UqVLltssdPnw4jz/+OABTp07lyy+/ZP/+/fTo0aPMZXNzc2PWrFloNBoiIiLo3bs3mzdvNul+dcOmTZvYv38/0dHR1KhRA4CqVauWiDtz5gxdu3bloYce4vPPP0d1yy/2Bx98QPv2hh+88ePH07t3b/Ly8tBqtXzwwQeMHz+eYcOGGZf/3nvv8frrrzNp0iRiY2Px9fWlS5cuWFtbExQURLNmzQCIjY3FwcGBPn364OTkRHBwMA0bNizztigrJ3s1Go2K9CzTk/yMLB0uTubT4q6OGo5lmTY40rP0WGlUONqrSc/Ss+9YHs72at4a6QEqQ9eBzfuyWbWz9P7JQ3o6c/piAXHXLJNat7cFtVpFdp7pSXl2noKj9t7PxM7HG7IZsdd0pGRBqK+K8ABVhZ7c3a5uDnb3XpBzN9ctE6r6qgivUrF1+zdz0IJGrSLrlt++zBwFp8B7b3C4O6mo5q/m8Fkd89bk4+mipn9bwwWBTZEV13UlPTMTnV6Pu6uLaflcnElOSzc7j6ebK6+PforwqqEUFhWybttf/HfyR3z57gQa1I4A4Gj0GVZt2s78T9+3eB3KytFejUatIuPWY2m2HhfHB+sL42inQqNWkZltejzJzFFwtr+/umht4INnnLHSgF6BpZtzORVbcftkXk4ail6HnaNpdz47Jw9yzyaVaRnHds2nqCCH0LplPz95oFTgAT4/P7/ExWtbW1tsbUuefyQlJaHT6fDx8TGZ7uPjQ0JCgtnlnz9/nl27dqHVavntt99ISkpizJgxpKSkMG/evDKVURoclWjgwIH07t2bnTt3smfPHtatW8f06dP5/vvvGT58OAAvv/wytra27N27F0/P0vvp3nBzY8XBwQEnJyeuXbt2V+WqXbu2SYbCz8+PY8eOmY2NioqiSpUqxsaGObm5ubRp04bHH3+czz///I7l9vPzAwyD6oOCgoiMjOTAgQPGjAaATqcjLy+PnJwcBg0axMyZM6latSo9evSgV69e9O3bFysrK7p27UpwcLDxvR49ehi7nJlj7kurK8pHY2W+0XBHKhXKbTL8t7516/EpIsSGvu0dWbgqnXNXCvFx1/CfXi6kZen5fVtWieU92ceZQB8r3v8++d7KexdurZeKkvW5G+sP6enTTM3o3oZ9LzULos4rNKj6DznJuI/KrT+op09zNWP6GOqW8k+r27+Eue/T7b5/d6JSQVauwvIdhSgKxCXpcHZQ0b6+VYU2OG4uz80UKHHx5oagAD+CAvyMf9cJr8615BR+/n0tDWpHkJOby3uff83rY57G1dnJgqW+N+X9Wf6T3O+xEiC/AKYtysLWGsKDrBjQ3o7kdD1nKzDLARgqczNFMTOxpHNHVnN481d0eWIWdo4eFina/5Np06aVuDHQpEmTmDx5cqnz3HrsUBSl1OOJXq9HpVKxePFiXFwMFz4+/fRTHnnkEb766ivs7O48dkq6VFUyrVZL165deeedd9i9ezfDhw9n0qRJxve7du1KXFwc69evL9PyrK1NU84qlQq9/u661tzNMsqyk9na2tKlSxdWr17NlStX7rjOGzv8jXXq9XqmTJlCVFSU8XXs2DHOnj2LVqslMDCQ06dPG3f6MWPG0K5dOwoLC3FycuLQoUP8/PPP+Pn58c4771C/fn3S0tLMlmPatGm4uLiYvI7/9eUd65iZo0enU3BxNP1KOTuoycgy/wOQlqXDxVFTIr5Ip5CVY6j7wM5O7D6Sy/bIXK4kFhEZnc+yTZn0aetY4uTjid7ONIzQMm1eCqlmBlaWl5x80OsVHG+54m+vNQzCvZ/l/rJTz4fLdHzxh47Zq3UUFkFaBd5s5kbdHG7J1DiUR9126Jm2VMfnK3XM/lNHQaGhUSXuX3Ye6PQKTrccjhztVGTl3vupXWaOQlK6YnKiey1Vj7ODCk0F/nq6ODmhUatJTjXNZqSmZ+DuUnIQeGlq1wjjSrzhCmZcwjXiryUxfupntH9kOO0fGc66bX+x68Bh2j8ynLiExHKtQ1ll5ejR6UseS53s1WRkP1gtjqxcxbBfOpgeTxztDYPc74cCJKXpibuuZ0tkAVFnC+nW7B4vjN0Drb0rKrWG3EzTbEZuVsodGxDnj65h54q36Pj4pwSElX0MwIOmIsdwTJgwgfT0dJPXhAkTzJbL09MTjUZTIptx7dq1ElmPG/z8/AgICDA2NgBq1qyJoiilntfdShoc/zC1atUiO7v4DKtfv3789NNPjBw5kiVLzA/Eqkz16tXjypUrnDlzptQYtVrNjz/+SOPGjenUqRNXr169q3U0atSI06dPExYWVuKlVht2YTs7O/r168cXX3zBtm3b2LNnjzErY2VlRZcuXZg+fTpHjx7l4sWLbNlifqCluS9tndYv3rGMOh1cvFpInWqmB/w61Ww4e9n8SOKYy4XUqWY6KK5OmC0X4wrR/d1esLFWob/ld0mvV8w2NhrX0vLhvGSS0ix7hUuvh/gUQ7egm1X1VXEl6f5PCHR6yMw13Eo2IlDF6SsVd5JhrJvfLXXzU3G5nOtWM0jFmQqs27+ZTg9x1xWqB5o24KsHqEu9LW5ZXEzQ4+GiMrle6+mqIiNbMX5HK4K1tRU1qoVw4Mhxk+kHjxynTkT1Mi/n7PlLeLi5AoYMyA+fTWX+J+8bX22aNqRRnZrM/+R9vD0q56qzTg+X4ouoFWp64atWqDXnrlT8bV/vh04PlxN1RASZdiaJCLbiwtVyzpD93eW2omisbPD0r01cjGmf/6sxu/EOLr3b8rkjq9nx60Q6PDqDoIgOFi7l/w9bW1ucnZ1NXua6UwHY2NjQuHFjNm40vSHIxo0bSx0E3rp1a65evUpWVvFVsjNnzqBWq+/Y1f8G6VJVSZKTkxk0aBBPP/009erVw8nJiYMHDzJ9+nQeeughk9iHH36YH3/8kSeeeAIrKyuL3WXpXrRv35527doxcOBAPv30U8LCwjh16hQqlcpk3IhGo2Hx4sU8/vjjdOrUiW3btuHr61umdbzzzjv06dOHwMBABg0ahFqt5ujRoxw7doz333+fBQsWoNPpaN68Ofb29vz444/Y2dkRHBzMqlWrOH/+PO3atcPNzY01a9ag1+sJDzd/+0dzfR41VmW7xL5udzbPDnTlwtVCYi4X0qGJHR4uGrbsN9xLdVBXJ9yc1Xy73HCVcsv+HLo2t2dIDye2ReYSFmhN+0b2zF6WZlxm1Ok8erRy4FJ8IecuF+LjoWFgZycOn8ozXnUd1seZFvXsmPlTKnkFxVcGc/L0xmd1lLe9p/X0b6HmaoqKuCSFhtXUuNhD5FnDWVin+oZnWPy+t/iszMfV8K+NlWGshI+r4Qc5KcMw3d8DnO1UJKQqONlD+zpqVCrYHV2xzzvYc0rPwy3VxCerjLf8Nalbg7/rtuemurkV183B1vC3TldctwAPcLI31M3ZDtrXU6MC/jpZ8c9yKCuNgz0OYcW3R7QPrYJz/Qj+x959R0dRvQ0c/25JL5teCRBIqKH3JkhvIkXADgo27FgQUUFEsWHvhaIiYEEEpaP03jskQEghvffNlvePlQ2TbGjuJvh7n885e0529s7MvTubmbnz3KLPzqM0MaUWc2bblsMGxvZ2IindREKaZVhcHy8VO49bKuADO2rReahY8nfFTWuov+UGzUWrwtPNTKi/CqMJ0nMs/1w7jhnpFqNlWDcnth01EKBT0buN5e+advstA3ntoy9pEhVJTOMolq/dSFpmFsP79wbgix9+IiMrh5effAiAn1asJiQokMiIcMoNBtZu3s7GnXt4/XnLAxQXZ2ca1FPeKHh6WJqaVl5e09btKmHCrV7Epxg4m2Tgpjau+Ok0bNxvCTOO7OWOj5eauSsqbn4igi2VTRdny/9aRLAGgxFSHDwf0ZX8tU/PvYPcSEgzci7FSLcWzvh5qdlyyDJc37DuLug81Xy/uqIDUnig5Rzu4mSJ0oUHqjEaITXbcr7o38GFhDQjGXlGtGoVzSO1dGrqxOINV9+B1x5iuo9j088vEBgeQ1Dd1pzc8xOFeSk06TgWgD1r3qM4P42eo98CLJWNTT+/QOehUwmKaEVxgWVyI62TK86ulmZ9RoOe3PQzAJiM5RTnp5N14QROLu54+9er0fL9L5s8eTL33HMP7du3p0uXLnz11VckJCRYp0+YOnUqycnJfPfddwDceeedvPbaa9x33328+uqrZGZm8txzz3H//fdfVUsXkApHrfH09KRTp068//77nDlzhvLyciIiInjggQd48cUXq6S/7bbbMJlM3HPPPajVakaOHFkLubbt119/5dlnn+WOO+6gqKiIqKgo3nyz6vjZWq2WRYsWMXbsWGul42oMGDCAP/74g5kzZ/L222/j5OREkyZNmDhxIgA+Pj68+eabTJ48GaPRSIsWLVixYgX+/v74+PiwdOlSZsyYQWlpKdHR0SxatIjmzZvb8ysAYNfRUjzd87m1lyc+XhqS0gzM+T6HrDzLBc/HU42/ruIJbGaukXe/z+GuQd706eRBboGR71fmW+fgAPh9UyFm4LY+Xvh6aygoMnHgVCm/rC+wpunTyTKM7rQJyieSXy3NZesBx1yAjieYcXM2cVNzNZ7/TPy3aJORvH/mqfB0pUqnyAcHVZxuwvxVtKivJrfQzMcrLN+PVg29Wqrx9QS9AeIumFm201jtULOOcvy8GXdnEze1sJQtPRd+3Ggk7596p6cr6Co1kXhocKWyRVrK9tHv/5RNY5mLw9cT9OUQe8HMb9trvmzXQtcuhi4bvre+b/au5byU+N1SDk+wHaqvTYfOGHF3hb7ttXi7q0jNNjN3pd46B4e3hwofL+Vxe3p0xUh5dYLUtInWkl1g4s2Fln5ceUVmvv5Tzy1dnXh6tAv5RWa2HjGw8WDNVzj6dO9MXkEh83/6naycXCLr1uGdac8QEmTp25eVk0taZkXfrXKDkU8XLCIjOwcXZ2ciI8J5Z9ozdGnXqsbzfq32nNDj4V7ELd3d0f0z8d+Hi/Osc3DoKp1LAaZP9LX+XT/Uic4xrmTmGnnh09qd6Gb/6XI83FQM6uyK9z8T/332W5F1Dg5vDzV+XsrGJlPvqehTUzfEMi9JVp6J6d9azvvOTjCmjys+XmrKDWbSsk0sWFXC/tM1e0Jp0HIwpcW5HPjrM4oLMvANjqb/uC/w8g0HoKQgg8LciocTJ3cvwWwysGP5a+xY/pp1eXTb4dx0m2WSyuKCDJZ9UnF/c2TLXI5smUtIZAeGPPBdDZXMTm7gUUHGjh1LVlYWM2fOJCUlhZiYGFauXEm9epZKXUpKCgkJCdb0np6erFu3jscff5z27dvj7+/PmDFjmDXr6gecUJnN/yvdsIRwjHtfvvGe5tpLdLMbY9x9RzDfuMGDf63dePtXmG8kmz4+WNtZcJjnehyu7Sw4zNRlDWs7Cw7j6vbfGpL3WtStd+MNGmAvz4+qvZ4DBXtW1ti+vDoMrrF9XS+JcAghhBBCCGFP1zEh3/8y+TaEEEIIIYQQDiMRDiGEEEIIIezIfAP34agNEuEQQgghhBBCOIxEOIQQQgghhLAn6cOhIN+GEEIIIYQQwmEkwiGEEEIIIYQdmZE+HJeSCIcQQgghhBDCYSTCIYQQQgghhB2ZpQ+HgnwbQgghhBBCCIeRCIcQQgghhBD2JBEOBfk2hBBCCCGEEA4jEQ4hhBBCCCHsSGYaV5IIhxBCCCGEEMJhJMIhhBBCCCGEHckoVUrybQghhBBCCCEcRiocQgghhBBCCIeRJlVCCCGEEELYk3QaV5AIhxBCCCGEEMJhJMIhhBBCCCGEHUmncSX5NoQQQgghhBAOIxEOIYQQQggh7MiM9OG4lEQ4hBBCCCGEEA4jEQ4hhBBCCCHsSPpwKEmFQ4gryE7Nqe0sOMzge1xqOwsO8/on2bWdBYcp/PhgbWfBoXo+3rq2s+AwH39zpLaz4DCubiW1nQWHKSnS13YWHMZgMNd2FsT/A1LhEEIIIYQQwp5kHg4FifcIIYQQQgghHEYiHEIIIYQQQtiRWZ7pK8i3IYQQQgghhHAYiXAIIYQQQghhR2bpw6EgEQ4hhBBCCCGEw0iEQwghhBBCCDuSeTiU5NsQQgghhBBCOIxEOIQQQgghhLAjM9KH41IS4RBCCCGEEEI4jEQ4hBBCCCGEsCPpw6Ek34YQQgghhBDCYaTCIYQQQgghhHAYaVIlhBBCCCGEHcnEf0oS4RBCCCGEEEI4jEQ4hBBCCCGEsCMZFldJIhxCCCGEEEIIh5EIhxBCCCGEEHYkw+IqybchhBBCCCGEcBiJcAghhBBCCGFH0odDSSIcQgghhBBCCIeRCIcQQgghhBB2JH04lOTbEEIIIYQQQjiMVDjEf1Z8fDwqlYqDBw8CsHHjRlQqFbm5ubWaLyGEEEL8/2ZGVWOv/wJpUiXsavz48eTm5rJs2bIa33fXrl1JSUlBp9PV+L4vGtzLl5ED/PHTaUm4UMbXS9I4FltcbfqYRu5MHBNM3TAXsnMN/Lomi1WbcqyfD+jhQ+8uPtQLcwEg7nwJ3/2Wzun4UmuaO28J5M5hgYrt5uQZuOfZ03YundK6P3/lj6ULyc3JIrxuJPc+8BRNmre+4nqnjh/itamPElGvAbM/+s66/K81v7Plr1Uknj8LQGRUY8be+zBRjZo7qghWA7t7c2sfX3y9NSSm6pn7ayYnzpZWm75ZlCv3jQggIsSZ7DwjyzbksHZbvs203dp68sz4EHYdLuStb1Kty8cO8mPsID9F2px8AxNeirdLmarTpbmGnq20eLmrSMsxs3xbOfGpJptpvdxhaBcn6gSq8dep2HbEyIrt5VXSuTrDwI5OxERqcHOB7AIzf+4o52SC7e3WNr/u7WnwzAR0bWNwDQti76hJpC3fUNvZuqIOjdV0b67B0x0ycs2s2m3kfLrZZlpPNxjYXkOYvxo/b9h1wsSqPcYq6bo0VdOhsQadBxSXwbHzJtbvM2Ko4UPXo5Uzfdq7oPNQkZJl4teNJZxJrppfAG8PFSN7uhIRpCHQV82mA3p+3aj8f20VpWVARxcCfDRoNJCRY2LDvjL2nKj6+7Wnm9u5MqCLOz5eapIzDCxeU0RsYvX7bFTXibH9PQgP1JJbYGLV9mI27a8oi0YNg7u507WlK77ealKzjPyyoZCjZyq2+dbjfgT4aKps+689JSxcXWjfAl5B2ygVnRur8HSDjDxYf8BEYqbttI3DoW2UmmAfLMcoD7YcM3Eu1XZ68d8mFQ7xP8PZ2ZmQkJBa23+P9t48MDaEzxemcDyumEE9fZnxRF0mTY8jI9tQJX1wgBMznqjLmi05vPtNMs2i3HnkrlDyCgxs318AQIvGHmzanceJM8WUl5sZNcCfmU/X49HpZ8jKrdjm+eRSpr133vre5OCbhR1b1vPdNx9w/8PP0ahZSzas/o23ZkzmnU9/JCCo+mNQXFTI5++/RvNW7cnPzVZ8dvzIfrre1I/opi1wcnLmj6U/8OYrT/H2pwvx8w9yWFm6tfHkvpGBfP1zBifOljCgm46XHgnjyTcSyMypetyC/LS89FAY63fk88F3aTRt4MYDowPJLzSy81CRIm2gr5bxwwM4Fldic98JF8qY8ekF63uT2fbNo720aqjhlq5OLNtiqWR0aqZlwhBn5iwpI7ew6r61GhVFpbBhv4EeLW1fLjRqeGCoC4UlZr5fpyev0IyPp4qycseW5d/QeLiTf/gUSQuW0u7nT2o7O1clpr6aQR00/LHLSEK6iQ6NNNzdV8snv5eTV1Q1vVYNRaWw6bCRrs1sN2ZoGammbzsNy7YZSUw34a9TMaKb5TivtlE5cZS2jZwY1cuVJRtKOHvBSPeWzkwa4cGsBQXkFNj6XUJBsZk1u8q4uZ2LzW0Wl5pZvbuMtGwTRiPENNBy9wA3CovNnDhf9f/aHjo0c+H2AZ78sLKQuKRyerZ15ak7dbz8eTbZ+VVPygE+ap66Q8fmAyV8s6yAqDpO3D3Yk8JiE/tO6gEYcbMHnWNcWPBnISmZBmIaOvPoaB2z5+eSkGopx2vf5qC+5CF3eJCWZ+/2Ye+JMoeUszpNI1T0a61i9X4zSRlm2kSpGHuTmq9Wm8i38dwtIkjFuTQzG4+YKdVDy0gVY7qrmb/eRFpujWbdIaQPh5J8G8JhevXqxRNPPMHzzz+Pn58fISEhzJgxQ5FmxowZ1K1bFxcXF8LCwnjiiSesn6lUqiqREh8fH+bPn29zf5WbVM2fPx8fHx/WrFlD06ZN8fT0ZODAgaSkpNixlBWG9/Nn3dYc1m7NJSlVz9dL0sjMKWdwTz+b6Qf19CUju5yvl6SRlKpn7dZc1m/LYWR/f2uad79JZuXGHM4llpGUqufj71JQq6BVUw/FtowmyM03Wl/5hY69WVi5bBG9+t3CzQOGER5Rn3sfeBr/gCDWr1p62fW++fQtuvbsR3STmCqfPfbsq/QbMor6DRoRHlGfBx6bitlk4uihvY4qBgC33OzDhp35rN+RT3JaOXOXZpKVY2BAd9uRsgHddWTmGJi7NJPktHLW78jnr5353NrbV5FOrYKn7g1m8cos0rJsP+E0miC3wGh95Rc6tqbYo6WWPSeN7D5pJD3XzIrt5eQWmuncrOrTUYCcAksEZP9pI6V62xWIDk00uLvAgjV6zqeayC00E59qIiXrxq1wZKzZzOnpH5C6bF1tZ+WqdW2mZn+cif2xJjLzYNUeI/lF0KGx7WOXW2RJc+isidJqHrBHBKlITDdz5JyJ3CI4c8Hyd7h/zTbR6N3OmR1H9ew4Wk5atolfN5aSU2CiRytnm+mz8838urGU3SfKKS2z/TuLTTJyOM5AWraJzDwTGw/ouZBhokG47e/LHvp3dmPLgVK2HCwlJdPI4rVFZOcb6dXezWb6Xu3cyMq3pEvJNLLlYClbD5YyoIu7NU2XFi78ua2YI3F6MnNNbNxXyrGzevp3rthmYbGZ/KKKV6toZ9KyjZw679hoTmUdG6s4dM7MobNmsgpg/QEz+SXQtqHt39P6A2Z2njSTkg05hbDpiJnsQogO+280ERLXRiocwqEWLFiAh4cHu3bt4u2332bmzJmsW2e5yP/yyy+8//77fPnll8TGxrJs2TJatGhh1/0XFxfz7rvv8v3337N582YSEhJ49tln7boPsDxxi6rnyoHjykeNB44V0qSh7YtNkwZuHDimDHfvP1ZEVD03NNVcE12c1Wg0KgqKlBWKsCBnFrwTzTezo3j+gXCCA5yuvzBXYCgv51zcKVq26ahY3qJNJ06fOFLtehvX/0F6SjKj7phwVfspKyvFYDTg6en9r/J7OVoNNIxw4dBJ5eO3gyeLaRLpanOdRvVdOWgjfcO6LmguOaOOHuhHfqGRDTsLqt1/aKAT37xWn8+n12PyuGCC/R0XdNaoITxQxelE5W8nNslE/ZDrvxQ0q6/hfJqJEd2dePleVyaPceHmNlpUcs9gNxo1hPqrOHNBWSGNu2CibuD1f9Hn08yE+qsID7Bsw9cTGoWrOZ1Uc+2pNGqICNZUiTqcOG8gMsx+/w+NIjQE+ak5k+SYhzEaNdQL1XLsrF6x/PgZPVF1bJejYbiW42eU6Y+e0VMvVGs9l2g1KsorBWT05WaiI2yf4zVq6NzCla0Hq28S6ghqNYT6wtlUZQXwXKqZOgFX/xt11kKJ/srp/gukD4eSNKkSDtWyZUumT58OQHR0NJ988gkbNmygX79+JCQkEBISQt++fXFycqJu3bp07NjxClu8NuXl5XzxxRc0bNgQgMcee4yZM2dWm76srIyyMmUY2mjUo9HYftJ2kbenFo1GRU6+8sqQU2Ckrc72v5mvTktOgfLil5NvQKtV4e2pJSevath/3KggsnINHLykYnPqXAnvzU0mOU2Pj7eW24cE8O4LkUyafqZKxcQeCvJzMZmM6HyUkRudjy95lZpJXZRyIZHFCz5j+ptfoNFc3Wln8YLP8PMPJKZ1h3+d5+p4eWjQaFTkVjoOeQVGfLxs1/p8vTUcrJQ+t8CIVqPC21NDTr6RJpGu9O3izeS3Eqrd9+n4Uj76IY0L6eX4eGm4bYAfbzxdhyffSKCw2P43fB6uoFGrKKzUuqug2IxXxPVXOPy8VDQMU3Mg1sjclWUE6NQM7+GERg3r9zmm6cr/N+4uto9dUakZT7frP3ZH4014uMKEgZYKokatYvdJI1uO1lyFw9NNhUatoqBIeaNaUGzG2/3f3Ui5OsPrD3qj1YDJDEs2lHAywTG/SS93NRq1ivwi5XeXV2QmxtP2MfL2VJNXqdz5RSa0GhWe7mryCk0c/SeacTqhnIxsI00jnWjd2EXRhOpSbZq44O6qYvuhmq1wuDuDWm1pgnmpolLLuedqdGqswkkLJxJv3OiouH4S4RAO1bJlS8X70NBQ0tPTARg9ejQlJSU0aNCABx54gN9++w2Dwb4XA3d3d2tlo/L+bZk9ezY6nU7xOnPw66vfYaXzpAq4bLP8Sh+qrIurrjRqgD89O+p447NEyg0Vn+87Wsj2/QWcTy7j0IkiZnxkucnt09XBnecrP8Kuppwmo5FP35nObXdOJDS87lVtesWvP7B98zqenvomzs6222jbU5WvW1VtcSzpqya3bsfVRcWT9wbz2aJ0Coqqv3E7cKKYnYeKSEjRc/h0Ca9/aenLcXMnx0V0wEbeVVf4jV6BSgWFJWZ+3VxOcqaZQ2eM/LXfQOdm8jyrJvybW7P6wSpuamnpF/L5CgOL/i6nUR01PVvW/q2Bin9XNoAyPcz+oZC3fyxkxbZSRvZ0I7qO45pUAVWvAVf8/7r8NWDRmkLSs428/ogvX04L4K5Bnmw7WIqpmm32aO3KkTg9uQ5unmlvzeqq6BGjYtkOE8U12/XEYcwqVY29/gvkiiAcyslJGfZVqVSY/unRHBERwalTp1i3bh3r169n0qRJvPPOO2zatAknJydUKlWVG+/y8mtrk2pr/7Zu5i+aOnUqkydPViwb+9TZK+4nv9CA0WjGt1I0w8dLQ26+7UpUTp4BX+9K6b21GAzmKpGJEf39GT04gJfeO0988uXPxmV6M/HJpYQFXT4qc728vH1QqzXk5WQplufl5VSJegCUlBRzNu4E8WdPM/+L9wAwm02YzWbuvrU7U2d+QPNW7a3p/1i6kN9/XsCLr31E3cgoh5ThooIio+W4eStvQnSeGvIKbEeHcvKrRj90XhoMRstxiwh1JtjfiRcfDLV+fvF68PP7DXns9fOkZVb9TZTpzSRc0BMa6JjmcEWlYDSZ8arUws/TTUVhyfXf2hUUmzGalDdV6TkmvD1UaNSWfiri3ykusxw7z0rHzsNVRdG/OHZ92mg4dMbSLwQgPRectEaGddGw+bDpX9/wX43CErPld+mhvGnydFdRUPzvcmAGMnMtZUvO0BPip6F/Rxdik6ofOfB6FRSbMJrMeFeKZni7V416XJRfaELnoUzv5aHGYDRbj2thsZlPfspHqwFPdzW5BSZu6+NBZm7V85O/Tk2zSCc+/dn2iHmOVKwHk8lcJZrh4UqVqEdlTSNUDOmgYul2E/FpjsujqF1S4RC1ys3NjWHDhjFs2DAeffRRmjRpwpEjR2jbti2BgYGKDt6xsbEUF9v/QnEpFxcXXFyUT9Sv1JwKwGCEuPOltG7qwY4DFW32WzfzZNdB2234T54toWNLL6DiDNummQdx50swXnItGdnfn7FDAnjlwwTizl85TK7VqogIdbnscLz/htbJicioxhw5sIcOXXpZlx89uJt2nXpUSe/m7sFbn/ygWLbuz6UcO7yXp6a+QWBwmHX5iqU/sGzJfF549QMaRDd1SP4vZTDCmcQyWjV2Z9fhimZqrZq4s/uIjaF/sDSFah+j7LTfqok7ZxLKMJogOa2cp2Yrm1LdMcQPNxe1tUO6LVot1Alx5vhZ2yNa/VtGEyRnmImO0HAsvuIGKDpczbH46296F59qonW0RvFEOsBHRX6RWSobdmI0QUqWmYahak4kVByrhmFqTiZe/5fspK369N1s/ucpuz1CDFfBaILENCNN6mo5HFfxv9GknpYjZ+zc6Vll6RPhCEYTnE8x0LyBMwdOVXRCaNbAmQOnbXdKOJNsoFW0M1BxrmnewJnzKYYq/zsGI+QWmNCooW0TF/Yer/rgqVsrV/KLTByOrflOECYTpORAZIiK08kVP5zIYOX7yprVtVQ2ft9p4oxjxnMRNwipcIhaM3/+fIxGI506dcLd3Z3vv/8eNzc36tWrB0Dv3r355JNP6Ny5MyaTiSlTplSJWNxIlq3LYvKEcOLOl3LiTDEDb/Il0M+Jlf/MqzFuRBD+vlrem2tpOrNqUw5Db/Zj4phgVm/OoWlDd/p19+Wdr5Os2xw1wJ+7bw3knW+SScvU4/PPk/jSMpN1dJb7bwtm9+ECMrLL0XlZ+nC4u6rZsD3PYWUdPPwOPnvvVRpENyG6SQv+Wr2MzIw0+gwaAVj6X2RnZTBp8nTUajUR9Roq1vf28cXZ2UWxfMWvP/DzD1/x2LOvEhgcSu4/ERRXVzdc3dxxlBV/5/LEPcHEJZZy6lwp/bvqCPDVsnar5fu76xZ//HUaPvrB0hRvzdY8BvXQMX5EAOu259E40pU+nb15f4Fl8Phyg5mEFOUFv6jEcvdw6fJxt/qz51gRmdkGdP/04XBzVbNxV/WdzP+tLYcNjO3tRFK6iYQ0y7C4Pl4qdh633MQO7KhF56Fiyd8VN3qh/4xY5KJV4elm6WRsNEF6juX3t+OYkW4xWoZ1c2LbUQMBOhW921j+vlFpPNzxiKpo3uceWQfvVk3QZ+dRmnhj3vVsP25iZHcNyVlmEjNMtG9kmTtjzynLsevbVoO3OyzdWlEhCfG1HDtnLbi7Wt4bTWYy/jk1nEo006WZmpRsM0mZJvy9VPRureFkoulfNbO7Vn/t03PvIDcS0oycSzHSrYUzfl5qthyy/L8M6+6CzlPN96srKuPhgZbIgIuTJUoXHqjGaITUbMv/Wv8OLiSkGcnIM6JVq2geqaVTUycWb3BMhR5g7c4SJg73Iv6CgTPJ5dzUxhU/nYZN+yz7HNnbA18vNd/+bvkf37ivhN7t3Rjbz4PNB0ppGO5EjzaufLW0IkIRGabF11tNQqoBXy8Nt/Z0R62CVduVD5RUQPdWrmw/XFZtcytH233KzLBOKlKyITnTTJuGKrzdYf8ZS4Z6tVDh5Q4rdlneN6ur4pZOKtYdMJOcVdHXw2CEspodYMshzOb/RlOnmiIVDlFrfHx8ePPNN5k8eTJGo5EWLVqwYsUK/P0tw8LOmTOH++67j5tuuomwsDA+/PBD9u3bV8u5rt6Wvfl4eWq4fWgAfjot5y+UMeOjBDKyLWdOXx8tgX4VFaa0zHJmfJTAxDHBDOnlS1aega8Wp1rn4ADLRIJOTmpefCRCsa8fl2fw44oMAAJ8tTz3QDjenlryCwycPFvCM7PPWffrCF169KUwP4+li+eSm51FnXoNeH76HAKDLM2IcrOzyMq4ttj4upW/YjCU88GbLyqWj7xjArfdOdFuea9s24FCvDzUjBngh69OS0JKGa9/cYGMfyIRvt4aAnwrjlt6toFZX17g/hEBDOqhIzvPwLe/ZlSZg+NK/H20TB4XgpeHhvxCI6fjS3nhvUTrfh3h0Bkj7q7Qt70Wb3cVqdlm5q7UW+fg8PZQ4eOlvEg+PbqijUSdIDVtorVkF5h4c6HlCWtekZmv/9RzS1cnnh7tQn6Rma1HDGw8eONWOHTtYuiy4Xvr+2bvWn5zid8t5fCEqbWVrcs6Gm/CzQV6tdLg5aYhPdfMDxsM1jk4vNxAV6lZ0qRhFb/b8ABo1UBDTqGZ93+1nBs2HTZixkyfNhq83TUUlcKpJBMb9tfcHBwA+0+X4+GmYlBnV7z/mfjvs9+KrHNweHuo8fNSNj2aeo+X9e+6IdChqTNZeSamf2s5fzo7wZg+rvh4qSk3mEnLNrFgVQn7TzvuvLjneBmebipuuckdnadl4r8PF+WRlWepBPl4qvHzrihHZq6JDxblcXt/D25u70ZugYkfVxda5+AAcNKqGNHLg0BfDaV6M0fi9HyzrICSSsMBN2vghL+PpsZHp7rUiUQzbi7QvbkKT1cVGXmwZEvFHByebvwzEIAl720aWgYMGNhOxcB2Fds5fM7EH7ul4/j/GpX5cg3ahRAMfeB4bWfBYV59rvYmSnS01z+xPWLW/4KoZuG1nQWH6vl469rOgsPs+qb6oaP/67KzHBc9qG0lRf8jY7Xa0LCR7bmi/he8ONbBgwRcRuyZ81dOZCfRDevV2L6uV+0PRSGEEEIIIYT4nyVNqoQQQgghhLCj/8qEfDVFIhxCCCGEEEIIh5EIhxBCCCGEEHYkEQ4liXAIIYQQQgghHEYiHEIIIYQQQtiRRDiUJMIhhBBCCCGEcBiJcAghhBBCCGFHEuFQkgiHEEIIIYQQwmEkwiGEEEIIIYQdmc0S4biURDiEEEIIIYQQDiMRDiGEEEIIIexI+nAoSYRDCCGEEEII4TAS4RBCCCGEEMKOJMKhJBEOIYQQQgghhMNIhEMIIYQQQgg7kgiHkkQ4hBBCCCGEEA4jFQ4hhBBCCCGEw0iTKiGEEEIIIexIJv5TkgiHEEIIIYQQwmEkwiGEEEIIIYQdmaTTuIJEOIQQQgghhBAOIxEOIYQQQggh7EiGxVWSCIcQQgghhBDCYSTCIcQVmM2m2s6Cw2w56VvbWXCYYSP9ajsLDjMkcFdtZ8GhPv7mSG1nwWE6TWxR21lwmE0fH6ztLDhM/Ink2s6Cwwzs51/bWfifJKNUKUmEQwghhBBCCOEwEuEQQgghhBDCjqQPh5JEOIQQQgghhBAOIxEOIYQQQggh7Ej6cChJhEMIIYQQQgjhMBLhEEIIIYQQwo6kD4eSRDiEEEIIIYQQDiMRDiGEEEIIIexI+nAoSYRDCCGEEEII4TBS4RBCCCGEEMKOTDX4uh6fffYZkZGRuLq60q5dO7Zs2XJV623btg2tVkvr1q2vaX9S4RBCCCGEEOL/iSVLlvDUU08xbdo0Dhw4QI8ePRg0aBAJCQmXXS8vL497772XPn36XPM+pcIhhBBCCCHE/xPvvfceEyZMYOLEiTRt2pQPPviAiIgIPv/888uu99BDD3HnnXfSpUuXa96nVDiEEEIIIYSwI7NZVWOvsrIy8vPzFa+ysjKb+dLr9ezbt4/+/fsrlvfv35/t27dXW5558+Zx5swZpk+ffl3fh1Q4hBBCCCGE+I+aPXs2Op1O8Zo9e7bNtJmZmRiNRoKDgxXLg4ODSU1NtblObGwsL7zwAgsXLkSrvb4BbmVYXCGEEEIIIeyoJif+mzp1KpMnT1Ysc3Fxuew6KpUyf2azucoyAKPRyJ133smrr75Ko0aNrjuPUuEQQgghhBDiP8rFxeWKFYyLAgIC0Gg0VaIZ6enpVaIeAAUFBezdu5cDBw7w2GOPAWAymTCbzWi1WtauXUvv3r2vuF+pcAghhBBCCGFHN+rEf87OzrRr145169YxYsQI6/J169Zx6623Vknv7e3NkSNHFMs+++wz/vrrL3755RciIyOvar9S4RBCCCGEEOL/icmTJ3PPPffQvn17unTpwldffUVCQgIPP/wwYGmilZyczHfffYdarSYmJkaxflBQEK6urlWWX45UOIQQQgghhLCjmuzDca3Gjh1LVlYWM2fOJCUlhZiYGFauXEm9evUASElJueKcHNdKKhxCCCGEEEL8PzJp0iQmTZpk87P58+dfdt0ZM2YwY8aMa9qfVDiEEEIIIYSwI5O5tnNwY5F5OIQQQgghhBAOIxEOIYQQQggh7OhG7sNRGyTCIYQQQgghhHAYiXCIG9b48eNZsGCB9b2fnx8dOnTg7bffpmXLlkDFTJk7duygc+fO1rRlZWWEhYWRnZ3N33//Ta9evazpf/vtN4YPH+6QPA/p5cfIAQH4+WhJuFDGV4tTOBZbXG36mEbuPDA2lLphLmTnGvhldQarNuVYPx/Qw5feXXyoH+4KQNz5Ehb8lsbpcyXWNKMHBdC1rTd1Ql3Q682cOFPMvF9SSU7TO6SMFx3d/iMHN35LcUEGvsFRdBv2ImEN2ttMe/bIWo7tWEzmhRMYDXr8gqNo3/8x6jbuoUiz/68vyctMwGQ0oAuoR6ue99G4XdVxwR1t38aF7Fr7LYV5GQSGRdN3zItERNsu26n9a9m/eRFpiZayBYRG0+OWx2jQvIci3e718zmweRH52Sm4efrSpO0Aeo14Bq3T1U3WZC9LV61n0e8rycrJo35EOE/efxetmjW2mXb/0RM88crsKssXfvQm9eqEVVm+futOZrz3GT06tmX2C0/ZO+tX1KGxmu7NNXi6Q0aumVW7jZxPt92Q2tMNBrbXEOavxs8bdp0wsWqPsUq6Lk3VdGisQecBxWVw7LyJ9fuMGEyOLs318evengbPTEDXNgbXsCD2jppE2vINtZ2tK+rSXEPPVlq83FWk5ZhZvq2c+FTbX7KXOwzt4kSdQDX+OhXbjhhZsb1ckaZdYw1jb3ausu6LX5dgqHqY7WZwL19GDvDHT2e5Bny9JO2K14CJY4Kt14Bf12RVugb40LuLD/XCLOeJuPMlfPdbOqfjSxXb8ffRMn5UEO1iPHF2UnMhXc+H8y9wJkGZzt52bfiRLSvnUpiXQVBYFIPvmkr9xrbPlfGn97F2yRwyUs5Sri/FJyCMDr3G0G3geGuatKRYNvz2MRfij5GbeYHBd75A1wHjHFoGR7lR5+GoLVLhEDe0gQMHMm/ePABSU1N56aWXGDp0qGK4toiICObNm6eocPz22294enqSnZ1dY3nt0cGbB24P4bOFKZyIK2bgTb68+mQ9Hnkljozs8irpgwOcePXJ+qzenM273yTRNMqdSXeFkldgZPv+fABaNPZg8+48vjyTgr7cxKiBgbz2dH0mvRJLVq7BmubPv7M5HV+CRq3i3hFBzJpcn4dfjqVM75hea3EHV7Jt+Wx6jHiF0PptObZzCX9++yC3P/sHXr5Vb0QvnN1LneiudBr0NM6uXpzcu5RV8yYx8vElBIY3A8DFXUfb3g/jG9QAtcaJ8yc28vdPL+Lm6aeomDja8T0rWf/TbAbcOZ06DdtyYPNilnz8AA/M+BOdX9WyJcTuIbJpV3oOfxpXN28Ob1/Kz58+wrgXfiKkrqVsR3ctZ+Nvcxgy7g3CG7QhOz2eP+e/AEDfMS/WWNk2bN3JR/MW8swD42jRNJrf1/zNs7Pe5fsPZxMSGFDtej9+8hYebm7W9z7e3lXSpKZn8un8RdVWXhwtpr6aQR00/LHLSEK6iQ6NNNzdV8snv5eTV1Q1vVYNRaWw6bCRrs1sB/tbRqrp207Dsm1GEtNN+OtUjOhmuWyutlE5uRFoPNzJP3yKpAVLaffzJ7WdnavSqqGGW7o6sWyLpZLRqZmWCUOcmbOkjNzCqucwrUZFUSls2G+gR8vqb2NKysy8s1h5w+3IykaP9t48MDaEzxemcDyumEE9fZnxRF0mTY8jI9tQJX1wgBMznqjLmi05vPtNMs2i3HnkrlDyCgxs318AWM7vm3bnceJMMeXlZkYN8Gfm0/V4dPoZ6zXAw13N21Pqc/hUMTM+TCC3wEhooDNFJY79jR7ZtZKVC9/klntfpm6jtuz5ewnfzXmIJ2avwMe/6rnS2cWNTn3vIiSiEc4u7pw/vY/f58/A2cWdDjePAaBcX4pfYAQxHQaw8sc3HZp/UbOkSZW4obm4uBASEkJISAitW7dmypQpJCYmkpGRYU0zbtw4Fi9eTElJxVP/uXPnMm5czT4VGdEvgLVbc1i7JYfElDK+XpJKZk45g3v52Uw/uKcfGdl6vl6SSmJKGWu35LBuay4jB1Tc+L37TRJ/bszmbGIpSal6Pl6QjFoFrZp6WtO88sF51m/PJeFCGeeSSnl/XjJB/s5E1XOztVu7OLR5Pk06jKJZp9H4Bjek+60v4ukTwrEdi2ym737ri7S5eSJBES3wCaxP50GT0QXU4/zxv61pwht2okGLfvgGN0QXUJeWPe7FP7Qxqef2O6wctuxeP49W3UbRuvtoAkIb0m/sNLx9QziwyXbZ+o2dRucBDxBWvyV+wfXpNWIyfkH1iDv8lzVN8tmD1GnYluYdb8EnoA4NmnWnWYehpJw/WlPFAmDxitUM7dOTW/r1on6dcJ6ccDdB/n4sW/PXZdfz1Xnj7+tjfWk0ykuH0Wji1Q8+Z8LtIwkLDnRkEarVtZma/XEm9seayMyDVXuM5BdBh8Yam+lziyxpDp01UVr1eQAAEUEqEtPNHDlnIrcIzlyw/B3uf+M+ucxYs5nT0z8gddm62s7KVevRUsuek0Z2nzSSnmtmxfZycgvNdG5m+9jlFFgiIPtPGym9wkOVwhLly5GG9/Nn3dYc1m7NJSlVz9dL0izXgJ62rwGDevqSkV3O10vSSErVs3ZrLuu35TCyv781zbvfJLNyYw7nEsss14DvUv65BnhY09w2MIDMHAMfzr/A6fhS0rPKOXSyiNSMan7YdrJt9QLa3TSS9r1GExTWkCF3vYjOL4TdGxbbTB9WrxmtugwhuE40voHhtO42jOgW3Yg/vdeapk6DFgy8/Tladh6C1qlqhOq/xGyuudd/gVQ4xH9GYWEhCxcuJCoqCn//ihNyu3btiIyM5NdffwUgMTGRzZs3c88999RY3rQaFVH13DhwrFCxfP+xQpo2dLe5TpOG7uyvkr6A6HpuaGxfZ3FxVqPRqCgoqv7JlYe7ZeXCy6T5N4wGPRnJx4ho1E2xPKJRN1LPH7iqbZhNJsrLinBx19n+3GwmKXYHuennCK2mmZYjGA16UhOOEdmsu2J5ZLNuJJ25+rLpS4tw9fCxLouIakdqwjEunDsMQE5GImeObiKqRS97Zf2KyssNnD4TT4dWyplhO7RuwdGTsZdd9/5nXubW+x/nyelvsv/I8Sqfz/95GT7e3gzt29Oueb5aGjWE+qs4c0HZBCfugom6gddfOTifZibUX0V4gGUbvp7QKFzN6aQbtD3Vf5BGDeGBKk4nKs9XsUkm6of8u1sUZyeYepcLL97tyn2DnAlzYEVRq4Goeq4cOK4Mpx04VkiThrYf/jRpYOuaUUTUNV4DOrXyIja+hBceqsMPcxrx4cuRDOjh86/KcyUGg54L8ceIilFeB6JiupEQd3Xnygvnj5MQd5DIxh0ckUVxg5EmVeKG9scff+DpaXmaX1RURGhoKH/88QdqtfJCdN999zF37lzuvvtu5s2bx+DBgwkMvPYnrWVlZZSVlSmWGY16NJrLP2nx9tSg0ajIzVeGzXPzjfjqbP+b+Xpryc03VkpvQKtV4e2pJSevagh+/KhgsnLLOXi8sMpnFz0wJoSjp4s4f6Gs2jT/RmlRDmaTEXcvf8VyN09/igsyr2obBzfPo1xfTMNWgxTLy0oK+G5WT0wGPSq1mh4jplep2DhScaGlbB7eyrJ5eAVQlJ9RzVpKu9bNRa8voWm7irI16zCE4oJsvn/nTjCbMZkMtOl5B10GPmjX/F9OXkEBRpMJPx9lJc9P501Wbp7NdQJ8fXj+kfto3CCSckM5qzdu48kZb/HxzKm0bt4EgMMnTvPH+k3Me2+Ww8tQHXcX0KhVVZ5gF5Wa8XS7/pvWo/EmPFxhwkAtKpVlH7tPGtlyVCoc9uLhavvYFRSb8Yq4/mOXkWPip7/LSc024eKkonsLLZOGu/DBL2Vk5tn/kbC3pxaNRkVOpWtAToGRttVdA3RacgqU14CcK1wDxo0KIivXwMFLKjYhgU4M7uXLsnXZ/LQyk0aRrjx4ewjlBjN/7bD9v/1vFRfkYjIZ8dQpm2J66PwpzLv8deDtp3pRVJCNyWik94hHad9rtEPyWNtMMkqVglQ4xA3t5ptv5vPPPwcgOzubzz77jEGDBrF7927q1atnTXf33XfzwgsvcPbsWebPn89HH310XfubPXs2r776qmJZVJtHaNTW9myclVUObapUlw93mm2tAGBjnVEDA+jZSccL75yj3GB7o4/cGUr9Oq4899bZq8rvv1P1ZKq6ihNs7IE/2Lv2EwaN/xR3T+WNvbOLB2Oe/o3ysmKS4nawfcWbePvXIbxhJ7vl+uooy2HGXGWZLcd2/8HWPz5h1KTPFJWW86d2sX3VFwy4czphkS3JSU9g/ZLX2ar7lO5DHrV35i9LVakYZioGX6isbngodcNDre9jGkeTnpXNot9X0bp5E4pLSnjtwy94ftL9+Hh7OTDX1+/f3FrWD1ZxU0tLv5CkDDP+3jCog5aeJWY2HZZKhz1VPk5XOndeSUK6mYT0izfzZs6n6nnyNhe6xmhZvs2BTY0qn9K5QjkqfaiyLq660qgB/vTsqGPqO/GKa4BKpSIu3tKZHOBsYil1w1wY3NPXYRWOKhm+yGyuepKpZOK0H9CXFpN45iBrf3oPv6B6tOoyxHF5FDcEqXCIG5qHhwdRUVHW9+3atUOn0/H1118za1bFE1V/f3+GDh3KhAkTKC0tZdCgQRQUFFzz/qZOncrkyZMVy8Y8GXfF9fILjRiN5irRDJ2XpkrU46KcfEOV9D5eGgwGM/lFynVG9vdnzOBAps05R3yS7cjFw3eE0qm1N1PePktWju192oOrhy8qtaZKNKOkMAu3SlGPyuIOrmTjzy/R/54PqNOoa5XPVWo1ugBLRTIgvCk56Wc58NdXNVbhcPe0lK0oX1m24oIsPLyr71QNls7mK7+bxoiHPiSyqbJsm5d/SEynYbTubnmSFxTemPKyYlb98ArdBj2CSu341q06Ly80ajVZOcobkJy8fPx0VTuBV6d5oyjWbtoGQHJqOinpmbzwxvvWz03/3Cj1vG08P37yFuEhwXbI/eUVl4HRZMazUssVD1cVRSXXf9fap42GQ2cs/UIA0nPBSWtkWBcNmw+b/lVlRlgUlVqOnVelY+fppqLwXxy7ysxAYoaJAJ1jnjrnFxpsXgN8LncNyDPg610pvbcWg8FcpdnsiP7+jB4cwEvvnSc+WXkNyMkrJyFFuSwxRU+3tlf/f32t3L18UKs1FOYqz5VF+dl4el/+OuAXWAeAkIhGFOZl8feyT/4nKxwySpWS9OEQ/ykqlQq1Wq3oIH7R/fffz8aNG7n33nvRVNcA9gpcXFzw9vZWvK7UnArAYDQTd76ENs08FcvbNPPkxBnbQyKePFNcNX1zT2LPl2C85FozckAAtw8N4pUP4ok7b3uIw4fvDKVLW29efPccaZmO7Sio0ToTGN6cpNjtiuVJp7cTUq9NtevFHviDv5ZMpe+d71Kvaa+r25nZjNHg2OF9L6XROhNStznnTmxTLD93Yjt1GlZftmO7/+DPBS9w68Q5NvtlGPSlqFTK061KrcESO6mZ21YnJy2NGtZnzyFlR/W9h44S0yT6qrcTe/Y8/r4+gCUC8t37bzBvzizrq3uHNrSNacq8ObMI8r/8jYe9GE2QkmWmYajyO24YpiYh4/q/Xydt1afTZvM/D3XlXsIujCZIzjATHaE8Z0eHq6sdFvd6hfmrKSh2zP+bwQhx50tpfUlnboDWzTw5ecZ2b/WTZ0toXeWa4UFc5WtAf39uHxLA9A8TbF4DjseVUCdEObx2eLAz6VmOuxZotc6E1W9O3DHldSDu2HbqRlV/rqzKjKEGz/Gi9kiEQ9zQysrKSE1NBSAnJ4dPPvmEwsJCbrnllippBw4cSEZGBt42huysCb+ty+SZCXWIjS/h5NkSBt7kS6CfEys3WobmHTcyGH8fLe/NTQZg5aZshvb2Z+KYENZsyaFJAzf6d/fl7a+SrNscNTCAe24N4u2vk0jPLLc+DSspM1FaZrkYT7orlJ6dfHjtk/OUlJqsaYpKjOjLHXNxbXXTeDYsnkJgnRhC6rXm+K6fKMhNoXmX2wHYuXIORXnp9LnjLeCfysbiF+h264sE121F8T/9ITROrri4WZri7P/rSwLrxKDzr4vRWE7CiU2c3vc7PUZOd0gZqtOx732smPc8ofViCG/QhoNblpCfnUKbmyxl2/jbHApy07jlvrcBS2Xjj3lT6Dv2RcIiW1GYZymb1tkV13/KFtXyZnavn0dw3WbWJlWbl39IdMveqNXXVzm+HrffMpDXPvqSJlGRxDSOYvnajaRlZjG8f28AvvjhJzKycnj5yYcA+GnFakKCAomMCKfcYGDt5u1s3LmH159/HAAXZ2ca1Kuj2Ienh2WQhMrLHW37cRMju2tIzjKTmGGifSPL3Bl7Tlnu3Pq21eDtDku3VtzJhfhaag3OWnB3tbw3msxk/BMEOpVopkszNSnZZpIyTfh7qejdWsPJRNMNOzKMxsMdj6i61vfukXXwbtUEfXYepYkptZiz6m05bGBsbyeS0k0kpFmGxfXxUrHzuOVYDeyoReehYsnfFTfQof90AHfRqvB0s3TuN5ogPcdyYPq205KQZiIzz4yLM3RvoSXMX8WyrY6L/i5bl8XkCeHEnS/lxJniimvAP/NqjBsRhL+vlvfmXgBg1aYcht7sx8QxwazenEPThu706+7LO19fcg0Y4M/dtwbyzjfJpGXq8fG2nC9Ky0yUllnK+vv6LN6ZEsnowQFs3ZNHo0g3Bt7kyyffX3BYWQG6DRzHL1++QHhkDBFRrdn790/kZaXQofdYANb+9B75OWnc9pDlOrBz/UJ8/MMICI0E4Pzp/WxdNY/Ofe+ybtNg0JORfAYAo6Gc/Jx0Us6fwNnVHf/geoj/LqlwiBva6tWrCQ21tCH38vKiSZMm/Pzzz9aJ/C6lUqkICLh8sxdH2rInH2+PVO64JQg/nZbzF8qY/uF56xwcfjotgf4V0ZK0zHKmfxjPA2NDGXqzH1m5Br5clGKdgwMsEwk6OamZNqmuYl8Ll6fz43JLe90hN1ueIr/1fANFmvfnJrF+e64jikpU68GUFueyb/2nFOVn4BcSzZAJX+LlGw5AcX4GhbkVF7vjO5dgMhnY8ttMtvw207q8cbvh9L7dMtZ6ub6ELb/NpDA3Fa2TKz5BkfS5422iWg92SBmq06zDYEqKctj252cU5qUTGNaIMY99hc7fUrbCvAzysytu3A5usZRt7aKZrF1UUbYWXUYwdLylbN0GPwKo2PT7BxTmpuHu6UdUy5vpOfzpGi1bn+6dySsoZP5Pv5OVk0tk3Tq8M+0ZQoIs/zdZObmkZWZZ05cbjHy6YBEZ2Tm4ODsTGRHOO9OeoUu7VjWa76txNN6Emwv0aqXBy01Deq6ZHzYYrHNweLmBzkMZlpg0zMn6d3gAtGqgIafQzPu/Wv5nNx02YsZMnzYavN01FJXCqSQTG/bfmHNwAOjaxdBlw/fW983etczzkvjdUg5PmFpb2bqsQ2eMuLtC3/ZavN1VpGabmbtSb52Dw9tDhY+X8tg9PdrV+nedIDVtorVkF5h4c6GlaZGbi4pRPZ3wcldRqofkTBOfL9eTWM1EkPawZW8+Xp4abh8aYL0GzPgowXoN8PXREuhX8ZtLyyxnxkcJTBwTzJBevmTlGfhqcap1Dg6wTCTo5KTmxUciFPv6cXkGP66wPNyIjS/l9c8TGTciiDuGBpCWWc7XS1LZuCsfR2rRaTDFhbn8/ftnFORmEBwezT2Tv8A3wHKuLMjLIPeSc6XZbGbtz++Rk5GMWqPBLyiC/qMn0+HmsdY0BTkZfPrKSOv7ravmsnXVXOo36cDEqd85tDz2dqM+lKgtKrOtnklCCKshE2t2roSa1G9Y89rOgsP4eP/vtnkZErirtrPgUB/vvZYmGf8tnSa2qO0sOMymjw/WdhYc5vi+c7WdBYcZN6FJbWfBYUZ3rr2eA+sOOWakSFv6tXK5cqJaJhEOIYQQQggh7MgsHb0UpNO4EEIIIYQQwmEkwiGEEEIIIYQdmaTDgoJEOIQQQgghhBAOIxEOIYQQQggh7Egm/lOSCIcQQgghhBDCYSTCIYQQQgghhB3JpBNKEuEQQgghhBBCOIxEOIQQQgghhLAjk8zDoSARDiGEEEIIIYTDSIRDCCGEEEIIO5I+HEoS4RBCCCGEEEI4jEQ4hBBCCCGEsCOZh0NJIhxCCCGEEEIIh5EIhxBCCCGEEHZkkj4cChLhEEIIIYQQQjiMVDiEEEIIIYQQDiNNqoQQQgghhLAjGRZXSSIcQgghhBBCCIeRCIcQQgghhBB2ZEaGxb2URDiEEEIIIYQQDiMRDiGEEEIIIexIhsVVkgiHEEIIIYQQwmEkwiGEEEIIIYQdyShVSlLhEOIK8tKyajsLDhN/vqi2s+AwR7efrO0sOMzWmIa1nQWHcnUrqe0sOMymjw/WdhYcpufjrWs7Cw5zauLy2s6Cwxw6aajtLDjM6M7OtZ0F8Q+pcAghhBBCCGFHEuFQkj4cQgghhBBCCIeRCIcQQgghhBB2ZDLLPByXkgiHEEIIIYQQwmEkwiGEEEIIIYQdSR8OJYlwCCGEEEIIIRxGIhxCCCGEEELYkUQ4lCTCIYQQQgghhHAYiXAIIYQQQghhRyaJcChIhEMIIYQQQgjhMBLhEEIIIYQQwo7MMg+HgkQ4hBBCCCGEEA4jFQ4hhBBCCCGEw0iTKiGEEEIIIexIhsVVkgiHEEIIIYQQwmEkwiGEEEIIIYQdybC4ShLhEEIIIYQQQjiMRDiEEEIIIYSwI+nDoSQRDiGEEEIIIYTDSIRDCCGEEEIIO5IIh5JEOIQQQgghhBAOIxEOIYQQQggh7EhGqVKSCIcQQgghhBDCYRxS4VCpVCxbtuyq08+YMYPWrVs7Iis3pMrlHT9+PMOHD6+1/PwX/H/7jQghhBDiv8tsrrnXf8E1NakaP348CxYssKyo1eLn50fLli254447GD9+PGq1pf6SkpKCr6+v/XN7GfHx8URGRnLgwAG73pjWr1+f8+fPA+Dm5kaDBg14/PHHeeihh+y2jw8//BDzDfKL2bhxIzfffHOV5dOmTWPWrFk1kgeVSsVvv/2mqIQ9++yzPP744zWy/3/r/jvqMWxAKF6eWo6fLuC9L2I5l1B82XV6dg1g4l31CQ91IzmlhK+/P8fmnVnWz4cPCmX4oDBCg10BOJdQzPzF59m5L9vm9p57NJpbB4bx4ddx/Lw82X6Fu0S3Flp6t3HG20NFaraJ37aUcfaCyWZab3cVt3Z3JiJIQ4CPii2Hyvlti77abbeJ1jJuoCtHzhr49s9Sh+T/3lFhDO4TgJeHlpNxRXw07zznky6/rx4dfRg/OpzQYBdS0sqYuySZbXtzFWmG9Qtk9NAQ/H2ciE8q4bPvEjl6qhAAjUbFfWPC6NRaR0iQC0UlRg4cyeebxclk5ZTb3OcbU6Lp2FrHK3Pi2F5pX/bSq50rAzq74eOp5kKGkcXrColNNNhMq/NUMaaPB/VCtQT5adiwp5Ql64ockq9r1aOVM33au6DzUJGSZeLXjSWcSTbaTOvtoWJkT1cigjQE+qrZdEDPrxuVx79VlJYBHV0I8NGg0UBGjokN+8rYc8L2sXK0Ls019GylxctdRVqOmeXbyolPtf0/5+UOQ7s4USdQjb9OxbYjRlZsV+a7XWMNY292rrLui1+XYLD9tdUqv+7tafDMBHRtY3ANC2LvqEmkLd9Q29lSGHSTDyP6+eKr05KQoufbn9M5HldSbfrm0W7cf1sQdUOdyc4z8NvabFZvybN+3rm1J6MH+hMS6IRWo+JCup7f1+ewcXe+ze2NGuDHvcMDWf5XNt/+nGH38lXWsbGaHjEaPN0hPcfMyt1Gzqfbvp/xdINBHTSE+avx94adJ0ys3K38oU0YqCUypOqz8FOJJr7fYPucJP4brrkPx8CBA5k3bx5Go5G0tDRWr17Nk08+yS+//MLy5cvRarWEhIQ4Iq+1ZubMmTzwwAMUFhYyf/58Hn74YXx8fBg7dqxdtq/T6f71NvR6Pc7OVS8c1+vUqVN4e3tb33t6etpt29fD09Oz1vNwNe4aFcHY4XV4/YNTJCYXM25sPd6f2ZI7HtlDSYntK3jzxt68+nwzvvnhHJt3ZnJT5wBmTmnGpCkHOX66AICMTD1fLDhHcorlwjWoTzCzpzXn/qf2VanM9OjsT7NG3mRklTmsnG2itYzo4cIvG8s4l2Kka4wTD93ixuyFxeQWVr3YaDVQWGJm3V49PVs7XXbbvl6Wykl1N4r2MPaWEEYNDuadL86RlFLKXSPCeOvFRtw3+SglpbZv4JpGe/DSEw2Z/3MyW/fk0r2DDy8/2YCnZpzi5BnLDXevzr48cm8EH81N4NipQob0DWT2C9FMePYY6Vl6XJ3VREd68MNvKZw5X4yXh5ZJ90Yw89koHp12oso+Rw0KdvjTqw5Nnbm9nwcLVxcSl2jgprauPHm7jle+zCE7v+p3odWoKCg28+e2Evp1dHNs5q5B20ZOjOrlypINJZy9YKR7S2cmjfBg1oICcgps/yYLis2s2VXGze1cbG6zuNTM6t1lpGWbMBohpoGWuwe4UVhs5sT5mr35adVQwy1dnVi2xVLJ6NRMy4QhzsxZUlbN/5yKolLYsN9Aj5bVX+pLysy8s1hZ0boRKxsAGg938g+fImnBUtr9/EltZ6eK7u28mDA6iC8Xp3HiTAkDeuh45dE6PDbzHJk5VX8vQf5OvPJoHdZuy+X9eSk0bejGQ7cHk1doZMcBy0OKwiIjP6/KIilNj8Fgpn0LD564N4S8AgMHTijP/VH1XBnQXce5Kzw4sZeY+moGd9SwYqeRhHQTHRpruLeflo+WlZNn4xmEVgNFpbDpsJGuzWw3sPnxLwMaTcV7dxcVjw7TcvS87fPyjcz038uyQ11zkyoXFxdCQkIIDw+nbdu2vPjii/z++++sWrWK+fPnA1WbVE2ZMoVGjRrh7u5OgwYNePnllykvr/qE6MsvvyQiIgJ3d3dGjx5Nbm6u4vN58+bRtGlTXF1dadKkCZ999pn1s8jISADatGmDSqWiV69eV7WeXq/nscceIzQ0FFdXV+rXr8/s2bMV+/Xy8iIkJISoqChmzZpFdHS0tXx5eXk8+OCDBAUF4e3tTe/evTl06JBi/TfffJPg4GC8vLyYMGECpaXKk0HlJlUFBQXcddddeHh4EBoayvvvv0+vXr146qmnrGnq16/PrFmzGD9+PDqdjgceeACA7du3c9NNN+Hm5kZERARPPPEERUUV//l6vZ7nn3+e8PBwPDw86NSpExs3bqxyLIKCgggJCbG+PD092bhxIyqVSnFcDh48iEqlIj4+HoD58+fj4+PDmjVraNq0KZ6engwcOJCUlBTF9ufOnUvz5s1xcXEhNDSUxx57zFougBEjRqBSqazvKzepMplMzJw5kzp16uDi4kLr1q1ZvXq19fP4+HhUKhVLly7l5ptvxt3dnVatWrFjx44qZbWn0cPC+e6nBDbvyORcQjGvv38SFxcN/XsGVbvOmFvD2Xswhx9+SSQhqYQffklk36FcxgyrY02zbU8WO/dlk3ihhMQLJXz1fTwlpUaaNfZWbCvAz5mnH4pm5pwTGAyOu1Pt1dqJXccN7DxuIC3HzG9b9OQWmunewnZlIrvAkmbPSQOll6kHqVRwT39XVu3Sk2XjZtdeRg4K4sdlKWzdk0t8Uilvf34OV2c1vbv5VbvOqEHB7DuSz6LfU0m8UMqi31M5cKyAkYMrju2oIcGs/juTVX9nknChlM+/SyQ9S88t/QIBKCoxMuWN02zamUNSShkn4or4ZH4CjRt4EOSvfGDQoK4bo4YE8+6X5xzzJfyjXyc3th4sZcvBMlKyjCxZV0ROvpFebV1tps/KM7F4XRE7jpRRUnZjRGYBerdzZsdRPTuOlpOWbeLXjaXkFJjo0cr2g5jsfDO/bixl94lySqspR2ySkcNxBtKyTWTmmdh4QM+FDBMNwjU20ztSj5Za9pw0svukkfRcMyu2l5NbaKZzM9t5ySmwRED2nzZSqr/8cSosUb5uVBlrNnN6+gekLltX21mx6dY+vqzfnse6bXkkper59ucMMnPKGXSTj830A3voyMgu59ufM0hK1bNuWx4btucxvG/FeehobAk7DxWSlKonNbOcP/7OJT65jKZRysq+q4uKyfeF8unCNAqLa+ZOt1tzNftiTeyLNZGRByt3G8krgo6Nbf8mcwstaQ6eMVFWTZCwRK/8LTYMU1FugKPxcvf+X2eXPhy9e/emVatWLF261ObnXl5ezJ8/n+PHj/Phhx/y9ddf8/777yvSxMXF8dNPP7FixQpWr17NwYMHefTRR62ff/3110ybNo3XX3+dEydO8MYbb/Dyyy9bm3jt3r0bgPXr15OSkmLNy5XW++ijj1i+fDk//fQTp06d4ocffrDe5FbH1dWV8vJyzGYzQ4YMITU1lZUrV7Jv3z7atm1Lnz59yM62NHX56aefmD59Oq+//jp79+4lNDRUUeGxZfLkyWzbto3ly5ezbt06tmzZwv79+6uke+edd4iJiWHfvn28/PLLHDlyhAEDBjBy5EgOHz7MkiVL2Lp1q/VmHuC+++5j27ZtLF68mMOHDzN69GgGDhxIbGzsZfN0LYqLi3n33Xf5/vvv2bx5MwkJCTz77LPWzz///HMeffRRHnzwQY4cOcLy5cuJiooCYM+ePYClkpiSkmJ9X9mHH37InDlzePfddzl8+DADBgxg2LBhVcoxbdo0nn32WQ4ePEijRo244447MBgc82QyLNiVAD8Xdh/IsS4rN5g5eDSXmCbe1a4X08Sb3QeUTaN2HcgmpqntddRq6NMjEFdXDcdOVoTVVSp4eXITFi1NvGITrn9Do4Y6QWpOJii/x5MJBuqH/rsbsQEdnSksMbPruOOeHocGOePv68y+IxXNFsoNZg6fKKB5o+qjaM2iPdh3WNmMYe+hfJpHW9bRalQ0ivRgb6U0+w7n0+wy2/Vw12AymSksriizi7OaaY834JN5CeTkOe670KihXqiWY+eUV/9jZ8tpWOfykagbiUYNEcGaKlGHE+cNRIbZbzDGRhEagvzUnEmq2RCARg3hgSpOJyr3G5tkor6N5ifXwtkJpt7lwot3u3LfIGfC/FX/anv/X2k10LCuKwePKx/tHzxRTJMGtiOBTRq4cbBSlOLA8SKi6rmiqeawtmzsTniwM8dilTXDh24PZt/RQg6ddNy5/1IaNYT5q4ir1Iw27oKJukH2+w21i1Zz5JyJ8v9gayrpw6FktzNxkyZNOHz4sM3PXnrpJevf9evX55lnnmHJkiU8//zz1uWlpaUsWLCAOnUsT3U//vhjhgwZwpw5cwgJCeG1115jzpw5jBw5ErBENI4fP86XX37JuHHjCAy0PEH09/dXNOm60noJCQlER0fTvXt3VCoV9erVq7aMBoOBH374gSNHjvDII4/w999/c+TIEdLT03FxsYTk3333XZYtW8Yvv/zCgw8+yAcffMD999/PxIkTAZg1axbr16+vEuW4qKCggAULFvDjjz/Sp08fwHLzHRYWViVt7969FTfy9957L3feeac1EhIdHc1HH31Ez549+fzzz0lOTmbRokUkJSVZt/fss8+yevVq5s2bxxtvvGHd1sXjcNHFfixXo7y8nC+++IKGDRsC8NhjjzFz5kzr57NmzeKZZ57hySeftC7r0KEDgPU4+vj4XLZp3rvvvsuUKVO4/fbbAXjrrbf4+++/+eCDD/j000+t6Z599lmGDBkCwKuvvkrz5s2Ji4ujSZMmNrdbVlZGWZnyEbzJqEetuXJzNT9fS5rsXGXfhJxcPcFBtp8WA/j5OJOTq7zhy8ktt27vogb1PPjinTY4O6spKTHy4uvHiE+suLjcNSoCo8nMzysc02fjIg83FRq1pVnNpQpKzHi7X/+FJjJUTedmWt5Z5NgLpq/OciNd+UY+J89AcED1x9nXx4mcvErHKa8cXx/L9nTeWjQalc00fjrbN+9OTiom3FGHv7ZnU1xSceF+5J4Ijp0uZPu+3Ksu1/XwdFejUavIL1TeNOQXmdB5/nduPD0v/iaLKv0mi//dbxLA1Rlef9AbrcYyzOWSDSVVKtuO5uEKGrWqSvShoNiMV8T1Vzgyckz89Hc5qdkmXJxUdG+hZdJwFz74pYzMvP/IXcwNwttTg0ajIrdA+dvILTDgq/OwuY6Pt5bcgqIq6bUaFd6eGnLyLRVMd1c1c2c3xMlJhclk5otFaYqKRY/2XjSIcOXZN6/+Ov1vubvY/k0WlZjxdLPPeEThASpCfNX8tq12+kwJ+7JbhcNsNqNS2T6x//LLL3zwwQfExcVRWFiIwWBQ9A8AqFu3ruImt0uXLphMJk6dOoVGoyExMZEJEyZYmw6BpQJwuf4PGRkZV1xv/Pjx9OvXj8aNGzNw4ECGDh1K//79FduZMmUKL730EmVlZTg7O/Pcc8/x0EMPMWfOHAoLC/H391ekLykp4cyZMwCcOHGChx9+WPF5ly5d+Pvvv23m+ezZs5SXl9OxY0frMp1OR+PGjaukbd++veL9vn37iIuLY+HChdZlZrMZk8nEuXPnOHr0KGazmUaNGinWKysrq1KGLVu24OXlZX1/LYMAuLu7WysbAKGhoaSnpwOQnp7OhQsXrJWp65Gfn8+FCxfo1q2bYnm3bt2qNGdr2bKlIh8X81BdhWP27Nm8+uqrimUR0eOo2/i+Kmn79QziuUcrvsvnZx6x/FH5Oq1SVV1Wia1BAyovSkgu5r4n9+LpoaVX10CmPd2Yx6ceIj6xmMYNPRk9rA73P7Xv8jtyIBVXLGa1XJzg7n6uLPmrjCI7Nz/u3c2PpydWPEiY9rYlClb5+72Kw1T1cxuFrnr4VTaPr0aj4qXHG6BWwUdzK24UurTT0bq5Fw9PPX6F3NiPzZ/s/8D95r/5TV5UpofZPxTi4gSN62oZ2dONrDwTsTUc5QD7H6eEdDMJ6RfLYeZ8qp4nb3Oha4yW5XKTd12qnFewfX6vLr1lDeWxLikz8dQb8bi5qGnZ2J37bwsiLbOco7ElBPhqmTg6iOkfJVHuwGa0V82OzynaR6tJzTGRnHkDlOs6/C+cQ+3JbhWOEydOWPtRXGrnzp3cfvvtvPrqqwwYMACdTsfixYuZM2fOZbd3sfKiUqkw/dPz5uuvv6ZTp06KdBpN9U04rma9tm3bcu7cOVatWsX69esZM2YMffv25ZdffrGmfe655xg/fjzu7u6EhoZa82YymQgNDbXZB8LHx+ey5avOxRNT5cqbrROWh4fyqYnJZOKhhx7iiSeeqJK2bt26HD58GI1Gw759+6p8b5U7ZEdGRlYpw8VRyC7Ni62+OE5Oyqe5l95wubnZr5Opre+o8rJL83LpcavO1KlTmTx5smLZwNt32Uy7dXcWx0/vtb53drJ8P36+zmTlVEQ5fHVOVaIel8rO1VeJZvj6OJFTaR2DwUxyiuVu/FRcIU2jvRg9LJx3Po2lZXMdvjonfp3b2Zpeq1Hx2P0NGTOsDqMn2i7D9SgqMWM0mfGq9OTY061q1ONqBejU+OvUTBxaEQm6eCjnPOrBG98Xk5V/fdvesS+Xk3EVTxGdnCwb9vPRkn1JZMnHW1slOnGpnNyqkQpf74qoR16+AaPRXCWNj7eWnHzlU0+NRsXLTzYgJMiF52adUkQ3Wjf3JizYhd+/baNYZ/rTDTl6spBnXjt1NcW+KoXFJowmMzpP5RNJL3c1+UX/natl4cXfpEel36T79f8mLzIDmbmW45OcoSfET0P/ji7EJtVM0xWwdLQ1msx4VTp9erqpKCyx33EyA4kZJgJ0/53o1o0iv9CI0WjG11t5W6Xz0pKbb7tymptvqJLex0uDwWimoLBiHbMZUjMs55lzSWVEhDpz20B/jsYm0bCuKz7eWt6bWvFQRaNR0TzKjSE9fbnt8dMOmYCuuMzym/Ss9Jv0cLXPb9JJAy0i1Ww4cIOOYCCumV0qHH/99RdHjhzh6aefrvLZtm3bqFevHtOmTbMus9U8JyEhgQsXLlib+uzYsQO1Wk2jRo0IDg4mPDycs2fPctddd9nMw8URmozGih/n1awH4O3tzdixYxk7diy33XYbAwcOJDs7Gz8/S8etgIAAax+DS7Vt25bU1FS0Wm21/T6aNm3Kzp07uffee63Ldu7cWW1eGjZsiJOTE7t37yYiIgKwPNGPjY2lZ8+e1a53MT/Hjh2zmVewdKg3Go2kp6fTo0ePy27LlovNnS4d9vjgwYPXtA0vLy/q16/Phg0bbA6/C5ZKwqXHsTJvb2/CwsLYunUrN910k3X59u3bFZGh6+Hi4mJtHndRdc2pSkqMJFcaeSozu4wOrX2JPWsZYUSrVdE6xocvFpytdp9HT+bTobUvP/1e0RSqYxs/jp6wPeyhlQqc/qnkrPk7jb0HcxQfvzezJWv+TuPP9amX3841MpogKd1E4wgtR85WlL9xXS1Hz15fU5O0HBNvLlTewA3p4oyLEyzdrLc5Cs/VKik1UVKpp3pWjp62LXTExVvaA2g1Klo29eLrRUnVbud4bBFtW3jz66o067J2Lb05Fms51gajmdPnimjX0lsxVG67Ft6KplEXKxvhIa48+9op8guVv6HFv6ew6i/lcJbfvBPD598lsnN/LvZkNMH5FAPNIp04cKqigtss0omDp6uvJN9ojCZITDPSpK6Ww3EVv8Em9bQcOWPnJ/Uqy++lJhlNkJxhJjpCw7FLOs9Gh6s5Fm/fG7IwfzWp2dJB91oZjHAmoZRWTd3ZeajQurx1U3d2XfL+UifPltCxpfJhX+tmHsSdL8V4hUOg1Vp+g4dPFvH4a8qBJZ64J4SkND1L12Y7bLZrowkuZJmJClNzIqHiN2h5/+9/PzGRajQaOHhWfov/K665wlFWVkZqaqpiWNzZs2czdOhQxU31RVFRUSQkJLB48WI6dOjAn3/+yW+//VYlnaurK+PGjePdd98lPz+fJ554gjFjxljb8c+YMYMnnngCb29vBg0aRFlZGXv37iUnJ4fJkycTFBSEm5sbq1evpk6dOri6uqLT6a643vvvv09oaCitW7dGrVbz888/ExISclURir59+9KlSxeGDx/OW2+9RePGjblw4QIrV65k+PDhtG/fnieffJJx48bRvn17unfvzsKFCzl27BgNGjSwuU0vLy/GjRvHc889h5+fH0FBQUyfPh21Wl1tk7WLpkyZQufOnXn00Ud54IEH8PDw4MSJE6xbt46PP/6YRo0acdddd3HvvfcyZ84c2rRpQ2ZmJn/99RctWrRg8ODBl91+VFQUERERzJgxg1mzZhEbG3vFSJUtM2bM4OGHHyYoKIhBgwZRUFDAtm3brPNsXKyQdOvWDRcXF5vNuZ577jmmT59Ow4YNad26NfPmzePgwYOK5mS14eflydwzui5JF4pJvFDCvWPqUlZmZO2mdGual55uTEaWni+/O2dd55M3W3PXqAi27MqkR6cA2rfyYdKUg9Z1Hrwnkp37sknPLMXdTUvfmwJpE+PDMzMszbjyCwzkV2o7bDCYycrRk5hs/2FnNh4s565+LiSmG4lPNdKluRO+niq2HbXc3A3t4ozOU8XCdRU3+uEBlsqRs5OlH0h4gBqD0UxajhmDkSo3OZYRkFQOuflZuiqdO28NITmllOTUUu4cHkqp3sRf2yo67095pD6ZOeV8uzj5n3XSeH96E8beEsL2fbl0bedD2xgvnppREXH49c80pjwayemzRRw/XcSQPoEEBTizYr2lAqFWw/SnGhAV6cFLb8eiVoOvznIaLig0YjCayckz2Owonp6lJzXD/pWAdbtKmHCrF/EpBs4mGbipjSt+Og0b91uiaSN7uePjpWbuioqbpohgS4TUxRm83FVEBGswGCEls/aeRv61T8+9g9xISDNyLsVItxbO+Hmp2XLI8p0N6+6CzlPN96sr/h/CAy2/SRcnS7QgPFCN8ZLfYv8OLiSkGcnIM6JVq2geqaVTUycWb6j5oZy2HDYwtrcTSekmEtIsw+L6eKnYedzynQ/sqEXnoWLJ3xUVrNB/OoC7aFV4upkJ9VdhNFnmSwDo205LQpqJzDwzLs7QvYWWMH8Vy7bemD10NR7ueETVtb53j6yDd6sm6LPzKE1MucyaNeP3DTk8NT6UuPOlnDpXyoDuOgJ8nVi9JReAe24NwN9HywcLLA+BVm/JY0gvX+4fFcjabXk0jnSlb1cdc+ZesG5z1AA/4s6XkppZjlajol2MBzd31vHFIsuDj5IyMwkXlOeFUr2ZgiJjleX2tu2Yidt6aEjONJOYYaJ9Iw06D9hzyvKb7NdWg7c7/Lq14rwQ4mf5TTprwcPF8t5oNJORp9x2u2hLxaXEcaO7O5yjKnv/Vddc4Vi9ejWhoaFotVp8fX1p1aoVH330EePGjbM2ubnUrbfeytNPP81jjz1GWVkZQ4YM4eWXX2bGjBmKdFFRUYwcOZLBgweTnZ3N4MGDFaM5TZw4EXd3d9555x2ef/55PDw8aNGihbWDtFar5aOPPmLmzJm88sor9OjRg40bN15xPU9PT9566y1iY2PRaDR06NCBlStX2ixLZSqVipUrVzJt2jTuv/9+MjIyCAkJ4aabbiI4OBiAsWPHcubMGaZMmUJpaSmjRo3ikUceYc2aNdVu97333uPhhx9m6NCheHt78/zzz5OYmIira/Udj8HSX2HTpk1MmzaNHj16YDabadiwoWK+kHnz5lk7bScnJ+Pv70+XLl2uWNkAS+Rh0aJFPPLII7Rq1YoOHTowa9YsRo8efcV1LzVu3DhKS0t5//33efbZZwkICOC2226zfj5nzhwmT57M119/TXh4uHXI3Us98cQT5Ofn88wzz5Cenk6zZs1Yvnw50dHR15QXe1v4ayIuzmomPxKNl6cTx0/n8/QrhxVzcAQHuipOREdP5jPj7eM8cE8kE++qT3JqCa+8fcI6BweAn48TL09ugr+fM0VFBs7EF/HMjCNVoho15UCsAXdXy6hS3v9MsvblihLrfAfeHip8KzXTee4Od+vfdYM1tG/sRHa+iZkLaq5pykVLVqTi4qzmifvr4uWh5cSZIl5447RiDo6gABfFcToeW8Ssj85y35gwxo8J40JaGbM+OmudgwNg484cvL203D0yDD8fJ+ITS3jxrVjSMy0X/kA/Z7q2t1Sgv3qruSJPz8w8xaETBdS0PSf0eLgXcUt3d3T/TPz34eI86xwcOk81/jplE8zpEyseAtQPdaJzjCuZuUZe+LR2fo8A+0+X4+GmYlBnV+tv8rPfii75Tarx81L+JqfeU9FPrW6IZU6SrDwT07+1HAdnJxjTxxUfLzXlBjNp2SYWrCph/+ma799w6IwRd1fo216Lt7uK1Gwzc1dWRP+8PVT4eCkfSj09uuKaUSdITZtoLdkFJt5caLmLc3NRMaqnE17uKkr1kJxp4vPlehKrmbittunaxdBlw/fW983efRGAxO+WcnjC1NrKltXWfQV4eWgYOyQAP28N51P0zPw0iYxsSwXOV6clwK+iyWV6VjkzP01iwm1BDO7pQ3aegW9+SrPOwQHg6qLm4TuC8ffRoi83k5yq5/15KWzdV/PnisqOxptwd4GbW2vwctOQlmPm+/UGcv85JXq5g0+lwSceG1ZR/vAAy/wyOYVm5vxS8T/l7w31g9XMWyP9iP6XqMw3yhTXolpFRUWEh4czZ84cJkyYUNvZ+X+n+y2bajsLDtO+f7vazoLDHN1+sraz4DD1Y+rXdhYcytXtvzMk77Vyd7ffBK03mp6Pt67tLDjMVxOX13YWHKZFp6r9b/9XzBpfe/9vn6ysudvrxwbf+P2u7DdAubCbAwcOcPLkSTp27EheXp51SNlbb721lnMmhBBCCCHEtZEKxw3q3Xff5dSpUzg7O9OuXTu2bNlCQEBAbWdLCCGEEEJcgbQfUpIKxw2oTZs27NtXe/MpCCGEEEIIYS9S4RBCCCGEEMKOLjPl1/9L9pl/XgghhBBCCCFskAiHEEIIIYQQdiR9OJQkwiGEEEIIIYRwGIlwCCGEEEIIYUcy07iSRDiEEEIIIYQQDiMRDiGEEEIIIexI+nAoSYRDCCGEEEII4TAS4RBCCCGEEMKOzDXaiUNVg/u6PhLhEEIIIYQQQjiMRDiEEEIIIYSwIxmlSkkiHEIIIYQQQgiHkQiHEEIIIYQQdiSjVClJhEMIIYQQQgjhMFLhEEIIIYQQQjiMVDiEEEIIIYSwI5PJXGOv6/HZZ58RGRmJq6sr7dq1Y8uWLdWmXbp0Kf369SMwMBBvb2+6dOnCmjVrrml/UuEQQgghhBDi/4klS5bw1FNPMW3aNA4cOECPHj0YNGgQCQkJNtNv3ryZfv36sXLlSvbt28fNN9/MLbfcwoEDB656n9JpXAghhBBCCDu6kTuNv/fee0yYMIGJEycC8MEHH7BmzRo+//xzZs+eXSX9Bx98oHj/xhtv8Pvvv7NixQratGlzVfuUCIcQQgghhBD/UWVlZeTn5yteZWVlNtPq9Xr27dtH//79Fcv79+/P9u3br2p/JpOJgoIC/Pz8rjqPUuEQQgghhBDCjszmmnvNnj0bnU6neNmKVABkZmZiNBoJDg5WLA8ODiY1NfWqyjZnzhyKiooYM2bMVX8f0qRKCCGEEEKI/6ipU6cyefJkxTIXF5fLrqNSqRTvzWZzlWW2LFq0iBkzZvD7778TFBR01XmUCocQV7BstnNtZ8FhlqV51nYWHKZnp6trV/pftGFrYW1nwaFKivS1nQWHiT+RXNtZcJhTE5fXdhYc5sFvhtV2Fhzm7NCTtZ2F/0mmGuzE4eLicsUKxkUBAQFoNJoq0Yz09PQqUY/KlixZwoQJE/j555/p27fvNeVRmlQJIYQQQgjx/4CzszPt2rVj3bp1iuXr1q2ja9eu1a63aNEixo8fz48//siQIUOueb8S4RBCCCGEEMKOzKbazkH1Jk+ezD333EP79u3p0qULX331FQkJCTz88MOApYlWcnIy3333HWCpbNx77718+OGHdO7c2RodcXNzQ6fTXdU+pcIhhBBCCCHE/xNjx44lKyuLmTNnkpKSQkxMDCtXrqRevXoApKSkKObk+PLLLzEYDDz66KM8+uij1uXjxo1j/vz5V7VPqXAIIYQQQghhR+YbeSIOYNKkSUyaNMnmZ5UrERs3bvzX+5M+HEIIIYQQQgiHkQiHEEIIIYQQdmS6gftw1AaJcAghhBBCCCEcRiIcQgghhBBC2NGN3oejpkmEQwghhBBCCOEwEuEQQgghhBDCjkwS4FCQCIcQQgghhBDCYaTCIYQQQgghhHAYaVIlhBBCCCGEHZmlTZWCRDiEEEIIIYQQDiMRDiGEEEIIIexIRsVVkgiHEEIIIYQQwmEkwiGEEEIIIYQdmaQPh4JEOIQQQgghhBAOIxEOIYQQQggh7MgsnTgUJMIhhBBCCCGEcBiJcAghhBBCCGFHZlNt5+DGIhEOIYQQQgghhMNIhEMIIYQQQgg7MkkfDgWJcIgb2vbt29FoNAwcOLC2syKEEEIIIa6DRDjEDW3u3Lk8/vjjfPPNNyQkJFC3bt3aztI1Wbp6Az/+voqsnFwiI8J54r47ad2ssc20+4+e4PHpb1VZ/uOHb1CvTliV5eu37mT6+1/Qo0Mb3nzhSbvn/UoObFrInvXfUpiXQUBoNL1Hv0idqPY2054+sJaDWxaRnnQCo0GPf2g03YY8RmSzHtY0i9+/h8TY3VXWbdC8J6Me/cph5bBlx/pFbP5zLgV5GQSHRzH07heIbGy7bPGn9rFqyXtkpJxFX1aKb0AYHW8eQ49B46xpdv/9M/u3/k5qUhwAdSKbMWD0U0Q0bFkj5blUj1bO9Gnvgs5DRUqWiV83lnAm2WgzrbeHipE9XYkI0hDoq2bTAT2/bixVpGkVpWVARxcCfDRoNJCRY2LDvjL2nCh3eFlubufKgC7u+HipSc4wsHhNEbGJ1e+3UV0nxvb3IDxQS26BiVXbi9m0v6I8GjUM7uZO15au+HqrSc0y8suGQo6eqdjmW4/7EeCjqbLtv/aUsHB1od3KNriXLyMH+OOn05JwoYyvl6RxLLa42vQxjdyZOCaYumEuZOca+HVNFqs25Vg/H9DDh95dfKgX5gJA3PkSvvstndPxyuPp76Nl/Kgg2sV44uyk5kK6ng/nX+BMgjLdvzHoJh9G9PPFV6clIUXPtz+nczyupNr0zaPduP+2IOqGOpOdZ+C3tdms3pJn/bxza09GD/QnJNAJrUbFhXQ9v6/PYePufJvbGzXAj3uHB7L8r2y+/TnDbuX6t/y6t6fBMxPQtY3BNSyIvaMmkbZ8Q21n67KObPuR/X9/S3F+Bn4hUfQY/iJhDWyfK88cXsvR7YvJSLZcB/xCoug44DHqNelhM/3pA3+y9vtniIzpw5D7P3VkMRxCRqlSkgqHuGEVFRXx008/sWfPHlJTU5k/fz6vvPKK9fPly5fzzDPPkJSUROfOnRk/fjzjx48nJycHHx8fwBIheeGFF9izZw8BAQGMGDGC2bNn4+Hh4fD8r9+2iw/n/cgzD9xLyybRLFv7N8++/h4/fPAGIYH+1a636OM38XBztb738faukiY1PZNPFiyhVdNGDsn7lZzcu5K/fplNv9unE96gLYe2LuaXTx/g/pf/xNuvauUoKW4P9Zp0pcewp3F19+bIjqUs/fwR7n7+J4IjmgFw64MfYzRU3NiVFuUy/41bady2ZqNbh3au4o8fZnPr+FeoH92GXX//xLx3HmLymyvwCahaNicXN7r0u5PQiEY4ubhz/vQ+ls59FWcXNzr1HgPA2RO7adVlCMOiW6N1cmHTn9/y7dsP8PTs5ej8gmusbG0bOTGqlytLNpRw9oKR7i2dmTTCg1kLCsgpqHpx1GqgoNjMml1l3NzOxeY2i0vNrN5dRlq2CaMRYhpouXuAG4XFZk6cNzisLB2auXD7AE9+WFlIXFI5Pdu68tSdOl7+PJvs/Kq9NQN81Dx1h47NB0r4ZlkBUXWcuHuwJ4XFJvad1AMw4mYPOse4sODPQlIyDcQ0dObR0Tpmz88lIdVSlte+zUGtqthueJCWZ+/2Ye+JMruVrUd7bx4YG8LnC1M4HlfMoJ6+zHiiLpOmx5GRXfU7DQ5wYsYTdVmzJYd3v0mmWZQ7j9wVSl6Bge37CwBo0diDTbvzOHGmmPJyM6MG+DPz6Xo8Ov0MWbmWbXq4q3l7Sn0OnypmxocJ5BYYCQ10pqjEdoX0enRv58WE0UF8uTiNE2dKGNBDxyuP1uGxmefIzKlatiB/J155tA5rt+Xy/rwUmjZ046Hbg8krNLLjgKWCV1hk5OdVWSSl6TEYzLRv4cET94aQV2DgwAllJS2qnisDuus4l2S/CpS9aDzcyT98iqQFS2n38ye1nZ0rij2wki3LZtNz1CuERrbl2PYlrPjqQe6c8gdevlXPlRfO7CWiUVc6D34aFzcvTuxeyp/fTmL0k0sIrNNMkTY/O5lty9+utvIi/nukSZW4YS1ZsoTGjRvTuHFj7r77bubNm2d9YhAfH89tt93G8OHDOXjwIA899BDTpk1TrH/kyBEGDBjAyJEjOXz4MEuWLGHr1q089thjNZP/FWsY2vsmhvXtSf06YTx1/10E+fvx25q/Lruer84Lf18f60ujUf6bGo0mXv3wSyaMHU5YcKAji1CtvX/No0XXUbTsNhr/0Ib0Hj0NL58QDm5eZDN979HT6NT/AULrt8Q3qD433ToZ36B6nDlS8V24efjgqQu0vuJPbsPJ2ZVGNVzh2LpqPu17jqJjr9sICm/ILXdPRecfys4Ni22mD6/fjNZdhhBcJxq/wHDadBtGo5bdiD+9z5rm9knv0KXvHYTVa0pQWANGTZiJ2WQi7vjOmioWAL3bObPjqJ4dR8tJyzbx68ZScgpM9GjlbDN9dr6ZXzeWsvtEOaVltp/WxSYZORxnIC3bRGaeiY0H9FzIMNEgvGoUwJ76d3Zjy4FSthwsJSXTyOK1RWTnG+nV3s1m+l7t3MjKt6RLyTSy5WApWw+WMqCLuzVNlxYu/LmtmCNxejJzTWzcV8qxs3r6d67YZmGxmfyiileraGfSso2cOm+/iM7wfv6s25rD2q25JKXq+XpJGpk55Qzu6Wcz/aCevmRkl/P1kjSSUvWs3ZrL+m05jOxf8WDj3W+SWbkxh3OJZSSl6vn4uxTUKmjVtOLhy20DA8jMMfDh/Aucji8lPaucQyeLSM2wX9lu7ePL+u15rNuWR1Kqnm9/ziAzp5xBN/nYTD+wh46M7HK+/TmDpFQ967blsWF7HsP7VnwXR2NL2HmokKRUPamZ5fzxdy7xyWU0jVL+FlxdVEy+L5RPF6ZRWHzjDSGUsWYzp6d/QOqydbWdlatycNN8mnUaRfPOo/ELbkiPES/i6RPCkW22rwM9RrxI294TCa7bAp/A+nQZMhmfgHqcO/a3Ip3JZGTdwufoNOBxvP3r1ERRHMJkMtfY679AKhzihvXtt99y9913AzBw4EAKCwvZsMESXv7iiy9o3Lgx77zzDo0bN+b2229n/PjxivXfeecd7rzzTp566imio6Pp2rUrH330Ed999x2lpY59ulVebuDUmXg6to5RLO/YKoajp+Iuu+59z05n2IQneWLGW+w7cqLK5/N+/h0fby9u6dvTrnm+WkaDntSEY9Rv2l2xvH7TbiSfPXBV2zCbTOhLi3B196k2zZHtv9Kk3RCcXdyrTWNvBoOe5PjjRLfoplgeHdOV87EHr2obyfHHOR97gMgmHapNU15WitFowN1D92+ye000aogI1lSJOpw4byAyzH7B7kYRGoL81JxJst9T8co0aqgXquXYWb1i+fEzeqLq2C5Lw3Atx88o0x89o6deqJaLdXqtRkV5pYfs+nIz0RFO1eajcwtXth603/lEq7E8hT9wvEix/MCxQpo0tF2ZatLAjQPHlM259h8rIqqeG5pq6n0uzmo0GhUFRRXHqVMrL2LjS3jhoTr8MKcRH74cyYAePv+qPJfSaqBhXVcOVirbwRPFNGlQfdkOVopSHDheRFQ9VzTV3MG0bOxOeLAzx2KVzbQeuj2YfUcLOXSy+qZp4uoYDXrSk44R0Uh5roxo3I3U+Gu4DpQV4equPA/uWfspbh5+NOt8m93yK2qfNKkSN6RTp06xe/duli5dCoBWq2Xs2LHMnTuXvn37curUKTp0UN7QdezYUfF+3759xMXFsXDhQusys9mMyWTi3LlzNG3atMp+y8rKKCtTNo0o0+txcbb9BLg6uQUFGE0m/HTK5lC+Pt5k5ebZXMff14cpD4+nccP6lJcbWL1pO0+++jafvPoCrZtb+n0cPhnLHxs2M3/OzGvKjz2VFOZgNhnx8FI2C/PwDqAo/+raQ+/ZMJdyfQmN2w2y+XlK/GEyL5xm4N2v/+v8XoviglxMJiNe3sqyeen8OZ2Xedl133jiZooKsjEZjfQd+Sgde1V/sVy15D10vkFENe9il3xfDU83FRq1ioIi5dOwgmIz3u6qata6Oq7O8PqD3mg1YDLDkg0lnExwXHMqL3c1GrWK/CLlU+q8IjMxnrbvQr091eRVKnt+kQmtRoWnu5q8QhNH/4lmnE4oJyPbSNNIJ1o3dlE0obpUmyYuuLuq2H7IfhUOb08tGo2KnHzl95dTYKStzvYl21enJadAWcHLyTeg1arw9tSSk1f1WIwbFURWrkFx8x8S6MTgXr4sW5fNTyszaRTpyoO3h1BuMPPXDtvnrWsrmwaNRkVugTI/uQUGfHW2m7n6eGvJLSiqkl6rUeHtqSEn31Jud1c1c2c3xMlJhclk5otFaYqKRY/2XjSIcOXZN8//63IIKCmyXAfcK10H3L38KS64/LnyogMb51GuLyaqdcV1IOXcfo7v+pXbn1lmz+zWCunCoSQVDnFD+vbbbzEYDISHh1uXmc1mnJycyMnJwWw2o1Ip7wIqd9AymUw89NBDPPHEE1W2X13n89mzZ/Pqq68qlj33yP08P2nidZWjch4xm6nu1q5eeCj1wkOt72MaR5GemcWPy1fRunljikpKmPnhl0x55D58vL2uKz92ZeP7r1JeG07s+YPtf37C8Ic/q1Jpuejw9l8ICGtEaP2a71QNXFfZHn7pe/RlxSTEHWL1T+/hH1yX1l2GVEm36Y9vObTzTx58cQFOzrb7RdQkFfBvr4tlepj9QyEuTtC4rpaRPd3IyjMR68AoB1Al4yrVlS7yyg8vHtGL545FawoZP9SL1x/xxQxk5BjZdrCUbq1dsaVHa1eOxOnJLXRA85zKZeMKZTNfvmyXGjXAn54ddUx9J55yQ8XnKpWKuHhLZ3KAs4ml1A1zYXBPX7tUOKrJ6j9lq75wVT+ylO7SxSVlJp56Ix43FzUtG7tz/21BpGWWczS2hABfLRNHBzH9oyRFeYUdVLnG2Vhmw+n9f7B77ScMuf9Ta6VFX1rI2oXP0XvMa7h5+jogs6I2SYVD3HAMBgPfffcdc+bMoX///orPRo0axcKFC2nSpAkrV65UfLZ3717F+7Zt23Ls2DGioqKuet9Tp05l8uTJimUFcVcXHr6Uj5cXGrW6SjQjJ68AP5+rb0bTvFFD1mzeAUByajop6ZlMmf2B9fOL43zfNPp+fvz4TeqEBF1zXq+Vm6cvKrWGonzlU6zigizcvQIuu+7JvStZ/cM0hk38kPpNutpMU64v4eTeP+k+tGpF0dHcvXxQqzUUVIpmFOZn4+ldfUd/AL8gS1vjkIhGFORlsX7pp1UqHJv/nMvfK75i4pRvCa1re7QyRyksMWM0mfHyUN4MeLqrKCj+dzdhZiAz13LTnZyhJ8RPQ/+OLsQmOabpSkGxCaPJjHelaIa3e9Wox0X5hSZ0Hsr0Xh5qDEYzRSWW8hcWm/nkp3y0GvB0V5NbYOK2Ph5k5latOPnr1DSLdOLTn22PhHS98gsNGI1mfCtFM3y8NOTm244a5eQZ8PWulN5bi8FgVjSZAhjR35/RgwN46b3zxCcro7k5eeUkpCiXJabo6da26sAV1yO/0GgpW6W86ry05Obbrpzm5tsom5cGg9FMQWHFOmYz1r4m55LKiAh15raB/hyNTaJhXVd8vLW8N7WeNb1Go6J5lBtDevpy2+On+Y80g79huHlYrgPFla8DhVm4e17+XBl7YCV/LXmJgeM+IKJRxXUgLyuRguxk/vj2Eesy8z/TdX/6bHPufmEVuoD/zkiVZvlRKUiFQ9xw/vjjD3JycpgwYQI6nfLm/LbbbuPbb79l6dKlvPfee0yZMoUJEyZw8OBB5s+fD1REFaZMmULnzp159NFHeeCBB/Dw8ODEiROsW7eOjz/+2Oa+XVxccHFRPnXWX2NzKgAnJy2NG9Znz6Fj9OzUzrp8z+FjdO/Q5qq3c/pcAv6+PoAlAvL9+7MUn3/1468Ul5by1P13Eexvu0OpvWm0zoTUbc75E9to1Lqfdfn5k9uJatmn2vVO7PmD1T+8yND73qNhi17Vpju1bxVGg55mHYfZM9tXRat1Jrx+M+KObiemfV/r8rij22nWtvc1bMmMwaDsL7Dpz2/56/cvmfD819RpEFPNeo5jNEFimpEmdbUcjqu4cW1ST8uRM3YewlZl6Q/hKEYTnE8x0LyBMwdOVXzPzRo4c+C03uY6Z5INtIp2Biqa5zRv4Mz5FAPGSnUUgxFyC0xo1NC2iQt7j1cdgapbK1fyi0wcjrW9v+tlMELc+VJaN/Vgx4EC6/LWzTzZdbDA5jonz5bQsaUXkGZd1qaZB3HnSzBech8/sr8/Y4cE8MqHCcSdr9oM7HhcCXVClOe/8GBn0rPs8/swGOFMQimtmrqz81BFn5PWTd3Zdcj2kMKWsnkqlrVu5kHc+dIqx60yrdbyGzx8sojHXzun+OyJe0JIStOzdG22VDaug0brTFCd5iSe3k7DlhXXgcTT24lsXv258vT+P9iweBoD7plD/Wa9FJ/5BjXgjueWK5btXPUh5WVF9Bhu6ZAu/ruk07i44Xz77bf07du3SmUDLBGOgwcPkpOTwy+//MLSpUtp2bIln3/+uXWUqosVhpYtW7Jp0yZiY2Pp0aMHbdq04eWXXyY0NLTKdh1h7C0DWLFhE39s2Ex80gU+nPcjaZlZjOh/MwCf//Azr31UMb/Ekj/WsHnXPhIvpHI2IZnPf/iZjTv3MmqQ5SbexdmZBnXrKF6eHu64u7rSoG4dnJxq7vlB+973cXj7LxzZ/gtZKWf465c3yM9JoVWP2wHYvGwOf85/3pr+xJ4/WLlgCr1GTiE0shWFeRkU5mVQVlL1Burw9l+IbtW31kLq3QeNZ8/GX9iz6VfSk8+w4oc3yc1KoVOfsQCsXvIeS754wZp+x7ofOb7/bzJT48lMjWfv5qVsXjmPNl1vsabZ9Me3rP3lI257YBa+AWEU5GZQkJtBWWlRlf070l/79HRt4Uzn5k4E+6kZ2dMVPy81Ww5ZbpqHdXfhnoHKzrvhgWrCA9W4OFn6gYQHqgnxq7h09O/gQpO6Wvx1KoJ91fRu60ynpk7sPmHfG/HK1u4soUcbV7q3ciU0QMPYfh746TRs2mfpKDyytwcTbq1oerhxXwn+Oku60AAN3Vu50qONK2t2VERhIsO0tG3iTICPmugIJ56+U4daBau2KyM1KqB7K1e2Hy5zyM3qsnVZ9O/hS79uPtQJcWbimGAC/ZxY+c+8GuNGBDH5/ophR1dtyiHI34mJY4KpE+JMv24+9Ovuy9K1WdY0owb4c8/wQD5ccIG0TD0+3hp8vDW4ulRUDH9fn0XjSDdGDw4gNNCJnh29GXiTL39uzLZb2X7fkEO/bj706eJNnRBnJtwWSICvE6u35AJwz60BPDWu4sZy9ZY8Av2cuH9UIHVCnOnTxZu+XXUsW1+Rp1ED/GjVxJ3gACfCg50Z1seXmzvr2PTPPBwlZWYSLugVr1K9JfqTcMGxv9NrofFwx7tVE7xbNQHAPbIO3q2a4BpRM9esa9W653iO7/qF47t+JTvtDFuWzaYwJ4WYrpbrwPY/5rDuxynW9Kf3/8H6H1+g+61TCK7XiqL8DIryK64DWicX/EMbKV4ubl44uXjgH9oIjfbaH/6JG4dEOMQNZ8WKFdV+1rZtW2tb37Zt2zJsWMVT8Ndff506derg6lrR3rpDhw6sXbvWcZm9jL7dOpFfUMi8n38nKyePBnXDeffFyYQEWZodZeXkkpZZcUNgMBj55LslZGTn4OLsTGREOO+8+DRd27WqlfxfTpP2gykpymH7ys8oyk8nILQRoyZ9hc7f0uemMD+DgpwUa/pDW5dgMhlYv2Qm65dUdHhv3nkEg+990/o+O+0cyWf2MfrxuTVXmEpadR5EcWEuG5Z9TkFuBiF1ohn/7Jf4BljKlp+bSW5WRdlMZhNrfnqf7Ixk1BoN/kERDBozmY7/zMEBsGPDIoyGchZ+9JRiX31GTKLfyJoZphlg/+lyPNxUDOrsivc/E/999luRdQ4Obw81fl7K51BT76m4aa8bAh2aOpOVZ2L6t5abBGcnGNPHFR8vNeUGM2nZJhasKmH/acdO/LfneBmebipuuckdnadl4r8PF+WRlWd57O3jqcbPu6IsmbkmPliUx+39Pbi5vRu5BSZ+XF1onYMDwEmrYkQvDwJ9NZTqzRyJ0/PNsgJKKg0J3KyBE/4+GruOTnWpLXvz8fLUcPvQAPx0Ws5fKGPGRwlkZFu+U18fLYF+FSNnpWWWM+OjBCaOCWZIL1+y8gx8tTjVOgcHWCYSdHJS8+IjEYp9/bg8gx9XWAZ7iI0v5fXPExk3Iog7hgaQllnO10tS2bjLfs3Gtu4rwMtDw9ghAfh5azifomfmp0nW+UV8dVoCLilbelY5Mz9NYsJtQQzu6UN2noFvfkqzzsEB4Oqi5uE7gvH30aIvN5Ocquf9eSls3Wc7InSj0rWLocuG763vm737IgCJ3y3l8ISptZWtakW3GUxpcS571n5KUX4G/qHRDH3gS7z9LOfK4oIMCnIuWNMf3WG5Dmz6dSabfq24DjTpMJy+d7xZZfv/dSbpNa6gMstUiOI/6rPPPqNDhw74+/uzbds2Hn/8cR577DFmzZp15ZWvQebRHXbd3o1kWVrNjZJU0/y9HNxhuRZt2Gq/Ga1vRCVFN85TZ3tLS7hxZra2N43WsXOv1KYHv6n5Jp415eyyk7WdBYd5fIjjmnZecd8f2Ld/1+V8/JR9WlddKAAAWMVJREFU+lk5kkQ4xH9WbGwss2bNIjs7m7p16/LMM88wdeqN9xRICCGEEP+/SKdxJalwiP+s999/n/fff7+2syGEEEIIIS5DKhxCCCGEEELYkUQ4lGSUKiGEEEIIIYTDSIRDCCGEEEIIO5IAh5JEOIQQQgghhBAOIxEOIYQQQggh7Ej6cChJhEMIIYQQQgjhMBLhEEIIIYQQwo5kXm0liXAIIYQQQgghHEYiHEIIIYQQQtiRSfpwKEiEQwghhBBCCOEwEuEQQgghhBDCjqQPh5JEOIQQQgghhBAOIxEOIYQQQggh7Ejm4VCSCIcQQgghhBDCYaTCIYQQQgghhHAYaVIlhBBCCCGEHUmTKiWJcAghhBBCCCEcRiIcQgghhBBC2JFJhsVVkAiHEEIIIYQQwmEkwiGEEEIIIYQdSR8OJYlwCCGEEEIIIRxGIhxCCCGEEELYkVn6cChIhUOIK1gQ17m2s+Awo2JO13YWHOarTfVqOwsOU7eeV21nwaEMhv/dC/XAfv61nQWHOXTSUNtZcJizQ0/WdhYcpsHwJrWdBccpP1XbORD/kAqHEEIIIYQQdmSSPhwK0odDCCGEEEII4TAS4RBCCCGEEMKOZJQqJYlwCCGEEEIIIRxGIhxCCCGEEELYkYxSpSQRDiGEEEIIIYTDSIRDCCGEEEIIOzKbTLWdhRuKRDiEEEIIIYQQDiMRDiGEEEIIIexI5uFQkgiHEEIIIYQQwmEkwiGEEEIIIYQdyShVShLhEEIIIYQQQjiMVDiEEEIIIYQQDiNNqoQQQgghhLAjs3QaV5AIhxBCCCGEEMJhJMIhhBBCCCGEHUmEQ0kiHEIIIYQQQgiHkQiHEEIIIYQQdmQym2o7CzcUiXAIIYQQQgghHEYiHEIIIYQQQtiR9OFQkgiHEEIIIYQQwmEkwiGEEEIIIYQdSYRDSSIcQgghhBBCCIeRCIewSkxMZMaMGaxatYrMzExCQ0MZPnw4r7zyCv7+/le1jfj4eCIjIzlw4ACtW7d2bIb/A47t+JHDm76luCAD3+AoutzyIqGR7W2mPXd0Lcd3LCYr5QRGgx7f4Cja9X2MiMY9rGlO7PqJ2P2/k50WC0BgeHM6DHyaoIiWNVKeS634409+XrqU7Owc6tWty8MPPkCLmOZXXO/Y8eM8O2Uq9evV4/NPPrIuNxgMLP7pZ9Zv+IvMrCzq1AlnwvjxdGjfzpHFsKlTUzU9WmjxcoP0XDN/7jQQn2b7aZWXGwzupCXMX4W/TsWOY0b+3GVUpJk42IkGoVWf75xMNPLdWoNDylCd4zt/5MiWuZQUZOATFEXnIVMJqeY3GX90LSd2Lyb7wkmMRj0+QVG07fMYdRp1t6bJSYtl//qPyUw+RmHuBToNeYGYbuNqqjhX1DZKRefGKjzdICMP1h8wkZhpO23jcGgbpSbYBzQaS/otx0ycS63RLFdr14Yf2bJyLoV5GQSFRTH4rqnUb1zNsTu9j7VL5pCRcpZyfSk+AWF06DWGbgPHW9OkJcWy4bePuRB/jNzMCwy+8wW6DqidY9exsZoeMRo83SE9x8zK3UbOp9v+n/N0g0EdNIT5q/H3hp0nTKzcrfyfmzBQS2RI1f+5U4kmvt9Qs/9zR7b9yP6/v6U4PwO/kCh6DH+RsAa2j9uZw2s5un0xGcmW64BfSBQdBzxGvSY9bKY/feBP1n7/DJExfRhy/6eOLMa/4te9PQ2emYCubQyuYUHsHTWJtOUbajtbDmU2S4TjUhLhEACcPXuW9u3bc/r0aRYtWkRcXBxffPEFGzZsoEuXLmRnZ9d2Fv9zzhxayY4Vs2nT+2FGPvEbIfXbs2rugxTmXLCZPuXsXsKjuzLwvq8Y+cSvhDXsxJoFk8hMPn5Jmt00bD2EoQ8uYPikxXj4hLLymwkU5aXVVLEA2Lh5C198/Q13jB3DZx99SExMc16aPoP09PTLrldUVMQ7c96nTetWVT6b/90PrFy9mkkPP8TXn3/GkEGDmPn6G8SdOeOoYtjUIlLNkE5aNh408MmycuJTTYwb4ITOw3Z6jQaKSs1sPGQkNdv2BWbh+nLe+LHM+vrgVz1Gk5mj52p22MSzh1ey6883ad3rIYY/tpSQ+u1Ys+AhCnNt/yZT4/cSHtWV/uO/5NZHfyGsQSfWfT+JzAsVv0nD/7V339E13/8fwJ/3JrJkyZQlQ2LEDEkr1IogoSWl1X6rRlEtaktR1ChVLRWjRRGrahRNjYhNzYZEJCVICAkSI5EhgyT3/v7Iz+XKkP2+N56Pc3KO+/58kjw/7r2593XfKzcHBiZ2cO8+AboGZtV1KaXS2E6Cri0lOB0tx9oDMiQ8kuOjDlIY6hV9vp2FBHH35dh2UobAgzLcfiBHv3cKChDRov4NRvDmH9DpvS8wcs4u2DdsjY2LvkBqctH3nZa2Lt727o9h32zC2Pn70Om9L3F451KcP7ZdcU7usxyYmNuh24cToG8k7r5r6iBFj7c0cDwyH7/uzsXtB3IM7KpZ7HNOUwPIzAFORBb/nPvjaB5+2PZM8bU0KLfgOXe7ep9zMReDcTJoPty9v8RHE/+CtaM79vw2HBnFvA7cu3EBdg3a4r3Pf8NHE3bC1vlt7Fs7Eg/vXCl0bnrKXZze/WOxxYsq0aith/TIa7g8do7oKCQICw4CAIwaNQpaWlo4ePAgOnbsiHr16sHX1xeHDx/G3bt3MW3aNACARCJBUFCQ0vcaGxtj/fr1AABHR0cAgJubGyQSCTp16qQ4LzAwEE2aNIG2tjasrKzw1VdfKY7Fx8ejd+/e0NfXh6GhIfr164f791+8iZ41axZatmyJwMBA1KtXD/r6+hgxYgTy8/Px448/om7durCwsMC8efOUsqWlpWH48OGwsLCAoaEhvLy8cOnSpUr8nyte5Mn1aOjRF43e+hB1LOujba9voG9UF1fObSny/La9vkHLTsNgYdcMRmYOeMtnAoxM7XE7+pjiHK//LUQTz09gZt0YxhZO6ND3O8jlMtyNPVst1/Tcrr+C0L1bV/h274569ewwYvjnMDczw97g/SV+35Llv6Bzp45o3KhRoWNHjh3Dx/364S0Pd1hZ1cV7PXugdSs37NwVVEVXUbR3mmog7LoMF67L8DBNjn3/5iMtU463G2sUeX7qE2DvuXxcjJUh51nRPzP7GfAk+8WXs40UuXlAVDUXHP+d2oAGrfugoceHMLaojzbvfoPaRnUR/e/WIs9v8+43aN5hGMxtCx6T7t3Hw9C0HhJeekya2zbDW77+qN+iJzQ0tKrrUkrlrYYSXIqT49JNOZIzgMMX5UjPBlrVlxR5/uGLcpy7KkdiCvD4CXAiSo6UJ4CLddHnV6fTIRvQukMfuHf6EBbW9dGz/zcwMqmL0CNF33fW9q5o4dkTlrYuqGNug5btesGlWTvcun5BcY6tUzP4fOyP5m16QrOWuPuuXRMpwmJkCIuR4WEaEByaj7RM4K2GxT/ngkPzEXFDhqe5Rf/MV59z9a0lyM0D/rtVvc+5iBPr4fp2XzRp8yFMLOuj/fvfQN+4LqJOF/060P79b9DKaxgs6zWDsbkDPHtOgLGZPeIuH1M6TybLx6HN/ni7+2gYmtpWx6VUyMMD/+D6zAAkBR0SHaXayGSyavtSByw4CCkpKThw4ABGjhwJXV1dpWN169ZF//79sW3btlJ1D4aGhgIADh8+jMTEROzatQsAsGLFCowaNQrDhw9HVFQUdu/eDWdnZwAF3Y5+fn5ISUnBiRMncOjQIdy4cQMfffSR0s++ceMG9u/fj5CQEGzZsgWBgYHo2bMn7ty5gxMnTmDBggWYPn06zp07p/i5PXv2RFJSEoKDgxEWFoZWrVqhS5cuVd5jk5/3DI/uXoatSzuldtsG7XD/9sVS/Qy5TIZnTzOhrWdU7Dl5udmQ5eeVeE5ly83NRUxsLFq7uSm1t27lhivR0cV+34FDBY+JTz/5X7E/V6tWLaU2bS1tXL5S+JO9qqIhBazNJIi5q/wHPPauDPYWlffn0r2BFJE3ZcitxpEd+XnP8OjeZdi88pi0cW6HB2V4TOY+zYK2nnEVJKxcUilgVQe4maT8dysuSQ5bs9IXEFqaBW9eRcrLe4Z7ty7DuanyfefctB3iY0t33927fQXxsRFwbOhRFRHLTUMKWJtKEHvvlefcPRnqWVReodfaRYqouOp/zj24cxl2DZTvN7uG7ZB0q2yvAzqv/I0/f/AX6NY2gWubDyotL1FV4hwOQkxMDORyORo3blzk8caNG+Px48d4+PDha3+Wubk5AMDU1BR169ZVtM+dOxcTJ07E2LFjFW0eHgUvfIcPH0ZkZCTi4uJgZ2cHANi0aROaNGmC8+fPK86TyWQIDAyEgYEBXF1d0blzZ1y7dg3BwcGQSqVo2LAhFixYgOPHj6NNmzY4duwYoqKi8ODBA2hrawMAFi5ciKCgIOzYsQPDhw8vx/9W6eRkPYZclg9dfeW5L7r6psjKKGYA+SsiT65DXm4W6jf3Lfac0P0/o7aRJWyc21Yob1mkp6dDJpPB2NhYqd3Y2BiPH6cW+T13795D4PoNWPTjD9DQKPpTy9at3LAzKAjNmjaFlVVdXLx0CWf/PQdZfvV9eqOnA2hIJXiSrfwmNSMbcNEt5pvKyNZMgromUuw6Wb3vYnOyUv//Mak8dEbXwBTZMaV7TEadWoe8Z1lwbOZTFRErlZ4WIJVKkJmj3J6ZA9TWKd3PeLuhBLU0gegEsWOxszJSIZPlFxr2VNvIFE/SSr7vfhzXCZkZKZDl58Pr/VFw7/RhVUYtMz3t58855fbMbDn0dSunyLcxk6BuHSn+Ol1Md0gVyc4seB3QM1B+HdAzKP3rwMXj65D7LAvOLV+8DiTGhePKvzvx8cSgyoxLlYyrVCljwUGv9bxnQyIp36dNDx48wL1799ClS5cij0dHR8POzk5RbACAq6srjI2NER0drSg4HBwcYGBgoDjH0tISGhoakEqlSm3P5xGEhYXhyZMnhSa8Z2dn40Yx8wKePn2Kp0+fKrXl5WpBs5Z2Ga74hVf/z+QFja/9vtiIvQg7tBzdBv1SqGh5LuL4GtyI2Id3v9hY7nwVUeja5HKgiEvLz8/HDz/9hAH9P4GtjU2xP2/EF8MRsHQZhn05AgBgbWWFbt7eOHj4cKXmLo1XXyYqc0CNe0MNJKXIcOeRoBejVy9GLi+isbAbl/bh4pFf4D1gebGPyZrEtZ4E7ZtKsOOUDFlPX39+tSjqvnvN35Nh037Hs5wsJNyIwMHtP8PEwh4tPHtWXcbKUolPOncXKZIey3BX2HPulYuRF9FWhOvhexF6cDl6DvlFUbQ8y3mCg5v94dXvO+jq16mCsERVgwUHwdnZGRKJBFeuXIGfn1+h41evXkWdOnVgZmYGiURSaGhVbm7Jnxq9OkzrVXK5vMhi5tX2Wq8Mt5FIJEW2PR/PKJPJYGVlhePHjxf62a9+Ov/c/PnzMXv2bKW2rh99i+4fzyrxGl6lo1cHEqlGoU+xcp4kQ+81b9ZuXArGiR3T0bV/AGxdiu65uHRiLSKOrULPzwNhatWwTNkqytDQEFKpFI8fP1ZqT0tLQ50i/l+zs7NxPSYWsTdu4pcVKwEU3LdyuRy+7/XG/Llz0LJFCxgbGWHWjOl49uwZ0tMzYGpqgrXrNsDS0rI6LgsAkJUD5MvkMNCV4OWyQ18XhT6BLY9aGkBzJykOh+e//uRKpqNnDIlUA9mvPCazn6S8toC4GRmMk7umw+t/i6u1N60isp4BMpm8UG9GbR0U6vV4VWM7CXp6SLDrjAy3qnc9hiLpGRhDKtXAk1Tl+y4zPQX6hiXfdybmBeP769o1wJO0ZBwLWq5SBUfW04LnnP4rLxO1dQr3NJZHLY2ChSCOXKz+55xu7f9/HUhXvt+ySvE6EHMxGEe3TYfPoADYNXjxnEtLTkBGyl3sXTtC0SaXF7zm/TKpCT6dsh9GZvUq8SqovJ7fL1SABQfB1NQUXbt2xa+//orx48crFQhJSUnYvHkzBg4cCIlEAnNzcyQmJiqOx8TEICsrS3FbS6tg4mF+/os/7gYGBnBwcMCRI0fQuXPnQr/f1dUV8fHxSEhIUPRyXLlyBWlpacUO8yqNVq1aISkpCZqamnBwcCjV90ydOhUTJkxQaltxoOyTKTU0tWBm0wR3Y87AsWlXRfudmDNwcPUq9vtiI/bixJ/T0OWTRajXuFOR51w6sRbhR1agx9A1MLdtVuZsFVWrVi24ODsj/OJFtGvrqWgPvxgBzzZvFzpfT08Pq35ZrtS2Z98+RERGYsbUqahbV7mg0NLSgpmZKfLy8nDqzBl0aP8Oqku+DLj3SA5nGymuvLSajbO1FFfiK/7i0cxJCg0pcDG2+t/8aGhqwcy6Ce7GnoFDkxePyXuxZ1CvhMfkjUv7cHLnNHT6eCHqNepUDUkrh0wGJD4GHOtKcP3uizeujpbKt1/lWq+g2Pj7nAw3Eos9rVppamrB2qEJYi+fgav7i/su9vIZNHYr/r4rTI68PMETUl6RLwPuJcvhbC1FdPyL50XB7Yo/55o6SqGhAUTcrP43fxqaWrCwbYKE62dQv/mL+y3h+hk4Nin+frsevhdHtk5D9wGL4ODaSelYHQsn/M9/t1Lbuf1LkPs0E+39CiakE6kiFhwEAFi+fDnatm2L7t27Y+7cuXB0dMTly5fh7+8PGxsbxepPXl5eWL58Odq0aQOZTIbJkycr9TJYWFhAV1cXISEhsLW1hY6ODoyMjDBr1ix8+eWXsLCwgK+vLzIyMnD69GmMHj0a3t7eaN68Ofr374+AgADk5eVh5MiR6NixI9zdy7/cn7e3Nzw9PeHn54cFCxagYcOGuHfvHoKDg+Hn51fkz9bW1lbM93hOs1b5PmVr3n4wjm2bDDPbprCs1xLRodvxJDURjdt8DAAI3b8ImekP0PmjBQAKio1j26agba9vYFGvBbIyCubMaGrqQEu3YChZxPE1uHBwCbz+txAGJjaKc2pp6aGWdjFrSFaBPu/74adFP6OBiwsaN2qE4JAQPHj4ED17FIwzDly/AY+Sk/H1xAmQSqVwcLBX+n5jY2No1dJSar969RoeJSejvpMTHiUn4/c//oBcJkO/vn2q7boA4NR/+fiwoybuPpQi/oEcHo2kMNKXIPRqwZuhbu4aMNSTYMc/L2afWpkU9MRpaRZ8MmtlIkG+rGAPj5e5N9BAdLwM2YKG6DR9ZxBO/DkF5jZNYVGvJa6e344naYlo9FbBAg3nD/yMrPT76PhhwWPyxqV9OPHnFLR5dyos7F56TNbSgZZOwWMyP+8ZUh8UDFGU5eciK/0Bku9Fo5a2HgxN7YtIUX1Cr8nR620JElOAu4/kcKsvgaEeEH6j4H7p1EwCAz1gz78Ft13rSfDe2xIcuijH3eQXcz3y8lHsakjVpZ3PIOxYNQU2jk1h59wSF45tR1pyIjy8Cu67g9t/Rvrj+/jgi4L77tzhzTA2tYaZVcHKgbevh+PU/nVo491f8TPz8p7h4d2C+y4/Lxfpjx8g8XY0tHT0YGpZfffd6csyfNBeA3cfyZHwUAb3Bhowqg2cv1bwnOvaSgOGesDOUy8KkrovP+e0C27n58vxME35Z7d2kQp9zrXsOBiH/pgMC7umqOvQEpfPbseTx4lo2rbgdeDM3oLXga6fFNxv18P34vAfU9D+/W9gad8CmekvnnPaugbQrKUNU6sGSr9D+/9fH15tVyUatfVQ2/lFz4ueoy0MWzTCs5Q05CSoSGVPVYoFBwEAXFxccOHCBcyaNQsfffQRkpOTUbduXfj5+WHmzJkwMTEBACxatAifffYZOnToAGtrayxZsgRhYWGKn6OpqYmlS5dizpw5+Pbbb9G+fXscP34cgwYNQk5ODhYvXoxJkybBzMwMH3xQsLrG86V2R48ejQ4dOkAqlcLHxwfLli2r0DVJJBIEBwdj2rRpGDJkCB4+fIi6deuiQ4cO1TJMp36LHsjJSkX4kV/+f8MnF/h+tgoGdQrmMWRlPFTa/yD6322Qy/JwOmgOTge9WKu8QWs/dOr3A4CCTdtk+bk4/PtYpd/VynsU3LuOrvJreq5Th/bISE/H5i1bkZKSAnt7e8ydPROWFhYAClY+K80iAy97lvsMGzb9jsSkJOjq6sDD3R1fT5wAfX39qriEYkXFyaCnkwcvN00Y6AH3H8ux4WAuUp8UHDfQlcBYX3kI4Oj3X/SC2ZoDLZ018DhDjp+2v/g02dRQAoe6UgTuF/cJs1PzgsfkxaO//v9mlC7oNmil4jGZnfEQT1JfvPhfDS14TJ7d/R3O7v5O0e7Syg8dPpgPoOBxHLT8RVEYdTIQUScDUdfRAz0/31hNV1a06AQ5dLWBd5pIoK8jwcM0YNtJGdL/v1NWXxcw1HsxfM6tvgQaUgl8Wkvg89J+k5FxMuwNFTsBtNnbPZD1JBXH/v4VGakPYWnjggETVqKOWcF9l5H2EKkpL+47uVyOg3/+jMcP70KqoQETi4L9Njw6v1j9L+PxQ/zy7Yv77tT+QJzaHwiHRh4YNrX67rv/bsmgpw10bqkBA10N3H8sx6bDeUjNLDhuoIdCz7mver34oMvGDGhRXwOPn8ixaMeLytDUEHCwlGLdAXHVootbwXPu/MFfkJn+EKZWLnj381UwNHnxOvDynhz/nd0GmSwPJ3bOwYmdL14HGnn4wft/P1R7/spi1LopPI9sUtx2XfgNACBh4y5EDp0qKlaV4qRxZRI5t0IkKtGioJr7FOnbNEZ0hCrz2wmxn65XJWPjWq8/SY3l5dXc55yLvfg9ParKpavVu4N3dbI0r7nPOSe/wvsi1RQ9c68J+909hkRV2+8KDqz+4dVlxR4OIiIiIqJKxB4OZdz4j4iIiIiIqgx7OIiIiIiIKpGMy+IqYQ8HEREREdEb5Ndff4WjoyN0dHTQunVrnDx5ssTzT5w4gdatW0NHRwdOTk5YuXJlmX4fCw4iIiIiokokl8mr7austm3bhnHjxmHatGm4ePEi2rdvD19fX8THxxd5flxcHHr06IH27dvj4sWL+OabbzBmzBjs3Lmz1L+TBQcRERER0Rvi559/xtChQzFs2DA0btwYAQEBsLOzw4oVK4o8f+XKlahXrx4CAgLQuHFjDBs2DEOGDMHChQtL/TtZcBARERERVSK5TFZtX0+fPkV6errS19OnRe92+ezZM4SFhaFbt25K7d26dcOZM2eK/J6zZ88WOr979+64cOECcnNLt88NCw4iIiIiIjU1f/58GBkZKX3Nnz+/yHMfPXqE/Pz8QhsgW1paIikpqcjvSUpKKvL8vLw8PHr0qFQZuUoVEREREVElqs59OKZOnYoJEyYotWlra5f4PRKJ8iakcrm8UNvrzi+qvTgsOIiIiIiI1JS2tvZrC4znzMzMoKGhUag348GDB4V6MZ6rW7dukedramrC1NS0VL+XQ6qIiIiIiCqRXC6rtq+y0NLSQuvWrXHo0CGl9kOHDqFt27ZFfo+np2eh8w8ePAh3d3fUqlWrVL+XBQcRERER0RtiwoQJWLNmDQIDAxEdHY3x48cjPj4eX375JYCCIVoDBw5UnP/ll1/i9u3bmDBhAqKjoxEYGIi1a9di0qRJpf6dHFJFRERERFSJZNU4h6OsPvroIyQnJ2POnDlITExE06ZNERwcDHt7ewBAYmKi0p4cjo6OCA4Oxvjx4/HLL7/A2toaS5cuRd++fUv9O1lwEBERERG9QUaOHImRI0cWeWz9+vWF2jp27Ijw8PBy/z4WHERERERElUguK9vcipqOcziIiIiIiKjKsIeDiIiIiKgSVec+HOqAPRxERERERFRlWHAQEREREVGV4ZAqIiIiIqJKVNYN+Wo69nAQEREREVGVYQ8HEREREVEl4qRxZezhICIiIiKiKsMeDiIiIiKiSsSN/5Sxh4OIiIiIiKqMRC6Xc5AZkYp4+vQp5s+fj6lTp0JbW1t0nErFa1NPvDb1xGtTTzX52oCaf31UPBYcRCokPT0dRkZGSEtLg6Ghoeg4lYrXpp54beqJ16aeavK1ATX/+qh4HFJFRERERERVhgUHERERERFVGRYcRERERERUZVhwEKkQbW1tzJw5s0ZOpuO1qSdem3ritamnmnxtQM2/PioeJ40TEREREVGVYQ8HERERERFVGRYcRERERERUZVhwEBERERFRlWHBQUREREREVYYFBxERERERVRkWHERUpfLy8nD48GGsWrUKGRkZAIB79+7hyZMngpNRaaWnpyMoKAjR0dGioxDVWM+ePcO1a9eQl5cnOkqlSk1NxZo1azB16lSkpKQAAMLDw3H37l3Byag6cVlcIsHCw8NRq1YtNGvWDADw999/Y926dXB1dcWsWbOgpaUlOGH53b59Gz4+PoiPj8fTp09x/fp1ODk5Ydy4ccjJycHKlStFR6Qi9OvXDx06dMBXX32F7OxstGjRArdu3YJcLsfWrVvRt29f0RHpDSWTyRAbG4sHDx5AJpMpHevQoYOgVBWTlZWF0aNHY8OGDQCg+Ds5ZswYWFtbY8qUKYITll9kZCS8vb1hZGSEW7du4dq1a3BycsKMGTNw+/ZtbNy4UXREqibs4SAS7IsvvsD169cBADdv3sTHH38MPT09/Pnnn/j6668Fp6uYsWPHwt3dHY8fP4aurq6i/f3338eRI0cEJqscmzZtQrt27WBtbY3bt28DAAICAvD3338LTlYx//zzD9q3bw8A+OuvvyCXy5GamoqlS5di7ty5gtNVzP379zFgwABYW1tDU1MTGhoaSl/qavfu3aX6Umfnzp2Ds7MzGjdujA4dOqBTp06Kr86dO4uOV25Tp07FpUuXcPz4cejo6Cjavb29sW3bNoHJKm7ChAkYPHgwYmJilK7N19cX//zzj8BkVN00RQcgetNdv34dLVu2BAD8+eef6NChA/744w+cPn0aH3/8MQICAoTmq4hTp07h9OnThXpp7O3t1b47fcWKFfj2228xbtw4zJs3D/n5+QAAY2NjBAQEoHfv3oITll9aWhpMTEwAACEhIejbty/09PTQs2dP+Pv7C05XMYMHD0Z8fDxmzJgBKysrSCQS0ZEqhZ+f32vPkUgkisepOvryyy/h7u6Offv21aj7LigoCNu2bUObNm2UrsnV1RU3btwQmKzizp8/j1WrVhVqt7GxQVJSkoBEJAoLDiLB5HK5YmjA4cOH8e677wIA7Ozs8OjRI5HRKkwmkxX5BufOnTswMDAQkKjyLFu2DKtXr4afnx9++OEHRbu7uzsmTZokMFnF2dnZ4ezZszAxMUFISAi2bt0KAHj8+LHSp5Tq6NSpUzh58qSiyK8pXh1eVBPFxMRgx44dcHZ2Fh2lUj18+BAWFhaF2jMzM9W+qNLR0UF6enqh9mvXrsHc3FxAIhKFQ6qIBHN3d8fcuXOxadMmnDhxAj179gQAxMXFwdLSUnC6iunatatSD41EIsGTJ08wc+ZM9OjRQ1ywShAXFwc3N7dC7dra2sjMzBSQqPKMGzcO/fv3h62tLaytrdGpUycABUOtns81Uld2dnbg1EX19PbbbyM2NlZ0jErn4eGBffv2KW4/LzJWr14NT09PUbEqRe/evTFnzhzk5uYCKLi2+Ph4TJkyhXPB3jDs4SASLCAgAP3790dQUBCmTZum+PRux44daNu2reB0FbN48WJ07twZrq6uyMnJwSeffIKYmBiYmZlhy5YtouNViKOjIyIiImBvb6/Uvn//fri6ugpKVTlGjhyJt956CwkJCejatSuk0oLPppycnNR+DkdAQACmTJmCVatWwcHBQXScSlPa8fDqOrEaAEaPHo2JEyciKSkJzZo1Q61atZSON2/eXFCyipk/fz58fHxw5coV5OXlYcmSJbh8+TLOnj2LEydOiI5XIQsXLkSPHj1gYWGB7OxsdOzYEUlJSfD09MS8efNEx6NqxFWqiFRUTk4ONDQ0Cr2oqpvs7Gxs3boVYWFhkMlkaNWqFfr37680iVwdrVu3DjNmzMCiRYswdOhQrFmzBjdu3MD8+fOxZs0afPzxx6IjVpr8/HxERUXB3t4ederUER2nQurUqYOsrCzk5eVBT0+v0PPr+bKd6kYqlSo+GS/uZV3d53A8L3xfJpFIIJfL1f7aoqKisHDhQqW/k5MnT1b7HsXnjh49ivDwcMW1eXt7i45E1YwFB5EKSE1NxY4dO3Djxg34+/vDxMQE4eHhsLS0hI2Njeh4VIzVq1dj7ty5SEhIAFAwEXLWrFkYOnSo4GQVM27cODRr1gxDhw5Ffn4+OnbsiDNnzkBPTw979+5VDLFSR8+XHi3OoEGDqilJ5TI1NYWBgQEGDx6MAQMGwMzMrMjzjIyMqjlZ5Xm+ElxxXu1tJCLVwYKDSLDIyEh06dIFxsbGNW6d8g0bNsDMzEwxL+Xrr7/Gb7/9BldXV2zZskVt3yDk5eVh8+bN6N69O+rWrYtHjx5BJpMVOfFTHdna2iIoKAju7u4ICgrCqFGjcOzYMWzcuBHHjh3D6dOnRUekVzx79gx//fUXAgMDcfLkSfTo0QNDhw6Fj4+P2k88rumKmlQNFPTeaGtrq/VeTAAQGhqK48ePF7l3ys8//ywoFVU3FhxEgnl7e6NVq1b48ccfYWBggEuXLsHJyQlnzpzBJ598glu3bomOWG4NGzbEihUr4OXlhbNnz6JLly4ICAjA3r17oampiV27domOWG56enqIjo5W26KpJDo6OoiNjYWtrS2GDx8OPT09BAQEIC4uDi1atCj2DZKqSk9Ph6GhoeLfJXl+njpLSEjAunXrsGHDBjx9+hSDBg3C7Nmzoamp/tM2b9y4gYCAAERHR0MikaBx48YYO3Ys6tevLzpaub08HK4otra2GDx4MGbOnFnksDJV9v3332P69Olo2LAhLC0tla5TIpHg6NGjAtNRdVL/vz5Eaq4mr1OekJCgmAQfFBSEDz74AMOHD0e7du3UelgOULBizsWLF2tkwWFpaYkrV67AysoKISEh+PXXXwEU7Iisjpvj1alTB4mJibCwsICxsXGRb+5qwjyA5+zs7PDtt99iwIABGDp0KH744QdMnDhRsbeKujpw4AB69eqFli1bol27dpDL5Thz5gyaNGmCPXv2oGvXrqIjlsv69esxbdo0DB48GG+99RbkcjnOnz+PDRs2YPr06Xj48CEWLlwIbW1tfPPNN6LjlsmSJUsQGBiIwYMHi45CgrHgIBKsJq9Trq+vj+TkZNSrVw8HDx7E+PHjARRcc3Z2tuB0FTNy5EhMnDgRd+7cQevWrVG7dm2l4+q6Yg4AfPbZZ+jXr59ic7Xnb+T+/fdfNGrUSHC6sjt69KjizfaxY8cEp6laT58+xc6dOxEYGIizZ8+iZ8+e2Ldvn9oXGwAwZcoUjB8/Xmnfm+ftkydPVtuCY8OGDVi0aBH69eunaOvVqxeaNWuGVatW4ciRI6hXrx7mzZundgWHVCpFu3btRMcgFcAhVUSCDR8+HA8fPsT27dthYmKCyMhIaGhowM/PDx06dFDrncb79++Pq1evws3NDVu2bEF8fDxMTU2xe/dufPPNN/jvv/9ERyy3mrxiDlCwLHNCQgI+/PBD2NraAih4Y2RsbKzWu6jXVKGhoVi3bh22bt0KR0dHDB48GJ9++mmNKDSe09HRQVRUFFxcXJTar1+/jubNmyMnJ0dQsorR09PDpUuXCl1XTEwMWrRogaysLMTFxaFJkybIysoSlLJ8fvzxR9y7d0+tX8eocrCHg0iwmrxO+S+//ILp06cjISEBO3fuhKmpKQAgLCwM//vf/wSnq5i4uDjREarUBx98AABKb+LUdQWnyMjIUp+rrj1Tbdq0Qb169TBmzBi0bt0aQMGu6q/q1atXdUerNObm5oiIiCj0xjwiIkKtF2ywtbXF2rVrC/XcrF27FnZ2dgCA5ORktVySetKkSejZsyfq168PV1fXQstQq/M8Piob9nAQqQiuU06qIj8/H99//z1WrlyJ+/fv4/r164qV0xwcHNRu2d/nk3Jf93Knzj1TpZlMrM7XBwBz5szB4sWLMWXKFLRt2xYSiQSnTp3CggULMHHiREyfPl10xHLZvXs3PvzwQzRq1AgeHh6QSCQ4f/48oqOjsXPnTrz77rtYsWIFYmJi1G5Vp1GjRmHt2rXo3LlzoUnjQMF+RvRmYMFBRFUqNTUVa9euVVpVZujQoWq9HwCA1y5XPHDgwGpKUvnmzJmDDRs2YM6cOfj888/x33//wcnJCdu3b8fixYtx9uxZ0RHL5HX7N7ysJi4CUFPI5XIEBARg0aJFuHfvHgDA2toa/v7+GDNmjFov/3v79m2sWLEC169fh1wuR6NGjfDFF18gNTUVLVu2FB2v3AwMDLB161bF0uj05mLBQSTA0qVLMXz4cOjo6GDp0qUlnjtmzJhqSlX5Lly4gO7du0NXV1ex+sqFCxeQnZ2NgwcPolWrVqIjlturwxtyc3ORlZUFLS0t6Onpqe2O1QDg7OyMVatWoUuXLkpLNV+9ehWenp54/Pix6IhUjOTkZMXQxYSEBKxevRo5OTl477330L59e8HpKk9GRgaAgje0NU1qaio2b96MwMBAREREqHWvlL29PQ4cOKCWi01Q5WLBQSSAo6MjLly4AFNTUzg6OhZ7nkQiwc2bN6sxWeVq3749nJ2dsXr1asUeAHl5eRg2bBhu3ryJf/75R3DCyhUTE4MRI0bA398f3bt3Fx2n3HR1dXH16lXY29srFRxXrlzBW2+9hSdPnoiOWG41tWcqKioK7733HhISEuDi4oKtW7fCx8cHmZmZkEqlyMzMxI4dO+Dn5yc6KhXj6NGjCAwMxK5du2Bvb4++ffuib9++cHNzEx2t3NatW4eQkBCsW7cOenp6ouOQQCw4iKjK6Orq4uLFi4U+3bpy5Qrc3d3VbsWV0rhw4QI+/fRTXL16VXSUcnN3d8e4cePw6aefKhUcs2fPxuHDh3Hy5EnREcutpvZM+fr6QlNTE5MnT8bvv/+OvXv3olu3blizZg0AYPTo0QgLC8O5c+cEJy2bVq1a4ciRI6hTpw7c3NxKHDYVHh5ejckqx507d7B+/XoEBgYiMzMT/fr1w8qVK3Hp0iW4urqKjldhbm5uuHHjBuRyORwcHApNGlfH+4zKh6tUEVGVMTQ0RHx8fKGCIyEhoUYOhQAADQ0NxfhydTVz5kwMGDAAd+/ehUwmw65du3Dt2jVs3LgRe/fuFR2vQooaDvZyz5S6On/+PI4ePYrmzZujZcuW+O233zBy5EjFZPLRo0ejTZs2glOWXe/evaGtra34tzrP03hVjx49cOrUKbz77rtYtmwZfHx8oKGhgZUrV4qOVmnYo0bPsYeDSLAPPvgA7u7umDJlilL7Tz/9hNDQUPz555+CklXcmDFj8Ndff2HhwoVKq8r4+/ujb9++ar02++7du5Vuy+VyJCYmYvny5bCzs8P+/fsFJascBw4cwPfff4+wsDDFymnffvstunXrJjpalVD3nimpVIqkpCTF8rAv90wBwP3792Ftba3W8wFqGk1NTYwZMwYjRoxQWuq3Vq1aNaaHg+g59nAQCXbixAnMnDmzULuPjw8WLlwoIFHlWbhwISQSCQYOHIi8vDwABS+mI0aMKLTmvLp59ZM7iUQCc3NzeHl5YdGiRWJCVaLu3bur9TyUsqoJPVOvfvpfk3oDAMDJyQnnz59XTIp/LjU1Fa1atVK7+W4nT55EYGAg3N3d0ahRIwwYMAAfffSR6FhVIiwsTLFSoaurq1rPS6HyYQ8HkWC6urqIiIhAw4YNldqf79CdnZ0tKFnlycrKUozjdXZ25uRBNfHs2TM8ePAAMplMqb1evXqCElVcTe2Zkkql8PX1VQw/2rNnD7y8vFC7dm0AwNOnTxESEqLWPRyv9uI8d//+fdjZ2eHZs2eCklVMVlYWtm7disDAQISGhiI/Px8///wzhgwZovZDTx88eICPP/4Yx48fh7GxMeRyOdLS0tC5c2ds3boV5ubmoiNSNWHBQSSYh4cH3nvvPXz77bdK7bNmzcKePXsQFhYmKFnFpaWlIT8/HyYmJkrtKSkp0NTUhKGhoaBklS8/Px9RUVGwt7dXyx2BXxYTE4MhQ4bgzJkzSu1yuVztN497dYO8V3umrKysBCWrmM8++6xU56njRmvPi0Q/Pz9s2LBBaQ+f/Px8HDlyBIcOHcK1a9dERaw0165dw9q1a7Fp0yakpqaia9euhYpkdfLRRx/hxo0b2LRpExo3bgygYNGQQYMGwdnZGVu2bBGckKoLCw4iwXbv3o2+ffvik08+gZeXFwDgyJEj2LJlC/7880+1nnTn6+uL9957DyNHjlRqX7lyJXbv3o3g4GBBySpu3LhxaNasGYYOHYr8/Hx06NABZ8+ehZ6eHvbu3YtOnTqJjlhu7dq1g6amJqZMmQIrK6tCQ3NatGghKBm9iZ4XiUXtFl+rVi04ODhg0aJFePfdd0XEqxL5+fnYs2cPAgMD1brgMDIywuHDh+Hh4aHUHhoaim7duiE1NVVMMKp2LDiIVMC+ffvw/fffIyIiArq6umjevDlmzpyJjh07io5WISYmJjh9+rTik63nrl69inbt2iE5OVlQsoqztbVFUFAQ3N3dERQUhFGjRuHYsWPYuHEjjh07htOnT4uOWG61a9dGWFjYG7FZV03qmarpHB0dcf78eZiZmYmOQqVkYGCAkydPFtot/eLFi+jYsSPS09PFBKNqJ339KURU1Xr27InTp08jMzMTjx49wtGjR9W+2AAKxo0/nyz+stzcXLWfm/Lo0SPUrVsXABAcHIwPP/wQDRo0wNChQxEVFSU4XcW4urri0aNHomNUiXHjxmHt2rUAoOiZatWqFezs7HD8+HGx4ahEcXFxLDbUjJeXF8aOHau0IMPdu3cxfvx4dOnSRWAyqm4sOIhUxLNnz3Dnzh3Ex8crfakzDw8P/Pbbb4XaV65cidatWwtIVHksLS1x5coV5OfnIyQkBN7e3gAKJoBqaGgITlcxCxYswNdff43jx48jOTkZ6enpSl/qbMeOHYohYXv27MGtW7dw9epVjBs3DtOmTROcjkoyZswYLF26tFD78uXLMW7cuOoPRK+1fPlyZGRkwMHBAfXr14ezszMcHR2RkZGBZcuWiY5H1YhDqogEq8kTdE+fPg1vb294eHgoPs06cuQIzp8/j4MHD6J9+/aCE5bfrFmzEBAQACsrK2RlZeH69evQ1tZGYGAgVq9ejbNnz4qOWG4vj5l/WU14TOro6CA2Nha2trYYPnw49PT0EBAQgLi4OLRo0ULtC6qazMbGBrt37y70YUV4eDh69eqFO3fuCEpGr3Po0CFcvXoVcrkcrq6uig9o6M3BfTiIBBs8eDA0NTWxd+/eIifoqrN27drh7Nmz+Omnn7B9+3bF/JS1a9cqbXSljmbNmoWmTZsiISEBH374oWI5Ug0NjUKbOKqbY8eOiY5QZZ73TFlZWSEkJAS//vorgJrRM1XTJScnK61Q9ZyhoWGNHQJYU3Tt2hVdu3YVHYMEYsFBJFhERESNnqDbsmVLbN68WXSMKvHBBx8o3U5NTcWgQYMEpak8np6e0NLSKvKYur+x++yzz9CvXz9Fcf/8TdC///5bY5+DNYWzszNCQkLw1VdfKbXv379fsaM6qZYxY8bA2dkZY8aMUWpfvnw5YmNjERAQICYYVTsWHESC1eQJuq+bg6LOG8gtWLAADg4Oip2B+/Xrh507d8LKygrBwcFo3ry54ITl169fP+zatavQnhX3799Hly5d8N9//wlKVnE1uWeqppswYQK++uorPHz4UGkJ8UWLFvGNq4rauXNnkcv6tm3bFj/88APvtzcI53AQCXb06FFMnz4d33//PZo1a4ZatWopHVfnzfGkUmmJQ8TUeS6Ak5MTfv/9d7Rt2xaHDh1Cv379sG3bNmzfvh3x8fE4ePCg6Ijl9vbbb8PV1VVpk7jExER4eXmhSZMm2LFjh8B09CZbsWIF5s2bp1j1yMHBAbNmzcLAgQMFJ6Oi6Ojo4L///oOzs7NSe2xsLJo2bYqcnBxByai6seAgEqwmT9C9dOmS0u3c3FxcvHgRP//8M+bNm4c+ffoISlZxurq6uH79Ouzs7DB27Fjk5ORg1apVuH79Ot5++208fvxYdMRyS05ORocOHdCtWzcsXrwYd+/ehZeXF1q0aIGtW7cW6vlQN0eOHMGRI0fw4MEDyGQypWOBgYGCUlFZPHz4ELq6utDX1xcdhUrQtGlTfPnll4WGwS1btgwrVqzAlStXBCWj6sYhVUSC1eQJukXtSO3u7g5ra2v89NNPal1w1KlTBwkJCbCzs0NISAjmzp0LoKBQVOciEQBMTU1x4MABvPPOOwAKNqZs1aoVNm/erPbFxuzZszFnzhy4u7vXuEUa3iTm5uaiI1ApcBgcPcceDiKqdjExMWjZsiUyMzNFRym3r776Cnv37oWLiwsuXryIW7duQV9fH9u2bcOCBQsQHh4uOmKFxcTE4J133kHXrl2xadOmGvHm3MrKCj/++CMGDBggOgqV0f379zFp0iRF79Srb1/UvdCvqTgMjgAWHEQqIysrC/Hx8Xj27JlSuzpPPn51TwO5XI7ExETMmjULV69eRUREhJhglSA3NxdLlixBQkICBg8eDDc3NwBAQEAA9PX1MWzYMMEJy6ZOnTpFFhRZWVnQ1tZWWjI2JSWlOqNVKlNTU4SGhqJ+/fqio1AZ+fr6Ij4+Hl999VWRvVO9e/cWlIxKg8Pg3mwsOIgEe/jwIT777DPs37+/yOPq/KldUZPG5XI57OzssHXrVnh6egpKRq/asGFDqc9V56V/J0+eDH19fcyYMUN0FCojAwMDnDx5Ei1bthQdhUrJy8sLu3btgrGxsVJ7eno6/Pz8cPToUTHBqNpxDgeRYOPGjcPjx49x7tw5dO7cGX/99Rfu37+PuXPnYtGiRaLjVcir81OkUinMzc3h7OwMTU31//OzadMmrFq1Cjdv3sTZs2dhb2+PgIAAODo6qt2nrepcRJRFTk4OfvvtNxw+fBjNmzcvtCrczz//LCgZvY6dnV2hYVSk2o4fP16o1x4oeB6ePHlSQCISRf1f8YnU3NGjR/H333/Dw8MDUqkU9vb26Nq1KwwNDTF//nz07NlTdMRy69ixo+gIVWbFihX49ttvMW7cOMybN0/RE2VsbIyAgAC1KzheFhwcDA0NDXTv3l2p/eDBg8jPz4evr6+gZBUXGRmp+IRcnfcTeRMFBARgypQpWLVqFRwcHETHoRJERkYq/n3lyhUkJSUpbufn5yMkJAQ2NjYiopEgHFJFJJihoSEiIyPh4OAABwcHbN68Ge3atUNcXByaNGmCrKws0RHLpKhNnorTq1evKkxStVxdXfH999/Dz88PBgYGuHTpEpycnPDff/+hU6dOar2ZY/PmzfHDDz+gR48eSu0hISGYPHlyoeWOiapDnTp1kJWVhby8POjp6RXqnVLnuUU1zcvDaYt6m6mrq4tly5ZhyJAh1R2NBGEPB5FgDRs2xLVr1+Dg4ICWLVsqPr1buXIlrKysRMcrMz8/v1Kdp+57jMTFxSkmir9MW1tbrVffAgpWp3J1dS3U3qhRI8TGxgpIVHGlWYJZIpFg586d1ZCGyoPLqKqPuLg4yOVyODk5ITQ0VGkZYy0tLVhYWCgtREE1HwsOIsHGjRuHxMREAMDMmTPRvXt3bN68GVpaWli/fr3YcOXw6kZqNZWjoyMiIiJgb2+v1L5///4i36yrEyMjI9y8ebPQsJXY2FjUrl1bTKgKMjIyEh2BKuhNmWdUEzz/u/imvB7Q67HgIBKsf//+in+7ubnh1q1buHr1KurVqwczMzOBycovJycHhw8fxrvvvgsAmDp1Kp4+fao4rqmpiTlz5kBHR0dUxArz9/fHqFGjkJOTA7lcjtDQUGzZsgXz58/HmjVrRMerkF69emHcuHH466+/FMvHxsbGYuLEiWo7DG7dunWiI1AFxcfHl3i8Xr161ZSESmvjxo0lHudeHG8OzuEgokq3atUq7N27F3v27AFQsJxlkyZNoKurCwC4evUq/P39MWHCBJExK2z16tWYO3cuEhISAAA2NjaYNWsWhg4dKjhZxaSlpcHHxwcXLlyAra0tAODOnTto3759kUtcElWHopbZfpk6D9GsqerUqaN0Ozc3F1lZWdDS0oKenh7n3bxBWHAQCZafn4/169crds99tQtaHdcp79ChA8aPH4/3338fAJQmVQPA77//jl9++QVnz54VGbPc8vLysHnzZnTv3h1169bFo0ePIJPJYGFhITpapZHL5Th06BAuXboEXV1dNG/eHB06dBAdi95gry5WkJubi4sXL+Lnn3/GvHnzSjVPh8SLiYnBiBEj4O/vX2glPKq5WHAQCfbVV19h/fr16NmzZ5G75y5evFhQsvKrW7cujhw5giZNmgAAzM3Ncf78ecWcgOvXr8PDwwNpaWkCU1aMnp4eoqOjC83hIKLqtW/fPvz00084fvy46ChUShcuXMCnn36Kq1evio5C1YRzOIgE27p1K7Zv315oCVJ1lpaWprSx38OHD5WOy2QypTkd6ujtt9/GxYsXa0zBsXTpUgwfPhw6OjpYunRpieeOGTOmmlIRvV6DBg1w/vx50TGoDDQ0NHDv3j3RMagaseAgEkxLSwvOzs6iY1QqW1tb/Pfff2jYsGGRxyMjIxVzA9TVyJEjMXHiRNy5cwetW7cutHpT8+bNBSUrn8WLF6N///7Q0dEpsVdNIpGw4CAh0tPTlW7L5XIkJiZi1qxZcHFxEZSKSvLqvkzP77Ply5ejXbt2glKRCBxSRSTYokWLcPPmTSxfvrzECZHqZOzYsTh8+DDCwsIKrUSVnZ0Nd3d3eHt7Y8mSJYISVpxUKi3UJpFIIJfL1X6PESJVVNSkcblcDjs7O2zduhWenp6CklFxXv07KZFIYG5uDi8vLyxatEgt95qi8mHBQSTY+++/j2PHjsHExARNmjQptHvurl27BCUrv/v376Nly5bQ0tLCV199hQYNGkAikeDq1atYvnw58vLycPHiRVhaWoqOWm63b98u8XhNGWpFpCpOnDihdFsqlcLc3BzOzs5KQzhJ9Tx8+BASiURtl3qnimPBQSTYZ599VuJxdd0/IC4uDiNGjMChQ4fw/M+MRCJB165d8euvvypWrFJHGRkZOHfuHHJzc/HWW2/VuBfRmrhyGqmvb7/9FlOmTIGenh4A4PHjx4WWWyXVk5qaimnTpmHbtm14/PgxgIJlcj/++GPMnTuXy2u/YVhwEFGVSklJQWxsLADA2dkZJiYmghNVTGRkJHx9fZGUlAS5XA5DQ0Ps2LED3t7eoqNVmpq4chqpLw0NDSQmJiqWnTY0NERERIRaf2hR06WkpMDT0xN3795F//790bhxY8jlckRHR+OPP/6AnZ0dzpw5w8LxDcKCg4ioDHr06IHHjx9j0aJF0NHRwezZs3Ht2rUatbyjmZkZNm7cWKNWTiP1JZVKkZSUpCg4Xt3Xh1TPuHHjcOTIERw+fLjQ0NmkpCR069YNXbp04YcXbxAWHESCubm5FTlZXCKRQEdHB87Ozhg8eDA6d+4sIB29ysLCAsHBwXB3dwcAJCcnw8LCAmlpadDX1xecrnJYW1vj+PHjaNCggegoRCw41JCDgwNWrVpV7MZ+ISEh+PLLL3Hr1q3qDUbCFF5mhYiqlY+PD27evInatWujc+fO6NSpE/T19XHjxg14eHggMTER3t7e+Pvvv0VHJQCPHj1CvXr1FLdNTU2hp6dXaK8RdTZx4kQsWbIE/DyKVIFEIkFGRgbS09ORlpYGiUSCJ0+eID09XemLVEdiYqJi49eiNG3aFElJSdWYiETjsg5Egj169AgTJ07EjBkzlNrnzp2L27dv4+DBg5g5cya+++479O7dW1BKeu75m5/ny/0+Xwb3+Rui5wwNDUVFLJc+ffoo3T569Cj2799fY1ZOI/Ull8uVetvkcjnc3NyUbnMpatViZmaGW7duFbvfUlxcHExNTas5FYnEIVVEghkZGSEsLKzQ5n+xsbFo3bo10tLScPXqVXh4eCAjI0NQSnquuL0Anrep65uf162W9jJ1XTmN1NOry+EWp2PHjlWchEpr6NChiI2NxaFDh6ClpaV07OnTp+jevTvq16+PtWvXCkpI1Y09HESC6ejo4MyZM4UKjjNnzig+RZfJZNDW1hYRj15x7Ngx0RGqxLp16xAfHw9bW9siNzUkEoWFhPqZPXs23N3d4eLiglGjRqFRo0YAgCtXruDXX3/F06dPsWnTJsEpqTqx4CASbPTo0fjyyy8RFhYGDw8PSCQShIaGYs2aNfjmm28AAAcOHFAaQkDi1OQ3P46OjkrLjxKpmhs3bmDdunW4ceMGlixZAgsLC4SEhMDOzq7EOQNUvWxtbXH27FmMHDkSU6dOLbQX0/Lly2FnZyc4JVUnDqkiUgGbN2/G8uXLce3aNQBAw4YNMXr0aHzyyScAgOzsbMWqVSROWSamqtscDqDwakBEquTEiRPw9fVFu3bt8M8//yA6OhpOTk748ccfERoaih07doiOSEV4/PgxYmJiANSMvZiofFhwEBGVUlHzN4qjbnM4ABYcpNo8PT3x4YcfYsKECUpL454/fx5+fn64e/eu6IhEVAwOqSJSAampqdixYwdu3ryJSZMmwcTEBOHh4bC0tISNjY3oePT/Xp6/cevWLUyZMgWDBw+Gp6cnAODs2bPYsGED5s+fLypiha1Zs+a1+4mMGTOmmtIQvRAVFYU//vijULu5uTmSk5MFJCKi0mIPB5FgkZGR8Pb2hpGREW7duoVr167ByckJM2bMwO3bt7Fx40bREakIXbp0wbBhw/C///1Pqf2PP/7Ab7/9huPHj4sJVgFSqRS2trbQ0NAo9hyJRIKbN29WYyqiAra2tti+fTvatm2r1MPx119/YdKkSbhx44boiERUDC5FQiTYhAkTMHjwYMTExCjN0fD19cU///wjMBmV5OzZs4rdxl/m7u6O0NBQAYkqx4ULFxAXF1fsF4sNEuWTTz7B5MmTkZSUBIlEAplMhtOnT2PSpEkYOHCg6HhEVAIWHESCnT9/Hl988UWhdhsbG+7EqsLs7OywcuXKQu2rVq1S29VXSjs/hUiEefPmoV69erCxscGTJ0/g6uqKDh06oG3btpg+fbroeERUAs7hIBJMR0enyNWPrl27BnNzcwGJqDQWL16Mvn374sCBA2jTpg0A4Ny5c7hx4wZ27twpOF35cIQtqbJatWph8+bNmDNnDi5evAiZTAY3Nze4uLiIjkZEr8EeDiLBevfujTlz5iA3NxdAwafM8fHxmDJlCvr27Ss4HRWnR48eiImJQa9evZCSkoLk5GT07t0b169fR48ePUTHK5eZM2e+dsI4kWh2dnZo1qwZ+vTpw2KDSE1w0jiRYOnp6ejRowcuX76MjIwMWFtbIykpCZ6enggODkbt2rVFR6Q3UGpqKkJDQ/HgwQPIZDKlYxwvTyJkZWVh9OjR2LBhAwDg+vXrcHJywpgxY2BtbY0pU6YITkhExWHBQaQijh49ivDwcMhkMrRq1Qre3t6iI9FrpKamYu3atYiOjoZEIoGrqyuGDBkCIyMj0dEqZM+ePejfvz8yMzNhYGCgNLdDIpEgJSVFYDp6U40dOxanT59GQEAAfHx8EBkZCScnJ+zevRszZ87ExYsXRUckomKw4CASKC8vDzo6OoiIiEDTpk1Fx6EyuHDhArp37w5dXV289dZbkMvluHDhArKzs3Hw4EG0atVKdMRya9CgAXr06IHvv/8eenp6ouMQAQDs7e2xbds2tGnTRmlZ3NjYWLRq1arIuXBEpBo4aZxIIE1NTdjb26vlrtRvuvHjx6NXr15YvXo1NDUL/pTm5eVh2LBhGDdunFovaXz37l2MGTOGxQaplIcPH8LCwqJQe2ZmJldYI1JxnDROJNj06dMxdepUDlNRMxcuXMDkyZMVxQZQUEB+/fXXuHDhgsBkFde9e3e1vwaqeTw8PLBv3z7F7edFxurVq+Hp6SkqFhGVAns4iARbunQpYmNjYW1tDXt7+0KTxMPDwwUlo5IYGhoiPj4ejRo1UmpPSEiAgYGBoFSVo2fPnvD398eVK1fQrFkz1KpVS+l4r169BCWjN9n8+fPh4+ODK1euIC8vD0uWLMHly5dx9uxZnDhxQnQ8IioB53AQCTZ79mxIJJJi90CYOXNmNSei0hgzZgz++usvLFy4EG3btoVEIsGpU6fg7++Pvn37IiAgQHTEcpNKi+/8lkgkHAJIwvz333/46aefEBYWplhgY/LkyWjWrJnoaERUAhYcRIJkZWXB398fQUFByM3NRZcuXbBs2TKYmZmJjkal8OzZM/j7+2PlypXIy8sDULAx2YgRI/DDDz9AW1tbcEKimiM3NxfDhw/HjBkz4OTkJDoOEZURCw4iQfz9/fHrr7+if//+0NXVxR9//IFOnTrhzz//FB2NyiArKws3btyAXC6Hs7MzJ1oTVRFjY2OEh4ez4CBSQyw4iASpX78+5s2bh48//hgAEBoainbt2iEnJwcaGhqC01FZ3LlzBxKJBDY2NqKjlNvSpUsxfPhw6OjoYOnSpSWeO2bMmGpKRfTCZ599hmbNmmHChAmioxBRGbHgIBJES0sLcXFxSm9SdXV1cf36ddjZ2QlMRqUhk8kwd+5cLFq0CE+ePAEAGBgYYOLEiZg2bVqJ8yBUkaOjIy5cuABTU1M4OjoWe55EIsHNmzerMRlRgXnz5mHhwoXo0qULWrduXWiBDRbCRKqLBQeRIBoaGkhKSoK5ubmizcDAAJGRkSW+4SPVMHXqVKxduxazZ89Gu3btIJfLcfr0acyaNQuff/455s2bJzoiUY3CQphIfbHgIBJEKpXC19dXaXLxnj174OXlpfTJ3a5du0TEo9ewtrbGypUrCy0R+/fff2PkyJG4e/euoGQVFxkZiebNmxd5LCgoCH5+ftUbiIiI1Br34SASZNCgQYXaPv30UwFJqDxSUlIK7cEBAI0aNVL7TRy7d++O06dPF5qcu3PnTgwcOBCZmZmCkhERkTpiwUEkyLp160RHoApo0aIFli9fXmiC9fLly9GiRQtBqSrHiBEj0KVLF5w5cwZWVlYAgG3btmHIkCFYv3692HD0xipusrhEIoGOjg6cnZ3Ru3dvmJiYVHMyInodDqkiIiqHEydOoGfPnqhXrx48PT0hkUhw5swZJCQkIDg4GO3btxcdsULGjh2Lw4cP4+TJkwgJCcGwYcOwadMm9O3bV3Q0ekN17twZ4eHhyM/PR8OGDSGXyxETEwMNDQ00atQI165dU2zA6erqKjouEb2EBQcRUTndu3cPv/zyC65evQq5XA5XV1eMHDkS1tbWoqNVigEDBuDff//F3bt38ccff6B3796iI9EbLCAgACdPnsS6detgaGgIAEhPT8fQoUPxzjvv4PPPP8cnn3yC7OxsHDhwQHBaInoZCw4iIsLu3bsLteXm5mL8+PHo1q2b0uT4VyfKE1UHGxsbHDp0qFDvxeXLl9GtWzfcvXsX4eHh6NatGx49eiQoJREVhQUHEVEpRUZGlvrc4lZ5UlWl3TdEIpEgPz+/itMQFaavr4+9e/eiU6dOSu3Hjx/He++9h4yMDNy8eRMtW7ZEenq6mJBEVCROGiciKqWWLVtCIpHgdZ/TqOObcplMJjoCUYl69+6NIUOGYNGiRfDw8IBEIkFoaCgmTZqkWKo5NDQUDRo0EBuUiAphDwcRUSndvn271Ofa29tXYZKq8e+//yIlJQW+vr6Kto0bN2LmzJnIzMyEn58fli1bprR3DFF1efLkCcaPH4+NGzciLy8PAKCpqYlBgwZh8eLFqF27NiIiIgAUfDhARKqDBQcRUTkkJyfD1NQUAJCQkIDVq1cjOzsbvXr1UtsVqnx8fNC5c2dMnjwZABAVFYVWrVph8ODBaNy4MX766Sd88cUXmDVrltig9EZ78uQJbt68Cblcjvr160NfX190JCJ6DRYcRERlEBUVhffeew8JCQlwcXHB1q1b4ePjg8zMTEilUmRmZmLHjh1quRu3lZUV9uzZA3d3dwDAtGnTcOLECZw6dQoA8Oeff2LmzJm4cuWKyJhERKRmOIeDiKgMvv76azRr1gy///47fv/9d7z77rvo0aMH1qxZAwAYPXo0fvjhB7UsOB4/fgxLS0vF7RMnTsDHx0dx28PDAwkJCSKi0RuqT58+WL9+PQwNDdGnT58Sz921a1c1pSKismLBQURUBufPn8fRo0fRvHlztGzZEr/99htGjhypWOVp9OjRaNOmjeCU5WNpaYm4uDjY2dnh2bNnCA8Px+zZsxXHMzIyUKtWLYEJ6U1jZGQEiUSi+DcRqScWHEREZZCSkoK6desCKFims3bt2jAxMVEcr1OnDjIyMkTFqxAfHx9MmTIFCxYsQFBQEPT09JTmo0RGRqJ+/foCE9KbZt26dUX+m4jUCwsOIqIyev6Ja3G31dXcuXPRp08fdOzYEfr6+tiwYQO0tLQUxwMDA9GtWzeBCYmISB1x0jgRURlIpVL4+voqlobds2cPvLy8ULt2bQDA06dPERISonb7cLwsLS0N+vr60NDQUGpPSUmBvr6+UhFCVJXc3NxKXdCHh4dXcRoiKi/2cBARlcGgQYOUbn/66aeFzhk4cGB1xakSxY2Vf3noGFF1eHnxhZycHPz6669wdXWFp6cnAODcuXO4fPkyRo4cKSghEZUGeziIiIhI5Q0bNgxWVlb47rvvlNpnzpyJhIQEBAYGCkpGRK/DgoOIiIhUnpGRES5cuAAXFxel9piYGLi7uyMtLU1QMiJ6HanoAERERESvo6urq9iE8mWnTp2Cjo6OgEREVFqcw0FEREQqb9y4cRgxYgTCwsIUe92cO3cOgYGB+PbbbwWnI6KScEgVERERqYXt27djyZIliI6OBgA0btwYY8eORb9+/QQnI6KSsOAgIiIiIqIqwzkcREREpBZSU1OxZs0afPPNN0hJSQFQsP/G3bt3BScjopJwDgcRERGpvMjISHh7e8PIyAi3bt3CsGHDYGJigr/++gu3b9/Gxo0bRUckomKwh4OIiIhU3oQJEzB48GDExMQorUrl6+uLf/75R2AyInodFhxERESk8s6fP48vvviiULuNjQ2SkpIEJCKi0mLBQURERCpPR0cH6enphdqvXbsGc3NzAYmIqLRYcBAREZHK6927N+bMmYPc3FwAgEQiQXx8PKZMmYK+ffsKTkdEJeGyuERERKTy0tPT0aNHD1y+fBkZGRmwtrZGUlISPD09ERwcjNq1a4uOSETFYMFBREREauPYsWMICwuDTCZDq1at4O3tLToSEb0GCw4iIiJSaX/++SeCgoKQm5sLb29vDB8+XHQkIioD7sNBREREKuu3337Dl19+CRcXF+jo6GDnzp2Ii4vD/PnzRUcjolJiDwcRERGprGbNmsHPzw/fffcdAGD9+vUYPXo0MjIyBCcjotJiwUFEREQqq3bt2oiKioKTkxMAID8/H7q6uoiPj0fdunUFpyOi0uCyuERERKSysrOzoa+vr7itoaEBbW1tZGVlCUxFRGXBORxERESk0tasWaNUdOTl5WH9+vUwMzNTtI0ZM0ZENCIqBQ6pIiIiIpXl4OAAiURS4jkSiQQ3b96spkREVFYsOIiIiIiIqMpwDgcRERGppdTUVNERiKgUWHAQERGRyluwYAG2bdumuP3hhx/CxMQENjY2uHTpksBkRPQ6LDiIiIhI5a1atQp2dnYAgEOHDuHw4cMICQmBr68v/P39BacjopJwlSoiIiJSeYmJiYqCY+/evejXrx+6desGBwcHvP3224LTEVFJ2MNBREREKq9OnTpISEgAAISEhMDb2xsAIJfLkZ+fLzIaEb0GeziIiIhI5fXp0weffPIJXFxckJycDF9fXwBAREQEnJ2dBacjopKw4CAiIiKVt3jxYjg4OCAhIQE//vijYiPAxMREjBw5UnA6IioJ9+EgIiIiIqIqwzkcREREpBY2bdqEd955B9bW1rh9+zYAICAgAH///bfgZERUEhYcREREpPJWrFiBCRMmwNfXF6mpqYqJ4sbGxggICBAbjohKxIKDiIiIVN6yZcuwevVqTJs2DRoaGop2d3d3REVFCUxGRK/DgoOIiIhUXlxcHNzc3Aq1a2trIzMzU0AiIiotFhxERESk8hwdHREREVGoff/+/XB1da3+QERUalwWl4iIiFSev78/Ro0ahZycHMjlcoSGhmLLli2YP38+1qxZIzoeEZWAy+ISERGRWli9ejXmzp2r2HHcxsYGs2bNwtChQwUnI6KSsOAgIiIitfLo0SPIZDJYWFiIjkJEpcA5HERERKTyvLy8kJqaCgAwMzNTFBvp6enw8vISmIyIXoc9HERERKTypFIpkpKSCvVqPHjwADY2NsjNzRWUjIheh5PGiYiISGVFRkYq/n3lyhUkJSUpbufn5yMkJAQ2NjYiohFRKbGHg4iIiFSWVCqFRCIBABT1lkVXVxfLli3DkCFDqjsaEZUSCw4iIiJSWbdv34ZcLoeTkxNCQ0Nhbm6uOKalpQULCwulnceJSPWw4CAiIiIioirDVaqIiIhILWzatAnt2rWDtbU1bt++DQBYvHgx/v77b8HJiKgkLDiIiIhI5a1YsQITJkxAjx49kJqaivz8fABAnTp1EBAQIDYcEZWIBQcRERGpvGXLlmH16tWYNm2a0pwNd3d3REVFCUxGRK/DgoOIiIhUXlxcHNzc3Aq1a2trIzMzU0AiIiotFhxERESk8hwdHREREVGoff/+/XB1da3+QERUatz4j4iIiFSev78/Ro0ahZycHMjlcoSGhmLLli2YP38+1qxZIzoeEZWAy+ISERGRWli9ejXmzp2LhIQEAICNjQ1mzZqFoUOHCk5GRCVhwUFERERq5dGjR5DJZLCwsBAdhYhKgUOqiIiISG08ePAA165dg0QigUQiUdp5nIhUEyeNExERkcpLT0/HgAEDYG1tjY4dO6JDhw6wtrbGp59+irS0NNHxiKgELDiIiIhI5Q0bNgz//vsv9u3bh9TUVKSlpWHv3r24cOECPv/8c9HxiKgEnMNBREREKq927do4cOAA3nnnHaX2kydPwsfHh3txEKkw9nAQERGRyjM1NYWRkVGhdiMjI9SpU0dAIiIqLRYcREREpPKmT5+OCRMmIDExUdGWlJQEf39/zJgxQ2AyInodDqkiIiIileTm5gaJRKK4HRMTg6dPn6JevXoAgPj4eGhra8PFxQXh4eGiYhLRa3BZXCIiIlJJfn5+oiMQUSVgDwcREREREVUZzuEgIiIiIqIqwyFVREREpPLy8/OxePFibN++HfHx8Xj27JnS8ZSUFEHJiOh12MNBREREKm/27Nn4+eef0a9fP6SlpWHChAno06cPpFIpZs2aJToeEZWAcziIiIhI5dWvXx9Lly5Fz549YWBggIiICEXbuXPn8Mcff4iOSETFYA8HERERqbykpCQ0a9YMAKCvr4+0tDQAwLvvvot9+/aJjEZEr8GCg4iIiFSera2tYtM/Z2dnHDx4EABw/vx5aGtri4xGRK/BgoOIiIhU3vvvv48jR44AAMaOHYsZM2bAxcUFAwcOxJAhQwSnI6KScA4HERERqZ1z587hzJkzcHZ2Rq9evUTHIaISsOAgIiIiIqIqw304iIiISCXt3r0bvr6+qFWrFnbv3l3iuezlIFJd7OEgIiIilSSVSpGUlAQLCwtIpcVPO5VIJMjPz6/GZERUFiw4iIiIiIioynBIFREREak0mUyG9evXY9euXbh16xYkEgmcnJzQt29fDBgwABKJRHREIioBeziIiIhIZcnlcrz33nsIDg5GixYt0KhRI8jlckRHRyMqKgq9evVCUFCQ6JhEVAL2cBAREZHKWr9+Pf755x8cOXIEnTt3Vjp29OhR+Pn5YePGjRg4cKCghET0OuzhICIiIpXVrVs3eHl5YcqUKUUe//7773HixAkcOHCgmpMRUWlxp3EiIiJSWZGRkfDx8Sn2uK+vLy5dulSNiYiorFhwEBERkcpKSUmBpaVlscctLS3x+PHjakxERGXFgoOIiIhUVn5+PjQ1i59yqqGhgby8vGpMRERlxUnjREREpLLkcjkGDx4MbW3tIo8/ffq0mhMRUVmx4CAiIiKVNWjQoNeewxWqiFQbV6kiIiIiIqIqwzkcRERERERUZVhwEBERERFRlWHBQUREREREVYYFBxERERERVRkWHEREREREVGVYcBARERERUZVhwUFERERERFWGBQcREREREVWZ/wM9E4GzgFFn9gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 7))\n",
    "sns.heatmap(data.corr(), annot = True, cmap = 'coolwarm')\n",
    "plt.title(\"Correlation Matrix of Data\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a11cd0c",
   "metadata": {},
   "source": [
    "# Regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "11cc6c2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "268"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df1 = data[data['Outcome'] == 1]\n",
    "len(temp_df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a2b02005",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "269"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = data[data['Outcome'] == 0]\n",
    "temp_df2 = temp[150:419]\n",
    "len(temp_df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "302102ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(537, 9)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upd_data = pd.concat([temp_df1, temp_df2], axis  = 0)\n",
    "upd_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cdd826d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>78.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.248</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>197.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>543.0</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.158</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6    148.0           72.0           35.0    125.0  33.6   \n",
       "2            8    183.0           64.0           29.0    125.0  23.3   \n",
       "4            0    137.0           40.0           35.0    168.0  43.1   \n",
       "6            3     78.0           50.0           32.0     88.0  31.0   \n",
       "8            2    197.0           70.0           45.0    543.0  30.5   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "2                     0.672   32        1  \n",
       "4                     2.288   33        1  \n",
       "6                     0.248   26        1  \n",
       "8                     0.158   53        1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upd_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2d6181b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0.238</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>2</td>\n",
       "      <td>111.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.343</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>1</td>\n",
       "      <td>128.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>0.115</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>10</td>\n",
       "      <td>92.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>25.9</td>\n",
       "      <td>0.167</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>5</td>\n",
       "      <td>104.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>28.8</td>\n",
       "      <td>0.153</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "631            0    102.0           78.0           40.0     90.0  34.5   \n",
       "632            2    111.0           60.0           29.0    125.0  26.2   \n",
       "633            1    128.0           82.0           17.0    183.0  27.5   \n",
       "634           10     92.0           62.0           29.0    125.0  25.9   \n",
       "636            5    104.0           74.0           29.0    125.0  28.8   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "631                     0.238   24        0  \n",
       "632                     0.343   23        0  \n",
       "633                     0.115   22        0  \n",
       "634                     0.167   31        0  \n",
       "636                     0.153   48        0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upd_data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2014ab4",
   "metadata": {},
   "source": [
    "# Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6fc9eae2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = upd_data[['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
    "       'BMI', 'DiabetesPedigreeFunction', 'Age']]\n",
    "y = upd_data['Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c9789993",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.15, stratify = y, random_state = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d9d424ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    228\n",
       "0    228\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53bddbb8",
   "metadata": {},
   "source": [
    "# Scaling the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a5d08820",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "mn = MinMaxScaler(feature_range = (0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4479b54d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_scaled = sc.fit_transform(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3bce22ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test_array = sc.fit_transform(x_test)\n",
    "y_train = np.asanyarray(y_train)\n",
    "y_test = np.asanyarray(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0c88feb",
   "metadata": {},
   "source": [
    "# Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "592fb5a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
       "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c6e0fcf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_rf = RandomForestClassifier(n_estimators = 25, criterion = 'entropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0e2bbfd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_svm = SVC(kernel = 'linear', C = 1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7c103933",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_lr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e9e3e608",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_dt = DecisionTreeClassifier(criterion = 'entropy', splitter = 'best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b2e7e62e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_lst = [clf_rf, clf_svm, clf_lr, clf_dt]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0be12fc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the RandomForestClassifier(criterion='entropy', n_estimators=25) model : 0.7654320987654321\n",
      "Accuracy of the SVC(C=1.5, kernel='linear') model : 0.7777777777777778\n",
      "Accuracy of the LogisticRegression() model : 0.7777777777777778\n",
      "Accuracy of the DecisionTreeClassifier(criterion='entropy') model : 0.7407407407407407\n"
     ]
    }
   ],
   "source": [
    "for i in model_lst:\n",
    "    i.fit(x_train_scaled, y_train)\n",
    "    print(f\"Accuracy of the {i} model : {accuracy_score(y_test, i.predict(x_test_array))}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "439ef4a8",
   "metadata": {},
   "source": [
    "Since the SVC(Simple Vector classifier) has the maximum accuracy, we can use that model to perform further process "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "30a749e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=1.5, kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=1.5, kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=1.5, kernel='linear')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_svm.fit(x_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "378b3570",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf_svm.predict(x_test_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5ae358a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.78      0.78        41\n",
      "           1       0.78      0.78      0.78        40\n",
      "\n",
      "    accuracy                           0.78        81\n",
      "   macro avg       0.78      0.78      0.78        81\n",
      "weighted avg       0.78      0.78      0.78        81\n",
      "\n"
     ]
    }
   ],
   "source": [
    "report = classification_report(y_test, y_pred, zero_division = 1)\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19dd27d3",
   "metadata": {},
   "source": [
    "# KFold Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4a18db70",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = SVC(kernel = 'linear', C = 1.5)\n",
    "num_folds = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ed2dfbde",
   "metadata": {},
   "outputs": [],
   "source": [
    "k_fold = KFold(n_splits = num_folds, shuffle = True, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "84a19fa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cross_val_score(classifier, X, y, cv = k_fold, scoring = 'accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8f68e72f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Validation Scores : [0.81481481 0.66666667 0.75925926 0.81481481 0.74074074 0.7037037\n",
      " 0.74074074 0.73584906 0.75471698 0.71698113]\n",
      "\n",
      "Mean Score value : 0.744828791055206\n",
      "\n",
      "Standard Deviation of scores: 0.04335002227192133\n"
     ]
    }
   ],
   "source": [
    "print(f\"Cross-Validation Scores : {scores}\\n\")\n",
    "print(f\"Mean Score value : {scores.mean()}\\n\")\n",
    "print(f\"Standard Deviation of scores: {scores.std()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "decfc473",
   "metadata": {},
   "source": [
    "# Deep learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7a11f8b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "                    tf.keras.layers.Dense(32, activation = 'relu', input_shape = (8,)),\n",
    "                    \n",
    "                    tf.keras.layers.Dense(128, activation = 'relu'),\n",
    "                    tf.keras.layers.Dropout(0.7),\n",
    "                    tf.keras.layers.Dense(28, activation = 'relu'),\n",
    "                    tf.keras.layers.Dense(64, activation = 'relu'),\n",
    "                    tf.keras.layers.Dense(32, activation = 'relu'),\n",
    "                    tf.keras.layers.Dense(8, activation = 'relu'),\n",
    "    \n",
    "                    tf.keras.layers.Dense(1, activation = 'sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3ea05f30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_7 (Dense)             (None, 32)                288       \n",
      "                                                                 \n",
      " dense_8 (Dense)             (None, 128)               4224      \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 128)               0         \n",
      "                                                                 \n",
      " dense_9 (Dense)             (None, 28)                3612      \n",
      "                                                                 \n",
      " dense_10 (Dense)            (None, 64)                1856      \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 32)                2080      \n",
      "                                                                 \n",
      " dense_12 (Dense)            (None, 8)                 264       \n",
      "                                                                 \n",
      " dense_13 (Dense)            (None, 1)                 9         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 12333 (48.18 KB)\n",
      "Trainable params: 12333 (48.18 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1fe7e83b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3),\n",
    "             metrics = 'accuracy',\n",
    "             loss = 'binary_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9b690066",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/60\n",
      "23/23 [==============================] - 1s 10ms/step - loss: 0.6782 - accuracy: 0.5833 - val_loss: 0.6390 - val_accuracy: 0.7407\n",
      "Epoch 2/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.6121 - accuracy: 0.7193 - val_loss: 0.5501 - val_accuracy: 0.7901\n",
      "Epoch 3/60\n",
      "23/23 [==============================] - 0s 4ms/step - loss: 0.5552 - accuracy: 0.7105 - val_loss: 0.5187 - val_accuracy: 0.7654\n",
      "Epoch 4/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.5175 - accuracy: 0.7346 - val_loss: 0.5216 - val_accuracy: 0.7778\n",
      "Epoch 5/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.5159 - accuracy: 0.7434 - val_loss: 0.5227 - val_accuracy: 0.7654\n",
      "Epoch 6/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4975 - accuracy: 0.7654 - val_loss: 0.5216 - val_accuracy: 0.7654\n",
      "Epoch 7/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4688 - accuracy: 0.7675 - val_loss: 0.5315 - val_accuracy: 0.7654\n",
      "Epoch 8/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4842 - accuracy: 0.7675 - val_loss: 0.5159 - val_accuracy: 0.7531\n",
      "Epoch 9/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4746 - accuracy: 0.7741 - val_loss: 0.5140 - val_accuracy: 0.7778\n",
      "Epoch 10/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4512 - accuracy: 0.7939 - val_loss: 0.5231 - val_accuracy: 0.7778\n",
      "Epoch 11/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4555 - accuracy: 0.7763 - val_loss: 0.5262 - val_accuracy: 0.7778\n",
      "Epoch 12/60\n",
      "23/23 [==============================] - 0s 4ms/step - loss: 0.4489 - accuracy: 0.8114 - val_loss: 0.5202 - val_accuracy: 0.7531\n",
      "Epoch 13/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4589 - accuracy: 0.7807 - val_loss: 0.5240 - val_accuracy: 0.7778\n",
      "Epoch 14/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4753 - accuracy: 0.7763 - val_loss: 0.5163 - val_accuracy: 0.7901\n",
      "Epoch 15/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4661 - accuracy: 0.8026 - val_loss: 0.5188 - val_accuracy: 0.7778\n",
      "Epoch 16/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4532 - accuracy: 0.8048 - val_loss: 0.5238 - val_accuracy: 0.7778\n",
      "Epoch 17/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4462 - accuracy: 0.8092 - val_loss: 0.5196 - val_accuracy: 0.7901\n",
      "Epoch 18/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4478 - accuracy: 0.8026 - val_loss: 0.5245 - val_accuracy: 0.7778\n",
      "Epoch 19/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4335 - accuracy: 0.8092 - val_loss: 0.5279 - val_accuracy: 0.7778\n",
      "Epoch 20/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4325 - accuracy: 0.8158 - val_loss: 0.5276 - val_accuracy: 0.7901\n",
      "Epoch 21/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4268 - accuracy: 0.8048 - val_loss: 0.5275 - val_accuracy: 0.7901\n",
      "Epoch 22/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4232 - accuracy: 0.8268 - val_loss: 0.5415 - val_accuracy: 0.7901\n",
      "Epoch 23/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4282 - accuracy: 0.8070 - val_loss: 0.5328 - val_accuracy: 0.7654\n",
      "Epoch 24/60\n",
      "23/23 [==============================] - 0s 4ms/step - loss: 0.4129 - accuracy: 0.8246 - val_loss: 0.5500 - val_accuracy: 0.7654\n",
      "Epoch 25/60\n",
      "23/23 [==============================] - 0s 4ms/step - loss: 0.3968 - accuracy: 0.8289 - val_loss: 0.5499 - val_accuracy: 0.7654\n",
      "Epoch 26/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4024 - accuracy: 0.8355 - val_loss: 0.5478 - val_accuracy: 0.7901\n",
      "Epoch 27/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4192 - accuracy: 0.8070 - val_loss: 0.5491 - val_accuracy: 0.7778\n",
      "Epoch 28/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4101 - accuracy: 0.8224 - val_loss: 0.5459 - val_accuracy: 0.7778\n",
      "Epoch 29/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4254 - accuracy: 0.8246 - val_loss: 0.5573 - val_accuracy: 0.7531\n",
      "Epoch 30/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3941 - accuracy: 0.8421 - val_loss: 0.5381 - val_accuracy: 0.7654\n",
      "Epoch 31/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3951 - accuracy: 0.8311 - val_loss: 0.5448 - val_accuracy: 0.7531\n",
      "Epoch 32/60\n",
      "23/23 [==============================] - 0s 4ms/step - loss: 0.3960 - accuracy: 0.8202 - val_loss: 0.5690 - val_accuracy: 0.7531\n",
      "Epoch 33/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.4039 - accuracy: 0.8311 - val_loss: 0.5469 - val_accuracy: 0.7531\n",
      "Epoch 34/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3973 - accuracy: 0.8355 - val_loss: 0.5487 - val_accuracy: 0.7654\n",
      "Epoch 35/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3917 - accuracy: 0.8399 - val_loss: 0.5567 - val_accuracy: 0.7654\n",
      "Epoch 36/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3869 - accuracy: 0.8268 - val_loss: 0.5610 - val_accuracy: 0.7531\n",
      "Epoch 37/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3855 - accuracy: 0.8399 - val_loss: 0.5615 - val_accuracy: 0.7778\n",
      "Epoch 38/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3845 - accuracy: 0.8399 - val_loss: 0.5572 - val_accuracy: 0.7654\n",
      "Epoch 39/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3857 - accuracy: 0.8443 - val_loss: 0.5718 - val_accuracy: 0.7901\n",
      "Epoch 40/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3873 - accuracy: 0.8333 - val_loss: 0.5845 - val_accuracy: 0.7778\n",
      "Epoch 41/60\n",
      "23/23 [==============================] - 0s 4ms/step - loss: 0.3949 - accuracy: 0.8399 - val_loss: 0.5646 - val_accuracy: 0.7654\n",
      "Epoch 42/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3926 - accuracy: 0.8224 - val_loss: 0.5684 - val_accuracy: 0.7654\n",
      "Epoch 43/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3790 - accuracy: 0.8531 - val_loss: 0.5920 - val_accuracy: 0.7654\n",
      "Epoch 44/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3805 - accuracy: 0.8618 - val_loss: 0.5984 - val_accuracy: 0.7531\n",
      "Epoch 45/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3580 - accuracy: 0.8575 - val_loss: 0.5978 - val_accuracy: 0.7407\n",
      "Epoch 46/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3558 - accuracy: 0.8443 - val_loss: 0.6251 - val_accuracy: 0.7407\n",
      "Epoch 47/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3518 - accuracy: 0.8553 - val_loss: 0.6139 - val_accuracy: 0.7531\n",
      "Epoch 48/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3697 - accuracy: 0.8531 - val_loss: 0.6000 - val_accuracy: 0.7531\n",
      "Epoch 49/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3573 - accuracy: 0.8640 - val_loss: 0.6313 - val_accuracy: 0.7531\n",
      "Epoch 50/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3756 - accuracy: 0.8509 - val_loss: 0.6025 - val_accuracy: 0.7407\n",
      "Epoch 51/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3537 - accuracy: 0.8509 - val_loss: 0.6153 - val_accuracy: 0.7531\n",
      "Epoch 52/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3683 - accuracy: 0.8465 - val_loss: 0.6208 - val_accuracy: 0.7531\n",
      "Epoch 53/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3619 - accuracy: 0.8399 - val_loss: 0.6261 - val_accuracy: 0.7654\n",
      "Epoch 54/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3628 - accuracy: 0.8509 - val_loss: 0.6141 - val_accuracy: 0.7531\n",
      "Epoch 55/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3456 - accuracy: 0.8531 - val_loss: 0.6413 - val_accuracy: 0.7654\n",
      "Epoch 56/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3353 - accuracy: 0.8640 - val_loss: 0.6267 - val_accuracy: 0.7654\n",
      "Epoch 57/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3375 - accuracy: 0.8640 - val_loss: 0.6549 - val_accuracy: 0.7654\n",
      "Epoch 58/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3361 - accuracy: 0.8706 - val_loss: 0.6317 - val_accuracy: 0.7531\n",
      "Epoch 59/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3206 - accuracy: 0.8838 - val_loss: 0.6633 - val_accuracy: 0.7531\n",
      "Epoch 60/60\n",
      "23/23 [==============================] - 0s 3ms/step - loss: 0.3356 - accuracy: 0.8640 - val_loss: 0.6870 - val_accuracy: 0.7531\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x21a9488ed90>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch = 20\n",
    "\n",
    "model.fit(x_train_scaled, y_train, epochs = 60, batch_size = batch, validation_data = (x_test_array, y_test), verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6c61e489",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 3ms/step - loss: 0.6870 - accuracy: 0.7531\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.686983585357666, 0.7530864477157593]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_test_array, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3b20752",
   "metadata": {},
   "source": [
    "# Saving the Deep learning Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "049e1a71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Diabetes_Prediction_Model.pkl\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: Diabetes_Prediction_Model.pkl\\assets\n"
     ]
    }
   ],
   "source": [
    "model.save('Diabetes_Prediction_Model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec054bd5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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