CAR_DEKHO_PRICE_PREDICTION.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "215027e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c:\\users\\admin\\appdata\\local\\programs\\python\\python39\\python.exe\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "print(sys.executable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ea5f4ac3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c06936d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"car data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ee386584",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car_Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ritz</td>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sx4</td>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ciaz</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wagon r</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>swift</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
       "0     ritz  2014           3.35           5.59       27000    Petrol   \n",
       "1      sx4  2013           4.75           9.54       43000    Diesel   \n",
       "2     ciaz  2017           7.25           9.85        6900    Petrol   \n",
       "3  wagon r  2011           2.85           4.15        5200    Petrol   \n",
       "4    swift  2014           4.60           6.87       42450    Diesel   \n",
       "\n",
       "  Seller_Type Transmission  Owner  \n",
       "0      Dealer       Manual      0  \n",
       "1      Dealer       Manual      0  \n",
       "2      Dealer       Manual      0  \n",
       "3      Dealer       Manual      0  \n",
       "4      Dealer       Manual      0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2a6338d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301, 9)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2997d7ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Dealer' 'Individual']\n",
      "['Manual' 'Automatic']\n",
      "[0 1 3]\n",
      "['Petrol' 'Diesel' 'CNG']\n"
     ]
    }
   ],
   "source": [
    "#Selecting Catorical Columns from dataset\n",
    "print(df.Seller_Type.unique())\n",
    "print(df.Transmission.unique())\n",
    "print(df.Owner.unique())\n",
    "print(df.Fuel_Type.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d3787db8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Name         0\n",
       "Year             0\n",
       "Selling_Price    0\n",
       "Present_Price    0\n",
       "Kms_Driven       0\n",
       "Fuel_Type        0\n",
       "Seller_Type      0\n",
       "Transmission     0\n",
       "Owner            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "01a53842",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2013.627907</td>\n",
       "      <td>4.661296</td>\n",
       "      <td>7.628472</td>\n",
       "      <td>36947.205980</td>\n",
       "      <td>0.043189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.891554</td>\n",
       "      <td>5.082812</td>\n",
       "      <td>8.644115</td>\n",
       "      <td>38886.883882</td>\n",
       "      <td>0.247915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2003.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>32000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>48767.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>92.600000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year  Selling_Price  Present_Price     Kms_Driven       Owner\n",
       "count   301.000000     301.000000     301.000000     301.000000  301.000000\n",
       "mean   2013.627907       4.661296       7.628472   36947.205980    0.043189\n",
       "std       2.891554       5.082812       8.644115   38886.883882    0.247915\n",
       "min    2003.000000       0.100000       0.320000     500.000000    0.000000\n",
       "25%    2012.000000       0.900000       1.200000   15000.000000    0.000000\n",
       "50%    2014.000000       3.600000       6.400000   32000.000000    0.000000\n",
       "75%    2016.000000       6.000000       9.900000   48767.000000    0.000000\n",
       "max    2018.000000      35.000000      92.600000  500000.000000    3.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f26c952c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Car_Name', 'Year', 'Selling_Price', 'Present_Price', 'Kms_Driven',\n",
       "       'Fuel_Type', 'Seller_Type', 'Transmission', 'Owner'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "78f5f840",
   "metadata": {},
   "outputs": [],
   "source": [
    "#droping the column car name as we don't want to predict price based on the car name \n",
    "car_dataset=df[['Year', 'Selling_Price', 'Present_Price', 'Kms_Driven',\n",
    "       'Fuel_Type', 'Seller_Type', 'Transmission', 'Owner']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2292b0a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0  2014           3.35           5.59       27000    Petrol      Dealer   \n",
       "1  2013           4.75           9.54       43000    Diesel      Dealer   \n",
       "2  2017           7.25           9.85        6900    Petrol      Dealer   \n",
       "3  2011           2.85           4.15        5200    Petrol      Dealer   \n",
       "4  2014           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  \n",
       "0       Manual      0  \n",
       "1       Manual      0  \n",
       "2       Manual      0  \n",
       "3       Manual      0  \n",
       "4       Manual      0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "645158cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deriving New feature from \"Year\" feature by adding new feature like current year\n",
    "car_dataset['Current_year']=2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5210ead0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Current_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0  2014           3.35           5.59       27000    Petrol      Dealer   \n",
       "1  2013           4.75           9.54       43000    Diesel      Dealer   \n",
       "2  2017           7.25           9.85        6900    Petrol      Dealer   \n",
       "3  2011           2.85           4.15        5200    Petrol      Dealer   \n",
       "4  2014           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  Current_year  \n",
       "0       Manual      0          2021  \n",
       "1       Manual      0          2021  \n",
       "2       Manual      0          2021  \n",
       "3       Manual      0          2021  \n",
       "4       Manual      0          2021  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "19eb54be",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding New Feature\n",
    "car_dataset['No_of_Years']=car_dataset['Current_year']-car_dataset['Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b503a41b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "      <th>Current_year</th>\n",
       "      <th>No_of_Years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>2021</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0  2014           3.35           5.59       27000    Petrol      Dealer   \n",
       "1  2013           4.75           9.54       43000    Diesel      Dealer   \n",
       "2  2017           7.25           9.85        6900    Petrol      Dealer   \n",
       "3  2011           2.85           4.15        5200    Petrol      Dealer   \n",
       "4  2014           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  Current_year  No_of_Years  \n",
       "0       Manual      0          2021            7  \n",
       "1       Manual      0          2021            8  \n",
       "2       Manual      0          2021            4  \n",
       "3       Manual      0          2021           10  \n",
       "4       Manual      0          2021            7  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d2dc7095",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropinng the unncessary columns\n",
    "car_dataset.drop(['Year'],axis=1,inplace=True)\n",
    "car_dataset.drop(['Current_year'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "23ce1174",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "      <th>No_of_Years</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0           3.35           5.59       27000    Petrol      Dealer   \n",
       "1           4.75           9.54       43000    Diesel      Dealer   \n",
       "2           7.25           9.85        6900    Petrol      Dealer   \n",
       "3           2.85           4.15        5200    Petrol      Dealer   \n",
       "4           4.60           6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Owner  No_of_Years  \n",
       "0       Manual      0            7  \n",
       "1       Manual      0            8  \n",
       "2       Manual      0            4  \n",
       "3       Manual      0           10  \n",
       "4       Manual      0            7  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7b0b9005",
   "metadata": {},
   "outputs": [],
   "source": [
    "car_dataset = pd.get_dummies(car_dataset,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c2a4e7f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selling_Price  Present_Price  Kms_Driven  Owner  No_of_Years  \\\n",
       "0           3.35           5.59       27000      0            7   \n",
       "1           4.75           9.54       43000      0            8   \n",
       "2           7.25           9.85        6900      0            4   \n",
       "3           2.85           4.15        5200      0           10   \n",
       "4           4.60           6.87       42450      0            7   \n",
       "\n",
       "   Fuel_Type_Diesel  Fuel_Type_Petrol  Seller_Type_Individual  \\\n",
       "0                 0                 1                       0   \n",
       "1                 1                 0                       0   \n",
       "2                 0                 1                       0   \n",
       "3                 0                 1                       0   \n",
       "4                 1                 0                       0   \n",
       "\n",
       "   Transmission_Manual  \n",
       "0                    1  \n",
       "1                    1  \n",
       "2                    1  \n",
       "3                    1  \n",
       "4                    1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "821fdf54",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.000000</td>\n",
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       "      <td>0.055687</td>\n",
       "      <td>0.124269</td>\n",
       "      <td>-0.050316</td>\n",
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       "      <th>No_of_Years</th>\n",
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       "      <td>1.000000</td>\n",
       "      <td>-0.064315</td>\n",
       "      <td>0.059959</td>\n",
       "      <td>0.039896</td>\n",
       "      <td>-0.000394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <td>0.552339</td>\n",
       "      <td>0.473306</td>\n",
       "      <td>0.172515</td>\n",
       "      <td>-0.053469</td>\n",
       "      <td>-0.064315</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.979648</td>\n",
       "      <td>-0.350467</td>\n",
       "      <td>-0.098643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <td>-0.540571</td>\n",
       "      <td>-0.465244</td>\n",
       "      <td>-0.172874</td>\n",
       "      <td>0.055687</td>\n",
       "      <td>0.059959</td>\n",
       "      <td>-0.979648</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.358321</td>\n",
       "      <td>0.091013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <td>-0.550724</td>\n",
       "      <td>-0.512030</td>\n",
       "      <td>-0.101419</td>\n",
       "      <td>0.124269</td>\n",
       "      <td>0.039896</td>\n",
       "      <td>-0.350467</td>\n",
       "      <td>0.358321</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.063240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transmission_Manual</th>\n",
       "      <td>-0.367128</td>\n",
       "      <td>-0.348715</td>\n",
       "      <td>-0.162510</td>\n",
       "      <td>-0.050316</td>\n",
       "      <td>-0.000394</td>\n",
       "      <td>-0.098643</td>\n",
       "      <td>0.091013</td>\n",
       "      <td>0.063240</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Selling_Price  Present_Price  Kms_Driven     Owner  \\\n",
       "Selling_Price                1.000000       0.878983    0.029187 -0.088344   \n",
       "Present_Price                0.878983       1.000000    0.203647  0.008057   \n",
       "Kms_Driven                   0.029187       0.203647    1.000000  0.089216   \n",
       "Owner                       -0.088344       0.008057    0.089216  1.000000   \n",
       "No_of_Years                 -0.236141       0.047584    0.524342  0.182104   \n",
       "Fuel_Type_Diesel             0.552339       0.473306    0.172515 -0.053469   \n",
       "Fuel_Type_Petrol            -0.540571      -0.465244   -0.172874  0.055687   \n",
       "Seller_Type_Individual      -0.550724      -0.512030   -0.101419  0.124269   \n",
       "Transmission_Manual         -0.367128      -0.348715   -0.162510 -0.050316   \n",
       "\n",
       "                        No_of_Years  Fuel_Type_Diesel  Fuel_Type_Petrol  \\\n",
       "Selling_Price             -0.236141          0.552339         -0.540571   \n",
       "Present_Price              0.047584          0.473306         -0.465244   \n",
       "Kms_Driven                 0.524342          0.172515         -0.172874   \n",
       "Owner                      0.182104         -0.053469          0.055687   \n",
       "No_of_Years                1.000000         -0.064315          0.059959   \n",
       "Fuel_Type_Diesel          -0.064315          1.000000         -0.979648   \n",
       "Fuel_Type_Petrol           0.059959         -0.979648          1.000000   \n",
       "Seller_Type_Individual     0.039896         -0.350467          0.358321   \n",
       "Transmission_Manual       -0.000394         -0.098643          0.091013   \n",
       "\n",
       "                        Seller_Type_Individual  Transmission_Manual  \n",
       "Selling_Price                        -0.550724            -0.367128  \n",
       "Present_Price                        -0.512030            -0.348715  \n",
       "Kms_Driven                           -0.101419            -0.162510  \n",
       "Owner                                 0.124269            -0.050316  \n",
       "No_of_Years                           0.039896            -0.000394  \n",
       "Fuel_Type_Diesel                     -0.350467            -0.098643  \n",
       "Fuel_Type_Petrol                      0.358321             0.091013  \n",
       "Seller_Type_Individual                1.000000             0.063240  \n",
       "Transmission_Manual                   0.063240             1.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2bafe73b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1bfd8fd3f10>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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cPfKLiMuBTMP627v60yaVTW9tHvPe0s1r1FxXXJXU6KR6oKNPX9m2R/dtulB9AzHNrRz71JvHY7S+tUFLN6/R0e5w2m0AuNf+jpCu+fffqqbcr6svapYxksdIb1xUo0encN4n8szVFzXr9m17ppRbx5uapdjyMjLb1d6tO3+2JxmX1kp3/myPmuuCWr1odqGLB4dyYv44eLJXX9mWGstf2bZH5zfVpJSp2NtcmS7up9LmzuW+nBgzufR6Z1i3PZ5aH9/2+B4tn1+t5rmVBS4dnMrn82jjyvlqmVuhI51hNVQH1NpYPW0L0ub7eCgO+c5vtE0xFflsZxw+FdZXf743JUa/+vO9OqNmuRbXUecjf+jcyIFMw/pDkWjapDL6Ia9CX9Dkcsj+SOmS6oGOPvUNxHRh85yMn/N4jJrrKoriAg9AqvausGrK/XrP+QtkhtLM97Yf0pvPrNWFzXOmfJNovHU7JtrnTJiaBRM70hXWgY4+3fnE3pT327uYMxaZOTF/tGeI5fHyoS2+AcQ5vbjP5b6cGDO51B1Of/3T3R8tUIngFj6fRyvPqNHKM4rzeHC/fOc32qaYiny2M7rD0bQxSp2PbOTyXrTrOzeMMWdIuldSvSQr6S5r7W3GmNmS7pO0SNJ+SVdYa09ORxkSw/pHz2m3cHYwbVIZ/beabKI53fnlR+9ruubjK/aLNwCT11gd0AfftDD51FOgxKMt61rUUJU+L4ys8OZWBuT1DC5clsh5iTwjTX3djkw5vFimZkF2GqsDWlhbpg0r5ic73h5+/nDG2AQkZ+aPbNtfxb72Q31V+nN6Ku3QXLZpnRgzuVRfXZr2/1V9ZWkBSwU3iEbj2tXWqbbOsBqry9TaWDWtIynyfTy4X77zG21TTEU+2xn1VenPibnU+ZhAPG617eV27Tg0vIbR8gXVWnv21NYwcn3nhqSopE9aa39jjKmU9Kwx5qeSPizpcWvtPxpj/lbS30q6bjoKkGlYv6S0ScXvM8kEMNlEk7gQvfWx3bpydVNyGpapXpDmYph9pt62Yr94AzB5sbjSDud+xzn1Y7ZNd+Nty7oW3fv0AZ3sjWjrFav0znPqtfWKVbr1sd3avLZlTE7MJt8U+9QsyM6cYIn+/A+W6IaHdiVj6MZLWjWnoqTQRYODOTF/ZNv+yuVUS060oLpMf/G2Fl3/o53J/w83XbpMC6rLJr2vXLZpnRgzuTS73K8bL2kdk0tnB/2FLhocLBqN68HnD+szDw6fr7dsXKaNK+dPS4dDvo+H4pDv/EbbFFORz3bGssZq3XzpMn12RFvr5kuXaTnrF2ECBzpC2tM+uIbRyHs9Z86p0OIpXIe4vnPDWtsmqW3o393GmN2S5ku6VNIfDG32LUk/0zR1bkiZp1LK1OkxlbnlpdzMLz/S6Q6zn+ipv2K+eAMweUe70+ecl9q71VxXkZIf0t14u+3xwXln73xir669/zk9unnNYJ5pqNSJUL/u23ThlEazMR0eDp0KJy8epcF4u+GhXbrnQ6u1cA5zxiIzJ+YPv89o01ubFbeD6xr5fWNzYbGv/bC7vSvZsSENfrfrf7RTZ9dXaOUZNZPaV67btE6MmVxp6wzrO786oM9fvlJ9kajK/D59/clXtbC2XItYBBcZ7Hq9M9nRIA2er595cKda6iq0smly56sTj4fikO/8RtsUU5WvdobP59H8mlLdddUbdLJ3QDXlJfJ5RScxJvR6Z1/ah15XLKiemZ0bIxljFkk6T9IzkuqHOj4k6YgGp63Kq9EjGppqylN+Xt00WwdP9uqZ1zqSN+LicatdbZ1q7wqrNliquKxqg6XJC6hczC8/0lSG2Y/8XuV+n259bHfGTpZivngDMHmZpgl5pb1b5zZWqbmuIplj9h7tTpvnljZU6tp3nCW/z6ijpz+ZU89vmk3nKaYs4zzKYeaMhbvs7wjp5kdeTObZuJVufuRFNc9JbY8VYvrQSCSmHa936khXWI1VAS2fVy2/35uyTa7m323rTN95c6QzPGaO/WyOSZs2O/VVAV2wqFpzK0t1rNtqbmXp0M9Mo4LMDnf2pT1fD3f2aaVy39mQ7+OhOOQ7v9E2hdPt7wjpo998dkxb8tEiGQWM6ZMpv/WEY1PaX9F0bhhjKiQ9IOkvrbVdxgxfkFhrrTEm7VKJxphNkjZJUlNTU87Kk25Ewy0bl+kr2/boQEefFtaW6RNrW1KGwt7xgfN0qncg5b3Na1t03/aDum79OVrf2pCT+eVHmuww+3Tfa/PaFn37VwfU1jm4sFUxPfXnBNMVo0AuZRunTTXlY6YJuWFDq/7j1wf05jNrtag2mJx672/Xn5M2z+052q3vbT+kD75poa76118X5VzxmB7jxWljhnmUG6uYMxb5k4s6vyPUP2bq0s1rW3Qi1J/SNsv39KGRSEwP7nh9zDRRG1fMS3Zw5HIdkMbqsrTn9Oi5yot97ZHpMF6czq3w6ayGGn34G8P1802XLNPciqK57MQ0mFORvg6eE5x6HTxenE7H8VD8piO/0TaF040Xo8U+ChjTZ0FN+nb6vJqp5beiGCtkjCnRYMfGd6y1Pxh6u90Y0zj0+0ZJR9N91lp7l7V2tbV2dV1dXc7KlG46lc88uFMbVsyXJG1YMX/MUNgdh8YOj7192x5tWDFf197/nPZ3hJIXog8/f1ib17akdHRM5YI0Mcz+0c1r9N1Nv5ec4iXTxVy673X7tj16z/kLktuwaHhuTVeMArmUbZwePNk7ZpqQGx/ZpT9YOldzKwPJHLNhxXz949A6GiPz3Gc3nKvvbT+k95y/YMwwxkSeBDIZL05jcWnLutR427KuRbG0j0YA0yMXdb7f6xkzdent2/aoxJva7J9sG/B07Xi9M+00UTte70xuk2kdkKnk9lnlPt1wcWvKOX3Dxa2aFUydqzyXx5wpxovTF4+EdP1Do/7OD+3Ui0f4/4nM6qtK056v9dVTv4k7XpxOx/FQ/KYjv9E2hdONn0uHH75O4H4gshGJxtPmt4Ho1BKc6x+hMYNDNO6RtNtau3XErx6S9CFJ/zj03x/ls1yZejATA0rSTSsVt+mnmkpsm+j9zMX88iNNZph9pu+VuF5m0XAA48mUQ86qr9Si2qCeea0jmfcOdPTp2786oKsvapYxkrVSd3hAbZ3hnE3NByQcPhXWvU+nxtu9Tx/Qgppynb+w0KUDstcbiaXNj72RscO88znV0pEM+b+9K5z8OZdPAL5+Kqz/eGbs3OiL55RrYe3wvnjqMLfau/oz/J37C1QiuMEZNUHNmxXSFy9fqVAkqqDfp8oyr86omZ5rynwfD8Uh3/mNtimcLt+jgFE8cp3fXN+5Iektkq6S9IIx5rmh9z6twU6N+40xV0s6IOmKfBYq0zzGdkQn1Ojfe036qaasTe39nOhCNFdzFU/me61bOldvPrOWRcMdamBgQDt37kx5b9myZSopKcnwCWB6ZMoh5zRUyeMxY6bea+sM684n9iZ//uLlK3XN2iU6u74y73PFo7jVV5XqZG8kGW/SYEzVM/QfLpMpz9ZXTW9+nKj92ZhFuXK5Dkh9VUCvHO3R5v/47bj7KsTaI8Wsvir9NCrkUozH4zF6S3OddrV1qq0zrMbqMrU2Vk3bNWW+j4fikO/8RtsUTpcYBbx08xod7Q5zPxBZy3V+c/20VNbaX1hrjbV2hbV21dDrUWtth7V2nbW2xVr7dmvtiXyWK9GDOfIm3S0bl+mRHYclDS6ge8vGZSm/X76gesx7m9e26JEdh7Pu/UzMG/zu25/S++9+Ru++/Sk9tuuI4vHcjF1M9722XrFKy+fP0oXNc5KLiMNZdu7cqT+/8yH97QPP628feF5/fudDYzo7gHzIlEMWzwmm/D7d1Hs3XNyqL/zkJd2xba+++JOXxkwnwFMiOB3nNgR10yWpdfBNlyzTuQ3EFNwlU56dzvyYTftz+bxq3XTpqHPs0mVaMa96Wsqe7b4K8f+rmLU2VKTNpa0NjIJBZvG41U92t+vKu36lP/233+jKu57WT3a35+wattDHQ3HId36jbQo3SDx8zf1ATEau81sxjNxwrHMbK/Wtj1yg3khUTbODWji7XOc31ehod1gNVQFZq5TfL54TVDxu1TK3Qu1d/Zod9MvKav2yhqx7PxPzBteU+/XHv9ekuopSxa3Vi22dOrexetx9ZDPig55Z96pqWKiaprMLXQzMcIkccu6WNWrv6lcoEtXCmnIdPBFSW+dg7nnnOfU6t7FSx7r79ZU/Ok/BgFcBn1f/P3v3Hh9XXeeP//WZ+yWZ3Jpb06ZpmvSW9EIptbgUtRWs/Aqt3HW/gFzs7gq2K7roukCXiyiIuFRwFQUFVteirAgsIkhBYAGh3FtaaBqa0JKkSZrmMpO5f35/zCVzMmcmk3RuZ/J6Ph55tDlzZs5nJu95n885n9vXH3oLHf2jAEJTVv30r2144LJV6BvxoK7EipaZyXMcUTJHRvz4oHsAv7p0FXrD57en9xzGyoYyFFlzXTqi1E2mrpZK3c/vD8b1bjYYlP2jEq1bsXDLmuhIY5NJj7NaazG3wobuIQ9qHGYsqS2JLiY+2bKn63PQ6QROX1SNHZtXh9+jBS0T1JkpMbvVjPWtlWiYsQo9Qx5UO8xYWGOH3cqexpTYwX4nbn1yb3R6CgC49cm9WFhTnJHp4bJ9PCoM2c5vrJuSFqRSTyQa78iIH7sO9uK+L5+EvhEPKovM+MMbnVPOb2zcyIBI77Xx887NnWFHY2URGirsCR83GHRYNrss7vVSnWaqZ8iNMpsJX/5kA370lw+ir791XTO6h9xYu6A64cWtWpnUFpbM5vzMRFSY3usaVuSbreua8cDLHRhweXHXl06A1y8Vj9/yhSXw+iVqSyw4e8Ws6IXoW50DuOVP7yfNWUSpOOb0oqWuHC8d6ENQAm1HRtBSV45jLm+ui0Y0aanU1VKp+/n9QTzy9mFc+8ju6D43b2rFpmV1igvXSP0zNj8//PohxboVwaDEzv29E9Y101nPTPVzeGpvT0p1YJqY1xvAk3t6o4vHR0bobFo6U9GQRRSr3+nBZZ+ci36XF0EZmq75sk/ORb/Tk5FrzmwfjwpDtvMb66aU71KtJxKN1zfiQcvMMrzS3o+gBA4cGUHLzLIpn4fZuJEBB/uduO/FA6EFDD1+OKwG9I+48dwHR9BQYYeUiI6uOHvFLBRb9AgGJf76wRHMqbArGi8iF563PrkXG5bWQa8DTphdipoSM9y+IHRCwOMP/XvU6cGMIjNuO2cJPh504wfnLsPhYy7c/1IH7nxmPzaf2ogyqwkGvYhbhDyVHndEROnQedSJfd1DuGJNI6xGHcpsRjgsJtx27lIUmfVw+wLw+f2456IT4Q8G4QsAHX0juGFjCz7sdSoabr/7hSW4ceNiHBn24tYn96Ku1BKX37Ihk2sdUZYIwGbWYX5VMZweP+wWA4y8D0cF7GC/E/u7BnD/patwZDiUu17e36PoubynaxCH+4fj9tnTNajojFNbYsGlf9eAO54ey89XnzYfNTHraaRa10w1n6Yr7+ZzHViLvSHf+XgQdmMQD1y6Cj3hmOkdGsE7Hw9iZUN5rotHecqo08EvJe55vj2aQ/7lcwtg1GUm3rN9PCoMWc9vrJtSntvTNYgf79yvGAX345370VxVFNdpmyiWUSdQU2pGRZFZkd8MYmr3UNi4kQGDo16cs6Ie1/z+7WhladuZLfiPpz/AB0dGcPu5y1BmM+Gi1XOwY1cnLlhZj2/G7BvbWywyZDa2Z8lbHx1Dw6gdkMB/PPMBLlhZj+0796v2gN66rhn/eGojfvp8O2wmPfb1DOOmx9+LO1bPkFuxMBYQuriL7XFHRHS8gkGJNzqPKS4mt65rxo93HoDJIPDVTzdh26N7FI/ZTXqYDXp0HxuF0+vHFWsaAYR6Bf/bH97FHecvx70vfogLVtbjb+39WR/JMZmRb5S/LEYdXN4gPuxzRntxNsywR+cBJSo0/qAftWVFuOSXr471QD2rBYGgP7qPTgQxt8qB/4vpNTqvygGdUNYZ/QEZbdgAQnXIO57+AGsXVEX3SaWumWo+TWfezdc6sFZ7Q5bZdGjvFfjGuLgqs+VvmSn33P4AHnj5oOIG2QMvH0TLTEdBHI8KQ7bzG+umlO/6nZ64+5Fb1jbjqNOT66JRnkt3fmNWnKJgUKK9dwQvH+hDe++IYvGxQBC44fE9igu8Gx7bg298bkHoppwAzls5C9t37seGpXXRRBDZ9+qH3sLBfieA0AXXhSfVw+UL4J7n23HXzjb87Pl2dA+60TvsVn3+nc/sx8Unz8HlpzRi1BdARbEZl35yDmaV2qING+OPVe2wxAWRxahDVbEFRETpcrDfiTuf+QCXn9KIq9Y24Yo1jfjta504e8UsbFhaF23YAMbyWZ/TC48/AItRH82Dv3ihHRetnoMymwn7uoeiubCm1BZ9bmwuTSZZPk/1Pan1+k3l2JQ/nN4Ahkd9im3Doz44vYEclYho6lLJa8dcAVw/Lude/+geDLjGYt4XEDh8zK2ogx4+5oYvoGxA6BxwqTYQfDTgiv6eSl0z1XwaO19+5Fxy65N7p5R387UOvKdrMNqwAYQ+i2sf2Y09XYOK/Y73HJZu/U71uOp3MpdSYqM+Py775Fzow1/FyDRRbl9m4ibbx6PCkO38xrop5TuHxRR3P3L7zv0otphyXDLKdyMJ8tvIFPMbR25MwUS9xfpGPKoXeD2Dbty1sw1zKqy4+rQFcPuCEALq+w65AQA6ITC7zBYd2RF5/LevdeKWLyzB4WNuXLGmEQ+/fghdg6HnlNlMcFiNuPOZsdbTmza2JjzWkWE3VjVU4I7zl8e9p4aKqa1UT0SkJlHvDp0OGPEEVOdsD0qgttSGfxmXB7fvDE23Fwgimt8O9o3d2Eql5206ev/ma69fmhx/IAi3P6gYVXT1afPhDwQnfjJRHkk1r/UMeVRzbs/QWG+7YY8/Wp8Exhqdf3bRiYpj2k0GWIw6RS60GHWwmcYuNRoq7BPWNVPNp8l6Ck4276ZSrlzoGlT/LLoH3Vg2O/R7Po4c7BlKcB00xF6clFiR2QhPwBl3DrabM3O7ItvHo8KQ7fzGuinlO68vqPqd8PoZo5RcuvMbz95TMNHcvLPKrNiyrgmRjlMPv34IAy4vLOELvI7+UXQdG432ElO7GPQFJM7Y/gLKbCZct2GR4vHaEgsuWFmPy+/fpbige/CVDnQNunHeyllxIzSu++Nu3HH+MtVjVRVboNMJrG+pwcIta3Bk2I2qYs4ZT0TpZ9LpVHt33HHeMnQOuHDxyXMUDbNb1zVDLwCX169acaovs+HHz4ZGwc2psKJhhh1XrW0CADz29uEJe96mY671SK9ftdxK2iElVKfVue+SlTkuGdHkpJrXZpdZVHPurNKx3OX2BVRzr2fctqpiM7aua457rapic3SfVOqaqeZTk179XLJj8+pJf146ncDpi6qxY/NqxdoWua4D15ZYVT+LmpLJr2OSTTUOM+ZUWLFhaV200eyxtw+j2mFO/kSa1nz+YFbPwdk+HhWGbOc31k0p39kt6p1b7CYuDkPJpTu/cVqqKUjWqywYlOjoH1VMnXLxyXPwvS8swS+ePxDd//6XO3DLF5bgsbcPY8vaZkVDxy1fWILr/vgu3L4gugbdGHH7FcPlz14xS/WC7uwVs2Ax6lBfblMtX3uvM+5YsT3TdDqBxsoirG6cgcbKopxf1BFR4Tnq8qrmJ5cvACmh2kN47owilNtMqtOG9I54wmtt9OLKzzTjmt+/Hc29X1vbjPoyW9LyJMvnqYr0+k2UW0kbnB71m7hOD4f+k7aknteEas4VMQv5za2In/vWYtShocKqfCUB2E16bD41NE3U5lMbYTfpMX5NwInqmqnmU5dX/fvqmsJQ9mBQ4qm9Pbjgnlfwj//1Bi6452U8tbcn59M7tdQ6cPOmVsVncfOmVrTUlkT3Scc5LN1KrHp89dNNuPfFsWuhr366CaVW3uigxEY86p1YRjJ0Ds728agwZDu/sW5K+W7I7Y27x7hlbTOGPb4JnknTXbrzG0duTEGiXmWVRRbsPnwM3/jdW3EXiv9x/nKcGl5U8fNLajG7zAazUYcfX7gCoz4fHrh0FY6N+iAlAAF854zFCAYlhkZ9cHn9+M7nF+KWP+2D2xeEXqc+vdTi2mI8tHk1JIRq+dY0z4AvEMSOzavh8gZQ7eDoDCLKnmBQwmzQJeiVa4bdZFDNbRISOiFwyxeW4Dt/eDfaK/iWLyxBscWAEqsRpzRX4PyfvaLIvdc+shsnzC7DvKrEvVfTMeqCI98KQ1GCnkdFFlaVKL8EgxIH+53oGXKr1uVSzWvdCW6Mdw+N3RivL7Xh+2cvwbf/Zyz3fv/sJagvVTY2dA268fvXD+GKU+dh1OuHzWTAz58/gPk1xWiYMZaD/f4g9nQNKkZIxC6OnWo+rXZYEvSeVb7HiT4rID9HPwCAwaDDWUtmoqHCju4hN2ocFiydWaL4vPJx5GDPsC9u/axtj+7Bzy9eifk1OSsW5bkic4Lev+bM3DTO9vGoMGQ7v7FuSlOVSv0nHUx6PXbs6sTlpzRCiFBv/B27OnHinGVpPxYVlnTnN2bFKUg0N++hY070j/hULxQ/ODKCX7zQjps2tmLU64+uoRG5QVdqM2DE48d3/vAuymymuGkCbj9vGX755ZPwcns/mquKVYNgUa0DjZVFCAalavmW1JXyZhsBAIIBP/bt26fY1traCqPRmKMSUaGLzAt+6KhTdeoSXyCIfd1DqrktKIGuY6O476UPcfkpjSixGtBUWYQbHt+Djv7QFH+3nrNUNffu7R7C3BmJK3Ppmms90huZa2xol9cfwJa1zXFz+Hv97B1H+SOVNRbqy2yqjcHjR7JV2E2qObfCPrYIZFvfCABg86mNCEogkkrb+kbQOqs0ul+Nw4INy2bimpj67dWnzUd1zE12vz+IP7x1GNf9cXd0n5s2tuILy+sUN+w9Hj/6RjzoHfZCJwRqi8ywWpX1k/oyG762tjm64HZkVEPse0x97RH1Rp6eodyumxQMSvzl/SNJy5+P64V4/eo98ZhLKRm72aBaPyzK0BoY2T4eFYZs5zfWTWkqsrkeV1WxGV9Z04gjwx4EJWDQAV9Z06iYlpRITbrzG8/eU6DWq0wngP958zDmV6s3PDTMsCOy9sXmUxujj5fZTDjY70SruQQmvQ5f/XQT5s6w4/6X2qOtnwDwgz/vw4Un1cNq1OP2p/bFBcFt5yxVTC/FXsSUzEjvYdz2vx5U7QktfjbU3YGfXAmccMIJOS4ZFaqD/U7c+uReXHhSPRor7fjR+cvh9QdRZjfCqBf44VPv4/AxD67bsDi6ZlDkBPe9P+3FxuV16Ogfxd3PtmH7F0/AV3/zhqLXVHvviGru/aBnGIvDDb9qmC8pwm42qPY8+uG57HlE+SOVUQYfD7lgM+lw+7nL4PT6YTcZYNCHtteXj+XCgAzg5k2tcQ0EQTl2UTHi9UdHbURYjDr86tKTFOUa9vhU581d3Vge3Wf3x4PRho3IPtf9cTeaq4qwvL4MADA66sNju7tx/aNjZbrxrFac2VqjaODoHHBFyx15rWsf2Y0V9WXRzyHVERm2hIuh57YHdyrlz8dzWLHFqPp5FlvYgYYS0+sFakosiobUmhIL9PrMxHK2j0eFIdv5jXVTmopsjkjV6wGH1Ygjw57oNofVCD0HwdEE0p3f2LgxRbG9dINBiefeP4KgBA4NuFRbnz4+5gIQSixBGVoU/O8/UY9qhwUDTg8+7HPiR3/5IPqcbRta8NPn26K9kresbYYQwP0vdeC8lbNQV2rBbecuw8E+J/zBIGaVWhUXMuxFTBMpqpqNsvoFuS4GTRP9Tg++tGoOfvNqBy5a3YDbn3pf0VPu/BNn48iIF0UmveIE9+ArHegadCN26vNRlXmSH9p1CDdtbFX0CP76Z+fjVy8dxCfnVSTNhcyXBACDbh++tGqO4lz89c/OxxDnjKU8kmyNhUgO6xvy4sM+V/zi3kUW1I+1NcBhMcFk8Clu7pkMOhRbxkZu9I2or5PUP+JVbOsaTDDF1aAHS2eFfj90bFR1n8PHRqONG+92D0UbNiKPX//objTMsGHV3IpJfQ6p7AMA3oB6zzFfQPncbEu1/Kmew7I1RcWwx6f6eY4wl1ISA04f/uMvH0SnmgsEgf/4ywf47qYlBXE8KgzZzm+sm9JUpFp/SIe+IS8OD4TWHI6tc84qsSrqnETjHRtVz2/H3FPLb2zcOA6hxcOdOHRsFB5/ECtml8IbCOJ7f9ob1/q0YWkdgFDLvsOsx0Wr50S3L6opjk5TBYQSzw2P78HlpzTi7mfb4PaFFgy/7dxl6Bp0Y/szbbhqbRPu2tkWLcvCmuKcfAZERKkw63X40V8+wOWnNEYbNoCxdYl+dP5yXP/Ye7hiTSPufbE9rkfUqoYy/PuZizDqC8JmNmDruiY8tOsQugZDc8MPuLyor7Diqs80we0PQieAoJQYcHlzOvc4aUex2YjfvNqhOH//5tUO/IC94yiPpLLGwojXr7pQ+D0Xnah4LacngGt+/07caz142aro7xV2k+raFuUxU1cBQFEKox8STYNVFvNaPUOeBFNEeRTbqooTr383mX1C5TKr9hxb36qcQD2djQOpvFY619PI5hQVxWaj6ufJXErJjLj90RG6sYbd/oI4HhWGbOc31k1pKrK5HteI14/fvtapmHXmt691omWmI+3HosLisKQ3v7FxY4qCQYmd7/egvdcZHYZvMerwnc8vxFc/3YRtj+5Bmc2E81bOwtfWNqNnyI05FVZ8bW0zKuwm/Ptje3DBynps37kfV6xpVL2QE0L5+8E+J4BQYpIxvZgtRh3qSqzZeNtERFMS6f0rBBKujVFmM8FiCM3BfmjAhYd2HcKAy4sta5tx3R9348KT6mE36XHLE3sx4PJi67pmPPByBwZcXmw7swX7u4ehEwIPvx5q9Niyrinnc4+Tdox4fLjsk3PR7/JG54y97JNz4dRI7zivN4B3Ph5E95AbtQ4LlswsgSnH0+pQ+qWyxoLbpz4n+PhtiUZl9MWMyii2GKL12sjxbjirBcXjFvuzGHX4+cUrYNDp0TvsQWWxGf5gAFbj2FoaNpMe285swQ2Pjb3WtjNbYI+J02qHOcFC4cq5mw164IazWuLKZdAr91E7nmHc1yKd63ekItXXSud6GtmcokLruZRyo7JYvfFzRpEpybO0czwqDNnOb8ynNBXZXI8rKIPR+5qxo5mCsTcsiVQkym9THQnHxo0pOtjvxDuHBqPDr4DQhcItf9qHqz/bjPsvW4WOPieuj7nounlTKxxWI8wGHb69fhH2dg/hijWNsBp1qpWr8Q0YHn9k7uEW3P1cW3T7zZta0TKzJKvvn4hoMuzmUK9eAKr5rmVmCRynGBXTVV2/YTEGR3144OXQ1FR3PrMfm09txNkrZuHuZ9tw5zP78ZMvrYDLG4DL48P259ox4PLi8lNCoz/WLazCkrpSrp9BKSm2GOEJOBXDqq8+bT6KNDBPvNcbwCPvfIzrY6Zlu3FjKzYtnckGjgKTyhoL5QlGSJTblbFcbFEfbRHbcOH2BfCT59oUPfJ+8lwbfniesleVw6pHe58rbrHw5bPHGiWaZ9jRPehWrAVi1As0zxi72G6pKcKVn26OW3OjpUZ5A75v2AuPL6CYUsvjC6Bv2Is54dmreoY8+Olf2xQ9wn761zY0VS7DnIqx1+sccOHHO/cr9vvxzv1TWr8jFam+VjrX08jmFBVazqWUO2U2fdz0ojdtbEW5PTPnsGwfjwpDtvMb8ylNhU4ncPqiauzYvBpdg27UlljQUluSkWtim8kYbdgAEJ115oGYUcBEauwmAzyBYFx+s5um1kzBxo0p6hkKzQGvdqEw5AlgYMSL68OjN85eMQtCAB8ddaHUaoReJ9DnDLVO6QVQaTfh65+dr5hr7IazWvCTmAaMmza2wmQQuP3cZQjKAP7l9IWQAOrLrGiZWQKDQadSSiKi/FDtMOPfz2zB71/vxJ0XnIA9XYMIylCP3AtW1uPwgCtuuqobH38PV32mKTr1VJnNhOaqYrg8fly1tgkPv34I/U4vDva78D9vjE1RpdcBd5y/nA0bNCnBoMRTe7pw27nLMOrxw2Y24P6X2rFsVv53Hnjn48FowwYQXqfgj7vROMOOlQ2c8LbQTLTGgsWoxw1nLsbh8HpFegHMLLHAMm7Igtko8J3PL1TUSSvsJpiMY3lz2K3sVaUX4V5V46ZuOeYKqC4WHjvFVdewF/s+HsDqpmrIYYlKhxmv7O9BU1Ux5llDvaXbel24+7n9isaUu5/bjwXVRVhWP9ZQ4vYHcMuf9sU1zNx7ycro74OjPtVpZ46NKnuE9Qy5VfebyvodqZjMa6VrTahsTlGh5VxKudM16MPHR4dx/6Wroo15r7T1oLbEgnlV6T/ex1k+HhWGbOc35lOaimBQ4qm9PVmZinLQ5VOt0wy6OLqIktPrhGp+W1FfOqXXY+PGFFU7LNAL9R7IDrMeo/4AymwmXLR6jmKI1m3nLEH3kCduwR27SY/NpzZifnUxghJ4/O2P8INzlqF7yI223hHc8fQHGHB5ces5S1FXakO53ZyxhQCJiNKtpsiC5mo7zjmxHlt3vBnNf9dtWIwdr3bi7z8xR7ViVFkUuplVW2LBxSfPwb+E1yeK5M4Bp0dxQ8pi1OHU5kqsqC9jfqRJ8QUDOGdFPa6JibFtG1rgD+Z2UeFUHBlOfLOUph+3zw+dTqeoa960sRUev7JBwqzXw2zUK/a74awWWPRjjSDFFiNcvpH4euu4aal6U5jiqnd4FFUlRbjkl68qvmN9I6OYVxW6ed/r9KhOb9DrVK654fXHT7Pl9gXh9Y9tc1iM6vX0cT1eU7nxn87GgWw2NERkc4oKLedSyh2PPwCj0aTID1vWNsMbCGTkeN4sH48KQ7bzG/MpTUU2p6K0GPWqdRoLR47TBNx+v2p+8/indh5md/9JCgYl2ntH0DPkxsmNFfj2+oWKqVa2rmtGc00xDvY5cd7KWXFDtNpi1uiIbLvzmf0ot5thNerx3f/di2t+/zY2LJ2FFfVl2LB0JjYtr8OPLliGJ7aswZlLZ2JlQwUaK4t4446INGN39xB6Bj3Ruc+BUP676fH3sGZ+FWwx01ZFWIw62M2hG2jnrZylukBuc02xIgffvKkVy2dxxAZNnlFnwA2PK+Pzhsf3wKDL/8p5ZZFZ9fsTaRyk6SUQFKqjKPxBZV50egPRNSsi+217dA9c3rGLikiuHZ97PeMaFhwW9RweO8WVEDrV71js5YjDoj69gcOsbJAos5uxck4Jtn/xBNx69hL8+IsnYOWcEpTZxubLr3aYsXVdc1w9ffz6HZEb/7H7jb/xn8o+qUrna6UqMsXVE1vW4LebP4EntqzJSA9OQNu5lHIn0Xe/yJyZ6XeyfTwqDNnOb8ynNBXJRoimm8NiUK1rFZvZj56SS3d+Y8RNQmQBwFuf3IsNS+tQYtFjZqkNW9c1o9xmQmWxGe/3DOP9rhE8tOsQrj5tflxSSTSVlV4vcGJDGaodFjRV2tEaM51KOoajExHlUveQG063XzX/CQEcPubC1nXN0ZtokYpRZbEJd3/phIQLkb/fPYLNpzaiqbIIDRU2LK7lNH00NX0jngQ9zz0JnpE/JIAta5vjeruDbXzTUr9TPZb7x41+GE6Qk4dippxKtM/wuGmpyuwG1RxeZhu71EhUrqMx5XJ51Y/n8imPFwz6cf5KZW+vG89qQVCONczUl9vRXF2kWJejuboI9eXKRoRU1rZI5/oXAGAyCEW5TIbMf1nTNcXVRLScSyl3+hKM/jrq9CZ4xvEeL0GeZJxSEtnOb8ynNBXVDgvmVFixYWlddIrPx94+nJmpKCFR47Ao6jQ1DgtCVydEiaU7v7FxYxIO9jtx65N7o8PlLz+lEXeEL6oA4Kq1TfjFC+24Yk0jBlxeHBl2xw3RSjiVlcWAfR8PYUaxRdGwQTQVwYAf+/bti/6+b98+xQL1RNlW67DgtaNHVfOflMB/v9qJq0+br6gY1ZVZ8X73MO79vw/xzdMXqj63rtSK/UeGsXRWCRpmsBGYpq6q2Jxgqpj8H/1QYTdjx65OxYLIO3Z14nMtNbkuGuVAZCTP+FgeP5Kn3K4+bVO5bazncmWx+uLkM4pMitdyWI2YXW5T5PDZ5TY4Yl5rRoJyldvHylVsVi/T+N7U/oDA9eNGnVz/6B48cOnUFrBM5cZ/uhoHDvY7cdVv3ox7j09kYLqIXNByLqXcmVlqVY2bWkdmpmurSJCPKjjikZLIdn5jPqWpqC+z4Wtrm3HtI7ujHUBu3tSK+jJb2o9VYjXh1397Dxd/shGjXj+sJgMeeKkdt527PO3HosKS7vzG7q0piExF9UHPMC48qR5ufwBXrGlEfZlV8Yd4+PVD2LK2GY+9fRhb1jbjoV0fYcta5RCtORU2fPcLSxTbbtzYikAwiFPnV2ZsiDhNLyO9h3Hb/76Lbz/8Nr798Nv43o6/YnR0NOXn+3w+vPnmm4ofn4+LQtHUVTpMqC21hOZzj8l/121YjIU1Rbj2jMUQUsJhMaChPFTx+t4T+3DLn/bhyk834f6X2uPy6XUbFuOWJ/Zi+zNt6B6a3DDbSF5/+UAf2ntHEAyy9W+6s5oEbtzYGnd+tpry/5ys1wEXnlSPe19sx10723Dvi+248KR66FnLm5Y8/kBcvtyythnecXPY9jt9+Ppn5yv2+/pn5+NozCKQxRY9bhr3vbhpYyscVuWQ8Z5BL371f+1oqirG7DIrmquK8av/a0fP4Fiva5fXr1oud8yojEG3V3WfYbey93bPsHpvryPDY729Io0I259pw10727D9mTZc9Zs3cbDfOYlPM/2yOV1ErGyd97ScSyl3WmoduHmTMm5u3tSKlpmZWTi52mHGtjOVddJtZ7bETVtHFCvb+Y35lKaic8CFH4c7Y1+1tglXrGnEj3fuR+eAK+3Haqiw47JT5uGa37+Nbz38Lq75/du47JR5GZ1qkwpDuvMbR25MIDIV1dUPvYUbNy7G8tml6BvxosJuBMaNwugadGPHrk584/SF+PiYCzdubMVHR1342UUnonfYA7vJgN5hNxorbXjgslU4MuxBhd2EGocZcyq4hgalV1HVbJTVLwAADHV3TOq5u3fvxlfvfhSOmjnR5//kSuCEE05Iezlpeugf8WJmqRkCemxd14wZRWZUFIV6BI+4AwgC+OFf9qOjfxS1JRacvWIWzls5C01VxfD6fPjyJxsRkEH87KITsbdrGC6vH0OjPnQNuie9EGtsXo9dWJWNy9Obyysx5HThgUtXoWfYjepiC975qA8ub/p7OaVb16AbD7zcoRi58cDLHTihvpQjmqYho16vOpLnxDnLFPuV2oz4zavKuPnNqx247Zyx/QxCj0GV74VelCpe66jLi7ULaxTTRG1Z24wB11ijRLndjB279sSV68cXjtUtKuwW7Nj1Xtw+2y9Q1j9qS9R7e8XemEzWiDB+hEQwKHGw34meITeqHcc35dREcrGgeDbPewlzqSf/cynljsGgw6ZldWiuKkL3oBs1JRa0ZHCq0dlldswsdeL2c5fB6fXDbjKg2KrH7DLekKPEsp3fmE9pKvpGPPjOGQvhsJjQO+xBZbEZS+qK0e/0pH2EaLqn7aTpI935jY0bEzjY78QbB3tx/6Whxgi9TqCx0oo7//IBNiybhe99YQn+9Q/vRi8U/vHUJvzwqX3o6B/FnAor/vHUJvzDg68rLiQ+Oa+KX3bKe46aOdHGEaLj4fcHcaDXCb8/gOoSMyqLzdFhsnMqrPjHTzXhp39ti0751zXoxr0vhkZq/PCpffjG6Qvw8eAo7nj6g2guvfq0+fjl/x2c0kKsB/ud0Rs8QOhm19UPvYWFaZ4SJJs3y+j4zS7T42CfDRf/8tWYOfxbMbss/xdtrHZYMODy4u5n26LbMn2zlPLXzDKz6nQEM8uUPZJn2PW48tPNuP7R3YqYn1E0FvMGg0Cxzar4XtxwVguM49aIKLMasXNfN247dxlGPX7YzAbc/1I7TmpYFN1nYVUxtqydj397ZKze/N1NS7Cw2jH2OnYDrvx0U3TKqchaGmVFymmpWmtKcONZrXFlX1I71ss71Tmns93gHVlQfPzxMtnLMVvnPSBJLi3P/1xKuWUw6LBsdhmWzc78sXQ6gTVNVTjY7+QNOUpZtvNbbakeB/us447XgloN1E0pd0qsethMZrg8gBCAXidQV2qGUZ+ZuMnWml5UWNKdTwu6cUMIsR7AnQD0AH4hpfz+ZF/DZvTj9JY69Ax7UO0wY3aZHgf6PPjG6Qvw5DsfY3VzFf77ik+gZ9iNcpsZoz4/fnDOMhx1eVFqM8LrD2DHV1bD5Qvw5hYRTUt7ugZxuH8YaxbUYMTjR0WRwIOXr8KA0webSY9iswHf3bQE3kAA91+6CkOjPhRZDHD7/PjhectgMuhQ47DgnotOhNsXRLFFD5vRgFOaKuALSLi8ARzsd0ZvDE3UoDCZ3rxTxdEh6ZHNBiKPDzi1uXis94jDgvoyPdwamJGvocKO33zlJPj8InqTxmiQHBI+TR1z+rFstjKWy+x6HHP6MbtsbL+gBD7RqNyv2qGHN2bt7gGnFy21NsU+NqPEgNOLORVj++mFwDXrFwBShx5IVDvMuGb9Auhjvq9dQy40VVoUr2XWS3QNuTBnRnHoeCM+zCq3hDsVhWI5IAMYGPGhvnzseAaDDrPKzPjll09C34gHM4rMkDKo6OVdX2bDv/1/i+H1BeH0+GG3GLB01uK4OadTvfE/OurDu91D6BkKXRMsqXHAalU2ugChBv09XYPoGnSjtsSKllqHolw6ncDpi6qxY/NqxT5quS1dOTAb570ILedSml54Q44mK9v5rbPPh8pivaJns9PrRmefT3E+J4pl1oemFXV5xqYjLbfr4c1QnA6PurG32xmtHy2qsaPYyg5WlFy682nBNm4IIfQA7gZwGoBDAF4TQjwqpXwv1dc4NurG8/uH43qFrZ5XDLcPOGPZTBwacEOYjHBYTfiw3wm72QCL0QCrUYcZdjMaKzndFBFNb1IGMLeqBH/78CjufGZ/dMTGVZ9pxj3Pt+GcFfX46fNtuOyTc+HyjUT3sRh1+MG5S9E16FaM2vjm6Qvw4CsHcdVnmrHjtQ7s6hiExajDXV86AV6/nLBBIRtTgmSzl2yhynYDkcUI1XP+qc3FaT9Wunk8frT1jMaVfVGlX/XmKxW2EhvwyoGhuNEPq+c5FPtZTMDLbfH7ndw0tl+JVYd3D7vQ1utEUAJtR0Ywr9KOJXVF446pw+sdI3GvdeKcsdfyBgJ4v8cVt88J9WOXI3qdwEdH3dgWs88NZ7WgpVa5gPn7R4bw9qEhxfli67pmlNhMaJlZCgA4NOBE/4gXNzw29lrbzmzBoQEnGirHvtep3PgfHfXhsd3dcd+xM1trFN8xvz+IR94+HDdqZtOyumgDRzAo8dTenglzWzpzYDanwtJyLiUiSibb+a221Ioztu+Ky91PbFmTkeNRYchmnA6PuvGn3b1xx/p8ayUbOCipdMdpIS81uQpAm5SyXUrpBfBbABsn8wIfdDujHzQQutC5/tHd6B4M4JgzgAFnAFaTET3DHvSPhNbUWFzrwCcaK/CpBdVoqi5mwwYRTXvegEBb71ijBQBsWFqH6/64Gxd/shE3PL4HG5bWod/lVezj9gWx/8hItGEjsu32p95XPD+y/Z1Dg6oNCuMXj41MCRK7eFW6pwTJ1YKxhSRRA1GmFgPuHAionvM7BwITPDP33u0eUi37u91DOS4Z5UL3sUC0AQGIxMMedB9TxnLXgPp+XTExP+AM4vAxN+55PrRY/c+eb8fhY24MOJX5rX9E/bX6R2JfS32fAefYPk5vINqwEdln26N74PQqy37U6Ys7X9z5zH4MOMe6e3UNeaING5F9bnhsD7qGPIrXitz4jzX+xn+q37E9XYPRho3Iftc+sht7ugaj+6Sa29KZA7Nx3ovQci4lIkom2/ktm7mbCkc243Rvgnume7szc71GhSPdcVqwIzcA1AH4KOb3QwA+MZkX6BnyqN6c6om5KJJSoqbEAn9AwhcM8ERDRDROz5AHQQlFPhUi9Puoxw+3LwghELcPoL4tsr/bF8RozPwpifYdP+1GNhY+y8WCsYUmm9OohI438Tk/X2m57JR+PcMJ4mHYM+n9hr1+1UaEn1104qRfK5V9jiTYp3dc2UfcftX9ht1j54T+Ea/qPv1Or2JbKmtgpPod6xpUz1vdg+7oOgKp5rZ05sBsLvjJfEREhSrb+Y2LNdNUZDNOec6nqUp37BRy40ZKhBCbAWwGgPr6esVj1Q6z6s2p6mIzED6fBKVEhV0Pk9GAuhKeaCj9ksVougQDfuzbty/6+759+yBlRg5FBWqiXNp2ZFg1n9rMhmhvJL1A3D5q2yxGHaQM/Ws1GSbcV61BIdPzLOdiwdhCk4kGoimf8/NcwrI78r/spJSOc36q8ZDKfm5fQPXCwzNuWyqvlco+tSWplb2qWH2/ypjva02i1xr3nU7l5lGqn2ltiVV1v5oSS8xrpZbb0p0D03neK9RcSoUlG9dQNL1kIr9NFKdcG4YmK91xOqVzPq9BaALpjtNCnpbqMIDZMb/PCm9TkFLeI6VcKaVcWVlZqXis2mHAjWe1KIYB3nhWCyqK9Kgv06O2VI+GcgPmlDswu4xra1BmJIvRdBnpPYzb/vddfPvht/Hth9/G93b8FaOjown39/l8ePPNNxU/Ph9XipzOksXpkhoH5lUWYeu65mg+feztw7hpYyvuf6kd2za04LG3D6O+wqbYJ3KjatuZyjz8zdMX4PF3Qs9/4KX26PYls0ryZuh25GbZE1vW4LebP4EntqzhYuKTlImh+MnitKpY/Zxf5cj/fiBLahy48azWcWVvxZIaxwTPpHyTjnP+ghq7ajwsqFF+d+Yn2G9+zH4NFTbVKZvmVFgV21prilVfq7VmbN7clpoi1X1aaopiXqdEPZZrSxTHa6lx4KaNyv1u2tiK1piYT/W1gLGbR6sbZ6iul5fqd6yl1oGbNyn3u3lTK1pijplqbsvn6Uimcv1UXZL/uZQKSzauoWh6yUR+Y5xSuqU7TpPF6KIEdclFNbmvq1B+S/e1t5AF2j1bCGEA8AGAdQg1arwG4EtSyj2JnrNy5Uq5a9eu6O/BoMSbH/XDHxDRnlwGXRB1paGFA8ttxTCZ9Bl9H1TQJn2Xc3yMxnrzzTfx7YffRln9AnS8+hQMjirULVwOAMf9+0Dn+/j+OctwwgknRI/11bsfhaNmDgBgqLsDP7nyrOjjVFDSEqejoz7sOzKMEY8fo74gisx6OCxGjPoC8PoDMOj1cPv80Ot1OOb0wenx46jLiznlNtSVWTE46oPbF0SZzYghjw+lVhNaqh34eNit6GkLhOYp59DtwhAMylT/nscdp8GgxGsdfQgGBfpGPJhRZIZOJ3HSnBmaiKHRUR/e7R5Cz5AH1Q4zltQ4uJh4fknrOX8ig6NuvN/tjMbDgho7SlQWdjw26sYHMfvNr7GjNGa/YFDiid1d+Obv3o6OQrv9vGU4o7U27nvhGvVid/dw9LVaa4phsyoXAneOerCneyS6T0tNEexWZQ8tt9uPd7sGx2K5tgQWS/yFTir7pfpaqUj1O+b3B7GnaxDdg27UlFjQUlsSXUw8ItXcNokcmC5pyaVx1096iRNmV2gil5ImZDWfEkVMIr9NKdkxTikdMhmnajE6POrG3pi65KIaOxcTpwlN4to7pTgt2C40Ukq/EOIqAH8GoAdwX7KGDTU6ncAJsytwsN8JCYnKYjNvlBGFOWrmoKx+wZSe6/P5sHv3bsW21tZWGI28EVeorFYjTphTPuF+sTdyTiounzDnNlrih2lz6HbhyOZQfJ1O4KQ5M3Cw3wkhoLnGMavViFVzK3JdDMoTJVYLVs2d+MKydIL9dDqBM1prsbjWMeENdpvVNGEM2q1mrJqbfLi5xWLASSnEcir7pfpaqUj1O2Yw6LBsdll0jQ01qeY2LU5HwusnIipUzG+kBdmO0+IU65xEsdJ97V2wjRsAIKV8AsATx/MaWryoIMq28Wt2RKaoim2siG282L17t8rID0RHfrDxY/pizqVcYvwRxeP3giaLMUNEhYr5jbSAcUpakM44LejGDSJKj4kWHA+t2eFB1R4PAKBr9yvQF5WjqmE+gPjGCyD5yI/xjR/HPm7HP5+2DwsXLozuw8aOqWHDERERERERERERFQI2bhAVkKHuDgCAs68LercHAzZrWn7v2bsL2153orQ2dFO8v30PHHMWQ4RHjDn7uqAvSj7l0PjGkUhZI+Xet8+sui8AjB49gm2//DB6fFd/N/7t7z+raOyg1Ozbtw/f/fVfYKuoARD6LB+86Squl0JERERERERERJpSsAuKT4UQohdAh8pDMwD0Zbk4k8Uypkc2y9gnpVw/mSckiVEgvz/ffC1bvpYLyJ+yTac4TRe+x+xjnI7RctkBbZc/WdnTHaPpVqife77Lt7IXei7Np/LkU1mA/CrPRGVhnGZPPpUFyK/ypPWcD2gqTvOpLEB+lSefygJkt26qpfeebflUFkBb5UkpTtm4kQIhxC4p5cpclyMZljE9tFDGRPK57PlatnwtF5DfZTsehfq+YvE9ap+W35+Wyw5ou/wse26w7Pkr395fPpUnn8oC5Fd5sl2WfHrvQH6VJ5/KAuRXeaZznOZTWYD8Kk8+lQXIbnmm83ufSD6VBSjM8ujSVRgiIiIiIiIiIiIiIqJsYOMGERERERERERERERFpChs3UnNPrguQApYxPbRQxkTyuez5WrZ8LReQ32U7HoX6vmLxPWqflt+flssOaLv8LHtusOz5K9/eXz6VJ5/KAuRXebJdlnx670B+lSefygLkV3mmc5zmU1mA/CpPPpUFyG55pvN7n0g+lQUowPJwzQ0iIiIiIiIiIiIiItIUjtwgIiIiIiIiIiIiIiJNYeMGERERERERERERERFpChs3iIiIiIiIiIiIiIhIU9i4EWP9+vUSAH/4k62fSWOM8icHP5PGOOVPDn4mjXHKnyz/TBpjlD85+Jk0xil/cvAzaYxT/mT5Z0oYp/zJ8s+kMUb5k4OflLBxI0ZfX1+ui0CUFGOUtIBxSlrAOKV8xxglLWCckhYwTkkLGKeU7xijlK/YuEFERERERERERERERJqS8cYNIcRBIcS7Qoi3hBC7wtvKhRBPCyH2h/8tC28XQojtQog2IcQ7QogVMa9zSXj//UKIS2K2nxh+/bbwc0WyYxARERERERERERERkbZla+TGZ6SUy6WUK8O/fxvAM1LKZgDPhH8HgM8DaA7/bAbwn0CooQLANgCfALAKwLaYxor/BPCVmOetn+AYkxIMSrT3juDlA31o7x1BMJjylF9EWcM4JSI6fsylRJQI80Pq+FkRUaFifiMtyGac8jtB+cCQo+NuBPDp8P/vB/AcgG+Ftz8gpZQAXhFClAohasP7Pi2lPAoAQoinAawXQjwHwCGlfCW8/QEAmwD8KckxUhYMSjy5pxtXP/QW3L4gLEYd7jh/Oda31ECnE1N420TpxzglIjp+zKVElAjzQ+r4WRFRoWJ+Iy3IZpzyO0H5IhsjNySAp4QQrwshNoe3VUspu8L/7wZQHf5/HYCPYp57KLwt2fZDKtuTHSNlB/ud0S8pALh9QVz90Fs42O+c7EsRZQzjlIjo+DGXElEizA+p42dFRIWK+Y20IJtxyu8E5YtsNG6cIqVcgdCUU1cKIU6NfTA8SiOj45aSHUMIsVkIsUsIsau3t1fxWM+QO/oljXD7gjgy7M5YWYnGSxajAOOU8sNEcUqUD3jOp3zHXJqfmB+UmEtJC5hPKd0ykd8Yp5Ru6Y5TnvNJCzLeuCGlPBz+9wiAPyC0ZkZPeLophP89Et79MIDZMU+fFd6WbPssle1Icozx5btHSrlSSrmysrJS8Vi1wwKLUfkRWYw6VBVbJn7jRGmSLEYBxinlh4nilCgf8JxP+Y65ND8xPygxl5IWMJ9SumUivzFOKd3SHac855MWZLRxQwhhF0IUR/4P4HQAuwE8CuCS8G6XAPhj+P+PArhYhKwGMBieWurPAE4XQpSFFxI/HcCfw48NCSFWCyEEgIvHvZbaMVLWUGHHHecvj35ZI/PHNVTYJ/tSRBnDOCUiOn7MpUSUCPND6vhZEVGhYn4jLchmnPI7Qfki0wuKVwP4Q6jdAQYAv5FSPimEeA3AQ0KIywF0ADg/vP8TAM4A0AbABeBSAJBSHhVC3ATgtfB+N0YWFwfwVQC/AmBFaCHxP4W3fz/BMVKm0wmsb6nBwi1rcGTYjapiCxoq7FwYh/IK45SI6PgxlxJRIswPqeNnRUSFivmNtCCbccrvBOWLjDZuSCnbASxT2d4PYJ3KdgngygSvdR+A+1S27wLQmuoxJkunE2isLEJjZdHxvhRRxjBOiYiOH3MpESXC/JA6flZEVKiY30gLshmn/E5QPsjGguJERERERERERERERERpw8YNIiIiIiIiIiIiIiLSFDZuEBERERERERERERGRprBxg4iIiIiIiIiIiIiINIWNG0REREREREREREREpCls3CAiIiIiIiIiIiIiIk1h4wYREREREREREREREWkKGzeIiIiIiIiIiIiIiEhT2LhBRERERERERERERESawsYNIiIiIiIiIiIiIiLSFDZuEBERERERERERERGRprBxg4iIiIiIiIiIiIiINIWNG0REREREREREREREpCls3CAiIiIiIiIiIiIiIk1h4wYREREREREREREREWkKGzeIiIiIiIiIiIiIiEhT2LhBRERERERERERERESawsYNIiIiIiIiIiIiIiLSFDZuEBERERERERERERGRprBxg4iIiIiIiIiIiIiINIWNG0REREREREREREREpClZadwQQuiFEG8KIR4P/z5XCPE3IUSbEGKHEMIU3m4O/94Wfrwh5jX+Nbz9fSHE52K2rw9vaxNCfDtmu+oxiIiIiIiIiIiIiIhI27I1cmMrgL0xv98K4EdSyiYAAwAuD2+/HMBAePuPwvtBCLEYwIUAWgCsB/CTcIOJHsDdAD4PYDGAL4b3TXYMIiIiIiIiIiIiIiLSsIw3bgghZgH4/wD8Ivy7ALAWwO/Du9wPYFP4/xvDvyP8+Lrw/hsB/FZK6ZFSfgigDcCq8E+blLJdSukF8FsAGyc4BhERERERERERERERaVg2Rm78B4BrAATDv1cAOCal9Id/PwSgLvz/OgAfAUD48cHw/tHt456TaHuyYxARERERERERERERkYZltHFDCLEBwBEp5euZPM7xEEJsFkLsEkLs6u3tzXVxiOIwRkkLGKekBYxTyneMUdICxilpAeOUtIBxSvmOMUpakOmRG38H4CwhxEGEpoxaC+BOAKVCCEN4n1kADof/fxjAbAAIP14CoD92+7jnJNren+QYClLKe6SUK6WUKysrK6f+TokyhDFKWsA4JS1gnFK+Y4ySFjBOSQsYp6QFjFPKd4xR0oKMNm5IKf9VSjlLStmA0ILgO6WUfw/gWQDnhne7BMAfw/9/NPw7wo/vlFLK8PYLhRBmIcRcAM0AXgXwGoBmIcRcIYQpfIxHw89JdAwiIiIiIiIiIiIiItKwbKy5oeZbAK4WQrQhtD7GveHt9wKoCG+/GsC3AUBKuQfAQwDeA/AkgCullIHwmhpXAfgzgL0AHgrvm+wYRERERERERERERESkYYaJd0kPKeVzAJ4L/78dwCqVfdwAzkvw/O8C+K7K9icAPKGyXfUYRERERERERERERESkbbkauUFERERERERERERERDQlbNwgIiIiIiIiIiIiIiJNYeMGERERERERERERERFpChs3iIiIiIiIiIiIiIhIU9i4QUREREREREREREREmsLGDSIiIiIiIiIiIiIi0hQ2bhARERERERERERERkaawcYOIiIiIiIiIiIiIiDSFjRtERERERERERERERKQpbNwgIiIiIiIiIiIiIiJNYeMGERERERERERERERFpChs3iIiIiIiIiIiIiIhIUwyp7iiEqATwFQANsc+TUl6W/mIRERERERERERERERGpS7lxA8AfAbwA4C8AApkpDhERERERERERERERUXKTadywSSm/lbGSEBERERERERERERERpWAya248LoQ4I2MlISIiIiIiIiIiIiIiSsFkGje2ItTA4RZCDAkhhoUQQ5kqGBERERERERERERERkZqUp6WSUhZnsiBERERERERERERERESpSHnkhgj5f0KI68K/zxZCrMpc0YiIiIiIiIiIiIiIiOJNZlqqnwA4GcCXwr+PALg77SUiIiIiIiIiIiIiIiJKIuVpqQB8Qkq5QgjxJgBIKQeEEKYMlYuIiIiIiIiIiIiIiEjVZEZu+IQQegASAIQQlQCCyZ4ghLAIIV4VQrwthNgjhLghvH2uEOJvQog2IcSOSCOJEMIc/r0t/HhDzGv9a3j7+0KIz8VsXx/e1iaE+HbMdtVjEBERERERERERERGRtk2mcWM7gD8AqBJCfBfAiwBumeA5HgBrpZTLACwHsF4IsRrArQB+JKVsAjAA4PLw/pcDGAhv/1F4PwghFgO4EEALgPUAfiKE0IcbW+4G8HkAiwF8MbwvkhyDiIiIiIiIiIiIiIg0LOXGDSnlrwFcA+B7ALoAbJJS/m6C50gp5Uj4V2P4RwJYC+D34e33A9gU/v/G8O8IP75OCCHC238rpfRIKT8E0AZgVfinTUrZLqX0AvgtgI3h5yQ6BhERERERERERERERaVjKa24IIbYj1MAwqUXEw6MrXgfQhNAoiwMAjkkp/eFdDgGoC/+/DsBHACCl9AshBgFUhLe/EvOysc/5aNz2T4Sfk+gYRERERERERERERESkYZOZlup1ANcKIQ4IIW4XQqxM5UlSyoCUcjmAWQiNtFg4+WJmjhBisxBilxBiV29vb66LQxSHMUpawDglLWCcUr5jjJIWME5JCxinpAWMU8p3jFHSgslMS3W/lPIMACcBeB/ArUKI/ZN4/jEAzwI4GUCpECIyamQWgMPh/x8GMBsAwo+XAOiP3T7uOYm29yc5xvhy3SOlXCmlXFlZWZnq2yHKGsYoaQHjlLSAcUr5jjFKWsA4JS1gnJIWME4p3zFGSQsmM3Ijogmh0RdzAOxLtqMQolIIURr+vxXAaQD2ItTIcW54t0sA/DH8/0fDvyP8+E4ppQxvv1AIYRZCzAXQDOBVAK8BaBZCzBVCmBBadPzR8HMSHYOIiIiIiIiIiIiIiDRsMmtu3AbgCwitmbEDwE3h0RjJ1AK4P7zuhg7AQ1LKx4UQ7wH4rRDiZgBvArg3vP+9AB4UQrQBOIpQYwWklHuEEA8BeA+AH8CVUspAuFxXAfgzAD2A+6SUe8Kv9a0ExyAiIiIiIiIiIiIiIg1LuXEDoUaNk6WUfak+QUr5DoATVLa3I7T+xvjtbgDnJXit7wL4rsr2JwA8keoxiIiIiIiIiIiIiIhI2yZs3BBCLJRS7kNoCqh6IUR97ONSyjcyVTgiIiIiIiIiIiIiIqLxUhm5cTWAzQB+qPKYBLA2rSUiIiIiIiIiIiIiIiJKYsLGDSnlZiGEDsC1Usr/y0KZiIiIiIiIiIiIiIiIEtKlspOUMgjgrgyXhYiIiIiIiIiIiIiIaEIpNW6EPSOEOEcIITJWGiIiIiIiIiIiIiIioglMpnHjHwD8DoBXCDEkhBgWQgxlqFxERERERERERERERESqUllQHAAgpSzOZEGIiIiIiIiIiIiIiIhSkVLjhhDCAODzABaGN70H4M9SSn+mCkZERERERERERERERKRmwmmphBB1APYA+AaAmQDqAFwDYI8QYmZmi0dERERERERERERERKSUysiN7wL4Tynlf8RuFEJsAfA9AJdkoFxERERERERERERERESqUmncWC2l/PL4jVLK7UKI99NfJCIiIiIiIiIiIiIiosQmnJYKwGiSx1zpKggREREREREREREREVEqUhm5USKEOFtluwDgSHN5iIiIiIiIiIiIiIiIkkqlceOvAM5M8NjzaSwLERERERERERERERHRhCZs3JBSXprKCwkhLpFS3n/8RSIiIiIiIiIiIiIiIkoslTU3UrU1ja9FRERERERERERERESkKp2NGyKNr0VERERERERERERERKQqnY0bMo2vRUREREREREREREREpCqVBcVTVbAjN4JBiYP9TvQMuVHtsKChwg6drmDfLmkU45SI6PgwjxIVFn6nc4OfOxEVKuY30gLGKWlBOuM0nY0b/5fG18obwaDEk3u6cfVDb8HtC8Ji1OGO85djfUsNkwPlDcYpEdHxYR4lKiz8TucGP3ciKlTMb6QFjFPSgnTHacrTUgkhtgohHCLkXiHEG0KI0yOPSymvmvTRNeBgvzP6YQOA2xfE1Q+9hYP9zhyXjGgM45SI6PgwjxIVFn6nc4OfOxEVKuY30gLGKWlBuuN0MmtuXCalHAJwOoAyABcB+H6yJwghZgshnhVCvCeE2COE2BreXi6EeFoIsT/8b1l4uxBCbBdCtAkh3hFCrIh5rUvC++8XQlwSs/1EIcS74edsF0KIZMeYrJ4hd/TDjnD7gjgy7J7KyxFlBOOUiOj4MI8SFRZ+p3ODnzsRFSrmN9ICxilpQbrjdDKNG5FxIWcAeFBKuQcTr7PhB/ANKeViAKsBXCmEWAzg2wCekVI2A3gm/DsAfB5Ac/hnM4D/BEINFQC2AfgEgFUAtsU0VvwngK/EPG99eHuiY0xKtcMCi1H5MVmMOlQVW5I+LxiUaO8dwcsH+tDeO4JgkOutU+ZMNU4TYfwS0XST7jxKRLlVCN9pLdbHCuFzp9zIdrxr8ftFuZWL/MY4pcnieZi0IN1xOpnGjdeFEE8h1LjxZyFEMYBgsidIKbuklG+E/z8MYC+AOgAbAdwf3u1+AJvC/98I4AEZ8gqAUiFELYDPAXhaSnlUSjkA4GkA68OPOaSUr0gpJYAHxr2W2jEmpaHCjjvOXx790CPzgDVU2BM+JzJ32BnbX8AXf/43nLH9BTy5p5snIsqYqcRpIoxfIpqO6stsuHlTqyKP3rypFfVlthyXjIimIp11o1zQan1M65875Ua2412r3y/KrWznN8YpTQXPw6QF6b72nsyC4pcDWA6gXUrpEkJUALg01ScLIRoAnADgbwCqpZRd4Ye6AVSH/18H4KOYpx0Kb0u2/ZDKdiQ5xqTodALrW2qwcMsaHBl2o6p44hXcE80dtnDLGjRWFk2lGERJTSVOE2H8EtF01Dngwo937sflpzRCCEBK4Mc792NFfRlzH5EGpbNulAtarY9p/XOn3Mh2vGv1+0W5le38xjilqeB5mLQg3dfeKTduSCmDQgg/gFOFELHPe2ei5wohigA8DOCfpZRD4WUxIq8rhRAZbXpOdgwhxGaEpsBCfX296vN1OoHGyqKUP+Bkc4fxJESTlUqMApOP00QYvzQVqcYpUS4li9OeITc6+kdx97Ntiu3MfZRNzKXpla66US7kc31sojjV8udOuZGJeJ/onJ+v3y/Kb+nOb4xTyoR0xinrppQJ6b72TnlaKiHEfQDuA3AOgDPDPxtSeJ4RoYaNX0sp/ye8uSc8pRTC/x4Jbz8MYHbM02eFtyXbPktle7JjKEgp75FSrpRSrqysrJzo7aSEc9xROmUiRpNh/NJUZDtOiaYiWZwy91E+YC6liHzOSYxTSrdMxDvP+aQFjFPKdzznUybkcs2N1eGAvkRKeWn457JkTxChIRr3Atgrpbwj5qFHAVwS/v8lAP4Ys/1iEbIawGB4aqk/AzhdCFEWXkj8dAB/Dj82JIRYHT7WxeNeS+0YGcc57kjLGL9ENB0x9xFRPmFOoukk2/HO7xdpAeOUiApVuvPbZNbceFkIsVhK+d4knvN3AC4C8K4Q4q3wtu8A+D6Ah4QQlwPoAHB++LEnEFqwvA2AC+E1PaSUR4UQNwF4LbzfjVLKo+H/fxXArwBYAfwp/IMkx8g4znFHWsb4JaLpiLmPiPIJcxJNJ9mOd36/SAsYp0RUqNKd3ybTuPEAQg0c3QA8AARCy1ksTfQEKeWL4f3UrFPZXwK4MsFrRabFGr99F4BWle39asfIFs41S1rG+CWi6Yi5j4jyCXMSTSfZjnd+v0gLGKdEVKjSmd8m07hxL8KjMAAEJ9iXiIiIiIiIiIiIiIgoIybTuNErpXw0YyUhIiIiIiIiIiIiIiJKwWQaN94UQvwGwGMITUsFAJBS/k/aS0VERERERERERERERJTAZBo3rAg1apwes00CYOMGERERERERERERERFlzYSNG0KI2VLKj6SUl6o8tiEzxSIiIiIiIiIiIiIiIlKnS2Gfp4UQDeM3CiEuBXBn2ktERERERERERERERESURCqNG1cDeEoI0RzZIIT41/D2T2WqYERERERERERERERERGomnJZKSvmEEMID4E9CiE0ArgCwCsCpUsqBDJePiIiIiIiIiIiIiIhIIZWRG5BSPgPgUgDPAWgEsJYNG0RERERERERERERElAupLCg+DEACEADMANYBOCKEEACklNKR2SJqQzAocbDfiZ4hN6odFjRU2KHTiVwXiygn+H0gIqLpguc8mqxCj5lCf39ENH0xv5EWZDNO+Z2gfJDKtFTF2SiIlgWDEk/u6cbVD70Fty8Ii1GHO85fjvUtNfxS07TD7wMREU0XPOfRZBV6zBT6+yOi6Yv5jbQgm3HK7wTli5SmpaJ4waBEe+8IXj7Qh3cPH4t+mQHA7Qvi6ofewsF+Z45LmR6x77W9dwTBoMx1kShD0vG3PtjvTPn7wNgionzi9wfx9kcDeHJ3F97+6Bj8/mCui0R5bjLnPOJ5H0h/zOTbZ8rvBE1VtmM53747lP9ykd8YpzRZ2YxTnvMpX0w4coPijW+d3LKuKfpljnD7gjgy7EZjZVGOSpkebImdPtL1t+4Zcqf0fWBsEVE+8fuDeOTtw7j2kd3RnHTzplZsWlYHg4F9QUhdquc84nk/Ip0xk4+faaL31zPE7wQllu1YzsfvDuW/bOc3xilNRTbjtHuQ53zKD7xaT8H41vIP+5Stk0EJWIzKj9Ji1KGq2JKL4qYVW2K1Z6q9O9L1t652WFL6PjC2iCif7OkajDZsAKGcdO0ju7GnazDHJaN8luo5j3jej0hnzOTjaFmbyaD6/mwmfUaOR4Uh2/mB+YimItv5jXFKU5HNODUbdKrHMup5q5kmls66KSNuApHW8jO2v4Av/vxvOGP7C9jbNaRonXz49UPYsrY5+qWOtKg3VNhzVey0Sda7jPKPWrw+uac7pSSRrr91Q4Udd5y/fMLvA2OLiPJJV4KeR12DzEmUWKrnPOJ5PyKdMZPqZ3o89cPJ8gYCcddFW9Y2wxvgNH+UWLbzA/MRTUW28xvjlKYim3E66PaqHmvY7U37saiwpLtuymmpJqDWWr7/yDAsRl10W9egGzt2dWLH5tUY9QVQVWxBQ4W9IIYKRnqXxZ5U2SMxfyXq3bFwy5oJhwWm62+t0wmsb6nBwi1rcGTYnfD7wNgionxSWWRWzUkzisw5LBXlu1TPecTzfkQ6YybVz/R46oeTZdLrsWNXJy4/pRFCAFICO3Z14u+aKtJ6HCos2c4PzEc0FdnOb4xTmopsxmmF3YIdu96LO9b2C09I+7GosKS7bsqRGxNQay1/aNch3PKFJYrWyW+tX4QldaVY3TgDjZVFBXNRyx6J2nI8vTvS+bfW6QQaK4uSfh8YW0SUT3Q6YNuGFkVO2rahBVxugyaSyjmPeN6Pla6YycfRst5AABesrMe9L7bjrp1tuPfFdlywsh4+jtygJLKdH5iPaCqynd8YpzQV2YzTlloHvra2WXGsr61tRkttSdqPRYUl3XVTjtyYgFpr+YDLixX1pXhiGvTSY49EbTme3h3Z/lsztogon5RYTXj4jU7cdu4yjHr9sJoMeOCldtx27vJcF42oIPC8n375OFq2wm5W7TG6vrUm7ceiwsHrENKCbOc3xilNRTbj1GDQYdOyOjRXFaF70I2aEgtaaktgYO8wmkC666Zs3JhApLU8Mlwm0lpeX26P9roqdJH3OR3eq9YlitdUe3dk+2/N2CKifNFQYcdlp8ybcv4koonxvJ9+qXymx1s/nIyGCju+tX4RcylNGq9DKN/lIr8xTmmysh2nBoMOy2aXYdnsjLw8Fah0103ZuDEBtpaTljBeiYimhvmTiApVNvMbcykRFSrmN9ICxilpQbrjlI0bKZhOreXBoMTBfid6htyodjAJapGW4pXxRkT5SMpcl4Bo+mLdIDOyWT/UUl2U8ke2v/vMNTQV2c5vjFOaimzGKWOUpiqdcZrRxg0hxH0ANgA4IqVsDW8rB7ADQAOAgwDOl1IOCCEEgDsBnAHABeDLUso3ws+5BMC14Ze9WUp5f3j7iQB+BcAK4AkAW6WUMtExMvleC0EwKPHknu64YUHrW2qYnCjtGG9ElE+Yk4hyj99Doukp29995hrSAsYp5TvGKOWLTK/y8isA68dt+zaAZ6SUzQCeCf8OAJ8H0Bz+2QzgP4FoY8g2AJ8AsArANiFEWfg5/wngKzHPWz/BMSiJg/3OaFICQivVX/3QWzjY78xxyagQMd6IKJ8wJxHlHr+HRNNTtr/7zDWkBYxTyneMUcoXGW3ckFI+D+DouM0bAdwf/v/9ADbFbH9AhrwCoFQIUQvgcwCellIeDY++eBrA+vBjDinlK1JKCeCBca+ldgxKomfIrVipHgglpyPD7hyViAoZ442I8glzElHu8XtIND1l+7vPXENawDilfMcYpXyRizU3qqWUXeH/dwOoDv+/DsBHMfsdCm9Ltv2QyvZkx5i06TR/XLXDAotRp0hOFqMOVcWWHJaKUqHFOGW8EVE+qXZYMKfCig1L6yDC6fOxtw8zJxFl0XSpG2ix3jYZhf7+KP2y/d2fLrmG0i+b+Y1xSlOVrThljNLxSGec5nRB8fD6GBldtnOiYwghNiM0DRbq6+sVj012/rjIH6bf6YFJr4PLG0BVsQU6AXzY74TdZEC1w4z68vys4NeX2XDPRSuxq+MogjJ0U+db6xehocKe66JNa8liFDj+eQ6DQYnOo070DHng9Poxp9yOOeU2dA64Jp1kJpOcGirsuOtLJ+CdQ4MISkAvgCWzShhvGjVRnBLlg2RxWl9mwzWfW4j3e4ajOemazy1EfZktF0WlaWq659LJ1A3SeUGUymul63iTqbflayPBRNdPz+zrwbuHx/6GrXUlWLewOi/KTvkpE9cFyeKU1yE0FZnIbxPF6QOXr0QgINA77EFlsRl6vWScUlLpXgdjohj92UUrMDwagNPjh91iQLFFzxilCaU7n+aicaNHCFErpewKTy11JLz9MIDZMfvNCm87DODT47Y/F94+S2X/ZMeII6W8B8A9ALBy5UpFI0ii+eMWblkTt5p7JIHc+uReXLCyHtt37o8mkq3rmvHAyx0YcHmxdV0zmquLsHZBflXwg0GJp/b2KBLgrecsxemL8quc01GyGAUmF6fjBYMSO9/vwf6eEdz5zFjM3rypFT/euR8d/aMpnwynchL1+iXueb5dsT9p00RxSpQPksVpR78TnUddipy0dV0zOvqdmFddnJPy0vTDXJpa3SCdF+6pvFY6j5dqvS2fF+lMFqcf9o2g7chIXC5tnGHHvCrmUkos3dcFE+VTXofQZGUivyWLU683gIO9blz/6O7o8W48qxVLawKwWHLaT5ny2Id96vWMBV9bg3lVye8PqUkWo8GgRO+wF9c+sltxLykYlDmvq1B+O5ggn86bYUfjFPJpphcUV/MogEvC/78EwB9jtl8sQlYDGAxPLfVnAKcLIcrCC4mfDuDP4ceGhBCrhRACwMXjXkvtGJMymfnjIhcqG5bWRRs2Ivvf+cx+nL1iVvT/fr/EG50DePlAH9p7RxAM5v7aVe1C61sPv4POAVeOS0YTmeo8h8GgxLuHj8HtDUYbNiLPvfaR3diwtC76eyqLQk12MSkuPkVE+aRryB2XC+98Zj+6hjhnLFEqgkGJ9t6R46rfplo3SHW/VMqUymuls86Sar1Nq/WkrsEEuXSQuZQS44LipAXZzm/vdg1GGzYix7v+0d14t2swI8ejwtBx1Klaz+g8mv78tqdrMNqwETnOtY/sxh7GKE3g4wT59OMp5tOMNvcKIf4boVEXM4QQhwBsA/B9AA8JIS4H0AHg/PDuTwA4A0AbABeASwFASnlUCHETgNfC+90opYwsUv5VAL8CYAXwp/APkhxjUlKdPy4yZNztC0IIqCaSyPzdbl8QIx4//jlcmZpTYcVNG5fAqBc5HW6e7EJrot7/sfJ1+Hy25etcnJEpqI4Me9A95MaBIyPQ63RJYzby+0SxkCiGeobUn5dKzDGeMoufL9GYYbdfNScNu/05KhGRdkRGgo6f4mWyI5VTrY+mWodIpUypvFa66slA6vW2dB4zm5hLaSp6htyYX1WEK06dh1GPHzazAT9//kDC6witHY8KQ7bzW/eQJ8H1tScjx6PCYDcZVOsZNlP6b/92DarXVboG3Vg2O8GTiACMeNTz6Yhnavk0o40bUsovJnhoncq+EsCVCV7nPgD3qWzfBaBVZXu/2jEmq6HCjjvOXx43VVPs/NuRIePvdw/BYgwNhFFLJFKO/b9zwAW3L4jaEgsuWFmPzQ/uyvlw83QsBJTPw+ezKdufg1qc3nH+8rh5DhNNQfWj85er/u3nVxfjqrVNAFJbVNeW8CSqV91/ophjPGUWP18ipWqHOUFOMuewVETa0NHvxP4etaHlRZg7iRuFqdZHqx0WzKmwYsPSumhnjPF1lc6j6mVqqixCw4yxMlUVqx+zssiiOF66FsxMtd6WynvMR5XFpgSfpymHpaJ8V2oz4IufmINrfv929Hux7cwWlFozc7si28ejwpDt/FZbol43rXGwbkqJVTvMuPq0+bjj6Q+i+e3q0+ajOgNxU1mkHqMzihijlFy6r71zMS2VZuh0AqcvqsY9F63ElnVNuPyURtzx9Pt4am9PdFh7ZEjrQ7sOYcvaZjz29mF8/bPzFQ0dW9c143/eOASLUYfrNizGc/uO4MrPNOHq0+bD4w+gzBY6GeZyOGzkQiu23GoXWslweG9ILj4Hk0Fg86mNuGptEzaf2giTIf7mdOdRZ9wUVGU2Ez466sSNZ7Uo/vY3bmzFD5/ah7t2tuEXL7Tjqs80Y9Tnh98fep7aNA/eQABb1jYrXmfL2mb4AsG4sgATxxzjKbP4+RIp6QRw9WnK8/fVp82HnjUlogl9PDiaYGj5qGK/iaaJiixMedcXT8CtZy/BXV86AT+7aEVcfbS+zIavrW3GvS+2R+sqX1vbrOiA1DPkUS3T+B6vet3E3/101JMjdDqB9S01eGLLGvx28yfwxJY1qh0LUnmP+cioA27a2Kr4rG7a2Aqjel8XIgDA0GgAP/1rGy4/JXQ9c8WaRvz0r20YcgcK4nhUGLKd3/RCJDg/sSMaJTar1IbaEovi/lBtiQWzStNff/AFA9i2QXkvaduGFgSCzKU0Aale/55qdmPXhAl0DriiIysiYhf9iwwZ7xp048FXOnDxyXOwbFYJbj93GZxeP7y+IKpKLPjnzzajtsSC7qFRfH5JraLn/Ja1zXjwlY7okK7jHW4+lalmIhdaC7eswZFhN6qKJz9FjVaHz6dbtj+Hg/1OXPWbN+NaPJ+IWZgyGJR4o/NYdPo0AKgtseDr65pgMxsRCAZxz0UnYtjtx6wyK256fI+ip+Bdz+7HxuV12Ns1jLOWzMRf3j8S1+NwcW0xduzqxOWnNEIIQEpgx65OrG+tUS33RDHHeMqsXH++gUAABw8ejP7e0NAAvZ53Pih3jrq8qCkx456LTsSA04cyuxHHRr045vLlumhEOZVKvTLR0HKnJ6B4nVQW7k5lYcrOARd+vHN/tM4BAD/euR8r6sui5zCnV71MLq9yuHvviAelFkO07m43GeDy+NA34omO8EhHPTmWTifQWFmU9HzbOeBSncc69j3mI18AePq9j/Gzi07EMacPpXYjfv3Kh5hb0ZTrolEeG3T78KVVc/Cjv4z1NP76Z+djyJ2Zc3C2j0eFIdv5bWA0Qd101JuR41Fh6Bxw4V9+/07c/aGWmSVprz8I6PDwG5247dxlGPX6YTUZ8MBL7fiXzy1K63Go8BxLkN8GR6d2HmbjxgQS3QD8oGcYQOgGscWoQ5nNhItPngOH1Ygv/+q1seGtG1pwyxPvoaN/NDrc9bevdSouVLaHL87ufrZtykPcI45nqplULrSSSeeQfS3L9ueQbK2LyONGvQ7f+cO7uGJNY7Rs//SpRgQg8M1xw7EBYO3CGmzfqWyA0+mAax/ZjYYKu2qP///92hp8a/2iCadZiJUs5hhPmZXrz/fgwYO44u4nYK+ogbO/G7+48gzMmzcvK8cmUlNhM6NnyItrfv+6IifOKeNUKjR9pVqvTDQtQUXMVB2JRgwujOmMkWhhyuaqIiybXRZ9rX6nBxesrI+rqxx1eqKvNafcrlqm+nJlvcRq1KPf5cP1j72nmL5q8biuuMdbT46VSoNRrjshTJXL58dnFtTiHx58XXE9NOrnmhuUWLnNiC3hhgYgFOs/+ssH+K/LVhXE8agwZDu/zbAnqJuWc8ofSiyb9Qe7WY91i2oUU/xtXdcMu5mdFim5cpsZ3Wm89mbjxgRqSyz41voFKLeZYDMbcPiYC//9aifePTyEf97xFu44fzkeuHwlhkaD6B/x4ONjoyizmaKjMH76fBu+cfrCaGPIT//ahg1L63D3s23RY7h9ocWbLUYdbvnCkribwX5/EPt6hjDg8sHtC6Cxwo65lUWqjRWpXDhmSqpzCBe6bH8O1Q4LVs4pwcWfbIwuiHf/S+3wBSTO2P4C3L4gvvP5Bbj8lEaY9Dr8+MIT8GHfCGYUW/B+95AiXm94bA8evHwV3P4AvvrpJsydYcfhYy6MegNYPLMEbl8Q3QlOlr0j7rT2amQ8ZVY+fL72ihoUVc7K2vGIknH5Anizow/3ffkk9A17UFlsxh/e6ERDRX5PAUOUSQf7nbjvxQOhHnnhOsZ9Lx7AwppiRb0yICX+7YxF6B3xRBfvnlFkRlCOTTvVM+RGmc2Es1fMio62ePj1Q4oFfBMtTNk9bmFKk14XbdiI7LN9537s2Lw6us/cGXbc9aUT4hYUnztDeZ5zegKq01etqC9T7DeVkdFqUm0wSncnhHSVfyJmgwEPv7FfETP3sxcnTeCo06u6wPfRDI2ezPbxqDBkO785vayb0uQluj+UiU6Mo74AbEY9Np/aiKAMTfNrM+rh9nFaKkou3dfebNxIIhiU2PPxsGIKqa3rmnH1afPxyBuH4fYFceuTe7F13Xx85w/vxk0zBQAXrKxXtGJuWduMWWWW6ELND79+CAMuLxZUF2PzqY1YUV+quNDw+4P4054uHBoYVZTjtnOWorbUggq7OW+m8kn3kH2tyvbnMKvEivNPUi6Id+NZrfivV9qjI4pKbSbc8Zc9iji+6fH3MODyKqZFK7OZcLDPpVh8c8vaZjzy1mHUldnwnc8vQE2ChX90QuBgvxMNFfa0xBrjKbPy9fPldFWUKzqdxMqGGbgsZvTljWe1QKeTEz85D2TrxiVNL4OjXpyzQlmX3bahBYPjpsSoLDLjncCgov7wL59bgMqYBSWLLQb806ca0ef0Rhsb/ulTjSi2jF2O1JVaVesYtaXKC3KXN5BgyinlxbTHJxVl+uF5y+PeoyuF6auOZ2T0eKl2REpnJ4R0ln8ibp9fNWbcHLlBScwoNqku8D2jyFgQx6PCkO38pvW6KeWG6v2hja2YVWJN+7Eq7Gbc99KH0SnNA0Hgvpc+xC+/zFFwlFy68xuXyUziYL8T3/id8uLjzmf240CvE+edVI/aEgs2LK2LNmxE9tm+cz/OXjELZ6+YpdqrzKjXQ4ZbNa8/czFuP28pbn9qHxZUO6JD5SMLLr7ROYD9R0biepRd8/A7eO79Ppyx/QU8uac7uiBjVbEluiBLhMWoQ2VRdqaaiQzZX904A40JRpdMB9n8HPZ2D+H6PyqncLj+0d04f+UcfPmTDQCA6x/dExfHZ6+YpYhXADhv5SxcN+61tu/cjw1L63D9H3dj0B3AN373NradqVw0auu6ZrzeMRAXj8eL8ZRZ2f58A4EADhw4gAMHDqCjowNSJUwi01Vt/e0buOLuJxQNHUSZJKCLy5XXP7oHQgNVpciNyzO2v4Av/vxvac/FNH35AxI3PK78Xtzw+B74AzJuvx/8+X3Ffj/48/uK/bx+ZQOC2nYB4DufX4gt65pw1dombF3XhO98fiF045YXTFTfje2V2N47EleP/8bv3kJ774jieeV2s+prldvGhsV/2KfeIPFhn1P1PSWTrCNSLJ1O4PRF1dixeTV++v9WYMfmk3H6ouopnasTNagc7J98+SdiNRlUY8ZqZJ86SiwQAG54bFzcPLYHAfW0obnjUWHIdn7Tct2Uckf1/tAfd2Nv91Daj9VQYce31i/CvS+2466dbbj3xXZ8a/0izrZBE0p3fmMtM4meIbfqcFWbyYB93UM4e8UslFj0isUMH379ELoG3RAi1HihdvEy6gvg3hfHepF9d9MS/P2qepiNAp1Hnegd8eDjY2586+F3cMOZLQhK9dcxG3S4/JRG7OseQl2pFUvqSqDXAVvXNceNNtHz/FewPh5Uj1OzUY/fvNqBqz7TrBo/9eVWXLW2CQ+/fig6LVrjjCLVfYUY+7ejfxQ//Wsbbjt3GT7oGYbdpEcgKFHlsOCKNY249cm9cdNVEAHKdTb6DryLorr5KFbZj9NVUS70DHlU81/PkCdHJUpdLqekJG2baMRPwu/FsPJ70TngUt3vowEXmqpDmd4XCMLpDShGUmxd1wxfzN3EIyNueAPK0RbfPH0Bjowob/wLSNX6rsBYY8qH/U7VMn3Y74yWCQC8gQC2rG2OW7/DFxx7bsdRp+qUWp1HnZhXpfyOTfSZVjssmFNhjfZyBIDH3j4cN11EMCjx1N6etIy2yObI7t5h9ZjpHc7/XEq5053lc3C2j0eFIdv5Tct1U8qdRPeHuobcWJbmY8V2xOgadKO2xIKW2hJ2SqUJpTu/sXEjiVKbQXW4anOVHa8dHEBdqQXegMQdf9mtuBjasasTC8IXTWpD6w/GXGy5fUH82yPv4gfnLsO7hwbRfmQEARmau+6KNY2oLbXg48FR1ddpqirC1Q+9hbJwz7KPjrpQ5TDjT+92RRtcpAQeeLkDJ9SXomGGtm5wcIqN1MwstajG6ag3gA1L63BowKUaP51HR3Hvi+3Yuq4ZrTMdmF+9DP0jbtV9pRz7Fwg1cHzQM4yHXz+Ei1bPwV3Ptim+A7ELeh4PxkDhiTRcOPu7c10UIoXqBFPuVTvyf9FGrS48TLkVDErsfL8nbk2KtQvGRgdUJfheVBUpvxd2k0F1P5tp7FLDF5Cqa1v84uKV0X0cFhNuf+pNxT63P/U+/uvyTyiOd6DPiQde7oir7zZVFaGxKlQHtxr1qmWyjlsovMJuxo5dnYrX2rGrE+tba6L7lFiMuPjkOXGNKQ6LcgqbVKZ/qi+z4Wtrm6MLp1uMOty8qRX1Zco5htPZaJnu9TuSqSpOEDPF+Z9LKXcSTXubqXNwto9HhSHb+U3LdVPKnUT3h2pL0n/ODwYlntt/JFqX3Ns1hN4Rj6IuSaQm3fmN/fmTGHEHVIerRkZN6IQuemESeXz7zv341vpFuOWJvbjlib3YsrZZMX3PdRsW43e7DgEILVZ+5WeacMWaRgDAI28dRk2pFdUOC+55PjSs67o/7kZjpR1b1ylf54azWnDrk3tRZjPhotVzcM/z7bjqv9/Exfe9is8vqcX/vHEID78eOs55K2fBajRoanoKTrGRuti4jP3datJDrwMe2nVINQ6LLXpcsaYRv32tE28fGsSW/34T9zz/Ib67qVWx75a1zXj8ncPYdmYLXvjgCABgToUVC6uL8Z0zFiWYeu34UwtjgGQwiI6ODhw4cACBABclo8wyGwRuPEs55d6NZ7XAbMj/inmup6Qkbeo86sT+npFonfNnz7djf88IOo+OTVVUZtOrfi9K7coGgmqHOW7Kym1ntiguUBKvbTGW3/tG1Htx9Y8oe3FZjHoMuLy4+9k23LWzDXc/24YBlxeWmIYLu0kfV3/euq4ZdpOy7KlMqWDUC9WGGaNemR9Smb6qc8AVd/1w7SO70TngUrxWqtNXpSKyfkfsZzHV9TsmIiFx9WnzFce6+rT5kGD9jRIrtqjnmmJLZtZdy/bxqDBkO79puW5KuZPo/tD4OkU6dB514uOBUcW2jwdGFXVJIjVmg8AN4/LbDceR3zhyI4kjCYYdDrh8qCw2J7zoaDsygq7B0IXHWx/1474vn4TeIQ8qikywmnQYcHlRW2LBRavnKIbAX7dhMSxGPb771HvR1+3oH8UdT3+A285Zip9fvBJOjx/ldhM+6B5CR/8orvxMU9zN5Tuf2Y+t65ohJaKP3fN8e8YWDpzIZHvf+/1BvHXoGPZ1D+GKNY3Rqb44xYa63gQ3Agw6YFGtAwMuL57c3YXbzl0GAQm9Todbn9yLjv7RaOOFLnxPbMDlRUOFHdd8bgGqHRaU2owY8QRwyckNGB71Yn1rLUwGgXNOrMc3f/82rljTOOGNimT8/iD2dA2GhzBa0VLrgMEQKgynWZk+Io0YABRrcbgGjuDahw/BbN6DX1x5BubNm5fDUlKhO+ryQgjg9nOXwen1w24yYNTnx4DTl+uiTYhTUtJU9Ax5VG/Yr6gvi4729fiBUrtJ8b0wGXXwjls7dcjtw8Ovd+K2c5dh1OuHzWTA/S+1o2Xm2PRPpTaTag+tUuvY6IeEI0DMyksWs1Goxrwp5oLI5fOhxmHB5lMbEQyvdVfjsMDlUxY+fkqFUH0ktq7qTLCAuXNcfafjqPpUWLHTV6U60iqdoy10OoH1LTVYuGUNjgy7UVWcudGwTq8fZr1O8bmb9bqU64Y0PQ24/Hho11gOsZoMeOCldswuX1gQx6PCkO38puW6KeVOovtDfSPpn86sf8QLtz+omE706tPmo3/Ei4YZaT8cFZDuIQ+efLcLP7voRBxz+VBqM+JXL36IMuscYPbkX4+NG0lUJhh2WGI1wOUJYH51serjFoPAlZ9pQonFgKaqIvzr/7wTvZF841ktuP28pfigZySuUeKmx9/D5lMbccHKejz4Ske0gcTrl+g8Ohpd6Nli1OF7X1iKORXW6FoIsdy+IJqqivC1/34z7sZwxaWrUFlsztr0PqkMz4/l9wfxyNuHFUP1t6xtjn4enGIj3owi9ZsFep0OFqMOt5+3DB8ddeGHT+3DzRuX4CsP7oruW2Yzwe0PoLmqGFvXNaHcZsKwxwer0YBv/C5mGOOGFvj0AdzyxD48eNkqXHTfq9HXUB9KNvGFt9rf+uZNrdi0rA4Ggy7hxX/PEGOg0EQaMcpn9setxWErr4bFwqHXlHl2kxH//ux7cfPg/+DcdM9Om35dg27VKXq0OCUlpUeyzgMRTo/6SAqnZ+zmv8cXwJaY+iQQOs//+vJViucddXqxdmGNYgqE0DSV3ug+Ix4fvv7Z+fjRXz6I7vP1z86HM6alxGE1qDZaOCzKSxabwYCaknENFyUW2GMWdTXpDfj13/bj4k82Ruecvv+l0KiMWKmsbZGooWF8fSeV6blSbbSIjLYYX66pjrbQ6QQaK4syXocqMhlx30tjuTQogfte+lATuZRyp9/pxa6OQezqeFOxPTaHaPl4VBiynd+0XDel3El0f6jcbkr7sbyBIO54+gPFfcc7nv4Av/rySWk/FhWWMpsR7f1OvPrhQDS/tfc7UWqfWjMFGzeSqHaYceNZLdEV3CONE0a9gNmkw96uQVy3YTFuevy96OO3nbsUTk8Ad/xl7DnXbViMHa924p3DQ7j+0T24/9KTYNDpVC8og+HRFpef0oi7n20DEJpW6q5n9ysWLv/13z7E989eio+OurB1XRMe2nUo2hhiMergsBhVX//Njwbg8gYwv6oYi2odmDsjs40ck+19v6drUHWqr8tPacS9L7ZnZG5grSsyGVTjFAjiyJAHTo8fdz4T+gz3dg1FP9vxo4fmVFjxr59fBK9fYnDUi63rmqM9En/6fBtu3NgKty+o6Anw8OuH4hbhTPXCW+1vfe0ju9FcVYRls8tgS3iDgMPFC5GtvJprcVBOubx+XHFKI3pHPNH1B644pRGj47uo56GqYgvqSs1YUFMcvYlbV2rmtFTT1ESdByJKbUbVha1LbGMjKXpHvKoLafeNKHuNmg161WkqfxlzcWvS6/GbV5WNcL95tQPf3bQkus+iagd6hj1xI0UWVTsUxxvxBvDrVw6GGi5iRor8y+fGGi6GPT7VBpcRj7LsqdRVU21oqHaYVRtnYqfnSvW1sjnaIp2cXj++tGpOXCOWFnIp5c6MIpNqPiovSv/NuFwcjwpDtvObluumlDtWo171/lAm7qMMu32q9x2H3IxRSs5s0OPq0+bjQK8zmt+uPm0+zPqpxSkbN5KoLbbCYtIreoVZTHq8eqAXS2ZXYNAdQH2FHT/50gq88dExlFoNqCwy4Zrfh3rG15ZYcPaKWegZcuNfPrcQP/jzPrxzeAjHXD7YzQZsWdeEoER02qXIgs1uXzA6lYTFqMPiWgcsMReNcyqs+MdTm3Dpr15TXDg98HIHTAaBmzYugcvrj2v0mFNhRbHFiP/4i/JGdCanqprsIqddg+r763XI2NzAWheQQdU4FdDhJ8+14aufakKZzYTWmQ7odQJ3ffEEtPc5MXeGHbc/tS8aqxesrMc/73hLEVMPv34IAy4vtqxtxtCoDxajDjPsYyOaugbdePCVDmw+tREttSVori5K+cI70d+6e9CNZbMBbyAQ13CyZW0zfIH0zxVJRFRsNmDUF4gbVl1kzv+qktEAnLeyXnET94azWmDM/6JTBkzUeSAiKAO46jPNipHBN21sBeTY9BolVgP+6VON6HN6oxce//SpRjisyguPEY9ftREkdhRIkVmPr6xpxJHh0E0agw74yppGxRoYwaDE0KgvrmFm/HpbvkAAG5fPQtuR4Wi5Ni6fBV/M+kxFZqNqg8sDlypHnaRSV021oaG+3I7m6iJFnay5ugj15WP118k0WmRrtEU6WY3KRiwg1Ih12zlLc1swymvFFgOu/HRT3M248aO2tHo8KgzZzm9arptS7kgpVe8PRed+TqNii3qH1CKuX0QTcPsC6BnyxOW3mhRmgVHDrJjE3p4hXPP7d+K+qL+69CR4/QH8rb0Xy2eVwBeUeOztw/iHU+fh5faj0ZvFamtqmN74CCPeALbG3ETesrYZO3Z1Rqejshh1WDW3HD/50gkISqDUqrw427C0Djc8rlwg6M5n9uPBy1bh40E3NoenHYpt9BhwefGt9YuyvobB+KH3tSUWnLdyFlzeANp7R+Iu5mpLrKrJ8dTmSqyoL8v73mq54PVL1Tj9z78/AResrMfgqBcXnzwH339yLy5YWR/XWPDgKx04e8Us1bVbIiOIIr0v7zh/OapLlL0SB1xeWI16LKgpipv+JNl6K4n+1jUloWRWYTdjx65ORQ/PHbs6sb61JtMfKRFNQx6/+rDq+y5ZmeOSTaz7mAfbHlXWC7Y9ugcPXLoK9eXauSlK6TFR54Ex+mjDRmSf6/64G/91+Seie1gMeji9yhsrW9c1w2JQXrRW2E24+OQ5cSMWYqdAsBh1MOp1ite64awWWE1jo0l2dw+pNsw0VNixsqE8ul+ZzYTdnqG4cpXaxo6XaNqt8WtupDpNVCoNDTqdwNoF1WicUZS04UKLjRapcvsCqvVNt49rblBiLk8g2tAAhL6r1z+6Bw9etmqCZ2rjeFQYsp3ftFw3pdzxBdTvD2UivzksRtxwVkv0OiRStyuxGCd+Mk1rgaBMa37jUpNJfJzg4vCo0wuL0YCvrZuP373eAYfFgG+vX4SeITfMhtA6B2o3i296/D18/bQF+Lc/vBvXi+zmjUvw5O4uDLi8uHlTK3qHPbj6d2/jqv9+Ey+19yvKkWidjRGPH996+B3Fa9/5zH7cft5SbD61EW1HRhL2TMuUyNB7i1GH2hILLj55Du55vh2X/WoXztj+Ap7c063ojddS68DNm1phMYZCM9Jjb/msUjZsJNA34lX9u1qMBmzfuR/egMSdz+zHhqV1qj0YI70s1V4j0iPF7Qti2O3H+pYazC4b65V41dombD61Ma5XIjC23soZ21/AF3/+t7i/d6K/dUttCYBQ7Hxr/SLc+2I77trZhntfDM2TzdE7RJQJQ271G6HD7vy/IdfnTLBwoDP9CwdS/ot0HogV23kgIlHc9MfEjdMbUF10fPxC2on2i11ktXfEp9oIFzvFVe+wepl6h5WxPDjqUz3e4OjYa5WHR5qO/xxKrcppZ2LrqpF91EYLB4MS7b0jePlAH9p7R+JGk0REE0Pl8wABAABJREFUGi5WN85AY2XRtKu/Ruqf4+ubFg4loySOJPjuHxnOzHks28ejwpDt/KbluinlTqL7Q/0ZWFNoyO3HT55rw+WnhO4NXX5KI37yXBunpaIJpTu/sZaZRLE5wRArswF9Ix5UFRvxmQW1iumhvvuFVty8qRWdR12qf6gBl/qcdK91HMU/f3Y+XN4AqkvMuOjesQWbg1J90ebxv9vNBtXX1gmBhTUOvN89lLRnWrJe9lMVO/S+d9iDS375qqIyMH7kiMGgw6ZldWiuKkL3oBs1JRa01JbELYJJY4oSDAUcCScLtz8YbahQiw+9LnGMRUYuhuI+1Esz1V6JE81hPdHfWqtzTRORNtlNetU8GNurPF/NLLGpln1miTWHpaJcaal14AfnLsX+IyPRKZuaqoqinQciSqxG1bhxxPS2c3oTjH4Y17gxmsJ+IwkvYmIWFE9QpykeN1XMsFt9GqyRmAuiIbdXdXrL8WtupFLfiHTYSLboOIX0p9BoRjReRRYXwM3F8agwZDu/abluSrmT6P6QPQPTmfWPeNHRPxpdLzi6PQMNKVRY0p3fmBWTKDLrQ0PvY3pybV3XjGKzAdXFFgSDurjpof7tD7vh9vqxem65am+xCrtJdXsgCLzXNYS93cNo73Uq/sAPv34I121YHH3eY28fxrYzW+LKddTpxZwKa9xr+wISpy+qxhdOqMMtX1ii2jNtol72xyPSgy0oZUojRwwGHZbNLsPnWmuxbHYZGzYmUJwgTh02o2Jb7L8RFqMOTVXFaK6yK2Is8hr/88YhWIw6bDuzBWajPhoTqfRKTDaHdcREf+vp3vuRQmQwiI6ODhw4cAAHDhxAIMDeSpR+VpN6Ls3E4nvpptchrl6w7cwW6JkzKQmXx48ta5Uxv2VtM0Zjpm0qt6nXW8tsyukGKovVR0nMiFmct7JY/bVi9ymyGBLWvWNFRgNHRnf+4oV2XHzyHNTELNxtNuij01tGehPu2NUJsyH+Oz1RfSNRh42D/c6415ruyhNc6/CmMSVjNepxw1nK89gNGVoAN+nxjPl/zqfcyXZ+03LdlHIn0f2hogzETbVDvf5XXWxO8AyikHTnN47cSKJ3xIsis0GxEE+R2YBepxfFFj36htVHYXQPe/HIW+/jli8swXfCU1BF/lDfe2KvYr2C2DU3ImtiXLGmEXMqrNiwtC7aG00Gg4q1B3z+QLRcUiK6rsZP/9+J+Mf/el3x2tf98V388sur0FgZmjpo+ezSuJ5p7b0jU16PI9URH6nOaZyJESSFLFGcOj0+bFnbjJ37unHjWS24+7m2uB6MX//sfPzwqX0478TZ+PXfOrH51EYsrnXgw77Qxfo5J86ClMBP/9qGmzcuUcTERH+nVP/eRBNxDRzBtQ8fQvnMfjj7u/GLK8/AvHnzcl0sKjBmgw4zSy2KXDqz1AKTBhrYO4668N9/68Bt5y7DqNcPq8mAXzx/AJs/NQ9LZpXmuniUZft6hnBoYDRuPYp9PUNorSuN7ldqNaqubfXD85ZF9wlIiX/53AL84M/vR1/rXz63AIFxi1LqdcCNZ7Xi+kfHFgK/8axWxLYjBIJB/PuZLfj3x8bmZf73M1sQlGP1hH6nBzajchFMm1GPfpeyV6zbpz4N1on1Y+/PZtLjwpPq49YBsU7hoimVRccpxG7Sq86/zZtxlIxA6DpBsQCuUYdMXQEmPB4vOSmJbOc3LddNKXcS3R/qy8BoCpNB4LZzlqCt1xkdLTyv0g6TgcmUkkt3fmPjRhIOqxE3/+970UaGQBD4xYvtuPWcpRga9UdbKcffvJ1fXYwisx61JWb87P+dCLcviD1dg3jg5Q50DbrRO+LF5lMbMavUio6jo9ixqxPfPH0BOvqduGJNI8qsRvzzuvn415iGkZs3teLxdz5ER/8oAOCqtU24a2do6FdtiSU6LN+gE9i6rhm+gETDDDs+PubCmcvqcNTpifZEG7+AYeQm9VQu2iYaph97A7y2xII7zl8et2/snMYc9j95Dot6nN6yaQl27OrEd85YDItBYOPyOuh0wG3nLsPHx0Yxq9SKgAzi259fhGGXFyaDgNWoh06EGssiMSVEaNFylzc0BcSRYTcaKuzRv1OZzYTzVs7C/KpiLKp1YE65DZ0DLgyOenHTxtboYqWROK4vs+X6IyMNspVXo6hyVq6LQQVs1BfA/75zGH+/ei6OOX0otRvx61c+xBVr8r8hrbrYjA+OjGDLf78Z3cZeU9PXMZf6ehRL6pTTUhn1Onxr/UIEg6HFt+0WA1pmLoRJP3ZR4fb5YRBCceFhEALjF08d9Urc/dx+RUPJ3c/tx23njDWUQOiwc18XfnbRiRhw+lAW+Y6d0hTdpcRqwg0vKes09730Ie44b7nieImmeR1wjY06GXb78cDLHYoyPfByB5bPLp30Z8oOG6kb9QXwzN7Q3zk2l86dwfofJTbo9uFbD78b9x37+cWZWTg528ejwpDt/KbluinlTqL7Q4o6WZoMuHw45vIpOtR85/ML4bD6Jn4yTWsjXr9qfrvslKnlNzZuJCGDQfzjqU3RqacsRh22bWjB4KgPdpMBegHceFYLrn9U+fgDL7Vj7cIaXPqrXXD7gtiyrgnbnxmbg65r0I3tz7Th1rOXQAjg71fVRxd9ju1ZVmYzoSu8qPm1j+zGHecvx61P7sWGpXWoL7Ni67omPLvvCNa31kZ74//CqMPVp81Hhc2ItiPD0dbTvhFvdDqhWJHGhInW4xj/nEiDhc1kwK1P7lUd8RF7Azzyvu760gn436+tQe+I+pzGE63TQPF8Qb9qnBZZDPjHTzVBINR6H4nB2hILLlo9B9/8/duKeLt+w2J09rtgNxtwzfoFONjnxEO7DmHA5cXWdc0YcHrCUz5Y0HnUiX3dQ/jqp5vQXFWE7z+5Fx39o9EGjB/vDC1g/vg7hxU3FX68cz9W1Jfxb0lEecflDeDv5lXh9Y6B0LmzD/i7eVUY9eb/NGjmcM/B8T0Jxw8Tp+nB6VVfj2L8OhneQBCDo37cEDOSYtuZLagqHqsLWo0GfO/JfXH1w19depLitY4Me1TnXI5dCNwfCOAzC2rxDw++rqiv+IJj5TLoBP7xU01xZTLolfXXmaVW1XprbclYg161w4IBl1dRJotRh2rH5BskIouOJ+ugQyEubwDLZ1co/s5b1jZrIpdS7ri8AdW8lam4yfbxqDBkO79puW5KuZPo/pA/A1M724wG3PKnfYr7d7f8aR8euHRV2o9FhcXrC6rmN4+PC4rHEUKsB3AnAD2AX0gpvz+Z51cWW3H7U+8rpnl44KV2/MOpTdDrQg0GtSUW/PLLK9E77IVOCPzgqX3YsLQu2tgAxC/WXFtiwXkrx3ogByTwb394V5EQ7nwm1PstckHm9gXRedSFzafOw02PvxdNUj86fzm+Pq4x4Nd/68DmU+fFTUfQedSJhhnKm8qRxoQymyluyiK1iza1kRVb1jbjwVdCo1IiZYisqzC+oeKq37yJJ7aswerGGaqfuVaG/efT1FkmvQEPv7E/Lk7rKxbgp39tw40bW3G0z4Mt65oQlMCC6mLc/tS+uHj7xcUnwu0P4vL7d8X9be98Zj+u+kwT7nq2DactqsY7hwcV8RUbA9c+sjvaoKF2o2Oyf8t8+qyJqHAVm41x01EIARSZjepPyCNFZiNeP9iH+758EvpGPKgsMuMPb3TipIbyXBeNcqDUasTFJ8+J6zRTYlVW+30BiZ/+tS16zgZC01B+/+yl0X0GR9VHSAyN+hXbqhKMZq6MGT1k1Ovj1qq74fE9igvgniEPntod7hXr8qHUZsSvXvwQFePmM19YVYybN7Xi2keUo0MXVjui+6SzQSKVRccpxGo0KKY7A0LTnWWixygVjjK7et4qtWXmdkW2j0eFIdv5Tct1U8qdRPeHrlm/KO3HOuryJhhJy5EblFyxRT2/FU8xvxXs2VsIoQdwN4DTABwC8JoQ4lEp5XupvsbcGXb8v9VzcU1MD/cbzmrBoNuLCnsRKop06Hd6UWOxwGYOYNQbREf/KIRA3ILgkYaDMpspriJ13YbF0VEaEW5fUPGHthh1mFlqjZYlss/e7qG4ZLJhaV20ASSy353PhHrMj2/ciDQmdA268eArY0P31zTNwEkN5SktqLh9p7IhJjLiYyoNFVoY9p9vU2eZDTpccNIcRZzetLEVQ6N+dPSPYnjUDwmRsDECCP1dvH4ZN41F7N/W7Q/C7QviowFXdC0Z1f1iYvd4/5b59lkTUeHyBgJwegNxHQO8GljAvr7MhlWNlbjsV69xGsBpYKJGf50QqtNSPXiZshfdiMePC1bWKzq2bFnbjBHPWMOFw2pQPZc7xjWUJJqH3G4em4e8b8SjWi/sd46N7iixGrCqUdkrVq1h5vDgKFwev2K6LJfHj8ODo5gbrmNmqkFi3HIjNM6Q26caV8Me3uigxAxCp5q3fnPFJwrieFQYsp3ftFw3pdxJdH/IrE//iO5is3o9Mbb+R6TG41fPb54p5rdCnq9gFYA2KWW7lNIL4LcANk72RQz60DzDV61twuZTG2HQC9iMBoz6/TAZdLAZDRjy+AEJHB5wKVZ6j+gadGPHrk788tKTcOs5S+IqUjc9/p5iJEfk+bqYG8TXbVgMGQzGXRRGRoXE0uugevHo8ip72QFjjQmRct79bBt+8UI7KovNqhd/iRosInkytldc7GvHvq9kN7cjvexiP8d8G/afaOqsg/3OnJTH7ffBHF4QLxKnZqMOlcUmWIw6lNiM+NFfPohrjDh7xVjMWYyhhjq1v60IL7Anw7FmMxkS7hd5LSnHGvWO52+Zb581ERUufxCqNzoCwQmemAc6B1zRHuwAotNZdg64clwySrdIo/8Z21/AF3/+N5yx/QU8uacbweDYHfdjCUZbHBvXi85hMSpGGkfqBw7LWI8pgy403Wnsufzq0+bDMK6O2DfihtUgcM9FJ+LOC5fjnotOhNUg0Dcy1nBRXaxeL6yMqRca9Oo3HA3jLsg/HhzFLX/ah+3PtOGunW3Y/kwbbvnTPnw8OKrYL7LW3OrGGdG156Yilc+dQkqsCeKKPY0piWMJ1tEZHM3MTeNsH48KQ7bzm5brppQ7br9f9f6QOxB/P/B4FVkM2LpOec9n67pmFFsKth89pUmi/OafYn4r5IirA/BRzO+HAEyqK8bBfie++bu3FRUfSzhJnNxYgY+OjsIflDDpdfD6A5hZasHVp83Hr//WETfF09WnLcC3H34HX/1Uk2pFqr7MFm3xtBh1uHFjC2aVWRGUTZASuGtnG845cVZcq+hjbx/Gv5/Zgn+PmZv4pDnlqq2n9eXxN5UnO2Q/0ciKdQur8Ml5FYpecVOZDkALw/7zbuosqcc1v3897m+y4yurcdPG1pQapLad2YL+EY/q31YngC1rm7FjVyfuOH85qhNMPRFp/IisuRFp1LvnopUw6sWUppTKu8+aiArWqNefoGNA/veOY66cPlJZm8xi1Kuepy0mZS+6wVH1Tg2Do97o70edPvzy/w4q1s/65f8dRENFi+J5ZTYLtj06tnillMDj7xzG9gtPiO6j0wHbzmyJX08jpt3C44vvyBMaXarcNuxW/76OuDPzfeWacKkbTDBFBW8aUzIWk041b5kMmemLme3jUWHIdn7Tct2UckjqcM3v34nLb/dnYB0Mf0DCbtIrRtLaTXr4A+z8Qcklym9TXVOokBs3UiKE2AxgMwDU19crHkt0syAoQ3MQW416GPQCFoMeR4Y9MBl0aJxhCC+OKPHzi1bC5fWjuboY/c7QQou2BMO2uofcuPyURuh1wCfmluNHT7+P1fMq8YsX2qP7Pvz6IWxd16yY0uqClfX4zd86cP+lqyAhUVVsQX2ZTbVRYe6M+EaFyTYmJGqwWFJXGvecqTZURHrZ5evFYranzkoWowDQm2Cah4+OjWJ2mRk6nfpNjqaqYly1tik8yqITF39yblx83fKFJZhfXQS3L4D1rTXRhqnxMXDrOUtRV2rBOSvqUF9mw4r6srQ0TmlhmjIKmShOifJBsjitsKs33JaPm+s/HzFXFo6JcmkqDVkmvYg7n29d1wzTuEW5y+0m1bgps43FfE2J+qLcNQ6z4rVaah342trmuDUwWmpLovuUWE14+PXOuDmgbzt3ueJ4amUavwh4KguKpxMbEJWSxWlZgrgqtXHkBiVm1utV85bFMPWpTZLFaSaOR4UvE/mtUOumlDuJ7g/1xoymnYxkMfrxoBv/+dd2nL1iFoQAAkHgP//ajn87I/3re1BhSXd+K+TGjcMAZsf8Piu8TUFKeQ+AewBg5cqViubFRDcLdCJ0QegLhHq/D7jcsIZ7w137yJ7oOgYWow7/+7XYnnQ6/Pz5A7jxrBZcHzMn8dZ1zXjg5Q4MuLy49ZylOKm+HLeduxxHnR40VxXhWw+HWl0HXF7MnWHH1nXNcHoDkBJ48JXQ8yqLzYqLq8k0KkymMWGyDRb53lAxFelcoDIVyWIUAKqL1ZNCdbEZR10+zKkwxsXcjWe14odP7UNH/2io5+SGFvzh9UM4eV4F7r1kJbz+IOrL7Zg7Q/1vO1EMpOtvnu3PmqZuojhN23GCQXR0dAAAAuH5GPX6UP5taGiI/p9ITbI4LbboVBcodljyvxcnc2XhmPCcn0JDls2sV+1FZxs3cqPYosdNG1tx3R/HYv6mja1wWMf2W1zjUP1eLI5ptAAAg0GHTcvq0FxVhO5BN2pKLGipLYEhphd0Q4Udl50yL2mcphrLqZYrXdiAqJQsTkut6nFVZuP5mRKrKDKp5q2KoqnfxE0Wp5k4HhW+TOS3ZHFablevm1bY879uSrmT7P7QVCSL0dpEnWBKpmf9iFJXUZTe/FbIjRuvAWgWQsxFqFHjQgBfmswLNFTY8cPzluMbv3tL0RBRV2aFN+BHIBBajHHEG0SZTQ+9TmDAFRrKP360ROzF2n+90oG7vngC/EGJIosBRSYDls8uVUzbE7k5vCIosaSuJHoTub7Mhqf29kx40ZfJRoVCbLCYjHybOqulphg3ntWK6x/drWi8KLfrcdTlwTW/240rP9OIX375JPSNeDCjyAybWeDOC07ARwOjEADuef4APjgygvNOqsfJjTPyZnRNvn3WlHuugSO49uFDKJ/Zj74D70JnLUH5zHo4+7vxiyvPwLx583JdRNKo6hILZru8uP/SVdF8AwRQrYHKOXPl9JHKzf955XZ09o+izzk2vVRFkRnzxtUV55YXo3fYo6gfBIIBzC0vju6TSqNF7L7LZpdh2ey4hwCkFqepxvJkypUObEBMXZXDgsYZPjxw6Sr0DLtRXWyBXhdElSP/cynlTn25HTPLRhR5a2aZVXVaZS0ejwpDtvNbXUkxhqr8iuOZjRJ1JcUTP5mmrUU1dtX7Q4tq0p/fls4sUT3W0pmZ6WxChWOG3YJmlfxWWTS1fFqwjRtSSr8Q4ioAfwagB3CflHLPZF5DpxP4fGsN5lefgg/7nLAYdbCbDDCHL5yOjXpRarVgQbU5WhF6IsHFWDqnaOINjNzLpwYem9WEDa3VaJhhQ8+QB9XFZpTZ9dAJAxZUO/Cd/28RahwWBIKAEFDEzNJZpTjY78R3/r9FeRtL+fRZU36wlVejqHIWnP3d0NvLUFQ5SzGiA+AoDpq8EqsFC2uB97udAASEABbUOFBi1cYNOebK6SGV+qTNasJnmmegstgcqhc4zGitKYbNquyRbDLpsWrODLzz8SB0QsCo1+HE2WUwjRvhMVGjxWTLP1GcphrL6SzXRNiAmDqH1YLmamBfOJdCAM3VxXBoJJdSbuh0AmsXVKNxRlFWvmPZPh4VhmznN5NJj0XVZdHztE4nsKi6NO48TRSryGrBGa2VaJixKloPXFxjR1EG4tRk0mPTsplorLSjZ8iNaocFS2eWMEZpQkVWC+bNAN6LyafzZhRNOU4LtnEDAKSUTwB44nheQ6cTaK4uRnN1aq3jyS7G0nXjgTcwaDyb1YRVcytUH2uYMRYn86qUMcNYokIRO6KDozhoqkqsFqyayxtwlN9SOXcnqxfEMpn0WNlQns7iFSzWmVLnYC6lKcj2d4zfaZqKbOc3nqdpKoqyGKeMUZqqdMZpQTduEBHR9BEZ0UFERERERERERIWPjRtERFRQEi06HpmqKhAI4ODBg3GPT3WB8tjXi31eou2UmtjPj58dEREREREREY3Hxg0iIsoaZ383AGD0WC90Hi9GLOYJ/x8cHUx53xGLGf0f7sE39npQUlWLgc790FmKYDIZ8b2L12LOnDno6OjAvz6wE9bSGdHHY/ctqarF6LG+6P4TiX292Ocl2k6piXx+APCb6y7lNGNEREREREREpCCklLkuQ94QQvQC6FB5aAaAviwXZ7JYxvTIZhn7pJTrJ/OEJDEK5Pfnm69ly9dyAflTtukUp+nC95h9jNMxWi47oO3yJyt7umM03Qr1c893+Vb2Qs+l+VSefCoLkF/lmagsjNPsyaeyAPlVnrSe8wFNxWk+lQXIr/LkU1mA7NZNtfTesy2fygJoqzwpxSkbN1IghNglpVyZ63IkwzKmhxbKmEg+lz1fy5av5QLyu2zHo1DfVyy+R+3T8vvTctkBbZefZc8Nlj1/5dv7y6fy5FNZgPwqT7bLkk/vHciv8uRTWYD8Ks90jtN8KguQX+XJp7IA2S3PdH7vE8mnsgCFWR5dugpDRERERERERERERESUDWzcICIiIiIiIiIiIiIiTWHjRmruyXUBUsAypocWyphIPpc9X8uWr+UC8rtsx6NQ31csvkft0/L703LZAW2Xn2XPDZY9f+Xb+8un8uRTWYD8Kk+2y5JP7x3Ir/LkU1mA/CrPdI7TfCoLkF/lyaeyANktz3R+7xPJp7IABVgerrlBRERERERERERERESawpEbRERERERERERERESkKWzcICIiIiIiIiIiIiIiTWHjRoz169dLAPzhT7Z+Jo0xyp8c/Ewa45Q/OfiZNMYpf7L8M2mMUf7k4GfSGKf8ycHPpDFO+ZPlnylhnPInyz+TxhjlTw5+UsLGjRh9fX25LgJRUoxR0gLGKWkB45TyHWOUtIBxSlrAOCUtYJxSvmOMUr5i4wYREREREREREREREWkKGzeIiIiIiIiIiIiIiEhTNNm4IYSwCCFeFUK8LYTYI4S4QWUfsxBihxCiTQjxNyFEQw6KSkREREREREREREREaWbIdQGmyANgrZRyRAhhBPCiEOJPUspXYva5HMCAlLJJCHEhgFsBXJCpAgWDEgf7negZcqPaYUFDhR06ncjU4YjyGr8PpDWMWSIiypZCP+cU+vsjoumL+Y20IJtxyu8E5QNNNm5IKSWAkfCvxvDP+FXUNwL49/D/fw/gLiGECD83rYJBiSf3dOPqh96C2xeExajDHecvx/qWGn6padrh94G0hjFLRETZUujnnEJ/f0Q0fTG/kRZkM075naB8oclpqQBACKEXQrwF4AiAp6WUfxu3Sx2AjwBASukHMAigIhNlOdjvjH6ZAcDtC+Lqh97CwX5nJg5HlNf4fSCtYcwSEVG2FPo5p9DfHxFNX8xvpAXZjFN+JyhfaLZxQ0oZkFIuBzALwCohROtUXkcIsVkIsUsIsau3t3dKZekZcke/zBFuXxBHht1Tej2iWOmI0Wzi92F60lqcxmLMTh+pxmnd7HoIISb9Uze7PovvhgqRlnMppaYQzjnJ4rQQ3h8VBuZTSrdM5DfGKaVbuuOU53zSAk1OSxVLSnlMCPEsgPUAdsc8dBjAbACHhBAGACUA+lWefw+AewBg5cqVU5qyqtphgcWoU3ypLUYdqootU3k5IoV0xGg28fswPWktTmMxZqePVOP040Mf4YKfvTTp19/xD5+ceuGIoO1cSqkphHNOsjgthPdHhYH5lNItE/mNcUrplu445TmftECTIzeEEJVCiNLw/60ATgOwb9xujwK4JPz/cwHszMR6GwDQUGHHHecvh8UY+jgj88w1VNgzcTiivMbvA2kNY5aIiLKl0M85hf7+iGj6Yn4jLchmnPI7QflCqyM3agHcL4TQI9RA85CU8nEhxI0AdkkpHwVwL4AHhRBtAI4CuDBThdHpBNa31GDhljU4MuxGVbEFDRV2LqBD0xK/D6Q1jFkiIsqWQj/nFPr7I6Lpi/mNtCCbccrvBOULTTZuSCnfAXCCyvbrY/7vBnBetsqk0wk0VhahsbIoW4ckylv8PpDWMGaJiChbCv2cU+jvj4imL+Y30oJsxim/E5QPNDktFRERERERERERERERTV9s3CAiIiIiIiIiIiIiIk1h4wYREREREREREREREWkKGzeIiIiIiIiIiIiIiEhT2LhBRERERERERERERESawsYNIiIiIiIiIiIiIiLSFDZuEBERERERERERERGRprBxg4iIiIiIiIiIiIiINIWNG0REREREREREREREpCls3CAiIiIiIiIiIiIiIk1h4wYREREREREREREREWkKGzeIiIiIiIiIiIiIiEhT2LhBRERERERERERERESawsYNIiIiIiIiIiIiIiLSFDZuEBERERERERERERGRprBxg4iIiIiIiIiIiIiINEVzjRtCiNlCiGeFEO8JIfYIIbaq7PNpIcSgEOKt8M/1uSgrERERERERERERERGlnyHXBZgCP4BvSCnfEEIUA3hdCPG0lPK9cfu9IKXckIPyERERERERERERERFRBmlu5IaUsktK+Ub4/8MA9gKoy22piIiIiIiIiIiIiIgoWzTXuBFLCNEA4AQAf1N5+GQhxNtCiD8JIVqyWzIiIiIiIiIiIiIiIsoUzTZuCCGKADwM4J+llEPjHn4DwBwp5TIAPwbwSJLX2SyE2CWE2NXb25ux8hJNFWOUtIBxSlrAOKV8xxglLWCckhYwTkkLGKeU7xijpAWabNwQQhgRatj4tZTyf8Y/LqUcklKOhP//BACjEGKG2mtJKe+RUq6UUq6srKzMaLmJpoIxSlrAOCUtYJxSvmOMkhYwTkkLGKekBYxTyneMUdICzTVuCCEEgHsB7JVS3pFgn5rwfhBCrELoffZnr5RERERERERERERERJQphlwXYAr+DsBFAN4VQrwV3vYdAPUAIKX8KYBzAfyTEMIPYBTAhVJKmYOyEhERERERERERERFRmmmucUNK+SIAMcE+dwG4KzslIiIiIiIiIiIiIiKibNLctFRERERERERERERERDS9sXGDiIiIiIiIiIiIiIg0hY0bRERERERERERERESkKWzcICIiIiIiIiIiIiIiTWHjBhERERERERERERERaQobN4iIiIj+f/buP76t+64X/+uj37IsKY5jW4rTJE3rrJ3kNBSzjQJjJGvJdhM3FMi2C5QLl++ACzSXcLkMbkiWLNwvlx/h22482AYXWLkwFtiWJWWUlhbYLt1gWZfGdn8kXZukcSzbcRxJlnX063y+f9hSJOtIluSjIx379Xw89Ih1fn0+Ouelt6Xzic8hIiIiIiIiIlPh4AYREREREREREREREZkKBzeIiIiIiIiIiIiIiMhUOLhBRERERERERERERESmwsENIiIiIiIiIiIiIiIyFQ5uEBERERERERERERGRqXBwg4iIiIiIiIiIiIiITIWDG0REREREREREREREZCoc3CAiIiIiIiIiIiIiIlPh4AYREREREREREREREZmKKQc3hBB3CCH+SQjxshBiTAhxUGMZIYR4QgjxuhDighDi/lb0lYiIiIiIiIiIiIiI9GVrdQcalAXwK1LKF4UQXgDfFEI8K6V8uWiZ9wEYWHy8E8AfLf5LREREREREREREREQmZsq/3JBSTkgpX1z8OQ7gFQD9SxZ7GMCTcsHXAawTQgQN7ioREREREREREREREenMlIMbxYQQWwF8B4B/WzKrH8BbRc+voXwAhIiIiIiIiIiIiIiITMbUgxtCiE4AnwfwX6WUsQa38WEhxDkhxLnp6Wl9O0ikA2aUzIA5JTNgTqndMaNkBswpmQFzSmbAnFK7Y0bJDEw7uCGEsGNhYOMvpZRf0FhkHMAdRc83LU4rIaX8tJRySEo51NPT05zOEq0AM0pmwJySGTCn1O6YUTID5pTMgDklM2BOqd0xo2QGphzcEEIIAP8bwCtSypMVFjsD4FGx4F0AolLKCcM6SURERERERERERERETWFrdQca9D0AfgLAiBDi/OK03wCwGQCklJ8E8GUA7wfwOoB5AD9lfDeJiIiIiIiIiIiIiEhvphzckFL+XwBimWUkgF8wpkdERERERERERERERGQUU16WioiIiIiIiIiIiIiI1i4ObhARERERERERERERkalwcIOIiIiIiIiIiIiIiEyFgxtERERERERERERERGQqHNwgIiIiIiIiIiIiIiJT4eAGERERERERERERERGZSssGN4QQViHEL7eqfSIiIiIiIiIiIiIiMqeWDW5IKXMAPtSq9omIiIiIiIiIiIiIyJxsLW7/X4UQnwDwOQCJ/EQp5Yut6xIREREREREREREREbWzVg9u7Fz893jRNAlgl/FdISIiIiIiIiIiIiIiM2jp4IaU8gda2T4REREREREREREREZlPy+65AQBCiD4hxP8WQvz94vO3CyH+cyv7RERERERERERERERE7a2lgxsA/hzAPwDYuPj8IoD/2qrOEBERERERERERERFR+2v14MYGKeUpACoASCmzAHKt7RIREREREREREREREbWzVg9uJIQQ3Vi4iTiEEO8CEG1tl4iIiIiIiIiIiIiIqJ219IbiAA4BOAPgLiHEvwLoAfAjre0SERERERERERERERG1s5YObkgpXxRCfD+AtwEQAF6TUmaWW08I8acA9gKYklKGNea/B8CXALy5OOkLUsrjevWbiIiIiIiIiIiIiIhap9V/uQEA7wCwFQt9uV8IASnlk8us8+cAPgGg2nJflVLu1aWHRERERERERERERETUNlo6uCGE+AsAdwE4j9s3EpeoPmgBKeVXhBBbm9o5IiIiIiIiIiIiIiJqS63+y40hAG+XUsombPu7hRAvAbgO4L9JKcea0AYRERERERERERERERnM0uL2RwEEmrDdFwFskVLeB+DjAE5XWlAI8WEhxDkhxLnp6ekmdIVoZZhRMgPmlMyAOaV2x4ySGTCnZAbMKZkBc0rtjhklM2j14MYGAC8LIf5BCHEm/1jpRqWUMSnl3OLPXwZgF0JsqLDsp6WUQ1LKoZ6enpU2TaQ7ZpTMgDklM2BOqd0xo2QGzCmZAXNKZsCcUrtjRskMWn1Zqo82Y6NCiACASSmlFEK8AwuDODPNaIuIiIiIiIiIiIiIiIzV6sGNuwF8RUp5qZ6VhBCfBfAeABuEENcAHAVgBwAp5ScB/AiAnxdCZAEkAXywSff1ICIiIiIiIiIiIiIig7V6cGMzgE8JIbYC+CaArwD4qpTyfLWVpJQfWmb+JwB8Qqc+EhERERERERERERFRG2npPTeklEellLsAhAB8FcCvYmGQg4iIiIiIiIiIiIiISFNL/3JDCHEYwPcA6ATwLQD/DQuDHERERERERERERERERJpafVmqRwBkAfwdgH8B8DUpZaq1XSIiIiIiIiIiIiIionbW6stS3Q/gvQD+HcCDAEaEEP+3lX0iIiIiIiIiIiIiIqL21urLUoUBfB+A7wcwBOAt8LJURERERERERERERERURasvS/U4gH8C8IcAviWlnGtxf4iIiIiIiIiIiIiIqM215LJUQgibEOJ3ANyHhftuPA7gTSHE7wgh7K3oExERERERERERERERmUOr7rnxuwDWA7hTSnn/4r037gKwDsDvtahPRERERERERERERERkAq0a3NgL4P+RUsbzE6SUMQA/D+D9LeoTERERERERERERERGZQKsGN6SUUmpMzAEom05ERERERERERERERJTXqsGNl4UQjy6dKIT4cQCvtqA/RERERERERERERERkErYWtfsLAL4ghPhpAN9cnDYEwA3gh1rUJyIiIiIiIiIiIiIiMoGWDG5IKccBvFMIsQtAaHHyl6WUz7WiP0REREREREREREREZB6t+ssNAICU8nkAz7eyD0REREREREREREREZC6tuucGERERERERERERERFRQzi4QUREREREREREREREpmLKwQ0hxJ8KIaaEEKMV5gshxBNCiNeFEBeEEPcb3UciIiIiIiIiIiIiImqOlt5zYwX+HMAnADxZYf77AAwsPt4J4I8W/61bMpnBSCSGyVgKfT4nBgM+OJ02XJ5JYDKmoM/nwtZuDywWUVhHVWVhfq/XBasFmIgqCPpdyKnAVFxBh8OGdC6Hbo+zbH2iei3NaTjgRWQuXcjo5q4OXLk5j+u35mG3WRBLZhHwu/D2gA82mynHOMmEimtjPpdXZ+cxl8oglVExGU9h6/oOZFWJ69EkNnQ6EfA7kcku1M3iGqpVe4mIqD5L67JWXa1lGQDIZlWMTUQXP/O6EQqWf8bQsz09t6XXvtK7TbOKJxW8EkkUPpfeG/DA63a1ulvU5ox+7/C9So0wur4pShYjE1FEYikEfE4MBv1wucx6Go+MMp9MYzQSLzk/1OF2tLpbRCViSQWvFtXTewIe+Bqsp6asilLKrwghtlZZ5GEAT0opJYCvCyHWCSGCUsqJetpJJjM4OxrBkTOjUDIqXHYLjg+HcXefG//xj79RmHbywE7sCQVgsQioqsTTYxEcOnW+MP/g7gH8/cgE3jcYxOPPXSpMf2zXAD537ip+bc+9hfWJ6lUppxcjs/iTf30LW7rd+KVdA/j485fwgaHNeOL52xk8sT+M/ff1c4CDmk6rNp7YH8a/vzGN79y6AUfPjGF7byc+9M4tOHZ2rLDMseEQ/ubcVYzfSuHR795SUkOLay8REdVHqy4vrau1LAMsDGycfmkch0+PVvyMoWd7em5Lr32ld5tmFU8q+PvR6bLPpe8L93CAgyoy+r3D9yo1wuj6pihZnBmZKGtveDDIAQ6qaD6ZxlOjk2W52Rvu4wAHtY1YUsHTGvV0T7inoQGO1XpGsx/AW0XPry1Oq8tIJFbY0QCgZFQcOTOKTFaUTDt06jwuzyQAAJdnEoUPSfn5jz93CT/z7rsKJ+Xy0594/hL27ugvWZ+oXpVy+t7QQuT37ujH4dOj2LujvzCwkV/u8OlRjE1EW9Z3Wju0auPh06PYf/9mHD2zMJjxM+++qzCwkV/m6JkxPPrANjxy/6ayGsraSUTUOK26vLSu1rIMAIxNRAsDG/nlln7G0LM9Pbel177Su02zeiWS0Pxc+kpk7ewDqp/R7x2+V6kRRte3kYmoZnsj/P5OVYxG4pq5GY3EW9wzotterVBPX22wnq7WwY2aCSE+LIQ4J4Q4Nz09XTJvMpYq7Og8JaNiKq5UnDYZUzTXSaaymtOF0N4mUV61jAKVczq9mKl8xvL/Ll0uEmX2aOWWz6l2bbwxdzu/lepkMp2tmF/WTqrHcjklajUjM1qpLhfX1VqWARYuv7rcZww929NzW7XQs1+rQSPfnyZjKSO7SCbTjPdO9Zyujfcq6asZ9a1aTiOsp9QAvXPK70/UDHrndLUObowDuKPo+abFaWWklJ+WUg5JKYd6enpK5vX5nHDZS3eRy25Br9dVcVqfz6W5TofTpjldSu1tEuVVyyhQOac9RZnKz9daLuBn9mjlls+pdm3s6byd30p10u2wFX5eOo+1k+qxXE6JWs3IjFaqy8V1tZZlACDody/7GUPP9vTcVi307Ndq0Mj3pz6f08guksk0471TPadr471K+mpGfauW0wDrKTVA75zy+xM1g945Xa2DG2cAPCoWvAtAtN77bQDAYMCH48PhkhPDx4fDsNtkybSTB3Zia7cHALC124OTB3aWzD+4ewB//JVv4+DugZLpj+0awFMXxkvWJ6pXpZz+49jCeN7Zl8ZxYn8YZ18ax2O7SjN4Yn8YoaC/ZX2ntUOrNp7YH8YXX7yKY8MhuOwW/PFXvo2j+0IlyxwbDuHJF97A5795rayGsnYSETVOqy4vrau1LAMAoaAPJ/aHq37G0LM9Pbel177Su02zujfg0fxcem9g7ewDqp/R7x2+V6kRRte3waBfs71Bfn+nKsIBr2ZuwgFvi3tGdNs9FerpPQ3WU7Fwz21zEUJ8FsB7AGwAMAngKAA7AEgpPymEEAA+AWAPgHkAPyWlPLfcdoeGhuS5c6WLJZMZjERihbu3DwZ8cDptuDyTwFRcQa/Xha3dnrKbCebn93S6YLUAkZiCgM+FnApMxRV0OKzI5FSs9zjL1qc1o+6DrpVRoDyn4YAXkbl0IaObuzpw5eY8JqLzsFktiCWzCPiceHvQz5uJ03J0y2lxbczn8ursPBKpzMKfIMZT2LK+AzlV4no0iQ0eJwLrnMhkgem52zV0ek679tKapltOAUAIgQ986oW6O/G5n30AZvxcRYbQNaN6WVqXtepqLcsACzcVH5uIIhJVEPC7ENL4jKFne3puS699pXebLaBLTuNJBa9EEoXPpfcGPLyZOC2rjveOLjk1+XuVWqTG+tZQkLRyqihZjExEb5+PCvp5M3Fa1nwyjdFIvOT8kMbNxNvysymtHbGkgleL6uk9AY/WzcRryqkpBzeahW9UMhh/mZAZMKdkBhzcoHbHWkpmwJySGTCn1O50G9wgaiLWUjKDmnLK/7JNRERERERERERERESmwsENIiIiIiIiIiIiIiIyFQ5uEBERERERERERERGRqXBwg4iIiIiIiIiIiIiITIWDG0REREREREREREREZCoc3CAiIiIiIiIiIiIiIlPh4AYREREREREREREREZkKBzeIiIiIiIiIiIiIiMhUOLhBRERERERERERERESmwsENIiIiIiIiIiIiIiIyFQ5uEBERERERERERERGRqXBwg4iIiIiIiIiIiIiITIWDG0REREREREREREREZCoc3CAiIiIiIiIiIiIiIlPh4AYREREREREREREREZkKBzeIiIiIiIiIiIiIiMhUTDm4IYTYI4R4TQjxuhDiIxrz/5MQYloIcX7x8TOt6CcREREREREREREREenP1uoO1EsIYQXwhwAeBHANwDeEEGeklC8vWfRzUspfNLyDRERERERERERERETUVGb8y413AHhdSvmGlDIN4K8BPNziPhERERERERERERERkUHMOLjRD+CtoufXFqct9cNCiAtCiL8VQtxhTNeIiIiIiIiIiIiIiKjZzDi4UYuzALZKKXcAeBbAZyotKIT4sBDinBDi3PT0tGEdJKoVM0pmwJySGTCn1O6YUTID5pTMgDklM2BOqd0xo2QGZhzcGAdQ/JcYmxanFUgpZ6SUqcWnfwLgOyttTEr5aSnlkJRyqKenR/fOEq0UM0pmwJySGTCn1O6YUTID5pTMgDklM2BOqd0xo2QGZhzc+AaAASHEnUIIB4APAjhTvIAQIlj0dBjAKwb2j4iIiIiIiIiIiIiImsjW6g7US0qZFUL8IoB/AGAF8KdSyjEhxHEA56SUZwA8JoQYBpAFcBPAf2pZh4mIiIiIiIiIiIiISFemG9wAACnllwF8ecm0I0U//zqAXze6X0RERERERERERERE1HxmvCwVERERERERERERERGtYRzcICIiIiIiIiIiIiIiU+HgBhERERERERERERERmQoHN4iIiIiIiIiIiIiIyFQ4uEFERERERERERERERKbCwQ0iIiIiIiIiIiIiIjIVDm4QEREREREREREREZGpcHCDiIiIiIiIiIiIiIhMhYMbRERERERERERERERkKhzcICIiIiIiIiIiIiIiU+HgBhERERERERERERERmQoHN4iIiIiIiIiIiIiIyFQ4uEFERERERERERERERKbCwQ0iIiIiIiIiIiIiIjIVDm4QEREREREREREREZGpcHCDiIiIiIiIiIiIiIhMxbSDG0KIPUKI14QQrwshPqIx3ymE+Nzi/H8TQmxtQTeJiIiIiIiIiIiIiEhntlZ3oBFCCCuAPwTwIIBrAL4hhDgjpXy5aLH/DGBWSnm3EOKDAP4XgA/U29atpIKLkQQmYyn0+ZwIrrPCLhbmRRXA7wKu3VIxPZeC12lDp9MKm8WCeCqL2fk0NnQ6IaXE7HwGGzodSKSy8LvtyOQkJuMp9HidyOZycNltSGVyiClZ+Nw2+N12xJUsbsylEfS54LZbEFWyuJlIY4PXCSAHKS2IJbPwuqxw2ayYnksh4HfDY7firVvzcNqsiCYX2t3Q6QQATM+l4LBaMJ/Ooc/nwtZuDwDg8kwC0WQaORWYiivY0OlEn8+JO7puz5+MKejzubC5qwNXZ+c1nwf9rsI28vOu3JzHlZsJeBw2BPxOZHO352/t9sBiESX7PJ3O4cL1KCZjCnq9TtisAn63Q3PZVlFVWbJPlvZtufl6W5rTzV1WKFnAaQMmbqnwdlgwm8gV5mdzOVgsFmRyObhsNsRSGfhddiRSWThsVswpWXhcVggAdqsFHU4rlLQKSGCdx4HeThteXmyvf50LFgFcu6Wgf50LNosFb83OY0OnE8F1TtyMZzARUxD0uxEK+mCz3R5TTSYzGInECv0aDPjgdtsL89PpHF6OxBBTMlAyKu7c4MFdPZ1tkwOzMzKniWQKV28lkcpIZFUVM4k03HYrOp02KJksbFYL3HYrUlkV8+kc5tM5+Nw2dNituJXMwCIE1nscSKYXcrzB60CH3YqJWApdHXaoyMFttyOblZiKp+B12bDeY8fdG7y4Fk2Wvcbi175xnQu3EpVz2ui+BGBoHViNjK6lRGZQy/ti6eeC7QEP1rldZduqZTmjt9WKvs8n0xiNxAvLhQNedLgddS8D1HZ88p91IzEFQZ8Lgxv9cDisZdtSlCxGJqKIxFII+JwYDPrhcpV+fau1TmazKsYmopiINv67rla17neiYkbnhjmlRjCnZAZG5oYZpUbpmR1TDm4AeAeA16WUbwCAEOKvATwMoHhw42EAH138+W8BfEIIIaSUstZGbiUVPDM6jSNnRqFkVLjsFhwfDuE7t/jgcSwMbHz19Th+80vF898OQODImbHCtIO7B/Dk165gdj6NI3vvRSSWwtGi+R/dF0JWVXHi714paecP//l1XJlJYmiLHz86tLmwzpZuN/7Le+4u2UZxG7/xvnuQzKj4g3+8WJh/bDgEt11gIpoumX7ywE44bAKf/OfX8cP3b8axp25v8+i+ELZ0z+PWfA6HTp0vtP1LuwZw+PTt13xifxgff/4S0lmJR797Cx5/7pLmslu63fi5778bx86OlbS/JxQofAlLp3M4feE6jhTt06N7Q/j8i1fx0997V8myraKqEk+PRQr7ZOnrWG6+3irl9N0DPvz75QT619nxrSupkvn5ffrD92/G51+8hAffHsRf/fsVfGBoM554/lJhuUMPbofLZoHXbUeHw4L5lIovj1zD9kAXjpwZRVeHo+SYF2fRYRP4hffcXfJeOLE/jP339cNmsyCZzODsaGRJv8PYFw7A7bYjnc7h6VciGJ9Nlmz/9390J94Xbn0OzM7InCaSKXzr2i3MzmcRTWZKat3B3QMI+Fz42ren8D0Dvbh+Syk53oce3A633YqvXprE7nuDJXXv6L4QbBbgf375ZRx6cDtmEwn89tOvlmz729MJfOaFN3HuSrTwGh+6tw/PvDKJQ6fOY3tvJz70zi0ldak4p43uy0/8x+9AOisNqwOrkdG1lMgManlfaH8uCOOhcE/JF4ZaljN6W63o+3wyjadGJ8uW2xvuKwxe1LJMrcdH67Pu8YfD2L9jY8kAh6JkcWZkoqzN4cFgYYCj1jqZzao4/dJ42ef3en7X1arW/U5UzOjcMKfUCOaUzMDI3DCj1Ci9s2PWy1L1A3ir6Pm1xWmay0gpswCiALrraeRiJFHY0QCgZFQcOTOGmbkcrt5ceOQHNvLzOxz2wsnc/LTHn7uER+7fBCWjlgxs5Od/9OwYpuKpsnb27lh4SY8+sK1knb07+su2UdzGjcTtAYz8/KNnxrCuw1k2/dCp87hwLYpHH9hWGNjIzzt2dgzZHApfmPJt578Y5Zc7fHoUe3f045H7NxVOSmotu3dHf+EEYnH7l2cShX1+4Xq08GWv0I+nxvDoA9vKlm2VyzOJkn2y9HUsN19vlXJ69WYOr0/PIaday+bn92n+3z/4x4vYu6O/MLCRX+7ksxdxI5HGmzcSyGSBN2cSeG+ov7C9pce8OIt7d/SXvRcOnx7F2EQUADASiWn0exQjkRiAhSy8PjVXtv1f+Zv2yIHZGZnTscgcrBYrLk3NFQY28m0+/twlvDmTwP77N+Pb04my433y2YuYnkvhx951Z1ndO3Z2DG67DXt39OPb07cHNoq3fWlqDo8+sK3kNY5NRAuv/WfefVdZXSrOaS209uWFa1FD68BqZHQtJTKDWt4X2p8LRnExUvreqWU5o7fVir6PRuKay41G4nUtA9R2fLQ+6x750iguXC/9vTMyEdX+nFT0+6nWOjk2EdX8/F7P77pa1brfiYoZnRvmlBrBnJIZGJkbZpQapXd2zDq4oRshxIeFEOeEEOemp6dL5k3Gbg845CkZFZNxpfBYOj+RymquIxb/85QqoTlfXfL3JMXrJJdsUwjtbSzXxmwiU7HtpW0U1pnP1Nz20nnLPc+vOxVXCs8jsfJ9qmRUJNPZsmVbZbJCH/N9W25+vapldKG9yjlVJTRzWrxP88e+0vFR5UKmEuksVAlMF22vnjzk50Wi+f1Uod+xFICFLFTKcjvkwOyMzOlkLIXpeKpq/bsxV31+pfqVSGchRPXamkxnS6ZNRG+/9kq1L5/TWmjtS2Z35fTOKLB8PSVqteV/5y//vlju92s9yxm9LTP3fWG55Y9Ppc+6k7HS2hapqV+11cni33vFy9Xzu65YQ9+fluwromLNyA1zSnpjTskM9M4NM0rNoHd2zDq4MQ7gjqLnmxanaS4jhLAB8AOYWbohKeWnpZRDUsqhnp6eknl9Pidc9tJd5LJb0Od1LTx8rrL5HpdNc538xbCsAprzl15ho3idDqf2Nutto8tjr9h2pTa6OrTXqdR2Lcsufd7rvf0nR0GNfeqyW+B22MqWbRWt417ct+Xm16taRhfaq5BTnwtWUbk/+X1afOwr5cMiAI/DBosAer2l26s3DwF/fj9V6vfC/WGCi/3Xc1/SbUbmtM/nRI/XWbX+9XRWn7++Qv3yOGyQsnptdTtsJdOC/tuvvVLty+e0Flr7ktldOb0zCixfT4labfnf+cu/L5b7/VrPckZvy8x9X1hu+eNT6bNun6+0tgVq6ldtdTLod6/4d12xhr4/LdlXRMWakRvmlPTGnJIZ6J0bZpSaQe/smHVw4xsABoQQdwohHAA+CODMkmXOAPjJxZ9/BMDz9dxvAwC2Bzw4PhwuOfF7fDiE7k4rNq+3YnOXFR97uHT+fCqD48OhkmkHdw/gCy9eKxyoY0vmf3RfCL1eZ1k7T11YGK/5zAtvlKxz9qXxsm0Ut9HtceCX37u9ZP6x4RBuzafKpp88sBM7NvnxmRfewNG9pds8ui8EmxU4eWBnSdsn9pe+5hP7w3jqwjg+/81rOLh7oOKyZ18ax9F9obL28zfeBYDBjX4cX7JPj+4N4ckX3ihbtlW2dntK9snS17HcfL1VyunmLivu6umEVeTK5uf36dG9IXzmhTfwy+/djrMvjeOxXQMlyx16cDs2eBy4c4MHdhtwZ7cHz46NF7a39JgXZ/HsS+Nl74UT+8MIBf0AgMGAT6PfYQwGfAvzN/pxV29n2fZ//0fbIwdmZ2ROQ4FO5NQc7u7txOH/cG9ZXu7s9uCLL17Fth5P2fE+9OB29HQ68X++/mZZ3Tu6L4RkJounLoxjW48HH9lzT9m2B3o78eQLb5S8xlDQX3jtf/yVb5fVpeKc1kJrXw5u8htaB1Yjo2upWfXfsRlCiLof/XdsbnXXqQG1vC+0PxeEsT1Q+t6pZTmjt9WKvocDXs3lwgFvXcsAtR0frc+6xx8OY8fG0t87g0G/9uekot9PtdbJUNCn+fm9nt91tap1vxMVMzo3zCk1gjklMzAyN8woNUrv7Ig6z/e3DSHE+wH8fwCsAP5USvlbQojjAM5JKc8IIVwA/gLAdwC4CeCD+RuQVzI0NCTPnTtXMq3k7u1eJ4JdVtgX/8oiqizcVPzaLRU35lLodNrgcVhht1oQT2UxO5/Ghk4npJSYnc+g2+PAfDoLv9uOTE5iMp5CT6cTWTUHl82GVDaHuJKF12WD321HPJXFjbk0Aj4XOuwWRJUsbiYWtgmhQkqBeDILj8sKt82K6bk0Aj4nPA4b3ro1D6fNilgyg/UeB3q8C6NfN+ZSsFstmE/n0OdzFb78XJ5JIJZMI6sCU3EFGzxO9PmduKPr9vypuIJerwubuzpwdXZe83nA50JOBabnbs+7cnMeV28m0OGwIeB3Ipu7PX9rt6fsxrDpdA4XrkcxGVPQ63XCZhXwux2ay7aKqsqSfbK0b8vNX1T3i9HKKFCe083rrVAygNMOTNxS4e2wYDaRw1Q8hd7FzAlhKWQvnsrA57Qjkc7CYbNiTsnC47RCCMBmtcDjsBb+ZGxdhwO9nTa8vNhe/zoXLAK4dkvBRr8LdqsFb83OY4PHiWCXEzfjGURiCgJ+F0JBf8mNK5PJDEYisYV++5wYDPjgdtsL89PpHF6OxBBTFi5JdOcGD+7q6WybHJidkTlNJFO4eiuJVEYiq6q4mcjAZbeg02GDks3CZrXAZbcinVUxn84hmVbR6bLCY7filpKBRQis73AgmclhMp5Ct8cBj8OKSCyFdW47JFS47DZkcxJT8RS8Lhu6PHYMbPDiWjRZ9hqLX3vQ78KtROWcNrovAdSyf6mKGjMK6FhPAUAIgQ986oV6N4nP/ewDMPpzlZn6usbpltFa3hclnwt8TmwPeDRvzlfLckZvqxV9n0+mMRqJF5YLB7wlNwqvdRmgtuNT/Fm3z+fCjo3+kpuJ5ylKFiMT0dufk4L+ws3E62kPWLip+NhEFJFo1d91uuS01v1OVKyO3DCn1DI15qahD/zMKemlWTllRklPeubUtIMbzVDtRAdRE+h6Mo6oSZhTMgMObtSJgxuGYy0lM2BOyQyYU2p3ug1uEDURaymZQU05NetlqYiIiIiIiIiIiIiIaI3i4AYREREREREREREREZkKL0tVRAgxDeCKxqwNAG4Y3J16sY/6MLKPN6SUe+pZoUpGgfbev+3at3btF9A+fVtLOdULX6PxmNPbzNx3wNz9r9Z3vTOqt9W639tdu/V9tdfSdupPO/UFaK/+LNcX5tQ47dQXoL36o+vvfMBUOW2nvgDt1Z926gtg7GdTM712o7VTXwBz9aemnHJwowZCiHNSyqFW96Ma9lEfZuhjJe3c93btW7v2C2jvvq3Ean1dxfgazc/Mr8/MfQfM3X/2vTXY9/bVbq+vnfrTTn0B2qs/RvelnV470F79aae+AO3Vn7Wc03bqC9Be/WmnvgDG9mctv/bltFNfgNXZH16WioiIiIiIiIiIiIiITIWDG0REREREREREREREZCoc3KjNp1vdgRqwj/owQx8raee+t2vf2rVfQHv3bSVW6+sqxtdofmZ+fWbuO2Du/rPvrcG+t692e33t1J926gvQXv0xui/t9NqB9upPO/UFaK/+rOWctlNfgPbqTzv1BTC2P2v5tS+nnfoCrML+8J4bRERERERERERERERkKvzLDSIiIiIiIiIiIiIiMhUObhARERERERERERERkalwcIOIiIiIiIiIiIiIiEyFgxtF9uzZIwHwwYdRj7oxo3y04FE35pSPFjzqxpzyYfCjbswoHy141I055aMFj7oxp3wY/GgIc8qHwY+6MaN8tOBREw5uFLlx40aru0BUFTNKZsCckhkwp9TumFEyA+aUzIA5JTNgTqndMaPUrji4QUREREREREREREREpsLBDSIiIiIiIiIiIiIiMhVbqztgBqoqcXkmgcmYgj6fC1u7PbBYRM3ziYzEvNJqpld++T4gLdmsirGJKCaiCoJ+N0JBH2w2/j8QIqJ6sJYS0WpldH3jdxZqBHNDZqBnPeXgxjJUVeLpsQgOnToPJaPCZbfg5IGd2BMKwGIRy84nMhLzSquZXvnl+4C0ZLMqTr80jsOnRwu5OLE/jP339fOkHBFRjVhLiWi1Mrq+8TsLNYK5ITPQu57yE+YyLs8kCkUBAJSMikOnzuPyTKKm+URGYl5pNdMrv3wfkJaxiWjhwxWwkIvDp0cxNhFtcc+IiMyDtZSIViuj6xu/s1AjmBsyA73rKQc3ljEZUwo7O0/JqJiKKzXNJzIS80qrmV755fuAtExEtXMRiTIXRES1Yi0lotXK6PrG7yzUCOaGzEDvetq2gxtCiD8VQkwJIUaLpn1OCHF+8XFZCHG+wrqXhRAji8udW0k/+nwuuOylu8llt6DX66ppPpGRmFdazfTKL98HpCXod2vmIuBnLoiIasVaSkSrldH1jd9ZqBHMDZmB3vW0bQc3APw5gD3FE6SUH5BS7pRS7gTweQBfqLL+DywuO7SSTmzt9uDkgZ2FnZ6/Xt3Wbk9N84mMxLzSaqZXfvk+IC2hoA8n9odLcnFifxihoL/FPSMiMg/WUiJarYyub/zOQo1gbsgM9K6nbXtDcSnlV4QQW7XmCSEEgAMAdjW7HxaLwJ5QAPc89n2Yiivo9bqwtdtTuBHPcvOJjMS80mqmV375PiAtNpsF++/rx0BvJyJRBQG/C6GgnzfAJSKqA2spEa1WRtc3fmehRjA3ZAZ619O2HdxYxvcBmJRSXqowXwJ4RgghAXxKSvnplTRmsQhs6+nEtp7OhuYTGYl5pdVMr/zyfUBabDYL7rujC/fd0eqeEBGZF2spEa1WRtc3fmehRjA3ZAZ61lOz/heaDwH4bJX53yulvB/A+wD8ghDi3ZUWFEJ8WAhxTghxbnp6Wu9+Eq0YM0pmwJySGTCn1O6YUTID5pTMgDklM2BOqd0xo2QGphvcEELYADwC4HOVlpFSji/+OwXgiwDeUWXZT0sph6SUQz09PXp3l2jFmFEyA+aUzIA5pXbHjJIZMKdkBswpmQFzSu2OGSUzMONlqd4L4FUp5TWtmUIIDwCLlDK++PNDAI7r3QlVlbg8k8BkTEGfj9ewo9WBuaa1iLmnPGaBiGjlWEuJaLUyur6xnlK7Y0apHbTt4IYQ4rMA3gNggxDiGoCjUsr/DeCDWHJJKiHERgB/IqV8P4A+AF9cuOc4bAD+Skr5tJ59U1WJp8ciOHTqPJSMCpfdgpMHdmJPKMA3MZkWc01rEXNPecwCEdHKsZYS0WpldH1jPaV2x4xSu2jby1JJKT8kpQxKKe1Syk2LAxuQUv4nKeUnlyx7fXFgA1LKN6SU9y0+QlLK39K7b5dnEoU3LwAoGRWHTp3H5ZmE3k0RGYa5prWIuac8ZoGIaOVYS4lotTK6vrGeUrtjRqldtO3gRjubjCmFN2+eklExFVda1COilWOuaS1i7imPWSAiWjnWUiJarYyub6yn1O6YUWoXHNxoQJ/PBZe9dNe57Bb0el0t6hHRyjHXtBYx95THLBARrRxrKRGtVkbXN9ZTanfMKLULDm40YGu3BycP7Cy8ifPXldva7Wlxz4gax1zTWsTcUx6zQES0cqylRLRaGV3fWE+p3TGj1C7a9obi7cxiEdgTCuCex74PU3EFvV4XtnZ7eMMcMjXmmtYi5p7ymAUiopVjLSWi1cro+sZ6Su2OGaV2wcGNBlksAtt6OrGtp7PVXSHSDXNNaxFzT3nMAhHRyrGWEtFqZXR9Yz2ldseMUjvgZamIiIiIiIiIiIiIiMhUOLhBRERERERERERERESmwstSLUNVJS7PJDAZU9Dnq379uOJlN65z4VYig4mYgqDfjVDQB5uNY0nUHPXktJZ1VVVibCKKiSjzS+1BK6cAappmxDU/V/IepPahKFmMTEQRiaUQ8DkxGPTD5eJHJSKieqTTOVy4HkUkpiDoc2Fwox8Oh7XV3aI2Z/RnKX52o0YYXd+yWZXfy6luzA2ZgZ7fvfmNvQpVlXh6LIJDp85Dyahw2S04eWAn9oQCZR98ipfd3tuJD71zC46dHSusd2J/GPvv62dBId3Vk9Na1v3UT9yP6Xgah0+PMr/UFipl3GET+MW/+tay02p5LzSjf81ul/SlKFmcGZnAkTO3a9/x4TCGB4Mc4CAiqlE6ncPpC9dx5EtFtfThMPbv2MgBDqrI6M9S/OxGjTC6vmWzKk6/NM7v5VQX5obMQO/v3kx2FZdnEoUPPACgZFQcOnUel2cSVZf9mXffVRjYyK93+PQoxiaihvaf1oZ6clrLuvFkrvCLMD+N+aVWqpTxC9eiNU2r5b3QjP41u13S18hEtPDhClg4jkfOjGKEtY+IqGYXrkcLJ/6AxVr6pVFcuM5aSpUZ/VmKn92oEUbXt7GJKL+XU92YGzIDvb97c3CjismYUtjReUpGxVRcqbpsMpXVXC8SLV+PaKXqyWkt6yaYX2ozlTKuStQ0rZb3QjP61+x2SV+RWErzOE7GUi3qERGR+UQq/E6cjPF3IlVm9GcpfnajRhhd3yai2u3xezlVw9yQGej93ZuDG1X0+Vxw2Ut3kctuQa/XVXXZDqdNc72Av3w9opWqJ6e1rOtxMb/UXiplfOlVAypNq+W90Iz+Nbtd0lfA59Q8jn0+Z4t6RERkPsEKvxP7fPydSJUZ/VmKn92oEUbXt6Dfze/lVDfmhsxA7+/eHNyoYmu3BycP7Czs8Py1OPM3ra207B9/5ds4ui9Ust6J/WGEgn5D+09rQz05rWVdr8uKE/vDzC+1jUoZ37HJX9O0Wt4Lzehfs9slfQ0G/Tg+XFr7jg+HMcjaR0RUs8GNfhx/eEktfTiMHRtZS6kyoz9L8bMbNcLo+hYK+vi9nOrG3JAZ6P3dW0gpl19qjRgaGpLnzp0rmaaqEpdnEpiKK+j1urC121PxJmPFywb9LtxKZBCJKQj4XQgF/bx5Dy1V993qtDIK1JfTWtZVVYmxiSgiUeaX9MvpSmjlFEBN04y4MeRK3oOkC11yqihZjExEMRlLoc/nxGDQz5uJk17aopYSLUOXnKbTOVy4HsVkTEGfz4UdG/28mTgtq47PUrrklJ/dqBE11reGgqSV02xW5fdyqluNueFnU2qpGr9715RTfmNfhsUisK2nE9t6Outedks3cF+zO0iE+nJay7oWi8B9d3Thvjv07CVR4yplvNZpzbaS9yC1D5fLhu+6s7vV3SAiMjWHw4qhretb3Q0yGaM/S/GzGzXC6Ppms1n4vZzqxtyQGej53ZtDvkREREREREREREREZCptPbghhPhTIcSUEGK0aNpHhRDjQojzi4/3V1h3jxDiNSHE60KIjxjXayIiIiIiIiIiIiIiaqZ2vyzVnwP4BIAnl0z/Aynl71VaSQhhBfCHAB4EcA3AN4QQZ6SUL9fbgfy1OPPXVNzc1YGrs/OazzscNqRzOXR7nLxmJxlqaU6L7z1QPM1iEZrL6p1VI9qgtaGebFdavpbsMbME3L6OciSmIOhzYZDXiacasH4QlcpfQzkSSyHA+xcR0SpidH3L3zthIqog6HcjFPTxnhu0LCNzw8/B1Cg9v3u39adMKeVXhBBbG1j1HQBel1K+AQBCiL8G8DCAugY3VFXi6bEIDp06DyWjwmW34MT+MD7+/CVcmUliS7cbv7RrAIdPjxbmP7ZrAJ87dxW/tude7AkF+KamptPK6ckDO+GwCfziX32rZNpD9/bhmVcmy5bVM6uV+sP3A9WrnmzvCQUAoKHsMbMELHy4On3hOo586fbv9OMPh7F/x0YOcFBFrB9EpRQlizMjEzhypqiWDocxPBjkAAcRmZrR9S2bVXH6pfGS800n9oex/75+DnBQRUbmhp+DqVF6f/c2a0X8RSHEhcXLVnVpzO8H8FbR82uL0+pyeSZReJMCgJJRcfj0KPbuWNjU3h39hYKRn//E85ewd0c/Dp06j8sziXqbJKqbVk4PnTqPC9eiZdPGJqKay+qZ1Ur94fuB6lVPti/PJBrOHjNLAHDherTw4QpYyMGRL43iwvVoi3tG7Yz1g6jUyES0cOIPWKylZ0YxMsFaSkTmZnR9G5uIlp1vOnx6FGOsp1SFkbnh52BqlN7fvc04uPFHAO4CsBPABIDfX8nGhBAfFkKcE0Kcm56eLpk3GVMKOzpPyagQIr8uKs5XMiqm4spKukYEoHpGgco5VSXKpk1EtZfVM6uV+sP3w+q2XE4bUU+2p+JKw9ljZteOajmNVMjBZIw5oMr0rh/NqKVEeqteS1MVamnKyC4SsZ6S7ppR36rltNJ390iUn02pMr1z08g5U36PpuXo/d3bdIMbUspJKWVOSqkC+GMsXIJqqXEAdxQ937Q4TWt7n5ZSDkkph3p6ekrm9flccNlLd5HLboGUpc+15rvsFvR6XTW/LqJKqmUUqJzTpX8F6LJbEPS7NZfVM6uV+sP3w+q2XE4bUU+2e72uhrPHzK4d1XIarJCDPh9zQJXpXT+aUUuJ9FYtpwGfs0ItdRrZRSLWU9JdM+pb1c+mFb67B/z8bEqV6Z2bRs6Z8ns0LUfv796mG9wQQgSLnv4QgFGNxb4BYEAIcacQwgHggwDO1NvW1m4PTh7YWdjh+WvVPXVhYZzk7EvjOLE/XDL/sV0DeOrCOE4e2Fm48S1RM2nl9OSBndixyV82LRT0aS6rZ1Yr9YfvB6pXPdne2u1pOHvMLAHA4EY/jj9c+jv9+MNh7Njob3HPqJ2xfhCVGgz6cXx4SS0dDmMwyFpKROZmdH0LBX1l55tO7A8jxHpKVRiZG34Opkbp/d1bSCmXX6pFhBCfBfAeABsATAI4uvh8JwAJ4DKAn5VSTgghNgL4Eynl+xfXfT+A/w+AFcCfSil/a7n2hoaG5Llz50qmqarE5ZkEpuIKer0ubO7qwNXZ+bLnkzEFHQ4rMjkV6z1ObO328AY6tJy6A6KVUaA8p/lfJkunWSxCc1m9s2pEG2QY3XLaiHqyXWn5WrLHzJqeLjlNp3O4cD2KyZiCPp8LOzb6eTNxWlaN9aOltZSoRrrkVFGyGJmIYjKWQp/PicGgnzcTJz2xnlLL1FjfGvoSoZXTbFbF2EQUkaiCgN+FUNDPm4nTsmrMjS61lN+jqVE1fveuKUxtPbhhNH7oIYPxgzmZAXNKZsCcUrtjRskMmFMyA+aU2p1ugxtETcRaSmZQU0455EtERERERERERERERKbCwQ0iIiIiIiIiIiIiIjIVXvx0Gflr1U1EFQT9boSCPl7jkNoOc0qrlapKXL2ZwGQshUQ6iy3rPbhzA6/jSc2Rv2Zs/rqfZrpmrJn7TkSrCz+XEtFqxfpGZsCckhnomVMOblSRzao4/dI4Dp8ehZJR4bJbcGJ/GPvv62dhoLbBnNJqpaoSz782iUuTc3j8uUuFfJ88sBN7QgGeuCVdqarE02MRHDp13nRZM3PfiWh14edSIlqtWN/IDJhTMgO9c8pkVzE2ES3saABQMioOnx7F2ES0xT0juo05pdXq8kwCF65FCwMbwEK+D506j8sziRb3jlabyzOJwuAAYK6smbnvRLS68HMpEa1WrG9kBswpmYHeOeXgRhUTUaWwo/OUjIpIVGlRj4jKMae0Wk3GFKgSmvmeijPfpK/JmHYtNUPWzNx3Ilpd+LmUiFYr1jcyA+aUzEDvnHJwo4qg3w2XvXQXuewWBPyuFvWIqBxzSqtVn88Fq4Bmvnu9zDfpq8/nMm3WzNx3Ilpd+LmUiFYr1jcyA+aUzEDvnHJwo4pQ0IcT+8OFHZ6/Blgo6G9xz4huY05ptdra7cHgJj8O7h4oyffJAzuxtdvT4t7RarO124OTB3aaMmtm7jsRrS78XEpEqxXrG5kBc0pmoHdOeUPxKmw2C/bf14+B3k5EogoCfhdCQT9vwkNthTml1cpiEdj1tj7c3dOJ+zd3YT6dxeb1Hty5wcObJJPuLBaBPaEA7nns+zAVV9DrdWFrtzmyZua+E9Hqws+lRLRasb6RGTCnZAZ655SDG8uw2Sy4744u3HdHq3tCVBlzSquVxSKwdUMntm7obHVXaA2wWAS29XRiW4/58mbmvhPR6sLPpUS0WrG+kRkwp2QGeuaUQ3dERERERERERERERGQqHNwgIiIiIiIiIiIiIiJT4WWplpFMZjASiWEylkKfz4nBgA9utx0AoKoSl2cSmIwp6PNpX9+6lmWIVqpaTos1O4/MO1VTKR/VclM8r7fTiWQ2h2uzSQT9boSCvpJrMjJ/q4/Rx3Q+mcZoJF6opeGAFx1uR9Pa0xPzT0TtIpFMYSwyV6iloUAnPG5nq7tFRLRiRte3bFbF2EQUE1FF8/sPkZZazw/pIZ3O4cL1KCIxBUGfC4Mb/XA4rE1pi1YXRcliZCKKSCyFgM+JwaAfLldjwxSGDG4IIQ4C+DMAcQB/AuA7AHxESvmMEe03KpnM4OxoBEfOjELJqHDZLTg+HMa+cABOpw1Pj0Vw6NT5wryTB3ZiTyhQclJuuWWIVqpaTot/gTU7j8w7VVMpHw/d24dnXpnUzA2AsnUO7h7Ak1+7gtn5NE7sD2P/ff2w2SzM3ypk9DGdT6bx1OhkWS3dG+5r+wEO5p+I2kUimcLfjU6V1dL/EO7lAAcRmZrR9S2bVXH6pXEcPn27veLvP0Raaj0/pId0OofTF67jyJeK2no4jP07NnKAg6pSlCzOjEyU5XR4MNjQAIdRFfGnpZQxAA8B6ALwEwB+26C2GzYSiRV2NAAoGRVHzoxiJBLD5ZlE4SRCft6hU+dxeSZRWL+WZYhWqlpOizU7j8w7VVMpH2MT0Yq50Vrn8ecu4ZH7N0HJqDh8ehRjE9Gq22f+zMvoYzoaiWvW0tFIvCnt6Yn5J6J2MRaZ06ylY5G5FveMiGhljK5vYxPRwsBGvr3i7z9EWmo9P6SHC9ejhYGNQltfGsWF68woVTcyEdXOaYP1zajBjfx/G3w/gL+QUo4VTWtbk7FUYUfnKRkVk7EUJmOK5rypuFK0/vLLEK1UtZyWLtfcPDLvVE2lfExEK+em0jpC3P45ElWqbp/5My+jj2mttbQdMf9E1C7MXEuJiKoxur5V+p6U//5DpMXInEYqfAeZjDGjVF1E55waNbjxTSHEM1gY3PgHIYQXgFptBSHEnwohpoQQo0XTflcI8aoQ4oIQ4otCiHUV1r0shBgRQpwXQpxrtNN9Pidc9tJd5LJb0Odzos/n0pzX63UVrb/8MkQrVS2npcs1N4/MO1VTKR9Bf+XcVFpHyts/B/yuqttn/szL6GNaay1tR8w/EbULM9dSIqJqjK5vQb9bs7389x8iLUbmNFjhO0ifjxml6gI657TpgxtCCAHgCICPAPguKeU8AAeAn1pm1T8HsGfJtGcBhKWUOwBcBPDrVdb/ASnlTinlUEMdBzAY8OH4cLiww/PXABsM+LC124OTB3aWzDt5YCe2dnsK69eyDNFKVctpsWbnkXmnairlIxT0V8yN1joHdw/gCy9eK1xzNhT0V90+82deRh/TcMCrWUvDAW9T2tMT809E7SIU6NSspaFAZ4t7RkS0MkbXt1DQhxP7S9sr/v5DpKXW80O6tLXRj+MPL2nr4TB2bGRGqbrBoF87pw3WNyHz/wW2iYQQI1LKwQbW2wrgKSllWGPeDwH4ESnlj2nMuwxgSEp5o572hoaG5LlzpX/okUxmMBKJYTKWQp/PicGAr3ATHlWVuDyTwFRcQa/Xha3dnrIbd9ayDK1ZdQdBK6NA9ZwWa3YemfdVSbecVspHtdwUz9vgcULJ5jA+m0TA70Io6C+5mR7zt/rUcUx1yel8Mo3RSLxQS8MBb9vfTDyP+W97utVSoibSJaeJZApjkblCLQ0FOnkzcdIT6ym1TI31raEPYFo5zWZVjE1EEYkqmt9/iLTUeH5Il1qaTudw4XoUkzEFfT4Xdmz082biVBNFyWJkIno7p0G/1s3Ea8pp/bcgb8yLQojvklJ+Q8dt/jSAz1WYJwE8I4SQAD4lpfx0o4243Xa8485uzXkWi8C2nk5s66k8Ul/LMkQrVS2nxZqdR+adqqmUj2q50ZoX7l9X1/bJvIw+ph1uR021tB0x/0TULjxuJ95xJwcziGj1Mbq+2WwW3HdHF+67w7AmaRWo9fyQHhwOK4a2rjekLVpdXC4bvkunnBo15PtOAF8TQnx78X4ZI0KIC41uTAjxPwBkAfxlhUW+V0p5P4D3AfgFIcS7q2zrw0KIc0KIc9PT0412iahpmFEyA+aUzIA5pXbHjJIZMKdkBswpmQFzSu2OGSUzMGpw4wcB3AVgF4B9APYu/ls3IcR/Wlz/x2SFa2pJKccX/50C8EUA76i0PSnlp6WUQ1LKoZ6enka6RNRUzCiZAXNKZsCcUrtjRskMmFMyA+aUzIA5pXbHjJIZGHJZKinlFQAQQvQCcDW6HSHEHgD/HcD3L96YXGsZDwCLlDK++PNDAI432max/PWs89eSW3q9+MmYgqDfBSmBqXgKMSWDdW47lGwOG/0duHMDr39NzVUpo5Xmb+7qwNXZecwkUnBYLZhP5ypmu5bpRHqolq/8vJlECgICNxMpdHucsFiArg4HciowFWcuqTFmvucGEVG7YC0lotXK6PpW6701iYoZeb6G54aoUXrWN0MGN4QQwwB+H8BGAFMAtgB4BUCoyjqfBfAeABuEENcAHAXw6wCcAJ4VQgDA16WUPyeE2AjgT6SU7wfQB+CLi/NtAP5KSvn0Sl+Dqko8PRbBoVPnoWRUuOwWnDywEw/d24dnXpnEoVPn0dXhwM9//zYk0jk8/tylwnK//N7tOHx6FL+2517sCQX4RqemqJTRfOaWzt/S7cYv7RrAx5+/hA8MbcYTz1+qmO1apjPbpIdqOQaAp8ci+F9Pv1KW2f/3hwbx4vwt/O4/vMZcUkPmk2k8NTqJI2dGCxk6PhzG3nAfT8oREdWItZSIViuj61symcHZ0UhZe/vCAQ5wUEXLnRcya1u0uuhd34y6LNXHALwLwEUp5Z0AdgP4erUVpJQfklIGpZR2KeUmKeX/llLeLaW8Q0q5c/Hxc4vLXl8c2ICU8g0p5X2Lj5CU8rf0eAGXZxKFNywAKBkVh06dx9hEtDD9kfs34UYiXRjYyC/3B/94EXt39OPQqfO4PJPQoztEZSplNJ+5pfP37ujH4dOj2Lujv3CSuHi94mzXMp3ZJj1Uy3F+nlZm35xJFAY2lq5HVIvRSLzw4QpYyNCRM6MYjcRb3DMiIvNgLSWi1cro+jYSiWm2NxKJNaU9Wh2WOy9k1rZoddG7vhk1uJGRUs4AsAghLFLKfwIwZFDbupiMKYWdnqdkVExEb08XAlAlNJcTYuHfqbhiWJ9pbamU0Xzmls7PZzL/79L1irNdy3Rmm/RQLcf5eVqZrVR7mUuq1WQspZmhyViqRT0iIjIf1lIiWq2Mrm+sp9SI5c4LmbUtWl30rm9GDW7cEkJ0AvgqgL8UQjwOwFRDeX0+F1z20t3lslsQ9JdOtwpoLiflwr+93oZvOUJUVaWM5jNXaX7xv8XTl2b79nR31XaIVqJajovnLV2mUu1lLqlWfT6nZob6fM4W9YiIyHxYS4lotTK6vrGeUiOWOy9k1rZoddG7vhk1uPEwgHkA/xXA0wC+DWCfQW3rYmu3BycP7Cw5sXbywE6Egv7C9M9/8xq6PQ4c3D1Qstwvv3c7nrowjpMHdmJrt6eVL4NWsUoZzWdu6fyzL43jxP4wzr40jsd2DVTNdul0X9V2iFaiWo7z87Qyu7Xbg1/9wbcxl9SwcMCL48PhkgwdHw4jHPC2uGdERObBWkpEq5XR9W0w4NNsbzDga0p7tDosd17IrG3R6qJ3fRNSSj37V7khIbYAGJBS/qMQogOAVUrZVhdfHRoakufOnas4X1UlLs8kMBVX0Ot1YWu3p3Cj5vz0gM8FKYGpeApxJQO/245UNoegvwN3bvDwpjpUrO4wNJrRSvM3d3Xg6uw8biZSsFstmE/n0OfTznalzGu1Q6uK7jldTrV85efNJFIQELiZSGO9xwGbBVjX4UBOBabnmMs1SJeczifTGI3EMRlLoc/nRDjg5Q1wSS+G11KiBrCWkhmwnlLL1FjfGvoCopXTZDKDkUis0N5gwMebidOyajxfo0st5bkhalSN9a2mMNn07145IcT/A+DDANYDuAtAP4BPYuHG4qZhsQhs6+nEtp7OZaffuWQZIiNUymi1+fUuX0s7RCtRLV+1ZO+uXuaSGtPhduAdd3a3uhtERKbGWkpEq5XR9c3ttrOeUt2MPF/Dc0PUKD3rm1GXpfoFAN8DIAYAUspLAHoNapuIiIiIiIiIiIiIiFYRowY3UlLKdP6JEMIGwJjrYRERERERERERERER0arS1MtSCSGsUsocgH8RQvwGALcQ4kEA/wXA2Wa2rZdsVsXo9SjGbyWx3uNAh8OKdR123NG1cIOc/LXfHRYLbs6n0eGwIbjOidm5DCZiCoJ+N0JBH2w2S8m14h0a9zeolV7boebJH6PJmGLIsVGULEYmopiMpbDB60DA68SmLg+uzs4X+pC/v8btexWksHGdGy6bFdNzqbr6Wfz6Nq5z4VZiIe8b17nhddoQMeh108oYndPl+uB12TCfziGVzcHntOPmfBqdThviqQzsFgv6fA7EFBWRmIKgz4XBjX7YbBa8eSOBKzcT8Dhs6PM5sXl9e+SuHfYv1edWUsHFSKJw3c/tAQ/WuV2t7lZNmDciahfRpILXimrp2wIe+E1SS6l18t9nIrEUAj4nBoN+uFzNO11hdHu0Ohhd32JJBa8WtXdPwAMf6yktw8h7tWSzKsYmopiIlp7/JFqOnt+9m/3b+0UhxM8B+AiA/wxgBMDPAvgygD9pctsrls2q+OL5cfzml0ahZFS47BYc3ReC323D5ZkEMjmJjz31Mj4wtBlPPH8JSkbFlm43/st77sbRM2OFdU7sD2N4cCP+8bUp/K+nXylZ3mW34OSBndgTCtR8UvnpsciKt0PNkz9Gh06dN+TYKEoWZ0YmcORMaU67pxP4rb97GVdmktjS7cYv7RrAx5+/VMhNV4cDj373Fjz+XH0ZKn5923s78aF3bsGxs7fzfnD3AJ782hXMzqeZyTZmdE6X60M+j3/9jatlte2xXQM4/9YMdt8bLKmtxx8Oo8/rwM/+nxdL8jfQ14ldb+trae7aYf9SfW4lFTwzOl1SS48Ph/FQuKftBziYNyJqF9Gkgn/QqKU/GO7hAAdVpPV95vhwGMODwaYMOBjdHq0ORte3WFLB0xrt7Qn3cICDKkomMzg7GinLzb5wQPcBjmxWxemXxnH49GjJ+c/99/VzgIOq0vu7d7PT9mEAjwP4FIC/lVL+qJTyR6SUfyylbPvLUo1NRAsDGwCgZFQcOzsGVQXiyRwuXIti747+wkk4ANi7o79w8i2/zuHTo7hwPYpDp86XLa9kVBw6dR6XZxI19enyTEKX7VDz5I+RUcdmZCJaKAj59o6dHUM6o2Lvjn4AC7k8fHq0JDeP3L+pMLBRTz+LX9/PvPuuwsBGfhuPP3cJj9y/iZlsc0bndLk+5POoVdueeP4Sfuxdd5bV1iNfGkVcyZXl78K1aMtz1w77l+pzMZIoq6VHzoziYqT9jxnzRkTt4rUKtfQ1E9RSah2t7zNHzoxiZCK6Ktqj1cHo+vZqhfZeZT2lKkYiMe36Fonp3tbYRLQwsJFv6/DpUYyxltIy9P7u3dTBDSnlvwF4J4AXAZwTQnxCCPFE/tHMtvUwEVUKOzpPyahIpLNIpLNQJSAESpZZ+jy/TiS2sK1K86fiSk19mtRpO9Q8+WNUrJnHJhJLVcypWPwPu/m8FOem0QwVv75kKqu5jXy7zGT7Mjqny/VBK6PF/bqVyFTM+dJpqkTLc9cO+5fqM1mhlk7GUi3qUe2YNyJqF2aupdQ6lb7PNCs3RrdHq4PR9Y31lBphZG4qnTONRPkdhKrTO6dG/J3QegDfBWAawDeXPNpa0O+Gy166i1x2CzwOGzwOG/JXetBaZunzgM9VmK41v9db25/d9Om0HWqe4mOU18xjE/A5K+a0+O+jtHLTSD+LX1+H06a5jXy7zGT7MjqntfShWm1b57FXzPnSaRaBlueuHfYv1aevQi3t8zlb1KPaMW9E1C7MXEupdSp9n2lWboxuj1YHo+sb6yk1wsjcVDpnGvDzOwhVp3dOmzq4sXi/jX9ffHy3lPLPpJSfyT+a2bYeQkEfPvZwuOSE29F9IVgsgNdtxY5Nfpx9aRyP7RooLHP2pXEcGw6VrHNifxg7Nvpx8sDOsuXz18Xe2u2pqU9buz26bIeaJ3+MjDo2g0E/jg+X59Rht+CpC+MAFnJ5Yn+4JDef/+Y1HNxdf4aKX98ff+XbOLqvNO8Hdw/gCy9eYybbnNE5Xa4P+Txq1bbHdg3gL7/+ZlltPf5wGF6XtSx/Ozb5W567dti/VJ/tAU9ZLT0+HMb2QPsfM+aNiNrF2yrU0reZoJZS62h9nzk+HMZg0L8q2qPVwej6dk+F9u5hPaUqBgM+7foW8OneVijow4n9pW2d2B9GiLWUlqH3d2/RzFtfCCH+D4BDUsqpZZYLSSnHmtaRGg0NDclz586VTMtmVYxej2L8VhLrPQ502K1Y57Hjjq6FHX55JoGbiRTsFgtuzqfR4bAhuM6J2bkMIjEFAb8LoaAfNpsFqipvL2+1YD6dQ5/Pha3dnrpu+KnXdqh58sdoKq6g11vx2NR9sLQyCizcFG9kIorJeArdHgeCPic2dXlwdXa+0IfNXR24OjuPmUQKAgI3E2lsXOeCy2bFjUSqWj+rvr6g34VbiYW8B/0ueF12TFZ/3dQmjM5ptT5MxhR4XTYk0zmksjl0Ou24lUzD47BhLpWB1WJBn8+BuKJiMqagz+fCjo0LtfXNGwlcvZlAh8OGPp8Tm9e3R+5q3L+kD11yeiup4GIkgclYCn0+J7YHPG1/M/E85q3tNbWWEulEl5xGkwpeK6qlbwt4eDNxWlbh+8xibgaD/ko399Ylp3W0R1RQY31r6AOYVk5jSQWvFrV3T8DDm4nTspLJDEYisdv1LeDTupm4LrU0m1UxNhFFJFp6/pNoOTV+964pp00d3KiVEOJFKeX9re4Hv0SSwXiig8yAOSUzYE6p3TGjZAbMKZkBc0rtTrfBDaImYi0lM6gpp+0ynMb/WkhERERERERERERERDVpl8ENzT8fEUL8qRBiSggxWjRtvRDiWSHEpcV/uyqs+5OLy1wSQvxkszpORERERERERERERETGaveLSv45gE8AeLJo2kcAPCel/G0hxEcWn/9a8UpCiPUAjgIYwsLAyTeFEGeklLP1dkBVJb49NYc3Z+bgslvhddqwwefAtZtK4T4XAHDtVgKT0VThngcdDivWddjR7+/A5ZkELs8k4HHZkM7ksGldB+7s6eS1sEk3ipLFaCSG6XgKXpcN6z123L3Bi8hcEpFoqnANO5sFuDWfxbqOhfsZ9HoX7rkxPZdCn+/2fTkmYwo6HDakczl0e5y8djvpJp3O4cL16MI9WnwuvL3Pi0s35nA9qqDLY4fLasVETIHPZYPfbcfNRBoOmwU2iwXzmRyUTA7buj3Y0u3BlZvzuHIzAc/ivTY2rbudX96HiBqxGu65wfwTUauZuZZS6xj9eyx/nfiJqIKg341Q0MfrxNOyjK5vrKfUiPlkGqOReCE34YAXHW5HU9ridxBqlJ71ramDG0KI75FS/qsQwimlTFVZNK01UUr5FSHE1iWTHwbwnsWfPwPgn7FkcAPADwJ4Vkp5c7EfzwLYA+Cz9fRfVSX+fnQCv/I3L0HJqHDZLTi4ewD961z451en8OWxSZw8sBMbvDZcnk7iyJmxwnJH94UQ8DnwzSu38BtfHClMf2zXAI6cGcN//8F78b5wgG96WjFFyeLs6AR+80ujJTm9OZ/C9dlUWS4/+29XcHFqDv/j/ffi9akETj57sTD/xP4wPv78JVyZSRby+rlzV/Fre+7FnhDzSiuTTudw+sJ1HFnM6pZuN37xBwbKsvvk165gdj6Ng7sHsKHTgVRWRVzJ4vHnLlVdb1OXG7/3zGuF/J48sJO5pZrdSip4ZnQaR87cztXx4TAeCve0/ZdIVZV4eiyCQ6fOF/rO/BNRK5i5llLrGP17LJtVcfqlcRw+PVryPWj/ff0c4KCKjK5vrKfUiPlkGk+NTpblZm+4T/cBDn4HoUbpXd+a/Zv7icV/v1ZtISnlu+rYZp+UcmLx5wiAPo1l+gG8VfT82uK0ulyeSRQGNgBAyah4/LlLeH06gR/5rs1QMioOnTqPXE4UTiDnlzt2dgxWi6UwsJGf/sTzl7B3Rz9+5W/O4/JMot4uEZUZmYgWTvICt3NqFVbNXP7Mu++CklExPZcqDGzk5x8+PYq9O/oLz/N5PXSKeaWVu3A9WhjYAIC9O/o1s/vI/ZsKP7vtNkzFU4WBjWrrXZqaK8kvc0v1uBhJFD5cAQsZOnJmFBcj7Z+hyzOJwpcKgPknotYxcy2l1jH699jYRLQwsJFv7/DpUYxNRJvSHq0ORtc31lNqxGgkrpmb0Uhc97b4HYQapXd9a/bgRkYI8WkA/UKIJ5Y+VrpxKaVEhft11EoI8WEhxDkhxLnp6emSeZMxpbCj85SMClUCM3OpwvPpeEpzuZuJjOZ0IRb+nYorK+k6rRHVMgoAkZh2/irlMpnOAgBUiYr5XPqceaXlLJdTAIgsqan5bBUrzqCSUZFIZ8uyWmk9VaIsv8wtFav+O1+7Zk7Gqv3haXuo9HmF+TefWmopUaut1lpKrdOM32PVcjoR1W4vEuXvTaqsGfWN9ZT0pnduGjlnyu8gtBy9c9rswY29AJ4HoAD4psajEZNCiCAALP47pbHMOIA7ip5vWpxWRkr5aSnlkJRyqKenp2Ren88Fl710F7nsFlgE0N3pLDzv8To1l1vvsWtOl3Lh314v/5SQllctowAQ8Gnnr1Iu3Y6Fq9FZBSrmc+lz5pWWs1xOASBYoaYufZ7PoMtugcdhq5jVpc8tAmX5ZW6pWPXf+do1s8/nNLKLDan0eYX5N59aailRq63WWkqt04zfY9VyGvS7NdsL+Pl7kyprRn1jPSW96Z2bRs6Z8jsILUfvnDZ1cENKeUNK+dcAhqWUn1n6aHCzZwD85OLPPwngSxrL/AOAh4QQXUKILgAPLU6ry9ZuD37/R+8r7PD8dd3v7vHgb79xtXA9OatV4vhwqGS5o/tCyKkq/ucPDZZMf2zXAJ66MI7f/9GdhZuRE63EYNCPjz0cLstpTuY0c/knX/k2XHYLNnQ6cejB7SXzT+wP46kL44Xn+byePMC80soNbvTjeFFWz740rpndL7x4rfBzMpNFj9eJg7sHll1voLezJL/MLdVje8CD48OluTo+HMb2QPtnaGu3BycP7CzpO/NPRK1g5lpKrWP077FQ0IcT+8Nl34NCQX9T2qPVwej6xnpKjQgHvJq5CQe8urfF7yDUKL3rm5ByRVd1qq0RITYB+DiA71mc9FUAB6WU15ZZ77NYuHn4BgCTAI4COA3gFIDNAK4AOCClvCmEGALwc1LKn1lc96cB/Mbipn5LSvlny/VzaGhInjt3rmSaqkp8e2oOb84k4LJb4HXasMHnwPisgl6vq/CmvXYrgcloCpPxFLo9DnQ4rFjXYUe/vwOXZxK4PJOAx2lDOptD/7oObOvp5A12qO4AaGUUWLip+Ggkhul4Cl6XDV0eOwY2eBGZSyISTWEqlkKvzwmbBbg1n4W/w45YMo0erwsumxU3Ein0el3Y3NWBq7PzmIwp6HBYkcmpWO9xYmu3h3ldu3TLKbBwU/EL16OYjCno87kQ6vPi0o05TERT6OqwwWmzIhJLodNlhd9tx2wiDYfNApvFgvlMDkomhzu7Pdja7cGVm/O4ejOBDocNfT4nNq1byO9U/HZ9Zm7XDF1yeiup4GIkgclYCn0+J7YHPKa5YaOqSlyeSTD/7UvXWkrUJGu+llLr1PF7TJecZrMqxiaiiEQVBPwuhIJ+3kycllVjfWvoAxjrKellPpnGaCReyE044NW6mbgutZTfQahRetZTowY3ngXwVwD+YnHSjwP4MSnlg01vvA78EkkG44kOMgPmlMyAOaV2x4ySGTCnZAbMKbU73QY3iJqItZTMoKacGvVfE3qllH8mpcwuPv4cAC8kTEREREREREREREREdTNqcOOGEOLHhRDWxcePA5gxqG0iIiIiIiIiIiIiIlpFbAa189NYuOfGHwCQAF4A8FMGtb0it++5MQeX3YpOpw1uuwU5CSQzOfR0OpFTgan4wvXjg51OjEZiiMRS2Oh3YV2HHVPxFHo7nUhmc5iKK+jpdGE+ncPM3MJ9EJKZLHwuJ0JBX0uv86mqEm/eSODKzQQ8i9ew37ye18szA0XJYmQiikgshV6vEx6nFbmcRFenHbH5LGaTGSiZHPxuO2JKBj2dLoSCPmQyOYxEYoVr3A0GfHC77YXrJubvi7D0uonLza9V/lq3E1EFQb+75e8Bar58nbl6c+E+RNFkBr1eJ7I5iaiSRneHE/FUFnEli16vE2/v82IykcJkLIUbcyn0eJ1IprPo7nTCabPirdn5Qr3K33OjOJcAdMkqrQ1mvq6xXnWZiGilzFxLqXUSyRTGInOF3IQCnfC4naumPVodjK5vrKfUCCPrG8/pUKP0rG+GDG5IKa8AGK40Xwjx61LK/9eIvtRDVSX+fnQCv/I3L0HJqHDZLTi4ewD961xw2gSi81l86+otnHz2IpSMii3dbvzCewZw5MxoyfJPfu0KZufTOPTgdgS8DkzG0jh2dqywzNF9IXz+m5fwwXdswf77+ltSCFRV4umxCA6dOl/S94G+Tux6Wx9PkLQxRcnizMhESe6ODYdwx3oXrlxNYPyWgsefu1SY99iuAXzs3Mt4bNd22K0C//3zFwrzjg+HsTfch39+faYkCycP7MSeUAAWi9DMSvH8WmWzKk6/NI7Dp2/3+8T+cMveA9R8Wtk5vu/tuDGXxif/5XX89AN34rXIXElef/dHdiCZzuHImds186P7QnhrNokTf/dKSb3a1OXG7z3zGq7MJAu5dNgEfvGvvrWirNLacCup4JnR6ZJaenw4jIfCPW3/JVKvukxEtFJmrqXUOolkCn83OlWWm/8Q7m3KCTmj26PVwej6xnpKjTCyvvGcDjVK7/rWLmn70VZ3QMvlmURhYAMAlIyKx5+7hNenE/C5nehw2gsDGwCwd0d/4cAUL//I/ZugZFScfPYi1nmchYGN/DLHzo7h0Qe24fDpUYxNRFv2WvMnRYr7fuFaFJdnEi3pE9VmZCJalrujZ8ZgFVa8Pp0onCjOz3vi+UvYu6Mf/+P0CF6fniuZd+TMKEYj8bIsHDp1vpADrawUz6/V2ES08Eswv51Wvgeo+bSy0+G049jZMezd0Y+Z+XRZXi9NzRUGNvLTPnp2DFPxVFm9ujQ1h707+gvTDp06jwvXoivOKq0NFyOJslp65MwoLkbaPy961WUiopUycy2l1hmLzGnmZiwytyrao9XB6PrGekqNMLK+8ZwONUrv+tYugxtt+d8KJ2NKYUfnKRkVqgRuzKWQSGVL5gsBzeWFuP3zbCKjuUwyvbCtSFRpzotZRrXXOhVvTZ+oNpFYSvPYTcUVqLJyJvPHd+m8ySrbAypnpd6cTES1t9Oq9wA1n1Z28nVUCGjmtVKGtbKrShTqbbXlWNNIS6XaNxlLtahHtdOrLhMRrZSZaym1jtG5YU6pEcwpmYGRueE5HWqU3jltl8ENufwixuvzueCyl+4il90CiwA2dDrhcdk05y99LuXtn7s8ds1l3I6FbQX8rfnzwmqvtdfLP3lsZwGfU/PY9XpdsIrKmcwf36Xz+qpsD6iclXpzEvS7NbfTqvcANZ9WdorrqFZeK2VYK7sWgUK9rbYcaxppqVT7+nztf3kKveoyEdFKmbmWUusYnRvmlBrBnJIZGJkbntOhRumd03YZ3GjLv9zY2u3B7//ofYUdnr+u+909HsSSKcwrGRx6cHth/tmXxnF8OFy2/BdevAaX3YJDD27HrUQKR/eFSpY5ui+EJ194Ayf2hxEK+lv2Wk8e2FnW9x2b/IWb8lJ7Ggz6y3J3bDiEnMzhrh4PDu4eKJn32K4BPHVhHL+1fxB393SWzDs+HEY44C3LwskDOws50MpK8fxahYI+nNhf2u9Wvgeo+bSyM69kcHRfCGdfGsf6DkdZXu/u7cTx4dKa+dF9IfR6nWX1aqC3E09dGC9MO3lgJ3Zs8q84q7Q2bA94ymrp8eEwtgfaPy961WUiopUycy2l1gkFOjVzEwp0ror2aHUwur6xnlIjjKxvPKdDjdK7vgkpW/9HE0KI35BS/s9W92NoaEieO3euZJqqSnx7ag5vziTgtFvgddjgdliQk4CSyWFDpxM5FZieU9DrdSHY6cRoJIbJWAoBvwtdHXZMz6WwweOEks1hOq5gQ6cLyXQONxIp9HQ6kcpm4XU5EAr6W3rTHVWVePNGAldvJtDhsKHP58Tm9R7ejLR56t6xWhkFFm4qPjIRxWQshR6vEx6HFTlVoqvTjth8FrPJDJRMDuvcdkSVDHo8ToQ2+pHJ5DCymNc+nxODAR/cbjtUVeLyTAJT8YVcb+0uzcFy82uVzaoYm4giElUQ8Lta/h4gTbrlFLhdZ966mUCH04ZoMoPeTieyqkRUSaO7w4l4Kou4kkWP14lQnxeTiRQmYyncmFuomclsFt0dTjjtVlybnS/Uq03rOnB1dr4klwB0ySq1PV1yeiup4GIkUaiJ2wMe09ywUa+6TE2jay0lapI1X0updRLJFMYic4XchAKdlW5+q0tO62iPqKDG+tbQBzDWU9JLjfVNl1rKczrUKD3rqSGDG0KITQA+DuB7sXAJqq8COCilvNb0xuvAL5FkMJ7oIDNgTskMmFNqd8womQFzSmbAnFK7021wg6iJWEvJDGrKqVHDaX8G4AyAIICNAM4uTiMiIiIiIiIiIiIiIqqLUYMbPVLKP5NSZhcffw6gx6C2iYiIiIiIiIiIiIhoFbEZ1M6MEOLHAXx28fmHAMwY1PaKpNM5XLgexURUQa/XCY/TCqsFmIyl0OGwIeB3YtO6heu6v3kjgSs3E/C77HDZLUikc4glM+h02ZDMZLHO7QAApLIqMjmJZDqHDV4HLBCIxBQE/W68racTr03HMRlT0O1xQoVEt8fZ0mtn56/jPRlT0Ofjdbzb0dJrKnZ1WOGwWhH0deDVyRhm59NwO2yYjqfQ3elAwOvE5u5OJFPpqtdirHTs8++LSExB0OfC4EY/HA5r3f1mttYeVZV4azaByWgKNxIpbFi875DdaoWQEhaLBTfm0vC6bOjutENJq5hL5yCECofFhum5hQy77Vas67AjmwOm4ivLD3NIeWa+rjFzTETtwsy1lFonmcxo3gtwtbRHq4PR9Y31lBphZG70OjdEa4+eOTVqcOOnsXDPjT/Awj03XgDwUwa13bB0OofTL13HkTOjUDIqXHYLjg2H0Ot1wCIEfufpV/CjQ5txx/p5zKdV/OJffQtdHQ78/Pdvg5JVcfLZi4X1Hts1gOdfjeDH3rUVkaiCx5+7VJh36MHt+LN/vYzZ+TSOD4dx6twVnLsSLaz3uXNX8Wt77sWeUMDwExWqKvH0WASHTp0v9PfkgZ0t6QtpSyRT+LvRqZKcHh8O4W19HTh3JYonnr+IDwxtxhPP387c0X0hJLNZjFybW7JeGP8h3AuP21nx2O8a6MGZ0Qkc+VLReg+HsX/Hxrp+iTFba4+qSnz19Slcv5XCsbNjt/O4N4R/em0Cu+4J4qNF048Nh5DJqTj9rWv44fs349hTL5Zk2O+24X89/SquzCQbzg9zSHm3kgqeGZ0uq4kPhXva/kskc0xE7cLMtZRaJ5nM4OxopCw3+8KBpgw4GN0erQ5G1zfWU2qEkblJp3M4feH6is8N0dqjd04NuSyVlPKKlHJYStkjpeyVUu6XUl41ou2VuHA9WtjRAKBkVBw9MwarxQKHzYpHH9iGo2fGkM0BF65FoWRUPHL/JtxIpAsDG/n1nnj+Eh59YBvevJEoDGzk55189iIeuX8TlIyKI2dG8egD20rW27ujH4dOncflmYTh++DyTKJwsiTfp1b1hbSNRebKcnrkzBhSWYH/cXoEe3f0FwY28vOPnR1DPKlqrDeKscgcgMrHfmQiWvjlVVjvS6O4cD1aV7+ZrbXn8kwC8WSuMLABLObxqTH82LvuLAxs5KcfPTOGqXgKjz6wDceeGivLsKoCe3f0F6Y1kh/mkPIuRhKaNfFipP2zwBwTUbswcy2l1hmJxDRzMxKJrYr2aHUwur6xnlIjjMzNhev6nBuitUfvnDb1LzeEEEeqzJZSyo81s/2VisSUwo7OUzIqZuczsAggmc4VnqtyYb4QgCqhuV4yna04T4jS5ZbOUzIqpuIKtvV06v9Cq5issA9a0RfSNhlLaR6jybhSkp9K88umx1KL29WeH6nUXkyps9/M1lozGVOQSGU1j/utREZzuiqBZIV1EulsoXbmp9WbH+aQ8irW0sWa2M6YYyJqF2aupdQ6RueGOaVGMKdkBkbmptI503rPDdHao3dOm/2XGwmNBwD8ZwC/1uhGhRBvE0KcL3rEhBD/dcky7xFCRIuWqTbQoinoc8FlL91FLrsFXR12bOh0wu2wFZ4XX/XBKqC5XofDVnGelLd/djtsZfNcdgt6vcb/6WFfhX3Qir6Qtj6fU/MYFR+75eaXTncubld7fqBKe/X1m9laa/p8LnhcNs3jvs5j15xuEUCHU3sdj8NWqJ35afXmhzmkvMq11FlhjfbBHBNRuzBzLaXWMTo3zCk1gjklMzAyN5XOmdZ7bojWHr1z2tTBDSnl7+cfAD4NwI2Fe238NYBtK9jua1LKnVLKnQC+E8A8gC9qLPrV/HJSyuP1tjO40Y/jw+GSE8THhkPIqSrS2RyefOENHBsOwWYFdmzyw2W34PPfvIZujwOHHtxest5juwbwmRfewNYNHhzcPVAy79CD2/GFF68VrjH25AtvlKz31IVxnDywE1u7PQ3srZXZ2u3ByQM7S/rbqr6QtlCgsyynx4dDcFolfmv/IM6+NI7HdpVm7ui+ELwui8Z6YYQCC//Lt9KxHwz6cfzhJes9HMaOjf66+s1srT1buz3wuqw4ui9Umse9Ifzl19/ER5dMX7jHkROfeeENHN0bKsuwxQI8dWG8MK2R/DCHlLc94NGsidsD7Z8F5piI2oWZaym1zmDAp5mbwYBvVbRHq4PR9Y31lBphZG4GN+pzbojWHr1zKmTxf3ttAiHEegCHAPwYgM8AeFxKOavj9h8CcFRK+T1Lpr8HwH+TUu6tdVtDQ0Py3LlzJdPS6RwuXI8iElWwwetEp8MKq3XhT2g67DYE1jmxad3Czn/zRgJXbybgcy38D+REOodYMoNOlw1KJgefyw4hgHRWRTonkUznsKFz4ebkkZiCoN+Ft/V48dp0HJOxFNZ7HJCQ6PY4sbXb07KbgqqqxOWZBKbiCnq9rpb2ZZWpeydqZRRYuKn4WGQOk7EU+rxOdHmscFitCPo68OpkDLPzabjtNtxIpNDV4UDQ58Tm7k4kU+nb6/mcCAU64XHfHimtdOzz74vJmII+nws7NvobumEUs2UKuuUUWDjmb80mMBlNYSaRQnenE6lsFnarFQISFmHBjbk0vC4buj12KBkVc+kcLELCbrFiem6hNnY4rFjXYUc2B0zPrSw/zOGqoEtObyUVXIwkCjVxe8Bjmhs2MsdtT9daStQka76WUuskkxmMRGKF3AwGfJVu7q1LTutoj6igxvrW0Acw1lPSS7NyWu2c6UrPDdHao2dOm33Pjd8F8AgW/mpjUEo514RmPgjgsxXmfbcQ4iUA17Ew0DFW78YdDiuGtq4vm35vsHzZu3o7cVfvyq9tfd8dXSvehp4sFoFtPZ28bncb87ideMed2n++taNKnqqtB1Q+9pXeF/VittYei0VgS3cntnTrd8xXWneZQ8pb53bhHXea8wsjc0xE7cLMtZRax+224x13dq/a9mh1MLq+sZ5SI4zMjV7nhmjt0TOnzb7nxq8A2AjgMIDri/fGiAkh4kKI2Eo3LoRwABgG8Dcas18EsEVKeR+AjwM4XWEbHxZCnBNCnJuenl5pl4h0x4ySGTCnZAbMKbU7ZpTMgDklM2BOyQyYU2p3zCiZQbPvuWGRUrqllF4ppa/o4ZVS6nFBy/cBeFFKOanRdiz/lyJSyi8DsAshNmgs92kp5ZCUcqinp0eHLhHpixklM2BOyQyYU2p3zCiZAXNKZsCckhkwp9TumFEyg6ZelsoAH0KFS1IJIQIAJqWUUgjxDiwM5Mw00sjS63Guc1sRU7LIqQJ9Pic2r/dAVSXGJqKYiCoI+t3o8thwI57GfDqHuJJFj9cBVUrMzmfgddngsllxM5FGh9OKTocNMSWL+XQO6zrs6PM5kclKXJ2dh8dhQ8DvRDYHTMUXrmG33HW089fdzl/zzsjrbmu1DaBl/Vkr5pNpjEbihYx2e6yYjKWRkwJOu0BPhxM2u8DsXAZRJYu5xUx2dVgxk8hhKp5C0O9CqM+H63EFM4kU3HYrEqkcEukstnR1wGoVmIgaewyzWbXkfRUK+mCzNfsPzqiZ8sd0dj6FDZ0uJFIL+evzOSGhwmax4uZceuFeRdks/C4H0jkVsWQGW7o74LJbEYmmkEhlscHrRE5VkVOB2UQa6z0OqG1wnyIyLzNf15j1kojahZlrKbWO0blhTqkRzCmZQSyp4NWi3NwT8MDXpNwoShYjE1FEYikEfE4MBv1wucx+qpmMoGd9M23ihBAeAA8C+NmiaT8HAFLKTwL4EQA/L4TIAkgC+KBs4O7pyWQGZ0cjOHJmFEpGXbyDewibu1340//7Jr5jczfu7u2EksnhV//2ApSMii3dbvzKQ2/D+GwSjz93qbDewd0DePJrVzA7ny787LAJ/Nz3341jZ8cKyx3dF8In/+V1XJlJYku3u2z+yQM7sScU0Dxxp6oST49FcOjU+ZqW11Olth02gV/8q28Z3p+1Yj6ZxlOjk2UZ3brBjZNPv4bd9wYQ8DngsNlw9eZ8SSY/9nAY33hzGqe+ObG4Xhj/+Mp1fM9dvZjP5EqWPfTgdvzZv17G7HzakGOYzao4/dI4Dp++/bpO7A9j/339PGFnUvlj+vHnL+FX3juAyVgaR8+MFermL7xnoCTHj+0awOfOXcWj370Vf/LVN5etl8Xr/Nqee1lnqC63kgqeGZ1eUkvDeCjc0/ZfIlkviahdmLmWUusYnRvmlBrBnJIZxJIKntbIzZ5wj+4DHIqSxZmRibK2hgeDHOCgqvSub6b9xiulTEgpu6WU0aJpn1wc2ICU8hNSypCU8j4p5buklC800s5IJFbY2QCgZFQcOTMGASt+7F134vHnLmFkPIpLU3OFZfbu6MfrU3OFE8P59R5/7hIeuX9Tyc97d/QXTtTllzt2dgx7d/QXtrV0/qFT53F5JqHZ38szicLgQi3L66lS2xeuRVvSn7ViNBLXzCikBY8+sA2PP3cJdqsNr03GyzL5m18axf77NxetN4ofe9edmJlPly178tmLhfwacQzHJqKFE3X5Phw+PYqxiegya1K7yh/TvTv6sc7jLAxsAAu1bmmOn3j+Evbu6Mfv/sNrNdXL4nVYZ6heFyMJjVo6iouR9s8R6yURtQsz11JqHaNzw5xSI5hTMoNXK+Tm1SbkZmQiqtnWCL+D0DL0rm+mHdwwymQsVdjZeUpGxVRcwa35DJSMClUCatHfhAix8FxrPSFKfxai+nKV5k/FlQr9VepaXk+V2laX/L2MUf1ZKypldDKuIJnOQsmoSKSyFTM5M5cqeT6byNSU32Yfw4modp4iUWbHrPLHVAjgZiJTcnyr1cJa6+XSdVhnqB4Va2ksVWGN9sF6SUTtwsy1lFrH6Nwwp9QI5pTMwMjcRJhRapDeOeXgxjL6fE647KW7yWW3oNfrwroOO1x2CywCWHrlE6uA5nr5C2Mt/bnScpXm93q1/0ynz+eqa3k9VWp76b4xqj9rRaWM9nldcDtscNkt8LhsFTPZ3eksed7lsdeU32Yfw6DfrdmHgJ/ZMaviY7reY9c8vkufS1l/vcyvwzpD9ahYS33OCmu0D9ZLImoXZq6l1DpG54Y5pUYwp2QGRuYmwIxSg/TOKQc3ljEY8OH4cLiw0/P3M5DI4S+//iYO7h7AYL8fA72dhWXOvjSOu3o7cXD3QMl6B3cP4AsvXiv5+exL4zi6L1Sy3NF9ITx1YbywraXzTx7YWbhR91Jbuz04eWBnzcvrqVLbOzb5W9KftSIc8GpmFELFky+8gYO7B5DJZrG9z1uWyY89HMbpF68WrRfGX379TazvcJQte+jB7YX8GnEMQ0EfTuwvfV0n9ocRCvqb2i41T/6Ynn1pHLcSKRwbDpXUzaU5fmzXAJ66MI5f/cG31VQvi9dhnaF6bQ94NGppGNsD7Z8j1ksiahdmrqXUOkbnhjmlRjCnZAb3VMjNPU3IzWDQr9nWIL+D0DL0rm+igXtsr1pDQ0Py3LlzZdOTyQxGIrGFO7h7nVjXYUVcySKrCvT5nNi83gNVlRibiCISVRDwudDVacdMPI1EOoe5VBYbOh1QpcSt+Sw6nVa47FbMzqfhslvhddoQU7KYT+ewrsOOPp8TmazEW7Pz6HDYEPA7kc0B03MKer0ubO32VL1JrqpKXJ5JYCpe2/J60mobQMv60+bq3gmVMjqfTGM0Ei9ktLvTislYGjkp4LAJ9HicsNsFZucyiClZxJWFTK73WDGTyGE6nkLA50Io4MP1uIKbiRRcdisSqRzm01nc0dUBm1UgEjP2GGaz6u33ld+FUNDPm+MaT7ecAreP6ex8Ghs6nUikFvLX63NCQoVNWHEzkUany4ZUJgevy46MqiKWzGJLtxsuuxWRaArz6Sy6PQ7kpEROBWYTaXR5HJCQ6PY4WWfWHl1yeiup4GIksVBLfU5sD3hMc8NG1su2p2stJWqSNV9LqXXqyA1zSi1TY24a+hLCnJJeYkkFrxbl5p6AR+tm4rrUUkXJYmQiWmhrMOjnzcSpJnrWUw5uFOGXSDIYT3SQGTCnZAbMKbU7ZpTMgDklM2BOqd3pNrhB1ESspWQGNeWU/6WPiIiIiIiIiIiIiIhMhYMbRERERERERERERERkKrwQ2jJUVeLbU3N4c2YOLrsVnU4b3A4L5tNZ5FSB4Don+v0L13bP329iMqagw2FDOpdDwO9E5FYKkcVriHU4rIgpadgsVkzHU+jxOpFMZ2G1WLCuw45kOofpuRT6/W6ksiqm4ikE/S70+OyIRNOYiqWwwetAwOvEpi4Prs7OYzKmoM93+z4Iqirx5o0Erswk4HJY4LRasd7jgBDARLR02XaUv274ZExBt8cJldfQX9bSayr2eq24Na+iy+NAJishkcPsfA6TsRQCPicsApieS2Oj34msCozfUhby6bSg02FHKpvD5Zl5uBczb7dKqNKKUNCn6zXc88d6Iqog6HeXbb/4PdXuuaXlZbMqXp6I4npUwYZOBwQEpudSWNdhh5LJwmW3wSoEZufT6HDYYLcK2CwW3JxPo6vDDq/Lilgyh6l4Chu8TkhVRY/XjTs3MBekDzNf15j1kojahZlrKbWO0blhTqkRzCmZgZG5mUsqeLmorbcHPOhkRqkGeuaUgxtVqKrE349O4Ff+5iUoGRUuuwUHdw+gf50L6z12/POrk9geWIetPUncv6kbz7wyiUOnzheW/Z0fGcSb0wkcOTNWmHZifxgOq8B///xIYdpjuwbwuXNX8XPffzc++S+vI52VePS7t+Dx5y5ByagY2uLHgaEtOHJmtLDO0X0hrJ+aw8G/vt3eyQM78dC9fWX9OLh7AB6HFQDwR//yBmbn0zh5YCf2hAJtd+Ijm1Vx+qVxHD49WrZ/fm3PvW3Z51aLJRU8PTpdko/jwyEM9nvw9TduIpdNw2ZzlOTw6L4QPvtvV3Bxag4Hdw/gya9dwex8Gr/zw4NQJfCRL4yUZf7NqRguTcWx/75+XQY4tI71if3hwvZVVeLpsUhJlts1t7S84uPd1eHAT33PVpx89mLh2P76nnuQlRK/+w+vldWuP/qXN9C/zlleB/eG8HvPvIaf/t67mAtasVtJBc+U1dIwHgr3tP2XSNZLImoXZq6l1DpG54Y5pUYwp2QGRuZmLqngyxptvT/cwwEOqkrvnPKyVFVcnkkUBjYAQMmoePy5S3h9OgGrxYr3hvpx5MwYcjmBsYlo4aRCftl17tsnlPPTDp8exevTiZJpTzx/CXt39OPY2THs3dGPR+7fVBjYAIBHH9hWOOD5dY6dHUMmK0umHTp1XrMfjz93CTcSadxIpPHI/ZsKy16eSRi3M2s0NhEtnOwGSvdPu/a51V6NJMryceTMGOIKcPTMGO7u6yrL4bGzY/iZd99VyEc+F69PJwoDG/ll85l/1919OHx6FGMTUV36rXWsi7d/eSZRlmVmwLyKj/cj928qDGwAC8d2Zj5dGNjIT8vXrkfu36RdB58aw6MPbGMuSBcXNWvpKC5G2j9brJdE1C7MXEupdYzODXNKjWBOyQyMzM3LFdp6mRmlZeidUw5uVDEZUwo7Ok/JqFAlcGMuhen4wvwbcylMRMuXvZnIVFx/6TQhbv+b/zkvmcpqbieRzpZN0+pHvk1VLmw7P20qrtS8L4xSqf/5fdKOfW61yVhKc59NLuYz/+/S+cnF/OT3L7CQkUr5mVrcTiSqzzGodKzz26/0/mMGzKn4eC+tcUD17AlRuQ4m01nmgnRRsZbGUi3qUe1YL4moXZi5llLrGJ0b5pQawZySGRiZG2aUGqV3dji4UUWfzwWXvXQXuewWWASwodOJHu/C/A2dTgT97rJl13vsFddfOk3K2//mp+V1OG2a2/E4bGXTgv7KfbYIlGy/19t+fyamtR+L90879rnV+nxOzX2Wz2+lHLsX81OcO6tAxfz0LuY94NfnGFQ61vntV+o3M2BOS4/30mNbLXtSVq6DboeNuSBdVK6lzhb1qHasl0TULsxcS6l1jM4Nc0qNYE7JDIzMDTNKjdI7OxzcqGJrtwe//6P3FXZ4/hrwd/d4kFNz+MexcRwfDsFqlQgFfTh5YGfJsreSaRwfDpVMO7E/jLt7PCXTHts1gKcujOPovhCeujCOz3/zGg7uHigs85kX3sDx4XDJOkf3hWC3iZJpJw/sRCjoL+vHwd0D2OBxYIPHgS+8eK2w7NZuj3E7s0ahoA8n9oc190+79rnV7gl4yvJxfDgErxM4NhzC65HZshwe3RfCn3zl24V85HNxV48Hv/3IoGbmv/76JE7sDyMU9OvSb61jXbz9rd2esiwzA+ZVfLw//81rOPTg9pJju77DgV/9wbdp1q4vvHhNuw7uDeHJF95gLkgX2zVraRjbA+2fLdZLImoXZq6l1DpG54Y5pUYwp2QGRubm7RXaejszSsvQO6dCSrn8UmvE0NCQPHfuXMk0VZX49tQc3pxJwGm3oNNhQ4fTgvl0FrmcQLDLiX6/BxaLgKpKXJ5JYDKmoMNhRSanos/vRORWCpOxFHq9TnQ4rYgpadgsVkzHU+jpdCKZycJqsWBdhx3JdA7Tcyls9LuRzqqYmksh4HOh12dHJJrGVDyFbo8DQZ8Tm7o8uDo7j6m4gl6vC1u7b/fjzRsJXL2ZgMNmgctmxXqPA0IAkVjpsu0om1UxNhHFZCyF9R4HJCS6Pc627nOD6n4xWhkFFm4q/mokgclYCn1eJ3p9VtyaV9HlcSCTlZDIYTaRw2Q8hT6fE1YLcCOeQcDnQE4C47cU9Hmd6HBa0Om0I5XN4cpMEk67BV6nDXarhCotCAX9utxMPC9/rCNRBQG/q2z7+ffU0oyToXTLaTar4uWJKCaiKazvtMMCgRtzKfg77FAyObjsVliFwOx8Bh0OK+xWAZvFgpvzaXR12OFzWRFN5jAVT2FDpxOAig2dbty5gbkgfXJ6K6ngYr6W+pzYHvCY5oaNrJdtT7daStREa76WUuvUkRvmlFqmxtw09AGMOSW9NCunWhmdSyp4uaittwc8vJk41UTPnHJwowi/RJLBeKKDzIA5JTNgTqndmSKj/XdsxvVrb9W07MZNd2D8ratN7hEZzBQ5pTWPOaV2p9vgBlETsZaSGdSUU9vyixARERER0Wp3/dpb+MCnXqhp2c/97ANN7g0REREREVF1pr3nhhDishBiRAhxXghRNnQoFjwhhHhdCHFBCHF/K/pJRERERERERERERET6MvtfbvyAlPJGhXnvAzCw+HgngD9a/Ldu88k0RiPxwnXAujqsmE/nkMoC/V1OXJ1R0OdzYZPfjTdm4pidz2ImkUbQ58LgRj8cDmvJ9tLpHEYnopiKp+BxWOFx2pDKZmG1WDEzl8bGdW7c2+fF1dl5vDmTgMdpRYfdhvlUDr4OG5RMDolUFm67DYl0Fpu6OpDK5nBtNomN69zodFhx+eY8PA4b+nxObF5/+7rbxfcF6fO5CjcbvTyTwEwiBYfVgvl0rjBP7+t1a7XPa4Kv3NJr1QXXWZFKA0IAN+ZymM9k4XHYMB1PYeM6F+wWC2aTGcwpWfR4HRCQcNpsmEtnMTOXxqYuN1RV4totBX0+J9Z1WGGz2DSPV7VjyuNNeel0Dq9NxZHJqcjkVNxKZuC2WxfurWERmEtn4bBaEU9l4LbbEFMy2OBxwmpRkcmJhZrqdyMU9Ol63xeiYma+rjHrLRG1CzPXUmodo6/bzuvEUyOMrm+sp9QII3Oz9HxpOOBFh9vRlLZoddEzp2Yf3KjmYQBPyoWbinxdCLFOCBGUUk7Us5H5ZBpPjU7iyJlRKBl18Q7uIdzV48bXLk0j2NWJazfn8KULEfzG++/FzUQGx86O3V724TD279hYGOBIp3M4M3Idh0/f3t6hB7ejp9OJX//iSFEbYfzhP1/ClZkkXHYLDu4ewN+PTOB9g0H89Teu4gNDm/HE85cKyx/cPYAnv3YFs/Ppsp8H+jqx6219AICnxyI4dOp8Yb2TB3bCYRP42FMvl23z5IGd2BMK6HZyRFWlZvt6trEW3UoqeGZ0uiyj79zmw7+9Ecepc1fww/dvxrGnXizMP7ovhE/+y+uFfP3Oj+yAkpnHkS+NaWbq+HAIE7NzGAh2lRyvascU0M4bj/fak07n8E+XppDK5HAzkcFvP/1qSc4CfhdcNgt+++nRsjr0sYfD+MQ/3a6FJ/aHsf++fg5wkO60a2kYD4V72v5LJH+/ElG7MHMtpdaZSyr4skZu3h/uacqAg9Ht0epgdH1jPaVGGJkb7fOlYewN93GAg6rSO6dmPjskATwjhPimEOLDGvP7ARTfEfHa4rS6jEbihZ0NAEpGxZEzY8ipFrzr7j4cOTOG7xnow94d/chkZWFgo7Dsl0Zx4Xq0sL0L16OFgY38MiefvYg3ZxJL2hjF3h39heePP3cJP/Puu/D4c5ewd0d/4eRf8fxH7t+k+fOFa1Fcnkng8kyicOIjv96hU+dx4VpUc5uHTp3H5ZlEvbusokrt69nGWnQxktDM6GQ0hyNnRvHoA9tw7KnSXB47O1aSr9en5goDG/lpxTk6cmYM77q7r+x4VTumPN6Ud+F6FJmsxOvTicLABnA7Z2/eSCCnQrMO/eaXSmvh4dOjGJuIVmyLqFHatXQUFyPtX7NYb4moXZi5llLrvFwhNy83KTdGt0erg9H1jfWUGmFkbrTPl45iNBLXvS1aXfTOqZkHN75XSnk/Fi4/9QtCiHc3shEhxIeFEOeEEOemp6fL5k/GUoWdnadkVEzGFUzFFSgZFVNxBUIAiVRWe9mYUngeiSmay6gSZdOEKH2eTGcL07W2kV9+6c+qBKbiCiartF1pm1NxBXqp1L6ebaxGK8moklGRrJDL4nypcvlMFef9dtuVjymP99pSLaeRmIJEKlsxZ6oEEunssrUt/zwSZYaoMdVyWrGWxlJGdrEhrLerx3K/8xvVf8dmCCGWfRDVYrXWUmqdZuSGOSW9MadkBnrnhhmlZtA7O6a9LJWUcnzx3ykhxBcBvAPAV4oWGQdwR9HzTYvTlm7n0wA+DQBDQ0Ny6fw+nxMuu6Vkp7vsFvR5XZCLP/d6XXhtcg4el017Wd/tP6kJ+lyayyy9aoTLboGUpc87HAvbzz9fuo388kt/tgig1+uquJ5FLJzc1pqXX08PfRVeu55trEYNZ3Rxf3c4tXNZnC+rWD5TvV5X2fFa7pjyeK8d1XIa9LkwAaViziwC8DhshefVsuqyWxDwM0PUmGo5rVxLncZ2sgH8/bp6LPc7v1HXr72FD3zqhWWX+9zPPqBXk7SKrdZaSq3TjNwwp6Q35pTMQO/cMKPUDHpnx5R/uSGE8AghvPmfATwEYHTJYmcAPCoWvAtAtN77bQBAOODF8eFwyaDC8eEQrBYVX399EseHQ/jXS5M4+9I47FaBo/tCpcs+HMaOjf7C9gY3+nFif+n2Dj24HXd2e5a0EcZTF8YLzw/uHsAff+XbOLh7AGdfGsdjuwZKlj+4ewBfePGa5s87NvmxtduDrd0enDyws2S9kwd2Yscmv+Y2Tx7YWbjhuB4qta9nG2vR9oBHM6N9PiuOD4fxmRfewNG9pbk8ui9Ukq+7ejtx/OFQxUwdHw7h669Plh2vaseUx5vyBjf6YbcK3NXjwUf23FOWszs3eGC1QLMOfezh0lp4Yn8YoaC/YltEjdKupWFsD7R/zWK9JaJ2YeZaSq3z9gq5eXuTcmN0e7Q6GF3fWE+pEUbmRvt8aRjhgFf3tmh10TunQkrd/lOYYYQQ2wB8cfGpDcBfSSl/SwjxcwAgpfykWPjb+k8A2ANgHsBPSSnPVdvu0NCQPHeufJH5ZBqjkfjCHdy9TnR5rJhP55DKAv1dTrx1U0Gv14VNfjfemIljdj6LmUQaAZ8LOzb6CzcTz0uncxidiGIqnkKHw4pOpw2pbBZWYcXN+TSCPhfuDfhwdXYeb84k0OGwwuOwYT6dg89tg5LJIZHKwm23IZHOYlNXB1LZHMZnkwj6Xeh02nDl5jw6HDb0+ZzYvN5TcgPoyzMJTMUX+pw/8XF5JoGbiRTsVgvm0zn0+Rbm6X0jUq321/DNTut+4ZUyeiup4GIkUchosMsKJQ1YBHBjLodkJosOhw3T8RQ2+l2wWy2YTWYwp2SxodMBi5Bw2BbyNDOXRv86F6QErt1S0OdzoqvDCqvFpnm8qh1THu9VQZecptM5vDYVRyanIptTcSuZhctugdthhcMiMJfOwmG1Ip7KwG23IaZk0O1xwm5Rkc4JzCTSCPpdCAX9vJk4adElpyW11OfE9oDHNDdsZL1te7r9zm+ocSFq/suNWpbLL2vG7xFU1ZqvpdQ6c0kFLxfl5u0BT6Wbe+uS0zraIyqosb419AGM9ZT00qycamW05Hypz4lwwMubiVNN9MypKS9LJaV8A8B9GtM/WfSzBPALerTX4XbgHXd2V5zfv66z8PM9wXXLbs/hsOL+LeuXXe7uPi/u7qt9xDPcf7vtbb3a61ksAtt6OrGtp7Nkuta0ZqjUPq3MOrcL77hT+0POXb2Nb/c7tiy/TLVjyuNNeQ6HFYOb1rW6G0RVVaul7Y71lojahZlrKbVOp8G5Mbo9Wh2Mrm+sp9QII3Oz3PlSokr0zCn/+ysREREREREREREREZkKBzeIiIiIiIiIiIiIiMhUTHlZKqMlkxmMRGKF64D53VZkckAml0NWBTqdVgz0+GCxCFyeSWAypiDodyGnAlNxpXD/ClWVGJuIYjKmoNvjRE5KOG1WxJQ0Ouw22K0CiXQOvV4XrBZgei4Ft92KRCqHRDqLLes9uHPD7e1MRBUE/W6Egr6ya9Dnr709GVN0v3/G0m1v7urA1dl5zbYa6Ucz+66XdutjNKngtaJr1QX8ViTTgNthw8StFDK5HJw2K6bnUujudMJpBbKqwI25FNZ7HIgmM1jvcSDU58P1uLLs61JViTdvJHDlZgKeJfd2Kd43Wu+DevdTu+1raoyiZHHpRhy3klmkszmsczswk0jD67ZByWRhs1jR1WHHPX0+AFi2xhE1A69rTFQHiw0Lt7hb3sZNd2D8ratN7hC1C9ZSaoSiZDEyEUUklkLA58Rg0A+Xq3mnK4xuj1YHo+sb6yk1wsjc8P5F1Cg9c8rf3stIJjM4OxrBkTOjUDLq4h3cQ9jc7cJ8WsWckkFOCly9mYTLbsHP/sWL6Opw4NHv3oLHn7tUWOcT//E7cGs+g8Onb2/nsV0D+Ny5q/jpB+5EKqfi5LMXC/MOPbgd61w2zMxnSrbzqZ+4H9PxdMl2TuwPY/99/YWTf6oq8fRYBIdOnS8sc/LATuwJBVZ8Ulhr2yf2h/Hx5y/hykyypC0AdfejmX3XS7v1MZpU8A+j02UZ/c4tPvz7m7P4+POX8IGhzXji+Usl8//wn18vHLPHdg3g+VcjODC0pWQ7Wq9L6/Uf3D2Agb5OvGegF8+8MolDp85rvg/q3U/ttq+pMYqSxXMXp3D15jz++htX8RPv2orfe+ZbZbXwg9+1GW/eSAAAfvVvL1SscUTNcCup4JmyWhrGQ+EefomkttR/x2Zcv/ZW6zqgZuu6+TitDayl1AhFyeLMyERZboYHg00ZcDC6PVodjK5vrKfUCCNzM5dU8GWNtt4f7uEAB1Wld055pmgZI5FYYWcDgJJRceTMGASssFksWNfhxJs3EshkJeLJHJSMikfu31Q4oZtf58K1aGFAIj/tiecvYe+OfszMpwsDG/l5J5+9iA6nvWw78WSubDuHT49ibCJa6PPlmUThZHB+mUOnzuPyTGLF+0Nr24dPj2Lvjv6ythrpRzP7rpd26+NrkYRmRmfmcoVjkx/YKJ5ffMyeeP4SHn1gW9l2tF6X1ut//LlLuHAtirGJaGGe1vug3v3UbvuaGjMyEcVrk3E8/txCzfu9Z17TrIWPP3cJl6bmcGlqrmqNI2qGi5q1dBQXI6w31J6uX3sLH/jUCzU9Wm7xrzxqefTfsbnmzfbfsbkp26XGsZZSI0Ymopq5GWnS5z+j26PVwej6xnpKjTAyNy9XaOtlZpSWoXdO+d8SljEZSxV2dp6SUTEVV6DKheeqBBLpbGG+EChbR5Xl05SMCiEqz0uks2XTE6nyaUpGRSSq4L478n1WKvZ5W09nbS+8gkrbLr4qQb4tWeF1VetHM/uul3brY6WMTsaVwrGp5ZglK2Rr6euq9PpVCUxEb8+r1G49+6nd9jU1JhJLFepctTzmc7TU0hpH1AwVa2ks1aIeEa0iTforj/wAj97bpcaxllIjIgbnxuj2aHUwur6xnlIjjMwNM0qN0js7/MuNZfT5nHDZS3eTy25Br9eFrg47ujrssAjA47DB47CVLFPMKsqnuewWSFl5nsdhK5vucZVPc9ktCPhv/9l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dlh8p6NwAAAAAAAAAAACJwmWpAAAAAAAAAABAotC5AQAAAAAAAAAAEoXODQAAAAAAAAAAkCh0bgAAAAAAAAAAgEShcwMAAAAAAAAAACTK/w9XJJmP2y30/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1620x1620 with 90 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(car_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b8cd8da9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Finding Correlation between columns \n",
    "plt.figure(figsize=(15,8))\n",
    "sns.heatmap(car_dataset.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "da27d9be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat=car_dataset.corr() \n",
    "top_corr_features=corrmat.index \n",
    "plt.figure(figsize=(15,7)) \n",
    "#plot heat map \n",
    "g=sns.heatmap(car_dataset[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4c697133",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Selecting dependept and independent features\n",
    "X = car_dataset.iloc[:,1:]\n",
    "y = car_dataset.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "992b427b",
   "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>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "      <th>No_of_Years</th>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <th>Transmission_Manual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Price  Kms_Driven  Owner  No_of_Years  Fuel_Type_Diesel  \\\n",
       "0           5.59       27000      0            7                 0   \n",
       "1           9.54       43000      0            8                 1   \n",
       "2           9.85        6900      0            4                 0   \n",
       "3           4.15        5200      0           10                 0   \n",
       "4           6.87       42450      0            7                 1   \n",
       "\n",
       "   Fuel_Type_Petrol  Seller_Type_Individual  Transmission_Manual  \n",
       "0                 1                       0                    1  \n",
       "1                 0                       0                    1  \n",
       "2                 1                       0                    1  \n",
       "3                 1                       0                    1  \n",
       "4                 0                       0                    1  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1292eef0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3.35\n",
       "1    4.75\n",
       "2    7.25\n",
       "3    2.85\n",
       "4    4.60\n",
       "Name: Selling_Price, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "09375920",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor()"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "model = ExtraTreesRegressor()\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "12fedf0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.35767142 0.03773116 0.00050022 0.07884029 0.2429999  0.00972263\n",
      " 0.12344742 0.14908695]\n"
     ]
    }
   ],
   "source": [
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "033d11e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot graph of feature importances for better visualization\n",
    "feat_importances = pd.Series(model.feature_importances_, index=X.columns) \n",
    "feat_importances.nlargest(5).plot(kind='barh') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "921cc95a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ed4d3e31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(240, 8)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9cd630c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61, 8)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4ca689fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a9c5759e",
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor=RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "dec0d895",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200]\n"
     ]
    }
   ],
   "source": [
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "print(n_estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "65d0bb80",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f4d71d9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(5, 30, num = 6)]\n",
    "# max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10, 15, 100]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 5, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5b5d88e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200], 'max_features': ['auto', 'sqrt'], 'max_depth': [5, 10, 15, 20, 25, 30], 'min_samples_split': [2, 5, 10, 15, 100], 'min_samples_leaf': [1, 2, 5, 10]}\n"
     ]
    }
   ],
   "source": [
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf}\n",
    "print(random_grid)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "708ba7bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the random grid to search for best hyperparameters\n",
    "# First create the base model to tune\n",
    "rf = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "00d28877",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random search of parameters, using 3 fold cross validation, \n",
    "# search across 100 different combinations\n",
    "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid,scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, random_state=42,n_jobs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "3ab6a9df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.6s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.4s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.6s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.4s\n",
      "[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=5, min_samples_split=5, n_estimators=900; total time=   1.3s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   2.0s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.7s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.7s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   2.0s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=1100; total time=   1.9s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.9s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=100, n_estimators=300; total time=   0.4s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.6s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.7s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.6s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.6s\n",
      "[CV] END max_depth=15, max_features=auto, min_samples_leaf=5, min_samples_split=5, n_estimators=400; total time=   0.6s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.1s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.4s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.1s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.2s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=10, min_samples_split=5, n_estimators=700; total time=   1.1s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.0s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.0s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   1.7s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   2.0s\n",
      "[CV] END max_depth=25, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1000; total time=   4.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   4.5s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   4.1s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   3.9s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   3.0s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=10, min_samples_split=15, n_estimators=1100; total time=   2.9s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.9s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   1.4s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.8s\n",
      "[CV] END max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=15, n_estimators=300; total time=   0.8s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   2.7s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.2s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.4s\n",
      "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time=   1.2s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.3s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.4s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.5s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   1.1s\n",
      "[CV] END max_depth=20, max_features=auto, min_samples_leaf=1, min_samples_split=15, n_estimators=700; total time=   2.0s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
       "                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 5, 10],\n",
       "                                        'min_samples_split': [2, 5, 10, 15,\n",
       "                                                              100],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200]},\n",
       "                   random_state=42, scoring='neg_mean_squared_error',\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a3ba9552",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 700,\n",
       " 'min_samples_split': 15,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': 20}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "523c13ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.457876135604631"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0cca651e",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4b46d531",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.05080846,  0.63188885,  5.36605686,  7.83316067,  0.34178272,\n",
       "       10.29347253,  6.44297045,  0.48002642,  0.60339808,  2.95798357,\n",
       "        5.37275464,  1.17553476,  2.73772901,  0.22027252,  1.16430845,\n",
       "        0.43015266,  7.63748388,  0.56874967,  5.3606442 ,  2.9972083 ,\n",
       "        4.57182436,  3.43122411,  1.16546804,  1.16948666,  5.37450243,\n",
       "        1.18084148, 16.90887782,  4.96237039,  0.37208307,  5.34107924,\n",
       "       11.66572557,  6.14538588,  3.14417333,  7.414438  ,  4.65990869,\n",
       "        4.9937205 ,  0.23256814,  1.15837226,  3.78391741,  0.70177773,\n",
       "        4.80096461,  4.24729487,  7.63748388,  0.49089195, 10.30041419,\n",
       "        0.57718014,  4.63157716,  2.75532915,  0.30204894,  1.23909673,\n",
       "       10.33254806,  6.5752431 ,  2.80770029,  0.62893952,  2.81444981,\n",
       "       14.03061465,  1.183158  , 22.33792787,  0.25151908,  0.58773585,\n",
       "        5.09682826])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "1fc8581d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\admin\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.distplot(y_test-predictions,color='m')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "bf32beda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,predictions,color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "bd73b83f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e92d8e4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 0.8847819327253101\n",
      "MSE: 5.329022071961555\n",
      "RMSE: 2.30846747258036\n"
     ]
    }
   ],
   "source": [
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1609a5d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "# open a file, where you ant to store the data\n",
    "file = open('random_forest_regression_model.pkl', 'wb')\n",
    "\n",
    "# dump information to that file\n",
    "pickle.dump(rf_random, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4f03388",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}