Lesson_Script.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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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>Suburb</th>\n",
       "      <th>Address</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Method</th>\n",
       "      <th>SellerG</th>\n",
       "      <th>Date</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>...</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>CouncilArea</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Regionname</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>68 Studley St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SS</td>\n",
       "      <td>Jellis</td>\n",
       "      <td>3/09/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8014</td>\n",
       "      <td>144.9958</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>85 Turner St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1480000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>3/12/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.7996</td>\n",
       "      <td>144.9984</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>25 Bloomburg St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1035000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/02/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8079</td>\n",
       "      <td>144.9934</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>18/659 Victoria St</td>\n",
       "      <td>3</td>\n",
       "      <td>u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VB</td>\n",
       "      <td>Rounds</td>\n",
       "      <td>4/02/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8114</td>\n",
       "      <td>145.0116</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>5 Charles St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1465000.0</td>\n",
       "      <td>SP</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/03/2017</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8093</td>\n",
       "      <td>144.9944</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Suburb             Address  Rooms Type      Price Method SellerG  \\\n",
       "0  Abbotsford       68 Studley St      2    h        NaN     SS  Jellis   \n",
       "1  Abbotsford        85 Turner St      2    h  1480000.0      S  Biggin   \n",
       "2  Abbotsford     25 Bloomburg St      2    h  1035000.0      S  Biggin   \n",
       "3  Abbotsford  18/659 Victoria St      3    u        NaN     VB  Rounds   \n",
       "4  Abbotsford        5 Charles St      3    h  1465000.0     SP  Biggin   \n",
       "\n",
       "        Date  Distance  Postcode  ...  Bathroom  Car  Landsize  BuildingArea  \\\n",
       "0  3/09/2016       2.5    3067.0  ...       1.0  1.0     126.0           NaN   \n",
       "1  3/12/2016       2.5    3067.0  ...       1.0  1.0     202.0           NaN   \n",
       "2  4/02/2016       2.5    3067.0  ...       1.0  0.0     156.0          79.0   \n",
       "3  4/02/2016       2.5    3067.0  ...       2.0  1.0       0.0           NaN   \n",
       "4  4/03/2017       2.5    3067.0  ...       2.0  0.0     134.0         150.0   \n",
       "\n",
       "   YearBuilt         CouncilArea Lattitude  Longtitude             Regionname  \\\n",
       "0        NaN  Yarra City Council  -37.8014    144.9958  Northern Metropolitan   \n",
       "1        NaN  Yarra City Council  -37.7996    144.9984  Northern Metropolitan   \n",
       "2     1900.0  Yarra City Council  -37.8079    144.9934  Northern Metropolitan   \n",
       "3        NaN  Yarra City Council  -37.8114    145.0116  Northern Metropolitan   \n",
       "4     1900.0  Yarra City Council  -37.8093    144.9944  Northern Metropolitan   \n",
       "\n",
       "  Propertycount  \n",
       "0        4019.0  \n",
       "1        4019.0  \n",
       "2        4019.0  \n",
       "3        4019.0  \n",
       "4        4019.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_data = pd.read_csv('Data/Melbourne_housing_FULL.csv')\n",
    "housing_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Suburb</th>\n",
       "      <th>Address</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Method</th>\n",
       "      <th>SellerG</th>\n",
       "      <th>Date</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>...</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>CouncilArea</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Regionname</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>68 Studley St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SS</td>\n",
       "      <td>Jellis</td>\n",
       "      <td>3/09/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8014</td>\n",
       "      <td>144.9958</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>85 Turner St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1480000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>3/12/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.7996</td>\n",
       "      <td>144.9984</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>25 Bloomburg St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1035000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/02/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8079</td>\n",
       "      <td>144.9934</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>18/659 Victoria St</td>\n",
       "      <td>3</td>\n",
       "      <td>u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VB</td>\n",
       "      <td>Rounds</td>\n",
       "      <td>4/02/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8114</td>\n",
       "      <td>145.0116</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>5 Charles St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1465000.0</td>\n",
       "      <td>SP</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/03/2017</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8093</td>\n",
       "      <td>144.9944</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Suburb             Address  Rooms Type      Price Method SellerG  \\\n",
       "0  Abbotsford       68 Studley St      2    h        NaN     SS  Jellis   \n",
       "1  Abbotsford        85 Turner St      2    h  1480000.0      S  Biggin   \n",
       "2  Abbotsford     25 Bloomburg St      2    h  1035000.0      S  Biggin   \n",
       "3  Abbotsford  18/659 Victoria St      3    u        NaN     VB  Rounds   \n",
       "4  Abbotsford        5 Charles St      3    h  1465000.0     SP  Biggin   \n",
       "\n",
       "        Date  Distance  Postcode  ...  Bathroom  Car  Landsize  BuildingArea  \\\n",
       "0  3/09/2016       2.5    3067.0  ...       1.0  1.0     126.0           NaN   \n",
       "1  3/12/2016       2.5    3067.0  ...       1.0  1.0     202.0           NaN   \n",
       "2  4/02/2016       2.5    3067.0  ...       1.0  0.0     156.0          79.0   \n",
       "3  4/02/2016       2.5    3067.0  ...       2.0  1.0       0.0           NaN   \n",
       "4  4/03/2017       2.5    3067.0  ...       2.0  0.0     134.0         150.0   \n",
       "\n",
       "   YearBuilt         CouncilArea Lattitude  Longtitude             Regionname  \\\n",
       "0        NaN  Yarra City Council  -37.8014    144.9958  Northern Metropolitan   \n",
       "1        NaN  Yarra City Council  -37.7996    144.9984  Northern Metropolitan   \n",
       "2     1900.0  Yarra City Council  -37.8079    144.9934  Northern Metropolitan   \n",
       "3        NaN  Yarra City Council  -37.8114    145.0116  Northern Metropolitan   \n",
       "4     1900.0  Yarra City Council  -37.8093    144.9944  Northern Metropolitan   \n",
       "\n",
       "  Propertycount  \n",
       "0        4019.0  \n",
       "1        4019.0  \n",
       "2        4019.0  \n",
       "3        4019.0  \n",
       "4        4019.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_data_tsv = pd.read_csv('Data/Melbourne_housing_FULL.txt', sep=\"\\t\")\n",
    "housing_data_tsv.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 34857 entries, 0 to 34856\n",
      "Data columns (total 21 columns):\n",
      "Suburb           34857 non-null object\n",
      "Address          34857 non-null object\n",
      "Rooms            34857 non-null int64\n",
      "Type             34857 non-null object\n",
      "Price            27247 non-null float64\n",
      "Method           34857 non-null object\n",
      "SellerG          34857 non-null object\n",
      "Date             34857 non-null object\n",
      "Distance         34856 non-null float64\n",
      "Postcode         34856 non-null float64\n",
      "Bedroom2         26640 non-null float64\n",
      "Bathroom         26631 non-null float64\n",
      "Car              26129 non-null float64\n",
      "Landsize         23047 non-null float64\n",
      "BuildingArea     13742 non-null float64\n",
      "YearBuilt        15551 non-null float64\n",
      "CouncilArea      34854 non-null object\n",
      "Lattitude        26881 non-null float64\n",
      "Longtitude       26881 non-null float64\n",
      "Regionname       34854 non-null object\n",
      "Propertycount    34854 non-null float64\n",
      "dtypes: float64(12), int64(1), object(8)\n",
      "memory usage: 5.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Price</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Bedroom2</th>\n",
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       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
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       "      <th>Longtitude</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34857.000000</td>\n",
       "      <td>2.724700e+04</td>\n",
       "      <td>34856.000000</td>\n",
       "      <td>34856.000000</td>\n",
       "      <td>26640.000000</td>\n",
       "      <td>26631.000000</td>\n",
       "      <td>26129.000000</td>\n",
       "      <td>23047.000000</td>\n",
       "      <td>13742.00000</td>\n",
       "      <td>15551.000000</td>\n",
       "      <td>26881.000000</td>\n",
       "      <td>26881.000000</td>\n",
       "      <td>34854.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.031012</td>\n",
       "      <td>1.050173e+06</td>\n",
       "      <td>11.184929</td>\n",
       "      <td>3116.062859</td>\n",
       "      <td>3.084647</td>\n",
       "      <td>1.624798</td>\n",
       "      <td>1.728845</td>\n",
       "      <td>593.598993</td>\n",
       "      <td>160.25640</td>\n",
       "      <td>1965.289885</td>\n",
       "      <td>-37.810634</td>\n",
       "      <td>145.001851</td>\n",
       "      <td>7572.888306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.969933</td>\n",
       "      <td>6.414671e+05</td>\n",
       "      <td>6.788892</td>\n",
       "      <td>109.023903</td>\n",
       "      <td>0.980690</td>\n",
       "      <td>0.724212</td>\n",
       "      <td>1.010771</td>\n",
       "      <td>3398.841946</td>\n",
       "      <td>401.26706</td>\n",
       "      <td>37.328178</td>\n",
       "      <td>0.090279</td>\n",
       "      <td>0.120169</td>\n",
       "      <td>4428.090313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.500000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1196.000000</td>\n",
       "      <td>-38.190430</td>\n",
       "      <td>144.423790</td>\n",
       "      <td>83.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.350000e+05</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>3051.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>102.00000</td>\n",
       "      <td>1940.000000</td>\n",
       "      <td>-37.862950</td>\n",
       "      <td>144.933500</td>\n",
       "      <td>4385.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.700000e+05</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>3103.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>521.000000</td>\n",
       "      <td>136.00000</td>\n",
       "      <td>1970.000000</td>\n",
       "      <td>-37.807600</td>\n",
       "      <td>145.007800</td>\n",
       "      <td>6763.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.295000e+06</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3156.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>670.000000</td>\n",
       "      <td>188.00000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>-37.754100</td>\n",
       "      <td>145.071900</td>\n",
       "      <td>10412.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.120000e+07</td>\n",
       "      <td>48.100000</td>\n",
       "      <td>3978.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>433014.000000</td>\n",
       "      <td>44515.00000</td>\n",
       "      <td>2106.000000</td>\n",
       "      <td>-37.390200</td>\n",
       "      <td>145.526350</td>\n",
       "      <td>21650.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Rooms         Price      Distance      Postcode      Bedroom2  \\\n",
       "count  34857.000000  2.724700e+04  34856.000000  34856.000000  26640.000000   \n",
       "mean       3.031012  1.050173e+06     11.184929   3116.062859      3.084647   \n",
       "std        0.969933  6.414671e+05      6.788892    109.023903      0.980690   \n",
       "min        1.000000  8.500000e+04      0.000000   3000.000000      0.000000   \n",
       "25%        2.000000  6.350000e+05      6.400000   3051.000000      2.000000   \n",
       "50%        3.000000  8.700000e+05     10.300000   3103.000000      3.000000   \n",
       "75%        4.000000  1.295000e+06     14.000000   3156.000000      4.000000   \n",
       "max       16.000000  1.120000e+07     48.100000   3978.000000     30.000000   \n",
       "\n",
       "           Bathroom           Car       Landsize  BuildingArea     YearBuilt  \\\n",
       "count  26631.000000  26129.000000   23047.000000   13742.00000  15551.000000   \n",
       "mean       1.624798      1.728845     593.598993     160.25640   1965.289885   \n",
       "std        0.724212      1.010771    3398.841946     401.26706     37.328178   \n",
       "min        0.000000      0.000000       0.000000       0.00000   1196.000000   \n",
       "25%        1.000000      1.000000     224.000000     102.00000   1940.000000   \n",
       "50%        2.000000      2.000000     521.000000     136.00000   1970.000000   \n",
       "75%        2.000000      2.000000     670.000000     188.00000   2000.000000   \n",
       "max       12.000000     26.000000  433014.000000   44515.00000   2106.000000   \n",
       "\n",
       "          Lattitude    Longtitude  Propertycount  \n",
       "count  26881.000000  26881.000000   34854.000000  \n",
       "mean     -37.810634    145.001851    7572.888306  \n",
       "std        0.090279      0.120169    4428.090313  \n",
       "min      -38.190430    144.423790      83.000000  \n",
       "25%      -37.862950    144.933500    4385.000000  \n",
       "50%      -37.807600    145.007800    6763.000000  \n",
       "75%      -37.754100    145.071900   10412.000000  \n",
       "max      -37.390200    145.526350   21650.000000  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Suburb            0.000000\n",
       "Address           0.000000\n",
       "Rooms             0.000000\n",
       "Type              0.000000\n",
       "Price            21.832057\n",
       "Method            0.000000\n",
       "SellerG           0.000000\n",
       "Date              0.000000\n",
       "Distance          0.002869\n",
       "Postcode          0.002869\n",
       "Bedroom2         23.573457\n",
       "Bathroom         23.599277\n",
       "Car              25.039447\n",
       "Landsize         33.881286\n",
       "BuildingArea     60.576068\n",
       "YearBuilt        55.386293\n",
       "CouncilArea       0.008607\n",
       "Lattitude        22.882061\n",
       "Longtitude       22.882061\n",
       "Regionname        0.008607\n",
       "Propertycount     0.008607\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_data.isna().sum()/len(housing_data)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_housing_data = housing_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_housing_data['Price'].fillna(new_housing_data['Price'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Suburb            0.000000\n",
       "Address           0.000000\n",
       "Rooms             0.000000\n",
       "Type              0.000000\n",
       "Price             0.000000\n",
       "Method            0.000000\n",
       "SellerG           0.000000\n",
       "Date              0.000000\n",
       "Distance          0.002869\n",
       "Postcode          0.002869\n",
       "Bedroom2         23.573457\n",
       "Bathroom         23.599277\n",
       "Car              25.039447\n",
       "Landsize         33.881286\n",
       "BuildingArea     60.576068\n",
       "YearBuilt        55.386293\n",
       "CouncilArea       0.008607\n",
       "Lattitude        22.882061\n",
       "Longtitude       22.882061\n",
       "Regionname        0.008607\n",
       "Propertycount     0.008607\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_housing_data.isna().sum()/len(housing_data)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f28bdd7b38>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6UAAAGbCAYAAAA8zjayAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAdxklEQVR4nO3db4xeV14f8O+PmA3sAtkEO39IAgmqcZtUYhesdOlWyN0ACYXiWCKSqaAONUqVhoptK0FCX6S8sLSpKkSqskbRGuwW2GAW3FgrFUgNllXJjfEkNmySNTEbSNxkEs9SBhokk4TTF3MjJo4Tjz13fOaZ+Xykq3uf85z7e844x46/vue5t1prAQAAgB6+ovcAAAAAWL2EUgAAALoRSgEAAOhGKAUAAKAboRQAAIBu1vQeQJKsXbu23XTTTb2HAQAAwBKYmpqaaa2tO9d7yyKU3nTTTTl69GjvYQAAALAEqupP3+s9y3cBAADoRigFAACgG6EUAACAboRSAAAAuhFKAQAA6EYoBQAAoBuhFAAAgG6EUgAAALoRSgEAAOhGKAUAAKAboRQAAIBuhFIAAAC6EUoBAADoRigFAACgG6EUAACAboRSAAAAulnTewAAAMvNzl27Mz0zu+g61669Ivdtv2fRdQBWMqEUAOAs0zOz2bBpy6LrnDi4b4TRAKxslu8CAADQjVAKAABAN0IpAAAA3QilAAAAdCOUAgAA0I1QCgAAQDdCKQAAAN14TikAcEF27tqd6ZnZUWpdu/aK3Lf9nlFqATCZhFIA4IJMz8xmw6Yto9Q6cXDfKHUAmFyW7wIAANCNK6UAwIow5rLiqWPHR7saDMD7E0oBgBVhzGXFhw4fGaUOAOdn+S4AAADdnDeUVtWGqjo2b/uLqvpkVV1VVU9U1fPD/sp55zxYVSer6kRV3bG0PwIAAACT6ryhtLV2orX2kdbaR5J8e5K/SrIvyQNJDrTW1ic5MLxOVd2SZGuSW5PcmeTTVXXZEo0fAACACXahy3dvT/LHrbU/TbI5yZ6hfU+Su4bjzUkea62daa29kORkktvGGCwAAAAry4WG0q1JPjscX9NaeyVJhv3VQ/v1SV6ad86poe0dqureqjpaVUdPnz59gcMAAABgJVhwKK2qDyT5gSS/fr6u52hr72po7dHW2sbW2sZ169YtdBgAAACsIBdypfR7kzzVWnt1eP1qVV2XJMP+taH9VJIb5513Q5KXFztQAAAAVp4LCaU/lL9dupsk+5NsG463JXl8XvvWqrq8qm5Osj6Jh30BAADwLmsW0qmqPpjku5P8y3nNn0qyt6q2J3kxyd1J0lp7pqr2Jnk2yZtJ7m+tvTXqqAEAAFgRFhRKW2t/leTrz2r7cubuxnuu/juS7Fj06AAAAFjRLvTuuwAAADAaoRQAAIBuFrR8FwCACzf11NN56OFHRql17dorct/2e0apBbCcCKUAAEvk9TNvZMOmLaPUOnFw3yh1xrZz1+5Mz8yOUkvwhtVJKAUA4KJNz8yu+OANLC3fKQUAAKAboRQAAIBuhFIAAAC6EUoBAADoRigFAACgG6EUAACAboRSAAAAuhFKAQAA6EYoBQAAoBuhFAAAgG6EUgAAALoRSgEAAOhGKAUAAKCbNb0HAACsXlNPPZ2HHn5knFrHjmfDpi2j1ALg0hFKAYBuXj/zxmhB8tDhI6PUAeDSsnwXAACAboRSAAAAuhFKAQAA6EYoBQAAoBuhFAAAgG6EUgAAALoRSgEAAOhGKAUAAKAboRQAAIBuhFIAAAC6EUoBAADoRigFAACgmzW9BwAAAGPbuWt3pmdmR6l17dorct/2e0apBbybUAoAwIozPTObDZu2jFLrxMF9o9RJxgvLgjIriVAKAACXyFhhecygDL35TikAAADdCKUAAAB0I5QCAADQjVAKAABAN0IpAAAA3SwolFbVh6vqc1X1xap6rqq+o6quqqonqur5YX/lvP4PVtXJqjpRVXcs3fABAACYZAt9JMwjSX6rtfaDVfWBJB9M8tNJDrTWPlVVDyR5IMlPVdUtSbYmuTXJNyT5n1X1La21t5Zg/ADAAoz1bMQkmTp2fLTnPwLAeUNpVX1dku9Mck+StNb+OslfV9XmJJuGbnuSHEzyU0k2J3mstXYmyQtVdTLJbUkOjzx2AGCBxno2YpIcOnxklDoAkCxs+e43Jzmd5Jeq6umq+kxVfSjJNa21V5Jk2F899L8+yUvzzj81tL1DVd1bVUer6ujp06cX9UMAAAAwmRYSStck+bYkO1trH03yeuaW6r6XOkdbe1dDa4+21ja21jauW7duQYMFAABgZVlIKD2V5FRr7cnh9ecyF1JfrarrkmTYvzav/43zzr8hycvjDBcAAICV5LyhtLU2neSlqtowNN2e5Nkk+5NsG9q2JXl8ON6fZGtVXV5VNydZn8SXTwAAAHiXhd59918n+ZXhzrtfSvKjmQu0e6tqe5IXk9ydJK21Z6pqb+aC65tJ7nfnXQAAAM5lQaG0tXYsycZzvHX7e/TfkWTHIsYFAADAKrCQ75QCAADAkhBKAQAA6EYoBQAAoBuhFAAAgG6EUgAAALpZ6CNhAGDZ2blrd6ZnZhdd59q1V+S+7fcsug4spamnns5DDz8ySi1zHlhOhFIAJtb0zGw2bNqy6DonDu4bYTSwtF4/88Yo8z0x54HlxfJdAAAAuhFKAQAA6EYoBQAAoBuhFAAAgG6EUgAAALpx910AgFVmzMfLTB07PtpdgYHVSSgFAFhlxny8zKHDR0apA6xelu8CAADQjSulAAAsC5YVT76du3ZnemZ2lFrXrr0i922/Z5RaLG9CKQAAy4JlxZNvemZ2tP+GJw7uG6UOy5/luwAAAHQjlAIAANCNUAoAAEA3QikAAADduNERAAC8D3cFhqUllAIAwPtwV2BYWpbvAgAA0I1QCgAAQDdCKQAAAN0IpQAAAHQjlAIAANCNUAoAAEA3HgkDwKo35jMIr117Re7bfs8otQBgNRBKAVj1xnwG4YmD+0apAwCrheW7AAAAdCOUAgAA0I3luwCwTO3ctTvTM7Oj1Jo6dny0JcoAMCahFACWqemZ2dGC5KHDR0apAwBjE0oBYERj3snX1U0AVgOhFABGNOadfF3dBGA1cKMjAAAAuhFKAQAA6EYoBQAAoJsFhdKq+pOq+sOqOlZVR4e2q6rqiap6fthfOa//g1V1sqpOVNUdSzV4AAAAJtuFXCn9x621j7TWNg6vH0hyoLW2PsmB4XWq6pYkW5PcmuTOJJ+uqstGHDMAAAArxGKW725Osmc43pPkrnntj7XWzrTWXkhyMslti/gcAAAAVqiFhtKW5Heqaqqq7h3armmtvZIkw/7qof36JC/NO/fU0PYOVXVvVR2tqqOnT5++uNEDAAAw0Rb6nNKPt9ZerqqrkzxRVV98n751jrb2robWHk3yaJJs3LjxXe8DAACw8i3oSmlr7eVh/1qSfZlbjvtqVV2XJMP+taH7qSQ3zjv9hiQvjzVgAAAAVo7zhtKq+lBVfe3bx0m+J8kXkuxPsm3oti3J48Px/iRbq+ryqro5yfokR8YeOAAAAJNvIct3r0myr6re7v+rrbXfqqrfT7K3qrYneTHJ3UnSWnumqvYmeTbJm0nub629tSSjBwAAYKKdN5S21r6U5FvP0f7lJLe/xzk7kuxY9OgAAABY0RbzSBgAAABYFKEUAACAbhb6SBgAAGCZmHrq6Tz08COj1Lp27RW5b/s9o9SCiyGUAgDAhHn9zBvZsGnLKLVOHNw3Sh24WEIpAACsYmNedZ06dny0sOxq8OohlAIAwCo25lXXQ4ePjFIncTV4NXGjIwAAALoRSgEAAOhGKAUAAKAboRQAAIBuhFIAAAC6EUoBAADoRigFAACgG6EUAACAboRSAAAAuhFKAQAA6EYoBQAAoBuhFAAAgG6EUgAAALpZ03sAAAAAS2nqqafz0MOPjFLr2rVX5L7t94xSizlCKQAAsKK9fuaNbNi0ZZRaJw7uG6UOf0soBeC8du7anemZ2VFq+RdmAGA+oRSA85qemfUvzADAknCjIwAAALoRSgEAAOhGKAUAAKAboRQAAIBuhFIAAAC6EUoBAADoRigFAACgG88pBeCSmnrq6Tz08CPj1Dp2fLTnpwIAfQilAFxSr595Y7QgeejwkVHqAAD9WL4LAABAN0IpAAAA3QilAAAAdCOUAgAA0I1QCgAAQDdCKQAAAN0IpQAAAHQjlAIAANDNgkNpVV1WVU9X1eeH11dV1RNV9fywv3Je3wer6mRVnaiqO5Zi4AAAAEy+C7lS+hNJnpv3+oEkB1pr65McGF6nqm5JsjXJrUnuTPLpqrpsnOECAACwkiwolFbVDUm+L8ln5jVvTrJnON6T5K557Y+11s601l5IcjLJbeMMFwAAgJVkoVdKfy7JTyb5m3lt17TWXkmSYX/10H59kpfm9Ts1tAEAAMA7nDeUVtX3J3mttTa1wJp1jrZ2jrr3VtXRqjp6+vTpBZYGAABgJVnIldKPJ/mBqvqTJI8l+URV/XKSV6vquiQZ9q8N/U8luXHe+Tckefnsoq21R1trG1trG9etW7eIHwEAAIBJdd5Q2lp7sLV2Q2vtpszdwOh3W2s/nGR/km1Dt21JHh+O9yfZWlWXV9XNSdYnOTL6yAEAAJh4axZx7qeS7K2q7UleTHJ3krTWnqmqvUmeTfJmkvtba28teqQAAACsOBcUSltrB5McHI6/nOT29+i3I8mORY4NAACAFe5CnlMKAAAAoxJKAQAA6EYoBQAAoBuhFAAAgG6EUgAAALoRSgEAAOhGKAUAAKAboRQAAIBuhFIAAAC6EUoBAADoZk3vAQAAAEyKqaeezkMPPzJKrWvXXpH7tt8zSq1JJpQCAAAs0Otn3siGTVtGqXXi4L5R6kw6y3cBAADoRigFAACgG6EUAACAbnynFGAZ2blrd6ZnZkep5eYJAMAkEEoBlpHpmVk3TwAAVhXLdwEAAOhGKAUAAKAboRQAAIBuhFIAAAC6EUoBAADoRigFAACgG6EUAACAboRSAAAAuhFKAQAA6EYoBQAAoBuhFAAAgG6EUgAAALoRSgEAAOhGKAUAAKAboRQAAIBuhFIAAAC6WdN7AAAsjamnns5DDz8yTq1jx7Nh05ZRagEAzCeUAqxQr595Y7QgeejwkVHqAACczfJdAAAAuhFKAQAA6EYoBQAAoBuhFAAAgG6EUgAAALoRSgEAAOjmvKG0qr6qqo5U1fGqeqaqfmZov6qqnqiq54f9lfPOebCqTlbViaq6Yyl/AAAAACbXQq6Unknyidbatyb5SJI7q+pjSR5IcqC1tj7JgeF1quqWJFuT3JrkziSfrqrLlmLwAAAATLbzhtI25/8NL79y2FqSzUn2DO17ktw1HG9O8lhr7Uxr7YUkJ5PcNuqoAQAAWBHWLKTTcKVzKsnfSfLzrbUnq+qa1torSdJae6Wqrh66X5/kf887/dTQdnbNe5PcmyTf+I3fePE/AQAAwASaeurpPPTwI6PUunbtFblv+z2j1LrUFhRKW2tvJflIVX04yb6q+vvv073OVeIcNR9N8miSbNy48V3vAwAArGSvn3kjGzZtGaXWiYP7RqnTwwXdfbe19udJDmbuu6KvVtV1STLsXxu6nUpy47zTbkjy8qJHCgAAwIqzkLvvrhuukKaqvjrJdyX5YpL9SbYN3bYleXw43p9ka1VdXlU3J1mf5MjYAwcAAGDyLWT57nVJ9gzfK/2KJHtba5+vqsNJ9lbV9iQvJrk7SVprz1TV3iTPJnkzyf3D8l8AAAB4h/OG0tbaHyT56Dnav5zk9vc4Z0eSHYseHQAAACvaBX2nFAAAAMYklAIAANCNUAoAAEA3QikAAADdCKUAAAB0I5QCAADQzUKeUwrA+9i5a3emZ2ZHqTV17Hg2bNoySi0AgEkglAIs0vTM7GhB8tDhI6PUAQCYFJbvAgAA0I1QCgAAQDdCKQAAAN0IpQAAAHQjlAIAANCNUAoAAEA3QikAAADdCKUAAAB0I5QCAADQjVAKAABAN0IpAAAA3QilAAAAdCOUAgAA0I1QCgAAQDdCKQAAAN2s6T0AgB527tqd6ZnZUWpNHTueDZu2jFILAGC1EUqBVWl6Zna0IHno8JFR6gAArEaW7wIAANCNUAoAAEA3QikAAADdCKUAAAB0I5QCAADQjVAKAABAN0IpAAAA3QilAAAAdCOUAgAA0I1QCgAAQDdCKQAAAN0IpQAAAHQjlAIAANCNUAoAAEA3QikAAADdnDeUVtWNVfV7VfVcVT1TVT8xtF9VVU9U1fPD/sp55zxYVSer6kRV3bGUPwAAAACTayFXSt9M8u9aa38vyceS3F9VtyR5IMmB1tr6JAeG1xne25rk1iR3Jvl0VV22FIMHAABgsp03lLbWXmmtPTUc/2WS55Jcn2Rzkj1Dtz1J7hqONyd5rLV2prX2QpKTSW4be+AAAABMvgv6TmlV3ZTko0meTHJNa+2VZC64Jrl66HZ9kpfmnXZqaDu71r1VdbSqjp4+ffrCRw4AAMDEW3AoraqvSfIbST7ZWvuL9+t6jrb2robWHm2tbWytbVy3bt1ChwEAAMAKsqBQWlVfmblA+iuttd8cml+tquuG969L8trQfirJjfNOvyHJy+MMFwAAgJVkIXffrSS7kjzXWvvZeW/tT7JtON6W5PF57Vur6vKqujnJ+iRHxhsyAAAAK8WaBfT5eJIfSfKHVXVsaPvpJJ9Ksreqtid5McndSdJae6aq9iZ5NnN37r2/tfbW6CMHAABg4p03lLbW/lfO/T3RJLn9Pc7ZkWTHIsYF8C47d+3O9MzsKLWmjh3Phk1bRqkFAMDFW8iVUoBlYXpmdrQgeeiwbxUAACwHF/RIGAAAABiTUAoAAEA3QikAAADdCKUAAAB0I5QCAADQjVAKAABAN0IpAAAA3QilAAAAdCOUAgAA0I1QCgAAQDdCKQAAAN2s6T0AYOXbuWt3pmdmF11n6tjxbNi0ZYQRAQCwXAilwJKbnpkdJUweOnxkhNEAALCcWL4LAABAN0IpAAAA3QilAAAAdCOUAgAA0I1QCgAAQDdCKQAAAN0IpQAAAHQjlAIAANCNUAoAAEA3QikAAADdCKUAAAB0I5QCAADQjVAKAABAN0IpAAAA3QilAAAAdCOUAgAA0I1QCgAAQDdCKQAAAN2s6T0AYHnauWt3pmdmR6k1dex4NmzaMkotAABWFqEUOKfpmdnRguShw0dGqQMAwMpj+S4AAADdCKUAAAB0I5QCAADQjVAKAABAN0IpAAAA3QilAAAAdHPeUFpVv1hVr1XVF+a1XVVVT1TV88P+ynnvPVhVJ6vqRFXdsVQDBwAAYPIt5Erp7iR3ntX2QJIDrbX1SQ4Mr1NVtyTZmuTW4ZxPV9Vlo40WAACAFeW8obS1dijJn53VvDnJnuF4T5K75rU/1lo701p7IcnJJLeNNFYAAABWmIv9Tuk1rbVXkmTYXz20X5/kpXn9Tg1t71JV91bV0ao6evr06YscBgAAAJNs7Bsd1Tna2rk6ttYeba1tbK1tXLdu3cjDAAAAYBJcbCh9taquS5Jh/9rQfirJjfP63ZDk5YsfHgAAACvZxYbS/Um2Dcfbkjw+r31rVV1eVTcnWZ/kyOKGCAAAwEq15nwdquqzSTYlWVtVp5I8lORTSfZW1fYkLya5O0laa89U1d4kzyZ5M8n9rbW3lmjsAAAATLjzhtLW2g+9x1u3v0f/HUl2LGZQwMXZuWt3pmdmR6k1dex4NmzaMkotAAB4L+cNpcDkmJ6ZHS1IHjps5T0AAEtv7LvvAgAAwIIJpQAAAHQjlAIAANCNUAoAAEA3QikAAADdCKUAAAB0I5QCAADQjVAKAABAN0IpAAAA3QilAAAAdCOUAgAA0I1QCgAAQDdCKQAAAN0IpQAAAHQjlAIAANCNUAoAAEA3QikAAADdrOk9AFjtdu7anemZ2VFqTR07ng2btoxSCwAALgWhFDqbnpkdLUgeOnxklDoAAHCpWL4LAABAN0IpAAAA3QilAAAAdCOUAgAA0I1QCgAAQDdCKQAAAN0IpQAAAHQjlAIAANDNmt4DgEm0c9fuTM/MjlJr6tjxbNi0ZZRaAAAwaYRSuAjTM7OjBclDh4+MUgcAACaR5bsAAAB0I5QCAADQjVAKAABAN0IpAAAA3QilAAAAdCOUAgAA0I1QCgAAQDdCKQAAAN2s6T0AuFR27tqd6ZnZUWpNHTueDZu2jFILAABWM6GUVWN6Zna0IHno8JFR6gAAwGonlLKsuboJAAAr25KF0qq6M8kjSS5L8pnW2qeW6rNYuVzdBACAlW1JQmlVXZbk55N8d5JTSX6/qva31p5dis9bzca8kvhHf/TFfMu3/N1lUydxdRMAAFa6pbpSeluSk621LyVJVT2WZHOSiQ2lyzH8JXOh7Z998j+MUuvQ4QfzT0cIgGPVmavl6iYAAKxk1Vobv2jVDya5s7X2Y8PrH0nyD1prPz6vz71J7h1ebkhyYvSBsBqsTTLTexCwCOYwK4F5zKQzh1kJlvs8/qbW2rpzvbFUV0rrHG3vSL+ttUeTPLpEn88qUVVHW2sbe48DLpY5zEpgHjPpzGFWgkmex1+xRHVPJblx3usbkry8RJ8FAADAhFqqUPr7SdZX1c1V9YEkW5PsX6LPAgAAYEItyfLd1tqbVfXjSX47c4+E+cXW2jNL8VmsepaAM+nMYVYC85hJZw6zEkzsPF6SGx0BAADAQizV8l0AAAA4L6EUAACAboRSuqiqG6vq96rquap6pqp+Ymi/qqqeqKrnh/2V8855sKpOVtWJqrpjXvu3V9UfDu/956qqof3yqvq1of3Jqrpp3jnbhs94vqq2XbqfnJWmqi6rqqer6vPDa3OYiVFVH66qz1XVF4c/j7/DHGbSVNW/Gf4u8YWq+mxVfZV5zHJWVb9YVa9V1RfmtXWdszV3g9onh/Zfq7mb1V46rTWb7ZJvSa5L8m3D8dcm+aMktyT5j0keGNofSPLwcHxLkuNJLk9yc5I/TnLZ8N6RJN+Ruefj/o8k3zu0/6skvzAcb03ya8PxVUm+NOyvHI6v7P1rYpvMLcm/TfKrST4/vDaHbROzJdmT5MeG4w8k+bA5bJukLcn1SV5I8tXD671J7jGPbct5S/KdSb4tyRfmtXWds8Pvna3D8S8kue9S/pq4UkoXrbVXWmtPDcd/meS5zP2PZXPm/pKUYX/XcLw5yWOttTOttReSnExyW1Vdl+TrWmuH29zvov961jlv1/pcktuHf0G6I8kTrbU/a6393yRPJLlzCX9cVqiquiHJ9yX5zLxmc5iJUFVfl7m/GO1KktbaX7fW/jzmMJNnTZKvrqo1ST6Y5OWYxyxjrbVDSf7srOZuc3Z47xND37M//5IQSuluWFLw0SRPJrmmtfZKMhdck1w9dLs+yUvzTjs1tF0/HJ/d/o5zWmtvJplN8vXvUwsu1M8l+ckkfzOvzRxmUnxzktNJfqnmlqB/pqo+FHOYCdJa+z9J/lOSF5O8kmS2tfY7MY+ZPD3n7Ncn+fOh79m1LgmhlK6q6muS/EaST7bW/uL9up6jrb1P+8WeAwtSVd+f5LXW2tRCTzlHmzlMT2syt3xsZ2vto0lez9ySsfdiDrPsDN+725y5ZY3fkORDVfXD73fKOdrMY5azSzFnu89loZRuquorMxdIf6W19ptD86vDcoQM+9eG9lNJbpx3+g2ZW55zajg+u/0d5wxLeq7I3FKJ96oFF+LjSX6gqv4kyWNJPlFVvxxzmMlxKsmp1tqTw+vPZS6kmsNMku9K8kJr7XRr7Y0kv5nkH8Y8ZvL0nLMzST489D271iUhlNLFsHZ9V5LnWms/O++t/UnevhPYtiSPz2vfOtxN7OYk65McGZY3/GVVfWyo+c/POuftWj+Y5HeHNfe/neR7qurK4V9Yv2dogwVrrT3YWruhtXZT5m4i8LuttR+OOcyEaK1NJ3mpqjYMTbcneTbmMJPlxSQfq6oPDvPv9szdp8I8ZtJ0m7PDe7839D378y+NS3lXJZvt7S3JP8rcsoA/SHJs2P5J5ta0H0jy/LC/at45/z5zdxw7keHuYkP7xiRfGN77L0lqaP+qJL+euS+EH0nyzfPO+RdD+8kkP9r718M22VuSTfnbu++aw7aJ2ZJ8JMnR4c/i/565uzGaw7aJ2pL8TJIvDnPwv2XuLqXmsW3Zbkk+m7nvQL+RuauX23vP2czdZ+DI0P7rSS6/lL8mbw8cAAAALjnLdwEAAOhGKAUAAKAboRQAAIBuhFIAAAC6EUoBAADoRigFAACgG6EUAACAbv4/KB3c45OCJTcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(16,7))\n",
    "sns.distplot(housing_data.loc[housing_data[\"Price\"] < 1000000, ['Price']], kde = False, hist_kws = dict(edgecolor = \"k\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f28c3ac4a8>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "housing_data['Date'] = pd.to_datetime(housing_data['Date'])\n",
    "\n",
    "var = housing_data[(housing_data[\"Type\"]==\"h\") & (housing_data[\"Rooms\"]<=4) & (housing_data[\"Price\"] < 1000000)].sort_values(\"Date\", ascending=True).groupby(\"Date\").std()\n",
    "count = housing_data[(housing_data[\"Type\"]==\"h\") & (housing_data[\"Rooms\"]<=4) & (housing_data[\"Price\"] < 1000000)].sort_values(\"Date\", ascending=True).groupby(\"Date\").count()\n",
    "mean = housing_data[(housing_data[\"Type\"]==\"h\") & (housing_data[\"Rooms\"]<=4) & (housing_data[\"Price\"] < 1000000)].sort_values(\"Date\", ascending=True).groupby(\"Date\").mean()\n",
    "\n",
    "mean[\"Price\"].plot(yerr=var[\"Price\"],ylim=(400000,1500000), figsize = (15, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regionname                  Type\n",
      "Eastern Metropolitan        h       1.159429e+06\n",
      "                            t       9.593282e+05\n",
      "                            u       7.791411e+05\n",
      "Eastern Victoria            h       8.088998e+05\n",
      "                            t       8.961040e+05\n",
      "                            u       6.409067e+05\n",
      "Northern Metropolitan       h       9.894457e+05\n",
      "                            t       7.807428e+05\n",
      "                            u       6.512898e+05\n",
      "Northern Victoria           h       7.000599e+05\n",
      "                            t       6.480000e+05\n",
      "                            u       5.530000e+05\n",
      "South-Eastern Metropolitan  h       9.617943e+05\n",
      "                            t       9.095537e+05\n",
      "                            u       6.488721e+05\n",
      "Southern Metropolitan       h       1.591019e+06\n",
      "                            t       1.183943e+06\n",
      "                            u       7.674882e+05\n",
      "Western Metropolitan        h       9.321050e+05\n",
      "                            t       7.731458e+05\n",
      "                            u       5.839877e+05\n",
      "Western Victoria            h       5.390579e+05\n",
      "                            u       2.850000e+05\n",
      "Name: Price, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "gp_df = housing_data.groupby(['Regionname', 'Type'])['Price'].mean()\n",
    "print(gp_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skumarravindran\\AppData\\Local\\Continuum\\anaconda3\\envs\\basic_env\\lib\\site-packages\\seaborn\\regression.py:573: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f28c260da0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SjdCQFXgh8EwIl57xZDAlAwI8A9L33AsfvnFB+vG5xuLAhmOrWTg8cbiyf6pRlDL0AApDGIQ9geFJ/HqBGwradmxFRNiVa3A8ONp1PBaO+tnWaDQazWmLDuhfZzR3Xc/gbbcHYphIBFUooByH5o1rJ4teQ/EgfVscnyyAffrbk4WaoQR4RyDSEGjkqwo3Ae5RH+B2fkuUItUi8gHLpqGu1uIzYkZQUjGUH5pWyPmRsz8yueM5G4AvcWDnJ4OgGGomwzzlVfzmp/L88PM4b/O4/iFFmCAzH/bA8uEnFwuue0iSrUlsBpnblbm6mnZCkTpkMYOQCikCaYlSYIQmH2zShTSpfIrURIo7fIfDJnhAvtRdu/TqSnfGQNbt78doaKjZNleWdSY/803rNk3b1vRPf83LdqE8clAah2RaIZXklewrvHfle/nm2u1c/r0+yNk0JJZQ71sox+HwNRdz24NdNW2u3a9oKJi0DE3gm4KJsCDiBJUEPiBkcK6FMCx7aZx973o3N9dl+NllDi+fnyRTzDBcGMbxHaJ2lJ7+npogd86xqKpZuGCfy1WPebSmJalGQWb5jlkDb0VQN3H1bkVbSlFXhEwdjNWB7UiENPFMj6KlEIBSEqUUDYmlxy3TfaIKV2f9bHdd/+o7rTkp6KLm44MeR41mOlpy8zojvn49S2+9BSuZRI6NYSWTLO26iviRe4IsPCYMvQCp3waymsxgEMxf+LHJQs3W1bD+09C8alrhJkAyHiFPBLfq9lFAvYxTqGus6U/BLyAMMWMh5xODU+oSztmANKzJ7G8VpjBnzHj29PeQdbM8tdrg7vcYpGOCWAHS9YK7Nxg8fxaMNAQBfhkDgzOsJlpWnVs7du1n4NcFmWhDgW9Cph4ONrnc9dRd3PLILewf218JNntNyBA8BxmlMXAJgl3bsGcshLTb21GFWsnMbFnW2fzMAbZcuYVtH9nGliu30NHeQXzFGtpDSwgZocr7yzMUAHk/z5Znt/Crszwe+FA76XrB+NAA2YTN0I0f4nPqxzXHue+bf0PfZz9Ds5VgpNFEKUV8QuEYQUZeiGB8clFBLA8IgdHQQFuxjmt+lGLJ0/28MvFKqUgX6qy6aRKkucZiZcNKpJKcv8/h2m0OiawkG1EkJ6w5C0EvPmBxfak4eiQO41GI56ElA5m4xY+uSTK8rI6IbyClj2VYnBFbRr1vHZdM94ksXJ3xs33rLTqQWSTooubjgx5HjWZmdIb+dUh8/fraP/Ld75/0cx/eFwTyALlUUPTqEFhUdv5kSkszW1B+wvoJYyLGkGjFEAKpFGFV4OMqyld8F6iVg4SMEM2R5optIgSFnDPJMGzTxvO8adtDZmjaNmC6NrtKryNQXOB4bFiVY/CJeoQMIUyJUibKqp+W2Wx+Y4ZDB6EYB98C2wPTgx1vg53PfwvXd2tkIJ7wQCkkk0/GCvAEtITik5ac1cdYQJZ1ISvrNnddj3vb7ayOtPPb3H5CpRmKresCV36FoiiLRK0ovW+K0vumBvJeHtswOZz7F/JunpAZoiXSQjwc5/ce6WdUKtrjq1GRMMP1Q5AvomwTM+/imArHUpw5HAy4sSSJEIJYPLjGV/QM0fOxoBC1NdpKLBQj7+Vr+j7XWNy8SnDro7dy1WNHgmOFDExh0tSwDOGZs8pjup5pJWf2V+RV4zEohiEbt7nn42fi+i7/6Zwumv/hO8ixLMorIqxB/FiMJX91bCsIV3M0SdWrZdpnW7NoONH3xumCHkeNZmZ0QH86kO4LHGSgVPBqAGKy6LWq4HU+NDmHsBoaSeUcHE8Ssgxa6ht5b36U2Lq/nSYHmcnlZjYbP7P8sDHP7eVFk35nb47rH5J4JiXbSsV/vR/G602eLibwI5ImFHWOwK4v0vx2j/iv/xs9+5N0NzQwIPO0rRjirAvgyl9B1IF8KAjmD65UTLgTQFBgClSCZEQgy1GUgnoRVBasSKyY0eUmvn49L7zrzRjf+ymhoo8TNpF/8D7WlJxd7vj1HRwYOxBcHumzrH5ZzbhFzMj0NQMuvYn4+g288K43I/7tfpYXoRCCH18MT60uzx1Mx/M9Do4H190SFm96scBVj/VxxrhBIuOTTgSPKXE7RtyOoeKKiZFB7nyfx7sezdKalhhKMdxg0FxfpesOh2g54vPBRyRXPV4k6oxTDBnsXN/C/e+cLLSOr18Pt94y49R5B3DbZbdhffnPyIQldVaIlmhr0A9LzSqPWZJWjFkJxMg4lh/MtmTqTZJjkmQ0GTxgPf4sY4U0tu9jKjCUgThObl8LlVRpTh/c/n6UYeANDiIdByMUwmhuXlT3xqkgddGfMY1mZnRAfzrQuGKy6NUMgV9aeKic9a4qeJ1ve/HMIPHW+sltzgTEl89aDDhTIedMmnghBEYp3y2Rla/FVAuXEm2xNjzpcfXubE3hq+FDvBAE5sPNCsszGPMlE+vhksY0NCToCdezWaawR1IkEm3U9Skuew7SMThiQciDtz4HB5YpDrwxRN6bXOG12pPeEAaWaaGUojnSzIrEikk7zinsuu9O3K0P4McMvGYby/Extz7Ad88QfK3ucUaLoxjCQCiBr3wOZQ8hEMTDQbVrYWI4WDMgP1GzRsCuX2zA2bqNYr2kWJIYXfEsvLRMloL6gEwxU2nrSP4ItmFjCIM3vVigc5uLY8J42CeWhaa0jx/PYMaC/VWhQH/cpS5UT8TysA0Hz/YJScFwfoi4HSPjZhkePYRQio88oiozFiFXcsXPUoTNdqgqnZgr49zR3kHf2WunWTXOVQhqxGLU7XsFYYbANghJSXTcJ7xmDVuu3EJPfw9HvvF1ElGB1xipaOiXWcenKFYXrmpmQ9TX4+7fD4YBhoF0XeThw4RWrXqtuzYvylIXEQrVSF04ybIv/RnTaGZGa+hPBy69KShqdSagLgnKD/7XJ4NtVQWvC25PqaO20dHewab295JMH2I89TzJ9CE2tb93xsDfFjamYWIZFmEjjGVYmIaJLSYN5qsXHEoX0vjS54wxUbIoDGicCJxYLFnSe9uB5jv9lIJIM4Tq6LaK2MIgKgQil+Lyx6vccAS4NrgmvPNxRdSMTutrGYFAqCBjP1ocnbV4FwI3GN8S+GELIYJX3xKE/u2nZNwMpmFiChNDGJiYSCQvZ1/mpfRLDOeHcfPDdDp28HAmRPBqhEj/7wcoGhInZIAIzsEzA5ebalL5FEop8l4eT3q0RltpjbZy1WMejgmOHcwwjMcDiYs3eASlFDKfRzkOzy2HD20dqlg+OuEgmx8eL6CUYmxsEFtCQyFoR5Yng4zANeiynuFZx2YmmruuRzlOcPyqfsxWCKqUqsySKFV67BKC8nob3Xu6aRn18EMmguBhTAjBkBw/Lhm+hfZXc/ogRDCdJ0pfCwi+nyVZcapRLXURQgSvoRAjW+4+qf3QnzGNZmZ0hv50oOQgw84vw5HfTmbpxw9B69nwrs2VgtcFt5c+GGT3L71p9jb2bqdj1xY6jBDYTcGMwK4t0HTutPesaVpD3/ALZJxxXBQ2gngowYqmYIGrnv4evnP3p3hfSfIx1Giw7dIwI002deNFiragrqCIOEFdrRQgHEE+DNgQywi6Gi06lcOAkLxlv89luyVNaZf6HAzHQNjBexXgWoL2lOTP7hmiLSWx/CDIH0gGi1M9tdogbIVxZVArUPbb7yo5xlx2sI6rd0vqU1ns9nYaB8bJh6BxzMX2wTcF2ZhFy6iH6xs1mf/y1wJB0S9yZOIIMaXojkzKpbrNAgMhyV+PSTIRMBAIEfi2l11uAOJWnMZII4MTg4w744GFpbBxlUvUinLGuMF42Kds79jY2o5dL/EHB/EGB1GuC5bFFY9myVmS8ZAAHwpRgZSCqANybIzResWuy9v4WHcfnqgS+ygQhomRn5TczOTgU/2QV57el/k8anwcLIvI2WfPOc2vcjm8ZCP+yAimJ/EtAzPZjJ3LAYFEK90cJp7xcEOlRdGEAQUHe/Wrz/DNJSM6Fo42RnBqyCBO5f6cKshsFqvtTOTwCMpxEKEQ5hlLkdnsa921eXGqSF2O92dMo3m9oAP604Vy4PzAX0C0KdDNu3lwjvGPyTkb6KmLTAYbL32XzrrIzN7bO788WZQLwatT2j4loO9svJDNA4+zFIgIg4KSuIUxOhsvBOD+f72dP/jpGK4pyEYgkZX80QMT/OJ3Dd79nEVdThKfCMJIRRDQt47DUCLIEqcaIKVcNptZ1vYq3rfdR5qQjwqiE4pkBoYMkCVNeHS8SJ0DS0ck9SVDlrAHS4cV1z+kuPs94Fy8EqBUZGpXVgl9Sy9c8cMXGTFANJ2JONxHXQFiE8FsgQQMX9GYdhloFUim6PNL2IaNKv3zlEdKedxq51BSkZAeCSkZajBI5AJP9SDjJwh7waq4dVYdb2h4A3kvz9olaytyoLKLDsBYc5j6cQfHFpwRW0bcjiG9PMbKlaiJCURDAznTI5QewXbBtRT5SPDQMVoHUVnPmod/xv94sItUPkUxZGA7MnAsCk4KJX1kaZq8ejXVagefTQQLMFVP71tLl9YUzM71hzuXjDHSP4jXaiOEgVISq5imuX01EEi0ft7hcM2PUijfoWgpQp4gpKzjluE7XoWrRxsjOHVkEGVOtf6cSpSlItbKlZVtMp/HWpac/U2nEKeS1EUXh2s009GSm9OJ6sC6Sq7Bzi8vuKnZLBWnrowKBMWb9hTJyiyFuB3PP8Smgk0Si3EgicWmgk3H8w8BcNF/DOCaAjckEEbw6ppwXp/igQ+1E3IlQgVZdGkEAb1S0JQJXF/uX2cQVQpberzjcYlbktgo02Y8ERTeNmUDuYZZ9KifkIzXCeqLgWTEN4JAvN4JJC3X7KYiYXF9FxQVZ5rLfjmEtEy8sMlwYZghlQFR5chZNdNuCatS+KumFLEG0/MCS1h4hklUQUZJckISlRKB4JG3CWwfQq5CSUnYVRWXm5ZIS6V/1XKg6hVNt19WR1gaLLOaiVn1lWlsIURlmn24MIxrASqQNJVXlgp7cKTkVtp5Xieu73L/xUYgaZHB/qYMfP0f7QhccI62ouyxTu9vXWdgy2BRLaEUYRdsGWwv9+/JsxTfeA8li1PBWEzwrxvreGrVqSV9mM+qu6eKDOJU7c+pxGKXiiz2/ms0r3d0hv50otrtpswCHW7KLMRSsaYot8xshbjpPjoiTXS4VcGVoSp9TKYV2YiqrBwKkwso7Ts3wURYcKRBoQREC0HgaXtBMNm9weDZ1QZn+yEi0qVxDBIRi2HLwlUSWR/BsU3s4QyRCZdsaz3FvCQfM2nIOfilQyqCNh0LzhgXFQlL53mdfH735ysrpTaNOOTrTIQQQbBPabEqI8jQl11YRusNIo7kiv4mLvvlEMm05EhjIOl5ZrWFVEFxsERim2Gy4TqK+RQgOGBbtCiDQ2tMvidcrnhckRwLMvP/cVmCibVtSDdXcXiZem0qRcxXQuYd06USg5+7DWUaeAcGaS3k8EsLboU8QAWr8do+3P/2MBtL7W1iEzdP3IxEctXjkqjDpMvNO0z+FI66ouyxTu8/unyC/AfbuOyXQzSNOow2hXj0nWfw6+UTlfMNGSGeWmXw61WBhr451Ew8Ep/53n0Nmc+qu6eKDOJE92c+0qNTncUuFVns/T+RaJmZ5lRAB/SnEwsJrI/CfIKNCpfeFEh9yivRTll5diF9zLRGCacncEKTOvewB0ONQQY21ShoyCqKNuQjwf+wGyw09dxqAxtACAp2lOySEMv8VuJVU8gyn8daeS6/e283AFvft5aGLPiWgfAkqpRhdy2IeIKRJpvPrPtMJbho29NWsegcbQ4Rz3gUbYVtBkW9vhFkvUeXBFp7qSQRT+AZgo/8ZARp2bgxQeuER9dDHt/cKNhzto0jHYQS1IfqOVxMQ+mRxhOCVwTklc/eNQaPr4ZVredS8AuMF8aZdP4/OjNNY6diMYr79iFME2UYCBnIglwTYoVgvLeuE0ycNykb6GjvYO2Stfz6fSmev3pybPNenrZosF9bLBgnX/oM5YdwZeDxvyKxAjj26f22WBvPnZOi902rZzxuT38Pg/lBLMOqSJvG3DEi9iz37mtIeYzmsnu129sp9h1AZbIVXbaIx34xdRgAACAASURBVAivWPka9PjEyDLmIz1aLCx2qchi7/+JQMvMNKcKWnJzOnHpTVAYg9QLMLgneC2MLczhpkRbrI2CX7vK52ze8gDY9TDWF6xQa4VqVp6d1sc5HHRau7oIS5OQE0hLQo4i5At+/HaTF0Ze4Efrgsx3uOTMGXYnF1iSQLOEvFK4kQYar7t21inku566i0u/cyn/fpGLdIpkQ4GUx5TBhyYXAtNXbL/EqpFAlCUneS/Po+9sxfB8rKJPS6SFVhGnGDGYCAXHyrt5KBQJ+QbN0WZcoyT/EQIvZCItkw//yiRiRoJMcqSZrJOteOGXV6aVwEhQo4oj4PmR53lp7CWGi8O8NPbS0SVRc1DtGlNZC0DAYCP82Y0Wt33M5OnVZo18qKe/h3QhzcHxg/Sme8kUM9MkP53ndZIpZhjIDuBKF4HAlz7DE8P09PfQ3HU9+fw4/UO9vDiyl/6hXvL58drp/b3bAz/+Oy4IXvdurxn/ailU+bjde7qxDIvf7fX4638p8A//M8+mfymy7NnDs9+7x0C1E1PXg10LHnfgqOcCEL34YvzUELJYRBkGsljETw0Rvfji43YuC+FEyDLmIz3SaF4rtMxMc6qgA/rTjqkL6BzbgjpHCzYqAc1330nXjk/SQwFa3xhk4OcqxD1nQxDsx5dCIR28VgX/l3z4RkKfuhG/OU6DY1JsjPK9qxoYOG8JYWHy1CqDuzcYpOsF8UKQme9+j8Fzqw1W+aAMk2TzGja943Yu+fCNLL31FqxkEjk2hpVMsvTWW/iXxB6+9szXyHt5nlsT4u73GBxuEuQiQcCdDcNgM2zZINj5hkJNZrdal/7r1fDz3z+b5vbV1E34JJatYPd1F3PnVUZJvx3ouO98l4OXydDSdCaWYSGlj2VYtDa18cZCE7/8w19yx+V3sCKxgqJfRCqJKUqzDQSBfEmqjpoiA3elS87NHXMQpHI5rGXLMGwbIYOFu47EA3//MjErRs7NVa775t2bcZXLsvplCASHcoewDZtN6yYzqh3tHTRHmrGN4Cwsw2JZ/TISkQTde7p5apVgS+k6xkrXccsGY1Lnvnd7MOuTGazx4++YKFTGf9wZJxlN1hx3IDtAx8EY125zSGQluSg0ZCV/sq3Ix3NvXdDYzMaC6kvmoPpemulcAPKPP47Z2ooRDoOUGOEwZmsr+ccfPy7nslDi69fP+Jl6NZnK8uJx1cw6G6jRnGTc/n5EpPb+1AtdaV4LRNmfWTM7F110kXriiSde6268errfP13O4kwEQXPnTxbc3Gy61uop8ki6n4J0cYXBJq+ODhkKjmnaUNdcs9rpgqwzS3SVXFWiVhSG9nEABwcIASuVCUqSN0ySy94y62JPU7n0O5eS9/JYIlCk+crH9Scj2HLMrEpfv23ZumNuG8BTHrf8q8eFxvKaBZRkPo+VTLKiJP+Z9n7lg+/hofCqOzaFOquOlQ0rUUox7oyz7SPb5tVXgL5rOysLOx0YP0DOnSDsKtIxwW0fm1y995zGc/j+Nd+vvR4l8l6eZDQ5bYw2fn8jiVCixoe73MeZ5CY17Rzjvdz1YBcf/PL/IZZxyVuyMgMR8yxWrnpzzVgfKwsZg1fLvne9G6OhYdoYyrEx1jz8s+N6rNeKkzmeGs1Cqf4dWWam392aU5JTywnhVaI19KcTr6IodrbgfSYNa03BrO8SNUxA0W0WgoDed2G0F5pW12RXIcjE3/XUXXzr+W8x4eSoU4r1RUUqHGYgUk9b0xqS0SQ7BnYw4U4glaQl0lI6lkOrYXBISBwUyi1QMASre11+/8cHeexza0k1wtMbVnH5pZcHzjkzPFBMuBM1AbcpTFxgba/k6t2K5Fhgf1n2oZ9rIampTLgTvGU/XPVYnta05Ehj0M4PLoZzesaJEmR3qm0aay6XsClQQCqJwECZNgIF0p31mG7pZ3NKomahuet6Bm+7HQkU3UKNe04NpW8XUlsxl0b8qO3M817u2f0Vup//JgOySJsR5qI3XEHTSJF8WBKSgbxJYdDUeMZxy6jFn3yRDz86QdOIw2hziJ2XJ3nxjfETklE+lawETxSd53XOe6Vpzcnj9VCofDyo/h051+9ujeZEoyU3pxONK4IC02rmURS7UAlBzRS5GQIliQADeDC8D9IHQCpQ3jT7zLueuiuQuzgTWEoygeL+MLwgiyRyI+xN7eH+l+4n5+QqQfdQYYjURArMEDHp0+J7RKVk3DR42z6fP35IUnzlMNmIoiELV3xvL/f9+Gv0TByqfaDYux32bqdOSny/CF4xyIIDa3sV129XNOYgG4HGHFy/XdHRqxb0R+ziAxZ/sq1IIuuTiSgas4rrHwo08Vs2GGQT9pxShTVNa2iONAfSHCSWYdEcaZ7Ut8+AJ71gldljCIKqJRR1hSAzf/d7DJ5aPfmrQyAqkpvZaisuO1hH37Wd7HvXu+m7tpPMjh1zyraOWqMxj3u5Z/dX2Lzn66RkkYQwSMkiWw/8FKPeJ+KCFAJLwRm+R32+eFyC4MyOHfzn+8epHw8cjuIZj/f94BDL9wwdV41+mdPBSnA+0iPNyeV4ycpeD5wImZlGcyzoDP3pxELcZqpYkEUlUzKvsSUw1k9BSdpcJwiSUSBMGOuHhnYIJyrZ1W89/y2EEFgyCKTLgrCMAUIKxr1cJRssRODN7iqXofwQrfVnUhjrwwa+OJqhI1+k77E4B+0Qni0xjBBeCIQv+b3HofsPVWCPWV7o6uH/Dk6WP44IvhZWeChM38UXkqsfV3glz3qYfH3fEzYLofPpJnJmjqIdePSU2/nIryzu/kQDX3lrC1uu/OHs7y9lK5fWL63JVm5cuZEHXnqgsjhVNUII0sU0Xed3HVMQVHa2uPbfLme4MBy0WboIimAhq3Kw+vHcWznyja/TMupRjJgoKYllfWKuSbGpGaulpeICsfbWW9i0btOsWb45s7LzuJe7n/8mNhAtPexEhQnS4+G3KT76cxNRFAgLlCtRzjDNXbcteGymMrLlbmL1TWS8EQQKJ2RgKY/1O0Zp+i+fe9XtTyW+fj0vvOvNGN/7KaGijxM2kX/wPtZMCSYWezZ1ttlAzWvDQv8mvN7R7j+aUwEd0J9OnLMB+FKwkFT6YJDNnEW7nvnOHYzccy/uaIFrmgS73tlK74WT0/qzySgqDidjfVhKskSCJQSu9OgcG69IQ3oiFt0NCQbsIm1inM6CoKNxORNuPxf2St7/mMeSMcWRBsHWdQQZYd9DGiZrexVX7/JZMuYw1CDYeonBr1fDePYQbZ5L51iGjnyBnlgCKx9iPAqgUF6Q0c1b0DgGA6Iq+LWjgQNP4wpuoA68HN8yHSaEok5Jlo4JJiICqgJm3zZoPeKw7QPriA3lyLbW03jdtZzXev6snsRL0oqRRJIj+aFSKwI/ZNKalix5up+PPPwiT3/yXABeaTF44T9fzNNrjJpA7MLkhWw7sA1f+ZjCZOPKjXxh/RdYkVjBPXvuIV86T0tYnBk7k1goRt7L88TgE5VrdCzB3erG1eSH8kx4E5XFrwQC27AD15odO2i98wfU+xHGVZrkKx5m6YlMCIWfSgVFm/F44Myz5W467p05ACj72c/azznu5fL5PUkR0wCBjwRsoAV4+GyTpSl46xM+YQdECJIXeMflD7Lb3099QzPLxiT+0BCmJ/FNQasX5fzjEOhM9btOvXEJ+a0P4McMvGYby/Extz7Art9ZySUfvhE4ObaPJ9qHe7E/kLxeKF/n6/Y+wVhzhJ2XJ9l3biCN04XKGs1riy6KnQevh6LYX/z2CF/bsZ+XRyd4Q1Mdn1i/isvfuGTGfTPfuYPBf/gawhQI22DA9xE+PHD1EvZdeAYwc1FaJXDwHNzcEYYEuAJWe5Kbh4foKLigfHqiETa3NGErRUQpCmYIVwg2nfdxurfdw395oBBkw63AY97y4e4NAucNPuGXTa7bHmTLXStY4Mjy4dvvhn8OHQHpBX2JRtjc0syN/yaITExmwmHSl/4bH7V40CnpsJ2JwFaz9Y2BDKiMUlBI85Ptdsn/3qj43yeyknge0k02XsjEcnyieZ96u55oU7JGT1megi0XUB10B/GkhyEMjIJLQXg0TEA8X3KsEWAqGI/ANz8YY/TNZ1HwCwznh8k4GUzDxMTEx0cpxSd+9xPcsPYGYO5i08+s+8xkwXJV5nvTuk0AcwZNZTkUBIXCZa466yq+sP4LwbkNHMAoHqE4oqjapWrww0TWrDlhhZvVweuh9EsUS9ttgokdH8WFvT4ffwh8K1gczHIhKU1W/N2drzoI7bu2k2LfAeTwSHAfGQbK8xCmSds/feVVtV/td12+t3IHXyJbb5CPhyr7mUWPQmOUjT/eDZz4otKZ+lV9z79aaorsp9yzOqg/eVRf54POK4iii+0LfvqhM9l3bkIXKmsWI6+rolitoT8N+MVvj3Dr1j0cyRRojNocyRS4desefvHbIzPuP3LPvQhTYISCVU4bbBPXhEt+OTSrHzZUTcPm0yQQrMJkuTJo9H068oWSHl3Q3ZDAVoqoClZ8jUofu34J3emnuXpXlbRFBK+eCVfvVigEH6r6uSr93DWhc6cXyHjMUOkYcWyl2PU2Oasv/Siq1uu+eQ09MkuXPc7GUJoue5wemYXG5TyyvgXbp+R/rwg5ivhEEHD7YQshBH7YIlyQ+NncrJ7EZc1zq4ijpMQsehieD0IQLQbdMVWw+qqQweJN7340V/HfzjgZIMi+lyVHQgi+9fy3KtdhLv35bJ7ed/z6jqNqYp8YfILWaCthM4xt2NRZdSytW0oqnwJK9m3uKCBmDuYBnMAt6EQVblafn6p2Eiq9+sAHdoM0wbMFBgLPhlHbPC6+0c1d1yNH0yilgoJbKUEIjMbGV93+jH7XUlGXr5VZeSGT2FCu8v2Jtn080T7c2of+1KD6OrfUJXFsgWsqLv1Fata/CRqN5uShJTenAV/bsZ9L5a/5qPNDluYGGTSW8h3rg3xtR3TGLL07WsCITBZZxpUAyyCSlhVLwZmmvCvOJL4DRvD+CDBgCTDsitxmwLJIlDTyQQAOkboW9g39hsaRIpna2APPhCVjMG7ZnDHm4oUUHiYShYGgQXg0jwHCqBSxDlg2Cemzb6Xg4HtMrt6lSI4pUg2CH18ieG61hVIy8LovyTV6Rp9n856vYwMJDFJ4bI7ApnPfw1PxnzL83jreszNPMq1INQrq85CpD86xjCkJIvEqqj2Jq5dPF30u/Y0u33nLBF3bfCyv9glbALaEttTkLJqqWjfA8Z2KZj7jZLjrqbu4Ye0Ngc7+kVug8DIR36NgWriRBjov+hSf3/35ae4x5+91eMvDvSwfAtMHaQmOLI3w844E3bHuGg/3i1+yuOyXPk0jHqPNBo++0+LXq4PA0G5vx3thABGyABn44ledS/m1XLgZvfhi+q7tPK4yjWp3HGUILGkgVTBKFgJPQHIM3IgInp6EgTBs8oY6Li438fXrEfX1UCigXBcjFMJsacGIxV51+25/P0ZDQ8023zYw3dr7zXJ8sq31le/ns+Ls8e7X8fThXtCq1JoTRvV1jtsxiC1jeCJFw0iBZDR5UmVQJ1ripdEsRnRAfxpwZqqHm/xv4GKRIUaTHOEm55/5UkoCl0zb326K4I0XEaHJoL7eUTQ0Rub0MK8EDmYoCN6FQQFo88pCagOUos3zSJkmUSjJEmxGMofIuhlSDdCQA6ckkbGVIuRBPg7bMiZ90TxeXmAYkxID6bhY9X4gtylJbto8l5RpEVWKZ1YJnloVhMoGgUc9COrCcbh5Z6Wd7ge/i12/hGg+Db4TnEe0ke700yip2LXSYdfKyTG59ds+jVkFVXIe3wAhaie+pmajqwuozgf+/cEuxn71f2gez09b50sRZOvLlItRq4P5MmU5zA11a9g0PEx3xGDAMGjzJZ3Dw3RMFKYFd2ueH+e9PxxAOpJoOanvQPNQkQ9tHeLfpQdXBpsvO1jHFT98EWmZkw4uPxwg+vtnAyX7tk8/iXR8pAnCDwJ4zwheDQnKNLCSSaIXX8z4D35w3JdLrz4/27DxEBgimNFY2bCS3nQvQ42KZN7CDQXXSSlJ1Dewlx+fGQM7mcTp65ucyxXHZ0ZiJotKK57AT6cxi15F9mV6isbrrq3sc6JtH0+0deaJfiDRzI+p1zlux6gPmVhtJ1dmUy39OZ6/OzSaxY6W3JwGXCd+TFGZFEUEhKAoIhSVyXXix5M77d0eLNZzxwU0v9lC+RLpBPps6fgoX9FcFSTMRMWGMNoYSHOUjwt0Fgx6IiE+0raMN69o51eRMC/ZFi9aFuMo8tFG0s4YTb7PzrepQNrilhKonsDwBY3nZmHsZZrP9xnCYp/vsUd47PM9Rl0L37XY96Mm+v6jmcyhEJ1jGcYNwX679plVAgVAKp8/PvePa342kB0gUtcCrWtg6ZugdQ2RuhYGsgOMOqOs7ZXc+m2ff7rT49Zv+zy7PPBkl/k8eTcfvIZB1YUXZCPYeV4n//GOOFAr6BOAFOBbgkwxQ2+6Fwiy9FOD+bL05hvPfoNLd/6//Fk9PG8orvFttniNdMgI7Pwyned1Ml4YZ396Py+Ovsibf3aQvPCod4KxkYZAGYJoQeKasPGxYuUYV++WuEZZ7iQCuZMB1zw0Tt+1nQx+7jaMxiVgKPLh4OGmHMxLA7Ixk/u6zmHFvd3kH3/8mGQamR07ptlfzngPenmaI81IJfGlT0ukhbyXJ27HefiyGIbnYzkyWJW36NNkxGa8RpUVj7+/ka4Hu2a05avuU+/V1+AcOoR0XVzpUSzkyL98kPzo0DFZSe667062fWAdj7z9fA68/Cz50VTNvRWJxHD/6AMUGqNEJlwKjVHsT91QKYiFE2v7mNmxAz+dxunro9jbizc+ftytM4+2KrXm5HCqWKSeaImXZm6O9jtY89qhM/SnAavtYfrcMEIpDCGQSlEgzGo7sCBk7/bAAtAIQaSJeHse3i4Z+Y2NO+ZhN0Vovu5a4h+9uabdqdOea7uun7QhVB5thRyd2SI0nMGtZj2jfgG/KgXtCsGAAWdHGollD9Ps+7xwlsE9GwTvr1rA6Rdvgz9MuFAUfP7NCQZavIqEJm+DY8CgXc9SMnh5g8EnG7DDeUSrmJrwBoIAs17B+a3n12yfKxO49JkBrnsoCHKzEWjMKq54Fn5+AVxwUFRkON+/MkbUitL5jE19amxe08Ed7R3wJ3/L8M9upnFwAquUkXcscOMRhpoNDuUOYRs2LeEWhovDNdKb4JwEvvTx8EryEsgLxdesICC/wa6rLLokhECh8KVPa1qSi0DID/TllFZONT2JY5m0ZyanH+pTWUTTmQwXAk9727RpteNYB1/BE2GMhoagIDLUwr+8a4w6KbnsV9CUEYw2R3j0na38evlEcO2PQaYxn8zcVHecVQ2rQEDOzZGMJvnURZ+Cy+Dn8X/kwu37SaYFdctXs+LGT067RvNxh5naJ2f/fqTrMhZVRBywfIFvwlAoz+gqwUJC6F333Yn793cRsQSFOhvLccm5BaRKUDdWrNxbUy0qZ+JE2D5Wn7vVdib+kRTeoUOEVq9m6V/95XHLlh7V8UhzUqiWC76WUpcTLfHSzI6eHTm10QH9aUA0eRZvGD7EYMHA8SQhy2BpRBJtOSvYYeeXg2A+VBd8H6ojvjJH/MwxiDRAYxu89byaNisfbFwMbxTvhQEGP/0ka2/qYstHt1Rs5j6fHWDcGafg+Ughav3LEYTNMI2RRhpdl5RpMGSavLIKfrNKBIGpUiz1Jd1+go6RCbZZEn+1wdOrg3Zu+baHpRSOUeQMw0DYEulB+vkY8QsVS1yPA7aNV3J8sYCVUpA3zGmeyXNJE1L/+DhuxYdeULSDgPqCg4LbP2YRNsPBQk9CsLT+6H7yU+lo7yDz3/+xxi0kUnIL2fbuOMsTHlEryoGxA9imjeMHxaWCUnCu/ErW3ir58FgExaDfMh1uyAtoXE73nm7i4ThL6pdwYOwAQ40eDVmFa5ZmGwQgFb5lsMxuIb5sRaWPdns79akU8cTKyrZiby/YdmXZcxGNkvUmWP8r+NE6g4KhkGWZkHRoi51VaWuhMo3qzFz5WGX7y+o/JvMJXjv+vAP+fM5d5uW1PbVPyvfx8Ik4gpElpeoKpYhOeNy9EI/uvdtJf/V/ERES3zQQKii6zgOjVoaND+yeXzsnkOpzN4hixRPBkvdNTcf9j7v2oT81OBX81k+H1ZFPVeb7O1jz2qAlN6cDl95EveWzKiF449IYqxKCesufXIQn3Rf4sJcpjkP2CDi56SuplhjZcncQzBePIKSHEbIQ+Ix8/Z+DFTqrHFPybh5XutOyyuVAdCA7QOd4DlcIiqWQXxLox1t8n4hSDFgWeAWmmqcsSSuKVrlYNAgchQmxjMADDtg2E0LgEqxBNAEcEAo3nGAgO1Ajqeje083Vq68mGU0ymBtkKD9E3stzx5N3kExLitZkzyGw1UymVeW8ynKAl8f6eOrw4/T8zwsCGVPVuM3FbCsOPrp8gvP3OvzJXb383VcybPpWkbf0To4hUAnmDQwwLfzS+XpARih6jAJcelON44krXX76dhvbh2ypJMHwQSgwEw1EpVUznV6ecs9mhjkw9hIHBl/Acwq4zXEybpYD4wd4YfQFDjqDnJnyue5Bj0TGZyzkEUkXeP8PXyG0ew9dD3Zx+JqLFzx97/b3IyK1FdMnMjM3H3eYqX0yQsFAWlU3qu0q0s3h+RdxlmbMYmMSzyaYNfEdkP40B5tXy6uZPj/Z10OjgVNH+nM6oj/zpzY6oD8dOGcDvPdLEF8auLrElwbflxeUalwRrLRZJluys7QCzT2huiCDv/PLlV2qLQopFYEK28DNlFborLKZC5mTBazVCASmMGmLtdERW8mm4THqlMQXAkspzvA84kpREAZtng+GhTml4PRIoyDslYpF7SgYNkoK0gnFIcvEK80KVLuteKbJK+44SqppVo1be7dy0dKLqLPraI22ErWi7B/bzysNirBX2/+wB6nGoH1f+bglFx9DSQwFm+sNdv3mIH3/9Sb2rb9sXgFTfP16VtzbzZqHf8aKe7uJr1/PZQfreN8PB4hnPHLRwP/+uod83vqSwCiNhylMbMPGMAx8Ffj/l1X2Atjc0kJPXaTG0tI2bJ5eY3LPlRaDLYJcOJiByEXhpXieoRs/VJN1ia9fz9CNH+IlK00o5zDREGZwSYhUcYRD2QE86SGVJOQqTE8hTRM3ZKCEIG9LlGXw/t0eqXyKz6kfM3Tjhxa0XLrd3o4q1NpxnsjM3Fz2n7P1yWxpwQB8EVii2o7E9BQ/70jMv4izNGOWbTCwPErPqQKkN83B5tVQnmXzUqma6fP5BvUn+3poNDB74kNniE88+jN/aqMlN6cL52yoXRG2XASb7oNwIgj0IQiKvUIQpNcnJ/e3oxUNNky1KAxQHtgNFgOySKIqs9kSaaGYK+IrvyZLLxDUW/VBcdtZBXjg/2aZL+k1DKQQKAR5YeAK6Mzm4R2fZKMxyv0v3V9pZ+s6wfUPKWJ2HZlwlDGnCJjcd4lBOWOvqteJAjBMlPQZdUY5o/6MSUmF53LO0wMs/do/8TdjinRzhB9cIjiyyuCnb7e5dltgZu9YEPIEtq/4ySUmAlEJ5k0V9CyJ4E37fdQvfTwhMMLZY9YbXr1bMmKAY0mUFBTtIFT/4C7ByNoVlUV2nht6jq8+/dXJgtnSebdGk9ihGN17uvl47q0c+cbXaRn1GG6y+P7bgiAcwLEFA0nBo+9M0vumBMnok9M8kL5e/ySpP31DjUvOe77fh1kEI2JhO5KQL/BMRd6SwToDBUXjBIR8H5We4IIXXZ492+br0SfZcm/3vMehuet6Bm+7HQk1CxidqMzcfNxhpvZJWBYk4gyFC0QnPNLNYX7ekWDP2WE2zbeIM90HkSYaL5K4D08ACs8Cy1GYyqhxsJkvM9n8vdrp85N9PTSaMqeC9Od0RH/mT210QH86Ul0EK0wY7gW/CE4WTBtQ4LuTAbwdhVACWldXmmjuup6+Tz/BqOeRtyHqQpM0WLHWoM2wSPmFStAXD8dpla2MO+O40sWXPoYwWN2wmpvfejMd7R1BAeLSZdiFMc6UHkcMwWHbYLUPnzKT/P/svXmcXFWd//0+595bS1dVd/VS6SQd6JCECAYkyBIEO4CKICKIOOMyOjRmdB7wh4LPz3GGZ1wGZ5jx+Tnzg2dmjPoz2KijzqJIBAWCYYlCYECD7NnoLJ2k01t17VX33nOeP05VdVV6SWdBltSbV7+6u7h177nn3kp/z/d+vp9vz+V/BUsv4h/Kx7+3/1587fPMEodnOk/n/A1jjPdvYX+L5Odnw1OLTebatib87ysIrZmvYI/K4yZ3MGg5uNrjrVtcrrhfUbAhH9TExvN87JcCdZHkmaVBHn6Lx2VPCIJFRT6geez8DuIB+OsfjpBIavKBcsFtCfa3CCJ5TcaGiC2JKfeQA6ZKHcI1O7ZSCkm8siWnEJKSrWgdK+EMbeHzo0l6tvwJz76px3SHrVE2SSRhO0zIChF7agsd63cSKYbxs0maxz3+Zz8UApCMQDYsaM9KPnj3GPfYoaq/fC0DmQEkkv5sP65y6Z/vsPtii/dtVMxP++xttrj7HIv3PubRklFYvqYjXXbQ0aCF5tI796DfP2/K/dcyVRDaeZSK8mbjYz2bYsypCgW7//ILjC0S3F7zvpumKeKsXOO6/ce7IT3IOd1RNr4Tsk8WiI4rMi2S+P9V72Az23OdqpBN5fOIpia8wUFUqYQMBJBtbYf0+Fw0NeH29wMQOOEE5vzlF9i0SNB336pGAWuDBm8wXiuF0Q2mRmg9lQ9Ig1rOPPNM/eSTT77awzh69F1mdPHag/HdTJglatDKBPDFdM0bJEgJK/8CLvgCYAKRn97+ed6xIUV7SjPSIlh/tuAD8ySc+Ulu2f3LQ2rVFbDYyQAAIABJREFUfqTt6a+66yq2j29n+XbFex/zSCQV++OCe95m89sTJrTmAiPnaVMwLsATRncmgL/8oUdLFlwbHDQS0K4gGYV7Vkh612lEIEDR1oR8SWvJIetmyYctlOuSSJmxDDWDb8HcURhuATcIc4VDrH2p0XyOj7PkVw9MeR6VAG9bchsZN0M8GOfGviSBZI6SY2QylrRw8iXGmzx+dpVizdA4G4IWNyRaUdLCZ6Lo2JIWARmgM9LJdWsGWZQOo0ZGy/7/El0sorRmpN2hEBJ4ysMpKcajkh99+iR+cvlP6ud57VVsS27DkhYSiUJR8ks40uHE1hPJlDLsze5l2ZYin7hfEc8YTb4oz/FIi8CyA4xHJT/77OnTXtvaILQ2E3Q0Hq2/kvs+FGpddOo+JwveQ8/GNWbB7YSNHE6V6mVyh8COq3uNrKamiFDl85T27gXXNZ9tUW60pRSBRYtYvPauGfc53RwOX3clf6N/fkif/QYNGjR4lRAH3+T1Q0NDfyxSKYLN7AfExB903zW/l3Kms2tFry4ERDuhf8KDu++5Pra9ZQ4/+PNubru2iR98yGLbkgB985fQs+Izh+x7faTt6fvT/Szfrui91yWe0VVryavvdTl9+8SitSKJGZPQXqut14rEuCl01QJKQuAhULaxz7z0cY2ytPFgB1paOvEzWYIFRSkgaMmBL4xLTDxntOieBa2ZciBrmYZUM+kNV29azQ0P3cBTg08xWhjFUx6jhVEeOC+K42sCLnjKLeuyfR47UzPg2CAFfS0xU3ugFLYwD94q1pRFv4jruyxIO6hUuhrMm400AoilPVzfRWtNyRYkkoqR3Mhk33VdrlfQpggYbZ4CaHTVJxxg02LJ7e+WSA1SmwXOcLMgF4CirWkdK83oI34kXtMHK/R8rfhY17roCCFMMyzLoS/59Mw1LwdwsPOdrpAN161efyHK/lNam6c8B2G6OUx+946pz+m5vsObpAYNGjRoMCsakptjkfIjffySkdyAyc6B+V25YAVA2OZ1raCpo05DX23HbochZNqyh7RmoGTS1Ae1mdu8zhT/JXdAvJuulnCdTAcOsRukhvc+5uFaUAqYQlVjLal530bFcyeG0Vqj/RIOENIAgvkaRoSmKAT7W6Bz1EhmHM9k6vMBQbJVM38cskFwpE17uIOYEyWjFGhMVtsvy0rEhMPJWATmpCDg2xSlP6Mbw4bdG1jz7BqUVjjSoeAXqtr2R7vzpN8T5pJHiySSmnSHzaPn5Xl2UblYGBiwLQJa4wOWtECBpz00mianiZtW3ERs7RpyTz6JcGpa25YDOdsv+9NrU/w73h6iOdQ8ydoz62WZG5nLaGEUV7k40mFO0xxybg5HOuxM7TTFuZZEyhIlWxFyKx1uNQhJyJfEuk/gtBnuD3f3brQl8foH0aWSCR7bDy4HmY1P8mvFx7r6Gaqhuog9sOZlGmZzvgfa/PnpNN7+/eB5EAiUF/M+IhDAmtuJymQOetzp5jC6N3tEC/MGDRo0aHB4NAL6Y5FzP2s09MIC5Zs/6GgTxGvfvK6VydDr8utuHuLHV3dxRO3YD2hkRXqQ3myBW9rbIXR47ekXtiwkkXyeTLgaOtJULsacN6b4mx/BoxfMYWtiP3lVIlG+9YeEYqGCfgnPHQ8n7TbvVcIE9QEXQm9ySagwqQIMA/syexmxHNqkCYCV1rgWWL7JRnsWSCHxbcX+ziDZJklHUmEfn5hWb9j3XB++8rHKC6yKnEVphatc9p46j789YQBHOkTsCGOFAgrYpyWro2G6PB/PloxaFgqFJS201ljC4ms9XzM+96s0+U2b0J4HllVtIoUQ+MInIIPGoUZrHrxw7pSBWOW6L2xZWH0t7+VZEl0CwPHNxxO2w8z//SBX3DtIIQBBFyxP05GCrC+IRaJ0X/e5Ga+njEYpbN5spEMatOcicjlCS5fO+L7ZFHq+Vnysj+gzVGY251tbyKZcF2/vXpOJDwbRvvn82/PnYUVjxkd+XmL6A5aZbg4zHREKR7Awn01tQ4MGDRo0mExDcnMsUrGxbF8M+CZwj3VBuB3QEIqbgN73zO+BZqPhrfjWc4Tt2GsbWZVtMXtUiJsKclYynakkBje89QbG2gIEXCMFCRcUHWkI+BLXgkiqxKV3DnDcrhAu0FsQ9HpBXK3Ia01JSJbtEiQj4Nnmg+HZkIzC8P4Ae9+qSSoQhRJSSEShRM7R5IIQdDXJMFhleUmyiarbyy8uaeMbqzrx/uOfqzaUUzGQGSBgBapaf1tOyGYquvnWYCtNVhMjxRFUWRaRF4JvtkRIuC6OhjY7goWFq1ykkKw6ZVV1DmMrV9L2qU8hLMtkZG0bK5FAxuOMz2kinPNIx2x+ceV8tp7cPGUg1rusl+HcMC+MvMDzI8/zwsgLDOeG6V3WWyebetdvMijLYjwiGG4xcyk0BEqaNRdJNi2aWdaRTQ6Vnw6Va3w1oLV5fQZm45P8WvGxPqLPUJnZnG+tzZ8/OIiwbZwFXdidnVB+KuMNDR/SPEw3h/Frrj7sczpSG80GDRo0OJaxvvKVr7zaY3jN8+1vf/srn/rUp17tYRxd2hfDWatg3lthfJfR6radAKd9FLy8aSolJTgRSCyFd3ypTgLQ3dxNd3M3W8a2MJwfJija0COXcM8TLax7fpCOSICF0/llP/T3EIyVnwyUKeXoHt7GFalxPlYUXDE6RPczd8KLv4CmRHnxMfFHXxeL5IKwf3A7++77Ofe4v2PrcTbLXswhtKY1DZYyrirFtghuyML3PbpHLd75wV56xvfTnR6mO5xgS+t8Bt00Vz7ikg5DugnGI5AJQ8mBWBbWfORNbG716dxbIJbyGGmGH1woGY5pTnsZWnLgWmYBIIHRmODulYLM8YrrF3+Qnjf/8YyX48FdDzJWGCPv5cp+7n5Z2wwxJ8ZxseO48Ywbuaf/HjzlIRAIAcu3KT55r2L5EzZv6w8zEIWdLS4Okg5f8dKeJ3jw6dtpKxboXrCCyNlnEVz2Zrx9g+C6WM3NyHAYp+Cyp9nn4ZVxXn5za/UJyfWnX09380S32NWbVvPsyLN1Yy+pEt3N3WTcDKlSCkc6rLx/HxnHZH9dGzJNkvEmjSUt7r5yLlvGtnDFkiumnY/Bf/zHmlZh5gclTRa487pPT/u+9Pr1qFSqTlakCwWcefOIX/l+AILd3XjpDLnHHsPbuxftecQ//GHa/uiDM16jo82Bn6G5kblcf/r1dYvY9COPsO9LX2b4X/6F9Pr1WO1tBLsnrsdszhfMOcevfD/J//oJ9rx5yGAQGQyiPA+dy0GphPb9Wc9DsLsbZ+FCii9txh8awpk3j8SNN7D4kg8e9JymY9+XvowuFqu6fOE4oDXFlzbzzFnt3PzYzax+ejUP7nqQtlBb3X3Z4A/Dwe7HBg1eZ/zNqz2Ao0nD5WYWvOFcbo4yD724ny+tfQ7HEoQdi7zr4/qamy9fxgUnzZlwbhl5nlIpi6M8lrg+vX6IHisOhXGzqJAOxOaanwGajzM2mjUOHxXHjqztsy+zFyEEdtFnJKL42z9xWNHv8K7fZDlpp8a1IRkV5IOQaErQEeqY1mFm9abVdHzhn4lnNEWnItqBsGuRjwf54ocUnjaBtIUFAk7ZWuIT9ys8yxTTBj2jRf/eRYJPzAvRI6OzdihZvWk133x6NeqAz+N7S/APF/xT9b2n3nEqYDTpp23zJx3f8eH2d0t+t0gigS5tXH1c4KZln6RnxWeq+z7QqSSbGSWTHePf39tM+owTJ9kNbti9get+NbVloiMdbrvwNm55/BZc5fLn39lTnkvKiw+BU9KkYpJ///SbSZVS3HvVvdPOx9OnnAwKtDXxmvABCac9+8K075uNg81rxeXmYLwS51LreOOn07gV+U0ggDN37qs6D1vf+S5kS0tdUa7WmtzoIJ+/PtZwznmVeb18bho0OAQaLjcNGtTyrUe241iCpoCNEOa7Ywm+9cj2qjXfjtGXGC9lyKNJS4sdluSWQIkN3hik95kdNc+D7JDR8AsLckOTutRWJAYj+WHjriEkedsnkTTZ30e7C9z8UYsXjxeMNEMhZD6vI/mRGXXSTw4+yQPnRbB9I6FBl7Xfvs8vzwnga1N8qtF4eHja4/KN2gTTDsYsyId4Bm78qeL4n3qk+0tTdtmdigd2PlB1HKllk031vbWOMxrNRx80tpCdYzBvzBzfteB9G82iQAH7hSYsLBxMB99aDnQqicbamdPSxY0vLWLNxWsmBUszOZW4yqVnQQ+XL76cseIYa1eI8lyaoCyWUcxJaubv9/n46q2ct7NpxvnY124WJEJhammV+ccqGRUzOrrMpovk6JrbSeosm/M7eGH0BTbnd5DU2T+Iy83BHGlqmY0bz6F2zWxb9Qm88XGK27bh7tpVdbqxEx2vmttPhem6UO6OuQ3nnNcArxV3qAYNGkxNI6BvcMTsGssRdqy618KOxe6xXNWaL11MITFV2AJIW7YJMq2C0eu3HGc61volo+kXwvwMdV1qK3/0Xd9FlG01HVczFBfI8u0sENWAMlAywbld8mfUBw9kBhg7/QTWXt5JKmYRKwhSMYvvXuzw0kmRug63FRLjmmK5rDxcgES67LmuwMsqBh9Mk365OKnL7lT0j/dXg/mKZzvAgIQNmR2s3rSaGx+6sbr98m2KBcPmeL4wBbmJNEjPjKtCsfw9JCQDqkgts9FfHzhHB+PJwSfpinax+U1RvneJ8ZtvT0MsD+NhGG0WRFMuV64dmjGY/dk7Y6TDpjhZKvO9EICAqw+qsY6tXEn3HX0s+dUDU9YtjGx/gUFvrOwiJFBaMeiNMbJ9+sz/0eBQNeKzvT4HO98DqWbAq85WE8vIV8Ptp8J0uvx73xZsOOe8BjjUfy8aNGjwh6XhctPgiDmutYnu0d/wEe9ndKpBBmUnP7Lfz46286rWfC6aSsgvAFdAyAowIBUcd7ax0QTjqOO7Ez9DncNOxbEj7EsK+AQ8gfZNAymFsXnUaDYtlnz33YIrHoeOpM9Iq03nTdNnLiuOI3ve0sl/vKUTMO4tu1K7IDdknFYqG5fjn6EWQTxrZCXxnMmIC8CzBdIRKBdGn8wR6/LrHIKmRIDP5Od/Arg1atP/7HeqNpYAlz9ung5IbXzzNaYTa1sWts6f/BTx+G0e73oCtv7bu6ruIYfq9tIV7Zo2iApIc60q17s91M7Ti/fx+8U2f/WDAi0ZRckRBK0g7c2dhD1rxo65pRXL+JH1IhdsGKdjTDHcKonlIYwzo6PLbOiP5IilwQ1MLKEcV9Mfy/HWWe/l0JmNI02FDbs3kG1KE9w/COFg1Sr1SN14RtfcjtXcjNPZSam/H+W6CMAfHsGKxl4Vt58K03WhTOfvoJAf4tQtLuc+NETraImRVptNFy18VcZ5rPJacYdq0KDB1DQC+mOFA3zfOfezM2q6//Wxn/P9F+4gp4doEgk+fvLVfPpt75ty279csouODd/CxSYtorT4I1zvf4vhJcfz/7kmUHYQeJXuq1oj0WxHoaVkVYtDb7ZATwmIJEw2W/smsN//vMkk5keh7zJi536W5667kvE1a4gNF9gflzz4rhY2LSygfR+BqGbTf7dY8PRiiRA2lyy8hHvydzDwk1umbEffu6yXWx6/hZybI11KU/ALxree2ki+TLlac+05gk/cb/6n45VfFpCLaOJeAYHGHZNQyMPFt1RrCQYyA2YM8dPoeeF+SO5gYdxms4DTtiouf1yTGNcMtQh+sULw+xPBVz5STDxQSyQ1Y1HoSJugXgmTrbd9iBY0/7zaY6jFPKno1Ior1iniTXFkawupvTvY81ef4TfLg5y3tUjUayXi2OjkILrk0nZGztwvB9wfvct6eXH0RVLlXgO1/NmpfwZMLIxiwRgAI4UREklFLiw5rvk4Yk7UTKGtcXfvntamsHdZL7dkbmHHsvaqbvovvrad5o45dcc9nAzhXSvgmvsEoqQpOcaa1PYFd62ADxzSnqZm0nUu32uz9b/fsHsDP/3eX3NZKk9i1MOTHsnmAiKaIKzsI3LjcXfvJtdkMZLqxwoWaC0opLSQxeKr5vZTS2zlykmLm97dgp9+7695z92j+LZFNlzzlOeMRxr67VeAqT6XtfantRr6V/N+adCgwQQNl5tZ8Lp3uan4vrsF4y6TT8KWX0Lbkqp7TC3/+tjP+fbz/4hPCYswLlmeGvoNqtjO2ce9adL2cx/+ApZfJKMCeEoj7QAdTQ7HudtpO+d/8PCuh7GFIOcXUFqjyl1YEYJOX1OwHB4Oh+i2QnSnh8ErTjS8Uh4gIDIH3DwbXr6Hvw2+zKYVCR45t5l1b3bpbynSEmihqIrVLH0FjebMzjPZmtxKwS8QcSKkSike3vVw1WUEjONIupTm0b2PUvJLxstdWMxUND7a7rA77tO9H9rSoCWMNkPYgYjWaF/gRDTxpZINnYu45YXvTowhvZeHBzbQnc/R7bQwv+Syd6fHNesUAQ/yAYgW4cytsLddMtRm4yq3euyztpgGUIVAuRi3ctoC8sHa92vetAtag3GibV1kvCz7CsYSsiUv+cVFcVp2jBAdSRKOCBJvjxGbV5zy/qi4l2xJbqmbB4HgrLlncdbcs2gLtfHwrodRKJqcJkJ2iGUv5OgW7cTC8YnrUiggQiEyD/zKOJtEo6hUisz6B3EWLuTEU1dOcktZ+XKYaIGDOrps2L1hRkeU/7XvB+xs9Vi433QTHmuW/PuFFltOirHq1FXTXu/ZUKkZmepea3ty26wcab635kYu/dkAUloUA5JgSRPJK0abFCd++ZYjCmB3/fKnjO7fiWeBCti4lsYp+VjSIrR4CYkbb3jNBcjdzd3M+9e7KObSFGyNYzm0N8+lyQpTfGlz3dw1OHJqncRqP5fR888nev75k5yNXmv3S4MGh0DD5eZY43XvctN3mZG0BGoKEUs501K+9+5Jm59z+wcp6CSWCFZf83WRkIiz8RP/BRyQhRwboFdF6NET25Mfh8weiHayIZ6gr6WFbcltlLRPToADJLQkhgDpkI93kQgnWLN3cGKsI1tr5DcOtC9hlZ1kyJKEOyYaDOW9PMP5YVzfRWlVzWQrFAJBwArQEe6oa3aT9/LmeBevqb626r5VDOWHGMwO4mkPiaTg1RfpVZCABXjl9P1p2xSr7tfGlcXSHO/ZaKXpvDBGrMtnVUwzFJ8/MYbhrdUGV2tc0y10/X8N4eeMhKcivgm6mnxLkK98RNYF9Mu3qarDTcmGkCdpH1ekw5CKmMWSJSRddgfOvlECS5YghKA/1Y+nPCSCcN7ntptOJj+8mYSvWONNBNzT3R/n/uhc8l4eW0w83PO0R9gO8+hHHp18b0S7+GT2DDq+ceckdwwZiaBdtypBAUxjo0SC7jv6Js35bFw2KgH1TI4oqzet5lu//xZCGMciHx+tNX/+lj/n2uXXTnm9Z0vlHprqXrs1fPWsXELWXrqclgx4gYknMnZJMR6Fy3+x6YjG99X//QEu/M8tKNvCdYzUSHo+D/7RiXzxxp8e0b4PhUNtIDWdA850rlUNDp9aJ6QKM30uGzR4HfOGcrlpSG6OBZI7TEfWWmYo1MzpId62vchlT+yhbVwz2iK4++xmHltkAsraoKk50MyQPcgtKstNvqBHBYwNZapsQxlqpWd0Hz27nzOZd6eJS+a10yzkxB9nvzRR5JYcmBirXypn6nW1QHZAQrPv1Y03ZIXIuTmkkNXCWACpNZ5fIucVCeUzgDAFuFaAUCTBgF+vB6/q/ZVb7dh6ILWffrcczDta8/RiyZp3K654XNM5LrBjkrYzm4idEAStGVBjSN+jP9uPq1wc7dIuBAM1TxTCKU0qaLrMaq0RCJStaR8ponwbS0gUmtO2+Vz+uCZUMhIb3xKMzAtTLORJRybc221ho4NG264LBUQ4jOu7SGnhlBRjreb/hXyPAXlAfbwTZkNmB333rTILMVXCEQ7pUhobG2qmx8IiU8qw6r5Vk2QmFdIdp0wK4Ab/5uYZJShTSVeWT6Gxrg0EK0XYlYC68r3vub7qeK5dfi2tv+sn8O+/oH2swEirTelDl/LhIwzmYeIeqqVyb8cunlojfmAgm0hCJqTrHAtKtiaRPPy/PZUA+sLNmykFjRtVOO8z1hrgN+fP5bfH5w5734czlsrCprY4mBnceRr67T8cs5WGNWjQ4LVFI6A/Foh3T87Q1xSaHsj52xV//MA4vgW5EMSymo89ME7gXR3AFEFTdC6M76ZP5ujxnXobylKaDf44fR0tDNgWXZ5PxCtRsAKEpVUNsKtdSeP2xFinKJDtUjBk2YRrxlvwCzQ5TdUMvUCA9lG+iwUENBT8EuGyqwnKpZAq0NW2lFq6ol0MJftxfA8PF1lu3lT7EEtgsvOq5n+45YXJpsWSTYshoDW3ehG61URRbyTksC27B0taWMLCEy570CzWE2HbUIugJavxAkFQPvglbFcz0ixY7LrstG3e/DL0ljPzI7GK97zmV2+PcsEGn/B4ARwQSqN1kZHRAdq75mKXSijAkTai6GL5gkcvSJj5s2y6/Hqp0gaV4ZaohZvawXhxHAQUKLB8m+Lyx4vMSRYZjkvueZvNUyeYeRjKD5kFXn6IWx6/hZu4qa5L7YHB2ugMQdqkRWNlnytuomeGLOFAZgCBYDA7aBZO0qEt1FZXzJt+5BHO/LffIgLHI7pCLCgU0P/2W9KLj1yPXakhqM3Q13bcnWoeDqTp+IUUdm/DCwqEkGitsEuKpuMny+NmQ20A7UaCyKKL4wvuubKLrSc3k/fydIUTh7Xvw+FQioMrNPTbfzgai6cGDV6fNGwrjwXO/axpblTKmSC0lDO/n/vZKTe/5vFxI+Uo+6uXHPAs8zqYoKnORi7UTKi5iwFLmI6zNTaUG9wxbmltZsi2aFaKIcti1LJICZ+8NlKHfDg+0R6+dqyRhCmO1b7pFlvK0VtQuKEW01o+P05+eDPu6Mt8vCiICBulFUorfN/FB2IIPp7KkJKC7Y7DFsdhu22RQtG75XEjR9q8DoDe+Gks/v0e/urfXP5ptccXfuhy+lYfKUwgb1H+wAgJiNokdZ3VZKtS9MkD5rqpo7zQMFIBpGW21351u6fPEljKZM/xSjiuxvIFT5+huCGZocP3ed/GciMpR5SvjcC1BOc9PMymBSXmJGHefk3HKEQzGsfz+eHbi/zovTGeY4BA1iUZFdx5eTtbToqR9/K4oRZ6C6ru/ugLuDjhdtKlNFJKbGFz2jafVfcr4hlNOqRpzij+9N4ib9nm0RJsqfqE+8pnKD/EDQ/dwKr7VtX559cynU1h26pP1C0aD8V7PGJH2JfdV5YVSTzlsS+7j4g90bX4UPy0D8U3HkzhsOu75v7U2sxv5d4+CJVjhXcNkxjXhMeLFN08suDSIptYdN3nDrqPqag93/amRPme0Zz70NAhje9ocTj2h4fqt9/g8Jnpc9mgQYPXLo0M/bHA0ouAr5ddbnaazPzCHvP7Lz43yfWmedzFDcEoZR9wDS2WeR2myUI6AbrmnQELl8IjX4Oxl0FY9HW24wBhpUFIfMsmLRQ+UBICx4mwJL6wGlCsevnHDHTG6Spk6c340LmYPrvEgPDpkja9J3+SLw/YJL+9huhIjkyzIH5mmHPaR9kRdvllJGQKbtGctVXz3icUHeMhPt0Ca1doNi02BbmOKMtv0oOmYJivs/wn36P1QZ8xW+IjWLwHPv9fmsF2j3WXH8f9ncO4uOZ9YILfcna+ksR3gDbhMCAFG/Jj3LNfcOYTPtcnd1Uz2r89wej7EbBFwmo9xrWxbi44+938JPIT3vHrNO3DmpEWwfqz4QOtOUDQpBQd45JMiPIZmmVEyTYNm+YNlRcWChwFjgu5AJzx8AiPnK/4wbVLKfgFUoUU7U2tZEsplm9TXPSLMZqHPZ7WGQbbMjx+QZBtJ0cJ2wGWPpfh8sc1c5KapoIpws2FJWhF0TFnfcXj8P8uLbJlbAtSmCC6UscwVba+wqZFghdOhTMefJlgSSOawiSu+TNiK1cy8OMvcvJzI7z9cUXHuGZ/XPDzFZJNJw6wYfeG6TuEClMIjaDqeKS1rtNKpTc/j5vNYPnG+jMbc2hpm0fTAQHl4UhDehb0cBM38dB//m9OW7eZRNJk3BclNOnt0+vGa49VaItQyI/SmoXWLBSC8OB5guwiwVRnPZ2rToVaCUXMiSJowU+N0DGS5XO37aYt3EbTN/6GHbPQsh8NDjcDPJunGw2OnOnsQxtz36DBa5uGy80seN273AAb8nu4Ob+V1WF4MBykbftDdBeLU7re7PqPb5IqabDARiMR+B7YMYu5f3pdnZOJLexq4eH11ly6H/umMUTHfK2OtxBRRgaTsR32StBCIIRgbvwELCvA9adfDzDhDhKMkwqGuS9osz7o4DfPIRKdRyoYZvCJ33HOj56hM5ej2dK0FSVNW0vc316kb0G4Gugu36bofUDjeCaojRbhjK2wr1Uz0ipwpeSlgMOHrFZQCvY/y767d2G5EPU1TVmBozVCaJoLmhN2+vTHi+xrrYkM6340wbVEIEItxFqO4+WXC1xxXw6n7FoTKWhO3+Kzp02zr01U9/HbgIU+7SNcdeb/IHLCEn68aJC7l+5mz5t8PmIZbfMtrVFAsHQbRIpGN29LU18QcLXxaC8ZT3olwarEsOXfT3kxz55WQWFeHEtatIfa+b/dCzlp9a9oHS+X9mpozsNxOzRb2jwigymuud8nUJ7DjrTp/FqyNa4tEEg8qYlnNHefCY50qg5BlWLkRFMChWLL2BauWHLFxP1YtmY8/8EhimGH8WaLvPDhpa2Egym2PPtrLntATVy/ApyxVbO3De71nq5zKKrlu89+l+ZAM0W/iKc9HOnQ2dRJSZX42Js/xsaffgPrvg1I38yLVBAqKNKlNGLBPOZ+8MPVfe370peN00c5ky8cB7Q+qLNK26bmXDV7AAAgAElEQVQdnPCddcyxWonG5xDIlhi/5xdk1q8H35/k6BPs7q471sjwLqIZDy1MT4NMS4Dj9hR5iM2c/7YP1X+uZ3DVqcxPev36qruOn07D4DC2EkjLJpr1sZIZZEszulCoG9MrhdXeRmb9g2ZBbNtV+Uzixhte0eM2mD3B7m7iV76ftqv/lPiV729clwZvVN5QLjcNyc0blIde3M9Hvr2Rt39tPZf9n//DF3/91QmN83g/t4RhQ8g2GeZAE8iAydgDa1cIHF8TdDVCG6cVx9esXWGC0J4FPdy04iYS4QSpUopEOGG0zb//GSDACZqCWCHp8lwKApA2w3JCmuJYwToZxVQSi7SbJutl6157x6/TjKkMUroIaZkGTr7PkgeC3Lba5//5oV/WeeuyNMUcsFiWDb3vcXPTS63pd8oPqMoFwm5GIiyNX5DARFZXa0FK57h848RCZbI5vcnOIyzG/DxoeOdvMriWwA0YLXRlDJc/bgpeBYKADCCE4PsvfL86t2suXsO9pTbW7B+hJ1+kL9aEoyCsFb8+W+L4pmOqUj5NnkVI2YS0ZUYlwFITI3Q8c+6uJXj7I8MAnLK5xBW3PUXkS/9CNGc2tpSxvpQKwnnFZRs9Ltvo181hyTbZ/zlJOH6/Zt6oojkLQ3GBwkidKj0AfO3jKY90MT1lV8++5/p4x6/TKNvCC0iktPCCFmMqw+h37+Dc/9a4U1y/yzb6M0pvuqJd2JbNwpaFnNh6IgtbFmJbdlXDnvzuHaTKTzgczxQVWz7EMoq1K+r/OTzczphTSXp0NovKZKaV+dQeqylVMo3CpMDywQ1IfNvitHXbJx1rNtKkWgmFNzwMWpsFihAgJcKyUCOjM0qPjiYN+UyDBg0aHH0akps3IA+9uJ8vrX0OxxLEww57+CV+VtFkOwhbEPY9kJI+q2BcaaDO9eY3iyGPxXlPKFrHNWMtgt+cLfltTU1ez4KeybKHYsYE8gCWDdj0posmu2w7lPBMYylp0d5kGgTVBnsHuoP4yq+zqQNoH/PIhAARBN/FL4FfkASBfXGIZ+ET6xTBEozGDhieDQlTBlBuAlUO4MoFwk7keby8RFe6NJWRUlO0BYnxqS1eLSDoNOEql4A0dolZL0tHUpEJ6ZqiWkHR1iSSJqC3pY0UEqEFOfcAl5F3fgXu+jQUUwzYkmalQdrsWX48dzXlefsjw3SM+eTnhIlfczXyK98C7ZsF0xTDLNnQMaZY8kKK9/xsAO3YoDRST8xHBceHBUPGPjNTE8/mbfMUAA2eBNuDVg8eON28+8BrBbAvt4821TYpmz6QGaB9zCPfNFGJIIQkLz3cMZcWCen6WJqiDR1JNeUCoUKlQRhQZ1tZkXRFh7OUbCatx6SCkfxw3WuHKw2ZyiVE+359dTX1i4PaY9n+RKMw3zZzOp3LzUyuOhVqJRSlHTsQwSB2ogN3z16QEoRAl0qTxvRK0pDPNGjQoMHRpZGhfwPyrUe241iCpoCxp1P2KEI7DKWLZgMrQEgrBkSNs0mN602XDPLsYsn3PhrktmtDfO+j5vcuGZziaDUEo6bIs4aefJ6bUkUS896KtAJIO8TcyPxqJ9GKA0hXtIuCX+/5XnGEqWWk1SasLIjOATR+zpyDa1OXyXV84wBTNzwPhlqgKAS+ECxUsq5AuO0UtxrMa1WJvwRWSFffO+Vpa13NCHdGOlnSusTUGcQFgboxlPcTN3KUis7cx6fJMQ5EG3ZvYNV9q7jkmX9k1ZJlbFjwZrq0TcEJm+sTambP0jB3fNjhe5+0ueSqBOecciLOwoVI6oN5AbiW+SngaYZbJec8OIgroaWlE9+RddtOjNJkrofigqAnynaggrAHvijXVQCeDckInLrTbGMJC1va5f2Z1zSaZDE5qeiyK9rFSKuN404MWGtFWFk4rSH2t4ipr19c1LnGHMi0T4/Ki89MR4TWjJHbuI758ixz/1zyWLFuX4dbHOgsWIAu1N/LwrIQdn3+pHZxUHssGQggNQityURNobdxuVk46VhTfW6mmp/YypV039FH05ln4sydixWNIQMBc5MrhQhM2Js23Eymp/r5/MklMxZ8N2jQoMEfmkZA/wZk11iOsDMRCDuqnbBIM9fdBYPPg/IoaEWXot6JJTYf/m4+vYO7cJVL3iuglXGjcYHek/905gOf82mMZ7xndPS+B2h63vrnrLl4DbdeeCsd4Q5sy57kANK7rJfFz4/zsW9s4bN/+ywfu+33rHghS6SUJT/4LHrP0+T3/Z71ZxSJ5RWpHbvJjGj8co1qcsLEhKJtgjTbN5pvtPlu+0ZOpDFf7yoq0zzpPV+HpRcRWxig88w0pWaFj6AgJXtjgoGAJOxRlRwdiAtsGX6e7cMvkMoOVc/n5+fI8hg0aF03Bk97aK2r3z8+5zxW953HZ9Zdx3/vfYI9mQF+l9nBF0OKM8+8Djc2l7ztGGef8d24yuOT2wLs+H4/W//kM4hSBkIhlBRUnDAV5ilFyBXYPvzsbE3zSB7h+6gdu3C8qZ84KGnmz0ivIJpWdI5owiVTIJ2MwK4E7G2D8SZIJDXNgWZKfglZ/k+jKfgFfOUTkIFJT3N6l/Wy/u0xpOdjlxRK+dhFn1YZpe2aq1m7QtCUh65hOH6/+d6UN2M6mCvL8u2aL/3Q51++4fGlH/os3z5xnvFrrsZRZuHjuObL9k3h6YK0U7efw5WGTLUQEJGI0c5PszioPZYdjmBZDtlogFxAE/IECSc+pctN77Je0sU025Lb2Dy6mW3JbaSL6Wnnp27h0NYGSqF9H9nedlhuJofqAvR6plKvcKA967Ea1B9L175Bg9cDjaLYWfB6K4pd9/wg43kXxzKR3dLiPnLOM9j42MqjgMIVcL3XRHchAy1dkDgZnv0v8It0ex7drseWgMOwFMwVDtcv+zN6Vnxm5gMvfLtJ7+7dBF4BghE470a44AsA1UK9LWNbGM4PMzcyl+tPv56eBT20bdrBojW/glyWnKOIFTQ9WxQr7QwvtguGLYu5nsfKvQWiL0uj9y4fVglBPiRMlh6Tyd3fKvjx+ZLu/cYpZDQGP14p2LTYqur4ky3z+NCf/BLay1qiLet4ornAX5xj8dx8QSJlimn3Nwt+fL7g9yeYgt4DMVaWpiw26Je4MDSfnjf/MbcO/js74y7H7Ve0ZmCsWfLAxQmeWeLgWA5Fv0iT08Qn5l3AKS/ey1cDRfya3SsUJVUiWUjy+bM+b+ZtdAtzteBz2xw61hfQnkA6oIsFpNOMn2glI0oMxzSpqCDoC0aaYe07I2xaLFn5tM/cMbOkUVJg1dvP49qQCsPQnAB3XhgiVFC8ZbvCqRj7CAj64Ntm27AHzOlgz8qlDOYGq64yjuXgSKfqOnNy28l1spvu5m4iJyzhITYT3z1Ga0bQfNwiTviLvyZ22Ye4++FvccYWheObRYQpEIUnlwpuuOJr07rcTNe2vlLoueDks9j1ozsIZEumgFqAlhD2LcLzF9D6kQ/X7e9wigOD3d04CxdSfGkz/tAQzrx5zPn8/yT2znfWvZa48Ya6xUHlWB2rVhFatgx71z5a0j5txy9l3uf+55QLiZ2pnazfuR5XuWg0Ukgc6fCO498xZdFw7djU+Dh2Zyd2ezsUi1OOaSYONtdvNG5+7GYKfqFar+BIZ8qC72OBY+3aN3jD8oYqihVaT52lazDBmWeeqZ988slXexizplZDH3Ysbh37NPc1J/m3lig5KWlSio+nslwb6ILrHjVv+ofjTYdXxIT+QhurSbrPg967X9ExV9uNj28p20IKlKuxw4rud4xUt7v313MJ5QR+wAI7SDDv0TLm4ksYaIdAudDx9oskm5ZIzuo8i6f3/TdQ/ziqEsfe+q5vTFj+yTDJ0a3slApVs73CWGxKrbFrXi8JgQKatGah64MQ5KVFwgqxpvdJVt23apK9Z97LkwgnWHPxmonB9F3GKn8XT5Sj67olgxA4lsNTH3uqPOBTIdTKjv9K4mUV0qlU7vqo2AmT2rNXxjCYHcRTHn/37TxdIxpVXtVYlYAZ02+g0GTjW/CTy9pYeumH6brp2zSnfTxHEMiWiGdMdtu1YKQZmpSF9fnrOOcD17Fh9wZueOgGlFZYWCa4R9MWMhr6unM+CI9edRHZfbuN376peCDoaiJzF3DuT9ZN+76Dta3fsHsDxT/5NJ1Drgnmy1p1G4vg4sUsXnvXrMf4WmC299jBrC0Ph4PN9RuNS35yCc2B5rpaEa01qVKKe6+691Uc2R+eY+3aN3jDcvjtt1+DNCQ3b0AuOGkON1++jDmxEON5l12hYX4ei9ChNEtdjw6lWRttYkNu58SbiunyD0YeUi0a1KpaLDuJzetMY6ZbT61r0HQ4uLt3I0TJdEjVxqNFWBo3W6OhF4JoSuLZVL3gi2GbZMwEZYm8kYPcfpHg94sFLT7sze5FMdmTRmMC9bpH6MJnmy0plU0oK5/12icBtZ/+yqIgJwQvBGy22RJXeQyoPHAITYaSOxiQU//LotH1g493s//RMXK7XUpjPoUhDzfjgRWYsqCx0gTMVaaLbrik2R8zkhqrLD0RZmqxfQhnPR5+i8XSSz/MtcuvZVEugg46ZlETcRiJQckyDjHJqOCbF2k+V/hB1Rs+akcJyICxNJU2c5vmTurUOhvmJDUtzQksIREILCFpaU4wJzlzAuJgzjR9z/URLmnG4w7Klkgt8C3BWNxCZ7OHNMaZ+ENprSc1eWNyUewrJRU5XBeg1yuzrVc4FjjWrn2DBq8HGi43b1AuOGkOF5xknGRWfTNKQEO4HBmGtcl49kWbTKOaKQPxcuAkZLVYto7N60xDJhmAUGtdg6ZKg6pDwWlrwtu1pZz9MosK7QucSH2RbaZZlTP0E2tR34K98wX/eZXHkGURRnFSyYTxea+Eg6BUbsMkgVO2KS59XNM5rhlvH2TjhZ1sPTlM2A5jWw6uVuhKUK81CuNJ7wmBj8DG/HzgbBWFYK9js9g1Y640GTpoZjTeTZe/i/2W0eMfcAVY2LKw+tr+nUsZ/u9t1f8nNHg5IBTEmqKgsdIETApJURXZHxfEM5q9bYL5oxpbCRAWJeEznAgQcOEtOyXf2LaWUzpO4fjuJYTLmbj+VD+5kIdnaZJRwVc/apsFh5vm1t/eSs+CHpa0LpkyY3yoQY+zYAHtQ0Mk2uZUX1P5PPaCxEHfN5MzzUBmgGRbkFjaYyRhV+fZKrhHrRh09abVrHl2jakfsAJ4ypu2udaR0hXtovV3L3PhhhStoyXG2gI82NPM2OknVLeptbYEqt/7nus7ovEcrgvQ65WDOSgdSxxr175Bg9cDjQz9McBAMERIq5qsO8blJljOsDx6GwTiU785EDNdZA/k0dtMMK9cGN0GyX4T1D/w5cMaY9tJabQSKGUZ4w1PoJWg7aQMG8IhVs1NcEnXPB46C6QvsDyB1hqr6GEpiL85x9aAzaAl2WLb9NsWaSEJ5ccIOxFay5noN29TXL1O0Z7V5MKS5ozi0jv3sOSFFABzwnOgLKVRgI/AB1qU5sy80bgXhOAAA5Zqdt2HavdYqPGVv+pe1ly8ZuoA6tzP0ltQNCtjgVl1uRdw7o4QX/qhXy08G/rpr/AF+LLSLdbgjaXI51N8b3mqLitceUqglHmesHaFqBbq2h5mwaJ80lHLdHl1BO1Jr+pl3rbqE3jj4xS3baNjT5b5w7panFpL/3g/cAhPJQ52Pxymw8zB3tcV7eLBnmYsT+OUjJWRXfQIaeuotLbfsHsD33n2O/jaxxIWnvIYLYziKndK7/wjzeR/MnsGV9w1RCRVItdkEUmVuOKuIT6ZPaO6zWyy+IfD4V6j1ysHc1A6ljjWrn2DBq8HGhn6Y4Cu+GKGRl4iXJWzCArSoiteLgZN7oDW4yDtQG6oKmcBCVetmTrjntwBWJAewOjupXnf0AvwjXOhlIZ4t1kMzCJjH2sfhnfEGN04ykjWpr9VctcKwfMndBAA2n2fZuXz/CJJOhjkPf9tER8tkumIEH/fu3HHfkBGRPAx1oquEOSkBZTAdQmjafcUV23UWJagLdbBiJ+jpDwEgnMfGmLryc3YXpEl2gavQL9lmj91+j5CSJ4Kh5gIoauFBpTbUFVXx1k7MOO5Tqlnvuif+Oqjf8+t/hD9FiBtenbH+KN7k4zKbYyGAnTs9bEKJbQ0Ab3GdIStrADWXCR5ZkGKVD7Fvsxe1K838snHNP8rLXkxbhqDPb3E5rvC530bFXOSRks/FtXkAz7CV4Q9yVhrkJAVYuvYVm59/lbeXRwj5Jc968XEekXXaIF8PfmpROypLVzyWJEFaYfY2jWkV+lZF1webvv56vv++eu4OzfjRHzazuskNtdYUvYu6+WWsS/COzUXbnSJpzQjrQ5zPvWpKfd9qNrzvuf68JVvCoIx9p1Km060sae2sOOHvdXz2XvF2Xyx9B9kvSy+9hnJj/DFX3+Rr779q7MOEufd9QRDTTGGVArle7i2JBFoJnHXE/CB64CJpzS1T02OhlQktnIl+SuvZOyOO1DZLDISofXqq+vmMf3II4d8DV/LTNl/4xhkNte+QYMGf1gaAf0xQO/8C7lldDNISUgpClLiCkHv/AvNBvFuk11vnme+wFhZxjqnD8bj3bD7ScB0mwTK+ncFo9uhY+mhyXDi3cSsQX5wpcu3WqIIDRaakhAUMF7vYauJcHQOuxIOd76tvuhv1bd+TpNSJC1ryt2XhGDQtlgw7hFPdCFCLeBm2JfZS9GG1tESI+kBxorjRDUskUFuzQvQLl9qjjJc1c6KerG7FlTCd4WRwHTFT2A6Knpmx3Lq9Mw3rbiJnt719NRsN/apz+Ba4AcdtPbZ640yv9wJ1pfGXlJhfi868MyJDqOFUYTWvHWrx5+uU2QtiAZ8OtPwqXUWPw238tCSFM+dKDhlS4ne+zx8C9AaxwXhe/zq7e2MFkbJeBmWr0tTaHJItYCnPHztE3RNt9tNNY3GhBBVHX3Pgh6Wb9cMrv8qIhBDtIXwhoYYvPmrcAgdQQ+3+VBsbpHY23eADJEekIw+OsTgvZ/BWXgiyz/wDi4fH+H7i+CBxRZNGj5e1Fx+yomzv1YzSGcGMgMEZAAfH1le4gkEJ23O86EHingtQ8iWFryhIfyvf5Pui+DZJQ4SidKK8dI4tz5166yDxvSOrYyKDLa0EUKitWJUZQjt2Frd5pWSiqQfeYTUnXdid3Qgyt77qTvvJHzqKcRWrqw6oYhAoHrOh3oPNHhtcrBr36BBgz88DcnNMUDPC/dzUzFIQoZIOSESMsRNxSA9L9xvNjj3s8aHvpSr96WfSmpTofKeSgGtUqA8ELZpLiUEBJqMLOfR2w4+yPL+vh+LlF1HdFlSYuxYRiwbOpZAqHlKucCAY5OT093Oxm1FAP1xgU4OAhBzosyNziPkSwbjkCyO06qgE8mQUNwS9vm7MIz5BapelwdWrgpQWuNrhY8mZofoPWuyX3iFWj2zEIKwHa7KWw7crn3Mww8Ym00pJEIIkmEzhIA38SUV/PxsSBVTCCGQyufSxzWuZQL9AUsQtAWu9Dn3oaHKlPC7RYK+i22SUUGkAONRQd8lDo8clyFZTNIabKV9zMNzTJMoVSlEto33fC1RJ1p3DqNrbjeBXNicpwyHEYEAo2tun3ZujhplOVh6wGLwwQxeDmRQ4O3pZ8dt32HzbkUHkqVa0oFkbQA2PPr3k3Yz22tVS1e0i+Zgs5EhlMumfXzet9EnGmmtm4+c8HjfRr96baWQSCHpT/fP+lR3x1xzDwhZvU8Cnnm9wislFTnYNX5V74EGryiNa9ugwWuPRob+WCC5g55QKz1uTTQq9YR7zdKLgK+bQCi50xTBHkwqs/Qi410/ss0E8FagHMhbYNU06HHC07vkHLg/vk7u0c8htCpbQk6MV9UMvSoX2LyuPOYdRCKKAWfq7DxU5CmKu1YI3nKva5r9hEJEPIsmp4OH3h1jvv8SYWV01WEhQNr0WzOvepdvVVz+uCYxrhlpEbhvzdGzojDt9gOZAZoDzXWv1S5QKhKPpwaf4tJmTVvOQwXNfIYLmlh515XpUEAuBC/Pk5RUCUc6+JjxpEOVbTSjEgSajqTC810C0kYgeGaJze8WiRr5jEb4JaJOFEc67G1RNKddXEdWtzFdc0XFTRKAolesW2S5u3eTa7IYSfXj+i6O5dAeaqfpD+GCkdwBoVZGn0wipChbe1oI7TGmNec/IXh0icIFHCAG9HlDHBjeHuxaTUUlG94WaiNVTFFSJSxpsSgXJZJow8+k8YdH0KUS7WiihfqFkUYj9Oyd1O59W5A/ujuPLClcR+C4GsuHn70tyCU1270SUhF39260JfH6B9Glkgnw2tuqTifu7t3Ilvr2yofrhLJ602q+/8L3ybk5mpwmPn7yx7l2+bVH5TwaHDpH89o2aNDg6NDI0B8LxLvBzde/5ubr3WuWXmS85m/4vfk+G6ead37FyHLiC6FtMabDkQ+RGieSA48zE0svwrFDeFLWBfOVH+uKLOOnGTlPehBCrWSmzc5PUBSC3y0W/OiSIJlmp67752/mjRCqSIYEpIFBodBo/Gn2t3yr4hPrFK1ZjReC9qym60HFxu9PXxg8k/Vdrb1g0Aqy9m0S4XrIolv22y9hK/Ck8YH3LNjfCuMReP8TAktaeNrDA/a3CIJefbGu5QtGWgQWoJSHhGpDolpsYZP38uzN7uXucywcX+C4qtpx1yl3uxUYiZHELCZqNdnZRJSRsT14ykNKUxw6MraHbCJ60Ot0xJTvdzflIyopC63ACpB1oHVc42H+8fOAUQFb7clB9OHYFFay4d3N3bSF2zij8wxuveBW2hedjDc6grdnL9p1wbJwFISLcMrWEmAaiSmt6lyNDkb6jBO58/IO0jGbcN4nHbPN72dMlhAdbWQ0ijewp3o+2nXxBvYgo+YaO2UpRi2H44SyetNqNt75DT7XN85t/1ric33jbLzzG6zetPqonUuDQ+NoXdsGDRocPRoZ+mOBcz9rgt8SJmPu5qeX1NRkvQ9a1Lr0ItjzUdj4r1DMgB0CJ1IO7HX1OBtOfjd/+58Xsze3F40mbIe5Ztk1U2bY2sPtJgMqjPa4EmwGrACpUmqiMPGBrxk5T6AJgGGrUpo6NTUGP9x5Iqw/ZZybz7u5mrXs6svS2i9Z+YSmJQX7W2DtCs2mxdNnSy9/XBPKQ6xkHngoAZkADG9MAlMXVM6kZ+57rg9XuYwWRin6RX53Auh3W1zxuObEAR/fMkG0qpjHa4hnYW8r/z97bx5nV1Wm/37X2nuf+ZyaU0kqpJJUQJApjKEDhQqCYHNRsO3b0iKBtNqhPwrNT1sNLSq26f61reJtFfHXwSC3sbu5LS0qUxQwxRQGSZAxEwlUJanUeOZz9rDW/WOdOjUnlTCH8/Cpz0nts4e11167eNe7nud5WZiP81fHLGfNs2vwhc+dSwUr79I0p0c598Uw3HWWrLrjBJXJywhG+ltKQ6/xlc8zi0P87DzBhx9xaUlr9tYJ7lwq2NgxdgJlTD6Hy8Oc99/n0ZZoo+WEAhe8ApFcQLToYvkaLeGxU/IcP22PjqKru4sHb/8ex6/bTsswxOYvYNGV18yMn1sZ705S4BdA2JVKBIlZOEPd9NWNZjIEJqj37Mik0xws93yqbHh2habnc59HBxVhutZYAgph+NNHfTYukljSoiHcwNUnXr3/exzTxi8Pfpn75rgVWplLMlTmn94EK0WtNQhh3q2RAoXCuE+BcULpvf6bRlsSiaBLpf06oUz1zjx95xouv9fHtwT5KNTnNJff63ObtQZqWfq3BI0rrmD3qmvxdu1C+z7CtpGJBLO+/KW3umk11PCuRS1DfwhikhVeLALn/4vJppeGzef5UwhVR7zlK1nvqqh1uoJRm9fBptsg3gqtx0CqDeww2KHqdbpOW8Hfbf4Zu/K7zP/otfEl//GmH0/OsG1eB9k9NFerl5pS9s2RZlqiLeOtH4d3mskJ0CVd3HEnmj4IFwg8HTBUGuKGP9xQ3f7p54pc+FtNtADZiAmUr1inWbKt4vgzxVxhwR5NXdlkqTXmM1WG1l7NjRtvnLKYDzAtn3nr0FYGS4P4yscWNlJINnYIvvEJQT6mkY0WTjiELSyoVDm1A2i1G2hadBQrl6zke+//HslQEilEtWhU9d41RBHM0RKnckthK1ztF4HAFnaVKy+R2MJmY4fk25fYfP2vba6/xJoQzJvnZEkLT3nVe71nzl42nBAnng+wfU1gS/Jxm6Me2012/fppnw+Y8fuLn/09H7h9C3U5yEU0g93b2Pm1a/d7LGDG9fn/QuOyFrQfGCvUVBtKOThK8qvTZLUomAIQkpATm3Sa15N7njzzTLTjVIN5hEBYNknfon1A0hpv5fiW48dNMmeCZ/ufJetmx23Lulme7X/2gNt4oND5PPacOUjHAaWQjoM9Z061QFfyzDNpve6r2C0t41bDppuUTVcA65yHC3iWwA0ZiyU3JPAswTkPF97we6xheugxk7hxv9dQQw1vCWoZ+kMM+3ZR+fW+Dx7xlq9kvQnFTFb/ke9PnaWfan+AaAOsfBiAtXdcRCEwloHV4FKDEopbX7h1NEtfmUy0JaBPSmapSuBTN5ei7dASnVBQqOLM0xWxuc6ZqsLnWJd287sAE0hhhIoj3ukAczaF2GGV8Cr0/3Llc8TNRQJKUxUvhmSIiJedsgJtxINbX7iV5mjzlMV8pvOj97Shv1jCaAEcJL4KiGqYm3Lwcz7a8VGehSVCaAI8oSgWjP/8+ysuM/+7838zdOvnKccl+Uabsm/oVjEPlm0I2LrIwlLQLwXNibn05nvxtV91WrGlCeqlJUfpH6UM27JT8GMrD9ULvHH3akubBVtzDDeG8CpFwJRWRHzB4Jqb95lpX/vcWj76UBZlW/gh4xXjhwVDfo7kfo6t4ohzSH7zHDhnrG1iC78/JzesUvYAACAASURBVEzPvAy2m8VTHo50SIaStKfapzzN68k9l1KiLAthj/mz6/ukZIx7PnbPlMfsz/bx1hduxZIWthg9p6/98e/WG4SR4kL2ggXVbapYxG4ZfVcPxKlougJYs4Y1uUj1DQbAtTWt6Vo+6q3C4JqbsevqkLNnV7epYnG/73YNNdTwxqEW0B9ieE1VIStiwnHYl6h1Bvv3pF+uZkPHQUPBG5Nhq0wOliub1bIAQhBBM5jbzZBtkS6lWXHvCkO3KZSgMAhD21jb2kJWhyYk5afOFBmt4ZjvxhzjlWKU7RKpMsTyJvPtS0hUpAcSCGtIxWcxXB4m5sSQjM+Mjj1t1s1S9IpVBxOlFY50SJfSUx4DEJIhirqIEsoE14FxKgkJQePJMXofyCIkpOMB4XyAHcBgClxHc9Z/bWFg3ed57K8+TefFV/LsoI1fdLF8D98SpKMaL2z440Ud4AGXHvYh7hx+lmQoyUBxACUUAkEylMQPfIQQFP0ifuCzt9RHeSKraUz/TeThz4rOonn4Zcpx49KjtUJrTV2qdb/CuZ5cj3HXEYqGYXOfvgWZuDxg0d3EgPL93V0M/uzvOeshj6Yhn4EGuP8Mj4s/tXzK4w/Uh35fEE5llqiUsXqtFPuqbp+Amdg+FrzCuGAewMIa/27tA6/FJ/5gKDX7wnQi5IEGm2TWx3VGVNiakA9229yDuk4Nrx01Uey7F4dabYlDCbUUxyGG11QVcibi2QPcv80Pph1ksbE0hwqFplOFWOXHaNGSvUIwrAMawg20xlvNasNDX6Vr3TUQeJCaT49tEYh9LPWKSiWkKVg4zeHm6r+dhYeTKlvUZUwwryp0lqgLJ25TWBpiQvK+7hRrfj2PH9zoo8VoRVcqn0pAsWJM72sfV7mUgzJocJVLzs9NWw20o76DpmgTtrAJdICtoUkLOpRFcmGY1g8k2Zk0vvxb2wS3n2GuKQNFJqxIZQP0d37C3h/+CLvoEQ4kth0mgsOsvCRZgP46QYsMs+roT7Py7G9XBZx14TqidpSkY7LV3zzjm1x/+vU40mFXfhcCQUiGprfvBHJurvpv27LJNseIBBKlAmxpMzsxh3hg71c415ZoIx/SNAwHWJVnYQXQlFYUwvs8dL9Ysl2zYp2iPm+yvvV58/uS7ZPH0HQUkAOt5jqC8OLFWM1NJoAPAoTjYDU3EV68eMr9Z2INGHNiBBNk2wHB+HdrGoxMGPy+vnEThhnRmjhwSs3+MJ0I+bkPHU6TlSLiCdCKiCdoslIce9VXD+o6Nbx21ESx70681r8ZNbyxqGXoDzG8pqqQByKeneH+y+0Wnld95OT45K4ELj3q0tENI8WtQjE6VYhOFWKFPUyfZRGNNgGV1YbSq6yNSDp9E7C0iQz9+PhjPRTHYprNAkjk++CGY6G+ncbzPkj+6T8QEKAF1aqomQh8/DHNX82GJY2X0/ub3yBCIV6xiyQiknhBEWgILHOMwHjCjxX0ggnubWHTEG6YdrVkRITZGm81IsyBLXjKZ3lgJmjJhWGuOTLPyBTputsCfEtX6EEKPxxGlzyGbrkFLxWDwWG0b6xubAX1rs3Rf/9DLhwTcO2PUrL2ubXMT80nakfJlrP05Hqq/upjIZH0FnqJO/GqeLR5xQqaf3QHIhQ6oAzu8qOXk1OPjG4YM3AGi4P7PHYiJmbYP//TAaRj42sLAoUfstDCnpIqMFakPJaeM6PVrikwktEWra0z6o+ZZEEvPepSbnrmJnx8LCwzfrUe/25N0ycf+f5TNHiCulgrSSEQ0SgKDog2sXGRYO0lFj05m7aExfJFYpL950wxnQj5/R//IoefpEezggtqWcG3Gq/36kwN7wyMTTIAB/U3o4Y3DrWA/hDDtM4c9cfD2gvoyu1kbSJMTyROW8Pi8RSCsX70fS9BUDaONSOFoSby6Ce63IQTcNrfjNuvc9lX+Od11/APUdhdCeojWjM3s4T1j5/AUZG9vP/IWbDsKrrWXcNa22WbCHBR5KQkon1aev9IwopCYhaRwKdnjEXl8iDCl2WO8v46ZkxQaAvBnECTF15V/JtM/xQrGsYrKYQX4NmQjoHjwIKM5HtzF3PBTTfTMuwRcwVNgSawBKUQhDwTMBdDJpi/4wybibQfjSZiRWiMNE5aLRkbdGql2VPagxu4xOwQl+Z8Oj3fNMQrEoRHzitoSZss8wi8wIOQhTeYYWcUYikj7rUDKFvgWD7J2fvtqXEYS4NIak2bFrw68mVlohSzYzRGGtlb3EvGzRB34jjC4ev+nVx8LJy2vgdZLFMOCZ76QBtHLRKwDypL57xOujxJf0qRKhibzMASZBI2dsmd3MhpMJWeJLNjG7mI8ebXgKs8CjrPnO0FJrLotw5tJetlEQgkEl/5JrgPvKkut18kzzwTrvvqjJaru7q7yMeyhPf2QjRMU7SZpJOYlAUd4cnvz6N9ZJk8u3MrZTJcoqG1zyMQoAZ2ktWgbInV1ES0e2ZjpKu7i8f/8Ytc+XCWsKsoh17lwdOfg698+6AmPJ3zOlnFqqnHxTxqAcPbCAcylms4dFCjWr29UQvoDzFM+T/F+uPpfGwNXY7F6rjEUWVS+TJ9YsfkUvYjwfjdXwBZbzLvI243THDGGetyU7/AZOg33QZzTxzd74hz6OS73PvI9yn2vczzxQZuD13Es7FT2Zstcd2dz3E9YCUirG5qwisMkBajafUysFsK5gQlEuluSkLQNiZB3KlCzFaQkTA1d15UHHNGEVIBtha04IxWtHVhoK5E3g5wnRFOiSbwNJsbBDv9LLP3esTLJjhXgAw0QkM2Cn/zeRtLQ2kfNYGyXpZduV28p/E91W1jg06BoLfYi0YzNz4X27K5085wTEnSOdwP9fOx2EmABjR9dSZgHxHwBtrHcQOKjin+VAhTpaiEPSjHBMdMJ3CeBtUVH9+DdDclWX00xt1HQMEvEPJCLGlZwvKjl/PVh75K3s9z9NYyRzzq0RsBlbKJBBbv3bCH227+Ii+8J0oynBwv3B4zDvMtCSLDRYZnjf6Jsso+pfr4jNs+lZ6ktw7qcqraZ2Cq7W5LZMlXRMUjmChSFgh87ePpgwvoYWYi0ZExcfTpcS66s4hXctnj70I4TUSVPSkLunLJyn0KYLPr17Pza9cypHL45RKtGbNdCTNZAqNRkIGGPX0UFsxgNQ944TvX86EH0mhhbFFDnuJDD6R5JHQ9nd+bxhlrP3gjCmDV8MbgQATPNRwaGBHCi+goA6BGtXr7oMahPwTROa+TNR9aM2rz+MJ9IEOsjWgcICosU8q+ODx1Kfux7jUjAa8MjWbqD3S/StGqK+rW8NPBv+D0dV185davcPVdN3Dcrue4af12E3xFUmSFQmJmmlJrdMXnuk9CUYAnYHlJgVswLjhugTxgiekiaY2lNSP/WVrjCmNdOUJlAcCJsnapwglM8CvQhD2T3b7jVEXWzWIHgAYtATlavdauBEXVQrz7oPRn3Mw4H/OxQWdfsY9jtrpc++8uf/fPL/OpG7exeJvH2jkLqwW/ziv51WPvXCqwK+0dKfpkBZpfncqk7XYAt58m9lm1d5LdaXcXy49ejhd4FHN70AL6q8Ji058j95oup1l+9HJueOoG0m4apRUXPBrgWWbC4WuFH5Io2+Lsh3NkvSxR23DDo3Z00jisv/wyLF9jlX20Np+Wr6m//LLpO3cCptKT/HLp1H1z51Ix6T0IyZBxZKpQjBSmuFZIhmbchoPByJh49ZgW7rqojXwqRLyk6QkXDoqjvv1H32WvN0RGutQVzLhVAmRl3GqMRmHES36mtKaTHuhBC1DS6FSUFGhhttdQQw2HHhpXXIF2XVSxiNbafNaoVm8b1AL6QxSqXMbv60Pl8+ihHVBK06NKRLyioW74Pp5bYtdQwNbBCUHe8E5QHgxshb3Pw8BWumSZFf7OccHeWC/4KqZxxenq7qJ++xf480d/SH3hJcKhQVpye/iLR/6Tuj8+YYIvz8XDBPCuEASVYN6q/N6Czaq8pvOc747z1G9rWEywHwvksNaEtaFZRJVi1cAgncUxoi6vyIYOyZpzJENxiBU1Qwm4+VzJxg5JOSjjV5LFolKnSFau6VvgAG16/6+TQIzLQI4EndlylqM2F7niPkV9zlBp6nKKj/1qkNCG56r7/9Nwnj/NGQeTjR2SB46B+hws2AtNadOus56BkgO+gEQJhuOCm8+R/KFDsqI+NKWos+uuz7P63s/S98rDpAZ30tf/4njffN8ngwnjba1xtK788dBVO8HOeZ3syO5ACokUkpZhTbnSZyN6As8RNA8rAjVeyDlRuH3axVfifHElpfookYJHqT6K88WVnHbxlfvt4xFMJbLc2CG5+VzJcEKYvkmI6jOeSIWaJFIWNk3RJjrqO2bchoPB2InI1qNS/GxlB99fdRQ3LG+YMpjPrl/PzsuWs/XsD7LzsuWTBGq5ndspWIZbPyL4Vpg//p40qywCQ7kZrndmTGsKu7o6qR2BEmZ7DTXUcOjh9RbC1/D6oka5OVShFEEuR5DLIdICUexjYaiZXRGLiFBY+CgcfFUmk03x4IsVLjtAOAV9L4KwQFh0OYLVYSMKHEeRqG+hM5sd9Z+HKV1xbtx4I//2zE/48h9KuDb4NhTQzGIY7ddz4Yv3899nRekb3oqUmrIYdZw2lTwFHa7HmnIUkm0m4z+GNrK8u4vHfzuZcjDiRD/P94loTUmAJySrBtImmPd2Q3MSvCJdKoPGYWOHZGPHhJMAgQ7oaZa0DiriZapWivkwZBoEC7Q17prTIWyPt2kZobQMlAa4fIPGt0YpNGXHnO2cR8b47EuHfxrKsD4W4fCXNWf/EdIJyPnQnDU/fSkTyCcLTHKF6YvWVektYLLBPX3PkXEzxLQmpQEU0Xx/9fs1H1pDp90O3U+ybG4jRSGxKqsdAD6aaDhpLqBHg/f+ekkqF4yjtziepq/eVEUFWPxChmUP9lE/WCbbHCPbsr76P4fTLr4SDiCAn4ip9CS2tNnY4Y9/xhXEnfiUx1dFyjOsFPtacSDC9plYW+5K+dTnoOyY8WUFZoyqCrMsEOBZgsGW8AHRmkQsiiwWxwX1UpvtNdRQw6GJGtXq7Ytahv5dAF0cRHmCT/bmSGYglBOES4YGIURAvHQ2N63fPuaAsSGpZm0yhqM1UcR4ikRdnXG1GUN/mehy09XdxZpn16CUz6w0lG0TQGhg0BIkZZbDysMsT6fxBKhKBD3SAqm1KUQorGkddzoLJZqCyc4rGmgKFC2BT0ZKWoLAZOZdD8J1EJSg91nI97I2HiI1Md04tiEYukZgwUASds+yGUga+s0jp1aO04q6aVx1RtAQGu/bP0JpcQN3XEZ75OJlG1qGx9zbrKN4bLiZL/9ccc0vIJU31IlU0fSrEtCYg7qCCa7CnrFmvGKd4uTtgmi8GcdyuOGpG0YtGYtpCkIyaFlkx7Q9UhgczVovuwqUy6XZAlqYSZYGfCHRjDoWLahbgNIKheJXfyLH0Fs0suQh/YDfnZ4g6SQ57Nk+PnxHD/GMSz4iaCvHXlcLtKkqvX7m2M9UOfFjIZGTZmKvZ6XYA0GV5uSbZe2iX5x2IjETa8ux1KzhmBkXlobhqPm31JBP2AdMa2q5/K+wFNiexvE0tqexlNleQw011FDDm4tahv7dAN/lyUiMO5JRygh8JbDLcHjeJyTOR8gO9uTSo24rkSxtc+eyPJ2ls5Cnx7FJYYEeDSwjnktPfo8J4oMhw52fdaQJ/MZkz9c+t5Yg8LC0or8O6ioizqCyzB9WitDCdo4dfoqv9Si6Nyla0rC3Dn61FF5aKGgMNHknBOd/e2pB52+/xmmvBpz0lKYlDX11JojZ1CFpwmJ5tsjaJPTYDmvrklBQdObSYEWg+QjwivSIHGdugxOeYNw5NnZITtwGFzymaElrig4gIFrw6asX3LvUZu98j4hXpCQliUg9p89/H3e9fNc420opJHErTn+pn/P++7xRsfIL97Eq182XEoK+ekF9To/LaNcVTIXXrWd/EGfePPrmNuI9tI2UZag/QpqsvFBmcqEEOL5ZPRgRPY6c76InLf79VJOt3pbeRluizWSBtSKM5Mjtigs3COYMw1Cd5oFTNEPzK1nhI86B5iNZOWgmfrcm4xSkJKbhUpHimOZjWHHvCnZldxHogCAIeGohBOdKLtygmTUMe+sDnjp7Hp9a/FHUv99B/KVuFJBOSeqTzSSiTa97tcnOeZ0s2a4ZvO1mvO6dOPMCnllk8UR7wAixxBE2sxKzyfuTKw6/FSLNscL2bcPbcJWLI0Y1BuOEuzNwnXh2cYibcblwg6ZlWNPdDGiIuaBb2xgqD2EVy5Tq49RfftkB0ZqEkIjK3wUBiGiM6LHHVL+vFaE5dPF6Fl2roYYaXjtqAf27AE/GU9yUCGEDTUqboF7An2Y9bo0uouSmaU/s5Md334eKhmgMbPosn9WNdaxKzaFNlOjDJzoiBixlKGV6aENCcs54//kJAXfP0FZCSuEDv1kKl91ntpdtE6gKYVw7sr9+lub703ghi1JY0JrTfPY+uOsciz+evIiWaMvUwfzmdWSf2c75Tzfg2lSKBcEV6zQ3o3hhoWR1QwpHK1IqoM+yWZ0SrPJCdEZnV8W8p29J84EHJCVbjDvHg7sDPvBH8CrnDvtg+7DmXBPsmzUFGwdYEAi+mM7ROfts+op942gTOTdHT65nlLY0vIPVPY+zquTQaTdwaTnNnacKrlinq/1TVzDtyMShoUKnCD31Mm5M4joKZWtkZVVBYHjRIzW2tDD/9mzzrQ7ZNA8b3nopKIFmnGD09K0BF/5W4NlQiEA8L/jI7wTOySeN9vUHvwF3f4GVMsRKd7TuQNdp/zerN6zGUx5lVTZVbitC0k0dFt3vbWJWYhZFv8gpL9sk/vXn9HnDJCu+/U1pxZAYJGtHSUTir6sF2iRKSs8OPvVigfg5klc7HDNJ1T5F36WlfsHrdt3XipHAaPWG1aSsFBErMqUb0ExcJ2bFZrGxY9ckmtHc+Fzu/bN7D6p92fXrGfjxj0212xFButboUom+73yX5JlnzogOVMM7E1NZwk5yTKuhhhreVNQoN+8C3DFrHrbWRLRxkImgsLVmbd0cSm6ApzROwxOEsWnwQtQHTcwd1DTkAv49KLK8CB5QjNYbCkBuj3GbUTEoZyC7C9Ld8IsVxspyDNpKeZIatJBsWiS55VwYjhuOdyoqaL1qBckzz2TwD2WEBXWWQouKM4oFp20I8EqjzjCTnFge+UcGX0jg2pVM9JhjL9wAZckoXQhJVJvf19YljVagggs3aDwLAtvEJyPnuOAJExS7k849mn3XGEFsQQoQxrd/Im2it9CLQNASbRl1GALWRjQIwZOOEWbefI5kOG4EmxEXhmKmv6p0CqWJlTRYIXLxitZAG8bTCH3Cs0Aq83Kn4yZoDvkw1BCq0jcW1C2gFJRY/EKGT90uWf4/RlzrKFBCoG1NPYo5v3x8tM//+B2+WZzFI78q8ejNfdz5qzzfLM7iht7f41gOWdd4tocsM/GTSBzpUAiMiDdiRTh+3XZjnxi2COyKrFYIktmAgWL/626BNomS4g+TkHDm45oixkWpKMArDkxJadmf4HQqTOUWdDAY64A0nRvQTFwnEqEEFlZVvCwQWFgkQomDaheYfsWbYN8pBCiF+/LL1X32Rwd6t+FgxtPbETMZmzXUUMObi1pA/y5Ar2MTjjViHrcGJGWRpFdKGhNhrjrrcEp6kLA1YlqeQCZmU+fZ5HM+S91ZXNt2CXMjh5EtpWnxfVb5cToLRcj0QOCBsMHNG7/6MUH98lwZR0gaNdhInu6w+KdPSJ6/pMjxN36f5CeuBsBL+4iQTVJpZvs+toayLWhOa1Z1b6Pz51fQtfYsVj/0VcP7HskKqT6yBWcC99xkuFvShtAescNgR8AxPxEEPfZ4HnU8I2giIKQ1ViUwdm2IuiAss238ucdfL4oYDdCHX5nEv9ZoZsdnkxwRjwYuESHpESaT/bwwdpQbOwTXX2LxuZU2+YjJzo8ldweOxPI1SItyLEQ6JYxriQU9TdDTLChGDP1mOGqKXSUCh7CSrDs9VuWBX33i1XQ8n+ZDv3iVcAHQAqFNny3K+ywIIB6JkN25tcq1P3EbvO+Xu8nnfTJxm7pSiA/cuYfEU5vxAx9PeeOCRoVCIPCUCfxKQYmWYSjKACEkuYRVmZBobF9Dsfy6W6B53d2IyBjryqBM3LZYNAwtWpIB456UCyZlFg+mzPlI5nLcGN2w+qCC+qlsNye6Ac3EdSLv5ZmbmGuCLmmCsLmJueS9yRSjmcLr7h7NzO9jn3F9z7u7CM3BjKe3K2YyNmuooYY3FzXKzSGKol8kUxok4SRojbUyKAeJJIyLjQAifoljI41864zjAfjlrlYGS4NE7Mof6XCCsmXTGmlEn/EtTgFOwWSKxa/+Gun2o0t9JoCT0iy925FRH/oKPaYz0c6qwi7WRkzw2qYky0uCzrqF4yg0TkMEP1NGWIKk1iT9AOWDHQloLxTBClgbDODklaGx2FHzKW266wPChfHc87APffUm4BjUiqw0qwwOkJQW7b5n+P+OoY44CUW8KEgyaqeofPBtwcKS5pWIhV8JYMK+4dhPRAToET7Um5qjY/nXK+5dQV+xb3RnK0RJubRVXsFi1d99FH11gvq8Roy5LzuZIhgexir7+CEL37bJpjS/uWgOTy5UDJYGEUJw/NaADz/q0TKsqV+4kEVXXsOfTKA5WE+FyEuF6wh8W2MFECAol21iUYF2GuiOFquZuNN/vwu3sr9A4YccBHDhYz7/ePheHOngK99kgKVFoAICAkJydGUgNn8B/u6dlKSiHLVJA8mMDxrKDTFav/j60jEmUVKsMLrkkkzZrPEqKzRuwbgnTcDgmpspSp9+bwiv5OFYDs0yuU+O/1TFrEa2HygVYaZuN/tznRg5z4K6BdVtRb9oaGwHCWfePLy9e02WXo8ft86CBdV9akVoRjF2xQJARKOoyvZ3GgXpQJyYaqihhjcHtYD+EEbRL1L0i5w1/yxue+E2AMK+R7nYj68CLsoWYMcjsGAZF6WO5Ka+/49SoAhbNuVwHb7tcNHhF487p9YafcylqN99HVEsIiyNdDTSAhGtn+xDv+wqOu/+Ap1ByHyX64PSIDfqAreuPY6CEMRCcT5/wXGcfOvjKKURFujA/DQeVQRpg/LokRhbxdxeiJhgLBJv5Z6lr3LROk3EhXjJCEEDAV3vNQ45vWPWoTygYAlOTh4BBQ19L0FQpvEYi10POfh5gdKCQAo8RyOO84lstpG+IBpAY75iWSlhyTaTXf/oBpiT9hmoE2w8RdB14bmsvXcFW4e24mmPkAzRFG0iU8pApGKhGK3Hy+9leUmAHCufHc163rnUcOqbA236vVQiEkmw4fxWQs9spmXYoxgWJGMJPnlXgdOiWf5nqeaphZonFsITCyUJJ8F7m5pYvkgwEk6OiNk+1d2DF8XQYhIBdekABQQBdOPQEATceapgT24PvvZJ9pfIRHR1HBT9EiUJs9ICX/nUR+vpL/bjKiM3HXGTiVgRWqItLD96OYtaNDu/di3pzCCxssbxNYEUrHtfilO/8m2SY4LekXaO7cezdzVy4QZFvC9HviXBnUslD88vTCvKa1xxBb3Xf9PoCyIRtF2PDnqJztXsvH0IL+3jJKHx0x8kOeH9ye7cSo8YNpV5tcbTPj2UEDunrxTbk+shFUqRc3P0F/vxlIctbDLlzAELRKey3TwY28zX6zxj0bjiCsrbthH094//Ihxm1hf+V3WfcX1fKh30CsyhIMDcl4D5nSYefiPGVA011PDaYH39619/q9vwtsdPfvKTr3/mM595q5txQPDcIvlhkxFujjbTEmuhu/95hnN7aFFwqYpycsmFHeuhnGPus//DXCXZaQuGCGgpF/nkgg9zcsd5k08+/CpsuQ8CF60EyhcoT6DKZVAgmhYgTvhLs29TBzQuNtfpexHKaW6sS3JTMooP2GhcFfBQtJdF7zmJ9q2vEhQFTgJajsuSPKxCELdCPJBIkBHgKB8SJrtYEhp7Tgvl9ABH7QBbgWsZAWt7P+xuhD2NE6gBAl5WOY4+8s9o3/0shOtwywkGX/ZQgcBSVH4E9x4e5XcnR3nPdo/WYVASBlKGbbDsBTh5s6HnlB2IluHYnVH+097G5kSWtJs2QlG/jBQSjSbhJMh5OWan5vO5+R+mM70Xcr3cGhFj1gYM9jQKBhrg3P4wQVHizJnD4xcezj/PfoqHjrfZ22Rx8ks+ZeXiRx2cXJkTtwTsaZLsbjAc9pZYC6WgxO9f/T3tqXZeybzC6g2rKQUlDn92iFgJPKnwbUlZKiKemVL0zgrxH2dKHm0vo1DYwuaEzT7xkiYYw1YKe9Cfgs1L55IKpRgoDZgMvbBwpEPSSfIPZ/wDnzvxc7Sn2gm3t0O+hN7wB4Qf4FtQiod473CMI5ecZb5nlLoyWBqs9uN7Xipw0Z39FHJDBLEQmf5ddDw3SK41xY66cvUe21Pto+1rb8dZsIDyS5sJ+vpw5s0ncdpxZB7bjnZ9ZDSEspvJPb0NZ8GC6vUBHr/9hzh5l8Aydq0AjqsYSAmO+MvPTvnePfDqA+zJ76Gv2GcqEwsLX/u8d3OJ9659CMsNkIkEKpMhd/8Dk645FiP3smVoC/3FfmbHZ/O5Ez53wIHs63WesQi3txMUSxSffnpUGOs4WKkUybPPJtzePrnv58yh5W+vPuBAdWQslIIScSdOxs1M+azf7sjefz8qk0E4o0tuulRCRCLkfvs7dLk847HxVuONGFM11PAW4BtvdQNeTwg9Ybm0hsk4+eST9ZNPPvlWN+OAkM8O0rvj+fEb110HhSEsJ0JcC+KA9MpQ6IdYs+GYj8ArQbwJLrpp8snv+CzsedZwUsZV/NRgh+H8f0EeeRYyHkfGYojt9dxL0wAAIABJREFUDxhufbYXVMCyeS0UhcSucnAFvmURtaM8cpJxUkGGjNjWL5tgoW4eXdEIq60cjrSJNB1ezQqtWrqKvX+1klQ2qBRjMm0Je6YS6PWXjOfLCwQazak6zJqscbnZefsQu9Me8YIhvmgpkMpUwvzlMps/fVwRchWebSqvliKCuX0KDexqNuqENhzSgWA4afEPf2noJ1LIakDcGm+lJdrCmg+tmdSlN268kZs2/RihFRYQmN7ksyVYedZ3q/SkZT9fRtEtYGvFl2/zqcvr6j2HrBBhD/piPqs/GTaPQ9osqFswjmIxslSefGoLn7y7hGdB2daEfYETwP97foTsSYezbXgbqmpJKHjvlhJX3KdM8Su74vgTmGq6hZOPpD5cT1+xj0AF1ey0FJL2VDv/feF/V+9152XL8fv6yNsB2cE9xDIujq8RsSjzv3sDyTPPrFKUevO91X780q0F6nMaHQnhKx9b2oQ9yCZtfrayo3qPU/XvWIxcX46hgqhiEbulhfZb1la3Xf7147nsHhffErgOhDywA80t54X46dc3TXnuru4urn7wapRWWFjoyn/X3RbQVJDMax61mpnqmu8kzLQfXytW3LuCnZmdZN0snjIF7pKhJO2p9v0+67cTxrr+jF2xkPE42vPe8H6soYYaJmHfQqB3GGqi2EMQqlzG37V78he5vWCHCYCM0PQKzbAdwncLJhAfCzsMmSnOAZDZZQJ5aYFljxfHhRKwYBmqVMIfGMB99VW8e75D4DroIAABBWkqjY762mssLApewQSux18C+V7wCqADuhJ1rEjAt+wcMQ1ObHKhn+ahANc25xrhopuiTFNNWDVo6PHzZtJQSuNlAsKl0QqaWmsCAZYPFz7sE3YVSpoqm80ZTaSkTFEdBY42lpF78MhIl4bBMp7yUErhBi5u4FY55D25nildUFYuWclnj/9rorZZuYhq+CypccE8QMHNc8JWly/f5vGeVzWNGYiWTNAohMRzBM1DComsFiXaMrSFPbk9bBveNk7MtvvYOTx4nPG+b98L9TnNg8cJdh87B4CgMlmbHZuNjWDjovEuPMNxwc3nSjZ1WOS9PD25HvzAZ3d+N742It9yUGbz0GY+dufHqsJQr7ubvOUz3N9NaqiMFWgCCapQZOfXriW7fn21nWOFtqbwlrlPpVX1fhuGDMVnpqK8mYo1n1lsc8t5IdIJSbwI6YTklvNCPLN4eqZi57xOEnaCkAyZiZy0mR2bzaxhTVGOX4N5MwWi+3JXOVjnlTdL9LpteBsDxQF87VdXPAaKA2wb3va6XueNxnQCZpXL1cTDNdRQw2tGjUN/CGLX+vvIfu7vKLQkiBx3PKHjjkYecyQiMQsKQ9VMvALyQYl8NEHML5FwIjgjE1a/DKk5U18gNRcKgyYgl5b5UZUqR40LJ+2uBrtR4RTkbaQu05IPGAoLsIQ5h5AEBMScmHHI2XQbxFuhfgFdXj+rYxpHu6RkmFKiAc+yuHZCxc6BeotkLsCrrGYrxgtjx0JrI45tC5S5z0wPTqIFO8s4OomoVH21tBHHysAE+VIbf/hAmO+9SpEsiSDka/akDOfdxx933T35PcyKzZrWv3nlkpWsXLJyn8/21K0+l9yn8W3jwmMHprBUPxDEFSEP+hokvvbxtV+lv3jaI+flaIo2UQpKRO0o73kpz/ufUQwnRjPu739GkVmYp+fYBJY0VofJcJJktpcdGjZ2SDZ2jKY1Kox64k6c+nA9m/o2IYQw96/M/QsEOzM7q/c5f9480js2kcyp6mqI0ODbkFM5kmtupu0SI7obK7Q1hbdA28pQmLS536EGY5M5U1HeTMWaC5IL2LhoO8902NVVHaUVi5IL9nn+xQ2LJwkGBxps6vPjJ5dvlkB0X37wwEF7xb9ZoldXuSAq1Xwxn0qoqlbjnYSpBMyDNfFwDTXU8DqglqE/BPHH3/4HALG+HPJ3D+N/7ye4K67B/Y2H/1QZtTOPdpWh1Wgfjvq/KOCx1y/SrxVlrwjKgxOmKQF/wmUQiptgXCmjotTKZOenOiY11wTOmMT+X+7N05wR1Kc18YJG+CYAvvSoS41DjgxBKAZCsDYexhE2USuCaD6caLx5Sr9j99Qo8SLM6Yd5e6GtH2JFuPfUCQF9pZhRHYLlQcSsLmhN43syKGn827Ue3Q+Mr3s6ZoIxWcnGO74pwFQIVagYGkKeJuIbMetIhnoEljT0i6Hy0D79m/fnYb78YQ8ZQEPWiH8tBSKAprzGLgdIP+ChM5urwbSFZbLZCOrD9aCp+uOf/mA/ngWuI7CljesIPAtOf7Cfol8k6SSJ23HjpR+440Sjo+sgFfGrNkI5XxnHGl/51Uq5trBRWlXvs3HFFeB6OL6uFsASGvJJh6IM8Lq7qz7+yVASjcbXPr86zSKkJHY5oCnSWL3fh9/XXF0BmYkobybe7QBXn3Q1daG6Km1KCkldqI6rT7p6n+efWIOg6Be5/4wkDTKx32u+EdiXH/xr8YqfaT++VjjCMTaoWpnrVMazM9b+6R2MN6sfa6ihhkMbNVHsDPBOE8XqE47mrtY9/MHuQaFpyJnAj2wB3Q/qZUXwXBm1V6Kd+ZB+GXQG4RcI/BKFVCulEy9FzD8NRzqTWWb1h0FjB/RvhnIaECYzf+bfwYJlkxsUbaqIb7MgLI4tl0FrXnYcUBaNJbhs0SUcVSjzrV2/5YcxnwdUjsbA5zeOJi4EYowQ1hY2/cV+PvneT1Yv0f7r75PeoRGBCbq1AAtYsEtz4QbFKVs06SjsaQRbaz5VFnxMNtAVcbg+GeKH86IUgCN7THDuW5CNQCgY9aO3fZOttw1jh1+fCvedKDhir6A+D+kE3NUJjy2W6Ake3VprGiON5L08rbHWqshy7P20p9r3K/4rfPdfCZUFaOM/b1YQNLaC0BFHcO+5zfyhQ1P0ipz4Mqy42+Pi9R4nbQ7YHSqyOZ7lE0d+gnQ5zZ/c/Yrxns8Y2k28bATF8ZJm67Fl/vbwv2DW7ON4bPdj7Kk614wP5gXQGG5CCcW1p13LHZvvqPruL9mm+Mw9iou7PE7cHJCP22xPFVlx/rX8LPtbFj07iO1rlC3J1jkUI4JoIGmcfwTHXXYVWTfLU3ufouSXEAiys+I4C9pZkm2gLhtgz5nNvec28/hC74BEeeH2dvxsjsKjj+Lv3o32fer/4i9o/PifjduvPdVOR30Hu/O7UVpxeMPhXHPyNfu9xlSCwU+ccw1HLjlrxgLRru4urn/0em7cdCMPvPoAjZHGgxaA9v/gB8hEYtyYw7YJ+vpQmcy03zVe9ql9nvc1iV43r4NfXQUP/iO8eBfEWoyAfgo81PMQbuDiK59ABzjSoT5cz6L6RXxk8Udm0gVva7xe4uF3E7Lr17Pnuq/R/4MfkL3/fqymxretgLiGtzVqoth3G96potitW59g3cDDrNv9IA3bBzh2p+a4lzWL9pigdxwckLMkYjbIsz+OmN2AePGXWLm9JBKziS+5DLHwjINv0I5H4L5V4BUNPz/aAOFkVXz7ZMcZ3LTlv7BVgIMmGxLkwuCEbDwBURmC5sUAU4ofN517PIM5Fz9kGNdOWdOYNvaSu5pMpVQngF+eDdsWaFoCxfIgwuqYwNGKPUJRFJIl2xQf2aBoSRuv+YEEnP6CmSGMXc7KR8ENwb+dK2hS8IHHFQ1p2F0vuHOp4OkOy2Sfx8yGpJCErBCz47PH0TGmEqxO/G7kXp85/ijwIBjTGEuZ53fcpheq2775vYv5wO1b8CwoSJ9QRfD6s/NC7D52DquWriK49CqaeotoMz+ASkGtXY0QuyQK2mN1UxNOJEXEcyllenhVjk6WJKCkTYCmNdZKIpRg69BWFIol29QkAa0TwD0XH8Y//d19dHV38Yuf/T0f+/UggW3h2hrbVbQ49bR/41tsXCSq1KSxtnirlr720vLTiRMnFmR6qzDi6vJ63fu+xKvAmyJsHYfN60aF75U6ECgXzv+XcXqREbze/VHDOxtv9/e3hncUDilRbC2gnwHeqQH9iMtNoBVPpJ/h13sf4I+5zcRKmqNf0Sx5GU55WVE/NMUJIiBnS2RbGDlLYcUCYqd9nkTHBwzFYscjPPn0TdzhD9JrS1oTh3HRcZdzcutJk881sq/XxysWeEJiI2hXkovKipNP/wrXPv0dBpVLRENeBwxZAheBhcJyTGBvNc6lFArjlTKsKkk6h/tMEadlV/HoZf+LjOMRSJM9njNoBK0CeMXU0yLsQSYu+M8/U2SkoC3Q9DkOUQ1bLADNRIfx624LOHIgQBQkwkToBMLwvQeSgBREXE1gaXxboAPjhZ+OQtQzxaHuXCrYuFhiC5tZsVlIIacMTr614VukQqlx2VKtNRk3wz0fuweAR086injRBNWqwucX2kww/uSp0YD+kY+dQ35PD2XHCAEEhhJUrIvwb585jJZoC5/9zktEuwdMph/DxxdAyYE1H5XsWShJS0nWMeJUB0kpKKPQhCqUJC0EvgApbSxhVWkRq/69TH1utNiXQBLyFOX6GBf85inABGoP3v49jl+3nZZhiM1fwKIrr6m63LxRzibbLvwI3iuvoIMAGQphNTUhbHvKIPaxX/yI4Z/eQqI/T645Tv3ll3HaxVfu9xoH45s+cszGvo0IRHWSBFNPYmeKfQVAwJsfHK29wDhehWKj29wCJFth+a+nPORQ8KGv4fXBm+WuVMO7AodUQF8Txb4LYAnJafVLOK1+CTuLPfym7/c8GNvAE0e4/B8kjRnNn24TnLEdGroDU3CpBGqHQu0oAiCSgvLTPyZzfpFYs2bLc/+HNRGwLUlCKQbTO7npyRvg5KvHB/U7HuHJh/+RmyLg2TZZoQw/XWh6LLipvh6iYXqVS0JI8gL6BAitsdEEQhJ3BfVlKOR7aE218Oe5fs6wIhBpMIHB3V9gT50mkTUZeYEJTpUw1JkRlG1oTmtKlkOb79JjSVLSgUQrTmEPZTW5YFBLGopRQbwIrqT6+tuBOd9hAxovCVlHooQJmpNF40m/qwnq85or1ml+CvS9N4UWglVLV00ZnLQ9t//qi93NgtmDhh5jB+b+8uHxXvtd3V2IXbtREctoJIwHDoFj0TwcELEibB3aSjkzRC4FjTmzgqEBT5pM+sfXKX56DrzcAUL5SCQ+xsYTLbAxYmCnMqHwUShpBLgIaBkukYtQ6TBT4EuHHOoHy9V2ds7rpPNvO+FvJ4/ZrUNbyXpZBMJcW/kMlgbxgumLOs0E2fXrcbdtA8sCKVGeh9q9G3v27EmuIo/94kd4376RiC0oxRwiw0W8b9/IY7DPoH5sRnmi8Hm6IHTsMVob16Ld+d3MYQ6JUGLGDj5TIXnmmXDdV6uFi0xBrjAP962mLdHGp6+8iDm/fPzNK2o0vNO8u2MxsSDdBIytulzDuxv7KtBVQw3vZtREse8ytEfbuHL+Jdx8zGouz8dp9WEwJbj1BPjsx+CqlZKHLnYJlkaQ820wBiLorEY9X8D7zo2kv/JjuCfg/U8oFncr7EAQEQLbzXLHll+Mv+DTt3BHWGILSV5oJAJbCASCvB3CDie4Y8svaJUhyloxJFTFMcYEmI7WJIQkJSRrCmH+Zft2Ts2F8YphvLwi0BG0cLj7FIUTQNgzJO+g4k4zPCYJGPZhb53Ak5LlQZw2O06pfh5EUjQrJhV2AkO7ySiJZ42+LLLCsQ+bWJlsSLBACQ5XFi05025LAQJcx+z7Zxs0TjlDW6KNJduNL/kPfuRz3W0BS7abVbKpxJQThZ53nirwLbM68Eqz+fQtsx1MYPilri+xO6Ww3AAhJKLi0WJ7AUOJgFJhAE979NZBYAmUNMJf1zaZf9c2v1+wwchapZBGMCmkcf6B0bxGxeUnpBWOdFAYK9K+ekHYN2sDUkjCVpiQD7nm+IzGqac9NHrctTUaT7+2gH5wzc0Ix3g5CSEQUoIQlaJT411Fhn96C4EtCMI2QpjPwBYM//SWfV5j7XNr9yl83t8xjjQiUCEE/UVTiXWmDj7TIXnmmbTfspbdt3yNL3+8zBML/epk4xv6V7zyrRUs/t1vab9l7RtPW6hvNzSbsfCKUD//jb1uDYcEnHnzjCXyjh2UNm/G3bEDf2Cg5gpUw7setYD+XYqEHeejCy/hxl1wbZ9kSdkMhd0hwf/znhiXv99nzUU2vZ9M4lyQwFpiI+aGwDaLOrP64U82aS66O+Azt/icf5fPKU95WNteNX7zI8jsoldCGPAZHXAC44QStsL0Fnq5qOOj+ICLcZFxBXhCECDxtaJXu5DuNsLachY99Apqzxb8XTvwhlx6mzWPHAl1Wc38vSYwLRrNLUIbuo0dwK+XClYVLTq9gOVHfaoaQMcDv9Kq0Uy3wDjWWAoyYYHUo1x8q0Kt2dUEvq950VL0EWAFYGEoOCPQFjSlNZ7y+XT+JHqv/6ZZMh5jE5hdv57OeZ1cljmO5T9+mb/5hz+y/Mcvc1nmuHGZyb0LR7zgoSkL9TkIu/CRDZr/+OmX+OpDXyXrZk27A03INRSYkGfa/F9LYVdhLwTGNcYJNI5v+lxo83yGYxUP/7TJyHvKww1cSkGpKoYtVJ5nqfK8wspUJNZao1D8+jQLO4BUXjGnXzFrV5GGIY/mZe+b0fgMyRBoUCgCHVAOyvjK59wH0jx38om8cPQxvHTqUvb+8EeTjr1x440s+/kylvxsCct+vowbN95Y/c7r7saa1WKsjFSlDoLWaM+b5CqS6M/jh8YXJfNDFon+/D7bPtbrfwTTZtg3r4O1F9DT8ziR4W4oZWiKNBmHIM24GgYzcfCZDiNe8/aff44r1/Ry7BZvxpONAzn/jLzsl11lOPNuwTwHt2B+X3bVa2pDDe8ORE89laC/31QmlxJVLhP09xM99dS3umnvCuzPia2Gtw41l5sZ4J3mcgMgAwWFEkoH1Wqfk5Cag0zMpW3gFT4wmOMMGtENi+gOhigJxVZHcXfC58W6gEQzzPvIFdh/eRmy9BiPREvoAJIFkwlvzMKC3bDk2QLp//kl5ZdeJBhOI/Ov8qRTICcFJaGpJK7RgG2FCFthWqItXHrK3zLXD3h06Hm8yj6WNlnUvNA0BQEfLpaNPaZXAKVB2sYHs5imry/M6X+QlMIwmICyAzKAYhjiZRhKCu4+0yZoF3wufBicdR3tx18y6kaS30O2Eq6KMVLWPY2C3Q2CI3dp6nKmYUoYUapnwYPHQscec0NZC5JFjaUhGg2ISY2PwPIFhSScfXwr8+7JmRLvFZtA4TigNeWXNvOi3kX8X39OJBD40RDxkqbuiS3sboR5R50CwHNP3URXsyYdg2N3GuvMXBQSJUg9+RIv15XZ3VBpdyO074WGPAwmBf9xpuSZDkmjhrz26W126G5QnLRFEwqMLmAwAcWIWX0YTAnWH2tXq51Oh5iCsoS6aCMxO0bBL7C7AZqDGEdv87AUBI7Erqsj8ergjErad/V04SqXclDGVS4CwcceVnzsIYX2fVPQzPUoPvEEGoifavrnxo03ctMzN+FrH1vYuMrlyd4n0VpzyuxTyN5/P7pUQibi6HIZfB9hWYQXLWL2l780rg3P//IWQnkXbY/mPWw3oNAYY/Eln5627Q+8+gAZN2McoiooBSVmx2ePd2UZEYd6JR4IQUb7OKUM4XCKUChJ0TdZ7I76jhk7+EyFEQ69LpfZo9PEy/Ce57MMNIcYbAlP6Rp1sOeXiQQqkyF3/wPTP+emDmhcDHufhVwv1LXBWddNKYitoYaJ6P/hD1GeiwgU+D4yHEY21KPzeeov+uhb3bxDGiPUwH05sb3DcEi53NQ49IcopJAknDgJJ46rPPJejpJXqlIiqph3ovkBDgP+GvikX+D+7bfzm/Tj7LEUmyKaTRFoTf8PHw4N88GzP8r6529hd8Xi8PBdcHiPZvEuzaw06EKBwqOPUXj0MQAuj8JzbYJtbYJN8yAdFwghiTtxfOVz0eEXA3ByqoM5WLyCj4VAolFaV8NsY8NiG4987YPvV7eftUmTtk02PKI1gRS4Yf5/9s48Pq7qPP/fc5fZZzQaSZZsCa/YwWCzOpgAZgsmlBC2Zm8IBqdJaEtC2qZpkl/2hDTNUtI28AkJYIc0BFJIYihLzGqzGQyYLXa8gBct1i7NPnc55/fHGY1GtmzLgMHAPHzEyEf33Dnnzr3Se97zvM+Db8MNH7NJmx7FoMlXsj7ERqdf4eZuXMnZj/wjHQZV76df1h0qOP9J6GqQlSJP0Fn/+dsFN5wluGCNoHHIpzMlmJ7RHlsxXxF1fJQPzUcViJ/4n2y+6Qd75H8O3bie0AjFA/CDFuCRu3E5lDnbq0yJqeC8NUoryJTHUwzo8Zz7hM+zM3UQvm6WwbqZu9wXQFAY+ErilWsG+hLQ1qc1+KnazVhxvKi4nVrCouSXUEJx9GbJeWsUk4YVvXWCexYKXpoTpq/QR97NE7EjLJ23lHPuX4M3dffitYHrb9gnrWPJEUv4+qNfx/FHzYPev0bqBZUhUMrHtAIoz2Nw+XIm/b2+PjetvwkhBJbQv9osLDw8blp/E5cffTmppZdVikDtadMqRaBN//SPu40heekluD+8FvDwAiaW42N6iuSle/BnqBr7VWuuAhhT+Lxbhr3Kc2GJ73CVlQckoWw3VrKNxnDjhJRc9lUwWq01b7sBHEMbdZ34UC+b5yZeM52n+vwAIhxGltv3+DnPWVwL4Gt4VXDb27FSDYiGxkqbUqrGoX8DUE0NBCqvy15att8Jh8yqVZW6njekducdgFpA/w5AwLAJBOtRAc3Lznt5Sn5pj8fHrAjnzbmEc9XFPJN+iTt7H+LZ9J/pdvq5seM2fmMEiCRtGvMeThCemwkvzbKJh5LE0x7/HP4A8oX1yOfXw3AaowDzNyvmb1ZcAHQn4eUpPrmWbo5L+hyXuw5aF8CGOynEJJMQDAmFJ7RmfIOvKBgCYs2Q2cnaUJDfx6N0mzbNvseFmSyTsmXD2qzC8AXSVPhBRWRYIHMes3yfC4GFwTrk4E6Mu/8ZqJLJm7OY83eczbU77hnNRZdjehOTpiGPzFgWhaalDCnWzTLL7qk62/7ohggDT2VxhzzsmEfqCJf4jDDMWYzd9j+ku7bRpzK4vott2jSKOIm2acQ2rqUYMhBeqeKg61ma4rG6fTVXP3M1GaEH1jRMueh09/GMLIGO3iI57wlF07CiYOv5hB3oqxPcuVAgheDSsrRkbx3UZ2FSGtob4FeLdTbfMixsYVeMso7eIrlspe6TC0FdTvGJlYrlpkv/0VMIxXQAu2LLChb9pQ/LkSjXrajJGLHYhP/wFr1iZS4KRdgZdfKt7BiYJjI3SoHJu/lKMD8CE5O8mwd2LxDd2x+SEy76O347vI3ALXfRMFikv97C+cg5fHQfKjeL2hbxFcYvfB6DquLQRTLAVzxYZhbpkC6t4aZx++wavC9oXsCKLSv2WoBbXUTYEG5kZ7aLkgX1A68PnadWpPjOwcEQhL1RDsU17I6ObAeJQGJM26sp2N+be3UtqH/1qAX07yAIIYjYESJ2BE965LwcBaeAP245qM7yL6ibz4K6+bQXd3JX78Pc3/84RVmiFIDBACRkiGYZo16FdYDanMI87iTM00/Sah07Oul84gEG1z7BtHaXoKtoHoLmIeDPAjDpSO0g3LCJ8JQQU2bY9NrQprSDK75H0TRJYUIwxtpSPz9PxrEUxJRiwLT4eTLJP9sedoYyDb5c4ZoTJOIev+wahFASElH8ksIniPAUxsofY0xdhBHSkfFab4BJ0eYxUokFT1+fvjpI5Bibofd0IDwCrSUD66a6LJoxmj2qSPIBXecfj/vDpxGWwAiYiKLDkNdH7rMfIvvLFwgN5fFtUXGwtRyXoWSQHz/6dQZLo/qivXWQHHc8mjB01Ba/ogPvA20D5X4JHYQvWakoBgRYFiXLA1vTd4IuZCOCdbMMYsCUupm40iVshdk6vJXz12TLOwMCQwgcW9tMnb8Gbl4wmrWZsyGLzOZQmDroLqvJmKkUwenTgb3LVl799NUU/WK5nFchEBTKuxDKrNL3932M6GihbcSOUPAKWFW/2nx8IvZodXT8lFMm9Edjdftqlieex/7sjKpM+/O0tq/eZzZqQqosyWlj5BsXyQCLih7EW2Ececrx1HN++eIvqQ/WkwjqP7LjZcyqA6C4HYPYZIaHu+mrVzTtYeGwP6gFWO8MHCxB2Mgum4QxUqs1Z90Dj9bYvpXYJoJXtatXwz5RK4p9h8IyLOoCdbREW2gINexWxLcr2kItfPqQj3Dj/O/zt20fpsHSkWzaKLHJ6med1UmnkebkttGHUQiBMbWVW1tf5rbFims+Lrn5HMXjxyh2NINvaI300gAMbQrR9TBccpPLB+/yOHKdT1OfooQuvrywpAPc38cjWEoRKgfPIaWwlGLYKN/K1TamABia/7LLjoQyg/i9HbhdXTjbt+P19dE7sIPALnbyMUvzc1YsBNvTASVKF6Javi6arUadFWVZwN1jwd8vok/zx/ObyCUCRAo+uUSAP57fxC+iT5M8LqiLaj2FUgrTU5g+PLJAknEzmIZZ2TVYsVBg+VXjcXWx7oqFOgCupuQkC1r1RwpI5sGxBa6ptfpl0CIgrErNwEgx7BQJ/z73b7ny2CsrhcMNoQaahiSOVSY6KQVC4NkGk4bGcuxPX50mHRF4ysf1S7jKQymJHBoitfSyirHU6b/bRF0WsiHFQPsWtn3jq2RWrWJrZiuGMCrZdoXijuP1+xpSYQpTc+mVov6SUQrMxXMvRimFp7wxrxfPvXhvt/e4eDVqNfuFCRSHVhegfWn1lyqLq5Hx+NIn42TGnHbXjFlq6WUox0EWtHpS1DOZbDdy+r/+J9e/7/rXLAe56/lloVALsN6GqA7ChBD6NRBg4Pob3tBxxE85heavfw2rqQk5PIzV1FQzlXqDMBEltonAbW9HhMbGHLVdvdeOWoZ62orGAAAgAElEQVT+nYKtj8GzyyHdCYkpcMwlMP1EEBCyQoSsEL7yKbgFcm6uQrHYFREzzLmTTuecplP5Q/vd3NHzAAMijyMk3YECP+z8FacWN3Bu02lMj7RB+zP0Z7uIANJQdDYLOpsVHK3wPIOfbBik2Beg0B/ESVsIH2Z0KGZ0KHgKikHY0WbxZIvL/S3DbEtahBG0G+AJgaUUSSUwPYkZNZAFDyUFwhS4IUgrk2+1NNLs+VwoXBYoG5wsZHsBCb//DOqYS/Cnn0hLPsDW9HYCAYUMQMF2EcIgFUyxaUYfN5yl+NiDirY+HVh2pmBGl+K8J32ahhR9SYNHTw2xfmYQrLjW1U5O1QHanMVkVq3i/J8+zeRuF8OT2u015hPwI3RkOzihIcNv3xsksNahYVjRXydwFgRYN8PHlz6WUX5cBaybZXADmsuuXW0F66cqLlqj+NSfPOqy2uUWWy9E/LIbrOXrhZYKWAg8bFchbZMIFtN9hSyVsOpC3Hvyv8OcxaxuX03EirA1sxUpJb1Jg7qsriUQQNAIYDuS/vqxajCJ/iLDEYW0LRJZH8OTFA1wLMkPC8sZemaIJY9kMH2I5VxMTy9AsuG0DhDO1CpI1fj9ybqm4CPPhjEKDjIc4NFFDdzesoLWe59myRFLuPzoywHNpR/h81889+JK+wgmYlQ00e3lV01DmLMY+JHm0u9yr4yMsTojvzO3k6JXJGAEiAfjgFYDqq4zgN0zZvtDM3o1ONDnr+HgwMFErVo3U7Ds4yYdWYvWmMmSmYKaS8GBx4TphPtAbVfvwKDmFDsBvBWdYpVSeiuyVEJuuA+18tsobLCC4JV0YekpX9JB/ThwtjxM7vlfU8juRMUmwdwLKsWzu6Kz2MNdvQ9xf//j5GWx0j4vNpv3d3XzeCFNxoCAVxZuR+EACSX5p4FhwlIRlwrhCApDYQo9JoU+Gy9v7vZeg1HY2CrY0gpbWgXDEYEPfOEug1k0I4rd4HvkDMGQL8mH4Y9/ZVBC4hkmnykKFgx165PFWsC0K9di6Ybr6Sn0jF5DoBSAZN1kvlh/GLc/fh9/fZ/CL2u2hwqCZA4GI5CNCWwXbF9xz0WH8G//8qcx4x7Zru7LdBHNlClOQivU+CY8uNjitLpBrkrFsbEIGQZFwFWSiGGzzdI67GOCt6pH9+gtkqUrJa6ppTonDYAloSehs/Jm2QXWN6GvMUDAVUjLIFDycQ1oqJ9C1LfGuIRWB5Su77Izt5N5mx2W/klWdOsjvonpwy3nxNkxv6lCTfnktVtoyAlkKICvfFzfJeAq0nGT5Z+dyfb0dn7+U5dIQaIMgRJaNhOpCNU3cMUXonTmOnf7/KdEp3DvB+8dM7ZdHXcnUkQ6kb5L71262/byro6tB9KGftf33zq8FUc6BIwA0+umA9Bf6GeoNMSU2JT9vg411LA/OFgcWl/Ls1/DwYED+XtzP/G2coqtUW7ephjZEjWTSewNywgkgwSaotgJCysRwQjaiOd+NX7nrY8RePQn1OcGmWwnqMsNYT/1C2h/ZtzDp4Qm8alDPsz187/Ppw/5CG3BFgBezG7iB/E0zzVAZxjypokCHASuEJyVK6CAvGHQbZkMRQzsVo+mI/NMPTPLLR+Ee86Is3FunHxEB/f1OVi4UfHxBxVf+7Xkn3/nc+Fjio62KLJUQpkJFIqMJ7E8WHekNugJRVJYhsnvzZJWyom3QCgOdggMG55dzmCxH4PRh8IEIg74fT38YbCfxWtMlBB4FhhCEC3pmDrqgEJUqCwnP9y/2zUauP4GBC7xvIcSIMtqOqGSlr8879Eiy+rrsaUi7DsIKQkrha2ASCNxO44vd6l1qPpVdN4apYNsW0tqDuoELqksDIW1BKihIB0VTLZSBKXBisUJHvzQbFJtszAyOTaZffz3GSWuLCyvZLBHKCcDxQEMYfDcLJPrzzJIx0xiRcFADFacP4mdR06hKdzElBd28nfXd9PWK2kYkkQyLp7vEnDB9gV3nGBUzJMMT6vWqPI81EjpgLtn86jeQu9uY9tfOsxE+05ke/lA0hB21bNvDDdWtOlHxmMbNmdOPZO+Qh8bBzfSV+jjvFnn1QKbGl53HCzUqgNOhavhgKNGmzowqFFu3gkoq2kIIRAWYAnMYBSKXahDDkEWSyjXQTnlr2eX6yDXDiGAmB0m5grc9XeQm7GIglvYXf4SiJgh3t90Guc0nspzmQ3c2fMga4dfYNiE4Sj0RATxkkW84BN14U9RnemZV9JE8KIwKKamEVAQc3JsCbvE4mk2HRqGs5ppSNtE1+9gdodkVpcuAm0ZhJZBCQzhC4G0FCiJEzd48jjJ9kOCEJsEwRjBSCPdA1ugfrqOHEdgBSHdBQmFgQ7kR+ADvvLZMLCeC4d8ckE0Gd0QWJ7CR3PXQZWpLDbJAc3Xr6ZieD3dmCEP0weE7qcQWL6iQUgiacFmEzLYuGK0EKDRjhE0Db698Ntc/czVbBzcqAemKv8DNO89GxrV+M8HdQFsQ0bPp70BEBBzBInJ05i+9Fu8p/zL89p113L9i9fjS5+ACfH0Nq5acxV5N0/UjrI1t5W8l68UqK6bpRVwZFmxvy0WQno5loU/T/cD30EE4mxPeRj9eRJpj3gaCgG4a6HJ+tlhpqODU8/MggdCqkqG3hQmWBZ9xT5MYSKVHKOBf8SmEoM3f44lAw7phjCPndbE5rmaFjNRtYWObAcGBltzWyvFzw2hht36TmR7eX9oCPtLzWmNtbItvW1MkXYsEMNXPmknPUblpjHcOEZhaF7jPI5+WdVoMDW8bjhYqFWvl9JKDW8uJipOUMPEUQvo3wnYRU0DqFitC8vCjFnAqFKIohOVrEdJUL5C+aBUEDu9k2QwSTKQJO/l9yh/KYTg6MRcjk7MpWvrQ9y99Rbui0HOgHRIkA5ZxByJk7f5jWnx8WyBeYVyYSDguDkG8n3UB2IMC4OIdBHZbvqjdTw7X/DofJNpvqC5R3FIh6S1SzCl28dQoFwAg5ZB+KuHBZ1/cdkxI0P7LIsdDYpmI6ApR3ZVQY5XgsRkWv1OtpfrTg20A6pf/l4pRX8CEnlNNUFq6o3paRpL2AiiBJiOT7YxupsiBN0deGV1RVPpIFsphWEoLFeRTQiGBWW9oVEN/D4vyxSVqKimHH/TcRR8h10qf7XsZFn1xkAg0ePb2Cr4zsdNEALLsDhm0jEVugjo7etfvvhLpJKYwsSTHgPFAVKhFAW/wLAzzDEvK855XNcJ9NYJViwUPDdrdE3UmetkVnLWmGx1YzGKcvL4AjwThmJw6vM+Q9PDdB4JtmnTPzmC6HMJFjwsH4xAACuR1Co46gWOflny/sc9Jg0pepKCF6YqTn8BhO2TDUE07XDO7zu5C/ZLTz1qR9kytAXTMPWclVeZw67Yl1rNRLmgr0YhZEHzAp7ufhohBCZmxWjrM0d+plITsPTepbjSZaA4UAn644E4D/3uP5j8f5k3XZFkPGRWreLla35CfvtWepPw3OKZnPahL9R2Fd4COBiCsNdLaaWGGt5ueFM49EKILwCfQkclLwCXAhHgFmA6sBX4sFJqsHz8l4Gl6Hjnc0qpe8vtxwHLgDBwF/B5pZQSQgSBXwHHAf3AR5RSW8t9LgH+X3ko31VKLd/XeN9yHPqNK8uFdtt0MD99ETz+M3AyWt8cQJgQaYCmd40W4o302/GkVoYJJsDNge+gMFF1M1CXrUSt/xPq8etQg+08Ea/n9miUblmgIdzAWdGZzNvxDGR7dGZ8rnbuK6z/PV+ze9kRERSrlpG2r2jN+3y7Y5A6X2uvj4zxxWCAm+MxfGFQMA3csnxhUEEKgyBQ8l08FP/wUo7US0G8gs6vK393Nlk2CFunwFALzJsU4Ji6CPntOYb/7OJ5deTtDLceJXlumsAXAlMJpNDhtQvM3SH560fKHHqz7M6ah3RYc+EDPlhSEPjcUprvfY5M9w66/SFK0iFSlDQO68WBIaueOaEwAopbz7W5dbbBmEC9HDGHrTBP/s2TALxv2VF0jiMzevQWyeV3KcIltDNr2cm2Pw5hF3qTgj+dGOaTS68GqGSd006anJMjYAY4d1Wec9ZIQo42qrrzeMHWKRafucMl7IyeNx+Ae4/VplpNw4repODpM9r41J8kRkiQzXXj97tYnl68gN7UyISgd3KIX376EFzf5RviA9T/+DeoXA7l+wjTRESjTPn+VXzvie9y3i07xszHlHqnJxMzkEqrHAVcxXDM4AcXR4haUb5z8nfGzG+8rPqpN5/KgDMwepkRmMJkZt1Mbjv/tr08WLtjPC5ooZDm9+8L8WhLL62ezxKrian/F8PL+RPmH2dWreLRH36RaG+OniTccYLB+tlh4oE40xLTKouyU397KoOlwTG7GALBt26GI41DKu+XcbMMD3czGFX88fPHvWapyn1dkz1lcTOrVrHtG1+l1x3CCxgEPIHp+dx2boqLPvndWlD/FkLPz65hcPlyZC6HEY1Sf8klFYO3A4nV7av52iNfI+fl8JWPKczKs1+7f2rYT9Q49K8FQohW4HPAAqXUPHSy8qPAvwL3K6VmA/eX/40Q4vDyz48AzgauEUKMsCKuBT4NzC5/nV1uXwoMKqUOBf4D+EH5XCngG8BC4HjgG0KI+gM64TcaI3bymW5tWpPphieugdLwaDAPoHwoDELfFn38Qz8Y7ZeYAr4DuW5wi6BAKBej1I+59mdYj30LW/SzZnKMHyQG2SHbscM2A04fv9lxDy8W+yEQg/wgrP0FAOHF3yFoNTBftnLYsEF9UYFSuKZga9zisjmN/HRKgs3B0VtyXsnhPYUiWQNcFLZU1CkICLAUZIGU7/EPG3LUPx+igKAnAT0xg8GIYuVR8Pi7ymovQKwE816Bkx+H5B8dttw6RM+jHm4xBiGbaM7nY4/6vPcVj0ZfcpjjoKRihNG9/hDBbSfpAD5S0g6r9xwLfUn973QEbjtJsD5VIvvKJnbme3DdIkKqCgVGAqqK7iMMEIZBfxXNZvQz0i/VhbA9cnz1IdAbHILyDoPSY4qVtAFVMqv4xN1FOlbeyVVrrqK30EsikKjQp855OMdFj0iCLniGlsH84KOKz97hEi/q88kyLaYuDx98DJI5VTn3OX/oQFiS3EA7O5WLXRXMQ7lfASZ3FGkKN/GVhV/hiMZ5iJFrUU4sjPz7ovvyxAv6fX1Dv1oS4nm9szFGZnNI03KEELzY9+KY+Y0YLa1uXw1oelF1MK8vsyJkhsh5OfYXu3JBswmb6093eappJwnp02saXCV7yWzdhBBj1Wj2Rs3p/vZ3CA7mKUVtmvIWf/snwZmdKVKh1BhqQcbNjAnmR+aTGvQqsnAZN8vObBdFU9I4KHe7Jq8nRsbu9faO2RnIrFoF6JqDQZnFC5oYhokXMJCWyRmPZGoc6LcQen52Df3XXIPM58GykPk8/ddcQ8/PrnlD3l8ITf9TSlWe/RpqeKfjzaLcWEBYCOGiM/OdwJeB08o/Xw48BHwJOB/4rVKqBLwihNgMHC+E2AoklFKPAwghfgVcANxd7vPN8rn+F/hvoZ/49wErlVID5T4r0YuAmw/gXN9YVNnJA/q1Pw0IXQwqqwoOpQuFPqARnvgZRJtH+5k2eL4O/O2QzrYLa8xxy6w0pjCxDEWJQQhAISn4g4STihY538AvFlAb/gBtx9IQbiBdSlNnJalL91M0oSsMAyHwDMED9WEeqA/zrrzDuf15TkyX2Bi0afR8AgAYYEIa6BaSeJkxHtwYpGhCb9DUAa2hKAnBtB647lytgpPKCGZ3KN7VAYd16Ey2UdTBj+9mIZ1FmALbMPnAkz6ftQcwbJMPtKTKF0sHTeunGqyfOvaSryyHl0EziKc8tr38R9qiPvawRFmiQmxXAooWFKPQZpm6sa4NWXQ4e81OHjjUQCvzi8o7CgWRKqqUZ4z/h+u8NQonBMWEQUlAc79EKIiWdEbbsUGgCNxyF/ZnZ1S2qwNmAOUrzn3SQ6GDZ9Ayl6bUwbtn6ttHQKVywpTg2joj4NgCwwWR7ycrIeCVFWuqr1z5GoRdKtnlbV9dgplIYDc3j96ShQID199AuHMAJUCVNy2k0OexRzYnhG4IetBXbzArOYuCV+Cm9TfRGG7cozX5Tetv0t2rEjMKRd7Lc3js8HGv7b5QTUNYeu9Seru2EZYChEEYQBi0JwUze7pQchDlODqjH48RnDZ9t/ONUJcQQZT0cAOaAHbiQ728MNseQy3YVdpzBD1JaCsWEeEw/YU+hBAEXRhM2a/Jrn1X7Cr/+bkb+4ntxTDGbW+nYPgYVW6+ri1oGPRqHOi3EAaXLwchEFb5c7QslOcxuHz5Ac/SL3tpGfFgnEnRSZW2gld4Xe7nGmp4K+MNz9ArpTqAHwHbgS5gWCn1J6BZKdVVPqYLGHlaW4EdVadoL7e1lr/ftX1MH6WUBwwDDXs5124QQnxaCLFWCLG2t7f31U32zcDQNrDDuzRKdEQ5niOsACcNpezYftJjtMSyDDs85rgOIQlB2bjJAd8hpKBTuDQM/oVDctuYFJJEjF7E5GbOOOoiMrZPPmCjIg0YGEzKKb46GOPvO9NMK+rg5C+RAD8+JMmn5jSyPm6P3qWRJAUgLaAktKlUj2GRK9j0B8xKdho0JSaVAV8IlDDoTwgen2uw7EyDb33S4IYLDPJBobnP5eyO8vUax02bbL0nRccTKc5eK5nZpTD9fVPTXN8FCQW3wOpjApg+2J7eiQh4OviUBrgCFBYqWI/KdCOK3bQNKuJKz2DXXP1ETJGahsG3AMNCKVXRnreqPvKSpWgY9MYopzSEGjCEQcjRx49ACKGD6XFQ3TwyTjcgkEWHW84yyUVHw2W164FVm0R7MxeR5Q4C7UgrBHjlk0Z8E6Qk4CoCvqajgC6My7v53UzSqgvm8m4eE1Nn98r/6WFJFjQvqJg4Lb136YQz2JlVq9h2yRI2v/dMzv/p08zb5OhnYuT9gRengZ/1tRKTYSBLJfzePsLHH7/b+UauS0O4UauJKIljC5IDpd1UdnbNzo9gxUJRUSRxPYeAqw3LHjutCQDP91jXs26/51qNEfnA6t2QzLbN5Myxi4zqnQi7rY2wNFFVu4W2q+ivt2oc6LcQZC6nFaocB1UqoRwHRLn9AGNX9SeoFcXWUAO8OZSbenQGfQYwBYgKIT6xty7jtKm9tL/aPmMblbpOKbVAKbWgqalpL8M7yJCcpgtex8BA//YdZ6pCgFeEYGy0XykNUuuw6GDdheF2yPeNHlccptUpUfQc3V9zRyhKl1bXA2EifJfocDstgQRt9dM5be45XHzyFTB5EtsaTcT0uXz4tC9y3Ln/yVl5g5++MsR3XxngPekihlIM2iY9MZN1TTab6yyykQhpc8Q5FDoMGLRtBuJoM6mqaQV8dHtVtnsEnhC8MEnQNymI1dCAPXMmVp2JEdKlBSMdSj0+f/WM4vN3SL6/XPGZuyWnP6+Y0q8Q41xLicTHxzZsckdM5w+LbDIRQaQImYjB7YtsulOCoAigwilUbgDluEjXIBb2+bfuflrl6E0aUnA5iV1MkcbP0PfWgeWNfKQC19KUF69KsifoCfrrLYr+qFdAPBgnFUpRDIxSZIQYpe2M0GxEmc5jKn0tPXPkgTKwDIuotLHrQ2Smm/z8YzaFoC54qZaklAJEbHTRaLe1oYqjY4HRgtLBprB+T6kXRIYEQ2jaUj4RJF4SDMcEy862WT9bn7PoF4nYkTHzG2kfCRYDZgB/nBoEE5MVW1bskaqzJ+xKManPCc65X3LoltGdsCIwbxuYUQMjGAQpMYJBzMZGCk8+uds5R65L3I7REpuMZViYJY9MY2Q3re2RbLuo+g/gL++KVqhA8ZJBOmZw14VT2Dw3QdbJ0pnrxBDGfs11V4wnHzhYH2A43TPmuOoi4dTSy6g3YlglHyl9LEdieD4PnBzfb7fJGl4bqhei2y5ZUqFFTQQiEADXG/2bohS4nm4/wGiNte71Ga+hhncq3gwd+jOBV5RSvUopF7gdOBHoFkJMBii/jvxVaAcOqerfhqbotJe/37V9TB8hhAXUAQN7OdfbB+PZyYcSjBsIGpYO3A0LTvj70X6ZblaHwyxtaeLs1haWTqpnddCGfL8+rjgEwztYks7iCigASnoUlMQVgiWZPGNCaKWwTZtUKMWFsy/kusXX8etzf8O33vsDjpl9KqK5CeOUj2NEfY6ULl/eOcTPN/dyUW+OsC9RQtAfNnjJ6qEj7pMLAAgMw8Y3TO47RmB5YHiglMDyTQLS4IGjd892j9zwAoOn353UmuelEiLRiGmDGYKm0yI0nxogMccg3RgEdHb98B1wwROKL92m+O5Nikvuk7xnvSKVHhvcH9sV5OLbhrholYtC8btFgv86V/DnNlhzXIw6I4pK9+mx+QLlKxKHhzi+z+OOTX08O1TH88NBnkobXH7iN8ece3Zo8rif5SPHG5g+mCUHC5NcQB+VC4KSioArCUgD5yPn7Kat7kufe08I6IBdAhKEryN394zjCaYaCdghbGVgCigEIR+CgKszxLYjqTdipC69hAWupBPJHe/Ww5RCF+cqAwzDoOnST1XGvDdd69AVnyIXNpCG0AsLQ5ANC544NoJUEqOcAZdK0hBqqGjEXzz34r1qx9cHxy+ZMQ3zVWlb76pDX1fXjGuanLBGoZRPAYWrJG1DCqt5CoHp0wnNmUNg+nSshoZxOfTV1yVmRZlqN3NIqIWTvvjD3SgFlx5xKQbGmB0HA4NLj7iU+CmnMG35Mrxb/4trljbzwmwbpRTd+W4EgqZw02vS8d48uJnuXDebBjexdXgrmVKGx0+bBI67R63y+CmnMO1b3yPVNotYUTAcgwc/NLtWEPsGY1+1DvuCUV9+jpQa/apuP4CYiD9EDTW8E/GGq9wIIRYCNwDvRseCy4C1wFSgXyn1b0KIfwVSSql/EUIcAfwGXcQ6BV0wO1sp5QshngKuANagVW7+Syl1lxDi74H5SqnPCiE+ClyklPpwuSj2aWDE8vQZ4LgRTv2e8NZVuamyk+98Bh69Gtw8FT79yKZFYor+PhAHIVidfpmvNdWTEwIfgYkiqhTfSXssunIjXHsS9G8B5bM6EmVZXYIOU9HqlFjihliU7tcUHDMAkSZWWz7LZh+/m+qI67tk3Aw5R6sV8OQvYN3/aFoPCiWhZIb5dV2SlWHIW6OBrCEhVhLESwbK9zlsh88Zzyka0pBO2jy5IM6zkx0kkpJf0nQcpbO8lhEgGU6hUPw09VmGf/e/eDt3YkUkdZM7iKYyupbg6L+B4/+W/77zn+j/84YKB79+nF3lvjhsahV4YYt5mzzi8Qa6vQFMV2L6cNvJgg2HmJx6yKmcOTiZ9G9+TSQL+bggMS/IUa0Rnts2SPrPPpGcQT4uiL//dI5b+l2MaBSjnPlaeu9S1nU9iVO1TAlIydEEWbxZEVjr0DCs6Ks32Tg9yOxXijQNKTKNYRqXLuWEi/5uN95zbO1GTlk1yNSdPiFHX6dCEB4/pZHP/dfqMaol+QB05bpIZCWWrzP1XZMspnz6ctzj53PVI1/DLQ6RVj7nPOrx/qcg4gqsWHxcFYy9KaI8cfs1DN24nFhfjmxjlMYTTyX0p8cZlFkKhk/Q0zr+vzs3Sea42ZX7qnp+USsKAnJujtZYK1uGtjBnQ5bFj+W1DGdSsPLECE/O8JhTP2dMcZ1SirST5p6/vmePj9rm956JUVc3pl/GyZDr6+JbnzFek8rNRPW+r113LTetv4m8mydiR7h47sVcfvTlY66DUorB0iCO7+hFULCBSbFRDvJE5lqN1e2rufKhK7XcaRWFKRVKcWp7gk+uS9T07w9ivFbX183vPZNiPg1DmcpOHsk4oUiCQ++/78ANvIxdf4cdSNWmGt7WeFtVU79ZspXfAj4CeMCzaAnLGHArOrDfDnyoqnj1q8Bl5eOvVErdXW5fwKhs5d3AFWXZyhBwE3AMOjP/UaXUy+U+lwFfKQ/le0qpG/c13rdcQL83VAf7gRjk+iBUp3nxbgGkw1/HJC+bCqOsJqIAKQQzpcFtlz0PV8/XCjrVygJKQe8GTfmpKuJc7We4KiKxk1P3aNOtlC5KzDgZil7VVurWR2HVD8CwUVaQP8sCXwu5uCOcEPTgQo4iVhQ02/VkShl86TM5NpkLZ1/I/Mb5/PTpnzLsDBOuqhEoekVSoRTfO/l75fd6rPJeWEGtTS9dOOxc2HAnZHaCMFkbsLiVGLO6oKUTZnVpNZld4VmCog0FWyGFdmi99jy7rNAiQPnYSqv/2xhcsA2aH3ORFviBAKYrMXyF/amPcdTijyMsCyMS4cI/nEXYy4y99ECPZRPGwDYsQg2zJ2yH/sTt11D89//CNQWOpQtabV/xx/Mmsf5d4TEBXmbVKp7/138gL1wcS5SPhRvfZ5E9djbJUHI3feiCV6Ap3DRG+/7VYn+DkPEs4uuf3cql97q7zfdXZwfpO3rqfo99omN6o63Oq+fu+VpnXyBoibbQV+jDla5ezAViE55rNZbeu5Rt6W0MFAe0Qy4GnvIwhcl/nPYfteDqIMd4C1GlFHJ4eEIB+XMfvYCB9i14QRMhDJSSWCWfVNssjvrtHw7k0Guo4fXE2yqgf1NUbpRS30DLR1ajBLx3D8d/D/jeOO1rgXnjtBeBD+3hXDegdwjemZizWH8BLDtX8+OrFXEc2GrkMVQ1PQVQiq1GefG3J6OqxtngZMGhskBYFnaxw5P2qDoCmvMdtaNE7SiudMk6WbJuFv/ZX5Uda8MI4AgzymEyzRZTkWd0cMWgoBiEnMjRFEmQKllEA1HmNc5DoThj2hncvOFmfCRhM4QrXTzpceHsi0bHX+WOC+hXF71jEGnUQb7vssDzwc7y+9khVs+1KABz+mBuh6KtUzCtU18vy1PEPIgVdMCdyijOWuuzYYpi+ySBaZr4+AwDdUoSeMFHmuAGTEeovQEAACAASURBVDzlgqmwFWR/97+w+OMoz8NPp3lXT5rpL1ic8CKEHO3A+uC74Q8nKRIK/HCSbeltuNLFEAZXP3P1XoOroRuXY5oCJyAQSuHauv3kh3pJz33XmGMHrr+BvPBwbAMhwA2AcBQfeMLn+7O30uQ1HVAHx/1xZYWxHG/Q994HnvBxTfR8AScAOIoLnoRr57uVMY8siPa1jZ9aehnd3/4OsjyWkUB9hGIygjfaZbN67ltzWzENXfQwUBygKdxEV66L7nw3UTs64blWoyPbQSqUImgGKwsEW9hE7EgtmH8LYKKGaHvCioUGp28H0wXXVgRcvWu6YqHBUQdq0DXUUMNeUXOKfaeh2nQq2w2xKWN/boeBPEoYZV6kpuUoUVV0euLntWZ9VeCOdOC9V+mfV9F9OkIZEpGGMW+xtyDPNmzqQ/Ukg0kKQ51kQnVUl/jOlxYvmS6mMBFK4VWMsqCgHLYzgBEyCBtZBuKCBiPO/PC7UabBypfvob/QT1O4iQsPvZAFzceNnjjdCcEEa4XLciNPhyHBhtZAiEtchwWRlM7SS4l+bBQhBfVKkU3BqkZB8zzJxXf6hHM2acPCKnlawhGwJZy91udsoGjDy1MUm1sFG6coepOQzIATMvCkXyHTOCZEB4us7X66Mtaz1kom/7lcmFrWi/+rx0B5ikcWp+hyhrWzqDA5YnOJ9z++nhd/cjLxaYeOG0BGe7Okg6CUrFSNOxY0DiuWdG7W90t5Aei2t2uXXJSmzQJFS9E4pPB9/3V3cNyVSvKjZJTmQnHCQUhHtgOBYNJz7Zz9WIlJQ4q6rKIvBsoeUbgR+AGT1KDHVxZ+Zb+38fcnUH8jXTY7sh2VxZUr9fOiULjSJR6Mo1D0FHpIO+lXRVkY+axjgRixQIysk6U7303GzbD03qU1CsRBjokuRPeER6fmKVzQykkP91E/6DBYH+DRU1t4Zmp+351rqKGGA4JaQP9OwojplBHQlJlsL6R36CguVM58ugWmm5IttlHeSheU6ySZRTl9O2cx8KPdefojmf+RV6D13qWvKsgTQhBJTiOS6cYLhMmiyAjFC4ZHPYIcBp5yK4sMW0FAQdbQhZI5P8+nVv09J045kXNnnsuRiQhHyqwu7MUGL0O300+UIFE7ikhMYW2hh5/aHhlDYSitkLPdsvhPw+Nzjs2CeAs3k+V30SC+ENhK4em4n8+mcywoOuRm2fT/OUQwlqTL6SPg6kz69mYtLdkyBCEXDt8mOXybHns6rFVKrJwkGwK/rEwT8KE/Af/30rJKQN+40dYUqPL2iRQ6M/beZ2D1sRmaTIUTMDh0h8PfrPTwTOgNFwj39rLtG1/l9+c18ejUPK2xVhY0L6CpDuqyCsemUkAc9KCvTjBJ2PozLn+edlsbsZd7yZguI6XGQU+70ApDsKB5ASu2rAAmnuXeE1f82nXX8vPnf44QAktYFLwCP5+X4fMPRqijcUJBSNSKEntmE5+418M1tVNtPAeTMtBrKIohrftvOj5DqQjnti16VUHoGxmoTxTViyvbsPGUlkCyDf0M26bN0U1Hv2oq1JIjlnDVGr2A93yPpud3cOkTkpa0YLD+WW4/eTPUCl0PWrzWHaPWWCsvzully+GzKm0Fr0Br+C2kCFdDDW8zmN/85jff7DEc9Ljuuuu++elPf/rNHsZrxx2f186vgYjmv1sBKA7rDHukoZJpnzL9DJ7IbsURWnrQUJI6KfmyG2RaYgY0zIL+l2HjPTqgz3RA1/NQN1X/rAqpUIqHdzyMRGIJi2K+Hze7kyvaNzPtoR/B49fApj9BpGm3vkSaYNPdGFIRMmzqnCI/D3gkIi3E3QJJKQkAxfI4632PogBZ1i6XSLZntnPf9vu4p/MR/kek+Z+IYIWZwet6nHlTjqA0ZSZ5y0PGGvl+7yp6DZ19RggECgOBK6BPudRZER7olXxwteJ9TyvmboOeCGytN1gbtJnulJges9h63HsZ2L6dcMZlMA53nCBYeazF6iMEjx8maG+Aki2IlnRwHyxr1Ic8SBS1G2oiD6ESPDsT1k0q8JHDPsLa7qeJ3v4gUmjXVFONSkvavnattZRBwIWP3Vci4OpsuyNdBr00RbdIQ0eGp4+N8/LwyzzR9QTDEcVxmxWi7Mga9LR2/R8XwZ1NMC03zLTjdSGr2ZDCWfUoBTc/5thbTjPxp0wi7+W54pgr2DS4ib5CHyErhG3Y3L/jfh7c8SCpUIppiWmVjzezahXbvvRFCttewU+nKXR3kl71MOE57+KLL/8ET3lYwtILS2HQlYKuFJxWmoHf20suFea200P82H5g3PPfuvFWLvp9DwFP6+QjBJ6piJb0zkYmrJV6bB8eOLuFM0/et97/hLFxpX7eHvo+bLhr/Pt7AljdvppvP/5trn3u2nHnuCdUP3chM0TaSQPQFGlCInF9lyuOuWJC5xoP0xLTmJaYxqbBTUSf/gufudOleVARLioSGZ/DNhW539zEqe/5yOs6rxpePwSnTSN54QWkLvkkyQsvIDht39c/s2oVO7/+DY69cxMznu9jKCwZbopUFu+v5Z6q4a2BkXug77//m8wDD2A2pCZ07xyk+NabPYDXE7WAfgJ42wT0D30fgvHRYlYrqL9KaTAtqGuFM77OtBP+gVmOx86e55C+y2zP5wulAIt8CzbdDcU0PPBdGN4BI3ZO+V7Y8gA0zR0TuFT/4e9L76Al28cVGY9FmQHN3/eKWhFny/2QOnRs0NMwS7f1vKjpQXWtPJRsImuZBHP9YBjYCJRS+EqSMQ1MoFEJ6iQIO0JDpImMm6FoCPzyvD0heNE2YMca5s/7GFgmj8sebu17mEwI8kEdCHum0Ar+Sstf5tpd3veYNjMq2hBx4Iht0Fun2Jk02BAIkjMtbklkeenIJHfPLfLUbIO+OoEQAtu0KViKzgbBCzMED80XPHOoYDAVYFLdFKz+NKaqXFFMYFY3HLvRY7hrG3e9fCdzNhYIVJkzjRznWvDAcZaWmlQuZ69VlOxRPXkhFZ5Q1GV8bj+qgIdEodiZEnSlYFqPVu8ZiMNvTxGsm2USVD5bAzbnH/NZQAcALyeK9LzwFPVZGEgIfnuqwQuH2iSDSdJOmq+e8FXOP/R8piWm8cD2B0BA1I6SdtI8vOPhyv0A8JfLl0J3HwiBMoR2ly2WGHzxWW49fLgSzAP4yseXPjuSHpvfcwjFi87kZ5P/zM6U2OP5b3zxRt7/YJZCoEyuEULz503toCsNwWCdwcqzmnhxToBPHL43O4z9wMhOmFvUz1thSD83u97f+8BIYWvRL+5xjntC9XOXdtI0R5upD9bjSIeWaAtXHHPFa86eT0tM4/xDzyf2zWtJDcuyGZn+HAOOpL59mJlLLt+t32uZVw1vHkYKu1WpRCCeJFpUHPriINvrXKyph7wu91QNBzeq7wEjFkOm02QfeBB7+vS3alD/tgroa5SbdxLGK2Y1bDjkeFhy55hDF9XPZVHPADg5sEIQDUNIF83yxM/Ac7QLkzHC/QBKmTEUjcq5RqgMy86FQh5ynYAAUzt+UhyG+JRx+44p4gWWlIMBTIuQ9HCFQVD51EmJ4wuCAJYFSIRXIhlKMZDeQcnQ9AqEDs5Riltsl/l9L3JEwxHcvul2TKWnIQ1BMQDFAKSBoJRM9SMcdm8B19K66iNc8wBwxnPwl6kCy7T5Y1hS72QIRRuxDEubGAkwhUlbvI2tw1uRUmIY2pDJaK1n4yEma0qDLOnT7raWLGfuXf0+TWng7kf4KHtwQUPTSZLBJBkng+EbDCR8Gof1osP0NY0nH4C+OsWkYSghyQcURRvWzTJYN5PK2Q3AUpA2DDqqrV2BX0Sf5vmLo2O04E0kPYUejmoaLYdb9tIybM8hnOkB3yFsBiCcHGvPvr2z7GG8i6PV9k4idpKCV8DCwlc+nvQqGuu9hV5++eIvqQ/Wkwhqnvh4xdatsVYG63uoy5q4AT3Wkl/CN6F9eoRfXa6D69edKvDYTzWtbZdi83Hv771gvKLekfaJBE6LXiWFaH/R3OehxFgTMQRM6vPGPf61zquGNwfVngsAsXgDESvCF/7SxLSvvHYVqxoOfux6D4hwGFluP9hoh+9E1AL6dxL2VMx64ufHHjeSYXRyICydSU+Xi1iDibJOPIACX5UDMROU1BScPWFom+bu+452loWyE62jx7O3vmUsalvEV/gKy576CR2Dm2mVsKR/iO811HHYFli0VtA47NBXJ3joeMmfoz04hoFVlmf10UE7QuADX37ky8xIzKCv2EdKQo8hRgPMMqIKPj7/YvjldWRCMKLlKRT4NtSXL0cQKAho8bUTaTJYT1+hF6EErnQpekWkkiQCCYqyiCc9hkqDugDYLdCQ0UZQCL2QEEpTQhIFGEwKWvo0tx/GqHbiGhB2YGYmzKaIx5A/zMbJMLNrRHIUTA8SHjx2GBgYGL5LrKBVeKShA/uSBSVbIQ3B4S9LzlsDbUMu2x5aUuHXdmQ7aAw30p3vRipZcSb1lDfKld+4ko7Op0i4JQ7dKjjxKYP6dJ7BRIGVi3LwPp2lTaiq7QgoS3rqAP/iuRdzzXPX4DE2KEyFUoStML70yTgZGsKjBdcjxdYjGtWbBzdz+/E+l97rY2HhWIqQIzAl3Pxuj40DGzENk7gd54sLvrjPe29P2K0OoOEV4nPGFoJP9P6uRnVh665zPGiwcSWGUnjozw+ofJ6WYY7b5S0xrxp2w/6qTNXw9oPb3k4+YtKf3orru9imTUOogUjtHjgoUAvo30nYVzHrCEYyjFZIB/OGoTPpud6yTnsI3BxVkRgoVxtJJafu/r4jyjrZbl2IO6KgI4ReBJgBvbgYr+84qGQeR87rdHH0FsXZDwhcU5ENQSKn+Ov7FMGo4pEWQRGFCVhKh+ueUghhIAW8kn4FgJwBEQUlBJ4oF30qxRV+hOmHX8jzDb8iNFygZOupK6ED7u56wWA9RFzwPWh6WXDSyu3UDbnkLYmPJOTAcF07jx4TZl1rBkMYGBj40qc334shDPoTmjvvlJ9KJbTD6kCj4J5zLHIlnw/+n6QupwtmbZ38JyAh4MAnbthGLmaxfrKirRcyQQj5YHngWVCwYU4XPKh8dIWAwhQmSJ9IaURPXzC7Q/Kh1QrX0ouBwnPP0X753xGYNYuTTo/w1AyPlkgL/cX+ioLK9MR0beq05j9Z9sIv6DUlU3YYnH0/KFNSCAqiOcVH7kqTOW0VywrL+VBK0NanEP7oZ6sE9LWUsz/svrgaQcAI4PjOmLaiXyRqRSv6683RZnYcYbOMPj6wRtIybKAmT+bXR2V4YaaPGLkOr8GLo1pfvuK4+RcDjDTx2VXBz37c3yN4vVWDXneUF/5+EoxByrstZQM3BX5r87jdDvp51TAuXqvUZQ1vfeSaYhX/AcMw8aRH/2Anqm3/64NqeP1h7PuQGt5WmLNY02uufF6/jkcBGNqmM4qxSYAqSzUKzXeXjtZlFyajuijlgEipPWf7M2WJTOlquo7ydfSrpFbYqdopeGhDDx+77glO/sEDfOy6J3hoQ8/e5/Lh/+HkpwzNj7b1OsGxNTXmtEeG+XDrGaiy661CF80KIfhoyyJ+fOqPOf2Q0zHKwX3OAE9ACEG9FHypYLBg4RdQSmFd/EECviBYFnkJuroo9I6FgjiCgqVYul2x+FFFsODhGD5Ng5KWQRCmQV1ecM7DWeZs11waUaaYKLTZ1ANHCUxfmx2h9KvlQ2JekAtliFxIsGKhzshLtEy+K8Axypl9IJr1WLBJ0jIE9UVN3ykGIRvSxzRlDe3uKUwMYYwbMJ/1jEIZ+v3jGVC+D4aBs20bF97eTtvTW+hN78B185hoNZkrj7tSc6PX30CvAZM8yTlroGhB0RZIobXfYygGrr+BjmwHLx2Z2J1DpCB21pnctP4mTMMkZIYwMCo7AQMlbeqcCCYwDXM3+3cEFTqHEIKGcAN9R0/lzivfzXseW8f1n55K+5GTmJmcyez62cxMziQRSrDspWV7fGT2huotaCGEfo01MPCMA05ePxNOfvydsH3goLe4Ly/8/3i6Tb68c2UqvSjLhuA/Fvay9N6lrG5fPabbQT+vGsZFaullKMdBFvTnJguF/ZK6rOGtjxULDWxZpoMqRdDVkswrFtZCyYMBtQx9DbtjhGsfTEBdG2R7dDAfiMJf/Qju+ketaJPt0u17w658YoHWc/ddCEbADEJqZmWn4KENPdx5+6/4F/8PTFY9ZLtCqFsEhYhHuGnGaFA0oqWfnAYnfp5k5v+z9+5xclV1uvd3rb13Xbpufc2tExISCAQIBIgEog2IE4wKOKJ4YQ5DS87RkXlncBwdx6jMOfoSdcZR1JlRjgKN+qIOAwiKXEWGxkBigAQSEMg93Umnq2/Vda+991rvH6uquvqSG9eA9Xw+/anu3VV7rV1VXf2s33qe5yfIBcc62wokOmDROFTkMxd+h2f/68/ZkNmGXyaGiyPH8LFz/gGAz5z5GT5+8se5actNPNbTjYdPAShYcEtLG4MDGzn/6S6y2T7uWSE4bz20jWjyQZMFf9UDikzcp/Vkl5nbj2E0BgM6TSTloqRZYEQLMNhqo/Ie7/2DJvdMgdYUDMXh92eEeabdo++4Fm5nkAs2aZpHjUH1sdME/++MBnNRfpg7yWD7huhLQGnI2/DT8yEblXwwM5/0H7dx0m6NpU0V3/EhVjCnkOEga3qW8khiL2tj+2nfnR0bLw4PnypoThtj8LRsWdKjPbTWSB+ypSJ/thYenyvwbHD9EgF/EHqfpmtkE47vE5YWYTymj0AqTJXotSiIBAO4PT20R+eycEc/qZgkXFTVXYRiyGLWH/vJtec4Yzu87/E8rSOK/ka4e5lg4wKjeXekw6pTVrFh/4ZxufHXrbvuoHKOV1vuMaUMId5Cuj/HqpiiVw3THgrSuWgVNIToun/VYefcV+VlR2uL+7KE7vcLSjSeIVj+B03QNwvpB5ZabJwvmZNPsmbdGlYz1rH4qL+uOqbExKjLbFuUu5cF+X1yDe3331J/Df8EUO8/cHRDvJLt5j8VLF26VG/YsOGNnsbrh9q8+lqt/Xu+aariXRcZwq89SPVQld4ICbHpY/cDuH6x0c3XtBhHayiMmF2CCfj6d7/HXw5/D084WMpjGkkABuQ0ZjbFjIEWDaHGcXO7736b4GiRvKONFlsIwq6g2BRhx1c7q5nmFhZemaB+7MSP8dETPzpufFe5PL73cX69/dc8P/R89Xio3DnXLXdfOnU7dD7goaVJmAl7MMMTqJLGaZ+NCCXI/HELSgBCIBX0zwwhMnnaUtDXbDTrwXJs4i/Ot3mxXSJ9H6krSnKTYPMlt4Gl2gG3wO9/k6NtePzWmgL6muBbl1nmMZbDhx8s8LatlcVN+eWZ8Fz7EnwBRWeswm8p87OtoGXUSG6EAKGMsbY/bqQ5X73c7GgUHSMROkYK0vEW4oU0Qnmk0Vz1c49E1uyWBMqNqGbKRuLHnMDu61YhLvsU+bCFkJZpbKU1MyIzaMj5/PN5I1z+myyeJSg54Lga24ebLpQE3n72AcnDqin6HuS9PG3hNm58942H/P2RYteVnXjJZNUkBpBJD7LDHuFHn5hTzeNPF9NorYmH4uMy+lcvW/26kKCKr+BVJdDlz4Gv7i/yzgddlA0ZGxwPHF/w0/eESJ95/Ct6fus4OtHd080dP/4SFzyWpmXYY7DJ5uF3xLi03nvgLY1X+/PzKMDEf4tvatT3SeqYjIUrDCmPTTfEeyJJX36NIfij+2okExpiM8wiYO13xs7VONeQ7locRE+8YuTneMKhKEI0kUIhUUjiathU+YujJk2nkqUfaAAZYORMifB8nJICDU5JITyfkUvP5yfP/wShwfZ9hF/EUQqJ4K5td9ESbiFoBavjb0pu4r6d9zFYGOS4xuM4TQewtcm6zwkjcfGAk3b4lCzIB0BIQS4g8G2BVqBG+gDwbYmomGdt87nRlAHPMjn0CEExIHBtuGCjT39C0ddkYjBHIiaVRgm40yqywc/yxWCR1hFD8mthAdNHzHabALTSnLzLGIDLr4wp8GPGDhx/HAhjDg2Uq/czUjBt1HwfyRtNvsIQeaHKGyshc/+hWDkuUxszbmMOchk4qS+N7ccJFBVDWrPlGGgbgWP6oW0IojlBppSnedVVdMzuIDb3OEK+RCkfW9rMiM4k4ts4s2fTuakJ1zILBo2RIjVm4PN3Cq691WfJ9qkLEYeSc7zaco/mVVfhj45S3LaNwgsvmNvhQR49t6kq+wnbYdJumqyXHXfMsZyXLfU5EnT3dHPt769lU3IT+7P72ZTcxLW/v3aSFOaIUf4cuOQJH9c2C1QtoOQYL8sl68xrVDe8vvXwyG3f5oO/HiKRUeQbLBIZxQd/PcQjt337jZ5aHa8h6nK5oxt1Ql/H1DiY1r5C+Cs2OMuBeLvRwk9M81h+DRRSdKe3s0rvY6WdZFUoS/eiC6ccdo5IElZp5vvbCZPHwUXiE6iknSjPfNXCCfPg3AK3X9zMaMwiWoDRmMXtFzfz4KwBcqUslvYZW31oLO2Td3PEAjFmRmcyKzqL5zb/ghvWfpXhvmeJZoco5IboUyU+7YaIlBs5VcywP/kzi29cZvH4CYKCbWIetQ3aFrgll1LfC3hBZQi90gyHFbJQwvZhKDJ++iXbSHgkRp/j2oJcSJCKCfY2CR5u9vm7No/HY2WN8oTnTGOOVxJ8PO0Rck0F3rXMl2ebCrtQMPu732Xuz39GOmSMs275U0BqE5fZlIfmnDmnXQ6iyUQsfMtEYD582jjnRPX2g+kStmfjqBbOeVbx7ifLBkkFjgvRrOaRxVY13mz+1Z9hptPKgtBs5sbmEvGsqiZ32ogmEW9DCkm4oGhNg4OFpYUxnn7lq6QffXTS+6djdgerl62mLdzGaGmUtnDbuCp45fdv22HzP77/Il/45918/bbgARcIh4OxdJdyZr7yCMjAuPv4ysfX/rhjrxfRvf6p6xkuDqPR2NJGoxkuDnP9U9e/shOXPwciaUlrCdqGYU4/zBjSBLWkdcRc76tleO3u6WbV/atYefvKKbX5dbx+OO3B7fh2OQ5WCNyAxLctTntw+xs9tTpeQxzq87WONxZ1DX0dB0YlRaZGq14l9gtXwOyzJufaT1F97w4I1kSjOEoTV4qktFiz4w5Wt58+6YMgGrQJ51JVkigAG4WqxFzKKd6ybp7ekEX81Hb+87SxxAWtNaOZXhq0Js/4N7uPpsH3jCSocS6BeR3cvvWnKAmDFrh42IUhIsD9ssh8bTGET1bDSDlqsb9J8MvlFvedqTnvJXjfs5pZtsY6KcfwH2MUPBhqNlX2cAmSEU2h3OVV1kiQHFeTbDQ/L9mmuGSdpm3EHDO6cWP4zWMMu/YU3NMrf1VQcIypVdcs2aWmmsduxeMMt4UIpAsMxwxRD7lGTuP4Zo6VcaSCxrRP0YYXZ4GlBKGSphAYu4Z2T7E0MhtO+wR3vnQHK9f3EymYa/fK/mmp4czNObq3P0zH/AsO2n5+aPZsWpJJ2pqnUdq5E2W5JuHScZCHyD4+VP66s/5ZVt66FSfvYimQ2e3sW/1FWHPdEWcpD914E3YigZwxo3rMHdjGOY/0s+2kMW29Ja2qsbeC1yvZZWdqZzVVCSg3YvN5cfhFVt2/6pXJbxauQEyfj719O7aUKFuD79I0okhOc161Cl6lGZVjOcQDcZJTaPPreP3QNgIuPi0pF8vT+LZgNCJpG5k6qrSOtw5er/4WdRw56hX6OqZGbTpNqMnc3vtZc7yCivTmYGkea79DVzSEIwOE7RDCDhEWFk4hNaXcoMEdKX8nxlWiLe2b8wfjpvvmhDHbE8dS8McbdCuE6Yp8OaoSxt1eMZoZu7bHvsVWqRmWptJtYfTlKQm7hebSosBDENUwXcFJPXD28wrH0xSCgvtPEVzzUcHXL1E8c0KUb11us+avHP5jVYAffNzm+k+FufmT87jv3a3GqFpSaKVxSgrHh32XLOPUbR5XPaBozJjozcaM5qoHFEu2qfIzItjbOrk6roG9LTAchWwIlGPzyBJDH0V5Y6Jy++SZCTbsfxKApss+hOMLAl65mm+bdJIfXQhf/5DkznMkz82VlBxDRIMeLN4N/+sBzddu0fzdLxXvW69Y2Ku4ciQDp1/J0ulnct07rmNW2irn/ZsqvV3e4WgfgA23/Cul3bvxkklUoTBlbGRtooYqlcrRqBqr1eS7v9z86+6ebjLf/QGhrItEoCyB77t4w8Mk//VbR3w+t6cHEQqNO5aIT6NpuDRuWzrmxIjYkTdmq1pQXUxUmnSZw6JKjF9JtVsIAeUeApa0sIWFVNDWX+TqG/fzT+LiV0wAaptRvd6SpTomI9LYStOIh/Q1SoL0NU0jHpHG1jd6anXU8SeLeoW+jinRvfZrdEVhm1WkpIs4Qc1xStK59mt01Fbp915uOscWMxCMwtl/PV6eM7KL3oRkXK6IkIR8b2q5gVdASAeh/fH6fLTR8r97Dex9yoxZGDUZ+VaQzh1pPt8YJiNF1Qga1YLP7e+jY2A/xCP8JB4lJ6BBKa4YzfCpnAfhsg5febjoagqOYmy16zsh3iPbCIxu4+dhwX5pYbUHubR/Hv/zp5t5eJ7F/WdYDMYFG5oCbAAsrWjU4ICJh/RdbLfAltmjnH6eJP60piWlGWyyKX3kvfztx7/Bbe8+qayvN+NWbi95Qlc7ud76Tslf/VqZDrDKEPFcAG59l6QQFHghm5K0+cWFNvlAjpV/0IRLpuvtf58qeWhxFmvdGj50/If42IrL2QTkb/4pM4bNWP2NgBDsaxbsa4ZZl13OT/ufxd6+h+nbRzhxj8/sfkPO5/Wbrws3gpAN7Nvxa8JLegmfvgShRZXMW2rsVQQ4694dZFJ/AblBkn8QKE+C0rgDAxS+sJpZX1szqXqPlNjTp2FFY+ZcLzP/umtLF9dMYTwjLQAAIABJREFU6mwq8LRPaceOIz7fVNncEd9Gzz2OtnBL1YRaaVz1WiS7TGpsVd7lqGBebB7Rp17i4id82kYU/eWdn+eOD72iLq3pRx9l+398C+ulF/AFCK2wXYFQGmFZSCk53m9F/8edpFtPeUWdJOvNqI4uROwIBSHxtKKyHreEJGRHDv7AVwmHes/XUcefIuqEvo5J6O7pZo1K4kpBCiOiLgC7pGKNSrK6p3ussdOmWyEyHRrnGbnNplth1hljpL5xLu3+HpISqpRHKwqWPbXcIBg1VXd7zKiK7xnS3fnrsTHtCFh58zuVY3ODTUY2jKtcZ4Rmc66PjlAjn0oN8qnUKMTnwOheQEDjnLExrCAajaqZSvV7yyay7O+45N7P8p6CQ9oJkc3nKe5aj5tu4M+f87j4RZ8NxwjuPcXmudkSX8CggExJ8fbnNafv0mw6sY/98y3On98IxxQI+S7xFWtoWHQRANNGtOlEW4OiDW0pXb2mjfMFP3if4JJ1MC2lGU0IHj5LsPk4h0A5Wx4AIdg+x2Fnj8uMlGR/QvPcfIt0zMbyfH62/TaOb16Ig6m8D8WhZBnT6wcf09z+DsULc2w+tuhjsOhjMHctX4x+jWdOhVhJM2s/zN4Ls/cJpg2BVoL80xvJP70RbgakySuurb0Lynp9oUk9NYTvClReGU2PZYNSqOFh+r/+DcKLFxM56yyiHR1kurtN8ybLRmuNLhRedv51b6YXoTRSgfA1WoAvX37QQfOqq9j/la8aA3EoVJ3b/Ks/z40HkAO9mpiysdVXvgrXfrlKcD7nv4vSAy9Qkub91ZjRrHpAc3c4Rm/TyyPG6UcfZdc/fZEhd4RGW2D72izKBDiOhUAiD0MedbioN6M6upAfHWQoDrGswPaNrG4wAs2jg6/52Ifznq+jjj9F1Al9HZPQtaULR9oM4SEp550DaWC6tMeqeRMz5gMNUMIcr0nE6XzwM6wJYyrzWlHQ4IYSk+QG3T3ddM1baCqYnktnKk1HvmDiMM/+a3OnypilAcpaAtBwcyI2LqIRc5ibEzE+1TdimmFl+mFkB0gLgmV98+BWKHccFYBVjousnEsI0w1v1dov0BtXRLQLwiMTVPzdCxFmWIqQLbCBs3drlm0rsXuW5j/PD/N0VFMMmIZRD58GJ+1SnL0rzx8ah7kzGmF/2Gb6umv5sO3xrrnvor9R0JjR1co8GLKdTAgkY/KazfMFz84XJLSi1dcgBVr5lISPLWwsYXFRd55LH1Pl6rhPPA2zBhQ/uljy7HEOKeXy7b5bufKO7TTYRm4jtDHoBoALNmnyp9R4IZ6+hQ+4NjcEIe347JyteWG2wJPwVynJCY1/Tr4nR37jRry+PlAKUfN6VOZeCkAM8LIaL1923AqM0dkKmAZWe/bgj4zgj4wgpOSZeIpH/6yBk/97JzN3KxpwcIINDN14E8AR/RN/++6G6muryxOzysTeWTBv0v0PFfc4lQ9g3/vP4mv5W+i9fc1rnrFe29gKQExBoGfetZ7RWBsDOo3w8pQCYHuSdz2W4d8XNpDMJ1FaHZGefujGmxhWGbygRTYmSIy4ANiexrd8bCmwWl6ZPKoWnSd3smbdGoBxsZ/1dI03Bj0xl5BvMTRtjEJYRY+emMspr/HYQzfeRL6YxU/uxfIUvi2x4olXvGiso443O+qEvo5J6M30Eo9Mx832ViMSBSayMRSZPlbNKzeWGYeJKTcLV9DBt1i99mt0eUl6bYv2xLF0znonHQ99A0auhsa5dC+6kDU99+IITVxrkpbFmpYmVg8O0+FJU/WvHdMvGaJf3u/Ny6ntIHlZ7nDrl8qGWg0NbZDdbyI5hWXSSXyXgIa8NqReAkoIPBQlv0TSVwgJ26VCA7O0IDYq6A9ZzFCKsDbVfW3D3D2Sf/iNRX+pyH2nSLoXCYajgufmSp4jhKWDRH1Fk+8yrDX/tvHf8LTH3csEVz1grqdoGzJv+6ahUqMGF0FA+SxwS3SmCyAEaxqjOMpnJrDXtvG0x+KtLpc+prBrKuSOD7EcfPjhEpuOswjIADvSu2gc8RgNluUnlYhNB1oygitrydLoXpYG4+B7rO/NcOIz0JzWhBp85nzkL4i870qi5bu6fX3kN24k/duHKWzeXOXsAtNgiwKUbFMlr+kEVn6jja+W/2HfH7hh0w+wW2w4o5XWh/YzYJVoamxB9vcfcWXuXY+lSUVMBKYct30gmPbZvx9338M1YsbOPbc6fvUx+dfHvDllY6sJBNrt6SGSaCYqWki7Gfoy+/AcSAwV2Jfdh0YzKzLriObq9vSQlz5S2BTDkhQQTftYvsKXEJ45Eyv2yuRRtag3ozq6cN85QS77dR5ZUriOwHE1lg+/PCfIytd47PSLz6FTaaQUKCmQvkYPDpP2nj/0g+uo4y2MOqGvYxIq29uOFcTzi9XqsGMFKTgB2sNt5o6VjrKHSLlh4Qo6Fq6g+q+3tnFV2ZTa9ewPcWLTCGcHQGvCmJz2rkQCRjN0PfY5ep+dQXtjkM5chg4rYLrN1hjypgoeFGBIvyhX860AxKZBLgnaLx9zQAgWuB67HJu0ZeFi9O9ag7QdwlqzX5equvpBoRlJQCQLg7Zknmci+lwPRFMA322kNRHjih7B5T2aJxte4t6TJZtnW/hCkLItRrUkpjRh4I6X7uDZBZKbLmQs5SYxlnLzbLHZDLzvGbrDQboaE2wMOEg0bZ5PXClE+XW7eF0Wa4LcRWMI7Jx+zbe+l2e42eGuZUYzH08LSo5AC40WmoALI21h3rfsgzy2/WF+9syPGWixmF3M8NFdFiseh5QF2ZDAc232/uIBZk0/mcjSpQA4M2bgrFxJ/MQ4mV/uY99/D5LHtAgPlMxTLr2aiVV1UuagPWdMCnXnS3dgSxtf+5zyRB9FoXAtwVC6n0j8GLTnMfiDG2g440xkKIiwD/6RJvYlyUVtPMcnkTHNqnwJfsietCioNWICh6U3P9LHvNKGT1Np+CcS6Nr7xJwoRGeSSu2nL+5hSYvpDdOJBqLV+x+Ont6ZPZvwzkEKUiGEpBi2UZbElZoGVxCzX7k8aiLq6RpHD9JnHs+dcgfv7B6tdgz9XUec9OnHvuZjlwo5HGEStMAUI6SAUiH7mo9dRx1HM+qE/i2OR/7Yzw2PbmfPcI45TQ188tz5nH/itAM/4MUH6dy3gzX+fmJoBixJqSxtUX6R9PAOPjeSMaR8+TVw119Dao+RTEjbpNC8e82kc46Lv8wNTZLq9FoF4vlh8IrlB2lCSrPNsVnTnMDxi6biGXZZ4+9jteXRUSpWh5jpeux17EmkfqZrSGJ3KEBXrIHeQJB2RukMBegolOiecRxdVoFeXSDiKzxguuvhCUG/ZeEKsHyXbSiKwpxdICgBd54tWXWPJlSCtLJRErAVC/78nQyuT5Hau4s+NYyrXJpHBFft1LzU7tH1HkFeSrQQjFqC0dx+hrP7abPjbFwwysYFNRegYYGPWVm4ebrDQW5ON/Ge38IVKZ9kAu5eJhmcJ4gFY0SVYtpIZsqXtlL7bkpr4tkSn+iH+8+UnP+MyRQq2ppAeVfgx2cWeKz7Czyx9wmyXhY/IdjhK95xf4EmB5QFEk3JEgy5w/g3/xsnLu0CYMP+J7nzmZvZP7qL6bMlzedJlq9XNKfAk5BpgD3TYfogTBupkUkp41rw9u5l35e+zOBxraT8LexrVizc43PMPl1u0qUZDZfINuRoCIRxe3rwkv3mGp2AIfahEDIU4rG+x8cR5oviisYM5IOCXIByR2FJOj55h+flGDF7M70IBPuz+3GViyMdmkPNUz7mYDsAMLWBdqIZMHzWWYzeeeckDX8tgZ6o8xeFIran+dXZEktY41KGDldP37zqKtL/9EWSxRG8gCbgCaTn8+uLmvnIiR/Bvmt93bD4JsahTKedJ3eyJrOGF0+cNb7z8esggXKlwsH099Bl433leB11/CmjHlv5FsYjf+zn2ru30J8u0Bh26E8XuPbuLTzyx/6pH1CunHcM7GP10AgJ3zeVXTRSaWxt9OXkRkyFfe9TTN3maPI5x8VfDvzRVNdr0K5tCr5Lbcm2IAQlAY5WhLUy5Et5OErRlYiNe/yXhoaJlXXbYEhiTCm+NJKhu6GBNc0xkk6QuJAkhWJNS4LvJ2KssXMkhSJeNvQJNEUBe22JQGFr8LWiWD0raHRZgiSrSSmVK89Zknt6HqbvHdMYyQ4giy5oCLomuvHxEwV5IbG1xlKqKhkqCUj6o5NfEwF9FtzpDzMaa+PWdAtXPASNWRMx2ZiFqx7UtO4ECqMURnsZTBhtuxh/GsB4IfzyP8FoHpY9r3jkVEEiozmm30hRfrcYNi6Q3LPjHoaKQyitkMJGWRbNoyZVB0ujLIGyje4+39+P7F3Phv1PcsOmHzCU6SEKDEnoFxrHhXQMhprAsyWz+mFXi5mPYnyhHtcl/+STNPzifj7zXyWuu9mj80FD5jWmut+UgWxqAIpF7JoMeO2W8NNpvGSStRt+yQ/u/T/k+/fRpiIMZvr55TKN8Dycki53FDY/rz1v8iK3Pdp+wCjUAyFiR+jL9uEpD4nEUx592T4iU6R/HCiK8fonr2fNujUk88lxRP+JO/6D/V/5Kl4yWTUDjt55J/EPfAC7rQ2VSmG3tTF9ggQpdu65TL/2y9htbeSG9rPDHuH2i5t5/vgwJVWiL9dHupg+rOurPefc/3MdLfHpTO93aRwq4gUdPnLiRzj70quZe0sXx/32Iebe0lUn828yVEynte+ziY3c3sgGQ8OzYqQjEmVLpBYoW5KOSIZnxQ794DrqeAujXqF/C+OGR7fjWIKGgHmZGwI2uZLHDY9un7pKX2M47fBduuIw1/UJ63LlQ0jydoCukKbDD5joyMh0iM8aO0cpN94UO5VxVgYg0wfhMe1vZ0GwpsECLQkpn4Iwmn1HQ0hro5cHyPQT0ppeu+wcLUtuOvIFrkil+UkiRk5KE02Zcelwmlk1dwHO0FbCwii5zfVIfhJvoFUrwsIqy3xMUseAZXFM+bq3OXbZGlxBhdTDResU2RAMxcZWxo4Lgac0PzzxEUor4JJ1okY+Y4gyGEItAKE1qpy5X7s4qEVWwG3zFnP6O9ZwwYffO2W05cXrNTcs2MewBfcuE3x8UBPPTjYJ+9Jcgsbk7bcPwPnPaEaiY7r9dz4Lu9sFTx6r0ehqco4QDsnGUtm4OxYPGlbQ3yxwtv6Eh4ccPvzAIC0DJVJx2HgqnLxJU7JBlC0MBUfjAGdsN42v/EowDwJHCXAcXjgxwoztKRJpn/D4tZ95rjRER0oou0jzZR+a8nm786U7CGIT8gKQ8YlhsbXV4ufn+lz4NLSkNMkmya/OtsgsmkwGXpYRU5gFXyX7XaNNBXyK1/ZAOwDbUttoCjYxVBiqVvljgRgjP76FtkDrJANsfv165t7SdeA5MabzX3X/qmpaTFspXNXQDxYGsS37iI2mDSWBOOZYRChEtFB4VSIq63hjcThGa3jjJFCNH78S91++z0hc4AUs7JKP5WkaP37l6z6XOuo4mlAn9G9h7BnO0Rh2xh0LOxY9w7mpHzDBcNprW8RVpX5qDKghoFcoY34tZkxcZS0mmmKnMs7GZkBqtyH/ThjcPB3KZ/WiVXRt+gG90qLd8+hMjdKViJO0LEO6wUReCkm7V9sXVdAdDnJ3LEKr7xPyPApC8OJ+m03rFB/PvkQqIVl7hsvWecqk7UTayLkpQtoa0+ILm5BW5KQg5ANWAIXGxpDfiXsRbSOmAVQtSrYhir1+lt75NpvmUzV6aj22JWxrcIXARpDQkBTVYv2U5G/36G587U85ZtE2cxnC7KhsWCApvBcuf0Qxa6jsf/DNNaiatYnQJk9e2xZF2zyfRce4ES5+XPHksZMnUmvcdW1wPNO0au3bBO98ZgfnPy5Qjk0uImgoaDrW+tguDEUhYAJ5QGtcy4zt1sxHo018pety+wUhohe20JjyuPSWHQhtOtlaNcE4UoOfTDJ8400UnnqK8JLTCS0+pUpE9uf2E3Wi4+Zv+7BjhuT/vqe8W2FBQziCzKTRnjdOg/9yjJhZN8usyCwGC4NVMj49Mp2sO1nfe6AoRt/3GSoMIUz4I57yGCoMEUkWEbPGm0uPNEGmdhERDUSZyUySuSRFv0hbuO2INPwHIn47vv4V9v5LmuhAFi8UoDncbDoQvwL5zSv1GtRx+Dgco/UbibMvvZongOzNtxAdyJJpjdD48Ss5+9Kr3+ip1VHHG4o6oX8LY05TA/3pQrVCD5B3fWY3NUz9gIrJtWw4bfd8klIagyqmSl4A2rU05tdg1NwezBQ7lXFWOtC2CMJNhvw3HgPLrzHG2ecfgIFthuz7CrIuaxqDYDmEtKYgJS7QmUqPm3pXIoajNeEyK168XfOehzWjlo8XDhIZLfLe38JvLgyw9VhBIT9MQzBMITrDEKqBraBcCsKmQWsKtkXY93G0xkPji7EQxsp3ySliJgMeDCag3VPstcoBLlVTrimNS+BY3wdpoaQki5l39iBx6KlSik8++ElOPVty3jMKq2Z1EfTMXBSmw60ENi2QbFwgCGqNBfzvmxSzBw0JVsLcSsqNqSyPmiBHSja0jlTOBgqFRKJQbJwvuWmFNsbdFCQT8OtlgswxPkO3hSEAJctH+uA65nyhoqDBB+WAK218rQiXQAlVnY95XoWJuwyHmd4wnaHCEKmmEANtAUKZEkOWWZg0uJJQUeH4AqE1pZ07Ke3cSerOX4JlETrxREJLTuNUK8ILzQUCwTHCjDYpRTbG9Kx8cDMZjvHDlPbsQTgOMhSqavCPtApZIenzEvOqx/JenraKkbwGB9oBCNgBXOVilRexAoGnPZKNklmFwkENsLWYigRPXEREA1EsadEWbuPGd9942NcJUxO/XCGN7EsSanHQQtKyPw/0kp/RhniZeeGHmzZUx6uDwzFav9E4+9KroU7g66hjHOoa+rcwPnnufFxfkyt5aG1uXV/zyXPnV+/T3dPNqvtXsfL2laxKOHTLAgTigKYzlcYVkLcsNJCXJv2lsyBAlUw2vCoZ8q21uVUlY5atYPk1U9/nXf/bNIr69DPmtia3HicAsVnQtoiOQBurC4K2xDyj1Yy2s3ooRUe+bIjVRn3daxvCbyBYvkHiW5B3oEUpSo6pKC9f55MXAlfAFZ6D67vkvTw60kZea1ytuMIL4CqfvPZo1holRDklxoyl0Ug0vzrLNFUJumYajmsIZ+mUAp1ekIqIw0hEdGVqNFhB8paD9l2KfgmtNVeUhFkuHITU78/t58EzJF/5C4sHzpAIZSQzDXlTORdCVmm5XR6vKATNWnLr+ZJ02JBnS5nbdBh6WyHgiZphjTE22QgSSdgKG0KpPFR5t+aZ+ZKvXm7xN5+y+OrlFrvmKrJC4BYaSISiaN9FlZ0GRVvgWiB98DxwlU/IM9rXp0+LIBTYnqn0254G3yfxgQ/wgeMvxVMeBa/AxmWtBJQ0uwy2xAvZWE1NTL/2y8z8xtdp/NhHCZ54ouka7PsUtmxh5P+7lYt+/BJ/8+97eO9/7mbJ+iFi+0bRyq9+6I3bEMkPmWOuW9Xgl/bsodTTizcwgJ/Jon3/EH9xhqRX31Nak/fyB5SxHEiH3GA3gDYLKSjfanjo7RF0qYTKm3OrfP6ACTIVEjxRh790+tLDnt+h4MyejS6M9xio5ACeJfCDNrGsbzrySoE/OIgMhxGBQLV/wOHiQF6Dri1dRzznOg6N5lVXHfb7rI466jh6IGoTDuqYGkuXLtUbNmx4fQedmAyz/Jox0nsI1FbmGmQbpYFzSQ0tYPaElJvayle1QlgYZXVB0tG/G1SJ7nCYrkSMXts2MphMkY5ozXyq8xyrtE+a51T3gQNeX/e679L1/I/pVUXaZZDORX9Jx7K/HTvfI98w+v3CqCFxgSirGoPssm3SlsQVguu/75MNAqJStTaallARbv5kiE4vSEdqkO62OWP5+KFWlhbybCgNs9UyGv6AhhbfZ49j4wqBVMacW6lfL92qWLFB0jKiGEwISosLfHROHMIJumWJ6+wse4UxE4fsEFfNPJ9T/ngfXSFJr1C0uyU6R1J0eJLvJ2J8PxoYp6UXCD524sfoaO/gnu338NDOByjoUvX3i3Yp3rXFYu0ijdCCi57waRtRJBNw1zLYuKAiVYIl2yuVdU2y0URiLuiDD6xVSAWuBZkg+DZ0rZDMXPY23rdkFY/c9m1Oe3A7jUMu/XHFPctg83xDi40bAU4teVy79hy8rU+RtTwGBVhFTWMarHI0pCehEIDeNsE9y2yUUPw/d/k0FMw5hBDIWIyZX/8akbe9ja4NN3DX87dRKuY5tdfm0j/GaRnV2DNmkLjsQ9WYzApUNkv+2WfJbzQda93du5mITMj4A4ZCmvZ+SORhNAZPnCpY/el7Dvm3JRzHyExCIUQwiAgEEBOy8w8kD6kcX3T3Zi5YmyVU1FjRKE1XXsm0vx6rNq66fxW7RneRLqXHaejnxudyffjKw2p5X6uVr6CyU9B5cuerIl+pdIsdVhmywsVxNdOGFAMJcBsCzNzvorU2MikNViSCbG5GKMVxv33osMdZeftK4oH4uOdZa81oaZT7PnjfEc+7jkPjUCk3bzSO9vnV8abBy28TfhSiTugPA687oa/NaS9rzFEleM83D0nqpyTpvjtlAsHB/ukf6fb7EeEg19fdEDrs+dfi+z88gxuckiEOaD7/M0VjFjwHHARaa2wPmhtsTvtQM2STkBuExDHVOXTLAmsSUZziKCHfpSAErpCsHhwCJGtaEjhaG+mPELhCmMZXIgrTFpnFSSEFdsRk3VdQykFsutmJ6LqI7txeukLaEHpf0Tk0SEdJQetCulWGroBLb7SZmY3zuWzhZZzSegqqrL8f/pvP8rvEPu47Ic++2JhbtCkN5272WfqixvGpxk/etMJ0lfWrZEhUP8KWbFNc9YDC9iFSKOvsBdxxDqxdbuNIi3+Kf4LW/7gTEQiwLb8Hp+gifLjlQsHm+QIfgRLwvZTLktN/yP6//yTCtlC+wktrtAZPGO2+pWE0BD+4SLJxgeTaW30aM1AqS5baGtpoIYrd1sbu61ZV3wcRHcAv5iFf4pqT/4qlbWcc1tvMGxwkv2mTIfgbN+Enk5PuozG7Fa4Nc/7+88TOO++wzl2BEMIQ+2AQGTRxmcKyJt2v8nd5+W1JztyYrY4tLIlA0HL11VVSfyR/wwfC60GCu3u6uePHX+K87hGahlySjYJIzjQ0KzmC2UnzXqz8h5GBAChFYP58Ftx912GP84Z9TtVxVKKSwiMCgXFRrRPTneqo4zDwliL0dQ390YipkmFKjE+POQCOpLHNy8nZPiQm7izM64Cd3eMr8Qe5vq6Z03G8EuF0P/glwlYAwo3j5z/FGA9ZPn5Zq+5hqs9XPagRCETAJlBykeWuq6eVcobMh5rpDtl0WWl6g4pRoKGYIl6WVhg9vonIvLEvySVpm5sSMQrSSFtmeR6bAwG6woJevYv2uE2nFaVjeL+ZZ7RtbLFS3pXozuxiTUTiAHEgicea5gSrh0fpEIIOK0ZHPgd2DFbeDBgilnWzpEtpwnsGOH8wyOLnczw3A9YuEjw3WzMcg7vOsbjnbZrTt2nOeV4xrx/ev06zacF4ZV0lfeWSdaaxUixvJC9g8uXPeQEsPN73B4+G4vdwpUQ2N+OEA3jawy5pLiqfV2rNsT50JI6Hc8+FlTMYWpsk32diIb2aFBsfaCiZxlkbF0w2FffnkoRiIRp6esa9jxUgnCj5UJ6bhn/DOUsuRufzqEIBXZYFTAW7pYXYBRcQu+ACtNa4vb28cM//xbpnA0F3zAthabBcSH79G6T+8zbCp59OeMlphE45BRkKTXnuCirNkygUqAhyhONUyb0sV/G7tnRx8ktFztg0Zo4VYHwiUjJ8yy1VQv9qdEU9kOF2YiTlKzGbdm3pYtfxDg/PBqUtJJIl2xWd93uAru40CQDHNnIwrSftaBwKLytt6DBRr/S++XC4KTx11PGnhjqhPxoxVTLMxPSYA+BISPrh/tM/bEzsADuwDXY/DtHp0NBqzLH3ftak48QnjFG+vt5Qmnh2yBhIpQXKJZTpp7fcRXSqMbr7n+LFac1Q7WlrpCY3CcUl6zTtaRhOhPj9Up+njtUgp0N+iO54gjV2rkqu+wQUpCCgIFbmiJWIzO5wiF/Eo3hSEijn8e+zbX7QlKDN82imnG8fdVjNdDqyGSg4k2RIXdEgjipWU3vCyixCuuKxsU66E15rIQTRQJRoIMr+aQlSvdvQAYuT9lks2qsZCSk2LBA8cqogH4A/nCD4wwmSY/dpLtikatJ5DJGyhY2rXWYnNdF8OemmMrQPxyRhTrImEUcp1MAAbc1x9gYtPNtjZgpmawtXKz5dAFaYBUvsitXEWj/L1p8q3IwhdY5PNUNeC0PkYSpTsWZgqJeWOccd9H0sAwEIBLASiSqhrpB7VSwyFYQQBGbPZvEnv8K2u95rLqvmWalQzNL27ZS2byd1++1g24ROPJHw6UsIn346wYULp6y+T4R2XXzXhYxp8CUsi/TeXXzokVS1Cc44KIXKjG8G9kojAQ+HBL9Ss+nW4a2k3XR190ijeXq+QF9o8f51MHPIx7PBko6R3TgO1ozpk671UHg1FjhTobbSW5u3fqSm3TpeXxztKTx11PFGoU7oj0ZMlQwzMT3mADgSkn5Yla8j0fJPrLyXRgFhquGFVDkO0zKZ8gdIx2kv9JEUEK7kzgvJED65wjArb19Je3qATseiwxobo6u5NppwrPq3cYFk4wI4qWURYLbp28Nt8O4boesiuvw9OFBO8YGgVpSAQcsiVo7FLAhBu+fSlYiRlaYiXeG5pg4JaWnRgsA845quSJAO3zaG3wnoDUWIZ4uAic9EQEgrep0AVBQ0B3mtf3W2xTtvE0hPmNhIF2bmBe/ZbPPR7S10L/SJbiYuAAAgAElEQVS4d8EovTGXHTMFN860JuVtCiGwsLB9r5qWU3nWdM33yiSVmvQZrbFGMsw4bg6p4X0MJFzafEWn3UbHii+MvScWrgC+iXPPP+BmCjgTfKRCQz5gvq+NwKxk4Fu+4u5l8rDfx0IIRDhsqnVNTWjfNwS/UsF3J4TYM9ZZcqo6ceNHPkL+6acpbt0Knkdh82YKmzcz/JOfIsJhwqeeSnjJaYRPPx3nmGMOq9qsfZ9j7Gk0DvWNe34nXMghz3MkOBwSfCS7eVPB1W7ZIC6rBl6NZtNxFiNnzOXqG/dzvD+Wmw+g8nnsmZMTfw6FJds1197q4/Z4OLN9mldpeIWhK/VK75sTb4YUnjrqeCNQJ/RHI5ZfY6rQJcZrzGvTYw6AI9mePuQ//YnV8EqFnW9OMMSWyX7/8+Mr737ZvOkVwAqWv8+b2+EdEJ1mKvc119f5wGdYEzGV9hAwhGJAQqvSporo97ImLFntlehQAfBLPBcYX60ZBw0vDjyHBcQCMT639HPV57j3sc8QN0Jm0IpW5bPXsilJk2pT0cl3ptJc19KMz/hYqApPzknBTnxatSQC9ArPPB9MkDTIMLowwlap8YSR8ziOQ5OvmKsl/2iNcp/t4gfBYicru87ifYv+B10jm6qvz7ZZQwRODXDB4zlCJWMy/e05DeydE+bqh30ufC7Aiu2zeKKhl98uLPHssQJdJouivBvg+iVsIIBkrE5tICb8oC0L7Zl8e6EUxf4+4pkSdhZW/qfP7cuSsOh5OqhZ5C1cQfj9/4vc97436dyqZpCNCyQ3rWCcUfdXyyT9x+T44sl/97JkFsKyEJEIMmI6s+pSCVUm+LpQILN+/YEfbFk0d14JnVfiZzIUnnmmqr939+xB5/Pk1q0jt26duXtTE+ElSwzBX7IEe9oUzdrK+MDxl1IMPHvgsYNBvMFBo8UPBBA7//tlm+KnQtvGPaS++2keGypWc7t79SuT3AVkgKzKjiPz5W9wfZfGj1+J9/VbUOl0eWVoTM/T/vHzBzznVBKgJdv1a1JJr1d635xoXnWVMWNn95KXPmFl0SSjzF114PdVHXX8KaBO6I9GlKuch0yPmQJHuj190K39g2n5YTLZL2UgkxwzhFoBc6wSpuhXqqVlRpcbNMfaTqheX8fauayuMY3mELQqTYsMgBBGU69KdFkFOlSA7yfiZOQB0ldrKrFCgy6kofdpmN0BC1fQvuU4kqmdhH0PhCSqNC3CJycEo9Ki3XPpTKXpyBdo15JBJEqA0GqMBguB1KZav08omhXMVcC514yXNCjYldpKv6RKsAFKAvptSVzDesaqyT6Ce8jz0JYfMisyjXhDC7tGdzFnS5KzNqpxnV2XbczxyzkNzLj2WgZ/9CPc3l4W5cKc8EKB3dPg0cWSDccLCsGxcWf6sH6h5qwt4NRw+toKskTgC4WyBJZv6JqTLjLaAKkGaMxq3v+gx0+5wbyXapKI8uvXg5QIrasadw14FkRLFhYCX+vqLkoVAhbakcN6Hx+O/lsEAliBAFY8bkyhX/oyyrYQ3vitAwE4x4ztiljRKJHly4ksXw6ANzBA/umN5DeVDbaDg/jDw2R+9zsyv/sdAE77LEKnLTESndNOw4qNdZ9dtEezNysnVeeNvtwhdNxx+KOj5uDOtYjub5hEnVACOdiHuOfvEfzrESVdVd57i17Is/LuflwLciGH0Ege91++z5IPzOS5hYWXLblrCbUwUhxBaMFp23yzMBvRDDZZtH/iYubvg8H0+H4RKp0m/+zmKYn4gSRAX78tSPQ1qKTXK71vTmycL7hjheSCxwQtwzDYJLjjHZJL5wtemQirjjre3Kin3BwG3pDYytcbU0lrfvMZQ9Zr5QBaQ2HELDImyoImJsdkkpDZC9I2HXwqDFs65pyN88bSX2rnUZOAszI4TFyBSMyGUBwKo+hUD6MS7is2sTw4RE5QNiROli20acE52zXL1/k0pjTphOTtX/o+sXPPHZ8mMtJDwc3hCkxyTb4mX1vadLfM4suNUVJ+ASkkJVUaP1DNn1FLIMqClpMYKY6QKqZM/KCbM51aGdNsVx5iWw6uck3vrilOeRIBMvEZ7Mvu4ws/LUxqZhV0YTRm8cH7NleP3bFyMbG0R8kxi52irXh2nuC/F0uSjWOjNI9qVjytuGCjJlHTQFhjJDeVdBqhYSBm0mAmjj0SEdx3eQM3do79jWx915/hDQ+jPQ9Xe9XqrfQ1u4+N8O3LXPpR4xYQGrMDclzzQm6/5PZJr2UtXm4SzObz38FAcYiWlEaosbGFgBlf+cqkKMypoLXG7ekxBH/jRgrPPIPKTugEKwSB4xYQLhP84Z/9nOKLL5owfjV+VwTHYfq1Xx4b+85PQnYQnBpDrltAxFoRH/sJMhgYi8w8gKa/Nhnmw/+2hXhaUQqYfgNBK4hV9MjEHf79qmkvO03ng3d/kG0j2zh9u6bzPpeSBSVbE1UB2oPT8AYH0a47rvuu9jxkQwMnrF930DlXkPfyfOGfdzN9xoJJiT0qlTqi+MuJqKelvDlRTz2q41VEPeWmjrcYDiStCcQO3Al2KuNuQ6upyGf3G+NrMFo+lgXllTXjZQ295Uxt9J2wO9EeCpKMJgiHytKAUJyCP432fAoKI+RCAltIrLL+Haiy5VYN52zXvPcBj5INqRA4WcXmL/wt4u8/QcelV49VgYd20O77LM0X6ErEuK6leVyFvmPFt/hIbis3bb6Jgm/IviWsMUNgjdsxHGwkmU+yK7UTUe7UammFW/YFaKAsQDLE+VCvj19iID+AEGJSMgyYSn3rsD/2Wq79DjP7PcIlcDxzdteCxoxmxVM+vfMk954q2DAXhuKCX5xncfvbNcuf16x4ShEsweMnwsV/gHAJikHJ4+e2csq6for25LHbUppeNWZG7e7pJtuQpnk4T6Skqx8ylUXC2vPbaPZ7SFqmW2tlXyIITFOQdSeQ4ynwcvXfOyN5gq6mPw6NOZPu40sYag2w6EMfGjPYFgroYnHKBB0hBIE5cwjMmUPikovRvk9x69Yxgr9lC3gepZe2UnppK6n/+q/KA82XZUFNkyoZiYxfSIzuheB4KQx2ED2yF53LonI1STmVXPxydKYIBBBSjjMVtw4rMiETIarRFP0itm3ROFRk9bKXbzbNulmagk289/H9lCxN0RHY0qZomZ0Rnc9DMDj+QZY1efFTxoGM0MlGiGaGGNBpXN/FsRxaRYz47LmHNc8DIXbuuXDtl4+KlJt62s7h4zVJZ6ujjrcA6oS+jgNLa0S5I+xUWv6135lcoc8NgFc0FfrGeeb+hRQEy9ID5Zer/RoibQc2fy5cUZUWdJYrsXj5sSqiHaDzz74Nszto+Nly8l4eWwSo8FxPe2jfI6ZNZ9iSDblyVdl1BEVfMfqjH9J91uIxyVHXRXSnNrCmpRFHa+LKJ2lZrGlp4pJ0loc2/gvbCv3Y0mZOdA79+X6KfhELC0dKSr5brb4P5ZLMC7eB1vgY0gog0agJBQGNWRi4TDZvVmEFcJWLJawpkmGM7GagyaouzNJ7BJEC2Gqsyh/wYdqIyXw/pQ8WD8ILAZ/Hj5c8cqogGxY8uljw6GJJQ16TC8Gd7wAhJPMb5yORTH8hOeXYyYSgXRriVqmcXzjXYsVLetzVCsC3zZGCZRPRHq3IqikZrchLi7bDkHy83H/qty11+cSvTHympQyZzwXgx+dp3lVrsAW0UiY5p/yly0bpiRCWReiEEwidcAJNH/0IqliksGUL+Y2bKGzcaAy2Wo99VR8oQErsadPGxznGZ02u0HtFiM+cNLZ2XbTrjiPJwglwvGpjf2YIO9RAMgGJLBQdsx+itUa4Ln1xyb1bul52YkzEidCX7WPaCGTDAiEEvvKxLRsRCpnr832oqdDj+1V/w0QcyAi99rxpNP1yL0L6SAtEscSIKpD9q8t4ZZTekPo3mjjX03aODK96OlsddbxFcADxcR1/UhjZZQh7LZwwFNOmmVVsupHZxKaPNbea1wGp3bB/Mwy8NCa3CTUbki+EuQ0ljPm1ZQHgm+p8rN3Ibg7D6Nsxu4PVy1bTFm5jtDRKW7htnCTgikVXoLXG0x6e71H0i3jKI+FESQtoTGnyNXzCFhZ+wKJl2BvfOn75NXQl4jhaE9aGiIa1xgV+1Bhnd64PS1hoNH25viqZVPjgFcviEY2tFUW/wM50T7XyXgCKoqKfNoSuIr9RQhKxI7xt+tuMCZWxL4Cggny4EVvYeNrj7mUC2zdSF7S5tX14aHm0ujAb2qiQEzYSK9KWgAeOJbCUZk4RVj6t+fYNPv/zXp9pw2bUXFhUZVYaTa6Uw9c+v1ompxz718ugc9FfAnD9k9czkB9gzksp/JqAHSVMJr1WmjMe2sOoE+KKIrhakUejtU9ea9xQ4rDyxduj7dWdkgoO55+60v6kQBkhzPGJEFIiIxHs1lZTkW9vx25uRobDB023kcEgDWecQctVH6f9u99h7i9+TuKjH4FAYPwdtQbfp/Tii+y+4i/p/+Y3ST/4EN68D4BywS2Y+7gF8/PpVx78Samc1i3x0TnvJzRaJDiQZu1im4AL4SIIXxMomf4D95xjV3Xq3T3dh3Xu8QOZBKRko8ApvyeqvyoUsGfNAq3RnmfiRT0PtKbpyqmvo/PkTlzfJe+Z3gJ5L4/ru7w0H+66QJONCBqKkI0I7rpA80Px4JHP+ShEbdqOEMLcBgIM3XjTGz21oxIHep+8Gn0J6qjjzYx6hf5oxZHERU7AQc2CU523ca7JjC+NmmQaKwCBOLQuGFctHze3Tbca8l4aNSk2uUFD0qMTIumcsFkMfPqZmrF3m8XBERh9D1RB/NSSTwEYKQwFBIJWO0bMzTOqFcNxCOZMZd4WFpbl4BRcRmKK3t710HVRdR6965spFEbYZdsoMb62bKGxTZsqlFBk3SwBYVNSLm7NfSvf58cFjpvUHB+BrRUOAl8A0mZ+Yj6fPvPTdMzu4B8f/Ufu23EvvlZYwErCvO9kk3IzOrKN0dKoSYa5kKoBMdlommg9d6wLg0YG5Y7mytVnjdBijGgJjS0FwvWxQkEaI22UVIH06ACzhjT9jUyuIAP7cvvMzwsE9oWS9zyhaEtBMmHI/M5jBR1Ni+ju6WbryFY0mv+fvTePk6uq0//f59x7a+2q3tNJOiFkhSQEEhMIW4OKEVAEWZQZRkwLboiMMvtkBsfBr3Gcme/vp44bjkiL4zYqKPsiawISSDAEAoEspJN00p3ea6+6957z/ePU1unubICA9sOrqXT1rbPculX3OZ/zfJ5P05BCqpFJtlqYqHjTkKIgBL9tnMJZ9+9mxTMQKkAhKFGXn8KyPzt0tPhoiw1duE7jS5MTYCnzIzVc8cghhU+VBNvaWhO9L9ljZrNj2mOWYMViNK5cSWjhQiOr2LXL6OhLEhyl8Pv7ST30MKmHHgbAaWkk3Jgn3DBMaPYUrFM/BseefsgxlrCsZSmc9Glu33obL0zbiz7T5l0bfeoTmoE4PPwOm85JkulugIRQh21VWY20l2ZydDL3nd7DR+7NApq8DTqXJ6sSzPjXL5N9/gUGf/hDVDqNjEapX7mS8KIT6FzZPkpe0jatjQv7LuRHL/2IjJsh4kS4cv6V/GbjTeyaLbl1TiVfQGufxPCrRzTe1wOvpRDXeJhw2zkyvFF1CSYwgbc7JpJiDwN/8KTYAxJDy1KXUnT8IDhosmAmN3a705bD5l8BomjhWExgPevv4J1jWIF1XDBablPIGO18tGX08wcmvr4BKCdKeS4M7wEhyArBydt83vNbn7xj4QcdnLyH5XrcvkIyOCvAzWm7fG7Pfe4/2Zvey0jjdlH+vyVtbGnWwL72afAVA9o1MppyYu54qJD+9896P/921r+NedR4hGHNnjXcsPYG+vP9Y77OEhYb1TRI9tD5mzzZfYURbFojzKZJfQAVnFROBOzq34HIZbllhWD9bIkWolJkSmvcAx2EtKbW92lUZhcjj6ARyXedGXy0PsTWoa0A/Pv3PY7pHX0GPAt2zgjxL38GF6/1uGStV9ypwOQaIGn6zLVMuvYzhyRPR0Oufrd0PjVp01f1u6wFzLjppqOWOKhCAZVOH7S4FUB6/Xr6v/kthONAMGi0+rkckeWn4PcPkN20yWjPqyElwTlziu45iwkuXGCKax0m/mntPzGQGyBkh9g5vBOJRAuNJSymxaahtSbppbjlop+YSHEoZPT447lHFXHpHZfSmejEUx6LtrlctM4UDeurk6w9u5FLPvp/Rr0fB0tE3ThLjPndFRnagyurpFlAFk2zr7j54wexAn2dcbSJ2IdC58p2vN7e0X79zc3M+GHH6zDyCUxgAuNgIil2Am8wDmYXeQhCf9BkwX09Y7e79T5TzbVU/MkOGqnMzjXAGIR+vEq2VnB8zf0bjK5UF3EFDO0sR5lDQvDEbIcVrkNio0dj0mMopnjkFMnm2TarvJCRQRTP7aAzWEyoHf0Zl0Liax+ppSmmIySOcmlWigFL4muBjSY/rhSjyK4FPN71+JhHHKxyZ8fmDuKh+EEJfal+QcNiyb4+hZ8vV4Yy8oigoPlkDz5YSQQcjGqeemeEfdPSWJjjBeBh9N1RO2pkTKVKvUIwbNuktCaiNEGl+GAyQ5+1m53SqRQZEhXXmgOXR3csF0ghOH9dkcwXeaMvAKXoveX7vHzRohHnojPRyfWPXk+NU8Psutll8n6kRMrxBbLimD7i7XktNogyEDAku74e7XmoTAaVzaFzWXSVq83wL35JTvoMegm8goctbeoDUZw9XUz96r+hPY/81q1kf/97o8F/6SXwPPKvvEL+lVcY+vn/IgIBQgsWEF6ymNBJiwnOmX3QCrYXz72Em577Ljkvhy1tXOUitKA+bD7DeT/P5HALKpOBTKa8MBVOABkyybaymHBbwpo9a+jP9OMrH1/7ZftRS1hMjU7Ftuwxo/4HK+bUcYU15ncXlo1bTKoPYSRsrjZFzf6QeK2FuMZDw9VX0XPjl0wOTtUip+Hqq16PYU9gAhP4E8EEoX8rYjzCfKAjzBgYN1mwdzPs3IJhdpZJSo1NLmrlUyaJNVp1g9R6/P7Gq2Rb8pM/HP/81yApGgutMkzn0CskbQsXgYMmphQzCnlOnRphzTEWP5h7Cl1dT9OqYNXAMG2ZvUZeFGmGoV0UGqTRquONYqGWtNHKQwqJq1xm1s7k8wOD3EBPeYl/IKVavF0ViyYZecodp0o2zpZk3AxjoWNzB8lCkqSbRGmFFJKYEytHoQ98X6uhUGWHoNiTX4fUBl59rgYSFhrBYL0itDTDcXOmsWaWoOMKi66UzUAugKc8UEGE8k3iqhBEhIW2w2S9bLlwkDknxRwAIUhagrQUPBK0qIk0oRnCEhYWFpG8x1AU6tKVRB3fgmxY8uws0064YHT11b6VSoDIZOnY3IGrXAZyA+S8XHkRNfulYT749O8Rg+t4bsYcZn3mr46IhGvbQuCNJvSC103iIGwbKx7HihfzLPJ5VCbDhtu/R3jzJqQPjZQKbbmkI1mE65VfG5o/n9D8+dRfcQUqlzMJtkUHncKOHehCoVjwaiMAsqaG0Iknmgj+4sU4ra0jNP7V8ptkPomvfeKBOBE7Qs7L4SmPi+deMvpcuQV8twBFL3lhWWVy/z+/v5l4MEbEibAntQdt9oCwhEUsGENrPWaC8sHkJV0pe8zvrkQozj/1dNMRgi5L0uor2nPQdv4/HvV7dDR4o9xV3kpuOxOYwATevpgg9G9FjEeYx3KEOQBjOgAkumhNDVQO0gpS3ebfwbixlxzPnnIsHKyS7Via+wNxqAq0R4Flw/vZYImiTaSmAPRZFpcmUpBO0DZtGW3n3gzfPh36Xq7YZ/ouJHZD03FEHONnLLUs21CWiIoUElvanNh8YkUGc89fkurpwQdsNNUeKIu3K656UONZkAoZYnvVA4qOcyVbj4+MNQVe7H+RlJsq/660YrgwzIv9L7KgcQG92V4EYlR82RIWsmiJWTr/G/OnsHpZEkdDCE0OiSuiXKhz3FGMfEtkmSxbwgKruAuBZGrdHF5NvIoaw1QzoDQXpVPcH42QsCwej4Z5nD4CoujGg0U2KGhNanzLkHapQShIxRzj6qNcsgGTWFsp0gVSQT4g2D60neH8MFKWrD41i7a5XPmAQgaCZMMWel/nEbuBuDMm427Zg+VXNmOUAGFbb1hBIRkM8vTdN+P+9/8Q9kc6EUggloGkM7aVowyFiCxdSmTpUgD84WGymzaVCb63bx8qlSLz5JNknnwSAKupqUzuw4sXYzc0sKxlqSH2wPqeDdy+9TZ6Mj20RFq4eO4l5b8dDNr30ZkMKpMhs3c3U50alG3j5oJkbR/XErjFa3O8BOWDFXNqrbHGdi8JNtBW2EdbMmHsb6U92tbzD4DXw11lPJnYW8FtZwITmMDbGxOE/q2IgxHmQ2DMZMHcIO3DCSph5+Jjaj9rwgE6jp1HV6aHVl2g3Q/TlvcP3t9rqGS7Zs8aOp78R7riilbt0e67tInDlxQB8OhX4alvVbzu557H+lwvNY5FUko8IZBaE/MV68NBSKYrczkg2bVSTlZw5fyPcNOmm0zktCq35LQd8P6n0szq1Qj1OzL2U9zR4nDXUpfQTEFGmuOrW75wnSHzJYvH0uMFT/n0XXzlmNPKupmRxaWEIe9ZL0v7wnZuWHvDWGIRAI6NHTvi9w6RwhUwYMnKjoWW/Ei4RIqR76xX0Wr7VS4vCsX+7H5T7KoaxdPlCfjocJI/G07yeCTMXfEY2x27XGwrkHNZe7zgvGc19SkQftG5R0DeywMOUSfKvct9Ll7rIf2Khl5o2PCuVgoqDVojPbfoIGTOqWuBa7nY2GQtVXYDOVwy9NCZMc7bBpHidEXxfOeCkmOPUuJwOB7iQ7f8kJAtRu3ilBBImwJMJWvM8Ui3VVtLTVsbNW1G4uH29JhofZHgq+Fh/L4+Ug/+ltSDpuiSc8wxhtwvWUx40aIR5P5o0RJpMbp8EWKqqKM32WsWhlYARyXxpEf7wj9HKzVCi1+Sl6S8DH0qAbkCIW0hPv5+2hcuGjvROZliTbSGjsYoXULRqiXtOUHb4X5fvE442kTsEg4mqZtI6JzABCbwWjFB6N+KeA2EeUwHgL6naTsw0Q5YEw6wurERJxQhbrXSm+5htUizKtBM2+n/ePD+DicSf2B/pRuanycuJb1CsdrOsMqDtsOUFPHoV+Hxf8eEcx3IJeH5n7NteisZKbABoQ3tzUjBNicAzfMrY80noHa6sdksOfpEp0I+aRxz+nfwo133kTZyd07e5tP+gKLGF8g8+GhCrqa51+WKBxUdKySdsyQZIaqc5I3MZqwCUFOGJZObTuDq+68eGaXL5ND6gGh4Uf9eSlwfVZ22CKUV75nxnsoTrzzIdhuGhYUELDQegn6p8RHkcgNjRvqrMZQfKv97yXbNB9YpJg1p9tfC3cuh3vFJSov35Aqckx9kSyTOjxvm8rzbSSYk+M0ZgrtOFZzysua8DYp5XWY6jVmL5kgz6XyatSumoOji/esUYRfyAcmjZ8Q45a+/gPPY3zFvm8sF6zTNw5r9tYLWPs1AzLwvrnIJWsEjdgPpz/ahAxa+62OVKsVKeOyUMCcfRYT0cD3Ea/rS6LJ16Uj4AhwFgenTUYUCT257mG++eBNhZVHj1DCQG+Cm574LJ316FBF3Wlpwzj2X+LnnopWi0NlZJvi5559H53K4u3bh7tpF4o47TILt3LmG3C9ZQuj4+YiAM8aoDo5qXX7EjlAbrCVRSBAWASbLOrMAsWdT6Ow0Wvxiddvo8uX0XfNB9t/8fRoHPYYagtzdFmezvpNVLBqz0BW//gyroxIHiIP53gjDqlQnf0ga/FrdVd4oDf4EJjCBCcAEoX/r4igIcwmjkgXXNZjH6ui01sZ3PRQ3NxY7TDhcC16WjnAzbW9A5Kt8Q7MCoFzCwqRMdlg52nLe+BKfar19oguUBqdYgVIBGtxiEmYpAmoSO83znPPFSlslOVPjnMpzJSce4JrdL3FNKlSWH3Wu3YsHeHmLvBBoYaK64bwiHYML1mn+YzYcq03PLwmjz+6tFdSlRxZhiukgvXWK740VpUvmiWpIi5GVY6WGaDDK1zZ8jayXHU3ENcSVZv3GmyEyx1wzT36dQlGCI4vHSjRKC7SkIrE5DCzerrjqARMZT4XMnD7xgEYurWFaq0cGSCjF/EKBK36+n3xBsGaRYN1sRSIq+N0Cwe8WSGb0aM7doFi2TROwAuiApiHUwPJ/uJ7vvPIL9qb3jiBI79mS5ewHFAUbkiGoS2sieSPfSdZUTb8o1zhcnPe7PJmQJFlbSfC08h4nHMZaciwcLMmzmtCnmqJM3pkcsw2pgah5vQwEuGX3L8g0hEGGcF1NoGAjshlu33rbQSPrQkqCM2cSnDmTuosvRrsu+ZdfIftckeBv2QK+T/7ll8m//DJDP/s5IhgktHAh4cUnEV6yhMCsWYd0t4GRuvyeTA+tNa18du51Y46vrMVPGTnZLzJP0PvhydjBCJ4j8GyBo3N0bO7g5nNvHkVur64J4qg84eI1GwbQPh01wddM6I+0QuvRJGKXMFHhdAITmMAbiQlC/6eAQI1xsKlKagTocmzibgGqNKFv5A2mfEOrmWSsJVGEhKBLeMaXuyiLWbPuG3S8dCtdKk+rtmhPZWmTcaO3L0Xx3SzVkpmAhmyRDEsqpDiAgHkrKjfuVwdx7DQN73CJzYmb6raZfsgOGDvO/S9BvKKJdZMCGdBoBbrIczRgeaACFs3DPllgq/CxtLF6LAjJHcsFVz1oxpa3IeyChc+9pwfHjtJ5ezgLm67OQjGRVptE2uWC1uXzeHDoeaSQFbeZ0kCAhIT15Ln68b+iffATtA114tQKcsKsfUQpE2BE8umhfdcBrnhYUZsyfu2eDYkwxPHZvyHEwGZwUxKnxhCdZcMAACAASURBVKf2JIt4X5ZsxOL8jYIr73dZd7zg3mWSHVMEnS2C773P4idZjZ3tJx6I053phnW/57IfP0+8L0uqaRDnqudhWhsXPllgwAbtmCVMwYFkBOJZKAQ1KmBj5T20ODI3kGlJhz4vSd2wi+OBa0MyKpiWPPIoNRy+h3jdx1Zi//N/la/PEccDTR/7ePn30udEC4EbFLhBia6J8WK2H7upCZXJoLNZDmU5LByH0AkLCZ2wkPq/+AtUNkvuhRdI3Hc/2WefNW4q+TzZZ58l++yzwC3IWIzwSSeVCb49Zcq4RbSOVrpTs3kn527IER92SdQ6PLu8gZ2zogwNdOL19RnLzGAQ4TgIIegKRYmn84AyeS9aEdLQFRq74uzh4g9doXWiwukEJjCBNxIThP5PAZNPhJ4XDWktQ9LqKXoTXYSFgJCJHL2RN5jyDS0UB6ZBaj85v0CrFYTzvwLzVrBm3TdYvfm/zfa6kPSqPKujklWFgtHaF2/oB2K2W6DTcUhKOdLlxoqOvHE3TcZLOPSs7YdCN7FJw6ZAVk2zidwXUpDqhdgkAJyYxsuYboU2OnAB+DY4rmB/XaX+qxagkEQUPDdb8gMok/O+WsET72zgldmCSdZILU7ICtFlWzRvLXDVQ2pkIu2DmvvVM3BcAD1ih2X0P3slrH7pB6yqa2aO30+nVCSFMOdDQwxJJhAlYkdIFpIUGFvCU8LibYpj+o2tpBLg+DApCZZjUyho0D4yCF4uQP/TNl7YwXE9vIAEKTljs+LMzT7bpsA9p0ienidIhQXkhxjKD9GSDbD3gZ8wc0iQjVgEhzK4//FtHvPyTE1ZiIBHv4ZC8ZxnwxDyIV8XoW4gT7IpQsvfHhn5UuEQjfs1qpgMa/vQOKxRM0KHfvEYOFiSZzVOveQzPP3dnxDc04+sunyN5EcSXnRC+bnxiN+U2mlYsRhWLIZWylhjpjPobOaQ5B5AhsNoIShs347d1IS2LHQyicpmEcEgamgIlUySXruW9Nq1ANiTmgkvXlK0yDwJu77+EL0cHOn163nfoykKQpMLSaJpj7Mf3E/h3XXULJiGn0xWXHWEQAQCzLWOoceWWO4wvnDBCpAL19Fad+xrGsvh7q68XnitGvwJTGACEzgYrC9+8Ytv9hje8vje9773xU9+8pNv9jCOHpFm2PkYeHnj7iJtkJKGUD2PORrlZrDDDeUbzHVLrmNGfMZr6/OVB+HOz8GjX4Et90CkmYbWZTy2+zEUCtuJkgtGcIM1XHfWl5nhenDn57hxz73kBISRCCFxfBcFbKXARQM9xuViDO13g694KBImKwSeELhCkBOCC495L9M6nkDn8+XS6iIUASdKfn+euhPrIVJn5EiWY9rODpjHxF4sO0eqKwiWMNmgxSTKTNj4rf/8nRbOjGOYEmulIdJMX7YfG81cLRhqkNy/SHD3yZJHT5TYM6YTsSMUVAFHViLCOT/H5GADZ/+mj1ga6rJQn4KQZ7qsHYQ9JzgMCDWu7l1gCPt+FPdZBULKJyOgAckkrQlpINLAh+dfwbahbcSD8RE6+cXbFZ+8T3HZGsXJWzXDYbh4naYlVXGDKdvzexopNU5EIQBhC4hMwg5FKWRSCK3xHEEob0TqAdth7i6fM1/UbDlGkAqbGHXa8XnqeMGmmUZ20pSWWD5kXn6J+obJOENDNEhJWEgyaAIeDDUGuemT03j0tBrO+dSXmLvoyIjXlh98nUjCxQJsVYyWaxiI+By78lNH1BaA1dhA6uFHTIKAbZc9xJuv/zzBGZXP0Jo9a/jF8KMs2ZQuJ+JqAUiJPakZd8er1F38QQAaQg3c/+r9dGe66cv0mfdJw/VLry9/LoUQyEAAqyaKjMcRgWBxpemPrPZbxPqeDXxr47cI3PxLvEIOGQ4TdELIcBgZCOBMnsyUr6wmMONYRDCAPziEzudR6QyF7dtJP/EEw7+6jfQTT1Do6gLlY9XXmwJZR4C+r38DXXBJClN8S1kSlKK5t8DiD32aqTVTRxyvfZ8GK85zXc8SEDFqrGaUDKF9+PSJn+aYuhmHJREacyzf/CaypmbkDoRt4/f20rDyo0fV5sEwIz6DGfEZbB3cSl+2j8nRyVy35LoJ/fwEJvDm4V/f7AG8npiI0P+xo6Q/L2TAy2EYVgRqJtFmxVmVGaTDSdPV+xKtMkj7/I+OuME8umU/Nz2+g92DGabXR/jUWbN45/GTRrdf7ScPFVtKYcHudfCTy2iTNqtaZtExaRpdhcSIhNDS8V0Ri7ivgEKx9KompDVdtm0sJrXPSHeaUmVbmLkT3rdOVXzflwt+Hn6Gtk6PSEPLiNMiQiHc3TlwRhanWWP5fG1SLdsdgWpowJoK5zb5/PnvPMLKbA5kLElvveb202x65zcQS3SXE2wD0iYrXbZoPdKOEUlPuoe0l8ZTHqL4n2M5xJwYf3vGjdhf+iTRvJEL+QKkb8i906f5fMrlC81N9OX6jL/9UyVZjuCO5YLnZkv2COMHo7XHroDD4lfyXPSUT11Ck4pp6uZv59SdX4Hj2vhR9tXynI1OvmpnIKW56kFNqAD7YtCYNBxRFU+7pQX7YoJMQCI1NGpNU9AhktO8+Mn3Efj5PTQOeuxtkkStIGSypIvj7KkXWEpzSt1x7HtlC69OFuxtgP89XXPPEsUZL2rOfDGLde1VpL78VRIqTba4O4CCn53ssiuxi/OOPe+oiFBoOIuWxkITKi438f1j1wY4FMbyEN930Sl8JftDun61unyNd2zu4IS9LnZ1dF4DWqE9f5RER2tjlyqEuU5KEfixbA+BynPRqbTPvoLT6hcbaY7vs75nAzc9911TxCqhSQcV6fQ+JJKAFaAuWEe4uxtnyhScKVOIn3+eSbB99VXjnvPcRnLPv4DO5yns3Elh504Sv/41WBbB4+YRXryEXTPC/EqvZ1++96BWmF53N+FYLU1+gKHcIJ7ykAGb6bkQc8eR7xyo1y+3b8+msGsXwnFM4augSZI+3Cq6h7u78nritWjwJ1DB0VSJnsAE/tghDmer9k8dy5Yt0+vXr3+zh3HkqPZ7d8LQ9woo17i8hGqNrn54t3GLaZpXscc83/jBP7plP1+4YzOOJQg7FlnXx/U1N1640JD6A9svvT5QA14BtGc076qk+xYgLQg3wMkfN5VohzrNOAI1EG3marrplRCuui6zQtDs+9zclwKvqJ2vP9bMAWDfc3yuMJkPPiyMVaQNQc/IKW55r8UVv48y128aXVrd28uMD9jlBNg1uR5uqBEMWtaIxFQLqFdw48AwbcHJ5bleHUrTK4VJ1itKgTqFJj1GwFBUlOwjHh3pEHfiXH785Sxb+Q2CLvhVr7cUFBxYdv1s1rzn7/n+f19L+/3uqHn+YIUpWmWK0QqWvipYeV8BYUmmqzzaF2gl6Ds9x7+eGMUJN/CqlwDgCz/xqUuNTOANulCXgqEaM4a6tOnHL0bq++OMOL7RFUSmz+MfPpTHsRxCVohjNvdx6sPdtAxrErWCJ5ZLXp5lkdGKeulw7U0u/SGP+5ZKnlwg8C0TKRUKjms6nuDLu/jQw3kaBly6azV3LBc8P9sp+3p+6sRPGWeiI8CGxQsJ5NSY9pFN113HpGs/c0TtHYhqa8JqWcXMFwf59M8TWN5Ie1Mw0wkedxyz/uNaePLrXO110mvbhGsml6VwWS+LIxwyXmZE28l8Eq018VB8RH+rlhsrRJXL8Zd3f5LkcC9REeQDP36VUKqAawNC4Agby1XUthzD8f//t8adl3Zdclu2lAl+fsvLJu+lCgVbsHd6mJ3TA2yf7nDxOZ9l2ZSTRxyz9+//AX9gABGqSJx0LofV0MDUr/7bazn1lfMpREWHHwgah50xdhKqpXjVFVpb3iAN/QReH6zZs4Yb1t5A2kvjax9LWETtKF8680sTpH4CR4rxSru/LTEhuTkMvG0lN3d+DtycIaxCgB0w5NnNQqTRkGmtoHYa2CEjOVEK9r8AkWZSv/osH83/lDP9ZxgWdfQFp6G0Zkt3ksuWThvdfun1AzsgNsUkvnp5Kp+ZotWkmzVRe2FDMGacawoZcEI0ZId5LBxAYbaPckLiCsF1gwlm+L6RC6GMjEjapq18gtRTUQJekWQKU5VUapjRC4+cGeeUbaIii+jvQg/tpXlBL0G7Fwp5CMW50U6wx7HxilvwgspegI+m25JcZDeU59qQ6OGxsIOSdnGsgl5RJGyH+JoIWSGklDjSoTHSyFP7nmLF73IEvIocQ5Y80gPwibMddiR3centvQRcRT5gOvBK89wPj50oEQhsafPxezwcD7KOZpJSCGnkQnsTQZ5dCGE3R59laO1laxTZwMgx+9KQei3NuRyKmnEUHLjvHTC7x/TrS7OoUD78+l0RBpqDhO0wc7ckufQX+2gaVITzEE/B3B2a/mZBskEwqH0uekLSOORz8iuaczZqwnnNvgZBNiToy/bRE3VZe6LDHcsUjy2SdDeYAdrSRqN5sf9FPr7o4xwJ9nz/O4QKYwcx8lu20PSJTxxRewfixt/dSM7PEbaNvMuRDgrFJbfvp3FIlXcEqiEAqyZAg/gNuDm+E1JEtY/IJcAOgh3EFjavJl6lLlQ3ou3uTDeucpkUmTSiv62DW7lozkUI2+YbL92EFY9TCEp2i0EWbfcQxR2XsLKwlGbtmXWcvPh9485LWBZOSwvhk04ifu651H7wgwQXzMeqrWPfQCfhjGcWfkMux3bmeMfzGXhwLXrHTlQqjayJYsViyPo6Mk89ZT6LlgX5PNp1qf9YO4GpU8vSoJ9u+Snr9q2jNlg3SoZzONCeZyRDmTR+IoFKJlG5HNp1Td9FRyDn2GPJv/wKfm8vzpQpNF//+Qky/xbHXz3yVyahHrPzqdHk/BwvD7zM5cdf/iaPbgJvM0xIbibwNsFQp3GGKSEYN9H5xF7IDRXJ/PSRVRedMPS+DL+5ltmFQWx86r1hPpf+Gl/n82wILGXPYMZE5/c8bQi8HYRos4mYO+GiUXjWyFCqIYT58Ys6+FJlWjtkiH9qP21+gVVa0RGL0GXbtHoe7Yk0bQUfJi0wxN8OQLi+4tG/6DKa7+4Y0/e9eUiTXDqXljNXGlnEts049gANyzLEWjGsJj8AAx5dk2rwEaNU6obQC7ocm+pysG2ZLKuUT0dTS7ngzU6hqgVBY0IUKZ1E4iqXkBUi42bY0yyYPKCJ5k003LMgHYTuBkE82kJvtpfa/tyIeQqgYEPzsOnRljaWsGgaUqRCuuooo06qSRq//B5LlvX4vXVidITeg64mwR3L4cJ1jHDd2ThbAj4feAZCBUP07zwZHmztZ7Y1G4Bz7t5HJOVXyKsyzkDnPOLx/Eyb6TKI5eZQtgRfUZeGS5/UXPS05pl5mm9/MIir3Ir3vjbT0Ghc5SKRZNwMe1N7qQnUUOPUVKrljiUDK9qwdk+ymZOqFNGqfo9VeuxqrUeC8awJm4cUSprdjlGwLPRQL0hjl9qqPXoFhNGQ2g+hODk/B9q0VQ1f+aNcaEpOVSVZQm+2l75sHy2RFrYcL8g6Qd73ZIGWYU2yzub3J9fz4tTcEc1TRiNEly8nunw5/zDrGSblQ0zfnWXazgzTO9PEEx7hjEf68TWkH18DgD15MuHFJxE9+yxyL2zG7+vDnjyZ2g9dRnTZshHSoEP57x8pSlVuyWQovfvCcQgdfzzTvvF1E6UPBo9ajz+BPxx2JneyeIfigt+5NA0p+uokd51msWnOzjd7aBOYwJuKCUL/x4yS53qJOIOJkE8/BdrvMjaNyZ6Rr3GzUEiDn8dC4GMhUMR0gpXZDtaKxbw/vBl+842ipl1BwTOvqzvGtN8wB9y0YZDVTirCqiTsWcHK89FmE6X3cmAFacvmacsVDGnPDhUj60FD5lUBzlk9yqO/v+5W4ik1ipT21gnaF7YTK5ZX59+OMe1YNmbHIGAWGFLQ6iv6LYl3AKkXmOJMreqA2Kq0aSso2twKgVsaGKBwiOi8LBoXKhSOdMj5OSJOhDuWu1z1gKY/NlJOc8cpAtG/nUVdNYRzmrqExrVN1DwbhLAHiVqBpU1RLYSZd21KI2wosWHtw1Ctptuxjfd5EXcsF1z1QMVis9xvkbxvLFn2F21PF29XvOsFI8cpHf+uF2D3NI/+xSY63bw/j2TkwkYCzf3gAu3zP4qM/hgyGUQwUGxeE/A8ztgV4uFwCz3Du8hKSJa2SsrD0Pj4OMKh4BcYyA4wlBsi4kSIdT5N8P5/NO9rqN5c3/f+DWBkZM+tmMXsm14aM0qOZdG5sv2wPcnHwngONenmGhpSaWDkYkIA+L75qDjmNe1+iNV2BoQg5BfIeVlc3+XY2mPL0f8SLGmVF4jV/UXtaFn6Myk8ie50N12pLixh8fvZgo2zQ0yOTCYWjJF1M0y1GrHicaO79zyOBC2RFgbkANkFcV5ZEAetCfWlWLDX4r2pGaaCbSqF191N8r7u8usCs2YRnDMboTUqm+X2rbdhS5uQbRYtITtEzssd0n//aKFdF+26IxZy1UWwjGQnMK5t5wTeHJy4zWPlfQU8S5AOQ21KsfI+nx+e92aPbAITeHMxIbk5DLxtJTeRZth6r4mil+QpqgDv/gI0zh7/75lBACQKC49SLDemU3TYH+Jr4v8STO810fayhaQ2C4FgFM79Csx9L3RvMj7vAFhGP699Q+wDUVOtNdVtovNO2JBsJwzKZU28iRvrwnwnHub2aIj/jYa5JSx4pGUWDce2jXLh2eTtovaZlxFVMhDbh/99p80tw/dx64u3UvALnLz5PjPXETdpAW6GvcEIzwQs1AE3cAuolUGuz2pmeLpyroDvxMN8LlTgv+wst1pZsubEMWpFUHpLrAhaaBQKrTV1oToAPjzvw9xZWM++BiMTqk/BQAx+dpZg42yL07a5vO/eFK4wc7MURPPm3/G0iZQv3a4ZDiv2NghSNZJl26BJhnH8PNoDrQS/bhNsa7SQspIn0N0gTL/7oT4NAzHBz86SbJotK8425XkIPnmfGiVvsrRk3kCApxaHUShW/HagLFmq5uMCCF3xPi5tuwFVcMk+80yxIq4oOrRA5GNXUi+fZz1ZIkKQFlWns2o8QgjiwTjTYtOwpU3BL5D67T+T9fKIQARHgLACFRnZ4iuQ06fCz+40SbYHQvl0u/30kiIzuJ/8Y2sJz5ozwq3mUGgINVScnIRd1rSft+TDhB96ekwHGgHYNQHq52uwHGZoixlashWXPksyuX4O1y25jlOnnDqqbTQ40sGS1oj+HMvkGoTtMEE7SEAGyPk5fIzmuD5UT22w1hyvPK49+XPMnHw8Vm0tMhpFWJapuOyPdaIMSvKYXYldDOWHUFoRskLk/TypoOa893yKeedfTu2llxA99VScKVMAgT8wAL6PPzhI/qUtpB55lKFf3UbTtj4aUqb4WSZqo6XAEhaD+UEunH3hYb8HrwnKRxcKqGzWyHSGh80ip1BAq6Krk3V4Rdn+GLFmzxpu/N2NfOe57/DI7kdoCDW8dke0I4T9H99HFFwKAYkQAs8SCK2ZNxBk3l8cuVPVBP6k8UcluZkg9IeBty2hb5xtouX7X4BUD9S2GjJfim6P9/fNvwLtlYQaQKniqGDOpTdy3HOrAWkIuCixV23IyiXfN+03zoaTr4apS2H/i0biI4DGeTDzbNj3eyO8FpZJ1PXzcMb18Bc/Z00wyOrkJnLKR1s2u2yLIdshVjOFnBQ8tvuxsgVcCSdE4anB+/H7TQJniZQ+P9s2ZE8VWN+zHi1tTs5moHprXfmsCQf5fl2coNa4AvwiqbeA2b7kHzM+bQs/Atn+8rn6zvFnclOuEw+NjfFLVweS+RIExJwYrTWt+MpHSknUiTKzdibXLbmOS+ddyk3P3cS+BnjsBMm9J4sRuvGP3atxPEiGTYGnEqkPuJCKQqIGjslJFm1TpKbWUzjlBE45+SKaXt2P3z+AE/FpPjHJfy2KEgfydgi3qkhVd4PgsRMl9y6TPLZI0tcgaAnUk1L5EdOwhOTytT7pEZp7gS81U3NhTv3cl9g6uJVT79s9dhqBgP86vcCM+AwWvOcyNJB/8UVjKxqN0vCJT9B63ec4/tH/jylWhK1S0SP0yDVSsWGNZn3Peu7ZcQ9D+SGm1Ewh9vuf4AeiZASkhManKENK7YdTr2FGfAaD3/ouQo0tihqsM4sdT2pyhSy5l19m8mV/NuaxY2E8a8IT85MYuueesfu1JDISp2F+try4nlEocFHe5yPv/k8uWn59ud0D275+6fW8+5h3j+rvoV0PEXWi5ehy0A5SF6zDkQ5fPvPL7EzsHNc6UVgWMhwue96XEkqrI/cleUzezxMLxNBoEoUEru8ypWYKH1lwZTmqLqTEbmwktHAhsXPOofbSSwgvPgm7sQntefiDg+D71CZ9pu3OsuD5BIvXDzClK0sglacm1shpx7/3TYuUa98v6vEz+MkkKpGo6PGVAin/JKQ6pYTvnJ8j6kRJFBJjfhe/0Sj8948YtgvFDcNiMMCSHFOIMan98IvMTWAC/JER+gmXm8PA29bl5mjx5SngjmHj50Tgn/bBlyZVktpK8H3zxXrD/kO333EB9G2HQqJs90ggDk2zof0urr7/6rJsYefwTlMdVRtidmztsWS9LM3hZm4+9+aRbVbJi04PDJIVGhth5DqApz3CWvDkzk6MHUzJ8lJz9eRmei0HX/v0WaYYk0Qzw/P5lWoxMp1Yi5EqFXH6T08n62WxRUW5lvOMFrmagOri7zPqZo4edxVO/+npZPNJo4PTCoWgIMxrv/Ntn3TYLBps6WArn8Z+heVD9ySBreFYJVDKwp7zDmb8sGPM83K1k6AXj7AMsEV6qKoiXdUOPCdPNu4knYlOkoUkrnJxpEMsEOMzN/dQn9JkHW3sFYUg7Ary9VHOu3MdAC8tWAhKjVrbKAGrvjL3oOfhwHG3O8PsF4qA1kbSVX8MybwZU9pNl3X2AsEyHeCCnM8SGalIUdws4Wgzsb+4jYgT4aX5C0ZGykXFFnLftConJOVTkxOc9uTG8cd5mOhc2c6+Lc8SG3ZHLXSUgMi8istNOTekSvt/pKj+DJUw5ufmCKB93xSzymT4hweuZzA3UJbHgLn2G0INfPnMLx+0nfT69Qz/4pd43d3YkycTu+D9SNum88kHST27nqaB0ZIfq66O0OKTCC9eTHjxYpyWljFafvMgbNsUqSrJdILBP7pI/htxTR0NOle2k9jXSZ9OlnejmkSM+JQZle+9CUzg8PBHpaeb0NBPoIJSMqFXnSBXut610cx/bVFFciJEVeVWBU3HH7KLNeu+QUf+FbrqLVq9GO1egDa73hCsoV0AdA1uI55Lgu/iWmBZDrqYQAoQcgt0DW2Af59tovtWEPLDUOWGkRG6eHFXiJuFRQYPzvo7eOpbkE9BsAZOvZauV3+M9F26LQuBxsJEd7c7NmvcAm1OuDy+ch9uZgSZN30YlbQ6QHPToCrJiuPhyvlXctPGb+NBMXPBkPnJnk9frZHD1AYs0hmXeMZE55UAO6dpDJhUXFHXgrtnD2v2rOHun3yJZb/dQ/OwJlmbpmlZmPaZRX22XyjurlRQSpIVmLyDG564gRmb+7lgnU/zkKa3Lsddy1P8+mT41AMKSwvcgI3jSyyl+M1pQcoy1vECBfrQ5wEwZPbev4ECXC1CfMFJ0yPAFx4ysYsau4a/XPqXzKufxwM7H+DuV++mL9vHMyLPM2FoVQku8EO8O+8TUR7ZJVeSzexHPbVh9NjGGWvAE/TWHXyYYOwPq33ox9Leu3v2UJBjy1e0ML7zzFtx1AT+QLwRVUmFZZWj9i+G+mkM1SBcjVNQSA1BK0hPpuegbaTXr6f/m99COA4iFsMfGGDw5h/Q+NlrWfDXX2B9zwZ++uzPib68h+P3Sebs9pCDCfyhIdKPPkb60ccAsBoaiJy63BD8k07CiscP2m91/9WLiVJC7muF9jy056EylUCIcBxD8ksEPxB4W5P88RK+D/lZfp3RcPVVJP/ln7CVjys1dsFHSI+Gqyei8xP408YEoX+rI7XfEOZAFJzoSKnIkeAgzh/lv5c85e2wIfX6AEuOkkQmXFf5uy7q70N1cM4XD9rfmsGXWL35v3Esi7iv6ZWC1UGXVflB2lTQRCVfeZDW1IDxopcWDj6e74K0cKwg5BLkEl20KgX5RJGMmQRCEruLIu1aIlpUIvRF+PhEnAi88+/NTxVat/+U5yyJQFM6wwJwtKZDpGhzQ2Z8VYg4EROhr/oYCSCgFT5GtiM1NPgek2SArJ+jtaZ13LfomsXXwMb/4Uc6QUYIIsrnykSGaxIZknMCdG6IMVzwacxiFE7mgcYkuDUamqejlUO62eFnt/wdH7p7CLdYLCqY1hQeyhA4B1ZNs+iIOHRJjUajlEIVFfW2tJkZn0nbtDZmbxnmIw94IwpOtT+guWWF5Pb3WrxrnaJ+2GWwIcQjZzeRXDKzPBcZi6FSKbQ2Vo26qIPPByW5Q5wHoHht/qe5hlKd6IBAWnZxt8O8QxJJPBDnsnmXcfGci1nXvY47t9/JC/0v0CXhJpnjVlvwnuZ3cEHTTKYC7q3/C7YNYyR++hY4BYXrCBxXIz2f51bM5WDq7Wovc1lbi9fbS8+NX4IDvMxFNEpDd8UBqWqZTKI+QPh1cNipRtu0Nlax6g0rvjM1Ps1Ea4u1HWxXodJpJocmH/R1w7/4pSG6JR/64uPwL35JdNkylrUsZdn5S+F882etNYn77mfwlltMEmuhAErhDwyQvOdekvfcC0IQmDWL8JLFhBcvIbRwATIUGtX3WIuJ/m9+Cz577etC6g/EmEm3tl3xxn+bRfLHS/g+5Gf5dcbGWYLbVkjevVbQOAj99YLbzpRcMksw4UI/gT9lTBD6tzq0MpHkfMqQVidsijAFoibJ9HBQTdbHcP4ADHGSASPNiLUYD3nFSFIvLeNGUzsNYpOhkDJEEGBGgwAAIABJREFU/kB5QHV/woI96+Fnl9PR0oxjO4SFDbjF4lGCDjtPW06YNp78Ou0Fh9VhI4Vp1IK9QiOUT0O0gWyyG1dAeyINCCP7UaqSoJvshmCcKwuCm4IaT0gsbVxRtNZcOf/KMU9ReyrLdbUOltZloqyBJs+jS0qTLFyqglvElfOv5KZNN+HhYWGZPoCPD6c4wfVZ3RDDURDSiqzUhxUhveb0L3JN6dwpF5IDoD02Lp7BbVMsrvjVMEKDa0MiAPEiuRcZUMpBFwrcsTzIu+5L4FoC1wGBplCsx5R5Jst5LQ5tp3+VNZHQmEWQPr/08wCc92TeFLAqOgeVHj+wTvODP7d5ZZZFSCtyEtxYkFVVc6tfuZL+b38bIS1cfOOpr+HxM2sPP1JcjFh33H818WwvLQds9d+14y5WHLuCZCFJgQKnTz2d06eezqvDr3L3jrt5ZM8jZP0Cd/Zt4M7ffoqlLUs5V+zmxJYm5N6eUZH5+8+Oc9xOl8ZBj/56m4fPrOWSD11/0CEO3PwDQ+aLxFaEw6ji8yMIvRDI8r5PhdR7NsSjDThTXv/qpG9kVdL2he184YkvsDe1F1/5WNIi5sT4zBn/SKBlmkkqzWbR2SzVsk6vuxsRi41sLBjE6+5mLAghSD/6KFZtrSn+pLXRsycSRtJV9LEvbN9OYft2hn/5K7BtQgsWEF58EuHFSwjOm4uwrEMuJv4QqETyR5N8EaiqePsWJPlvxK7P0aBjcwe9C2rZe2Jl8Zj1snRs7pgoLDWBP2lMEPq3E7Q2Wu5CcVvXCRtiH6gp2jCOg2qyDuaxYJ5fEwmZKJ7qpDVq0+4XaAvGyTt1BHIVPbyHRGmJDSbBMNpsikqFxpArlPrTnlkAIEDYdFmSuO9SKdcEIa3psqWpTgvG2z5gE3Fq2WnbIIzkJKp80sl9NBcKtBds2pJDpg0lzQ6B1qxpbKXDytEVGKRVBjmv6QQeT75Cxs0QcSJcOf/K0ZVFizsJbYM9zA5Poma3xQXroHkYemvhoZMl7nQPzv+GOb7jgvKuwzWnfw5aV/Cj3feTwSOC4MpUgWuoB7eXVQMJOuIxupwArUrTXqzeebDdkqcevZ+hu1xqhguk4gq9sJHfTmrnCWcdNcdlSYeGSYahLmPIfMlJM+CB3dxMw9VX8UTvas4aUqRCI/NzczbEEpp3NkcobPgXHOEghKAr1WXkNhrCGjp+ez3M/yiThjTJsbz9hzUX+g4/sgpkJES0x5WzLxxxM5107WfI79xJ6p57cHzzNq1fFOaFD8xnVSlSfKhdoyLG2+rfm95LLBAjFoiR9bIk8gmyXpaZtTP57JLPsnLhSh7sfJC7d9zN/ux+NvRsYMMKmDKc5tyn4exNEKmyuV82eRnZXc+gdAHHcrj8+Ms59RAEwd2zBzeZhESi8mQ8js6PTCZWqRR+SwNqfx+OZxaNngRHSwLKflvKBbQ2+RZCCJN/USTuwnGwHAcrHkcrhS6Se5XJYE+ebFxuqiPo+Tz25PEj+9WLACEEIhRCBIPoZJJp//Mj8i++RHbj78lufI781q3geeQ2bSK3aRODt/4IEYkQPvFECjt2IOuNvK+cYHuQxcQfCiWST6ZiavpWjOS/0bs+h4uuVBfv2A5nPLaX+oECgw0Bnji7iWdn/2GlPxOYwFsNE4T+7Qw3a37SfSbxs0Tu7cDI4w4sMAXghFmT6ixHaOPSoVe5rLYz/HUixVm5AROl1iW3F4XCp6AlQTeHHN5tEhTHiviX+hvYjvF6l6A1rZ5Lr2WZyLwwGoycgFZR9KS/929YEwqyuq4GR8Nst0BOCFwh+HwiR1swDoN7jPSnBK3BL7CmJs7qsMaRUeKNc+n1c+zN9/DVtq+Of8Op3kkAzt7qs2iNVZGYpOHyh+D5Ng3vZvQux28+wzUIrikV1HKzMNwDIQmNc2gDswWcLibUlkjsOLslTz16P+7NdxOyIBeCYEZiP6VoOHMT2Sm9ZJMRsgGY1m+IvF+sJis17G6C84sJYa33/5Deul3UjlEsan+dYNjLgYC0SpelNqVodVbAK36G1S/cxFXjFJzqrYXv2jkcrZmqBLZ0uGP7HZzQdAJgImixDVu5/MkENa3NRGsa0LkcZ/YXuDS8ktghzsOBpP5wtvrDdpiwHcb1XYYLw6TdNLFAjEvmXsJFcy7i6X1Pc9eOu9jUt4l9tYqOFRY/O0tz9vOa8zYopg5A888eRk6fDi0tNOcLiG/fRqJxIfGzzx77+gH8bHYkmQdIJPDtkV+t6eYa0q9uI1r8XQC2Ah2yaTlAnvN2QMfmDmzLRvoSX/tIIbEte1SUVEiJiEaRUTPzxk99kp7/82XI5yEQKFeKrf3QZeP2dbBFgAwGjdRmyWIA/GSS3KZNZDduJLvxOdw9e9CZjKlOi1lYUXTxEZGIceA5yGLizcKYkfxqTX5xUfOHdv55I3d9Dhdn7Irwrl9vRdkW2YhFLOnxvl93Ef7Q3Dd1XBOYwJuNCUL/xwIvb34yA2A5htgHawzRH6vAlJulIxbEsRxDlGpaCA/vART/EyxwVjFiayqnChx8bO3hCRtd8pKPTTbEvCriz7wVlf78QrG4FKA17cMpVjfWAZoQFMk6tLtOOarfUVePo0tyHCqynLpa2vwIxZqeVRM3x3XUhHE0hGvMmErk76DbsNU7F0Iy7bnAmBKTac9VxrcmZNNhJekKKloDkvZkhrbAFHNgIAKRRsj0m3NfIvnVcp2D7JYM/fJVQhb4AYHW4Dlmfoufe5ZfLzgeVwyXQ+7FGk+VCHxVKL59YTs3n/IUKx8co1jUKQIpJRJJjtyI15b2TRKWpN71uOMUi6vGamO5KEuSugVMccI4lsPXnv0aGTeDYzlc8kSGvFQkvQEme0Fi4ZqRUpQnvw5uAQp9I52OStcQI+dz263/zLvX7qmSw8S45KN/O+otdSyHpnAT9cF6Um6KRCEBCk6behqnTT2NzkQnv/7Pz7DmBEEuKLh/meD+ZZITdyjO26BZageRCHQoiMrm2Pe97+CecgKxQIyAFRjVnxoaGvPSOvD5dVPSnL7R58AMmLw+siJOR4JSpdiuVBdRO2oWcW76dYmsbh/aznB+GCkllrDwtEd/th9PHXw+8XPOQTgOAzf/gMLu3ThTplD74Q8RPekkxnNcq/3QZUbrDhAMHnQRYMViRM84g+gZZwDg9fYVyf3vyTz9jCH0vm8eUynAaN37vvktwksWEzrxRKwDJUF/AKzv2cDtW2+jJ9NDS6SFi+deMqqY1oGafCGEid6HQiaaHwoi7D/+W/qF6xQD0nw/CiHIOxpbmecnMIE/Zfzxf/r/FOG7kB00P5YDS6+Ch75oiGMVyewK1REvlZIPxYFphFI97LM9PBwGdA31Io0GXCQOCguFj8SKT4dQrUlMTe03i4mhThN5LTmUCKuYSGuoYluuUJShROlybFo9TbsboM3LQ9ZE9busAnFlg/KK+n1BSNp0SQGZhHkeqJbtgKTLsYjHW4vzKE7pUA4M1TsXWtM4bCLz1SjY0Dhsjl0TibLazuAAcTBJvXU1rNIF2lSR7EWaeKrLZ+jxDDXDKVK1krrLzufUEkkdZ7eEoV3UDCtyoZE+Wp4NtQlFPL+C3tCthF3YH4O6LDie0dL3hyHswpp7/pK2/TtoG+rkhpmaW1dI3rdO0zys6a0V3FWs/BoaRSsrREpglkshDRtnC34gJBc+VWqjUj3WnHWBEtDnpphhTWP70HZaY62E7TD1AwWyEeMY1J/tI+bUIEIh3D17TEf7XzLXDkWnJN+FbB/sd0e9TYt3aOofVAwqjYfPMbt9PvHjPKF1/5fkX+sxo9uWtKgN1hIPxEm7aRKFBAXf+N9/4n7Fnz8Gjy4SPLBU0lMv2DRLsmkWtKT2cN6OOO/aVUNEB9F7u0kWkiQLSUJ2iBqnZoS/O2ocEnHA87Oe7R511gEsV43S278eKHmGO5aDQLBjeAcazdToVHqzvaxet5pVrDpqUl9QBbP5VpWgrIQqW4geDLGzzho13wOlOdUFraLLlsFnr/1/7J15nFxVnfa/5y61V++drZt0SELYIUAwbM2gEAzIouKMo6JE46DD+/qKzrzDTHxdRiUjKiozyriBjToOooIbyibENIQlCSQCBrJ2J91J792136p77znvH6equqq7E5IQEJh+Pp+i6Mq955y71K3n/M7ze36H5U5jNTcRX3YR8WUXsaFvA0/+6GaWPp0hnvYxfYWhwB8eJnnvvSTvvReEILhwIaHFOuofOuEEjGDwJft5OSh5+luGRcyOMeKM8J3N34ZTP3rACrmlfAIq5F3CshDBoNbih0JvyEq30cE0on4Ow85w2baysX4mkcH0y277YByrpjGN1yqmCf0bHb4Ls0+B8/4BnvkRpPbpCPp5n6Bl153VUoZQDY5lU9e/hwHRSFoEcQjRRIIgLimi/FvoH7ha/oqTzJwmZIkeyqRMGJrIX/JV/frD52BwC4gAxGZBuo92J097cAaIWt2nykJd0VM61U9LwCg63ATL9pmOYdCiDD1xAN2fXWTeUoIwaDGjDNoBxkUZB+HAULlyIQRDtbrqaqXEJODBUK3etsPfgw2Ei5S71FeH6ZQJ/RM7xyg8ZmCaikQIAhlF4Qf38UTTfM5653X7XS2hbi7p2l2E0hK/IhBseZCoGaeCg7WCuoxiX8P4NkEXRqJw+94HWZUTtIeaWOD2MzhX8PN5NhgmXcKngLbBlMgyEZuIkme+IwRhqdg832DT/NL0qUT8RaloLAbgonTVUqEnUQCjDQEiiTx5W1LwC3Qlu7RXdGuxAI10q2sZCAG+p1czJmDkttsJh2sIeRHckX16W0NR6O6e0lGmEkIIYoEYsUAMx3NIFpJkgJgDl61XXLrB55kFgvuWCP50tEF/zOOOU0a484RR/mpnmEsGZ7Ow2JbjOTiew6gzSjQQJW7H9fhLNRjKJ3FCjQZg5tDUlpWWZHyScwTR8XxHefWtP9OPIQwQMOwMM692XnmbwyX0trBxcJBKVtUvsIX90jtPgYnSHOk4Zc975bpElyx52Ymr92y/h5HT6th+ppbYCKmo60ly3F7BeWPNOM//GTyP/LZt5LdtI/GznyFsm+AJJ2h7zNMWE1y48Ihr2e/ZdjeWYZU9/UNWCMdzuGfb3Qck9FOhLNWZGMUv6fCDQS3deR2TfLu1FbGvu+ozkS9gl54th4mDdayaxjReq3jjl7d7PaNnI/Q9W60Zf1mQupCS70C6nxUL3o7rFch52oki5+VwfZdLW/8GG4+oyJNSYfpUA/to5LPW9awzTsc/62OaeCX3VWs+4rO0nKQkm/j7x+BvfwqtS3TfjQsg3KC196UE35Ic5ZyPgyywwhG4QE75KGGSM0xc5bNiZES76gAgNJHXIn+93/EfwPXdScdyQAeGYp8UtO3l2iUKy9cEGaXfLR/WLlFwzsfpFZJQyfVHSRAmIQx6hVc+nqFnPPImFGyFEPo9b/gM3Xbb5D4nnIO6d12C6YNZUAilsFw9nk2nns6o/QBCRXjgTWLKMT54ZtFiM6Adf1Y44ApBTrooL0/c13Q8bkWQUuIpr4LUi9IVBKDGl7gCPpjKES0dbsVpK1l7SnQSnwG4vsu8+DxN7IGHzo0hPA+7oK+Tl80ynOzjP0/YS2dPp64dAOPXsRTRNidHQ92eHkQohD88DEJo8lck0iIQYOS22/d/jSsQskLMiMyo+sxQcMZ2xafulNz8XY+Lt0YIeoK8pXhgUZZPnLuD//fY/+PJfU/iK/099JVPMp+kN92LdfGbiydIjb+A2KWXVvVj7ieQL9AE5UijN91bnly50kUgMCprObxM//CF9QtpCDVgGRYSiWVYNIQaWFi/8KV3PggYoRBWQwOB1lbslhbMujqMwGTJ06GgP9tPsOL+UoZg5KgaHjhdMOdLX2Lez+5i1o1fpPZd7yKwcKEuOOa6OJs3M3rHHey9/hN0v/tv6fv8F0j8+jcU9uzZr0zo5YwLDs7T/2CglELm8/ipFN7QEG5vL253N25vL97gIH4ioave7m+l6TWIfVe+ibHUEMIpYAgD4RQYSw2x78o3vax2Kx2rhBD6/RCeL9OYxl8a0xH61zIeuRF2/EHr4eecrolxyxnQtGhSUaADousxWHuTJtLBWsgMw5rVtJ9/A6tO+CAdO39Fb25Qa2tPXkl7azubwwsxn/gPGgr76KGZH4srGJp5Pv+xcA+n7r5DE1Hf0QbjgYh2vQnVQi6hnWq+cfK4c0lFddVxZ5OpqmF+lfZ1t7Aq3U1HLEhvJEoLNit6XqQ9my1WdlV0RsJ01MbptQxaPMkKamlf+n+4Imjzoy0/qnK1aW9tp7Onky8+/kX2ZfehUIStMB888YNFx5ui13mimxfnC34u4IL14y43a86EHQtsWLSMlucXMpjoIux7WvNd04LjF4jmRllpjdIbFPxLUjvDVFLlggXxoaIz0aJldI6upGPLD+mVo7SEgqw4fiXti5bxTHY7G/YGWL7OpTmhGKsVbF18Jvc1XoNvfQrb8tm4UOAIwRVPqvIYHzgTkm0+IWXQawlIJ2hPDLHKsYrnyabNLXBVw5lsiIQIPvU8Fz2WoXlMMtpgs6nV5fguWW7vt0th9mzJ3+cFJ4WjfMPK0WXoyrUoSaMvObnb4LwnFU0JRaohSNPfXY57xsllW7t1bTlGlttc/rhP85hksM7g3rNtnmsZZfOTq7mieQYb3ACBbsmyp/T5ztRA3Vn1nDXh9rVbW/EGB5GFwngdBinL+uFDiXCn1q6tquJbggKOSphc2zWP967fx5ozAtx3YoE+f5TNg5vZPLiZmZGZvO3ot7GsbRmxQEyP7XP/wEBugNq1f8KQ2s3HffObOP4rX65qX5imdjKZAAGviMNNZSKxbdh40kOhsA0dQX+5/uErTlzBpx/9dLnSsFQSz/cOysJw4Fu3MnrHHchMBiMapf6aa5jxv67b7/ZGMRGU+nqtIc9mkZkMcoKT0EthZmQmIxOq247lx3B8hw8/8OFx7frp+nr4ySS5zaUE2014e/ciMxmyjz9O9vHHATAbG8v2mOHFp2I1NR3SmPY3rryfZ2bklamGq5TSfv6F6tWwKmedUjT/cOuevIL4XnQjJ50R5/xHEwQLLvmAwdrzankuunHSs6OEgy3+ZtTWVn12qM+XaUzjLwlxJCIMb3QsWbJEbdiw4dXtVCn49nnQ/9zkfwvWQMvp0LJEvxoXHJjg33OtJvF2hUDcdSDaCO/4bvW2Qown1NqRailBpTOJHYahrVo6UVvU0zsJKLnfNC0aTwi9ZLJzyUGj47JxiYqToLMwyOr6GmwgZFg4CtxoI1cc925+vePXkzzVr1hwBT98/oekvWp9pYHBR0/96LiN5ZqbWPnC9xk0jXJCLkBOGDQ3Hc9t77inSpdc6iOVT6GUokaYhNID/PVPPeoylH3fAWwXElHBux7485RtuL7Lqc2ncl/XfQghxj3tleIjp3yEk5pO4vo112t5g+chUfiidBwwx/WoUYqcEDRLuG1wtChXEkV5iq1XZpqPI3Xiv5WXlUUohDc8jDs4wEhUkYxoiZHtwQ8uFpw1N8TfO2aVPKjTGWRNn+SChwVYgtpYI1GzBlUoMPMzn2bTfEHH8x1s7N9IwAggkSgUBoaOFiJpCDUwmhvmnK0FrnpI4pp60hNxwVIm9v+9TsuTiigthXtDQyipizOhFNac2QjTwmpuPuiS793XrCC1cT2GJysERMXXnFmI2TNQe/sRc2Yi3ncVf1pg8Judv+HpgafLbQTNIBccdQGXz7+cwdwg3978bWzDJmAGKPgFPOnxT2f+Exe1XVSWNuy44koKO3ZoeU4lbJvWb33zFdXQu75LX6avrKG3TAvXd1m19PA19J09nXzmsc+QclNVPvSfP/fzB2xz4Fu3Mnzrrfq+LMmVlKLxuusOSOqnQqkyq8xmUY7zktHySq160Awylh9j1BmlPlRPXbCOvJ/Hkx4f2Y923e3vJ7dpM86mTeQ2b8YfHZ20jX3UUUWCX0ywjcVe8jgmjuulxvFqQtgBTfCLrjrpxx9n9PYf/EU15tevPp+//u0YvmWUi8CZnuRnl9XxjVVrJ21fKaURoZC+V4rPq8qxd1+zAm9wsFxTAkDmcof0fJnG6w6vX+3ZFJgm9AeBvwihL6F3I+z8oy7O1LsB0lMsw4bqdOS+pRjBb5hfTcTvuExH5itvXYXWwF/zG/1312PwzB2Q3As1c+C0a/Qk4fFvwshOva9hQbheR+Mp7j/arRsTpiaNCmiYpycdoCP5VkDvN9YNgbgeW3oA/LwmmzOO368HOV9eoMlp0QllZXMtg0IRllJPPGIzyFk2Q7khmsJNOh/A0Ym6Ob/AkCnICJBFje/44SvigTjr3rNOf9BxGZ2jL7A6bmErRUipoguPwaqT/572pf8HqHYOaYm1MOaM4SqX8FgvSJfoLpf3PwhekaQGPLB9uO9Cgy/d+Dwr719J/TO7eHNnsuyh/Eh7DQ/NGUEIwWk7fN62zqOpmMj6wDlB/LNPpzvZzYgzguf7nLrDrYrQ37tUsLsNgiitoR/eS7l0kak19CWdd/f2S6t+tApdXeRzGTwL9hZ1+UEXxqKCG99rMktCizJY4Ydpz/sgC3Q/2oaX8Q/4w1c6zgvv3cecEb3NvkbBzy8M8exCm5yX49M/8Zg5rIgW9CTCs8AJGSRnxlj+myerboPU2rUMfPVmTYoNo6i59xGWRcO11x40GXy6fSnZbJKGKQqzmo2NGDVxZDCAzOXAc7H/8Tqsc86kJ9XDvbvu5Q+7/0DOy5X3idpRQmaI2mBtmbznvTz1oXpuar+prLV3Hnucvf+yCjk2Cn5lsTYDs6GB2atvfEVIfeleVVIxWhil4Bf2X5PhELDy/pWTrERzXo7mcDO3vfW2/e734puWIrPZKjcW5XkYkQjHPvXkpO/XwbrxKN/XCbWZLCqX3S+5r3STSbtpQmaI+ookdcdzaAg1cON5Nx64P6Vwu7t19P6ZTeSefRaVy1VvZBgEj1lYjt4HTzhhv7Khg3G5+UujXGm3mHCrCgWU6zLrs595VUn9fZcvJTSWww+O30Nm3sOpC096bsDBE/WDJf7TeENhmtD/T8NflNCn+nSVWNBR+2SvJva9GzXJzwxM3ifcoIl9azGC/8iN2s5yfxH6SkmOFdIEOp/UCbVuFigSKFkABNTPG3e4Ge3WenLD0tF6wxqP2EMxar8b6hfo9pJ7xit0imK74SawA5Mj+VsfhLuu1jkEhpbbLJ/TSI0yEVYQmhYWT4ti6+hWFtUvQuRTOlFXCJQQbBVSF2sRTCL0pjDZ9IFN+oNvnAzJfjqDoixTafFcViRStMcXwHXrprw8y3+xnJpADWJgCxgmab9AY7eupjojoRNq//Amg/csnEn7iof3G1369kU+tjC55n63HLG2i5OBX15ay97T5pJxMzQ808WHHpTaXtOCkKcTZ3+8zOCacy6kfWAndHVSReahmDwM2x85BaN2nHxmX9xCQUlMYHdz+eQQc+Bjf29xfLQFJ9OPKz1WGc20n/MvbP/7m6raKF0DmUiw8A8PAfDE3bfCjd8impO6+JXSqwnZiMm3LoVnFgi+c4tHLF8sSKzvMr1NSLB045+nPN8D37qVke9+F1XUzxvxOIZtH/SP7n3nn0DLoMKc8NhTgD1zJnZRMqFQeNkMqqGO4K1fKm+XdbP8YfcfuHfXvVUadEtY1ARrqAnUYAiDtJvmtovHiW3ICuHddiepW78zeVBCEFy0iPm/+uVLjv9w0NnTyepHP43tJAj5Ho5p4YZqWXXeFw47Ql+67zNuhqHcEK50sYRFxI6w5t1r9rvflhNPAsuadO/geQzd/59Trl4d6krCgRxzKvHhBz5MzI5NGkvaTfP9i79/0P2BnpTkt24tE3znhRdggsRKBAKETjyhTPADCxb8xYtFHQr23vDP+CMj45V2AeU4mI2NtP77v79qUp3nLjiPvSKpc2mEgVISJSVzVA0nrXl00vbbL7zoJZ9XJUy73PyPwxuK0E9r6F9PEAJqW/XrhLdrYpzYowl+KYKfHYbcCGx/UL9AR8t9V0toQrWaICtPR+FBR+YNWxP+fFrv7xY138ICs6RbBlB6lcCOaCkMQhe0alwIw9u1fWVmcJzQp/rGfdeHtxetLIvaTSugiWYhCcE5kz3I192ifd0zg/pYhUGL6zNoQTg2ntzo+A4RO8KIM0IqM4Brgo0iriCiBBmhkMWAdSUidoXTTF0bjO2hPadod4ra3BLpc7dPeTk6ezpJFpL0ZfoImoImKYkJk73zPL5+tKBGKa3x9yzaz/kXAJY/nsc1wQ/oc+oGBFJJrnhSIZSnyXzRKKRga4K7/NEUt57iEAvEuPJJpaP/tk7oVJYApbj8KUX7F4vVbG89B4Ze1AdcTtb0ofHYsh5dFKNVeUNiFaPjJejiUQIDEOFawuFa8HJ0hJtpX7QMu/W/qtoA/cNemdw5+1dPkfYsfFFAifGhBB2fy58U/GlhANv3tJd+xe1lKLDl/slA7qmnsFtaJkXbDtb6USltVVid5FtsJ5WCIqEXCKxwFNk3RH2onlQhhSc9InaEyxdcztvmv41nBp7h5g03k3JTeMpjxBlh1BklbIWZE5tT1a/jOeSffLzY4QRHHKCwa9dLjv1w0bH+a7jZYUYMit8Nj3h2mI71XztsQt8SaymvGgkhMIWJq1zSbprOns79tmtEo8hsFir90n0fIxqtcuYBDq6WxBSY5JiTz2tin83qPIwijqR2XVgWoRNOIHTCCdS/971Ix8F57jlymzaTe+YZCjt3ogoFHc1/RgcRjFiM0KmnEFl8GuHTFmPNmfOadp+prNhbRjCIt28fMpuZUAQrUE3wj6B9ZrxtIbP3dTOkUmXbyiYzTnz21C43E595MPl5VW57CkvVaUzj9YJpQv96hhA6sbRuLpz4Tk116OCeAAAgAElEQVQOxrpg809hx0M6Oq5k0e8b8HKQGyZvWjwdq+fxjV+jv/tuLsvto92q0WR+oqRHeTrSZNia3CtPk/3BF/X/C1PLaaSvpTiJHh3hV2pcQ18zV7flFyq0/kqTfyX1576rE2VhPHG2e51eMQg3aIcbv8CKdJ7V9TGwbC2LKUbxzm85n/t2/R6hJCaKAoIhAct9iz8aHmlRabuoNfTvP/7948d5zsfp7F8/OTqfc8Ar6DFVTDZKGuWIMnCkTwHFXgMaJdhY3DQyRns2A03HwbJ/Le/bmrLZa+UQSpajS74FbekwOS87yQc/bgoioxLX1+4kMxI66RbAQiFQFCzBjLEKinrRv8KvrqNTOHTEwtrzX8KS+ecylN/BBXd1Q8amtmYG6QDUe5AJ6ktSWTyqMTDZ07+zp5M1i5Nc/F9dhPIKWwoMO4ARizHjn28ob+/29GC4HoYSOhpWUdl2xph28fEsLe8xlK58axTnHoFQlIkoSTE+uHUDbjRIo9VM3Nb65ENJXIsUxlcDStDVDvSPfKGrC1ko6GqcsRjBefOoDdZSG6wl62ZJFpI4noMhDM6YeQafXPJJfn/3zVjJDE/PUzhBRdbLsn1sO/98z1Vcni1wVmQ25mnXoPbq79akVVGlXpEwUemcbUhsRxrjExcPcAzwEjsPu+0VJ67g+jXX6/wIZaBTpgV1wboDEvD6a65h+NZbdYJwhYa+/ppr6E3/mpqKew5evhsPgFH0Zae+Xuvui5H7dxzzTr6z+ds4nlOlXX/HMe98Wf2BdumJLFlCpGi16Y8lyP1psyb0mzbh9fUh02myj60j+5he/TObm6sTbBsaDtTFq44DVeydCOUW8N1CuXhX2T6zSPCNopXm4aBh5YdwP/8F5gZmIuLj0pj9JZc3rPwQ/Z//gv6eh156+2lM4/WKaUL/RoIQkNgLu9dpzXxkBhRS4Gagbp4m+06CoO9xdmKQsxODsK+LftOk30ow0yt6g1cuTZbepYtEYFKSrpRKlvo6Co/SmvZIgyb8uREd9TZtTdZBO8P4bsW+FVri5B5oOnZy4q2X123VtkKwhvZCllVmnI5wc5XOtmP912jyJSmhcBEElCLuSwYRfDln88WIj/a4mehyo9EZCbG6eQa2X6BG+gyaJqsb61k1PEp7QeoxMS4J6ni+A9srUJNLEBAwbAgKKLIobkq5tDedOmVewP6iSzVz2+jdt5FQxsOxddGmRiVoLCis+hCrlq6i4/kOhmu3E8sopEXRrUVgexCpqVi6X7SMzvM/xuott2P7PjWmTXckwsbeB2lqbSL3jlbOXjOAGOplsMnk4bmKE7sVzWNat//rpYJNCw3mhurKTTq+Q9SOsvrJ1ZyYz5fvAB+JUhIxgaSKaBSkLFeUFQpsBa6ho/8xpdjbbDJj2Cea15MIzwS7tp74/EVVbVUmeCYaQkSTBfrkPojNJm7H9httmwq5gMCY4HFTnmJKqZ1TTFO/Ow7hv/mb8nYRO0LEjpD386QKKTJuhsU7FMf80oWMR8FU/PFkgwdPN9jbAM+LAs9HoUnu49InvsQFs2YR6ROTzhVAvi6KJz0s48g8kivPWfk7TMVqBJCt/P4dItpb24lZMT2hli62YdMYaiQWiB2QgJdyHaZyuWm5f+MkXf7LdeOZCGFZmPE4ZjzOOTOuQjbWcdemOxgZ3cvs8CunXTfraomdfz6xYvTX7esb199v3oxMJPAHB0k/+BDpB7UMxJ47l/Bpi7UH/sknl1cc/lI4lIq9EzFlESzDqCb4wYOrdBs//3z4zKcPWhpzqNtPYxqvV0xr6A8CrxkN/cHgQI42b/82n33kE7SO9LA4m+b4zBgxf/+l2tWE/xdo33EZaCDgpYpSjuL+wtJk3rTh/Btg3rl6YrBnPTzyRTBDusprYreeBFShWJhqxvE62l/hakOyt1ioR9tE7s81Z3nHYmqkj1CUJxAKSJoG96Xtl3TaWXn/SgbHugin+orVaJV2uPElt+UjeoUiPrNswbn8F8upSQ0gpFdedVAokkjuE23VVp0VOFDiFb1P0/317zBqQc7W1V/rPWj7xEeIv+d6vf8/LaH7kSSjliBnG4RdSb2naDszR/z628vHODFpsSvRRUEWCBgBmsJNDOWGyPt57U2uJldnBe3mcvHeJs5eM0D9aIGxhiBr2mt5c2eSeMrDDRhIpT3I59ozq5LMdl75dpxtW0FWlqMC34DvXmXQucDg9C6TD9yXxzMFnm3QEmgmLK1JevjKY1m4Jcml9+zFNRXKNpibl6iCy8zls4i/f9VLuin9/vzjmTswtXDSNyAQjqIKBX194jGCbfP263DhSY+dV74df3sx0l2cDCulePZkwQOXBFlveFpyBNgSzn1Wsnyj5OiKhTAJDM4OcfQv7yFshYkH4tVysMNA5Tn789DU+QgmsGnFs0ekjxIOJjEWJieXl+wuj4SG/lD7bW9t14Qzl9N2mNnsq+rLrqRk08bf8cLaX9G4fYi2XhfbndC/YRA89thyBD903HGIwOEV8Xo5yGzYcFgVew8W5Uq3gde2deY03jB47WrcDgPTEfo3GpJ7dXS+ElZIF4ESBk/isGXmPB4SOlLYlktzQnqUYxMDnJVJVe1WutNLZF4BIzRQU8josKZpaoavNAHGzULL+fDAKu1uE4jA4vdB+//VOv3hXUz9/VE6Wp8e0PKgkutESYefGdTynfjM/brhtHi+tpwsrS5IDwdFi+fBJbfsn+gV5T29spua0sSieMQhoDcQ1FVtlaIz3U3H/SvpTfeSLCTxpEujGI+MO0CLsujMjG830anjQNGizh7B3S9GeMtjWRoTiuFawd3nRnhn+xmU6Ey8ro/0WZDZEiWWhHQN2MdniTemqlYRetO9VfIFV7qYmOT9PPsy+xBCYAkLX/ks3qE1/M1jisE6wZpza9l5Yh2znt3LJff3QMCmpqkFd3Qv7/j1EIG8z2hM4mkJPAW/QCbkEenpofPJf6djyw9Z0ZfBCENdVkfnATwBuRBsP1rHyP+00OIHb5Vc/oTPzIRCzqpl5nWfnBQ52zG2g6ybxVMeXXNscpfXc+HaMWpG8myrEdz3FpPUvBFWPPhJ2vma3mndLdpVqVQLoXj9w/lxec/E+xsBgXnzxu9KpQ4o5bEMC39n13iSt94JAZzynGLpsih9SO418zxoFsgYsOZUgzWnGhy7R3HJBsmZWxWGBCunJ1U5L0fOy2EZFvFAnKgdPayofeX1F6J6iCV6ZL7M1YAVJ64o1x2oJOAv5UVfuXpQE6hhMDfI6idXs2rpqvJK1KG63BwM9tsvesIgIhGMSGSc3JcsMfeTVHuk8Kc/3MnoXXdxbNInVRvgV5c2kAvCu/InU799QCfY+j75LVvIb9nC2H/fiQgGCZ10UpngB+Yf/aoQ3yNRsXcSutaVHdZUzRzUadcg551T/ueyHr9E8gOBaZI/jWlMgWlC/0ZDzZzJEXrPgZrZALREZjDojBK2Qigh6IrE2RKwaZ5zLGfNvBjW3KgTa5UsE50S8RHADEZ0xFUZ6ExTdIJs7VxI9sC2+/WWhqVXBtZ/H878sHbTKa0ejHVNMXClI/LNx45H6EGT+gnR8amwwmpmtRwEYRAyDBwjgKskK+zmA5P5orynJQyDqlDhQa9wELQULQY7ZZrVMRM7N0hNoAZPegwZKZA+DcLAAVxgiSurtptIGg7kotCx/msMHlVg73tsPbFQkpwq0PHov9LuhGCsm07TZfWp9dinKELKL1prxlg17NJeUaW3srAQgG3Y5P08vvLxlY+BgRCCJbsMrn5AJ9r6sRAzc4r33pflnkCYizaGmFPfVE5AHQ4HcXMOIU8iPFDlAKFieKSXdGMdq5//HjaQDcCcFHiGrj0mlCbRY1EYEno8C+oWMHhKiq8sGqbgFwjbA9w0X1BJ3zp7Okm7aXzlYwkLT3msaU2y/t2SnLKYg0EIPZlaHYZVj3xKJzUbAT0xTPVXTXQG6wT1aYVRdFilQtvvG9WTzYOS8pQiuRMTXYu30SwMVvph3ucoHthY4HcnQm+T4MWjBC8eZdKQVFz0jGTJruq+Pekx6owy6owSsSPE7NghRe0rr3+gqA+nOImxAF8YzKubf9DtTYX21nZWcegE/EDJr7e99bYjRuAPpd/KPoUQZXIP40m1MpNFuYXJDb8MZDZsQN3+U2JCkQ+ZRDMeF61J8OBb6vjJSWPc+NGvILNZnOeeJ7fpGXKbNlPYtQuVz5PbuJHcxo0AGDU1hE89RctzFp+GNXvWazrBtoyudRVFD2v078Pam4AboEjqy3r8Cgjb1i5X05H8aUyjjOlvwGsd8VlQ36bfIw264JMVqCYQlTjtGm0f6TqaVLiO/rvoaLNi/ttxlUfO04VYcp6DqzxWzH87HN0Ob/40FMuQK8BF4GGUrQWhSPCV1NIU5emk1mQvFDJ6I9MEo1g4BgGb/kvvmNyrVwv2Bz8PZ/0vLaspZDUxKmT13+d8/ICnqf2cf2FVDpolJNHvq3KU3WWmxLpbyg48KxIpXCHICa1LzwmBK2DFyBBkBukIuNh2lPBYL2JgC42ZUZqsCFkBSSTNymBVzmSDKbHDjYQtXT48bIWxTZuO5zvGCyQNDmLU1uINDtL/+S+QWquLofQmdhESjCcOCwNPKDbl+liuullZY/GN+jpcIei3LLbbAfZaFv2myfUzmlgZ9ehMd+vrfOIKXN8l5+VQShEwA/gVUieJxFc+lz3uoyyTvK29+gsBA9dUnL92lNaUXWVR1xhuomD6FExtqRkopkNECmD7Ph2njmIDYWEilSbxlq+3tXz9t1TgGgZBK8yfh//MnvQesp72Dc+6WVY/uZrOnk5Ak/kbOm/A9V186eNKF6EECkUCRR0QRiAQhBHYwqBDjY27KoliFePSRAconLIIU1asEymwpLYAzdkKmdPnS+ZyB5c4tz/bQQOihfHk8JD0eGuila9+z+fTP/E580WJkIqRGsFdf2Wy6mr4+savs21026Smsm6WgewAe1J7GHVGceXUEqlKVF7/5nAzpjCLdcYMhBWkPtTA9adf/5LtHAiH6xnfm+4lZFY/B45E8usr1a8RDGLV1xNobSHQ0oLV0IARDh8Rwpz42c/JC4kfMEEIXNtAmnDWhjT9Wa3LMiIRIm86k8Zrr6X11m/R9t8/YcYNNxB/68VYM7Tbl0wmyXQ+ytB/fJM9K1eyZ8UHGfzGN0ivWYM/NjZl3xv6N/KpRz/Fhx/4MJ969FNs6N/4so/nkFHpsCaEfjds/fkBoFwXmcngjY7i9vVR6O6m0NOLNziIn0ggD6LQ2DSm8UbDdIT+9QDT1q9KKKW14n5eu7D4eR2Jn3cuDFymSXSl7AXgnmtpT+5lVW0jHXW19LppWowgKxI52n//2fGCUuE6oBGZHUb5BVxMUtQg8LnfvIC/MR8hWKj4kZCeTr4FwAdPjnvMI/Q4YHz14EBoOgYWXQqbfgT5jJ4ARJvhd5+EddXyibIbTkla0XgcJJ/VIWGp4OiLx7f9xbXw/M/HPe1PfJferyjvac9mWOU5dNTG2GEHyApBQcD1M5qY50mGgyFmZEeKibwK/AINrsAyTO7b06s/M4Pc2DITkRmgPzeIKwS2UjRIRa+xm5G7dyJyw6iki+cIlDTAshm8+WvEzz+fFreA1a246CmvXDTq10sFg/MFNZgMCkl3wKZaXatJhQkMyryOUn/zZLBDROwCXYbe2jBsImaErJ+t2FNQP1oou+bkfZ2wJgxoGPHpajSIDmwtF3CRSmK70NMEgzFo3wKm0hHup4+DpxYYzJbQj0tdlnJEuPKWrctqUpl0k1VH4eMjlMCVLh3PdwBaT511s9iGjSc9pJJ4eCzZZfDWx3xaEorRWp91S022zzcJKUmvbQLhqraxw2UHpfMG6uiOQo2jJxqgE3UH6uDJ4w3es2moKllz03yxX/kUQHDBAvJbt1ZrWoQgOHc2zbEwdWPdJOtayZz2fvaOPsbM57s5qVtxUrdioBYeON3g4VMFmbDg4T0P8/Cehzmu4Tgum38Z58w5B9sY/9770ieRT5DIJwhZIa21tyJTEsv21nauGLqCH235UfkczgprN5IjIWV5KfnKgVCyvEwVUuWE2nggTltN22FPEg4GE1et4NCTbkUggBkIYNbW6iTqbA6Vy+oJ4GFIc7y+PgjY2I5LLCsxfIU0BYGct1/7TLOujtgFf0Xsgr9CKYW3r68Yvd9EbtNmZCqFNzBA6v4HSN3/AKClZKUE29BJJ/F0aku5Om3MjjHijPCdzd+GV7s6bXLveBHCEqyglogeIqaK5BuVMp0jbJ/5RsQr+f2bxisP83Of+9xfegyveXz3u9/93LXXXvuXHkY1hNDE1Apq0h6M64TS3U/Bk7dCoEZH9U0b9j4Du/6oyWwgRpuT5cqxEa6ecwFX7niKNld/Tj4FXWu1hEaAEWsiZ9Ux4keQ0idlNxO9+DO0XfRRmHESDG2F3Ji2rjTtCcmuRV08Uo+1kNb2moNb9P9PBSusnW423qbHE4iCM6Z19YEaraPfdh80LIThnVpK4ToQjNOZ6WU1AziGRdS0SRoGf0x30VbwaHvyNnj2p+OkSykYeF5Hbg1Lj91J0JbP0uBL1kTCZIyiDzswYhrkpY9AEqlwBnEEzPJcrsx5ekvpck80zB7LLPqd66JKKSGY6fm85eExlFT4GcrWL0oq/JERQg0F9j23jgvXmAQ8yAUglocztsNQvSLfYGEjGBBTJ+spoIDCAjbZBg8LB6THDEwiwAgST7mYhoWsOIYl2xQxB/yKQHPQg6E4/PJNkjO2g+e7FPC0xaRUdDdC+wt63uQW7RDnDANKsqFNoIB3PKawZXVidWk58O5z2C/yXp6sl2V3ajeO7+B4DhKdeGsIgzN2CVbc7xJwFQUbIg4cu10yXK/YWyeYhc2VeVk9AXZzUNsCi99Lzy1fpd/OkYzCWEwwFhMkI9CQgbnDgrrGOVizZmEEAiT+tJGfpB+hp94nakdJFpL8cc8faatpo61Ge167IyPk1q+fIFI3qLv6GqLXfh3zrOuILL6a+MwT+fOXP0PdqFee5ETzcHKXYvkGRX0Wnj8mgFSSodwQ6/au44GuB3B8h9ZYaxUJBS3JybpZ0oVxOZJpjF/Ezp5Ovv/s94kH4syMzCRsh5FK8qmln+Jjp3+sPP7Dxecf/zyO75RXomzDRiLZNrqNKxdeecB996b30tnbiURiYuIpfSynNp/Kz7f+vOyoNNX5fjloCDXwxz1/1PeTsMqa/4+ddnjnQwiBEQhgRKMYNTUYoZAuFCXluBTrJZB54gms0STBpF5RVQIMqV/HnXERsxac/JJjMONxgsccQ6y9ndqrriJ69tnYc2aDYWibSd/HHxsj/8ILpNesYewXvyCx/glqUh62FSAbszEtG6kk3ckuLpx7YfUYN2xg6JZ/Z/THPybzxBMY9XUE5szZz4gOEbv+qH93zIrYopeH+Aw4/vKX3bzyfVShoK1KUylkIoHMZHSV29IEzDSnST7jk/RX6vv3GsW//qUHcCQxLbl5o+GJb2pHmVBcS3PCdTpCXshouY5hFmUIto7il5c7GV/uFEKT6JFdxFK7OEr10Rr2WPTW61g6v1H3M+9c3XZdm46qN8yHUKVvcqWmWOq+1n+vWGhqKncGAad/oHoJNjdSjPQbkBvSD30FrP0KPHozCLssregISWylCEtfSzCEiQ10bPmhjsyDPq7SCyA3PC7vieoyqR21cVKGgYmOehuUEiglo4ZBThgVkhzBikQKDINxN/OS371ConX1HrDHhEyNxM+q6sQE5SNsm5Ef3EHr5oCuAGvrf8/b2spx2VMHd+k9IRgxDXYaEluBLwTdhqTH0KOTSlc0DRgBjOJX/9dLBZav/eBR+r3kQ//cfMH3L4ZEzCDmCIZjkjuWCc7cUbSsLM54fEP/fen6cY91y6sm8yUEXfjMT3Qi7pQQUJCFsjyiKdykJTBFn/Pl6/K4BsRrm1GmRT4g8Ew46ylwo42sOOlDB5Rs9cRdwl5FLQSU9t73IBqpLUspjHCYUZnmLY+mppRPlZB76im8xlryQUNfu6CB11hL7qnqi2YIg/iww2iksmeNkAdGfR1HxY+iOdysq81iMJof5Scv/IQP3f8hbt5wM1tHt046Xb7ySeaT9KZ76cv0kS6kUUpV6cX3N/aXg5cjm9nQv4GmcBMBI4BElp2X1vaufUXH3N7azqqlq2gON5MsJGkONx8xB53SPWM1NBBobcVuacGsq8N4Ca/12r9+F2bawRQGGOO1rI2aGpof+tOhj8MwCC5cQN273sXsL36BeXfdxeybbqLuPX9L8Pjj9bPK95nVk+HcxxNc9ZM9/N0t27j8Zz2c9XQW1d1T5fCT2bCB4W9+S1eIjcfxR0YY/ua3yBwp17cqiaiaJBE90lBKIQsF/FQKb2gId+9e3O5u3N5evKEh/GQSmc//j5TrvNLPjGm88piW3LzRUCEjKaMUOa90tQjZWoZQH0eHhooUwwpBZoDJbjRTRDAmOurEZ+jtnGH9bkdg/gUQm6mr2A48r1cJJmqAhQGzToFZJ8Offzk+ft8dd50pedlbITqzPXSEBb1xkxbpscIL0GsJavxKmgQhYdAr87rPqaCktrNcd4s+F8Kg17LxEVgV7YgiRY9JRZPv02tZtHguH0ykWLxD0f1iHDcdx476HNVukDvao980cYsTB4EiJwS3nGfzDz9Xmv9XmIObzc24o3toNCAdGp9lS8D0YVEvfPw/HUZrBXeeJdi0YOp5uAF4RSLvCeizTITSE5PSGfekhylMLMOiIAtsWmBw+zK0y01ClWU+mxYILKVQSlXVHRAIQgWFN2EIUkC4AHN8n2HT1DnTPuVVjkrUpRUf/a0iGZWECzBYV/S+X2AgENjCpsUIMzi8jZjvMdu0GDIN8spjRgLqmucQD8QJWWGGc0PkRYGmtMGq876gyVn98ePXtG5ulUzrvrODXPWbLNKV5K3xQlquCbFIdRXMnOHTOFo99omkNdW9nX1WGtFkV5ShTyO6J1cXzgVhZmZ86ieK522gBtZc1IwA4oE4whV8/YKv8/tdv+f+rvtJuSnW9KxhTc8aFtUv4vL5l3Nuy7lVchzQ1Wgdz2HEGWF3cje1E9yujqRO/UCymZdCb7qXhlADjeHG8mdKKYZGhwjFXlltfXtr+6siITCKCZvU1+sIcdExR1b4sIN2jTGiUaTjYHseImBj1tchwhEtxyliQ/9G7tl2N/3ZfmZGDt4vXwRswqecTPiUk+EDIDNZcs8+y8O/u5UZO8doHtarXfN2Zpi3M0M70P2b92EfdRT+6Chefz8YBlZTI4YQ5aJSiZ/9/Mi43cw7B7ih6HKzT5s3nHZNOSH21YBSClUoQGFi4m0AEbAxSlKdQECvwLxBMdEZDV653JYDmUNM4/AxTehfz5ioIT/n4/q90iUGqol8CW5OR/G9fPW2flZr4iPNEJ8NyHJiH8/coSPzJUzlqBOKQ+M87WozEdsfhrVfKmr/C1rzD5pY79sEv/7fgNAynmBMk3np64h6UULRSZbVNTa2EroAlJCsDjhEPYUj0C41bg6EgWOYtJghvSoxFak3TE30Fi3T5/Kuq2nxPIZMnQRcGcc1gQVuge/3DQGapKf3BunfWIswwQgovJzBux9U/OIiGF4IPgpfiLJb0DPzDXY3SuaOFZfVTTCjFsK2sepDDOMQz4Bb5GnRPDQltVNMNiSIZhQfehBuR05J6qUeGKaCPtPAK/4tqrbRkW7bsClI/QO2aYHBpgWqbF+58gHFYK3Ps3Phzc+BZwrSIU3EP/CgwrU0CfYrGjaUlglZCOa5Puko1CfFpCi9Z4Dhaw17pAC9jbrdDz2guGO5Sc+JDbRZcVbs3c7qMGAYRKWH6Xu40UZq59URTerpSdyOEbdjyFwOa24zbSWiVrqmUyB1xjH8oLCRSx+XNI8phuoMfnu2ydse95iZHCDeNE7qw9JkuL56OjJRc90TdwmNgR/U10MIA9OT9NS5nFTZ79q11GV0Aq5nFl1/gFQYOpbbGEJ7+hf8AjMjM5kRmcE1J17D3x73t6ztWctvd/6WnYmdbB3dys0bb+a2525j+bzlXHL0JTSEqiuKSiVpjjQz6owStsIYwsAQxhEt0rRk5hLW960H9IqUJz1yXo6rjrmqarupfrjP3R1h8YPbaRz1GG0IsO6CZp49xiZiR8oynv2d79cjyrr7ujpdqbbodS8d/fwLzJ2rI+AVCejKccoVWDf0bzxiencjGiF61lJmH23xnc3fpjYLC3p85nSlmLvboTblIxMJ8olE1X7evj6MulrMunoIBqsmGy8b8845ZAJ/uBOcQ4FyCyi3gMxkyp8Jy0IEgtU2mm8Qkn8kckwOBpW1WCrNIZhQf2Qah45pyc3rFSXLxVR/tT3fvPbJkoNgXCceTZQhVDrKOAkYfBFGdxQlOmltLzn4otbKJ/ZA37Owd3NRsmNAyxJI7YWhbTC8Q78Su8FJQtdjVcPtHNjIyi3fZnlTlJUzGuhsaoXGYyA6Q0fwm4+viMbntXWmn6egPHYZSuv0s6P8OOBjW2HCSiL8AmGvgC11zNMVkBNCS2JQuMpnRf3pcFxR06vU+At0YmwJ624BO8KSnIMPFITQlpCAFIKIlHwwkaIkllDAyAsxMBSGVUxpsBQx4XP+ekHBEPgTdJkCuPPNgmQUAjUCuxZErFG7qXzwGnoWS+0K42p5TF0xzzgZBWkIlG1gGTqaPhElO8JGH2YoLQfSIxXjSbRCkz1XavcTE7P8+eIdkg89qKjL6FWCugy883EwPSgUJUB+wMI1IRHW/ZnF/OCSa8xDSwO4wiAnIB+oFCGNQwqdGOsXJx4laZEy4aqnLGzDZkUiQbsMscqP0awMksKgGYNVjsH86z5Z1sQekhtNEStOXIGvfKJZSV1asbDH590Pu3QvjFn0vY4AACAASURBVEPBrWq33ojx8HnxslNQzstN8lm/7+wgpqdQOQfHzaFyDqanuO9MAR2XwTdOho7LGPmPr6JMA98A29fEXgKJCDy70NI1AaSPL/0qUhw0gyxrW8Y3LvgGX2r/Eue1nIchDMbyY9z54p186P4P8ZX1X+GFkReqZALvPOad5evsSpdUIYXjOrzvuPfR2dPJyvtXsvwXy1l5/8qyq9Ch4KHuhzBE9c+HIQwe6n6o/Hdq7Vq6P/spdndtZqccYHfXZrpv+EfecVcvsaRLJiyIpTwuuaeHBX9O8P7j31/lzDTV+X69Q1gWZm0t9uzZBI46Cqupibr3vRflurrInFL6vaIC6z3b7sYyLEJWCCEEISuEZVjcs+3uwx7Hkpln8JFTP0qwaQYbFxk89fZF+P/+WVq/912slhZkKICckNEuR8dwd+3C3b0bJSXZDRvKk5JXE6UJzogzUjXBeTVcepTnIbNFd53+fgq7d1Po6cEdGMAfGzvs5OjXAiY6o71S37+R227XZL5C3igCAUZuu/2I9vM/EdNJsQeB12RS7G8+rvWGJXs+09aJWLlheMtnYOA5SPfrZMBlX4Dj3lb92Vs+A2d8QCeYdnfCyA5NmuMt4Ka1ht4vMK72VTqkvO9pmH0aZEfhiVt1Um7BAeXqSHu4Qa8IdK3V3vR1c+kc2Mjq57+H44wRFQZJAX+0JG3KoM2M6oj9+34Op76HO7Jd/KkwTEApGn2JBdRLqcfiZnhrOs3iTJJmz0MZJmNCYChJ2jT4VDLPNttgyDSYJRUfyyraXQ8u/QqM9ehjROmJw0nvgnd9b/x8PvhZOsnx/boagkriCU3IlRDM8nxWDSdoz+XIiigWPlkRof+5WrAElihSVyuArSSxjOI3Swy8CYReGgZD9Qbpejh3KIjv12DPXUDzJ65nU3M/P5DPsKdBMWcQ6jM6gp2NQUvIolEZ1GEQRxJIS+490yxXII1KyQJlEgrWgvJRSpEqOQyVMIViSiBYWLuQ0fwon7zbpykJcUevDLg2xBxN1t14qFzcyDAFpie5dwks6IOAp8n7PWfD3e0W/3bC37FtYBNnP6aw/cldl/zoAUKmIBY28dBFp+qzJud+4t9of/pnEIzThsWVMsjVfogGJehQY9wmN5OYGePoIQN7JIU9ezbNn7j+oCM7DZu6Ofqbv6UxqfT5U1CbhdkDHs+dO5tFxiz8wUHs2bOZ/cl/pP6CC9k2uo2h3BCzorP42Gkfq5JsfG/0tzwXGaZtAOrTMFIjuPN8GDrK5d2ZvJ5M58bof6gP09FFp3yz6M0vNLH/3TkBLMNidnQ2Hz/94yxrW4YhDFzpUqqzK4RgRmQG57Wcx7K2ZQTNIHtSe3B8h+5UNw92P8j6/vUEjACtsVaOih/FnNgcupJdjOZHaQo3cfUJV5Pzc3xtw9fI+TliVoyke3iJb1/e8GUMDGzDxjIsLMNCIBgrjPGRUz4CwAs3fJzhsX0UAgLDMPEMRXg0h+lJQrNbyPt5CsLHEiZLs7O47H/fTFtN29Tne+uD+pm35t/ghd/pFcTGBQc93tcihGFgBIOEjzsO++h55F/cij88jNXcTP2Ka8qSlv9+4b8nuRmZwmQ0P8oVC6447P7nxOZw4dwLuWLBFVw490LmxOZg1tQw8LM7GQjmyUYMfENgu5XyQ0BKVDZL+pFHGPvFL8g98wze0CDCNDHr619xP/hvbfoWeT9fnuBYxUT/qRJ6XxVIqW00HQeZTmvbzEwGmXPAc/XvsmG85n3yS8+AAz3vjgSGvvlNjFisOhHZsvAHB2m45gNHtK+DwBsqKXZacvN6xVRa+ZI93/4kB/v7bN0tUL9gXHqT2LP/fis8vTGD2lknn9IhV6W0jCY+WyfhPvNDmHcuHTt/ieu7jJgmLgpbQVxBh+3R7njlolcE43y90IXfWI9AUOP7LHHyLHFyvMnJs6jgElCKk/J5TsrnIQl5IdgSCLDTtmg3G2l3w5odgr67S/ZnF39Bv6rO4W6t87fD4Dl01MewgRopaSwmhuWEoFnC+TmHLFHe23BXefeVNV+hIZ/iqNnjkgeVGMJXA4iK6KUo/kcBmDap9tNo++JtVUPp6PgkNoKxNvj5XAVCcPVdWo5SCVXwmBlRbCro5fhOo0CHkaXXFLQ0HceKE1dw/ZrrMaVf5WZTIoYBI1COrHrKoy/Xx2NHf5PuoY8gha4XZvla6iMFBH1YUDdOnGTfVp6uFdxznskvz6sYFyCUpL3+eNozBi/6UBBam24U/ehBk1jP0ETWjBrElSCuBFJaWMcs1rKZkmxMupAZpNOG1Q012EJbJK4/2mHdXJdVSz97yD82I7fdTtS18EVB10pA37ZBx2fpvihtd3ZUbd8OB+5DwZ8WWDy3UOv/FQrpF5gvGf8+BSIg0zpVxRTl+0FIhS0NFjcv5ra3Vt8PdaE6aoO1pN00iXwCT3rlf2sKN/H+E97Pu499N529nfxmx2/YkdjB9rHtfP3pr3P7c7fz1nlv5dKjL2X1ebqS64b+Ddy97W5eGH4BIQQNoYYyGZdKTiqu9JJQ4/fU+EcKocZ/pLO7u/BCRvl+E8LAlArpu2W5FBQTFQe1xGNKjXtFAbipioW9EVBzwQXUXHABQNEOs1jIKpdlZmQmI84IoYoaHnk/v19by5eLvqhLMAl+wMSJgrQE0bSHkBCZN59Aaytefx/5bdvB83Ceew7nuecY/dGPEeEw4ZNP1gWuTluM3dZ2xB1k+rP9xIr3TglBM1j27X8tQLmuJvnZiXKdQHVBLOu1RcFejRwTu7UVb3AQER6X9hxUEb9pvCReW3fTNA4eU2nl3ZxOAjxUTJwcqP0sGfpuhae3qkheLYy70UhXu9GEaiAzCHVz2Z7uJeWmEMLAUJ52YxFKF8hJ7CtG9B+DeefiV5DQpGnycDTCH6L6i//sW25ny0//mu24nJrPM9d1CSrF4nyexfk8kKGcjBuIgLDGJwtTwSvoV9F6s9c2qfGr3VdCStFrmpgCHmUJnx/7Z2arAfaJGaw54QwWPPcsMpdDhEIoxyHn+3S8uQZXZCh53yiho+EmAum7rNj2FOy7rCpZs1fmqPGL9pcCUIrHlyguedhAFnyEbaBcifKhYUnxQegkaM8M0u7ltQTqnK9Aa3uVvUwl6Vq8Q/L2J/M0jUkG6krJrx5PfeZjzGRcQuMbilIqrDDNquNTBZffvsUotl0N0zBJ/Wg1I+t8fRsU5TiVUEBfHRyV1eReSR/lKVSoflw2c87H4VfXFV2OTDoa6rClIix8UpkBhv0cBb/ADZ03cFP7TYf0A+T29GBIBaaNr3x9foQgIAWBwf3YqR4AGS/DrOgsRpyRcnJog1sgM0GOIiyB6SrwlXZNUsXzZ6v9LmkLIYgH4sQDcbJulkQ+Ua4VABAwA1w490LectRbeGHkBX6787c89v/ZO+84Oao7239vhc7Tk2ckzQglJBAIBRAijoBdC7CJNsZxMTLYa+xdr8NzZNesn73Ws9+zd51xAgvba68DYJIRCBM0SCCiBIikgMKMpAma0LG6K9z3x61OMz2jSPQcPvNpUV1dqau7z/3dc85v91qG88P84eU/cPPmmzltymnMrpvNXdvuIqCrWEwNjf6s8oJEA1F0obMjsYPh3DAxM1YRfzkWptdOZ+vQVjVljoaHhye9isFfXx3UpgROWdCLpwk8Kdme2I7t2pi6SZOoId4+zuxAWQM4deIRyFPsivxWg9A09FgMPRZDeh6XLlnOD9b9P6RrEdJU11/Hc3jn7He9KvvvXBTg/AdyaLaHbQikJshEDVadU8O1H/thcT03lcJ65hmyGzaS3bABe9cuZDZL5rHHyPgpT3p9PeEFCwgtXEhk0cJiI6zDQWukle5UN2k7jeM5GJpB1Iy+4b0W0nGQjgOZDIVfWKHrxXx8rWC8NaulwL110HD1VfR87etKkln8XTlw2eQExsYEoX+z4vRPqSpVHkWy7ewBdVStilGDg3Eiu8oHDYXX6AE/hUaqf5evZwSwpeM3kDVBagjXxpESGwGxKcqE67f71oVWQeoL0IUG4Xrm1s6kP9fH/67XSEuHs7M5LklnmZzP+ek5Euy0+gOwBuCOf1F6/7bF0HyMkg6NRONM2pxu+jQIl52+JQRtjot2/GWc/cojpLNZwl6SJvqZ3/oymbnvZeDxZNH0d+vCIFtnOATSPTieUzQMgsTwJNM8QYdRN6rK2Oa49Pkyn35dwxaCrXMknvD4+BNB7EELsz5Ew6IoNe0oz8PwLt/sK8HT4JaPQLyN5lCebj9eplA1XrjV46p7PRxdkAxJ6lJw1b2SB07waOmhaNxFgu4KPCHRgab3nkt260DJ1HhShr0z+qveGkt3xuhZtRdhjE0IExH4xaemsvIZm4G1PSTTGl2NQVadqZHM3sTyLkHHnGUQa1EzP9Kl2zSIo5MUsNfah9AD6EIvdpY9kGZGBZjt7Tj79qF5HnrhXvU8MLVDqhAVjGTTa6cXl2V7X6A5b0Hv8+rzEG0mWK8pjbulPBxSqBmQkGcwZ5uE/ew6YkaUadSxSOQTZOyyBmFCMLdxLnMb53JV9iru3q7ScYZyQzzc/TAPdz+MqZnUBetURR4PIQVDuSGigSh5N09LpIVBa5Ch3BARI0IsEBuVfV+OT5/4aa5bex1JO6mSkzSd+mB9RffZjctmcs4fNyMA21TSjYx/yYWVRwvoCCvPkNNP+prLGZPSjzcb+RaH0DTOPOZcZDTMymd/ye6BLqYbLbxn2qWvWgOo9PHTWS26OPWJFPGETSJu8ujiGOnjKm9SPRYjevrpRE9Xhlanv79I7rMbNuDu24c7OEjqwQdJPfggAMaUKap6v3Ah4QXz0ePxkbvfL+Y1zuO5/udUBj86tmczaA1y3rTzDvvcX2tI10VmMqNJfoHcB4JoAROxn/jTNxNqli6F674ykXLzKkD8LeatHiwWL14snzhSubtHEsWUm9HxfAe9ncKUthmGvc9QndQLqJ+moh6h9BrXhoQv06mdqnLkvbxab84yzv7taQznUyrTHYHnZ7TXIngwV+8bddMQbeRL04/lrq4HRu35gslL+eaJn1GV/DXfUvswQkri49mw9IuK5D55o/IJeK4y145EIAZTToS2k6B9MTTNUTML29fS+dcvsKKxHlNKQlIqU6zQuHY4S0d8Jgx3QWZAXQdNVwMRTYf3/KZ43c+/+Xw0NHoyPeS9vKrMCx3pOUz24Fo3RofnfznnM1DTCsvvpPO7s7muxqRfr6zsBj3Jf537kxJhLbxXid2+x8GfzhaamlnRA1zWNoWtmqd+IPynr/utS11Kqoz7wrZtqEspOUyhaqz7j0JAoF4w6x9n0vm2L5a6B2phXhjawtvWOlz0OITyYAXgjiWwqMtkfgI0zcXq86reQR4weJ9KQPruk99l6/BWDM2gJdyCoRvYrq2ywf/0CUXihOBqM0Gf8OgRHg6gGaFic6DWaCvN4eZRkpWxkFyzht1fvhZveFjNakgJnodeX8/kFd846B+Vzq5OvvLwV0g7aVzpogNRO8fX+wfpsGx/GsIl2d9I9wNB5cfW9aI5W29oIDh9OtNuWnlQ+7U9m0QuQdpOV0iris+7Ng/vfpg7tt7B5qHNxeWFpHMNDSEEk6KTsD2baxZcw+LWyhhCQzOoCdSMWbW/fsP1xU60ETPCFXOv4OMLP15xbW751b/xdw8naRx02FdvcMsSl5gZ4/x1OeoH8wzWB3igI87gohljv4crL6woOCRfyTHweAo7JTCPPelvkgxIz0Nms0qac4SNmOWpOkE9yFBuiGQ+ScSIcFT8qANKlJFSYnd1kX1akXvrmWcq0mIANTM2a1aR4IeOPw4tFKq+wTL868P/OmaF/htnfuNwTv0NC6FpFd1uC9X8CRw23lIdxSYI/QHgDUvoDwbVIi7Lyf+D34JHfwS5lC+5KXY+Kq2jmfC+35VeVz6gCMT8hlTJysHFy6u5es1n2aFBUsPX0EsCnop0jKPRhsFyJ0hHJgOffoYvrfkSq7avUgRJ6Jw/uYNvLiqbedi+tkpucVmcZmGdB7/hE3tbHRejiQ9Ch5bjVXOsTTfTGQqysi5Ot2HS5tgsH07SkbWU2dfO+iReK10a11YRm6FaqJ/OZdE8W7O96Aik5+D4q4Wlx3fSOh2JfYqI6wFl7MODTz8DKy/kLO8VBrTR+f9TolO45/J7Kt/L315O0eCrGeA5JLtNBl6MsSsXpL9WcLOf7Y6AH/zYIRWi8utLwvRe6K2FluGSWRV/tfZL42xoz7BicjumbhLSQwxYA5x+bzeXr/VP30+rEYBlQOPUdkSiG6tvpMK6hIf+51+4fevt9Gf7FRkVigBMjk5G13RF0Pf0QP9WyCd8DX0te3VNXVcjUFy/0NFw1WWrxtjbaCTXrKH329/B3r4dgMCMGTT/r8+yYaY46LbnnV2dfO6hz5FxMsVrGgG+ndHoGOr35Wg6NM7ipV8rrajM5YpJSyIUQoRCHLNu7dg7GQeu55KyUyTyCdwx+i18+oFPsye9p3SMPgSCGbUz+Ie5/8DJk04ecx+FJjM1gZpi1b7QVbL8vhjMDRIzYhxdf3Tx2o1sJb9lcAtn7AxzxkP91A/kGWwIsPasJp6aBasuW1W99XzGKhYPkt0aPfcnEZpENLYjZQCZz9N6gJF3b9TW9oeby+1Zlq+7TytZx2GiEAu5Y3gHGTdDPBCnLlhXlPt87CAjM6XrktuypUjwc88/j7RH9CMxDELHHVck+ME5s6tGQn7k3o8QMytNlVJKUnaKX5z7i0M+5zcbiiS/jOgL05zoentweEtdrAlCfwB40xP6kRX4gjzHr6CPer7/ZZVPbwRUco0egEAcmmbB8jtL2xxvgFDAygvpzOxmRdjF9DxCrs2ALujXdZoclwbPxRI6tq5zrdZCx/L7q5+D56rjdiz16OZL8ZPVcOs/ljLy0/2QqS4TOTD4KUKuox4LXwGuowY/QlcRnE6Oy2octhkampTFdkweMDOf5+bdvf7rfQOxdKHpGPjEOnh5NSes+2zVXQM8e+Wzlcv/o0Vt3B9cJHd4KhNfk+wMmziuqrzfuExj49E6X/mt41foBUKoOq2Z96hLKZNqbXb0rpuWGHz574L01U0hbIRJ5VPsSe/hp9/OErT9TrE+dE9V9iMzZqJpNtkt1Y3VEvjQv0Voi7XRlewqVooLFfdp8WmKoDeeA2v+r7oAQqczqPPFpjoyukHQCNMUbiIWiJF1sgdVoS9gJIHac8kS/re8o0hQLdcqzRaMQ/jOv/n8YuMVgShGR7ZJwaq8LxOREqwhdjx9Irnt23EHBih2LHZdhK7T9oPvH1aVuUBoEvkEtt+ErWCE3ZXYRdpJEzNjSCSJfKKiqj89Pp0LZ17I2VPPJqgHx91PoWr/mQc+Q7/VX3FfSCQBLUBrtHXMa/f1/3oX5/xxM56hF2U4muPywOWzOfvyz1QMEireg4wF677Hjl9tw8kZaPWTVBQvqD4Ezc37neUYOQg50Pf41UZ5Lne5pvhABykj4VmWSllJpw+7cv+vD//rKEOu5Vg0hBoOqxru5XJYzz/vE/ynyW/ZOur7XEQihOfPLxJ886ipCCFetWN6K0AIUUnyg8EJkj8+3lIXZkJD/7eA/ZnKRj5fM8lPuhEqH36kPv9gUieGdtARqudax2alN0i3IcgIQZPjFJNkwtIFV7IyojHmz6qmq0p4sCzdwMkrWU3h0c6WfhTKu9hmBsbYqFAZ+LlkSXNfDboJWlANIopRnmUwguprwQySFi6TbEcl+ggwJTQAab28PWyp8yqFL9o5y+CREZse46ums6uTlZOn0E2ONttleSrLUS+aCE2imYJGobPTz4y8eL1qQnX7KYKr7lXxo3lDYjoC01VSmfd2jhZYSWDf4w4fftljuHE3685u5r4pAyoHO8+oTrGuf64yn8cbZypYCsi7eZJWEle62NJGQ0MXOrawS41Mtneq98YaBjdPh2PwrTSsqNEwo62E9NAh5yRXa2wiv/Mzjr+ogV3zFEksVKL3l/6yO7UbKElZCu/sblF2RX0/ScPVV9H9yX8BKVXl0fNACLT6OgZuuPGwCP1IA+3qHav5ycafKJNuuAE9p5PIJ4joEeY2zOWY+mPYtG8TLw2+xPbEdn644YfctOkm3maewLmremnaNoiY0or+D+/GOL1UvXc8h0FrkJ3Jnejo7E3txXJVFrkhDGzPHvfaXbzeY0ADx1THnDMlhqeWf/+4Uuv5Ue/BeTfAnGXYv34bWmtt6XODmuWwu7r2e43KW9uP2v7rSOjLc7kBRDiM5y8/lHtCC4WUdKWxES+bLTayOhRy/2olymjBIJFFi4gsWgR8GDeZVAZbv4Jvd3cjMxkyjz5K5tFHAdAbGggvXMB7Z85gJXvI1qpjebVNwm8mSCnVDGAuB0nVyGQUyS/8e4Lkv+UwQej/FrA/U9nI50O1ipWkdoM1NFqfv+57SmqS7i/JR0K11VMnfMNth4jQ0bsPhMb5kxqIe1XSZLK9ADz4Yi8/XbONXYMZptZH+NjSmZx9bJV0BCOg/sqLio5P7GuPUlp6M0RVqQ0AEsJ1EKqD/hdRHwffZFqOIpFn9HOFa+mjTWrsoDTl7QA9qOnRqyc1+xKeLKBBtNmXApVhP9+xnV2dyoxoarieTr+mcZ1p8IOMixfw2Gca2L69KmdA87DqHrdhpsaN56qmVC1D0k+5KbByr7hbz//T/VO1YyGiiTzvuHU33edKNh0dxAoo/X15p1hdghUUTPPNTta2baNOx6M0EOjP9ZctVwkpBkaJoL/wCYg0qWsEXK+n+bWeJ42LSOwkrIc5rum4Q5JMVCNQVsLlnM4Ev5rXXFzvQNqeF4RFRYGRz+gllDos+4PhmjlL0WIx3GRS/ehC8cf1QAjpgSJiRrh96+2EjBABLYCHR12ojrARpj5UX4yyBNg8uJk7t93Jmu41JO0kt9rr+POZcPKsCOe/sJfjv/0j+Nw/VZB6UGR4Z2InuqYXz92WNkFNfRjHunbRvhSifgr7rH3FlJvG+lYifSm6U9Z+W88fTuRdd6qbE7fCGQ/tHiH3OfKt7cdCNcnP5K4utNraivUOdJAyFkbOQNVf9WFiJ5+sZDkHQe5fq8hMvaaG6BlnED1DSSedvj5lrvUJvjs4iDswQOr+B4jdD/8M9NTBy1MEm9s1Whaf8aqZhN/sOCCSHwyiTWjy3/SYIPRvRYyUwwRqFLEYK+KyWgSmbkL7kpLEphx9L0F20I+q1BW5T/X6STcjUJ7GowfAydHm2PTp+og0GY22vMWDL/Zy3e2bMHVBXdikN2lx3e2b+BpUJ/UjYQTV39LPq/26BT9ANXmOzy4dC4ywr3dWevRSBV33ZzTSSn5UDdkh5S8IRFic93gyrBeJrO3/o8mV7DANPt3SSExKjs47ityb9cq3YISK8X+jj7JUDv/uU99lwBooxi26CAZ02NYANWkDR0g06SEQBBzorxWYqMu/YabGhrJePIXkm5GTAsW9aRrRQIw+t5+A53HBI4KnZwjuWAKXr1Uym3IN/d2nGCxeupSapUu5u2Mu7fv89gT+dnUJexoYE0KIkvyh7J68Xk/zEyNXujLSJeNkWNy6+JAqq3YVAkUoQN1ApYn6QNqeV33PhO9HqDIYNpqalOQGVNpNPo+zq4vgnDkHfR7joTvVTTwQRwglA3KlW7WyOrt+Np856TN8eN6Huev7n+Wetn0MRjwea8vwWFuGqUMGb3/w5yxbckIFqUP6sxIjPlaepwZnllP92pnt7WhbX6I+qRpMuYaDVpPAnHUMbTF9v63nDyfy7oydEc75s5L7ZCM6NUmHd/y5m/Dlsw/soh4myiU/8UCcvmwfK9av4JvNMWIJ64jlclebger9+n8gfAmPlLJkqN0PuX/n7Hfx040/wXKs17QabjQ3U7NsGTXLlimD7c6dRYKf3PAUes6mdQhahyQdz7t4q9fwzK830X7a3xFeuIjg8cdNENRxUJXkVxhvg2jBt36E5lsNE4T+rYZqchhrmOIvrxmGVJ+Kc7SGVILE9A54/AY/BtFRJstgHM5bUX0fhfSYgjlUaOB6laky5YOKYFxVK40weIrIrmhUMwIqTUbDFrA8keHHa7Zh6oJIQN2akYDBoNzIl9b9iLpNqbGNbNU0/W//tlqW6aVT5FlZW0O3YZTMrsRUt13PhhM/BM/+CfLlWeQa1EymM6CxMhDHcB2W5mzOy+ZoymdLBF+6kNoLwOW6zoxwhPVBk7XhIHsNAx1IagIPgQQsBH26xorGOq7N5+hIqtceHZvKy6kdoy730XVHA4oQvDz4sr/P0vNSwB9PFXx0te//NQQBW2nobztF4PexHTWkuXi9xNFhKAYNqdLzBUKfiEJqoJdJGTAdaB6SzN/qcOuZBsftcDhhp/oCkcCzR8EfT4ev+K+95+3NXPmbPoJlXCGnwW/PKQ1ORpJh27N5rv859d6WDQR/GcsVz6HAIT08fvLMT5jXNO+gSX26OUZizzayuqcqxOEmmrQ4W+sG2Dq0Fddz0TWdGrOGzy/+/LjbMjSD4zZbXLxe0jwk6asT3H6K4PnZIfj06Fb0ue5uKnxL/r+dZHLUulVxgN6VQpxm2FDt1Q1hkHfztMXa0IWOO6LXRF2wjsvWelxcO5XH2jLcPSvBS405dtU5/KxuD/99z3KWTVvGBTMuYFJ0Ehk3Q1OkieHcMK7r4qHy7T3pkcqncDyHd89+dzGbv4C+Y1sIPb4e3Y/t1B0PBobou6CF5cdfxHVrr2N3aveY78HhRN6NJ/d5LTBS8uN6Ln3ZPv7z2DxX3ePiDgtEOEiTFifsGYecy70/CY8QAhGJoEVUAaeouc9kRhlqF7eeBAuu4dbNt9CT6SFshDGEwU82Xk9rpPWAEm8OF0IIAtOmEZg2jdpLLuGzd7yX5t2SY3YLZne5TNvrYXgQ27WPZMElBwAAIABJREFUoV1/ZOgPf0QEAspgu2ghoQULCR49q6rBdgIlSM9DWhZYVnFZMULTr+C/EZthTaCEiXfmrYZqenlQ0pRwPfS+CPkkRBqVpCHZo8i8Y43Y0DiGU803dvoaYKSfc6j5FZGRg4qC7ODS6wHo+OOVXLtvkJW1cbpNQ+nAEyk6ambw5cEMdeESAcjoz5EK/QkpdY4KNBSrWhXZ42Np+t/+bRUJ2dXJige/gGkNEfc8+gyTFc0tXJuBjmg7nPJxmH66irN8/OdKf29GQAg6ZYYVMQ0z2EDIGub3AZff1NZwrWXQkXch1Q9OgdxLmlyX81NJzvfHBV2GzhOhIOtDIZ4OBuk3dGwhCINqmmTaRd/A2yafxubNqmlXMRcewdsmn0rntlWseOq71d8aCRtmafwcj4vXQ8sQ9NUK7jhF8OwsjUlSY6/w0AG37LXNQ5JUiGKUZV26lHSTCkFa92hO+s2xUAOHq+/1eL7N4/guZYotVOiP74JLH7bhSvX6hRuSBEfwpKAHp21Smn5d6AjEqMjFnz7zUwA//lANyLL4g5wRBWFPegedQ9/Z1ckt8wd53xabeM5Dc/O4WhqiEe69IIzA9U3DggMJDDhus+Xn+6trVpeSXHWv5EZGfp6g90c/RqaqN6+y9+7d/8EfhHdl+fHLWbFeDcgLBlDHc/joCR+lvaadpJ0kmU8WDbQAYkorRv8gZ3THOKM7xta6HKuOGmTtdIs0af685c/ctuU2lkxaQtRQ+fVtNap6ns6n1cyRlNSH6nnX7Hcxt3Eu3cnuioSc/nUPEa/RiVgS3ZG4hiATEiTWPUT0iouQUrU0G+89qPFngQ4W48l9XgsUZk2AopHYkx5PzZBwns5Fj7i0Dud4pWGIlo98lKMP0VNRbQZqPAlPheY+lyt1qbWVzHBx60ksbj2pIs4yZsYYsAb46cafwEEm3hwu0q5FbpJJ92TB/SeZmLZk+h6XWV0O56dmkN+2DZnPF/PwAbRYjND8+YQX+QbbtrYJDfkBQLouMpuFbLaUk28YpQp+geRPDJbeEJgg9G81jKWXt4bg42tHZToTiPgGWFTTpQLymbE7MbbMLUYKFjX0hRQcGN+Eu/xOuPwmOm77Jzr6E5UzAn//VaY+GKE3aRUr9IngapA6AS1UjM+DEUa2/Zh+V25aiRlrJuxLjMIATpaVbc3KbFfAwvfBgvf6OvwMOBYr13wO0xogbITAcwin94EHK7UcHTkPwrWwdAUcdSr0v8zv1/0HM4Z7mJcaIiIl7Y5LeyrDpSkVGbjDMHgqFGJnMMRzQY1dgdLg5YmB52gJNpB00tieg6kZ1BhRnuh9iid6n8KEKuMsWXzYMEvjmVmCgJQgVWZ7xHNZtbuP06c0ktEE5ar2vlpBXVpl0w/F1F/QVjr3cB7qUxQr40LAYI0i8R3PK3JfSLlxhZLfXPRY6ahOetaqOiTseB5uuDSAK10cWVkNLBC5X7/wa0Xo5yyDOcsQN81HUj0G09TNgzI1rty0knqtJIkqnFzWyRAJTGZmXUlDn3Wy+912YZajMCgqPF68fvTRDt5005jbEd4BVIkPomNqR3sH13LtmBGN8UCceCBOxs6QyCewHAv9H96N9+0fI7NAKMjMPZJP7Iqy/JRP8NfmXu7efjf92X7W710PgC50arQgdU4Ow3Wp1zWumXUZi+d/qOJYsk6WrJNF13RifWkyUYNsTWmmRkpJrD/NLzetJB6K02q0Vrz2SJlWzfZ2on191MSnF5d52SxG+zjdpI8gymdN+rP9Kt3JU/K4548O8twsD0Mr9FZ4klMPcT+H4zPQgkG0YBDq65XB3ZfleLkct26+BUMzitKrkBHCcixu3XzLa0row2YYy7EwfPpim4IXpsIrM2J85IIf4A4Pk33mmaJEx9mzBy+VIrNuHZl16wDQGxsJL1pEeOECwgsXYjQ2vmbH/2ZHoeOtlykFSRRIvgiYaAXj7UQl/zXHxBV/q6GaHr5cL1+N8Hs+sbKGId1XIunZwer7KMghglOqd6kd2gHosG9LZeZ6wYQ7Zxlc8qPKpljTO2Dd97hx+BWez9bzR/udPBdZQl70gxehuabkfC0Y5YoGM/cV2gIuyxO76bBRRspgvLi/8srYyG2Mwub7KiQN3aEk8UgL5BLq+mg6IenSrWuQG6Bz9gWs3LuK7m2/oi3SwuLjLub6HXdjZaPMtG0WWzlOtixOtHJEpWSa4zAtlQK/UrvdNNl992eZMvvtpBLdmNJTAwo88FxMEaQ7o8zCcTNGSZFehd5Kiec7MhU9LJB7VVsZqZS9/RTBVavVdnIGBB0l0/nV3ym6+7lbJEKCY8BQFDLBUvMpW0Cg4B8W4Ag1CChAl9XneHQJMTOGJjT6rcooUUMYaGgVXVABJkcmszuze9S2DGEckHG13CB4SbCfSNojh4suwEQNTmwBS9cM8o2p6aJMpDHUuN9tF2Y5ypEz1PKRGNVY52BxkB1TO9o7qhLhasbMJZOXkDjrbIYB9zd/Qu7uKabcNJ5+Mu8BLpt9GY/ueZQ7tt3Bpn2bcKXLkJthGGjQBB+wBIs3/Anis4p9IXK/+G+8/7kVMlmIhBGGQMs7WKZU1XghCNuCVFP04D6nh4DXu+V8+axJ3s2jCQ2JxBRqFCgQ2J592Od8pM5TBALogQB6XR3Sttkq+mgKRiu+SI5E4s3B4pKZl/A/L/0PDg46Oi4uUkoumXkJAHptLbGODmId6t63e3r8DrZPY23YiDs0hLtvH6n77iN1330AmFOnlgj+/Plo0ehrek5vdhRIPhlGd7wNBhFm4C3X8faNiAlC/1ZDuQm1GtmuRvj9xkQkulG535oilZ6jpvlHVunnLKMgh6japTZQA/0vKUOp0EqdZJuOqdxGeYMqX0oQrmnkWC3FZzI/4/8mPfpjzcRqstSESreq5VpEjagymDl54p5LnwYrGuJcO5CkI9EN4VxxxqC8Mla+jVGmvSqShjbHok8YhLNDfhMnDQuDNinobJjCiuGnMWOTiZsx+qxB/rBjFXnXxtE0XggGeCFg8pt4jKgnmWXnOTWbY34uz6JcjrCUTLdt2PoQbH2I3wNbTIMnQiGeDIV4KhRkL0PM1Jupi02izxokgCDPaBTMrIVmT6aEGs9lmu2CZlKt1cyGWRo3oqrJzcOSvlpF8jfMUtXTzW0utWkqussGHKV9Nr0ywi7V/nJmqfrvaSCqFJ1dQbHx0Cfv/ySudIuxlbqm40iHiBmpeM2/nfZvfOGhL5ByStIIgWBKbMp+jasjDYK1fX009tlITZ2HJ0BzJXVZMPdaOFIdiyMddqd3M6tu1pjbBuirE6M78Dpq+Uho0ShuIjHu9sbF/gbrB4CxjJkFM3LdeZcyfPY5pO30KLmLrumc0XYGZ7SdwSvDr3DH/V/iITLkBewTkh+GXB4xBRc9/TMWTjsN+4bf4d34O98RbUDWos51SZvg1kDeANOWCBcG3rmUttjAgX1ODxGvd8v5ilmTZDea0AhogeKkmURiauZhn/OrcZ7CNGloPoqebB9RESKQ9wjkXNysdcQTb/aH9899PwC3bbuNrJ0lbIa5ZOYlxeUjYba2Yp53LvHzzlUG2+3biwQ/++xzyGwWe9cu7F27SNx+O2gawdmzi/Kc0NzjEGUzqRM4MJTLdQoQmlYWnTlhvD3SmGgsdQB40zWWKu/gOpJsV2syZQ1D1k/e0IyiHpxoMzTMrJ50Mx6uPwP6XiwReumpKnHzsUr2U3GMO9T+AzGVTpPqreiu2XnRN6s2g4mYEZV5PdStzLiuQ1ZAs+tyw95etd+zvgRnf3HshjLtb6fjhXtLBsPMgBp8lBGmTjfJioiH6bqENA0LsIFrnQgrdYs+6RBumavO0UqwNbMHISUzXZTZFsgKjWbXZflwki9OnkRaehhSMi+X49SczSlWjnnZDIEqKTovmSZbQmFmzP8g/77vUfbkUwx7o/XZEc8jp2k0uS4NrseArjGoacVEnceDAWTBVQpU1M8LPogyCODErR5XrvYlJX4F33QhkIO67OjI/IEakP80mZW1tZz+222c9mx+1DqPnBBgzn/9kI72Dq7fcD0/feanCCEqKm0fm/8xX0NfQmdXJ9998rtsHd6KoRm0hFswdGO/jYF2XLkcp6+vaBDcOrSVSbsthAS7rJyheeDosG2KVjS33nGqTurE2dx8yc1Vtw2w/Kvz+ejtNpG8n/qjQSYAP7/YZOVXn6lYt/dHP6b/Bz+ouh0PmPfiC2PuB9h/g7gDwNX3XD2KNFdrzuV6Lol8gmQ+OcrnUMRNF5EIxrjXcPiLnqOvLHu/LdbGeXf1cNbTNmFZutCOncPRYNsUnaYhl/46nb+eGWPoxJm875j38aMNPyKgBwgb4aqNpfbbyO5NgsJ3ku3Z7MvuU3UUBA2hBkzNfN2bXVVDte9RN5/ny/M/wym18/Gs0d9LB4JCV9qeTM9rZrQtQDoOuZdeKlXwX3wJRhiDRTBYNNiGFy4kMGsWQtPG2OIEDgZaOIw5adLreQhvKSPFRIX+rYjy6nc1BGLQv1ndyg1HK/nLbZ9Q0YyerSQysRZVaR9jOn9c5BJQO7VSvhOdUspcf3k13PZPaj3PUX9WUqXmCF39eS70vUBHxuLa8GxWdt1LtyZo8yTL4yfwjeRm4k5erecjJCXdhoEvjIaNv4UpJ9IxZ9loPXHdAjoevaHSYDi4FUJNkNytTMJS0iEl18airGxoohuXNmmw3A3R4QX4hpEijoB9W4vGWNc0lNnKK5gNBSHp0W0adGgxTM9BF6qWvilg8lwwwM9ra2jQoyxM9LIwl2euZXFCLkcAOMa2Oca2Ye31/EHCLhHggXiQx0JBngoFSfk/LDrwscFhnggH2RoIMOx3g00KwTNBEylg4VaXi9erbPqKanzB1FzGvg0peXaWzi/xuGi9pGVY0l8LDy+GS/8KKRei+ZL4JxUAqcEKrw9zoI973tNGfbKbY7Y7xXW2zAwx57++T0d7B51dnTzR8wRhI6waREmbaCDKFXOvGEXmoSQfKZeLNIeb95tDP9Ig6ElPkSep/jw/YlJINftQm/JIhqA2Jbn6XsEtwT64ZOxbPaJHEGK4YpkQavlI3HyGYOkPqv+CHNCvyv5mxsZB4bo92fMkQT1IY6iRmmANUF3Woms69aF6aoO1JPNJEvkErjdCtBWfQjzdz7tFmHe6AdZrDneKLM8aku5UNzeeBb87VXD28x7nbxBMHhK4AgI2TO+RBPMQy0nm9OjckelhfvN8Pjr/o9y6+VZ6s720x9q5at5ViswfaCO7NwnKq/WO5zDnpRRvX5dnUqKfyFHTmdks4dBSK181VPVlLF7Omf7nT+mqMyox5wDJ/etttBWGQej44wkdfzz1H/wAXjaL9dxzvqF2ozLY5nJkn36a7NNPA6DV1KgOtj7BN6ZMmTDYTuANgQlC/7eE8gpf87GqwlfokNp8zOjp/HzmoKbziyhIAxqPrtxWjT81+9evQnafIu4FuQ8eeBIK029CgAjAX/4XHYluOhA+0bdh9720TZ5Mn+anxfhCbksI2hxXDSB0U52nbxgcpSdeeWGVNCANMj1lx6TQkbHokGlAQCikjtHO0Ka56hg8FCmWEt1P6fBPAoTA0oO0SQHhOLaXRGoS3X9OAI50ycg8g+E4fwjahGvrMT2Ps7dnOHe7RW1jjnitjdDgKPJcmchzZSKJC7wQMHkyFGRTIMjHh5OQyHDZlFaGTYGQEqGurMqcX00pjSUNV62W3IhkwyyNhVs8Ln6sLHpxiaBvhmBopsGfZgolwZIeWU0jswYakmDrJUV/2IF9JphCI4zk6Gf20JrQ6Gs2kUGTowKTOC6fp3WbpJNSpW9qzdTKSux+qpJj6cLHwkiDoKmbOFoeIcDVlWfA8QMaPAF5U0MAeROwXd62dnzd+8WPSXJhnaF4pSb84sdGz3z++oVfc8Cih7HiKfc3WK+C8spqQAuQ9/LszahUnZpgzbgSD01o1AZriQfipO00iXyCfKHJ2qIPwZpvgQ26EeL0nMPpHmw/+SruzHXzwOZ7yAbh7kWSuxdJFm6TnPe4YOE2SchSzcxClsfb/jqAqbfCubC4dTGLWxcX9x8yQqTvu4qIZiIOwAz8ZkLhXk6uWUPPz5UsTEwKIRMWPV/7Ovi58W8kLNwmue63LnaXg9nu0nB1aeAhDAM9HkePxxW596MwxyP3bxSjbQFaOEzk5JOJnKwaqblDQ2Q3biwZbHt68JJJ0mvXkl6rZpv15mbCCxcSWbSQ0IIFGA3jNNuYwAReRUwQ+r8ljJeSMb0DHv5PRWT1oOqgqpsl7f3BYH86/v7NgFbKsdcMv6Iti8QYJMQmwdB29bzu36qeIkrLhwZZ0diAqoBLLAG2ECwf9jP3Yy3jGgarGgw1f8AwshIpXXUtQnUQaShWR5fnelihWyD8Y9A0ajyJFC5ZTRDypJLoCFjuhkE6BIAsKhteo8y86uZZPv1drHjlTyA9pCZoXR9gazpA4w6I6xCszWPVWsSbctTGbQxgXt5mXt4GfH15pIELU0meCAZ4JhjA0hRBHT+NxeOau7yibKQ2LbnmLskNF0j2zFDHFxIaFhJbaAhROGplKC0qLQSEAITG6Y/mcY0ATkDH8xy0sizslR/QK/K4qyYXHSGMNAg2iRoSgTSmp7L1kUrrr3uQDMPkAa9I8ociUD9QpVlaGdqTJl1maXJDoq5te3K0LjRjZ7B130w8Ak556ttBxFMeCMrzz5sjzXSnunGkQ1eqi2A2SNSI7jdvXwhBLBAjFoiVknGmnwF8EZ7+lYp6jU+BRR9i+vQz+GfgvbcPc3/fWu45SaOvTrBhpmDDTJ3JA5JlT3qc/awkklMzJGc9NFB1v5ZjYSV2oQfjRPCoQRBAjP/ZfpNhf7nxbxRUa1g11sBDGAZ6bS16be245L4n00PMjFUsez2MtmNBr6sjdtZZxM46CwB7zx7VvXajquB7iQRuXx+p1atJrV4NgDl9GuEFC1UF/4QTinn/E5jAq40JQv+3hLFSMvpeUtGVoQYVRelYSsteMwX+8llYd5Ca1f1JAyq03CiyXpCoSM/X3aOMtMiSLEeIYjOnjqzFtUMpVtZE6Nb1UrOorKUGAKleCOWUB6AaCrMI0inp9l1bzQLIkYTeK3XC/cS64uKO757AxVqQXxt5MgGDiOdxRTLLvHyelfUNdGsOba7HcjtMR84BL88sRxLZ5XH2eknTsKS/VvDgKYLMUdAx/wquHdrOyt5H6dYFTcOSqAk1QlcS/cEQLw0GCL8EX/iYYGEux5JsjiWWxXF5Gx0g3cuHgQ+jtP7PBQM8Hgox2zDZHQjgC+kBP41lWPKBByRxSxlWXZ+gxy24/EFJbFqclXJI9QsINLE84+DlB0iEoTajzLCegOGISrmxgLDnUD8syYZySEdg6iqhqJCF3Z1SaTbb09sPKlHmUDDSIBhvn8Yfp+3lvCdVwk3xLZZQm1VmXk+A7kJzAnr205zYndREYNcQVqA0WxHIgzu1adS6ETNCJpjDyJby/kHtLxMsW/Eg4ikPBCPTYwozSNL/72DlAhEzQsSMkHNzDM9eRsZPtBmJ+N4EF2+OceGTaZ6c6bFqscaz0wV7GgS/Wqbzh6WSpc9JznvCo328gVN8Cm66n6QZJokkICFmW8TqpvJWUDLbXV1kZR63e7vfPVdTsYpduf2/+DBQLe1ovAH1oQ48RpH7MllOa6SVAWugogtxzs295kbbA4U5eTLm5MnE3/F2pOeRf+WVkv7+2eeQuRz29h3Y23eQuO020HWCx8whvFAl6ISOPXbCBDqBVw0ThP5vCWOlZLg50Oqgpg5oUSbV4V2Q7oWmOeNXCA9GGlBY1/N8kq4rMi8lqmKvKyNuqnfEgRey1itlDB2pBB2pZOn5AoShJCKpHjjxyurX4vRPKR1/dp/at/CV3iPJfDncyh/Yzrpmbnd7aXJdQp6HJQS3R0PMsz1uyIZU9n+sRXkH/EHNR3/979j3DuIaYIegMS15z70S89x66N9Kx44n6RARMALsCPfhZP1j82czwo6kt1aQ1TTWhcM8Eg6jAfMsi98MezB5AVv2PMaMbAoTWJTLsyiXhyUw34PBRIB9QwH2DQXJ7AvQV6sxu1viihLXl6josSmDsGDhP9Lx9E2wb7eaJlh0FfcEvsmUpDKAOoUBQBa6G8H2HPBcBmsF9YMQzkmCrkVm38skg9DbqDFgBcg7eXRdP6hEmUPFyEZEJ5x3HJmwZLDIcQVTeyWmp4h8AVKoaMzxcPspGufsFBiugW0KTFuiSZfbT9FYMGLdK+ZeQXfzD3D2SaJ5NUNgG5AOqOtSxEHGU+4PI/PPNU0jIAMYmsH02umHnPUe1IO0RFqwXZvh/PCoZBy5uwcmt6ILwRJgydOwY/Vm7jlJY808gRUU3HuS4N6TNOZv87hk72Msbl2MJkbQ9DJpD0aIvGMxgM3gog8SzfYTM2MVhPDNhkwQxPY+NE3gaQLNlbC3j8z0I5PuUw1jph2N06TtYBtWVcNIWc6lJ1/JD9d9GxyLoB4k5+ZwPId3zn7XYZ3fawGhaQRnzSI4axZ1l70LadtYL75YrODnXnwJXJfc8y+Qe/4Fhn77W2WwPWFesYIfmDFjwmA7gSOG1+VOEkLUCSH+JIR4UQjxghDiNCFEgxBitRBis/9YX7b+l4UQW4QQLwkhzitbfpIQ4ln/ue8Lv9QkhAgKIX7vL18vhJhe9por/X1sFkKMwfbeojj9U+Dl6XSTXG0mOD8wwNVhi85QUBGGAtJ9pUq1EGoAUNCjl6MgDUj2VEoDXl49et/l69a2+1V4B5y8+ne0CZZ+HvIpPxXHq9CxK1SrJI4g85oJeCoxJ9IC2zurX4s5y9Q+NdOP1TMZ9+MgvVInXB8rQxqm5xD2m8OEpcSUHivjYeUXuOTHKtXn08+opKA5y5j8YhN1GkhD4AmBNAR1Gkx+sQke+T7oIQjVgBGk4aQw0gUv7yA9iZdXcpA7TyldhwL53hgKcVlTlM58L3su+k/OntrGf7oNvLw7SjJhIKUaEzTW5ZkzPcVpC/dx9tl7OP60fibNTRJrzCHKUkqEROn813wL0vsgWKse13yL+vLrUHb5G1yPa4fSNEuNF6ZBPA1BVxFjL28TS9rsmBnDcW1cXDzPRXrqHhCeCwPbqt87RxCdXZ20DElyFTxdolcJ+xISmpPjb2/tURn+cmkbyRqDcNYlWWPwl0vbWHtUZtS6H1/4ce4/I45rqESgnc3q0TPgib8rI29109RAuxwHGU9ZjuXHL8d2bbJOVunfparON4ZUM53DzT03dZOmcBNtsTZqg7VFQi6mtIJVOQie3A8fucfjRz9yueI+l5ZBdeGfmanx9Ue/zjX3XcOft/yZVL6se+v0M2DpF9XnNZfwvyu+iJx+Oql8ir3pvXSnuhnODY827r4JMFBIGCvIDf1BUXE56r69+p6rOf/m87n6nqvp7Brje+0AUS7DKjTsKzRpGwtmeztyhGQmnRpgSyR5SMclDIMzjz2Pfzr3OrT2KewJZqmJN/Gx17jz7JGCME3CJ5xAw4euoO0732H6H/5A61f/nfill2JOnwagDLZPPMnADTfQ/c+fZMf7P0DP//k/JP5yN/aePa/zGUzgzY7XJbZSCHET0Cml/IUQIgBEgGuBASnlN4UQXwLqpZRfFEIcB/wOWAJMAe4D5kgpXSHEY8CngEeBvwDfl1LeLYT4BDBfSnmNEOJ9wDullO8VQjQATwCLUVTkSeAkKeUYHZQU3rCxlWNVx8daDnT+5V9Ysec+TM8jhMCKNGDbaa7NaHToKvWC3ud9/YBQRLeQVGOE4QtbSvu+5WqVjGOEVGU9VIvd8zK6l0EALjp90y5k8od/NbpDbS4BiT2AB+1LlIZ/w3/7TakOBSObLQl1zME4XHp99evx3RPUQEQINSsx+Aqd4RAra2voNsxKGY8egKmnVER4nr9yIZprs08DG4GJpNFTEopVXXuUFl/T4fh3w2U/A2DL37+N7W4PVlkyfAiD6V6co5dt5kv1UVZFI7hCoEu46kWXSx7NY2cCmPUhvrXEYu3ROpJRQxl0CfWex9dkPb/aPsAHV6sKcM6AmPRojeUIT87TVJsjFh+dTO85gky/Sbo3SKonyHbd5N8+bJL1A3CiEq6w4JRfOogkBMsOwNEg0tpK/4k9DD0jmLxboqEurSeVRjwdhJ4Gjf/4oOlXciULtjhctB5ahiV9tYL2BYJTP/rdUbM7BysPGOszcPWt7+Si618imq7Mjp+xd4z0GSGY+8LzY+7m6nuu5qWBl0jaKt5RExo1Zg3HNBxTEQNZwKO3/JjAV35AqIx37miG2O9+VjqfIxBPORKF67ehdwOa0GgONxdTbqrFVh4OPOmRzCfZ9+B9pL76TWQqjXAlUhc4ukA4HgFXyY4cDdbPEfzugih9gRJZDOkhzjnqHC6ccSFHxSsHMk/0PMEtZTGH75r9rqKRtkBOY2ZsVD+DNyoePm0eoYxLOFf6FssGwYronPnIc2PH7h5GtOX5N59PPBCvkFtJKUnkE6y6bFXV15Rr6EUoRDo1wFCyn9suaWbn8U1H5LhA5ZcXZTnZbNV1Nv7hWyRWrSGSlGRqBPHzl7LgPV885H2+FnAGBrA2bixKdJzevlHrGK2tSnu/YCHhhQvQ6+pehyN97TARW3lk8ZoTeiFEHNgIzJRlOxdCvAScLaXcI4SYDDwopTxGCPFlACnl//HXuwf4KrAdeEBKeay//P3+6z9WWEdK+YgQwgD2As3A+wrr+K/5qb+f3413zG9IQj/Wj/6CD6i4xmpkALh6zWfp0yAsdL8SLsmG4jRnBrmhZ0BVxaWrqkSaHyEpRImcvuc3av93fw6Gu5S8xaeXttQxPPUFXOaTZO+0S5g8/HSJPBcgpZKlvOM7cMtH1L/HgtDUn+dQSd79RljSHYOM51WmcJuqAAAgAElEQVRFL1Q3+nqs+15pkNH3Ip0mrGisx5TSN9oKbCG4dt8gHTkb3vf7CkJ12S+OZ6su0AENiYfABWbZNjfv3lt5/Ce8Fy77GXe+4ySCw5nKRkQ25CKChy+xuCs2moRc4Oh88yMbATj9lyeQxcMTgpHp4AE/1WaaI3n/HyW1aT+tpbCfPAzG4Gsf0Kn3PNXFNmvx9wMWLeZogp/zBI9HgjweDvF4KMhLgQB5ATd8xyVWRfLsmBrDUQ9Xg8Yh/x0S0B/3NeISYhZ88hMGOjrzt9hctdrD1lWToYADIQfMt8U49fO3qFkWPUBn75OseOL/YeqBAyM04xDi89d9nplbbC6/z6vI12/bV7ybRmHuOPnwX1rzJe565a5Ryy+YcQHfXPrNimXJNWvY8YmPozmjc93tBXNY8PvbKs/hEOIp94dXgxyOhUdu/jFixQ8J5UH3JK4myJqSu0+CE3YKWoagtw7uOEVj7gUf5My2M7lz253cv/N+LLdE7hc0L+DCmRdy8qSTebr3aX6y8SeYmklAD5B389iezTULrqlIxwEVvVlj1hANRDG1N65u+ZHTF1A7MLpnw3BDgNPWbTzg3gEHg0PdZnnX5S2RJPedEWXXvOYjdlwjUY3cb/zDt7D/+BCeDq4BugOaC+blZ73hSX0BUkqcPXuK6TnZjRvxkqOnAwMzZqjutQsXETphXtG/8FbBBKE/sng9NPQzgT7gl0KIBagq+aeAVinlHgCf1BfsaG2oCnwBXf4y2//3yOWF1+zyt+UIFRTdWL68ymveXBjLOPfoj8CIQr6/VFkPxItymW4d4gVpiS97CWUG6BYojbhEEXdpU9RpSM8P124syW60gKrMu7Zax/OKZN7fOKCm9pt33AnTTx+7y+W674GVYHSVvQyyrDWpZviDDp8Y+WS+QMbjnkufrrOisZ5r9w3RkUuqBI7y67Tue5VpPE6OlU1NmFIS9seZhceVtTV0WJHRhEp6CKGDlP6hFQyGcvTAZdOf4LKf8fvFFlfdqxYXyKThwq9OFTwbjZRdOf+lwCrDo0ANTemS0bRRZB6UYMhBsN2A1iFJIlx6JwrpK5OHQEMwpOmsjkZYHY1g3OsyNeXQGs8Tb8oRbckTqnEIapIzLYsz/Wn2lBA8FQpy1KwAmZ4A1pBZYbI1bA9X13ENF1dXenTNhdZByAVUhX5vg1rfxeWi9T6Z97mWNAWulKQfSykPhKPkGiuf/QWmhDA6eA5hzQTPY+VzN1YnoeOYStscl7VHayRFeYdcwZR9svo3+34Mo2u616Ch4ZW9Ixoaa7rXjFp34IYbq5J5AGPjy5ULDiGe8kBQNUt8f7MdY2B/sybDK28ibmjgSPBAagJHl8zfqfGtKyI4noOhGUTNKE7/s7z/2Pfz8QUf50PHfYi/7vwrd267kz3pPWzs28jGvo20RFqKXYWDhnIRB40gOHDL5ltGEXrXcxnKDTGUGyJkhKgJ1BAxIm+4zPD4aHVWxfKRpmY4fJnU8uOXs2L9iuK2CgO75ccvH/d15X6Ufy5U+Q/zuMoHCSO72gpdR6+pQa+pKRpqh+9ZQ0gH1//eKDwmVq2BNwmhF0JgTpmCOWUK8Xe8Qxlst20rEnxr0yZkLkf+lVfIv/IKw7f+GXSd0LHHFiv4wWOPQRgTNsgJlPB63A0GcCLwSSnleiHE94AvjbP+WMLp8QTVh/Kayp0K8Y/APwIcddShaVdfVYxlnMslff2tX7l2bcj2Q68NgTBtcYM+4an8ds8F18YSqPz2QlW+tl1VBqVvEh3VaEqqfcdaVJXe8yi/tCOpkY47fpTlXz6LCnAc64fWp6Sa7q/mD0gKHW1RpLs6GY/R0Tui8l8wGJan8ex7mW7DJD5Cg6uaVZnqeF9eXUGy0prGJNtlQNewNYHpSRocm7SmMwqeCy+vZsNMjRuXVZLJ209BNXiqeiuqqn8BTY7LUKC61t8DdNTgIlEriKQ9sgFRvPFDNgzUwRQPBoQk4xOb5iFJf0inPxuGXWHYBUHTpS2a4/kzbZZYOY5yHGJSsjRrwUJF8N28INMXIN0TJN0bJDdk4AR1BBo506bGLp2R4UCdA6sXyuI72jKsMvEFYKAkRo4BsUTleXVneombMUqxphASOt2JnaqplxFUUatGQD0ObofwiCxo/z1fXtfMY/SxYZbGxjIP7vevd2gp9Icq65xrTJlS9VoXkM6n8fDKeg+Ah0c6Pzq/fjzz4GtJMQ82y78aDsRUWb87RSSrIlg9DXRXzRoZrkdbTamOIqWsiCmMmlEunnUxF868kKd6nuKObXfwVO9T9GaUWV4gqAnUEA/GCepBAnpgvzGHlmNhORa60IkGotSYNZj6G6Nqr9ku0tDBdRHSHyPrOpqtPvnlpuYCxusdcCA4EgO7I3FcBx2FGY8TTkusoCpgFBrEuQZEkq+9fPhIQWgawaOPJnj00dS9+93IvI314guqodWGjeRefhlcF2vTJqxNmxj8zX8jwmFC8+aVOthOmzZhsP0bx+tB6LuALinlev///4Qi9D1CiMllkpvesvWnlr2+HdjtL2+vsrz8NV2+5KYWGPCXnz3iNQ9WO0gp5c+An4GS3Bz0Wb7aGCuxpiCPKZAfIQChyHPdsSzP7GZFGEAS8uyy/PYUxYjIwVeU1Aag5bjS9ssbTSV7lD69tl2l0jhjNw9x0THGi7JcNw2GuhizOo9QxxFpUMbJdH+ZYVaR/XHJuDbiNi83GBYqoT8+nTanlz5dLw4GAL9ZlaMkOyNiA9v0MH1YTHddVYUUgizQ7FTRo2h6cXZjwyyNDRWBLmp/ulSxkSMxcngwFvlzEdR7Lg0ePHCqzjvvleh5iW2A7giCLqw9WceUMF2YbNU0cl6evjpBXapSU05GZ5MW5WvNOkJCq2OzxMqz2LK4YK9FIOaiByQ1bTlq2lQl3c0JmodDDCTDyLxBPquj+xX8QprLCTsFtwKzY9PINnQTHbaKFTaQGA6kGioTS9oiLfRZg4TLkkwsN0dbpEXd47al/gqItUB6QH02Cve/bUHdUXSc/imMtZ/FEaW7TQd+eZ7BP9/uEMuJ4udGq6lh0r9fN8bV9t+LER12K5aPgNneTr77yEdzvh4oN1VC9V4CpiegPD1J+IM3F0zNxJMernTJu/mqMYWa0Fg8aTGLJy2mK9nFXa/cxV9e+Que9EjkEyTyCUJGiKgRZXJ08gEdtytdErkEiVziDVO116JRvFQKITR/1k8DT6LFVD77oVbT94fDHdgdieM6lCjMVK1GKOXh+sUKpMTIQTr+xpp5ORyIgKm60c6fD1eCl06TffbZYgXf3rkTmc2Sffxxso8/DqjM/NCCBUWCb7a+MaM/J/Dq4TUfzkkp9wK7hBDH+Iv+HngeuB0opM5cCRQEpbcD7/OTa2YAs4HHfHlOUghxqp9u86ERryls693A/b5e/x7gXCFEvZ+ic66/7M0HP7GGfEYRkHxG/b8eLGngoVRl///snXmcXFWZ97/nbrV3V6/ppDvpkIV9CbIKdMCVsASUZZTRQEsERcfXddTJjLyKTkZHx1dHBWFEo3FEVBDZFZElgAGDJpAQyEa2Tnpfaq+7nfePU1Vd1Vs6IZAQ88unPt25fevec5e69TvP83t+T+E9bY7HkqxOg9RICEGD5ymN+JjFRz4kuyu3f9YnK/dtxZRffagWx1RpYYEse0FP68Vqc0e+S703PkNlGJ75rop6n/VJCFYxPqH3oX8T7H5BTR58V2UNNKug89dodh1yI76Uc0LQ7EsIxEafp2KTqw2PqILddA/tQ0n6dJ31lsVLlsV6y6JP12lPO8NR/Q2PwM1nwdcaae/eRQKfLYbORstki2GQ0HXah5KjHCs47goY3MbcsRUXzHVhQV79UZa9QLKgftjxoc8wxk01zXYcbuod5FMZwbpZOr99l0YyIgjmBYmoQLwtxHnNBo5QdRONgVo0NO49Q2B4SsuPVD8ND+4tuOlIAZ2myQOxGDc21HH3milsvK+RjmfjDL4awkmrKYcekDQ0Zjlqdj9Hv7ObYy/tovmcfowT0yRbHBIR1YnWEDqfOmYR8Q9cRigvqOuDxm5JXR+E8oL4Byot69pnvYeEk2JLcgcbE9vYktxBwknRPus9Y5/Mk68p3J9p8FyVtXIzcML7uGXgb7gjnnqegL/NFmz+5CWETz8ds7mZ8OmnM+1b39xjY58ikZVl/8qXl6N28bXj3uETt686+NCR6sDxHLYObWXjwEa2Dm3F8ZwKuYUVKJA0X30OhC9BqA69eS+PLnQ838OTHpfPvXzC/bXEWvjIiR/h86d+npgZK9mJ5twcfbk+diR38JsNvyFhJybcTjlybo6eTA87kzvpz/XjeAfmKoTPPbdg5Vt4OBR+DxeaGbW1tLHkjCU0hBpI2AkaQg2vS83D3mJ/jMvZuRMRrJzA78kKM37FBege6Lbqzqw7qsA69v4LsaZPx6itRQsExn3/mxFaJELkzDOp/+hHmX7rD5nx8+U0/PPniL7rnej1queFNzhI+okn6P3Od9nR/iG2X7uYnv/+HqkVK/CGhvawh8M4FHCgBFifAP634HCzBdUHRwN+JYRYDGwHrgSQUq4TQvwKRfpd4ONSlszCbwCWASHgocIL4HZguRBiEyoy//7CtvqFEF8F/lJY7yYp5dgtCg92jBfxvntxoe1keYReG37Prn+kbeUPaOtJFYi+NtyFtRyaDlYc3DTkzDEK88r2bUUBgRWK4TuJEtmUgKcFmXr2IrVgvA6YF3wLLvsRPPjZ8V1u3HyBIPuFCYtQGv6C7Kd9KMnSOiVBGi5o1Wg/8QaoOWbszED5eKwIa/0cyREpy6SmsTYao83OqonB7z4G2f5CBkMJLVwkHirT4Wkma4MR5YxThBGGE66E5C4+ndnFP4WdCg28huDTJ1wHq5fzJz9NtmwMIQkXNbwFUDKHQU3DRznImFIWdPNKYnRXJgBnfAq2rmBJahvL5oT43vERmgO1tA8NceZgL4RnsOSYd7NscDUdyZ3MqZ7Ji7O38uN3u0oGNCjpiQvuPUMUZEDDsHQTQxh8/YMZvvhzOOFVg6FXw0gkr8712H5pNVcOGsT6NxC0PIygT7wlR5wcRwH5vEZ3MsCidA1tZh3J6jlsM8J4dgY0VX8QMcK0xueOuvwCFY2TUhYivRNE40odTH+qnJSqpiqS3/pWlv/+gxhCx0dW6N4NdN7//i/CBwJKwjOWbGoMTItMY+PgxhKRL451WmS0VGf1LEGVUM24RkJOMrSy124/rxMiRoQtQ1vQhIaGhuu7dKY7mVU93MgtdtRxJDe/gpdMlBomGbEqQi1TaAjVVRzDWdPOIuWkSOQTeBP0gji7+WwCRoC7NtzFjtQOfN8n6SQZsof46Us/5Y6X7+Dc6eeycNZCjqg+YlLHMjJqHzWjRMzIGxa1dzZsqJB5ASCEWl7A/pBJvR54reMyW1pwe3oQZcWeMpfDbGkZ9z1nXv8tVgLp3zxEdMgnVa0Rv+ICzrxeGT+Umlg5znCH2vzr26TrjYZRV0fs7W8n9va3I6XE6ehQ0fvVq8mteQE/lcLdvZvk7t0kH1K0yJo9m9A85X8fPO44tBETqcN48+OA2Fa+2XBQutyMh/+crSwhEWVfElL5iL/nlkr3j571qnBWGCBHuJxEmyA6RTnPfOqFifdZtKRM7qookkU3VfQ+NkXZPZZbV+YTw1IdKwKX3a5I9lcblWRo5HhAZR88W3nHGxbUzVHLu9aBlKyIxlgWsVRXU6nTnoO2f3pxz+O2wtC3ibOaqskKUTHLdYEQgmcSOpgRJfmRPmgai+ur2abr9BsaAoHQA3heHg34jhOlzS/4tdsZdQ7O+uTYLkPRRhriM2H3Gnr8vPpbAVnp0aAFaH/n/2Pps0vZldqJr/LMABgq6ElMwhNJvdLacAL70nJ8aul8zn6idxSZN4VBU6ievvwgtu8QEgZTXZdXcXGEGJUmODLayl3nfgekJPXEg+y+8/8Rq8sRr8kTDIxOTbhugOxghHymjmwyjmsH8XN5jLoaWv97aWm9xSu/NEpyk3VzNARruP3Mr457eVd0P8+yLffQkemmOdxI+6z38IlVSzHQK7Sm0vdx8Vh94V3Db9b0ksuOsmwt/D6C5F1+7+VsHtyMruml4ljP95gdn81dlwxvr6g5/9a/bh23kOe4Cdx0yrfxRjjU7AmTOe6RNocyl0PaNlPG0EcXIaVUxN5OTDpiviu1iwe2PMAft/+RjDtcYXpc3XEsnLWQM6eeiT7JCVoRutAJm2FiVgxLt/b8hteAl088CSklQh8eo/Q8hBAc/cKa13XfBxr7co/sC0rkPp3Gt+39tt2DEdLzyG/aTG5NWYGtM+KzZBjDBbYnn0zgyCMr7r83CoddbvYvDpdIH2poPAZ6N4OdqHS5qZ892v2jugUGtjGqO6pmqii0pkPtrNH7GIliga5nD2vvhVD/L5eq7HyuQNYLjaMQSt9up9VEY9c/qveMJ0wokqnitkvjVbdxW7SVNlAsvEiiJ0LPK2rfvgOeQ0arwRgxwdWBjEDZga7+ucoUCMCTdOg1JDWBkBJNqAmUgZJPLPMHaOvuU+c/3FAqwu34ay353ADb8PAFaEJQK33sgU3gpanyyoiv0AhqBh1+vqRZDhph8m4WKSU+yvO+wXVpdT1IDMIf/+/o7EMhG7Likc+wbN0cOvxsKTI6b4vkPff24uiSVBDiKcm1f5D8+N2werZLZ7YXUfDEz/g5+qTA0Yqi6Eo3n978YOn6/O3YJn50bIyLV4Zp+JtPrsmj8ySbqTGbM3MO1Z6DYeSJ1eeJoZJkrm2RTcbJ9HZCYhf0vwp/+ykdejdVwoBwPQSUrjioB+jIjOwoPIwV3c+zdN3/YAqDKjNKT26g8H8T27dVkqfsPgvpAVY+fAeDd9xLtC9Lqi5E/KpLOHPBVWXXQ1SSeyNA2k5x9tYQ5z09VJoQPX52NZuPqyyKLV6/ouPQSCejyXyrTEa3/kYh7aSZFplGX64Px3cwNZMpkSmkneHjjs2fDzd+aZSDyc+r1rH8ji+ScTKEzTCLjlnEDfNuAFTtQcyKEbNiZJwMCTtBboL6HIBp0Wlcd+J1fOCYD/DYjse4f8v97EztZF3fOtb1raM+VM+FR1zIu1vfTXWgesJtFeFJj6SdJGknCegBYlbs9Y3aj9zuQebE83phvHtkT2R+ImecsSBMEz0eR4/HkbY9bIV5CJJ7oesEjzqS4FFHEv+Hf8DP58m/tJ5sgeDnN20C1yW3di25tWsZWP5zRCikNPvzTiJ08smYM2YcdG5Qh7FnHCb0hxqKbjKBaWO7yZQ74wSqlARlYCtKllNwktF0RbwzfXDxd8fZURmKBbq6pSL0QlNkT7fU/q2oGpOUI7q/SkWmjSA4Njz1bcbX0aPIdNGyUjMLBZFZdRwU9PEjj3k8bHhEZQl8rzAhcAj7/qgIvYcgLIXqOGvFUP576m/NrkNXMIguhymZD1jSp8MocxlK7IB6VTIijSC9ZR1ZfSS9+V6medDieGwzdZKaVmpUFfNdWgmUrOvqgnV0ZjoRro1A4iIwgfahwgSuZ/1wZL5s8rYiaLBUB3NoK1V1c0uuJF/6hYujFwtiRcFCUnLJs5LVs0EIDU1o+NIhIAVVCPrKzaTK2GimzDt82ZZ7WDNL58XZBp70CuQ5iKVZ1FvVPDzvC/R/90YsvYtQdRLdcDEsm1hdN7E64GcL1eTQDHFlNMjTAYu03wk0QSA6XBQ7DpZtuQdTGKWofsgIgpsjrAfJ+aPT78duyGM/8kuCukYurBMcyuH84E5WwjCpl7LCThNg3gabBQ8O4uiQDgriKbjywUEeLjRtKqJ4/XKmchsqhwCccZyLxtpGOV6rfeG+ouhwMrN6ZmlZ0YO8HOU2hwC3rL6FW1+4FSEEhjDIullufeFWgBKpLyJshgmbYfJenkQ+UTFZGAthM8xFsy7iwiMuZHXPau7fcj9/6fwLvdlefvbSz5Qcp+VcLp51MbPjsyfcVjnyXp58Nk9/rp+wGSZqRgka+0+uYM6cib1lS6FvQyGr6vuYsyYRTDkEMPIe2RP2xhlnLAjLQresErn30mn8dAbpHHrkHkALBAqR+HnQDl4ySe6FF0sE39m5E5nNknn2WTLPKq8SvaamJM8JzZuH0dAw8U4O46DAYUJ/qGFPbjIjnXGK8pX6oxXBTfcUZC3K85sHP6PeN1Fjm+IkIlitZDRewbfeqlLE2igUsIpxSIuUKqMwqmnUqBUVAQ/EID5dyYHiM+CEK2D9vdC7Ub29dg6cv3RiH+9nvgvBWmXpKX0QGouGktxaU40rJTrgoaT6i5wApLer/Qq9kNFQzkBrAgE8wNANfHwkUOX7NDte5bEUoh0DedWUuFwDLqVkQMB78g7PBy2ElOhIbKBX17nc0aBAoIrdPfsS27CFIOz7yms/l6dEsIsyG6FD3ybwbJY11mFqJiHPhUI3zYyTwdm5C7vETRTttg1lZVkcZ/G46tEICkZcn2FG75bJpDoy3Viage07ZaWiKFed/CArvEHmXfhpOr99K8LUCcRdgsE+grEhIlNshJdV59lOsbg/xWJgp2GwKtjP8+EoL0draT/2Q+Ne3mGry2EE9QAdXjc6Ol6ZGaiOxkXPOuQ10AK6SsAEdMh7pO+4F8qj9CNwzlODBS99gUBgFwTy5zzRCx/dWZDsmDSHm+jJ9dFdozGje7T8KDdlzx0hXw/7wn3FvjqcLF+/vETmAQwMXFyWr1/ODfNuGLdGoCHcQNyPk7STpOwUvhx9DosQQnBy48mc3HgynelOHnj1AR7Z9ghpJ80ft/+RP27/I8fUHsPCWQt567S3Yox0wRoHvvRJ2SlSdgpTN0ta+8m+fzw0fu6z7PqXJch0WkltdB0Ri9H4uc++pu0eqtgXZ5zxICwLw7KgpubvgtwD6LEYkbPPInL2WQC4Pb1Kf18g+F5/P97AAKnHHiP12GMAmM3Nw/r7E09Ej8Um2sVhHCAcJvSHInb9FTpfgHxKkd5dfx12mBnhBZ/NZemS06CzF98M0xCdSYw0DO1QZL+8eJVxWs+XTyI8RzWo0iwl8ynPDEhfRcP9Efp4z1bLNAOll9kDQnG44Wl4/Bvw9HdU9BwNIg1qUrGHSB4A3euVft93KTaoumFIOWQsr46R0TTCvs+iwSTHO/0sjlfRYQzSPK2J9v5+2rJZ2rJZ3pnO8mA0jOs5CKDa9zGFQXs6O+zhH5mmnFYA27MxhIEvVVmmkKBLH1sIVgUMor5PUtNw1RER9AXLDQdrcDMpJ0U8EKc2WIvhgyPdgkNRWcRZCDWRC1RBz8uK1AudDkNQ5dkqG1JAIp+gOw7xlCBfVqlpudAb1wgZIWzPJqAFqMcnKj2y0gO9PBU7dlq2OdxI6/pB3vmMM0qbb0uHT6/6T1qjU5n+dpsFf7Zp2W0QazmGwILLEKefDMsuVNt2M7hOBkNKWlyXlpTLe1Jp6O4i0/1v0HoONJ8CzaeqjsBl+x/L6rI44tNe1bjozy71g5KeuE9zjyRrQW2Pje5JPF2QiupE+8ZuPV9EvN8hEzIQ+EoHLQTS0on32xV2mu2tF7B03f/Q3DM2EY107Lk2//WyL9wX7KuPecbJqImPbyORCASa0Mg4mYm97TM5zGe+S+3gNuLxGSRPu47EjFPw/PELaAGaIk0sPn4xHzh6WI6zPbmd9f3rWd+/ntpgLRcecSHnzzyfeGDPk6pVXau4e+PddGW6mBKewmVzL6OtuY2YFSNkhPZJphCbP5/wWWeRevBB8Dyk7xM566z9qiE/lODs3IlWXSmd2pMzzmRQTu592y5o7g9tcg9gNNQTe9c7ib3rnarAdseOQvfa1WTXvIDMZHA6OnA6Okg88AAIQWDOHIJFgn/ssYecq9CbFYeLYieBN1VR7OPfgCf/E1UUW4wmS5j/eTjvCxUt5Qesqfz74LswNMEn7dvIS50cAeZouzCkq6LfgUKKv6hJb79/78dULD4d2j6hX31J9jNOgyWMYMFj31M2kOvuGvbcL0bDo01qzBONdcMj8KsPqsnHWI4aQi/4+buFDrS1mEIn6DkF9xzBksEU+B5La6tw0EgaOjaKnC/OSG4wylKUZefurDvOIutmMRBq/4ArJSHpY0kY0jQ0QEPiCoGLQAOOqjuG/lw/A/kBokaUObk0pw71sipg0mEYNLsu7Yk0bbYPLadCph96Xykdy+L6anp0jZAegMajAXi572VO3Oyy+A+qY2t559rfXBTn+IVXc+/me5V237HJDW2nV2OUE1Ax+xAyQjx3xZ/ATrPydz8i9/2fj9ruj9+tldxzAprFrGgLOS+vJifHXUdbY8Gi87fXQ7oPzCBXBtNYrs3puTyn5nKcnMsTHuu5VTNTEfuWU1lpGXxl852YwiCoB0r7COtBql/YwdUP27i6khiZjmTKgGpQI3WhXHSkslvsnxLk3F//euz7CHj4mqsIDuVURL8APe+Rqw6y4Kd3VKy7ovt56i778rjbOvaZB4aLcHVTTaiLvxfO8cHicrOvOP1/Tyfrjp4khYwQJ9SfMCoDkXWzNEid27dvGS7mL0jq5IJvkj7iLBJ2AtubHOmSUvJC7wvcv+V+ntv9XMnpyNAM5jfP5+JZFzO3ZrTLEigy/8M1P8TUTCzdwvZsHN/hoyd9lFOnnFpqWhU1o3tVSNv9g5vpu/nmQo2GajCFlNR97GM0fvxjk97O3wu2XdOO29NTitAD+NksRkMDrT9dtt/3N0zu06OLSw9xSM8jv3FjieDn1r0EbmXQTZgmgWOPITTvZELzTiIwd+6kC2wPF8XuXxwm9JPAm4rQf32GIpDlVpSeq2Q2X9xesepVt62kO5kjbBm8Jb+KazI/odnvwMJBGEGITVURb1CymMk43ozlrAIqwp/YVVnMWkJBby5HyFTKoZnqmHwlj8G3UXYvIyKeQocpx0881qkqrRIAACAASURBVGUXK7eaxK6yhWN/DhY3NdJjBglpeqEDryQrdBp8Vdjbo2uEpABTRYKzvkuD63C7XVWp5y+4z5Q0xL43LOsBPjKUZHksTEbTRvjQKzcZUzMxNZOYFaO1qpX2+EksXXsrpu8RlBQmGrAk7dN2wffhno+CnSrpvVdEoiytrcb0fYKaQU432CrUdOGkzR4Ln/VLkfT7ztQYPHkWDaEG2o9rLxHISP9Wthg67lhRSCGoDdTytXO+xrJ1y7j0u88TGsqj+xBPKzLvCeishc8vVvemKQzmVrWq8zbStWbr0/DkN0AzOSWurrGGBM1EFxpHZTOclsvxT5G5sHvNmBPFdNU0VhqSZ3SXXt3gCqMOZr+Lvm/cTSTpYJvDx9HSLTF85ZFeIvQS5PRGTvzf28e+j4CVD9+B84M78XSBa2kYto/uScyPv6+yoLaAdecsHN/l5qn7xt6JKBSPlxXjogfGtps9CDBRweK5vzyX/vzobERtoJaQGaLKqqqIckspSfRt4OGkWSkVHBFgyLpZEvnEmJOF8dCV7uLBVx/kD9v+QMpJlZYfVXMUC2cv5KxpZ2Fqw93Wljy1hIHcAAFjOBqZd/PUBGtYes7Sim0H9ABRS0lytPGkhgW8cvoZ+JkMwhi+ntJ10cJhjnru2Qne+feJN8oZZyz8PZN7AD+XI/fSS4rgr16NvXlzpd0qyjM/eOIJJYJvTp8+bubqMKHfvzg4vxEOY9+RTynyWw6hq+UjsGMgQzw0vG6IHF3aFOr8HiK+B4lCsV2wurK76niYyGf+gm/Brz5QKbkR2nA03CvO+ieYYPo+pSj84KtjryO9PY91cJtyTEl1lawjx0OHZVFV8YUsCAIdhgZSo8qvnFAENZ0OU0JgyugaBoYL/5b/7WYyAsJSsMizuEEY/AybynzBsDWkLnRc6dKX7cP1XZYBZrSJULoXvLzqbqtbLJsxWzn92CqDgBEAKWnLZlnS67IsXk2HodHs+fRokNNUU6XVs9WjQCLRhc6RhWLLcp/pxT+cC6Yx5pRLSKjXAyXJRP2AT05CfbLQj0CA5kNLD8zb7CtbzDLt8SjXmnIvebpVizLNBE3HA14MBlkbCvFPF9ysMh3d62DnKuhYpRqQeXkiiV28A9W5DgCtF7pfZVuwild9A9tW1y6gWZiag5Q+rg6GC64BsipCtT3x8/7MBVcpT+yJ3HHKMJ6jzYRhFSkLUraR1bRCRe/HiuofoBbweypYLCfO5Ug5KebUzBm7RsD1wKwsBi65ZxUQMkKEjBCO5zBkD5F20uwpWDUlMoUPHf8hrjr6Kp7Y+QT3b7mfrYmtvDLwCq+seoWaQA0XHHEBC2YuoCZYQ1emi+iIugxLt+jKdI3adrGQdiA3ULK/DOhjyxL8dBqMEV/Fuq6WH8YoxObPh8UX0f+Tn+LsyGHWBKldfM0bIlHSLAvt71CWU4QWDBJ+y1sIv0X1RvESCbIvvKDsMdesxunYhZ9Ok/nzSjJ/XgmAXlen9Pfz5hGadxJGff1EuziM14DDhP5QQyCqolfll1Z6Jbu/ckyvCZci9Jfl7sLBIEuAhF5HhF5FJNI9iiSMdI0ZKxI/0hbTCiu9/jPfVZG0ltPH96v33QLbKTjkCMqItpK/YEWUTn7khGUk9uRwU3TlMYKFCLY3gtSL0jiaHY8eyyAEJdlDzrBolhq4WXo0QaiMouWkT7MWmlCadMO8G7hh9QOVBcoBEKJftQATWkXToyI0NHzhY/u2cjwJ11XoxoNS0mEn1PkO16lrJ2XBbSdPW9ajLdgEqKzLYmOQbULQo2n40kdDOdoE9AD9uX4yboYFdy0oSTs6TBPLl+QFeCMiLhpAugczPp2QEWKg1mLaNgeJstYEFfl2NbjkWcma2YL6YO3weRvLtWbm2TDzbGY++Sk2J3fgSg/puUp3jWB2dLpaTzdh6jz1Ou3DKgvUuRb+eKOSHhWtUH0HbIfWeWlmSLCzUXKparLJMAODQ9g29NVbSgcvJYadw6jbs7vD32ZpLP9HnYynE9Z1Fh2hceY46/qFMpFyuu0Xl+8tpATXVq+R0PSyaL5ZiOi//kR/jwWLQk1OpZQlDb0o9DQYt0bAaIBssjJCP86k3dRN6kP11ARqSNgJUnZqwkZVAEEjyPkzz+fdre9mbd9a7t9yPyt3rWQgP8AvXv4Fv3rlV5zTfA4xM0bey1dE6G3PZkp4fHvc8kJaS7dK9pflUXstEsHPZCpJveehRSITjvtA44DJvzY8Qqz7J8QWWmA2qHuh+yew4biJjRD2M8rJfckK8xBsYjUR9KoqouecQ/SccwBwu7sLDa7WkF29Gm9gAK+vj9Sjj5J69FEAzOnTS+Q+fPoZmAc0QH9oQf/yl798oMdw0OO222778vXXX3+ghzE5uDZse7pAUMWwxvzsT8PMcxQRv++T8Ph/8A7xPBvTIXaIJq62f4nv+0yRPcRFCqHpyq3Gy6lob2wqzDoP6mYrnf5D/6wiZK4NbhY2PwrJTgjVVHooa4aKhJ95g/JjX3e3KlotFsIKodxmnFSBRBf86Uso6zurW6oJloaS1IzAilCQm+pruCWk8VjHE9Tmc7S2nDH6HIUbYOND6tw4KSZy1an1fJ6IRPClj4Egp2k4SD7RP8S8bJonwkF8JIbnkhMSR2h84thrx97vmGMoFAo7WX5mumS1Sn/0InShl5br6Ni+TVe6i6SdRBc6AUOR8JST4tdON78NGvwqFuEnsRCPBU1qPY9WD4jPYIVmc5ORZrPmM4CPFKLgRKP+eb5HxskQD8TRhc6rQ69y/5b7sXUdy/PIC4EcQehNBK7vE49ORQhBOmpw4l8HS2ReoC5bXxRiOcFzbQ0ErTBZ16Yj08mAPURYDzEt1EDriC6r/fkhVvWvrTgvEklAs5gZnTZqfTRddYdd8wuVzQnXghlWxFaiJgUCDNMmGEkSre2h/sgEVdNy1Ig8sZSL0S+RnuChBTWce/IF417GWzbcyQ833km+UORp+w7P968D4LS640et/+J9vyaS8St96AV0NxjMet/7x93PXqNoEevm1AT/lYdUJ+bHvw4v/Q6MiHKKKk5e96GYc+XdN7P28/9E5/e+y0u/+ym9QYfgo8+iRaOlFHvSSdGZ6ybVuYOvTH+eVD7FkRtSXPewxxUrPE7d6DMY9pHTmvjXt/4rrVWtbBzYSG+2l6ZIE584+RO0Nb111GcF34a336ieR2NAE6qou8qqQtdUdmsiZxxQ7jhTwlNoa27jHTPegaVZ7EjtIOfl2JrYykB+gIybwfd9AnqgpKFfdOwipkVHdwYeCU96ZN0sSTuJ4zvomo6hGfi2Q/a558BxlD65oFGuve46IqeftpdX5Y1BsYA55+WImBESdoIndjxBa1UrrQUZ3d5s66Y/38Qta27hsR2PURusnXgb932SFX6am8I+txg5HjMltVKjtfMlmPePr/HI9g1C19GCQfRYDD0WQ5gq6CTdSZg8HELQIhECs2cTOftsqi+7jOg552C2NCN0Hbe/H1wXP5Egv2ED6SefZPCOO+j/yTKsObMJHBib1q8ciJ2+XjhM6CeBNxWhn3mO4qe7V6sv80BEkfliQexDn1POG4EYITdJm1xFjzWd2ZkXmE4npiZUV0XPBVxFohuOUZHsjQ9BLqH84v2iF7ynCLoRVMReL0QEi3CyUN2sHrR9W+CVByqlA9JXkbZoY6HJVAYo6ORFoUhWaChLRk856Jx+PXT8VUVcC1DFqzXkhEZEt0hIlyd6/kqr7Y4m13WzlbXl+nvUcRb3MwZaXYdWEWBjKEJvOE6TVc0nujpoSydpdRxaHZeNlkmvbtDkunwidjxtC/5rz9epOIbutWrCU93MLwMaSX/somFNaJia6hCa9/NUWVVk3SweHmknTc7NMZgfpDpQje5m2SF8+jWoEjo5w+SJkEWrJ9keirDUyJATEl1Kkhr4YvSxa6hIfV+uD1BkxxeCvAB/REMpEHiAKyAciGFqJtuqbI55YZBoDnSpJCz9UQiHIkyfczJvaf8sf9n9F7antqMJjabwVNB1nuhaRWv1LFojU0pZkx9uvBPHd3Glhye9QoReI+87/HXgJVojU0eTeoBXH1fuQkVZihUBI4ioPYLMtPeT2dCNdGyMgCL4VsgnWu9Q05plypEp4lNy+PoQc5rmKomWPjoz9PG/fA1nRFdjCawd2sh1c64Ytf7da3/H3K12pexGwFOnhTnr7aPX3y8o1iO4eXUOckOw+Y8QqlNyutygmrQ7OTXRlj6gTRjNX3n3zTjfvAXddrGDBlbaRj71F/RIFNNThXJJJ0VnajeG7ZGMB3jyRJ2pa3fzoT94WC5kLYjm4JRNkt56kwvmX0trVSuXzrmUDx77QS6dc6kidmN8Vnj7jZOKxgohCOgBqqwqLN3Ckx7uSJetMRAxI8xrnMfFsy6mKdJET7aHgfwAvvTJeTkS+QQBPcAHj/0gZ087e2+uhpr4eTYpO0XGzaDZDqnHHi8ReSnADwepv/pqAq17R47fKNz055vIebmSs4+pmfj4bBzYyKVzLp30dvZlYrDiqf9gaRhyQhIBEkLyhClpTQ/SevqBLyIWmoYWCKBHo+hVVQjLQjl2/X2ReyEEejxO8Oijib7tPOKXX0741FPwXVc5EhXlqkKQ/dvfMGfOPBD3+yFF6A8XxU4Cb6qi2IlQdJsZq7gsO1Bmc6gVigylIuoNRw+vm+4CO42HjitVXaqOj6abGIGImkCMcKMoFoROuP+ipabnKC97GPal1w3V1TZQNbz+wHYY2lbazLVNDfTqOkEJtghgaoK88GnQAtzePs61++bc4U6xZfaVCgWluG7B9DOGJTTFY+jfPNohR+hK2jSi+HiyKDqAFD3qixFpgeDo2qPJeTl2pXYRD8SpC9WRslP0ZnvJF+wY60P11IXq2DqwGbewzABm+oKs9GjwVVFpjwYhKdkqJK5u4BSOI6AH8KWP4zsYmmoIZQijJA/wpEdtsFZphov2+oWxSxTHrw3UkXbT5L08J232WPwHH2ka+JaBbnsEfI3j/+O/ic2fz+LfLx7b1STUwO3n367uHzvNggfeR5UZZVt6F6701HikxJM+LeEplcW05SgrrKXQWArfgflfKGj0C7AzPP3p9zLDylFbkydc44wOWGs6NB6vHISaT4WpJ4IR5IQH3jvu9Xzxot+OWvabD1xKY59PxAbTBceAtAXddRpX/O/vxt3Wa0KZY1AJTg4idfDe28Z/nxDDhbjlxbiaxsMLzyA4mMULDMtE9LyLME2atVqEZbHd7kTkHUxP8OB7p7HpmCqu+N6LxFOSfFlBcsCRDEU1rvj9utfj6Ech7+UZyg+RcTKTfo+Ukpf6X+K+zffx591/LkX7daFz9rSzWTh7IUfVHLVPtpU9H/4YyV3byZqFUnghCDmCupY5nPTLe/Z6e28EFty1YOwCZjvBw5c/POnt7PEZMNZ7lp1Kj58nJIadVLLSm/hZfxBA+j4ymy1Jc6Q/ccboUMWuL3wRr78fLAvyedA0hGm+bi5Fe8DhotjDeAMwlkb9teoDB7dVdoqFsuIyCdXThxtLQSEC71eum0/h6kFc1wY0BOAjEF6egeiJ1Lzzc2M3tdrT/sfyss8l1N9jU4btM4vrCwmBasgPIYEOw6TKVy45UoLtSSxd0OHnx9Z6ZnKVnWLlCB2yEEreI2VF8V3pGMbS5Uqv5De/L7A9GwOt4FFfMPEUithvGNhA2AxjaRa1GNC7iahnE9UtZKSJDdlOaguadAcfTTMQvodTcIYJRqbR4WUhN0SVp/zxHc1D1wykNxw5Kk4mBAJf+qUvbB8fUzOHCX1hviOH31gg9cPyndWzNX5yvs7lf9GpH/QYqLG45+wwby0Ur+2x86kZAjNEc/VMejLdONJFK6jPfSSmZowupi1HeWFtYreS4Zx8TSWZB7DC6JsCDGYsEhI00ydcbxOekifUZBONu+o+6VyjXqtuV5+NpuP5WGaQ/n6Lo5+zqBugwm9/LNQP+mg+BBzQJGgOZA21/HVDYpf6rJTDCKpzMhGkrPDSL0HTifakyYUNRFm2xrV0gpk8U77xJfpv/zHmhm0M1QZ55rwGNh2jrnPDoCQ1oslqeSOzNwIBPUBjuBHHd0jkE6Sc1B4LaIUQHFd3HMfVHUdvtpeHXn2I32/9PUP2EE92PMmTHU8yJz6Hi2ddTFtz217ZVto7dyB9j6ZkcZInGQxBatuW13qorxv2V5Ozfel+3BGMIDJ5uvBwBJgSagvLD2YITUNEImiRiKohyeVUUW02+3clzXE7OxGxGEIIRCSCME3lf/8a+wgcxmFCf3BiPLeY8Ro7TRbFYtDxisuSXVA3R/3et0ml6Mu/mJwsBKIM5E2qySPxkQg0PHw0bnUv5otHvmv8Me5p/yPfO1ZEv7h+pl8RFT2A7UmaXacUoS+6sGR8n4hpjd2sJpmnrbxTbLl3y0iLzPLiu+IxjId9iNAVEdYssm6W4hn3AVuCJgRH1hypIvSJHfTnktQhCt18HXKJDsKBUCkFbmomru8iDQ1TGFA9k5ybpblKfdkWv4jNoa240q3sWotU6XPpIyjT1ktJfbienFdG7sY41FggRmOkka1DW3F9l7Vz4eUjDWZWzyxF3oqYLCkoFkvqxYZcUuUu6oO1YxfTlqNQWLsnBB0lDZKA72ikdgdJ7w5iW4J5D/4cdv9t2EWnd4OK9O/6GzcAaOCdBgMJi8G+ALOfs/gewTH3I4HazLAtqZDq/13VY66+f1A1bXSE3s2pCc6+wPdI1YWG/fcLZNjIe6TqwsROPZ7YGd/npsc+QU+ur+L69sRFIUI/3DvCcmGobvIEeH/B1EzqQnXEA/FJF9CCyoQtOnYR7zvqfazoWMH9W+5n0+AmNg1u4jt//Q4/WfsTzp95PhcecSF1obo9bi9huLT0qeJxXyiL18Yk7Kxz6M32TuiQ80ZgLBvS/dXkbF8mBpFQPZvy/cjC59UVsEuDOaE3j3uKEAIRCpWKx/18XpH7TGZSdpjpVasY+vVvcDs7MZqaqL7yCiKnnvp6D3u/wGhqUhH64PDzSOZymC0tB3BUhwYOjLfZYUyMcrcYIdRPzVLLXwvO+qSSwNgZ9SVsZ5SeNtOvOqcObR92RtEtRVqcLPRuVMt9G878OLbUGaQaDx0DD4nOnYEreSB73N7vfyJHmonWLyPOPnD1UBpHCLKFCLEtfBwhIVyPqZslrWfICGHqJsvcHog2QFVzwe6vvBGGUGReehCIVY6vOKbxPjrmaDehyWKRa6ovKNQXVTFnUCspjT3u+wxoqOP0PbK+gyNdFg0M4OSUD3dtsBZf+ni+R12wjqybxfEcrkufwv/5SR+f/8YWPnjzRs7aFsLzlYRFIHCli0RSE6yh2qpmamRqSbPeFG5C13Qcz0Fn/KYhQV09pOuCdUoyJFXmoTiG8i/89uPacTyHrJtFSjnmOlDoSnrGEmbEZqjxiMJ4jACOgPZjr64kq/sA0x17Ima6AoJVcMS50PZZeP8dsPhRuOCbcOL7SWQUEdV1qK+xmTMnyVvO7ONHchf87mOw6scFG031JV09jspjvOX7BSdfU/gs59SN5RSkRydfs8+bjF91Cbon0fMeUqqfuieJv3+hep4kO2mfsQDHyZDNJZCuQ9ZOc/+ZBoanMhRIScAB04Nnzp1gUvY6Q9d0aoI1NMeaqQnWqBqiScDSLd4x4x18+9xv883532R+83x0oTNkD/GrDb/i2j9cyzf+8g3W9a2bMANQIdMRlctTdordqd3sSu0iYSf2WNi7v1G0IXV7eipsSOdtkSw5YwkNoQYSdoKGUANLzliy1y43k30GlCPlpPBR2UtEoeagsPzNCi0QwKitxWppwWxuRo/HlYPOGEivWkXf93+A19+PiMXw+vvp+/4PSL9JZMHVV16BdBzVP0BKlaGwbWoXX3ugh/amx2EN/STwhmvov3OCisyXP+gn29hpTyhKebpfBiczXMgaqIJsv/qiFzpKT1H25SF0OPcLcN4X+Pp/f4/zh+5kquymS5vC3cHLeUrMozEW5I7rxzPsK+Dxb8DKH6iJRBGarjq/Xj6s53385W5ufXIL03pW8CFxH7PNPkINRwxLeL5zghpTugfHzuFg8ngowq+rdXYaJjWuyRT/XWyc8iJVPoiilEi3kJEGEunO0c1qBndAbkAdtxBQNR0u+vaojMOKZ/+bZWtuZrOuYRdSvnMch/aspK3m6JLefjxbt3Ht3r5zAr/s1rFW2dQNSbqrBY+dIeibqcGUYwGQXS/RJWCOY7PZ0NX+gTm2y6kEWDVlDh1+logRAQFpJ01ztJnr0qdQf/NvEZZFWncZSnSD7XDXwlqenpHF9mw1adBDHFt/LO3xk2hb/wduyW5jeUiQEYKwFWHRMYv44ZofculTNgufg6ANOQvuOx1+e47BEdVHlKJtyXySznQnrnQ55VXB5X8xmJkOEWudU2o2NFnru1tW38Ly9ctJ2Sk0oRE2whxTd0zl+r6nijvttJqITvBsW9H9PMu23ENHppvmcCOf/ZcXwRkdndUsk6P/dPe421l72SJ2W/3U1uSpi+epi9vEImOkz80QTJ1H531rSfQEsPtNhK+60npCZQeOXzFOY6n9ga1P71l6tJdY+fAdDO7Bf3/ked6U2MFbtkjOWTFE/aBHb43OU201rD8qrLTXryHDtb8gpSTtpPeqA20Rfdk+Htqq5DiD+WEnrlnVs1g4ayHzW+aPkuN0LFhIXrrEMyo67+owGAZLGLQ8XHlPaEIjbIaJmlGCxmubxE4G265pJ7F7G70yieM5yhZUxKia2rrf9M57a38572fzOGGTwyXPylIzvHvPELw4x2T11av3y5gOFkjHGbbDzKnsaFGDLkZEuPXaWqZ94+sHaqh7hVKGobsbq7W1ovncG4wD/8DZjzhM6CeBN5zQT1Q8OoG/+aRRlPQkuwq2ln7BQtJQ5Nobx0e3egZ8+kUef7mbG+9dh6kLQqZO1vFwPMlNlxzHeUdPEGkr7jfVW7CLHIET3geX3za57Zedo2TOpWMoS1DmGNLr+Ezoq6X1l6//CD39mwiJore8rwqopMbtuzpU5iPapKLuqS4IN6rI/ciC3gKKrgyOnaQvryYlAqj1FbFectx1tJ3xf0rrmbpZkZK+ZPYl3Lv53lHLl5yxhHm33kjX77sQuoYwoMPzEB489G6LTWeowuRs93oa7BztQwmW1tVgSklQ+uQ0HUczWKI10Nb+p1Gndqx26alkH68ag/zo+umVY2m5gLaVt7PC1Fka8jClr7rRRhtJSI/5j3Zx5dPDDaP0gszp7nM0Vp4/o3Rsu1K7GLKHmLfZ59o/qKZNnqXTbDYQ8o1Jd3YsddcVAh0dDxUV/siJHyk16hoF31fuS3bhVfacW9H9PDeu+T5JN4Pne+iazrd/kCvp2MubP5lTpzD31z8ad2zPfPjDpHu6yJcZ4MSER1NriKMuOkVJdAa3jXqf5whSvRapngCZLovBlMlpj+6Hz/ZBjsUrv0RPboBQGRmt6BKsGyrAUO6fb7zxcpwiMk6GhJ0gN0Yn4ongeA5P7XqK+zbfx8bBjaXlMSvG+a1KjtMQVvKzng9/jKFdW8mZULz7gg5UT5tJw49uHncfpm4SNaNEzeikswp7i7XnncMukVD6b6EhpY/0fabJKo5//KnXZZ97wqIbjys9T/IGBFw1EfrxuzWW3/TGFFYfCEjPw89k2LLwEqU9H1GQLJNJZrzxRaWvCYc7xe5fHJbcHIzYW2nK3qIo6ZGeIvClSLw/OkImxPCyxA4Azju6kZsuOY7GWJChrENjLLhnMg/w6JcVCR8vNbruNwDc+uQWTF0QtgyEUD9NXXDrk2VFYmXnKBbQmR6BkObxM3FJxXjah4ZwBCUpTlZKHOnTPjioJigAie3K5SfcCLHGCWVOy9Ytw9RNkr6DphkYBQV6UhOYkUaWDa6pWG+k1Gf5+uVjS4DWLaP/5RhCk2iGRAhBtSFwdDhzFcPpaAHtiRTLqqOY0icklad5yJeYQlNSojHg7NxZEdEB6PUT1A24o8ey/megWSwLSkwgJHT19+wgaTfNwucKZL7gJupp6v8XPzechu9Kd5GwE4BqJOXqkDfBlT5d3iDCsui//ccT3y8FLF+/HCEEhjAqfi5fv3z8N2makkvFmqB2lopKB2Kgafz72tvotQfJ+zYePp70SJs+XiF1D4VmTwKy1sQBj2UnJzE9CDqq2VXQEciszn8dYcDblsAH74b2h+FdX4Nj30ufq4iXbkqqp+ZpPjHB3Hf1csKlnXD/p2H1/0LPKyMclw4dtM96D450ybq5wj2dw5Eu7bPeo1bwXDUBy/SrZ8XgdujbrH4mu9Tn1E6XdZZ+fRE2wzRFmpganUrEnHzRpambvG362/j2ed/mW/O/xXkt52EIg6Sd5Dcbf8OHH/kw//Hcf7C2dy097z0b01NuP0j10/Sg97JzJtyH4zkM5AbYmdpJT6aHrJt9rYdbQvLJJ9l2TTt+bz+NPQ6hvFS9JISG5cLO2J513q8XLn2WwvNEfTflTYGrq+WHMoSuo8diWK2t4PsIw0AUrWXzeYwDS4wP4yDA4aLYgxHlji9jucW8VhSdWnRLaXuL0UspJ5QplP/tvKMb90zgy7HhEehZDxPor1W2AHYMZIiHKj2/Q6bOzoEyofGIcxSpm0Fk4Sf5rxHnqG2whyXhCMuMPB3Cp9l1aE+kaMvlIVatIvGpbvAzqghYesPFgiPay8OwK4NqDGOAppw+HHyC4To6Uh2s2LmC1T2rkVIVmNYF64gFYgT1IBknQzBaSayLrg5Ov4tW11JyGorpFsRiBIeSJOyESkfvfJU2EeXfDb3g6gPFIENQ+nQYY59fs6UFt6cHURahJ2czWFtZbBfUg3T4eTBr6BBDCHy6BAU3CRvH0wjaquNrOTwBAVtySksbbS1tLP79Yp7rYqtj2gAAIABJREFUfA6AxkFJMlgcpyTv2YhgcNKuBhkngyEqH1U6+h5tB4vFfMltm9gZc3j4rQF2HN9AR1a54hSLgT3pEbKhuwri2WE7ycEQBIb6JtzHc0e4uOebXLTSo35Q0hsXPHCmwV+PKCOc0QY46gI46gIuzq/n8qcHuGxnnnhtnsgUm0DEI6hJ2PqkeoGSwDW/RVlkNp+qvNjFmz/+0tZ4CkugQobTPus9tDWeMv6byjvilicPNa1gqVmI4hd/fx264Qb0AA3hBuJ+nKH8EGknvUdnnCKOqj2Ko2qP4trjr+XhrQ/z0KsPMZAf4Jldz/DMrmcwNZOpl+t88DGfaX1KQnLfGRr9gT/zPfbcKKkoEUo7aQzNKHWjNbR9+3ov6uaFZTEYE9QMSar7bYZqTHxdQ/fgnrcGWLBPW3/tmJrQGbSKE16V0bANtfzvAbWLr6Xrpq8ihUAEg8hsFun71Fy9CKHrSG/Phd2HcWjiMKE/WDGRW8xrRdGpJdoIQyNJ1bD7ROm/RexFhGoUSlmBCSKPhbTx9Jow3ckcYWv49sw6Hi014cr1J3OO4q20JbtokwVrtO6X1DEZlupsm+ocXld6ymcfFKkfo7180ZXB1Ezcgo1i0Rmm2Bxl6bNLSw4xru/SmVH7MHSDsBkuudEUUXR1MFs8RbqLTkNAJJuleu5sHr58mVqw5WzIvkxzrI4eXSMkS34p5CQ0Vx8x5mkofgn4oL4EcjmCUueBtkrLuJyXo1kLgJMlYsEWTaXxNJSbhC99cpYqavTKkjm6hLw1TKQ6Uh3M2+xzybOSeAqq0tAflWSDqEK2vXA1CJthsm4Wo+xx5eERNsPjvqdISrKay26RIDgIV96f5XY7AaNOkSi5r+yurfRH3x2dOFIe1kP8dVaW52epTr6i8C+sh8Zc/9iaWfz67I381B9mprM9jUu0Kq4NTFcSnVSXslTd8rh6AQTj0HyKIvctp0LNEQeF3nxf0NZ4ysQEfrLwffDHttWs8M8vvvYD0Tc1k/pQPTWBmr1yxgGoCdZw1dFXccWRV/DMrme4b/N9vDLwCo7vsL0Rlr5PQ0NDL/qrp8e3bhwPru8ykBtgIDdAyAgRs2KlDNxk0X/7jxGWhRYK4Tkh+kWWqoRPbNBh03Sdx9uqyZwyd6/Htr8QbZ1Ffucm5dtfaIARcjWirQek0+gbjtj8+XDjl0Y5DxXli34uN+x1PwnHnMM4dHCY0P8dYs2Ma2h6+t/IejoBrY5GrRfddwA57EFfQpHRa3D2JCQ/I/zz18y4hq9vms5/7X4FIeI0+l0UzAlKKP1+nOqU+ZH5s7jx3nVkbJeQqXN85jmutH/Lsf4ALDti77IVZ30SfvdxGNpRaB7lFbwJtUoyXz6JSXepKDkS+jbAV+KlAtn2+Eks3f1jYr5Hj6ZkGQDSs0n0v0qdZmCaYaa4HrtRUVqh6fRke5Td3TGLuPflOyG3g6DnktMNnGA17af+M7WLJRv/7Z/pH0qSMyQBF2J5jbDMs+kd71QP7il5/n1GNasCVmnfMd+n3pc4kTraT/tM6YhGFptd97H3MvV3z5W+BMSHL2KdvA/TzTL7pQRnP9FDw6BPsjbAyuMdOFovXf2ys8N9p8GVz4DuV2ron26rJVPY59QXd3N1QefaF4OGBDQmoFuCYRklV4PJFMQtOmYRt75wKy5uhYZ+0TGLxr3s/bf/GN9x8AZ7mOr6eIZGNqhx4Z8dHJ9RBXX3niH46P2S+iGpjkuDjAU/e4fgyglur/kNp/DA7idL/5cFQ8r5DWMT1lNrj+f5/vUYQkcvGL5uNSA/ZwEc+T4VjU7sHLbI3LkKMr2qIH7zo+oFEKpVBL/Y6Cre+qYh+MmVq+j/xd04u7swp06h9h8vI3bma7fcG1mAOyryX0H0zTKiv/eR3aIzTnWgmqStMmiePzlib2om57acy7kt57JxYCOffeKzIGXBrcVHeh5VWUEqoiOl3KdmVaAaNGXdLLqmK629FcXURnc7Hgln5060auWjGjEjdFsZknWqq+9XrtKAJDdMOXAWibM+9hm8f/tnpJ0iZ0iCjiCuR5j1sc/s+c2HCGLz549bf6QFg2jBINTW4ts2fjqNzGTw7b0r8D6MNx/0L3/5ywd6DAc9brvtti9ff/31B3oY+wWPv9zN5x9L06FNYy7bifhJkjJElPQY1SGFJaKgRz7joyr1Px6KRa9ODgIx0kN9aJse5hWnkVnsJOoNEJJ5hJAV+5JoiBP+oeRyM7M+whF1EV7uTHLEwDN8zvsfpkUE4WgcEh1KZ7zyh7DxEQg3TDymvi3wygPDLe2lBIo/y9MPI49+RDo9PwTr76e1Yw2tIsBqA/qFLIweDAQBBGnpEc+nCAABoZMTEreQmv/3tqVcThWta+9how69mqDJl3wimaFtxjv4sfYKv8k+zYweSU0KbBOsvCThp7Gq4hipHNvWd/Nok1URSbaFiuz937f9V4kMj9VS/U/+ek68+pOc/IkvEX/ve2g55jRaq1oZfPxPXHzPLiwX3KBJKCeRW+HVWokXF+SFwNV0TF/ShODZVtX8anYnWC7kLfjtW+Huc3Se73qenJfj/fcMYjg+eVPJVxxDRfXDNvitU5n1hS+xepaYVNv305pOK3XqzHt5wmaYa4+/dvyCWKDrm9/EHxpCeh5SgOZLArZPLCM5brvyPs9aiqScskkyGJbM7VQWippUVniuAWuOtrj09H8Ydz93bHuQlJPB8ZXlp4ZGlREhZAa4tOXto9b/4cY7mbM+xdUP5HjvCptTN4KsCrMt7qj1hYBgNTQeA7PfAfM+CEcuUPe4GVbE3skqd6r+LbD1KXjxV7DubtXpOZcAK6okOwchwU+uXEXXt29F5m20cBg/mSL19F8wp08j0DJtn7e7ovt5lq77H3KeTcQIk3BSPNG9itbIVFojhe1KqSSGbk7VJeWTkB1U59QuSO58txD11SZ1/oQQBI0gVVYVpmbiSGevrCXrQnX0PfYI196VwHIk3XFBPiDIW2py+MzuZxAIpsem77OERkpJ3suTtJOlPhKmZo47UUj+6U/4iQTCNOlMd+JJj4AL/VWCJ0/Q0ITGYG6Q9x39vn0az2vFc/p27kw/SUu3R21akIhb/O7tEWrOe0fFc+MwlO5eC4XQq6rQo1GEoZolHiyNrIRpokf33ep5P+ArB3Ln+xuHXW4mgTfc5eZ1xFW3rRwlZ/lF3z8QJIdpFtwk3HzBulGHphPUssm47Ixw59nSm0ZzMwzpddwdvJx/SX4NgY+PjiYkAskA1fRYLRy/5Mk9bzOfUBIhKVWkLTZtTCeaicZE3yZ1LEKM0e21PA49DowQNBzFYjNBj/AJuQXphBEki6RXutR7HiGEGiMj2pJP4GB0VqCfpJ1UUwsJX/qFRzwtyZuCiPn/2TvzOLmqOu1/z12qbu29ppN0Z4FAkE1AAmGxWZRV9sA4iDJG0XEbR4b3HZ2Jo+MHR1zGmVdnxgFUtB0ccCNCArIpS8IOwUAIhISEhKST9FpdXfvdzvvHubV0d3U2wiL2w+dS6dtdd6uqW8/5ned5flHmJufyys61jMQEX7+iVlWUKGfC6sVrquv2pqX6PRcsxBop4oVr7wm97DIck/z3VR1k7SyO72B6Lglf0qfVrlb1GIIfKrGVn/v6GrKWrK6vHGiiJPjWF+ewZOESetb27HXb9z3Fy8cvxC8UcIQfVM0VqRcSdjQTpNKo1zvsQFMORuKB0S6QMIUdSWx6Fyf9+p5J4zDPeeBTJM34hMSJUSfHPe+7ccJxXX3DR/mLu0bxDIFjCkxHoruSX5+X5Huf/tnuT0xKSG+G3qeDKv4qRUjHI95R0993Hav6LbwNsOVvlzDat51BoQZBpmbQJqMkO2Yy5z+u2+ft7jY9Z1+gGw00+qHdEv2CUyBTzlCeLC1sHAY+8Vmy21+jGBizX5greOJdglen1/YTM2OcOedMzjvgPKbHXr/5sRJ/2ahpVb2Gfl3hVUIOhDz42Tkh1hykms4hYNVHVr3u49gX7M29bQqNUUnMqXSqfaswlXKzfzElufkzw3jD6WjRISKL2Oj4ro+hCfSqSdZTJDrXr0j+yBZVhZ+MPFfMtgFs10cXFh1+H8+GF5DNRolQIoSHi0laa6ZAhBZnFy3o67eZ61eExnfAdpQcBgF3/R+YP0k+/7hjqmTRgw+R9nGymz0Y3LpFKI/SG/JJwhhyZwEhJI4QIMFCUgIcobE4V258PFA13xZatDGHMS1TMZNKnGDgUDagPSOrJLV6WuNIRm+uF80psbm0GQeJiaDVaqG3Qa52fDBPwdJwvXJ1it8wdNpGPAYKA/hB9osjoKTV9lv9V92+K42l+psglQPNg6aCMpp6GuxslgwUB/jes98j7+T3uu37nkKYZtDYSl0rEfi9NamuoUADpIriC+tE0g6DpoYuBL700YRGKtlCy4hUkgwrpZZxcZid0Wk0P9fL6Y/maE47pJtNHjw5TvqoxgT6nMdtHB28wG/ghAS+9DjncRs+vScnJqDlALUc+UE18B7eVJPo9D6rZpNyfWpm6uW71PMSM2r6+85j1c9vAbLbtrJDzyGEqvS6vscOmUFse31Gvt5CP8lxTd0sPUxvoX+Xz9ulTMdzgzSdOvO1EKAZNXLfgOhHzajyyrglMuXMbtNnEoMFnHiKkp1Bkz5Hbxactj3FSFxw398sYMW2FeSdPLe/cjt3vHIHx08/nvMPPJ+j2o/aZzmOL31ydo6cnSOkh4iZsWr8Zb1GO77uVQohiacLPnq3zWCTy/ITNdbM2710541CJZigHvvrvvHngkpijp5IIF23Ru5LexfPOoW3F6YI/Z8Z6g2no0WH7ZkiecPCooyUGrYnCSPQkGraObMNCDLchaYkNUxSEa+YbYPqc8jQ0NwCfXoHAJvoolWmKQmLsK4IjekXGTZnMOlke/023ZKaEh8DCZktqmHVaV/a7TGhh2oV+uJwkPQTkFyhKQlPxRg7GTLb6IzOYEAXKt8+QAmY5/gszozSk0rSC3RKjcUlQXd8VuPjgar5NmoOky1nq6v7UyKo0IMZTOOHXRhITTwkbVyhIeb7bCwNoaOq9y6SbaUhLCfEObedM0avPtISJjxSQIY0hBCqPm079Ke0qtGz/lEDxY6FwK/uWyCEqBp+lx8v+Ou7JMmS0tlLwPBVR9QjXrFZfeBG5jXNm9QgPB5723ymOKuNvDtCpKgyqh0d8mGIOhoJ38AOCbQgNca0fcohjbgM0VUn3/KLRYyu9nEXOpCfhRMgJZ8sLsS588d4uqAQ0YiNOlx05zDmnMaD3q6swXYTRDBwklLimWr9PkFo0HqQWo66XBH8oVdqBH/7s0pakt0B65arBVTFvqK/71ygDPJvArYlXKwMeGH1fhVCoNuwLeVyxOvYbmd02oQKfckr0xmd/LwqMh1TGCTNOAOlNNet/RFLYHLTbkW24zlAfuzv6iv6uomlh7Ai7djSrSbjNEK2LUp5x2sYIR2hGUoik8/QnpzN1e+5mo8d/jHu3Xwvd2++m8HiIE/ufJIndz7JrMQszj/wfE6fdfqYz9DewvZsbM9mpDxC1IgSM2NVjfb13zmLc5ZuVZ+fCCRzPovv8bhn0VtXVa0EE+zJfWMKu4cwDCXJSSYVuc/n1VLesxmmKbx98KefgzaFvcI/HLSV7+T/iR8Of4zv5L9Mt1jNT/zz0AVouIEkJqi9Cq2uDCtVpneDbPYqxuXnd1g+Ji63GhcjpeQX5sWYwiUmyni+j3CLCN/hF8bFPLRukkpa/Tb9XWhTn/jB5M8vjahs7761ajvVtHEtKNsaqqlV02xVhd0VNKVBXDySwZE+RU1HCp2i9HCAxSWN7lKZm/Ia95RT3JQ36Ha8Wg+BXfQYuPLQK4O6sVqWLRQYHkQcaHV9/GKRsAfLFgYB8HVL03jpXH6gSvEl4AWPrmeTDCUZKA5w3ZPXsXLbSh45pRXTg5Dt4/s+IdvH8OCOhWBoBmE9jKVbhKXyCkiEqrAGvgEtIKfnzD2n2sb9j/M0RmMq3lIDPEMNRPIWnP+4p8x5EkZLo2wa2cSG9AY2jWxitDQ6oe17xQ8wUByYcOyTYdlCDV8XZJtC9E+3yDWFEabBqtNn0mokMcoevu9h2D6a6/HQyQliMsS2wY1sGF7PtsGNFIuju25HLgQz7lxNU6IdGQnjI5FWiKZoMzN+t6bhUxJds5ihpTA0HV/6GJrODC1FomvW5PvZGwgN2ubD0VeoLsdX/QE++HM4+WqY2w2hIKlqtBdevAPu/wr0nAs3XwIPfgPW3wv5wf1zLA1wz4khTE8NopAS0/YxPbX+9WC3+fYN0LPpdkxhEDGsoA+DhSkMejbdvm8H4bnq81xMq9nEzDYY3kRodDvtrkOnZpGQIFx7TNrXHQsFhicIOWoaKeRIDE9wx0L1CU6FU3zwkA/y4zN/zJeO+xKHtx4OwNbsVq5/7no+ds/H+NGaH7E9t33fjjtAJf6yv9DP1uxWRkojXL4qgm/o2CE10LdDAt/Q+dCzkydMvdFYfPji6n2m2p/DcybcN6aw9xCGgZ5KYc6cSairC6OlBS0c3v0Tp/C2wJQpdg/wJ2eKXX8/LP8CPPRNWPe7mnF0/f1Mf/SfSBkeI55FxMvyfu1ZHoicgy7gAH8LJp6y9s3phtz2QK8eUmTeSilCm+vjoaZL+cela/je79dz/4t9tMVCzD3k3dByEPS/ALk+Qi1dvPrua7ireAQD2TJa20G0zj6MyPBLpLxh+mQrP7M+wtPmAv6wrp8DWmPMbRsXjdk6r7bNzFYmlcW4ZZi5oHqe1fPf8pgi9L4XfIkGsZyarqQ70lfn5Htw9jfr9tMAQlOk3yszp5RnTvM8NkSTDHpFpttlPp8ZpdvqgAVXqaporg9SnfC+r9ZmNOrPZ/hVsEfVNR5+heMO/ABsfZwXZAlXQF+LoJySnLwDYiUDc85B/PSYHTwzT6sm3ACYEpokfOSYz1bX/fTZH5AAykKReR+45BGX/3Obzxn39HPSI8PguvyhZQfPhwfYnCwxux+a8zCcEPzqVJ0/ztNUBVl6uNLFDbZjAOd4Bq9oPo66mpxzwAf41infYk5yDhvSG+jN9XLhkz5DScjEIBtRxlhPg+YcPHvaTDLlDJrQKPtlfOkjhCCshzl99uljzG3XPn4tw6VhhkvD9Bf6GSmPMGqPsqJ3BfOb5zc0wn1z+0/JdSTp2FEikXXJpEweOGc6Dxyt8cnzvkbp5ZcRQxmGk/DEBQeRunQR97rP0769QDLr0R/3+fWpJm3vO2uXRrvB//ovnIhJ0S3iSx9d04lEUugjeVqu/EjwHqu9Z9eJfpyHniaecUhmfcJ5Fyl9Ov/mk6/LFDophAaxdphxlDLXHnMlzD0FmmYpj0xhUM16lTOqT8TGP8Dqn8OG+5SUxylCpFnJwvYBK/tXce2a67l+/S95sO8p+ps1drZozOxzSWR9MimDu89IkTl6VkMT8Z5iTmwmc2Iz2JDdwmA5zfRIG58/5MO7jMe8fv0viRnRMbIVQ+gMltN85IAL9vlYJkD64Dnonk3U90lIH62cxSmlkU6BH2TuJmHrHPxKkaaMi+7BEyek+MNRcNG8i6qb0YTG7ORszphzBifOOBFXumzLbqPklXg5/TJ3bbqL9en1JMNJOqId+yzHAUXuS14JbvolRiKBGyT4WEaYtkQHsUyZte8/gGsfv5brn7ueB7c+SIvV8qaYUium+Q3pDQwWB5kem87nj/n8LmfsprD3ELqOZllKmhOPI3QdfJ+ntz/FD1b/gFvX3cqTO54kFW5iZnzf7l1Tptj9iylT7B7gT8oUW0ma0ULqS9gp1oyjj32/oWnV10JEZBEHg6IMEdMcupK6yp33nAkGzrTewkW5f8DUBRFTp+h4OJ7cs26xNDbmFmyXaQmLW//6hMmf2HO+SvVoROorZPuoK+C5W2rnP7hekaqm2Sr5o/9F8AKKW9G9SgnShct/qbb1iw+p54zL4kEzoOOImkH4pC9Mfq13F6s52evUtRDW3qb2LfTAuCvhlC/CaV/i7J6j2M7EmYqZaNy7+Lnqz1f1LGDALxMJMq0PfbTMpY8FiZ1CackFcMcpIX5zInjjkjkMVO78GNRd9jYECSmwpE9JM3AS01mycEn1S/X4m4/l728uViVDFYQdKKYsfvzXsxgsDtIWadutue3UX5xK1skipcSVNcmVhsbM+Mwx+62e/14a5y5ddikbRzaiazoaGj4+nu8xr2ket11424S/r+C5yy9meNtG3LCu9PjSxyh7tHTN46hf3K7eW04ByjlWbnuY//nlt/jY8iKRMtV4zGIYXvvk6Vx+2VsQu+c5Kh1n29PKYLtjtZK2jUfLvECec6xqeBVpnvg341AvabH0MCWvTNbJI5EkzXh1nSNdlhz+yf2TTb8X2Bcj7f6M3PSlT84t8t3/+XvOvH07nl5nlPYk917SxScWf0fdI4xQw8Zio/Yo92+5n7s23cVAsdYlujPeyfkHns/7Zr1vl/0adofyZ7+EP5hGRCxlHxA6olQmnwzxD39RxtRNLN2i5JVwPKfhZ3EK7xys3LaSbz/6DeKuTtI1ce0Sru/yqaM+zYKOvf/8Tpli9y+mJDfvNFQaOIWiiqyGojWZzMiWMZW29niYEmE6vW2KzBNGCkEqmVLPEaKhPORG93xMXRANGQihHk1dcOOKTXt0iFvTBSLm2OznCZ1gG+GkLyhS3gixdnXMT/xg7PlLD9DUFDjUDLFIpYeuVLG0EPzhawHJVh1gFeriLTVD5dNnXoP+l2DpVbUBz/hrvTtM9jptuEcllBhhdZxGWP28WclL0roByED4ohaQwfoaFh/6VziohB2J5Lyna/H7aODr6uezHx87/V+BCxjjx01B/I5AcsBGh0/dUubqG1w+9Us4fEOZnrU91T9tttpYdoKSDIWDFgdhR+nZ7z85iuM5hLRQ1URbQSNzmyMdJFJV8YP/Ksdj6uaY/VbPfy+n5TdnNqvz9lzKXhnXc8esnwzLFmqYvjo3IVVijulXZFEEr20MEh30vHYPZz1eIm9Bbxu8Nk095i0I3T5JytMbDd1USVYLPg4X/QA++RBcehMs/Ax0Haf04ADDG2HNL+GeL8JNZ8Ctl8PK76rGV6XRhptuJGlJmDHCWojB8gjrs5sZLI9wYefpbzqZh72X6VQiN92hNFoigTuUpu/fbyT7xL4VezShkTRjXPq0jqsrOQtCYIcErg4XPeFCIa1m+ka2qpnD3E4oDKmwAqdE0ohx6cGX8qMzf8SS45dwZJtKJevN9XLj8zey+N7F/PD5H+6zHEf/yGXgOshiCd+XuPk8TrnIbcd56JqOpVde28ikn8UpvHPQs7YHLRxCpuJkWsOU2hPY8TC/2fTbt/rQpsCUKfadh8lSVPrXKXI+uh0MC+LTSFhJZsUFIgsFP0TI1GiPh0lYBkhd/W0oCkMbAKlysE++mjufPJz36X/kM0P/zTQGEEhKWPw8fzEwrsK+/n74/T/D8CuKQbYdzPmRy7k9dyjZkovt+YR0jYRlcEDbbqbe5p8JJ34OHvoWjK9SlzJgRKGcg6a5tfV6KDCxBcbXWLuKIISAqwfkPjEdBjco02piemN5j1tSA4NoO8TboT/Q5Bvh2kAjSKzZLSZ5nVYKh56mML2iTRlqPYtuz6xu0/ZdDKmq7JWevrpU6+vRvfBvWTL0Cj3b7qNXE0RspWevnjdqG5atTLM+E+c9ak2lKmZYlYB0+Cafv7pPqZaKliQx6nDJskF+7btwdvAcTbD1sHZ+wsCEJk4vzinxiXkf4Zm+Z/bI3BbSQhRlER+/RuaD45os3aK7q5sLBy/k5pdupuAUiJpRrjz0ykmrhz4+nvSq268OIHYjW3h0doHixZ2c/PAgzWmbdHOIR0+dzrOzJw5Oe3PbaR+R5MaOYSgb0Jp+m7Rr102YcbRajvuEkrL1vVCLyNz5vJq9GtqgluduBQS0H1LrZDvzGAgnGibPOL5LX2mI2bEZWHoHJa/Mst4HOaLpoD0m9furSt497ViWwK6bUdVh+JalCNNQTXtQHZd9SgzfsvR1NcaKDRXQjCTeUAY9aIJGKo41NG6mxPfBLwPjzIqahq6bnNg0nxOP+wc253Zw52v38uC2hym6RZZvWs7yTcs5tuNYzj/wfN4z7T1VQ/juYJx0HPzfz+L9/DfI7X2ImR3oH7mMR3M3EBdxHN9BE6q77VTSzDsf4xOGPENDJmO8ZA9hdnZWDbVTHWrfGkwR+ncaGqWo5AbAzoLVEjSlKauKT3waMcOEafN513hpTW4AyiNQSoNmqkqjZ8PTP+bz+lbOzd1GgkKV9FmUuMr/FTx0YC1tZv39cMdnVZqM0FWVf2Adn9f/Hy8XPsEjHI0mwPZ8BnI2Vxzfsvvz27xSJXqMbIH6nGe3DKNbFbnODShtumcDQmmE9VBggDVr5yM9tT4+DYSh2LEZARFVg5mqlVRA81zIBhGXiUBWZFhqv7n+GqEPEmtg8mSWldtWKtIu03RKQ5F2P8RKP8d1bc2YuCTRGBA+1xkFljg63cE2o75PEUmojn27wfoxWH8/3RsepltLgBnh2dAAhqMSZyrQJZRCQQINMP4WrAOaMJVNWkqk9HGF6rLq1E1iOIaL7xmc83jt9eiMd7JldAvPzdNZPW9sUs60cDPP9D3D4sMXc92TKnu8ftp+fBV9XtM8toxuYaA4oCIlUZGHYT28y1ScZRuX0RZpw4qrbS/buIwj2o5oSOoNYeDiTogDNcSub5Gd8U5emD/AxsNq6ThFt0hnpL3h3w40vUZTbpwMyYX+JqGSc+zcGM39Ww4jHBD1gOS6JUXqKwS/7wX1+RpYp5bV/6ukIe2H8gWtyJPhHK8m2ykFM0iD5TSGZlRlLhHDArdEz6bb94jQV6rkwjTGVMm5hn0m9Xs6kHB29KElEmPWiXAYZ8euozF3By39dDJ0AAAgAElEQVQaxdi8FVPXwTAxpY8cyqPNmVlNQtolxhH9uZrF38y9iI/OOov7+57krq0P0V8aZFXfKlb1rWJmbCbnHXge75/9fmJmbNfbBlbPEyy9QqevoNMR1Vk0T9CxoYN0KU3YCONJD0962J7N9Oj019Xddgpvb+wqYUgLhdBCIWhuxi+XFbkvFKbI/ZuIKVPsHuBPyhQbbVddI0e3q6naYlqRhFi7Iq56SOVoe44ybiY74bCLYOdq9cWgGYqU5gPyKjTQ9UCaIsBzOMTbQNivRLDVklY0JGLn8/Dev1O/Wv4F1QQHEXRp9UB6GH6R8/THOUl7iX6ZZKc+k5aYSa7scdmxXbs+v4e+qYhPcSiIsKzsP4jZtFJQHFQmV6EH+/UhOVORkVQnHPUhGNkMkVZ1TXxfzV7Ep6vBR3aHypsXGmg62f4EOx8LM/hHnew2Az0eJtxsqBJ1aUQNHApD6loj4cyvs7K4vWEX1Kyd5cfP/iclO0fMtRnF42HdZY7j0hNyKEWaiLhlNbYQqiPrBs3novf+M7TOw17xHZ62wjhC4AYLQnDVSIbjWo+sdc1d/gXVsTeQ9DwryzRt8xEBN9ADDf29742yZpZEC+IoK9QhIuGq2eewenQTTiDbqfz2skck5RDV/btC4AtJlx1n2uKP89C6fu5fk6fXXQH4Y1SKOjqe9HB8hy+f8OU9Mre1WC08tv0xLMOi7JYRmoqcbAo3AfD5Yz4/wYx37ePXViMxhRCYmhqYbEhvoMVq4ZqHruE7T32HH675Ife9eh95J89hG0r89T0+l630OW6DJBOFQkeKjx3xsUnfji1WC/dsuof+Qj+DxUEy5QzSl1yz4JoJx9RitXBL312cskbSPgotOUjlQffg5+/XueScLwYGVCv4zLhvL3IP6v6Q7FRynMMuUp1sO49Vnx3pKZOt9CE/wMG5NKdlBrmo7zXOfXk7x68dRHdcNqVCbC0PMVBOM1BO43ketnT2yIi68zv/pbrMWkrqoTpf+pQ3bqbp3Pe/oaeefeRJ/GxO7TOALJcxp7W9rn0P3/473OE0vueD56lHJGZLM7MuuwwhRLUT8d4grJkcmpjL+Z2nMC8+i1EnR19piKyT5dn+Z7lr050MFfrpiE0nFW6c7vVM3zPc8NwN2J5NxIgwWBzk96/9ntHyKKP2KFJKwnoY27NxfIcrDr2CZCiJJz0MYaBresPtTuFPEy1WCw9vfRgfH0MY1SLM+HuwMIxqh1otGkVoWvA9O7bwNGWK3b+YqtC/IzHuxl+pRFd/DgyhQfoCz92izJgb7lGSlXBcVebsgiKtFQhV7TalzfjWRhVaTTlXWzmypZYb7ztj/tbE5wCxk2+EfsYN0SirQsfuXkMPtRkI6atKu/RqXW1Ts9RAJt6hJDierc7DSqmqeX2X25nvCXwFr6nfnfQFldm94ju1s5E+2V6Tvj9GEYaPFpK4RY2+B1VWfK03T301Sv27Z20Ppm5WKxmVx5tf+Clt5aLKr9dDRHyVad1j6fRGW0jGOlS1P9cPno2lm/RGE1WT7RGlEtFElIKmBRnwqjp/hG2P7REwTtKz9GSDQ4TL6U9DxFZGzAdPivP02bNpLg2TLWfwfYeQhITQuPawqwDw5MQW4UMpSObBMWuyn5AD3uw2HlrXz1eXrcXUD4ZmHYRbCxZCw9AMbM+uVtW7u7p3a6Lr7upmCaqzrOu72L6NKUzmJOdMmkc/WfOZV9Kv8NVHv0q6nEYTGkIKNmU2ccQrNh+/z8fVIWdBU07y8fskvzJ2n8WssvulmsUQk1cnu7u6+cNOiNqoRleox6gNB+6sRMUGmvtQrGaotfNqUL6r2Na3CmYEZp+gFlD3jB2rofcZvJcfRsttRhOQaLJJNNn8PfB3rw7wvBXm6UiYpy2L1WGfor5nX0VvVJV8T9ByxSL6/v1GfEqIcBhZLiMdl5YrFu35RjY/Cn/8mbpPJWfCMR+lODiADIzqFXgCioMDGJpOcyhJyoyTdQuMOjk8f+/kWbrQOKHt3ZzQ9m5ey+/gzt6HeaDvKYpeibu23MtdW+7l6OZDuWD2GSzoOBbNCKt7qxFi6YalmJpJ2AiTt/NqwIrE9V2SoSSj9iiu5zI7NZtFBy9iQccCPOkpwl8exTIs4macmBmbqtq/A1B/L97TniBaOKyiL1taVOW+UMDPF5DOxCaHU3h9mCL07zQ89n2wmtSXRQUDLyu5iJVSps4KATXCqoKbz8NLd0BqttKfO0Vl/BRBZb2it6xkthshRDmLkHXV18qXUbhutN00R8lfGqRm+ECcAoO0sqh0G4+Io+lqVpKfh9b1c+OKTWxNF5jVHOVTpxxYS8+pJMuIQPktgrpyqqsmm4m2qRmJCqScqGuff+bEJJrHvg/RaUqu40rwPYbXxRHCQzN0kDpCB9+VDD9TIHFav7pGzXNqkhu7AI99n96E25BQFtwiliBo1AVoOpb06NWgs/kgNZ1pJcFSzy2Nk2/0pBK0ex4Rt0a0i0LQk0rQ7QSG3PlnTpBe9QqfHScZ3PZegaMbmJpJq9WK7+a59uRrG96gr+pZgER1wFVQXVTvWAgfu1+R0bIJIVc1jVq2UOPFFZswdQGRdRz9qs2FT3q0ZyQDKcGyE3yen6eMrHubGb0nxL8ek00NO9LBdu1qmk0FFz4pcXWqUpjK4/sebWz4rKBnbQ+JcIJpsVq6U9Et0rO2p+HxnveUjy/Aqxsn675aPwFjyH27+lzaubcvuQf1fptzEsw5iW2/2Ik30kq0tUQknsFKjBCK5DAEvKdc5j3lMp9iFBt43hqAJ29U1f7pRwam8IkwZ3TgDqURVu1dKctlzBlvfGOsxAkL4BoC/X4/5oxpe6ff3/worPi2IsvhFOSHYMW3scsCU4Br1giv5kvsusY+mtBImXGSRoy8WyTj5HD8vZcyzI7N4LPzL+evDryQ3+98grt6V7CzNMjq9EusTr/EdKuN8zpP4YzpJxA3ovRltxM3Y+D7jJRHEKjZMU96NFlNRIwIzVYz1733uob7K7klSm6J4dIwUTNKIpQgrE/lmv8pY2/vxfWokvvmZnzbRtpTcpz9iSlC/05DI7NlfDqMvqbIpltGRSJS6w5ZHFGV9IqGPhSlZDShlwYBifQEuqah46tBwXGfgMd/oLKr6yUBQocTPlf7+aQvKA19g9bnGmBRZIa/kw6/j8sLv+CyyKsU/3UbiWIzc0KXkI0eT3+2xFeXreVaUKR+/pnAd5XRdnCdSoZJTFdkvpQBXyqNLyhSkJihvkADDfousX21qojKQK4TTuLkTbSQp8yCQVVZZPtwMkE6TGrW2OSdwBTbmZrHwNAGIp5b1emXPJuo71HyfCKy1p22pBl0OjaLd7zKdf4AaAZWrIOSGZqgKe81DIT06TMMHCEwpaTF8+g1TCg7tYFLZeBjq2OK+ZKNemCm9R1c32V7bjvzmudVb84VUl9Jquj1y4Q05SSoUF+B4I/zdCS+MrtmYCAlWH6CYMuMIbwtBZoiJtN6l/JXv/dwDGUCbcqrivdPznI5adGn3/Bou8WHL+aLD3+RglvAR+nuo0aUsBGm6BQxtNqtTyBoH5EYLswYVpVSX8BIFEx31zKHvW1DHymDM86P6Am1fgzW319Lpmqao17P+WcGn9FpityXK+T+bWKoHYdKNb04GqM42grAy7n1dEZtVp/ucFypxLtshxCwoFSGp38IT6OSdaYfqch913EqKlZXI6z9UiV/HUicsGDfDbB//Jm6F5nBYMS0wAFHszFlMGsj1CMSHH3ie08IQdyMEjejFNwSo06Okrf3HT3jRpSLu97HBZ2nsWp4Lct7H2Z1eh07S4PctHEp//vqnZzecTypUIw5L47w/scKJIZKDDRp3HmCztqDQ+B5hIRBX36neg/uQl7jS5+cnSNn5wjpIeKhOHEzvsfm3Cm886CFQhB6fQ3lpjAWU4T+nYZGpljdhLZ3QbQFMlsADZIzakTUKyuDZ4BsyaW3HKWdGBm9mU6vF9eXFFLzSJz3DUUsZr4H7rpGGVGlVJn1J3+hZogF9XfHfQIeGlu9CRTvCFSlXiC5yv8VWmk6m8oRWuUwX7B/yA2GzrPhBRRslxtXbKpV6SvV9SrpeU3lNLulWm47qJ9HXoNoK5zduIJUxUPfVsbhCqQH5QxmwsJ1oojWg2q/8k3Mue0wa1Bd63o4RQgnWLz9Fa6LAJqG5duUMq/hAFdm8yyLq6qxJSUlpIrKS2fojsRYEo7RoxfpzfbS2XwQixf+/RjyG5OwyTTRpNK8u0Kw0zQ50LZVMk/7u2rXiO9Wr0/OEngVpoBKcfHwyNm5aidWUzfHdGKNaSau7zCsUZX3eMG0+ep5OqsrPtCgsGi5BQ5qjtKfLXHu0ztxdA3b0EF4lE2JABY9rXPJtZ/Z9WuxH/DC4AtVMg8qxabgFkiGk+iaXiX5lWshgZZ8LaRUSPVz324aB+9tG/pyWCNU9tWegw+BH6yvor5HgdWs3mP1cipQA0czAlQq93nliXkbkftG1XS9pLHOiPCdVjWTl/I8ji2VWVgqc4XZAUOvqPtR7zNqeepGNTCfcTR0HktizgK4+hMM/+KOfauSv5UY3a4q8/UwLNKtNl5GJ1ry0T01e1OI6oy277qZV9SwiBoWZc8m4+QoNCic7A660Di+9UiObz2SrYWd3NW7gj/sfIKSb3P3jkcA0IRkYzPMKwhSOZ/F9/r81oiz5TAP2yvTEW5SXXE1TX3XaCEwgvABfWJ+vu3ZDBeHSZfSxMwYcTOOZViNDm8KU5jCXmCK0L/TMK4yW21YdPZ1NRJ89/9VFW0p1e81A+qqjAO5MhZlthlz+KfUN4Gg8VPM4tb5gVZ2/pkwv3F7+zHYvLKuQVINlcnlsK6pG77vQSmD7VnoWgRNllhUuo1nwwsmZtSPr15+4N/Uz0Mbg+gVXc04SNVenvi03Td6euIHNRNtFZKWQzL0vTwNv1hEWBayVELaNi1XfRymlxtfa2nS7Vss8Qx69BK9AlWBzxboLqnK/s2pBAVNI+r7XJnJ0l12IBWluzRKd25QDUb6M3D4OLlSOIn0ctVrKCFIwQjSfCqZ4OOu0aDWW9Xo1pPZweLgpHp/om24uR2qWhjEZKrkG4EfJNbUrpRq+vSpUw7kq8vWMm2EIJ5RIKSBqWvIsM/0zL7paCdLDJoMN790M5qmERK1CpArXdLlNAkzQbqcVnr3QEaUyjfezmTrK9jTpJ4K1h0a45g/Zmv+E6mu6bpDY1SzVup7FIB6tKnJqcajQu5jbcoIXZHleBP9D28mGlXTw57GsoW1v8noOg/Eoqxums4VZ/1MGct7Vykyv+0ZSL+qZhW3PqkWIGFGSJxxNHSerGIypx36Fp3hXiI5U8lszDry6pZoOj6O80CRkaSOG9IwbB/dkzR96MI92mxYDzFNb8HxHTJOnrxb2H0yTgPMik7n0wd/kCsPuIA/7HyCO7evYEdxgJe7BC93QXNW48R1ku61gjMeK/Lfh0RwpMeiWWeoDdSn7dRPGmi6IvZ6CHQjeDSRkmrV3tRN4qaq2k8Zad/eyK5YwfBNP8HZtg2zq4uWqz5O4pRT3urDmgJThP6dh3GV2arhs0IEGv3+yMuUMdYugBlBcwqENY+l1qW8p/wMi0q30eH30Ztvh/VfU9sZly3P+7+mtv3QtxU5rphrQZENt1RLnKlA6Er7ntmm/u3ZhAwNx/MpE6bDV9XvouNV9fVjqpd2GbY8Wm26VLfhuke/1lSqgko2/uB6RYI1A3wHDwMPAx03aNoEiRklmGYyvE7HGc40uIE1uNa/uwasZrp9QbcfEMq+tQCsjIRZlojT5nlYrktJCJYl4hzheHSXR9W1QKgBl52fUJnN+2VmepIhXeAgMJF0eB55LegYlXkNvtZMtRNuslNVeOMuhq6jabW8RD/wQEwmG+nzM7ihCK5brM6ohPUQRd/h6I3+xHz5gzVOe9c0rgVG75+Ole+lHPIRSFwpsGxBdPZB7C0mm0FYwuRdKQtOASSUqA2IdHRsafNvp/4b33jyG2zPbUcisXSLkKcGLTo16YMLhHZT8N5bk5iVLlDSwKr7GJQ0iI7UDdwm6yWxJ/0NTEstVXKfVzNP9eT+qR+qeEm7oAYLR38Yjt//KV6NNOdLuwtsnOOCXxugJ7RorVtrpBkOOkMtAPnBGsHvXaWujVOE1x5XC6jZwZlHK3LfdRy0zd+l/OMtwzEfVRp6hyDytgS+wwmXXMMvZj1J6PYVtKZthpp17ItP4fJzPrRXmzc1k7ZwE01mgqybJ+vk1Wd8LxEzIsyMTqM1lGLRb/p44CiNl7ognRD87jjB/cfA8esdIobFVXPPY0HL4bveoO+BX1SvWwVCqPtuQO4dzSTtFBnRR4gYEeJm/HV1uJ3CG4PsihX0Xft1RCiElkrhDgzQd+3X4atfmSL1bwNMEfp3IhoZPnf3+7rUl1yojRu0iwgDny5cj4NBRsbo0DJwx+fUF5GTH5Mtzx2fgwNPg7W3oSJNzKCzrKu+cIWm/l6Imkm2abaS/eghPKeMjUHJ8/AlxESZPqODgu3ieJJPnXKgek6lelnKQGGc3IWaoKTyfwGI8qgi8ZUZijs+q2ImK7MGvoMENNyA1OsYuIH8QpCYliPRNgznfnfidWtorm0gewo02z2JGKb0iQQVtIgE8OlJxuke7g+uXRDxZVi1zrPBPjptmwEhmevUpEVFIWj3XGrNtoJHL5DhNM1mroRNQZRnJRPelz4HJg+kyWpqKBspekWVKGOE0NBwpUvBtzl6o98wEeaWgEOd9q5pPPGZi7H/9b/B8bENiemA4QuKHzxrwmu2O0w2gzCZ8RRA13Rsb2yKgodHSFMDLIFgbmputapeDG0g7IzVt+s+FE12iz01iV2/+nqO3e5h+WNzqCwfunrrzGGNZHN1/Q32GFVy36qq3OUcrPguPP1j1PvMUKT/6R+rv3+DSH29HCb7xFc46bleTn/UoTntkG42efDkMOmjJjG1xtpg/tlqATU4rxL8Z9QA2Mmrgf2WR9XfhBPqflZpdNV28ATZx1uCuScDXwpSbnYo2eMxH2Vl1OKG1PPkPxzGkz660IgZz9PZv2qfOui+3mScZ4bXcsOGX2EKnc5siM/c7dDbJnj2sDCPHGhTNCWPHiYg+xpLX/s9tuewsO1IdLEXgygpg4Z/Y02RUggKuklBD2EYEeJWM/FIC8YkJukpvLkYvuknisxH1D1YRCL4wfopQv/WY4rQT0GhjpgOruvnsWVr+X7xKzgYFAkjBaSSKchuVBpXzVTEE5SDsDwKa38TMBVfEflKTJmTh/iMID2mFBjDokHspKSkxTFkkVGimJrA8Evo0uHH3nlMS1hjU24q1cvhjepnIarG3PGTzALw0HGNJqwKKX7s+0prLJV6v6JbkUFFXhF5UX1+WmumZXeSh/FoJHsKJwFJr6mT9Dzqoy4tCb2Grq6NMIIEE6mSesZVZhfnSlyXDAMalvQoCYEjBIszWSZCqOpYrp+rrVl8xciTN1RChS50EmaCq4+9GoDrHvkKlLZieS4l3cCxUtUukBV5jh8kq0yWCHPWY7UK3I9iq4ien+K0lRna0j6DzRoPdacoxFaN7yW8W+yt8RRA+o0lB9KXDQcIy4+Hv3hUkXhP1HL6lx8Px+3l8U6Gm1+6mfc6jTvyWvW8ZjLZ3Elf2PedG2G1PPe/gFDSh4qIyvNUxf4NIPTj8cmRw3DuXIOnCwoRjdiow0V3DmPO2YPPFSj53CHnqgVUele9RCe7XX2+X31YLaB06511BL913ltH8OeeHBD7Gr738NWk7dGggKBms2zb4Xsv3bxPhL6C+mScnFtg1MnvUTLO0q2/xxQ6YT3Myu4UFy4fonNQMuMJlw+vbmLFnBL3nhimV46wJrOBNZkNtIeb+cDMUzhrxokTugPvFaQE1wbXxi3nGMkPMAJEzCgJq5mo1aR0+nrwfp6KwnxT4WzbhpYa6wMRloWzbdtbdERTqMcUoZ/CBFRkE52/7mfYjxEyNdrjYRKWARmlTfd8Hzy7rv+nQKtWiIObrKyZMHHzAbGNqyScuhmBV/wOnjLfx7HyBTr8PvqMDm41LmZDeAHNwD/d8QKzVgTxldUc+okVJ0k9TVY/76AdwxbM2PYUfO/IuueO/2vw0RD4aMExOxjkZZgWmFzyMFkaSX0ST0WWdOiFdK67iQFdq1boqyk3vqfiCe28qszH2lWikF0YU5nt9nSWDGfoScbZaBrYAkyp4iwBuotlxlJGCXaO7v7N/GXHTG7WTRzHwTIs/vJdf6kqy+vvZ8nQED0hn15N0lkqs3hwkKvbW3j3Jp/zHy/RlpH0pwTLTlCJMLlxHrayAe0jtf2+kn6FOeUMvvSrswEj5RGe2/k0V9171diuubuRq+yt8VS9lj7HbJRc8KRflQUtX6jx/EF+wwHCb99rAC4XPAWWrTroLj9erb920r3sHQpOodavYRzGrNudbO71oJyrdUomGAwLXb3P3gTM+N0aRqNNDIoCvu8iLZMmGSX5uzWwlxITQCVcves8tYAynm57plbBz/WpNK5ND6oFVKxvhdx3LYDmA95SYvhK7jX8us9sxaS9Mbd1v2xfCEHCjJEwYxTcIqNOfpfJOH3FIeKGmh1af0iUZUD3ygxNaZt4ezMXvO8sLlpwGKvT61je+zCrhl9koJzmZ6/ewa1bfsep0xZwQeepHBDfTZPAvUDRKVB0Cuj5ncSNKHEjiqkZweBsitS/WTC7uhjdsYVBmcXxHEzdpE0kSHbN2f2Tp/CGY4rQT6EhTtOeA7PENDkEwgLagRRoBr7vIqRDpQGTqNaWAmIynlUDxDpqGffP3aIIfdDo6dPffoCmiMlv627Mo0WbbQM55vqSpohZja/8zwUf5ag1/6IqbHKsdqHRbT1CmZQ/WksMyQ0oKco4SMBFAwx8BDoOOh5dcgcMpMFqgbZ5Y5+0qzQSUDMTqTm1Kutzt7DYauI6XXWhtYAS4EifxeZ0WPSPte2ZkUCyNK4y234IZDcxoktGdA1TSlKex4BucF1rM0uG0nSXnLFpPwhWhjSWaWXatGas5i5KXollG5dxRNsRdD/2fbqLLt3Dg+oqClXZP3NdiXMe1HAq0pogerIYhrBbq8yD+nmgqfYKHLhuhL+6z5sgy/nJWTCQUhr4CwcvZNnGZbvVxu+t8RTg+M0GV9xXrh17TvLx+zxuMSy8hRMHCKDI+2/fO+kmXzeiZhTbKI+txgewx9+Jdyeb21eE4wF5D3Yo1DwWVkL1Uyhn1aDS3fsoxD2Bs6OPWKKJuKh5BKSU+68xVHImHHahWqSE0W0BwQ+q+PkB1d154x/UAioFq/PYGslvmvOmkcSV/avwkQ09Kc/N2/3z9xZRI0LUiFDyyow6+YbJOB2RVtLlTDUvfv0hUdYcpNMcTnHdUbV7kY+k7Ns0h5IIIcg5Bcq+zf07H+f+nY9zeOogLug8lRPa3r13cpxdwPM9MnaWjJ3F0sMknOlEp5pWvWnYcdHxOP+6CmEItJCOKNmMuIPkP/0XTFH6tx5vA2HhFN52qBDVUBwQ6st9tBey/RBO4lfzTWoVeL+uPo+UtaWCUDRolBNVhPX3/ww958P3juQG72scUXhqzCH0ZcuYmkY0ZCCEIBoyMHXBt16ZpbTsyUr1J6gwNoAAmhlRx5qcofafmM6YbliyopTXEfikiZMniokfDFRQUphCP8wdp5OuTyOpP7fHvj/p77o9gyVFaPdhFPW4pAjdJ/2jInDnfhcSHYp0JDom6PZXHnoW18V0tpiGmk8Qgj7TwBVaUKlPTrweRpiellZMoREpjiCEIGJEMHVTZc6PbFFyqIp+PzCsfehxD19THWERYJvgBv28DA/CjrqEYUf9vHxh7XbygcedmixHqEdXV3Kdyr5vfunmqvRlwjHVoburmyVd59I+sp3RgZdoH9nOkq5zd6lbv3xVBGfc/h1drV98+GIcz6HoFpFSUtyHuL99wZWHXklmEp/fZOv3O074HCCVSdYPHpFqvW6qaNumWYrcR1tUHOx+hDmjA1keO1h4wxpDCaH6RBx+CZz1L7D4bvjwUjjty3Dw2YrIg/LTbLgPHvom/O+l8NNz4L4vw9qlMLJ17H1sP6Nn0+1VT0pTTtYNPn2O2vjGNQ+z9DDTrBY6o9OIG9ExhHjRrDNwpEfZKyOlpOyVx6bZUNPZp8sZmswEES1E0ozzgZndzIpOB2Bt5hW+9eJNfOLJf+bXr91LxslNOI7Xg5JXZqA4wLbsNtKlNI63eznRFF4ffhRbxR0XtZNPhogWPfLJEHdc1M6PYqve6kObAlMV+ik0QoWMWk1Kp5jrV6TWzcMJn8N/6Nto1GITbUIM0UIbQ+jhBNiZWnMm6QUDgzp4DqQ3QvM8sJo5wMvx+dyN/CfwQvR4jig8xRLxG+YYgwxkprPUurQaXzlzYCU89rDaeWq2+jKu60SrvnprhF1DUox1UpRRBgbz2K5GSutgmj+Arolqyo3WNh9/cAtxv0BUlpHBVEP1ay7eodJ01r+nJrEZ3QFoKhYlaB5FKKE685Yz4DqKVAihZDTRdvDKdB/9Ebrrk4BO+FyVtK+MWvTM6KA34dIZ72Bx1KKetvaMPIcTimK7+cDIqxohDRkGc7QIvU4WfJvrU0l+mkpS1LTqOXRJMWZ2oqpDb5qjpAp1zZaQPrGsoKT7NOU0DE8R8mJEpb/8+CwtaCxVqyhuPbyt+vT2EYkvYGaW6nNHojVZjqVbFJwCVnysdqehNn79/XQ/cRPdWgjMZjXb8cRN0HzopFXsyECWdEijZhQGx9SID2Q5sUEyzdbsVlx/YsxjSN9/hPYzR3+Gx5z/qGb6V+ATDI7eDFT6RIx//9X3j4AauY+2qM9rOauiMN3a+2dl/yp6Nt1Ob6Gfzug0Fh948W413y1XLGLLv/4H6eIARd0j4uk0iyhzriwEsBsAACAASURBVLhqj08h+8QzQXJOH+aMjj3Poa90dW6eA0csUkQ9vRl6nw4q+KtUbGZhENbfoxZQn/16iU5ycqnX3qK30M9Vk3hSLnzyjRtIVGBqJm1WM01+klEnR84tsKDlcD598AdZuvX39BWHiOoWhmZw/fpf0hFpZdGsM8bo7IHq49bCTn6w4Ms8N/Iyd/Y+zFNDLzBYHuF/Xl3OrZvv5pRpx3J+52kclJi1387Bkx6ZcoZMOYNlWCRCCaLjBilvNPY2VvdPFb25XrKHt/E/R9S6l0spGd2Fn6kR/lyu15sNsS95tX9uWLBggXzmmWfe6sN48/C9I5WEpP6GKGXQGCVGKb0DgTJ2CiQDoh0HDfQQcxOyJhlxiipG0WqBRF0FbuBl9dh+SHVVPpfl1XKcn8oL+KL/Y0rSoCRDRISNicsN0c9Qdj3+zvkhHU3JsWbBc78Lv/qwKjwicH2JL0HHx8ClmDiQrXlF8zUhMP0iQ6KF7AeXKmnRY9+nvONFzPIQME4xJAw004KWedXzRwuB76iMbAg0yRorLZOeVJJeXdDpuCzOZOguBoONCllOzFRV8PprFJzDyqhVjWesl5Ys6TqX7pfug5EtnJaCjJDUOwgq+fCzHZd2dBYQ4QazFFDZsV9qszGIt80HoOgWaY+0c9MBl8OvPhJ0ezQCw7DkqXuaiaR11V026GCpSdjRCl+8yiCw7qqCvtCZl5rHbRfdBsDdpxxK16DyS/tCPU+TsK0Ner74bopukcHiIG2RtjHSl+oxnX1T7aB7zp+Y+mIX1AxGINsaj9+e+27iow52SKsOPEO2Ty5pcsndz0/4+7N/czbb89snrJ8Zm8m9l93bcB/7gmePPBRrnDFWACUT3rPmpf22nzcMrg3lLCu3Psh1a27AFAaWHqbklXGky5LDP7lLUr+yfxVLf/sfvO+RPK0jHkNNOg+8N8aiS/52jwyg2Seeoe/fb0SYxphOsR3XfOr1N5eSUpntqxKdVWpgPh6JGYrcdx6rCH5ixj7v8qonvsKVX1+tPCnjpIqJEpx61/J93va+wJc+WSfPqJvH870xiTchLYTt2zjSo+SWaQ03jSHNUkpyboGbTqi5TnYWB7lr+wru3/k4+bqZsEOTB3JB56mc2HY0xuuJGG2eTSOxpS50YqEYCTOBqe9BVNXrQH2s7pj79sLJY3X/VHHVvVdNkCs2vGfvAm+z6/WO0mpNVeinMBGNYvNyA1AahlIaU9eRXsU8Kpkud5ImSe9J/wZdTbvMuK8S2NTYCL5YLM4R+gj/1vQwZJNk/RC9mSJFVOXn4uJv8CWkwqgkC89WVXErRfr33yUjZ9LpKdNqqF4uE20jV8hjSR1HWIRlCVN4/Dp0CU33/ZLTyv8J5VFMt9j4ky1dVXl3ijhOmb6yRc73mMNOwsJAky74LivjCa5rimNKn6QHA7oWaNpHFKmvtEYvDKkKX4OmQT0zOibGM5bz9Kz5Ed1FC6xmbKkGHQaqS2wFPgRpN3m+1KIHsqiaJKpCIPs0QUzKsTr0rm547zXwyL+rgYoehkgTaSGp3rbrGKgnGTugkCoSspF3Qgb/k3Xri24Rx3O48tArWbZxGbAbbfw+5LLffWKIK37nIGyJbdZkQXefGOISJjZHOeYwybT8RB1z4djXkdjRAOYk6YGTrX/bwQiB0UrPlrsxjQgRPQS+r3Lk3RI9m27fJTHv2XQ7A4ck2H54rcJX3IPnVTB8y1KEaaAF3WeFZeFTYviWpa+f0AsBrQep5ajL1cB26JU6k+0qNUuR3QHrlqsF1Oe5a0FA8o+DePuu91OHxQdeTF/Tc6RycoInJdv65ndP1YRGKpQgacbJuQVu3/qHiZV4r0xOeti+XV0PYPs2HZHWMdubHmnjqnmL+PDc83iw72nu7H2Y1wo7eGl0Ey+NbqIllOIDM7s5e8bJNIUSe3yczwyvVTMI5QwdsQ4WHbyIBR2119+THqPlUUbLo1iGRdyME3uDtPb7Eqv7p4p98TONx5/T9XqzMUXopzAR42PzcgNKQy4BzUCXHrIul0EgSVgmR3U17TbjnqbZihS444yplZztgLwlhKCTCAO5MgUnRKfeT8p0CZdHa5n2Tglp50mObmdIm4ONTgS7mh+vIUDo3CFPY6H+okrQ0TpYal3KC6Fj+X/pz4EYCrLZa5iQfyMMiqUiBUeQFya6JtA9hzIaIWGiS5eeuIUpZZBeoxEJYjF7UolalT41S10Dc1xL94Cc9ibcifGMxTS9OtUBgCklJaGkQLqUeMEXlCYlS9I5ukslCq2TfzFKJKP26MRpztO+NOF1spyNDCQgVQTTBceAkQhEJpGHbM5srv47YkN/AprqnjsUUevbI+3VfR/RdsTup173IZf95fkxenyb8x/3qgT9zhMNXp0fa9gc5aJfbcOXkkJkXLa+sRkumnQ3ew0/0NoIWWtg5Yva+vF4aF0/N67YxNZ0gVnN0bERrm8hqklBQij9kPSx0Ogt7Nrc2lvonxBraOnh3T6vAmdHH1pi7PtbhMP7z1Q7ZsOaalLVNh+OvkINygfX1yIyt/9RGd9He+HFXnjxDvW8pjk1iU7nsSpLfxJ0TzuWX1xyGmaPSuApG4rMh32NxBWL9v857SEqyTiD5RHiRhQfHz+YzQ9pIUxNx5EeeOUxlft6nX09LD3MuTPfyzkzTmbNyAaW9z7EU0NrGLYz/Hzznfxiyz2cMu09nN95Kgcndm2vrJ81iIfipEtpbnjuBj591KfHkPoKSm6JkltiuDRMzIyRCCX2q5RuX2J1/1Sxt430GuHP6Xq92Zgi9FOYiPGxeW5e6UhLGaWnlZ4KqhRBAyndJBxrmTynfTzJr5huG+VsP/Z9yPaR9UMM5MrYrk9cdzBbDyA8GkgSNE2Z+aRb1cyHcAnh42KgIVV1WtOgPMpZ2pPcYFzJh9zb6fD7WFS6jbLrMUduD9JgxmqnKxKNyr/RTfrKDjMoEJMlhmQrLiY6Lp4EPRSj1zTr8uUDnbiU9BqGkrEYYXWtwnF1vg3IaWe8gy2jW8jaWRzfwdRMQr6DJwTnhEbolBptrocpBVlNwxEaEV+S8D3muC7dxSIYYaJSkBXjDXUCDTja17np0nvGvhYNYjdXbltJ/62foikn2dFS+/OwA8OxxlUu268N0gaaROPnxsUeT81WsQ+57Ac1H8SWw7fwrwfXrmUilOCg5JyGzVGssnrN0kl1bqpaKjnr0T0wzE4aXToR6fYIrX1FfK0mYxJSrR+Ph9b189VlazF1MSbp6Vp4y0n9hChRoVGiTGdqrjLV2vkJmnuAzug0BkrpWmdYlLmxM7pn52PO6MAdSiOs2vPfMFPteGg6TDtULcdcqfw3A+tU5X7bM7Djj+q9ObJFLWuXquc1H1CT53QuUN1w63D5ZdfwRHwGpVuX0TRUJNcaIfShCzlhX2I89zPqXy9f+njSp+gVmRWbUdXS9xWHqtr63XWNFULw7ub5vLt5Pv2lYX63fQX37XiMrFvggb6neKDvKQ5JzOX8zlM5uf0YFU05DmP0+0IQNsLgwtIN/5+9N4+TsyrT/r/nWWqv3pcknY0k7IEECBKBRkAUkICyOIMLGoiK6OAy7zDyw4EXt7zOqDOijoiI9oCjOKPIEhBBEdKAQQIkQNiykK076b279nq28/vj1Npd3elOukMIufgUlXr6WU6dqnrOfe5z3dd1T8WAPg9PesStOHErjl/3E/GprL22j54EeyOr+3bGeI30RsM7rb/2Jw4F9O807Cn4yP093fMmm+16fiGv4Cve7QQDNUTDPpWNytuJS6lMpkBRNQa3FzKKM3rauVI8wHyzj2CkRu2b7MG2MiQdQVL6qGM3fmGj5QrzHvcWsSZ2Liv6v0tYppgpXDwMkm6Qbw2+j2+JjfiFUJMKTwXzQqoVgpnuTqW2iMBD4KKjawZ4LrO9rfyT8wMSIkxcRKh2+/j/nO9iMnolYlnIGusAJ0wv9TTTzXSvM6e9L/HQINRIi51S+vJCUwM9kBGCFsfO0Viq1ITIX017agdtZpQOw6DF9VjuQeupK1mS2sRzXc8hhEBHJ+tmSWlQ7UmqhKBHeMR0naQAS1OqQq4QaGgsH0oAEgLVXJGR/CRYWg4KIAl5kuWpbLEgaWATLYl+llsmrSWym+0DK1i58w/MPi3M5Q8pZYp85tBw4f5T1PkKPVXSWflzW6cIrnpEFo6tTkE0BZG0ZNsnl1O34irWzRMFLuVYspV7o8ueXxpuDjePWBq2d35thDmKXpRoKmzLGtBUoq1fEW88yq2P/SN3+SFVAyG5jSse+0eu4d8rtu/FyxZx0h1rCFk5EysNMgFB4NpPjdj3ttVbMHWl8AQQ8hmkLIfbVm95ywP6MZfe8yZWoTratz9O24af05HopCXYxJK6hdzf8RdwMmXc++XzPjSu69Z99BJ2rfwB9u4epOsgdAMtHKLp2hV7Xyy7t9AMaF6oHid+Ut2Xel6Dnc+qLP6u9apgf+BN9Xj5t7k3Mb9YYDvjRAjWTF0b9xL5vvzyzh1sCiW5f6nghcMEuqYT0YN8/sgLOK7m8D0G8GOhKVDH8nkf4siqufzXlvvpyvThSJfX41t5/bWt/HzL7zl/+umcN+N0aksyuqU6+Xn4dB9dFZzDR0PWzZJNZxnIDBAyQ0R90TL60EQwGTSUdxIO9dfUQb/55pvf6jYc8PjpT39682c+M/UuilOOfGbczih79PQgbPwD1C1QBh25vyeTSbYndUJenDPkcyQIks2k0ANV+P0B5QqbD3zyJjWZQeK+Jj796iKOS/2Nf3RuR3czZCyXaGobItWLZ6XwPBsfWSKkEdJjh1dPTIYQnev42asaluNxqr0GX04V3hMCG5M15lJmyl00mRmwUwVudh75mFIIcmZXGrqu5/7m4dMlg3oDjieplTFqiJcF7aMxKwWAkyGtR/GAMMncCoAyngLQdYM61+WJkB9PehgSMkLDFoJrB4aY4+Yaqpu0G4KV1QEyQhCWHjFN5wm/zpyqOTzU9wK2Z+N6Lq508fBUIS9QK8EUGkO4JDWt8NYliks/W+qc/K5rIRvn5K43wHV42e/HydFzZtg23+gbgHADKweeI+NmCCd6ieHxhAlz0JmjBcDz+HpsPZlQDbsiGTZXW8zphtok9Efh7jME6+aXZLSGcVLX96wn42bYGErQWSeZ0wPTBiCchVgQklUmNZZB4rG/sMp+nt11oiBbaWomHh4bBzbywQXDeC718xXtYek16rl+bJHuOVVzmFM1h40DG+lN9zItPI1rT7iW1pmtxB97DC8WQ5hF0nK6vwcpIF4SJ/gdGKjWOebjnxv1Orc+8AluM7I4QmVHLAFrDZA7/srJi8uVW25ddyu3DzzAjkZJYyxXJNwk+PVZBosuupI5VeVUg+//6Q2ifqOM92togp54lqtOP2zM9z/VGKt/82jf2c7Kv32bjGcT9kWJOSlejW3hotnvY8gaojc7wLRgA9ce+bFxO6JaOzuJt68B2wEpEZoGPgOjoZ7+u36LzFpooRBePEHiqWcxZ83AP3PGVHVDOTRdrWTOOEEZXZ3wCZi9VHHshYBkn1oRTA9A9wbY9Ci8cBfJdffgX7sG07ZJaiZGwkU+8zK76iQzFxy3f9pegnzhscxaWAENN5bghI0uu+oFPXU6Pt3k/BmtHFO9AABbOjkXkoljbf8Gbt/0O3ShUWtWYwodV3p40iPtZnlpSNFzOlLd1PtraPDX8EzfiySdFIZmqD4HLNeiIdjAe2e/d0LXl0gs1yJhJUg5KSQSUzMnxLUfz2/hEIo4wPrra2/FRacKh1RuxoGDRuVmT2ohub9viUls10MTAr/M4GAQkGk8zcfMxjroeVUVpQpDWchLCdJlq5jFitD3+ff0jdR6/WRFgBa3AwMHHw4eSuLSxAIpsTFx0NnKDAJkGdRqMXWNsNVLRhSX1Kt0iwGtjjvFRXxPfhdcGyldRhtD8pKTmu4DJHieGlCbc9mk3S9VdJodC44RJuso1RyZc8Xdos1hR43L3VUmHYEw4WQ/uBZJXVcqN/Ekrb5G8FepzF3NHFb44vRogmC+UlQI0rpBo+anI9pAVc6kBWDjwEY0NDzpcLing2vxmq4MXQIlUxAHVVj09MdzWv55R1w3S5nmPrBi1mH01M9Wy51dr4Cmk0bSKDXusKtASs7zDVDVeDSv971akuUf3tmi4j8Pqz6MoBHk1b5XC4P8Tb9yqckV/WlC4+i6o/HSaTbQwS+vOWKEUkbMivFwKS1oklHKoReBADKToad7K67nkQqWr0bce2EDt3y1fdRznfqL40iL8qVOBwhKePrKl8r3/fWpxK14xfMcUXsEv7vod2XbPvLTNXTHM4UMPUDKcmiKBvj1Z5ZO+H3vb4ypiPG+2xX/PJsAOzVurfdtX7iBWFcnvSKF7TmYmkGDDOEbymDU1xaKZQG8jNo25wcrJ/297RVcS9178hSd3S+q1bsSSAnxhI+hfj/d2SgnfedXKvmyH7HtCzfg9A2gBQJsTXbgeC5+WxKPGtz5mdmknQyNgVruWPoNoEQZx07gyolp59+w/pYyEytQ2fOwGeK46sP5466niTvJwt+OiM7h2OoFPNXzAj7NwOeLYLkWtmePyqGfKIQQhM0wETNCwNj/hcmHsF9xSOXmEN6m2JNaSO7vlpNQGu1AFj9REvw4fA0fTP2WmZlBFRyHmpXefN5RUvMRdfsJmjrNyS7iqMI3EzuXay8O2EKqDLcfGz82R7GVXqoIeBaG0OgVoUJlqpSSLH6a3N10Tm+F3lvUNccY/5V/rVRa2uFGiO8u32GCwTyA4aTQkTlKj4YtfGyvN/iPkIvpZqkKzyMjDOzELr7aN0irI9S1A9Vq0iRUX3cYCao8WWyolASERoeXHcEtNDWTjJPBw+MV4aKZGl6u2BYvr2CjlsFTJfz1gq687lP0H5nrTMNPh3Cp0nODlK7kNwNC0JHn3NtpWgJ+etwMYw/Nw4UXFRzXYWtya1nGrnFQGeaAIJ9AEIEAjbsVd3LKuJSj0MuiZ5wBN91YpnJz9+lxbMfmzKeGCkW0j59WzeajwmNeIiVG3kT13PYR+9qpUc9TWlCcx9VnzOOm+zeQshyCpk7adrFdydVnzNvzez8AMGbxm6apQNUfVd9PKzGu4D6+cwe79ARCaGhCw/FcdskhZqZczBnTyvadsmLZvYXuU6ujHWuVUte0hTD7NLb/8k6ijVmqo1k0DaqiFlVRi1nE4WdnQ+PRRQ7+9MXgG/s7OV6M5iNQWnhsew6a0LBNqB1QVMLhRczDlXFidhLbG53OWIqK9BnNx0A2xifnfZDL55xPe89zPNDxBFsSO3kjvo034tsIG0EgSDo7xIzIjBEqN/sCKSUJK0HCSmDqJhEzQsSMoO+LvOYhHMJ+wKGA/p2EPamF5P7uM7Rihp4sXVozT4kT2Djt3Soz2LYM+rdA1lODVI43HiHNwtTf6NKaVYaeADZmgasuQGXnh0HDo5lBdshGurRmqmUfcdenTGCFwJQZOkWzCmTu1UAzkJ6NqDDuSyHQNF1Rgermq/fnj6pJQM/rBX77xKFY8zoSHRczVMVvgjam9JR0nxAEc2oWbVJXMpNmUAXznqVoTXaaFiFHZOgz0qNF84/gFgoEXklY7eWyX0IW5ShB4kqXkF7ymZ76RcXj9Vz1+eT3D9TRoqlgPWgElRzn0E4ySFowCm1dfrTi0JfbMpXDkGCU8OgdoeMJSWeyc8TAly+QzZoSkStAk5kModlzsXM1GJPOpczTyzSfmsTm6gPgu4WgPnrGGYXd47ls8m9PLCrnpJ00LcGxJQhDRpC0ky67kbq57SP2NUOjZujdCpPMM49q4usoLv3OgRQzDyCVm/Fg3MVvQhSDe88bM3O/M+oQGALXL3KHCnQLMj4Nfzb71hTLjhdbn4LV/5orjq+G1AC88nte7K3D/6aF65fUVNs01GRoqM5SVWWjaZ6i53RvgBfuVOpeTceUB/jDVbPGgfbu51i54XZMYVBlRujJDLByw+3cAMwuKTw2NQPHc/HZkoFa9S0frYg5r4wTNcOknDRDdoKsO/J+X4rmYD2dyR6SbgrHczE0nbAeYkZY/e78uo9zpr2b9zYv5ZXYFlZ1PMHTPetIOmmSThpd6BxbfyxhI4yUctJlKW3XZsAdYDA7SMgIEfFFyr7Ph7B3OGQsNTU4xKEfBw4aDn2oUXHmPU8VdOXVQs6+SXGSc3/3aYIhS+KXGXzC5Wfmx9jGdK57/5HMbQir/db9tyqO1TQ16Apw/HU0Wtv5jXkxp7rPKloMkgipUfnqhZBQgInLf/o/xVnOkzSLfhoZolrEMQTEW29i6cnvgr/+GJwMUnoV18o8oaOFGqC6RXH9q1uU9fvulxQFJZ/hnijnMxe0k5OlRMCtUT9hCaJquioCBAwzSK/w+LheB4kudf2zb4J5Z8HGP1DnSJ7wqUDZQJLRDcW1P+YqWo/5uzJu4WB2EAAt5yuqoVFKkROo4FEKwVVZwcknXa3+UD9ffcY7nlETGMMPgVragwaP1zaxNdlJLBtDN0N4RgDbzXBt0mVOVLV1zqKPMqdqDs/vfJq4qyQ3DQRabsBcks6yyzTUFEdoqg1Iany1WJ6FQOBKl8WbPT7zsMfMHkl1CvDA8xvUEkJaFrP+6XoOP+6MqeFSPvBFlQ31hdTnppuqT7pfVhz8YagL1PHEjifw8DCEUZhcXHvCtSO47aWwPIe1Xc8hc3UV6vPQuOr4T3PytJPL93Utnu16ttAvl7V7nLxRMhSCwcYgnz7+0yPOP7chzGUnzeSq0w/jspNmqt/f2wR71adCrSThjyqn6rwpUG4i/qOuBzju9SxCKqlP05YYLrSfHOSo7pwpmq4XDKcaP3PF/uPQ7wl/+aZKLJgB9ePN0RV/Xutx5CYP6QmSWYNYj5+eHWF+cew8zvrAv6iJt+soDwvpQbIbdq2D1x9SQf72v6rCfSSE6ssdn0fB11+6lYxrETQCufoVA096bIxv4+LjLibx1LMgPQzTRzaVwHAlj53bQGeNxJYO1x75MeaER+9XUzOJmmECuh8Xr6ILM0B3pp+1/RvwpIeGhitd0l6W9zafwnE1hxf2E0LQFKjj9MYTeN/0pfg1HztSXWTcLNvi23h0+6M82/UspmYyMzJzSrLptmeTtJMk7AQSiaEZ+6yQ805E3lgq42YIm2FiVowndjxR4NbvZxzi0L/TcNBw6IH1j/0v+pofUmfvot+cTqx5KVVdawqvQ0ecybzE8yUqNxfS2dg6MjP4b/OVgkPe4CmsuOLpeB9XVd9RULk52nkNXSjKh+c5eFKiS69geaRCa4GNwBTwnbqv86n+7xIhjU+4aLqpOOgf/E+lGtK2THG/031lZkUIkGhohgl/99/lCiOVagcGt0NmUA2QQkDVLJx4D5qXrjz50H1QPVP9O94FToYVc+bRE6wuZOZBZXUt1yKW7iflpAlJuEJUcc2pN6sdnr6F9sE3aAtpdBg6LVqQ5Ud/gtZTvlC8aI4qsphtGIDQTZWZA6SdIisEUc8jpWmEPI8rhuJcE0/DTb3lH3aBcrKd9poGVgY8zEAV8WycvkwfEklAD3DVwqu4ZvE1ZYfmMyiv9rxEyknjAREJVwzFuCaW5Naaau6KhkgJCEnBFRnJfU0tCAT9mX6OeD3BVY94ytK+ROUm44fpx51C3YqryjLk48W4MzujuR1nBuFLL1Y8D7BXWaPrV1/Pw1sfxpUuutA5b+55fPuMb1fc98qbF/GJh61Cv+S5+r/+QJif3nRw3GNKMWmZOM8DK8GKP3+O2nXbOeupBLUDDgO1Bn85LcLAoha+zwdzKjfdmNObpl7lZqL4r2UqMz8so3FeKMnRnTWc3j5Iw6BLb43Ok601vHqEn4fPvq24bzauAvm80VXP64xITGgmTDu+mMGfdpy6dw3DeY9dTZUZGVm/Yid4+OzbShSDuknWB7n/FMFTc7Jl1JyJwHJtYnaCpJsuS0rcsP4WOpPdJN10SYY+yIxwEysXjS5LC2B5Nu3Jraza8iCbBjcVtlf7qjl37rl84LAPUD/M6GoyIYQq6I+YEUJmaM8HHAIwOW6zk4iDikN/KKAfBw6WgL5U1zpo6iwY+itfdm7HEyaOFsCUGUwcelu/xaKzPzz2yfZUYJtHSWC1pTeJ7XrMd7egIclQHGh04WKLIJE5J4x93sf/FVb/m6KTlEwJiExTgf/w6w9rQwElwV2+b6L/cwn1sp8m2Y+Og4dGACunRe9T2cL6BYX2tJ/zlREW1n3pPuJWDF0qLnW+lVdn4Jqzc1KGY0mHllBFTo1kSQup6By5oN6xUwSl5OntFUw4bh5mU19ynRU1frb5Agx6GbJuFoGSuzR1k4ZgQ5nt9pjW3H/611E/nxXTmws36st++FKhEDYPvw1DEY3L/rhhZNv3gPad7Xz/ue+zeWgzhmbQFGzC0I3RLcPH+H5W+tz21np8ojbmvztvIVVxd0S/xKI6lz788oSu/U6E6u9vYQqDgGaScdLY0uGGYz894SBzv+P3n1FKN2ZJoaWdYUW1Tk+0qUybf3jhaUVkYkr7Ph/g974xch/dr4L6vAZ+80LQTVasuXGEH8C4rrmPsD2HmJ0g4aSQUrJizU1EjNCIiUXCSXHH0q/v+YS1s5ESXh94nQe2PMBTHU8V6Gua0Dh1xqksm7eMY+qOmRKX2Dx0TVdce18EUzP3fMA7GOf97rwy8QfYP2IIo2BcXwohhAu8hKKpvwlcIaUcnMqG7Q0OrRe9g1Cqay2E4CPOvdjSICmVQYetBbEx0Nf8cM8nO/WLiq5j5Xiuea74cLOfmjmK2gNYjuLlD+YKZvVcuKvjoknJnfIDKsgdxUkVgK3tEGoqzzoJQy1HD21XyhHfnq1WENqWqaC2pA0FDHMavW31Fv7XdzF+4eLDKijZuGiqRgDUcnnJ+2yd2coNp9xACGHgqAAAIABJREFUY7CRmBWjMdiI5VroUv3qBcXnu/yo4DofsMe7yrndbzyqrvH0LYr37QtxhetTspSAdG0c6SCF4IqhyjzsMgy7ziaZpd8awspx1iUSFxfLtTB1k7YNbYVDS62581mowj6nflFNhHpeh64N6jkzCKd+UWm8u7bKtgxKssNW/rMGNAxOTAUDikHz9vh2dKEjkexO7cb13BFtL2CM7+eY72+CmOi5Ggbcyv0yMPFC7Xci1G/uqzSGm4m5aRojM7hhyXVq8jSFAduk4IRPKlUbO6Nm+XYGPJvlcy/Clg5pJ4OUkrSTGZ82f6AKDnsPtP4fuPzXsOLPcP534Pi/V/VDoGiGHWvhmZ/APZ+C298D932OGzM+5icHsezUxK65jzA1g3p/DTNDzdT4okwL1pcZ0oEyqGueQGZdCMFRdUdx3ZLruOP9d3D5kZdT46/Bkx5PdjzJ9e3X86XHv8Sj2x4lm/dNmWS4nstQdoiOeAe7k7tJWAkOJUsroyXSQiZH5czjbWAslZZSLpZSLgT6gc+/1Q2qhENFse8g7BhIURMsZg9m0M2QCJfdeGwRoM7eNb4T+iLQu1FFrHUL4NyVI810Slw+fbpAc9MkZZBX5UyWaq/jy6ng3O+dyk/0y/hcza6xC3cHtymOrW4WC1ylpx5mQJk3ASBg1wZ17UUfhfW/GtNpdMdAik7tBKY7rXxO/A4/FlIoOhBaTv5SejCwWS3/37MCln6e1hkn0rqrCwY7oMZgsbBGVz0Z3K4CdteGZG+RrhSoLrrsligRXeMqvvRdukVKSEJGkCt6e7lmKFb583jj0WL/l0wMAOycOn++0BhUUI8YabtdUCfJxEgkdtOFQxZ4c+hNLh3awZd0l9Z8PYJ0wVGTq9aZrVzUexF3vXpXSSFssXl+RxXIThT5oNmVLhqaKhYWHr3pXuZUzalsGT6GGVXHS99DQ2NrcmvBRbY+UL9X1uMTtTFP+aClT5lK2QYMhsDToafmUG5lvBjVqdJzlVqOlVS/8QMtoJp7GvAVeOG/ILYLqqbDCZ+kde5p3NB9dEXFmQkhWAPzz1YPUHr3Hc+pgH7nWmVu5WRhxzPM3QH/AWQ0nZeDYbbWTGf+UZdwQsOiyX7XFaELnRpfFZ9Z8HfcuP6HdGcGFI9eaIQMPysWXLpX560P1vOxoz/G3x3xdzzZ+SSrtqzijYE32DK0hR+88AN+seEXnDvnXM4/7HyaxulOPFFknAwZJ0N/pn+fTav2FQdi8elBYCz1V+B4AKEG038DzkdN078ppfzNGNvPRPH2u4DFwD2ozP8XgSDwISnlZiHEh4H/i1rkH5JSjoufeiigf5tjIpX9s2pDdMczuJ6kJ55lh2ykUQySLdF8N2WGfnM6hXKnSvQQKCqINB6lBk87OeJ6QFlgNT3zJi+nannCPYoP66vZKqeRxkcIi5O1N3iPtq5sAlAx+PZFofd1xSkvuLLmBu5sgjLd9eyACvK3tsP5YzuNRv0G03qe5GLjCWKEqCWRC35lTvoxl1kWhppMWCl4/NsqSxZuKmTbQxFvhC65C4Qk6ro9r6uBVmjqPbg2JLrVM4xQIrrGDXNNWigq0UdWwdeGyY6WokTFZbhEqU9AusIhAjEiO9ISaaFncCtusptOTa0QgHLl3ZLaxY3VAb7hhmnN2ipospPwp/9LeyjA/ZvvpyHYwP2nDHHNg5KGoaIjatoPvzxHZw9krhHIB82mZuJ4ToEuZHv2mJmd9lCAtunNdEQdWiLNLA8FaAXCZpjNg5vRNR1d6DjSoTPZSXOomRV/XDGhwW8iNubx1aupTWsYntIu0l1oikEsAKs+UMdlE+yXQxgGTVeT40B1LrhPqgD/QAru556WC+zL8fLgJl4depOUmyZmJ3l5cNO+U4iCtbDgHPUAlUTofL5I0RncRsBzWZKMsSQZg47/B6t/ADMW55xsT4aGIwrmTVMBTQj0vPJV7j4uJoHWbOomZ806i7NmncXr/a+zassqnux4krgV57cbf8s9G+9h6YylLJu3jIX1C6eEjuNJryB/6dN9BW37/SV/WUoHHNOJez+jdWYrN3DDATfRGA+EEDrwXiBP9r8EFZgvAhqAZ4UQq4FTR9lObtvRqEz/FuBnUsp3CSG+CFwLfAm4CThXStkhhBi3lfShgP5tjoztEcvYNET8Be340XD1GfO47rfrGUjZaAJul8u42WhDyAyOFyBAFhMHd+m16oBS6T90NRDc/fegB1SW3LUqZ5mH44j3wRHv45nXurnut+u5RbsJC4M0KmuRwg8IPu7dx+m/P5lr9VYuyd6H6STBH4Glny+eN3/j9VyQ5coJ7UE/bdVROgyTFsdm+VCcVitWpOsAICHVD3++GR76x8IkRUofn9EfwJImdcLBQUNHKj37/HGglCkAMFSgn4lB7Vy1yRfiCsviNr9XCILzz1EJ7ce8n9bdLxYz21Lm3o/ImUCx5wnNwg/DS78Z2cfBWvU55T+DYROD+Z7OG8JhSBQHTg0NUzNHZEeWH7ucm/70D/Rp+Xdd/L+UkqQm+H5NhDZPqsJe22F5YnsZ/QRGxlBKDGniA2c+aK4P1LM7tRtPesoLWGijZnbad7Zz45M3knSSuNKlL93HjU/eyDdO/4aaqKkq6kJfSCnpTnfjN/wTGvyWH7ucG5+8kV3OrkJRbNgIc92S60bs23/Hz8kGDRxpUZMCzQNPgGXCzuMOIHnFKca+Zg3Hdbymq8l2oKpQUKsy9+M3sdpfuPWN3/DjjXcXXsedZOH1NUf8/eRdKNwAh79fPQASPSqwzz+GdqrJ+ban1APAF6GvYR6PajarfQKrdg6fnH/xpNUrtG25l6gZpqmEYpO0U9y7488sqTt2Uq5xZN2RHFl3JFctvIqHtz7MH978AwPZAZ7ufJqnO59mbtVcls1bxntmvmfKjKQs18Jyrf0qfzn8fpx/btvQ9rYIng8wBIUQ64C5wHNAjiPL6cCvpZL06xJCPAGcPMb2GPCslHIXgBBiM/BI7lwvAWfl/v0U0CaE+B9UFn9cOBTQHwRIZh2ytkdj1E/QN/rs/8yjmqgP+4hnHVxP8jfjJL5nGHzUuY8Wr5sh/wzcpdeqgtg3HlW0EiupMsnSzSmt6OqmbyeL2erhWeZRcNvqLVQFTeY4vfR55aoAKWkyU/Rwtr6e1uQjdBClvnoWUd2GZ++AV+4DK66kIIP1SrqtBO3BACvrazGlpMpz6dF1VtbXckPfEK2+SPnEpPd1dVD1rAKH/djUx5mj99LvhfHh4KLjCmWIFdQAN8dxdp1iMF7Q6iniGlkN6QF+EfGRdi00oBbwh5tYufMP3OAzaM2U8KXzwUX+NGNQRQAlSVkJur+81mDYxGCJ7fGcXy11IwSOdPDwqPPXVSzglJ6H1Eoblnv7QuBK2GwazHZcqjxP9XVNlNTAJprDzQBc9IwkFYSBEjaK34YL1kzcByC/RGvqJs2hZnrTvdiezWHVh/GlE79UcXD6/nPfZ8gaQhNazm3XY8ga4vvPfZ+kk6TaV82AVVzm14SSBN2bwU8IRWeSUiLF6Ctm9s6dCMehOqNWLBzUelJjHGa93AMXTbhrDngMD76XNC/h/s3373XWsH1nO/fc+S986Mk49QMOfbXd3HP6JvjEN0c/XtPKg/u9cKidSty+6bejbp/UgH44Io1w5PnqAcqAr5SiE+8EK0F954tcDlwOxPXXeemNv7J5/rnMP+pidT/aB+nGjlQ3VWakbFvICNKbHaQ52MCQFSczSbz32kAtHznqI1x2xGU83fk0q7as4rX+19ga28qP1v2Itg1tvH/O+/nAYR8o3McmG1JKknaSpJ3E0IxCIa0xDqnRiWKidMD9hQN15WAPSEspFwshqoFVKA79Dxi9qHaszFXpF9orea3UrAEp5WeFEKcAFwDrhBCLpZR9e2rkoYD+IIHjeewaSlMT8lEbMkcNKhKWy4LGolzZm5zGN+WpDKVt2r+S417mM/NWLmh3s4AEXVOZr0Jdo5ezZdXA9YpZ5lGQ5/DvlI3UM0AafyFcDGGxUzZyaeYeHGGSxU9P0iIadSHdpzTlG45QWaV0H4VAM6cN31YdxZSSYG6Azj+3VUdolaLIJ+/bVJCAJNmTU62BK8UDdGnN1IsBHM9Ex0EpzksVLOdpN5UcEDNDaoUCwE5zTXQOa5uaR1AxcNK0RXy0xrSCS6xqP+U//9yKRkWs+U8lTSfdouSmzL0Xf1Wx1mDYxGBtjY8GX4C4tLE9m5CuuJ0zq2aOuIm2bWijSjMYwK5sLCVAk5IuXcMWAlNKomjY0i44vxbdYYsHZQ1J0+DEA6jhS7THNx6/x6zu1vjWQqCuri4K2+dG59KV7MIQRiEYtzwLn1Yu7zeewa9tQxtRf5SmcDHDnnbSFScC5syZVD+/C7xcsbQEKcARcN5fp6ZY761EpYH7Zy//jKAexPbsQv1C1Bfd48QpPzHw/rqW5Q/beIZOOmRQnfC4dFU/fwn+B61fHkcwUMmh1kqqx1sU3Nuy8iR3tO1Thug0OOoC9QCIdfLz1Tcwd3AXxyfjNNhZoq7LqfFBWPcb9QjUKInMliVKSaf2sAkVJ7eEmkao7eSNq4K6n2DQT9a1GLITpJxKpMGJw9RM3jPzPbxn5nvYOLCRVVtWsbpjNQk7wT2b7uHeTffyrunvYtm8ZRzfcPyUqeM4nsNgdpDB7CABI0DUFyU0TPFnXzAROuD+xNt55UBKOSSE+AJwnxDiVmA1cLUQ4r+AOuAM4DrULb7S9qPGcx0hxHwp5TPAM0KIC4FZwKGA/p2GwZRFxnZprgpUpODkefQhX/GjT9suM2tLMub5gkojUJ519xwoPaf01CCYD0y1kXrHla59m7uMm/VfAIKM9OEni0843OYuY6XeRpwImhBYjqcy/2i5FQKhCslKKTS5QbjDMKnyypVCAlLSYZoQjxX55K6VyygJ9W8AM8h8s48buZJ/tH9KghA1DCFwMIRQwbrQIVtScKsurtoW362C6RJ6TMdL36ucHdH1HG1HlBhVyfK+K61b8FflAo+4otFk41zfUM/DIT+uEOhScl4yxbd7+6Fvo5LvbFs2QhKz43fnUeeron6YVFilgLUj0UFVuBmR3DnqZ2kLgV3y75RQtCI7930ZWRQr8TvQV7t3t5xRCyFHQwmdprhJIqQAofitw91ZHc9h69DWskBzT0YnE8mC1a24iuTfnilb9BA5edOZPRNX/znQUWngtl0b21X9q6HheA79mf7C96YSSicGn/irg6WDZbiYUgOfjgEsenQLfHmCDSx1qJWyGNhbiQMic/+Wo2oG/xPyU1W9EAE0WxmOTQxwTHyAYxID1Du2Urja/Gf1AGVslQ/wW5aoBMMYAeryeR9i5YbbwckQ0P1k3OwItR2/7qNJr8P2bIbsJMmc5OVk4PDaw/nySV/myoVX8setf+ShNx+iP9PPml1rWLNrDbOjs1k2bxlnzTpryug4UCyk1YVO2Ke49r4K/gETwYFafHqgrhyMF1LKF4QQ61GLVr8E3g2sR93Z/1lKuVsI8ftRto8roAe+I4Q4HBVs/Dl3nj3ikLTCQYiM7dIxkCZjj5TCu/qMediuJGU5SKmebVdy9RnzijvlpSMjTZRFH9JTr41ArihVzzkzmhBsgKaxv6v5az/NCXzNvZJeWUO1SNBHLTe7V2JogrBMcJi3lRa3g2otnQvARVGm0l+lqDLD0OLYZIYNHBkhaHG9YrAN6jwyp1iTP6edJth4GMsu+QR31l7LDm06KRFGmGG0UB3UzYNL78hl4RUNB6Gp4Ll2jjpXZlAVrp7/XdWeeC+Z7lehd5Pi2ZPLjuhB1Ve6WbnvSuUmhQ49r+UoQjrEu7i+voYHwwHc3Ht1heDBSJjrG+pUu7atVsZbwyQxJyIV1hJpIWP68EuBLksXDwQmOc5/6SPXlqSd5IaZ59M42MlD7xKYrqLZINWz39PYcO7hI643FZhbPRfXc8m62cLD9VzmVs+lL91XyNiXBv0eHiknhe3ZpJwUPakeljSPbUw0kX6NnnEGmjlykNYkGNmDT7ayI9FBQC8PgkRObUkTGkIIRXVCYsvRA/rSiUHToMQy1ZmcXAbbMiSNe6sI/cajagJ8y/Hw68th14vq9141XQX62tQPkdooq/Ojbd+faAk1KcqLEHT5gzxWP4PvzJzPPy86Bz72OzjzBsXJD+U48Kk+2PgIPL4S/vsSaDsfHvkqbPg9DO0YMVFqbTqJG479NI2BWmJ2gsZA7aieAqZm0uCvoSXYTJUZmVSX1hp/DX9/5N9zx/vv4J+X/DNH1x0NwPb4dn68/scs/+Ny7nj5DnYnd0/aNSvBlS6xbIzORCe7EruIW3E8uXeT/UqyynvjtTHZeDvKVkopI8NeXyilvEsqXCelXCilPE5K+Zvc30fb/riUclnJec6UUq4d/jcp5SW54xZKKb8oxzmDnVC6TAgRllKOImdyCAcSFAUnQ33ER1WgqB145lFNfB3FZ985kGJmbWikC2y+oNJfpdxRhzpydBoNqlpUkJ3oUnrwkcaKMpCVkL/2vz78Go93L+YpcQLNET9Zx+Po5N+4SfsFaRnATxYDi2avWw2o0oVSm3HXomAolcPyoTgr61UWPiAlGSGwhcbylACrm0JYGmqE2A717/CMMn3yM49o4kztKHi6FgZjI02fph1f2axo1ruKZla5gHy5qbMyqIFnERjaScZtwjZ8LD/6E7DmDvDPqFz0Wio3WUoPSil60MM+JWU5zGySh8Mhvt03WJxc1MxS57DUOZcf/X5W7vo5uC4B3SQTrFXtqZCtyWd2ov5qbGuocK16D0xg5yhlGh4erat/RGs2RntU4+73VnPWs9A4pGQZ/3J6FZd/eKJp1L3DObPPYdPAprKAXSA4Z/Y53PXqXWoeIkVhe6nChkQicv/9afufRrjolmKiWTAvt4pUencWgGdbFfd/O6PSkn/+Z+vhqdoGPJCMoDuVojSjN1jvJziUwTJVbOhJD8PyCM0epbZkLJQW/ZdOgPNKUb5wTsUpVczce5O/kmJoBlYFKt9U8KonilEz6PMvVmIAtXNh4aWqnwa2QsezORWd59R9KNkDbzysHgCR5mL2fuYSqJpBa9NJEyqyNTSdOn81Nb4ocX8tMTuO603OhNjQjMJq4KbBTYqOs3M1STvJvZvu5b5N93HytJO5cN6FLGpcNKVmVVk3SzadZSAzsNfylxNe2dwPOFBXDg4GjMspVghxKvAzICKlnC2EWARcLaX83FQ38EDAgewUm7Zcdg2NzS2MBAwawn60sVRwcjSPdM+bdGVMIu4AGT1KXUAQyvaAkwEzpAa5xiNhbquSgxxWuPn4a93ctnoLOwZSzKo0Wchh/WP/i77mh9TZu+g3pzPN203IGcCQnqKSCIFPeIq/7gsrrqYZhFSvmkzIHN1F5mQrNZP2gI+2qlBO5cZluRug1aiFeDdOagDPU7z4Aa2OcLSGqMiUF52WDvClwfaij6r32v2qGtRD9RBqUG1J9Sk9/kizogeletQbFBrtNc20RYN0CIcW4Wf5Of+hbq4lfb3ZrucX8kI6G1tVXz383qKrbfcruYA+t6LQdAzH+fsZXqiax0tbdxRfTF+snqWEWCf4w7SbOm0BqdrjwvLjPk3rKV8AKhcvru1ayyvd60m7WSQQRnDFrHP58c5R3PwkvLS9E4ROe9DPP9RF8bQcvUkoVZ0fvfdH+2WAWfHHFWyLbSNuxUdQaF7pe4WEnah4XD7I1HKLl4Zm8NwVz415rVvX3cpdr95Fyk4RMkNccfQVo04CXlx4DHgSPV9PLcAVgCY4/uVX9uEdH3io5KLbmegkaASxXGvE5zKa7XupVfyCV2Ocd89OMpqLZUKV9FOrRZjztW8RPWNcUs1FtC2jPdWZ+014tEiN5RlBa2jGSLdpyAX36RzvfvKC+8UPXZpTbipCfSU01n3gd5NyjX1Be/dzE9fIlx70bylKZHY8X0JZLEF0hqLo5J1so9Mm1rj6+UggYSeIWbExqVt7i6HsEI9se4SH3nyI3nRvYfus6CwuOOwCzp599pQr1uTxVshfTgUOIH38t34ZbBIx3oD+GeAy4H4p5Qm5bS/nXLMOerzdA3oAQ9Ooj/gI+ytkfXKBbNLR2ZGQBMgSlUmywk+T7AHdhxGdnjNzshStpELR5uOvdXPT/RswdUHQ1EnbLrYr+fpFx5YH9cMC58xgF/7Mbmyp46ChIdEEOKEZhHwCPvC9oupLZhCMMFgxxe/XNDWw6ib4qiC5SxXyGn4INwLgDe3E9Ty2iDkEhYUhbf7d/AzLLvlEebvalo3MwMe7IdMP1bNVkJ/oUa8NvzJpCdSpfw9upWKgHZmmgv3MIHzpxT321X2Rb1Pr9hcz9PkBSjehfgGL/P2qUDVfVJuDJiXrt+U470LLrbLsUm1EKnpR9Uy16gJqZSHaDMtXVQy8bNfmovkXcf/m+7E9m1g2huVZ6JqO7doj+OmFNmzvAl3n3dPrSJTSFXKZrIge4a8f/+vIfppkjGUvHrNiJC210FiWKa4AXeis+8S6Ua8zWt+NtrT9h9ajmdmngvh8LbQuYWc9nN/+6j695wMRY6ncjKe/8uco7ePZG3o5Y/UA81JhonMWULfiqokH80D7j45jZVjDBAJABrCBG5Ierf/w0p5PYJVm7vc+Q3zyHy4n440sig5ofp49/+4KR7wNIT11PysE+M+pfhuO6pnlHPzIHuRc6+eXcfRTdoqh7NCUOMK6nsuaXWt4YMsDbOjbUNgeMkKcM+ccLjjsAmZEZoxxhsmDEGK/yV8e5DioAvpxr+lJKXcMW146+EifBzEcz6MrliHiN6gfrlmfo3l0ZSRCeNgiSFIKauUAO8V0PC3EvKCieuQpHJUC+ttWb8HURaHgNuQzSFkOt63eUh44D3MxdTNDeBJ0PNyc+rsnPUSqC5reVa768v3jVNY6bSsakCtAM9Tg6mRyCjCoQDjWAQhcz8PGh6ZpZFGc3o+59/Jvq08vb9fgNnXuvk1FfX3PUY98kB9tUtr4yS4V5OcD71Gy5iPUZ/bUV84yrpc/U/0cblS8U4CQogfVmNBfITFT45YEpBJ1nFeikuFaxdUUf1WZxOVoqgN3vXoXISNEf6YfIYQydpJOxWAe4PxkujC4JkbhHifcypnxyUZLpGXUDH16II2u6QVaTV7lBsp18iVyj4VpE1Vs+NVZGp9d5RGyioZbMb/afv5kvfkDCJWW/Bc2LJxQdm64ytHACYdR+/GvsXAfM3ptET+mlyWYo7UFAaRLW8TPaGceNbNoZ4qZe3di6jRhI0DGGhmAhqewCHO/Q2hKpazhCFj8UTUB6n2jKJHZ+YKSEx3aqR6v3KeOq55dzN63nKS09MdAyAwRMkOknTSxbIz0JCnjAOiazmktp3Fay2m8OfQmD2x5gCd2PEHKSXH/5vt5YPMDnNR8EhfOu5DFTYsnleM/HMPlL6O+KGEzfEDQtA7hrcN4P/0dOdqNFEL4gC8AB1866SDG37b0c/ezO5gz8BQrtAdYYPYRapqn6CY5V1HLSRQC/Sx+gjKNrU3DdUqCxZJAcDi95o2uGNOry7MFQVNn50CqvDHDXExNaeOg48MtMJjz20fw8v1VqlBU6Cp49xwlJSk0lQk3/GpAAPJKNhKNAa1otpbFz3TZPbJdpefOq17k86il0pRmUOlY18xVr93K/Of2YEAZXfkytFSbLN/ZXghc8hKew/vqwfSxXH9xiQ5941FFeb1oM8FAnKhrE7fjheOinkex1zUlneJVCCo8V9GC8kXCuUlGR6KDKg/o36Yy+gICuo+ULnBcRxUv5igoOjoI9ZyVxSDk8OrD+XZyN8hBRia7938SZEnzEp7reg4hBDo6lmfRm+7l0sOVpfzwYN/2bBZtdrnoGUnjoKSnRnD/KYIdx4bGvE5HogMNja3JojpOfaB+VMWGdfM1frKMEddZN/+do0+wN7zeqeACdwTCVCWzKPldDaRHQKrtlbBH/WwzoAJOO1PM3I+DAhJ3UhPaflBA06HpaPU44Qp1v+p5vRjg73pB3aOGtqvHhpy3Tu3cIv++ZfSC9aARLFC7hrJDJEdzMt9LHFZ9GF844QssP3Y5j2xVdJyedA9ru9aytmstLZEWls1bxtmzziZkjn0P2Vc4nsNAZkDJX+pK/jJoBKeU338IBybGG9B/FrgFaAF2opytPj9VjTqEycXftvRzy2MbOdV7nv/j/QzLNdhm+5nZ00HkoX9C+KJgp/EZGrbroQmBnyxpEcSUGXSj5IaUCwRLKSM1QZPueIZE1qU3kaUxWswsjZDEhBEuphYGJjYZfLho+FBOrRvlDI4evhJQShHT9NxA7AJC8dqFUMu2ie4c3QRiWi0JQgVJJz9Zdommke3Kn3uEC63MZftRQb2dVll6O63eg+4bEdQXja6gKtpCj3DLBv8x5UOPWDqqDn3LH1fgS/cwq3coF7RL0kKj0c31gRAoJZ5KWUKp+qSkEBigRQvSM/gGQTdndSQh41qEhE4KMEuy1HkDpSxZDM1AR8fFZUtsC7ce+T6uSfZAVk02Fm/2VOA6xH4PXNd2raUh2FAI2n2aj6gvytqutYWirOZwc4H2UfvCVpY/4uHokAhATUKy4hHJw5GxM/RhM8zmwc3omo4udBzp0JnsZH7N6EWa6+ZrrNuLGs5DmFy01C6gR2wlmB4srMhlgjW05CfqwzDu1RgzkAvu63O/t1xw71Se+DuVJt9jbD/Y8MyWPu5eu4PdQ2mmVS/i8iXLOOWCXHJl57MqyN+1TvXlwFb1eDlnxtV0DHzwRyp7XwE+3UdjqJEar4ZYNkbCTkya5CVAla+Ky464jIsXXMzfdv+NB7Y8wEu9L9GR6OC2F2/jzlfu5L2z38uyecumXMVFSknaSZN20gX5y6gZxdTNPR98CAcFxhXQSyl7gY9NcVsOYYpw97M7MDR7PHBWAAAgAElEQVTBx537sDFJCz+O57E1DlHdxhVJAl4aXTPJeCZ+YWEKl6fFybzHewqfI6EvoDjqpg9O/SK3PT6SMlIXNulP2oT9Rhkv/PoFO6Dtm0Vt9LmtsP5XBRfTpAhTJwfoI0o/VUynlxoSHKFth2/NUEWoAnVsoguqZqni0zwtJjQDEp3FANtfpR5WCgwf4cQQiVSGrPQXOPT/bXxISXWWar4nuiBQDyWFTwV4rqLPaLk6gqWfL76HUGNJNl9BGV1BMNIMwWqVQc8mafvTl2kdzPJj3wy+lX0fL7Bk7L4qVdmhVCHAU7xfIbAFLB+KUSieNUNgjwwG1IpBNZsDFpbhx3z+myx4826WDHXzSwFxn1ngdUc9yRWxFD+rjuBIBx1dBfPIAt/c8zxcXEVcEYK7up9i4RnX0vbqnSzenOaqR2VZgHzVI5K29++fQq6ORAemVj6QmZpJR6KD1lSGG+JZ2pyddBg6LdWHcdHfNBwdLFOJBSppRI/Tn+iDfx7jQjJH0ynRvc+/roQZ4Rl0Jjsrbn+n4EApiCv8lmpaxqW2MdHVGECtGBp+CNWpbL2VUKt7zsFnJLY3eGZLH7c8thFTF0QDJv3JLLc8tpEvnn04p8w7DqYdB0uuUn3X9XLRyXbXi4py2f2KqlPaA0zNpD5YT42/hrgVJ27FR/hQ7At0TefdM97Nu2e8m61DW1n15ir+suMvpJ00q7asYtWWVZzYdCIXzruQE5tPnFI6DhTlL2PZGAEjQMSMEDbDh7L2bxMIIX4OLAO6J1KrOq5vlRDiv4QQNSWva3MXPIS3AXbF0gRMjeleFynpw3G9XAACcdfE5yT5d/PT9IlaqkkyqNXxAGeyWLwGwXo0w6/46Zl+pfhyxPvYMZAiaJYHZ/VhP9GAQVM0wFDapika4IdLeln00jdVRj4vDbf+V+o8UVUs6ms6nNvFh9kupzGLLmqJ4wlAmDle5XawsupYKzcY1i8gXnUEW+R0tg5abJUzSGfSKoiXspiFfu/NBD/4H1Q3zqJWS9JDDXfWXqsKYrX1Rc33QC2gQabEhbZUa126Sr4urzV/5lfUc7QZUAo0hBrJ00s6TB+BcFNRtSETI5DopkNmIVBLrdvPN8w23me+NHZf5XTk8yhoC3uCmK7R6Epu6BukNZ1R1/ZFVU3BMORXDLZVNzGkaaSlou1si23jlzJGbJgCUlwTID0+lVaFoa50MTSDukBdIXAtfXalS8JKsHLnH+ipmcGHnhU4OspYSqhnRxd85IX9U8AVNsLsTu7G8ZyCgdHu5G7Crgd/+Cda43HucGp5OG5yx/Yt1A/YSJ+ZKwGQyvPLZ1LTP3bglXSSTAtPw9AMPDwMzWBaeBpJp/IS/4cWfGhC2w825GkrPemeMtpK+872/d6Wiep0h80wnclONcEtWY0Jm5UpOiOgmxCsVZKytXMVPcd8Zxc03r12B6YuCJjKtTlgGpi64O61O8p31E2YcQKc/Cn40E/g03+Bi2+H938Lqsef+dY1nZpADS3RFuqCdVPCOZ9bPZd/WPwPtJ3bxpXHXklTSNVpPd/9PF9b8zU++6fPcv/m+yedBjQaMk6G3nQvO+I76E33TknB8CFMOtqA8yZ60Hi/zcdLKQvWHVLKASHECRO92CG8NZheFaRjMMlWt5F6BrDxF1jNQSx20shDmYW8XHcKtuvSXBXk175vQrwqR4vJBaVWSsk38pVRKSOHN0X59WeWFi/e9s2yAtiCNvrW9oI0XC1wzGvd3LJ6C7d1XoKLjqab6F6J3ny6F6qnq2x9qo+kCNKRU+Txay63mFfg2JKvhh+lNrOrXI4SqM09NwGFxdm28uLcggvtcCMPPSd67QuVy9mVFusOQ0teai+/IdFNRkCLNBQtxhciCFwf/SPXf+Hasfvq6VvU69xKQqu/itb+gfIsny+iVlAa5sPuF4umX7mgu606iomg37PRNE0pu0iPuBUnrVQl8ZcsRTsI7qqK8LQ2h4VnfqUso9qT7qlodiKRBUpC85BgyEche29qJlJ4zIzvp+XfCm6wAqGkRiv0caLKIZAG3V/UedazDomGsYO1fH/MrZ5b2JZ20jQGGyvuv7ZrLc2h5hHFumu7DkwVrcnGZNi+T2aGf0Lc/AmuxowJ3YBgDQRrCJsRUnYKiVfmgRB6B6iX7B5KEw2U3xP8hk7XnpTbDD+0nKhUbvYCmtCo8lURNaMk7SQxK4Y1Si3U3iLqi3LJ4ZfwwQUfZO3utTyw5QHW96xnV3IXt790O7989ZecPetsLph3AbOiI80SJxue9EhYCRJWAp/uI+KLEDbCb2v5ywMBc69/8DzgOuAw4E3gO1u/fcEo2s7jg5RytRBi7kSPG29ArwkhaqWUAwBCiLoJHHsI+xn5AthdsTTTq4I0RExe7LC5TbuAm/U2ANL4CGLhw+EnzjLSnsf2/iS1IZPt/UkIlBeuAmUFsVefMY+b7t9AynLKKCPvnlfHR366plAo+/OhNwlG60c9Tx5nHtXEmbt/AZ0qayFdN0fsyJn/SFf9K9QArs2b2Qj17KJbn8Y9gUvZ4F9CynL4nHE6v/7cUsaFYcW5BRfagTeL2zSDvI46w8xvxgosRppnWNiaxnKnRLlieD8Mb09+n+7XijKfeffYvNttPppwsmA6ahJz3+dApFTbc8V+HaaJhlZQfRAol07bs5HlHl0A6EhSmoBTvzgi6Hn3r96tuKgVIhnbtelKdrG7yqO5H8JZieFKHD2LiIaJLlgw2qcxqci7weZpQvkgqU/aYEbKdzaD1CxKYT8pAQfHp2NYLrojqbnyk2NeZ6ImKR2JDpJWklSu4NH2bDS0t43t+b5iX23f91iYOoVIOknO3FnF6av7aBjw6K3VePKMel4N71um9YpjruAn63+CV7LipSG4Yt5Fk9HsAxrTqoP0J7MEzGI4kXVcmqv3z2RGCEHEFyHiU5OqmBUj42T2fOAEoAudU6afwinTT2FbbBsPbnmQx3Y8RtpJ8+CbD/Lgmw+yuHExF86/kCXNS6acjgNguRb96X4GxABhM0zYDB+Sv9wL5IL5/wSyQD8wHfjPudc/+Pl9Der3BuP95nwPeFoI8Q0hxDeAp4F/m7pmHcLeIl8A25fMUhUw6EtmeWJjL2GfzhrtRG5yltMja6glSY+s4UZnOU94yoTIcSVdsSx+XcOKzkLaw7IkJcooZx7VxNcvOraMXnPZiS389vkOuuOZQqHsK+laksnEqOcp4PF/hdXqK1VKeMm/ctGIZxx1bOORfFa/mU/X/px/qf5/PO9XagcVFXXGQs0cdb5SaKYqfI3MUIZWQuSWyhug6ajCbnuiDoxYztf93JDWafVKJgXD+6FSe/LmVvmscrJHBfVCIAEPgQe4nkM2L13ZeKTSb9ZNRRXSTcJodOrFn7tE4kgHTWgFYlEpXCCk+SquQMwIzyiTdwQ1QTA1s0BzeXk21CTBcNS5DAeMgSQ9FUzGpgKWZyE0gV/3E9AD+HU/QhNYQlTs46XHHoZ53TVkaoIEUjaZmiDmddew9JKxvfMmSttIW2kSTvnvIeEkSFuTJ693IGNfbd9LM/xCCIJGEFM3advQNgWtLcdp20NcuqqfxpSBE/HTmDK4dFU/p23fNxWThQ0LCRmhgpKUhkbICLFwVqtaNfRHld/GQYjLl8zCdiUZ20FK9Wy7ksuXTH3GejhCZohp4WlMj0yfMmWaOVVz+Nziz9F2bhsrFq5gWkitfq/rWcc31nyDqx+9mns33Uuikkb/FEBKScJK0JXsoiPRwVB2aNJcd98huA4VzOcDj1Tu9XVvRWPGWxR7pxBiLXA2auy/REp5cNkaHiTIF8Dm+e1BU8f1JBnHw9AEq73FPOEtRhfgliRYzZyhp6qKFPQt+iwNq7+KbiXRMoOqAEkz+P/ZO/M4uao67X/PXWrtqt67k3SHbBDWyJYQBBoQRRYDMgoOCkhLXhZFDe4zcVQEJ8OMOKK+yDJGAvgiOCwKEYigQAIYIIARYoBAtu5O0vtSe93lvH+cquqq7uot3dmgn3w61X2r7jnn3qq693d+5/k9D/MuzO1zurae0z0/A+828Mzgpn+chanPKyiU/V/rn/ha/E6CHr3QfXWgHOXaW3GkQKKjZ2kSoj/Q7CZEqq+XUFiHk5Yw/dkAWzuj9CVs0o6LR9cI+w1mVg7Ivg6Hk5aozHemOBcroeQpvaUQaVFBdLiuvxA2b8zDUQec6KHcsXozm2PrEaW9eP2OyvzH3lW0JceC6C7VpuFRHPm5ZxYfj5tWbrlZrq2TBqEjnbTST8cLSDRcdqa8lD59M+Uf+6ZqJzStvx2SCKGhCw07T8HHdV1CUhABbCHQUQG4BC5z/UVXIUhH0KREBzQErqbjZLLgLi4IOHK7pDsAwbRaOLAMiHmg7/k/j/79GQdMYRJzY6RIFWToTTMIbnzwOT5pCVbAx/8Gj8o71nmj6msstI0eq2dM299vGK/t+3gz/OPB+S+5RC2XUNTBcMDWIe4VnP/S+BxjV2xYQXWwuiBDmrAT/TQkT3CPudTuayycXckZkTe4Z+sfiNJNCeV8fuYnWTi7cuSd9xC8upeaQA2WY9GbVpKXE6mMA1DiKeGCgy/g/Dnns651HSs3r+T1ttfZFd/F8jeX5+g4i2Yv4qDwQSM3OAGwHItuR8lf+g0/JWbJHpfcfB9gFiozn494Zvtex7ABvRAiLKXsy1BsdgH35T1XIaUceCCT2MfY2Zcg7Ct8W3UBViZ6Nw2B48hcMG9qatnxZPk6V2orma6309xXTdtbp1PpuohoC1mXetsFufZOvNOOUztnaSCZAs7Lun5BvOSLvM6CXN9vBk7gvyIuP9EfUYoErq0mBn/8BjxdAukIlM3ATUZIoyMwkICBk5OAT2GgA62ylPpzroe5Z/Lh7e+wdnMnZMfmOMTTDj+p+RPccD5knRd1LzR8QxWxDsTcM4E8zXdvSLVm+MBXBfG2fvqN7lVUlupD4aQlhYFFsg+ibficNE3adla+eg9NJX4I30fYiRGM2LQmTJaVlrA0ZtPQul2dN38VdL4H9/8zVB8OH72eNScuZsXGe2hxu6nzeWk8fDENG//UL/Ope1hjCu6qqqLFMKmzbRp7+zg5YaNLG6tzy+DjKjuImC/CFFOZRElH0VCQ6r2/KSp50+vlXiNNXEgCUnCZ7eGoVB/fX/0dpQgBdERa+P6Ol5GuQznQpeu4SDTXpsIM0e3EmRacRmeyk+oeSV8Q+vLnVxJCHXtHW7vKX0V3qntQAW9VyTRYcE3BueGkJawJ+PYKlaNY7cFw299vGGgSNVYOfLZmIT/4HUuGfyDGwsf3bW/HSIAtJQ6gOVCWEBjb23er7yxaoi0c9x6c/NwOyrvSdFd4eOG0Kl6bkzdJydTdqNqPmglzqd3XWNP2Kg913U9ZqcEUvZKkk+Khrvs5tC1EQ01xKcq9BVM3qfJXFSjjTPT3VBMaJ0w5gROmnEBTpKmAjvPE1id4YusTHF19NItmL2LBlAXoYs/z3aWUxK04cSuOrumEzBBBT3CQatgkAMWZn0p/hh4gkNm+1zFShv4+lHTOqxSybLO53Nl7aFyT2E1MDfvpjKUGKdAAuXdQE+rtM3WN6pCXBfarXJdeQVoa9BBkhthFzbt35O8CgEAikt1E/vhdQuU1g4oLpZ7gnxIP8bq3P6BPWI7SpY91qNY0E3Chdxug5TTpJRITBxsNF5Hr15UabfoUDGnxVNnFHJ+hgDz+xk40AW7eAL+sP8zCpgcLj9lJwbP/oX4fKqjP0kpWLFJa0a41QO0m0068E7o2wxPfpO6g2bQ7Sfy2pYyshCCpaVSnbL5u3cmVvhqk24tXClw0PLaNLaOsMHUayueoPjJOtqBD53useerrLKusxCybRlj30e4kWdb8BEsPP4eGtcshDWtKK1nmtTClS6nr0KFrLKss5zudPZyS6GCrU0/NwOOiv0i3WPFmg9MK8R0cruu04FInNY6yXG4JaHSnI+ioC4UrXboBXRM4CAwp0YSi/HRbETymH0M3mFk6k/ayNyiLSqVyk4HXhrYywZo8g609hZgdKyhczHLpY3asaDHzilWLx12sORpkFYMGOtLujRv1/oLxmESNN8Ofj7Hy8e10QgV0Wv+757ou9jjpUidvD/CR32/CNXQSAZ1QxObc37fgv+iQoXfKBffVKnOfih6Qwf2Kzb/HFAb+jCuu3/CBnWTF5t/v84A+C0MzKPeVU+otJZKO0Jfu2yO0lOmh6Vxz9DVcdsRl/Hn7n1m5eSU7YztZ376e9e3rqQnU8IlZn+DjMz5OiWcMK9HjgOM69KR6lGmVoUyrAkZgUv6yHz9GcehBBfUBwJvZvtsQQvwWOB2oEkI0Az+QUi4fab9hiXlSykVCvXOnSSln5/3MklJOBvP7GG2RJJ/65Qs8tn4HSUtdYC5eMB3blSQsB4l6dCSUBwwMXeBKiaELasNe/KaG7UouSj1MWhok8AKCUhHHhX7qS4ZlreHiIvD2blYFnAMk10pDYWrdVuJpxYeMpxUf8mpjJaT6FPdbz6qvAEilJ+8J0EsQDRcNG0P0U0I6ZIgEXtIYqp0MtnTG0TWBz9Txmzo+U+f/GI8jiq6MSlh7a7EnCpE9plh75saYf9FSMo4ke0Hz0Njbi+VYJKK7kAISQmABn+jVsYWJpXXhkSJT3iZw0PAJlGxltg+E4sZqurKc92mYyd7B/OCe9TmJzBVeiamZ+KQanV+CKeGeUrVaYBrFv9KNRzaq8dqJnAFJNhBac/jHWeazaMcmDLRjs8xnsVlz0chcJKTM/Z4WIifvKGV/6XK5tzzXx6MLBYYDXkudfq8FhgOPLhR7he/cHm9HF3oBL1kXOu3x4tnUlmgLlmOxtXcrm7o3sbV3K5ZjTTiV4+yZSolM5v3L3z6J4THWmoXhMFY+fkLYalUrc40RmaXLhBifAdT5L7lYmpJ2lUKQMsHSGD2Vx/RDSTVUzFKmev4ypaJzAKAl3oZP9xZs8+leWuJt+2hEQ0MTGqXeUupL6qn0VxYYNq1rXcfS55ey+E+LWfr80nGpVgXNIOfPOZ/bP3Y7P/jwDziuRq2It8XbuGvDXVy+6nL+7+v/l629W8d7SGNC0k7SHm+nOdJMV7ILaxQuyO93ZApfrwV2AhWZx3EXxEopPyulnCqlNKWU9aMJ5mEUHHoppRRCPEKe2t8k9g/8Zu12Nu6KsHFXhOXPb2HRh6byyWPqWHLGIdz/ShO7+hJMCfsxNYHlSqpL+rOACcthRqWfixdMp/7xdnqkMp3QNYEpbVQoV5iFyObNXcAOH4QRa+vP0KNMquzq2dQYPpq749SXB7j61Nn4HmvGstPY6Giui0f20yDsdJLmjhi2Vovhukp1QMaQmex7iUghRYKScDmh9M6cEdSf9bdoktX8Wp7P8ygF1SDJQcWdOaSGKDLKN5ZK9oJtZ1xfsxZLWWTu4ukYOBYNPR0s/ditrHjyS7ToGnVSo9HxUWdbJKRGvR1lh+7DJ3NlCSSFRp3lqIyak1YKNKAmCrqHFg3CTmFwkOMHZ7LKLQ+dTdgTRrb+A8uVGDj4pKTFMNhFNbVea/Bxlc2g4aQlLF1YnOqweMMKzGBNzjHTr3vAX4aTaMMccPzZMzLFsunSNSwhMKWkQuhIIXJ9vHrwdn6NpZxie2TOKXbDwV5qdidIHnA8Aw23BkGAjo6WV0zoSndw9W8GQSPI5t7NaEIr0K2fXTqxeYubTr0JgCe3PokjHXShc/bMs3PbJxRjPWcHCMaT4c/HWPn4O6o1qjUDf9JFtyWOIUgEddorx1ewGmyPIsoVVc1yLEzdpLK8lkD7bhRG5lxqq8BKZmg5EXD2T9fZukAN7cnuXIYeIOmkqAvsneL53YEQgpAnRMgTIm7FeWrbU9y+/nZMzaTELKE72c3t62/nmqOvYX7t/N3uRxMa82vnM792Pi3RFv64+Y88vf1pEnaCVdtWsWrbKuZVzWPR7EUsnLJwr8lPTppWFSITvO91RZtiGO00fq0QYoGU8pU9OppJjAnnzptCc1ecx/6+g76kzX0vN/HAumZOm1vNlafO4vCp6maVVb5JWA4+U6M7lqY3aRNN2dz/ShNz9SlUuF2kNZVxt6SJSRqXQqUZCWhImrR6tld/jsOabyDpxnANP7V+SdBwKD/rm/x2br9s5LNvtdEcN5iNiw8HV/bTaSQCGxPLcTHdJG+L2Tzk/RQ/TN6EwMEROn5NUkIHuEKZsjzxTXAsKkSEWro4VvyE290LuE1+mhg+wmIIjra3yBLlO08V1gG4jnKLLar7koe+Jqg6VAUWxox+fjsQKdHo7Onm4t40P670kgQ8EhxN4EiXRkpVEWZWJ15m0nwlNdS5Lu26Qf66x0B+cD+H2IthW1hSJ4mkyhFUhHz4K6cNPq6MQVXDOTfTcNbgiX5LtIVwoFIFARn4pERLtJHNEWZpK64Q+F0XE5hp2+o8CUEiXEt1SV0u2FrTvIav8lXWz3EKilKr/RVj5zsPcTxw85AB6szQTDb3bi4cu3SZHR4iQBeKO2pJKzdeDW3Yj8Hu4qZTb9ozAXw+duOcfdAwVj7++jNnc9rv3iFWIkkZEq8NHley/szZjEdg0qyvJ9jeTig8M7fNTSQw6qeOo1XygvtKJWubpeXsR5nVxtkXsGzD/4CdxKd7STopLGnTOPvAMFoLmAEefe9RfIYPj+bBxcVreMGGhzc9PK6APh91JXVc9aGruPTwS3mm6Rke2/wYLdEW3uh4gzc63qDaX825s87l4zM/PmiSuieRtJMk7SRdyS4CZoCQJ4R3wIrLJPYeRpta+AgqqH9PCPF3IcQbQoi/78mBTWJkHDYlzL//0zx+e+WJXP7hGZQHTBxX8pe32rj2vtf58n2v8+zbbRw/s5wlZxxCZdBLRyRFb9KmzG9QVeKhM5ZiubsIAwuPmwDpEsGPBiSMUpysDjxgY9BDCS/O/DL/+uYUfqz/H7r1CvxOH2/FAqyf92+DgoW1f3qACtmNzOT3BRKkRGQy192U4ieFT9j83v9pLnX/QDclCAQeTfG1kVLx16VUN6NYO15NYgsdgcvV2u/5sPsav7LPJU+ZvRCHFKE0vJhnLCUEBKuhpBZ0j/q7WEtZZ8FsNuKkJSpAzzjUhrQ0tQGNbv08ruuMM9VJkdJsau0USyNpGj6yTNFnKmaDtFWWPlwHwqAx6WL5SgtoMZFUhJ5kD2c/dDaLVy1mfu18RWvxlyGExNUlwtD4omMQNBw1noHH5Qmov7MGVQMwlJTgnMBUSl3QJLhCoAGljsMXeqNYQpAQAokgoelYhqeAx9xQ38BVH7oKUzfRhY5P91EdqMbUzLHzncd4PADXHX8dpZ5SNKGIYprQKPWUct3x1xV9fWeiEwen0P0Wh85E59jGur9gN87ZBw3D0dCKoeojZ7L844KeEkFJUj0u/7ig6iPjmyBVLL4CmU7jJtQ43EQCmU5TsfiKcbVbAMOrAvvyGcqpNlChZG33MRpqjmfpkVdS7Sunz4pS7Stn6ZFX7jf8+dGgJdpCwAhg6iampq53Xt1La7x1wvsKmAE+MfsT/PKjv+SHJ/2Q+bXzEQjaE+3c/Y+7+cKTX+Dnr/+cLb17tyYza1q1M7qTHdEde6zOYBLDQ4xGjkkIMaPYdinltgkf0X6I+fPny3Xr9k8nx0TaYWfGVS9tu/zlrTYeeq2Z99r7zU5qQl4uOLaOT8ybwg/+8I9BRbMJy+Ek93UucX9PpbWLTnMK7RULqO56hWnpbRjSIo3BFjGdTXO+wIN9RxS2IVTfU0v9hS6xwKs3nEw1PQTcGOX0oGXUVSwgLT34hE1U+vittoj7/J9ljXeJyiim+hTP3EmrANvwg8cPsU6wkyCdvMJZwcvycBqd7/En/SvUiw60nOSlACMIhq705fOpB7fMU33lLxUmeiG6QyndpCJ5fP9M1l5kfjQdTvmGcrxt26iKXHVvTgUHgD9cq44jq+zjDcMnb+2f9OQoEYVqK1laTNAM0hnvJOwLFxQAnt8XYV1qV0blxqKxN05D7XHDH5eUkOyB6wbPw9c0r+F7z3+PmB3L0UCCRpAbT7kRWl7PqO6kqLPSNPZFaUimWRMIsqI0RIsOdcJL48d+WpQGMSGunmM8nixu+9tt3LvxXuJWnIAZ4LLDL+OLx3yx6GsX/GbBoEkNKArGK5cegAuTu3nOPmgYy+dz8arFbOvbNsjld0Z4BsuLrHyNBW23/pLuu+/GjcXQgkHKL7+cmmuH90CYENjpfilMe2KdUvcKKucUfsb3ARZnXcHzVnriVpxyXzk/OvlHe1zBakd0B3/c8kee3vZ0zrAO4MjKIzlv9nmcOPXEfeIGK4QgYAQo8ZTsz6ZV7yue0EiylT7gGuBg4A1guZRy/yTjTQKPoXH2UVM468ha1jf38tCrzbz4XidtkRR3rt7MPS9uRQgV4OfDZ2o8lzyaq6+6mhTwjwxFx5Zn0522ch/5Mr+J2aLTG+9FIhRVRtcoD3gIenW2dsawHRcjz8Boumgn5UCYCDYGEoGGg4lDOyVslmH8pDnXfZZ3rUOgNkNh8ZWqH1DZ71Ct+r2niXw+twQ0ITmcLSAEAZEmgQ+PtNFML4a3BJLdkHahbGYh9aCskC5DslfRaYSm+hR6hhIjIJv7lxlVGimVEVagRmXY8/X1556pFHN8pcoYJot0XAXw2YC+iNpKA+QCisWrFmO5VqHySk8T61IRlu8aUNwphWrrnacyx7GjfzVBuupYhrFJFyKjBCMlUsgcH7Jh4VdpWPhV9aIsjcPnocH005DIHPM5/wFDBEETwnce+D5BcXOyPKxpXsOj7z1Klb8KX4maDD363qMcVXVUcQUTV13WBqrPZLcPi/2Rq74b5+yDiLF8PluiLVT4Kqj092ukSynHXTgdWb2a5gfuptuMkqxy8dlRYg/cjX/eUYROPXVcbY8IwwNGhcrYH8OScwYAACAASURBVOjB/T5CMeUl27W5ct6V1JXU5SQvHblnMtbTSqZx5bwrc3SclZtX0hRpYkPnBjZ0bqDKX8U5M8/hrJlnUeot3SNjKAYpJTErRsyKYWgGIU+IoBnE0A6Mgu0DESNRbu4G5qOC+XNQjrGT2M8hhOCY6WXceMFR3LP4BD51bB1+UydpuyQsl21dCVp6EsTSWXc+lynh/hl01pwqmnIQmkDXlFJLLO0ovXdLYjkumhDYjqQtkqQ7lqY25KO5O0Ffsp+jaVbOooos5UZDzwTzAFNFF9V051RsLrJ+r4KhZA+0vw2tG7Db3qK7q41vtJzGTX1n5SgRkAnrFYOHMAle1q+gkj4CJNFxkHYCYm2KG2/4ctSDmK3z5oM38o2W02jt6SMWjahGIruUYYuTka50MxItDMiw5Oa0ApJd0PUe9GxVAdTTP1BPFVEBwvSrbPwo0RJtwaf7Crb50jFajCJL5dtWK7fdJ76pViRArWTYSRXQuxZE21TwOQArNqwg5A0xp2wOcyvmMqdsDiE0Vjz9NZXpXbGo3/wqo7ZDskc9nrMXONkDaE2k48XNyQYc01gUTDShVHBEbl1HKJWckWzYs5OcSGshV73Iec5iTfMaFq9anKNRZd2FJxS7cc4mMTzG63I7FN742Y10On0kTKkUs0xJp9PHGz+7cVztjhmGRwX2ZQcpak6wUlF1JjEshlNe0jWdMl8Z9aF6KvwVezSY9Rt+zp11LreecSs3nnQjC6csRCDoSHRw78Z7+cKqL3DLa7fwbs+7e2wMQ8F2bbqT3TRHmmmNtdLxl6fYdvnlvPvRj7Ht8kYiq1fv9TG9HzFSQH+ElPJSKeUdwIWoBOIkDiDUlfn58hkH88DVJ/LF0+dQHlDBYCzt0NKTZEtnnL6kxaeP678p7exL4DM1LMfNrWYKAZbjEknaKFPZDCc+8wnqTdpcvGA6rpR0RFLs7E1gOS7lH/smXuGgPGBtZRiVgQBqRS81dJHEQx1tec+AKyWWI3ElBL0GT1nzchz5/sLa/hLWEPHc7xoSI5dVd6BEqSZEkjZNUUmltZO3QyfyU/Mq3ooFSEQ6M+o2IyyPCqG09LOFZXZS/S40FTh3vKWCubIZGYfWPIwxQ1o0gBCCOnuIorbn/1uNJVSjMvLZbLOUyqnWV1aUQz1o4pDswxdtUxKbA4PUuWdC40rWXHgri6fWcvYbP9lzQWkWuzGRKDoZGkbBJKvPb+gGXt2LkZH9y9ftL4oxctVv+9ttfO3Zr/Fa62t0J7vZ1reNZS8tm/jzt68mX+9jjJVzP1pYzS2kDMi/qqUMtX2fQTeVCEHZdCifqYJ70zfibh9UNNQ3sPys5Tz56SdZftbyQas+QgjCnjD1oXqqA9V4dM8eG4sQgmNqjuHfTvw37jzzTi44+AKCZhDLtfjz9j/ztWe/xrdXf5s1zWtGtwI5wYisXk37vy8j1daKVlqK3d5O6w03Tgb1eRBCTBdCPCOE2CiE2CCEGFUmZqSAPhc5TFJtDmyUeA0uOr6e3139YS494SCCHsWps11Jb8Lm5qfe4dcvbKEzmmJq2E/SUnQa15WkbZe0I3FcSNkSr6lRE/ZiaALXlRiaoMRrcMLsilx/ibRDS3eC3vrT0WoOx2OYmNkS2wGstSoRwU+aHaJGBUK+Uqg+lK3GLLZr9cS0Ej6dfJiAxyBOMFeoC4X0yYJmCyTkdcVfB9qjKXykaNOnIIRgQ3Ah3w7+iCtKlzNEOW0hDG8/lcW1yGnJZweSDeYmIENaNIAQgsbeSPEdXBsSPZk/pBqr4Vd8f1/pkCsEgyYO0TaSAuqkUTRIzRrytCfaCwx59nhQ37hS8b8bV44YmI41m3rdcddR7i1HILBdG4Gg3FvOdccVL6LNoWebmkR1vquckDvfVX8XOc9rmtfwqzd/latTsF1b6Tm71p7R5h/jOZvE8Giob+D8OefTkejgne536Eh0cP6c88dNKWsrU6Zr/akJkTFhm4BBTwR0QwX3pfWZ4L5qMrgfB4JmkGkl06gN1uIz9ux5nBKcwuKjFrPirBVce8y1HBRSCaWNXRv5r3X/xeI/LeaBtx+gJ9UzQksTB+c3D4JhIvw+hBBofj/C46Fr+a/32hgOANjAN6SUhwMnAtcKIY4YaaeR1n+OFkL0ZX4XgD/zt0BJ1O89faRJTAh0TXBFwyyuaJjFO60RHnqthWfeaqM3YfGbtdu5/+Um5tWVkrAcPLpGwirMWEvA0ARBj0HQoz4+CcuhMjh4adaVks5YivSJ/0rlc0vRerYNagtAx8UUNs70U6B5haK9GF5MqwRHLyElvdS6SjHgJWMBH7Wfze1b1EhqYJlLlhNv+tGsOF7N4WHfpwGIJC3a+pJs7YzjetyRBCvV2KRUGSwnDWiZJYPMakBoigrm5p4J3Dyo6JW5Z/YX4nW/S10yRmM0RUPJYO51Q30DSxmgHd/5Mg2JwcWbgJpo2Mn+oDK7LbtsPsQKQeORjSx7/nuQbMLn2CRdG0s3aHTybjZ5k4F8OgsM7ao6lqLUicZYHUUb6hu44eQbxl7A6wlBx9tq0ig0dd4zsqYDsWLDChzXQUqJTX9+pDPROckrPQAw1rqM0eLRhYIv/lFS1SvRXXA0SHjhno8KLprA8U8IdEMZV/nLlLZ9Oqq07geuRk5iRPgNP37DT9pJ05vqJWbFRt5pjFjXuo6HNz1Ma7yV2kAtjUc24tE9rNy8kpd3vkxXsovfbPwN9799Pw11DZw3+zwOKR/GoXgCIHe0QjhUsE34fFjNzXu03z2G60vPBr4FzAK2AD/m+t7xGkvtRJlUIaWMCCE2AnXAP4bbb9i7iJTyg+NH/gHE3NoQ/3rOYVzVMItH1+/g0fU76U1YvN6kZuvagOy3z9QwdUHCcnOa9knLxXYlFy+YPmQ/kfrTsU/5EVP+2AjSQQgtQ7yRCCmxhUbL9H/ipOgq1KKRkqecQge7HJCaQaumimIrZSe9hAgRQRshoZ4bfmiaohz0bCfqqeJ27ZNs8M4nkrTY0ZNEIvHlOawOtJRSjekgjAy1Jg1VhysqSrwT7IR6Plit6DjZAt4iRa9rmtew7PnvYca7CNsp2jWNZQGDpfEdNBTRCR9UtPfKMIYrdibQt1NqrDJTB2BWDLtC0BBPsrSzkxU+jRZNo852aezupMFngDezNJw3GRiNIc9tf7uNO/5+B0IIDGGQsBPc8fc7APZKUF90MpQN0CeyiLVAYSNP6rSI8kZLtAUkuANoXbZUtSyT2L8x2onsWBEyQxh2L6ajJGI1CZattu/XyA/uXUcF96lopm5n8vM8Wnh0D9WBasrcMvpSfUSt6IRcD9a1rhtkeHXH3+/gmqOv4bsLv0trrJUntj7Bn7b+iYgV4ZmmZ3im6RkOLT+URbMXcXLdyZjaxEubimm1yI5uCPYX7MtkErO+fsL72uNQwfytQAroAqYCt3J96bXjDeqzEELMBI4FXhrptZNpoUlQWeLlCyfP4pKFM/jzxlYeeq2FzR0x3Mw1xdAE5QGTsM9E06AjmqYy6M050V68YHoB3aYYEjPOoGvB16h45adIoSkZLekAEu3Ub3PS1jWK1hGeCr1qpq5pGlVuF92ynId8nyKetql1W9HLDkL0bCjekRhIvTHUhsaVAHS81caLj27ATNu09SVzRbZVJV6suIknwzLLv5xKQAoD3V86QJbySxm6jZnRyu9Qj2ctG/I8rHjlvzFjnfgzdB2/K0HarPAIGhxvoQpOMQSnQO9QarFCTTgMb2b1wKtGn+yB6rlDB64v/owG10eDnaf2k+yA9E6oChUq+DA6Q557N96bC+YBDAxsbO7deO9ey9IXVTAZwnBpTfdiljU/gambBTSipSwdPlhL9an6hHyJ1eA0JXk6AHUldeyI7ijaTHeqezyHOom9gJZoC8e9Byc/t4PyrjTdFR5eOK2K1+aMj+t+4Z+T+C2VmbeECuj9ltrO9yZo8HsaWVqfrzQT3MdUgG8lJoP7UcLUTCr9lZR5yyZEGefhTQ9jaqYyuoJBhle1QZWxv/jQi1ndvJrHNj/G1r6tvN39Nm+/+ja/fvPXnDPrHM6eeTblvvJcuwOz/p865FNjMtDSL70Q9+ZfIhNJpD+ITCYn3ndh7+FbqGA+qxcaz9s+7oBeCFECPARcJ6XsG+n1kwH9+xAvb+7i/lea2NmXYOooA25QspeVQS8hr0FZwCSSsHCk4tm3R9N0xNIEPTrTywNcvGB6ro/7X2kCGLKP7Wv/wEGv/xchtwmJg5AOEgvMIOLkJXD6d+CW3/TrZpfWQ7QN3U7h0TTuKfsKzySOpL7ch98/m5DTpQLugd5PA0n0QlPZ8jyayemH1XADcMfqzWztjOMzNKpKvIT9JrF4AIMI2sAMKjpLjW+z6LzPc/phmQz5ikWqyBSRCeSd/mB6uGLN3i2Es+POjNcnJS2aDWbZyCo4xRxvQfUtVLkylQf3b89qj2cmNIBSw1l7q8qmeUsUjagiz0HVV6rGF92h9s2jC/HOUzTu3MIytx00A1+wlqTpydFZsnSiSFoFtJqm5ZRidHTi1hBOvnsL+UWsoB7TsGLjPZhl08aefc3KQ+af83yZ1Tw0HtnIy7teLtpMyk7t3vFMYq/h5O0BTnvkHdKaS7dH4u2xOfuRZvyfmTuudqs6UkgBUlMUQpmpr6/qOEA/E5oOvrD6cd08Wk58MrgfBbLKOKXeUiJWhL5U324Vr7bGWykxC+8XHt0zyPDKZ/j4+MyPc+aMM9nQuYHHNj/G2p1r6U51c99b9/G7t3/HKXWnsGj2IiJWZFDW//b1t3PN0deMOqg3TloA3/wS3Pd73B2tmPX1VCy+Ys9LtO4ZzEJl5vMRz2wfF4QQJiqY/39SyodHs89kQP8+w8sZDXlDE4R9Bp2xFD/7yyaWcMiIQX3+vtUlHkwNOqIWXkMjZbtICdGUw8ZdEX7w2AbCPoNS//B9bF/7B4589V8pzwuU1SVdxzX8xCrnEXIlWr5utjesftJx9FAt/9L4Ff4l2+A734QnvtkfD2eFXHK6OyjeuO5VS8G6OYhmcvphNZx+WA2fvXMtbZEkgUwtQJMxg7TdTAlxPNjYmEQJsNOo43XvfFpWb+4P6Hu2AbrSuNeM/ix9pKVfDaYI6myHdl3DLzIOuAKSmqDOskengtM7FM8wo74zEHltrmlew4rnf0hLrIW6Mh+NEUlDIq5oOd1NUJHXt25C/QmFE4FMdrtB87DUG2SFnqAl0kJd+cE0LvwWAMteWoapm8qhVbo5Ax5NaDg4BMwA+xQ929TEMR+mnxa3m/AYVHFyOGmJyvinVTsDVzPyMdzEQI6mIHsS+xQffT5CTHNIm0pHK2VKpOXw0ecj8LXxtKxm+KJgebEo8e/Ag6YVBvdWTCUSJoP7EZFVxgl7wsSsGL2pXtLO6P0BagO1dCe7cxl6gLSTpjYwONmQ7e+oqqM4quoo2uPtPLH1CZ7c+iSRdIRnm5/l2eZn8Rt+fLqPoBlECDEo6z9aGCctIPyRj1LhGznRuJ9jC4pmk5+pCmS27zaEMoJZDmyUUv73aPcbSeVmEvsxnn2rjct//TKf/Z+1fP2B9bnMvKEJ/KbS1PabOoYmcln04ZC/bzztKB16IO24hH0600p9lPlV0JiyXdqjaZq6k6RtF11QtI+Kv99OCQlEJpjv13Fw0FJ9mGt/QVN3nNiCa/tVYRK9Soe++z0i3W3c9PNfcMp//oXP3rmWZ92j4ZybSXv6LwQy7/+u6hPhoA8rJYaK2cNK9V196mwsRxLP6PH/1rgAR5i0Usk7cjo73HJSUuf/6Z/Eb+o0d+d9Z8tmQHQXQ6rcDIFGoxpLuiQ0AwkkEFhAY3ToQDCHd56CVG/x56QD3lBuIjRQWSenTBNtIey4tBs6yypKWBPwK/5/umdkRZ687HaD9LLcLuPJmI/lvRYN9Q0FHOOKzPsjkaTddI4nftnhlw19fHsDZTMg2l6oShNtp07z7p7G+ATJQ04G9KPHXtHxLwKxsx3XY2RM15T5musxEDvbR9x3WEyfhpAgXGWqIVypiv2nT5uIYe8/0DR1jQpPhfJZ6rviLdnnTq8HAnZHGedTh3wKy7VI2SmklKTsFJZr8alDPjXivtWBaj5/xOe566y7WHLsEmaXqhXchJ2gO6XkdrsSXdiuXTTr/wHCj1Hc1mymKpD5+8fjbPdk4DLgDCHE3zI/5460k5gsxhoZ8+fPl+vWrdvXwyjAs2+18f1HN6BriuOeLU5NpG2qQ95Bjpcd0TQzKoLD0nB+dvsvaeRRap1WtrlV3Gkv4hn3GABMXXDpCQfxuRNncOHtL5KyXZJ5Cji6AJ+pc/cVJ1AR7NfY9d56LFPoGERjycLxVbF98XoAwi3PUf7Cj9A738YWJq1uGQlHwyNsfuG9ite8C7AcyQ3nH8nph9Ww867PU71tJToODjq91QuoDJqDix0HFEGuP+hybnp3Ok3dcUJeQznapR3qywP8c9lbTHnzTupEG1GpaBdVogevcHA1k7KD5vUHuvddxCDOj+FVSiczTirOWX/nKdY89XVVgCpc6qw0jb19NIRmw0evHzoQzHK/u7cWfx7g9KUw7biiyjo5e/K2t8kWGiSEoNpxWd7WwxoPrCgN02IY1Nk2jWXH0HDx/xa2f8s8ld1O9RVyxg0/fPtdzn7obMKecM5ltqm3iYgdyXw+dM6eeTY3nXrT0OOfYOTUhPKLYt99UTn8IjJOwKqOY838S1iW2ISpmwWqOFmDmInCvLvnDfncG5e/MWH9vF+RnZju6fepGB499xiqOx38SRfdljiGIOHTaK/UOf/xv+12u5HVq9l83VfQE2k0Ca4Ax+9h9i2/OFBpCGODlP2c+3RsbJn7yjkfyAnBaJVxxst3z0JKyT+6/sF/vPQf9KYLk0p+w8/U4FRuOf2W3LV/NAh7w/tDhn78H549oHKzu5gM6EeB/TGgz9JFfIaO5ahgOWE5dMXSVAQ9+M1+gaLueIqehM2UsK9AmWbJGf0UGf+2v8Dj3yLl6kSlB59MYWLzfbuR59xjMDS1JHfj+Udx/ytNdMYUv7MnbhFJ9fP7TF1wxmE1fPq4eg6uKSFw23E5ycnBELieENuu3JjbMuX3FxHvaGFnQsuFygFStFPGv5Qsw9AFNSEfv73qxMKm8osd86kPR38O1t+X2x6LRemJRvmF92reDJxAwnIKJgmfvXMtWzqiHBF/mev1uzCwqaQPkJiaQAvVZugoC+GNB4ock6b6D9ep/otla3MTjMKge1isWKQoSZ2bKK6XL5S74xDZ4VywvesNRdAViqDUp2l8t6OLZZVlmFLik5KkEFhCY+m0M2k49+eFY+h4DxIdqj8hVAGcpsNnfsPiLffnimUjqQi74ruQSExhMqVkStHAK7J6NV3Lf43V3DyhXMohA79IioauXarwNzsh8ZVCxWzWfOw7Y5ethFGr5qxpXsOX/vylok2YwuS1z7823sN+3yM3Mc0ryE7YCar91Sw/a/ke7fvOr53JSauakUIF3ZpUnPcXz6rnqp8O7Qw8EtY+/Ev491vxpSW6K3E0QdIj4LvXcuKnin9e3reQUtFxUlEV4I8Un3xAA/osLNeaUGWckbCudR23vn4rKSdFzI7hyv5E3ZzSOZw35zwa6hpGZZz1vgno9yNMUm4OUDR1x/GbOpomMA0NQ9cIeHQ8hqYy9ZaDRD32JGz8pkZHNMWWjhgd0RS24xRQZEpfv42A309C+JRgC17SGFytr1SBrCZwXMn9rzRx8YLp2BkJnCmlXqaW+gh6dQKmjuVIVm1o5ap7X+Xrv/sbr9mzcGTx74xEIPVC/Xq3axs7E4Wvj+OhXrTTEU0Npr5kkV/smI5AZIfim6/+T3AsIq6HzZ1xtvRJEq7BZ9KPIIQg4DEwdcEdqzfnzmtViZcvex4njUmYOAKJIVw0aUNkl8rAb3iQ4l8fVy0lD+cYujuGPz3b1ERhOL1yxxqS7pMzWso45iIlSRR3f0VpCaaU+KVyAPZLMKXLiqZVhY2ctASSXRnuv5Z5FBCohBd/VmCE1ZnsRGZ4wdWBavyGH1M3CwyUIqtX03rDjdjt7RPuGJhP/xFC9Pdvt0OgShWx1hyhHgNV0LN9RLfHoshOJCOtg111i4xJF8WVgGuCw8iRTiKHlmgLtmOztXcrm7o3sbV3K7Zjj1zrMAFYuDNIX0jH1gWaFNi6oC+ks3BncFzt9tx1Nwm/YEeVYHu1ekz4BT133T1BIz+AIAR4guoaWjFb0XN8YUXXmcQgZJVx6kvqKfOWDXl9mSjMr53Ptcdey8zSmVT6VL9Tg1MBeK/3PW557Ra+sOoL3PuPe+lIdOzRsUxiMCaLYg9QTC8P5Ao6NaEoFClbctiUMItPmckdqzfT0p2gNuyjK5oinnIQmcDcdiXdMQvb7V+uM/ua0P1l1OoOO3qTypkUD9O1djQpcKXEowt29SU4YXYFSziE+19pyklXLjnjED40vZSnN7by0KstbOuK87emXr7EVzlItHK5vorP6M9SIvp5yk5wClbZzILj2mxXEqCDBP2Bvp80TW41aVfp39eXFymszBY7pvoyhaNCSVY6SdxIKz24WCKQOS6TamcXfQmLsN8smCRkz+s02ojoJfhcC6Wan2H/SweibSozrfvU39LJGEtlkHGlHcqZdbeQLRoeUsZMqnFlDaUGIGe05C9VWfh4u+Lux23+Pewl7GaPUcEnJS36gJvD3DMzHP2YKqTVPWqC4AnlAuKs9vv2vu14NKWvXOJRSgsDi0y7lv8a4fGg+VW2Vfj9uJnt483SD6mVb+iQSPSr3MDoipGHwhCqOcXkR1uiLQiUlKedZ7ytTeZVRo2gGeS9nvfQNV257UqbHbEdzCmbs+f7bo8iauroTHZiORamblLpqyTQHh13u31eV8nbIFRCRXcIj7PdAx7Z4N4TBFmtvqfpTObeLU7h/KBiopRxRoP5tfMLaDtSSt7ufpvH3nuMF3a8QF+6j9+98zse3PQgH576Yc6bcx5HVBwxJjrOJHYPkwH9AYqrT53N9x/dQDxt4zf1HHXk6lNnc/phNXzsiCm51877wZMgQM/QLIQAR0jSdv9F0QpPx4i1EfQGmFbqo6UngZ80zbIaV0plguo3mBJWwdcJsyuKquYs+tA0PjFvKuu2dfPQq828vLWb7bKWG+3Pc4t9IZ/Rn+VyfRXT9U7QDXqPLdQkX+6exzfE/4CEBB58pPFgc4ezCE0jd4yDkA14o23kClVdF4SOIyWV9JDUVGW+jzQtsoaOaIqw3yyYJFx96my+9eB6um0vM0QTOk7hopzIC76ko8xVMJSZk1T99Z/UcQSKA5FVU5Ej3Mic4lJ3BUZLTpq6ypNylJK62w9Ryjt5K7ZJIaizi9wQqg/tVyPKIh3PHWdW+33xqsVs69tGR6KDnbGdmJpJyBNiRnhGbjeruRmttLSg+YlyDBxSK790FvRuHpUqzagwhGpOsYlcXUkdHYkObNdGQ8PFRSDQhT5y8e0kFDKrSMj+QuLs33saZn096eZ3SLpJXOniSId0PEJp/fhkK9vLNEIRFyuPpWBaavsHHUXrYOob1DUnPfHOqgc6sso4ITNE3I6PWRlnd/s8rOIwDqs4jCsSV/Dk1id5cuuT9KR6eGHHC7yw4wVml85m0exFnFp/Kl59sKv8JCYGkwH9AYp8PfXm7jj15YFcMD8QHkMjmZGdFALcDF3GawgMTcN2XV6ecimHvn4DSTeBo/soMy1cy+Z2exGWK9EE2C45R9jhtO6FECyYWcGCmRV0PXcbj7y+g4ecBiIEWO6cy13O2XzE2IAnVcarq/xMK12f239b+cms7NjEJawkSJKY9PE/zrk85x7D7HI/131sLg1zqwefkJkNsPrHKnMMkE1kCwMNBx8JZjpbsYVGRPr5D/dS0tLlyNhLXJR+hCPcblgxi/KDLudEdzOVohcDJ088LusAqquTaAaUI6I1ICPuK1P87MguFSjq5mAZy+E410M9N/dM4Ga470LW+H2sKA3RYpjU2RaNvREaEkkV7LtScd2LtJ0zWsr2sfFLUDaDxiQsK1HKHf0cekGjXURNYZQyjfNr5/Nq66sIIdDRSbtpOhIdfPqQT+deY9bXY7e3I/z9QfdIjoFD3uAHILciAQUc+saF34Ijk2OvYRgK+XKrWQwxkWs8spHvrP4OqbxJl0TiSGe3CtU+iIjZMaYEp9CV7MpJolb4KojZez64++vptcy+5SWmp8kUrzrEPd389dJaZoy8+5B4+uQglzzSS6DXRXeVwVTcA4+cHWRkPZL3L/LrYIqavXn2sQTufgwhBEEzyGutr7H8jeW0RFuoDlTvdlHsaFHpr+SSwy/hM3M/w/M7nmfl5pW80/0Om3s38/PXf85dG+7irBlncc6scwh7wyM3OIkxQb/++uv39Rj2e9x5553XX3XVVft6GIMwsyrIhcfXc8Ups7jw+HpmVhXncj77djsp28F2JbYr8Rga5QEPB9eEWdwwi3Vbu/jB80maxVQOYTtlThfb7XJudj7DcxmVGwnowGlza2jpTvCfq95iR0+CWMqmM5bi5a1dzKoMUlfuL+i7QkRY1PQTLuOPlIko77l1RAiwxa3l3VRpTpnnlW1dTC8P8DHz73x4591ERZCdVJLG5EP6FuYc+iGuvegsasI+IkkbV6qJiiaEClD/8iOlcVwkVScyuTyJQKJjC5On5Xxmil18V/yaaUFBoKQMEj2ITU9wrPwHUjNJaEFKiGX2zAT2ZkBlZEunQTLSP4EARb0IVkJfkwr8Q5ms66YnoOJgVcCV5VxbSSXhlujpf75z89DPVc6ByjmsWXc7yypCJIUgKF36NI3nAn5mWDYzrAz9RzOL7w9F+5+R6GNGIsomj0mHbjDFsflKdy8Nx10NM08pPJmVc1R7bW9CtBVK6+CM7w8KiG9ffzuWa+G4Do508Ggeyn3l50Ry9wAAIABJREFUxO04nzz4kwDolRVE//KM4uIbRs4xsPpr1+GdMThEyt7gk06SoBmkL93Hc03PMSM8oyDzD+S2bereREeigynBKXzl2K+oQKByDhzzOTjxi+qxchx0jUC1Oseuq+obshOcM74/qN0Z4Rk8vuVx+tJ9SKnqC7y6l0p/ZcF5mcTQeKbpGZJOkupAtXLV9JXh4jIlOGWPn78/3PEt5m12lKQk6npguvBXYyunLrpmt9t96oV7OGpjHNNREwUpwDbgrSPDnN3whYkZ/AGIG/56A0knmauDMTUTF5dN3ZsmvyujQPZ6mXbThDwholaUl3e+zNSSqUwr2bOSqLqmM6t0FmfNPIv5tfNJO2maI80knST/6PoHK99byXu971ETqGFacNq+pOP8cF91vCcwqXIzCuyPKjdjQVbi0tSVxnxHNEV33CLkMzikJkR3LIXlSgIeJeG4uT1KPCNJ6TXUsq8rVVB7xNRS+pIW2zpjaJog64/kupIZlUF+dXnh7H/K7y/CiLUhM4ZCltT43/Y67rNOZ5PbTzPQBNSEfDxY8X8JRbbQnlIFtqYuqPY6mGVT2XWBklH0b/sLpa/fhtnXhCw7CCPVg9a1WWWonTQDg3oJpPCwTZuOJgSmm6BTVDC7Kki501WQ6dm6s52ptLFdOwiEIODGqJbtuIBPE0o/2U2rAC6yoz9jL2WGguOBcP1gSkqoVhXAZtVqij0PQz+XMXha/Kt5tMs0/rzvrZKfdFi+q131X3PEkPsX7b/97f6xZ5VfPGGomlNoLJWHkTLlAyUsQXEt+9J9PPnpJ3OrBJG/b6HrTQ9WMoA565BhVW72pcLJsBiDatGI52USw2Jfyla+cvTh+NKZUvjM0p0LJD2wYP3G4XceBg+ddRThqEMqzxfOa0Ffic6nV705zlEfuJj8rowPQ10vK32V/Pi0HxOzYntFGSeL7mQ3T259kie2PEF3qhuAIyqP4P5P3L8vA/r9ktgvhPABq1Ga9gbwoJTyByPtN0m5+QAgn56zqbWPSMqhImhSGfTSFkmytTNGfVmmMFEILLf/S56yXQRgaOBI2NWXoDOWRhOo7DgZ1TAhaSqiPmP2NeF6y/r/Fi4nio2c7X2Fz/juyMleuhJ29SU5o+9yzg9s4POhVznUkzFskRKtTyny+Lf9harV30VqHlxvGSLSiujdgislaUw8mTutyGTVAYSmtpu6pkywdD9HeLvxp3sH8Z9dww82eEmRwkdcC9LqSKrpVl/9UK0K2H53iQrms+oLInN3t+KqMLXz3f7gOFDdz6kelnMtR+Rjt2gQdjT6OUWZAlbDBDJ1A0P1PVT/2eKpyoP7t0k5ZEHviEvhDMNhL6krkBgNza0kNCsBbhTOuQTmDl0MO2Sh615QOBkWOUrUyBj2vExiRBTUgoxVXnSc8KfJZecBkCq494+TolzV4+AImBYBwwFbh56A2v5BxuR3ZXwY6nq5M7aTKn8VZd4yIukIkXSkQH5yT6HcV85nD/ssF869kBd3vMjjWx7nc4d9brJYtjhSwBlSyqgQwgSeF0I8IaVcO9xOkwH9BwSnH1aT01nPquMASrZR02iNpAj7VVWWlnFChEywLsFywdTgoIogXbH0qGvQssW22Qw9QImeZoeswW/q+Et1qhyXjmiaeNohLU0ejB/Dg/Fj+LB3K5eHXuF0/U2ssOLul75+G1Lz5NqTZgBbmGgyhcDNGqjnKDZapohVM7zMrsxQktJxCM1Svw/IVtf6JdujdQRlAoCE9OAIHRmogU/+tD9wkwzWSM7+naXcCE0F931NUHWoem4kzvUIfOw6dNqx8NP/HvUXsLqZVQqreN9D9V9MCnOYgt58SUgg97hiw4pcYDUkh/3IRnj6P0etDJOP98MNftjzMolRIVcLspchsitxxbaPAwmvoK5DKkMpAboD1X3QUrWfBDqj9FmYaEx+V8aHka6XhmZQ7itXyjjpCH3pPhx3z08iTc3ktPrTOG/OeZR7y0feYT/HvLvnDTKWeuPyN8a1hCTV0klW5srM/IwYdk2W0X/AkNWvz0dt2IvlSOJpGyklbp4kWEbgRkEIvnT6HGZXBRUfPWNVLl2JK2F6WSF/HqD32C8i3DTCiitbcytOmQd+o12Q08q3XUmJz+AHi47gW8e6zNbbAPhraibXdFzE2R1fZnnoSyTSjqLZGIX9tEu1ApDVDcly3l0EjiesGPS+0owjYby/iPOkJer3dDz3XNBwiDf8G/eUf4V2yijXYpRWT8efH8wDVB0CuCojLsnIqLnKKTaHvBty9qZfpM/hxjOw4LTRMrGEIJER9khkC1h7+wac+SJ9D9W/N6z49MP0m4+WaAs+vbBgdmCmvKG+gaULl1Ltr6Yv3Ue1v7qfFpHV1M/HKCQ+83XupZQk7MQBd4Mf9rxMYr+GHggOWp8Xme3jgZFRxpKZ/+SA7fsUY/BZmGhMflfGh9FeLzWhUeotpb6knkp/JaZuFm9wD+BAz85ngvlbgalAV+bx1sz2cUEIoQsh/ga0AU9JKV8aaZ99lqEXQujAOqBFSrlICFEBPADMBLYCn5FSdmde+6/AYhTP4KtSylWZ7ccDKwA/8DiwREophRBe4B7geKAT+Gcp5dbMPpcD/5YZxo+klB8o9458/fosDF1jbk0JUko2tUVVNl4XOI4kG9oLAT5D5FR0vvLb14ik+mfzGnDuvKn8vamHFX/dxs7erPrNMZx26r/nOO9WeDrRU75I5M0ptLzdjisVf/6MQ6s5ZW4VzP0Inyn5KX9f9wArUqexxv0QW51qfvI3+On657nUdz5X9v0v9UIF/Uj1DXKAHlFCmDigYaETx0eg6nCSdSdRsvOvGH1NaOUDMkw7Pgdrb1XOhN4SOPFajj79Io4+A+ArQ5/Ij14P919SKBOpecFbqnSTY+39tJfgNEhF1GuyajVDca6LjCd/ItHQ28X5Zpp7S0PENY2A63JZVuUGTb1Rulm876H6P2sZ7Hht2H7zUVdSR3vPVvyJnlw/SX8ZdWUzC7J5DWUzaCiWzRuDMkw+9iXdYiKxrzLM7xeMVuloouE/8kgSb7+F7ItkiqA1RDiE/9DDxtWuLy3pLBWEYhLTBsuAnqDAl959fvOEnaMx+CzsCUx+V3YfY71eCiEIeUKEPCHiVpy+dB9JO1n0tZPI4VsoekyWbxzP2z7eLL0DHCOEKAMeEUIcJaUctqhmnxXFCiG+DswHwpmA/r+ALinlTUKIfwHKpZTfEUIcAfwWOAGYBjwNzJVSOkKIl4ElwFpUQP9zKeUTQogvAR+SUl4jhLgY+Ccp5T9nJg3rMv1K4FXg+OzEYSgc6EWx+RhYIJvVr7/wuDoefK0FUxfs7EmQsl0cCYZQAb8jJZoQ3HHp8QB89bev0ZcX0OsCSnwGpq4R9hl4DZ24ZZO2JUvOOKRAs/7eF7dy99ptaKK/ntSVcPmJM7iqbnOOIx91TP7W6+cR52QedxbiZBaUdBzO0l7hCv0JjhObcvR1F407xYX8IXwpCcuhMujlv//5aKC/kNYTaUKWzUA7ZQk6IsflLpBgPOfmkW9WD10FbzwweHuwRklXDlPYOiTyuOVDjWfNzQexrCyAKQslJpd29tCQFdypzqPYjKbvUfSbjzUv/ZxlG/4HE/AJjaR0sYClUz5Cw6bnRm5njP1NYhJZ7Mui2KyzsfB4ED5fTpWp9vvfG5cR2pPnLcTXk8Dx9idZ9JRNsszP2Y+NmJQbhAk9R7fMU5n5/EyqlJDsUU7Xk3hfI+Wk6E31EreKuLOPE2FvmArfYC+bvYxxLRHMu3veZlRmfiAq3rj8jSKGObsHIcQPgJiU8ubhXrdPKDdCiHrgE8Cv8jZ/Eshmy+8GLsjbfr+UMiWl3AK8C5wghJiKmgz8NcM3umfAPtm2HgQ+KtTazlmopYuuTBD/FDDupZEDCacfVsMN5x9JTchHb8KiJuTjhvOP5K+buzB1QcBjUBP2ka2LdSFXZloRNLlj9WbuWL2ZtCvx6priwZs6hq7Rl7CJpmyCXhND1wj7PPhNjYdfb6HUb2Lq6uP2u1eb0QTomoYmtMyj2p7Pke9KWEzTe/mm5xF+Hfg5l5jPUkM3DjqPuydyofVDLrBu5A/OSThSx0VwibuShKUkOrOa+dlCWiPWhuMpQ/btwl35DdKrvoejmUhPIONKGFBB5os/G/lEbnjw/7P35vFxlWX///s+y2yZSSZrlyRNCYst+yoFbKlVUQTL/giKWhapCor6xQ21j4Lw6Fcff/L48MUiheLCIlUR6gqU0iKUpUDB0spSWpK0zb7Mfrb798eZmcxkadMkbdL2vF+vdpI759znnkkyuc51f67P5T4K0f8P3Mx8TtKS7nUdZLrfhmTX7repC7Nhw6xneTiALiVB6b4TBaVEl5LlZWFXNuMvHbF0Zk+uW8jcTf/gxrRONRp9QDUaN6Z15r768MjmOeJDbvAemeIGBpEpXjDvMSIK6zeEEAS1ILqqs3zj8r1+7ci8eUxZ8l206mqc3l606uoxB/MA0Ss+g2pJ1IwreVQzFqoliV7xmVHNN66vUbTBveEuZDwb5nlMavyqn5pQDbXhWsK+8H4vkdkLvAMMbIgQyo6PGiFEdTYzjxAiCHwQ2Ly78yZKcvMz4OtApGBsipRyB4CUcocQItchqRY3A5+jOTtmZj8eOJ47pyk7lyWE6AUqC8eHOKcIIcQ1wDUAM2YcWG9euQLZQr7zp38RDbrauUhAR1UEdlYbrymC6oifsF+juTuJBGxHohb8cousptt2ind8Qj6N+s6nqVxxC+Edr2MaGZ5E4S21jrucj/FPcUL+/FOs9QR2rgfpIFUfPiuCo5SQcnwc6rzD55R3WOJbxp/lHO6xzuY12cir8lC+bF3Hf/EJPqk+zifVx1mR+RxEG3DUL5JiwZCFtJig9WzBLD8CLAdFEa5zjx5E9Ly7+0KwYYuHpBucPvE9aH/bDWhLZ7gFqn+9AdhF4DqCrqMtukqpXexIkHe5Oe9/3IE9bZq0B91Oc8fPDZQz1yx4c1ckZLZCdGZ+aK1isLzEosXZRu3fryre7t0DZxgPjxwT7XT0SqNg+SdUWuIatWGVRY2Cse4LzLnwC7z4760ov/sLvoyJ4Vdx/uOjnHzhF0Y137i+RiNsJOdxYKOrOlXBKsr95fQZfcSNOLY8uF2YsvwYV0MPrtwmhGs1+eMxzjsNuDcrTVeA30kpd7PFPwEZeiHEuUCblHL9SE8ZYkzuYny05xQPSnmnlPJkKeXJ1dVDdCY9wKgvD5Ey+39B/ZqCpgpKfCqN1WEiAZ2UaVNXHqK+PISqCCxHkrFs0qZNxnKDTFUpfomPTj7P1527SLe+icj04JMpykSSBrGT/1Tv4Qz5MgDv4xVu1pfnWzgJx2IaHfidOJqTpklW0yyrUYXD+co/+ZP+HR7Sv8fZynMoOLRSwU/t/+AM4+f8VHyaTkOhas23CW5bNWQhbe5zYbnZJ8eRWLbEzCSw9BLkXwYXgm1Y9RCX3bmO9/1oFRbq0D84iuoGqsFyKD/Ulb8Ey/qz1Y//p+sF/7Nj3MfCrP0IsmFTpI+UEFkXHwWJICUUpuDrD5IXrXS3wxetHFnQvKdZuOGO94fz42sVg1u1JO1YlCp63tpybfPa3a/Hw2MYasO1pO1iXe++cjrKSVnaU+1Fdq1j/ZmOrVlD9ImXKZvWQGjWkZRNayD6xMvE1qwZ1Xzj+hp5u2keBaiKSnmgnNpILeWBclRlEhRuTyBZN5trgR1ARfbx2nFwuXlVSnmClPJYKeXRUsqbRnLeRGTozwAWCiE+CgSAUiHEb4BWIcS0bHZ+Gm5lL7hZ9PqC8+uA7dnxuiHGC89pFkJoQBmuzqkZmD/gnNXj99T2XxbPa2TJIxtJGhZBXeX9yit8Qv6Jetppaa/hQd/5rPedwuJ5rizsc795EaswG5/9UFcEScPCsh1aYxluFCtIaxqRVCcajhuCCphGF10yzNXKo6x1juezykqCgSBOoAQ15maShFCodrrpEFHusj+GlDBb20aZcNu8nyze4GTlDZplFffaZ/GgvYAYIR5MnMCDiROYq2/mU//8M2dE6tGTxdaZwkphRhtRzASYboCfTsVJplLEHD/lIk5USaFJEzQ/GSWM9fRttJX8gGhQ54nMXM5yVmc7fhZw1MXu41BZb9t05TflhxY7RuSy9iPIhpUaZ9HqW4lAEpBOXkNfapw1+m/+bq5bVGCnBFmUaWVu55tZqZHS35l2zrWw4T4wYHmJhSkdugSYioOeaCXiixRZW0429lWx5UQVdR4ITKSV4UjsWkdD17K7SSkWHWY3Ztp0s6FKhK5ld49KzjPur5G3m+YxgJwzTqmvlISZoM/ow7DH2JBhPyUbvE+KTmf7PEMvpfyWlLJOSjkTuBRYJaW8HHgEyIkGPwP8KfvxI8ClQgi/EOIQ4HDg+aw8JyaEmJPVx396wDm5uS7OXkMCfwfOEkKUCyHKgbOyYwc9hdr6Q3qe4etyGVPUXnpFmCq6+Yr5S746c1teqmPYg/PTisDVyiuC5p4USGhQO0hLHz6ZRi1s9iSgQsQ5UtlKUFc5KtRDVXkUPVQGZXWgaCg4KEJys3MFTznH8axyAr8V52DJ4h/bOtHBt7X7eMZ/HUu0e2kQrQCsNWdxzY6FnNN+LQ8kjidtmHnrTOEYdJ12Ix3zbsEqqcFMdPFmsoSfqFdTSQ9lshfbNrGlgrRN1HQHM513CegqQgj+t/xr/EOZj002Q6GocMzH4aI73c+HymLHd+5aYz6CbNgxHX18q7ObKtumV1Gpsm2+1dnNMR0DbSv3gF1ctygr6UB711vcqqVYGwq5nveO5XaZRcD0E/PzvC0sOhWwVA1V0bGkRWeqk7d73h79Ovcieyv7OlHXOVCZSCvDkdi1jobYtrfYYXZiORaKomI5FjvMTmLb3hrVfJ7do8e+QghB2Bdmeng6NaEaAlpg9yd57DUmzOUGQAgxH7gh63JTCfwOmAG8C1wipezKHvdt4ErAAr4spfxrdvxk+m0r/wp8MWtbGQB+DZyAm5m/VEq5JXvOlcCN2SXcIqW8Z3frnBCXmwlq5gHwr1vnETY7MJV+mYrupIjrVRx94xouu3Md67Z0uhqmXISe/THyaQonzijPW2P+oPdblDtd1NnNbjBfaJYAOKho3+ty5ScDrAwdI8nGvgDX6t8noLmB800936KKbqaILnQsJAoCiQ8ThOtAL9UgT9rHcrfxAZ51jsrPV6akudT/LB+v2UrwlE+TaliQ/9pXH9xAZyJDUFf5fd9l+EljoyGyz8myDDIEuKj0foQQbnIaiKVNnv7mBwa/iEM5uXS/DWUzXE/8/IuwZ44R8e/X4pMpnILNNQULQwQJ/+f464iL2od3vAWOScoxqbZslnX2ufZ9qg6R6UVuOqfffzopK4Um+tdpSYugFuSZy54Z93WOleHapFcHq1n24WX73XU8xp+99b0bb5cbD4+JJGNn6Mv0kTATuzzuQHC5mWxMaKdYKeVqspIXKWUnMERkBFLKW4Bbhhh/ETh6iPE0cMkwc90N3D3aNe8TCoPBoaQZe5kKcwd9hLEsh9wNXxqdqLOd1ZvbaOoewsKqoIliU3cyX2D7h8BFfC55R77jbI7ch0ruoyFkH4pj8Pbh17JzQxrbkfg0hemiDRtBCcUa0dz0AonA4QPqy3wgsJ5XeQ9X8j26kga9ToClqfdzV5PgzGA1F/n6mD3NLR7b0ZeiNOD+OphCxy/T/Y2qsm2xjeyvi5QSKSFl2vg0lQtu/yc7+9LURoN89n2H8MGjpqIO5feu+cAyINMH8TbXw12oUHnoiL83IVJkUIrehWwUQqSGPWcsFBXY2QYoKgFH8pZP46qqMlo0lVrTYpGiMregiFYXOmnSONJBIJDZHr662HdNS/aEfVVs2RJvQSBoTbRiOia6olMRqNhnRZ0eo2fRUYtY8s8lbI9vx3ZsVEUlokf42slfG9O8fzvNz0WPJrGcNBlN4rcEqqPyt9P8k9uCbQKTTh6TF7/qpzpUTdSJ0pfpI27GmcjE8cGE1yl2MrKHNoLjTZsyBc1Ou4ErbqAcwKBZVrPkkY1E/Bq6mg0pZcE/oLGqpKjA9iX/yfwi9HnXQ77oFAGoKIGs0dEQso8Nx3yHn25toDLsw68pGLZDXAaoUzqR2Qx57h+AyHZZtG2LlC1ocyIYjqTEr3JIVYjSgIYiXCeeVZvbuPa+l7nuvpd4cnMbUyMB0qYbuG9T6ukmioWKhoNUNEx/Fe+I+nx325RpE89YdCcNOuIZwn6V1r4031v5Og+90ERTV5L2afOIXfpHzC++4mauP/A918qy5103sEeAY7rB/Qg7Lyr+ML6cfz/ZHw/hju8NigrsVB9Ihy5VIS4E7apCqe3QrqncqiVYG63Kn3dY+WGEtBCWtMg4GSxpEdJCHFZ+2F5Z51jZV8WWJVoJOxM7XXkFCpZjsTOxkxJtbN1GPfYNbs2McHfpEOMSqDQdXc1dZ0FPWBBOC3rCgrvOcscnLRPYQdZj/0BXdCqDldSF64j6o6iTofPxAY4X0E9Gera5WepCdmUjOM6s8F+IT1gEyACSIBl8WCyTH0NX3T9ipUGdgTF9WVDjGx+ZxeJ5jZi2JGm4vspPi+O5R/04UqgIRUdRA6iKhqIIt5AyxwCHlh++VY+uCqrCAQ6tiXDktDJ0TUFKN3gvJPepJTXeZgYdohJDatxhnIMjJafZL3OvejOr/P+HLwdWcnjUPeP1HTFu/vMmtnYl6UmZJDIWv/NdgIVGG5XEIodjl0zD5/cTP/ELVJb4iaUtKkv8lAd1wn6NoK4icBt1aYrg30//gcrfX0T0zpNQf72QjpdX5gN8O1SJVHQQYAmNHVSzNaHzrxU3s3pzG7tlzrWoQuIXDkFV4BcOqpDFr+M4UtQ+vKSalJT0KArljkPQcRBIgkJFl7C8rF9KdPKUk4mbcRSh4Ff8KEIhbsY5ecrJe2WdY2WkbdLHjACJ20RAZHVbuc89JjfLNy6nNFBKY7SRw8sPpzHaSGmgdOwe+BJePVTjvy4P8H+uC/Fflwd49VBtGP+1ScIEJ5089h9URSUaiFIXqaMiWIGmTKgw5IDGe2UnI9GGQXryfdnMY7U8nj71ai41H6ZOtNMsq/mlfS5Pi+N5j67SmzL59JwG7njqbVKmgwDqyoPcfN7R+aLZm4Cla7bQ3J2krjzE4efchLLzEFh3O2TirsXhnGth/jf6LzxgC3d6+5nEInOK1hYRaZIiQJkcqM8TtFJGlARlIs5OZQr/Y5zN0xzP/MRzXKfdi4lGRgQ5Tz7FF/SnefzMW/htSw3PvNVJb8ptsxpLW9zPLN7VP8cPyh4lQjtmST29J3yeGQ0L+GnBci775TpKAxonm+u5xPgDM+x3Ccg0fgysmI90oBp/oo2qNd+mA4g1LCCajmFFDyNh2rT1ZRACFAUqzR184ZGNXNzcw7NbumjqTlJfHmLxvMbingHzvwHvPA3b1uC2YgBUP2xdC2+cOO5b3kXtw+0WaisOo7fnLcqzXvg5+8xA6XRanBS88Rjdj/+EZ5QmynSVPkXHUm38qo+IL8KLrZOz4/KetkkfLQkzQbkaosuM4eBmVCr0yG71ph4Tz96SSyWsBFFflC6jC0c6KEKhwldBwprEPxN72rvC46BHCEGprzTvjOPJcHZP1of+RaBFSnnu7o73AvrJyAQ384j4Nf7SewyPOv3lCRLwa4KUaVPiU1nxUgvTo0GCukrKtDEHuN4M1byKWd8oDuALGaJu4OvOXfx/SYWNJafmD3MklJHoF81nMVGJyRDvyOl8L/ojpJQ0GUmwHa4Uj2aD+QCOlNhaEFSY33wHs85/iO09Ke5Y/TbPvN2Zn/Kf5nv4SM9sLj+1gU+cWj9kh7xppUEO7X2G66y7UKRBWCbQsLJiIgslvZOMmIbf76fs5TtINSzALK1HS7TRlf1bLRD4SNOqTiFjWfzvk29RXxEiGtRpi6VZ8shGbsq+ngCs/hE0PYMbCmYbTNkZaH1993UWo9S8zq2bmw9sN6x6iB+3/iedqo5fKggchG2TyKSpKykn9aev0Jt02F6rUm5LKu0MrVY1ZeWVhP3qpNaKFz7PvUWJ47DTjKHR/x3sNmMc6ts7kimP8aNEK2FL7xYUoRTJpRrLxtbhvUR3ZViaoqFkK3e6jW4ODY28tmafM8FJJ4/9mxLdkxiOkOuBTUDp7g4ET3IzOZngZh65O2dVEYXyeBzHwbQlQgh0VRDyaQjhPuqqYOmaLaO/6BBbuOFQCZcYf8xLd5KGRZnsya6x+HQNCx8Wv7DPRVcFmqpQHvLhSKgT7WTw4WRrAipCPqQWRO9zmwZPjwZJZGymRwNUh33o2eZYGcth2T/f4ap7X+TPr+4gYxZ3xrv0lHousx7GkCphmezvXCbIF9Sq6Y6ia/We8HmEY6BaKRQkfplGlxa/0y+kN2m6HXizTbv8moqmUPy6rrvddZahuFss6Z5db3mPk+ZVXfdzzu8FUwjSQuIgSAmBlelmUW8vnWnIiAA1liAt3BLlCtlDeyyzzxoATWoS7Xl1TVEXvET7xKzHY+TsLbmUdG/skdn33oLPJy2nX+8mmYyk+2ZsJL0Osh4HJZtmzf7Iplmzn9g0a/aW7OO41LILIeqAc4C7RnqOF9BPVkbT8XOciBs2tdEAfk2hsPGr5cDFJ9YSy7jNpwoJ6irNQ7nfjJQBdQOxtEVrSjDVaaU9lmFnb4qaSICgsBBCd+0p6dfvC2CJtYinnOPZtLOPrR1xHOlQFtLp0KaiywyaKpgS8VPi19zGUqX9/cp29KUI+VTKQz5mVoaYW9LCkYq7fby1M8l/P/YGH79zHcuefoeOeAaA9zZWMCvQjaUG8GG5a8pm8kV2VZo0i66ValhAx7xbiOmVhInTpZTzv4HFvKifhGG7Tj7IXOdaB1URbO2I0xHPkMjEZpodAAAgAElEQVRYyHQfEqfouUtwveB3teU9TprXCnMHx2UCfLZbpdwWxFWI2gpf6kgzt6eduK2jCMH5MQVLQEoINEwMZ981AJrMJByLqdLdGnVwH6dKd9xjcpMwE0wvmY4mNGxpowmN6SXTxyyXykluCgvIo77o5JbceB1kPTzIBu+3A9NwLdKnAbePU1D/M+DrDMreDY8nuTlIWL25jaVrtgyvzS6gvjxEWyxNdcTP9p6066giJYoiWPFSCxG/Rsq0Cfn6f3xSpk1deWjI+UZEwRZuLG3R0psiINN0aFOpDvoxbcnieY0oD4fBSGII3S2OBRRs0rgNLe7Tf8AMpZ0mWc2y1Mc49syLOXbGEvjzDdgCpKbmG0v1nvD5/OWnlQbzPvSnWC9xnbMUw6eyiUP4O6fx5/Qx9KXht8+9ywMvNPH+91Rz0Yl1TK1oYEaiDatXR5EWNhoaJu7KHBxU1AHXSjUs4PUPHs9tq95EUwQBXSFt2qiKoMRXfKOUNh2mlAbpS5n0pUwayN6FF2bvco43u9ryHifNa5c+jbDZwYnpICem3bW6PQqmQLSUcGwrCenjxLTKZ7vhkYjFdl0lKKLceOr1B31zm1rFT7uTYSb93+eUtKlW/BO4Ko+RUBuupT3VzsyymfmxnA/9WCjRS9gZ3+lm+3Hfa7szk1xyA14HWQ8P+BqQAXLZzGTB+Ki7xwohzgXapJTrs/2aRoQX0B+ADAzeT2usYMVLLeiqGF6bXcDieY18bcUGOuJGPvutKIJpkQBa1uUm52JTqKFfPG9oLelQNxNA0dg3D/sMx62/EXqb8FsmM6RCTAb5XuaTxIVFOKCxdM0W5s+5FlbfSmE/Ogd4zD6eH+tLCZNCw6ZC9HKr+AW3vRRA/eBXgf9GPHMbsmsbRqSOnhM+T6phAc9v6eKBF5rY2hknnnH1799XH8KvJKgRCRpo4SPiGb4RqedX2gXcnz6DnpTJ45vaeHxTG8dVXsvV5m85U48TMDoAiYOCjYKOja1oCDNJ2ct3AOSbWb23sYLrOZwHXmhiZ1+KqaVBPjirhr+93krKtLNBvoPlSC49pX8nQRYa/vcPApCOd6F+6BaGdHofJ82rPeeL6Gu/DU4KUwTQZRodC3vOF6EuSuWfvoKVTJORfk5LmcxNmvxUv4ZzL/w0c+uGvoHcW6xtXrvXi1z3lEWzP82tG38J0iYgFNLSwcyOe0xuFh21iFufuxVwexSk7fHZdYobcWz65XwSiY1N3IiPaV4PD4+9ziG4mflCktnxsXAGsFAI8VEgAJQKIX4jpbx8VydNaKfY/YUJ6RQ7SlZvbmPJIxvRVZEPtpu7U5SHdKoj/WFw0rCoiQS4/5o5Q85xQzagh6zXu3Az95GARm/K5Obzji5ysRku4z/UevpSZtbmUs+PnZB5kVuUO1CMGI5tYaMSJ8jXzMU85RxPTdiHX1dZe9gD8NqD/Qnq7Ae9MkRIZHBQsi2MJAoOb8la2j7xRNHaLNtxg/LXW7ntCTdLbjsOO3ozSGCD/2oiJF2tbIHi2fGV8uYVG3licxu/X9/Mlo7+LfFarY/L1cc5T6zGj0mfWsEUpQ+tpBypBRFWCuEYdMy7pahD7UByNxi5IP/SU+p5b2N/N73p/zsTFQttCIFthyhj40n/xSGnn49fUwnoCgFdxa8piDcfH9y51jFGtU2+YdVDqOt+ToW5gy59GvacL3Lcgmwft6zLjdn5Dk2ymseilzLnrI8Puxu0t1jbvJZbn7sVXdWLgq8bT71xwoP6tc/9D8s3/YoWJ0Ot4mfR7E8z99QvTeiaPEbG3rhJPP7e44sC+hwqKq985pUxze3h4bFLxlQBs2nW7CdwZTaFeuMQsGP25k1DNkrdU7IZ+hs8l5uDkKVrtuQLVgFCPg3LcYilLaoj/ccN1LwXZtH7UiYlfpUSn4rlSBQhcBxJR9zVoRdKa3Z3OzjUelq6UyBgWlkwP/bJ5MO020F6nVLs7E1mkAyL1ZWscY6nPW4wp7ESNq7IXjf7eyjcz8pIkkHDyYbgbr9YQaPYwc1rthQFlJqqUBX28/DLLfhUgV9Taep2nxsSQqTz1a35QldAmAl8msLZR0/lI0dN4eWmHlasb+a5LZ20WKX8yLqQn4tzOedQjSuN36IZbyJ197WSeghM8m43w/HexoqiAH4g78hpHCpasiW3/a9+Gp0EYSpe/QX2nPNIGhZJ934MRQgCU+YS+eAPCT5/O0pvtnPtKDs7HrfgEsgG8NMHfvGID1GenbMGOGmPZx8flm9cjq7qBDX3Zyz3uHzj8gkP6Oee+iUvgN9P2RtOSE5WIlvYW0OK/nEPD49Jy49xNfTgBvUhwJ8d3+d4Af0BRlN3kmiwWHThVxVSps2W9jiG7eBTFaSUZGzJoTf+Bb+moAqoKQ0QDers7E2TMmwqSnx0J023BFNIMpYrtTmtsSKfdd+dhKepO4kqYEt7nFOdl1isrmS60kaTrGZ5+0KetI/DpyrMUN4lQJoaaWMIjXZZRowgdaI9H7YuntcIDwzOZAFFNpZF7iGCYYt132yLkTYdMpaN5YAmXGmRkv9DWny7ImT/H1ghBCfOKOcM+TLxnp/yq/Rcfp86kaT08dBbsIJP8v7AW3wm8iKn+t916xAK3G5Gy/LgIr6V/m/XujOLjWCnrCAj/FSaO8kMOMfJOgQla+bCuXNRFfcmxqcp+DIWfk1BVw+s+viWeAulvmKnr4AamNS2mR4HJ6pQsJwB72sSVOXA+p302PdMRtnhgcTszZv+tmnW7GtxNfOHAO8AP569edOo9fMDkVKuBlaP5FgvoD/AyBW0FhasBnSVpOlg2A6KgITh/vFQBOiqIJn9PJAyCfk0/JqCYTvEMxbTowHXctByKPFp3LTwqCGz7knDcjXuAwL6iF/jzbY489VXWKLeg4FONyVUix6+I+7GUa4AB0IiiSocbKGiSZvpopMOWco2ORUBBHTFnVtRYeAfP9zQW0VCNnetIFGRvOlMG1RoCu6ORDxj40iJpihYjoMlQbUlca2EEEkUsjl64XanlUN4hZe9fAeVvhjfKVnNl5xnWRE/jt/ETqDFKWdV+ghWpY/gPXobnwm/wMd861ELnHVGw9lHT8Vcr+FIV1aU24kQgO6kadWnEN3NHLYjizL4AJqiEPC5Ep2gru73AX6ugDGXmQc820yPSUmjo/AWdlH6QMmOe3iMlkLZYamvlPZUO7c+dys3MvGywwOJbPA+bgH8WPDeMQ4wFs9rzBes5rzbE4ZNeUjDpyo4AzQyilDykpLOhBvhVYVdx420ZRP2a0wtC1AbDfE/l57A/Fk1NHUnR2xbmavRuFqsxEAnLf2AIIUfA52rxUquVlbSLSOAQBc5jyZJpYhxl/wYiiL4/JlZx4ejLgZyYhj3nwCetY+kS4ZxUNBwcFDoJsyP7U8M2RRq6ZotVJToWbU9+FT3GBu42/koSIElFSx8SKEihKDn+GsGzaP3NSGzQWOpkuHK0uf5x7Sl/G/J3ZykbwXg32YNN3afw5ltX+Unvi/QlTAGzTNS3rvzN+CP8q6swUTDRMNBYYroRsfiXnkuX31wA5f9ch1ffXADz28ZWK8zNJbjEE9bdMQyNHUlaepK0h7LEM9Y2AN/aPYDFh21CNM2SVkppJSkrJRnm+kxKfly3OR9bzssuc/m53dYLLnP5n1vO3w5bk700jz2Ywplh0IIgloQXdVZvnH5RC/NYy/hZegPMObPquEmKCpY7UkaTCsL5gPb11p6gX6zFJE1TsnFbaVBnYxlkzRsFvb8hk/IlYRIuZaRc66lvvxMtnbG6UtZeQlPaVBjZuXgDHbO074u0Ua3LEEIVysqgZTUmU4bAugWZRhSp5pedGFhSwUVm5u0u5FlDdTP+CZwBFx0p7vmjSvAsbFReNg+je/IL3Ka/RLXaCuppZ1mWc1ysZAtZaeSiGe47M51RS47Td1JKkv8+DWV9lgGw5b5zp23mRdQr23nPO1ZNAwcqRI//Hx6T/nKoOeX6/ya08oD6HaSBVOTnFbbxbvr72N5+kz+bM+hS4a5exP8+o11LJhVw0Un1nFYzZ51CHW6tpHKWFSLXlQcFJysQaZgpTKfR1PHUJvIUBrQ6ExkuG3Vm1zP4bvU5QMEt62i7OU70PuaMEvr6T3h88QaFhBLu0GFriquRCf3OEKZzkRt+c6tm8vCjoX8etOvSZpJQnqIT83+lJeZ8ph0HN9aRfnfd9KtSlJ+wZSYw+f/Lmj48L4tJPc4sPBkhwcfnsvNCNifXG6G4rI71xXJcDZu78WRruTGr6nYjsSw3by4rgpURRD2a/xu1loaX78d1+5FdZsXIXmm9io+9fZ8t+mUlJhZaXldmZ8fXHBskewmd+2fpr5LudNFRgRIm+72cgiDDqJIKakSPaTxu5IPJ8EU2YZQdbTq9+zSkaXwuW1pj2M57t2Cpgoaq8N0xNN0JUzqyoNFFpslPhXDdvKvSV/K5N0ut9vrB/UNLFHuwZAapuInrJpMLVGGdKgJbltF1ZpvIxVfkZtN36z/oHTz7/LjbRmN+xIncZ/1fnqN/h2D4+vLuOjEOuY0VqIqg3cSBnHHGdQ7TUgJWoEzhoFKG5V8376Cpooz8uMp06ayxM9PP37csFMO9xx258ijCIFfd4N8f9ZNpzDIn0inmcnscuPhUci2he/H2tmMouVK8CWOJdCm1tHwyJMTvTyP/ZSr/n7VINlhrm/Csg8vm8CVTSrG2ud5UuFJbg4CBspwSgNuECsEONLBzhZ7KhS2Hof6f98DCFA1N/pXNUBwbPNvqQ77UIXAdNx5VAEdSZMlj2xk9ea2Qde+XzsfHQvdSQGSEBl8wuQueS6/sM/Fh0VQGCAlVdKVibTL8t12NS2U/1RH/K7WHfcGJWlYdCVMykM6IZ+GEK7uXx/gpS+lpDWWzmtYr+RRDKmRwo8tBQnHD6ov7yVfSK7zq1VSg5LpwSqpoWPeLQRbnnEDZN3tzFoTsPlK6WpW1S7lqx86goZKN6P/SlMv3/3TRj599/P8/qVmEplddwy1stsomih2wJAoGFLj8/qfi8YDusLOvtQu5yx7+Y6itUo9hFSGfr6FOFKSMmx6UyZtfWmaupJs60ywozdFV8Jg2Wv3oCkTs+XrbTd77C+YrR04jobRp5LuUTH6VBxHw2ztmOileezHeLLDgw9PcnMQMFCGM3taGVNLfTyxuT1bICuIBlXqK0ry5yQNCyUWB9VXPJlQCThJqsJ+YmnXIUURboBsOxJdFUXFsf3XDvKTdocrxKNUyB20KjX8wjqXp5zjsBzJ9+1FfFZdySF0InBoo5qYDDEtd90hupqu3txGX8pkR2+KgKZSHfEzPRrgsL5nuVqspDHZwTatikd9l/Ayp+TPC+rqIC992+7fqaoX7XSTfS2kxKep6P4QSrzZtfDM2WoOkKl0nHlrPqNd9dSNOP7i8lSpBSmNv8O5x07jnGOmsn5bNyteauH5d7rY0Zvm9iffZvk/t3L2MVO54ITavK1nIWVKmp1ONbW0uh1ipcBCQUViqwGm01Z0fNp0mFo6eJ5C9L6mIdc6Gkce23GD/JRh0xRrJqyVYlgOihDuvZkSoDm297d8ve1mj71BbM0aupbdjdncjF5XR8VVVxKZN29McwrVwoiBLRQcAYoUqEnwR3d9c+/hsSvm1s3lRm70XG4OIryA/iBh/qyaYZv7vO9HqwZZXQZ1lQRBSqVB0Y+JtEmLECnTxrCdvExESvCpypDFsfNn1TBf2QDPPAU93fyLafxOOY8d5acyC9fScq19As9xEo3lYX7Q+y3K7E58WsEG0oCuprmGVSGfml9LS3eKc0P/4gZtOdFwmJKSqRjtXXwucQdLEbzkPxlwZSh15aGi1+TY7/0dO2PhSGiS1VSLHlL4kUB12A9mCrW8gbryoNtB981/5GUqjj+Klmijas236Zh3CwBKphctvgNH82MHq5H+UoSVwsy63AghOHlmBSfPrODdziR/eLmFf2zcScKwWbG+hT+81MLph1Zx0Um1HFtb1l/YG21A73obmXXykSLb+Ev1MTUgeTNZs8tOs0MxVB1A4VpHy5TgNLoynQREMN9bIG2nqPRPoakriV9X8GuuVMevKUMWL48Wz+XGY7yJrVlD6003I3w+lLIyrPZ2Wm+6GZZ8d0xBfULVEFjuDmG2nsnOjnt4jIW90TfBY/LiSW48qC93A/RCUqbNX8MXAhJsy62YtS1A0nbUVZi2RM02nHIciYOkKuzPB8tFvPGY26U01gqBcg7xx/liZilHJZ5DSkkkoOFIKA1qSNkvz5kScNy/bkbS1dCffn1+ypx1ZnUkwPSyoOutD1xqPewG8+EICEFZaRkGGuenVuRdf0xbup72BeRqBzQBd+YkQGRQBUQUI399TVWYWhag+rWlSHWwTKXi2VupWvNtHD3sdqy1DbR4C0qiHeEY9J7w+UGv/4zKEF/+4OE8cM0crpl7CNVhP46Ep9/q4CsPbuBzv3mJf7zeimk7aIfNo4YeZL7plUTHRqh+QqpN/MQvUFnipyOWoSthkDZtHnihaZduN70nfB7hGAgzCVIizOSwa90TLmi8HMsxSdvulm/aTmE5Jhc0Xo5pu646nfEM23tSbO1M0tKToiOeIZY2MayxNdXxtps9xpuuZXe7wXzQlXEpwSDC56Nr2d1jmjdhCboj4KigSPexO+KOe3h4eIwULwVwkFDYCTbn9JLLTi+e18iSRzaSNKyiwtEpC5fAznpYdztk4uB3XW4a53+Dmza38aO/beaNtji6CtMjATRVDBks88xtrgbe5wb6JeEIsYzJeckVPNAzC1URHF4d4t3uNDt6M2wUszBLruY7wScoSe8YsqtpYQOt0qBOaVBHSkl9dzslJXmhDpGABmVRQrE2elMmdQOee44jppTyTkec7oTBU/bxLLEW8TltJYcoHRB5z6Dra73vIgPlWBKcrK5dakH07jewA5UoRq8bcksHpECxErQtuH2XRaalQZ1L3zuDi0+qY+2bHfz+pWZe3xHjzbY4P/zrZu5cs4VP+gw+Livz7kDgOvOkTIP1x3yTGXPO49KaLm5b9SZhRRDQld263eTqAAa63KQaFgzpfrOr57C+7Vn+uOU3tKZ2MCU4jQ/UnsNrXevzn1/QeDkn1Zw26DwpJRnTJlNwY5kruvVrKgFdIaCpKCMpHGZ0281eExaPXWE2N6OUlRWNiUAAs7l5TPO2lytEelW2VzjZzhKCoKnQU+bl2zw8PEaO53IzAvZ3l5ucPEVXRVHAftPCo/KBbS7gz1ldDhX0Djf3bs/72TEQyBa4Aq2xDG19KcpIMM/oL3RVcN1pwN0QiIZ0fnLxcUOuY6BzD7i6/9sySzg6ksrfPACQaAcjDoEyiDYMCs5zz+OL979ELNMfUKoCKkp8LPpAmpf6Hi4O9B7/kbvj4AthS4nlSISRROvb5npyKgoIxQ3oHQcnEOXdK1/Z7es5kNe39/H7l5p56o32vK2oD4PzlH9yhfo3Zimuzr2PIGcHfsv1Cw7ngRea6ExkinoFjMTtZiB76n6zvu1Zlm78CZqi41cCZJw0lmOy+KgbhgziR4Ouus2vAtlAv0iWNQY8VxyP3bHtM4vIbN2KjMdxDAPF50OEw/hnzqTh3uWjnvebPzqLj/yxCVMVGBr4LNBtyd8uqOeH3/jH+D0BDw+PgRxQ22Behv4gYCSdXQdq7Fdvbhvk3T5UYL0rbX6eaAPEWok5PtrjbrOiIAbNsjrvgQ9upjnX7lw4knhm6O6zMPyugj3ni/DaD8DALaRNdkC8lbSviu1xH0rPFoJN17HzjB9w3IJLiuZMF8g8BK7OXYY2sWzzH6krjxR325t9NnPXLQMDVD2IYqexpYkUGlKaWDbIrEe8JiTCzuz6NRqGI6eXcuT0I1ncl+bhV7bz1xc20UuYh5z385Dzfk4X/+IK9a/MU17Fsl15zY6+VN7JKMdI3G4GUuR+A+6j6Y4PFdD/cctv0BSdgOrq1gNqkHR2fLwCetN2MG2HWNr9XFUEgaxlZu5xNFr8QlccIP+4fOPy8Q3o33jM3bHq2TbszaXH5CT43veSfPHFrK2XipPJQDpN8D/+Y0zzvjk7QuBthXOecwgakPLBn09VeHN2ZJxW7uHhcTDg7ekdBOxJZ1foz+i3xdJEgzptsfQgO8o94vTrSaVTdPZ0Y1o2QTL4sFhqnwvDbBAJ4TqmDLfG+bNquGnhUdREAvSmTGoiAW5aeJQbpJ/9E4hMgXQPGHHSviq2GiWYjsRUg6QcFevp24qez9I1W5ASAppb2BvQVVRFkAquwnbUwfaHPRuKriMiU9DO+W9MNZjNpDtZlbuDI8GQY7t3rikNcM28Rp72fYmbtWU0iu0APCOP5rPW1/iw+X/pjJts7YwzrTRI2izWoI/E7WYghV1wc+zK/aY1tQO/Eiga8ysBWlM79ui6e4LtSBIZi66EUaTF70oYJA0rL4faHS3xFgJq8drH3RVnQC0JsVb38zceG79reOw1Us8/j1pdheL3IxwHxe9Hra4i9fzzY5q3/l/tfOA1hd6wYFsN9IYFH3hNof5f7eO0cg8Pj4MBL0N/EFBfHhokTxmyeDXL0jVbMCybznh/J9hIQBs2W75bjvgQj4j5fFT+gSAp4gS4y/4oTznHD3uKlG72dbg1wi52B474UH/W82fHsD3uQyBRsplbUwSYLlv5asHzaepO4tcULFvmlEHuo9aFrhRnyvKBXuF1srxFPVU0ESGFjomJTg9B2qhjz3rCDo0jVC5Vn+RS5UnWOsdwj302a+WxbJVu3UBPyqI0oNHS62bj98TtZiB76n6Td7VR+28CMk6aKcFpQx6/NxhKi+/XVQIFGXxtiA63+8QVZ0AtCb6Qu5P0zG1eln4/wGxuRquoRFRW5ceklGPW0H/k2Qy2JhD+AAEAHeyMxUeeHd2unoeHx8GJl6E/CBjYWGo4p5ccb7T20ZkwsLJONpYt6UwYvNnaN7oFvPEY74v/g04RZZMzg3aiXKKu4UxlsKbcdhz3n5SE/dqwaxwx0QYUK5UP5gH8ZGhTpxZl/+vLQ+iqwLAdUqZN2rQxLQesCqID7il2FejdIz+GVHx0KpW0iypUHKroIWD2Ety2ao+W/vyWLr764AYu++U6vvrgBp7f0sWLUy9F4KAIh/nqBpb7fshf9K9xpHgnf96atzrojGeIpS064wYVIR/XLxi6IDZHcNsqpj58CfW/msPUhy8huG3VHrvf7MrVZiLJmG7zq9a+NO92JXm3M0lbX5relEnGcgP/feKK07PNlYEVMkR/BY/JiV5Xh0yni8ZkOo1eVzemeetiOobmNvmTuI+G5o57eHh4jBQvoD8IGE6eMly23cw2WVIU4dqzZZ1FDHuUBdTP3IZUfcQdPwKFNAEMNBarK/OHBDRBfbkraxFCcFh1ybAFsXvE6dcTVG23Q62U+GUaHYv7tfOLsv+nNVYQy9gI4WbmJWBLOLH0PHTNGXGgt716Lrf5rsESOlNkGxLYQTVB1aZqzbdHHNQ/v8V1qulMZCgNaHmnGrXuJAwtkvXCAIlgutJDrdZHRUhjWmmAyrAPR0JPyqQraZK2HWIZE9Me2goyV/yqJdqKPPWBIbvgDudyc1LNaSw+6gYq/JXErT4q/JXjWhA7XliOQzzjWma2dKfY2pHgsMjJfOm4r1Phr6Iv00d1sHr8C2KjDW4/hUIG9FfwmLxUXHUl0jBwUu57gZNKIQ2DiquuHNO8kYbDmKZXoikajmOjKRrT9EoiDYeN08o9PDwOBjyXmxGwv7vc7Ckn/+AxepNmNqB35S+OIykL6bz4nVFIA352DDEibO1yM+JCCKR0KCPB+83b0FSFpZefNPbgfRge/t1ypv7rTmpFGy2yhgd957Ped0rRTc1ld65ja2ecvlS/zKg0qDGzMswXPmqO2M4wV39wW+a7VMputzmVhKllAUJksEpq2Hn+Q7td81cf3DCkU83t1n8y29cB6R6kbWCiEaeEZmUa1+k3cf2CwzmhIcqaN9pZsb6Ff7fG8udXhn1ccHwt5xw7jbKCRmJTH75ksLTGTI54rQcaQgh8+VqKPbPL3CU5Db3iczPzZsrtb3D2TzzJzX7Cuj/8P3ruuZdwR4J4VQnRKz7DnAu/MKY5CxtWiUAAmU4jDYMpY2xY5eHhsVs8lxuPA5vDayKDg9sSnZmVo1SBRxuIxFrx6yqG5SClJChMOtSpzCwroSYS2GvB/OrNbfx0awNG4CZiaYuM5aA6gmvn1BZds6k7SWWJn6pwf2GklG5R7ty6Bcytm5u36PzWK0nqy9cN6fwzf1YNP9/+FLPXbAYkltAhVEMoqGM7KiI2dEHpQIZzqqlNbUMz+8hVE+tY+Mngk3aRrOYDs6ewYFYNG7f3seKlZp5+s4POuMFdT7/Dr9dt40NHTuHCE2uZWVmC3teE448WXWtXxa8jZU897CcLQ+nwfVrOLlMlmC2Y3mOO+BDwk6zLzbtD9lfwmLysbV7LrfJR9KumFFibPsqNzceMaScnMm8eLPkuXcvuxmxuRq+ro+KqK71g3sPDY4/wAnqPQeQsIaeWaVi24/rGxwyiQYPVm9v2PPg+/Xr46w3UhlSa4hDAwC9s7vJdsEst/3iQs+wsCwaozta2Jg2LZ7d08aWC43ZXOFzo5V/o/HMTFL8ebzzGca/9ADQNHBufcCC9A3wKqqIjKmYS0FXSAzrzDmRaaXBQhj5tOoRJMtAaSEVSpqQGaeSFEBxdW8bRtWXs7Euz8qlnefQtk5jlZ+WrO1j56g5OmVnO1doZnGmuR/j69d27Kn4dCYUe9oUynl1JdiYzhuVgWA59KRNw/fCDvlEE+EMUUnvsH+xNa9PIvHleAO/h4TEmPA29xyBymntdETT3pEBCXTSAYTujs6884kNw9k8oqZxOQ8ggrlfxE/VqtlWcsUst/2jI+ee/70eruPLGz5MAACAASURBVOzOdbzR2kdQV+lLmWxpj7N5Zx87e9ODCnx3Vzhc6OUvhPuoq4Kla7YULyDnZFI6rWAzT0BsJzgGyunXMz0apDzk26Vf+qWn1GM5kpRpI3EfLUeiYw55/O587g/pfobv99zImqofsyT6d2aqHQC8sLWbxTsW8tGOa3mg90iStrbb4teRUORhLwRSDyEVH2Uv3zHqOScTpu0G9219abZ1JmjuTtIZz+yRVabH/sU+sTb18PDwGCWehn4EHCwa+pykJNdMqjuRwXTkoG6sNZEAi+c1snTNFt5o7cO0JT5N4fCayG47zOau8WZbDMNy0FXBEVNKR9SZdnfnrt7cxg0rNhDPWNiORFUElu3wZd/DfEqupIQ0CYIscz7K/7Mv4PCaCLGMlW+cBQzb9fZ9P1pFNKgXBeFSSnpTJmu/UZBxLuyKm+mDeBtYGbdr7Md/W5SdzVg27bEMhjV0serzW7p44IUmdvalmFoa5NJT6vn4X48pPkj25+tvPvGffOr0mfkvFUpelEwvjh7GCbmWe46ENfE67s6cyXOp/kx8mUhySdkmzj39eEpnjz6TXv+rOa6Mp/CmRUqUTA9Nn1436nn3F/zZzH1Ohz+aZlcek4ur/n7VIGvTlJWiOljNsg8vm8CVeXh4jJID6o3ZC+hHwMEQ0BdKSoK6Smciw47eDAoQ9KlUR/xEAjpSSnb2udIUw7LpTBjuBBKqIj50VR026567hmnbdMSM/K9SZYkPnzb8eSM994d/3cRb7QlU0V/Mew0r+JL2BxzX7BEFBwXJ/1gX8ktxMYfVhPNdZnd1/cvuXDdIkpO7ubn/mjn9By4/120Y5CvwujSSbgOqRSsZiJSS7qRJb8pkJL+LM3/R2J+NLzg8jc7szL1ccVoDnzp9ZpHkRWpBfJ2bkNIB1QfSRqo+7EAVAoenzn6cP7zUwmObWvsdjgSceUQ1F59Ux+xppbtd10C8Qtt+hBD4s0W2Qd/ou9l6TCxrm9dy63O3oqt6gYbeHH83JA8Pj33FAfVG7EluPIBiSYlr6edKOyRgOZLtPWliaZOUaeez47G0hYJAUxQURdCXsoaWoQy4Rl/KQlGy5+HOs6vzRnruO51JFFFst3m19pdsMK8is48O7rgt5a7lMwWM2Mv/9Otd5xLD9W7HSLqfn379kPMKIago8TGtLIA+RMOjgVjBaveDAbF/mywD4L4XXE/zgZIXR6gIHPdmQKgIx0JPtGDrYRqrw9zw4ffw4DVzuOKMmVSUuLaXT/67nWvve5nr7nuJJze3Ye+BlGRPPewPZKSUpE2b7qTbzXZbZ5KdvWl6k+Zuayk8Jg9z6+ay8NCFdKQ6eKP7DTpSHSw8dKEXzHt4eEwKvIDeA3BdXnIFmO2xDEKArgg3bsz+t7M3jWnLfBbfsJ2irqqG7RDU1aKGTUNdY0/PG8u5JaSxcTOiStbj3kahhDS+ggB6d9cfsZd/tl6AyBRI97iPI7AlDOgqtdEg4cCu69SFEKT0KmwU1ysfhVZZlnWlh4yVdb/pa0IWSAP6M8IyL4ORslgREw35+NScBu67+lS+efYsDqtxXY1e3xHj5j9v4pN3PccDz79LLD20jr+QVMOCPfKwP5hwsjeEnYlMNsBP0JptdDWc/Mpj4lnbvJZH3n6EqmAVR5QfQVWwikfefoS1zWsnemkeHh4ensuNh0uhy4thO6iKGwD7NYGmCAxbIoGbFh7F0jVbaIu5AbFly7y8xacqRc4ww13DpypYzsjPG+m5Yb/Gm21xhOz/eoIAQZFxg/rsXCoOCQJUR/z5+Xd3fXCD+hEV8I7SyURRBDWRAEHdpDNu4AwhwenSpyFjbxPGh46FiYaNSrOsLjrOLK0vlrxIGyk0BA44liu5KZmGYsQHXcOnKZx15BQ+NLuG11p6WbG+hX++1UFbLMOda9/hV89u46yjpnLhibXMqBj+NUs1LPAC+BFgO5JExiKRsQDQFIWAruDXVHyagl9TxscH32NM7E2XGw8PD4+x4gX0HoDbKfX21W9jOQ6O42YRFSGYXhqkNKjn9eK5gPaGFRswLBvTAWxQBVSU+PMylIEFtovnNebtMEuDGh0xA0e4AWskoBfLV954LOvVvc3trnn69Syed1z+3La+DLmmtY6EvpTJd885EoCvrdhALG1h2Q6aonCf+BiLxQpUHCwpENgoAn4lz2FnX5qWnhSqIgj7tfwcMLhAeCRFu+NFJKDj11TaYulBGdvHkodzKeuxcbPzOiY1oof7bTdwnlrq3qSkak+nfP3PEY6No/XfuJilDUif698pzCRmyfRh1yGE4Ni6KMfWRdnek+LhV1r4y2s7SRo2j2zYziMbtvPeQyq4+MRaTmoo93Th44TbydbtZptDV93A3g3w3UB/VF74HqOmJd5Cqa+4nmS8XG5ia9Z4PvQeHh5jwpPceLB6cxsrXmqhPKQT0FSEANuBsF8lEtCG1IsLQFUUFNwCSglEgz5uWngUAEse2UhbLF3k2Q5uhn9mZZiykE5QVykLaBxSFe6Xr+S6acZaXbeYWCv89QbmKxu4aeFRRIM+ZPaaigBVVfJy8vmzavjxxcdxwoxyppUFOWFGObMv/QHKmd9A9YfwCxtfIMy7R1/Hr/SPuw4x0rWJEQNej6HWv8d2nWPApynURoNFHV0BZmU20EkZltDRkBjotMkopyuvE/arfPkDRxDctorSzb/DDlTiqD6ElQbI6unVUWnap0eDfGH+YTx4zRy+cpxDvdYLwPPvdPH137/Glfe+yMpXdxQ1Y/IYP0zbDfC7EgY7el2ZTlNXkrasVCdt2iMqqvYYPbXhWtJ2umgsbaepDdeOad5cp1irvR2lrAyrvZ3Wm24mtmbNmOb18PA4uPBcbkbAge5yM5SDS3ssTdKwKQvqgywcd+f4MhJHmGEz4LtxiRmx28wePt89Xf++JGlYdMQMLMeh5I4T6XZKUBQFW0psRyKlQ7lI8PRHV/HexophHWakqmMHysfUuTXnnmMJP6usY7i37ySeNw/Jf700oPGx46az8LjpRZImj72PEIKArhDQ1GxXW89NZzzZWy432z6zyA3mg/01L04qhVZdTcO9y8dh5R4eHsNwQL1BepIbD5q6k0QHZIKrwv7BHuu7OL6wqHR3X99l19WebW5mPufhbhug6G6B6e7mXv0jWHc7ZOLgD8Oca2H+N8Z9/fuakE+jtlylPZaBaAO+rhYSli//9RLFRK84JN8pVu9rcj3gC5BaECXTQ8ulj49pLTn3HEUP8kHfW3ww9BabkqUsN97Pn5NH0pe2+O1z7/LAC03Mz9pevmdqZEzX9BgZUkpShk3KcHdJhBD4NIWApmQD/D3oaOsxiLl1c7mRG1m+cTkt8RZqw7UsOmrRmPXzZnMzSllZ0ZgIBDCbm8c0r4eHx8GFF9B75AtOLVvSEc/ki2JnDlPwWFhAm6OwqHR3Xy+0yAQ3YE0aFkvXbGF+tAG6tkCiHRCuRMQ2IGPDG49RXx4Zcu7r1D/AmvvdcxTdzeqv+b/uAQOC+rGufyJQFcHUsgDPz7qK6c98F0dIUtJHUBj4sNh0yCJmZI8dVBALCCuFWVo/9OR7wFA3C7ODvfxYWc7ln1zDIxu28+iG7XQnTZ7Y3MYTm9s4enopF51Ux/sOq/ICyn2IlJKMaZMxbXpTrjORrrrBfdCnEtAUtBFYpXr0M7du7rgXwOp1dVjt7YiCDL1Mp9Hr6sb1Oh4eHgc23ru5B4vnNdKbMmnpSWHaDgKwbEnn/8/encfHXd33/n+d7+zaJVvryGAMBrPbMgmGEAqUEmhCCJjekG5Js0BvbxOae9Ob3tw2N01v82vu7W2b5tEFmqRJShPSYhMggSQklJiEmMUym1lsMNiWbEuy1pFmnzm/P0aSZVmSR+t3lvfzkTyEvx5958x8rdF7znzO54wlZ6wbP1VP9lP9/dQWmRMmZ8Avv5PM6DFSmQyxjCWRTpPMWo6mK3jpvj/jsnUNM577lsQDgAGPd7y43pv7886/W/Lxu+mv31rLlwN3MOpbxWpPlIh3FX/tu52/ObB28jbL2QM+VbMGk46dcGzizUJDpZ8PXb6Wb39sC//9XeewrrESgJcOj/CnD73Mb371Kf7t2UOMxtMznVpWQCqTJRJP0TsS5+BANFeHH4kzElfLTLc0fOTD2GSSbCyGtTb3NZmk4SMfdntoIlJEVEOfh1KvoQe44W928OaxMTLW4vc4NFYH8Iy3UZypbvyT93by4AtHyWQtHsfw3ota+OvbOib/fqJGvmswmncNvm98Q6hvDv4GIZL4TZqk9dJna0l4KqlllPeH7ubWjjC/2D9w4rm/syE3Mz91BjhrIZuC/zVw0vjnGl8+j88tV3zxMepCvlw//awlnc1irSUST/Otjx2/TqEDj1G7+x8WVS8/k4kaetIpnNQITjqBdTwMbv44w2/75Am3tdby3KEhtnV284s3+icXLwd9DtePt71081MPt8zn2jy9f4B7nznEkZEYrTUhbnvbmsnSquXgcUyuPMfrIejPddSR5acuNyKuKKmPjBXo81AOgX5qUJxgrZ2xjv5vf7yXLz32+mSnmazN/f/Oa87iE9eefcr7mlpDH/J5iI2XBBggEk/zz87naWSYKH4MYDAETYIx3yr+a+jPZn6T8Ren5cpsPFOqyDLp3OLaPzo4r+disY9vOU1/M5S1lkg8RUNFgL96/8UrMobaZ/6a+l1fHu9nH8iV4Hi8c24c1T0YY/vubn7w0lFi451wDLBl3Sq2bg6zaU1dWSzgnHhDZB0/1hvCpGOYbHLG5+7p/QN86bF9eJ3cYtd4Kks6a7nzmvXLGuqncoyZXGAb9HkIeLXQVkRKRkm9mKnkRoBc3XhsSsvBSDzF672j9EYSfODunSeU3nzlZ2/imNwGOI5xxr/mjudjpl1XG6sC1IR8ZKzlq/ZG/CZFiARgCZoEflJsD26dfXHqlv8C2FyIz45/xY4fn5/FPr7lNL0cKJ7KAIaPvvOMFQtaoe4nSVevIbX6fNL1Z5GtWI11/NTu/odZvydcH+Lj1+TaXv7nX1pHc00AC/xifz+f+vcX+Ni/7OKRl46WfNnHxKLiXAtRg/VVzPrc3fvMIbxO7k2vIffV6xjufebQio13YlfbgbEkh4divNUf5fBQjIGxJLFkhmxWE0IiIoVAi2IFYHLTp2gytylT91Cu33K4LnhiF5oNTYwlM3invRV0DIwl8+9BPn3X1YlPCPwehyeym/izrOF3eJB208dh28TXuJEjgUuIJdMzl2lc9WmOvLmHxgMP4bEp0jg87r0SX8vvcNU8n4uJx3e8pCV3fDTrfu33VRua+Dy5hcX7ekZIZix+r8O/PnWQD79jLee21SxpKN7V+wvu338PPbEjNIdauXndb/K+kUNY48E79AYmk8ztOhtqxDdy6qBZFfTya5es4ZaOdn7++jG2dXbzYvcw+/vG+L8/fI1/2rGf917cxns3ttFQ6T/l+YrNbB2IZnrujozEqAme+BId9DkcHYmddNuVMvEmMp7KkBgvHfJHDpGtPY3sZZ/At+FdWvgsIuICzdALcOKs+dGRBF6PIVwXoibkp8Lvxecx3LVjPwCVfg/TJ+ayNnd8oSY+IWisDmAt7LAb+e3MH/NLyS/xm+k/5rWqS+dcnPr8Y/9O5sBO3sq28JJdywHbwjnpl9n+b/887w2hKv0e0hlLKnM8zNvx/6/k5lKzuWpDE3dcuY6KgI/G6gAtNbk3XX/2/VfYdzRCfYV/SWbrd/X+grv2/CUDiX6qvDUMJPq5a89f8kQoiDfShcmmwfFismm8kS6y/qq8z+1xDFee3ciXbtvIP/5mB9ee24TXMQzFUnxz5wFuu3snf/HIq+zriSz6cRSSuRYVT9daEyKeOvHNWTyVpaUmdNJtV9pE6ZB3rJeMvw4iPXh+8If0dj7EoYEofZEEkXiKVKa0P3ERESkUCvQy6aoNTXz79i00Vgc4q7GKmim92KeWunz0ijPI2twW9VmbHf+aOz6bx1/t5QN37+SKLz52UgkPHC8l8TiG1toAhlyADtcFOauxkqyFpurg5E6008/l2fll4tZLnCDGOMRNkCQ+fj39wOQbkVOZGGPWZsnY4yF+4r1LXch70rlO9biWw7/fezv//Pg7CTX8Hu0Vd7Ap+eXJN113P/Em9ZV+WmuD+PJsSRg68Bgt3/011nxzCy3f/TVCBx4D4P799+B1fAQ9odymRZ4QXsfHv/hTTL5fGH/HY8zkf87b2c3VfOZXz+VbH7uU37j0NGpDPtJZy49e7uGOezr55Hee42f7jpEpgfKO+XQguu1ta0hnLbFUBkvuazprue1ti28/ulhzlQ5NdNLpiyQ4NBDlYP/4jrZR7WgrIrJcVHIjJzlVH/aJhaFf+dmbjCUzVPo9fPSKM2ZdMDrnRlLjZTdTS0m6BqNsOq3+pM4zc53r28kjDNgKpk5Mx/ETNr15bQg19bxrV1Xx0uGRyb9zDKyu9NNUEzzhXPk8rqX27/fezldHn8DnMVRnswx4DT11L/K2ob9lt//jk+ML+jy014cYGEtO9iCfydRFmtlAHd6xXlbv+J8cu/LP6Ykdocpbc8LtA06Qw06GVGUYT/zYZMlNOtiCJzW6qMe2uirAR644g9+89DR+/Eov2zq7eKs/yvNdwzzfNUxrbZCbN4W54YIWKgPF+dIVO/0ajl3553l1uXn7ugbuZD33PnOIoyMxWlagy02+5lM6lM5mGU1kGU3kStaMMfg8hoDXQ8DnEPA6+D1abCsishjF+VtRltXUevqJLjTTS10+ce3ZeXd8mXMjqSnBd3pd/XzO1UUjq8wQcRuYXLceJEm3bcqrNeL081b6PSQzWfweh3WNuVKS6LT6/Xwf11L63tAOfB4IWgAz/tVysPJ5YtETN78yxrCqKkCF30tvJD7jDPcJM62Q+5rKHW9uaWUg0U/Qc7zEI5GN02qC4LGk6848fl+pKKnKpXnMAZ+Hd1/Uyq9e2MKuA4Ns393Nzv0DHBmO8/ePv8HXn3yLGy5o4eZNYdrq3C8/ma/Y6dfk3UL07esaCiLAT7eYzcustSTTlmQ6SyQ+/r3GEBjf0TY03lVHAV9EJH8K9HKS6bPlM/Vpn49Dg1HqppTvALN3q1ngue4x7+Wz/D1t9OElQxoPo4T4W+8H89oQavp5V1cFODwcI57OlQjM9KZmsY9rohf+ocEoa/J8jo96DTXTgnnAWo76HFIZy2XrGvjA3TtPOmf7+KcusWkLl+eaab358v/KXXv+kji5mflENk46m+Lmte/H7P4mpDih9eJSbFw1lTGGS9Y2cMnaBg4ORLm/s5sf7jlKNJlhW2c32zu7ufysVdza0c5F7bUKgCtoeNN/zu1HsET/BqYuth3ixIAf9DkEvR6cEl5s+0TXE3x9z9fpHu0mXBXmQ+d/aMl3pBWR0qZALzPKZ7Y8X6cq4VmKc4XrK6mJ+rDx+GTRu9dx+MgV67g4j8cx/bw1IR+JdIZoMtcjf6Y3NYt5XAst12lJWwY8ELLHa/sTxtCSzrK5I8x9nd2znrO1NsRQNMlgNDVZxzzXTOvmpsu4g0+d1OXmwqbLOFZ9zrJsXDWb0xoquPPa9Xz4irV8/8WjfHd3N72RBD9/vZ+fv97PWY1VbN0c5upzmvBPb8EkS24+pUMLMTXgT/BPBvxcP/x814gUuie6nuALT30Bn8dHjb+GvlgfX3jqC3yGzyjUi0jetLFUHsphY6nlNNNGUiOxFKsq/YwmM3nPTs92rlTG8kDVX+CP9dETd0ims/i9Ds3BLJWr2nh8y9dmnQmfmCXf1xshEk9TX+FjdVVg8ryff+/5s45rtrFM7GQ718z7bLvlzrYzLwB7H+XHD9/J/6vK4rOWgIW4MaSM4SNV72T7yIfzOmc8laEvkiCVyc5ro6NCks5k+dnrx7hvVxcvHzneCae+wsdNG9u48eI26itKr+2lHOdxcnX4QV9uR9uA1ynKWfyP/PAj9MX6CHmPl4/F0jEaQ4189V1fdXFkIiWv+F4w5qBAnwcF+vmbXk5y2boGfrF/gK7BKFUBL32jCWpDvhOC8FzheaZzTy0HuvShqzkQzbVrdIwhay3WWlr9cW70/P1JoXuiW87UQN4/lmBgLEV1wMP65prJMc8VzKeP5bJ1DZOz5HM9ttl25j06Emd9U/XJ97n3UXjkU+D4eSw5xD2VcNjrpSWdoXn4Ig7Wf4p9vRFaaoJ57fabzVr6RhOMJdIc3PkADS/8I6tSR+n3tTBw0e9y2pabFnTd3fDKkRG2dXbz0719k+sEfB7DL29oZmtHmDOb8m+nKcXN73UmF9sGvZ6i+LTm+m3XU+OvOenndiQ5wg+2/sDFkYmUPAX6cqNAPz+zzVxPhNoFzU6fwktfuJKq1DFSzvFZLl82Ro+t4zPVX5jxvoBZxzGxMNjnMaQzWXoiCVIZy9lNVXz6+g2zvvHI97HNdLtjo3EGxlK014dOft52fhgiPeDPlcZE4mn6hwYZdBr4k9r/j1gqQ9dgjIZKH6urgnk/r99//jD/++FX8DqGoM8hnsqSzlruvGZ9QS7GnEtfJMF3n+vm+y8cYSR+fBOwjWvquHVzmC3rVuGozr6sOMZMhvtggS621Qy9iGsK68VgkQp/+kKKztTuL8aYkzamOjQYJeQ7cROqhS6ShdwbiL+JXY+TTWHSMTLZLAEbJ2Ay/GPqPbPe11zjmHgM6Yzl8HAcmwWPgTePjfHZB/fM2m8+38d2x5XrGIml2NcT4ZUjw+zriXBsNEl9hW/m523oAPiO/8LvG02QIECr7Z28bX2Fj4GxFNFkGmvtnBtxTbjnqYOEfE7uPsm9AfM6hnufOfWur4WmsTrAx965jntv38Inr13PaQ25Nz/PHRrij7+7hw9+7Rnu39190sJgKV1Za4klMwxGkxwZjvFWf5TDQzEGx5IF0xP/Q+d/iFQmRSwdyy3AT8dIZVJ86PwPuT00ESkiWhQrS+5U3V8Wu0h2ajlPld9D/1iSiN3En2bgY873CGd76fO08G/+m9mVvYDGVGbW+5ptHBOP4c3hMRwMjmOwQCZrJ0P2TLP083ls450nczOGBtJpS2BaicDk89Z8+gkz9Ml0lgqTpMdpnrzt6qoA6UyWpupg3t2JJh6nMYZM1pLOZgn6HI6OxGb9nkIX9Hm48eI23n1RK7sODHLfri6eeWuQ7qEYX37sdb728zf51QtaubkjTEtN8NQnlJIxdbHtYDQ3gx/0eQj5c+0y3SjReWf7O/kMn1GXGxFZFAV6WXKnCrX59LmfzfTuMK/3jZLOWFZV+vnp2EYey1yMtbnNoBocPx+94nTu6+ye9b5mG8ddO/bTG4mTzGTxjH9Eby34Pc6cnybk+9ju2rGf2pCP1trjs+77eiP0jCSoCR1fzDn5vF1+Z66GPgn4QlQ5SZxsiu3BrSfcdn1zzbzKlqZeK49jMMZhNJ6ipab4+rtP5xjD29Y28La1DbzVP8b9nd386OUexhIZ/n1XF9s6u7hi/Wpu7Wjn/LaagivFkOWXHf8kK5rMlWh5HYegP/czHiiSGnwREQDP5z73ObfHUPDuvvvuz91+++1uD6NorK7085NXe8lai9cxk6H2D687h7WrK1m7upIzVlXy6tEIfZEErbUh/vC6c/JaEPs/tr9IfHzG3RhDXySBY3Iz1u/gOf7c8xXu9G7jWmcXw04tV112GVef0zTjfc01jonHEEtlyI5/LG8ttNQGyVhLa22IWze3nzS+fB/b3/x4L9UB7wkh0ucYhmIpKgPek5+3cy6ChrOg9yUY7SFT3cb/Sf0nnvZ2zPgcL/RaxVMZMhY+cc1ZNJXQ7HVdhZ/LzlzFjRe3UR3wcnAgylgyw4H+KI+8dJSn9g8Q9DmsaajAU4SdUmRpZG1uw6toMsNIPMVwLDX5s5W1FmNY8n8fE20r45k4lb5KRpIj/PTQTzm95nROrzl9Se9LRE7wp24PYClpUWwetCj2ZKfaFGmmTjT59rWf69zTu8Ps7xsllclymd3N571fJ4mXGH4qSBJ0MtzT8HH+6BMfX9Dj++IPXmVfb4R0FvweQ2ttEK/HmVdHntnMtnjW5xjqKwN5PW9/++O9fOVnbzKWzFDp9/DRK87Ie/fe6Y91pmuVSGd46LnD3LPzIEdGYrTWhLjtbWuKbrHsTNKZLD/de4z7Ort47ejxtperqvy8b2Mb77mwjdoK3xxnkHLlccxkN53c18X1xNeiWBHXlNTsjQJ9HhToT3SqLjbLee7pQXgklqJrMMq/eP83jWaIGIHJc1WQYNCp522fe3JRYzg2mmAwmqI66GV9U/Wids3N93Eu9/fnO8Y/eeAlPI7B5zFF3QFnNtZaXj4ywn27unliXx8Tm/D6vQ6/cm4zt3SEOWMen3hIeZoI+X6PQ2CeG1+pbaWIa0oq0KuGXuY002z51C42ABV+L9FketaFovmeF+AT9+5mNJ6eXCwa9DpUB72T555eo+71GIyBdtPHECcGryh+Wpm5G81cpj++xuoglQFv/m019z7K4I//klT/mxyyjTxadxtbrnv/Cc/NVRua+Pz4fS3kU4yluAb53Iffm+uAk81ajDHEkhnufeZQyQR6Ywznt9VyflstPSNxvru7m++/eJTRRJrvv3iE7794hM2n13Pr5jBvW9ugtpcyo0w2100nRgZiKeB4y8yJkO/3ODPW5IerwifN0MczccJV4RUbv4gUPwV6mdX0Bai9kTiffXAPY4nUCYs5YX5tJ2c67x/e9zyWXH91C7kWMNaSSGdJjiVJZ0aAmYPwUDRJd6qR1Zw4Qx8iyRHTzMmV7nM7VZeeOe19lNgDn2Q4miVBJY1miN8e/DJ/tT0Bt/z2SaF+oeF7UWNcwH04jsFvHPBT1B1w5tJcE+SOXzqT375sLT96+SjbOrvpGoyx68Aguw4MsqY+xC0d7Vx3fvNJrUlFMP1HmQAAIABJREFUpptomTk95E+U6UyU7Xzo/A/xhae+AEDQEySeiattpYjMm5bwy6xm6yefylhiqRN7ec+n7eRM543E04wm0kyfAE1nc4vRkpnjpWFXbWji27dv4YlPX8O3b9/C2c01fNXeSMCkqSABWEIkCJg0P6m/bd6Pe019xcIf35Nfoj8OCRPEcRwSJkja+PiNzHcn+/AvhUWNcYH3MdHacinvoxCF/B5u2hjm67/zNr5w8wVsPq0OgEODMb70k328/66d3L1jP70jcZdHKsUmO942cziWoi+SoGswyppgB797waeo869iKDHMquBqPnPpZ9S2UkTmRTP0MrO9j/Kpo58jTB89sWa2B7fSGbhksldzKmMX1HYSZp5dTmezs7YNzGTnPt8dV67jU/dF+F8xuN3zEGH66LKNfCV7IxvPe1deY5p+voW21WToAKMZHx7P8ccysQHUUs6en2qMp1q0fJK9j8KTX8ptYFV3Olx+J3dcefGM9/H7V59FS22QvkiCTLZ01+A4xrBl3Sq2rFvFm8fG2Lari0df6WE0kebeZw7xb88e4sr1jdy6uZ3z2mrcHq4Uqay1XNhwKRc2XDp5zOMYjg7HCXgdguM1+Y66L4nIHLQoNg9ltyh276PwyKfoGskwlvURMkl8pPnHiv/Mz8xGmqqDk7X0C6n/nqnDy76eCIy3n5yeET0GKgNeXvjc7OH88Vd7+eP7X6BrOAFAwOtQX+HD7/UsaKHogrv0fP09dB16izHrn6y3Dtg4/aae/9P8l/PqEb/QMc57wez49cbx53ajTcUgm4Qb/pLHsxfP+jykM1l6IwniqfLZeXUomuShF47wwHOHGRhLTh4/t7WarR3tXLl+Nd5FdDwRmY3P4xDwHQ/4Aa/KvkQWqaTeJSvQ56HsAv3X3wORHiJZP93DMQwQIkG/qefOwJ8tupPKTIFzJJbCAv2jSab+i/Q6uUWvtUEvz/7JdXOed7ZWkHkvZp3NDLPXnP0rs9429sAn6YlmSRAgZJJ4bYq/8t3Oe6bV0C+XeT8P49d7YhdaAJJRqG6GD31vzvuy1jIYTTEUTc55u1KTymT5j9f62Lari329o5PHG6sCvG9TG+++sJWakNpeyvKZqMc/HvAdvZkUmR8F+nJTdoH+by6EYD0YQySepm80QTKVocEzxsu/9rMlCaUzzS7D7F1uzlhdNRlGZysnmd6jHnKB8+hInPVN1fmXn0w1x+z1XKH+VF1ultNsz8NwLMUTn77m5G+Ycr2nfAPEh+APXsjrPqPJdMmX4MzEWsuL3cNs6+zm568fm/x0KeB1uO78ZrZuaue0VaW95kAKh9dxCPpys/cBXy7kawdkkVmV1A+HAn0eyi7QL2LGdrFOVS4y19/ftWP/STPTx0bjDIylaK8PLaxfu4vPxUKt5Az9VOlMlp5IgkQZleBMdWQ4xv27u3nkxaOMJY8/B29fW8/Wze1ccnq9wpWsKDOlq05wnv3xRcpASb0g6ydbTnb5nblZ6GQ0N1ObjOb+fPmdy37XV21o4vPvPZ+m6iDDsRRN1cETwvdsnXfu2rGfO65cN7lY19rc14GxFPUVvhlvn5ehA7mZ+al8IRg6uMSPfOnM9DzMuah3ia631+PQVhuktkxLTVprQ/zeVWfxnTu28PtXn0lbXRCAp98a5NPbXuTD33iW771wuKzWHIi7rLUkxksae0fiHBqIcqB/jKPDcYaiSWLJDNky+1RNpFRphj4PZTdDD1Pqxg9C3Wlz142voFOVk0wv5dnbM0JrbejkMpzhGOuba05dhrOCM/T5dqbJ53bzXtS7xNd7LJHm2Gj5leBMlcladu7vZ1tnN88dGpo8XhP08p6LWrlpY5jG6sAcZxBZGT7P+Az+eJmOFtxKmSipGXoF+jyUZaAvUPMtJ5np9n2ROIPRPMtwptXQ9w8PE41F+Wzqd9jl28xHrziDT1x79qIfV76daebdwcZFqfEuOOVagjPV672jbOvs4rFXe0mN76ngcQxXnd3I1s1hNrSo7aUUjoldbgNez2RNvkdtM6X0lNQ/agX6PCjQF475BtqZbt81GKOh0sfqquDk7easMR+fvR4+8gYvx+r4p+yN/JyNZC1kLdx5zVmLDvX5vlGZzxuaefeiXwbWWvrHkoyM75RZ7gbGkjz0/GEefP4wg9Hjz8n5bTVs7WjnnetXKzhJQZpomzk15IsUuZJ6sVWgz4MCfWGZbznJ9Nvv643QUhPMvwvMuIs+90NiqQxe5/jSk3Q2S8jnmbNHfj7y7UyT7+0KbSZ/NJHmWCRBVq83QG6/hcde7WVbZxdv9I1NHm+qDnDzpjDvvrCVqqD2/ZPCNTGLH/R6tPmVFKuS+ger3xhStPKNhldtaDohxM40yx1LZWivn7u94Fgyg3faMnLHcEJHk4VaU1+R15jyvd3UxcMAFX4v0WSau3bsdyXQVwW8BLwOPSNxkulTbP1bBvxeh+svaOFd5zfz3KEhtnd28+Qb/fRGEty1Yz/f+MVbvOv8FrZ2hE/571LEDVlriSUzxKa8/k3tix/0edRRR2QFKdBLUZk681wX8tEbifPZB/fwecg7qN5x5To+++Aeosn0CbPXU7vAzFSu4vcY4qkskMEY8BgDBir9i//oOZ8xTb9dOpOlZyRBKpvF5xgef7UXyIX5p98aIOh1WF0VmNzgKOTz0DUYnXkA89k8a4F8HodwXYhjo0kicZXgQK6t4KbT6tl0Wj3dQ8fbXsZSGR547jAPPneYS9c1cGtHO5tOq1PbSyloyXT2hDfsXseZnMVXX3yR5aWSmzyo5KZwLNVusHOV7cxUrjIcSxFNZohOm403wCevXT+vGvrZatvzLSV6/NVevviDV9nbO4rPY2iuDuD1OAzHUhigJuTjyFCM1HiHmbbaEDUh3+zP00I2z1okleDMbjSR5pGXjnJ/ZzdHR+KTx89YXcnWjjDXntuMf/pHRSJFwBgzOXuvxbZSAErqH58CfR4U6AvHvHdBXYCZ3jTs642AhdqQj77RBFmbeyVorw/N636XqrZ9rjGub64mEk9xeCiOxeL3OLTUBme/H5c2z0qms/RGVIIzm0zW8uQb/dy3q4sXu4cnj9eFfNx4cSvvvbiNVVVqeynFzedxqAx4aaj0uz0UKT8lFehVciNFJd8a8sU4NBilbtrmSJmsxVpLU02Qpppcd5yJNxLzsVS17XONEaA66KOtDnpH4sTTWZqqg7MvHh46AMH6E4+twOZZfm+uBEddcGbmcQzvXL+ad65fzd6eCNs6u/mPV3sZiqX4l50H+fbTh7h6QxNbO8Kc3Vzt9nBFFiSVyTKWSCvQiyySPreVojLvXVAXYE19BbFpvdM9jjmhuw0s7I3EocEoId+JNfdz1rYvYozVQR+tdSHevraBb9++ZfY3DHWn58pspkrFchtMLTNjDKurArTUBvXR+xzObq7mf9ywgW9/7FJ+a8tp1IZ8pLOWR1/u4Xfv6eQPvvMcT+w7VtYbeYmIlDMFeikqV21o4vPvPZ+m6iDDsRRN1cElbcX4+Ku9DI4leKt/jH09EUZiSaLJNFUBL9VB76LfSMwUxBfyxmCmNzYLHuPld+Zq5pNRsDb3NZvMHV8hFX4v7fUVJ3zyIidbVRXgd95xBvd+7FI+dd3ZnLG6EoAXuob5Xw/u4be++jT/vquL0UTa5ZGKiMhKUg19HlRDvzLc3ghpan17OpOlJ5IglbGc3VTFp6/fADCv/venuo/F9oefaRHtgsc42eXmYG5mfhFdbhZ7HYdjKQbGkui16dSstew+OMR9nV3s3D8webzC7+H6C1q4eVOYcF3IxRGKnJrP47CmQe1ZZcWV1MfCCvR5UKBffoWwEdJSddA5lflujFVMluo6asHs/B0aiLJ9dzc/fOko8fHnzQCXn7mKrZvbubi9Vi0DpSAp0ItLSuoFUYE+Dwr0y2+lwvRcVqKDTqlbyutorWUwmmIomlzqYZa0SDzFwy8e5f7d3fRGEpPHz2qsYuvmMFef06S2l1JQFOjFJSUV6PWqLgVhqRaLLsZS1beXs6W8jsYYGir9tNaGTlqQLLOrDvp4/9vW8K8fvZTPvuc8zm+rAeD1vlG++IPX+MA/7eQbT77FwJjeKImIlAr9lpSCUAhheiU66JS65biOIb+HcH1IC2bnyeMYrjqnkS9/YBN/9+ubuGZDEx7HMBhN8Y1fHOAD/7ST//OD13ijd9TtoYqIyCIp0EtBKIQwvdwddMrBcl1Hj2NoqQ2yqiqgOvAFOLe1hj9+97l866OX8utvX0NN0EsqY/nBnqN87F928V//7Xl+/rraXoqIFCvV0OdBNfQro5QXi5aT5b6OiXSG3pEEqYwWzC5UPJXhx6/0sG1XNwcGjpdDtdUFuWVTmOsvaNEnIrJiVEMvLimp2SEF+jwo0BcHt9teysrJZi3HRhPqt75I1lqePTDIts5unn7zeNvLSr+HX72wlZs3hWmpDbo4QikHCvTiEgX6cqNAX/gKoe2lrLyReIr+UfWsXwoH+6Ns293Fj/b0kBhve+kYuOKs1WztaOeCcI3KnWRZKNCLS0rqBU2BPg8K9IWvENpeijtUgrO0RmIpvv/iEe7f3c2x0eOdcM5urmJrRztXndOIz6PlV7J0FOjFJQr05UaBvrA9/movd9yzi6y1+D0Oq6sC1IR86iFfRlSCs/TSmSw79h3jvl1dvHo0Mnm8odLPTRvbuPGiVuoq/C6OUEqFAr24RIG+3CjQF66JUpvekThZazEYsljaakN4PaYkZ+i1VmB2KsFZHi8fHmFbZxc/3dvHRCMcv9fh2nOb2NrRzhmrK90doBQ1BXpxiQJ9uVGgL1wTpTaZrOXwUBxjcgv9HMeUZNtJrRU4NZXgLJ/ekTjffe4w33vhyAmfhmw+rY6tm9t5+xkNOKqzl3lSoBeXlNSLlfqSSVE7NBilLuTDGEN9RYa+0QRZCyZjubUjXHIh964d+0llMvSPpklmsvg9DjUhL3ft2F9yj3WhAl4P4bqQSnCWQVNNkNuvXMdvXXY6P9rTw7bOLroGY+w6OMSug0O014fY2hHmuvNaCPk9pz6hiIgsCa1skqI2sTNpJJ5iMJrC53HwewwBn8N9nd08/mqv20NcUvt6IxyLJElnLR7HkM5ajkWS7OuNnPqby4jjGJpqtBHVcgn5PNy0sY2v/87b+MLNF7D59HoAugZjfOknr/P+u3dy10/foGck7vJIRUTKgwK9FLWJnUmPDscBO/E/mquD+DyGu3bsd3uISyqZzoIBxxgMJlfeYMaPy0lqQz7a6oLqyrJMHGPYsm4V//fWi/jqBy/h3Re24vc6jCbSfOfZLn7jK0/x+YdeZs/hYa1rEBFZRiq5kaJ21YYmPg/ccc8uLODzGFZXBSe73HQNRk91ihMU+oJTn8cQS+W6uuTWC+SO+z2ahZ6NSnBWxhmrK/lv153NR684g4deOMwDzx2mfyzJ43v7eHxvHxtaqtna0c4vnb0ar95giYgsKb2qStG7akMTHafVc1pDBesaq6gJ+QCIpTK01+e/0GqyY04kTl3IR28kzmcf3FNQZTtnN9ewqtKP12PIWIvXY1hV6Wd9c43bQytoKsFZObUVPn5zy+l862OX8plf3cDZzVUAvHo0wp8//Aq//pWn+NZTBxmOpVweqYhI6VCgl5IwUXoTTaaxNvc1lbHcceW6vM9x1479+DyGCr8XY3JfC61s544r1+H3emipDXJOczUttUH8Xs+8Hmc5UwnOyvF5HK49t5l/+I0OvvT+jVx59mocA8dGk3zlZ29y2907+etH93Kgf8ztoYqIFD21rcyD2lYWh4lyma7BKO0LKJe54ouPTXbMmVCIm1Mt9nGKNqJyy9HhOPfv7ubhF48wlsxMHn/72nq2bm7nktPr9QlKGVLbSnFJSb3YKNDnQYG+PEz0tK/wH19aEk2mS3JzKsnRRlTuiCbT/HBPD9s7u+keik0eP72hgls6wvzKec0EfWp7WS4U6MUlCvTlRoG+PGjTpvKkjajck7WWnfv72dbZze6DQ5PHa4Je3nNRKzdtDNNYHXBxhLISFOjFJSUV6Fe8kNQYs8YY8x/GmFeMMXuMMXeOH28wxjxqjNk3/rV+yvf8D2PM68aY14wx75pyfLMx5sXxv/tbM/5ZrTEmYIz5zvjxp4wxa6d8zwfH72OfMeaDK/fIpdBdtaGJWzvC9EUSvHI0Ql8kUZKbUy3W46/28oG7d3LFFx/jA3fvLKhFwwsx0QWnKqCmXyvNMYbLz1zN//u1i/mn397M9ee34PMYRuJpvvX0IX79K0/xv7//Cq8cGXF7qCIiBW3FZ+iNMa1Aq7W20xhTDewC3gd8CBiw1v6FMeaPgHpr7aeNMecB3wbeDrQBPwbOttZmjDFPA3cCO4GHgb+11j5ijPk94CJr7e8aY24DbrbWvt8Y0wA8C1xCrl35LmCztXZwrjFrhr48aIb+1Er9OVIJjvsGxpJ8b7zt5WD0eCec89tq2NrRzjvXr8bjlNTEWtnTDL24pKReSFZ8ht5ae8Ra2zn+3xHgFSAM3AR8Y/xm3yAX8hk/fq+1NmGtfRN4HXj7+BuDGmvtL2zut+83p33PxLnuA355fPb+XcCj1tqB8RD/KHD9Mj5cKSLF0OXGbaX+HNUE1QXHbQ2Vfn77srV8+2Nb+PT153BmYyUAew6P8PnvvcxvfOUp7n3mEJG42l6KiExw9bfWeCnMJuApoNlaewRyoR+YmO4LA4emfFvX+LHw+H9PP37C91hr08AwsGqOc800ttuNMc8aY57t6+tb2AOUonJoMEpo2kK8kM8z782pSlk5PEcqwSkMfq/Du85v4e7f2sxf/aeLufzMVRigN5Lg7h37ef/dO/nSj/dxaKB0/u2JiCyUa7+xjDFVwDbgD6y1I3O0KpvpL+wcxxf6PScetPZu4G7IldzMNjgpHWvqK07qcjPfzalKXbk8RxMbUQVVguM6Ywwb19SxcU0d3UMx7t/dzSMvHiWWyvDA84d54PnDbFnXwNaOdjpOq1PbSxEpS67M0BtjfOTC/L9aa7ePH+4ZL6OZqLOfWGnXBayZ8u3twOHx4+0zHD/he4wxXqAWGJjjXCJLsjlVqSu356gm6CNcF1IJToEI14X4/avP4jt3bOH3rjqTlpogADv3D/CH973AR7+5i4dfPEIilTnFmURESosbi2INufr2AWvtH0w5/n+B/imLYhustf/dGHM+8C2OL4r9CbB+fFHsM8DHyZXsPAx82Vr7sDHmvwAXTlkUe4u19j+NL4rdBXSM320nuUWxA3ONWYtiy4c2bTq1cnyOrLUcG02qbrvAZLKWJ9/oZ1tnFy90DU8erw35uPHiVm66uI1VVWp7Wei0KFZcUlIf57kR6K8AngBeBCYaP3+GXCj/N+A04CDwaxNB2xjzP4EPA2lyJTqPjB+/BPg6EAIeAT5urbXGmCDwL+Tq8weA26y1+8e/58Pj9wfw59bafz7VmBXoRQRgLJHm2GiCTFYlOIVmb0+E+3Z18fhrfaTHr4/XMVx1TiO3bm7n7OZql0cos1GgF5co0JcbBXqR4yZm6A8NRllTJjP0U2Wylr5Igmgy7fZQZAb9owkefP4wDz5/hOHY8U9ULgzXsnVzmHecqbaXhUaBXlxSUi8ECvR5UKAXySn1PvTzMRJPMTCaJKvX0IKUSGV47NVe7uvs5s1jY5PHW2qC3LypjRsubFUnowKhQC8uUaAvNwr0IjkfuHvnSV1uosk0TdVBvn37FhdH5o5UJkvPSJxkOnvqG4srrLXsPjjEfZ1d7Nx/fLlUyOfhhgtauHlTmHB9yMURigK9uKSkAr2mJ0Qkb4cGo9SFfCccK7U+9PPh8ziE60L0jyUZiWnBbCEyxtBxej0dp9dzaCDK9t3d/PClXNvL7bu7uX93N5eduYqtHWE2rlHbSxEpTgr0IpK3culDPx/GGFZXBQj5PPRFEirBKWBrGiq485fX8+F3rOXhF49y/+5ueiMJnnyjnyff6OfMxkq2drRzzYYm/F61KhWR4qGSmzyo5EYkRzX0c0tlsvRGEuqDXiQyWcsT+46xvbOLlw6PTB6vr/Bx48VtvPfiNhoq/S6OsDyo5EZcUlIfxynQ50GBXuS4cuxDPx/WWpXgFKFXjoywvbObx/f2TbYl9XkM12xoYmtHO2c1Vbk8wtKlQC8uUaAvNwr0IjJfo4k0x1SCU3T6IgkeeK6b771whJH48dakG9fUsrWjnS3rVqnt5RJToBeXlNQPsgJ9HhToRWQh1AWneMVTGX78Sg/bdnVzYOD4ou/W2iC3dIS54YKWE9aSyMIp0ItLFOjLjQL9yij3DYukNKkEp7hZa3n2wCDbOrt5+s3jbS8r/R5uuDDX9rK1Vm0vF0OBXlyiQF9uFOiXnxZbSqlTCU7xO9A/xvbd3fxoTw+J8U9dHAOXn7marZvDXBSuVdvLBVCgF5eU1A+rAn0eFOiXnzYsknKQTGfpjagEp9iNxFJ874UjfPe5bo6NJiePr2+qYuvmdq4+pxGfR20v86VALy5RoC83CvTL74ovPkZdyHfC7Ja1luFYiic+fY2LIxNZWtZaBsaSDKsEp+ilM1l27DvGfbu6ePVoZPJ4Q6Wfmy5u48aLW6mrUNvLU1GgF5eUVKDXih4pCNqwSMqFMYZVVQEqA176IglSGc3WFyuvx+GaDU1cs6GJPYeH2d7ZzU/39jEwluSfn3yLe546wLXnNrO1I8y6RrW9FJHloxn6PGiGfvmphl7KUTabWzAbiWu2vlT0jMR54LnDfO+FI4wmjre97Ditjq0d7Vy6rgFHdfYn0Ay9uKSkfhAV6POgQL8ytGGRlKtoMs2xSJJ0VrP1pSKWyvCjPT1s6+yiazA2eby9PsQtm8K86/wWQn6PiyMsHAr04hIF+nKjQC8iyy2TtfSPJk6Y1ZXil7WWp98cYFtnN7sODE4erwx4ePeFrbxvU5iWmqCLI3SfAr24RIG+3CjQi8hKGU2k6R9NkMnqtbnUvHlsjO2d3Tz6Ss9kpyPHwBXrV3NrRzvnt9WUZdtLBXpxSUn9sCnQ50GBXkRWUjqTpTeSIJ7KuD0UWQZD0STfe+EIDzx3mP6x420vz2mp5taOML90diPeMmp7qUAvLlGgLzcK9CKy0qy1DEZTDEWTp76xFKVUJstP9/Zx364u9vaMTh5fVeXn5o1h3n1RK7Uhn4sjXBkK9OISBfpyo0AvIm6JJTP0RuIqwSlh1lr2HB7hvl1d/Oz1Y0xcar/X4brzmrmlI8zaVZXuDnIZKdCLSxToy40CvYi4KZ3J0jeaIJZUCU6pOzoc5/7d3Tz84hHGplzvS06v59bN7Vyytr7k2l4q0ItLSuoHSYE+Dwr0IlIIBseSDKoEpyxEk2l+uKeH7Z3ddA8db3t5WkMFt3SE+ZXzmgn5SqPtpQK9uESBvtwo0JePyI4dDHz1a6S6uvC1t9PwkQ9TfeWVbg9LZFIsmaEvklDP+jKRyVqeerOfbZ3d7D44NHm8OujlPRe18r6NYRqrAy6OcPEU6MUlCvTlRoG+PER27KDn83+G8fsxwSA2HscmkzR/9k8U6qWgZLKW3khcJThl5o2+Ubbt6uYnr/aQyuR+dzsGfunsRm7d3M65rTUuj3BhFOjFJQr05UaBvjwc+OCHSPf14YRCk8eysRjexkZO/8bX3RuYyCyGokkGxlSCU24Go0keev4wDzx3mMFoavL4ea3VbO1o58qzG/E4xZNVFOjFJcXzQ5IHBfo8KNCXh9d/+Vqc2toTNnax1pIdHuasn/zYxZGJzC6eytA7ohKccpRMZ3n8tV7u29XN633H2142VQd438Y23n1RK9XBwm97qUAvLimpQO91ewAihcLX3k66rw8zZYbexuP42ttdHJXI3II+D+H6EH2RBNFk2u3hyAryex2uO7+FXzmvmRe6hrmvs4snX++nN5Lg7ife5Ju/OMB157dwS0eY0xSYRUqaZujzoBn68qAaeil2Q9Ekg9EUel0vX4eHYty/u5tHXjpKdMoai0vPaGBrR5jNp9ef8ClkIdAMvbiksH4QFkmBPg8K9OVDXW6k2KkERwDGEmkeeeko9+/u5shwfPL42lUV3NLRzq+c20SgQNpeKtCLSxToy40CvcxFbwKk0GSylmOjCcYSKsEpd5ms5ck3+tne2cXzXcOTx2uCXm68uI2bNraxusrdtpcK9OISBfpyo0Avs1GZjhSy4WiKgWhSJTgCwN6eCNs7u3ns1V7S2dy/CY9juPqcXNvLs5urXRmXAr24RIG+3CjQy2zU6lIKXTyV24gqlVEJjuQMjCV54LluHnr+CEOx420vL2ir4dbN7bzjrNUr2vZSgV5cokBfbhToZTZqdSnFIJu19KkER6ZJprP85JUetnV2s//Y2OTx5poAN28K86sXtlIVWP5meAr04pKSCvRqWymyCGp1KcXAcQzNNUFG4in6R1WCIzl+r8MNF7Zy/QUt7D44xLbObnbu76dnJME//nQ/33jyANdf0MItm8KE60OnPqGIuEYz9HnQDL3MRjX0UmyS6Sy9kTjJtEpw5GRdg1G2d3bzgz1Hiady/0YMsGXdKrZuDrNpTd2St73UDL24pKRm6BXo86BAL3NRlxspNtZa+seSjEypnxaZajSe5vsvHuG7z3XTM5KYPL5udSVbO8L88rnN+L3OktyXAr24RIG+3CjQi0gpiibT9EUSZLL6PSAzy2QtP3/9GPft6uKlwyOTx+tCPt57cRvv3dhGQ6V/UfehQC8uUaAvNwr0IlKqMllLXyRBNKkFszK3145G2NbZxX+81jf5JtDnMVx9ThNbO8KsX2DbSwV6cYkCfblRoBeRUjccSzEwpgWzcmrHRhM88NxhHnr+MCPx428EL26vZWtHO5eduWpebS8V6MUlCvTlRoFeRMqBFszKfCRSGR59pZdtnV0c6I9OHm+tDXJmOfJrAAARoklEQVTzpjA3XNBCZR5tLxXoxSUK9OVGgV5EyoW1lsFoiqFo0u2hSJGw1rLrwCDbOrt56s2ByeMVfg83XNDCzZvCtNXN3vZSgV5cokBfbhToRaTcaIdZWYiDA7m2lz/ac5R4+njby8vPWsWtm9u5KFx7UttLBXpxiQJ9uVGgF5FylM1ajo0lGI1rwazMz0gsxcMvHuH+3YfpGz3e9vKspipu7Qhz1TlNk20vFejFJQr05UaBXkTK2WgizbFIgqx+X8g8pTNZnth3jG2dXbx8JDJ5vL7Cx00b27jx4jaaqoMK9OIGBfpyo0AvIuUuncnSN5oglsy4PRQpUi8fHmFbZxc/3dvHxNYHPo/huvNa+Pgvn8WGlhp3ByjlRoG+3CjQi4jkDEWTDEZTam8pC9Y7Eue7zx3m+y8eITKlnGvHH17Naas0Uy8rpqQC/an7SYmIiIyrq/AT8nvoHdGCWVmYppogt1+5jt+67HQefbmH+zu7aW8IKcyLLIJm6POgGXoRkRNZa+kfSzISS7k9FClyHsdQHfTRUOl3eyhSXkpqht5xewAiIlJ8jDGsrgrQUhvE6+hXiSycY4zCvMgi6VVYREQWrMLvJVwfymtHUBERWR4K9CIisigex9BcE2R1dQDHlNSn2CIiRUGBXkRElkRN0Ee4PkTA53F7KCIiZUWBXkRElozP49BWG6S+QjXRIiIrRYFeRESWlDGG+ko/bXUhfB79mhERWW56pRURkWUR9HkI14WoCmrBrIjIclKgFxGRZeM4hqbqIM01QTyOFsyKiCwHBXoREVl2lQEv4boQIb8WzIqILDUFehERWRFej0NrbYhVlQGM2luKiCwZBXoREVlRtRU+2uqC+L36FSQishT0aioiIisu4M0tmK0N+dweiohI0VOgFxERVxhjWFUVoLU2hNfRryMRkYXSK6iIiLgq5PcQrg9RFVB7SxGRhVCgFxER13kcQ1NNkMbqAI4WzIqIzIsCvYiIFIzqoI9wfYiAT+0tRUTypUAvIiIFxedxaKsNUlfhd3soIiJFQYFeREQKjjGGhko/LbXaYVZE5FQU6EVEpGBV+HM7zAZVgiMiMisFehERKWhej0NbXUglOCIis1CgFxGRotBQ6ae1NqQSHBGRaRToRUSkaIT8uR1mQ36V4IiITFCgFxGRouL1OLTWhqhXCY6ICKBALyIiRapeJTgiIoACvYiIFDGV4IiIKNCLiEiRUwmOiJQ7BXoRESkJEyU4Xke/2kSkvOhVT0RESkbI7yFcrxIcESkvCvQiIlJSPI5RCY6IlBUFehERKUkqwRGRcqFXORERKVkqwRGRcqBALyIiJW2iBKehUiU4IlKaFOhFRKQs1FX4aatTCY6IlB69qomISNkI+nIlOBV+r9tDERFZMgr0IiJSVjyOoaU2qBIcESkZCvQiIlKWVIIjIqVCr2IiIlK2VIIjIqVAgV5ERMra1BIcY4zbwxERmTcFehEREXIlOK21QZXgiEjR0auWiIjIOJXgiEgxUqAXERGZYqIEZ1VlQCU4IlIUFOhFRERmUFvho7U2iM+jX5UiUtj0KiUiIjKLoM9DuC5EZUAlOCJSuBToRURE5uA4huaaIKurVYIjIoVJgV5ERCQPNUEf4boQfq9+dYpIYdGrkoiISJ78XodwXYjakM/toYiITFKgFxERmQdjDKuqArTWhtSzXkQKgl6JREREFiDkV896ESkMCvQiIiILNNmzvkoLZkXEPQr0IiIii1Qb8tFWp571IuIOvfKIiIgsgYDXQ3t9iBotmBWRFaZALyIiskSMMayuCtBSG8TjqARHRFaGAr2IiMgSq/B7CdeFCPk9bg9FRMqAAr2IiMgy8HocWmtDrKrUglkRWV5lGeiNMdcbY14zxrxujPkjt8cjIiKlq7bCR2utFsyKyPIpu1cXY4wH+DvgBuA84APGmPPcHZWIiJSyoM9DuC5EVVA960Vk6ZVdoAfeDrxurd1vrU0C9wI3uTwmEREpcY5jaKoO0lQTxFEJjogsoXIM9GHg0JQ/d40fO4Ex5nZjzLPGmGf7+vpWbHAiIlLaqgJe2utDBH1aMCsiS6McA/1M0yL2pAPW3m2tvcRae0ljY+MKDEtERMqF1+PQVheivsLv9lBEpASUY6DvAtZM+XM7cNilsYiISBmrr/TTVhfSglkRWZRyfAV5BlhvjDnDGOMHbgMedHlMIiJSpiYXzAa0YFZEFqbsXj2stWljzO8DPwQ8wNestXtcHpaIiJQxxzE01QQJxVP0jybJ2pMqQUVEZlV2gR7AWvsw8LDb4xAREZmqOugj6PPQG0mQSGXcHo6IFIlyLLkREREpWD6PQ1ttkDotmBWRPCnQi4iIFBhjDA2VflprQ3gd/aoWkbnpVUJERKRAhfwewvUhKrVgVkTmoEAvIiJSwDyOobkmyOrqAEY7zIrIDBToRUREikBN0Ee4LoTfq1/dInIivSqIiIgUCb/XIVwXojbkc3soIlJAFOhFRESKiDGGVVUBWmqDeByV4IiIAr2IiEhRqvB7aa+voMKvBbMi5U6BXkREpEh5HENLbZBVlVowK1LOFOhFRESKXG2Fj7a6ID6Pfq2LlCP95IuIiJSAgNdDe32I6qAWzIqUGwV6ERGREmGMobE6QHONFsyKlBMFehERkRJTGfASrgsR8nvcHoqIrAAFehERkRLk9Ti01oZoqPRrwaxIiVOgFxERKWF1FX5aa7VgVqSU6adbRESkxAV9HsJ1IaqC6lkvUooU6EVERMqA4xiaqoM01QRxVIIjUlIU6EVERMpIVcBLuD5E0KcFsyKlQoFeRESkzPg8Dm11Ieor/G4PRUSWgAK9iIhImaqv9NNWF9KCWZEip59gERGRMja5YDagBbMixUqBXkREpMw5jqGpJkhjdUALZkWKkAK9iIiIAFAd9NFWFyKgBbMiRUWBXkRERCb5vQ5ttUHqtGBWpGgo0IuIiMgJjDE0VPpprQ3hdRQVRAqdfkpFRERkRiG/h3B9iEotmBUpaAr0IiIiMiuPY2iuCbKqKoDRglmRgqRALyIiIqdUG/IRrgvh9yo6iBQa/VSKiIhIXvxeh3BdiJqQz+2hiMgUCvQiIiKSN2MMq6sCtNQG8TgqwREpBAr0IiIiMm8Vfi/huhAhv3rWi7hNgV5EREQWxOtxaK0NsapSC2ZF3KRALyIiIotSW+GjrS6Iz6NYIeIG/eSJiIjIogW8HtrrQ1QHtWBWZKUp0IuIiMiSMMbQWB2guUYLZkVWkgK9iIiILKnKgBbMiqwkBXoRERFZchMLZhsq/VowK7LMFOhFRERk2dRV+Gmt1YJZkeWkny4RERFZVkGfh3BdiKqg1+2hiJQkBXoRERFZdo5jaKoO0lQTxFEJjsiSUqAXERGRFVMV8BKuDxHwacGsyFJRoBcREZEV5fM4hOtC1Ff40WS9yOKpmE1ERERcUV/ppzKgKCKyWJqhFxEREdf4vYoiIoulnyIRERERkSKmQC8iIiIiUsQU6EVEREREipgCvYiIiIhIEVOgFxEREREpYgr0IiIiIiJFTIFeRERERKSIKdCLiIiIiBQxBXoRERERkSKmQC8iIiIiUsQU6EVEREREipgCvYiIiIhIEVOgFxEREREpYgr0IiIiIiJFTIFeRERERKSIKdCLiIiIiBQxBXoRERERkSKmQC8iIiIiUsQU6EVEREREipgCvYiIiIhIEVOgFxEREREpYgr0IiIiIiJFTIFeRERERKSIKdCLiIiIiBQxBXoRERERkSKmQC8iIiIiUsQU6EVEREREipgCvYiIiIhIEVOgFxEREREpYsZa6/YYCp4xpg844NLdrwaOuXTfsvx0fUubrm9p0/Utbbq+pS1orb3A7UEsFa/bAygG1tpGt+7bGPOstfYSt+5flpeub2nT9S1tur6lTde3tBljnnV7DEtJJTciIiIiIkVMgV5EREREpIgp0Be+u90egCwrXd/Sputb2nR9S5uub2krqeurRbEiIiIiIkVMM/QiIiIiIkVMgV5EREREpIgp0BcwY8z1xpjXjDGvG2P+yO3xyOIYY75mjOk1xrw05ViDMeZRY8y+8a/1bo5RFs4Ys8YY8x/GmFeMMXuMMXeOH9c1LnLGmKAx5mljzPPj1/ZPx4/r2pYQY4zHGLPbGPO98T/r+pYIY8xbxpgXjTHPTbSrLLXrq0BfoIwxHuDvgBuA84APGGPOc3dUskhfB66fduyPgJ9Ya9cDPxn/sxSnNPDfrLXnAluA/zL+M6trXPwSwDXW2ouBjcD1xpgt6NqWmjuBV6b8Wde3tFxtrd04ZW+Bkrq+CvSF6+3A69ba/dbaJHAvcJPLY5JFsNbuAAamHb4J+Mb4f38DeN+KDkqWjLX2iLW2c/y/I+SCQRhd46Jnc0bH/+gb/79F17ZkGGPagXcDX5lyWNe3tJXU9VWgL1xh4NCUP3eNH5PS0mytPQK5QAg0uTweWQLGmLXAJuApdI1Lwng5xnNAL/CotVbXtrT8DfDfgeyUY7q+pcMCPzLG7DLG3D5+rKSur9ftAciszAzH1GNUpMAZY6qAbcAfWGtHjJnpR1mKjbU2A2w0xtQB9xtjLnB7TLI0jDHvAXqttbuMMVe5PR5ZFu+w1h42xjQBjxpjXnV7QEtNM/SFqwtYM+XP7cBhl8Yiy6fHGNMKMP611+XxyCIYY3zkwvy/Wmu3jx/WNS4h1toh4HFy62F0bUvDO4D3GmPeIlfeeo0x5h50fUuGtfbw+Nde4H5yZc0ldX0V6AvXM8B6Y8wZxhg/cBvwoMtjkqX3IPDB8f/+IPCAi2ORRTC5qfivAq9Ya/9qyl/pGhc5Y8z/397dhNZRhXEYf/6mQWIbu9B2ISKh0qIIVaHZ+IERYhfFhQtLQXQlKKKCLV2om4JQqAhdaBEXIoK0xaLiQpG6UKFEJJGaWpWsFKQo1VX9wChNXxczxUuIUYkxzu3zgwvDOfcwc+5ZzMt735mzrs3Mk2QIGAdmcG37QlU9UVVXVtUIzb32vaq6F9e3LyRZnWT4/DGwFfiMPltfd4r9H0uyjaaubwB4qar2rvAlaQmSHAbGgMuB08Ae4E3gCHAV8DWwvarmPzirDkhyC3AMOMkfdbhP0tTRu8YdlmQzzUNzAzSJsCNV9VSSy3Bt+0pbcrO7qu50fftDkg00WXloSs0PVdXefltfA3pJkiSpwyy5kSRJkjrMgF6SJEnqMAN6SZIkqcMM6CVJkqQOM6CXJEmSOsyAXpI6Kslckukknyc5kWRXkovavi1Jnl1k7EiSe/67q5UkLRdfWylJHZXkp6pa0x6vBw4BE1W152+MHaN93/byXqUkabmZoZekPtBuaf4A8EgaY0neAkhyW5vJn07ySbtr4j7g1rZtZ5uxP5bkePu5qR07luSDJK8lmUlysN0VlySjST5s/x2YTDKcZCDJM0mmknya5MGV+k0k6UKxaqUvQJL076iqL9uSm/XzunYDD1fVRJI1wCzwOD0Z+iSXAHdU1WySjcBhYEs7/kbgOuAbYAK4Ockk8Cqwo6qmklwK/ALcD5ypqtEkFwMTSd6tqq+Wc+6SdCEzoJek/pIF2iaA/UkOAm9U1ak2yd5rEDiQ5AZgDtjU0zdZVacAkkwDI8AZ4NuqmgKoqh/a/q3A5iR3t2PXAhsBA3pJWiYG9JLUJ5JsoAnGvwOuPd9eVfuSvA1sAz5KMr7A8J3AaeB6mnLM2Z6+X3uO52juHQEWeggrwKNVdXQJU5Ek/QPW0EtSH0iyDngBOFDz3naQ5OqqOllVTwMfA9cAPwLDPV9bS5NxPwfcBwz8xSlngCuSjLbnGE6yCjgKPJRksG3flGT10mcoSfozZuglqbuG2hKYQeAs8Aqwf4HvPZbkdprs+hfAO8A54GySE8DLwPPA60m2A+8DPy924qr6LckO4LkkQzT18+PAizQlOcfbh2e/B+5a4jwlSYvwtZWSJElSh1lyI0mSJHWYAb0kSZLUYQb0kiRJUocZ0EuSJEkdZkAvSZIkdZgBvSRJktRhBvSSJElSh/0OMwp9dZGrhVsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 762.375x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(\"Distance\",\"Price\",housing_data[(housing_data[\"Rooms\"]<=4) & \n",
    "                                        (housing_data[\"Price\"]< 1000000)\n",
    "                                       ].dropna(),hue=\"Rooms\", size=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Practice Questions: <br>\n",
    "What is the average price of property by postcode?<br>\n",
    "Number of properties by regions?<br>\n",
    "Average property price by suburbs?<br>\n",
    "What is the most popular suburb?<br>\n",
    "What is the relationship between the number of rooms and property price?<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predicting using Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>Suburb</th>\n",
       "      <th>Address</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Method</th>\n",
       "      <th>SellerG</th>\n",
       "      <th>Date</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>...</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>CouncilArea</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Regionname</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>68 Studley St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1.050173e+06</td>\n",
       "      <td>SS</td>\n",
       "      <td>Jellis</td>\n",
       "      <td>2016-03-09</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8014</td>\n",
       "      <td>144.9958</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>85 Turner St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1.480000e+06</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>2016-03-12</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.7996</td>\n",
       "      <td>144.9984</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>25 Bloomburg St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1.035000e+06</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>2016-04-02</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8079</td>\n",
       "      <td>144.9934</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>18/659 Victoria St</td>\n",
       "      <td>3</td>\n",
       "      <td>u</td>\n",
       "      <td>1.050173e+06</td>\n",
       "      <td>VB</td>\n",
       "      <td>Rounds</td>\n",
       "      <td>2016-04-02</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8114</td>\n",
       "      <td>145.0116</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>5 Charles St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1.465000e+06</td>\n",
       "      <td>SP</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>2017-04-03</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8093</td>\n",
       "      <td>144.9944</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Suburb             Address  Rooms Type         Price Method SellerG  \\\n",
       "0  Abbotsford       68 Studley St      2    h  1.050173e+06     SS  Jellis   \n",
       "1  Abbotsford        85 Turner St      2    h  1.480000e+06      S  Biggin   \n",
       "2  Abbotsford     25 Bloomburg St      2    h  1.035000e+06      S  Biggin   \n",
       "3  Abbotsford  18/659 Victoria St      3    u  1.050173e+06     VB  Rounds   \n",
       "4  Abbotsford        5 Charles St      3    h  1.465000e+06     SP  Biggin   \n",
       "\n",
       "        Date  Distance  Postcode  ...  Bathroom  Car  Landsize  BuildingArea  \\\n",
       "0 2016-03-09       2.5    3067.0  ...       1.0  1.0     126.0           NaN   \n",
       "1 2016-03-12       2.5    3067.0  ...       1.0  1.0     202.0           NaN   \n",
       "2 2016-04-02       2.5    3067.0  ...       1.0  0.0     156.0          79.0   \n",
       "3 2016-04-02       2.5    3067.0  ...       2.0  1.0       0.0           NaN   \n",
       "4 2017-04-03       2.5    3067.0  ...       2.0  0.0     134.0         150.0   \n",
       "\n",
       "   YearBuilt         CouncilArea Lattitude  Longtitude             Regionname  \\\n",
       "0        NaN  Yarra City Council  -37.8014    144.9958  Northern Metropolitan   \n",
       "1        NaN  Yarra City Council  -37.7996    144.9984  Northern Metropolitan   \n",
       "2     1900.0  Yarra City Council  -37.8079    144.9934  Northern Metropolitan   \n",
       "3        NaN  Yarra City Council  -37.8114    145.0116  Northern Metropolitan   \n",
       "4     1900.0  Yarra City Council  -37.8093    144.9944  Northern Metropolitan   \n",
       "\n",
       "  Propertycount  \n",
       "0        4019.0  \n",
       "1        4019.0  \n",
       "2        4019.0  \n",
       "3        4019.0  \n",
       "4        4019.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing cases where there was no price\n",
    "housing_data_filtered = housing_data[housing_data.Price.notnull()]\n",
    "housing_data_filtered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.api.types import is_string_dtype\n",
    "#Tranform categorical data into numerical data for training purpose\n",
    "for col, col_data in housing_data_filtered.items():\n",
    "    if is_string_dtype(col_data):\n",
    "        housing_data_filtered[col] = housing_data_filtered[col].astype('category').cat.as_ordered().cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Suburb</th>\n",
       "      <th>Address</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Type</th>\n",
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       "      <th>Postcode</th>\n",
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       "      <th>Bathroom</th>\n",
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       "      <th>Longtitude</th>\n",
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       "  </thead>\n",
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       "      <td>...</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "      <td>-37.8014</td>\n",
       "      <td>144.9958</td>\n",
       "      <td>2</td>\n",
       "      <td>4019.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
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       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>2016-04-02</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>31</td>\n",
       "      <td>-37.8079</td>\n",
       "      <td>144.9934</td>\n",
       "      <td>2</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>9768</td>\n",
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       "      <td>2016-04-02</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>-37.8114</td>\n",
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       "      <td>4019.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>2017-04-03</td>\n",
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       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>31</td>\n",
       "      <td>-37.8093</td>\n",
       "      <td>144.9944</td>\n",
       "      <td>2</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Suburb  Address  Rooms  Type         Price  Method  SellerG       Date  \\\n",
       "0       0    29458      2     0  1.050173e+06       6      155 2016-03-09   \n",
       "1       0    32512      2     0  1.480000e+06       2       33 2016-03-12   \n",
       "2       0    15389      2     0  1.035000e+06       2       33 2016-04-02   \n",
       "3       0     9768      3     2  1.050173e+06       7      296 2016-04-02   \n",
       "4       0    25128      3     0  1.465000e+06       5       33 2017-04-03   \n",
       "\n",
       "   Distance  Postcode  ...  Bathroom  Car  Landsize  BuildingArea  YearBuilt  \\\n",
       "0       2.5    3067.0  ...       1.0  1.0     126.0           NaN        NaN   \n",
       "1       2.5    3067.0  ...       1.0  1.0     202.0           NaN        NaN   \n",
       "2       2.5    3067.0  ...       1.0  0.0     156.0          79.0     1900.0   \n",
       "3       2.5    3067.0  ...       2.0  1.0       0.0           NaN        NaN   \n",
       "4       2.5    3067.0  ...       2.0  0.0     134.0         150.0     1900.0   \n",
       "\n",
       "   CouncilArea  Lattitude  Longtitude  Regionname  Propertycount  \n",
       "0           31   -37.8014    144.9958           2         4019.0  \n",
       "1           31   -37.7996    144.9984           2         4019.0  \n",
       "2           31   -37.8079    144.9934           2         4019.0  \n",
       "3           31   -37.8114    145.0116           2         4019.0  \n",
       "4           31   -37.8093    144.9944           2         4019.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_data_filtered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.037402</td>\n",
       "      <td>0.156229</td>\n",
       "      <td>-0.012749</td>\n",
       "      <td>-0.120861</td>\n",
       "      <td>0.004872</td>\n",
       "      <td>0.103235</td>\n",
       "      <td>-0.038651</td>\n",
       "      <td>-0.071677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
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       "      <td>-0.224187</td>\n",
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       "      <td>0.365058</td>\n",
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       "      <td>-0.094509</td>\n",
       "      <td>-0.191110</td>\n",
       "      <td>0.173248</td>\n",
       "      <td>0.067626</td>\n",
       "      <td>-0.052935</td>\n",
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       "      <th>Method</th>\n",
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       "      <td>0.003203</td>\n",
       "      <td>-0.010660</td>\n",
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       "      <td>-0.006353</td>\n",
       "      <td>-0.006498</td>\n",
       "      <td>0.001522</td>\n",
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       "      <td>-0.018645</td>\n",
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       "      <td>-0.037141</td>\n",
       "      <td>-0.030190</td>\n",
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       "      <td>0.040146</td>\n",
       "      <td>0.005844</td>\n",
       "      <td>-0.068638</td>\n",
       "      <td>0.026275</td>\n",
       "      <td>0.044486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distance</th>\n",
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       "      <td>-0.026620</td>\n",
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       "      <td>-0.235798</td>\n",
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       "      <td>-0.041120</td>\n",
       "      <td>-0.025243</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.481566</td>\n",
       "      <td>0.269524</td>\n",
       "      <td>0.126201</td>\n",
       "      <td>0.241835</td>\n",
       "      <td>0.060862</td>\n",
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       "      <td>0.323059</td>\n",
       "      <td>-0.122343</td>\n",
       "      <td>-0.100417</td>\n",
       "      <td>0.200946</td>\n",
       "      <td>-0.136102</td>\n",
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       "    <tr>\n",
       "      <th>Postcode</th>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>0.120080</td>\n",
       "      <td>0.067886</td>\n",
       "      <td>0.040664</td>\n",
       "      <td>0.042437</td>\n",
       "      <td>0.089805</td>\n",
       "      <td>0.013252</td>\n",
       "      <td>-0.231027</td>\n",
       "      <td>0.362895</td>\n",
       "      <td>-0.031745</td>\n",
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       "    <tr>\n",
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       "      <td>0.269524</td>\n",
       "      <td>0.089292</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.614892</td>\n",
       "      <td>0.388491</td>\n",
       "      <td>0.037019</td>\n",
       "      <td>0.154157</td>\n",
       "      <td>-0.002022</td>\n",
       "      <td>-0.114298</td>\n",
       "      <td>0.003447</td>\n",
       "      <td>0.106164</td>\n",
       "      <td>-0.047550</td>\n",
       "      <td>-0.053451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bathroom</th>\n",
       "      <td>-0.054201</td>\n",
       "      <td>0.005210</td>\n",
       "      <td>0.611826</td>\n",
       "      <td>-0.224187</td>\n",
       "      <td>0.365058</td>\n",
       "      <td>0.003203</td>\n",
       "      <td>-0.030190</td>\n",
       "      <td>0.126201</td>\n",
       "      <td>0.120080</td>\n",
       "      <td>0.614892</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.307518</td>\n",
       "      <td>0.036333</td>\n",
       "      <td>0.147558</td>\n",
       "      <td>0.167955</td>\n",
       "      <td>-0.069062</td>\n",
       "      <td>-0.059183</td>\n",
       "      <td>0.106531</td>\n",
       "      <td>0.009718</td>\n",
       "      <td>-0.032887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Car</th>\n",
       "      <td>-0.031082</td>\n",
       "      <td>-0.002473</td>\n",
       "      <td>0.393878</td>\n",
       "      <td>-0.264314</td>\n",
       "      <td>0.174743</td>\n",
       "      <td>-0.010660</td>\n",
       "      <td>0.002421</td>\n",
       "      <td>0.241835</td>\n",
       "      <td>0.067886</td>\n",
       "      <td>0.388491</td>\n",
       "      <td>0.307518</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.037829</td>\n",
       "      <td>0.104373</td>\n",
       "      <td>0.128702</td>\n",
       "      <td>-0.127012</td>\n",
       "      <td>-0.009020</td>\n",
       "      <td>0.047213</td>\n",
       "      <td>0.011351</td>\n",
       "      <td>-0.009617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Landsize</th>\n",
       "      <td>0.000708</td>\n",
       "      <td>0.007260</td>\n",
       "      <td>0.037402</td>\n",
       "      <td>-0.022672</td>\n",
       "      <td>0.031958</td>\n",
       "      <td>0.008753</td>\n",
       "      <td>-0.000636</td>\n",
       "      <td>0.060862</td>\n",
       "      <td>0.040664</td>\n",
       "      <td>0.037019</td>\n",
       "      <td>0.036333</td>\n",
       "      <td>0.037829</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.354530</td>\n",
       "      <td>0.044474</td>\n",
       "      <td>0.002830</td>\n",
       "      <td>0.025318</td>\n",
       "      <td>-0.002582</td>\n",
       "      <td>-0.014342</td>\n",
       "      <td>-0.018195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BuildingArea</th>\n",
       "      <td>-0.011000</td>\n",
       "      <td>0.015120</td>\n",
       "      <td>0.156229</td>\n",
       "      <td>-0.087115</td>\n",
       "      <td>0.098883</td>\n",
       "      <td>-0.006353</td>\n",
       "      <td>-0.000101</td>\n",
       "      <td>0.076301</td>\n",
       "      <td>0.042437</td>\n",
       "      <td>0.154157</td>\n",
       "      <td>0.147558</td>\n",
       "      <td>0.104373</td>\n",
       "      <td>0.354530</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.067811</td>\n",
       "      <td>-0.031212</td>\n",
       "      <td>0.017155</td>\n",
       "      <td>-0.002143</td>\n",
       "      <td>-0.000757</td>\n",
       "      <td>-0.024523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearBuilt</th>\n",
       "      <td>0.028735</td>\n",
       "      <td>-0.059246</td>\n",
       "      <td>-0.012749</td>\n",
       "      <td>0.281698</td>\n",
       "      <td>-0.288049</td>\n",
       "      <td>-0.006498</td>\n",
       "      <td>-0.012125</td>\n",
       "      <td>0.323059</td>\n",
       "      <td>0.089805</td>\n",
       "      <td>-0.002022</td>\n",
       "      <td>0.167955</td>\n",
       "      <td>0.128702</td>\n",
       "      <td>0.044474</td>\n",
       "      <td>0.067811</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.029595</td>\n",
       "      <td>0.091592</td>\n",
       "      <td>-0.022175</td>\n",
       "      <td>-0.043509</td>\n",
       "      <td>0.022420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CouncilArea</th>\n",
       "      <td>0.112177</td>\n",
       "      <td>0.013175</td>\n",
       "      <td>-0.120861</td>\n",
       "      <td>0.069836</td>\n",
       "      <td>-0.094509</td>\n",
       "      <td>0.001522</td>\n",
       "      <td>0.040146</td>\n",
       "      <td>-0.122343</td>\n",
       "      <td>0.013252</td>\n",
       "      <td>-0.114298</td>\n",
       "      <td>-0.069062</td>\n",
       "      <td>-0.127012</td>\n",
       "      <td>0.002830</td>\n",
       "      <td>-0.031212</td>\n",
       "      <td>-0.029595</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.084693</td>\n",
       "      <td>-0.131714</td>\n",
       "      <td>-0.077030</td>\n",
       "      <td>0.020691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lattitude</th>\n",
       "      <td>0.128608</td>\n",
       "      <td>-0.011085</td>\n",
       "      <td>0.004872</td>\n",
       "      <td>-0.085209</td>\n",
       "      <td>-0.191110</td>\n",
       "      <td>-0.015751</td>\n",
       "      <td>0.005844</td>\n",
       "      <td>-0.100417</td>\n",
       "      <td>-0.231027</td>\n",
       "      <td>0.003447</td>\n",
       "      <td>-0.059183</td>\n",
       "      <td>-0.009020</td>\n",
       "      <td>0.025318</td>\n",
       "      <td>0.017155</td>\n",
       "      <td>0.091592</td>\n",
       "      <td>0.084693</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.345589</td>\n",
       "      <td>-0.281039</td>\n",
       "      <td>0.011112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Longtitude</th>\n",
       "      <td>-0.149092</td>\n",
       "      <td>0.000547</td>\n",
       "      <td>0.103235</td>\n",
       "      <td>-0.009198</td>\n",
       "      <td>0.173248</td>\n",
       "      <td>-0.018645</td>\n",
       "      <td>-0.068638</td>\n",
       "      <td>0.200946</td>\n",
       "      <td>0.362895</td>\n",
       "      <td>0.106164</td>\n",
       "      <td>0.106531</td>\n",
       "      <td>0.047213</td>\n",
       "      <td>-0.002582</td>\n",
       "      <td>-0.002143</td>\n",
       "      <td>-0.022175</td>\n",
       "      <td>-0.131714</td>\n",
       "      <td>-0.345589</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.508331</td>\n",
       "      <td>0.016326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Regionname</th>\n",
       "      <td>-0.018914</td>\n",
       "      <td>0.009380</td>\n",
       "      <td>-0.038651</td>\n",
       "      <td>0.058296</td>\n",
       "      <td>0.067626</td>\n",
       "      <td>0.016915</td>\n",
       "      <td>0.026275</td>\n",
       "      <td>-0.136102</td>\n",
       "      <td>-0.031745</td>\n",
       "      <td>-0.047550</td>\n",
       "      <td>0.009718</td>\n",
       "      <td>0.011351</td>\n",
       "      <td>-0.014342</td>\n",
       "      <td>-0.000757</td>\n",
       "      <td>-0.043509</td>\n",
       "      <td>-0.077030</td>\n",
       "      <td>-0.281039</td>\n",
       "      <td>-0.508331</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.114134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Propertycount</th>\n",
       "      <td>0.149115</td>\n",
       "      <td>0.007403</td>\n",
       "      <td>-0.071677</td>\n",
       "      <td>0.074679</td>\n",
       "      <td>-0.052935</td>\n",
       "      <td>-0.028175</td>\n",
       "      <td>0.044486</td>\n",
       "      <td>-0.018140</td>\n",
       "      <td>0.017108</td>\n",
       "      <td>-0.053451</td>\n",
       "      <td>-0.032887</td>\n",
       "      <td>-0.009617</td>\n",
       "      <td>-0.018195</td>\n",
       "      <td>-0.024523</td>\n",
       "      <td>0.022420</td>\n",
       "      <td>0.020691</td>\n",
       "      <td>0.011112</td>\n",
       "      <td>0.016326</td>\n",
       "      <td>-0.114134</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Suburb   Address     Rooms      Type     Price    Method  \\\n",
       "Suburb         1.000000  0.015609 -0.064271  0.022750 -0.121037  0.001405   \n",
       "Address        0.015609  1.000000  0.022116 -0.070503  0.034200  0.006326   \n",
       "Rooms         -0.064271  0.022116  1.000000 -0.550992  0.404908 -0.029778   \n",
       "Type           0.022750 -0.070503 -0.550992  1.000000 -0.329911  0.043113   \n",
       "Price         -0.121037  0.034200  0.404908 -0.329911  1.000000 -0.009431   \n",
       "Method         0.001405  0.006326 -0.029778  0.043113 -0.009431  1.000000   \n",
       "SellerG        0.034903 -0.002137 -0.040063  0.030186 -0.041143  0.002896   \n",
       "Distance      -0.007840 -0.026620  0.271511 -0.235798 -0.186849 -0.041120   \n",
       "Postcode      -0.014178 -0.000163  0.085890 -0.030485  0.040512 -0.009798   \n",
       "Bedroom2      -0.055460  0.016586  0.946755 -0.505942  0.368067 -0.017121   \n",
       "Bathroom      -0.054201  0.005210  0.611826 -0.224187  0.365058  0.003203   \n",
       "Car           -0.031082 -0.002473  0.393878 -0.264314  0.174743 -0.010660   \n",
       "Landsize       0.000708  0.007260  0.037402 -0.022672  0.031958  0.008753   \n",
       "BuildingArea  -0.011000  0.015120  0.156229 -0.087115  0.098883 -0.006353   \n",
       "YearBuilt      0.028735 -0.059246 -0.012749  0.281698 -0.288049 -0.006498   \n",
       "CouncilArea    0.112177  0.013175 -0.120861  0.069836 -0.094509  0.001522   \n",
       "Lattitude      0.128608 -0.011085  0.004872 -0.085209 -0.191110 -0.015751   \n",
       "Longtitude    -0.149092  0.000547  0.103235 -0.009198  0.173248 -0.018645   \n",
       "Regionname    -0.018914  0.009380 -0.038651  0.058296  0.067626  0.016915   \n",
       "Propertycount  0.149115  0.007403 -0.071677  0.074679 -0.052935 -0.028175   \n",
       "\n",
       "                SellerG  Distance  Postcode  Bedroom2  Bathroom       Car  \\\n",
       "Suburb         0.034903 -0.007840 -0.014178 -0.055460 -0.054201 -0.031082   \n",
       "Address       -0.002137 -0.026620 -0.000163  0.016586  0.005210 -0.002473   \n",
       "Rooms         -0.040063  0.271511  0.085890  0.946755  0.611826  0.393878   \n",
       "Type           0.030186 -0.235798 -0.030485 -0.505942 -0.224187 -0.264314   \n",
       "Price         -0.041143 -0.186849  0.040512  0.368067  0.365058  0.174743   \n",
       "Method         0.002896 -0.041120 -0.009798 -0.017121  0.003203 -0.010660   \n",
       "SellerG        1.000000 -0.025243 -0.005506 -0.037141 -0.030190  0.002421   \n",
       "Distance      -0.025243  1.000000  0.481566  0.269524  0.126201  0.241835   \n",
       "Postcode      -0.005506  0.481566  1.000000  0.089292  0.120080  0.067886   \n",
       "Bedroom2      -0.037141  0.269524  0.089292  1.000000  0.614892  0.388491   \n",
       "Bathroom      -0.030190  0.126201  0.120080  0.614892  1.000000  0.307518   \n",
       "Car            0.002421  0.241835  0.067886  0.388491  0.307518  1.000000   \n",
       "Landsize      -0.000636  0.060862  0.040664  0.037019  0.036333  0.037829   \n",
       "BuildingArea  -0.000101  0.076301  0.042437  0.154157  0.147558  0.104373   \n",
       "YearBuilt     -0.012125  0.323059  0.089805 -0.002022  0.167955  0.128702   \n",
       "CouncilArea    0.040146 -0.122343  0.013252 -0.114298 -0.069062 -0.127012   \n",
       "Lattitude      0.005844 -0.100417 -0.231027  0.003447 -0.059183 -0.009020   \n",
       "Longtitude    -0.068638  0.200946  0.362895  0.106164  0.106531  0.047213   \n",
       "Regionname     0.026275 -0.136102 -0.031745 -0.047550  0.009718  0.011351   \n",
       "Propertycount  0.044486 -0.018140  0.017108 -0.053451 -0.032887 -0.009617   \n",
       "\n",
       "               Landsize  BuildingArea  YearBuilt  CouncilArea  Lattitude  \\\n",
       "Suburb         0.000708     -0.011000   0.028735     0.112177   0.128608   \n",
       "Address        0.007260      0.015120  -0.059246     0.013175  -0.011085   \n",
       "Rooms          0.037402      0.156229  -0.012749    -0.120861   0.004872   \n",
       "Type          -0.022672     -0.087115   0.281698     0.069836  -0.085209   \n",
       "Price          0.031958      0.098883  -0.288049    -0.094509  -0.191110   \n",
       "Method         0.008753     -0.006353  -0.006498     0.001522  -0.015751   \n",
       "SellerG       -0.000636     -0.000101  -0.012125     0.040146   0.005844   \n",
       "Distance       0.060862      0.076301   0.323059    -0.122343  -0.100417   \n",
       "Postcode       0.040664      0.042437   0.089805     0.013252  -0.231027   \n",
       "Bedroom2       0.037019      0.154157  -0.002022    -0.114298   0.003447   \n",
       "Bathroom       0.036333      0.147558   0.167955    -0.069062  -0.059183   \n",
       "Car            0.037829      0.104373   0.128702    -0.127012  -0.009020   \n",
       "Landsize       1.000000      0.354530   0.044474     0.002830   0.025318   \n",
       "BuildingArea   0.354530      1.000000   0.067811    -0.031212   0.017155   \n",
       "YearBuilt      0.044474      0.067811   1.000000    -0.029595   0.091592   \n",
       "CouncilArea    0.002830     -0.031212  -0.029595     1.000000   0.084693   \n",
       "Lattitude      0.025318      0.017155   0.091592     0.084693   1.000000   \n",
       "Longtitude    -0.002582     -0.002143  -0.022175    -0.131714  -0.345589   \n",
       "Regionname    -0.014342     -0.000757  -0.043509    -0.077030  -0.281039   \n",
       "Propertycount -0.018195     -0.024523   0.022420     0.020691   0.011112   \n",
       "\n",
       "               Longtitude  Regionname  Propertycount  \n",
       "Suburb          -0.149092   -0.018914       0.149115  \n",
       "Address          0.000547    0.009380       0.007403  \n",
       "Rooms            0.103235   -0.038651      -0.071677  \n",
       "Type            -0.009198    0.058296       0.074679  \n",
       "Price            0.173248    0.067626      -0.052935  \n",
       "Method          -0.018645    0.016915      -0.028175  \n",
       "SellerG         -0.068638    0.026275       0.044486  \n",
       "Distance         0.200946   -0.136102      -0.018140  \n",
       "Postcode         0.362895   -0.031745       0.017108  \n",
       "Bedroom2         0.106164   -0.047550      -0.053451  \n",
       "Bathroom         0.106531    0.009718      -0.032887  \n",
       "Car              0.047213    0.011351      -0.009617  \n",
       "Landsize        -0.002582   -0.014342      -0.018195  \n",
       "BuildingArea    -0.002143   -0.000757      -0.024523  \n",
       "YearBuilt       -0.022175   -0.043509       0.022420  \n",
       "CouncilArea     -0.131714   -0.077030       0.020691  \n",
       "Lattitude       -0.345589   -0.281039       0.011112  \n",
       "Longtitude       1.000000   -0.508331       0.016326  \n",
       "Regionname      -0.508331    1.000000      -0.114134  \n",
       "Propertycount    0.016326   -0.114134       1.000000  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrmat = housing_data_filtered.corr()\n",
    "corrmat.head(21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Bedroom2</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rooms</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.550992</td>\n",
       "      <td>0.271511</td>\n",
       "      <td>0.946755</td>\n",
       "      <td>0.611826</td>\n",
       "      <td>0.393878</td>\n",
       "      <td>-0.012749</td>\n",
       "      <td>0.404908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <td>-0.550992</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.235798</td>\n",
       "      <td>-0.505942</td>\n",
       "      <td>-0.224187</td>\n",
       "      <td>-0.264314</td>\n",
       "      <td>0.281698</td>\n",
       "      <td>-0.329911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distance</th>\n",
       "      <td>0.271511</td>\n",
       "      <td>-0.235798</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.269524</td>\n",
       "      <td>0.126201</td>\n",
       "      <td>0.241835</td>\n",
       "      <td>0.323059</td>\n",
       "      <td>-0.186849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bedroom2</th>\n",
       "      <td>0.946755</td>\n",
       "      <td>-0.505942</td>\n",
       "      <td>0.269524</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.614892</td>\n",
       "      <td>0.388491</td>\n",
       "      <td>-0.002022</td>\n",
       "      <td>0.368067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bathroom</th>\n",
       "      <td>0.611826</td>\n",
       "      <td>-0.224187</td>\n",
       "      <td>0.126201</td>\n",
       "      <td>0.614892</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.307518</td>\n",
       "      <td>0.167955</td>\n",
       "      <td>0.365058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Car</th>\n",
       "      <td>0.393878</td>\n",
       "      <td>-0.264314</td>\n",
       "      <td>0.241835</td>\n",
       "      <td>0.388491</td>\n",
       "      <td>0.307518</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.128702</td>\n",
       "      <td>0.174743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearBuilt</th>\n",
       "      <td>-0.012749</td>\n",
       "      <td>0.281698</td>\n",
       "      <td>0.323059</td>\n",
       "      <td>-0.002022</td>\n",
       "      <td>0.167955</td>\n",
       "      <td>0.128702</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.288049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>0.404908</td>\n",
       "      <td>-0.329911</td>\n",
       "      <td>-0.186849</td>\n",
       "      <td>0.368067</td>\n",
       "      <td>0.365058</td>\n",
       "      <td>0.174743</td>\n",
       "      <td>-0.288049</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Rooms      Type  Distance  Bedroom2  Bathroom       Car  \\\n",
       "Rooms      1.000000 -0.550992  0.271511  0.946755  0.611826  0.393878   \n",
       "Type      -0.550992  1.000000 -0.235798 -0.505942 -0.224187 -0.264314   \n",
       "Distance   0.271511 -0.235798  1.000000  0.269524  0.126201  0.241835   \n",
       "Bedroom2   0.946755 -0.505942  0.269524  1.000000  0.614892  0.388491   \n",
       "Bathroom   0.611826 -0.224187  0.126201  0.614892  1.000000  0.307518   \n",
       "Car        0.393878 -0.264314  0.241835  0.388491  0.307518  1.000000   \n",
       "YearBuilt -0.012749  0.281698  0.323059 -0.002022  0.167955  0.128702   \n",
       "Price      0.404908 -0.329911 -0.186849  0.368067  0.365058  0.174743   \n",
       "\n",
       "           YearBuilt     Price  \n",
       "Rooms      -0.012749  0.404908  \n",
       "Type        0.281698 -0.329911  \n",
       "Distance    0.323059 -0.186849  \n",
       "Bedroom2   -0.002022  0.368067  \n",
       "Bathroom    0.167955  0.365058  \n",
       "Car         0.128702  0.174743  \n",
       "YearBuilt   1.000000 -0.288049  \n",
       "Price      -0.288049  1.000000  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "important_cols = ['Rooms', 'Type', 'Distance', 'Bedroom2', 'Bathroom', 'Car', 'YearBuilt', 'Price']\n",
    "corrmat_v2 = housing_data_filtered[important_cols].corr()\n",
    "corrmat_v2.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.050173e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.480000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>1.035000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.050173e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>1.465000e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rooms  Type  Distance  Bathroom  Car  YearBuilt         Price\n",
       "0      2     0       2.5       1.0  1.0        NaN  1.050173e+06\n",
       "1      2     0       2.5       1.0  1.0        NaN  1.480000e+06\n",
       "2      2     0       2.5       1.0  0.0     1900.0  1.035000e+06\n",
       "3      3     2       2.5       2.0  1.0        NaN  1.050173e+06\n",
       "4      3     0       2.5       2.0  0.0     1900.0  1.465000e+06"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Feature Selection\n",
    "important_cols.remove('Bedroom2')\n",
    "cols = important_cols\n",
    "housing_data_filtered = housing_data_filtered[cols]\n",
    "housing_data_filtered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "#Replace null data cells with means value using Simple Imputer\n",
    "y = housing_data_filtered.Price\n",
    "cols.remove('Price')\n",
    "X = SimpleImputer().fit_transform(housing_data_filtered[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "#Split the data into training and validation data sets\n",
    "train_X, val_X, train_y, val_y = train_test_split(X, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression()\n",
    "model.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_y = model.predict(val_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1f28a692be0>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the actual Price value against predicted Sale Price \n",
    "g = sns.jointplot(x= val_y, y= predict_y, kind='reg', xlim=(0,8500000), ylim=(0,8500000),\n",
    "                  joint_kws={'line_kws':{'color':'darkorange'}})\n",
    "g.set_axis_labels(xlabel='Actual Price', ylabel='Predicted Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>900400</td>\n",
       "      <td>785511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1050173</td>\n",
       "      <td>1201195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>755000</td>\n",
       "      <td>1088767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1050173</td>\n",
       "      <td>996168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1590000</td>\n",
       "      <td>1137575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Actual  Predicted\n",
       "0   900400     785511\n",
       "1  1050173    1201195\n",
       "2   755000    1088767\n",
       "3  1050173     996168\n",
       "4  1590000    1137575"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_y_df = pd.DataFrame(predict_y)\n",
    "compare = pd.concat([val_y.reset_index(drop=True), predict_y_df], axis=1)\n",
    "compare.columns = ['Actual', 'Predicted']\n",
    "compare['Actual'] = compare['Actual'].astype('int64')\n",
    "compare['Predicted'] = compare['Predicted'].astype('int64')\n",
    "compare.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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