Admission_Univ.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "893c9fee",
   "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>Serial No.</th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>337</td>\n",
       "      <td>118</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.65</td>\n",
       "      <td>1</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>324</td>\n",
       "      <td>107</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>8.87</td>\n",
       "      <td>1</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>316</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>322</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.67</td>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>103</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>396</td>\n",
       "      <td>324</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>9.04</td>\n",
       "      <td>1</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>397</td>\n",
       "      <td>325</td>\n",
       "      <td>107</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>9.11</td>\n",
       "      <td>1</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>398</td>\n",
       "      <td>330</td>\n",
       "      <td>116</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.45</td>\n",
       "      <td>1</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>399</td>\n",
       "      <td>312</td>\n",
       "      <td>103</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.78</td>\n",
       "      <td>0</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>400</td>\n",
       "      <td>333</td>\n",
       "      <td>117</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.66</td>\n",
       "      <td>1</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Serial No.  GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  \\\n",
       "0             1        337          118                  4  4.5   4.5  9.65   \n",
       "1             2        324          107                  4  4.0   4.5  8.87   \n",
       "2             3        316          104                  3  3.0   3.5  8.00   \n",
       "3             4        322          110                  3  3.5   2.5  8.67   \n",
       "4             5        314          103                  2  2.0   3.0  8.21   \n",
       "..          ...        ...          ...                ...  ...   ...   ...   \n",
       "395         396        324          110                  3  3.5   3.5  9.04   \n",
       "396         397        325          107                  3  3.0   3.5  9.11   \n",
       "397         398        330          116                  4  5.0   4.5  9.45   \n",
       "398         399        312          103                  3  3.5   4.0  8.78   \n",
       "399         400        333          117                  4  5.0   4.0  9.66   \n",
       "\n",
       "     Research  Chance of Admit   \n",
       "0           1              0.92  \n",
       "1           1              0.76  \n",
       "2           1              0.72  \n",
       "3           1              0.80  \n",
       "4           0              0.65  \n",
       "..        ...               ...  \n",
       "395         1              0.82  \n",
       "396         1              0.84  \n",
       "397         1              0.91  \n",
       "398         0              0.67  \n",
       "399         1              0.95  \n",
       "\n",
       "[400 rows x 9 columns]"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "df=pd.read_csv(\"D:/Data Analysis/adm_data.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "6520517e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 9)"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "8aaedd6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3600"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "dc5878e2",
   "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>Serial No.</th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>200.500000</td>\n",
       "      <td>316.807500</td>\n",
       "      <td>107.410000</td>\n",
       "      <td>3.087500</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.452500</td>\n",
       "      <td>8.598925</td>\n",
       "      <td>0.547500</td>\n",
       "      <td>0.724350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>115.614301</td>\n",
       "      <td>11.473646</td>\n",
       "      <td>6.069514</td>\n",
       "      <td>1.143728</td>\n",
       "      <td>1.006869</td>\n",
       "      <td>0.898478</td>\n",
       "      <td>0.596317</td>\n",
       "      <td>0.498362</td>\n",
       "      <td>0.142609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>100.750000</td>\n",
       "      <td>308.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>200.500000</td>\n",
       "      <td>317.000000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>8.610000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>300.250000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.062500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.830000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>400.000000</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.920000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.970000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Serial No.   GRE Score  TOEFL Score  University Rating         SOP  \\\n",
       "count  400.000000  400.000000   400.000000         400.000000  400.000000   \n",
       "mean   200.500000  316.807500   107.410000           3.087500    3.400000   \n",
       "std    115.614301   11.473646     6.069514           1.143728    1.006869   \n",
       "min      1.000000  290.000000    92.000000           1.000000    1.000000   \n",
       "25%    100.750000  308.000000   103.000000           2.000000    2.500000   \n",
       "50%    200.500000  317.000000   107.000000           3.000000    3.500000   \n",
       "75%    300.250000  325.000000   112.000000           4.000000    4.000000   \n",
       "max    400.000000  340.000000   120.000000           5.000000    5.000000   \n",
       "\n",
       "             LOR         CGPA    Research  Chance of Admit   \n",
       "count  400.000000  400.000000  400.000000        400.000000  \n",
       "mean     3.452500    8.598925    0.547500          0.724350  \n",
       "std      0.898478    0.596317    0.498362          0.142609  \n",
       "min      1.000000    6.800000    0.000000          0.340000  \n",
       "25%      3.000000    8.170000    0.000000          0.640000  \n",
       "50%      3.500000    8.610000    1.000000          0.730000  \n",
       "75%      4.000000    9.062500    1.000000          0.830000  \n",
       "max      5.000000    9.920000    1.000000          0.970000  "
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "f6ea017c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 400 entries, 0 to 399\n",
      "Data columns (total 9 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Serial No.         400 non-null    int64  \n",
      " 1   GRE Score          400 non-null    int64  \n",
      " 2   TOEFL Score        400 non-null    int64  \n",
      " 3   University Rating  400 non-null    int64  \n",
      " 4   SOP                400 non-null    float64\n",
      " 5   LOR                400 non-null    float64\n",
      " 6   CGPA               400 non-null    float64\n",
      " 7   Research           400 non-null    int64  \n",
      " 8   Chance of Admit    400 non-null    float64\n",
      "dtypes: float64(4), int64(5)\n",
      "memory usage: 28.2 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "b19970a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Serial No.', 'GRE Score', 'TOEFL Score', 'University Rating', 'SOP',\n",
       "       'LOR ', 'CGPA', 'Research', 'Chance of Admit '],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "10d7dcde",
   "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>Serial No.</th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Serial No.</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.097526</td>\n",
       "      <td>-0.147932</td>\n",
       "      <td>-0.169948</td>\n",
       "      <td>-0.166932</td>\n",
       "      <td>-0.088221</td>\n",
       "      <td>-0.045608</td>\n",
       "      <td>-0.063138</td>\n",
       "      <td>0.042336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GRE Score</th>\n",
       "      <td>-0.097526</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.835977</td>\n",
       "      <td>0.668976</td>\n",
       "      <td>0.612831</td>\n",
       "      <td>0.557555</td>\n",
       "      <td>0.833060</td>\n",
       "      <td>0.580391</td>\n",
       "      <td>0.802610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOEFL Score</th>\n",
       "      <td>-0.147932</td>\n",
       "      <td>0.835977</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.695590</td>\n",
       "      <td>0.657981</td>\n",
       "      <td>0.567721</td>\n",
       "      <td>0.828417</td>\n",
       "      <td>0.489858</td>\n",
       "      <td>0.791594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University Rating</th>\n",
       "      <td>-0.169948</td>\n",
       "      <td>0.668976</td>\n",
       "      <td>0.695590</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.734523</td>\n",
       "      <td>0.660123</td>\n",
       "      <td>0.746479</td>\n",
       "      <td>0.447783</td>\n",
       "      <td>0.711250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOP</th>\n",
       "      <td>-0.166932</td>\n",
       "      <td>0.612831</td>\n",
       "      <td>0.657981</td>\n",
       "      <td>0.734523</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.729593</td>\n",
       "      <td>0.718144</td>\n",
       "      <td>0.444029</td>\n",
       "      <td>0.675732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOR</th>\n",
       "      <td>-0.088221</td>\n",
       "      <td>0.557555</td>\n",
       "      <td>0.567721</td>\n",
       "      <td>0.660123</td>\n",
       "      <td>0.729593</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.670211</td>\n",
       "      <td>0.396859</td>\n",
       "      <td>0.669889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGPA</th>\n",
       "      <td>-0.045608</td>\n",
       "      <td>0.833060</td>\n",
       "      <td>0.828417</td>\n",
       "      <td>0.746479</td>\n",
       "      <td>0.718144</td>\n",
       "      <td>0.670211</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.521654</td>\n",
       "      <td>0.873289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Research</th>\n",
       "      <td>-0.063138</td>\n",
       "      <td>0.580391</td>\n",
       "      <td>0.489858</td>\n",
       "      <td>0.447783</td>\n",
       "      <td>0.444029</td>\n",
       "      <td>0.396859</td>\n",
       "      <td>0.521654</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.553202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chance of Admit</th>\n",
       "      <td>0.042336</td>\n",
       "      <td>0.802610</td>\n",
       "      <td>0.791594</td>\n",
       "      <td>0.711250</td>\n",
       "      <td>0.675732</td>\n",
       "      <td>0.669889</td>\n",
       "      <td>0.873289</td>\n",
       "      <td>0.553202</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Serial No.  GRE Score  TOEFL Score  University Rating  \\\n",
       "Serial No.           1.000000  -0.097526    -0.147932          -0.169948   \n",
       "GRE Score           -0.097526   1.000000     0.835977           0.668976   \n",
       "TOEFL Score         -0.147932   0.835977     1.000000           0.695590   \n",
       "University Rating   -0.169948   0.668976     0.695590           1.000000   \n",
       "SOP                 -0.166932   0.612831     0.657981           0.734523   \n",
       "LOR                 -0.088221   0.557555     0.567721           0.660123   \n",
       "CGPA                -0.045608   0.833060     0.828417           0.746479   \n",
       "Research            -0.063138   0.580391     0.489858           0.447783   \n",
       "Chance of Admit      0.042336   0.802610     0.791594           0.711250   \n",
       "\n",
       "                        SOP      LOR       CGPA  Research  Chance of Admit   \n",
       "Serial No.        -0.166932 -0.088221 -0.045608 -0.063138          0.042336  \n",
       "GRE Score          0.612831  0.557555  0.833060  0.580391          0.802610  \n",
       "TOEFL Score        0.657981  0.567721  0.828417  0.489858          0.791594  \n",
       "University Rating  0.734523  0.660123  0.746479  0.447783          0.711250  \n",
       "SOP                1.000000  0.729593  0.718144  0.444029          0.675732  \n",
       "LOR                0.729593  1.000000  0.670211  0.396859          0.669889  \n",
       "CGPA               0.718144  0.670211  1.000000  0.521654          0.873289  \n",
       "Research           0.444029  0.396859  0.521654  1.000000          0.553202  \n",
       "Chance of Admit    0.675732  0.669889  0.873289  0.553202          1.000000  "
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding correlation with data\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "7af6413e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA24AAAOcCAYAAAA2C+J1AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzddXQUVxsG8Gfjbht3YkgECCFAgjulSItDcS0Uh2JFikOhpWhxL1LcSnGKe7AEl0Dc3bPfH4ENm92E8CVLJuX5nTPnsLN3Zu8Mm7v7zvvOXZFEIpGAiIiIiIiIBEulrDtARERERERERWPgRkREREREJHAM3IiIiIiIiASOgRsREREREZHAMXAjIiIiIiISOAZuREREREREAsfAjYiIiIiISOAYuBEREREREQkcAzciIiIiIiKBY+BGREREREQkcAzciIiIiIiIiunff/9FmzZtYG1tDZFIhAMHDnx0m/Pnz6NGjRrQ0tKCk5MT/vjjj09+XQZuRERERERExZSSkoKqVati+fLlxWr/8uVLfPXVV6hXrx7u3LmDyZMnY8SIEdi7d+8nva5IIpFI/p8OExERERERfclEIhH279+P9u3bF9pmwoQJOHToEIKCgqTrhgwZgrt37+LKlSvFfi1m3IiIiIiI6IuWkZGBxMREmSUjI6NU9n3lyhU0b95cZl2LFi1w8+ZNZGVlFXs/aqXSG/oiaFf/oay7IDhvLiwp6y4IjraGall3QXBUVURl3QUqB5LSssu6C4KTnpVT1l0QHI4nsmJTMsu6C4LjYaNX1l0olJC/S05oZ4qff/5ZZt306dMxY8aMEu87PDwcFhYWMussLCyQnZ2N6OhoWFlZFWs/DNyIiIiIiOiLNmnSJIwZM0ZmnaamZqntXySSvejy/m61guuLwsCNiIiIiIi+aJqamqUaqH3I0tIS4eHhMusiIyOhpqYGsVhc7P0wcCMiIiIiIuUTfZnTa9SpUweHDx+WWXfixAn4+PhAXV292Pv5Ms8eERERERHR/yE5ORkBAQEICAgAkDfdf0BAAIKDgwHklV326tVL2n7IkCF4/fo1xowZg6CgIGzYsAHr16/HuHHjPul1mXEjIiIiIiIqpps3b6JRo0bSx+/vjevduzc2bdqEsLAwaRAHABUqVMCxY8cwevRorFixAtbW1li6dCk6dOjwSa/LwI2IiIiIiJTvEybiELKGDRuiqJ/C3rRpk9y6Bg0a4Pbt2yV6XZZKEhERERERCRwDNyIiIiIiIoFjqSQRERERESnfFzqrZGnh2SMiIiIiIhI4Bm5EREREREQCx1JJIiIiIiJSvv/IrJJlhRk3IiIiIiIigWPgRkREREREJHAslSQiIiIiIuXjrJIlwrNHREREREQkcAzciIiIiIiIBI6lkkREREREpHycVbJEmHEjIiIiIiISOAZuREREREREAsdSSSIiIiIiUj7OKlkiPHtEREREREQCx8CNiIiIiIhI4FgqSUREREREysdZJUuEGTciIiIiIiKBY+BGREREREQkcCyVJCIiIiIi5eOskiXCs0dERERERCRwDNyIiIiIiIgEjqWSRERERESkfJxVskSYcSMiIiIiIhI4Bm5EREREREQCx1JJIiIiIiJSPs4qWSI8e0RERERERALHwI2IiIiIiEjgGLgpUcOGDTFq1Khitz937hxEIhHi4+OV1iciIiIiojIhEgl3KQe++MAtMjISgwcPhr29PTQ1NWFpaYkWLVrgypUrJd73vn37MGvWrFLoZT5HR0eIRCJcvXpVZv2oUaPQsGHDUn0tofH3dsaeJYPx4sQcpN1ZjjYNvcq6S6VGIpFg/eoVaNuiIRr5eeOHQX3w4vmzj2539vQJ9OjYBg1rV0OPjm1w/swpmedTUlKwZNE8fNu6KRr5eWNw3x4Ienhfpk1qagoWL5iN9q0ao5GfN7p3aIP9f+0s1eMrDRKJBH+sXIbmjeuhjk9VDOzbE8+fPS1ym+fPnmLc6OFo3aIxvD0rYfvWzXJt/li5DN6elWSWZg3rKuswSpVEIsGqFcvQtGFd+Hp7oX+fnnj2kXPy7NlTjBk5HK2aNUZV94rYtmWTXJv3zxVc5s76WUlHUnq+9HPyfixp17IhGvsXfyw5d/oEvuvUBo3qVMN3ndrg/FnZsSQ7OxtrVv6OTm2bo7G/Nzq1a4GNa1ciNzdX2iY1NQW/LpiNb75qjMb+3ujRsQ327yn7sUQikWDzupXo0qYJvmpQE2OG9sOrFx8/J/+ePYl+3dqjVf0a6NetPS6eO11o2z83r0PTOl5Y+dsCuedev3qBqeOHo21TP7RpUhs/DOiBiPCwEh1TSUkkEmxauxIdWzdGi/o+GPV9X7wsxjk5f+Yk+nRph+Z1vdGnSztcUHBOoiIjMGf6RLRrVhct69fEgO864nHQQ+nzm9auRK/ObdCqgS/aNPXD2B8GIPDBvVI9vv/H8YO78X33Nujaog7GD+6BwHt3imz/8O4tjB/cA11b1MH3Pdrin0N7Cm178cw/6NC4BuZPHSOzft+fG/Dj9z3Ro3U99P22KeZPHYOQ4FelcTj0H/XFB24dOnTA3bt3sXnzZjx58gSHDh1Cw4YNERsb+3/vMysrCwBgYmICfX390uqqlJaWFiZMmFDq+xU6XW1N3H8SgtHzd5d1V0rd9s3rsXP7ZoyZMAXrt+yCidgUo4YOQEpKSqHbPLgXgOmTxqHFV22xecc+tPiqLaZOHIuH9/M/AOfPmoYb165g2qz52LprP3xr+2Hk9wMQFRkhbbN08QJcu3wR02bNx597DqNLj5747Ze5uHDujFKP+VNt3rAO27dswoTJU7F1x18Qm5rh+0H9kJKSXOg26enpsLG1w4hRY2FqalZoO2cXV5w4e0G67N53SBmHUOo2rl+LrZs3YuKUadi+aw/EpqYYMqBv0eckLQ22drYYMbrwc7J91x6cPndRuqxetxEA0KxFS6UcR2n60s/J9s3rsevPzRjz4xSs27wLYrEpRg8bgNSPjSWT88aSTe/GkmkTx+LhB1+mt29ej4N7d2P0j1Ow/a/DGDp8DP7cuhF7dm2Xtln26wJcu3IRU2fOx/a/DqNz955YIoCxZNe2jdi7Yyt+GDsJKzb8CROxKSaMHFzkOQm8fxezp/6Ipi2/xuote9C05deY9dN4BD2UDzAeBT7AsYN74OTiJvdc6Ns3GDW4N+wcKmDxivVYvWUPvus7GBoaGqV6jJ9q59YN+OvPLRgxbjL+2LgDJiamGD98UJHn5OH9AMz8aTyatWqDddv2oFmrNvh58jiZoCspMQHDB/WCmqoa5i9ZhU07D+D7keOgp28gbWNr74CR4yZj/Z97sXTNFlha2eDHEYMRH/f/f+8qqUtnT2DjisXo0KMfFq35E5U9q2POxOGIilAcYEeEhWDOpBGo7Fkdi9b8iQ7d+2LD8l9w5V/5QDYyPAyb/1iCyp7V5Z57ePc2WrbrhHnLN2H6LyuRm5ODmT8OQ3paWqkfI/03fNGBW3x8PC5evIgFCxagUaNGcHBwgK+vLyZNmoTWrVtL2yUkJGDQoEEwNzeHgYEBGjdujLt370qfnzFjBqpVq4YNGzbAyckJmpqakEgkcqWS27Ztg4+PD/T19WFpaYnu3bsjMjLyk/s9ePBgXL16FceOHSu0TW5uLmbOnAlbW1toamqiWrVqOH78+Ce/lpCcuBSIn1cewcEzdz/euByRSCTY/edW9O43CA0bN4OTiyt++nkuMtLTcfL40UK32/XnVtSsVQe9+g2EQwUn9Oo3ED6+tbB7xxYAQEZ6Os6fOYlhI8aimrcPbO0c0H/wMFjZ2MhcBX9w/y5afd0O3j6+sLK2QbtvO8PFtSKCAh8o/diLSyKR4M9tW9B/4BA0adocLq5umDlnPtLT0/H30SOFbufu4YnRY39Ei1atoa6hXmg7VVVVmJqaSRdjExNlHEapkkgk2L51CwYMGoKmzZrD1dUNs+cuQHp6Oo4VcU48PL0wZtwEtPqqdaFfHk1MTGBqZiZd/j13FnZ29vCp6auswykVX/o5kUgk+GvHVvTqOwgN3o0lU96NJSeKGEt279gKn1p10LPvQDg4OqFn34Go4VsLu//cIm3z8P5d1G3QGH51G8DK2gaNmraAby0/PA7Mz6Q8uCc/lji7VsSjoLIbSyQSCfbt2obufQaiXsOmqODsih+nzkZ6ejrOnCj8M3Tvrm2oUbM2uvceAHvHCujeewCq+/hi365tMu3SUlMxb8YkjJ44QyY4eW/D6mWo5VcPg34YA9eKlWFtY4va/vVhbCIu9WMtLolEgj07t+G7vgNRv1HeOZk4fQ7S09Nx6p/C3yd7dm6Dj29t9OgzAPaOTujRZwC8a9bC3p3552TH1g0wN7fEhGmzUdndE5bWNqhRszZsbO2kbZq2aI0avnVgbWOHCk4uGDpyPFJSkvH82ROlHndRDv+1DY1btUPT1t/A1qEC+v0wDmJzi0KzaCcO74WpuSX6/TAOtg4V0LT1N2jcqh0O7d4q0y4nJwe/z52CLn0Gw8LaRm4/UxcsR+OWbWFfwRmOzm4Y9uMMREeG4/mTIKUcpyCIVIS7lAPlo5dKoqenBz09PRw4cAAZGRkK20gkErRu3Rrh4eE4duwYbt26BW9vbzRp0kQmK/fs2TPs3r0be/fuRUBAgMJ9ZWZmYtasWbh79y4OHDiAly9fok+fPp/cb0dHRwwZMgSTJk2SKVP50O+//47Fixdj0aJFuHfvHlq0aIG2bdvi6dOiS4bo8wsNeYuYmGj41vaXrtPQ0EC1Gj64f7fwUo2H9wJQs7afzDrfOv64fzcAAJCdk4OcnBxoaGrKtNHU1MK9gPz9elXzxsV/zyIqMgISiQS3blxDcPAr1KrjD6EIefsW0dFRqO0ne45q1KiJe0Wco+IKDn6N5o3r4euWTTBx/Bi8ffOmxPtUtvfnpI5/flmnhoYGavjUxN07JT8n72VlZuLokUNo/20HiAR+D8CXfk4KHUu8ffCgiLKvB/cC4FtLdiypVdsfD+4FSB97VquOWzeuIvj1KwDA0yePcO/uHdT2rydtU3AsuX3zGt4Ev4JvGY4lYaEhiI2JRg3fOtJ1Ghoa8KpeAw/vBxS6XeCDu6jhK3tOfGr54+F92QuHSxfNQS2/eqjhW1tuH7m5ubh2+V/Y2jlgwqgh6PhVA/zQvzsunS/bDGRY6FvExkTD54P/cw0NDVStXkPu+D4UeP+uzDYAULO2n8x5vPzvOVSsXAUzJo3BNy0bYGDPTjhyoPASwqysLBw5sAe6evpwca34/x9UCWRlZeH5k0eo5iP7f1jVpzYeK8iwAsDjh/dQtUD7aj618fxxILKzs6Tr/tq6FgaGxmj6Vfti9SX1XWWAvoH8RQAi4Av/HTc1NTVs2rQJAwcOxB9//AFvb280aNAAXbt2hZdX3v1TZ8+exf379xEZGQnNd1+AFy1ahAMHDmDPnj0YNGgQgLygbOvWrTAzK7wcq1+/ftJ/Ozk5YenSpfD19UVycjL09PQ+qe8//fQTNm7ciO3bt6Nnz55yzy9atAgTJkxA165dAQALFizA2bNnsWTJEqxYseKj+8/IyJALZiW5ORCpqH5SP+njYmOiAQDGYtkrsCYmYoSHhRa6XUxMNExM5Ld5vz9dXV14eFXDpnV/wKGCE0xMxDj1zzEEPrgHW3sH6Tajx0/C/FnT0b5VY6iqqkFFRYSJU2eiavUapXWIJRYTEwUAEBc8R2Ixwoo4R8Xh6VkVs+bMh72DI2JjYrBuzSr07dkNfx04DCMj4xLtW5mioxWfE7HYFKGhJTsnHzpz5hSSkpLQtv03pbZPZfnSz8n7v32TAsdvLBYjooi/k9iYaLnxx1icP5YAwHe9ByAlORk9On4NFRVV5ObmYNDQkWjWMr86ZdT4SVgwezq++Sp/LJnw00xUrVZ2Y0nc+/G1wFhpbCIu8j6zuJhoucy7sYmJdH8AcPbk33j6OBArNyi+jy8+LhZpqanYuXU9+gwajoFDR+HG1UuYMWk0Fi1fj6rePv/vYZVIbEwMgE8/J7Ex0Qq3+fB9Ehr6Fgf37Uanbr3Qo89ABD28j2W/zoe6hgZafNVW2u7KxfOY+dN4ZKSnQ2xqhkXL1sCwjMbbpIR45ObmwNBY9tiMjMWIj41RuE18XAyMCrQ3NBYjJycHSQnxMBab4dGDAJw+dhCL1/5ZrH5IJBJsWvkrKntWg30Fl//vYOg/74vOuAF597iFhobi0KFDaNGiBc6dOwdvb29s2rQJAHDr1i0kJydDLBZLM3R6enp4+fIlnj9/Lt2Pg4NDkUEbANy5cwft2rWDg4MD9PX1pZOJBAcHf3K/zczMMG7cOEybNg2ZmZkyzyUmJiI0NBT+/rJXOf39/REUVLz0+7x582BoaCizZEfc+uR+krx/jh1B07o+0iU7OxsAIILslXuJRPLRq/kFn5dIZNdNnTkPEokE7Vs2QqM61fHXzm1o1rI1VFXy//T/2rEdDx/cw4LflmPD9t34YfR4LJo/CzeulXyCnv/XsSOH4e/rLV2ys/LOkaJZn0qa8fCvVx9NmrWAq1tF1Krjh6UrVgMAjhw8UKL9lrajRw6htk916SJ938i9BySlOjnW/r174V+3PszNLUpvp6XkSz8nJ/4+gmb1fKTL++OXO9i8gaHIfRUcf1BgLDl94m+c+PsIps9eiA3b/8KUGXOxY9tG/H3kgLTNXzu34+H9e5j/63Ks37YbP4waj8ULPu9Ycvqfo/i6cS3pUuR74iP7Uji2vFsXGRGOFb8twKQZ8+WqGt57XxFTp14jdOzWEy5uldCtV3/U9q+PIwc+373aJ48fQauGvtLlfUZI0fF99DNHwefUh+8tSW4u3CpWxsChI+FasTLaftsZrdt1wKG9u2S2q1ajJtZt3YPla7eiZm1//Dx5HOIKCZI+F0XvkaLeJPKnSiJ9Ii01Bb/PnYrvx/4EA8PiBaTrli7A6xdPMfqnucXvdHlU1uWQ5bxU8ovOuL2npaWFZs2aoVmzZpg2bRoGDBiA6dOno0+fPsjNzYWVlRXOnTsnt52RkZH037q6ukW+RkpKCpo3b47mzZtj27ZtMDMzQ3BwMFq0aCEXeBXXmDFjsGLFCqxcuVLh84q/vBTv28ukSZMwZozs7Efm9b68CVGUoW6DRnD39JQ+zszM+xCNjYmG6QfBf1xcbJH3QYjFpoj54Epn3jYxMtvY2tljxdrNSEtLRUpyCkzNzDB14lhYWdsCyLsPbvWKJZi3aCn86jUAALi4VsTTx4+xY+tG1KxVB2WhQaNG8PDKnzU0693fSEx0NMzMzKXrY2Ni5LIrJaWtowMXVzcEB78u1f2WVMNGjeHpWVX6ODMr75xEFzwnsTEQi01L5TVDQ0Nw7epl/Pr7slLZX2n70s9J3fqNUMVDwVgSHS0zyUpcbKxcdv5DJmJTmaxJ3jayY8nKpYvRo3d/NG3xFQDA2cUN4WGh2LpxHVp93R4Z6elYs2IJ5i5aCr+6H4wlTx5jx7bPN5bUqdsQlarkn5Osd++J2JhoiD84J/EfGV+NxabSzNR7cbH52zx9FIj4uFh837er9PncnBzcD7iFA3t34u/zN2FoZAxVVTU4VHCW2Y+9oxMelEKJd3H512uEKu7542lmIeek4P95QSZiU8TGyr5P4uNk31tiUzO543VwdMKFArOUamvrwMbOHjZ29qjiWRXfdWiNY4f2o0efAZ9+gCWkb2gEFRVVxBc4toT4WLms2ntGxmK5QDMhLhaqqqrQNzDEm1cvEBkeinlTRkufl0jyAvlOTX2xbPNeWNrk3/e3bulC3Lj8L2YtWQuxmfAukpFwMHBToEqVKjhw4AAAwNvbG+Hh4VBTU4Ojo+P/vc9Hjx4hOjoa8+fPh51d3h/rzZs3S9RPPT09TJ06FT///DPatGkjXW9gYABra2tcvHgR9evXl66/fPkyfH2LdyO9pqamtDT0PZZJlg5dXV2ZQF8ikUAsNsWNa5fhVqkygLwvGwG3buL7EWMK2w3cvarhxrUr6Nqjt3TdjauX4Vm1mlxbbW0daGvrIDExAdevXMLQkXn7zc7ORnZ2NkQqsleaVFVVkJsrKclhloiurh50dfPLhyUSCUxNzXD1ymVUqlwFQN45unXrBkaMGluqr52ZmYmXL56jurdwSkWBIs7J5Uuo/P6cZGbi1s0bGDlmXKm85sH9+2BiIka9+g1LZX+l7Us/Jzq6utApzlhy+yaGDC98LPF4N5Z0+WAsuX7tMjy8qkkfp6enQUVunFBF7rsvo9KxpMBVaxUVFUg+41ii6JyYiE1x+8YVuFZ8f06ycO/OLQwcOqrQ/VTxqIrbN66gY7f8WxFuXb8M93cXCqr71MLabXtltvllzjTYO1RAl+/6QlVVFaqqqqhY2R1vC0zv/jb4NcwtrUp4pMVX2Dm5eV32nNy9cwuDho0qdD9VPKvi1rUr6NStl3TdzWuX4e5ZTfrY3asa3ry7D/K9t8GvYPGR45VAIg2yPzd1dXU4u1XC3VvXUKteY+n6e7euoaZfA4XbVHT3ws0r/8qsC7h5Fc4Vq0BNTR029o74bb1slvHPDSuRnpr6buITSwB5/xfrli7E9Ytn8fNva2BhJT+BCdGHvujALSYmBp06dUK/fv3g5eUFfX193Lx5EwsXLkS7du0AAE2bNkWdOnXQvn17LFiwABUrVkRoaCiOHTuG9u3bw8eneDXq9vb20NDQwLJlyzBkyBA8ePCgVH7jbfDgwViyZAl27NiBWrVqSdePHz8e06dPh7OzM6pVq4aNGzciICAA27fnTd0cEhKCJk2aYMuWLcUO5sqarrYGnO3yrw462ojh5WaDuMRUvAmPK8OelYxIJELn7j2xZcNa2No5wM7eAVs2rIGmlpbM/SOzpk2CqZk5vh+edwWvc7fvMGxgb2zbtA71GjTGhfNncOPaVaxanz+r1bXLFyGBBPYOFfD2TTBW/L4I9g6OaN0m794cXT09VK9REyt+X5T3O4ZW1rhz6wb+PnoII0b/+HlPRBFEIhG6f9cLG9athr2DA+ztHbBh7WpoaWmhVeuvpe2mTp4Ac3NzDH8XzGVlZeLFu5LmrKwsREZG4PGjIGjr6MD+3X1+vy1agPoNGsHSyhqxsXn3uKWkJOPrdu0/+3F+CpFIhB49e2H92tWwd3CEvYMD1q/JOydffXBOpkz6EebmFhg5+t05ycyUlnlnZWUiMjICj4KCoKOjA3uH/Hsfc3NzcXD/PrRp1x5qauXjo+JLPycikQiduvXE1o1rYWvvADs7B2zZmDeWNC8wlpiZm2PID3ljSaeu3+GHQe/GkoaNceHcGdy8dhUrPxhL/Os1xJYNa2BhaYUKTi548jgIu7Zvxldt88eSat41sfKDsSTg9g0cP3YIw8twLBGJRPi2y3f4c/N62Ng6wMbOHn9uXgctLS00bv6VtN38nyfD1MwCA4aOBAB827kHRg/ti51bN8CvXiNcvnAWt29cw5LVmwDkBUMVnF1lXktLSxsGBoYy6zv36IPZU8fDs5o3qnn74sbVS7hy6TwWr1iv/IMvhEgkQseu32H7pnWwtXOArZ09tm1aCy0tLTRtkf8+mTtjMszMzDHwXTDXoct3GDmkD3ZsWQ//+o1w6d+zuHX9Gpauyf99zE7deuGHAT2xbdNaNGrSAkGB93HkwF6MmTQNAJCWloptG9fCv15DmJiaITEhHgf37kJUZAQaNGn+Wc/Dh9p0+g5L502Fc8UqqFjFCyeP7EN0RDiat+kIANi2dhlio6MwYtJMAEDzNh3w94Fd2LjyVzRr/Q0eB97Dmb8PYtS7MkcNDU25+9R09fJ+HurD9Wt/n48Lp49j4uxfoa2jg7h3WT8dXT1oamop/bjLhIpwJnQqj4T3yfMZ6enpoVatWvjtt9/w/PlzZGVlwc7ODgMHDsTkyZMB5A1wx44dw5QpU9CvXz9ERUXB0tIS9evXh4VF8dPZZmZm2LRpEyZPnoylS5fC29sbixYtQtu2bT++cRHU1dUxa9YsdO/eXWb9iBEjkJiYiLFjxyIyMhJVqlTBoUOH4Oqa94GSlZWFx48fIzU1tUSv/zl5V3HAiXUjpY8XjusAANh66CoGTd9W2GblQo/e/ZGRkYHF82chKSkRVTy8sGTFWpnMXER4mEypq2fV6vh57i9Ys3IZ1q5aBhtbe8ycvwjunvklMcnJyfhj+RJERYbDwMAQDZo0w+ChI6Gmnj81/s9zf8Efy5fg558mIDExAZaW1hg8dATad+zyeQ6+mHr3G4D0jHTMnz0TiYkJ8PD0wsrV62UyLuFhoVD54BxFRUaiW6f8CSS2btqArZs2oIZPTazdmPelNCIiApMmjEV8XDyMTYzh6VUVm7fvgrWCqZuFpm//gcjIyMDcWT8jMTEBnl5VsWrthgLnJAwqH2RBIqMi0aVje+njzRs3YPPGDfCp6Yv1m/K/qF+9chlhYaFo/22Hz3IspeVLPyfvx5JfPxhLflu+VibjEhEeBhUV2bFkxpxfsHbVMqz7491YMm8R3D3yx5LR46dg7R9LsXj+LMTFxcLU1Bxtv+2EvgO/l7b5ee4vWL1iCWZOzR9LBn0/Au07lO1Y0uW7vsjISMfSRXOQlJSIylU8MX/JHzLnJDIiXCaj6O5VDT/NXICNq5dj05rlsLaxw0+zF6LyByWHxVG3YROM/HEqdm5ZjxW/LoCdgyOmz/0VnlW9S+34/h9de/ZDRkYGliycnXdO3D3xy9LVBc6J7PvEw6saps1aiPWrl2HD6uWwtrXDtDm/oMoH75NKVTwwa+ESrF25BFvW/wEraxsMG/0jmrXMu3CiqqKKN69fYvqxQ0iIj4OBoREqVnbH0tWbUcGp7Cbk8G/UHEmJ8fhry1rExUbD3tEZk+ctlWZG42KjER0ZLm1vYWWDKfOWYuOKxTh+cDdMxGbo98N41Knf5JNe9/3PDUwbPUhm/bAfp6Nxy5J9P6T/JpFEIim7eigqV7Sr/1DWXRCcNxeWlHUXBEdbgyW1BanyCiMVQ1Jadll3QXDSs3LKuguCw/FEVmxK2ZRYCpmHzafNVP45aTcqebWZsqSdnVrWXfioLzrjRkREREREn0k5mb1RqHj2iIiIiIiIBI6BGxERERERkcCxVJKIiIiIiJSvmL8nTIox40ZERERERCRwDNyIiIiIiIgEjqWSRERERESkfJxVskR49oiIiIiIiASOgRsREREREZHAsVSSiIiIiIiUj7NKlggzbkRERERERALHwI2IiIiIiEjgWCpJRERERETKx1klS4Rnj4iIiIiISOAYuBEREREREQkcSyWJiIiIiEj5OKtkiTDjRkREREREJHAM3IiIiIiIiASOpZJERERERKR8nFWyRHj2iIiIiIiIBI6BGxERERERkcCxVJKIiIiIiJSPs0qWCDNuREREREREAsfAjYiIiIiISOBYKklERERERMrHWSVLhGePiIiIiIhI4Bi4ERERERERCRxLJYmIiIiISPk4q2SJMONGREREREQkcAzciIiIiIiIBI6lkkREREREpHycVbJEePaIiIiIiIgEjoEbERERERGRwLFUkoiIiIiIlI+lkiXCs0dERERERCRwDNyIiIiIiIgEjqWSRERERESkfPwB7hJh4EbF9ubCkrLuguDY1RtV1l0QHBWXGmXdBcFxdLMp6y4Ijp6eZll3QXD09DTKuguCc+3C47LuguB413Yt6y4IyoM7r8u6C4ITu7V7WXeBlISlkkRERERERALHjBsRERERESkfZ5UsEZ49IiIiIiIigWPgRkREREREJHAslSQiIiIiIuXjrJIlwowbERERERGRwDFwIyIiIiIiEjiWShIRERERkfJxVskS4dkjIiIiIiISOAZuREREREREAsdSSSIiIiIiUj7OKlkizLgREREREREJHAM3IiIiIiIigWOpJBERERERKZ2IpZIlwowbERERERGRwDFwIyIiIiIiEjiWShIRERERkdKxVLJkmHEjIiIiIiISOAZuREREREREAsdSSSIiIiIiUj5WSpYIM25EREREREQCx8CNiIiIiIhI4FgqSURERERESsdZJUuGGTciIiIiIiKBY+BGREREREQkcCyVJCIiIiIipWOpZMkw40ZERERERCRwDNyIiIiIiIgEjqWSRERERESkdCyVLBlm3IiIiIiIiASOgRsREREREZHAsVSSiIiIiIiUjqWSJcOMGxERERERkcAxcCMiIiIiIhI4lkoSEREREZHysVKyRJhxIyIiIiIiEjgGbkRERERERAL3RZVKhoeHY968eTh69Cjevn0LQ0NDuLq64rvvvkOvXr2go6MDAHB0dMTr168BAFpaWnBwcED//v0xbtw46Ww4r169QoUKFRS+zpUrV1C7dm2Fz509exYzZ87E3bt3kZ6eDhsbG/j5+WH9+vVQU/sy/jskEgk2rFmJg/v+QlJSItw9vDBmwk9wcnYpcruzp09g3aplCHn7Bja2dhg0dCQaNG4qfT4lJQVrVy3Fv2dPIy4uFm4VK2PUuImo7O4pbZOamoJVy37DhXNnkJAQDysrG3Tq2gPfdOqqtONVJn9vZ4zu1RTeVexhZWaIzqPX4PC5e2XdLaUY2LwSRrXzgKWRNoLexuPHjddx+VFEoe271HXC6HaecLYyQGJqJk4GhGDylhuITc6Qa9vRrwI2j26Iw9dfo+svZ5R4FKWray079K3rCDN9DTyLTMH8o49w+3V8oe3VVUX4vrEz2lS1gqm+JsIT0rHm/AvsvxUKANjY3we+TiZy251/HIWhW+4o6zBKVQdva3xXyxZiPU28jErBb6eeI+BtQqHt1VVF6O/vgJYeFhDraiAyKQObLgfj8L1wAEAFUx0MrueIipb6sDbSwm+nnmHnjZDPdTiloq2nBTpXt4ZYVwOvYlOx8sIr3A9NUti2qo0Bfv3WXW59n2138CYuXfpYV0MV/evYo66zCfQ11RCWmI4/Lr7G9SLef0IysHlFjGzrAUsjHQS9jcOETddx+VFkoe0713XC6LYeMuPJlK03peNJW197jPvGC06WBlBXFeF5eBKWHn6AnRdefK5DKpH2VS3RzccGJroaeBWTimXnXuJeSKLCttVsDbC0s6fc+u823kZwXJr0cafqVmhX1QoWBhpISMvGuScxWHPxFTJzJEo7DmXr18QVw1tXhoWhNh6FJGDytlu4+iSq0PYd/RwxonVlOFnoIzEtC2fuhWLqjjuIS878jL0WFs4qWTJfRqQA4MWLF/D394eRkRHmzp0LT09PZGdn48mTJ9iwYQOsra3Rtm1bafuZM2di4MCBSE9Px6lTp/D999/DwMAAgwcPltnvqVOn4O4u+yEnFosV9uHhw4do1aoVRowYgWXLlkFbWxtPnz7Fnj17kJubW/oHjbwgKScnR1BB4fbN67Fz+2ZMmTEH9vaO2LR+NUYNHYAd+45CV1dX4TYP7gVg+qRxGDBkOBo0aoLzZ09j6sSxWLV+K9w9vQAA82dNw4vnTzFt1nyYmpnhn2NHMPL7Adi+5xDMzC0AAEsXL8Dtm9cxbdZ8WFnb4PrVS1g8fzZMzcxRr2Hjz3YOSouutibuPwnB1kNXsXPxwLLujtJ08KuAhX19MWrtFVx9HIn+zSpi/5RmqDF6P95Gp8i1r1PJHGuH18OETddx7NYbWJvoYOlAP6z43h/dCgRmdqa6mNurJi4Ghn+uwykVLT0tMPGriph1OAh3Xsejc01brO7tjba/X0ZYQrrCbX7tVhViXQ1M3f8QwTGpEOtpQFUl/0N01J8BUFfNL8Qw1FHHvh/q4MT9wgNkIWla2Qyjmzpj4T9Pce9tIr6pboXfunii69obiEiUD9gBYG77KjDR1cCcY4/xNi4Nxjqy50RLXRUh8ek4/SgKo5o6f65DKTUNXcUYWs8RS8+9xIOwJHztYYF5bSqj3/YARBbx5bH31jtIycyRPk5Iy5L+W01FhIXtqyA+LQs///0EUckZMNfTRGpWjqJdCU6HOo5Y0McXo9ddxdXHkejXtCL2TW4Gn9EH8DZGwXhS0Rxrf6iLiZtv4NjNvPHk94F1sGKIH7otOgsAiEvOxC/77uFJaAIys3PRytsWfwyti6jEdJy+G/q5D/GTNHYzxfCGFfDr6Rd4EJqItl6WWPhNFfTafBuRSYW/R7pvuIXUD94j8R+8R5pVMsOgeo5YcOIpHoQmwc5YG5NauAIAlp9/qbyDUaJvatlj7nfeGL/pJq49jUKfRi7YPb4h6kw8ipCYVLn2tdzMsGpwbUzZfhvH74TAylgHv/atid/710Kv3y+UwRHQf8EXUyo5dOhQqKmp4ebNm+jcuTMqV64MT09PdOjQAUePHkWbNm1k2uvr68PS0hKOjo4YMGAAvLy8cOLECbn9isViWFpayizq6uoK+3Dy5ElYWVlh4cKF8PDwgLOzM1q2bIl169ZBQ0ND2u7SpUto0KABdHR0YGxsjBYtWiAuLg4AkJGRgREjRsDc3BxaWlqoW7cubty4Id323LlzEIlE+Oeff+Dj4wNNTU1cuHABEokECxcuhJOTE7S1tVG1alXs2bOnNE7tJ5FIJNj951b07jcIDRs3g5OLK376eS4y0tNx8vjRQrfb9edW1KxVB736DYRDBSf06jcQPr61sHvHFgBARno6zp85iWEjxqKatw9s7RzQf/AwWNnYYP+endL9PLh/F62+bgdvH19YWdug3bed4eJaEUGBD5R+7Mpw4lIgfl55BAfP3C3rrijV8K/dsfnMU2w+8xSPQxLw46breBudgoHNKyls7+tqjteRyVj1dxBeRybjyqNIrD/5GN5OpjLtVFRE2DCyAWbvvoNXkYozEELV298Re2+FYO/NELyISsH8Y48RlpCOLrVsFbav6yqGj6Mxvt9yG1efxyI0Ph333yYiIDg/G5WQlo3o5Ezp4uciRnpWLv55UD4Ct26+tjh0NxyH7objVUwqfjv1HBGJ6ehQ3Vph+9pOxqhub4TRu+/jxqt4hCVkIDAsCfc/yDQEhSVh2dkXOBkUhczs8pcp6FjNCn8HRuJYYCSC49Kw8sIrRCZnoI2nZZHbxaVmySy5Hxx6yyrmMNBSw7Sjj/EwLAmRSZl4EJaEF9HyX16F6Iev3bHlg/FkwubrCIlOwYDmFRW2r+lmlj+eRCXjyuNIbDj1GNU/GE8uBIbj8I1gPA5JwMuIJKz8OwgPXsfBr5LF5zqs/1vnGtY4+iACRx9E4HVsGpade4mopAy0r2pV5HbxaVmITc1fPnyPuFvr40FoIk49ikZ4YgZuvI7H6UdRqGih+OJseTC0VSVsO/8CW88/x5PQREzefhuhMano18RVYfuaLmIER6VgzYknCI5KwbUnUdh05hmqV5CvaiAqri8icIuJicGJEycwbNiwQjM6haVuJRIJzp07h6CgoEIDsuKytLREWFgY/v3330LbBAQEoEmTJnB3d8eVK1dw8eJFtGnTBjk5eVe1fvzxR+zduxebN2/G7du34eLighYtWiA2NlZmPz/++CPmzZuHoKAgeHl54aeffsLGjRuxatUqPHz4EKNHj8Z3332H8+fPl+iYPlVoyFvExETDt7a/dJ2Ghgaq1fDB/buFl2I9vBeAmrX9ZNb51vHH/bsBAIDsnBzk5ORAQ1NTpo2mphbuBeTv16uaNy7+exZRkRGQSCS4deMagoNfoVYdf5AwqaupoLqTGKfvypannbkXiloVzRVuc/VxJGzEumhRPS+IMTfUQvs6jjh++41Mu0kdqyI6MR1bzjxVTueVRF1VhCrW+rj8LEZm/eVnMahmb6Rwm0aVzfEwJBH96lXAmQn1cXS0P8a1dIOmWuEfA9/WsMHf98ORVg4yKWoqIlSy1Me1l7Jj4fWXcfC0NVC4TT1XMYLCkvBdbTsc/qE2/hpcEyMaOxV5TsoTNRUR3Mz1cDNYtlT0VnAC3K30i9x2dVcv7O5XA7+0r4JqNrLnz6+CMQLDkjCiQQXs6V8D67pXRXcfG6iUgwooddX344lsFuz0vVDULmQ8ufZuPGle3QbAu/GktiP+ufO20Ndp6GEFV2sDwWfy1VREcLPQw40CJa43XsfDw7ro98j676ph/6Ca+K2jO6rbGco8dy8kEW7meqhsqQcAsDLURO0Kxrj6Mq5U+/+5qKuqoKqjCc7eD5NZf/ZBOHxdTRVuc/1pNKxNdNC0at6FIzMDLbT1tcOJAGFnYJVNJBIJdikPhFM/p0TPnj2DRCJBxYqyV9NMTU2Rnp5XUjRs2DAsWLBA+tyECRPw008/ITMzE1lZWdDS0sKIESPk9u3n5wcVFdkP+YSEBKiqqsq17dSpE/755x80aNAAlpaWqF27Npo0aYJevXrBwCDvg3HhwoXw8fHBypUrpdu9L8VMSUnBqlWrsGnTJrRq1QoAsHbtWpw8eRLr16/H+PHjpdvMnDkTzZo1k27366+/4syZM6hTpw4AwMnJCRcvXsTq1avRoEEDub5mZGQgI0O2tCgjSxWaBQKjTxUbEw0AMC5QTmpiIkZ4WOGDWUxMNExM5Ld5vz9dXV14eFXDpnV/wKGCE0xMxDj1zzEEPrgHW3sH6Tajx0/C/FnT0b5VY6iqqkFFRYSJU2eiavUaJTouUh6xvibUVFUQGS9b/hcRn4amRtoKt7n2JBL9lp7H5tENoaWuCnU1FRy5EYyxG65K29SuaI7ejd1QZ/xBZXZfKYx0NKCmqoKYAvfrxSRnwlRP8d+orbE2vB2MkJGdi5HbA2Cko4GpbSvDUEcdU/c9lGvvaWsAN0t9TNsv/5wQGemoQ01FhNiULJn1MSlZqK2roXAbGyNtVLUzRGZOLibsfQgjHXWMb+4KAy01zD725HN0W6kMtdWgqiJCXKpsuVtcWhZMdBRfiIxJycTiM8/xNDIF6qoiNK1ohl++qYIx+x5K74uzMtRCdVtNnH4cjUmHHsHWSAsjGlSAqkiErTcKD2aEQGzwbjxJSJNZH5mQBvNCx5Mo9F/6LzaPKnw8AQADbXU8Wd0ZmmqqyMmVYPT6K3Jf9IXGUDvv7yauwN9NbGoWTHQU/93EpGRh4clneBKRDHVVFTSvbIbfOrpj5O4HuPsuW33mcTSMtNWxvIsnRADUVFWwPyAM28vZ/aHvvf8cikqU/RyKTEiDuaHizOT1p9EYvOoy1g/zl75vjt16iwlbb36OLtN/1H/jsmIxFYymr1+/joCAALi7u8sFKePHj0dAQADOnz+PRo0aYcqUKfDzk834AMCuXbsQEBAgsygK2gBAVVUVGzduxNu3b7Fw4UJYW1tjzpw5cHd3R1hY3uD+PuOmyPPnz5GVlQV///zskLq6Onx9fREUFCTT1sfHR/rvwMBApKeno1mzZtDT05MuW7ZswfPnzxW+1rx582BoaCiz/L54gcK2Rfnn2BE0resjXbKzswEAogI/5CGRSD56taPg8xKJ7LqpM+dBIpGgfctGaFSnOv7auQ3NWraG6geB9V87tuPhg3tY8NtybNi+Gz+MHo9F82fhxrUrn3xs9HlJIFumJhLlvW8UqWRriEV9a2P+ngDUnXAI7Wb/A0dzPSwdlPc3rKelhvUj6uOHPy4hJknxvU/lQcHDF4mAwor53j83Yfd93H+biAtPorHw2GO0r26tMMP0bQ0bPAlPwv23iicoEKqCx1/UOVF59x6adigIgWFJuPw8Fr+ffo7WXpb/maxbYQo7J2/j03HsYSSeRqUgMDwZS8+/xLVXcej8QbmpCvKCv1/PPsfTqBScfRqD7TdD0MZT+GWB78n97aDwN0olG0P80rdW3ngy8TDazTkBR3M9/D6wjky7pPQs+I0/hPqTjuDnnbcxr5cv6lUpuiRVKOT+biA/5r73Ji4NR+5H4ElkCh6GJeG3My9w5UUcuvrYSNtUszVAz1q2+PX0CwzYfhdTDgXBz8kEvQop5S4v5MdcUaGfQxWtDTCvZw0sOvAAjaYdR8eFZ+Fgpotf+/p+hp7S57By5UpUqFABWlpaqFGjBi5cKPrexe3bt6Nq1arQ0dGBlZUV+vbti5iYmCK3KeiLyLi5uLhAJBLh0aNHMuudnJwAANra8lfZTE1N4eLiAhcXF+zduxcuLi6oXbs2mjZtKtPOzs4OLi5Fz4ZYkI2NDXr27ImePXti9uzZcHNzwx9//IGff/5ZYV/eez84yAcw8kHPhyWh7yc+OXr0KGxsbGTaFZZBmzRpEsaMGSOzLilLcUBalLoNGsHdM3/2qczMvKt6sTHRMDUzk66Pi4uFsYniSV0AQCw2Rcy77Fr+NjEy29ja2WPF2s1IS0tFSnIKTM3MMHXiWFhZ531QZKSnY/WKJZi3aCn86uVlGV1cK+Lp48fYsXUjataS/RAmYYhJykB2Ti4sClwNNzfURmQhk3CM+8YLVx5HYMmhvHsXHwTHISXjCk7Nao2ZO27D3Egbjub6+Gti/t+zyru/oYSdvVFt5D68jBDuPW/xqZnIzsmFqb7s36+JroZcFu696KRMRCZmIDkjW7ruRVQKVFREsDDUQvAHN9drqauglZcllp9SfGFHiOJTs5CdK4FYVzaTZKKjjtgUxRMsRCdnIio5EykZ+aWgr2JSoSISwVxfE2/i0hRuV14kpGUjJ1cC4wKZE2NtdcSlZhWylbzA8GQ0rZhfDhaTmoXs3FyZe5qC49Ig1tWAmooI2bnCvRcwJlHxeGJmqCWXhXtv7DdeuPo4Er8fzss+PwyOQ2r6VZyc9RVm7ryDiPi87SQS4MW7ceP+61hUtDHE2PaeuCDgcsmEtLy/G5MCfzfGOp/4HglLQvPK+Z/p/f3scSIoCkff3R/7IjoVWuqqGN/UGVuvvS30woFQvf8cMjfUkllvZqAll4V7b1Qbd1x/Go1lx/IurAe+iUdKRjb+ntoMc/66i4hCPr/+68pLSeLH7Nq1C6NGjcLKlSvh7++P1atXo1WrVggMDIS9vb1c+4sXL6JXr1747bff0KZNG4SEhGDIkCEYMGAA9u/fX+zX/W9fUnxHLBajWbNmWL58OVJS5GeM+hhjY2MMHz4c48aNK/TKyv/L2NgYVlZW0n55eXnh9OnTCtu6uLhAQ0MDFy9elK7LysrCzZs3Ubly5UJfo0qVKtDU1ERwcLA0GH2/2NnZKdxGU1MTBgYGMsv/Uyapq6sLWzsH6VLByRlisSluXLv8wTFkIuDWTXhWrV7ofty9qsllxW5cvQzPqtXk2mpr68DUzAyJiQm4fuUS6jVsBADIzs5GdnY2RAVKW1VVVZAr4C8aX7qs7FzceRGDxl6yE0w08rLGtceKp+/W1lBDwf/SnPcrRMDjkATUHLMfdcYflC5Hbwbj34dhqDP+oMKZ5YQkK0eCwNAk+LnIXuzwcxEjIDhe4TZ3guNgpq8JHY38CzAOpjrIyZXIfYFo6WEJDVUVHA4QdpnXh7JzJXgUngTfCsYy630rGBeaNbz3NhFmehrQVs8fE+xNtJGTK0FkOc7EvpedK8GTyGTUKHD/UQ17QzwMK/6FCVczXZkS1IdhibAx1JKpm7A10kJ0cqaggzYAyMpRPJ409rLG1ULGEx1NVeQW+Ox/P54U9R1UJAI01T/9gufnlJ0rwZOIZPgUuDfWx8EIDwr5yQhFXM11EfPBBRItdVW570u5uRKIREWfM6HKysnF3VexaOghm0Ft6GGJ60+jFW6jo6kq990iN1fxBXgShoyMDCQmJsosBSvy3vv111/Rv39/DBgwAJUrV8aSJUtgZ2eHVatWKWx/9epVODo6YsSIEahQoQLq1q2LwYMH4+bNTyud/SICNyAvnZmdnQ0fHx/s2rULQUFBePz4MbZt24ZHjx4VWt743rBhw/D48WPs3btXZn1MTAzCw8Nllvf3zRW0evVqfP/99zhx4gSeP3+Ohw8fYsKECXj48KF0VstJkybhxo0bGDp0KO7du4dHjx5h1apViI6Ohq6uLr7//nuMHz8ex48fR2BgIAYOHIjU1FT079+/0L7r6+tj3LhxGD16NDZv3oznz5/jzp07WLFiBTZv3vyJZ7JkRCIROnfviS0b1uL8mVN48ewp5kyfAk0tLTRr2Vrabta0SVi17Dfp487dvsONq5exbdM6vH75Ats2rcONa1fRuVsvaZtrly/i6uULCA15i+tXL2P44L6wd3BE6zbfAAB09fRQvUZNrPh9EW7fvI7QkLc4emg//j56CA0aKS5PFTpdbQ14udnAyy0vk+poI4aXmw3sLI0/smX5suzIQ/Rp4oZejVxR0cYQC3r7ws5UF+tO5GXRf+5eA2t/qCdt//etN2jn64ABzSvC0VwPtSuaY1HfWrjxNArhcWnIyMpB4Jt4mSUhNRNJaVkIfBOPrGzl/DxHadp86RU61LDBNzWs4WSmiwlfVYSVoRZ2Xc+7x2hUcxfM7eghbX/0bjjiU7Mw+1t3OJvpooajMca1dMP+WyHIKHC83/rY4HRQpMwU8OXBjutv0a6qFdp4WcJRrINRTZxhYaCFfXfy7p8d2qACpn+df6/zPw8jkJCWhamtK6GCWAfV7AwxvLETDt8Ll54TNRURXM114WquC3VVEcz0NOFqrgtbYy2FfRCaPQFh+MrdHC0rm8HeWBvf13WAuZ4mDj/IywL1r2OPCc3yq0a+rWoJfydj2BhqwcFEG/3r2KO+ixgH7udnjQ7dj4CBljqG1XeErZEWajkaobuPDQ7eF25m6UPLjzxE7yau6NnIBRVtDDG/d03Ymupi/cnHAIAZ3byxZlhdaftjN9+ira8DBjTLH09++WA8AYCx7T3RyNMKjuZ6cLM2xA+tq6B7fRfsvCD8rPXuW6H42tMCX7mbw8FEGz80qABzfU0cvJv3/zmorgMmt8yfObFTdSvUdTaBrZEWHMXaGFTXAQ3dTLHvgws9l1/Eop2XJRpXNIWVgSZ87A3R398el57HyV1UKy9W/v0IPRs6o0d9J7hZG2BOD2/YiHWw8XTe5FZTO1fFysH5lTvH74Tgax879G3iAgczXdRyNcW8njVw63k0wuPLdzb/v0rRbULz5s2Ta5eZmYlbt26hefPmMuubN2+Oy5cvy7UH8ubEePv2LY4dOwaJRIKIiAjs2bMHrVu3Vti+MF9EqSQAODs7486dO5g7dy4mTZqEt2/fQlNTE1WqVMG4ceMwdOjQIrc3MzNDz549MWPGDHz77bfS9QVLJwFgx44d6NpV/gedfX19cfHiRQwZMgShoaHQ09ODu7s7Dhw4IJ0gxM3NDSdOnMDkyZPh6+sLbW1t1KpVC926dQMAzJ8/H7m5uejZsyeSkpLg4+ODf/75B8bGRX9RnzVrFszNzTFv3jy8ePECRkZG8Pb2xuTJkz967kpbj979kZGRgcXzZyEpKRFVPLywZMVamfLOiPAwmStSnlWr4+e5v2DNymVYu2oZbGztMXP+IulvuAFAcnIy/li+BFGR4TAwMESDJs0weOhIqH0wG+jPc3/BH8uX4OefJiAxMQGWltYYPHQE2nfs8nkOvpR5V3HAiXUjpY8XjusAANh66CoGTd9WVt0qdXsvv4SJniYmdqwKS2MdBL6Jw7dzT+LNu99wszTWhq1p/vtn27ln0NNSx+CWlTGvly8SUjJx/kEYftr+37kp/Pj9CBjpaOD7Rs4w09fE04hkDNlyB2HvJnEx09eE1QdlPamZORi48SYmt6mMXUNrIz41C/88CMfSk89k9usg1kENR2MM2FD+ztWpoCgYaqujn78DTPU08CIqBaN330f4u99wE+tpwMIg/5ykZeVi+I57GNvcFZv6eiMhLQungqKw+t9X0jZm+hrY1j//nuHvatvhu9p2uPU6HkP/FP7PcJx7GgMDLTX09LWV/rjypMNB0t/nEuuqw1wvv5RSXVUFg/0dYaqngYzsXLyOTcWkQ0EyP6wdlZyJCQcD8X09R6ztVhXRKZnYdzccO2+Vj4kn9l55BRN9TUzsUA2WxtoIfBOHDvNOfTCe6MDOVE/afvv5Z9DXVsPglpUwt1fNvPHkYRimbrslbaOrqYbfBtSBjVgHaZk5eBKSgAHL/sXeK68+9+F9sjNPomGgrYbete0g1tXAy5hUTNgfiIh3WWexrjosPijLVlNVwdAGjjB79x55GZ2KH/cHyswYueXqG0gkwAB/e5jpaSA+NRuXX8Ri7aXXn/34Ssv+a8Ew1tPE+PYesDDSRtDbBHRZdA5v35WZWxhpw1asI22/48JL6GmpY2BTN8zq5o2E1ExcCIzAz7sCyugIhEHI2UZFtwkpqjaLjo5GTk4OLCxk7+u1sLBAeLjiC1h+fn7Yvn07unTpgvT0dGRnZ6Nt27ZYtmzZJ/VRJCnt2j/6z4pOzv54oy+MXb1RZd0FwVFx4QydBTm62Xy80RdGr5DZL79kenqKZ/H7kl278LisuyA43rUV/27Yl+rBnfIbDCpL7NbuZd2FQol77SjrLhQqZku3YrULDQ2FjY0NLl++LJ2tHQDmzJmDrVu3ys2pAeRNFNi0aVOMHj0aLVq0QFhYGMaPH4+aNWti/fr1xe7jF5NxIyIiIiIiKglTU1OoqqrKZdciIyPlsnDvzZs3D/7+/tKf7vLy8oKuri7q1auH2bNnw8qq6B+8f++LuceNiIiIiIjKkEjASzFpaGigRo0aOHnypMz6kydPKvzpMABITU2V+93n9/NrfErxIwM3IiIiIiKiYhozZgzWrVuHDRs2ICgoCKNHj0ZwcDCGDBkCIO9+uV698ifQa9OmDfbt24dVq1bhxYsXuHTpEkaMGAFfX19YW1sX9jJyWCpJRERERERUTF26dEFMTAxmzpyJsLAweHh44NixY3BwcAAAhIWFITg4WNq+T58+SEpKwvLlyzF27FgYGRmhcePGWLBgwSe9LicnoWLj5CTyODmJPE5OIo+Tk8jj5CTyODmJPE5OIo+Tk8ji5CTyhDw5iWmfnWXdhUJFb5KfEV5oWCpJREREREQkcAzciIiIiIiIBI73uBERERERkdIJ+Qe4ywNm3IiIiIiIiASOgRsREREREZHAsVSSiIiIiIiUjqWSJcOMGxERERERkcAxcCMiIiIiIhI4lkoSEREREZHysVKyRJhxIyIiIiIiEjgGbkRERERERALHUkkiIiIiIlI6zipZMsy4ERERERERCRwDNyIiIiIiIoFjqSQRERERESkdSyVLhhk3IiIiIiIigWPgRkREREREJHAslSQiIiIiIqVjqWTJMONGREREREQkcAzciIiIiIiIBI6lkkREREREpHQslSwZZtyIiIiIiIgEjoEbERERERGRwLFUkoiIiIiIlI+VkiXCjBsREREREZHAMXAjIiIiIiISOJZKEhERERGR0nFWyZJhxo2IiIiIiEjgGLgREREREREJHEsliYiIiIhI6VgqWTLMuBEREREREQkcAzciIiIiIiKBY6kkFZu2hmpZd0FwVFxqlHUXBCf32a2y7oLgZDhYlnUXBEdFJbOsuyA4OjrqZd0FwWFZlTwrsU5Zd0FQHqrwPVKe8G+6ZJhxIyIiIiIiEjgGbkRERERERALHUkkiIiIiIlI+VkqWCDNuREREREREAsfAjYiIiIiISOBYKklERERERErHWSVLhhk3IiIiIiIigWPgRkREREREJHAslSQiIiIiIqVjqWTJMONGREREREQkcAzciIiIiIiIBI6lkkREREREpHQslSwZZtyIiIiIiIgEjoEbERERERGRwLFUkoiIiIiIlI6lkiXDjBsREREREZHAMXAjIiIiIiISOJZKEhERERGR8rFSskSYcSMiIiIiIhI4Bm5EREREREQCx1JJIiIiIiJSOs4qWTLMuBEREREREQkcAzciIiIiIiKBY6kkEREREREpHUslS4YZNyIiIiIiIoFj4EZERERERCRwLJUkIiIiIiKlY6VkyTDjRkREREREJHAM3IiIiIiIiASOpZJERERERKR0nFWyZJhxIyIiIiIiEjgGbkRERERERALHUkkiIiIiIlI6VkqWDDNuREREREREAsfAjYiIiIiISOBYKklERERERErHWSVLptwEbh/7j+7duzc2bdoEADhy5AgWLVqEW7duIScnB+7u7hg2bBj69Okjbf/q1StUqFBB4b6uXLmC2rVrY9OmTejbt6/c82vXrsWAAQOwadMmjBo1CvHx8cU+jtWrV2PlypV49uwZ1NXVUaFCBXTt2hUTJkwo9j7+ayQSCVavWo59e3YjKTERHp5emDhlGpxdXAvd5vmzp1i1YimCAh8iLDQUY3+chB49e8u0+WPlMqxZtUJmnVhsipPnLirlOErTwOaVMKqdByyNtBH0Nh4/bryOy48iCm3fpa4TRrfzhLOVARJTM3EyIASTt9xAbHKGXNuOfhWweXRDHL7+Gl1/OaPEoygb/t7OGN2rKbyr2MPKzBCdR6/B4XP3yrpbn0UPP3sMaFgB5vqaeBqRjNkHg3DzZZzCtgu6eKJDTVu59U/Dk9BqkfD/RgrTxdcWfeo6wkxPA88jU7Dg78e4/Tq+0PbqqiIMaeSEr6tawVRPExGJ6Vhz/iUO3A6VttHXUsOIpi5oUsUcBlpqCIlPw6K/n+LC0+jPcEQl187LEl28rSHW1cCrmFQs//cl7ocmKWxb1cYASzp6yK3vteUO3sSlAQB+6+COaraGcm2uvozDpENBpdt5JRnQvCJGtnGHpZEOgt7GY+Lm67j8KLLQ9p3rVsCoth5wtswbY0/dDcWUrTelY2xbX3uMbe8JJ0sDqKuK8Dw8CcuOPMTOCy8+1yGVSPOKpmjjbg4jHXW8jU/H5utv8Sgy5aPbVTTTxfSWrngTn4YJhx9L16uKgPaelqjvYgITHXWEJWRg+60Q3C3kfSdE/Rq74IevKsPCUBuPQhMwZfttXH0SpbDt8gG10K2ek9z6RyEJ8J98DACgpirCqK+roGvdCrAy0sGz8ET8vPsuztwPU+px0H9LuQncwsLy39i7du3CtGnT8Phx/iChra0NAFi2bBlGjRqFCRMmYOXKldDQ0MDBgwcxZMgQPHjwAIsWLZLZ76lTp+Du7i6zTiwWS/9tYGAg8zoAYGgo/4FVHOvXr8eYMWOwdOlSNGjQABkZGbh37x4CAwP/r/0VR1ZWFtTV1ZW2/9KwecM6bN+yCTNmz4ODgyPWrfkD3w/qh/2H/4aurp7CbdLT02Fja4dmzVti8cL5he7b2cUVq9ZukD5WVVEt9f6Xtg5+FbCwry9Grb2Cq48j0b9ZReyf0gw1Ru/H22j5D9I6lcyxdng9TNh0HcduvYG1iQ6WDvTDiu/90a1AYGZnqou5vWriYmD45zqcz05XWxP3n4Rg66Gr2Ll4YFl357P5qqolprStjBn7HuLWqzh0rW2P9QN80PKXCwiLT5drP+tgEH45lj+2qamo4PAYf/x9r/y+N1p4WGBCq4qYfeQR7gTHo5OPDVb1rI52y64gPEH+HADA4i5eMNHTwPT9gQiOTYWJrgZUVfIvFKqpirCmtzdiUzIxZuddRCRkwNJQCymZ2Z/rsEqkkasYw+o7YsnZF3gQmoQ2nhZY0K4K+my7g8ikzEK367n5NlIyc6SPE9KypP+eduQx1FTzz5GhlhrW9aiGc+UkkP22jiMW9K6JMeuv4erjSPRt6oa9k5qi5piDeBujYIytaI41w+pi4uYb+PvWW1ib6GDJwNpYPtgP3RefBQDEJmdg0f77eBKagMzsXLT0tsWq7/0RlZiO03dD5fYpJHUcjdC7pg3WX3uLx5HJaOpmiklNnTHmYBBiUrIK3U5bXQVD6zngQVgSDLVlv052qW6Nes7GWH35DUIT0lHVxgDjGjlh6t9P8Co2TdmHVGLtfe0xp4c3xm+5ietPotG7kQt2jW0Av0nHEBKbKtd+0vbbmPnXXeljNRURzs9uhYPXg6XrpnTwQic/R4zacB1PwxLR2NMKW0bURatZp3A/WPEFNqKCys09bpaWltLF0NAQIpFIbt2bN28wduxYjBo1CnPnzkWVKlXg4uKCsWPH4pdffsHixYtx7do1mf2KxWKZ/VhaWsoEOgVfx9LSUhokfqrDhw+jc+fO6N+/P1xcXODu7o5u3bph1qxZMu02bNgAd3d3aGpqwsrKCj/88IP0ueDgYLRr1w56enowMDBA586dERGRn4mZMWMGqlWrhg0bNsDJyQmampqQSCRISEjAoEGDYG5uDgMDAzRu3Bh3795FWZNIJPhz2xb0HzgETZo2h4urG2bOmY/09HT8ffRIodu5e3hi9Ngf0aJVa6hrFB6YqqqqwtTUTLoYm5go4zBK1fCv3bH5zFNsPvMUj0MS8OOm63gbnYKBzSspbO/rao7XkclY9XcQXkcm48qjSKw/+RjeTqYy7VRURNgwsgFm776DV5Hl56rnpzpxKRA/rzyCg2fK/v39OfVrUAF/XX+L3dff4nlkCuYcCkJYfDp61LFX2D45PRvRSZnSxcPWAIba6thz4+1n7nnp6eXngH23Q7DvVgheRqVg4d9PEJ6Yji6+8plFAPB3EaOGozGGbr2Dqy9iERqfjgchibj7JkHa5htvGxjqqGPkn3cREJyAsIR03AmOx5Pw5M91WCXSydsaxx5G4tjDSATHpWHFv68QmZyBtp6WRW4Xl5ols+RK8p9LysiWea6GvRHSs3Jw/mmMko+mdPzQugq2nHkmHWMnbr6BkJgUDGheUWH7mq5meB2Zgj+OP8LrqGRceRyJDaeeoLpT/kXei4EROHwjGI9DEvAyIgmr/g7Cg+A41Klo/rkO6//Wuoo5zjyLwZmnMQhJyMDmGyGISclC84qmRW43qI49Lr2Iw5Mo+WC3nrMJ9t+LQEBIIiKTM3HycTTuhibi6yrCPx8AMLRlRWz/9wW2nX+BJ2GJmPLnbYTGpqJfE8WVQElpWYhMSJcu1SqYwEhHA39+kHHt7OeI3w4H4tS9MLyOSsHGM89w9n44hrVS/Nn+XyUSCXcpD8pN4FYce/bsQVZWFsaNGyf33ODBg6Gnp4cdO3aUQc/yWFpa4urVq3j9+nWhbVatWoVhw4Zh0KBBuH//Pg4dOgQXFxcAeUFO+/btERsbi/Pnz+PkyZN4/vw5unTpIrOPZ8+eYffu3di7dy8CAgIAAK1bt0Z4eDiOHTuGW7duwdvbG02aNEFsbKzSjrc4Qt6+RXR0FGr7+UvXaWhooEaNmrh3906J9x8c/BrNG9fD1y2bYOL4MXj75k2J96lM6moqqO4kxum7ITLrz9wLRa1CvgBcfRwJG7EuWlTP+3JqbqiF9nUccfy27LFO6lgV0Ynp2HLmqXI6T2VGXVUEDxsDXHwim/G4+CQa3o7GxdpH51p2uPw0BqFxijNTQqemKkIVa31cfiYbPFx+FotqdkYKt2lYyQyBoYnoV9cRp8bXw+GRfhjbwhWaavkfjY0qmeHumwRM+boSzk2oj30/1MGA+o5QKQcf8moqIriZ6+FmcLzM+puv4+FhpV/ktmu7V8WeAT5Y/G0VVLM1KLLtV+7mOPskGunZuSXtstKpq+aNsWfuyWbBTt8NRS03M4XbXHsSCRuxDppXswEAmBlqoX0tB/xzp/CLHA08LOFqZYBLQYWXuAuBqooITmId3CtQwng3NBFuZrqFbtfQxQQW+hrYc1dxmZ+6ighZObLvh8wcCSpaFL5PoVBXVUFVRxOcfSBbfXD2QThquhQdzL7Xo74zzgeG421MfnZOQ10V6Vk5Mu3SsnJQy7V4+yQCylGpZHE8efIEhoaGsLKykntOQ0MDTk5OePLkicx6Pz8/qKjIxq8JCQlQVVWV/ltPL79cT09PD+Hh/18p0fTp0/Htt9/C0dERbm5uqFOnDr766it07NhR2ofZs2dj7NixGDlypHS7mjVrAsgr67x37x5evnwJOzs7AMDWrVvh7u6OGzduSNtlZmZi69atMDPL+xA6c+YM7t+/j8jISGhqagIAFi1ahAMHDmDPnj0YNGjQ/3U8pSEmJq9e/MPyVAAwEYsRFlay8hJPz6qYNWc+7B0cERsTg3VrVqFvz27468BhGBkV78vs5ybW14SaqgoiC5S2RcSnoamR4kzvtSeR6Lf0PDaPbggtdVWoq6ngyI1gjN1wVdqmdkVz9G7shjrjDyqz+1RGjHU1oKaqgugk2XsaY5IyYKqv8dHtzfQ1Ub+iKcb8WX6zlMY6eecgJlm2/C8mOQNifbHCbWxNtFHd3ggZ2bkY9eddGOtoYEqbSjDUVse0A3kl7LbG2vCtYIyj98IxdOsd2It1MOXrSlBTUcEf54R9/5KhthpUVUSIS5Utd4tLy4KxruL3RWxKJhadeoYnkSlQVxWheWVzLP7WHaP3PMS90ES59pUs9OBkqotfTj1XyjGUNrHBuzE2QbZcLyohHRaFjrFR6L/sAjaNaiAdY4/eCMa4jbIVPAba6nj8RydoqqkiJ1eCMeuv4qzA718y0FSFqooICWmypb8J6dkw0lZczWKpr4lu3taYcfypTCb2Q3dDE9Ha3RxBESmISMqAh5U+fOwMy8UFD+nncIHy6qiEdFgYan10ewtDLTT1ssKgPy7LrD9zPwxDW1bClceReBmZjAZVLNGquo1MaTbRx/ynArePkUgkcpOc7Nq1C5UrV5ZZ9z5oAwB9fX3cvn1b+rhgkPcprKyscOXKFTx48ADnz5/H5cuX0bt3b6xbtw7Hjx9HdHQ0QkND0aRJE4XbBwUFwc7OThq0AUCVKlVgZGSEoKAgaeDm4OAgDdoA4NatW0hOTpYLjtLS0vD8ueIP24yMDGRkyH4JzBZpSAO//9exI4cxZ+Z06eOlK/7I+4eCHHVJZx7yr1df5rFX1Wpo+1VzHDl4AN/1lp90RkgkkP00FIny3r+KVLI1xKK+tTF/TwBOBYTA0lgbc3rWxNJBfhi66hL0tNSwfkR9/PDHJcQkyU9WQv9hIhEK+V4lo0NNGySmZ+PkA2FnB/4fIpEIhZ0ElXfnZ+JfD5CckffF9ZfjT/BrFy/MOfIIGdm5EInygpmfDwYiVwIEhibBXF8Tfeo6Cj5we0/h2FHIePImPh1vPrhwFBieDDM9DXSuYa0wcPvK3RwvolPwKKJ8lI6+J3f4okJPCSraGOKXPr5YsPcuTt0NhaWxNmb3qIHfB9TBsNX5X86T0rPg/+Nh6GqpoaGnFeb2qomXkUm4GCj8vyu5zxwo/rMRiYAR9R3wV0AYwhIL/zzZdD0Eg/3s8Fv7ypAAiEjKwLlnMWjoovgiihAV/LsRFfEe+VC3ek5ISM3CsVuylTOTt9/Gkr6+uDq/NSQS4FVkMnZceKFwUpP/MhUGqiXynwrc3NzckJCQgNDQUFhbW8s8l5mZiRcvXqBx48Yy6+3s7KSliIqoqKgU+fz/w8PDAx4eHhg2bBguXryIevXq4fz58/Dx8SlyO0WBp6L1urqypQi5ubmwsrLCuXPn5LY1MjJS+Frz5s3Dzz//LLNu0k/TMGXqjCL7+DENGjWCh5eX9HFWZt7V8ZjoaJiZ5ZcCxsbEyAWaJaWtowMXVzcEBxdeqlrWYpIykJ2TK3fl19xQW+7q33vjvvHClccRWHLoAQDgQXAcUjKu4NSs1pi54zbMjbThaK6PvyY2lW6j8u79krCzN6qN3IeXEf/de96+BHEpmcjOyYWpvuyFFbGeBmKKmIDivY41bXHwVgiycooT5glTXGreORDryWaSTHQ15LJw70UlZSAyMUMatAHAi6gUqKiIYGGgheDYVEQnZSI7N1cms/AiKgVm+ppQUxUhW8DnLCEtGzm5EpgUyK4Za6vLZeGKEhiehGaV5MsINdVU0MjNFJuuCrsE/UMxiYrHWDMDLbks3Htj23vi6pNI/H74IQDgYXAcUtOzcWJmK8zcdQcR8XnbSSTAi3dj6f3XcahoY4ix7T0FHbglZuQgJ1cil10z0FKTmZDmPW01VTib6sLRRAf9auVdRBaJ8j5T/uxZDXNOPsPD8GQkZWRj0dmXUFcRQU9LDXGpWejubY1IBTMdC01hn8OmBlqITPx4KXn3ek7YffmlXKloTFIGei69AE11FZjoaSIsLg3TO1dFsIJJx4gK85+6x61Dhw5QU1PD4sWL5Z77448/kJKSgm7dupVBzwpXpUoVAEBKSgr09fXh6OiI06dPF9o2ODgYbz64TyswMBAJCQlyWcMPeXt7Izw8HGpqanBxcZFZTE0V11ZPmjQJCQkJMsu4HyeV4Ejz6Orqwd7eQbo4ObvA1NQMV6/kX7XMysrErVs34FW1eolf70OZmZl4+eI5TE0V38cgBFnZubjzIgaNvWQvPDTyssa1x4qnqtbWUJMrV8l5v0IEPA5JQM0x+1Fn/EHpcvRmMP59GIY64xXPokblS1aOBA9CElHXTfZiR103U9x+VfRsZbWcTeBopovd18vvpCQAkJ0jQWBoEuo4y56DOs4mCHgTr3CbgOB4mOlrQlsjv8rCUayDnFwJIt59QbsTHA87Ex2ZogAHsQ4iEzMEHbQBQHauBE8ik+FjbySzvoa9ER6EFf9ijauZnsLZBRu6iqGhqoKTjxRPkS5EWTl5Y2wjL9lbKhp7WeNaIVO962iqIbfAIPt+jC2qMEQEETTVhD2TcU6uBC9iUuFV4J5HL2t9hZOOpGXlYNzBIEw4/Ei6nHocjZCEdEw4/AjPomVnXMzKlSAuNQuqIqCWgxFuBifI7VNosnJycfdVLBq6y07g09DdEjeeFT1zqn8lczhb6mPb+cKz8RlZuQiLS4Oaqghf+9jh79vle+ylz+s/lXGzt7fHwoULMW7cOGhpaaFnz55QV1fHwYMHMXnyZIwdOxa1atWS2SYmJkbunjUjIyNoaX28jhkAcnJypBOAvKehoSENyD70/fffw9raGo0bN4atrS3CwsIwe/ZsmJmZoU6dOgDyZoUcMmQIzM3N0apVKyQlJeHSpUsYPnw4mjZtCi8vL/To0QNLlixBdnY2hg4digYNGhSZrWvatCnq1KmD9u3bY8GCBahYsSJCQ0Nx7NgxtG/fXuG2mpqacmWRKZml/yVFJBKh+3e9sGHdatg75AVzG9auhpaWFlq1/lraburkCTA3N8fwUWMB5AV3L96VeWZlZSEyMgKPHwVBW0cH9vYOAIDfFi1A/QaNYGlljdjYvHvcUlKS8XW79qV+HKVp2ZGHWDe8Hu48j8m7f61pRdiZ6mLdiUcAgJ+714C1iQ4GLr8AAPj71hssH+yPAc0rviuV1MHCPr648TQK4e9+dymwwBfXhNRMhev/C3S1NeBslx+cO9qI4eVmg7jEVLwJ/+9Oubzh/Ess6lYV998k4s7rOHStbQcrIy38eTVvOupxrdxgYaiF8Ttlf9Ouk68tAl7H42k5mSWxKFsuv8a8Dh54GJo3M2QnHxtYGWpJg9KRzVxgbqCJKXvzMidH74VjcEMnzP7GHSvOPIexjjrGtHDF/tshyHg30cau62/QvbYdJn5VEX9efQN7sQ4GNqiA7eUky/TX7VBMauGKxxHJeBiWhK89LWChr4nD9/OyQAP87GGmp4F5J54BADpUs0J4YgZexaZCXUWEppXM0MBVjGlHHsnt+yt3C1x8HovE9PLx0wjvLT8aiLU/1MWd5zG4/jQKfZq4wdZUF+tP5v08xoxu3rAy0cHgFXm/Z/j3rTdYNsgP/ZtVxOm7eeXo83vLjrFj23vg9vMYvIxIgoaaCppXt0W3+s4Yvf5qof0QiqOBkfihrgOex6TiaVQKmriZwlRXAycf5wUp3bytYKKjgRUXX0MCyJTSAnn3w2Xl5MqsdzHVgYmOOl7FpsFERx0dq1lBJAIOPSj8t/KEZOXxx1g1uDbuvIzFzWfR6NXIGTZiHWx8N7nX1E5VYWWsjaFrZP9/v6vvhJvPovEoRD5AreEkhpWxNu4Hx8HKWAcT2ntARSTC0mPl47cPS0t5mb1RqP5TgRsAjB49Gs7Ozli0aBF+//136Q9wr1q1SuGPaTdt2lRu3Y4dO9C1a9divV5ycjKqV5fNDDk4OODVq1cKX2vDhg1YtWoVYmJiYGpqijp16uD06dPSssDevXsjPT0dv/32G8aNGwdTU1N07NgRQF6Qc+DAAQwfPhz169eHiooKWrZsiWXLlhXZR5FIhGPHjmHKlCno168foqKiYGlpifr168PCwqJYx6lMvfsNQHpGOubPnonExAR4eHph5er1Mr/hFh4WKi3vA4CoyEh06/SN9PHWTRuwddMG1PCpibUbtwIAIiIiMGnCWMTHxcPYxBieXlWxefsuWFvbfL6D+z/svfwSJnqamNixKiyNdRD4Jg7fzj2JN+/KKSyNtWFrml8Ou+3cM+hpqWNwy8qY18sXCSmZOP8gDD9tv1lWh1CmvKs44MS6/Ml9Fo7rAADYeugqBk3fVlbdUrpjd8NhrKuBH5o5w9xAC0/CkzBg/U3pLJFmBpqwNpa9IKWnpYYWnpaYfVB5vyX5Of3zIAJGOuoY0tAJZvqaeBaRjKFb7yDsXZmxmZ4mrD6YXCAtMweDNt3CpNaVsHNILSSkZeGfBxFYduqZtE1EYgYGb76N8a3csHdYbUQmZWDblWBsuPDqcx/e/+Xs0xgYaKujVy1bmOjk/QD3xINBiHh3v6tYVwPmH5TYqquK8H09B5jqaSAjOxevYtIw8WAgrr2Kl9mvrZEWvGwMMG7/w895OKVi35VXMNHXxIQOVWFprI3AN/HoOP90/hhrpA07cf4Yu/38c+hpq2Nwi0qY29Mnb4x9GIZp2/Pvf9fRVMev/WvDRqyDtMwcPA1JwIDlF7DvyqvPfXif7MqreOhrqqFDVUsYa6vjTXw65p9+juh3WVYjbXWIdT/t92DVVVXQpboVzPU1kZ6Vi4CQRKy48AqpBWZVFKoD14NhoqeB8e3cYWGkjaCQBHT99bx0lkgLQy3YmOjIbKOvrY6vfeww+YP3xYc01VUwuYMXHMz0kJKRjVP3QvH9mqtI/ISyZSKRpLAZD4gKUEbGrbwz77GprLsgOLnPbpV1FwTHpknrsu6C4Ghr/+euG5aYqanwp0r/3G5e5s+XFPTVV14fb/QFOXVGPhv8pYvZLKzbgj7kPuVEWXehUA/nNC/rLnwUPzmJiIiIiEjpSjpj+JfuPzU5CRERERER0X8RAzciIiIiIiKBY6kkEREREREpHSslS4YZNyIiIiIiIoFj4EZERERERCRwLJUkIiIiIiKl46ySJcOMGxERERERkcAxcCMiIiIiIhI4lkoSEREREZHSsVSyZJhxIyIiIiIiEjgGbkRERERERALHUkkiIiIiIlI6VkqWDDNuREREREREAsfAjYiIiIiISOBYKklERERERErHWSVLhhk3IiIiIiIigWPgRkREREREJHAslSQiIiIiIqVjpWTJMONGREREREQkcAzciIiIiIiIBI6lkkREREREpHScVbJkmHEjIiIiIiISOAZuREREREREAsdSSSIiIiIiUjpWSpYMM25EREREREQCx8CNiIiIiIhI4FgqSURERERESsdZJUuGGTciIiIiIiKBY+BGREREREQkcCyVJCIiIiIipWOlZMkw40ZERERERCRwDNyIiIiIiIgEjqWSRERERESkdJxVsmSYcSMiIiIiIhI4Bm5EREREREQCx1JJIiIiIiJSOlZKlgwDNyo2VRX+tRXk6GZT1l0QnAwHy7LuguCEnD5a1l0QHFP/ZmXdBSoHDEwMyroLgqOrya9uH+J7hL4kLJUkIiIiIiISOF62ISIiIiIipeOskiXDjBsREREREdEnWLlyJSpUqAAtLS3UqFEDFy5cKLJ9RkYGpkyZAgcHB2hqasLZ2RkbNmz4pNdkxo2IiIiIiKiYdu3ahVGjRmHlypXw9/fH6tWr0apVKwQGBsLe3l7hNp07d0ZERATWr18PFxcXREZGIjs7+5Nel4EbEREREREp3X+lUvLXX39F//79MWDAAADAkiVL8M8//2DVqlWYN2+eXPvjx4/j/PnzePHiBUxMTAAAjo6On/y6LJUkIiIiIqIvWkZGBhITE2WWjIwMuXaZmZm4desWmjdvLrO+efPmuHz5ssJ9Hzp0CD4+Pli4cCFsbGzg5uaGcePGIS0t7ZP6yMCNiIiIiIi+aPPmzYOhoaHMoih7Fh0djZycHFhYWMist7CwQHh4uMJ9v3jxAhcvXsSDBw+wf/9+LFmyBHv27MGwYcM+qY8slSQiIiIiIqUT8qySkyZNwpgxY2TWaWpqFtq+4LFIJJJCjy83NxcikQjbt2+HoaEhgLxyy44dO2LFihXQ1tYuVh8ZuBERERER0RdNU1OzyEDtPVNTU6iqqspl1yIjI+WycO9ZWVnBxsZGGrQBQOXKlSGRSPD27Vu4uroWq48slSQiIiIiIioGDQ0N1KhRAydPnpRZf/LkSfj5+Sncxt/fH6GhoUhOTpaue/LkCVRUVGBra1vs12bgRkRERERESicSCXf5FGPGjMG6deuwYcMGBAUFYfTo0QgODsaQIUMA5JVd9urVS9q+e/fuEIvF6Nu3LwIDA/Hvv/9i/Pjx6NevX7HLJAGWShIRERERERVbly5dEBMTg5kzZyIsLAweHh44duwYHBwcAABhYWEIDg6WttfT08PJkycxfPhw+Pj4QCwWo3Pnzpg9e/YnvS4DNyIiIiIiok8wdOhQDB06VOFzmzZtkltXqVIlufLKT8XAjYiIiIiIlE7Is0qWB7zHjYiIiIiISOAYuBEREREREQkcSyWJiIiIiEjpWCpZMsy4ERERERERCRwDNyIiIiIiIoFjqSQRERERESkdKyVLhhk3IiIiIiIigWPgRkREREREJHAslSQiIiIiIqXjrJIlw4wbERERERGRwDFwIyIiIiIiEjiWShIRERERkdKxUrJkmHEjIiIiIiISOAZuREREREREAsdSSSIiIiIiUjrOKlkyzLgREREREREJHAM3IiIiIiIigWOpJBERERERKR0rJUuGGTciIiIiIiKBY+BGREREREQkcCyVJCIiIiIipVNhrWSJMONGREREREQkcGUWuDVs2BCjRo0qq5cHAJw7dw4ikQjx8fFl2o9P1adPH7Rv376su0FERERERJ/JJ5VKNmzYENWqVcOSJUtk1h84cADffPMNJBJJsfe1b98+qKurf8rLlzo/Pz+EhYXB0NAQALBp0yaMGjWqxIHcq1evUKFCBeljAwMDVK5cGVOmTEGbNm0+eT937txBtWrVpOt///33TzrXQieRSPDHyuXY+9cuJCYmwtOrKib9NA0uLq6FbvPs2VOsXLYUQYEPERoagvETJuG7Xn1k2rRq1hihoSFy23bp2h2Tp04v7cMoVV1r2aFvXUeY6WvgWWQK5h99hNuv4wttr64qwveNndGmqhVM9TURnpCONedfYP+tUADAxv4+8HUykdvu/OMoDN1yR1mHoVQ9/OwxoGEFmOtr4mlEMmYfDMLNl3EK2y7o4okONW3l1j8NT0KrRReV3dUy5+/tjNG9msK7ij2szAzRefQaHD53r6y7pRS96jlicBMXmBtq4UlYEn7eex/Xn8cqbPvrd9XRqba93PrHYYloOucsAKBlVSsMb+EGB1NdqKuK8DIqBWtOP8O+G2+VehylqYuvLfrUdYSZngaeR6Zgwd+PPzqeDGnkhK+rWsFUTxMRielYc/4lDtwOlbbR11LDiKYuaFLFHAZaagiJT8Oiv5/iwtPoz3BEJde7fgUMaeb67n2SiOl/3cf1ZzEK2/7Wyxud6zjIrX8cmojGs04DADrXtsdvvWvItXEafhAZ2bml23klaORigpaVzGCkrYaQhAzsuBOKp1GpH93OxVQHExo7ISQhHTP+eSZd71/BCP1r2cm1H7T7AbJzy8f3l+/8HTCokRPMDTTxJDwZsw48xI0Xij9jfunmhY6+8sf7JDwJLRb8CwBwtdTD6JZu8LQzhK2JDmbuf4iN/75S5iEIEislS6bM7nEzMZH/ElnasrKyigwONTQ0YGlpqbTXP3XqFNzd3REfH4+VK1eiQ4cOuH37Njw8PEq03/eB5n/FxvVrsXXzRsycMx8Ojo5Yu3oVhgzoi4NHj0NXV0/hNulpabC1s0WzFi2xaME8hW2279qD3Jwc6eNnz55i8IC+aNaipVKOo7S09LTAxK8qYtbhINx5HY/ONW2xurc32v5+GWEJ6Qq3+bVbVYh1NTB1/0MEx6RCrKcBVZX80XHUnwFQV81PsBvqqGPfD3Vw4n6E0o9HGb6qaokpbStjxr6HuPUqDl1r22P9AB+0/OUCwuLlz9Gsg0H45dhj6WM1FRUcHuOPv++Ff85ulxldbU3cfxKCrYeuYufigWXdHaVp422N6R08MWXXXdx8EYsedR2xZWgdNJ59BqFxaXLtp++5j3kHA6WPVVVFODGpEY7eyQ9Q4lMzsez4EzyLSEJWTi6aeFhi8XfVEZOcgfNBUZ/luEqihYcFJrSqiNlHHuFOcDw6+dhgVc/qaLfsCsILGU8Wd/GCiZ4Gpu8PRHBsKkx0ZccTNVUR1vT2RmxKJsbsvIuIhAxYGmohJTP7cx1WibStYYMZnbwweWcAbjyPRc96jtg2zA8NZ55S+D6Ztvse5h54KH2spiLCySlNcOS27IXBxLQs1J9xUmZdeQjaatoZolt1K2y9FYpn0alo6GyC0fUd8dPfTxGbmlXodtrqKhhQ2xZBEckw0JL/OpmamYPJx57IrCsvQVvralaY2r4Kpu15gJsv49Ddzx4bB/mi+fzzCFXwGTNzfyAWHPnwM0aEY+Pr4VhAmHSdtroq3sSk4tjdMExtX+WzHAf99yilVHLGjBmoVq0atm7dCkdHRxgaGqJr165ISkqStvmwVHLSpEmoXbu23H68vLwwfXp+ZmTjxo2oXLkytLS0UKlSJaxcuVL63KtXryASibB79240bNgQWlpa2LZtG16/fo02bdrA2NgYurq6cHd3x7FjxwDIlkqeO3cOffv2RUJCAkQiEUQiEWbMmIGZM2fC09NTrm81atTAtGnTijwPYrEYlpaWqFSpEubMmYOsrCycPXtW+vzx48dRt25dGBkZQSwW4+uvv8bz58+lz7/P2lWvXh0ikQgNGzYEIF8q2bBhQ4wYMQI//vgjTExMYGlpiRkzZsj05dGjR6hbty60tLRQpUoVnDp1CiKRCAcOHCjyGJRNIpFg+9YtGDBoCJo2aw5XVzfMnrsA6enpOHb0SKHbeXh6Ycy4CWj1VWtoaGgobGNiYgJTMzPp8u+5s7Czs4dPTV9lHU6p6O3viL23QrD3ZgheRKVg/rHHCEtIR5da8hkjAKjrKoaPozG+33IbV5/HIjQ+HfffJiIgOEHaJiEtG9HJmdLFz0WM9Kxc/POgfAZu/RpUwF/X32L39bd4HpmCOYeCEBafjh515LMnAJCcno3opEzp4mFrAENtdewpR1mTkjhxKRA/rzyCg2fulnVXlGpgYxfsuvIaO68E41lEMn7e+wChcWnoWc9RYfuk9GxEJWVIl6r2RjDUVsfuK8HSNlefxuD4vTA8i0jG6+hUbDj3AkGhiajpJP5MR1UyvfwcsO92CPbdCsHLqBQs/PsJwhPT0cVX8Xji7yJGDUdjDN16B1df5I0nD0IScfdN/njyjbcNDHXUMfLPuwgITkBYQjruBMfjSXjy5zqsEhnYxAU7L7/Cjkuv8Sw8CdP/uo/QuDT0ql9BYfuk9GxEJWZIFy8HYxjqqGPXldcy7SQSiUy7qMSMz3E4JdaikikuvIjDhRdxCEvMwI47YYhNzUIjl6IvsPfyscG11wl4HlN4Zi4xPVtmKS8GNKyA3dfeYNe1N3gemYxZBwLzPmP85TOvQN57JDopQ7p42hnmfcZcz/+MufcmAfMOP8KRO2HILAcBPQmT0u5xe/78OQ4cOIAjR47gyJEjOH/+PObPn6+wbY8ePXDt2jWZoOXhw4e4f/8+evToAQBYu3YtpkyZgjlz5iAoKAhz587F1KlTsXnzZpl9TZgwASNGjEBQUBBatGiBYcOGISMjA//++y/u37+PBQsWQE9PPovj5+eHJUuWwMDAAGFhYQgLC8O4cePQr18/BAYG4saNG9K29+7dw507d9CnT59inYusrCysXbsWAGQygCkpKRgzZgxu3LiB06dPQ0VFBd988w1yc/P+oK9fvw4gL3MXFhaGffv2Ffoamzdvhq6uLq5du4aFCxdi5syZOHky78pfbm4u2rdvDx0dHVy7dg1r1qzBlClTitV3ZQt5+xbR0VGo419Xuk5DQwM1fGri7p3SK+HLyszE0SOH0P7bDhAJOE+vripCFWt9XC5QsnP5WQyq2Rsp3KZRZXM8DElEv3oVcGZCfRwd7Y9xLd2gqVb4n/e3NWzw9/1wpGXlFNpGqNRVRfCwMcDFJ7IlWRefRMPb0bhY++hcyw6Xn8YgNE5xxoHKH3VVETztDPFvgSzYv0GR8KlQvAqPLnUccPFxFEIUZF3e83czhbO5Hq49V1xWJyRqhY4nsahmZ6Rwm4aVzBAYmoh+dR1xanw9HB7ph7EtXGXGk0aVzHD3TQKmfF0J5ybUx74f6mBAfUeoCHdolVJXFcHL3gjnAyNl1p8PioBPMYPxbn4OuPAoEiGxsu8TXU01XJvdAjfntsTmoXXgbiv86hhVFREcjLXxsEDQ/TA8GS6mOoVuV7eCMcz1NHCwiIt/mmoqWNimIha1rYSR9Rxgb6RVav1WJnVVETxsDXHhsexYcuFxFGoU8zOmS207XHoaXeRY8qV6nxwR4lIeKK1UMjc3F5s2bYK+vj4AoGfPnjh9+jTmzJkj19bDwwNeXl74888/MXXqVADA9u3bUbNmTbi5uQEAZs2ahcWLF+Pbb78FkJeNCgwMxOrVq9G7d2/pvkaNGiVtAwDBwcHo0KGDNGvm5OSksL8aGhowNDSESCSSKZ/U09NDixYtsHHjRtSsWRNAXuavQYMGhe7rPT8/P6ioqCAtLQ25ublwdHRE586dpc936NBBpv369ethbm6OwMBAeHh4wMzMDEB+5q4oH2YnXV1dsXz5cpw+fRrNmjXDiRMn8Pz5c5w7d066nzlz5qBZs2aF7i8jIwMZGbJXCyWqmtDU1CyyH58qOjpvYBSLZT8wxWJThIaGKtrk/3LmzCkkJSWhbftvSm2fymCkowE1VRXEJMue+5jkTJjqKT73tsba8HYwQkZ2LkZuD4CRjgamtq0MQx11TN33UK69p60B3Cz1MW2//HPlgbFu3jmKTipwjpIyYKqvOPv6ITN9TdSvaIoxf/63s09fGhM9TaipqiAqSTYYj07KgJnBx78wmhtoolEVcwzfdEvuOX0tNdyY0wIaairIyZXgp133cOGR8MskjaXjSabM+pjkDIj1FQcptibaqG6fN56M+vMujHU0MKVNJRhqq2PagbyyUltjbfhWMMbRe+EYuvUO7MU6mPJ1JaipqOCPcy+Uflwl8f59UnD8iE7KgLnhxz/fzA000cjdAj9suCmz/llEEkZvuY1HIQnQ01bHgEbOODi+PprNPoOXUSmlegylSV9DFaoqIiQUyIYlZmTDUEvxrSbmehroUNUC80+/QGGVj2GJGVh/7S1CEtKhpa6CZm6mmNTUGdOPP0Vkgfej0OR/xsj2M28s+fh7xMxAEw0qmWHUtgAl9ZC+ZErLuDk6OkqDNgCwsrJCZGRkoe179OiB7du3A8grN9ixY4c02xYVFYU3b96gf//+0NPTky6zZ8+WydIBgI+Pj8zjESNGYPbs2fD398f06dNx796n35A/cOBA7NixA+np6cjKysL27dvRr1+/j263a9cu3LlzB4cOHYKLiwvWrVsnc2/f8+fP0b17dzg5OcHAwEBaGhkcHFzYLgvl5eUl8/jD8/348WPY2dnJBH++vkWXC86bNw+GhoYyyy+F3Ev2KY4eOYTaPtWlS3Z23odFwSsdEomkVG9g3b93L/zr1oe5uUXp7VSJCs49IxIBhd0Z8P65Cbvv4/7bRFx4Eo2Fxx6jfXVrhVm3b2vY4El4Eu6/TSz1fpcpkajQc/ShDjVtkJiejZPltEyUilbwPSASiYo1mVOn2vZITMvCP/fC5J5LzshGy3nn8PXC8/jlcBCmfuuB2q7lo1RSEVERA4rKu7+jiX89wIOQRFx4Go1fjj9Buw/GE5EIiE3JxM8HAxEYmoTj9yOw9vxLdC6k/FKI5MZYBesU6VzHAYlpWTh+V/bC4u2Xcdh3/Q0CQxJx/VkMBq+7jhcRyejbyLn0Ov0ZiQBIFLxJRCJgcB07HLwfiYikwgOwFzFpuPo6Hm/i0/E0KhWrLgUjIikDTd3Kz99NwXFDBFGx3iMda9oiMS0bJ+5/GfdQ0+f1SRk3AwMDJCQkyK2Pj4+HgYGBzLqCk4KIRCJpCaAi3bt3x8SJE3H79m2kpaXhzZs36Nq1KwBIt1u7di1q1aols52qqqrMY11dXZnHAwYMQIsWLXD06FGcOHEC8+bNw+LFizF8+PCPHG2+Nm3aQFNTE/v374empiYyMjLksmWK2NnZwdXVFa6urtDT00OHDh0QGBgIc3Nz6X7t7Oywdu1aWFtbIzc3Fx4eHsjM/PSrUUWd77wg6NOioEmTJmHMmDEy6ySqJc+2NWzUGJ6eVaWPM7PyjjU6OhpmZubS9bGxMRCLTUv8egAQGhqCa1cv49ffl5XK/pQpPjUT2Tm5MNWXPdcmuhpyWbj3opMyEZmYgeSM/CumL6JSoKIigoWhFoI/uP9AS10FrbwssfzUc0W7KhfiUhSfI7GeBmKK+CLxXseatjh4KwRZOeXjJnkqntjkDGTn5MJcXza7JtbTkMuuKNKltj32XX+r8H0hkQCvovOyJoEhiXCx1McPzd1w9emV0um8ksS9G0/EerKZ6LzxRPHfSlRSRuHjiYEWgmNTEZ2UiezcXJlsy4uoFJjpa0JNVYRsAf9tvX+fFMyciPU1i3VPWlc/B+y99uaj44dEAgS8jkcFc90i25W1pMwc5ORKYFhgchF9TTWF96RpqamgglgH9sba6FHDGkBeMKciEmFtZw8sPvcSjyLlM4wSAC9j02BRSOWIkLz/jJF/jxRvLOlUyw77bykeSwjloqRayD4p41apUiXcvHlTbv2NGzdQsWLFEnXE1tYW9evXx/bt27F9+3Y0bdoUFhZ52RELCwvY2NjgxYsXcHFxkVk+nHa/MHZ2dhgyZAj27duHsWPHSu83K0hDQwM5OfL3/KipqaF3797YuHEjNm7ciK5du0JHp/Dab0UaNGgADw8PaaloTEwMgoKC8NNPP6FJkyaoXLky4uJkp5l9P+mGoj59ikqVKiE4OBgREfkZhg/v2VNEU1MTBgYGMktplEnq6urB3sFBujg7u8DU1AxXL1+StsnKzMStmzdQtXr1Er8eABzcvw8mJmLUq9+wVPanTFk5EgSGJsHPRfaqpJ+LGAHB8Qq3uRMcBzN9Teho5F/EcDDVQU6uBBEFZo1r6WEJDVUVHA6QzyqUF1k5EjwISUTdAldu67qZ4vYrxVM1v1fL2QSOZrrYff3LmJTkS5KVI8H9NwmoV8lMZn29Sua4+VLxzwG8V9tVjArmethZYLKJwogAaBRxD6lQZL8bT+o4y/6t1HE2QcCbeIXbBATHw0xfE9ofjCeO4nfjSWLeeHInOB52JjoyVREOYh1EJmYIOmgD8t4n94LjUb+yucz6+pXNcfNF0fct1nE1RQVzPey4/KpYr+Vua4jIBGFPUJKTK8HruDRUsZS999/dUg/PouUnHUnPysXUv59gxj9Ppcu5Z7EIS0zHjH+e4kURE5XYG2khPr3wWSqFIitHggdvE1DXTXYsqetmilvF+IypYKaL3VffKLOL9AX7pIzb0KFDsXz5cgwbNgyDBg2CtrY2Tp48ifXr12Pr1q0l7kyPHj0wY8YMZGZm4rfffpN5bsaMGRgxYgQMDAzQqlUrZGRk4ObNm4iLi5PLDH1o1KhRaNWqFdzc3BAXF4czZ86gcuXKCts6OjoiOTkZp0+fRtWqVaGjoyMN0AYMGCDd7tKlSwq3/5ixY8eiU6dO+PHHH2FlZQWxWIw1a9bAysoKwcHBmDhxokx7c3NzaGtr4/jx47C1tYWWltb/9VMAzZo1g7OzM3r37o2FCxciKSlJOjlJWd+MKRKJ0KNnL6xfuxr2Do6wd3DA+jWroaWlha9afy1tN2XSjzA3t8DI0WMB5AV378tks7IyERkZgUdBQdDR0YG9Q/6sT7m5uTi4fx/atGsPNbUy+/WLT7L50ivM7+iJByEJuBucgE41bWFlqIVd74KNUc1dYG6ghcl7HgAAjt4Nx+CGzpj9rTtWnH4OI10NjGvphv23QuSmov7WxwangyKRkCb8D8+ibDj/Eou6VcX9N4m48zoOXWvbwcpIC39ezSszHtfKDRaGWhi/U7Y0upOvLQJex+NpOZn9rrToamvA2S7/S4ijjRhebjaIS0zFm/Civ4iUJ2vPPMOSXjVwLzget17Gooe/I2xMtLHtwisAwIS2lWFpqI3RW2/LbNe1jgNuv4zF47AkuX0Oa+6Ke8HxeB2VAnU1FTR2t0CHWnaYvLN83CO55fJrzOvggYeheTNDdvKxgZWhlvTixchmLjA30MSUvXn3vB69F47BDZ0w+xt3rDjzHMY66hjTwhX7b+ePJ7uuv0H32naY+FVF/Hn1DezFOhjYoAK2l5Mvq2tPP8PvfXxw93Xe++S7uo6wMdbB1gsvAQAT21WBlZE2Rm6Wvd+xm/+790mo/PtkdOtKuP0iFi+jkqGvpY5+jZzhbmeIKeXgffLPo2gMrG2LV7FpeB6TigbOJjDRUce5Z3kXPDp4WcBYWx3rrr2FBEBIgWA0KSMbWTkSmfVt3c3xPCYVkUkZ0FJXRVM3MeyMtbHtVundu65M6869xK89quH+m3jcfhWPbn52sDbWxp+X8z5jxreuCEtDLYwtcK90l9p2uPMqTuEMq+qqIrhY6L/7twosDbVQ2doAqZnZeK0gSCZS5JO+yTo6OuLChQuYMmUKmjdvjvT0dLi5uWHTpk3o1KlTiTvTqVMnDB8+HKqqqjLT3QN5gZOOjg5++eUX/Pjjj9DV1YWnp6f0JwUKk5OTg2HDhuHt27cwMDBAy5Yt5YLC9/z8/DBkyBB06dIFMTExmD59unRafVdXV/j5+SEmJkauXLO4vv76azg6OmLOnDlYuXIldu7ciREjRsDDwwMVK1bE0qVLpVP+A3mZvqVLl2LmzJmYNm0a6tWrh3Pnzn3y66qqquLAgQMYMGAAatasCScnJ/zyyy9o06YNtLTKfpanvv0HIiMjA3Nn/YzExAR4elXFqrUbZH7DLTwsDCqi/CvckVGR6NKxvfTx5o0bsHnjBvjU9MX6TfkXEa5euYywsFC0//bjpa1Ccfx+BIx0NPB9I2eYvftx6SFb7kh/n8xMXxNWhvn/b6mZORi48SYmt6mMXUNrIz41C/88CMfSk89k9usg1kENR2MM2CCfNS9vjt0Nh7GuBn5o5gxzAy08CU/CgPU3pbNEmhlowtpY9r2tp6WGFp6WmP3B73Z9KbyrOODEupHSxwvH5f09bD10FYOmbyurbpW6w7dDYayrgZGtKsLcQBOPw5LQe+VV6cxuFgZasDHRltlGX0sNX1WzwvR3F0IK0tFQxZzOXrAy0kZ6Vg6eRSRj5OZbOHy7fHwB/edBBIx01DGkoRPM9DXxLCIZQ7fekf4mpJme7HiSlpmDQZtuYVLrStg5pBYS0rLwz4MILDuVP55EJGZg8ObbGN/KDXuH1UZkUga2XQnGhncBstAduhUCY10NjG5dEeYGWngcloieKy5LZ4m0MNSCtaL3SXVrTNt9X+E+DbXVsbBHdZgZaCIpPRsP3sSjw+ILCHgt/AsjN94kQE9TFW09zGGolfcD3Ev+fYWYd7/hZqitDhPdwn8TVxEdDVX0rmkDQy01pGXlIjguDQtOv8DL2PIxy+LRgDAY62pgRAtXmBlo4klYMvqtuSEdS8wNNGFtLP8eaellhZmFTPxlbqCFY+PrSR8PauyMQY2dcfVZDLqtuKq8gxGYsk4YlHciSXHu2iZIJBJUqlQJgwcPLjLDV15cunQJdevWxbNnz+DsXLybp8vRT7B8NjWmnyjrLghORkb5+4kBZQs5fbSsuyA4pv6Fz2r7pTIy/rQS/C9BbPSXlR0vjhYNXMq6C4Jy9tqnT+j2X/fyt9Zl3YVCffXH9bLuQqGODRH27/wCSvw5gP+SyMhIbN26FSEhIejbt29Zd+f/sn//fujp6cHV1RXPnj3DyJEj4e/vX+ygjYiIiIiIyg4Dt2KwsLCAqakp1qxZA2Pj4v34otAkJSXhxx9/xJs3b2BqaoqmTZti8eLFZd0tIiIiIvpCsFKyZBi4FcN/oZq0V69e6NWrV1l3g4iIiIiI/g/Cn8+YiIiIiIjoC8eMGxERERERKZ0IrJUsCWbciIiIiIiIBI6BGxERERERkcCxVJKIiIiIiJROhZWSJcKMGxERERERkcAxcCMiIiIiIhI4lkoSEREREZHSifgL3CXCjBsREREREZHAMXAjIiIiIiISOJZKEhERERGR0rFSsmSYcSMiIiIiIhI4Bm5EREREREQCx1JJIiIiIiJSOhXWSpYIM25EREREREQCx8CNiIiIiIhI4FgqSURERERESsdKyZJhxo2IiIiIiEjgGLgREREREREJHAM3IiIiIiIigeM9bkREREREpHQi3uRWIsy4ERERERERCRwDNyIiIiIiIoFjqSQRERERESkdKyVLhhk3IiIiIiIigWPgRkREREREJHAslSQiIiIiIqVTYa1kiTDjRkREREREJHAM3IiIiIiIiASOpZJERERERKR0LJQsGWbciIiIiIiIBI6BGxERERERkcCxVJKIiIiIiJROxFklS4QZNyIiIiIiIoFjxo2oBPT0NMu6C4KjopJZ1l0QHFP/ZmXdBcGJvnSyrLsgOKK6zcu6C4KjrqFe1l0QHP4OlixNTX6VpS8H3+1ERERERKR0KrzuUCIslSQiIiIiIhI4Bm5EREREREQCx1JJIiIiIiJSOs4qWTLMuBEREREREQkcAzciIiIiIiKBY6kkEREREREpHSslS4YZNyIiIiIiIoFj4EZERERERCRwLJUkIiIiIiKl46ySJcOMGxERERERkcAxcCMiIiIiIhI4lkoSEREREZHSqbBSskSYcSMiIiIiIhI4Bm5EREREREQCx1JJIiIiIiJSOs4q+T/27js8iqKPA/j3ckkuvffeQwohhJLQCb0qvShNAUXwVcCKiAoqVYpY6E1QAQWkd+m9JBBISAgQAum959Lu/SNw4XKXAIYjG/1+fO55vLmZzcywd7uz89vZuuGMGxERERERkcBx4EZERERERCRwDJUkIiIiIiK1Y6Bk3XDGjYiIiIiISOA4cCMiIiIiIhI4hkoSEREREZHaaXBVyTrhjBsREREREZHAceBGREREREQkcAyVJCIiIiIitWOkZN1wxo2IiIiIiEjgOHAjIiIiIiISOIZKEhERERGR2okYK1knnHEjIiIiIiISOA7ciIiIiIiIBI6hkkREREREpHaMlKwbzrgREREREREJHAduREREREREAsdQSSIiIiIiUjsNxkrWCWfciIiIiIiIBI4DNyIiIiIiIoFjqCQREREREakdIyXrhgM3AUtNTcWMGTOwf/9+pKSkwNTUFE2aNMFXX32FVq1aAQDOnj2Lb775BufOnUNRURE8PT0xZswYTJ48GWKxWL6tJ59Ub2BgAG9vb3z22WcYMGDAS29XdTKZDMt//hHb/tiC3NxcNA5ogmmffwEPD88ay8TG3sbPPyxFVORNJCYm4KNPpmHEqDEKeXp27YTExASlskOHvYbPZnz5opvxQg0MssOIYAeYG0hwL60Ai4/cQfjDnBrza4lFGNvGGT38rWGur43UPCnWn43H7uvJAABXCz283c4F3jaGsDPRweIjsdh8SblvhGxoSweMaesCSwNt3EktwLz90bh6P7vG/FpiESaEuqFPE1tYGEiQkluMlSfu4a+rifI8hjqaeK+LBzr7WsFIRxMJ2UX4bv9tnLqd/hJaVHej2rng7c4esDLWQUxSHmZui8DFO5kq8y4a0RSDQ5yU0qOTctHl22MAgB5NbPG/7l5wttCHlliEe2kFWHk0FtsvPVRrO162NkHumDKqC4J8nWBraYwhU1Zi9/Hr9V0ttRnZzgVvd3aHlZEObiflYeb2GzXuJwtHBGJwsPJ+EpOUiy6zjwOo3E/e7eapsJ+s+vtOg9pPRrR1xtud3GFlJEFMch5mbY/Epbuq++S715pgULCjUnpMUh66zT0BABjWygkDWtjD29YQABDxIAcL9kTjWny22trwInX0MEN3bwuY6GoiMUeKzWFJuJ1e+NRyHhZ6+CjUFQk5xZh16I7KPC0cjfF2a0eEPczFT2fiX3TV1WZ4iCPGtneBpaEEsSn5mL3nFq7EZdeYX0sswqTO7ujb1A6WhhIk5xRj+bG72H656lg7qo0zhoc4wtZEB1kFJTh4IwWLDtxGSVnFS2gR/Rtw4CZgAwcORGlpKTZs2AA3NzekpKTg6NGjyMysPLjs2LEDQ4YMwRtvvIFjx47BxMQER44cwccff4zz589j69atCgO2devWoUePHsjOzsaCBQswePBgnD59Wj4IrC/r1qzCxg3rMOvbuXB2ccGqFcswYdwb2Ln3APT1DVSWKS4qgoOjA7p274Hv5s1RmefXLX+iorxc/j429jbeHvcGunbvoZZ2vChdfCwxpYs75h+8jesPc9G/qS0WD22MYasuISVXqrLM7H6+MNPXxrf7ovEwqwimetoQa1T92+toiZGQXYyjt9IwuYv7y2rKC9Pd3xqf9PTGN3tuISw+G4Ob22PZyKZ49YdzSM4pVllm4dAAmBlo48sdkYjPLISZvmKfaIpFWDk6CJkFJZi6+RpScqSwMdZBQUnZy2pWnfQNssOXAxtj+pZruHw3E6+3dcEvE1uh0zd/IzGrSCn/l39GYM7OSPl7sViEQ9NCsTesaiCbXViCHw7EIDYlD6XlFejsb4OFI5oiI1+KE1FpL6VdL4O+rgQRMQnYuOs8Ni8cX9/VUau+QXb4coA/Pt96vXI/aeOMDe+EoPO3x1TuJ1/9eQNzd0bJ34vFIhz8tCP2hiXJ07ILSvDDwRjcScmv3E/8rPHd64FIz5Pi5C3h7yd9mtrii/5+mPFHBC7fy8LrrZ2wfkJLdJ1zHIlZyr8nM7ffxLzdt+TvxRoi7P+kPfaFV/VJiIc5dl1NxNV7WZCWluPtzu7Y+E4wus49gZQafqOEooWjEYYF2uDXq0mITStEew9TvN/eGV8ciEVmYWmN5XS1NPBmsAOiUvJhpKP6dNJMTwuDA20Qk1qgruqrRc8AG0zr0wizdkbialw2hgY7YuUbzdBn0Rkk1fDvueT1QJgbaOPzbTcQn1EIM30JNJ845vQJtMUHPTwx/c+bCIvPgouFPuYM9gcAzN0T/VLaRQ0fB24ClZ2djdOnT+P48ePo0KEDAMDZ2RktW7YEABQUFGD8+PF45ZVXsHLlSnm5cePGwdraGq+88gq2bt2KoUOHyj8zMTGBjY0NbGxssHz5cmzevBm7du2q14GbTCbDrxt/wbi3JqBL124AgG9mz0On9q2xb+8eDB4yTGU5/8YB8G8cAABYunihyjxmZmYK79euXglHRyc0b9HyBbbgxRve0gG7riVj17XK2bLFR+4g2NUUA5va4ecT95Tyh7iZoqmTCQYsu4Dc4spBR1KO4gAvKikPUUl5AICJHd3U3IIXb1RrZ2y/moDtVyqvXM7fH4M2nuYY2tIB3x+OVcrfxsMczVxM0XPxaeQWVfZJYrbiwbZ/kD2M9bQwctUllFXIAKDGA7IQje/kgS3n7mPzucor2DO33UAHHyuMbOeCebuilPLnFZchr7hqUNo9wAbGulrYeq7qCvj52xkKZdYev4tBwY5o4Wb+rxq4HToTiUNnIp+e8V9gXKg7tpyLr9pPtt9Eex8rjGzrgnm7n76fdHu8n5x/Yj+JrbafnLiHgcGOaOFu1iAGbuM6umHr+XhsOf8AADBrRyTaN7LEiDYumL/nllJ+pT5pbA1jXS38ceGBPG3yxjCFMp9uvo6egbZo42WO7QKPbujqbYHT97Jw6m4WAGBLWDL8bQzQ0d0M2yNSaiw3srkdLtzPhkwGBNobKn0uEgHjQxyw60YqPC31oKclVrEVYRrT1hnbLj/En4/+7ebsuYW2XuYYHuKIRQdvK+Vv62WBFq6m6Dr/FHKKKge7CdUuAjR1MsHV+9nYcy1J/vnea8lo7GCs5tYIi4ixknXCxUkEysDAAAYGBvjrr78glSrPshw6dAgZGRn48MMPlT7r27cvvLy88Pvvv9e4fS0tLWhqaqK0tOaraS9DwsOHSE9PQ6s2beVp2traaNa8Ba6FhdVS8vmUlpRg755d6DdgoKB/NDQ1RGhkY4gL9xRDdi7ey0JjByOVZdp5miMqKQ8jQhyx+90Q/PF2C7zXyQ0SzX/H11tTLIKvnSHOVjtZPBubiUBHE5VlOjayRGRiLt5s64IjH7XD7vdb44Pungp9EtrIEtce5GB6n0Y4/kl7bH+3Fca1d4GGcHcPOS2xCI0djXGy2mDqZFQqmrua1VBK0dBWzjgdnYYEFbMuj7XxsoC7lQEu3MmoMQ8Jl3w/uZWqkH7qVhqauZo+0zaGhTg9835yMVZ1qKGQaIlF8Hc0xqloxXDoU9Hpz9wnQ0KccDomvdY+0dUWQ0tDA9m1zFgJgVhDBGdTXdxMzldIv5mcD3cLvRrLtXE1gaWBNnbfTK0xT19fK+RJy3H6XtYLq+/LoCUWwc/eCGeqXcg6czsDTZ1NVJbp5GOJGw9zMbaDC05M64ADH7TFx728FI45V+Ky4GdvJB+oOZjpor23BU40gIsdJByccRMoTU1NrF+/HuPHj8fy5csRFBSEDh06YNiwYQgICEBMTAwAwMfHR2X5Ro0ayfNUJ5VKsWDBAuTm5qJz58415qk+YJSJJZBIJHVolbL09MofLHNzc4V0c3MLJCYmqiryj/z99xHk5eXhlX79X9g21cFETwuaGiJkFige7DMKShGir62yjL2JLpo4GqOkvAKfbLsJEz0tfNTNE0Y6mvhmn+p9oCEx1dOGplgDGfklCukZ+VKYG5qrLONgpoumTiaQllVg8m/XYKqnjel9G8FYVwtf/FU50+JgqouWrqbYez0ZEzeGwclcD9P7NIKmhgaWH7+r9nbVhZmBBJpiDaTlKV7RTc+TwtJI56nlrYwkCPW1wv/WX1H6zFBHE5e+7Q5tTQ2UV8jw+ZbrOMUTiwbJTL/yu5Oep/hbnvYc+0lHXyu8t+Gq0meGOpq4+E23qv1k63Wcihb+fmL6qE/ScpX7xMLw6cc3SyMJOvpY4v1far+w+ElfHyTnFONMtLDvlzXQFkOsIZJHazyWW1wO4xrCH60MtDEwwAbzjt7Fo2AFJR4WemjrZopZB5UjIoROfszJq3bMyZPCwstCZRlHMz00czFBSVk53t0YDlN9LXzZzxfGelqY/udNAMC+68kwM9DGrxNaQiQCtMQa+O1cPFapiKQhqsm/45L8v9TAgQORmJiIXbt2oXv37jh+/DiCgoKwfv16eR6ZTPWvpkwmU5pZGj58OAwMDKCnp4dFixbhu+++Q8+ePVWWnzNnDoyNjRVeC2q4l+x57N2zCyHNm8pfZWWVB4vqda2sf53/nNyObdvQpm17WFlZv7iNqlH1f1WRSDntMQ1RZX99sSsKkUl5OHsnE98fvYPeATb/mlk3VUS1dIqGSAQZgE//uIEbCbk4dTsdCw7E4NWmdvI+EYmAzIISzNwZicjEPByISMGqE/cwpKXDy2tEHSnvJ6IafxOeNDjECblFpTh4PUnps3xpGXrMOY4+809gwe4ozBjgjxBP1QNkahiq7xIiALIaf1GqDA52rH0/mXsCfRecxII9tzCjvz9CPBrSfqLY/mc93Axu6YDcojIcikiuMc/bndzxSpAdJqy9DGkDWXRC6bdERRrwKPyxlQN23khBSrWLaY9JNDUwNtgBv1xKQH5Juco8DYHSd6SW31eNR4ejDzdHIOJhDk5Gp2PunlvoH2QvP+a0dDPF26FumLUzEgOXnsO7G8PQ0ccS73RqeLcv1IWGgF8NAWfcBE5HRwddu3ZF165d8cUXX2DcuHH48ssvsWTJEgBAVFQUWrdurVTu1q1b8PX1VUhbvHgxunTpAiMjI1hZWdX6d6dNm4apU6cqpMnEdZ9t6xjaCY0bN5G/Lymt/OFPT0+HpWVVnTIzM2BurvrK1vNKTEzAhfNnsej7H17I9tQpu7AUZRUymOtrKaSb6Wkhs0D1QTI9vwRp+SUokFYdIOMyCqEhEsHKUIIHtYTzNARZhSUoK6+AuYHijKOZvrbSLNxjaXlSpOZKkS+tuop8N60AGhoiWBvpID6zEOl5JSirqFC4Ynw3rQCWhhJoikUoK3/6iW19ycyXoqy8AlaGirMm5gbaSrMrqgwNccL2iw9RqqKNMhkQl165kEBkQi48bAzxbjcvnL997sVUnl6azILK746lkeJvt4WhBOk1LHT0pCEhTth+qeb95P6T+4m1ASZ181S6/01osuR9ovjdsTCUPNN3Z3CIE3ZcVt0nADA+1A2Tunrg9Z/P41Zi3gupszrll5SjvEKmNLtmqCNWmoUDAB1NDbia6cHJRBevBdkBqBzMaYhEWDHYD4tPxKGgpByWBtr4XztnebnHF2JXDPbD5/tuI62G45kQPD7mVJ+BNTeo/ZiTkqN4zLmTWnnMsTHWwf2MQrzX1RO7ribK75uLScmHrpYYswb4Yfmxu0oXWIhUaSgDTHrE19cXBQUF6NatG8zMzLBwofLCHLt27cLt27cxfPhwhXQbGxt4eHg8ddAGABKJBEZGRgqvFxEmqa9vACdnZ/nL3d0DFhaWOH/2jDxPaUkJrly+hCZNm9b57wHAzh3bYWZmjnbtO76Q7alTWYUMt5Lz0LLavRYtXU0R8TBXZZnrD3NhaaANXa2qr7OTmS7KK2RIfYYTEaErK5chMjEPrdwVr+a3cjdD+INslWXC47NhaSiBrnbVzfAu5noor5AhJbcyvDAsPhuOZnoKM7vO5npIzZUKetAGAKXlMkQ8yEG7RpYK6e0aWeHyvdrvMwrxNIerlQE2n7v/TH9LBED7Xzxz+29W437ibYkrT7nvKMTj8X7ybMu3i0QNYz8pLZfhxoMctPVWvDDY1tvi2frEUl++qEl1b3Vyw/+6e2L08guIeFDz41uEpLxChvtZRfC1UVzB2dfaAHdUPA6guLQCXxy4jZmHYuWvE3cykZQrxcxDsbibUYikXKlSnmsJeYhOLcDMQ7HILBL2fX+l5TLcTMhF62ozyK09zBFWwyNort7PhpWRBHpPHnMsK485j1c+1tXSUAotrXgUXdQAbq0mgRD+r+x/VEZGBjp16oRNmzbh+vXruHfvHv744w/Mnz8fr776KvT19bFixQrs3LkTb731Fq5fv464uDisWbMGY8aMwaBBgzBkyJD6bsZTiUQivD5yFNasWoGjRw7j9u0YzJg+DTo6OujVu4883/RpH+P7J1aPLC0pwa2oKNyKikJpaQlSU1NwKyoK8fcVT0YrKiqwc8d29H21HzQ1G8YE8+8XH+LVJrboG2ADF3M9TO7sDmsjHWx/tGz7xA6u+LKPtzz/wZspyCkqxYzejeBqrodAR2P8r5Mbdl9PlofpaGqI4GmlD0+ryucuWRpI4GmlDwfTp9/nIgS/nL2Pgc3s0S/IDq6W+vi4pxdsjXWw9WLlc6Pe7+qBbwf6yfPvvZ6MnKJSfNPfD26W+mjmbIKp3T2x42qCvE+2XHwAEz0tfNrLG87memjnZYHxHVyx+aLqkzKhWfV3LIa1dsbQECd4WBvgywH+sDfTxaZTcQCAT17xweKRQUrlhrVyxtV7mYhOUp4NmNTNE+0aWcLJXA/u1gYY38kdA4Mdsb2B9Mmz0tfVRoCXPQK87AEALvbmCPCyh6PNsy1O0ZCsPnYHw1o5Y0iIIzysDfDFAD/Ymeli0+k4AJX3Yi0eqXyRbFgrJ1y9l4kYVftJVw+0867aT8aFumFgS0fsaCDPcVt9/C6GhjhhcLAj3K0NMKO/L+xMdfHrmcrjx8d9GmHh64FK5YaEOCIsLktln7zdyR0f9PbGx79fw8PMIlgaSmBpqHgiL1SHo9PRztUUbVxNYGsowdBAG5jpaeH4o2f9DWhsjTeDK78rMgCJOVKFV15xOUrLK5CYI0VJuQxlFTKlPIWl5SgurcxTXtONcQKy/vR9DGrhgAHN7eFmqY9P+3jD1kQHmx+tJDq1uyfmDvGX598TnoTswhLMHuQPdyt9NHc1xcc9vbHtctUx59itNAwPcUSvABvYm+qitYc53uvqib8jU2u8V/DfSCQSCfbVEDSMM9n/IAMDAwQHB2Px4sW4c+cOSktL4ejoiPHjx+Ozzz4DAAwaNAjHjh3D7Nmz0b59exQVFcHDwwPTp0/H5MmTG8xO+MbY8ZBKpZj99Uzk5uagcUATLFu1VuEZbslJSdAQVV1nSE1LxdBB/eTvN6xbiw3r1qJ5i5ZYs36jPP38ubNISkpEvwEDX0pbXoQjUWkw1tXCm22cYWGgjbtpBZiyNQLJj0KbzA20Yf1EmE9RaQX+9/t1fNDNE+vfCEJOUSmORKVhxck4eR5LQ21sGttc/n5EiCNGhDjiyv1sTPzt2ktr2z918EYKTPS0MKGjm/xhqBM3hsmX77c0kMDW+Ik+KSnHW+uvYFrvRtg8IRg5RaU4eCMFPxypulE+JVeKtzdcxUc9vbBtUghS86TYdC4eax8NfIRu99VEmOpr4/2e3rAykiA6KQ+jfz4vX+nO2kgH9ma6CmUMdTTRK9AWX/55Q+U29bTF+HZIAGxNdFFcWo7YlHy8v+EKdl99cQsFCUGQrzMOrX5f/n7+h5W/Dxt3ncdbX26qr2qpxe6riTDR18b7PSr3k5ikPIxeVrWfWBlLYGeqvJ/0DLTFV9tU7ye62pr4Zkhjhf1k8i9XG8x+sicsqbJPunvC0riyT95YcbGqT4wksFfVJ01sMXP7TZXbHNnWGRJNMZa/2Vwhfcn+GCw5IOxFoi49yIW+JBl9/axgrFP5AO7vT92XP8PNWFcT5nqqF8f6t9p/PRkmelqY1NkdloYS3E7Ow9vrr8ofK2NpJIGdSdU+UlhSjjfXXMHnrzTCn++2QnZhCQ5EpGDJE48OWPZ3ZTjk+908YW0sQWZBCY5FpSnkIXoakexZ7mQnAqAi3P0/r8OCE/VdBcEpLBTuvQv1JTtLOeTovy79zOH6roLgWLbtVt9VEBwNDQYGVdeltUt9V0FQTocJ+zl59eHW3O71XYUavfeX8rMShWJpv0b1XYWn4i8iERERERGpnYZIuK/n9fPPP8PV1RU6Ojpo1qwZTp069Uzlzpw5A01NTQQGBj733+TAjYiIiIiI6Blt2bIFkydPxvTp0xEWFoZ27dqhZ8+eiI+vfUGnnJwcjBo1qsbnKD8NB25ERERERETPaNGiRRg7dizGjRsHHx8fLFmyBI6Ojli2bFmt5d5++2289tpraNWq1T/6uxy4ERERERGR2tV3OGRtL6lUitzcXIWXVKr8WKWSkhJcuXIF3bop3pfcrVs3nD17tsa2r1u3Dnfu3MGXX375z/vvH5ckIiIiIiL6F5gzZw6MjY0VXnPmzFHKl56ejvLyclhbWyukW1tbIzk5WeW2b9++jU8//RS//vprnR5PxccBEBERERHRf9q0adMwdepUhTSJRFJj/uqP3ZLJZCofxVVeXo7XXnsNM2fOhJeXV53qyIEbERERERGpnZCfMSyRSGodqD1mYWEBsVisNLuWmpqqNAsHAHl5ebh8+TLCwsLw7rvvAgAqKiogk8mgqamJQ4cOoVOnTs9UR4ZKEhERERERPQNtbW00a9YMhw8rPo/08OHDaN26tVJ+IyMjREREIDw8XP6aMGECvL29ER4ejuDg4Gf+25xxIyIiIiIiekZTp07FyJEj0bx5c7Rq1QorV65EfHw8JkyYAKAy7DIhIQG//PILNDQ04O/vr1DeysoKOjo6SulPw4EbERERERGp3T950LUQDR06FBkZGZg1axaSkpLg7++Pffv2wdnZGQCQlJT01Ge6/RMimUwme+FbpX+l4rL6roHwdFhwor6rIDiFhSX1XQXByc4qrO8qCE76mcNPz/QfY9m229Mz/cdoaPCOjuq6tHap7yoIyumwhPquguDcmtu9vqtQo4/2RNd3FWq0oI93fVfhqfiLSEREREREJHAMlSQiIiIiIrUT8KKSDQJn3IiIiIiIiASOAzciIiIiIiKBY6gkERERERGpnQZjJeuEM25EREREREQCx4EbERERERGRwDFUkoiIiIiI1I4zRnXD/iMiIiIiIhI4DtyIiIiIiIgEjqGSRERERESkdlxUsm4440ZERERERCRwHLgREREREREJHEMliYiIiIhI7fgA7rrhjBsREREREZHAceBGREREREQkcAyVJCIiIiIitWOkZN1wxo2IiIiIiEjgOHAjIiIiIiISOIZKEhERERGR2mkwVLJOOONGREREREQkcBy4ERERERERCRxDJYmIiIiISO34AO664YwbERERERGRwHHgRkREREREJHAMlaRnlldUVt9VEBwDA+36roLg6Olp1XcVqAEQte1W31UQnLTTh+q7CoLj1uuV+q6C4Bjp8tTtSWIxQ+8aEkZK1g1n3IiIiIiIiASOAzciIiIiIiKB43w7ERERERGpHR/AXTeccSMiIiIiIhI4DtyIiIiIiIgEjqGSRERERESkdiIwVrIuOONGREREREQkcBy4ERERERERCRxDJYmIiIiISO24qmTdcMaNiIiIiIhI4DhwIyIiIiIiEjiGShIRERERkdoxVLJuOONGREREREQkcBy4ERERERERCRxDJYmIiIiISO1EIsZK1gVn3IiIiIiIiASOAzciIiIiIiKBY6gkERERERGpHVeVrBvOuBEREREREQkcB25EREREREQCx1BJIiIiIiJSOy4qWTeccSMiIiIiIhI4DtyIiIiIiIgEjqGSRERERESkdhqMlawTzrgREREREREJHAduREREREREAsdQSSIiIiIiUjs+gLtuOONGREREREQkcBy4ERERERERCRxDJYmIiIiISO24qGTdcMaNiIiIiIhI4DhwIyIiIiIiEjiGShIRERERkdppgLGSdcEZNyIiIiIiIoHjwI2IiIiIiEjgGCpJRERERERqx1Ul64YzbkRERERERALHGTeBGDNmDLKzs/HXX3+p/LyoqAhz587F5s2bERcXB0NDQ3Ts2BEzZ86En5+fPN9XX32FmTNnAgBEIhFsbGwQGhqKuXPnwtHR8WU05alkMhnWrvwZu3b8gby8XPj6BWDqJ5/Dzd2j1nLHjx7C6uU/IOHhA9g7OGL8xPfRIbSL/POysjKsXfkTDh/Yi4yMdJhbWKJXn1cxeuwEaGhUXqMoLCzA8h8W49SJv5GTkw1bW3sMGvY6+g8aptY2P69XGltjSFM7mOtrIy6zED+fikNEYp7KvE3sjbBogJ9S+phNYXiQVSx/r68txthWTmjrbgZDiSaScoux/PR9XLyfra5mvFCvBthgaNCjPskoxI8n79XaJ0sG+Sulj/olDA+yigAAiwf6IdDBWCnP+XtZmLYr6sVWXk2GtnTAmLYusDTQxp3UAszbH42rtfx7aolFmBDqhj5NbGFhIEFKbjFWnriHv64myvMY6mjivS4e6OxrBSMdTSRkF+G7/bdx6nb6S2hR3Y1s54K3O7vDykgHt5PyMHP7DVy8k6ky78IRgRgc7KSUHpOUiy6zjwMAejSxxbvdPOFsoQ8tsQj30gqw6u872H7poTqb8dK1CXLHlFFdEOTrBFtLYwyZshK7j1+v72qpzbBgR7zR1gWWhtqITS3A3L23nvrdeaeTO/o2sYWFoQTJOcVYeeIudlyp/O6sG9scLd3MlMqdiE7DxF/C1NUMtWrtYoJQdzMY6WgiOa8Ef91Iwb3MoqeWczHTxaTWTkjOk2LhiTj1V1RNuI+QEHHg1gBIpVJ06dIF8fHxWLhwIYKDg5GSkoI5c+YgODgYR44cQUhIiDy/n58fjhw5goqKCty5cweTJk3CkCFDcO7cuXpsRZVfN6zBlt82YPqX38LRyQUb1qzAlEnj8Pu2vdDT11dZ5sb1cHz52YcYN+F/aB/aGSePHcUXn36An9dshJ9/gHy7O7dtxfSZs+Hq5oFbkTcwe9bn0DcwxJDhIwEAPyyah6uXL2LGrLmwtbPHxfNnsGjeN7CwsEK7jp1eWh/UpqOnOSa2c8HS4/dwIykPffytMaevD978NRyp+SU1lhu9MQwFJeXy9zlFpfL/19QQYX4/X2QXlWLm/hik5UthZSBBYWm5qk0JTqinOSa1d8GSY3dxIzEPfRtbY96rvhizKQypeTX3ycgNV2vsky/2RENTXBWzYayjidWvB+J4AxmgdPe3xic9vfHNnlsIi8/G4Ob2WDayKV794RySc4pVllk4NABmBtr4ckck4jMLYaavDbFGVR9oikVYOToImQUlmLr5GlJypLAx1kFBSdnLalad9A2yw5cD/PH51uu4fDcTr7dxxoZ3QtD522NIzFI+4fzqzxuYu7NqkC4Wi3Dw047YG5YkT8suKMEPB2NwJyUfpeUV6Oxnje9eD0R6nhQnb6W9lHa9DPq6EkTEJGDjrvPYvHB8fVdHrXo0tsanvbzx9e4ohN3PxpAWDlgxOgivfH8WSTV8dxYNbwJzfW3M2HET8RmFMDdQ/O5M/i0cWuKqICZjPS1sf7cVDkWkqL096hBoZ4h+/tbYdj0Z9zKL0NrZBG+FOGLesbvILqr590BHUwOvNbXF7fQCGEoa7ikm9xH10WCoZJ003G/Vf8iSJUtw7tw5hIWFoUmTJgAAZ2dnbNu2DcHBwRg7dixu3LgB0aPAYU1NTdjY2AAA7OzsMH78eLz33nvIzc2FkZFRvbUDqJxt++P3jRj1xlvo0KkrAGD6zNl4pVt7HDqwF/0GDlFZbuvvG9E8uBVGvlF5QjHyDTeEXb2Erb/9gpmzvwMA3Iy4hrYdOqF12w4AAFs7exw5uA/RkTfl27lx/Rp69nkVQc1bAgBeHTAEO7f/gVtRNwQzcBsUaIv9kanYF5kKAPj5VByaOxmjb2MbrDkXX2O5rMJShUHKk3o8mj15788bKK+QAUCtAx6hGRxkh303U7HvZmWf/HQyDi2cTfBKYxusPvvP+iRPqnjy0cnLAsWl5ThxO+PFVVyNRrV2xvarCdh+JQEAMH9/DNp4mmNoSwd8fzhWKX8bD3M0czFFz8WnkfvoxCsxW/EEpH+QPYz1tDBy1SWUPdpPajpJEaJxoe7Yci4emx99T2Zuv4n2PlYY2dYF83Yrz6LmFZchr7hqP+gWYANjXS1sPV+1T52PVdwf1p64h4HBjmjhbvavGrgdOhOJQ2ci67saL8XoNi7YdiUB2y5Xfnfm7otGa09zDA12wJJDyt+dtp7maO5iih4LTyGnhu9OTrXBTM8AGxSXVuDgjYZ5Ut7B3QwX4rNxIT4HAPDXzVR4W+mjjYsp9kbVvN8PbmKDqw9zIQPgb2Pwkmr74nEfIaHiPW4NwG+//YauXbvKB22PaWhoYMqUKYiMjMS1a9dUlk1OTsb27dshFoshFotfRnVrlZjwEBkZ6WgZ0kaepq2tjcCg5rhxveZQgRvXw9EyuLVCWnBIG9y4Hi5/3ziwKa5cOo/4+3EAgNsxt3D9WhhC2rST5wkIDMLpk8eQlpoCmUyGq5cv4EF8HFq2agMh0NQQwcvKAJcfHSwfuxKfAz9bw1rLrhgWgK1vNsOCfr4ItFccoLd2NUVkUh7e6+CKP8c2w+rXmuC15vYN4spXVZ9kK6Rfvp8N/6f0yarXmuDPcc2xcIAvAh1qv2jRy88Kx2LSUVxWUdcqq52mWARfO0OcrTaoOBubiUBHE5VlOjayRGRiLt5s64IjH7XD7vdb44PunpBoVh0GQhtZ4tqDHEzv0wjHP2mP7e+2wrj2Lg1iP9ESi9DY0Rgnb6UqpJ+6lYZmrqbPtI1hIU44HZ2GBBWzc4+18bKAu5UBLsaqDr8kYdOq8buTgUAnE5VlQn2scDMhF2+2c8Xfn7TH3ilt8GEPL4XvTnUDmtljf0QyihpIVMOTxCLAwVgHMakFCunRaQVwMdWtsVwLR2OY62nhUEzDiFqoCfcREjLOuDUAMTExCA0NVfmZj4+PPE9gYCAAICIiAgYGBqioqEBRUeUJyHvvvQf9GsIQVZFKpZBKpYppJWJIJJJ/0IIqmRmVP+hm5uYK6abm5khJSlRVRF7OVEWZx9sDgBGjx6EgPx+vD+oDDQ0xKirK8dbE99G1R295nskfTcO8b75E/16dIBZrQkNDhE8+n4Umgc3q1K4XxVhXE2INEbIKFWfDsopKYaanpbJMRkEJFv59B7dTC6AlFqGLtyUW9PfF1O035feA2RrroKmDBEej0zFt1y04mOjgvQ6uEItE2Cjwe3Wq+qRUIT2rqBSm+toqy2QWlOC7I7GIedQn3XyssHCAH6b8eRPXE3OV8jeyNoCbhT4WHLmjlja8aKZ62tAUayCjWuhsRr4U5obmKss4mOmiqZMJpGUVmPzbNZjqaWN630Yw1tXCF39VzrQ4mOqipasp9l5PxsSNYXAy18P0Po2gqaGB5cfvqr1ddWGmX9kn6XmKv1tpeVJYGuk8tbyVkQQdfa3w3oarSp8Z6mji4jfdoK2pgfIKGT7feh2nov89s23/JSby747ifpKRXwILA9XHNwdTXQQ5V3533v81HCZ62pjxig+M9bQwY/tNpfyNHYzgZWOIL3Yof9YQ6GtX/ubmSRUHFHnSchjqqL4AbKGvhT6+lvjx9H08mqxvsLiPqJcGl5WsEw7cGjiZrPIXUvTEF8Hb2xu7du2CVCrFzp078ccff+Dbb799ru3OmTNHvsjJYx9+OgMff/bFc23n0P49WDD7K/n7+UuW4VGFFTPKZE9dI1aE6mUU23300H4c2r8HX34zH67uHrgdfQtLF82FhaUlevbpBwD4Y/OvuBlxHXMX/QgbWztcu3oZC+d9DXMLS7QIbvVcbXvZajoWPswuxsMnQjIik/NhZaiNIU3tEJEYDaByaj2rqBSLjt1BhQy4nVYAc31tDAmyE/zA7bHH+3q1RJV5H2QX40G1PrE00MaQZnYqB269/KxwN70At1LyX1h964NIJKpxR9EQiSAD8OkfN5D/KEx0wYEYLBoagG/33IK0rAIiUeWgd+bOSFTIgMjEPFgZSjCmrYvgB26PVd8lRABkNX57qgwOdkRuUSkOXk9S+ixfWoYec09AXyJGG29LzOjvj/j0QqUwSmo4lPaTmr868s8+2Roh/+7M3xeNxcOb4JtdUZBWm6Uf0MweMcl5iHio/FvTkFT/3oig+idXBGBEkB0O3EpHWkGpcoYGivsICREHbg2Al5cXIiNV33tw69YtAICnp6c8TVtbGx4elSs0+vn54fbt23jnnXewcePGZ/6b06ZNw9SpUxXSckueP9SybftQ+Po3lr8vKan8Uc9MT4eFhaU8PSszE2ZmqmcKAMDM3EJhdq2yTAZMnyjz89KFeH30WHTp3gsA4O7hheSkRGxctxo9+/SDtLgYK39agtnfLZXfB+fh6Y3bMdH4fdM6QQzccorKUF4hg6me4kySqa6W0oxTbSKT89HF20L+PqOwFGUVFQpXQuOzimCurw1NDZH8fiYhetwnZvp17ZM8dG1kqZQu0dRAqJcF1p9/UOe6vixZhSUoK6+AuYFin5jpayvNwj2WlidFaq5UflIBAHfTCqChIYK1kQ7iMwuRnleitJ/cTSuApaEEmmIRysqFu59kFlT2iaWR4hVxC0MJ0nOlNZSqMiTECdsvPUSpijbKZMD99MqwsciEXHhYG2BSN08O3Bqg7EffHQtDxf2k8rujej9Jzyup+btjrIP4jEJ5uo6WBnoG2ODHBjJ7r0pBSeVvrlG1xUUMJGLkS5XD+iSaGnAy1YW9sQ4GNLYGUDmQ0RCJsKCPN1acf4DY9EKlckLFfYSEjPe4NQDDhg3DkSNHlO5jq6iowOLFi+Hr66t0/9uTZsyYgd9//x1XryqHANVEIpHAyMhI4fVPwiT19PXh4Ogsf7m6ucPc3AKXLpyV5yktLUH41cvwD2ha43b8AwJx6YLiqpgXL5yFf0Cg/H1xcZF82f/HxGIxKmSVV7rKyspQVlYGkUgxj4aGBmQCGbiUVcgQk5qPZo6Ky9Q3czLGzSTVS9+r4mmpj8wnrnzeTMqFvbGOwpylg4kO0vNLBD1oA6r6pHm1ewuaOZngxnP1iQEyVFwN7uhpDm2xBg43oIUmyspliEzMQyt3xYsdrdzNEP4gW2WZ8PhsWBpKoKtddQHGxVwP5RUypORWzk6GxWfD0UxPYfLb2VwPqblSQQ/aAKC0XIaIBzloV21w3s7bElfuZdVaNsTDHK5WBvJFTZ5GJAK0a7l3hYSr9NF3p7WH4nentYc5wqvdR/tYWHwWLA0l0Hviu+Ns8ei7U23xnh7+NtAWa2B3uPLMbUNRLgMe5hTDy1Lx9govS33Eqbj/U1pWgfnH7mLhiXvy17m4bKTkSbHwxD3E13LPqBBxH1EvkUi4r4aARx4BycnJQXh4uMIrPj4eU6ZMQcuWLdG3b1/88ccfiI+Px6VLlzBw4EBERUVhzZo1CiGD1bm5ueHVV1/FF188X5ijOohEIgwePhIb163CiWNHcDf2Nr79ajokOjro9sS9aF9/MQ3Lf1wsfz942AhcunAWm9avxv24u9i0fjUuXziPIa+Nkudp064jflm7EmdPn0BSYgJOHDuCLb9uQPuOnQEA+gYGCAxqgZ+//w5XL19EYsJD7Nu9Awf27UL70M4vrxOe4s/wJPTys0IPH0s4merinbbOsDKQYPeNZADA2FZO+KRr1TPvBjSxQRs3U9gb68DZTBdjWzmhvYc5/opIlufZFZECIx0tTGrvAgcTHQS7mOC15vbY+UQeIfvjaiJ6+Vmhp68VnEx1MbG9C6wNJdj9aBnlca2dMK1bVZ8MDLRFGzcz2JvowMVMF+NaO6GDpzn+uqZ8oOzlZ43TdzKRW9wwlrx/7Jez9zGwmT36BdnB1VIfH/f0gq2xDrZerAx9fb+rB74dWPV8v73Xk5FTVIpv+vvBzVIfzZxNMLW7J3ZcTZCH8Wy5+AAmelr4tJc3nM310M7LAuM7uGLzxYYxG7n62B0Ma+WMISGO8LA2wBcD/GBnpotNp+MAAJ/09cHikcoXiIa1csLVe5mIUXEhYFJXD7TztoSTuR7crQ0wLtQNA1s6YkcDCTF+Vvq62gjwskeAlz0AwMXeHAFe9nC0ebaFXRqSDWfiMLCZPfo3s4ObpT4+6eUNW2MdbHn03ZnczQOzn3gO5N5rycguLMU3A/zgbqmPZi6m+LCHF3ZcSVAOgWtuj6NRqQqPHmmITtzJRLCzCVo6GsPKQBuv+lnBVFcLZ+MqL4L09rHE8Ka2ACpDBJPzShRe+SXlKKuQITmvBCUCv+ijCvcREiqGSgrI8ePH0bSp4knF6NGjsX79evz999+YM2cOPvvsM9y/fx+GhoYIDQ3F+fPn4e+v/KDh6j744AO0adMGFy5cQHBwsLqa8ExeHz0WUqkUi+Z+XfkAbv8ALP5xlcIz3FKSk6DxxFJ2jZs0xVffLsCqZT9g9fIfYO/ghFlzvpM/ww0Apnw0HauWL8XCuV8jKysTFhZWeGXAYLwx/h15npmzF2DFT0swa8YnyM3NgY2NHd565z30Gzj05TT+GRy/nQEjHU2MbOkAs0cPm562O0q+fL+5vhasngiR0xJr4O02LrAw0Ia0rAL3MwsxbVeUwoO10/JL8MnOSLzTzgWrhjdBekEJtl9LxuZHS8kL3bHbGTDS1cKoYAeY6VX2yac7o5DyaCEKc31tWD0R1qIlFuGdds7yPonLKMKnOyNxIS5bYbsOJjoIsDfChw3wBvGDN1JgoqeFCR3dYGkoQWxKPiZuDJMv329pIIGtcdWiHEUl5Xhr/RVM690ImycEI6eoFAdvpOCHI1VLW6fkSvH2hqv4qKcXtk0KQWqeFJvOxWPtqbiX3bx/ZPfVRJjoa+P9Ht6wMpIgJikPo5edl68SaWUsgV21VfEMdTTRM9AWX227oXKbutqa+GZIY9ia6KK4tByxKfmY/MtV7L5a82JKDVGQrzMOrX5f/n7+hwMBABt3ncdbX26qr2qpxYGIFJjoaeOdUHdYGkpwOyUfE34JQ9Kj+2ItDRW/O4Ul5Ri/7jI+6+uDLRNDkF1YioM3krG02mM3nM310MzFFOPWXn6p7VGH8MQ86GmnoJu3BYwkYiTllWDV+QfIerSkvaFEE6a6qhfM+jfgPkJCJZKpvOOfSFlaXsOakXgZhq2/VN9VEJwKgYde1of09IKnZ/qPycluWOFTL0Pa6UP1XQXBcev1Sn1XQXC6BzvVdxUE5eCFZwtx/i+5+W23+q5CjdZcFO6/19iWwv9uMVSSiIiIiIhI4DhwIyIiIiIiEjje40ZERERERGrXUFZvFCrOuBEREREREQkcB25EREREREQCx1BJIiIiIiJSO84Y1Q37j4iIiIiISOA4cCMiIiIiIhI4hkoSEREREZHaibisZJ1wxo2IiIiIiEjgOHAjIiIiIiISOIZKEhERERGR2jFQsm4440ZERERERCRwHLgREREREREJHEMliYiIiIhI7TS4qmSdcMaNiIiIiIhI4DhwIyIiIiIiEjiGShIRERERkdoxULJuOONGREREREQkcBy4ERERERERCRxDJYmIiIiISO24qGTdcMaNiIiIiIhI4DhwIyIiIiIiEjiGShIRERERkdqJGCtZJ5xxIyIiIiIiEjgO3IiIiIiIiASOoZJERERERKR2nDGqG/YfERERERGRwHHgRkREREREJHAMlSQiIiIiIrXjqpJ1wxk3IiIiIiIigePAjYiIiIiISOAYKklERERERGrHQMm64YwbERERERGRwHHgRkREREREJHAMlSQiIiIiIrXjqpJ1wxk3IiIiIiIigeOMGz2z4tLy+q6C4Fw4FV3fVRAcXk1TZmRmVN9VEBwtba36roLguPV6pb6rIDh39+2q7yoITqzz6PqugqDcvR5b31UQoG71XQFSEw7ciIiIiIhI7RjqVzfsPyIiIiIiIoHjwI2IiIiIiEjgGCpJRERERERqx/vg64YzbkRERERERALHgRsREREREZHAMVSSiIiIiIjUjoGSdcMZNyIiIiIiIoHjwI2IiIiIiEjgOHAjIiIiIiK1E4mE+3peP//8M1xdXaGjo4NmzZrh1KlTNebdvn07unbtCktLSxgZGaFVq1Y4ePDgc/9NDtyIiIiIiIie0ZYtWzB58mRMnz4dYWFhaNeuHXr27In4+HiV+U+ePImuXbti3759uHLlCkJDQ9G3b1+EhYU919/l4iRERERERPSfJpVKIZVKFdIkEgkkEolS3kWLFmHs2LEYN24cAGDJkiU4ePAgli1bhjlz5ijlX7JkicL72bNnY+fOndi9ezeaNm36zHXkjBsREREREamdBkSCfc2ZMwfGxsYKL1WDsJKSEly5cgXdunVTSO/WrRvOnj37TP1QUVGBvLw8mJmZPVf/ccaNiIiIiIj+06ZNm4apU6cqpKmabUtPT0d5eTmsra0V0q2trZGcnPxMf2vhwoUoKCjAkCFDnquOHLgREREREdF/Wk1hkTURVVvRRCaTKaWp8vvvv+Orr77Czp07YWVl9Vx15MCNiIiIiIjU7p+s3ig0FhYWEIvFSrNrqampSrNw1W3ZsgVjx47FH3/8gS5dujz33+Y9bkRERERERM9AW1sbzZo1w+HDhxXSDx8+jNatW9dY7vfff8eYMWPw22+/oXfv3v/ob3PGjYiIiIiI6BlNnToVI0eORPPmzdGqVSusXLkS8fHxmDBhAoDK++USEhLwyy+/AKgctI0aNQrff/89QkJC5LN1urq6MDY2fua/y4EbERERERGpnQj/glhJAEOHDkVGRgZmzZqFpKQk+Pv7Y9++fXB2dgYAJCUlKTzTbcWKFSgrK8OkSZMwadIkefro0aOxfv36Z/67HLgRERERERE9h4kTJ2LixIkqP6s+GDt+/PgL+Zu8x42IiIiIiEjgOONGRERERERq929YVbI+ccaNiIiIiIhI4DhwIyIiIiIiEjiGShIRERERkdpp/EtWlawvnHEjIiIiIiISOA7ciIiIiIiIBI6hkkREREREpHZcVbJuOONGREREREQkcBy4ERERERERCRxDJYmIiIiISO0YKlk3nHEjIiIiIiISOA7ciIiIiIiIBI6hkkREREREpHYiPoC7TjjjRkREREREJHAcuBEREREREQkcQyWJiIiIiEjtNBgpWSccuNWj5ORkfPvtt9i7dy8SEhJgZWWFwMBATJ48GZ07dwYAhIWFYe7cuTh58iQyMzNhY2ODxo0b4+2330afPn0gEokQFxcHV1dX+XZNTEzQuHFjfP311+jQoYM8/eHDh3Bzc4Obmxtu3br10tv7mEwmwy9rlmHfzm3Iy81FI7/GeO/Dz+Di5lFruZPHDmP9yp+QlPAAtvaOePPt/6Ftx84q8/62YTXWLl+KAUNex8Qpnyh8dj/uLlb/tBjXwq5AJquAs6s7ZnzzHaxtbF9YG+tqfDdvvP+KP2xM9BD1MAufrL+Is7dSa8w/pK0bprziD3dbI+QWluBweAKmb7yMzHwpAOCVlk74sH8A3GyMoCUW4U5yHpbuvoHNp+6+rCbV2bhu3ni/r9+jPsnGpxue1ieumPyKP9xtKvvkyLVEpT75oF9jhT75Yc/NBtUno9u7YkJXT1gZ6yAmKRdf/hGBi7EZKvMuHhWEIa2cldKjE3PR6eujAIAhIU5YPLqZUh63/+2EtKzixVZeTUa0dcbbndxhZSRBTHIeZm2PxKW7mSrzfvdaEwwKdlRKj0nKQ7e5JwAAw1o5YUALe3jbGgIAIh7kYMGeaFyLz1ZbG160YcGOeKOtCywNtRGbWoC5e2/h6v3sGvNriUV4p5M7+jaxhYWhBMk5xVh54i52XEkEAKwb2xwt3cyUyp2ITsPEX8LU1YyXrk2QO6aM6oIgXyfYWhpjyJSV2H38en1XSy16+lpiQIAtTPW0EJ9VhNXn4hGZnK8yr7+tIWb3baSU/s6WCCTkFAMAujWyQKinBZzNdAEAsWkF2HgpAbfTCtTXiBfsrV5+mDKgKWxM9RAZn4mPV53BmcikGvMP6+CJKQObwsPOGDkFJTh8NR7T1p5FZl7lMefg7FfRvrG9Urn9l+5jwKy9amsH/btw4FZP4uLi0KZNG5iYmGD+/PkICAhAaWkpDh48iEmTJuHWrVvYuXMnhgwZgi5dumDDhg1wd3dHRkYGrl+/js8//xzt2rWDiYmJfJtHjhyBn58fUlNT8dlnn6FXr164ceOGfFC3fv16DBkyBCdPnsSZM2fQpk2bemn7lk3rsO33jfhoxtdwcHTGr+tX4ZP338a6zbugp6+vskxkxDV8M+NjjBk/CW07dMbpE0fx9ecfYcmK9fDxC1DIeyvyBvbt/BNuHl5K20l8+ACT3x6Nnn37Y9S4idA3MER83F1oa2urpa3/xMBWLpg3piWmrD6P89GpeLOLN7Z/1hXNp/yFhxnKB71W3lZY9W5bfLrhEvZdfgA7Mz18P74VfprQGsO/OwYAyMovwYLt1xGTmIOSsgr0DHLA8oltkZZbjKPXEl92E5/bgFYumDe6BaauuYDz0al4o4sXtk3rghZTd9bYJysnVfbJ/isPYWemhyXjQ/Dj263x2sLKPsnMl+K7HRHyPukR5IBl77RpMH3ySjN7fDU4AJ9tDselO5kY2c4Fmya1RsdZR5CYVaSU/4ut1zH7r5vy95oaIhye3hl7riYo5MstKkX7rw4rpDWUQVufprb4or8fZvwRgcv3svB6ayesn9ASXeccR2JWsVL+mdtvYt7uqotYYg0R9n/SHvvCq07OQjzMsetqIq7ey4K0tBxvd3bHxneC0XXuCaTkKG9TaHo0tsanvbzx9e4ohN3PxpAWDlgxOgivfH8WSTXUf9HwJjDX18aMHTcRn1EIcwNtiJ+4TD75t3BoiavutDDW08L2d1vhUESK2tvzMunrShARk4CNu85j88Lx9V0dtWnrZoZxrZyw/PR9RKXko4ePJb7s6YVJW28gvaCkxnITtlxHYUm5/H1ucZn8//1tjXDyTgZunc1HSZkMAwNtMLOXF9794wYyC0vV2p4XYVBbDywY1xbvLz+Jc5HJGNfDF3991QdBk37HgzTlAW1rXxusntIZH685g70X42Bvro+lEztg2f9CMXT2AQDAsNkHoK1Z9b0xM9LBxaVDsf1M7EtrFzV8vMetnkycOBEikQgXL17EoEGD4OXlBT8/P0ydOhXnz59HQUEBxo4di969e2Pv3r3o1q0b3N3d0bJlS4wbNw7Xrl2DsbGxwjbNzc1hY2ODgIAArFixAoWFhTh06BCAylmudevWYeTIkXjttdewZs2a+mg2ZDIZtm/ZhNfGjEe7jl3g6u6Jj2d8g+LiYvx9aF+N5bZt2YRmLULw2uhxcHJxxWujx6Fp85bYvmWTQr6iwkLM+Woapnz6FQwMjZS2s3bFDwhu3Q5vvTsVnt4+sLN3QEib9jA1M3/hbf2n3u3jh1/+vo0Nf99GdEIOPtlwEQnpBRjXzVtl/hZelrifmo9l+6NwPy0f56JTsfZINJq6WcjznIpMxu5L8YhOyMG9lDz8vD8KN+5noXUj65fVrDp5t7cvfvk7Vt4nn264hISMWvrE0xL3Uwuw/MCtJ/okBk3dqv6dT0emKPTJsv1RuBGfhVbeVi+rWXUyvrMHNp+Nw+9n7iM2OQ9f/hGBxKwijGrvqjJ/XnEZ0nKl8leAsymM9bSw5dx9hXwymUwhX1qu9GU054UY19ENW8/HY8v5B7iTko9ZOyKRlFWEEW1cVObPKy5DWp5U/gpwMoaxrhb+uPBAnmfyxjBsOn0fkQm5uJNagE83X4dIA2jjJZzfjNqMbuOCbVcSsO1yAu6mFWDuvmgk5RRjaLCDyvxtPc3R3MUU7/xyFefvZCIxuxgRD3MRHp8jz5NTVIb0/BL5q7WHOYpLK3Dwxr9r4HboTCRm/rwHO/++Vt9VUatXA6xxJDodh6PT8TC7GKvPPUB6fgl6+db+W5hTVIbsJ14VsqrPFh27i/2RabiXUYSEnGL8eDIOGiIRmtgrH5eF6L1+TbD+cBTWH4pC9MMsfLT6DB6m52N8T3+V+Vt62+B+ah5+3h2B+yl5OBuZjDUHIhHkUdWHWflSpGQXyV+dAx1RKC3D9tN3XlazBEEk4P8aAg7c6kFmZiYOHDiASZMmQV/FDJOJiQkOHTqEjIwMfPzxxzVuR1TL4+f19PQAAKWllVe2jh07hsLCQnTp0gUjR47E1q1bkZeXV8eWPL+kxARkZqSjWctW8jRtbW0ENG2GmxHhNZaLvHENzVq2VkhrHtwGNyMUD6hLv/sWwa3boVnLEKVtVFRU4MLZk3BwdMYnkydgUK8OeHfsazhz4u+6NeoF0hJroKmbudKMz9HriQipYUBxIToV9ub66Na0MgTDylgH/UJccDDsYY1/p6O/LTztjHA6MvnFVV5NHvfJ39er9cm1RAR7WaoscyEmFfbmeugWWNknlsY66BfsXGufdPC3gaetEc5ECf/kU0ssQoCTCU5EKoaKnohKQXO3ZxtQDG/tjFO3UpGQqTg7py/RxIVvuuPy7B7YMLEV/ByMa9iCsGiJRfB3NMap6HSF9FPR6WjmavpM2xgS4oTTMelIUDFj+ZiuthhaGhrIbgCzBlpiEXztDHG2Wvjs2dgMBDqZqCwT6mOFmwm5eLOdK/7+pD32TmmDD3t4QaJZ8+nCgGb22B+RjKLS8hrzkDBpaojgYaGPsIc5CulhD3PRyFp1BMxjSwb4Yf2IJvi6tzcaPwolrolEUwNiDRHypGW15hMCLU0NNPWwxNGwBwrpR8MeIMRH9cXO81HJsLcwQPdmTgAAKxNd9G/jhv2X76vMDwCju/rgj5O3UdgA+oSEg6GS9SA2NhYymQyNGinHiD8WExMDAPD2rppRuHTpEkJDQ+XvN2/ejD59+iiVLSgowLRp0yAWi+X3uK1ZswbDhg2DWCyGn58fPDw8sGXLFowbN07l35dKpZBKpdXSAIlE8uwNVSEro/KkqvoMl6mZOVKSa44dz8pIh6mZ4j0VpmZm8u0BwLHD+3E7OhI/r92schvZWZkoKizE5o1rMOat/2H8xMm4dP4Mvpo2Bd/9uAZNgpr/02a9MOZGEmiKNZCao3jimJpTBCsTXZVlLsSkYezSk9gwuSN0tMTQ0tTAnkvx+GDteYV8RrpaiFkxBBJNMcorZJiy5hyORdTc50JRU5+k5RTDurY++eEU1k/uIO+TvZfi8eG6Cwr5jHS1EL18sLxPpq453yD6xMygsk/S8xS/o+l5UlgZP/07amUkQaifNd5de1khPTYlD1N+uYpbCTkw0NXCuFB37PyoPbp+8zfuCfzeFFN9bWiKNZRmCNPypLAwfHqfWBpJ0NHHEu8/5R6tT/r6IDmnGGeqDRCFyESvsk8y8hX7JCO/BBYGqvvEwVQXQc4mkJZV4P1fw2Gip40Zr/jAWE8LM7bfVMrf2MEIXjaG+GKH8mckfEY6mhBriJBdpDh4yCkqhYme6tmxrMJS/HjyHmLTCqElFiHU0wJf9/HG9N23cLOG++JGtXRAZkEJriXkvvA2vGgWRjqVx5xsxWNOSnYhrE2U74kFgPO3kvHGd4ex8eNu0NEWQ0tTjN3n72HqilMq8zf3tIK/izneWXrshdef/t0441YPZLLKeILaZsxUCQgIQHh4OMLDw1FQUICyMsUf2tatW8PAwACGhobYvXs31q9fj8aNGyM7Oxvbt2/HiBEj5HlHjBiBtWvX1vi35syZA2NjY4XXT0vmP1d9AeDowb3o0ylY/npc5+ptl8lkT52kVtlfj9JSU5Lx0+J5mPbVXGjXMLisqKi8T6dVu1AMGj4SHl6NMHzUWIS0aY89f219voapmUym+F4EESBTnbeRvTEWvBGMuX+Go+2nu/Hqt4fgYmWA78e3UsiXV1yK1h/tQvtpezBz81XMGdUS7Xxt1NSCF696n0CkIu0Rb3tjLBjTEvO2XUO7aXvQb/ZhOFsZ4Ptxyn3S5uPd6PDZHszachWzR7VAW9+GET4KqNpPau6TJw1p5YzcolIcqDaze/VeFrZffIDIhFxcjM3A26sv4m5KPt4IdX9xlVY7xQ541l/ZwS0dkFtUhkMRNc9Cv93JHa8E2WHC2ssN5r4/QMV+UvPPifyzT7ZGIOJhLk7FpGP+vmj0a2qnctZtQDN7xCTnIeKh8E/IqWYyVT8mNUjIKcahW+m4m1GI6NQCLD9zH5fjc9CvierjyYAmNmjvbo45h2NRWv4MP1ACUb1PRCJRjd+bRo6mWPhWO8zZfBmtp/yJvl/shou1EX6Y2EFl/tHdfHAjLgOXb9e8wNa/lUgk3FdDwBm3euDp6QmRSISoqCj069evxjwAEB0djZCQyrA/iUQCD4+aV17csmULfH19YWJiAnPzqhmt3377DcXFxQgODpanyWQyVFRUIDIyEr6+vkrbmjZtGqZOnaqQlvoPLri3atsRjXwby9+Xllbe6JyZkQ5zi6owt+yszFrvMzM1t0BmhmK4T1ZmVZnbtyKRnZWJd94YJv+8orwcEeFX8Ne2zdh/4jKMTUwhFmvC2VXxJNTJxQ03rgljJbSMXCnKyiuUZpIsjXWUZpwe+6B/AM5Hp+L73ZVXvG/GZ6Gw+DwOf90LszaHIeXRVUOZDLibUhkeG3E/E972xvigX2OcEni4ZI19YlRLn/RrjPMx1fukDIdm9cSsLTX1SZa8T05HCjtcMjO/sk8sjRQvUpgbSp7pnrRhrZ2x7cKDp55EyWRA+P1suFrVHjIlBFkFJY/6REch3cJQojQzqcrgECfsuPywxj4ZH+qGSV098PrP53Er8eWHmf8T2YWVfVJ9xtFMX1tpFu6x9LwSpOZKkf9E+NbdtAJoaIhgbayD+IxCebqOlgZ6BtjgxyP/rXt0/k1yi8tQXiGDqZ6WQrqxjtZzhQNHp+ajo4fyMbxfgA0GBdrii73RiMusOQRZSNJziyuPOaZ6CulWxrpIzS5UWeajwUE4F5WExTvCAQA34jJQKD2Bo/MGYOamC0jOqiqnK9HE4HYe+PrXS2prA/17ccatHpiZmaF79+746aefUFCgPBrKzs5Gt27dYGZmhnnz5j3zdh0dHeHu7q4waAMqwyQ/+OAD+WxdeHg4rl27htDQ0Bpn3SQSCYyMjBRe/yRMUk9fH/aOTvKXs6s7zMwtcPXSOXme0tJSXA+7Ar/GgTVux9e/iUIZALhy8Sz8GjcBADRtHoxVm7ZhxYat8peXjx86d++NFRu2QiwWQ0tLC94+fngYH6ewnYfx92ElkEcBlJZXIOxuBjoF2Cmkdwqww/lo1Vfm9CRiVFS7Mlhe8XhWt+a/JRIBEi1x3Sr8Ejzuk9AAxX+jTgF2uBCTprKMnkQTFRX/oE8ggkSzIfSJDNfjs9HeR/G+x/Y+Vrh8V/XjAB5r5WkBVysD/H427pn+lp+DMVJzhL9ASWm5DDce5KCtt4VCeltvC1y5l1Vr2RAPc7ha6mPL+QcqP3+rkxv+190To5dfQMSDHJV5hKi0XIbIxDy0rnZC3drDHOE1PM4gLD4LloYS6GlXfQ+cLfRQXiFTWkWzh78NtMUa2B0u/PBiUq2sQobY9AIE2iveyxroYIRbKc9+tdbNXA9Z1QZ6/QNsMDTIFjP3xyA2XfWAR4hKyyoQFpuGTk0VwyI7BTrgfA33QOtJNFHtkIPyctXHnIFt3SHREuP349EvrM7038GBWz35+eefUV5ejpYtW2Lbtm24ffs2oqKisHTpUrRq1QoGBgZYvXo19u7di969e+PgwYO4e/curl+/jvnzK0MWxeKnn2CGh4fj6tWrGDduHPz9/RVew4cPxy+//CJfwORlEIlEGDB0BH7bsAanjx/FvTu3Mf/rz6Gjo4NO3XrJ882d+RlW//y9/P2AIa/j8sVz2LxxLeLj7mHzxrW4eukCBgytDP/U09eHq7unwktHRxdGRsZwdfeUb2fI62Nw/MgB7N35JxIexOOvP37HuTMn8MrAoS+tD57mxz03MbqzJ0aGesDb3hhzR7eAg4U+1hyu/JH/angQVk5qK8+/7/JDvNLSGeO6esPFygAh3lZY8EYwLt1OQ/KjRRY+6NcYoY1t4WJlAC87Y7zb2xevtffA5lMN40r5j3sjMbqTJ0Z2rOyTOaOU+2TFE32y/8oDvNLSGWPlfWKJ+W+0rNYn/k/0iRHe7e2L4e3dseV0w3iO26qjsRjexgVDWznDw8YQXw1qDHtTPWw8dQ8A8OmrvvhexTPZhrdxxtV7mYhWMWs0pXcjdPCxgpOFHvwcjLFwZBD8HI2x8eQ9tbfnRVh9/C6GhjhhcLAj3K0NMKO/L+xMdfHrmcoFAj7u0wgLXw9UKjckxBFhcVmISVLuk7c7ueOD3t74+PdreJhZBEtDidLARsg2nInDwGb26N/MDm6W+viklzdsjXWw5WLlQj2Tu3lg9qCqlfL2XktGdmEpvhngB3dLfTRzMcWHPbyw40qCUnjogOb2OBqVipwi4S/U8k/o62ojwMseAV6Vixy52JsjwMsejjbPtthNQ7Hzegq6NrJAF28LOJjoYGwrR1gaaGN/VOXFwlEtHDC5Y9Vqta/4WyPY2QS2RhI4mupgVAsHtHEzw96bVRcXBzSxwYgW9lh6Ig4peVKY6GrCRFcTOrUsciMkS/+6hje6+mBUl0bwdjDF/HFt4GhpiNX7bwAAZo0KweopVc+R3XsxDq+2csX4nn5wsTZCKx8bLHy7HS5FpyApU3HQOqarD3afvyd/vtt/TX2vHNnQV5VkqGQ9cXV1xdWrV/Htt9/igw8+QFJSEiwtLdGsWTMsW7YMANC/f3+cPXsW8+bNw6hRo5CZmQljY2M0b968xoVJqluzZg18fX1VLoTSr18/vPPOO9i9ezcGDBjwwttYk6Ej3oBUWoyl332LvLxc+Pg2xtwlyxWe4ZaakgwNjaofeL+AQHw+ax7WrfgR61f+CDt7R3z+zXylZ7g9TduOnfH+xzOw+Zc1+GnRPDg6u+DL2YvQuEnQC2tfXW07FwczQwk+HRgIG1NdRD7IwsA5R/AgvfLqp42pHhwtDOT5fz0RC0NdTbzdoxFmj2qBnIISnLiZhBmbrsjz6Es0sXhcK9ib66GopBwxCTkY98NJbDsX97Kb949sf9Qnnwxs8qhPsjFo7tGqPjHRhaN51f7z64k7MNDVwtvdG2H2yObyPvni16vyPHoSLSwaGyLvk9sJORj34ylsbyB9sutKAkz1tTGltzesjHQQnZSLkT+dla8SaW2sAzszxfBSQx1N9Gpqhy+2RqjcprGuFua/3hSWRhLkFZfhxoNsDFx4CuH3a5+xEoo9YUkw0dfG+909YWksQUxSHt5YcVG+SqSVkQT2psp90rOJLWaqWHgDAEa2dYZEU4zlbyouXrRkfwyWHIhRT0NeoAMRKTDR08Y7oe6wNJTgdko+JvwShqTsytkzS0MJbI2rwksLS8oxft1lfNbXB1smhiC7sBQHbyRj6WHFZ005m+uhmYspxlVb4ObfJMjXGYdWvy9/P//DgQCAjbvO460vN9VUrME5fTcThjpiDA2yg5meFu5nFmHW/hik5Vfe2mCqpwVLg6pnnWqKRXgzxBFm+tooKatAfFYRZu6PwZUnZqN7+lpBS6yBaV0Vb+/4/UoCfr8i/Odk/nk6FmZGEnw2rDlszPRx834G+s3cg/hHz3CzMdODo2XVcXjT0WgY6mpjQp/GmDu2NXLyS3D8egI+X68YKeRhZ4w2fnboPWPXS20P/XuIZEp3pBKp9iDzv3l1qDY+E36v7yoIzvMuuvNfYGTWMJ5d9DJpaWs9PdN/jP4TJ8dU6e4+nuBW1/Wd0fVdBUE5vJv3ilVXtHtifVehRsejM+u7CjXq6G329Ez1jDNuRERERESkdhq8tlsnDSPYmIiIiIiI6D+MAzciIiIiIiKBY6gkERERERGpXUNZvVGoOONGREREREQkcBy4ERERERERCRxDJYmIiIiISO34xKC64YwbERERERGRwHHgRkREREREJHAMlSQiIiIiIrVjpGTdcMaNiIiIiIhI4DhwIyIiIiIiEjiGShIRERERkdppcFnJOuGMGxERERERkcBx4EZERERERCRwDJUkIiIiIiK1Y6Bk3XDGjYiIiIiISOA4cCMiIiIiIhI4hkoSEREREZH6MVayTjjjRkREREREJHAcuBEREREREQkcQyWJiIiIiEjtRIyVrBPOuBEREREREQkcB25EREREREQCx1BJIiIiIiJSOxEjJeuEM25EREREREQCx4EbERERERGRwDFUkoiIiIiI1I6RknXDGTciIiIiIiKB48CNiIiIiIhI4BgqSURERERE6sdYyTrhjBsREREREZHAceBGREREREQkcAyVJCIiIiIitRMxVrJOOONGREREREQkcBy4ERERERERCRxDJYmIiIiISO1EjJSsEw7c6JmJNfhtqy4oxLO+qyA4tuZ69V0FwdGX8Ke2Og0evZUY6XI/qS7WeXR9V0FwDi/bUN9VEJS2Y0fUdxWIXhqGShIREREREQkcL+8REREREZHaMdaibjjjRkREREREJHAcuBEREREREQkcQyWJiIiIiEj9GCtZJ5xxIyIiIiIiEjgO3IiIiIiIiASOoZJERERERKR2IsZK1gln3IiIiIiIiASOAzciIiIiIiKBY6gkERERERGpnYiRknXCGTciIiIiIiKB48CNiIiIiIhI4BgqSUREREREasdIybrhjBsREREREZHAceBGREREREQkcAyVJCIiIiIi9WOsZJ1wxo2IiIiIiEjgOHAjIiIiIiISOIZKEhERERGR2okYK1knnHEjIiIiIiISOA7ciIiIiIiIBI6hkkREREREpHYiRkrWCWfciIiIiIiIBI4DNyIiIiIiIoFjqCQREREREakdIyXrhjNuREREREREAseBGxERERERkcAxVJKIiIiIiNSPsZJ1whk3IiIiIiIigePAjYiIiIiISOAYKklERERERGonYqxknXDGjYiIiIiISOA4cCMiIiIiIhI4hkoSEREREZHaiRgpWSeccfsP6NixIyZPnlzf1SAiIiIion+IM25PGDNmDDZs2AAAEIvFsLOzQ+/evTF79myYmprWc+3+PWQyGTasXoY9f/2JvLxc+Pg1xvsfTYerm0et5U78fRjrVvyIxIQHsLN3xNh33kO7jp0V8qSlpmDlT4tx8expSKVSODg546PpM+Ht4wcAWL/qZ/x9eD/SUlKgqaUJr0a+GDvhPfj6B6itvf9EvyY2GN7cHmb62ojLKMQPx+/hekKuyryBDkZYOqSxUvqIdVcRn1Ukfz+4qS1ebWILayNt5BSV4XhMBlaejkNJuUxt7XiRunlboK+fFUz0tPAwuxgbLj7ErdSCp5bzttTHlz088SC7CJ/sjpani0VAv8Y2aO9hBjM9LSTlSPHrlQRcS8xTZzNeqFAPM/RoZAkTXU0k5Ejxe1gibqcVPrWch4UePunkhoScYnx1MFae3sbVBGODHZXyv7X1BsoqGsZ+0tHDDN29LWCiq4nEHCk2hyXhdvqz9clHoa5IyCnGrEN3VOZp4WiMt1s7IuxhLn46E/+iq/7StHYxQai7GYx0NJGcV4K/bqTgXmbRU8u5mOliUmsnJOdJsfBEnPorqkY9fS0xIMAWpnpaiM8qwupz8YhMzleZ19/WELP7NlJKf2dLBBJyigEA3RpZINTTAs5mugCA2LQCbLyUgNtpT/+NakjaBLljyqguCPJ1gq2lMYZMWYndx6/Xd7XUoq+/NQYH2cJcTxtxmYVYduo+biSpPj4E2BthYX9fpfQ3N4XjQfbjfcQSH3VxV8rTa9kFlDaQ4zDVPw7cqunRowfWrVuHsrIyREZG4s0330R2djZ+//33+q6aSiUlJdDW1q7vajyXzRvX4o/ffsEnX3wDRydnbFy7Eh/97y38snU39PT1VZa5GRGOWZ9/hDffehftOnbCqeN/Y+ZnH2Lpyg3yQVdebg7+99YoNA1qgblLlsHU1AwJCQ9gYGgk346DkzPe//Az2No7QCqV4s/fN+Lj997Gpm17YWJq9lLa/zSdvCzwv46uWHT0Lm4k5uKVABvM7++LURuuIjWvpMZyr629gsKScvn77KJS+f93bWSJt9q5YN6h27iRmAdHU11M6+4JAPjxxD31NeYFaeVigtEt7LHmwkNEp+aji5cFpnVxx9SdUcgoKK2xnK6WBia2c8aNpDwY6yr+3A1taod27qZYcfYBEnOK0cTeCB+GumHG/hjEPcNJbH1r4WiM4U1tsfFKImLTC9HR3QxT2rvg8/23kVlYe5+MC3FAVEo+jHSUDwGFJeX4bF+MQlpDGbS1cDTCsEAb/Ho1CbFphWjvYYr32zvjiwOxT+2TN4Nr7hMAMNPTwuBAG8Q8w8UCIQu0M0Q/f2tsu56Me5lFaO1sgrdCHDHv2F1kF5XVWE5HUwOvNbXF7fQCGEoa9qlDWzczjGvlhOWn7yMqJR89fCzxZU8vTNp6A+kFNf/GTthyXeE3Nre4qr/8bY1w8k4Gbp3NR0mZDAMDbTCzlxfe/eNGrfteQ6OvK0FETAI27jqPzQvH13d11KaDhzneaeeMH07cw82kPPT2s8bsvo0w9rdrSMuveR8ZsylcYR/JKVL8ty+QluGNX68ppP3XBm2MlKwbhkpWI5FIYGNjAwcHB3Tr1g1Dhw7FoUOH5J+vW7cOPj4+0NHRQaNGjfDzzz/LPyspKcG7774LW1tb6OjowMXFBXPmzJF/npOTg7feegtWVlYwMjJCp06dcO1a1Rf4zp07ePXVV2FtbQ0DAwO0aNECR44cUaifi4sLvvnmG4wZMwbGxsYYP77yh/PMmTPo0KED9PT0YGpqiu7duyMrK0terqKiAh9//DHMzMxgY2ODr7766kV33TORyWT4c/MmjHhjPNqHdoGruyc+/fJbFBcX48jBvTWW+3PzJjRvGYLXx4yDk4sbXh8zDkEtgrFt8yZ5nt83roWVlQ0++eIb+Pg1ho2dPZq1CIG9Q9UMQpfuvdGsZSvY2TvC1c0DE9//CAUF+bgTG6Pqz9aLIc3ssPdGCvbeSMH9zCL8cPwe0vKk6NfEttZy2UWlyCysej15ru1nZ4gbibk4cisdyblSXLqfjaO30uBtrXqgLDS9fa3wd2wG/r6dgYQcKTZcSkBGQSm6eVvUWu6tVk44czcLMSquerdzN8OO6ykIT8hFan4JDken41piLvr4WqmrGS9U90YWOHU3C6fuZiEpV4rfw5KQWViKUI/aL0CMam6PC/dzcCej5lmo3OIyhVdD0dXbAqfvPeqTPCm2hCUjq6gUHd1r75ORze1w4X427maoHrCLRMD4EAfsupGKtFpO7BuCDu5muBCfjQvxOUjNL8FfN1ORXVSKNi61R5UMbmKDqw9zcT+r+CXVVH1eDbDGkeh0HI5Ox8PsYqw+9wDp+SXo9ZTvfk5RGbKfeD35G7vo2F3sj0zDvYwiJOQU48eTcdAQidDE3qjmDTZAh85EYubPe7Dz72tPz9yADQy0xYHINOyPTEN8VjGWnb6PtPwS9G1sXWu57MJSZD3xqn7NSwYofJ71LxrU08vBgVst7t69iwMHDkBLSwsAsGrVKkyfPh3ffvstoqKiMHv2bMyYMUMeXrl06VLs2rULW7duRXR0NDZt2gQXFxcAlQOW3r17Izk5Gfv27cOVK1cQFBSEzp07IzMzEwCQn5+PXr164ciRIwgLC0P37t3Rt29fxMcrhuQsWLAA/v7+uHLlCmbMmIHw8HB07twZfn5+OHfuHE6fPo2+ffuivLzqqs+GDRugr6+PCxcuYP78+Zg1axYOHz78EnpRUVLiQ2RmpKN5cGt5mra2Npo0bYabETUfCCIjrimUAYAWIa1xMyJc/v7syePw9vHFV9Omon+PDhg/cjD2/PVnjdssLS3Fnr/+hL6BITw8vf95o14gTQ0RvKwNcOl+tkL6pfvZ8LczrLXsmhGB2PFWCywe5IemjsYKn11PyIWXlQF8bAwAALbGEoS4muL8vSxVmxIUsYYIbuZ6uF4thPFaYi68LGseeHb0MIO1oTb+vJak8nMtDRFKyysU0krKZQ1iMCvWEMHZVBc3q4V23UzOh4eFXo3l2rqawspAGztvpNSYR6Kpgfl9vfHdK43wfjtnOJnovLB6q1NtfeJeS5+0cTWBpYE2dt9MrTFPX18r5EnLcboBfF9qIxYBDsY6SrOG0WkFcDHVrbFcC0djmOtp4VBMurqrqHaaGiJ4WOgj7GGOQnrYw1w0esp3f8kAP6wf0QRf9/ZGY9vaf48lmhoQa4iQJ204Fz6okqaGCF5W+rjyIFsh/cqDbPjZ1P7vvmxYY2x+IwjzX/VROWjX1RJj06im+G1MU3zdx7vW3yYiVRp2vIMa7NmzBwYGBigvL0dxceWVxUWLFgEAvv76ayxcuBADBgwAALi6uiIyMhIrVqzA6NGjER8fD09PT7Rt2xYikQjOzs7y7R47dgwRERFITU2FRCIBAHz33Xf466+/8Oeff+Ktt95CkyZN0KRJE3mZb775Bjt27MCuXbvw7rvvytM7deqEDz/8UP7+tddeQ/PmzRVm//z8/BTaFRAQgC+//BIA4OnpiR9//BFHjx5F165dVfaDVCqFVCqtliaS1/2fyszIAACYmpkrpJuamSMlWfUJdmW5dJVlMjOqTiQSEx9i5/atGDx8FF4fMx5RNyPww6K50NLWRvder8jznTt9ArM+/wjS4mKYW1jiux9WwthEGPcwGutqQVNDhKxq4X+ZhaUw01MdEptRUIr5h2MRk5IPLbEGuvlYYvEgP7y/9QauPbov7u/odJjoauHHoY0hAqAp1sCO8CT8eilB3U2qMyOJGGINEXKqhXHlFJfBRFdLZRkbQwmGB9nhqwO3la54PnYtMRe9/awQlVKAlDwp/G0N0dzRGBoNII7DUPtRn1SbDcuVlsFYR3WfWBloY2ATa8w9erfGPknKlWLNhYdIyCmGjpYGuj4KSf3ywG2k1hIeJAQGj/qk+gxhbnE5jGsIf7Qy0MbAABvMq6VPPCz00NbNFLOeuBewodLX1nw0mChXSM+TlsNQR6yyjIW+Fvr4WuLH0/dr7KOGxEinsg+qh4XmFJXCRE/17FhWYSl+PHkPsWmF0BKLEOppga/7eGP67ltKFwoeG9XSAZkFJfLfYGo4jHUr95Hqs2FZhaUw1VP9+5pZUIJFf9/F7bTK43AXbwvM7+eDD3dEIuLRRccHWUVYcOQO7mUUQk9bjP5NbLBkoB8mbK66V/I/oQEcY4WMA7dqQkNDsWzZMhQWFmL16tWIiYnB//73P6SlpeHBgwcYO3asPDwRAMrKymBsXDm7MWbMGHTt2hXe3t7o0aMH+vTpg27dugEArly5gvz8fJibKw4+ioqKcOdO5Y3wBQUFmDlzJvbs2YPExESUlZWhqKhIacatefPmCu/Dw8MxePDgWtsVEKC4+IatrS1SU2u+wjxnzhzMnDlTIW3qJ5/jg09n1Pp3qjt8YA8WzZ1Vtd1FPwEARCrWg1WVpvB5tW+7TCZTWFdWVlEBbx8/jJ/4PgDA09sHcffuYNe2LQoDt8BmLbB645/Iyc7Cnp3bMPOzD/Hz2l+VBob1qfr5kQiATCm10oOsIjx4YhGSm0l5sDKUYFhze/lJQ6CDEUYGO2DR0buISs6DvYkO3uvohoyCEvxy4aGaWvFiVW9/ZZ8oE4mA99o744/wJCTlSlXkqLT+YgLebu2Ixf18IAOQkifF8dgMdPQQzn7wvGraT0Qi4O1WjtgZkYqUWu6TvJtRpBAuGJsWjy+7e6CLlzl+u1rzhRUhUf3dUSYSAeNbOWDnjRSk1DAolWhqYGywA365lID8knKVeRoild8lFZ0kAjAiyA4HbqUjrZZ7SRsiWfUG13L4ScgpVjixjk4tgIWBNvo1scHNZOUB/YAmNmjvbo7pe2795+5f+jd51t8SAHiYXYyH2VX7SFRyPiwNJBjc1FY+cItKyUdUStVA/2ZSHpYNbYxXA6zx86n7L7by9K/FgVs1+vr68PCoXN1w6dKlCA0NxcyZM+UzXqtWrUJwcLBCGbG48kplUFAQ7t27h/379+PIkSMYMmQIunTpgj///BMVFRWwtbXF8ePHlf6miYkJAOCjjz7CwYMH8d1338HDwwO6uroYNGgQSkoUTyr0qy3goatbc4jLY4/DPR8TiUSoqKioITcwbdo0TJ06VSEto+j5L5O0aRcKX7+qQWNJaWVbMjPSYW5hKU/PysyodeBkZm6BzEzFMJ3srEyYPVHG3MISzq6KKzY5u7jh1DHF+wR1dfVg7+gEe0cn+DZughEDe2Pfrh14fcy4527fi5ZTVIqyChnM9BX/vUz1tJ4rFj4yKQ/dfKr6d2xrJxyKSsPeRyFyd9MLoaMlxkdd3LHxwsMaD0ZCkCstR3mFTGl2zUhHU+nGbwDQ1RTD3UIfLmZ6ePPRCokiEaAhEuG3kYH49nAsbibnI09ahu+O3YOWhggGOprIKizFa0F2SM2vebAnFHkllX1SfSbJUKKp8p40HU0NuJrrwclUF683swNQ1Serhvhj4fF7KlfolAG4l1kEa4O6zbS/DPk19YmOuOY+MdODk4kuXgtS7JMVg/2w+EQcCkrKYWmgjf+1q4qeeHytaMVgP3y+73aDuuetoKQM5RUyGFVbXMRAIka+VHlgKtHUgJOpLuyNdTDg0b09j/toQR9vrDj/ALHPsGKnkOQWV/ZB9ZkTYx0tZD/Hb2x0ar7Kizz9AmwwKNAWX+yNbhCLHJGynKLKfcSs2j5iovd8+8it5Dx09ras8XMZKvcje5Onn8MRPcaB21N8+eWX6NmzJ9555x3Y29vj7t27eP3112vMb2RkhKFDh2Lo0KEYNGgQevTogczMTAQFBSE5ORmampry+96qO3XqFMaMGYP+/fsDqLznLS4u7ql1DAgIwNGjR5VmyOpCIpEohUXmVzz/CYqevr7CSpEymQxm5ha4fPEcPL19AFTea3Yt7AremjS5xu34Nm6CKxfOYfDwUfK0yxfOwq9xoPy9X0AgHtyPUyj3MD4O1ja1L+ohgwylpcI4+SqrkCEmJR/NnUxwKjZTnt7c2QSn72TWUlKRp5U+Mp44odTREitdYa6okEEkqjwRU3W1XSjKK2S4m1GIAFtDXIqvui8lwM4Qlx/kKOUvKi3HhzujFNK6eVvAz9YQi4/fUwr5K62QIauwFGIREOxsgnNxwr+PqbxChvtZRfC1McDVJ0Kx/GwMEKYiNKu4tAIz9isuwBPqYQ4fa338fCa+1lXSnEx08LABhPE82SdhCVX3Q/paGyA8QXkJ7+LSCnxx4LZCWqiHGRpZGWDZ2Xik55egQgalPP39raGjpVG5GIyKCwdCVi4DHuYUw8tSHxFPhPh5WeqrDPmTllVg/rG7CmltXEzhYaGHDZcTGuRqiWUVMsSmFyDQ3hjn47Ll6YEORrj4xPuncTPXU7qY1j/ABkOCbPHVvpgGN6ClKmUVMsSkFiDI0Rhn7lYdD4IcjXH2Oe5zdbfUR0Zh7ecW7hb6uFfLQlH/RtWjp+j5cOD2FB07doSfnx9mz56Nr776Cu+99x6MjIzQs2dPSKVSXL58GVlZWZg6dSoWL14MW1tbBAYGQkNDA3/88QdsbGxgYmKCLl26oFWrVujXrx/mzZsHb29vJCYmYt++fejXrx+aN28ODw8PbN++HX379oVIJMKMGTNqnRV7bNq0aWjcuDEmTpyICRMmQFtbG8eOHcPgwYNhYVH7qnsvm0gkwqBhI/Dr+tVwcHSGg6MTNq1fBR0dHXTp3lueb/ZXn8HS0grjHw3mBg4dgfcnjMHvv6xBm/ahOHPyGK5cvIClKzfIywwePgrvjhuJTetXIbRzd0RFRmDPX9swddoXAICiokJsWrcKbdp1hJmFJXJzsrFz2xakpaagQ+duL7UfarP1SiKm9/REdEo+bibloW9jG1gZSrDzWjIA4K22zrAw0MbsRyeUg5vaIilXiriMQmiKRejmY4WOXhb4fFfV4OXs3UwMCbJDTGoBopIqQyXHtnHCmTtZDeK+lb2RqXi3rTPuZBTidloBOntZwEJfG4ejK2dhhwfZwkxPGz+dvg8ZIH9uzmM5xWUoLa9QSPew0IOZnhbiMotgpqeFQYG2EImAXTdqDiEWkoO30jE+xAFxmUW4k1GIDu6Vz6M7/mjAPzDAGqa6Wlj9aEY1IUdxJjFPWobScplC+it+VriTUYjUPCl0tMTo4mUOR1NdbLqS+DKb9o8djk7H2ODKPrmbXoT27qaVffLooseAxtYw0dPE2gsJkAFIrN4nxeUoLa9QSK+ep7C0XGV6Q3HiTiZeC7LDg+xixGUVoZWzCUx1tXD20QWL3j6WMNLRxO9hSZABSK4WWptfUo6yCplSekOy83oKpoS6Ija9ALdS8tHdxxKWBtrYH1X53R/VwgFm+lpYcrzyUSmv+FsjJU+K+KwiaIpFCPWwQBs3M8w5VBUmOaCJDV5vbo/v/r6LlDwpTB49fqS4tALFZU8/jjcU+rracHesmkVysTdHgJc9snIL8SBZ+Be9ntW28CR80tW98piZnIdeftawMpBgz6OolTdbOcJCXxvzj1Te6tK/iQ1ScqW4n1m5j3T2tkB7D3PM3Ff17NARLexxKyUfD7OLoa8tRr8AG7hb6OGHBvBIHhIODtyewdSpU/HGG28gNjYWq1evxoIFC/Dxxx9DX18fjRs3xuTJkwEABgYGmDdvHm7fvg2xWIwWLVpg37590NCoXLxz3759mD59Ot58802kpaXBxsYG7du3h7V1ZQjK4sWL8eabb6J169awsLDAJ598gtzcp9/Y7OXlhUOHDuGzzz5Dy5Ytoauri+DgYAwfPlxtfVIXw0a+CalUiiXzv5E/gHvB0hUKM3OpKUnQeGKVCP+AQHzx9XysWfED1q74EXYOjvji2wUKD85u5OuPr+cvwaqfl+CXNctha2ePSVM+RtcefQAAYg0xHty/hy/37UJOdhaMjE3g7eOHpSs2PPXh3y/T3zHpMNLVxOgQR5jra+NeRiE+2RGJlLzKE0VzfS1YG1bNhmqKNTCxgwssDbQhLavAvfRCfLwjUmHFyF/OP4BMBoxr4wRLA21kF5bh7N1MrDrTMOLqz8Vlw1CiiYFNbGCqq4UH2cWYe/QO0h/dd2OiqwVzfdU3jddES6yBoU1tYWUoQXFpBcITcvHTqTj5ibnQXXqQAwOJGK/4W8FYp/IB3EtOxiHj0SyAsa6WUsjt0+hpizG6hT2MdTRRVFqB+KwizDt695keziwElx7kQl+SjL5+lX2SmCPF96fuy2eGjHU1YV7DIj//FeGJedDTTkE3bwsYScRIyivBqvMPkPVosQ5DiSZMa1j059/i9N1MGOqIMTTIDmZ6WrifWYRZ+2PkM8+melqwNKjaTzTFIrwZ4ggzfW2UlFV+L2buj8GVJ2b8e/paQUusgWldFY8lv19JwO8N5MLHswjydcah1e/L38//cCAAYOOu83jry001FWtwTsRmwEhHEyMeDeLjMgoxfc8t+bNUzfW0YfXEcVhLQ4S32lReVJWWVeB+ZiGm776Fi0+sEG0g0cTkjm4w1ddCgbQcd9ILMHVHJKIb+LMh6eUSyZTu0CVSLTG74V5hVZdhay/VdxUEx9acyxtXp9/AH1isDhpPWYzov8hIl/tJdbHJymGu/3WHl214eqb/kLZjR9R3FQTn8Lsh9V2FGkUnCzc01NtG+OcvfI4bERERERGRwHHgRkREREREJHCMyyAiIiIiIrVjkHzdcMaNiIiIiIhI4DhwIyIiIiIiEjiGShIRERERkfoxVrJOOONGREREREQkcBy4ERERERERPYeff/4Zrq6u0NHRQbNmzXDq1Kla8584cQLNmjWDjo4O3NzcsHz58uf+mxy4ERERERGR2okE/N/z2LJlCyZPnozp06cjLCwM7dq1Q8+ePREfH68y/71799CrVy+0a9cOYWFh+Oyzz/Dee+9h27Ztz/V3OXAjIiIiIiJ6RosWLcLYsWMxbtw4+Pj4YMmSJXB0dMSyZctU5l++fDmcnJywZMkS+Pj4YNy4cXjzzTfx3XffPdff5cCNiIiIiIj+06RSKXJzcxVeUqlUKV9JSQmuXLmCbt26KaR369YNZ8+eVbntc+fOKeXv3r07Ll++jNLS0meuIwduRERERESkdiKRcF9z5syBsbGxwmvOnDlKbUhPT0d5eTmsra0V0q2trZGcnKyy3cnJySrzl5WVIT09/Zn7j48DICIiIiKi/7Rp06Zh6tSpCmkSiaTG/CKR4n1xMplMKe1p+VWl14YDNyIiIiIi+k+TSCS1DtQes7CwgFgsVppdS01NVZpVe8zGxkZlfk1NTZibmz9zHRkqSUREREREaicS8OtZaWtro1mzZjh8+LBC+uHDh9G6dWuVZVq1aqWU/9ChQ2jevDm0tLSe+W9z4EZERERERPSMpk6ditWrV2Pt2rWIiorClClTEB8fjwkTJgCoDLscNWqUPP+ECRNw//59TJ06FVFRUVi7di3WrFmDDz/88Ln+LkMliYiIiIiIntHQoUORkZGBWbNmISkpCf7+/ti3bx+cnZ0BAElJSQrPdHN1dcW+ffswZcoU/PTTT7Czs8PSpUsxcODA5/q7HLgREREREZH6Pd9zrgVt4sSJmDhxosrP1q9fr5TWoUMHXL16tU5/k6GSREREREREAseBGxERERERkcAxVJKIiIiIiNRO9G+KlawHnHEjIiIiIiISOA7ciIiIiIiIBI6hkkREREREpHYiRkrWCWfciIiIiIiIBI4DNyIiIiIiIoFjqCQREREREakdIyXrhjNuREREREREAseBGxERERERkcAxVJKIiIiIiNSPsZJ1whk3IiIiIiIigePAjYiIiIiISOAYKklERERERGonYqxknXDGjYiIiIiISOA4cCMiIiIiIhI4hkoSEREREZHaiRgpWSeccSMiIiIiIhI4zrjRM8ssKKnvKgjOjbD79V0Fwbmpwctp1RmZGdV3FQRHIuHhpzqxmN+d6u5ej63vKghO27Ej6rsKgnJ6zab6roLwvBtS3zUgNeGRk4iIiIiI1I6Xp+qGoZJEREREREQCx4EbERERERGRwDFUkoiIiIiI1I6rStYNZ9yIiIiIiIgEjgM3IiIiIiIigWOoJBERERERvQSMlawLzrgREREREREJHAduREREREREAsdQSSIiIiIiUjuuKlk3nHEjIiIiIiISOA7ciIiIiIiIBI6hkkREREREpHaMlKwbzrgREREREREJHAduREREREREAsdQSSIiIiIiUjuuKlk3nHEjIiIiIiISOA7ciIiIiIiIBI6hkkREREREpHYiritZJ5xxIyIiIiIiEjgO3IiIiIiIiASOoZJERERERKR+jJSsE864ERERERERCRwHbkRERERERALHUEkiIiIiIlI7RkrWDWfciIiIiIiIBI4DNyIiIiIiIoHjwI2IiIiIiEjgeI8bERERERGpnYg3udUJZ9yIiIiIiIgEjgM3IiIiIiIigWOoJBERERERqZ2IDwSoE864ERERERERCRwHbkRERERERALHUEkiIiIiIlI/RkrWCWfciIiIiIiIBE4tAzeRSIS//vpLHZuud4WFhRg4cCCMjIwgEomQnZ39wrZ9/PjxF7JNFxcXLFmy5IXUiYiIiIiI6t9zh0omJyfj22+/xd69e5GQkAArKysEBgZi8uTJ6Ny5szrqKCgbNmzAqVOncPbsWVhYWMDY2LjGvEVFRbCzs4NIJEJCQgJ0dXVfSh0vXboEfX19+XuRSIQdO3agX79+L+XvP82BnVuxc8tGZGWkw9HFDW9M+hC+AU1rzH/z2hWs/3kRHsTdhamFJfoNHYXurwxSmff03wex+JvP0KJNB3z69SJ5+vbf1uL8qWNIiI+DtkQCb78AjBz/HuydXF5089Tizc6e+F9vH1gb6+JWQg4+23QF52PSasw/qLUL3uvtAzdrQ+QWleLv64mY8XsYsvJLXmKtX6w3O3ng3V6P+iAxB9N/vVpjH/w4LhjD27kppd9KyEGbz/YBADTFIkzu44thbV1ha6KH2ORczNx6DX9HJKm1HS/SiDbOeCvUDVZGEsQk5+Prv27i0t0slXkXDA/AoJaOSukxyXnoPu8kAMDTxgBTenihsaMxHMz0MGvHTaw7GafOJrxww0McMba9CywNJYhNycfsPbdwJS67xvxaYhEmdXZH36Z2sDSUIDmnGMuP3cX2ywnyPKPaOGN4iCNsTXSQVVCCgzdSsOjAbZSUVbyEFtXdsGBHvNHWBZaG2ohNLcDcvbdw9X52jfm1xCK808kdfZvYwuJRn6w8cRc7riQCANaNbY6WbmZK5U5Ep2HiL2HqasYL9VYvP0wZ0BQ2pnqIjM/Ex6vO4Exkzd/9YR08MWVgU3jYGSOnoASHr8Zj2tqzyMyTAgAOzn4V7RvbK5Xbf+k+Bszaq7Z2vCh9/a0xOMgW5nraiMssxLJT93EjKU9l3gB7Iyzs76uU/uamcDzILgYAdGtkiY+6uCvl6bXsAkrLZS+28vWsTZA7pozqgiBfJ9haGmPIlJXYffx6fVdLsBgpWTfPNXCLi4tDmzZtYGJigvnz5yMgIAClpaU4ePAgJk2ahFu3bqmrnoJx584d+Pj4wN/f/6l5t23bBn9/f8hkMmzfvh2vv/76S6ghYGlp+VL+zj9x5tghrPtpIca//yka+Qfi0O5t+PbT/2HJuj9gaW2rlD8lKQHfTnsPXXr1x/uffYNbN8Kx6vu5MDIxRav2ihcKUpOTsGH5Evg0Vh4E3rx2FT1eHQwPbz9UVJTjtzU/YdbHk/D9uj+h85IG1P9U/2AnzB4RhI/WX8aF22kYE+qBrR91RKtP9yIho1Apf7CXJZa9HYLpv17FgbAE2JrqYdEbLfD92GCM+v5UPbSg7vq1dMK3rwfho18u42JMOkaHemDLBx3Qeto+JGQq98G0X69i1h/X5O81NUQ48U1P7LwYL0+bPjAAg1u7YPLai7idlItOjW3xy3tt0fPrI4iIVz34EZLegbaY0c8XX/x5A5fvZeG11k5Y91ZLdJt7AomPTp6eNGtHJObtiZa/19QQYd9H7bAvvOpkVVdLjAcZhdh3LQkz+imfmAldzwAbTOvTCLN2RuJqXDaGBjti5RvN0GfRGSTlKPcJACx5PRDmBtr4fNsNxGcUwkxfAk2NqlOLPoG2+KCHJ6b/eRNh8VlwsdDHnMGVv/9zn+hPoerR2Bqf9vLG17ujEHY/G0NaOGDF6CC88v3ZGvtk0fAmMNfXxowdNxGfUQhzA22In+iTyb+FQ0tcFbBjrKeF7e+2wqGIFLW350UY1NYDC8a1xfvLT+JcZDLG9fDFX1/1QdCk3/EgLV8pf2tfG6ye0hkfrzmDvRfjYG+uj6UTO2DZ/0IxdPYBAMCw2QegrVnVJ2ZGOri4dCi2n4l9ae36pzp4mOOdds744cQ93EzKQ28/a8zu2whjf7uGtFou9o3ZFI7CknL5+5yiUoXPC6RleOPXawpp/7ZBGwDo60oQEZOAjbvOY/PC8fVdHfqXe65QyYkTJ0IkEuHixYsYNGgQvLy84Ofnh6lTp+L8+fMKedPT09G/f3/o6enB09MTu3btkn9WXl6OsWPHwtXVFbq6uvD29sb333+vUH7MmDHo168fvvvuO9ja2sLc3ByTJk1CaWnVD4NUKsXHH38MR0dHSCQSeHp6Ys2aNfLPIyMj0atXLxgYGMDa2hojR45Eenp6rW3ctm0b/Pz8IJFI4OLigoULF8o/69ixIxYuXIiTJ09CJBKhY8eOtW5rzZo1GDFiBEaMGKFQr8f27dsHLy8v6OrqIjQ0FHFxcQqfr1+/HiYmJtizZw+8vb2hp6eHQYMGoaCgABs2bICLiwtMTU3xv//9D+XlVT+eT4ZKuri4AAD69+8PkUgkf19fdv+xCZ16voouvfvDwdkVb777IcytrHFw158q8x/avQ0WVjZ4890P4eDsii69+6NTz1exa+tGhXzl5eX4fvZ0DB3zNqztlK96zpj3Izr1eAVOru5wcffCpI+/QnpqMu7ERKmlnS/SxJ6NsOnEXWw8cQcxibn47NerSMwoxJudPVXmb+Fhjvi0Aqw8FIP4tAJciEnD+r9j0dRV+Qp5QzGxhzd+PXkXm07cRUxSLqb/dhWJmTX3QV5RKVJziuWvQFczmOhp47dTd+V5hrR2weLdkThyPQn30wqw7u9YHItIxqSejV5Ws+pkXEdXbL3wAFsuPMCd1Hx8/VckkrKL8XobZ5X584rLkJ4nlb8aOxrDWFcLf158KM9z/UEO5uy+hT1hSQ1mNulJY9o6Y9vlh/jzUgLuphVgzp5bSM4pxvAQ5ZlGAGjrZYEWrqZ4e91VnIvNREJWMSIe5iAsPluep6mTCa7eG5QlGgAATHVJREFUz8aea0lIyCrGmdsZ2HstGf72NUdbCMnoNi7YdiUB2y5X9sncfdFIyinG0GAHlfnbepqjuYsp3vnlKs7fyURidjEiHuYiPD5HnienqAzp+SXyV2sPcxSXVuDgjYYxcHuvXxOsPxyF9YeiEP0wCx+tPoOH6fkY31P1BdmW3ja4n5qHn3dH4H5KHs5GJmPNgUgEeVjJ82TlS5GSXSR/dQ50RKG0DNtP33lZzfrHBgba4kBkGvZHpiE+qxjLTt9HWn4J+ja2rrVcdmEpsp54VVQbk8kAhc+zCktVbqehO3QmEjN/3oOdf197emaiOnrmgVtmZiYOHDiASZMmKYThPWZiYqLwfubMmRgyZAiuX7+OXr164fXXX0dmZiYAoKKiAg4ODti6dSsiIyPxxRdf4LPPPsPWrVsVtnHs2DHcuXMHx44dw4YNG7B+/XqsX79e/vmoUaOwefNmLF26FFFRUVi+fDkMDAwAAElJSejQoQMCAwNx+fJlHDhwACkpKRgyZEiNbbxy5QqGDBmCYcOGISIiAl999RVmzJgh/5vbt2/H+PHj0apVKyQlJWH79u01buvOnTs4d+4chgwZgiFDhuDs2bO4e7fqpPHBgwcYMGAAevXqhfDwcIwbNw6ffvqp0nYKCwuxdOlSbN68GQcOHMDx48cxYMAA7Nu3D/v27cPGjRuxcuVK/Pmn6oHPpUuXAADr1q1DUlKS/H19KC0txZ2YWwhsHqKQ3qR5CKJvqg4riL55HU2q5Q9sHoI70ZEoK6s6CPyxcRWMjE3RpVe/Z6pLYUHlVVVDI6PnaMHLpyXWQBMXMxyrFr537EYyWnpaqCxz8XY67Mz00KWJHQDA0kgHr7R0xKHwRLXXVx3kfXAjWSH92I1ktPBQ3QfVvd7eHScik/HwiRlKbS0xikvLFfIVlZYjuIZ+FRItsQj+DsY4Fa0YKnoqOg3NXEyfaRtDQxxx5nY6ErKK1FHFl05LLIKfvRHO3M5QSD9zOwNNnU1UlunkY4kbD3MxtoMLTkzrgAMftMXHvbwgeWLm5EpcFvzsjdDYoXKg5mCmi/beFjhxq+ZQZaHQEovga2eIs7GKfXI2NgOBTiYqy4T6WOFmQi7ebOeKvz9pj71T2uDDHop9Ut2AZvbYH5GMomrfJyHS0tRAUw9LHA17oJB+NOwBQnxUD1TORyXD3sIA3Zs5AQCsTHTRv40b9l++X+PfGd3VB3+cvI1CadmLq7waaGqI4GWljysPshXSrzzIhp+NYa1llw1rjM1vBGH+qz5oYq98LNXVEmPTqKb4bUxTfN3HG+4Wei+y6tRAiUTCfTUEzxwqGRsbC5lMhkaNnu1q9JgxYzB8+HAAwOzZs/HDDz/g4sWL6NGjB7S0tDBz5kx5XldXV5w9exZbt25VGFiZmprixx9/hFgsRqNGjdC7d28cPXoU48ePR0xMDLZu3YrDhw+jS5cuAAA3t6p7WpYtW4agoCDMnj1bnrZ27Vo4OjoiJiYGXl5eSnVetGgROnfujBkzZgAAvLy8EBkZiQULFmDMmDEwMzODnp4etLW1YWNjU2v7165di549e8LUtPIkqkePHli7di2++eYbef3c3NywePFiiEQieHt7IyIiAvPmzVPYTmlpKZYtWwZ398pY8UGDBmHjxo1ISUmBgYEBfH19ERoaimPHjmHo0KFK9XgcNmliYvLUOj9JKpVCKpUqpJVIS6EtkTzzNqrLy8lGRUU5jE3NFdJNTM2RnZmhskx2VgZMquU3NjVHeXk58nKyYWpuiVs3wnF0304sXPXbM9VDJpNh/c+L4NM4EE6uHv+sMS+JuaEEmmINpOUqhjSl5hTBylg5tBSoHLi9vews1kxqAx0tMbQ0NbDvykN8svHyy6jyC/e4D1KrhXWl5RTD2ljnqeWtjXXQJcAWby0/q5D+d0QSJvZohHPRqbiXmo8Ovjbo2dReISRMqEz1taEp1kB6nmIYU3qeFJZGT/+OWhpJ0KGRJSZvCldTDV8+U73KPsmo1icZeVJYeKkejDua6aGZiwlKysrx7sZwmOpr4ct+vjDW08L0P28CAPZdT4aZgTZ+ndASIlHlhYTfzsVj1Yl7am9TXZk87pN8xd/yjPwSWBio3k8cTHUR5GwCaVkF3v81HCZ62pjxig+M9bQwY/tNpfyNHYzgZWOIL3YofyZEFkY6lb8n2YoXLFKyC2Ftonpm9vyt5P+3d99RUV3v18D3gMDQBQUUQigiBlBU7JJYiQWNEo3d2GLsrwU1lthrolGJJfaCNcGuiTWAvaBBilIFe0QpAkoTYd4/+DJxHDT6i84ZZvZnrVkrc+9ANnfJMM895zwHA386iW3ftYFUXxd6FXRx+NIt+K8te+p5/erWqOlYCcOXh773/O+buWEF6OpIlEbDnuQWwsJIr8yvych5jqUhyUhMfQY9XR341KiMRX5umLA/BtF/l6yLu/ckD4v/TMKt9FwY6eviy9pVENDVA8N+jcaD10zRJaJ/99YjbjJZyRi45C1LUk9PT/l/Gxsbw9TUFI8fP5YfW7NmDerXrw8rKyuYmJhg/fr1uHv3rsL38PDwgK6urvx51apV5d8jIiICurq6aN68eZn//7/++guhoaEwMTGRP0qLzqSksqcuxMbGwtvbW+GYt7c3EhMTFaYi/puioiIEBgaib9++8mN9+/ZFYGCg/PvExsaicePGCtezSZMmSt/LyMhIXrQBgI2NDRwdHeUji6XHXr6278PChQthbm6u8Niwcsm/f+FbePXfkEwme+NqVeV/cjL5ibzcHPy8YDqGj58GM/O3G2nYsPxH3ElOxLhpC/79xWpC9soUFIlEIv+dfFUNWzMs/LoefjpwHS1nHMNXi0LhYGWMpQMbqiDph/PqzyuRKF+XsvT6zBlZuYU48tcDheNTd4QjOeUpLv3QASkbe+DHr+th19lkFL0630eNKV0TSN7qmnzV4CNk573AieiUf39xOSOD0i/La39XdCQl7yYTfo1G9P0snIlPww+/x+FLLzv5CFNDZwsMbemMOQdj0HX5RYzadg0t3KwwvJVy8xt1pfz+gVevktK5SUHRiL6fjbMJaVh0JB5+dW3LHHXrUs8OCSlPEX0/+73n/pCU308kr70mn9hbYMmQz7Dw16toOm4PvphxGI42ZlgxouzPH/3buOH67XRcTXy/f5c/pFd/dkkZx0rdz8zH0ZjHuJmai9iUZ1hx+jYu385Et7r/3EyMffQMwQlpSE7PxfWHTzHvWCIeZOajs+ebp18S0Zu99Yhb9erVIZFIEBsb+1bdCfX0FO/USCQSFBeXrJkICgrCuHHjsGTJEjRp0gSmpqZYvHgxLl++/Nbf4986NBYXF+OLL75QGsECSgrAsshksrKLind0/PhxPHjwQGkErKioCCdOnED79u3f+vuWdQ3edF3elylTpsDf31/h2M20/zY/3dS8InR0dJGZobjOMCszQ2lUrVRFi0p48spoXNaTDOjq6sLUzBz3bifjccrfWPj9OPl5mazkWnTzaYgVgXtRxe6fu6gbli/ClQtnMDdgPSpZqf8fkPSnBXhRVAzrV0aWrMykSqNwpcZ+4YGwxDSsOFKyfi/mXiZyCl7g6PTPMX93JB6Vs7udpdfApqLi73xlMykev+YavKz3Z84IunALhUWKvyPpTwvw9fKzMNDTgaWJAR4+ycPM7rVxNy3nveb/EJ7kPMeLomKl0bVKpvpIe1rwmq/6R7dG9tj/132NahTwJLfkmlQ2feWamOgj/TUNFlKfFuBRVgGevTSdLelxDnR0JKhiLsWd9FyM/rw6DoX/jT1XSgr/hEfPYKinizldPLAmNPmtCmVRMl9zTSyN9ZVG4UqlPX2Ox9mK1yQ5teSa2JhLcfel6cZSPR2096yClX+q/zquUmnZ+SXvJxaK0/aszQ3xOFO50REATOzmhYuxD7FsfwQA4PrtdOQWnEbwj10we/tlpDz55+sMDSqg22cumLtD3LKEd5GV9wJFxTJYvjK6VtFID5nvsCYtLuUpWtd4fWM0GYD4x89gV1G9m4HRhydhX8n/5K1H3CwtLdG2bVusWrUKOTnKH2zeZe+xs2fPomnTphgxYgTq1q0LFxeX146CvU6tWrVQXFyM06dPl3ney8sLN27cgKOjI1xcXBQeZa3RAwB3d3ecO3dO4diFCxfg6uqqMPL3bzZu3IiePXsiIiJC4dGnTx95kxJ3d3elhi6vPn9f9PT03mnEEAAMDAxgZmam8Pgv0yRLc1Rz/QSRfykW6FF/XUYND88yv6aGhyeiXnl9xNVLqFbDHRUq6MHuY0cs2/gblqzfKX/Ub9oMNevUx5L1O1HJumR6qEwmw/qff8TlsyGYtWQNbKoqNzBRR4VFxYi8nYEWNRWnubaoWQVhiWU32jEy0EXxK6NGpc/fdsRcncivgccr18CjCq7cfHOzIe9PrFGtiim2n05+7WsKCovx8EkeKuhK0LG+PY6G33/ta9VFYZEM1+9n4VNXxQ9Kn7pWxl+339wRs1E1SzhZGSPo0r03vq68KSyS4caDbDR1UbwJ1NSlEq69pvV9+J1MWJsZwEj/n/d3RysjFBXLkPK/GxyGejpKTReKZbKSNRHv9Sd4/wqLZIj5+2mZ1yTipQYsL7t29wmsTBWviUPlkmvy6k2fdjWrQF9XB4cjys8WGoUvinHtZipa1VWcFtmqzke4FFt2cxUjgwpK/waKikrfUxWPd/20Ggz0dLHrlPp3HAWAF8UyJDzOgZe9YrMdL3tz3EgpezuAslSzMkZ67pu3m6lW2RgZOeV3SxoidfBOXSV/+eUXFBUVoWHDhti7dy8SExMRGxuL5cuXlznN73VcXFxw9epVHD9+HAkJCZg+ffo7N81wdHRE//79MWjQIBw4cAC3bt3CqVOn5A1ORo4ciYyMDPTq1QthYWFITk7GiRMnMGjQoNcWMePHj0dwcDDmzp2LhIQEBAYGYuXKlZgwYcJb50pNTcXhw4fRv39/1KxZU+HRv39/HDp0CKmpqRg2bBiSkpLg7++P+Ph47Ny5U6Hxyvvk6OiI4OBgpKSk4MkTsW3Ov+jWF8FHDiD46EHcv3MLm1ctQdqjFLT5omRftu3rV2D5whny17f5oitSHz3E5l+W4v6dWwg+ehAhRw+iU/evAQD6+gb42MlF4WFsYgqpkRE+dnKRj06u//kHnPnzCMZOmw9DIyM8yUjDk4w0FBSo/+jTL0fj8HWLaujTzBmutmaY38cLdpWMsDk4EQAwvXtt/DL0n9+/Y9ceoGN9ewxs7QIHK2M0ql4ZC7+uh7+S0pCSWT4bUfxyLB59mzuj92fOcK1qhnm965Zcg5D/XYNutfHLkMZKX9e3mTOu3kxD3IMspXP1nCuhY72P4GBljMauVtg9vgV0JBIsP6L+nUYBYMOpW+jR2B7dGn6EatYmmObnBlsLQ+y8UDLlfGKHGljSu7bS1/VobI9rt58gIUW57bmergRutmZwszWDnq4OqphL4WZrBody0lRgy7k7+KrBR+hS3w7OVsaY3LEGqlaU4tfLJUWqf9vq+KH7P50Df494iMzc51jwVU1UszZGfScLfNe+BvZefYCC/3XVDI1LRa/G9vD1rAI7C0M0damE0Z9XR0jMY6UP8+oo8PxtdK1nhy/r2cLZyhiTfGugqrkUv/2vm+jYNi5Y8NU/1+SPyBRk5hZiXhcPVLMyRj1HC0xo54r9f/1zTUp1qW+H4NjHSm3g1d3yA5EY+Lkb+vl8ghofWWDRYG/YW5liw9HrAIA5/Rpjw7h/tpv5I+w2OjdxwrftPeBoY4YmblWwZOhnuBL/CA9f2Y5kwOduOHzplnx/t/Jgb8RDtHe3Rls3K3xsIcWwTx1gbWKA3//XJXRQE3t899KebF/WroKmThawM5fCwdIQg5rYo5lLJRyK+mfqdd8Gdqj/sTmqmBmgWmUjjG/ljGqVjeTfU5MYG+rD09UOnq4lN4Qd7SrB09UO9lXebvkG0bt4p33cnJycEB4ejvnz52P8+PF4+PAhrKysUK9ePaxevfqtv8+wYcMQERGBHj16QCKRoFevXhgxYgSOHj36TuFXr16NqVOnYsSIEUhPT8fHH3+MqVOnAgBsbW1x/vx5TJo0CW3btkVBQQEcHBzQrl076OiUXa96eXkhKCgIM2bMwNy5c1G1alXMmTMHAwYMeOtMW7duhbGxcZmbkbds2RKmpqbYtm0b/P39sXfvXowbNw6//PILGjZsiAULFmDQoEHvdA3expIlS+Dv74/169fDzs5OadsBVfJu2QZPszOxe+t6PMlIw8eO1TB14XJYVymZvvokIw1pj/9587epaofvFy7H5lVLcOxgECwrWWHQqIlKe7j9m9LtBmaMG6JwfOR3M9GqXaf/+FN9WPsv34WFiQEm+tWETUVDxN7PQo+fTsk7JNpUNMRHlf75YL3r7C2YSPXwrY8r5vbyQlbuc5yNeYTZv0UI+gn+uwNhd2Fpoo+JnT1KrsGDLPRcevqfa2AuhZ2lYnFhaqiHjvXtMXVHeJnf00BPB1O7esLBygQ5BS/wZ9TfGL7uErLLScvqPyIewsJYH6PbVoeVmQESHj7DoHVX5F0irc0MYGuhOC3JVFoB7TyrYs5rGklYm0lxZOJn8udDWlXDkFbVcOlmOnqt+jAzAt6no1EpqGikh5Gtq8HK1ACJKU8xdEu4fF87KzMD2L40VSv3eREGbfwL0zp9gj2jmiAz9zmORT9CwPFE+WtWh5RMhxzTpjpszA2QkfMcobGpCq9RZ8eiH6GikT6Gt/zfNXn0DMO2XsPD0mtiaoCqL03Fzn1ehG83X8XUL9zw24jGyMwtxPHrKVh+UnE/ModKRqjnaIHBm8pf06M9527C0swAU3vWRxVLY9y4kw6/2b/j7v/2cKtiaQR7q3/WkW8PjoepoT6GdayFH75piqxnz3Eq6gGmbbmo8H1dbM3h7WGLDtMPoTw5fTMdZtIK6NvgI1ga6+F2ei6+/z0Oj//X6KeSkT6sX5puq6cjwRBvB1Q20UfBi2LcycjF94fjEPbSyLaJQQWMbeEMC2M95BQUISktB/77YxD/WP2nor8rL3cHnNgwRv580YSuAIBthy5hyMztomKprXI48UetSGT/l0VcpJWuP1C+Q6/tmk0uX3+gVUFSDroyqpqZpXpvOyGCgcE73TfUCrq6/N15VXKU+m9grWqftvUSHUGtnNvI4uhVeddWio7wWk9y1XfbEAujt18WJco7TZUkIiIiIiIi1WPhRkREREREpOZYuBEREREREak5Fm5ERERERERqjqvDiYiIiIjog2NXyf+GI25ERERERERqjoUbERERERGRmuNUSSIiIiIi+uAk4FzJ/4IjbkRERERERGqOhRsREREREZGa41RJIiIiIiL64NhV8r/hiBsREREREZGaY+FGRERERESk5jhVkoiIiIiIPjjOlPxvOOJGRERERESk5li4ERERERERqTlOlSQiIiIiog+PcyX/E464ERERERERqTkWbkRERERERGqOUyWJiIiIiOiDk3Cu5H/CETciIiIiIiI1x8KNiIiIiIhIzXGqJBERERERfXASzpT8TzjiRkREREREpOZYuBEREREREak5TpUkIiIiIqIPjjMl/xuOuBEREREREak5Fm5ERERERERqjlMliYiIiIjow+Ncyf+EI25ERERERERqjoUbERERERGRmuNUSSIiIiIi+uAknCv5n3DEjYiIiIiISM2xcCMiIiIiIlJznCpJREREREQfnIQzJf8TjrgRERERERGpORZuREREREREak4ik8lkokMQvYuCggIsXLgQU6ZMgYGBgeg4aoHXRBmviSJeD2W8Jsp4TZTxmijjNVHGa0KqwMKNyp3s7GyYm5sjKysLZmZmouOoBV4TZbwming9lPGaKOM1UcZroozXRBmvCakCp0oSERERERGpORZuREREREREao6FGxERERERkZpj4UbljoGBAWbOnMnFvy/hNVHGa6KI10MZr4kyXhNlvCbKeE2U8ZqQKrA5CRERERERkZrjiBsREREREZGaY+FGRERERESk5li4ERERERERqTkWbkRERERERGqOhRsRaYzMzExs2LABU6ZMQUZGBgAgPDwcDx48EJyMiIiI6L9hV0kqt65evYrc3Fw0a9ZMdBQhzp49i7Vr1yIpKQl79uyBnZ0dtm3bBicnJ3z66aei46lcVFQUfHx8YG5ujtu3byM+Ph7Ozs6YPn067ty5g61bt4qOqFKHDh0q87hEIoFUKoWLiwucnJxUnEqsy5cv49ChQygsLISPjw/atGkjOhKVM0VFRTh8+DD8/PxERyEiLcTCjcotNzc3JCQkoKioSHQUldu7dy++/vpr9OnTB9u2bUNMTAycnZ3xyy+/4Pfff8eRI0dER1Q5Hx8feHl5YdGiRTA1NUVkZCScnZ1x4cIF9O7dG7dv3xYdUaV0dHQgkUjw6lt86TGJRIJPP/0UBw4cgIWFhaCUqrN//35069YNUqkUFSpUwNOnT7FkyRKMHTtWdDQqB+Li4rBp0yYEBgbiyZMneP78uehIRKSFOFWSyq3g4GAkJyeLjiHEvHnzsGbNGqxfvx56enry402bNkV4eLjAZOJcuXIFQ4cOVTpuZ2eHlJQUAYnEOnnyJBo0aICTJ08iKysLWVlZOHnyJBo2bIjff/8dZ86cQXp6OiZMmCA6qkosWLAAAwYMQGZmJjIzMzF79mzMmzdPdCxSYzk5Odi0aRO8vb3h4eGB8PBwzJ8/H3///bfoaMIEBwdj6tSpGDx4MAYNGqTwIKIPr4LoAET/V7a2tqIjCBMfH1/mFFEzMzNkZmaqPpAakEqlyM7OVjoeHx8PKysrAYnEGjNmDNatW4emTZvKj7Vu3RpSqRRDhgzBjRs3EBAQoDUfuOLj47Fjxw5UqFDyZ2/ixImYNWsW0tLSULlyZcHp1NOVK1fQoEED0TFU7uLFi9iwYQOCgoJQvXp19OnTB5cvX8by5cvh7u4uOp4ws2fPxpw5c1C/fn1UrVoVEolEdCQircPCjcqFpKQkBAQEIDY2FhKJBG5ubhgzZgyqVasmOpoQVatWxc2bN+Ho6Khw/Ny5c3B2dhYTSrDOnTtjzpw5CAoKAlAyJfDu3buYPHkyunbtKjid6iUlJcHMzEzpuJmZmXykunr16khLS1N1NCGePXuGihUryp8bGBjA0NAQ2dnZWl24PXv2DLq6ujA0NJQfi4iIwPTp03HkyBGtm4ru7u6O3Nxc9O7dG5cvX5YXapMnTxacTLw1a9Zgy5Yt+Prrr0VHUQvOzs64cuUKKlWqpHA8MzMTXl5eWjsjiD4sTpUktXf8+HG4u7sjLCwMnp6eqFmzJi5fvgwPDw+cPHlSdDwhhg4dijFjxuDy5cuQSCT4+++/sWPHDkyYMAEjRowQHU+In376CampqbC2tkZeXh6aN28OFxcXmJqaYv78+aLjqVy9evUwceJEpKamyo+lpqbiu+++k4+iJCYm4qOPPhIVUeWOHz+OQ4cOyR/FxcUIDg5WOKYt7t+/D29vb5ibm8Pc3Bz+/v7Izc1Fv3790KBBAxgYGODcuXOiY6rczZs30axZM7Rs2RJubm6i46iV58+fK4zga7vbt2+XeWOjoKCAnYzpg2FzElJ7devWRdu2bfHDDz8oHJ88eTJOnDihtWu6vv/+eyxbtgz5+fkASkYQJkyYgLlz5wpOJlZISAjCw8NRXFwMLy8v+Pj4iI4kRHx8PDp37oxbt27B3t5ePgLp7OyMgwcPwtXVFQcOHMDTp0+14g66js6/36eUSCRaM8LUt29fREdH49tvv8XevXtx5swZ1KlTB7Vr18b06dO1ruNoqQcPHmDLli3YvHkz8vLy0KtXL/Tp0weNGjVCRESEVk+VnDRpEkxMTDB9+nTRUYQqvcHj5+eHwMBAmJuby88VFRUhODgYJ0+eRHx8vKiIpMFYuJHak0qliI6ORvXq1RWOJyQkwNPTU164aIuioiKcO3cOtWrVglQqRUxMDIqLi+Hu7g4TExPR8YR48eIFpFIpIiIiULNmTdFx1IZMJsPx48eRkJAAmUyGTz75BJ9//vlbFTGk2ezs7BAUFARvb2+kpKTA1tYWCxYs4JTAl4SEhGDTpk3Yt28f8vPzMWHCBAwePBiurq6io6mMv7+//L+Li4sRGBgIT09PeHp6KjTGAoClS5eqOp4Qpe+fZXXt1dPTg6OjI5YsWYKOHTuKiEcajoUbqT17e3ssXboU3bp1UzgeFBSECRMm4O7du4KSiSOVShEbG6u1d8XLUq1aNezbtw+1a9cWHYVI7enq6uLBgweoUqUKAMDY2BhXr17l9MAyZGVlYceOHdi0aRPCw8NRs2ZNREVFiY6lEi1btnyr10kkEoSEhHzgNOrFyckJV65c0eo1sqR6bE5Cau/bb7/FkCFDkJycjKZNm0IikeDcuXP48ccfMX78eNHxhKhVqxaSk5NZuL1k2rRpmDJlCrZv3w5LS0vRcdRCcHAwgoOD8fjxYxQXFyuc27Rpk6BUYu3evRu7du1CQkICJBIJqlevjt69e+Orr74SHU3ldHV15f+to6MDqVQqMI36uHPnDk6cOIHCwkK0aNEC7u7uGDFiBEaMGIGIiAit+t0JDQ0VHUFt3bp1S3QE0kIccSO1J5PJEBAQgCVLlsj3z7G1tcXEiRMxevRorWxJfOLECUyaNAlz585FvXr1YGxsrHC+rG6Cmq5u3bq4efMmCgsL4eDgoHRNtG0t5L+17t6/f7+gZGIUFxejV69e2L17N1xdXfHJJ59AJpMhLi4ON2/eRLdu3bBr1y6teT/R0dFBzZo15dsjREVF4ZNPPoG+vr7C67Tt9+bMmTPw9fVFbm4uAKBChQoIDAxEr169BCcTLysrC0VFRUo3xjIyMlChQgWt+LuzfPlyDBkyBFKpFMuXL3/ja0ePHq2iVKRNWLhRufL06VMAgKmpqeAkYr28RunlD5oymUyrGiy8bPbs2W88P3PmTBUlUQ9Vq1bFokWLtKLxyNtYunQp5s+fj8DAQKW1J4cOHcLAgQMxffp0jB07VkxAFfu335dS2vZ707x5c5iZmWHt2rUwNDTElClT8Mcff+DevXuiownXvn17fPHFF0qdi9esWYNDhw7hyJEjgpKpjpOTE65evYpKlSq9ccaLRCLhdgD0QbBwIyqHTp8+/cbzzZs3V1ESUleVKlVCWFiY1u51+CpPT0+MHTv2tRuOb9y4EQEBAYiOjlZxMlInlpaWOHPmjLzJUU5ODszMzJCWlgYLCwvB6cSytLTE+fPnldZBxsXFwdvbG+np6YKSEWkPrnEjtdWyZct/nbYkkUgQHBysokTqg4XZ6/3111/yjdrd3d1Rt25d0ZGEGDx4MHbu3Kn1rbtLJSYmvnFrCB8fH4waNUqFidRHVFSUwpo/T09P0ZGEyczMhLW1tfy5sbExjIyMkJmZqfWFW0FBAV68eKF0vLCwEHl5eQISEWkfFm6kturUqfPac9nZ2di1axcKCgpUF0jNZGZmYuPGjQpFyqBBgxT2lNEmjx8/Rs+ePXHq1ClUrFgRMpkMWVlZaNmyJX799VdYWVmJjqhS+fn5WLduHf7880+tbt1dytDQEJmZmfj444/LPJ+dnQ1DQ0MVpxIrLCwM33zzDWJiYuRtzSUSCTw8PLBx40b5Ru3aJiYmBikpKfLnMpkMsbGx8qn6ALSyuG3QoAHWrVuHFStWKBxfs2YN6tWrJyiVODKZDHv27EFoaGiZDaD27dsnKBlpMk6VpHLlxYsXWLVqFebPnw9zc3PMnTsXPXv2FB1L5a5evYq2bdvC0NAQDRs2hEwmw9WrV5GXl4cTJ07Ay8tLdESV69GjB5KSkrBt2zb5VJ6YmBj0798fLi4u2LVrl+CEqvWmNt7a2Lq7Q4cO+Pjjj7F69eoyzw8bNgz37t3DH3/8oeJkYsTExKBRo0Zwc3PDuHHj4ObmJi9Qli1bhvj4eFy6dEnrNpzW0dEpc38u4J99u7R1HfH58+fh4+ODBg0aoHXr1gBKOtdeuXIFJ06cwGeffSY4oWqNHj0a69atQ8uWLWFjY6M0Q2jz5s2CkpEmY+FG5caOHTswY8YM5ObmYtq0aRg6dKi8I5q2+eyzz+Di4oL169fLr8GLFy8wePBgJCcn48yZM4ITqp65uTn+/PNPpVGCsLAwtGnTBpmZmWKCkVq4cOECWrRoAT8/P0yYMEHeVTI2NhZLlizBwYMHERoaCm9vb9FRVaJbt24oKirC3r17lT5wymQydOnSBXp6eggKChKUUIw7d+681escHBw+cBL1FBkZiUWLFiEiIgKGhobw9PTElClTUL16ddHRVM7S0hLbt2+Hr6+v6CikRVi4kdo7duwYJk+ejFu3bmHChAnw9/dXavWubQwNDXHt2jV88sknCsdjYmJQv359eStrbWJqaoqzZ88qTbG9du0amjdvjuzsbDHBSG3s378fQ4YMQUZGhvyYTCaDpaUl1q5di65duwpMp1pWVlY4evQo6tevX+b5K1euwNfXF6mpqSpORuqosLAQQ4YMwfTp0+Hs7Cw6jlpwcnLC0aNHlf4OE31ILNxIbYWFhWHSpEm4dOkShg0bhu+//x6VK1cWHUst2NjYYNu2bWjTpo3C8ePHj6Nfv3549OiRoGTidO7cGZmZmdi1axdsbW0BAA8ePECfPn1gYWGhFfuWdenSBVu2bIGZmRm6dOnyxtdq6/qL3NxcHD9+HImJiQCAGjVqoE2bNlq3vk0qlSIxMRH29vZlnr937x6qV6+O/Px8FScTLzs7W74n2ZEjRxQacujq6qJDhw6ioglVsWJFhIeHs3D7n8DAQBw7dgybNm3SuvcPEkc755lRudC4cWMYGhpi+PDhcHR0xM6dO8t8nTZuctmjRw988803+Omnn9C0aVNIJBKcO3cOEydO1NqNYleuXInOnTvD0dER9vb2kEgkuHv3LmrVqoXt27eLjqcS5ubm8mlvZmZmWrOZ9Nu4fPkyMjIy0L59e3z55ZcASj54jR07Fjk5OfDz88OKFStgYGAgOKlqODo6Iiws7LWF2+XLl7VyOuDvv/+O6dOn49q1awBK3mtzcnLk5yUSCX777Td89dVXoiIK8+WXX+LAgQPw9/cXHUUtdOvWDbt27YK1tTUcHR2VGkBp2+b1pBoccSO15ejo+FbbAWjjJpfPnz/HxIkTsWbNGvndYD09PQwfPhw//PCD1nz4LMvJkycRFxcHmUwGd3f3N7aAJ+3Rvn17tGjRApMmTQIAREdHo169eujfvz/c3NywePFiDB06FLNmzRIbVEVmzpyJLVu24I8//pDvWVYqOjoaX3zxBfr37//WG3Vrik6dOqFz58745ptvAJRMwY6MjJSPMi1atAinTp3Sis2mXzV//nz89NNPaN26NerVq6e0ZEHbbqJ2794doaGh+Oqrr8psTqJtm9eTarBwIyrHcnNzkZSUBJlMBhcXFxgZGYmORGqiVatW2LdvHypWrKhwPDs7G35+flrXVbJq1ao4fPiwfE3X999/j9OnT+PcuXMAgN27d2PmzJmIiYkRGVNl8vPz0bp1a1y+fBmff/65QifWP//8Ew0bNkRISAikUqngpKrl6OiIPXv2yP+dvFq4RUdHo3Xr1nj8+LHImEI4OTm99pw23kQ1NjbG8ePH8emnn4qOQlqEUyWJyqGsrCwUFRXB0tIStWrVkh/PyMhAhQoV5OsztMno0aPh4uKidNd35cqVuHnzJgICAsQEE+TUqVN4/vy50vH8/HycPXtWQCKxnjx5AhsbG/nz06dPo127dvLnDRo0wL1790REE0IqlSI0NBTLli3Drl27cPr0aQCAq6sr5s2bh3HjxmnlyH1KSgoqVaokfx4aGqowndTExARZWVkiogl369Yt0RHUir29vVb+rSWxdEQHIKJ317NnT/z6669Kx4OCgrRyXzsA2Lt3b5mt3Js2bYo9e/YISCRGVFQUoqKiAJSMnpQ+j4qKwrVr17Bx40bY2dkJTql6NjY28g+ez58/R3h4OJo0aSI///TpU6U1KppOX18fkyZNQkREBHJzc5Gbm4uIiAhMnjwZjx8/xqBBg0RHVDlLS0skJSXJn9evX1/h30ViYiIsLS1FRCM1s2TJEnz33Xe4ffu26CikRThVkqgcsrS0xPnz5+XTm0rFxcXB29sb6enpgpKJI5VKcf36dbi4uCgcv3nzJmrWrKk13fFKNxAGUOYmwoaGhlixYoXWfSgfOnQooqOj8eOPP+LAgQMIDAzE33//DX19fQAl+0QGBATgypUrgpOqh8jISHh5eWndRtM9e/ZEbm4uDh06VOb5jh07wtjYGL/99puKk6mH+/fv49ChQ7h7967SiP7SpUsFpRLDwsICubm5ePHiBYyMjJRu/Ly87QjR+8KpkkTlUEFBgUKL6lKFhYXIy8sTkEg8FxcXHDt2DKNGjVI4fvToUa1qX33r1i3IZDI4OzsjLCwMVlZW8nP6+vqwtraGrq6uwIRizJs3D126dEHz5s1hYmKCwMBAedEGAJs2bVLaXoO0z6RJk9CkSRN069YN3333HVxdXQEA8fHx+PHHH/Hnn3/iwoULglOKERwcjE6dOsHJyQnx8fGoWbMmbt++DZlMBi8vL9HxVE7bpt+TeuCIG1E51KJFC9SqVQsrVqxQOD5y5EhERUVp5RqmTZs2YdSoUZg4cSJatWoFoOSDxpIlSxAQEIBvv/1WcEJSB1lZWTAxMVEqXjMyMmBiYqJQzGkzbR1xA4CDBw9i8ODBSiMmFhYW2LBhA/z8/MQEE6xhw4Zo164d5syZI2/aYm1tjT59+qBdu3YYPny46IhEGo+FG6ml7Ozst36tNi4OPn/+PHx8fNCgQQO0bt0aQEmRcuXKFZw4cQKfffaZ4IRirF69GvPnz8fff/8NoKRD3KxZs9CvXz/BycSJiYkpc1pTp06dBCWi8kCbCzdAeaP26tWro02bNkot8LWJqakpIiIiUK1aNVhYWODcuXPw8PBAZGQkOnfurBVrvfjZhETjVElSSxUrVvzXPdxkMhkkEolWfrDw9vbGxYsXsXjxYgQFBcHQ0BCenp7YuHEjqlevLjqeMMOHD8fw4cORmpoKQ0NDmJiYiI4kTHJyMr788ktER0dDIpHI17uV/l5p4+8N/aNLly5vPJ+ZmamaIGomJCQEo0aNwqVLl+QbtZfKysqCh4cH1qxZo5U3x4yNjVFQUAAAsLW1RVJSEjw8PAAAaWlpIqOpzNt8NinF91j6EFi4kVoKDQ0VHUHt1alTBzt27BAdQy1ZWVnh9OnTyM3NRePGjWFhYSE6ksqNGTMGTk5O+PPPP+Xr3dLT0zF+/Hj89NNPouORYObm5v96XhtHqkunVZc1WmJubo6hQ4di6dKlWlm4NW7cGOfPn4e7uzs6dOiA8ePHIzo6Gvv27UPjxo1Fx1OJlz+b3L59G5MnT8aAAQPkHWovXryIwMBALFy4UFRE0nCcKklUjhQXF6O4uBgVKvxzz+XRo0dYs2YNcnJy0KlTJ63bDHTx4sV49uwZZs+eDaBkJLZ9+/Y4ceIEAMDa2hrBwcHyO8PaonLlyggJCYGnpyfMzc0RFhaGGjVqICQkBOPHj8e1a9dERyRSOw4ODjh27JhSx95ScXFxaNOmDe7evaviZOIlJyfj2bNn8PT0RG5uLiZMmIBz587BxcUFy5Ytg4ODg+iIKtW6dWsMHjwYvXr1Uji+c+dOrFu3DqdOnRITjDQaCzcqN3Jzc8tcq+Pp6SkokeoNHDgQenp6WLduHYCSvac8PDyQn5+PqlWrIiYmBgcPHoSvr6/gpKrj5eWFSZMmoUePHgCA3bt3o3///jh58iTc3NzQr18/GBkZISgoSHBS1bKwsMBff/0FZ2dnVKtWDRs2bEDLli2RlJSEWrVqITc3V3REIrXzum1FSt28eRO1atXS2u699A8jIyNERkYqLU9ISEhAnTp1+B5LHwQ34Ca1l5qaio4dO8LU1BQeHh6oW7euwkObnD9/Hl999ZX8+datW/HixQskJiYiMjIS/v7+WLx4scCEqnfr1i2F4v3IkSPo2rUrvL29YWlpiWnTpuHixYsCE4pRs2ZN+UbcjRo1wqJFi3D+/HnMmTNHq7ZHIHoXdnZ2iI6Ofu35qKgoVK1aVYWJ1EtmZiY2bNiAKVOmyLtuhoeH48GDB4KTqZ69vT3WrFmjdHzt2rWwt7cXkIi0Ade4kdobO3Ysnjx5gkuXLqFly5bYv38/Hj16hHnz5mHJkiWi46nUgwcPFO7uBQcHo2vXrvL1Kv3798fmzZtFxROisLAQBgYG8ucXL17EmDFj5M9tbW21ZuH8y6ZNm4acnBwAJXuYdezYEZ999hkqVaqEX3/9VXA6IvXk6+uLGTNmoH379pBKpQrn8vLyMHPmTHTs2FFQOrGioqLg4+MDc3Nz3L59G99++y0sLS2xf/9+3LlzB1u3bhUdUaWWLVuGrl274vjx4/I1fpcuXUJSUhL27t0rOB1pKhZupPZCQkJw8OBBNGjQADo6OnBwcMDnn38OMzMzLFy4EB06dBAdUWWkUqnCFJ1Lly4pjLBJpVI8e/ZMRDRhXFxccObMGTg7O+Pu3btISEhA8+bN5efv37+PSpUqCUwoRtu2beX/7ezsjJiYGGRkZMDCwuKtu6IRaZtp06Zh3759cHV1xahRo1CjRg1IJBLExsZi1apVKCoqwvfffy86phD+/v4YMGAAFi1aBFNTU/nx9u3bo3fv3gKTieHr64vExESsXr0asbGxkMlk6Ny5M4YNG8YRN/pgWLiR2svJyYG1tTUAwNLSEqmpqXB1dUWtWrUQHh4uOJ1q1a5dG9u2bcPChQtx9uxZPHr0SL7ZNAAkJSXB1tZWYELVGz58OEaNGoWzZ8/i0qVLaNKkCdzd3eXnQ0JCtG5K7etYWlri4cOHmD9/PlauXCk6DpHasbGxwYULFzB8+HBMmTJFYRuNtm3b4pdffoGNjY3glGJcuXIFa9euVTpuZ2eHlJQUAYnE++ijjzB//nyFY+np6QgICMDYsWPFhCKNxsKN1F6NGjUQHx8PR0dH1KlTB2vXroWjoyPWrFmjdWsNpk+fDl9fXwQFBeHhw4cYMGCAwjXYv38/vL29BSZUvaFDh6JChQr4/fff0axZM8ycOVPh/N9//41BgwYJSidGTEwMQkNDoaenh+7du6NixYpIS0vD/PnzsWbNGjg5OYmOSKS2HBwccOTIETx58gQ3b96ETCZD9erVtXJbkZdJpdIyN6COj4+HlZWVgETqQyaT4cSJE9i4cSMOHjwIMzMzFm70QbCrJKm9HTt2oLCwEAMGDMC1a9fQtm1bpKenQ19fH1u2bJF3E9QWMTExOHnyJKpUqYJu3bpBR+efHkPr1q1Dw4YNUadOHXEBSajff/8dXbt2RWFhIYCSaZLr169H9+7dUbNmTYwfP15r1+gQ0f/dkCFDkJqaiqCgIFhaWiIqKgq6urrw8/NDs2bNEBAQIDqiyt2+fRubNm3Cli1b8ODBA/Tu3Rv9+/dHy5YtoaurKzoeaSAWblTu5ObmIi4uDh9//DEqV64sOg6RWmnSpAkaNmyI+fPnY926dZgwYQKqV6+O9evXo1mzZqLjEVE5lZ2dDV9fX9y4cQNPnz6Fra0tUlJS0KRJExw5cgTGxsaiI6pEQUEB9u3bhw0bNuDChQvyNX69evVCZGSkwlR9oveNhRsRkQapWLEiwsLC4OrqihcvXkAqleLw4cNo37696GhEpAFCQkIQHh6O4uJieHl5wcfHR3QklapcuTLc3d3Rt29fdOvWTT6FVk9Pj4UbfXBc40Zqyd/fH3PnzoWxsTH8/f3f+NqlS5eqKBWR+svOzkbFihUBABUqVIChoSFcXV3FhiIijdGqVSt5U6zMzEyxYQQoKiqCRCKBRCLhdEhSORZupJauXbsmX6MTHh7+2vblbGtOpCwmJkbe5U0mkyE+Pl6+p1uplzctJyL6Nz/++CMcHR3l68q7d++OvXv3okqVKjhy5Ahq164tOKFqPHz4EHv37sXGjRsxZswYtG/fHn379uXnEVIJTpUkIo0WGxuLDh06IDk5WXQUldDR0YFEIkFZb+2lxyUSCYqKigSkI6LyytnZGdu3b0fTpk1x8uRJdO/eHb/99huCgoJw9+5dnDhxQnRElUtKSsLmzZsRGBiIBw8eoFevXhgwYABatWrF0Tj6IFi4kVorXaMTERGBmjVrio4jXFhYGOrVqyf/g1D6IbxUQUEBDh48iO7du4uKqHYiIyPh5eWlNYXKnTt33up1Dg4OHzgJEWkSQ0NDJCQkwN7eHmPGjEF+fj7Wrl2LhIQENGrUCE+ePBEdUZji4mIcP34cGzduxOHDh2Fqaoq0tDTRsUgDcaokqbUKFSrAwcFBaz50/5smTZrg4cOH8g3Jzc3NERERAWdnZwAl6w169erFwk2LsSAjog/BwsIC9+7dg729PY4dO4Z58+YBKLmBqO1/o3V0dNC+fXu0b98eqamp2LZtm+hIpKF0/v0lRGJNmzYNU6ZMQUZGhugowr06QF7WgDkH0YmI6H3r0qULevfujc8//xzp6enyTrURERFwcXERnE59WFlZ/WtTNaL/K464kdpbvnw5bt68CVtbWzg4OCjtFRMeHi4omXriAmkiInrfli1bBkdHR9y7dw+LFi2CiYkJgJJmHSNGjBCcjkg7sHAjtefn5yc6AqkxCwuLNxarL168UGEaIiLNpKenhwkTJigdHzt2rOrDEGkpFm6k9mbOnCk6glp5tdV7XFwcnj17BgBauRh62bJlHGUkIlKBbdu2Ye3atUhOTsbFixfh4OCAgIAAODk5oXPnzqLjfXDZ2dkwMzMTHYO0GLtKUrmQmZmJPXv2ICkpCRMnToSlpSXCw8NhY2MDOzs70fFUhq3e6W3NmjULAwcOZLMSInovVq9ejRkzZmDs2LGYP38+rl+/DmdnZ2zZsgWBgYEIDQ0VHfGD09XVlTcIa9WqFfbt24eKFSuKjkVahIUbqb2oqCj4+PjA3Nwct2/fRnx8PJydnTF9+nTcuXMHW7duFR1RZdjqXdmmTZvQp08fGBgYiI6iVurVq4fIyEg0b94c33zzDbp06QKpVCo6FhGVU+7u7liwYAH8/PxgamqKyMhIODs74/r162jRooVWzPgwNzfHpUuX4ObmBh0dHTx69AhWVlaiY5EWYeFGas/HxwdeXl5YtGiRwh+LCxcuoHfv3rh9+7boiCTQy3dAAcDW1hYXLlyAo6Oj2GBqICoqCps3b8bOnTvx/Plz9OzZE4MGDUKDBg1ERyOicsbQ0BBxcXFwcHBQ+FucmJgIT09P5OXliY74wXXt2hXnz5+Hm5sbTp8+jaZNm0JfX7/M14aEhKg4HWkDbgdAau/KlSsYOnSo0nE7Ozv5Wi8qsW/fPnh6eoqOoVKv3nt6+vQpiouLBaVRL56enli2bBkePHiATZs24cGDB/D29katWrXw888/IysrS3REIionnJycEBERoXT86NGjcHNzU30gAbZv345Zs2ahfv36AAAPDw/Url27zAfRh8DmJKT2pFIpsrOzlY7Hx8dr5RSF9evX48SJE9DT08OYMWPQqFEjhISEYPz48YiPj8fXX38tOiKpmeLiYjx//hwFBQWQyWSwtLTE6tWrMX36dKxfvx49evQQHZGI1NzEiRMxcuRI5OfnQyaTISwsDLt27cKCBQuwceNG0fFUwtDQEMOGDQMAXL16FT/++CPXuJFKcaokqb0hQ4YgNTUVQUFBsLS0RFRUFHR1deHn54dmzZohICBAdESV+emnnzB16lR4enoiNjYWAPD9999j6dKl+H//7/9h5MiRqFy5suCUqqWrq4uUlBR5EW9mZobIyEg4OTkJTibeX3/9hc2bN2PXrl0wMDBAv379MHjwYPlmuUuWLMGiRYvw6NEjwUmJqDxYv3495s2bh3v37gEomfkye/ZstG3bVqsahb2q9KM0OxzTh8bCjdRednY2fH19cePGDTx9+hS2trZISUlBkyZNcOTIEaUNuTWZm5sbJk6ciEGDBuHUqVNo1aoVWrVqhT179mjtXT8dHR2Ym5vL/2BmZmbCzMwMOjqKM8EzMjJExBOmtLhv06YNvv32W3zxxRfQ1dVVeE1qaipsbGw4tZSI3klaWhqKi4tRVFSEBQsWYMOGDVqxxu1VW7duxeLFi5GYmAgAcHV1xcSJEznzhT4YTpUktWdmZoZz584hJCQE4eHhKC4uhpeXF3x8fERHU7k7d+7If+4WLVpAT08P8+fP19qiDQA2b94sOoJa6tatGwYNGvTGu+BWVlYs2ojojTIzMzFy5Ej5FP3Jkydj1KhRmD17Nn766Se4u7tj06ZNomOq3NKlSzF9+nSMGjUK3t7ekMlkOH/+PIYNG4a0tDSMGzdOdETSQBxxIypHdHR0kJKSIu+g+HJnL6KXzZkzBxMmTICRkZHC8by8PCxevBgzZswQlIyIypMRI0bg8OHD6NGjB44dO4bY2Fi0bdsW+fn5mDlzJpo3by46ohBOTk6YPXs2+vXrp3A8MDAQs2bNwq1btwQlI03Gwo3U1uXLl5GRkYH27dvLj23duhUzZ85ETk4O/Pz8sGLFCq3av0tHRwfz5s2DiYkJAGDSpEmYOHGi0rq20aNHi4gnRFhYGOrVqyefBli6CXmpgoICHDx4EN27dxcVUYhXt0kolZ6eDmtra27STkRvxcHBARs3boSPjw+Sk5Ph4uKC0aNHa9X68rJIpVJcv35dvma4VGJiImrVqoX8/HxByUiTsXAjtdW+fXu0aNECkyZNAgBER0fDy8sLAwYMgJubGxYvXoyhQ4di1qxZYoOqkKOj478ufpZIJEhOTlZRIvFeLVDMzMwQEREhH4V89OgRbG1tta5Qed3msCEhIejRowdSU1MFJSOi8kRPTw937tyBra0tAMDIyAhhYWGoWbOm4GRi1axZE71798bUqVMVjs+bNw+//fYboqOjBSUjTcY1bqS2IiIiMHfuXPnzX3/9FY0aNcL69esBAPb29pg5c6ZWFW7cbFzZq/eeyroXpU33pywsLCCRSCCRSODq6qpQ6BcVFeHZs2fydtZERP+muLgYenp68ue6urpa1RTsdWbPno0ePXrgzJkz8Pb2hkQiwblz5xAcHIygoCDR8UhDsXAjtfXkyRPY2NjIn58+fRrt2rWTP2/QoIG8JTHRm2hTi+aAgADIZDIMGjQIs2fPhrm5ufycvr4+HB0d0aRJE4EJiag8kclkGDBggHxZQn5+PoYNG6ZUvO3bt09EPGG6du2Ky5cvY9myZThw4ABkMhnc3d0RFhaGunXrio5HGoqFG6ktGxsb3Lp1C/b29nj+/DnCw8Mxe/Zs+fmnT58q3AXUBnl5eQgODkbHjh0BAFOmTEFBQYH8vK6uLubOnQupVCoqIgnWv39/ACUL55s2bap1vyNE9H6VvqeU6tu3r6Ak6qdevXrYvn276BikRVi4kdpq164dJk+ejB9//BEHDhyAkZERPvvsM/n5qKgoVKtWTWBC1du6dSt+//13eeG2cuVKeHh4wNDQEAAQFxcHW1tbrWtDHBMTg5SUFAAld4fj4uLw7NkzACX7DWmL7OxsmJmZAQDq1q2LvLy81+6tVPo6IqI34ZYrROqDzUlIbaWmpqJLly44f/48TExMEBgYiC+//FJ+vnXr1mjcuDHmz58vMKVqNWvWDOPGjZNfh1e3A9i+fTtWrVqFixcvioypUjo6OpBIJGWuYys9LpFItKI5ycuNWkqvy6u06XoQERFpEo64kdqysrLC2bNnkZWVBRMTE3m791K7d++Wt8XXFgkJCXB1dZU/l0ql0NHRkT9v2LAhRo4cKSKaMNwr5x8hISGwtLQEAISGhgpOQ0RERO8TR9yIyhFDQ0NERESgRo0aZZ6Pi4tDnTp1uH8MERERkYbR+feXEJG6+Oijj3D9+vXXno+KisJHH32kwkTq48qVK/D390fHjh3xxRdfwN/fH1evXhUdS5hjx47h3Llz8uerVq1CnTp10Lt3bzx58kRgMiIizXHz5k0cP35cvp6Y4yH0IbFwIypHfH19MWPGjDJH1PLy8jB79mx06NBBQDKxvvvuOzRq1AgbNmzA/fv3cffuXaxfvx6NGjWSb+CubSZOnIjs7GwAJZvX+/v7w9fXF8nJyfD39xecjoiofEtPT4ePjw9cXV3h6+uLhw8fAgAGDx6M8ePHC05HmopTJYnKkUePHqFOnTrQ19fHqFGj5Bssx8XFYeXKlXjx4gWuXbumsP+dpgsMDMSwYcOwePFiDB06VN7+vrCwEKtXr8akSZOwdu1a9OvXT3BS1TIxMcH169fh6OiIWbNm4fr169izZw/Cw8Ph6+sr78JJRETvrl+/fnj8+DE2bNgANzc3eaOwEydOYNy4cbhx44boiKSB2JyEqByxsbHBhQsXMHz4cEyePFk+JUMikeDzzz/HL7/8olVFG1AyBXDBggUYNWqUwnE9PT2MHj0aL168wMqVK7WucNPX10dubi4A4M8//5T//JaWlvKROCIi+r85ceIEjh8/rrQ8oXr16rhz546gVKTpWLgRlTNOTk44duwYMjIycPPmTQCAi4uLvJugtrlx4wY6d+782vN+fn6YPn26ChOpB29vb/j7+8Pb2xthYWH47bffAJR0JtXWdZBERO9LTk4OjIyMlI6npaXBwMBAQCLSBlzjRlROWVpaomHDhmjYsKHWFm1Ayd5lz58/f+35wsJCpa0ktMGqVaugp6eHPXv2YPXq1bCzswMAHD16FO3atROcjoiofGvWrBm2bt0qfy6RSFBcXIzFixejZcuWApORJuMaNyIq11q2bIlPP/0Uc+fOLfP8tGnTcO7cOZw6dUq1wQR68eIFduzYgTZt2qBq1aqi4xARaZyYmBi0aNEC9erVQ0hICDp16oQbN24gIyMD58+fR7Vq1URHJA3EqZJEVK6NHz8efn5+KCgowPjx4+Vr/FJSUrBkyRIEBARg//79glOqVoUKFTB8+HDExsaKjkJEpJHc3d0RFRWF1atXQ1dXFzk5OejSpQtGjhzJG2b0wXDEjYjKvRUrVmDChAl48eIFzM3NAQBZWVnQ1dXFokWLMHbsWLEBBWjZsiXGjBkDPz8/0VGIiIjoPWDhRkQa4f79+9i9ezcSExMBAK6urujatSvs7e0FJxNj9+7dmDx5MsaNG4d69erB2NhY4bynp6egZERE5d/mzZthYmKCbt26KRzfvXs3cnNz0b9/f0HJSJOxcCOicm3QoEH4+eefYWpqKjqKWtHRUe49JZFIIJPJIJFIUFRUJCAVEZFmqFGjBtasWaPUiOT06dMYMmQI4uPjBSUjTcbCjYjKNV1dXTx8+BDW1taio6iVf9tHyMHBQUVJiIg0j1QqRVxcHBwdHRWO3759G25ubsjLyxMTjDQam5MQUbnGe09lY2FGRPThWFtbIyoqSqlwi4yMRKVKlcSEIo3HfdyIqNyTSCSiI6ilbdu2wdvbG7a2tvIRuICAABw8eFBwMiKi8q1nz54YPXo0QkNDUVRUhKKiIoSEhGDMmDHo2bOn6HikoVi4EVG55+rqCktLyzc+tM3q1avh7+8PX19fZGZmyte0VaxYEQEBAWLDERGVc/PmzUOjRo3QunVrGBoawtDQEG3atEGrVq2wYMEC0fFIQ3GNGxGVazo6OggICJBvA/A62tbhy93dHQsWLICfnx9MTU0RGRkJZ2dnXL9+HS1atEBaWproiERE5V5CQgIiIyNhaGiIWrVqcZo6fVBc40ZE5V7Pnj3ZnOQVt27dQt26dZWOGxgYICcnR0AiIiLN4+rqCldXV9ExSEuwcCOico3r28rm5OSEiIgIpbu/R48ehbu7u6BURESaoaioCFu2bEFwcDAeP36M4uJihfMhISGCkpEmY+FGROUaZ3uXbeLEiRg5ciTy8/Mhk8kQFhaGXbt2YeHChdiwYYPoeERE5dqYMWOwZcsWdOjQATVr1uRNRFIJrnEjItJQ69evx7x583Dv3j0AgJ2dHWbNmoVvvvlGcDIiovKtcuXK2Lp1K3x9fUVHIS3Cwo2ISMOlpaWhuLiY6wCJiN4TW1tbnDp1iuvbSKW4HQARkQaaPXs2kpKSAJTcGWbRRkT0/owfPx4///wzp+uTSnHEjYhIA3l6euLGjRto0KAB+vbtix49esDKykp0LCIijfDll18iNDQUlpaW8PDwgJ6ensL5ffv2CUpGmoyFGxGRhrpx4wZ27NiBX3/9Fffv34ePjw/69u0LPz8/GBkZiY5HRFRuDRw48I3nN2/erKIkpE1YuBERaYHz589j586d2L17N/Lz85GdnS06EhEREb0DrnEjItICxsbGMDQ0hL6+PgoLC0XHISIionfEETciIg1169Yt7Ny5Ezt27EBCQgKaNWuG3r17o1u3bjA3Nxcdj4ioXNuzZw+CgoJw9+5dPH/+XOFceHi4oFSkyTjiRkSkgZo0aQIXFxfs3r0bAwcOxJ07dxASEoLBgwezaCMi+o+WL1+OgQMHwtraGteuXUPDhg1RqVIlJCcno3379qLjkYbiiBsRkQaaOnUq+vTpAw8PD9FRiIg0zieffIKZM2eiV69eMDU1RWRkJJydnTFjxgxkZGRg5cqVoiOSBmLhRkRERET0DoyMjBAbGwsHBwdYW1vj5MmTqF27NhITE9G4cWOkp6eLjkgaqILoAERE9H74+/tj7ty5MDY2hr+//xtfu3TpUhWlIiLSPFWqVEF6ejocHBzg4OCAS5cuoXbt2rh16xY35aYPhoUbEZGGuHbtmrxj5LVr1177OolEoqpIREQaqVWrVjh8+DC8vLzwzTffYNy4cdizZw+uXr2KLl26iI5HGopTJYmIiIiI3kFxcTGKi4tRoULJGEhQUBDOnTsHFxcXDBs2DPr6+oITkiZi4UZERERERKTmOFWSiEgD5eTk4IcffkBwcDAeP36M4uJihfPJycmCkhERaYbMzEyEhYWV+R7br18/QalIk7FwIyLSQIMHD8bp06fx9ddfo2rVqlzXRkT0Hh0+fBh9+vRBTk4OTE1NFd5jJRIJCzf6IDhVkohIA1WsWBF//PEHvL29RUchItI4rq6u8PX1xYIFC2BkZCQ6DmkJHdEBiIjo/bOwsIClpaXoGEREGunBgwcYPXo0izZSKRZuREQaaO7cuZgxYwZyc3NFRyEi0jht27bF1atXRccgLcOpkkREGqhu3bpISkqCTCaDo6Mj9PT0FM6Hh4cLSkZEVD4dOnRI/t+pqamYM2cOBg4ciFq1aim9x3bq1EnV8UgLsHAjItJAs2fPfuP5mTNnqigJEZFm0NF5u4lqEokERUVFHzgNaSMWbkRERERERGqOa9yIiIiIiIjUHAs3IiINoqOjA11dXaWHhYUFGjdujH379omOSERUboWEhMDd3R3Z2dlK57KysuDh4YEzZ84ISEbagFMliYg0yMGDB8s8npmZibCwMGzevBmBgYHo1q2bipMREZV/nTp1QsuWLTFu3Lgyzy9fvhyhoaHYv3+/ipORNmDhRkSkRVatWoWtW7fi8uXLoqMQEZU7Dg4OOHbsGNzc3Mo8HxcXhzZt2uDu3bsqTkbagFMliYi0SJs2bZCQkCA6BhFRufTo0SOl1v8vq1ChAlJTU1WYiLQJCzciIi2Sl5cHqVQqOgYRUblkZ2eH6Ojo156PiopC1apVVZiItAkLNyIiLbJ+/XrUrVtXdAwionLJ19cXM2bMQH5+vtK5vLw8zJw5Ex07dhSQjLQB17gREWkQf3//Mo9nZWXh6tWrSEpKwtmzZ1m8ERH9Hzx69AheXl7Q1dXFqFGjUKNGDUgkEsTGxmLVqlUoKipCeHg4bGxsREclDcTCjYhIg7Rs2bLM42ZmZvjkk08wYsQIODg4qDgVEZHmuHPnDoYPH47jx4+j9GO0RCJB27Zt8csvv8DR0VFsQNJYLNyIiIiIiN7RkydPcPPmTchkMlSvXh0WFhaiI5GGY+FGRERERESk5tichIiIiIiISM2xcCMiIiIiIlJzLNyIiIiIiIjUHAs3IiIiIiIiNcfCjYiIiIiISM2xcCMiIiIiIlJzLNyIiIiIiIjU3P8HbTlcmfY8GQQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# making a correlation matrix to find the relation between the columns\n",
    "plt.figure(figsize=(10,10))\n",
    "correlation=df.corr()\n",
    "sns.heatmap(correlation,annot=True,cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "f6706fbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Serial No.           0.042336\n",
       "GRE Score            0.802610\n",
       "TOEFL Score          0.791594\n",
       "University Rating    0.711250\n",
       "SOP                  0.675732\n",
       "LOR                  0.669889\n",
       "CGPA                 0.873289\n",
       "Research             0.553202\n",
       "Chance of Admit      1.000000\n",
       "Name: Chance of Admit , dtype: float64"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# making a correlation matrix to find the relation between the columns\n",
    "admit_chance = df.corr()['Chance of Admit ']\n",
    "admit_chance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "7de770d5",
   "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>Chance of Admit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Serial No.</th>\n",
       "      <td>0.042336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GRE Score</th>\n",
       "      <td>0.802610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOEFL Score</th>\n",
       "      <td>0.791594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University Rating</th>\n",
       "      <td>0.711250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOP</th>\n",
       "      <td>0.675732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOR</th>\n",
       "      <td>0.669889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGPA</th>\n",
       "      <td>0.873289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Research</th>\n",
       "      <td>0.553202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chance of Admit</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Chance of Admit \n",
       "Serial No.                 0.042336\n",
       "GRE Score                  0.802610\n",
       "TOEFL Score                0.791594\n",
       "University Rating          0.711250\n",
       "SOP                        0.675732\n",
       "LOR                        0.669889\n",
       "CGPA                       0.873289\n",
       "Research                   0.553202\n",
       "Chance of Admit            1.000000"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "admit_chance=pd.DataFrame(admit_chance)\n",
    "admit_chance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "fa377533",
   "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>Chance of Admit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GRE Score</th>\n",
       "      <td>0.802610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOEFL Score</th>\n",
       "      <td>0.791594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University Rating</th>\n",
       "      <td>0.711250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOP</th>\n",
       "      <td>0.675732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOR</th>\n",
       "      <td>0.669889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGPA</th>\n",
       "      <td>0.873289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Research</th>\n",
       "      <td>0.553202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Chance of Admit \n",
       "GRE Score                  0.802610\n",
       "TOEFL Score                0.791594\n",
       "University Rating          0.711250\n",
       "SOP                        0.675732\n",
       "LOR                        0.669889\n",
       "CGPA                       0.873289\n",
       "Research                   0.553202"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "admit_chance = admit_chance.drop(['Chance of Admit ','Serial No.'])\n",
    "admit_chance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "bca65658",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting heatmap for 'Chance of Admit ' vs all other columns\n",
    "sns.heatmap(admit_chance,annot=True,cmap='crest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "1f1cf62d",
   "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>Serial No.</th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>337</td>\n",
       "      <td>118</td>\n",
       "      <td>4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.65</td>\n",
       "      <td>1</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>324</td>\n",
       "      <td>107</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>8.87</td>\n",
       "      <td>1</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>316</td>\n",
       "      <td>104</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>322</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.67</td>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>314</td>\n",
       "      <td>103</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Serial No.  GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  \\\n",
       "0           1        337          118                  4  4.5   4.5  9.65   \n",
       "1           2        324          107                  4  4.0   4.5  8.87   \n",
       "2           3        316          104                  3  3.0   3.5  8.00   \n",
       "3           4        322          110                  3  3.5   2.5  8.67   \n",
       "4           5        314          103                  2  2.0   3.0  8.21   \n",
       "\n",
       "   Research  Chance of Admit   \n",
       "0         1              0.92  \n",
       "1         1              0.76  \n",
       "2         1              0.72  \n",
       "3         1              0.80  \n",
       "4         0              0.65  "
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MULTIREGRESSION MODEL\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "b93e6a3d",
   "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>Serial No.</th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>396</td>\n",
       "      <td>324</td>\n",
       "      <td>110</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>9.04</td>\n",
       "      <td>1</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>397</td>\n",
       "      <td>325</td>\n",
       "      <td>107</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>9.11</td>\n",
       "      <td>1</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>398</td>\n",
       "      <td>330</td>\n",
       "      <td>116</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.45</td>\n",
       "      <td>1</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>399</td>\n",
       "      <td>312</td>\n",
       "      <td>103</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.78</td>\n",
       "      <td>0</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>400</td>\n",
       "      <td>333</td>\n",
       "      <td>117</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.66</td>\n",
       "      <td>1</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Serial No.  GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  \\\n",
       "395         396        324          110                  3  3.5   3.5  9.04   \n",
       "396         397        325          107                  3  3.0   3.5  9.11   \n",
       "397         398        330          116                  4  5.0   4.5  9.45   \n",
       "398         399        312          103                  3  3.5   4.0  8.78   \n",
       "399         400        333          117                  4  5.0   4.0  9.66   \n",
       "\n",
       "     Research  Chance of Admit   \n",
       "395         1              0.82  \n",
       "396         1              0.84  \n",
       "397         1              0.91  \n",
       "398         0              0.67  \n",
       "399         1              0.95  "
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "f2049b5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Serial No.', 'GRE Score', 'TOEFL Score', 'University Rating', 'SOP',\n",
       "       'LOR ', 'CGPA', 'Research', 'Chance of Admit '],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "9cf54c67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Serial No.           0\n",
       "GRE Score            0\n",
       "TOEFL Score          0\n",
       "University Rating    0\n",
       "SOP                  0\n",
       "LOR                  0\n",
       "CGPA                 0\n",
       "Research             0\n",
       "Chance of Admit      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "5fa54e7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.  , 337.  , 118.  , ...,   4.5 ,   9.65,   1.  ],\n",
       "       [  2.  , 324.  , 107.  , ...,   4.5 ,   8.87,   1.  ],\n",
       "       [  3.  , 316.  , 104.  , ...,   3.5 ,   8.  ,   1.  ],\n",
       "       ...,\n",
       "       [398.  , 330.  , 116.  , ...,   4.5 ,   9.45,   1.  ],\n",
       "       [399.  , 312.  , 103.  , ...,   4.  ,   8.78,   0.  ],\n",
       "       [400.  , 333.  , 117.  , ...,   4.  ,   9.66,   1.  ]])"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df.iloc[:,:-1].values  #input data cols\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "5cbeb7f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.92, 0.76, 0.72, 0.8 , 0.65, 0.9 , 0.75, 0.68, 0.5 , 0.45, 0.52,\n",
       "       0.84, 0.78, 0.62, 0.61, 0.54, 0.66, 0.65, 0.63, 0.62, 0.64, 0.7 ,\n",
       "       0.94, 0.95, 0.97, 0.94, 0.76, 0.44, 0.46, 0.54, 0.65, 0.74, 0.91,\n",
       "       0.9 , 0.94, 0.88, 0.64, 0.58, 0.52, 0.48, 0.46, 0.49, 0.53, 0.87,\n",
       "       0.91, 0.88, 0.86, 0.89, 0.82, 0.78, 0.76, 0.56, 0.78, 0.72, 0.7 ,\n",
       "       0.64, 0.64, 0.46, 0.36, 0.42, 0.48, 0.47, 0.54, 0.56, 0.52, 0.55,\n",
       "       0.61, 0.57, 0.68, 0.78, 0.94, 0.96, 0.93, 0.84, 0.74, 0.72, 0.74,\n",
       "       0.64, 0.44, 0.46, 0.5 , 0.96, 0.92, 0.92, 0.94, 0.76, 0.72, 0.66,\n",
       "       0.64, 0.74, 0.64, 0.38, 0.34, 0.44, 0.36, 0.42, 0.48, 0.86, 0.9 ,\n",
       "       0.79, 0.71, 0.64, 0.62, 0.57, 0.74, 0.69, 0.87, 0.91, 0.93, 0.68,\n",
       "       0.61, 0.69, 0.62, 0.72, 0.59, 0.66, 0.56, 0.45, 0.47, 0.71, 0.94,\n",
       "       0.94, 0.57, 0.61, 0.57, 0.64, 0.85, 0.78, 0.84, 0.92, 0.96, 0.77,\n",
       "       0.71, 0.79, 0.89, 0.82, 0.76, 0.71, 0.8 , 0.78, 0.84, 0.9 , 0.92,\n",
       "       0.97, 0.8 , 0.81, 0.75, 0.83, 0.96, 0.79, 0.93, 0.94, 0.86, 0.79,\n",
       "       0.8 , 0.77, 0.7 , 0.65, 0.61, 0.52, 0.57, 0.53, 0.67, 0.68, 0.81,\n",
       "       0.78, 0.65, 0.64, 0.64, 0.65, 0.68, 0.89, 0.86, 0.89, 0.87, 0.85,\n",
       "       0.9 , 0.82, 0.72, 0.73, 0.71, 0.71, 0.68, 0.75, 0.72, 0.89, 0.84,\n",
       "       0.93, 0.93, 0.88, 0.9 , 0.87, 0.86, 0.94, 0.77, 0.78, 0.73, 0.73,\n",
       "       0.7 , 0.72, 0.73, 0.72, 0.97, 0.97, 0.69, 0.57, 0.63, 0.66, 0.64,\n",
       "       0.68, 0.79, 0.82, 0.95, 0.96, 0.94, 0.93, 0.91, 0.85, 0.84, 0.74,\n",
       "       0.76, 0.75, 0.76, 0.71, 0.67, 0.61, 0.63, 0.64, 0.71, 0.82, 0.73,\n",
       "       0.74, 0.69, 0.64, 0.91, 0.88, 0.85, 0.86, 0.7 , 0.59, 0.6 , 0.65,\n",
       "       0.7 , 0.76, 0.63, 0.81, 0.72, 0.71, 0.8 , 0.77, 0.74, 0.7 , 0.71,\n",
       "       0.93, 0.85, 0.79, 0.76, 0.78, 0.77, 0.9 , 0.87, 0.71, 0.7 , 0.7 ,\n",
       "       0.75, 0.71, 0.72, 0.73, 0.83, 0.77, 0.72, 0.54, 0.49, 0.52, 0.58,\n",
       "       0.78, 0.89, 0.7 , 0.66, 0.67, 0.68, 0.8 , 0.81, 0.8 , 0.94, 0.93,\n",
       "       0.92, 0.89, 0.82, 0.79, 0.58, 0.56, 0.56, 0.64, 0.61, 0.68, 0.76,\n",
       "       0.86, 0.9 , 0.71, 0.62, 0.66, 0.65, 0.73, 0.62, 0.74, 0.79, 0.8 ,\n",
       "       0.69, 0.7 , 0.76, 0.84, 0.78, 0.67, 0.66, 0.65, 0.54, 0.58, 0.79,\n",
       "       0.8 , 0.75, 0.73, 0.72, 0.62, 0.67, 0.81, 0.63, 0.69, 0.8 , 0.43,\n",
       "       0.8 , 0.73, 0.75, 0.71, 0.73, 0.83, 0.72, 0.94, 0.81, 0.81, 0.75,\n",
       "       0.79, 0.58, 0.59, 0.47, 0.49, 0.47, 0.42, 0.57, 0.62, 0.74, 0.73,\n",
       "       0.64, 0.63, 0.59, 0.73, 0.79, 0.68, 0.7 , 0.81, 0.85, 0.93, 0.91,\n",
       "       0.69, 0.77, 0.86, 0.74, 0.57, 0.51, 0.67, 0.72, 0.89, 0.95, 0.79,\n",
       "       0.39, 0.38, 0.34, 0.47, 0.56, 0.71, 0.78, 0.73, 0.82, 0.62, 0.96,\n",
       "       0.96, 0.46, 0.53, 0.49, 0.76, 0.64, 0.71, 0.84, 0.77, 0.89, 0.82,\n",
       "       0.84, 0.91, 0.67, 0.95])"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df.iloc[:,-1].values #outputdata cols\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "475bdd1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.69, 0.57, 0.72, 0.96, 0.88, 0.82, 0.68, 0.89, 0.94, 0.64, 0.96,\n",
       "       0.88, 0.76, 0.69, 0.67, 0.81, 0.83, 0.94, 0.65, 0.42, 0.65, 0.79,\n",
       "       0.63, 0.77, 0.65, 0.77, 0.8 , 0.58, 0.36, 0.76, 0.8 , 0.7 , 0.86,\n",
       "       0.87, 0.92, 0.78, 0.54, 0.78, 0.93, 0.84, 0.47, 0.49, 0.83, 0.7 ,\n",
       "       0.8 , 0.74, 0.91, 0.95, 0.49, 0.61, 0.64, 0.58, 0.7 , 0.62, 0.78,\n",
       "       0.94, 0.66, 0.62, 0.54, 0.85, 0.8 , 0.95, 0.71, 0.66, 0.69, 0.82,\n",
       "       0.73, 0.84, 0.63, 0.72, 0.8 , 0.81, 0.66, 0.96, 0.64, 0.68, 0.85,\n",
       "       0.82, 0.7 , 0.93, 0.59, 0.79, 0.86, 0.93, 0.71, 0.6 , 0.65, 0.8 ,\n",
       "       0.76, 0.34, 0.79, 0.92, 0.75, 0.78, 0.89, 0.38, 0.47, 0.52, 0.89,\n",
       "       0.72, 0.67, 0.89, 0.64, 0.64, 0.74, 0.61, 0.91, 0.86, 0.93, 0.81,\n",
       "       0.86, 0.76, 0.42, 0.87, 0.97, 0.48, 0.47, 0.52, 0.64, 0.78])"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Set as Training data and Testing data\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.30)\n",
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "83587eda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.84679949, -0.02898139,  0.47409116, -0.01253038,  0.64528924,\n",
       "         1.1800472 ,  0.34272857,  0.91766294],\n",
       "       [ 1.47887947, -0.4700026 , -1.49494012, -1.76678412, -2.40847394,\n",
       "        -1.0600424 , -1.92373199, -1.08972474],\n",
       "       [ 0.61784566, -0.91102381, -0.34633854, -0.88965725, -0.88159235,\n",
       "        -0.50002   , -0.61880015,  0.91766294],\n",
       "       [-1.14860516,  1.73510344,  0.80226304,  1.74172336,  1.66321031,\n",
       "         1.7400696 ,  2.02540384,  0.91766294],\n",
       "       [-1.37939773,  0.50024406,  0.47409116,  1.74172336,  1.66321031,\n",
       "         0.6200248 ,  0.89217356,  0.91766294],\n",
       "       [ 1.72742531,  0.67665254,  0.47409116, -0.01253038,  0.13632871,\n",
       "         0.0600024 ,  0.78915262,  0.91766294],\n",
       "       [ 1.3901131 , -1.35204502, -0.51042448, -0.88965725,  0.13632871,\n",
       "         0.0600024 , -1.18541529,  0.91766294],\n",
       "       [ 1.51438602,  0.67665254,  0.47409116, -0.01253038,  0.13632871,\n",
       "        -0.50002   ,  1.09821543,  0.91766294],\n",
       "       [ 0.74211858,  2.08792041,  0.80226304,  0.86459649,  1.66321031,\n",
       "         1.1800472 ,  1.85370228,  0.91766294],\n",
       "       [-1.28175472, -0.02898139, -0.83859636, -0.01253038, -1.39055288,\n",
       "        -0.50002   , -2.02675292, -1.08972474],\n",
       "       [ 1.63865894,  1.6468992 ,  1.62269274,  1.74172336,  1.66321031,\n",
       "         1.7400696 ,  2.12842477,  0.91766294],\n",
       "       [-0.10116196,  0.67665254,  0.80226304,  1.74172336,  1.66321031,\n",
       "         1.7400696 ,  0.85783325,  0.91766294],\n",
       "       [ 0.97291115,  0.32383557, -0.51042448, -0.01253038, -0.37263182,\n",
       "         0.0600024 ,  0.27404795,  0.91766294],\n",
       "       [ 0.0319876 , -1.61665775, -0.34633854, -0.01253038,  0.13632871,\n",
       "         0.6200248 , -0.06935516, -1.08972474],\n",
       "       [ 1.75405522, -0.38179836, -0.67451042, -0.01253038,  0.13632871,\n",
       "         0.6200248 ,  0.34272857, -1.08972474],\n",
       "       [ 0.7243653 , -0.38179836, -0.1822526 , -0.01253038,  0.64528924,\n",
       "         0.0600024 ,  0.35989873,  0.91766294],\n",
       "       [ 0.60009238,  0.94126527,  0.96634898,  0.86459649,  1.15424978,\n",
       "         1.7400696 ,  0.96085418, -1.08972474],\n",
       "       [-1.55693047,  2.08792041,  2.11495056,  1.74172336,  1.15424978,\n",
       "         1.1800472 ,  1.75068134,  0.91766294],\n",
       "       [ 1.01729433, -0.73461533, -0.51042448, -0.88965725, -0.88159235,\n",
       "        -0.50002   , -0.87635249, -1.08972474],\n",
       "       [-0.93556586, -1.0874323 , -1.16676824,  0.86459649, -1.89951341,\n",
       "        -1.0600424 , -1.27126607, -1.08972474],\n",
       "       [-1.51254729, -1.44024926, -1.65902606, -0.88965725, -0.37263182,\n",
       "        -0.50002   , -0.82484202,  0.91766294],\n",
       "       [ 1.38123646,  0.94126527,  0.31000522, -0.01253038,  0.13632871,\n",
       "         0.6200248 ,  0.32555842,  0.91766294],\n",
       "       [-1.61906693,  0.14742709,  0.47409116, -0.01253038,  0.64528924,\n",
       "        -0.50002   ,  0.37706888, -1.08972474],\n",
       "       [-0.05677878, -0.02898139,  0.31000522, -0.01253038,  0.13632871,\n",
       "        -0.50002   ,  0.30838826, -1.08972474],\n",
       "       [-0.30532462, -1.26384078, -0.83859636, -0.01253038,  0.13632871,\n",
       "         1.7400696 , -0.42992844, -1.08972474],\n",
       "       [-0.61600692, -1.17563654, -0.34633854,  1.74172336,  1.66321031,\n",
       "         1.1800472 ,  0.11951655, -1.08972474],\n",
       "       [ 0.73324194,  0.41203982,  0.6381771 , -0.01253038, -0.88159235,\n",
       "        -0.50002   ,  0.54877044,  0.91766294],\n",
       "       [ 1.25696354, -0.73461533, -0.1822526 , -0.01253038, -0.37263182,\n",
       "        -0.50002   , -0.58445984, -1.08972474],\n",
       "       [-0.9444425 , -1.17563654, -1.33085418, -0.01253038, -1.39055288,\n",
       "        -1.0600424 , -1.58032887, -1.08972474],\n",
       "       [ 0.84863822, -0.55820684, -0.01816666, -0.01253038,  0.13632871,\n",
       "         0.0600024 ,  0.15385686, -1.08972474],\n",
       "       [ 1.05280088,  0.94126527,  0.96634898,  0.86459649,  0.13632871,\n",
       "        -0.50002   ,  0.18819717,  0.91766294],\n",
       "       [ 0.33379326, -0.55820684, -0.51042448, -0.01253038, -1.39055288,\n",
       "         0.0600024 , -0.36124781, -1.08972474],\n",
       "       [-0.42959754,  0.41203982,  0.80226304,  1.74172336,  1.66321031,\n",
       "         1.7400696 ,  0.82349293,  0.91766294],\n",
       "       [-1.397151  ,  1.38228648,  1.62269274,  0.86459649,  1.15424978,\n",
       "         0.6200248 ,  0.89217356, -1.08972474],\n",
       "       [-1.05096215,  0.32383557,  0.47409116,  1.74172336,  1.66321031,\n",
       "         1.1800472 ,  1.09821543,  0.91766294],\n",
       "       [ 1.59427575,  0.50024406, -0.51042448, -0.01253038,  0.13632871,\n",
       "         0.6200248 ,  0.44574951,  0.91766294],\n",
       "       [ 1.02617097, -1.61665775, -1.0026823 , -0.88965725, -1.89951341,\n",
       "        -1.6200648 , -1.23692576, -1.08972474],\n",
       "       [-1.31726127,  1.55869496,  1.4586068 ,  0.86459649,  0.64528924,\n",
       "        -0.50002   , -0.99654358,  0.91766294],\n",
       "       [ 0.75099521,  1.29408223,  1.4586068 ,  1.74172336,  0.64528924,\n",
       "         0.6200248 ,  1.16689605,  0.91766294],\n",
       "       [-0.64263684,  0.85306102,  0.80226304, -0.01253038,  0.13632871,\n",
       "        -0.50002   ,  0.89217356,  0.91766294],\n",
       "       [ 1.29247009, -1.0874323 , -1.65902606, -0.88965725, -1.89951341,\n",
       "        -1.6200648 , -1.61466918, -1.08972474],\n",
       "       [ 1.28359345, -0.02898139, -1.49494012, -1.76678412, -1.89951341,\n",
       "        -1.6200648 , -1.97524245, -1.08972474],\n",
       "       [-0.47398073,  0.85306102,  1.13043492, -0.01253038, -0.37263182,\n",
       "        -0.50002   ,  0.90934371,  0.91766294],\n",
       "       [-1.29950799,  0.50024406,  0.47409116, -0.01253038, -0.37263182,\n",
       "         0.0600024 , -0.99654358, -1.08972474],\n",
       "       [-1.75221649,  0.50024406,  0.47409116, -0.01253038,  0.13632871,\n",
       "        -1.0600424 ,  0.15385686,  0.91766294],\n",
       "       [-1.10422197,  0.94126527,  0.80226304, -0.01253038, -0.37263182,\n",
       "        -0.50002   ,  0.23970764,  0.91766294],\n",
       "       [ 1.43449628,  1.91151193,  1.29452086,  1.74172336,  1.15424978,\n",
       "         1.7400696 ,  1.11538558,  0.91766294],\n",
       "       [-1.57468375,  1.55869496,  1.95086462,  1.74172336,  1.66321031,\n",
       "         1.1800472 ,  1.9223829 ,  0.91766294],\n",
       "       [ 1.66528885, -1.79306623, -1.65902606, -0.88965725, -1.89951341,\n",
       "        -1.6200648 , -1.33994669, -1.08972474],\n",
       "       [-1.19298834,  0.94126527,  1.13043492, -0.01253038, -0.37263182,\n",
       "        -0.50002   ,  0.75481231, -1.08972474],\n",
       "       [-0.97994905,  0.14742709, -0.1822526 , -0.88965725,  0.64528924,\n",
       "         0.6200248 , -1.13390482,  0.91766294],\n",
       "       [ 0.7953784 , -0.82281957, -0.34633854, -0.88965725, -0.88159235,\n",
       "        -0.50002   , -1.59749903, -1.08972474],\n",
       "       [ 0.44918955, -0.02898139, -1.33085418, -0.88965725, -0.88159235,\n",
       "        -0.50002   ,  0.720472  , -1.08972474],\n",
       "       [-1.6101903 , -1.17563654, -0.83859636, -0.01253038,  0.13632871,\n",
       "        -0.50002   , -0.13803579, -1.08972474],\n",
       "       [-0.31420126,  0.50024406,  0.47409116,  1.74172336,  1.15424978,\n",
       "         0.6200248 ,  0.66896153, -1.08972474],\n",
       "       [-1.1574818 ,  1.38228648,  1.78677868,  1.74172336,  1.66321031,\n",
       "         1.7400696 ,  1.81936197,  0.91766294],\n",
       "       [-1.00657896,  0.05922285, -0.01816666, -0.88965725,  0.13632871,\n",
       "        -0.50002   , -0.51577921, -1.08972474],\n",
       "       [ 0.91965132, -0.29359412, -0.1822526 , -0.88965725, -0.88159235,\n",
       "        -1.6200648 , -0.25822688, -1.08972474],\n",
       "       [-1.64569685, -0.20538988, -0.34633854, -0.01253038,  0.13632871,\n",
       "        -1.0600424 , -0.4814389 , -1.08972474],\n",
       "       [ 0.31603999,  0.76485678,  0.80226304,  0.86459649,  0.64528924,\n",
       "         1.1800472 ,  1.01236465,  0.91766294],\n",
       "       [ 1.13269062,  0.67665254,  0.80226304,  0.86459649,  0.64528924,\n",
       "         0.0600024 ,  0.32555842,  0.91766294],\n",
       "       [ 1.52326266,  1.73510344,  1.95086462,  0.86459649,  1.15424978,\n",
       "         0.6200248 ,  1.78502166,  0.91766294],\n",
       "       [ 1.1770738 ,  0.23563133,  0.14591928, -0.01253038, -0.37263182,\n",
       "         0.0600024 , -0.06935516,  0.91766294],\n",
       "       [ 0.05861751, -0.55820684, -0.83859636, -0.01253038,  0.13632871,\n",
       "         0.6200248 , -0.96220326,  0.91766294],\n",
       "       [ 1.12381398, -1.88127047, -1.0026823 , -0.88965725, -0.88159235,\n",
       "        -1.6200648 , -1.23692576, -1.08972474],\n",
       "       [ 0.25390353,  0.67665254,  0.6381771 ,  0.86459649, -0.37263182,\n",
       "        -0.50002   ,  0.73764216,  0.91766294],\n",
       "       [-0.0390255 , -0.91102381, -0.34633854, -0.88965725, -0.37263182,\n",
       "        -1.0600424 , -0.55011953, -1.08972474],\n",
       "       [-1.68120339,  0.94126527,  0.6381771 ,  0.86459649,  0.64528924,\n",
       "         1.1800472 ,  0.720472  ,  0.91766294],\n",
       "       [ 1.11493734, -1.52845351, -1.16676824, -0.01253038, -1.39055288,\n",
       "        -1.6200648 , -0.96220326, -1.08972474],\n",
       "       [-0.14554515, -0.02898139, -0.1822526 , -0.88965725, -0.88159235,\n",
       "         0.6200248 , -0.44709859, -1.08972474],\n",
       "       [-0.55387046,  0.85306102,  1.4586068 , -0.88965725,  1.15424978,\n",
       "        -0.50002   ,  0.85783325,  0.91766294],\n",
       "       [-0.491734  ,  0.32383557,  0.96634898, -0.88965725, -1.39055288,\n",
       "        -1.0600424 ,  0.10234639,  0.91766294],\n",
       "       [-0.75803312, -0.55820684, -0.1822526 ,  0.86459649,  1.15424978,\n",
       "         1.1800472 ,  0.78915262,  0.91766294],\n",
       "       [-0.62488356,  1.99971617,  1.13043492,  1.74172336,  0.64528924,\n",
       "         1.1800472 ,  2.02540384,  0.91766294],\n",
       "       [-0.99770233, -0.20538988,  0.14591928, -0.01253038,  1.15424978,\n",
       "         0.0600024 , -0.75616139, -1.08972474],\n",
       "       [-0.33195453,  0.05922285, -0.34633854, -0.01253038,  0.13632871,\n",
       "        -0.50002   , -0.03501485, -1.08972474],\n",
       "       [-0.22543488,  0.32383557,  0.6381771 ,  0.86459649,  1.15424978,\n",
       "         0.0600024 ,  0.49725998,  0.91766294],\n",
       "       [ 1.61202903,  0.67665254,  0.47409116,  0.86459649,  1.15424978,\n",
       "         0.6200248 ,  0.97802434,  0.91766294],\n",
       "       [ 0.67998212,  0.32383557, -1.0026823 , -0.88965725, -0.88159235,\n",
       "        -0.50002   ,  0.06800608, -1.08972474],\n",
       "       [ 0.46694282,  1.6468992 ,  1.29452086,  0.86459649,  1.15424978,\n",
       "         1.1800472 ,  1.88804259,  0.91766294],\n",
       "       [-0.76690976, -0.4700026 , -0.34633854, -0.01253038,  0.13632871,\n",
       "        -0.50002   , -0.22388657,  0.91766294],\n",
       "       [ 1.04392424,  0.67665254,  0.6381771 , -0.01253038, -0.88159235,\n",
       "        -1.6200648 ,  0.37706888,  0.91766294],\n",
       "       [ 1.46112619,  1.20587799,  1.13043492,  0.86459649,  1.15424978,\n",
       "        -0.50002   ,  1.01236465,  0.91766294],\n",
       "       [-1.13972852,  0.41203982,  0.6381771 ,  1.74172336,  1.66321031,\n",
       "         1.7400696 ,  1.49312901,  0.91766294],\n",
       "       [ 1.69191876,  0.14742709, -0.1822526 , -0.01253038, -1.39055288,\n",
       "        -0.50002   ,  0.11951655, -1.08972474],\n",
       "       [ 0.35154654, -1.79306623, -1.0026823 , -1.76678412, -0.88159235,\n",
       "        -0.50002   , -1.54598856, -1.08972474],\n",
       "       [-1.62794357,  0.23563133, -0.1822526 , -0.01253038,  0.64528924,\n",
       "        -0.50002   , -0.99654358,  0.91766294],\n",
       "       [ 0.94628123,  0.76485678,  0.80226304,  0.86459649,  0.64528924,\n",
       "         0.6200248 ,  0.720472  ,  0.91766294],\n",
       "       [ 0.19176707,  0.67665254,  0.96634898,  0.86459649,  1.15424978,\n",
       "         0.6200248 ,  0.35989873, -1.08972474],\n",
       "       [-0.96219578, -1.61665775, -1.49494012, -0.88965725,  0.64528924,\n",
       "        -0.50002   , -0.94503311, -1.08972474],\n",
       "       [ 0.4846961 , -0.82281957,  0.47409116,  0.86459649,  0.64528924,\n",
       "         1.1800472 , -0.36124781, -1.08972474],\n",
       "       [-1.04208551,  0.50024406,  1.29452086,  1.74172336,  0.64528924,\n",
       "         1.1800472 ,  1.33859761,  0.91766294],\n",
       "       [-1.72558658,  0.41203982,  0.31000522, -0.01253038, -0.37263182,\n",
       "         0.6200248 , -0.65314046,  0.91766294],\n",
       "       [-1.16635843,  1.02946951,  1.29452086,  0.86459649,  1.15424978,\n",
       "         0.6200248 ,  0.99519449,  0.91766294],\n",
       "       [-1.36164445,  1.99971617,  1.95086462,  1.74172336,  1.15424978,\n",
       "         0.6200248 ,  1.9223829 , -1.08972474],\n",
       "       [ 1.54989257, -1.0874323 , -1.0026823 , -0.88965725, -1.39055288,\n",
       "        -1.0600424 , -1.58032887, -1.08972474],\n",
       "       [ 1.56764584, -2.32229168, -1.16676824, -1.76678412, -1.89951341,\n",
       "        -1.6200648 , -1.75203043, -1.08972474],\n",
       "       [ 0.64447557, -0.38179836, -1.33085418, -1.76678412, -2.40847394,\n",
       "        -2.18008721, -0.97937342,  0.91766294],\n",
       "       [ 0.76874849,  0.67665254,  1.13043492,  1.74172336,  1.66321031,\n",
       "         1.1800472 ,  0.85783325,  0.91766294],\n",
       "       [ 0.40480636, -0.02898139, -0.34633854, -0.01253038, -0.37263182,\n",
       "         0.0600024 ,  0.25687779, -1.08972474],\n",
       "       [ 0.99954106, -1.35204502, -1.16676824, -0.01253038,  0.13632871,\n",
       "        -0.50002   , -0.92786295, -1.08972474],\n",
       "       [-0.58937701,  1.47049072,  0.96634898,  1.74172336,  0.64528924,\n",
       "         0.6200248 ,  1.20123636,  0.91766294],\n",
       "       [ 0.06749415, -0.99922805, -0.1822526 , -0.88965725, -0.37263182,\n",
       "        -0.50002   , -0.72182108, -1.08972474],\n",
       "       [-1.29063135,  0.32383557, -0.67451042, -0.01253038, -0.37263182,\n",
       "        -0.50002   , -1.51164825, -1.08972474],\n",
       "       [ 0.92852796,  0.41203982,  0.31000522, -0.01253038,  0.13632871,\n",
       "         0.0600024 ,  0.37706888,  0.91766294],\n",
       "       [ 0.83088495, -0.02898139, -1.0026823 , -0.88965725, -0.88159235,\n",
       "        -1.6200648 , -0.44709859,  0.91766294],\n",
       "       [-1.38827436,  0.85306102,  0.96634898,  1.74172336,  1.15424978,\n",
       "         0.6200248 ,  1.40727823,  0.91766294],\n",
       "       [-0.2520648 ,  0.50024406,  0.47409116,  0.86459649,  0.64528924,\n",
       "         1.7400696 ,  0.94368402,  0.91766294],\n",
       "       [-0.82016958,  1.29408223,  1.4586068 ,  1.74172336,  1.66321031,\n",
       "         1.7400696 ,  1.37293792,  0.91766294],\n",
       "       [ 0.39592972,  1.02946951,  0.47409116,  0.86459649,  0.64528924,\n",
       "        -1.0600424 ,  0.75481231,  0.91766294],\n",
       "       [-0.91781259,  1.29408223,  2.11495056, -0.01253038,  0.64528924,\n",
       "         0.6200248 ,  0.65179138,  0.91766294],\n",
       "       [-0.57162374, -0.38179836, -0.67451042, -0.01253038,  1.66321031,\n",
       "         0.6200248 , -0.22388657, -1.08972474],\n",
       "       [-1.25512481, -0.4700026 , -0.51042448, -0.88965725, -1.39055288,\n",
       "        -1.6200648 , -0.4814389 , -1.08972474],\n",
       "       [-0.23431152,  0.41203982,  0.6381771 ,  0.86459649,  0.64528924,\n",
       "         0.6200248 ,  0.66896153,  0.91766294],\n",
       "       [ 0.01423432,  2.08792041,  2.11495056,  1.74172336,  1.15424978,\n",
       "         1.1800472 ,  2.28295617,  0.91766294],\n",
       "       [-1.43265755, -0.82281957,  0.14591928, -0.88965725,  0.64528924,\n",
       "         0.0600024 , -1.51164825, -1.08972474],\n",
       "       [ 1.27471681, -1.88127047, -1.823112  , -0.88965725, -1.89951341,\n",
       "        -1.6200648 , -2.12977386, -1.08972474],\n",
       "       [-0.36746108, -1.70486199, -1.16676824, -1.76678412, -1.89951341,\n",
       "        -1.6200648 , -1.16824513, -1.08972474],\n",
       "       [-1.60131366, -0.38179836, -0.01816666, -0.01253038, -0.37263182,\n",
       "        -1.6200648 , -1.16824513,  0.91766294],\n",
       "       [-1.67232676,  1.02946951,  0.80226304,  0.86459649,  0.64528924,\n",
       "         1.1800472 ,  0.89217356,  0.91766294]])"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Normalization ---->standardscaler--(values are changed to similar values else higher value be evaluated)\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler=StandardScaler()\n",
    "scaler.fit(x_train) #collect data\n",
    "x_train=scaler.transform(x_train)\n",
    "x_test=scaler.transform(x_test)\n",
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "01b6a7bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Training Model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "model=LinearRegression()\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "57871399",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.76, 0.55, 0.66, 0.94, 0.8 , 0.84, 0.62, 0.85, 0.96, 0.53, 1.01,\n",
       "       0.86, 0.77, 0.67, 0.76, 0.75, 0.86, 0.93, 0.63, 0.56, 0.56, 0.81,\n",
       "       0.7 , 0.73, 0.66, 0.71, 0.79, 0.67, 0.52, 0.73, 0.8 , 0.68, 0.84,\n",
       "       0.82, 0.83, 0.8 , 0.56, 0.69, 0.9 , 0.8 , 0.54, 0.54, 0.81, 0.64,\n",
       "       0.7 , 0.75, 0.94, 0.93, 0.55, 0.77, 0.63, 0.58, 0.73, 0.62, 0.79,\n",
       "       0.93, 0.64, 0.67, 0.62, 0.86, 0.81, 0.97, 0.76, 0.65, 0.55, 0.81,\n",
       "       0.62, 0.8 , 0.59, 0.68, 0.8 , 0.73, 0.78, 0.96, 0.63, 0.69, 0.78,\n",
       "       0.87, 0.71, 0.95, 0.67, 0.77, 0.87, 0.87, 0.75, 0.53, 0.62, 0.84,\n",
       "       0.79, 0.55, 0.71, 0.87, 0.68, 0.83, 0.92, 0.57, 0.51, 0.6 , 0.87,\n",
       "       0.73, 0.61, 0.87, 0.62, 0.57, 0.79, 0.66, 0.86, 0.84, 0.9 , 0.81,\n",
       "       0.83, 0.67, 0.6 , 0.81, 1.  , 0.55, 0.49, 0.53, 0.58, 0.82])"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=np.round(model.predict(x_test),2)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "dee36b4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.76 0.69]\n",
      " [0.55 0.57]\n",
      " [0.66 0.72]\n",
      " [0.94 0.96]\n",
      " [0.8  0.88]\n",
      " [0.84 0.82]\n",
      " [0.62 0.68]\n",
      " [0.85 0.89]\n",
      " [0.96 0.94]\n",
      " [0.53 0.64]\n",
      " [1.01 0.96]\n",
      " [0.86 0.88]\n",
      " [0.77 0.76]\n",
      " [0.67 0.69]\n",
      " [0.76 0.67]\n",
      " [0.75 0.81]\n",
      " [0.86 0.83]\n",
      " [0.93 0.94]\n",
      " [0.63 0.65]\n",
      " [0.56 0.42]\n",
      " [0.56 0.65]\n",
      " [0.81 0.79]\n",
      " [0.7  0.63]\n",
      " [0.73 0.77]\n",
      " [0.66 0.65]\n",
      " [0.71 0.77]\n",
      " [0.79 0.8 ]\n",
      " [0.67 0.58]\n",
      " [0.52 0.36]\n",
      " [0.73 0.76]\n",
      " [0.8  0.8 ]\n",
      " [0.68 0.7 ]\n",
      " [0.84 0.86]\n",
      " [0.82 0.87]\n",
      " [0.83 0.92]\n",
      " [0.8  0.78]\n",
      " [0.56 0.54]\n",
      " [0.69 0.78]\n",
      " [0.9  0.93]\n",
      " [0.8  0.84]\n",
      " [0.54 0.47]\n",
      " [0.54 0.49]\n",
      " [0.81 0.83]\n",
      " [0.64 0.7 ]\n",
      " [0.7  0.8 ]\n",
      " [0.75 0.74]\n",
      " [0.94 0.91]\n",
      " [0.93 0.95]\n",
      " [0.55 0.49]\n",
      " [0.77 0.61]\n",
      " [0.63 0.64]\n",
      " [0.58 0.58]\n",
      " [0.73 0.7 ]\n",
      " [0.62 0.62]\n",
      " [0.79 0.78]\n",
      " [0.93 0.94]\n",
      " [0.64 0.66]\n",
      " [0.67 0.62]\n",
      " [0.62 0.54]\n",
      " [0.86 0.85]\n",
      " [0.81 0.8 ]\n",
      " [0.97 0.95]\n",
      " [0.76 0.71]\n",
      " [0.65 0.66]\n",
      " [0.55 0.69]\n",
      " [0.81 0.82]\n",
      " [0.62 0.73]\n",
      " [0.8  0.84]\n",
      " [0.59 0.63]\n",
      " [0.68 0.72]\n",
      " [0.8  0.8 ]\n",
      " [0.73 0.81]\n",
      " [0.78 0.66]\n",
      " [0.96 0.96]\n",
      " [0.63 0.64]\n",
      " [0.69 0.68]\n",
      " [0.78 0.85]\n",
      " [0.87 0.82]\n",
      " [0.71 0.7 ]\n",
      " [0.95 0.93]\n",
      " [0.67 0.59]\n",
      " [0.77 0.79]\n",
      " [0.87 0.86]\n",
      " [0.87 0.93]\n",
      " [0.75 0.71]\n",
      " [0.53 0.6 ]\n",
      " [0.62 0.65]\n",
      " [0.84 0.8 ]\n",
      " [0.79 0.76]\n",
      " [0.55 0.34]\n",
      " [0.71 0.79]\n",
      " [0.87 0.92]\n",
      " [0.68 0.75]\n",
      " [0.83 0.78]\n",
      " [0.92 0.89]\n",
      " [0.57 0.38]\n",
      " [0.51 0.47]\n",
      " [0.6  0.52]\n",
      " [0.87 0.89]\n",
      " [0.73 0.72]\n",
      " [0.61 0.67]\n",
      " [0.87 0.89]\n",
      " [0.62 0.64]\n",
      " [0.57 0.64]\n",
      " [0.79 0.74]\n",
      " [0.66 0.61]\n",
      " [0.86 0.91]\n",
      " [0.84 0.86]\n",
      " [0.9  0.93]\n",
      " [0.81 0.81]\n",
      " [0.83 0.86]\n",
      " [0.67 0.76]\n",
      " [0.6  0.42]\n",
      " [0.81 0.87]\n",
      " [1.   0.97]\n",
      " [0.55 0.48]\n",
      " [0.49 0.47]\n",
      " [0.53 0.52]\n",
      " [0.58 0.64]\n",
      " [0.82 0.78]]\n"
     ]
    }
   ],
   "source": [
    "#Checking how much predicted value is correct,compared to a actual value\n",
    "print(np.concatenate((y_pred.reshape(len(y_pred),1),y_test.reshape(len(y_test),1)),1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "bae90eb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04766666666666667"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mean absolute error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "mae=mean_absolute_error(y_pred,y_test)\n",
    "mae"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}