SARIMAX (2).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fe4ee3d8",
   "metadata": {},
   "source": [
    "### Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "712978b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install yfinance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f8471cec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "# Libraries for Data extraction \n",
    "from datetime import datetime\n",
    "import yfinance as yf\n",
    "\n",
    "\n",
    "#Visualization Libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6196644",
   "metadata": {},
   "source": [
    "### Data Extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1edb76e3",
   "metadata": {},
   "source": [
    "Objective is to get the data of RELIANCE INDUSTRIES from 01/01/2015 to till date.\n",
    "\n",
    "Columns required are Open,High,Low,Close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a719c339",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>438.988831</td>\n",
       "      <td>441.812073</td>\n",
       "      <td>437.676270</td>\n",
       "      <td>439.781311</td>\n",
       "      <td>417.988922</td>\n",
       "      <td>1367720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>439.880371</td>\n",
       "      <td>443.818054</td>\n",
       "      <td>437.998199</td>\n",
       "      <td>438.617340</td>\n",
       "      <td>416.882599</td>\n",
       "      <td>3383424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>438.344940</td>\n",
       "      <td>441.267212</td>\n",
       "      <td>432.946106</td>\n",
       "      <td>433.812897</td>\n",
       "      <td>412.316223</td>\n",
       "      <td>4662967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>430.915344</td>\n",
       "      <td>432.401276</td>\n",
       "      <td>412.093750</td>\n",
       "      <td>414.124512</td>\n",
       "      <td>393.603516</td>\n",
       "      <td>8596809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>414.619812</td>\n",
       "      <td>425.367950</td>\n",
       "      <td>414.570282</td>\n",
       "      <td>423.139069</td>\n",
       "      <td>402.171295</td>\n",
       "      <td>9562421</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-10</th>\n",
       "      <td>2606.000000</td>\n",
       "      <td>2606.000000</td>\n",
       "      <td>2546.000000</td>\n",
       "      <td>2558.399902</td>\n",
       "      <td>2558.399902</td>\n",
       "      <td>3713755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-11</th>\n",
       "      <td>2552.500000</td>\n",
       "      <td>2558.000000</td>\n",
       "      <td>2521.100098</td>\n",
       "      <td>2526.149902</td>\n",
       "      <td>2526.149902</td>\n",
       "      <td>5311657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-12</th>\n",
       "      <td>2524.850098</td>\n",
       "      <td>2532.500000</td>\n",
       "      <td>2465.000000</td>\n",
       "      <td>2471.600098</td>\n",
       "      <td>2471.600098</td>\n",
       "      <td>8163366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-13</th>\n",
       "      <td>2458.399902</td>\n",
       "      <td>2472.899902</td>\n",
       "      <td>2434.600098</td>\n",
       "      <td>2467.600098</td>\n",
       "      <td>2467.600098</td>\n",
       "      <td>9515473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-16</th>\n",
       "      <td>2472.699951</td>\n",
       "      <td>2479.649902</td>\n",
       "      <td>2427.000000</td>\n",
       "      <td>2444.100098</td>\n",
       "      <td>2444.100098</td>\n",
       "      <td>6285468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>1987 rows × 6 columns</p>\n",
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      ],
      "text/plain": [
       "                   Open         High          Low        Close    Adj Close  \\\n",
       "Date                                                                          \n",
       "2015-01-01   438.988831   441.812073   437.676270   439.781311   417.988922   \n",
       "2015-01-02   439.880371   443.818054   437.998199   438.617340   416.882599   \n",
       "2015-01-05   438.344940   441.267212   432.946106   433.812897   412.316223   \n",
       "2015-01-06   430.915344   432.401276   412.093750   414.124512   393.603516   \n",
       "2015-01-07   414.619812   425.367950   414.570282   423.139069   402.171295   \n",
       "...                 ...          ...          ...          ...          ...   \n",
       "2023-01-10  2606.000000  2606.000000  2546.000000  2558.399902  2558.399902   \n",
       "2023-01-11  2552.500000  2558.000000  2521.100098  2526.149902  2526.149902   \n",
       "2023-01-12  2524.850098  2532.500000  2465.000000  2471.600098  2471.600098   \n",
       "2023-01-13  2458.399902  2472.899902  2434.600098  2467.600098  2467.600098   \n",
       "2023-01-16  2472.699951  2479.649902  2427.000000  2444.100098  2444.100098   \n",
       "\n",
       "             Volume  \n",
       "Date                 \n",
       "2015-01-01  1367720  \n",
       "2015-01-02  3383424  \n",
       "2015-01-05  4662967  \n",
       "2015-01-06  8596809  \n",
       "2015-01-07  9562421  \n",
       "...             ...  \n",
       "2023-01-10  3713755  \n",
       "2023-01-11  5311657  \n",
       "2023-01-12  8163366  \n",
       "2023-01-13  9515473  \n",
       "2023-01-16  6285468  \n",
       "\n",
       "[1987 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Defining Parameters\n",
    "start_date = datetime(2015,1,1)\n",
    "end_date = datetime.today()\n",
    "\n",
    "#Data downloading\n",
    "data = yf.download('RELIANCE.NS',start=start_date,end=end_date)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "44277a67",
   "metadata": {},
   "outputs": [
    {
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       "      <td>438.988831</td>\n",
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       "      <td>439.781311</td>\n",
       "      <td>417.988922</td>\n",
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       "      <td>430.915344</td>\n",
       "      <td>432.401276</td>\n",
       "      <td>412.093750</td>\n",
       "      <td>414.124512</td>\n",
       "      <td>393.603516</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>414.619812</td>\n",
       "      <td>425.367950</td>\n",
       "      <td>414.570282</td>\n",
       "      <td>423.139069</td>\n",
       "      <td>402.171295</td>\n",
       "      <td>9562421</td>\n",
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      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2015-01-01  438.988831  441.812073  437.676270  439.781311  417.988922   \n",
       "2015-01-02  439.880371  443.818054  437.998199  438.617340  416.882599   \n",
       "2015-01-05  438.344940  441.267212  432.946106  433.812897  412.316223   \n",
       "2015-01-06  430.915344  432.401276  412.093750  414.124512  393.603516   \n",
       "2015-01-07  414.619812  425.367950  414.570282  423.139069  402.171295   \n",
       "\n",
       "             Volume  \n",
       "Date                 \n",
       "2015-01-01  1367720  \n",
       "2015-01-02  3383424  \n",
       "2015-01-05  4662967  \n",
       "2015-01-06  8596809  \n",
       "2015-01-07  9562421  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c4f68e03",
   "metadata": {},
   "outputs": [
    {
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       "      <th>High</th>\n",
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       "      <th>2023-01-10</th>\n",
       "      <td>2606.000000</td>\n",
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       "      <td>2546.000000</td>\n",
       "      <td>2558.399902</td>\n",
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       "      <th>2023-01-12</th>\n",
       "      <td>2524.850098</td>\n",
       "      <td>2532.500000</td>\n",
       "      <td>2465.000000</td>\n",
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       "    <tr>\n",
       "      <th>2023-01-13</th>\n",
       "      <td>2458.399902</td>\n",
       "      <td>2472.899902</td>\n",
       "      <td>2434.600098</td>\n",
       "      <td>2467.600098</td>\n",
       "      <td>2467.600098</td>\n",
       "      <td>9515473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-16</th>\n",
       "      <td>2472.699951</td>\n",
       "      <td>2479.649902</td>\n",
       "      <td>2427.000000</td>\n",
       "      <td>2444.100098</td>\n",
       "      <td>2444.100098</td>\n",
       "      <td>6285468</td>\n",
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      "text/plain": [
       "                   Open         High          Low        Close    Adj Close  \\\n",
       "Date                                                                          \n",
       "2023-01-10  2606.000000  2606.000000  2546.000000  2558.399902  2558.399902   \n",
       "2023-01-11  2552.500000  2558.000000  2521.100098  2526.149902  2526.149902   \n",
       "2023-01-12  2524.850098  2532.500000  2465.000000  2471.600098  2471.600098   \n",
       "2023-01-13  2458.399902  2472.899902  2434.600098  2467.600098  2467.600098   \n",
       "2023-01-16  2472.699951  2479.649902  2427.000000  2444.100098  2444.100098   \n",
       "\n",
       "             Volume  \n",
       "Date                 \n",
       "2023-01-10  3713755  \n",
       "2023-01-11  5311657  \n",
       "2023-01-12  8163366  \n",
       "2023-01-13  9515473  \n",
       "2023-01-16  6285468  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b6270117",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2015-01-01</th>\n",
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       "      <th>2015-01-02</th>\n",
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       "      <th>2015-01-05</th>\n",
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       "      <th>2015-01-06</th>\n",
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       "      <th>2015-01-07</th>\n",
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       "      <th>2023-01-10</th>\n",
       "      <td>2558.399902</td>\n",
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       "      <th>2023-01-11</th>\n",
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       "      <th>2023-01-16</th>\n",
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       "                  Close\n",
       "Date                   \n",
       "2015-01-01   439.781311\n",
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       "2015-01-05   433.812897\n",
       "2015-01-06   414.124512\n",
       "2015-01-07   423.139069\n",
       "...                 ...\n",
       "2023-01-10  2558.399902\n",
       "2023-01-11  2526.149902\n",
       "2023-01-12  2471.600098\n",
       "2023-01-13  2467.600098\n",
       "2023-01-16  2444.100098\n",
       "\n",
       "[1987 rows x 1 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop({'Adj Close','Volume','Open','High','Low'},axis=1,inplace = True)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "618f7958",
   "metadata": {},
   "source": [
    "### EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "98d9047b",
   "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>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>439.781311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>438.617340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>433.812897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>414.124512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>423.139069</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Close\n",
       "Date                  \n",
       "2015-01-01  439.781311\n",
       "2015-01-02  438.617340\n",
       "2015-01-05  433.812897\n",
       "2015-01-06  414.124512\n",
       "2015-01-07  423.139069"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9c55c244",
   "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>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-01-10</th>\n",
       "      <td>2558.399902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-11</th>\n",
       "      <td>2526.149902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-12</th>\n",
       "      <td>2471.600098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-13</th>\n",
       "      <td>2467.600098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-16</th>\n",
       "      <td>2444.100098</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Close\n",
       "Date                   \n",
       "2023-01-10  2558.399902\n",
       "2023-01-11  2526.149902\n",
       "2023-01-12  2471.600098\n",
       "2023-01-13  2467.600098\n",
       "2023-01-16  2444.100098"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aee05628",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1987 entries, 2015-01-01 to 2023-01-16\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Close   1987 non-null   float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 31.0 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "23fbc318",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Close    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "42cdc665",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b89a093d",
   "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>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1987.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1321.584657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>754.876245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>401.568542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>532.675751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1177.983887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2054.599976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2819.850098</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Close\n",
       "count  1987.000000\n",
       "mean   1321.584657\n",
       "std     754.876245\n",
       "min     401.568542\n",
       "25%     532.675751\n",
       "50%    1177.983887\n",
       "75%    2054.599976\n",
       "max    2819.850098"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b132fc8a",
   "metadata": {},
   "source": [
    "Correlation matrix shows that data is highly correlated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dacd63da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Reliance stock Close Prices')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distplots to check the distribution of data\n",
    "\n",
    "plt.figure(figsize=(17,15))\n",
    "plt.subplot(4,1,4)\n",
    "sns.distplot(data['Close'])\n",
    "plt.title(\"Reliance stock Close Prices\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbdc2ea0",
   "metadata": {},
   "source": [
    "#### To check normality of the data \n",
    "Shapiro test(p-value > 0.05 -- Normally distributed data)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ef901364",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stats=  0.8922933340072632 \n",
      "pvalue =  3.5689167914103643e-35\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "shapiro_test = stats.shapiro(data)\n",
    "print('stats= ', shapiro_test.statistic,'\\npvalue = ',shapiro_test.pvalue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "da551883",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Close')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# QQ Plot\n",
    "\n",
    "import statsmodels.api as sm\n",
    "\n",
    "sm.qqplot(data['Close'], line='q')\n",
    "plt.title('Close')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cf635c0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x198695c4a00>,\n",
       "  <matplotlib.lines.Line2D at 0x198695c4cd0>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x198695c4fa0>,\n",
       "  <matplotlib.lines.Line2D at 0x198695d32b0>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x198695c4730>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x198695d3580>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x198695d3850>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6af923b9",
   "metadata": {},
   "source": [
    "Since pvalue < 0.05 & from the QQ plot, we can infer that data is not normally distributed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27485ecd",
   "metadata": {},
   "source": [
    "Hence to make data normally distributed we need to do minmaxscaling to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5223da24",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.preprocessing import MinMaxScaler\n",
    "# minmax = MinMaxScaler()\n",
    "\n",
    "# data1 = pd.DataFrame()\n",
    "# data1[data.columns] = minmax.fit_transform(data)\n",
    "# data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f9cb49a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Close    0.427729\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.skew()\n",
    "#Allowable range is from -2 to +2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "66e7ec23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Close   -1.240144\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.kurt()\n",
    "#Allowable range is from -7 to +7"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1027144",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c4416bc",
   "metadata": {},
   "source": [
    "https://colab.research.google.com/drive/1M9oLQgGiBubg6LK3XSraJoSyniNMht3_#scrollTo=bxuyYmJVTaUT"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b92e5f2",
   "metadata": {},
   "source": [
    "## Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f1e46381",
   "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>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>439.781311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>438.617340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>433.812897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>414.124512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>423.139069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-10</th>\n",
       "      <td>2558.399902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-11</th>\n",
       "      <td>2526.149902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-12</th>\n",
       "      <td>2471.600098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-13</th>\n",
       "      <td>2467.600098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-16</th>\n",
       "      <td>2444.100098</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1987 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Close\n",
       "Date                   \n",
       "2015-01-01   439.781311\n",
       "2015-01-02   438.617340\n",
       "2015-01-05   433.812897\n",
       "2015-01-06   414.124512\n",
       "2015-01-07   423.139069\n",
       "...                 ...\n",
       "2023-01-10  2558.399902\n",
       "2023-01-11  2526.149902\n",
       "2023-01-12  2471.600098\n",
       "2023-01-13  2467.600098\n",
       "2023-01-16  2444.100098\n",
       "\n",
       "[1987 rows x 1 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0495104d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Close'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c7417b2",
   "metadata": {},
   "source": [
    "## Rolling Mean & Rolling Standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "be3ac1a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of AD-fuller test\n",
      "Test Statistics                  -0.556305\n",
      "p-value                           0.880553\n",
      "No. of lags used                  5.000000\n",
      "Number of observations used    1981.000000\n",
      "critical value (1%)              -3.433655\n",
      "critical value (5%)              -2.863000\n",
      "critical value (10%)             -2.567547\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "def test_stationarity(timeseries):\n",
    "    #Determing rolling statistics\n",
    "    rolmean = timeseries.rolling(12).mean()\n",
    "    rolstd = timeseries.rolling(12).std()\n",
    "    #Plot rolling statistics:\n",
    "    plt.plot(timeseries, color='yellow',label='Original')\n",
    "    plt.plot(rolmean, color='red', label='Rolling Mean')\n",
    "    plt.plot(rolstd, color='black', label = 'Rolling Std')\n",
    "    plt.legend(loc='best')\n",
    "    plt.title('Rolling Mean and Standard Deviation')\n",
    "    plt.show(block=False)\n",
    "    print(\"Results of AD-fuller test\")\n",
    "    adft = adfuller(timeseries,autolag='AIC')\n",
    "    # output for dft will give us without defining what the values are.\n",
    "    #hence we manually write what values does it explains using a for loop\n",
    "    output = pd.Series(adft[0:4],index=['Test Statistics','p-value','No. of lags used','Number of observations used'])\n",
    "    for key,values in adft[4].items():\n",
    "        output['critical value (%s)'%key] =  values\n",
    "    print(output)\n",
    "\n",
    "test_stationarity(data)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1d59b2b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ho: It is non stationary\n",
    "#H1: It is stationary\n",
    "\n",
    "def adfuller_test(sales):\n",
    "    result=adfuller(sales)\n",
    "    labels = ['ADF Test Statistic','p-value','#Lags Used','Number of Observations Used']\n",
    "    for value,label in zip(result,labels):\n",
    "        print(label+' : '+str(value) )\n",
    "    if result[1] <= 0.05:\n",
    "        print(\"strong evidence against the null hypothesis(Ho), reject the null hypothesis. Data has no unit root and is stationary\")\n",
    "    else:\n",
    "        print(\"weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary \")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b80c53a",
   "metadata": {},
   "source": [
    "## Seasonal Decompositions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "66c94937",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PS2eAa2YXda/5ftkJBTy9rJzJg9NIjotcQ0VzV+R7Uye1rT4+Na2AP31mcvd68kfC6TBHHKwDzIqOZPj4xLwjPtb+xHuc/OzjY/n9y+v51lkj+M45owiEwjiN4ZKpBcS5j/w6epMCdhERERGRA/jjnI1UtUSCoXvmbeOEwjQumLB3wNHcEeCONzbx+ZOK97vklfQ+ay3battwGMhKiqytnug9OmHQqNxk/n71tO7nbqeD8fkfBuPXzCriVy+sA2BwegKD9+5U30O8x8mc75xKpz9ERqIHj8vBqrJmfnfJBH53yQQSPC52Nny4dvlvLx7Pzc+tJRgOkZHoZXj2kQ0xP1ZdPasIY+DT0wbjdBicDidX7Dbd4FiiLPEiIiIiIgfQ2hXgkin5/PfzJwLwu5fW4wuGqGzupLHdT22rjx89uYq7397CfR+U8NSyvROPyf6t2NnEwm31WGsJh2M/XfcPL6/nv++XMHFw2iGtiX40XB7tef/iIQwFT4lzk5sSh9vp4DvnjOI/nz+RBI+re3RARqIHgClD0rhyRiG7LjEjoeclyQaqeI+TL586jLQET19X5Yiph11EREREZD+CoTDt/hBDs5I4c0wOf7vqBL7x0DK+8N/FrC5vprUryNljcnhzQ033PjUtvj6scf9nraW8qZO7525hRE4yv30x0uOck+wlLzWOJ7520j7XKz8cv3txHfe+F8kM/+tPjo/JMY9EgsfFut+cj9cVu2HZCR4nn5yczyVT83E5HZw/fhBz1lSRleyN2Tmk7yhgFxEREZHjQiAUxuUwh5Qxe1dG65T4yL/NM6PLa32wtb57m13B+tfPGM7f397K9rp2AqEwbmf/H8z60MJSnl1ezpljchidm3zEGa0Pxq9fWMd9H5TsVV7b5qOm1cfLqyu5ZGrBEZ+nuqWLe9/bTqLHyZKfnxuTOdixEMt58xDJAH/nlVO7n//x05M4aXgm5/RCW8rRp4BdRERERAa8+jYfJ/7+DS6ZWsAfLp1Ihz9Eary7xyHSLZ2RpGCp8ZHhxZlJXi6cmMdLqyv5yxVT+OTkfDbXtOF0GIZnJxEOW/45bxuzb3mTN79/Rvd+/dWuzNmLSxoxBl77zml7rTEeSzWtXXsF6/+8Zhp5qXGMzUth6m9eZ2lpI5dMLcBaS12bn+xD6Ckub+rkqn8toLShg/PHDcIYeOnbp/abYL03pMS5D3ldeem/FLCLiIiIyIDW0hXgtNvmErbw9LJyOv0h5qypAuCsMTn8/tIJLNhWz+D0BGpbfawsa+Ibp49g7sYaclIiweKuZGUAd3x2Ct89dyQjciKB7ajdAtzrTxtGZyDEA/NLeX5Feb8NnO6Zt5W/vrWFETlJbKlp49lvnszl/5zPn9/czF+vmIrjKM31vvutLbgchuduOJmsJC9LSxs5b1xu96iHcXkprK1oxh8M882Hl/H6umoevX5Wd3ZxgMZ2PwleZ/ew8m21bTy+pIwfnj+aa/+9kJL6SBK2V9ZW8dnpQ5QAUI5pCthFREREZEDbUd9Buz/U/XxXsA7w1oYaZt/y1l77/POdbQBMj66NPWbQh0G5x+XoDtY/KjPJy68/OZ53N9dx83NrOaEofY8s4UfT2opmalt9nDE6p8dt//DyBgB8wQ6uOHEIU4ak8Y0zhvPnNzaTGu/mD5dOpLUrQHJc7EYINHcEeHxJGRdPKeh+Tz66vNe4/BQeW7yTUT+f01325zc28b1zR/ODJ1aSleRhTUULY/NSeOz6WcS5nXzhvsWU1newqbqVrbXt/OoT4zh7bC51bT6mFvaQhl2kn+uViTXGmCHGmLnGmPXGmLXGmBuj5RnGmNeNMZuj39N32+cnxpgtxpiNxpjzdyufZoxZHX3tTnMok5BERERE5LhTGV2f+mMTBnWXTStKZ/HPzuGRr8xiSEb8fvddUtrIsOzEQxqWbYzhF58YB8Aji3Zg7YeZz//yxmaG//Tlo5IN/cI73+Pz/128x/l28QVDNHcEKP7xSxT/+KXucq/LwSen5ANw49kjuWZWEQ8v3EHxj19i4q9eY3VZc8zqN29zLZ2BEFfN2v/yWp+cko/lw/r/+pPjWbCtgcv/OZ8dDR0s29GEPxhm5c4mbnh4OavKmiiN9qi/Fc0l8KlpgxmSkaBgXQaE3uphDwLft9YuM8YkA0uNMa8DnwfetNbeaoz5MfBj4EfGmHHAFcB4IB94wxgzylobAv4OXA8sAF4GLgDm7HVGERERERGgvDES0P36k+PZVtvOxupW0hPcZCd7yU728ub3zqC6pYtX1lTR1OnnkikFvLOplrLGTjr9Ia6ZXXRIieoAzhydw4WT8vjfgh38b8EOfn7hWM4fP4g73tgEQF27j5zkuP3u39ThP6QlqUK73QAoa+xkSEZC93NrLTP/8CZNHYE99kn2unjvprNIjS7/ZYzhokl5PLigtHubeZtryUzyMCgl7oDD5HfdJDjQ+1TXFsmeX5y5/yHqJxSmM/cHZ7ByZxPnjRtEMGx5Y3018W4nw7KT+OIpxcS5nTy8cAe3ztnAG+urGZQSx08vHMu8TbV89bRhMR0VINLXeiVgt9ZWApXRx63GmPVAAXAxcEZ0s/uBt4EfRcsftdb6gO3GmC3ADGNMCZBirZ0PYIx5ALgEBewiIiIish8bq9tIjY8E6N84czg3PrqCtRUt3a97XA6GZCTwldOGdZfFIvHaZVMLeGlVJQB3z93Cm+s/XPrtqaXlfG5GYXewvLu3N9bw+f8u5nvnjuKbZ47A6TBYa9lY3crWmnZOH51NknfPf+NXlTV1P16wrX6PgL2uzd8drE8Zksb6yhZ8wTA3fWzMXuefUPDh8P2UOBd/enUjf3p1IwB/uWIKXYEQF08pIM69ZxK3O97YzP0flLDk5+fslR1/a20bje1+Gtr9GEOPifjyUuPJS42MevA4DA9+aeZe23z+pGJunRMZ1n/X56YyvTiDT07OP+BxRY5FvT6H3RhTDEwFFgK50WAea22lMWbXhJsCIj3ou5RFywLRxx8t39d5rifSE09h4f6H3YiIiIjIwLZ8RyPj81OiPcj5PLRwB5+ZNvion/fssbl8/qRi7vughJzkONaUN3NicTqLSxr54ysb+PMbm/jtxRO49ISC7iDXFwx1B/m3v76J21/fRHFmAueOy+Vf727vPnZqvJvfXDyei6dE/hWeu7EWAKfD8P6WOj4zfUj3thVNnUAk4D5v3CBeWVvJPfO27zPATfS6OH98LicWZ/Deljrejh4X4MZHVwDQ2hXky6d+eHOjod3PnW9uBuDJpWVcOePD/72fW1HevR9ARqKnx8z8ByPO7aTk1gsJhW1MjifSX/VqwG6MSQKeAr5jrW05wJCZfb1gD1C+d6G19wD3AEyfPj32k4REREREpN8KhML4gmF8gRAbqlr50QVjgEhA+/hXZ/daPX71yfF4XQ7+OS+SxO6iSfkUZyYS53aypLSRP8xZzy+eX0OS183po7J5almkb+qsMTnMHJrBLXM2UFLfwb/e3c7QrETOGpPD1to23t5Yy30flJDkdfGl+5cAcObobFLi3by3pR5rLcYYugIhbnt1Aw4DU4ekE+9xcunUwVw6df83LP55zXQATh2ZzUWT8rloUh7+UJi7527hn+9s48EFpeSlxnNCURp5qfEsLmkAwGHgl8+tZcbQDIZlJVJa38Edr0emAAzLSmRbXfseyftiQcG6DHS9FrAbY9xEgvWHrLVPR4urjTF50d71PGDXOKEyYMhuuw8GKqLlg/dRLiIiIiLS7aYnV/Hy6sru3t7JQ3onU/u+jMhJ6n48MjeJ604qBuCO1zfxl2jPdFfA1x2sTytK5/vnjWJ8fipfPGUot87ZwL/f284Zo7O5+aJIMrtfPb+W+z4o6Q7WIXJzYHFJI8+tqGD5ziaeWlrGQwt3APD/PjOZwswPh8kfjNGDkhkdDbDj3E5+8rGxjBmUzHcfW8k3H15GYUYCd145lX+8sxWP08Hz3zqZC/78Lmf/v3e6j5ES5+KRr8xienE6r6+r5syDyGAvIh/qlYA9msn938B6a+3tu730PHAdcGv0+3O7lT9sjLmdSNK5kcAia23IGNNqjJlFZEj9tcBfe+MaREREROTYsKWmjWeWlwNw3wcljBmUzOTBaX1Wn93nw+8evBdFA+gfXTCGjEQ304rS91ouzu108NOPj+UTk/MZl5fSXX7WmBzu+6Ck+/m8H55JYWYCmUlefvHcGv42dwtvrK/B63Jw26cndQ+dP1Ifm5DHz55ZQ3qCh9pWH5fc/T4At1w2kdG5yZw0PBNjYFttO5XNXTz8lVnd8+I/uoSbiPTM7GvZh5ifxJhTgHeB1UA4WvxTIkH340AhsAP4jLW2IbrPz4AvEskw/x1r7Zxo+XTgPiCeSLK5b9keLmL69Ol2yZIlB9pERERERAaANeXN3PH6Jt7c8GGCtw2/vWCvJGm9qc0XZMIvXwVg+y0f786k3u4L8uyKci6fPmSvRG0Ho7kzwNsba8hNiWPWsMzu8pueXMnjS8pwOgxvfO90hmbtPyv74ejwB/G6nGyoamFJSSMTB6dywj6WUAuH7QEzy4scL4wxS6210w9n397KEv8e+55/DnD2fvb5PfD7fZQvASbErnYiIiIiMhB0+INc9Nf3AJg8OJWVZc24HKZPg3WAJK+LkTlJuJyOPZY9S/S6uGpm0WEfNzXevc+e88+eWMjjS8r43IzCmAfrAAmeSAgxPj+V8fn7n2qgYF3kyPV6lngRERERkaPhiSUfLiY0KDWO310yEY/r0Huuj4aXbzx1v71XsTatKJ37vnAiM4Zm9NIZReRoUcAuIiIiIsccay1vb6plyuA00hM9WGt5YH4JxZkJDE5P4AfnjY7JWuqxcjhD3o/EGUruJjIgKGAXERERkWPOqrJmvvDfxQB4nA7OHZfL1tp2fn/phCMaZi4i0p/0jzFCIiIiIiKHoKKps/uxPxTmpdWVQCSLuYjIQKEedhERERE55tS2+QD4+1UnUN3SRWlDB6GwJSPR08c1ExGJHQXsIiIiInLMqW7pwmHgvPGDcCobuYgMUBoSLyIiIiLHlIXb6rl77lZG5SYrWBeRAU097CIiIiLHIGstLV1BAqEwWUleANp9QbbXtfO1/y3lV58Yzznjcve7fzhsj6l1sp9cWkZThx+nw/DbF9eRmejh8ycV93W1RESOKgXsIiIiIseQUNjy/Mpyfv7MGtr9IeLcDq6bXcyC7Q2sKmvC2sh2X35gCY9eP4tZwzL3OkZzR4CTbn2T7583mi+eMrSXr2Bv1S1d3PzsGrxuJ3+9cuper9e3+fjBEyv3KHvz+6eTlqD56iIysClgFxERETlGvLCygm89snyPsq5AmH/O29b9PDXezQXjBzFvcy1X3LOAYdmJJHpcfGJyHttq23lhZQXt/hAAv3lxHaGw5ZrZRcS5nb16Lbv761ubeW1dNQB/+eyUvXr+b3tlIwDj8lJYV9kCoGBdRI4LCthFREREjhEPLSwF4PbLJzO9KIM2X5DWrgBhC9OK0nE7I4GuMYYH55dw83Nr2VbbDsDq8mYAXLsFwylxLn7/8nr++/52/nLlVE4sztjrnKvLmimpb+cTk/OP2nWtLmvufvzCqgounlLQ/XxTdStPLN3J1bMK+fmF43h2eTknDc86anUREelPFLCLiIiI9CMN7X5ueXk9151UzJhByRhjWLajkaeWlrFgWwOXnVDAZScM7vE4V80swhcMM3pQMo8s2kGnP8TcjbU8d8PJpCd4cBhDboqX+Vvr+dL9S/jMP+az4hfnkhrvxlp4bV0V33t8JR3R3viMRA8nj4htoLy+soW81DhWljXjcTnwB8Pc+OgKZg/PJCc5jtL6ds67Yx4A3z93NHFuJ1fMKIxpHURE+jMF7CIiIiL9xNLSBm5/fRPvb6nniaVlAN2B7C7TitIP6lgOh+HLpw4D4NSR2UAkUZ0xew43P2lEFlfPKuRf727nuRUVPLuinPo2P9UtXQTDtnu7rzywhMe/OpsJBalHdI27rCpr4pN3vd/9/OYLx3Lzc2sBmPH7N0n2uhiblwLAby4eT7rWVxeR45ACdhERETlmVDZ38vbGWi6dWtCnc65jyVrLr19Yx5LSBtaUR+ZnX3ZCASNzkvnjKxu6g/XvnDOS8fmpnDbq8Hu5Pxqs7/LTj4/l5dVV/PL5td1lyV4Xb/3gNHKSvTS2+zn/z/P4+9tbufuqEw77/Lv8+KlVPLp4Z/fzsXkpXDGjkMHpCTyyaAevraum1RdkSWkDl00t4NrZxUd8ThGRY5ECdhEREel1Oxs6WFXWzMcmDKIzEOLed7fzwqoK/vipiUwrisyj9gfD/P6ldcwalsnXH1oGgNNhCIUtjy7awWNfnT0ggvYPttZz3wcl3c9/d8kErppZiDGGq2cV4guGWV3ezBmjsvcbcB8pYwyXnVDAX9/aAsALN5zC4PT47l7tnJQ4Lp5SwP3zS9hQ1cKYQSmHfa7K5k4eXbyTCyflceHEPM4akwOA2+ngzDE5nDE6m3+/t53zxw8iO9mLx+k48gsUETlGKWAXERGRXlPZ3Mn8rfU8tngnC7c3MHNoBgu3N3S/fvk/F/DE12azqaqVzTVt3D+/lPvnl3a//o0zhpPodXHrnA0sKWnklJGR3mZ/MMzq8iYmDU7D7XTwvwWlPL2sjL9cMZUhGQm9fp0H49nl5dwyZz2+YJjsZC/PfOMkXltb3R2sAyTHuUkGzhydc9Tr891zRuF1OfC6nEwcvPew9+tPG8Z9H5Tw2OKdfHxi3j4T1B2MhxfuAODrpw/f5/B6Yz4cyi8icrwz1tqetzrGTZ8+3S5ZsqSvqyEiIgJEhkA/tHAHp4/K7rfB5KHoCoRo6QyQkxLX47ZX3rOA+dvq9yr/8cfGUJyZyHcfW0FnINRdPi4vhVDYkpsaxz+uPoEEj4vWrgCTfv0a3zl7FDeeMxKArz64hFfXVu913FG5ScRH9/n7VdMYPSj5CK70yNS3+fj+EyupbvHxi4vG8YMnVlLe1MkF4wfx/fNGMTK37+p2sM7+f2+zNZp1/q7PTeWiSYeeOf76B5awrrKF9350VqyrJyLSLxljllprpx/OvuphFxER6UXWWp5eVs7Pn13DmEHJvPKd06hp7SLe7SQ5zt3X1TtkDe1+zr39Herb/Sz62dnkJO8/aF9S0tAdrH/77JF8+dSh/P3trWQmerp7VKcMOYN/ztvK4PQEpgxJY/SgZBI9TsI2MhweIr3Oo3OTWVIa6ZlfXNLAq2urmVqYRkldO40dAS6cmMclUwu48dHleFwOAsEwn71nPkPSEzh9VDYry5r47cUTKGvsJN7jIM7tpLyxk/nb6hmfn8qUIalkJHrJ2Eeis+U7Gpm7sZaali4unlLAhIIUXA4H8Z79D89v7gxw1b0L2VDVCsCV/1qAMfDHT03ksyceO1nPf3j+aH71/DqqWrp4cWXlYQXsTR0BBqfHH4XaiYgMPOphFxER6SXWWm6Zs4F75m3rLpv/k7P4xF/foysQ5nMzCzl7TA4zh2UCkSWv3tpQw2UnFJCX2n8CnKYOP82dAQozEvjy/Ut4c0MNAEMy4vnpx8ZyzrhcOnwh4jyR4dUA/3xnK7fM2UBRZgLP33AKqfFHdnPiZ8+s5vkVFXzr7BHc9spGMhI9vPPDM7FYWruC5EZ7+/3BMG6nYW1FCxff/T6h8MH/3zMoJY7Xv3faHjdSgqEwM/7wJg3t/r22//d10zl7bO5e5UtLG/jJ06vZVN3GbZ+aRKLXRVKci8KMBIZmJR7G1fe97z++krkba1j683MOeV79Sbe8ydTC9JgkrxMRORYcSQ+7AnYREZGjJBgKs7KsicmD03A5Hfzt7S3c9spGrplVxCVTC/jU3z/Y537fPHM4GYlefvviOgDyUuN44muzGZze98Pnb52zgX+8sxWA/NQ4Kpq7uPmicQzLSuQL9y0GwOUwxHucdAVCjMtPJSvRw/tb68hM9HL/F2cwIifpiOvx7uZarvn3ou7nj3xlFrOHZx5wn7o2H6vLmnljfTVvb6ylqcPPJ6fk88HWekrrO/jLFVMA+OXza2nqCACRHuWvnT68u3f/nU21XPefRfz042MYPSiF6/6zaI9z/POaadS3+Vm+o5H3ttRxzzXT+fQ/PsAXDDOtKJ2nvn7SEV97f/Dg/BJufm4tH/z4LPLTDnwzqaKpk1DYkp8Wz8+fXcMji3bwmWmD+dNnJvdSbUVE+pYC9h4oYBcRkd60qbqVmhYff3lzE4tLGvd47eIp+dxx+RSMgYcX7eBnz6wh0ePksa/OZmlpI4tLGnhxVSUAI3OS+Mppw/jtC+uYNCSVh748qy8up9uGqhYu+PO73XXbXNMWKf/tBcS5nVQ0dXLu7e/Q7g/tte8F4wfx84vGxvSmw/Idjdz73na+f+4ohmUf2k0Aa233MHtrLV2BcPeQ9qYOP/EeJ9f8exGLoonx/nrlVHJS4vj+4yt5bV0VS35+Dl6Xk83VrRRnJfLB1npueGgZrb7gPs+XnxrHA1+aGZObFf3B0tIGPvX3+dx77XTOGbf3qIKPbgfw6WmDeXJpGTOHZvC7SyYcE3P2RURiQQF7DxSwi4hIbwmGwoz42Zw9yhwGwhY+f1IxP7twLO4DLFMVDlteX19NTrKXqYXpAPz+pXXc/0EpG357AQ6Hoaa1i/e31HHJlIIehyPvHpguLW3koYWlnDoyi5OGZ3UPGz9Yu4LVd286k9R4N1//3zJGDUrme+eO2uN8nYEQ1S0+hmYl0tDuJz3BfdSWIzuaGtv9/PaldTy9rByAh788k68+uJQLJgzaZ+9wXZuP37ywDpfD8MMLRvPm+hp+++I6fMEwv7hoHF88ZWhvX8JR0+YLMvFXr/KFk4byk4+P2e/P9DcfWsZLqyv3KFv8s3PITvb2RjVFRPoFJZ0TERHpQx3+IO9urmNqYRp/m7u1u/z/PjOZk0dkkuBx4XIYEr09/9l1OAznjx+0R1lhZiL+UJjaNh/+YJgv3reYzTVteJxOLpyUt99jlTV28PX/LSMYtjzzjZO4+dk1rKts6Q5AX//uaYzMTWZNeTNPLi3j6llFFGUmsGJnE+kJHoZnJ3YH2jsbOnhqWRnnjM0lLSGSiO0f10zb65zGGBI8LoZmRa51X0nbjhXpiR5+cdE4ttS0saqsmc/duxCAz0wfss/ts5K83Hnl1O7nV88q4soZhTy7vPyA7XQsSvK6SIt385/3twPwi0+M22ubcNiycHs9F07K43cXT2Dqb18HULAuInIIFLCLiIgcppK6diqaO3lhZSWPLNrRXX7VzEJ+c/GE7nnPR2pXRu0L/jyPxo4Aruhx/7egtDsQDIctDofh0UU7eGdTLV8+dShX37uoe4m0j/3lXbbXtfOnT09iQ1Ur/35vO+feMY/izARK6jsAWFnWRGaihzfWR5LIFWcmcO64XOrb/WyqjmQ3/9iEPW8mDHRpCR6ev+EUFmyr54p7FgAwY+jBrz/udBg+NW3w0apen0rwuGjsCPCf97dz0wWjiXN/mCX/8SU7+csbm6lr83PeuFzSEz08/Y2TjjjZoIjI8abXAnZjzH+Ai4Aaa+2EaFkG8BhQDJQAl1trG6Ov/QT4EhACvm2tfTVaPg24D4gHXgZutMfDuH4REel3rvnPQnY2dAKR9b7H5aWQleTlZxeOjekQ8CHRgL0xmgjt6llF5KXGccucDTy4oBSvy8FNT65i0uBUVpU1AzBnTRUAXz9jOOWNnSwtbeTy6YO5dGoBLqeDoVmJ/PzZNZTUd/Cdc0ZiMNzxxiYAPjk5H4eBZ1dUcP/8UvzBMBAZ0j9Qg8+ezBqWyQ/PH01hRt8n/usv/nnNNC7663sAPL2snM/NLOTN9dX8851tLCpp6N7ujFE5AJwQneIhIiIHrzd72O8D7gIe2K3sx8Cb1tpbjTE/jj7/kTFmHHAFMB7IB94wxoyy1oaAvwPXAwuIBOwXAHtOFhQREYkRXzBEW1eQzKQ9h/GWN3Wys6ETr8vBRZPyueWyiXhc+5+bfiR2T9T2owvGcOWMISTHuVm0vYGbn13T/dqqsmaum13E5ScO4YklZYzPT+GyEwbvs6f/6llFjMtPYWhmIumJHqy1nD02h4Z2PyePyMJay5dPHcbYvBQCoTA1LT7y0w5tzvtA880zR/R1FfqVCQWpjM9PYW1FC+9tqeVzMwv534LSPYL1G84cQWqCetVFRA5XryadM8YUAy/u1sO+ETjDWltpjMkD3rbWjo72rmOtvSW63avAr4j0ws+11o6Jll8Z3f+rBzqvks6JiByfgqEwrgMkeNufcNjy3MpyXllTxatrqzEG/nH1tD3mlt86ZwP/nLeVV79zGqN6Idv17a9t5JSR2XsMx27zBbnozncpqe/g5ovGkRrv5lMn9JyITiRW2nxBfvjESl5bV82M4gxafQHWlLcAaOk2EZGoYznpXK61thIgGrTnRMsLiPSg71IWLQtEH3+0fC/GmOuJ9MRTWFgY42qLiMjBstbyr3e3UZyZyOzhmSTH9U5v24qdTXzpvsWcP2EQV55YSLzHwV1vbWFDVSszh2bwi0+M32fP89PLyrjrrS1sq2vf7Rrgqw8u5RcXjSPO7eSnz6zG63JwztjcXgnWAb533ui9ypK8Ll789qk0dwYo6GEtbJGjIcnr4ttnj2TOmirmb6sHYHx+CndeOZWsJCWXExE5Un0dsO/PvroG7AHK9y609h7gHoj0sMeuaiIix7YVO5tYubOJ9EQP87fWcdrIbM4Zl7vHskyd/hB/fWszta0+xuencN1JxQfdaxsMhXl6WTnrKlsYnB6P1+XgDy9v2GOb+T85i7zUSIDpC4YIhyHe46TdF+TxJTvZVN3K108fQWHmwc8XDoTCrKtoYWNVK0MyErjuv4vwB8M8vHAHDy+MJIRzGBibl8L980uZODhtj97oYCjM8ysr+OGTq8hK8vDts0Zww1kjeXN9NaeOymbCL1/lNy+u6z7foNQ4frmPzNi9LcnrIukgss+LHC1j81L2eJ6W4GZ49sBYb15EpK/19V/4amNM3m5D4mui5WXA7mumDAYqouWD91EuIiI92FLTxh9f2cDr66r3KH9k0U5mD8vk4a/MxFp4YVUFv3lhHfXtfgCeWBq5M/roop2kJbiZWJBKc2eAxg4/jR0BWjoDJHhdzB6WicPAi6sq2dHQscc5Jg9J4/Lpg5mzuor3ttTx0IIdfOvsEayvbOVrDy6lqqWLBI+TDn+oe5+nl5Vz7ewiNla3say0kVsum8gFEwbR2O4nZ7f1w7fWtvHW+hpumbOe8Eduz979uRNoaPcxd2MtBWnxXDK1gClD0vjU3z/gB0+s5OfPrmby4DTG5qXw9sYaSuo7GJuXwqPXz+rOZv2xiZEs7Ld9ahI3PbWKK2cMYVxeCp+aNpgET1//GRXpH5LjXLR2BQFI6aVRNCIix4O+nsP+J6B+t6RzGdbam4wx44GHgRlEks69CYy01oaMMYuBbwELiSSd+6u19uUDnVdz2EXkeBYOW7bUtvHdx1awtqKFc8bmMjw7kWHZiZwzNpeHFu7g9tc3kZvixeVwUN7USWaih2+dNYIrZhRy2m1zqWn1AZGes6aOAEleF6GwxR8KM70onc5AiHUVLYStZebQTE4ansk543L5w8vr6QqEuOea6aRH1+P+5F3vdWcy32XXet8ZiR6+dMpQxuen8NUHl7K2IjIXdkhGPNXNPvyhSLZyt9MwPj8Vl8OwpLQRiPQ0f+eckQCsrWjh/PG5XDBh32tfN3X4eXJpGfd9UEJ9mx+HgdR4N7/4xHjOHZe73+XYDndOvMhA9+raKr764FIAzhmbw73XndjHNRIR6T+OZA57rwXsxphHgDOALKAa+CXwLPA4UAjsAD5jrW2Ibv8z4ItAEPiOtXZOtHw6Hy7rNgf4Vk/LuilgF5GBqqyxg9auINZGeriG7GPJqV88t4YH5pcCcNGkPO763Al7vN7hDzLuF692Pz9/fC5/v2oajmjQ2tTh5/V11QzLTmJaUTr+YBiPy4G1lg5/iMTocOw2XxBfILRXNvWP2lTdyosrK3hrYw2TBqfxowvG7HNt5q21bfzwiZXcctkk0hPc/OOdbby2ror6Nj9DMuLZXNNGUUYCYwal8IPzRzE8O+mQk611+kM4HOCJBuFK1iZy+MqbOjn51re499rpnDMut6+rIyLSbxwTAXtfUsAuIv3Zrl7btzZUkxLnZlpR+gEDx3DY8tq6SObym59d0937DfCts0Zw0aR8Rg9K5h/vbOXvb2+luTPA2LwUvn7GcE4flb3P4BigpK6d6pYuZgzN6PeBazhs8QXDxHucfV0VERERkQNSwN4DBewi0h89u7ycXz6/lubOAFlJHuraInPGB6fHc+PZIynMSCAQsjgMNHYEGJ6TyFNLy3hmeXn3tgAXTsxjeE4Sd765GYCUOBdXzyrib29vJTXezejcZG779CSKsxL75DpFREREjmcK2HuggF1E+ovS+nZuf30Tz62I5Mvclahp8pA0spM8TChI5aVVlWyuadvvMU4dmcU5Y3OZUJBKdpK3O5N6RVMn722u48dPryJsYVpROg99eSZxbvVCi4iIiPSVY3kddhGRXmetJRi2eyxjFmvBUJh2f4h4txOPy8HzKyv42dOrafUFSYwO406Nd/P8DSdTlLlnz/dVM4u4663NzB6ehcdlaOkM0u4PsqWmjarmLu68cuo+656fFs/lJw5hXH4KnYEQ03sYWi8iIiIi/ZsCdhE5rviDYb71yDI2VLVy15UnMCIniaWljbR0BXhoYSnljZ1855xRnD9+0CHPj7bWMm9zHW+tr+b5lRU0dgQoSIsMb7/pqVUAfPecUXxm+mCyk724HGafAXV2spdfXzzhsK9xQkHqYe8rIiIiIv2HhsSLyHGjKxDi6/9bytyNtft8Pd7txBcMEbaRx5eeUIDH6WBjVSsp8S6GpCfwySn5BMOWRxbuoKUrwGUnDGZTVSsZSR7e21zHnDVVxLkdnDUmh7QEDw8v3AFE5pU/8bWTGD0ouTcvWURERET6mOaw90ABu8jAFw5b7nxrM48s2sHJw7No7gywtbaN8fmpFGclkBLnpqS+g0cW7eD3l07gokn53DNvKzUtPganJ+ByGq6eVUSix8nC7Q389/0S3lhfDUCy14U/FMYXDHefz+UwhKzlo79Czx2Xy1+vnNo9b3xbbRury5sZl5fCyFwF6yIiIiLHGwXsPVDALjIwhcOWdzbV8vr6auasrqSxI8CInCQa2/0kel1kJnnYVttOc2ege58LJ+Vx90fWId8Xay2ry5vxB8NMK0oHYOH2BtZWtLC2vJkbzhpBarybjdWtlDV2MrEglU3VrZwyIqvHdchFRERE5PihgL0HCthFBqZ3N9dyzb8XAXD2mBxOG5XNtbOL9pgX3tThZ1tdO9bCxqpWLp1aoLW7RURERKTXKEu8HLd2NnSQ4HH2ao+mLxgiHAavy4HD0f8zcFtr90ps1twZYHVZM9vr2/lgSx1tviAnFKYT53Yyf1s9Q9LjuXBSHjnJXjZWtXHi0HRykuNiWp+uQIi3N9ZQ2+ZneFYizZ0B6tv9DEqJ44zR2bh6yOBe1+bjlTVVAPzzmmmcP37QPrdLS/BwQqEHoLunXERERETkWKCA/RgXDlscDkNNaxeN7QEykzwkeV1UNnexuryZJ5bsJMHj5BtnjGBcfgoGegyE+otAKIy14HYaOgMhdjR00NIZJC81jvs/KGFNRTMLtjUAMDo3mQe+NIP0BA8PLijFFwxx5YmFpCd69nt8ay1VLV2sLmvmg631nDE6m+bOAGWNnawua2bmsAyqW3yU1LVTmJmAPximvt3Pq2uq8IfCDMmI5+wxucwensm0onSyjuJNg8Z2P40dfjbXtOF2GsoaO8lJ9lKYkYjTYVha2khpQzuJHhdFmQkMTk+gpSvAnNWVPLuigo9PGMRnTyxkzppKyho7WVXWTF2bD4DcFC+BkOXdzXUAJHqcLCmBh6LJ0nZJjnORkeghLd7NVbOKOH/8IIKh8B43S1q7AiTHubufdwVCPL+ygrXlzby2rprWriBdgRBpCZ7u8+/LrGEZXD59CE5H5FoXlzSQkeDB43KQmxJHaX07c9ZU4QuGGZaVuN9gXURERETkWKYh8cegtRXNfOvh5XQGQtS0+kiOc9HUEeh5x6hh2YmMy0th0uBU0uI9DM9JYkROEqnx7p537kFzR4AF2+tJjXczNCsRj9PBwu31uBwOXE7DsKwkCjMTAKhs7mTuhlrmbqyhKCMBXzDM2WNzmFCQyrqKFn7/0nrKmzqJczv3Gdwle110BEKEwvv+GR6dm8xVswrJTYnjrDE5VDV30dIVoKq5C4/LwRNLynh+ZUWP15TkddEZCOF2GjISPIwvSGVSQSovra5kW107/mCY9AQ3EwpSGZaVSGcgxJLSRqYMSaMoI5HPzSyk3RfkyaVlpCW4KcpMxOmAFTub2V7XTocviAXauoLMGpbB+IJUFm5rYGlpA4leF/VtfjZWtx5We7idhqLMRLbUtAHgdBhG5yaTkejhS6cOxWkMJxZn0OoLsL22naLMRLKSPDR0+Pnv+yUYIDcljsYOP1XNXTR3Bpi/rX6Pn7eCtHgKMxJoaI/UMznORZzbibXs0W6TB6dyQlE6wZClttXHpppWfnDeaOI9Ttq6gmQmechNieORhTv47wcle7RrcWbk+IGQpTMQwukwXDB+EJ+ckn/Ub5aIiIiIiBwJzWHvwZQTptnlS5fsMSx4bUUzGypbmTg4lTvf3Ex9m59tdW1cNCmfgrR4SuvbKUiPZ3B6AueNy8XldNDpD7FsRyPTitK7M0AfSE1rFw8t2MGyHY34AmE+N7OQi6fkA2CMod0XpLbVR2OHn7QED1lJHt7ZVEtFUyejcpOZUJDK+soWVpc3kxLnxmEM2cle7pq7hQ2VLVw4MQ+X02AtFGclkpcax5ryFtwuQ1q8hxlD02nuDFCUmci7m2qpbO7CAktKGtjZ2Elt64fB1MicJJ782kmkJuw7aA+FLWvKmxmRk0Si98OBGSV17by4qoIZQzOpbO7k9tc3UVrfccD3JTfFS3XLh+f2uhwEw3afgXd6gptpRemMzE1mWFYibqeDbXXtJHqcfG5mIclxbroCIe6Zt40NVS2MyE5ifHQN6p89s+aAvbhxbgcXTsznshMKSPS6aOsKkhTnoiAtnvQEN7VtPoIhy5CMBIKhMBZwf2R0Qoc/yLxNdTy0sJSKpk7KGjuJ9zhp6QyQ5HXR6guS6HHR5gvusw5FmQmR6w9ZgmHLjoaO7vdkypA02nxBMhI9zByaQU5KHM0dAUbkJuF1Omjo8FPT4qOyuZNzxw1iZ0MH54zLZUNlCx2BEC2dAaYXZ5CfGsfikkb8wTBFmQkMyUg4YPv0pMMf5MWVlbR0BWj3hdhS28amqlby0uJo6QyQmeTFFwyTk+zFYeC0UdmcPip7j573nviCITZXt9HcGWBsXgoZ0ZESgVCYQCiM02HwujQPXURERET6PwXsPfDmjbR51/2ZGUMzyE7ysr6yhW117fvc1ukwewWOEwpSmDk0k1fXVlHW2InLYRiWncjEgjQCoTDNnQFGD0pmVG4yF0/JZ9H2BrwuBz95ejWba9qYUJBCVyDc3cvpcTnISPDQ0O7HHwrvqxo9+s3F47l2dvFh7bvL40t2Ut/mZ01FMy+tqgRgeHYimUleCjMSmJCfwoMLSknyuihv6qKuzYfbafjmmSPISvLyyKIdrK1o2eOYuSlefvyxMWQnxbG5ppUOf4iphWmsLW+h1RdkWWkjealxrK9qYdLgNC6enM+MoRkEQpGA/bV1VfxvQSkXTyngnLG5DEo9/HnT1lqqW3zc8PAyNla3MiwrkUunFpCdHEfYWk4fnU3KIQSRB3tOoPvm0OqyZh5aWEqc28lFk/LIS4unuqULayE13sWInA+X+QqFLQu31xPndjIuL+WgbgqJiIiIiEj/poC9BwUjJ1j3p/4IgMNEhjhfM7uIIekJJHhdDI8OEQdo94eYu6GGU0ZkYYFHFu3g+RUVkYAvO5FhWYmMGZTCB1vrWL6zCWsjQ7N9oTD+YBivy7HHWs27kmGFwpanlpaxo6GDujYf726u4/zxg0jyOvG4HJQ3dZIc5+aMUdnkp8VT1dLFkpIGCjMTmVSQSiAU5rHFOxmXn8KsYZnkp8XH9D26/4MSfvn8WoZlJxIMfdjTC5FEXYUZCYzNS+Y/75VQ1dIVeV/T4rl2dhGjBiXT5Q+RmxrHhPxUPK7+N0d+11x/ERERERGR3qSAvQfTp0+3r7/zAYleF13BEDbMfod+70+bL0iSd88cfdZafMFwdL6u5cmlZawsa2JSQVokOVeCm+nFGbG8lKPGWsvG6lZG5iTjMNDSGWR7fTvFmQmkJXj22G57XTsd/hAjc5M0LFlEREREROQAFLD3YKAlnRMREREREZFjw5EE7P1v7LKIiIiIiIiIKGAXERERERER6Y+OiyHxxphWYGNf10NiJguo6+tKSMyoPQcWtefAovYcWNSeA4vac2BRew4c+2rLImtt9uEczNXzJgPCxsOdMyD9jzFmidpz4FB7Dixqz4FF7TmwqD0HFrXnwKL2HDhi3ZYaEi8iIiIiIiLSDylgFxEREREREemHjpeA/Z6+roDElNpzYFF7Dixqz4FF7TmwqD0HFrXnwKL2HDhi2pbHRdI5ERERERERkWPN8dLDLiIiIiIiInJMOSYDdmPMEGPMXGPMemPMWmPMjdHyDGPM68aYzdHv6dHyzOj2bcaYuz5yrLeNMRuNMSuiXzl9cU3Hsxi3p8cYc48xZpMxZoMx5lN9cU3Hs1i1pzEmebfP5QpjTJ0x5s99dFnHrRh/Pq80xqw2xqwyxrxijMnqi2s6nsW4PT8bbcu1xpjb+uJ6jneH0Z7nGmOWRj+HS40xZ+12rGnR8i3GmDuNMaavrut4FeP2/L0xZqcxpq2vrud4F6v2NMYkGGNeiv5fu9YYc2tfXtfxKMafzVeMMSujx/mHMcbZ4/mPxSHxxpg8IM9au8wYkwwsBS4BPg80WGtvNcb8GEi31v7IGJMITAUmABOstTfsdqy3gR9Ya5f08mVIVIzb89eA01r7c2OMA8iw1mpNy14Uy/b8yHGXAt+11s7rjeuQiFi1pzHGBVQA46y1ddEAr8Na+6tev6jjWAzbMxNYDkyz1tYaY+4HHrDWvtn7V3X8Ooz2nApUW2srjDETgFettQXRYy0CbgQWAC8Dd1pr5/T+VR2/Ytyes4BSYLO1Nqkvrud4F6v2NMYkADOttXONMR7gTeAP+nz2nhh/NlOstS3Rm6JPAk9Yax890PmPyR52a22ltXZZ9HErsB4oAC4G7o9udj+RNxJrbbu19j2gq/drKz2JcXt+Ebglul1YwXrvOxqfT2PMSCAHePfo1Vz2JYbtaaJfidE/UilEAnjpRTFsz2HAJmttbfT5G4BGNPWyw2jP5dbaXZ+7tUCcMcYb/Wc0xVo730Z6ch7YtY/0nli1Z/S1Bdbayl6svnxErNrTWtthrZ0b3cYPLAMG99qFSKw/my3RchfgAXrsPT8mA/bdGWOKidz9Xwjk7vrlFP1+sMPb/2siQ25v1hCwvnUk7WmMSYs+/K0xZpkx5gljTO5RrK70IEafT4ArgcfssTgkaAA5kva01gaArwOrifa0A/8+mvWVAzvCz+cWYIwxpjg6euISYMjRq6305DDa81PAcmutj8g/nmW7vVYWLZM+coTtKf1MrNoz+r/uJ4j0sksfiEVbGmNeBWqAViK97Ad0TAfsxpgk4CngO7vdrThUV1lrJwKnRr+uiVX95NDEoD1dRO44vm+tPQGYD/xfDKsohyBGn89drgAeOfJayeE60vY0xriJBOxTgXxgFfCTmFZSDtqRtqe1tpFIez5GZORLCRCMZR3l4B1qexpjxgN/BL66q2gfm+kGaR+JQXtKPxKr9ozeHH2EyHSVbUejrnJgsWpLa+35QB7gBc7ax657OGYD9ug/f08BD1lrn44WV0eHde2aa1DT03GsteXR763Aw8CMo1NjOZAYtWc90AE8E33+BHDCUaiu9CBWn8/otpMBl7V26VGprPQoRu05BcBauzU6UuJx4KSjU2M5kBj+/XzBWjvTWjsb2AhsPlp1lv071PY0xgwm8nfyWmvt1mhxGXsOsR2Mpqz0iRi1p/QTMW7Pe4jkJPjzUa+47CXWn01rbRfwPJFh9Qd0TAbs0WHr/wbWW2tv3+2l54Hroo+vA57r4TguE81SHG2Ei4A1sa+xHEis2jMaBLwAnBEtOhtYF9PKSo9i1Z67uRL1rveZGLZnOTDOGJMdfX4ukTlg0oti+fk00VVVTCQr7jeAe2NbW+nJobZndDjtS8BPrLXv79o4OpSz1RgzK3rMazn439ESI7FqT+kfYtmexpjfAanAd45urWVfYtWWxpik3QJ8F/BxYEOP5z8Wp4QaY04hMgRvNRCOFv+UyFyCx4FCYAfwGWttQ3SfEiJJjjxAE3AekeyZ8wA34CSSNOd71tpQL12KELv2tNauM8YUAQ8CaUAt8AVr7Y7euhaJbXtGX9sGfNxa2+MvNIm9GH8+v0YkC3WAyO/fz1tr63vtYiTW7fkIMDl6jN/YHrLcSuwdansaY35OZCrK7qMhzrPW1hhjpgP3AfHAHOBbyhvSu2LcnrcBnyMyBakCuNdqVY5eFav2JPK7dyeRwG7XPOi7rLW6SdpLYtiWBniRyFB4J/AWkRWQDjil7JgM2EVEREREREQGumNySLyIiIiIiIjIQKeAXURERERERKQfUsAuIiIiIiIi0g8pYBcRERERERHphxSwi4iIiIiIiPRDCthFRERERERE+iEF7CIiIiIiIiL9kAJ2ERERERERkX5IAbuIiIiIiIhIP6SAXURERERERKQfUsAuIiIiIiIi0g8pYBcRERERERHphxSwi4iIiIiIiPRDCthFRERERERE+iEF7CIiIiIiIiL9kAJ2ERERERERkX5IAbuIiIiIiIhIP6SAXURERERERKQfUsAuIiIiIiIi0g8pYBcRERERERHphxSwi4iIiIiIiPRDCthFRERERERE+iEF7CIiIiIiIiL9kAJ2ERERERERkX5IAbuIiIiIiIhIP6SAXURERERERKQfUsAuIiIiIiIi0g8pYBcRERERERHphxSwi4iIiIiIiPRDCthFRERERERE+iEF7CIiIiIiIiL9kAJ2ERERERERkX5IAbuIiIiIiIhIP6SAXURERERERKQfUsAuIiIiIiIi0g8pYBcRERERERHphxSwi4iIiIiIiPRDCthFRERERERE+iEF7CIiIiIiIiL9kAJ2ERERERERkX5IAbuIiIiIiIhIP6SAXURERERERKQfUsAuIiIiIiIi0g8pYBcRERERERHphxSwi4iIiIiIiPRDCthFRERERERE+iEF7CIiIiIiIiL9kAJ2ERERERERkX5IAbuIiIiIiIhIP6SAXURERERERKQfUsAuIiIiIiIi0g8pYBcRERERERHphxSwi4iIiIiIiPRDCthFRERERERE+iEF7CIiIiIiIiL9kKuvK9AbsrKybHFxcV9XQ0RERERERI4zS5curbPWZh/OvsdFwF5cXMySJUv6uhoiIiIiIiJynDHGlB7uvhoSLyIiIiIicpxq8wUpa+ygpSvQ11WRfTguethFRERERETkQ1tr2/jJU6tZuqORUNgCcMmUfH75ifGkJ3r6uHayiwJ2ERERERGR40hXIMQ3H1pGdUsXXz99OEMy4tlc3cZ/Pyjh+ZUVTC/OYOygZCzQ7guxo6Gd1q4gNhLXk5Pi5fzxgxiVm0xJfTsbKltpaPfhC4aZMTSDq2YW4XEdu4O5G9r9/Pu9bSwtbWRTdRuBUBivy0lWkoehWYkMy05kWFYSQ7MTGZ6VRGqC+6jVxdhd7/oANn36dKs57CIiIiIi0pNgKMxbG2rYXNPGjvoOdjR0UNvmo90XxOty8KVThnLJ1AKS445ekHY0balp46dPr2ZRSQP//cKJnDk6p/u1dRUtvLKmkhdXVVLf7scYiHM5KcxMIC3ejcMYwtayva6dzTVt3fsleJxkJnlwGENpfQcTClL4yqnD+PjEPNzOww/cW7sCbKlp4/4PSlhV3kxFUydFGYnkp8UxvTiDCyYMYnh20hG9H7tr7gjw2JId3Pvudurb/UwoSGVcXjJelxNfMERNi49tde3saOjoHpUAcNXMQn7y8bEkeffdH26MWWqtnX44dVLALiIiIiIiAqyvbOHmZ9ewpLQRgKwkL4UZ8eSmxJHodbGlpo0VO5tIiXPxlyumcvqobBwO08e1PniBUJiL7nyP6tYubjp/DJ+bWXhYx7HWsraihcYOP4NS4hiRk4QxkffhtbVV/OzZNdS2+jhjdDb/uHoacW7nQR97fWULDy4oZcG2erbVtgOQEudi9vBMcpLjqG7poqS+nU3VbSR4nDzwxRlML844rOvY3ZryZn7wxEo2VLUysSCVWz81kfH5qfvc1h8Ms6Ohg+117by7uZYH5pficTr48xVT+PjEvL22V8DeAwXsIiIiIiKyP/5gmJueXMmzKyrwOB38/tIJXDgpjwTPnj2m1lqWlDby06dXs7mmjawkD3deOZWThmf1Uc0PXjhs+dvbW/i/1zbxj6unccGEQUf1XA8v2sHPn13D2LwU/nDpBKYWph9wn+aOAM+tLOcPL6/H5XAwc2gGJxSlU5AWz5mjc/Yadl7e1MnV9y6kuqWLS6cWHLCH+0C6AiF++OQqXlhZQVqCmzuvmMppow5tBbalpQ28vLqKa2cXUZSZuNfrCth7oIBdRERERET2ZdmORn781Co2Vbfx7bNHcs2sIrKTvQfcp90X5LV1Vfxt7lZ2NnbwrbNG8rkZhf02WVtpfTvfeGgZaytaOHtMDvdeN727R/xoem5FObe9spGWrgD3fWEG04r2HbQv39HIVx5YSl2bjxOL0/n71dPISjpwGwDsbOjgjjc28ezycoZlJ3H9qcO47IQCXAcxDH9JSQP/eGcbi0saaO4M8O2zRvClU4YdlfnoCth7oIBdREREREQ+qt0X5Lw75gFw80VjuWDC3sOZD6S+zcc3H17Ggm0NDEqJ4+Ip+Zw9NpcZQ498iHYsVDR1ctfcLbyypgprLT+/cBwXTc7D6zr4IepHqrypk0///QMqm7s4sTid7507GqfDsLq8mfLGTlaWNbG0tJHCjATu+Oxkpg5JP+RpBu9vqePHT69iZ0Mns4dl8qOPjWFcXso+E9/tqO/g248uZ8XOJrKSPJw1JodLphYc1VESCth7oIBdRERERER21+4LcvOza3hmRTlPfm0204oOP8heXdbML55fw9ryFvyhMDOHZnDhpDyumlmEsw/muDd3BCI9zyvK8QXCnDYqi++dO5rRg5J7vS4Ata0+nltRzp1vbqalK9hdnuBxUpiRwIUT87h6VtERjVCw1vLUsnJ++sxq/MEwqfFuTh6RyezhWYzITqKxw8/7W+p4aXUlAF8/fThXzyoi8TCG0R8qBew9UMAuIiIiIiK7+INhLv3b+6ytaOGbZw7nh+ePiclxuwIh7vughKeWlrG5po3Jg1P59LTBXH7ikD16tZs7AiwpbeDp5eXsbOgg0eMiweOkzRektSuI22lI9LpI9LpIin4lx7m4eEpBj0H35upWvv3oCrbUtHL6qGx++vGxDIthJvUjUd/mY2VZEy6Hg5G5SQxKiYv50Pya1i6WlDTy2toqFpc0Ut7U2f1agsfJqSOz+MF5oxmZ23s3LxSw90ABu4iIiIiI7HL765u4883N3PW5qVw0KT/mx7fW8vzKCu54fRMl9R1kJ3vJTvJS0dxJKGxpjfYypyW4mTw4jQ5/kHZfiKQ4FylxLgIhS7svSFv0q90XpKUriNNhOHdsLjecNYKxeSl7nG9JaSP/fGcbb6yvJs7t4J5rph9y8rSBxlrLlpo2alt9pCa4GZ6ddEgZ62NFAXsPFLCLiIiIiMiWmla+9cgK1le2cPGUfP5yxdSjfs55m2p5bPFOOgMhclPi8LocZCd7OaEwnSlD0oj3HFwAWdvq4663NvPcygrauoKcMjKLsXkp1LX6WF3ezIaqVlLiXHz+5KFcNbOQ3JS4o3xlcrAUsPdAAbuIiIiIyPGtpqWLq+5dSEO7n2+dNYIrZhT2SW/rkWps93PX3C28taGG0vp2spO9FGYkcOnUwVwyNX+vpeik7ylg74ECdhERERGR49fS0kauuncBobDlv5+fwSkj+/+66QcjHLaHnFFdet+RBOy6/SIiIiIiIgNWU4efGx9dTnayl/99aSZFmYl9XaWYUbA+8ClgFxERERE5RG2+IDUtXXhcDganJ/R1dWQ/nl5Wxs+eWYM/FOaJr80eUMG6HB8UsIuIiIiIHISuQIi3NtTw9LIy3tpQQzg6s7QoM4FvnzWST00b3LcVlG6hsOXFVRXc9OQqphWl8/MLxzFxcGpfV0vkkClgFxERERHZj0AozNsba3lrQzWvr6uhrs1HosfJ104fzsjcJGpbfby8uoofPLmSrmCIz04fgsvp6OtqH9eqW7r40v2LWVPewvj8FO69bjrJce6+rpbIYVHALiIiIiKym/WVLbyzqZZ5m2pZWtqILxgmNd7NtKJ0vnTKUKYXp+N1fZhd/NrZxXzhv4v52TNreHZ5OQ9/ZRZuBe0HxRcMsbqsmX+/t52aVh9FmQlcODGP6UUZpCYcepC9bEcjv3p+LVtr2rn98slcNCkfj0ttIccuZYkXERERESGyzvUdb2zi4YU7ABiZk8SpI7OZOSyDs8fkHLDnPBgK88iiHdz83FounJTHF08u5oTCdIxRUrCPstby6tpq3tlUy4urKmjtCpIa72ZsXjIbqlpp6ghgDEwdksasYZl86ZShZCZ5D3jMmpYunltRwW2vbiDJ6+IPl07kYxPzeumKRA5MWeJFRERERA5Bpz/E2xtrmLOmisrmThI8LhaXNOALhvnSKUP52unDyU4+cJC4O5fTwTWzi2nqCHDHG5t4aVUll04t4LvnjKIwU0npdrHWcvNza/jfgh0keV2cOSaH88fncurIbFLj3fiCIZaUNLK4pIG5G2u5Z942Hl28k6tnFXHKiCyKMhMIhMJ0+EPUtvrYWNXKgm31vLG+mrCFs8bkcMflUw6rd16kP+qVHnZjzBDgAWAQEAbusdb+xRiTATwGFAMlwOXW2sboPj8BvgSEgG9ba1+Nlk8D7gPigZeBG20PF6EedhERERHZpbypky/dt5gNVa2kJbgZlZtMY7uf0YOS+d65oxiWnXREx2/q8PPf90v4y5ubAbjpgtF8/fTh/aa3PRS2vLOphieWlLG9rp2yxk68LgfZyV6KMxMZlp3I6aOymVaUHtP5+Ct3NvHdx1ewrbad608bxk3nj+7x+BurWvnl82tYtL2hO8nfRxWkxXP++EF8cko+kwen9pv3WWSXI+lh762APQ/Is9YuM8YkA0uBS4DPAw3W2luNMT8G0q21PzLGjAMeAWYA+cAbwChrbcgYswi4EVhAJGC/01o750DnV8AuIiIiIu2+IDc8vIy3N9WS4Hbyf5+ZzLnjco9akrjS+nb+9OpGXlxVyTljc/jZheMYmtW3y4rN3VDDT59ZTWVzFznJXiYNTmNwejz+UJjq5i6217VT2tBBKGwZlBLHHZ+dwuzhmUd0zmAozHtb6vjOYytI9Lj4/nmjuHRqwSEF1s0dARZsr6euzYfb6SDB4yQryUthRgL5afFHVD+Ro63fB+x7ndSY54C7ol9nWGsro0H929ba0dHeday1t0S3fxX4FZFe+LnW2jHR8iuj+3/1QOdTwC4iIiJyfFtT3sxvX1zHktJGrj9tGFeeWNgrQ9XDYcuf39jEfz8oIdnr4smvn9TrAaa1lgXbGnhjfTUPzC9hRE4yN5w5gvPG5+4zOV5LV4C5G2q4883N7GjoYHpRBt8/bxTTizMO+dxVzZGM7WsrWhiUEsfjX52tKQJy3Dmm5rAbY4qBqcBCINdaWwkQDdpzopsVEOlB36UsWhaIPv5ouYiIiIjIPn2wpY6r/r0Qj9PB7ZdP5uIpvffvo8Nh+N55ozlv/CCuvGcBp902l09Ozufmi8aRnugBIj3Q5U2dpCd6SPa6Yjaku77Nx3/e384ra6rYWtuOy2E4Y3Q2/+/yKaTG73+Od0qcm4unFHDqyGz+/vYWXl5dxaf/MZ+81Dg+M20wl584hIK0+APWs7UrwJ/f2MzjS3ZiLfz5s1M4d1wuiV6l0BI5FL36iTHGJAFPAd+x1rYc4EO+rxfsAcr3da7rgesBCgsLD72yIiIiInLMq27p4vtPrKQ4M5FnvnESaQmePqnHhIJUnv7GSTy0cAf/W1DKC6sqSEvw4HE6aPMFae4MADAkI560eA+BUJhAKIzDGHJSIsnvwmEIhsNsq23HGBibl8Lpo7JJS/CQFu8mJd5NZXMnZY2dPL+igo3VrRgDp4zI4vMnD+Uz0wYT53YeqJp7yEj08LMLx/GNM0bw5NIyFmyr5863tnDnW1sYlBLHiUMzmDE0g0kFqSR6XXhdDrxuB+srW7n9tY2sLm/mE5Pz+drpwxmbl3JU3leRga7XhsQbY9zAi8Cr1trbo2Ub0ZB4ERERETkK5m+t5wv3LSIchse/NpspQ9L6ukoAbKpu5cmlZbR2BfAFw3icDiYNTqO5M8CyHY2EwhaXw+B2OgiEwtS3+zGAwxiMgcKMBJwOw7ub6yhv6tznOcbmpXDRpDzOHZfLqNzkmNV9S00b72+pY2lpIwu311Pd4tvndukJbm65bCIXTNDSaiL9fg67iXSl308kwdx3div/E1C/W9K5DGvtTcaY8cDDfJh07k1gZDTp3GLgW0SG1L8M/NVa+/KBzq+AXUREROT40tIV4II75uF2ObjvCzP6PNnb0WCtpaUrSHNHgKZOP00dAXJSvAxOTyCpF4aeW2vZ2dDJ+qoWugIhfMEwvmCY7CQPZ47Jwes6+N58kYHsWJjDfjJwDbDaGLMiWvZT4FbgcWPMl4AdwGcArLVrjTGPA+uAIPBNa20out/X+XBZtznRLxERERERIJIJ/efPrqG61ceTX5s9IIN1AGMMqfFuUuPdFNL7idyMMRRmJiiJnMhRdFA97MaYyw70urX26ZjV6ChQD7uIiIjI8aG1K8Bpt80lPcHDzReN48wxOT3vJCJyFPVGD/snot9zgJOAt6LPzwTeBvp1wC4iIiIix4e/v72Vxo4AD3xxJhMHp/Z1dUREjshBBezW2i8AGGNeBMbtWootmiju7qNXPRERERGRg/OHl9dzz7xtXDwlX8G6iAwIjkPcvnhXsB5VDYyKYX1ERERERA7ZmvJm7n13G5edUMD/fWZyX1dHRCQmDjXp3NvRJdYeIbL++RXA3JjXSkRERETkIK2raOGHT64iI9HDLy8aj9t5qH1SIiL90yEF7NbaG6IJ6E6NFt1jrX0m9tUSEREREenZu5tr+eJ9i3E7Hfz5s1NITXD3dZVERGLmkJd1i2aEV5I5EREREelT722u49uPLGd4dhKPfGUW6Ymevq6SiEhMHdJ4IWPMZcaYzcaYZmNMizGm1RjTcrQqJyIiIiKyLy+srODqfy8kwePi7qtOULAuIgPSofaw3wZ8wlq7/mhURkRERESkJ1tq2vjZM6s5oTCNR66fhdfl7OsqiYgcFYeakaNawbqIiIiI9JVHF+3gvDvewQJ3fHaKgnURGdAOtYd9iTHmMeBZwLerMDqvXURERETkqOj0h3hyWRm/fG4Ns4dncvvlU8hNievraomIHFWHGrCnAB3AebuVWZSETkRERKRXWWspqe9g4bZ61lQ00xUIEwyFSY5zk5bgJi3BQ06yl3i3kwSvk2FZSeSmeDHG9HXVD9mO+g6uf3AJG6pamVaUzj3XTCfRe8i5k0VEjjmHuqzbF45WRURERESkZxurWlm4vZ7HFu9kbUUk929KnIskrwun09DaFaS5M4C1e++b5HVx6sgsLp8+hNNGZeN09O/gvaq5i58/u4a5G2tI9Dj55zXTOHdsLo5+Xm8RkVg5pIDdGDMK+DuQa62dYIyZBHzSWvu7o1I7ERERkeOctZZN1W08ungH726uY0tNGwBFmQn85uLxzB6WyYicpD16zsNhS3NngNo2H12BEK1dQbbVtrG+qpVX1lQxZ00VuSleZg7NZExeMsWZiWQleRmbl0xyXN+uY97cEWDOmkr+/s5WSus78LocXH/aMD43o5AhGQl9WjcRkd5m7L5uv+5vY2PeAX4I/NNaOzVatsZaO+Eo1S8mpk+fbpcsWdLX1RARERE5aNZanlxaxh9f2UBdmx+P08Hs4ZmcOTqbc8blUpAWf1jD2/3BMG9tqObZ5RWsLm+mvKmz+zVjYGJBKt87dxSnj8ru9eHzzywv46YnVxEIWSYPTuVjE/M4d1wuw7OTerUeIiKxZIxZaq2dfjj7HurknwRr7aKP/PIOHs6JRURERGTfXlpVyd/e3sLaihZOLE7npvPHcPro7JgkWfO4HFwwIY8LJuQB0NwZoKKpk6qWLlbsaOK5FeV8/r+LGZadyJ1XTGVCQeoRn7MnXYEQDy/cwe9fXs+Jxen88PwxTB2SpqHvInLcO9SAvc4YM5xIojmMMZ8GKmNeKxEREZHjjLWWtzfV8r/5pby5oYYxg5L55SfGce3s4qM61zw13k1qvJuxeSmcOTqHr54+jOdWVHDnm5v5zD/m89OPj+GqmUVHLXj2BUN85YElvLu5jlnDMrj3uhNJUkI5ERHg0AP2bwL3AGOMMeXAduCqmNdKRERE5DhR09rFrS9vYElpIzsaOshN8fLts0Zww1kj8bgcvV6fBI+LK2cUctaYHL7x0DJufm4trb4g3zhjREzPEwyFuXvuVuasqWRDVSu/u2QCV80sPCaz2IuIHC0HHbAbY5zA16215xhjEgGHtbb16FVNREREjgddgRAbq1rZUNXC4pJG6tp8+INhRuUmMzQrkeKsRCYWpJKR6OnrqsZUuy/InW9t5oklZbT7gpwzNpfrTxvGZ08cgtvZ+4H6R+WmxPHk12Zzw8PLuf21TWyvbecH54+OybD8QCjM719az30flDC1MI0/f3YKl0wtiEGtRUQGloMK2I0xLmtt0BgzDcBa2350qyUiIiIDVShs2V7XxrxNdby3pY6F2+pp94cAyEryUJAWT8hanliys7vcYWBsXgqJXhdxbidxLkfku9tBgsdFYUYCRZkJTB6SRlaSty8vr0ftviAPL9zBI4t2UFLfzhmjc/jxx8YwKje5r6u2F2MMf7hsIinxLp5eVk5Fcyf/+9LMI+oFb+0KcOnfPmBLTRvXzi7iNxf369zFIiJ96mB72BcBJwDLjTHPA08A3UG7tfbpo1A3ERERGSCstby/pZ63N9bw7Ipy6tr8AAzNSuTSEwo4eXgWowYlMywrsTsYtNZS2+ZjW20772+pY3V5M53+EM2dAWoCIboCIboCYVq7At2BPcDg9Hg8Tgcd/hCdgRCBUJjCjASG5yQxOC2egvR4ijMTmTwkjdT43lvCrKyxg6eXlfPA/BLq2vxMLUzj3584kTNH5/RaHQ5HarybWy6bxNi8FH7x3FqeX1nBxVMOvzf8/17dyNbaNv5yxRQ+MSk/hjUVERl4DnUOewZQD5xFJPGciX5XwC4iIiLdyho7mLuxls3VrWyubmNTdSv17ZGlyU4blcUFE/KYVpTO0KzE/R7DGENOchw5yXHMGpa53+2stTS0+9lW187S0kbWVbQQspYEt5MEjxOHw7C9rp11FS28vq4afzDcvW9KnItpRemMz0/lrLE5TB6cFtMEb4FQmMXbG1hS2si/5m2j1Rdk1rAM/nbVaGYMzYjZeXrDVTOLeGTRTn74xCo6/SGumFF4yMd4eOEOHlhQynWzi48o6BcROV4c1Drsxpgy4HY+DNB3/0tmrbW3H53qxYbWYRcRkf5iZ0MHayuaqW3z09Dmp6HdR3lTF4FQmGlF6Xx62mDy0+L7upoHLRS2bKpu5Z1Ntexo6KChzU9NaxcrdjYRtpDsdTEiN4lROcnMGp7BBePziPc4+6y+4bClrt3Hpqo21lQ0U1rfwaLt9ZTUdxAKWzwuB5+eNphzx+XiNAan48OvOJeTrGQPWUneveaYN3cGeGdTLat2NrG+qoWG9gAtnQHq2310BSI3CGYPy+SWyyZSfICbFP1dXZuP7zy6goXb63nhW6cwZlDKQe87b1Mt1/5nEaeOzOIfV08jUZngReQ4cSTrsB9swF4J/J09A/VdrLX2N4dz8t6igF1ERPpKZXMn726uY0lJA/O31bOzoXOP11PiXOSlxuN0GNZVtgAweUgaN5w5gulF6aT3w0RrgVCYRdsbmLeplkcX76S5MwBAZqKHjEQPmUkephamc8WJQyjMSDgmsn63dgV4fV01i7Y38NSyMgKhA/9/lJHoISvJQ9hCpz9ETWsXgVAk4B+bl0J2koeUeDdp8R5mDstgRnFGv2zLw9HQ7ue8O94hJd7NHy6deMDRD7t0+INc+a+FVDd3Me+mM/sk+72ISF/pjYB9mbX2hMM5QX+ggF1ERHrbhqoWnl5Wzn/f304gZEmNdzNzaAYnDc9kWlEGuSle0hM9e/TUbq9r5/V1VfzjnW00tEfmeF8wfhCXnVDA6aOz8br6rmcaItf01NIy5qypoqyxE2Mi9TtnbC4nj8hiUOqRZw/vD6pbuihv6sRaSzBkCVlLOAydgRC1rT5qW33UtHZR1+bD6TDEu11kJXs4f/wgJhak9osM70fb+1vq+P7jK6lq6eIXF43ji6cM3e+2jy7awR9f2UBTZ4A7r5jKJyZr3rqIHF96I2Bfbq2dejgn6A8UsIuISG8Jhy3/W1jKr19YR9haLplSwNfPGM6I7CQcBzk32hcMsay0ife21HLf+yW0+0MMTo/na6cP5+MT83p1eTNfMMTi7Y28tLqSJ5fuxGA4cWg618wqZvbwzF5N2ib9S1cgxA0PL+etDdVcMGEQv7l4wh4Z+mtbfTy5tIz/e20j0wrTuemC0UwvPrbm7YuIxEJvBOwZ1tqGwzlBf6CAXUREesOCbfV897EVVDZ3cfaYHG779CQyj3CJMV8wxAdb6/nNC+vYXtdOWoKbL5w0lFnDMphenHFQCdI6/EHqWv2UNXZQ1thJ2FrCFiwWpzHEe5wkeFwUZyaQ4HXhchiW72jkP++XsGJHE/5QmASPkwsn5vHTj48dMEO75ci1dgX461tbeGB+CdnJXr548lBGD0qmsT3AL55bQ327nxOL0/nXtdNJS9DPjYgcn456wH6sU8AuIiJH086GDu6eu4WnlpUxJCOBG88eyUWT8mOabdxay7rKFn7/0no+2FoPQFqCm5OGZ3LxlAK6AiEa2/00tPtp6PDT2B6grKmTzdWtdOy25NmhKMpM4ILxg5hWlM6pI7P7NFmc9G/LdzRyw8PLKW/6MEfDiJwk7vrc1ENKTCciMhAdEwG7MeY/wEVAjbV2QrQsA3gMKAZKgMuttY3R134CfAkIAd+21r4aLZ8G3AfEAy8DN9oeLkIBu4iIHA0tXQHmbqjhty+uo80X5OMT8vjlJ8aTmnB0h4k3dwR4d0st8zbV8ura6u6kbwDGQFq8m/RED9lJXsbnp5KT4iUrycuglDiKMhNwOQ0GgzGRLO8d/hCtXQF2NHTQ6Q8RDFsK0uI5dWQWruNgPrbEhrWW2jYfG6tacRrDCUXpxLl1k0dE5FgJ2E8D2oAHdgvYbwMarLW3GmN+DKRba39kjBkHPALMAPKBN4BR1tqQMWYRcCOwgEjAfqe1ds6Bzq2AXUREYsVay6qyZuZvq+eeeZHkcEWZCfz7uumMyEnu9fq0dgXYVN1GaryLjEQvqfHumPbsi4iIyJE5koC91xbAtNbOM8YUf6T4YuCM6OP7gbeBH0XLH7XW+oDtxpgtwAxjTAmQYq2dD2CMeQC4BDhgwC4iInKkSura+c/725m3qZaS+g4Aphel84+rpzFlSFqfLVOVHOdmWlF6n5xbREREjq5eC9j3I9daWwlgra00xuREywuI9KDvUhYtC0Qff7R8L8aY64HrAQoLC2NcbRERGeiCoTDb69pZsL2BB+eXsKm6Da/LwSkjsvjKacM4Z2wuOcneY2KNcRERETk29XXAvj/7+u/HHqB870Jr7wHugciQ+NhVTURE+jNrLWWNnVS1dFHX6qO2zUdrVxCXw+ByOnA7DW6nA5fDEAxbKps6qW/309QRoKHdT2NH5Kuh3U8gFPnzMXlIGj/9+BgumVJATsrAWGtcRERE+r++DtirjTF50d71PKAmWl4GDNltu8FARbR88D7KRUTkMPiDYTZWtbKtro2WzgDxHhdJXieJXhdJ0S9fMExLZ4DaNh+N7X4ssCv9SeRx5Im1UFLfTkVTJ/5QGH8wjMGQnujuTly2667rrl7pXc8L0uPJT41jUGo8qfFuvC4HXrcDr8uJx+WIPHdFnrudkb1auoLUtnZR0+KjptXHtto2Vpc3s6m6bY9M1T1xGEhL8JCe4CYj0UNhRgJThqSRnuhhVG4SI3OSGZ+fop50ERER6XV9HbA/D1wH3Br9/txu5Q8bY24nknRuJLAomnSu1RgzC1gIXAv8tferLSJybKpv81FS38H7W+pYVdbEou0NtHQFY3b8ZK+LoqyESKDtdBC2lu117YTC9sPhUHaPb4TClpdWVxIKH9xgKGPAaSK947tzGBiVm8ykwal87YzhFGYkkJ3kJTvZS0q8i1DYEghaAuEwwZAlEArjcBhykr24lQldRERE+qFeC9iNMY8QSTCXZYwpA35JJFB/3BjzJWAH8BkAa+1aY8zjwDogCHzTWrtrEdmv8+GybnNQwjkRkX2y0WB5SUkjr62rZn1lS3fPszEwIjuJc8cN4swx2YzKTSY9wUOnP0SbL0i7P0hbV5A2XxCvy0FKvJusJA8ZiV52JSA3dD9gV+dzkseF4zAylIfClvp2HxVNXbR1BfEFQ/iCkV76PR+H8QVCBMKWzEQP2clecpLjyEnxkpcaR4Knhz9rnkOumoiIiEif6bVl3fqSlnUTkeNBMBRmW107H2ypY8G2BlaVNVHR3AXA4PR4TihMZ0JBCsOzk5hYkKq52CIiIiK94JhY1k1ERA5dOGzpCIRo7gzQ1OGnuTNAS2eA5s4Abb4Qnf4gpfUdrK9qYVN1G/5gGIDizASmFKZxw4hsJg9JZVye5mCLiIiIHGsUsIuI9JHaVh+ry5tYX9lKWWNHNBgP0twZoKa1i9auIB3+UI/HyUz0MC4/hc+fVMzYvGQmFqQxIiepF65ARERERI4mBewiIr2kzRdk+Y5GXl5dydqKFlaXN3dnW89O9pIa7yYlzkVmkoexecmkxLlJ9LpI9DpJjnOTnuAhJc5FaoKb1Hg3yV438Z5IFnURERERGXgUsIuIHEUtXQHe21zHi6sqeGVNFWEbyaQ+oSCV75w9ipNGZDIqN5nUeHdfV1VERERE+hkF7CIiUdZaugJhvC7HYWU6t9ayqbqNzTWtzNtUy6qyZrbVtuMPhUmOc/HlU4cxa1gGJw3PIs7tPApXICIiIiIDiQJ2ERnwwmFLdWsXZY2dbK9rp77NT0O7j/p2f/Rx5Ku+3UdXIIzTYUiJc5ES7yYlzk1KvIuUODf5afFkJnnwupz4giHqWv3Utfmoa/NR1dxFZXMXnYHInPNkr4sZQzM4fXQ2547NZfKQNK31LSIiIiKHRAG7iAxIzZ0B/v3edpaUNLCusoWmjsAer8e5HWQmeslM8pCZ5GFkbhKZiR7SEjy0+4K0dgVp6YpkZG/pClLd0sZbG2rwRbOwAyR6nGQne8lI9DA2L4WzxuQwalAyE/JTGZadqF50ERERETkiCthFZEAIhS2ry5v5YGsdTy4to6SuHQtMKkjl/HGDmDA4lSHp8QzNSiQ72UuC59B//Vlr8QXD+AJh3C5zWMcQERERETlY+m9TRI5ZzZ0BXllTybLSJuZvq2dHQwcAM4dmcNGkfM4fn8v4/NSYnc8YQ5zbqZ5zEREREekVCthF5JjS1OFnzpoqnlpaxtIdjVj74Trk3z13JDOHZpKfFt/X1RQREREROWIK2EX2wRcMdc9VNkR6Vh0G4lzOw8oe3hfCYUtdeyQZWlVzF02dH87HburwU9Pio7kzQFcwRChsCVtLKBzZLxRdHDwryUNBWgKD0+MpSI9ncFrke15q/EGv/W2tpbkzQG2rj5pWH7XRr5rWLpo7AzgdDjxOg9vpwO1y4HZGnntdkfXFPS4HwVCY7XUdbK5pZcG2egIhy7CsRG48eySnjcpm6pA0jDk22kVERERE5GApYD/GBUJhyho72VDZwvb6dtq6grT7grT5QoStJSXORXI0y3VqvJth2UmMHpRMSlz/XfM5EApT0+qjuqWLpg4/zZ0BWjqDtHQGIo+7Ah+WdQVwOQwZiR4yk7xkJkYSiGUkeslLjSM/LZ44t4N4txOnwxAOQyAcpq0rSHNngIqmTsoaOylr7KC8qYua1i4qmjqpa/Pvs27GQJLXRZLXRYLHSZLXRaLXRXayl7zUePJS47rPOyg1jsxET4+BpLWWzkCINl8w2n6Rx+2+IO3+YHd5a7TOuxKhdQZCdAXCdAUiNxd2fffv+gqF93k+YyAlzk1Ospe0BDdJXhdOh8FhIl9OBzgdBmuhptXH+1vqqG7tIhrDdx8jI8FDTkocKXEu3E4HLqfB5XDgdhoCIUttm4+6aHC+r7p4XQ7SEtyEwpE2//DL7rUtQLzbSXFWIp8/qZiLpxQwPj9FQbqIiIiIDGgK2I8B4bClqiWyJNWWmjZ2NHSwubqVHQ0dlDZ04N8ta7XTYUiMBpIOh6G1K0hrV4DwR2KggrR4xuencM64XCYPTmN4diKuGCw5FQpbGtr91LR2RXpTW3zdj9t8wWjvbeSaguEwwZCl3R+M1jNS16bOwB7B4e4SPM7uZbZS493kpsQRDEeCww1VrdS3+/d4Pw5WSpyL/LR4clPiGDsohYL0eBI8kXnK1oLFErZ0Zw9v9wXp8EeDbF+QpaWNVLdU7hVsuhyG5DgXNvreWEu0J3u3x9bu93p35zCQEu8mNbrUWLzbSXJc5GZBnNtJnMuB1+3A43Tidhm8TgdZyV4GpcSRlxpPWoKb1AQ3SR7XIY8S8AfDVDZ3Ut7YSVlTJxVNndS0Rnrv23xBOvxBgmFLIGQJhiLLomUnexmenUhOchzZyd7IV5KXnJTI42Sva58Bt7WR4/hDYXyBEP5QGKcxZCZ5cR4joxtERERERGLhuA3YG9r9/G3uFoLRwDEUtgRDFkskMHIYgzGRYCvZ6yIt0fNhsJHkJSvJS3w0oAuHI0N+23xBwjYS2IWtxVrb3QPa4Q9Fe0Sjj6PPO/2RNZvdTgddwRAdviDt/hAd/khPa22rj50NHbT6gt11dzsNQ7MSGZqVyBmjsxk9KIXh2YmMGZRCnNuxVxBkraXdH6Kx3c/mmlY2VLWyobKVpaWNvLauGogEwsOyEynOTGR4dhLDshPJS40n3u0k3hMZptzUEaChPbLu9K51q+ui61nXtvmoaYmsax366N0B6O7pdzpMtDc3cs2RGwwuclPiGJET6bnOTIr0jg9KiSM90UNKXCQ4T45z9zgM21pLmy9IQ7ufnQ2d1LX5ut/3YMjicBjcTkOSN7Kudl5aHIPTE0iNP/IRB7uGoFc2RdbjrmzupLbVR2tXMPIz1d2LHfn5ckTfB6cxJER76pO8TpK8bhK9H/be7+rRj3f33XB8j8tBUWYiRZmJR/1cxhg8LoPH5SDJe9z+ihIRERERwdiD6do7xk2fPt0uWbJkj7KdDR2c/+d5uBwGl9OBKxpIGsDyYS9omy9IV2DfPbZxbgdel3OfPdgHa1f8FbaR3thEr4tEjzMSwHmcZCZ5GZwez6jcZArS4xmZk0ReanxMehqttWysbmV9ZQsrdzazva6d7XXtlDV2HNT1eFwOshI9ZCR5um9i5KR4yUmOIyf5w8e7eoBFRERERESON8aYpdba6Ye17/EasB+KQChMY7v/w4RZbZHvTR1+fMEwafFu0hI8JHlduJwm2jsf2TfO7STe7STBE1kKKsHjJN7jJMHtIs7jwBMdhh4K25gMSY+FrkCI0voOalt9dAUiIwH8wTCp8W4ykjxkJXrJSPKQ6HFqDrGIiIiIiMgBHEnArvGmB8HtdJCTEkdOStxRO4fL2X8C3zi3k9GDkhk9KLmvqyIiIiIiInLc6h9duiIiIiIiIiKyBwXsIiIiIiIiIv3QcTGH3RjTCmzs63pIzGQBdX1dCYkZtefAovYcWNSeA4vac2BRew4sas+BY19tWWStzT6cgx0vc9g3Hu4kf+l/jDFL1J4Dh9pzYFF7Dixqz4FF7TmwqD0HFrXnwBHrttSQeBEREREREZF+SAG7iIiIiIiISD90vATs9/R1BSSm1J4Di9pzYFF7Dixqz4FF7TmwqD0HFrXnwBHTtjwuks6JiIiIiIiIHGuOlx52ERERERERkWPKMRmwG2OGGGPmGmPWG2PWGmNujJZnGGNeN8Zsjn5Pj5ZnRrdvM8bc9ZFjvW2M2WiMWRH9yumLazqexbg9PcaYe4wxm4wxG4wxn+qLazqexao9jTHJu30uVxhj6owxf+6jyzpuxfjzeaUxZrUxZpUx5hVjTFZfXNPxLMbt+dloW641xtzWF9dzvDuM9jzXGLM0+jlcaow5a7djTYuWbzHG3GmMMX11XcerGLfn740xO40xbX11Pce7WLWnMSbBGPNS9P/atcaYW/vyuo5HMf5svmKMWRk9zj+MMc4ez38sDok3xuQBedbaZcaYZGApcAnweaDBWnurMebHQLq19kfGmERgKjABmGCtvWG3Y70N/MBau6SXL0OiYtyevwac1tqfG2McQIa1Vmta9qJYtudHjrsU+K61dl5vXIdExKo9jTEuoAIYZ62tiwZ4HdbaX/X6RR3HYtiemcByYJq1ttYYcz/wgLX2zd6/quPXYbTnVKDaWlthjJkAvGqtLYgeaxFwI7AAeBm401o7p/ev6vgV4/acBZQCm621SX1xPce7WLWnMSYBmGmtnWuM8QBvAn/Q57P3xPizmWKtbYneFH0SeMJa++iBzn9M9rBbayuttcuij1uB9UABcDFwf3Sz+4m8kVhr26217wFdvV9b6UmM2/OLwC3R7cIK1nvf0fh8GmNGAjnAu0ev5rIvMWxPE/1KjP6RSiESwEsvimF7DgM2WWtro8/fADSiqZcdRnsut9bu+tytBeKMMd7oP6Mp1tr5NtKT88CufaT3xKo9o68tsNZW9mL15SNi1Z7W2g5r7dzoNn5gGTC41y5EYv3ZbImWuwAP0GPv+TEZsO/OGFNM5O7/QiB31y+n6PeDHd7+XxMZcnuzhoD1rSNpT2NMWvThb40xy4wxTxhjco9idaUHMfp8AlwJPGaPxSFBA8iRtKe1NgB8HVhNtKcd+PfRrK8c2BF+PrcAY4wxxdHRE5cAQ45ebaUnh9GenwKWW2t9RP7xLNvttbJomfSRI2xP6Wdi1Z7R/3U/QaSXXfpALNrSGPMqUAO0EullP6BjOmA3xiQBTwHf2e1uxaG6ylo7ETg1+nVNrOonhyYG7ekicsfxfWvtCcB84P9iWEU5BDH6fO5yBfDIkddKDteRtqcxxk0kYJ8K5AOrgJ/EtJJy0I60Pa21jUTa8zEiI19KgGAs6ygH71Db0xgzHvgj8NVdRfvYTDdI+0gM2lP6kVi1Z/Tm6CNEpqtsOxp1lQOLVVtaa88H8gAvcNY+dt3DMRuwR//5ewp4yFr7dLS4Ojqsa9dcg5qejmOtLY9+bwUeBmYcnRrLgcSoPeuBDuCZ6PMngBOOQnWlB7H6fEa3nQy4rLVLj0plpUcxas8pANbardGREo8DJx2dGsuBxPDv5wvW2pnW2tnARmDz0aqz7N+htqcxZjCRv5PXWmu3RovL2HOI7WA0ZaVPxKg9pZ+IcXveQyQnwZ+PesVlL7H+bFpru4DniQyrP6BjMmCPDlv/N7DeWnv7bi89D1wXfXwd8FwPx3GZaJbiaCNcBKyJfY3lQGLVntEg4AXgjGjR2cC6mFZWehSr9tzNlah3vc/EsD3LgXHGmOzo83OJzAGTXhTLz6eJrqpiIllxvwHcG9vaSk8OtT2jw2lfAn5irX1/18bRoZytxphZ0WNey8H/jpYYiVV7Sv8Qy/Y0xvwOSAW+c3RrLfsSq7Y0xiTtFuC7gI8DG3o8/7E4JdQYcwqRIXirgXC0+KdE5hI8DhQCO4DPWGsbovuUEEly5AGagPOIZM+cB7gBJ5GkOd+z1oZ66VKE2LWntXadMaYIeBBIA2qBL1hrd/TWtUhs2zP62jbg49baHn+hSezF+PP5NSJZqANEfv9+3lpb32sXI7Fuz0eAydFj/Mb2kOVWYu9Q29MY83MiU1F2Hw1xnrW2xhgzHbgPiAfmAN9S3pDeFeP2vA34HJEpSBXAvVarcvSqWLUnkd+9O4kEdrvmQd9lrdVN0l4Sw7Y0wItEhsI7gbeIrIB0wCllx2TALiIiIiIiIjLQHZND4kVERP4/e98dbsdRnv/O7jnnNvVmyVXuBTdsY2NwpcWUhJoAgRB6SCD0IgKmhBRCfgQCIYBpptp0Gyxb7r333m3ZltXb1e2n7Pz+2P12vylbZq8tK3je59Ej6d4zZ3Zn5utlPDw8PDw8PDz+1OENdg8PDw8PDw8PDw8PDw+PHRDeYPfw8PDw8PDw8PDw8PDw2AHhDXYPDw8PDw8PDw8PDw8Pjx0Q3mD38PDw8PDw8PDw8PDw8NgB4Q12Dw8PDw8PDw8PDw8PD48dEN5g9/Dw8PDw8PDw8PDw8PDYAeENdg8PDw8PDw8PDw8PDw+PHRDeYPfw8PDw8PDw8PDw8PDw2AHhDXYPDw8PDw8PDw8PDw8Pjx0Q3mD38PDw8PDw8PDw8PDw8NgB4Q12Dw8PDw8PDw8PDw8PD48dEN5g9/Dw8PDw8PDw8PDw8PDYAeENdg8PDw8PDw8PDw8PDw+PHRDeYPfw8PDw8PDw8PDw8PDw2AHhDXYPDw8PDw8PDw8PDw8Pjx0Q3mD38PDw8PDw8PDw8PDw8NgB4Q12Dw8PDw8PDw8PDw8PD48dEN5g9/Dw8PDw8PDw8PDw8PDYAeENdg8PDw8PDw8PDw8PDw+PHRDeYPfw8PDw8PDw8PDw8PDw2AHhDXYPDw8PDw8PDw8PDw8Pjx0Q3mD38PDw8PDw8PDw8PDw8NgB4Q12Dw8PDw8PDw8PDw8PD48dEN5g9/Dw8PDw8PDw8PDw8PDYAeENdg8PDw8PDw8PDw8PDw+PHRDeYPfw8PDw8PDw8PDw8PDw2AHhDXYPDw8PDw8PDw8PDw8Pjx0Q3mD38PDw8PDw8PDw8PDw8NgB4Q12Dw8PDw8PDw8PDw8PD48dEN5g9/Dw8PDw8PDw8PDw8PDYAeENdg8PDw8PDw8PDw8PDw+PHRDeYPfw8PDw8PDw8PDw8PDw2AHhDXYPDw8PDw8PDw8PDw8Pjx0Q3mD38PDw8PDw8PDw8PDw8NgB4Q12Dw8PDw8PDw8PDw8PD48dEN5g9/Dw8PDw8PDw8PDw8PDYAeENdg8PDw8PDw8PDw8PDw+PHRDeYPfw8PDw8PDw8PDw8PDw2AHhDXYPDw8PDw8PDw8PDw8Pjx0Q3mD38PDw8PDw8PDw8PDw8NgB4Q12Dw8PDw8PDw8PDw8PD48dEN5g9/Dw8PDw8PDw8PDw8PDYAeENdg8PDw8PDw8PDw8PDw+PHRDeYPfw8PDw8PDw8PDw8PDw2AHhDXYPDw8PDw8PDw8PDw8Pjx0Q3mD38PDw8PDw8PDw8PDw8NgB4Q12Dw8PDw8PDw8PDw8PD48dEN5g9/Dw8PDw8PDw8PDw8PDYAeENdg8PDw8PDw8PDw8PDw+PHRDeYPfw8PDw8PDw8PDw8PDw2AHhDXYPDw8PDw8PDw8PDw8Pjx0QT5vBLoT4oRBivRDirpzfCyHEN4QQDwkh7hBCHMF+d4oQ4v7kd8vYz78ghHhSCHFb8ucVT9fze3h4eHh4eHh4eHh4eHg8k3g6I+ynAzil4PcvB7Bv8ue9AL4NAEKIEMC3kt8fBODNQoiD2LivSSkPT/6c+3Q8uIeHh4eHh4eHh4eHh4fHM42nzWCXUl4BYHPBR14N4CcyxnUA5gghlgA4GsBDUspHpJRtAGcmn/Xw8PDw8PDw8PDw8PDweNag8QzOvQuAJ9j/VyU/s/38GPb/Dwgh3gbgJgAfk1JusX25EOK9iCP3GBoaOvKAAw54Ch/dw8PDw8PDw8PDw8PDw6McN99880Yp5cI6Y59Jg11YfiYLfg7EafNfSv7/JQBfBfBO25dLKU8DcBoAHHXUUfKmm26a7vN6eHh4eHh4eHh4eHh4eDhBCPFY3bHPZJf4VQB2Y//fFcDqgp9DSrlOStmTUkYAvoc4fb4SepHEVLdX60EnOz1EkSz/oIZuL0K7G9Wac6Ldg5Ruc7a7ETo9t/mklJjsuK9LFNUbR5ho1xtbZ1xvGs86nT3cnvtRZy5gevtYl6amuj30atBT3TNTdw+llLXmnOy40y5Qfw/b3Qjd7TRuOuel7v7VPS9156xzXuqelbr0DjwzezHZ2b60W3c+AOj06tFTHdkL/N+SiXV5Yt1nBOrzRSDmAduLV9V5x7o8uM5c0zln25t+aU7Xfa/LF+vSfN1xdfei7rjtrTvVHbu9+cv2Pi/bU7fneCYN9j8AeFvSLf75AIallGsA3AhgXyHEnkKIFoA3JZ9FUuNOeC0Aawd6G/7upzdj/8+ucH5IKSUOOHUFPnt25alSvOxrV2C/z57nPG7j6BQO/NwK/OCqR53GHfkvF+KEr1zqNOablzyEA05dgW2THadx/3nB/Tjg1BW1DuAtj2/BgZ9bgUvvX+807pqHNuLAz63AdY9schr3np/chANOdd97APizr9fbQwB47j9fgJP+8zKnMV+76EEccOoKjE51ncY95/MrnOcCgC8tvwcHnLqilhB4709uqkVT+392Bd7/81ucxlz+wAYc+LkVuPkxawVMIV7+31fW2sMfX7MSB35uBdZtm6w8ZnSqiwNOXYGvX/Sg83wHf+F8vPS/Lncet99nz8Offf2KWuNe+7/XOI353B/uwgGnrnBWwG54dDMO/NwKXPPQRqdxQHxePnjmrc7jfnXTEzjwcyvw2KYxp3EvrcG3z7ghnuuJzeNO435w1aM44NQVWD9S/YwR/u3ce3HAqSucFYf7147gwM+twPI71jjPecCpK/C+n93sNObqhG9f78i3ab5P/uYO53EAcOy/X4wj/vlCpzFbx9s48HMr8L+XPew8H/HSOoYbrdENjxa1/TFx6X3rceDnVuDWx9344ptOu875jN/+xFYc8oXznekJAEYmO7X5IhDzgNd/51qnMV/449044NQVTsGWhzeM4sDPrcDvb13lNNd+nz0Pr/rmVU5jiA7PuWO107gfXbMSL/p/l9Vyfhxw6gp8yJGX1j1jADCWyMOvOe57Xb549L9ehCO/5EbzAHDkly7E8f/hpjsDwOf/4H7GAOCTv72jlk66/2dX4G9+cL3TmNue2Brr2/e56dsAcN6da3Dg51bgntXbnMa9+ltXO/OXs297Egd+bgUeXDfiNA4ATrviERxw6gpsGp1yGnfEly7EMf92sfN8h//zBTj5/13mNOZrFz5Q2w4hPJ3Xup0B4FoA+wshVgkh3iWEeJ8Q4n3JR84F8AiAhxBHy/8BAKSUXQAfAHA+gHsB/EpKeXcy5itCiDuFEHcAOBnAR6o+z0X3rqv1HtsmY8PpF9c/7jz2kY3ugg0AHtkQjzvvrrVO40Ymu1gz7MbgfnLtSgDA6KSbgfjLG+M2A2OOhiUA3LwyZvxXPeimvF+bKHzXP+Km1FySMKo6WRIPb6i3hwAw1u7hya0TTmN+fM1KAMC447p2etJ5LgD49U2xYrJ5rO089uIaAoCw4m63s33TynjPr3xwg/NcD64fdR4DAOffHfOMB9dVH0/0cHqyjy5odyOs3ORm8BHqntM7nxx2+jzR/dZxNwff5Q/EZ+WmGg4XALWMy9/dEp/tVVvc6OLRGnz73DvX1Br7h9tjZX31VneD/eeJTHJ1mj68YTSZ+0mnceTUu/AeN1l6zcMxn3c1Rolf//YWN+OJsHG0jRFHPjqSyMGfXuuetfjz6+L9mKoRWbr43pg+bn9iq9O4yx+I+eEtj7uNq0OHq7ZMoBtJ3LvGTXkHMn7xm5vr7SXgvjY/Sfaw7eBAuW9NbCxccLe7vnjfWjdD4+7VMe+lva+KVVvGsXp4EhtH3WQ2OfbOceSldc8YgNTZfdatbrymLl/cMt5JdXYXbJvsYq2DY57w0+vczxiQ0UEdp8s1D7s5PsnYXuFoTwCZjneXo55wt6OBDwAXJHLlXkc6AoDfJ+fLdQ9HJru1dN/xds9ZrzjrNjfHnA1PWw27lPLNJb+XAN6f87tzERv0+s//5ql5uurYmHhsZvZvv3L/kSTavT3mJObmGqWhRgPdGkawSAa78iqaU6JeWt3m8TYWzOirNXZ7gfa+jtJXB7MHmhid6mL9yCQWznz616ZuSuTsgSYAYHjCzVCcDmjOrRPVGTqlgW3P59yemD3QxMbRNtaNTGLuUKvyODKEXHlaHSebPmd/M6z9HVWR8jTXccnfdeiC5pzqRpjpMI72wNXpsmXcXbEBAJG8pesbuirBTwVIntWiX7YfQ46sdHSqnszPZGl9OqmKdi92DD3umEUyXbhm/xGEiHWMqU5UmQfU1U3qQNi6NVUAyZjHN487yexNjgY+YTpnbMt4zXON+nM+E5js9GrJmalu9bNZVxZOR3ears5da65asjCRMTvwcaF9mA6eyZT4ZwSu6WobRxKDvW/7GewkoGY4zFm3Pp+Yf10DcTo1GduDCXCs3+aWLjMd1K5LTZak7n66gpjIdNbGhaY6vXp7TgJ/2NHImA7mDLoLurp0VCdT5ZlA3fNCGTxDLTc+Oh2jjcpK6vagqINnQsF0PXO9mkbpRsd0Qx2uS7O9nJbqnDHfnZiGXKvDu8m5NNB6+p1LdTHViffjic3umVxEx3Xo40nHSBaBDACX/dieBgrBdU2ILlZtcXOcEP02w5qeghqgOV10WY7tzU3r1DMD9XkV0VQVjLXr6QhhEO+3S+BBx45sCD+TcCklJX1yOnjWGexlhLV5rI2ly5bjV0nqJ6UdzewvXuw1wxNYumw5/nh7vbSHpcuW4yO/vA1A5gktm3PZb+/A0mXLlTFluGPVVixdttyoBS4zMJcuW46PJs8HqNGdMrz1+9fjsC9eYPy8jAlc8/BGHPUvF6ZRZzh40V745Uvw6m9dDQAYSpSgsnqoddsmsXTZ8jQdyxWf+PXt6X5sGKmm3N72RLwft2npfpMljPzDZ96azuWCKJJYumw5vn7RAwCYAVahVuzV37oaL/zyJcbPy87AhpEpLF22HL+/dZWT8nTcf1yCN3w7rrGmyNfWCkbGkV+6EG/8rlu9I2HpsuX4+K9vBwDMStamjLZuT/bwlse3OL3fR355W7qHLsbQNy9+EG/9vlsdGxDXoy1dthxrhlUFeLxEEfjL71yD5/3rRQCAOYNxVH19yfme7PSwdNlyfOfyuBaYMnnKMnImOz08/98uxiX3xelxLgrNqi3jWLpseZr6R0bQZMme/MeK+5xpaeXGMSxdthwXa6VWZazp3869V50r4WlVBP81D23E0mXLcX+SMkhqdxXn4F9959qUdqccskD+4n+uwvFficdtHInpoK9RrDaMTHawdNly/DQpt8qyD8rf8cmtsRw97841tQzfpcuW49O/u9NpzE0rN2PpsuW468lhp/P2/p/fouxlth/l3/H6b1+TriuQOZfKeOnweLy2Z9zweDJndePryC9daNS/lhmLX7/oASxdtjxpMJpFdsvw5fNUmqJ1rareLl22HF/4Q1wJWdVgv/yBDVi6bHla8kFRt7L9WLpsOf51+T3JmPhnZTrGu06/Efs71uhKKfGir16GX98U65YumSev/p+r0prZdB8qlE+97GuX48++Fvc32ZBmixbrlb+4/nEsXbY8dY6nz1lx8/b77Hl4949vBMAzVIvnfGDdCJYuW571OKnIFz971p219KDrHtmEpcuW4761aup2mRy+QjtjhDIefNsTW3HYFy8w0q+LZJOUEif/v8vS0q4Rh1T/V33zSrwk6YdDfHR4onz8y752OU5h/XBcMscO+fz56b5XBZe9wiHl5At/uNvKe8v456d/d0et8wLEfOJL59yj/KysZv7dP74J+30m5hOzfITdHWWE9UhCiGfeGAvE1ENYktJDdSK/q1lvB2R1GMRYWyWe0DNvzK6rr6rwX3pfXI90udbwrYrh/TulDomEYblSddVDGxXlsCphPrxhDBtH29gypiqWVZjHk1sn0pq3qlFBqs37bc06u1+zcVX3gxT+Kx9Qa7PLlNW69TCbk7TW718ZNzR0iZje/sRWa5182dmhfTj7ttVO0c5VWybSWktS+KoYGZvG2rjesV4WyFLO9PqysqZzVOd1+f0bnN7v94yeXAz2B9eP4sH17nVe30+aWD6+aVxR1Mv2/saVW1IHFJ2XsjWhffphMiel/Jad620THazdNpn28ZjqVTfa7lgV19mdfVu8rhTVLzPCvp00GHOJdJHDk+pBiaf1SjJITrviEWUuFyPvj8lcNyb9HGjOKkbmDSs3p7RLZ7RKSvwdq4bTiGrV8rDHEkPiFzeQYRKjyvLekfCKs257UqGlKpEvyvQhY7YqyMFzzcMbnSJly+9Ua4EzJ3b5mb35sS1KpJocWmXzP5FEVanG3iWFe9NYG1dqfWPKMlioSVw3yro+V2msSI66qvPYQH1AVicOxrII1dkJP701qbWuEmEnOvzelWqD37LlvPi+9c5R1W4k8ciGsbTPhcve3b5qOB3X7lYvTXhg3SjuT5p4UbZoWbT79GvitaB6YBeHW/x8ES5K6vLJyTejrzhz5OrEUD8/6W2T7l0Jb/vZde69pYC4ISkA3PGEWptdJgvPSmTLbVo9f9lZeGTDKIYnOobcLHq/qW6ERzdm58XFYL/ryW14KOndkzpoK5Q0PbBuVOnF4OKsGZnqpvtOKJOpXPa6yAm9R1BV3nvGDU8U/j4P9B56I/CywMVF965Led/QU5A99awz2MsIi4RSK4kikJIyWLLYpFA0Q3NJXVOeiMm5CLmqCj/VoTXDQHkul8gC4BZhN8ZW/NxU4gygdaibyDWrYhSZ9t62h671Q1UzHtI5G9Pbj6oggbFgRhwpJSFcxniKUMYkyQG1aGZf5fOi0wydga0162iB8tR9vUaXnrVsbTjP4O/nYrxvGKn+XlPdXlpa4MJbUqN7sKnwFpe9T7LrSjNIdFoaqWiQ0O/p/VzoQJ+T3rFqpFZfyqK1zfh9vCDEm6rybIocuRh5umyqk/Ibfz7+Hte076rRMirpmkWGvUPkhNav1QiVs1JFvrk24TLmDAOFZl1vziDltg7vHqXeJSV7EiVnMghozmrI433V+XF2zlZtmagsD4mGyt6LQ39WirCXGZptnSYr6Cj679o1SraqnpOMt0XKONcZXTIdOLJs0eJ17CZrQKnU00mg3zQW023ZEun6swtfrAOSX3oPgKqyvtkIlGBVGc3re5/9PP/9aAyd67q9HKbTV6cq684L3FXlL5HM5qpzY9F07JEqyPtel1sM6paDcniDXf89UxiATFEoU76J0Tct6YJlc+qKIc1ZJeqSjqmo8HfYc46xOw+LIuVFiuu0athLjA1iVMTkhIsLjoEiUWXMmNam1TC5lCsjqO5AyQQV72hclsbL4WK0ZQZ7LKjovVyuLtNRdk7Ja71wZl9lI1aP/rlE2PNQtocUUSBPaNvRYO9rqAr/BoeouUuEfaoboaMZtlVAwkVKlZ+5CJ3MiVHi/Er5qGqwl/ELUmCI5l2clpyW6ji/Io2Ois5LnoJZlR/SvlVN5QOy90tT0tM5i8fqxlVdo5QU/pbFocmxbYIaDDb5Y1YyTGgddON5XYUMoLWaM7IqMkdIqCjQrlcEVT0DtjWvmhJPQwOhGqX62dWRx4tcaKPNDIh1FXkGPW9Ww14+Rl8DPTMkDx3meAEyB0rRftBc5IikOVxUjKoyra3x7Gyu4sl0I4/GuXapJhkTlFhhnSj+/kagnrE6dcyZLltMEx1Nf3bJPALcjTwy2PWGb2V6UHbGBDax9PYyfY0cVvpeFr1fKgu78buNVDTYdScAnfGxds85eEh7342q6Xjms1TnL65Ob+t8FeWvc9+ITmaD8LFV5FL6HU+B8+lZZ7CXMY52V2X6FPkqO3hdTVhwlAlFXekmJueywVwgFx1GHoXayAyRovezEVDVlKWi7ysjGfrujibs3T3S8TpWZca2CLsrsW2suB9cACj74bCuLkbb2uF4jtRg71QzSotQtjZk3M0bqh5h16/noDk2j7VreWDj7yiem87Holn9yufXl5wbylrRI+wuThAXg73djZyjx0CmHERSas/p5iyoMkaPQFPtXvUIe/J+TnRAhpdQnF9V10g/VsUGe6Jghnq0u6LybihuVSLsyTnT+FPZ+20a0zNHmFE65u5UKlOoSLGcNaBF8iooSXnZKmU0CABrkytNF87sV35elk6fO6cjT6x6BnRHgJQydUSWKZxZZoZQ/i7rDZFHr1UdTJFU36tK/XQ8Ts2UqZJWnWewl+tgKk3mfR+HnpWT0UZ1GVOVv9DnMt5dTQ/SZQONWz08Uegs0DMVquqVVNZDe1f1jNmwsaL+bDpARaVn1ccTyrLpiLallMpnqwZ1DP25RE7Rnre7ulM4//3amiysmhKvG898jlHn5rbVsoZIX2tpAcuqBjR3ONYJAqbZTRXlbzeSil5eZsBPMtmryiUH3ekpyJp91hnsVVPi+7SU+HIPoeqVVOesFlUi1Iqwj1YzvjOlNlDGVPFCc6TRhBpeo1R4l/B/XXmvE3Xj85VGSgsMdpe9ANT0zKLnJe9pMwyUMS7rys9PmTFLjJWu5KJnq9okz4bSCPsIefarC2B6Tj3yEUn3yFf2nMVzkzOD0uToWTeMTBWmgHInH38/F2bOabFsD6e6UUYLNVLApKZ8Px0RdjrXrSR1kBSF8gi7SuMuDokO2weuTFWlXSPCXvCsukMim8tNwcwU0woR9pyU+LL3M+omayocVWUh1WPPSiLs6V5W4Ntc/vL1r2I8pw63mX0KvVbPqgtqZ55wlPIZbT/G2r10DycrGjZ07Og9y3kbOU3V7IOiteFrqDv5qqZjpwa7Q+R6SnNMrU4M9tJgC+lgtDgVMh5ortRgr6ibqN9Rjb+kAYiuxrtL5qK9y+brpc9o6ylD0O8jr6pXkmFOcqjqGeNjzDmr6c/NQOen1da204sUw7uMjsiBrDuiNpTJNaYjVtWfATP4lI3Lf05d/91WMbtQ5/cqT3uadCfGezmq2CJAfJZdZGE2Ti0tc5G/XC8vm5O+t9UItHLC6jLiqbim9FlnsFeOsGsGe9li6ynxUvEYVYsqATFzpPpnF2WcG3uVDPZQKAyn6P1snqEs3cz9EFJEssxjm6XHqkK/zFOVX/9czPC4M8N4ZkfDiGc8FI3Nc6C4zOfSnGm91kiG1rjuHcv6/DaQMyCS1d+LnjMznrNxWwr2sUp2SR5I0M0nZ0by+W4kMVrQSZ3zDD6HS709L2kpe86pbg+RhNK5GSjus8DXhae3AsDWseplBmnDspIxvFcGdwaVvltC210tbbQKlOhHRYcZh5TqOrmkxKdzVfXwaynxlSLsGn+qGoUqUuBc7mKvWh5GiiXVsKep1A5p/7piVIWW6D3naj0aXLLq+FrqzU6rouy86dHujS70kfye6ouzyF013rZwhqpQF60Nz8yQUTw3XW9bVfEnciJ+UCVISzygGQq0u1E6V9m66inxhKK1ofUkx1vaL8ehcLsqzU8xeRL/P1n7krls9JvuQ0HmiU4zaW+kkuclo5ues+oZs31mQ0X9WU+Jz76vmgHW7Ulljqp7osvCMppXMlQr6s8A33v1c+2CpqqZkR+vDTlgyq7l0/nLVE2dBMh0irL30/U1QhF/4XpB3Qh7mnVLfKbivnd66r6XG+zckVxPfvoIew2URtg1pk/KR5nHVU+J7yke/mpRJSBuBESMsqwujYN734pTsDOldhu75qFoLtvzkxd7osbdkJOpF7ukhl3zMGZ3xlerh9K/p2w99XKInhZhcAHfjyIlZYop/lXH6ODe5LLnJE9oTzOI6qaZV5lzA0s/q8pQ9Wh31bpbXbBwr3vZc5JiFASmp1cWPLZiZCh7UTidAt5Qpuo57fQibb78cTwVTnecuJxtUqDKxmRKd6C9W8n3TyObJs2QaWi0VHEjIqkqfUWvqKfEd9I00mrPqvflcImwk7M0VaxLSmKI5lOjpObek7woG0P7TWtDCliVqTgPVmm+fCyPXE85KGOZw02oczry/KpngJ5zIKmfdaF9Wkty1lSVbcTb9E7rRWvDDUUybPqT/h5Va0DpucbTfjnVU+KbjQBbxtuQMpYDukNNR+oES9aGaKxoP/SUeFrfRlBdNa5qYPAeAPz/ZTXlZIDRDR18H4reTe/3Ques7KzoTfHo/arwCr4WUso0jbuU72v7UGXv9Geuytf0zBHO88tonjt0qurPfJyeEl9UGq7LwrLrVwk6f3Hlo/FzSWXuUr0yyQLRG0MW8Ze1Cn/hjqzqMp+ea7rnpTQlniLsoepIdpGfU70IL9h7fuXP2/DsM9gdI+zEtMoMGj1FkhN+aUoeeyYedXYxonoVx/G0bz5XkVJre35an/F2dW8YgRhPldRfgAkQrR4uD3o6eVRzD7k31FV561Vc2043m7Pu3o8zY6w0JT5hrHraW52GMoQyg4jShiIpK68jMfMwUZz4uGLnkno2eGPFqs4MYt7880XPTTwjFEL5nMuZUfa+zBhmdFF1nK589xzm46BhVZ+xFQaVaQHIeKGuNFYBV/pc1jN9NikxPsXKS6r0nkj44ISDUguw9N1ATcUtHqOeS/qOsoZA64YpcyR2ftU9o1VlYWbYx/8nGVFlL7n8dXWY0hmPpHrOqsp8KdVnrGyUalGosjO+TktPd+H7epO0jI8XP6Oehp99X/7a6DwjkpI5s4vny8bFf5PhVsUQ5mnq9H5Uoli0PGmvG6gyreiMT2mGYh19RpExhXoUNRFTI+1V5RI5W3psH4rGbtUM9qrz6RH2sZR+C4cBUOWvC98nHtZKA0Hu/LSqPruR9e1wpXluwDrRbU7TucKzqY2h+To9WTgf0S1dRV2Vj9rKiJ5W/jKs8Rcy2B0i0fTIJH+r6gudXlTZZgLUlPjIYZzyrJHMGsbWxLPOYC+rbTEMdlnV2It/3yBGyqYp88BSxDkQpvevCqSDAs69uoriVvB+tudvVRRwNgY4lijFVZV+8kqmtVQlEXb9eWmeMqGhe3n5Hrpe61Z1Pzo53joXz52qMBR/ltILe5HqkXR1SHAUjZ3s9JTIU1XhuF43nivShb733CtdJnQokkFrWJWh8+ZBUUWlQYfLOJ4qp5yZgvfjaXJS6uPy59P3KKtrLHzEdB+aoXByDhg07xL9Z+U+Vd+PI5LAWLua80vvWUKKiYvCwFGlZ8WU5hTIshBKDMRtWQ8JoPpZy937klekSB4ZTmOJQ7EKj0nrlxuBMz9M6VdzDJaVbbVZdIYvZVWjVH+vsjOwTitLcpH5dAYoKpvtSfE46lWgf6xIOeY8g3i3LSBRBHouynipFqXNnH20NrasNx1EB/oUhUY041OA21klKA7zCs4BPQBR6uDRjKEoMgNKNgwn6brEoyoHLtLPJZHdqWqZNYDWT6eifgnYmv+5OWw7PakaUoW6hZqK7WK4UW8WKd30tbxr3aoEytLzwphTFUdbZOENVQNzqQFsCV7YsJ7xXj5HEe/l51pGLKPDoW+N3tSyqr7Q6Uonp/7kNBzJ6RyRTEuZ6uJZZ7CXedspjYjuqiTFtGqaKjEcpwg7U1JcmBxBSi0CWZRmw1LoVAZX9HzmF2apbsVpOmMWg56M/HID2h5tK/PADWt1JWlkqDRtKf497eF0IuxVmWSeMesUYW9XUxiALDVaj7CXzVf0DkVjx6bUtDG+LkU9DEY1JaEqXehngzuUyt5xTJtTcdgUrCtXRuvuoQvd86vPeDp00d7rKfG9irSvp1VWpaUpJuBcjGfzrmJz7jx0coRqVdqVUqaRHaB4z/WUeFJMKiuYXXUdq0QV6CzTY9HfnRL5Qv0XUuecoyMq/WxF53WWQhv/P80+qLA21L+lvxE47T2Q0a+UerSomsw3ZWLR2uTzlbLzRk4h6bgf8bxJNk+gdu4uG6fz0+z78teG826ZOEGCQBiBhSJQKRE5cSYqRLAVfShS6ayYJu1Ke9WMQ/58Ls7WqjJGv9aNDDBXuVTVcUI9CKjBbFX61UttXDJk1J4q2c/L0/Dj3ze00oSqvNvINqugW8TPpWfSlc8Tj9NlffG4PIO9ioOH9FLFpiiQF2M5Ol75fNk5Tg32imc05S+RbvsU8BdN1rrIQgJNNenoMG9rEfayAATV/s/qb1bW1fTyz0jK0vKXMjz7DPYS5YaYnG54V02nppSeOjXs3KMMlEcyCBLVvfRZ2lj1aKeNgIgZlEXYN1qa06Qp8SXvpzfqSAm6rMmZ1kU8E3TF8+lprnWNL6mlSxUN5ffT9mrsPQAljbdo73kNeWQ514Wd0AsWr0im6jXB/GuqXCVoU2pdUuKrrg3AUmOT/1dlzMQzYsdZ9nMX72tVQ4E/p57WVTSfWq+lO/jyx+lXCtnOjQ2Z0h2qhlfFd0tpnknSqvcwN0M9Ols4TPncWFXlWyufcU3hpLuO0zTAClEFrrhzfl2WEp+da1Pxc6GlqtHcKa1HiW5wFGFNkiYpteesorjz93RxmG9W6LcaXfDO3b1INfSr9max0VJVxZ8UP1sUrWicritUvc6VxjUCgTAQlc85nTlKiR/vlN8FPcUi7LQfDYtepYPXsfLeJcVpx6rBPtZ2j7DzrJwqvYD0FOcy/qTfDd+TMn3eorUk3j1TS40uezXdKZsa7FUi7ExX5Hypat08vc9kBScff3ddFhbqJFrtskvqPtdLuhVlL59T769UxeDrps7ranRLv7PxhqoOWlr/qg7B7KpmdT2LjG+lRxDcZGE2TjsvBY+py0yXSDk1sZ031Koc6NysXafqI+w1UJaevnmM0mKlQsxlTFXvGswPQ9Uu8a1G4MQE+Oeqehc5Y6weaaFGN9nP6PNlHnPbPb9jFZXb7BqUZA8ilTjzQMTVSiPlJKiqMVU9ehGPLRyqQI/wVClR0D22Lh5+RWGoaHR3LZ7TKoqNDVUj3vGZM4VC0Th7VDB3mMHsqypTfCydk6oMPbsiRn0/F4Pd5axNMeGvKEUVhbFx1gom3DCiCh31Ocvni3sz8LlzhyTj4j2glHi+11WvdZJSbcRWORoopZLeWuj4NHollBulfM31OtYqt22Msggy/7yuBOpIFTgLLRUtjZ6NVtlg1yKd4w41sGtZHXqv4tlO500VVXX/y2U+GezVZSK/TqsbyTQiFY+r+JzEZ2Q1mgKyd8lq2O1RZR28WaRaLlDEg1VnZzeKI0RCK6crAi0hZSH1Ilno/AV407msnKZShD01bCXGteZn5XOpJX5VFX8+pmycmT3k6Gxh9Nus0EsgDZZodF917+jzWUZI+Tiezsz9iFUDXlnEVOVZNnCZFstCd8M05jPVxgH5GarlmWP2GvaiJc1z8ADVrmGmzyuysKKON5HybNWRUvas+noWpbdzXY3bMC43T9FUkxVS4jmdGinxJfuXOb+alXUgPdjhDfYaKPO207UOkVSZRdWUHoJThD2towqcmAd/tqr1NLxxUdUIBq0Zv2NeT5fKg67wA6wmqqLXrq0xrNIsiYRQ5g41IaWs1HwGMIWGy17o12ZVjdQoBrtjRInmVVLyKkZZbcK7SnSAz1tpzp4uHPl3VomwuxnP+ndOOKTEt4sMm4Ijx1OV1TTe/DF8/VwMhS5L/XNpnKJ30K06Tne4VW24MqXUodrHW8fl1Hny3+WB173XKS2KZPV+EHpXWsrkKNpznorZYXwYqBZV4IYed0SVXeXYLnBEuaTEV62BndKiHVWcGYQ1wxPps/LXqrKFijLtcG74HFX5zOqtaoR9zKEsqciBUnWs0GrYq6bS6+9YGKnTeHeUKJyhEE43LwDqneBlTn7edI6elbLeirtqx5+VUs2squLgbWk17C5NeMcqNn3ljlbAIXppycioUtO/iTmS+Wer8sM0izLlbeXjiO83AqHpFsXjOD+MoiwTsGhOfrtCLAurZVUYEfaKOhCgZUQ66Gu5KfEVzktbK6Hgv7M+o7Z2VctluQwyIuwVg136ehYFenSdxEUW8nF8TYvWc4TxICMlvuT9yKbQMwiK5jMMdukN9sqoen0OKajOzSg0BS6qeGj5M+mpnEVz6uk/VZ+VvNBRpBsk5YyDHzYaO14SvbBF2KvWROkexqoETYQye6BZcw9VgQqUMyw9Bb5q/SV3ElQ1hni6n5TuNXT8eVUvce5QU3ln01RxSMRjdGaev4+6klK5cYpGay4R9ra2/1UUad1RU9Vxpnv5q9K9ftds1TPK77PVz1rRc+olLdWdfEzpdjCes4Z6xKe4klJM97m05OT8qmZ4UWMmUhj0s2PDiKZgAtl6uF1loxo95QZ7vmxycdJlDYiKIwv6WkxU5PeTnZ4SKXHpxiulVJvHVTRKdaWtusHOI+yR2vugogFmvY3CUSZW7eLM56yqmyi8O5KpwhkGolK2BJC9Gz/7ZU5+rg+lBrulN5AOnm1QlY713kNVdBM9Q6Dq3psR9orZERo9RZFEsyFKx5IepNN91cyvXhqUoVt9ysfY+g/EY6vrzxNaZkceuAHW6VbvEs/vPndx0tE89Lm6jWI5KnWJ1yLmQLEs1MvKqvflyDZY78lSlb+QY8/2nXljANWGcZGFMqqu/+ryV+lRVbJ/lLUrHXQ1GsM/G/oa9mqgNS5rOreJpce5NEvSjT01Jb6qgBKVmUdRxKxqYxZVISp4PsvdpMQ8yu5h35hE2Pl1BuMVIy668k7vWJYys4kRStVIN5BF6NJIlENarZ5yXJVJqk1Msp9XTW2PpJbGWxiBKI6wV2XmQPWGfIZhqtBFOTN3jULpgqxqpCWKpHGPchXGzCNHuvCvnpZXnX453Rs17BX33iUCTfyQ7gCuOh+/fsqpPr9nbzQJlNO9QksOdJ9+TlYX/h12PqsrDKqHn3++6l3OQMyfuCOq7B72QkdUwfPq6109lT5znADVbwVZM6zey1sn8hXPq8ruorXdMp7JCt3hVsQvntwywT4n03ek/xc+q8Hbst/VlYlV06p1XlO0NjaFOhBJ07nKTrD4720TXfQ3q90sQ/pGiwUwKtWw59BkFSPaMNgLDSndKVwtw43eS88YdD4vUqa6WKHBMJIFn/hnXSLsPIOvyp7XCT4BKu+uelOHzk+rNmA1IrsVgyvxc2Y6gotDgrJVjEZkFfQ83cEDlOhOzPkRO9qqzWdrOmcL7FjnnCZ/4ePKbDRdVxqvGJThuppL/x8gi7C76Gqb9P4/Udy0czpoFP1SCHFE0e+llLdMa/ZnAEXem04vwtbxLCXeJVWtk6ZjmUyxzGOU3fEXatGP8jH0uarj8lJ6ig4e1ROR0AQy5lEmfCnCztPpxypH2FWvJCk2VSPsMcPKfl4mp/QsCRcP3JRSM6eubVE0ShEAFfeQKwyRrH7XuBphNw2iYgPTXs9a+qxKZFdrSFKhvimr87TPrYMEGTkyq0bYdYVfnydvD3nakwsz1+tDo4rvN6Xsod7csJqSGUk9MyB3mNl0ruJ8xJ8kqmfy8OdMaV5WOy/xGLuXv6J+GiuLVWvYWcnGeA0Fs2ukxBfLCP19uIFYVhOcKlOWaEkx7WrKpSYvQtiVD34feS/K6rvL6iB51No1w810gOW/Bwc/3zzKQ9+T+6zDLMLeq640xs+j8bYa70nnRz9HecjOgCoTqzSvou+Pkgh7UNJ0rqs5B4E4fXnxrH6s3DRemhJvu0KpWpf4bC2qp6lTJlB8ltNggoMsHK/YQ0bfO1dnC5dLZeshpcRG1kxRMhlTlR/2Iqncb14pJd5SzsCfPQ9cf1ac7EUGGLvBpKvdTV7lpiTA4rwuecV2nlO4ZNxUagBrTtAKMjutRa+oOymljw7BI+4E0O81dynzqVrGlpdlWPVWrXic6iwtNtjV81K1MTSQRctdbC2SLWT69OT0I+yFBjuArxb8TgJ40bRm307ge1h00HlklhNkla6ourHnYrBnXeKzVLMwKK4Ts0Xo4lQ1WchYbSk9ZR7zLMLOatiTsWXCV0/L6vQiw2OcB94Nm54ZKF/PDaNZ7RYxyDAob5RT2EG95FknC/cjf5wa+YiSvXCJWldP/+PjUmUhijv/diNZ+bwBqJ5+VhBJrpIuRa8TsfWsksarR0zKnlNfUwBpCmgvkrnrylPGKbInhJmWXTQfGQrZ++UOUwzvTler26vgraf3o2cr423Ug4KXiZStCaCdawc+mteYif+u0pzs/QrTaPVslYrnJevwrCoMRYqpLSW+al+OUc0gHHeoYTebVkXZWSs6o1qkQ9n7SKIZFo+LJLRmbMV7zxu5ceO5UeHctDXlTzk3BRGbjSOmzK8iR/mz9iJZ2WkKqA4NGg/QexYOzdJkNfqomkrPZSL/ufU5NZ7Y7WU17NWzh+JnHW/3sFNisJddBcvroGktW1UMdtYga9zRiZ12iZ8qjybrUVLVYMgdZugzWfQyfwwfx3XLVklK/MhUV+vpEP+c031ZtK8bRU63rACZ0ddi2aJVdC+uP3Mne9GwES1iWtl5bcjeTEd0aVCqyuxq/Em/L76Kw9RWz1/cfV3luT2m41UNyug17EXvF7HAgZRScwhW5C/s2UrlvBZ8UPsU5Y8rqmEvWhcpZXrzFHcG097nIU2jZ8/2tEbYpZQnT+vbdxDwJS1KJVGjZVlKdDMUpUzVSIlnB7w8JT4zMkiYNsNiJqdELiPyupYrGm0W0SUFTKBaVI+nxNPny691UxX+qoI0nteunJStZ9ogQmbCsBUG5bWeRoS9mgAAzIhpL4obw0xEvWIHitK1P17jbhRV9vBLqSkMFY1n7jmNz12vcoQWUMsFCs+brkhXpAtbGi+d7yqp2K3UYHerZQSyMxNVoKlNY1zhRyocy50u5nlJ36/iOD0NsKqSyTMdWlokRAdlyKSOE5md6yq9BLjxFPO03CHxuByaB8rpntNSl81ZtbxEaspitdsdqp8xW0p8epVNybuNTKoGO6f50pR43REpkfGmyopt3Lyz1Uj2vmhtGO2OK5HOwsdUI+yM5ppa40LrnDkNjPT30MF7rERSpfmi6OXqrRNYNLMP60em0HN8T9s1XQC9ZzWHFsnxKl3iu70olWnS4MHlTtP4++M5GkFQGmGf0hTx0eTsLp7dD6C87w2/lYbWslkhJT7jUS5XrcUTBEkjvTSyWMGQIrg6zFPeZskiKhrHdctWSZd4Cj71NQLFSZPKMykR5GTIEPT09Eop8dRAuRFoMqZ4HOdRdWqSXQywvGyclpbGXwSSa1V0PD5nuxtVLtey9asgw7uo+7qu53Edr+j1uI43mUbYVTllna+g3K5K6j5AfEmdO/c5NblU9YwaNewVddhR5vziulMzLOaDG5kdQsGcxjQN9so17EKIg4UQfyWEeBv9mdbM2xVceBd420fVaBkXpNWjs/H/qzZeAJhHmTkG9JQiY4wlrada2pga+aIUtyqChp+17B72khp2TeF36dqtp8TzCHteJEtKyaL62R42Shwg8feqqZv1UzIzA5M/tw1KGq+UCIJyj7SunE503BSGWBFSDeGysXrabdVmZ0W12nl0odaTZ89ZpfEQ7wcBoHJtqW5A0zwpTeWQsC0lPhAUhcqdThFk2Xkpr0vUr7KppaRE2Ts2wjIvsVkHWaWelNOSanhV46P6/gPFUQV1TDaulI92NFpy7hKvK5j5Y6xN5xhPKwIfKyUUmq+cEs9kk8se8s+VGU5SSiWF18VBu3rrBBbO7EufNdvDKrxbVf6U9NECBXDDiEq/3UiiGRDN2+fcMt7BZCfC7vMGAcRr4eaIVg2wKHJ5T/cIu9lfxfy+orkANaJYHmFXHcqUirrTrNhgL+8Sn53XzNAs7xJPcKHJ9KxCvZqvenZU9ayc7MpKNcJeOTsikqnjrBEWn1Hi2wtn9iWOqPjnZTKGZxx1e9JJX4ufNduvHqPfMprgd9LX6QmiX3FaXX9iGS4lspCDnIJVdCdAddZUldl6RkYvkhhsxWlNZc0iKfOaoupVHF78XNO+p+UbFQ39KILWzb4af6EMU/05ysehVkmaSw07bx7Hm3WX6RZ8nJQxbW2XLvFCiM8D+Gby52QAXwHwF9OaeTuC70XRQd88pjWgSTajr1G8MYClzkhWO7QAq/eU2bhWGBR6wyY0BSGKssZuRY/KFekoimsqQiEK50q9bRbPYFmEfbN2tUjVhiJAxrBSAceubcm7e3hC6zTMvbzk6crDhNZcRWGsJQx5oqMqN1FFB4qexhsKEXv7HYxgNcKe/4y0joPNUFEWqlyZk3cnM1DSMVpTpKtEvhSPbfLPnuTPmT/fZFtNied0UrSF+nMCseApS8VUy2iy1PY4Lb7gOfWMDClT+q1usGve+kK6z48qFK3LZlZeQnWQ2ZrkjyN64M4BFz5qTQOsKMjNqEn5fPE4zcFT6GjLeFHVGtYRfq0bqykGyiPs2ybsEfahVugeYWfOr0J+2DHXolmy9+1eVCvSCcRN53aeM5B+NuXdjeqOHn0sUHxuuMwnmRhQJ/Sc56Wr53adGz+rUcNe8VnpU/w9y+hjUms6xw2dPNic+7bfGc/ZU2mjJ+OUTirbyoOum9DZJYOd15dbx3e4jhL/jO5Jr3LjQyShNmItkqWMT1XVTfR+NVUNhjxnZFl9N8/I4MYC/w4dJJcWzuxTgk8Z3dvn4qV9eqlHlS7xtHfcOas3oLNB6QlSce8UftqLFP2lyi0W9P287MI1wt4sWU8Cd7RVNRTNHk4SM/oayu9saPciDCb1SpQd2KpAP5zf6/eaFzqieqYuk/7O4Vq3XkVZqN8iUNVZOqrcKqA6eIr42eYxLSijnJfq4yIZZ/JMB1Uj7G8A8GIAa6WU7wBwGIC+ac38DKEolUStQ7MoKRWYsc3YK+tuTJ1qdYZcxPzV7rbVI3STSqoq2DUtBXMlSg19hlJOAxErDHpNF8e4VhNWtSaK3zedNkhjn8/bRz1CFul7WEBgvOFgPK/poMgfW28/6Hc0JlaIyvoXMIUBqNzwiMYN9jXQizIDrOkQuSaoDflyh2nGd7UOonoEI55DVnpOoouhRLBVVcJ0IU7zlM05rtTbybSxSClNjbOIaYQkFbeKgydbMxfhn1eaUJQ9FKebZfXINFWV59yS0pIaYa8aibA1RSxLleP0y+m++rpUN7z4tXNVjXwuB/To2mSJM2KzVnpBzzl7oOlewy6r8aYtY9kZ1SN0eWuzlZ9rVE9vBeL9HWqFqbOrToYboCrh9L15GLfI/CyKbB9D+z1nsJW+V9VO4fxZ9fK5KobNaGKkGI3LCtaWG8hSqp916eIcO/hjo71oPs7bIinTCPtiirCX0nHW4Ek3NKtEQDnfKhujlm9UrHvXZEVV+td5W5U7rtXrCrmxUBzZJf4wo6+hRJHL5NkWxme6UXXjmUD0HwhNxpTsW4fxqKp7xw27WF+sppNwI1LPACuaT6Wj2MCswkcBtvdas7Mqznklwp7oNXl0S9mJ9LluFKnBo4o2hd4lvmrZI7dhgGK5ZpTcVJSFilzS6bbQ9mG6U1Q900HvUaE4ogrGjWvj4mBO7scroerwCSllBKArhJgFYD2AvaY39fYDX1Ii1rtXD+PUs+5SPHG2OmQg645ODOvKBzfgX865R5mDp3EBarSSfrd1vI0Pn3krhplAA9QodKpoNjIP/6X3rcc3Ln7QOgbIlBv+nJvH4rl4Z0Q9qhfJ2OgOWIr0Vy+4H1c/tNE6l+7Nn9kfX/U00e5BSokv/vFu3LFqazquF0mjLpwMKN1L/7PrHsPvblmV/p8LxU7XVEqmOhGmuj184te344nN49ZxCjNuZClBtz+xFV/8492GF3aTlg2gGkPx31c/tBFfveB+6NAbFkZSvSt10+gUPnzmrdp9tKoAiKR5z62UEl/4w924+bHNyrun46KYoZDjjp75ya0T+OivblNrninC3ooj7IYBFkk8tmkMn/j17YYRoBvstuyDqW4PH9f3o6gZVPK7069+FH+4fXXumPhvfr7js/yxX92u1L0C2VmlvR1v95QunQBwxQMb8B8r7lPG6cpp/I7ZuYmijKZGFJpSHRJphE5k9HvhPevwrUsf0p7T9Nry99swEp8XPRqlXusmrXVYZ9/2JH58zUp1nH7tFaMLGvebm1fh59c/ln5Od5zYaOnRjWP45G9uV1Ip4/djSjfjo5zmfnDVowrN8/fr9EwapOf54VWP4pw7VkMH3/ue5f0A4P+dfz8uvW89+041M2aMnRc6B5fevx7/eb52XhhfG1d4Wjxm0+gUPnTmrYoCzGkqvdaNaCfhzbYzxt+NnouUiFkDzSxiF0l8/uy7cNeTw8o7Fd3j3Ev25zO/vxP3rx2xzjnQDDPlu5EZCg+uG8Gnf3eHsr56xgmdX6E1Nv3hVY/ij7ere9juRmg14spaQzFirHrt8CQ+eIYqR4vuVqZzs2FkCh/55W0K3zUyXaI4ChIEGQ/58nn34fpHNhljhvriKFbcnKub8mAup3514xMKTfUiachRVQHM5vjEr2/Hyo1jyhqRwW5cDZY8q40vUipofzPIzT64+bEt+Nflqk5jy1oIteyD71z+MM6/e60yTncu0VlePDuO8ZCTZP3IpLEfQHaGeJYjpfTSnvzqpidw+tWPwgZyvOl0/PCGUXz0l7cp78V7bYxZ6LgXSfzT7+/EnauGjTE0js9Fz2fjizxiGvMoMsSQzvXZs+7EvWu2pWPiq9WyuWyOs24vwqd/dyceWj+ajiO5NNgK1XVkvPveNdvwmd/fqZxXvnfcEaU3Jr5x5Wb8+3n3QscmC99vNlQHz4X3rMPXL3pAGddmWQfjFh1x/bZJfEjTZ9sKP7WXh11kkb0Gr2C8jV5xxV1r8P0rH1HGrd3Gr52Uik5C89Fe3L1a48Fp7wndyR7//fCGUXzqNyovNZsURhiilPjkd2fe8Dh+deMTxppQ6jxF2PWA1U+vXYnf36rK3s2jbfQ3A8zqb7Aa9mxfCN++7GFceM864zn5uhBIrl3/yCZD5zL0C0Zz3V6E0akuPvLL25SyJUC3fTK6FcxJtHY4Pi+2MjQgdhDZelSt2xbzpEkl28AsDQJUG235HeZ5mdRT/uX0r3WrarDfJISYA+B7AG4GcAuAG6Y183bEwxsyRkaH5HX/ew1+et1jyt18tDEDzRAbRqZwytevBKB6JXuRxN/84AZ8/ypVWJDit254Ei/72uVYuSkTtHRov3HxQzjrttX4jaakckPxr757bTonMbl3nH4j/utClcHxQ/vtyx/GhpEpxdv39YsewFm3rcbZtz5pHXPfmhGcfs3KWEFNmsh0ehG+eclDeMv3r7c+X68n8a1LH8Ky394BAJjZH3vxxjtdbBidwo+uXon3/uRmY00GkvScfzv3Xvz7eTHRzkiivITPnnUXPvqr29maZcR1wT1r8VffuRYdRdHo4ZJ71+PXN69SBAcZ99T460Nn3pquJ63xq791NX509UqF4Xd7EYaTa0LWDU/ixV+9TNlDeta3fP96fPMSVQDoa/u/lz6MjaNtth/AVy+M9+OPt6/J1pUpt/es3oafXPtYamASA7nl8a04/ZqV+NI52TtOsef+74sfxD1rtmFm4lElBvLxX92O393yJG5+bEv6WXrfgWaIXiTxpcTpxGvt3/ezW/Drm1cZyjtnyhfesw6v//Y16f/pnF5w9zr85uZV+M/zM4cGbyD0w6sfxXcuz5ga0cUX/ngPPnjGrdYxkZS44O61uGPVsKKk/OH21fjtLavwNY0uuMJw5g2P45L71qeOJXrOt/3wBnz7soeVcbxPwy2Pb8WHz7xV8Uz3IomvrLgPZ922Gufcke3hZKeXnu9714zgx9c+holOT4lCvecnNylrwp8TAL55yUPYOt5R5vryefFc5965RhnHhdx/rLgPX/jj3en/6Yx+6Mzb8Pk/3K2O62SG6MhUFx/+5W0A1Kjex399Oz7z+7vYmOy8RBLpd/LnfM9PbsKvblqFRzTDguhhZLKDT/zmjnQcPeO2yQ6+dM49Cs3H75cZrqd8/Qrcx84h/e6fz7kHH/jFrcq4yU4vNQSe3DKR8stmkEVnH9s0hv+59CG8/xe3WNfzZ9c9jise2JCmHdKzvuNHN+Jbl6rnhYT/45vH8anf3gkgiWYlYz712ztw9m2rcSVzfra7UapIPbhuFC/72uWpMkLP8dUL7sdZt61WHFjxemRKy62Pb8V/Jw7cvkbWTPPJrRP48bWP4QPs/YjmiZYuvX89Lr5vfdpANEoU959f/zg++Rt1LzaziMunfxe/I6fBd5x+I8644QnFOORRmuHxDt7+oxsBADM1fv/P59yDfzxD3cOpboRWGCAQAk9uncD/uyDZQ61x4Nt+eD3+cPtq3PJExtvorAoB3PDoZnyMy5Jkbf/5nHvw+1ufVJTNyU4vvR/8zlXDOOOGxzHV7SFI6rQnOz185/KH8cbTrlPGAFkWz40rN+MblzwU967QUuk/+ds7FJpqMz4jpcSl963HsnRts7F/vH01fn3zKvzvZaqsobTODSNTeMl/XZ4aabS2f7TwRTLyZ/Y3sXLTON72w0x1o3V7/bevwfeufFQNYHSjlGe88htX4ZGNo0YN+5fPuw9/99NM5gPqWf3ZdY/hfT+Lz+OimUnTuYROv/iHeD8uvW+DNj4+Q51ehHecfmOyNpkO1ulF+ORv7sAX/pg5GPhz3/DoFnzr0ofRCIP43CfP+uffvAq/u/VJPLElcyjT2Zjq9vDKb1yVrhO936X3rccvrn9cMTA5z/jKivuwctN4KmNINBNffHiDqQcCwMv/+0pc+3DsBKLne3D9CH523eOqHsT6zkx0eviL/4mfscnKp25+bAvOuOFxfPasO9NxXJeNIqnwYJrzrd+/Hj+//nGlDwun+Y2jU3jPT24CYOprf/mda/Hdy1UDBcgi9FJK/O0Ps72jcy1lLDO+fpEagCIe9vCGUXzu7LvTOWlt3v+LW3D2batx80qmzzB++u3LHlbWjWTauy2yl9d4t7sR3vEj8znf97Nb8C/LVYfE2uHMYL9x5Rb8/tYnEUVScUZecM86nHHD4/jmxRndcj3z8gc24FXfuNJ4zneefiN+edMTeHwzP5tZdtspX78CNzy6GYOthvK7Zb+7E59M9PH455nMBmJD8pqHN2WOhWS+U8++Gx/5pcbvx9qYP9SHgVaIVVvG8eKvXobHNo0rzwnEegedCz6nEMCD60fx7h+bv3vjadcZOhffh5d+7Qrt3SP89NrH8Ptbn8QPNccc5y8/vPpRfDHhA7GMiX/+dz+7GWffthq3Pr41m6+XnZf/ufRhfPI32brR2f7sWXfh97c+iSseyHjSJFvTqW6Edyfvzm209/9CPS+9KM50oH34z/PvR7sbPe3XugEApJT/kPzzO0KIFQBmSSnvKBqzI4EbG8TIUqbLPPeTnVhAtRqBopSnNZsRsPxuVYHW5/hdYiD/iB0y2vB1iYduwYyWMjYTUJk3tRkUp8dxhf9HV69Un1PK9PckSPg8APDLm2KvHNVNR1IqDMn2fN1IKswv/u4JjLd7qSIxdyh7N+7lnej0cNoVjyhji2q4OJPbMt7BDSs3Y++FQ8p3k8dzHpuTvKf9zdizfGPC4G21yDwLgqfxnXVbrCzzKGVZOhhfW2IwfE6qBZ410LCOOTPxkra7sReV5iMv6KG7zk4/y50ZNNfM/ia2TXbTtaS1mTPA1oZF2MfaXZyevB83wNYm9ZnE2NI5mbfwvT+9SanZ4t5JQNsPxuzG2z1FOZjqRkZkVh8TSeDie+OIKE+LXJec1fkz1Mocnq1CivCMvgaGJzqIpOq8k1JCJAyU5uxPnvOs21Zj9kAza0AlszVdyOacTJSGiU4vpal2N0pTEfOwebTkvCRCadZAUxnHlUXd81x0RNu9KH03nkFTVLdHHuLBVojJ8R7OuOFx5TmlzJSY/kZ2XnqRTA23G1ZmmSGtMEib16y4M47KDRnnLHu/+9aOKAb7ZCdSeDkHp6XfMSdlGGRR/d8nPz9i97nW+YgeiJaiJIpsAzkGf8ucrzP7m+jJ+FxdlJxZcmoC8bnoT4T+79kzAjFv7fYirE/2dO6gKiM4v6d9ANSbReh8zrXQX38jPqPvTAwg3l18zbA5DlD7F1A0nPcvIIcjj1zw57z0/iyTIeb3MD7P0e7FEfZACGV9GkHm6Fk7PIkH1sU0zFWfqZ7KZ/i1ayTziWcs4PTbiTDYamCy00558MhkN1b+pEy/Z+5gRockz8mx82/nxk5o6g1A76ln/wAqn5nqRnjnj29Mf8cVwNue2AoA2GP+kDKeZ9zwiCqR8FoLXxydimXbrP4GNoxMKYqxXloWSSBkxgzxDForalIrZX4tOj8DpzMZOnuwif5mgIkkIka9AObn6ENrmD7SYLyfK9ME3tOG6KPdjdBIHCjcocdB9M+zwjjvJsfZgUtmpb/nEVoK3JCM4ecUyAyneJzK2wjEf2nMvKHsrOn0+2Cy5zxiSnQ/f4jLpcRgbzUgJXDRvbGTKpNnmY7IeSCXS+fdlWVOcH2Ng8tQQA0+kaznNHH36m3GdwAZT/jVTRk/HUpoUDI9rp+vZzfCUF/D0Cto/jy0u1G6no8wfaAVBhjt5vdX4OfxF9fHZ2z18KRy/eAfEt3xgCUz08/y9R2Z7CrNz+i80HdT8EQfR+elEQq0GkFumS3X8QCkRiR1J6csQBs2jbUxb6iFkclOKr/059Qzv/icxHsV/lJQ/jLVjdIx/Hto3Lr0XOfLQrJ9GoFAfzNMswdvT/gnPy9TFc5Lxj+5HZPpQZznNoMgtz8ZHzPR6eEHCZ/YLk3nAEAIsYsQ4gUAdgcwRwhxwrRmfoZQVIsbe9tDY1G5d/cXLL1N90YDSD1GfB7yrJKCzZU4IGOS/HDrqZyAmubHGav+nFEEbEuixTwFgx90jiBJwSbGryvRJED1uid6j4l2L2U4i2ZyZYiEhnlh78z+zGO7ZdxkArZmFZxJTXV76XqS5z7+OSlEgSIgbbVGfO83W9aGO8PKapRsa8u92RS9F0zNtM0JII3uAHHUOn4fznhMJkF7Qa+8PtlLLrgyZt5QOnny7ANee8yhd2HmoGclY2PRrOwM0JzU9ET5zk5P6aSZjullSq2UMvX6czqkezH5eQN4WnT2s3RtpFSiazwliivSBJ46HEmZnjf+mclOz3q+eQ0fgfML297zM0qZP7ozq6ghS0/mC2NSvgFV8WgmV+7YDGFOv/wx+D6kNbWMlraOt9PP8+/lBskF98TK4MG7ZI6o+P3siggQn/v1I8VORR02WlrIzkwZLV3Azov+LDr6m/H7rdyUKSycztpJBDnvapepbpS+B60xYfNYO21KyNEIReo8IAWH00Sb8UMAikMYSGhppDot8b2nOkduCG1O6LKvESg8g0fLdEcTf9ZWI4AehOCpjtxBwr/fRr/654hn8PWf7PYUo4pAGTJrU7mWyZg0wt4y4x08oktGt/IsmkNZX1t6z3uStGiuwEeRxGjOrSwGD2Z7SQaC7vwDTHpT72iPjPUMhEhL6NbkOfgtPF0IYEargcFWIz0vtB9c34qYs4/zDqrZjqKMjpfOH2TPaueLQdKL4PpHM8ehWhKgBXCg6iYXJ4Yuf0YbjyKeQXLTxhfzeBvtOe3dTuys6fRLaIWmXFo4U3VEAeT0ZrKelbQQFPplfJTro3xNlGfXfkaOZv5zhe9r5RMEmxOP7kW/nzlNlZRxFjEtey6O2Hkdr6cuC4tqkimYoYPOmJQSlyTlVjySWiTT6Dmr3rXeCETCW3vpGdPfDUAaiSeEQaZb8CwKjs2JwW7joSTGbEE9rq/p0O0trs9QEMGGqW6UyvqFOXKJY6AVpn2D7l3LS0pUGaHbNgT6WKZf8P3L9CA+N0+J15Fn+2yvLvH/AeBqAJ8F8Inkz8enNfMzgFYYGB4fvZHZQDOEvqYtln7Em+rYasFCLWLXCoPUI9/Rapbicb202yX3dts6EPJaL5uByDtB0sGYYGN4KglHmDQ5IwbBp+WMQWeCs5Lo/Xi7lwo+bpgVKTZcANg89TYBPDrVTb9/spPVoPA5ad37GqGyV00m8AlK7WXO2hBcIuz6nHw/yvYQgHLPLdVs2c4ah57GS2dOqSciT2grVMoLsvOdfV9X2+sioUNzkNBtMWNjKoma6alARBc2D2wWVQggka0t7xKf3rOtGTGb0rsvs+dPlalIpo4sPg/AHD3s+9T7brPaJd3JZ1MabI0D+b4V0S/d3gCYtzAUXeHVi6T1bAAUVYi/f62ipCS0b6E3Uvp0+rVdEaM4E3PqtnkXbHJI6HSV937NUGCyE+W+X5kzEgBGpqrREnfwEP3pRrZtHCkMPArR0xSUViNQ6AMA42lmvwnC5rG2EhkmNMPMMTk+ZfLbqRxDNmARF6ql1Z0ExBMlkwgZH81+xpuIbR5rQ4g4y4av88z+RroWeZHZdjdCXxJh5yDFHVCbe7ZtBntDN2wymZ/XyJBq0fU5ezKjCzJ0gEyBo5R4QkNr4EpO2llalgU9py5SeDd8XZEH4vvLpTSNN0CtYQdURZHk96x+1WCPebAWYWfHrt2LjPUMk6aoFLW2wSYLZ/Q1EAQijjhpV0bxPhxbk0wowOQd9J5Ex/yc2OhxsBUiCGIeo9Ckhf75XKSbqE03K/IMjeer4/IbhQHZuRrsU6PIgEm/3Llr04OmklKPMFD1OZ4tSsjTSXQnhu0WGc7fpJTp3qvBksywIb4/Q6Md260/RINKRFoLQLRCk58CxfraVCczFHldetm1ijYHVV8jSM8Y79mk8/086M+pnE2rEyNAfzOMZaGF/qZyDMU0wi7zdYQ4Jd5usKcN4RKdgIvDKrzX9n5cJ+Fj4nl6uTdB2WT9UKuR8iXlRhXOzxIHpM1opv3qajc4xM+SZS3wvWyxniM6SP/WdcPppsRXjbC/BsD+UspXSCn/PPnzF9Oa+RnAQCssbJ41mRCy0BaVDjs1BiPYInQkRNJ64VZYeCUW73jII8i2jpWqJ9Q0MHnaC0VjeIfDTRbPN5AxRtudwMSAF83ssxjsSQ17u2tlkKmX12LQzOhrZHez2xiPhamMTXWzeyi7PdiOfp5H2tYdtZtjZBB4NFx/P90YK4qw99h+lO0hAKVGML3WzHLWOHQPf2Zc2iLsYWrYxs+pNlXU5wOyNEAbvyEeJiw7MtWJ0BcGBk31NwNMdXpWAcmNjIgpAZnQMZ8BiJWuzBDMfp7VF0pD4GdzJpGvlqos2c6N2lQlwoDFIWXrEp8XySDw9+tLzu+YZrCXOU7yOjBPdaP03VQlJUgi8+aYPC+xrUs8X59NisGe/ZynDkaW8wnYMwgCEaeETnV7uRGQXFoKeLO15FlLaImUSd6cT39O2zi6jjHPkUERZN3JlPG07J5WXbnfNNrGAi3SMLOvgRZLiedXKunPqUeR6awVdVPmjQMJnKfRd3ADfNNYG3MHW2nNZPqOLMKed0bbPaphV38esnOjOrstDjftPa0yX1PGbPQb76V9fUiu6UbHrIGm0myS6EcxpOg5LTIxvq86/rdeqgNk9et6uQSQ0ZN+Pzsfp0fYY92kIMLe6RnPSTdgRJHMNYpsspCcBYOt0HBC8u/hdKxn59Bni2Qbx8z+RhqMKKJJQDdOm0pzqWpzNY13McblEJuud3H61fsAEcq6vVO2qH5FrG3chKaTUHYGN7ZaDXvpFF+jkaluyu91XVZKVX/m58xWFgckgSSpO4ZVJ12rEShZKNnn8qPs7V6UrqcRYS+Iy6wdnjTSs1thkOprvRz9qSgrrtBgt4wLA6QRdtve603nsnGZjpefEj+FeUMta8aRfqMFR6o7WXivTiucHqa6PeuY+HdRqk0aWRwWO2YwibDrfEmJsPfyzwutSXp/PdNbMj1ID1zENpptPfPGbK+mc48AMPOp/o9hqBUaipDube9rmgoDj7CrHR5N72mSecKiy9mhtR3AvBQk2xUvXNDZ0sjTCJ2U6EtqSrlytCUnFYaaZujeff58PM2ZMHsw6xKfdTXPfk9z68wjEDFh6ooNh81g70YyjR7lpWjnpefwPSTkdUfl8xF0maIzri0FBrtkBphqtJl7CJCCqs5TFhUkRlfkWODpUnlKOEFnkmn2hYXXF9aL9SKDpsKk3miqG1mZHY8qUHd2QI2y0Nfx8epVh9nPSQDpwsqaUstqsbuR/ToUhZl3e9Y0q0CY3vpxJdulOEOGHDwTWgps0f2rUZQf9aK6Pfo3nzPSlA3CZA792miJr4mNFtK5NMFflIVAaAQB+ppxinWegprrjBTc2HOjpZjfZz9PjfdeZBjwQGaw5zm+qKmarjBwnka/s0XYF2q1vn3NUEmJp/3ixjLRfJ8ltRlIyiiSR+Rv1Isktk6Y5THccMrOqOqImjfUMqLkDcbTiuovW5YIe0xLMJ7FGmHX3pPLfHIo8r2b7PTSO4vVOaEYevzY6V3iCXG0TRiOaJsDhfMZAnfSp1FXbrAnkWWrwZ5mOUnl//G4uIP9jD7b2qh7YUbYTcWfHCh5BpFNzyCaGmg1MN6h/YihOs55V2fm7GN8mHdVJ9jouJXsR6+EJgH1LNGtCIqTVjM0jPdr2uVvWYpz/N3x35OW80LPp9MvOf2kzNaRZ5VNdHrob8TBJ5usp2ZpgOoU3jzWxvwZfQiE+rw2eaa/HzekdF0WgGJ8225R0kHlJXzte9r5tDlA47nsWWM0H60nL88hWZhn0K4ZnsTOcwaMZ6SMyLzszSKZXaRX2hxDrUaYlhvZnOy87JEjZA5aG91OtHuY7ESYN6Nlz+DRZCf/hmLeq/EX7f1sYwB1zfTntfGXViNI9eY8G62dyt/yjAwlwt7txaUI2jgKdhTpTrpuuL1q2McB3CaE+K4Q4hv0Z1ozPwMY7GtgshMpjE032PsboaEwcK8kPzv8jsJMUMa/owMXz0nRSVIYzAj23MGmwiBjb3u+wr9pdEpphKM8J4t+6EaCPgbIUg5thJxF2PuN31GK5njbHvmaKlD4ecqwTYEjZqunoqYeuE4PthB7WQoZ33uuKJDCP5tFIZRr/iKppM2ZtVvm2vLUYerKrJco2PaD10Haogk2QdQITCPKHFecfZCXPcLH2qB7J21MkpNUKESS1pXjJWbP2Wbd+23Khi1LYs5gU6FTft2hIvC5wt/LW5ss6k30y9d4op2TEh+Yzg3dsMmj3yiSqVGsR6Py6AJAYZoqr9vT58xTvvMcbtYIO6clxtP0cbpBYjtn+rsFQVziMtnp5RoJm8faaAQivS2BIFi2Spr2VhLF4HzURhN5kTLKqujlKG6Zh1/dB+Jpk51eWmuoX8O3eTw/JZ5KrYiPWg1Ebe+DANl95xajdEvSh2DuYFOrs85oiZRefkY35Rjs/OrQIqeSrYY9sPDD+PM949/6ew60wnRd0qsvteiJjX5pL7PGqNnvJjq91OmoQ33PKP0u/o625wRUJz19h96wClCbehLoldIroNg7jkx245R0bWEH+xrG+ddrPvXnDAMR05TMd9RuGjV5G9H0YDNMZWDGT7k+RE0Xm9Yadr4ntlp0HaRn5JV70Tgu2xthXEYT5Y6xOBXDzCDi0A03G9+m99HPKZAvl1qWsiTVqRTvHacdQKPfVMaoGTLzh1oIA6HwIFuJlz7/pgJdlj5ri7Dn6RYpP1XkjDou1wCLZKFjMI/+8gwwIM5M23mOqgcLIbIzVuIUKtp7Aqdb23npa8Qp8XnZiXrTOQKlYkfS7oii8qf5Qy1rRmyapVaYEalljjHbJ30/5vSOpH0MEPNl/apiwqaxNuZo/CUQIu11omTR6j1kLCVp6hymU3eiTZnX6phmck2tTSfJC3Zsly7xAP6Q/Pk/DfJ27Pnpc9OfmSnxpoefGM6vb1qFR9n1RXTYbZ5++nuoFWLLWBuPbxpPr9iy1U4vmNGHLeOsA2Gi3PLrREjhp9TffRfNUCLt9JwPbxhLOyUrytRoO5lHjeyGiQB+N7uqgUBG0EKLskiKw6ln38XmUT1TgJkW0tcI0zT8S+9bn17dwkHrN7O/oTwv7SFdFQPYoy55hhe/Uk8pTRiPmQAZvoAqSNZtm8S+nzkv/b8u9EenzP0go+vB9aNpt1k9Ldq6H8navPDLl2TvWOKp50bpEV+60DqOGJYuBGhtrn5oU/ozI8KeoxABMZN8309vxoqkoYxuFLcaAUZZaUaQpHX98Y41aUd+da7M6cKFLj335rE2fnbd48lcTNlLnC4LZvQpUV7a0w0jU8oVIbY76vMcPQ+uH0nvx1UidN2ePSU+MRT5/d209xPtHiY6Pew6d8BKv/etHcG5SRf1Cb2GPYnCtsIAXe13d64axl+fp17JyMfZDAyq2/tIcs0bR1bSYq9hv/LBrNu8zXEyXzvblPJ70T3rcHtyr7FRetGNMKO/ofSfCIVAXzPAWbc+id/donZXJ2wZb2PuUMuISlA08PSrH02jKWX1qHRe1m2bVLpc07i8SFkczUN6Naf+fu1uz2qwE097+X9n1/3wOcbaPbS7kWGwC5EY7L0Inz/7Lvz42seMZ8p6M1iMUiHQ6Un88zn3GL+jPdT5E/G0i+5dn35mnCllW8ba2GfRDKMLL2UfnHvnGvzDz2PezfkQ9aVohaGRNkiK+7ptk/jpddk7KhkyOZlVQ30NbBlv4/61I+kVRV1N5tubRsZ0Qc/KMdmJay9tCjjx7lPPuit91ioZYEBCi5HEirvWpk3n1Ai7eRMLIYok3vGjG3Dp/XEHdT3CPtNisA+1QoxMdbF02fL0Z1ktJynUZu+DMDECX//ta2HD5rG2hbfF52awFWLNcAf3rtmWduDnSvUm5dyNGuNvfWJr2tiLK+Z5NEn78fFf357+TC9pin+WjWkkN/R89qzsOr6yiCl3mO//2fOs49rdyNBn6DP/9Ps7087juswGTPql+SY7vfQKS85JeUo8P++0jufdtTbl71zGbBlrY7e5gwgDoWQ40Dpe89BGhV+o5Z32vSOe8cSWcZxxQ3wTQySzDvOFDQNlfI1yNl/2e1rPpoUOH900hr+30C6Nm21pwEj090V2VSphstPD5rG2PcKePCddfRk/p7mHeXu/zz9l9ogum/QxfY0AfY0Alz+wQdEP9bn0tPYswi7Ta+yAWH8ZbDVSXj5vqM/edC6S+PVNT6TXAyo8rSDC/tD6tsJfUpspj18nvPhN7BpNvU/ClrE2li4YUnWE5LrJSHs/xTboxbqFXq5Fn3vhly9J5Vak6Xh2uzC20T5/tu282HWn7ZISL6X8MYAzEN/BfjOAXyQ/+z8Fm2A2IuxWT0q8TPp9jmnExcLAeYOvqW6ES+7Lug3bUs51ZazZiOtp+H2VpPBvyRuTPOeF92SdOPWoni1Kw++M1kGRZ1tKPNXz2K5LAVizBo0o+xpZ3c93r3jYOpbWT2/so6f6AGYUCzAFHK3NV1Zke6hE2NPIEH+G7L1ueXxL7py5e5jMeT7rjMqV2005+0Frw68mKquFy5qjqenWumOhrxGkUTz9OenqCf39APsZJ0SRTI31eCz3gvfQ1wgVjzpF2POipXl1e8RoSZmN58p+nyl7LYVRt5KGUXrXZlsNrFErmCgb592ZvZ/ekMSaUpsoOPz+bqIT7qRT5kr2Yfmdq40x/DlbYZCmiHLwDto6pnK6YTeTxilXsave+BjApF96zu+xaxrVOtQ2ZvU3DE82CbjTrszG6SUq7V5kNLkLAoH+RlhYX7hpNI4MGfXPCS19kSmZZfWodF5uXKnSfGmEXYtmAZYadktKvI2n8TnIEaVffwXESma3JxVjvavRH2DKPoH4ei71mp7qPO27l2fnekLL4rJF2Mno/p9LsvuJlUwAaliZmxIvcZO2H0oPio6dfgeTOu0/3J45evR0x7wIO99LxRjq2m+TEcgikdyxYOsUbqPFRhLh+zZbW3sNu2ls9CKZGuvxnFIZN6O/YdBGkT6Uxw+phj2Pd5Mz0jw3Ip1zotPDWbfZ9yPvrNNZ+VVy9R5/ViCfJqmDN4cSdbPJ0uSGnt/mOPeLeIbe1EvPAtD1GRpDxjpgj+YbTcTCOKKoO8YIk0nat77nJM84HdoyZBqajkDrqF+16aIHXaldx0dj83QLnQYBMwOkFdpT4i/RriXTSxpsZ5+6xFMwgGP9tnidd9ENdmRrQ1dfxs9pZnHY9r4XSeWz+jh9TCuJsOc1ZMsye+26Uy+SyrVrmU5CBnt+l3hyDOXNqfMK2xrT++XxwTIbbdtEF91IGldjC2HvG6TL+ryMjF6EXH17shPrsBpJoJk00aXrfDnSCHtO75i6qGSwCyFOAvAggG8B+F8AD/xfvNbNauxpwpu8khy2FAogYwLbLHcT0sGJ73jtKYKBHyryEukpHnq9BJARF9UWzh0yu74CqreYM+OtEx1jHkBtckag1PF0LkvdnE2BtNX66UTY18xq/XK7LCZj9cY+NuXKVidm1LAXpsEAw+MdzB5oKnvPo7u6fmLbw/z94Ok12X4Mj3eMMYDaJT6bP/u/7S5MWx06oEUhunEXZp1pkAecp0fq38M7terQU7P0xlCtRqAoSUFyNUkecjtbJ3szqTxnNhk/q/zx89aG02TeuSE65L0g9IYkNiFD3l6OiU68hqXnha2fbrDHfTbCnDos40cpeGdcfc485TuPflsla7J1vI3Zg01DwNnufNfPJ2DSfBiItAdEHrZOdDBLo18gzuaIIpUv8X2xnetGYNItkJ0fG/3Rcxa93+hUF0N95t7ZeBqnxa0TmTJlPGsSYVefk31PTsaREPEZ5eeLP3o5T8t+Rt8hZVz3HvNR9TnJgaU+e/Yhor9WIzAqncJAGNcbhoEwHG6UccBB8pcruEYNe17TSDallOqY/mZoGDXpOMNpk/07r0ETEO+HnnHKeRRF2mxO3qLa6bF2F4OthtH4s8j5nceDeZd4G+is6ueGHIxx07muYqTx/dg60cFgK0x78BDKynBsNCkg4i7pObJUSpkrS4vo2Kbv5cpfLZtD5222MWo2nb3UIxBxRHGC6Si67tXfMJu99tl0kmSOXhR3ZJ890DScUbSORfrFsINeysfm6RY2fqpnjgz1NSrVJOtyxpYSb7uZiUBXEOpXjAkRnzFd1tu6jNv2XufdeialPqYvqWHPQ56TXSQp4zotkD46nPCW2QNNqzNRshIKc047r7DxF703R5Uxiq5N/EWzRyIpjQaL+lgqDbLaAgXjpnIyr/sKzktew+3pRtirpsR/FcDLpJT3A4AQYj/EEfcj8wYIIX4I4FUA1kspD7b8XgD4bwCvQFwj/3Yp5S3J705JfhcC+L6U8svJz+cB+CWApQBWAvgrKeUW/bvzYPX49FTC6m8GyjU2QH6jABI0vOuyjsFWiG4kleilrjD0N82Ime1KB6o1IiLTiZkYF48k8vqkuN429rTzg3b/uhHlrksgaRaW1I0ONEOrF9PmMVQiEew6BI6+RogNI1N4cuuEwgRsd53qV9EMlgg8SsM3Gt3Zan4jVWgNtdQoxGQnTmFtdyNcrnmHuaeXBJ4eGSSF9I4k/RdQOyrTnDoeWj+Kh9aPKj/jZ8Z2FybN9SvN26fXa9oiQ3Qn8wh7Nr22OO/OXQD49mVqlgQ3oifasUHLBVMYCMWbqSOvqRM5GvJSsScZXXDGTe/LI1CAmkY50eklkWt1bej/fD9MZm7ylQfXj+JBbQ/pxoY8ZxTRL88gGNeazk124g63uuDVn1HHRM71VZvG2rn8K49+aU24sWenJXUtN4xOYdWWCeXM6xlOADBrQDPYhcATm/PPC42dP9QyFNQH1o0aDf447a7dZn5vw5Lyz581jxbuWzOi0FA8V/Z+a4Yn8YK9F2DdNjUqZuNpnP/n8XuBWMncqDXc4/RHNGHjMxOdHn5/q73EgJxL+py0NsPsesTxtFQrVuiHLOnXa7ZO4JENY9htXhah4ooT75egd5Hv9iRuXLkFR+w+VxnLjdmJpHmcTVZvm+ziAeUu53iclDI3Jf7RjWNK+RvHVCdupBlaUivXDE/i1zermS49m6yw0OKa4Qk8unEMey8cYnNla7F2eAKBMKN8AJSoZzyn5lS0ZA7aFHAal/ecgRC4f92Iko7Kka+bJDXsrQbWbZvCL3Mi5ROdTEfhoAj2yk3j1nF5NLlu2xTOvFGViUSTwxMda53z6GTXMCK5HLXK30SGfvDMW9VxUeYcmOz2DH0GMBsId7X1AEz6DYM41Z1HddVU/x7mDFqaP1p4N8kYLnd1p/4Tmydw39oRhe4BVX/OaNjOM350zaPKz8v4Kdeb9PmklFg7PIklB/cbchYAbnl8qzEXienJHL1rrN01eA+l7efJbCA+Y3qUVdfxAVOXBYCfs8wKPq6XZJvpY/oaQe4VpkC2BzMtc0kJfINlVgDxO/NxQ32h1WC37QUhT1+zdWPvlvCXsoDcRM4+RFGsN3HdiY+NorikavHsfjRXm3zvg2fY6Rbgmdfq+4xMddMyJR1PVw17pQg7gCYZ6wAgpXwA5V3jTwdwSsHvXw5g3+TPewF8GwCEECHiSP7LARwE4M1CiIOSMcsAXCyl3BfAxcn/K2PYImRsG6N/jqeQ2MauLjA+yEDjSpru3bTd/X4BSzEmpDWwJYzRNgagaKDpKbKBBC8ZXLZUDhthc+QdWqrBAYBHmFLE5yDDe2a/+o629GP1mh77nE9uMfeoqxmWOlF2I2llsoAaBU2VYkO5NcmLC4SJJDpbhYajEuWE9pTq+wj62sSRIXVCWw2Z7jFeM5x/xvM85wApYKEiwBqBKHQAjOesp83pYrsPerAVKtGGvDQkReFvdzHQMrNrbHvIO/fnNXOzYaKEfm3vZ4uw9zfNyEkR2t24q7nNe607ovS5AHMfJixKrrrn9usxyQC+XzGeTGebTnNhIHJTP9M5Ez6lL6GtGz+n3dVbzXOYl1GVKpiWMQAMYz2eKx4zOhUbAYtn9xvfP2A5P7ozCbBHH2wdc7mvN4+WbOdHaazWtpck8TtuszlUpc+296RAc8cLj4zRd9iu/bph5WYAwHdZCUarESjG7Hg77iWhvxbxw8uYs47WJy27siiJRaDmtDpfyaNJfsbz9gPISjAe3pDJRM6jVg9PYtHMfquDoWhOkjM6b3tsk6nXpAp1uh+mjpFnrNNctnG019RHIs/ZN9m26yjWO8C5TMyhSRuiVG+zj/mdxYnF+ZtNdpFxoq8NjWv3Ikhp6jPW57Nk5OnnxeaY0qO6Nr3SdpuGrlcOWBxfZAhdrOkXui7bDIUR8SaeoTtdaf/WFugWOmi+zWNttHtRwk/LZaFOgzb6O//udcbPaBjtgyGzc6aOLAZ7pb1PI9D2MX3NoNB4pr3Ux+WtEO+rA8R7X4W/qN/RjbPgtKyYe9eMGJ+lfSB+r++DzZGiN/m1jctrgEljN45OoRtJLJndj2ajwnmxZl6rn7nwHvO8UCAlL3CYo1pURtXhNwkhfiCEOCn5833Etey5kFJeAWBzwUdeDeAnMsZ1AOYIIZYAOBrAQ1LKR6SUbQBnJp+lMVQ7/2PE98NXhs0zrd/J2t8M07ucCbvPG7R+X8ZwYgZuSzdfPNvsrq54JdtksKun4ag95hnjdIW/ineKC8bMOVB+YDkTtzFwwJ6xoF8tEn9OT+sJsN9OM4yxSsSlTdE2zcNYcOVEPM7e7IFHdmzjKLVZz3LUa2UItgi7vv42RVDZj5y9L5tvzfCkcdbylE49JW+gGRqRIVsDFt1DvGXcXk5hf1bzfPPnKHvfTDhqHltLCqoejWiGAq2Gmubdykvl0p0nliiUzUHU1YSqLcJug54ho9OvbQ/1pnPkdOF0ZqN75TsKonq2vc/msteZ2Wto1aiuTVk8ZJfZlnGmMNaVjSpXoUx07OVMNigRdgst6Vco6c9KCvusCgpYV1NKl8zuNxyrNqcgN9TyIhiAvRlolYiubZW40pMqVBoftZVBEU+bZAq/vmV7zDdlKF8Hbuzr2GeRKSv6GoGxRjZD70UHLDLG0vqkdZQt3ZlhDFEw2bUbwHmwZZHYFFMbuONmzfAElszpt6YAF8+ZdQznOGDJTGNclBrs5LCxlyXloSw7ate5phzWefiAxeEzw0JreoRdp+O8R80yI2Oa1Me95MCdcueSUmLN8IQxZnaObKS5JpP11PUZ6xhL1FpfT1ukTl/H/qT0kMPWi2hCM9psTn3Sg/TglJktatL+vBwdKg14DU+iEYhKjrOuxoOXzB5QnOp5zvMu27+8bLMDFpv0QLIiX4YWZ98CPHOswt6n9Jej/1qah3KkGS66wZ5DC7pN0d8sTrm3f0dktREO222O8Vna8zw+qGfXAfaME13nybHXDZm9ZPaAUsqUF3RU9JmEf+rlIDa5lL1fTtO57RRh/3sAdwP4IIAPJf9+37RmBnYBwPNIViU/y/s5AOwkpVwDAMnfpjROIIR4rxDiJiFE2vr8tUfsYnxOTW/tWbvpvvmY3a1zkLG/ZngSM/sa1nSZ9xy/l8EIdO93fys0yP5LrzGqCDLFKMfbt/OcAey1YEj5GREkMaqBZmjwmH96xQG5cxHjtwmIgWaIdx+3p/FzwlRyNYPORPsaIX73Dy80Ps/T7vNSXwIh8NKDVIGqR+lsjZ3e+DxzD40oRDNI7+olzBtq4ZKPnVg6FjC9frvNG8RSTVGldY0imdRSmQJu2cvN/eCMbvNYGzvPVhWfvRbOwNtfsNQYpxvPtr08du/5BpPuaQ4CAMacAHDJx07EG4/aTfkZ9y6Pt80abyGA77/tKOO70ufMOXNBIHDCfgu159TfL1b2eCZDfyPEuyznlCv848n1bPr+7zp3AHtqNNVLxhFT7rcIuE9b9pAaDuZFoZYuGDKciuMd1XlINMz3dd+dZuL0dzzPmI+M+jyvNAD87F3HGD8jEC3pgvj4fRcaUWJdyRyw8LQz3/t8w3lioyM9nS8QAt956xG5zwnkp/1+/Y2HG5/ltcmrhyeMcz3YCq20lPH7CSyY0YeWJivO/7DZ1kWP5i2ZPWAYXLYzZo+wm3T0sZftj7do8om/X15kCAD+6qhdre8HsEwlTUF92XMWG9+jK/wDLfNqtnP+8bhCQ5hHeHT85n3H4ti95is/iyPsnH67GGw2oOtt/2KRo6kRlXMVHAB87KX75T4rKXC2zJ3f/8MLjJ/1pMoPAXNdgZg+dHAetWZ40urw+c83HIqXHKiqQibfNx0M73yhyRNpXF4ELAyAr73xMGNcNheV0dgV1Y+8dD8jAGLKYZOOD911tsn7lai3SccA8K2/NvlGT6NJfdw33/zc3DFbx+M0en3MPgtn4MKPnICdtQBN6gDJibLa5IT1vFhS4l/7XFWf1aO6NufXnx+6s/Ezw+HWMm9quOTjJwGAkRJvm1Pfu3e+cCnefLSqIwDcmTmJnWb1G3Oe9X5TRzQNsH5Ff1o8qx9nF4yb6saZDjZ+WLTvRVlOv/17C81b5Zo69px/PM7g6bT3eWP6GgF+9XfHGvOlc+WUpADAqw5dYvyM770Q8ffr+tp/v+lwQ1Yoc3a61uDdB07ex/gZvV8eH5zR18QRu89RfmbLVtDXLZLSvg9MZgNxAJXLiJ3n9OfsX/ZvyqjSb6L49ltM3tJlerrtObfLPexSyikp5X9JKV8H4F2I09KLcxTLYXtyWfBzJ0gpT5NSHiWlTC2DRiAMIasbe9b7GYPAHp1nKfFL5vRbvSd9zdA02DXFyOZRnjPYNBgfKe95ClwYCBy48yzrGGJU/Za00V3nmtGPCeYcGGiF1nT7wVYDe2iKJt+oyaTJmW549TUDzOhrGB4qqWU7tJIrLPR33FcbpxttfZaU4WYojD3U06RskaGhvgb2Wmh60nSFCLDUzQuBg7T9oFTDSda9WX9WWxRCz+awXS/ysueYkQHzegrTExoIgYO16CdnWGsSr7o+JxA7CvRrhnTvsq6E9yJgp1lm5gmBovL6uggB7Few93lGGwC8cJ/5xs944yNSNvTUqjAQOGiJuodkEBFTtilGhTRVQL/P0enXEmEfaKkZC32NwBq9TtP5CqJ6togpn6vPEpkLAxjn2qZ0G9dI9TVw/D4LcseRA8SoYQ9E4XkB8rNVbBlO5IyiNHXbuX7B3uZ5oTrW1cPxfbz6uuy7aIbpyIhUhWHJbDNCajtjarmG3QgCYv5h8kNbfw3T2Nf5mu5oawTCeJ9QmM9qS6nV92FmfxPHFez9eA4PBYA5gy0cq+1HXyNQ6/w7EfpboXI3Oz2LHt2PNKVKd9ILxM5WDrOhl5mpBACH7zbHWGtbFoktc0dvaAVkPEpKiTVbJ7Fk9oCxJwtm9mHpfFUOq9dD2fmpTaYbNey6oSgEFs8y6YVQpJsAMa/Us+tsTlejaaEQOGFf8/xIFjSx0fERe8wxftZjNBkGwog6D7RCw2jIosF2WRgIgX13mmlk5+gKvG6AzRls4tBdddmryrQ46GH25OG9DvhcNM7mpAks+pNe0jJgibDP6m/imD3NrE9bNp3RnKsR4gV7L9CHMmfmRMJP1XEHLZllnIOezk/n9GuyMDRkKB+Xp6sB8TWkZeOMTCUBPHe3ORYdx5RruhG918Iho3kayRgaYzPYbXyCMJ5kGernRcAeEeZZf8Qj9HdZOKMPBy4x15SQt++2GnZjPQ1HFHCANpd+JzpgysJIShy8S/6+p865OQOKfOxrhDnjTN1QLx21nZfs6tfMAaK+33Yw2IUQlwkhZiVN324D8CMhxH9Na+Y4cs7dbrsCWF3wcwBYl6TNI/lbLagpQRCI3AhPtxeh05P27q2BsBryqRG1bRKLZw9Y61lCIQzvUxVDsRGYhEOHfDz3sOeP4RGMrnYlhC2qQYRMz6cftFYjsM7HkSc06BDrhqnRT6BhXtlhWxf96hTbegZCGEzaSMWzNMqaafFUAvbaaVudmX6exrU9HGyFxrUmtjXVU8F2maMaIrEjw5bSZSpDujBuhOaa6in4gN2RYHteXfE3vMhRhIFWPusZT5sjagY7zDKMrmUuY5ywG9C8yzSN1ZlyGATGHva0NDkbz7C9n773NvrV10pPiZ/oRMk1earBbr9CJUrms0fLAPtZ4waNrR45sAh1pYY9J6IHAPvoyrolqqtH2ON1yU8hllJiPKcfhO39yM6jNHWdlgTs54Wede3wBBbPMhXMIBDYRaORiBkUQsSOqpZWQ2c7Y2p9dv7+ASZN6PLFdvWRgKm0mvzQpvCb8+lyacDSkRwwFUZbFCqvvEQ3MlpaxGOi3cVgMzTuyA5DYXTX1xsfGcqtMPk2b0RL5Re2CLsQovQ9+5sBQktau/W2gIQfbZuIG2LZHD42+tfL7mxGsC1rzjRQzD4bRTWueaUUPHpq8tPyc9cIhJUmI5mlqdvoeNFMi8OO0eROM/us3f71uXSHuU3+Amb/CzPCbqY4F+lB4+2kmaJl74rWMb7WzXS2Niz7p8ulgZa9BHK/nUz9wl56YfLFolKIPP251QiwWHPU8gh7MxRYMNSnGuyWBs583HiOQwmwlxyVRdhF8n5lNN+wyPZWGJj2iMxkL2A5L017U7h0rnbOlZMW/Zc+D8TrQr83bsppBtYbSgg2HQ+wG6dmU0sLf9H1SUuZre78kDKmpZ005xu30foaAeYONhXe2NcMcm6Qyv49mTQZ1WWLLYuBy5a+hpmduF0MdgCzpZTbALwOwI+klEcCeMm0Zgb+AOBtIsbzAQwnae43AthXCLGnEKIF4E3JZ2nM3yb//lsAZ7tMGIp8g30y5/obIF9h5N6bnWfbI+xBAAw01bG29FEbk9OJQO8ybRiIFqKkMdzzrXcAtwlgpYa91TCUEyKqoloSikSY3WnjMWUG+0DLvALJpjAYXl5LarPNGKL5epFEuxvX4ehEmack2yLstmiEPqfe5MNu7FmMKC21RzcMQiGwYEbL8Gqq949SDbt5RUyR0s+jgzbkKf5KGYb2e722h4Maq+m8TYhi4ySrYza/05Yhwx0lRId62lMY5L9fUYRdp3nAjGTY6Fd/vzGtkdmUNcIeWkt5yOeS1YtVO2uZ0mBXwIpoid5vMKeZ4n6aU0m/xQAwa7oDkV+nL5N7j6WM30VfL3vX2XhhyOuu01Lez2jcmq1xNM92znSeRrx+zdbJJI3evAfWdsZsNex5a6DTktlI1d5otMjxmUVN1DFWGaN1mbaNA0yF3xp5znnHfbWxrYbZJd5Kv0JgvqZs6nWGxpV3lufgci3tEp+jfOnPSkYlkL+ugN25RDxqdcqDBwwe39cwG0Xxq8smE76vX11kvTlFS8nVeYZt/zmKdBNCoaMox+ka5hp9UZqmbqPZMBCG053T8ZIcOta/i55x9bCdZ9Ba6vqlLitsXb+LMv9S/VDPigtM45HmihJ9pr9h1yv1c2bLkLE5Mfa19B2qUs4QCruzhTIk1gzn6895jpM1WyfSNHrdeW1DWoteoHc1AzOjU68pz+dP5Zl/Ot9vhKaxqNd4285LkcOM6MdoiAk7fxljmbS0Jvrn+hoh5g/lR/VJ72r31OCCbT/NCHs5f1H5Q9IMs8/O7/LOy+qtE1gyux9CCE13irNxTbtQ649isQ1aliuSeYaELatpul3iq17r1kgi2n8F4DNVBgghzgBwEoAFQohVAD6PpLO8lPI7AM5FfKXbQ4ivdXtH8ruuEOIDAM5HfK3bD6WUdydf+2UAvxJCvAvA4wD+suLzA4iZt41Ali5bnqbM2iPs9sN+4T3r8KpvXgUgTr207UUjCIxoW7vXw7H/fjE+dcoBmGj3sHBGn9HszOZB/e0tq/DbW1bhrc+PaxZtV5cZEfZOD0uXLcfrknonu3Fh/uwdp9+IBTNaWDCjDzvN6kOoPSDNbfOGHfUvF2GnWX3Ye+EM+/2FaYRdIy4psey3d+DMG5/AXxy2szWKYTNqomQPX3XoEnR6saDqam1lG5Zo9/WPbsZbvn89fvHuuI53oBUYV1zlGey9SOLXNz2BT/zmjrSG1JYGqK/P6FRX2Q/b2lsj7D2JY//94qye3EiJFxBCYK8FM5Qu3GuGJ7F02XL85xsOxdhU11p7aXtOYpJLly0HEKfu2a6fAuIyC45IxvtxSlLvajMobO/44TNvxVm3rcbJ+y+MI+wVbiboRhKv/MaVeM3hu2SNAy2EaNvH8XZMG//4on1SOtRT0G3OjLFkD6k5kc3JZxOqZ9zwBM644Qm85Zjdrb0d4n3QO7L3sH5kEkf/68XxHra76G+od2/3WZoLAfHtFn/29SvwpuftlrsGNgXn3869Fz+6eiVefvDiROlTfx8Gwtjzbk9iv8+eh+ctnZs6zca0K+kAi2IjJb5/5SP43S1P4u9P2huAmQbYsESgAeBblz6EC+5Zhx8n9fsDzdC4Isk2rhdJvOHb1+Cmx7YAsJd62Jrx/eG21em1OIsT4a9DNyravV5KQ5T6qjfIsZ2xqU6ES+5bh4/96na85rm7oBHE3ZcDYd7KYDO8D/3C+dh70Qzsu2iG9Zos25zdSGKffzoXR+85Dwtn9sWGkyUbR1/Tu1dvw9Jly1M+mNdI00jdlxIf+MUtOOeONfj31x0Sj81RRvW09r5GgHa3h1O+fgU2j7Uxo7+BgVaIrVrX/DAwI+zDEx0sXbYcLzuI6NdMc7U9x4u+ehmmOlHuPez0yvp7AsC/LL8XP7jqUbz+iF2t2UN57z7VjfChM2/F2bfFSYaL8yLslgyppcuW49WH74xeJNHfDDA6qtKjLUPgygc24nX/e026l4bhbcnGAoBL71uPd5x+I/7m+XsAMBVq7tzQx49NdXHEly7EV15/aKr8644XIQR2sxh9X73gAZyW3B5go2MgTpseWZdd/fX4pomUJl956BLFaUjQ6TiSEod8/nyMTMUdsfXIPb2erviv3DiGpcuW42+PjdfF1vXbZmR86Zx78IOrHsVrn7uLtWzOpleSHkT9Hmz15Db96bYntqr0mxNhtzbZSmT9u4/bM44qWmg/DIS1ee8vb3wC/3NpOT+9YWX2f5K9APC8pXMBqAGwvAazW8c7OO4/luN1SR8rWwCOMnC5Yfa9Kx7Bd694BG85Zner7kTvqmc3diOJf11+D7535aN40/N2Q18ztEb+9ed9aP0oli5bnvZQsaXE2+jvC3+4G6dfsxJ/cdjO1ozYmKeZ7/ylc+7Bl865By8/eHH6vUaEvWGPsL/thzfgigc24Kg95sbZiRrN2kpuLro3tply+YtFT48iiTefdh1ufmwLPv5ncW8RPThGMnHnOQO4OZHrALAt4fUA8Py94pIO1cETz9UXBkoApxfF+vas/maWEt9RdUN6R/593778YZx2xSN45aFL7KU904ywVzXY/xmxAX2VlPJGIcReAB4sGiClfHPJ7yWA9+f87lzEBr3+800AXlzxmQ2EBSnxVz+0CYBZzwYk6dSWdJnfsPtWZw80rUI49hipyzw21cOa4Uncs2ZbamSYkT2R20n21se3pt2wy+Yi0FUlNmK3pQIBwMbRNob6GtaaJvoeW8Rz4+gUNo5OYZc5A/Gh1fgUdXrXBWwvkumdqWuGYw+qXlNsyzwggjnnjjU4euk8zBlsGt4w27iLkmsZLkj+1ruZA8CMnLXpRRK/uyVe0ztWDcfettAUVPX2wxwTJZ5oIE6lsqVTA8DSBYOKwf7g+vjfP7p6JdaPTGG3uYNmmo7Fq6lf6xafb+urGPWYJEBXJFcT6tdW9SJp9VSflSilelS+GQp0ehIC9ud8dOMY7l2zraD23f7go1OxcXfaFY9g8ez4uiT9miybM4OMwovujc+NzSgsikLdtXpbbNQYURNh0GKnJ3H36vhKnZ9f/zjWbZvCbvMGDS+xdZ4nhwEgpSkbndvW5kdXrwQQO5dsqe2hEMae92Qc1Un5aDPE2mGzzYmu9HUjifvXjuChDaO52SM22gViZfiuJ4fTKEFVWupFMjXWARh1hHlO8J+xO3NnDzQNvgaYDax4kyZyAuhZAGEgjMaF7V6ERzaMYct4B09sHk/fLRAi5YmURWTjh9smu7j18a3Yde6govDTtWnCkrUQRRLdSOKahzfhpQftlFsOkdfNmc5Z3K3d/L2RNtqTOOeONQBYiZAlMwWA1Ugdneqm949TqZTB94XAPC06RGnNxPfNCLtJ8xLAI8l1a0Ot0OrIyntPAPjBVY8CQOo0tY1thbFBwBXAdjdKjXUgPkO6jOpvBpaMl/hvGrtoVj+e1K4xC4Qw5jvvrng/6Bo8W3dym2Ph/ITX0zWRRko8O0e64bB5rI3NY+1MH2qG1rvObR2kT2NX/Zl0HM+506x+PMAM9lsez2h/9kDTcPIApm7SjWR6bePM/oYp64U9Jf62J7YCAH587WMATN7W1witehCdl/F2N5Zp2vOFtszNJEhx7SPEgwPDOVikV5I+O9C09yyynWvSW79/1aPYd9EM7LVwyDqnTc6cfs3K9N95+sUSrfSAB1SIn/LgTN4NCtTdnnS2vLRy/RF+eHW8D6u2TOT21QHMtYmkxPeujMc+uXUCi2f3WWu6dR2e5BKtje446muEVqcEfZ6utzQzJIq78PPmwLYm0bMGzDmvSGh9otPD7AFT57ZFk/9we8yPrnk4PqMGf8nR7+lMZ7dA2SPsi7WU+FXsVgPbeSHdqa8ZYISpK90o1rfXDE+mJdH6+9nw/StjfrRueDJXd5oOqjad+7WU8lAp5T8k/39ESvn6ac38DCAMzKZzenq47dqwMBCYwyIutOajTPEatKRvA3Hqks7EaeM3jbZT42SDds9wIETuVSGbx9r25mGB/Wo5DpuwnT2QX59C0TLdKKDvyUsBA5DrcSWvVlFznsc2jWOPeYNGNCkUAq3QTqxAfN/igpl9Rl14UTkEpSrbDC9bnQqA5F7r+PObx9pWr2YQiFr7MafkmjVbqQD919aMAwDuWxsbfQcumWV6iS2OrG4kFaN9oBnm3k+dV4dF0J+pF8nc7wKyM0dpYXRehLCft24ksWkso6We7dJeDYHIjASJrA6rrUVbrE6+nq4wm3tY9H6bx6asTrBQCGtkd3Nyd+7jm+N7kw9cMks583lXvej7MKOv2rV8hNHEuNBR5PgkDDRD693pxvmMJEYmu2h3Izy6cRyzB5pYpDXVCQN75KQXSfQiiSe3xELZRku2q9f0dSm62o6jCr/Xs1D4vcykJOqZB2EgjAymdjdK0wbXbZtKMxpsc+pro97jrCr8/MTpfFRvWjXYCg0FIwwE5gwWX9PUb0n/BExeautf0F/Q24IjjrCzHhTJ8/LME3pe/QzrZ9VGP7q8UxqiduNSESGE1VmWJzMAYNtkB4NWhTo2MBdojYx0JXHQIottSrzOA/eYN2hdG11m6TWmtn4SNlocYLKQnlMdl/1bH0/vuHmsnTpd14+YvCPP8cqfwWYU6fvLHWaDOWfViOCxM2MbQ85X3dDlZzwQwN5ao8e+RmDqQWzMyGQXA62GcWYDy9kzHTlmhL1Ir6R9GGjar/aaaZEffC03jk5h4cw+0+md6zAv56fm3mVRTnL8cHaeZ7Ab8inHeNX1TZ4SX1Q7rtM856WPbBjDHvOGrN20dfrj56wRxBmTHDb7hGNsqouBphk8AuLmlHnYNDaV6jG6jO5rBtZAJoH0NZ2/2KLJEdO541I3M/BUdpNMKzRLF+gM6LqYQuvJXJw1Um8X/ZxxGdqLJPoboVKClb2P9n+ZPeczVsMuhOgXQrxfCPG/Qogf0p9pzfwMIBDCOER6BHegGaapE3wc74RNDIgfhv5miA++eN/k39my2g4gNZLZPDaVNonQ74sNA4F5OYrRpsRA1Dt7AzA6dusYaIaGQlxkVFKK2lFL5ylXD5FAzmuyAsTe14FmgCN2n6t8hojNVDSzf68fmcIe8wdxvNYZ1qa8c+a4YWQKC2f04aT9F2rjhHENE+09b7Zy9FJ174tS4onpb0oMMH0/AoHCZh1AfG70GkubQqwbz8/bc66SJkrKjO4cIWZHww/ceRaO3EPdj7xSEW5YDLRCvPyQJcr60FRlxlt/K8TRrMNsN5KFqUFkeD8/Se3jtfO6kdGLInR7UazsdeIrEun9irqpthoBxpnwj+8Ob+DVh6nX3tgUo47GpfubgVEnmZeaB8QGeH8zxKG7zlF+Hgb280IKECnDBy6Zibccs0f6+7wIu54lQel1VZ28o5NxhP0I7bzkOXg4BppBmn5YhDhyFRu196/dhj3mDxpR6lCYjlYA6CRzrtwURz77m6Fx92sjDAyDnDtcGkHcmf9oSwdkHdxA7G+G+PBLzKu/WpqixCPsxC9efrB6vU4ghKFgTXV7qdG0fmQyVRY/9jLbnNpeWHqkEE3smXSWFihWjCji8lytW3YghBHJ1DHQDPF+y5U+ekqo2jCym/Khoo7EBD11lRTq12hXXQUWJ58h81uaM9IyhoOX9HyUXf+Wxw85tk12MNAMcXIi73VFfyetT4itIanewb6vEVj4ojrvHvOHjPvFbQZ7JhO7aIbCuCY2EKasADJ9YHQqdhDppS98Hp1fkT6UOl1bIV5xiHkFVRkGcmhSf1xuKA60Qrz7uL2MMSZ/Y7TfCnHCvgsV50pQIn+BuP+AzovszpZsTHxeAoM/2fRKkweHhu5VpFfy97PRr+1c87XcMt7Bghl9hs5my0Qy5myGCi3x5+XYNskdoPEXczmT5yi3lRHZjCe9lJJYxXina5SBAZkD1OAxbMInt05g9/mDOHrP+cZ43ZnB936XuQOGc6XsjvSRqdghqOuUQsBo4MexfttUytP20m4fsDXH4yB97c+0Kz9tjhp6PWpmrTuwbPzF1pNFlxHEt3T6G9VsNAB4w5HZFXU0V1FfLBpLpYXq5+zBIbKbdPqbbkp8NXc28FMAiwH8GYDLEXduHykcsQMiDIRxiGwb85N3HoOLPprdqdsIVSZHh4KPHWw1cMrBi3HZx0/Cv732EOWzuteP0og2j3dSAfX2FyzF8g8ex8blG9/tboSBVogZfQ1c8YmTU2M/EBWYcTPERR87ET9559Hpz/K8kkDi4W3GQuXCj5yQ3oFOhz+vSQeQXSO227xBXPKxE/GXCaGQN6toXgDYff4Qnrd0Hi78yAmpEyUMTObBmePIVBcLZ/bhpP0X4YKPZHsYWLIrbFd9/ORdR+Ocf8z2IT/CHqUMbrITYbAVYlZ/E5d/4qT0vliBcuV2sBXi0k+cpNyjbWOOPPIbR1lCXPzRE1NnC51rfUk5s5vV38DOs/uxx/whXPKxE1PHTZ7wH5lUPeAz+hr45d89H5/4s/0BZPtXxGCBOCLxs3cdg8//+UEFK5GBot0v3GcBuSqi+QAAbvVJREFUzv/wCXhjwihjWlL3sNOTiGQWnRlshnjRATvhvA8dj9cfkX93aCsMMEoOCYmky3iAdx63J8770PFpKhrVDnPoUa/+RoirP/2iyjQ1lhhDswfi80I1h7pjkLBpLItYzOpvYJc5A/jiXzwndUrled113jbYCnHzZ1+C6z5draqIUuL3WTQDF39MPWvGnmsWwkArxFdefyhWfPj40nnIqL1v7Qj2mD+EIBC4/p9ejA+/JHaABjkpld1kzscSg32gGeLM9zzfuL9Xd4LwCBali5/5nudbry3Kw2ArxF8ctjN++/fqnbi6QObRJjJq3nbsHjj3g8enSlUjEIaiMdWNUoN93bapdOy7j98LX/3LwwBkBqJ+1rhBSrS018J4Dz/04kQpFua1O1z5mEgcWAcumYULP3JCut9xTXhxRsJgq4EX7rMAV33q5MLPKTdttLMmPb//hxdY71LmaIWBcqPDVCIT//7EvdOeJEDsyNTPaseSIXPjZ1+CH7095sECtjWFMQYA/u7EvfGVNxyq/I6P1eVjHDENMW+ohRv+6cXG3et6Sqce1aH64ms//SI2h2n08WhXfzPAopl9OOXgxTj/w0wmClMf4g35BpK622uWvSjtv2Bz7gJqpGwgkU9Xferk9D57fr71c0f60NrhCUQyXtuPvGRfnPvBct6hPkNsaH75dYcoP9cNM90Rfcius3HDZ1SeqD+jHg0eaIW4Zlm2B7QkeQ4QIC5XA4CbPpv1a+5rmk0oufwcmexisBXT4UUfPTH9uU2vtJVKHLbbHEUPCgO7jOEYaIY4af9FuPKTKv3aDNxRrbxn4cw+vO6IXZW9qxJVHGw18Pojd8VP33W08nOdL/KMJTpz7z95H7z68NjRbossA6ZhNdgKcctnX4qr2R4C5hoSxqeyCPutp740vYOb5JJ5naeZ4bL/4pmKbQCYa8Nl0+6JY+7Gz7DzUhDpBuLbJKiU6YZ/ejH+5TUHx88JGNcXcqwfyWRMMwxw2cdPyuZsBtbMFQLJmNc8dxec96Fs361NLXnGQquBViPA1ctelF5RaI2wazfQDLYa6G+GuPKTJ+M7bz0SQEZn+npyhzm934dfsm96Jz29V9FNR0BMS//0igOV94vnNdcDiM9LfzO+gu/Sj5+UXu1tCzy4oKrBvo+U8lQAY1LKHwN4JYBDSsbscAiFLQVMj5bFwm8f1kQi1BRpm8eQNnzpgiGlJsl2AInJbdg2iU5PporKc3bOIrRCFCtGNN/u8weVKPDcEmVqIDEs9SY+PC0rr169EQY4NIkic+LmiLQ0R0ql2WvhjHTtycAo8toBMZMD4q67lM4bBqYw1VNxFiaeb96VOGS1nwQyvPj1PmSgEGZYUmqBmCB52h8pcHvMH1LWsizCPtCk/VC9mrqit40xnn62H3TnKMk1PbWWC6ADl8xKBcxeC2ek6x9ahH+7GykGO50BIQQOXBKvazPZzyJlEUgiWI1qkTMgqfNM1nD/xTMVoa3PRcrXZhadyd41f45mGKRNRDpRlNTVN9Kxac2whX71GuT+ZA93Z5GvsrM9wM4LnSOR43DjSsoByR4GgUjXM0+I6+e9vxli/oy+0jvNCRRhB+JUTm606e830TH5aCMMcMDi8j2n9xtv91Ka32lWfxppz0upJIfEyk1xqcBAokjr9xPP1aIUnCb4Ptvuss/DAFsXDp138r3rZzR00M6zUn4YBMJwtE12emmaOB8LAAcsMTumc9iucqRn5YpJXmoyAIWW9t1pZqoIV1H485qK6tAj7FkNZWitl+VohIFRmkJy9FAtyyKPZxD6G3EWxm7z4vNmK73RGw7xLDr9XnrOu/UUzW0TnXQPFs3qN+qXy2iTvnsJy0KxKdT87Ow+bzBVnvdfzGRiIIy75HVnFhDXc5McCwIzSxFQ35Peb9e5g2lWD9e7dOOG9KG0tCXZx4Ms92kXgebVx+kycdQi1/Qmcrr8tZW2tBoB9k90jLwMN57tsvu8mC/xyHxfwwxAcLrg54XThDVzU+PBdCe8ogeVONyaYaYL6JkcNugGO70b3wNbbycd9I568zadn3IHKOenpAflOcr1tRlohZg92DQ69OtObsK2JHAFxIE0vReBGURSx+8+n3TZ/MwTQJVNpKPzTMHSCPtkJ9UnFs3qT53CQsR6ApeF+txcNvD3a4X2q/IIlNkIQNHxbCXI6TXakUybJu4yZyDtQm8PBpgZY0B8Pokv0bLp72SjW85b6L3ymi4T+hKeVFWHpWAHAOy5YCilxSq0UISqBju99VYhxMEAZgNYOq2ZnwGEoWmw64Rsq9kMAvVqGJsCyVMquCFnq50eSdJ66IqQvNqYougsF5C8vq6KgQiYB4enauleSv5uJKy50OLoKtc0Rcp6UrQgTYkvibBzpwI9gzVFWTMQF8w01yDUmuvEz2deZ0JzEPJS4ocnOkrzHL5GWXpOhRKF9L2g/D1LS52zeQpt0Je0qxnsHJIxOdMA66XnNH7O7F1pfaj+p0jxj8faz0oetk12laZmfNdMWsquSts63smlJR1hINJrnWz1T/TMNgHCHRnxuMyQJZQJ1gHL+wnY6Xd4IlNSDmQKNwm/3KtsGC0GovyZdIxMdZVmaFwo6oqR7sSoug+AKlR3ZzRPSkBehKZjibDbPq+v6STj+ZyWsn2ooGAyJwuHHklUI0LqmpAyEwozwj7R7qWZP4C6nvr76fuqRK47qmNRMD6jj+PzUbZK+p0sglGUxdWfc2NBGShVnFCm2DQCYemVYS/TKqdfdS9t5QITmsHe1zTXlM4NH6vTwbbJrqofaGtVZLDnNe+ypaxyHkyGou37dF2GGxo8ak5nNS9CxM8cl/nEglSnq7omhj5UIN+KwHUEIEtX1oODvA46T5bqJQaK4s/WRSZcIy/DjTtN9SAJYM+OiLQIu209GpZMB/2M2uqOw5KSFls/liKMTpoRdh1Voorp3mnrx3lJKwwwnMNPU50kR6cc15x0eWdMb5hHGJnsqHownbGcMhgjwp4EZPSlKDTYLXRbVsPOHQuAKdM4f9GdfP2a3UIoMtaBOLhjaxbasDj3OM/mY2gdwkCUlE71lDNKtoq0pMS3wkCjW/P9iIb0dbX1g3DB6FRXpb+cDABXVNXgThNCzAVwKuK70O8B8JVpzfwMwBZhN9JbLRtjRtgtBjsbR4JOiCRCV5BeBeQzjyLj26poVqgvHEwZo/pM3EDUhfiAReEjoVVUf0XXIRAoFZEigmUGHK/lJsMhTslT12tSc7rojXtonB6RIQE3wWrYAZWp5qXED090FEFkM1CEQKUShXhOEgDx33qtm153Z8yFTPnn4DWQB2qROUIghBGNnmh31ZR4izJNwjGv5CN9XnLulDB+Au8PwCGETfkuv8bLhjAQSkotoNIUMdswEKljomxOvoZl5R5572dNiR/NDPYDmNOF9ja3hp1Hy5pmh9sqsF9zaWar6Dwt73YEG/hYnqXAad4GOmePbcwi7IBp7Ok80VbX5orMuaf+XI8kKnSrzRWyqLWu1E50eooCblUWk//rZ01XcPi8Iv3bdLrw+XjEW3nmnLKN9DlrrufwhGqwlx3VIBC5SpWuiOrvmUe/XAnX+Yx+3WN/gXOBz2dreMcVY/1Zi+pM89JSg8B0YusRdhtCm0KtGN6mYyEv24WvKad9Mj45XRSlm8fjp0eTRqM17R3bSrmAfa5mI/8Z9VtPANXg4OC0aNuHuP9APv12I/sVqHHml8kzOOzBp2K90pV+jQi75b7uKkZKHu8OFZkq0k79ADR+IdLP2DCm0a/re051Iyt/ysqSdMNUHU90XeTgBVSZbctwKEuJL9KdgPj6PILe+NKF/+rQrxgE7CU3imOPZ/UyWVjk9Ipvkcg+QHIzCyZk45qhKJS/9Bnb7wzZ4hjsAFT6o2+rqALnopJWJaX8fvLPywHsNb0pnzkEgUkgutJuvdYtUO9ytXn/uZChqwpSL05OVJCQG2F3ZKwC5Qpof46iyQ12XcAp0Z3kndJoRkG9yQhLqQWymtMqEXb9e+l7ipr4ERbPNpWeUJgRdhJwadM5S4SuKMLezVFuuC1TFi3gKbkctu7W+hgb9O+ZZAb7fIswBfKdGUqDF5s3m1LijRpvdT/I6+4S4eWeevKe2iJfBi217PsAxEKIfhYIYTh6eHM78rgGlkiGPie9F+cLZUqK4gVnD2q7eoXXsPNMH/Li560rV0yLGvAVQVX6Mi+2LtCMCHvFbt86eAPMfgs9ctA5IyUuT2Gv6vh0ARklRV1gm40A4FFyzYlBClOcEq9+T6cnrSUpgMm7i5ymm5LOzYSibBVulI5MdRWZwLNxnkqFn7Bm66RRSlaE0HIG6T11pbiIZzRZ1h1XLvNudiFwBU5/Vj6fTcFeyBzKutJcFGFvFnTvMpzY7HnzmsqGNoWa8SJ+bvhZtSHPkOpZIktlPLyuE40ic7ZrS/OQ51gs0k1sY7LMlXwDxXb7S18zQLunRdil/VxzhEIg0M6DrVTLGBcUB3Vcsxt0g91WQljFUTyoOc0IxfzUlKF5jnKePTRnsFnqULdBidAW0DxgltrqPCYrYVTn4DzNRrdVdChVd1J/txMr/dC3xeYAqQrrGbXINW4fcD7Iy8OMspKc0ikg40u2pnP63FzW03emKfH6TQ3TjLDnjdkuKfFCiJ2EED8QQpyX/P8gIcS7pjXzMwBrhL1CSrzO5GzKhJISn6R6BKlBo6XFTage/twIe1HqEh+Tk8ZjAxG8LsRmsetb8mrYgezA5UVN9SsYeKoJpbBWqWE3noF5YI2mNUw5aYbC6uUNApOJph1Ak3QpW5orRdj548zsa2DbZCd1QAD2jAfzVk8V3PlA308j9JR4jsECo1RJZQqEUnupMxCZKlNmzc5EW6thV8oi4r/zUuK5IdwKg9TIrJoSD6g1mhz6d+i0ZDPAUuGopHmZEXZ+hUrqvLGcN7WfQJCeF5d0pwHLeRHCLig3M4Odj+t0i5UUztvy1rMMNkeUrbzkqVK6Oa/Ji7oQjNILtmccedcY8TmA7P2qyNS80iI9Jc82hsCVOJshlLfv+pxFBnsk1b3n72hEkZnyLaVa+0/fWHZVZd105sc3jysOszLFJrRE2PV6cIL+VQr9MoNaMB5cltbfb3Gg2NJjbfrEznN4lEuLsM/Od6zlNdTSfyeEWnOf50QJguKU3J3ZfmQRZPv8nP65bkLfx/dTl1mGPlSTdxACvpEo5st5cxXJqjyeaJuL8yhbl/FWaImwa87zJbYAhCVwoa9jXrboU+lwm04plDKuZefdnA6LIsLknM5Pic/ooa4stOnBlNnYp1+RqYfYtXHNHOcSH2dbyyoGOz8vVLJBy7pTAY+ty7vjOc01DYTpXFb4i4UP2mw07vSKb6HInEKkm9kyefTUev5+tM60D2UR9jprY+MT26uG/XQA5wOgO48eAPDhac38DMBWs6UrfX05TI4rKTb+zzdUZz76HlVNz6nKWHWiLEJap6crtQP5zSi4oMlS4ssNdkBViNpaSnyRl1NXZJSU+IJ6vZ1m9VuVLZuRQZjQIuxAtscU8eRrMmugaUTY81Liq0KviSq6G7qIefDnjNO+s7XRo56pEi7MFOfJTq8gwhfPQXtUFGFfPLs/66TqZLAzocOMDKN2uiBVVQc/F6FQ16YVBkqjlTQTJTDPNJ+Tz6enyRXBxszzaqe54aaWmMTPn1djNtnlSkq1RnNFz0mwRTe3sjp7oL7ixufjZQk25PVK0IVi0b3YRTyoaB8z5576czX1V/3uvBr2ILBHAXmDpaL67rImnLa9FwJGqYdOS7ZIXChEYVpmXaWvG0nNYC/+vM1gt2VWAcX0y+U98Ycq0SWV7jUHCu8Sb6GDxUy51WVVUYS9qJaUy8v+Rqgaijl1r7GjSP0Zj4Cp6bOkGNvnH7FctwWoTmGCLocNfWgahgNgnh2+xkZUMWeuIr5gdQpXKEmzZW82wsCgQ/28Wo2hwHQkm7LQlhIvCrOKphthd+2Too8T2nA9AMExaHVe22mX17A/FbJQd9LpJRS6PkVIP0+8Xz8vJeUaVXjTYpvulJxPHgkvajrnCruMMY1vviyLLc6D0CILeT8AvacDfb/uONP/DajvRzKS9kFfa+P2sJJSBBv6a9poRahKXQuklL8CEAGAlLILoFc8ZMeDLfVQj7LZDq3eZMnmJdG777bCIJ0rz1Ak5BkZVVPiyxR+G/R1mDVgNhWzzUXj8lK5izxTRkp8UYRdE5ik+NiulZmqYJjY0ngJvEs8IQwE+puBNc1u1kAT2yY6CiMpSsXOA38efc15xoOOAUuDD/7c/N880qIr2vScjSCwlguodYnmGUi9xAUZD+od6jUNdmJ2Fb7D5sgicIEfBEKp89xj/qCydqTsNIIg99wAKiNPHXSFT2h7zgQOkV0gu4c8T0lRIuxzpq+kZFFWM4LAm7sA9ZVuxWCnsoTcCLvWCIyyVTSeNFRQT69HbdTf5W8IrbmuXBSl/up8PnN8BVanRF6E3UjHLEnf5nvPaaKMlpRIOkUISkh4Wkofi5DbFFP+ozBQm84NJbef2JAX7QJUJ2Zd+jUyHpQadnM9di4wGIqcS0X7pfOuoswqPkY//5ESYTcdC3m8MM+5S/YHXyM9003HdCPsOjgdG/158iLsBWtd9Hw6bU7m9KEomktvpmjj3bZmxlWesxHYGxcWjSkCN9j7GvUaTgIZvRfx06JAEuli1SLs9WSh2supmAfn6fyp7tQg/aLgvNSkA063um6xcAYv7VXHTcdRludoLLq1iD8nZQhF0pb1k/07vmY6+07ab9u1bkW9uDp0XpJ90HmkecOOuyNKqWGvYaPZUPUpxoQQ85HsvxDi+QCGpzXzM4AGM9gpgsnTdwNhV371A/TVvzrM+IzOKOi+VEAVULZ0wjxC4fWsbzhSvVO6SirnB1+0j/V7AdNDqHSJr5ASn+el1aM7vNavoxns3Bv79hcsVcblPYMtfbRK6m+DRdh1hSmS6nUmQMyQufJExP/vrzsEswcaGJ7o5DbosXWsBIB/rLQf6vkEkN4xSuBKpr73mTEdn/XJrt2pAGTKux5FbgQCE201wm7zLrdyyityDXYH7zvPLuHvyFM/d7LcK6oIOY1J0nP+y2sOjssF2HPuuUDtyJr1M1CFr95bgDNlvXEg4ZWHLsHrnruL+v3KHmYOCY4vvfo5xvvx+TrJ8+vKwt+duBcAPdOhPA3whfvML5yPhFjsxMjWbvGs/lpprTrN6/Ol1xfmRdi1cqY8RZT3oXjT83ZTfsf5ve7gof187wl7Gd+Zd41T1YgQwBvtqDKGnpHfF14UbeHvfcpzFpsGO9t7OsqBUB2fdKUZB4+w873neNdxeyr/r5ry97fH7mH8rEyRprX9r786zIiwLyqITPPPzdTpt2HyNZ0QbVfM9VuiyDSMy6fXaLwbyM8EANS9fMcLlyq/K0qJ5+dnn0UzFL6fZ7ALIdJ3XpRE3ridYYuw64YI3S+cVz5FfII/H/+OmRYHRZVyGhtNEjLlOAadyQUzWoV1rRx8D095zmLld3nlTED2nnsuGDJkTJ7Sz3nQSw/ayRJhL0+J168ni+cz303Xnd75QpV+q0bIn7/XPABql/iqZVDvP3nv3N8V1bAbe1chJf5fXxvfQ84j7HmlMxwvO2gn42fW5n/kdGXzvuygnRTHF38lPSWe9oPoqMp54bDpVGqEXeVNvCGzztem4yjLdwrGf9P55Gd7sVJyE3+wG0mLwZ6tSZwSn83FDX1Alb9FtK6XE+rvbtgxJWvzkgMXGT+zZlFOz16vbLB/FHF3+L2FEFcD+AmAf5ze1NsfgRDYNUl7/d+3HAFAVWr7m/ZOyrq+uNdCU4DrGGIGOzUpecsxu+MvDjMFeB6h8GeZP0ONtlsVuORvMipeYmE6+nfTGOpg+qlTDjCbzrVMpUY3/hqBwJuP3r2w6z6lxJNXi4hlrwVDOHTX2cb32b6HC6rDd5uDk/ZfmGsgcoSBSA310952lHF3rhn9UqMdeyfM7c8P2xmzk5T4Tkm9Eb0BOV5edIBJ1Nl82n4kV8B85hUHGs1qijpwU8rTqa86yIiwF0VaqM78q395GI7de35hhD2LzMdPS0L+qD3m4vh9Fyhz8qhZnsH+5dcdYvzMHmHLHG7H7jUfbznGVPqLvMR7Jfdzv/rwnZOmc9lz7qnd3c07R1MjnXcftydeeegS6+fos4BpeH/rr49QbjwA8koo4pG0Ti8+0KTfKinxx+2zAIBq0BZF9Qjf/ZujzPmsTTgzwf/fbzocL9h7PrZWqJ/UcbDl3nNbdFIXvKSETXWjSlkbixLHzhuP2s1QbItSX+mOdd1hxqEfU6qXf/PRu5spedrZ5L8n4/i9J+yFUw5WDQSgmjI11Aoxs7+hnOvBVpjbwJL4/MKZffiHk0xnYl5KPId+d31ZNhlh/8XmfbZltaWkYL7y0CVGWcaCGfnZaGSkf/BF++BlB6lra0tt1+n3n15xgPGdVc73rP6GNWI+syB7ikO/hrPorNL5eMHe89EMg0o17ACwNLlu6ttvjfWhvBrTNPigRZ6+8oZDAeTfvGBTpCkT4sMv2RcvtRlGFSJ9x+5tOhfzQNG/ZS8/sJQmdczsaxiN1IrWk4yQj71sPyOLq0zPWzCjDzP6GobBbpP3YSDSJoS7zxtMnbQcNuNbp1/95piq9Pvfb3ouALWGvYxHkY75ykOq81POF4v2Tk+JJ2f+yw+O5TWPsBfdxEC88rS3HVUYuEqfV/v/3MEmZvQ1lHVUMvuSf1IqNumrH37JfnGAhetrFejgvyzBwyLnLuljJ++/0OAnVR0un3nFgcr/i0p3KRBC9hbnH0qEPS25kSm9vfLQJThstzlGx/1+S7YZOZQpK/n9J+9tZmTYUuJDWpd4H/7pFQdg0cw+oyFx3nV65FT53ttsulO98pAiFHaJF0I8D8ATUspbhBAnAvg7AK8HcAGAVU/50zzNCAOBz//5c3Ds3vNx9J6xh3CqoicaAM75x+MAVLtXcrCvgXYvVmKP2XMe/vtNh+PPnrMYX7vwAfOzBYT5y/c+Hwtn9uE3N6vLrTY5U0/0+R85AQ+sGy2MduiH+bVJBPBVh+6MM298XPmdEmHXUuIB4OfvPga7zxvEaVc8ojAcQF1TSolvsTTzH779KBy8y2xc+/AmZZxuhPSzrt2z+pv43tuOwhG7z8GnfntHpZR4IQT+5bWH4MUH7oSDd5ltCGqjIZQQSmTuR29/Hm55fCtm9DUwe6CJbRNdpemcEjHV5r7woyfiofWjhem3upPkDUfsikYg8OeH7Yx/P/c+5XdFXvC3HLMHhloNvOa5u+AbFz+IER5hN5rOxX8LCLzruD2xYEYfXvvcXXD+3WuxYWRKvdaNC0etYcfcoRZO+5sjcdTSefjYr26rnBJ/3oeOR7sb4YF1I8rP9agLX89FM/vxnbceiefvNQ+/vslkQUVC50fvOBo3P7YFM/ubRn3/XlqEnZdgnLTfQnztjYfhFYcswZfPu8/6OcBMF/7N+45NDXVd19Y7hnNc+vGT8MiGUauDQxE6Wkr8FZ84Gau2jqe/n9R6CRTNt37bpFUhsTZYEgLvO2lv7DJ3AH9x2M64aeUW49qrojN6/odPwHi7i8c2jRu/444aTvMAcO4Hj0c3inDvmm3x+3V6WDKn3/o9HEfsPhffePNz8eIDFuHH165UflcUtfzJu47GbQnN50F3LD1vaczrX3bQYrzhO9cov9PXl+RILwJO2n8hvv7Gw/HyQxbjtse3GvPYrpLi+Nm7jsHSBYP41qUPGfTHn1FqBtfp73ge9l88E9c/stn4zjlDtqZz8d/nf/gEjLW7eHLLhDKmq0UmdJz3oePR6UW4b82I8buyso2fvusY3LFqK/oaIYJAKNHgolsQTjl4Mf7rrw7Dqw7dGf98zt3K73gUS5eJv/q7Y7FgRgtrt00a31l2H/JP33U09lo4A/ev3Vb4ORvO+9DxmOpGeHj9qPLzIr1j0ayML/7jGbdWirADwNfedDiufnBj2nBTjYCZCnUv2d8/fuA4CJHROXfuckOAFHQu31516BJ0ehH+/LCd8fk/qPsBqJFTHZd/4iSs3jpp3HNdhPccvycWzYxl2xf/qM5XZLDTHn7z4gfV5ysY8/6T98Hu8wbxykOW4JO/uaOSfgkAP3nn0dh3pxn46gUPFJZgESjT8Edvfx6es/MsXPbABuX3fY3A6vSmM05n7JEN6hkrKh8BMh2YvmdEcdQU08Rv3ncs7l69rXKmCJDxxVMOXoxL7luv/E7t+q1G2M9+/3G4b+22VIYqKfEFfOa8D5+QrslAM1Tfr6Ts8SfvPBr7LJqBr134gBJh53pfev1c8lwfevF+2GfRDPzZc3YydJKiuumLPnoito63sXF0KvczNuwyZwDfeeuROHrPefjor25TflfmvPrDB16IRhDgukdUXX2h5RplwnfeeiSue2Rzqn/ws73TbLOevhtJHLB4Fr7110fghP0W4O0/utEooeFylJxWtB8vO2gnfO2Nh+GVh+yM8+5cq4zjji/9vLzhiF0RCoFXH74zTr96pXl7WA7tXviRE/HYpnEIITDQDJWrFW3lhNNF2bVu3wXwkuTfLwDwGcSR9cMBnAbgDU/Rc2wXhEKg2Qrw6sN3SQmKR6HKPCIUEdowUk4kQ60wFWJCCLz68NggthFFESM/Zq/Yk6wLa/5/Pd1iyewBLJk9gPsKlAU9BVsIgdcdEafdF3kys5T47Oi8MIno6QwnfjczJZ4L9BcdsJN1zqKUeACpZ16/nmtJQbrTjL4G/jzJcNCFrv7/QDPY58/oS+ec1W82nePvlKYhJa+w85wB7DxnAHevHs59Nr1EIQj4fqifVeZi9d3xZwVen5RP6HX7ukDl6XyNMEjHDbbCJMJuj5rQPnKD8mVJ2mAYBJrBkF+XShGkRzaqSsMCTfnWUxwpAmmjJVu3fpp23lAr3cMwEIrQ3XOBPT0svqNe4LXPzdaGg/MMvXHgUUvnpb/Ta7m4Qau/3y5zBrDLnAEMa1Fr/lyAmRK/+/xB7D5/ENcnAlWNsOfTxZ4LhlJPeKsRqI2SrA2k4vIROp+2qw+L6iT3T9L/ntCMPR1phD35qoN2js/L/WtjY2+qG2Hn2QOlBjuANLNJjzDxq7IyPppFvF5y0E54YnP593MQr68aYe9GEYQQeE3iNLVF1MqUqeP2jXmwTvN6+mdGE/HcJ+2/yPr9jUAojjOp1QjSHq4dVo3ZTonCTzT/wLpR43dFfTuA2CinrBNdPiwoUBq5bNPXVomws88DSJ3627QbEPRx+poCwPH7LgQAPLTefM8y0Bo9tmlM+XnZVVTEF/VygaIrFmf1N/HyQ5akvIY3vVJKwljKKgAckmTEEW3wNeLppLbuzcp+WHSforO+x/wh7DF/CNc8tDH3M/p+cNmmG1pFUWHaw6KMQz3lmPNF/SrZIifmCfstTMfwvctruEls6+Qka8+QSznvRTIq74yVOUJIB96S9NcYy8mssGHRrH4smtVfSBN6kInzRZ132yOm8cIsnt2PxbP7U1mmpMQXyEKSvUC8z9xgV8+qJrTB9jAQuRF2/fq5ViNIdYtQCLTZ+uf1pgGybKML71mn/NzQ8czHTPlEWcmWjkN3nQMAuOkx1bm7YGZ+hH3OYAunHLzY6ljgPZUy53X8/pTJaGsuyp8zS4knnTvT1Yqup25rKfEB05vD0LzyN895stu8QeyWZI6Q3pyOsZTJPt0p8aGUknbnjQBOk1L+Vkp5KoD8gtwdFHwDg0DE158wT8oBWgpaHvSDfpiWzg0kNeyW3eGHZrd5A2gEQmn4VuXZgSxFW4X6GX3+VxySpQPmpe/q48Ikop2Ni/+2KQE24bKURS5JmbN5WPU1zUs91nUW/bv0FM086MJFTz8Uwl5fB8T15ROdnnK3596WMgl9+/UI+6vYO+rXhHDoxt6+1r03YXbaz8kg0f4/0Aox0VbvYad0NoClYlv2W9+fg3cppyldSP/10bvbP6hNx2mJSl2Kuixz6Gf1AC01kARfWSdVfm6K91D9v14zD5jnRVfwmqGwdjrVs1GIJijC3t8MCq/i4tDfb4/5g8Zn9Peb0aeO2XVueY0goJ6fPeYPpiULhGYYoBGYPSs4zS+Z3Y85g00cu1e1FFl9P8koK3xOjcccU2GMbS49Ckz8wGzUme0nZX5Q6nLps2pzHrH7XOvnDJrX9v2QXWfnlIdpMkabz5biXOU5n7v7nErj8uY9uaDciEPn+1Q7yqG/tr42i2f1FzYy5NBlcFHttQ59rf/sOfXWtso1VvqYw3ebo/4+JIXarjxzJ99hu2Zj6fN5d6Fz43e3eQMYaoWV6qj1/X/z0bvlfFIFN9wGW2Gl+7j1talKi7wZ60BOuaVtTDeSqbMk77yUySXbubaNMyPa1ego1GqHAVOHyoO+nicmhi5QfDuE/qzz2fW9ZIDpvJqM3qoRdg7dcaTLJyBHfw7iW3covf71rP9Uu0B34j+b1d+odl604/ue4+3nxfZVOn+pUttvG0cO6iLo9K/L3bwG3boDqxEI7LtoJvt/vAC2rvz6nFwH0lPi9XHcLlw6f7DQeULQZYtVx5tm07kyqRMKIRpJV/gXA3ivw9gdHiGLzn79jYcX1ipycAXyjPc836r0DbUaRvdsQPUOffetR2GfRTMqNeOiw7f3wiEs/+DxOR7+/Oc894PHK/VK9FkbIdP3ffgl++IfTtpHeb7sHnZ7XRXh0y8/AO944Z7K2OwqBfN9ORN43RG74GMv21/5fWawB7njvvPWIyszHdqHZihwy6kvNeoKw0BYI4dAVqe6aayNA5fMwu//4QX2/dAdKOzRV3z4eOy/U7myyMcducdc/Pzdx1SuNeL7f+cXXmYYPjJ7UAX9SWpPuxvhlYcuwVf/8jDNm03C0dxH7pT43tuOqqgsZmNeffjOeI+mpGRZBPke4X977SE4Zq95hVdOcXBmfuFHTjCie6QAGZkeTFE/6/0vVBTb9Ctte5j88uBdZuE379PPi/39uDJ56qsOwtuO3UPZQ0qJ15UUooltE7FyevvnX1ZJ+APxmg5PdHD0nvPw83cfo2Vz2N+P08lX//Kw6nxU2YMT7ddlNkNjD/i4vmaI6z794so3EBCPmtnfwI2feUklWuJn5Xf/8AIczgySKuP+7sS98LGX7m/w+SBHSeFn7K+P2R1vff4etWj+3157CP76GM35ldP8htPSF/78IPyt1hCQnlDfC7429/7zKZU7DfPn/Msjd01roauCn4EPvmgfnFzR0ODvufyDx+E5O2cO3hx2qIz5+5P2xidetn/lbthcJh6/7wJ8+uVmPXyVsfd96ZTqZ4CtzRnveX4lmRhqxsJv//4F1u80z2r2TLvMGcDFHztRec5eSWSJj//5u56PJXP6Kxoq2Wdu+MyLC9NyOejx/+U1B+ONz6tm5NO7H7/vAnzvbUdV3gdOG3d84WWVx0RSYvZAEyfutxCfOkU9L0LEcrsoe+dfX3twrtNbP7b8GV3OGB+38+x+nPuh4wuvolXGsodY/sHjcCDrZ1G09/SrD714X7z/ZFUvzVKchTYmbq5JwZXLP3FSZR2BdIADFs/E2R94oTJOz8bioAj7rIEmjtt3Ab7wF1nzWP3+b47sWkng5lNfWukZuT784gMWGXqznoGpPycQN8b8jzccWnlduAx46/N3x18dVU5H3BZaMKOFM97zfOuz6A7BRigwmThj/vbYPfCZVx6k7LvedI6DjPiPvnQ/vO/Eva3nxVamGgYidfB8+uUH4L0n7FWJJ5GcOGjJLPz+/S+wrufTHWE/A8DlQoizAUwAuDKeVOyD/0Nd4vMii3EKd7wxC2b0GZti65wLqAeWd4PnmDfUsnrhOUPsawaVO2fTgRdC5EYJFhkRnOy5ZvSpHjsSQkftYTob6KD3NULj+Ug5t0Xs9CZK+lhqLKc3cQFUo2O+pYnF3KSeckiL5vF31H8H2COEQMaMZ/Q1rE2ABlsNo9EfgQTT5rE2mqFtP2IBpEfUuCI11FL3g9buyD3mGvMRU46vmlPnOmhJvKbzLQoLrU0YCGsdLkW19O8caMYR9k4UN/XSf78wSYHSG/cBqiKel2KlN8HissvmJNltbryHey5Q95LXlLUagcEgycPJswOyObPn1BvC8Z8Nas/DIxn6Oc1oqmAPLfRLCot+3vgz9jUCQ8jT+uvPQUJo81gbswealQUxkL3f3MGmMR9Fa3XjmO/ZQCssvC9afc7s/VqNwDpuzlDTOBNcyDYSmrAZULbOyTSngHnu904iKLtpGQKhdqZtc9miL/S5vtDO5w/e2b5/ShdcC80TXR1uiUrzNV082+QJlDp+gNb0jc+xYKYpC5+XyImiCLvLtUDqc+YbaXnNElW+Xz12oNKvuj60R3pWgrI2M/qM/adMLL1xKqCuj87zy6Be1WZfW5ujistSW0MoW/mAvo+6TkO0oWfPcN2qYZGFexXwYEDdj/6myePyEBboNtRk0LYfBBs/zQOdNRstUjaBTYfgzkGX9+r2IgxPdNJmmRzP3zPOJCq6ns6my1IWqP7zKmes7OakZiPAnMGW8d15UUmdJmz81LZ3ZCS3GiY/pQzHXeaY56zVCDCWZFa4dEInfjZn0JShpMMeasnopDTu4fEOFs1U+RedzcN2M8fROZs9YMrePPBzYOODi2fF9LqvJTjEr+fN0xH0WzUAVcezzWnLTOXPaeMv9Hx6gCcQInW2LJ49YOw7rZlN5yIb3nZe9kmi9DvbrkwMBManaM5qDkQgOy9zh8zzQqUksyo6tfJQKOmklP8qhLgYwBIAF8isY02A/0Nd4vdaOIRffOh44+e85tpmdP/xA8dh3Taz9oJ/Nq8RzCdO2V+58oLAa+hskebr/+nFSqp1OmdyaGzTfejF++Kk/RfiuZqiwZUrPdrfDAOc84/HKSnrKbQu4BwvPWgn/PbvX2D12hddqQAAX/3Lw/G+E0esSgT3FNqYwAGLZ+FXf3esQZicedjmPPv9L7TuIRFXnnFx+jueZzWCgazWcvNY29qd+OMv2w8vPWiR0QWbGxq2Dp3n/ONx1jQaWldbWuGylx+AVx66JK0pVcYl6zHUsqfj/febDsfD68es0eVuJDHZiaxreuQe8/Cb9x1rnLf4HYtp46KPnmg4e/jeNy1jXnXoEuw0qx/PW6rOx2vKbMrBm563G/ZeOMMYF8+pKnw6XnLgIvzmfccaRp/tPnpCq5FPU7Tdtn341MsPwMsPWVKYUmhby8//+UH4q6N2xR5aiiYt5+axdm4zris+cTKaDfM7i+jif99yBB7dOGYYZjOVWlfzOy/7+ElWZdCWgaTjR29/ntGtnD9aXgOj8z98guHAjMfGg2378Nbn74H9F88yMqZ0B4GOFR8+HktmmfyQPqpnBRE++tL98KIDFqW1gQTljFn2Yf6MPpz9/hdiP6sSxvioxWF8yK6z8du/P1ZJW9bntEUdfvD2o/D45nFDuc6rsSVc8rETrfQVlih9AHDBR07IjZyW8f3rPv1io3EQUHyf/Yy+Bv74geOw9yKVntS1MedaNKsfZ7//hYU8GMg/q5d+/CTlCtd0bImiePHHTrTW/SuOYYsT+4KPnIBNWk2peh2huf9vOHJX7D5v0KCNIBDoa8R9S2z7kEdTBLUe1Zz36mUvUhp42Z5Rn3fJ7AGc9f4X5qaFx3OZz3rVp062fpb4lI2O/+11h+BvX7DU2tSTnst2/q/85MlWQzUMBNq9CJOdyBqx/t7fHoWVG8eMtVL0Ssu7/fTdx2DVZrNnSBn95p2xouvWCNcse7FSVld17LkfPB67WoJleVk+QNzs7wV7z1f6xhBajQCjY93c+fLOGJ1NGz0UnbFQCLS7Eaa65h7utXAGfvcPL8DBO+c792x8+8pPnmx99jJb5Ni95+PX7zvWWhpFY/N04Is+eoL1phA+j82GufQTJ2HreLvgOc0xbz1md+y/00yL7BWps8VWKiOEwPIPHmd1CJK5aluXD75oH5y43wKrDhsGQdp/zDbnVZ862dp0MLsK2BzzL685GG99/h7WIIILSl3TUsrrLD8zW53vwAgDYVWGucFuY3JzBlvW6FsVIbxoZj8WWeQFr0+0jc2rwaU5bTUQjTDAkZZIueIFtRxa27VKAGOMludrhoE1CqzPZxs70AoN5TQdyxSGvK7MNqGvMA9bhC5nD/NSngk2jySBvGSjU10r88nbD/5RG/PN249Gml1h/q7VKN+PvPUcbDXS5kEcpNSOTnZzveQ2wQioyqLtDNjuNA6VdbEzZdveD5QYGXnjgEzhD4S9u20jDKzvyA1P2/vl7SFPd9NRRFP8eWzPYneaxJ+d6PRy0xR3L8k8sdHFUF/D+n7c4LKdF6tTEOUGCZB5wpVxJQY0AKvxFI+N/7ZNnXde+NGynTM9Wk1Irz/MoaG8M8aVBBvfBoDDtBpjQlm2CgArbxooMUpn9jeV9PFsvuI9zLsCtSwyBMDqkMjGZ/+28f28WxEGSpx8Nn5YRvNA0X6UO/dtTlp9rA22vin6PDbeP2+oZTjNqaePlPZ3FEKkDXB1DLRCTHUjq/JexIOB8rXNU3D5VLZ59Rp8HTY63nWunSfSWbWdl/5mWLr31qyxnIyDRpCVadp494wcHszPtU2Gzupv4qCdLc6dmmeM069t/YE4w9DmMA5LdDZqLpoHmywMA5Grk/B9s43NO2OpLMyh+bwzFoaZXWHbw7y+IkX6Wt55qWKLPC9nXWgP8/iSTfbqc9p41IIZfUYWTxkfzJe9WbQ7LwPEJpc4rP0CcnT0+Fmzngd9ljnz+ATJUJvM7m+GpTypCqrlXfyJgqfEV03jBHRvdLnSycFrv13GhgUKfx6UaKfD+6WeKYcxQDkjrjrWJcWxikJkQ+o9rRDl08GZsMt4dX2qjyu6nq94vngPXNYTyBjjRKdXqjTqUCPsVdMAeeaB+x7G31HvOYf63NJU+Zx5ikrRfPV20pVXZP+uWldIGCiIKuSBny+Xca68k6BG2F35TBJhdxhTFM0rQlFEqAi2Upmq4LRUdB2djkEul7YHb2Jz6E0Lq40vdnznoSyia50riSIDbjQP1JfBNG8dlKWs5s5XosDnocywKQKPDLvMy2myaj8BdbzLXPFn3Wkx32DPA38XF949oMgl93dzBTl4gBrrUpO/FUVMi8DToV30C+ol4iLTAPX9XPYwOy/V+WH4FPAXV7qto8cqNpPDc5ZF2ItA8teWpVaEMAjS2w9c5pyOTVEVz2qDvRGI9OojFyagpuO5LeFADYUhnocM9rrP+fQrmlXSpKqMrW+wV19PMkpdFTBAN9jd99B13HQNG1eDXa2hdVufQHnHOsy8+rv2K9Eyx+cU+d7sIigptTUMm/rOFxeDPVuL2RW7wxNo710Umxl99ZTuOoo2UF5CUTw2/tuNj2b/dhLGdG99zfd0ng/VItc29Le4UuvAm2oqJ8pzVuy4njfe5czp/QEqj2tRuqPb+3J6d6GpOnMR6L1aDnXafD5XXlqWrVZlbDze5Vmdp9LGu/OpujqNi0OKr2HVmz2AaeiV0+iCldb2u57rsB7tpuNdDXa2Hi5jB5LMO9dn5J+vZ7A//QErPtY5uFbD+OYOHlcdgTJOqjZETFFQ2luEUCDNkHCZs7/pHuxwxbPaYA8DgckORdinJxyrom5UsOgatjyENZWFqKYnU01Pr6+cuAm4upGWhjFvVfBr+JwinzWVzKJ06sL5krWZllG6XSLs9RxgSnTG+bzFf0/HmeHi7KmTIcNR95y5RtgHaxgmPCKwPRxRjZrnhX/eZWa1ht1dZLoqRByu79dQItfVz3YrDNI9ryOXXFGWtu0y3mVPylL/8zBYM4rMPz8dY98FZaVQeagbSU4V1RrnvO5+uNKFDienVM3sKOLDLg6poCbvVlPin36nKZ/H3SBiNOFAT7KmAdZq1JPZpF+40rySJeHgdKlDt3X5IDB9R5Q+fxnqOHj499eOsDvrsNk8PsK+AyG+by+/vX8VuG5OWQ1d7jw1FH7Vk+mSEh//PZ20duc0IjZ2sKaAc0t3SrynNZSMvkaY1j3XTeOrw+hc73Ck5cjr1p6HgQo1m7lz1omwc0fPdoiWxZ9PygVc16ZVb23q7mE2l4NzgD1X3ZR4F9pXIuwuWQd10zFrZmQA9SJK/DFdzhldqTOdCLtrBgFfG1tvhjwIIWplV0wnpZag38TgOt7lDNR1RvbXoAtAO6vTcL64gN7Lle8HqUJdL2twOhH2QLjxg+lEhoHtY9TWiZhOJzpLqd/bg34BXkIxjdJJF70U9Uo1W8l6uJ6xNAvTcT4lS8JlDwXR7dOv//L53LPUagae0vnqZYA4R9gTuK4LfzyXOQd8hP3pBW8OUT/t2NFgL2mUlYesSVY9oeakaNZM5ZxOek7diIsawXCItCQ1m67ETCBh6sLMy5rO5aFudLZRM8LePw1DuI4wrlsqwA0SVyEe1FCmgPKO0WXzbY8Ie12lD8jowmW+oZop8bUj7NNIqaxDS2I7R4T4mLoZLkK4pf0D9foXTDdtG6hZw87LFFyiZux81+ldMZ2MI2fFsXb2Qj2+T8/qqsBPr4a9XnTehQ5tqOUwd9wPWg+nmmT2Xq5XQBV1Nc/DUxFhnw5N1OEfdWvYXQ2pujSvlKTVSIl3yjCdRgZPmqI+ncBcjUyVOrovEF+DXQfOWWo1I+xZz4Pp8aYiPLsN9mlEatJxrjXsNY0helanCDv7fhfGTM09ppPW7mxA1ay9DGo6JerWJBKIEddlPnXv43UBHU3ntG8lClWfmVdVquo6epSooHNaXvx33ZT40FHhr0O/yviaacruEfZ4v10U4ia7Z9xJWay5GGqEve75rDe3W0QoRp0sHpJHddMxXe4bJvCzXXm+unsY1OP3BOWmAJe+F2lmVT2jdDoycTq81AW0J67rWtcAm07tZt3o/HQj7C40WaccEWAp8TUa/wF1nK3ujpPprONT0bRsewSSUtm03Wg++7eL06VWk8JpyMLtHZgLapwXPld/zl3xeajbpJDLploRdp8S//RgOinc6biaTMA1PSe92sthrroCP6v9mIax5up1Zevo4pFWu/C6R0zqNJ0DMmFal/m4oE7DQYClfU+rTrs+M686tu4eAtP3gtet768r5OqnxLtHhAC3lDwgS8dzpQtaR6fnfAr6hrgqU3VKizi2R1djIMvccU6jTuaqkz44WCPt+6nYw1oGe01H7WDNzs8Dac+TaaT/1jyrrsgiu/UMduemc9NJia9rsE8ziuUyPn02xylJj5rhkOKsRBQdDZQ6azmddSRamM4tGE6BpORvVzqi81w3G3Y6EXYXPtxL7oKvW0Lhug9UYrC9AnNpDXvN8+lS4gXUb57dUOasvn+D0+CDVeEN9gTbq+lcEMRXxDg3iEgjdE+/97Ts/uAq800n/c/FiFJqGZ1S4qcXYZ/VnxjsNbIkXFF3HAkA15RTpYHNNM5A1bWtmyUB1G94lDWdc1ubVhggEPUdGa7+oayxT700R9coTX9NuqB13C70MA3HYFqaUGtmx4hQ8ncdHtPXqKcMTyfCXmfvn4osiVpd4nlKvEsZTe1oW1IfPA3Dcns1nctuwHA7A0SPzoZNs96axmPr8u/pKcW1rtR15BrU0LhO1+/pZMhsj5KWeCz9vX3MCHKAus5HXeLr337geDaFOm9V0Hlx0X+nY8NkEXbX96unr2UZGfXOp6sDq64dw/muU0r8NJpvVsWz2mCfO9hK/1030lqH4Q20QudDRGk9ZIRVQf2mTvHfrmvC039cmRwnDBdhNas/Y24uToL+aaavzB1qJeOffuGY7X3kNK7Tiz/vGmmZO8T30e2ZlQ76FdemzhhC1iStHj25rg2l4Ts3vknm6/aq0y9QL02V05JrHWTdNMAZfe49HVqOXV8J5CwD3HkUKVEufJSjTmOsOn0y6kaF+igyPw3jyeV5XTv32sbVuqlDOQMuWR0BWqG7w7yuMjZTkU/1eIYraBz1o6iKWWlfFkeeT87rGopqMwzQCETtuuS6cKGPuvK3nfRHcrqtIZnLNZoI1EvJnc46zuyvd17qom5X+jQbsmaE3V23iMe51lzTeXHRSaYjC8kudd0+Rc93GDyrxnnh7+dKE7Tdrg4J0kfDQDjx7H7fJf7pxYFLZqX/rtvEpE4q/WAzdGYeFIUgon46QUzcVZHaY/5g+u+6qc2AWxbBHvOH0n+7PG+d9E+OhTP74jkdntU1pZ1A0cu6e+9qlM7sb7IGIW7rw/ejKmNeMnsg/XfdaxJdzyrtu0u6ImGg1ahPvz23PawTaeXGUN1r3VyVb4rmudGg+9oDwM5z+tN/u/KZ6dKSC0hhqxOBatY0vAeT96vDaersvWsn8nRcjTR4jj0W1OP7QOIwdx5T7xrQwVYDM6lcpGbWiiuGUp7oNo7kt6tOQ7JwqlOPpgZaobMcHqwRgeZwalRYk3eTS9BF/g6lV4lNox+AA8+ok91CWJqcl+lmO1RFXb10acIrXPcvS4l32wuiW9cIO8ElM4ZfG+fs3E0cCx3HIIKi5zuszR7peXEfA7hH2Onzrnx3aaLDUkZHVaRXf/qU+KcHB+2cGezbq0s8EHtiXBkyKWKuTKcO6lwPAqjGmrtyUk9wLGUE7SL0p5sSv3BGrKSMTXVrjXfBYE1nTVpDV2Ntd50bG9GuZ2BpDQdKGIjaDWzSq1dqZsgM1DA4BlpBDUOx3h6mQsdhPu4YmuNwDyzAG0jVez+X56xzLuM56kdn6TmntofB3qhvPBPduRpP9H51nIP9NWrw6vLtOo4yjiWzuNPGPVq+vRpQAcAuCS+te1ZdQU4U1xRu4t2urJQM9q3jbbeBCQaaYe3SFlfUMfqIJ9Z1SLg4Xmjv6hh7dVK4B2s6hYBM39teBntdA4yMvq3jHadxdWm+7v3tdRw8ALDLnHq6Gjn2p7o9p3Hc0e6yF3ReXDKiljLHrGvGQl+Nq5eB7Ly4JuHVuWXFFc9ug51F2OtGWutgsIaHf3tG2IkxugpFroC7rmfd1Mrd5nFvX/XnrZvuRCAlZUtNJcUFtPeuRgZ5COsofrvOrec9353th4sRTQ6COlevBML9rDo6T9U5m43aV8S40m9/KnTqeuvrXVtX12B32fM6aZ+EnWfHBpsrn6mb6VAHxNPqzEVpla51zPR+tSLsNZw1dfl2HUcZB6d3d4W6RkR3GspY5vzcPlHkuldIkkK9ccRNpk1XFg7WKBGsi74awYi6dEwyxoUH13F8EgZrRNink6lAwZLNY0+/DgRk++BqSPEgggvq0vxQzXF0Xlz1tVR3cta562ebpQ5lh7NG52XrRHXHiRph3z42Rd3zMl2bogqe1Qb7vjvNSP+9nZyEABIPf83UX1dvWB3UrbflcL8Tst4G8C6OrnW+QtRvmLIoUVI2O3pt62Ag3ft6RkadtFXy2rrW+nJF3MWIpvncGx6FT6tH04b+GgrmYM09JFqsy5/q3sUduqbEU8pwzUwAV9SNWk4nouSKVs0IBgC0knXsOtbN1k1RB+pFCOru4XRrkDnqXENWt2N0HUOKeJurYly3eVFdZyQp1Ku2jDuNywz2erKwv4Y+VBd9NSLsKR07RtipC7dL6c90Ok3X6RI/nQZZ5OBZtWWi9ne4oC4/3Z0ZfS6oe00XlfvUNdxcyxRIFkaOhJ+tp7teSTzNKSV+AZ2X6vyFMlkBd1nTquloq22wp1m7PsL+tIDXRExHeXTFQKvhzCi3ZyonpWNNJ5q/vTzmHC57KITAYDN07vpLWDDNNEAXpB7+minxdV6RvLart066D64BEgCuUZo6DRyBLPugDt3X6UFBjqW6KfHbIyIMsDTA7RBhnw7ovAw7eOuB6de+uqCvpqIPZNFY1/rCNGuhBs1Pxyh9JrA4SYuvk+3iXMM+jfpEUqif3Lp9DBs6Mc4R9kShXj3sxvNJqZ5WhH078Q3ipy62Td3UYZrDxYmW3hle5077Zr0rC+ti6Xx3A2w6qKsH8cZlLqh75WAr7T/iGGEHZUS6yahdE1m41pFup2WwJzyt46CX0Hl50sHBMx27jGjdVf7OdiwjJAz6CPufJgabYa00PsC9JrUOFs2KBXAdWtkvyVqoeyXN9sRAq1G7FGLBULxG/Y6NMOqA9p53HK6C+Ukn+1bo/ox7LYz3cXtkdADAfjvNBABMtF3rqdzTW4Gsw63r1Ud15ySF37UJ3E4JLU4nhd8FdZsxzuivn8pZB/sm52XcsYcEKVHbI6I3PzFk6kSxKGrpykZJWV/AIhNVMdjcvns4XTwn6UFD1yFVxUArdI7wT6dJ6V4LYl7qotxOB+SUcr0hgvoCuOomdNtO3b4Eg60Gmk9hxkURFielNC78lFLaZzjKX6JBl3cjvYl0MBdsj3ugOaj55/YqJ6X13F7BNcqMcH0/yt7YyXEPiee78qa9F5Gu5sZf5iT8weWuccL+O8W8d8RB/qalQduJ1smRuL0SMAemISMqQ0r5tP0BcAqA+wE8BGCZ5fdzAfwewB0AbgBwMPvdhwDcBeBuAB9mP/8CgCcB3Jb8eUXZcxx55JEyDw+s3SZ/e/MTub/Pw31rtsk/3Pak8zgppbzlsc3ywrvXOo8784bH5OObxpzG3LRys7z4Xre5hifa8ruXPySjKHIaJ6WUa4cn5I+uesR5nJRSXnTPWnnTys3O4x5ct03+5ib3PVx+x2p515NbnccRfnF9nf3YJC+5b53zXD+7bqV8csu405iNI5Py+1c+Umsfe71Ifvfyh+TW8bbz2Do0NdXpyW9d+qCcaHedxt2/dps8uwYdjk915f9e+pDsdHvOY29auVlecq/7Hv78usfkKsc93Dw6VWsPr3loo7zygQ1OY6SUMooi+b0rHpZbxqacxj2+aUyecf1jzvNdePdaectj7jRf97xIKeXvb1klH1y3zWnMnau2ynPvWO00ZqJd/4xtHWvL713xsPO+R1EkT7/6Ublu24TznE9srreHF92zVt5cYw/Pu3ONvP2JLc7jCFvH2vI7l7nLqRse3SQvu3+905gtY1O19kPKmJfWoSkppTz/rjXytse3OI3pdHvyfy99SI5PudPGWbeukveuGXYe99ubn3CmKcJ1D2+Ulzvuh5RSXv3gBnn1g248bv22SXn61Y86jSGaWr9t0mncmq0T8ifXuM/1gysfkRtH3OaSUsrHNo7JX974uPO4OmeM8PtbVsn717rv+22Pb5EXOOrAo5Md+e3LHpK9njsNXvvwRmeal1LKn1y7Uq4dduOlURTJH171iNzguIert47Ln1670mmMlFJ2e5H8zmUPyeEJN12t24vkty97SI5NdZznHJ/qym9d+qBsO8q2393yhHzA8bzcuWqrPOd2N9krpZQjkx35nZrn5eqHNtTiST++plz2ArhJ1rSphXyaQjdCiBDAAwBeCmAVgBsBvFlKeQ/7zH8CGJVSflEIcQCAb0kpXyyEOBjAmQCOBtAGsALA30spHxRCfCEZ8/+qPstRRx0lb7rppqfq1Tw8PDw8PDw8PDw8PDw8KkEIcbOU8qg6Y5/OZIGjATwkpXxEStlGbIC/WvvMQQAuBgAp5X0AlgohdgJwIIDrpJTjUsougMsBvPZpfFYPDw8PDw8PDw8PDw8Pjx0KT6fBvguAJ9j/VyU/47gdwOsAQAhxNIA9AOyKOBX+BCHEfCHEIIBXANiNjfuAEOIOIcQPhRBzbZMLId4rhLhJCHHThg0bnpo38vDw8PDw8PDw8PDw8PDYTqjXJaQabJ0h9Pz7LwP4byHEbQDuBHArgK6U8l4hxH8AuBDAKGLDnrobfBvAl5Lv+hKArwJ4pzGRlKcBOA0AhBAjQoj7p/tCHjsMFgDY+Ew/hMdTBr+ff1rw+/mnBb+ff1rw+/mnBb+ff1rw+/mnA9te7lH3y55Og30V1Kj4rgBW8w9IKbcBeAcAiLj146PJH0gpfwDgB8nv/i35Pkgp19F4IcT3AJxT4Vnur1sz4LHjQQhxk9/PPx34/fzTgt/PPy34/fzTgt/PPy34/fzTgt/PPx081Xv5dKbE3whgXyHEnkKIFoA3AfgD/4AQYk7yOwB4N4ArEiMeQohFyd+7I06bPyP5/xL2Fa9FnD7v4eHh4eHh4eHh4eHh4fEnhactwi6l7AohPgDgfAAhgB9KKe8WQrwv+f13EDeX+4kQogfgHgDvYl/xWyHEfAAdAO+XUm5Jfv4VIcThiFPiVwL4u6frHTw8PDw8PDw8PDw8PDw8nik8nSnxkFKeC+Bc7WffYf++FsC+OWOPz/n539R4lNNqjPHYceH3808Lfj//tOD3808Lfj//tOD3808Lfj//tOD3808HT+lePm33sHt4eHh4eHh4eHh4eHh4eNTH01nD7uHh4eHh4eHh4eHh4eHhURP/Jw12IcRuQohLhRD3CiHuFkJ8KPn5PCHEhUKIB5O/5yY/n598flQI8T/ad10mhLhfCHFb8mfRM/FOz2Y8xfvZEkKcJoR4QAhxnxDi9c/EOz2b8VTtpxBiJqPL24QQG4UQX3+GXutZi6eYPt8shLhTCHGHEGKFEGLBM/FOz2Y8xfv5xmQv7xZCfOWZeJ9nO2rs50uFEDcndHizEOJF7LuOTH7+kBDiG0II2/W8Hk8jnuL9/FchxBNCiNFn6n2e7Xiq9lMIMSiEWJ7otXcLIb78TL7XsxFPMW2uEELcnnzPd4QQYen8/xdT4kXcKX6JlPIWIcRMADcDeA2AtwPYLKX8shBiGYC5UspPCSGGADwXwMEADpZSfoB912UAPi6lvGk7v4ZHgqd4P78IIJRSflYIEQCYJ6X0d1puRzyV+6l9780APiKlvGJ7vIdHjKdqP4UQDcRXex4kpdyYGHjjUsovbPeXehbjKdzP+QBuBXCklHKDEOLHAH4ipbx4+7/Vsxc19vO5ANZJKVcLIQ7+/+3dfbRcVZnn8d9z69aNN+HlEklouBAi6QwsEAh4x0TTPYN0AwrapEFENIPaa7WtvXSJzjADiuILLSg20o69xkX70r4iIJoGAeluhdFmCJqQQESIvBggNywJkEgggbw980edk9Ste96q6pyqU3W/n7Wycuvt1D61z95nP3vvc7ak2919NNjWLyV9SNJy1e4/9CV3v63zezV15ZyfiyQ9Lulhd9+nG/sz1eWVn2Y2XdJCd7/Daqtr/VTSZymfnZNz2dzP3Z8POkV/IOkGd/9+0vf35Ai7uz/l7vcGf2+R9KCkUUlnSvpm8LZvqvZDyt1fdPf/kPRS51OLNDnn519Jujx4326C9c4ronya2XxJsyX9oriUI0qO+WnBvxnBSWo/1QJ4dFCO+XmEpN+6+8bg8b9LYkZTh7WQn6vcPSx3D0h6hZlNCxqj+7n73V4byflW+Bl0Tl75Gby23N2f6mDy0SCv/HT3re5+R/Ce7ZLulXRox3YEeZfN54PnByUNqbbyWaKeDNjrmdlc1Xr/75F0UFg5Bf9nnd7+DatNuf04U8C6q538NLOR4M/PmNm9ZnaDmR1UYHKRIqfyKUnnSbrOe3FKUB9pJz/dfYek90tao2CkXdLXikwvkrVZPh+RdJSZzQ1mTyyRdFhxqUWaFvLzbEmr3P1l1Rqe6+teWx88hy5pMz9RMnnlZ9DWfYtqo+zogjzy0sxul/S0pC2qjbIn6umA3cz2kXSjpAvqeiua9U53P1bSnwb/Wlk2DjnIIT8HVetxvMvdT5R0t6Qv5JhENCGn8hl6u6Rr208VWtVufppZVbWA/QRJh0i6X9LFuSYSmbWbn+6+SbX8vE61mS/rJO3MM43Irtn8NLNjJH1O0t+ET0W8jQ7SLskhP1EieeVn0Dl6rWqXqzxWRFqRLK+8dPfTJB0saZqkkyM+OkHPBuxB4+9GSd919x8GT/8+mNYVXmvwdNp23H08+H+LpO9Jem0xKUaSnPLzWUlbJf0oeHyDpBMLSC5S5FU+g/ceL2nQ3VcWklikyik/F0iSuz8azJS4XtLri0kxkuR4/rzZ3Re6++skrZX0cFFpRrxm89PMDlXtPHm+uz8aPL1eE6fYHiouWemKnPITJZFzfl6j2j0Jri484Zgk77Lp7i9Jukm1afWJejJgD6atf03Sg+5+Vd1LN0l6V/D3uyT9S8p2Bi24S3GQCW+W9Ov8U4wkeeVnEATcLOmk4Kk/k/SbXBOLVHnlZ53zxOh61+SYn+OSjjazWcHjU1S7BgwdlGf5tGBVFavdFfdvJX0139QiTbP5GUynvUXSxe5+V/jmYCrnFjNbFGzzfGWvo5GTvPIT5ZBnfprZZZL2l3RBsalGlLzy0sz2qQvwByWdLumh1O/vxUtCzexPVJuCt0bS7uDpj6p2LcH1kuZIekLSOe7+XPCZdard5GhI0mZJp6p298yfS6pKqqh205yPuPuuDu0KlF9+uvtvzOxwSd+WNCJpo6T3uPsTndoX5JufwWuPSTrd3VMrNOQv5/L5PtXuQr1Dtfr33e7+bMd2Bnnn57WSjg+28WlPucst8tdsfprZJapdilI/G+JUd3/azMYk/bOkYUm3Sfog9w3prJzz8/OS3qHaJUgbJH3VWZWjo/LKT9Xq3idVC+zC66C/7O50knZIjnlpkn6s2lT4iqSfqbYCUuIlZT0ZsAMAAAAA0O96cko8AAAAAAD9joAdAAAAAIASImAHAAAAAKCECNgBAAAAACghAnYAAAAAAEqIgB0AAAAAgBIiYAcAAAAAoIQI2AEAAAAAKCECdgAAAAAASoiAHQAAAACAEiJgBwAAAACghAjYAQAAAAAoIQJ2AAAAAABKiIAdAAAAAIASImAHAAAAAKCECNgBAAAAACghAnYAAAAAAEqIgB0AAAAAgBIiYAcAAAAAoIQI2AEAAAAAKCECdgAAAAAASoiAHQAAAACAEiJgBwAAAACghAjYAQAAAAAoIQJ2AAAAAABKiIAdAAAAAIASImAHAAAAAKCECNgBAAAAACghAnYAAAAAAEqIgB0AAAAAgBIiYAcAAAAAoIQI2AEAAAAAKCECdgAAAAAASoiAHQAAAACAEiJgBwAAAACghAjYAQAAAAAoIQJ2AAAAAABKiIAdAAAAAIASImAHAAAAAKCECNgBAAAAACghAnYAAAAAAEqIgB0AAAAAgBIiYAcAAAAAoIQI2AEAAAAAKCECdgAAAAAASoiAHQAAAACAEiJgBwAAAACghAjYAQAAAAAoIQJ2AAAAAABKiIAdAAAAAIASImAHAAAAAKCECNgBAAAAACghAnYAAAAAAEqIgB0AAAAAgBIiYAcAAAAAoIQGu52ATjjwwAN97ty53U4GAAAAAGCKWbly5TPuPquVz06JgH3u3LlasWJFt5MBAADQUy5ZtkbX3vOkdrmrYqbzFh6my5Ycm+t3LFs1rk/e9IA2b9shSTpgelWXvuUYLTlhNNfvAYBuMbPHW/3slAjYu4mTENAZy1aN68rb12rD5m06ZGRYF552ZGQ5y/o+oEgch+gFlyxbo+8sf2LP413u+s7yJ3T9r57U9l0uqf12zbJV47rwhvu0Y7fveW7T1h268Af3SVJu5WLZqnFd/MP7tW3HbknSgEnvWDinkM6HbpVt6hXkpRMddcjO3D39XT1ubGzMk0bYi6rgGk90oWrFdOVbjy+8Eu10xd3p76MygTS5Uyxkkt65aGJjbNmqcV34g/u0Y9feeq9T5bEsutGJ2MuNyCLSXgsc1mjbjl17nhuuVnT5WcdO2HavdPh2si4uU73fy8d1VkdcfIt2Z2gmDph01dsWZN7/xuA5zujIsO666OTE7WTtqP3IdasV9W1LF6UH7ZcsW6PvLn9C9T9FVHmMel9U2S5Cq+e3qXAc96Ju5Utc7CJJi+fN1Hf/+nWTno8qzzOGKvq7vyz+uO8lZrbS3cda+uxUD9ijKteohn6zkg54Kf0klCQsxOObt6lipl3uGm0ozFE91tUB05XnHD+pQZhHhRDVAM3jd4wT9/sunjdT657dlnl/mt3/XmlATwVxgXqjq8/d24g84dP/qk1bJ78/zMdOnxzrgw+TNH2ooq3bd2X+/laO3wuuWx352uCA6Qvn5N9x0WwjMs9GSuO23nDULP34vqcyld8iO1zjjsP688KyVeP6yPWrI4OlLAFGlDx+28ZzZsWkXS2ksdnAu9lGZLv7mvT5pLTk1UhtJ/15zTZKqi+ijAxXtfrSU1Pfl9Y+qmeSfnfFGZGvZe34kqTFV/xM45u3xX5P/Xmi2fSGn122alwfvm61olrVjWW7iHNN0vlt1Sei8yWqrRiKC87iNLNf9eW/UZEdcc204ZLaGM3+Ns2IO94GJF0VcZzm2Y7NUjYby0pSPVHfkVd0B0QvbJ+APUVcwJ7lZBQGwpKaKuRp2406CWU5GKI6GOqFjaQFn/rXyEpmuDqgBz/zptiKKG6UJy1dSSfDGTFBSDvB79yLbkl9jxTfaRC3/2mBRNyJrdUGdJHSft+0gPeA6VUdffC+Wv7YprZGs+KOn6TvnzY4oM+dfVzssRDVUEsS5k/W46bxc0V45z/drbsefa7l72+2oZW1kXz1uQskKTJvWmmkxDUipcn7FxXcR70vi6zHSNTIYFrehA3gqEbn6Miw5r5yOLbcpG07POd87Edr9OL2+LRPrw5oazCakXY+Cjt442TN12YCLUlaFxFo1Y7b1UoZWN2zT5IyjcTWNyLb6UCOO8eGHd4rHn8u02/QTqdK1P6G52ZJe/LTpEkjvmccd7BuXDmeuu9Zgt1jPvGTxGMwSmOeN9bz9cdtFkmdAHHtjqgBkbS6P+n4mHfxrZGBZWi4OqDLzzpOH75+tZKa1OuuOCNzB2YrwUHSPtbnS5Y6ITR/9gz920dOSn1fM50nWc59oTzPwUmdoFFBaNz5NVTfTsnSns3axk+qX6oD0sOf3ZuXUb9l1OBc0vtDSxfN0ffueSJ1Rk1jmUw6x0t766VmZp5EDUzWazxnRf1uSb9DlKQ8XLZqXP/9hvu0q+HHSWurNiJgTxEXsMcFtc0YMGm3a8IId9rBK+094OsPysaTb+MJJKn3tl7ciEdW4ckuy1TjZir+UFh44yqGaYMDOmfsUN3x0MY9FVt9A7hZJumLDY25qKAgLq1hozHtt69/b1qlHPfbNl5TlzZtMHz/2OEzJ40kXvfLJyedbML337hyfWoDOE54nDbO6ojSbGDdKO5knTZaEmXxvJmZGwn1okZekkYHo06I9aNuWaeC1qe7/sSU9fP1n2sm0KoOSDt3K/ZYj0pP4/EuRQf8UebPnqGt23drQ5CfaaUy6wjmkZfcppd3ZvuN6xsgWX+rpYvmNBW8SrURktZKXbqw0S/trX9eUR1oqZzHlfG0wKVRVCM4rsEcp1qxTHV1dUCpnQChqEZWliBiZLiqLS/tzPwbRO1/Y/A6rVrR5q07tP9wVS++vCNxH0aGq3p55+626tOxw2emls0ZQxX95YmjTR/f0uTAMC3oSVMfzDbWu0n5MDoyHNvYTxLVeZWlo7c6YKn7uXTRHP3o3vHYTpClde2quDbKSMNxUt8GSBooWnfFGU13uNUbbehsb6zz49qBjcFds7M2pIl5kmWGaZyktnnY6RLul0yJnS8hkzR73yH9fsv2pvap9p0TA9ZmOtXTRsMbA3tJOuWqO/Xw0y82nc6kNEjZykdjfBOK6lxrpkMnaduhrJe8xP2W0wZrK6AntSfq2zFJHWx9E7Cb2dclvVnS0+7+6ojXTdI/SDpd0lZJ73b3e9O2GxWwt1NxJWmlEZdV2DnQCVefuyD2ei+pVkD+ePaM3Ap/0eorhSwdKt32iorppXZ6XTqkftQnqmfyUzc/0NZvbZJeP2/mpNHKpFkm3TJ/9gw9+vSLhQRk9Q25Zho77TbSyqxxBKPZk3yjcPQr6+/bbCDQCSPDVW15eeekUYB2hZ0kzTa0GztLe6HuzVM4o01q//jMS1rjtl31AXsrHatRRkeGNX1ooCvtjdGRYT31h20da3vNnz1DGza/1PTMhjRFtk3TOiuyXJbWrgFJ+0+v7un82r5z14QZSGccd3Apz4Nm0hfftiDzzB2puf0ZDQZw2hmkiTJcHdDOXbszd5LGCQcAi27TNQbtabOV2xV3+UI/Bez/RdILkr4VE7CfLumDqgXsCyX9g7svTNtuY8Derw3YPLU7Sl9GQxXTjGmDU6rB2K9mDFVyb9D0o/k91KnWbVefuyB1GvpU1k6HcXh5TRkC1nYVHfD2svprpVsZSUUxplcH9NLO3R3rdIhLQzOXQgC9rrHDWmovYC/Vsm7u/nMzm5vwljNVC+Zd0nIzGzGzg939qWa+59p7nmwnmVNCvwXrkrR9l2s7wXpfIKjKhmA9O4KLZO009jdt3dEXwbpEsJ5m2apxSdLFP1zT5ZQgVIZAuQxpADrJtbcezOPmd6UK2DMYlVQfba8PnpsUsJvZeyW9V5LmzJkz4bWyTWME0LzBAdPObg4ZAJiSBlRrjFH7TLRp6462r1kHgH6xbccuXXn72lwC9oEc0tNJFvFc5JnB3a9x9zF3H5s1a9aE1yoWtRkAvSQuWK/2Wq0GoKfs1t6b8i1dNGdK1TnD1QGNDFdjX281WJ8+lX5EAFPGhhzu4yH1XsC+XtJhdY8PlbSh2Y2ct/Cw9DcB6DmL583Uw589Q0sXzUl/M+i8BNowvnmbblw5rivPWdDtpHTM5Wcdp9WXnhq5ZF87DpgxTQNURyixg/Yd6nYS0IMOGRnOZTu9FrDfJOl8q1kk6Q/NXr8uSZctOVZLF82ZMFw/Y6iixfNmRg7hozX9+ltOGxzQ1ecuICjMWR7Hy/LHNklSYWunF2nxvJkabbFib2V0qlqxKdV5STBQTovnzUwcsW00Y6jSVF5Orw40XT6qFctcv4dTHlstu2UwnPH3WbpozoSpnXmWqQ2bt+kdC/vnnFodsD3LQfWSpYvm6IDp2ctjXtZdcYbWXXFGacvR0kVzdM/HTunob2Mqth09VClm64vnzVSlgBPuUMU6NhMnS+qzpMSkPUvdtqtU17Cb2bWSTpJ0oJmtl3SppKokuftXJN2q2h3iH1FtWbf3tPpdly05NrJRX7++ZNLEruqANHu/4cxLllTM9Ef7vyKXJU4ahWtQRq1v27j0USjrfraicf34LHflT7uD6OJ5M7Xu2W0Tfr+KmRYdcYB+uW7ThPVKB6x2l/tm7nHSuORD1Dqjjb9h+LjdFQfmz56hdc9sTZxK2LjOaF4rHYTr8qatox0eR80sPRIlXI7kx/c9NWnbUvs3/qq/P0XWZbcaR4paWSu6XeE6s62sWx+mv5nlckzas7ZxHkubVMx04D7VSWvRNt5RO8td668+d0Hsmr6taKyPpOaW2Fq6aI6+d88TmY6HxjXsw+XQospNuL70isefm7C29HkLD5uQ1ryWxqp3wPSqXt6xK5cbQVXMtNtdhwTLB31v+ROZlzYcGa5OWvM6KW/CejrLur/S5LKdJd/rz5m33P9UpjK1YfM2ffHcBbneuDDrUmb165Q3WrZqPHF51vDzl591XGrao86RedaRh4wM67Ilx+p3G1/oyA0KqxWb0G6oDpiqFculTIzWnUfzOCbC82bj0lx5rlhQX0+OHT4zdh14Kf9VhOpnel142pGZ7oOQx13n95tW0fMvx59ro+rjS99yTOI5Oq88CdsERa1qNTJc1Sf/4phMv3XWtlT977Vs1fiENmQ7ouKYZavGI4/RZs8RUcI6VYovv+H3XLJszZ7zd6OwTOVx/bpUsmXdihK1DnsWR3/8ttgKoX5dyT/+6K2pN78KA6PGgh6uM73u2W0TAqb6RlyS+pN1liAzyqsuuiW1ggk7y+J20yT9LmGKXFLhDfchbs3ukeGqVl96auK2o/Y7a4FtbIg0qxZk3T/hRDpjqKIFh+2v//foc5N+27AibtxG4+8zYNI7FsanLaqiaAzsw21feMPqCR0YUWloRlzDNzyeH9iwZcK+xHUcNYpKazMqZnr08tMlZevUiDu2ovIjqrMhyYBJ7tEn76RjrvF4nvvK4dgG7OjIsO666OQ9n8vSOKwMmP7+nOMzd/7Edeq0ehLKEpBFdVwMVys6cU50mZL2lpesac0SvIXlJGtdcnXEuquhVuvnWplo/kZecevAJm03XMu4mY6fxmVrourDKMPVii4/69hMedNYX2XpxKivCxo1lu+kju0sZSosh802rrNMK086TrPUq0nn3vrPp/2mjWnNsyOp8VhIanslfT5ru2nxvJk6Z2xObHlMOw+Fdc0dD21MLc/NHhNhwBd1Lm8Ud57KsoTiQfsOabBSiU1/WF81DpKEAVlcoBLXBkgS1RmUpT0UlU8H7TukZ17YoV3uMtXal9uDoC4u8Kuvr9LaXXG/jbT3uErqJBsdGdbGLS/tSVOjqO8/5ao7Yzvuwk7hZoLj6oDpynP2xg1J9fVwtaKzXzOaegzPGKrogU+/cdLz7a533kxbqf4YzjqAMX/2DG3csj32XNDM8ZHlHN8367AXpdWAPW6kLapySTqp17+/lUZbXGGdNjigz519XNu9N2mN0foR0LiC3UzQG/cbxDXS4xp1adIKbJYTYrtabaRPdY2/2xuOmpWp0dN4HCY1lqKCjVbTGtVoCstMlsAgy3dkKRtHXHxL4qhX3PfHBQXtduzEacyXqFHwpLKTV7lKOj7qj6UswclwdUAPfuZNTachi2ZmT0jNdZCl/cZJ+51W78f9vu3WvVk6MdrtiA1lCbjqO2oaf9NnXnhZL++cfM6s72xL04nzyLJV4/rwdasjG9ZRac3S0Z9FGHBM7mSOz9/FEYMccbMI4wLOLKKCjVbq8LQ24ujIcO55e8wnfhK7/GleZSNN1Hm8fpZAluC4F6V1CEYd3/VBdJT6DpKo47iZjt2ojuW0AaC0OKGZzuo3HDVLdzy0UeObt+0ZvR+tez6PshAXvyXNSOoEAvYUrQbsUvYTZVTF3k6wGbX9pMLajrjGaFyDosi05NkwiZoy0+3CitaljXokNUIay2dUI7HsspSNuOAiS+A9lTuX0vY97VKFtMZWu5KCo/ASgqKDuTw6nvJOU1wHct4dTWlBe9JIed4d0UVqph3T6gj7gNVm6qW1HeoD7vpGfa/WS0kBT9438JN667ibioo432bpZG2mo7BeUqdxUR377SrjeYuAPUU7AXszerXB288Ve6/mCeIV2WHUD/h9ilFfl+w/XJWZtHnrjo7UK812qk4lnarj40YsD5he1apPxF+y1ck05iFrWpNG5KNEzaKZSuICnizHT6t66bhDvvIeRIy7ZryswXpZEbCn6FTA3suo2AGgnPq5U7VXMGNrsqzXZ/fyyHheOH7QaXm364kT2kfAnoKAHQDQy2gsdR95MFna/RXoWNqL4weY2gjYUxCwAwAA5CvLnfS5dAMA2gvYO7MCPQAAAPrKkhNGNb2a3JTckNPybwAwVRGwAwAAoCXTqpXE1w8ZGe5QSgCgPxGwAwAAoCWbE65hl6Q3HDWrQykBgP5EwA4AAICWpI2g3/HQxg6lBAD6EwE7AAAAWnLhaUdqOGFaPNewA0B7CNgBAADQkiUnjOrys45VxSzyda5hB4D2ELADAACgZUtOGNXfv+34SSPtw9WKLjztyC6lCgD6w2C3EwAAAIDetuSEUUnSlbev1YbN23TIyLAuPO3IPc8DAFpDwA4AAIC2LTlhlAAdAHLGlHgAAAAAAEoo9xF2M5uZ9Lq7P5f3dwIAAAAA0G+KmBK/UpJLMklzJG0K/h6R9ISkVxXwnQAAAAAA9JXcp8S7+6vc/QhJt0t6i7sf6O6vlPRmST/M+/sAAAAAAOhHRV7D/p/d/dbwgbvfJum/Fvh9AAAAAAD0jSLvEv+MmV0i6TuqTZFfKunZAr8PAAAAAIC+UeQI+3mSZkn6kaRlkmYHzwEAAAAAgBSFjbAHd4P/UFHbBwAAAACgnxWxrNvV7n6Bmd2s2lT4Cdz9L/L+TgAAAAAA+k0RI+zfDv7/QgHbBgAAAABgSsg9YHf3lcH//zd8zswOkHSYu9+f9/cBAAAAANCPCrvpnJndaWb7mdlMSfdJ+oaZXVXU9wEAAAAA0E+KvEv8/u7+vKSzJH3D3V8j6c/TPmRmbzSztWb2iJldFPH6SWb2BzNbHfz7RAFpBwAAAACgq4pch33QzA6W9DZJH8vyATOrSPpHSadIWi/pV2Z2k7v/puGtv3D3N+eaWgAAAAAASqTIEfZPS7pd0qPu/iszO0LSwymfea2kR9z9MXffLun7ks4sMI0AAAAAAJRSYQG7u9/g7se5+/uDx4+5+9kpHxuV9GTd4/XBc41eZ2b3mdltZnZMTkkGAAAAAKA0irzp3H8ys5+a2a+Dx8eZ2SVpH4t4rnEt93slHe7ux0v635KWxXz/e81shZmt2LhxY5OpBwAAAACgu4qcEv9Pki6WtEOSgiXd3p7ymfWSDqt7fKikDfVvcPfn3f2F4O9bJVXN7MDGDbn7Ne4+5u5js2bNan0vAAAAAADogiID9unu/suG53amfOZXkuab2avMbEi1AP+m+jeY2R+ZmQV/v1a1fXg2pzQDAAAAAFAKRd4l/hkzm6dgSruZvVXSU0kfcPedZvYB1W5WV5H0dXd/wMzeF7z+FUlvlfR+M9spaZukt7t747R5AAAAAAB6mhUV6wZ3hb9G0uslbZL0O0nvdPfHC/nCBGNjY75ixYpOfy0AAAAAYIozs5XuPtbKZwsbYXf3xyT9uZnNUG3a+jZJ50rqeMAOAAAAAECvyf0adjPbz8wuNrMvm9kpkrZKepekRyS9Le/vAwAAAACgHxUxwv5t1abA3y3pryX9T0lDkpa4++oCvg8AAAAAgL5TRMB+hLsfK0lm9lVJz0ia4+5bCvguAAAAAAD6UhHLuu0I/3D3XZJ+R7AOAAAAAEBzihhhP97Mng/+NknDwWOT5O6+XwHfCQAAAABAX8k9YHf3St7bBAAAAABgqiliSjwAAAAAAGgTATsAAAAAACVEwA4AAAAAQAkRsAMAAAAAUEIE7AAAAAAAlBABOwAAAAAAJUTADgAAAABACRGwAwAAAABQQgTsAAAAAACUEAE7AAAAAAAlRMAOAAAAAEAJEbADAAAAAFBCBOwAAAAAAJQQATsAAAAAACVEwA4AAAAAQAkRsAMAAAAAUEIE7AAAAAAAlBABOwAAAAAAJUTADgAAAABACRGwAwAAAABQQqUL2M3sjWa21sweMbOLIl43M/tS8Pr9ZnZiN9IJAAAAAECRShWwm1lF0j9KepOkoyWdZ2ZHN7ztTZLmB//eK+n/dDSRAAAAAAB0QKkCdkmvlfSIuz/m7tslfV/SmQ3vOVPSt7xmuaQRMzu40wkFAAAAAKBIg91OQINRSU/WPV4vaWGG94xKeipuo2vXrtVJJ52UUxIBAAAAAChe2QJ2i3jOW3iPzOy9qk2Z17Rp09pPGQAAAAAAHVS2gH29pMPqHh8qaUML75G7XyPpGkkaGxvzO++8M9eEAgAAAACQxixqzDmbsl3D/itJ883sVWY2JOntkm5qeM9Nks4P7ha/SNIf3D12OjwAAAAAAL2oVCPs7r7TzD4g6XZJFUlfd/cHzOx9wetfkXSrpNMlPSJpq6T3dCu9AAAAAAAUxdwnXf7dd8xsi6S13U4HcnOgpGe6nQjkhvzsL+RnfyE/+wv52V/Iz/5CfvaPqLw83N1ntbKxUo2wF2itu491OxHIh5mtID/7B/nZX8jP/kJ+9hfys7+Qn/2F/Owfeedl2a5hBwAAAAAAImAHAAAAAKCUpkrAfk23E4BckZ/9hfzsL+RnfyE/+wv52V/Iz/5CfvaPXPNyStx0DgAAAACAXjNVRtgBAAAAAOgpPRmwm9lhZnaHmT1oZg+Y2YeC52ea2b+Z2cPB/wcEz78yeP8LZvblhm3daWZrzWx18G92N/ZpKss5P4fM7Boz+62ZPWRmZ3djn6ayvPLTzPatK5erzewZM7u6S7s1ZeVcPs8zszVmdr+Z/cTMDuzGPk1lOefnuUFePmBmn+/G/kx1LeTnKWa2MiiHK83s5LptvSZ4/hEz+5KZWbf2a6rKOT//zsyeNLMXurU/U11e+Wlm083slqBd+4CZXdHN/ZqKci6bPzGz+4LtfMXMKqnf34tT4s3sYEkHu/u9ZravpJWSlkh6t6Tn3P0KM7tI0gHu/r/MbIakEyS9WtKr3f0Dddu6U9L/cPcVHd4NBHLOz09Jqrj7JWY2IGmmu7OmZQflmZ8N210p6cPu/vNO7Adq8spPMxuUtEHS0e7+TBDgbXX3T3Z8p6awHPPzlZJWSXqNu280s29K+pa7/7TzezV1tZCfJ0j6vbtvMLNXS7rd3UeDbf1S0ockLZd0q6Qvufttnd+rqSvn/Fwk6XFJD7v7Pt3Yn6kur/w0s+mSFrr7HWY2JOmnkj5L+eycnMvmfu7+fNAp+gNJN7j795O+vydH2N39KXe/N/h7i6QHJY1KOlPSN4O3fVO1H1Lu/qK7/4eklzqfWqTJOT//StLlwft2E6x3XhHl08zmS5ot6RfFpRxRcsxPC/7NCE5S+6kWwKODcszPIyT91t03Bo//XRIzmjqshfxc5e5huXtA0ivMbFrQGN3P3e/22kjOt8LPoHPyys/gteXu/lQHk48GeeWnu2919zuC92yXdK+kQzu2I8i7bD4fPD8oaUhS6uh5Twbs9cxsrmq9//dIOiisnIL/s05v/4bVptx+nClg3dVOfprZSPDnZ8zsXjO7wcwOKjC5SJFT+ZSk8yRd5704JaiPtJOf7r5D0vslrVEw0i7pa0WmF8naLJ+PSDrKzOYGsyeWSDqsuNQiTQv5ebakVe7+smoNz/V1r60PnkOXtJmfKJm88jNo675FtVF2dEEeeWlmt0t6WtIW1UbZE/V0wG5m+0i6UdIFdb0VzXqnux8r6U+Df/8tr/ShOTnk56BqPY53ufuJku6W9IUck4gm5FQ+Q2+XdG37qUKr2s1PM6uqFrCfIOkQSfdLujjXRCKzdvPT3Teplp/XqTbzZZ2knXmmEdk1m59mdoykz0n6m/CpiLfRQdolOeQnSiSv/Aw6R69V7XKVx4pIK5LllZfufpqkgyVNk3RyxEcn6NmAPWj83Sjpu+7+w+Dp3wfTusJrDZ5O2467jwf/b5H0PUmvLSbFSJJTfj4raaukHwWPb5B0YgHJRYq8ymfw3uMlDbr7ykISi1Q55ecCSXL3R4OZEtdLen0xKUaSHM+fN7v7Qnd/naS1kh4uKs2I12x+mtmhqp0nz3f3R4On12viFNtDxSUrXZFTfqIkcs7Pa1S7J8HVhScck+RdNt39JUk3qTatPlFPBuzBtPWvSXrQ3a+qe+kmSe8K/n6XpH9J2c6gBXcpDjLhzZJ+nX+KkSSv/AyCgJslnRQ89WeSfpNrYpEqr/ysc54YXe+aHPNzXNLRZjYreHyKateAoYPyLJ8WrKpitbvi/q2kr+abWqRpNj+D6bS3SLrY3e8K3xxM5dxiZouCbZ6v7HU0cpJXfqIc8sxPM7tM0v6SLig21YiSV16a2T51Af6gpNMlPZT6/b14SaiZ/YlqU/DWSNodPP1R1a4luF7SHElPSDrH3Z8LPrNOtZscDUnaLOlU1e6e+XNJVUkV1W6a8xF339WhXYHyy093/42ZHS7p25JGJG2U9B53f6JT+4J88zN47TFJp7t7aoWG/OVcPt+n2l2od6hW/77b3Z/t2M4g7/y8VtLxwTY+7Sl3uUX+ms1PM7tEtUtR6mdDnOruT5vZmKR/ljQs6TZJH+S+IZ2Vc35+XtI7VLsEaYOkrzqrcnRUXvmpWt37pGqBXXgd9JfdnU7SDskxL03Sj1WbCl+R9DPVVkBKvKSsJwN2AAAAAAD6XU9OiQcAAAAAoN8RsAMAAAAAUEIE7AAAAAAAlBABOwAAAAAAJUTADgAAAABACRGwAwAAAABQQgTsAAAAAACUEAE7AAAAAAAl9P8BXlIwpHbd3dAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "result = seasonal_decompose(data, model='multiplicative',period = 30)\n",
    "fig = plt.figure()  \n",
    "fig = result.plot()  \n",
    "fig.set_size_inches(16, 9)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28b7626a",
   "metadata": {},
   "source": [
    "For time series analysis we separate Trend and Seasonality from the time series."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26a7d5e7",
   "metadata": {},
   "source": [
    "## Logarithmic model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5b9c484e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 10, 6\n",
    "df_log = np.log(data)\n",
    "moving_avg = df_log.rolling(12).mean()\n",
    "std_dev = df_log.rolling(12).std()\n",
    "plt.legend(loc='best')\n",
    "plt.title('Moving Average')\n",
    "plt.plot(std_dev, color =\"black\", label = \"Standard Deviation\")\n",
    "plt.plot(moving_avg, color=\"red\", label = \"Mean\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "291e9104",
   "metadata": {},
   "source": [
    "## Train & Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f8810bda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1986a31f970>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train=data[(data['Close'].index.get_level_values(0)>='2015-01-01')&(data['Close'].index.get_level_values(0)<='2020-01-01')]\n",
    "test=data[(data['Close'].index.get_level_values(0)>'2020-01-01')]\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.grid(True)\n",
    "plt.xlabel('Dates')\n",
    "plt.ylabel('Closing Prices')\n",
    "plt.plot(train, 'green', label='Train data')\n",
    "plt.plot(test, 'blue', label='Test data')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "61a39e0d",
   "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>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>439.781311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>438.617340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>433.812897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>414.124512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>423.139069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-26</th>\n",
       "      <td>1501.170410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-27</th>\n",
       "      <td>1527.867310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30</th>\n",
       "      <td>1529.699951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>1499.833130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>1495.424927</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1230 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Close\n",
       "Date                   \n",
       "2015-01-01   439.781311\n",
       "2015-01-02   438.617340\n",
       "2015-01-05   433.812897\n",
       "2015-01-06   414.124512\n",
       "2015-01-07   423.139069\n",
       "...                 ...\n",
       "2019-12-26  1501.170410\n",
       "2019-12-27  1527.867310\n",
       "2019-12-30  1529.699951\n",
       "2019-12-31  1499.833130\n",
       "2020-01-01  1495.424927\n",
       "\n",
       "[1230 rows x 1 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8fe754cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(757, 1)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58591686",
   "metadata": {},
   "source": [
    "## Shift Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "840e7609",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Close First Difference'] = data['Close'] - data['Close'].shift(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "eeea8a40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2015-01-01            NaN\n",
       "2015-01-02            NaN\n",
       "2015-01-05            NaN\n",
       "2015-01-06            NaN\n",
       "2015-01-07            NaN\n",
       "                 ...     \n",
       "2023-01-10    2502.199951\n",
       "2023-01-11    2524.050049\n",
       "2023-01-12    2544.699951\n",
       "2023-01-13    2544.449951\n",
       "2023-01-16    2543.300049\n",
       "Name: Close, Length: 1987, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Close'].shift(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a9c7f8dc",
   "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>Close</th>\n",
       "      <th>Close First Difference</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>439.781311</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>438.617340</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>433.812897</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>414.124512</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>423.139069</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>417.071564</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-09</th>\n",
       "      <td>426.110901</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-12</th>\n",
       "      <td>421.157837</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-13</th>\n",
       "      <td>417.616425</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-14</th>\n",
       "      <td>413.554901</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-15</th>\n",
       "      <td>428.067352</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-16</th>\n",
       "      <td>430.766754</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-19</th>\n",
       "      <td>435.695038</td>\n",
       "      <td>-4.086273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-20</th>\n",
       "      <td>447.285187</td>\n",
       "      <td>8.667847</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Close  Close First Difference\n",
       "Date                                          \n",
       "2015-01-01  439.781311                     NaN\n",
       "2015-01-02  438.617340                     NaN\n",
       "2015-01-05  433.812897                     NaN\n",
       "2015-01-06  414.124512                     NaN\n",
       "2015-01-07  423.139069                     NaN\n",
       "2015-01-08  417.071564                     NaN\n",
       "2015-01-09  426.110901                     NaN\n",
       "2015-01-12  421.157837                     NaN\n",
       "2015-01-13  417.616425                     NaN\n",
       "2015-01-14  413.554901                     NaN\n",
       "2015-01-15  428.067352                     NaN\n",
       "2015-01-16  430.766754                     NaN\n",
       "2015-01-19  435.695038               -4.086273\n",
       "2015-01-20  447.285187                8.667847"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b15cf4e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Test Statistic : -7.598178327937587\n",
      "p-value : 2.428791769219177e-11\n",
      "#Lags Used : 26\n",
      "Number of Observations Used : 1948\n",
      "strong evidence against the null hypothesis(Ho), reject the null hypothesis. Data has no unit root and is stationary\n"
     ]
    }
   ],
   "source": [
    "## Again test dickey fuller test\n",
    "adfuller_test(data['Close First Difference'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb5a24df",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a756064",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a02338c3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "085075cb",
   "metadata": {},
   "source": [
    "##  ARIMA Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "159be0f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # For non-seasonal data\n",
    "# #p=1, d=1, q=0 or 1\n",
    "from statsmodels.tsa.arima.model import ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b302e242",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    }
   ],
   "source": [
    "model=ARIMA(data['Close'],order=(1,1,1))\n",
    "model_fit=model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7f714a1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Close</td>      <th>  No. Observations:  </th>   <td>1987</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARIMA(1, 1, 1)</td>  <th>  Log Likelihood     </th> <td>-9376.755</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Mon, 16 Jan 2023</td> <th>  AIC                </th> <td>18759.511</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>18:17:26</td>     <th>  BIC                </th> <td>18776.292</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th> <td>18765.675</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td> - 1987</td>     <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>   -0.5099</td> <td>    2.159</td> <td>   -0.236</td> <td> 0.813</td> <td>   -4.741</td> <td>    3.721</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>    0.5140</td> <td>    2.151</td> <td>    0.239</td> <td> 0.811</td> <td>   -3.702</td> <td>    4.730</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>  739.4427</td> <td>   11.222</td> <td>   65.892</td> <td> 0.000</td> <td>  717.448</td> <td>  761.437</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.56</td>  <th>  Jarque-Bera (JB):  </th> <td>4028.58</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.45</td>  <th>  Prob(JB):          </th>  <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>22.35</td> <th>  Skew:              </th>  <td>0.02</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td>  <th>  Kurtosis:          </th>  <td>9.98</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                  Close   No. Observations:                 1987\n",
       "Model:                 ARIMA(1, 1, 1)   Log Likelihood               -9376.755\n",
       "Date:                Mon, 16 Jan 2023   AIC                          18759.511\n",
       "Time:                        18:17:26   BIC                          18776.292\n",
       "Sample:                             0   HQIC                         18765.675\n",
       "                               - 1987                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1         -0.5099      2.159     -0.236      0.813      -4.741       3.721\n",
       "ma.L1          0.5140      2.151      0.239      0.811      -3.702       4.730\n",
       "sigma2       739.4427     11.222     65.892      0.000     717.448     761.437\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.56   Jarque-Bera (JB):              4028.58\n",
       "Prob(Q):                              0.45   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):              22.35   Skew:                             0.02\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                         9.98\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f058ac05",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1b97603c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['forecast']=model_fit.predict(start=1620,end=1980,dynamic=True)\n",
    "data[['Close','forecast']].plot(figsize=(18,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fcaeb37",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ab72a866",
   "metadata": {},
   "source": [
    "## SARIMAX Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4832a244",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aMnbsqyfL7QTM93zlYOaNLgrvD2WYff5Ai0mHqUQBs4iIiEiEUAb3P1ceDjhZUGstVz/8MW+s3t0tY9hY5kzAGzugKWAOZYtDNb+RbeMyXE0hXW5GU4YZoLy2gQff3cxht73O2t1NEwNDvMGyjoC1ZLldDO+fw2/PmxXe/9UH41/t0FrLW2vLorat21NDVnAsgwuzGFWcE94XCphvf3UtF933AUu27Iv7tbqTAmYRERGRCK5gdnl0cKENnz9Agy/Ac8tK+dqDi7tlDOU1XtJdhn456eFtoQUHY630549YjTAnM7qnw7xbX+flFbsA2LE/uoYYwOsPBcxEvd4vvjAt4XF7GgOU1zS02J7lbgo5Q+UZAPVe57VD/Z6rPI0Jv2Z3UMAsIiIifZ61TQGnK1jyEMraev02anJao7/r+whX1DZQlJsRVRoSHl/kpL/guAIR4w81nYhc0fv9jRUA4dKISFURC6RMHJQffhxaHjxS5H2KpXlNclFwOezIa6VFvKcd++sprawPB/KZrtQMTVNzVCIiIiLdyBcRXYYyzOlu5/8+fyBqsY3dVbFrgTtTtcdHQVZ6zH2RK/01+i2N/kBUwNwv2wlSAzGC28z0lqFfWXVTRnjykKaAOTITHOJt58NC864XJXlObXVuRNY7ciLi9n11HH7bG+ESFH87AXmyKGAWERGRPs8b0Z4tKxhUpgeznY3+QFTmtNHf9UGdp9Efzso+8NVDovZFZphDxwYsfHneSF757jGMjKgRbi4jRga3IeK9TR4cETDHyDC315M6dJ9OnjqIv10yNzzWyFULMyLKMyI/qDjvJTVXAVTALCIiIn1eZMAcKoMIBXnNSzK8MXofdzZPYyAcuB8/aSDHTWpa9S9cw5zhCh/rD1gyXCaqpKIwO3aGurmGiPcTqtuG6ID558F65tqGttvUhTqKnHvwcE6aOoiVpc4CMP9btjN8zHdPnMiX540AYGezmuoGX2q2mVPALCIiIn2atZaN5U5JwJmzhoa3G2NIdxka/QHqvB2rYV63u5pL7/+w3UCzOY/PH1X3O6xfdvhxZFs5CGWYLWnNVsy74JARLa4bq0wjMmAeGvE6WRElGaHykOYZ4ebe37gXaOrQEfLTz08NPy7Oy+TWs2YAtOjaoQyziIiISAp6+MOtnHu3s7T0l5oFmemuNHz+APvrvOFt7dXxRvrGPxfz1toyVu+qav/gCE6GuSnoPGXa4PDjyIVLwCmDCARs1GQ6wFkeu1lZRax4tzqiM0Xka0aeGwrS2/uwcMvzzuIvgwuyAKecZP7kgZw1e1jUcWlpBmOaJiOGpOrKfwqYRUREpE+LXDBjRP/o+t86r5+/vbMpqnQg3pKMQMCyZW8dAL4E657rvb6o4PWYiSX87MxpHDa2iMxgDXAooN2xr55arz8q8A354amTosdkm9cM+6MyzJFC189wp0XVc7cmtLQ4OP2WwSknuf+yQ2J2+4g1v6+1sSSbAmYRERHp0yIzs0P6ZUXtC5UWvLW2KaiOtyRjQ1lN+HFdAqsF+vwBdu73MLQweiyXHjGaRy8/PFx6EQpof/XiagD+92lpi2tdMG9k1PPmbeEi+x43z0aHJuz1y04PTxZsa8Lj6tKmLHp+Kx0+Ypk4qGlxFmWYRURERFJQekTniPRmXST+/Y1DAdhWURfeFm/AXBqxFHWsBUNaU+Xx4fUHGNIsYG4uVGO8bZ8ztoEFmS2Oaf5+mpdkVNU7tdXXnTCBhT88Pmrf4MIs5o0u4k8XzImrJGNNsB55ZFHrXTpiCbXBg+iOHamk3YDZGDPCGLPAGLPKGLPCGHNdcPvNxpgdxpilwf9OizjnR8aY9caYNcaYUyK2H2yMWR7cd4eJlZ8XERER6UaxWq2F5AX7B2+NCJjjLcnYW9vU3/jTbfvjHk8oKE13tx2mRU76g6b+0ZFcaYbIuYCBZhFzZb2TYZ49sh/FedEBd7orjcevPJzDxxXHVZJRE1wA5fXrj21z3M2FFjeBnl2S4QOut9ZOAQ4DrjLGhKY6/sFaOzv43wsAwX0XANOAU4G7jDGhHP/dwOXAhOB/p3beWxERERFJXGgp51iBXihgrm/0h2uHvXHWIzd0sONDKCBvnh1uLlTj3E7jiqjrtMwwOwFzey3oQtf461sboyZARmoMXtydllg+9Ly5w8OPe2xJhrW21Fr7cfBxNbAKGNbGKV8AHrXWNlhrNwHrgXnGmCFAgbX2PesU0PwTOOtA34CIiIjIgdhd7eH4SSWMK8lrsS9yhbpBwc4PjXFmQUMBZH6mG08CmdNQ67a2Mt/QsuY4Vss4iA5gQzXM/oDlwr+9H+6PnBfxPmNJD5ZkvL22jD+8urbF/tLKery+AO40E3OCX1tOmDKIV757DAPyMlM2w9z23WnGGDMamAN8ABwJXG2MuQRYjJOF3ocTTL8fcdr24LbG4OPm20VERESSZndVA9OHFsbclxvRT3jWiH5srajD6w9grW03MAwF1rmZ7oRqc0NlD6G64dY0D5hbW1a6NmLCYeiIV1fu4t0Ne8Pbs9wtV/WLFNlXOS8rOnxcs6uaU/74NpnutHbHHGlsSS5ThhQAMHFQPjkZrp6bYQ4xxuQBTwLfsdZW4ZRXjANmA6XA7aFDY5xu29ge67UuN8YsNsYsLisri3eIIiIiIgkpraynrLqB4ryMmPvdEVneH58+BYAXlpcy5kcvsH5PdcxzQnwBJ/DNy3InlDmNuyQjI3q/P45SkVAWunnP5sz0tl9rXEked3x5DgCutOhjF29xeik3+AKkp8XfT+KN64/jLxceFH6elZ7WsxcuMcak4wTLD1lrnwKw1u621vqttQHgb8C84OHbgciu38OBncHtw2Nsb8Fae6+1dq61dm5JSUmsQ0REREQO2E1PLQdg2fbKdo/NDmZZ31nntJi75flVfLJ1X6vHh1qw5WW6E1ry+YXlTnu49koyMlxpURP6WsswRwrVMDcvwchsZ4KhMYYzZw0lL9Mds99ziCuBDHNzmW5Xz10aO9jJ4u/AKmvt7yO2D4k47Gzgs+DjZ4ELjDGZxpgxOJP7PrTWlgLVxpjDgte8BPhvJ70PERERkYTNGdkfgCuOGdfqMd8/eSL3XHxwiwD2zTVlnH3Xu62eFyqtyHSn8f7GihY9kGPZU+3hrjc3AO2XZBhjohY3mTu6qN3rhzLMzSf/ZbZTkhGSle5qM1u+v671YLr9a6duhjmeGuYjgYuB5caYpcFtNwFfNsbMximr2AxcAWCtXWGMeRxYidNh4yprbejjwreAB4Fs4MXgfyIiIiJJkR+sx50+rKDVY66ePwFwJsrF65lPdvDG6j240kw48PX6A+0GppGdNUzMatZo2emu8KIot583q93jQ0F78/ZwGe1kmEMy3Wktun90tBtIc1nprgMKuLtSuwGztXYhseuPX2jjnFuBW2NsXwxMT2SAIiIiIl0l3PO4nfIHaFr5rjmvL9Ai4PzOY0sBJ2s6f/IgFq3fS73X327A7IsIykvyWy5E0lwow1ycmxGVbW5NsKw6KmB2p5lW31tzmelpLcomPJ1URjF9WCH3vLWBeq8/XP6SKrTSn4iIiPRZoTrjeALm1kz88YtRC5NEZqLT09LCHSbiWR7bFwxkf3n2DCYNzm/3+FBgmdZGwPvElYfz63NnAE0lGZFLXMdT+xyS4UprUZLRWRnmWcMLCVhY185kymRQwCwiIiJ9VlNHivgyrOcdPDzm9vc3NrVoi2yNlpXhCgfM9XG0TAtlmPvntL2QSEiotVxbi4XMHV3EjGH9gKba5VAHD4AE4mUy010tVjrsrAzz5MFOWczqUgXMIiIiIkm1sayGz3Y4XTG8/gDprvgX28htZYGPyCAystyhODcjHNTWN8sw/+x/K/jfp9ENw3zBzK87zox3VrAdXPM2cc2Fur3ZGBnmRGS6W5ZkdFaGeWRRDtnpLlbtquqU63UmBcwiIiLSp8y//S3O+PNCwFlcJJFyjNZWxKtu8IUfRwbPA/IyyclwzmlekvHAos1c88gnUdtCmd94FwAJ1S23d3wooF4e/KDQfNJfvLLSXdQHA+TaBh+eRj8NPj8FWW6GFGZF9VVOVFqa4atHjmbm8NiLyCRTQiv9iYiIiPQWLywvZfGWfW2WMzTXWoZ5b403/Diyxveq48eT4XauX+f1tTivuVBJRrxjCrW6c7WXYQ7uvuvNDRw9oaTD3SgKstxsr6gDYNpPX2bCwDxmDCukMCedd26Y36FrRrrh1MkHfI2uoIBZRERE+qRvP/QxAEMKs+I+Jy8zdveGvbUN4ceR2dtDxxSxepdTkxtZktG8DjgktCiIO84V85YGJxtmttMhI7Lk5Mt/e5+hCbznSIXZ6VTWN4ZLO9btqWHCoLy4+zj3VCrJEBERkT6jeR0xtF5mEUteVvsZZm9EwJyWZmJO+ntzzZ7w48gFTb724GIg/pKM0DX/8KW2ezA3v9rOSk+4bV1+K+8pllDA/NmOpjrjmgZ/uJa6QwIBePIbsOKZjl+jiynDLCIiIn3GripPi22tBcGx5Ga0FjA3ZZhD2eNTpg0CiNlWblVEJ4jyGi8l+ZkEItrRxVuSEWph1y87o83jYk0KnD9pIN8/ZVJCJSn9ctLxBWxUwL9udzVD+2XHfY0wa6G6FB48Ayo2wIhDE79GN1GGWURERPqM0sr6Ftvys+Jr4QatZ6N3Vnr4y4L17Kr08PV/OFniCw8dBTT1So7Mbo8qzgk/Xr+nhrLqBjbtrQ1vi7ckIxQw57ZSKhIyuDCLI8cXR22bOaKQkvxM+ue2HWxHKsx27tXOyqYPHqWVHgoS+NCBtU42+fdTnP8qNsC0c+CQb8R/jW6mDLOIiIj0GbtjZJiH9Yu/njdy0t9hY4t4f2NF+PlvX17DJ1v3U1btZJtL8pySh1BbucgMc2Sd8+OLt/H0Jzs4ccqg8LbMOEscvnTICB76YGurme+QrHQX//raoYy9qWmh5rmjiuJ6jUihgPmRD7dGbR8ST4Y54Ic9q+Dt38LKZyCrHxx+NZRMhoMuTngs3UkBs4iIiPQZpcHM6NQhBawsdepwDx83IO7zI8s3jp5QEhUwg9NqLWRYMIh0u9LIcKWxp9rDgjV7OH7SwKjVAJ/+ZAcAr63aHd6WFeckup9/YTo/Om1Kmyv9hTQ/ZlxJblyvEamwldKPdicR7vwE/vNV2Lepadv1ayC9Y5MPu5sCZhEREekzajw+0l2G0QNyWFlaxazhhZw5a2jc50eWZAwqaBnsRa6gV5DddGx2houHPtjKQx9sZXBBVriWujg3g7213hbXiXcSnSvNJDRp8WtHjuH+RU7QGu/iKJFaK/0YXBgjw2ytk0le9xos/TfkFMPxP4bsfjD1Cz0mWAYFzCIiItKH+AMWd1oaBcG65c/NGJLQ+blRAXNmi/0fbd4XfhzZyi0v001lvdMyLnLi4ZB+WTED5vbaxHXU/31+Kg++u4lAxxb6a7XeO9RxI6xyBzz3XVj3svM8PQfOexDGHNOxF04yBcwiIiLSZ/gCFneaCa/u1y87/gl/ADkRgWysDHNrpg8rYMf+lhMOi3JbBt0Qf4a5I9698QSqPB1buGTMgNhlHCV5meCphCUPwvbFsOpZZ8eJP4PxJ0DxhB6VUW5OAbOIiIj0Gf6AxeUyfOfECdQ3+jll2uCEzo+sAx4QnNQ3eXA+voBl/Z6apuOalRS3FlxnumMHxhkdKJeI1+DCLAZ3cOGSSFcdP46/LNgAwNQ9z8O9VzbtzC6Cw74NR33ngF8nFShgFhERkT7DFwjgMobivEx+d17bi320pyg3g1vOms5xk0r4RrCV3KjiHLbsreP6kydFHds/J/ZkueYB8+jiHG45a0ZUOUeq+uLUHAa98wDnp78DzwTLTC56AiaclNyBdQEFzCIiItJn+AMWVwILdcQyf/JA3ljtLNzxlcOcXssZwcB3ZFEOb1x/XIsMc3Fe7IA5o1nAPLRfNkdNiL9rR7cLBBhldjHZbGPM3y9kjBv8I4+CojFwxDVQMqn9a/RACphFRESkz/AHa5gPxN1fOSg8gS8kVBOd4UqLGZC3lmFuPpYDDea7VEMNPHIBb2W+07TtC3/BNecryRtTN1HALCIiIn2GL1jDfCAy3S4G5kd3sQgtTpLeSu1xazXMzQPkWEtYJ932xbDmBXjndgCe9R/OZjuIyy69goIJRyR5cN1DS2OLiIj0Ep5GP+t2Vyd7GCkt1Faus4V6LjcvsQg5ZHR/fnPuTE4NTjI8Z84w1t7yuRYBckplmD2V8PglcN8J4WCZI67l2sZr+L3vfFwj5yV3fN1IGWYREZFeYP2eGk78/VsArLnlVDLjXCmur/F1Qg1zLKElo1sLmI0xnH/ICAqy3by0YherdlWT4W5ZvpH0DLO18Mm/YPXzsPYlZ9u4+XDKbZBVAAVDudqu4c4F68nqol7RqUgBs4iISC/wlfs+CD+u9vjIzOs7wUwi/H6LqwuC0tCS0a2VZIQcNaEEcFrRQcsAuQu7ybWvYiO8cAOsf9V5blww/kS48DGIGOf1J0/k+pMn9ohOHp1FAbOIiEgv4PH5w483l9eGewRLtK7OMAfaWUIvL9PN2z84ngH5ToBtbfTx3V6SUbsX3rwNNrwBFU5PZeb/GI78rhMkp7X84NWXAuUQ1TCLiIj0AqGMJcDVD3+SxJGktoC1uA9w0l8soYDZ6w+0e+zI4hxyMpycZZ3XH7Wv20oyPFWwdwP8diwseQDcWTDmWDj7HjjmB+ByxwyW+yplmEVERHoBQ1OgNWFQXhJHktp8AdslQWluphNcxhMwR6prdALmDHcaXl+gezLMH90HL/0I/F7n+em3w8GXdf3r9mAKmEVERHqBgLUcOqaIHfvrW5Rj3PfORvbVefnBKZOTNLrU4fH6wy3gOlNoxT6vL7GA2RPMMA8qyGRbRX3XBcx1FbD1fXjhB1C13ckmD5wKM86D4Qd3zWv2IgqYRUREeoGAtaS70ijOy2RvrTdq3y3PrwJQwAxUN/gY1i+7068bmuyXaMAcKskYlJ/Ftor6Th8XAO/+GRbcBo21zvMxx8DFT6vkIgEKmEVERHqBgHXqX4tzM9hd5Un2cFJSIGBZVVrFpC4oWZk4yKkh/9z0wQmdVx8sySjJd/4q0JBgwN2qqlJY/HdY/QLsWQEDJsLcr0PRWBh/goLlBClgFhER6QUC1mIMFOdmsHJnVbKHk5LufsvpArFpb12nX3tEUQ6rfn4qWemJ9VOo90YHzJ5mkwA7ZMMCePj8phplgAsehgETDvzafZQCZhERkV4gEGyX5pRkNGCtxRjDgtV7kj20lPHehr1A4mUT8crOSDxrW9foA6AkWHce2R4wIY0eeO/P8OljsHeds+0rT8GIeZCZ3/a50i4FzCIiIr1AqCSjKDedRr+lpsFHaaWHrz74UbKH1qlW7Kxk/MC8Dq1kOLDACUp/cvqUzh5Whx00sj/bKuoZ1t+pq65PNMMc8IO3Fn41wnlePAFGHw1HXOuUXkinUMAsIiLSC/iD7dKyg/19X/psF/vrGpM8qs61fk8Np9+xkCuOGcuPTks86PX6AowuzuGI8QO6YHQd8+tzZ3LV8ePZW+OUT3gaE8h+f/g3eOvXUFvmPD/iWjjxZ5CmZTY6mwJmERGRXiBgLWmmqb3ZD55YluQRdb7Vu5za7Cc/3s4F80YyZkBuQufvqW5gYEFWVwytw7LSXUwclM/yxkoA6ry+tk+o2ASbF8Ibv4Ca3TDiUGf56qFzYN7lUUtYS+dRwCwiItIL2GBJRihg7o127nfarpXXeDn37nf5+CcnxX2utZYPN1Vw0tRBXTW8AzJqQA4As0f0i33A3g3wzu2w7HEIBP9yMPMC+MJfnFX5pEvpDouIiPQCfutM+vP5bcz97u5YQa4LXfbAh7y5piz8vKJZr+n2rCx1stMfb9nXqePqLAVZ6bz5/ePC3TIA51PQZ0/C2pdg+X+cbeNPhEOvhNwSGDo7KWPtixQwi4iI9AKhtnKxuiwcPWEAC9eXJ2FUnScyWAZnZbxEVNU7pQ43daD2ubuMjiwxqd4Fz30X1rzgPHdnw/n/gImnJGdwfZwCZhERkV4gEJz0F6vLQqY7DWudiYFdtvRyFyqrbmixLRQAx6vK45QxTB6Swi3WvHXwwd2w6R3YuMDZdtxNcPBlkN0f3BlJHV5f1nsLnURERPqQgAVXmuGMmUPD2y4/ZizZ6a7w5LhGf9f0H46lsr6RPZ204uAht74W9Xz8wDzqG/00JNCzuKreCZgLstI7ZUydylqnNdxtw+H1n0PZGsgugmN+AMf9EPIHKVhOMmWYRUREeoFQScbgwqYuED88dTI3nTaF+xduAqChMUBWevcsiTz/d2+yt9bL5l+d3qnXvXb+ePrnZvCz/62krsEfdz/mKo+TkU6pgLmuAt78lbOEdSCYMT/hp3D095I7LmlBAbOIiEgvEAhYXM1aioXKL3IznaCy1uujMKd7Asa9wUl5tzy3kptOm0JaJ5WCpKWZcJDsTSBjHsow52WlQOhTWw77t8LfjneejzgUCobBrC/DxJOTOzaJKQW+akRERORAhVb6iyW0mEldoqvIdYL7Fm7ignkjGD+wc2qHXcaQEWydl8gS11WeRvIz3cmv4X7z1/DO78Af7PJx5HfgpJ8ldUjSPgXMIiIiKe7xxdsYXJDFMRNLWj3Gb22rC7zlZjgZ2TdW72b8wLyuGGKbtuytO6CAuX9OOvuCqxZmpqeFA+aGRALmeh8F2Ukqx9j2EWz/ELa8C6ufg4mnwsjDoXg8TO7ckhXpGgqYRUREUtwNwVX72qoHttaGM8wf3HQCGa6m6HlEkbMoxl1vbuDyY8Z14Uhjq6w/sCW667x+inMz2Fvr5fCxA9gRXMAkngzzsu37OfPORQBMHtzNHTICAXjkS7D+NbDBsY46Cs79O2R2/wcX6TgFzCIiIr1AZEnGoGbLP08clM+X543kmU92dNt4jho/gK0VdWytqDuggLnO66PBF+Ca+aM5f+4IBhZkUV7jtJmLp0vGyyt2hR93W4Z512fw4T2weRFUbIDRR8Mpv4SsAug/unvGIJ1KAbOIiEgK8wdir9wX67i2ynOHFmZR3+in0R8g3dX1XWX9AcuAvAy2VtQl3DM50qbyWgDGluQxMPhBIJEa5saIlQ+jVtHrKksehP9d5zx2Z0G/kXDJfyGte7qTSNdQwCwiIpLClm5rWsr5thdWccOpk2NOXPP6AuFAMpZQd4zK+kYG5HV94OgPWDLdLnIzXOFFQzoitAR25JjDAbM/wL/e38LBI/szdWhBzPM3BwNugKPHD+jwONrkrYXnvgfLHnWemzT43mqnf7L0Clq4REREJIU9saSpjOKetzcy7qYXYh7X6G87YM4PtlOr9nQ82xuvP7y6lg83V+BKMxRkp4dbunVEKDtdGFFOkRl8nxW1Xn7yzGdcdN/7rZ6/KSJgPnZS65MmO6SmzFmV7/YpsPxxGH8izLwArl+jYLmXUYZZREQkhYXqdSN5Gv1kpbv41/tbGJifyUlTBuEL2DZLLfIynYCz+gCyvfH60+vrAKcPdEFW+gHVMIfOLchuCllCHww+2bofcOq3m6vz+thb42V/fSMXHDKCH58xlbzMTgp7vHXw4g3w6SNNC45c+DhMPKVzri8pRwGziIhICou1nPXht71OflY6WyvqAHjgq4cAxJVhrumGDHOIO80wqDCLnZX1Hb5GqJwjMsMc6gCyZItTrjKiKLvFeV978CPe31hBfqab7AzXgQfL1sLGBbDlPVj0R6eP8uijYfRRTf9Jr6WAWUREJIXFmti2r64x3JcY4KsPfAQQ1UquuVDAXNWNAbMrzTCiKIclmyuw1mJaWVilLZX1jbjTDNkRS3qHPhis2V0dfJ2mSYDb9tUxriSP9zdWAM7qhjkZBzjhrnI7vHYzLP9P07biCXDZcwd2XekxFDCLiIiksFgZ5ta0mWEOlmSEJtF1B1eaYVxJLrVeP7urGhhcmNX+Sc1U1TdSmJ0eFWw375JRWee8p5v/t4KHP9jKkh+fGD42YKGhMf57GOb3OQHy5oXw6cNOH+V5V8CM8yA9G0omJX5N6bE06U9ERCSFef3RBbpfOWxkq8fGk2G+6enlnTOwOLjSDGNLnAU6NpbVdOgalfWNLfonZ7qiM8b76hr57mNLefiDrQCU10R/KHj2052JvWijB/58EDxzJSz9txMsn/RzOO03MOIQGDwdXElaNVCSQhlmERGRFNa8JOOiQ0fx7/e3xjw2nhrmzlLb4CM3Rl2wtU0BfoYrjbEluQBsKK/liA60davytFzSOjM9rdkxjTwdsSjL7ipP1P5fnzuz/RdqrIf374YP7wV/I9SVw+FXO4EyhlbXHZc+QQGziIhICmtekjGqOKfVY9ua2OZ2pTFlSAGJVxG3tLGshvm3v8Vvzp3J+YeMiNoX2bFi1oh+DC7IIsOdxvZ9dR16rcr6RgqaBfuZ7jTSTNNr2WZdMkKTIUOmDYvdoxlwssmNdfDb8WD9UDIFBk2FSafBtHMUKAugkgwREZGU1jxgjrVoSUh+VttlAmMH5OKJYznp9qzb45RXvLJyd4t9vkDTeI+ZWIIxzoS93ZUePt66r8Xx7alr8LX4IGCMYfxAp9QjVuY8svcyQG5GKx8kFj8Af5gKvxnjBMszL4BvvQtfvB9mfFHBsoTpK0FERCSFeX2BqJZqacbw7NVHxjy2vbKL3EwXtQ1Ol4w6r4/RNz7P88tKEx5TKKPbvOnFwnXlnH9P0yIio4PZ8Aafn2eW7uScu96NKtmIhz9gY35IOHvOcAAuPXx01PaxJbms3xNdLx3ZYYO9G2Dpw/DSTfDcd6BonDOZ77TfwVl3K0iWmFSSISIiksIa/QHys9zhBTxcxjBzeD/Omj2UZ5ZGT2Zrq1wDIDfTTW2Dk2EOlS388bW1nD5zSIKjcoLe5mHsV/7+QfjxZUeMDne28ER0qfD6A2S642/z5rexA+avHTWao8YPoDEQ4M4F6wH4/fmzeHXlbt5eWxZ1bFqacaL8126GD/4KvmCN88jD4YKHIaco7vFI36SAWUREJIV5fQFK8rMAZ/GPUFb3jxfM4Y8XzMFay7/e38KcEf3bLcnIy3RT6/VhraXR5wS9ba0O2J622iqPGZAbc7vXl2DAHLC4YrxQptvFjOGF4e4bR40fwDkHDWf5jkpqvc6HgksOH8WZOZ/BSz+CPSth45swcCp87teQNwgGTGz7TYgEKWAWERFJYV5/gMKIZaGbL/5hjOGSZmUJrcnNdGMt1Df68QZro9vqrNGa1qoqjGna11qtdYMvQH4CrxUIWCdD3IoxA3K5/bxZnDxtEAD9sjMAyKKB6/fdQuEnL0afcPmb4M5MYAQiCphFRERSWqPfBjPMBy43uOLdvW9v5NAxxUDbvZtb4wuESjKaAtnHP9oWFUinu2IHubFWLmyL38bOMIcYYzj34OHh56MC23g14wdMSNsBm4GB0+Cbrztt47L7K6MsHaLKdhERkRTlD1j8AcvA/M7JiIb6Jv/xtXUsXO/U+XYkw+xpdEoeImPPG55cFnVMVnrssouGRAPmAG1mmJvLzs1lhx3A0sBYArMvgivedlbmyylSsCwdpgyziIhIigq1lCvKzeiU60UuNLJmVzUAw/tnJ3ydUMDcViCb3UrAnGiGOWAtiSTB63KGcUXjDwHYfNbpCb2WSGuUYRYREUlRoTrjzA5kgWOJ7Ge8ZrcTMPfLSTwYr29sv5dz6xnmxPpAtzbprzUF7Ux8FOkIBcwiIiIpKpSN7ayAOdK2Cqfrhj/QMuP70mel7K/ztnpuvTd4TkTNcr+c6EA1OyN2wPy1Bz/ikwQWMGlv0l9z8ycP5OBR/fnFF6bFfY5IexQwi4iIpKhQScaBtH6LNHd0/xivEd3yYk+1hyv//THf+vfHrV6nrtFZ/CSyHjk3w81JUwcRim2zWmkdV17j5ey73m2xfdbPXuFrD37UYnt7k/6aM8bw5LeO4OI4O4eIxKPd70BjzAhjzAJjzCpjzApjzHXB7UXGmFeNMeuC/+8fcc6PjDHrjTFrjDGnRGw/2BizPLjvDtO8N46IiIiEdUav5Eix+h/7A9EBcyirvW5PdavX8QT7HHsiSjN8gQDFuRmELtc849xcoNnrVtY38sbqPTHH19Zy4CLdIZ7vQB9wvbV2CnAYcJUxZipwI/C6tXYC8HrwOcF9FwDTgFOBu4wxoe/Qu4HLgQnB/07txPciIiLSq3j9TkDakU4W8fI1C1xDQXBoZcFYQjXMkbXMzQPbknY6ezy/vLRFsB5LwCZWkiHSFdr9DrTWllprPw4+rgZWAcOALwD/CB72D+Cs4OMvAI9aaxustZuA9cA8Y8wQoMBa+551FpL/Z8Q5IiIi0ow3IsP89LeP4Nfnzjjga/7r6/Oinj/y4Va27K0NPw/VJzf6LYGAZf2eambe/DLvbdjbdExwqet6b1PA3Oi3pLvSeOzyw7jsiNFRk/5evO5ovn7UmKjXveaRT3h15W4AbIyVULZV1HHan96h0Z9YSYZIV0joI6sxZjQwB/gAGGStLQUnqAYGBg8bBmyLOG17cNuw4OPm22O9zuXGmMXGmMVlZWWxDhEREen1IrtkzBnZny8dMvKArzmksGUbuR8/81n4cZ3XF378zNId3Pjkcqo8Pr78t/ep9jhZ5/oYJRmhDPOhY4u5+czoCXdThhRw2RGjW7xuaaUz8bCmwddi39G/WcDK0iogsT7MIl0h7oDZGJMHPAl8x1pb1dahMbbZNra33GjtvdbaudbauSUlJfEOUUREpFfp7El/EHtlv8Wb9+ELvlZkmcX3Hv+UxVuaOlpcev+HQFOgHHlsoz+Au5XV/QAKsp2a5vMiVuULJZY3l9e1OWZlmCXZ4voONMak4wTLD1lrnwpu3h0ssyD4/1Cl/nZgRMTpw4Gdwe3DY2wXERGRGBp9oYC58wLGdHfLa9U3+imt9DiPva33Sf54634AaoNZ6OY1zO42MsGF2em8c8Px/PrcmQwucJb6rvY419kX0cIuVra5Ez8viHRIPF0yDPB3YJW19vcRu54FLg0+vhT4b8T2C4wxmcaYMTiT+z4Mlm1UG2MOC17zkohzREREpJmGYNa3Myf9RWari3IzyEp3nu+pDgbMMRYluf6kiZw/dzgFWc7CJxW1ToAbCq6ttfgCFnda2+McUZRDWprhle8dA0BVsMQjcvW/0v31WGuJjL1d7VxXpKvF8xV4JHAxMN8YszT432nAr4CTjDHrgJOCz7HWrgAeB1YCLwFXWWtD333fAu7DmQi4AXixM9+MiIhIb9KUYe6agPmGUybx2OWHA7CqtBprLRvKagD4RsQkvax0F4MKsqhp8GGtpby6AXD6MAcCNhxAF2bHt8peQVY6gwoywzXRkf2ct++vd64bUbQ5ZkBuB96pSOdxt3eAtXYhseuPAU5o5ZxbgVtjbF8MTE9kgCIiIn1VaFGRzswwR9Ywp7vSwnXHP37mMx79aCuf7XCmKV174gQe/nArdV4/Ge40cjPdBCw89MFWar1++ueks6+uEY/Pz+a9Tg3y6AE5cY8jPys9XJIRap8HsHN/fYuyjBnDCzv2ZkU6SbsBs4iIiCRHuA9zp2aYm3Jg6e60qIxzKFgGyEl3kZvpDgfMoZ7JoY4aI4py2FdXSb3Xz9YKpy3dqOL4M8H5We6mgDkiw1xW3cDO/U73jN98cSajinIY1q9lZw+R7qSiIBERkRQVXumvEzPM7ogAOcNlWp2o53alkZfpDh6X1iLLPby/E8TWN/rZXF6HMU3b4lGQlc66PdXsqvREBcyexgCrgu3k5o0u4tCxxXFfU6SrKGAWERFJUaE+zJ2ZYY6U7kqLWR/9v6uPinrdnAxXi4C5ONdZya/e62fL3lqGFmbHXHq7NflZbnZXNXDYba9H1TA3+PysKq0mN8PFyKL4SzxEupJKMkRERFLQyX94izpv55dkRIqsYQ55/IrDwzXDh48rZs3uaiYPKWDZ9v1Rx00ZUgDAvrpGXl25m9kj+yX02pHBdW2D8z5zMlw8sGgzADOGFWrBEkkZCphFRERSjD9gWbu7Jvw8Vu/kzpDuSmvRCm7WiKYJdj85YyrnzR3OmAG5rC5tqm/+4amTmTAoD4BvP7SEWq+fgqz4OmSE7K1tCD9et6eakvxMyqqbtoU6b4ikApVkiIiIpJiq+sao512VYc5wm6hJgAu+f1xU5teVZpg21AmgI0s3BuZnhuuby2ucwPawBGuNI9vGvb9xLyOa1T+X5GcmdD2RrqSAWUREJMVErnw3fmAeri4qTXBKMppCgbb6HUeWbrhdhvyspj9SD+uXzUWHjkzota1tipjLa7wM758TXkQFiLq+SLLpq1FERCTF7KtryjBffNgonAVyO59TkhHftUOZZoCGxgD5mU0lGGNLcqMC73hccMhI3llXHn6en+Vm4Q/nYy3c9sIqvnvSxISuJ9KVlGEWERFJMfsi6nfjXT2vIxIJmEvyM7nimLEAlNU0kBeRAc5Oj787RsjpM4ew6bbTws+PHD+AAXmZlORn8vsvzWaEOmRIClGGWUREJMVElmQ072LRmTJcabjSDJcdMZrPzxrS7vGnTB/MPW9v5OBR/XGlGXIyXNR5/eRmdiycMMbw768fSqM/wPGTB3boGiLdQQGziIhIitkfUZLRvItFZ0p3G4wx3HzmtLiOP2hkfzb88rRwTXV+lrMSYL+cjmfBj5owoMPninQXlWSIiIikmMgMc0YXtZQDYi5a0p7ICYihjhpFORmdNiaRVKSAWUREJMXUNPjCj7s0w3yA7ep8wZUI++cqYJbeTQGziIhIivFGLBXd1TXMB8Lrd1rDFStgll5OAbOIiEiKiQyYDzQL3Jb0AwzGG5Vhlj5CAbOIiEgK2VZRx1Of7Ag/78qA+UAXRAmVZBQpYJZeTgGziIhICvnuY0ujnsfbJzkR1580kTTDAS+I0hgsyVDALL2dAmYREZEUEjnhryDLzZDCrE5/jWtOmMDG204/4OucOn0wAP26cHEVkVSgPswiIiIpxB+w4cfLbj4liSNp32/Pm8mPTpuc8LLYIj2NvsJFRERSSGTAnOoy3S6GFGYnexgiXU4Bs4iISArx254TMIv0FQqYRUREUojPr4BZJNUoYBYREUkhAWWYRVKOAmYREZEU0qgMs0jKUcAsIiKSQmqDbeX+e9WRSR6JiIQoYBYREUkRXl+A+kY/1580kVkj+iV7OCISpIBZREQkRYSyy3lZWiZBJJUoYBYREUkRoVX+cjMVMIukEn1HioiIJFkguFhJrTeYYVbALJJSlGEWERFJssn/9xLffujjcEmGMswiqUUBs4iISJJ5fQFeWrGLak8wYM5wJXlEIhJJAbOIiEiKuP2VtYAyzCKpRgGziIhIili+oxJQDbNIqlHALCIikmKUYRZJLQqYRUREUkxupmqYRVKJAmYREZEksta22JbpVsAskkoUMIuIiCSRP9iD+bQZg5M8EhFpjQJmERGRJPIHM8zThhYmeSQi0hrNKhAREUmiUIbZlWZ44dqj2V3tSfKIRKQ5BcwiIiJJFA6YjWHq0AKmUpDkEYlIcyrJEBERSaLIDLOIpCYFzCIiIkmkgFkk9SlgFhERSaLQpD8FzCKpSwGziIhIEinDLJL6FDCLiIgkUeSkPxFJTQqYRUREksjrCwCQ4davZJFUpe9OERGRJPI0OgFzVrp+JYukKn13ioiIJJHH5wcgM92V5JGISGsUMIuIiCSRp9EJmLPcCphFUpUCZhERkSSqqm8EVJIhksr03SkiIpJEP//fSgCyVJIhkrIUMIuIiCRRdYMPgAkD85I8EhFpjQJmERGRJGnw+anz+rlm/njcLv1KFklV+u4UEZE+42f/W8HfF25K9jDCNpfX4Q9Yxiu7LJLS3MkegIiISHd5YNFmAL5+1JjkDiRo/Z4aAAXMIilOAbOIiEg3s9by7Yc+5uOt+wAoys1I8ohEpC0qyRARkV6r3uvn1ZW7W2yvDU60S5b6Rj8vfraL3VUNAGSoflkkpek7VEREeiV/wDLj5pf55j8X8+6Gcs748zvhfZvKa6OO/d+nO3l/495uG1u91x/1PMOtX8ciqUzfoSIi0is9sWQbvoAF4O43N/DZjqrwvuYB8zWPfMIF977fJePw+gKMvvF5Hv5ga3hbfaMCZpGeRN+hIiLSK22MCIrfWVcetW9rRV23jWPx5goA/v3+lvA2T2Mg6hiVZIikNn2HiohIr3TPWxtb3Rdajro7vLWuDIB5Y4rC2zzNMszGmG4bj4gkTl0yRESk16nztpzUd9Xx4xhUkMXvX11LlSf2pD9rbacHr7sqPQBkpjflqGqSPOlQRBKjgFlERHqdS/7+YdTz8QPz+MEpkwGnF/O2ijp27q9nc3ktd725IXxcrddPXmbn/mr0+pzyC7/fhret2VXdqa8hIl1LAbOIiPQ6i7c4/Y3f+9F8stNdGJqyxvVePwvXl3PMbxZw3twRLFzfVN+8t6ah0wPmhlDAbJsC5j3VHlxphpU/P4X0NFVHiqS6dr9LjTH3G2P2GGM+i9h2szFmhzFmafC/0yL2/cgYs94Ys8YYc0rE9oONMcuD++4wKtgSEZEuNqQwm345GRTmpIe3eXxO/bAvYHnkw61Rx0dOzOss4QxzoClgrqhtpH9OBpluF2lp+nUokuri+Vj7IHBqjO1/sNbODv73AoAxZipwATAteM5dxhhX8Pi7gcuBCcH/Yl1TRETkgKW7DOfPHR5zX3a6K+Z2gOeWlXb6WEIBsy8iYN5X66UoN721U0QkxbQbMFtr3wYq4rzeF4BHrbUN1tpNwHpgnjFmCFBgrX3PWmuBfwJndXDMIiIi7SrOy4y5PauVgPnYiSWUVnqoqPV26jga/C1rmCtqvfTP0XLYIj3FgRROXW2MWRYs2egf3DYM2BZxzPbgtmHBx823i4iIdCp/wNLot2S2shjIzOGFLbZ99P9O5LoTJwDwTrANXGcJZZhXljYtnFJR56UoVwGzSE/R0YD5bmAcMBsoBW4Pbo9ViGXb2B6TMeZyY8xiY8zisrLO/cElIiK9WyhAbS2T/KtzZnLlseOitpXkZzJreD/656Tz1tro3zvLtu+nytPxvs0NwZrp5Tsqw9v21Xrpr4BZpMfoUMBsrd1trfVbawPA34B5wV3bgRERhw4Hdga3D4+xvbXr32utnWutnVtSUtKRIYqISB8VClBbyzBnZ7i48XOT+eOXZgNw4aEjAXClGSYOymd7RX342EDAcuadi7j0/g9jXSouoQA+8pr76rwUK2AW6TE61DvHGDPEWhuaGXE2EOqg8SzwsDHm98BQnMl9H1pr/caYamPMYcAHwCXAnw9s6CIiIi2Flp3OdLc+uQ/grDnDyMlwcdSEAeFtORkuymuaaphDHTU+2bq/w+NpHjBXeRoJWFTDLNKDtBswG2MeAY4DBhhjtgM/BY4zxszGKavYDFwBYK1dYYx5HFgJ+ICrrLWh9T+/hdNxIxt4MfifiIhIp2ovwxzp5GmDo55nZ7ioDy5b3egPMOtnrxzweLz+6ID5lRW7AeiXoy4ZIj1FuwGztfbLMTb/vY3jbwVujbF9MTA9odGJiIi0o9rTyI+eWs7/fX4qA/OzwguFRC5FHa91u2tYv6eG2gYf++q8NPpbnW4Tt8gMcyBgueHJZQDhwFxEUp+WFxIRkR7tv0t38tyyUu54fR27Kj2c/Ie3AchqpyQjlnV7agBYvauKrXvrOmV8DREB8+It+zhxyiAAPjd9SKdcX0S6ngJmERHp0dzBlfLqGvzc/eb68PaOZJi/euRoAIwxLN2+P2pfZV1inTJKK+s5886FUSv8/b+nl1OQ5WZ4/2y1lRPpQRQwi4hIj1ZZ7wSyT32yg3+817S0deTKevE6JVjT/OLyUu5fuJncDBfnHew0edpSUZvQtf6zeDvLtldGbVu3p4aymgbys1S/LNKTKGAWEZEeray6ocW2K44Zy+FjixO+Vk6GU8bxt3c2UV7TQFFeBl8/egwAmxMs0RjWLzvm9nfWlZOf1aEmVSKSJAqYRUSkRyuraWBoYVbUth+dNqXVhUvaEgqYQ4pyMxlZlAPA1r2JZZj9tinD/f2TJ0bty89UwCzSkyhgFhGRHmtXpYf/Lt3JkFayuYlqHmQX52aQk+EmP8sd1Z85Hp6ILhjnzx3B6TObJvmVVnoObKAi0q0UMIuISI/1f/911s1ypZnw5L8DkZMRnfmdPrQAcDLCNQ2+hK715JLt4ccF2ekEgjXVA/IyuPaECQc4UhHpTgqYRUSkxxrW38ks/+6Ls/jsZ6dgDBQcQH1wZEnGiVMGct2JTilFbqabGk/8AXNFrZdPgxP+Vv38VLLSXeFJiLecNZ1Tpw9u63QRSTEqohIRkR5p0fpyHli0GYCRxU6d8Wc3n8KBLDUSuTrgsRNLcAWz1nlZiWWYX1mxC4DfnDuT7GAQ/pXDRvHqyt3MHtH/AEYoIsmgDLOIiMRtW0Ud//t0Z7KHAcAdr69rsS03003eAUyoM6aprCM7ojwjL8GSjI3ltWS60zhv7vDwtmMnlrD5V6czuNkERRFJfcowi4hI3M6+axHlNV6OnzzwgALTzuDp4qWlcyPKM/Kz3AlN1Gto9JOd4YoKwEWk51KGWUREWgjEWPSjrLoh3Cli7e7q7h5SC6EFSzrboIJMgHApBQQzzAnUMDf4AlHlHSLSs+m7WUREouyp9jDtpy+Huzw0+Pys3lXFIbe+Fj5mza7eGzAPDbaoS3c1/YrMz0pnf7035geJWJyAOfE+0CKSmhQwi4hIlE+27qe+0c/fF24C4KK/fcCpf3wHAHeaIcOdlvSA2VpLlcfHFw8ezhvXH9up1z56QgkQPQFw0qB8PI0BtlbEt9pfg8+vDLNIL6IaZhERAeC2F1dRnJuBpzEAwIB8pzRh8ZZ94WNOnT6YT7bup8rTNdndeNV6/fgDlgkD8xhbktep177uhAkcNraIuaOLwtsKstMBqPPGVzft9QXIUMAs0msoYBYREQDueWsjABnBUoSGGJPqDhldxKrSKhp8gW4dW3N7axoA6J+T0enXdqUZjhg3IGpbhtuZvNfoj+9913mVYRbpTfTdLCIiUbzBoHBfXculoC85fBSZblfMYHrB6j3UeRNbDa8jNpfXctzv3gRg4uD8Ln89aKpnjjdg3lRey6ji3K4ckoh0IwXMIiKCtS0ns63dXUMgYMOLd4DTpzgrPa1FhnlzeS1fffAjbnpqeZeP9fZX1xIa7uRuCphDWXdvnJn1yvpGinM7P/stIsmhgFlERLjvnU0xt//8uZX4m3WGyHS7WvRA3rbPmQwXWg66Kw0LdrE4feYQstK7pxNFerC8whtnhtnf7IOGiPRsCphFRPq4HfvrufWFVVHbnrvmKAAefHdzeFtJcBJgZIb5g417eWF5KZvLawFId3V9kNg/x5mA95tzZ3b5a4WEMsyXPfARW/bWtnt8wFrSFDCL9Bqa9Cci0sdVxehnPG1oQdTzx684nKnBbZEZ5i/d+37U8T6/5YONe5k3pqjTV7nzNPpZvas63MWju7LLEN2T+YFFm7n5zGltHu8PWBQvi/QeyjCLiPRxsRYAiQx2/3f1UcwbUxReCjtWDfOKnVUAbCyv5Uv3vs8zS3d0+ji/8+hSzvrLInZV1ZPhSuvWkofIFnFpcXwQCFhwaVlskV5DAbOISBf7ZOs+dld5kj2MVkVmmIcUZoUfP3b5Ydx02mRmDC+MOj6UYbbWtto67bMdVZ0+zpdW7AJg+756cjO7dxW9yPviauc3Z2g1QJVkiPQeKskQEeliZ9/1Lv1y0ln6fycndRx1Xh85GS1/7IcyzNfOH883jxlLtcdpDXfo2GIOHVvc4vhQhrm+0d9qP+Z4V8TriHfWlXPomKL2D+xEWekuMtxpeH2BdktN/MEWHsowi/QeyjCLiHShULu2/XXJXRnvqY+3M/X/XmZjWU2LfaGA+etHjyU/K52hwS4UrclMdzLMFbXRfZr/dslcLj18FJMH57NjX33nDR5nqelIs0b069Trx+PtHxzvjCVGD+pIfmWYRXodZZhFRLpQslfEC3npM6ecYc2u6hZLSe+vayTNQH5mfL8SstxOhjkUMP/pgtnMGdGfkcU5nDR1ED9+ZjlPf7yDmgYfOemuTgkc91Q1hB8PLsjiquPGH/A1EzW4MIshhVntLo8dCGWYFTCL9BrKMIuIdKGahq5f+S4enmDg7gu0XKCktNLD4IKsuAPbzHQX1sLuYBA7vH82I4tzwvuH9cuh1utn+k9fZuKPX6Ta42SwG/0Bfv/KmpiTDP0By98XbmJfbcvVBQH2VDuvdfdFB/H+TSdQGGwt192yM1zUxZlhVkmGSO+hDLOISBfqylreRGwLjmN/jGB15/56hrRThhEpNNGvtNIpuyjKzYzaP6x/07V8Acv7GyuYMDCP1buquOON9eypbuBXET2UK+samfXzV5xr7q/nx2dMbfGaZdXOpMnIwDwZcjJceNrLMAf/qKCSDJHeQwGziEgXemPVHgAKsty8s66M4tzMcD/j7lTndTLd5dUNLfaVVtYzfVhhi+2tyQz2Py6tdILYopzoJaBHFkUHtd/85+Ko549+tI0TpwzixKmDAFhZ2tRRI0YCHIANZc5iIaHFU5IlO93VbklG06S/7hiRiHQHlWSIiHSh9zbuBcDtSuPiv3/IaXe80+1jCARsuN44lBUO8fkDbN5bF15uOh5ZoQzz/npcaYb8rOjcy8w4gu9vRATRvkBTnff9izYx+sbn8TVbgnrZ9v3kZ7kZkJvkgDnDTX07JRmhGmZlmEV6DwXMIiJdKLSMcqiONxkq6rw0+p0gbs3u6C4Zf31rA5DY5MRQhnndnhr656S3CAzT0gyLf3xii/MmDcqPeb0aT8s6713N+lbXef2MK8lLehCanZ4WXuWwNeE+zKphFuk1FDCLiHSR2gYf5TVe8jLd4YAVYE+wHnfn/nqe/XRnl49jV7B0YkhhFp9u2x/Voi00me6KY8fGfb1QhnnFziqKcjNiHjMgr2UmuK4x9gTI6lgBc2V0wNzQGCArPfm/sjLcLrztfLjwq0uGSK+T/J8+IiK9VGjCX/Oa5aN/vQBPo5/T7niHax/5JGbXiM4UCtAnDXYyvL9/dW14X0NjgEEFmQwpjL8kIy+iBGPsgLxWj3v/RydEPd9WEV0OEuomUR2jk0h5TVO3jP8s3saHmytIb2+JvW6Q4Wq5LHhz6pIh0vsk/6ePiEgvFQ6Yh0QHzA2+AKt3VYcXM1m/p+ViIp3B5w9wwu1v8ovnVgFw7QkTALjnrY0c+as32FxeS02Dj9w4+y+HzBreL/w4dM1YBhdm8ZMzpvLo5Yfx9aPG8OtzZ0St0BcqxQj9PyejabnrK/+9JPw4VDayqrTzl9tOVIY7Da+/7YA5NCkw2eUjItJ5FDCLiHSRrXudgHnm8JaT4Nbsagr+NnRRwLxgTRkbymrZVO7UUY+LWLBkx/56HvloKzUNPvISDJgjA+z2zv36UWM4bGwxPzljKl86ZCT3X3YI3z1xIgBVwbruak8jORku/nfNUfzoc5Ojzg8ELPPGOMtzR2adkyUzuDx2W375gvMBZcmWiu4Ykoh0AwXMIiJdZGtFHQVZbk6YPKjFvueWlYYfr9tT3SWv37ydW25EBhecTPNba8sYVZyb8LXdwexpTqarnSOj5Wa6mTLEKQ0JlaKEgvZxJXlccew4Zo/oR6Y7jccXb2PsTS+EW+L96+vzEh5nZ8uII2AOTQo896Dh3TEkEekGCphFRLrIloo6RhXnRq1K95svzmTKkALeWVce3tZdi5u4W6kBvvzo+Cf8hfz36iO56NCRLXowx6Mg27kfTRlmX1Rrui/MHkqDL8ANTyxzXmvpTvrlpHP0hJKEX6uzZbjaL8nYXdXAyVMHMXd0UZvHiUjPoYBZRKQTWWu59fmVvLlmD9sq6sIr06342Sn8/AvTOPeg4Uwc5JRGHDyqP0dPGMCuqpaLiXSGWL2Vr50/vsW2GTFKRtozbWght549o0N1ugVZwYC53skcVzf4yMtq+lAxZkDLjLc7LTV+XWW40/AHbHhiX6TfvbyG0+94hy17axk/sPXJkCLS86TGTyARkV6iotbL397ZxGUPfMSm8trwqne5mW4uOXw0rjQTrvs9cvwABhVksavZYiKdpabBx/lzo8sCvnfyJI6fVEJRbga3nzeL1b84tUteuy2hVnSPL94GODXMBREZ5sha6xCvr+3ex90lI9hSL1ZZxp0L1rNiZxUBC1npiZWqiEhq09LYIiKdqPmyyaOaLRMN8JXDRrGxrJavHjGa+xdtoqy6AZ8/0GrJREf4/AEq6xtjtou7/7JDADBJans2uDCL02cO4fllpdR5fdR4fAwuyArvHxojM14Vo1dzMmS4mgLm7Iia8OYBdCiwFpHeQd/RIiKdqPmyydNjLBM9ZUgBj1x+GP1zMyjISidgW553oPYFW9YV57WsMTbGJC1YDjly3ADAqV9u3qkjcsGPJ648vNvH1pZQINzgj/732l8f3cEjFXpGi0jnUYZZRKST/Ou9zfzkvyvCzzPdaTED5kih4DBWTeyBqKh1Arj+ORmMLMqJWc+cTAXZzq+fyvrG4KS/9Kj9j15+GJvLa9u9f92ttZIMj7dZhtmlHswivYkCZhGRThIZLANcdsTods9JDwZWkUtnd4ZQwFycm8HbNxzfqdfuDKGJf7c8v8rJMGdF/zo6bGwxh40tTsbQ2hRZkhGp+bLfKskQ6V0UMIuIdIEF3z+O4f3bz+qG6pZ9gbZblSWqNrjcdKKr+HWXUGu5t9eWOc+zUnOczYUzzM1ayzWvXVdJhkjv0jN+QomI9CDXnzQxZmu0WEIlGb44M8ybymvZvq+u3Z7EoYAuMz01A7fmAXJbKwa+9J2jaWjs3A8UHdVahrm+WcCsDLNI76LvaBGRTva5GYPjPjZUkuELWP713mYe/mBrm8f/9NkVXPz3D9lY1vZy2g3BNmwZKZrpbF6z3LwkI9LkwQXMGtGvi0cUn9ZqmEMZ/dC/Z6ZbbeVEepPU/EkqItIDDeuXzRcPHs74gflxnxNakMPnD/CT/67gpqeXt3n8jn3OqoBfuvf9No8LBXSZKdoPuCQ/kyuPHRd+3pEVA5MhFDA3X50x1OUk1H+5MDv6A4GI9GwKmEVEOklNg4/sBANUd1pThrktj320lQcXbQpnLsuqG9hV6Wn1+FDAnKoZZoATpgwMPw6tiJjqQgHz9x7/NGp7qIY5NJkxXV0yRHoV1TCLiHSCPdUeKusbGR1n7XJIeNJfRA2ztbZFn+QfPulknocWNi3wUdPQ+mIeDaGAOYVraUvyMsOPC3pIRra1DyChkoy7LjqIxxZvY0aKtcMTkQOTuj9JRUR6kBU7qgCYPrQgofNCGebIhS+aL2LiiXi+v76RscGgfOf+eh77aCtX/msJ1kZnqJ/9dCfg9IJOVSX5TQFzVg+p+W3tfoYm/U0ZUsAvz57Rqas2ikjyKcMsInKA7nxjHSt2VmEMTEsws+gO/un+1ZW7w9tqPD5yMpp+PK/ZVR1+XOf1U5yXwcbyWi65/8Pw9s176yjJzwx3m1i2vRJI7YA5suVdTylhaC1jX9foJ91lUjqjLyIdp4BZROQAeBr9/O6VtQAMyMtosz1aLKFJf4vWl4e3VXl8DIxIVG/eWxt1TqzX+MY/PmJDWS0Pf+NQjhg/gMLsdM6aPTTpS2DHq6eMs9WAuQP16yLSc+ijsIjIAaj2NNURl9d42zgytlCGubK+6TrNa5NrG6JLNM45aHiL62woc4Lqu9/agLWWOq+P7AzlRDqbK0Zgv6fKw/PLd0X9VUBEehcFzCIiB6DK03hA54dqmMtrGsLbqoPX3FhWw+gbn2fBmj1R5xTnZnDy1EExr7dseyVjfvQCjX5LToYynp2tMKfl5MQL7/uA8pqGlF0kRkQOnL67RUQ6aH+dl2eXOpPrZg0v5Nmrj0z4GrGWUK4JZq0XbdgLRNc3A8wa0a/Fwh8hlfVNAXxPCJifuPJwfnferGQPI26ZbhdThjj1Ms8tc/7ttwRLZtJ6SFmJiCROAbOISAf94Ill/On1dQD88pwZzBzeL+FrhJbGjhTKNtd4okszfnDKJN76wXHkZrppDC59ffPnp7Z67UTrqZNh7ugivnhwyxKTVLZ+jzMJc+nW/UBq97oWkc6h73IRkQ7asa8+/HhkUccW3mjeHSLDlca2ffXc9uIq3ljdlFmeN7qIq44fz6hip6XckGA/5ilDCsKvffXx4wF46ttH8MuzZ/C5GUM6NCZpW2jxmNBiM+qMIdL7pX76QUQkRQ0syGRlqfO4tRKJ9oS6ZAD86pwZ/PWtDby1pow1u6ujjjtoVP+o5989aSJzRxdx6Nhi/vm1eby6cjffOHoM1588EWMMB42MPl46T6Y7jZoGaPQH+OULq9hX55TBhHoxi0jvo4/FIiIdFKv+OFGRJRkXzBvJsP7ZUcHyVcePY2RRDmfNGRp1Xla6i5OCE/9GD8jlm8eMxRjTY9qz9WSh3tYNvgD3vr0xvP2wsUXJGpKIdDFlmEVEOii0/PQr3z2mw9doHnQP75cD7A0/nz95ED84ZXKHry+dL7SKX+jfP+Snn5+WjOGISDdQhllEpIMaGv3MG1PExEH5Hb6Gu1kN87D+2VHPi3MzOnxt6RqTBjv/3s3na/bXv5VIr6WAWUSkgzy+AFkHuLpbqA9zaPLfsH7RAXNRnoKwVPOHL80GnLZ96pAh0jfoO11EpIMaGv3hetaOCtUwTx9WCMDoAU3dNu65+GAKOjiZULpOXqab0cU51Db4GRzsViIivZtqmEVEOsjbCRnm/Kx07rtkLgcHu2CE2sYBnDJt8AFdW7pObqabOq+P/Cz9GhXpC5RhFhGJ01Mfb2fH/qbey1WeRvIyD3w1vROnDgrXvxblqASjJ8jNcFPb4Mcf7MUsIr1buwGzMeZ+Y8weY8xnEduKjDGvGmPWBf/fP2Lfj4wx640xa4wxp0RsP9gYszy47w6j3kci0oPUeX187/FPufi+DwAnu1xe42VwQXY7ZyYmLVii8bnpyi6nspxMF7VeHzYYLx9oaY6IpLZ4vsMfBE5ttu1G4HVr7QTg9eBzjDFTgQuAacFz7jLGhNIvdwOXAxOC/zW/pohIygqt6hfKMO+v8wJQ3AWT8tbf+jn+cuFBnX5d6Ty5GW6Wba9kze5qTpwykE9/enKyhyQiXajdgNla+zZQ0WzzF4B/BB//AzgrYvuj1toGa+0mYD0wzxgzBCiw1r5nrbXAPyPOERFJaZvKaznpD28DEPoDfE2DD3AmgHU2tystnGmW1BT5756V7jrgWnYRSW0d/RvSIGttKUDw/wOD24cB2yKO2x7cNiz4uPn2mIwxlxtjFhtjFpeVlXVwiCIinePlFbvCj72+AGXVDdQ2OMsg53ZBwCypb2xJ0+RMlz7ciPR6nV10Feunhm1je0zW2nuttXOttXNLSko6bXAiIpE8jX6Wbtvf7nHvb9wb9fyDTXup9ToZ5txOmPQnPU/kIiVpmpIj0ut1NGDeHSyzIPj/PcHt24EREccNB3YGtw+PsV1EJGlueX4lZ/1lEVv31lHb4GPFzsqYx322oyrq+Z6qBv7x7mYAirS6W58UOclPAbNI79fRgPlZ4NLg40uB/0Zsv8AYk2mMGYMzue/DYNlGtTHmsGB3jEsizhERiduSLRVs31fXKddaVVoNwPb9dfz1rQ2cfsdC7ntnY9Qxb60to7ymgWvmj+ej/3ciAD9/biUvfuaUaYwvyeuUsUjPkulu+suCFvsT6f3iaSv3CPAeMMkYs90Y83XgV8BJxph1wEnB51hrVwCPAyuBl4CrrLX+4KW+BdyHMxFwA/BiJ78XEekDzr37PU78/Vudcq3QohOl+z18snU/AK+u3B11zO2vrAFgypACSvIzyc2ILsFwK1rqkzLTlWEW6Uvana1irf1yK7tOaOX4W4FbY2xfDExPaHQiIhHqvc7nb09joFOut6eqAYDr//NpeNvOyvqoY2obfBw3qSTcF7nW6w/vG6plkfusqJIMTfoT6fWUGhGRHqM0Ipj94RPLDuhatQ0+VpZWtdi+t8Yb9XxPdQOji3OJtdZSf9Uv91lRJRnKMIv0egqYRaTHKK30hB8/tngbr0S0ewPw+QNsKq+N61qhLheRrjthAnVeP3XBfZ5GP9UeHyX5mTGvcceX58Q7dOlloif9JXEgItItFDCLSJez1rJ+Tw0H/eLVqCB3U3ktX7rnPe58Y12b5wcCloc/2Mq/3tsStb15hvj/nl3B8b97k7LqhnbH5PE6ZR2/OXcmALNH9GNYP2eZ61CWeWdwVb+BMQLmN79/HOM04a/PykrXr0+RvkTf8SLS5f761kZO/P1bVNR6ufxfS8Lbn/p4Ox9squB3r6yNKrdo7tlPd3LT08t5KRhs332Rs2x081X2XlxeCsDizc0XJ422ZMs+jvntAucaWW5e+96x/Psbh4aXud5b6wTMy3c4beamDi1ocY3RA3JbbJO+I7IkozHQ6rICItJLKGAWkS4VCFh+/dLqqG2hDPCOfU1B8ppd1a1e4zuPLQ0/LsxO56SpgwCo8/rxByyPfriV7fvqcKU5P9IWb9nX6rVW7qzi3LvfDT/PTncxfmAeeZluBhVkRY1r4bpycjJcTByUHz7++WuP4lfnzGjzPUvvF1mS4fN3ziRUEUldCphFpEttrWjZM3nBameto6c+2RHe9rtX1nDNI58ATgDy4vJSnlyyvcW5+Vlu3K403GkGT6OfJ5ds58anlnPUrxdQXuME4os3V3D945/yo6daTgy86uGPo56PKMoJPx5ZnEOGO41fvrCK8poG/rdsJ1+YPZT0iNZx04YWcsG8kYncAumFIjPMSjCL9H7ttpUTETkQK3Y6dca5GS4evfxwPn/nQnZVeaisbwRgQF4m5TUNfLajis92VDGsXzZ/fWtD+PxdVc5EvxnDClm+o5LtwexvVroLT2OgRUB+1fHj+MuCDXy63SmnuOWsGbgiZmWFJgWOKs6hf04G4wc21SEXZKXz089P5f89/RmXPfAhnsYAp04f0tm3RHqByD7MAauIWaS3U4ZZRLrU3xc6K+d9/H8nMWN4ITkZLirrG8N1xrecNZ3IrlyRwTLAb19ew6zhhdwWLIOYN6YICAbMPj9bIgLmw8cWM2lwdL3x3pqmCYD1Xj9FuRmcP3c4r373WJ648vAW4z1i3ACgaTnsyYPzWxwjkhHxVwfFyyK9nzLMItJl/AHLx8EV9EJ/wi7MTqeqvpEH393MsH7ZnDBlIFluF/WN/hbnHzSyHx9v3c95c0cwfVghC75/XHhiXlZ6Gp5GPyt2VDJ/8kDu/spBZLjSWtQvP7N0BydMGcS4kjxueHIZ++q8fG76EDLcsfMFgwqaOmKce9DwcF2zSKS0NMOhY4r4YFOFMswifYACZhHpMp5gEDwpYtKc22VYsKYMr8/P52c59cEDCzLZsreO33xxJufPHcGeag9YpxzjH+9u4aw5wwAYE9GZIifDxVMfOzXQX543MhyQFzVbTOSXL6zmly+s5p0bjud/n+7ksiNGc/zkga2OOSfDzaOXH8a0oQXkZ6V3zo2QXunCQ0cGA+Zkj0REuppKMkSky4QC5i/PGxHetq2invKaBqo8Pob1d/oe33HBHEYW5XD42GIABuZnMbAgi5nD+3H7+bNatI8D+OqRY8KPZ43oF35cmB07yD36N04bOXccq0wcNrZYwbK0Ky1YS6QMs0jvp4BZRLqMx+e028rOcMXc3y/byQbPGtGPt284PqpjRXvOOWhY+PEho/uHH4cC5nSX4cxZQ1ucd97cES22iXREKGBG8bJIr6eSDBHpMqEMc1Z6U8A8bWhBuHOGP9Dx/rWZbhf/u/ooxpTkYiJmDaa70rjh1EkcM6GEYf2yKc7L4KmPd1BZ38ifLpjNJE3ik05SkO38Ch2Ql9HOkSLS0ylgFpEu0eDzc+afF7bY/tgVhzP9py8DcPZBww/oNWYML4y5/dvHjQ8//unnp3H0hAF8tqMqZsZZpKOOGj+A33xxJp+fqa8rkd5OAbOIdIll2yup9ToZ5hnDmgLbvEw3f/zSbFbtqopZm9wV5k8exPzJg7rltaTvMMZwvkp8RPoEBcwi0iWqPc7CJM9cdSRjS/Ki9p01ZxhnMSzWaSIiIilHk/5EpEvUNjjZ5ZxWJvyJiIj0FMowi8gB2V3l4Yp/LWFIYRYXHzaKI8Y7K+XVB8sxstMVMIuISM+mDLOIHJAFq/ewdNt+XvxsFxfe9wEA1lp+9PRyQBlmERHp+RQwi8gBidVj+V/vb8EfXP6stYVEREREegoFzCLSpq1769i6t67V/dUeHwCfD7ZsCwQsb60pA+C4SSW4XfoxIyIiPZtqmEWkTcf81llSevOvTm+x73uPL+Wpj3cAMKbYWaXv/z3zGR6fn4IsN384f3a3jVNERKSrKGAWkZg8jX6Wba9s85hQsDx1SAHjBjqt4x75cCtFuRmcNmMI/XO1ApqIiPR8CphFJKbJP3kp6vlDH2zhokNHhZ9/vHUfAMdMLOHPF8whL8vNki37+Od7W6io9TIgL7NbxysiItJVVFwoIi3UNvhabPvP4u1Rz3/27AoA/u+MqRTmpONKM8we0S+8v1+OJvuJiEjvoIBZRFp4b8Pe8OOcDBdXHDuWpdv2s7emgdLKepZt38/a3TV89cjRjB/YtIpfcURWeUhhdreOWUREpKuoJENEWli7pxqAN64/lpFFObyzvpx73trIwbe8FnXc4IKsqOfFETXLR08c0PUDFRER6QYKmEWkhU1ltaQZGDMgF2MMRTmxJ++V5EfXKY8KdspwpRkKslSSISIivYNKMkQkyr/e28x/lmwnYMEYA0TXI99w6qTw4+Z1yvlZ6fzpgtm8eN3R3TNYERGRbqAMs4hE+WlwMl+kEf1z+PpRY7j4sFGMHpDLx1v28dqqPbjTWn7m/sLsYd0xTBERkW6jgFlEwipqvQRXtI6Slmb4yRlTw89//oXpDC5cz+HjirtxdCIiIsmhgFlEwj7dvh+A4yeVcMvZM1o9bmi/bG45q/X9IiIivYkCZhEBYENZDd/69xKMgTu+PId8TdoTEREBNOlPRIIWb67A0xjgB6dMUrAsIiISQQGziABQ7XFW97to3qh2jhQREelbFDCLCABb9tYBkJelSi0REZFICphFerDXV+1m7i2vUe/1A7Cr0sN/l+6I+/yy6ga++sCHXPz3D3h7XRngLDoiIiIiTZRKEunBbv7fCsprGti+r44Jg/L52oMfsbK0iiPHD2BAXma759+/aBML1jQFyoeOKerqIYuIiPQ4yjCL9GD13gAA++oaafD5WVlaBTgT+NrjafTz6IdbCSWU/QHL9GGFXTZWERGRnkoBs0gvsGTLPr75zyXh5+9u2NvuOc8tK2VfXSPfOXFieFs8WWkREZG+RgGzSA9lrcXrc2qXf/3Sat5e65RWzB7Rj2c/3Umd19fm+fcv3MSEgXlcfszY8LaSfAXMIiIizSlgFumh9tZ6qfL4WgS5N35uMvvrGvnFc6vYvq8Oa5vWuq72NAJOsL1+Tw3zJw8kK93FtSdM4IhxxaphFhERiUGT/kR6qI1ltQCcOm0w/3p/CwA/Pn0Kh40tZsawQh75cCuPfLgVgHsuPpi9NV5ueno5T337CCYMzMPrD1CclwHA906aGPtFRERERAGzSE+1qbwGgEsOH4UvYLnuhAkMLswC4PBxxSzfURk+9pcvrMLT6JRv3LVgPZ9s3Q9AUa5KMERERNqjgFmkh9pYVkuGO42xJXncds6MqH0Dm5VphBYlAXht1Z7w46JcLYEtIiLSHtUwi/QAZdUNlFU3UF7TQKPfaSW3fV89w/tnx1xo5LQZQxjWL5uzZg/liohJfYeM7h91XL+cjK4duIiISC+gDLNID/DFv75LRa2Xao/T+WLjL0+jytNIYXbsDPHQftks/OHxBCw0+gPc8/ZGAL574kS276+ntsFHZX0jM9V3WUREpF0KmEVSXJ3XF1VSATD2phcAOGZiSavnGWNwGXCluXjoG4fyz/c2c9Co/hwxfkCXjldERKS3UUmGSIpbvas6/PgLs4dG7cvPjO8z75HjB3DPxXPJSnd16thERET6AmWYRVLc6lInYH7nhuMZUZTDf5fuDO8b2i8rWcMSERHpM5RhFklh/oDl3rc3ADCsXzYAL1x7NDkZTqZ4wsD8pI1NRESkr1CGWSSFvbxiF5v31lGQ5SYt2A1j6tACPrjpBO57ZxNnNivREBERkc6ngFkkhX2ydR8A/7nyiKjt+VnpfFer84mIiHQLlWSIpKjXVu7mb+9sIjfDxaTBKr0QERFJFgXMIinq9dXOiny1Xn+SRyIiItK3KWAWSVGDC5wOGBceOjLJIxEREenbFDCLpKiS/EwArp0/IckjERER6dsUMIukqEZ/AIAMt75NRUREkkm/iUVSVChgTneZJI9ERESkb1PALNIOa21SXtcbDpj1bSoiIpJM+k0s0oaF68qZ/fNX+dvbG5l7y2v84dW13fbajT4nUFfALCIiklz6TSzShhue+JTK+kZufWEV5TUN/On1dWwur+WttWUEAh3LPH+ydR+L1pfzvceX8vAHW1s9rtEfwJVmcKWpJENERCSZtNKfdJrt++q4f+Fmbjh1ElnprmQP54B4Gv1c9sCH7Kz0tNh33O/eBOChbxzKkeMHJHTdxz/axg1PLgs/f+rjHcwYVsiM4YVRx1V5Gnn0o63kZepbVEREJNmUYZZOc+7d73L/ok1897GlB3yt55eVcsG97/Gn19Yd+MDi9MfX1vLgok0AvLZqN+9vrAAgM9ilYu0tn4s6/pbnV+FpbH9RkW0Vdfz+lTV86Z73ooLlkBc/K22x7T+Lt1Ne4+WIccUJvw8RERHpXEpf9WCvrNiF1x/gjJlDkz0UAHZXNQCwoawmofOstTy/vJR5Y4rYsKeWw8cV88SSbby/sYIPN1XwzWPGkJPRtV+qdV4ffwwG52fNGcYdrzuPX/7OMYwoyqbG42vR3m1VaRWTf/ISvzl3JucfMqLVax/9mwUttt34uclcccxYDv3l61TUegF4Z10ZnsYAJ00dxOurdjOkMIu7Ljqos96iiIiIdNABRSHGmM1ANeAHfNbaucaYIuAxYDSwGTjfWrsvePyPgK8Hj7/WWvvygbx+X3f5v5YAcNr0IaQluc71g417w4/X7q7h7bVlbN5by5HjBzCuJC/mOfNvf5P5kwYyY3gh1z26NGqfMVCYnU5lfSPXPvIJ9116SKeP+Z11ZazfU8Olh4/m3fVN4//mPxezdncNs4YXMmlwPkA4YH/3xvnUNPiYMDCPqx/+hOeXl/LP9ze3GjDHykDfcOokrjx2HABFuRk8++lOvnH0GC7++4cApBkIWPjyvJEYo/plERGRZOuMtN3x1tryiOc3Aq9ba39ljLkx+PyHxpipwAXANGAo8JoxZqK1tv2/aUuYzx/A7Urj9lfWhLftrKxneP+cpIwnELA0+AJ86d73o7bf9eb6cEnDpttOaxH4VdR62VhWy8ayTTGvm5Pu4l9fn8eZdy7itVV7WL69skWdbyIqar388oVVPLFkO/PGFHHy1EHc8vwqAN5cU8aq0qrwsR9t3seR44v5w5dmt7jO0H7Z4cd/uegg8p9cxqMfbcPT6G9Rt22t5bjfvgnAnRfO4YyZQ8P/fiGTB+ezelc1J/7+7fC20FzCYyeWdPj9ioiISOfpihrmLwD/CD7+B3BWxPZHrbUN1tpNwHpgXhe8fq/j8wf4bEcl33tsKeP/34t8699L+PMb68P7j/r1AvZUt5yc1lWstdz+yho+3FTBcb97kyn/91J436nTBgOEg2WA/XWNUefXNvg46BevRm07flIJxbkZXHfCBG44dRIf/fhEZg7vx7NXHwnApQ982OHx/vn1dRz0i1d5Ysl2AD7cVBEOlgHeWlvGnuqGqHMuOnQUA/Oz2r326AG5AJx/z3u8tnI3nkY/v39lDZX1jazfU8OuKg9jBuRy4pRBAFHBMsAvzpoefnzajMEs/b+TOHRMEbedM4MTpgzs2BsWERGRTmUOZFEGY8wmYB9ggXustfcaY/Zba/tFHLPPWtvfGHMn8L619t/B7X8HXrTWPhHjupcDlwOMHDny4C1btnR4jD3du+vLufC+D+I69n9XH3VAWdjWWGup8/rJDXZs2Lq3jmN+27Iu99yDhvPLc6Yz6ccvRW1/7pqjmD6saVzvbijnwr857+n6kyYyeUgB8ycPbLV92rl3v8uSLfvY+MvT2FlZz+4qDwePKop5bKM/QFl1A0P7ZVNZ18iCNXv4TsQkxGlDC1ixsymbXJSbwcD8TFbvquYvFx7ErioPY0tyOW5iSVzlEGXVDRz16zdo8DmLjFxxzFjueXtj1DH3XzaX+ZMHtXqN8poG3li1h3MPHq4WciIiIklijFlirZ0ba9+BlmQcaa3daYwZCLxqjFnd1jhibIsZrVtr7wXuBZg7d25ylllLEX9f2FSykOFKY+LgPD7bUcX8yQO59+KD2by3lq89uJitFXV8/s6FfPazUzq9Fdn9izbzi+dW8qcLZnPKtMGU1TRlY4tzM/jNF2dy95sb+MkZU8h0N5UlhILHnfvrowLm1aXVAHx40wkMLGg/i/vFg4ezZMs+xt70QnjbsptPJi/DTcBa3K40KusbufBv74eD4Ye+cSj/fG8zL6/YDcCfLpjNGTOH4kozvLZyN1c9/DF/umA2x04cSHZGx1vgleRn8r9rjuLhD7by4LubWwTLQLut5wbkZbY5aVBERESS64AiK2vtzuD/9xhjnsYpsdhtjBlirS01xgwB9gQP3w5ERgXDgZ0H8vp9QWmlh4NG9uOvFx9MpssJ7D7cXMFJU52M5fiB+bx9w/Gcc9ciPt66n6N+/QZPfesIxrYy0S6WQMCysrSK6cMKafD5eWDRZs6ZM4wBeZmkpRke/sDJ8F/36FLOO3g4Y0qcMoRr54/n0iNGU5yXyQlTmjKov/3iTIpyMzhkTBH3L9rE3xduYs7I/pTkZ7Jjfz2rSqsYkJcRV7AMcNyklrW8M29+hf87Yyo/f24lf7tkLk9/sj0qc3zNI5+Eu0/MHF7ImbOGhjPGJ04dxOpfnNppE+omDsrn5jOnsWJnJR9t3scPTpnE3hovXztqdNJqy0VERKTzdLgkwxiTC6RZa6uDj18Ffg6cAOyNmPRXZK29wRgzDXgYJ6geCrwOTGhv0t/cuXPt4sWLOzTG3uCoX7/BIaOLYk5Ai2St5aqHP+aF5bsAePbqI5k5vF9cr/HfpTu47tGlHDuxhKlDC7j7zQ0AfP/kiXz9qLEc/Zs3KK/xho93pRlK8jJ5/6YT2r32vW9v4JcvOH94OGXaoHDG95Rpg7jn4ph/9YjJ6wtw5b+XMHFQPit2VvLOuvIWxxw7sYSz5wzjj6+tZfPeOkYUZfPSdceQ4U7rluWlrbVUeXwUZqd3+WuJiIhI5+qqkoxBwNPBLJ0beNha+5Ix5iPgcWPM14GtwHkA1toVxpjHgZWAD7iqJ3bIWLu7GneaSSiDeyCqPT7ys9r/ZzLGMKSwqYPDWX9ZxMbbTo/rNdbudkok3lpbxltry8Lb31pbxmc7qiiv8fL3S+cypDCb0+54B3/A8sWDh8d17W8ePZbbXlyNtYSDZYBvHTc+rvNDMtxp3H+Z01puW0VdVG/j/Cw3xbkZXHrEKOZPHsTSbft5Ysl2fn3OzHDddXcwxihYFhER6YU6HE1YazcCs2Js34uTZY51zq3ArR19zWT78+vruP3VtQBs/lV8wWhIg8/PbS+sZvzAPL5y2Ki4zrHWUtMQX8AMTulByCGjY0+Ki2XDnlrGDMjlc9MH88/3tlDT4AOc9mqh60aWXAB8/5RJcV3bGMP3T57Eb1922uDNG1PE6TOGMHtEv7jH19ywiNZu3z95IlfPnxC1/+Yzp3HzmdM6fH0RERGRSFrpLw7WWirrG8PBMsDVD3+MBe788py4amFvfX4V/3zPqQX+8ryRLbohbN9XR6bbRUl+Ju+uL6eizsuhY4rxBywD8jLjGueZs4bSPyeD37+6lnjKc5/+ZDu5GW7W7K5m0qB8bjh1Mtee4ASf3/zn4nDZQ20wgO6onIhJdY9dftgB1w6npRmeuPJwXl+9h28eM/aAriUiIiLSHgXMMVhrCVjCQe31j3/KU5/siDrmuWWlAOyp8vDo5Yfzx9fWcuzEEubGyOzur/Py2Efbws/X7Kpm6tACGv0BSvd78AUCzL/9LQD++pWDuPLfH0edPzjOyXHGGI6ZWMIrK3fx5JIdWGujgtOd++vxBywjipyJaN997NPwvlCJRWjxjb9dMpfaBh9n3rmIH58xNXzcvRcfjC+QWN17Sb4T8P/uvFmdNtFu7uiimPdaREREpLMpYI7hnrc38tGmCv5y0UFkpbuiguUffW4yT3+yg9W7nLrfjzbvY1eVhz+/sZ4/v7E+ZqnGR5v30eAL8PvzZ/G9xz/lheWlDMjLYN4vX29xbPNgGeDICW23JWtufEke9Y1+Kmq9FEdkp0/949tUeXysveVzUa3hjHGy3pGy0l1kpbtYdOP8qO0nBxcmScTpM4Yw6Mos5o7qn/C5IiIiIsnW9a0DeqDcTDevr97Db15aw+by2vD2z00fzAWHjOTq+c6EtR+eOhmANbua2pk9uWQ7q3dVsauyaeW93VXO4yPGOYHvnQvWxwyWLzrUCVonDmqaUPiDUyZRkJXYRLLBwcl/33v8UzaV1/Layt386KnlVHmc0oqXV+zi7+84/Z3njS7iyW8dQVFuRkKvkQhjDIeMLuq07LKIiIhId1KGOYaLDxvFM5/s4P5Fm/jHe5vJz3Lz3DVHMarY6T98xsyhHD9pIHtrvPz6pdX87uWm2ubr/+OUObjSDBt+eRrgrAYHUJyXwR+/NDtq5bmQX587g3MPGs7QftmcPmMIw/tn89QnOzhnzrCExz+k0CnheGttGWfftajF0tTvbtjLk0u2c86cYfy+nXZ1IiIiIn2dMsytWLLF6RARmnQXCpZDcjPdjCzOYfaIfqwsrWLKkAJuP6+paYg/YDnyV2/gD1jKaxron5NOuiuNs+YM499fPxSArx05hk23nca6Wz/Hlw4ZiduVxlXHj2f0gFzcrjTOnzsCdwf6Bw8ubKp5jgyW//zlOQA88uFWvP4AXzk8vm4dIiIiIn2ZAuY4eH2BVvddHGwRd+nhozj34OHc/Pmp5Aa7QuzYX8/Mm1/moQ+2RvUDPnJ8MX+6YDY3nDoJY0ynL6oRq6vGRYeO5POzhtI/p6m8Y/Lg/E59XREREZHeqMMr/XWXZK30t62iji1769hb28CkwflMHlzQ6rGN/kBU0Guts9T06XcsDG87a/ZQ/njBnC4dc6Sz/rKI+ZMHMm1oAa+s2M1XDhvFjOGFVNR6+ff7WxhcmMX5c0e0fyERERGRPqCtlf4UMHeRBp+fST9+CXBasR02rjjhyXsiIiIi0j26amlsaUOm28Wr3z2Gilovh44tTvZwRERERKSDFDB3oQmDVCMsIiIi0tNp0p+IiIiISBsUMIuIiIiItEEBs4iIiIhIGxQwi4iIiIi0QQGziIiIiEgbFDCLiIiIiLRBAbOIiIiISBsUMIuIiIiItEEBs4iIiIhIGxQwi4iIiIi0QQGziIiIiEgbFDCLiIiIiLRBAbOIiIiISBsUMIuIiIiItEEBs4iIiIhIG4y1NtljaJMxpgzYkoSXHgCUJ+F1eyrdr8TofsVP9yoxul+J0f2Kn+5VYnS/EpMK92uUtbYk1o6UD5iTxRiz2Fo7N9nj6Cl0vxKj+xU/3avE6H4lRvcrfrpXidH9Skyq3y+VZIiIiIiItEEBs4iIiIhIGxQwt+7eZA+gh9H9SozuV/x0rxKj+5UY3a/46V4lRvcrMSl9v1TDLCIiIiLSBmWYRURERETaoIBZRERERKQNCphFRHoYY4xJ9hh6Ct0r6Ur6+opfT79XfTpgNsaMTvYYegpjzFxjzMBkj6OnMMacaIw5ONnj6CmMMYURj3v0D9Vu4k72AHqQ9GQPoKcxxriSPYYepE/HUQnq0d+LffIf2hhzkDHmNeDn+sHQNmPMNGPMu8BPgX5JHk7KM8bMMca8CDwNjE/2eFKdMeZQY8x/gfuMMV8zxmRazURulTHmMGPMQzg/uybo51frjDGHG2P+A/zOGDNV96ptwfv1cwBrrT/Z40l1xph5xph/A7cZY2YYY/pkPBWPYMLtP8BvjTFH9dTvxT71D2wc/w94BHjUWntJ6AeDslqtug542lr7eWvtWtC9isUY4zLG3Av8DbgHeBiYEtzXp77P4mWMmQn8BXgC+A8wH33IaJUxZjrwZ+A5YDdwOXBJcJ++JyME/xp2J/ACzlK71wFfC+7TvWrGGHMp8A/gx8aY84Pb9FeMGIwxacaYnwL3AS/i/LXnKmBWUgeWgoIx16+Av9L0c+tqYGRSB9ZBfeoXeTBzlQ4stNbeB+GMoFtZrWjBALAIsDi/eDDGnG2MGQ5kB5/rF09Q8IPXS8DR1tpngCeB440xWdbaQFIHl7oOBtZba/8FvApkAVtDO/X11cJhwGpr7SM4H8zqgIuMMaOttVb3K8osYK219gHgduAp4AvGmIm6VzHtwPnAeirO/cJa69N9ain483w7cJm19iHgVmAU0COzpl0pGFe9A5xkrf0H8ABOTFGW1IF1UK8PmI0xxxpjDo3Y9FtgmDHmd8aYj4BfAP8wxnwxOSNMHZH3KhgA1gHHAPODf3q6ArgF+GPwmD79IaP515a19ilrbX3wl0wAWAvkJG2AKSbG9+LzwNnGmFuB5cBw4A5jzA9BX18x7tdHwAhjzDhrbS3O11gl8E3o2/fLGHOWMeYmY8zpwU1LgbkR9+ojYDHOz7A+fa8g6n6dEdy0ANhtrX0F2GKM+UVwu7LMxPz6egT4NFhCtheoBoYkb4Spo/nXlrX2eWvtPmPM0cD7wGjgFmPMSckcZ0f02oDZGJNvjHkKp5b0CmNMf4DgD89/AbOB6621ZwBvA6caYyYma7zJ1Ma98uB8IvwL8LK19lTg/wHTjTGfS9qAk6y1+xX885MJ/jJeDZyAkzXt09nSNr6+9uBkAt3ATdbaw4AHgaOMMYcna7zJFuN+FQV3bQA+BB4wxjwDzMUpZXEbY7KSMtgkM8aUBO/F94AKnHvzRWttGc5fea4JHrofeA3IMcb02cAmxv263xhzdjBBEvoZdQVwrTFmkLW2MUlDTQmtfH2dba2ts9b6rbUNxph0nA/7a5I51mRr7Wsr4pAKnKz84cCnwIXGmMndP9KO67UBM+AF3gC+AuwEzgvtCP4Z5Xxr7dvBTa8BJUBNdw8yRbR6r4C7cEowSgCstTuAhTjZrb4q5v2yQcaYNGvtduAD4IuhfckabApo63txNTAZ2BbctATYAzR08xhTSWtfXzXW2htwagAftNZ+HlgPzAx+uO2LxgGLrLXHWGv/ClwPfDe47xFgsjHmhOCf0fcCw3Cy8n1VrPv1AwBrrdcY47LWrsD5IPYrgL6cHKGN+xVhCk52fm3ww+68bh9lamjzXllrV1hrFwSfvoXTRKBHxVy9KmA2xlwS/DNmP2ttA05R/ms4fxqfG8ogB7OAFRGnnoRTV9Oj/vEORLz3ylpbg5OludQYM9sY8y3gRGBzkoaeFAl8baVZawPGmTCzDqhN3qiTJ977FfQKcHMwC38BMA0nuOkz2rlfB0feL2vtsmCdPDh1p+/3pb9gBO/VccaYHJwPWP8MbncBK4P/gVPm8yjwJ2PMeJy/+Bggo/tHnTxx3K/lwecG5/cg1tpv4PzM3wfMMn1o4nIC9ytUrlIE1BljLgPeBWb0le/HeO9VDCfjxJ/V3TLQTtLj65OCX5iDcboSBHD+bHm5MeY6a2158Jj3gInA+cAtoSwgcBTwJ5yJRj+01lYl4z10l47cKwBr7ePBc8/HCWYuttb2+j8/dfBrKxAMmn3GmHyceq0+IcH79SWc+QMA9+NMaHsJZ+LM16y1W7p5+N2uo9+Pwe0H40zO8gOX9/a/YLRyr74JXGet3R3MjPqNMVOAQghPznrQOB0zbsT5S8Y3rbX7k/EeulOC9ytUImUBa4wZBfwBZ7LWVdbaz5LyJrpRB++XL3j6KcCXcf4qdpG1dlm3v4Fu1JF7FTwvA2dO1K9xJpneYK3tUX/t6dGfGoP/MBbIB3ZYa08Avo1TK3NP6Dhr7TqcTz9DjTHjg/V+Fucf7afW2i8E/zTcax3Avco1xqRbax8D/l/wXq1IxnvoTgf4tZUd3P09a+1PunnoSdGB+zXEOH2Ec6y19cBXgUuttSdaa1fGeIle5QC+vkJfW5txfnadYK3d0L2j717t3Kt7mx1+Mk6bQowxgwGstb8Bvm2tPcpau6r7Rp4cB3C/SoLbKoFfWWuP7SPBckfv16DgtueAL1trv9YHguWO3qsB1lovTrndT621Z/bEmKtHZpiDfwr5OeAyxrwAFOBkWkKtcK4FdhpjjrXWvhXc/nTwE89LQB4wP/iLubf/sumMe3U8sKq3Z7FA9ytRB3i/XgTyjDHHBwOZXcl5F92nM76+jDGhn11vJedddI+O3CucsrpNxlmA4xxjzKnW2u3BX9a9Wifdr9OstVtxJpf2ap10vz5nrV2UjPF3p068V8uAHvuhosdlmI0xx+JkXPrjTHj5BdCI0/N2HoT/tPRz4OaI887D6fCwAGeSTF/IYnXWver1WRnQ/UqU7ldi9LMrfh25V8apm/waTlarADjeOpNve71OvF9bW1y8F+rE+7WtxcV7Gd2rJqanJcGM08tvtHUWO8AYcxdOYXk9cI219mDj1CcPBO7AqU3eFDwPa+07SRp6t9O9SozuV2J0vxKj+xW/DtyrH+D8xfQa4J/W2o+TM/Lk0P1KjO5X/HSvmvS4DDPOJ53HTdNa5IuAkdbaB3H+XHCNdSZ7DAf81tpN4Pyy6Uu/cIJ0rxKj+5UY3a/E6H7FL5F7FbDWbrHWbrDWfqc3/YJOgO5XYnS/4qd7FdTjAmbrNAxvsE6jdXBawoWWWfwqMMUY8xxOD85e9Y+VKN2rxOh+JUb3KzG6X/FL8F4tgb69OJDuV2J0v+Kne9WkR076g3CNjAUGAc8GN1cDNwHTgU3WWWSjz9O9SozuV2J0vxKj+xW/RO6V7Wn1hV1A9ysxul/x073qgRnmCAEgHSgHZgY/4fwE508CC/ULJ4ruVWJ0vxKj+5UY3a/46V4lRvcrMbpf8evz96rHTfqLZIw5DGdlnXeBB6y1f0/ykFKW7lVidL8So/uVGN2v+OleJUb3KzG6X/Hr6/eqpwfMw4GLgd9bZzlZaYXuVWJ0vxKj+5UY3a/46V4lRvcrMbpf8evr96pHB8wiIiIiIl2tJ9cwi4iIiIh0OQXMIiIiIiJtUMAsIiIiItIGBcwiIiIiIm1QwCwi0kMYY/zGmKXGmBXGmE+NMd8zxrT5c9wYM9oYc2F3jVFEpDdSwCwi0nPUW2tnW2un4SxRexrw03bOGQ0oYBYROQBqKyci0kMYY2qstXkRz8cCHwEDgFHAv4Dc4O6rrbXvGmPeB6YAm4B/AHcAvwKOAzKBv1hr7+m2NyEi0gMpYBYR6SGaB8zBbfuAyUA1zjK1HmPMBOARa+1cY8xxwPettWcEj78cGGitvcUYkwksAs6z1m7qzvciItKTuJM9ABEROSAm+P904E5jzGzAD0xs5fiTgZnGmC8GnxcCE3Ay0CIiEoMCZhGRHipYkuEH9uDUMu8GZuHMT/G0dhpwjbX25W4ZpIhIL6BJfyIiPZAxpgT4K3CndWrrCoFSa20AuBhwBQ+tBvIjTn0Z+JYxJj14nYnGmFxERKRVyjCLiPQc2caYpTjlFz6cSX6/D+67C3jSGHMesACoDW5fBviMMZ8CDwJ/wumc8bExxgBlwFndM3wRkZ5Jk/5ERERERNqgkgwRERERkTYoYBYRERERaYMCZhERERGRNihgFhERERFpgwJmEREREZE2KGAWEREREWmDAmYRERERkTYoYBYRERERacP/B08PfCssjAAzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model=sm.tsa.statespace.SARIMAX(data['Close'],order=(1, 1, 1),seasonal_order=(1,1,1,12))\n",
    "results=model.fit()\n",
    "data['forecast']=results.predict(start=1620,end=1980,dynamic=True)\n",
    "data[['Close','forecast']].plot(figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "225e1d4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zSQGziIiICHDMExfiMVGMr5irLr0ssT2cg4DZH4rgDTVSUzqbPbYKgII2J+sdzzB7fbE662iEVpsMUInVOOOLBczBlm5frzUYxgYaKTEBDtjS9J3GRZnJvhWctZYjWpMBvK/N6bxB6ShWNhSmHRuOl5PkOQXMIiIiIsCE4EYAjK+Y0oJkW7VoDvoGb9zXTAUtuIqHUdIuWI13yfB64wFzGD/JSYrxSYEmHjCHWrt8rafe3sLOm+ZRtu4BAJ4f/v60/Ta1rV64+3ruh19dy5W1P008H9O61nlQNoYjjzk57dhQNDerKPaUAmYRERGRVL4SAH7rvgQAG8vQ1u3bTfSV25zlsvvZhr3NjDG1FAwbQynpAbMr1ofZ53X+i40yqiQZ0rliMagr9j4itZtp8od47J93YUMdV/57Z9lrTHft5OONvwbgvAs+zBtXbkrsN/dcmjw4i4zw6qVPpT2/pu6HzoPyMXz7vQv5x8jPJvYFYwFzze4dcEMFoZX/6vb6uaCAWURERCRFPNB87+d/zg5bjSvUTCRqefOWS3A9/lXYs6Lfx7C/ZhejzQGKJszvUA4RX7jE54tlwaNhfCnt47xR53hXgfM+3Pddxp133cG7l13Drvu/0uG1XOH0DLR72DgWTa7imtJfdBxYsKnLcUeiFluzJvPOsjEAWG9yUZh4wPzXhx8DoOm/v+zy+rmigFlEREQkhYkFmh6XocGW4A3U4w9FmGBiPYqzKEs4WL79KwHwjptPGekBbTLDnKxhdtvkin++Fqe9XJtJ1gvP3+FMritq3NLxtYL1icctpgTKnUVbzjlyWseBdZNhbg2GGWH3J54viSx0hogLvEVAesAcCrRBUw1jWtcB4CZKPlLALCIiIoe8aDRZZhGv/fV6XNTbUnyhBvyhCAU4QWlrw95+H09Jo1NPzcjZFJr05a/dHiezXOhzAmcbjaQtUOKKOAF9yJUMmI+LvgUks9Npr9WyPfG4vnB8os+zz1fQ4Vgb6DrD7A9FGW2SC52841sAkLZYSb2nKvE4tHsl/luP47KG3znjI9Ll9XNFAbOIiIgc8kLRZGbTXegEdz63i3pK8IUaaAtFKDNOpre1ueugsS+4grFMblElW6Mj0/clAlonwxyJhPCmZJhtrO1cyFXU4brG0zFgdgUak09Kkm3fCny+DscGWho7bEvlD0XSejm7ipx7GXInx7LBm+wX7W6rpTCQbGVno8owi4iIiOSlYDgZqPnKRwHgdbtosKUUhBrxh6JUGSdQDof6vyTDFWkjjBvcXsqvXcLbnrnJfbEscTygDYfCeIllmK97C/M/bwMQcnXMEJMhw+xKyU4XlCeDc19Bx/MDrQ1djrstFGG0qaO2dDqNZ/+ChiYn8K8M7kkcM33ciORrh9JLPGxUGWYRERGRvBRsS+lVXOoEjW6XoYFSCsIN+IPJFemiPaxhblj696y6S6RyR/wEjROwVo6ayD9HX5fcF8sSx0sywuEQnniGuXIKlI8F4Iip4ztc1xpPh20mmnxvRZWjEo89JcMTj9+e9SXntboJmP2BIKM4QNOE0yg/7kqKwh0z0lcdP5ll73+e7dERWH+7bL0CZhEREZH807x3C1W/mATA9lFnJNrKATSYMtw2QqB+Z2JbtAcZ5of+/TAV//4ETQ937E7RFXekjWBKDXLB+Pnstk4A64oFx0U+FyHrJhwO47MhQsYHJtnX+JjpY/hW6Kq060ZdGQLmSLKco6giGTAXpWSYG6vmA2D9XQfMjXdeiddEoHwcABNLncx9w5HXJo5xuQzz5x7BcjuVSf7Vaedbq4BZREREJO/c/fizice+kemdIVpcTg1uuGZdYls0nP3Kf/tfu9c5p3FPN0emc0cChEwyYL3iuMn8MnyRs8/n1AMXed1EcREOhyiP1hPNkD3eWTIn7Xn7DLO1FpsSMLu8ydcs9rkTjyPF1UStgUDnNczRqOXEwHMAlIyYCMDp7/sEABXHXp52rDGGFtuxxhrVMIuIiIjkn8ZgMkgrLatI21cbcuqEK1b+JbEtGu64+EcmLf4QH3f9GwB/QVU3R6crD9fR5kmOZXRFIZ/64nf5z7sewDf9VAAKvW4iuNi/cxMfcD9HQbTjin7f+PjF7LDJiXztg+qWYAQPyZIMUt5boTcZMJuCMpophLbOM8zb6pKvP2yUk7EfPuMEuKEBRs7scHwzHQNmaxUwi4iIiOQdT0rv3+LS8rR9lbNOch401yS22XB6m7fObNyZPKfVU5n1eFqDYaojewmUptcgT6oq4ezTz0g8L/K6CeOisPadTq81qaqEppRMrjXpS1E3tIXwxgLmFs8wmJtcFtvncbE56pRouHxFNFKCCXaeYV69O7nPWzGmi3foaMYpOVnnPjyxraUH92kgKWAWERGRQ1qpKxkAm4oJafsuPuUoItakdXmwWdYwN9TuTjweteGerMfTEohQYEKJBVQ6UxgryYh2UcbgdRtCJLPKqRP8ABpaQ3gI01w2jZJvbYXKyckxlxey8dy7aTvj/zBFlTTZIkwXfZi31Layx1bSXDwByrsPmAPWyd4H3cmFTCJ5Gpp2OypjzARjzDPGmNXGmJXGmM/Htt9gjNlpjFkW+3duyjlfN8ZsMMasNcacnbJ9kTFmRWzfLca0+5gjIiIiMsA8EaeU4Kny98PsC9P2lRR6aKGQUaT0Co5kV8Oc2q+52V3RxZHpwtEoPsLg6tgCLlWRz00YNz46z3gbYwinBMy0G3ujP4SPCHg69lwGOOPYRRSd+Gm8bhdNFFNW8xq07M94bDAcxUOE4llndjnu9iJFyXIVdyS7cpeBlk0YHwa+ZK2dBRwLXGuMmR3b9zNr7YLYv0cBYvsuBuYA5wC/NsbEi2B+A1wNTI/9O6fv3oqIiIhIz/lbncD2lMu/ndZlAqC00ENLrNY2iMcpb8gyYI5PDmyzPrC2m6OTQmHrlEm4MwexcUVeN0E8FNF1xjtskoF3WoZ53ROMfvk7jDD1uDwZejan8LoNFoM32EDb787qeEDTHgi1UEYrrsLyjvszcu7JtOmzeNvtTE6Mr1KYb7oNmK21u621b8YeNwGrgXFdnHIhcI+1NmCt3QxsAI42xowByq21L1trLfAX4L0H+wZEREREDkYg1oPZU1jaYV9pgYfmWA1woykniAcbDkDjrm47OsQz0S2mCHc0+84awUg0FjB3nWEu9LppswWU0HVW1h9NmbwXG9OeBj/c9UEmr7+DBa6N2KLhnZ0OOIu4HONaA0BRw8a0fZFIFH4yg4ve/jQFJgwFZZku0UH8o0lpoZd5336JZ1zH4o4O0oA5lTFmMrAQeDW26bPGmLeNMX80xsSrtMcB21NO2xHbNi72uP32TK9ztTFmqTFm6b59+3oyRBEREZEe8fpj5Ra+4g77irxu3Di9gRvGnkgID4XNO+Cns7D//V6X1433a24zxXhs9gFzOBrFSwTTXYbZ5yaAj0LT9STEgtT9Uefxq5v2ph9U3HXAPKo82RN6R8WitH3PL18LwMS2WE/lwuzKT44/PjahcvQ8AMKugh59sBhIWQfMxphS4AHgf6y1jTjlFdOABcBu4CfxQzOcbrvY3nGjtb+z1i621i4eMWJEpkNEREREDtpTry7nGu53nng6tjkzxjDV5Uz4G3HypwhaD837nfxfy/KHurx2vL+x31WMpweBYCjkZJhNJ3XFcYUeF366zkIDVJHsXhFfpGRYu0ubkmq6MqKsAPvJZwAI23adNupq0g8uyK4k44TzPgrXvgZz3+dc11WAdzBnmI0xXpxg+U5r7T8ArLU11tqIdRrm/R44Onb4DiB1iul4YFds+/gM20VERERy4rllK5NPXJnDomgsQHRNOIoQHkKx56UN61jx84s6vbaN1TD7XSWU2Jas6piD4Sg//NPduIylpGVbl8d63C4CdB1UAww3KQFzLMNc6EpfUc9V2n2C0ow7kmdZREEovRez17bLcGdZkgHAiBmJh1FXQY8y8QMpmy4ZBvgDsNpa+9OU7an9Qi4C4k0AHwYuNsYUGGOm4Ezue81auxtoMsYcG7vm5UDXH81ERERE+tExE53grv64r3d6zJYPP0Hdh/6J1+shiJcjXFsS+46of7rT8+I1zPtxqlajz/6o2/HsbfJzetC5ZtWuZ7o9PuRKTtZbtvC7GY8Zbpqda9thiYA50q6XtLs0u4VVGlwVFIcOpG0z4bb0g7Ke9Jcu4i7Al6cZ5o5rKHZ0AvBRYIUxZlls2zeAS4wxC3DKKrYAnwKw1q40xtwHrMLpsHGtTS4Mfg1wB1AEPBb7JyIiIpIT8U4WnomLOz1m6mznj+jWWma5MmR9m/dCww4Yd2Ri0xPfOZcLoy8CsPnwj3HiOy9gajd0m6mMRiGCM0kvVDC820AtZAoSBa4LTrqwy2N32GqmxgPmUHom15NFhhmgwVVJafiAM9BYRj4SbDfpMMuSjPZcvkJ8LUFsNIrpJNufK90GzNbaF8hcf/xoF+fcDNycYftSYG5PBigiIiLSX+JlE15v123VwKlnfic6mbkpGWYAfjzd+e8NyVKFs2LBMkBl5TB22mpGh0PdB8DRKLXWyXpHLr2v2zFFXAUQT0u63BmP2WcrGGEaaLAlnWaYTUl2AXOTuxJ3OAL++sREwWiobzLM+xdex+GPnskLzQFGlnesJ8+l/ArfRURERAZQPGB2e7uvBQb44agfd3tMIJxeH+wpKCaCi2i77ZlEopYK00LEXUjp2FndHh92J7tX4Mocjlf+z4tseM8/COFJTPqLhNvVCmfZ2aI5vnR1S7KLWTTYLmDuZYZ5xrhqgnhZs6e5V+f3JwXMIiIicmiJRiGWFbWxhTKyDZjHjRnd7THBcHp/Zk9JJWHcRCLpWV3/Y98icvdladtCkSjDaCHkyy6ATQuYTeYMs6dyAqGxRxHEg7HOwiXRcLtaYW92Gd1mbyxgbk62pbPhvinJmDnayayv2dPYzZEDTwGziIiIHFJW/u1LcPNoCAcTGWbj7r4kA6C0IHNQCkAsIG4fMBcUlRHF1aEMovDVX+Je+++07hnhiKXSNBHyDctqPNHUcXdSkgHgMoYwblyxMUZD7TpbeAoznNVRmzc2ObAlJWAOtQu+3dlMkeuossTH6PJC1uxu6v7gAda7dyQiIiIySI3aeD8YsOseo7Rtt7Oxm0VC4koKugidQm3g9hKMpAfMRT4vYVxEI52UZDTuggpnLbdwvCSjILsMs00ryeg8YDYGPEQpadlKYMNzhEPtssLe7ALmSMEw50FbPc8vX83MVb+kzO/0gn5xzg3MG1dBD5rKdXD7FYsZXZHdWAaSAmYRERE5pGyzI6k2jZj7LufM+MZuFgmJK+0qYI6VOYTCyYxxYOFVFPvcTg1zSklGNGqTf+bfuzoZMEeiDKOZcMGkrMbTFElZuKSTGmYAl4GJxllgpOBv76Fw4ufSD8iwaEsmxSWx1RCjYcofuJQRrk3MKDgcgBPe8/FeT/iLmzsuuw8KA00lGSIiInLIsNay1Y7quCPLutuuA2anLjqYkkkuGHtELGB2Y6PJ7U271ibPq92QePjO268zw7WDaDyT243tTSmLofhKOj3OGINJWWD59G2/TD/A3f2KgQDFRU5gHQj4mWs2A1AQbXF2ZlnWMRgpYBYREZFDRl1LkFLaOu7IcnW6rksynDKHQGoN88hZFHrjGeZwYvP9b+xIPI601Scef+QtZxJgNMuuFW3WCXTbJp3W5XEuYyjB3/kBJlMH4Y5Ki50M86aaA7iNE4CXheuIYrIOugcjBcwiIiJyyNjT6Kc0U+DYRf1vqq4yzMH/XA/W0rJ5KQB+33CYeJyTYbZubErmeWxFsgSksb4WgNZgmALjBNXWl10A74ktS23Kx3R5XKHXRZVJ7z4RNj745h7437qsXgugvNjJMNfVJc8ptm0EjS/roHswUsAsIiIih4w9DX5KTWuvzy8tTAbMm6LpLeZ8Gx7luSf+wdFPvg+AmjmfAGMo9nmIYLDRZIbZQzIL3VxfS9P3Dmf7U7clL+Ytzmo8I3GWqfZUjO3yuDEVRbRMOj1tW1vZRKedXJYfFgAqSnyErBvbVJO2PWyy6zIyWClgFhERkUOGk2FuI0T2QWKqEl8yYH559rc77F++NlmPXFbm1EUXel1EjBtSapij0eQEwL3b11MWrGHGa99MbPN4sytvOPWDnyHoKsJ95GXdHjv28j8QtCnv+5RvZPUaqSqKvHhNhBObH0/bbrPsMjJYqUuGiIiIHDJqGgOUGj+trjIqovUA7D3/L4zM8vzUkoxhpRmywCmdMMrKnYDZGAPGTUnLFrihgpqjv8H6feM5G4haw5jIDmhXzeDxZBeizZp3NMzbk93gPQX4TDJoLx09LbvzUlQUZQ7kzRCuXwZlmEVEROQQEgxHKcZPm8eZVNfirWLk4guzPr8kZeGSypKOZQhTQusSj72Fpckdxk25dWqIR732PT67+TMA7HaNZKzpWEPs9fQuA96dbSd8PzmkLrpqdMbrzhw6lvp393pMg4ECZhERETlkRCJhSkyAgNuZVOcvqOrR+aldMqoKbYf957c+lHySUodc4u14LMABb+altr2e/snYTjzzM9w77QfUmwqomNDj8ydV9TzIHgoUMIuIiMghw8R7JVc5i23sOeLTPTq/wJMMnYb7khP3nows6nhwSbLQY2P1GRmvF3Vlrv31evuvavbDH72GYddvy3p1v1QVRV7uDp/aD6PKbwqYRURE5JDhjgXMh807nrc/tpHZZ32sR+eblNZpFb4IdbaUrWVH8mrp6R0PHndk4mFT5azM4zEdM8/7bAWuw8/p0bgGUig2Ba5uakopy8eeyNFoBoYCZhERETlkeMJOSznjK2XexOq0ADhbPw87beN8JZV4v76F8V94mjZPsm/yI4fdyK4L703rS1xSkrmvsqddwOw3BYy4cRuUZViNME/EA2ZPaTWrCxewd8ypMPGYHI+qf6lLhoiIiBwy4gFzV8tId2fBZTfzu5eP4+qppxAPg/3uZEB83kf+p8M58RZz7bWP1wttoNfjGnAlVcz62rO5HsWAUMAsIiIihwx35OAD5lNmjeWUWVenbQt6Sjs52lFSmnm/i8yTAfNZfGlxU9yzCZODmUoyRERE5JCRzDB3HeD2VEFhUZf7Z0yewPLoVP4UPjuxrdk3gg3lR/fpOAZCfKVEd3FljkcycBQwi4iIDBErdjTwnX+twtrBl7UcKJ6Ikx3Fl93S09kq6SZgHju8jFn/+wZNp96c2Fb6jQ20eob36TgGwj6cMXsKun7PQ4kCZhERkSHg9uc38Z5bX+CPL26mKRDO9XDylrcPSjIyKS7uPnj0eVycNrPdmoKu/lmgpD+965pbeWH6l/HNeneuhzJgVMMsIiIyBNz0yOrE45ZAmPLCob1UcW8lA+a+LckoKcqup/GcseXcXX0dC6eMZiZgB2HAPG3sCKZd9q1cD2NAKcMsIiIyBMwYlezS8I83d+ZwJPnNlyjJ6NsMc2kWGWZw+jhf8tnvMvO8awEIRXre1k4GngJmERGRIaC6LLli3C+eWp/DkeQ3b7SVKAY8fVt/W1rSu5pof6RPhyH9RAGziIjIEBCJWhZMGAbARQvHpe2rbw2yp8Gfg1HlH2+kjYApBFffhkCFvt6VwPhVbj4oqIZZRERkCIhaKPS6mD6ylEZ/KG3fgu88CcCWH5yXi6HlFXfYCZj7ur+D1927ALxNGeZBQRlmERGRISAatbhdhuElPmqbg7keTt7yhFsJuvu+HVqht3eT95RhHhwUMIuIiAwBUWtxGUN1aQH7WwbR8soDaNeOrZwefpaRoV19fu0Tpjmr3gXHH9+j89piAXPAZNdlQ3JDAbOIiMgQELXgMoaqUmWYO/PXx57rt2t73C74ymZ8V/6zR+cFIlEA6oom9cOopK8oYBYRERkCnAwzDC/x0dAWIhSJ0hoMc+ZPn8310PJGcyybG6Gfeh8XDwdPQY9OqQzVANBSPq0/RiR9RAGziIjIEBAvyagocro1NPnDrNnTxPq9zTkeWd8JhqN88b5lrK9p6tX540qdnsfBD93Vl8M6KKeffREAB474WI5HIl1RlwwREZEhIBIFl8tQ4HGyp2f97Flag0OrBcOrm2v5x5s72dcU4K8fP6bH5zc3Ox8eikoq+npovXbUUcfCUQ24ttbleijSBWWYRUREhgAbK8ko8Di/2vc3B4dcwLzzgLNK3/Pr93PHi5t7fP5Jdfc7D3pYNjEQKop83R8kOaOAWUREZAiIl2R43ENzqeVVuxpZsmZv4vkN/1rVo/MbWoIcHXrdeeLJv44U00aU8OdRX+O1c/6V66FIBirJEBERGQIiUYvLZfCHOmaVF02qZNn2+oEfVB8695bn056XFvQshNmwaw+L4k8qJ/fJmPqSMYYrrvl6rochnVCGWUREZAiwsbZygXC0w77yQg+RqMVam4OR9Q+fp2chTLD5AABbj/8++Ir7Y0gyhClgFhERGQIi1uI2UFncsRa22OdkY8PRwRkw3/f69g7b6luDRHvwfvzN9QAUlA7ro1HJoUQBs4iIyBAQr2E+f96YxLYlXzqZG94zmyPGO10hQpGO2ef+8uSqGv74Qs8n5mXylQfeTjw+Y9YoPn3yNKIWWoLZrysdiAXMhaWVfTImObSohllERGQIiEadOlhjkpP+po0oZdqIUv768hYAWoORRLa5v33yL0sBeP+i8Yne0H1h9pgyRpQ5XS4C4ShlWZ4XaW0AoKhcAbP0nDLMIiIiQ0DUWtyd/FaPB8mtgYFvM3fUTU/R0Brqs+u5XCZRvxzMUK/dmUhbPQC+4mF9NhY5dChgFhERyXP+UKTbcop4SUYmJQXOYiY9KWHoK8FIlHV7e7cyX1xZSkcMt0kGzJkmOHYm6ncyzKYwfxYtkcFDAbOIiEiem/ntx7nw1he7PCYSK8kAuO706XznwjmJffEM85fuW95/g+zC3sbAQZ2fOrVvRFkBPrfzASCbDHMwFOGlW65k8a67nQ2F5Qc1Fjk0qYZZRERkEFi1u7HL/TalJOOLZx6etm/RJKdud8eB1n4ZWyZTqkvYvL8FgEZ/70syrLW0hSK8/8jxeN2G9y4cx/Pr9wPZBcz3v/A2l9Y9mNzgVUs56TllmEVERIaASJclGR4+c8o0WoKRAevFPHZYIbPGONncxrbeB8y7G/xEopYFE4fxg/fPo9DrTiz/HYx0X5Pd1FSfvqGTeyTSFQXMIiIieSx1wlxDF4FnJGJxuzoPBocVe4lELc2BgaljDkcsZYUePC7T5bi7s7O+DYCJw5OZ4dQa5mA42uWHgNamhl6/tkicAmYREZE89uBbOxKP59/4RNrzVIFItMvV78oLndZuTf7+D5j3Nvlp9IfxuAzlRd6DKsmIf2AYltKaLhEwh6Ic/q3HuPFfqzo9f39tba9fWyROAbOIiEgee3VzXdrzL9ybnLjX0BaiNRjGWksoEqWgs75yQNkABsxH37yE1bsbcbsM5YUeGtt6/5rxYDu1l7Mv9j7Xx7pv3PHSlo4nhtrgzb8QbtoHwHMTryXy8ad7PQ45tGnSn4iISB7LNLHt5Y21WCyX/v5VqksLePnrp2EteLsImEsLnV/5TQeR7e0pj8tQUeyjriXY62vEyznKUwLmeA3zG1sPADBheFGH83Y9dANj37mN9zEXgJM+9D9QOrLX45BDmwJmERGRPBbM0H/5kt+/kni8vznAKT/6L0CXJRlliYB54Hoxu10uJlcVs3TLgV5fI56dLi9Mhizx9/nWtnoARpYVdjjvv8vWcakHjrDrCbkK8JaM6PUYRFSSISIikseyaZ0WnxjXdQ2zE3AeTD1xT7ldMLW6lF0NbfhDvVtlsKEtRInPjSclex5/n3ubnP7OLbGJjKt2NfLlvy8nErW04iyfXWwChNxF6o4hB0UBs4iISB7rboW/VF2VZMRrmB95e/dBjylbHpeLqSNKsJZET+aeavSH0uqXAQo87rTn9a0hlm2v59xbnufvb+xgW11rImAGKA7X9+q1ReIUMIuIiOSxUCS9Zdqfrjqq02O7yjCXxpaXfmJVTd8MLAtul2HqiBIANu3rXcDc0BZKq1+Gju/zQGuQ9/7qxbTnEZsMqvd6x/fqtUXiFDCLiIjksfYlGXPGdr60c7HP3e0+XxdZ6GxFopYf/2dtxsl8qT2Rq0sLmFpdCsCmfc29eq3GDAFzYbuAOdDuHu2qb6PY+BPPXWfd2KvXFolTwCwiIpLH2pdkxPspZxLPImdijOHM2aMSGd+D8ey6vdz6zAauf3hlh33haDJgftfh1RT53JQVeKjv5eIljf5w2oQ/AI/bRXVpQSdnwJb9LVSb5FLihSWdf8gQyYYCZhERkTzWPnvqMobTZ2Zuj1bWRTANTkDdEkx2yXhyVU2vVuELhp2guP1EvpZAmG11rYnnx06pApwSirtf28Z5tzzfi9eKdKhZBlg0aRgA580b02Hfpv0tVJNc4a+guKLHryuSSgGziIhIHgtFoowsS2ZTXQb+cGXmOuaqEl+X1yr2uWkJOEHu9rpWPvmXpXzpvmW9Hlv7vhMf+u3LnP6TZxPjLIqVgdS2BGkNRli5q5FItPNlrDOJWnBlWPL72lMP4/SZI3lPSsA8f3wFx04dzjs7G6g2yYDZWz21R68p0p4CZhERkTwWikQTi46AM5EO4KFrT+CXlyzklksWAnDiYdVMqiru8lqlBR6aYy3Y4v2Ydxxo68WoMge9K3clyyC+ff7sjMdk0yYvVSRqcWfoCDdv/DD+cOVRjK9Mvuc/XHkUk4aXsK6mmWrTwJvRw7i94Aooqe7Ra4q0p4VLRERE8lgwHE2rWzaxfsLzJwxj/oRhAJwzZzRet0ns60xJgYdgOEooEk0siNJVZ43udPVyngxZYYBAOJLIPGcjErUZM8xxk6qKKSv08KMPzKO6tIDhpU6WvZxWQnNO4coP/Eg9mOWgKWAWERHJY6GI7bbUItugtyQ2KbAlEE5MJuyqd3Nn4lUVJqUoo6EtxIxRZaytaQKcVf4y6WmGOWot7i4C3rJCLytuODvxfKTHz9mu1ykyQUpKy9IWPBHpLX0ViYiI5ClrLcFIlJHlnXeE6InSAiezu+A7T7I9NjmvN23mAuGOq/bNv/GJRLAMUNBJEN9+EmN3wlGbKEPJxjj28VvfzwAoKSnt0WuJdEYBs4iISJ6KL1oyoqywT65XktJ27pVNtQCUFfb8j83+kBP0dpJEBui07KKnAXO0m5KM9mxxZeKxr/DgW+iJgAJmERGRvBUvm4hnhg9WasC8tsZZSGRydc+Dyragk2E2HfpkJBV6O8swd8xOdyXSTUlGh7G5U1rItdb26LVEOqOAWUREJE/F6317U2ecSWqZxNo9TkeL3kyHawt1H/QWejMH+W9uPZAIuLMR6WFJxrEzUpbBNn3zQUNEAbOIiEieCvVBJ4tUo8qTpR3xsop42UdcWzDC5K89wn1Lt3d6nXjAm9pTeXxlUdoxRZ0EzN9+aCWfvevNDtuXrK5h+fb6DtujPQyYRw8rou7/7aXlwj/Cif+T9XkiXen2O9AYM8EY84wxZrUxZqUx5vOx7cONMU8aY9bH/luZcs7XjTEbjDFrjTFnp2xfZIxZEdt3i+mu/42IiMghLHgQnSwymTai4yS4SDS9priuNQjAT55Y2+l14hlmf0p5RSRq+dDi8XhjTZNLulime8mavR22ffzPS7nwVy92HJ/tWcAMMLy0gJKF7wdvUfcHi2Qhm+/AMPAla+0s4FjgWmPMbOBrwBJr7XRgSew5sX0XA3OAc4BfG5P4m8hvgKuB6bF/5/ThexERERlS4iUZnXWc6AvhdivvhWKv2dXkvHjAnFpa4XSzcCUnKpZ23dkj2yW5o1FnOXCRXOr2O9Bau9ta+2bscROwGhgHXAj8OXbYn4H3xh5fCNxjrQ1YazcDG4CjjTFjgHJr7cvWWgv8JeUcERERaScefHrdLk6ZMaJPrnnJ0RPTnj+9Zi/RlKA5HgzXtzoBbTgS5a+vbKU+lnkG8McCZX9KLXM4EsXrNpwxaxQAFUXJxVbef2RKXXHM0Tc/lWhtl0kkanlyVQ3haBS1UpZc69GXoDFmMrAQeBUYZa3dDU5QDYyMHTYOSC182hHbNi72uP12ERERySCeYfa5XfzhiqNYf/O7D/qaV50wOe357gY/v3l2Y+J5a0rWuLY5wK+e2ci3//kONz2yOrE9kWEOtc8wG269dCEvf/20tFZwP/7gPJ7+0slprxsIR3k6VpoRr9VO9eW/L+eTf1lK1NKjLhki/SHrgNkYUwo8APyPtbaxq0MzbLNdbM/0WlcbY5YaY5bu27cv2yGKiIgMKYkaZo8Lt8v0SS1zpmvc+3oyz5WaNV5001P87Kl1ANz/xg7+/NIWoJOAOWLxul0Uet2MqUivHTbGJLpmZGo3t6fB32HbP97amXjckz7MIv0hq+88Y4wXJ1i+01r7j9jmmliZBbH/xiv4dwATUk4fD+yKbR+fYXsH1trfWWsXW2sXjxjRN3+CEhERGWySy1f3XcCY6Vrb6loTJRetXbR8u/7hlUCydrktmMwMd9f+bXR5IVceP5l/f+5diW3x19p+IFmaEYl2zKUpwyy5lk2XDAP8AVhtrf1pyq6HgStij68AHkrZfrExpsAYMwVnct9rsbKNJmPMsbFrXp5yjoiIiLTTH5P+OlsKe3csy5upx/J75o8FoLrUByQn7KVmo0PRKN4uAmaXy3DDBXM4bGQpt1++GIAmv3OdQCgZeO9t6phtVoZZci2b78ATgI8CpxljlsX+nQv8ADjTGLMeODP2HGvtSuA+YBXwOHCttTb+HXUNcDvORMCNwGN9+WZERESGklAft5Vrf63PnnoY37voCAD2NQUAaAuGO5wze0w5nzppKk1+Z9/+5tixoQjWWgLhCNZm3y/6jNmjqCz20hgPmFM6cuw80Ea4XU1zecoEQpFc6HYBeWvtC3S+ENDpnZxzM3Bzhu1Lgbk9GaCIiMihqq8XLml/rYlVxcweUw7AtXe9yV8+djRffWAFAE9/6WRO+8mziXPKCj0EwlEOtATZ3xzE4zKEo5ZQxLK9rg2AcZXZ9z0uK/QmAvBgSoC8s76N6aPK0o6dO7a8F+9UpO+oUYuIiEieCvTx0tjtr+Vzu/DEapqb/GEu+vVLiX1jKooYVuxkdn0eFwUeZ9Lewu8+CcCE4cWAk2XeWtsCwKSqkqzHUVboSQTMgZTSjl31fhpiLe0+sGg875pezZyxFT17kyJ9rNsMs4iIiORGvA9zZ3XHvZE66c/rduFxZb52kc9NaYGH+tYQBR5XWq9mcJbC3ry/BX8owpZaZ9Le5B4EzOWFXhpjtdCpGeaWQJi1NU0AXHL0BBZNGp71NUX6izLMIiIieSrRh7kPSzKMSQ2YTcauGR9a7DS1Ko0tb13gcXUYw+jyQsDpmLGttoWyQg+VxdnXGpcVeli69QC3Pr0+8T4BAuEIq3c73WtnjFYphuQHBcwiIiJ56Nl1+9gSK3Xoy5KMVF6PC0+7a193+nS+/755aa87sqywQ5Z7RJmz9HVLMMyW2lYmVRWnBePdKSt0gusfP7Eu7YOBPxTlmbV7mVRVnAjYRXJNAbOIiEiesdZyxR9f43fPbQL6NsOcyud2dWgFd9kxExP9lH/24fm8b+E4Fk4c1mEMx0+rBuCpVXt5dt0+xg3LfsIfOKUXcXsa/RR53QTDzjLcb22r77Kns8hAU8AsIiKSZ9r3Qu7LhUvSr+tKC0yvPmkqo2KlFgCHjSzjpx9eQKHXnRYw//AD8ygvcrK/8ZUAh5cU9Oi1U9/jm1sPML5dh42+rNsWOVj6ahQREckzB2JdIsCpH/Z2MjHvYHndJq0k4xvnzur02NSA2ed2dSiX+MS7pvTotaM2OYlw+Y6GDgFzeaF6L0v+UMAsIiKSZw60BBOPLz5qQr+tdOd1u7LOXg9LWTzEmGQNMsAxU4YzbURpj157TEVh2vPxlcX83/uP4EtnHg7A2XNH9+h6Iv1J1fQiIiJ5pj4lwzxv/LB+ex2fp/O2cu0tmDCM8kIPjf4wtc1BygqTIURJLybnXf+eOSyaVJlYKKW00MOHj5oIwIeOmsDIsp6VeIj0J2WYRURE8syB1mSG2dtPE/4gPcN8/rwxXR5rjOELsexv1FqnVCR2brHP3ePXLilwAuTzjnBe9xMnJks6RpUX9qjjhkh/U4ZZREQkz9SnBsz92C3C6zYYY1j6rTOoKOq+Zvijx04C4NJjJmKMobTAw4HWUFq2uad+ddmR/KrXZ4sMDGWYRURE8kzqpL/2fZL7UrwTRXVpQVa9nj1uF1edMCWxTHZpLFAeXuLrtzGK5AMFzCIiInkmtYa5v1rKOdc+uDAgGlugr7JYAbMMbQqYRURE8ow/nOxR3F+r/MHB10eHIk7EXFWqgFmGNgXMIiIieSa+VDSAp59rmA9GOOr0UlaGWYY6BcwiIiJ5JBK1bNjbnHjenzXMB7sgSigW2Ff1cJU/kcFGAbOIiEge+dF/1rJse33ieX8sET2pqhjgoBdECcZKMipLtCqfDG1qKyciIpJHnl23L+15ka/vA+YHrjmeTftaDvo6xT43gXBUJRky5ClgFhERySORaLJ++RcXL+jxktPZqC4toLr04Mso7rn6OJ5aXdOrlf5EBhN9hYuIiOSRSGwiHcCFC8blcCTdmzG6jBmjy3I9DJF+pxpmERGRPJIaMItIflDALCIikkciVgGzSL5RwCwiIpJHIhEFzCL5RgGziIhIHgmrJEMk7yhgFhERySP+kLMs9qXHTMzxSEQkTl0yRERE8kQ0amkKhPnUyVP56tkzcz0cEYlRhllERCRPtATDWAvVJQUHvQqfiPQdBcwiIiJ5oiXglGNoIRCR/KKAWUREJE80B8IAlBS4czwSEUmlj7AiIiI5dvvzm5g7roIirxMol/j061kkn+g7UkREJMduemQ1AHd94hhAJRki+UYlGSIiInliS20rAKUKmEXyigJmERGRPPGNB1cAqmEWyTcKmEVERPKMMswi+UUBs4iISJ5RDbNIflHALCIikmeKfSrJEMknCphFRERyKBq1HbYZo1X+RPKJAmYREZEcCscC5i+fPSPHIxGRzqhISkREJIei1gmYXcbw2VMP4+2dDTkekYi0p4BZREQkh+IZZo/L8P+UZRbJSyrJEBERyaFILGB2uVS3LJKvFDCLiIjkUCQlwywi+UkBs4iISA4pwyyS/xQwi4iI5JAyzCL5TwGziIhIDkViXTLc6r0skrcUMIuIiORQOBIFwONWwCySrxQwi4iI5JA/5ATMhV4thy2SrxQwi4iI5JA/FAGg0KtfySL5St+dIiIiOZQImD3KMIvkKwXMIiIiOeQPOyUZBcowi+QtfXeKiIjk0H2vbwegQBlmkbylgFlERCSHnlxVA8D4yqIcj0REOuPJ9QBEREQOVdZavG7DpcdMZlixL9fDEZFOKMMsIiKHjJc27mddTVOuh5Gwu8FPSzDCYSNLcz0UEemCMswiInLIuPT3rwKw5Qfn5XgkjvV7mwGYroBZJK8pYBYREcmBfy3fxTNr9wIwdpjql0XymQJmEREZ0iJRi9uVvux0OBLF485dVWIgHOFzd7+VeK6WciL5Td+hIiIyZN3x4mamfeNRdta38acXNye27zjQlnbcngY/9a3BARtXWzCS9rzArZZyIvlMAbOIiAxJr2+p44Z/rQKc8ocbY48BNu9vSTv22O8v4djvL+mXcVhr+cVT61m9uzGxrS3ULmBWhlkkr+k7VEREhqTn1+9PPP7BY2vS9rUPmAH8oWi/jGPjvmZ+9tQ6vv6PFZ2+li+H5SEi0j19h4qIyJC0ZHVNp/tqWwIDNo7/rt0HwNTqksS29iUZrnY11iKSXxQwi4jIkBONWlbuauywffGkSgCa/OEBG8uq2DhGlhcmtvnDkc4OF5E8pC4ZIiIy5Nz9+rYO2zZ//1yMMZz4f0/THAuY9zT4E63dAILhKD5P3+aSAmGn/CJqbWLblgwlISKSvxQwi4jIkPPNB98B4H/Pn43P42JSVTHGOGUPXreLf7y1k9ZghBmjy/jFkvWJ8+pagoyuKMx4zd6KB8zhSDJg3l7ndOn48QfnM7KsoE9fT0T6ngJmEREZsj524pQO2/Y3O/XLj6/cw+Mr96TtW76jntEVo/t0DMGIEzBHosmJfnUtAcoLPXxg0fg+fS0R6R/d/t3JGPNHY8xeY8w7KdtuMMbsNMYsi/07N2Xf140xG4wxa40xZ6dsX2SMWRHbd4uJf9QXERHpB4d1stx0kbfznsc3PbKq0329FYi1kAtHkxnmutYQw0t8ff5aItI/sinUugM4J8P2n1lrF8T+PQpgjJkNXAzMiZ3za2NM/CfTb4Crgemxf5muKSIictAKPC5Onzky477CLgLmmsYAgT6ekBfPMKfWMB9oCVKpgFlk0Og2YLbWPgfUZXm9C4F7rLUBa+1mYANwtDFmDFBurX3ZWmuBvwDv7eWYRUREOmWtJRCOUtBJYFxa0LEa8bvvnctvLjuSYDjKG1sP9Ol4ghlqmOtaglQpYBYZNA5mKvBnjTFvx0o2KmPbxgHbU47ZEds2Lva4/faMjDFXG2OWGmOW7tu37yCGKCIih5r4JLuCTrpd/OqyI2nf9vijx07iXYePwOs2PLu2b3/vxAPmv7+R/DVY1xKkslgBs8hg0duA+TfANGABsBv4SWx7prpk28X2jKy1v7PWLrbWLh4xYkQvhygiIoei7gLmKdUlrL3p3Rw7dTgA75peDTiZ5zljK3h7R0PiWGstk7/2CD99Ym2vxxMvyUi9Zl1rUDXMIoNIrwJma22NtTZirY0CvweOju3aAUxIOXQ8sCu2fXyG7SIiIn0qXoPcWUkGOK3l/vKxY3jfkeO48YI5ie3lRV5aQ8ka5njwfcvTG3o/nnbLYLcGIwTDUdUwiwwivQqYYzXJcRcB8Q4aDwMXG2MKjDFTcCb3vWat3Q00GWOOjXXHuBx46CDGLSIiklE8QC3sZgESn8fFTz+0gKkjkt00irwu/LFlq621XP/QyoMeT/sM89qaJgBlmEUGkW77MBtj7gZOAaqNMTuA64FTjDELcMoqtgCfArDWrjTG3AesAsLAtdba+Ef1a3A6bhQBj8X+iYiIHBRrLf9+ezfvnjsaj9uVLMnoIsPcmUjUsramCWst+5uD3Lt0e/cndSNewxwf60dvfzX25KAvLSIDpNuA2Vp7SYbNf+ji+JuBmzNsXwrM7dHoREREuvHYO3v43N1v8eWzZ/DJd03l1qedlfs6q2HuylOrnWWyl+9owN1HywWkBsy1LUFGlheyeX8LM0aX9cn1RaT/HUyXDBERkZyrbw0BsGlfC79/fhP/XOZMkelNwDw2tiy2Ad7clt5ezh/qWX9mfyjCH17YnFaS8f/+vpyjJldSWuBh/oRhPR6fiOSGAmYRERnUgrFJfg+8uYN/LU/OJ09dKCRbP/7gfAC21Lbw6uZaAHxu51fljgOtPbrW3a9t47v/Tl858OWNtTT5w4yJBeYiMjgoYBYRkUFtb1Mg8XjNHmdCXWmBh7ljK3p8rSKfU/f8+XuW8eiKPYwqL+CeTx0LwJb9PQuYM60oGAhHeeydPZQVdlsRKSJ5RAGziIgMavtSAua4FTecxcjynmdx4wFz3PCSAiZXlQCwta5nAbPXnfwVe+qM9DUFygq9PR6biOSOAmYRERm0guEoy3fUM298ejbZ9HLCXrE3PfNbVeKjsthLkdfN7vq2Hl0rteb5xgvmMqW6JPF8T4O/V+MTkdxQwCwiIoPWT59cx7qaZnpRrpxR+wzz6IpCjDGUFXpoDoR7dK0t+1sSjytLvMxM6Ypx9tzRBzdQERlQKqISEZFBa3eDk/X97GmHMW5YEef/8oWDul5xu4D5y2fPAJya6KYeBMzBcJTbX9gMwJ+uOoqyQi+RqBPV//KShZw/b0xXp4tInlHALCIig9LeJj9N/jAel+HsOU7G9uHPnkAo0vt0c+pEvevfM5tRsTro0kIPLT0ImF/YsA+Ao6cM59QZIwGYP2EYT6yqYUp1Sa9LRkQkNxQwi4hI1gLhCPWtoUQgmUvXP7SSp9fsTds2b/ywg7qm25UMZEt8yV+RpQUemv3ZB8yvbzmA123428ePSWy75uRpnDpjJLPHlh/UGEVk4KmGWUREsvb5u5dxzPeWEEpZjCNXdhzo2SS8nkqtZy4t6FkNc1swQpHXjS9l8RSXyyhYFhmkFDCLiEjWHl+5B4DNKRPacqWthyvvZWtYsdPyraQgJWAu9NDUgwxzIBzF5+nYh1lEBicFzCIikqYtGOG6u99ibWwREHBapE3+2iOJ52tS9uVKQ1uoX647tqIISK9nLi3w0OTP/vUC4UivluYWkfyk72YREUnz5rYDPLx8F1994G0A/vTiZmZ++/G0Y9bsbszF0NI0tIU4YlwFv7h4QZ9ed9rIUgACoWTZyaSqEhr9YfY2Ztc/ORiOUuDVr1iRoUKT/kREBICnVtVQUexNZI/jLdZu/NeqxDHnHjGaZdvqM66uN5D8oQjBcJRz5o7mwgXj+vTa37lgDmMrCjlxenVi29gKZ5JjXWswqxUEA+EoBSrJEBkyFDCLiAgAn/jLUgDOmj0KSO8YETe6vIgCbxP+cG4n/cXLMcqL+n6J6coSH18/d1batvgy16Fwdi3rnIBZGWaRoULfzSIikuaJVTUA1LUEO+z70lmHU+BxEcgw4a45EMb21ZJ7XWgOhLnpkdUATKkq6ebovhHvdhGMZDfRcOeBVqpLC/pzSCIygBQwi4hIRit3daxTLinwUOB1d8gw728OMPf6//CH2Op2/eknT6zlX8t3ATB33MC0aYtnmINZZpj3NPiZOLy4P4ckIgNIAbOIiLBmT+ZJfC9vrKXIm16LW5ghw7xpn9Nm7q5Xt/XPAFPEe0AX+9wMK/b1++sB+Dwm7bW7E7EWj1ur+YkMFQqYRUQOcXUtQc75+fNp2751nlPDe8nvX+nQ7zg1w1zfGmRnfRtba52AOVPdc1+bNNwpw1jypZP7/bXi4hnmHzy2hrZg92UZ0Si4tPy1yJChgFlE5BBX19Kx48UVx09Oe/6ZU6bxyHUnAukZ5jN++iwn/OBpXtiwHwBjIBrtvzpmay3+2GsPZI1wvIZ51e5GfvPsxm6Pj1iLW79hRYYMfTuLiBziMi0A4k2J9r5z4Ry+cs5M5oytAJwFPQKxDPP+Zmdi4EPLnJridTXNTP3GoyzfXt/n4/z9c5uY8vVHqWsN4nGZtDH2t9TXymYBk0jU4laGWWTIUMAsItLP/KEI4SxrX3OhsxXzvnz2DEoLPFx69MS07QUeVyLLW+LL3Gv432/v6ttBAt97zOmMsfNAW4e66v5WVZKsle4uEI5n2I0CZpEhQ32YRUT62cxvP87pM0fyhyuPyvVQMooHzBOGF3H75Ucl6pGvPfUwrj31sA7HxzPMgXCElk7qeWsa+35hk3jHurd3NDCxamA7UKROLuyuTjsaG+hA1HOLyMBQhllEZAAsWbM3p6+/encj5/z8uYzZ5IZWZ9s/P3MCM0aXcdac0V1eK55hrm9Nv9blx01i6ghnQt72A619NHJHJKUuek+jn7mx8pCBdO/VxwIkylE6E1HALDLkKMMsItKPgjleES/up0+uY82eJl7asJ93HzEmbV9DWxjIftW8eIY5vrDJl8+eQaHXzcdOmIwxhi//fTnPrtvXp+Pf35yesb7yhMl9ev1sHDO1ipFlBYlylM5EY//L1SVDZOhQhllEpB+1BMK5HgIA8dAtnKGDxZ5GP1Ulvqwn0RV4XESilr1NThC7aFIlHz9xSqJmd1xlEXubAkz+2iN86b7lafXbL2+sTcsWp1q5q6HTWu99sde69JiJbPzeucwaMzALlrRX7HPT2k1buWSGeSBGJCIDQd/OIiL9qDZDy7Zc2LTfqUvOFMDvbmhjzLDCrK9VGJtwt6ehDYDhJemLh4wbVpR4/MCbO1izp4lI1PLKplou+f0r/Oa/G9KOD4ajXHjrC5x3ywvc8dKWjK+5t8kPwAcXjc9pqUORz9OhL3V78Q8EyjCLDB0qyRAR6UfPrnP6E7tdhr2Nfgq8biqyLH3oS62xQLk2VkaRald9G5OrSrK+VoHXFTvPCWI7BMyVRWnPz//lCxR53YlA88dPrOOcuWM4bGQpAMu217N8RwPgLCmdyfY6JzgfWZ59YN8fin3ubhcuiXfJUA2zyNChDLOISD/671pnsl9lsY+jv7eEd/3f0wM+BmstB2IT9GoaOwaku+r9jB1W1GF7Zwo98Qyzc61h7T4ALJxQ2eGc9lnZM3/2bOJxOJosw7j9hc185s43sDa9bOPNbQfwuAwjBnCxkkyKvG5ag12X2WjSn8jQo4BZRKQfbdrnlEI0B5yAtdE/8DXNTYFwImDdsLc5bd9/Vu6hORDuEKB2JZ5h3lbXSkWRF0+7Yt0in5s7P3FMl9dIfbnmdvfk0RV7EguixNW3hpg1pjyx4l6uFGVRwxxVSYbIkKOAWUSknwTDUXY3tGEM+EPJLGpzrDwiEI4keh73p5pYJrjI62btnqa0fUtW1wBwwYKxWV+vIJZhfnlTbVq9cqoTDqvusG3m6LKMxzZl+BCxq74t7bk/FKGok0VSBpLP4yLYzSI08TmNyjCLDB0KmEVE+snO+jaiFuaMTe/o8KX7lmGt5dN/fYOTf/TfbtuUHaw9sTKMOWPLqW0J8vel2xP73C4X1aU+Fk0anvX1Cr3JXx3RLjLT7bPMa9oF6/GsdnOGiYh1rckM81vbDvDq5rrEZMNcKnC7um0VGC/JULwsMnQoYBYR6Sfx7PGcMemLbPxnZQ3r9zbzzFqnV/Hm/f2TZbbW8o0HV/DQMmeZ6njv4jtf3cb3Hl1NSyBMSyBMaUHP5n/PTvkAcP175nR63AmHVXPGrJF89ZyZ+Nwu3n/k+LQJj/FVApv8HRdT+eWS9YnHX//HCgB21PXtYii94fN0HzCrJENk6FGXDBGRfrI9FuDNHtuxZ3BqacT6vc390ld4475m7np1W+L5CdOcMoll2+tZtr2eYp+b5kCYkh4GzCPLkp0qRld03bXi9iuc5cCvPmkqLgNba1u56ZFVPLV6L41tIUoLPDQFwvg8Ln55yUJufXoDK3Y28Oa2+sQ1Zo4uY82epkRrvFzKpiQj3hqvvz4IicjAU4ZZRKSfbK1tpdDr4rSZIzvse3VzbeLxhpqmDvv7wod++0ra8/aB8c+fWs9z6/ZRdRCdJ0oKsiuTcLsMxhgmV5fw/iPHA9AYyyw3+cOUF3o4e85o/vW5ExPnrNrVyJV/eo3SQmfc3zx3Vq/H2Vd8WZRkvL6lDoAJw4sHYkgiMgAUMIuI9JNtda1MHF6cFjj9zxnTmTi8mL+9ksz8bq7tn1KDunY9lzN1mAhHLZcdM7HH1/7++46g0OtiWJGv+4PbiS/B3RhbkrvZn14W8pVzZgBw/i+f579r9/HI27upKPLyyZOm9vi1+lpWJRnWMnVECR9ePGGARiUi/U0Bs4hIH/vPyj1s3t+SCJgBHv7sCcwdV87HT5yS6BZRWuDh6MnDEyvm9bVhxR0XSJkxqmOnijNnjerxtS85eiJrvvvuXrV5Ky90xtXQFs8whygrTI51arWzoEm828SB1hBed37UA/s8LsJRm6hTTrVkdQ2/e24j62qaOX3mSFya9ScyZChgFhHpQw1tIT711zc4++fPsWZPExOHOyvozRs/jH9/7l2UFXoTK+N9cPF4xgwrTHSx6Gsel+E989Pbxf37uhMT7c4uPmoCr3z99AEP7MqLnGzyixucVRCb2008nDoi+1UHB1r8A0KmOuaP/3kp33t0DcFwlNKCgV/NUUT6jwJmEZE+FG+RFv+z/aSqjnWsZ8QyupccPZHR5YXUNAR6tHBINqJRZ3W/icPT+yR73S5e+8bpPP+VU/nB++d1O2mvP0yoLGb++Ar++spWAuEITf4wZYXJgDnTPWu/kEmu+GKLtATalWW0///n9Si7LDKUKGAWEelDbe2WTZ42orTDMWfMHsW6m97N4aPKGF7iIxiJdrt6XE81+kNEopbhJR0n9FWVFuR0QprLZfjAovFEopbGtjBN/nBiYh8kF0YB+OOVi3MxxE4VxDPM7QLm9gG9z61fryJDidrKiYj0kbe2HeDKP72etu34aVUZj43/aT++rHQ40rcZ5trYhL+qWPlHvrUETkz884ecGuZ2HTxueM9s1tY0M3/8sByMrnOdlWQEwpGMx4nI0KCAWUSkj1z065fSnp8zZ3S39cHxyWyhaNedF3rqQCxgrizx8do3Tk8E5vkiPvHvn2/tpDkQTpv0B3DlCVNyMaxu+TrJMLe1+wuBN8/ut4gcHAXMIiL94OaL5vLuuWO6PS4+AS+SoevCwYj3OC4v9DCyfODrlLsTzzD/8ukNAGklGfnM53bKRdoHzC3tAmaVZIgMLYPjJ5SIyCDy2VMP47JjJmV1rNflBFahblaPi2sOhPGHIlR3s9hIIORcL7UeOJ9UFKX/+inrImC+7SNH0hzo2xrv3uosw9zarnbdq5IMkSFF39EiIn2ss7rlTDyxkoxwxPLyxlpe21zX5fHff3Q1i296itrmQJfHxWtsC7z5+WO+yJceIJd2sTz3OXPH8IFF4/t7SFlJ1jCnB/Dxkox4rbgyzCJDi76jRUT6yLhhRcyfMIzjD6vO+px4SUY4arnk96/wod++3OXxT6/ZC8D/Pb6my+Pibc/yNXAbN6yIoyZXJp5XFA2OvsWdtZWLdzkpiX0QGCzvR0Syk58/SUVEBiF/KMLcseU9Oic+OSzczaS/pVvqeGnDfkaUOaUY9y3dkZjYl0m8ZKAgj0sDvnDm4YnHE3PY5q4n4hnmS3//atr2eElGodeddpyIDA36jhYR6QNN/hC1LUFG93CCnceVLMnoygdue5lLb3+VA63JILk+trR0JoFwftcwA4wsS9ZhV2XoF52POvsAEs8wf+Pcmfg8LqaP6th/W0QGLwXMIiJ9YOWuRgDmjq/o0XnxGua2ULIm1h9Kr48Np0wIrG8NMW6Ys3pfY1uI17fUcduzGztcd8WOeiC/M50jSpMfLvK11rq9zu5nPGA+94gxrLvp3Ym2eSIyNKhLhojIQXpm7V5e3eRM1jtiXA8D5liXjOfX709saw6EE3/aB9i0vyXxuMkf5vBRZeysb+N/7l3G5ti+Dy4aT1VK54x/LtsF5HfAXF6Uurpf/o4zVWc14a3BMC4zeN6HiPSMAmYRkYMQikS5Kra637Bib7ft3tqLZ5iXrK5JbGvyh9Ous2Fvc9o58YmCm1MC6a8+sIL9zQFuuXghE6uKKS/0cOzUqsSx+cikLD9o8m0pwk50lWEu8XkGzfsQkZ7RR2ERkYPQ5E/2361v7bymuDPxDPP+lDZxzf70nr5N/vTrvm/huA7XeWp1Dcu21/PdR1YBTg3zlOqSHo9HuubJ8AHEH4qwvqaZIl/+1ouLyMFRwCwichDaB7M9Fc8w1zQmA+b4Netbg1z+x9dYV5OeYR47rIjTZo7MeL11NU1cdvsrBMJRBXD9oDhDv+jP3vUWL2zYn1ZGIyJDi0oyRER6KRSJsnp3U+L59y46osfXiK/0l6op4GSY//X2bp5bt4/n1u1L2z9rTDnDijNPKtta28rW2lYAigdBwHzzRXNZFZswORiUFnhYNKmSN7Ye4I2tdSyaNJz/rnV6Y+dx9YuIHCQFzCIivfT9R9fwxxc3A/DPa09gwYRhPb5GphrjeH/lQLtuGWfPGcXlx01mRFkBNtaF7rrTDuOWpzdkvHb71fTyUbZLiOeT5dvrAXhsxR4WTRqOz+MiHIyofllkCMv/n6YiInnqxQ3JzhYTKot6dQ2vOz3IchnYWd/GI2/vZvmOhsT2Yp+b3350ceJ5PNCeN34YXrchFLGcdPgInlu3j5veO5eVuxo45fARvRqTdC0eIIejzqcWZ/GZSNcnicigpoBZRKSXRpYXsLbGKcmo6mF3jDhPSpuy02aOZO2eJl7aWMsv22WNrz5patrzr54zk7EVhZwyYwR3fuJY/vbKVn70wXnsPNDG1BFaNKM/FXhctAYjhKNRHl2xm4bYAjJR2/XiMyIyeGnSn4hIL/VFjXBq14U/XnkU44YV8XZs0RGA844YA8C8dguijCgr4ItnzcDjdnH0lOHccslCCjxuBcsDIN5aLhiO8pk730xsH1vRu78yiEj+U4ZZRKSX4stP/+mqo3p9DU+7koxxlUW8tqUu8fzy4ybx4w/OV8eLPBIPmOP//+NuvHBOLoYjIgNAGWYRkV7yhyIcPXk4p87I3OItG552XTLGt6uFriotULCcZ4YV+QAIR9JLMA4fVZaL4YjIAFDALCLSS4FwlALvwf0YjZdkxCfxjRuWHjBXdtI+TnLnd5cvAqBC/29EDhkKmEVEeikQilLgObjsryvWimxEbNLgxKrixL7Lj5vU68mE0n/GVBQxcXgxrYEwYysKcz0cERkAqmEWEeklfzhy0Bnm8iIP/++swzlv3lgAplYnJ+1958K5B3Vt6T/FPjctwQjDin3savDnejgi0s+UYRYRydLbO+rTlsJuCYQpPsjlkI0xfPa06UypLgGgutR3UNeTgVFS4KE1GEaN5EQODd0GzMaYPxpj9hpj3knZNtwY86QxZn3sv5Up+75ujNlgjFlrjDk7ZfsiY8yK2L5bjJZEEpFBxB+KcMGtLybaiIUjUfY3Bxndx3+Sj/dlnjaipE+vK32rpMBDSyBCNKqQWeRQkE2G+Q7gnHbbvgYssdZOB5bEnmOMmQ1cDMyJnfNrY0w8/fIb4Gpgeuxf+2uKiOStA63OctWvbq6LPQ8RiVpGlPV9jfELXz2VB689oc+vK32nxOdmw95m9jT6OXLiMJ7/yqm5HpKI9KNuA2Zr7XNAXbvNFwJ/jj3+M/DelO33WGsD1trNwAbgaGPMGKDcWvuytdYCf0k5R0Qkr+1rCnDc958GIP6nsZZAGIDSgr6fCjK+spjyQnVgyGfFPg/NgTANbSHGDCtiwvDi7k8SkUGrtzXMo6y1uwFi/403IR0HbE85bkds27jY4/bbRUTy3mPv7E48DoSjtAbDtASdgLmkHwJmyX+TU7qZuFVhKDLk9fWkv0w/NWwX2zNfxJirjTFLjTFL9+3b12eDExFJFY1a6mOlFl15ZVNt2vPn1u2nJRABoMSngPlQNKo8WbvudilgFhnqehsw18TKLIj9d29s+w5gQspx44Fdse3jM2zPyFr7O2vtYmvt4hEjRvRyiCIiXbvtuY0s+M6T7G3yE4laWmNZ4/aWbatPe17XEuS1zU4QXVGk0olDUWo7QSWYRYa+3gbMDwNXxB5fATyUsv1iY0yBMWYKzuS+12JlG03GmGNj3TEuTzlHRCRruxva0lq7HYwnV9UAsGV/K396cTOz//c/PPL27rRj1tc0savBz8dPnMJ9nzoOgF8sWcePn1gHwPRRpcihp8CT/PWpkgyRoS+btnJ3Ay8DM4wxO4wxHwd+AJxpjFkPnBl7jrV2JXAfsAp4HLjWWhuJXeoa4HaciYAbgcf6+L2IyCHguO8/zft/81KfXKssNrFuT6Oflzc6GeN7Xt+Wdsw3H3Q6ah4+qpSjpwyn0OuipjGQ2F94kH2YZXBKXeFRJRkiQ1+3xXfW2ks62XV6J8ffDNycYftSQMtWiUivBcLO5+91Nc19cr22WAnGdXe/ldi2ra417Zi61iBjKgq5aKFTVeYPRRP7tMjIoSs1w6xlBUSGPq30JyKDxt6UzO6/lnc6DSIr/lCE17cc6LC9rjl9EmBtc4DTZ43E5+n443JEWd8uWiKDR2oNs1u/SUWGPH2bi8igsau+LfH4c3e/xRtb01vEW2sTWejuNAc6TvD7+IlTaAqEE9cIhqMcaA0xojRzYPz1d8/MdugyxKSVZCjDLDLkKWAWkQERDEe55m9vsHJXQ2LbvqYA33xwBQ8t29nt+a9truOZteltJp9fvz/t+e3Pb2bGtx6noa37SYFtQSco/u6FcwCnvGLaCGcCX12Lk2Xe2+QHYGR5x9X87rn6WE46XF18DlUFGf7iICJDl77jRaTfPbZiN4d/6zEee2cPF/0qOWHvvqXbufPVbXz+nmUcaOm8H/LSLXV86Lcvc9uzGwH4/OnTgY49kG95ej0Ab27rWGqRavP+Fj5/j1O3XFni409XHcXDnz2RqlhN8v4mZyzv7GwEYMbosg7XOHZqVZevIUNbaoY5FO10WQERGSIUMItIv7LWcs2dbyaeByNRGmNt4bbVJifYvZOSeW7vA7e9nHhcVuDh2lMPA5xJgNZaXttcR0sgnAigl26py3gdgO11rZz64//yZqy3crHPzakzRjJ2WFFiMYqaRiez/OrmWrxuw+wx5Ynz//yxo/nUSVOzeu8ydKXWMIcj0S6OFJGhQEtUiUi/2t3g77DtmTV7uXDBOO5duj2x7e9Ld/DOzkauOWUaAMu21+MPRTpkcsuLvHjdBrfL4A9F+c/KPXz6b2+mHfP6lgPc+vR63C5X4npx33hwRdrz1PrkCZVFGAO/fHo9xx9WxYNv7eTM2aPSWsedfPgITlYpxiEvtSTDKsEsMuQpYBaRfrVyV2Pi8a8uPZJr73qT7XWtHVbVe3j5Lh5evotpI0r42j9WJOqIb710IQCTqorZWttKQ1sIYwyFHhf+UIRVu5vSrvPxE6fwhxc289pmJ8v8qZOm4krpk5ta9+x1G+aOS2aPq0oLuO606fxiyXpuemQ19a0hLlwwro/uhAwlqSUZEUXMIkOeSjJEpF899o6zct7KG8/mvHljKPS6aPSHWbHDKcH42Yfnpx1/9V/fSATLAJ+96y2mVJfw3QudNu7xpagLvW7aQhF2HEiWdXhc6QEwOH2U46JRy8iyAs6cPYqXvnYab3z7zA49dN8zfywAd73qLGAya3T69USAtDaDipdFhj5lmEWk30Sjln+86XTAKClwftyUF3ppaA1xx0tbqCz2ctbs0RR536Et1LEd3GEjS9mwt5kPHzWBkw4fwZ2fOIbJ1SWAEzD7Q1FW7Wpk5ugybrlkIVOrSzr0Vn51Ux3HTB1OdWkB339sNXubApw1exRjhxVlHHNqR4wjJw5jwvDMx8mhze0yjC4vZE+jn6giZpEhTwGziPSbQNiZDFVWmPxRU1rgYW1NEzsOtHLGrFGUFHgoK/TQFopw80VzOXvOaPY3B4hGoabJzy+XrOf9Rzqr7J1wWHXiOoVeFw+8uQOAL515OIePcjpZjChLX33v2ruc+uYVN5zF7S9s5r0LxvLBxRM6HXN5oZcffmAeJxxWzbhOgmoRgK+fO5PP37MMNckQGfoUMItIv4lnjb945uGJbZv2tyQeT6oqBuCm987l6r++weJJTia4utTJ8s6mnFNnjMx47Qvmj+NnT60DYP6EYYnt5bGSjfaOuOEJAMZkEQR/qIuAWiQuXs5jlWEWGfJUwywi/cYfC5hTu0ykGlbsZIPPmjOadTe9O2O/48588qQpicfHT0t20qhICZiPmTK8w3nvSslSixwMdyJgzvFARKTfKcMsIv0mGTBn/mweSflbtq+HK6cV+zzc9pFFzBtfgcedPLfA4+Z9C8dx4cJxTBtRwhfvW57omPH1d8/keAXM0kfiX7M9/doVkcFHAbOI9ItI1PLZu97qsP2Nb53BopueAuDdR4w+qNc4Z27m83/64QWJx/d96jjuW7qdVzbV8sl3acER6TunzRzJtadO4xMn6utKZKhTwCwi/eLtHfWs2u30YJ4+MllqUVVawBfPPJy1NU2MLCvs7PQ+9aHFE1SXLH3O7TJ8+eyZuR6GiAwABcwi0i/qW53lrx+45jjmjqtI23fd6dNzMSQREZFeUeGViPSL1qBTvxzvvywiIjJYKWAWkYPS0BriS/ct565Xt7F2T3KZ6vjS1yU+BcwiIjK46TeZiByU/6zcwwNv7kgsIrLlB+cB8Jv/bgSgyJe5pZyIiMhgoQyziByUwgwB8UPLdiYWKCkvzLyQiIiIyGChgFlEulTfGqS+Ndjp/ma/U3pxdGyREGst/1q+G4B54yvUo1ZERAY9/SYTkS4t+M6TLPjOkxn33fr0er7x4AoguarerU9vIByNOo8vOXJgBikiItKPVMMsIhlFopYdB1q7PObHT6wDYEp1CeMriwD4yZPrqC4t4P1HjmdiVXG/j1NERKS/KWAWkYxm/e/jBMPRxPNHV+zm3CPGJJ5v3NcMwMThxdxx1VGMKi/k8Xf28MzafexvDjCqvGDAxywiItIfVJIhIh34Q5G0YBngry9vTXv+jX84pRi3XrqQSVUlFHrdnDdvbGL/sGJN9hMRkaFBAbOIdPD6lrq05xcfNYGXN9XS0BaioS3E9rpWVu1q5LJjJjJv/LDEcVWlvsTjgVr2WkREpL+pJENEOli5qxGAv378aI6cWMnLG2u55/XtzL/xibTjxsXqluOqS5JlGO+aXt3/AxURERkACphFpIP61hAuAyceVo0xJi1znGpEaXqd8viUALqqVDXMIiIyNKgkQ0TSPLpiN7c9u5GoBWMMAMOKkwHzBxaNTzyuLE4PpCtLfHzj3Jnc/+njBmawIiIiA0AZZhFJ86X7lnfYNr6yiPOOGMM1p0xj7rgK9jT4eWHDftxu0+HYq0+aNhDDFBERGTDKMItIQkNbiLZQpMN2r9vFry47krnjKgC44YI5nDZzJMdOqRroIYqIiAw4ZZhFJGHFjgYApo8s5VeXdb5K32EjS/njlUcN1LBERERySgGziACw40ArX7xvGQD3f/p4KtRHWUREBFBJhojEvLSxlr1NAT5x4hQFyyIiIikUMIsIAE3+MACfPe2wHI9EREQkvyhgFhEA9jcHACgtUKWWiIhIKgXMIoPYm9sOcPkfXyMYjgJQ3xrk+fX7sj6/oS3EV+5fzhfvXca/lu/CZcDj1o8FERGRVEoliQxin7/nLbbXtbG1toXpo8r41F/f4NXNdSz73zPTFhvpzB0vbuG+pTsA8HlczBpT3t9DFhERGXSUShIZxPwhJ7Pc0BYiHIny6uY6AN7YeqDbc0ORKHe+ujXxPBiOcvSU4f0zUBERkUFMAbPIIGatBWDFzga+/o8Vie0vbazt9tz/rNzD3qYAnz45uTJfdWlB3w9SRERkkFPALDKIRaJOwHzjv1bx9zec0ooZo8p4ePkuAuGOK/al+sMLm5k4vDitK8aIMgXMIiIi7SlgFhmkDrQEOdAaoqwwfSrC1949k31NAX70+FpaAuG0ffEA21rLyl2NnDV7FKUFHq44bhIzRpWxaFLlgI1fRERksNCkP5FBatP+ZgDOO2IM97y+HYBrT53GKTNGMH1kKbe/sJnbX9gMwJ2fOIYDrUE+f88yHrr2BKZUlxAMR6mOZZRvvHBubt6EiIjIIKCAWWSQ2rSvBYCPHDuJA61BvnnubCZWFQNw8uEjWL+3OXHst//5DoFwlEjU8ocXNrPzQBsAw0u676QhIiJyqFPALDJIbdrfgtdtmDm6jN9+dHHavpHlBR2OjXvwrZ2Jx1UKmEVERLqlGmaRQaAlEKY5EKYtGCEaq0PeVtfK+MrijAuNnDV7NIVeF2fOHsWVx09ObD98VGnaccOKvf06bhERkaFAGWaRQeD9v3mJJn+YnfVOKcXm759LY1uIiqLMAe/k6hKWX38W1kI4arnjpS0A/O/5c1hb00RDa5BGf5i54yoG6i2IiIgMWgqYRfKcPxRhzZ6mtG0Lv/sk/lCEoyZ3vtBIgcedeHz75Yv53fObWDhxGCdOr+63sYqIiAxFCphF8ty6mmSw/K7p1Ty/fj/1rSGADi3lOnPG7FGcMXtUv4xPRERkqFMNs0ieW727EYCnv3Qyf/34MWn7RpcX5WJIIiIihxQFzCJ5LBq13P3adlwGJleVAPDANccn9h82srSzU0VERKSPKGAWyWNL1uxl2fZ6fB4XLpcBYNGkSt769plcefxkLlwwNscjFBERGfpUwyySx97cdgCAuz55bNr2yhIfN1wwJxdDEhEROeQowyySp15Yv5/f/HcjZYUejpxYmevhiIiIHLIUMIvkqUdW7AagyR/O8UhEREQObQqYRfLUyDJneeuLFo7L8UhEREQObQqYRfLU6IpCAL589owcj0REROTQpoBZJE8Fw1EACjz6NhUREckl/SYWyVOhiBMw+xQwi4iI5JR+E4vkqWAsYPa69W0qIiKSS/pNLNKF17fUcdIPn+GvL2/h5B89w++f2zRgrx0KW0ABs4iISK5p4RKRLnzh3mXsONDGtx9aCcDNj67mnLmj2d3gZ/GkysTqez2xencjrcEw/357NwsmDOPCBZm7YIQiUdwug7sXryEiIiJ9RwGz9Jk9DX7uW7qdT588bdDX3QbDUa752xvsONDWYd+7fvgMAHd98hiOn1bdo+v+480dfPG+5Wnbpo8sY/bY8rRtLYEwD7y5g2Kfu4cjFxERkb42uKMaySsf/O1L/PTJdXzl/uXdH9yNJ1bu4co/vcZtz27sg5Fl57ZnN3L3a9sAWLK6hiVr9qbtX/Pdc9Ke/9/jaxOdLLpS0+jn9uc3ce2db3YIlgEeWbGrw7a/L93O7gY/R08e3pO3ICIiIv1AGeZB7Jm1ewmFo5w1Z3SuhwLA9jonG7tmT1OPzrPWsmT1Xo6cVMnW2hYWTqzknte389+1+3h+/X6uOG4yRf2cafWHIvzgsTUAvGf+2ESg/uBnjuewkaU0tIUo9KaPYfn2eg7/1mP8/MMLeG8Xi4sc870lHbZ97rTD+OKZh3P095ZQ2xwE4NVNtfjDUU4+fARL1uylstjL7y5f3FdvUURERHrpoAJmY8wWoAmIAGFr7WJjzHDgXmAysAX4kLX2QOz4rwMfjx1/nbX2Pwfz+oe6q/70OgCbvndur2pp+9Ly7fWJx2v2NPHyxlq21bVw3NRqJlYVZzznwl+9yMnTqzl8dBmfveutxPZin5tAOEqJz01LMMIX71vGbz6yqM/H/MbWA6yraeLioybw8sbaxPZP/Pl1lu9oYO64chZOrASgrNALwPNfOZW6liBzxpbzqb++wZI1e/nDC5s7DZjjreFSffbUw/jSWc5iJMOLfTz2zh6uPmkqH/7dKwBUFns50Briw4snqH5ZREQkD/RFhvlUa+3+lOdfA5ZYa39gjPla7PlXjTGzgYuBOcBY4CljzOHW2kgfjOGQEY1aXC7DrU+vT2zb0+hn7LCinIzHWkvUOsFvqluWrOflTU4Quvn752JMeuBX3xpk+fb6tEA7rjUYwes2/OXjx/D+37zEY+/sYe2eJmaMLuv1OBtaQ/z0ybXc9do2TjysmlNmjOT6h52JfC+s3887uxoSx76yqY6jJlfyi4sXdrjOhOHFTBjufAD4w5VH8dX73+bepdvxhyIdMtDWWk77yX+d+3HJQi6YP7bDcYePLmNtTROn/eTZxLYDrSEATpkxotfvV0RERPpOf9QwXwj8Ofb4z8B7U7bfY60NWGs3AxuAo/vh9YecSNSyvqaJr//jbaZ+41G+eN8yfvzEusT+d/3wGRpiQdZAsNbyyyXrWb69ngtufZFp33g0se+0mSMBEsEyQENb+thag2EWfOfJtG3HTh1Osc/Np06aynWnHcZb/3sWiyZV8uBnjgfgo394tdfj/f1zm5j/nSf488tbCUUsz6zdlwiWAR5ZsZutta1p53z0uMlZfQiJZ8+v+ONrPLN2L4FwhF89s4Emf4hN+1vYXtfGuGFFnDlrFECHoPq7F85JPD595khe/+YZzBlbzrfPn83psXNEREQkt4y1tvcnG7MZOABY4LfW2t8ZY+qttcNSjjlgra00xtwKvGKt/Vts+x+Ax6y192e47tXA1QATJ05ctHXr1l6PcbB7dVNt4k/13Xn0und16LbQF6y1BMLRRLC3va410Ski1Xvmj+VHH5jHzG8/nrb9ketOZM7YisTzlzfWcsnvnfd07anTOHxUGecdMSZRftA+G33+L5/nnZ2NbPreudQ0+dnbGGD+hGEZxxqJWupagowoK6DRH+KF9fv5zJ1vJvaPG1bEzvpk54tCr4uqkgJ21rfx0w/Np6YxwPjKIs6fN6bDODLZ2+TnxB88k1hk5HOnHcYvn96QdswfrljcZfC7t9HPv9/ezeXHTcKjnssiIiI5YYx5w1qbcfLQwZZknGCt3WWMGQk8aYxZ09U4MmzLGK1ba38H/A5g8eLFvY/oh4DfP5++UMaMUc6f8I+dOpy/fvwYNuxt5qN/eI39zQHOveV5Vt54NiUFfTuX82+vbOXbD63kto8cyakzR7KvOZDYV+R18+MPzue3z23kxgvmpGVQrzx+Mne8tIVd9f60gHnNnkYAXv76aYyp6D6Le+nRk/jGgyuYmpLJfufGsymJTQQ0xtDkD3HVn15n6dYDANz9yWP52ytbeWTFbgB++P55XLBgLIVeN4+/s4dr7nyDn394AWfMGnVQ92tkWSEPXns8f35pC/ct3dEhWAY44bCuW8+NLC/kYydO6fUYREREpH8dVGRlrd0V++9eY8yDOCUWNcaYMdba3caYMUC8N9cOYELK6eOBjv20JM3uBj9zxpbz248uotDrxm0ML2+q5dwjxgAwa0w5S791Bufd8jwrdzVy+k+e5d5PHcukqpKsX8Nay7qaZmaMLiMYjvLXV7Zy0cJxVBZ7Mcbwp5e2APDpv73JJUdPYOJw59qfOHEKV588lZFlhZw3b0ziet9/3xEML/Fx7NQq/vbKVv780hYWT6qkssTH3kY/q3c3UlXiY3R5YVbjOzlDLe/c6//Ddy6cw/8+tJI/XXkU97+5IxEsA1x3z1vsa3IC+xmjyvjg4vGJjPE5c0ez8ea+myg5Z2wFP/zAfFbvbmLFzgauPXUa+5uCXHPKNCZXZ///QURERPJTr0syjDElgMta2xR7/CTwHeB0oDZl0t9wa+1XjDFzgLtwguqxwBJgeneT/hYvXmyXLl3aqzEOBe/64dMsnjScn314QZfHRaOWT/3tDZ5cVQPAvz93InPHVXR5Tty/lu/ic3e/xRmzRjF7TBm3xLKkXzlnBh87YQon/t8z7E/JKvs8LoYVeXntm2d0e+1fPbOBH/1nLQAXLhjLQ8ucz0hnzh7F73vQMi0QjnDFH19j5uhyVu1u5LXNdR2OOX5aFe+ZP5bf/Hcj2+paGTesiEevexdFPveALKQSjVoOtAapKi3o99cSERGRvtVfJRmjgAdjWTsPcJe19nFjzOvAfcaYjwPbgA8CWGtXGmPuA1YBYeDawdghY8PeZrxu06MM7sFobAtTVtj9/yaXyzBpeLJ920W/fpH1N5+b1WvESySeWl3DU6trEtufW7ePVbsa2d8c4HcfXcTYYUWc/8sXCIajXHRk532HU33mlGmJgDkeLANcc8q0rM6PK/C4uefq44CONdSFXhfDi31cdcIUzpw9ipW7Grj39e384P1HUFHs7dHrHAyXyyhYFhERGYJ6HTBbazcB8zNsr8XJMmc652bg5t6+Zq6lZku3/OC8Hp0bDEf50X/WMG1EKRcfPTGrc6y1NAeyC5gB5oxLTviL9w/OxqZ9LUyuKubsOaP56ytbaQ06n2Ne2eRkcY8YV9FhcZSvv3tWVtc2xvDls2ck7tv88RW8Z/5YjuzB+NpL7V7x+dOn84UzD0/b/90L5/LdC+dmNWlPREREpDta6S8L1lqaAuFE0AfwhXuXYa3lZx9ekFVg9r1HV3NHrBb4gxkWpNhV34bP46K6tICXN9bS0Bbk6ClVRKKW6iyzlu9dMI6KIi+/eGo97izG9NCynZT4PIkex18/dxafP2M6BsNVd7yWCJhbg+GsXr8zxSmr9P3z2hMOOpB1uwz3Xn0sS9bszZipVqAsIiIifUkBcwbWWqwlMSnsy/e/zf1v7Eg75sG3dgLOoiF3feJYfrFkPScdPoJFkzpmThtaQ9z92rbE83U1TcwaU044EmV3g59I1HLKj/8LwO8vX8wn/5Jesz0qy8lxxhhOmzmKp9fs5YE3dmKtTQse9zT4CUejjK90Sjc+f8+yxL73LxoPQLHP+ZL405VH0xwI8+5fPM83zk1mk2/7yJGEIj2re48H/D/8wLw+C2aPmVrFMVOr+uRaIiIiIl1RwJzB75/fxGubD/CryxZS4HGnBctfPnsGDy3bybqaZsApW9jd6OcXS9bziyXrM5ZqvLaljkA4yk8/NJ8v3recx9/ZQ1Wpj6NvXtLh2PbBMnTflqy9aSNKaQtFqGtJn4D27l88x4HWEOtuendaaziADx81Ie15kc9Nkc/N0m+lT+w7Z+4Yeur8eWOoLi3g2KnDe3yuiIiISK5plYQMCr1unlpdww8fX8vW2pbE9jNnj+KyYyby2dOmA07wDLA2NmkO4MG3drB2TxM1jf7Etvjj46c5ge8vlqzPGCxfHAtap41ITij80pmHU1HUs4lrYyqcjPSX73+bLftbWLK6hm/9c0ViyeUnVu3hjy9sBuDIicO4/9PHZV320RvGGI6bVqVSCRERERmUlGHO4PLjJvPQsl384YXN/OXlLZQVePjX505M9NS9YP5YTp0xggMtIX70n7X89MnkMtVfuHc5AF63SXSpiLdkqyr18bMPz08ck+r77zuCDy4a73SimDeGicOLeeDNHbzvyPE9Hv/o2GIgT6/Zy/Lt9dS2BNP2v7SxlvuX7uB9C8fx027a1YmIiIgc6pRh7sQbsUUwQhFLdVlBhwUoygq9TKwqZsGEYbyzs5GZo8v48QeTTUNCEcuJ//c0kahlf3OAymIvXreLixaO528fPwaAq06YzObvn8u6m97NJUdPxON2cd3p05k6ohSP28WHj5qItxdLJcczzEBasPyLixcAcNer2whGonzkuEk9vraIiIjIoUYBcxaC4Win+z5yrBN0XnH8ZD6waDzXv2d2oivEjgNtzL/xCf72yra05ZdPOKyKn394AV85eybGmD5fVCNTecVlx0zkwlgXjbgZo8r69HVFREREhqJer/Q3UHK10t/2ula21LZQ2xxk5pgyZo4u7/TYYDiaFvRaa1m5q5Hzf/lCYtt7F4zl5xcv7Ncxp7rw1hc4beYo5owt54lVe/josZM5YnwFdS1B/vbKVkZXFPKhxRO6v5CIiIjIIaCrlf4UMPeTQDjCjG89DsBvP7qI46ZVUV44cKvOiYiIiEj2+mtpbOlCgcfNk184ibqWoPoFi4iIiAxiCpj70XTVCIuIiIgMepr0JyIiIiLSBQXMIiIiIiJdUMAsIiIiItIFBcwiIiIiIl1QwCwiIiIi0gUFzCIiIiIiXVDALCIiIiLSBQXMIiIiIiJdUMAsIiIiItIFBcwiIiIiIl1QwCwiIiIi0gUFzCIiIiIiXVDALCIiIiLSBQXMIiIiIiJdUMAsIiIiItIFY63N9Ri6ZIzZB2zNwUtXA/tz8LqDle5Xz+h+ZU/3qmd0v3pG9yt7ulc9o/vVM/lwvyZZa0dk2pH3AXOuGGOWWmsX53ocg4XuV8/ofmVP96pndL96Rvcre7pXPaP71TP5fr9UkiEiIiIi0gUFzCIiIiIiXVDA3Lnf5XoAg4zuV8/ofmVP96pndL96Rvcre7pXPaP71TN5fb9UwywiIiIi0gVlmEVEREREuqCAWURERESkCwqYRUQGGWOMyfUYBgvdK+lP+vrK3mC/V4d0wGyMmZzrMQwWxpjFxpiRuR7HYGGMOcMYsyjX4xgsjDEVKY8H9Q/VAeLJ9QAGEW+uBzDYGGPcuR7DIHJIx1E9NKi/Fw/J/9HGmCONMU8B39EPhq4ZY+YYY14CrgeG5Xg4ec8Ys9AY8xjwIHBYrseT74wxxxhjHgJuN8Z8zBhTYDUTuVPGmGONMXfi/Oyarp9fnTPGHGeM+TvwY2PMbN2rrsXu13cArLWRXI8n3xljjjbG/A34vjHmCGPMIRlPZSOWcPs78CNjzImD9XvxkPofbBzfBO4G7rHWXh7/waCsVqc+DzxorX2PtXYd6F5lYoxxG2N+B/we+C1wFzArtu+Q+j7LljFmHvAr4H7g78Bp6ENGp4wxc4FfAv8GaoCrgctj+/Q9mSL217BbgUdxltr9PPCx2D7dq3aMMVcAfwa+ZYz5UGyb/oqRgTHGZYy5HrgdeAznrz3XAvNzOrA8FIu5fgDcRvLn1meBiTkdWC8dUr/IY5krL/CCtfZ2SGQEPcpqpYsFgMMBi/OLB2PMRcaY8UBR7Ll+8cTEPng9DrzLWvtP4AHgVGNMobU2mtPB5a9FwAZr7V+BJ4FCYFt8p76+OjgWWGOtvRvng1krcJkxZrK11up+pZkPrLPW/gn4CfAP4EJjzOG6VxntxPnAeg7O/cJaG9Z96ij283wHcKW19k7gZmASMCizpv0pFlc9D5xprf0z8CecmGJfTgfWS0M+YDbGnGyMOSZl04+AccaYHxtjXge+C/zZGPOB3Iwwf6Teq1gA2AqcBJwW+9PTp4CbgJ/HjjmkP2S0/9qy1v7DWtsW+yUTBdYBxTkbYJ7J8L34CHCRMeZmYAUwHrjFGPNV0NdXhvv1OjDBGDPNWtuC8zXWAHwSDu37ZYx5rzHmG8aY82KblgGLU+7V68BSnJ9hh/S9grT7dX5s0zNAjbX2CWCrMea7se3KMpPx6+tuYHmshKwWaALG5G6E+aP915a19hFr7QFjzLuAV4DJwE3GmDNzOc7eGLIBszGmzBjzD5xa0k8ZYyoBYj88/wosAL5krT0feA44xxhzeK7Gm0td3Cs/zifCXwH/sdaeA3wTmGuMeXfOBpxjnd2v2J+fTOyX8RrgdJys6SGdLe3i62svTibQA3zDWnsscAdwojHmuFyNN9cy3K/hsV0bgdeAPxlj/gksxill8RhjCnMy2BwzxoyI3YsvAnU49+YD1tp9OH/l+Vzs0HrgKaDYGHPIBjYZ7tcfjTEXxRIk8Z9RnwKuM8aMstaGcjTUvNDJ19dF1tpWa23EWhswxnhxPuyvzeVYc62zr62UQ+pwsvLHAcuBS40xMwd+pL03ZANmIAg8DXwE2AV8ML4j9meUD1lrn4ttegoYATQP9CDzRKf3Cvg1TgnGCABr7U7gBZzs1qEq4/2yMcYYl7V2B/Aq8IH4vlwNNg909b24BpgJbI9tegPYCwQGeIz5pLOvr2Zr7VdwagDvsNa+B9gAzIt9uD0UTQNetNaeZK29DfgS8IXYvruBmcaY02N/Rq8FxuFk5Q9Vme7XlwGstUFjjNtauxLng9gPAA7l5Ahd3K8Us3Cy8+tiH3aPHvBR5ocu75W1dqW19pnY02dxmggMqphrSAXMxpjLY3/GHGatDeAU5T+F86fxxfEMciwLWJdy6pk4dTWD6n/ewcj2Xllrm3GyNFcYYxYYY64BzgC25GjoOdGDry2XtTZqnAkz64GW3I06d7K9XzFPADfEsvAXA3NwgptDRjf3a1Hq/bLWvh2rkwen7vSVQ+kvGLF7dYoxphjnA9ZfYtvdwKrYP3DKfO4BfmGMOQznLz4G8A38qHMni/u1Ivbc4PwexFr7CZyf+QeA+eYQmrjcg/sVL1cZDrQaY64EXgKOOFS+H7O9VxmchRN/Ng3IQPvIoK9Pin1hjsbpShDF+bPl1caYz1tr98eOeRk4HPgQcFM8CwicCPwCZ6LRV621jbl4DwOlN/cKwFp7X+zcD+EEMx+11g75Pz/18msrGguaw8aYMpx6rUNCD+/Xh3HmDwD8EWdC2+M4E2c+Zq3dOsDDH3C9/X6MbV+EMzkrAlw91P+C0cm9+iTweWttTSwzGjHGzAIqIDE56w7jdMz4Gs5fMj5pra3PxXsYSD28X/ESKQtYY8wk4Gc4k7Wutda+k5M3MYB6eb/CsdPPBi7B+avYZdbatwf8DQyg3tyr2Hk+nDlR/4czyfQr1tpB9deeQf2pMfY/xgJlwE5r7enAZ3BqZX4bP85aux7n089YY8xhsXo/i/M/7Xpr7YWxPw0PWQdxr0qMMV5r7b3AN2P3amUu3sNAOsivraLY7i9aa789wEPPiV7crzHG6SNcbK1tA64CrrDWnmGtXZXhJYaUg/j6in9tbcH52XW6tXbjwI5+YHVzr37X7vCzcNoUYowZDWCt/SHwGWvtidba1QM38tw4iPs1IratAfiBtfbkQyRY7u39GhXb9m/gEmvtxw6BYLm396raWhvEKbe73lp7wWCMuQZlhjn2p5DvAG5jzKNAOU6mJd4K5zpglzHmZGvts7HtD8Y+8TwOlAKnxX4xD/VfNn1xr04FVg/1LBbofvXUQd6vx4BSY8ypsUBmT27excDpi68vY0z8Z9ezuXkXA6M39wqnrG6zcRbgeJ8x5hxr7Y7YL+shrY/u17nW2m04k0uHtD66X++21r6Yi/EPpD68V28Dg/ZDxaDLMBtjTsbJuFTiTHj5LhDC6Xl7NCT+tPQd4IaU8z6I0+HhGZxJModCFquv7tWQz8qA7ldP6X71jH52Za8398o4dZMfw8lqlQOnWmfy7ZDXh/drW4eLD0F9eL+2d7j4EKN7lWQGWxLMOL38JltnsQOMMb/GKSxvAz5nrV1knPrkkcAtOLXJm2PnYa19PkdDH3C6Vz2j+9Uzul89o/uVvV7cqy/j/MX0c8BfrLVv5mbkuaH71TO6X9nTvUoadBlmnE8695nkWuQvAhOttXfg/Lngc9aZ7DEeiFhrN4Pzy+ZQ+oUTo3vVM7pfPaP71TO6X9nryb2KWmu3Wms3Wmv/Zyj9gu4B3a+e0f3Knu5VzKALmK3TMDxgnUbr4LSEiy+zeBUwyxjzb5wenEPqf1ZP6V71jO5Xz+h+9YzuV/Z6eK/egEN7cSDdr57R/cqe7lXSoJz0B4kaGQuMAh6ObW4CvgHMBTZbZ5GNQ57uVc/ofvWM7lfP6H5lryf3yg62+sJ+oPvVM7pf2dO9GoQZ5hRRwAvsB+bFPuF8G+dPAi/oF04a3aue0f3qGd2vntH9yp7uVc/ofvWM7lf2Dvl7Negm/aUyxhyLs7LOS8CfrLV/yPGQ8pbuVc/ofvWM7lfP6H5lT/eqZ3S/ekb3K3uH+r0a7AHzeOCjwE+ts5ysdEL3qmd0v3pG96tndL+yp3vVM7pfPaP7lb1D/V4N6oBZRERERKS/DeYaZhERERGRfqeAWURERESkCwqYRURERES6oIBZRERERKQLCphFRERERLqggFlEREREpAsKmEVEREREuvD/Acr5xkpHEvh6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.tseries.offsets import DateOffset\n",
    "future_dates=[data.index[-1]+ DateOffset(days=x)for x in range(0,21)]\n",
    "future_datest_df=pd.DataFrame(index=future_dates[1:],columns=data.columns)\n",
    "\n",
    "future_datest_df.tail()\n",
    "\n",
    "future_df=pd.concat([data,future_datest_df])\n",
    "\n",
    "future_df['forecast'] = results.predict(start = 1620, end = 1985, typ='levels')\n",
    "future_df[['Close', 'forecast']].plot(figsize=(12, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "18582180",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
      "  return get_prediction_index(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(31,)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions=results.predict(start=1988,end=2018,typ='levels')\n",
    "predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0d866f35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-01-16 18:17:29.083558    2446.481555\n",
      "2023-01-17 18:17:29.083558    2450.537965\n",
      "2023-01-18 18:17:29.083558    2450.439435\n",
      "2023-01-19 18:17:29.083558    2452.809574\n",
      "2023-01-20 18:17:29.083558    2453.751427\n",
      "2023-01-21 18:17:29.083558    2454.857595\n",
      "2023-01-22 18:17:29.083558    2455.307819\n",
      "2023-01-23 18:17:29.083558    2452.572678\n",
      "2023-01-24 18:17:29.083558    2451.283976\n",
      "2023-01-25 18:17:29.083558    2453.398399\n",
      "2023-01-26 18:17:29.083558    2454.353384\n",
      "2023-01-27 18:17:29.083558    2458.075057\n",
      "2023-01-28 18:17:29.083558    2457.178666\n",
      "2023-01-29 18:17:29.083558    2461.481666\n",
      "2023-01-30 18:17:29.083558    2461.768804\n",
      "2023-01-31 18:17:29.083558    2464.207911\n",
      "2023-02-01 18:17:29.083558    2464.931618\n",
      "2023-02-02 18:17:29.083558    2465.452123\n",
      "2023-02-03 18:17:29.083558    2466.289359\n",
      "2023-02-04 18:17:29.083558    2463.848232\n",
      "2023-02-05 18:17:29.083558    2463.090092\n",
      "2023-02-06 18:17:29.083558    2465.265424\n",
      "2023-02-07 18:17:29.083558    2466.464017\n",
      "2023-02-08 18:17:29.083558    2470.191952\n",
      "2023-02-09 18:17:29.083558    2469.293721\n",
      "2023-02-10 18:17:29.083558    2473.599177\n",
      "2023-02-11 18:17:29.083558    2473.890157\n",
      "2023-02-12 18:17:29.083558    2476.329951\n",
      "2023-02-13 18:17:29.083558    2477.051485\n",
      "2023-02-14 18:17:29.083558    2477.566156\n",
      "2023-02-15 18:17:29.083558    2478.407247\n",
      "Freq: D, Name: predicted_mean, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "from datetime import timedelta, date\n",
    "Date_req = date.today() + timedelta(days=31)\n",
    "# Date_req\n",
    "\n",
    "index_future_dates=pd.date_range(start=datetime.today(),end=Date_req)\n",
    "a = len(data)\n",
    "predictions.index=index_future_dates\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2b425001",
   "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>Close</th>\n",
       "      <th>Close First Difference</th>\n",
       "      <th>forecast</th>\n",
       "      <th>predicted_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01 00:00:00.000000</th>\n",
       "      <td>439.781311</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02 00:00:00.000000</th>\n",
       "      <td>438.617340</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05 00:00:00.000000</th>\n",
       "      <td>433.812897</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06 00:00:00.000000</th>\n",
       "      <td>414.124512</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07 00:00:00.000000</th>\n",
       "      <td>423.139069</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-11 18:17:29.083558</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2473.890157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-12 18:17:29.083558</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2476.329951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-13 18:17:29.083558</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2477.051485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-14 18:17:29.083558</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2477.566156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-15 18:17:29.083558</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2478.407247</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2018 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Close  Close First Difference  forecast  \\\n",
       "2015-01-01 00:00:00.000000  439.781311                     NaN       NaN   \n",
       "2015-01-02 00:00:00.000000  438.617340                     NaN       NaN   \n",
       "2015-01-05 00:00:00.000000  433.812897                     NaN       NaN   \n",
       "2015-01-06 00:00:00.000000  414.124512                     NaN       NaN   \n",
       "2015-01-07 00:00:00.000000  423.139069                     NaN       NaN   \n",
       "...                                ...                     ...       ...   \n",
       "2023-02-11 18:17:29.083558         NaN                     NaN       NaN   \n",
       "2023-02-12 18:17:29.083558         NaN                     NaN       NaN   \n",
       "2023-02-13 18:17:29.083558         NaN                     NaN       NaN   \n",
       "2023-02-14 18:17:29.083558         NaN                     NaN       NaN   \n",
       "2023-02-15 18:17:29.083558         NaN                     NaN       NaN   \n",
       "\n",
       "                            predicted_mean  \n",
       "2015-01-01 00:00:00.000000             NaN  \n",
       "2015-01-02 00:00:00.000000             NaN  \n",
       "2015-01-05 00:00:00.000000             NaN  \n",
       "2015-01-06 00:00:00.000000             NaN  \n",
       "2015-01-07 00:00:00.000000             NaN  \n",
       "...                                    ...  \n",
       "2023-02-11 18:17:29.083558     2473.890157  \n",
       "2023-02-12 18:17:29.083558     2476.329951  \n",
       "2023-02-13 18:17:29.083558     2477.051485  \n",
       "2023-02-14 18:17:29.083558     2477.566156  \n",
       "2023-02-15 18:17:29.083558     2478.407247  \n",
       "\n",
       "[2018 rows x 4 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pred = pd.concat([data,predictions], axis=1)\n",
    "new_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "330aae5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "d = new_pred.index\n",
    "new_pred.index = d.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "614d20e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>Close</th>\n",
       "      <th>Close First Difference</th>\n",
       "      <th>forecast</th>\n",
       "      <th>predicted_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>439.781311</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>438.617340</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>433.812897</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>414.124512</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>423.139069</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2473.890157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2476.329951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2477.051485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2477.566156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2478.407247</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2018 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Close  Close First Difference  forecast  predicted_mean\n",
       "2015-01-01  439.781311                     NaN       NaN             NaN\n",
       "2015-01-02  438.617340                     NaN       NaN             NaN\n",
       "2015-01-05  433.812897                     NaN       NaN             NaN\n",
       "2015-01-06  414.124512                     NaN       NaN             NaN\n",
       "2015-01-07  423.139069                     NaN       NaN             NaN\n",
       "...                ...                     ...       ...             ...\n",
       "2023-02-11         NaN                     NaN       NaN     2473.890157\n",
       "2023-02-12         NaN                     NaN       NaN     2476.329951\n",
       "2023-02-13         NaN                     NaN       NaN     2477.051485\n",
       "2023-02-14         NaN                     NaN       NaN     2477.566156\n",
       "2023-02-15         NaN                     NaN       NaN     2478.407247\n",
       "\n",
       "[2018 rows x 4 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "67b9ad05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>Close</th>\n",
       "      <th>predicted_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>439.781311</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>438.617340</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>433.812897</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>414.124512</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>423.139069</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2473.890157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2476.329951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2477.051485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2477.566156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2478.407247</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2018 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Close  predicted_mean\n",
       "2015-01-01  439.781311             NaN\n",
       "2015-01-02  438.617340             NaN\n",
       "2015-01-05  433.812897             NaN\n",
       "2015-01-06  414.124512             NaN\n",
       "2015-01-07  423.139069             NaN\n",
       "...                ...             ...\n",
       "2023-02-11         NaN     2473.890157\n",
       "2023-02-12         NaN     2476.329951\n",
       "2023-02-13         NaN     2477.051485\n",
       "2023-02-14         NaN     2477.566156\n",
       "2023-02-15         NaN     2478.407247\n",
       "\n",
       "[2018 rows x 2 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pred.drop(['Close First Difference','forecast'],axis=1,inplace=True)\n",
    "new_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1ff17e1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19870273cd0>,\n",
       " <matplotlib.lines.Line2D at 0x19870273c70>]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,15))\n",
    "plt.plot(new_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a2d1ae49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1987feabcd0>,\n",
       " <matplotlib.lines.Line2D at 0x198700c00d0>]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(new_pred[1958:2018])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c995823",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}