{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get the url of the dataset\n",
    "url = \"https://www.openml.org/data/download/22102255/dataset\"\n",
    "d = requests.get(url, allow_redirects=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"dataset.txt\", \"wb\") as f:\n",
    "    f.write(d.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "\n",
    "#To get the raw data\n",
    "with open(\"dataset.txt\", \"r\") as f:\n",
    "    for l in f.read().split(\"\\n\"):\n",
    "        if l.startswith(\"@\") or l.startswith(\"%\") or l == \"\":\n",
    "            continue\n",
    "        data.append(l)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = []\n",
    "\n",
    "#To get the Column names/features\n",
    "with open(\"dataset.txt\", \"r\") as f:\n",
    "    for l in f.read().split(\"\\n\"):\n",
    "        if l.startswith(\"@ATTRIBUTE\"):\n",
    "            columns.append(l.split(\" \")[1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"ds.csv\", \"w\") as f:\n",
    "    f.write(\",\".join(columns))\n",
    "    f.write(\"\\n\")\n",
    "    f.write(\"\\n\".join(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_left</th>\n",
       "      <th>ct_score</th>\n",
       "      <th>t_score</th>\n",
       "      <th>map</th>\n",
       "      <th>bomb_planted</th>\n",
       "      <th>ct_health</th>\n",
       "      <th>t_health</th>\n",
       "      <th>ct_armor</th>\n",
       "      <th>t_armor</th>\n",
       "      <th>ct_money</th>\n",
       "      <th>...</th>\n",
       "      <th>t_grenade_flashbang</th>\n",
       "      <th>ct_grenade_smokegrenade</th>\n",
       "      <th>t_grenade_smokegrenade</th>\n",
       "      <th>ct_grenade_incendiarygrenade</th>\n",
       "      <th>t_grenade_incendiarygrenade</th>\n",
       "      <th>ct_grenade_molotovgrenade</th>\n",
       "      <th>t_grenade_molotovgrenade</th>\n",
       "      <th>ct_grenade_decoygrenade</th>\n",
       "      <th>t_grenade_decoygrenade</th>\n",
       "      <th>round_winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>175.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>de_dust2</td>\n",
       "      <td>False</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>156.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>de_dust2</td>\n",
       "      <td>False</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>de_dust2</td>\n",
       "      <td>False</td>\n",
       "      <td>391.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>76.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>de_dust2</td>\n",
       "      <td>False</td>\n",
       "      <td>391.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>174.97</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>de_dust2</td>\n",
       "      <td>False</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18350.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122405</th>\n",
       "      <td>15.41</td>\n",
       "      <td>11.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>de_train</td>\n",
       "      <td>True</td>\n",
       "      <td>200.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122406</th>\n",
       "      <td>174.93</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>de_train</td>\n",
       "      <td>False</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122407</th>\n",
       "      <td>114.93</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>de_train</td>\n",
       "      <td>False</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122408</th>\n",
       "      <td>94.93</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>de_train</td>\n",
       "      <td>False</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122409</th>\n",
       "      <td>74.93</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>de_train</td>\n",
       "      <td>False</td>\n",
       "      <td>375.0</td>\n",
       "      <td>479.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>466.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>122410 rows × 97 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        time_left  ct_score  t_score       map  bomb_planted  ct_health  \\\n",
       "0          175.00       0.0      0.0  de_dust2         False      500.0   \n",
       "1          156.03       0.0      0.0  de_dust2         False      500.0   \n",
       "2           96.03       0.0      0.0  de_dust2         False      391.0   \n",
       "3           76.03       0.0      0.0  de_dust2         False      391.0   \n",
       "4          174.97       1.0      0.0  de_dust2         False      500.0   \n",
       "...           ...       ...      ...       ...           ...        ...   \n",
       "122405      15.41      11.0     14.0  de_train          True      200.0   \n",
       "122406     174.93      11.0     15.0  de_train         False      500.0   \n",
       "122407     114.93      11.0     15.0  de_train         False      500.0   \n",
       "122408      94.93      11.0     15.0  de_train         False      500.0   \n",
       "122409      74.93      11.0     15.0  de_train         False      375.0   \n",
       "\n",
       "        t_health  ct_armor  t_armor  ct_money  ...  t_grenade_flashbang  \\\n",
       "0          500.0       0.0      0.0    4000.0  ...                  0.0   \n",
       "1          500.0     400.0    300.0     600.0  ...                  0.0   \n",
       "2          400.0     294.0    200.0     750.0  ...                  0.0   \n",
       "3          400.0     294.0    200.0     750.0  ...                  0.0   \n",
       "4          500.0     192.0      0.0   18350.0  ...                  0.0   \n",
       "...          ...       ...      ...       ...  ...                  ...   \n",
       "122405     242.0     195.0    359.0     100.0  ...                  2.0   \n",
       "122406     500.0      95.0    175.0   11500.0  ...                  2.0   \n",
       "122407     500.0     495.0    475.0    1200.0  ...                  4.0   \n",
       "122408     500.0     495.0    475.0    1200.0  ...                  5.0   \n",
       "122409     479.0     395.0    466.0    1100.0  ...                  3.0   \n",
       "\n",
       "        ct_grenade_smokegrenade  t_grenade_smokegrenade  \\\n",
       "0                           0.0                     0.0   \n",
       "1                           0.0                     2.0   \n",
       "2                           0.0                     2.0   \n",
       "3                           0.0                     0.0   \n",
       "4                           0.0                     0.0   \n",
       "...                         ...                     ...   \n",
       "122405                      1.0                     1.0   \n",
       "122406                      1.0                     0.0   \n",
       "122407                      3.0                     5.0   \n",
       "122408                      0.0                     3.0   \n",
       "122409                      0.0                     2.0   \n",
       "\n",
       "        ct_grenade_incendiarygrenade  t_grenade_incendiarygrenade  \\\n",
       "0                                0.0                          0.0   \n",
       "1                                0.0                          0.0   \n",
       "2                                0.0                          0.0   \n",
       "3                                0.0                          0.0   \n",
       "4                                0.0                          0.0   \n",
       "...                              ...                          ...   \n",
       "122405                           0.0                          0.0   \n",
       "122406                           0.0                          0.0   \n",
       "122407                           1.0                          0.0   \n",
       "122408                           0.0                          0.0   \n",
       "122409                           0.0                          0.0   \n",
       "\n",
       "        ct_grenade_molotovgrenade  t_grenade_molotovgrenade  \\\n",
       "0                             0.0                       0.0   \n",
       "1                             0.0                       0.0   \n",
       "2                             0.0                       0.0   \n",
       "3                             0.0                       0.0   \n",
       "4                             0.0                       0.0   \n",
       "...                           ...                       ...   \n",
       "122405                        0.0                       0.0   \n",
       "122406                        0.0                       0.0   \n",
       "122407                        0.0                       5.0   \n",
       "122408                        0.0                       4.0   \n",
       "122409                        0.0                       3.0   \n",
       "\n",
       "        ct_grenade_decoygrenade  t_grenade_decoygrenade  round_winner  \n",
       "0                           0.0                     0.0            CT  \n",
       "1                           0.0                     0.0            CT  \n",
       "2                           0.0                     0.0            CT  \n",
       "3                           0.0                     0.0            CT  \n",
       "4                           0.0                     0.0            CT  \n",
       "...                         ...                     ...           ...  \n",
       "122405                      0.0                     0.0             T  \n",
       "122406                      0.0                     0.0             T  \n",
       "122407                      0.0                     0.0             T  \n",
       "122408                      0.0                     0.0             T  \n",
       "122409                      0.0                     0.0             T  \n",
       "\n",
       "[122410 rows x 97 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"ds.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time_left</th>\n",
       "      <th>ct_score</th>\n",
       "      <th>t_score</th>\n",
       "      <th>map</th>\n",
       "      <th>bomb_planted</th>\n",
       "      <th>ct_health</th>\n",
       "      <th>t_health</th>\n",
       "      <th>ct_armor</th>\n",
       "      <th>t_armor</th>\n",
       "      <th>ct_money</th>\n",
       "      <th>...</th>\n",
       "      <th>ct_grenade_smokegrenade</th>\n",
       "      <th>t_grenade_smokegrenade</th>\n",
       "      <th>ct_grenade_incendiarygrenade</th>\n",
       "      <th>t_grenade_incendiarygrenade</th>\n",
       "      <th>ct_grenade_molotovgrenade</th>\n",
       "      <th>t_grenade_molotovgrenade</th>\n",
       "      <th>ct_grenade_decoygrenade</th>\n",
       "      <th>t_grenade_decoygrenade</th>\n",
       "      <th>round_winner</th>\n",
       "      <th>final</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>175.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>de_dust2</td>\n",
       "      <td>False</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CT</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>156.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>de_dust2</td>\n",
       "      <td>False</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CT</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>de_dust2</td>\n",
       "      <td>False</td>\n",
       "      <td>391.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CT</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>76.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>de_dust2</td>\n",
       "      <td>False</td>\n",
       "      <td>391.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CT</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>174.97</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>de_dust2</td>\n",
       "      <td>False</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18350.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CT</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122405</th>\n",
       "      <td>15.41</td>\n",
       "      <td>11.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>de_train</td>\n",
       "      <td>True</td>\n",
       "      <td>200.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122406</th>\n",
       "      <td>174.93</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>de_train</td>\n",
       "      <td>False</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122407</th>\n",
       "      <td>114.93</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>de_train</td>\n",
       "      <td>False</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122408</th>\n",
       "      <td>94.93</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>de_train</td>\n",
       "      <td>False</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122409</th>\n",
       "      <td>74.93</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>de_train</td>\n",
       "      <td>False</td>\n",
       "      <td>375.0</td>\n",
       "      <td>479.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>466.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>122410 rows × 98 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        time_left  ct_score  t_score       map  bomb_planted  ct_health  \\\n",
       "0          175.00       0.0      0.0  de_dust2         False      500.0   \n",
       "1          156.03       0.0      0.0  de_dust2         False      500.0   \n",
       "2           96.03       0.0      0.0  de_dust2         False      391.0   \n",
       "3           76.03       0.0      0.0  de_dust2         False      391.0   \n",
       "4          174.97       1.0      0.0  de_dust2         False      500.0   \n",
       "...           ...       ...      ...       ...           ...        ...   \n",
       "122405      15.41      11.0     14.0  de_train          True      200.0   \n",
       "122406     174.93      11.0     15.0  de_train         False      500.0   \n",
       "122407     114.93      11.0     15.0  de_train         False      500.0   \n",
       "122408      94.93      11.0     15.0  de_train         False      500.0   \n",
       "122409      74.93      11.0     15.0  de_train         False      375.0   \n",
       "\n",
       "        t_health  ct_armor  t_armor  ct_money  ...  ct_grenade_smokegrenade  \\\n",
       "0          500.0       0.0      0.0    4000.0  ...                      0.0   \n",
       "1          500.0     400.0    300.0     600.0  ...                      0.0   \n",
       "2          400.0     294.0    200.0     750.0  ...                      0.0   \n",
       "3          400.0     294.0    200.0     750.0  ...                      0.0   \n",
       "4          500.0     192.0      0.0   18350.0  ...                      0.0   \n",
       "...          ...       ...      ...       ...  ...                      ...   \n",
       "122405     242.0     195.0    359.0     100.0  ...                      1.0   \n",
       "122406     500.0      95.0    175.0   11500.0  ...                      1.0   \n",
       "122407     500.0     495.0    475.0    1200.0  ...                      3.0   \n",
       "122408     500.0     495.0    475.0    1200.0  ...                      0.0   \n",
       "122409     479.0     395.0    466.0    1100.0  ...                      0.0   \n",
       "\n",
       "        t_grenade_smokegrenade  ct_grenade_incendiarygrenade  \\\n",
       "0                          0.0                           0.0   \n",
       "1                          2.0                           0.0   \n",
       "2                          2.0                           0.0   \n",
       "3                          0.0                           0.0   \n",
       "4                          0.0                           0.0   \n",
       "...                        ...                           ...   \n",
       "122405                     1.0                           0.0   \n",
       "122406                     0.0                           0.0   \n",
       "122407                     5.0                           1.0   \n",
       "122408                     3.0                           0.0   \n",
       "122409                     2.0                           0.0   \n",
       "\n",
       "        t_grenade_incendiarygrenade  ct_grenade_molotovgrenade  \\\n",
       "0                               0.0                        0.0   \n",
       "1                               0.0                        0.0   \n",
       "2                               0.0                        0.0   \n",
       "3                               0.0                        0.0   \n",
       "4                               0.0                        0.0   \n",
       "...                             ...                        ...   \n",
       "122405                          0.0                        0.0   \n",
       "122406                          0.0                        0.0   \n",
       "122407                          0.0                        0.0   \n",
       "122408                          0.0                        0.0   \n",
       "122409                          0.0                        0.0   \n",
       "\n",
       "        t_grenade_molotovgrenade  ct_grenade_decoygrenade  \\\n",
       "0                            0.0                      0.0   \n",
       "1                            0.0                      0.0   \n",
       "2                            0.0                      0.0   \n",
       "3                            0.0                      0.0   \n",
       "4                            0.0                      0.0   \n",
       "...                          ...                      ...   \n",
       "122405                       0.0                      0.0   \n",
       "122406                       0.0                      0.0   \n",
       "122407                       5.0                      0.0   \n",
       "122408                       4.0                      0.0   \n",
       "122409                       3.0                      0.0   \n",
       "\n",
       "        t_grenade_decoygrenade  round_winner  final  \n",
       "0                          0.0            CT      0  \n",
       "1                          0.0            CT      0  \n",
       "2                          0.0            CT      0  \n",
       "3                          0.0            CT      0  \n",
       "4                          0.0            CT      0  \n",
       "...                        ...           ...    ...  \n",
       "122405                     0.0             T      1  \n",
       "122406                     0.0             T      1  \n",
       "122407                     0.0             T      1  \n",
       "122408                     0.0             T      1  \n",
       "122409                     0.0             T      1  \n",
       "\n",
       "[122410 rows x 98 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"final\"] = df.round_winner.astype(\"category\").cat.codes\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final                           1.000000\n",
      "ct_armor                        0.336382\n",
      "ct_helmets                      0.308255\n",
      "t_helmets                       0.297458\n",
      "ct_defuse_kits                  0.291557\n",
      "t_armor                         0.290753\n",
      "ct_grenade_flashbang            0.253868\n",
      "ct_players_alive                0.216798\n",
      "ct_grenade_smokegrenade         0.209975\n",
      "ct_weapon_awp                   0.198626\n",
      "t_weapon_ak47                   0.194147\n",
      "ct_health                       0.190662\n",
      "bomb_planted                    0.187101\n",
      "ct_weapon_m4a4                  0.178008\n",
      "ct_grenade_hegrenade            0.168781\n",
      "ct_grenade_incendiarygrenade    0.168517\n",
      "ct_weapon_ak47                  0.166855\n",
      "t_grenade_flashbang             0.166839\n",
      "ct_weapon_sg553                 0.163963\n",
      "t_weapon_sg553                  0.163709\n",
      "ct_weapon_usps                  0.152893\n",
      "t_weapon_awp                    0.149878\n",
      "t_players_alive                 0.142518\n",
      "t_grenade_smokegrenade          0.140348\n",
      "t_weapon_usps                   0.136694\n",
      "ct_money                        0.129326\n",
      "t_grenade_molotovgrenade        0.116754\n",
      "t_grenade_hegrenade             0.116336\n",
      "t_money                         0.098362\n",
      "t_health                        0.091361\n",
      "Name: final, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "feature_cols = [c for c in columns if c not in ['round_winner', 'round_id', 'match_id']]\n",
    "df_for_corr = df[feature_cols + ['final']].copy()\n",
    "\n",
    "for col in feature_cols:\n",
    "    if not pd.api.types.is_numeric_dtype(df_for_corr[col]):\n",
    "        df_for_corr[col] = df_for_corr[col].astype('category').cat.codes\n",
    "\n",
    "correlations = df_for_corr.corr()\n",
    "print(correlations['final'].abs().sort_values(ascending=False).iloc[:30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bomb_planted</th>\n",
       "      <th>ct_health</th>\n",
       "      <th>ct_armor</th>\n",
       "      <th>t_armor</th>\n",
       "      <th>ct_money</th>\n",
       "      <th>ct_helmets</th>\n",
       "      <th>t_helmets</th>\n",
       "      <th>ct_defuse_kits</th>\n",
       "      <th>ct_players_alive</th>\n",
       "      <th>t_players_alive</th>\n",
       "      <th>...</th>\n",
       "      <th>t_weapon_usps</th>\n",
       "      <th>ct_grenade_hegrenade</th>\n",
       "      <th>t_grenade_hegrenade</th>\n",
       "      <th>ct_grenade_flashbang</th>\n",
       "      <th>t_grenade_flashbang</th>\n",
       "      <th>ct_grenade_smokegrenade</th>\n",
       "      <th>t_grenade_smokegrenade</th>\n",
       "      <th>ct_grenade_incendiarygrenade</th>\n",
       "      <th>t_grenade_molotovgrenade</th>\n",
       "      <th>final</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>500.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>391.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>391.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>500.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18350.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122405</th>\n",
       "      <td>True</td>\n",
       "      <td>200.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122406</th>\n",
       "      <td>False</td>\n",
       "      <td>500.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122407</th>\n",
       "      <td>False</td>\n",
       "      <td>500.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122408</th>\n",
       "      <td>False</td>\n",
       "      <td>500.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122409</th>\n",
       "      <td>False</td>\n",
       "      <td>375.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>466.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>122410 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        bomb_planted  ct_health  ct_armor  t_armor  ct_money  ct_helmets  \\\n",
       "0              False      500.0       0.0      0.0    4000.0         0.0   \n",
       "1              False      500.0     400.0    300.0     600.0         0.0   \n",
       "2              False      391.0     294.0    200.0     750.0         0.0   \n",
       "3              False      391.0     294.0    200.0     750.0         0.0   \n",
       "4              False      500.0     192.0      0.0   18350.0         0.0   \n",
       "...              ...        ...       ...      ...       ...         ...   \n",
       "122405          True      200.0     195.0    359.0     100.0         2.0   \n",
       "122406         False      500.0      95.0    175.0   11500.0         1.0   \n",
       "122407         False      500.0     495.0    475.0    1200.0         3.0   \n",
       "122408         False      500.0     495.0    475.0    1200.0         3.0   \n",
       "122409         False      375.0     395.0    466.0    1100.0         2.0   \n",
       "\n",
       "        t_helmets  ct_defuse_kits  ct_players_alive  t_players_alive  ...  \\\n",
       "0             0.0             0.0               5.0              5.0  ...   \n",
       "1             0.0             1.0               5.0              5.0  ...   \n",
       "2             0.0             1.0               4.0              4.0  ...   \n",
       "3             0.0             1.0               4.0              4.0  ...   \n",
       "4             0.0             1.0               5.0              5.0  ...   \n",
       "...           ...             ...               ...              ...  ...   \n",
       "122405        4.0             1.0               2.0              4.0  ...   \n",
       "122406        2.0             1.0               5.0              5.0  ...   \n",
       "122407        5.0             1.0               5.0              5.0  ...   \n",
       "122408        5.0             1.0               5.0              5.0  ...   \n",
       "122409        5.0             1.0               4.0              5.0  ...   \n",
       "\n",
       "        t_weapon_usps  ct_grenade_hegrenade  t_grenade_hegrenade  \\\n",
       "0                 0.0                   0.0                  0.0   \n",
       "1                 0.0                   0.0                  0.0   \n",
       "2                 0.0                   0.0                  0.0   \n",
       "3                 0.0                   0.0                  0.0   \n",
       "4                 0.0                   0.0                  0.0   \n",
       "...               ...                   ...                  ...   \n",
       "122405            0.0                   0.0                  0.0   \n",
       "122406            0.0                   0.0                  0.0   \n",
       "122407            0.0                   2.0                  0.0   \n",
       "122408            0.0                   2.0                  0.0   \n",
       "122409            0.0                   0.0                  0.0   \n",
       "\n",
       "        ct_grenade_flashbang  t_grenade_flashbang  ct_grenade_smokegrenade  \\\n",
       "0                        0.0                  0.0                      0.0   \n",
       "1                        0.0                  0.0                      0.0   \n",
       "2                        0.0                  0.0                      0.0   \n",
       "3                        0.0                  0.0                      0.0   \n",
       "4                        0.0                  0.0                      0.0   \n",
       "...                      ...                  ...                      ...   \n",
       "122405                   1.0                  2.0                      1.0   \n",
       "122406                   1.0                  2.0                      1.0   \n",
       "122407                   4.0                  4.0                      3.0   \n",
       "122408                   1.0                  5.0                      0.0   \n",
       "122409                   0.0                  3.0                      0.0   \n",
       "\n",
       "        t_grenade_smokegrenade  ct_grenade_incendiarygrenade  \\\n",
       "0                          0.0                           0.0   \n",
       "1                          2.0                           0.0   \n",
       "2                          2.0                           0.0   \n",
       "3                          0.0                           0.0   \n",
       "4                          0.0                           0.0   \n",
       "...                        ...                           ...   \n",
       "122405                     1.0                           0.0   \n",
       "122406                     0.0                           0.0   \n",
       "122407                     5.0                           1.0   \n",
       "122408                     3.0                           0.0   \n",
       "122409                     2.0                           0.0   \n",
       "\n",
       "        t_grenade_molotovgrenade  final  \n",
       "0                            0.0      0  \n",
       "1                            0.0      0  \n",
       "2                            0.0      0  \n",
       "3                            0.0      0  \n",
       "4                            0.0      0  \n",
       "...                          ...    ...  \n",
       "122405                       0.0      1  \n",
       "122406                       0.0      1  \n",
       "122407                       5.0      1  \n",
       "122408                       4.0      1  \n",
       "122409                       3.0      1  \n",
       "\n",
       "[122410 rows x 28 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_cols = []\n",
    "\n",
    "for cl in feature_cols + ['final']:\n",
    "    try:\n",
    "        if abs(correlations.loc[cl, 'final']) > 0.1:\n",
    "            selected_cols.append(cl)\n",
    "    except KeyError:\n",
    "        pass\n",
    "\n",
    "if 'final' in selected_cols:\n",
    "    selected_cols.remove('final')\n",
    "\n",
    "selected_cols = [c for c in selected_cols if c in df_for_corr.columns]\n",
    "df_selected = df_for_corr[selected_cols + ['final']]\n",
    "df_selected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dDazq4IrtL2JwPj4V3U0N8EUbZ2SXlOFRunjxfLarDR6kZmFvWDyKKiowzdES67q4Y87Np4jJFwzg+5gb8HPtDo3D1+2cm7xctKuxARa1csC6oDD4Z2RhuLU5VrRthUV3nyC+UPBd9RlhZQ4zdVX8HhKJnzu0FvvdM289EtwA1ThoaWJT5zbwTRV//0piXicbzO1sg/nHAxCWlo+Pejli/1Rv9PnjDnKLyxvMO8zNBB6m2sgsLJN4jaVJU0Nhahr8ftkMm0H90PaDhcgMDkXQtl1QUleDaecOTc9TWcWFtd5b14nklVeom9BPuvuAt9GtF8yFip4OsoLDEPjHn7yNZueWxOaFXaCppoRx312GvLwcNi/sivXvdcac9bck5lFQkMPiLb58EqCGCqEb38lCG7s+6okd50OwYPMdPqG4dm4nrHjLG5/vfAhpUZSTw9pO7ojOL8TbN/xhoKqMVe3duPj86/MosXlGWptyQeN7/1A+SeZtqIuv2jrxPCdjBM+7tpIiFrnb40xMMn+GDVUli8cNwQT4iA3roN+5K2znzUdRdBRidmyDvIoyDHv2FpunMCoS6Vcuw6BHTy6aovJlMSj1wnkoGxjCbsEiKGhqIev+PUT/sRX2i9+HVqtWUpfPUUsDy9u6YVdYDC4lpKCLsQE+9nBGTmkZ/DLEC2U9TQ3R1Vgf+yNi8VlrF7H3/BePgkSeXW0lJezt1QG+KaKTd9LC7ttDK/6AqqY65mz+BMX5RTi66k+UFpVg5IfTxeaprKjEP5sOoO2QbpCTl0NCcHST0jTEnAFOmDvACQu330doYh6WjWyFPUu7o/+3l5BbKF6cZvfTYd8YLD/4pO731RsvSZNGGuIPH0Tes2dwWLgEipqaiN2/FxG/boTrl99ATqhtEM1zCGpWVjAZNAQJhw82OY00bPymHzQ1lDB+4Sn+m9nndV/0xdwvL4pNf9U3Fh6Ddors+35ZD3RpZ47AakHWQE8N44e4YMteP7wIz4CpkQa++6A7fls5EGPnn5S6bGzss6WPByJzCjHhn8cwVFPGuh6tBG2OX6TYPOOcTLkg/aVvMJILS9DRVBffd3HhbcvhsCSexklXHWt7tMLWgGgcDQuClrIivuzgiE293THm7GOpy2ekp4Y93w3A8asRWLj6Olo7GWDjRz1RWFyOAxdDJebb/Ekv2JprY9n6WwiOzkZnTxOM7euA344K+kzf/H4P3/5xvzY9uy5X/xiLKw9k6wO66mtgQ69W3DDpTGQqelvp4/uuLsgqLsO9ZPFty6I2tvBNzMKOoDgUlldgprsVfu/ngSnn/BGVK+hLLGtnj75WBvjkdjDCswvQ29KAfw8bV73IlN5gZ2Abc6yc1g6f7HqI289TMKOPI/5c3B3Dv7+MyOQ6Awhh2HV8EJqOtzfekvhcGmip4OhnfeAfmYmxP15FTmEZJvewQ2cXI9x8Jjp53BBvt7LA260s8dHNF4jIKcAiL1ts6++JkacfI6/05X6LvqoSnxTZ8yIBzzPzoKOshK87O+K3vh6YdM6Pj1PYPc0+pxWW4p2LT1FYXokFXjb4o78nxp15jFJZO6cAEuPT8fWSHRgztSdWbJiJgMcR+PmbA9DQUkPvQW3F5rl2wR8VFZX4YvVbfCKATQys+epvyMvL8/MIk5NdgHUrDsGzrT3SUmSfvPn/ipwcOYBpKdCVIFo8KSkpePfdd2FqasqjSs+ePRvZ2YIGl+1jtG/fngvQ3bt3b/R8mpqaPK2KigqcnJywfPlylJXVdYoTEhL48W3btqFdu3ZQV1fHnj178Pfff8PCwgJr1qyBh4cHtLS00K9fP0RHR+Onn37i0bD19PQwadIk5OaKWtCcP38e3t7e0NDQ4Om+++47VAoNoNi5LS0tsXXrVtjb2/OyxcTEvJL6O/33dXh2cMKgsV2ho6eJ4ZN7ckH/9IEbEvN88css9BriDUMTPW6JP+ejsSgvK8eTeyG1aZiQ36ajM1xb20FNXQUe3o5wb+eI8OeNd8gGORjCRkcNn18NRVJ+CZ6m5GHD/RhM8zSHhpL4AYB/ch6+uBqGgJQ8bqHP/p4MTkV7c+3aNGNcTHA/IQcHniUjt7SCn/f3x3GY523FOxmvg/t+YRj11k84ffERKsQMiv8NLDRV0d/GEL88jkJETiHi84vxw4MItDPRgZeRltg8ZZVVWHztOe4nZyOvrAKxecXYH5zIrV1Yp+1VwCzuM6Li0X3uZGgZG8DQzhIdp41E2M0HKMzKbXYe1jFjYlLN1pAlaA0syawx7th16jl8nyQhLasIy7feg4qyIsYPcJKY7/NZHfA8MhOrtj3geTKyi/HTnw+5qM9o5aDPLfrZ8fiUfGTmluC73+7BxEAdw3raSV1nWkqKGGZtwoXVkJwCLgb9/DQc9toa6Gwi3vLtWVYevn0cgqcZufxasnx/Bceii4keF5WkTdNUZg1xxd3nKThwNQKZeSXYdTGUC/ozh7g0mEdXUwWLNt9BdHIe8ovL8feV8GaJ+oyBzkaw0VXDFxeDkZRXgqdJudhwJwrT2lhAQ7nh37mkmx1yS8px4OnL1vP+ibn40icEAcl53EKf/T35PAXtLV62FG2IQZZmKKusxI6QSGSXluFheiYuxidjnE3daqj6hOTk4Vv/Z7iXlsEnlBqCifpsvFSzycp4O3OE5hTgYGQickrL8U9sCu6nZmGKo4XEPOy+OheXyq2RWZ6tz6P5KqxuQvcrs/hn6fwyJFvWS8M4WwvcSk7H9eQ05JSVc8GPPSOjbEQtToU5Eh2PTc/DEZ4reSDOWm7heutvboyM4hI8SJdeHGStz+xONtj5MBZ3ojORWlCKry8GQ1VRHhNbSy5fzSqT5f1dsPRUoIgoLWsaYeKuXIeKrg4cx42CsrY2TDu2h1nnDog+7/NK8gi3vcKiPsOydw/YjxwKDXNTKKqpwahta+g6OyI7TPzEPcPdRg/d3E3x3T5/RKfkIzIpD6v+9ueTfY5C73lxsMeC1UnNJszgDlbIyivF+uNByCko5eddeyQQ43rYwkhHFdLSw1Qf5hqq+CUgAmnFpQjOzseu0FiMtDGFmgTh8mBkAnaExCIyr5CvjLqZnIGrienoZ25Ymya3rBwL7gTwiTQ2Kd9UMm7fgryiIswnTISStja0W3tBv3tPpPmIFy4ZGvYOsF/yPnTatmMvVLFpLCZNgVH/AdxSX1FDA0Z9+0Hd1g7Zj2UzABhjY8GfwaPRCfzZvZCQgofpWRhvK7ltYdb8q56GNLgip/6z29vUiO+/nCi9sCVM5JMQJEfEY+iiSdA1MYCpgyX6vDMCgdceIj9TfL9FXkEeszd9Au8h3aCsqtLkNA31W2b3c8JfVyNwJzgNabnF+ObgE6gqKWBCF/EGOrUwAyihZ0Ns0yFNmgYoLyhAxp07MBs1Guo2NlA2MIDV1OkoTkxEbpDkVRP27y2A2bARUNLRbVaaxmjlZIBu3hb4fvNdxCTkIjIuBz9svYc+XazhaCP5vMJ1oqysgMG97HD0XAhvbxgZWUVYsuIKHgYkI7+wDOEx2Thw+gU8XYygLGEsI44+lgaw1FTDqofhSCksxbOMfGwLisU4JzMu3ouDiapbAmIQnlOI/LIKXI3LgE9MGgbZCO5/houeJn8v7XkRz9MkFZRwi34bbXVoylC+SQOcuHHKqp0PubX+tUcJOOQThjlj3CX/pvYW6NfRCkvX3oR/SDqKSsp5vhpRX1y73dXLjK98PXxJtlVy010tuNDO6oStUj4RnoI7iZlcfJbEJ7eCcTIihVuPZ5eUY51fFO+3MPG+hqF2RtgXnMCt+Fn9nY1KxYPkbC6Ey8KcQS449ygOpx/EITO/FBvPPEd8RgHe6fvyagVhqtDwc7l4uBsqKqrwwY77iM8oRF5RGbb7hMok6rP74y03S+wPTuDjwfSiMvzwIJyvFh7tYCI2T2ZxGd67GgTfpCxedzF5RXz86aSnAQcdDZ7GTU8Tjroa+PlxJBIKSvh1+elhOIzVlDHItu4elYWzh325OP/2giHQ1ddCzwFt0GdwWxzdc11iniFjOmPKrP6wsjWGhqYqOvd0h3sbOzx/KjqxyiZNfll+AMPGdYGjm+T7hiBaMiTsEy2aoqIi9O7dG7Gxsbh8+TJevHiBrl274tixY7UiPOP+/fsoLi7GjRuSxeoacnJyeFr2d9++fVxUX7du3UurAFavXs2F9qysLMyYMYML8YmJiXjw4AEX6oOCgpCWlsYnFQICAnDnzh08evQI/v7+XOiv4dmzZxg5ciSmTJnCf8eOHTuwadMm/Pzzz7Vp2LnZbzlz5gz/nQUFBbCxaaSzLiXBAdHwaCfaefBs74SQAOmthQoLSlBRXskF/Bq69PNC4ONwRIUmoKK8AmHPYvHiaRS39G+M9mbaCMssQLqQldHtuCwugHgaa0pVJgc9NQx1MsSF8HSRwQ/rCAnDOkPMJYKTvjr+V2lrJBA9HgpZprBObm5pOdpUH2sMQzUljHUyhV9qDu+0vQqSQyKhYaALHbO6TpyFpwuqKquQGhbV7Dw+P2/HX9OX4eiyH/Hs/A1uadcY1mbaMNJXx72nAosmBhuw+D1PQTs3Y7F5mJuHrm3NcfqaeMspRo0lobDwyv5jH9u7iz+vONz1tKAoL3C/UwMTTOPzi7gLE2nRUVbkkzelDQhF0qSRBm8nQy7sC+P7LAXtnOrEq/oMbG+Bq/4JKGjEillW2lvoIiyDtS11Ky/uxGRCVVEBniaS649Z409ubY5Pq1f/NAaz2B/qYoQLoZKXUovDTUcbz7JzRFqpp5nZMFVX49b4zWVXj074u3dnrG7fGt4GArcossDuMf96lvl+6dlw15OuHWGwAaGKggK3gnuVMOtFZ21NPM0SLd+TzGy46Uj/bDSGkpwc+pgZ4VJiqkwCl42eOl8hcjemzqUXE2ofxWfD21KyeMQmnTeN9sSG2xGIzCxscpr6MBFd39VZZJ9+K1fkxcajoqS0WXkqSktxY8nHuL5wGR79+AsyX9RN+tensqKCW/7nRETB2LtNg+1IYUm5yCqf+8FpKK+obLAtYax6tz0C/hiL86sGY+5QVygq1E3ysjn9+hNilZVVUJCXb/S8wnjoayMmrxBZpXXvR9ZOs/vdRVcgZkgDsygvrHi1zwajICIcGk7OIivXtFxdUZqejrKc5k2o1aeisADyMorTbNVYfYGeP7u6r+7ZZQyyNMHdlAw+edAU4p9HQctQF/rmdX0QOy9n3gdJCJHNyv5VYWOowSeh7gq9b5irjccRmWhnXydEimN4e0sErh+JGysH4afp7WCopdKkNA1REBXJHnRoOtdN5qsYGkLZyAj5kZIn8/4tvD1M+SrKJy9Sa/c9eJrE25a27uLFy/oM620PNRVFHD0v2UKdWeyPGeSM6/diZVql6WWkjcjcQpG+NxOQWdvipi/dmIiho6Ik4lqRnSOvrBwTnMz5akY9FSWMsDfhojcTqqXF280YD5+l1E5oMHwDkmBtqgVDXfGTowM7WyMkJgvBjbi4FIa55XkWkYFnkbKttmHjnIcpom0LE6lbSzBskuSyj9W3cP2xfn39dwezRm9rJL1Bh5KCHFrb6uFePbc7vsGpaOfQ8LPb1t4ATzeOgu/qYdgyrzNsjETfMwPbWODc43iUiVvKKyVWWqp8hcjD5Lr6Y9b0bDLDy1D6fp+uisAJSK37z+pX8EtjIqExq6w8fxoFr/aiekabDk6IDE1EsQRXpcIwrYJZ+QcHxaBrH1F3xCf230RRUSnGvyV+dRtBvAmQKx6iRXPo0CHExcXh1q1bMDQUDMBmzpxZe1yh2kqK/WVW9tJQk4el79SpEz755BP8/vvv+PTTT0XSrVy5Ep07dxbZxyyCmTCvqysYpL/11lv44osvsH37dm6Nz5g6dSquXr1am2ft2rV8JcFHH33EP/fv35/n+f7771/6zj///BPm5g1b9slCeXkF8nMLoVOvY6itp4HsTPHL/8Sxe9NpaOlqoF03gVsbxsDRXbjP/o/eWldbNxNnD+SW/o3BXO9k1POZm1n92Uij4WXoxya0QTtTbb5k82JEOr6/XSe2XohIx9Yhrbgv/vPhabDVVcOcdla13/kivQD/ixipKfPlkvWXNrJlqI0t6/+0vT0mu5jz+gzOzMeCq9K7FWmMwqwcqNUT3FS0NLjwIMliX5o8rMPo3LsTWo/sB3U9HcQ/eY5b2w6hKDcf7ScNa7BMxvpq/C+zOhImM7cYlhKEXwsTTSgqyHOB6Oi6YXC01kNyegH+PheMPacFQjCz5o9LzsMn77bHl5t9UVRchiXT2kJFWYFPJEgLc+/AYK5PhGHW3foN+PcVhgnEbzlb4WJcqkTfz9KkkQYmoOlpqSAjV9RnZmZecYPWsNbGmrj+NAlbl3RDNw9TZOWXwOdRPDYcC+LiXlMx1lRGRoFoBz+zWuQ30hQvVuioKmLD8FZc1M9qwJc34+g0b7Qz1xG0P6FpWHVNNssyPRVlJGWLuoxhrij4MWVlfp2bQnllJfaGR+NakuB69jc3wTdt3bHC/xn8MrJkuv+EhUsGcwfFrAbVFORRJMUk0BxXGz6heiNJNjc2jaGtrMQnvWrqqwb2mdXrq6KriQE0FBVxMUG8a6SG7j2GuPvPSlfQ7ojj416OyCgow9/+Cc1KU5/S7Bwot3IV2aekpclnG0tzc6FmZNikPPLKSnCaOBamndrz43GXr+Pxmg3o8MVH0HVyEMl7eeaCWvcB9qOGwaxLR4nlNdJVRVaeaDvC7iNmZd9QW3LxUTx2+YQhLi0fnVyNuchvZqCOFXv9+HHmomfBiFaYN8wVuy+FQV9LBe+P9eDivrEEUUocBiring3BZ2nbZm9DHb5KavljyRMhTaU8JwcqRqKTyApagndaeW4OlHRkW10kifTr11CSlg6bztLFs6mB1VH99o19VldU5KIai1nQXNjEn72WBrYFS56Ebwxmla+hI9pnVtMW9EHyJfRbXjc1939GvecjM78EVgaS+xdhSbk44hsNv6hMWBtqYMXkNtj7fneM+vFarQ9uadJIc+8xWFwHYRQ1tWqP/ZcY6ashK7f4pbYlN69E6v7Z+KEuuPkgnvf96rP6014Y1d+Ru+L0f5aCOV9IXiUjDtZPZ/11YWriYxmoSde2dDTRRQ8LfXxyu844IbWoFB/dfI6fe7TCp+0FbXNAei4WX5MthgdzxROTJHrvZ+aU1B5Lz37ZXz8T/aPYSsdZ7TG6lwNKyyvwICgFa/Y8RpKY+AP6Oqro094SK3dI7x6tBiZMZ9YTdll9aigpQk1RnscsaAzmmofdE5dj64zF2CqIqa4W3Dc8Ww3N4hV0M9dr1A2eMHqaKlBSkEd6/WeX3Xvakt8/ydlF+GzPI9x5kcrP8elYTxz5tA8Gf+vDrf5Z/ALmrz+3qAx/LekOb0cDpOeW4MyDOGw590LqZ5fVHS+PmPpjq8GlQVleDkvb2vGJpITq2A0hWQX8//fb2eG7e2H8Gsxvbc0nT9iYtSlkpuehTUfRtpl5ImB9jJzMfKiaS47nMazTJ3xyljFtzgD0Gdyu9ljYi3gc2XMNG/cs5SvBCdkgVzwtB7p7iRaNn58f3N3da0X9V8HevXu5WxwmzjNx/7333uOW9PVh7nbqw1wB1Yj6DPa/mZlZrahfs49Z+Qtb7NefIGCrDliapKQ6q2HmxqcxUb+kpIS7+RHeShsIjirJT6YsjfCxXZdx5/ITLFv1FjQ068QJFoz3+rlH+G7rAuy7sgrLN8/D+aN3cHq/5CVxDVGjSTfWXZpw9Alct97G6EP+sNNVw6+D6yYbzoen46trYXi/kw2ez++OP4d74E//eH7s/2MYBn79G6nQNY8i0eHAHYw69YgHPdoxoDXvpLVkFJh/50UzoG9tDlUtDTj26IA2YwYg4MyVJsfbYMb+kvrqNZ34xVPb4Oe/HqP7jENYu/sxPp3ZHlOHCizUSkorMHv5JT6wu7RtLHz3TYaqsgLuML/PTShTlZjnQ5qrwtzqrO7khvTiUmwOimxyGmmRk1CqxiydWZ3OHuLKRbduS09jya++GNjeEivebnxiUFZqhjeS6m/NYDf4hKXjZlTjVmIT/34Mt/XXMWbvQ9jpq2HzSMlByKWlZpWRDGPFlwjOycPhqDikFZcgs6SU//8gLRNjbZu/pLjO4qrxAo62McUYW1N85x/ykgj6umCle5Ut1mALU25JzFbKvAoEz674Enaz1ccYDzN8ck6y0CJNGmmRxl1ZY3kMPd1hO2QAD4jLNqeJY7ibnZgLl17K23f7ZvTZug5ei+Yh9uLlBt0ASaKx19jH2x/gWUwW9zN+yS+BB86d3tcR6tUWhEHRWVi4+Q6Gd7KG35YxOPZNfxy7HYWyispm9wuk7bcwHLTU8W07FxyNSuIuef4Naq7dq+r/5AYGIPHIIVhMngw1K+tmn6/mfS3p+ZCVwZYmSCoshn8DrnuaRQvrSPK2uYFnesPZF7j+LIU/G0Gx2Vi0/T5czHXQx8NUpjTSUr8oTWlv/k3Y5J5Uz661Lrf6P/RPXUBuYT5bcwOth/6FYbOOckv9v9YM4ZP//1bb4qSrgdXdXXEgJJGL0XX71bGxtzt3xdP76F2MOPUQOSXl2NrXA0KLmppYvoafXXbtB3S0Qll5JQYsPImpX1zkhjXbvuzL46fUZ2wfB76C4sxN8at5ZS5fTTmkqEEWhHiysxk+uxMismrip4cRuJWQiV/7euDu5K6Y4mKOfS8SXklcP94vaKBoe65F4OzDeGTll3I//Et33OcGRuO72fLjNXW4cKgrDt6KRNdP/sGnux9hSk87fDS6+f1SaVcrsvvop+6u0GbxG+6EiKxWXHg1iMcKODWyPa6M68RF/XtJ2S+Nb17NO67hs56+8yOOXFuJr35+G8f338TRvQKtgln6//TFPsz7cBSMTWVf5UoQLQmy2CdaPK+yY3jt2jXMmzcPf/31F/r27ctFeOaKZ+nSpS+lVVZWlqos4vbVf8HUTyPuRSTu++rz448/YsWKFSL75n8yBQs+m4on90Ow6sMdtfvnfDyWW9WzoDK5WaK+hXOz8vgsd2Oc2n8NR/+6hM9+noVWbeqClDLOHryJMTP6wr2dQ617n8Fju+LU39cxclrDS9nSCsu4KwthDNUFbiiE3fNI6mywDoNfci5+uhOFnSM9YKyujNRqq9z9QUl8q6G3jWAGP7aetc7/EhnFpTwoFltqy1yr1MB85deshJAES11eWcWDRS2/G4qr4zuji7kebsTLthT20JKVyE0WLDXVtzbDuLWfQ01Hm1vRC1OSX8Bd5tS3yq+hKXkYBraWKC8uRWFmDnflI4n0rKJa66CIav/4PL+uKtKzxN8jadWuLw5dDMX9QIEV7+W7sTh/O5r7z//7nKAzy87HxH1hrv45DlcfCCaXpIGJsgxdFSV+XWtgS6gDJfj3rYFZVP/SuRW3an7fN1CsdbU0aWSBiWPMopYFpBSGfa5vxS9ManYxt7A9dkswiAuIzMSOc8H4dHIbfLL9fpP1k/SC0pfaFgN15dpj4uhopQttVUW81c5CZCDov6QHfr0bjfW3o0Tbn/JK+CXm4qfrEdg53ouvBmL+1KUhu7QUOkqiLndY4DHBsVcrhEfnF6CfuXRuBoStBHWVRbuG7N5jbnVYYNyGGG5tgkXudtyX/v3UVx94LLe0jFvU1a8/tvrkVdWdiZoKWuvr4KcA2S2qa+4vffYuE9JtDTWUkV4o/lnoYKULI01lPFxSF8SNPZsf9HDAzA7WaLvhhlRpxKGso43SPNG2tDQ3j6sJytparywPQ8vKElnBL9cZ97+vpsZd8ORG9ebW/bZDBoo9B1tFxVb/iOSXk4OupjLSZXh/v4jL5qIHWxUUHCe4Dy/7J/KtBlsTTagoKfA2SFqySkphLWTcUPNsMDIbMLBg2GupY11nD1xLTMcWCYF2m4uilrYgAK4QNZ+Zz/3mkvssCNHbfofZ2PESg/E2BFuFVt/dmK6yMvdr3VjbIg0q8vLcv/7hKOnft7/N+x6ZiQILXWMbM8z59VNo6GmhMFfUKrsor5D3QTR0m1+PTaHG2ldfUxkR9YJnZsjwbCRlFSG7oBS2Dbi8lCZNfRSr76/yvHwoCRk/sftPw6FhP+L/BswXvl4962jWRujqsH6f+KDr9a31U9ILuIsdcbD+SmlZJUKjsrB8wx1c2DWBu/h5VN1fbAwmJttpi/ZbamJd1bekro+jjjp+7+sJn9h0/FIv0O4YBzPuQ373C8Ezwdxzrn0cgVMjO8DbWBcPpAwQmp5dxPvMwhhUf2bHxMFiUeUWlGHNbsHKqey8Ev7/sZ+HwsFSB2Gxot89vp8D/rkdg/xGxiyS6k+vXmwwfdZvKato1CXgWEdTfOxtzwPk3kkUXd3I8v74MIJvNXzewQEJBdI/c2w1KpuwYM+qMOyzLO+14tIKxKTm1z6X7DPzqX89MAkXq99tD8PSse96BCb1sMMPRwOkOm9G9UQGq7+aoMG148dG3LKyuYUfurmilYEmZvkE8BUiwrDzLaq3OuTsqPa4mZDZaJDcOePW1H4eO60nZi0ZDl0DTeRki76zs7Pyuaai24jLKgVFBWhoKqBrbw+ETuyGM4duY/yM3shMy0ViXDp+Xn6Abwxm2c/0GWblv/yXd9Gxe50BIUG0ZEjYJ1o0bdq04SI8s25nFu31Uaoe4AsHom0IX19fHhCXBbgVXhXwOnFzc8PDh6JLC5mffh0dHZnd7nz++ef48MMPRfaFF17hf706OuPgjTrf/ixQF8PF0xbP/CMxekbf2mPMNz7b3xDM8v7gtgv4dM1Mfu76sBfpS9Y58nJSLWN7nJSDGa3NoaeqiKxqH9tdLXW5YBaYKr2LoBqjj4bmfkY6GyE0owDREjqf/wswX4gMbxMdbg3BcNXTgLaKUu0xaWCWFYymTKVNWP+lkJ254AwmLnbwP3YBuSnp0DYRrLpJDAzlF8zYWfz915Q8jMzYRB44UEWz4WXV0Ym5yMguQkdPUzwMEviFZ0tamX/9Xw88FX/u3BJEJeTULuMUHsw1JEB7Ohlyn/6XJQwGxfE8K49PtLQ10OYBRxnGqsqw0FBFUAPCPhPsf+7sDlUFBSz1DeIBcpuSpin4haWjo6sxtglZs3VpZcL3S86TBoN6A+1XYQz5OCEH09taQE9NqdatTlcbPUHbkiy+/rx/vSVi1TXAyRB/jGkN7823kNNADIDqJlamByY4O5cH0GVZan5ua31dpBQV107qvCqsNdS5GCkLQZl5aGMg6rKjnaEOvy8bggV8ft/DHt/5heBWsmyTgtJSXlWFsLx8eOprw0coMKaXvg6CXpGLjIHmJtwn8d1U2S2qozILubjf2VoPD6oFZWUFObSz0MHmO+LF3I23IrFZaOKIcWdhd/z9JAG/3omUOo04dBztkRH4XGQf84WvaWkBBRWVV5aHkZ+QCOVGXL3w9rOBh/xxeAa3sve000dg9eqZji5G3ErRL0z66+FcHdA6LUfyO59Z77MJyQfB0sfICMrKw0gbM+goKdb6b2fPBotREpojeYLAjov67riRnIF1zVwh1RAaDg7IuHWLC9A1fvbzQ4KhpG8gIrY2WdT/fSsPjmrUr3+TzvE8OxeeeqL3CGtr2GqjV0EPU0NuEeqTWOdHvTHmbf3ipX6LpZsdbh+8iKzkDOiZCnxgRz8V9EEsXBvuN78uolPzkZ5XjE5ORngYnlHbb/G2N8Dmc+KtyMVhoqMKXTYRXc8Voaxp6qNhZ8eDL+eFhUC/Qye+rzQzEyXpafy+/K/xe5YCdTUleLoYIjBE0C/p0NpU0LY0EmiUuRsc3d8Rh8+FSBW0vMaSWhabtKfpuRjvZMb9lLNgpLx8Jrq8bWHxsiThwET9fp64EpeOHx+Gi18NWK/INT9BFheM/sFpmDTQSRDHrDpbl9amiE/NR6qEiZHHL1J5mpfKw/8R/e52rkZwtNLF57/6oimwcY63sWjb0tFUl7sdaogxjiZcqP/sdrDISgdJsNXMfa0M8E+U9G0M83/PVsJ0dDLEkTt1MTq6uBrhUQN95PqoKMnD2kgDN57VTRY9Dk9/ybK+sXFJfWJzi/jkUXsTHfilCuqLGYmxuA/bAmMbEfVd4GWkhVmXAnmA3MZwN9CElZYarjdS1+aWhtzCXlhbYLRqbYtHvqIGBE8fhsPOyQyqatLHBWGaUU0dmVsZ4uy91SLHd209j3s3nuH3Qx/VaikE8SZAdyvRomECvLGxMaZPn46IiAjk5+dzC/vdu3fz40wcZxsLnivN0jhnZ2ce6JYJ6yyALvPh/8cff7zW38CEeLZSgAXiZUFxWZDdVatW1frclwUVFRVoa2uLbMrVFmNMaGcz0jVbzaqAEVN64un9EFz75yGKCorhc+IuD6g7fFKP2vOe3HsV0/p8LmKNf2DbBXy2ZibadKoLhiVMx14eOHfkNg+ay3z5BwdE4eIxX76/MZhv/MS8Yqzq4wR9NSW4GmpgSScbHHqehLxSgdhooqGMyMU9MdhBIO7ObmuJ8W4mMFJX4mKJt5k2Pu1mh1uxWUiptpTUVVXEtz0deF7mV5HlGeFsjOU3Xu7w/i8Rm1fMO0oftrPjPhGN1ZTxSQcHBKTlwl9I2L80tiMWtxEEZe5lqY/ZHlaw0FThQSnttNXwbWcnJBeU8KBTssI6P9w6k2+CV4ullxt3l3Nn+2HuHz87IQUPD56FQ7d20NAXCA1lxSXYPnEJgq/clTrPi0t38OzCTRRkZKO8pBRR957gyXEfuA3oCsVGfB2zZmLXyed4Z1QreLcyhramMr6Y25Fb1By/XOcv/dcv+2DPD4NqP287EojJQ1zQxsUISory6OltgcHdbHFWaNnwwile6OBhwgeCTNT/5eOe+OdmlEig3sbIKS3nfu9nu9rAVkuNW4R+2NoBcflF8E2psyb6q3cbfOwlGDAzMWNNZ3fuYkcg2L8sRkuTpqnsPB+C7h6mGNvdFhqqipjSx4EHwvzrYl0HfO4wVwRsH1f7+c/zIejoaoThna25QOFipYNZQ1xw7n5sswR+5vc+MbcE3w90EbQtRppY0tUOhwMS69oWTRVEfNwHg50FwRHZwIgNcmu2miXmXIesPu/sDlYY52HKY4Dw9sdCB5/2csSt6Ayk5ksvnl9ISOb+pN92suV+65moP8jCFKdi6nynt9bTwcl+3bkwLy1vO9qis5HANzzbxthYoKuJIU7HSu+TnXE0KhFuupoYa2sKNQUFDLAwQmdjPRyOrLN2HmFtgqvDuvLBH2OIlTE+qBb1b74mUb+G49EJ6GVqhK7GBrx8E2wtYK6uhjNxdc/YTCdb7Ooh8P8uC+zXDLAwweXEFD6JICssx86HMdyKvr2lYBXI1/1duBh0JKCu/n4f2xp/T21Xm0f43qsRW1h/pmbALk0acVj1643i9AxEnj6H8qIipPkHIPnuA9gMrhNmUx75cz/4xdXuA6XJE7zvENKeBqKssIhb90ecOIOs4FBYD+hTl2bvQaQHPuNpyouL+ffEXb0Bs26S/bIzMf9BSCq+ntYWZvpqsDBUx+dTvHArKBmhCXWrq1iQXBYglzG0oxXmDHGBuYE6lJXk0dPTFB+N98SZezEiK4Z+eLc9bIw1eVszorM13hvuhh8OPEFRdZsgDbeSM5BaVIIPPB144HFmhf+WkxXOxaXUBgxkvrKvDO2KHqaClYI2mmpc1L+ZlIF1ga83iKhB956oLC1B0onjqCgqRF7wC2Tcugmj/gNq07B9TxfMQ3Gi9O0CyxP9+28wGzUGRv3Fr7aQhpOxiXDR0cRIazP+7LIA1R0M9XAsuq4sQyxNcG5ANx7AWlYGW5jgQVqWTBOk4vot9m1dYWxrjgtbD3N/+xnxKbi+9x+492wHrepJz9LiEqwa8T6e+Aj6La8b3m+5GoF3+zqgvYMBtNWV8OV4T95vOXY3pjbd1rmdsG9pd/6/sY4q1szwhou5Nr/vncy0sHFWRyRmFsLnSaLUaaSB+dLX79wFSadPoSgxEWW5uYg7+DdUjE2g4+lZmy74+xWI3b8X/zZMzH8YkIQvF3bhAW5Z3KTP3uuE24/iESYU3PXJP+9gzqTWInn7drWBvq4ajp5/eUXS8L4OeGe8B8yMNXi/z8VeHyve747IuGw8eS69+HstLh3JhcX4rL0jX63JXOiw/vnJiOTaILfML/mDyd3R11Iw2cT67MxS/0pcBn4QI+ozrsdnwEFXnbuQYf0OQ1UlPk5g/fznMsRZO+gTCnVVRXz8VjtoqitxwX7SQGfsPFU3CdzZ0xTBx6bDyUrwjBy/FoHy8kq8P7UNDzpsoq+GD6e1xfOoTEQkiAruE/o7ITQmC/7Vky6y8ndwAjwMtbg7HdavGmprhO4Wetj7oq5tGedoisdTu9f2W0bZm+CLDo5c1Gd1KA4m4rN0bDzJxlXMOp2tiv7zmfSrghg7fEIxvIMVBrYxh4aKIuYNcoGNkSZ3t1MD86F/84chtZ9ZsFwvO33+XFoYqOOXmR35RNRRocmBbT6hGNDGHL09TGuD9E7tZY+zD+OkLhvrQux7kYhprhY8qC1b/f2Rtz3vZ5yOrJv0+qWnG7b1EzzLrAa/7+rCgxZzUb/ar3595npaoZ2xNh9jMlGfTQRcjE7Dg3qBjsUhrGfUGAwOn9ANqclZOLDjMgryi3Hv5jNcu+CHMVPrVjPeuRrILe3TUwXfsXXNCTy+G4KC/CIUFZbw42eP+qL/8PZiv0tYPxH+n5CMwNDzzdv+FyGLfaJFw3zXX79+nYvjzHqfBb4dMWIENmzYUJtm/fr1+Oqrr7B48WLuy/727dsSzzd+/HjcvXsXgwcP5pMEzHp/yZIl2LZt22v7DV5eXjh69Ci+/PJLXkYWL2DWrFnc+v7foHUHZyz8ajIO7biIrasOwdjcAO+vmAY3Idc6TMiqEHLJwfznl5WW43sh1z6M0dP7YNr8ofz/mR+M5hb9P3++iwet0TXQQs8h3pg8p04MlURJRRWmnwzED32c8GBWZx5U52RwClbequvksDZXka0AqG57TwSnYElHG3zcxY5b4yblF+NsaBp+e1zXgckuLkd4ViGOT2zL3fMw6/8ZJwNwT0gUeNUoKysiI3gX/19RUQGdvZ3x7uQ+CIlIRPsBn+Dfgvk2/LyjI44Nb8frzDcpC9/fF+3sM5+fNf7imWW/o646fuvrwScD2HJMlue7++FSBZqSBjZQHvz5e7i1/RAOLPiGW9Tbd2mLru+Or0vExFO24qZarJImD/vsf/wiTn35C3fbo2VsAO+JQ+E+pK5z1xC/HwmAqqoitn7VF9qaKngWno53v/ZBlpAQxOpK2EfqEZ8waKgpYdPnvWGop4aE1Hz8vOsRDgoN9s7fisY38zuhg7sJDyx27HIYtkhYBdAQvwREYKmnHX7v4QVleXn4Z+Tgo3vPRCys2OqKmmvJrEbbGurw42cGC6zlanj3uj+i8gqlStNUfJ+n4NPt97F0rCd+mtMRcakF+GDrXTwOrRuksU4Ucx9Sw4vYbCzYeAefTvbC2nmduTuBf+7HYv2xQDQH5qZrxmF/rBroivsLu6OorAKnnqdg5dVQobIIXJnIEgDtxLNkPkHwcU8HQfuTW4J/glPw2/06UUUamOj0rd8zzHW1x2hrS+SWleFYdDzOxNWJKHySVujeY4Oio3278f/ZflddbQw0N0V8YSEW3RWsOPNJSMZUBxssauXEB66x+YX4/skz3E+TTWh/kZ3PA3vOdbPBYg97LmSuDYjAvdSseteybo3DO85WUFaQx7feokFXz8QkY321hbKtphp29mor+D3ycpjXyhZz3WxxLzUTXzyU3uL0Vko6d100z8UOBioqvA6+e/ICMfl19y+ruprVR4xORvr42qtuKfWXbdx4u8OExh2hdRNz3oZ6MFJVwYX4hi04G2KrbzTUlBTwxzgvHpSZrRKZcdBfJCgzu+9kufeaioapCdp8sBChB44i8uRZ7mbHccIYmHcTivlTVVnd/kqfx7JPDy7mB237i89+aVqao92yxTDwaFWXpm9PQZrfd6KyohxqRkbcF79l7zqDAnEs2uzL42xc+mkoL9JV/0Qs3/NYJA17dmuqjwXgnj3EBfs+6wNTXTUkZBRgz+Uw7LggKsJde5qEP97vDhsTTUQk5uGTHQ9w7oH04geDBaf/6MEzfOjhgGP9O/Bgr5cT0kRc67BiCfotcrUxJ1jQ2GHWpnyrgcXCmHy17nft79MOpmqq/N5lednkAKPfOektWJX09GC/aCkSDh9A2pVLUNTUhPGgwTDq268uEXuHCFkrVpaVIXDpouoPlSiMjEDGndtQNTWFyzcCt4+pF86jqqwUiceP8q0GTVdXOCz5QOryheTkY9XTEMx0ssF7rvZIKyrBxucReJhe17bIo7rtq17SZKOhjt+6CtoNtn+msy2fuHuQnolv/euChJqrq8JTXwdfP25+HArWB5m0fC7ObzmMzTO/5eKOW/e2GDRv3Ev9FmGDoj+X/ozkyGr/21VVXPhnLDv4I1Q11KROI4nfLobwuD1b53WCjroytwJ+Z/MdZAm5geNtS/W7g1nc33qRgjVvecPZXJsH3LwTnIr3dz5EQbVVuDRppMVq8lTEHzmE0DU/oaq8DJrOznBYvARyCnVSA29rhFZYxx3Yj/RbN2v7f0+r70WHRUug3cpd6jTSsOjby1ixtBt8dk/k9X/1bixWbLojkoaJ8zXWwTVMGOKCu34JiEt6WQi/djcWsyZ6Yt+64TA11EBaViHft3WfP/ctL0vbsvBaEBeaL47uyNuW89GpIq51atqWGkFqgpM5D6w7xsGUbzWkFpZg+GnBKvGHKTn4wjcE77ayxCIvW74CIDAjD4uvB8nUz0/JLMLM767gmzkdMXNkK95X3nY8CHuEVmmye48JzzWNc35hGd5efgnL53bC3H2TuIsd36dJ+GyzL49tUAObMBjazQbr9vujqQRl5OOTW8FY0sYWn7R3QHJhCR//3BZyrcMuq3C/ZV5ra95vWdND1M3K0bCkWtc7bJz0QTs7fNReMF6+nZCJd3yeIq9Utmfj3ON47nrnm8lt+GRaZEoe3vvtLkIT6yY42HOrIBT44MDNSHw2zhOeNnooLCmHX2QGxv10FfEZdX2deyFp+GTXI3w10Ytb86dkF+Hw7Wj8+o/oqrvG2PksDqqK8ljXy40L+y8y8jH/SlDt6pGa+qvpwltrq2GYnTHXDk6PFDWi+PDm81pXrj4x6fisgwPaGevwYLxsouCPBlYBNIaljRFWrJ+JbevPYN92H+gZaOHdRUNFRHpmjV/J4+cI7rFh47pg7x8Xsfqr/Sgvq4CZpQFmLh6GoWNFYx8SxP8CclWvIgIIQbQABEurqrj4Lwvc6q2yUiRfeXk5D6zbWDpp9zWlDNISlHVW5jziv5/VneCtzUV+MU2DtK52hBm6R7Jf3sZgfZyK19xCpf3ye7PPUVNv4uq0uTitWoBXBeuY8fHkKzsj8Hab5scuqKyo4K4DXvUM+m+fymZVIwwrCivPq7iGkjCd59TkvOyOYyWrKZ2kQGjCz480aYRJOtr0+hMHG/jVBWJtPuUeAov7psCE34aWojd2XBpat2s8bkpDz6rwrSc2Dh/T6Jr8DUBOsfwraZvZWcQ9usIrHmryNJZGGA2VptW/XPUmXDfi6k9cW8h+izR1+vxR01278fLVu74iZWjgmLRpRvSRzm6HvaeY2MYslv9Nzv8uvTXr625HxGE5wfKVPBtyDdx7IvenhGdIUttsotE0N2p8yMdc9Qj3WcX5t2cWbdV9PeGJd0lp6pNTKv9a2z6xz64Uz00N0x1Efei/in4LE5PEtWbCz5Y0aZb/UtC8fosMgS+bQseJglW0TYHfS3ws0ci9JVSv0qQR5vFK2eOj1J3y5T4f28fbyVdUp1qzRSfAX3nb0szrn3u46TFA2OSbNO6KmpNPY7xdE0ompt9S/azI0ieRhrybKS322dXqKVvMpYb6LbKOJ6ThxKjmx2biY++KSj4h21RqXPXUH9vba41odvn+F9G2n4k3kdzInfhfgyz2if8ZZBWcRawj6w1q64v6ktJJu68pZfg3EXy/XINC9X/B6xb1XxXCqx1aMq+zw9gc/m1RSRoEBnUttMLEiI/SPCv/9fP0usU4WWhMtG+uqP+qn9WW9uwK30v8XmxB95/whJes9Vf5b5WvgfJIU9ZXdT/wJcktsP19U9qRxu5zgRulxvNI+ww1Fy6E1u+zNnL9JYn3La3t+7fbyPr9Fml8Mb9uf82v2nDjVVP/XpLm3vo37z9xhhyv07jj32hb/k2aIuo3J1+z+i0trO5a+rNbv9/S0u69GmrcEv8XehJB/NfQnUv8T7F8+XIuyovbevfu/V8XjyAIgiAIgiAIgiAIgiDecDn5Tdz+9/jf/FXE/2thnwXFFbdduXLlvy4eQRAEQRAEQRAEQRAEQRBEsyFXPMT/FGz5FC2hIgiCIAiCIAiCIAiCIAjifxkS9gmCIAiCIAiCIAiCIAiCIIhGkZMjBzAtBboSBEEQBEEQBEEQBEEQBEEQBPEGQcI+QRAEQRAEQRAEQRAEQRAEQbxBkLBPEARBEARBEARBEARBEARBEG8Q5GOfIAiCIAiCIAiCIAiCIAiCaBTysd9yIIt9giAIgiAIgiAIgiAIgiAIgniDIGGfIAiCIAiCIAiCIAiCIAiCIN4gyBUPQRAEQRAEQRAEQRAEQRAE0ShyZCfeYiBhnyDecBILW/bCG0srBbRklD0GoiXjbFGFlszhELX/uggSSQjyQUvGXcMRLZnUiGy0ZPRHWKIlY6NZhpaMT3TLbpvbtOTHQ6ll152lejlaMhX2umjJpKZUoCXj2apll89FuxQtGTWFFtyvKmvZ19ZNt2Vf28do2bSyqERL5p5Syx5TOpqhRfOkuOU+v22tW/a9V14p918XgSDeaFp2600QBEEQBEEQBEEQBEEQBEEQhAgk7BMEQRAEQRAEQRAEQRAEQRDEGwS54iEIgiAIgiAIgiAIgiAIgiAaRU6O7MRbCnQlCIIgCIIgCIIgCIIgCIIgCOINgoR9giAIgiAIgiAIgiAIgiAIgniDIGGfIAiCIAiCIAiCIAiCIAiCIN4gyMc+QRAEQRAEQRAEQRAEQRAE0SjkY7/lQBb7BEEQBEEQBEEQBEEQBEEQBPEGQcI+8cbi4+ODUaNGNfs8t27dwsCBA19JmQiCIAiCIAiCIAiCIAiCIF435IqHaDFcv34dq1evxvnz56VKn5qaivv37zf7ezMyMuDr64uW+BtfFf7Xn+D8novISM6EobkBhr07BK27eUpMX1ZaxvPcPu2LmOBYTF42EV2GdBJJU15WjnO7LvB0uVl50NbXhnffthjy9iAoKCg0WiYzdRV84OWAtobaKKqohE9sKn57FoOKqiqx6dUVFTDO3gwDrIxgqq6CpIISHI9Kwqmo5No0cgA/ztLZaasjp7Qct5MysP15LArLK2SqMw8nA3w1txPc7PWRkV2M/Wdf4M8TzxrMo6woj/mTvTCqjz10NFXg+yQR3297gJSMwto0M4a7YcpQF5gbayI5vQC/Hw7AyasRkJXcoECknDqOktRUKOsbwHjIMOh2FL1GwlRVlCPH3x+ZN6+jICICpmPGwqi/6IRWRWEh0q9cQo7fI5Rl50DZ0AD6PXvDoEcvmcs3wtYEM1wsYKymgujcQmwOjMbjtByJ6V31NDHd2QJtDLUhBzkEZebht6BoROcV1aZ5z90GM1wsRfKlFpVgzPlHeF3o62rirYm9MGtaP9haGaPj4M/wIjQer5PKsjKknTqG3EcPUFVWBnVnF5hMnAolPX2JecoyM5B95xZy7t5CeV4enNdtgbySkkia6NUrURwfJ7JPt0t3mE59S6by6Wip4KsPuqFXV2uwx/Xq7Wis2uCL/IJSsen7drfBrz8MEnts8ryTCHiRKlUaaTHXUMFHbR3Q1kgbxeWVuBCbil8DJLctGooKmOBohoHWRrxdSiwswdHwJJyIrGtbamDpxjmYwkRdBS8y8/HLk0hE5NQ93/WprKhAxPHTSLpzD+XFJdB1coDr9ElQNzFuVp7Hq9cjKyRMJJ9x+7ZovWBO7ee8mDhEnT2PrJBw1gJA29YGDmNHQtvWusH662Wlhw872MFGWw2J+cXY6h+LsxFpEtNrKSvgXU9LDLEzgrGGMuLzivH38yQcCk4Sm36grSHW93OFX3IuZvwTAFnJuHMbaT4XUJaVBRVTM5iNHQctVzeJ6SuKi5H98AHSb15HcUICbOfNh45XG5E0hTHRSPW5iILwMFZVULezg9nosVA1M5O5fIs622BKawvoqikiMDkP314NRXBagVR5fxzogomeZlh3Owpb7sfU7u9uo4fZ7a3gZaqNovJK+MZmYc3NCKRKeObqU1FWhkf7TyHqzmOUl5bBzN0ZnWZOgKahXrPypIVFI+CED1JDIyEnLw8jZzt4Tx4OXcu6ejv92WpkRieInFvRvjPKO05+6Tt1VJWwfLAr+joboaqqCpdD0vDdxWDklZQ3+hvVlBRwanZn2BuqY/SO+whKyhU5PrSVCeZ1tYODoQbC0wuw+nIo7kZnQhYsNFXxZRcHdDDVQVF5Bc5EpGL9w2iUS2hbWJ9kmIMxprqZwVFPA9nFZbgWm4GNfjEoLKuQOk1La1uEebr5D6T5P4XrW1Ng2bsHyouK8XD3YSQ+CuDfZ+rVCm3fngBVbS2J5SorKsaTfccazCNNmtRnoQg+ewmZETFQUFKEoasjPCeNhKaxYW0aduyvtaeQGB4POXk5uHfzwuDZo6CiplKbJjcjB2d/O4YI/1AoKCnAo0dbDJk9EkoqyhJ/Q0JYHB6cu4PAG34wsDDGws0fSUxbmFuALYt+Rm56Dj7862vomUh+twuzYJgrpvS0h46GMgKjs7Dy4BMEx0vuV22a1xmD21mI7HsQmobpv9ys7bMGbRkjPu+Z5/j17ItGy8TaiSd/n0KM7yNUlJbBxN0Z3u9MhEYjbUtjeQrSMxF+5Q4ir99FcU4eJu5eB4V6fZqchGQ8PXAK6WFRqCgvh465KVDcFlC1F99vWdQFvTtZCfotd2Pw/Za7yC8oE1vGvl2tsWXFALHHJi0+jYBgwTuxZ0dLzJzQGu5OBigqLsddv0Ss2/kQKemS+wXSknL1KlIuX0ZZXh7ULCxgPWECNB0cJKYvLyxExr17SLt5E0XJyXD54ANou7jgVeBhr4+v3m0PN1s9ZOQUY/+FUPzZyP3Bx0TjPDCqux10NJXhG5iM73c9QkpmkdjzH/huIHLyS9D9vRMyly8vKAApp0+gJDWFj4mMhgyHboeGx0S5/n7IuHUdhXxMNA6G/cSNiXyQy8ZEOWxMZAj9Hr2h34QxUX2MdVXxzfR26OZugtLySpx7EIefDj5FSQNtPmN8Tzu8PcAJVsaaCI7Nxqq/nyAwSrb3WGFiIqIPHkReZBQU1dVh3L0bLIcP5+/w5uRJ9fVF0qXLKElPh5K2Nkz79YVZ3761x8vy85F0+Qoy/fxQmp0NVSMjmA0YAKPOkq9TfYL8IrBz0xnERaVAz1AbY6b2wpBxXSWmLy4qwZnDt3Hjgh9Sk7NgaKyLASM7YvTUXpCTY29foKKiEg9vP8f5Y74IeBSOMdN7460FQ6Uu0/93yBVPy4GEfaLFkJ6ejrt37+J/mf/iN4Y9DceuVXsxYfFYePX0wqPLj/Hnt7vw/sbFsGtlKzbP/YsPERkYidHvjcDmZb+hqvLlQSsT9e+ev485382EhYM5YkPisGP5X1BQVMCQt8QLdDUoyslhfTcPROcVYtplPxioKuOnzm5QkJfDxoAosXlG25lycf/bhyFIKSxBB2NdLO/gDAU5ORyPFAhIrfS04GWgjQ0BkYjJK4KNlhq+ae8MAxVlfPMwROo6M9JTw55Vg3D8SgQWrrqK1s6G2PhZbxSWlOPAOcnn2fxFH9haaGPZzzcRHJWFzl6mGNvPEb8dFohXb49qhQ/faoelP13Ho6AUtHUzwqbP+6CwuBw+vnUiTmMUxcYg9vctMBk1BnqduyL36RPE7d4JBS0taLm1Epsn5/Fj5D71h/HQ4Yj7awf4yKYemXduQV5NDTbzF0NRSxN5QYGI37ML8soq0OvUWery9TI3wKftHPDdwzA8SMnCeEdzrOveCm9dfsKvizgWe9riWEQS1j+N5Nd0SWs7bO3lick+fsgtFYg68nKAf3oOlt4Kqs0nQU95ZXz94XgUl5Rh+ZpD2P/b+1yEed2kHjmA/BfPYLVgCRQ0tJB8YA/itmyA3efLISdh0izl6EGoWlpDf8AQpB49KDZNVUUljEaOgX5focFLdcdWFjZ+PwCaGsqYMPsEFBTksGHlAPzybT/M+1j8hOXV2zHw7LNdZN/KT3uhS3sLBAanSp1GGhTl5bCppweicgsx+YIfDNSUsbabG7+n1j0R37aMcTCFmqICvr4fguTCEnQ01sV3nZz5uY6E14nTS1rbYritCVY8DMXj1Bw46WpglJ2JxPMyIo6dQuLte/BaPA+qhvoI2X8Yj3/eiK4/LIeCsnKT81RVVsJ26EDYjxlRm69mkFJD5JnzMOvWCa5vT0VlSSkiTp7F4zUb0P3nlVDS0BD73a0MNLFlgDvWPYzGibAU9LcxwJrersgsLoNvQrbYPBNczJBXWo55PkHILCpDL2t9rO7lwoXP0+Gi185cU4ULo/4pubx+ZSXH3w8Jf++D1dvv8rYu/fo1RG3ZDOcvv4aqqXgRnqUpTUuDxcTJiPjlZ7GNRtLxozDo2RsWEyfxuk06dhQR69fC5ZsVUNTUlLp88zpYY24Ha8w/HYSw9AJ81N0e+ye0RZ8/7yG3EXF6mIsxPEy0eB3KC11LQ3UlzOtogx0PYxGQkgddVUX8ONAVeye0wdDdDyVOWAlz76+jSHz6Av0/nw9VLU34bjuASz9uxag1n0FeQpsiTZ6nxy7AdVAPdF8wDaWFxXi07yQurNiECVu/qxXhWLvjPWUE3IfXDfC3nRZf5i0TvKCpoojRO+7xZ/bXCV7YMNYTsw74N/obvxvihvjsIjgba77UTk/1tsQ3g1zxxdnnuBicAnMdVS7yyyLsK8nLYcdgD4RnF2LE8ccwUlPGr/1b8f7Mj/cjxeZpbaQFbxNt/HAvApE5RbDXUcOaXq4wVFfGsmvBUqdpaW1LDXGXr6O8qIiLOzX9xGc7dqM4OQm9v17K74H7W3bDd/129F3+ocTf8uD3vchLTG4wT2NpmFgccu4KXIb1g56dNUpy8+G/+zBu/vgrhq7/lqcpSMvA9R82oeOgTpj46VsoLijCiQ0HcWztPkz9ehZPU1lZiT3fbIO6ljoWbfkYRQVF+HvlTpQWlWD8R9PElr+yopKfp+PQrryu4oIb7s8dW/c3jG3MkJ2aJXUnZs4gZ8wd5IKFv91FaGIulo32wJ4Pe6L/VxeQWyhenGbP0OHbUVi+v+75YRNmNTBB0W3+cZE8g70tsHleF/j4iU7GSeLxriNIevoCvT9dABUtDTzYcQDXf9qCIas/l9i2SJPn8e6j0LO1RKuRA/j/bMJVGPY7rv34K/RsLDFo1adQUlVB8PmrSDt2ElXG7wBKopMlG7/pB00NJYxfeEow1vimH9Z90Rdzv7wotoxXfWPhMWinyL7vl/VAl3bmCAwRiPoGemoYP8QFW/b64UV4BkyNNPDdB93x28qBGDv/JJpD2p07iD92DPazZ3MxP+niRYRs2ACPFSugoi9+IijZx4eL0VbjxyN00ya8Kox01bDnm/44fiMSC9feRGsHA2z8oIdgTHRJdBJQmM3LesLWTAvLNt9BcEwWOrubYmwve/xWz0hKQ1UR65d2x8MXqXziQFb4mOiPLTAZORa6bEwU4I/43X/yd7emm7vYPDl+NWOiEYj/azsgZpybdecWFNTVYc3HRFp8TJSw9y/IKytDt1MXNBXWnG7/sCdyCkow/OuL0FZXxtYl3Xg9fLL9gcR8C0a4Ye4wV3y64yFuBSbDzlQLk/vYyyTss/b6+br10G3VCm2/n8kngEJ++x1y8gqwHD6syXnS7t1H5J69cJw1E7ru7iiMj0foH9sgr6gEk549eBom+ssrK8H5vXlQ1tVFpv8ThP/1F+QU5GHYoUOjZU+KT8e37+/AqCk98c26WQh8HIF13/4NDS019BzYVmyeGxf9UVlRhU9/fAsGhjoI8o/A2q/3Q15enp+H8eRBKC6dvo8Rk3ogLSWbvwMI4k2EXPEQ/yrMOv7bb7/F4MGDMWXKFFy7do3v9/f3x9dff438/Hx0796db7///rtU53z06BHmzJnDz/nFF1/wc9T/TnZu5m5nwoQJ+Ouvv0Q6tpJc/Fy9ehWzZ8/GgAED8OWXX6KwsJDvmzx5MoYMGYL169e/1Pg39F2SfmNZWRk2bdqE0aNH823z5s0oL2/cOk1arh6+Dpd2zug+shu0dDXRZ3wv2LaywbUj1yXm6T6iK976YjrsPV62eqkhNjQOru1dYO9hxy2dnNo48i02OLbRMvU0N+CWb6v9w5FaVIoXWfnYGRyH0XZmXLwXx99hCfjjeQwicwtRUF6B64kZuByfjv6WdZZYz7Ly8POTCH4+ZqHP/vrEpcHTQBuyMGmwM0rLKrFq231urX/tQTwOnQ/FnHGSVzn06WiJfp2tuWjvH5yGopJynq9G1GcwS35mnX/9YTzyi8pwyy8RRy+F4b2Jks8rjvSrl6FqZQ2jAYN4Z1O/ew9ouXsg/ZL4AQqDWfNbz3kPmg1YtrLzMSt+FWNjKKipc2sXVQtLFEYwi1/pme5igctx6bzus0vLseN5LF9hMdHRXGKehTeDcDUhAxnFZfye+PFxOJ/w8TbSEUnHHqcKoe11d78++GYXPl+1H+FCK0NeJxUF+ci+ewdGI0ZD1doWSgYGMJkyA6VJich/Figxn+XchTAcOgKKOroNf4GcPJ8cqN0asNARRytnQ3TtYImV628jJj4HkTHZ+HGjL/p0s4FDAwOyioqq2k1ZSQGD+9jj6JlgEU1DmjSN0dvcAJYaqvjxUThSikrxPDMf25/FYayD5LZlX0gCfguK4Zb3BWUVuJaQgUtx6RhgVde2WGuqYpqLBdb4ReBOUhaKKyoRmJHXoKhfUVqK2MvXYTdyCLeMVdXTg9s701CSlYWUh37NzyMnx0WQmq3+tfRaNBfGbb2grKkJVQN92A4bxC36ChLFW9Iz3vG0wPP0fOwMjEdWcRmOhCTjZlwmZre2kpiHpf0rMAGxucXIL6vAPxFpCM7IRzsT0XZXQQ5Y19cVv/rFICZH/ARfY6Re8oGOd3vodezE2z7TESOhbGCA9KtXJeYxGTwEVjPegpqV5JUKDh98BF3v9lDS0YWynj4sp01HeW4u8kOlnxBm0ufsDlbY+Tged2KyuDX915dDoaokz63wG8JKRxXL+zph6dnnqKgnMKQXlmHGkSe4EZ2JrKIyRGUV4fvr4XA10oSzofgJGmFK8gsQdu0u2k0aDkN7a2ga6aPLnMnIjktCvP+zZuXp/9l7sGzrDhVNDWgZG8BjZD8U5eQhNzm9wXuVtUP1cTfVQnd7A6w4/wLRmYWIyCjAyovB6OdsDMdGfudIDzO4mWrhl2svi0waygr4fIALNt2MwPGARBSUViAsrQAfnaqbIJaGfjYGsNJSw7e3w5BcUILA9Dxs8Y/FJFdzqCuJb1uepuVhhW84gtLzufU9+3s2IhVtjXVkStPS2hZGXlw8XxHkPuft2n2FqWlIfewPr+ljoWNpDk0TI7R9ZyLSQyKQHip+8iM/JR0JD580mEeaNEzs7/HxfJh4uEJZQx1aZsZwGtQb+SlpKM4RrN5IeBTAn9Mhc0dDQ0cTBuZG3Fr/2Z0ApMWl8DTMSj8pIh6jlkyEnqkBzB0sMfDd4Xhy9SHyMkVXgdQgryDPJwE6DusmYvkvDt+T11FSWIzu4/o0mK7e5cDsgS7463IY7rxIRVpOMb7Z7wdVZQVM6CbeQKcW1leqrKrd6uuXwsfYNq6rLR6Hp/PJg8Zg7UTE9btoPWkE9O2toWFkgA6zpiAnPgmJDbQt0uTpuWwuPMcNhZqe+OeAWfEXpmfBaUAPbumvrKmOVqMGQo71CstEJ5RbORmgm7cFvt98FzEJuYiMy8EPW++hTxdrONpI7jMJ14uysgIG97LD0XMhtX2SjKwiLFlxBQ8DkpFfWIbwmGwcOP0Cni5GvA/THJhIb9itG/TatuXWz0ysV1BTQ9qNGxLzWI4eDZupU7l1/6tkUn9HlJZVYNWuR9xa/5pfAg5dDsOcUeINiRh92lmgX3tLLF1/C/6h6SgqqeD56ov6jJXzOsHnQSzuPxM8g7KSwcdENjCsGRN16wmtVh5Ia2hM1KETrGe/B00XyWMidj5mxa9ibCI6JoqUbUxUH2al72Grh6//eoyE9EK8iM3G2iMBGN3NBoY6qhIt/BePcedW/RcfxfNJlWcxWfh612OZvjv93n1UFBXBbtpULq7ruLrCfOAAJF2+jKqKiibnSb9/D3ptvLhAzyz6tZ2dYdqnDxKEPBRYjxkNy2HDoG5uLrD679YVeh4eSH/wUKqy/3PkDvQNtTBj/hDo6muhx4A26DWoHY7tFWhJ4hg0ujMmzewPK1sTqGuqomMPd7RqY4cXT+v67t5dXPH1L7PQvpubxElsgngTIGGf+NdIS0tDhw4duJg/c+ZMLmKvWrUKz549g729PaZNmwY1NTX89NNPfGPieWMwIZ2J7/369cN7772HEydOYN68ebXHMzMz0alTJ8TGxmLp0qUYN24cd4Xz4YcfNuji59y5c1i2bBkGDRrEz//nn3+id+/e+OSTT7hgP336dHz//ffYunWr1N8l6Td+88032LJlC5/oYBMUUVFRfPLjVRH5LIoL7sK4tHVG1LPoZp23bS8vhPqHIT48ARUVFYh+EYOIwEi06yN+1lyY1gba3D1LVkmdldGj1GyoKMjDRVd660htZcUGXezYaKqhj4UhbiRmQBa8W5ngYVCyiKDo+zQR1mZaMNRTE5tnYBcbhERncUt9STArzPqTSpWVVXB3MICqivSDALZsVNPFVWQf65wWRsru0kcSleXlyA0MQElyErTrua1oCGa92EpP8yW3O+z6tjaQvBS/PjoqggVl9a+vp4EWrozqjDPDOuDHzq5cxP1foig6ipn/Qd257voqGxhCydAIRc0cTDAyfM4h5IMFiFjxJVKPH0FlcbFM+b1bm6KwqAxPn9UNnB88SUR5eSXaeZpIdY6h/RygpqqIo2eDm5VGHF6G2txaP1OobXlY3bYwd0/SolOvbelpYYCSikou+ktLXmw8KktLoe9WtxSeieyaVpbIDotodp64KzdwZe4S3P7kawTvPYiyAsnuXtgxZl2rZmQILWvJIj0T4+8niVrm303MRhtjLamtmntb6cNeVx1XYkTraqm3LTKKBJMFTW2TCqOjoVnPtQBr+wpewbMhTHm+oC4VVKVvX2x01WCsoYK7sXXvAHbPPErIgbe5TsOrTIa7Y4NvFCKzpHPfoKcmsIbPr17N1BDMXQ6zmjf1cK7dx0R4LRNDpIZEvbI8xbl5CPa5BV0rM+iYibqDCTzpg70zPsSxpSvwcO8JoKzkpfztrfVQWFoO/4S6d8f96CyUV1bC20qy+Gatp4avB7lg6fEAlLHZ3nqwyQItFUWcCpQ8oSUNbU10EJFdyCefa7iXmAUVRXm4G0jXttjpqGGgnSEuxaQ3K81/3bZUlJQicOsOuEybxCcIaqg5j7Fb3X3DxFsldTWJwn56aON5pElTn6KsHERe94Whsz1Uqt318P6XnJyIeMMsNxkxzwTniXkeCR1DXS761+DQxpmvSIgLbl6/mbn/uXHoCiZ8PEMmAcnGSBNGOqq4K7R6jVnbPw7PQDuHuglocQzvaIXAX8fgxo9D8dPb7WGoLXniwVRPDT3cTXHoluQJa2EyqtsJ5kqnBk1jA2iaGEq8Lk3JIw41XW0Yuzki8vo9FOfmo7y0FKHnr6NKXh1QEX3HeXsI+i1PhFz6PXiahPKKSrR1l67fMqy3PdRUFHH0fKjENMxif8wgZ1y/F8uF8KZSXlCA4qQkaAm969j9wj7nR7y6fr60eLsYcWt6kTFRYDKsTbRgqCv+HTmwkxVCYrMQHCN+pV8N4/s4wN5cG+sPPm1y+dj7X8NZtF+g4eqGoqhXOybKqx4TabVufJzbEN5OhkjKKERMap0hou+zFCjIy6Otg4HYPH3amPOVJmfvNW481xB54eHQtLOFgkpdO6Dj6sbvOWaJ39Q8rH2s75KFTQgztzxs8lgSZQX5UvezXgREw9NbVM/w6uCEqNBEFBc37paworwCgX4RCAmKQefeshnUEcSbALniIf41mMjNuHTpEpSrl/xOnDgRJSUlUFVVhaurK/fNzizZpYVZth87dgwO1T4HmcA8Y8aM2uNr1qyBra0tdu/eXbvP2dmZTzAwQV1PaEAi7rxMjGcEBwdjxYoVXLS3tBT4+H7y5AlOnTqFRYsWSf1d4n4jC97LJg8mTZrEPw8bNoyvDngVsJdYYW4ht9QXRlNXE7mZec06d7fhXZGRlInVc9fWdjqZf/0OA9o3mtdAVUlE1GdkV39mx6ShvZEOupnq46v7Lwt/v/X0hIeBNl+GfDMxA5sDpRuk1GCkr4aYev55M3OKa930pGe9bG3KRP+ohBx8ObcjRvd14Bb/DwKTsWbnIySlCwbFPndjMWecBy7cjsbj56nwcjHC2P6OUFCQh4GOGhKEOnkNwXw9MqsUYRS0NFFZUsL9ScsiRtWnPD8fLz5dxmYcmBkazMZN4KsBpEVXRQmK8vIvXV/2mVngS8sHXvaIyyuCn9AEQVpRKVY9CsOD1Gz+PYs8bbG9T2tM8fHjKwP+FyjPEfxeRc3611eLWxA3B3VHJ2i1aw8Vc0sUx8ci+e89KElKgNXC96U+h5GBOrKqn4UamIV9bl4JDA3UpTrH+BFuuHkvDikN+B2XJo04DNRebluyZGxbOhrroLu5Pj7zrWtbLDVVEZtXhKnO5pjibA4leXk8z8zD5oBohEvwsV+SLbiWyvWeVfa5tNqCtKl5tO1sYT9qGLRsrFGQmIgXu/+G//ot6PDFRyLWtbGXriL0wFHuXoNZ7bd5f6HI4Kw+RurKXHwXhrmG0VRWhLqiPArLxa+R0VVRhO/0LlykLqusxE/3InErvm4w18VcF6OdTDDquGzWZcJUsNV4lRVQ1BJdCcDchjX32RCGiX+JRw5B2cgYGk514lNjGGsK6jWjUHSAmVlYxi3yJfFxD3ue5++niVJ9D5uk+qynA3xjshBX71kUR2GWoG5UtUX7AexzUXZus/M8OXqeb+we0zYzxoDP50NeaHWMiZsjbLu0hZ61OTKj4nFn2wEoySWirM/8l+qP1ZUwzM1QTlE5jKrrVtxE0uZxXth4IwIR6QVwNnpZYLfWU+c++jtY6+FAH0foqikhJDUf666Fy+SKh7neyagnHjAXVfyYesPvtn3DvNDGWJsLM5dj0vGzGNc90qRpKW1L8L6D0HG0h0mHdiLnKmV9EzVVKCiLtrUqWpq1VvP1Kc7OhVIjeaRJU4P/7iMI97nBn2NdGwv0+GRhrYhu1sYdgYdO4dKus+g1eSC3nPf56ww/XmONz/5q1Oszq2tr8AmAvOrnoimUFJXg0I+7MXz+WOgY6SIjUXLckvowUZ+RkSc6IZaZVwKrBlazhCXm4MidKPhFZMDaSAMrprbD3mW9MGrlZT4xUJ/x3Wy5a8h/HorG4pFEUfU9pVKvnWATKZLalqbkkUT392fj+uqtOD73U/5ZVUcLVQZjAAWNl/r0Wbn1+i2Vgn6Lkb6U/ZahLrj5IJ7HxqrP6k97YVR1X97/WQrmfCHZUlzaPj6DWeoLo6SpySe3/23YuCcmWXTMmFldn8xNT3r2y+8hJvpHJebhy3e8MbqnPZ/oePA8FWv2+XFRm8EE/U+mt8Xkr31QLmZSVpZ+c/0xEetDv6oxUfBnHzZ5TCQOY101ZNS7H7PyS/lEkySLfWtjTW7dP6qbLd4b7gp1FUU8j8nGz0cCEBAp/XuMtdFK9fpQSlqatcfUxaz2kCaPftu2iD50CJlPn9a64km+fr32flYRo7ekP3yI/MgoWI8RH+ejPpnpuVzIF0ZHV4O39TmZ+VA1lxyrZFSXj2vdxU2ZPQC9B4u+u4imw+LiES0DEvaJf40bN25g5MiRtaI+g3WmmajfVPT19WtFfYa1tTWKioqQk5MDHR0dvjogKSmJW9uzhp9tTPxnLnRCQkLQuXNnieetEfUZ5ubmMDMzqxX1a/ZduHCh9nNTv6tXr17YsGEDrxfmwsfNzQ3q6uI7mmwShG3ClJaUQVlFvGAlyeXQq1hqxoLx3vd5iCXrF8LKyRIxwXHY9f0eKKsqo99E6ZcY15a1pmxSpHXU0cD3nVxxODyRu+Spz8KbgVxcdtbVwGftHPFdRxd8KWYCQBZqvC5JKh8LwjagszUPsDtgznEeKOqHpd2w7dv+GLXkNLfM/+NwAJQU5LD6wx4w1ldHWEwWth0NxKczO6CqvgNRGXlVyweZT0qPTVu5JXfus0Ak7NsDeVVV6Hft1qzzsl8nbQmXtrZDW0MdzL8RiDKhdeNHIuosLllg5K/uh3DLfeb3fF+odP5g3xjkxF3f5t0jLABvDRrOrjxobtzGtShOiIOqhWQrbmmorKqS6vo62Ohyq//5n15oVhpZqGkGpSmfk44GfujiioNhiSLW+fKQ4+1OrF4R3r78lP/epV522NLLAxMv+PH7UVqaci3r53GePK72f10nR7jPeQf3l//ALWb1XOoGPlb9+8CyTy9uMRV5+hz3sd9l5VdQ0ZVsQV6fyurvbaiNyS4ph+fOW9BQUkQvK32s6unE3RodD03hq2/W9HbBZzdCkSVFEFRZ4VZirzDYRuLRw8gPC4Xjso9fCkDd5GdDQtV1s9bDmFamGLxLsl9dYdhk9abhraCjqoiZx582a/DF/aI3Um/S5PEaOwitRw9EfnomHh84g/MrNmL0z19ARVPQj+k8c0JtWjMPZ3SbOwUXvtsEuawEVOlZNKv+lvV1QnpBCfY9kixEslAOzFXOFG9LvLXvMbKLyjC3qy12TWuHoX/c5RMCTUVaV3BvnXvKJwPdDDSwsrsz1vZxxftXX8icpiW0LSkPHvPgup2/+1L672AXQcZnVJo84tK0mTEOraeNQUFKOg+2e33VRgz84XM+KaBtboLuy95D+LFTuHPiBneb03vKQCSEx0kXb6YZzQwLxmvlZgPPns2z9q3/bDT0Yttw+nnt/0Ex2Vj0+13c+Xk4+rQ2w0UxPvSZsH/6QSyKSmWzNn+pnZCiLpuSRxgWU+HK95ugZWqEHh/MhqKqCkJ9biLg6FFUGU17yce+OFi/XKp+i7Uut/p/7ysfscc/W3MDX6+7BVtLHXyzuCv+WjMEExaeesmtWrOR0XXi66Tmp0kcE8kBAzpa4s8zLzBgySnBmOi9Ltj2WR+M+vQcz8f89G849BSRUrh9kplXOCZy3/gbKkuKBT729+/mYyK9LtIbIb6KYrOmzsJQHX28zDDp+6soKa3A0rEe2PNJLwz+/AKSxRicSf+l8s3OY9KrJypKihF98BBKszKhamQM80ED+WdxPyo3LAwRu3bDatRI6DQjwDN/BzSgd9Rw/NZPKCkuw9NHYdiw4iBU1JQxdrrsWgVBtGRI2Cf+NQoKCqBdz/qguSjVG3TXdAxrGvi8vDwutM+dO/elvExAl+W84vYJv0ia+l3MpU/r1q25G6Eff/wRGhoa3Pc+8+1fH3acrRwQZvoHUzFj2TS8eBiM3z+vCz458f1x3KpeTVMN+TmiluB52XnQlMEthTiuHb2BAVP6wclLsCzOpZ0TeozshquHrzUq7DM3GTZaopMXetWTE8IuNMRhr62Ojd09uH/9TRIs8dlgu7SyEkGZedx39s9dW3FrXeEl9A2Rnl0E/XpWEwbVy03ZMXGkZRYht6CUW+gzsvNKsOavRzi2fgQcrHQQFpPNO/kb9z/hWw3vjm6F4pJynl9aFLW1UJ4naj3DPsurqPCtuTDf6woaGtDr2BmFYWHIuH5VamGfrbwor6yqvZ41sM/S1P8CDxuMtDPB4ptBCMtpWHBhbi7i84thpSnePdKbiGJ1G1melw8lXV2R66tmL7oEtbkwX6GM0tRUqYV9tlpFr96zIS8vB11tVe5vtjHGj3DlVvjXGwgWLU0aSTALWtsmti0OOupcqGf+9dfX852fXlzKLWnX+kfWnof52782pgu8jXVwNf7lCUYVHYEFWWleHpSrXUHUfJbkDqcpeRialhZ88FSYnCoi7LP3lJyiAnfB0+rd6bi+8EMk338Im0H9xZ4nvagM+vVWNrC2k4n0bGsIZnDHAl2fiUhFBzMdTG9lzoV9Wx01mGioYPvgOis3NhZjrsmez+qByaefICCt8RVkCiyIrbz8S21fWV5u7XPTXBJPHEPmnTtwWPoB1Cxlm+xKLxBYc+sz6+3MulUcLAhqzbH6dLDUhZGGMh4uqGtf2aT0B93sMNPbEm233Bapsw3DWsHTVBuTDvohJf/lcyrd2AX58IfYVR3zcdqun6Gmq1XrKkddv65NYb7wjV3Ex9GRJQ8T+9kYX5sJbAunY9/bHyH24VM49REfXFDfViDmy+WliQj7TKDXVxe999hv1lNXQrqY38roZKOH1uY6CP9atL90YnYnnA1KxvsnApGaX8Kf3TVXwrjvfgb7f2JbCwx0McZv6VK6HSkqhb2u6LumZhVQ/VUu4kQw9r56kpqHXx5G4beBHjBSi+Cr0GRJ0xLalqyQUBSlpePae6IrvYL3HuDnLS8q5qJrTfDkGn/oqjrin1FmZV3WSB5p0gjfj8ylhbaFKTrMnY4zC79ASmAwzL0FrhdMW7thUG9BG7l+9iqc23aSrzZ5dN4XfaYMhKauFgrq9ZmL8gq5kQ471lSiAyOQlZKBp1cFq5ZqjDnWzfwenUZ0x/D5dRMq9Umvtu7V11RBBOraPwMtlZcsfxsiKasI2QWlsDV+uf/fzc0Y1kaaOHjzrti8CiFHIZcagAN3BJ8n7FxbW/f12wn22UhC29KUPOJIDgxBTlwi+ny+sPY8zCd/wPE7kCsMQJVO79q0rG+ipy2m36KjKnYFrjhr/ZT0Au5iRxxsKMhW6YZGZWH5hju4sGsCd/HzKLBpbueUdAQT72X1+/m5r+5dJwvpOcXQr1d/BtX9QHZMHGnZbExUhjX7BIGbs/NLubX+sR+HwMFCm1v5s0C538zswDcGG8KzZzf44FR8/KsvztyWbnUCqxNmWf9ax0TqGtDt2BkF4aF8TNQcYZ89z/r1XGLpaipDUUG+9lmvT2p2Ma+bVX8/QWL1ioeV+/wxvqcderY2w+Eb0rmxYqtAyuqtcGR9qJpjTc3D+poWgwbxrYa0u4K2RMVA1L1QXkQEgjdthln//tznvrggue9NEHh5YIyZ2gvvLB7O/ernZouOC3Oy8vl36+o3rGkoKCpAXVMBXXp7Iux5HM4evkPCPvE/Bwn7xL+Go6MjAgMlB3+s8XP5KmHW/CkpKTK593md3yXuN7IXEnPDwzY2cGC+/ZlrnpiYl0Wtzz///KX4ADfTBUFjWCDbdRfXvPRddu62CHsagf6T+9UeC/MPh717IwG3pJglrz8Jz/ZJcx2DMnIxxs6M+7GusXRlQVJLKyoRnJXfoKi/ubsHriekY+0T6XwnsoCNsi4V83+eiklDXPjvq5m76eJlhviUPKRKEOAfP0/haYSpnfdpwJBgWC973HiUgDIJLi7EoW7vgIIwUT+f+SHBULO1f+WBf9iAVxbKq6oQnJ2PtobaOBOdIuI66Ul6w1Y58z1sMM7BDEtvPcPzBu6DGlTk5bmP/XvJkv03vmmo2tpx8bIoPARK7TvxfWVZmShLT3vlwn5JosD1h2L1IFIa/AOToa6mBE83IwS+ELgS6NDGDIqK8vBrZBDLBi2jBjvjyJlg7r6nqWkaIiA9l99Dwm1LBxNB28KCaUvCQVsdW3t54GpCOlb7vdy2PK2+d2us14Wfb0mGQlrW1pBXVuLWrZoWgsDRzFc183Vt1bfXK8vD4AFxq6qgrCt50M9XkjH1sIFq9U/J5aK8MJ3NdfE0VTaLOtbu1jRFT1Pz4Lbjpsjx73o4w15HDdPPPuUTAtIgr6gIdWsb3vYZdOsu0vZpOoouz24KSSePI+PGDdgvWQp1OzuZ80dlFXIBv7OVLh7EC/wKKyvIoZ2FDjbfFS8eb/SNwua7ouLFnXlduFueX+9F1xP13eFtocNFfUkueMp6vgX0mIF5AwSTMCzwqZGjLRc7k5+Hw767wFVefnoW8lLSYews/nc2JQ+j5v5qyHguK1aw8qpKTfRefRyXDXVlRbQ210ZAtQVnJxt9PtHBjolj3M77Iu92Z2NNnH+vK8bvfICn1b76H8Vmi7XoYx9laWH8U3Mx2c2cu51iK1Rqng3WtjxLl961IZtk4Mg1Pc1/2bYwv/rOUyfyvkFNnV6f/wFcpk6AfitX+H7+LdKCw2HqKTBoyYqOQ1lhEQycxN83Bk4CMbehPNKkaaj/ImlF5NI/Psfd0zdxcecZzN+0jO+zbmWH6wd8kJmcAX1TgSAV8SSU962s3Jreb/5gx5ci5YgOCMfOz7fi/R1fQs+0Yevy6NR8Lvh1Yr7OwwSxF5QV5eHtYIjNZ+us8hvDRFcVuhrKSBXTfkzsbofAmCw8q35e6lPhPBZwHoMZ8/Vq2xaD6nYi9UU4bLsJ2omC9Cwe7JjFNhBHU/KIRa6hA6LjEL9nKYJ+i4shAkME9dehtSnvb7BjDaGoIIfR/R1x+FyIVBb4bMKAl6IZXXFFDQ2ompggLzQU+u0ELkPYs5YbGgqDToJ+4b+Jf0gaJvV3Eh0TeZggPjUfqRImRh4Hp6GLh6nIvtraqwKy8krgMmm/yPG5o1rhraGu6D7vuEyrHdiYqDBMNNB9QejrGRPVLt9uBn5h6Vg0yh2WhhqIr14x1sXNmK8geRIuPo7T4+rnXvg9xtsT/r6Vvq60HBwQe+IEKsvKalck5gaHQEFdHWqmpq8sT42rHS0nJ+5CSljUf7FhI0z79uHBdMVhZmnILezrW+W7tbaF313R1fcBD8Nh62gG1UaClgvDtBZZ6oxomPqxFYj/DroSxL8GC2p7+vRpnDlzpnbfxYsXER4uCHpnbGzMrd7Z9qpYsGABfHx8sGvXrtp97Pw//PDDK/sOWb5L3G9cu3Yt0tMFL2wmijNf/MwPvzhUVFT4qgfhrcYND+u8sHw1W01npu+E3gh+FIL7Fx+guLAYt8/4IjIoCr3H1Q3kLh+8gmVDBT4qpaV1N0/cOHGbB81lvvzZOW+dugPPbo0HpGHBbFOKSvBRGwfoKityUe0dVyucjUlBQXXASkNVZdwc3Q29zAUDKxstNWzq7oFriRn4WYKoP9nRHEOsjaGvosR973roa+E9d1s8SMniFrfScvBCKNRVFfHxu+2hqa7EBftJg12w88Sz2jSdvcwQfOZtOFkLLIWOXw7n/hHfn9GWB9gyMVDHh2+3w/OIDETECwSG1s6GmDveE7paKnz7al4n2Ftoc8t+WTDs25/72Uy/fpX7j8x6cA95QUEw7F9ntZh56yYCF87jHTFpid+7C/mhIfycFUVFyLp/F9kP7kGvs3jLS0n8HZqAAVZG/NqpKypghosFLDXVRFzpLPS0xYkhdfEY5rlbY7yDGZbcCuIrLcTBguW662tCWV4OZuoqWN7Rmfv0Fp5AeNNhfkF1OnZB2pmTKElKRHluDlIO7YeysQk0PeqeragfVnAf+dJSGBmO1BNHUJqawu8JFlQs+cAeqNrY8cGPtDAx/+GTRHyxtBtMjTVgYaqFTxd1we0HcQiLrJtg8b88C7OniQZd7tvdBvq6ajh6RrJ7CWnSNMT1hAykFJbgU28HLsA56qhjlpsVTkel1FqcM1/Zd8d3Qx8LQdtiq6WGLb09uNX9T4/Fty0PUrK5T30W+4FNGmgpKeCDNnbcilc4DoQwCirKsOzTE1FnziM3JhalzFfr3oNQ1tGGSUfv2nSPfvwFAVu3S50nPz6B7ytITBYEuY6OxbMdu6FhbgYD91Y8DRPrQv4+jIIklqYCRekZeL5zL9c8jNtLdgWxJygBrY21uLW9hpICRjgYo5e1Pv4KjK9NM8nVlFvaszaWsaqnMzqa6XAf/CzPKEdjjHQ0wYnQuueSiffCW406IOvcjVH/Ach+9BA5/n68nUq9eB6laWkw6N23Nk3i8aN4/sVnMp036fRJpF+/Dvul70PDvs69nyywn7LzcRxmeluhvYUOtFUU8XUfJy5QHAmqm/T6fZQH/p7YpjYP8yMvvPH9VaKuDtYNbcXP2ZCoL0gsz/0AM9GNbTV+8R17doTfobPIjk/ifqzv/XmIB7i1ale3iuLUxz9y//fS5kl+EY7Hf59GbnIaKisq+N/bW/dBWV0V1u0FaVJCIvFw30nkJKVya+vU0Cj4bj+ASgMbVBmKiqRMzL8fk4lvBrnCTFsVljqq+GKAM25GpCM0rW5S7tnn/TCvqyAvqyOR+quuNPa35taKyy7CP8+S8VFfJ35eNSUFvN/LgQfUvSQUjLQxmN/7pPxifNPVCXqqSnDW08B7baxxLDQZ+dVti7G6MgLf7YEBNoK25W0PC4xyNIGhmqBPwnzof9DeDr4JWUirjsUgTZqW1LYwUZbdW8xyXlFZmW+8vyknDw0zUxi2aY2Av09wobYwMxtP9hyFvqMtDF3qnqszi75E4KHT/H8tM2OYt/NsMI80aRIfB+L5yQvIT03nbV5uQjIebd8PNX1dmLjXuXp4vPMgUmOTeb81+H4Qruw9h0HvDq+1xndq5woTWzOc/vUI97efFpeCy7vPwbNXW2gbCCY9S4tL8PXQD/Do4j2p7x95BXnRPnq1EYy8FAYxrD3YdTkM7/Z3QntHA2irK+HLiV48sPQx37oJwK3zu2Dfsp78f2MdVax5tz1cLLT5JICTuTY2zu3MrX196rnhYecb2M4Ch25GNty2yL3cttj17IiAw2eQU91OPPrrEI+1YdG2rm059+kPeLD9b5nyNIaRsz1fyeG35xg/R3lxCZ6fvgRUZKNKVbQNZ2L+w4AkfLmwCw9wa2Giic/e64Tbj+IRFl3Xb3nyzzuYM6m1SN6+Xav7JOdFhWPG8L4OeGe8B8yMNfgEgIu9Pla83x2Rcdl48lz6tkUcJgMGIP3OHeQ8e4bywkIknDyJioICGPcUXF9G9N69CPruO7xuDl4Og7qKAj6e1lYwJvIw5UL/zrN1/bTOHibc0t7JUvCMHL8Ryf3mvz/JC2oqCjDRV8OHU9rgeXQmIqonblk7LbzVvPNkdWFk0KcfHxMxS3rWL2DjFuY2x7Cf0Jjo9g0ELZrbhDFR8EtjIt1OXdEcbgWmIDg2G9+97c196tuZauHD8Z44ez+WW+Yz1JQVEPLXBG6Rz2B+9O89T8Gnk7xgoK0CTVVFfDa5DYrLKnBThpUhRl06Q0FZGVEHBEHR8yIikXjpEhfamfEEoyAuDnfnvYeckBCp8xSlpCD2xEmU5uby+zXu1GnkhoTAdtLE2u/Oi4rCi41M1O/bqF99ZmFfs9W0j8PGd0NqchYO/XkJhfnFeHDrGa5f9MPoqXV6hu+1AO5PPyNV0C//4+fjfDKgIL8IRYUl/Pi5Y77oN6zxeIAE8aZBFvvEvwYLCrt+/XpMnTqVC9xstpQFkz1wQDCQZD7ovb29+T47OztMnz4d7733XrO+c8iQIdi+fTu3cmcBbJnffWZVX9/q/VUgzXeJ+41MnGeueJigz3zy5+bmikwONBeXds6Y/skUnNt1Aft/PghDMwO8/dV0OHjWiXnMSqCyos4KITIoEhvf31J9rBIH1x/GofVH0L5/O8z4bBrfP37RGJzdeQ5/frsLeVl50NbXQscB7TH0ncGNlqm0sgof3A7Cx20dcWpoRxSXV8InLlXEtQ4bJzLRtma4M9beDPqqyhhpa8q3GtKKSjDuokAYvxiXxicI5rnbQEdZCalFJbgan469oXWilDSkZBRi5jeX8M28Tpg5xp0HC2W+8PecruvEMk2LWfvUWA3lF5bh7S8uYvn8zph72JN/9n2SiM/W3eb1y2Aif5+OVrjw+xioqyniQWAKJn10DjEy+pdUt7WD9Zy5SDl5AkmHD0JJXx8W06ZDW0j4raqqFLEsKU5MQNiq6gFAZSWSTxxD8snj0PLwhO18QQBo/Z69kXr2NAoiwrnipGJiDItpM6DXWbZO7JX4dO7+5MM2djBUVUFsXiE+ufsCkbmFIha9itWTT+waz3Sz5v5it/X2EjnXr4FROBAmsCw/EZmMRZ52cNPTRFF5BQIz8jDr6lMkFYrGnXiVLJk9FKu+qPNNf/+CwIpk2fLd2Lb30mv5TpNJ05B69CBi1v6IqvIyqDu5wHLBUsgp1L2yqyorBNe4muRD+5F9+0atYBr64UL+12rBUmi4uUPNxhbFsTGI/2MLytJToaijC02vtjAcMkIk2Ko0LP7SB99+1AMXD07mX3ftTgxWrL0lkoYNcuuflrnYufsoAXGJkidvpUnTWNvC3Dh95u2Ic8M7oriiEhdiU0Vc67C7jrUtNYZc4x3NeGDn0famfKuBtR+j/hG0LaxWP7z9HJ+0c8DZ4R24uyk2AbXoZlCD/vWdJozl6iPzbV9RXAJdR3u0+2iJSABbbvkq9Kw2loeJbJrWlgj4bTsKk1K4MGfY2gMOY0bUBi3VtDTn6ZioV5CUAiUNdeg6OaDDlx9DzVB0WbQwzCXO+1deYFkHO3zZxQGJBSX45lYYbsTViR9MxBOuvwPPE7HY2wbeJjp8X3ROEb6+HYqTYc0TNsSh274DX3KfcPgQynKyoWJiAtv3FkBNOOAba28r69wG5Tx9gug/fqv9zP+Xk4NR334wHy+wPE499w/fF/5z3fJvhvnYcTDqP1Dq8m29H8OF4z9GeXI/+IEpeZhx5AmyhFy1MBdEbJMWOz117oeftY/XZ4vG6pl3MgiXIwSGAQ3RefZE3N91DGe//AUVZeUwdXfEgC9Eg9xW1rsPG8tj7GTHg+FeWfMHcpPToaqlAVN3Jwz7fhlUq129GDnYICMyFld/3sbTqOvpwKZja6SqDRLr13fB4adYOcwNVxd1533EK6Fp+Pqc6CQf7xfIaIX58akgfDPYFRfnd+X5g1Py8c7+x4jIkN6/fmlFFeZcDMTyrk64PrkT77ecjUjFT/cjxLQtgvKdCU/F/DbWeL+9LX8nJheU4HxUGnYE1MUEkCZNS2pbGsNjzjuI/PsAfD5bxVdwmLR2Q+eZk0UsZ6sqKkTK1XH+W/DbdbjBPI2lMfZ0QU58Im6t3oKC1Azueoi53fGeNYX7X6/Bor0XD2LLBHsDcyMMe28svAd2FhHg3/puLk5tPoK1b6+AgpICPLq3wTAhVzl84o3XbZ0AuWXxWiRHJNTG2GLCP+PLIz9AVaP57gJ/Ox8MVWUFbF3QFTrqygiKycI762/xoJu1ZeeTBIL6YFb5t56lYM27HeBsocMD7d55kYr3t99HQb1YJ6M723Ax9fR98a5mGqL9zEnw230UPl+v5e2EcSsn9P5sgcj9UlUhWlfS5Hm48xDCL9+u7dMcfkcwlur96QKYeblBWUOdu+Hx//sUzi77DpVl5dA2N0WV/ghA5eXYHYu+vYwVS7vBZ/dEfn2u3o3Fik3VfoWE+i011sE1TBjigrt+CYhLerlPcu1uLGZN9MS+dcNhaqiBtKxCvm/rPn+ZVuGKgwn4FYWFiNq1i7vkUTM3h9PixVAxNKyrV8GNWPs5/e5dRO3eXfs5ZN06/l4zHz4cFsOHN7ksKZlFmPnDNXwzsz1mDnfj1vbbTj7DHqHJDtYmvzQm+u4yls/qgLl/TUR+URl8A5Px2Za7tWOiV4W6rT2sZ89D8qnjSDpyAEr6BrCYOgNaHq3r9QtEx0ThP6yoGxOdPIrkU8d4Hpv3BGMiAzYm+oeNicIEYyJjE5hPfUvmMVF92Lt89vpbWPm2N27+Mpyv+jr/II671hHpZynIi7zrFv7qi+Uz2uHa2mH8sQiKzsI7P99Aigz+9RXV1eH2wfuI2v83Hi9bBgU1NRh36w6rESPqErHLI1RX0uRRNTKCgroaAlZ+j/KCAmjZ28P9o4+haWNTmybp0mVUFBUj4cIFvtWgYWWF1l81HrPFwsYI36ybhT83nMbfO3ygZ6CFtxcOQ18hkb6qWs+oscgfPLYr/t5+EWu/3o/y8gqYWhjgnYXDMXhsXZuflZGHd4YLxscsb1xUCk7svwEXD2us2b5Y6roliP8auSpai0L8y7Dgts+fP+fivpWVqF9P1lEOCwvjFuwsUK2N0AuhPmlpaYiKikLHjh1r9+Xn5+PJkyfo0qWLiNV7WVkZXrx4wfc5OzuL+MvPzMzkwW1ZHknnZQJ9XFwc2reve3kkJiby/W3bilo/NvRdkn4jE/SDg4P5rDRzWVQ/T0P4JJxDc6lx0cAGNDWwMtWHdTRkdZn07f2m+4Nk4m+NRSdfXCtmLM/7tU3+BiB1R1jTyycv9+qDY9WjzUdNtB6t7vAz35C1+8RcU9bpl1XYFSYus2l55aq3ynouk+rTBG8stQR8KJicai4KQs9FDRVCE2FNZeDOBU3OWyOK1Fw7/lnc0lLm/7qJS5FffFO3QkVWmLhQf/DG9glEj4bzNZamBp33pbfua1LbIhQgril0sipr0rX8t/AJkk6oa6z+mvz8yzVcv20cX9GzUU/8ENf2iW0bG3h+7j8qf22/XV7MMRY0Vxw1Fv71mT9QtuCXNX0TuX/pPvz9ZNPbT3H1U7+uJNWLtKhZqv1nz4Y09GtV0aLaFv497Hmqvk/1lCtazL0mDi/95gfxZn1k1h+u+c1cTBLj8kfSClzWLrF6qH/8k1VNX7XMF0404b0lS763Fwpc8chCU663sKunl2J6NNCn2bdMupgZr7LfIgudlju/mhM18V3XGPd+bXhC8XWPicRdA2G83pOsC7SEMdGT3QJDpFfBqx5j9pgt2W1OfXjdNGP8IPacksYp1XX+hVfzPTbwdrWiklv6ywJbwSWuXMLjP2edpk+O/S9j7CpwY/emkRr8C/7XIIt94l9HTU2NW62Lg3WSXVxc+NYYRkZGfBNGU1NTrI97JpQzq3hx6Ovr14r6ks5rYmLCN2HMzc35Jst3SfqNrGPv7u6O/woeYLGesippMPJvIjw4FrgsQIvidYv6zYF3xupdQ+EO7X8N98Yh9LmlXdtXLeK/auoPPP4rgUQS4gZm0lhpvWpLrjehbWlp104amltfQt54Xv+zIaYtfCnPv9g2NvbbxT0CzRWqpeF1xDl6HTTWRPwbddXw96PF8G+1LbJ+z5tyrzVE/T6ysGGMNNS4z3yVyBo3orn5Xuf15gI+3ox+y39FU951LX1M9Lrqu6WPiVraGPN11M2/8T7i7aqMoj6jKXmImjp/89/n/yuQsE+0WJjP+oCAALHH3n77bcyZM+dfLxNBEARBEARBEARBEARBEMR/DQn7RItl/vz5yMkRH5TQ2tr6Xy8PQRAEQRAEQRAEQRAEQRBES4CEfaLF4ulZFwiUIAiCIAiCIAiCIAiCIAiCEEDCPkEQBEEQBEEQBEEQBEEQBNEo5GO/5UDRDgiCIAiCIAiCIAiCIAiCIAjiDYKEfYIgCIIgCIIgCIIgCIIgCIJ4gyBXPARBEARBEARBEARBEARBEIQUkJ14S4GuBEEQBEEQBEEQBEEQBEEQBEG8QZCwTxAEQRAEQRAEQRAEQRAEQRBvECTsEwRBEARBEARBEARBEARBEMQbBPnYJwiCIAiCIAiCIAiCIAiCIBpFTo7sxFsKJOwTxBtOUbkcWjKKSi27fFUGamjJKLbs6kNmRiVaKlaWvdCS0VNuuXXHiIu/gZaMkZIHWjKKclVoyZSXtezyteS2Tz4xDy2Z5CIttGjkW/a9V1zcwstX0YIfDgA5pQpoyVhplKDFotKy606phb/XoNyy66+lS2BV2ipoyagptux+MypabvkqW/ijW97Cy0cQLZ2W/n4hCIIgCIIgCIIgCIIgCIIgCEIIstgnCIIgCIIgCIIgCIIgCIIgGoVc8bQcyGKfIAiCIAiCIAiCIAiCIAiCIN4gSNgnCIIgCIIgCIIgCIIgCIIgiDcIEvYJgiAIgiAIgiAIgiAIgiAI4g2CfOwTBEEQBEEQBEEQBEEQBEEQjSJHduItBrLYJwiCIAiCIAiCIAiCIAiCIIg3CBL2CYIgCIIgCIIgCIIgCIIgCOINgoR94j/h0aNH6N27t8z5nj9/jrfffhu9evXC+vXr8V/j4+ODUaNGSTx+69YtDBw48F8tE0EQBEEQBEEQBEEQBEEQ/9uQj31CZq5fv47Vq1fj/PnzTa697Oxs3L59W+Z8TEQfMGAAVq5cCUtLS/zXpKam4v79+xKPZ2RkwNfX95XWXVMIuPkEl/ZeQGZyJgzMDTDo7aFw7+opMX1ZaRnPc+/sHcQFx2Lc+xPRYXBnkTQ3j17DuR1nRPYpKivi+9NrpCqTmZoKlnjYo42BNoorKnEpIQ1/vIhBRVWV2PTqigoYY2uK/hZGMFFTQXJhCU7GJOF0TIpIula6mhhta4ZeZgZ4npWHD+49w6tAQV4OH4z3xJhuttBQU4JfaBpW7PFDTGp+g/m8HPTxwbjWaOOgj7ScYvxx9gWO3oxqdnlyAgORdOo4SlJToaxvAJOhw6DfsZPE9FUV5cj290f6jevIj4iAxdixMO4vOulUnJKCVJ+LyHvxHBUlJVCzsoLZ8JHQdHSUuXzjXUwwp40VTDVUEJFdiDX3onAvMVtiemN1Zcxra4U+1vrQVlFEZHYRtj2Jw+XojNo0H3Swxdw2ViL5kgtK0OfvBzKX772322HSKDfoaKkgKDgNqzbcQUhEpti0SkryeHpltthjv+58hK27/KRKI4nc0FBEHzmKoqQkKOvowGzAAJj27tVg+RvLU1VZicRLl5B2+w5KsrKgamgIyxHDYeDtXZumJCMDiRd9kBUUhPKCAqibm/M0uq1a4VXh6mSBOdP7Y/KY7sjLK4JrtyV43Zipq+D91g5oa6iNoopK+MSl4vdnDbct4+zN0N/SCKbqKkgqLMGJyCScik6uTSMHYIClEcbam8FOWx05peW4k5SB7S9iUVheIbEslRUVCDt2Bgl37qG8uAR6TvZoNWMSNEyMm5UnNyYOEWcuIDM0HKiqgo6dDZzGjoCOrXVtmoLkVIQcOYGssEhUlZfD0MMNblMnQEVXp8H6622tj48628FWRw0JecXY8jgWp8NSJabXUlbALC8rDHU0hIm6CuLyirE/KBEHnifVptk3sjU6muu+lPdJSi4mnngCWUi/cxvJFy+ijN3XpmawGDcO2q6uEtNXFBcj88EDpN28gaKEBDi89x50vdrInEZaFvZ3xpQuNtBRV0ZgXBa+OxGE4KRciek3v9Ueg1ubiey7H5GB6b/V9R26OxthZi8HeFnroqisAnfD0vHzPy+QmlvcYFkyg8MQfPAY8hOToaKrDbvB/WHdt2ez8pSXlCDm0nUk3n2AovRMqOnrwbJnV9gO7gc5OfakAP5bdiDlkf9L51Y3MUbPn5ZL/G4dVUUsH+SKvk5GqKqqwuXQNHznE4K8knI0hpqSAk7N6gR7A3WM/vM+gpLzao8FfNwH6soKIul/vhqOP+5GQxYstVTxbQ9HdLTQ4dfhVGgqf7eVV4pvW1htjHQ2xgwPczjpayC7uIy/09bdj0ZBWV270d/WAAu8reGgp46Siko8SsrB6ruRiMmRfH2rKioQffI0Un3v8ftX29EBDlMnQ62BtqWxPOxd8eCTL8TmZenM+7z8Xnq+5XdkPHkKx+lTYNZL6D4pKkbwgSNI9X+KqopKGHq2gtu0SVDW1pJYPlnyVJaX4/6qn5EXEwfvj5bAoJVrbfuZ5Hsf8TduIz8hCcramjDt2B72I4ZAQVkZ0pCVnoN9G07g2eNQKCopomMfL0xeMALKKuLzlxSX4upJX/j6PEZqYgb0jXTQa3hnDJzQA/Ly8lKnaYwFg1wwpbsddNSVEBibjZVHniI4UXLbsmlmRwxuYy6y70F4OqZvui1TmoaoKCvD4/2nEOX7GBWlZTB1d0andydAw1CvWXnSwqIReNIHaaGRkJOXh5GTHdpOHg5dy7q2Mj89E2FXfBF2zRfFOfmYtucXyMLgHrZYPK0tLE01EZuUhw27/XDlXqzE9Mc3jYSbg/5L+289isfc5Zdl+u7kq1eRfPkyyvLyoG5hAesJE6Dl4NCsPGx/3IkTyHn2DOVFRdC0s4Pt5MlQMxN9v9TA0iZevAjTfv1gM2FCg9/taaOHr6a0gZuVLjLyirHvagT+9AmVmL6jsyH2fvRyezH820sIE7pnp/Syx4y+jrAwVEdeYRluBiXj52OByMovRXNg7Vvs4cPIfvKE94d13N1hPXkylLQktz/FqalIu3kT6b6+vK1st3EjXhXje9lj3nA3mOqrIyIxB6sPPMXd56Lj1/qY6avjo0le6OFpirKKShy/FYXNx4NQWl7Jj6urKGJkVxtM7e8EFysdLNhwG1f8EhotS2FiImIOHkR+VBQU1dVh1K0bLIYP589Zc/Kk+fry+7M4PR3K2tow6dsXpn37ipwj8LvvXjq32wcfQNvFBdLwzD8CuzadQXxUCvQMtDFyWi8MHttVYvq05Cyc3HcNfneDkZ9bCCs7U0ycNQBtOtV9X0VFJU7/fQNXzj5ARko2TCwMMGnWQHTp21qqMv1/R06O7MRbCiTsEzKTnp6Ou3fv/us1xyYDwsPDcfToUXh5eeFNoGfPnrh06dJ/WncRAeH4+8c9GLVwHDy7e8H/6iPsXfkX5q9bAhs3W7F5Hvs8QPSzKAybMxLbPtmKSjGCGBt0m9iaYsmWZbX75PgwtnEU5eSwtrM7ovML8fZ1fxioKmNVBzcoyMlh8zPxovdIG1OoKSpgpV8oUopK0N5IF1+1deJ5TlQLcNpKiljsYY/TMcl8v6GqdIM4aVg2oTXGdrfFgk23kZBeiK+nt8Puz3pj0KfnUSI0QBemnZMB9n7WB9v+CcaHv92FkqI8Fo92x7UnicjILWlyWQpjYxD1+xaYjRoD/S5dkfP0CWJ27YSipha0JYiyWY8fI+eJP0yHDUf0zh2oEiNEJJ06AW13D5gMGQoFVRWkXLiA8I3r4frFV1CVMDAQxwBbA6zo4YTProfgTnw2prmbY/sQd4w65scFe3G829oCL9Lzsf1JHIrLKzHG2QS/DmiFKaefwj9FMAhQkAMeJGVj5j+BtfnEyykNM2uqF2ZP9cKSL30QFpWF9+d2wK5NIzBo8gHk5r08mCgrq4Rnn+0i+wb1tseGlQNwqXqSRpo0kgYSLzZugln//nBbvAg5IaEI37kTiupqMOzYscl54s/+g6QrV+A8by40bW2RGxKCsB1/8g65jpsbTxN35iw0bWxg2r8fFDU0kHL9OoI3bYL7x59Ay8Eer4Ktq+fg2Nl7+OvAVUwe3Q2vG9a2rOvmgejcQky/4sfblp86ufH9GwPFX4fRdoK2ZcWjEKQUlqCDsS6+ae/M25DjUQJxupWeFrwMtbEhIBKx+UWw1lTD8vbO0FdVxvKHIRLLE3r0NBfo2y2eCzUDAzzffxgP12xCjx+/kSgySZOHifrmXTvC/Z0pqCgpRdiJs3iwegN6r10JJQ0NlBUV4cGajVzw7/rtZ1xwZWLto3VbBJ8lDODcDTXx+xB3rL0fhePBKehvZ4C1/VyRWVSG2/FZYvNMamWGvNJyzPknCJnFZXxiYE0/Vz7hwYRPxltnAkTeDnqqSrj1VmdcjEyHLGT5+yN2/37YvvMOtNxaIe36NYT/uhmtvvqKi/ziYGlK0tJgNXESQn9ZyydCmpJGGub2ccTcvo5YsOshQpNzsWyIG/bO74p+P15BblGZxEnjw/dj8c2xAJH3aw2Gmir8nDuuRyAwLhu66kr4YWIb7J7XBcN/uY4KCaJyQUoaHv3yK2wH9YX3hwuQ+SIMT7ft4u2EeecOTc6TdPchF0raLpwDFT0dPhHw9PedgLwc7Ab142nazJ8p8hsqy8pw7YMvYNKu4b7blnFe0FRRxOid93l7/+s4L2wY7YlZh16eJKjPd4NdEZ9dBGcjzdoJBuE6XngsAJdD0mr3SZrok4SSvBz2jPREWGYhBh14xCej/xjqzs+98naE2DxeJlroYKaD725HICKrkAv36/u7wkhdGUt8XvA0rgYa+G2IO365H4X9ZxL5xPaqXs74a7gn+u5/KLE80cdPIsX3HlotfA8qBvqIOHAYges2wHvltxLblsbyqOjpofvvv4rkSbpxk6czaPOyyJF49RoqiooE7Um9+gzcsRsFScno+NmHkFdSQsAfO+H/6x/o9MVHEn+TLHlCDx+HspYWvxeFvzsj8DmyQsPhMnk8NMzNkJ+QiIDf/0RxVjY8Z7+NxqisrMS6T3ZAQ1sdK3d+hML8Imz68i8UF/4fe2cBHtXxtfE37u7uRgRCcHcvUKQUKC1QoEBLKVXaUhcoNYpVoEgprsXdHZIQkpAQd3d3vufMZpPdzW6ym4T+Q7/5Pc+F7N2Ze2fn3nvuzJkz71RhwcfTpea5cfo+Gwx4dfk0mFubIDo0Hhs+34GqyiqMf3m43GmaY/4wNywY5obXN99BVHox3nmuE/56sx+GfXGuedtyKxGf7W0cPBV9LuVN0xx3tx5A6sMIDFu+CBp6uri1aTfOr9yI51Yvh7KKSqvzPDx0Gh4j+qPvopmoLqvA/Z1HcObLtZiy4UuoqKmxNPe2HYSxoy18J4zA3W0HFGoQ9vC1xE8fDMJXG2/hzI1EjB/sjHUrhmDGuyfwILLRTogy5a1jYu8xa3NdnPtzMs7eTJT/xPS+uXEDyQcPwmXePOaYJ+d65Jo18PviC2gYG7cqD12zqA0bWFrPZcugqquLjHPnEPHjjywNtfFEKXz0CHlBQSzoA/QMNYOZgSb+encADt1MxOKNN+HnaIy1C3uhrLIGu6/ESc1DNlhVRRlerx0Ue0eJ/j0ywAafz/THO5vv4tLDdNiYaOPn+T2xak53vLbuBtpC/NatKM/IgOe770JJVRVxW7Yg5tdf4fX++zLzJO7cCX0vL1gMHYr006fRXozoZouv5nbD+7/fwY3QDLw03A2b3xuA5z46jbj0xgFoUUz0NbD/i+EIjs7BpM/OorC0Ci8OdkGvTha4+lDQLp01wg0OFrr4akcg9nwyjF7BLUIDPpE//wyDTp3Qee5cVkfRv/0GJRUV2Iwd2+o8OXfuIH7HDrjMncsGUcpSUhDzxx/Mjpv37y84ENkVajt89x3U9PUbTyDnwGZ6Sg6+emsznps+AB//+CrCg2Kx5vNd0NHVQv8R/lLz7N18Fi6ethg3bQB09bVx+tBNfL1sM775/Q14+DqwNAe2nsPxvdfw7jcvw7WTHcICY/Dzp7ugo6cJv+7ucpWNw+kI8CEWjsxI888//xyjRo3C9OnTcenSJbY/ODgYn3zyCUpKStCvXz+2/fbbb3JJ6MyaNQujR4/G8uXLmZNe2jnp2CRdM3XqVGzdurWhcUlR7yNHjmR/z5kzh503NDQU+/fvx8svvyx2HIqgF5X5qa6uxtq1azFx4kS2rVu3DjU1NXKdV1Fqa2uxdOlSzJ07FxUVFQgPD8dnn33WbN21VL62QpH1rv7u6D2uL3QNddF/0iDYezni2sHLMvP0GtcXL37wEhy9W3buqaioNGzKKvKZlP6WxrDW0cQPIbHIqqhCREEJtkUlCZz3MjoCe2JTsTkyCXHFZSitqcWV9FxcSM3BEGvThjRF1TVYdP0hTiVnsai39kJDTQWzhrthwz+PEBSdi8z8cqzYeg+WRtoY00M8glyUT18KwJWQdPxyKAx5xZX1+e63yalPZF04Dy17e1iMGMmiT0z79WcO+axzZ2TmoWh+pwULoecpcOpKg7436dsPGqambJDAZspU5uAvfKhYRO28zrY4FZeNYzHZzNG3LjARqcWVeNnHRmae727H48DjTGSUVqGgsgZbQ1OZs9DfQjy6hh7NWpFNhk9LJuTvmTu9M7bvC8XN+6nIzi3DFz9eh4a6CiaPbSbqt/aJ2DZpjAeCQjMQHZevUBpJMi5dhpqBAeyfn8gauqbduzHnPHXa2pKHGtkWAwew6Hty5hv7+7No/dRTjZ0V19mvwHLwIGiZmzNnsO3YsdA0M0dukOzZBYoyZNLn2LDlNAoKS/FvMMDaBDY6mlj9IAZZ5VWIyC/BlshkTHCyYpH50tgVnYo/HiUirkhgWy6n5eJ8Sg6G2TbalvD8Ynz/IBaRBSXMYU3/n03Ohq+xSOdEgtqqKiSdvwzX50bDyM0FmsaG8JkzAxX5+ci4G9SmPP5vzGdOUnVdXWiZGMNl3EjUlJWjJFUwyJn/OAYVuXnwfvlFFlGtaWQI75ens0j/7IeyZzHN6WyD8OwSbH6Qwp7dfREZuJKUh/n+smfIUdo/Q1KQWFSB4qpa9txH5pSgm2XjzIA6ied2nJsgQvjw4+Yj1iTJPHsGRgHdmD0j22f93HhomJgg6+JFmXksR42Gw6yXoW1v36Y08tiWeYNcsOVKLG5EZSO7qJI56zXVVDC1R/PHfYInzOEh3ETtWk5JJV7+7RauRmYhv7QK8dml+OafMHha68PdUnb0YdKFK2x2hvuUCdDQ14dVzwDmnI8/ea5NeewG9YPr+NHQtbaEmpYWLPz9YOzuioLoRgcPOXrJQSfcMoMesmhs24GyI+u8LfXQz9kEX5yJREJeGWJzy/DV2ccY6m4GV1Nxp5Qk430s4WWhhx8vx8hMQ+26WpFNUUY4m8JeXwsfX45CekklQrKK8cu9RDZwraMm3bY8yCzGiivReJhVzCL06f8jUVkIsGy0G51MdZmz8I/gZPb80LtyV3ganAy12WwYWXYi7eJl2I8bw6LuySHv9vJMVOXnI+d+YJvykLNGdMu8cQvGvj4svSilySlIPnEa7q/ObnKusqxsZAUGM+e6ro01tM3N4DXrRRRExSA/WvogiCJ5sh+EIudhODynT2lyHLMuvvB59WUYujpDTVuL2VGn0SOQeTdQMAjQAuH3o5EYnYo5706BmZUxHNxsMPW1sbhx9j4KcqVHxw+Z2Acz3pgAR3dbaOtqoXPvThg4tiduXwhWKE2ztmWoG7ZeisWNx2RbKvDp3gcC29Jb4JySyRPZtkWhNFKoLClF9OVb8J82DibO9tA1M0bveS+iICUdKcHhbcoz9IOFsPX3hoauDnTNTeD93FBUFBajKKNxMHjwu/PRecpoaBk1PwtNGnMn++DWgzTsPvkYeYUV2HbkER5EZmHOJB+ZeepE6oi2CUNdUF5Rg5NXFJuFm3b2LIt4pnYZteHsp0yBipYWsq5caXUeGpguiYuD3fPPQ8vSEmq6urCdOLFhUECU6qIixG3fDpdXX4WyHLNYpg1wQlV1Hb7e84D1X8gJv+dKHBaMbjnKWrS+JAehaRYAzXo+fjcZpRU1iEotwrG7yfBzlD3bQx4qsrNZEADNaNCytoamuTkcpk9HSUwMimOl2x/CY9kyWI0axQZF2pP5Yz1x8nYyjt1MZH3BtYfCkJJdildGyq6/N573YZHkb2+4xdLSbIZNJyIbnPrE78ci8NHmewiPl93HkCT3zh02GOs4YwbUDQ1h4OnJZvxSpD3NUmhtnpzbt2HUuTNMundnfQ59d3dYDB6MNGkKBcrK4u8ZiYF4WZw6cANGpnqYuXA0DI310HdYFwwY0ZVF5MvijRXTMHpKX1jZmULPQBtT5wyDpZ0pbl1qDKS4cioIIyb2Ruce7myQoOdAX/QZ6odDf8luV3I4HRHu2Oc0ITs7G927d2fOfHJQk7P5m2++YU5qZ2dnzJw5E1paWli1ahXbyFnfHOnp6ejbty8z3EuWLGFTTmfPFu8I5OXloWfPnkhKSmKO8cmTJzPJmrfffpt97+XlhY8//pj9vWzZMnZeBwcHpKamIkjCCZWfny8m8/Ppp59iw4YNbIBi/vz5iI+PZ4MW8pxXESorKzFlyhSmq0/l09TUFJPikVV3zZWvPUgMj4dLZ3EpFdcubkh81HY5mOyUbHw2+SN8Oe0TbPnkD6THp8mVj5xhicVlyK9qjDIKzC6EhooyPAyb78CLYqCuhvJmZDDai04OhmzK4+2IRidUQUkVIpMK0NWt0fknOYXS19kY/ygYySMPpbGx0HMXd0KTw740TnaDtTXUVVWirqoKyhqaCkU1+prp4XZqodj+m6n56Goh2wkqCt0HUzwsoa6ijGvJ4g1Wfwt9BM3ug2sv9cS64V6w15e/bIS9jQHMTLRxO7BxumpVVS1zwPv7Wsh1DAszHfTtYYt9RyPalIYojo2Bgad4497Ay5M5TUgOqbV5njypa9pYVlZmnRpZzg3aT5E5KpqK1WlHgmwLRevnVzbalvvZBfW2Rf7OmoG6arMSOxSxP8jGFFfSGqWiJClKSmHONGOvxogfcsTr2dkiPyau3fJUl5Yi8fxlaJmZQs9B4IBvGJwWvQfqw7nyo2TbiQBLgyaSWTdT8tlzJ+/zP9jBmEUmn4uXHY3/gpcl+54GD+SFpDdKExKgJzFlW8/Tk8mL/a9xMNGBmb4mbsc0/m6aMh8Yn4sAJ+lRmEKe87dF2KqxuLpiGFZN68Ki9JvDSEfgjClpRqKGnKGi9xFh0slDcI9VVrVLHpI9yY2MQkFsPCwCZEfjp1y9ARMv92YlqLrZGaKsqhbBIu+OO4n5qKmrQ4BtUxknIfZGWvhkuAeWHglFNY0ayWDlWG+EfzAEZ17rg4V9HKEqT3ijCOSMj84vRY5IdDQ9GxqqyvAxk8+2OBtqYYyLqdhMFTpGUVUNk+tRV1aCiZYapnhZ4nJiHnP0S6M0KZm9m0XfA+TI07G1RZEMO9GqPMkpKElIhOWAfmL76V6I+H0TXKZPa+LwJwrqHfHGno33koGjA1S1tFAQI/1ZlTcPRd6Hb/sbvq/NhYpG88+JkKrSUpa2OakJIRRJb2xuCAtbs4Z93gFubJZjbLj87TmSfdDU1mxzGsLBVGBbbkVli9uWuDx0dTZpNu+4AFuE/jQeV74YiVUzu8JUT6NVaaRBcjkkmURSOkLICa9nYYrsqPh2y1NRVIzHZ6/B0M4KBlaybYgidO1kjjshjU5S4taDdHT1kv/4k0e44fiVOJRVyB+MRbKHFenpYtIj1Fajz7KcznLlEb7zRe5x1gakdl9M44AntQ1it26F+YABbDanPAS4muJudLbYpJybEVmwN9OFqX7z98qNH8YhcO0E7F0+GAN9LMW+uxCSxgKkhnS2gqqKEuxMdTA6wJY5+tsCOfAJ0baCjoMDGwj5t9sKairK8HU2wW0J2Z2b4Zno6i69/yiM8j91J5lJ8LQndC/oODqK2U6apUD3GEXitzoP3RyS9lVZGZU5OWzwWJSwr77C/aVLEfbtt8i9f1/uskc+TIBPgLg/w7e7G+Kj0pjUmTzQYElZSQW0tBt/C6kSKEm0B8hXFfkwkc3g4jQP2ZlncfsvwqV4OE0gxzZBEjLq9SP5L7zwAnNck7Pa09OTRWdTxLk8/PDDD3BycsJff/3FPo8ZM4Y55Hfu3NmQZvXq1XB0dMT27dsb9rm7u7MBBnJ8GxkZoUe9xIS/vz98fGRHVEhCjvZ58+Zh2rRp7PPYsWNRVlYm93nloaioiOn/U4OJdPT1RaeY1WNgYCC17porX1upralFWXEZdA3Fo/oocr84T/r0P3nR1tfB80umwKtHJ1SWV+LsX6ew4a1f8Pbv78PYsvmOBslXiDreiIJ6J7+xDA1TSQJMDdDbwgifBcqWwWgvzAy12P+SkfZ5xRUwM5TeKbM3F3T0dbXUcOzrkbAz10VKdgm2no7CwWttG1SpLiyEqoROJEWY1FVWMl3J9nLMph05wqIpDLs26rK3BElsUEM2T6KRRQ48U+3mr21ncz3smdCFOVxKqmrw7sVIROc3PguZpVX46EoUGySg87zX0wn7JnbBmH2BcjsIzUy1BeXJF5cEyi+ogK2V7OhXUSaP9UBZeTVOXYhtUxqiqrCwQRpHiJquHmskk16qNMeFPHkoqivz2nUY+fmxDg1J8VADmhw7dI9QRI0kqadOoba8DKY9pUsAPQuYaKo1tS31n401BNP3W6KbmQH6WBrjk7uRTb7bOMAXPsb6TKbnalou1ofJfpYrCwQOSkl9aHU9XVQWFrU5T8LZi4jcfZANyGiaGKPbssVQrb9fKEqV8kTuPgCvmVPrpXgOMUd/ZaH4oJsoJC+SWybx7JZXQ1ddFdqqyiir13aVxFBDFXfn9GHPLnVEv70Zh6sSg3JC/Mz14Gmii29uKNbBrikpYdO4m9o+PRaB+L/GvH6QMadY4j1RWgVb46bPmxCS7Nl3JxFBCfmwN9HGl5P98PfiPhj/4xVUSenUq6sq4/1xnXAzOhvJubLbCnQvmXqLDwCz++rJE1QWFUPbzKRNeU7Peb1hAMl1whhY9+4hMxKb5Ho6L5yD5jDX1UCexL1HkfWF5TUw01WXOZC0bpIffrkah9icUibDI43TEVnYcjcRyQXl6OVgxJz8Vvqa+Ox002dcZvl01JErIXlCzwZB0jrNse/5Luhqqc9kT87G5bDnQwjNUlt4MpzJ8Xw+wI3tC8oowpzjjZJz0t4B7PdL2AmaxULtg/bKk3HtBovQNPYTX5Mpdudu6Ds7w7RbV6n5yMaoamlCRV3c5pLefZUs2ydHHrJ1oX9shf3QQUxmrCKv5UjV8uwcJJ65ALvB9XIQLUBR+foSg8C6BjpsRmqhnO1mGgC4fT4Is9+d0qY0olIoRK6kbSmphJ2JbNsSnV6E/bcSEBSXB3tTHXwxrQt2vNkPE7671KDTLU8aWZQXCK6Lpr54fdFn4XdtyRNy4BRCDp5i113fyhxDly+CsoyZd4pATmQjfU3kFoivYUGR+6bGgvZ+S/T1t4athR72nlKsDyJ8DlUl+ok0yEYD163No2FmBi0bG6T88w9c5swRSPGQHn9hYUN+Iv3MGdZXsG4hKE8UcwPNJuuJUeS58N7MkTILubqmDj8dDsPhW4moqa3D1H5O2Ly0H1795Rquhgmc3MGxeVjxVyDWLewNzfrZSafup2DV/sZo6tZAv5n6QSQDI4pqM7buaWGkp8FkWHMl1sOh+hM+19Le8aTFX1RWha3vD2IDALmFFTh2KxEbjoS3+Fw2B/1+NSn3kfA72Ni0Ko+Rvz8S9+5FfkhIgxRP1mWBMgDdf+pGRmxglWZEmPfrx65P7r17iNm8WbAGVC/xdfykkZ9TBL/ugnekEAMjHdYOKcwrgbl18wEUBEXhlxaXY8DIxndXr4E+OPfPHXTr14nJ9oQHxuLGhRA2WFBeWgkdPflsAofzv4Y79jlNuHLlCsaPH9/g1CfIKUBO/dZw9+5dJnMjCsnqiDr2aXYARfaThA4ZaDZluraWjZQ+fvwYveQw+LIYOHAg1qxZw34PlYOi/7XrnVrtcV6S3Bk8eDBbzHfv3r0K11Nz5ZOEBldoE6W6shpqMhxWsiSF2mOksvvIxoVadY308MJ7M7B69te4dewG0+ZXFOEMTXlK5qKvjS8CPHAgPp1J8vyvaG66sjBw4Y2J3li28RZi0oowrKsNVi/owXQpT7UxIqXpCdt39Jk0p3OuXILTotebNOhaA92KLZWQpA18Nl+DgYYaJrqbY80wL8w9GYo7aYKG+I7wxhkh+RU1WHYhEtdm9sRkDwtsCklpU/loirVYdHMzkGTPsbMxbAp2W9K0eC2ftD6P/cSJ7DmP+u13VBcXQcfeHtYjRyLl6FGp2XPu3kXKsWOsQ0jSPP8lhNUoz+V11dfBVz08sT82jUnySPLG1VCoKivDzVAHH/q74ovuHlghZQCgOaTpUbcmj8PwwbAfMpBFsMYePck09vt+vQKahgZQ19VBt3eXMF39y++sYPcCLYAqWFxXMVshXGOlufcGyWd5/X4VOuqqbAHslYPdUVpdgwORmVKj9ZMKy9n6G+0Bi7RqpXzevwGLBmumytecbnQMhaUU4vXt93Dzs5EY3MkCZ0LFI0rJMfzLrAAYaqtj3uY7Cpel8Ro+aXOeEZvXstkleY8e4+Gm7WxA0XlMU63wlKs3oaajDYuALm2oP+kV+M5gV+SUVOHvwObfp+8cDWv4++zjbBhrR+PbsZ3w3YVolMlYI0e+sslnW1488gBqyspMdmfVEHesHe6FxWceNWjs/znOB+vvJ2HPo3SmsU8L9P71nC8mHwxm0lVyo6Sk+LozMvLQughZt+/AavBA8QUS7wWiMDoaXT9b0ZqHVfFHVSRP/IkzTPrBaaxAmrMlqopLEPjzBug72MH1+efQVuSR6cxIzsaaj7agz4gAtjhua9PI3W5pxp6vOdE4YzAsuQBvbL6DG9+MxmBvS5wJSZM7TUs0WV+LFlNsoa7kyeM3aSR8J45gi+QG7zmGM1/+gvGrP4KGruzBDLnKK+OBlbb2lCymjnJHRGwuwqLbqQ8ip864rDz0jHq8/joS9+9H6FdfseeX5BeNu3ZtiJguTUxE+tmz8PnoI7lmr7SlXRAUm8s2IRuOR8DX0QgLRnk2OPaHdrbCytndsHzrPVwKJY19HXz/anf8OK8H3vpD8fdbS3SkKF2612SVR7m+Pb94gjfe3ngLS9Zdh5eDEda/2Q+aGqpYubNl+a5/894jaAYIBQ2Rc78qLw8a5uawGjGCfRb+TlrE2X7SpIY8tGgzDQCQjKg8jn1pCI9NcoYtce1sMPZuOoM3P5vOpHmEzFg4mrUjf/jwLxTkFcPZ0xYTZw7Cnk1nFG0uczj/U7hjn9OE0tJSqRHnrYWi2XUl9Or0JCLuiouLmXN9wYIFTfKTo7stfP311/Dz88Phw4excuVK6OjoMG374cOHt8t5qaFPzn2CovHbs3yS0PdffPGF2L5pS2dg+rKXEHU/EltW/NGwf+KSKeg1tg+0dLVQWigeaVFSUAIdBWQp5IF+u7m9JXJSpS86JUp+ZRUcdMVHwI3qBycko20lcdbTxs+9fHAxLQfrZSy0295QpARhrKfREKUiXNzoUaJ051RWfSTQ9jNReFDfuD1yIwHje9tjbE/7Njn2KeKupkQ8cqymqBjKGhpsays5164i9cA+OM5bAAMf8Ui9lsivqEZN3RMYa4oPNhlrqSGnvOWpkuTEoOj7LQ9TMdDeGNM7WTU49iWhRXYTi8rhYCB/NEVuniDC1chICxC5diZGWg3fNUefbjaws9FvVmJHnjRC1PT0UV0s/nxWFxUzZ4tkZKUieShSyWHKFLYJSf7nKFR1dZpE6+cGBiJm6zY4z5oFs56NA3bPImQ/HPS0pdqWlmZ1OOtrY00/H1xIycFaGQvtUpxUVV0dwvOK8Wt4Ilb37gQTDTXkSrFbpFEudC5piFzLqqJi6NtLX5tDkTxsOqmqCoui9pkzE+fvBSH99n04jRIsYEpO/J7LlzWkp6jH86+/C3P/potgCqFn1FhLPPrYRFud6YPT1tKzW1RZg3+is9Dd2gCzfGyaOPY1VZXxnKs5fg9W3P4x3Vtl5Sa2j+799hh8bCvCSH0TXQ3EikQ30mfJKP7mSC+oQEFpFRzNxGXpqK//88yu8LUzxIvrbyCz/r0kCw0DfVQViduJyno7ITkjpDV5mH4+aewHdIFDfCKTg5J07NM9l3L9Nmz69mpY8FIWOaVVMJaIfKffbKStxtYZkEZPeyP4WRsg5uNhYvsPz+2B4+GZeOuI9Kj3iMxiKCspwcFYCxGZ4r9XZvnKquFiKG5bSDZH+F1zkL+Q1v2hheBX34rH5rE+MLumjuyyKrzYyQopxRUNz0RhZQ2+vB6Dyy/1RE8bQ9yUMgAmvN9phhYtICuEPuvKsC2K5skJeoCasjJY9hdf9LwwKgoV2Tm4uaTRttBMmpi/dyP13AV0++ZLqOvrszUVyLkoGjVLdkzDQPq9J0+evMfRTC7q3Lw3xPLe/2EtTH29EfB24/6qkhLcW/0zm7nU9a3XoazatMv7wcxVyEwVyCLZOlni663vQt9YD0UFEm3mojLU1dax75ojMyUbK5duRKcAN7ZIbmvTSCKMiDbWU0esiEk10dNAbnHzdkCU9IJygW2pn1GqSBqV8H1QygiBUHZ6+tbvoVV/XUgqR9u4US6LPpt7SF+jS5E85Hwmf7++pRn6Ln4Ju2a/i6R7IXAb3BttgaLJC4srYSwRMW1s2DSKXxqGehoY1tse3/5xV+FzqxsI1gOoKZZ8jxXJfI/Jm4fWx3JftEgsDcmdkMY8QbI9JJ/yYIXIoFxdHcpSU5Fx6RJ6bNwo8/6j/o8o1P8RfCf//ReZXIgp/Rrlf14e6oaLD9OYrj7xOKUQPx8OZ5H9K/eFIFOOayENqhNyMkvaErJ1NEPp3yS/uJLNWDCWkA010ddEjox3eEVVLYrLqnD5QTrO3hcELd2LzMbf56MxbbBLmxz7VDeSMxxr6j/Luv/kyUPtUQoeok1I9q1b7H91E9mz+LVtbVlgkeQiuW+8IFCPICbMGIiX3xjHdPWL8sXX6yrML2HnNjBq3qdx80II1n65G4s+nCoWrc/Kp6GGV5Y8xzYhu/84zTT5SXOfw3lW4I59ThNcXV3ZwrSyIN0xRSBteYp+F0Xys4uLCzIzM+WW9xFCke3l5eKSGnQcUcjgk8wNbRSJ/8477zDpm8TExFafVxTSzL948SKL2qfFd2lBXzUZHVhpdddc+ST58MMPm+j/n80QTHVzC/DANye+b3Iuh05OiHsYi0EvCJw9RMyDaDh2ckJ7Qp2e7OQseHSTvQCpkLC8YkxwsGI61oVVgojmrqYGTHbgscQghChO5NTv7c2i9H8Kla4J+zQIT8xHeWUNeniZs8h7Ql9bDZ72hvj7vPTF+uLTi9nUS2FUi5AnckYWNIeOswtKoqLE9pU8joS2k3ObI1Jyrl9Dyt7dcHx1Pgz9pU+zb47quicIzy5GdytDthiukN7WhrifodgUWJI7aRLVJQLpptNihpI6/M2RkFyI3Pxy9OhihfsPBNGwamrKTF9/w9aWF42d8pwnwiKz8Sgqp01phOi5uqAgTHyRucLISGjb2MjUD25NHnKwkQOfFrcShfZFb/4TTjNmwLyvuAPnWSQ0rwgTnSRsi1m9bZFw1kjalrX9fHAlLYct6q3IJAlZzxxFiSqrqyEvMgp6NlYNevikV24/dGC75SHYjDMWbSjbtuSEPmIL7Dbn2CcJkB7W4gsR9rYxxIOMIsWfXSnVMsbFjDn3D0RK13JtDnLM0eK2xVHRMO3b+M4ufhwJXTfx6dn/C+JzSpgDv6eLCe7GCQZzaZ2Qro7GWH9WfrkGCwNNGOqos8V3xZz6LwWgq5Mxpm+4gRQ5BiFp8dCcUHE7kRfxGHp2Ng2STe2Rh2DrdkiJ0KWFmivzC2A7sGXbEphcAG11FfhZ6eNhelGD455myASmSH93TN52V+wd4W6ug1ML+mDK1nsIkTEgzNLVS/Zkl8iny8vKl1GImT7WMNJUZTPGiD62hsxhH5Ytv7Rhg92o/8zaAxJVJwwcllxsUoiuvT2zE4WPo6Fjbd1gJ0gT33rwoHbJk3n9Bgw7eUHTVFwHmnT1XV58QWzfjcVvwnnaVFgNGtBwHwkd8aY+ndjfRYlJbKDA0NVFavnkyUOOe9Goebq3rr77MQLeWQKTTo1tz6qSUtxf/QvUtLXR9e03oCJD4nHlX+83HE9ox918HHF0+zlkp+XCzFrgkIoIimbfu3rLXqiWBghWLv0VHp2d8drHM6S29+VJI42EbLItFejpaoZ7MfW2RVUZAc4mWHcqUmHbktXMoKCsNLWdpgBekzF3Xn3wgIoKTF0dmfM9IyIGzn27sf2lOfkozsyBuYf0PkZr8hDs/daOE7OCI7LQw88Smw409nd7d7ZCsIQWujQmDHEhfziOXlRcr11VRweaFhYoiopi0fQE3YPFUVEwkRFY0Zo8REVWFovSp8hpghYztRgo3o4I++Yb6Lm7w2HqVJlR/EGxOXhxAPUvGs18b09zpOSUNgQxyYO7jb5YerJ9kq8N4fMoy/bJg66LwF4UR0fDoJPAlpQmJaG2rKzhu38LkiYMi89DT09zHLjS2G/t5W2B+4+zZOYLjMpp2n+kz22cnUi/P/nwYbFBj8LHj6GirQ1NS8t2y0Pk3bsHPTe3BtkeaZSlpTUMXAmxsjXFvqurGj4L9e89fB0RfFvc3oXej4GDqxU0tWS3T25dfIifP9uJ196fjKHPtSw1Sr6Ymxceont/7xbTcujdyZds7SjwK8FpwmuvvYajR4/i2LFjDfvOnDmDmPrFaMzNzVmkO23y8Morr+DgwYN48OAB+0zSN+vXrxdLs3jxYpw9exbbtm1r2EfH//bbb5s9tq+vLxISEhASEsI+FxQUYN26dU00/nNyBA42akSTbr4wsr6155XEwsKCyfpERUWx9Qiqq6VHb0mru+bKJ4mGhgabTSG6CWV4qNNB+YSbsJPSf/IgRAVG4v7Zu6goq8Dt4zfYgrr9JjU27i7vu4AV499X6Dfv+e5vxIfGoqqiCoU5BTi4Zi+KcgvRc2yfFvNey8hFVnkllvm6MAccReG/4m6HE8mZKK1fsNJUUx0Xx/bBAEuBZh5F+Aud+j+G/ruLH1H0xK6LMVg8vhNbSNdQVx1fvNINuYWVOHk3qSHdzo+GYO3rgt9PDbLNJyMxZ5QHGwCgTtjoHnbo52OJ47ca87QGsyHDmK5m9qWLLCol785tFIaFwXzocLGo++DFr7GGmLzk3ryBlD27Wu3UF/LnwxSMczXDcEcT6KipYEFnWxZVvyOscVo36eNfmtHYwFo/3Au+ZrpQV1Fiet3zO9uiu5UBjkQ1drJosVzS4ac0Nroa+H6wB9NYPigygNAS1CbevvchXpnmhwA/S+jrqeOjN/ugpvYJDp9sdL6t+2YEtq8dJ5aX0g4f4NRsJL48aUSxHDQYlbm5SDl+gi1cmxcSgpw7d2AtMmMnNygIt15biMr66dTy5CmMiET6hYvMKUKRNrF/7WAa5XbjJzSkyQsOZk5955kzYNG/9YObHQnSvc8sr8Q7nV1gqK7KZLtme9jheKK4bbkyoS8G1jttHPS0mFP/cmouvn8g3bZMc7XGaHtzptNP95yPsR4WejviXlY+cmQs2kXOJPvBAxB77DQKE5NZBGn4X3tYVLRlj8bn687KnxC8YbPcecjJH7FzP0rSM1BXU4vynFyEbfmbeQotu/k3HPfxviMoiE1gaUjjPGzbLtgPHQB9e8ECu9LY+jCVPWO0kCc9uxPczDHI3hh/ikhdUYRx1MIBbKFPYtVgd/S0NmAa/LpqKkxGa6K7BQ49li7DcykxD1kSWuryYjF8BPLv30N+cDCzfRmnT6MyOxvmgwY3pEk5eBChH32IfxuyLVuvxmLOQBd0czKGvpYaVkz0YQ6KA/caZyhsnN0dfy/q06DLv/pFf3hYkV1ThpulHtbOCkBqflmDDA+9yn+c0ZUtwEtO/eZ09UVxGDYQ5bl5iPnnJKrLy5EZ/BBpt+7CaWTjIH/G/WCmlV+RVyB3nkc79iI79BGqy8pRQ9fgfjASL1yFTb9eUmV4aL0H4SBVc5AznxbL/XSkB6z0NWBroImPhnvgamwOorIbB+VoAdzXeguiPsn3Qzr8DVu99DD9LXR/jO1kgQW9HWFjoMkGgwe6mOD9IW44GpbOZgnICy14m1Zcga8GurEZaSSh80Y3B+x7lN6wyK2FjjqiFw3ASGeBM/zVzraY5GEBU2019ryQzv77vZxwPTm/4Rk4G5cLdxMdzPazYYNepNe/oq8LO1eojAEDshNWAwcg+cRJlCQmobqkhOneU/SkaffGNXEerv4REb/9oVAeoiInFwURkU0WzW2IoqY2psgm2K/U4BjUsbSAWRc/RO09xNZYqMjPR+Su/TBwdoKhW6Nj7fKy5Yg+cETuPHR8NlOkfhOej81eqv+bBivuf7+G6fWTU7+5ASnSzVdRVWEb/U34dveAnbMVtv90kOntpydl4cCmU+g5tAuMTAUOKFpXavagd3HlhEAuhAYBVr65kTnsF66Y2XAsUeRJ05xt2XYpFnMGu6CbiwmzLR9P8mWRwAdvNwYBbZzXE3+/2a9BF331SwHwsNZn7U83Kz38MrcH0vLKcLZeYkeeNCIXniqsoe6FuvguA3rgwd7jKEhJZxr5t7fsZXr4tv6Na6EdfX8lbv2xW+48mRExCNp9FEUZ2WyBbvr/xsa/oa6tCbsA+ddYa44th8LQt6sNnh/mCh0tVbw4xgNdvS2w7YhAIouYP8UXDw7PapJ38kh3nLoWj5IWZurIwmr4cGTfuIGC8HDWRks5coRF0lsMEAyMEfE7diD0yy8VypN+7hz7ntr9JHES/fvv0Pf0bBgMYM+JtGe3fr8s9lyJg5aGKt6f7AtdLVX08TLHiwOdseVsY4BRb08zPP5jMtysBRHc70/xxXB/axYERdu8ke4YGWCLbeejG/KcDUrFsC7WGOFvze4/RwtdLJ3gjeDYXKm6/fJCgyCGfn5IPnAAFdnZTIooee9e6Dg5Qde1cfHVB++/j5TDh/G0+fNkJMb2tmcL4upoqmLBOC84WOhix9nGunj/xc64sqYxYnzTiQgMD7DFoM5WbN0yP2djzBjq2ub+I0neKKurI2HPHnb/FMfFIePcOVgOHtwwq6k0ORl3Fi5ka3PJm6ciMxPJR46w/ga7P48eZfkdXmgcBE49fpxF59PMCeq7ZF29ipwbN5gkjyRCu8xsc71tHz21L7Iz8rF/yzm2AO69a+G4eiYI42c0+jNuXXqIyX3eQ26WYGD/zpVQ/PTp38ypP2y89EGwh/ejcWLfNaa9T1I8G7/dj+LCUkxfMKpNdc3h/NvwiH1OE2jx1p9//hkzKILT3JyNENOir7t3CxplpDsfEBDA9tGiuC+99BIWLlwosyYnTpyIOXPmoGfPnvDw8EBaWhpGjRqFPXv2NKQZPXo0Nm3axKLRadFaWmiWIuklo9Ml6d27N1599VX2P5WH8gwbNqxhEIEg5zdJ3ZDDnPTzSRpI6Mhv7XllOfeFkfvk3N+3b1+TNNLqrrnytQdu/u544Z0ZOLvjFPb/tJstbDv9w1lw8nEWi4ShiHshCeFx+O2d9Q0j14d+2YfDv+yH/5AATHt/puC3jOuDczvOIDEiAWrqqrB1s8Pin5fC2qXpwjuSVNU9wbt3wvG2rwsODe+Oito6nE/JxvpHjfIX5DKihRiFAxQTHS3Zwrrj7C3ZJiS7ohLTLgQ2fN41pCsstTRZVBxNtafBAWLIiZttqsfVe0PY8XYsHwxtTVUERefildWXUV7ZKE9B5RVqIxJ/nIiEuqoKtr43kA0GJGaW4KM/7+FM/dTK1kKNU6f5C5B25DBS9u2BurEx7Ge+BANfEdmcJ3Vsiq2Q8rRURH5d30moq0Pa4YNIO3KISe04LxZMX884cYwtYhS/6Xex85n2HwC76YLrLg+n4nJgrBWLFX1cYK6jgfiCMiw6Ey62EC5Vk6pISO+eiAws7+UMHzM9QXR1XinmnwoTW4CT9Iff7+nE0pAuMskaTD38gMkYKMLvO4KhqamK9StHMkd8eGQOXl12AvkiEWoqKnQtxTveE0a6MyfdMZHGuCTypBFFy9ICnkveQOK+/Ug+dozJEThMngSzPr3Fe/UsIlb+PBTVnx8WiuCPP0ZddQ1bbNd7+QfQMG5cEDzlxEl2vWN3/M02ISZdu8L9tabyZK3h6I7lGNzXp37gURnFcYLz+A1+G/GJsqOVWgvZlrdvhOHdLq44MroHk2s6m5KFdaFSbEv950lOVmxB7+ccLdkmJLu8ElPO3md/n0nKxhxPOyzo5AADdTU2MHkxNQd/RzX/LHu88DyePKnDve/WoKaiEkZuzuj+3hIxZ9OT2jpBxLOcefRsraFjbYkHGzajJD0T6jrazPHV6+N3oWXaOOXZsmcAInbsRUF8IjSNDFjEvzQNdFEeZhVjydlHeK+XMz7t54q0kgqsuBKNy0l54s8u/VNfgTvD0rC0uyO6WRmwXfGFZWyR68MSjn1HAy30sDbEqydkzwhsCeNu3ZgcQfLePWzRNurAuyxaxBYNbKzQOqbDLaQg5AFif/ut4TP7W0kJ5kOGwm7qVLnTyMOvF6KhpaaCX+f0gIG2GtOsfuX3W8gXcSCTTj5tRFZRBa49zsLq6V3hYanHFtq9EZWNpTsCUVopiAp3MtXFxG52TE/74ofiHeBFW+/hfLj02Q/kKA1YthiRuw4i5sgJqBvow33qRDEHvGCmR13DLDJ58tAipNGHjyPk1y2oq62BtrkZPF6YCHuJxUkri4qQ9SAUPnPlf3csPhCCr8Z44eLifszsXYjOwicSEcnsPavAxLRL0TnMsb/rpW6w1NdASmEFtt1LwuZbTWdFNkdV7RO8ciyUOfZvze7FbMs/UZn4+npsE9siLB8NTC/p5oB3ezmxwQB6nk7GZOO3oMaBnlupBVh6NgKLAuyZ018o2UOL55ZVy14k0XHKZHb9Qn/4GbWVldB3dYHv20vFZm3RcyCqGy5PHmG0PkVZmnRp3boIhO/82Yj4ew9ufvI1u8coCr/TKzPEZjgJbN8ThfK0RFbwQxQnJrPn9+KiZeJrWv28ig2SNgc53JetnoftPxzAOy98zRxLPQZ3xktvPi/+ShYp+5WTd5GXVYA7Fx+wTYimljp+O/Wt3Gma49ezj6GppoKN83vCQFsdYUkFmL3hhphtofYntVNZPRRW4FpEJnPcu1vrI6+kCjceZ+GtrfcabIs8aVqi56sv4N62gzi54kfUVtfAspMrhn0ovsit5DuupTxmbk7IS0jBxe9/R3FGDjT0dGDZyQ2jv3oHmiKSYLf/3Iuo8zcaoph3vvIOlOqe4InFVEC7+VnJtx6kY/lP17D0JX+sXNYPyRnFeHvVFQSGN763aLCKFtoVxdfdFF7Oxvh8fev7FKRHTs7PuG3bmJNT29oaHkuWMCkdSdusSB5y4Cfs3o3ojRuhoqUFkx49YDthQpv19EkS59U11/DpdH/MHeGO/JIq/HHqMbZfaJyxTM+oqopyw0y9vVfjmJP+m1e6Mac9zXZ+bd0NnH/QOGC063IcWzT3/Sl+WPNaLxSXVeNGRCa+a+PiuYTT3LlI3L0b4V9+yeqRIvedZ84Utz/07hOJgI/54w/kBwUJ7qcnT3Cv3rfR6cMPoeMge7ZOS5y8k8ykeD6Z1RXmRlqISy/Gop+vIUpkJhprF4gM9t1+lIUP/riNj1/qCnsLXWTml2P/5TisP9w4o25oVxtsfKtx8HXDW4L35rYzUTLlekiK0/Ott5CwaxeC3nmH3Sdm/frB5jmJNUhE7j158tDizbQ/9OuvmfNfz9kZXu+9J1Zvpn37IvXYMSTt28fePyQR5TxnDkzllAC1sTfDxz++iq2/HMWezWdhZKyHWYvHYvAYwcwfUX+G8Loe2HoBNdW1+HXVAbYJ6T3YF+9+8zL729PXEUE3IrBo8reorq5F5+5uWLlpCUwtGqXCOJxnAaUn8qwExPl/CUncPHr0iDn37ezE9TfJ2RsdHc0izWnRWAc5XngpKSnIzc2Fu7s7qqqqEBYWhr4S0g8U6R4REcEizimdqKQNfXfnzh107dq1yeKydOy8vDw2cEDlDg8PFzs2OcwjIyOZo46khiSlcpo7b3NkZ2cjPj4ePXo0Rh7Tb6RjkUY/NSBIdogGHpqru5bK1xz/JJ5CWxHKOIhGEFGZJKHfo6gU048PW699TO1p4cJx1BST1plnHSyRz1Q6aX1AWQvQpe5rv0VsqUMlbWppW+g+VzBlXlGYaa+rE4vCEXV2NSAS6SZLTkE0jSSB4opAcqNUf53aMNu25XNsf9T6vOS3VFKqX5xO9j558smizyqvVl/L9qCl6733pUZHZ2sR2IymD2StyEBia+n6y+v/qm1RlO5W8kUDCzvvbe18K8rRIJV2qb+nla+XYAa9wkjWp/D5adb2yZFGlFv/tH7hX2YjWlh8XRLhQIAksuQKJkyTT0dY6DwSRuC2J80d+8i5ViwqXg9VRXN1RzJQFLHfFpSMNf71Z0MRhnZ+8lRti6L5WHomvyW4T7VVnm75RKGIbjaLQLiIooRDVBThvTjTRb5ZL81RW1PL2syC9734AIXY+6++XS1PGmL6F5X/qm1RlHnzFdcop9+u9BTecZKOWWL7RzTYzhpi7XIOar9ItufIHrdWKqb3hy4dql0geZ4bf7U+4KIt9SIvvV8WrBfQHjRpK8hoEyvS9r71h3wLTkt/dpWayO+0pm1Ax5B2mL4L5O9Psv6iiF39N1jeWX5JO1mQPSDHPg3INvQ1pF1TFoin2LPVyVB8FjdHgH3nr5/JqkgKEVlr5D8Cj9jnNKsdT9Hl0iBjSE502uSFnNi0CY8t6dQnyKFN0evSoO9kaeGLHpvkaiSPTQ57b2/ZWmnNnbc5zMzM2CaKiYmJWDlFnfqy6q6l8j1tBNMwxV/erVkIuL0R7RzTn/J0llkz7X80XKlog+xpwhpjEtewpcbpv+lYZGrgHae6miAI2HnS4j558j2Na9kux/0XrjfVRe3T9nL9S7blafFvO/Tbg9bW179Rz5L1Kc/z87SeMZk2QsE8T8tZ0pIEQ0c8dktV0VanflvpSOautbZF0Xz/1nmkITlw9DTvaVGEjiNWBvodLfwUedL8L2zLv4GiDjR5YQM6TXe26zmkBWk8bef1v9kuaM/z/Bv18lTbCv/Dtpjg2VW8/p5a26AD+ABaA5sdLGKbRWdBcJ5SnXNl9w4Dd+xz2gzp1D98+FCmvv78+fOf2Vr+L/82DofD4XA4HA6Hw+FwOBwOh/Nswh37nDazaNEiFBY26sSJYm9v/0zX8H/5t3E4HA6Hw+FwOBwOh8PhcDicZxPu2Oe0GV/RBTv/Y/yXfxuHw+FwOBwOh8PhcDgcDoejCErtLH/GaT38SnA4HA6Hw+FwOBwOh8PhcDgcDofzDMEd+xwOh8PhcDgcDofD4XA4HA6Hw+E8Q3DHPofD4XA4HA6Hw+FwOBwOh8PhcDjPEFxjn8PhcDgcDofD4XA4HA6Hw+FwOC3CNfY7Djxin8PhcDgcDofD4XA4HA6Hw+FwOJxnCO7Y53A4HA6Hw+FwOBwOh8PhcDgcDucZgkvxcDgcDofD4XA4HA6Hw+FwOBwOp0WUeJx4h4FH7HM4HA6Hw+FwOBwOh8PhcDgcDofzDMEj9jmcZxwr7Tp0ZHKya9GRUU4tRkcmJlMJHRlPZ3RYIjVV0JEJTe3YY+u2nYajI5OX17FtX7iWBjoyRkYdu/6SCjuw7VPv2LaloKpjl08lOhsdGcPR1ujIpJYpdezOpdITdGTSyjruu1cltgAdmaA8U3RkUh6dQ0fmfqwrOjJKIWnoyNyLtkBHRjm5CB2V+7E26MhU+vyvS8DhPNt03JYNh8PhcDgcDofD4XA4HA6Hw+FwOJwm8Ih9DofD4XA4HA6Hw+FwOBwOh8PhtIwSjxPvKPArweFwOBwOh8PhcDgcDofD4XA4HM4zBHfsczgcDofD4XA4HA6Hw+FwOBwOh/MMwR37HA6Hw+FwOBwOh8PhcDgcDofD4TxDcI19DofD4XA4HA6Hw+FwOBwOh8PhtIgS19jvMPCIfQ6Hw+FwOBwOh8PhcDgcDofD4XCeIbhjn8PhcDgcDofD4XA4HA6Hw+FwOJxnCO7Y57SK+/fvY9CgQe1ae48ePcIrr7yCgQMHol+/fmybOnVqux3/7NmzmDBhQpvTcDgcDofD4XA4HA6Hw+FwOP8fUVJSeia31lBbW4vS0lKF8tTV1aGsrAz/Blxjvx2hi01O6XXr1sHf3x//ZQoKCnD9+vV2PSY51IcPH46vvvoKysrKOH78OPbs2dNux8/KysKdO3fanOZZ5M7FBzi89Syy0/NgYWOCyfNGI6C/j8z0KXEZOL7zIiKCY1BdVQMnD1tMXTAGjh62DWmKC0pwfNclBF0NQ1FBMSxsTDHyhQHoO7KbXGWy0dXER71c0N3SAOU1tTgem4Wf7yeg5skTqenJBI91Nsd0Lyu4GumgoLIal5JysTYwEWU1tXKnkRcfd1N8/HpveLmaIC+/HH//8whb9ofKTP/Vsn6YMsajyf68ggr0nbqT/a2upoIZ470waaQ77Kz0kJlbhqPnY/Dbrgeoq5P+u2XR18oIi/0cYa+nhfTSSvz5KAlnErNlprfT1cTLXrboaWkEbVUVPM4vwR9hSQjJKWpIs3GwL7qaGTTJG5ZbhHkXHipUvmHWFpjsaAtTTQ0kl5ZhW1Q8HuYXykyvqaKMgZbmGG1rBUddHXz78BHuZueJpZnl4oDJjnZi+3IqKzHv+j0oyqKZXTBtnCcM9DQQ9jgHX2+4hcdx4ucToq6mjJCTc6R+t357EDb8Hcz+drYzwMKZ/ujd1RrqqsqIiMnFur+CEBiWqVDZ1JSV8GYXJ4ywN2P1cj+rAKsDY5FZViU1Pd33w+xNMcXVCu5GOiiqrMGV1Dz8EZaIkurG+95RTwtLujjCx0Qf6ipKiC8sx5/hSbiRno+2Mqq/I5bM9IetpS6S0ouxZnsQLtxOkpn+0Nrx8HIxbrL/2v0ULPjsfJvKQrbl496NtuUY2ZZ7LdgWF3PMENqNCoHd+CUoEWX19SdPGnlx19fFIi8nuOjpoKCqGkeT0nEgIa3ZPN1MDfGcnSW6mRnhdHIm1kXENUkzzs4S4+0tYaGliZLqGgTmFGBzVAKKqmsUti2vdxaxLeFJON2CbXlFaFvUBLbl91Bx26KsBLzayR6jHc1gpqWBvMoqXEzOxa8PE1CloO0ba2+BmW42MNPUQGJJGTaGJyAoR7Zt8TDUxQxXG3Q20WdX8lF+MX5/lIDEknKF0rQWV3NdfDbeG13sjVBUXo1995Kx9kIUZNyOcLPQxcmlA5rsf2nTbdyJl26jZFEUFYWE/QdQnp4OdQMDWA0fDstBA9uU50ldHdLOnUP29RuozM+HpqkpbJ8bB5OAgIY0lbm5SDtzFvlhYagpLYW2tTVLY9ipExRldHdbvPm8D2zNdJCUWYKfD4XifFDzz4uJngbemeqLwV2soaykhOO3k/DD/ocor1LsWZXEWkcD7wW4oKu5ASpq6nAqIQvrQ2TbFh1VFbzgboWRDuaw0tZAWmkF9ken41BsRkOamR42zN435QlGHr6Dgir5n183fV285uEEZz1dZluOJ6fjUGJqs3kCTAwx1s4K3UyNcDolAxsjxW3LEi8XjLSxbJKvsLoaM6/clbts7FymRnjZ1RHW2lrIKq/EvvgkXMmQbVtUlJTQ29wEY2yt4GWoj23RCfgnKbVJW+MNL7cmeadcvCHzurREUW4hjm48hJjgx1BVU4Vv/y4YM3881DTUZeZJjU7GnRM3EXIlCKY25liy/h2ZaUuLSrHu9R9QlFOI97atgJFF03ehPLjaG2LFol7o7GGG4tJK7D8TjfU7g2XalmWvBGDBC75N9ldX18F/0g7UKmiLRamrrkbG4YMoDLyHuqpq6Hp4wOqF6VA3lv3bqvJykXf9GvJvXkdNcTG816yHspqaWJqy+DhknzmF0thYKCkrQ9vZGRbjJ0LTyhpPC083G8x/aRhefL4fiovL4dn3TTxtnkafSIf6HF7WGONsBmtdTaSWVGBPRDr2PU5XuHw+XuZY8c5AeLmbIjevHDv3h+DPnYK2ryymT/bFS1P9YGOlj+LiSly7nYjv191AfmFFQ5oBvR0wY4ov+3/fkXB8vvqywmVjx7E1wtvdneCgr4W0kgr8+iAJx2Nl2xY9dRXM8bHFKGczmGurI6W4Arsj0rE3UnrdjHA0xU9DPBGUWYSXTyjWHyKofzh/RhdYmukgNrEAq3+7jdvNvMcu7Z3B0kpy9Fw0PlgpqKNl87pjwYwuYt9nZJdi8LRdCpfvtc52mOphBQMNVYTllGDl7VhE5ct2YDoZaGFBZ3v0sjJkfZbIvFJsfJDI6keRNIoSERyLv9cfQ0pCJoxM9DF2+kAMf76PzPQ5Gfk4tusSHtyKRElRGWydLTF5znD49Wjsr9+/FoafPtreJO/6QytgLKVPzPn/R2VlJd544w3s2LGDOeo9PDywefNm9OzZs9mAZcpz8+ZN5tc0MjLC+++/j6VLlz61cnLHfjvy5MkT3LhxA4WFsjuYHOlQncXExODAgQPo3Lkz2xcZGcmrqx0g5/yvX/yNWcsmoftAP9w8F4h1K7ZhxYY34OrjKDXP3t+Oo9fQLpi6YDTr1BzcfAorl27EN9vehamloJF+7tANmFoY4b2fFkBHTxv3roTgj2/3QEtHC137eTdbJnrBbxrpg9iCMow/HAgzLXWsG9YJKspKWHWnqcOK8DXTQ4ClPlbeiUVcQTmcDbXw3UBPmGqp493LkXKnkQczYy1s/2EMDp+Jxhufn4efhxnWfDIE5RU12H0sQmqeT9dcx+e/3Gj4rKKihAs7X8TFm4kN+0b0d4Shvgbe/vYS0jNL0KWTOX5eMYSlXbc9SO7yeRjp4Id+nbDhYQKOJ2RhoI0xvujpwZyNdzILpOahQYBb6fnY8iiZOSJnd7LDhkE+eOlsMBKKBM6rNy6Hss6CEAN1NRwf3wOXUnKhCL3MTLDYyxW/hEchOLeAOQ0+9ffGW7eDkVIm3VE2zs4allqa2PQ4Fqu6d4ayWEkEkIMmLL8QnwaLDLC0oh86b5of25Z8fh7RCflYNrc7tn8/BiNe2YeikqbO86rqOviM3CK2b+QAJ/zy6VCcvZ7QsO+16V1w8VYiVv12m93L81/sjK2rx2Dk7H1Iz5J/hJ8cR70tjbD0SjjrrH3cwxVrB/pgxukg1Er5vT4mehhsa4rfQhMRU1DGOohf9faAnZ4mll191JBu/WAfPM4vxctng1FWXYcZHtb4sX8nTDsVhMTi1jswe/ha4qcPBuGrjbdw5kYixg92xroVQzDj3RN4ECm9YzXlrWNiV9jaXBfn/pyMsyLPS2sg27J5lA+rh+cOCWzL+mGdoKqkhJUybIsf2Q0LfXx7OxZxheVwNtDCarIb2up451Kk3GnkwVhDDd9198a51Cx8ERwJDwNdfNzZAxW1dTie3OjsE8XLUA+THKxxIjkDhhpq7DmQpJ+FCd7wcsaqh1G4k50PCy0NfNjZHe/6uuHTIOk2SxqeRjrsniDbciw+C4PItvTyQH5lNe5kSLctNAhwMz0ffz5KRnm9bdk42AczzzTallmetnjJ0wbvXn+EsNxiuBgIzkMO/5+D4+Uu3wArE7zb2QXfBkXjXnY+JjlZ4/tenTDn8gMkyXDCL+7kiCMJ6VgbGsfq7nVvJ6zt64tZF4MaBj3kSdMadDVU8ff8XrgenY2h31+Cs5kufp0VgNq6Oqy/GCM1jxKUoKqijF7fnEdOSWXDfkWdbhVZWYj4ZS2shg2D15I3UPg4CjFbtkBVWwumPXq0Ok/K8RNIv3AB7q8tgK6jI4oeP0b05j+hqq0NAy8vlib52HHoOjjActhQqOroIPPyZUSuXQvv996Hnouz3L+hh4cZfl7UG1/sCMKZ+ykY38cB69/oixe/uYgHsdLfSzqaqtizYghSckox/duLyCmoYPmG+FvjxJ1ktBZVZSWsH+SD+KIyvHAyEKaa6uweJlv/Y5B02zLJ1RJaqipYcTMSGWWV6GFhyGwz5SEHP7HzcSr2RIk7q38d4sf6FIo49Y3U1fBtgA/Op2Xh65BINoD4YWdPVNTW4mSKdNviaaCHiQ42OJWSAUN16bZlfUQsNkTGNnxWgRK29O+GOxID7y1BA5kfd+6EHTEJuJCWhV7mxljm7YHCqmo8yJNuW/pZmLL2xJ64JLzr68nshSS0jwb4F9wQH+BvrY+aOu3bPt0EbV1tvLnxPVSUluPvL7eisqISL7w7U3qe2jocXLMXPcf0ZpGASZGN7QJpHPhxNywcrVCQlc+uc2vQ1VbDtpWjcCMoFSO/vwInWwOs/2QoamvrsHF3iNQ8a/4KxNod4m3NY79ORExSQZuc+kT6vj0ofhQOx8VvQkVXF6m7diBh/S9w+/hTKKmoSM+zfy80be1gNmI00vdLD+TKOnUCJgMGwWbWbNRVlCPj0AHEr/kRHl+vajII0F5s/G4+Dh6/ja27L+LFiX3xtHlafaJpHlZssP39K5FIL6lEb2sjfDeQ7A+YE1tezEy08dfGSTh0/BFef/8E/DpZ4JeVo1FGfaKD0gOeRg5xxWfvDcI7n57B5evxsLbSx89fj8Kqz4bjtbePsTT+vpaYPb0Ldh8Kg4mRNpSpYK2gk4kuNgz3ZgMhh6MzMdTBhNVFXkU1bqZKty3kxC6qqsHCs2HIK6/GQDtjrBrowQZVjsZkiaW11tVggy7BmUWsLakow/s74ot3+mP5ysu4cS8FMyd5Y9N3ozHh1YOIS5JevqHTd0P0VBRkdnjTZJy91theor7j3ZB0zH33RMO+1piTuT62mONjh2WXHiE6vwxLAxzx5yhfjD14n9WRNOb52eFyUi6+vytoM73qa4tNI30x7uB9FhAibxpFyEjJwap3NmPMtAF4//tX8Sg4Fhu+3AUdXS30GS49oPbAlrNw9rDF6BcGQFdfG+cO3cR372zG57++ATcfB5bmSd0TqKmrYsuZr8XyqqhKt1uc/3988MEHOHPmDB4+fAh7e3vmoB8zZgyio6NhLGPwesqUKXBxcWFBw/r6+szHSUokXl5eGDFixFMpJ5fiaU2Db9s2TJ48GWPHjsX69evZPmLcuHHs/yVLljAZmZkzpTcAJR3adLNQpPq8efMQGBjILjaN7hDl5eXsWFevXmUjPEOHDsX+/fvZd7m5ufjkk09YerpRtm7dKtZAFMrKkGzO/PnzMWrUKHz00UcoKSkRG00Syt4MGzYMixYtkupQp3SzZs3C6NGjsXz5chaxL0lL5ZEF/VbhDT5nzhxWltDQpg0FecpaXV2NtWvXYuLEiWyj2RM1NeIvpebqQ7RMc+fOZdfls88+Y9dBkXLIU/cEGYiXXnqpoV7Pnz/f7hJHp/ZcQacANwyd2Af6RroY9cJAuHg74NTeKzLzvLN6Hou8N7EwgoGxHl55ezKL3H94u/F3Tpo7EsMn94O5tQl09LQwaFwvmFuZIDqsZScNNbrs9LTw+Y1oZJRWIjSnGBuDkzDNw5pFk0vjYXYxvrgZwyIJKBqF/qeIFn9zA4XSyMO0sZ6oqqrFNxtvITe/HJduJ2HviUjMn+YnMw/d6tQxEm59utrA0lSH5RNy/GIs1mwNRExCPkrLq3EjMBXX76cgwNtCofLNcLdBZH4J/n6cyhy//8Rl4mZ6HmZ5Nc6okOTDm5E4Gp/JGlOFVTX45UE8q6OB1iYNaahfVyuyjXAwY/tPJIg3cltikqMtrmfmsEi8oupq7I5LYtF5z9nLjrA6kJCC9RExiC1u+jyK8gRPWDkbNoWnDAJzp/pi28Ew3AxKQ3ZeORuQ0dBQweRRTWdcCBG9trTRrIug8Ew2MCDkg9VXcPpKPJulQcfdsCMYWpqq6ORqKnf59NVVMcHZAhtDE9g1JmfQt/di4Gqog77W0hsPobnF+OhmJIKzi1BcXcPyUbR+P2vjhufJWFMNVjqaOBCTziL/Kd22iBSoqSizKP+2MHeyD249SMPuk4+RV1iBbUce4UFkFuZMkj0rqE6iPicMdWEDZyevyO/kbda2XG+0LRvItnhasw6uNEJE7Ua1dLshTxp5GGNrieq6OvwaGc8iaskJfzIlEy842cjME1FQjI8CH+FGVp5MZ5W7gS5Sy8pxOSMH5bW1SCgpw6X0bObcU4TpHgLbsiNSYFuO1NuWlz1l25blNyJxNE5gW8gRuUZoW2wabUsnY10EZxfiXmYhymvqmHP/eloevI31FCufqw0upubgXGo2O9eWx0lIL6vEVGfZtmXpzTBcSstFbmU1siuq8H1IDEw01eFvaqBQmtYw0d8GepqqWHE4FFnFlbgdl4vNV+Mwt58zc9o0h6TNUZSMS5ehZmAA++cnQk1fH6bduzHnfNqZM23Kk3PnDiwGDmDR9+TMN/b3Z9H6qadON6Rxnf0KLAcPgpa5OdR0dGA7diw0zcyRGyT/ADbx6mgP3HqUid2XYpFXXIltZ6KYQ3/uKPdm8xjqauCNdTeRkFGCkooa7LoY2yanPjHYxgS2ulr45l4Ms6HheYJZb5NdrWS2W+g52vgwETGFZWz21MWUXJxNysZIe8G7VYjoe5fstL+ZPg6LRPXLw+h62/LH4zhmW+7m5DOH/RRH2c9uZGExPgkKx82sXNTJaK/TXtF3bhcTQzYTj46tCOPtbRBbVILDiamsXXA2NROBuXmsvSALakN8FxrZ7Gw/IWLtgjb4qGOCo5AWk4KJb06FsaUJrF1sMXLOWARfuI/iPOmRpuSQfHPDu+g5ti/UtTSaPf71w1dQWVaB/pPb1tYfP8QFejpq+Gz9TWTllePOwwxsORiG2c97y7Qtku1UHzdTuDkYYd+px20qC83Kybt5g0XSazk4QN3EBDYzXkJlehqKw2TPdHV4bTEsxj4HNUNDmWkcFy+Bno8vGyBUNzGF6fCRqCkqQlW2Yu1SRRgy6XNs2HIaBYWKSS60lqfVJ9oSloJfAhOYs5bsz7nEHJyKy8YYJ3OFyjfteR9Bn+inq8jNK8Ol6/HYezgM82c1ztKSxNfLHIkpBThxNgqlZdWIjs3F8TOP4dupsb8THJqBuW/+g3OXY9s0sPSKjw0e5ZZgS2gK8iuqceBxBq6m5OFVX/EZvqJQ2m1hqUgqqmB1cyIuG5G5JehqQbP2GlFRAn4c7In1wYlIrA9UUJR50zvj1KU4HDsfw9rI67YGIjWjGC9PbqGNXNu4PT/KA5k5pbh8S3wmLPlZRNMpOvubLMVsH1vseJSKW2kFyCmvwle3oqGpqozn3WT3TT++FoUzCTls8ITy/PYgiQ1ie5noKpRGEc4evAFDEz1Me2008030HtoFfYd3xdGdl2TmWfjRNIyY3BeWtqbMsf/87GGwsDXFnctNZ12QI19043CIiooKFp1Pznx3d3doampi1apVbP/OnQJFBknINxwVFcX8xeTUFzr66e+nGbjMHfsKQhrw5IAlh+7ixYvZRfv+++/Zd+TUJhYsWMAu+DvvyJ6GKYScz6dOncLChQvRt29fPP/887hw4QLy8vIa5H1oFgDtt7a2ZuegdPQ9Tf9ISkpiDn+6cb777ju8/fbbDcemEaKTJ0+yAQMaEKBzHD58GK+99lpDGjs7O1ZW2miAQUdHBwEBAUhIaIw2SU9PZ+ekKBQatKDpJLNnzxb7HfKURxY0cvXxxx+zv5ctW8bK4uAgGEUVRZ6yfvrpp9iwYQOmT5/OHOrx8fH4/PPPxQYfmqsPYZoZM2Zg8ODB7Bi7d+9mzndFyiFP3ScnJ7PBAapPqlcVFRV2P7S3xFF0aDw6dXUV2+cd4IbosOYjikQpL6tgEUma2tI7LOT0J7mfvJxC+PdpPlqfoIYnRabkVlQ37Ludlg8NVWV4m8r3wqcpfjQ18nxiTpvSSKOrjyXuhWaIRT7cCkqDnbU+TI205DrG1DEeCI/OwaNo6VGFyspK6Oxphl7+1mJR3/LQ2Uwf97PEO7r3MgvgayK/k0xDRZltpc1IFE1wtsTl1Fzm4JMXimYhOYBQiQi8kLwCeBqKN5hbAx1jz6De2Na/B5b7ecFKS1Oh/PbW+jAz1sad4MYpsFXVtQgKy0RXb/k6OzRg06+bDfaJDNpIi6abPdkH2XllCAqT3wFC0feqysq4n9l4fclhmlxcjs6m8tcfRV9W1z1BVa1g6IMa1oFZhRjvZAFDdVV27V90t2aNbdFztYauncxxJ0Q8+uvWg3R09ZK/8zh5hBuOX4ljEWBtwd+iGdsiZ2eC2Q0nU9YJbksaaXgb6SE0v0hsognNarHS1mQRt63lVlYek6bpZWbE5CsstTTQ38KUOfoVoYtpU9tyl2yLqfy2heSj6P4SlSg6n5wDX1N9dn9TR9nVUBu9LI1wJkn2VHlptsXLULeJ7E5gdgF8FBggMFAXTFZtTp5NnjTyEOBojJDkAlRUNw5B3ojNgZGOOlzMmr8fT77VHyGfj8Th1/tijK+Vwucujo2Bgaf4YKWBlydKk1NQW1nZ6jxPntQ11SdVVkZxbCyT6ZEG7a8pL4eKpmL2OsDdFLcixB14Nx9loqub7MHSEQG2uBichtI22hJp712K1idbKvps0L3uZSy/o4JmwjX/3rVgUZIXkxV7djsZ6iNMwrY8aAfbIslIGwvEFJUgtlgxpydJ6YRKOOipXeBhoNjgnjSMNdSxc2Av7BjYE1929WFtkNaSGB4PA1NDmNo0Dr64+ruzqM6WIvFbIjUmBVf2XcC0919qtcavkIBOFnj4OAcVlY33Eg2wG+lrMllAeZgy0h3JGcW4IdIeag3l8XE0bYHJ7wghJ7y6mRnK4hpne7QVkuvJu3oZGtY20LBQLCCmI/Nv9YkIA001lCooHxjQ2Rr3glPF+kQ37ybD3tYApibaUvNcuBoHS3NdDOnvxGag2dnoY9RQV+bob2/IGX8nTbzPcTu1AP7menLPmKCIfRdDbVxIFO+zvRngiNxywWBBa1BTVYavpxluB4vPyroZmIquPvLdwyQHOn64Kw6efMyc96L4e1sg6ORsXDv4EtZ9ORz2Nor1s+z1NdnM0zvpjfVXVfuEyeV0MZfvWCT5NMvbBjllVQiS0Z+QJ01LPA5NgLeEP8OnmxsSo9NQWSFdqlQS8mWUl1ZAS8KfUVVZjdfGfoZ5oz7BF69vRHiQ9BmVnKYosTn2z94mLxRsTLr65LMToq2tjW7dusmU7ybfHvmDf/31VwQHBzP/6MqVK1m+SZMm4WnBpXgUgByuf//9N+7du8cuJkFR+8IFEYQ6S76+vmIXXxaXL1/GtWvX2DQOJyeBvqaJiYnUxVvffPNN5kQWQoMLjo6O2L69UROMRpG6d+/OnNuk40RQtPrBgwfZVBDhQAFF3gvR09MTKytFqNNIEo1Mff21YErSDz/8wMr3119/sc809SQ1NVVslGr16tVylUca9F2P+inetDaBj4/0EWx5ykr1Sc70adOmNbk+8tSHMM2WLVswZMgQ9tnT0xNdunRhDyaVT55yyHOuH3/8Ea6urmL1SoMows/tQU1NLdOU0zcSb9zoGeqiMK9Y7uPsWn8UuoY66NJHXB83MzUH789YxV6UNI1t9jtT4O4nTStWHDNtdeRJvISFnWWaRtocO8Z2RhczfRaRdCExh03xa02aZstnrIXEVPGGR15BecN3OfnNR20YG2piUC97fL3+ltTv7xyeBUM9DebcJ93+XUfll8ogSAIgX6L+SCpDR00VWqrKLCK2JV73c0TtkycynQcUYetmqIOfgxWrO311NeaYpohBST1eI/Xmr21L5FZWYW14NELy8qGvpoZX3JywukdnvH6T5DLkG3wwr++E5BY0anwSFEVjaylfJ2DyKHeUldfg5OWmdfPcEBes/nAQ68iQU3/xp+eQXyT/lFPh/U/XUxT6TBHE8mCkoYa53nY4GZ8pps/6wfUIrB3ojQuTe7PPuRVVePvqoybnUgRVFSXmSJBWn6bG8g2C9fW3hq2FHva2MWqQoCns9Luk2RayO83xN9kNc4HdoM7x91KmwMuTpiUHVFqpeNQnSVEIv8uXeG7khaL614THYkUXD2jUyx5czcjBH48VmwFB95+kbSaJL0Vsy+J623IhpdG2nEvKgY2OJrYM69wQTfp3ZEqDHIk8GGjU2xaJ+5VsDdWdvCzxcUZySTke5Ba2KY08mOtpIFdC3iuv/rOZngaiMpu+h6nufr0Ug733kli0+XOdrfHLdH+2bscRCedAc1QVFjZI4whR09VjYbvVxcVQ0dBoVR6K0M+8dh1Gfn7QcXBgUjy59++jrqoKtRUVLIpfktRTp1BbXgbTntIlgGTaFl0N5ErYz7yiSpgZyB4gsDfXweWQNGx8sy/6elsgv6QSZwNTseZgGMoqW+/sFzwbEvde/b0or20mKZ7+NsbMFkuDHo1xThY4mZCl8NoTzLaUFTZ57xJGbbAtkoMSPcyM8ZuEDr+85Susqmpi+7RVVdlgIMmRtQYafPvjcSxuZ+VCTVkZU53ssKpbZyy7E4ykUsUXrKOofF1DcWeqtr4O66gXK9BulqSyvBK7V27Hcwufh4GZIXLS5B/UlAa1RXPr26Wi713Bd9qITpQu8SFES0MV4wY6YdOB0FbJd4hSXSS471TIVohAn4XftYXME8eQdfI4hUFC3dwCjq+/CSWV/44L42n3iYT0tjbEIDtjLLuoWJ/DzFQHicni91NeflmDTE9ObtPnjKLxP/n2ItauHANNTcG1On0hGqt+uabQueUqnza1+6qb1J+Ouiq0VZVRJqPdYqihihszezOZNZrt9N2dOFxLaZyF28vaEBPdLDDxUGCry2ZkoAk1VRXk5Uu0kQuojSx9UESSEQOcoK+rgQMSwUSZ2aX46LsrbJCAzvPewp7Yt3Eixry8r8EWtIRp/QyjvHKJPmVFNWz0mh+IH+tshpUDPFn9kcN+yQWSD61ROI28FOQWwbeb+Hoq+oY6bNZCYX4JzK1aXqvknx0XUVpcjr4jujbsU9dQw8zF49B7mGC9gjMHruPrN3/D57++Dg/fln0anGdXN79SIshFQ0ODbaJkZwve1WZm4jMt6bPwO2l88803LNCZgn/pmOrq6sx3amsre5ZiW/nvvBX/Ba5cucIiyYVOfSE0+tIaSKaFHLtCp77QSSwNyYGCS5cuMScwybaQQRNMxaplUz8eP36MXr16sXSk+yR0LBOkC0WyMiQBZGAgiOigRWr37dvHnPV0g8fFxTHntZC7d+820YIaOXKkmGNf3vK0lZbKSosXr1mzhj08VGaaDSB6feSpDzU1NTE5HD8/P1haWrIBHeGiyC2VQ55z0fWXrFe6/s059qUZIRplppeSVGQtutSCBIAoR/86jzsXHuD9n16Dtq64o44WzN1y4TuUlVbg/pWH2PrDfmjpajItf0UR9mFbCmJ65WQI67x5Guvgq37u+H6QJ5ZdilA4jeLlExRQnigrkmmpqanDsQvSR/x7T/6bdaoCfC2w+oNBqKiswc9b7repfMJLTfrMLUGLrE51tcI71x8hT4ZTd6KzJVJKyllEYnvQWh1ZUY4nN0aVke71D6GR2DqgB1s871BiSpuOTdNX5Q2gI8f+sYsxTDpGkmMXY5nDn5zac6f6Ycuq0Zi0+AgSUhTs3D5pev/JUz6atv3zgE7ILq/CjyKDMhSR9NsQXxb5/971CKYj+oKbNdOMfvnsAyS1UmNf1vNA0Y3yMnWUOyJicxEmY3ZLW5HXXfRyvd3wMhHYjR8Ge+ItiQ6wPGkULl/bHw30NjPG2z4u+DEsRqCxr6mB9/3csNzPHd+GRLVL/cljW8iuvOBmhbevPRJzgtLsEFq8+80rYUxmgKSlvu3jyRYg/TU0sc31J+8b7Q1vJ3QxNcCS66FsRktr07TPu0T697FZJVh9urEDv+1mAjyt9fHaQBeFHPtSEb77n7Q+j/3Eiey5j/rtd1QXF0HH3h7WI0ci5ehRqdlz7t5FyrFjcJkzh0nzyIus+02WZExjcZUwb7QnPt5yD+9vugtnKz2sfb03jHTV8d4fii322l7tFoIGylf19cTux2lMkkcafa2MYa6tgSMKyvC03C5oH4ZZm7OBp8vpbXNKC3migG2RBcn+ibIxIgZeBvpM+m9DRDtHXbahHXN040HYezrCb6B0Lej2QCjDIU9tjhngxByuB89Gt9v5JZ8D1j5oBxNqPnoszEeNRlVuHjKPHkb8mh/g9vFnUNFpm4xgR6Y9+0SEh7EOfhrshR3hqQrPNGy+fNILOHSAE75dMQzLvzqHy9cT2AK6338xHD98ORLLPm6UbXtayNNnIwez39ZrLHCBIva/7u+G0qpaHIrOZIvIfj/QA8uvRiG/DQPCsniiQJ9j6jgv3ApMRXK6+MDijkPhDX/TgsTLvriAawdnYvIYD2ySsc5Ge7arSL7odHw2TLTUmZzPHyN98cLRYDHJInnStIUGf4Y80s/ngnHgzzNY9Ml0Js0jpHMvT7YJmbF4HKLDEnF81xV4rOSO/f8qK1euxBdffCG2j+S3RZU+RG0I+TVFoQBeGvCXBn1Hyh8U9EyqJuT3I+UOcvSTD1GWv7etcCkeBaBpGEKdpPaguLgYurriUSGk26Sq2nS8heReJPOS85kixGlE6Ntvv2XSNxSxTs5sIeSklnZzCp1spINPMjMUkf7ee+8xeRmKshfVlC8qKmpSTkkntrzlaQvylJXO/9NPP+HWrVvsgSLH+rlz5+SuD4IGAiQfVPq9VA/ylkOec0m7/pKfpRkhMg6i2/Zf9rHvQu8+xisD323YLv5ziy18S874ogJx3fKi/BLoS0QkSePErks4su0slq16FR6dpS94Rzp0egY6GDy+NwL6++L8ocYFZGWRW14FI03x+jHREnymKY8tNTYqa+uY5vVP9+Mx0smsSUSLPGmaLV9+OYwNxQcxTOo/txStT0wZ5c6cuyVl1TI7X6Sxf/VuCjbve4hZE1uWLxIrX0U1DCXqj+qTpC9ako4gh/3b/s5Mc58WvJQGSQuMsDdj2v2KUlRVzbQyKbJPFAN19SZR/G2lsq4O6WUVsNaWX95BeP1oVoXk9aXr3hJ9ulozSSbRtRMkod+fmVOGlb/eRmFJJZ4fIR5h0hzCqC3J62us2TQSXRKKqF43yJt18l6/FCYWXd3T0pA5U78LjGW6/bTOwqbwJBZx+ryLJVpLdU0dCosrYSwRQUv1KxnFLw2auTKstz32tEO0vtC20HoCophoKmZbHmQV48d7ArtBMwAUTdMcNDtC8tmgBXGJtjwfExysmEP/UnoOswHxJWXYFp2EwVZmMFEgmp1sC834kFzwVx7bQvfR212l25YZHjY4FJuB2xkFTIYkJKcIfz9OwUxP2WsLSFJYWY2auicN9SWEyitrgFKUhZ0cMM7BAm/fDENMUWmr0ygCLX5rrCNe/6a6gmignGL5Z/JEphXB0VQxJ5aanj6qJdYsqS4qZp4iNX29VuehBSsdpkxBwPer0eu33+D70UcsklZVV6dJtH5uYCBitm6D86xZMKuf1Sov1bV1KCytgomeePSUCc0QamYWVFZBBe5EZuHg9QSUlFfjYVweNp98jFHd7eR2pMh8NprYZcFnyWhbSVwNtPHrYF82c+WnZmbBTXSxQEh2EWILFY80z6+qkvLeFXxuj2h9YoS1BZsJVCbRwZUHsm/6kuVTU2ODzLQuSHuSWFIKa+2WZ4z9+Oq3+Gj022z7ZdFqtk/XSA8lheLPQFlxGQtUou9aS0JoHIIv3m843+blG9n+H+Z8g6O/HlL4ePR+lXzvNrRTJSL5ZbVTr9xLQaaUaGtFUdUT9I1rJGwHSeeotkO/WUlZmUXoa5ibw/blOaguLEJhyAP8V3jafSI3I238OdKX6et/p+AMZiInrwzGEjKkJvWfpUXrE7OmdWFa/MfPRKGktAqPY3Lw86+38dxID1iYte+ADNVRk3aflkByqCXZIVK2IemzY7FZOBKdhZnegvV6HPW1YK6jgT9G+iB8bn+2TXa3RIClAfvbz0w+W0AOdwr0kuxzUH3K05+0tdJjkq17j7ccQEJBYompRXCwNVDo3iOMJGadUf21dO8J6y+rrAqr78ahqLKGzXBoTRrJRXJn9H+vYdu54Tjbb2Ckh6KC0ib+DPKv0BqCzXH7Ygg2fr0b8z+Yin4i0fqycHC1RkZy+wxg/9dRUlJ+JrcPP/yQBduKbrRPEpJCJzIzxX0i9NnKSrpMJq2ZGhISgi+//BKGhobsHiUJHpI2J1WQpwWP2FcAiq6nyGySdpEWpS9r1EYWFKlPGvA0qiN05gs/twQ5rOmGkkfypzloIV7SfRfVwifpHNEBDGdnZxZ1L4rk5/YqT1vLSg8OyfDQRo1wWueApHkSE+WPCKQHm36LRb1+I11v0sMXzqyQpxzyQMejNRpEIVmm5iCDI7luwcOii+x/n+7uLHpedDEvws3XEZEPYjFupkBaiHgUFM32N8fJ3ZdxcPMp5tSnY8sD1bk8kdnBWUVsMUuaBimcktfTypDpgYfnyD/VmbSkCaU2ppGEFkWdNs6LOQGEP4caVikZxchqoRPU1dsCLg5GWP79VbnORXI8ijobHuYUIcBMvOHW3dwQobnSF3YT1e59P8AFH9+KZNr5shhmZ8oWTjrWCsc+Sb/EFBfDx8gA59Ma83c2NkB4QfPlUxR1ZWVYamkiKFf6AIU0KHKeHPg9/Kxw72FGg34lXbcNfwfJtXZCWFS2zLUTJFFR8PqG5RQz52WAuQHOJAoalRba6rDV1WSLpTXn1F870AeaqipYdDGULY4rivA+fiIlqqmlCNiWCI7IQg8/SzalX0jvzlYIftTy/TNhiAv5BHH0Yvvo8JJtedFL3LbQdGqFbYswCkipbWkkeVRQxBbQpSzCWvc3NkBGeQWTmmo9NFNOYk/9Z0WuL9mWrhILAnezaNm2TKy3LbSI8yUp0ci06LXku4E+KlI2si1RhSXoYqKPE0mN91aAqQFCWijfAi8HTHS0wju3whEhMdCtSBpFCUrMx3ujPJmMTlX9QFtvFxMUllUhJkv+c7hb6iGrSL6p9UL0XF1QENYY0UcURkZC28ZGqgxPa/OQfj458I06dxbbT/uiN/8JpxkzYN63L1pDUHQOenia4Y+TjQOpvTuZs/3N5TExEC9re8y7oGeDZrvRGiW0cDPR3UJgWyLyZF9LF3LqD/HFheQcrLwvO4KcHFO04PnXd2NaLcc1WsK2dDY2RGZ5BfLaZFsEdDLUg72uNn4Ob12Ed0RBEWsXiOJnbIjHhe3bLiConEklLTusl/2xnNkm0VkDDp0ccXHXWeRl5LLFc4nYB9Gsf2Hv1Xy7uTne+fOjhnMR8Q9jmXOf9htZtiwhIUnQo0y8M6cb1NVU2DpBRK8uVmygPTap+ZmWTrb66OZtgdc+awx8agva1D9SVkZp9GMYdhcM4FXl5aEqJxvazo0zl9sF9s4Qbv8NnmafiNaz2TLKj0Xpf3mrdbYlOCQd0yb5iPWJene3Q0paEbJyZAyA18/eF0X4vm/LQrlSy5dZhB5W4raF2n0hWYrZFuo2C+uOBko6/Snej/uynztby2DWiRDmrJY3+CU8Khvdu1jhwMlG30nvrta4X98HaY4pYzxQUFSB89daXt9DQ12FrSN2TYGF4ilynpz73S0NEFivfU8zfElykha7VQSaLafUDmkomn7HpVVNovLdfR0Rckc8qCosMAb2rlbQbGbR8juXHmL9Fzsx993JGDROPjnA5LgMGJq0XzAvp+OhIUV2Rxre3t5MheP8+fMskJegKHxy3pOOvhBhADD5A4V+YlE5cGGQuGSwdnvCI/YVgEZatLS08O677zY432kxhGPHjgkqU1mZaeSTPIs8kJY+HYekY4ROUcnpH7KghXvPnj2Lbdu2NeyjCHCKlFcEuvnCw8PZuQmaJkKyOpILBpNW/IMHgugIktxZv379UylPW8tK6wHk5OQ0XA/S76dFaRWFHPXCBgnNAqBzjx49Wu5yyAPp7VO9PnwoWJmdBhNo4d/mIANE5xfdhDI81OkQXc1dOENg9LSBCLv7GNdO3WOL4F48chPRoQkYOXVAw3GP77yIecOXN3w+ve+KwKn/3avw7SG+kJ6QX7/8G1EP49mCNaRXd+HITQReC0P/0QKj1xykTZ1eWoFP+7ixKBV3Ix0s6mKPg1EZKKnvoJhrq+Ph7P4Y5iDoWL3ibYMJrhYw1VJraHS81c0JN1PzmeyIvGnkgaKxtTVV8d78HtDVUUNvf2tMG+eJrfsbHZfk6I849ypcHY2aOH6j4vPw4JH4gn/E+wt6YEhve+jrqkNTQwUDe9rh1Rf8cEjBqdB7olLhbaLHJC9IemWUgxn6Whth1+NUsejZ2y/0Y/VAPOdkgQ8CXJlTX5rjTXLR3OtpeciRczEiSY4kpmKApRl6mZlAS0UFkx1tYaWtheNJjVI6r7g6YnO/lu8VUWixXHd9PfabzDU1sMzHnek2nk+VX7aAHuttB8PwymQfBPhYsGvx4aJeqKmtw6HTjQNt6z8fhu0/jBHLS2mH93PEvhNNo8utzHXw9Tv94eZoxBbLIi3/Fa/3ZhF0xxVwWpPD6ERCJhb5OsBJX5s5e+i6kVQOXRMhu0b546PugkWkSJ94zQBvdi+QU5+ijyShCGlaKPcdf2d2TMpD0ii2ulq4mtp43Naw5VAY+na1wfPDXKGjpYoXx3iwgZJtRx41pJk/xRcPDouvZ0JMHumOU9fiZc5uURRmW0rEbctCKbYldE5/DBfaFp+mdmOZ0G6UVcmdRh5OJGeyQbNX3R3Y9SKn/hg7SxxMaHw2uhgb4PSIPnCQkD5rjuuZeehtYYy+5sZQU1KCjbYmXnazY84+RaJ1dz9OZQvcCm3LaAcz9LM2ws5IcdtyZ5qEbenmKtOpT1xOycXzLlZMC5ieWU8jXcz0sMEVBe+9PTGpGGpjhgFWJtBSVcFMVxvY6GjhYHyjVv+iTo7YP7xRMnGepz0mOVnh7VthCM+X7iSRJ7eeq6oAAQAASURBVE1rOBSUgvKqWnw+3hsGWmroYm+IV/s7Y/vNBDaAR3hZ6SH62zHo6SRw7i0Z6sZ09SnSX1dDFdN72GNKNztsvaHYwp2WgwajMjcXKcdPsIVr80JCkHPnDqxFpgDnBgXh1msLUZmfL3eewohIpF+4iJqyMlQXFSH2rx2oKSmB3fjG9aHygoOZU9955gxY9G99wMeW04/Rz8cSk/o5QkdTFdMHuyDAzRRbRRZgXDDGEw//aFyM7M/Tj9HDwwzjetqxARUPOwO8SrPo7iS3SUv8UnIOMsoqsLy7K5s1Qg6zed52+Ceu0bbQ7B16NgbbmjREfZJT/2JybrNOfYK09Uma6pwCC0qLciolg9n1OW6O7NklOzLalmTqUsUG2I8P6wt7HcUlREfaWCKhpBSRha17Po4mpbL391g7K9YuGGhphm6mRjiSlCp2jiND+7GFsuVlSSc3NkBAv50WjZ/n7gx7HR2cSG55/Q4KgKE+Am0NwTBdPWHpZIUj6/Yzvf3s5Eyc3X6CSejomwich1UVlSzq/t6Z23KXU/RctAmdVfS/ooFhxJELsUwO8JNFvWCgq44unmaY+7w3dhx9hJp6r6OnkzEiTsxGD1/xWXlTR7ojI6cUl++1TcJQiKquHox69kbmsX9QkZ7G7ELanl3QMLeAvq9vQ7rob75A6s4dch+3NDoKGUcOoTIrC09qa1GZnYXkv7ZCRUsL+r7iA4nPMk+rT+RsoIUto/1wLiEHX9xsvSzVnsOhgj7Rkr7Q1VFnTv1pz/tgy87GYJhe3WwReXsJ3JwF77Gzl2IxbKAzhg9yYYNPjvaGWLqgFx6EpsuM8m8t28NT4Wumh5mdrNkirc+5mGOAnTG2hjbe3y94WLJIe2G75Zv+7mwwgDT4Kc8EV3OMd7HAkejGoAF6jEQ34cCcvE59IX/ufYhxQ1wxvL8jdLTUsGBGZzjYGGDHobCGNKSPf2nvDLF8ZAYnjfZgfRMaIJCEFsvt7GUOdXUV2Fjq4vuPB7P+x0EFZsDSTyF5ppe9bdgixPrqqvigpwsbfDkc3di3WjPEC1tGCZ5lKx0NfNHXjb0DqT7pvfdhTxcW5X8iLkvuNM0h6s8Q2scRk/siJyMfh7aeYxLAgdfDceNsEMa+OLAh393LD1mUf162YJDi3pVQrPv8b7z63mQMeU76rMFtPx1G8M0IlJWUM4WD/ZvP4FFwLEa90F/ueuT8d1FVVWXrnJJCB/l8Hz16hNmzZ8POzg4vvviimJ94xgzBM0xqJaTssXTpUiZpnpCQwHyiJOstmqfdy/rUjvwfhKZSnDhxAjNnzmSa6zT9guRXdu3a1ZDmjTfewMKFC7Fu3ToWxS6qQy8JOZ03bdqEuXPn4o8//mCjOCRnQxIu0uR4RCEnM+Wl6G1yQpMkCzmGJaO5W+KTTz7BqFGjWPQ4jS7RyFL//uKGbOLEiZgzZw5bHNjDwwNpaWksz549e9q9PG0tKzm6SROf6pa0sGj0THSwQR4oLw0OkCY+XYv8/Hx2jYWjb/KUQx7IANBgCI3+0QK9VK/Dhg2Te2BIXry7uWP+Ry/i0J9nsGnlHphbm2Dxpy+JSeuQ1h8tgCvk8JazqK6qwQ/vbhI71rgZQzD1NYGzc8iEPji89QxiwhNZ1Lm1oyUWf/YSeg4RLD7THFW1T7DgTChrxF6e1pNJhhyPy8J3dxodoNT0IgcQje4TNE2SGrpLAxyZ9EJGaSVOJ2Rj88PGyAR50sgDyai8uvw0PlnSG3Om+rKplJv2PMRfhxsjGaltSAukinY/qeE7epAzfv7znswBg6WzA/DtuwOgpanKZgD8ujNYTCdRHsLzSrD8RiTe6OyId/xdmLTKt/dicENE/kK5vv6E5ZvnbQ91FWWs7CMujXUoNh2rAxvr3U5Xk0XsLr3S2OBsjeYtLW67wMMZxhoaSC0rwzcPHoktZEeRRcLoIoIW5PvIr3Fx5uWdvViL82hyKrZECRYAPZ2SjjnuTnDV12XT9ynS7927IciskF/Sgvh99wNW/xu+GA59PQ0WTTP3g1Nii9zSPd0QkV3PhOFuqK19InXthPSsUtx9kIbvPhgIdycjFJdUIfRxDma8dQxR8fLPKCC+ux+Ld7o6Y9vwzuyaBWYVYsnlMLGFcEXrr5uFAYuqpob4uefF1zOZfioIcUVlrHNIx3izixMOjg1gsx0Sisqx/EYEc/q3hVsP0rH8p2tY+pI/Vi7rh+SMYry96goCwxs7SOS8oMUwRfF1N4WXszE+X38T7QXZlvlnQvEZ2ZYXezJH2fHYLKySYluEg5/HYgR2461ujXbjVLyEbZEjjTxQVP7H9x9hsZczpjjasMUt98al4EhioxOKisVmetQ/veSoPzZcsOAx7afIWXLY0fO04IZgwP14cgaT0Jrn4YiPunigtLoGQbmF2PQ4odW25d2uAtvyjaRtURK3LfN9BLZlVV8J2xKTzqSfiA0hCUyv/qteHqyDR9I5NAiwMUSx8l1My2FO1aU+TjDR1EBySRk+uhuB+OIysfIJnw2yg7M97Fmk4MZ+4o6gXx/FY29smlxpWktxRQ1e/vM2vpzoi3ufDGef991Lwi/nRWfr0bNBoYKCMu+/n4y3hrnjk+c6QVtdFfE5pXhvXwj+eaBY20DL0gKeS95A4r79SD52DOr6+nCYPAlmfQT3UuO0ibqG4Fd58lBUf35YKII//hh11TVssV3v5R9Aw7hxkDvlxEk8qalB7I6/2SbEpGtXuL/WGOHUEjcfZeGDzXex9HkfrJrXHclZpVj2620ERjVG7Csp0/3Y6BiNSCrA4rU38MG0zvjhtV7ILarAibvJ+FlkRlFroMVs37gUhg+7u+L0hB5ssddTCVli0jqS7ZapbtZsYd2JLpZsE5JVVonnjt1rMqPudGJ2qxeRJdvySVA4Fnq6YJKDwLbsj0/F0SQR2yIxi4xsy+GhfdjftN/LUB+jbCyRXFaGRTeDG/KR07y/hSm2x7R+PYzoohJ89zASr7g5Yr6HC3IqKpkGfmBOflPbVz/tgAYg1vYSyCbQ/lfcnPCKqxPu5eThmxDBwPHJ5HTMdLFHJ0Mv1OEJ4opK8eH9h4ho5UwAcsDP/nIBDq/bj+9e/hIqairw7d8Fzy2aJPHY1ImtJbPujR+RHpvasM4YOf6JTw98A00d+Qdp5aW4tApzPjqDz9/ojVt7ZrDPB85EYd3ORokaqkeBbWnMR/U4cagr9p2OatDkbw+sX5yB9P17Ebt6FZ7UVEPHzR2Ob4gvcvukluqs8f5O3b0TedevNoSBh7/1BvufFsfV6+QNbWdnlCcnI/G39ajKzoaKji503N3h8u5yqEpIwrYnR3csx+C+FKGuBBUVZRTHCWyY3+C3EZ/YslNSUZ5Wn2i6lzWT5ZniYcU2IZmllRi+X/71RmiR1rlL/8Gn7w7E3BldkV9Qjj/+uo+/9oaItZlVVRvfY7sOhrI1HN5f0he/fDsKRcVVuHE3CavXXm/IQw7/h9cWs78pb1c/K7wwwRtxiXkYM022/0SS0OxivHUhAu90d8LHvVyQXlqJz65H46rIQrhUb4J2n+Dz7og0vNHVAQEWBmxfQmE5PrkehX9i2v/6nroUx+RdV7zZB+YmOohPLsCij88gWqRvoCyljdy/hx2szHWx/7h06c89Rx/h/UU94eNhhrLyagSHZ2LqwsNIkdDib4lND5PZbN9fhnSCvoYqHuWUsHa06CK31KYS3ntUv/fSC/HtAA+2dgzNEA7LLmZrUEXXL6osTxpFsbY3w/vfv4oda4/iwJazMDTWw/RFYzFgdGMgR129P0MYnHl4+wXUVNdi03cH2CakxyBfvPXVy+zvYc/3xr5NZ1hUP+Wzc7bE8p/mo3NP6YGNnP9/vPfee2xAfvny5UzZo3fv3rhw4QIL+BZCvk+SVCfIj3vy5EkmxUN+Xgp2dnNzw9GjR5mv72mh9KQ9VjT8fwZVWWRkJGvUkUNWMiKcHLQk3UJO4a5dW9bxIuczSbLQAgsUwU8DBhQd37lzZ3aOmzdvsr8lde2J6upqREREsDK4u7uL6brTSs0k7dOjR+O0o5KSEnZsuiGF5abFWOn30GcaYSLZGtonqY2fkpKC3Nxcdp6qqiqEhYUxrSh5y9MclO/OnTusvoQOdBoYoHoUXaxYnrKSQ5/S0AgvyScJyyBPfYimEZ6fji05baalcshb9wSdg6b0UH1t376drU1AeeXlbvYJtBXWEal70hCxJPpSFKU1kUWzj7W+8U1tHEUjIxSl+u/Wa3tTB0lySqm0fW3BYEHjgj5tqT9ydkmLgaOiPmlDvVsJgocURql+E3VhSFvTmW7D1tZm5ErZOvgtQe1X6tSJdnyl7WsL+vNbf22prkTrRqI/0EBbnp+i7e23qJ6w41LXjs+L6lTBjIWOaltsbRSfLSZ6fUWrRep65+RcavUZgLz8un/dtiiClrZSu9gWeZ4NRZ+f1Jvts6j403pvDBwr33uXvefr6qDUipmNzR6XDRZI+T00DV9ZGdc2t8/isELI4dBWSTFRDEcLNFXb+mwoyXqvSVk0WxGbZG6i9K/bFsl8zaGq9OSplq8t7QJirlvbpbaor0HtYeEAMWs3SymVrFnD9OxRH0/y+/cXtjzT4H/ZTu38fuvfu0KnPtmAhs/SnluRelWUE7N/RVuhc1N7RZLaVg66ieL0+aJ/rd2iiP0RUrUxuPXlU1FiwS+K5pFE8FqSfhyVhf7/s3Yfa1u0YAfr/opo3bFl9C+ktZtbi9psr6f629vKzgltl2VjdrW2jkX6t7c/w99kXJvL91/EvYdgvZhnjai7gkHF/xI8Yr8VkOFtbkFYWmRBuNBCSxw5coRF6ZPzmpz6pN3u4OAAHx8f9j0ZneZ068lpTRHq0jAzM2Ob5OKskscjeRcaOBDV1JeGra0t2wgaoZJ06rdUnuagfJLlIo17oc69ImWlRjLpYbWmPkTTSDu/vOWQt+7/+usvJslD03lI4ohkmcaNG/c/uaeVRBpXQgf//5qn7XhrK9I6Ru2tHdle9adIsf6NepemkNqBqk7gOJCiCd5RxsIl66qjPytEXQd6Xjp6fUlWS0d6NtpiW/4NJG2LPNf6f30//K+eA+Y8a2enPjtuK6RF2kJ7OvXbiui99ESBe+vfugdba1v+rVu0o9s+IZIOeUXbzYJo8PZ/9jpyO1XSLvzbdkJeqJ2nqIP630DRIilif9qD1tTZv1nPbT0Va1s8peLK6l+056yajvrb2xOhLHFH82dwOP8G3LH/FCGpGkktelFHMDl11dXVmROanMAkwUKLMxw4cOBfaez9m5AGv1BLXhLS8J8/fz7+PxIaGtog60SzNkjihzT9ORwOh8PhcDgcDofD4XA4HA5HFtyx/xQh3XVhhLskQmmXMWPGsAUVHj9+zCK6KVq/tdMPOzKLFi1imlTSIC37/698//33WLFiBeLi4lg90OLLHA6Hw+FwOBwOh8PhcDgcDofTHNyx/xSxsbFhmzwyNELpnf8qvr6CldQ5TaHFNvz9W68ZyOFwOBwOh8PhcDgcDofD4fwrcLWjDgO/FBwOh8PhcDgcDofD4XA4HA6Hw+E8Q3DHPofD4XA4HA6Hw+FwOBwOh8PhcDjPEFyKh8PhcDgcDofD4XA4HA6Hw+FwOC3zH1wb9FmFR+xzOBwOh8PhcDgcDofD4XA4HA6H8wzBHfscDofD4XA4HA6Hw+FwOBwOh8PhPENwxz6Hw+FwOBwOh8PhcDgcDofD4XA4zxBcY5/D4XA4HA6Hw+FwOBwOh8PhcDgtwzX2Oww8Yp/D4XA4HA6Hw+FwOBwOh8PhcDicZwilJ0+ePPlfF4LD4bSFqA5dfXpOK9GRMV74Ejoy3bqpoyMTnYYOi4aGEjoyve2q0ZEJzFBDRyZo6QZ0ZKz7PoeOzMDF1ujIGKvXoaMSW9yxn43uphXoyJxJ0UZHxsWgY9tmC61adGT2/ZiJjszL75ujo3L4kQY6Mg4W6NBEJXZst4aRUceOqczP77jvXUJPv2PXX1VVx73/OnqfaM2AQnRkepuP/V8XoUPi3uc3PItE3VyI/xpciofD4XA4HA6Hw+FwOBwOh8PhcDgt07HH2v5fwS8Fh8PhcDgcDofD4XA4HA6Hw+FwOM8Q3LHP4XA4HA6Hw+FwOBwOh8PhcDgczjMEd+xzOBwOh8PhcDgcDofD4XA4HA6H8wzBNfY5HA6Hw+FwOBwOh8PhcDgcDofTIk+UOvaizP+f4BH7HA6Hw+FwOBwOh8PhcDgcDofD4TxDcMc+h8PhcDgcDofD4XA4HA6Hw+FwOM8Q3LHP4XA4HA6Hw+FwOBwOh8PhcDgczjMEd+xz2pVr165hxIgR/y9/6/+n387hcDgcDofD4XA4HA6Hw/l/iNIzuv0H4Yvn/j/m8uXL+O6773Dq1Kl2O2Zubi5u3ryJ/w9I/taO/NtPnbqODRt2IyUlE3Z2Vli6dCaGDevVbJ7c3AL8/PMOXL58D3V1TzBmTH+8/fbL0NbWZN9HRydi06aDuHMnFFVV1fD2dsGyZbPg7e0qV5kcbE3x/ecz0beHB8rKK7Hvn9v4bPUB1NTUyswzfmQA3l40Fq5OFsgvLMW23Vfw468nGr5/49UR+Gr5C03yPXnyBG49lyE3vwTy4muph8+GuqOTuS5yy6rxV1AKNt1Lkjvv/hkBKKioRq+NNxr2a6goY6a/Dab4WMHeUAuZJZU4Ep6BDbcTUPcEClEc9hCZRw+jMisT6sYmMBs9Dobde8pM/6S2BkXBQci9dhllsbGwfH4yTIeKD0TVlpUh58JZFAXdR3VhIdRNTWHcfxCM+w9EWzHTUsfy7i7oaWWI6to6nE3MwU9B8aisrZOZZ5i9CWZ62sDNSAfFVTW4kZqPdQ8SUFhV0+byzPGyxfPOltBXV0VEfgl+DI5DTGGZzPQ9LAwxw90a3sZ6qKipxf2sQmwITURORZVYOhcDbSz2cUBnU30UV9dgb3Q69kSnNVuWvMfReLznAErTMqBhqA/HkcNhN2RAu+XJjXiMwB/XQcfCHH2/+bRhf8jGTci8H9wkvbaFOfqt/FzmufXUVPGWnzP6WBqBbtvr6Xn45WEcSmU8u2rKShhrb4GxDhaw19NCbkUVziRn4++oFNQ+abzxe1kY4UVXG3gY6qC8pg6B2QX4/VHTOm5Pevi7Yv6s4Xh+TA/cDY7BmOnf4Gnj62yMFbO6wsvRCLmFlfj7XBT+PBHZbB51VWUsnuiN8f0cYaijgRthGfj6r0Bk5pcLvldTxoxhbpg8wBl25rrIyi/DPzcS8OuRR6gTqWN5yHsYiqTDR1CemQUNExPYjRsDs5492pTnSV0dkk+cRPbtO6gqKISanh5MAvzhMHEClNXUZB63Ir8AYX/vQ3Z4JJRVVWHdoys6vTgJKurqrc5TU1mFxItXkXLzLsqysqFpbAT7gX3hPGIwlJTF412SrtxE/LlLKMvKgb69DbxnTIGhs6PMc9O7JufMCRRcv4raslJo2jvCcuqL0LSxk5mnpqQEBbdvoOD6FVTl5sD5w8+gaW0jM33arr9QcOs6zMZOgNmosZCH2upqBO78B/E3A1FbVQ1Lb3f0nDMVOqZGbcqTHZ2A0CNnkR0Vx+rOzM0J/i+Og6GtVbNpagZNgaql7N8oyiArE7ziZgcrbU2klVXgz8dJuJGZJzO9o64WZrjawt/EgNmeqMJSbIpMRHRRKRQl7cJFpJ+/gKqiYujYWMNx2lTou7i0KU9NaSkSDh1BfmgoakrLoGFiDMuBA2A9dEhDmoqcHKScPIWCsEeorayEtrUVTLoFIOfefZQmp0BTXxcuwwfCbeywZsuSH5eIhzsOoCAxBRoy8siTJu78VcSdu4qynFyoaWvB3K8TfF6cCA09XannVAn+EVDVQq3v22LfDepijXemdYajpR5Sc0qx4XAYjt1MbPY36OuoYdnUzhjezZbZuXP3UrByZzBKyqvZ93SsBc95oa+vJbQ1VRGRUIBfDjxEYFQO5CX20k08OnoOZXkFMLCxRJcZE2Hp49GmPNUVFUi8fh/R56+hICkN/d+eD9tufmLHqCotw8N9x5EaGIrKklIYO9mjrt90KFvZSz3nPG87THKzhIG6Kh7llmB1YCyiC2S3W3pZGuIlTxt4m+ihorYW9zIKsS4kAdnlje9Ucy11LPV3QncLA2ipquB+ZiG+D4xFWmklFKGuuhrZ/xxE0f27eFJdDW13D1i8MANqRsYy81Tn5aLgxjUU3rqGmuJiuP+0ocn7IOG7r1CRkiy2z7B3P1jOeFmh8lGdfdTbBQPsjEENl4tJuVh5OxYl1bL7HP1tjTDHxxZeproor67D7bR8rAlMQFZZlUJp5MFKWwPLOrvA31Qf5dRGTsrCr+GJYm0kUbRVVTDZ2QrD7cxgqa2B9NJKHIpPxz/xGQ1pyHdF31M6J31t1na+np6LTY+SUNZMX0sa3qa6+LCnCzxNdJFXXoVdEWnYFpYqM726ihJe9LTGRDcL2Olpsvo4FpuFP0KSGvo78qSRF2qPLvZ1gJ2uFtLLKrH1UTJrZ8rCTlcTszxsWdue6jKqoBR/hCfiYW6xWDo9NRW85uOAgdYmUFdRxuXUXKwNiZfZ3pXFBGcLzPG2hYW2BuIKy7AmOB73Mgtlpu9krItXOtnC30wfSkpKCM0pYv2f+KJysbIt7uyIgTbGMNBQRUReCb4PjMPjfMXfdbM9bTHRSdAniswvwU8hLfSJzA0x3c0anahPVCvoE/0aJt5e/7qnBwbbmIrlC84pxBtXw9BaHj+IxZ6NR5GWkAlDE32MnDYIQyb2kZk+NzMfJ3ddQuidCJQWlcHGyRLjZ4+AT3cPhdJwOM8C3LH//5icnBzcunXrf12M/wwDBgzAuXPn0NG4ezcU7777A1aseA0jR/bB0aOXsXTpKuzcuQpdunhKzVNSUoYZM5bD1tYcO3ashJmZEctHTn5y8BM//rgdo0f3Z858NTVVrF27E6+8sgL//LMWNjbmzZZJTU0FR/56B5HRaegxcgUszQ2w+/clUFFRxvKvdkvNM2yAD/7asBhvrdiOwyfuwdXZElvXLhSUpd65v/7Ps/h123mxfMd3vscGJhRx6pvrqGPnNH8cDMvAwsOh8LPSw4bxviirrsXOB7IbsoSOugrWPueDO8kFbFBAlJHuZjDSUsPS4+FIK6qAv7UB1o/3gYqyEtbciJe7fOVJiUj6fQMsxk+CYa8+KHoYjJTtf0JVVxe6Xt5S8xQGBaIoJBjmY55DytZNkNZqzr9xDSra2rBftASqenooDgtF6o6tUFZXh2HP3mgt1LlYP9gbhVXVeOF4EPTUVfHzQC9oq6ngk5tRUvM4G2hjoK0Jfg6KR0xBGWv0f9nHHav6eWDRxXC0hZc8bFiDfvnNCMQWlWGRjwM2DPTBlFOBKJbSyTPWUMMsDxvsikrFo7wSQQexmxvWDfDGS+eCUVtflS762tg8xA/H4jPxbWAMqD82y9MGTvpaYo1xUcixGPTjOjiMHIquy15HfmQUHv6xDaraWrDq1b3NeaqKSxC2eTuMPVxRkZsv9p3fwleZI1K0Y35l2Ycw79q52fr7qocndFRVMP9yCJSVlNjnz7p74P1bj6SmH2BlAhcDHfwYEovkknJ4GOrii+4erB7XhgrueyMNNeb43xqZhOjCUuZseK+LK77r5YVXL4fgaWBsqIvvP3sZm3degKqKMqwtZTsg2gszQ0389dEQHLoaj8VrrsPP2Rhr3+yHssoa7D4fIzPfurf6wclSD+9suIXIpHz06mSBSQOc8Os/gjof2d0ORroaeHvDTaTllKKLmyl+WdIXKsrKWHswVO7ylSQmIXLDr3CYNBHmfXojNzgEUX9uhZquLgy9O7U6T+qZs0g7ew6eixdBz8kRZalpiNj4K1BXB6dpTQdjhYMBd37aCHUdbQz86mNUl5Xh/trfUVNRCf8Fr7Q6T8qN28z53+XVWdA2N0VedCyCNvyJ2qoquI8f3XCsqKOnEHviLDrPmwVz304oychC0qXrzTr2c8+fYZvtvEXQsLJG9rEjSFz7E1w/+xoq2jpS82Sf+AdKamowHz8JKX/+1szVAQoD76EiOREqOro0igB5ubv1AFIfRmDY8kXMGXtr026cX7kRz61eDmUVlVbneXjoNDxG9EffRTNRXVaB+zuP4MyXazFlw5dQqXfQSUuT99v3MFvxPZRUZQ/qEJ2N9fGJvzt+CYvHlYwcDLcxw5cBHlhyMxSPCqS/01/zcsTFtBzmzK+pe4K5Hvb4ubcPXr36AJnl8jsrM6/fQOLBQ3CfPw96zs5IPX0G4T/9gq5ffQ4NY+NW54n562+UpaWj05tLoGFqgoKwcERt+pM9L8LBMHpe9N3cYD9+PJRUlJFy6jTid++Fac8e8Fy0EKppcbi7/k+oaKjDeZj0Ad3y/EJcX7kW9v17oudb85Efm9gkjzxpUu8GI2T7PnRbPBuWXXxQlpOHexu2IPCPHejzziKxc1aXV+Dehq14oucApbJGByPh7WiEX9/pjx/3hDD7N6ybLX5Y3Bt5xZW4ESqeVnRAc8fHQ1FeWYM5qy4hNbsUo3raYXRPO+y/HMfSvD3ND1dD0rHhcDizowvHd8L2j4ZgwkenEZtW1OJ1Tr77APc270GvRbNg6eeJqDNXceW7XzFq1XLmsG9tHtpXkpmNgFem4PwXa8TetUJurN3KrsHA9xdC29iIDQJk7fgBmou+hJKeoVjaV7xsmKPv3WsRiC0oxeudHfH7UF9MOBbIAh8kMdZUY+l3RFC7pRj66mr4tKcrNg7xwYsng1i7RVVZiX0mR//ssyFsQH2RnwN+G+qLKccDUaWAdzVr/26URITDbvGbUNHRQ8buv5C8YQ2cPvwMSjJsTOaBPdC0tYfx8NHIOrBHapontXUwG/88jIeIBKIoKR5u+fMQL9Y+f/FoMGu30OfvB3li0Tnp7UkTTTVMcrfExgdJiMgtgaWOBj7v64YNw7wx9Wiw3GnkQZXK09cHCcVlmHk+CCaa6ljVy4v1DX55KL1vQE5Yckh/fu8xMssq0d3cEJ91d4eKkhIOxaWzNJ2M9NDZRB9rHsYhsbgcDnpa+LSbO0w01PHpvccKBeZsGeWHIzGZWHrhEXzN9PDjYC92v+yNFJxLkmEOpjDUUMX7lyPZIFGX+jxUvg3BiXKnkQcKBvm+rxc2hibiREImBtqY4LMe7sivrMbdrAKpeajdfyujAFsjktkgxyuetlg/wAcvn3+AhGJBe50GhjcM9GXBPEuvhSO9tAJDbE0x1NYURxMy5S7fYFsTrOjhik9vR+F2egGmuVth3SBvvHgqGAky+gZv+Tthf3Q6vr8fy+7Xt7s6Y9MwP0w+HtgQ3LSynye7Nm9eCUdmWRWmulli01BfTDoeiJwKwcCnPLzkboOX3G3x4e0IxBWVYaG3A9b198ELZ2T3iV6q7xNRYBQNBnwY4IZf+nvj5fONfSK6jscSMvB9cGxDXgXjTMTISs3Bj+9twsgXBmDZd/MQGRyLP77eCW1dLfQa5i81z5GtZ+DoYYcRU/tDR18bF4/cxM/vbcKHG96Aq7ej3Gk4nGcBLsXzH4eiyD///HOMGjUK06dPx6VLl9j+4OBgfPLJJygpKUG/fv3Y9ttvzXcoz549iwkTJrCo9Llz52L48OH47LPPUF4u/aVEPHr0qOH4w4YNw6JFixAZ2RiZWFxcjIEDB+L+/fti+UJCQtC/f382+CD8HVRekrqZOnUqtm7dKtZIFpbtzp07mDFjBjtmRkYGqqursXbtWkycOJFt69atQ01Ny9G+LZVbGuHh4aw+iKKiIuboDwoKEksTGhrKfldWVpZcv6s92LLlCHr16ozp00fD2NgAs2dPQOfOHti69YjMPPRdQUERfvllOZycbKCrq40ZM8Y0OPWJ3377FBMmDIaVlRlMTY3w6aeLWOT+tWuBLZZp3IiucLI3x5sfb0dqeh4CQ+Kx8pejeHXmYOjqCGYESPLCxN64cfcxtu25isLicpZnze+n8OaCUVBVbew01NbWNWw0K6BPd3eWRxFe7GyDqton+PJCFHLKqnAxNhe7QlKxsKdDi3lXjvTE6ags3E4Sd6ISRyMy8eO1OETnlKK0qhbXE/JwNT4X3WwNFCpf7sXz0LRzgOnwkcwBb9x3APQ6+SD73BmZeSia337eQuh6eMlMQ8ejKH4NcwuoaGmzPJo2tiiLk+1wlIdeVobwMtHFV3diWOOdokl+CU7AOCdz1jGSBkW0kNP/YU4xa3RTnn9iM9HFXL9NZVGqb8Tujk7D3axC5FZUY3VQLJtN8ZyThdQ8eZXVWHI1nHUCqEGdVFKBNSFxcDXUgbN+o7NuaWcnFvXz04N4dlzK90uIeISNJEkXLkPd0ABukydAQ18flj26Med8/Kmzbc5DtiR00zbYDR4AfYem9y5Fz5KDTrhlBYWgpqICNgP6yjy3u4EO60BSRzGltAJJJeVYGxqHvpbGcNTTkprnAjXGQ2IRWVDCopyCcgqxPzYNQ23NGtJQB+yTu5EIyS1i15s6VkfiM+BppAd15aczZzKvoAQDJ36KHfuvoPwpzgoQZdoQV1TV1OLrHYHILazApeA07LkYgwXjpDvNicH+1hgWYIs3195AcHQOyitrWT6hU5+gqNef9z9EdEohSitqmKPs2sN0BHiIR0u1RNr5C9BxsIfNyBEsqt5yQD8Y+XgzR2Nb8pQkJDJHpaGXJ1Q0NaHn4gwjX1+2XxYUcV+UmAy/OTOgbWYCAwc7eE6diJQbd1BRUNjqPI5DBrDIewNHOxZ9bNHZB3YD+yDtdmM7hBz/UYdPsEh/6+5doaqpCUNHe3ZcWdCgQu6FszAePAy6np2gZmAIy2kz8aS6CgW3GmduSWI1bSYsJ70AdbPmB8SrcrKReXAPbGbPl+kokwZFAkdfvgX/aeNg4mwPXTNj9J73IgpS0pESHN6mPEM/WAhbf29o6OpA19wE3s8NRUVhMYoycppNU1dchJocQTuoOaY52yAwpxBHkzKY7T0Qn45H+cV4wVl2tP+H9yJwLjUb2RVVyK+qxpqwWGZDepiJO0pbIvXMOZj37QsT/y5QN9CH4wtToKqlhYzLV9qUpyQxEabdAqBja8PuK/pby8oKxfEJDWlcZs6Aee9ezM7TM0XPDGHg4c6Oa9XVF06D+yHquOxgkoRLN9iMFb+XpkBTRh550lAEvo6FGex6d4OaliYM7KzZ37RfkuA/d8G6exc80W3qDJkz2hPh8fnYfCKSOfP3XYrFlQfpmD9OdptkxnA3OFnpYfFP15htI8c9DQoInfrEm7/cwIHLcWwGQH5xJYvmL6uowdAA+WaERBw7D/veXeHYrzs09fXgN3UsdMyMEXX6cpvyeE8YgZ4LZsLISfpsncriEqSHPILfC2NhaG8DdV1teE8cCSUdfdTcFz83vQFnedliZ2Qq7mQUMKfdynsxrN1CkcDSyKuoxqKLYbiZno+CyhokFZfjx6B4uBnqsIF2wtNIl7VjfgiMY20zeg9/dz+GDayPdGh8P7dEbWkJs3Fmz01ks5TUTExgMX0WqtLTUBIue2DZdsHrMB3zHFQNWng2lZSZzWvYJGZWtUQnE130tjHCt7dikVhUgfjCcqy6HYdB9iZwMdSWmofacMsuRuB+RiFKq2sRW1CGPRFp8DHTY5Hm8qaRhwHWJrDR1cR3wTHIKq9iztItkcmY6GTFnPfS2BWdymY0kiOW2lWX03JxPiUHw2wb3/nh+cX4/kEsOx61q+j/s8nZ8DVRrB091cMSVXV1WHU7lv3my8l52P84Ha/62srMczIuG2uDEllgDgVF3UwrwPXUfHS10FcojTxMd7NhUeY7o1JRUFWDf+IzcSsjnzmfZfHR7cc4lpDJovvp3bL2YQKrowHWjYO2k12s2CzT928KHN40k+JEYpZCTn1ididbnE3KwamEbPaM/RaaxJ63GR7WMvMsuBCKc0k57Fmne+Lru9Ew1VJHNwtBf5ECY/pZG+PXh4L6o8G9LeEp7Pq84C77uJLQXTrD3QZ7YtJwr75PRI54si3jHGX3id68Fo7bmYI+UXJJBdZSn8hAvE9EkEujVmSTPUe7ZS4cugFDEz1Mnj8G+kZ66DGkC3oN64pTuy7KzPPq8hcx9Pm+sLA1g66+Dsa/PBzmtqa4f/mhQmk4zUB9tGdx+w/CHfv/YbKzs9G9e3fmzCdHPDm2v/nmG+aAdnZ2xsyZM6GlpYVVq1axbfToxkg1aZAz+uTJk8xxPnjwYMyfPx+7d+/GSy+9JDOPnZ1dw/E/+OAD6OjoICAgAAkJgg6Mnp4edHV18fvvv4vl27RpE/vf1NQUeXl56NmzJ5KSkrB06VJMnjyZSQi9/fbbTcpGv3PcuHHsdxoZkbP5U2zYsIENalB54+Pj2UBHS7RUbmmISvHo6+tDWVkZ27ZtE0vz119/oaKiAubm5nL9rvYgODgCPXv6iu3r3bszgoNlD1ScO3cLQ4b0YA59eaEo/9raWujoSHfuidIrwI1F62fnNEZTXbn5CJoaaujiI915ThELFHkvSl1dHYu69XST3oiZNbU/CgrLcPS0+MBRS5Cj/W5yPpMZEXIjMY/J55jpyJaAeMHXCs7G2sx53xL0TulipY/e9kY4/Vj2dFFplMbFQMddfIqgjqcXyuMboyLaSl1NDYpDH6IyIx16ftIjIeSFppJmlFYiubiiYR91Tikayc9Mvga8ra4mRjua4UJSbpvKQscx0VJHoEgUD0Wl0QCCnwKdHQN1wYBEWf1AoZaqMrpbGOJ0omLXsiA6Fsae7mL7TDp5ojgpBbWVVW3Kk3jmApNxcBoj39ofqddusuOSZI8sqI7Ka2pZh1HIg5xCFhnra6xA/WmoNTsVnBwLo+3NcTMjT6GowY5OgLsZ7kZki0Ut3QzLgL2FLkwNpA9qjuhuh8dJBYhMkh55Js1WdnYxQW9vC5y5Ky5h0BLFMTEw8BC3LeSML4qNa1Mek4CuKI6NY9uT2lomJ1IQ/gim3bvJPG5+dCyTyRG9H029PVlPMT82vt3yENUlpczJKiTzQRhz1Fv3kl0+aY732uIi6Lg3zoQjWQktZ1eUxbXNNpOUWurWTTAbPR4aFtKjiGVBUjgU9UpSOkLIwa5nYYrsqPh2y1NRVIzHZ6/B0M4KBlbmzaYhGR5VM+lOA1F8jPUQnCs+iBOYWwhvIz3Ii7aqKoscJLslLySXU56eDgPPxvua5BDIsV4UE9emPOTIzw1+gPKsbPYskIxVRXY2TLpKf8+SDc++cw9QVoZhp0YnuJm3B8qyc2UOcuVGxcLU01XMCSqZR540VgF+KM8rQHrQQ9TV1KI0K4dF8dtJPBsJl2+iJD0TnaY8J7U8XT1McfuRuEPsZngG/N1kDz6O6G7LBilpIEBeNNVVoKGuwgY4W6K2pga5cYmw6CT+PrXw8WDSUe2VRxoNQTxKEl1xZWXUJUWL7aIZi+TUE5XuoPfig+widJazDUUYqgsm6gtlRIS+DVG5tif1m78CQRTlCfFAXS20RWyfuokp1EzNUN7GwBAi9+xJPF62GLFffIysQ/tRV9HYlpQHfwt95jgOyW5st9zLKGDtFnl/J0Xjk2TMleRcFvjT2jSy2lUJRWXM6SvkflYBc67SDEd5ocjp5tpVDrpaTBrlSlquwvVHgxeiv+h2WgHs9LVgqtX8rCvhfeZnpsekOM8l5LQ6jSz8TPUQmCVuB+9lFcDXRP73BNU1baISO4NsTHA3s4ANFrQWmhVDA0v3MsXbb3czCphkpyJtZoLuY2E7j5BsHtOzTH0uhfpEmlL6RLkK9onqy1cqETxJUlCXJvbG4dHd8HGAK4v2by3RofHw9BeX++0U4IakmDRUyhmcU1dbh/KSCmhpa7QpDYfTEeFSPP9hyElMkDyMer227AsvvIDKykpoamrC09MTKioqLCpdXijafcuWLRgyRKAFSsfo0qULmwHg79+0U0KOe9HjU5Q/Rb5v3rwZX3/9Nds3b948vPLKK/jll1+gra3Nyrdr1y78+OOP7PvVq1fD0dER27dvbziOu7s7G7Qgxz058IVlo3ydO3cWW9CWjj9t2jT2eezYsSgrk60Zp0i5W4IGTlasWIGffvoJqqqqzAlNAyHvvfeeQr+rLVRX16CgoBgmJuLRMBS5n5PTNKJcSHJyBgYO7IY33vgWN248gJGRHoYP740335wp03H/3Xd/wshIH4MGSZcPEYWkd7JzxadI5+QJGtwWZtKj14+eCcRf6xdj6vhe+Of0fbg4WmDJvFGC45kZICxC3IGlrKyEGZP7Yvfhm6hUsFFGUjyJ9drVQvLKBA1ucuxnlzZtQLgYa2P5IFdM3RnIOgvNEbykPwy11FjDbPO9JPzdgryPJDWFhSxSXxRVXT3UVVaitqKiIbqvNZDec+Tyt5lEBpRVYDV5KvS8fdAWTLXVkSfR6KIIMqonUxkR+0JIwqevtREbBLiVno+v77Stk2iqqd4QzSYKdaisdeRrxFH05xt+jqyhnlqvQ2utrcka8NQ52TLUD8762mxqLE2JJp19WVQWFDKnvNjxSbf4yRNUFRVBy8y0VXkKExIRf/IMen32oVzRbSTvkxcZBb/X5jabjjoAJKkkCvVfaT0B4/q6bQmaDj7B0ZLJ7kiyIsANI+zMmSMuLK8I796ULu/zrGJupInETHEN17yiygaZnpzCpg4LcvrHpxdhxctdMbGfE4v4vxuRhe92PkB6nvj77N4fk5gGP9k/0u3f1Yy8jzSqCgX696KQrWnOtsiTx6xHd1Tm5OLhqtUNc7Gthw+D1ZDBMstSUVDENL9FUdfVYfdzZWFRu+Uhh3/q7Xvwmz1D7HnQNjVG6o27iD5+hjlXhdH/Ri6OMu2y8LeL1YWuHtPObwtZRw9DRU8PRq1Y76S8QPC7SZddFPos/K4teUIOnELIwVNsIETfyhxDly+CskSUqWQawznLoKTSfBeEbAANoBaIOLsI+mysIZ+tIV7v5MiiCm9lyW7zSLunCWn3NUXctyWPw6TnUZmXj6CPVrDPSqqqcJ31Egzc3cTy5Yc/wqNf1gnexUpKMPLzhaZp4/tAeJ/TPa9p2LTdRI55XQvxqGvJPPKkMXFzhv+r03Fn7Z9Mro2w7u4PnxnPN+QpTstA2J4jGPjp202uvRBzQy02S0nS9ulqqUFbQ5VF40tib66Hs4nJ+On1PhjS1QZFpVW49CAVP+wJQXF9m0wS0vCvrX2C03IMalYWlbABLA0DvSb3Of3+9sojDYr0N/NwRtihU0y+R8vYALEXbuBJdjpgIh7XSk59QrIdRe0WGxmzXKW1W970d2IOxdQSwXWgmZD0N2nsf3UnmkmrLPSzZw5O4TnlocH26YrXCdmsmiL560Qa2q5u0OvaDRrWtqhISULGrr9QmZ4Ku9ffkvsYJFciaUeo3VJUWc3ap82xcoAHnnMxZ23QB1lFWHgmrFVpmoNmroo69QlheWXNapWkm5kBmzm54k7ToK1fB/jCx0Sf2dSrablYVy+BKC9m2upIyhC/jsI2NN0nOfXrXUjj5szeTP+d+jvbwlKwR4p0jzxpWmrXS9YffdZRU4WWijKLtG8JWhuL7olLKY2DHvRsRRfk4sse7iw6nq31lZ6PjWEJza7NIArJTKopK0vtc1CQkby829UZSUXlTMtemD84qxALfOzZDGeS05rkagknA20oK8meJSytTS88nuT9p0ifaLGPIxuMEl2bg2Y50KyI0NwiVpfv+bswuaNXLjxAdSsCdgpyi9Cpm/iAqp6hLhskLcovgZlVy3Kax3deQFlJOXoN79qmNBxOR4RH7P+HuXLlCsaPH9/g1BdGD5FTv7Woqalh0KBBDZ/9/PxgaWmJe/fuycxz/PhxvPzyyxg6dChzlpM8TWxsY/Tac889xxz6+/fvZ58PHz7MJHRoEIKgGQdRUVHsvCSxQxI3S5YsYY7yx48bNQIpql7UqU9Q+jVr1rBBg4iICLaPziUPLZW7JUhap7CwsEF3nxYrJnmgF198UaHfJQoNevwfe2cBHtXxtfE37u7unhAguLsXpxQqtEVLadEKFdpSoQ4tUKVIW9wp0OLuBBKSEHd3d/ueM5vNCrvJbhL6D/3m93Afsndn7s6de+/cmTNn3lNSUiKxVcvx6iXkyfrQfdASVIatWw8zr/3Ll7dhw4a3mBf/2rWy5Zp++mkfTp68wtIZGMjWEW4NoTe+vLId+ycYy9//A6uXTkRm2I/Y88vrTFOfkHWWo4d2ga21CXbsVU6GR275mupSVumos7xpkj++vZKAeClDmyyCNl2B/4ZLmLM/BJN9rbFqoGv7C9gGzVFZkE6/33c/wufr72D/wkvIOnIQhTeu4nHR2r342oUI9N59Dc+cuM+8kb4bKl+ypD00oLHVshC0uvqTPl6sLB/cFsUHEGad7+uITQ+SMf6vO/ghLAmvdXHBNDflPGyFB2tsYx7y2n/w4xZ4z34aOmaK6canX74GDT1dWAV1RVugtkZFwQHYV339cDe3iC0ll+bT4FgMP3Ydz5+7xzzeNvT3Y3X+X6a5bZFz/9Fgd2RPe9TWNWDEir8w66OzsDDWwa9vDm722hLSe+FhdJ27Hy9/fgGTBzhjxdOSwRrbgkob7kfpPBlnzzO9cd9lr6P3xg0IeOsN5N0NRvKRo20rVFsk62TkId38Oxt+gn2/3iyArihpI9MSzw4NR//3VmLoFx9Az9oSN7/8DpUFihuIRW6xbV91UhYVieI7N2H77ItoDyrSTyh5CrdSj4rk6TJ1NJ77Yz2mfPcBTJ3tcWrtd6guq2gxTcEPX6KhouUAf/Iee2Vq8jl3ewy1NceH96KVDnYos0wqKkrfetJ5YrftQEV6OgLXvMueBc95LyN+507kB0tKN5J3fr8fN6HnN18y/f3CsHAJuZ42vfMVySOVJiP4Ae79uhNBC57DU79+g+Hr3kF5Ti7u/ihwSmmor8ftjb/Bd/oEGNgq964T9lHlFYvmpJ8b5cEkyAa+dgQLv7mE3j5W+OoV2TF/nh3pwdIv33TtkUkEpZD2on9MeQYsmwcDKwucfv8rHJj7BnIi46AW2Ff+zS8FVZ8il5Teoev6ezP5jveui8YX1fUNWHIhnGm8H3mqB85O7Q1tNVWmA94mVVDp5oIVrn0r7igAr667J4v/RKuhKGhu+cNwVKUrtxpNFmTuba363rkcjW47rmLiobuoqW/AljEBj/RJFEmjLMJaU+QwJIHySW9v7IvLYJI80rx6OQzDj97AgouhzEN7bS+vDuy3tJxuwK4b6PnHdSw4FcYmP5YGObcpjbKI2pbWa5D66NPdbfD+rWgmMyP+6qb9YfmlmHjiDlZdi0R3SyMWy6Aj6k/RW2RldxcmwUPxNcQN4m9ciWRxq/4Y3RWXZ/RlZfsrIbudT5yofIrcfXSfr+0tGBN9KDYmIihI8/WsQqbTH1VUjtU3o5gMGE1AdRQqwmVHCjRYN8/ex9Gtp/DSW08z2Z22puFwOivcsP8fpry8nEnCdCRkFCeJGWnvdjIwy4I040mqh7z6yVOdpG3II11cl5+82V988UW2EoCg/8nDngz1Qh1+Mn6TpzxJ7Hz22WdsNQJ54/v4iJYlk6SPNJSHPOYpSDDJB7m5uSkU4FaRcreGsbExxo0bh507d7LP9D95/ltZWSl1XuKsW7cORkZGEtu6dQIZo6tX78HXd1LztnfvP9DU1IChoR4KCiSXKNJnU1P5mpYWFqbo1SsAU6eOYIb6Ll08MXfuVJw6dY0Z/cX57bdD+OGHvfjxx/fRo4fswK3S5OSVwFxqiaRF02f6Th7bdl9E9+HvwMxrPgKHvIW0pg5sUsqj8icvPD0It4LjEBmjnDc8Qbr6prqSXjJCr57cikcnUgy11eFraYCPRnoi/o2hbHtjkBss9bXY35N8JCUHqF9GGvsXE/Lxy+1kzAmSrcEqD3VDQ+ZZL05daSlUtbTY1l5Iw5QCPRr36sOC5uZflK9fqAj5lbUwkfI6ogEmebjnV7a8fLKxaVkoBa396m4C+tqYsGC0bUXYaScvGnFoeai0N5w0qk0dWB8TfbxyMYx5yAjJa8pLuvD3couZEelSRgHOpeaxpajyIL1kCnArTk1JKRstaRoatClPVVERKrJzEfbLNpx+eTHbSH+/PCub/Z0bKql5S1606dduwrZ/HyYd0lr9CWWImuuFLQHXQEELk4xCo/7GgQFILq1gXmWyuuG0jwYu5OnzdWgc09j3V0Lip7NDHvmmhpLPqFmTBI8sb30it6gSJeW1+GJXCIrKapCcXYYvd4XAx8kE7naGjwzGSILiUmgmfj0eiRdGKzcA1WBti+SKgtqmtkVNTtuiSJ6Ms2dhNXgQTPx8mea4oYc77EaNQMbps3InoLUMDZh3rMRxy8rZ/UqxJdqbpzw7BzfWrYe5rxcC50pKCmqRB3RjI/xmT4OuuRnL6//c02iorUPOgwi57TJRV/po26xu0PZ7uCI+hnm9xryzEg9fW8C2uqJCFnQ3+k1Jr9X0339j3/8+63W21VZVQ6fJs5hkcMShz6SrLgtl8rBYHepqMLS2QP/FzzGP/pQ7oS2mIY39qnBJQ7Y0dY2NzEPSWKqtNiYvfqlVQ7KY6WqLFzzs8c6dSDwoUM5rWMPQqPnaSd/X8tplRfLUlpYh98ZNOEx8CvqOjuxZIGkes27dWKwKccggRe9iTSMj6Nrbs5Uv2VdEk+zVTddG2nNcCF0n0nEXRzqPImkSTl+ETTd/OPTryWJSGDnaM7mdtBt3mURPbUUlilPSWYDdw88vYZtqxnmo1JVD7d5aqBSEibV9ko5FZobaKK+qlSubk1tYxXT5/zgdwzz0I5OL8MORCAzrbsskd8SZOcwN7z7fHUu/u8baP2moHFQe2nbPfo0FwKXVCWw1j9R9Tp+15dRrW/LIQ8fECP1ffwnTfv0Sz/zxHQaumI/G0mKoGFs80ocipPtR9Fn4XUv9lk/7eTM5kHlnHzC9bnEoBtCSixEYuP8G+u27js/uxDPpH6FXvyI8rrZPFhT3iahpilWmCPlVNY+0I1QvtC9PgT4o9UliCyvw0bVYpp8vHetJkTSt9aukyyfso4obmmVBq0O/G+DP9PW/l+OJT6M20sgPLyjFj+HJTI5H0ZUA8vrwQm/zlrz12W83CuRjrqQVYmtYGp71tW1TGuXrT5PJErUkTURMcrHC8kBXvHsjmunyi0P69tGFZdgfn8k89GOLy1mwXfLep1UtikCe8LQ6+ZExh7Ym07Nvjde7OmOymzVeOR/GYnhJl2/1tWgMPXgTvfdcY4Z+Cx0tpZ5d4UqCR+pPu/U+PdXAR70EY6LFl8JYXJuWoLaHVvxS+9JiuvQ8vDxkVfO278e/2H4jUwOUFkm2MeSpT+9KQ5OWJavuXAjFls924cU3nkbfkUFtTsORAU2ePYnbfxAuxfMfxt3dnQVrlYe0gV4RyAM9Ozu72ThNsjapqalwcXGRmZ688BcuXCihG08yM9ITDiSXQ9I058+fx7lz55hxWwgZ4+k3lZEMEkKNPU0S0EYG6ZUrV7LfSpazlFrZcrcGTQ6Q1z/p6R88eJDp/bfnvFavXv2IBr+WlkDOon//bnjw4FDzfrWmTke3bj64cycc8+dPa/7u5s1QdO8uKeUhTvfuPsjPb13Pmbz6v/tuJ3744V3066e4py8Z3Oc9OxRmJvrILxS8pAf380F1dS1CwuXHMZBm+lO9meE+XiqQkYW5IfPYf/0dkcyRMgSnF2N2oB3zVRCanPo5mSC1uBI5ZY92XAora+H6paTxe1FvR7wY5IA+P1xDfQueBORxq+zrRdfVDRWxkqs6ymOioOPsqpB3ilJITeS0hdDcEizs4gg7fepwCpZp9rI2ZkZI0rZXFKF3cnvOkQLIkQGfPFvuN00iaZDWv5khfpOSc5Jl1O9iZoBFF8MklpsKpYWSSysf0bsU6NXKv/7G7q7IC5OUmymIjIYBBVaUY0htLY+6lSVG/iZqa4jYg0eRey8U/T5d84g0T96DCFQXFsG+haC5QmhJrY66GryN9VkwXKKruRGbpCGvJnmYaWlg4wB/pJdV4p1bkcxo1xqqSj8ZnZ97MXl4Zpg761MKq4C08NNyy5AjJf8lJDg6F339JScHhbXXUi2SHI/Crp9NGLi5oThaUt+5OCoaBq4ucp87xfKoPNqPJg/XFp5lEw9XxB77GxW5edBtkqTKi4wWyJK4u7QrT3l2Lq6v2wAzL3d0W/jiI8+EqYebqIyiAgv+ySkzBb9V0zdARVw09DwEEyokXVKZEA/zsRPQVizGPgWLMZL5Y9e8DZP+g2A+ZrzEftvnXmKe/T3MBQN7Copt7u7Mzi8rMg6u/QW66OV5hSjNzoOll+x6bEseopEawFYebZaGEingVhheWIJAU0PsjhdN0Hc3N2L7W+JpV1vM9XLEO3eiWPBdZdHQ14O2lRWKY2JZfAhW7sZGFEfHwKJ3r7bnEb7DpDPT/deCx7ehuytKoqPR2Ch6H+dGxDC5KB0T2U4aph6uSLxwlU1qCe9v6TyKpBEvt7SXJP2vZaCPyb9vlPj+4NuHoZpzC/UB1F9VaW77evtIxl7o42eFkFj5et/BMbkIdDeT2CdrIvDpoW744MUeWLbxGk7fTZN5rEYTf9SbCJxPZq2yYOdLz7KpiwPzlHcb2q85bXZENCy8JbWchaipqyudR1Eqi4rRkBwNjTGzZPZbgiyNcC9H1G8hje5fwx+VtJM06nsh0MIA884+2m+RhZ+pPhwMdCQkSVpD29mF3cOVcdHQ6NGb7astLEBtXi6LMdKRVGdksP/VjWTLdsrifnYJdDXU4G+uj/A8Qb+lh7Wg30LSOYpCUjut9UEVSSNNeH4JprjYMIcXkg0jgiyMmPc/BYVtyahP/aqL6Xn4OkSxFeXClQTKjDyo/p72tpYYE5EWfnpplUxnp/aMd9oyJnqQV4LuUjKuPSyNEN5Cn1Ro1Cd5mPduRctc6UDH9TOVmqxT0h2ejPqRBaXM4/5ogmis2svKCPdyW773Xgt0wgwPGyw+H47wfPn3gRCarAmyMsKXdxVXFyCPfzYmsjBCiNSYaGtLY6Imoz7FMXjlUhgLQtwaFtqazDGotckgSztz/HpWICct/r5x93dGmJTUVOS9WNi72UBLR75T252Lofj54z/x/IppGDi+V5vTcDidHe6x/x+GDNPHjh3DX38JZjqJU6dOIS5OoLlLAVzJa5w2ZSADt7BjTd7mZOyWF3iXvqNgvUIvb5LZIQkaWZMQJEdDgXk9PDzQr5+os7x48WKcPn1aIhAtlZk83Fvj66+/Rl5eXvNEBunWU1yB1lC03K1Bmv4kX0SBeykGAAUwbs95aWlpsbKJb1pNWrPUiVRXV2vehJ3Kl16ajGvX7uPw4XMswO2ePX/j3r1IvPDCxObj/vrrQXTrNqP5M+W5cycCJ05cRk1NLaKiErF9+1GMHTugeUKIPn/33Z/48cf3MGCAcjp0f50KRmpGPr79+HmYmxrAz9seby6ZiN/3XUFJqcC4ZWNljMLYLZg4WjBrbmqshy/WzGb7dXU0sWTuKEyb0AtvfCRYESHOs9P6o6KyBodO3EZb2BWSAR0NNaaZb6Cphv5OJpgVaMf08IX0czRh3vge5oKVJWS8F9+EBl5xo/7qIe4Y7mYOQy11aKurYqirGRb0csTBcOX0JM2GDkdFUhLzpCcN66LbN1EaHgbz4SOb0xRcvYTwJQuaNXEVIe2P7SiLiWLHrK+sROGtG+zYxr1Fz2NbuJ5ZiJjCcrzby511PJ0NdfBaVyf8k5Tb7PVOwWfvPTsAk90EBszpHtZsoyCqLPiUqT7eCHJFWF4p05NsK3Q1dsdm4BkPWzYoNtBQw/KuLszQ/FeiqNP9RT9v/DBYEFuAnqQPe3kisMmoL9TVl+b3qDRMdbVmAR+pzH2tjTHc3gynU+TrazsOH4KqvHzEHzuJuspK5Nx/gMwbt+E0ZkRzmuy795mnfVVhocJ5yKAnvgmHSuxvqQFn2pVrMPZwg75d615SZMynYLlLu7iya2Otq4VX/V1wO7sQiaWi63Lmqb6Y7WHH/iZPpe8HBiCtvAqrb0kuJRYywt4cM91sYaWjxaSt3Ax18UZXN6SUViCiKf7Gf4E95+Kgo6WON2d1ZdrS/fysmKF/6wnRYIUM/dF/PgMPe8FA9eDlRNTVNWL5jC7Q0VKDlYkOVs7sgodJhYhPFwzE3prdlWlQG+ppME/WIV1tMW+CNw4rEMhbHNvhw1CWlIjM8xdQV1WFnJu3UBgWxvTwhWRduoxrC14R6W0rkMesW1dkXb7KjJ4UmLssKRkZZ87CrGugXAOIZYAvDBzs8GDHbqYFXpaZhagDx2DXOwjaTUbHuupqHH/xVaRcuq5wHjL6X1+3XmDUX/SSzBgUpKNv5uOJyL2HmDZ/bWUlHu45CDUNDVgEyF6ZRscxHToCBRfOoiIuFvUV5cg+tA8qaqow7iNqQ1N//QFJ332t8DVhBkh6bsU2wRcqj5RdmFb43Au1v90G9ULI3uMoSstkHvU3t+5lWvf23UTxU469uQ43ftmtcJ7syDjc230MJVm5TI6F/r/2w5/Q1NWGQ1DLaVS0daDtJymdKIt9CRnoaWGM0fYW0FFTw1OOVvA3McSBBIFxj5jlZoe/x/Rp/jzdxabJqB+Ju3mKBZyWBa0oyb56DYXhEairqETK4aMsQK71kEHNaeJ+/xMhH32scB4y/ht6eiD1+ElUZGaxZ6Eg9AHy7wbDrJugPiieQ/QvW1CWksqeMYqbUldRxYzvFLeCjpsTHoWkC1fhPnZ482/nREQzb/mSNEHduAwbgPqqGqZ9XysnjyJpbHt2RWbwA6TfCUF9bS1KM7Px8OBx9owItf2l3zfNUxdik3fb/4lGFzczPD/KE3ra6pjY35m1U1tPitq+Z4a5sbZPU11wX/9+Kgae9sbMcK+poQpXW0MsmuSLc8HpqKoReOJOG+yKD19qMurfkW3UF5SFZuUEEyji70HvCcORfD2YefDXVlbh4dHTKM3Kg+doUTyL+zuP4OiS95s/K5JHEWJPX0ba3Qeor6lFSUY2rq7fAhVrR6h1lZxkpzfmzqgMPOtth64WhjDQVMeqIFfWvzwmZiz8eqAPfh4eIDhdAB/39WLpyagvz4t3vr8DulsaMjkeMup/2t8Lp5JzJQL1tgZp6xv16ovcv46gOjMDdSXFyN67E5qWVtD3F5SHSPzsI6aRrygVCXHIObwfNTnZgknSxHhk7f4d2k4uzJFFUciYT8FfV/dxYwFubfW18GYvV1xLL2Re9kLuvtAfcwMEKwLGuVpgjp8dbPS0WN14muhhTT8PJBZVILRpMkCRNIpAwWyzK6uxqqsbC3BM/Z8XvR1wPDm7WUKMVjxentwfg23NmmMVfT/AHxcy8vGVHKP+M+62GOtoyVakkrGW+qaL/JxZf024ylQR9kVnMoeOlT1doK+hhj42xnjay5rp4UPM0B/20kC4Gwvkblf1dMEQB1Mm0ULyToPsTfBygD2OxIruV0XSKMKe2Ax2785ws4GuuhpGO1qgv40Jdomt2J7sYoXr0/qzeiAmOFuKjPrpsiexSNrIzUiXTQCQjjzV+Qve9riSUcBkrBTlj8h0jHaywFB7M1a+F33t2eTZnmjRe2xpV2ecnCSKUbe4ixOe9rTFK+fD5TrNkNF/sJ1pc9no+acVBkfjFa8/alv2xGWweyXQTDAmWtbFBfUNjTgu5jT3eR9vpo9PUA2u6enJghaTUV/WhCHdr+/38GD3MtW5i6EuPu7thayKKoWCN6upqzVvQrvD8Cn9kZddiGM7zqCyvAr3r0Xgxpl7GDNT1O7evfSAefkX5gre/cGXw/Dz2j/xwsrpGDxB1E8QR5E0HM6TAPfY/w9DRuX169czYzkZ8ckYT8FuKYAr0adPHwQFBbF95HFP3uWLFi1q8ZhkGCdDuaOjIzNYFxYWsoC18nTr33//fYwZM4Ydn9KQh//AgQNlpiVPeiqDtEc6TRr8+uuvbD9NKpD8DHm6S6eTBRm+KQ4AlbueBpUlJRKGdHkoU+6WIEP89OnTWdBdOjehvFB7z0sZ+vYNxGefLcXGjbvwzjvfw8HBCl9/vUpCNoe8wOrEliv6+Lhi48bV+Prr7XjzzW9ZsN1x4wZi6VKRXMHmzbuZh/38+R9K/B6tDFi+/IUWy0TBbKfM+RbrP34e0Te+RUVVDfYdvYHVn+xpTiOcqBDO1BcUlSM6LgNnDrzLguXeD0vC5Dnf4OqtR+MRPD9jIPYfu8mM+20hu6waL+4PwYcjPDGvpwMLnPvTrWRsDxZ1YmlMqE5GFCWOuzs0HSsGuOLLsT7Me4hWAGy6kSRxXEXQdXaF47yFyDp6CJn7d0PD1Ax2s5+Hgb+YnjYZT8W87asy0hH32UdN3zUg68gBZB09yPI4LVrCdpsNGoKcE8dQHh/Lentallawnf0CTMSMUm2BirLkfATe6+2Of6b2Qm19AzN2f3FHcjAiDD5LnE7Owzx/B/w+JpAF58qpqMa5lHz8Gt5+XdUdkWlsEPFlP282oIgsLMPrlyOaPaWEXkPCFQKOBjoY62TJVhgcGCvwXhXy1vVIXM4oYH8fS8yGnroa1vX1YgGpMiqqsSksCYcTsuSWRc/aCt2Xv4qo3QcQf+Q4k9nxmDEFdv37SDyfZNQRegopkkdRqktKkBsSBr+XJKVIWuLdW1FsALp7RBAr0rWsAnwTKnkt1cTqb4S9BZwNdGGvp4OzT0neSyP+us4M/aTDOcvdjnmf0VJiWgJMx90RlaqQd39bCb+8Hk72Fsy7nQYPpQl/sv0GrorXhzJkF1Zi7hcXsGZOD7w8zhuFpdX45a+H2HFKMl6DupqobSmrrMULn53Hhy/2QPCv05l0xfWwbLz1081mndu95+OxdHoAPl/Qm00c0AqAHw5HMMOYMpCXvdfCBUg+dBgJu/dCy8wUbs8/B9MuIuMMm9gXa1sUyeM0dTILEhr72zbUFBVBw9CAGfsdp4gmu6UhI3Wv5YsRtmM3zq18D6pq6rDp1R3+z4kmoWnZAz0bQk9mRfLQJEBVQSEybgWzTYiathbG/vRt8+ceS+Yj/I+9OLdqDRvJGjs7ovcbr0OHZOzkzDWZjxqLxtoaZryvr6iAjqMTHF9dLhlUkj3LovrLP3ca2UcONH9OWCdop61nzILpIPnBhZWh99yncWf7QZx87xvU19bB2tcdI1ZLBrmlgKCsnVEwj4WHCwqS0nD+q5+ZUVPLQA/Wvh4Y+/FKFhi0pTRmr70DVf3WV0Deyy/G56FxeMnTAW8FeiCzogqf3I9GWKHoAqg0tTdCXvRwhKaqKr7oJTkBszs+DVui5Xs3S2M9eBAzopMmPsnp6NnZwnfpaxIBbNm9J2bgUSSP18L5SDpwCOFffcOM/lqmpnCYOAE2IwTGdJKvMu/ZAwk7d6E8NRWqmprQc3CEy6yZyLl2A7eWrWBe8p4TRsF9zFAZz4LgI92n/d98FaG/70Pc3+dZgHXpPIqkcRk+kBmeI/YcwZ1NW5kcj6W/N/xniYLnKsKD+Hy8/t1VrHomEO/P6Y6MvAq899ttXAwRGbeov0dtn7DxS8oqxdwvL+Ld57pj7cs9WXt55m4avtoT0pzntan+0NJQw8alkqtfd5+Lw4fb7rZaLqe+QUy+K3jHAVQWFsPQ1hKDVi6AsYNooptNqog9G4rkIYP9lW9/bf58lf5WUYHX2CHo/rxgBa19z0AE79iPaxu3QV1LE459uqO063SZgaW3RqQyh5BvB/kww35kQRnz5KXVguLe2EKPbOq3jHcR9FuOPiXZb1l5+SEupQv6LWdS8vB2Dzd0szRCYVUtmyj4pYVVAPKwmvkscg7sQfLX69BYVwtdDy/YL14qcS6NDfUSq06y9u5E0dVLzcvXYla8yv53WLwUej5+0HFyRlVKMtJ+3ozavByoGxlDP7AbzMc+JXNCtiWWnnuINf3ccXJ6D/ZzF1Pz8fF1yeDyZJwXTvhcTC3Ay/722D6uC5sMIMmeiykF+Ckkpdk5QZE0ikByk8uvhuONbu44Oq4XquoacDo1R0JaRzDmUGn2yJzqasPkXCY6W7NNSG5lNaadEtz3p1Jz2QTBQj8n5imdU1mN82l5+CNGuTFHTkUNFpwKx7t93TDH357dJ1vC0vDnQ9Gzq9pUPmFTvD86E0u6O+OTgZ5sUoAmln4OScGfD0XGdkXSKMLDwjK8cyMaiwOcsKKbK7IrqrEuOJ71K0XlUxGUr+nzPF9HaKqp4rM+kqvXDydk4qv7AoeI1LIqLLsSgWVdXfFWdzf2rJFRenOY4ivLCRrvkLTNmz1cWf82uaQCyy8/RLyYkxKVT7jag/5bEODInt3toyQnwDfcT8SfUYL6uZCahzd7uOHzAd6orKtnz/LGkCSl+8zklERjos/7CsZEtErk9avyx0QO+qIx0f7Rkm3L2zcicSWzgE0c3couZMZ9VyM9FFfX4nZOEdbcjm5VHkke1o6WWP7FPOzeeBRHtp1i0jwzFo5H/zE9JVYENlBfpqkKjv9xFnW19dj+1X62CekxuAsWf/SCwmk4LfDfW1z9xKLSKE/clPOfgXThHz58yIz7Dg6SWt7UUY2NjWXGent7ezg5Ock9zp9//olVq1axALBkgCYJHtKCFzdWk+QMBX7t27evRMDXqKgo5ilP6UkGh/ZJ68iTBA8Z0+m4FJBXGgqoSwFw6Tienp5sYkFIbm4uEhMT0avXo8unyKBPv09GG1oZIJ6vJVort/S5yjp3guqK6ph+W9nzUgzlDDeyoGaA7gVFVjOI16us1kNoIBNi4LKuzeUiOaF6Jbwi2pLPdFHbDXhkTGhZZkdg1G4PPXqIgl8rg9D41uzdyQw3MjpTMrw+lSFW1K9vNzQgre/AN5KWVtt7G0zEROz6yQuE1p7y9nWoVfhakmFB6IHbFoQGO+lr3dKxg7OUbYtE0K+IP4GPo/7uLZWUG2oL1F7J8hhvS7sjjW3/p9qclwZ45DH1OBm8WDkd25balseBqaZi14A8wYWyGoogMH4+WrcqSrSF8aUaHfYsSk+UNNPCObFjiBmhpOnZJMWjDNQHYO1eO94HinIqTbYzSEe0N/S3rGpRQCWoGTej2na1q48bK536x/Ke6Cj2fU2T2RTZVfWxt31MukPm9W6UG0/xhTclJYEUajOUlDAVXg9l25nDD7U6db/FSVIVrn1tH5vklDmQaLPkYkxy20+uI/rsrWFiotohfWSqHaEjjDjsddKO8hUWNnSaPrwsDAw7pv464j0hi5qaxk777LZ7TCT2nn0c5dswSHnZPJn2jPoG5ulPCIz8Mvp7YrYKRdIQfS0lpQ85AjyGiSawnyRiz8/Hfw3usf//AB0dHeaZLwtqsLy8vNimDKSxL9TZF8fU1PQRwzZ5rQcGimacXV1lL5/87rvvmHe7LOM3QQZv8r6XhYWFBdtkQcZqPz/FgroqU27pc5V17i3VlSLn9W9BHWhljPqEsunbQluNax1hlFPod1qZF33cA4SWYIMiqWv0uA1x7eVxDwiUgalAN3aOsgmDKLbrGHIMCR1xbFk0dOJrK06DUO+7k/G4jfod3bb8L1HWkMkmAR5baRT7/fbW5+MwJLcl7lJnoUH673/p8fm3Dfot8bja8jbBLGYq/0rbx1YtPebr3ZY2439xPTpTv0UWsqTDOhOd+LX7yPVs7ITXt7OVp6Xy/Zvvif/CsyvdU+5s5ZOwZ4itRFRVIMixImk4nCcBbtjnSGi+P3jwQGaNzJkzh00QPA5IjmbDhg3Ms/3mzZud4lxJE5/D4XA4HA6Hw+FwOBwOh8PhcDoj3LDPaeaVV15BcbHsZVCkqU+GfZKK6WhGjx7NPNZ9fX1hYCAVff5/dK4cDofD4XA4HA6Hw+FwOBwORwpZmmCc/wncsM9pJiBAFOhOHvLkbtoDGdL/bWO6IufK4XA4HA6Hw+FwOBwOh8PhcDidES4qxeFwOBwOh8PhcDgcDofD4XA4HM4TBDfsczgcDofD4XA4HA6Hw+FwOBwOh/MEwaV4OBwOh8PhcDgcDofD4XA4HA6H0zoqXGO/s8A99jkcDofD4XA4HA6Hw+FwOBwOh8N5guCGfQ6Hw+FwOBwOh8PhcDgcDofD4XCeILgUD4fD4XA4HA6Hw+FwOBwOh8PhcFqHK/F0GrjHPofD4XA4HA6Hw+FwOBwOh8PhcDhPENywz+FwOBwOh8PhcDgcDofD4XA4HM4TBJfi4XCecG7kxKIzY/vuS+jM1B9PQmcm2ckFnZkujg3orNzclIrOzLWpTujMFJ3NRGfG+YNX0JkxNe3cvhOxhejUqKh03vW9yWEV6MyYDNRAZyb1RDY6Mw1jLdGZya7s3MM39xcd0Zlx1CtHZ6X854fozBS/5Y/OTO2Pd9GZyZ7bDZ2ZjHU/ojNjsXwhOjNaO8PQWSl+qQs6Mw2N/+sScDhPNp27Z8jhcDgcDofD4XA4HA6Hw+FwOJzOgWrndcL5/0bndifjcDgcDofD4XA4HA6Hw+FwOBwOhyMBN+xzOBwOh8PhcDgcDofD4XA4HA6H8wTBDfscDofD4XA4HA6Hw+FwOBwOh8PhPEFwjX0Oh8PhcDgcDofD4XA4HA6Hw+G0DpfY7zRwj30Oh8PhcDgcDofD4XA4HA6Hw+FwniC4YZ/D4XA4HA6Hw+FwOBwOh8PhcDicJwguxcPhcDgcDofD4XA4HA6Hw+FwOJxWaVThWjydBe6x/4SwZ88ezJs3D086mzdvxqpVq/D/kbt372LIkCH/62JwOBwOh8PhcDgcDofD4XA4nCecJ9pjPzo6GnPnzsU///wDfX19/JdJS0tDSEgInnQSExMRHh6O/48UFRXh6tWr/5Pfvn0hBEe3nUZuZgEsbc0wdd5YdB/oLzd9emIWTuw8j6j7caitqYOzlz2mzR/H/lcmTUvY62vj3X5u6GVthIq6evwVl4Nv7yShrrFRZnqaD57gZonZvjbwMNFDUVUtzqfk47vgZJTX1j+S3lJXE4endIexlgZ6/3EdZTLStIS/mxnem9sTPi4myC+uws6/o/Hb0Yct5tFUV8UrMwIwabArjPQ1cT00E5/8dgfZBZXs+49f6YMZI9wfyVdQXIV+Lx9Qqnz9bUywuIszHA10kFlejd8epuBUcq7c9A762njBxx69rU2gq66G6MIy/BKegtC8kuY0PwwNQHcLo0fyhueXYN65B2gPNUVFSNmzByWRkVBRV4dpUBAcpk+Hqqam3DzlycnIvXwZ+XfuQNvKCn7vvouO4pWp/nhmhDuM9LUQHp+Pj7fdRXRKUYt5PB2NsXJWVwR5W6CkrAY7/o7CjpPRMtN+srA3pg91w3d7Q/Hj4QilyqahqoLXu7pglKMFtNVUcTenCF8GxyO7okbuszHC0RzT3W3gaaKHkuo6XEovwC/hyRL3vbOBDl7r6gx/M0NoqqkgsbgSv0Wk4FpmoVLlM9LTwPuzumFoFxs0NjbiXEgGPt4TgrLKOrl57m+cBF0tyS7HN4fC8cs/ovrr7maG5VP84ONgDGoGIlIK8e3hcDxIVK58dtS29HVDT2sjVFLbEp+D9a20LeOpbfGxgXtT23KB2pZ7yahoqj9F0iiKt7E+Xu/iAg8jfRRW1+JQQib2xKXLTW+mpYFnPOwwwNoUxlqaSCmrwB8xabiaWdCcRlNVBZNdbDDO0RK2etrIq6rBqdRc/BGdigalSgf0sTTBAh8n2OvpILuyGjtiUnE2XX7bYq+njdnu9uhhYQwdNTXElZRja3QywgpKm9OoqahgsI0ZJjvbIMDUAD89TMLehAy0hXEOluz3LLQ1kVxWyY51L79YbnovI33McrNDFzNDdh0jCkvxa1QyyytER00VI+wsMMnZGq4Genj/bhSuZYvqVxmW9HPBrG52MNbWQFhWCT48HY2o3DKF8q4b64OnA23x7eV4bL6e1OY0QopjYpC07yAqMjOhaWQE21EjYDNkcLvzpJ8+g+wr11BdUAANPT2YBAbAaeoUqOvosO/rq6uRee4Ccm/dQnV+ATRNTWDVvz87lkoL3l1GuhpY83QghvpbC9qWB1n4eH8oSqvkty0h30x4pG35+mgEfjkTq1QaRbDR1cJSfzd0NTNEVX0DTqfl4OfIZNTLaVvoXTvV2QYj7C1gpaOFrIpqHE7KxLHkLIl0viYGmOxsjSE25nhYWIplN9rWT3Y31McCLxe4GuijuKYWx1MzcThZfttCBJkZY5yDDYLMTXAqLQs/RiU8kkZdRQXPuDpgiI0FDDQ0EJJfhF+iE5BfLfudJI+e5iZ4ycMJdro6yK6qxu6EVFzIlN+2ULsxwMoM4x1s4GdsgN9iknAoWbLd+LyHP7qYPNpviSwuwcrbYegoQs/cwK2D51CaVwQzBysMeXESnAM95aavqazGw0t3ce/vq8hNysTUd+bCo3dAu8rwyvPdMPMpHxgZaiE8KheffH8d0Qmy2ylNDVWEnpor87tN24Ox+fd77O8NH47A6EEuEt/fDs3EnOXH0V66mBriFR9nOOvrsntlX0IGjqVI3vviWGpr4hk3O/S2MIGBhjqSyiqwIzYVwXny23dFoTp7f9VgDOnvImhbLifik28uoaxc/j3crYsNli/qCx9Pc9AT/jAqB9/+eAMPIrKb07i7mGLWtABMGuvNjjVk4rY2lc/eQBtrBrihl60RKmvrcSw2B1/dSkJdg/x+y1MelnjeXzAmor7EuaR8rL/96JjoeX9bzPazgY2+FsJySvHxtXjEFFTgcTFycCDmPz8CowYHYtueC1j+ftvqpD3oaahhzWA3jHIzh7qqCi4mFeCDi3EoqKxVqA9+cGY3+Fsa4LlDobie2vLYoC1Ymuvh/TcGoV8vB9TU1OPvc3H44rurqK6W35/s39sBr87rBU83U9TVNSA0PBtfb76O2Pi29VXEeTXIEbP8bGCkpY6wnDKsvRqHqPxyueldjXVYnn72JtBQU0FkXjm+u5OEu5miMaWFribe6uvC0uhrqiG1pApbQ9NwMEr0/ChLdEg89v1wDBnJ2TAyNcToZ4Zg6KR+ctPnZxfin90XEHYrEuUlFbB1scbEOaPg19OrOU1DfQNCrz/EhaPXEHkvDmNmDWX2DA7nSeOJNuyXlpbi2rVrqKuT3+HncP6/Q4b3nz/6E88um4oeg7vgxplgbH5/O1ZvXgJ3P2eZefb9dBy9h3XFtPljoa6hjsO//Y0vlv2Aj7etgrm1qcJpWuo0bRnrj/jCCow/GMxe/j+M8GWdr89uPjqoJLpYGKCHtSE+uxGPhOJKuBrp4Ksh3jDX0cSKC1GPdHi/HuKN8NxSDHE0g7KrxCxMdPD72pE4dD4er35xEV08zPDdqkGoqKrD7lMxcvNtfHMwnG0NsXL9FUQlFaFPgBWmDnPDjwcEg/Q1P93Ehz/fak6vpqqC8z9PxbnbqUqVz8tED18P8MXmB0k4npSDwXam+Ki3FzM23sqW3QGlSYAbmYXY+jCVGSJf9HXA5iH+eO70fSSVCAxcSy6GSQS3N9LUwPGJvXAhLR/tobGhAbEbN0JdTw9+77+P+spKxP34IzP+uL70ktw8Sb//DotBg0AXsCxB9n3RFuZN9MX8ib5Y8s1lxKYWY8WsQPy+ZgRGLj2GEjmDPA8HI+z7eBT2X4jHez/fQkNjIzuGu70R4tIkB53j+jrCz8UUhaXVLRqx5PFGkBv6Wptg6aUIFFXX4t1e7vh+sD9m/3MP9TLGeP5mBhhqb46fwpIRV1TBDNsf9/WCg4E2ll8WTUZtGuqP6MJyvHD6PipqGzDbyxbfDPTFzL/vIblUZORsjY2L+kJfWwPTPjkHVVUVbFzUB+vn98b876/JzaOmqorXfrrJJgGE1IsNWE30NbFtxUAcvp6MpT/dhIqqCt6YFoAdKwZh4BsnUNaCYe+RtmWMP6uHpw4Fw0JHE5uobVFRwbpb8tuWICtDfHZT1LZ8Odgb5rqaWNnUtiiSRhHISL+hvz/+TsnBu7ei4GOij7U9vVFVV48jSbINHs97OSCzvApv3nzI7ofRDpb4rLcPVl6LwJ1cwfM+yNYMRprq+OhuDDPG+5kY4KNeXswwtjUqReHyeRrp4bNePvglMhn/pGZjgLUZ3u3miaKaWtxt+i1p5ns74VZOEX6PSUVlfT2edbfHt338Me9ySLPxfKitOQZam2J7TArWdPdq03NB0DFWdnHDupBYdu5kNP2itw/mXg5FipihXpxXfJ1Y3W6MSICqigoW+zpjQ19/zLl4HyW1gvtqqosNbHW1sTE8ERv7Byj9zhCysLcTFvRxwiuHHiA2twyrBrtj5+wgDP35GkpauYfH+1jB39oQBRW1rJxtTSOkMicXDzdshO3I4fB5/VWURMcg5rdtUNfVhUWvnm3Ok33lKpIPHYHXogUw8vZCVU4Oon/8BfVV1fCcK2jPc2/dZm2416KF0DQ2YseJ/vU39lzbjhwht8yb5veGvrY6pn5xgbUtm+b1xvqXe2LeDzdablu23MbZB5ky2xZF07QGtSHf9PFHUmkFXrhwD2bamuxZoWdsY0SizDyTnKyho66GtcHR7Lmkya/3u3uyPGTgJww11PG6nwsz9tN+C20ttAUTTQ18GuSPcxk5+Cw0Ch6G+ng70BtV9fX4O0122+JtZIBJTnbse3rfy7un3gn0hq2eDr4Oi0FiWTkzpA+3tcS+xDSFy+duoIcPuvlge2wyzqRno6+lGd7w92QTEPfyZbctg6zN0c/SFDvjU/B2Fy+Z5Xvnbrig09eEoYYG/hjcE9fbODEni+gboTj1wz6MX/osXLp5IfjEFRxY+zNe2vAmM/LLIvj4ZRRl5WHE/GnYtfp7NCp5v0kz75lAtr225gxikwqxfF5P7Fg/AaOe3cMcDaSpqW2A/8gtEvtGD3bFdx+OwOkriRL90AMno/DhepHjkZx5KqWg9vSLnr44kJiB1Xci2WTYO4GeKKutw/nMPJl55ng6Iqa4DAcTM1nbTM8PHeP1G2F4WKTY5Kg8vl83Dvr6mpj24l52zt9/Pg7ffjIGC5Yfk5nexEgb2zZOxpETkVj2zt+s7Vq1pB+2b5qCQRO2Nk8IfPrecJw8E4u9R8IxaYzIWKgM1G/ZPsEfcYUVGLsnGBZ6mvhpjC8r5yfXZPdbAq0M0NPGEGuvxiOhqJIZWr8d4c36PEvPivokb/d1wVQvK7x5Pga3MorgbaaHp32s5R63vfTu7oElc8fit51nYWlmCDW1/404xDejveFuqouZB0JQXdeADWN88MtTfpi+r3UnydUDXZFfUcvGpK29Z9sCHfKXDRNQVFyNibN3w9BAC5u/HAc9HQ289dFZmXkc7Azxy/qnsOXPe3hl5XHoaKvjvVWDsG3TJAwavx0N7WhfFnSzx4JuDlj8TwSb8FnZ2xl/TOyC4bvuMEchWbzS3ZFNJH12XdCvWtjNAb8/1QUjdt1BRlk1S/PdSB/oaKjiuaOhyCmvwTh3C3w5zAu55TW4nKqcww6Rk56H9W/+ilEzBmHpF/MQdT8eWz7dCV19HfQe3k1mnqPbTsHZywEjpg+EnqEuLhy5jg1v/oq3Ny2BW5MNJOJuNK6cuIUR0wYiP7uIGfo5nCeRTi/FU1FRga+//hoTJkzAtGnTcPDgQbY/KyurWZpm7NixGDBgAD788MNWj5eUlIT58+dj9OjRWLp0KW7dusXy5uYKPEbCwsLYZ/r/xRdfZNIp9+/fZ9/FxcVhyZIlGDFiBJ5//nmcOnVKpszMsWPH2Pfjx4/Hhg0bUF8vmn09evQoOz5tVO4333yz+bfFOXfuHDvfSZMm4csvv0Rt7aMzzK2VpzXv8Q8++ADjxo3DM888g/379z9yHjt37sSzzz7L6mrjxo1oaGjAn3/+yco0ceJEiTxCbt++jRdeeAHDhg3Dyy+/jAcPWvbyLS4uxsyZM1lZCPqNrVu3YvLkyaxs7733Hksjneett97CyJEj2T0QHByMUaNG4fr16+z7yspKVr+XL19m13j48OHNZc3Pz8f777/P0s+YMQPbtm1jnhtCTp8+zc6PZHPoPhkzZgzeeecdlJWJOpQPHz5svoZU96+88gqioh417FA6ui50nd9++21W59K0Vp6O4J+9l+AT5IFhk/vB0EQfo58eDDc/J5zae0lunuVfzEO/0T1gZmUCI1MDPL98Gupq6hB2K0qpNPIY4WTGPM3XXItFVnk1wnJLsel+Cp7xtmVeFrIIzS3FB9fiEJZXxrxR6H/yxO1u9ain1uJujiitqcO+aPmeQS0xc6QHamrr8enWO8xb/8LddOw9HYv5U/zk5hnaww7Dezlg6deXcT86D5XVdSyf0KhP0KUlY4Jw6xdoA2szXexT0mtwtqcdogrL8Gd0OjP0HU3IxvXMAjzvI3+1xOrrUTiWmM28+4tr6vBdSCJbKTHY1qw5DfUL68W2UU4WbP+JpBy0B/LSr0hNhdNzz0HL3By6Dg6wmzIF+Tdvolbq+RaioqrKJgEsBw+GqlbbDB0yj6sCzH3KB9tPROF6WBZyiyrxwZbb0NJUYx728lj9QhAeJhXi0+3BLA/dF5//ce8Ro769pR7efbEHVnx/DfVt6BwaaqpjkqsVfghLYteYPDw/uxMHd2M99LeVPWEWll+Kd65H4X5uCUpr61g+8tYfYGvKPEYJU20N2Ohp40BcJvP8p3TbI9OgoabKvPwVxc/RGP19rbB2930k5ZQhIasUn+4NxbBAW7jbGLSYl9o28ftfHFdrAzZZsO1MDArKapBfUo2tp2NgqKsJZyvFV+QNdzKDg4EOPrza1LbklWLz/RTM9LaFbgtty0fX4xCeV8Ymvej/4/E56GZppFQaRZjobI3ahgZ8H5bAPOyuZxXiWFIWZnvKf3Y3PEjA3vgMpJZVobS2HgcSMhFZWIph9ubNac6m5eHXyBQkllaw55qM3reyC9HFrOVrIs0MVzvEFJVhT3w6imrqcDwlG7dyCpnHuzw+CI7GydRsZFUK2pYfHiYxA39/sQle8vindPfa6XlJ5biQkYez6Xnst7bFpLJnZJqLjdw8y25E4GJmPvKra5FbVYOvH8Qzo2w3c9G12xmXjq8exDOjUluh4f+83k7YeicF15IK2ED2/VNR0FZXxdNdbFvM62CkjQ9GeGHp0TC5BmdF0oiTdeEC87h3mjIZmoaGMO/Zgxnn0/851a48ZUnJ0HN0gFnXQKhra0Pf0RFmQd1QlihaPWA9aCAcJoyDro018+I37RoIIw8PlMTFy/1tPwdj9Pe2xEf7QpGUW46E7DJ8cuABhgXYwN265fu4oYW2RZk0LTHQxoythvnqQRxyqmoQWVSGbdGpmORsw1aqyGJ3fDpbHZJQWoHyunpcyszHufQ8DLcTPbtkwFx09QFOpuagph0GhTH2grbl1+gENhF3J68Q/6RlYbqz/LYlqrgUa+5F4EZOPqsfeV72vS3N8EVoNEtfXd/Ajq2MUZ+Y4mSHuJIyHEhKR3FtHf5Jz2bHme4sv20hb/5PQ6MRWiC/3aAao8sp3IZYC/otZzPa7hUqze1D5+AzoBv8hvSArpEBBs4eByNLU9z9S34/uu+MkRj72ixYuzl0SL/l5ZldsP1AGK4HpyM3v4IZ4rW01DBtnHxjcn19o8Q2dYwn7oVnIVZqFRzrm4qla4+RUMhkJ2sUVNfgt5gUdj9SG3wuI5d55MuDnq2jyVlIr6B3XR3+jEtjf9MET3vw87JA/96O+PirS0hOLUJCciE+/fYyhg10YR73snBxNoG+nia27bqPAurzFVRg2877zAjr5GDcnG7m3P3YsScEJSUCY2ZbGOliBkdDHbx7KZb10R/klGLj3RTM9pU/JgrJLsX7l+MQlts0Jsotw9GYHATZiN5rzkY6mBtojw+vxOFiSgEq6xpwP7v0sRn1iVv3YjHphc9x7NRd1Df8bwykjkbaGOdhgY8vxSMmvwLJxVVYcyEWveyMEWRj2GLeYS6mGOJsho8vxz228vXv5QA/b0usWXcB6ZmliIzJw9ebb2DSOC+Ym+nKzOPpbgZNTTVs+f0eikuqkZVTjj/2PoCVhT6sLfXb12/p6sA86a+lFSG3ogZrLseyfssMb2u5+d44H42T8XnIr6xleTbeTYaOhhr8LERlCbDUx+HobDbxRKuH90VmsX5RgKVy/VIh5w9fg7GZAabOHwdDEwP0GtYVvUd0x9+7zsvN8/Lbz2DYlP6wsreAvqEennphJCztzBF8SWSfCujtg9c/n4sufX2ZQwFHSajOnsTtP0inNuxXVVUxw/qOHTuY8fm5557D7t278ffff8PExASvvfYaS0dG4c8//xyzZs1q8Xjl5eXMGJuens7yOjg4MKMref1XV1c3G43pMxl3Ke3HH38MV1dXZqDu3bs3DA0N8cYbb6B///6sPNu3b5eQmfnhhx+wadMmZqQlg/Unn3yCb7/9tjkNHYPKShuVgfLQPjpXIVeuXGHl8vb2xoIFC9hvUznEUaQ8LUF1RYZvMko//fTTOHz4MKtb4XnQOZBhn9JRXZChv1+/fsxATmUiYzl9Jy4tc/PmTQwcOBAWFhYsvaamJvr06YOICNkyFNnZ2ez60ooLMp4TJK1EkwhUd1Q/MTEx7Bji9UNGf7oHFi1axM57ypQpbCKkoEDgnUMTKXQNab+trS0znFM6+p7qLCUlhRn8aeLkiy++wIoVK5qPnZOTg5MnT7IJAzpH+g2qm4ULFzanoftGeA1pgkFPTw9BQUFs0khIZmYm+03ySqTzUFVVZRNF4ihSno4gNiwRPt0l5V/I0B8X3vISfnEqK6rYDLa2rla70gghYzx51FKHQMiNjEJoqavCz1yxDgp5zI52MceZJEnPH/KqneFlg/euyPesb40gH0vciciW8Fq6/iATjtYGMDfWlplnVB9HRCcXIipJcS8EkuWJiM9HhJyl1PIItDDE3RzJge6d7CIEKGHE01JTZRsZG+QxydUaF9Pz2eRBeyiLj4eGiQm0LS2b9xl6e7PRZEd64iuCo5UBLIx1cDMiS8Kz7V50Lrp7yR486mqro1+ANY5dafmZUVdTwYalA7BxfxgSM0TLUZWBvO/VVVVxN1t0fWmgl1paiUDzlgck4hhraqC2obHZUFRQVYvgnGJMdLGCsaY6u/bPeNoir7JG4rdaI8jdHBXVdQgRu2dvReeirr4B3d1bHnx/OqcHHmyegr/XjsKCsV6svoREpBQhKbsUs4e4QVdLjXnt0t8x6cWISVe8LrtZGSGe2pYq0T17U9i2mCnWtrgY6WAUtS3Jee1KI4sAM0OE5JewZf1CyBPeTk8bploaCh+HvHzJgC+vY+droo8gC2NczFButQ3J5NyXkrUJziuCv6nybYu88rUV8pgmGaP7UpMDJNPgb2Kg1OQZ0dHlczLRhaW+Fm4ki94BZAS9m1aEIHuRIUga8gr8fnIANpDnpRx5BEXSSENGdPKoF8fIxxvlqWmolyOhokge0+7dUJmRiaLISDTW16MiKwsFIaEw79VD5jEpTXF0DEoTEmDWXbZXHRHkZipoW8SMjrdi8wRti5toAloWnz3bHWHrJ+Lv94dj4ShPVl9tSdMSAaaGzFufJuTEnw26172M9ZW6/zr63iN8jA0RUSjZtpBkjrWuNvPmbyv9LM2QVFrOPPXbA8kNSRvoQwqK4GPcNiOPPEbbW+FGdj6bPOgI6mvrkBmbAscAD4n9ToGeSI+SvVKjo3G0M4SFmS5u3ROteCP5jnth2ejuL9/4Jo61hR4G9LTHvr8edb4ZP8wNIf+8jPN7Z+GztwbDzEQgqdUe/E0MH3mXkGSau6EetFQVMz9QKj319j8v3bvaoqKyFiHhon7f7XtpTM6kexfZk8IPo3KRlFLEZHZ0dTSYkX/2tADExOchNr59q1ilCbI2Yt764mOi62mCfou/mKG0Jchjf6ybOU4l5ElMGFTVNeB0YseWt7PTw1YwuXEzTeRQR/IyxVV1CGr6ThZWepr4YoQXlv0TyVa1Pi66B9oiM6sUKWKOQTdup7LVDV3lPM/BIZnIzi3Dc093YRN6JC319GQ/BIdmIDNbJHuoLE5GOmzV/M10UV3V1DciOLMEQdaKjTn0NdTwcqA9M/DfzRSd04m4XExwt2QSULQqZYK7BZPkIU//ttozvLtJ2jN8gzyQGpeB6irFZOHITlFRXqWQrYLDedLo1FI8W7ZsYYbd+Ph4mJkJOvVkrCUvfi0tLQQGBrJ9ZPg1NpY/aBLy66+/MqPvkSNHmNGZVgHQsYTe4uKsX7+eGbSFkGf9Sy+9hM8++6x5Hx1j7dq1EgZb0vonr3ydJp3R2NhYHDp0iBnfCWtra7YJIY9wZ2dnHD9+HNOnT2f7aOUBGek//fRT9pk8/7t27SpRPkXLIw+aPCCDNXm9E1OnTmV1IcTIyIiVW1tbYMQ8c+YM7ty5wyYD6HcIKrNwBQLx7rvvMuP0N998wz6Tx31CQgKr3wMHJPXDafKAfpsM+z///DPU1NTYxABNLlA8AXNzgYGIPNk9PDywb98+thLg4sWLrOxUry4uAk1IujfEr5WQ119/nRnehZDXPNU1TRQJ8fT0RM+ePbFmzRo2WUTQRAOtDHFzE3jv0j1DnvdCDAwMms+ZoPMgj326X2kih6BVJlS+33//vbkuaEKJJkuE0EoMRcrTHurq6pmmnKHUwMnAWB/FYvrHrbFn0zHoG+khsK9vu9IIoU5EQaXkS1ioe0jSOi2xa0IguloasiWqZ5Py8IWYvAZpA5I8Dxn1C+UsH1RUiidZTCeQla+4uvm7vCLRRJMQMvqTMffduT0webAbaurqcTs8G1/+HozMvEcNMaZG2hjawx4fb7mjdPnMtTVRKNWJIWODnoY6dNRVmVdOa7zaxZnpAp9PlW2Y9DXVh4exHtbfb7/hnbzyNQwk70F1iouiqorakrYZwNuKZdOAlTzuxSkoqYK9HK8XOws9qKupUnFx4NPRcHcwRlZ+OXadjsXvf4s04lfM6so8zfecVW4FhjjC+1/ceCT8TF7GimCipYGX/RxwMjFbQlf+rauR+H6wH85N68s+51fVYMXlh4/8VktYGGujsGmZrRDyfi2uqIWFkexJL+JUcBq2n41Fal45entZMCO/jYkuPtolWBFXVVPPpHx+WzYA80YLDIu0GuDl9VdQo8D93Fw+HU12XuLQpAb7Trfl+vtzvFjbkpyHr2RI9yiSpiXoGqZJGTuEE2f0XYEC14LJxuhp45+UR1fSnBjXmxkOaWk06fYfSVRu1ZKZluYj9wOVT1ddnenQVyrgUbzA2wmUjLyTOxKSCqFJr8IaqfLV1MJUS7Fng3jNzwVpZZUIaUGXvy1Y6gvKkC8l51VQUQMHY/mGsjcGuyO/vBa77qe3K400NcXFMPbxkdjH2uHGRtSWlkBNy7xNeUz8fOE8YxqT7CGjPWHZry8cnprwyPGuLXilWdfD4anxsOjdS255LY10UCglKdLcthjKH4T/E5KO7efjkZpfjt4e5syAb2Oigw/3hiqVRhEZLelJ7uZnV8FJuSBzI/SzMsWau4rLdykKPQMRFZL3dEnTal8Teq6lnhtFsdbVQXpFJeZ7uWCYjSVbFRBeWIKtMYnIU0Jjn8pHz6o49JnaFoolQzEL2ounoT6LkfGLjDgBbaWipJwZg3SlJm90jfRRXvjv9F8szQSr6vKLJOXGyJPcvpWVckKmjfVixu2TFyRXzZD3Pknx3A/PhoOtIT5cPgC/r5+AKfMPMqeHtkLvs2ApiSV6XujdRH0UWuHVGhRLhYyG5Onf3vorlKo7WplQUloFc3PZHtJV1XVMpmfLd5Mw7/kgto88/ee+foSt6O1IqG+SLzUmylew37J3ciC6WQn6JGcS87DuhujedzTURmJRJV7uYocXu9hBQ1WVrZD+4kYiogvaN1HXmbHU02QSMjSxLg6NO+XVJ03zklzP9pB0PMguhbX+4zP8WpjrIr9Q8n4sLK5iE030nSyKiquwaMUJ/PTteKxYLOjDR0bn4uXXj7VLOovi0RF5UrEHqN9McR9aYqKHJb4Z4c0mycmov/BkBArFJAfJ8//HMX64PqcP+0wrS5afiWxRu78livNL4NvD8xF7Bq0GLi0sg5ZNyzLAxImd51BZVsk8/Tmc/xqd2rB/6dIlZvgVGvWF6OrKbvRag+RVBg8e3GyYFhplZRn2xQ231GCQQZk8q+kY9Jk28u4nL22SfhEa8gMCApr/JhwdHZlskBAyGpMhlwzltJ8+k0QLTV4IIQM6ycCIQwbu8+fPK10eeVA9kFc9eYeTbA55oYvXq7+/f7NRnyDPd5pIEa872id+blQW8vQXhyYlxFcsEMnJycybnYzl5KEu5MIF0lRVZRMcQjka+r+wsJDJ2gh/w93dvdmoTwgnJ1q6hsLjkyc93VPCOiOjPcn/UCBmmiAiTE1Nm436wmtIdUr1SxMewkkNmmwgYz2t9qAJDDL4i0sS0TUThySNxA37ipZHHPot4eoSITXVtdCUN6iU87ZXRjPwr9/P4vb5EKz8ZiHTsWtrmtYQlrS1kj13IpR1Tn3M9PDJQE98M8wbS89Fsu/o87nkfFxNV167rzWEy9RV5JSQVmeM7OWA3449xMhXj8BITxOfLemLX94dhkkrTzyytHnqUDfmifjX5Y7x+BJeannlE4eCrM5wt8HKqw/lGhInu1oz49dtOZr9HUYHS0+1Fbo+8mpO+Ly8NqMLlq2/iojEAvTxt8J3ywaya0gG/r7+1pg00AVPvXGiYwrU+Oj9p8hjS9I76wf5IreyBt+ITcqQt8xPwwKY5/8bVyNZUNmnPWyxaYg/XjgdghQlNPZlFreF+iPe2CqawDpzPwOmBmH45PkgfHXwASqq62FmqIWdbwzGqXvp2PjXQ1bnyyf7YeebgzHhwzMoqWj7qhFFTRMvnBS1LR8P8MTXQ72x7Hyk0mmULp8Sj0BfKxMWeHd9aAJiix8dHD118ha01dVYoNj3gjyYdj9J9LSrfAq3zmABQKe42GD17UilJozag6LPBvGqrzMCzYzw+vUwtqLl34B+Rl673N/ZFFP8bTDmN/ka8oqkUZTm2AaNbc+Tc+MmEvcdaNbYr8zORuyWrUj4cxfcnn9WIm+/HzehvqYGxVHRiN26DWpaWrAbLdk3ag3qG7X0XntjR3Dz32dCM2GqH4FPZ3fDl0fCWduiaJq20HwLKXD/uRnqYW0PbxxIyOjwSS95dMQtTgsb+lia4XBSOhZcC4a+OsUE8MAH3f2w9MZ9pYNziyPs6yvSb1GEMfZWyKyowv0WpHs6Cnou/te9F0Hbp1jdkWTPX2fjUCkV6+P7bXeb/46IycNrH5zBlQPPYUhfJ5zuoP5pc3mVqLFhNuZ40cMB60JjmRzP44CUYuTde2amuvjz52k4fT4OG7fcZs/BskV98edP0/DU7F0oKW279I5yffqWmXVU0CfxNdfDZ0M8sWGEN5acFvRJqB9FmvpJxfqYcvA+aw9Ic/+PiQEYtfsuitrhAPUkwt7Fcip0cU9HZqD+4U77+kvtRaWFVTukp7/rQBi27wqBlrY6Vi8bgD9+nIJJz+1hK3j+7X4VBXg+EZfDYk2RnM/2pwIwef89JBYLxhM/jfWDkZYGRu66g+zyaox2Ncf3o3zw0vFwiRUC7UHY/ikiY3zr3H0c23YKc9+ZxaR5OB3Ef1PV5omkUxv2STpH6LndUcF2SSZGHPKwlwXJqwghfXsyppL0zNChQx9JK27s1tDQeLTjJ9bYrFy5EidOnGAe9yTxQ8ZzknkhwzFBhl06b+lyiRuNlSmPPMgjnbTsSVqHpGJ8fHyYjA/9L+88Wjq3lsotrZFPhuuamhrmpS99faysrJq93sWxs7NrTiP9G1SH6urqLV5DYV4yopOUkDTC85Z37oTwXEkHf/ny5cyrfvbs2aw8X331VfM1JEpKSlq8hsqUR5x169bho48+ktj38qpZmPfGswi/HY1v3/y1ef8LK6ZhyMS+zNBeKhV0qqSoDIYKLB3/e/cFHNtxGsu+mAevQNc2p5GG5D9o2ag4pAFOiC9FldcxIy+MkJxSfHMnET+N8oeFTjwzZPa0NoKBpjrTpRR/19x8rh9+CknBxnvJipWvqJJ51Itj1vSZvpNFbmElSspr8eWOe+xzUWk1+/vgV+PgZm+E2BTJTsz04W44cTUZZa2cryzIk8e4qb6EmGhrMO3v1pYsk8F+RTdXprl/PVP2BAhJC4xytMCOKOU0dOWhbmiIulLJFSL15eVsVEXf/ZvkNXnqmxpqI15M4oWur/A7aUhTn9h7Ng63Hgp0e8/eScPfN5Mxvp8zM+z39LFkEj/Xf5nWnI+8/JfO7IIXx3uj11zJVUvyKGjyNqfrS8+JEFNtTYTmtewdSKs1Ng7xY4O8RefDJFZu9LY2Zjr9Sy6Gs2eF+DUiBeNdLDHFzZrFXFCE/JIqmEh5MtGA11hfE3klig++I1OLmJalo4U+otKK8VQvB+hpa2DtLsHgk/jgz3sI3TwF43s6YPclxTww82W0LWZtbFt+FGtblEnTEoXVNUwmSRzyXhR813L5elsa49Pe3vghPEluoF264tQG3MwuxK7YdMzxclDKsE8TfY+UT1OTHZN081viKUcrvObnig/uRjNd/o6GgmzWNTTKrD9FJhEWejthvKMVVt6MQHyJYnI2ypDX5KlvqqsBiNltzfU0kVch2wjU08EYFvqauPP6oOZ9tCph+UA3vNzTEd02XFIojSxII7+2TLLdraF2mPpzhgZtzpN57jzT3TfrJlhJauDsDPvx4xC7dTvz5FcTcwpRUVNjGvuUtmzoUGSeuyDXsE/tBwXRfqRtoforVaJtSSsWtC3m+ohKL25zGmnoHnMy0G3Ts+tqoMsCNp/PyJMbaLe9FNXUsFUt4gifFWlPeWUgnfTy2jpsixVI0bH4LLGJ+LZ3Vzjo6yK5TLFniby1pZ9dY01NNsncWtuiCCTvQvr6ymr/ixNx8S6Or/+z+fPkt16Ce08/FvOnQir+RnlxGfQ6WEZIHnlN8lumxjqITxb1Jc2MdR7x/JVFvyA75o2/93jrK0VIu7uopArO9u3rm8l812lqMoNha/fjYGszvB3oga/D4nEuQzm5O3n1ZyLVL6Dn35j6fXKkzSaM8oSergbWfn2p2THnwy8uIPTyKxg30gN7DoniZ7UX6re4SckfmeloyPSklkbYJyHt/K9uJuLXcf6w0I1nHtQ5FTXMk/+jq/HN/R/S2w+Z2x997Izxj5hsz3+JvIoaGGqpQ1NNhcnKCDHX1WDfyaK3vTGT8IlfOlhi/x9TuuBiUgFeOhrWYeWjeA2mUteb7kV1dVXkFch+nqc+5YPKylqs//GmYEcx8OHnF3H73HwMHeCMU+flx69pCeE4g/rJ4kcw06F+S+v9Wqre7PIafHotHmNczTHN2wpf30pi/fChTmZ45nAIk5kiDkRlM2keWj3SkmGfguS+89znzZ8pNuCMV56CoanBo/aMwjJmp6EYgi1x92IofvtsF15442n0GSlYgcPh/Nfo1IZ98swm2RV5kHe3MpCX9717AqObEJJ0aQ0ylNvb27MAqtJe4MpChnSSqhHGAyCDuHjwXDJ2k4c4eWyTVJAQ+tyR5SFj+OLFi9lGBmmSOCKpGpLWaQvi5RaHJGrEvd+F12Hv3r1Mw54QyglRuoyMDLZaQJ60EuUlGR9a6SA05gs/twYdn3T9O+Ia0mSMuBY+Gfkp3oEQmrSRrgvpz20pz+rVqx/R4L9fLFjJ4dfTE7+eFa2AUFUTPB/u/s6IConHuGeHNX8XGRzL9rfE33su4tCWv7F03Vz4SS19UyaNLO5nl2CWjy1MtNSbJXP62BozPfDwPMUlgoSe1EKvgn47b0hMHA9zMsOmEX7ov/MGipXwTLkflYuZozzYcYXzcn27WCMtpww5cgZRwZE5LI04jULvJClPgu7eFkzOZfUmQcBnZXmQV4IgC0mdyJ6WxgjLb9nwS0FZ3wxyw7s3oph2vjxGOJizwEl/JXRM8Dl9V1dknjiB6rw8FjyXKKGA0yoq7Lt/k6TMEibD08vXCnciBVImmuqq6O5lgU0HZHfcC0qqmcyStEcIfRTu23jgATYflMx/6YfJ2HM2Dj8oMQCk+5+Ml0GWRjiVLHg3WOlqwl5fGw9yS1s06n8/2J95a79yPowZX6TLyv6XykeDbHlBE2URHJcPXS11BDibIKwpnkQvTws2iXFPCd1ZTzvB/ZvbNBnA6lL0xEiUVZlAl/dzSvCMjy2MtdSbvdGEbUuEEm0LDYZb80RRJI2sQMcTXaxZFuFZkRZ+ZnkV8lrQCO1laYx1fXzw88Nk7IsX6Sy31j4q60hDEhtdxYLKEt3NjfCwsOW6m+BohWUBbvjoXjSuZD0eb2SSlaLgtl3NjFigUVH5jPGglbZvvrcjJjlbY9XNCBb09HGQWFDBjPt9HE1wO1UwaCXDQnc7I2y8JtuY+92VBGy8KvndtVcHYFdIOjY1BThUJI0sDNzcUBguGeOIPOf17O2Y53zb86g8cs+T4VPwh/w7rrGB/HXlP8v3EgpY29LFyQQPmuIU9PIwF7QtSsSh8bIV9MVammhUJI00YYUlLFCukaY6C9wslNahtiW6hXvKhYz6/fxxMTMP3zxom/FFESKLSlkAXfG2pYupMbIrq5hxvq08LCpBoKmx7PdJo3LHCTCRbFvoWaaAvB3BQGtz5pRwOuNRiTJF8R0cBJ+B3STuazIaWbs7IDU8HoEjBRIYREpoDOz9JMc3j4uktGJmwO8VaIM7oZlsn6aGKrr7W2Hz75LjWlnMGO+N8OhcPIxp3ZBrZa4LY0Nt5OS3b/IzvLAUvSwkJUUpYHlCSXmLskuDrM3wbldPbIiIxz9pbb+W4tx7kMl08gN8rRDW5JzRq5sdM6TSd7IQrKSW9AQWfCad7o5dq3EvqwSz/Wxhoq3eLGXS186YGexJOkdR1IRjoqbPpJMuLHdrfcH/EnebYlz1tjPGlRTBu4SCuhppayBYTvyrOYcfSKxmt9bXxI15ffHC4Qe42nSMjuLegyy8Oq8X7G0NkdZUnj497NkEUkiYbKcNdt9JNbjCzyQr1VZIqokM+L3tjHC7SR9fU1UF3a0NsemuYs5wQliXuNkZUlRuiTKj9eDcFNz25zMie4ZKU1/b3c8ZYbclJycj78XC3s0GWjpaLRr1f/n4Tzy3fBoGjpMvB8jhPOl06uC5FEg1PDwcmzdvlpCpIZkTwrIpECPJoSgCSb9QsFeSwSHIME4654rw6quvsnJQUFYh9LvS0jOtQcbfsDCR8YeC4oob9ok5c+aw3xLK3NBkhLRGfXvKQ4Z8ksARBqQlIz95l0t70CvLyy+/zIIHp6amss8UH+G3335j+6WhmAFnz55l+vqkzU+QBA8Z9Cmgr7Bs1KGiSYDQUIEOKmnpkxF/w4YNzd7/FJNAEWgS4/Tp0xIBhslrXjxOgaLXkAIC028TFKuAZHWkryGtiggJCWGfSXJH+tq0pTwUW4J+X3wTyvDQ4ENNXa15E640GDNzMCLuROPq33dYgNsLR68jNjwJo54Wef+d3HkeC0e93fz51L5LOEwG+8/nwr+XZCA9ZdLIgwJOZpZV4YP+HszT3MtUD4u7OeJAdBbKmrQrSfcv4uWBGOkskOJ60d8Okz2sYK6jwWRFulkaYmVPF1xLL2ReKQT1FerFNmHfgbTklen27DkdwwKmvvFCd+jrajCD/cxRnth6VCAJRfQJsEbUwefg4SAYqB66EM/0EZfN7godLXVYmepgxbPd8DCxQMIznJgxwgMxyYW4H902b5k9MenwMzPA0x42THpljJMF+tuaYFe0qC0kL+ybTw9gdUU85WKFt4LcmVH/QlrLhjcKmns1o6BFQ6MyGPn5QcfODsk7dzK9/cqsLKQfPQrTnj2h2TSJV19djTuLFiFXLCD344A6mdtPRDIv+iBvCxjqaeKdOUGoq2/EoYsiI9mmlYPw+xrB5CPxy9EIPDPSA109zKGhropBXW0wpo8jjl9Lbj4uGaDFN4I6sK11YsUpqqnDiaRsvBLgBBdDXbaSha4bSeXQNRGya0w3vNNTEESK9Ik3DPJj9wIZ9UuajE7ikLc/eeas7ObKjkl5XvCxh72+Di6nK240I2P+7ZhcvP9MV6ZRbWemi9VPB+JKRJZEkFsKkrtgjKBdGNfDHvNHe8LWTJdNogzyt8KqqQH461YKi0lAXArLYl7Ib8/oAn0ddRjqauDdmV3ZdbnWNBBXhLNNbcuafoK2xdNED4u6OuJgjGTbEvbSQIx0ErQtc/ztMMld1LaQhv7yHi64nl7IvN4UTaMIR5OymFb9K37O0FNXYxN0k5ytmB6+uCH90qT+zCBIUJp1vX3wc0Qy9sbJNuov9nNGf2tTGGioMeMWSfbMcrfD3zJ0+FuCZEJ8jPUx1dkGOmpqGGlnwY61N15UvqecrHBhQv/mtmWcgyWWNxn1Lz9miREqxzBbcwy0NmXlm+1mBztdbRxKEhlnFvk4Ye9wkUfWXC9HTHG2YZ76Dx+TUZ+gp3zrnWTmRd/D3hiG2up4f4QXawv2PxBdt5+mdsGu2d2b89D7SXxj+9mEGxROIwuboUNQnZ+P1OMnUFdZyQLc5t68BduRI5rT5AffYzr41YWFCucx694VubfusAmAhro6VKRnIO3E3zD29Wk2/ifs2s2+r6uoRH1VFfudrIuXmBa/PMiYfzs2D+/N6CJoW0x1sXpaAK48zEaMmDGGAuAuGCkIZDquux3mj/SAramOoG3xtcKqSX74604q8pqkMhRJowhXMvORU1mNFQFuLAC5m6Eu5ng64ERKdnMQeop/Q8/GIBtB2+Kkr8OM+iS98ziN+sQ/aVmsXX/Rw5m9CwJNjZg0zeFk0bPbxdQIx0b0h6Oe4pKm5zJy2D33nJsja1soDscLHk5IKClDWrnixt8jKRnwMtLHREdB2zLUxgI9zU1wMElUvrH2Vjg5sj80lJCLFDLGzgq3cwvbNYlB/WZVNbXmTdiP7jV5KCIvByP6RiiqK6pw88AZFGbmImjCwOa8F7YfxQ9zFRuPtKnfsj8Mc2YEICjAGob6mli9pJ+g3/J3THO6TR+PxI71krEuKO3Igc7Y99ejknGWZrr4/O0h8HQ1ZRMF7s4mWP/BCGRkl+LMZcEKjbZyJDkT1jpaeN7dnt2PfS1NMNKWVlSI2kJqx8+N7QfzphgpA6xM8V5XT6wPj5eYvG0vZMy/fS8d760cBBsrfdjZGODtZQNw9WayRCBc8saf/4Lg3XHpejKbVHx76UAWONfQQAvvrhzE5Bev3e5YuZZTiXnIKKvCRwM9WP+MxkRLghyxPzILZU0SKxTYNXrhQIxyEbQtpJs/1UvQJ2GGWCtDrOrjgqupojHRtbRChOWU4p1+rszZgeLvvNvfjfVZbnWQFEpnJKmoEmfi8/DOQDc4GmmzuvtgiDvuZZbgToZohdateX3xRj8XOe9ZQZrGxzAJcvVmCqJi8/DhW0NgbqYLFydjLF/cBydOxyAnTyCxqKOtjsibr2L6RMEK/nOXE2FloY9X5/Zkk1Smxtp4f9UgFmcjOEQxZw9Z0Llte5COlwLt0cPGkK10eG+AG6uDA1GiSYYfxvjiz0ld2N+2+lpYN8QTnqa6rB9Ifeo1A9xgpquJv2IEz21ScSWi8sqwso8zHAy1mZPDUx4WGOhgitOJrY99xe0ZQkfeYVP7Iz+7EH/tOIPK8iqEXIvAzbP3MOpp0SqL4EsPMG/oKhTmCu7ve1fCmFH/+RXTMWjCoxLHHM5/iU7tsU+a7qRJTkZsMkSTBjwZfsljmiCv9cmTJzM5Ewo6SlrrLRl5g4KCmIwJecJTQFYynFNQ01u3bsmUchGHpHNIC59+g/ToaUBFcjKKTgwIoaCqJN9CAXxJuoakgby8JI2iFGj3+vXrbMUCeX4XFBQwfXbxCYz2lIeMw6RbT/I25GWfl5fHZGLIQN0eSLOfJi28vb1Z2cmwT+dK3u2y6NatGzPuk+c+dZ5JgufUqVMsSC4FGCbvfNLjpwDDQu9+CihLQZBpsuCXX35hdUjXn+RzWruGY8eOZXnJ45087EkvnzzmpT3gW+P9999nZaLy0T1JQYcHDhR17gm6Lym4ce/evdn1pZUIlGfPnj0dXp7WoEAzc1c/g8NbT+G3z/fA0tYMi9Y8B88urhKz/vViHjRHt51GbU0dvn1DJO1DjJs9DNMXjFM4jTxoaeTcf8LwYX8PXJ7VG1V1DfgrPgfrbooGvjScIr1D1Sbfk2NxOcz4v7yHM1v+nllejb8Tc/FrqGAiqSPJLqjEy2vPYc38Xnh5oi8KS6rxy6Fw/H5C5ClAnh3U6Rd6J5RV1GLOB2fwwYLeWPDnTCaxcz00E29vvC5h2KUJg3H9nfDtTkHQ0LYQUVCGt69FYUmgM1Z2c0NWRTU+uxOHa2LSOqpN9SccHs/zc4SmmirW9ZOUeToUn4kvg0X17qCvje6WRlh6qeOWGZPXm8eSJcywH7p6NVTU1WEaFATHmTOl3FEaJNw7Ij79FBU0UdjkJkWGf6Lbt99CvY2xVoifjkRAW0sdP6wazAz7EQn5eOmTcygUM/KQN7aa2Kqw/efjmVTM9ysGwtxYG+k55fhq5/12BcqVxxd347Gyuyu2jwxk1yw4pxivXQyXCIRLnllC76weVkboYWXMDIhnpkh2XGf9fQ8JJRXMqE3HeL2rCw6OD4KmqiqSSirx9rXIViV+pFnyww189Fx3nPl0DBsUnA/NwAd/St7P6moqzc67F8MyWUDcP1cNhrWJDtLzK/D7uVhsOSUySCTllGHed1ewYoo/rn09gV3yqNQizP3uCkuvKNS2zD8Vhg/6eeDiM4K25Xh8Dj6/9WjbIjTa/BWXg1e6OmJZU9uS1dS2bHkgalsUSaMINFm28noElnVxw0x3OyYv82dsOg4kiAzTZC+n8gl53tOercRY7O/CNiHBuUVYcV3gXX0sKQtzfRyxurs7tNXUmM7079GpzFCvDOTNvuZuNBb6OOH1AFdkV1Tjq9B43BST1qE2WbxtmeMpaFs+CvKWONax5EysDxNMljkb6GLbYIEnLOVd5OuMhT7OuJFdgHfuKB6j4EJmPoy1EvG6vyszMKaWV+Ldu5FILK2QaJuFzwY9wWR8pXfc5v6CQamQnyKTsK+pfvpbmeLjHqLy0990Dx5MzMDmh4obuH64ngQdDTX8PC0QRtrqCMsqwfN77qNQTE6ByqdMnJu2omNtBd/XlyBx736kHDsOTSNDOE+bImFcb2xudxXPYzdmNIupkbBzF6oLCqGurweTgACWToj14MFIOfYXYn79jRn/dSwt4DRtKqwHS/aVpHn111tY+0xXnP1wJKv/c2GZ+GBP6CNti7D+LkZkYf4IT+xcNhDWxjpIL6jAjovx2CLWLiuSRhFqGhqx8mY4VnZxx6FRvZjX8Zm0HGwSk9YR9VsE0IQSBY2d4GjNNiG5VdV4+qxI23z38CBY62izZ5/OjSYHiKHHRc47rZFfXYM19yKwyNsNU5zsWODcA4np+CtFrG1percJbz91FRUcGt6P/U37fYwNMdrOGqkVFXj1uqBNJxmud++G4xUfV0x36cM+h+QXYX14rFL6+tHFZfg0NBovezhhkbcrciur8d3DeNzJk2xb2EqopmUHTnq6+LGfoN2g/S97OuNlD2fczivAh/dF7YatrjYCTI3wfrDkapOOwmdgd1SUlOHcL4dQWlAMMztLTH13PiycBLKPRGN9Y3MwaSL2VhgOffZb8+fDn9PfKugxcTCGzxU9K4ry8877zNi3+ZNRMDTQRER0Hl5edYIF3RSXl2leSdbEpFEerG9A+vrSkFf+1TupAuO+iwk71vXgdKxYew7lbZCJFCetvAqr7zzEYl8XvOjhyCZcfolKwun03Eeut/B+fM7dnr1LVgW4s03Ipax8rL0vuepZWZa8dQJr3x6K0wdfYG3L+SsJ+PCLixJpqE8vbFuSU4swb9lRrHilL66enMv6oJGxeZi39CjSM0Ve9Ns2Tkbfng7sHNTUVBF18zW2f9S035GioMwX9VteOh6GtYM8cO0FQb/laGwOPrsmY0zUVD76noz/K3o5s8mAzLJqnIzPxc/3RX0Satbn/x2BDwe64erzvdlqUJIRnPNX2GPT19fUVEd+lMBhTV1dDX2CPPHSM0MRHZ+BHiPfxL/F8lNRWDvUA6ee6wkaql1KKsS750V9TlE//98XCKcx4YJlf2Ht6qG4eGwOamsb8Pe5WHzytUilgvqntKJE2E8Nj8zBq2+exJJ5PbHgxSDmSBb2MAdzXzsm0Qa0hR+DU9iqXwp0a6SljvDcMsw59kAiEK74mCOjrBo3M4rw1XBvNglVWlOH0JxSJrsjDMpM9968k+FY3c8Vh6d3g76mOlKLK7H2ShyONBn/lcXawRJLP5+HvZuO4uj2UzAyNcD0BePRf0zP5jTM4aG+oXkoefyPs6irrceOr/ezTUjQoC545aMX2N/FBaVYOU0gc0x5M5KycWrvRbj6OuGdzYLnmdMC/0J/lqMYKo2KRJv4H0Oa8uQhTcZnaVkXggKXkpGeguxKG8llQcZTMpKTcZ883idOnMgMxGQcJm30Bw8esOCusgISUTqSl6EJBjK6i6ehwLX0vZ+fn8RvkQd7jx49JLyyyehN3tZUBjKGk1GXjOzS0i0k1UOTFnQcMsDTZIei5WkN8oqnvFSv4nllnQfVMen6i2u/k4wR3T5UPnHIiJ2WlgYnJyemmS+OrGNTEGDaevbsySYdhL9Hkw90bGlteoKuE9Whs7Mz8+C3sbFh3vFUP+RJTxMj9LesvHQ/RUZGshUKdHxxTX1aPUHSPr16iZZq0coOOnbfvn2bVzVQXVDd0WeqE5qAkK4fguohPz+f/Q5NvNAKFLq3FC2PItzIaX+wTrbktKGxWb5H8FJ8tGmg5XDCmXNF0hAvHmu7BqmaisDzvj20doz64233SqIOoTIyIW3JZ/yMyJDXnnMXjpWloWI0tqPevW3aEzpPEjYoblr6zj5LGfrFdZsV4eamtk/6UBGoHMp420tDg+yW8htNdWr7sZtkohrFrpks2vP8FJ2VvSRY0fI97tikmv0k3y//dtvSGqamqh1SPrq0ssaesp5dZVBtxyI9ibZFTr9eunxqSp6DMv0ZiXxNW4OSz4Yy5UsOa7tMhXC1urznQ5Fnp7U0wwcq1o8QGvYVbVM7iou7FF8l9L9oW+zGClYEP65nl1W72Gd5z5C8NspQMgRQu+pPpl1LqnzKUtf4eMsn/u6Tl68lnnF5NPB4e2FSU40iGUxBv7pBZrvWLF0lg8/mJXVov6Uj+jLi2L3lj46kLe+Slkj7QDRh1tF9NllQeSmfNPLkURrniqSeOmO/JWPdj+0+Bk1wSMMMrh1wD1osl+0s2BH9aGnIkC1cIacoWjs7TotfTU2lXTI70tS9JOnc0JH9lo7gz+ntX1EpNOyTp78ytop6GfHpBCu4RGn6W41vd/n+i7hP3IEnkbhjc/Bf44kw7Hcku3btwowZM5jxlIK6kvc+GcX/+uuv/3XROApCqx3IS5+uGxn1aUXAuXPnEB8f3245oSeRjjDsP07aY9j/N2iPYf/foD2G/X+DjjTsdzTtMez/G7THsP9v0B7D/r9Bewz7/wbtMez/G7THsP9v0FbD/r9Bewz7/waKGvb/V7THsP9v0B7D/r9Bewz7/wbtMez/GzwOw35H0R7D/r9BRxv2O5r2GPb/Ddpj2P836AjD/uOkIw37j4OONOx3NO0x7P8bdIRh/3HCDfuy4Yb9zkOnluJRFtKrJykXWZCUDUmokEc2SdDQRoZgkoP58cfO/RJrC6NGjWISMbIgyRsyjD+pUPBg8vgnGSNaeWFqaspiEPx/NOpzOBwOh8PhcDgcDofD4XA4nP9//KcM+zNnzsTQoUNlficMtLt06VLMnz+fybiQTAxJuPwXoVgCJOMjC2m5mCcNiotAkj4kVURBf0nypzN79nE4HA6Hw+FwOBwOh8PhcDj/Cf4HcSo4/w8M+6RPLq33LgsKeNq1a1f8lyE9+P8yJKXk79+5l4NyOBwOh8PhcDgcDofD4XA4HM7joHMLwHI4HA6Hw+FwOBwOh8PhcDgcDofD+e967HM4HA6Hw+FwOBwOh8PhcDgcDucxwZV4Og3cY5/D4XA4HA6Hw+FwOBwOh8PhcDicJwhu2OdwOBwOh8PhcDgcDofD4XA4HA7nCYIb9jkcDofD4XA4HA6Hw+FwOBwOh8N5guAa+xwOh8PhcDgcDofD4XA4HA6Hw2kdFS6y31ngHvscDofD4XA4HA6Hw+FwOBwOh8PhPEFwwz6Hw+FwOBwOh8PhcDgcDofD4XA4TxBciofDecIJK+jcj3GAKzo1d3rZoDPjatWIzkxaWee9/2p72aIzY2rSuefWCww10Znxdurcyz/9TarQmXlYpIXOTFU9Oi05P21FZya1+xJ0ZrR6mKMzY67bud+7Gmqdu3ymmp344QVgoNF560/leR90ZrqbV6MzkzzcDZ2ZLh6du99St+hldGb8/TrvmIMIm+2Pzoqrc+euu7qGzv1scOTApXg6DZ3bqsDhcDgcDofD4XA4HA6Hw+FwOBwORwJu2OdwOBwOh8PhcDgcDofD4XA4HA7nCYIb9jkcDofD4XA4HA6Hw+FwOBwOh8N5gujcYlscDofD4XA4HA6Hw+FwOBwOh8PpHHA38U4DvxQcDofD4XA4HA6Hw+FwOBwOh8PhPEFwwz6Hw+FwOBwOh8PhcDgcDofD4XA4TxDcsM/hcDgcDofD4XA4HA6Hw+FwOBzOEwTX2OdwOBwOh8PhcDgcDofD4XA4HE7rqKjwWuokcI99DofD4XA4HA6Hw+FwOBwOh8PhcJ4guMf+/3MOHTqEBQsWIC8v739dFE4HUVlajgtbDiLx7kNABXDrGYCh86ZCS0+nXXnSIxNwfddJ5CSm0fQsrNzs0f/ZCbDxdGLfx90Kw7HPf3vk2I0A3N54G7rOLhL7a/JykbFvD8pjY6CqqQXjXr1hPXkKVNTkN0sVCfHIv3wJxfeDoeviCtdlKyV/q6EBRXdvo+DSRVRlZEBNTw+GXQJhNXEy1LS1larHAGtDfDDSE75WBsgvr8HvwWn49Xaywnn3P98DRZW16LPpSpvTtERJWBgyjx5CTU4ONEzNYDVuPEx69ZabvrG+DkX37yP/0kWUx8fDdupUWIwYJZGmOjsbOadPoTTyIRqqq6Hj4ADrCROh5+6O9mKmpYnX/VwQZG6MmoYGXMrMx4+RSexvWWipqmKikzVG2lnAVlcbuVU1OJGajYOJGeyeai9L+jhhVhc7GOuoIyyrFB+ej0FUbrlCedeN8sLTATb49moiNt8S3RMDnEwwr4cDAq0NUVnXgOsphfjycjxyymuUKpuGigrmeTljiI05q4eQgmJsjkxgdSAPC21NjLO3xlh7KxhrauCpMzdQ2yhZUw56Opjr6QQfYwNoqKoipawCu+LTcDuvUKnyGelq4IMpARjqY8WuxdnwLHx8JAylVXVy84R+Ng66mmoS+74+GYmfz8exvzXVVDG9tyOe7uUIF0t95JZU4UhwGn46F4u6BuWuOLUtWft3oyJO0LYY9uwNq0lTW25bEuNReOUiSu4HQ8fZFc5LV8lNW1tUiITP16K+vBxeX26Amo6uQuUqSEhGyO8HUJScBi1DA7iPGgSv8SPanae1NPU1tYg/dwVJl2+iLCcfOsZGcBrQE42DJ0JF9VH/jtLwB8g+dhjVOdnQNDWDxdgJMO7ZcttScv8e8q9cREV8PKynTIP5cMm2pb6iAnnnTqPk3l3UFhdD09wcpgOHwHTgYCjLSFsrTHexg7m2FlLLKrA1NgkPCorlptdWU8UQawuMc7CBs74ePg2NxK3cgkfSmWtp4kUPZ3QzN2b33LmMHOyKT0Gd1HPUUZga6+OFpwdj7rPD4exgiV5j3kZkDL1fHx+dvm3RVMc7fd0wyMGUdR7Op+Rj3c14lNXWy80z0N4EL/nbw8dcH5W1DbiZUYgNwUnIqahRKs2T3LY0/1ZyEtL37UVVWirUDQxgNngoLEdKPoviNDY2ovRhBOsX0P+m/QfAftazkmUqLkLumTMoCQtFXWkptKysYTl6LIy6dm2xLHWVVYjbux9590JZ38zU3xces2dC09Cg3XnyQsOQfPxvlKdnQMfSAq5TJ8KsS8Ajx2uoq8O9dV+hLDkVgStfh4mPNxShJL8Yx388iPj7MVDTUIP/wG4YO28iNLQ05eZJj03F7ZPXEHbpHszsLPHqRvnXuaKkHJuXfIWSvGKs2PY+TKxMFSrXoq4OmOltAyMtdYTnleHTG/GILpDfb3E10sGibo7oa2sMDVUVROaXY9O9ZARnl8hM//EAD0zzssb3wUn4KSS1xbLkR8Uics9BlKVnQcvYEK5jR8Bp2KB25amrrkbS6YvIuHEblXkF0DYzgcOgfnAZMxwqMrxC8x5G487Xm6BrZYHB69ZAWcYF2OD1ER5wMNVFSn4Fvj0djTMPs+WmnzvQBW+P9ZHYV13XAP81/6C9UNuSvX83KuNioKKpBaOevWHRSttS2dS2lDa1LY5SbUtdSTHi3n3jkXx2cxfCoGvQf2pM1MPcBHPcnWCrp4OcymrsSUjFpaxcuenVVFTQz9KM9Qt8jQ2wLTYJR5IzHkmnr66G59yd0NfSjL3bbuTk47eYRFTUyX8nyeJpP2u80tMJNgZaiMsvx2dX4nE9tUihvIt6OOLNAS448DALb56Obt6vq6GGyd6WeC7QDt7melh4LBxnEvLRETjp6+B1P1f2Ti+rrcPJ1GzsiE2VO/4y1FDHdBdbDLQ2Y32G9Ioq7E/IwNkM+degI6itqcOhn//CnfP3UVNTC6+u7pi1dCpMLU1kpm9oaEDwxVBcOnoNafEZ0DXURWA/Pzz14hjo6su3k3A4TwLcsP//HGrg6urkG2M4Tx7Hv9yGmsoqzP5qBRoaGtnnk9/+jinvL2xznoqSMhz88Ef4Du2J8avmsMHgld//wsEPf8D8LR9CS1cHbr38sezANxLHPb15D2Lux0LHUWD8Fx9sJW7cAG0bW3i8/xHrfCb/tBmN9fWwffoZmWWsKytDxv69zBDU2FCPuqJHO0SVSYmoiIuFzYyZ0LK2QXVWJlK3/Ya6khI4zlugcB1a6mli5+zuOBiWiUWHHqCLjSE2T+6Citp67LzfsuFFT1MN30/yx62UQtYBbmualqhISUbST5thPWkKTPv2Q3FoCFK2b4W6vgEMfH1l5ikKDkZxyH1YjZ+AlK1b0CjDWJp59DAM/PxhNXYcVLW1kPPPP4j/bj0833kP2jY2aCs0HFvX0weltXWYdyUE+hrqWBvkDR01VXz+QGDYlWa0vSXMtTXx5YM4ZFZUwd/EEGu6eTIj3Z9x7TN+LezpiAU9HfHKsXDE5pVj1QBX7JzRDUN/u4mS6pbbw/FelvC3MkBBZS1UxQaa5roaWNjLCVvupOBBdimMtdWxbpQ3/pjRFeN23EG9EsbBxb6u6GFujPeCH6K4pg7L/NzwaZAvFl0PgTwb92JvV8SXlmNPYhpe9XEVVLpU2nU9fBFfUo4lN0JRWVePqc62+LC7NxZcC0FaeaXC5ds0pycMtNQxZcNlqKqqYPOcHlj/XBDmbbklN4+aqgqW7LiLsxFZzfvqxU5mVBcbeNsYYs3BB0jMLYOfvTE2vhAEYz1NfHIkXOGyNdbVIWXzBvb8u723FnXFRUj9ZTO97GA9XX7bkn1gL0wGDEJjfQPqiuUbI8nYlL59C3QcnVEWEfZIHcujsrAYlz7bCOeBvdFv+XwUxCfjxsatUNPSgvuIgW3Oo0ia9LuhqC4tR+9XX4SumSny4xJxc+M2GJWrwGrCJMnfTElGys+bYTVxKoz79EPJg/tI2/Eb1PX1oe/jJ7OcxfeCURJ6H5bjnkLatl8h6yYtvHYFarq6cHzlNWZwLA0PQ/of26CqqQnj3n0Vq0SADa5f9XXD+vBY3M8vxARHW3zYzRev3whBWoXse/gpB1tY62rj5+gEfNmzC3s0pDHS1MA3vQMRVVSKFbdC2UB2tL01Ak2NEJyv2OBbWd5fMR1V1bX44Mu92PnjMpnl6mg6e9uyfpgPez8+c+w+a1/p81dDvPHKmQiZ6c20NTDV0xo/hKQgMr8M1npa+LC/BzaP8MOMY/cVTvMkty3iBvj4Dd/CtE8/OC98hfWHkrf8AlUtTZgPGiIzT0ViAvLOnYXZwEHMaE/nIk3OP39D08wcLouXQE3fAIW3biLp5x/g+toyuf0NImrrDpRnZqHbWyugqqGBh79uRfjmn9F99ap25cm+dQeRv+2Ax6ynYdW7J2pKS5B45LhMw378/kPQMDBgdSurzyNvXPT7ml+ga6CLJZvfQGV5JXZ9vBU1ldWYvupZ2XnqG3B4wx70GtePGaBTo1o2eB78dhcsnWxQlFNIsysKlWtuF3vM6+KA188+RGxhBZb1dMb2cQEYve8uSmpk91sWdHXA+eR8fH4zgRky5wXaY+u4AIzZdxeZ5dUSace6msPXXB+FVbUyjejilGfnMoO6y5hh6LliMfIjYxH683Zo6OrAtk/PNufJuH4HjY0N6L5kPrRMjFAQFYv7P25l5SHjvjg1pWV4sOV3mHq5o7JAuQlEoreLKTbM6oYPj4bjn/AsTOpmh83PBeHpn64jJEV2m09tUkx2KSZuvNq8j8ZD7YXaltSmtsWlqW1J/2Uzu2+t5LQt9U1ti/GAQdSZQq2stoWK1tAA59UfQMvGVuxElBNs6OxjIjcDPbzX1Qe/xybjbEY2+liaYaW/J4prahFSIPtaDrAyR19LU+xOSMGbAV5QlfEGVldRwac9AlBVX4/3gyOQU1WF/lbmLO/pdPkTQNKMdjfHZyO8sPJUJK4kF2JOVztsn9IFY/+4i/jCihbzdrU2xHOBtojJr2DPsDgvdrWDk7EOPrwQiwMzu7f63CqKrroavuntj7t5Rfjs4j046OtgbXcvNo75Q874a4aLLaobGvDhvSjkVdUwA//qQA+W50Lm43Me3bPxEB7ejsJrn8+HvpEe/vxmP75/8xe8v2UV1NQlnYmIxMgU3L8Shokvj4Wdqw3yMvKx9bOdyE3Pw5J18x9bOf/TcCWeTgOX4vl/QHBwMMaMGQNjY2O4uLjg888/Zx3XS5cuYebMmSguLoa6ujrb3n333RaPZW9vj3379jV/HjJkCExMTJonB+Lj46GhoYG0tLTm3x45ciQMDQ3h4OCAhQsXokjMIHvr1q3m39bX10e3bt2wd+9eid/ctWsX+91NmzbB19cXBgYGGD16NJKTJTvOf//9N4KCgqCnpwdnZ2esXbuWnaf0cX755RcEBASw+hg2bBji4mQbFqVRpKzK1I+i56UM2fGpSHkQg6Hzp8HE1hJm9lYYMncKEu5GID8ls815CtNzUFtVjaBJQ6FrZAA9Y0METRyK6vJKFDXNxlOHQlVNrXmrr61DzLUQmPQb8IhXKBmBanJzYTv7eWiamjJvfsvxT6HgyiXUV1XJLCcZltzfegem/fpDVUO215SuqxvsZj/Pjkce+vQ/rQSoSFDsGgt5pps9auobsPZMNPLKa3A+Lg+7QtKwqI/kBIUs1o31wT/RObiZUtiuNC1BA3EdR0dYjhot8MobMBCGfv7IOXNKbh7y5ndesAgG3pLeRuLQ92b9BzBvWpoksJ0+A2raWih5EIL2QF76nkb6+DYsHlmV1YgrKcevUckYaW8JUy0NmXmOpWQxj35KW15Xj1u5hTiZmoNhNubt7n/M6+mArcFpuJZcyLzp3z8bA20NVeaF3xIORtr4YJgHlh5/KGGUJvIqavH8/hBcSipAYWUtEgsr8cnFOHhb6MPTXE/h8hloqGOMnSW2x6QgtqQcOVXV+O5hPFwM9NDbXL5X30chUfgzPhX5cjxvydPWSkcbf6VmMe/csrp67ElIZx5INChSFD97IwzwtMCHh8OQlFeOhJwyfHwkHMP9rOHeyoCsobGR1ZtwE+f4/XRm1H+QWsQ8/2/G5WH75UQ81c0OyiBoW3JgM+t5aJiYMi82i7ETmVdbS22LyxvvwLjvAKhqyr4fheT9fRxqOjow7t+yZ6I0CReuQVVdHV2fnwZtI0PYdg+A67D+iP7rTLvyKJLGsV8PBDz9FIzsbaGhow3rAB9Yd/GR2S7mnz8LbQcnmI8UtC2m/QfBwNcfuS20LeTN7zhvEfS95LctdDzy4teytGJeyJRH285e6bZ5mrMdrmblMU+8kto65lFP3nkTncSMFlLsT0rDxodxiC8pk5tmlqsDG4B+FRaN7Mpq1uYcSkp/bEZ9Yvma7Vj96U7EJYomux4nnb1t8TXTR187E3x2Ix7JJVVILK5kBskhjmZwM5btuZ5fVYvl5yNxN6sY5bX1iC+qwJ7IDPhbGEBTTUXhNE9y2yIk/+oV1hbYzngaGoaGbLWi6YBByD0t/9nVc3WD6+vLYNStu1xjn93MWbAYMZJ56qvr6cFi2HDWtyoKviv3uJU5ucgNvg/3mdOhZ2fLvOo9n30GxbFxKI6Lb3MecgqJ3b0P9iOGwm7oIKjr6kDXygp+C+c+cjzy6i8Ii4DHzOlQBvLSz4xPw6TXn4aJtRls3ewx6qUJCDl/B6UFsj3dVdVU2SRAr/H9oaWj1eLxrx+5iOqKKgyYNlThMtFd+nKAPXaEp+N6RhFyK2vw0bVYaKmpYpqXldx8b1+KwT+JeSioqmV5frifAh11NWbAF8feQBvv9HHDqgtRj7ybZZF89hK0jI3gNX0StAwNYds7CLZ9eyL+xJl25XEcOgAeE8dC39YaGjo6sOrWBaae7iiITZA4FhnTQ37ZAcehg2Dk7Ii2MG+QK65T3/5WCgrKa7DtaiJCUgoxb6Brq3nF+zFKLiiUSWnofdTm5sBarG0xGzsRRS20LWr6+nBualtUWmlbaAymoqYm2pQ0AHf2MdEkJzv2fj+UnM76BWR0v5tfyPoL8qA+xOcPoltc7TfewQZ2ujr4NCQSKeUVqKpvYCv5lDHqEwt7OOJ4TA6ORuUwp6D1N5KQWlyFl7rZt5jPQFMNG8f54s3TUSiuqn3k+x/upOCtM9EIz5Hft2kLtFJaT10N68PikV9dg5D8YuxLzGAe+fJemb/FpDCnq+SyStZ/+icth43bhtm2b8zWEuUl5bh28hYmzR0HJy8HmFmb4tkVM5CRlIWwW5Ey87j5OWPBBy/AM9ANega6LN+EOaMRdjMSVRWynzUO50mBG/b/4zx8+BCDBg2Cj48PHjx4gIsXLzJDfmhoKNu/e/duGBkZoaqqim0ff/xxi8fr168fLly4wP6urKzEjRs3oKqqygz4BH3n5OTEDNbR0dHMcD5lyhQkJibiypUrzKD97LMij5fevXs3/3ZmZiabWHjppZdw8+bN5jRknE9PT2dlPXr0KCIiIqCmpobJkyc3e0rQvokTJ2LWrFlISUnBli1b8P333+Orr7565Dg0AfDXX38hKioKmpqamDdvnkJ1qUhZlakfRc5LWUguR11LEzaezs37HPzdWacuPSqxzXksXe1hbGOB0H+uMa+l6opKhP5zFWaONjBzkm0Mjbp6D3U1NcwQLw3JNZD3CA0+hZBRiLxWyGO0I6A6rMrKZMvfDbt2VypvD3sj3E4pknCYu5ZUAEcTXVjoyV+K/XQXW7ia6uGby/HtStMaJKWj7ym5pFzfmwx1bT+mLBpqqtFQUwNVLeVkjKTxNzFgxjdaminkXl4R8z6hJbCKYqipjop65Za/SkPeLZZ6WrghNoCorm/A3fRiBNkayc2nrqqC7yf4YcP1RCS04mEjxERHMNgqk+NNJwtvI32oN0lkCCFDY3p5JXxNlPdkElJUU8sGMKPtrNiSWU1VVUxxskFBdQ1CWxjYSNPDxRQV1XUISRbV3634fNTVNyDIWfbSVyHrZnZF+Ofj8c+bQ7FwmDur05Yw1tNAeQvyPrKoTIhjbYu6oeha6nl5s7alKiUJ7aE8LgaF16/A5tk5SufNi06AhbegXRVi5eeF8tx8VBYVtzmPssel9w557OdERMtsF8sT4qDn6SWxT8/bhy357yjIOFca9oCtqDLo0k3hfOQ952GojweFkudFHnk+Rm1/NoQrAa5m5z022Z3OQGdvW7pZGTIP0NDc0uZ9d7KKmCxSN0tRX6ElyBt/socVLqXmo6a+sc1pnqS2pfkY8XHQ8/CUaAsMvL1Rk5fH5K86kvqKcraqTx5CQ7yxt6eoLM5OUNfRkWvYVyRPcWw8aktKYd1HvjQYUV1YhOjtf8Jn/stQ1WrZ0C5N8sMEGJkbw8zWonmfW1dP5vGfGtW+65wRl4ZLe89hxhvPK2VcdTTUhoWuJm5miCYa6d69l12i8LOhp6GGOf52yK2owb2sYol29duh3th0L4VNpilCYWw8zHxE14kw8/VCSUoa6qtrOiRPQ3098qNiUBSfCOugQInvEv85h/qqariNH4m2EuRkihvxkrIl1+Ly0d2p5X6Mq4U+7n8wCrffG4EtL/aEl3X73j2Pu20hUr7/GjErlyDx87UounFV6TFmZx8T0ThC2kAfml8EbyXGF/L6Bffzi9hkQVshCaxAKwPckJLduZZaiCDblp/dz0d64e+4XIUlezoKWiEdVVzGPPDFx2y0stFRT1e5MZuSkkXKkBCZwlZLeXUTycWa25jCwtYcCeGybR7yJgjIu1+9lQkyDqezw6V4/uN8+eWXzDt9/fr1zfvWrVvX/DcZnQnyQlcE8kAngzlx7do1tgKgV69ezGBNhm+aOKA0BBnVJ0yYgMWLF7PPZmZm+OGHH5g3fUZGBmxtbSV+mzzWp0+fjsOHD2P//v3o06ePxG+Tp72Hhwf7mwz3jo6OOH/+PIYPH46vv/4aAwYMwKpVguW6I0aMwDvvvINPPvkEb731VvMxqCO9detW5kVPrFixgpWxvr6eGdVbo7WyKlM/ipyXspQXlkDHUE9iwEDe89r6OqgoKm1zHtIVnfzeAhxe+xOCj5xn+8i7f+oHi6CuIftFGH7mJly6+zLvE2lomSl5g4qjbiDwIKrrgAFo/NdfCIzcjY0wDOwKm2nKeWxZ6mshuVCyI1XQpMVLndhcGZrpbma6eHuYB2b8cUeuJrgiaRSBBumP1J++PtPFJ+8eZeMJyCPzyBHm3WPUXTktTmnMtDVRWCPpbVJcW8e8nUxb0KwVx8dYH8NtzfFtePsMjHRtiXwpbeWCilrmkS+PNwa6sjy7Qh/V4JQFedK9PcgN15MLmWeOogjrg5YQi1NcWyt3dYOifBwSzWQ3Dg4XGEUKq2uw5l4kM8wpioWBNgql7n+6jsWVtbAwlF9//zzIwLbLCUjNr0BvNzNm5Lc11sEHh8Jkpnez1Mfsvs74XkxPVBGo/SC5CHHUmp4VkuRqK/XlZcjYvgW2z85hq1mUpaqoGPrWIkMRoWUoaPOqikqY7n1b8ihz3CML3kRNeQVrFz3HDoPmoKEy6+/RtsWgQ9oWkiWJenuFQO5DVQ0202Yw6S9FMdTUYIZp6fuVnhUTBdsRWZBhi/T6y2vrsLa7H9OUpd+4lJnLdHr/K8b+Tt+26GiiqFoyPdndS6prYa7b8vVdN8gLT7lZMsmvkJwSLDoV3qY0T2LbIl4+LQtLOeUrhoaR/IlrZci7eAHVuXlw6iNfQqua6kpHG2pS/UMNA33UFJe0OU9lbi515FGRlYXwH39BTUkJ9Gxs4DxxXLMUD8mXPPx1G+yGD4GhixOqlJRpIa98PWNJj3ZdQz02XiotbPt1rq6sxt51OzDhlakwsjBGvhLa0xa6gn5LgdSqGZLNIW/7lpjgZoEvh3iziXQy6r96JgKFYpKDy3s6s1Ute6Nkr+qVBb13zPwknUu06F5rbER1SSl0Lczalefki682G589Jo+DXb9ezd8VJyYj/sRpDPjwbZkxYhSB6sJETxP5UnJEBeXVsGjqI8qisKIG7x8Jw4XIHOhpqWP5SE8cWNwfY9ZfQnqh4pJjyrQt9e1oW2iph/GgoTAZPIx5+Jc9CEH23p1oqKyE6bCR/5kxEb3/H3mv1dRCV12dyXeSp31bsNHVRmJ2PlYFeKK3hSmT6LuTV4gdsUnMK10RTHU0oKGmKnPM0dKkyOwAG7ia6GLZ37I9zx8nZloaKJR6Fxc1OShRXyFRgQUCQ23M2YTLllZkydpDcb7g2TCQaq8NjPVQXCDb5iFNSWEpTv55Fn1G9YC6DOkeDudJgnvs/8e5d+8eBg9WPjidPMgoTZ745LFORuqhQ4eyffQ3QfI+QsP13bt3mVSNtrY2tLS0mHe8e1MQzoQEwbLK2tpavPfee/D09GQSOmQ4Jw928roXR0dHB35+In1fmhQgaR9akUCQt7v0RAB5zxcWFrKyCrGwsGg26hOmpqasDGVlrb+lFCmrMvWjyHlJU11djZKSEomttqb14G8qKqpKe2iI5ykvKsH+9zbCubsPFu34BAu3fQx7Pzfse28jqsoe9V7OT81CRlQiAkYprp0MFWFz1H4jiuuKN+C3YRNc33ibBYBM+e3Xdh9T2OeU5WVFnuebJnfBt5fjEZ8v25tbkTTtohXv57YM3vMvXYDjy/MkVlb8L7DT1canQT44k57L5HgeByQTI8+Brr+jCab4WuPNf6IUOhZd6+8n+MJIWx3LTsrWhm4N6adA8CiqtCto5pc9/Zj8xrMX72LquVtM7mhdDz9Wv+2F2oqWSrdq131EpBWjpLIWZ8Kz8PWJSDzX3+WRgLqElZE2ti7og2sxufjlgnJSLY+rbcnYuQMGgd2g76u4IbrVYjWXq2PzyEsz8afPMWXL1xj45mIkX7vNAuQq+KPoCGjy0e+7H+Hz9Xewf+ElZB05iMIbIq3ittLeN4ZQt/ZpVwf8k5aFFy7dwYaIWIyxt8YcD9FKtv8KT1rb0qBA6d65HI1uO65i4qG7TDJiy5iARyQDFEnzX2lbhAj7Kx01N1US9oDFOrJ75hnoODi2rb4a25GnqSOW8s8ZBCxZhL5ffgrzboEI+/5HlCQKPJuTT55icZicxo1Gh9OOeqRgvA4+TggYpPgqpdZQxBZ6PD4XAVuvYODOmzgen4PfxgbA2UgQJJKC6k50t8Q7l2M6sA/a2O48Y377HqN++gZBry9g3vnxJwVyPeTZf++H3+D77AzotCAf1tZXWmv1eeBuGtsoeGxKQQVW7Q9FUXkNnu/zGN4THdC20AoA65nPMu1+mjQ07jcQJkOGo+DsP//5MVFjU72ptOPdRjnHO9qw2DsvXr6Dj0MiEWBihBX+kitP2jzmkFM2VxMdvDXADa+ffIjajtB66gCovAwFqjPAxABvdnHH1pgUhMiRL+tQpO5BFdautF5vJL2z+Z3fYGRmiKdfnfwYC/jfplFV5Ync/otwj/3/B3RUMBWCtOCtrKyYoZq2119/HT169GD/R0ZGMqkdoeGavOCXLVvGNP2lEXrHf/rpp8z4v2PHDnZs0q5fsGABCgoKWj0H2ieuoS+dRjSgaWy1LhQxeitSVmXqR9HzEodWW3z00Ufsb5oAoEkTeoHpmxph4daPoWdsgMqScoGRrenYtEytsrScfScLRfJEX7nHgusOmz+daYkSwxfOwMZn3kD01fsIHCMptxN29ib0TAzh2sMPCTKkCNUNDVGdLakrXFcqePmrG7TfiMz0JDU1mX6s9eRpSP5xEwssp2FkrFB+0pA01ZX0GhN6DMryTDHUVmcBnz4a5cU2VgaoMM/A+LeHY8WxCFxOzG81zdGHimktaxgaoK5M0huhrqSULTlXdtm5LPKvXEbGgX1wmrcAhv6PBqRTFvL8IB1mcUiygc69sJWJKTIMbejjj+D8YhZIt73QtSVM6XoWVEhcX+F30vS0N2aeNXcWi+5z8hxe3t8FLwfZo9tmkXGS+gobxvsiwNoQM/fcQ3ZZ6xNv4ghXNlB9kbalEPoc3g5vwW7mxkxLe/Xdh83HJd3sEbYWGOdgjV+jFVvunVdWzTzdxKFzNqb6K5P0fmuJyIxiFnjXyVwPkRmi87I01Mbuxf0Rl12KV3dQID0ohRprWyQ9D+s7oG2hoNz1lRUouCyQWhMWLPqtZTAfPR6WTUFoySs9ZvUKwTkCcBnSFz3mzWb69+SVKE5V02dtOTIyiuRR5rjkcaqqow2brn7wGj8C4YdPwWrilEfaZjoHcSioZke1LbQCSE1XD8a9+jD5kfyL52HSd4BCeUtqatnqECMpj156NpTxDJeGlpuTt/7dvEJczxFIM0QUluBEaiYz7v8Wo/iS7s5MZ29bSMPfWGrlAGtbtDSQV9lyO0pPIxlAKKgoaY+fmN4TXS0NEZxdolSazty2tAaVgQXAFUP4uSMm50siwpH0y0+wmTpdbjBeIZqGhqivrEJDbS0LgiuktrQUGnLaO0XyaNIKpMZGOE+awHT4CafxY5B55Rpy796HoYszimNimWTPpQVLJI4f+u33MA3wQ+Ay0f718z5FQYYgsKOlsw1e++FN6BsboLxYsg2sLK1g/XL6rq0khcWjMDsfoeeDJQyP3778CXo/NQATXpkmN29+0/1vok3vXpFnuJmOBvIrW2/7aOVLdkUN1t1MwEhnc0zxsML6u0noYW3EJH6uPdtHwpv99e5OeNHfDr3/uCHzeFpGhix4rTg19N5RUYGmoUG787BYXTo6sA7qiqIRyUg+cxFu40aiqqgIFdm5LOgubc3jt8ZG5uXfY/krZBZttT5q6xtRXFEDMz3Jd5qZPnnxK95no/dRbE4ZnJWIoySvbamR07aodcCYSBwtOwfUFf/DVuEp+k7v7GMiev/Tij5xjDQ1WSD3ynbId9I7k95px1MF1yahtBx7E1KxIsATWqqqElI18iBN/bqGBua5L44Z9ZmlvPiFBFobMinPUy/0lBhzkHF9io8Vuv90DUVKylQqO2YzkXoXCz9Le/JLQ176n/f0xYHEDKa531FsW7cTt87ca/783YnPYGgi8NQvKyqDsbloRVppYRnc/F1aPF5VRTW+e/MXFgNxxbeLod20KorDeZLhhv3/OF27dsXVq/I94iiQqzwjsjxIm//kyZPMI5+M1JaWlkxmh6R33NzcmH688LfPnTvHjPjyDOokV0MBfMm7XnyVAcn1iFNRUcEM4xQrgMjOzkZqamrzZ/r/zp07Enlu377N4gcIJX/ai6JlVbR+FDkvaVavXs3kg4QTJ9Sh3Zl8GRqagheSrbcL6qprkB2XAmsPQVCjtIg4tjSZvpOFYnno+qlITorTBxWVpplxEfV19Yi8cAcBI/uyzrm8ILf5ly8x47Rw6Xl5dBRU1NWh49R6MCZloPMQ/KF4nuC0IszuZs/OWpitn7MpUosqkSPDeEnBUl3XnZXYt6ivE17s4Yg+G6+woIyEImkUgeqvLEbSw6osOgq6Lq7tnsijAHzpe3fDce58QUC9DoCMRi94OMBaR4sFzyW6mRmxTurDQvmrZWx1tbG+jz9CC0qwLiSmA9ZyAImFFWyQ0sfBGLfTBEuLKYBidzsjbLwh24D33fVEbLwhaZy6trAvk+XZdDNJyqjvhyA7I2bUV0aCRwh5BtFgsYupIS5kCgwOFtqabEnwQzlyWgrRKH93syeOAtxLLICuljq6OBizQLdELzdzqKupIjhRckK2JTytBYPV3BLR82RhqIXdr/ZnQXlf2XqHDb6VhZ4NCmYp0bbECNoWbce2ty2en38r4fZaGhaCtF9/hOe6b6Gmqyvhle6z4Uf2t59JdfPzaObpgoTz11h7JJQPIJ17XXNT6JjInnBUJE9bjiuazG6UWX8VsZLyR1R/FMyvI50EGEr2PUgSJ660DP6mhjiTIZox7mJqxAzx7eFhUQkaZNRH5/CV6xg6e9tyP7sEuhpq8DfXR3ie4L1Ahkf1JukcRSHDENHS/apIms7WtrSGnpsb8q9ckWgLqF+gYWoGDWPFnBpaNOr/9ANsJk2GxfARraY3chcYV4tiYmHq58v+Lk1OQV1FBYzc3Nqcx9DVReC4IX3dyLu5aVeXZUsknHVqCotw4813Ebj8NZj4SkrBLP159SOevY6+Lri4+zQKsvJhai2Qh4kPiWG/6eDTds/s5Vvebf4tIulBHLau/gHLtrwLE+uWvc+TiiuZcb+XjRELAi3U7qa4FJvvSa5wbg3ylBbW3qZ7yfjhvqRUxoVnejNZnh9D5B/XxMMVuQ8kVyPmPYyGoYMd1OUYi9uSh9HQ0Hw99awsMXbbJomvow8cQ/a9UAz67H3BfR+vmCROcHIhermY4udLInnHfm7muCcWP6g1qBlxs9DDpRjFZZXktS1FUm1LRQe0LbKoyUhnkybk/PRfGRNFFpUwT3pxAk2NEF1c2r5+QWEJvKQm85TtE9Bkclh2GfrYG2NfhGiior+DCW6ny9bOPxyZjWNRkquTd8/oirTiSrxxOlqpumkL4UWlWODlBE1VFdQ0rRigMVtpbR0LjtuSUf+rXr44lJzJgul2JHPemoUX3nim+TNp4rv4ODGHlZjQePQaLhivFuQUIjcjH25+Li0a9Te+/Qtqqmqw4ttXWBBdDue/AJfi+Y+zcuVKZmCmQK+5ubnMcEwe3xQ8lyA999LSUiZloyhkrN6zZw8zUpPRmiC5nz/++EPCG/3NN99ksjSvvvoq09QvLy9nUjQUTFcIydqQEZyCyBYVFTHDtbBs0pBWPxm9c3JysHDhQnh5eTEtfYKM3aRjTxr+9DtkhCcPe6HmfkegaFkVrR9FzksakjQyNDRkG0kKkZSQlo5Osxc9GebtfN1wYcshlOQWojg7H5e2H4FTVy+YO4kmOL6fuQq3D51VOI9LkA8bLF7afhTV5ZVMfufib4fY7zoFSgZaTLgTjoqSMviPlJRGEoeCNpL2fsbuncyjrDItDTknT8Ck3wCo6QhesLVFhQh7dSGK74tm6Fsj9+xpFN64zjToyeuLgsllHz3EAssqM7DddT8dOhpqeHuoBwy01NHf2RSzutphy23RAKifkwnzKvFo8tShjpb4Jlw9Kd4BUySNIlgMG4GKpCTkXTjPdK8Lb91ESXg4LIaPlPC6D128kNWDohRcv4b0PbuYUd+4g4z6xN3cIsSXlGO5vxvTZ3TQ08FcLyecz8hr9vAkHcxzY/thrL3gmaFJAKFR/7OQGCbH0BFQTW8NTsXLQQ7oYWcEQy11vD/Ugxm89oeLOt0/TfLHrqe7NueRvnZsf6PYcmTyvBvny47ZVqM+QUG6zmTkYI67Ixz1dJg37Wu+bkgrr8StXNGA86d+gVjqJ9tAIouIohIWzHKRtws7JtX30y52zKh3M0dxgzwZ82/H5+H9Kf6wMdaGnYkO3pnohyvROYjJEg2iKEguBcglxne1xYKh7iytproqBnlb4o3xPjh2L63Zy99cXwu7FpNRvwyLtt5mUhltwSBQ0LZk7RG0LVXpqcj9+ziM+0q2LQ9fW4CSEIHnpCIwYxJNUjdtwmXybL+Uzq8wDU1sCr9zHTaATaA+2HMUNRWVyA6PYgZ5z3HDmvNlR0Rj/3OvoTgtQ+E8iqQJ3XUYGcFhqCmrYGkz74cj+vhZGPcWTVILMRs6nLUt5ElPbUvR7ZsoDQ+DuVjbUnD1EsKXLFCqbUn7YzvKYqLYMesrK1F46wY7tqwytMShpHQMtrZgQe101NQw3dkOtro6zR51xEseztg6sIfSx6Vj9jA3YZr7nob6GGtvjctZ7TPYdCY6e9tCxnwyWq7u48YC3Nrqa+HNXq64ll7IvOyF3H2hP+YGCBwkxrlaYI6fHWz0tATXzUQPa/p5ILGoAqFNkwGKpHlS2paWMBswiAW8zzx8iK0AKI2KZP0AixGiZ5f2Ub+gKiNd4eNSnqSffoTNpCmwGDFKoTy6VlYw69oF8fsOoTInF9WFhYjdvZ8Z5o08RPfW9ZVvI+HgEYXzkGe39YC+SDp2gqWpr6lByqmzqMrLhUWQoM9CdcY8vps2COuQOaJI1if1Ycn5iDZhP9qjuzesnG1wbNN+prefm5qNsztOImBwNxiaCYyHNVXVeH/cctw9dVPhehT/Lebw1FQWWrkmjHcmD+pm7AhPZ8Fvg6wMWVDKd/q4MV3ywzGifsvGET7YMU6wypLu908GesDDRJdNAljqauLdvm7My58keYTHpflz8Y2gt29LCiBOwwejMr8AsUdPorayEtn3HyDjxm24jBHFBsu8c5950VcVFCmcJ/z3vch98BC1FZWoq6pix0g+dxn2A0TjCfFry96xYvuVmaj77UoCBnpaYGp3e+hpqmFWb0cEOZlg61WBVCyxcLAbwteOaf787cyu6OlsCm0NVVgZauOzaV3Y/7tvtU9HXL+pbckWa1vy/j4OI6m2Jeq1BShVom0pOH8GRdevMA3/hqoqFN+5hYILZ2EyeLhSddXZx0THkjPgaaSPCQ42rF8wxNqCvcsPJ4vauTF2Vjg2oj97ByjKX6kZcNLXxSg7K/YM2evqYIaLPW7lFCjkrS/k1+AUPOVlhdHu5iyI9Ss9HeFkrIPtIaLyrR7oimtz+8itG5rcEo5FHjdn0nLYSofX/FxhoKEOX2N99k4/lJTZ/PtuBrpszBZoKnDS8TbSx5dk1E/KxG/RHWvUJ6iNJGO+cBNq6/cZ3QNHf/sbGUlZKCkoxa4NB2Fpb4GAvoLJYWLt3K/wxzf72N/VVTXYtPpXVFVWC4z6hu1bbcMROXo+cdt/EO6x/x8nMDAQZ86cwdtvv808xsnQTPIxQl33bt26McMyBZ4lAz8FmiWDeEuQcZqWLokbqelvacM1Be0lQz4FsSVtfdKkJ1ka0qkXQpMML730EgseSx3dkSNHYsaMGaiRkuegco8bN44ZyMkITnr6FLhW2Bmm8zxw4ACbwHjttddgbm6OuXPnMuN7R6FoWRWtH0XOqy1MfOtlnP1pH7Yt/oQ1XG49/ZlsjjgktdMo1mtvLQ8Fyp3y/kJc/fM4fn55DWsPLVzsMHXNIhhZSQbJCjtzA45dPGFsbS63jLTM2uX1ZUjftRNRq99gniPGPXvDZvrTokTMatog4ckWveYd1OTnC/Y1NjLDPxGw+WdBOXv1Qc7fx5F17DDqy8pYR9koqAcsRok65oqQXVaNF/few4cjvTGvtyMLcvTTzSRsv5vanIY6xbQ08n/xbtB1cYHz/AXIPHIY6fv2QNPUFPbPPgfDAJFsTmNjg4RHLA3koz9ZK/jQ0IDMwweReeQQk9pxWSxYmp594i801tUh+defIT5MMRs4CPaznm1zeakUq+88xPIAN+wZ2gO1DQ24mJWPTRGiAZRKkxelalOFjnOwgpWOFobZmLNNCHU2J5y+hfbww61kNkj5eVIA08EPyy7F8/tDmJeRECqHsCyK4GKiy3T4yUP14jzJSa2FR8JxNl7gIasIGx8m4BUfF3zfpwsLuvWgoBjvBj+U6NBT2URDWuA1H1eMd7Bu9lr8a4QgvgXlC84vYoG+3rn7EPO8nLB1YHc2SEktq8QnIdGIUNJbd/H2O/h4eiDOrR7OHsVzEVlYc/CBRBryshUOHC88zMb8oe7Yubg/rI20WZC5HVcS8etFkbTShO52cLcyYEvaI74YL3Esv7dOKGzop7bFcclyZO35EzHvroKqpiaMevaB1VQZbYtYGxj7wWrUFojaFjLOEb4bf0FHoGtqjEFvLsb9HfsRc/I8tAz04P3USHiOEQtg29AoWGHUqHgeRdK4Du2PiIMncOfXP5lhX8/CDD6Tx6Cu96NGOl1nVzjOW4iso4eQuX838/a1m/08DPy7SJRTum2J+0wgEUf7s44cQNbRgyyP0yJB22I2aAhyThxDeXwsOz8tSyvYzn4BJn2UM+xfzc6DkaYGFni5wExLC2kVFUz3Nlks1gt5UQp18wkKevduoGgV3DtdfVgZjqZkNMvsPCgsxvrwWMz3coGNjg7yq6txOj2bBc99XLw+bxw+fWd28+db/whkC1d+sAO//CHQlO5oOnvbsvTcQ6zp546T03uwR/Fiaj4+vi4pwUaGGWHbcjG1AC/722P7uC5sMoAkey6mFOCnkJRmbWJF0jzJbYsQDRMTuC5ZivR9u5F77gxbPWQ5egwshg1/pHzCy02Tc2FLm6RpGhpQkRCP/GtXoW1tDa81gmc655+/0Vhbg4xDB9gmRN/bG26vL5dbHp95LyL2zz2488EnrF0z9feF5/OzJQyKjVJ9UUXyeDz7DOL3HcTdj9ehoaYWerY2CHhtMQuU2xGQAf6FtQtwdON+fD3nI6jRKpIBXTFeTCpHUI2SZd/82tfIik8XGOAaG5nhn3h3/2fQ1hNo2reHn0NSoa2uhk0jfZlDQkRuGeb+HSYRCFdNrN+SWV6NWxnF+HywF5vMKq2pQ1heKWb/FYoYsYmytqBvY4Weyxfj4e6DiD18gsnseM+YLGGAp0qiayhcpaBIHqdhAxFz+Dju/7gVDfV10LO0gPfMyXAcOhAdzfX4fLy5PxTLRnriyxmBTDN/2Z77uJskmuSkqqS+jJCdN5OxdIQnujmZoLq2HmHpxZj+43VEZrbPM5zaFoemtiWuqW0x7NkHljLaFvF7Ll6qbSHDP+Hd1LYY9uzNJgjyThxDfXk5NC0sYDltJoz7D/xPjYliSsrw+YNozPFwwgJvV+RWVWPTw3gmrydePhpjsPI1Ao56utjUVxDvgva/6OmMFz2ccSevgPUpiIyKKnxwLwLzvVzxqo8bmxy/kZPPgucqw/GYXJjqxOHDIR6w0tdEQkEl5h8LQ0x+eXMaem7F7zVFGOlqhp8nimKz/PyUP3vett5LwyeXRStRlKWsrh5v3IrAMn83HB7Rk73jT6RmY4eYF35zfTZ9nuFiC30Ndcx2s2ebkP9j7zzAoyq+Nv6m99577w0IvfcuRQQUxEIVlGIX/dsV7AoIdgULIEWkC0gnECCN9N577718z8yyye5mN9lNgga/8/O5kr07c+/s3Hvnnjlz5p3kyhqsCZIesNkXLNm0AL9/+Sc+eHobmhub4D7AFRs/Wi22EC73edztQySEJ/EIf6Y28Py8N8SO9cYPL8DayeqelZUg7jVKbYquqEn8Z2HSLqyhlsepzBzXohI77DZi+ZnzvrcIpYGE5fj111955H1+vnxae7JgZWTHFur7i/6WnpZbsqyK1E9f/a5v488oXu4WNlW7o3Pcl/Bjs8HQu3VyNrfnHZo2po0oMu1a4PTq3GTxKLcecjtYfl1wSVgnqqvoCWajdec36C7NsBE90/3jTTubki9SN7w+JRGJYpNVv9Ii3YQU1va87iVhCxkKI8bYFZdm4/KfJefx0mJ7fm25+FQX10badRN1Jooi6x5x9+u5piO7GqJiKsLyStKbdbeSzxT3vHxy3Puy5DEkYbMppDFglvG/1rZIe74k8TVS7P7jDqHWVpkSZn1NbPk/17b0hPqWnr2fBMJx4u2ErLakp4/HhZU70Reo3I0SFqWlh7NVRBn9rbi+eH9rW7JzWv7RtkVRPB2U+nXboqbS1q/tAmP1FoWkEnvTTshDK+vfiFyTydY9tw2EMJue2f6ia1OJSu0IkexzdNcnefVv7Xtmt/TF8zXbv7lfvc8k76Ejf/TtQqx93d74TxGXjOlvfaKIWz2bbfpP9Yl8B/e8Tyl57L62CxhR0U33rM8hrWzS+h0swEjaYZw95Jdf+jf6bG8PEsiM9TWsbWYFZO01b3dl2FjCmQCyGGc1856U737HZck+3I+k7H0E/zUoYp/o1viUeuNIOML5SH0fOPUZvYlW7wo+uizlN/am3LLKei/rpy8QTjnu78eWNE7vdedPUbqbEilPZ+BeOSi44S9Zf9084/92/YpKqguniP9bcMdWm2LX7Z+YItt+fgXL+0/Tk/talgP/XtDbtkXa89XrMjFH2T/k1P+n25Z/EmmrB/yDt5ZC9IUTv6/5L7Yt/yT9sW3pr3bBP2Vz3AvnsmR/QlHbV1afpDf0xbPaV8/XP/U+u+eDQv2ovaE+Ud9ey/50bXvS5/in+x2dz91/+mxdIdo283a3Gwc+Qdyv9C8vGfGvw6RimANa2vbmm2/iv8z/599OEARBEARBEARBEARBEHJPk73ftv8g/SeEmOgXnD9/XjBV9x+MpO+OpUuX4pFH7v10mX/6t/9Tv4sgCIIgCIIgCIIgCIIgiP8W5NgnxOjraaF9wb2Yrtoffvs/9bsIgiAIgiAIgiAIgiAIgpAftn5mcHAwKioqEBgYCEtLS7nyFRcXIyQkBGZmZhg0aNA9Wd9SCDn2CYIgCIIgCIIgCIIgCIIgCAJAdnY2pk6dirq6OtjZ2XFH/WeffYannnqqy/phUt6ffPIJhg0bhpaWFqipqeHo0aPQ0dG5J/VKjn2CIAiCIAiCIAiCIAiCIAiie5T/o4L1Iqxbtw5GRkaIiIiAuro6fv75Z6xYsQITJ06Eu7s7pLFz507u1L98+TIGDx7M9125coUPDtwrxz4tnksQBEEQBEEQBEEQBEEQBEH8v6e0tBQnT57E+vXruVOf8eijj8LExAT79++XWj9szc4tW7Zg9erV7U59xtixY2FqanrP6pQi9gmCIAiCIAiCIAiCIAiCIIj/LA0NDXwTRUNDg2+ixMTEoLW1Ff7+/u37lJWV4evri8jISKnHTk5ORm5uLqZPn47ExEQkJSXB0dERPj4+9+jX3C3XPT06QRAEQRAEQRAEQRAEQRAE8d+ALQZ7H25bt26FgYGB2Mb2ScIWy2UwKR5RWMR+eXm51CopLCzk/+7ZswczZszAl19+iXHjxnHpnqqqKtwrKGKfIAiCIAiCIAiCIAiCIAiC+M+yefNmPPfcc2L7JKP1RffV1taK7a+uroampqbUYwvzMAd/QkICVFVVUVJSwqP+33vvPXz44Ye4F1DEPkEQBEEQBEEQBEEQBEEQBPGfRUNDA/r6+mKbNMe+s7Mz/zczM1NsP/ss/E5Wnvnz53OnvjDCf8KECbh9+zbuFeTYJwiCIAiCIAiCIAiCIAiCIP7f4+LiAnd3dxw6dKi9LuLi4hAdHc1ldoRcvXoVQUFB/G9jY2OMHDmS6+uLwrT2bW1t71mdkhQPQdznrPZ0Qn9m49Q30Z8xe/4p9GeaWpTQnykpaUV/xcZFsHp9f8XDoBH9mYaJpujP/L3qe/RnonzHoT8z8WVH9Gd01fpv26K1ey36MwOM6tCfudimhf6MiWYT+jMmGi3ozxz6XKAv21+xetEM/RVNzf5t8yVU9G+7Ssujf7s28sv79/XVNO8csdqfKChtQ3/G0Kz/3n81Nf277lr6d/EIWfTvJq1P+OyzzzBv3jwuvcMc/Z9//jlfGFfUsf/222/z70+cOME/f/rpp5g6dSrU1dXh5+eH06dP8wGB3bt3415BEfsEQRAEQRAEQRAEQRAEQRAEAWDWrFk8Gr++vh7Xr1/Hpk2bcPToUbG6GTt2LEaPHt3+efjw4bh58yZaW1tx7tw5eHh4cMe+l5fXPavT/jusSBAEQRAEQRAEQRAEQRAEQRD/MEOHDuWbLN54441O+5gTn0Xu/1NQxD5BEARBEARBEARBEARBEARB3EdQxD5BEARBEARBEARBEARBEATRPcr/D0T27xMoYp8gCIIgCIIgCIIgCIIgCIIg7iPIsU8QBEEQBEEQBEEQBEEQBEEQ9xEkxUMQBEEQBEEQBEEQBEEQBEF0D0nx9BsoYp8gCIIgCIIgCIIgCIIgCIIg7iPIsU/8v+Hs2bOYO3fuP3Kuq1evYurUqf2mPARBEARBEARBEARBEARB/HcgKR7ivuXSpUv48MMPcfr0abnSFxYW4ubNm/gnKCkpwfXr17ss6z9ZntOnr2Hnzn3Izi6AnZ0VNm5cismTh3eZp6SkHJ9//gsuXbqN1tY2zJw5Bs899xi0tTX590lJGfjuu8O4eTMKjY1N8PFxwbPPLoOPj2ufl3/oQFesWjYF82cOxa3wZMx85H3ca/zNdfHmOFd4m+qipK4Re+7k4rvwbJnpNVSU8KifNR7ytoS9viYKahpxJL4AO0My0drWke5xf2uezkZfA3lVDdgVkoXD8QUKl68qOhKFx46gsagAasYmMJs+GwZDhslM39bSjMqIMJRdvYTalBRYzFsAk0myB58qw0OR/eO30HZxgeOmlxQu3zxXCyz3sYWFtgbSKmrxWVgabuVXyExvpqWOlX52GGtjBH11VaRV1uHH6GxcyCppT6OlqoxZTuZY5GEFN0MdPHspFpeyS9ETHvewxVwnS+ipqSK+vBpf3ElFSmWtzPRDzA2x2NUa3kZ6qG9pQWhRBb6JyUBxfWN7GgddLTzmYYfB5gZQVVZGUnk1fozPQmRJZZdlKUtIQuL+Q6jJzYe6oT4cpk2B3cSxvcpTGH4HkTu+6ZRv9KfvQ9PIiP/d1tqKoohIZF+4gtK4BDjOmALXh+ahO1idbfB1wghzI7BbOyi/FDti0lDT3CI1vZqyEmbYWWCWnTnsdLVQ0tCIc9lF+C05By1tbWL1N9fBElNtzfixFp8PRU9wsDXFx28txaihHqita8CBo8F486NDaJZRPsacaYF4bu0suDpZoKyiBrv3XcanX51s//6ZFVPx7iuLOuVra2uD27BnUVJWLXf5fF1N8L9VQ+HlZIySinr8djIeP/wZ02UedVVlrF3sj7njnGGgp4HrEXl477tbKCjtuGcfme6BR2d5wsZcF1U1jbganoOP94SirLJB5nGb6+qR8vtBlIRH8PvByNcHrksWQ11Pr9d5Su5EIfPkKdTk5ELLzAyOD86Fib9f+/dVGZlI++NPVKdn8OPo2NnCce4DMPRw77Iusi5fR8rJM6gvLYeutSU8H54PU2/PXuWpLytH8vEzKLwThaaaWuhaWcJl9lRYBg6AIrD7oeivUyi9dgUtNTXQcnCE1cKHoWVrKzNPc3U1ym4EofTqFTSWFMPttTegaW0jlqY+P48ftzo+nrflWrZ2MJ/1AHRc3eQqV0tTE8L2HkX69RC0NDbBwscdQ59YBB1Tox7nYdcsIzgcieeuoiwzG+o6OrAN9EPAwllQ19ZqP05FTj7C9h1FcVIaWpubYWBtidYJc6Hl7S+1bXnaywnDzATnuF5Yil1xstsWYbsx284Sk63NUNvcgqWXO7cb3oZ6eNLNHq76OrzNSqqsxo+JmUio6Pq5zTl/AbnnzqOxsgo6NtZwXrwQ+q4uvcpz/ZlNaGno/ExaTxgPlyWLO+1PP3wEWX+dhc3kifxYQlpbWhB36Diyrt1Ec10DjN2d4f/YIuhamsssmzx56ssqkH4pCBmXrvPnYtqOLdA00Be7x/PDIpF69hLK07OgqqUJiwAfeD30ADT0dLusm/EDrPD8ogA4Wuohp7gGO/+MwfHrGV3m0ddRw7ML/TEl0Bbqaso4F5KNrb9FoLquiX/vaKmL1bO9MMrPEtqaaojLKMO2Q1EITSyGvKRcvI7YY+dQW1oOAxtLDFgyD5a+Hr3K01Rfj4xrIUj6+yrKM3Mx5rlVsB0sfs831zcgYv8x5IREoqGqGtomRmj2HA2VEdOhpKTUbbmdDbTxyhBn+JnqoaqxBUeS8/F1ZCa/x6WhqqSEuS4WWOBmCUd9LZTUN+FcRhG+isxEk6ih2gPYsZ90d8Q4SzOoKysjsqwCX8eloLihw0aSxExTA9NsLDDVxgKG6up48Px1NIvYBEJ8jfSx1MUBLno6KG5owL6ULFwtkP/6MgzUVbF5uAvG2RmDneJiZgm23kxBdZPstmWMrRGe8LWFt4kuaptaEZxXhm2h6SisbVQojTxYaWvg+QEuGGSmj7rmVpzJKsTOqAwxG0kUHVUVPORihSl2ZrDS0UBeTQMOp+ThSFp+expjDTUcnzW0U97XguNxKbfDtpYHX1NdvDrcBZ4muiita8Rvcbn4KSpHZnp1FSU84mmN+e4WsNPT5PVxLLkQ39wR7xOJHv/X2QGoaGjGuH2K941HWRlhnb8j7PW0eF38EJuJMxlFMtPb6WriMS9bDLM0graqChLKqvFtdCbuFHfY67sm+GGQmUGnvNEllVh5PhK9gfV5XhnigmFWhmhqacXZjGLeT2poaZWZZ7K9CZZ62sDNSAdVjc0IyinDjoh0VDQ2o7c86WWL+c6WvP8VV1aNT8NTkVwhu0801MIQS9yt4WOsh/rmFoQUVvD7VbRPxHAx0MY6XwcEmOqjqqkZvyflYX9Sbo/LmXQnBYe/Pob89Hzom+hj8qLxGDtnlMz0rS2tiLoRgyvHriMhPAlTFk/A3JWzOqX5++Al3Dh9E2WF5TC1NsHMx6Zh0LiAHpeTIP4NKGKfuG8pLi7GjRs3cD/wb5b11q0ovPDCJ1i6dDYuXPgBCxZMxsaNHyAiIl5mnurqWixZ8gry8orwyy9bcfbsN3B2tuVOfiGffroHo0YNxP79H+H48R2wtjbD44//Dzk5hX1afmNDXXz85mO4ciMWx8+EQEX53jdb5trq+O3BAEQVVGHMnpt483Iynh/hiEf9rGTmmeZiCkNNNWz8Kw5DfwjmeVYOtMXGoQ7taZYPsMEro5yxNSiVp3n7SgreHu+K6S6mCpWvLjMDWd/uhMHQ4XB750OYTJyCnJ9/QHWcbOdgZVgoqsLDYDbjAajo6PDOuSwaS0qQf2g/tJ1d0NaFkSmLiXYmeGOYK+9gTv/jNi7nlGLnRB846Xc4eyR5zNsGcaXVeOJMJGb+GYKz6cX4bJwXAsw6nIVLPK3hY6qLD2+lQFVZCcpydHylscTNBkvcbLElNAkPnwtFbk09to/2hZ6aitT0RhpqWOpmg4MpuXjkXCg2XouGlbYmPh/lAxWRIjzqYYtr+SV44kIEHjsfhtTKWnwxygcWWhoyy1JbWISwT3fAxNcboz9+F64L5iJh7wHkB9/uXZ7WNiirqWHidzvENqFTn1ESHYecK9dhN2UCtC3M0CZnx/7tQA/Y62jhqWuReDooCh6Gunh9kGxn7BhLE7joa+PzqFQs/DsEn0Wm4kEnK6z1dhRL91KAK3Jr63EiswAqPby2amoq+PPn59HS0oqh0/6Hh1dvx8I5w/De5s5OeSGTx/ri553rsHv/JfiMfgFPrP8Kjz88Fs+v7TD+v/zhLEw9V4ttwaFJuBGSpJBT38xICz+/Ow3RySWYuPow3v32JjYuHcCd8l2x45XxmDnaCc9/ehWjnziIPy4k48FJHc7CaSMd8OZTw7DrQCRGPfE7lr91Dv5upvhgg+wODyPhx92oTElBwEvPY9Drr6K+uBgxO7/udZ7Cm7cRs+trWIwcjuEffwCvtatQGNzRWW9pbETkp1/wwYDB77yBoVveha6tLaK+2IGGctkDgPkhEYjavRdu82ZhwmfvwXyAH25/ugvVufm9ypP613kYONhhxGvPY8LH78Bq6CCEbv8WpUkpUITic2dRdO4MbB99HO5vvwd1U1OkbfuMO/llUXDiGJorK2E5dz7Q2gppXrmiM6eh7xcAt1dfh9v/3oSGtQ3Stn+BxlL5nDO3dx9E1u07mPjyOsz+6FW0NjXh/Ac7uaO3p3mKk9ORefsOAhbNwrxt72DsphXIi4zDtS93tx+DvWfOb/2S/YGZ77/M01n5e6Lkhx1oKsjrdM7XB3jATkcLz9yIxIbgKLjr6+LVgK4Hep7zdUVebT1OZRdARYrWq76aKj4c4oOsmjo8cTUMK66Go6i+ER8N8eEOHVnkXwtC+qE/4LRoIYZsfY8756M/24aG0tJe5Rmx/TOM/vrL9s332Q38upsEDux0vLLYOBSHhUPLzJQPpIgSd/AYMq8EY8j6lZj00RtQ1dDA9Q938GdLFvLkifn9CB+A8Zg/Q3BOCVuhKjcfGVduwH3OdEz59G0Mf34tylLScevzzgPJovg4GuGr58bgaFA6xm08hh9PxeOTtcMxyteyywHNX16dCC97Qzz5wUWM33gcoQnFmDHMrj3Nc4sCEJZUgiXvXsDUF04gLr0MezZPgIt1x2BEV2TdisDt7/fDb8FMzN3xDqwH+uLyh1/xAane5Ek8cwUlqRkIfPwhXo/SbK7Qnw8hJzSKO/3nf7MVAx99EC1XjqM19FK35dZRU8G3k325w3TO0VC8FpTAbSQWICGL4VaGcDLQwtvBSZh8+BbeuJ6IB5wt8EKgM3rLU57OGGFugrfCYvD0jXDu3H870KdL+eXVHk7ccX0wPZs/u9Je+4NMDPFeoC9Cikux/GoI3gqLxTBzYz6QoAifTfSCo4EWHj4ejqUnI3jQzkfjZA8Im2iq4UE3S+wKz8TkA7ew+mwU7PS08OVkH4XSyAP7LdvH+KK1rQ0Pnw3Di9djMdXODBv8xW0kUZgTVktVBW/eSsCck7fxQ1wmNg1w4s5+sWMrK+GJ8xEYeySofVPUqc+c0D/N8EdMcTWmHLiF926kYMMgRzzsKbtPNMWB9YlU8cLFeIzddxPv3kjG4742WDewo08kei9/MsETIXkVPbL9PIx08Mlob5xOL8QDx2/jt4RsvD3MA8MsDGXmYYMAUcVVWHMhEgtOhiCxvAY7x/vyAS8hz1yKwqiD19q3mUdv8mt0MVux+pOE/cIvJ/hAT10Fi06E4anz0RhjY4T/DXPtchBvnK0JPg9Lw9TDt7DpUiz8zPTwweiubUd5eNTDBss8bPHe7SQsOB2KnOp67Bwnu0/EBoyWedjg96RcPHQ6FE9fjoaVjiZ2jBXvEzG7//uJ/sipqcfiM2FYcT4SFtrqXfYFu6IopxhfvvItvAZ74J29/8PcFTNx8MsjuH0hTGaeuNAEBJ2+iQkPjoGZjSlaJd6jjFO/nsWZveexaMOD2HrwLcx+cgb2fLAX8aGJPSrn/zfalO7P7b8IOfaJfg2LfH/rrbcwffp0PPLII7h48SLfHx4ejtdffx3V1dUYPXo0377+umtnhJCQkBCsWrWKH/PVV1/lx5A8Jzs2k9JZuHAhfvrpJzGDPDY2tv2ckydPxtq1axEfL9tJ3l1ZuytPb/nxxz8xfHgAHnlkBoyNDfDEE3MREOCBn376U2Ye9l15eSW2bXsFTk420NXVxpIlM3nUvpCvv34Dc+dOgJWVGUxNjfDGG2t55P7Vqz2LspVFaXk1xs17A78cvIw6iUiAe8UjvlZobGnljvfi2iacTyvF3ug8PBUou8N0LLEInwanI7G0FjVNLbiaWYbLmaUYbN0R7THf0xx/xBfgQnopqhtbcCWzDAdj8/H0ENnHlUbpxb+hZecA08nToKqnB6NRY6Hr7YuSv8/IzMOi+W1XPgUdD68uj93W0oKcn76F2cwHoG4uO/KvK570scWZjGKcSitCWUMTvrqTidzqBizxspaZ59PQNPyZXICC2kYesfNzXA6qm5oRYNbROf8hOhtv30hGbGnPnxH2Ln/EzQYHUnIRUlTBI9Y+jUiBuooyZjlYSM3DfsOmoBjcLCjnkTFZ1fXYEZUKFwMdOOnrtKd7PzQJF3NKeHp23J/is6CpqgJ3w440kmSdvwQNQwPunFfX14fl0MGwGj4E6afP9kkeZRUVsU0UU38fDNy0DmYBfoCSfOaAu4EOBpsZYlt0KrJr6pFZXcej9UdaGPPIWWlcyC3mTv34imoeeRteUoHDaXmYZC0+oMUGCQ6l5fGonp4ye+ogONmbY8Nre5CTV4rQO2nYuu0YViydAF0dwWwjSRbNG4GgWwnYvf8KKqrqeJ4vvjmNDaunQ1XE8ccGC4QbmxUwcog7z6MIi6e5o7GpBe9/fwsl5fW4eDsbv59JxKoFvjLzTBhii0nD7LHxw0sITyhCXUMzz/fVwaj2NH6uJsjIq8LJq2moqWtGUmY5TlxJg5+b7EHDuqIi7jR0WfQQjyzWMjeD29JHUJmUjIrklB7nYU7B5P2/8whj6/HjoKqtBW0LC3itXtl+nPqiYjTX1MBm8gSoGxhATU8XttOnoLWxEbW5sqO5Uk6dg9WwQNiMHAoNfT24PzgbWqYmSDt7sVd5vB9ZALtxI6FlbAR1PV04z5gMNW0tlCenQV6Y8674/FmYTpwMXS9vqBkYwvrhpWhrakTpjSCZ+WweXgKrBQu7bG/tHl8Og8DBUNXX58c1nzmbH7c+K6vbcjVU1yD50g0MWPwATJztoWtmgmErHkFFdh5ywmN6nMfM3RljNy6HhZcbNHS1eTr/BTP590119TxNfUUVaorL4D5lDI/0Z+l85k5lDxOacsTL7qavg0BTQ3wZl4qc2nruiN8Vn4bh5rLbFsbG4Cj8kSG73WADBcyBfyg9l7ffZY1NOJyWC101VdjcnYEojey/zsFi9CiYDhoAdQN9OC16CCpaWsi7eLlXeZSUlaGkotK+FQbdgJaFeaeZKo2VlUj6aQ88Vi6Hsrq62HfMEZ967jI85k6HibsLtIwNMWD5I6grLUPOTelODnnzBD71BLwXzoG2qYnU4+jbWGH4s0/BzMcD6ro6MLCzgfeiuShJTEFdabnMunlyhgdi0srw/cl4lFY14MClVFy+k4dVs2U7V5dMdoOTlR7WfX4NSTmVqG1oxh9X03DwUmp7mg3bg3DociqfAVBW1YiteyNQW9+MSYHis15kEXf8b9iPGATH0UOgqa8H/4WzoGNmjMS/LvUqD7vPh61eCiMn2fZdaWomj+I3draHmqYmbAb5QsnaEa253bc7s53MoaeuivduJqOorhG3Cyrwc2wOlnnZiDnXRLmWW4ZPQtOQUFbDZ7eEF1Xi76xiDDCXbxBEFrqqqphiY4FfkjOQXFWDovoGfBmbDEddHQwxNZaZ7/078diXmoXSLqL6n/J0wcW8IhxOz0F1czMK6xvwSVSi1Mh+WXiZ6GKEtRG2BKcgo7IeaRV1+PBmKsbbm8DFUFtqHmbDPXsxDqEFFdymTymvxe/xufA11ePR6PKmkYdxNiaw0dXEB2HJKKxrRGxZNX6IzcJ8ZyuZg4+/Jubg65gMPtOU2VXM9mTXcopd53c+GzxpaUP7piiLPC3R2NrK66+krgmXskpxID4PK/1lz0Q7mVqEbaEZSC4X9Imu55TjWnYZBll0vtfeHuWGc+nFuJkne1C/K5a42yC+rBq/JuSgvKEJR1MLcD2vFMu8ZJdv8/V4HEsr4NH97L2wLSKNPxPjrDvaPRbrIlpvUx3MBL8tvXcBbGyAjd2T795MRm5NA38et4Wn82eaDRZJI7WiFq9fT0RkcRUvJ8tzNKWg188uu0sfdbfBvqRc3CoU9Ik+CkuBhooyHnCS3icqbWjC+isxuJEv6BNlVtfzWc+uhjpwFukTbQxw4gMmn0Wk8eOyfNvupPMZ2T3h8p/XYGCszx36+kZ6CJwwEEMmBeLc/gsy8/gM9cK691fCd7i3zFlQt86FYswDI+AV6AEtXS0MGO3Ho/XP7Dvfo3ISxL8FOfaJfktRURGGDBnCnfnLly/HvHnz8P777yMmJgbOzs5YunQptLS08MEHH/BtxowZ3R6TOe1XrlyJSZMm4amnnsKRI0ewZs2a9u9LS0sxbNgwZGZmYuPGjViwYAGX0Hnuuefa09jZ2bWf8+WXX4aOjg4CAwORnp4u9ZxdlbW78vQF4eFxGDasQ/6AMWJEAMLDZQ9GnDt3AxMnDuUOfXlhUf4tLS3Q0enZSHx/YrC1Pm7lVIgFTgZllcHeQAtm2tKNLlFYhNIACz2MtDXC6eSOqaDKUOLRHpIGt6+ZHjRV5W+Oa1OToe0uHqXBHPZ1aYpFl0qj8MRR7jxigwU9gUUHsaj6W/niHfyb+eUYIOKk7wpmUM53tYCasjKu55ahL7HR0YSJpjpCizrK19jahqjSKviZyG8gG6gL7oNaGc4k1hlj0j0l9Y1dSvGUJ6XA2FPcoWPs7YmqzGy0yOjsypuHOXIub3gRl55+HiFbP+VyO72FTYmva25BbHnH4Mqdkgo0t7bB11j++mNRtKxz0tcMD3RDfFIuikSmU1++HgtNDTUM8O0cKcZgMz+Y3JgoLKqHzRbydJM+GLVs4RiUV9Ti2F8hCpUv0Msct2MKxIJgr9/Jg72lHkwNpbedU4fbIyG9DPHpsp+F8zezYGmijYlDbKGqogQ7C11MH+XAHf2yqLwbjW7o2dGW6Dk6cEdkpQzHvjx5mIO/qbIKFsM7SwAI0bKw4NI7eZevorm2jkuT5F64BE1TU+g5OUnNwwYMKtLSYeIlfu+b+nigPDm1z/Kw5ybzchBam5ph5tf1QKgojcVFPPJeR6RtZrNmtF1cUZva+7a5vXx1dSi58Ddvp5lUWncUJ6XzmVeWPh11oGtuAj0LUxQlpvZZHuGAgLKqClTUBO2jlqE+zL1ckXIpGPWV1WhubET86UtQ1tOHhpu4Q9fnbtsSJ9q2lFagpbUNPoY9d14kV9Ygu6YOD9hZQlNFmbfND9hbIq2qBunV0qUGmmpqUJeXB0OPjmvJnAIGnu6oTEntszzs3i8OC4Pl2NFi+1kwSeIPP8Fy3FjoOXWO2q3IELT1pt4d14cNSBnY26I0Sfq5epJHXhqra9iPhaqm7Nlpg9xNERwr7hC7Hl2AgV0MPk4dYougqHw+ECAvmuoq0FBXQc1dqZ6uaGlu5lH1FiJ1wrDw9ZB5n/ckjyzshw9CTng0j/RnM2EKYhLRVpAFZe/B3eZlDj0WcVwvMquS2VmGGmpwMujedmcuLncjHYyzMcb5TMVkbSTxMNTj8oORpR2OWeaAz62tg5ehbFm37nDQ1YaNjhYu5fXOkTrIXB+1TS2ILKpq33c7v5zbLfI6Ri11NDDX1QKXs0rQKMM7Lk8aaQSY6COtspY7PtvLV1jObWFPo67lrURhMirsd0qyc6wvLswdjj2TBmC2g+IBOwMt9Hk0vegvupFbDjt9LZhqydcn8jfTw3BrQ+7AF2WBuwWcDLWwLaRrSa6uYAFATApGlNsF5fAzkf/eY3XNtq5k3+Y6W+JSTgkfPOgNA830kV/TgKwqwQC48Nlls1b85ewn2epqYoajGc5n9m72ADuOiZY6QgvF+0RsAMG/J32i5uZ26dQhFob4qws5JEVJiU6D+0DxWQ0eg9yQnZyLxl4E/rH3raTTX1lZGakx6VIj/Amiv0Ia+0S/hTnUGefOnYP63WilRYsWoaGhAZqamvD09ISKigqPgJeX5uZmHD58GC53O8PMEb1s2bL27z/66CM4Ojpiz5497fvc3d35AMMbb7wBIyMj6OnpiZ1zypQpPGL/+++/x3vvvdfpnAYGBlLLyhbY7a48vaWpqRnl5VUwMRGfjsgi94uLZTuJsrLyMW7cYDzzzBYEBUXAyEgPU6aMwIYNS2U67j/88AcYGelj/PghuN8x11FHuoQcREmtwJAz01ZH0d2/pRGxeiSffsqchd+FZePXqA65gb9SirEm0A6nkosRkluBgZb6WOhlyY05Zhxny9l5ba6o4JH6oqjq6qG1oQGt9fVQ1pQdhdgV1fFxqLh1A86b30BPYbI1zCFfWi9eR+yzqZZ41KEkTCf25+kBfHCgpqkZm68l8GifvsRUU1AGFlUvCjPUmcapPKgrK2GtryM3hHNrxa/ZFFszvD7Ynf8G5tTfHBzXpf5lY3kF1CX0wVn0MvP8sohNJsHQkzzK6mpwW/QgLIcJHARZf19C6EdfYMirL8DQrXtnoCxYNFFFo3jdsf4rm11hotF9B4/Bom/nOFhiT2L30caKYmlugCKJgZTiUkFn3kKKVirj2JlQ/PzlOiycMxxH/wqBi6MF1q+cLjiemQGi48TLqayshCULRmHfketoUFDblEnxZOSJl6+0or79u+LyzpFM9lb6SMutwGsrh2LeBGc0NrXiVnQ+PtodgrxiwfPBIvlf33kD218eD00NgWn3V1A6PvhR9sBDQ0UFVLQ0ufNZFCaP01hR2eM89YVF3MlXm1+A2K++5fektpUVHB6Y1a6xz5y/vs+s49I7Qes38X0axsbw27Sea3ZLo7GqmjubWdS9+Ll10SCjvIrkKUtJw413P+GR9yqaGhjw1JPQs5Uv6lfYLjNU9fQ7tc1MO7+3lN+6iaw9P3LZFubUt1+zjh+7O+ruvss09MUdRKxO6sor+y5PRRWijvwF5zFD+fUVMnbTSlz4cBcOrn6Zf9Y00IPJig1Qkagn1n5UNom3LWy8raq5mb9XekpDayv+FxqHLYO9schZcD2zquuwOSRGpq54491rqSZx36jp6fE1IfoqT+HNW/z+NB85Qmx/9l9nuRPebqagHZKkvv36dL6v62U8Cz3JIw9NtXWIP3wC1kMG8FkusjA31EJJZYcji1FaVQ9dLTVoa6jyaHxJ7M11cTajDJ89PQITB9qgsqYRFyNy8cnvd1Alww57fpE/Hwz665bsNZGENFTebR8MxOtEU18X9TLu857kkYX33KmoKSrByeff5Z+VVJShOvlhqLh2Xn9CmjyKNDuLwYIXkiHbdjo6JxAO+lrcTmVRv99G9e5dbHy3jyZpG7DPRhKzTRTB6u67gOnv7xo5kGvyM7m+Q2nZCmnsM7td0hnL7JbKhu7t0i1jPPCAizm30yMKK7H2bHSP0nRnV0napMLP8tpVQ8wNMNrKGK8GiwdtHUrOxYGUPP77x1qb4MVBLny20v7kXIXkSUMqK6Xea6z+irsYRAt+dAQMNAR9op+isrEvrqNP5GyghReGOGHpiTsKzcCQZteXSTh2Wf3pqKlyBzNbs6A7nvZ35IFWF7Kk31fexrp8ba/Pw3s3CMrLq82eXfHyljc084EmUxkR+0KYhM8oayN+r93IK+MzdvqiTyTZlrD6s9aRv0/0jL8jH0zJqRH0iay1Ne9KpgI/TvKHs742n5H9R2oe19nvCRWllfAMFB9Q1TPQ5Y75qvJqmFjKnh3UFSxC/9rJYPiN8IG9uy0SI5IReimCDxbU1zZAu4sZg8TdkTuiX0COfaLfcvnyZcyZM6fdqc9gI6rMqd9TjI2N253oDHt7e9TV1aGiooI74NnsgLy8PIwfP56/KNjGnO1sxDYhIQHDhwsWnD1x4gQOHDiAnJwcPtCQmprKHf59XR5J2LnYJoqGRiM0NKQbprJ01LtblIv93h9/PIJ3330GW7duRFpaDjZt+pAPEnz44bOd0n/99QGcOnUVP/zwDvT0ZMuO3M+0yll3g767znUvh1gb4LOpHjwC8ZMbgtkcu0IyoaaihE+neMBCR53L9nwVmoVXRzvLXOxM0RdrT4/TUlON3J+/h/Wy5XI5i3oUEdFNGhZ9Nvi3azDQUMNsZ3N8PNYTT/0dzaeY32vYTAp5Fqtjs6vfGuLBI86ZDqokbFHYCzlFMNJQ51r+n43ywYqLEVy+R17kKUd3eUz9fPgmxG3RfJSnpCLjr3O9cuzLolXOO491Ij4c6o3Q4nLsS5G98FpfIozGl1Wvx/4KxbOv/4LNG+fg649XIDuvlEvxbN/yuNRfNW2CP6wtjbDn9yt9U7677bSsy872TxlmzxfYnfLUERjoqmPL+lH49o3JmLvpOP99k4baYcv6kXhlexAuhWTzBXQ/3jQanzw3Bs9+omA5eUHaepynra213Tnp88xaLrWTd+kKonfswsBXX4a+kyOPVL7z8acwcHeD//OboKSqgsyTp3Hnk88Q+ObrXGpK/lMrd7lmiLwjlrsJAAEAAElEQVR5jFycMP2H7TzyOifoFsJ3/YAhLzwDU+9eatiyuumF00KIwZChXI6nuaoSRX+fRfqObXB95VVoWFjKVwyJFliedkbePEx659LHX0PbyACDH1sgtgDv3+9th56lGcY9uxJqmhpIOHsFkV9/BvPnXoeauWWfvDu6wkhdDZ8O9cW1ghL8kpzFL8cTbvb4dJgvVl+LQLUCM4f471f40ZCdp+DqNZgMCOCSakKq0jP4szPw9c1ctqevztWXeYSwRZVvfvEtlFVVMWD5EoXz320qZLZ97Oc/OtUN7/4chjd/CoGtmQ4+f3okPn5qOJ767Gqn9Esnu/L0T316tdMggkLIKU3X2zyhew4iPzoBU999gS/AWxifgstf/ASoa0AloOv1UaTRdvdCdvdozz8eyqOTfU308M5Id67t3VsHoeD8Ep/Zjl48vMJ1lB52tsMnUQnIr6vni/O+5O+B6rBmhJfIln6Sh1Y5ivfa1QS8GZTI9flfG+6K76b74ZHj4WKSNvKkURRhVnnMQTcDHbw/3BO/J+dySR4hbAbAxxEdjujj6QV8cVmmqa6IY19q+bqxW4SM/O0G7xMFWujjg3EeqG9uxReh6dyW/nyiF7aHZSC1omfSLF2XT/o7TBoPuVphoasVnr8WKzZrQpR5zpbIrq7DrYLe3XPd0d17ef3FGKgpK3HZm9eHu2LbBG+s+VuxgSR5bXp5+0TvDffgs0Weu9bRJxJmXeVtj1eDE/jixIPNDfD+CE8+gHE4RfYaJoqgJOz79sLGmrNiJv+t3721G5VlVbB3s+WL7J7Y/VevbA+C+KchKR6i31JTUwN9kc5OX6AmEWUofGkJXwhVVVXcqc8i75nsz5YtW/jMARZd7+UlmJLPNPcfffRRDBgwAC+++CKX1mER/cwh39flkWTr1q3c4S+6bd0qWLDs2rUweHvPbd9+//0vqKurQV9fB6Ui02MZ7LOxsexFhczMjDF0qB8efHAyd9T7+7tjxYoHceZMUKdpaT/88Ad27fodX331OgYPVmzBqP4K09U3kZheKpxuWlzb9XQ/5kNkepKXMkrxTWgWngjoiPhkRv5nwRkY+dNNuHx5FTP2hqKptRX1zS0orJF/GiGL1myuEteZb66qgpK6BpQ15IuwkKShsIBHnGbu2obY9av5Vn4jCHWpyfzv2jT5olRYlAcz2owkok6MtdS5xmJ3sDpikSNMKzassBKLPGQvztUThEY7c/aIwiJCJSNopL0w3xzswadGP3M1CsUy0rPfwL7bHpWGqsZmzLSXrlPJYDrMLKpYlMbKKm4Vq0tEVvYmD0PPzha1Bb2b0s6ur3DKrWi96Kt1jjiTxERDHdtG+CKjuhZvhCT0fjBLCoXFlTCVmH5tdvcz+04WP+27hEGTXoWJxyoEjH8Z2XcXl0vP7DyN+LFFY3EzNBlxiYoPTBSX18PYQHxw2sRAEA0kLVqfUVRWxyNVP9odivKqBq6lz6L1vZyM4WIrcIAvm+3FdfeZrn51bROX7vn8t3A8MM4ZFsbSZRmYM7Glrp4viipKU1UV1GS8e+XJwxz5rFftMOcB6FhbQ01HB/azZkDL1BTFoQIt7+LwCNSXlMLt0aXQMDLkEf9Mt7+tuQUF16UvNM8ii5mjs7FS/N5vqKyChoF+n+Rh61Bo6OtzjX0jdxdkXujsPJSFMFK/pbpD7kHYNrM2u7cw+4BpsqsZGsH6ocVQ0dZGWbB4XWXt/hFRT6/Br0vW862pvgGad39nPWsjRGCfWfS8NBTJ01RfL1hUt7kFk15dz7XCheRFJaA8KxdDn1zEdfo19HS5Dr+yrh5qbogPOLH2g7UjUtsWiUhgRZhgZcrld76MTeXHYe+A7TGpfPB1vJV0GRh1fYP2+1rst1ay+1yvT/JUZ2WhOiMTlmM71i5iVKWk8vUnbm9+HVdXreVbTVY2cs9f5H+zGSXCe7ehStp9Lb18PcnTrVP/829QX1aOUZs3cL39riiuqIexvrh9YmKggZr6JtTUS5/5VFRez3X5fzmbxCP04zLKsetoDCYOsuaSO6IsnuCC15YNwsbt17l2vyRKpZFQCX2bb/uWrOcL4LIZKax9YHUgSkMXz0ZP8kiDDXolnb0Kn7nTYOrmxGc7MI19Zf8RaAmWvcaOkJK6RhhL2ll3o2/Zd93ZqSyKmQVNfB2ZiQWuljzqtqeU3118WdI2YJ97I1si1N4/lJ6NlKoaLpNyKjsfCRVVGGMhW8JJkuK6Ri5RJAr7uWwfm1nZFcxOYTN7kspq8c71JK6fLynfI0+arn9nEwyl2KSM7uxmtkDpjrG+XF//izvdr82QVF4DMy0NLksmLywiv9O9dnemA9Pcl6dPdCW7DD9EZuNRH4G8IXMEe5ro4n8jXBGzfAzfnh3syGdXsL9nuwj07OWB1ZGhRPlYH4TJEnUn+cgc9s8NdOaa+9fzpM9kZ4NgU+3NuHZ/X8DqTLKPZKCuKpjp282z23ZXKoetNfZxSCpGWBn1eDFasT6RxPNhLE+fSAl4Z5gHvIx0sfZSFF/rQ4iwf/RnWj7Ciir4s3s5txTn+ToQZt0ukvv0pOfbtyPfHOf7ma5+tYhUH6OqrJrbRnqG8ktWSaKmroYHn5rDF87d+fenePmrZ7mvQ0dfh2vuE8T9AkXsE/0WV1dXREV1LA4oCdM/62tY9HxBQUGX8j4HDx7kOviiuvtMpqerQYi+KuvmzZvFzsvQ0Mjk/44aNRCRkX+071e5a7QNHOiF27ejsWpVRxRdcPAdDBoke8GyQYO8UCJHJAyL6t+27Tfs2vUaRo4cgP8KoXkVWOJrxUfqhc7HkXaGyKqsR4ECDng2VbK7rtIcd3NcTC+VKQkgDS0nF9Qmi+ul1yTEQ9vJuUfR3gxtJxd4bRcMEgnJ2/cLGgry4bjpRe5Qkgfm1I8tqcJgCwO+GK6QoZYGCCtQbJo6iwTp62gJJsHApuwOMDNAxF3JFhb9wvT12WK3XTr1h3jwCLdnrkQhT0KCR2Y+pa5/hIGrM0qixCP/mRa+rq0NVGQM0vQkD6M6J1fgdO0F0aVVPPrK00CXL4bLCDAx4B2S6DJxR4cobDr5tpG+yKmpw/9C4ns17bormMN95dIJMDHSRUmZoHzjRnqhoaEJEdHS10GRxkMPDOOO+5R08Y6cmak+j9jf8GqHXJsihMcX8gV0RYO4R/hbIrugGoWl0h37oXGFGBEgPsDVufpYrKbkOgGCz0yWQhr6rs783/LEJBj7ePO/qzIy0VxbCwNXlx7n0XdxFoTaSjqK+LNwdx/7R/LZuPu9rChlFhVs4GiPkvhEvtCtkJK4BBi7u/ZZHiHMgapIKDNb/FZFVw/VSYnQcRNMF2cDIExf33zmLPQ5LNxZ4kawfewJ2C57HAFGje0DFaaujrxOC+KS4TRKIM3FFrStKijmC+BKQ948zKl/4YNdaG5oxJT/beCL44oiexYKu87iX8aUC9oWDwNdJNxtW/yNDfh7NKaLtqU72kQ20X3sf7Jeu2q6OnwdiIqEJJgGDhLkaWtDeUIizGWsHaFonvwr16BhagJDCVk1q4njYTVefI2b8He3wMDdHc6LH+LXxcDBDirqaiiJS+KL2TIaa2pRkZkDpynjpJavJ3m6ckrf/OIb1BaXYPSrm6ApxwybsKRiDPMU1/ce7m2BiGTZGtGhCUUIcBVfxFfaq2PReGe8+UQgNn15HWdDpEvwtBn5ocVIEHzyyPNmgkWMlZRg7GSHwrhkuEzoaB8KYhJg5im9fVBRVVU4j1SE7V+nZlIJbXL0GSKKqrBxoCN3yDNHn9DOYvIybHFYRewsHljfk5lad2GOdvae8TPSx+V8gZSJqYY6rLQ1EaegPJEoqVU1fNar5NpUULCkTB5HW00Fvqa6iC4WtC3MRmV2S7gCdqlwBkFXRp08aSSJKqnEg85W3LkrlG5k0c2NLa18UVhZMHmTL8f64mJ2MT4Kl28dFxcDbR5wIro2Q3ewOmIL6IreIUwvP7uqHoXdBDtJ1o2wVsoamuH9g/jg7kp/OyzzscG4fcEKzXaILK5EoITU4hBzQ16vXTHX2QIvBbrgtRvxXDtfFpPtTPk6aMf7yLF/p6gSa/ztYaOrgZxqQV9iqKUhv8+Ztr28CO+1nvb7GJlVddyBP8jcAOHFHX0ipq//g4QMpTSnvr+JHp66FMUXAZaUFsqoquv0jhW8i7u+uGY2pth+9qP2z8Lf5+zjiJjb4lJTCeFJsHG2goZWz4LapMGc+mGX78B/5H8jWJH4/wNF7BP9FuY8P3bsGI4fF4zUMs6cOYPkZMF0UXNzcx5hz7a+Yt26dTh79ix2797dvo8dn0XuC2EOfLaArzBynS14yyR8uqKvyqqhocHPL7oJZXjYi09VVaV9E74In3xyHoKCwnHkyHm+wO3+/acRFhaHxx6b037c7747jIEDF7Z/Znlu347ByZNX0NjYhPj4NOzefRQzZoxuH6Rgn7dt+xVfffU/jB4t6MT+V/gtKo87GDaPcoaeugpG2RlyR//3YR0dxpG2hkhdPxbud6NhXx3ljMlOJlxLkhmAExyNsXqQHQ7FdRiCARZ6WBtoxzX42fbWWBc4G2lja1D3UTaimEyYhLr0dJReuoCW+npU3ApGdUwUjCdOaU9Tdu0yj7SXjKztCua8F92EFri8Tn0hLNqeLeo00c6ER0ou97Hl03/3JXRM/X12kCP+erBjPYbPxnnBx0SXd1JZHT7pY4tACwMcS+ldhLkkzJz8PSUXi12sueGqp6aCDX5OvFN6KqPjWr0/zBPbR/vyv1k1MN18v7tOfUldfYaFlgZeGejKO1qqSkpccmaTvzOPgjmXJXvxKLtJ41FfXILUY6fQXFeHovBI5N+4BYfpk9vTFISE4+/l61BfViZ3nvhff0fRnSiuf8yi+1OOHEdZfCLsp0zoVf0xZz5bLHe9rxPMNdVhqaWBdd6OuF1UhrSqDk3fv2YMxyMuNu2zI74YIXDqvxYSr9AglqIcPxOKrNwSfPbuMpga68HH0xYvPTMHPx+4isoqgbPDysIQZUnfY860QP7Z2FAHH76xhO/X1lLHMyumYsHsoXjx7d86HX/pglGorWvEHydv9ah8+/9KhLamKl58PBC62moY4W+FxdM88OPRmPY0w/0tEf/nY3CzF8yq+uN8MpqbW7Fp6UBoaajyCPznlg1CbGopUrIFs7HO3sjE5GH2mDLcHuqqynC01sfGpQMREV8kcyaAtoUFTAL8kXrwMOqKitBQVoaU/Qeg5+wEfRHH/o0XXkbaH3/KnYdF31uOGomMYyd4GrYYbdaZc6gvLoJZ4ECexsjLEyoa6kjZ9zuaqmv4fczOwRbRNfYTPHfScJoxGXk3Q5EfEoHmunqknDiD2oIiOIo4JuN+P4ILz72mUJ7Q7d+gPDWdRyHz5+XkWZQmJMNmlEB+Tx6Yo9B04iS+sG1NchJaamqQd+gA1802GtHhAMz45iukfvGp3MdtLC1B9q8/oz4nhy8G3FRejtwD+/lMAMMh4g5j7qxUUeEOfbYJtb+dxw7FnQPHUZ6dxzXyb/30O/StzGE7sKOuT7y8BcHf7ZU7D3PmX/zwazTXC536nSO22SAAi2IO+fkwPwabQRBz7Byaiwuh6SMeDMCc+WzxzXVeTjDTVOdt6hpPR4QUl4ktcntiynAsdpJ/7YNbRWXcecmOpaOqAl1VFX4Opqcc2kUAg820yci/FoSy6BguHZVx5CiPpBd1uif9/CvC3npXoTwM9l4uCr4Fy9GjOg1kCWdmiL2L+Rcd72JVDXU4ThyDhKN/oTw9i9+zkbv387q2Gdphj11973Pc3vG9Qnm6g92Dt7Z9J3Dqv7YJmkbyDRbv/isB/i4mWDbVDTqaqpgzygHjB1jjx1MdjpqHJ7og4ZfFvA1j/Hw2Ee62htxxr66mDGdrPTw1xxvnQ3NQ3yiIxF0wzglvPTlY4NS/nd2NI53ZNYJnQ2gje86ehIzroTyCn0lKxR49i6r8YrhP62gfwn/7E0efeb39szx5uoMNEFgP8EHc8b9RlpHDF+XNj4pHS+QNKLt3HyhzIrWAO71fGerCo5/9TfWwzMsWexPy2gfO2eK4YUtHI9BccI2e8rfHVAdTGGqo8ihkNhDAHIznMorQoICjV5LKpmaczyvEo64OsNPR4tHn67xckFNbh9si63ntGD4Az3jJLwXY2NqK45m5eMjJlh+XORwnW5vD3UAPV+8OIMgDc+aH5FfglWEufIFba10NvDjUGUE5ZWLrOIUsG4Xlfrb875nOZnjMxwZWOhrcpmN1+eZIN6RV1CKyqFLuNPLAnMoFtQ14aaALDNVV4WqgjeVedjiWXtC+mCtrE4MeHIXx1oKBLkc9Lb4oLpPe+VCGU/9hV2s84GjBo6+1WNS5nRnfdzBFMRme3+PzuC3/wlAn6KqpcKf+Yg9L7I7ueN6GWxnySHtXQ0Gf6MWhTphgb8zvTTY7YKytEVb42+JIUoedzZz3opvQNFRUwmh/Yg58TPSwyM2Kl3O6gxnXod+b0DGrcr6LJYIXjeb3EOMBJwu8HOjKnfoXs7tegJYtmnstt1TmLF1FYTMDEstq8NpQV76+gqO+FtYPcMBf6UXtUe9sbQD27M5zEcz4fcjNkm/mWup8QIpp/r8Y6MwlTFMrer4WGavqfUm5eNjNGgGmgj7RswOceBtyPK3jWn040hO7xnX0id4a6o6Au059oa6+JD/HZ+NBZ0v4GrPFtZUwwtIQk2xNcFaOxbrZuoTCTeh3GDdvNErzy3Dql7Ooq6lH5PVo3Po7FJMWjm/PF34lkkf5lxfJL5kUH5aEi39cQW11HZfi+e2TA6iprMEDy2fIfYz/1wiDdu637T8IRewT/ZZZs2bh888/x5IlS7hjnEU+sUVo9+3bx79neveBgYF8n5OTE5fHeeqpp3p1zhkzZuC7777jUfEsCp9J3bAIftEo+ddffx3Tp0/n59TW1kZtbS3GjBGfSi2JtLLq6vZ82pgijBgRgC1bNmLHjr149dXtsLOzwCefvCAmm8M0kZtFpit6eTljx47N+OST3Xjppc/4YrszZ47Bxo2PtqfZuXMfj4JdteotsfOxmQHPPvtYn/6G6Cufw8HWjC9ayV7wVam/8v16zh3l6UtYVP7jR6Pw9jhXrBxki9K6JnwVkoWf7nQYicw2ZIaK0Pm9NyYPzw13wEeT3XlkUFZFPXbczsDuiI48MUXVmORkgvPLhkBHTQXBOeVYcCAc6TIcb7LQcnSG7Yo1KDz2B/IP7YOasQmsHlkGPd+OBde4nJOIbFJ9bg5St74t+NDaisI/D6Hw6GHo+vjD/qln0JecySjm00xfGerMp/ymV9Zi48VYsQ4UizRhHSEhBxPz8EKgM7xNdLk8ETN6nz4fg2u5HZ3C8bbG+Hy8IEpYOBjAjNLf4nLwSaj8gyO/JGTzjsbWYZ7QU1dFfHk1ng2KEVvkVkWJLfokKJ+drham2ZvzaJr9UwXRq0JevRmHa3mlKKhrQFhxBV4LdIOzvg5f/DeurBrrrkQitVK20a1jaYEBzz6NxH2HkPrnCS6z47pwPqxFHYptrYLo4Tb589hOGMOd+dHf/sR7S7q21hj0/HqY+HbUH1s89Oqzr9w9RStqcvO4Br+BixOGvPaizDK/HpKA5/yc8evEQTx68npBKT6PEpdqEtSf4O9JNqZw0NOGjY4WzswQXyRy2ukb7Y7+T4Z5Y5CpIbe3WP4LswTO0EcvhvEF8+SBLWY7//HP8Pm7y5Bw4zPU1jfiwNEb2Pze/vY0wkFQYcRwaXkNEpJzce7Qa3yx3PCodMx7/FNcuyk+K4axbOEYHDwWzJ37PaGgtBbL3/obb6weiuXzfFBW2YBvD0fh5+Nx4s+GyDR5Jq3z+Otn8eaaYVi97xFU1zXhekQeXtl2rT0qf+/pBC5N8dITgdj20jhUVjci6E4uPvoptMvyeK58Ekm/7UPIm+/ydsHIxxtuy5aKRYCxhSIF0evy53Fb+jBSDhxC2DtbuBNT29oKPuufhp6jI/9ew8gIfps2cGf+zVdeRVtrG3RsrOG7fh3/VxbWwwLRWFWFmF8PoKG8AjpWFgjcuEZskVtWVlZmRfLYTxiDuH1/oCItA8pqqtCzs8GQ59bBPED2IIM0zKbNQGtjI3fet9TWQsveHo7rN4mvW8Ii7UXqk+nl5x853P45acs7gnIvehgm4yZAzcgYOu7uyP75J96Oq2hpQcvBEc7PvQhNG4ETqjuGLl+MkD2H8Nfrn6ClqRkW3m6Y+Mo6sUVuW/l1bpM7T35MIgrikvh1P7ha0I4Imf3hZhjaWUNdRxuTNj+NsL1Hcez5d/hxDKwtYfzEWmg4u3Uq59vhCdjo44zdYwVtS3BhKbbHdm5bRPtkHwz2xiATQ/4uZt+dnSZoNx6/Goa82nrk1Nbj1ZA4LHe3x/4JQ3i0YGplDTaHxPJ2WxZW48Zy53zij3v4/cPuS59N66FpairzXpMnD6M4LBzN9fWwGN0x4KMoPg/P5+/5oK3b0FzfAGM3Z4x8eT1UNTXEyydyTeXJE3vgKJJOnGuXhDyzQTBINnjdk7AZHoiy1AwURETzjrHwOyGj//csTNylO24jU0qxYXsQXng4AK8/Ngi5xbX43w+3cSkiT7xtZm3f3eubnl+NFR9d4hI77ywfgrKqBpwLzcbH+++051k/3xcaairYsUFck37f+WS8tbvr9o/hMCKQL4gbuucQ6soqoG9tjrHPr+b3r2g9ispQypMnOyQSVz/7rv3zNfa3khI8ZozHoGWCGbTD1y7DnX1HcemDnfx4WsaGUBk6GSpjZndb7qqmFqw5H43Xhrrg4sJhqG5swZHkfHwd2bFQM6tGZqcKnxf2/doAB7w82IUHreTVNOBQYj5+juv9ejdfxaVgtYcTPh0aAHUVZT5I92ZYDB9AE8JteJGHd62nM2bYWrVf78MTBc/Dm+Ex7fr5vyQLfs8HQ/ygraKK7No6fBSZgIhSxbTON12IxesjXHFywWDetlzKKsG7N8TXFRC1+y5llnIn/08z/PlgAJPzuZRViq8vZrbbLPKkkQc242LjtWi8NNAVJ2YP5Tr0ZzIL8UVkh13LiiVcjJSxwMWKSy/NdbLkm5DCugbMPx3C/z6TVcQHCFZ723MHO1vricn1MHkURWBR+avOROO1ES54wtcWZfVN+C4yG7/E5HYqn/DyHojPw/pBjnh/jDsPlGLR/V9FZOLXmL5fWymmtBqvBMXjmQBHPD/QBfm1DdhyOxlBItI6zKLi5bv7eaWPPb9Pt44UyOwK+SMlDx+FdgyU2Olq8mj2jZf7Tsee3RrPXIjha1v89eBQNLW0cmf3h7fFB2hEr/fZjGKs9LXDz9MD+ILFhbUNOJ9Zgu+ie7fwNWNPnKBP9NFIT36fsL7LhivifSJlkWeDBWrNcBD0iQ7NEO8TvXw9DldyS/nfx9IK+GD61hEefEFvFhT1ZVQ6jqT2TF/fws4c67auxOFdR3Fy9xnoG+th3urZGD5tiJg/g7XVwqevsrQKmxcK/BRsf35GAf7+/RKcvB3wwo4NfL+LryNibsbijaXvo7mpmS/Qy74zMpMtWUwQ/RGltt6sNkEQ/wBMuz42NpY79+3s7MS+Y410UlISiouLYWtrCwcHB5nHKSoqQlpaGoYO7Yhwq66uRkREBEaMGMFHhYU0NTUhLi6O73N3d++khc8WsI2Pj+ffM+39jIwMvk+ow19aWsoX22XHlVVWNiggb3m6JhG9hTUDrHzynxN8UWFprYfQ+S5Ey/7NXpePHVPaVMOWXkQYCTF7vueDQSwKsDeLY8lDoE/PJlYJHfui0fZtLVK0JruQvhA687pawK+n628JlTgUDd5mdS4JO4a0w+jp9nxEXjhDvrWL8zJ6c/0HWck3m4Iv5N3a2h6B25e0SrknBDIZyogslr4otzywO0b06ZSn/lgSaTK/suo4fIMgErUnMKmynrQfiuSz8FVM3kLsPMpKMiV0+oqJLwuc7N0hTztwL9BV61n7/k+UN7Om53ExkuWTHIRt565USE8YcFeKRxGYDaCkQL0J2yVpdNdWXczX6rO2RfgukaQ3j4+TXtM9vdcUzSeZ3kSj5Z6Vjw8GSDHuhNI1st4botf90OeF/1jbx+U9pF7/NplrVj/2ovz63e11oqCspqznQ/h+lcXh6L6TlLgXdqqdRc/tKmUJWax78eymZfe8X8Dsj3v82oWevnKfXEtZ9hJ/nfSifOXlrf/qvdbdNeiNXS9aPpG4rG77E4r8rl4sB3PPn10NjV72iUSuzb3oE304uueyXV35M5i/QhI2xKOswDoTjInWM3tdvv8izms6AlPuJ1K/6ZCo/q9AEftEv0dLS4tHu0uDGdkeHh586w4zMzO+icKi5qXp6TNHvr9/R/SzNEmcgICA9s/OzuI6tcbGxmJOfVlllbc89xrW0VDEqc9QNH1vEESl9r8xyHvt1O8NvAMucY0UldS5l44xfkXb+m+dS95x/+a1Fkoz3AvuxWABQ7JrKE/9tf2D9dzTQcG+GEyU6zz32rugAP+0Q/+/Xl5p0iuSbfW/gaJrAd3LdqkrWvvoXfJv3muK5vunziPMo/QvvTd60vZx/fV7fP3lqZP+8nz0Zzu1Pz270uhHr91ur+U/aS/JS1+U515eA9HyKXKef6ue+9P1lXxW+1PZuvNn/JP+CoL4NyHHPvGfgenjR0ZGSv3u8ccfx6pVq/7xMhEEQRAEQRAEQRAEQRDEfwZpU4eIfwVy7BP/GdauXYuKCsFCgpLY29v/4+UhCIIgCIIgCIIgCIIgCIK4F5Bjn/jP4Ofn928XgSAIgiAIgiAIgiAIgiAI4p5Djn2CIAiCIAiCIAiCIAiCIAiie/r3slb/r6BLQRAEQRAEQRAEQRAEQRAEQRD3EeTYJwiCIAiCIAiCIAiCIAiCIIj7CHLsEwRBEARBEARBEARBEARBEMR9BGnsEwRBEARBEARBEARBEARBEN2jpES11E+giH2CIAiCIAiCIAiCIAiCIAiCuI8gxz5BEARBEARBEARBEARBEARB3EeQFA9BEARBEARBEARBEARBEATRPcokxdNfIMc+QdznHM1IQX9m5DfPoD+Tvj8b/ZksC2v0Z5YNqEd/ZdeRVvRnysu10J+pzalBf2bQjpXoz/gYN6I/Y6Tegv7MpSwN9FdKS/t321JS37/bluCnvkR/JuDgKvRnLmf232eDYbfUHv0ZfbVa9Fcqcvv3e8PJSh39mYZLeejPqE7p3zZ9U2gx+jONI83Qn6lMqEZ/RdtZB/2Z5rZ/uwQEcX9DUjwEQRAEQRAEQRAEQRAEQRAEcR9Bjn2CIAiCIAiCIAiCIAiCIAiCuI8gKR6CIAiCIAiCIAiCIAiCIAiiW9qUSGO/v0AR+wRBEARBEARBEARBEARBEARxH0GOfYIgCIIgCIIgCIIgCIIgCIK4jyDHPkEQBEEQBEEQBEEQBEEQBEHcR5DGPkEQBEEQBEEQBEEQBEEQBNE9FCbeb6BLQRAEQRAEQRAEQRAEQRAEQRD3EeTYJwiCIAiCIAiCIAiCIAiCIIj7CJLi+Y/xxx9/YPXq1SguLv63i/L/gqysLAQGBqK0tBQVFRXQ0dH5V48ji8grETj3y18ozS+FibUJpj0+Ez4j/WSmb2ps4nmCTwQhKz4TCzYtwpDpw8XSXDl0Eae+Py62T1VdFe8d+0iuMllqaeAZb2f4G+ujvrkV53OL8H1iBlra2mTm8TLUxRw7K4yxNEFceRVevB3TKc0Ic2MscbGFvY4WGltbEV1Wie8SMpBbWw9F8HM0wv+WDoSXvSFKKhvw6/lk/PBXgsz0Qz3M8MvL4zvtn/36GSTlVLZ/XjbZFUsmuMLaRBt5pbX45mQ8jgSlQ1FGWRlhnb8j7PW0kFfTgB9iM3Emo0hmejtdTTzmZYthlkbQVlVBQlk1vo3OxJ3ijrLtmuCHQWYGnfJGl1Ri5flIucqVeOEGIv88h5rSchjaWGDIo/Ng7efRZ3kij55DyN7jcBs3DGPWLRX7rrq4DCF7jyHnThyUVVSgYjUULX7TARW1Tsd5eowzHhlkC0MtNUTlVuLtv+IQX1gt12/cMtsbiwba4LOLydh1LU3sOw9zXbww0Q1D7I1QUdeE3bcy8NPNTCiCmrISnh/ihJlO5tBQVcatvHK8H5yC/JoGmXn8zfSwyt8OA8z10drWhojCKmwPS0dKeS3/XgnANCczPOxpBQ9jHVQ0NONiZgl2hmeguqlFofIZaKrizWmemOhmhra2NvydWIR3ziagqqG527xaaio4umIYnE20Me+Hm4jOr2r/LvLFCdBWVxFL//GFZHxzQ/HnQ5IHHa3wkJMVjDTUkVZVg50x6Ygp7zi3JH5GeljsYgMfIz3eJkWVVuHb+HTk1cq+BtJobWlB6h/HkH89GM31DTB0c4H70sXQtjDvVZ6wjz5HeUKSWD6zwIHwW7dKbF9FShpSjxxDRWoaNAwMYDtxHGrzC1AUfgdtzc2wGRwA/yUPQk1bS2pZLm/ZhuI48fMwjF2dMOHN59Fb8iKiEf37MVQXFELbxBhe82bAftQQsTSttTWoPfcnGmPC0dbShEVr12PF4oUw19ZERlUtdkanI6y4QuY5PA11scTNBgEm+oCSEmJKq/BNbDoyqura05hqqmOZuy1GWBpDT00VmdW1+DUxG1fzShX+TWsC7LDI0woG6qqILq7GluAUJJbVyEzvZKCFNQH2GG5tyJ/9+NIa/lyGFVQqlEYeWN7VHo6YaG0KdWVlRJRUYHtsKorqG2XmMddUxyw7S8y0s4CRuhpmnL2Bplbx97SjrjZWeTjwd7SykhJuFJZiV1w6qpq6bxN6iqebDVY9OhkPzx+Nqqo6eI7a0OfnSDt7EelnL6Chogp6ttbwXvIQjNyce5WntqgEycf/QnFULH++9Wyt4PrADJj5e7enid6zD5kXr4kdlx1rzHuvdXludq8/G+AEN0NdlDU04XBKHvYm5chMb6Kpxp+N0VbGvG1kz9PPCbLve3b8r8f5obKxGXNO30Zv0VJRwdPejhhtYQIVZSXcKizj92NFo/T7RkUJmGJjjtn2FvyeK29swsXcYvySnM3tvb6C2SJBPxxCdmQCVNRU4TxiIEY8Ng+qGuq9zhN//gaiTl1CZUEJTBxtMPKJB2Hu6iBXuZ4Z4YhHAqxhqKmGqPwqvHU+EfFF8tktW6d5YpG/FT67moqdwRnt+12MtfH0CEeMcjCCmooyYgur8MW1NITkyG5TpdHa1ISio4dRGXILbU1N0Hb3gMWiJVAzMpaZp6m0BOVBV1Fx4yqaq6rg/tlOKKt1ttcYba2tyNr5OWoTE2D9xCroB4q/J3rCjCG22DDfF7ZmOsgsqMbnf0Th77DcLvOY6Gng+YV+mDDAmrd1J4Iz8cnBSNQ1KmZHycMjHtZ41MsaJprqSCqvwcchqYgslm23WOtoYKWfHUZaGUFbTYXbf99FZeJ6Xnmvy+Jnb4jXF/rDy9YQJdUN+OVSCn44nywz/VA3U/y6cXSn/bPeP4+kvCq50/QU1s95cbATJtiZQlUJCMotwwe3U3m7KIshFgZ4wscWfqZ6aGhpRVhBBbZHpCOnWjG7TxoLB9niqTFOsDLQRHJRDbaeiceNVPnsizWjnfDiFHccjsjBy0eixezpl6a4Y4qXOYy01ZFfWY+DYTn4+mpqj+yWhR5WMNAQ2C1b5bBbVjObxKrDJtkVIW6TjLA2xOOsPs30UN/cguC8cnwRko6iOtn2Rnck30nBka+PIT8jHwYm+pi4cDxGzxklM31rSyuig2Nw7dh1JIYnYdLiCXhgxaxO6S4cvIQbp4JRVlgObT1t+I7wxgMrZ0NLR7PHZf1/gzLrYRL9AXLs/8dobW1Fc/O960gRHbS0tGDp0qUYMmQITp06xR1c/+ZxZJESmYy9W3/G3KcXwG90AMIvhOCXd3/C2s82wMHLUWqe0LO3kB6Thlmr5uDbl3ZxR6EkrJwWjpbYsLPDuaPE3Yfdo6qkhA+H+CCjuhYrrobDWEMd7wzy4h27XXHijlIh+mqqWOfljBOZ+VBWVoKplA6Ws5423h7oiR+TMvBKZj501VSxyccFWwd74/ErYZAXMwNN/PzSePwRlI51O4Lg72SM7U+PRG1DM/ZdTJGaR0kJUFVRhteKg2gRcXqI/v3EFDc895AfNuy6gZCEIgx0NcWOp0egpr4JZ0Nld74l8TDSwSejvbEzMh0n0gsxzsYYbw/zQHl9E24WSDfg2SDAjbwy/BibhdqmFjzhbYed433x6NlwpFcKHFzPXIoSu4IG6mo4MWcoLmaXyFWu9Jt3EPTtfox9+lFY+3si7swVnN36NeZ9/DIMbSx7nacwKR1xZ67ByM6Kd/JEqauowvHXPoW5uxPmbHkB6rra2P1FEJQKktBm3eEwYawe6Yg1Ix2x9kAEEouquSP+18cGY+KX11BZ33X7OcvbAr5W+iirbeKdOlHczXRxaPkwHAzPwasnYsAeG3YuV1MdJBfLNpAleW24K0bZGOGpc9H8mr45yg3fTPXFg3+GokVG88Ccfvvjc/G/q4nQUVfBC0Oc8dN0f0w6cJM74Zjjf7KDCXaEZSCprAa2epr4cKwH7PW18PTfnQfIumLnggDoaqhi3o83uaPlywUB+GKeH1b8Ht5t3nemeyK7vI7XlZJE/bHn/+nDkfg7oWOAqquBPnmZYWuONV4OeDcsETFlVXjExQafDPfBY5fCpDo02VTG1Z4O2J+ai08iU6CpooyNvs74fLgvnrwcjroW+R1IqYePIi8oGH7PrIGmqTES9x5A+CfbMPz9N6Girt7jPOz+d5g5FU7zHmjPJ1mf5UkpCP/4CzjMmAqf1cvR3FCPkHc+gJa5GQY8vwFWVobIvhWB7FthcBovvWM09pX1Yu+kxuoanNrwPz4g0FvK0jJx/bNv4Lt4DhzHDEduaCRuf70HGvq6sPDzEvzO5iZUfP8JlNQ1oL/iWUzw8cTrI3zx1uETiNC1wIPO1vhkpDeeuBCBzOoOR70o63wdcSQ1D9siU/kz+4yfE3aM9sOj58O4g5Kx2NUaiRU13Jnf0NqKGfbmeH+YF9ZdiUR0qfwOhuV+tljuZ4dNF2KRXFaLjYGO+HGGH2YeCmk/lyRsQI4Nsn10KxUqSkpY4W+L76f7YdahED5oK28aeVjv7YwhpobYfDuWO0+f83PBh0O8sfJaBCR89e087e2MlMoa7EvJxnqfzk5tNoDx2TAf3Cwqw4qrEbyTv8HHBe8M8sSzNzucEH3Nrg9X4fCJYPy07wIenie7Y99Tsq5cR/yBIxi4djmMXJ2RcvIsbn68HeO2vgEtE+Me50k9dRbGHq5wf3A2lJSVkXH+Mm5/vhOj33kV+nY2PE1baxssBvlj4NMrZT7f0pz0O8b44lRmITYHx8PLSBfvDfNEXXMLjqTlS83zuIcdcmvq8cL1WJQ3NPH7/oMRXnj2WgxuFZZ3cpS9M9QD4cWVcDfsm+CTVwJcYa+rjU3BUWhsbcNrA9zwbqAnNtyQft8MMTPiwSC72MBcdS0c9bTx+gB3PjD3YaRsJ6MisLb19JavoaGrg4c+fQWNNXU489F3aKpvwMT1y3qVJ/TQX7hz9G+MX7cUdgO8UZ5XiLhz1+Ry7K8Zao/VQ+2x9s8oJBXX4IWxLvht8UBM+O4GKrsZVJ/laQ5fCz2USrFb1g53wN/JxXj/YhJvW9YMc8Cviwdi4nc3kFslf9tSeHAfquNiYLduA1R09JC/72dk7fwCTpvfhJKK+IC9kIJD+6Fpaw/jKTNQeGh/l8cvOXsKSiqqrKPLOiDoLSwY5/O1I/D2L2E4E5KNOSMd8OUzo/Dw+xcQkSLd5tXRVMX+/01EdnENHtlyAcXl9TzfxIHWOHkzC33JXBcLbBrkiFeuJeBOUSWe9LHFN5N9Me9YKApqpTtGmVM6tKACu+5kgJkpiz2ssGOiDx4+GY6ku0EePcFMXxO/bByNw8GZWPftTfg7GGH7yqF8MGPvVel9NnaXsT6R5/o/ZfaJ5EnTU94b6Q5HAy2sOBvJnfRbR3vg83FeeOKs9CAl9t56zNsGP8fmILakCkaaatg8xAVfT/LDA0dDelWWaV4WeH+OD174IxLXkkvw2HB7/LRsMGbtDEJKN32DAbYGeHSYPZIKq/nzKcobs7ww2sUET+0LR2pxDYY5GmPXwwNR3dCMX2/JH1C03NcWT/ra4dmLsUi6a7f8wOyLw7LtlpX+driUWYKPbwnsqhV+tvhumh9mHxbYJOx9xGyhPdHZiC6ugoGGGt4e5cbTLDgquy/TFUU5xfhq87eYsGAcntqyEkl3kvHL1t+gpauFwImDpOaJD03AjVM3MW7+GJQVlnFHvyTMoX/8+5NY/sbjcBvoys/z49t7UF/bgMc2iweQEUR/hqR47kNCQ0Mxffp0GBoawsnJCR988AF36F++fBmLFy/mEd+qqqp8e+21rqN7bG1tceDAgfbP48ePh5GRUfvgQEpKCtTU1JCdnd1+7ilTpkBfXx92dnZYs2YNyss7jP+bN2+2n1tXVxcDBw7E77//LnbOvXv38vN++eWX8Pb2hp6eHqZNm4aMjI4IEsbp06d5FDuLXnd0dMQ777zDf6fkcb799lv4+fnx+pg4cSKSk+Uz7ll+GxsbfPTRR/D19eXlmDRpEtLT03mdsnOyumB1WlnZOSrurbfegrm5OZ588slO38lTD/Icpy9gkfWuA90xYvYo6BrqYsyD42Hv5Yirhy/JzDN89ig8/PKjcJTSiZdEhUVG392UVeRrUkZZGMNKWxOfR6dwx1pCRTV+Sc7EA3aWPIJLGpVNzVh/IxJncgrRKMOx5qqvwy3FA6k5qGluQUFdA05k5cNWRws6qtKPK43F453R2NyK9/aG82j9i3fysP9SClbP9Ow2LzNIRTdR5o50xJ/XM3DpTh6q65txNTofh66mY+1sccdzdyxxt0F8WTV+TcjhnfGjqQW4nleKZV62MvNsvh6PY2kF3OBiDp1tEWmobW7BOGuT9jSsuC0i21QHM77/ZHqhXOWKOvY3nEYOgsuYIdAy0MOgRbOga26MmFOXe52HdZQvbduNMWuXQF2nc3Rx+MHT/P4bv+Fx6FmYQkNHGy3ekzo59ZlZvGqEI34MzkBQWimKqhvxxsk4aKqqYOEAgVNFFraGWnhjuic2/RGJZikdj9emeiA2vxLvnInnxy2uacSWc4kKOfWZk2y+uwWPto8tqUZuTQPevp4ENyMdjLWVHfnGnPNXs8v4tc2tbsBPUdkw1VaHg76gru4UVeGFS/G808cMdXbsnRGZGGdnzB028uJjqYfRziZ4+0w80ktrkVJSi3fPJmCSuxkfwOiKOb6W8LLQw6eXZLfRzIncIrL1BQ+72OBUViGuFZSirLGJRxLXNDdjnoP0wSbWuqy/EY2gglIeEZpf14AvolN5m+VlqCf3eVsaG5F1/hIcH5jBo+41jYzg9fhSNJSVofB2WB/kUeIzU4QbcxKKkrj3d5j4+cB5/gNQ19dDcXgkWpuaEbDxaejaWENVUxOOY4fLdOrzMygri50j64agg+swZphYuoyrwTj3yns4svxZnHnxbSSdvtBp8E2SpL8uwMjRDh6zpkBDXw9OE0bBMsAbCSfOtaepD76EluIC6D/6NFQtbLDU2wkXcktwSd0E5Y3N+DE+k8+iWOhiLfM8G65F42JuCUoamvj75qPwZB4BOci0Y3YSi/o/mVHAv2fPx+/JuahpaoavsX6Xv0GsrphjxdcWP8fk4EZuOY9Ke+dGEh8YYs+0LF69mogz6cUorW/ieb4Kz4SWqgq8THQVStMdbCYCG+T6ITETiZU1KKhvwGfRKXDS08FwM9lty5th8fg5OQvFDdKdSUNMjaCvrsafEfZ8FdY3YkdsKgaYGMDXSP7nRVEmPvgWdv74F8or5G9fFSH11DnYjR0Jy8AB0DDQh9cjC6CqpYmMC1d6lcf38UdgO2oYNA0N+H3vNncm31+eKjEzSanr51uSuY6WaGptxRd3UlHa0ISg/DL8mZaPRz1k2wWf3UnF/uRcZFXXo6qpBQdS8hBbWoVJtqad0r480AWXcosRVqRYJLcsrLQ0MM7KFF/FpSG9uo7PqtwWnQp/YwM+U0oawYVl+CgyGbHlVdzGYwO1+1NzMN7KVM6wku5hEffFadkYu+Zh6JubwNTJFsMenYOkK7dQW1bZ4zwsoj/04GkMXzaPR/OraWnAzNkOY9c80m2Z2G9bOcQeP4ZkISijDIU1jXj9bAI0VZV5FH5X2Blo4s2J7th4IkbqO/WFU3E4lVCIktomftzt19N4JLCPhQLvuppqlN8IgtkD86Bp7wg1ExNYPLIMjXm5qI6JkpnPdvXTMJ35AFQNDLs8fm1qMsqvXYHlksfQV6yY4YEbsQU8WKe0qgG7zyRyh/7y6e5d5jHU1cAzO64jPb+a2/F7L6T0uVOf8YS3Lf5MLsDFrBLe7n8amsZnVy52l329t9xKwcm0IhTXNfHI9G8iBc5dX9PetcMPj3YU9IkORaK4qgEXovOx/1oaVk+VXVfy9IkUSaMINrqamOxgyusspaIW2dX1vG4GWRggwEx6XbAAmPUXY3Ezv5y3hZlV9fgtPpfb0Maa0meRyMvqMU44GZ2HY5F5KK1txBcXkpFdVocnRnQ9oKenoYptiwbgpSNRqKjvPNPA39oA5+IKEZ1bidrGFlxMLEJkTgUCbDvPvO7ObvklVmC3FNc14l1mt6gqY76bbLvlNRGbhOX5OkLcJimpb8KqM1G4llOG8oZmZFTW8cAEd2MduBr1bGD46tFr0DfWx+wVM6FnpIdB4wcicGIg/t5/QWYe76FeWPP+SvgM95Y5OJ6ZkAVbVxv4jfKFprYm7NxsMWCsPzITFJttTRD/NuTYv8+IjY3F2LFj4eXlhcjISFy6dIk78u/cucP379u3DwYGBqivr+fbu+++2+XxRo4ciYsXL/K/6+rqcOPGDSgrK3MHPoN95+DgwB3oCQkJ3HE+f/58pKWl4erVq9zhz6LNhQwbNqz93Hl5eXxggTmsg4OD29Mw53xOTg4v69GjRxETE8OdwvPmzWuPDGT75syZg0ceeQSZmZn4/vvvsX37dnz88cedjsMGAI4fP474+Hioq6tj5cqOCKeuYPlzc3Nx69Ytfozo6GgUFRVh8ODBvG6DgoIQEhKC8PBw7ugXhdX7nj17+KCCNOSpB3mO0xdkxKTBJcBVbJ/rADdkxEqPslCEouwivLngVbyz+HX8+Pq3yEvregqrEF8jfR5txZwAQsJKKqCuogx3g55HgrFjMGfMHAcrHn1hqK6GaTbmuFVUxjuB8hLoZopbCUViQUHXYwpgb64LU4Oup+UFff4AQnfOw++vTcQ4f3HHIeuXt0oYrcyI9XE0hKaEBElXBJjpI6RQvHN9u6AcfibyG+8aKsp866pe5jpb4lJOCR886I6WpmYUpWTAysdNbL+1rwcKE1J7nefaN/vgODRApkRPxq07cBo+kE+B7woHY22Y6WrgRnrHFFgWzROaVY5AO9mdS1VlJexY4I9tl1OQWtI58olNex7lbIyjUXnoDf7melBTVsZNkanTzFGfWVmHARbyORlZpBGT3EksrUF6hfQoZoahhiqaWtpkDpRJY7CdIe9AhItM07+ZUYbm1lYE2squP3sjLbw+xQMb/4zi55TF1lk+iHl5Is6sGYmnRjryeu8NumoqPKozXEKqhX32UcBpy2YMMWpb5G9HqjOz0drYCCOvjntWTVcXuna2qEhO6XWe7AuXcXHNBlx/+XUk/LofTTUdDs76klJUpWfCcsTQ9n1FYREw9vHkTv6ekn7pOqwD/blDUkjaxSBEHzgG/0cfwuwvt2Dw6mXcaZ9yTvaAHqMkMRVm3uKOAXMfT5Qmd7ybGmLCoO7qDWVdPT7Ti0UhS8ruhBaWw89YT6HBMwYb2JQGk6iZ7WAheA4LyuQ+rr2+Jsy01bl0lpDGljaEFVZioLl895qOmgoe87VBUW0jlwHoaRppMJkc1bvyO0LY4HdOTZ1MR6o88Ce0jQ2IdTzXwll+7F1/P8KepercfJh4ddyfzClg4uWBsqTUPsvT3NDApXtUNDRg6i0eOFAUFYszq5/F+U2vInzXD6gr7lq2wd9Un0fTi7auIYXlsNHRhLGG/I4pfXXVTs8Gex4c9LTxTUzfOTqEg2ai9yMbcKpmA2oK3I/6amqob2kR+929IT8+FTomhjCwEgQ2MGz8PPgsioLEtB7nyQiN4YOdrqMDFS6Tg5EWzJndklkmZrcwuZxAm67tlu1zfPFFUBpSS7uP2NZVV8GKwfYorG5QSIqnLj2NaV5A273jHlY3MYWaqRnqUns3k6KlthZ5u7+H1dLHoaIj/0BmdwS6m+JGnHjQyvXYAgxy6zyoJWRqoC0uhOeippuZnb1FT10VLoba3K4X5XZ+Bbf/5UFLVRlLvaxR19wqZk/2hEAXE9xKKhbvE8UXwd5UB6b6Gl3mvb5lBsI+mY0Dz4/FOB+LHqdRhIF36+h2fsfvjiut5oP2A+SsP1MtNTzoZomwwgruvO4paipK8LcxwI008fY7KLUEgfZGXebdOs8Xf8Xky5TsYYMFE9zN4GKqw2e8DncyhpelHk5GS5+hJctuYUFANyXtloJKLu0pD8wmWeZjg+JubBLDu++hGgXlP4WkRqfBbYC4P8NjkBtyUnLR2IWcYHf4j/bj0j4JYYlcRaEgsxCRQdEYNGFgj49JEP8GJMVzn8Giy1l0+ueff96+b+vWre1/M6c8g0WKywOL0GcOcwZzZLMZAEOHDuUOfeacZo5nlobBnOqzZ8/GunXr+GcTExPs2rWLR7YzB7m1tbXYuVkE/EMPPYQjR47g4MGDGD5cXKOdObPd3AQOPea4t7e3x4ULF3jU/CeffILRo0fjhRde4N9PnjwZr776Kt577z28/PLLYp2mH3/8kUfWM5577jleRtYws8GC7mD52blZtD/jscce4+f57rvv2nXulyxZwsslhK1fsGzZMvz0008wNpYd5dZdPch7nN7Q0tyC2qpa6EpEmbLI/SoF5AWkoa2vg/nrH4LXUG801DXg7M+nsXPTNjz3zUswtuyIApcGk96RdBZX3HXys+96SnF9I94Ii8fbgzy55AAjtqwSm0NiFTqOuaEWMgrE9epZRI9Qpqe4orNef1NzKz47HMX18ptbWrFwrDO+f3YsVnx2BVeiBEYWk9tZNcMDf93ORmhSMQJcjLFgjCNUlJVhoq+BnGL5psqyaedlEkYMi87RUVPlxjwz5LvjaX9HHr11IUv6ehzexrpwM9TB5+HyaTXWV1WjraWVR92Lommgi7ryyl7liT93DRW5BRi3Xnq0VktTE2rLKngk/1/v7eSDApoGelAxDUSL71QxjX3WOWawaHpRSmoaYWckXWecweR6WJ69oYLZS9Ki+ZnTjE1JPbJiGNzMdJFXWY/fQrKwW4EpsWZagvu/TKIjwToWpne/k8XaAfZYO8CBd+bTKmqx9mw0mmVEvbMIJCbfcyylQGYaabD6YxFHorD7qKKuGWa60svHBtl2POiPbVdS+bRjJsMjjb/iCvHjrQxklddhuIMRd/Jb6Wvizb/i0VNM7rYnLPJeFBbt7WEon5OAvVWf9nZCUkU1Esvl0zNmNJQLOjjqeuL3N/vcUFHZqzz6To5wmjMLeo72qMnJRfyevbjzxU4Ebn6BR/bWFQnar+b6etx84z3UFRWjtakRhh7uiP7mBxRHRPFZLZYDfeC7aC7UdbS7/T2lKemoyMrlDnxRYv84yY9h4Stw6pi4OcNzzjTu2HedNkHm8erLK6Ah8ewzGR6mO87KDWigtaQIqr52qNr/LQyLc6E672/kXT6LVgs3KGsJyswGiI015X9vbPB3RlZ1XafBHm8jXXw1NoA/P7VNzXgnJBFpVfLLF5hpC9qWEgn9WPYsswjCrpjlbIYPxnnyczOH/YbzMTzKTdE0PXsWmhRy/EobUK9racHTXk58PRtWvrWeTty5Lzzn/Ub93feP6AAW/6yni4q0jF7nYU7725/u5I5eVW0tDHp6JbTNOuwm9veA1U/A2NMN9WXliP3tIG68/ynGbPkf1LS0ZNoF2dXi97TQzmIzVFgUf3cscLbiAwGnMjocng56WnjazxFPXY7ss1lUvEwa6tyJzyR4Ot+P6nKv1bTQ2RrHMuR3ZHUHsyU62SR6OrxdrZVhy8iTpzK/GLpMWu3KbUQcOcdlelhk/9Clc2DhJl0WU4i5zt22ReLdy97Fdoay7ZYXx7pw22bvna6lHud6WeCz2d7chmFO/TVHIlFWJ78zs7lCcN+p6orXgYqeHpqlzHhWhLzfdkM3YCB0vHy4jn9foKqiBCNdDT4jV5TSygZu48vC3lwHl+7kYteGURjlY4Gy6gZu139xOJpLdfYVQjtQ0qHM7HyfbmZpjbAyxM6JvrwdrmxowotX4vjMz95grq+JDIm1HJjOPi+rviaKJeqR0dTSik+PxeDIzUw0t7Rh4UgH/LBuJJbvvI4rsQVyp+lp/VU1dm5b2LuYtZNd8fJgZzzsYc0d5fGl1Vh3oXdyckz7nq1dUVIt8ezWNPIgI1k8MtgOzqY6ePbQHZlpdl1Jha2RFs5vGtten++eisOlRNnrrUliqiUoQ6k0u0Wve7tl61iBTcKc+uu7sEnUVZTw3GBHBOeWI7tKsTXvhFSWVsIj0L2TP4MFhVaVV8PEsme+FK8hnpi7Zg6+euVb7jdhDJs2FDMem9aj4/2/oxuZQOKfgyL27zPCwsIwbty4Pjsec9qzSHwWVc6c+BMmTOD72N8MJu8jdOyz6HUmJ6OpqQkNDQ0eHe/qKhg5TU0VOP+amprwv//9D+7u7twxzpzbLDKfRd2LoqWlBR8fn/bPbFCASfuwGQnCiH3JgQA2u6CsrIyXVYiZmVm7U5/BHOSsDNXV8jlfWH6hU5/B/rayshJbvJbtY+cVsnz5cixatIgPNshCnnqQ5ziSNDQ0cFkg0a2pi86aLL3+7rRa5WHItGF80zXSg4m1KRa9uAQ6+tq4cTyoR8frg9mXXGN/y2BvHEjLwby/b+LRSyFcwofp+fd2bRdh+WQdJiy5BDuPxSK3pBaF5fX87/MRuWLyPV+fiMNPZxPx0aqhiPp2Ad58dBC+PSlwWva2vyzML886Bw+5WmGhqxX+dyNBZmd/nrMlsqvrcEuGZr+8sHutrRd5ynMK+IK44zc+ITMan0XFMSL+OAPPKaPw8DfvYcy6R6GSch0qd07IdU72rMiquZFOxpjvb4WXj8k28IX318ZxLvjw70QM/+wSPj6fhM1T3PHoYDu5yiBeHvHPzEnW3bVlU2EH7bmG6QdvIb6kGj/NCODRl5Iw6Z2dk31QWNuID29KjxxXFF4+Ge3K8xNcUVzdiF9Du56u/vyxaMTkV/F1Ds4mFOHji0lYNtiOz4boawTXW75G4Xl/VzjqaeGtsAQu09NrWD0p+sBL5HFbvADG3p5Q09aGoZsrfFY9gYrkVK6rL/pMpB07Bc/Hl2D0p1t5VHBZbDxfnHDUp1sw8rk1KIpNwu2vf5arCGmXrkPHzATmPh5izvk6JjHx/W/444kN+OPxDTj82HqE7/4d1YXyrc3R6XeK3v9trai/cQGq9i4weOZ1vqs5PxvVB35oz8LSytu8r/dz4hIx/7sZ32kB2Niyakw8FoRZJ2/ih/gsvD3EQ0yup6ew03T3yj2ZWoQBu69i3L5gnEwtxLfT/OB4V0ZLkTTyIHnnCeqv5y/IkoZGvHI7Fs56Ojg4cQh+HjsIadU1yK6p67Mo6n5DHzy7DFNfL0z/YTsmbf8ATtMmIuSLr8WkeFxmT4Pl4AFQ19XhuvuB61ehobISecGKaT0L2yt5ru5ISyNsCnDCpxGpSLorb8TWUHlvqCe+i80QW2z6XiKvLWigpoqtQ7yRWFGNnxL/IcmEtp7nYe+c6uJSZIbGYO67z+Lh7a/DwMocJ9/5EtUl8s8M6tS2yPiOLYY739sSL52O6/Y4R+MK4P7JJQzdeQ3H4gqwZ9FAOHUR6CATicIIbIKetwLl1y6jqagQZnMeRF8iq72Ttp6YKCxwY+UMT5wPy8GoTcexYecNTA20wduPS9f37msEdmDX3Mgrx5C91zDhYDB+jc/FtvHe3Q4G9K7PIZ2w1FLsPJ2A3NI6FFbU87/PR+Vh9VQ3hdL0bZm7NxY+CknFkH1BmHs0hMvefT/FH+r3YHFQfq/JOCxz6L801R0bDtzpcobrm7O8MNLZBPO+vg7fd89h9W9heHGKBx4aaNMH5ev+vcFskoF7rmL8/mCcuGuTCOU/RWHvkY/HeXGd/Veu9DxQRxrCfkdv1ie8fS4ER746iiffeBwfHd+KF796Fmmx6TjwxaE+LClB3HvIsX8f0hdOWSFM497CwoI78kUd+9euXUNcXByX2hE69lkU/KZNm7jTvKamBrW1tVy+hzmxR40SaPS+//773Pm/e/duLpPDpGieeOIJnqa738D2iWroS6aR1njLqgt5G3hZ5ejqeFeuXMG2bdvaNfSZBr9wAICtAyBvPchzHEnY7AwmtSS6Hdol0O5PDInHK9Ofa9+CT16HKovg1tVCTYX4QEd1eTV05IxWlRc2Q8Lc3hLFOd1HCpQ1NHKZHGlT9FhESk+ZaWuB/Lp6rrla1dSMvLoG7IxLg6ehHgKM5XfQsIh8Y4nppSyinn9XKX+kQXxmORxFdEqZ7M62IzEY+/wJeK44iAfeOMsjLOobm1FULv9xmXahoYTmI5NfYYviypKXEHXYPzfQmWvuX8+T3plkEj1T7c24dr80miJvourt1Xz7cfEGpN+MgKaeLo9Oq68Uv9fqK6o6RbIJkSdPcUoGGqprceSFrfxcbMuPTebatexvFvWvqqEONS1NOAzxh+OwAVDX1oKVtyta3EZDJUNcl7z4buSSibZ41I6JjnqnKH4hQ+2NeGTNrefHI/n1KXxjkeTPjndF2IuCiGQW5cbYF5aN4IwyVDe24GxCIU7GFGC2j3Qtd2kwbVTh9RQrn6Z6p0hgSVgrxaLvmTYo0+Q21VbDJAfx2TPaqsr4Zpovj6Jn+pe1cszuECtfTSOMJeqO9XmMtNVQfLcOJBlmb4TxrqZIfm0y306tFgzaHlk+lC+6K4u4girekXYw7oGD4S7CgSuh/IoQIw013g51x3O+zhhtYYxng2OQXaNYlJH63Xu4sUp8dlRjZRXUDfT7LA9Dx9aGOxDrCgpRkZKG8E+38f31xSUoT0jiUcFaZmZQ0dJEc12dYEDA0Q5e86YjLzwKzd3UBfue6es7Thgl9o4UDiCMemEt5v3wOeb9+Dnm//QF5u/ehnnff8q/K0lOw+Flz7RvCcfP8v1Mg7xB4tlvqKziAxCqmoL2VknfEKo2DtAaOQmVSqp8bQuL4ePQGH8HbY0N7ddSnmjkp3wcuKTIs9ejkVwpXZed9aFZxPD+5BxEllRinpMiz66gDo0lZtaw2TElckS/snMLBttS+WKYc6Xo28qTRhbCd6u0d688z0JXxJRXYUNwFGaeDcbcv2/xd7CllibyansWmfdvw+5NhuT9yZ5D4Xe9ycOeIaabz3T23efPhq6NJbIuyw6KUNPRgZaxMWryZa93U1rf2G5HCWHPBv+um+djuIUhtgz3xJdR6WIL7eqqqfKZe88FuODa/FF8e8rXgb+P2N9T7TqkZxSF3XPs+OxdJFnm7uxAJo326XBfPtPz1dtxCs06E2X/+nfxzcINfDv4vGD2s5ahfiebpKG6hs+u0JKxxoo8ebSN9Hl7OfKJB6FnbsxtnFHLH0JzYzOywrt2vrNoWGlti6mOmky7ZYitIZ9Fd/uZ0Uh5cQLfrPQ08exoZ4SvH9MpPZuNUVDdgHcvJHE974d8u9buF0VVX3B/N1eJ10FzVRVU9Xoux1WbnIiG3BwkPvc04tev5v8ycnd/h/SPt/T4uMzurqhphImepJ2v2SmKXxQWtHMzvhCHr6Wjuq4Jkaml+P5UAqYPsevToFXhu0T4/Iq9S+SQhWHvCZbu68hMpFbU4kFXy96Vp6oexhLR5cK6Y5r78hKXXQFHGTM2FUnTHSX1jVzOSLJtYfVX2s27mNvRrW1Iq6zDmzcS4WygjRHWXUvmdEVZbSOfxW2sI6XPIcNmZtI9LNL/r2dGIfntaXwb7mSCBwfY8L8NtdS4dO2yofY8aj8iu4IvmMs09v+IyMHKUU5yl0/YrzCSmMlgoqWY3cL085lNMk/CJmGX4MNxnvA11cUTpyN52u5gi9dunPx8+3b02+N8P9PVZ/4LUVikPnuf6vXCp3Hpjyt88d2A0X7Q0tGEvYc9pj86BUEnbnBFAoK4XyApnvuMAQMGcKe7LNhCt6LOcXlg2vynTp3iEfnMic8WcmUyO0x6x8XFhevrC899/vx57sCV5VBncj7MQc2i60VnGTC5HlHYoAAbOGBrBTAKCgqQlZXV/pn9e/v2bbE8TAufObKFkj//FiUlJWKO/j/++IP/Zrafye7IWw/yHEeSzZs3c7khUc7mC2ZXuAV64P2TH3eSZXLwdkJqZArGL5rU/l1yRBIcveV/8csDW2m+KKsQHoM95XICzLG34p0zFlHPGGhsgMbWViRJdI4UgdemRP9OWMfdReKIEpZcjIfHu4gF2o3wskB2UQ037OXF3dYAhV1onDNmD7PH5ch83tGQl8jiSgSaiQ9UDDE3RFRJ11Oe5zpb4KVAF7x2I55r58tisp0pXzjpuAzHvqrfUOj6DOZ/L/Su58551iaYOtshLzYJ7hNHtKfNjU6EpZeL1OOwCPzu8rBFdZ1HiWvSnn57B19gd8zapdw5wrDwdJbeLknsSyup5c79YY5GuHVXr5ZNER1kZ4gdV6TLDjFdfcnvrm0ai32h2fjyqiBCurS2CaklNZ0GFdvYfwr4HCKLKnmnYoiVAU6lCgbJLHU0YKeviYhC+ae0M2OaRaUpSTj1v57qxyP2l5+O5HqjisLWItBWV4G/lT4i8yrbHfdsCn9otnRtzQW7b4mVxN1cB6dXj8RDP93GnVzZepxCyR62EHFPYQN8mdV1PEr7an6HTin7fK6bQchNvs58ccdng6MVkmQRomdvD2V1Ne5YZ4vVtutwZ2XDdtK4PsvDqMnN440Vc/4buDhh/Nfbce3ZV+A0ZybspkzkaQzcXFBXWCRxV8jnjci+GcYX9mWL7YqiaSRYALQwJgEWfoL3tyQmrk7c0d9+xrvvJibZUxyXJJa2MCYRJq6O7c+ymoMrmrMEzx5z3iWUVyPQyQ57RfKwtvBON23fGm8HzHeywnNBMYgrk+8dwwaVFCGjoo53kodYGiAkX3BfM8fCQAt9vtitIrBzK/VBGlHYgqNscDnAWB8X8gQSbGaa6rDW1kR0ee+k+SQZY2HCpQxuFHatC99fYZHyOpbmKE1IgtUQgc4ua9tL4pNgM2JIn+Vpp7Xr90RTXR3qSsugYSg7QCGypIoPRInGSA82M0ReTT2XKZTFMAtDfDDCC9/EZPBFo0Vhi7GP+kO8z/Gouy0WuVpjzqlb3KnTU6LLBPecv7E+Qu/KYrnq6/BFnmNkLFLLYN9/OsyHy/iwmSINCvZ5RFn0xWsitSV4miw9nBB26C9UFhRD30Kgt54TlchtCQt36ZI58uSx9HTubJOwZ1iOhzitlNktjRhub4hb2eUddouNIXZcl677vy0oDTuuiy/IHLR2JPZG5OLLG12vsaXCCqVA+6fp6MQXkapLToDaYMHC6k1lpWgqLoKWs7gmtiJYPbYCVsuWt39ua27mzn2rx1dCP7CbZ6obwpKKMdTTDN+e6oggHuFtzvd3lcfEQNzBfS9mJTHbLL2iFoMtDHA+q8NWH2JhiJNpsgf3ZL4nejnowCLrHx7tJN4n8jBDdkkNj7SXFw9rfRR1ExwlT5ruiCgStB+BFgYIvqsd72mkA30Ntfbv5IE/B3JbSdJhEfdscdvhTkY4GNYh5cki7W9nSA+u+vNOLo5LrNe198mhyC6vxUtHovl7nGn3t8mY3atIf5ctaiu0W0ILOuwWpq/PZgErgqRNInTqszWGHj8diZxq+a6rmY0pPjvzUftnoT3o5OOIuFviEf+J4UmwdraCxl1JoZ7R+RlREnSiCHm4BzNaiJ5BEfv3Gc8//zx3wLPFWNlCr8wh/vbbb/PFcxlMp76qqopL2cgLc+bv37+fO/GZU5/B5H5++eWX9mh9xksvvcRle55++mmuqc+i9plUD1tMVwiTnmGDBCxKvby8nDuihWWThGn1M2d+YWEh1qxZAw8Pj3ZZGua8Zjr/TMOfnYc5ylkUvFBz/9+EDWwIo+zZJnSgs/3Cv+WpB3mOIwmTQNLX1xfb1O5GdLAXH8sr3IQvwjELxiMxNB4hZ2+hvrYewSeC+IK6ox/scBJdOnAe/5vzkkL1sP/DX5EWlcIXrKkoLsfhL35HZUkFhs3qGMyQxbWCEhTUN2CjjwuPpGUSOo+62uF0VkH7Yq6mGuo4O20kj5aVl6CCUr5Q5oMOVtBQVua6wWu9nFBY14AEiVkLXf62S6nQ0lDFSwv9oaulhpHe5nh4vDN+PJPQnmaElzkSflwINxtBNNJLi/wxZZAN9LXV+LZyhgemDbbF7rMdjit/Z2MuzWOoo86315cOhLOlHj46IFtDUWr5EnPgY6KHRW5W3Ek73cEMo6yNsDehQ0d1voslgheNbo9YecDJAi8HunKn/sXsriUy2KK513JLZToD2L2lxO4xdq+K3Gu+D0xCalAY0m/eQWNdPe78eY7rynpN77jXbv36J35f90b75+7yCCMbRTdmbDHzUejUZ/jNmYyM25HICosRLMqblA6V5CC0OIhPkWbm7o/BGVg+zIEvBKuvqYr/TfPkhvKhiI76+2rhAPy2bHB7HhbNJrp1GNAdx/46KA1LAu0w0MaA1/s4F1PM9LbE8Rj5F9Rl+pTHkguwYZAjXzzNRFMN/xvhyhfBvZzV4ST7Y+4gvDVSMF050EIfmwIdYa+nyae82ulpYssYD1Q3NuPi3TyaKsrYNcWXO/eZU585bHoCc+azxXLfmOYBK30N2Bpo4tUpHriSUoxEEQ1WtgDumhEChwarI7H6u+uHYX8Lq2+WtwVWj3CEjYEmnzEyzsUEL010w7HoPJkRifJyIDUHM+0sMMTMEDqqKljpYc+dQ6K6zM/7ueD7MQHtnzf4OGHCXad+ag+c+gwVDXXYjB+L9BOnUZWRiabqaiT8sh/q+vqwGNIxWBX6waeI2vWd3Hmqs3P4Yrk1eflobW5GZXom4r7fAx1rKxj7eAuOo6YGhxlTkfX3RVRn5XBtYi1zM0G0vp4uX5eiMjcfCcfP8MVw2awXRuqFazyqnn0vuWiu1QBfaBmJL9TInk+v+TP5YrlMqqepto7PuMkMuoXIfUfa04k+v8L2wm36BJSmpiP57CU01dXzPPl3ouE2s2MQmkXqN+fnoP7WFbQ1NeG30GhM9XbD9CdWQUdbG0vdbGCjo4VDKR3P2FofRxyaKnh2Gau87PGgsxWeDYpGzF1noiTvD/WEp6Eun3LP5KuWuNlggKk+/sqS34nC7uWfY3LwmI8NBlno8+O8MsyFty1HkjrutW0TvfDjDMFMFSsdDbwz2g1uRtq8zWCL724e7sKj5Zjcjrxp5IENop/NKcRyd3s46GrBSF2Nv4OZZE5wYYeD4bvRAXjOV/pgrCzWeTnCRU+HO0IGmxriGW8n7EvJRv59HO3mPGMysi5fR1FkLL+vEw4dQ1N1DewnCvSMGVE//Yar/3tf7jxssVy2EG5FRhZ/xpi8TsLh46jMyoHNSMFC12x/6I7vUJGeyf9mUfrhO38QtA1300jjaFo+X2PnaV9H3s4NNjPAXCcL7EvKERsEY5H2TnqC9SlYmg+He+Hr6AzsSxJ36gthznvRTfjO641Tn5FTW89ttrWejrDS0uA2H7tvYsuqECXynB6cOBgrPOz537qqKtypz+xE5tSvVyAgQhrKKsoibZPA7rYN8IKxvTWufncAtWWVXA7w1r4TcB01CDrGgvaP6eOzKP/48zfkzsN09K193BD8y59cd7+xtg7BPx+Biroa7AYK2m1ZcLslJBPLB9tjsI0B9DVU8fpEd962HBRxAH49zw97Fw/s0m5hg03Ca2itp4EPpnvC3VSHty3muup4c5I7n9F4LFb+dQuYtr7B0BEoOv4nGvJy0VxZgYLff4O6uQV0fTtm5aVteRv5e+WTfmPwoJG7tibb2OBBuw0qo58kLz/+lYDRvpZ4cLQjdDRV8cgEFwS6mXK5TCHMXo/8tkMG6Ie/EjDUwwyzh9lBXVUZHnYGWDHdHaduZvVaTlOSPXE5mO9qiZFWhtBVU8H6AQ4w0FDFwcSO6/36MFccmCW43uzZ/3C0BzyMBNeSRaevC3DgM24UeU9IY9/VNGipq+Cleb7Q01TFSA8zPDzaET+c71gYmTn6E7+cBzcrQWDay/N8MCXAqqNPNNkN0wbaYPeFDvlHedL0BDZr9VJWCZ4b5MTXtzHXUsdLQ1x44Ey4iGP/3INDeb0yxtkaY6WvHWx0NaCqpAQnfS28NdwN+TUNuCmyCG9P+C4oDbP9rDHNywI66ip4aowzHEx0sCe4Y+2VV6Z64NrzHX0l9myLbsIgIfa3cMCAaek/NcYJnhZ63NHPBgsWDLTB2Tj51ydgR/tFwm55WYrd8gWzW6Z32C1vj3KDq+Fdm0RLHZuHidskzMrbOsYDg+469RXV1Rf1Zwh9ImPnjkZpQRn++uUs6mrqEXU9GiF/h2LCwg5fVcSVSB7lX14k/zULGOOHkPNhiLsdj2Y22z8tD2d/+xuegR69HDAgiH8Witi/zwgICMC5c+fwyiuv8Ih65ohfvXp1u179wIEDucOcLTzLHPxsoVnmEO8K5rxvbm4Wc+KzvyUd+2zRXubIZ4vLMm195owePHgw15IXwgYZnnzySb4oLmuMp0yZgoULF6KxUdwxw8o9c+ZMPoDAnPtMT58tLitsvNnvPHToEB/AWL9+PUxNTbFixQruIL8fkLce/gncBrpj0fNLcPaX0zj42T6+sO0jm5fByddZTE6BRdwLSY9JxdfPf8n/ZjNA/th2AEe2HcTAiYFY/NJSvn/47JE498sZZMSlQ01dFbZudlj3+UZYu3Sv7ce0jV+5HcOdCr9PGIKGllaczy3CV/EiUURK4BF/olGTTLuXTe9nu9h+5vhnTD1znf8bUVqBLXcS8bCzDVZ4OKCxpRVx5VV88VxFOoAFZXVY8ekVvPHoQCyf7oGyqgYe1bPnXIeTnnUsVFWU26Nff7+Uio3zffH+k4OhrqaC5JxKrPniKv4O7+gsx2aUYWKANc5snQFtTVXcii/EovfPI71AsVkKMaXVeCUoHs8EOOL5gS7Ir23AltvJCBKR1mFPElvQSFh7K33s+dTNrSPFo2r/SMnDR6EdRrSdriYGmRtg42XFF4xyHjkI9ZVVCP7pEF9MzsDaHJNfWsU7vELY1HTRe02ePPJg7evOdfVv7j7Mo+a0jQ3Q4jwCLb6dFz/66loatNRU8PXiATDQVEN0XiUe+zVUbLE41sdn958iHAjPga66Kr5cGMCle7LL67jevqwFd2Xx7o1kbiTvnT2AO7lv5ZVjjcRCuIJnQ/D3ncIqeJno4svJPrDX1+KLXt3Or8CSExHti/AOtTLkGzPWry7pmB3BePDPUCSXy++8XnfoDt6d6YUL60bzjsb5pEK8flo8iobde4pU38WkYu7Y3/voYFjqayC7oh67b2fi+xvSF6pUhOOZBVzy4ZUANy5DklZVg5dvxYo5HVl7IozOYjOJFjhZ84Gb78YMEDvWp1HJOKWAs9d14YM8pCrs4y/QUt8AA1dnDHxhA5ebEX0m2tpa5c7DHPi6draI/uo71OYV8Ch9E39fOM97AMqqHYNdDjOnoq2lGRGfbefORS0LM9jPnIqymHhcXruRL+ppHRgA38VzxcvCImBFnBRV+YUoTkjmcjtSf+PUcXxgIPHUeYT9uJcf19zXEz4LZndZN8Yujhi+fiWifz+KiJ8PQtvUGIOWL+EDCEJUTC1g8OQmVJ/8HdV//oo/dHShu/5ZbF6/DqZamsiqrsXmm3FiMyrYfSd8dlk7+ISnPb+WbGFcUXbFpLVHKB9Nz8czfk7cud/U2orkilq8eCMWwQWKaV9/dycLWqoq2D7JmzvfYoursfJMlNiCcsrKHfdaXk0DbuVV8IE4NyMdPsMkuqgKy07eQVJZrdxp5OWLmFTuPP1yBNMNVsad0gq8fDtWbFFUVjbR9+5GH2c8YGfZHr12eqqg/XglJBYhxYLO8+W8Ej44xiKuC+oa8GtyNv7IkH9Asycc++UVTBjlezeoQRlVqb/y/f4TnkOayOKvPcV+whjunL/z/c9cTkfP1hpDXnhabJFbwfusRe48qhoasBoWiOg9+1GZmQUVdXUYONhh2MsbYeLp1j4oxxz40T//ztMw2Sxjd1eMevNlmTJAjKL6RjwbFMNlcx52s+EyNb8m5uCAyKAXeyy4XXD3Wj7mYQtNVRV+77NNSEhROTZdkz9AqKdsjUjERl9n/Dh2IC/b7aJyfBYt7tRj96PK3ZtvlIUx3A10+f16cqr47KGHzt/mC2n3Fubgn/HqU7j67e/4be0bUFZVhcuIgVw2p502YbvdJn8eZqu+uALXvj+IvU+/xW1HUxc7zHr9aeiaiA+YSmNXcAa3W76Z7w8DTVVEFVRh2YFwMbuFPbesfZGX3KoGBGeW4dOZ3nA300FVQwsfvF+0NxQJxdLlymRhsXgpCg/tR8YnW9HW3ARtNw/YrtsIJZUOV0Nba4vYuy7/99+4jr7QKy6U2rFbt5EvlnsvuR5biJe/v8Xt9g9WDkFWYQ2e/SoYoYkdEftKyux56RhAiMssx7rtQXh5cQA+WTMcJZX1OHkrC58fiurz8h1Oyue2yDsj3fni8MnlNVh3PkZsIVz2bLDnmVHX3Iq/Morx2lBXeBjroL65FfFl1Vj9dxRCFZjtKY2Cinos/zIIbywOwIpJriirbsQ3ZxOx52KKeNvC+kR3G5f9QenYNNsLW5YO4oMgyflVWP3VDfwd2dEeyZOmp7wWlIDNQ11xePYgXjYmPfrezY6BCIZoH5NF9jNH9VcTfflgAJMyYnneuZnM67Y3nIzO51I8b8zygoWeBlKLa7Dmt1AkFnb0/dhtpqLgYNULf0Th5anu2PP4YC7dU1BVj5+up2PHJcUGRr6LzOLvgW0TO+yWVRJ2i6hdwGyS28wmGevBB46ENsljpzpsEqa1/4CrBbe9Ti0Qn12z8XxMe9CRIpjbmWPNlpVcD//0njPQN9bDnFWzMWxqx/FZ+8L8FkKLprK0Cq8veov/zfbnZxTgwoFLcPR2wLPbN/D9kxdP5H4QpqlfVlTO1wv0HubNj00Q9xNKbb1ZbYLo1zBNfB7tKseLgjn2RaO82W3B8jPnfW8RSgMJy/Hrr7/yyPv8fPmjQaTBo05aW3m5JX+LPOWWll/efZLHkaeuJOuhp8eR5GjGaYXSyzo3e6kJI5YYrCySyHs/ibI9tuf6mswYE42I5mdW6tuFd9P3K+Z0lTQKhdET9wqjGT2XnmLR28KoOlmzClnx27rI1x2LvBWLwhB0hgUd4Z7CBgb4TPFu7sVdR3pujLO6YueQdXkl782eoG3Xc+14dn5Wj4oUgV1Xaci61rU5PZ8O3V39sE6CqDOxJzgM7FjkvLfw6y2y2KSidSUNH2P5BnK5I11Eouafwki9Vca7oFVsNoy0ff8El7I0+s21lKS09N61LX2BpVXPrxW7C/kUf4nyStKb8gc/JQgc6A0Ce6RzwVp6GcXNWHtwVb9+dq9ni+shK4Lo+53VnjQ/sDS7QBT2a7uqZU3NvpuaL3k/9sXzPNehZ7OwRGEDOkIZwr7ki5PK/dpuGRDY83tP8nkRDCJLKVAX9drG+iZdfB/9s/SZJz2FOTMVkTbpDp0pfScney/eJdXXu18n7d/sE+mO7Pm6Hn1hR3dHZULP5GR5n6aL9yovqxTpHUXQdtb5R+81RdvqL6b1XIq3K5+NVH8Gn/WtWFs71WZmr8v3X8TppRO4H0n76L83cEMR+/9hZDmipSHpTObRyH3g1Gco6giWF6H0jCTylltafnn3SX4vzzm7q4e+rHNFEUirKPX4/rlXSBoQvEvQj4Yi77UB21tEjSdFitrbKfZdwTvCvTxGbwYF5KU7A/rfvvQ9Of+9vK6Klq+3Tv2+RtRx9E/X1T/tFOwKocxWd/v6M//mtZSH3nbO7zWt90l5BcEQ/27B+tOzKy+iVSaQa1H8GL0fOun5ufrL8/xPD3T+F+wWyeelJ8/PP/0u6kunfl/T39rm/t4nkqQ/Fbe7AYZ/u6w9udf+jbZams+mP/gz/tOQxH6/4f6zSAmFYFI6ojruotubb775n67N/8+/nSAIgiAIgiAIgiAIgiCI/y4Usf8f5/z58+0akP9UJH13LF26FI888sj/y99OEARBEARBEARBEARBEATRW8ix/x+nP04/6k7a5r/82wmCIAiCIAiCIAiCIAiCIHoLOfYJgiAIgiAIgiAIgiAIgiCIbmljKzsT/QLSIyEIgiAIgiAIgiAIgiAIgiCI+why7BMEQRAEQRAEQRAEQRAEQRDEfQRJ8RAEQRAEQRAEQRAEQRAEQRDdQ1I8/QaK2CcIgiAIgiAIgiAIgiAIgiCI+why7BMEQRAEQRAEQRAEQRAEQRDEfQQ59gmCIAiCIAiCIAiCIAiCIAjiPoI09gmCIAiCIAiCIAiCIAiCIIjuUVKiWuonkGOfIO5zDNXb0J8pKm5Ff6ZNWw39GVW1/v3CvFmkif6KclY++jPGA3TQn6kPrUZ/pqJCC/2ZyJb+3bYo9/MFr7T78eUt6t+vNXgYNKI/k/r0SvRnwks00J9xNmlGf6a5rX+3LYbq/fcBLtz5I/ozzd8/jf6McmEN+jOtLejXqMQUoT/TEGiK/oz6pQz0V5RcvNGfae3n7w2C6O+QFA9BEARBEARBEARBEARBEARB3EdQxD5BEARBEARBEARBEARBEATRPRQm3m+gS0EQBEEQBEEQBEEQBEEQBEEQ9xHk2CcIgiAIgiAIgiAIgiAIgiCI+why7BMEQRAEQRAEQRAEQRAEQRDEfQRp7BMEQRAEQRAEQRAEQRAEQRDdo6REtdRPoIh9giAIgiAIgiAIgiAIgiAIgriPIMc+QRAEQRAEQRAEQRAEQRAEQdxHkGOfkMnUqVNx/fr1Pqmhuro6jB49GrGxsf9vazwkJATjx4//t4tBEARBEARBEARBEARBEMR9DmnsK8jLL78MGxsbbNiwAf91mFO/tLS0T47V0tKCoKAgVFZW4v8r5eXluHbtGvoDNZU12L/jCKKC47g0WsBIXyx6Zh60dbWkpm9qbMb10zcRdPoW8rMKYWCsj+FTAjFtySSoqqr0Wbke8bDGo17WMNFUR1J5DT4OSUVkcZXM9NY6GljpZ4eRVkbQVlNBSnktvovKxPW88l6Xxc/eEK8v9IeXrSFKqhvwy6UU/HA+WWb6oW6m+HXj6E77Z71/Hkl5VXKnkZcRlkZY6+MAO10t5Nc24Kf4LJzNKpKZ3k5XE4+622KouSG0VFWQVFGD72IzEFki/bwTrE3w7jAP/v26K1FQlLLr11B09jSaysugYWkFy/kPQdfDS2b6lvp6VITcROmVS6jPzYH96nXQ9x8glqapvBxFZ06hKjoSLbW10LC0hNmUGdAfMFDh8q2b5YlHxjrDQEcdUelleHd/BOKzK2Sm375mOKYPshHbdyuxCI9+eoX/ra6qjOid86XnPR6LL0/EyV02NWUlrPV2xCQbM2ioKCOsuAJfRKagsL5RanqmbjjB2hTznazgqq+DyqYmXMsvxY/xmahpbpE7jbwYaKvhjYUBmOBjiba2NpyPyse7h+6gqr5ZZp6Ij2dDW0Pc5PjkWAy+/TtJoTTywNqFlwe7INDCAHXNrTiVVogdEelobmuTml5HVQWLPaww3cEcVjoayK2px4HEPBxOzhdLN9HOBCt87GCvp4XKxmYcTs7DjzHZUBQPQ11s9HWCq4Euyhua8EdaHvan5MhMb6KhhoddbTDK0hhG6urIqK7Fr0nZ/PopkkZehpsbYZWnPWx1tFBQ14Cfk7Lwd06xzPS2Opp4xMUGg80EbUtyRQ1+TMhEdFlH26KipIRxViaY62AJP2M9fB2XgQOpuegJ02zMsdjZFqYa6siqqcN3CemIKJX97GqqKGOilRkesLeEk64O3g6Px42i0k5plrs5YISFMQzU1FBU34CzOYX4PU32dZHFUwPssNjTCgYaqogursb7N1KQUFojM72zgRaeGmiPEdaG/NmPK6nBl2EZCC2Qbi+9O9oNCzwssT00HV9HZHVZlvKEJCQfOITa3HyoG+jDbtoU2EwY2+s8LQ0NSDt6AkW3Q9Fc3wATPx+4PvwQ1PX1Ox2vLC4Bdz7fAW0Lcwx9940uz22gqYq3Jrljoosp2NN6LrkI7/ydiKrG7tsoLTVlHHtsCJyNtTH35xBEF3Tcf+OdTbBqiD18LfRQ29SCoIxSfHwlFQXVDVAESy0NrPd2RoCxPupbWvF3bhG+S8hAi4y2heFlqIu59lYYa2mCuPIqPH8rplOaMRbGWOJiy5+5qqZmnMjKx94u2gRZ1KSnI+vA76jLzoaqnh7Mx4+HxZSpMtOz9rsyJgZFVy7zf01HjYb9kiUKp5GXyqgo5B39A42FhVAzNoHFzFkwGjpMdvlamlEeHo6Sy5dQk5IC6wcfhNlk8d/TUFCAwrNnUBUXi9aGBmjZ2cFy9hzouLrKXa47527g5uHzqCouh4mdBcY/MReOAe69ylNfXYsrv51C8q1o1FfVwMLFDpNWzoeli117msb6Blz++QSSbkahrrIaeiaG0FNVRVWzxd23ds8wNtTFY4vGYcXSSXC0M8fQ6a8gLlHxd5UiqCop4Ul3R4yzNIO6sjIiyyrwdVwKihuk2y0MM00NTLOxwFQbCxiqq+PB89elvqd9jfSx1MUBLno6KG5owL6ULFwtkP1OkpfpYxyxfulA2FrqIjOvCl/sCcP54EyZ6f/YPgdeLsad9l8NycbqN//uVVlsdDWweZgLBt+1W06mFuKLUNl2C7s7Zjqb4RFPa7gYanNb4lJWKXaEZaBWxKbzN9Pj9s0UB1NEFlVh5VnF7XmGr4cpXls/El6upigtr8OvR2Lw4++RMtO/+8IYPDTTs9P+0oo6jJr/K/97aIAV9nw+u1OaB5YfQnJ6mULlG2trhOeGOMFBXwu51fX4KiITJ1Jk94n01FXwpK8tpjubwVxbHdlV9dgXl4ff4/Okpp/qaIrPJnoirKASj52U/btlsWCWB1YtHQhLMx2kZJTh413BCA6TbQNdOLiUp5Xk2NkkvLLlYvtnS3MdPL9mGEYPtUNzcyuOnE7Ajp9C0NTUqlD5VvvbYaGHwG6JKa7G1pspSCyTbbc4GWhhlb89hlsZQk1FCfElNfgqIgNhhR12CzvWfDdLLPKwgo2uJh48Gsr76b0hOTIFR78+ivyMfOib6GPCwgkY/cAomelbW1oRExyDa8eDkBSehImLJmL2illiaXa/uxt3rnS+pma2pnj1p1d7Vd7/FyiTxn5/gRz7ChITE4OGBsU6AgTR3/jmrT2or23A5q828Zfet2/vwQ/v/Yr1H6ySmj78aiSyU/OwZNMCmNuaITMpG9+98zOqK2uw+BnpzkxFmetigU2DHPHKtQTcKarEkz62+GayL+YdC0VBrfSOwRM+tggtqMCuOxloaQU3XndM9MHDJ8OR1AvjwUxfE79sHI3DwZlY9+1N+DsYYfvKoahrbMHeq2lS87DXmqqKMjzX/4mW1g5DXPRvedLIg4ehDj4a4YWvojNwKqMAY61N8MZgd27Y3yqUPqixxscBwfnl2B2fxY3+xzxssX20L564EIH0qjqxtJbaGvg/9s4COqrjbeMPcXd39yAJ7u7uhZYWLVDqLW1pKVRoqdEWa6FAoYZLi7s7MeLu7u7hO++ETXY3u8mN0D/0m985e5Ldnbk7d+69c+c+884zb3RxYKK+Yhvu10WB/kjb+zss5y2AlpsHcq9eQuLWjXD64GMm8ssi7+olVOVkw3zGc4j//mtARp3kXDwHdWsbGI8cjU7KKii4ewtJv2yFw1vvQcNR+EP84lEuWDLKFa/8dBtRaUV4e7IXfntrIIZ/dAZFZdUy85AweeBGPNb8GSAheIioqqmD+7IjEnlG+1pi08t9cM6/dQLNG94O6Gmij5V3QlFQVYOVXZzwbR9PzL8SgFoZp4qHvjYGmhtiR0Qi4opKYaGhho98XZlI9N7dMMFphLJ5YS9oqSlh6jeXoaDQib3//qUeWPTzbbl5FBUV8OrOe7gQnC73vBeSpiWUFDrhp6FeiCssw7QTfjBWV8GGgR7s82/84mTmme5sBg0lRay6FYGM0kr0MtPDur6uLM/+qPqy0ODhN/3dse5eDM4nZbOHx/X96h9aWyPukwD/Q18vnEnKwof3I5jo90l3N5TX1uLvBMmBBBHPu1gjvbQC790JQ0FVNUZZm2BdT3e8fTsUD7ILBKcRgouuJtb1cMMv4Yk4nZKF/maGWNW1vm15kCNbPF/kZot7Wfn4PTqF7cdcJyts6OOJxdeCkFhS3jBQSNvaE5WM1T4ubZ4u2s/EAK97OuKb4Gj45xRgoq05Pvd1x7JbQUzklwWJqnS+bw2Px4Ze3jLX+VrmZo9uhnr4NCCCbaezgS5Wd3VlbeXxZNnHRRYLO1thUWdrvHYhDNH5ZXijhx12j/XGqAMP2GCQLJZ0tcalxFysvxPH2plFXaywa6w3Rh94gPRSyf7mGAcjeBhpIb+iGp1aWLCsPCubCerWI4eh8+uvoCAiCmE7dkNJQx2mvXq0Oc+jujo8/HELasrK4bViKTTMTJEbHILMO/dZPnGqiksQsWsP9FycUJnXslCzdZI3tFQUMen3+6wutkzywo8TvLDgcFCLeT8d4YqUwgq4GGlJHGMjDRXM9DbHjzfjEZZVDHNtNawb5YodUztjwm/30Rrh8uuenkgsLsP86wEwVFXBZ77u9eUMl90v0FFWwgp3B3YOUTojNZUmaXoY6WGNjxu+D4nFlfQcWGuqY3U3V/Zda8T96sICRP/wPQz79IHj0mVM5I//ZTsUVFRhPGiQzDylcXHIungRRgMHoqaoGI8e1bUpjRDKkhKR8PMWmE2aAoM+fVEYFIik3bugpKUNbQ8PmXkK/PxQGBgA03HjkbRrBx7JuB+k/30U2p5eMB0zFgpqqsg6cwaxP34Pl1UfQc1cdn9DnMjbQTi79QDGvT4X9t1c4XfyOg59ug3zf1jJBPu25vnnmz0ozivE9NVLoGOkh4DTN7D3w81YvHUVtAx0WZqLvxxBQmAkpn6wAAaWpkgKiUHOJ9vx6JEiSmqN0VZWvzUdFZXVWPP1fvz50xvtGCIQzlI3B/gY6WOtfygKq2vwqocTPvH1xKu3A2R15xhLXO0RV1yKgwkpWOrmWH/dSqX1MdTDx9088HtMItYFhrN79TxnW9zOypUreguhp7cZNrw3GJ9tvY2zNxMxcYgDNn00FHPeOYnACNmC8PQ3jkvUpYWJFs7vnIZztxLRHqivsW2EF2ILyzD5mB9rszYO9YCiQid8dU92v8XbWJsFL6y/G4u4wnI2QPzlQFcYqavg3asRDcLqez0dcDCyvv0x1Wza/gjB2FADezaMx9GzUVix+jw6uxnjh7XDUV5ejb3/yA5a+fi761j7fWMwG+3LxX1zcOlmQmOiToCSkgI8h/0i+Uwkq6PbDB6GWtgywhPfP0jA0ehMDLM1xFeD3JBXUY1bqbL7PyRi0z156bkQ5JVXY5C1AdYPckV5TS3+icmSSGuhpYpVvR0RkFnE7gOtZcRAO3zyzkAmyN+8n4Lnp3ph+zdjMXn+IcQlyS7f8Fl/SdzHaEDlyM5pOH+t8V5joKeGAz9PQUBoJmYsOYLC4krMnOCO3j6WuH63+UF/cWiAY76XNd68HIaYgjK85mOHHaO8Mf6I/H4LBdVdSc7Ft/fjoNCpExZ6W2H7KG9MONLYb1nRzRaVtXX4wS8e3w/xaHc7lJOWg20fbMPgaYOwZN1ixATF4Pf1f0BDUx0+Q31k5on0i8Sd03cwcMpA5Gfmo66u6f1r3ofz8GhV4zlXU1WDNbPWwLufdztLzOH8u3ArHhncuXMHCxYswIgRI/DWW28hO7v+Bv/FF1+wKPaDBw8yWxl6JSXJH9kXsXPnTowZMwazZs3Cb7/9hs8++wxfffVVw/dvvvkmtm7dis2bN2Ps2LENswGo8dm1axcmT57MPv/oo49QWFjYxN7m+vXreP/991mal156CeHhkjfZSZMmsXQDBw7E3LlzceSIpPhEFBcX44MPPmD7vGjRIvj5+TVJ01J5hEAzAJorq5DfoPc0c0K8rOK2QaJ6uXbtGl5//XUMGzaMHTMiNzcXq1evZulnzJiBX3/9VUKcO3fuHKsvss1ZvHgxRo8ejVWrVqGkpKQhDdkJiY7/8OHDsWzZMkRE1HeixKF0L7zwAjv2tM8UsS9NS+V5EiRGJSPCPxqzX50CUytjmNuaYuYrkxF8JwxpcoSlnsN8MPfN6bBzs2FR/W7dnDF06kDcv9QocraXlzyscCwmE5eTc1ln7Du/eJRU12KWi/wHsy/uxeJkfDZyyquRX1mNbQ/rr0cvI+12lWV2fzsm1H5+6CFyiitxKSQD+27EY8nI5iO4COqcir/amqY5ZjlZIjK/BH9FpzLh95+ETNzJzMdcF8mIcnE+uhuJE4mZSC+rRGFVDTYFJzDRaoC5ZPQRCfmf9nTFzvAkpDwW5VpL9vmz0PHpDr0evVjUoOn4SVAxMETu5Yty8xiPGgvLuS9CzdpGbhrzaTOh37c/lPUNoKSlBaNhI6Goro6y+FjBZaOO8qKRrvj1QjRuhmchu7ACH//pDzUVRczoZ9d85keSx0760Ekf12l97eAXk8MGD4SirayEcTamTFiNLCxlEdPfBMXAQUcTvU2bRooRofnFWOsXiaDcIhRX17J8v0YkoY+pPougFppGCJ7WeujnZoJPDgYhIbsUcZkl+PzwQwz1NoeTWfPXXd2jls97IWmaY6iVIay01PHZvRg2IBiSW4JtwUmY7mzOBAFZ7AlPxeagRPZAQ23OxeRcnE3MxijbRmFlrL0x/LMKcSQ2g9UfbXd3eApedLdq1YPeBFszVNfWYWNIHGuzbmXm45/EDCaGy+PH4DgW3Z5cWsF++1BcOsLzizHUwqhVaYQw3d4CUYWl2BeXxtqJk0mZuJuVj+ec5LctdF6dSs5CRnl927I1LIE9HPcTO18p4p/S+ee2rs8gzQx7S1zNyMHl9BwmHv0ek8yukcm28u8TFHX/fWgsoosa7+PSuOhq4XZWHqKLSlkk9r3sfEQVlsBVV0tw2egsWOBthT0hqbiVVoDs8ip8cjOazbqZ5ipbICTevxqFM/E57L5HebYGJLFrkgR8cay01Zi48M7lCEHXRsqlK1DV04XD1Ekskt6kZ3cmziedPteuPJl376MwKgZey5dA29YGiqqqMOnu20TUp75M+M7dsBg8ENq2ti2Wl6Lp+9sZYO3FKCTklyM2rwyfXYrGMCcjOBs2jVwUZ5K7KTyMtfDd9aYiWE5ZFZb/HYJ7KQUoqapFdG4p/gxIRWdzHXZshNLf1IANEG0IiUV2RRUiCkvwW3QSJtqYQV1RdttSVF2DV24/xJmULFTKEBSI4ZbGeJhXhJPJmWz2FG13X1wqZtlbMjFOKNnXb6CTkhKsZsyEso4O9Dp3htGAAcg4d1ZuHi1HRzi//jr0u3WTG3UnJI0Qci5egLqNDUxGjmL9AsP+A6Dj6YWs8/LLR9H8dkuWQttN/mw/+t6wX3+oGBmxQQKL6TOgqKaKooeBgsp178hFuPfvBs/B3aGhq40Bc8ZC18QAD45fbXOe8qJSxPmHY8DcsTCxs4Calgb6zBgJTT1t+J9qFDvTY5Lg3NsbZk42UFFXhVMPT1TVaUJFQX6krBDe/Hg3Plj3J2LihQ9KtgctJSWMsDRl4ntMcSmb8bQ5LAZ2WproYSS730KsC4rA3rhk5DUT1U+C/+X0bBxOSEVJTQ2yKirxbXBUu0R9YsE0L9wOTMPeU5HIK6zA7mNhCIzIwvypXnLz1En18SYNc0R5RQ1OXZU9sCeUYTaGsNZWx6e3YpBB/ZacEhZxTpHO8votFH3/6e0Y1hehvjz9PRmXjW4mjbOmCitrMPdkEHu2IoG1rcwa74aq6lqs23QLufnluHw7CfuPh2PxHMlZteLQ4SGBXvTq62vFItD3n2j6vMzqUyxta3nRyxJhuSXYFZzCBr0PRWbgWkoeFno3zo6RhtLuDklFUlEF6/dR3UXklsDHVKfJM9F3Q9ywOSARiUVteyZa+FxXnL4UixPnY5BfUIFNux4gNb0YL8zwbv5cE6uTKWNckJlTiiu3GweRVszvjpraR3jnk4tISS9GcUkVdu4NapWo3+lxkNzvYam4k16AnPIqfH4nGmpKCpjiLL/f8uGNKJxLqO+3UJ6fA+v7LW6Gjf2WdXdi8e39eCS1sd6kuX7sOnQMdDBuwTho62uj2+Bu8B3qi4v7L8nN497THYs/XwzPXh7oJOf+paCgAEVFxYZX8M1gFvzYe0zvDik3h/NvwYV9KQ4cOIBBgwbB0NCQCe7W1taYP38++46EV3d3d/Tr1w/r169nL2Pj5iMqfvjhByYuk0g9Z84cJtx++eWXiI5utBYIDg7GO++8g7t37zJRnwRlYuHChdi0aRMbEHj11VcRFRWF3r17o6KiQsLeZvr06awc9DvV1dXMx52EehEffvghKysNKFDZX375ZWzfvl2inLSNkydPYunSpSwNCeskkIvTUnmEQHXZXFmF/AaV7fTp0w1lnTJlCi5evNhgGySqF/rcwsKCCeeUjr7v1asXG4yh3582bRobYKHBGxFZWVk4deoUGzCgAQH6jaNHj7I6E0HnhOj40wCDpqYmfH19kZDQGIWQnp7OfpMi6mg/6KZBAxniCCnPkyA2JB4qaiqw92h80Hbp4sjKGBsiFkkhwM5HTUOtQ8qkraLEppLez5Qc/LifUYguxk2n9stCXUkBc90t2BTWu+204vF1NMS96BzWMRVxKyIbNkaaMNJRbTbvrS/GwP/b8Tjw9kAM8jRtc5rm6GyoDb9sSYHsflYBvAyED2iQoEEv8Sm7xGIPW9ZRo8GCtlBXU4PyxARoukhOwdV0c2+VAN/i71RVIe/mdfZ7Wh7yH8SksTXWgrGuGm5HNEbk0CCOX0wufBybF0HH97RG8OYpuPrlWKx/sXuz54KZvjoGeJphv5wZHvLw1NeGkkK9/Y4IEkxpkIUsTISiq6KE6rpHqGrmYU5IGml8HQxQVlmDQLFp0nejc1BTWwcfB8Nm834xxwfBGybi9IfD8PIIFxal1pY0zdHVWAdxRWXsHBZxL6OAneseBsJFWl1VZWbZIUIBnSBdS6TTUToHXQ3B2/U21GGDK+KPrn7ZBbDQVIOBqrLg7eioKDW5dtuSpkn5DLQRIBWZT5HxnvrC2uHGtkWx1b/dEjSAQkJ7kNTgQEBuITz12jeYS4MFvYz1WbQ0nXKdDXTgoK2Jaxm5grdho6MGYw0V3ElrvP9U1T5iU/fFxZbm0FRWZCJFdlkV/DMKJfZ9A4kL/kmILxT2kFwYHQs9N8nBaH0PN5Qkp6BWjpAmJE+OfyB0HB2gbtJ8/zf53EXUVlTCZox8KxhxulvqoqyqFgFiA6F3kgpQU1cHX8v6CGdZ2Oip46Ohznj9RKigtsxcWxXTvM1xKTanVWIX2YGQxVV+VWPb4pdbCBVFBTbTpa1Q2yId0EEDnDoqyrCTY48oi9LYGGg5O6OTQuOjHQniVTk5qG5lEM6TgKx0tKT6BVrUL4jruH4BUVdVyfoHCqot909rq2uQHp0EG29nic9tu7ggNSK+zXlEsxqkhSTqZ6eENe4vDQ7E3g9DbnIm6mprkRgcDRWFcpTV6uNZwlWvvt/yUMwSjQT4tLJyuLejbbbV0oClpjqupEtGUHcEPh4muBskabtyOzAdPu4mgrcxbaQzTlyNQ1kzNoRCoPsDRevnivVbSGSle6mn1ABvc9YoI22NcCGx/RZF0vh4m+F+ULrEM9Ftv1RYW+jAyEBYGzVjvBtCo7IRFtW0fNcPPY/7J17E3s0TMbCXdevLZ6qDu2L3XeJOagG6mQg798gCjyL26Tn0YqLkPf81XzvkltcPFrQFZSUFeLsb466U7Q7Vn4+XsOc/ZWUFTBjpjCOnIiUGPkYMtMeZy7Gormn7oI21jhqbIXIvXbLfQrMTurai3/KCpyUbRA/IfHL3mvjQeDh3lZyd7dLNBamxqaiSY1XaFu6cvgunrk4wtmz7rKn/V9B97ll8/QfhVjxikCBMwvrKlSuZCE6QIC8SdZ2dnaGvr8/EYorWbomamhqsW7eORfqTuEsMHTqUCcPSuLm54ffff5eYNbB3716kpKTAyKhebKKobioDDT7MmzevIS2V9+2332b/U1S+np4ei14fNWoU+6xnz54NaWnQQkVFhQ04LFmyhH1GEf8XLlxATEwM7O3t2We0jalTp7apPM3RXFmF/MaVK1dYeWlgRFRWGoShKHtp6FiS8C6Coubt7OywZ8+ehs9cXFzQo0cPfPzxx+zYio7b4cOH4ejo2HBeUOS9CG1tbYnjTzMHKGJ/x44d+Pzzz9ln3377LSsfzdAQnUepqan4888/G/J9/fXXgsrT0RTmFkNLV1NiGr+ikiI0yDc6T1hkcXpiJq4dv43x84Q9sLcE2WUQ4mIcQRGtnmKj/7LoY66HLUO9mABYVFmNd6+FI03KvqC1mOioITFbMrqTfPZZWXXUkFPUdPsUhfvdP6E4ejeJRVDM6GuLncv7YsGWW7gWlik4jRBoOj/VjThklaGprAR1RQWUCxArlnraMluXy6mNndjuxroYa2OCFy62fSZGLc1uqatlEXniUCRdTVH7O3xl8XGI+249U1UVVFVh/dIiqFnIjyaWhkR9IrdY8hjmFVfC2ki+OBOdVoiDN+PhH5sLG2NNfDLHB7+/PQiTPrvABgakmd7Pjj3snbwvPHKGoPUlRMdTHLJXMVAVNo1aT0UZ81yscTY5S673s5A0sjDRVUd+aVWTaKvCsmoYNzPQcSYgFbuvxCI5pxS9nI2YgG+ur461B4JalaYlaBq6rHaEMHzczrQEWfEMtDTAu9cbZ5RdTM7B1/3dMcbOGBeScpjP/gvulg2/GVUgLMKS7DtSSwubHFuCjm+e1HGXxVT7emuZM8lZ7Uojs3zUtogJl/Xlq2FRg0LblsVutuycuJouXBQXgq6KMhOPCpqUrxr6Aq8NeVCEtKm6GnYNqJ/OTWLyzxHxuJcj3OfXWKP+/M+TesCkCEKKtm+O8Y7G+HqwG7uPkaj/yvlQ5Fc2ikVv9rBjoo88/19ZVBUWQsVDUkhV1tZioZRVxUVQVzVqUx6y69GysULE7j+Yxz5FR5P47zh9KvPkJ4oTEpF0+iy6r/5AQmhuDmMtVeSVS7Utjx6hsKIGxnIsJEiQ2TzRCxtvxSMmtwwuzbTh3431wBRPM2YJ4Z9aiJcOCYvoFr82pNvlQrFrt61cz8zFx91cMczCiA0kWWqoY6a9RcN2Y4uF2QqSeK9lbNL02NF3RUVQ1pU/OPJvQOVr2i/QYr74tMaOolrHBIqkHzuGToqK0PXxbTFtWVEps6PU0JPsZ2roaqE0v6jNeSiK38rdAbf2nYWRtRmz3gk6exs5KZkwEJu50Xv6CBRm5uGX5evYewVFBRRUW6Ci7n97rFqLgYqKxPUggt7Tmi9txVy9/pwg//2tfbsxT/60sgocik9pl8e+kmIn6OuoIbdAMjCNIveFCtX9ulnAylQb+09Hor2wfku5VL/lcT+GvmuO38Z0ZgFQzOomKYdFSHc0ZMWTmCp5PeQ9rjtjAw3k5DU/2EyWMYP72ODzH29KfE6C9Pc77uHY2WjmDz99nCt++WoMFq08jev3hPedaUBdfFCEla+iGpoqStBQogAm2f0WPVUl3Jzbh913q+vq8NXdOFxPabzn97bQw2RnU0w+0tTFQCj6umpQVlJEboFkHdE6BUYGwoJCRg5ygI6WKg6JrdVFYr+psSaKSqqw47uxbPAlN68cJy7EYOseP8Ee+8bqsvsteQL6LeMcjPHFgPp+C4n6r10KRYFYv6WjKcwrgotPvU2dCC09TTYwXlxQDEOz5oOLhNr9kMXPvFWNug+H86zAhX0xSJzNzMxkkdPiaGgIj8YThyK4c3JymDgtLgpTlLY0FN0tzuXL5F2swCLpRZE89Dc/P59ZvIgjLtyrq6szUTwjo3FkOTIyEj/99BOLfqfFa8kSJja2MWLk3r17TDwXCeWEaFCgLeVpjubKKuQ3yCLHyclJoqwkrMtCevCFtk+R9DRLgLZLLxLtyf6H6ohmBhAGBgYNoj5hY2PDZi+QBZDu4wejEydOsMEGEutpzYW4uDh2bMXrVPy4i+pUXNgXWh5x6Lek13ioqqyGSisiPeVBkUVC5L387AJsen873H1dMHL2EDxJKGqtpTHV2+kF6PHXDeipKmOGizl+HOyB+eceIjRXvu1CWxBpn/LK4x+Xx14itpyOZN78S0Y6N4j2QtK0p65Y+QRM3Z/mYIbpjuZYeSu8QUgkL+A1PVzw2YNoZqfR4XRqGpXYFjTsHeD540+oLStFwd3bSN61HbavvAEt16aLdLW6/pqpuh/+aWznQhILsOLn27j5zXgM6WyOszI89EnY/+deEluXoS1I1xQ5bwiJL6CpsF/1ckdORRU2hcS1OU2ry8uuVfklfPf3xgej8w/TYaAVinXPdcPXf4egrLJWcJq2IHItEVJ/LrR+RX83/BWRxix5RND/X9yPwcveNvi0twtbsHp3WApW93LGI0Etp4DyCSggWSe96mWP74PjEFNU2uY0rSufaP9aLuBkWzNMsTPDqvvhTQYInhRU/+2NvVnubo9uBrp49XYQkkrK4WWgg1VdXNhABi2i2x6EOErRIn+n47KZiLOgsxV2jvHGtGMBSCgsZ4vqTnQywcQj/mgvDfeHR23PQ9d6xq27sJ88Ab2/+hxVRUXMcid4y8/wXbWSRfaH/rwDzrNnQs1Qvg2HUFjbJ+cAvzPQETmlVfg9oGUv+ndOheGDs+Gw19fAJyNc8cfMbpj8+4NWDWzKKhvRnvOPxPwfQ2Ixz8kG73V2RlZ5JfbHpeItb+Hrxsil0+NBlSds8dhmOjhqLufKZeRevQy7Za8wO6K2Quf8o3bmmfz+fFzaeQy/vb2BLZJLNjveQ3siLbJxZuyF7YeR8DAK8759i3nzJ4fG4cAnv+ARFFFa236R6t9Gus7YadeOQ0ze3cRsB2t8GxyJjPIKtjjvys6uKPGvQUBu22bnyusny1rDQR4zRrsgPDYXIdEdO4AtQmhRXjrzEMoKCnAz0MQnfZ3x9SA3vHVFtu99R9L4zNFy2qmjXZlwf/xCjMTnASGZ7CVi628B8HYzweI5XVol7DdfPvkFJBG686/XWVAURex/PsAZpVW1OBKdydYn+GaQK96/FiUxyN5RkNWOUKe1mePdWIR/clqjw4Hi4wHzZfO64d1PL+H11efh7myEjZ+NgLqaEtZvlr/mlRCEXLpkX3QmPpsFzZCdz7aR3ph1PKDNlkVtoVPDPa7jovU1tDXQuX/njtkgh/MvwoV9MUpL6x+AddrRGRRHZDGjpSUZ1SH9niA7F+m8pqamDRHg4lhaSkanKisrN+1cPr6h0eACienPPfccs5WhCPn79+8z6x8RJPZLl4lEdyUlpTaVpzmaK6uQ36A00mVVU1OTKGtzdUoiumimgjhksdRcGQlROclOiWyaKKqe7JWoPN98842EdZGsOhUX/ltTHnHIxumTTz6R+OzFt+Zg/jtzZab/asVGxIfV+/HpGGjj60NroaOvhZLC0noh7vG+UfRRaWEZ+645CnIK8d2bW2Fma4qX177IBmI6AvLnI/SlBigM1JSbRGHIgiLPKd3PD5Mw2MoAU53MEJob0/byFFfAQEsy+thQu/49ee4LJTylENP72LY7jTQkxtNAhjj6airM+qIl+4tJdqZ4vbMD89y/ndkYmUL+nhS5saGfZ8Nn9bPVOuHGlH54+UoQQvNbHixRpPNeQaE+cl+MmuJiKGl3TNtK0Xi0LaPho1AcGoy861cEC/s5RfVRRnR8Y1EscXxzH38nhPT8chSUVsHOpOk108/dBDbGWth3rfUda5HXLB3fXLEIGro2gluYUUMR1d/1poViFfDGrWCZ0dVC0rRUf/pS0bN0nuhpqrDrRijhqYVs4V0bIy1EpBa2OY001A7Y6Wg0aUfqv2t+qq6TngZ+HuaNs4k5+Na/6YDH4ZgM9hLRz6J+VlVqifD9zq+sYrMlxBG1ey1F6/cy0cPnPdywNTRB7kK7QtI0X75q6KkoNSkftSu0MG5zjLcxxQpPe+alf1fOIt7tgaI/aSYARe6LQ/XZnkEE5U6dMNHaHD+ExjB/c4I89i+kZmO6nYVgYT9XdB9js24a+wOG6spsKr+Q+xitC/HlnTiMsDNi/ra0GGB3M10WkXhzbuNgP0XIveZji5e8LNHrd9ntDHnk0+K14lQVFTMlRkVHu815VHR1WYS13YSx7L2ylibsp0zEww2bUJGXj7rqahbVH77jV/Zq6D89eoQri5bD+7VlZDrW5LdJoDdQb9q26KsrIVtqlpCIntZ66GKmg9h3JYMMjr3QHSfCs5g9j4hHjy0GInNK8dG5CFxc1Ac+lrq4n1IguG0mqyZZ1257B7GOJ2eyV8N+GeuxvxSdLBQlHR3UlDTe04ia4qKG7/7XKOtoNy1fUTGbeUev9pJ7/RrSDh2A7aIl0PFq6l2df+8uW6yXeEjC+3vzmdhOM0rKHl/3IkoLS5gfviw0dDQF5aEo/YnvviiRZv/HW6H3OKq0prqa+e2PfmUWLFzr1/eh8pCgr62U+UwJ+wVV9dcntc3ifvn0PqxA+BpD0oi2dSghBbHF9c/op1IyMNTCBANMjdos7FOkOC00avB4Bqd4ZLl0FL8s9LRVMbyPDb7Yfg8dAfVbpC396L7BvpOaxSRrAIAsxYKyi/G9XwK2DPeEkboyW3uso6BIcOm6MtSrbwtbitYnKBL/1KVYlJS1XKaImFxMG+vauvKVVzf08xrKp66M0upa9mrpvksLxB6PzWL32rmeFkzYt9NRh4mmKraP8mryTBS6YACeOx7I1jloifzCCjaoQeeWRPn01QXVnZW5Nnr5WOLNtRckPq+orEFxSSWu3knCuccL6j4ISsdfR0PZArpChX2JfouYzR/VZ47A5++ssip8fT8Ow22NMMnJFBv9hdv6youaX/fiFw3vh8wYjIlLJjJf/RKpdrekoJhpGVpSM6jaAmkh987dQ48R3aEk1Q/mcJ4F+FkrhoODA2scyPOe/pdFa4RMslkhKFLeyqpxYTyykRGPXJcFRYynpaXBy8uLifFthRaDNTMzw88//9zwWWCg5PRj2leKOCfPe5GoTRH9ZEnT0eVpDiG/QZH68fHxrGwiMV/0Xsj2aUaGEBul5qCFeMmeSdwLn0R+8QEhqlOKuhdH+n1bykMLHEt78N/Nuyw3/bs/rmgYkBCJ+I5e9syLLjEymS2GS0Q9jGUzBRw9G2dCSFOQS6L+FphYGmHZpwugpNxxzQd1qhIKy9DdVFciSraHqR5OxrcuWpI6Xa1Yb04mFFU/u789244o0K2PqzFSckuRVSj8QdvVQgfZLYjFQtJIE5xbBB8jyanaZKMTmtd8J3OinSne7uqIj+9F4mqaZJQR5e13pHFRN+K9bk6w1VbH8mvBrPMmBAUlJajb2KI0OpItdCuiNCoCGo6SnrQdwSM5CxLKIyGrhInTvVyNcT+6fiq3ipICfB2NsOmE8NlHpnpqTMyWdT7M7G+P4MR8hCa1/qEzLL8YNXWP0M1Qhy04SpioqcBSUw0hzQj7JNh/09sTaoqKeP1WCFtAtS1phFwbGqpK6Gyjj4dJ9QNDPZ2MoKSoIDEbpSXovBcfaGlrGmmCsosww9mcTbEWTQnuaarHvLfD8+QPTDnqamD7MG9muUOR+UIYbWuM2IJSJLViQCM4rxgT7cxYJJTokvI10kN6WUWzAw8k9H3R0x3bwxNxMC6tzWlaIiSvGF0NJdsWamvovGwOWvD5DS8HfOofiesZws+D1kCLJUYVlaCzgS7OiontXQ30ECLHNkMIjx5H/TeZJQNaIFt4GBhF19NDck9zXTx47I9PVjHdTHWwxb9+YXeh0KKpotvYZv9EbA1oXDCPuDy7F7PloQUW5aHr5IDcYMk2rSA8ElpWlmzB27bm0XV2RHmW5H1ZVFq6Z2qYmmDQL1skvo8/8jdyAoLQ47OP6615bjatV7/UAmioKKKzmTYeZtSfb72s9dkgpJ+cgb1pfzyQmClEVjxnFvTCtD/8EJRe1GIkcGu6CrQA+SQbcza7jRbFJboZ6rK2hRZa7kiGmhsjobgMqa0Q9rUcHJF9/Rq7J4rsj4oiIqFiaAiVJ9Rvbw0aDo4oiYqS+KwkMoLNwhMy07A5cm9cR+r+vbBZuBi63erttKTR69ETer7d2f+jLctZHdHvmjlZIzkkFl1G9GlImxQUBSvPxpm74igqK7U6D1GSX4SkkBiMWFI/K5zOW7bbMnf92fL/jSwsZoOu3vo6bL0SwkhVBeYaaghvh7AfV1zKFmKX1Q63N0A3IDwLPTub4ZdDwQ2f9elijgABs2cnDXVka+z8c6lj1ocIzCrCLFepfotZfb+FFoUVCtnxEM3NnmwL/iGZmDXRTeKZqLevBVuwNSu3easw8pF3tNXH++vlL0YtjouDQYvblIb84Om+Kw7Z6ARlte7co7XURTVHAyUeO69JfP9pfxe2lsELJ4MEPxPRIBKtLdCzqwUOn2zUAXr7WjIhviWmjXNDQWEFLjwW78XxD85k56E4j8fQBUPR9dRv6WGmC7/Mxn4L+ev/HNS6fgsF4nTEmWdkYYRvz3zT8F50f7D3sEfEfcnZKFEB0bBwMIfqY0uh9hB+PxyFOYXoM7axXee0zKP2ii6cDoMvnisG2cKQDQ+JtGSRQlRVVeGXX35pSGNiYsLsV4RAHunjx49nHvuixV8PHTrEBg5aguxoSNxetmxZQ14SaPfv34+gIOF+wyQ204Kw9CISExPx3XffSaQhf3ra9oYNG9h7soOhBWefRHmaQ8hvUFlJxKc1AggSo9euXSto+8uXL2cDHbt375aImqfj0xqoTkNDQ9lvE7S4LtnqiPPiiy8yn37RIAqdT5s3b253eVRVVdnvi7+as+Ehv07yz6cX/U+QmO/c2QH7Nx9FXlY+ctLzcOinf+De3QWWDuYNeV8d/R7O7L3I/i/KK8aGN7fCmET9zxZA+QmMZO8JT8UUJzP0NdeDlrIiXu1qy6ZCHoxq7Pis7uWEA+O6NSyW+1V/V7jqa7JOCEUXLO9iC2c9TZyMa591wt7r8VBXUcTKyV7QVlNCX1djzO5vh50XGwU/EvqjNk+Gs3l9hNZ7kz0xoos5dDSU2WvRcGeM6maJ3WIdfyFphLA/Jo0tBEp2OuR9PcraGH3N9PFXdGPbNMnelEXaU90Q42xN8M5jUf+KlKgvgjqq4i/xz1uD0bCRKPS7j6JAfxbZmX3uNKqys2A4eGhDmoyjhxD5UeMaGEJI2r4VZQnxLCK0pqSEbbcsJhp6vYR3wqjDu/tCNOYPd0Z3J0N2HD6c2YV5ah++1RhlsnVZH/zx9kD2v4muGr6e3x2uljpsEMDZQgc/LumNtNwynJOy4aHtjfSxxP5rbbO4IRsk8r1f5GYLO211FhH6VmdHJJeU45bYDItfB3fFu13qRQRaYO3r3p7MYqdesG860CkkjRBIzL8Xk4OPpneGuZ46LA008MFUb1wPz0SUmJBGC+AuGV4/kDPWxxKLhzvDwkCd1d9AD1O8M9ETxx8kN8yAEZJGCJeSc5BRWoFVPZxY3TnraWCxtzWOxWag5PFAhom6Ch481x9DresjIu111JmofyEpF+vuxchdaHilrwPLq6aogOfdLDHK1hhfPWjdcf4nMYMNsCz1sIOmkiJ8jXTZgNv+2FQJIf3KhH6w166P4KM0JNhvC0vE/ljZgr2QNEI4FJ8Gdz0tTLUzg7qiIkZYGqO3iT4OiA0UTLAxxaVxfRvaljHWJnjzsah/7QmJ+g3lS0jFYHMj9DMxYOWbZW/JFh7+O6nxPrHIxRa/D2rZX1t8wIAi9GfaW8JeS4MtVEu2PCMtTHAzS/j+UDO5JySVLX7ra6rDFi9e1duRDdQdjWqcPbFpuDv2jK2PKDbXVGXT/531NVh9mmio4MM+jiza8ERsVsN2ZbXNdS3YNVgOHYzK3FwkHD+FmvJy5AQ+ROade7AeObwhTbZfAIuir8zPF5zHYlB/1JSWIen0OWa7U56Tg/i/T0DX2Qmqj9cGUlBUlHiJ5BL6X56IS2L+3eR8rBnmwha4tdJRw4dDnHAtPhdROY2WUmFvDsLLPeuDEmj/yUpH/EXQX1HVTHQ3xcLu1rDQVmUzHdyMtbBupBtic0sRkFbYKi/8zIpKvOnlyNoDB20NvOBkjdMpmSh9PFOOxMwLo/tigKlwCyIaKFjhbs/yUtsyw94CQ8yNsCmsdW2L0YABbNHY1KNHUFtehqLwcORcvwbT4Y3HrigiAn7LlqI8re1tRFsxHjocZWRRevkS6xfk372DopAQGA8bIRF1H7T8ZXaPF0rerZtI3fcXE/X15Ij6BJ13NNuPXuLnYc/JQxB+zQ+Rt4NQWVaBO4fOIz89G77jBzTkvbz7b2xd2PisISSP/6nriL4bjJqqauSmZOLYl7tg6mAJ7+G9GwYIHLp74O7hi8iKT2WL8iYERUJTMRfltc+Wxz4NdF1Mz8LzTrZsVgvNolru7ojUsnLcF1unZFPvrljhLn/wQ5qqujocT0rDdHsrtl1qI4dbmMBFVxvXHw8gtJVdR0LQz8cSU4Y7QVNdCbPHusLH0xS7jzUObC6e7o3Ao029tqeNcsHp6/GCItCFQN746aUV+Kh3fb/FRV8DS7tY40i0WL9FQwWB8/pjuE19v2WehyUmOpqwewUTYo218YaPHW6n5SO7hSj/1rL/eBg01JTx7su9oKWpgj4+lpg1wR2/HqC5L/X09rFA+KXFcLKTXB9uxjg3RMXlITC06YDJu0t7YXh/O+hoqbDXwtmdMXKgPX4TG2wRwp7QVHgba2OuhwVbyHWCowkGWhvg1+CUhjQzXc1YpL2o37JugAsbDCAPfsoziezuHE1xLLqxnNL3XdFdpbXPRLv2PcS4YU4YMdAOmurKWDy3K2wtdfHH4ZCGNO8s64VLByVn31MTNXWMK46eiZS5QO6OvwIxfIAdBvW2YYv0dnY3wezJHjgp9qzaErQrv4el4gUPS/iY1Pdb3uvpyAbqjkU39lt+GOKOnaMa+y1k+0SzXKk+aZ2893s5wlBNGadaGYwnD0VFxYaXKKh2wKT+yMvMx9k/zqGitAIht0Lgd9EPg6cPbsgXdD0Ib454CwXZBW2y4bH3soeZnVmH7AOH82/DI/alIBGfRFmKuKaFTEmQ/eijjxq+f+mll9hCqF27dmVWK3/99RfzYJfHxo0bMXr0aLZgrrGxMWucKFpflnWMOOTlfvbsWbZgLEXcU6Q6ifK0rWHDhgk+wDRQQdYx5KFP+0TWPJQ/KalxFJbE9J07d7L9pgVgyZJoyJAhzOKmo8vT3n2mwRI6RgsWLMD27dtZWcnOhmYatFSnY8aMYXkp4p0Gb+j3KGJeOgK+JWjQg8pE5aP1F8rKyjBgQGNnnpg8eTLmz5/P1lNwdXVlMxEoz759+zq8PG3h5U9ewl/fH8LqF75kDzed+3hizhuSa0vU1tY1+E3ev+SPjKQsZKXkMMFfnE1nvuoQof9wdAZ7wP20rwsM1FQQU1CK5RdDJRbCpShGejAnymvqcCYxBx/2dIKrgSYqauoQkV+CJReC4dfKKA1pMgsrsGDzTXw8qwsWDnNCfkkVtp2Lwp7LjQI8FYOilEUPh/tuJuCN8e74Yq4PEyZjMoqx5KfbuPCwUXASkkYIYfkl+PBuJJZ52eLNLg7ILKvEev9Y3M5ofIBSQH1diSSUhe42UFFUwOe9JC1rjsWn49vAjvFZF6Hr24NNuU8/uA/VhQVQNTWDzcuvSCxyS1GF4tH2RQ8DmXAvIumXraxXazhkGMynzWKf6fcfhIwjB1GelMBmBqhaWsF2+WvQ9mw67b45fjodATUVRWxd3he6GioISczHS99fZ8dZPPKEXgRF5V8PzcTX83vAxVKXLbR7MzwLb/xyF6VS3puTe9cvHPrP3dZFuojz3cNYvO5tj58HdIGKggICcgvxzp1QCS9ouhZEUackBHcz0mXfHx8tuYbL/CsBiC8uE5RGKK/8chefzu6KC2tGsIGSi8HpWCO1wC2de6LyXQnNwOJhLvjztQEw01NHal4Z9lyJxY6L0Q3phaQRQlXdIyy/HMKE/XNTeqKitg6nErLwrV9c0/I9vjpmulgwj1Cy8KKXiKyySoz9+37DgEtcURl2j+zCfNDD8oqx/FIIHmS1bkFoWteAjuUb3o6Y5WjJ7GX+jE7Fobh0ybZFzHv6eWcrqCkpYrmnPXuJ8MspwNu3QwWnEUJ4QQnW+EViibstXvVyYH7f3z6MxZ2sxraF2jzxtuUlF2vWtqz1lWxbjidm4PvHazjYaalj16D6QVnK+7KHHZa42+FOVh5W3Y9olR+5nko8lrs7sIWIk0vLsdY/HAkljecvnXd0fYjoY2yANd0ay/Yx/f8IOJqYhm2P/a6/DYnBAhdbfNndkz3Y0nE6kpiGP2Nb5/O7LTCZHYfNIzygo6qE0OwSLDwdLOHRK37tppdW4m5aIdYPcoWLviaKq2oQnFOMOceDEJXfuqhFaTTMTOH9+iuI2X8ICX+fYAvbOkyfArN+jZY+jx7Vt8OipkVIHlVdXXR5+3VE/7UfcUf+hpKGBgy9PeE4cyray7Jjwfh8pBsuL+7DynQhNgerz0U0iUoV1Z8QLsbmYElPG+x7zgdm2mrILq3EpdhcbLoVj+pWeGpT2pX3Qpmwf3BoD2Z/cSEtG1vDxSIpO9WXT3zw4o9BPjBTV2MiDZWbhH9i+JlbDaJoYmk5NvXxZovlUvT/yvuhCGrBek0aFX19OL/6GpL370PmhQtsYVqzUaNhMlSsj/6oji08LzrgJKAHvPZq/Xd1dSiNi0POjRtQMzOD55q1gtMIQcPeHnaLlyD92FGkHtgHFQMDWM19Hjre3hLno3gIakVaKiI//7Tht9OPHkb6sSPMasd++Qr2cebJ43hUU4PEX7ZBfF6L4YCBsHpOtk2lOO4DfFBWVIKL24+gOK8QhpYmmPrhYhjbWjSWq/YRHolZkQnJ49K7M/PQ/+ebPVBWU4Fbv64YOG8CC7QRMe71ubi65zgOrP2JLcqrbaCL4hoTFNY0Btm0hdcWjcW6VXMa3t89s579fXvNHmz//TyeBD+Fx2KJqz2+69mF3Q8e5hVijb9kv4X1q8SujWVuDhhjZd4QJn14aP21sSYgtMFm5/eY+qO6voc3NBSVkFJWjq8fRiIwr312b7cD0/H+hut4/flu+PLN/kjOKMZb66/CT0yAprXHaKFdcbxdjODuYIC1m+uv346ALMJePheC1X2ccGlmT2aReDI2i9mbNJTl8b1T1LaciMvCy11s8JqPHfTVlJFRWomz8dnYGdIoZhOnpnaHhZZag40MDQ4QXX+TnKHbHJk5ZVj47imsfr0f5s/szOxlfvkrEL+JCdO0bSUleiZqzKehroQxQxzZArmy2H88HK8v6I51KwdCRVkRMQn5WLrqDC7ekJyh1hLB2cV442I43u5hjw97O7L76pob0bgmthAuKx+rv/r3e8PTsMLHFr6muuwzmnG3+kYU/o7pGGFanNOXYpkVz4ev94OJoSbikwuwfNUZRMc3lk9Rxrk2oJc1zE21cPC47D7S3YA0fPDlFXzwWl/YWOggM6cUh05EYOvu1i32u+NhMgv6+WFofb8lLKcES84FSyyEqyDVb7mXUYh1A1xZMB0FCoXkFGPeqSBEi/VbXvS0xFvdGx0wDk+qD7j48m4M9kW07rmXMLE2wZJ1i3H0p2M4s+cMtA20MWHxePQc2eiCQbqFKPBSFJi4ZtYa9j99npmYicsHLsPWwxZv/Ph6Q7ri/GKE3gnF7Lfqnzc5nGeRTo86YjXD/yAkxJKo7+bmJtOrPSYmhonKvr6+zI++OSgCPiQkhHmskxjcp08fJuquWVPf0Ii+s7WV7bNNNjm0gCwNNIj7tFMDdevWLXTp0kXic/LQp23R7AIRVF5aNJdEZspHswakLWBov8guhiyEaPbC7du32f6TmC6kPM3RmrIK+Q3ysCeLIyorRfCbm5uz6HjavrzfEkGWQ+Hh4WwUmLYv7qmfnZ3NrH3ErZJKSkrYtum4UR6CFrClxZbpPfnh0wAEfSbtjZ+SkoLc3Fz2OzT7g4619ELJzZVHCFfTT+FJQF5z1KkVrYNA72Uh/pAii1cvCDtHhMBmLXcSvqiUEEpuZbc5L3XESMR9kpgMl7w2WgP1EUWRJRTvIEsLoeLL2wPRLPHmzG4s9dq2/yJRX2QZwG5Fsmx1KMqujWs5BOxtvce42M/W7/ujJ5fPYkLbH94VGixE6pF6HmhAPLJISBpx0s61/QGHHiKf8KUBnb7GHXJtdJKzhiN93Z590NLq9ETLJ37tCkkjjWjg6t9qWxRbWb62rgkvq92SeXyb+e2WSEyu/Z/ex1q6voZ6CCsftbvUFtdH1T85pNv70zdaZ6HWmn2ngZP2LIhLOHhL+iG3p3xsj+Wc+23FXKNtM65E91mKWm/4TNbaGVL3XSFpxKl51OlfKx87t2Qd72bKN8aydYs7iga/RDNfnyTLhzfO4m0PijLKSgE77WXojlc6rN8iags78tqI2iBp99Re6D5JC512VN9fdXbbLSnF77utaY9kXY3ytlO5S7glpTSKip1QK7VhWZ+1B4V5stege1L119p796Pdbas/9uzQqem5Juv8ayvKCz3+9X6LrH6fvGPw3YjGmXlthekVdXUNeg37PRn3EApNEW/PZeWTZrTVmHaX77+I7edPZrD4SZP4UeNswf8KPGJfDhYWFuwlCxKLu3WrjzxrCZEVC0X4E2TPcu/ePQlbFvKUb47m/P5l+bP36NGjyWdOTk4S72Xlo/3q3r3ef5IgIbs15WmO1pS1pd84duwYi9KnspKoT373NDggqkd5vyWChPPOnWWvdk6zKuglDs3MkN4eWeLQwEFL5aW1FUTrK9AAkLSo31J5/peI3/Cos9GSgP9vwB4InqKhyCct6rcX8c4Te5xrZXHFH8A6GukHbhaF9ITFpdbQVtGvPWJha5B+PBfysNKBz1Yt8pRfGhJ1IbI6eZbL92/vQ1valn+rfLLarafpfOyI+1hH7Y/InuRJ09bB2bbse3tF/Y4uX1vuvU8KWfdZIcf/3zhH2lq+jjy3mvuNZ81FuCNE/I6m7inv08tClqj6v+r7t+Ue+m8WVZaA35Gifntpb1Ge5Pla743fdOMdJeq3l7bu+799+JleIXXPaE6sby4fRyDc2P2pgQv77YC89mfNkj9lh7zhSSQmWxaKMKcRw4yMDGzZskVCQP8vQBYz0h7y4qL3b7/91mG/paKiAk9PT1a3dAwMDAzY2gW8QeZwOBwOh8PhcDgcDofD4XA4/x/gwn47MDQ0xPr165v9nnzqyW6GvO3Juoci5yna+78GecyLItOlkbYyai+0xgHVJ9kGUTQ9RevLW4yNw+FwOBwOh8PhcDgcDofD4XD+a3Bhvx2QaN+c5Ys45AX/X8bS0pK9/i3IvqYlCyMOh8PhcDgcDofD4XA4HA6H04Hw4NqnBu6KxOFwOBwOh8PhcDgcDofD4XA4HM4zBBf2ORwOh8PhcDgcDofD4XA4HA6Hw3mG4MI+h8PhcDgcDofD4XA4HA6Hw+FwOM8Q3GOfw+FwOBwOh8PhcDgcDofD4XA4LaPQidfSUwKP2OdwOBwOh8PhcDgcDofD4XA4HA7nGYIL+xwOh8PhcDgcDofD4XA4HA6Hw+E8Q3ArHg6Hw+FwOBwOh8PhcDgcDofD4bQMt+J5auAR+xwOh8PhcDgcDofD4XA4HA6Hw+E8Q/CIfQ7nGaf20f+6BM84tXV4mql7uouHkpqnd9GcTlVPd+XV1OCpplNB5f+6CJz/x9Q8xZdvRXEtnmaq6p7edpnoVFyFp5naWjU8zZTXPN1xWYoKT3fHVFXx6S2ftc2g/3URnm3KqvE0U/u0P7SVPd1tc3XV011/6UlX8bRiU+vxvy4Ch8N5gjzdPUMOh8PhcDgcDofD4XA4HA6Hw+FwOBLwiH0Oh8PhcDgcDofD4XA4HA6Hw+G0zNM9QfT/FTxin8PhcDgcDofD4XA4HA6Hw+FwOJxnCC7sczgcDofD4XA4HA6Hw+FwOBwOh/MMwYV9DofD4XA4HA6Hw+FwOBwOh8PhcJ4huMc+h8PhcDgcDofD4XA4HA6Hw+FwWuSRwv8Pk/2YmBjs27cPhYWF6N27N6ZOnYpOnYTt+8mTJ3H69GlMnz4dgwcPfmJl5BH7HA6Hw+FwOBwOh8PhcDgcDofD4QC4ceMGvL29ERYWBnV1dbz66quYN2+e4AGBpUuXYseOHQgMDHyi9cmFfQ6Hw+FwOBwOh8PhcDgcDofD4XAAJuTPmDEDf/31Fz799FMcO3YMf/zxB65du9Zs/VRVVWH27Nn46quvoKam9sTrkgv7bWTkyJG4detWhx2I/v3748GDB3hW9/9ZK///AqqfJzn9hsPhcDgcDofD4XA4HA6Hw3mikB3Ns/gSSFJSEou0f/755xs+69mzJ1xcXPD33383m/f999+Hq6sr5syZg2fOY/+9996DpaUlXnvtNfzXIVE7Ly+vw7Z38+ZNFBQU4Fnd/2et/P8LqH5oKs//Ar8rgTi55yxyM/JgbGGICfPHoEt/b7npq6uq4X8lENf+uYWE8CTMfXsm+o7tJZGmsrwSp347B/9rQSjOL4GFvRmmvDwBzp0d21zO51wt8Ly7BQzVVBBdUIpvHsThYU6x3PQWmqpY5G2Nvub60FBWRGxBGX4JTsKt9Pafi962+lg9qwvcrfSQW1yB36/EYuf5aLnpe7oY4Y83Bzb5fNynFxCdXtTwXkVJAcvHumFSLxvoaajgZkQWPtsfiMyCilaVr6+ZPpZ728JaSx3pZZX4NSwZZ5Oz5aa31lLDC65W6GmqBw0lRUQVlGJ7aCIe5jbW7wQ7U3zg69Qk76Cjt1Bd96hV5RtpaYqZ9pYwVlVFUmkZdkQlICivUG56NUUFDDE3xnhrc9hraeLTwHDcyZZsY/cM7M62J83F9Cx8FyL/2AjByUIHH7/QDV0cDFFcVo0D1+Kw6VgoHjWz2wqdOmHxOFdMH2APY1013I/Kwed/BCAxq6RdZVFW6IRXvOwwwtoYqooK8MsuxHeBscgqr5KZnronw6yMMNXBHE66miiqqsb19DzsCEtCaU2tzPQ/9veEj7Ee1tyLxMXUnFaVT1dTBavn+WBIVwtWPxf9U/HZ7/4oKa9uNt+YntZYMt4dDhY6iE0rwtf7AnEnLKvheydLHcwZ5oRJ/ezYtga9cRxtgdqF97o7wtdUF+U1dTgVn4VNgQmokXMwNZUUMcvVHKNtTWCuqYq00gociErH4ZgMiXRDrQ2x0NMaNtrqKKqqweGYdOwKTWl1+Vz1tPC6lz2cdLVQUFmNI/Hp2BebKje9oaoyZjtZop+ZAfRVVJBYUoY/olNwIyOvVWmE0ttEH4vdbGClqY7M8kr8Fp2MC82cI1aaanjO0RLdjfWgrqSImMJS7IpMQkh+Y9ui2KkTBpkbYpKtGbwNtPFzeCIOxKWhLYyxMsFsBysYq6kgqbQc2yISEJDbfNsy3MIYE23M4KCtiY/9I3ArK6/JObDAxQZ9TQygo6KMqMISbAmPR0xRaavLt6KnLeZ0toCemhIeZhZj7eVoROQI28764a6Y6WWG724lYMu9xIbPaVszPc0xt7MFrHXVMPr3+4jKLWtxe4VRUUg4cBhl6elQ0dWFxcjhMB88qN15Us+dR+b1m6jMy4Oypib0u3jDduoUKKmrs+8rc/OQevYs8oNDUVNaCg1LC1iPHwc9T49mf1tXXRlrxrljqIsx6Gq9EJGFT0+Go7iypsV9VVdWxN9L+8DBSBOTt91GSFrjfdfHWg9vD3eGh7kOa7NC0ovw3fkoBKXKP29kYa6uite8HNDVUAcVtXU4n5qNbeGJqJXTttC9doqdGYZbGsNUXRUZZZU4lpiOfxIzJdJ56Glhsp05u0bC8ovx5p1QtIXyxARkHNqHipRkKGlrw2DgEBgOHyU3/aNHj1AaFoq861dQGh4Cvb4DYD5rbrPbT/j+ayhqasJl3TetLl9RSDAy/z6CyqwsqBgYwmTMOOj17CW/fLU1KAwIQN61KyiNjYXZlKkwHj5SIk1tWRlyLp5Hof8DVBcUQsXIEAYDB8NwQPPneXMU5xbi9M+HERcYBSVlRXj074YRCydCWVVFbp70mGQ8OHUTIVf9YWhpgiUb35H4vrqiin3/8PID5KfnQMdID91G9UbvSYPQSUFYnN3SeT6YNdEdujqqCInIxrofbyIyVnYbr6ysgKDzi2R+t/nXB9i6x7/hvYuDAd5c0hO+nc1QVFyJ3w6F4LeDwWgNSp06Yb6LHQaZGUNFQQEP8wvxc3gscipl91sIYzVVjLI0Zf1FPRUVTL14S+Z92ktfB3MdbeGorYmcykrsjU3G9cxW9lu0VfHRa30xuI8NawMu3UzE55tuoaRUdvmG9rPFls8kzzURs145hofh9f1tv5MvQUNdWeL7Db/cwy97g1pVPkstNXzYxxE9zKjfUovjsVn4/r78fgv148Y5mmCOuzmc9DVRUFGNy0m5+NE/EWXVtYLTtBVFxU54Y2lvTB7nBi0NFfgFpePTb64iKUV+m2ploYN3X+2L7t0soK6mhPjEAmzf44ezl2LRXgbbGOCd3vaw01VHanEFtvgl4Z/oxv6lNNoqiljYxRpjnYxgqqGK5OIK/BmShr1h6Q1p/pjYGT0t9JrkDcwswsyjT8ZSQ1NDFbMm98Pi50fAy80as1/+HifP++FJs7SrNWa6mkNPVQkhOSVYdycWkXny+y0Ouup4uasN+ljosWeW8LxSbPFPhF9m431XnE/7O2Oaixk2+iVgW1Bym8sZ8zAWf//8NzISM6BjqIMhM4ag/4R+ctPX1dYh9E4obhy/ieiAaAydORTjF46TSLP7s90IuvawSV5jKyOs+nVVm8vKebqprKxkL3FUVVXZS5zo6Hp9wd7eXuJzBweHhu9kcerUKRw9evSJ2+88MWE/NDS0SQVxhHH9+nXm3fSs8qyX/79MVGAMdn3+O2a9NhXdBnbBvQt+2L52N97e+CocPOxk5rlz5j5iQuIw9eUJ+OHtn1Ano2P51/cHER+aiEUfz4OxpTEeXPLH5ve244Ntb8PMxqTV5ZzkaIo3fOzw/o1IBGUXYb6nFbYN98Lkf/yQWSa74/2SpxX8MguxNSgRtXVg4tymoZ6YfTIA0QUtix7yIJH29zcH4PDtRCz/6TY62xlg45JeKK+sxV/X4mTm6YROUFJUgNvyI6gVE8HF/yc2v9wb9qZaeGvnPUSkFKKPqwmm9bHD1tMRgsvnqqeJb/q5Y2twIk4mZGKQpSHW9HRBfmU17mXJHtRY5mWL2xkF+DU8GWU1tXjRzQqbB3ph3oVAJBSXszS0/k1WeSWmnZacfVPbOk0ffU0M8ZqHIxPb/XPyMcHGAp/5eOCV24FILq3/LWkm2ljAQkMNP0XE4buenWUOps+/9qD+SeUxjtpa2NynK25l5qI9aKkp4bf3BuNmaAZGvHcKDuY62PJqX9TWPsKWf8Lk5vtyYQ/09TTBu9vvISg2F10dDTF9oD2+O9S6B2Jp3urigN6m+njrZigKq2rwXjcnfN/PE/MuBsg8Fp4G2hhkYYjtYYmILSyFhaYa1vZwZYM+79xqWv4XXa3YQI2SQqfWBC00sOm1ftBSV8a0j89BQaETNr3WH9+/0geLv5U/JfG5oY746AUffLTzPs49SIGFoQYWj3eXEPa/WNQTp+4kYf/lWEzsJ7ttagnap5+GeiGusAzTTvjBWF0FGwZ6sM+/8ZN97U53NmMC3KpbEcgorUQvMz2s6+vK8uyPqn/Io8HDb/q7Y929GJxPyoatjjrW93Nj37VG3CcB/oe+XjiTlIUP70fAXU8Ln3R3Q3ltLf5OkBxIEPG8izXSSyvw3p0wFFRVY5S1Cdb1dMfbt0PxILtAcBohuOhqYl0PN/wSnojTKVnob2aIVV1d2ADEgxzZD+yL3GxxLysfv0ensP2Y62SFDX08sfhaEBJL6q/3IRaGbFt7opKx2selzdNF+5sa4E0vR3z1MBoPcgow2dYcX3Z3x+IbQXLblim25qxt2RwWjx/7eMs851d3dYWRmgpW+YUju7wSE23NsKGXF1665o+8yuYHrMR5ubs1ey07EYqo3FK8088ef03viiG/3kVhC+L0eBdjeJloIa+8mrXF4rzV1x6VNXX4+kYcfprg9ViyaZ7yrGyE/bAJFiOGwf21V1AUGYWonb9CSUMDxj17tDlP5vUbSDxyDK5Ll0DXzRUVWVmI/Gk7aisq4bJwPkuTdPwEtGxtYD5sGJQ0NZFx5SrCNm6C98p3oe3oILfMW2Z3hZaqEhPmaTBo8+yu+GFGFyz8o2Vh49MJHkgpKIeLqbZE7ehrKOO3l3rgSEAqXt0fxOp25UhX/P5SD/T99gpKBAwaiITLb3t7IqGkDC9eCWABCOt6uLNybgqNl5mHziMa7PrMP4oNktHg10fdnFmeo4+vdx1lJbzq5YB/EjPY53QetoXqwgIkbvoOer36wnrxMibCp+zahk6qqjAYIHu2aHl8HHIvn4d+/0GoLSkC6urkbr+2ogKpu3+BhpMzGzhoLeVJiUj6eQtMJ02Bfu++KAoKRPKeXVDU1oa2u+wBn0I/PxQFBcBk7Hgk/7qDRiKapMm7eR0K6uqwXfYqlLS1UBwSjJTfdkNBRRX6vXq3upyP6urw19rtUNfWwNLN76KipBz71+1CVUUlJr81V6549M+P++A7pi9bTC8lsnFQTkTQxXsoyi3AxDeeg76ZIZLD4nBo/R5UV1Zj4GzZArI4C5/rgkXPdcFrq88hOj4fbyzugd0/TMCoOXtRVNy0j1xdXQfvYb9IfDZqkAN++HQEzl9rPF+d7fWx76fJOHQyAh9/cw11dY+waE4XONrpIzYhX2CtAUvdHOBjpI+1/qEorK7Bqx5O+MTXE6/eDoC8uJAlrvaIKy7FwYQULHVzrG+bpdL6GOrh424e+D0mEesCw9m9ep6zLW5n5coVvWXx4yfDoaWpgulLj0JRQYG937B6KJa8f0ZmehL+vUbskPjs83cGoo+vJYIjGoNoFBUV8PraC7h4M6HhM+o7tgYSRneM9kJMQRkmHKnvt2we7sHanC/vyu63dDbWhq+pDr64E4u4wnImtH49yA1GGip4+3KE4DRt5a3lfTBlnBteWXkKaenF+Oidgdi9ZRLGzPwTlZWyBw1+3jAOObnlmLXgEAoKKzBzsid+/HI0Zsw/iGCxvmBr8TTSws9jPPHt3XgcicjEcHtDfDvMjd1Lb6TIPodneZijuKoGi0+GIK+img0MfD3MjT0f/R1VX5Z5xx9K3kvUlHF9Xm+cjWvdoFJrWPbSKNjbmuKdtXtw4dAaFkD0pFnobYWF3tZ47WIYYvLL8EZ3O/w6xhujDz5gQSyyWNzFmg0SfXU3jt23FnW2ws4x3hhz8AHSSyW1wDH2RvA01EJ+RXW79icnLQfbPtiGwdMGYcm6xYgJisHv6/+AhqY6fIb6yMwT6ReJO6fvYOCUgcjPzEedjHvcvA/n4dGqxmu2pqoGa2atgXc/rmv9l/nyyy/xySefSHy2Zs0arF27VuKz8vL6ZwttbW2Jz3V0dJCbK1t/SE9Px4IFC3Dw4EHo6uri36LVz1Z37txhBR0xYgTeeustZGfX39y++OILFsVNO0C2LPSiqQstsXPnTowZMwazZs3Cb7/9hs8++4z5EIl48803sXXrVmzevBljx45tmA1AF+auXbswefJk9vlHH33EVikWPwhUBhKcaRoEpXnppZcQHh4u8fuTJk1i6QYOHIi5c+fiyJEjTcpYXFyMDz74gO3zokWL4OfX9AGjpfK0BJVRNOojtOz0m7t378a0adMwbtw4VkfiDVZH1ZGQ/Rcvv5B6Ff02eVO9/vrrGDZsGDt36P8NGzY0iSiaMGECG/UiaB9odoh4ecStgeRtm6ALcPXq1Sw9eWX9+uuvbPsizp07x8pOtjmLFy/G6NGjsWrVKpSUNEbg0sIZonN8+PDhWLZsGSIimnaQKN0LL7zAzm+qH1kzGloqT0dw4eAVuPm4YODEftDW08Kw6YNg72GLiweuyM0zYGJfzF/1PBy9ZT+A19bUsoj+UXOGwdbVBhpa6mz71k6WuHT4apvK+ZKHFY7FZOJyci7rZH3nF4+S6lrMcjGXm+eLe7E4GZ+NnPJqJmpve1jf5ngZSTa+rWV2f3tU1dTh8wNByCmuxKXgdOy7Ho8lo1xazEtCvvhLnKHe5hjexQKvbr+LgLg8lFfVsm23RtQnnnO2RER+Cf6MSkVBVQ3+js/E7Yx8PO9qKTfPqjuROJ6QyaL7SSze+DCBdWAHWhg03YdHkq/WMsPOEtcycnA5PZs94P0Rm8REjUk2FnLzHIhPwQ+hMYgpkh/tTq0bVanoNcLCBLkVlbib077ZUxQhrq2hjNW7/ZBVUIE74VnYeToS80e7QFFaYXuMSMRfuf0eS0/H8nZ4VrtFfRJ5aObEttBERBaUsgjPrwNi4Kirib5mTY8VEZJXjNX3IhGYU4Ti6lqWb2d4EpvVQQ/B4lC09CR7M6z3j2lT+Tzt9NHPywyf/uaHhMwSxKUXY90f/hjazZJF3MtCU00J783phs1HQ3H0RgJKK2oQnVqEldvuSqSb+ckF7D4bhUI5EXRCGGplCCstdXx2L4YNCIbklmBbcBKmO5s3qQsRe8JTsTkokT1UU5tzMTkXZxOzMcrWuCHNWHtj+GcV4khsBqtj2u7u8BS86G7FHr6FMsHWDNW1ddgYEsfarFuZ+UzQIzFcHj8Gx7Ho9uTSCvbbh+LSEZ5fjKEWRq1KI4Tp9haIKizFvrg01k6cTMrE3ax8POckv21Z6xeJU8lZyCivb1u2hiWwiMN+po3nK0X8Uzr/ZiLrhTDLwRJX0nNwMS2H/dae6GT2u9Ps5N8n9sal4ruQWETJaVvomutloo9fo5IQX1yGkppa/BWbivzKKkyykb9daegsWOxrjV0BKbiRlI+s0iqsvhgNNSUFzPBsfjvWOmpYM9gZr50Oa3LfID6+FI1112IRXyB78EIWGZcvs4h72ymToaKjA6Me3Zk4n3rmbLvylCQkQtPGGoZdu0BJTQ1aNjYw9O2GkvhGYcv5pXkwHzIY6qYmUNbShPX4sVAzNkGOf2OUsDSe5jro72iET06EIyG3DLE5pfjsVASGuZnAyVir2X2d2Nkc7mba+O5C0+gpiuCnwYKdtxKQV1aFnNIq7LwVDx11ZdgZakAoA8wM2KDpt0GxyKqoQnhBCXZHJdWL94qy2xaaibMjIglxxWVs9tTV9Fw2Q0r8uiyqrsGyGw9xOjkLlRSh0EYKbl5HJyUlmE6bBSUdXWh7d4F+v4HIPS9buCQ0HBxhu+JN6HT1ATo1/0iYvvc3aHfpBk0X1zaVL+fSBahZ28B4xKj62QT9B0Db0ws55+WfjxTNb7N4KbTc3OWmoe1RFL+qiQkU1TWg16MX1CytUBbbtnscRelnxKZg/IqZ0DM1hJmjFYa9OB4PL91HSZ7saFQFRQW8vOlddB/bDyrqTWcVEt3H9ceoxVNg7mgFNU11OPfwZBH7oVflXxMi6BazYHYX7DkYjFsPUpGdW4ZPNtyAqqoipo2tH2CWBQnM4q+pY13hH5zBBgZEvLeiD8Kjc/DFxltsu7n55fhqy51WifpaSkoYYWnKxPeY4lJkV1Ric1gM7LQ00cNIdr+FWBcUgb1xychrJqqfBH/qSx5OSEVJTQ2yKirxbXBUq0R9Dxcj9Otuhc9/vInElCLEJRXgi023MaSvLZxsm0ZkixCvOxVlRYwe4sAGQKR/mgZDxNO2lmG2hrDWVsfaG9EsuCA4pxhbApIwy82CzUaWRVB2MT65FcOiqyn6nv6eiM1CNxPdVqVpC3TePT+zM7bueoCAhxnIzC7F6i8uw8xUC2OGO8vMo6KiCFcnIxz8OxQpaUVspsSuPwNQU1MHT7fGvlZbmN/FEqHZJdgRmMKeHw+EZ+BqUh4Wd5Pfr6K0O4NSkFhUgeKqWhyPyUZETgm6mzXWTZ3Us9B45/qgtaORkjOuOpJvt/6DV977BYEhsgeLOxrqt8z3tsKe0FTcTitAdnkVPrkVzWY6TnUxlZvvg2tROBOfw+qb8mwNTGKD2B6GkvdqKy01rOrtiHeuRMid2SaU68euQ8dAB+MWjIO2vja6De4G36G+uLj/ktw87j3dsfjzxfDs5YFOcp7nFBQUoKio2PAKvhmMirJK9B7T+oFhzrPDBx98wDRF8Rd9Jo2WVv05La3j5efnM3FfFhs3boSKigr279+PFStWsBfpk4cPH2a631Mh7B84cACDBg2CoaEhE9ytra0xf359dA6Jku7u7ujXrx/Wr1/PXsbGzTfUP/zwAxNeSVAm7yESNWn0RFwgDg4OxjvvvIO7d+8yUZ/EVmLhwoXYtGkTGxCgBQ2ioqLQu3dvVFTU21nU1tYye5jp06ezctDvVFdXM49zEqpFfPjhh6ysNKBAZX/55Zexfft2iXLSNk6ePMlWNKY0JJSLRm9EtFSelhC3shFa9hdffJEJxiQuL1++nP3mN980To3tqDoSsv/SVjwt1avot6dMmQILCwt2klM6Ly8vtg81NY0jxBcvXsSZM2fQt29f9p5+//Tp0w3loW1QGpE1kLxt0/e9evViA060rzQgQoNINEAlIisri02doQEDGhCg36ABBSq/CDrvRec4DTBoamrC19cXCQkJEiN19JsUuUN1TzcNGjQRR0h5OoK4kHi4dJO0VyGhPy60sbythQYfqCMrfZOk/YwNbn1nRFtFCY56GrifKdlo3s8oRBdj2Y2mNOpKCpjrbsGsN+6204rH18kQ96JzJDrvt8KzYGOsBSMd2Q9tDem+Ggf/7yfiwMrBGORlJvHdyG4WiEwtZJH67aGzkTb8siS3cT+rAN6Gwgc0yOKFXtJWLRQpdG5iL5yZ0BMbB3jCXb95QUUaEjlddLWa2O4E5hXAQ699Ay7iKHfqhKEWxjiXmiU3Gkwovs5GeBibi4qqxrq4FZYFfS1VOJrLPv9G+loiM7+cifkdCUXfKynU2++IoMGY5JLyVh1fXRUlFpUvLhRpKSvik56u+NI/BoVVwqOQxfF1MUJZRQ0CYxqjFO6GZ6Gmtg4+zrJFZBoI0FZXxt+32t7mCKWrsQ7iisrYw4aIexkF7Fz3MBB+LuuqKktMVVdAJzawJA6No1M6B13h4qC3oQ6CcoskghL9sguYYGigKjmdvzl0VJTYwFx70zQpn4E2AqQi8/1zCuCpL6wdbmxbFFv920LaFjddLQRKDQ745xTCU7/tbYtoXKZOKlSU2hVvA+H7baunDhNNVdxKbrz/0PX3IK0Q3S3kb4dmhmwa54EfbicgLl+4cN8SRTGxLKJeHF13N5Qmp6BWjpAmJI+BTzeUp6WjIDwcj2prUZaRgbzAIBj17N5sFHRNeTkUm1k8rLutPsqqahCQ0lh/dxPyWNviayNffLMx0MDqMe54/WAQGzSTJjS9CPE5pZjb0xoaKopM5J/bwwaRmcWIyhRum0bnQmJxGfLF2k5qp+l8p1l0QtFVUWYDXx1NWVwMNJxcJGxdNF3dUZ2bg5pWBBrJIv/WDVRlZsBkwuS2ly82FlqukiK0lqs7yuLab8Mhoq6mBkXBD1GZkQ6dLl3btI2ksDhmk2Ng0fgM69DVBY/qHiElomPvYeVFZVDRaL5PSdhY6sLYUAN3/Bst26qqaplI381LvvgmjqmxJvr1sMKB443BWxrqSujra4V/mrGZFIKrXn2/5aFYv48E+LSycri3o99nq6UBS011XElvXz/L18sUZeXVCBSLCr8XlMZE5W5S/XR5jBvqCHVVJRw6Fdnku3UrByLwzAKc3D0DS+Z0YbN3W0M3U11mJZor1m+5k5YPVSUFFuksBHtddYy0N8L5xJx2pRGCu4sxsx+686BxtiJF4EdE5cCns+z6pPP1wtU4TB7rBkMDdagoK+C5aV6oqKzB9dstB4I2h6+ZLu6kST733UrJRzdTHcEzJobYGsBRXwPn4+XXzUx3M/a9eP/yWcdGRw3GGiq4K1Z/VbWP4J9ZJLj+NJUV8aKnJbLLquCfWSjRZ/tuqBs2ByQhvrD9fZv40Hg4d5XUM1y6uSA1NhVVFW0PCJLmzum7cOrqxNwIOAIgLegZfKmqqjJhXvwlbcNDeHh4MD1POvCZgnrpO1mMHz8eK1euhJubW8OLBo1MTU3h6Nh2y+oOs+IhsZSEdSokibUECfIiwdPZ2Rn6+vpMSKVI5pYg4XbdunUs0p+ET2Lo0KFMNJWGKuP333+XmDWwd+9epKSkwMioXkigiGcqAw0+zJs3ryEtlfftt99m/1P0uJ6eHovsHjVqVMPiByJo0IJGV2jAYcmSJewzima/cOECYmJiGryVaBtTp05tU3laQ3NlJ692Wo35/v376N69/mGKovbLyso6tI6E7L8sWqpXEXROkTgu4rnnnmPCNgn3FKVP0KwDOtfoYrhy5QorEw3+iMpDA00UZS+N9LZpEMTOzg579uxp+IwWvujRowc+/vhjdv6Kzk0aURNdeHTuU+S9CJqKI36O08wBurh37NiBzz//nH327bffsvLRLBSCyp+amoo///yzId/XX38tqDztgSLrS4vKWKS+OFq6WijKk+9d3xJKykrw7u2B8/suwd7dFkYWRvC/EsDsezS0hItc4mIyId1ZoojWljq1fcz1sGWoFxNHiiqr8e61cKRJTQNsLSa6akjMkuzc5ZbUb9NYRw05RU23X11Th++OheDonSQmRMzoZ4edK/phwaYbuBZaH91BAwPxmcVYPbMLJve2YbMC7kVlY/3hYKS3QtChqfpUN+LQe01lJagrKqBcQNTfci9bFoFyOaVRoC2trsV3AXG4mpoLZUUFvORmhe1DOuPFC4FMLBUC+VPTAx7ZgYhD7/Wb8adtLf1MDaGppIQzqbLtS1qDiZ4acoslj2lecf0AqJGeGqJk+DDbmGgxn/g3p3nhuSGObBAoICYH6/cFsUj2tkL2DoT08SUrFEOB9aevqowX3ayZ3Yt4hMyHvs64mpbL7JpU5ESutISxnjryH18LIijCmKLs6TtZUF0Vl1ejh6sJ3vjQG3qaKohKKcT3hx5KWPF0BEbqKjLbEcLwcTvTEmTFM9DSAO9eb+zEXUzOwdf93THGzhgXknKYz/4L7pYNv0lrVgiBjmFqqeT5JLpWDFRVBNm+TLWvt5Y5k5zVrjQyy0dtS5Nrt4bNdhDatix2s2XnBEUndyS6j9uWpuWrZnXXVijyPzivCC84WTProJyKKoy3NoWNljoUWmGxb6JZX4ZcKeu4vLJq5osvj5X9HJBbVo0/g9u25oA8qgoLoecuGemsTFOJHz1CdXERFFWN2pRH39MDdjOmMcseEvYJk759YD1hvNyypJw6g9ryMhiL9Q2lMdFWZXXVpG2pqIGxtmzxU1mxEzbN7IIfL8cgNrsULiZN+wsV1XVY9Icffp3XHUv6189CjM0uwUu/PUBVKyLkDWTcd8WvXSH4Gumij6k+1vg1FQfbS01hATSMJWcVKmrVi6o1RYVQauN0cBLJs/4+BLs330MnxbY7uFYXFrJIfYnyaWuhrrKS2fw0N+jTEjUlJQh/7+360VYFRZhPm8FmA7QFisrXlOozq+tosgGTknzZEfttgQYJQq76YdyKmS2mJVGfyJPqJ+bnV8DKQphwPm2sKxO3T4v5mVuaaUNJSYHZYxzYNgVO9vrIyCzB3r/D8PuhEMH7YqBSf/5LBwzQe1rzpa2Yq9efE+S/v7VvN+bJn1ZWgUPxKa3y2Dc21ER+oWSgHUXWF5VUwthQdr9Fmunj3HDtbjIysiVvCmevxWHPoRAWhd6zqwUT+c1MtPDpDzeFl09dBblSwqSoH0Oia3P8Ma4LuprosNmlFxJz8I0M6x4haVqDiVH9QGZenuRzQV5+BYwM5Q9yvvvxeWz/fjzunFvUMBiwYuVppKYXt688GipN77vl1dBSUYKGkgLKamS38+Qnf29+X/b8SIPCX9yKw7Vk2TNVOptow81QC+tudtxA5NOA8eOBRVnnn5V2823yeEdjfDXIjdUfiforLoQiX8za7s0eduw47I9oXLegPRTmFcHFRzLwQEtPkwUbFhcUw9DMsN2/QXY/ZPEzb1Wj7sP5/42JiQkLet62bRvTGymI9Z9//mEaKwW2iyDnFCUlpYbAY3qJQ84ppB9KB/p2JIJ7aCRcZmZmsqhicTQ0Wi/iERTdnJOTw8RmccGUIpilka6Yy5cvs0qlSHKRbQn9pSkRZH8iT2BWV1dnIndGRqMQFBkZiZ9++olFsxcVFbGo89jYxkb73r17TAwXXzBBNCjQlvK0hubKfvXqVdja2jaI+tLHo6PqSMj+y6KlehUhPQhE011mzpzJxHwS9ikfRczv27ePfU8WOU5OThLlIWFdFtLbpjqhSHq6OKku6EWiPVkWUXlpNgNhYGAgMZpmY2PDZijQFB2RT9aJEyfYAAmJ9bSuRFxcnIT3FtWb+LktqjdxYV9oeVpa6KOqshoqciI95dn6yJuO1hpeWDkbR34+ju/f2oKy4nK4+jhj0KT+zGu/oyBv/5ZKeju9AD3+ugE9VWXMcDHHj4M9MP/cQ4Tmtm8BU2koWougUVtZ+MflspeILacimDf/klGuDcI+VfuIrpbYcT4Kw1afha6GMr6c1x07VvTDhM8vtCvyXHSs5ZVPnGmOZpjuZI53boZLCIkXUiQflNb7x6KzkQ5mOVvgS7+2TWtvLJ8QV2jhjLYyQ2BuAbPheBKIXM3klZm85Xu5GyMpqwRjPjjDphmvfcEHu1cOwugPzkhE/7cF6VOBHV4BFUgC7Dd93JFTXoUfghof3ibZm7LFUD++1/GCkqh8zdWVhqoiZg91xPz1l1FQWoVF49yx693BmPDhGTZA8iQRXVdCzj8XPU183d8Nf0WkMUseEfT/F/dj8LK3DT7t7cIsknaHpWB1L2c8anK02lg+AQUkUfBVL3t8Hxwnd2FXIWlaVz7R/rVcwMm2Zmyx0FX3w5sI8E+K9tY/sTYgAsvd7bG1b2dmq3I7Kw9nU7M6ZJZR/X1Mdt31s9bHFHdTjPr9Pv4NGu4Pj9qeJ+v2HcQfONTgsV+emYnoHbsQ98dfcHyhqQd59r37SD5+HM4L5jNrno7sB7w93AU5JVX44578iE8jTRXsXdgTZ0IzsfFyDNuft4Y7Y+/CXhi7+QaKKoR57Msum/C2xVFHA5/4uuJQfHqHD3rJQ9TXa+sVQgM35NNvPG4SVM2E21IJRUh/RQhKWlrw2rgVdRUVKAoNRuofv0FBTQ0GfeUvqtgWOsooMzc1C/s+24HOQ7rDZ1SfdrbNwupw2jg3HD8fg3Kx853uzcSK+d3x5toLCI3MZh7y368dzqLZ9x4L+1f6LfIQ+XHPdrDGt8GRyCivYIvzruzsihL/GgTktm92Lpt5LKCAjrZ68PU2w9JVTW2t3vui0db0wo0EGOip4bN3BuLbbXdRVt6OtkVgunmngqCsoAB3Q0181t8F3w5xwxuXwludpiOg5xB5lzSda7s2TURpWTWGTf6NifpTx7sx3/05i48gLLJx3YKOQNRvaa6NKaisgfu2a9BUUcIQGwN8OcQFpdU1OBSRKTNaP6mwHDfFZpT9l6F7W0vN84nYbJyOy2ZBMwu8rbBjtDemHwtAQlE5elvoYaKjCSYe7ThNQBadRBZyjzouWl9DWwOd+3fumA1y/hP89NNPGDJkCNOpSYMkdw/y4u/SpUtDmmPHjkFNTY0J+/8rBAv7paX1D4fyvIRai8jqReRbJEL6PUFWJ9J5KXpbFB0tjqWlpA+ssrKk4EkNvEgAo8EFErUpSpwOAkWiUwQ8Wf+IIFFaukwkftOITFvK0xqaKzsdj+aORUfVkZD9l0ZIvco7tgTZLdHMAbLFOXToEBPTaTaCaL+ky0MXkazyyDpvSESXnjVAkI1Uc/VBiOqELKPIioqi6slCispD9kHi9kSy6k160Q2h5WlpoY95b83Bi2/PRdj9CGx5v3GhrNlvTMOACX2hrqWOkgJJkZtGtrVbabEijaaOJhP3xaFFeo3MWz9iTiKkKNJYHAM1ZYmpqfKgyHNK9/PDJAy2MsBUJzOE5rZdiKaIfAOpCEHDxxY8OUXC7LWI8JQCTO/buAhoVmEFisqq8NXheh92Eji/OhKMox8MhZO5DqIECpwkxtNAhjgUDU/WFy3ZX5DA+2YXB3x4O5L58rcELcZqrSU8kq6oqppFWVJ0rTh6KsodJvSZqquii4EuvgzqGJE6p7ACRlIRtS0d76yCcib+f/K7P5t5Qaz7KwCXvx2PLg4GuCu2uFprEHnN0rVAkcPii3Y9zG3+/KCI6u/7ebAHuBXXgyWiq7sZ6TLLmMuT6i3NRJA1z2wnCyy68lBQ+XILK5hFkfTDt56WCqtHeXVFi9V9u79xNgP9P3OQA4b7WnaosE/tgJ2ORpN2pP675qfqOulp4Odh3jibmINv/ZtGtB2OyWAvEf0s6mdVpZYIbxPIt52uBXFE7V5L0fq9TPTweQ83bA1NkLvQrpA0zZevGnoqSk3KR+0KLYzbHONtTLHC05556d+Vs4h3eyh83LboyWpbWrHArSyo7j8PjJL47OseHkgvE35scx5HDBqoS5bPUEOFRbPJoqeVLow1VfDg5cbrkmYlvNXHHgt9rND1J+FRn9KQR351iWQkZBX1vTt1grKOdpvzpF+8xHz3DbvVW51o29nBatxYRO/azSL5xSOvcx74sc8dX3gexr3kR+uztCWVMNCQrDvSHfXVlZl4L4tedgbobKmLmE8kA06OvtwHJ0Iy8MbBIEzobM6Em7UnwxqE+I+PhyJk9UiM9zbHX/eTBV+7tlrqMq/dls4/B20NfN/bC5fScrBZzkK77UVJRwe1YjaaRM3j90rabXt2o1kWlakpyDi4l70Y1Bd+9Ahhry6B5byF0O3RS2D5tBvKI14+BVVV9movncgfWVMT+j17oyw6GrlXLrUo7G9esg55afUBDSZ25li6eSU09bRRWijZZy4vLmN2UlodMNCXl5aNPe9vhkMXF7aQrhByH0dG69OsuMTGttVQXx25+S3Ppuzb3RLWFjoSNjxEVm59Xvr8XkD9jKEL1xNw5nIcxg5zEizsF1TVX5/U7xP3y6f3YQVtv7+LtnUoIQWxxfU6xKmUDAy1MMEAUyPBwj7Vkb5UH4+EZj2agStgtixF62fmlOKKAMuYiNhctm0bCx1ExApb/ym3vAoOUjMeDUX9lvLm2xZq08jyLTCrGN/dj8dPI71grB7LfM9bk6Y15Dw+Hw301ZFX0HiPpPfyBPqu3mbo1tmcLa6b9NiOdPfeIEwc44o5073x0Tr5Puktlqe8CgZSMzLpvkuzkOnV0vNjUWUN/o7OQg8LXbzgZdlE2Kd1ciY4mWBbQOsXDX/aoXNPNCMtDo3XgiHdd1s490T1l1VWhfV34zDCzgiTXUzxw4MEtlYBLdJ8Y05jkCJF9r/qY4sXvSzR+4/bzUbNr3vxi4b3Q2YMxsQlE5mvfolU21xSUMx0Gi2pWVZtgRZBv3fuHnqM6A4lqX4wpxme/PrO/3NcXV1Z0C1Zg1OgL2mAZB8uDjnQNKePklYoHkz9JBB81jo4OLALhzzv6X9ZUIS4UMiChKCIbiurxsVNyGKlpZ2maOq0tDRWoSQatxVaKNXMzAw///xzw2eBgYESaWhfKRqbvOdFgi9Fnot7wHdUeVoDRa1Tuch6R9asiY4qk5D9b0u9NgdFqtMFRPZLZCdEtkGiC4VGyeLj49nviz4TvW8JqhOadSLEKqo5aCFesqAS98KnC1x8oIXqjRoAcaTft6U8tKiHtAf/rdzL7K97d1dsPPd1k+vR0dMO0UGxGPncsIbvovxj4ODZKDh3BOWlFQi5G44Rs4a0Om9RVQ0SCsvQ3VRXIkq2h6keTsa3zkqCRMb2BoH5x+Zi9kB7th1RsGofVxOk5JQycV4orha6yBZL7xebi77ukhGLjTNqhJfvYU4RfIwlp9V3N9FFSG5xi6L+u90c8dHdSFxJExYx6KCjibhWRP3SgmbRRSXorK+Dc6mNneOuhroI6aBp7KMsTdmCg7eyOibq0T8mF+/M8Gaen1XV9WJ4Hw9TZi8jT3T2i87BrMEOEsdN9H971ocKzStGTd0jdDPSwfnHMyhM1FVgqamGh3IW7hOJ+hv6eUJNSRGvXgthC6iK88n9KHz2oFG4JPH/yuS+7PMLycIHIWi/NdSU4O1ggOC4+ofWnu7GzFPWP1r21Hi/xw96FB0nDr3r4LXCEZRdhBnO5myKNUVjsfKZ6jHLjfA8+bN4HHU1sH2YN7Pcoch8IYy2NUZsQSmSHts2CSE4rxgT7cxYP1i0675GekxAbm7goaexHr7o6Y7t4Yk4GJfW5jQtQQsx07Uqjo+RLsLym29bxtmY4g0vB3zqH4nrGe1bzLq5toUWwKVBvdMpjfcFH0M9POxAiwxCX0WZ/c6mMOGWBfH55Uzc722lh3uP7btUFDvB10IXG+/I9uYmX/2NdxIlPru1qDf+Ck7Hprvt8/PWdnREfkioxGeFEZHQtLKEohwhVVieTk0e5Bp83cVuvjl+/ojasQuOc5+Daf+WI6f9kgqgoaLEhPqHj+uPhHtqW/ySZA9CT9t+WyIik6x4Tq/oj+m/3EHQ420wHVoqoI+1OzRLshXT5OjamGRrztYvIfsm0bVBbUuklNggjj2J+n08WZT+huD2WWA0h7q9EwpuXmMCtOh4lEaGQ9nAEMptfA5Q0tKG+8ZtEp/lnD+DvCsX4bLuG+poCt4WLdRbGi05eFYSGQF1u/rny46E6kAIy3+ihfIkZyRZu9vj+r5zyM/Ihf5ja4f4oCh2blu5ta/fnJeew0R9Wy9HTH77eYn1EJojIaWQLWrbs6s5HgTV21ooKyswf/0te1qOiJ0+3g0hkdkIi5K8R+cXVCA+uaBhRqr4LCh5M35lEVlYzK4lb30dXM2o/w0jVRWYa6ghvB3CflxxKVuPonHWmHgZheMfksk84b3djBH8OOiiRxdzZkPkH9L8ADi1P5NHOuPAiQhBC+O62NcvFpydJ9xeMyCrCLPdLST6LRTxTG1LaI5wmxqy2mF0al+algiLyEZ5RTV6+lgi5vFCzDraqnBzMcKfB2UHiTScT1LHkk49mqneHvwzitDTQrLf0sdSD4EZrTv3FOU8P451NGbi/qGI9lt/Pm0kFJYzcb+nmS4eZBQ2rDlA/vpbA5JaX3+P/9/in4ifAiT7Npdm98KBiHT8FNj8dsne99szjWtGiu4P9h72iLgvOTgZFRANCwdzqMpZtLw1hN8PR2FOIfqMbfssKs5/F21tbQnrHWlk2YKLI1on9kkiuEdG9ixkw0MCJtmHEFVVVfjll18kPIjImkQI5B9OCwuQx75oMVeKzqaBg5YgexkSq5ctW9aQl24YtPJwUFCQ0F1iQixFhdOLSExMxHfffdfkING2N2zYwN6TVYr0asYdVZ7WQB73FDlPUfAiUZsWYT1+/HiHlknI/relXluCFq+lkS0/Pz8sWLBAojy0v+TXT1BngKbCCIEWGKZBh927d0tEzdM52Bpo/0JDQxs6ImQVRLY64tDCxuTTLxrQoGuGvLfaWx5ZC32IbHjoxie+qrvoRjhsxmCEPYjE7TP3UFFWgev/3EJsSDyGTh/UsN1zey/ijTGN6xEI4e75B7h/0Z8tWJObkYcdn+6BrqEOhk4biLawJzwVU5zM0Ndcjy3y+WpXW+iqKuFgVKM33+peTjgwrlvDYrlf9XeFq74m64RQVO7yLrZw1tPEybj2+XbvvR4HdRUlrJzqDW1aXMzNhAn9Oy80LjDWx9UYUT9NhfPjxVXfm+qNEV0toKOhzF6LRrhglI8ldl9szHPkdgLz339zoifUVRRhqqeGdyZ7ISy5ALGt6IDui06Dp4EWZjiaM+uVUTbG6Geuj7+iGtvfyfamuDWtH6sbYrydSaOonypbEP/Q1wk9THSZSEwLeb7ZxR72Oho4GNM6f8TDCakYZG6MviaGzNZipr0lzDXU8XdS43YWuthhz0D5iy3Kg/ZmpIUpLqRmMqGvIzh6MwHllbVY87wPdDVV0NXREAtGu+C389Goefwg52ath8hfZ6Cna/1iSmcfpCAtpwyr5nSFlroyDLVV8f7sLkjMLMHD+LYLmyQYnU7KwhJPW9hpq7Pj8G5XRySVlONmeqO49duwrnivW71lGC3e+F0/T3YukKhPgx7S0F7Qroheogdl+tuaRyoS8+9FZGH1Cz4wN9CApZEmPpjTDdeD05lvvoiHO6djyfj6mUfJ2aU4dTcJb8/swvKoqyritale0FJXwgW/xsXXOoJLyTnIKK3Aqh5OLJrWWU8Di72tcSw2AyWPBztooOTBc/0x1LpetLHXUWei/oWkXKy7J1vUJzFvpa8Dy6umqIDn3SwxytYYXz1onVD3T2IGu76WethBU0mReW5PtDPF/tjGa5fEwisT+jFBkKA0JNhvC0vE/ljZgr2QNEI4FJ8Gdz0tTLUzY9fuCEtj9DbRxwGxgYIJNqa4NK5vQ9syxtoEbz4W9a89IVFfxIG4VAwxN0J/UwNWvtkOlmwtgaMJjW3LEldb7B3s26rtTrQxQ18TA7ZP1prq+MTHjdkYiQ8gtARdUTv9U7DQx5pF/tH9a/UgJyZ4HQxtFAR+nuCJvdO7il2XjyReDddlO5s38yGDUZmbi+QTJ9nCtbTAbfadu7AYMbwhTa6fP24uWYbK/HzBeQx9uiL77n02AECLlZalpiHl5Gnoebg3iP+5AYGI+mUnHJ+fA9MBwoIXSMynxXI/HusOc101WOmpY9VoN1yLyUFUVqNwHrp6BF7uX2/HSHVE9Sv+Iuiv6PZwJTqbRQquGuUGbVUl6KgpYfVYdzaAejNW+ODw9YxcZJVX4k1vR9YeUBT+iy7WOJmc2bAIPa1/Q9fGQLN6cY8i/EWi/nfBT9abWb//QNRVVSLr78Ms0r4kIpwJ/QZDG+0qSeinSPuKNGHPaqJIeInX4+ue/d8KQd5o6HCUkQ3rlUvMUz//3h0Uh4TAaHhj+fKuX0PwKy+jrlr4DJyU33ejJCqSbbO2vBz5d2+j4N4d6PduWZxRUFSAgqLi49fjYBgfNxa9f2rLQea3n5OSicu/nYLXwG7QfjzoWVVRiU/Hv4mAc3cEl5MGCva8v4mJ+lPeeaHh94RA5/KeAw/x4szO8O1sBh1tFax6tS/rnxwVW8x10+cjsecHybUuKO2IAfY48I9s65Vf/gjErEke6OJpAmUlBQzoZY1Rgx1w8qLwWa/U57iYnoXnnWxZ+0mzqJa7OyK1rBz3cxr7LZt6d8UKd+ELB1bV1eF4Uhqm21ux7VL7PNzCBC662rj+eABBCCTm3w9Kx4ev9oWZiSYszbTw/vLeuHE/GdGPhWmCFsBd/FyjtQIxtJ8tDPTUcehkRJPtjhnigEWzu8DCVIvZMQ7oaY23FvfEiQsxbCBGKOR7n15SgY/7OrPZmS76mlja1QaHo8T6LRoqCJ4/ACNs6/stFPU8yckURurKrF7IQ//N7va4lZrfMENMSJq2QAve7j0cgmULesDD1Qh6umpY+94g5OaW4fSFxvPmj21T8MMXo9n/FMmfnFqI917vzxZyVlVVZIvnerub4MLV9s1i+vVhKrqYaOMFLwu2kOskZxMMtjHAzqDG/uVsD3NELR3YsL7U+iEu6GWhyzz46ZlzsosJizY/EinbhudyYh6LTP+vQbfJPSGpmOdlCV9THeioKOGD3o7s/ng0qrHfsnGYO3aP8Wb/m2uq4rP+znDW12DnFZ2bH/Z2ZFH+J2OzZD5ziMbE2ECOgL6NuJ4hClQcMKk/8jLzcfaPc6igYMJbIfC76IfB0wc35Au6HoQ3R7yFguyCNtnw2HvZw8xO2ILaHM7TRqvmmZCIT4IlRSPTIp8kVtJCACJoMQBaJLRr167MhuSvv/5i/uTy2LhxI0aPHs0WzDU2NmYXLkXrNzeNgSBrlrNnz7JIbooMpyhuEo9pW8OGNUYltwQNVJCtCnnI0z6RhQzlJ4FcBInjO3fuZPtNi6OSBQ55LJH9S0eXpzVQuU6ePIm5c+ey3zQ3N2dWMFTnHVkmIfvflnptCVqslha+7dOnD1s8WXxAiM5DEvu3b9/OykN2NjSboKXzZsyYMSwvRbzTABXVEUXMS0fAtwQNbFA9Up3SbAmaNTFgwACJNJMnT8b8+fOZFxfNPqDZE5RHtFZAR5anJdx8XTDvvedwYvcZ/P7NPmaVs+Cj5+Hk3Tjzpt7fv1Heiw2Ow4Y3trD/aQDjrw0HsHfDQfQY7oOXPqj30PXq5Y5DW/9m39FDUZd+Xnjx/TltHjU/HJ0BHWUlfNrXhU0JjCkoxfKLoRIL4VI0AD2gE+U1dTiTmIMPezrB1UATFTV1iMgvwZILwfDLal/0ZmZBBRZsvIGPZ3fFwuHOyC+pwrYzkdhzqbHDSlNtKZJH9Fy773oc3pjoiS9e8IWKkgJi0ouwZMtNXHgcXUUUl9fghQ3XsXZOV/iPmojSyhrcDM/Cyt0PWiXihOWXYNXtSCz3tsVb3RyQWVaJL/1icUvMWkfhcV2JHrsXedhARVEBX/RuvJ6Io3Hp+CagXpw8HJvOBOWufXWYwBSVX4qlVx7iYQszAaS5lpkD3QhlLHOzh6GqKlLKyvBJQDgSSxqnjCs8Pp4iehsb4OOujRZUq+n/R8CxpDRsj2zs9Psa6cNYXRVnxGYDtJfismq8+PVVfPqiL+5umsQWej14NQ4bjzZGrlJRxY83Rfa/9M1VfDLPF/c2TUJFdS3uRWZj/jdX2+2v/21ALN7oYo8dQ7pARUEB/jmFePNGqMRCuFR3ovrzNdZlMzjo+1PjJW0RXrgQgPhiYQsfC2XFjzfwyfzuOP/tOCY4XApIxZrdDyTSkJ2IuObz3ra7+OgFH5z+agyz5YlILsCCr68iTmzRtF/fG4y+nqYsH6WJ/G0W+3zEOyfZWgZCqKp7hOWXQ5iwf25KT1TU1uFUQha+9ZMU4OnaUHh8dcx0sWAeoWThRS8RWWWVGPv3/YYBF1pAevfILmyx3LC8Yiy/FIIHWU0XVm4Osld6504o3vB2xCxHS2Yv82d0Kg7FNbYT1MSJ2jnieWcrNhNjuac9e4nwyynA27dDBacRQnhBCVvYc4m7LV71cmBC5rcPY3Enq7Ft6STVtrzkYs3alrW+km3L8cQMfB9SX+92WurYNah+UJbyvuxhhyXudriTlYdV95sKJvK4kpELPdV4rPBwYBGhyaXlWO0fjgTxtkXs2iBIsP/Up7FsJNpT23I4IQ0/RdRHxd/IzMWrHg74qKsLKmprcTUjFzsiEyWuOSFsvZfIBp23TfRiwn5wVjFeOBKEfDFLOSpba5e5WeRjjVUDG+/ZZ16oHxRdczkavwfJHshRNzOFx2srEL//IJL+OQEVXR3YTZvCFrqViKKkIIVHwvNYjh7FInzj/vwLlXn5UNLShL63N0snIuXkKTyqqUHMb3+wlwhDn25wW9rUflDE8r0B+GyCJy69PpBFDV+kQcTjYU2v3VZUYEJuGeb//gDvDHfB7ZVDWJsVnlHEPkspEC6+Udvyzt1QvOXtiCMjerC25UJKNjaHNd6fqFTs2nh8/k22M2ML6463MWMvEdkVlZh10a/h/V9DfWCmrsbOCzp/aXCAGHryluDyKevpw2b568wyJ/fSeShqasFwxBgYDhne9Hg/hgT0iLdeefymDuXxsci/dR2qpmZw/OhTdCQadvawWbwEmceOIv3APigbGMBy7vPQ8fIWK1+dRPloACJ63eNy1NUh4+hhZBw7Am0vb9gtW8E+Nhg4GFkn/kFpbAw7j1VNTWA59wXo95a0nRMKCe5z1i7ByS0H8cP8T6CopAiPAV0x5mWxNeZoFkhdnUSk+/bXvkVGXGp9HT96xIR/YuX+L6Cmqc4GAYqyCxByLYC9RKioq+L9g+tbLNe2PwKgpqaEzetGMbE+NDIHC986KbEoLEVjSw8YTBrpwga6jp9vDDIRhwRrTQ1l/PjpCBgZaCA1oxjf/nwX+/9unQf7T+GxWOJqj+96dmH3g4d5hVjjL9lvoetW5JtPLHNzwBgr84bo8cND64/ZmoDQBpud32Pqo37X9/CGhqISUsrK8fXDSATmtU64W7H6HD55awDO/TGrvt9yKxGffH9DIo2SYqcma5DNGOeK236pSJaxwOvVO0lYOLsLfv9hPEyNNJGaWYLfj4Rg535h1oYiqmofYfHZYKzp64wrs3ux55kTsVlYfzdWbttyPCYLy7ra4I3udiyIIaO0Eqfjs7HjYaNdjJA0beWbjbfYsdyzdQo0NJQR8DAd81f8LbGGg6KiAhQV68tbWVmLRa8dx8rX++L43uegrqaMhOQCrFx7HtduSUZ2t5aHWcV49VwY3u3tgI/7OyGtpAIfXY3GlaS8pv2qx4f3z5A0vN7DDt3NddlH8YVlWHU1CkelhH07XXX0tNDDwpMtB552BONG+GLftvq2g9i77U3WpmzZdQbvf954L+1ItgclQ11JEZuGe0BHVQmhOSVYdCZYYiFc1m95fG2kl1biXnoh1g90hbO+JoqraxCcXYy5J4IQJcAarK2YWJtgybrFOPrTMZzZcwbaBtqYsHg8eo5sdPqgNll8BkhRXjHWzFrD/qfPMxMzcfnAZdh62OKNH19vSFecX4zQO6GY/Vb9cweH8yzS6VFr5to9hkRKEvVJcJXlYx4TE8MEV19fXxZV3hwUAR4SEsKmN5BQSkIuCZ5r1tRfhKLvaKFYWZBNDC0ISwMN4h7mdPHeunWLLWog/jl5vdO2aHaBCCovLdJKAizlo1kD0vYotF9kpUIWQjR74fbt22z/SWgWUp6WuHHjBry9vZm425qy0+GjhY0pD5WHRjafRB21tP/i5RdSr/J+WwTNBrGwsMD69etZ9L405GFPNk5UHorgp4ENio6n7bW0bbIVCg8PZ3VFdSLuqZ+dnc2sfcTtoEpKSti26dwU1S8tYEv1Tu/JD58GTegzaW98WjE7NzeX/Q7tE53P0otBN1ceIVxKO4X2whbvrXsk8UBA16Y01KFsjeUW8cbF9nuSNvz+Y4G1vZGM4pRcb7tATA9RrZnK3xaMRrQ9coD606IoCTpqsoLrqPjt2QN9nXYcS6kFwmRpNSIbBXFoX4REmcf8KTwq8X9xvI0ntX0NFqoDcXuJx89OTWhu5rj4+SGL7CNtf/ijBz5Z0+ebg85P8Yd+EfLqXHtso41faxHf907yzj2xxTHbgpZWpydaPvFrV0gaaVojjnZE26LYyvLJ6M488bZFKLERVU/sPqYg5zvxwQoR8gYehvcQdmxFQi9FX3e4FYqsstFUfQUFXDorXFAXWj8d2U7b9NJ5sm0LVbvYe3nXkLz22VC9bdYVso43LZAr7zjJ3QYtktlMX1BR4dH/rHxCmGrbfvGprraWlUEktJI/s6wWhQJfCDYQIPN6lRTj177YdgsuKgqVR9z2TtZn7cH5Y0lf4fb0W0RtoTTtKWrU56FtL59C03qiz9gzUgd1/ZRelL2GmhBa6rN1BDU/dZzTQEfXHVG7oPO/Xn+tyZe+vtGGuK3QgIg0dF62QbKTwObjZf/T5++W7t0/jhZu+yoPqiPSgcT1MJl6hlS7KyufNKOtxrS7fP9F7DZfxbNIwopG54r/18J+RyGyKaEIf4KsS8i76N69e+jevfUWDZz/Drt27cLKlSuZYC49eESrTlOUPs0mIFGf/O4vXrzIvP+ba5D/q3SEsP8k6Uhh/0nQHmH/36A9wv6/QVuF/X+Djhb2O5r2CPv/Bu0R9v8N2iPs/xu0R9j/N2iPsP9v8DTfztsj7P8bCBX2/1e0R9j/N2iPsP9v0FZh/9+ircL+v0VHCPtPivYI+/8G7RH2/w3aI+z/G7RH2P836Ehh/0nQHmH/36AjhP0nRXuE/X+DjhD2nyRc2JcNF/afHp7Yks/ktT9rlvzpLOT1TvY7ZFlC0dc0mpaRkYEtW7b850R9sl+R9lcXQVY1v/32279epqcVmhVAtj+0DgCdC9KiPqGiogJPT092/tB5ZmBgwNZn+P8o6nM4HA6Hw+FwOBwOh8PhcDic/388MWHf0NCQ2ag09z35tJPtC3mwk3WPk5MTWxz0vwb5r1tZyY4slCVc/3/G0tKSLbRLVkDy6ozWcaBzhgYBaC0HStuaBbw4HA6Hw+FwOBwOh8PhcDgcDudZ5okJ+yTaS/vUy4N80v/rYjW9OC1DQr20B70syIfey+vpng7K4XA4HA6Hw+FwOBwOh8Ph/JfgsbVPD21feYjD4XA4HA6Hw+FwOBwOh8PhcDgczr8OF/Y5HA6Hw+FwOBwOh8PhcDgcDofDeYZ4YlY8HA6Hw+FwOBwOh8PhcDgcDofD+e/ArXieHnjEPofD4XA4HA6Hw+FwOBwOh8PhcDjPEFzY53A4HA6Hw+FwOBwOh8PhcDgcDucZggv7HA6Hw+FwOBwOh8PhcDgcDofD4TxDcI99DofD4XA4HA6Hw+FwOBwOh8PhtEgnbrL/1MAj9jkcDofD4XA4HA6Hw+FwOBwOh8N5huAR+xzOM869bGU8zfS2r8XTTISeOZ5mZjuW4mlmd6gGnlYUBzzdx/Y5tzI8zfzYzwJPMz42T3fb4qRdjaeZzybuwtPM4v1L8LRi0OXp7j676VbgacZ8sgqeZmoePd3152v4dLctCSWKeJqpfdQJTytm73njaWac1dPdJ81Y5oGnmXmdy/E0c8i4G55mpjo83fV31HoFnlamOTzd127to/91CTicZ5un+8mEw+FwOBwOh8PhcDgcDofD4XA4TwXciefpgVvxcDgcDofD4XA4HA6Hw+FwOBwOh/MMwYV9DofD4XA4HA6Hw+FwOBwOh8PhcJ4huLDP4XA4HA6Hw+FwOBwOh8PhcDgczjME99jncDgcDofD4XA4HA6Hw+FwOBxOi3CP/acHHrHP4XA4HA6Hw+FwOBwOh8PhcDgczjMEF/Y5HA6Hw+FwOBwOh8PhcDgcDofDeYbgwj6Hw+FwOBwOh8PhcDgcDofD4XA4zxBc2Odw2sC5c+cwadIkXnccDofD4XA4HA6Hw+FwOJz/N3RSeDZf/0X44rn/T7hy5Qq++uornD59+n9dlP8EWVlZuHv3rqC0ZWVlmD17NvLy8nD+/Hmoq6uzz//880/89NNPMvOcOHECenp6bSpbZUkp7v56CMn+oWxBE+vu3uj10nSoaKi3K09KQChCTlxCblwylNRUYOHlCp/nJkDToL6cSQ8e4tK3O5ps+xGAbh+sRHF8PFLOX0RVUTE0LS3gOGsGdJ0cm92X1IuXWsxTnpmF+CPHkB8RAUUVFZgPHACbsaPRSVGRfV9dWor4w8eQFxyMmtIyqBoawGLwQFgOG9rsb7vqauFVL3s46WiioLIaRxPSsT8uTW56Q1VlzHK0RF9TA+irKCOxpAx/xaTiRmaeRLqexnqYbGeGnsb6OJmUie9D4tAa4v1CceuP4yhIz4aOsQF6zBgFt4Hd25Xn8MebkBoa0ySfmYsdZn75Jvu/rrYO9w+dRcTV+yjNL4S6rjacenfFo64z0ElZuUneCXammOdmCRN1VSQUlWHTwwQ8yC6UW0Z3fS0872qJrkY66IROCMkrxtbgBCQUlzekWeZlixdcrSTyZZVXYvKpB2gtS7taY5abOXRVlRCSU4J1t2MRmVcqN72DrjqWdrNBHws9KCt0QnhuKTb7J8Ivs0hm+s/6O2Oaqxk2+iXg58BkQWWqra7G7d/+QfRNP9RWVcPSyxn9F82AtpF+u/Okhkbj/r5TyIlPgaahHrrPGA3n/r4N3x9892vkxKdK5FHw6Ie6YS80+U2qs4/7O2GIrQG7vi/G5+KzmzEorqqVW85BNgZY1MUKnsZaKKupxa2UAnx3Nx6ZpVXs+2F2hvhptKfMvNOPBOBhVjGEUpGdg7i9+1AUFQ0FVRUY9+oF26lToKCk2K48uYFBSDl1GuXpGVBQVoaOkyNsp02FuqkJ2kJdbS0C9p9A7LV7qKmohImrA3rOnwEdM+N25clLSMHDo2eRGV5/TRs62KDbzPEwdLBGR6KkpIjJY3pg8fMj0Ke7Cz76ci827jjVob9RkV+AiD/3Izc0AgrKSjDt4QPXWdNYW98ReapKSnB7zReozC/AgK8/g7qRYZvSiHj06BGyz5xE/s1rqC0thbqNHcxnzIaalfy6rykpQcHtm8i7cRVVuTlwWrUGahaWEmkqM9KRfeYUSiLD8ai2hm3PZOxEaDo5QwjUTtz/42/E3fRDTVU1zL1c0GfBDGi10LYIzfOorg5nPt+C9NBoDH79JTj09Wn4Lis6AUFHzyErMg51NbVAJ9p2LXSszOE9ezJMPF1l/n6a30Pc/mG7zO+GrH0HBo52aA9VJaUI/O0g0gND0KlTJ5j7eKPrCzOgLNbviT13GVHHzqCyuIS91zI3RZeXZsHYQ3aZRXliz1xGZWExdKwt4D13GgycHRq+ry4rR/KNu4i/dB0laZno98FrMHJ3kdhG0O59SLh0Q+IzbSsLDP1ilaB9q62txcXfTiHw4n1UlVXAxtMB45ZNg6GF/LalOK8QD87cgd/Z2yjOKcS7f3wCLX0diTTpsSm4uv88EkNi2blu6WyDYfPGwsKp+baF+pYP/voHRRnZ0DIyQNdpo+A4oEeH5Ym/E4DLP/wKU1cHjPvkjRbTLPrmtWZ/uzi3EGe3HUJ8YBSUlJXg3r8rhi2YBGVV+e1Oekwy/E/dROg1PxhammDhj++2KU1zvOBshQk2ptBRUUZEQQk2hsQhrrhMbvruRrqY6WABN31tVNbWwT+nANvDk5BbWX/fJXSUlTDW2gQTbc1gpqGGBVcDkFDS2O9qiZrqatz87R9E3ahvJ6y8nDF4cfP9lpby5Cal488318vMO+qNF+A6oHuT9ufop1uREhwNlamLoezV9DzRUVHCOz4O6G9R32+5lpqL7/zjUFItu99C/byJDqaY6GAGO2115FRU4VRCFvaEpaDmEW2hns5G2ljmbQcXfU32PiKvBFsfJiA0r77NaImizBxc33kYaaEx7PxyHtgdvZ+fCMVm+itC8wSfuorQszdRnJMPEycb9F8wDYa2Fs2mqRkxF0rmkv1s0TPRa/RMpKvFnomO0DNRrGSfUdYzUT8zeiZSYc9Ef8ak4EaGrGcic/Qy0ceJpAx8H9y6Z6JEv1Dc++sfFKZnQ9vYAD7TR8G5hbalpTwRF2/j6s97m+Rb9Nd3UHz8vHN87UZW/9Io2TjAYEXTdpqusTlOVjBWo7oox89hCfDPlf9MRPX9nKMlOhvSMxEQml+MXyISWV4R6ooKGG5pjEl2ZnDQ1sTqBxG4KfXM2RzUhvsdPoewczdRWVIG48fniJGdZbvz5Cam4e6fx5EeEQdVTQ14jxuEzuMGsXstUVFcivCLdxB2/iaKMnMxa8P7gFfLfenYh7H4Z9vfyEzMgI6hDgZPH4K+E/rJTU/PrmF3QnHz+E3EBEZjyMyhGLtgnESa3z7bjYfXHzbJa2RlhPd3CbvncjhPA1zY/39CTk4Obt++/b8uxv9LVqxYgdjYWISFhbGHLRGDBg2Cra2tRNq33noLFRUVbRb1icvf70J1WQXGf/EO6+xe+X4Xrm7cjRHvL2tznvKCIkRfvoOu08bAwM4SZXkFuPXLPlz8ehsmrn+PpbH29ca8P7+X2O6tbXuR9DAKpampiDt0BO6LF0HH0QHJZ84ieMOP6P75WqgZGMgsU8aNmy3mqcjJRcAXX8Ggszd813zEhJvUi5dRFBcPXWcnliZqzx8oS0uH1+uvQs3QEPkhoQj/ZSeUNLVg2run3A7phj6eOJuchdX3I+Cmp4W1vq4or63DP4kZMvPMdbJCenkl3r8XhoKqaoyyMsFnPdzw7p1QPMip77x56mtjhoMF24aeijIUWrmUfFZsMk6s/wV9506A+5CeiLsXjHM//g51HS3YdnVrc54pa155PARTT3lxKX5dvAZOvTs3fOb/90X4/3MJ499bBFNnO+QmpeHEVztQk60A9bGzJX5zsIUh3vd1xKf3onE3Mx8znCywYYAHXjgfiEQxoV6cVzvb4XBsOjYExkGxUye81tkePw32xuyz/iisqmFpFDoBAdmFeO16SEM+secrwSzsbIVFna3x2oUwROeX4Y0edtg91hujDjxA0ePfkmZJV2tcSszF+jv15SORetdYb4w+8ADppZUSacc4GMHDSAv5FdUNnVghXN9xGMmB4Rj34VKoa2vhys/7cPKznzBzw3tQeDxQ1ZY8SQHhOPXlNvR6bjxGr1yEqvIK3P3zBBx6dW54SKHOb++5E9Bl4pCGbW+6ribzNzeP9ICWiiKmHg5gx2TzKA98P9wdi041HhdxjNSVMcPNDBsfJCI8pwRmWqr4fJAzto/xwqRD/izNxYRcuG+7JpHvi8Eu6Gulj+BWiPp1NTUI/eFHaJibo9una1BVUIjwLT/hUV0tHGbPanOe0uQURGz9GbaTJ8HsjYGoKStD7O9/ImzjJviu+wxtIWDfccRcvYshby+GprEB7u8+hPOfb8akDR9CSY5wLSTPwyNn4TCwB3ovmoXaymoEHjyJc59vxNSNn0BVSwMdxbRxvTBhVA988eMR7N64Agp0MnQgdC/y/2ELlDU10ffTD5kgGrh5G2orKuG9+KUOyROy8zdoWVqgIjdPbmMiJI2InAtn2ctm8TKomlsg859jiN+4AS5rP4eiRr3wI03Wyb/RSUkZppOmInnHzzLTZJ87De3OXWA2bQbbx5zzZ5Cw6Xs4r/kMKgbyBxpE3N51CKlB4Rj5wTKo6mjh5ra9OPvFVkz55n25bUtr8pBwr6CkxMr26FGdxHeBh8/AfdQAmLk7EUiIPAABAABJREFUwm/vcejbWqAoKx8+C59DzJkrcoV9Etqn7P5R4jP/nX8hKyQS+vY2aC93Nu5AdXkFhn66kpX77sYduLvlV/R/dzn7PvHqbYT8eYT97/38dJRkZCHp6m3EXbgmV9inPGH7jsF3+Xwm5kefOI9bX23C0K9WQ8Owvt8SffI8qkvL4PXcVNz+ZovMU4rKY+bbGT1WLGz4rDX3kgu7TyLw4j0899FC6Jno4+TPR7B71Va8tu0DueL02V3HoWesjyFzRuHYD/tkluvqvvPoMqw7Jq6YierKKlz6/TR+/WAL3tr1MdS1ZbctOXHJuPD1dnSfMwFOg3oh6UEwrm7+HWo6WrDs4t7uPMXZebi7+zBMXRzwqFby3GtNGvG63//JNqhpaWDxppWoKCnHoXU7UVVeiYlvPS8zD90/T27ci25j+qGTQiekRiS0KU1zzHa0ZK+PH0QgobgMi9xs8X0fT8y97C9TnKYAk+ecLLE/Ng2RgdHQVlbCu52d8F1vDyy8Fojax8d3gasNqurqsD0iEZ92d2vVeUZc3XEYiQHhmPhRfR/k0s/78PenP2HO9/L7LS3lMbQxx4oDGyTyPDhyAfcOnYWdj0eT7d0/fJ6J2nTs5LXR6/u5QVNZCS+eC2R9uPX93fF5H1e8cS1MZvohVkZw1tPEVw9ikFRcDjd9LXzRzw26KsrYEFAvPuupKGHzYC+cjM/CB7ciWH9oRRc7bBnijXF/30MpDWQ2Q211DY5/uhUG1maY/eMqlOUX4vT6X9g52n/htHblubXnGCIu38XQFXNZwAcJreEXbreY5vTJ69Ce9FyTZ6Lv+3rhTHIWPnoQAXf2TOSGippa/C3nmeh5Z2ukl1Xg/bthyH/8TPR5D3e8Q89E2QUNz0QzHS3xd0IG9FVb/0yUHZeMs19vR885E+A6uBcS7gfj0sb6dsJaTtsiJA+J1xQAM2fLGom84ufzuNUrJJ6bSKj+4+WPoerVOKgtYoCZAd7u7IgvA6NxP7sAU+3M8VUvdyy8FoQkOYNoyzxscSwhA5tC41i9LPewww99vPDilQAUVdc/p0y1N4eFhho2hcRjUz9vFpjXGgL/vojAYxcx6t0FMLA2x929J3B87WY8t3k1a//amofOoyOrvof70N4YtHQ2a+8Cjl1EfkoGy0Pc23eSPX/0mjsB577dxeq8JXLScvDLqm0YOHUQFn2+GLFBMfjzqz+grqWObkOa1jsR6ReJu2fuYMCUgSjIymftsDTPfzhP4vdrqmrwyew18OrrLbguOZyngf/oRIT/n+Tm5mLt2rUYPXo0nnvuOVy+fJl9HhAQgNWrV6OkpAT9+/dnr59/lv3wKGLMmDG4fv16w/s33niDfSYesU7bSUtLa/ht+o2RI0dixowZ+PXXXyUaSRK1Rb89fPhwLFu2DBERETLtbW7duoUFCxZgxIgRWLNmDcrLJW968fHxeOWVVzBs2DAWCX/27FmZ23nw4AEWL17M6mPVqlVs/4UgpKzSkGD/+uuvs3KTMC9i7969CAwMxIcfftgkj5WVVcPv0MvFxYWlpTK3FXoYSg+ORK/506FrbgI9SzP0fHEaUvxDUZCS3uY86no6GPLWQph5OLEofj0rc7iO6M+i9ynahqCHAer0iF7U+aQIKbMB/ZBy9gLM+veDkU9XqOjqwGHmdCiqqyPt8lW5+5J85nyLeeKPHoOyjg5c589jYr+ylhbsJk1oEPWJksREGPfwhZaVJZTU1dj/JOAVJ8h/qBpvY4bqujpsCo1nHdLbWfk4npSJOY7yoxg2hsbjYFwaUkor2EPW4fh0hOcXY4iFUUMairh4924YrmfkoflHS9kEnLgMEwdr+E4eBg1dbXiN6MsecvyPXWxXHgVFBYljF3W9Xmx1G9w48JEZkwQLd0dYd3aFiroqzF3t2XZqU5vWI0XeX0jOwdnkbBRU1eCXsCQmfs9ykowUEmf51RBcTMlFbkU1ssqr8KVfDAzVVOBjrCuRjloVeiAVvVpbj9TvXeBthT0hqbiVVoDs8ip8cjMaqooKmOZqKjff+1ejcCY+B3kV1SzP1oAkqCspMgFfHCttNazq7Yh3Lkegtk74qAM9FERcuo2ec8bDxNEG2iYGGLR0FvKS05HoF9bmPNQOX99xEC4De6DblOFQ09aEjokhRrz5YoOo31g5ktewrLmKnkZa6Getj09uxCChsBxxBeX4/EYshtoZwklfjrBTXo0V58JwP72QXRsx+WX4KzQd3ibaUFFsfBIRP64qigoY42iMA+EZYo9OLZMXEIiKrGw4vvA8VA0MoO1gD5uJ45Fx5RpqxNrm1uYpTUpmgoHl6JFQ0tCAmpERzAYNREVmFmrKhEc3iqipqkLE2WvoPHU0i7qnmU+9F89GaV4+Em8HtCvP4LcWwqZ7Z6hpa0HTSB9ek0agqrQchXLuAW1l/9+38PzyH3H1ViieBLlhEShOTIbHi3OgbmwEHVtrOE+fjLRbd1FZUNjuPInnLqKmvAJ2o4fLLYOQNCJIVMq9eA6GQ4ZDy80Dyrp6sJg9F4+qq5B/+6bcfBaz5sJ82kyoGMuPVrOatwC6Pt2hpK3Dtms8ZjzbbkVyUovlqiwuRdTl2/CdPR5G1E4YG6DfktkoSE5nUdHtzZMZGYeICzfR/2VJMUjEyPeXwtTNEQEHT6Pr9DHou2Amm4VHAwE9ls6TW27pPkVddQ1S7vrDbnBfdFJobJtKs3Nx+8df8M/L7+L4svdw+4dfUJbTfNRifnwSskIj0XXeTGibmUDHwgydn5+OjMAQFD2+TqJPnGN/3aaOg+OoIeg8byaUNdWh3cwMnZiT52EzqC8suneFmq4OvOZMZf2OhAuN/WmPGRPR5aXZ0LGSfz9k+w/J/Rff5+Ygwf3u8esYNHskbDzsoWOkh0mvzUJRTgFCrgfKzTf9necx/MVx0DORHWxBzP5wPtx7e0NDRxO6xvoYMHM4E76zkmSLe0ToyctstpD3xOFspp/rsL6w6uaBh/9cbHcemsF05Ydf0W3GWOiYy56NICSNOHGBkciITcHYFbOgZ2oIM0crDHlpAoIv30dJnuwZetSHWrRxJXzH9IOKmmqb08iD7pKzHCxwKC4NfjmFyK2sxvfBsVBVVMRYa9n9Fuq7vn0nDPeyC1hwBPVNt4TFw0FHE3ZigzA/hMRha1gC+761UB8k7OJt9J0zHqaONtAxMcDQpbOQm5yOhGb6LULyiJ/79Aq/fBfOfbuy6F9x0iLiEHL+JoYtk93+EG76muhlpo9v/GKRXFLBZoJStP4AS0PY68jut5xLysb6B7EIyyth/ZYHWYXYF5mGUbaN55CtjgYbLPgzMpVFsVP/8K+IVGirKMFaW/4saRHx9x6y6HsSP2m2AgXN9Jg5BqHnbrAAjLbmKUjLQuA/lzBw8QzYdfeCspoqzFztJUR9eWmkRX1igu3jZ6KQOORXVuNWZj6OJ2awCHR5/BgShwNxaUiWeiYaKvVMREL/9Yxc1LUhSif4xGUY2Vuj66T6dsJ9eF/YdPNA0N8XOySP9Dko+Z3kc1PMDT/2uXr3vk22Q5H3l9NycCE1h12Lv0YlI6OsEtPs60VuWbxxOxRX0nPZtZ5dUYVvH8ayZ6JuRo3PRH/GpOKbh7GIKhSmb0j3V+j4dx4/GNZd3KBpoIuBi2eyZ/qIS3falYcGjIzsLdn5Rmk09HTQ76UpDaI+QfnoMz0BbbKIm39fh7aBDou419bXRtfB3eAz1BeX9l+Sm8e9pzsWfrYYHr082ACDLBQUFKCoqNjwCrkVjMqySvQa01tw2f4/QwNKz+LrvwgX9v8jZGdno0ePHkzMJ3F58uTJWLduHUJDQ+Hg4IC5c+cyC5j169ezl7hILwsSg8gORiRak1B/6dIlhIeHN1j7REdHw8LCglnM9OrVC0lJSUzcnjZtGrP9oehzEdbW1g2//d5770FTUxO+vr5IEBNXabDg1KlTmDNnDoYMGcIEbhLGn3/+eYk0tJ+0v7R9Ly8vTJgwAYcPH26ynUWLFjHxf+nSpTh69ChefvllQXUppKziVFZWYvr06WwghPKoqdVHuVKU/ptvvsksd1SasQ0QsWfPHnZDeeGFptYXQsmKjIUS2Ug4N05RJzGeHgizIuM7LE9JTj6LGrXq5gkllaY2LET8LX/UVlbB2KcbytLToefqKvHArufmgqL/Y+8swKM6vjb+End3d0cSILi7OxQpUIqUChX6p4UqpU4NqSDFKcWLu7sFixB3d/fke85sVrObbKRt6De/h31INnPu3r0y98yZM++Jlr/kkuRzGrNhQZRHT2DRrWuDA17zLp2RFfQIpRmZqK2uRvaTpyjLzIRZgL9Cm/Ym+nicXSAVUHyQmQcbXS2YaMr/vvKg5dIkO9JapIbFsiXLkth38ECqgvPUXJuQC7fg0q09mwgQQgOqtPBYZkcD5cy4ZCQ8egb19tKrHtTatYOPiR4eZEgH0e5l5LEly8pCci+E7PFrb6qPi+O74/jorviqhxfsdOVnlSvCwUAL5joauJ0iyBgiKqprEZReAH8LadkBReiqq2KOny0ySyoQlJYv9d1/GOCF9UEJiM1vWrA3PTKOZZJQ1pQQCsAbWJkhLTym2TY5CalsubFHI3JNRNChs9g4fSl2v/YZc8xRUX9g2dnaACWV1XiULs6iv5OSh6qaWgRYKXf8rPU0MdHTEpfis9mxl8doNws2cbI/rGnB6IKoaOjYWLPJQCGGXl6orapCcVx8s20Mvb1YQD/10mXUVFaioqAAGTdvwcjPF2oNyJwpguRyqsorYOUrluCgQLyJoy0yImJazYaWStNkgJ6lGYydFA/C2yJ5kdHQMjGGrkQg1dTHi02w5EXHtsimID4BMSfOoMPClxRmqCrTRpKKrExUFRRAz0O8eookm3Rc3VASE43Worq0FNkXz0PNwIBtuzEyouJYNqe1xHWjb2EKfUszxX6BkjblxSW4vGY7+rwyA5r68lckEClPwll2vF0nb4SdvcaC2vpWiidS5ZF4+wG7/p369RC9V0Gfv+oH6FmaY8jXH7IXrS649s16thJHEdkR0VDV1ICJm9jvMfd2Z75EdmQM225RajrzGex7CSQa6BogyRz6uzzIpjAlTUpWpzGbhsh4Gorj89/BmSUrcP/nLY1OVghJjUlmwX3njuLnAgXirVxskRim+LnfVEoLS9gEgrGVKawbSHqgiR9rX2kfxKa9JzIjYltsE7T3hCjwrwhl2kiSFBoLfTMjmEjIFjl39EBtTS2Sw5uWZd9a2OpqsaCepHRHRU0tnuYUwM+4CX5Vnb/eWn5pWoTAB7FrL+2DGFqZsYB7a9mQVCRJSVJiiiTU/5z5cTsGvTqDJS0ooqOZIUqrqhGcLfZbgjIEfkvHJvqlxXXZ0kR4bhHL5p/sZg1tNRXoqqlikps1ovKKEZ2vWNpRCMmUGNtZscCnELsOniwxKjM6sdk2FPynsZlzt44KP1uZNkLamxjUHxNlNWdMpNaqY6K0ZzFS/i9h28ET6Q2Mb5S1oZUQ2+a+h20vvY/jn61HRpR8/1FSvsc5sANU9KT9YBoX0Krvh3Urt4XQBF1T7l06dmy/Wun45adloTSvELYS9yFdD1ZeLkh7Fttsm8rSciQ9CYeHjFxWaxAbEgu3jtI+j7u/B1Kik1FRJpYXayl3Tt2BWyc3mNsqP+nA4bQFuBTPfwQKpBOk4S4MIk+dOpUFnSnQ7OXlxYLGlBmuDP3798ehQ4IlyA8ePGDBbQq208SBt7c3C+yTlAzx7bffwsnJiQWmhVD2OQXgP/74YxgbG0NfX1/qsykbn7LgN2/ejM8//1z0flVVFbZs2YKBAwX657TfnTp1YqsO/P392fe0tLTE3r172YBp1KhRKCgoYAF4mlCQ3A4F+11dXUWTE8oGzJXdV4I+m1YH0EQIHRMDA8EDvbKykq2aoBUHdLxogqUx6HvTaoeWyPCU5hawwbVkEIIyCTR0dVCSV9Bim6vrdyDm2j32fWkiYMhyxfI+kZduwdbfR6CfQnqVBtIOjLq+PgoVBNoq8vMbtaksLER1WRkLnDz69nsUxcVDw8gIlr26w2H4MJHGvvOkCSjLycXd5R+y39upqcFj9iwYeSjWJqYBVHK29HcneR3CRFMDOeWCnxtigpMVbHQ0cSYpE62FQNteOkOcJHUqy8rZMnHKpG+pTVpkPFtG2felCVLve/TujPz0bOxf8SMb4BL+YwcgJlC6VoGRpjrUVFTqHSPKZqLjqixvd3RhA6YgCV1+ypT//H4k7qbnsc95vb0TNg/swOR6aGWAMpjrCL5vjowTSLI5lG3fEKNdzfFtfy+oqbRjQf3XzoUgt1z8uW93dWIrDvY+a3pmdElugejcSEK/K7p3lbHJTxNcfyV5hdjz5hcoyspjA2jK3pfU2LfxcYNrz05MfzUrJolJ+qhqpKF6/JtS27bQ0WTHShKKzeeXV7IJk4ZYPdAT4z0soarSDkFpBXj5xFOFbad4W+FKQg7S6jT4lYX6DuonJBH+TsH45tpomhjD67VXmBxP7J697D3K7Pd58w00B+p3Cdl7U8tAnw2cWmoTduoy7u04xCZAKWt/0HuvsIy85wnKsNeQPS96uiz4WpFf0GwbWoXx+Nff4T1zKpsEKEnPqLcdZdrUs6l7bqnKfL6qnj4qs7PQUvLu3UHS9t+BmhoW1HdY+BrU9BoPDAivG5IakIR+J5m9lthc/3U3HAM7MHkU4eo9eTw7K8hYP/Let2inqsJWC1354if4Th6tUIpHlrjLN2HV0Rc6pmLt7tjLN6Cpr8e0+oV0mjMVRxe+i6zwaIXbpu+gqadXz+9R19VBWV4ByupWd5C8X/azKFw/+BML3NPvlN0nD6GNpozfQr/nxTa+skISHXMzBCyaAzNvd5Tm5iF41wFcW/UDBn71gVQNAHkU1mWV60lMzBO6hnoozFVe1kwRt45cwemNf6GmpgaGFsaY9enCBjPQKUBGgXXZ64h8EAoCqcvxW5SxSXkajsgrd5k0lCKUaSMLZeXTsZJE20DQhxTV3Rf/NKZ18knkR8n6pdY6yiU3aKi0wyJvR6azn1oiLR/YXIqb4bc0xybk/C0Y21rA1kc6qHdu/R9w7daRyUo21P+QHKDssSO/pbCiCqbayvmlTgbaLGi/KVh8L5dV1+DtKyFY098XL3oLJs6pptSSyyGoVGLFJvlw8q5z9rcG/L7GbCgAa2RjgSfHLuHx8Uustgnp53d/cZxIC11Rm6r+06BmI10zg3z3JBk9eOHxVH5MJJCNITmf1oL6Ca1m9C2N2ahra6HXvMlwDuzIJkweHj6DIx/+iEnfvgcTh/pZ9hT0z0lIQc+5E3FPzmQajYloBY3svUvHTlne8HVGUlEpHjWgy98UROMHmWNB/iVp3jfXpiAju06SrxYH3/uerSCmujy+w3qh/Uixxn5zKMgugEeA9DNdz1CXfVZRXiFMrBqXJWyM7JQsJvEza0Xzkyw5nH8LnrH/H+HKlSsYO3asVGY4dZ7C7PGmQoH9oKAgFrimgDUF9ek9+pmg/+l3goL9ERER7HcK9vft2xdvvPEGc/rDw8NF26QVALNnz2ZZ9BQ4p+1TVrsk6urqou0SHTp0gJWVFe7dEzwq6X+S1pF8MFBwn7aTm5sres/ExEQU1CccHByYpE9+3cC7MZTZV5LcoeNCwfzTp0+LgvrE+++/DwsLCybjoww3btxgkweNyfDQRA2dE8kXyTM0Blt+1sRljvJs+rw6Cy/u+gHjvlsOVXU1nPl8PcveliUvKY0VyvMYpLigDTuHTVx5KWkjDC7HHzsO++FD0f37b+A28wUknjqLhJOnRTbhW7ajJDkZAR9/gF7rf4L3gnmI3LUbmQ8EcjPKIjwUyrgk3S2M8bqvM34KjkVUQeNZOy1BvLSwtlVsaABlYGnKJHckeXT8Mh78dQHjPlqMV3Z/iylfvo3IGw9Rdv6wUp/ZBFUavNXRmUnwLL/1TGpwtC8qFecSBUtZSav/wzvhLEg8xrlpmZ/N3b/j0Zlov+Ua+uy+jePRGfh9RHs4GQqCLFRUd6ybBVZcjWjZjsg4vUrdJw3YCCXRHhw4g8FLZmP2xpXwGdIT537cjoRHghVYRJ/5k1lwn5a527b3QP9XXoBKfAiQmaTUbtPHNHZvLLsYDt+N1zDiz/uoqK7BjjEdIKHEI8LVWAddrA3xZ2grSccIr/em9IEyNqSxH7pmPWyHDUXgTz+g81efQ01XFyE/rmFZva0GO5e1LbbxGt4Ps3b+iEnrVsK6vRfOrlrHBrT/FWqbeowkbJ7t3gcjV2dYBSrOKFOmjbII7sdmFAORwbBLIHzX/ArPL1fDsEs3xK3/CeXpaU3bD8nfVVQaPY4N2Tw7dx0FaVnoOnNso59NhXdZ9rqrA2zbe0FNSxPGLo64vvpnFCQ3fp9TG8p6dxkg7VPkxiQgLz4Jh+YuwaE5S3Bw9hs4/NJbrLB0cUbTJ1NoHyWPSXVFBeIu3UDP997A0O9XQtvECKU5uShUYp8lNtrk8+8xZihsunaChp4uDO1t0XXJApQXFCD5tkDu4d+8DruP7YuPj3yHd7Z9DNdOHti2/GdWeLfJ+9LE+1jShqSirqzbjj6vzqw3+SREmTZNphWOX2uizHOXoOfsxwGeTGf/84eRrb8j9fqJZvgtCmzKS0oReetRvWz9p2dvsMSFni+OafZu16BWqeNnrq2BNf18cSctDzvCxD6RiZY6NgzqgJspuRhy+DaGHb6Nhxn52DCoPTvWzUHknzfhWqtnU1uL7PhkZEQnYPK3/8O0H5ezBC7SQicppIba5G78HjXFjUu7CGUwlTl+PSyM8YafM356GvP3j4la2LcQbr0C4DusD3SMDZjEZZ+FL8DQ2hxPTwgkjmUJO3+TrWijzH9lIekhZWPcr/k4oaOpIT56ID0m+jtoznNC0kY45ri37xS6vzgWczavQreZo3FrxxGEnLne+vtbN9HeWl3zndN3oKOvg/a9xDXmOJznBZ6x/x+huLhYKrDcUrp06cKke0hehoL4EydOZO99+eWXSE9PZ5I8wgB8YWEh+3nhwoX1tkPZ6gRJ+ZAsDWXwk9SOnp4eVq9eXU8/X0dHp142FGXQUwCboP/JVvbvBAXtaXWAcIJA7kNbiZ5f2X2lbQn19Gk1hGz2va2trSjzn2oQEFSDgCSCSC5Jkt9//x2enp7o06dPg/v21VdfYeXKlSLJIJq4IYdOx9gI0377HFpG+mwwQ/sm/M40wULvaRvJz+xrig09QFVVVGDiYIse81/A4Xc+R0ZELKy8pbNoIi7ehLaxAewDfBGZUS3KsJeENHY1ZDLbhGgYGDZqo66vxzL/LLoFwrSDoMCNia8PrHr3RMbd+3AcMwqVhUVIv3UbPosXQd/RQSzN8/ARks5dgHln+cV2KPvEUGZ5KWWIC//WEIHmRljVxRO/hcYpLLTbGJXx0cj5+Sv281oAPWeNQZcJg6FjpI/SAmmHuySvCOpaGgqzcZtiQ8v3I64/QOcJg+sFdB4eu4T2Q3vBsZPgnrbxdkHAuIG4uvM4NAeNF7WnLB5a3kyFsCSh5bqkP9oYr7V3xFhnS7x+JRiRjSxlLq+uQVJRGez1lJdCyS4VTIQZs9UD4nvaVFsd2aWN7x9leaWXVOCr2zEY4mSGCe6W+PF+HLpYGbKM9RszxZqMlNm/JMARc/1s0W2nRPHyk1uAsDv4ta423Pxd34oyYMoKipgmpRA6d1beLnL3RRkbXWPB+5Shb+4qyMLyG94H4ZfvIvrGQzjUnU9ZTOuyutrlpaPWXCzhklVaAWMt6XNLY0ojLXWmpd8QtXXyAeE5xfjoaiTOTe+KACtDpr0vyVRvK6QXlzOpnqZC/UNpqvR9V1kg6EeoHkdzbdKvXYOWmSnsRgwXvK+nC+cXpiHog4+QHxEBo7pnnTzubjvA5HCETPr5M6lzRxn3QsryC2HiLF8ypyk2dD+2U1OFnoUpeiycjj/nPUTszQfwHSW9wqYto2FggAqZZwDJtFE2mKbCc9m4TU54JEozs5B6666UX3DtvY9hP6gfvGdOa7SNwYSZ9T6bsuiJapl6PlWFhUwbv6WwPpayyo2MYT15GgoeBSHv9k1WdFcIZfRTZr9wjeCL21eLshNLCwpZXQbJ68bSU37fooxNWlg0chNSsH3WUinbyz9tYy/yF6g+z/QNn0PHxIgdw+5zJ8HU2R475rwLQ3sblhmZ8uAJDGwVaw0TsZdvQsvIEFb+flLv03m17uSLHm8vUjjgv7Tye+RECWQCSPd+1Pov2fcrLyqS8ntoWxVFxawNvYT4TBsHPSuBtJOetQXy4xOR+uAJ9GX2WWgje/2VFxRCU2J7zYFWUGqbmrACvpKc+O0g7h4TB0uWbv8EenV+W3F+EXSNxP5ycV4hrF1bLsdFx4sKlRpbmmLckhfw5dTleHolCPbezti8VFDsmO4WKnzbYdwQaBsasD5LktL8QiYBqchvacwmMzKOZY+e/fJX0d/ZPVpbiy3TlmD05++wnxW1+WLMW5i7+i0c/XEXclIEE0AWjtZYsP496Brro0Qm+EiyQ2z1k4T8yT+J0O800lBHvITfQn4paZ43BN0FH/p7wNNID0tuPEVWMyUr8vdsRtnD28wnJRbv/pb5l0KfQ0/CBynJL4KNl/y+pak24VcfMFkwyZpPQnme7IRU/PLCu1Lvlx/ahMrb56GzYIXoPfI9hT68pN9C2dS0yrIhzLQ1sGFge1ZT6L0bYVLh4mEO5tBRU8XqoGhRcsjXD6JxbXJPDHEww6FosV9RvPod1JYU4dd2gLW3C8avepPJXeYmp9e7zglFYzZlbCggTclPlLAhlOwhLf3fX3wPyU8j4NrTX2GbyKvvoSL6GbQ6iCe1c8or2LUnW5xZ6TFRVy/8GhqnsNBuY1QmxCC3bky0gbY5cwz8xw9hzxe5/UQDY6Lm2FCfZ+Jgw2Qt6+1beQWibzxAp/FD5Gaj51cIxkT1jp8S9y6xyMsRoxwssfR2CKILStAcis4eRfH5oxD2hFNW/090rZSRPr+d9LFQdO0pY6NrLLiWfAb3FEkeuXTryK65qBtBbPyhTJHcr+d+Kfq935T+GLNgLNPVL8qTPneUqU/HXU/iWddcSCLs3tm76DKkC9TqpI84jaOgdAHnX4Bftf8R3Nzc8PSpYlkDRUuHFaGmpoZevXrh/PnzLJt87dq1LAOeJG1+/vlnlo3u4+PD2tL7FOxvSOZn//79TONeUnefAueykxEUnKdtkdwOUVJSgsTERDg7O7PfqV6A5CoAgjLdKZBPge7WQNl9pYkPqjtAWfskoUN2wgmFU6dOMTkgITQ5QsWFKShPAXxJqKjvvn37RAH7hli+fLlov+hc0CBlfcR1qGkKnBELDxemQZsVnQBzN0f2XnpoFBuQ0N/k0Rwbgv4u+EH6fVrOGX3tHjwG9hAsbdfTgralJfLDI0WBdNrv/PAIWHSXdtSFUNCsMRsqvKfv5CTXkRK9JfxBpkm7dioN6vIH5xRgrKMVMxN+vc5mhkgtKUN2ueJBUVdzI3zR1QubniVgf2zzs43VHV1h8RW5r8A012LRvlp7ubDBjCRJTyNg5SH/ODTVhjLw6VrwGSinYBCrNiP7Vv0KNFW1tXiWW4QAcwMcixMPPrpYGOJRVsPL2Bf7OWKSqzWWXA1BaG7jGUOaKipMY/9Wmni1TmPQ4IyC+4HWhrhfp4+vrtIO/pYG+DmoaXIJqhQ8rft5fVA8fnkoLS116YVuTJbn10cy2x0xFxg+B4t6CCYG6T6xpPOhooKUkEi412lTFmblsuXSVNBMHsrY0LJrGrDUuz7knE9JSJufoSstDUYSOjrqquhgoY8nGYLBZKC1EZvEkKw30BjyMvUJ2s4ED0vsDUtlkyhNRd/VFamXr7JJPZr8I/KfPWMSXHqOji2woR2Wk2FY1580RNfZE9HlRbG0FZ1vWhmhqqGOtNAoVoxcqIefG58Mr2F95W7HxNm+yTaM2lpBf93Gsk0bw8jdBTHHTqIkM4tJkxA5oeHs2jV0dW62TZ+vV0pN8ueGR+L+tz+h99croW1mqlSbFDl1Dan4LcnuFEeGQ9ddoLNO9RhIX99ixGi0Nmy5u8x7ti++BNtZcxFoLu5bLNwF/QRdN669u4jq5FABRgsFfYsyNv1efxF9XxPXQCLJgh0vLmXvO/UIYMUFhfeMpTBgRxnxLLgqtGq8ihn5FAnX78J5QM96BQyNHO0QefoSqisq2SSBPPp/9LboXAr7QVN3F1YDKDcmHiauAp39zLBIdkzpb5Qpr2NuipLMbKnEkOywSKioqsndZ7KhCYCssEjYdPWXsImAXU/5vo6yVJaUojQ7l01uSDJi4QQMXyCWIaIkEy09HahrqiPuaRQsHK1Egem02BQEjmk8qNIUKMNVILsA2Hs54eOj37H344vExX4tPZ2RGirtg6QGR8DCw1mh39KYDf3/0p+CSQQhNzb8ifyUdIxc+aboOlHU5uXVb7A2i36h4K/wYhTsi523M67/eQa5admsfgAR9ziCnXNbL3FNhn+SpOJS5JZXoKOpAR7XSS2R30Ia3Tsi5Guxi4L6AR7wMzHAkptPkVrafAkeg2nzYDD1JeaTsm2rqgr8SBUVJIdEwlPGB7H2kt+3NNVGXs0nYtibL2LoEun+55fp70Jzwnyo+QnqYgh5nFXAavZQ/ScqhkvQqlDyN5404JeaUUb+wPas4O6710NZgFaSWuE9IPl2XfcmWwxWZ+l37Bn8YodS0bVm5eWM4DPX2SSHUJqIAu80vjF3cZB//JSwIc1zQvL+Ev1c93+DbWQIzimsNyYKMDdqdExEQf0vA72xKSwe+2NS0FzUHVxg/uVv7OcJLqVSfUuKzPgmJTgClu4N9y1NtaF+PDcxDcb2gv5UkpibQWzc5Dmgm1xbGhNRcdtOpoY4KSFDFGBmhCcykq+yLPBywDgnK1ZgOEwmoN0UdAePhu6gUZjoIr53qd+m64eOhY2vIDmvurKS1VLrPFmQwCILFbptzIYSUGh1g9xCtUouUTCzMcM3p1ZLmAnsnHyd8eyeeKUxEfkwEtYu1tCUI7vUVGjb+Vn56DZSXMeHw3me4FI8/xEoEH306FEcO3ZM9N6ZM2cQFSV4eFEgnjLr6aUslIVP2esU0KaJA+pYKaOcgvxCfX3i1VdfxdmzZ7Ft2zbRe/Q5lN0vhLZBOvOUCU5QMVuS8JEHBdGFgzDStCdbYbFfynQn27t3BVl0JL9Duvtz5syplzXfXJqyrzQBIZQiopoGpK1PdO/enU10CF9UK4Do0aMHkwWS5M8//2R2JP3TGJqammz/6EWrE0hySENbWzRxQ4F5S29X3N1+kA3CCzOycW/nYdh08IKxg41oOztffAdPj5xT2ibm+n2EHL+IoqwcNsjOiU/Grc17YWBtAXMP6YFOwoOnLBvCY6B42azdsMFIvX4DOcEhqCopRdzhIyyL0qa/OBgVsWMX7n+6qkk29iOGIf32HeQ+C2eF8vIiIpF+4xbMA7uKJggMPdyRQIGe1DTWJvvxE2TefwAzf8UFo44lpENLVYXpkVIxrAAzQ4xxtMI+Ccc0wNQQF0f1hFNdtji1oaD+xmcJUu2aC9UIoBc5YEKnptOofkiPjMfjk1dQUVqGZ1fuIS4oBP5jB0otT1476U1U1V2LythIDqCcOvtKZVEJce3WASHnbiE5NIoNoNKjExB09BLUvf3rOcN/RCRjiL05+tuYsmym2Z62sNPTZlI6Qkgf/6+R4oygRb4OmOJmjTeuBrNBhDyoWK6viR7TirXW0cSngR5Miud4rHT2UkNQz7I9OJkVv+1sacAKUq3o7soGbIcjxNlE6wZ7Y/tIwUoQa11NfN7HHe7GOmwwbaGjgQ96uLIsf5LkEW6XAtGSL4J6kXorZykQrCI4t8IABDnLnv0DcWfPCaZJSVqp1zbug5GNORw7i7NU977zNS7/+qfSNpThSLqWDw+fR05SGtOhDbtwGxmRcXDrGSAqxnZzx1/IS8kQOemksV9j6YRaa+mBNgXz76bk4cNerqwIrq2+Jpb3dMG1xBxE5IiziZ4u6I2FnQSTrWPczDGvgy1s9DTZQNrLVBer+nogJrcEj9KlBzaDnExhoq2OfWHNy+wyDfCHpokJonf/wVb8kIRO4vETsOzdS1Tktjw3FzcWLkZ2nRyXMjYm/p1QkpKClPMXWECwIi8fsXv3Q8PYGHpO8icMxKdbRXSuheebzovnkN54evgMsmMTUVZYhDtb9rIVVI7dxYW9T6/8CVd+2qK0DRXYpb6cglfUVxdl5uDmhj/YgMqxm+KC4W0RMz8f6NnZImzHHqadX5yahshDR2HVrQu0jAUTTlXl5Tg771UkXb2htI3s+RD2X2yVQ92zVJk2stD7pgMHI/vSeRRHRaK6pBhpB/exlWVGPcTPw4SNvyB2jSAAqgwVOdlI3r0dZSnJ7BlWmZeH1H17UF1UCKMugfX2oZ3MtUYyJG79AvHgz+PITUxl+vL0/Da0toBDgLhvOfzuV7i+YY/SNvWOkfDYqamygnr0HhV1vv/HUdYJOnXrhDvbD+HiD1ugpq3FCs1SQV2bzuIl74fnvY3wY2elvlPqwycoLyyCU//60n4ug/qwz7n78xaWzU4B/tzYBNz5eSvTppfdT+E+UjDfzNMNj3ceQEl2Doozs/Hkj0Ow8PNiKwkIjzHDWPtHW/cgPykFIXsOsxUL5Bta1+3zo9//wMUVYl/XbeRgxF+5hfQnoSwYH7b/KNPmdxqkfECd+uC7azcxXX76mb4XFc+l+9+urpCvEPL9yPcVvggNLQ10HdkLV/48i5SoJJZ9fuyX/dA10odfn04i29+XrcPer8R+uzJFeU9uOITMpHRUV1UjLz0HR376k/Utvr0E/pRwPyTvGd+R/ZEZFYfQUwIfJOraPSQ+DIHfaLEPQrJOlGlP31dZG8lrj13rdW6I5ORPY21o8kn8d8G14eLvBQsnG5z+ZR/T289OSsflnSfg2zcA+qYC36iirJxl/T86K7ES72+EXIj9MamY4mKD9ib60FNXxes+zqiurcWpJHGwkFaL/tjdl/1MX3WFvzsrfMqC+i3U1ZfXt5AP4t0/ELf+OIHsxFQU5xXg0sZ9MLYxh5OE37L77a9xQcJvUcaGyIxLRkZUQj0Znob6H8jpoymYH5SRj6UBLrDU0WA+3Vv+zridmovofLHfQpn2s+u08klm57eBHZBUWIZ3r4XKlUC5kZLDEjxoW3ROSH7nnQAXVNfU4k56XgPHT7B/VLhW39wYVzfuY1nPWXHJuL//DLwHdYemrsD3KMrOw6+T30T07UdK25CUJq3QvLHtMJPeKSsqwfWth1hWtTCLWlEbKv6q4SouAE/Q6mMaE73i48TGRJToNNbREnujk0VtaAx0aXQvOOnrsN+pzRddvbExLB57/6YxUftRgn4iuG58E3H1HhKCQtBhjLifCD17HRumiPsWZWwu/7KbFYAlzX3yq29uPch8bN8RfeUWzaVnouTKNlnoOA20MUMfKxNoq6pihqstbHW0cChOPCZ6xdsReweJ61697OnA6hJQpn5oC4L6iu5deq/D6P6svkJqWDQ7/ze2Hmbvew0UT1Kc/nYzjnyyrkk2/hOGIOTsDTaWoPFiQlAoYm49gnsv+avk5SH5XBPGOHqN643c9Fyc23UWZcVlCLkVjKALD9BvkljC+cm1x3h36DvIy5K+/5SV4XH2dYZV3YQ4h/O8wTP2/yOQzvyPP/7IpGMoiE+BcQom79mzRxRo7ty5M3uPst9nzZqFV155pdHAPmWIjxkzRuq9I0eOSOngU9B906ZNLJOcgvKGhoYs614y452y1Ukbnz6b5HYoE1+e7AwFq7Oysljwm7LfKXD/xx9/MBti9OjRePfdd9nEAk02JCQksO8lLB7cGii7r5LBfWHmPgX3KfteVgqoIUiGZ/z48TA3b53q6wPemc8G4Ife+ow5P/ad/dD95alSbYSFbZS1od+Dj13A6ZVrUZydx5xD+wA/9J80HKpq0t1I5MVbsPHzgL6lIFuSsOnXlwXnSe+elqnr2tqg/VtvQMvMTHqfqmuaZGPm3wluL0xFxLYdKM/JgaaxCeyGD4FDnVwG4fPKAsQcOMQK7FYVF0PT1ASOY0fDdvAghceQlir/704o3vRzwVQXW7aU8o+oJByUyMInv5KClEIHc6abHbRUVbHY24m9hFChsnfvhLKfKSh8ZoQgE0CtLtuKllgmFJVg7hWB094QlOk04t15uLnrKK78fhD6ZsYY+MoLcO7sK3Echdm5ytsQuSkZSAmNxtgP6ksaED1njmHn+sxPO1lBXsqeomB/bID0tUWcT8piy57f8XeGmZYmEgpL8L+bYYiRWEZKyRw0IGI/kxPr48AynDYNkJ5wWf8kFn9ECgYFh6PT8EYHZ3gb66GkqhrB2YV4+cJjpDRxwLrhUSK01FSxfogPDDTVEJJZhJdPPZUqhEv7plK3f6nF5biTko+v+3nCw1iXFVx7mlWIGcceIyK3eUtj5UHLoK9vOYRDy39AVUUVbP3cMPrDxUz2QAjdI6LVMkradJsuyBY+8uEaNpChgmlD3p4L+06CwZuFmyMyoxNx6ptNyE/Ngq6JAVs6m2k7TjAJIcNrZ0LxWV93nJ/elV1mF+Ky8clVad1eNXb8BD9fjM/Bgk522D2uI6z0NJFVUoGLcdlY/yCh3mCZZHhuJuUhsUBOSrQSUDFt37eXIHrXH7j37ntQ0dCAefdAOE+dIm7E0unEfaAyNkbeXvBY8DKST51G/KHDUFFTh76LC3zeegOqzaxl03nGeHa/kv49aYKbezpjyIrXpZaDy57vxmyMHGxgZGuJKz9uYcF9TT0dWHi6YMRn70DP3AStibeHHe6e/pr9rKamis+Xz8Cq96fj1MWHmDr/+xZvnwaLAW+/htDtf+Dqux+wFRRWXQPgNXOqwtUIStn8jZgPHYHaigoWvK8uKYG2gyOcXn9bqsgte9ZJnNOsC2eRdviA6PeoLwWr96ynTIdpvwFQNzaBrrsnknZsQXlKMlS0taHj6Aznt5dBy1Y5aZWeL0/F7W0HceyD79lA29rXDcNWLIaKRD9RI7Nfytg0hoW7M7Jjk3Du2w1MxoACWnRN03mi4ra9l70GAxvxILq2RrASUVaGh4rg6lmIn/1CqHBu/0+W4ukfh3H+w6/ZZBYF5t1HDKyX3S5L9zcX4OHWP3Hm3ZXsgW7t3x7+c6eJ/u40sDfK8gsQeewMLr3/OWujZ2OJjnNegH7dPgvOZbWUTWVJCR5u3MkkePTtbNDj3Vehay4u6pdw7TYebtol+v3GV2uYH+E5YSS8JoxkhYXtewXiyY69yI9PgrqODkw9XdFv5TIpiaCGGDpvLOsnSP+egtAkkzPni8VSRW6pPhJJDwg5t+04ru+/INKZ/u7FT9j/k9+bjfZ9/WHpaA1zeyvs/XIbshLToa2vCwcfZyz4/k0YWSruW8zdnTDw7ZfZBM/trQega2aC3gunM6lGIXTOhZn/ytr8XdA1Ou2ThTj18z6sm/cpe5Z69/bHsEWTpLOyZfzo399cjbSYZCm5H2Lpn19Bqy7gqkwbReyOSmLB1c+7eLEAcnh+Md69HcpqDon2XcJvsdPVxlA7C+ZX7R4gDhgSH91/hhvpOeznqS42eEXCZ/29r2DyZ21wDP5SQjplwMIpuLLlEPa9/wOq63wQqsXUkN+ijA0RqqDmU3NYdj0M73dxw6FRXdgVfjU5B9/cl87cpkQRFQmZHWdDHdjra+P6FOlJxd77bzDfhTL537oagsXtnXByXCDbbmRuMZZcCWZ+Y2PQBOjoj17F1Y17sX3BR1DT0IBH3y7o9dKE+s+4Ol9JGRvqX0cuX4Rrm/djx4KPWJ9Nq7DGfPwatPR1G2xjtOAdqOjqyRkTheBNP1fRmGh3ZLL0mKhuXCNM85npbsf87MU+zuwlOSaiYLVwTHR2ZE/xmMjEAKMdrNiYaM7lh40eP0t3JwxZ+jLu7DqKG1sPQM/MBH0XTYdjZ8V9izI2pK9/b+8JJjdHx8nM2Q5jV70Jay9x7T6CkmEowD1iRcPxlEup2TDSjMUSPxdWCDuxuBQf3A9DbKHkmKid1Jhojoc9u3d/ltF6/y0sTpQ81svSBKu6iCdh6Gf6ngdjU/BzaFyjxy9g4hCW7HP6m80oLy5hKz7oGpEsbC3rryhj4zO4B5u0P/v9VjYxom9uwvT2fYeJlR0eHb3IdPeF7Fv6DfbTiozXJrIAvjws7Cww//MFOPLbXziz4zT0TfQxav5odB0aKHW+WWJm3fmmgvErpwmeZfR+enw6Lu+/BEdvR7yx5k2RHbULvR2Cqe+IfQCOcrSgHjKnlWlXq4zoOOe5gXTgQ0NDWXBfVpqGOrTIyEgWOLezs4OjAlkCIST1cuvWLTg5ObH2RF5eHoKDg9G+fXsWwJeEss5Je59mVz08POoFt6nwK8nm0N9Jez8+Pp69J9Th37VrFwvap6WlsYkBkuChv+nqCpwQSSjgT9+FguFCmR4hmZmZiI2NRWBgoJTczaNHj1jGvDKZ/Y3tq7zPIB19+v7UxtRUujK78G89e/aUkkWi2+/mzZtMYsjaumGNWUV8/ViQed8U6FpgmYd/Q2/Mti2hbxtTqNwkh9BxaEgi5+/gWa7ykzDy5ESEmdn0neWtPCRfvLYRCRLqhcVukzQv1C17VhahEysrWfB32W0LEUy6NRWhsElNI9IszZFjEZKXp+ioKrl/7ZpW9FcWlUbsX+vZ9OA1BWKYis4/cJ+suaL5t333huyklDoaoH/75p8cVvCWspj+Ro/UTb9x/dSm9qetyaqxgpUALUG1LvNQkhqaXGwF13LB3vp1e+RBAcp2LTiXyvR9sm2SS5qfFyP7rGPHSmLgLKIF36lbnRTPP923KHsu8itUFB8bGd/k3/ANqpS8fJuzb8JrSZaGVoPI0tm0FfoWCuq3E0t1sokdOfetSjOuw7iif8b/kNpGXfBOmA3dUBtb+RLSLbrWBZMkco6fxHdSps2msOZrRTfXr5LXTpHv1VSftCV9S1Pt6Jxsearb7H5T1m/5O/zS2UyKp2m09BnXFA5EN8+n/6fGRBNdSv/xvqUp2z4c+8+OiVSVOMZCJtVJ8bR2v9oc5D0HXQ2qm/W8kd0uPcskYz0Uz5KFpqAkv5M8O1lG2QvUIzjS+GwR1xB7ngid14CE6XMKz9j/j0G675TBLg/qLEnfXVbjXRHUucnq5hsZGSnU0qdAfocOHRqUkenYUZyNS8HshrLghTr78qDMfsmguiQU7JfNfqcCuA3VAGjqvsr7DArmK/oMRX+jhxfVMvinaWrNhX9i2/90QL81kHSwhFIsTbH5O2DBgWY4sM21ay4CbdJ/7rg0a/9auE8tmRRQRGs7138Xzf3uf8cxk8c/ea231b66NaiWyPj9t2jpgF2Zvq81+0fZZ52wKO6/TWv0LS0+F3Ku97bsGzRn3/7pZ62y5/vf7Gta45iwwKfSbWpa/VpX5v75u5/fzfWr/m7/q7nfu6l2kjItreF/tBW/9O8ISv8d/JfGRP/0ttvqvatMv9pax641DiUr7l5vu6rNsuNwnjd4YP//KaSL/+TJE7l/I736BQsW4L/K/+fvzuFwOBwOh8PhcDgcDofD4XCef3hg//8pixcvRn5+vty/yRZ3/acYNmwYk/D5//jdORwOh8PhcDgcDofD4XA4nLYO19hvO/DA/v9TSCO/rSFP3ub/y3fncDgcDofD4XA4HA6Hw+FwOBxlabvClRwOh8PhcDgcDofD4XA4HA6Hw+Fw6sED+xwOh8PhcDgcDofD4XA4HA6Hw+E8R3ApHg6Hw+FwOBwOh8PhcDgcDofD4TRKOy6y32bgGfscDofD4XA4HA6Hw+FwOBwOh8PhPEfwwD6Hw+FwOBwOh8PhcDgcDofD4XA4zxFciofD4XA4HA6Hw+FwOBwOh8PhcDiN0o6nibcZ+KngcDgcDofD4XA4HA6Hw+FwOBwO5zmCB/Y5HA6Hw+FwOBwOh8PhcDgcDofDeY7gUjwcznOOSju0aWrQtmnrxdwra9GmqWrDO6jaxqeuq2ra9sVX23ZP7XNBZW3bPr9LDs5HW8bPuAJtlcS4tu0+17Txe7e0um3fG79O2YS2zJSdr6At42PUdu/dtn5/qKqiTdOGXb7ngrbu97V1yqv/7T14filv489dDofTMtr2yITD4XA4HA6Hw+FwOBwOh8PhcDhtgraeIPn/iTaez8jhcDgcDofD4XA4HA6Hw+FwOBwORxIe2OdwOBwOh8PhcDgcDofD4XA4HA7nOYIH9jkcDofD4XA4HA6Hw+FwOBwOh8N5juAa+xwOh8PhcDgcDofD4XA4HA6Hw2kUrrHfduAZ+xwOh8PhcDgcDofD4XA4HA6Hw+E8R/DAPofD4XA4HA6Hw+FwOBwOh8PhcDjPEVyKh8PhcDgcDofD4XA4HA6Hw+FwOI3CpXjaDjxjn8PhcDgcDofD4XA4HA6Hw+FwOJznCJ6x/x/h0KFDWLhwIbKysv7tXfnPk5ycDEdHR7nnYOzYsUq3qa6uxh9//IFffvkFoaGhMDU1xfjx4/HZZ59BT0+v2ftXXliMW1sPIDEohM2iOnRpj+5zJ0NDV7tVbCrLynF0+Wrkp2Rg7JfvwszVQfS3xIchCD52EVkxiVDX0oCNnyeMx0yAprExki9cRMq5C6goKISurQ1cpk2BgZtrg9+lMZubr7+F6vLyenY2A/rDdca0eu/HHTyMxNNnYTt4INtWQ3ga6uF1X2e4Gegir7wSh+NTsS8mRWF7E011THOxRU9LExhrqCOhuAS7o5JxIz1Hql2guRHGOVoh0NwYJxLT8VNwDJpC/IMQ3N59DPmpmdA3N0HnycPg0bdLi23Kikpwd89xxN55iuqqKrh064Aes8dDs+4aKC8uweOjlxB18yFKcvOhb2EKv2G9Aashcj9znIsl5vjYwVJHE7H5JVjzKBb30vMV7qO3iR7meNvB39yATf8/zSrAz4/jEFtQKmqjpaqC1zs5ob+tKYw01ZFeUo6jMenYHpaEprKooz2melnDUEMNwVlF+PJ2NCJyixW2dzbUxqKODuhuYwR1lXZ4llOMnx/GIyi9oEltGqK6shJ3dh1B9I0HqKqohI2vB3q9PAV6ZsYtsjm07BtkxyVL2XkO6oG+i6aLfi/KzEHY+ZsIv3gTpflFmLf7ewAacj/TUFMNn/RxwwBHE9QCOB+bjVXXo1BYUa1wP/s5mGB+Jzv4meuhpLIaN5Py8N2dWKQXV4jaBFgZ4J1uTvAx00NtLRCSWYjv78ThcUZhg8ct5cJFpJ4X9xNO1E+4urbIpqq4GHGH/kLu06eoKi6BpqkJrPr1hc2ggaI2ZVlZSDp5CnnBoawf0rGxht2okTD285X7mdnR8QjacQC5cUnQNNSHx9C+8B49uMH9bMymtrYWqY9DEXnuGlIfh8F1YE90nSfd993d9AeiL92qt20NfT0M/u5TBO8+iNQHj1FbXQ2LDr7oMHsKNA30Fe5TZWlZozZFaRmIu3gdCdduo6a6GqM30vUkzZm3PkRZTp7Ue+6jh8Bn6jgoQ2F2Ps5sOIDYRxFQU1eDd+9OGDRvHNQ15V+3RGpUIoJO3kDI1QcwtbXAy2v+J/3dyirw4NR1BF+6j9zULOibGcF/aA8EjuuHdiqN58LQ+cg6cwJ516+iuqQYWg5OsJryArRs7RXaVBUVIe/2DeRdv4KK7Cy4LP8EWja2Ctun/LEDebeuw3zUOJgPHwVloH7i/u4jiK3rJ6x9PdBtXuN9S2M2mZFxeHL4LDIiYtjxMfdwRucXRsPIzlrUJi85DQ92H0VmZCx7thjaWsFpzEhYdPRD6t0HiDh8AqWZ2dCxNIfHxDGw6tyxwe/SmE1FYRGiT5xF+oPHKC8shK6FOZyGDYRdr26iNjGnzuPZ3sNS21VRV8fwTT+htQj0d8OCF4dgwshA3H0YhZHTv0BrQvde4pEjyLp9G9VlZdB3dYXT9OnQsrBokU1ZejoSDh1CUXQ0auh8+fjAcdo0aBgaitqE/fADCiIipLZtEhAA94UL5X4u+ZZBe46iIC0TumYm6DhpGFx7d23w+zVmQz5JyPGLiLv9ECU5Ap/Ec2gfeA3pLWpz+rO1SAuNqrdtWy8nzP72rQb7lrMbDiDucQRUqW/p1QkDG+lb0qhvOXUDoVcfwMTWAvN++l+z2jTETFc7jHawhIG6OsLzi7A+NAYxhSUK2xuoq2G4nQXGOFjBWlsL868/RFyR2KcizLQ0sNDTEZ1MDaGtqorHOflYFxqL9NL6vrWifuLWjqOIvPEA1RWVsPVzR+/5U6DfSN+ijE1ySCTu/XkSWbFJ0DU1Qpcpw+Heu7Po7+kRcQg6dBZpz2JZ/2Pp6YSazpOhYmFT/1hoqOHdABf0thH4LVeTs/F9UAyKKuX7LeTDjXWxxFgXKzjpayOrrAIn4zKwPTQJVeSgAOhra4LVvX3k2s879wghOUWNHr+C9Czc3HIAqaFRUNPUgHufLgicOQ4qaqqtYlOcncd8wLLCYszZ+o3UuC7u3hM8OnQWecnpUNVQg6WnC6r6T4OamaXcMdESP2e4GeqxMdGhuFTsjZb2KyUxpTGRqy16WdGYSAPxRTQmSsL1tPpjovFO1uhmYYzjCWn48WnTxkT/Vt9C1zA9+2Ju3GdtDG0t0Xn6WMBYfH1KMtLeAjPc7GCuRceiFL+FxiEoW/GYiI73dFdbdDA1QDsAIbmF2PQsntkK0VZVwWBbc4xzsoKLvi4+uv+s3pizMX/l8aEzeHb+BiqKSth4vvtLk2HiaNtqNtd/+wMRl24jYNoodJo4rME2I+aOwOAZ8seUQqKfROPohiNIj0+DgakB+k8egJ5jeilsX1Ndg9DbIbhx7AaiHkViwNSBGDmvvt9UXlqOszvP4NGVhygvKYdXoDfGvTIe+saKfWEOp63BA/v/EWpqalBVVfVv78b/C+ihRkH54OBgeHp6it5XVVVtUptbt26x1/r16+Hh4YGwsDDMnDkTGRkZ2LVrV7P37+KPW1BRUoaxX72L2poaXPxhCy6v3Yahyxe3is2t3/dBz9wUeUlpqGXusYDSvAJEXLyNTpNHwNTJljmTNzb9idR1v8B6YH/EHTgEzwXzYeDqgqTTZxD8wxp0/vxTaJqYyN2ntOs3GrXpsfYHOtgim7zwCAR//xNMO/vX215uaBiygh5C29yMfceGoCD99919cSYpAx/ffwYvIz18EuCJsqoaHE1Ik2szy80OqSXlWH4vFHkVlRhma4FVXbyw7E4I7mcJnDdfI31MdrbBsYQ0GGuqQ6WJ69cyoxNx6ptN6DZjDLwGBCL27lNcWLsT2gZ6sO/k1WwbclCPfrKOTcaM+fhVFvyPvv0I0bcewWdwD9Ym9NwtNiAYtWIRtAz0kBAUggvrdkN9jB7UOgjaCBlgZ4oVXd3wye0I3E7LwzQPa6zp54sZpx8iTiJQL8lbnZyxPzIV3z2IZsfl7QAXbBjUAVNOPEB+haBve7ezC7pZGeN/18IQV1iCzhaG+Ka3N0qqqpmtssxrb4d57e3x1sVQROWW4M3OTtgyoj1GHriPgrrPkmVBB3tcSsjGt3djoNquHV7uYIfNw9tj1IH7SC0uV7pNQ9zYcgBJj8IwfPlidoyvbdiDU1/8gknfvQ8Vib6jqTZ0vXedMQYdxoiD0u1krr2bWw/C1NkOncYPxc2tByRvq3qsH+YDfQ1VTDjwECrtgJ+H+eDHId6YfyJYbnszbXVM9bbCunvxCM0qgpWeJr7o746NI/0wbn8Qa2OspYbtY9rjcHg6lpwJY9v9Xw9n7BjbAb2231Y4+E6/fgPxBw/BY8F86Lu4IPn0GYT8sAYBqxT3LcrYRO3YhZKUVPgseQOaZqbICw5BxKbfoa6nB/NugaxN8pmzMHB3h8PYsWinqoLUS5cRtm49On70IXTtpAc5pbn5uPjFOjj37Ybeby9ATnQ8rq/dAjVNTbgP6SN3P5WxyYqMRfipS3Ab1BtlBUVy+7auL7+ALhLBfmpzdMknsAzogKCNO1CUko7eH7wFVXV13P91G+7+tBF9Pl4KRShj83jbn7Dw84LrsAGIOHZW7nZqq2vQ6eUZsO/dTeF1qQj6DntXboCWng4WrFuGsqJSHPjid1SUlmPsO7MUDvBOrN0D/xG90E6lHZKfxdVr8+TiXRbUG/3mdBhZmSEpNAaHvtmGyvIK9H5B/qBUkuzzZ9jLbv5iaFrbIPPYX4hf+wPcPvkcqjq6cm0yTxxBO3V1WIydiKTff2tw+/kP7qEsMR6qunpSz77GuL31AFIeh2Ew9RP6eri5cQ/OffULxn2ruG9RxubxwdPwGtYHvV+dyXyI+7v+wumVazHll8/YtUG+0NkvfoaJkx1Gf7UM6lqaCD15Cfd/+g0d5s/Ck8074fviNFh18UfKzbsIWr8RPT5YCmM3F/nH91kEHv66pUGbuPOXoW1mgsBlb0BdRwep9x/iyaYdUNfRhqV/B9aG9kvfzga9Vr7f5GtPGUyM9LD6k9nYvPsC1FRVYGMlvy9qCYl//YWsW7fgvngx67fi9u7Fsx9/RIeVK6GiodEsm6rSUoT9+CN0nZzg+8EH7JgkHDiA8LVr4Ue/101u0f1nM2wY7OoSVRgKjh8lelxcvREB08fAvX83JNx7imvrd7LnlW0H72bbRFy4yXySwe+9wt5PCgrBtV92seC7a19BHz30w9el7hNKYtn36sfw7C64DuRB323fZxugraeDl9cuQ3lxKQ5S31JWjjFvN9C3rNsD/+G92DFLDo9rVpuGmOZsixdcbPFJ0DPEFZXgZQ9HfBfoi9lXglBUJf/5ONfdARU1NdgcHo9PA8jfkz5Hau3a4duuPixoveTWU5RWV+Mldwd8H+iLl649RGVN433Mtc0HkfgoDKM+eAXa+nq4/NufOLHqV0z94T2FfYsyNgkPw3Dyqw3oNn00hi+bj4rSMtzZfZwlnVDfQtw/cAZ+w3tjwGsz2d9v7TiC0u3fQeftb9BOTdBGyNe9vKCrroY5Zx8x/+zr3t74vIcn3roaKncfB9iZwd1IF9/cj0JCYSm8jPXwZS8vGGqo44eHguDz1eQc9Nx3Xcrug0B3BFoaIVSJoH51ZRVOff4zmwid8uMHLGnm7Lcb2Zi+50uTW2zDxnNrt7PgKwWzJcdslOxxbvVmdJ0+Gt5De6OiuBTXNv6J/N/XwPS9L+sF6X/s6YfTiRn48P4zeBvp4dPOXiirqsaReAVjInd7pJaU4f07ocilMZGdBT7v6o13b4fgfqZgQt/XWB9TXW1xJK55Y6J/s2+5t/Mw4u8+Qf+3XoKxgw0S7j/FhW83wvC15VC3c5L6zD5WJljawRVfPYrEvcw8THSyxjfdvPHy1cdIkJloE7LYxxF/xaVhXUgMOy6v+jjhpx5+mHP5IQoqBeOUic7WsNHRwrrgWKzr1b7JkihPj15gr4FLX4axvTUe7DmOU5+tw+Q1H0NTT6fFNjE3g5AdmwgtfV32zJWHVJtG+puslCxsWrEBfSf2w/zPFyD6cRR2f7ML2nra8B8QINcm/EE47py+jT4T+iIvI5f1w7LQe5s/3MT8yJc+nQdzOws8uxeGoIsP0G9S/wb3icNpS3ApnueIBw8eYPjw4TAyMoKzszO+/vpr9iC/cuUKpk2bhvz8fKipqbHXBx980OC27OzssG/fPtHv/fv3h7GxsWhyIDo6Gurq6khKShJ99pAhQ2BgYAB7e3ssWrQIeXniTLs7d+6IPpuyzf39/bF3716pz6TsdPpcCmT7+PhAX18fw4YNQ3x8vFS7U6dOoXPnztDV1YWTkxPLYKfvKbudjRs3on379ux4DBw4EFFR9bNy5EGrGmbMmAErKyv2omB6amqq1Pbd3NzY8aDj8v3330NLS6vedihIL/zO9JI3KGyoTe/evVm2Pn1XOhaBgYGYPn06bty4odT3kPvdYhKR8jQcPeZNhqG1BYxsrdB97iTm0OUmprbYJvraPeTEJ7OMPFm0jQwwaOnLsPZxg4aONnvgew/pjaL4BCSdOgvL3r1gFtAJGoYGcJ46Gara2ki9dEXhd0k6fa5RGxpktlNVFb0ybtyCtqUFjDw9pLZVUVCAyK3b4Tl/nsIBrySU3VRZU4P1IbHMIb2VkYtjCekse0IRa0NisT82BUnFZSwIeTAuFc/yCjHAxkzUJiSvEMvuhuJaWg6qlY/JiHh8/BLMne3hP34QtA314TOkJxwCfPDwyIUW2QSfvo681Aw2eDJxsIa6tia8BnQTBfUJsu80diAMrc1ZFj9lCJk52aA6of59N9vbDucSsnA6PpNl9mx4msAC2y941M+iErLo4lOcT8xCVlklMkor8MXdSJhpa6CzpThL0MdEH1eSshGWW4TSqhpcT8lFaHYRfE2UX+FCd+BcPzvsCEnGrZQ8ZJZW4LNbkWw1wASP+llKQlZci8CZuCzklFUym18fJkBbTRXepnpNaqMIyqYKv3iLDbLMXR3Y5EqfhS+we5AmUVpqQ30PDZiFL9ns46HLFqDzlBHQMREfb3n4mumht70xPr0Whbj8UsTklWLVjWgMcjKFm7H8gUBWaSVeOxOKu6n57N6gyZTdwanoYKEPDVVBn+hipAM9DTX8/jiJHT+y2fIoGQaaanAyUrzaKPnMOVj06gVTf0E/4TR1MtS0tZF2+UqLbIri42HWpTML0KtpabGfta2tURgrDsa4zpwBix7doWFkCHV9fdiPFmQBFcXG1vvMqIs3WOZn59mTWF9p27k93Ab2Quixcwr3Uxkbcw8XDFj+OuwDO0GFZkPkQOda8tynPQ1nkwaWHXyReu8R/GZMhIGdDXQtzdFxzjRkR0SzlzyKM7KUsun1/hK4jx4KDX35wWzxzjV8XSoi5lE40qKTMPL1aTCyNIWVqx0GzB2Dp5fuoShH/goZFVUVzF+7DJ1H9IKGlqbcNp1H9saQ+RNg5WoPLV1tuHX1RaehPRByVTAB1RAURMm+cBYmAwZDz8sH6oZGsJo2E7WVFci7pfi5bj1tJqwmToWGueJMa6IiKxPpB/+E7dwF7HmnLOVFxYi8dAsB00bDzMUBeuYm6LHgBeQlpiLpYUiLbAa//wrs/H2hqacrWMU1dhBK8wtRkCZYPVqWX4jirFx4De3DMv1p4N9+/BCWOR575hLMfLzgOLAvW+3hPHwQjFydEXta8fOMMu0bs/GYMBpOg/tDx9wM6ro6cOjXi/2cG1k/E7Q5154y5OQVod/4j7Fz/xWUlolXJbUWNRUVSL90CTYjR7Ksew1jYzjPmoWK3FxkP3jQbJvCyEhU5OTAecYMttJSw8gITjNmoCQxEXnBMhO37dpJ+V+Kjh9N5Ji62KP92MHQMtCHx6CesOvkg+Cjis+zMjb0N7/Rg2BgZc58TpfeXVjWaHq4+DyryPR9MTcF39NvkOKM3thH4UiPTsLw1wR9i6WLHfrPGYNg6ltyFfctL69ZhgDqW7Q1m91GEdSzT3WxwYG4FJblm1NeiZ9CoqGpqooR9or9lrWhMfjtWRzzS+XhbqALZ31d/BIWi7TScpZEQTaUxT/QWuy7KoJ8kGcXbyFwxmhYkA9iYYJ+r0xDTmIq4h+ENtuGgoDXNu+HR9+u8J8wmAX9DCxMMeTtOaKgPkGJJo4BvqK/dxo3ELXFBajJyZD6TC9jXZYUsvpBNBKLyhBXWMqy9fvYmsLZQL7fcjYhE1/fj2YBevJb7mfk48/wFAxzNJdqR7688KWuooLB9mY4Ep0uEUJXDGXMU/Z9n0UvsP7Rwt0JAVNGIuzsdTZR0VKboAOn2b3hPbhnve1kxyXSkUaHsYOhqavD+m8as1VnpaOmVHoVyBhHwZhoXXAMcssrcTM9F8fi01gGuiLWBMewVc6JwjFRbCrCcgsxUHJMlFvIAv3X0rJR04SJ6rbQt0Rfuw/fUQNg6eXK2rj1DYR1ew8UXz5d7zNp7HgpJQvnk7PYPbY1IhFpJeWY5Cxe2SbLW7dCcDk1G9nllcgsq8B3T6JhqqUBfzOxj06rwlc/iUZEfuOTSPL8leBjF+A7qj9sO3hBx9gQPeZPZStoIuSs8GyqTWF6Fu5sO4B+S+Yq9FeUaSPJjSPXoG9iwDLuKZO+U39/BAzsjIt7Lyq08Q70xsurFsCnmw9L6JBH0KUgxD6NwdxPXoKduz00tTXRsW8nHtRXEjqsz+PrvwgP7D8nkFRL37594e3tjSdPnuDy5csskP/48WP2/p49e2BoaIiysjL2WrVqVYPb69mzJy5dusR+Li0tZZnj5ABTAJ+gv5GUDAXQw8PDWeB8woQJiI2NxbVr11jAnwLiQrp16yb6bAqS08TCSy+9hNu3b4vaUHCeJGpoX48cOYKQkBAW+Cb5GeFMLr1HUjUU4E5ISMDmzZuxdu1arF69ut52aALg2LFjePbsGTQ0NDB//nyljuXSpUtZVjxNRtDnjRs3Dtu2bWN/o+//4osv4p133mGTG9R25cqVcldD9OvXj010UGB++/btcj9LmTYEfX/K2CepnokTJ6K5pIdHs2WZ5u7ibAErHzc24MqIiG2RDS1ZvLP9EPovmdvgElEhRVm5iLxyB4Y+3ihNS4ORxMoFCjIaenmgIFr+ksvK4mKUpqY2yaaqpBRZQUGw6iteKik8thG/b2UyGvrO0lkUivAz1seT7AIpxzwoKw82ulosm19Z9NXVWTZ5a0HLjW3au0u9Z9feA+nhsS2yib3zBPYdvFjgXxkoYyjufjByk9Kh6iW9OkJNpR18TPVwL11aYuNueh46ksyOklBWFEGyLULOJWSit60JnAy0QfFgytj3MNbFuUTlJcgcDLRgrqOBu6ni/auorkVQRgH8LZTbP111Vcz2s0VmSQWCFMgLKdNGkozIOJbBTFI6QmigZWBlpvD8NsXm0eGz2DLzHex9YyVu7ziMSiWX2cvSxdqAnZNH6WJ5nDvJeaiqqUVnK+WOn7WeJiZ5WeJiXDY79kRIVhFi80ow09cGOuoq0FNXxUw/a4RnFyMiW75EUlVdP2HoJdNPeHqgICqmRTYUyM9++AilGZksCJnz5CnKMjNhGlB/NRBBUjwp5y9CRUMTht71V89khsfA3EvQrwqx8vNEcUY2SvPkXx/NsVGG6Es3Yexkj8pSQaaYmbf4+jFydoCajjZy5ARBCWHwvik2DRHyxyEce/ltnF+2EmEHj7MBojIkhcYymRwTG3GQxbmjB8v4amombGOUFhazgV5jUOC9urAAuh5eUvIu2i5uKImRP1GiLLXVVUjeugnmI8ZC09KqSbaZdf2ElZ90P6FvaYYMBX1Lc2zKCgrx7Ow1GNlbsyQBgiakLL3dEHnpNltRUlVRgdCTV6BpaIDSjCyYSlxHBAXt5QXgheRGxDTJhlajkXRPWW4eLDq1l/pbUWo6zi5eivNvvId73/+MgkTFshJtjeLERBaoN/ASX2u0mkjH3p5J6DTbRhhck0xUqfu5UCZ5Jv3yZdx7/XU8+uADxO3Zw/pWeWSEx8DKV9oHsW7vqdAnbY4NSTwlBgUz2SeHroqz8SMv3mJ/123A10kKq9+3OAn7FjmrfP4JKCvXRFMDjySkOyibPji3gK0EbS7CZCOpRNla9g9+xo0/z9Mj41i2K0npCDGo80HSJIKgTbXJSUhl0pGNyUxKQhOKIaevQ8XCFiqm0pMdHc0MUVpVjeBssd8SlCHwWzqaKX/8SIawuC5bWh5DHc2hpaqKIzHys9hlSX8WAyM7K+gYiY+1bXtP5mNnRSe0yIZkep5duIl+i2fI3Y5Ne08WkA49c5X1k3T8SA5Fw9MPKtrSkx3tTQzwWGZM9KAZYyKSQ2rNMdG/2bdQkFs2oY98tcrYyHqrYmjV98O6ldtCHmTls7FmU44d0VrHjyaH6JyTxJ5oXzXUYeHpovAZr6xNTVU1Lq3ZBv8pI2FkK3/iUZk2ssSGxMKto5vUe+7+HkiJTkZFCybQn15/AkcfJ5hKTDpxOM8jXIrnOeHbb79l2ek//vij6L2vvvpK9DMF5QnKClcGykSngDlBGeK0AoAyximgT0F6mjigNgQF1UePHo1XX32V/U5a8JRpTtn0KSkpsLGxkfpsyj6fPHkyDh8+jP3796N79+5Sn02Z9u7ugocqBe4dHBxw8eJFDBo0CN999x3LZH/33XfZ3wcPHowVK1bg888/x3vvvSfaBj1Mt2zZwrLqCQrE0z6S/I2k3I08YmJiMHToUJEG/tSpU0V/W7NmDfub8LuOGTMGr732mtTEAn3266+/zl50LP766y9W36CwsJC9p2wbIXR87t27xyYsKKj/zTffoLmU5hZAU19XytmgLCXKxijJK2i2DTmMl37aik5TRjCHMidBsdb8lfU7EH31Hguo02SB4+SpeBIaBnUZzWbKcC2Kk16tIaQiX+AANcUm485dFoiw6CktC5N0+iyqyytgP3I4lIWyIpJlsj5JXoegwRVlSzXGeEcr2Oho4kxSJloLWnKrYyid/U2SOlT3gAK1lGnfHJv8tCy4dO+Icz9uZwF7ysh37OyL7rPGsOtAMtNq60srmENL2Wc9507AQzs/qW2T9j1lLVFWjyS5ZZXsuCrL0s4ubPnzgwyxM7w1NIkNJA6NFgz2KIPoh6AY3EjJVXq75jqCY5RdWlFv/2z16q/MkWSUizm+7ufFJi8oYL/kQgjyyqua3EYeJXWZgLTsVxL6nWSuWmJj5e0Glx7+MHG0QXZsEq7+tge5CakY8aGgn2sK5rqa7FhJQrH5/PJKNmHSEN8N8sR4D0uoqrRDUFoB5h1/KvobyVyRlM/W0e2xwF+gRx6TW4K5x5+iQsHyXFE/oS/dT6hRPyGzEqypNo4TJ6A8JxdBKz5kv7dTU4Pbi7Ng6CE9IMwNCUXomnU04wxVHR14vrIQWmb1BwdlefnQt5LO9NOsO29leQXQNjJsFZvGoAzqlKBgdJ47BSV5+VDT1oJq3SSa6DP09VCeL/+aK2+GjSIsO/rCsX9P6FlbIjcqFg8370Zxeia6vPpSo7aUla9br1/TZQNrRVm1zYECeSFXHmDEa/VrtshSVXdt0bUkiZqePtPObwkZRw9DVV8fxn36Ndn27+xbiEcHTrEXPRcMrC0wZPliqcn/Ae+8zCR89swXyN5oGerD/42FuP3599CQecZrGCi+jigQQJP+ytjQdXTlvU8Fzyp1NfjNmQ4TD3ENDQ09XfjNeQEWnfxQVVqOiMPHcXPVavT94iPomJuirVOpqB/T0xP9rTk2+m5u7PqN37eP6eoLpXgouC+5XT0nJ9iOHg1dBweUpqQgdtcuhNNq3P/9r17mPvkg2jLnjK6jKvJBysqZPJMsytqUFRbhzwUCn4Tk0AJfnMiyb+WRGRWP3IQUBM6e2Oy+pbgV+5amYFqn7S/rV5FfStr5zSWqoJjJpSz0csL3T6NQVl2Due720FBRUcpfE/YT5FNKQr8rGnMoY5OfJvCbS/IKsefNL1CUlQdDKzOWvS+psU/c23cK9/edZteAkY0FtKa9iXaqavXkAGn1qKzfUlhRBVNt5fxSSiiZ5GaNTcHyA+7C2lI3U3PYqlNlkHedC4+L4uPXuA356pfWbkffxTPq9eFC9EyNMXTZQpxdvYnp9RMWHk4wmPlOvbZ0LSTJ6MELj6eyY6IJTgLZGJLzaS3+zb7FMbAjQk9dZpMENC6mVe7Jj8KkFAaESUpqNCaqqH/v0rFTljd8nZFUVCo1udcSRPehYf1jUZSR3SKb+3uOsWtSsiaBLMq0kaUguwAeAeKkHELPUCDzU5RXCBOr5j27s1OzYOtqi30/7GUa+5TI4RHggdELxnKNfc5zBc/Yf04ICgpi2d+tBQXtKROfsuspiD9gwAD2Hv1MkLyPMLB///59JqtDcjSamposO56kaoRBcqKyshIffvgh04onCR0K8lNmPmXdS6KtrQ1fX3FhQZoUIGkfWpFAUAa97EQArS7Izc2VkssxNzcXBfUJExMTtg9FRY0vR1uwYAGbFKFVAVu3bmWTE0Ioa75rV+klul26SGeM2NraYt26dUw738zMjK0UoGA9Tb40pY0QmlihgP/169fZ9581S76GJ1FeXo6CggKpF2XANQqtOWrqMkcJmwd/HhfIuAzr26hZ31dnYc7uHzDhu+VMRiJq20657dhEQhN3qSGb9GvXYdqpIzQMxFkshXHxLLDvOf+lFi+zFyWyKdG2u4UxK7y7JjiWDZz+ToRLCyW1M5tqU1tbg+DTV2Hl6YTZG1di5PKFSAmJwoW10rUeaLnzor0/YN6ObzDw9Vm4tfMoKh9K64sqoga1Sh074p0AZ5aNT1r6khqv/+vsgkArI8w+8wh99t3E0quheK2jE8Y4NyxhodT+1SqUCBZxIiYTnbZdQ789t3EiJgMbh7Vng72mtmmIehlA7VQUalMqa0PFdEkiiyZpbPw80PeV6Uh8FFqvoG5LqFXi+P3vQjh8NlzD8D33UVFdw/Tz65R42MB7z/iOuJKQg65bbiJw6y3cTcln75EcT1Og49HU7k7WhqS7SpKT0fHjD9Bt3U/wmD8P0bt3I/uBtCSLkY83ev66Hl2//xY2gwfi2fpfpOR6GvxMoV51E/a1OTaSxFy9jXZqqnDq1eVvPX7K4D9/JkzcnKGhq8OC/O1nTUbSzXsozZVe7dNkmntwZMhJzsC+VZvgN6Ar/IdJTxg3CdbfNn+fip6FIf/ebdjMnNv8fWDPrvrZhY32LUrYdJw4DLN3/YhJaz9hWvqnVq5BeZFAyoEyQamAqa6pMab8/Bmm//41vIf1ZRnycj+vwU6kVmkbkoga/vtaDPl5NXxnTUPwtj1IvfdQ9Hf7vj3ZS9PAQCAntWA2C/bHX1As4fU8wO7DFtio6erCa8kSVObl4dHy5exFgX5dSoSROM4OkyfD0MsLajo6bDLAdd48lvUvm9Xf0Gc29V6VZ0O1H2bv/hEztnyLPq++yIo9k4SUPCIu3oSehSnLzm0uTfGz/gnYc7cF9qS///69UPYc3tEvAAcHdYWmqgrLxm5SN1rPB1HCv2/ARtjHPDhwBoOXzGZ+KclIUvJJwqMwKbsuk4dh4Z8/YObPH7P6XqSxX1ta3Kp+qbm2BqsTdSctDzvCBBK1spCf18ncEIejlcvWV0hzNCJkbK7++gecAjvCXsEkF0H+3+kvf0XHsYMxe+s3eOHnT5mkWt6mH9gqxcYQhq+V2dseFsZ4w88ZPz2N+fvHRP9Q39J93hQmQ3fuq1+xc/ZShJ2+wqR5lBW6J+khZTXxX/NxQkdTQ3z04JlSdS9aAo0Pa1tgQ9K+0dfuovdisaqDLMq0+ad8YmZbU4v75+/D2MIYH+z8CK98sxhp8enY+umWFu/f/wfoOn4eX/9FeMb+c0RrFvYijXtLS0sWyKfXkiVLWACb/qfgNkntCAP7lAX/1ltvMU1/WYTZ8V988QUL/pPcDG2bdPYpQz0nJ6fR70DvSc5w1w9S1QUhJXptRceisUEqMXv2bLYq4OjRo0z6hrLzKdBP37Ghz2+Ijh074ocffmCBd5r8aEobOoY6Ojro1asX2w/K2qdJAZq8kIX+TtJABE2I0GQLPVB1jI0wfcPnLBOOioPRcRAtsa2uYe8pkllRxiYtLApZUfHYMm2JlO2xFd/DpWcA+r85V+ohq6qiwvQIey14AQff/py9X1koXgLLfi8orJeRL0TDwLBJNkWJiUzL32niBKn3C6Nj2PLwe8s/Er9ZU4PipGSkXLyM3hvkBxcoK8pIJiOVMtGJxjJTAs2N8FlnT/wWFqew0G5jVMRHI2e9YEXOr+RAzhzDMpW0jfRRKqOlWJJfBDUtDbmZKYQyNrrGhtA1MUL7kYLJQ01nHXSeNBQX1u1kRSOpYJTkag4qWOnRrytSQqPx7N5FqPv3lsrioeXNVAhLEpbVI5PpLY83OjphnIsVXr30FJF54gGAuko7THG3Ydr7wuXUpLF/IjYDs7ztcCxWuSygrLpMKhNtDcTki4tWmWipI7u08f2jLK+Mkgp8cycGQxzNMM7dEmsexDWpTc2JLUDoHWyq61rm7lgNnbrl9KUFhexcCKFlr5Ze8gtJNseGfVdHQa2I/LQMNhBuClklFTDWUq83pjTSUkdWScPHj3pnyr4PzynGh1cicX5GVwRYGeJeaj5Gu1tAV0ONafcLxy0fX43E04W9McrNHHtC6tcIUa/rJ6pk+4nCwnpZvU2xqSwsQuat2/BcvAh6Dg4iaZ6ch4+Qcv4CTDuLi3SxPlNVFRqGhnAYOwbZDx7iyRdf0TI63CQd/gE9ELhgBrQMDVBeUFgve17YB8ujOTaNEXPpFhx7BLBCohTUrCotY8FXSd1i+kxF22+OjbIYOgiuxeK0DGgbi6/nXxd9jpwUQca7haM1Fqx/D7rG+iiRCRCUFpaw7DpdCYmC5pKTkomdy9fBqZMHxrw5XSkbtbpJ5arCImhKqOXQtaam3/x9KomOQFVBASJWSBQ0rqlhRXdzLp2H57c/id5O3vE78u/dgTD0NXPbavYMEErl6Mj0E7SEXh5NsWG1blTA9Ij7vDYLu+a8i4R7j+E+oAcbwJMu/9APXhP1UZ0mj0D45bsoq6xCRaH0s4muI0X3rgrVKtLRVtqGnlUa+npwGNAbWSHPWNDeuqt8KS1qq2djjeL01ssm/TtRr7vWqN+SzMCn33Xq+qzm2lAQ33up+Fqje+rBO+/AuINiiRsdW8G9G/b992zEfJfqVUwfg/bjhkDb0IDJMElCzyySgFRT5Lc0wYatMNXTgWufrsxXDTt9lV17klSVVyD2xgO2PwI/V/CQ+e2Vz5Fb17eYO1pj/rr3oGv09/YtzUGY7Ut+aUKx2G+h33OUlC9TBG1v+X3pYPmufgG4nSG9EjJ392aUBt1mPikxf9e3ojECnStdifo8pQVFsPJW0LcoYUM+KUF+r7mrYAWf3/A+rN+IvvEQDp28ZcYcYPWfBr0xC9Ezl6Hq2UMpv5R8T6EPL+m3UDZ1diN+KdV62jCwPasp9N6NMIVBz/EuVsgsKcf1FOlxr5Di1e+gtqSI+X1WXi4Y89lbrJ/NS06X+4xXNGZTxiY1LIoVww05fbWuhWCvd7z0PvwnDUOXaaPw7MIN6FmYoNOEIaLEHSq+S3KNlTHh0HAXTwrklFfUGxMZayg/JlrV1Qu/hsYpLLTbGJUJMcj9WTAm2tZG+hZ1bS30mD+NvYTc2rwXqibSKzbzKwRjonrHT1O93goceSzycsQoB0ssvR2C6ALp2gfKUnT2KIrPH4UwTD3um2Wia4XuOyOpa6lI8bWnhA0dJ7bSZqG41iP1nQ/3nkDIiUuYteUbhW1O7ziFq4eu4M31b+PrueICzv2m9MeYuuz5ojzpc0eZ+tSn6xkpX2tNFgMTA5SXlmPIrKHsd10DXYyYOwIbl29AXmYejMwlvy2H03bhgf3nhE6dOrGMbkVQoVvZ5V+NQdr8J0+eZBn5FMS3sLBgsjEkO+Pq6sr09YWffeHCBRaAVhTkpqxzKuBL2fWSqwxIrkeSkpISNnFAtQKI9PR0JCYmin6n/0mWRpK7d++y+gFCyZ/WwMXFhQXy6UVBdAqW08+UYU/7LYns7/IIDg5mqwYUBfWVbUOTKA1NUCxfvpzJDgnbUrtfIq9DTUOwTUtPFzaAIZ1FczeB1BA9QOmBqWgAr4zN6FX0meJ9ouKcf/3va4z+/B1WtFMRwmuSirTlh0fCrC4gRvudFx4Bi+6Bcu3U9XShbWmptE3a1evQNDOFkY+0trX1wP6w7i+9yuDhqi9h6OEBl2mTFWbxk24pFdCVzLMMMDVkS5azyxWvkOhqboTPu3hh87MEHIiVX6xYGTQcXWH59Qb28wTnYtF+Wnm6sEx6SZKfRsDKw0nhvamMDQ0ySPdUCiUmtOgakR3lkAMbllOIAAtDHIkRDz66WhriYWbDy9hf7+iIye7WeP1SMEKypZ23WgXZcpT10pSiW/H5pUyGp6uVIe6n1UkTqLSDv6UBK3bbFFSoeGAz2rQbORcYMQcvdy0XDR6oABqdZ9JFdevdRVSngjQtrTyd5W67OTaEsCi2TjOkXEhCR0ddlRW+fZIhGEwG2hgx6aEHdcdTGYSZ+pLHhu5xyVPJfqylfqRWYT+hRf1ERKQo2E7byA+PgHm3wObb1F379c4t3YcUwWwAmmi16NMLbrNmwkGvSnSPmXk4I+rCDcGy7rr7OT0kHLpmJlKBU0maY9MQmeHRKEhJR7dXBKvCTDwE/Xv2syhYtBc8g/PiElFZUgoTd/nPi+bYKEtBkmD1nJbMdbnolxUSPbHgeNp5O+P6n2eQm5YN47rl13GPI9i5s/VSrpaKInJSBUF9Bz83jHvnRaVXe1HxW1U9fZREhUPXXaBBW0PaxTHRMBtRv+C8spiPGAPz4dL2kR+/D+NefWE2XFCwWYjNrJdYZn+geZmobzF3E/QTaaFRrAigsJ+gwnUWHvL7iebYCDPfmEa36JaVfy/RfaFlbITsZ5FwHSUYTBPZoREwbuA6MnZ3bbIN269aelYpfk7QPVaUmgbz9uIVpW0Z0sWn+g2FERHQqfONKYmhJCkJlgMGtJoNkR8SguqSEhh36qSwDdUtITxefRVGfn7wNqoQ3TcWns7Mp5QkNTgC5h7OCv2W5tgIz6M8PyH2VhBb3erev5vU+wt/lt+33Nh7Bnlp2TCq61vin9T1LZ4t61uaS1JxKXLLK9DR1ABP6uQwyG/xNdbHzigqgtp6eBrqwVZXGzcypGs1GE2fB6MXXsI4x2JR32JJfqSKClJCIuHeR9BPFGblMnlHRT6IMjZmTlS0XqP+uWaplor3nY2d2Ev6/cdZBdBWU4WPiR4rhksEmBsyv+VJlmK/1ExLnQX1qeDuu9dDmX8rD9JRH+lsgb+i01hihzx0ln7H9m26H/XN7UTjr9Az11mgWSiZQ/45TWKau8gfWylj8+JmCoKLdyT+3lOc+24zXvz9Swl5zXZyVmTV/S7zzAvOKcRYR5kxkblRo2MiCup/GeiNTWHx2B+jWMK1MdQdXGD+5W/s55GOpW2qbxFSVVGJhHtPoNlRZlKR6rzlF6GTqSFOSsgQBZgZsVpuDbHAywHjnKxYgeEwmYB2U9AdPBq6g0ZhlGOJ6N6l70PXT1poJFvRS1DCBhUI9p8sX7qWJs8as/GfPAKdJknb7331E3gN7sVW9zXUps+Y7hgyYyiTev3mlLQEMuHk64xn96QnISMfRsLaxVqpOkiKcPJzRlbdBK/sZ7ZoSRSH8w/DpXieE6iIKwXgqShtZmYmC4hTMJqK5xKkU09yLiTloiwUzP/zzz9ZEJ+C+gTJ/ezcuVOUrU8sW7aMyfaQ1jzJ1hQXFzOpHiqmK4QkeGiSgIra5uXlsQC0cN9koQx5CuZTAdtFixaxYDpp6RMUtCadf9Lwp8+hCQNaDSDU3G8NSOrm9OnT7HjRvtLEAdUYIN544w1WlJeK6dIkBE1o/PyzdFb3999/zyR80tLSmPTPrl272OQArXZoShuaQKFjTeeSsvjpu1I9geHDh4vOhyw0KUDFeOlFUkQ0UaChrc0eggQF5i29XXF720HBIDwjG3d3HIZNBy+YOIgnRrbPegdPjpxT2oa2T46A8CV0qsgJFP4cff0+go9fRFFmDtPCzYlPxs1Ne1mAnvTt067fQG5wCCtyG3/4CBtQSgbdI3fsQtCn4qLPtsMGN2ojDJxk3r4Lq9696gVg6MHcjvZX4iX4A8Q/y+FYQjq0VFWwyNsRumqqLKg/xtFKyjH1NzXEhZE94aQnkFmhNhTU3/QsAftim+/Aiva9bn/Z8a5zMDqM7oeMqHg8PXkFFaVliLhyDwlBIeg4ZqDILuTsDfw6+U3mbClr035kX1asLPTcTeac5ialIejwOTh1aS/K1r/4824kB0cyXf6KklKWORVx9R7UOoon84TsepaMYY7mGGBnCh01VczxtoO9vjb2RoiPy5JOTjg+Vix7tbiDI8vIf+1SMJ5KFDgTQgMq0tKf7W0HdyMdNiALtDTCaGdLXE6SrwcpD3LLd4QkY7avLQIsDVhBqve7uaK6phaHI8XZRGsGemPLCEGxRWtdTXzW2x3uxjpsME1a8su7u8JUW53J7SjbRnRu21GGq/h+IshZdu8XiPt/HmeBd9JKvb55LytC6RAgrmNw8N2vcHXDHqVt0p7F4PbOv5CfmiFwwCNicW3DHsF979H0IAUF8++m5OGj3q6sCK6tviZW9HTBtYQcROSIs4mCF/bGojqt/DHu5pjX0RY2eprsvHmZ6uLzfh5MQ/9humBgczk+h/1tRS8X6GuoMvmdj3q5skHR9STFNRRshw5GukQ/kVDXT1hJ9BNRO3bh0cpVSttQ8N/Awx2Jx0+iJDUNNVVVyHn8BNn3H8DUv6OoWG74xs0oSkhkfVBFQQHi/zrKVgNZ9Oguvnfr+iS3Qb3ZBOqjPUfY/ZP29BkL2nuNEt+HacHh2DPjDeQlpiht09SiuYZ21jCvC87rWVnAyr89gvccRnFGFkpz8vB0134YuzpJ6ZGffmMFQvcdaZJNY6Q9CsazwyfZNui6zAwNx9PdB2HRwYdp7ksi/fwRHE8Xfy9YONng9C/7mCZ2dlI6Lu88Ad++AdA3FUwMVJSV44sxb+HRWfnSHPKgiYJdy9fDwc8V45a+KPo8ZaBzbTJgMMuiL4mKRHVJMdIP7WP6vEbdxf1k4qZfELfmuyZtV/5zTPz8lW0r27e49Q1E0N7jyEtKZRr5t38X9BP2En3Lkf99hRsb9yhtQ8GRB38cRUFaJmqqq9n/13/ZBQ0dLTh08RMFUSiT7872Q2wbpGH89Mg5FGdkwmnoAGQFhyLp2i22CiT+4jXkRkbDeaj4+o4+cRanFwhWUxIuwwc1avPwt63IiYhmtXUqi0sQf/Eq0h88hl1vsczjI2oTHsnalOXk4cmW3ax+hOPAPngeUNXUhEW/fkg+eRLFCQmoLCpiBWwpK9+ks1iDPPS77xC5cWOTbBIOHUJRbCzr9woiIph+vkX//tCpS/YhiTKyo2A+tSmOj0f01q3QtraGoa9vPb/FZ2R/ZEbFIfT0FVSWliH62j0kPQyBr0Q/Fn7+Ora9sETktyhjc/3X3UgNiWDXFPWPUVfvIubaPbj1kw7eEyShYe/vV29CVF7f4uzvBXPqW37dx+p1UN9yZecJ+Mj0LV+NfQuPm9C3tATyWw7GpWKSkw0ruKmnpopXvZ1RXVuL00li/2JlgCe+D2za5NSLbnboYGwA1XbtWFB/RUcPXErJwkMZLW95fQtpZHv2D8SdPSeQU+eDXNu4D0Y25nDsLO5b9r7zNS7/+qfSNpQ9TStIHx4+j5ykNOaXhl24jYzIOLj1FEzIp4RG4fauo6zILvU/pMt/cd0uQEsbap6C57QQCuYHZeRjaYALLHU0mL/2lr8zbqfmIjpf7Ldcm9yT+ZjCVZy/DeyApMIyvHsttEEJlD62JiwD+0i0dCa94uMnuNZILkfPzBjXN+1lq6FIHifo4Gl4DeoBDV3B2KI4Ow+bpi5B7O1HStvUH7O1k1hdJfzsDuz4Pz1xifkZpD1/a9shqBgaQ81O2jc8Gp/GxkSv+DixMVFnM0OMdbTE3mixnGOAmSEuje4FJ33BxAG1+aKrNzaGxWNvC4L6DY2J/s2+Jf7uY9avUJvinDxcXbed1ZbRHSg92U7QcRpoY4Y+VibQVlXFDFdb2Opo4VCcOAHsFW9H7B0k7odf9nRgdQkoUz+0BUF9RfcuvUfSQZRFnxYWzeTz6DlN1457f/Gz8sJ3m3By5VqlbaiN5LUn/DxaIiO87hW1UWmnImpDyaTCl7COZK9xvZGbnotzu86irLgMIbeCEXThAfpNEsesnlx7jHeHvoO8LOUlHXuM6omSwhJc3HuRFeHNScvGmR2n4eznAiMznq3PeX7gGfvPCSTjcu7cObz//vssIEyBX5K6EerV+/v7s4A5ScxQwJoKzVJAvCEoeF9VVSUVxKefZQP7VLSXAvkUdCZtfdLPJ9ke0tQXQpMML730EiuKS53wkCFDMGXKFFTI6L/Tfo8cOZJNIFBwn/T0qciusNOm73ngwAE2gUFBdtKnf/nll9lEQWtBwfVPP/2UFc0l56BPnz6sHgBBhYOpuC99t8WLF6NDhw5sPyhQLzkx8Nlnn7E2NDFAx4QKEZOOflPavPjii1i1ahX7bllZWWxyhlY90ERKSxi0dD4LqB948zP2/ew7+6HnfHGBYFHmgYSTqoxNY5BN8NELzAEgR5SWizp09oPTgjHQMDJkgbCILdtRUVgIXVsb+L71hlSRSbZP1eJVJ9b9+rKgW0M2RFbQQ1SVlcGyd/0Ac3PJKqvAsruhWOLrginOtmwp5R9RSWxgJYT8ZApECh3MmW520FJVxSveTuwlJCgrD/+7K6ghQQHf08MF2RxkSwO0UfaWSCgqwUtXBU57Q1i6O2Ho0nm4vfsorm85yJz7fq+8wArdSmU907GsVd6GCo6N+vAV3Nx6GFc37mXaks7dOrDiuUL8hvXBvb0nkRpGdTVqWYCHtnPHRNxXCDmXkMUGOe92doG5tibiC0rwztVQqQEUZbJTEVXhsVzg58Ay77cMkR6QrXkUi93PBIOHT29HMKme9f392LJqKlC2JzwZmxsoZiaPTY8TWfbW2kE+LIAcmlWE+WeeShW5VVFpxwa6RGpxOe6m5uPLPp5wN9ZFYWUVgjML8eKJx4jMLVG6TWP0nj8VN7cdxF8ffI/qiirY+LphxAfShShJJoutlFDSxsLNEVkxCTj7zUaWEUdL32lAFzBlhFRQkAaJYeduiPKxts5aCvVaoGrsG6h1lA4UvHo6FKv6uuPCjK6s9YW4bHx8JbJeBpsw4eViXA4W+Nvhj/EdYaWnyeR8LsZlY939BNFgmZa5zzsejKXdnHBzTne23WdZxZh37CmSCwUrG+RhVddPkCZ+ZV0/4fNmw32LMjaeixYg7sAhBK/+ngX9NU1MYD92NKwHDxIFycy6dkHM7j9QnJgIFQ0N6No7wHfp22xFkCwUUBrw/qu4v20/np24yAqW+4wdAs8REpmydfduU2xosLpvjmAVF9lmRsQi+uJNGNhYYtR34md0ZVkZEm4FocO0sVL7FfDKHDzZvhcXl38hWKXVwRud5k6XylyTfV4oY3Nv3Wak3HskyqA8MltQNL7fymUwcnaAuY8nChKScfPb9SjNyoG2iTHse3WDxxhxJnZD0OBv2icLcernfVg371OoqqnCu7c/hi2aJHE86/ZdIlP79zdXIy0mWbRfFPgnlv75FbR0tfH43G0UZOYi9OpD9hKioa2J/+1rvLC92dARqK2sYMF7ynLWdnCEw2tvswK6IthKJ/F5zr5wFul/CYoXEjFfCeT2rKZMh0lfxZnUTaH7/Km4s+0gjlM/UVkFK183DFkh07ew81yjtI2FuzNyYpNw4dsNKEjLYlIOVEhw1OdLoVUnjUPZoUM+eA33d/+FQ2+vYtsxtLVEwOvzYd01gE2iRR4+jsebd0LHwgydFs+DiacgE5Cg6662Rqz3bObrhQ7zZzdo4zigDyL/OoG8qFj2cCGJHf9X58E6UBw0cRjYl7XJjYphklKGzg7o+eG7MHAQBPVag+CrP8LRzpw9S8jHLYwR1KzRd1FcR6kp2E+cyK7hsB9+QE15OfRcXeH11lusbxJCWtmS51QZG9MuXVjgngL2GkZGsOzfH9bDBJmWBAXwKcgftWkTStPS2MSAUfv2sBs7lmUNy1v9MeDtl9kk0N2tB9iKo54LpsM+wFfmPIv9FmVsvIb2xsP9J5HxLIbdz4Y2Fui5cHq9wD5NaqeHRWPw+68o3bdM/XghmzT8WaJvGbKw4b5ly1urkS7Rt1Dgn3h7j6BvUbaNIv6ITmL6958FeEFfXQ0R+cVYdi8UBZUSfgvaMd9KyBRnGyySWGWwubdg1cW60BgcqZOKvJyazXxdCu5TQU+aKGjKKoC+C6bg+pZDOLT8B1RVVMHWzw2jP1zMjpvocMn4LcrYdJsuWKV05MM1LCmF/NQhb8+FfSfBylxLD2dkxSbh5NcbkZ+axQoc2/i6Q+fl5WinW19KZNn1MLzfxQ2HRnVh/sXV5Bx8c186c5t8UqFXNMzBHM6GOiwp5fqUXlLteu+/IRXoJxmeu+l5SC4WrJRSFjUNdYz86DVc37gXuxd9CDWqYdenC3rMFRd4pixxyWtNGRtlsG3viYFvzsHjv87h7u6jbLu0CtRo/ltQ0dSqNyb6350QvOnniqkugjHR7shkHJRYmUxXHRsT1f0+090OWmqqWOzjzF6SYyIKVgvHRGdH9hSPiUwMMNrBio2J5lwWP38V8W/2LTbtPXFv11+4u/0Q+93O3wcjP3sbV4p16+3npdRsGGnGYomfCyuEnVhcig/uhyG2UGZMVHfv0jU4x8OejYl+7iUtgUYyr/vqJkp6WZpgVRfxSnX6mb7nwdgU/BzaeK2njuOHsEkdCt6XF5fAzMUBwz58TargMq2Ylbx3lbH5u7Cws8D8zxfgyG9/scC7vok+Rs0fja5DxSt06XwxtYC6812YW4iV0z6p+y41SI9Px+X9l+Do7Yg31rwpkuIhXf3DPx/Cqa0noK2nDa+u3hizUNpf5sjnv6pX/zzSrlYZUXLOcwVJtNAAWxgsbwgK7EtK7NDlQPYUvG8pQhkW4X5Q1jpl3lMWe0sQdtpCfX/J79Ia+y1P1572vSmrIf5Jvn0iyLxvChQcZNn2LeiNKUNGNBPfABEF0rqCihA6Dk0tcttUO9n2kXnK7Z8iORHhstt2Cmpe1SkTSNnU2yeSG1HwGROdm1ZoShjYV+bctAbbHytfGFYSOgx0+QnHRvKOC6FoWbMylJY231h2//4O5gcqDlg3eO/SipMWFoOWRTZIQfxyTUMgPdOMfoLuhb+5xhcGdKj9W/uWluKoJw64/J33LvXF9ZDjA8j22SVV7f624yfveiKa8t38jJt2/ORB35llq0nUj5FX1VG4X4r2m3oEyez9Q3FCOYOmI3s82efJk1KU2G+522CTZ/L/3q1OiqcpkF/V7h+6TzLKlLsOhAHQf/re/XXKphZvgwL68s5PtcQkY3OZsvOVNt33+Rgpluf4p/0Wedu21qn5x/sWZdtsi2x+gIzdvxKFTdGAX9pcxtdJ8bQFv0UeO540zyeV57co65eSnRwFILnMYFI8LbvWmkpT7q+/YrXb9JholKO4xkRbHBOdTNBplXtX2WtPVYljLGR0nRRPUxBOijRl9WJzYg7Uxs9UEL9q7RiRUOZYEpqCaup3GmU/okX79l+ly5/X8Dxy/4XnY4VmU+AZ+/9BZAPeDSEbCKcOtbWC48pMLDQH2kd537G19psy+l9//XWWQU+FhSlbv6VZ9G2NljygxdtoXSepuQ5/U+1ac2Ah6WDVKhmEbkmgWhmE8kNtHaaZX/vPHZeW7t9/6d6VBxs01v+wZm/v7w7qN4V/Oqj1T9+7yvbFze2zm3P85F5P/wKy37mx++ef2G95knFUgLkl22jLPltLYMfmOU0HE9QG+Xc7wv963/dvbrupfYuybVqCvCuuLTyL/+7v3VrIHitl/dK/+xi3dLz1T40L+Jio9e5dZa+9v31M2Qo+kbJ9Y7t2NX9LjKgpcTEO53nm+XjScpoMSelQoFve65NPBEuS/qu09Lv36tULEydOhJGREQvwU1CfahxwOBwOh8PhcDgcDofD4XA4HE5bgGfs/0ehoq+KVJb+raysmTNnYvr06W3+u5POPb04HA6Hw+FwOBwOh8PhcDgcjhhhcW7Ovw8P7P9HaYvLjhRJ6Px/+O4cDofD4XA4HA6Hw+FwOBwOh9NacCkeDofD4XA4HA6Hw+FwOBwOh8PhcJ4jeMY+h8PhcDgcDofD4XA4HA6Hw+FwGqUdV+JpM/CMfQ6Hw+FwOBwOh8PhcDgcDofD4XCeI3hgn8PhcDgcDofD4XA4HA6Hw+FwOJznCB7Y53A4HA6Hw+FwOBwOh8PhcDgcDuc5gmvsczgcDofD4XA4HA6Hw+FwOBwOp1G4xn7bgWfsczgcDofD4XA4HA6Hw+FwOBwOh/McwQP7HA6Hw+FwOBwOh8PhcDgcDofD4TxHcCkeDuc550W3MrRles/PQlvGbY79v70LzzW9navQVjHUqEZbxs2g7R47YkxA23YRTqxNRVumsrst2jKvD6hAW6a0Dd8eltpteOcA1NSiTROUpYm2zCfH5qEto61WjrbM+yO2oi3z9amX0FYx127bfktaqSraMjZmaNNklLXtnEqdtt01I7+ybR8/C922e/8mFbdtn97TsG37VRz5cCmetkPb7h05HA6Hw+FwOBwOh8PhcDgcDofD4UjBA/scDofD4XA4HA6Hw+FwOBwOh8PhPEfwwD6Hw+FwOBwOh8PhcDgcDofD4XA4zxFtW2yLw+FwOBwOh8PhcDgcDofD4XA4bQKVdv/2HnCE8Ix9DofD4XA4HA6Hw+FwOBwOh8PhcJ4jeGCfw+FwOBwOh8PhcDgcDofD4XA4nOcIHtjncDgcDofD4XA4HA6Hw+FwOBwO5zmCa+xzOBwOh8PhcDgcDofD4XA4HA6nUdpxjf02A8/Y53A4HA6Hw+FwOBwOh8PhcDgcDuc5gmfs/z/k0KFDWLhwIbKysv7tXeH8Q1w+9xjbNpxFWnIObOzNMG/xMPQe4KewfV5uMf7cfgnXLwUjN6cItvZmmDyzD4aO6ixqU1lZha2/nmXbzskuhImZPgYN88ecRUOgpqba6D7Zmevi4zmdEehlgdLyKhy9EYfVex+jqrq2QbsXh7pjxiA3WJvq4mlMNlbtDEJEYj77m562GmYN8cCYno6wNdNBUmYxdp+LxJ6L0Wgq7gZ6eMXLGa76esirqMSxhFQcjE9u0KaLmRFG21ujs5kxTiel4eewGKm/L/FxxXBbq3p2eZWVmHH5rlL7Ff8gBLd3H0N+aib0zU3QefIwePTt0mKbsqIS3N1zHLF3nqK6qgou3Tqgx+zx0NTVZn8Pu3ALl3/9s962B29Yg4qiYjzbvRfZIc+goq4Gy64B8Jw2CaoaGgr3qSw3T2mbiqIi3PrkS5Tn5qHPt6ugbWbK3q+prMT5RW/Wa+8zdybs+vZCflIqHm7fj5zoOKjraMO5f0/4ThyBdiqK57SVsWmsTU1VNWKv3kbMxesoTM2AloE+7Lr5w3fSSKiqq4u2c//sXVw7dBk5adnQNzZAr/F90WtcH6n9iXkSjRObjiA9Pg36JgboO3kAeozupXD/a6prEHYnBLeO30D0o0j0mzIQw18aJdUm5FYwdq7cUs92+a6PYWhmVO/9tLsPEH3kBEozs6BtYQ73iWNgEdBJ4T401Sbp6g2EbtsNs45+CHjzVdH7RckpiD1xBjnPIlBTWQUDJwe4TxrH/m+8b+mCQG+JvuXPR0r0LR6YMViib9nxQLpvGSrsW3TFfcuFKLQU1XbAuz2cMcnbErrqqrifWoCPL0ciPr9MKfvfRvlgqIsZPrwUiT+CU5u9H4lBIbj/x1EUpGVCz8wEnSYNg2ufrq1mE3v7IS79tBWWni4YtfKtRtvM/GqJ1N+enr+FewcvoDA7D6Z2lug7dxwcOng0uH+N2VSWlePazuOIvvsUpQVF0DM1gt+gbug6cTDa1aUhUd9444+TiLkXjNKCYli62cNq0lToOzqgODkFkX/sQ0FMLNR0dGDdpyecxo5qsI9Rxqa2pgYJp84i9fotVBTkw8jdHW7Tp0DH0qLe9krS0nF/1VeoraxCv43r5X5mdWUlHuw+gtibD1BdUQkrXw90e2kKdM2MFe6nMjaZkXF4+tdZZEbEsP03d3eG/wujYWRn3WCbygGToW5lC2XoZ2WKuR72sNLWQkpJGbZEJOBGeo7C9k562pjuagd/U0Ooq7RDRH4xNofHI7KgGM2lMCMLd7YcQFpYFNQ0NeDSuwu6zBgHlQZ8ncZs6BzH3HiAsDNXkZeYCk19XTh0aY+AaaOhrq3F2gQfv4D7u47U2zZdmnN+/wLaBnpyP7umuhp39pxAxOW7qCgth7WXM/rMnwJDa3OF+6usTXpEHGuXERkHHSMD+E8YDO9BPdCakA85fkRXLJg1BD26eODDr/Zg7eaT+Lsg/+bh4fMoys6DsZ0lesweB7v2ni22KczKxe1dR5H06Bk77579A9F12giRH1Cck4+gg2cRHxSC8qISGNlaQnvgSBh28q/3ebW1tcg4dRLZ16+iurgY2o5OsJ36ArTt7BTuY1VREXJu3kD2tauoyM6C54cfQ8tG+r4rDA1F5oVzKImLg4qGBvQ8PWE9fiLUjcS+APlaqYcPIu/+PYRUVsDcxwMd50yFjqlJg/1H8J9/IekW9R/ybZRpc/HDr5EfnyS1bcf+PRHw8gy5bYRPecMevWE5fbaojZ6aGhZ6uqCLuQlqa4G7mdnYFBGDkqpqhd/BXlcHI+ys0N/agrWbf/1evTZ7+veAtqp0P7AjKg6HZPa5MYozsvBk5z5kh0VBVVMD9r26wmfq+Ab7GGVscqJiEXv+KlLuPYSxqzN6r6jvM7f1MVFJyBPkHj+Eqsx0ZJuZwGv8CPZdGyLtUTBC9h5FUXoGu57k2TTW5tqXa5AVFllv2yZuzuj3yVJRP/7sr9NIvHEXpbn5UNHTh6F/ACzHjoeKhL+fc/M6Ms+cRmVeLrSsrGE1YRL0vLwV7n91WRny791F9tXLKEtJhuPCxTDoKO1DV+blIfPMSRQ8fYrqkhJoWlnBfOhwuf2HJORbh+3eh/yYWKjr6MC2T0+4jmvYb2mKTXFaOm6v/Ir57UM2S/skdLxiT55FMvk2+fkw9nCH54wp0JXj28ijLYyJOJy2Cg/s/z+kpqYGVVVV//ZucP4hHt2PxqoVu7Fk2Xj0G9QB504F4ZNlO7D299fg28FRrs1f+27A0toYq39ZAD0DHVw9/wRff7IXenpa6NnPl7WhoP7Jv+7g8x9egouHNcJDE/Hx0u1QU1PBnEVDG9wndVUVbHu/P6KSCzBi2UmYG2vht3f6QFVVBZ/vDFJo9/6MTpjY1xnLfruDO2Hp8HIwwtT+riKb6YPcoKulhrd/vomUrBL08rPED6/1YNvdda6+c6YIEw11fN3FD+dSMrDq0TN4GuphRQcvlFVX40RSmlwbb0N9THC0xYnENBhqqENVztq0daHRWB8mnmRQRTts69sFtzMUBykkyYxOxKlvNqHbjDHwGhCI2LtPcWHtTja4t+/k1WwbGlwd/WQd1LU0MObjV1nwP/r2I0TfegSfwYIBe21NLQt6zfzlY6ntxxSqIuinn6Guq4uen32AypJSPFq/AdVl5Wi/YK7cfSLHrik2wb/vgJ6tDcqyc2h0K95OrWBbXd9/B0burqL3ydGsKi3FjS/XwbK9F0a8+jEKUzJw86dNUFFVgc+EEXL3i/bjSiM2yrRJD36GwuQ0dJk/A3qW5siLT8Lt9VtRVVqGgJemsTbx1+/i/sa9mLZsJjy7eiM1OgW7vtgGNXU1dBspOObZKVn4/YMN6DOxH176bAGin0Thz293QVtXG50GBMj9DpFB4bh7+jabJMjLyGX9vbzjr6ahhpWHv5J6X1VmcEpQUP3Jht/hPfMFWHbxR8qtu3j080YELn8XRm4ucvehKTZFKamI/us4DJwdgRrpwHvEvsOw6tYFbpMoGKaGqMPHcO+bH9Fz1YeiyR25fcvyAYhKyseI/52AubE2fltKfUs7fL6jkb6lnwuW/XYbd0KpbzHG1AGuIpvpg9yhq6mGt9fX9S3trQR9i0q7JvUt8ljWk4L6Vlh0IgRJBWVY2c8Nuyd0wKCd91FeXf/8STK7gw0MNNRQVVMLlRYsh82KScT5bzeiy4wxcOvXDQn3n+LK+p3QMtCDbUfvFtsUZubgzraDsPRwQa2C79RQm8hbj3H+130YvmQmHDt54tHJazi8agNm/biMBezloYzNpc2HEP84HGPfmwdjW0skhUTh2DdboKGthU4jBZNsJ77fzgJw4z9YCH0zIzw+fR23V/+EgA/fw6Pv1sDE1xvdvlzJAuzBv2xAO1VVOI0ZKXefqF9SxubZ1p3IDXsG75fnwsDFiU0CpF6/CddJ46W2RwG3kN82w9DdDbnBoVDE3a0HkPwkDIPfXwxNfT3c2rQH57/6BWO+fR8qcu57ZW2eHDoNz6F90GvxTFSWlOH+7r9w5rO1mPzzZ6Lgpbw2Ob+uhsVHq9FOTRz4kEdHEwN85O+BtSGxuJKWhSE25lgZ4Iklt54iNK9Irs1CLydcSs1iwXy6L17ycMCP3f3w8rVHSC8tR1Ohie6zX/wMI1trjP/+A5Tm5ePC6o2sH+02d3KzbTKj4pEeFsV+p4BuXnI6rq7bjtK8AvR/ax5r4ztqIHxG9Jfa9unP1rH+TFFQn7iz+zieXb6D4cvms+f59d8P4ujK9Zi+5gM2ydBcm9RnMTj66Xr4jx+EIW/NRnVVNe7vOw3Hzr4syN9aTBrVDWOGdcWXaw5h29rXodKSzq0RYm4/xpUNezHw9Vmw7+iJp6eu4cQXGzD1u2UwtrNqtk1JXiEOvf8DrDydMOmbpdDU00HouZtIfhoJhwAf1ubR0Yswc7FjkyN0jJ9duoObG36F27vLoOvqJvWZmefOIuPcGTgtfAVaNjZIO/IXYtb8AM9PV0FNV1fufqYdO8qCi9bjJyB+0wZJ94lRWVDAtmk+eAh0HB3ZhEHirp2IWfsTPD74iPVLRPK+P1EYEgLn15bA1VITQVv24Oa3P2PglysU9h+Pd+xHxpNQ9Pzfq9DU05Vro0wbehb4Th0Lt5GDRNsWTroKkWwTklt3fcu0ea+DF3TU1LD07iPmey/r4I13/Tzx2SPF/ebrPm64npaFM0lpLLgvD/L1v3kahjuZ2aL3ZFyZRqmpqsLNb9dD39YKA7/5EOV5Bbjz4wYWlOzw4pRm21QUFuHpzv1wGtiH9T2lOXloDv/mmKg8MR4Zm9fDePRE6AX2glnsPdz/bTs0DPRg2V6+b5Ibm4BbP2yA77SxcOzTHSkPntSzUaZN7/ffYBNqQiiJ6fSSD2HTpaPovYgT5xF58jy6v70IJq6OCHmWgfgNv6K2tgY2kwX+fv6jIKT8sQt2s1+CnrcPsq9cQtwv6+C24iMW5JcHtanIzITN1BcQ88Nqqf0QknXhHLTsHVgwv526BnJv30TCxl/hsrR+/yHpg9xfvQamvt5o/9VKFoSn8Rfd665jR7bYhnySx79shpG7G7Ll+CTBW3YiJ/QZ/BbMhaGLE/KjY5F87SY8Jkv7NnKPSRsYE3Hq047rv7QZ+Kn4j/LgwQMMHz4cRkZGcHZ2xtdff806sytXrmDatGnIz8+Hmpoae33wwQcNbsvOzg779u0T/d6/f38YGxuLJgeio6Ohrq6OpKQk0WcPGTIEBgYGsLe3x6JFi5CXJ3Ym7ty5I/psPT09+Pv7Y+/evVKf+ccff7DPXb9+PXx8fKCvr49hw4YhPj5eqt2pU6fQuXNn6OrqwsnJCZ999plUpy3czsaNG9G+fXt2PAYOHIioKOWyLNeuXYtOnaRnyM+ePQstLUE2FUErH2bMmAErKyv2mjlzJlJTU5X+Hg3Ztwb7dl5BQKA7xk3pCSMTPUyZ2Rc+7R2xf9cVhTZzFw3FhGm9YG1rCn19bYya0A02tiYIfizebwrkd+nhCb9OTtDR0YR/Fzf2ehaa2Og+DeliCwdLPXyw+R5Sc0rwJDoH6w4Gs0x8CszLw8lKHy+P9MKnW+/j8qMUlJZX42FkttREwKbjz/D9vicsy7aotBJn7iXh+K0EjO7RcIavLCPsrVBZU4MNz2JYZsqdzFycTErDFGfFmVFh+YX44EEIbmZko0aOA0bU1jn8wpe/qRHMtDRxSoFjLMvj45dg7mzPBtXahvrwGdKTDRAfHrnQIpvg09eRl5rBBvQmDtZQ19aE14BuoqC+JDTYknxlhz5DYXwifObMgLa5GQwc7eE+eTxSbt5BeZ4g21mWptjEn73AAuJOwwcr/I4UyJfcJxr00bYqS0vRed4L0DY2goWvBzxGDULEqUssM1Ee8TfuNWqjTBvrTr7o9OIkGDvZs8xLcy832HbtiKwIcbYSbcenhx869Q9gTqlLB1f0GtsHl/48L2pz8+g1GJgasOwSPWN9dOznD/8BnXF5/0WFx4ImCcjh9Q70qTf4lYWcVsmXPOJOn4eJtxfsB/aFhoE+nIYNgqGrC+LOiPezuTaUBfz4183wfGEytIzrZ8UEvP0abHp2g7apCTQNDeD94gtsMJv1NEThZw/paifoW34X9i3ZdX2Le8N9yyhvQd/yUNi3ZElNBGw6Hibdt9xNxPGb8RjdU/7kqLJoqqpgTkdbrLsbjwepBUgvrsD7FyNgraeJ0e6Ks2sJLzNdvN7VAW+ffYaWEnLiEkxd7NF+7GDWT3gO6gk7fx88OXqhxTZ0X1z+aSv8p4yEgYKM4cba3Dt8AZ69/OHdrwt0DPXRc/pIGFiY4OFxxc8xZWzSohLg1q09LN0coKGtCZcuvrByd0BaZAL7O2XoxwWFoeeMkTB3soGWng66TR7KruuoP/axAa/HizOgaWwEY29P2A8djMSzFxT2Mem37jRqkx8dg7Qbt+D98hz2d1VNTRh7e9UL6hNR+w6ylQPmDaygKS8qRuTlW/CfNhqmLg7QMzdBj/kvIC8pFUkPQ1pkM+i9V2Dn78uCcnoWpvAdMwhl+YUoSMtqsE1NYQGqsjLQGFNdbBGUlY+jCWnIr6jCgbhUhOYVYoqz4mz/FffDcC45E5llFcitqMSakGiWuR9o3rzMu4S7T1CYloWeC1+AnpkxzN2c0GnySISfu47K0rJm21h4OKPnwukwd3MUPCfcHNlql/Rw8XOC+nDJZ1tRZg7Sn0XDZ3BPhftbVV6Bp6euosvkYbD2cmET8v0Xv4Di7DxE3XzYIpvrvx+Ag783Al8Yxe55YbvWDOoTe4/cxKxX1+DKTcV9fWtBvpBrT3+2ipG+U+ALI9nExpMTV1pkc3//KTbhP/itOTCwNIOmrg78xw8WBfWJXnMnsNUOdI1o6eui09iBUNXWRnFMTL3AU+b5szAfOBj63j5QNzSC7fSZqKmoQO6tGwr30276DNhMngJNC/lBaXUDA7i++TYMfP2gpqcPTUsr2EyeyjKEy1JTWJuq4mLk3LgB63HjWfBfx9wU/vOmoyApFWmP5J8fCoDGX7kJnyljYOzsINdGmTYiVKTvA7mZxXVt2glfEm1c6diaGmNjeDRSS8qQVFKKzREx6GpuyrLyFfHevSc4lpiC4kaS4cjll/Ttm0rq/ccoTs9Ep3kzWOa4sasTPCeMRNzFawr7GGVsNPT10G/lMjj26yG1WrSp/JtjooLL56Bh5wjDQcOhqq8P5wG9YNnRB5HHzyn87OjTF2HkZA+PUUOgaSDfRpk2smOLpFv32fsOfbqJ2uTFxMPM0w0Wvp5Q09KCjosr9P38UBoXJ2qTde4sDDt3gVFgN6jp68Ny9FhomJoi+5JiX95i2AjYzZrNAveKsJ40BSY9e0Pd2ARqenowHzyU9R8lsdL9hyQ0NqoqKYXP7BnM7zb19oTTsMGIP6PYb2mKTfifB9mqWsvO9X2SvKgYpFy/Bb/5c9g21DQ1YerjpVRQv62MiTictgwP7P8HCQ0NRd++feHt7Y0nT57g8uXLLJD/+PFj9v6ePXtgaGiIsrIy9lq1alWD2+vZsycuXbrEfi4tLcWtW7egoqLCAvgE/c3R0ZEFsMPDw1ngfMKECYiNjcW1a9dYwJ+C1UK6desm+mwKYNPEwksvvYTbt2+L2lBwPjk5me3rkSNHEBISwjrZ8ePHi2at6b2xY8di+vTpSEhIwObNm1kgfvXq1fW2QxMAx44dw7Nnz6ChoYH58+c3e3WD7HtLly5FRkYGm7CgfRo3bhy2bdum9PdoyL41CH4cB/8u4mxmIiDQTSpI3xAVFVVMbicrIx89+oqzI/oP6YiHd6MQGZ6MqqpqhAUn4MnDGAwY2rBEB9HZ0xxRSQXILhA7rDdD0qGpoQo/ZxOFkwFlFdU4e79py1uN9DRQXNa0FSo+RgZ4mlsgWtJLPMrJg7WOFow1mu8cyzLMzhKRBUWIKlROIiDtWSxs2rtLvWfX3gPp4bEtsom98wT2HbzYALUhinPzsWXO+9g6dzmOrvwZ6ZHxyIuMhpaJsdQySnLUaKSTFy1/v5S1KYhPQMyJM+iw8KUGHbJH6zbg/Ctv4sZHq5Bw/jIbBNNnUPaMZHaipa8nG0wWpqTL3U5WeOM2yrSRhO1LfBJSg4Jh21V8b9AKiHYyWYj0O8ny5GcJJkLjQmLh2lE668atkwdSo5NRUVaBllBZUYnPpn6ITyetwG/vrkP0Y/lZ5+w4ekvLC5BDTg66IpS1Cf9zPwwcHWAVKJb4agia4KmtqYaqxMSqLJ096voWCRmbm8FpjfQtdoK+5V7jk5KSGOlrori0ZavffM31oKOuiltJ4snvvLIqhGUVo7ON4mCZlpoKfh7hjU+uRLHJgJZCwURrX+l+wqa9JzIjYltsE7T3hCjwr4iG2lRXViE9KgH2Mv0YSeqkPJO/f8raePb2R8z9UGQnpbMBauLTSGTGpcCjl+Bepaw7ol7/o6KCwoQElk1PEghCjH28WCCsJFX+ZG1+VHSjNllBj6BhaMiC+Q2R9fAxcoJDmURPQ5AUDmW0kpSOEAqw61uaKTy/zbEpKyhE+NlrMLK3hqGC7FZhGzUrW6iZy19pIYmfsT4eZktP9lKgn95XFsrSpWzRhiQ3GoKuc0M7K2hLBK/pOqdrLCsmoVVsyB/MS05D3O2HcAxU7ENFXrzF5PFcuoszRmXJiktmgXpbP/G5o6CxqZMt0hT4CsrYkKwMrQD06NuwBMbzBJ0PWjkh+b0Juw4eCo+VsjbkV1HwX1VducXxdPxDz99CbVUV9H0EK2OFUOZuVUEBk8kRQpn4lJVbHN10qcmGqC4WrIRR0RQ8Z1mQsKYaeh7iz9Y1N4WupTlyIuX7ATlRgv7D3EexjTJthEQcPYsj897C2aWf4ukfh1BVVj/YLWwTu/IDZB7ej5pycRtvI0OWXR6eXyh6Lzg3D9U1tfA2Ur4vUcTrPu7YN6An1vcIwCQnO7nZ6Q2RHRENA1traBmK+wtzX08mZZIXm9BqNs3l3xwTlcdEQdtd+llo4evFJIYUkR0Rw2SdGrJRpo0scZdvwrpzBzYRIMQ20J/Z5ETGMh+iNCkJRWGhMOoSyP5OySgkc6Urcf8Qup7eKIlpuZSjEJrky7lxjX2evrd0/yHrn8v6IDT+qiwuRrECv0VZm4yHj5EVHAqvGfJ9kvSgR9A0MhSM95pBWxgTcThtGS7F8x/k22+/ZdnpP/74o+i9r74SLy+ioDxBGfPKQBn6FDAnbty4wVYABAYGsoA+Belp4oDaEBRUHz16NF59VaCTbGpqil9++YVl06ekpMDGxkbqsymDffLkyTh8+DD279+P7t27S302Zdq7uwsG5hS4d3BwwMWLFzFo0CB899136N27N959913298GDB2PFihX4/PPP8d5774m2QQPyLVu2sFUGxDvvvMP2sbq6ulVmZGNiYjB06FA2uUFMnTq1XpuGvocy9s2lqrIaBfklMDaRXrJtZKzHdPEbIjkxCy9O+JYt61TXUMM7KyaifSdn0d/HTOqO1OQcLHjhR9FxnrtoiJQOvyLMjbSlgvpEdoFgiby5kfygnYOFHmLTCjFvhBfmjvCAupoqnkZn45s9jxGeKH95aU8/Swz0t8Xra66jKZhoaiClRDqYQFkqhLGmBssCbCm0NLWbuQl+ldGcbIiS3HzoGEqfS1qOTzrRlaXlLNO+OTb5aVksUHDux+2Iux/MAge0tL77rDEsy4zQ0NFC75cnwyWwAxvYPjh0Fn99+BPMOnaAhr70wEhdT5dlulTkF8j9HpSV35gNDdwe/0pyLlPZJEBJev3sTho72fTqDqcRQ6BpZMQyuUO3/4GKwkKU5xdAV2aiQrNOuqAsrwCG9oK+SBJ6n/TwG7JRpo2QU0s/Q2FaBpuwcOrbDT4Thov+Rhn8T3YeQOitYHh08UJqbApuHRNk3hXkFDBdx8KcArgHyAwGjHRZEKgorxAmVvLlaBpDXVMdI+ePZdkuwiyYje/9gsXfL4GTr/gep1oB5LhLDmIIOneKzq2yNun3HyLraSh6fvah0vsd/ucBqFNWUsf2CtuQrJfivkVQL0IWyvCPTSvAvJHUt3hCXU0FT6Nz8M2eRwhPaN2+RRYLXcFAKatUuk/JLq2EuY7iGhWr+rshKLUAp6Jap1YO9ROyE3taSvQtjdmkPA1H5JW7mLD6fYWf3Vgbypqn51C9fsxQD8W5BS2yCZw0BAUZOdj22hfsd8qw7f/yRJa5T1Cmv423C27vOwNTeyvomhji6dlbLAhPtUFk+zHh7+xat6ufVU5yCY3ZlGZkQsfGCjGHjiLlyjWgHdiKF9epE6FjKQiGl+XkInz7bvi9sZhlCTYESbsIz40k9Lvwby2xeXzgFB4fPMUmMQ2sLTDo/cX1dKFl2xi/9DbaqTbsg1JwjJ6Tss9behbTM1pZXvV2Ytn+tzNy0RxKFVzn7G+Kjl8TbE589AObLKF+3aFLBwS+OEHuNilRJOrKHbj2DYRaA8E0moAXXuuyz31F+6uMjXAVBmUD733naxSkZ7MVMB1GD4D3IGnf/XmhrFBBP2GghxIFx0oZG5I3JPkuDV1tHPvsF6Q9i4G2kT7ce3dGlynDpLKnqWbBoRU/sntDXUsTDi+9DG1b6b6jskBwftT0pSd7Kfu3vBXrpQm09A+xCQRNc8HKqcp8wWdTtrQkJM9FPo88yupWXgp9I3k2yrQhzGjFY7cAGNjbID8uEUG//4GCxBT0eu91uW0eBachfc8OlKcmw+5VQS0XY011FMj0I5QhXlRVBaMGakEpw82MLBxLSEZaaTnaGxuyIL+ZpiY2hCs/4ULfl1aBSSL0ociXbS2b5vJvjomqC/KhIvvMNNBDVVk5GyfIe/7RtaVp2LCNMm0koZpadN11mCUtv2bXowuKM7NxeeV3IqlQs0FDYNp/gGD/i4rYxFi9e1dPj03WtRSaeIv+7ht6QEBFUxP2L70MLZn+QxK6Nur55waN+C1K2JBPErJ1N/zfVOyTkG+ja22FyINHkXRZ4NsYubrAY9pE6Fo1PtH/b4+JOJy2Ds/Y/w8SFBSEfv36tdr2KGhPmfiUXU9B/AEDBrD36GeC5H2Egf379+8zWR2SqtHU1GTZ8W5ugtlVCmATlZWV+PDDD+Hh4cEkdCjITxntlHUviba2Nnx9xbPONClA0j60IoGg7HbZiQBaXZCbmyslZWNubi4K6hMmJiZsH4roYdsKLFiwgE2c0MqBrVu3sgmMpnyPxuwlKS8vR0FBgdSrvFyxQ1UrlV8hRkWJbBIqmHv29lc4cmkl3nxvPH744iCuXngq+vv2DWdx5th9/LRpMU5e/xzf/7YQh/fewN4dguuiqQhXMLSjJ728fVZpBy8HQ/g5G2PCh2cx8r2TyC2qwM4VA1hWviykv79uSS9sOx3OJHlainAlaWspvQ6xsWDZQpdSM1u0HWHWt6JzrYwNZaUGn77KtGBnb1yJkcsXIiUkChfW7hLZuPUKgN+w3tAxNoC+hQn6LZrGiuoVJSsuntWUfZK1ebZ7H4xcnWEVqLgwMGWskSa/vp0tNPR0YdMjEC6jhzMpGHl6lELN1SbtlzI2CtoMW/0hJv7+Pfp/8CYyQiPxYIu4+LDroN4YOmckjv56GB+OXYa93+xCvykDGrwHBB9V99huxnJvIZ5dvNFv8gAYmRux18iXx8DJ1wVXDwhWZolR8CEN6h03blOWm8smYDosmge1uiKRjRFz7BTS7t5Hp1cXsILFzepbFOw29YfUX1BG/4QPzmDkMupbyrFzxUDFfcubvQV9y92mZfkrCy1dV3SUR7mZoZutEcvW/zsRZqk3qW+RsCkvLMaVddvR59WZ9QLEQpRp0+BnNfE+kLW5tOkgEh5HYMa37+D1Pd9g3IoFuL7zGIIv3BG1GbPsJRhZm2P3/37AzzPfZzr8Vj3J95BzhkQXWdP7GKENy95+FoGKggIErvoIXT5azt57/P1aVJcLstJCN/4O24H9YOiq/KCzXr9CfYkCiYSm2HSYOAyzdv6ICWs+gYmTHc58toYVAm2oTfav36KmpOGVaoqu/6ZIXcx0tcMAGzN8GhSO4mZm7MtDtIKjtuU2I1a+hVk7v8eoz99BQVoGLq/ZKtc+KSiETaZ5DFS88qWRHWjsdDdoI1y9cn//afb8n73xM3QaNwiXf/1DocTP8wp7ztY236am7iINOnAGPkN7Ys7mVRj42kymsX971zEpO0sPJyza+wPmbvmSFdaN/30TisKVlFdj/VkLnAEJaqurkfD7JrZ6yGHuy3I/qv4bjX12OyVsGm5DxXQpcK+hq8My0qlobvqTUOQnJMlto+PhxYrmloQGozw5sdnPWWX5KSQC0YXFTK7ndmY2K5w70t4aWqotDLGI+otmPEta6Zpoq2MikZ/RjP63IZuG2lC2PslF0TUoSdTpS4g4fg69lr2GMZu+h8u77yE/6D7Sjv7V8A5RkmUrnCcdZxf4rf0F3t98D4vRY5HYlP5DiOh71zbb5slvv8NhUD82blMEtc2p8216fvERun8i8G0efCf2bZrKPzsm4sg/B8/n678ID+z/R2lMR6wpkDa8paUlC+RLBvavX7+OsLAwJrUjDOxTFvxbb73FgubFtLS8pITJ91AgvVcvQdXyL774ggX/SW6GZGpIkmfu3LmsTWPfgd6T1NCXbSN+KNc2eiya9ABrwG727NkIDg5mqxcOHToEV1dX/PTTT0p/j8bsJaEJAJJRknyt+24/+9u9W+EY2GWZ6HXs4G2oq6tBT18bebnSA+jc3CIYGzceRFFTU4WhkS5GT+yO3gP8WFFdIft3X8OkGb3RqYsrdHS1BDr+U3ti707FuqRCSCbDxEA6oGda93uWhISGJBm5pVBVUcHK7UFIzy1FZl4ZPt12H6aGWujuIz3T72FviB0rBuDE7QR8savpA8688goYyWTECX9vjcwUYpitJSsGWCJH07A8LgZxby1gr18nv4mHhwXa5JTxVZovPSFVkl8ENS0NluklD2VsdI0NYeZsj/Yj+7EMfTNnO3SeNBTxD4JRqcDZomvY1NGG6aRThrwklLFN2WeaBvKlRDQMDBq1yQmPZLqOZ+e9yl73vhXcE9fe+xhhu6Vrcgjb0Cvz0RNUl5ezgHF5gfT3Li8QfKbk0mVJKHunMRtl2kiujlLT0mQ6/L4TRyL24g12vITHr//UgXh/x0f43+8rkJWchWO/CQYC98/dZf+ThmSRTIFIykohWz2jpgVBG8PGxQZZydIDKipYq6ajU+9cVRQU1ssUa4pNYWIK+/vdL78Tnbf0B4+Q+SSY/Sy7HDj21FlEHz2JgDdfhbGntLSK/L5Fs2l9S56wb3kg7lu2NtC3fDAQJ24l4IsGCn0rS1aJ4P4y1Zbub8x01EV/k6WbnREcDLUQ/EpvxLzRl71ImmdVf3dcfLFhmYx26THQ+vUV9toybQmeHBHoyWobGqBM5rouzS9kklMK+5ZGbPJTM1CSW4CzX/7KPoteEZduM5kS+jkjMq5em9+nvoGIi7dYm+/HLcHdA+egbSBYzVNSUL8f01Ego6CMTVVlJR6duo7AyUNg7ekETR1tlqnvMyAQD46INVv1TAwxaukcvLbrK7x14AeMff9ltupITVubFSeUvc6FfZw86B5ozEbT0JBNvnrMnMbe0zI1gfsLU1CWlY2CWIFMQH5EFOKOnsDl+a+yF2XvU/95ad4ruPTyYuyYvoS9aPWEMHOcZHAkod8V9YVNsWE6xGqqMLAyR69XZ7EM74R7jxtsU1NQgLLghu+fqtpaFFZSRq30vUHZt8o8h6e52GC2ux0+uBfGZCSUIf2Tt5D67nxse2EJTn36k+gZKu86J7QUXX9NsGHPCQ0NWHi4oPOMcYi/84gF8OXJ8Jh7OMPYQbwq7NrvB5iPIHwVZedBp+78lNb7fMX3izI2OsaG7P/2o/rDytOZrezz7B8I+45eiLre8r7w70ZHNQf2WkHsRccq+vYjJjdE12b9713IzqE8lLFR19RgqxydAtvDtXsnaOhow8bXDX7DeyPqRn2flPS76TjTRAnJ62Rdlfajhdm+VUXS92NVYSHTyW8p1HckbP0dJfHxcH1nKdQlkqGE268qrO/3yGbxChFmQpfL+AGSNsq0kYeho0DXvShNcQBY01bQpiJDsNIzr7wSBjL9CAVADNTVRZnnrUVsYRFLFrDSVj4BgY6FPJ9J8DeDVrNpLv/mmEhV3wA1RfWvEao9Q/61POj7y/PTJW2UaSMpk0X6+k4DetUb00edvgjngb1h2cGHJZ3Q/Ws2aCgrbEvxAlU9PRbEl713qwsLoNYK9y5BNSVoW6Sxr+PqhpxrisfhDfkgiq4bZWxyI6IQfeSEyKcP2SbwSejn+HMXpXwb71nT2FiP6meRbE9pVjbyYyRkklKy8P7wd0Svk5uPtokxEYfT1uFSPP9BqNgrBd0VQYVu5VUFbwjS5j958iTLyKcgvoWFBZPZIekdCkSTvr7wsy9cuMAkbhQF1EnOhwr4Una95CoDkuuRhCYFaOKAagUQ6enpSExMFP1O/9+7d0/K5u7duyzYLZT8aSmU6S9Z+JeQV3jXxcWFTWjQa926dVi5ciX7WZnv0Zi9JMuXL2dSQpLkVAuCvl26e7AMeyGqddkifh0d8fhBNKbPFWQDE6SN79uxaUUfq6trpCY1KINe9hyTMyuUemqIoIgszBjsBmN9DeQWCoJXPXwtUV5ZjaexOXJtHkQIlhtL7oPwZ8n3POwMsWvFQJZJ+/EWQaGjpkLF+ahYlGTeUCdTI6SVliGnmVkFkvgY6cNBTwc/BMvX8NN0coHj97+xn0fYlYiKgFl5urBMekmSn0bAysNJ4f2mjI2VlwvSI8WFnhiNTA6yzIuEVGibmSI3PBIlmVnQMTdjf8sJDWf2irJJjdxdEHPsZIM2fb5eKXVe6TPuf/sTen+9kn2mJIM3rRP9HHf6HPLjElgAOObwMRZIV60bgGSERDDH28DGSu5+mXm44OmfRxq0UaaN/ONVI8h+lJNWYmpjhi9OrMa2jzejvKQMYxcLpBgcfZwR/n/snQV4VMfXxt+4u7t7QggJ7u4ORUuBQrFSWmpQ2tIW2lIKpaWUFnd3dw8EAgkJSYi7u7t9z8xmNbvJRmjD/5vf8yxk7865O3tl7syZM+95ES5UNjYoGsY2JjTRZ3uSkZABDd3GnXpte1vkRUTDZtRw3ra88Ei6XRLN2eh7uGLonr+EbIK376YyAGQZL3FycEm4dgsxZy/R7Xpu/Daz6bbFAToaSsgv5kjw9HRvaFviJLQtkdm8vAeN2haBcrRtWTO4oW0Rfva0ltDsEpRX16K7mRai8zhRzlpK8nDRV8eBYPGrt9bej8H3ItH64Uv7Yt2jWBwJkbzii1BvZIuKRZxjv3RAFa9tMXKyQfpr4X2mh0bRBJ+S2pbmbMj/847/IfT54x3HUZiWiVHfr+CdZ8Ey5Lg//uc4CtMzMePHZVSyguzLyM4CKaGx8BBI6J38KgpmrsL5Y7gQXevmbEg0Ov1pjQJGZcQnZ2yASPkURkZB36szcl+FUNkNrrRGQUQkndhSNTERa6tlb4e4M+ebtNFysEPaQ1/xbXDDtv67hO+fjEdPEHX4GPr+/QctY6XOcY6QY6xvb01/T0Z4DGx7c1ZAlebkozgzB4ZO4tvo1tjw7qFmYiY491nz5Qih+UXw1NPE8Tj+yjAvPS2ENeOof8fGFPMdLbHmRQQCRDT6m8Jw7W80inK4eRnvWBOHe8RNX+qo564qIdc5mcTUtxWf2LA1NgTiCOH8IXxwyGRJ8stQ9P5ghtD2PvMm0QSsXMj5VlJXpVI95Lmva8G5pipKypCbmAqPkX3Ffq++rXmzNtomhvS3NGoPyP3yFkS/ldXq0Bfhp8vv0eub/BYDOwv6u0kSWy4poVEwcZHctkhjQ/pVvGhSLjRUsOl60mtA5PyT5LdEdqc0KgrqDhxdcPK8JPr6RqNGt+AoiP++pL276b7sVn4GJX3h5OWqNjbUMVkSHQmdrpykoWW5eSjNyoGeo/h+gK69DT2+OeHRsOjVVayNNGXEQeRQCMpNJGyubEj8K6/FmYyKKCyCspwcHDTVqYY7wU1HC3KyMgiXICfUWqzU1ej/BVXSjxX0HGyRcOcRKotLqBQRIft1JG0vtG0s282mtfyXYyIlGztUxEQJbcsOi4IueUZJaHjIsSHXVVM20pThkvosELVVVbDqJ0FyTGybyNlGzoeKpRW9d0miWy4kql7NvulAlVYhpv0QRNveDtGnzwuNYUj/nPRB1CT0W6SxEe3Tpz56gvCDx+j4jNuf0nawQ8qDpvs2gmMi/kcyHWZMxGB0ZFjE/v8gJBkrccCTpLTZ2dnUkUwcxSR5LoHouxcXF1MpG2khzvzjx49TJz5x6hOI3M+hQ4d40fqEL774gsr2LFu2jErKkKh9ItVDkulyIRI8ZJKAROsTpzlxVnPrJgrR6idOcJJcdtGiRXBycqJa+gTi4CY6/0TDn3wPmTAgqwG4mvvtAZH6IbI+Bw8epDI4T548wYYNG4TKzJ49G9evX6fHlPweMrlA8hBI+zuksedC5I00NTWFXkpKCrwHH4mw5764D8Kps/vj+dMoXL/4HGWlFbh42g8hwQmYMqsfb7/H9t/DiF5f8d6vX3MUIUHxqCivQnFxOS6cegLf+2EYPoYvi9JngDvOHvelSXOJln9oUALOn/JDn4GSk/ZwufEiGWm5Zfh+XlfoaijByUIbH050w6n7cShp0Jo20lFB5KFpGNaVM2n0ODQDIXG5+Gq2F5XH0FRTwJrZXZBdUI5n4ZyoHDtTTRxaMwjXnyfjm1Y69QlXUzLoMtr5jtZQlZdDZ10tjDQ3wtkEvnPBU1cLV4f2hlWDBn1LGGFmjITiUoQLJPISF4FBXmSgzj2Xncb0R1ZMIkKuPkBVeQWiHjxHUmAYPMcO4tmF3XxMo9GI40haG49R/aiTniwTr6mqRn5KBgLP3YK1jweNPCPc++soUl5FUu1soiP7eO9Z5CVnwH7SWKibm9EOHIliJdHW0Wcvwri7D5R1tKltTWUljdpIechZ8aHv7tqsDY3wbPj9gsdA0OmWfP8Rku7cp8s662tqkf3yFeIv34BF/z4w79ubRt68PHAKlSWlyI2OR9TVu3AYPoCn/0yS2p6avRxZDZ17qz7dmrWRpkzYmStIfhpAo4FqqqqQGRaJ12evwby7F43MJBSnZ+H6visozi9GeWk57hy5ibhXMRi3ZBJvcqzXuD4oyMynn1WUVlA9/pd3A9BvMr/NDfENphEt3IS70nDhrzMI939Nv7e0sAQ3D16j3917Ar9N4GI9Yghyw14j1dePJq9NvveQJtKyGjaYVyb+6g3cXrRCahtyDgXPLef88rdzSbhxB9HnLlKnPrlmpOHG82Sk5ZTh+/k+nLbFkrQt7jh1P1a4bTk8vXHb8m4XftvyrjenbXmdyW9bvh6E6/7J+GZP+zj1CRU1dTgcko7lXa1oIl1tZXmsG2hPo/WvRPOjhU5M9qTJcglkyFZbL/yi2+vrpZMpkZWjL8H7ym3UAGTHJOD1NU47EfPoOZJfhsF9DL+diLjlS6PquW2LNDai55nr1BI8z4Kfy8nLQ0aOMygm9wq3fj4TBiLyUQCi/YJRVVYB/zO3UJCeDa/RfEflwwMXsGvhd7z3zdkQB52Ntyuen72D7IRUmjskMTgSYff8Yd+dn8ch6NojxDwLoW1jXkomLv6yF+qWFnCYNQ2ySoqIPnIC1SWlKIyNQ/KN2zAbzG8LSpJSaER9fiTHMWHUq3uzNvreXjRKP+b4KdSUldP2LfbUWSgb6EOzIQCi0XFtkLoikwXkGPK2N2i7E132oBOXUZCSTp3ET/eeoFr35l7uvN958Yuf4bfzmNQ2meExCDx2EUUZ2TRpIPn/8fbDNFLZwrvpMjLKKlByk5wAlsupuDR01dfGcDMDqMjJYaylETx0NHE6nj+BNd3WDNeG8x0uU6xNMN/JEl+9CMeLFrSLBOrs5R7XhnbYspsn1PR14Lf7BF2xkJeYiuAz1+E4qCeNxCaU5hXQKP+EZ0FS24RevoPo+0/p85TcU2SVSsCxizTJrqou5znIhWjrk/vBumeXRvUVug4aosXdhvdBwOmbyI5Lpprwj3adpKswSDJXLue/+QM3Nu2V2obkn+g8bhBeXb6HnPgUWmciwZMcFAH7PtIlQP9v4c7icZ4z3LaF/KZo30DEPQ2m7Vjg2VsoTM+Bx0j+89Dv4AUcWrSW914aG6/xgxHv/wqJAWGcZN7RCQi74Ut19rlc37gbmdGJtG0hx5ysziyNiYZuj57CNZeVhf6gwci+exslMdFULift9EnIyMlCVyBIKmHH34jdsrllTv39e/lO/QZdfUHk1TWg06MnMi5eQEVaGioKixC0/wTUjQ1h3JnfTt5Z/RPVvycQR7Nl3+54ffoyilLSxdpIU4YkiCXJcksysuj1RhKUvtx7FDp21nRiQFyZ8vhYZB07CGUrGyhbcyYIiDM/NL8QC5xsoa+kCENlJcx3tMHL3HwklfJlw0gC3EkNKwKkoY+RPiZamcFAWQkKsjLooqeDOfbWeJCR1aKVACY+naGip4Pgfcdp1DiRGYo8fw1W/XvypAfL8wpwYc6HSHv+Umqb9uK/HBNpDhiCysR4FD28g7qKciQ99kdGcCgcRgn0Qe/64ty7H/L6JnYjBiI/LgGxN+/TnCDibKQpIyjDY9zZHSoN4xNBTH06I+HeY+RExNDEteWJiTRaX9OzM6+N0R8yFIUBz1EYFIjaigpk37hGE2Lr9ef3ldLPnUbEGsm5iMSRuONvlCXE00m+mpISZN+8TtsPbZH2Q6i+vbrTJLgRR06gqqQUBTFxSLhxG5ZD+H2QoqQUOmYjsjnS2jTu0zdMbAj8beTtBRV9XUQcO4XqsnJUFhUh8sRZqBjoQ8tGOLiTBIhyXx1pTMRoDB2Xv4Wv/0VYxP7/IJ6enrh16xZWrVpFI+qJI/6DDz7g6bx7eXlRRzNJPEucySTRLHGINwVx3tfU1Ag58cnfoo59krSXOPJJEluirU/08318fKimPhcyyTBv3jyaTJY02EOHDsXUqVNRJRLdQOo9atQoOoFAnOLEyU6S7HIbePI7T58+TScwli9fDn19fbz//vt0oqC9IA74f/75h37HkiVL0KVLF3z44Yf45ptveGU++ugjfPfddzTpLWko+vbtS3MGSPs7mrNvK97dHfDld9Ow/5+b2Pj9SZiY6eGbn2aik5dAksy6ehqRz2XclJ44sOMWXock0jpZ2xrh259nYcBQ/kD8wy/GY+/26/j2swPIzy2Brr4Gho3ugnmL+VG6kqiqrsO8Dffww/yuePzXeFRU1uLC4wT8JCCbQ9pceTlZXj4AEoCwcNNDfDfXB75/jkdNbR2CYnLx3s/3UVDCuXZmD3WAvpYypg20oy8uGXll6L9CWNe0KXIrq/B1QBiWOtthspUZCqurcTI+FReS+LkbyNkj0T5cR5WCjAwuDOEMsMh2V21N2llNLi3DIgH9WdI57mesj/3RiWgpRg7WGPbpfDw9chG+e89AXV8H/RdPp4luuRDnHhmscQM2pLHRNjXE6K8X48m+c3i48wSUNdRh070TTZ7LxW1EHzw/fhXp4bGcQaaNOSasW4EyM0d0+WQZ1Ux/+NkayMjLw7hrFzjPEkgC3VAnbqWIfbM2UkBs4i5dx7P1v9JloSoGerCbMIbT2ZSTQ//VHyJg3wlcXLIKCioqsBnYC66TRzU6Vtx6EX3W5mykKWMzoBd17r88eBrVpeVQ1deF7eA+cBzF78SrG+lDRk0FfyzdhLLiMli5WGPxpuUwd7TglTEwN8S8dQtxacd53Dp0HRq6Ghj5/hh4D+3G/w119XQFFvd8k4mCH2dwHBBke1ZSJh6eugdLFyss3cJxvvcY0xs3D1zD8V8O099vbG2C939cRJP4iqLn6gz3999DzLlLCN1zEKqG+ui0eD50HO2F6kD0eVtiIw1kWW9ddQ0Cf9smtN1m1DA4TJnQfNuyfQK/bRGQzRHbtvz6gNO2bJsg0Lbc47ctw0jbooJpg+zoS6ht+YizTLi1/Pw4jvplj03qBFUFOQSkF2H2uRCU1/DbZHlZGZpM9E1h4GCNQZ+8jxdHL+LpvtNQ09dFnw9mwKKL5LZFGpv2wqlPFyqjc2/3WZTkFULXzJDq4etbmQo9x4jzuCU2wz+aRTX1z3z3N8qLS6nsTpexA9DjHf5zzL57J9zbfQZXNh+AgrIiHHt1ht3oyZBXVUHnlSsQefgoHn/yOZXmMenbGzbjx/CPGYTbGAVV1WZtiHPec+VHiDp8HI8//hyyiorQdrSn28jgujV0f/8dPN9/Ble/3kydjMau9hiyWjjJbT1ZlSewmrM5GwMHG+QlpODurztQnJEDJQ01GLs6YOS6T3kJxiWV0f/oK8ipNx8NF5hbiF9exWCugwW+8HRAelkF1gVFIiSf7wAi947gvfGeoyUUZWWxsavwdXg0LgV7IoVzOUkDiWQftmYZddKfWPQ1lZqy7eODbu9N4hcSeZZIY2PXtxtNKBx4/DKNulXV04FNTy94jB/aqA7R955Se0myWKL0eHc8bZcvfreNyjER6Zyxa5cJ2ZMEsIKrlKSx8Zo4hCZIv7zub1SUlELL2AADl86AXc/OaE9cHM3hf50TREMCVdavnol1q2bg2t2XeGeB9I5raSD5g4isDukfkYS32maGGLlqAZUa5EKepeR4tcTGzMMRgz6chcf7zqIwI4cm3iYR/j5TR/DKuA7rDb8D55EVm0QnGom9zbLl0HRvnCDecPhI1FVVUWdeTWkZVK0sYfvRx9TxzoW2zwL3cPbtm0g7e4b3PurHHzh1e2c69AcMRGVWFgr8n9GHYsRa4UT21ouWQMuTc17Np89E6qkTiN64AdE11dB3caCa4kLtR12t0Hd3njsNwYdO4/53v9LnuDib5sro2FqjID4Jfr/tQGlmNpR1tGDq4wnniaN4AR6iZWS1tKHeyQt6I8cKrbzaEByOJS72+Ke3D50c98/Owz8RwivOSDsimELoey93eJJJtoY25vxgTsT14icvaLT6i5w8TLQyx4/eHtBTUkJWRQUuJ6fhXGLL8nqRKOheXy5H8L5juL78K9rOk1UM7rMm8wvxnr/10tsAuLVyLcpycjl29fV0coAw/qBwv6qjjomUrGxhMG8x8i+dQd6ZY8jR14XX/JnU0d7oum9oznTtrNFt+QKEnbiA4IOnaB9c1EaaMgQyYZQbGYOeny0RWz+3d8bR6/XF3/tp0nSSZFqrsxeMxvEDGrW9u6K2uATpJ0+gurAASkZGsFq0VDjJLelH1/H7L0XBQUjc+TfvfRL5W0YG+gMHw2QKZ6yk27cfMs6eRlliAl0ZoGxmDuuly6Ehpv3goqCmCu/PViD84FHcX8Hpg5j16w37CWMkPs+kspECcs16f/oRwg8dx/2POH0bHSd7eH/G7duIl8vsSGMiBqMjI1PfWqFxxv8ERBNfRkr5FOLYF5TYIZcOsSfO+7bClQbi1uPw4cM08j4jQ1hruaXQCMa6Olpv0d/S2npL2qc42ut3NEV6mfQO66Z/Uz1Pvqc96bOg9UmKSWeQJFOSFnJpiksMTFo5kiRLHPbv8R2pLYX0YQWrJzanKPnuZuyaYhSRBGgBXOebYETsmyS2SFh3UxLE4cZd+t4aWvO7tBRr33i92oKPftu1ScW1R6RdFoXIjpCIy5ZwJ026xLbcAWNT8iXN7qPhGSC4D8FBrCDcVRtXtvIHlR2xbanuITBoayOkzaCHuYnPW5JUlPDhwKp/vW3hTgw0dS1yy+iqtP++24vAXOkd7P9GGyN6D1qq1bR4H6QdofHMbbiPpeVmasujOpuC1LhO4G9xh1oKlSAeI1r43H3T51l03yrybR+6EUc1XSn1Bs73qpHiEwC3BHH9UdJPbY9h64Zr8zps2/I4S7rnrjTPUXqsxEmvClxLgpPyksoIYqdRLfUz/L8gNL91k5+i7QhBpom2pLW4ardeuqY1bYykfpSk53hQnlKHHhN10av8V8c9Ld13tJRjolbfu81IBTaFqYr0CeTbY8zW0nHBYNOmHfv/9ZhovNXINtfvf5F+l/j5F98mHo7l5P78X4JF7P8/RxrnNBdRRzhH+qV9LiFpJhZaA6mjuN/YlnpL2ufbDOc3dbxlSS1xvBFIP6L2X5yrFK2etNVty6CgOajTswNen23tcL+p3/VvTYC8KcS1R/92+0Q7/m10Zonr/NNBBf5/ti2iNFfdN9mmtOc9KM055ZdpWS6gN3m9dPQ2pj3uwTfVD/s3qBP9u/5/6zy/iX3/G07qtiC4ivS/pqO2Lc09R2m70My10179qv/aod8eiF5xNBtIBwp/bE078G9eu//VmOhNjnv+rTFVa+7djj5m+y+Sr3SEMRHjf5MjR45g3759KCwspOoba9eupWohkoiKisJff/1F5dHV1dUxfPhwqpiirNz6yfvmePufwow2Q6R0iKNb3ItctP/L/H/+7QwGg8FgMBgMBoPBYDAYDEZL4M7hvG2vlrBr1y4q901ks3/55RcEBQXRXJlEAUQciYmJNL8okSUnsugkv+a2bdswbdo0vElYxD4Dd+7ckbi09b+K4Jo1axZmzJjx1v/2f+t3MBgMBoPBYDAYDAaDwWAwGIy2QaSdSLDv559/TnOWElxcXGBubk5zfU6fPr2RjZmZGUJCQoR8iSRSf/To0UhJSaG2bwIWsc+gS5QkRa3/V479f0vu5k3/9v9F2R4Gg8FgMBgMBoPBYDAYDAbjf5GIiAikp6dj1KhRvG0mJibo0qUL7t27J9ZGnB+RKwPOzSv6JmAR+wwGg8FgMBgMBoPBYDAYDAaDwfifpbKykr4EUVJSoi9BkpOTec58Qch77mfNQRI4r1+/Hl27doWlpSXeFCxin8FgMBgMBoPBYDAYDAaDwWAwGM3yX2vlt/b1888/Q0tLS+hFtonC1dFXVFQU2k4mAKqrq6W6QpYtW4bXr1/j8OHDb/SKYhH7DAaDwWAwGAwGg8FgMBgMBoPB+J9l9erVWLlypdA20Wh9gp6eHv0/NzcXpqamvO3kvZGRUbPf8/HHH+PkyZM0r6ejoyPeJCxin8FgMBgMBoPBYDAYDAaDwWAwGP+zKCkpQVNTU+glzrHv7u5Otz979oy3raqqCoGBgfD29m7yOz755BMcPHgQt2/fhpeXF940zLHPYDAYDAaDwWAwGAwGg8FgMBiMZvmvJXVa+5IWdXV1zJw5E5s3b0ZOTg7d9ssvv1CJnlmzZvHKLVy4kErucPn8889x4MAB6tQniXb/DZgUD4PBYDAYDAaDwWAwGAwGg8FgMBgA/vjjD+rcNzc3p1r8srKyOH36tFBC3fj4eCgrK9O/g4KCsGnTJujr62POnDlCx3D37t3o0aPHGzmuzLHPYDAYDAaDwWAwGAwGg8FgMBgMBgANDQ1cunQJ2dnZKCwshI2NDeTk5Bo57InDn+Ds7IyQkBCxx47YvimYY5/BYDAYDAaDwWAwGAwGg8FgMBgMAQwMDOhLHNbW1ry/SeQ+0eb/t2GOfQbjLSe8QHjGsKOhOsIcHZlhpuXoyDhp1aIjs2rkPnRUOv3G17rriFird+xze+1Vx25b1EZZoCOjo9Ox0xidjuzY53eSY8dtmyMKFNGRMVDu2G3LKIsydGSOh3KWU3dUJrlUoiOz8MQH6MhoKlSho1Jb1wLx3/8AQ+W6/7oKbzXmah27bU4t69j101Hs2NdfcXXH7ffZGHbs50Z5Tcdu+xjikWWnrcPQcVsfBoPBYDAYDAaDwWAwGAwGg8FgMBiNYI59BoPBYDAYDAaDwWAwGAwGg8FgMN4imGOfwWAwGAwGg8FgMBgMBoPBYDAYjLcIprHPYDAYDAaDwWAwGAwGg8FgMBiMZmEa+x0HFrHPYDAYDAaDwWAwGAwGg8FgMBgMxlsEc+wzGAwGg8FgMBgMBoPBYDAYDAaD8RbBpHgYDAaDwWAwGAwGg8FgMBgMBoPRLLIy9ewodRBYxD6DwWAwGAwGg8FgMBgMBoPBYDAYbxHMsc9gMBgMBoPBYDAYDAaDwWAwGAzGWwST4mG8NZw9exYffPABcnJy/uuqvHUE3A/ClQM3kJuRBwNTPYydNxKefTwklq+uqkbg/SA8vPgECeFJmPXpO+g1qrtQmcrySlw9eBOBD4NRnF8CUxtjTFw0Fg6d7KSqk5m6ElZ1tYOPkRbKa+pwJT4LfwQmoKZe/JIuGQCjbAww3ckUdtqqKKysxr3kPGwLSkRZTS2vXCd9DbzjZIKhlvp4lVOMhbdC0Bbqamvx4vhlRD3wR3V5JYydbNHr/anQMjFok83l77Yi/XWMkJ1Nj84YsnJ+i+pXUVaB83+fx6vHIairrYNLV2dM/nAy1LXVJdpkp2bjyeUneHbDH3U1tdhwcYPQ5zHBMfjrs+1ibRf9/AGcfZzRXqipKmHahN5YOHso3J0tMH3RFly5FYA3zVxnc0ywMYamojwi8kvwW3AcYgrLJJbvZqiNGQ6mcNXVQAU5v1mF+Ds0ETkVVbwy67s7YaCZvpDdy5xCfPgwVKo61VRX48H+i4h4FEjvQUsPBwz5YAo0DXTaZJOdmIaHBy8hPSoR9XX1sOvqhoHvT4SKhhqvTFpkAp6evoW0iHjIyMrC1MkacJoM6Jk2+k4tJXl83csOAyx1Qe7Wu4m5WP8kFiVV/PtQlH4WOpjfyRxu+ur0fvdLzcdv/gnILOMfv4C5vaCqICdk95t/PHYFp6CtzHAyxWwXU+gpKyK6oBS/voij7YMkTNWUsMDDAr1MdGidYgvKsCskCU/SC9DeyMkA852sMNzcAKpycgjNL8YfoXFILauQaNPbSBeTrE3gqKWG0ppa+GflY09kEgqra9pcn17GOljqYQULdRWkl1Vi3+tk3EjOlljeQl0Z7zqZo5uRNlTl5RBVUIqdYYl4lcs/vmOtjbDa276Rbf9zT1BdJ90y3qi7fgi5cAtleQXQMjOCz+wJMHV3apMN2f7q/E0kB4ahqrQMWqZG8Bg/FFbdPHllqisqEXDsEpJfvEJFUQnU9HRQ59UXaoNGQkaGPJmaxkpdBR+52cJFWwMl1TW4mpyJA9HJ9N4Rh6aCPKbYmKKvsR4MlBXpdXAqLg230ySfA1Gyw6MRfOQcilLToaytBcfRg2E/pG+bbVqy36ywKDz85S+oGxtgxMavedtrKioRfPk+Yh89R0lOPtT0tGHb2xuFaVn0PJBDaunjgR5zp0BRTUVifSuLS+G377RUNuQcXlz9K/2OcT99Bn07S6HP4/0C8erCbRSmZkLLzBi1vadCzsZZbLvwhY8duhhy+i3XErKwLUhyv0VNXg7vOJpghJUhjNWUkF5agVPR6TgTkyFUrp+ZLt53s4C1pioqa+sQnF2ErUHxSC6R3AYIkhubiOcHTyM/IQXKWhpwGtYPrmOGtMmmLL8Q4ZfvICUwBBVFxdA0NYb7+KGw8OHfG4Ta6mqEnLuBeN/nqCothbG7M2ymvwN5ZRVEHDuFrJfBqK+tg76HK1xmTYOipobEOtWUV0htU1dTg2c//orixGR4f/YR9Fz55+vBp1+hMl+4rbYZNQwOUyagtQTf8sOzM3dQnFMAPQsjDJg7HtaejhLLV5VX4vWDFwi85ovshHRM+up9OHSX3O9ujvr6emRfv4L8xw9RW1oKFUtrmEydDmVzC4k2NSUlKPB7jDzfB6jKzYH9V2uhbGomVKYyIx3Z16+iJDIc9bU1dH+Go8ZBzd6h1XVNC4uB38FzyE9Jh6q2FjzHDYLb8L5ttiH38YuT1xD7JBDV5RWw7OKGXnMnQUVL8jXFRV1eHh842cLHQBfkdvXPzsWuqDih8YMoFmqqGGlujAEmhrTcAt/njcocG9ATKnLC/ZaDMQk4myhdv6UoMwcPd59BalgMFJQV4djPBz1nj4OcvFybbV5dfYDQ649RnJMPI3tL9Hl/MvSt+H26WL8gBJy9hYK0LCipq8JtWG/U9x0j1XOtvrYWKRcuIPfpU9RWVEDDzg6WM2ZA2dBQok1VQQFyfH2R7etL/+68cSMUNDXRFkj7/+LoRRRlZENdXxedJw+HXd+u7WYT//Ql7v2+D0ZOthj9/cdCfQvfHccalbf8dTtkFBQabR9mZoR3bMxgoKSEpNIy7I5KQHBeocQ6KsvJYqCJAcZYmMBGXQ0/BIXjaXaeUJkD/Xzo/kS5k56FzaHRkKZNCTxzA+G3HqOypAwGdpboPX8K9KzN2mRza/NexD0NErIzdbPH2O8+arKMtYcd5v78YZN1TgiJwc09F5CdlAF1XU30mjgQXUf3kViejIuj/MPw/Kov4oOj0WvyIAx5b4xQmaqKSjy7+BCv7gWgMCuPjp+8hnWn+5bmXmAwOgrMsc94a6irq0NNTdudFv/fiAqKwd71hzDto0nw6ucJ/9sB2Pndfny6dTlsXa3F2jy9/hwxoXGYtGgsfv/0b9SJGbQe3XIK8WGJWPDtHBiYGeDF3UBs+3InVu/4FMaWkjt2BHlZGfwzxB1xBWWYcDEABiqK+GOgK+RkZLDxRZxYGw99DTqY3vA8FvGF5bDRUsHPfZygr6KILx5F8ByOX3S1xemoDMjLyMBQVRFt5fnRS4h68AxDP1sIDQNdPNl3GlfXbcPULWsgr6TYapv6ujp4ThgCn2mjeXat6UAc2XgUWUlZWP7bh1BQVMChnw5hz9q9WPEHvwMlysnfT8HZ2wn9J/XH7WO3G31u18kOm67/KrTt4o6LeH7rBew7NXbOtYUlc4fDxsoIn313ALdPr4Xsv9CJmu1ohtmO5lj9NBxxRWVY7GaFP/u6450bASiubjzI01VSwGwnMxyNSkV4fgmdDFjt7YA/+rphzu2XqG24Pcj1eykhA7++jOXZSvD3iOXOzjOIfxmByd8ugoqmOm7+dRynv/8Hc//4ArIiA0dpbcoKi3Hi622w9XbF3K2rqDPk1o7TOP/zHsz4iX+N+J28Aa9RfTHyo5moKqvA/f0XIHNqM+oXbADkhQcpfwxxgbqiHKacewk5WRn6/rdBzvjgepjYOuqpKGCKkzH+CkxCeE4JdXD90M8Bf49ww6SzL3nlyL5W3H6NOwm5vG3cY9sWxtsZ4eMu1ljlG0kdZvPczLFjiDttewQnFgSZ62aOgMxCbA9ORG0dMM3JBH8OcsP0Ky8RXSB5Aqg1LKBOfUN8ExCBzPJKLHezweYebphz/yWq6urEOopHWhjicEwKYopKqfN3lacD1vs4Y7mfdJNIknDSVsOvvV2wPSQRVxIy0d9MD2u7OSK/shr+WeInNZa4W8EvowD7wpOp8+M9Z3Ns6+eOObeDkFBcTsvIygBZ5ZWYfO2FkK205zfRPxh+u46jz7LZMOvkjPDrD3H7538wbuOX0DYzbrVN6OW70LW2oM58OUUFxDx4hnubd2PkD5/QgTzBf/8ZpIVEYuCnC6jjP+N1NG5v3gsZZWWo9R7UZL3JRMfm7u54kVOAn+4H0smSH7o4oba+HodixDt+ptqYorKuDt8FRtCJQ+LgX+3pQG3upTcf2FCTk4WHm7fDceQg9Pl8CbLDo/Bs+0EoqqrAspePWJuSzGw83Ni0jTRluFQWlcB/xyEYONujLDdf+Lw8eUE6dBj06ftQ1dFCxusY3P51J1R1tTHu58/os/Hub3txf+t+DFu9ROLvvLtlL22rpLHx23MS6gZ6KEjJQL3IlErELV883XcGvRdNpxM6pTn5uHjgXiPHPum3/DXQHXGFZZh6JYD2PX7r60r7GpsCxfdbJjsY02tgzZMIZJRVopuxNtb3dKLPipPR6bSMg7YqNvV1xfZXCTgdHQoNRXms6WqPrQPcMPFy85PcxAF/68c/YdevO/p/spA67B9u3Qt5JSU4Du3bapvQCzegbqCPgZ8vhpKmBuIePcODzbswaPUymHZy4e3r4e97UJSehT4fvgdtSzNkhkUhxfcpChOSUJqegW6rVkJWQQGvduzFy2070P2rzyT+lpDdB6S2iTp5FooaGvTciz5oyTbXubNg2rsHb1tbHDSRfsG4sf0kRq+YBRsvJwRceYTTP+zAvN+/oE5+cQRcfoiCjBwMWTgZR1dvpZPqbSHn9g36sly4BEompsi8eB7xW3+D43frIafKn6QXJOvKBcjIK8Bo/CQk7/5HbJnsm9eg0ckTxpOn0uOWc+s6Ev7cAoe166Coq9fiehZmZOPqj9vRacxAjPpqMVJDo3F360EoqanAvo9Pq22Ig+7aT/+gsqwcw79YCG1TQyS9fI3oRy+oXXN82ckZqvLy+NQ/CHKQwRedXPCZuxN+CHot0eZDV3v4ZuTgRkoGde6Lg9zLv4SE41k2v98i7amura7Bhe+3Q9fCGDO3foWyvEJc2bCLTmr1fX9ym2we7z+P8HvPMGT5LJi5OyAnIQ3ht/14ZRIDX+P6pr0YsGga7Ht7oSA1Czd+2w+NUnmYjhzZbN1Tzp9Hjp8f7JcsgZKuLpJOnEDkli3w+P57yCqKHxulnD0LRV1dmI4Zg4SDB1vWQRZDTlwybm/cCZ+ZY2HfvzuSXoTgwbZDUNZUh5mnS5ttirPz8Gz/GRg52tLjK+rgJpPSU/9cK7T9XlZjp34vQz185GpHne2BOfkYa2mKdV1cscwvCMmlnD6SKOMsTWGqqoy/I+KwuVsnOnktyryHLzhRbw3YaahjW8/OeJLJvxabIvjCHfoa9tn70LE0wfOjl3Hp+z8x489v6URPa21IO+I8uCf6LnxHwFL4B4grY6LSuL8rSF56Dg5/uwM9JwzAzO8+QMKrGJzddAjK6qrw6N9FrE3sy0gE3nyK7uP6oTArn/O8EOHV/QDU1dXjna/mQUNPC4khMTi98SBkZWXpdzGahvTvGR0DJsXD6FAEBARgxIgR0NbWho2NDTZs2EAd+g8ePMC0adNQWFgIeXl5+lqzZk2T+zp69CjMzMywceNGuLu7Q0NDA4MHD0ZCQgLdr7W1NXR0dOh+i4qKhGyvXbsGb29vqKmp0XI//PADrYfgvs3NzbFz5054eHjQ+g4aNAgxMTGNfs/QoUOhqakJCwsLLFq0CAUFHOdIcXExtLS0cPHiRSGbGzduQFVVtd1WJtw+dR/OXRzRb1xvaGirY/CU/rBxtcKdk/cl2vQd1wvzvpoNOw+OY0OU2ppaGtE/fOZgWDlZQlVdhe7fwt4Md888aLZOgy30OA6OpzHUsRaaW4J/gpNopD0ZBIuDRNeuexaDsNwS6jwi/1+Nz4aXIT/ao7CyBrOvBeN8bCYqRDphraGmsgph1x/Ca/IIGDvb0k5c30XTUZKbjzi/l222IQNN4nzlvkikdEvIScvBq0evMGHxeJhYm0DfVB9TPpqCuNA4xIfFS7Rb9utSDJ4+GGpa4geDpF5ycnK8F/GDvLgTgK5DfSCv2L7zwZu2X8SyL3chKFRyfdsT0v+Y6WiG4zFpeJ5ViNyKauqIV5KTxRhr8QP0vMpqfPQoDE8zC1BYVUOjKLcGx8FeSw22msLHkIxTagVe0l6F5cWlCLn9FH1njYaxvSW0DHUxbOk05CSlI/bF61bbEKc/KTd0yTtQ19GEpoEudTQkh8Yg5TXfGTX5m0XU+U+i+LWM9NBt4mDIlBUBBVlC3+mqr47e5jpY/zgWiUUViCsox09+cRhopQd7bfEDgdzyanx0OxzP0wtRUl2LmIIyHHudBg8DDSiScHUB6kSOX3sw19Uc52MycS85F3kV1dgcEE/rMc3RRKLNT/6xuBKfjZzyaurU3vEqiW53128+MrAlKMrKYqK1CQ7FJCMsv5g6cje/iqXO+oEm4p0qiSXl+PpFBF7mFqK4ugZxxWXYFZmITnpa0Fdu22TmDAczuoLlSFQqCqpqcCE+E34Z+XRiSxJfPY3EpYRMGt1P7o+trxJoG93PVLdR2dpWnt+Qi7dh3bML7Pp0hbKmBrzeGQ11Q12EX3vQJptucybBcVBPGoWvrKEO9zGDoaCqguwofnuUE5cEy64e0Le1hIKyEiy6uEPRwhrVSc23WUPNDGjU9paQWORWViEotxAn49NoRL7Ipc9jT1QSnbQh55msxriekoVn2fkYZCq8GkgSZY/vQkVbEx7vjKWR2BY9vKnjPeJy40lcLjE3HzZrI00ZrsPD/5+DsBvSFzo2jaOJ7Qb1RufJI+jkCpkUIA594vTUMDKAlokh3d5j7mQaVZmfzHF+i3POkMmWnvOnNGtDVgbkJabCe7pwlB6hqrwC/ofOo/OUEXDo3x2KKsrQsTCB0oTGK+cGmuvBXF0FPz7n9FtIH2RnaBImO0jutxwMT8VfrxLpajDS5txNzsXNxGwMt+Kv3nPSUafPpYPhKbRMemkljei30lSFusgKJnHE3H0MOQV5+MyZTM+PubcHHAb1RtilW22y6Tb3HbiOHgRNUyPqsHEZORB69tZIfBrIK5MSEEJffVfMh4GjLb0/yL6Mu/sgK+AlnKZPgbqZKVQNDeDy7nQURMUgP5o/8S1IWVa21DbZQSHIeRUG5xlTJP7GtvavBPE/ewcufbzgNsAHqloa6DtzFH3evrgkuf3pOXUoRi6fAWM7yRH10kKcUbl3bkJv4BCoO7tCQUsbptNnob66Cvl+jyXamU6bBZPJ70DRQHKwjfmc+dDq4gN5DU26X4ORY+h+K5I5z7yWEnbtIVR1NNFt5lgaSW/fuwsc+noj6PztNtnE+L5Aengshn+2AAa2FvRas+vpJZVT305DDZ31dLAzMhbpZRVIKSvH7qg4dDXQo1H5kvjy+StcSk5DaTNBZaTfVyfwkpY4/1c0+n7gkunQ0NeBkaM1uk0bidAbvrRtaq1NfmoWXl68i/4fTIW1jzs9VibONkKO/6iHL2Dqak+j9JXUVOl+ukwYgowbN2gASFPUVVUh6949mI4aRSP1FXV0YD17Nqrz85EXIHky0nb+fJhPmECd++1B2JV70LO1gMe4IfS6cRrcC+Zernh18U6bbcjK6/u/74PX1FHQbGKFtmAbIykAZ6q1GR5m5OBeejZdVXk4NokGcYy3bLwilsvJ+BT8HhaDmKISiWXI+ELwuhtqaohcEn2eIxzZL6lNCb50Bx5jBsDc0xlqOlros/Ad1FZVI+KuX5ttyESE8LFp3P5KU0YQ/0uPoKGricHvjYa6tgbc+3nBY4A3Hp+WfL4dfFwwc+1COHZ1g4wED7TPiF7oP30YDCyMoKyqDKfu7rBys0XS639nXMpgtBfMsc/oMLx+/Rr9+vWDi4sLXr16hfv371NHfnBwMN1+7Ngx6givqKigr3Xr1jW5P+KIT0tLg7+/P3XUh4aGIjs7Gz4+PnT/jx8/xosXL/Dy5Uvq6OcSFhaGcePGYcaMGUhKSsLu3buxdetW/Prrr0L7Tk1Npfu9dOkSIiIioKioiAULFvDKREZGUmf/xIkTER8fj0ePHiElJQWzZs2in5OJhkmTJmHPnj1C9SbvyeSGvr50g/jmiAuNh6OXcJQ1cfTHhSW0ep9k4E5mt0UfkmR2Ozak+QdhZ0NNGvVGnKpcnmYUUOeqq55kCRlBbDRVMNRKH7eT3pw0U25CCnXUm7rzl1sT54+elRkyI+PabEMmAPbOWonjH36Hx3tO0mWNLYHrvLfvzD+/Fo4WUFFTbtKx31JC/UJRUlCCnqN64m3HXF2ZyrEECEQfV9XVU9mQTnrSLwnWUuJE5YgO+IZaGODehJ44N9IHa7ztabS/NBCZHBKRZtmJv/ydONi1jfWpPE6rbWhElIxQtCLXwZEaLsHBUliMl9d8Ua9nBugIT3Z4G2mirLoWQVl8mRX/tALU1NXDy1i640ci9ic6GuF+Yi6qRLy7P/ZzQND83rgy1RsfeJrTKNm2QKJfiXTX80zhaPPnGYXwNJCuviryspjlYkqlN561sxSPg6YaVOTlqGQTl6LqGsQUlcFNtwXXo4I8XVlV3oSsgDQQKbOALOHl4c+zCuChJ/2EBmnHyYs4pQUhK7NujuuO62O7YWtfN7joSNfW19bUICc2ESbuwtIQJu5OyJLQDrfGpqaqClF3n1CnhmDknk1PL+owLkjNoIP99LAoVKclQ9mz6eX+BHcdTUQUltAIfC6BOQXQUlSAZRMOJVHIKqGmJCMEqYqPhoGrsESIkbsTChI5zyZx5ETFNmsjTRlC1NW7VG7HuRkpGAI5ntH3n9K/nQbxo6uNXe1pO5UlMMEiSGZkLF39ZuBg3aQNkVp4duAsBnw0F7JinO9pryKppEdz0g0E0l6QFV5kcpCLf0O/xUVXumuZ++wQvDfIPsgE3VQHUyjIykBHSQFjbY3wOC2POvqbg1zPhs6c3y54nZdk5aK8oLDdbAhVJaVQUFbmvU96HgxtC1PoWpkLlStocMTrOvOvFy1rK8irqKAgRvxzR1qbivwChO0/DI9F8yEnRoaCS+SJM7i9aAV8V3+HmHOXqPOpNZAI6fToJCpzJ4iVpyNSJTyb25uqnGzUFBVB3ZG/ioSsaFC1s0dZnPjj2Rpqy8uRe/c25DU16b5bQ0ZknFD/l2Dm4YSchFRUS2h/pLGJfxYMIycbaBq3fGzkoq1FJRQjC/n9ltD8AtTW1VOJtLbyoasDTg7shW09u2CytTmN4peG9PA46JgbQ1Wb/6w37+REr7ms2ORW28T7v4K8ogJsuwvLZomO40RXsZAxXW1ZGSrSxU+ocilLTqbOfU1n/vUor64OFQsLlMS23/XYHGRMZeImfF+aejgJTcy31ibwxBWe418SZLXE4flf4sj7q3Bt3TZkxyQ2KkNWdDlqqTeS3QnKK4BrO1x7XBRkZDDI1AA3U7OkmlwqzMhBeUExzATuO3LNkIC0zMj4NtvE+AZi96yVOLJkLe7/dQRlBcIBlOLKlOQ3LiMIcbRbC4x3CLadHZERl4oqAVnUtlBbW0vlflIiEuHSs/XSaQzGfwGT4mF0GEhkPYl+37JlC2/bzz//LOQ0JpBofWkhnRbimCcR9YQ5c+bgq6++wq5du2g0PmHmzJm4e/cuz2bTpk3o06cPPvuMs/R3yJAh1Gb9+vX48ssvhfa9d+9eGvVPWLlyJcaMGUMfCiTKmUwEkPdLly6ln+vp6WH79u10BQCZcDA1NaUTAQMGDEBmZiaMjIyQl5dHI/jPnDmD9oBE1pcWldFIfUHUtdRRlCdZX7o55BXk4dHDFbeO34WNixWNFA+8/5LK96hKWL4n6uARdOoT8hvek+XtTXFgRCd46mtS2Y67STnY/OLNDazKGjoZROJEEBVNDZQVFLfJxsDeCl2mjoS+rQWNMHy08ziuE5mIdR9LHVlWlFsEZTVlKsEjiJp2286vKE+vPYONuw2MrcVLXrxNEKc+gURhC1JQWU31k6VBUVYGS92t8SKrAGmllbztxOlDIpdDcotgpqaMz73sqCTJe3eCmtURL2no9KuKXDeqWuooldDZlcbGspMT1WC9u/ss+r83jjo87+07T0NlSvKE9/v4+DU8OX6DRuXomBmiftwKQE64vTVQU+Tdq1yIb76ospre103xywAnjHcwpPfuy8wiLLwmLBtzIy4HB0JTkVJUgW6mWtTJb6yuhB8et36gyK2ToDOOe/7dmplE7Gmijb8GudPJBfL7Pn8YLnS+2wNdZc69my/idCqsqpZ6UohEhM9zssTD9NxGzvSWQiL+Re8N8l5NQR4qcrIol2Il1FJ3K3pN3EvhLwcvra7F5pdxeJCaCwU5WZrjYufATnjvdhC9b5qCyLqQZfAk6l4QsnS+XMxgsaU22dEJuPLNb/S6l1dWQr/l70FHIJLOY8IwuiT/3Cfr6XsZOVlojJsBZddOzR4LPSWFxm1NFWcykJzfeMnBeDwGmuhTB8DuiMZOA3HUFhVCWVPYSaakoU4n+SqLiiFv0HglSEVBEYzcnZu0kaZMXlwSIi7dwpD1XzT7HNs77SPqXCL2CqrKsO/XjfcZid4jUaTinAGE8vwiKGmoCTmmRG2Io4toI3eeOhLa5sbIS0prtJ/izGwoqCgjMzwG1767QifXScR+bfdJjaR46L0hph0h6DXT9nEheSj6muniC99w3ras8ip89vA1fu3rii99ODmKXuUUYfk98dJmjY5FQSE0jIUjSpUangnkWlfR1moXm8gbD1CclQNbgfNUkpkDTRNDPD9wGnGP/OkqACMXe8joGEBeRZnKWwmiqKmOqkIJ9yxZkduMDblHQ3bug+XgAdCysUJFnrDMExd9DzeY9+8DNWMjFMTGIWzfYboioNOiluUwIpQVldIJdFVt6Z/N7U1NIedZL6ch3J7JqWugOrftwS0Fz58h5cAeKpFFnPqWHyyDvHrrnI6kD0yc8oLQ/jCZeC4shoKhXqtsSJS6nrU5Hvx9FLFPXtIodLNOTujx7nghJ7c4yGRZkcgzlnTJSmpqoC1BNkZanmTl4FJSKjLKK+Gho0Wd/PpKStgR2Xy/hfxusgJEEO7YgTuWaI0NccASqaLgS/cQdOkezaVlaG+JXnPGQ79BC922hydubNqLyIcvYN/TEwVp2TTKn1BNrjcLyStNqhquR3mR61FBXZ1j+y9BJMVE8yuQZzzJxUDymymoKLXKhqwGi37gj4m/rpL43eS50WP+FFh19aT96lfnbuDKt1tg9Nm3UDThr3DUVFSAvKwsCkSuP/JeR4Kca2vobaQHNXl5XE8Vzt8iCd5YVcyxKM7KbZMNeYY6DeoJYycbet8+2nUSl777E1N+/QJyDfkHxJU58NVfWLT1c+pjEEdJfiFsvYT7Nmpa6rQfUVpYDEXllkuHCfL9mE84fRKS/2nmcHQaKF46jCEMixLvOLBzwegwBAYGon///u26TwMDA55Tn0D+NjEx4Tn1udvy8/OFIvZ79OBHjhF69epFy6QLRDGQfXOd+gRdXV1UV1ejpIQzUierAU6cOAFlZWUoKSnRiH57e04ETFwcJ2Kwd+/ecHBwwEGiNQjg8OHDdAKAROyLo7KyksoGCb6qRJwGgnAfUKJIWo7WEt79Yjps3WywZeVf+GT0KvjfCUT/8X3E6gBKA9fv2Zz5vBuv0P3YE8y+GgRLTRX80q/9ErlKDTl+LdWGFLHpMWciHcgQR4Sxsx0GfjiHRpJkRIiPJm3ZV8lIPPctpSC7ABHPI9DrfyBavyk4eSSav3iJfMYP3Z1oBO13/lFCn+16nYQnGflUpz+ioBSrn0bATksNvY1bsOxYNIKKnsvW2xC9yKlrFyM7MR3b536D3Ut+hIGlCXRNDRrZ9XpnOFae3oyF/3wDQxszyJzaBFSUSn3/Nnfvr7ofiU57fDH61AtU1dZh32gPITmSL+9H4nVOCYqqanA7IReb/RMwy80UqvKyb+R8N3e2/dIL0PWoLwaeeorDEWn4Y4Brs5MB/2b9uJNM63yc6cTRppA3EynHbUuk0aiebGeMKfYm+OZZJJWw4nI7JQdn4zKQW1lNtcY3BMYiuaQc0xwkL0VvDnqdt4MNifqec2QLpu/6mS67J8vv00P59/azfaeRHhKJMT9+hln7f8Xgzz9A8dUzKPP3bVW9eTlrpDjBHjoa+KKTPfZGJSFIZCKuRTQ881v0XJDGRqAMidp/+udeeM2ZAjX95tu8uUe34N39G2HVvTONmifSSe35nA04fpk6IFyH95NYnNa7ohIRtx9j+NfLMHXbWhi52KHiyO+oy248EdDafgvBQVsNv/RxxrHINCrJw9+uij8GuFEpngGn/TD2wnMqK7h9kLtEuabm4E2q1LePTUpgKF4cPEPleUhOCi719XVIfh5MV0NM2PIthq39mE66pPo+kfQlLZfVFrCJv3KDJu20GT28SRP3+e9C284GCmqqMOjkDucZU5Hu50+j/duL1rQ/7Q1tk9uhr6fl0w1uf/wNp59+hZZPdyRs+x2VmdI5CKWCO+ZoRfvDtSFyXdEP/aFuoIuZ27/DmLUfIj8lAzc27n7jz9mm+D0sCrHFpXT15tPsXJo4d5SFCU1+2hr447P6VtuQdi03MRVZsUl459fPMeP31VDWUMOFtdtQXsTp09n37Iz+H7yD5yeuYcfMz3Hl513oPLZB1qi1A7kOkGiU208RzaUirQ1Jyv7gzwPou3QWdVhLwrZXF7gM60slpEgutV4Lp0PT2ABFDyRLTglCLuv2PFojzI0RlFtAJ5jaArmWWtqkiNp0nT4aVt5uVMKNJNclevwkgC0xIKzJMlmJGTTRbYu+m3vNtUNj/PWFTVh1agOmrZkPv3P38fgMP+iTwXgbYBH7jA5Fe2cfF7c/cdtEB66NlidyH/oC5STVlVuGRO5//PHHQjI/XKhueQMkap+sKvj888+xb98+uqpA8HNByAqG77//XmjbnJUz8d6ns/D6eQT+WrWLt336x5PRd2wvqKirUBkVQYoLiqEhpQyCJNQ01ahzXxCSpFdfgi60ICRa30ZLVWzkam4zy+nIQLqytg7BOcX4PTAB2wa5QV9FgWpht4Une09TaRwuM//+ASoNyyQrikqEIhQqCouhZyO89JxLa2wIulamtFNcmJ4FE9fGS6B3fLWTOtgJROf+1ysboaGjgYrSCtRU1Qhp3xcXlEBTp32WeD674Q8lFUV07t8Z/wtwI7e1STR0Q2JPgo6yAvIkLBPnQoZp33dzohIii++HILuZa5VEYpLIawsNvnQBofT0HlQHPwVX3GvF8V+g1hBxVlZYQp3xvLKFJTBzEZ/rQlob8vesXz7mva+prqbJcrWN9Ro5dshYVMfUAKNWzELEtC+BmCDAvTevTG5ZFT1WQsdFBtBWJvdg08ejvkH2KCqvDGsfReP6tK7wMtLEiwzxDsuI3FI6SUUm8CLypJtgEIVbJxKxJ9reiK4aEgeJPCfl/nmVhAHmuphkb4ywXOFcKm2BG/GrrahA9em5kPpGF5Y269Rf7+NCI4k/9gulch5thTjj6b0hAIkqIzIwzUnBjLcxwieetljjF0l1+ZsjtrAUFurC90ZV8FOUn+HI0+0HMOCT+bDwdqfXJmlTBakoKm4UOSYYfdwSGxLtTba7jx2MlJdhNKGqibsjaqurEXHzEXp9MJ0n+0I09lV8eqH0/g2oduvT7PkVvfa470Uj+UUhUfoburridHwa1dwXlyQ3e8NXvPdq/YdDc+xUyGlo0hULglQSCQoZmUYrGLgoaWk0a9NcmfL8Appg99n2A/RF4Ebkn5q9HH0+XYTwCzeQG8ORASTa7jN2rIexix2SX7xC6NX7VPeYQCKkiYNF0vklGv/kc0E5CVGbjPAY5MQk0pUBglz6ajN1ygxYMZdGppN9dJ05jmr1E3xmjsOrm09RExkERQNToWeHjab4fkteM88Cey1V/DPIAzfJKkORRLsT7UzoSqAD4ZxzTCY2NwXE4sK4rvA21IZ/g4xYfVkJyjatpH8flgHsB/ZEj4UzoaxFzrfwCj3S3+AeJ/HHT3qb1KAwPNyyG11mTWiUjJccP+I87zJzAj0PZAWH18zxuP7NJvp5XXU1lYzhUlVUTK8jcShqaqKmvKJJm7zIaKq3f2vBh0K2LzZtpVH63iuFt3PRsOT0vcoys6Csww/2ESXs/gtc3nKY937Cl/NgT7WZZelzVhDynFVrRymNpiBR9ITahsAhLjXFxVQbv63Qe0hODgraOjCZMg1FQYEoePqEJt2VBEl4e3w5XxLVc9wg9Hh3Ar3/RNvdcnLsZGQk3s/S2BAHKomq9p7CCXwijupu00fjyvrtNIeVuh4/0EoUsiKTRE6L9uc0FRQaRVK3lfjiEtpvMVZRQUIJ/xkeu3olaktLEE1kr1xsMWn9CjpeyE/NFP7dDSt7RaPyuUhjo6ajSSdC+i2YwlvNQPT2d83+EqmhUbDv5UW3uQ/vQ19cuI5XpWakYBUarkdy/SkIRO1XFxdDzdIS7U1JXBzCN26kf7+gbfRYdBo/FCpammKum2Iq00ZWdIijORuygo9Ept/86W/e59znGHmWjFm/EoYCEnCC9xBZ6ZeWKXxuyEoRIvlE5PcEIX0+0ZWarcVIRQmeulr4OThS7OcF1y6i4MYl7Gh4P/nXL6Da0HaR+0xHYGhK3nM/E6U1NgR1fR3qwC9Mz26yjIq6KnLTsmmS3D8X/sj7rOekgRg2fxxtb8W1w+TYk5XqbYXmlVOVg0uvTkiNTsKziw/Re/KgNu+Xwfi3YI59Roehc+fO8PWVHAGnoKAglMD2TUE0/p8/fy60jej0E31/Ip/Tkt9z584d+qBoasKCOPJXr15NZXqCgoJw8uRJiWVJOSL5I8iT3Hucevs4YetNTsdHULrIzs0a0cGxGDZjMO+zqMAY2Lo17pi0hfLSCoQ+C8fQac0nsgrKKsI7jibQVpJHQSXHGdXdRJtG8b7OlUKboAGujqVMO8Q99Jw7CT3emyi8rF9dlS4LT38dQ5cNEshSfRIJ4zpCfBSgvo1Fi20I+ckZtOOoqtN4CTxh4foF/MjZht9r7WZD/495FQtnH84y5pToFJSXlPM+awvk+55dfwbvwT5QbGNSzo4CiRImTpguBloIyuE4lImuMdHX3xsuXtNU0KlPtMaXPAihiUKbgyRAJZ150Ukn1UnzgIlzsdCjlHetmThZUecBSWrr2t+bbi/KzkdBRg7MXMSfy9bYEKKevKJtqZ2Pu8Qy3IGMaBhMYGYRVBXk4GGgjpBszr3a1USLytUESnDQi4MMfAlNtY2OuhwnWnYzEwZNQZxkCYVl8DHSwh2BKNmuRtq4Ei+cGFiaOrd3QBpx3hPt3856WjRZKoEkzLTTVMO5BMkRkwoNTn1jVSXq1BeMjm8LRAKE3BuC+BhqITS3uFmnPpGe+vpZJO6niV/GLQpJPB3XED3IRaFTdyi4c/TOJzqW0+ubXCMk2V3G62ia6JYLiao3cuZIl4giJy/fYhsuRO6DF35GTjg95zJisr41H5EZWlCMD5ys6CQMmdQieOlp0UkY7vmW5NT/tZsrziam02S64pDXN4TxLzsaRUsqWNsjO+KVUNms11HQtjSlUkPi0HewRcar103aNFdGw9gQUw5tFfo85OQlpL14heEb19BzaeTpSo+thgJ/hZSRky11yhO5CC7EKU/Og6GT+ElNYkNWCOTEJlFZO3E2Y9atFGq/SMTg+c83UMcMiRCk+3Fu2L9gDhIJ5zw4pwhTHIT7LaQdIf2W8DzJ/RY74tQf7IE7yTn4+XnjSUEaWVovfiVArWAwiao6VL/mOJsmOlfy2k5DRxtE33lMfzs36j4jLJKumiCJicUhrU1a8Gs82LwLnaePhcuoxg4OAydbaieIYJtOHPH67q7076LEJNSUlUHbXvz9p21v26wNcdwLBthU5hfg4Wdr4P3pcui5Sl69WZLCWX2hJEZiSBDyHHXp68X/LQ3tj7G9BZJDY+E5lN+WJAVHwdyt6bakvSDJb4nsTml0JNQcOFIUZAKE6OsbjmycFLqtkOuiuQBYLWMDLDy+pdF5J/dmcpBwO5EWEgU9K1OJzlZpbMjK1qIMEdkh3rXW9IM5orAIynJycNBUR3SDU9dNR4vKAoZLkPtqLVbqnBXhBVXC/RbbHzfRtm+oWTmvviShbeh1X5STQKCG6PCUkCjIysvz2ihRpLEhEwcEodxK3L+b6MRE+QZC2cQEykbCuZVEUbWwoJNvxVFRUGkYF9eUlqI8JQVGA5sfA7YUdVtb+Pz1F/3bQ6eK12aRnAtkrCUIecaT9k1S37I5G/L/vON/CH3+eMdxFKZlYtT3KyQmyCXtUkFKBuT0hQO4aurr6TXXSUcTNwUmZEifL7SdpLyGmxnR3ExPJEjoaA0fA61hozGcXnucMQe5x8mKhPTX0TB14wSTkUAGsnK8S8PkWaP9mBi02IZQkltAx8Fkcq6pMuUlZTQ5rq6JPo2e58I9lxYuNogJ4AS4cYkLjoKRjSkUJbQtrYX2A//zNVkMRstgUjyMDsOnn35K5WvWrFlDk9wS3XkSnU6S5xIsLS1RXFxMpXLeJMRxfu/ePepoLy0tpUl2f/zxR57mvrR88cUXNIHusmXLqKY+2deDBw9oMl1BSJLcCRMm0O8l2v5EmkcSRNJHU1NT6KXYEP1HHnx0trnhxX0QDp46AK9fRMLvuj8qyirw6OITxIbGY9AUvuzRzWN38PFIfv4AaXh26wWe3wmkCWtyM/Kw+4cD0NLTxKDJkp3XXO4k5SCjtAJfd7en0YuO2qpY1MkCZ2MyeMniDFUU8XJ2Hwy25EQVz3ExwzhbQ+gpK1CnlqeBBlZ4WcMvLb9Njj8upKNIOjvcF4FEcLgO64OXZ24gJy4ZFcUl8N19gkYQ2fbkD/4uffs7bv+2V2obovVLkuWSZIxEB5iUu//XIZoQy7yT+MEpmajhnlvZhiW+huYGcO/phos7LyAnLYfK5pzdfg5WLlawdec7dr+dthaX91xp8TGJDopGbnoueo4WlqZ6myHdtOMxaZhubwpPPU1oKMjh4042NKLmcgK/072hhzPVxyeQO+nbro40sShx6ovTWSdR09/4OMBOU5VenySyc113J2SUVeCBiKOTOgtErjWik+8+sCt8j1xBTlI61e69teMUlcyx9XHj2e5f8Qtu/HW8RTZ395xFZlwKzbkR/zIcd3efQffJQ3kR+8lhsXh48BLy07JpMsv89Gxc+/0IoKQC2AonXyPO/OfphVjTy44mwTVTV8KqHrbwTc5HdD5fK50kwF3oyRngjLEzwFwPM5ioK9EJACddNXzf1wFxBWUIyuQMbEba6mOBpzlM1ZWgKCeDvhY6WNnNBpdjspDbxtU4B8JTMdHeGL1MtKnTfHlnK2gpyeNUFF9a7Zvu9jg52ouXLPeXPk5w0lGj55JE5S71tKJSGlfiWjYZ0BwkqerFxAzMtjeniXQ1FeTxibsdjea+n853Yvze0x1ruzjxkrH94O0MY5X2deoTjkenwU1XHVPtTKAqL4fhlgbobaKDo1GpvDITbIzwZHJvemwIY6wN+U79VPEDS5JIuquhFtXpJ9ryn3ja0HvkVIxwkj7y3CL3Bvf+4D7H3McMRvyTQCT6B1PZllfnb1Enj8sI/nPs+eHzOLXsW957aWzubtpFE97VVFXTtjrkwi1khMfCvn833gSBuZcbQi/eRl5iKtXSJfq75S+eQNmd/wyQxK2ULJTX1mK5my00FOThqq2Od2zMcDYhnee0tdNQxZ2RveDZkCzZWUsdG4lTPyEdeyLFO/V5x6vhWNFXg7NDrfcglObk4fW5a6guK0daQAgSHz+H40j+5H6KfxCNoi/L40SD2w/r16yNNGUEn6H0/AlsJ+eSPMeCDp2hx7CmQdO4NDefLuWXk5dDSU4+1er1P3gOpp2coSuQ6+DA7JV4deEW/Zs484lkztP9ZyTakOekUF0ajg/5Lu7fmkb6sO7hhYBjl+l+SERw4MkrQGUF5J2FV6ndS86h7fkqH3u6qoVI6Cxwt8D5WH6/heT08J/eB4PMOW2rjaYKjdQnk4o/iXHqE+6n5NIE3zOcTKl8h76yAlZ2sUFGaSVei+TKkZElv0P49zgM7kMnOQKPXUBVWTnSQyKo095lNN8Rnx4aicMzl6MgOU16m5AI3N+8E52nj4PraP45FsSufw9al+CTl+mxI9fTyxOXoGFhDoPOHog6cZZq21fk5yPi6Clo2dpA24HvDL//ySpEnz7PuW6NjWDQuVOTNqL9NN45Je1Gw9/ZwSGIuXCF7oM4v3PDIxFx7BSN6Cff0RT0GhXcf0P7023CQIQ/DECkXzAqyyrw9PQt+pz0HsNfwXBv/wVsf/87vAnIb9MbNAS5926jNCYatWWlyDhzkub70O7JT+6ZtHM74v/gO8SaoyovF6lHDqAiLZUmDa8uKED6yWOoLSmGtg8/l4IkxJ0L95F9UZKdh4DT1+m1lfA8BNGPnqPTWP61Ffc0CDumfkSdedLauA7tjYqSUgSdv00T6hZl5eLFyat0xY+6nuRVGATiWA3NL8QCJ1voKynCUFkJ8x1t8DI3H0ml/H4LSYA7SSQRdFP0MdLHRCszGCgr0edhFz0dzLG3xoOMrEYrAYT7fZxjRZLbahjo4MGOkzRqnCQLfn7qBlyH9ICSmgotQ47RX5NXINYvSGobi05OMLCzgO++c1R6p6KkDI/2nqVR1WYNiWPJ9oe7T9P9k3v35YW7iPYNhNV04ZXY4iBJqw3690fa1asoTUpCTUkJEo8doytLdLw5QSaEiE2bELNzJ9oDcf0Ct1EDkB2TgNfXHqCqvAIxj54j+WUY3Mfwrxuy+o5E2hMHtLQ2os8x7oNM0Kn/6O8j9DlGnmEkL8mz/Wfo5LFG38YTG2cSUtHfxAC9DPWgIidH+wAmqiq4kMTv/7zvaI0D/Vqu6U6qNszUCLdTM+kkgtgyYsYcZFunMQPx6vI9pIfHUsf7k/1n6Xangfwx342Nu3Dpu61S25TmFeDetkM0mI0c87ykdNzZso9G5Ns0JHOWVEbLQBsuPTm5iwT9GdxAxW5j+qIwKw8Pjt2g/ozIZ6EIuReAnhMH8Or7+nEw1covypFedu3K36cRExCOitJyVJZX0n08v+wLz8HNt4EMEvRU/1a+/hdhEfuMDoOnpydu3bqFVatW0cSzhoaG+OCDD+DmxnFQeXl50US0xPlNHPwkkS1xuL+Jepw+fZpOMCxfvpw63t9//30aLd8SSCJg4sgniXeJtj5J+uvj44Ovv/66UVkix0Mi9cn3tDfO3o6Y8+UMXN5/HYd+PU6lcuZ/PRv2HrZCkQa1AkkRY0Pi8NvHnOgIEtl79LeTOPbbKXQd0gVzV8+i2927u+D09gv0M9JR8OztjvdWzYSSmGRFopDIxUW3Q/FND3vcmdKNJmS8GpeFjS/4S9RJv404AWUbelSX47OwqJMlPvKyprIfZNB7IyEbe8OEJQquTPCBqboylQchEbZkcoDgdbh1esjdZ0+gx+fKD3/Szq+Row1GffOhUOQROUac2X3pbLTNjKFnZY7bv+2jUSDE6W/RxQ0+00ZTrdqWMGvVLJzeega/LNxIl96SyP2pH08Vilah0ZAC9du/bj+CH76ikYLE5pOhnFUgK//6BBaOFkJJcy2cLGBuL/1Ap6WMHuqN4zs+4b0/toOTvOivvdexaj1/SXx7cjAihTpQNvR0plr5Efkl+Mg3TEgKhVw73KhyC3UVjLQypHqsp4YLd7xX+YXjUXoeciqq8Cwznzr3bbXUUFhZDf+sAnzrH9mshAmXIYun0iS3h7/Ygloi4ePhgClrl1CHl6RzKY2NU6/OdDIgKz4Vmga66PnOcHiP5Ts3TZ2skRWXgrM/7kJBejaNArPwsEf99FWAauPltR/efI3v+9rj5jQfOpe8znoAAQAASURBVFFyNzEX3/sKO62I85l7Dd5LysP7nuY4PKYTTYabXVZFt20PTOIlFX5Ay1jg0JhOMFJTQmpJBQ6FpmLPq8YSJC3lTHQGdZj/0MsRusqKiCkoxdI7YUITNGT1D2lvCOU1dbiemIM13ezpJERFTR29Rj64HYKArPZPlvhPeCJdhfNbDzeoyMshNL8Ynz0LQ4VAm0zqx5XRddZWRy8jXeoYPjWYE93OZYVfCLVvLa/zS/CVXySWelhhpZctMssq8XNALM0dwUW24VhxW5gFrpZQlJPFTz2EJyXPxaXj15ecNv1MbDo+cLNC516atN5R+aVYfP8VXjWzEoCLTa8uVEaH6N2T5HdapoYY/PlCoSS3pA0m90dLbJyG9sHzQ+eQG5dEIx6JJNrQVYupM59L36Wz8eLoRdz86S9UFpXSiGa1fkOhPnRss/UuqanF58/C8LG7Hc4N6UqTG19JzsQBgSh8OikvcDyn2phCXUEeM+3M6YtLTFEpFj3mBDo0hbyhMfp+tgRBh88g7MxVKGtrotP08bDu151/rOobnlkNjgANE6NmbaQpIw12Q/og8swV3P9jP43S1zDSh/eMcciJScDpFT/Q40Hkl3oteKdxFLFAEvLBny7Ak10nmrSRhn5LZ+Pp/tM4u/JHTuJwS1Moz1oBWX3OijvBfsuye6H4qqs9bkzoRu/PawlZQtI65BzSe6Oh7ZvqYEoT6060M6YvLllllRhzkbMy9HlmIb56Eol5rub40NOargAIyS3G8vuhtC1qDnI9Dlq1FM/3n0L4lbs0qbDbuKFwGSngYKqvF4rClsYm9OIt+kwJPHKOvrgYuzthyFccyRtFVRUMWbMc/vtOImzBFzTxpLG7M5zmvUedf+GHj+PJN+vpd5MofNf3Zgr1TUiCa8Fz6rFwbrM2zaHr4ozi5FQEbP4T5bl5UNbVgWmvHrAZ07Quf1O49O2CsqIS3Nl5FsV5hdAzM8SkNQthQCQUeb+lnur/c4l+FoKzP3FkxQjnNpC/ZeAzrj8Gvy8c4CMNBsNGor6qijrva8vKoGJpBesPPxFKcsu5R/jXTM6dm8g4d5r3PuYnjpSnydQZ0Os/EAo6ulBzcELKwb2oTEuFrIoKVK1sYPPJF1A2a12/T9vUCCNXL6YOvxcnrlIpmO6zx8NpgGD7Uy8UESuNDYn2HfPth3i89zT8j12i155lF1f0nCPdsdwQHI4lLvb4pzen3+KfnYd/IoT7LYLPWcL3Xu7wJCtYiFKRjAzOD+aMJxY/eYGM8gq8yMnDRCtz/OjtAT0lJWRVVOBychrOJUrXb5FXVMC4b5fi/o4T2LfgG8grKsKpnw/6zJ/Y+N5taKulsSGO1jFfLcKDXadwYOE3dFxh5GCNcWuXQUWTs6KA/K9rbowzq7fQoBBDewuMW7sUqUYuUtXdfNIkWrfI335DXWUl1O3s4PTxx/S+51Wd3A8C12PKuXNIv3GD99wJ+pITUGa3YAF0fVru1CbyeIM+eZ8+n5/uO01XHPX5YAYdT/EPH/f4SW8jDURfn0wCk0AAOvFmY47R369AqFbj4LyHmTnQilDAEmcbep2klJXh+5fhSCzhTyrJCqxCJ/Qw0MW3nfnn4hvydz1wPikNOyPjedu99XVgoKKE6yLyTNLgNXEoneAlzvuq0jLo21pi9DfkGuHL2pD2WXDM0ZwNWXVu7umC+38dpg57ZQ11muR68Cdzoaii3GSZGavnQElVWJ5REH1zQ8z87gPc2HUO949ch7qOBobMG4vOAg54cr7pmLzhfUl+ETa/u5b+TbZnJ2XiyZl7MHe2wvubOBKlXUf1xr3D13B640EaAEVWDAyZPw4+I/mTpgzG24BMfXtlWGQw/kWIfj03+ksS3MZdUK9e2m3NIcmmpqaGOvBbyvHjx+kkBknOK5jYVxrupl1FW+F0fOp5USTcYyxKc8dcHCtut16DlCSOIxrXLYF0ysUNASXtZ65XBdoKcSbR1fstPDbS4KbTdt1s6uyiyhGc+tFOj5imX/R6FrUTx8RenMTPbUFOTJKxurr6NicA7vTbslbbyjS8uN1ZSUkMW3p9CvJBgxRPR7nWRNlwnxP91RrIfSjgr3kjqKi2ny4OPd/tXGcdnfY7R2RPgsJI7XE9lpfXt0vbTOomzu9GjmVbDuckR8lSNeLgOi5b+oxqDddThLXW24rg8SSHUlx+e9IcSitG2Ne46WPHdXZIkhVoLySdE44UTyvaPgGneWsgK5Kk+c3HQyU7F9rlXArI7bSGSS4tS5DYmvMt6EwURDA6XhJZFdJ9D9cJ3ZbnGTmnXMkcafHSa/sqT1G4zkNuP5rvvG7Z8buc3Pq2RfR40vMnTsK0hcdLkD5Gbe8zv8n252566+9dctTqxPQLRGnLvTusQQ7lTV/jreVZdvtJmjS6HgVl7gRpwW/rpFP1r19rovd2U9xJV2mXMQea6AfUN3PdSmJEK669lvz2tmKiIl0QVEv9M2L9GZBp8W+abidZbuj/M2NvPcLbyCWRnEH/C7CIfcZbiTROeK40TWu2tWbfhNY49YuKirBx40YsXLiwxU799oIjfyDcg2jpMXkTtMZp+qadieL4Nzo87Vk/aR1f/9bvElwt0lEQVZdviwP//9O19l/eh20+3x24zqJ3yH99PQp+P61bBzh21PGBtxPB41n/L5xfruTR23RO2qPte9MTGf/FuXxT5/vfuJ/aY4L63zinrTle/9Y9JloHcUlxOxr/xbFpzXO2o/QLOso13ubr8V+YdH8T19q/0RY2zmglfT/6TY6g3rZ+lTj/TEfwZzAY/wZvh4eAwRDDgAEDqCNd3GvtWs6yq44Oqaeuri7U1dXx7bd8bWAGg8FgMBgMBoPBYDAYDAajo8GRPn77Xv+LsIh9xlvLnTt3JEp1/BtL8dvLsU9eb0t9GQwGg8FgMBgMBoPBYDAYDMZ/D3PsM95a/heWVjGHPoPBYDAYDAaDwWAwGAwGg8FoKcyxz2AwGAwGg8FgMBgMBoPBYDAYjGZhmhMdB3YuGAwGg8FgMBgMBoPBYDAYDAaDwXiLYI59BoPBYDAYDAaDwWAwGAwGg8FgMN4imGOfwWAwGAwGg8FgMBgMBoPBYDAYjLcIprHPYDAYDAaDwWAwGAwGg8FgMBiMZpGVYQepo8Ai9hkMBoPBYDAYDAaDwWAwGAwGg8F4i2COfQaDwWAwGAwGg8FgMBgMBoPBYDDeIpgUD4PBYDAYDAaDwWAwGAwGg8FgMJpFRqaeHaUOAnPsMxhvORnlHXvhjaVxxxZfe56jhI7MH7c6djP909X56KgUVVegI7PuZMe+Nz4YW4mOzLMcZXRkCjr25Ye5bmXoyGz3V0FHZZhLx743/Dv4vaEs17EHgu95lqMjcyJSFR2Z/pYd+/64l95x74+K2o7dL/hq1F50ZEbvX4KOzG97OnbHwGqQGjoyj88WoCPjOrrj9lv+/KcEHZluk/XQkZlu91/XgMFomo7tEWQwGAwGg8FgMBgMBoPBYDAYDAaDIQRz7DMYDAaDwWAwGAwGg8FgMBgMBoPxFtGxNR4YDAaDwWAwGAwGg8FgMBgMBoPRIZDt2Opx/69gEfsMBoPBYDAYDAaDwWAwGAwGg8FgvEUwxz6DwWAwGAwGg8FgMBgMBoPBYDAYbxHMsc9gMBgMBoPBYDAYDAaDwWAwGAzGWwTT2GcwGAwGg8FgMBgMBoPBYDAYDEazsCjxjgM7FwwGg8FgMBgMBoPBYDAYDAaDwWC8RTDHPkMiw4YNw5MnT9rlCJWXl6NPnz54/fr1/9sj/uLFCwwYMOC/rgaDwWAwGAwGg8FgMBgMBoPBeMthUjwt5Msvv4SZmRk++ugj/K9DnPp5eXntsq/a2lo8fvwYRUVF+P9KQUEBfH19/5PvDnv0Eg+OXkdBRh50TPQw8N3RcO7pIbF8TVU1wh4F4cVVX6RGJmHsR9PgNayHUJmsxHQ8PnUHCa+iUVNdA1N7Cwx6bzRM7C2kqpORihIWOdvBQ0cTFbV1uJ+ehf3Riaitr5do46SlgVEWxuhjpI+IgmKsCQgVW26MhQlGWhjDUFkJUYUl2BkZh8SSMrSEssQEpJw8gfKUZMhraEC//0AYDR0msXx9fT2KX4ch58F9FL0Og17vPrCYMavFZaRloI0uvuhtCxsdVaQWVWDrswRciMiSynakgwG2jXLFi7RCTDsVxNsuLyuDkfYGmO1pCh9TLfz8KA67A5PRFkrzCvB4z2mkvIqEnII8bHt6oeecCZBXUmyzTcQdP4RcvY+izFzoWZuh19xJMLS3krhfcvzDzt9AzB1fVJWUQdfWEt7vTYWOlVmbbCqLSxB3/ymi7/iiNCsXI39ZDW0LU6H9lOcXIujYBWSERqK6vALqhnqQNR2AOjvh+4qgKCeDVYMdMdbNGErysniamI+118ORXlSJ5pABcHCWN3pa62DFuRBceZ3J++zi+93haqwhVP7ky1R8dTW82f3S31lSCv/9p5ESGAYZGcDc2wPd5k6BoqpKm2xSXobh9eW7yI1PhryyIkzcneA1fSzUdLV5ZQpS0hF5yxdxj55DQVUZU7b9IPE7q3KykXnqGMpjoiCjqAStrt1hMH4SZOQkd3nK42OR/+g+il8GQMXaFpYrPhP6vKaoEDFrPm9kZ/b+Imh09kZbkJMB5jtZYbi5AVTl5BCaX4w/QuOQWlYh0aa3kS4mWZvAUUsNpTW18M/Kx57IJBRW17S6HvEBYXhy+BIK0rOhaaCLrlOHw7mfT5tsznz7J1LDYhrZGTta452fP2m0PdovCNc374OJsy0wbpXY75xob4T5HuYwUlVCfGEZNr+Ih39GocQ6GqgoYkEnC/Qz14GmojziC8uxNzQFd5NyeWVU5GUx2tYQ05xM4KCjho/vvcb95Ob7P8Wp6Qg7dAIFcQlQUFWFRb9ecJgwCjKysq22qaupRcojPyTd90VpeiYUtTRg0q0LHCaMhpyCQqP9laRn4vF3G1BXU4ORe/5ssr4KMjJ438ka/Y0NoCQri+C8QmyPiEV2RZXk46eshJHmRhhhZgRtRUWMv/0E1QLPaSs1Vfzdy0us7caQSNzPyIG01JaWIPPUcZSEvQJpMDQ8PGE4ZTrkVFQl2lSmp6Hg0X0UPn8KWWUV2K/7RWLZuspKJGz8EVVZGbD+/CsoW1pLVa+izBw82nMGaWExUFBShEM/H/SYPQ5y8nJttgm5+gBhNx6jOCcfhvaW6DN/MvSsOM+P+ro6RD8KQMj1R8hPToeyhhqsfTxQ33UKZJQat7vO2upY0ckGDtrqyK+sxtnYdByLSZVYRz1lBcywN0NvE13oKCkiqbgMh6JS8Cidf+0ryspggo0JRlkZwkxNGTkVVbiRlI2DkcmoE9hXVcQrlN08i9qcTNwy0IXzhJGw6N21yeOaERSKsBMXUZKZBVU98TbNlUm4/wQv9xxttO9xe7fw7hfRMvTqra+HjqcniiIjISMvDz1vb1hOmQJZRcl9k6qCAiQcP46i8HCJNgUhIUi9cgXl6emQU1aGfq9eMB87ln9/V1fj+fLljfZtM3s2DPv0afJ4TbMxxwhzY6gryCOmqAQ7IuKQ0ETfVkNBHkNNjaiNsYoyPvR7iaTSxuWt1FXxnr0VXHU0UVJdg4tJ6biYlIY3hYWpHubNGIS50wfCQE8Tes5zUVXV+meXNNSUliL95HEUh3LaFs1OnjCZOq3JtqUiPQ15Dx+gwJ/Ttjj/uKFRmeLQEGTfvoWK5ETIKilBzckZxuMnQkFbp0X1M9dVxXcT3NHNTg/lVbW4EJiCjVfCUVMnfkykr64Ev2+HNtr+4aEA3AhJp3+rK8nj3d7WGOdlDlMdFaTml+HQ4wQce5qItkL7LY5WGCbQb9kaJl2/xUFTDWU1tXiWnY+9bey3cBngaojPR7vC2kANqfnl2HYzEhcDJLd9U7tb4qdpno22u31+BVW1nJZNQ0UeCwbYY1RnUxhqKSMltwyHH8fj2JOWHz9yH06xMYO+shKSS8qwNzoBr/Ik91uU5WQxwNgAoyxMYK2uhh+Dw/Esu3GfRF9JEXMdrOGlr02vlTtpWTgam4SaJsbS4lg63BEzettAS1UBIUkFWHf6FSLSJPtsts7rihGdhcc5/jE5mP3nY7Hlf5zRGVN7WOH3K+HYfjOqRXUjbWbauTPIf/EcdVXV0HBygvm0GVDU1ZVoU5WXixzfR8h97Iua4mJ4/rENsgL9J/JsLQgMoOPx8tQUyKmqQYu0CWPHNtkmMBojK9Oya43x5mCO/RYSFhaGysrmnSsMRkchISQGZzcexMjFk+HSpzNC7r3AqZ/2Yt6vK2DuLH5QG3TbH8lhcRg6fzwOfvUX6sV0LO/suwS3fl7UmU8GqvcOX8OB1X9h8bYvoG0k+WFLkJeRwbou7nSAseRxIHSVFPF1ZxfIyshgV2S8xAHKB042uJaSATkZGegpKYktN9/RGkNMjbAlNAqv8gtho66GYWZGEvcrjurCAsT8/ht0e/SCzaIlKEuIR8LunZBTUoR+P/GrLsri45B15zb0+/ZDdXEx7TS0pow0uBuqY9c4D2z0jcPp1xkYZqePLSNckFdWjUdJ+U3ammkoYe0AewSkF1FHviCjHQ0w3M4AfzxNwNZRrhD5uMWQ33ftp3+gpK6GKZtXoaq0HDc27kJ1RSUGLX+3TTYBp68j+MJtDFg6CxadXVGQnoXwW75NOvbDL99G+KXb6PvJAmiZmyD4xCXc/XErxv62Forqqq22eXXqCnUgdJ4+Dr6/7xG7n8d/7kNtZRUGf/0RVLQ1kfT0JfJ3HkW1ihbqTV2Eyn433Bn97PQx71gg8sur8dMoV+yf4Y1RO/2anPgiLO1jg+raOsjLytL7SRByvn+9G4PdAoO6uhZ0/h9s2Yuq8gqM/vEzep4e/L4Xj/7cj8FfLmm1TXlBEWLuPUWnySOha22GsrwC+O06jnu/7sCYn7/k7efJjmOw7ukFx8G9EOf7QuL31dfUIPmv36FkbAKbr39ATWEBUneSNqwORlOmi7WpLSlB5ukT0O7TD6itQ3WhmHuIHKa6OlivXgslE4HBTBMOXGlZQJ36hvgmIAKZ5ZVY7maDzT3cMOf+S1SJaSOs1FUw0sIQh2NSEFNUCgNlRazydMB6H2cs9xM/2dkcWbHJuLxhF3rNGguXgd0Q5x+Cm38cgoqmOqw6O7faZuLaZVw3GqW8uBT7Fq6FfY9OjfZXlJWHh3vOwMTJFnUNA2tRBlvq4due9ljjGwW/tALMcDHB9iFumHrpJXXYi+M9NzNE5JZgb0gynUQeb2eELQNc8N61YARlF9MyM11MYaGhjA3+sTgw0rPRvSOO6vJyPPvlD+i7OWPAL99RB3vgnzshIycLh/GjWm2TExaOkrQMeMyfBTVDAxQlpeDl9j2oKa+A+xzha7i2uhov/9oNHUd75IQ0vyJyiYstvPV18E1gGIqqarDCzR7ru7hhid9LSPAfYbGzDeKKS3EiPgVLXew4s4cCZRNLyzDm9uNGDsgZthZ4ntP080iU1D07UFdRDuvP19B7Nm3PP0jbvxsWSyQH02QcPQCNLl2h3bsfip4/a3L/mSePQEFPD1UZacSvKxW11TW49MN26FoYY/ofX6EsvxDXNuxCfW0d+rw/uU02Tw6cR8S9Zxj04SyYuTsgNzEN4bf9eGUyoxORFh5Lnf065kbIT8nEnT8Ooiz5ADRmLG7kpP+jjzuuJWXhq2cRcNFRx7puziivrcX5+Ayx9ZzjaIG0sgp84fcaBZXVGGFpiJ96uGDl4zA8zyqgZfqb6kFLSR7fP4+i7ZObrgZ+6OoEOVkZ7AlPomVqUhNRfHgbVIdNglKX3rDOfI4X/xyAoqY6jDyEn29c8uOT4PfbDrhNGwervj2QFvCqkY00Zcjku4quNob99r3Q/mXl+BMoomVSi2URtmEDaisq4PHNN6gpL0f033+jtrISdvPmia0vuR4j//wT8mpqEm2KY2MR+ddfsJw8GQa9eqEyLw+xu3fT5xLZxqOuDi6ffgoNe3upnyWTrM0wydocPweH00CVOfZW+NHHHYt8A1BSUyvWZpadJarr6nAwJgGrPV3o5Lo4p/6mbp1wMzUTf76Oobf2ZGszWKipILlUfJvaVn5dOwfBrxOxafsFbP5+LmSkaG/bStIuTtti9+VX9Fwm796B5L27Yb1MctuSevggtLx9oNOnLwr8G7ct1UVFyPN7DKPRY6BsboHqgnykHj2MxL//gv3qr6Wum4KcDA5+0APRmcUY/us9GGooY8e8bvQeW3chTLyRDCAvJ4tRm+4jKpPzHCPUCjTkM3taQU1JHh8fCUBaQTl6Oxhgy6wukJeToQ7+tvC+kxVGmBnim8AIZJZx+i2burvhvQdN9FvM+f0W/YZ+yzofZ3zUyn4LFzdzLex4vzs2XX6NM/7JGOphjM2zuiCvpAq+kdlibcj4JqOgAgPW3xbaLnj8pvewQlF5NRbseoa8kkoMcDXCr7O86MTL+RcpUtevp6EelrnaYUtoNF7m5mOMpSm+83LFR35BSCkTf4+NtTCFsaoydkTGYWPXTvSxK4qWogI2d/ekgW4rnwXTSbnh5sbw1NVCQC6n/ZaGhYMd8MFgByzb44+o9CJ8OsYVB5f3wZAfbtHfLw5ybZ70S8Tak8FC7aw4RnmZwd1CG/mlVa2611PIhFxYGOyWfQR5dXUkHTmE2G1/wHnNt5ARaOeFbU5AxcICRsNHIvXk8Uafk3F9QdBLmIwbD2VTM1Tl5CBx3x5UZmfBblnjiVcG422ASfG0gJ9++olGsZ86dYrKypBXUhKnU9sUe/bswciRIzFt2jQcPHgQ69atwy+/8COKPvnkE2zfvh3btm3DqFGjeKsB6urqsHfvXkyYMIFu//rrr1FYWNhI3ubRo0dYtWoVLTN37lyEhwtHX44fP56W69evH2bNmoWzZ882qmNxcTFWr16NoUOHYsGCBQgICGhUprn6SANZAdBUXaX5DvKerJwQrKugbBD3uDx8+BArVqzA4MGD6Tkj5Obm4ptvvqHlp06din379gk9iG7evEmPF5HNWbhwIUaMGIGvvvoKJSUlvDJEToh7/ocMGYIlS5YgIiKi0W8l5d5991167slvJhH7ojRXn/bA7+w92HR2hM/oPlDTUkePCQOoQ59sl4TPqN6Y+Pm7sHSzlVhmxncfoNOgrtAy0IG6jiZGL51CB7MxAc1H//Yw1KMdlm2vY5BTWYWoohIcjUvGKHMTqEh4SBdX1+BT/1e4nZbFi6YQxVRVGROtzLA9PJY6FCpr6xBRWNwipz6BzPKTaCyzqe9AQVOTzuLr9emHzJs3JNqo2drB/qOPoe3VRWK0pjRlpOH9LhYIzSrGzoBk5JVX43hoOu7H52GRj2WTdmRC5M/RbvjdLwHx+Y2jtkjE/9IrYXiSLH2HsClIxH1OfAr6LZoOTUM96NuYo/vscYh+6I+y/KJW25CI/oBT19Dj3Qk0ml9BRQkGthbot2iGxLqQgVzE5TtwGjkQxh7OUNHRQtf501BbVY3YB35tsiHburw7CRrGBhK/Py8uCdZ9u0HT1AgKqiqwG9QLUNGEbK7wM0RLWR5TO5th0/0YhGYUI7WwAmuuvoaToToGOug3eby7mGthhpc5Vl+R7OAjjvxagZe0rU1uXDLSQyPRfe4UaJoYQsvMGF3nTKaR+CSavrU2ZJJjwMr3YexqT6P4tc1N4DS0D7Ulx5nLqHUr4TpqIBTVmo6kKQ5+iersLBjPeBcKOro0+l5v5Dga0UucOOKQU1en0bvaPftARrFxRLQg5L4lAwneq40OCUVZWUy0NsGhmGSE5RfTaNjNr2Kps36giZ5Ym8SScnz9IgIvcwtpuxhXXIZdkYnopKdFB8ut4eXlezC0tYD3hMFQ1dKA+9BesO7iisDzd9pkIysnSx1s3FfUo0C63XlAN6F91dXW4vpv+9F92khom0q+j+a5m+NGQg6uxmfTiOTtQUlIK6nELBfhyDFBNr2Ix7mYTGSWVaGwsgYHX6fSAbCnoSavzJ6QFHz3JAavc/nP+uZIfeyPmrJyeMydCWUdbei7OsFm5BDEX79Lf09rbQw93eE6awq0rCwgr6IMXSd7GPt0Rn5UbKP9hR87Cy0bSxh7N448FDc5PtzMCAeiE6ljJauiElvDYmCtoYZu+pIn49cFReBIbDJyKyVH9RNfiOCLTK4/ysilq0mkpSIpEWWR4TCaOgOKhkZ0cs5w8nSUhr6iUfmSsPp0NXQHDoGcatNtQ+HzZ6hITYHBmAloCfH+r2j0ff/F06GhrwMjB2t0fWckwm760knL1toUpGUh6OJd9Fs4FdY+7lBQVoKxk42Q45+8H7B4OowcrKCookz/J5H/NYmNV8GMszamTtw/XsXRe+NJRj4uxGdglqO5xN+25VUcTsSkIbmkAsXVtTgVm47wvGIMNuM/a26l5GDX6yTEF5fRKFvi8H+WlQ8PXf7qr/LHtyBvagWVfiMgq64Bm4G9YeTpiujLtyR+d+z1u9C2toDj6KFQ0hRvI00ZLoLtjKBTX1yZoqgolCUn0yh5JX19qFlYwGLiROQ8fYoqCeOawvDwZm1y/f2hamYGk6FD6QQAKWM2ejQy795t9PxpybOEfDLJygwXktIQlFeI/KpqutKGPD+GmBlJtPsnIg57ohLo5I0k3nfkTNyRfjLZb0FVNbV5U059wvRFW/DzH2eRntmyib/WUp6UiNLIcJi+Mx1KhkZQNjaByZRpNNqeROVLwu7zVdAfRNoWNbGfk/GB1cLFUHNwhJyKCpRNTKHXtz/9PhJlLC3D3E1gqaeGNadeIb2gAsHJBfjjZiRm9bSGmpLkVUEE2per478E2Xk/FpuuRSAyoxjFFTW4HpKOSy9TMbaz5FWqUvdbrAT6LZVV2BzC6bcMaKrfEsDvt5D2ZHdEIjrpatGo87Ywv78dwlIKsOteLPJKq3DiaRIehGfhg0ECE2cSEDx2oseP7G/P/Vgk5pTS43cpMBURqUXwtmk6eE0UMlHmm5GDBxnZKKquoRH1WeWVGNewMkscpxJS6ERbbJHkPgmZPCfn/9eQSDrpSp63ZxNSW+TUJ83OgsH22Hc/Fo8js5FdVIlvTwZDWUEWU3tKDpTiInjsxAUHmOup4pspHvh4/wvU1Na3aqVN7uPHMBk/AapWVlDU04PFzNmoSEtDUWiIRDvbxUthMnosFLT4K39Fx+M2Cz6AuoMjbavJvo3HjKX7lDRWYDA6Osyx3wKI49XFxQW9e/fGhg0b6MvAQPIAlPD7779T5zJxUs+cOZM6bn/++WdER0fzyoSEhOCzzz7Ds2fPqFOfOJQJ77//Pv788086IbB8+XJERUWhR48eqGhocLjyNlOmTKH1IN9TXV1NddyJo57LmjVraF3JhAKp+6JFi7Bz506hepJ9XLlyBYsXL6ZliGOdOMgFaa4+0jBv3rwm6yrNd5C6Xbt2jVfXiRMn4s6dOzzZIO5xIdtNTU2p45yUI593796dTsaQ7588eTKdYFm5ciVv31lZWbh69SqdMCATAuQ7zp07R48ZFwsLC975JxMMampq8Pb2RkICP/ohPT2dfifpqJPfISsrSycyBJGmPu1B8us4WHdyENpm4+mI5PCWObubo7KsgjomlFTER9IL4qqtSZcikgEEl+DcAijKycJeU73VdSATBiRSxC+LL7HQGkpjY+jDXtD5ruHsTGf0q1s4mfUmIDI5fiLO98dJ+ehiyndUiePTXjbIKauiEwH/BhkRcVDT04aWCb+dNPNwoitAMqPiW22TGBBGne72faSXQCnOzEFFYTGM3Bx52+QUFWDgZIucyLh2s5GEZY8uSPILRGlOPmprapDw5AVQXYE6c3ehcl5m2lCQk4VfAn/JbUphBRLyyuBtLr6DStBUkscfEzph1eUwFJRJHlAu7W2D118Oxt2lvbF6sCNUFZoeNHLJioylUkj6DvxVPkau9vQeyZIwcdYaG3J8Yh88g5mXGz3WLaU8LoZG1MtravG2kaXxJGKyIqltEWqEpK2bEPXph4jf8AMK/HzbPBFLlqSryMvhZQ6/XSEDv5iiMrjpNn0/C6KlIE8nbcpb4EgVJD08Hubuws8Ji06OSG9iUrQ1NmF3/GDb3YNOBAjid+wKVLU5kwOSICtO3PTV4Z8h3PY9TS9AZwEnfVMoyclikoMRFGRl8Ti1bU6l/OhYaNlZ05VcXIijvrq0FKXpGe1iQ9o5ErGfGRQCYx9huZvMwGAape86a6pU9SVSdvIN8ju8fVRUIq2sHK7awuejLbjraMJcTYWurmsJZXExkFFUhLI1P6BA1cGRRjKT+7otVGVnIevMCZjOXUAn7VtCekQcdMyNoarNv8bMOznRQIbs2ORW2xDnv7yiAmy6Nz8pQyBtTX5KBmL9gqDo1qXR5530NBGUUyQ0Wfsiu4DK5+gqSd+WaijKUwe+OEiPyFVHHd4G2niQxu9nkYkGeVvhlT2Gbs7Ii5HcFuRGxcHA1bFJG2nKcKXuLi/6HFeWfAHfn7ciL7ZxWy9YJv7IESqxqGxoyPtc09mZyvOUxIl/tpfExkJRR6dJG/o8EHXQy8pSJ29porB8R9Q//1BJnpAffkDGvXtNruA0UVWmUkmv8vhtX3VdPcILiuEicI21FCL30VlXG/fSxUc1/69A+vSkbVGx4bctxBlPzk1ZbOMJ09ZSlZeH/KdPoOHuIST70Rze1ro0Wj+nhK8I8CQ6B0oKcnBvot9HOLK4F179OBKXPumHKV2bl0LVUVNESWXbpG/suf2WXOF+S2xRGW3/pYVI49F+i4SJcGnxttWFX7Sw5NvjqGx4WTftgCfyOgE/jsDz9SNwcElPdLLUbnJVxUBXI9gZaeBWiPTPNrJC3UFTna4gFyQorwAuIv2glkJWAvhm5rRYdkcQK301GGgqwy+K3wZU1dQhIC4PXZqZwBjTxQwhm8fiwXfDsGGmF/Q1lBr12f6Y2xVbr0YgPkv6oAlBSuPjSOQH1B2deNvIxKqigQFK4trv3iXUlJTQSVZZBSZowng7YVduC3BwcICOjg51FpNo7eaoqanBjz/+SCP9iXOXMGjQIOoYFsXZ2RmHDh3ivX/69CmOHTuGlJQU6OtzImdIVDepw8mTJzFnzhxe2S+++AKffvop/ZtE5Wtra9Po9eHDh9Nt3brxI+P69+8PRUVFOuHwwQcf0G0k4v/27duIiYmBjY0N3Ub2MWnSpFbVpymaqqs033H//n1aXzIxwq2rnp4ejbIXhUySEMc7FxI1b21tjQMHDvC2OTo6omvXrvj222/pueWetzNnzsDOzo43UUAi77loaGgInX+ycoBE7O/evRvr16+n2zZt2kTrR1ZoEMjETmpqKo4cOcKz27hxo1T1aQu1NbUoLy6jkfqCqGqpoySfP6HSHtzcdR6qmupw6ObWbFkyyBR06hMKG6JbdFowABXFREUZqaXlmGBlivGWptSJEV1UjH1RCU3qkIpCnPdKBvzBG4EMAulnRYVQ0OI7Df8LDNUUqYNekNzyaqgrylNHbVl1405ybwsdTHY1wohDkmVM2hsiP6Ai0nElGsHEsVtWUNRqm6KMHKjr6yLq4XMEnbtFZXpIZH+3WeNoZKQ4Kgo4nWplkX0raaijNDu33WwkQaL6H23ZhQsfcpZnE4d3Te85qNcRjpwyUOc4/EjUkSB5ZVW8z8Txy1g33IjMhG98HnVeiuNZUj6uvs5EZFYJ3Iw18NNoV7oSYO4xThR1UxDJHCVyHgScFiTykUTQk8/aauP710Gqn08cIwYO1hi8SrK8T1PUFBZCTl34fMk13Lu1bcnxIgNo9xsInf6DaIR/yasgZJ44grrycugOaqxzKy26ypz2jkRKClJYVS21M05NXg7znCzxML1lEdKClNL7Tvg5QSR1yL1VVV4JRTETti21yYhOpFIj/eZNFNqe9CoSEfefY+Zv/Oe1OMizgTjkSVsnSH5FNfRVmo7489DXwKFRnnSgWVpdg9WPIhFT0LK8K6JUFhRCqeHa4qLY8L6yoAga5mZtsrn/5XcozciijkPzPj1gP34k77PyvHyE7DsK748XQ15ZWar6Esk77rUlCHkWE6dhe0G0+MnEfaiEdkESteTeVVMXai/IIJtE4te04d4lk3ppe3dCf9RYKBmbojJNsu6yOMhKsUbPpIYABMnPseZtCjNyoG1qiFeX7iH48j2aW4Fo7Pd4dzz0rYWvnbNfbaET22SC26abBzDqnUbfqaesiJQSYecRkdfhfpbX8HdTTLI1oRMBRM5HlKuju1NHHJGpOh6dinMC8j51xYU0Ul8QIpdTU1GJmooKsdcoeb4qaTVtI00ZBRVleM6ZClMfT9RV1yDy4g08XPcbBq1fBU1zTkSsaJn7G/6igRplaWlQNeWUIRIPxNFL5FXEQaLyuf1ALqI2up07I+vBA2Q+eMCT4km7do1+JhgUot+zJ0yGDYOitjYKQkPpRAPRgTYfN07sd+tIuHfJe5KvqrUYqShTSQ3SY9jcrRMs1VXpqrGrKem4lPTvBID8G5B+ATlX4tuWtgfrJO/fS3X4SVtNJg+akvcRh6GmEnIFnPqEvFLOewMRZymPeuDQ43js942nEidD3YyxbnInaCgrYN8j8ZNTvR30McjFCMsOtm0cQGS/xPVbClrab3G0xMMWruyS+viVVEFdWR6qinIoq2q8/5KKGnx/NgQ3X6XTXFZLhjrg5Ed9MHbTA0Rn8MfH2qoK1PFPZI+IzOVP58PwUMp8ZgRNRQU6Fm005m3jc5dMGBC9ftKf+aGLG1y0Neh3PEjPxvG4ZKmd/cSpT8gtFhlzlFTCQk/8ShVCdHoRTvklIjA+D5b6avj+HU8cWt4H4zfeoxMDhE/HutLzcqwNsk/k3qW/t1Hbq8H7rD0gbXjmtavQ7d6zyXxcjMa0VbaX0X6wK/cNQiK4c3JyqHNa0ClMorRFIdHdgty7d49GeZNIem5EII3Wyc+nEi+CCDruVVRUqFM8I4Pf4Y6MjMTff/9No99J8loiCRMrEKHg7+9PnedcRzmBOynQmvo0RVN1leY7iESOvb29UF2JY10copMvZP8kkp6sEiD7JS/itCfyP+QYkZUBBF1dXZ5Tn2BpaUlXLxAJIK0Gh+7ly5fpZANx1pOcC3FxcfTcCh5TwfPOPaaCjn1p6yMI+S7RHA/VlVU0MZtYJDzYZdq5FX504iZCH73E7HVLoKwmOZFmU3Cr2paakQEnkRVILSvHJ8+CaSQIWWZMdEgXPw6kyz9bDXdA0EFzxHC10sXqMCrL4/eRLlh5I5w6wToE9a23IfdKSU4ekgLCMH7dJ5BXUsDz41dw5YdteOf3NVDXk35SjEwYtDTYpTU2D3/bSfMFjN70NZX0SX4ejKe7DqB64GLUGwtHJBLqxZxfGQl3xwwvM1jrqmLFuVdN1uH7G5G8v/0S8/HVldc4NqcrnA3VEdHKaBpOW1LfZpveS2aj5wczUJSehWf7TuHWj9swav1nEiUVWviFaOvNS1YAGE/jJ7rW7tUXVVmZyLt9vU2OfUlwznfzkMSWRKOWRG9uCmnf6CX+c6K+XWzCbvtB00gPFp34kVcVxaW4+ftBDP1oNp0UeFPHKiSnGN6HfKGlpICxdob4tb8zFt0KxfMmku62Bu7vr28Hm/4/f0s19AtiE/Bq90GE7j8Gj3kz6WdBf++F1eD+0LHj94WkRfR7aJAx2geSRLGvkT4OxjQvU9myZ2/r793sS+cgp6kJnX4D269K3Ou8BQ+CRjb19chNTKWr06Zs/Jw6HZ8cPI9L323DjD+/phPaXCasW0GTI+ckpOLe9qMoPb4TGrOWNvud3NpJc357GevQxLu/BcchurC00edjrjyDsrwcXRnwjbcDKmprsfO15PPMdaK25FkpjY1oGfMewiv3Os+fQSP9Y67fQ5cFs8SWIdr2lbm5yLhzB7YCgTstrrCIjZarK9XbJ878xOPHqVSL2dixiCdBPg31JpHcgjr++t27cyYArl6lZVtCHST3C6SBaznTzhK/vIqkCXmJPveXnZyptMbVFq66edsg11J7dOnN58yF2ax3aZ8g7cQxJGzdArsvVkvU/25Rn16CRBOJ7v/2LF+K5KR/EmwM1LBooL1Yx76LiSa2zfGhnxFJnjcBuR4hZb/lB29Ov4VI+LyRujRz/K4ECUswfX3yFbxt9DCvvy2+OsHXjScrYJ0/u0xzFZCI/Q3TO9MVD6efte0Z19brjkirEt6xtcDmkCj8HBwBW001rO7kDCU5OeyRsCpaWiTl3OHy+1W+BHFocgE+3OOPx+tHYKCbMW4Ep6GXowEmdLPA6J/voj0QPY3tmZeDSO/E/b0NCtpaVIKXwXhbYY79NwhXYkadRHQIIPqeQORcRG2NjIx4EeCCmJkJR/IoiCz3ox2VhgcamVwgzvQZM2ZQWRkSIf/8+XMq/cOFOPtF60Sc7vICy5VbUp+maKqu0nwHKSNaV2VlZaG6NnVMiROdu1JBECKx1FQdCdx6EjklkheBRNUTeSVSn19//VVIukjcMRV0/LekPoIQGafvvxdOEDZp+SxMXjEbsYEROPLtDt720cumwntkLyirq6BMZJBWWlAMNe3WS94I8uTMXTw8dhMz1i6ElTt/QqQpSFSBuYhWNkkCJC4CpCXkVVbRzs6OiDhedMTfEbE4Oagn1XF8nCldlLWChiaNnhKE+54M1P5rssuqoCcSoaqvqojSqhqUionWt9VRhZG6Eg5M7CQ0CUJecR/3x6TjLxGU0YZoZgDHl69DYQZnKaeupQmmbl5N9dMrRPQhK0tK6ZJzFQnSD9LYqOpo0sjFXnMnQcOQs1S09/wpiLj7FMkvw1GaV0g1+LmM+PlLKGtxzltlUTFgZsz7jEjtiEbkc2mNjTiK0jKRHvQag7/9mCbgJdgN6Am/CwGQi3yAGgHHfk5DpL6uqiKyBCKQ9FQV8UJC7oNuVjpwNFDH61WDhbb/PsED87tZYuI+f7F2rxsSrpFJAUHHvsKNLZDJScDBo5zzMfXv9fT3VhaX0naQ2yaSSUiyTTQylUtLbMhkiZysLHQszdDj/em48Ol6ZEfFw8ileY1UQYgDrypTeNBaW8y5tuU02vfeVTKzQE3hddRVVkJWQjLv5iBa2ARtRQUUVtUIRaeLc66JDo7X+7hQXf2P/UKlmrisSoxF3raf6d9biTNv9lj4TBxCZXDKRe67soISKCgrUu1vcbTEhkxAR/kGwHviEKEBWX5aFkrzi3Bh3T+8bfRZS14bPoDcu6sga2bLO1Y1dfW8VQ5cdFUUG0Xxi4PIu+ZVVONAWCr6mulimpNJmxz7SlqaqCwUbjdpW0E+09Rosw25J+SVlKhUj8OE0Xi15zCV3SESVXmRMciPjkP0+StCx+zq3GVwmTkZ6Dai0XcXNGjkk2cteVZyIddeWAuj6yUx0MQAsrIyuJ2W2WJbcu/WlpYItRek3a8tLYV8G+7dsphoVCTGI2K5cF8rcdNP0OzSFabzOPKXhOhVK2kdyBSoiYstdagT2aj8VOHfU17IOWeSnmPS2HCfY30XTOFJ9hC9/T3vfonUkCjY9fISyVWhSDX3e84eh6s/70RdUQFkNfnSEXkVVdAWiZblroBsLlq/u5E2fuzujL9CEiQm2iUxmESi52lmPo5Ep2KuswXPsS+rron60uJG17WckhLkJbQf9F4QfdaL2EhTRhRy7WhamKKErHaRAFl1ScpVZGYKaTmTpLaS+nhke6N+oRgb/R496IsLSahLf0sT0q1q5ub0OVJNcnGpNs6lw115QaJ/IaB9T+7dgirJuS+ag9sOXE/JQEiDVMjT7Dwq79HX2OB/xrEvT85dSeO2hZw/0t9vKzRfgqwslM3MYTpjNqJ/+BZlcbEcuR8pIE56O0PhtkRPnXN95xQLB3M1RXhaERYPUoaKohxN8MrFyVgDhxb3xOWgNKy/KCEZb3v0WxQVEF0kRb/F24Xq8X/8VLp+S3OQY6TbcLy46GkoobSyhr6kJTK9CNYGjaPUySQXSSJ7ISAF3ez0MKevjdSO/aKqamqvJeJb4Ny7rR/vVtbV0Wj9Fzn5eNIgPxuWX4QryekYYW4stWOfe33pqisiVuCRpaeuiNwWXHvpBeUoKK3iHb+u9now0FDG0x/5Kw3JqoePR7tg3kA7+Ky6KvW9S6gpLoGCNv95R9piNbuWjQ8kOfVj//wD9TW1sP94JeSkXAHJYHREmGO/hZCIcmkhMisEEilvbs5PXkVkZAQj18VBIsbT0tLg7u5OnfGthSSDNTY2xj//8AfNQUFBQmVsbW1pxDnRvOc6tUlEP5Gkae/6NIU030Ei9ePj42nduM587ntp9p+ZmSmVjFJTkES8RHNfUAufOPk1BTr25JiSqHtBRN+3pj4kwbGoBv+5lPuc7/RywtcXNvG2c/XhLVxskBgag95T+U6/hOBour2tPDl7F/cOXcX0bxfQ75eW8IIimihXU0Ge6jISSJQQSfxGIoZay+sG5wQ3UoPzd8uDsNTs7JDz6BHt+HOPY3FkBBR19YQ6Fv8VAWmF6CGiu9nLQhsv08U7Z8h2my2c64TLz0MdYaejindOBtHkS22FRMqLxgcSJ0Tg6es0iaCmEWewShwWJPTCyFG8ZI40NsbODTqpghEbMjK8t95ThqPL5GEoqm6IkJOTo+dSSVMdma9jYOjC0QUn0bA5UXFwn8TveApCkuG21KYpRANM6skGXjQ5h6DUQtTU1aGHlQ4uhnEG1SaaSrDSVUVginjH/srzofjsAn+wpigvi/BVg/HJhRBcbtiHOJwaBpKCEwiE6qEr6LlcMEaGV2lDR1vUVFYhNzYJ+vachFrkuJDjauAoPsl2a2zocWnQGm7NZalqa0cT5daUFNOluoSyqAiqq61s2XwisJZQlZYKWRUVqt3bWojznkS+dtbTosnlCOoKcrDTVMO5BMnnTqHBqW+sqkSd+tLIbBAUrexgtIEzAfyObSmvfTNxtkVqmLCOeUpIFIwdrSVGRrXEJvrxS3otuA4SXolm4mSD5ad/F9p25+/jyE/NQvb4LyEjy494JE7917nF8DHWoslwuXQ31kJgVssc0xLUqlqEtr0tIk9doEmeufkgcl9HQl5VBeqmJu1mQ6DPIoHI9ZH7tgl9nvLwCUIPHMeIPVvpOX0tZr6CJJInDoZOOpq4n8HRIybOFaLfzX12tpXh5kbwy8xFYSucNSq2dqivqqK5MFSsOP2Tspgo6jhVsW39AN7q01VCjQlJxJvw8/ewWrkKylbCzyH7nzbRsgNNyvnPMWcbhN7wpZNY3FUl5JkkKy8PA1vxCeulseE+x4TkQbh/NxGNSCZHOQg3kCF5xRhvY0xrzf2EaOGnl1ZQeRVJdDfUxoYeLtgRloiTsZITiQpCAigEayhvaYfq+CihMtlhUdC1l9x+6DnYIic8ukkbacqIQpy3xanp0DCTfD+p29qirqqKJjzlUhQRQY87+UwcGra2SLtyBRU5OVBukAttzoaQ+/w51eZXsxR/rRDKUlPpM4okcRQXykvyYBAHvoeOFnXecaU4XLQ0qexGayH9byJf2WgVj8C//wuoNrQt5YkJULXmtC2l0Zy2hXzWrjSRK0ESAQn5NFEu0b8nsjqEXvb6qKypRaiEfp84HI01qANa0KlPth1e3AvXX6XjmzNNr+psab+FjNt4/RZ5Tr/lfGLT/ZZ13g39lqfS91uaIyA+D93thJP29nLQx8uEluXRIccqSkCGRxxEuqolseJEEiemuATuupq4JTDhTYLNuPdyayHPbXGrJFpy5yZkl1DnfncHfTyPzeWNIbxt9fDndX5UfnMYaSlDW00RWUWcnIhbr0Vg23Vhv8fD74fj+ON4/CWwerg51IhCg6wsiqMjodu1Oy+XRWVONh2rt9mpv20r6qoqYf/xp5z2l9FiWMLWjgM7Fy3E0NCQyq9IA9FIHzNmDNXY5yZ/PX36NE2W2xxEjoY4t5csWcKzJZ3VEydOIDiYv0SsOYizmSSEJS9CYmIiNm/eLFSG6NOTff/222/0PZGDIQln30R9mkKa7yB1JU58kiOAO8D57rvvpNr/0qVL6UTH/v37haLmyflpCeSYhoWF8QZXJLkukdUR5L333qM6/dxJFCK5s23btjbXR0lJiX6/4Isrw0MGOcSByX1xBz09Jg6g0fxBt/1pgtsXVx8j6XU8ekzoz9vv41N38NOkz1t0HJ6ev89z6tt1EU6a1hwkuiC7ohJLXeyoc99aXRXTbS1wMzWTl7RNT0kRF4f0Ri9D4c5aUwTlFiC6sBgLnWyhoSBPO5ofONkgv7KKF40kDXp9+tEHfdq5s6gtL0NxRDhyHz2EwRC+5AbZ9nLpIpS3UK+3Pdj7MgWexhp4r7MZ1BTkMMHZCANt9LArkD/Am+lhQqPxFeU41wFx3gu+uH6O9nDq8yMKudcf59Fi7ukCXUtTPNp1kmoOF6Rmwv/YZdj37gI1Xc7EBNHj3jH1I0Tc8ZPahujom7o54Omh81SvuKqsHE8PnqOOMgsvV+rYErwXCGSb88iBiLx2D1kRMagqKUPgobN0qbRNf76z8dFvu3Bn3R8tsmkOMkGgbWmK4BOXUJKZQ516CY9fQDY9AnXm/FUUhPzyapx9lY6VA+xhr68GfTVF/DDSBfG5ZbgrkBzs6sIe+GkUZ2UPOYOC57auYTaLnFruxFYXcy2sGuxAo/PJNeFlpoWfRrvQiYSXKSL3BnH2yjYcvwbHL3HMGzrbwf/AGZrgtiQrFy8On4NJJ2foWHI0iglH5qxE6IVbUtvEPX6BsCt3qbQS0ZjOS0zF0z0noGliCAMJkz9Noe7ZBQo6usg8ztEsrkhNRs61y9Dq2QdyKpxVQtUF+TR6tzgoQOr95t29hYInj6imZ11FBQqfP0PevdvQ6T+4TUuCSdTVxcQMzLY3p4l0SXv4ibsdjYi7n84/37/3dMfaLk48hw5Zxm6s0jKnPhdy/cqIPCc6j+6PzOhEBF99gKryCkQ8eI6EwDB4jRvEswu5+RhbJ69ATUM+FGlsBJPmWnu7QV23cX4SwXtVsE6CTn0uJNp+pI0BBlvqQVVeDvPdzWGpqYKj4XyH5Cfe1rgxpSvv/ZYBLnDXU6fXvZaSPOa5m8PbSAuXYqXXyRWHee/uNAlu2OGTqCopRX5MHOKv34H10IGQlefUnSS+JVH0uRFRUttEnbuMtGcBqCouQW1VFXJeRyD6/FWYdOsCuYZJJNFjxpP5EDh+4px4t9Oy8J6DFSzVVGjE4DIXO+rY88/mO0C29+yMj1xbPmC2UVelCXpbmjSXC3Hmq9g5IOv0cVTn56EqNwdZZ09C1dkVSqb8laGRnyxD7i3+iiypomkbrnn64gboNETZiisr+BwjyW01DHTwcOdJGnVP5HBenLoBl8E9oNQgPViSW4C/p6xA7NMgqW2IJJWBnQUe7z9HJakqSsrgu+8sjeg3a0hKHXTxLiLuPaPPQjKpTJLyPj18CQr2rpDVFJacuxCfARU5WSxxt6b61d4GWhhnbYTjMfw+Ctn2cEJv2Gio8t7/3MMF/4Ql4niMeKf+Undr9DbWhYaCHM3f0tNIBzMczHBVQIdfufcQ1CTHo/zJHdRXliPpsT8ygkPhMIofUBJ/1xfn3v2Q/g6C3YiByI9LQOzN+6gurxBrI02ZgF2HkRUaQTX3KwqL8OrwaRSlpMNuaH+JZQrDwni6ykQ7vzwjAykXLkCva1eqe0+orazEs8WLkeXrS99rublBxcwMCUeOSLQhE3BxBw5QmR8ycZD18CF9WU2fzrvWsh49Qub9+6jKz6ffkRcYSKV7DPv2hayESWLyGL+QmEZzR7lqa9K+7UInG/q8F1wd85WnM370dkdLOJ2QgpHmxpzk2jIy8NbTRm8jPTxomPz7X4A481XtHZB+6gR1CpK2Jf3MKai7uEJZYNV52McfIvvmdan3W/D8GXLu3EJVXi7qa2tQnpKC1GOHoWhoJJSotzluhKQjraAc6yZ5QFdNEc4mmvhwqCNOPktCcQVnktRIUxnRG8dguAdnwmpeX1u8082SJiwlOvLjvMwwv58tDvjyZXjsDNVpcl0ivfN1Ozn1Bfst79qb00S6pN/ysbh+Sw93fOsl0G/p4tzuTn3C/gdx8LTSoZH0RDJnvLc5BrgaYc99fuDBjJ5WiP5tLBQb2nUiqdPLUZ8eO3IMv5noDgeysuERP9J9w4zO6G6vR8uoK8ljoo85JviY48zzlk2mnU1IRX9jA5rsVkVODlOszWCqqoLLyfzVpfMcrLG3r0+L90v26aOvQ4+vo6Y6vZcfNqyelgYyTth/LwbzBtrDx1YPmioKWDPJgwZSnHnKX5WwfUE3HF7em5d0eOPsLnAy1aSTAOS4/TGvK9LyynAzOJ23XxJIIPgikP+ak/kRhATo6PboifSLF1Celkbb7OTjR6FkaAQtDw9euYj13yPpCD9XZXOQMX7cX3/SlVLMqc/4X4FF7LeQuXPn0kSonTt3plIrR48epRrskti6dStGjBhBE+YaGJAlyrI0Wl+cdIwgRMv9xo0bNGEsibgnkerEKU/2NXiwsNxCU0yePJlKxxANfRJFTqR5iH1SEr+xJs70PXv2UGc0SQBbWlqKgQMHUomb9q5PW38zmSzZtWsX5s+fj507d9K6EjkbstKguWM6cuRIaksi3kmEPfk+EjEvGgHfHGTSg9SJ1E9VVRVlZWXo27evUJkJEyZg3rx5NJ+Ck5MTXYlAbI4fP97u9WkO285OGP/JTNw/fA0Xfz8KHWM9TP5iDizd+AP3+vo61NXyo0ySwuKw/8s/OZ/V1eHythO4vO0kOg30xoRPZ9PtD45eR01VjZD8D4GsDBj83pgm60R0Fb8JDMUyF3sc7N8NlbV1uJ+ehV2R8Y0jIwR8FLv6eMNIWZluIzIyxPFPGHf7MaeuREf85WsscbHD/n5daUcisrAYXwe0bLknia6y+3AFUk4eQ9adWzTpltHwETAcxL/WqfQBmdxp6KDUVVcjeMWHDW/qUBoXi9zHvlA2NobLt99LXUYagjOKsfRKGL7sY4vvBjggtbgCq29H4l58Hq8MOT4kYRMntkS6XpSjniquv8txiBHb1X3tsKqvLe7G5WLBxVCp68erg5wsRn61GI92nsCRJd/SaEW7nl5UNocHUZCoq+NJXUllQxJrf/4+fHefwtFl39HfqG9ngdHfLIO6nuQVFa7jh6Gmqpo676tKy6BrY4mBq5fxkhrS6pC6CPQ6pbEJv3IHQUfO895f+5IjdeI9dyoch/WjA/p+ny1G0NFzuPH1r9RBoW6ohxqfyaiz5TsguXxzLRxrhznh7LxuUJKXw7PEPLx3LJB2tLkQ2RoieSEtr9KK4GGiiZ3vdIaVjiqyiitpst2tD+Okju4ZsHIBnu05gXOf/ECdh+Zd3NH9fWE9yjqBcymNjUUXd4RduoMbP2xFWW4BdWqRMp0mjYCcQJtONPfTQ6PoaIHs/+CMj2i9bb9dD0WBRNdEv9jiw0+QcfwwYtZ8Rh0lml17wHCSQD0b7l3B8xy7djWq83I5n9XX82Q7nP/cSf/X7NqdThDkXLlIpUEUDQxgOHkatHsLt/2t4Z/wRHoN/9bDDSrycgjNL8Znz8JQIdAmkwhZ7ul21lZHLyNd6tQ5NVj4+lnhF0LtWwqJsh/52Xw8OXwRD/acgYa+DgYtng4bb34ydHK86GqKeultuHI7aa9jMW7NIrSVGwk50FFWwKputjBQVUJCYRk+uvtaKBEuOVZc/VnCqah0fNrVFm566nRVWFR+KZbeDoNvKt+ZPcBCF78PdBWaDCA/8/DrVGx6IX5Ju4KaKrp/8RFCDxzD7eVfQF5FBRb9e8Nx4mj+MasXPmbS2Fj064Woc1fw+vBJVJeVQ0VfF5aD+sJ2RNv7WtvCY7HY2Qa/d/eEgpwsXuUVYk1gmNAEL+da4x+/ZS62GE0kxBo2XRjci/7/dWAYAnP50aTDzY2RXlaBl3mtlzcyW7iETsrFff81/T51D08Yv8PPbUGpqxWKwE/etgWlkeGN7l3RtqG1yCsqYMw3S/Fw5wkcWPgN5BUV4djPB70Fk0Bzz3NDmyKNDXkujFq9CI92n8LBhd/QiR1DB2uM/XYZT1/fsV9XBJy6jmdHLqOiuATq+jpUoifevXGSVRKVv/JxGD7xtMN0ezOanPFIVCpOxfKdR+QUkgTS3NP7rqM51c1f5m5DX1wCsgvwyWOO8/tifAYWuFriqy72tCxZAXAgMhmnBaL7FSxsoT5zMcpunEHZ5WOo0NeF1/yZMO7sLvJs5d8LunbW6LZ8AcJOXEDwwVNQFWMjTRnbIf0QfvoyciJj6DHVtrZAv68/gZ6TncQyKhaWcFi8GNm+vghavZpGy+t5e8Nq2jShc8rp4zVodcvKwunDD6ljX5INKaPh6Ijw336jDmRVCws4LlkCbQEHlG6XLtSRH/bLL6guLoaSvj7V1jce2HQOiFPxKXRiZU1nZ6jLy9PVrd8EhvFWvQpKLHKZYGWK+Q788/pnD468047IWFxJ5kzAkYAa4mxc1cmJJvPMKq/E/ugEKs/zptiybh4WzOJPiueE7+PU971fcOdR88FvrcFq4WKkHj+CqO++oTeCpocnTKdzcpZwqa+tFeq/xG/dgpLICF7bErKM8wxz/G4dlAwMoeHhiZxbNxC/ZTMNGCCyIRrunWA4ajTtu0oLSTb63s6nNPmt37fDUFFdiwuBKVgvsBKTHCoiZcLtB1x8mYLlQx3x8XAnmuA1MacM6y6E4fizRJ7Nu72tqdN6endL+uKSUViBvj/eRlvYEZFIr7XfunP6LWGk3+LfuN8iJ9Bv6cnttwwS6bc8bV2/hUtwUgE+3P8CX4xxwbeTPJCWX4Y1J4Jx/3WWUI4Tcvy4z7HDvvH4eJQzutrq0WCYsNRCTP/zMY3+53LENwErRjqhq40etYvPLqH6+2db6Ngn0lZEAo8EmukpKSGlrAzrgsKRWMLvt5DzKthv6W6gizWefFnerzq70LbzQlIaT2bnVX4htoRG00k+ExUV5FZW0vu5pat4/r4VBWVFOWxf2B1aqgoITSrA3L8e81aPcOonwxtzZBVW4FF4FnXuO5po0kTFjyOz8PH+5y2SPpIWi+kzkXLqBKI2bkB9TTXUHR1ht/wjoSS3nOcv/9pLPnYEOY8e8tpv7tjb7sOPoOnqhuLISJSQVTsyMgj5XNjv4vz1t1ARCCZgMN4WZOoFn2AMqSBR1TExMdSp7O3tTfXom4JEwIeGhlKNdeIM7tmzJ3Xqrl27ln7O/czKSrxMAJHJIQlkHR0dhXTaiQPlyZMn8PT0FNpONPTJvsjqAi6kviRpLnEyEzuyakBUAob8LiIXQySESFJbPz8/ODs7U2e6NPVpipbUVZrvIBr2ROKI1JVE8JuYmNDoeLJ/Sd/FhUgOhYeHQ05Oju5fUFM/OzubSvsISiWVlJTQfZPzRmwIJIFtREQEfU/08MkEBNkmqo2fkpKC3Nxc+j1VVVX0XIsmSm6qPtJwNFb66BJJcJwP9bwINUJdbWOtdtIJ50YdcSYC6pssQzgS23otf9KHEJzZp3sV48tsyey/KBoKLV82K+jYF0yORQYFjRA5HtKUEeTJSzHlpYR0EiVF5JPDSPqQosdOsGPJhUgbSTrEnwxreyeOXGs0UrIdkyERuFI8LYE6H1oou8Zz3onQ3G/ad7l11x733qBjzVace2n5YGzLjx9pf2kb0M7nUnDyh4t/jnKrk9PR+1Dg/HAcTmLaszYkvyuoaL9FkWRPpHbcGnIHy+J05KVlmm3TWriSrvOWJjRuqR3nXAP/BLRuWbSktq05xB1Tsg9xuxnmUv2ft2+CcDX2/4+9s4CO6uji+D/u7u4uEBLc3d2lFIdSSqFQSgstUIUWaZFCKe7u7g4BYkCIu7u7fWdms8nuZjfZTQKEfvM7Zw/sZu7b2XnvzZv5z517par7rcBs+Wa71rjtKYhg+wra1YeiTOP7Jnqv8vQxjbl3yf1f39970VA8kvE+zjOX4yH8OYokvc65/QSpqbQE1724dDcvabb+412QUCDbqOfE+yCjpPHtQ+/V6nwIXESd38ZyZ9Y2NBW6w1hIxSp4ROHGMnjfZx+0b2kIn8vih9YRhDg7cT2fxYHrBCXsXhR1GIte4u+S/hDjlpgb6e+t/RqD82Cdd3rv1jfub4igC+J78gtCI4c2se8QnNML0m5046897hyMOwYSde9y+/Ma5zwhiLq/j/Xs1uj6/ZeZev8+PkYOdK/d1fdfgXnsNwIiFnt41Ca1qg9uKBbi4U8g4VmeP3/OF5aFxJSvD+JpLwwiPAmLz962bV0PUFtb/vikwuzI7/Lyqt0GRoRsSepTH5LUtaHvOHfuHPXSJ3Uloj6Jd08WB7jtKOq7uBDh3N2dP/wFF7Krgrx4ITszBI9HQuKQhYOG6ktyK3DzK5AFIEFRv6H6vC+oECcw4mpo8sW7CPCuEBwA0EdwC1mKpAN/gTYSZ7DflAmBpNQn7NLBtpA/N1doHkloCRN9LlSYadT9835/Q0OD6w9xHiHhgkhTz0tT2lzQVtTCWkuhsgkT4eaisde5pHaNuQfF6dsa4l216fvo3+jzqJnEx8pGtmfTJTnxEBY+R+JjvINz0pKeY+Je55wwbu/nez/Ec7KpfGz1Fbaw9o51zEZBRLWKD/EQew99y7tEUlGahkP5gP6bLWHcwvf9LfFm+Eju3aYsKLyP3yPpvStsDs9oHBJsHme8Y5iw3wRIrP3xvNs2BSCx4YlITMKyEA9z4rmfnJyMbdu28Qno/wVIiBnBGPK8oveBAwea7bvk5eXh4uJC25acA21tbZq7gOtNz2AwGAwGg8FgMBgMBoPBYDAY/2WYsN8EdHR0sHbt2nr/TuLUk3AzJLY9Cd1DPOeJt/d/DRJjnuuZLohKM2cZJzkOSHuSsEHEm55467/PbbIMBoPBYDAYDAaDwWAwGAwGg/EhYcJ+EyCifX0hX3ghseD/y5iYmNDX+4KEr2kohBGDwWAwGAwGg8FgMBgMBoPBYPwXYcI+g8FgMBgMBoPBYDAYDAaDwWAwGkRaqgUlg/g/p2VlhWEwGAwGg8FgMBgMBoPBYDAYDAaDUS9M2GcwGAwGg8FgMBgMBoPBYDAYDAbjI4KF4mEwGAwGg8FgMBgMBoPBYDAYDEaDSEuxRmopMI99BoPBYDAYDAaDwWAwGAwGg8FgMD4imLDPYDAYDAaDwWAwGAwGg8FgMBgMxkcEE/YZDAaDwWAwGAwGg8FgMBgMBoPB+IhgMfYZDAaDwWAwGAwGg8FgMBgMBoPRIMxLvOXAhH0G4yPHRq0CLZnUbLRo4ktbdtYX+acJaNH0M0BLJSpPDi0ZuScRaMkEd7NBSyY5u2Xfu4WFlWjJlFmhRZMfkouWiq+eJloy8XEte1wgL48WTWf9lt23JESUoiVTYNSyp/qqci23b/aJbNnXntGwcWjJvI1Gi0bqbjhaMjHWGmjJSD+KQksm3E0LLZWkCyfQohn92YeuAYPxUdOyR14MBoPBYDAYDAaDwWAwGAwGg8FgMPhgHvsMBoPBYDAYDAaDwWAwGAwGg8FoEGmpKtZKLQTmsc9gMBgMBoPBYDAYDAaDwWAwGAzGRwQT9hkMBoPBYDAYDAaDwWAwGAwGg8H4iGDCPoPBYDAYDAaDwWAwGAwGg8FgMBgfESzGPoPBYDAYDAaDwWAwGAwGg8FgMBpEWoo1UkuBeewzGAwGg8FgMBgMBoPBYDAYDAaD8RHBhH0Gg8FgMBgMBoPBYDAYDAaDwWAwPiKYsP8B6devH548eYKPgS5duuDly5cfuhofNaT9evTo8aGrwWAwGAwGg8FgMBgMBoPBYDA+clpcjP1vvvkGJiYmWLhwIf7rEFE/MzMTHwOPHz9Gdna22OUrKirQvXt3bNmyBR4eHu+0bh8LpP0ePXr0Qb77+V1/nN17A2lJmTAw1sGoWQPh2dVVZPn4qGRcPnwHQX7hKCsth5WDKcbMHgRLB1OJytSHsYoClnrYwENPHcXllbgWm4qtr2JQUVUltLyKrAzG2hqhn7kejJQVkFhYglPhSTgbmVynLCk32sYQBsoKCMrMxwb/SETkFEISnLVVsczLGg5aqsgsLsXx0CQcCEoQWV5XSQ5TnUzR3VQb2gpyiMotwp7AONyL57/HJ9gbYay9EYxUFJBcUErLXIpKhaR09zDGkkmtYWmojsT0fGw7/QYXH0XXa6OuIo/FE1qhb1tTyMvJ4MbzOKw96Iv8wrI6Zfu3N8Ofi7rCNyQNk1ffFLtewbefwu/cTRRkZEPL1AAdPhkBEzeHJtl4H74A/3O3+GxUdDQxZceP9ZaR19JCm7W/oTAxEdHHjiEvMgqyysrQ79IZpkOGQEpa9Nq2ODapT54g6eYtlKSnQ05dHYa9e8GoV6+av5fl5yPp1m1k+vqiNDsbinp6MOrbF3od2kNS2roa4JtZ7WBrrom0zELsOfMGR6+EiCz/5/Ie6N/Fss7nMQm5GDD3jETfnfHyJRIuXaK/U0FPD6bDh0O7desm2VSUlNC/Z/r4oCwvD8omJjAbPRrqdnY1ZZJu3kTs6dN8x5WWk0PbLVvq/W7SPyxpbYM2euooKq/E9bhUbHtdf98yxsYIfc306H2ZVFCC0xFJOBtV27eQe/ri4HZ1bFc8C8a9xAw0BRkpYEFrSwy10ad18U/Lxa/PIxCXVyzSxkFLBbPdzOBpoAES3jIwIx9b/aMRlFnQ6HrE+ATi2eGLyElKg5qeNjzH9Id9N68m2xTnF+L50UuI8n6NivJyWLd3R8epI6CgokT/XlFegchn/gi8/gjJwZHo8MlwtB5Wex8JMtbTFPO628BIQxHhqfn47WownkaKdw7mdrPG1/0ccNo3Ad+ceVXzubqiLL7q64A+TvrQVJbHm4Qc/HTpLQKTctFULFSVsNDFGk6aasgvK8eVuBTsD4uD8KsRUJeTxRgrY3Q11IGeojwSCotxMjIRtxLTmlwXeWkpfN3BCkNs9aEoI41nidn48VEEveZF0UpfDfM8zNDGUB0VlVXwS8nDphfRCM/if6a2M9LAl20t4KyjiuSCEmzxicWVCMnqrCEvi2872KC7mTbI7Xo3NgO/eUcgv6xCpE1XUy1MczWl31tYVolnSVn4yycaqYWl9O9y0lIYZWeIkfYGsNJQRlphKS5GpGL3qziUi+gTBMlLScfTvaeQ/DYcsgrysOnqhbaThkNaVqZZbMiz79w361CSV4Ape9ZBvvreEFUGU9YD8nXLuBmoYVUvOzjrqyKjsAwH/OLx78s4sX4jsT05sQ2yi8vQYUet05G+ijxmtzVHbxsd6CrLIyKzEH97x+BmeHq9xysvKkbw0ZNI9QtAVUUldN2c4TR5POTV1ZrFprK8HN6//IG8mDh4Ll0IHWdHvr+n+b9G5KWryItPhLK+HuxGD4NeK7d665z44DFirt5AcWYWVIwNYTt2FLQFjtsYm+KMTEScOoeMwCBIy0jDsHNHWA8fTJ9nkjDH3QxjHYygoSCLwPR8em+EZonu8600lDDb3RwdjDQhJyOF4IwCbPePgW9qbb9GjjXSzhDjHIxgoqqIUed9EJEt2XiZIC8rjW+Gu2CIpykU5WTwNDQNa06+QlJ2kUib88t6wMlEg++zE09jsPKYf52yUlLA/s87o4OdLhbte4krfqLH4++rbxG3jDiYGqtj1bKeaNfGFEVFZTh/NRh/bH2E8vJKkTa9u1lj/sz2sLHUQklJBXwCErFu80PExHHm66oq8vhkXCsMHeAIE0N1xCfl4tBJfxw9/RqS4qaniu+72MJZj/QtpTj4JhG7/ONFlpeXkcJkF2OMcTKEmboiUgtKcS4kBX/7xqKSp9ud5GKET9yMYaqmiNyScjyMy8K6p5HIKi6XqH49OltiyeedYWmuiYSkXGzb/RwXr4keI0tLS2H+zHYYMdAR+noqyMgswvU7Ydi4/SlKSznXhKysNPr3tMWkMe7wbG2E3zc/wp7DfmgMo+0NMNvdDAYqCojMLsQfz6PwLEm0rqKvLI85rczQw0wb6gqyiMwuwr+v4nA7RvhYZ5a7KRZ7WeFcWApWPAzFu0JbUxVTx3XHzMm9YWmmj3YDliMoVPR10BxUlpUh8expZL18gcrSMqg5OMB0/ETIa2uLtCnNzED6o4fIePwI5Xl5aPXXVr7+tqqyEtm+Pki/fw9FCfGQUVaBhnsrGA0dChkl5Xf6e/5rsBj7LYcWJ+wHBgaipET0BIPxcVBVVUUXA3Jycj50Vf7vCfYLx/Y1h/DJolHw6u6OJzd9sPX7fVixbQFsXeoKf4QTOy6hfa/WGDN7IGTlZHFm91WsXfQ3ft67FLqG2mKXEYWstBQ2d3NFVG4hJlzzhY6SPNZ3doKMlBQ2+kcJtRlpYwglWRl87x2C5MIStNPXxI/t7emxToYn1ZRb6G6JIZYGWPMiFD6pObDTVMFwKwORxxUl0v/Txw0XI1Pw1YMguOqo4feujigqr8DJsLoLCYSZLmZIyC/GwrtvkVlShqFW+tjYzRnz77zBs2TO4G2SgzG+aG2JZY+C6MSqlS45rhM97u048cVBFyttbF/WHRuO+OPs/Uj08TLDHws6ITO3GI9fJYucdB38oQ8KS8ox/ec7SEgvQP/25hjYwRwn70TwlTXWVcHKaV7wC02DDFEdxSTKOwAPdh5DzwVTYOruiDfXHuDKrzsw5o9voGVq2GibqsoqGLvYYvD3n/NY8tdLWJlXWQooLyrC242boOnsDI+fZ6AoORkh23dASloGpkMGC62TODZpz7wReeAgbGfOgKaLCwrj4xH6z05Iy8rBoFtXWoaI/tLycrCfNxfymprI9PNH+N69kJKRhm7btmK3q7mRGnb91A/7zgZizqqbaO9uhPVfd0NuQSku3xd+XS9edw/Sv9e2kYK8DB4eHI+bT2MgCbkhIYjYtQsWEydCu00bpHt7I3zHDjgtWwY1a+tG20QdOoT8yEjYzpkDRX19ZLx4gZC//oLrypVQMqy+VqqqoGxsDNcVK/hn9/UgKyWFzV2r+5YbvtBVlMfvnTh9y6YAEX2LNadvWfW8um8x0MTqdvaQkZbCqYjavoX0NVNu+iEyt1ZIqRBPF6yXhR6WGGZjgEX33iIpvwTftLPBzj5uGHHBByUVwif1c9zMcDEyFT95h1OB9vPWFtjV1w0Dz75EbqlkE2JCWkQcrq77F+0nDYVjz3aIev4atzcfhJK6KsxaOzbapqKsDBdWbYGcojyG/jCfiv8Rz/wR8dQfzn060jIRT/wQ9fwVvMYNwM1N++m9LIr+zgb4ZYQblp4KwKPwdEztYIG909pi8JaHiEirf1GjtZkmprS3QFhqPmQE1vQ2T/CAgboiZu5/iaScIlruyOwO6LPpPtLyGj8mVZaVwYb2rniZno1f7/nCTFUJP7ZxoItMB8OFT4bHWhmjpLISq32DkV5cSgX+b1vZUZu7SfWLqQ1BhJmuZlqYdeUNsorL8HM3O+we7IqhJ31EXsuftzHHocBELL8XClV5GXzTwRoHh7qj+yFvlFafKyJu/TPQhQr+86+/peW+ameFW1HpNWXEYWMvJ6jIyWDCRT9Ik/FATyf83t0R828FCi2voyhHRfu//WIRnJkPQxUFrOpkh619XDDuAkeA6WOhC3ttFfz8NBzROUVUgFvf0wmaCrJY9zyywTpVlJXj2i/boGlqhFEbV6AoKwe3/thJhecO08c02YYIDPe37IeutTni/QJRJWTJR7AMVSYFIAL84XGtcTowGfPOv4a7oTq2DXVFYVkFDgck1vsbSZtvHuIC7/hsuijAy/z2FojLKcbMM6/oYsEoF0P8M9wNU0/541FMlshjvt61HwVJyWi3/CsqqLz6Zw/8tv6D9t8tbRab0BNnIK+mRttGsD2Snr2gx3KaNA6GHdqiNDcX4ecu1Svsp/r4IfjAETjP/BTaLk6Iv3MPAX9uQ7s1K6BiZNhoG/LdL3/5HRo21mj7/TeQVVFG4v1HyAoOhY6bC8RluqsppruaYfHdtwjPLsTCNpbY1d8NQ86I7vNnuZnhXlwG1r+IpPfTTDdT7OzvhqFnXtYs5i3wsKDPmT99orCpp7PAyEp8fhjjjm5O+pi5/Sky80vwy0QP7JnfCUPW3qELgsIgz9j1F99i953wms8qRSy2fdbPHmXllZCVkUY9PhnvtW8Rp4w4yMlKY/+20QiLzMCAsfuhp6uKfzYOo8LyT+vvCbVxtNPF9vXDsPHvx5h26hXU1RTw84o+2LN5JHqP3EvLTBztDmVleSxecRWJybno3M4CG38eSNvw4IkAseunpyyPQ8Nb4XRwMuZfC4S7vhq29HemfcuRwNoxEi/9rXWhpSiHRTeDkJhXAg8DdWzu70TP+V8vOOPQAda6WNPNDotvBuFuTCZM1BTwV18n/N7LAbOvCD8vwnBx1MP2DUOxYdtjnLkYhD49rLF+TX9kZhXhsXesUJtZn3hi1pQ2+GzpRQS8SYG9jQ49hrS0NH7d9ICWGdjHDn172GDrrmfY9PNAuhjQGPpY6GB1Zzt8+yAEjxOyMdnZGP/0d8HIs76IzBG+8DXN1QRBGfn4NyCO3p8j7AywpbczJl8KgB/PwhzBXU8NE52MEZ5V8M5F1u+/GoPikjKs+v04Dm9f1Oj+QhLiTxxDXmAgbD5fCFlVVcQePoiIrX/BccUPkJIRvrgef+I4lMzMYNB/IBJOHKvz98LoKGT7+8Fo2HAoGpugND0dMXt3oyQtFTaff/EefhWD8R8PxfPrr79SL/aTJ0/S0C/kFRsrvEPmZffu3Rg4cCDGjx+PAwcO4KeffsK6detq/r548WL8/fff2Lp1KwYNGlSzG6CyshJ79uzBiBEj6OcrV67kE6KLiopoHR4+fIjly5fTMtOmTUNQUBDf9w8fPpyW69atGyZPnowzZ+p6Q+bl5eHbb79F3759MWvWLPj4+NQp01B96oNb1+vXr+Prr7+m7fHpp58iJCQEcXFxWLBgAf1u8m96Ov/EsLi4GL///jsGDBhAv/evv/5CWVldD15JbIYMGUL//eKLL2i9SLuIY0fK//nnn3UWCYYNG4bT1Z6bpE3Izg7etuQNa8RtiwcPHuDLL79E79696TVFyMjIwPfff0/Ljx07Fnv37qXH53Ljxg16PknYnNmzZ9N6fvfdd8jPz68p8/bt25rrs0+fPvjss88QHBxcp41IuU8++YSeC3L9CNvx0FB9moOrx+/D2dMOvUZ0grqWKgaM6w4bFwtcO35fpM1X62ahc38v6BhoQUNbDVMXj6Ze+a+8gyUqI4oexjowVVHEby/DkVJUireZ+fg3MA6jbIyoECKMQyEJ2P4mhnreF5RV4G5CBm7GpaOvmW5NGXNVRUx2MMHvvhF4nJSF4opKvM7Ik0jUJ4y2NUJZRSX+eBmJzOIyPEjIxKmwZExzFr0bYd3LSBwKTkRMXhHySstxJCQRbzLy0M+itn6DrfWpd/7DhCz6G54kZeN8RApmuJpJVL9pgx3xNjITuy8GITO3BCfuhOOBXyJmD3MWaTOpnz0sjdXw+fr7CIvPQWFxOV0UEBT1yaD7z0VdsOXka0Qn5UlUL//zt2DTqQ3suraFkoYa2o4fTIW811fuN4ONFKRlZHhewh5f/GWId336M29UFBXBavIkKq5rODrCuF9fJN26haoK4Z5a4tikez+DVutWVKAnHv3q9vYw7NkTCVev1hzHfOQImA4eTMVp6vXfuRO0XF2R/vyFRO06eYgT0jKLsOmALzJzinH1YRQu3o3A7DGiRQrSjZDJNPfVp6M5VJXlcfK6ZJ48xGte3ckJBt27Q05NDUZ9+kDV2hrJN2822oZ4WhJPfeOBA6FqaUnbhpRVNjND8u3bdY5HBu41rwZm9N1NdKjX4VrfcKSSviUrH7vfxmGkdT19S2gCdgTGICK3EAXlnL7llkDfwoWIq0QA5b6aioKMNCY4GGPnq1gEpOXROv/4LAwGKvJ8fYcgSx4E091AOSXlSCksxe7X8VBXkIO1RuM8jQIu3YWelRk8RvSm96Fz304wb+MMv/O3m2Tz5tojZCelYsCyWdA2N4KckgIce7avEfUJxMO//9IZMG1gZw9hTjcbXH6dhAsBicgsKMWft8MQn1WEaZ2s6rVTU5DFX+NbY9npV8gp4h/faCrLoYeDPjbeDEVISh5yi8vx9/0IpOeV4JMOFmgKfU306C6MTa8jkFFSCv+MHJyISqQe+aLWTHeHxuJQeDxi8ovo9XgtPhXeaVnoZSz6ehAH4pk7xsEAm55HU2/fxPwSfP8wjIrePcxFL8bPuRaIB3FZ9FpLyCvBroB4KvRYaNR6jK/qaovzYan41z++ptyS28ESifpOOqroaKyFX59FICa3GFE5RVjnHYke5jqw0RR+XWcUl2Hx3SD4pOTQZyrxND4enAhXXTXqMUq4GpWGn56G4016PvXOfZ6cg8NvEzDIWk+sesW8eIW85HR0mTMBqrpa0LOzhMfYQQi++QilRcVNtvE7fQ1yykpw6NNJZB3EKTPB3RilFVX48U4Y0gvLcCcyA0deJWBeu4av4d/6OeBaWBqexdYdp66+E4bdPnGIyiqiHrX7fOMRkJyLIQ4GIo9XmJpGRW+HCWOgamJMPeadPpmA7NBwZIVFNNmGeOOnvwqE48S6Cyvk+RJ85AQs+vaEWa9utN1UDA3Qat7Metsg9upNGLT1hGHHdnSHgPWIoVDU1UHczTtNsok6f5k+t1zmzoCSni7klJVhMbCfRKI+uZKnuZji4NsE6uWbXlSKn5+FQVFWGiPtRJ+HFY9CcSM6nY5jic0O/1i6iO2oU7t488uzCKx/EYXYXNGe9Q2hoSyHsR0tsOFSEN7EZSMxq4h63TsYq6OHi+j6cYV83rGKsClQGyttTOxshe+ONs5b+l31LeKUEYe+PW1hbqqBlb/cQlJKPl4FJmPzzqeYNNodKsrCd3U4O+hTP4edB14iL7+EeqkfPf0KVhZaUFOVp2X+PfCSit0h4enIyy/FtTthuHQjGEP6N/ys5WWCsxFKKyrx06MIpBeV4U5MJo4GJmGuh+i5y8WwNGx8Ho2wTM6c7VF8Fh7EZsLLqHaHhpu+GmJyinApPI2WCc0sxIWwVLpwIAnTJ3ogMDgVuw76IjO7CCfOBeL+k2jMnuop0sbNWR8v/BLw9EU8CovK4P8mGfceRcGd53olHv8Ll1+hZZrCTHdTXItKw6WINLqgvtU3Bgn5JfjExUSkze/Po3A6NIWO77JJv/smgc4tWxvwt42qnAw29HTEigehyGmEU4ekLP5hH7795TDCeXa0vkvKCwqQ8fgxjIaPgLKFBeR1dGA2aQqKExOR+0b0zhPrefNhNHgo5DQ0hf5dxdoGVrPmQNXOHrIqKvTYhkOG0mNWFIveLctgtGRalLBPhE0nJyd07twZa9eupS89vfoH3kQEJuItEYknTZpEhdHffvsNYWFhNWVev36NpUuXwtvbm4r6RLAlzJw5k4aKIQsCRFAODQ1Fhw4dqPjMDSdDvM7HjBlD60G+h4jQJE46Eeq5rFixgtaVLCiQus+dOxc7d+7kqyc5xuXLlzFv3jxahoj3RIDmpaH61Ae3rkRI5oYySkhIoKI2EZYdHR3x1Vdfwd/fHxMmTOCzHTduHF0cmT59OhXgN2zYQOtSHw3ZELGaMGfOHNo2S5YsEcvOxsYGmzZt4hO3iWB/6dIltGvHCYNA2u7q1as1bTly5Ejcvn27JqwRty3I58bGxrQupBz5e/v27eliETmXo0ePpgtApF24pKam4sqVK3TBgLQd+Y6zZ8/Sc8rFzMys5vokCwwqKirw9PREdHRtGJSkpCT6nVJSUvRcEg8AsijEizj1aQ5CX0fBqY0t32dE6A97U3/YFl6KCotRWVEJRWWFJpXh0kpXnXrUEs92Li9Ss6nI5ajF7y3W0NbawvJacbabiQ71bCDCXFNoradOvf155xfPk7NhqqZEPXTErp8Cf/2khXgjkfdOWqrU61ZcPB308Cwwhe+zJ6+T0dpedH/Zr70ZHr9KogsB9bFofCuk5xTRxQJJIB6KaeExMHapDaVCICF1UkIim2yTEhqFPZ8sxYHZK3Djj1009IcggmWKUlORFx4OVStLyCjUXpcajk50sEg88YUhjg3xKpaS4j9nZPJOQs+UZIn2YiwryIeMoiIkwdPZAM8C+L2invgnwclaB4oKokNB8DKmnz2evUpCrISLNfkREVB34J8Eqjs6Ii8iomk2lZW0fxRsvzyeZzehKCUFLxctgu/SpQjZsoXujKiPVjrN07eok75FyBb9bd1ccWd4B+zv3RpDLPTRVBy1VKAsJ0PFRi5EGA3NLKD9kLh1nehohLi8IoRk1S5CS0JycBSM3fjvQ1M3e6SERDXJJsr7FczcHanw31RIKAl3U406YXceR6TD01z45I3Lb6PccC0wWWjIHuK9CSF9M1nEaWtR/+6zhnDVUkdwTj71wOfim54NDXk5mKsoS3Y98jxLGgMJqSNXHX6HCxHgibBCwuyIg7aiHCY5GyEko4CKYwR7bWVYaihRQaYptNFXp/fcq7TaPupFcjbKK6vQWl+8+hGP2eG2Brgfl0FFblFoKMhRIUkcUoIjoWlqCCXN2joYuzrQ51dGZGyTbJKDwhF6+wm6zpsk8vvFKUPwMtHA8/hsvnHL45gsmGsqQU+FI/QJY5yrEay1lbHhUcO7F7hoKMqioEy0iJRdLcRrO9rX2lhaQFZJCdnhEU2yKc7KRuC+Q3CbO4PvGc2FLAKU5ubBqKP4Ie/IYkBudDS0nPifW9pODsgJj2ySTZqvP/TbekJatvGb5EkoExIG6TlP6A5yfful5Ip9bxBvdSIkpheWwi+leXdTt7bU5vQtobXjsoTMQkSn5cPTSqde28/6OeDNhqG4/UNfLB/hCmV5/jGNmpIcNn3qhW+P+CKrQLLwNu+7bxG3/xHEs5UxwiMzkZ5ZGwLpyfM4KCjIwtVJ+MLIk+exyM0rwZSxrWhoTR0tJYwe6ox7j6OoiC8KTQ0lFBTU77wniJeROp4n8s+JnsRnwVxDCboiFh54IV7kRJDuZKqFazzh2W5FZcBQVQG9LXXojkhynQ+00aOLApLQprUxnr3kHxuS9vNwMxJpc+1WOP17a1dDujPZwVYXXTtY4NJ10eF7GgMJBeempwbvRP577mlCFt3FIA5k/DrG3hDyMtJ0gYSXn7ra4WZ0er1hfT5mCqIigcoKqNrX9rMKurqQ19NDfqToeUhjKM/Ppw5E0nItLqBJi0b6I339F2lRV66dnR20tLSoGEu8oRuivLwcv/zyC/X0J+IpoVevXlR4FYQI2wcPHqx5/+zZMxw9ehTx8fHQ1eV4QRGvaVKHEydOYOrUqTVlly1bViNME698TU1NKjb379+ffsYVnAkkrry8vDxdcCCiNoF4/N+6dQvh4eGwsuJ4lZFjjBo1qlH1qQ/iGb5o0SL6f9KOrVu3ph7n8+fPp5+pq6vTti0oKKCCNIn5TkRz4l1O2ohA6khEaeL57+ZW1yNUHBsiWBPI/7nnUhw7suhAFmFI2a5dOeEsDh8+TNuVnNd79+7R9iQLN9y21NHRoV72gpDFDSK887aNpaUl9u/fX/OZvb092rZtix9++IFee9zriuwOIIsM3IUCsmDCRU1Nje/6JDsHiMf+rl278PPPP9PP1q9fT+tHdpAQyMITWWghv4UL2bkgTn2aQnl5BQpyC6GuyS+qqGuqIidTfIHv6NYLUNVQQeuOzk0qw0VHSQ5ZPMIbgfteXOG8nb4GuhhrY/mT2h0CpqqKiM0rwiR7Y0y0N4actDTeZuZhy6tohEsQY19XSZ4eh69+xWU1fyNeOg0x3p4Tr5SEy+ByJy4Dnzqb4lZsOvzScuGmo0ZDcBAveSKYJNYT55gXPS0lZOTwL/iRMDyqSnJQVpSl3viCmBuoIig6CxsWdkZvTxPkFJTinm8C1h/xR151jP2OroYY2d0KQ7++AkkpzsunCzuCAp6ShioKs3ObZKOio4Ee8yfBxN0Rxbn58D50Hme/24Dxf66osRVW5s3adVAyMKBe97zIqXEE3tKcHBrbXRDyuZyaer022h4eiD5+HJkBATWheJLvcbZMl+XkQEHI/Zv+4gXyI6NgPnIkJEFPWwmP/QXOd04x3Raso6GEhNT6xVwzQzUavuer34Vv6RYFES7IYgbxuudFVk0NZbm5jbYhAoemmxsSr1+HipUVzT1AYvKTBRXivV9jo6ICq0mTaFniQRN/8SIC162D+6pVdGAvDNJ/iOxbFMTrW9qSvsVIG9894999dCo8EScikpBdUoZuxjr4uo0NVOVkcSy8/jAX9UHEGgLxqBSsMwkJVh+THI3xtZc1nRAn5hdjwZ1AmlOgMRRm5UBZg3/hg4TUKSsuQVlRCfW0b4xNTnI6rDu0oiF2ol++oXH1LTxd0GHKUChIIGwTtJTlqXiUkc/fTxLPfT010QvKE9uawVpXBYtP1I3bzLV/EZ2JL3rZ0TA9KbnFmNjOHLZ6qpCRanzOAu41J3g9Zld71dFcLGKsw/Q00oWzphp2BUsWRktYzF5CpsCOBfKeex2KYoGnORZ4WtBrjcQHnn3lDRXFCObqHM99cr1eHecJI1UFuljwj3+8RDH2yS4Acm/xQrSxXHov1F+/X7s60BwV5Fnqn5qLz268EVnWWkOJxhMnscbFoSg7B4oCzyhFdc51L+rZJo4NiZV/b/N+dJk3qeZvgohThos+afd4fnGHe66JsJ8mRBS10VbG8u42GHvUt+Z8NsRUDxMq6JGQP6IoycmBrJIiZOT5+zB5dVWU5uQ22oaE3Xm9cy/Me/eAhpUFjWsvSFFqGg3ZVpicjIBtO2koHBUjI9gMHyQyFE9ZXj4NkyQnEMufPMdE1VccGxIbuiQ7B7LKSvDbsBk54RGQV1eHQfu2sBo6UOwY+3rV/S/J98QLeW6Q2OT1MdhaD792daT3LhH1F94JpB7AzYm+OqcOJAQPX/3yS6GrLrpvfh6egat+rxCcmAsXUw38OtED9kZqmLH9aU2ZdZM9cONVEh4Fp9GQko3hXfctkvQ/wtDXVUGGQM6SzOr3ejoqQm2SU/Mxb+kFbP9jGFZ/w8lJ4/sqETO+OCvyezq3M0evrtZYsOySZPVTlkeMQAQB7piFtC3ZISQKnxmdoKkoSxfQSUz+wzyhe8jC1Ip7odja3wmK1Tsqr4an4bcnEZK3H8+iCK1fdiHNMaCsJEc98gW5fDMUpibqOLFnHGSqHat2HfLBoZO1uXeaAxKOiMxFM4Tcuw1deyTEztGhrem9W1Bajq/vBSOM5zoZ52BI88Ysu9e8ixEtifLq647MIXiRVVWr+VtzQOYnKVevQLt9R0jJtCh5lMEQm4/6yiUe0iSsDBHAeUVXrqjMCxGPebl79y71oiae9FzvcPJvVlYWFZ554RXulZSUqPCezOPpScLdbN++nXrY5+bm0pArETyeic+fP6cCPVeIJnAXBRpTn/ogojAXIuyL+ozUnwjXpG6kXlyhndCpUye68EDC0QgT9htjI66doaEh9ZQ/dOgQFfbJDgmysMENrUTK2dra8rUlEdaFIbg4RNqYeNKTHRekbcmLiPYkBBI5h2R3BEFbW7tG1CeYm5vT3RUkBJCGBmcLIVmgIPUiYj3JCREZGUmvPd7fyntdcs85r7Avbn14Id8lmIOitKQM8qIEKxFhfQQ9ZevjwoFb8L7jj683zIWyqlKjyzQEt6ri1MxOQwW/dnTEsbBEPu98aUjBVkMFsVpF+PRWAPXA/LKVFbZ1d8W4a75N2qZYWe2rIk79uppo4WtPa6x7EYEQnsRmJFEuGaD92NGeDobJtt29gfFY3MZKZCJFsevXQPuRcz5lgD1+3vsSq3c9h6m+KjZ92ZnG5p/3+31oqMpj/Red8M22p8hqQkxpYd8r6Y8TtHEb1INPOOy9eDoOzVmJ4LvP4DGir8gy+2atpEK8oLAPAU978SrFb2PQvRsqSooRfew4SrMyoainD+P+/eh7YXHgc8PCELFvP8yGD4OGgDd7Y+A+J8S5lcf0t0d2XgluPmmaMNiY/kOUjfW0aYg9dQpBGzagorCQevQb9OyJjOfPa8ro8Ty3SXJim2nT4L9yJVLu3YP5GOGxrYXBvZTEqTbpW37p4Ijj4fx9C9kB8Id/rRfmxegUmKspYYqDSZOE/fru54ba+UhwIk6EJNFJ91x3c/zb1w1jLvnSrfLNgVR1sFZhMb/FtamqqqR5M7rMGI1uc8YiLzWTivy3Nx/CoG85zg9NheNpL7ytiKC/bIAjxv7zFGX1eFDOP+KLlYOccG5+Z6jIy+B2cCpO+8bDw7zpC+zC6yvew8RNSw3L3G2xJzQW/plNT+RLqBLybJNqoDLbfGKxwy8OJqoK+KqdJQ4Na4XBJ3xobG9uTF8Si3/JnRAa0mOorT7+7ONIQ7cIehhKSqUYTbXiYQhWPQ6lOwdWdLDFvwPcMPGiX51wWeRe2d7PFc8Ss7DndRPCK3B/dFXjbR7uOAKLtq1g2lq0M4Q4ZcS51oS1H8k7snWoCzY+jqIJccWhl7UOvu9ph1W3Q/G2gQVloUhJixqWimUTdfk6DYdnNZh/DiXs2Rh19SY8Fs6DgqYG4u4+hN9f29F+5TJoWFtK8NVSEvV/gjbcfCHRl67CZfYMuM2fjbzYeLz+eycqS0thN0H855gwRPd8tVyOTKNhQEguKxLO559+bhh/0Q8xTQi9I7I+Ak1VSXc2iq7hj6dqRdRnYek01M6RL7vC0Vidiv0TOlvCQleVJst9FzRX3yJu/yNR3aqvHVHNR2Ls7/5rJLbt8saxs69pjP3Vy3riwN+jMXraUVQIfDkpv2XdEOw76ktD8qDZ5hz1t2DbvU9o+CcSgmd9bwcUl1dggzdnx3gfSx2s7WmPZXdCcI/G2FekZTb2ccKXN/nDHktKVbWPg6j2+3RCa8yZ6oUZC8/B/3UyHGx18Nevg1BcXI5NPAtL7wpyrzQ0JiU7TNz3PqQ7zIbb6WNTLyfMuvYa3kk5NDH2V22taMz9MglC3n2sCLZVY+YhoiCOQ5Hbt0JOUwMmY8c123EZjPfNRy3sc8PhqKrye7EIvicQ73RBWwMDgxoPa15IKBte5AQ8Kkhnwh04ksUFIvxPnDiRhm0hIvWLFy+o1zkXIvYL1oksEMjybMuUpD71wVtXbqcn7DNu/YXVjUBEalHx/RtjI4kdCdFDdh2QsEQkZwDZXUAWPLjtJHgMRUVFvras75wTEZ27k4IXEgKqvvPN22Yk3BPJ20C86kn4J1KfP/74gy+0krDfyiv8S1IfXkiYqTVr1vB9NnPpRMz+ejJePw/BhmX/1nz+6Vej0XNYRyq052XzT8Bys/Op135DXDl6F+f338DidbPg0Mq60WUEIZ4Klmr83ppa1YsTvCE0hGGjoUyFehJff5NA7HySaJAm4/KLrDkOibd/d2RHeOpr4E68eCF6iGcF8bLghXjUc/5Wf/06GWlhfVcnbPKLqpNol4yzt7+KpS8uUxyN6UA3rUj8LcbpOcXQrvaQ4qKjoYCC4jIUCPHWJ6RlFyElswgHr3FirBPv/b9Pv8EfX3SCorwMrIzUYaCtjF3f9ayxIR42xCs8+NgkjF95HQHhnPaTynoNqbjz9P//jJVC3yUzYOHpSkOpEG95Xopy8kSG4VBUU5XYhiCnIA91Qz2h4Xh4y5CkrOUF+SjjCZ1GKMvLrRGLhdqqq9fxSBe0If2CSf/+9MUl7SlnMqCgw7/1nISgCd68hcaaJzH3JSU9uwjaGgLnW1ORTvwysusP1UbO36g+tjh3OxylZZJ5cxPPehllZSHtl1fHI19SG+KNb/3pp3xlwnftEumJTyBbZJWMjFCcKjrkB7nvLUT0LQ3duzbqytjSzZXG1/9TRKJdXsKyCzDVwZSG0SL5PBpDRrUIT/obXs9u0t+QZHwNUV5VRXf6rHkWhkfjO2KgpR4OBoleaKhMiET5/rX0/9ulgA6Th8JjZB8oaaqhKIf/+wpz8iGrKA85ReEel+LYqGhpQEVbE26DutP3ClbK8BzdD7e3HERZSSm9T8Ulq7AU5RWV0BYIK6KjooB0AU9RLu6mmtTT/9pCzg5AAkkcWFmpjRGtTeD16y1kF5bRBLlfHuf36D8wvR1ixRQ8Rda5pKzm+uPCfS/oyS8I8dJf29YZp6ISacz9psJd8CHXVmph7fOGCH4+yfUvGpDRD/HoJvGpl90Nhf+MTuhrpYPTISlIqz4W8dAnsfsJR94mYaS9AQbZ6Iot7JMY4JoCbUW0cPKZoLejsPoRcYN4M/74JAyXRrelITZ8Ump/l56SPPYNdKc7DkhcbGFSSMXWJUBhPvZIAQaO1hi8ZhF9FmUn8Ie+K87h9G+inlPi2JAQO6UFRQi6/oBPFT08Yzlaj+qPNuMHiyyjcOhrVLQegHLPobXtV1AKbYFdPtydGMK89dUVZeGsr4Y1ve3oiyvSkTFUxJIe+OpKEM4H1f6G7pba2D7MFb/dD28wGS/xSi8vKqYe67xe6SREjoKINhPHJjMkjIbauTlrAZ/ty/WboevmAs+vFkCBOOBUVcF2+BAaq59gPWQAEh4+RspLP6HCvlz1WKQsl/+5Rb9b1DhBDBsZBXnIKClC190V+l4e9DMtBzuY9uyOxAePxBb2M6rHh1qK8gBPsk1yL6eLsYOUjD3JPf/7i0iaTJqEi9nsK344zoZIz+OMQ7TVFJDKs5tUV00BPkLCn4kiKIEzF7TUV6XCfjsbHdgbq9NQPbxs+rQtpvewxegN91tE3yJOmXrrl1kIGyv+sG862pxxDG94Hl7Gj3RDQmIu/tnPydmUk1uMH9ffw93zM9De05SGouFCwswc3DEGl2+E4Gcx24yvfkVldfoWsvOa/o3nWSIMojmTsGf3YzPxr38cPve0qBH2P3U3wZ3ozJrQO8EZBdjoHY3dQ9zw65MIpIgZeik9oxDaWvxOZTraSigoLEWBiN0E0yd54OiZ13j0jDMf8wlIwq5DvljyeadmFfbJbm/y7OTOIbmQ9uTe1w3du2TOvPd1ArqbamOCkxEV9kl4HzJuvDCqNo8AcRyrNKzCMFsDdD78tNl35nwIZKv70vK8fMjxOGmV5+VBxYY/zHBjRf2ILX+hqrwCtou+kjhUKoPRkmhxwj7xWhcXEsaEQDzlTU1rk1qSMC28XvbCIB7ZiYmJcHV1pWJ8YyHJVomX+Y4dO2o+I3HsebG2tqYe3cT7nCsaE49+EvKluesjKaRuZHGCeIErVMeqJCI78STn9VqX1EbYeRT3u0iIIpKQlsTRJ+GJSCJerqc88dSPioqibccV87nvG4J8R0pKilhhnuqDJOIlMfd5Y+ETkZ+EOeL9rcTrnhfB942pD0nALBiDPyCHk6TLta09dt+qTRrNTS5q52qJYP8IDJ7M2apJeOsTRj+vj6vH7uH0rqtY9NtMuHrZN7qMMF6l52K0jRGNkc/1om9roEGTMwXVEyOaCG9/d3fFnYR0rPOtu1UzIL16+zPPlJ3rQSSJl1hAWi5G2xlRPxSuWTsDTSTkF9crwHcy0sSm7k7YGhCDw8HiefEOsNTD48QssbfBE3xD0tDOmT++Nwmj4x/KnxibF5/gNLSy4xdNeb/RPywdDuNrd5QQfp7bHlbG6pi86iZNalZjp+mKKk1O4re53+vTyS0RuvVszJD4NgyOvWsTYya8DoWRs/C+REZOVmIbQnlJKXKT02De2qneMiVpaVCxtEBeeASfWJAbHELFZyVDQ6G2ajY2iD17ViIbbqgdNTs7yPEs6hFRP+jPv2DYqydNptsY/N6moqsXf+LmDq2MERKViaIGBu7dPE1gqKsicdJcLmrW1sgLDQUGDKj5LDc4GKoing+NtSkvKkL2mzcwEtjpxAsJw1CcnAwNF9FJB19n5GKUNX/f4qXP6VuC6+lbrNWVsbWbK+7Gp+N3P/G2gZNFRpLMrLGiPoGI90XlFfAy0EBkdbgwEk/dQUsFx0LE3wkgVe2B25D3nLSJNeSWb6f/n9mmqCYZsaGDNRID+fNqkPvQ0N5SpGeUODaGjtZICRMQkBrpaUU87t8k5qCDtQ5O+tQK3Z1sdGgoHWGc80/AxVf87XhkZnvEZxfRRLq8/RoveqoKaG+ljdUXA9EU3mTnYY6DBeSlpWoSyXroaCCvrJwmx61P1P+jnTPOxCTRZLrNQUBqLn3OtDPWoMkKCUYqCjBXV4RvA8K+oCBGzi/3LL5NL6DCDW9uJALxWpbEj5CEsCD5Jlx1VWmiWwK5L4hYQUI2iF8/bs1qrzMS8mDfIHfE5hZj4e23Ij0cpef/QWv+qWdJjb2+gzWCbjyiC9DccDiJb0LpIqautbnQ44hjM+nf3/iewrEvX+P2+l2YtOvXmjBVosqUTF4HKPAvYPok5mCSuzHfuKWTuRbicoqQKkQgyyoqg/X6u3yfzWtnjmmepuiw/QnNMcGlm6U2do5wwx+PIrGH594ThaatdY0Qr+vK2W2QGxOL8sJCaNraNNqGCPe811lJVjYeLF0BzyVfQMeZsxtY08aK069xd0jw7roT0fWQ86JmaY6skDAYd6vdKZYVFAJNe9sm2dC613U3FXP/JwfiXU9EwLaGGjRRKzd2NxGPdwRI1j+Qxf7m83Xl4B+dRRdd29vq4mL19WGkqQRzXRX4Rgnvm4Vhb8SZR3EXB5Yc9MHXh3xr/k5C8QRuHIYlB17ikhjX4fvoWxpTRhDfgERMHtMKWpqKyKp21ujY1hwlpeV4w7O4xgu5DQR7WK6XP6+3vr2NDhX1r98Ow/e/1Sa2lwSf5BxMdOafE3Uy1UR8bjHfInFDkLbhvRXobnXB31D9L2//0xC+r5LQvo3AGNnLjHrii4K2n+Azq6qqpg2bC/KsCUzPQ1tDTZoMt6Z+xpq0XSW/dzkNeCE8FZcj+J1cyDOO5M0hO0iaslukJUHCdYLm3wqBdltORI7SzEyUpKdBpZ45hdii/tbNqCwtge2iJdThiCE50lL/kYvtP0CLyx2gr69Pw5uIA4lBTkRfEmOfm2D21KlTNFluQxAPcCKgEwGZa0s69OPHjyMgIEDs+hIxlyRcJS9CTEwMTQjLC4n/To69ceNG+p6EW+Eml23u+kgKqRtZbCCJYLmQuhkZGYkMcSOODRH2Sex73nMp7ncRT/dhw4bRxZILFy5gypQpfMcgIj7JYUAgYWtWr14t1m8leQbIQsy+ffv4vObJ9SMJ5JwHBgbS7yaQ5LokrA4vn376KY3Tz13kIYsXW7dubXJ9yIII+X7eFzcMD5loy8jK1Ly4wsqA8d3x5kUIHl59QRPc3jn/hCbO7TeuW81xLx++g9n9lte8v3biPhXsF6+dCbd2wsOGiFNGFPcSMpBSWIJvPG2gqSALWw1lzHQyw4WolJqEdsS77umYzuhpwvF+tlRTwrYertTrfq2PcOHteUo2jam/uJU1FfbU5GSwuLUVnRD5pok/gDodlgwlGWl86WEJVTkZtDPUxGg7QxwKqr2e2xlowGdSFyru0feGmtjY3Rlb/KNxkKccL646qpjubErrRl7LvKxhoa6MP/0a9g7mZf+VYLjb6uKTAfZQUZTFsC6W6O5hgj2Xareuju9jSz3tuTFJD1wNgb2ZJsb1soW8nDSsjdUxd4QLbr+MR3Epp82JyMX74o5564hf5Nqik2RpSMvUXmvuQ3sj/JEvorwDUFpUDL+zN2mMbdeBHG9dwrOD53Bo3g8178WxIYlwU0KjUV5ahtzUDNzZcpDG5nfo1aHeMmTLvvmo0ZCRl0fU0WMoKyhAXkQkEm/epEI7N5ldQVwcns6dh5zqxTe9jh0atCFJXWPPnqPxe4nwEHf+AnJDQmA5vnYbZ15UFIL+IqJ+L4nj6vNy6FIQTPRVMH9CK6gqy6FXezMM62mNPWdrRcd+nSwQdGkaDHT4xZ6x/e3xMjAF4bGNS6xl2Lcvct6+RdqTJ3QQnHL/Pl2sMOrdu6ZM4rVreFGd50Zcm/Rnz5D+/DkqSHix9HSE79wJOQ0NGPKUCd+9m4YwImVKs7IQdfAgSrOzod+ttu8S1bcs87CBpjynb5lB+pboFBRUJx/VU5TH41Gd0cOYp28hon5CBtaJEPUn2BpjqKUBjYtO+oZ+Znr0s5MRTQvDQxYFToYmYY6bGRy1VWjC7RXtbejughsxtQt1e/q54Y9uHOHKXkuF9h2W6kqQlZKi4uyajvZ0gnwzVvTiHhcpaRn64rt3h3RHangMXl+5T+/D0PsvEOsbiFZDaxeEA288xvYxX6KirExsG7dB3ZAZm4S3N5/QezMrPhm+Z2/C0stNIm99Lv8+jMIQdyP0dzagIXPmdbOGhY4y9j+tXTxYPsARj5bV7jyq069Vj694+7Up7c3Rx0kfCrLSNHzP35Pb4G1SLt8CQmO4GZ+KoooKfOFiDTU5WThrqmKclQnORCfVCBc2asq4PbATWmlzRC1HDVX8TkT96CTsDmkeUZ+QVVyOs6EpWNzWErZaytTjcnVXW0RlF+FebK34dmFMG/zUjePB7WWojiXtLGGhrggZKRJPXxG/93SgC1p3Yjg2JGH9wdcJmONhBhtNJcjLSGG0gwFa6atLFGOfCG4vk3OwvL0NTUJprKqAr9tZ43FCFsKza71WX37SGTPcOCLOIGs9THUxofcAuRfIvbGqkx2icgrxKi23Ju/G3oFE1C/CF7cD6w1bQBepq+8NrnOERbtWUNXVwpNdx+lussyYBPifugb7Xh0hr8LxEi3IyMae8QsR7e0vtg05Pud7OC+u4sWpg3S9ZbjPX16OBCRASU6GxsxXk5dBZ3MtTGxljF0vaz13idBPvPHtquN2k2uQ98VtGl5RjRyHiPq/P4zkO1Z9qBgaQK+1O0KPn0FhahqKs7IQfOQkNKytoGlXK8bcW7wcYafOiW1D2oW3PbjtRBeaqv8vr64G4y4dEXHuMj1ORWkpoq/dQlFaGgy82oiss3n/Pkh5/hKpPn5050D05evU3rR3bZi/8BNn8HjpdxLZmA/shzTfAKQHvKY5aHIio5Bw7wEM2ntBXMjZOPg2AZ84m9BEsGTx95t2NrQPO8ezM/TPnk7Y3Z8TDpU+FzrZwVZTmS4CkDE1ubfI/XAlqmmJrgUhSW3PPo/D4iFOsDVUo576a8a3QlRqPu6+qa3fpeU98fOE1vT/bay08c1wF1jqqdBxamtLLfwy0QP+0Znwq16oJZehYP/NDTElif76rvoWccqIw/W74UhMzsWPy3tDW1OJhs35YlZ7nDz3piYRroGeKkKfL0L/XpxFo5v3wmFvo4tpEz2gqCgLPV0VrPiqOxKT8/D6LUdAJrsADlWL+it/bZyoTzgamET7lm86WtO+hYj6E5yNsDug9vnY0UQTYZ91g131ToPlHa1pUlx1BVkoykqjh4U2Zrc2w5ngWnH7emQ6+ljpop+VDl38JuFlFrW1gF9ybr1x+wXZd9QP7q4G+GRcK6goy2HYQAf06GKJPYdrF4UmjHRFiPdCmmiYcONeOMaPdIVXa2PIyUrD1VEfMya3wa37zZuQlbD3dTwG2+ihj4UOTWI9290UFupKOPS2dvy4tK0Vbo+vdUrd3NsJbrqq9HlK5smz3E3pwt65sNr2I+I974uObUD6cvxnILH0tTt0RNKF8yhKTKS7qeOOHYGCvgE0eEI/B/+8BrGHa3NpNgQR8yO3bUFlCRP1Gf8dWpzH/rRp02iiUZL0lQi8R44coTHORbF582YMGDCAJlbV09OjgjLx1hcWmoUX4gFOwryQpLTE4554ghNRnhyLxHgXl9GjR9PQLCSGPtcjndjHxtZOxohgv3v3bir2kgSrJLRMz549aQiZ5q6PpJDvJXHfyfeSJK5ENCeC9bFjx/jq1xibBQsW0PBEJKQO8U4nNuJ+FxHzyaINiXc/cOBAvsWcf//9FzNmzMDOnTtpW5JwNmTBoKFzTo5DbInHO/GwJ7+DeMwLesA3BFmMIOeFnCNlZWUUFhbWJPrlMmLECEyfPp3me3BwcKC7MYgN+a3NXZ+GcPGyx6xvJ+DsnuvYtfYY9I118NkPU+DgXhs2hw6SebxOz+29gbLScqz/uja0D2HwpF4YO2eQ2GVEQTwXv3jwBss9bXFlSDsqbl2LTeULrUOmr8SbhjuPHWNrBB1FeYywNqQvLqlFJRh+mRN/k4xlvnr0Fsva2ODSkLbUO/FNZh4WPHgjUXz91KJSfH43EN94WeMTJ1OadIvEwj/C40FLJpK0ftXvZ7iY0jiSi9tY0xeX58nZ+OwOJ5FWcGYBupno4OxQT+o99DIlB9OuByC2ehuzuLwKz8DCTQ+xdFJrrJzuhcT0Qny/0xv3/RL5PGNIyAluBWOS8zDrtzv47lNPrJndjsbRv/kiDusP+6G5sO3cBsW5eXi85xQKMnOgaayPAd/MhrY5Zzs81+uaJJyTxMapX2e6IJAWEUtDfBjYW2Hkb0ugbqBbbxnXb5dD2dgYTosXIerwEfgsWQIZJSXod+4Cs6E8W7vJhVO9UEcgCVwbsiEJX2WUlfDqp59psljipe6y9GuoWljUlEm6eQsVRcVIuHaNvriomJnBfeUKsds1OiEXc1bdwrdz2uGLKR5IzyrC+r0vaXgdXk8ecr55vaFI+J4e7cywcvNjNBYNJycaDz/+wgVE7t8PBT092M6aRXcm1EC8rioqJLIhCXFjTpxA1KFDVJDRat0aNtOnQ6Z6NxfBoHt3mjA3PzKS7pxQsbCAyzff0Parr2/58tEbLPOwxSXSt5RX4npsKv58xdO3SHH6Fq5DJ9k9pK0oj+FWhvTF27eMvMrpW67HpdEFgjnO5lRUicsvpuF6zkWJ9ggTl00+0dQLa1dfNyjLysA/LRdzbr3mS4RLwmNwPQHDswuod//6bo40cVpOSTlNxj31agCSxEzALYiBnSX6LZmBZ4cv4NGe01SU7D5vAk10y+ddR+7fKvFtyP08eOU8PNl7Fg92Hqfht6zau9PkuVyI8H/8K86CPzn+s0Pn8ezQBcgaOKG840y+el5+nURD8fww1AUGagqITC/A3IM+CE2p3Y1B2om0lyRcf5uCVUOcsXmCBwpLK3DldRL+uB4i0U4qYeSXV+Br70AscrXB2T5t6eLS5bgU7OfxwqeL8jzPkrFWxjQp8yQbU/riEp5bgLmPm+bosfphOFZ2tsHJEa3pIoZ3YjZm8iTCJZB7g4j4BP/UPDjrqmLHABdYaCjRkADeiTkYd9afL+HzxhechZUjw1vRutNwN7eD8FggmWtDLLrzFt93tMXl0V70OrsXl4GfnvLvCiE7U7j3AlmQIEIcEe6JYEdCbtyLy8SOu7E1Av5Aaz3YaCpTIcVnKv8OSc8DjxqMTywrL4f+Kz/Hk3+P49jclZBVkIdNVy+0/3QUT6nqe6P6WOLZNC8p+aWYdjoAq3vZYZaXGU2cu8M7Fvt84wX6Pv7nREN81t6Cinrf9bChLy6PY7Iw9ZTo69Ft9jQEHTqGJ9//TNuGeOE7fzqJbwcQGQtw20xcG3FwmjIBocdP4+ma31BZWgZVEyN4fDmfJtwVhUE7L5Tm5SP0yAmUZudA2dAA7l/Mg6ppbThUzjmulMhG28kBzjOnIuzYKZrYV0FLE8ZdO8NqWP3jZEF2vYqjY8w/ezlTsfRtej7m3HjNF26Dhk+sbivyLHienINfujrATlOF7hJ6k56HqVcC+BJwfupigq+8asesp4dzQnv85h2OY8G1iU4b4ocT/vh+tDtOL+lOhXrv8HRM3/6Er2+RkZau6ZtfxWTB1VwTO+Z0gIWuCvXSJ0lyN18NljwPwwfqW8QpIw6lpRWY9vkZ/PRdbzy5PgfFxWU4fzUYv/CEzaH3rqx0Tf2evojDou+uYN70tvh6QReUlFbA/3USZnxxpiZZ7JSxraCro0LD9pAXl+TUPHQbslvs+pGQONMvvsaqrraY2dqUhpf5xzcO+17VOjGR08o7Jzr2NgmL2lnQGPpkvhOXW4xtPjHYz2NDEumSpLnLO1ljc39n5FXnZFn7pDafkTi8CkzBwuVXsHRBZ3y/lLO4sfKX27j3OJov9wVpP24FN2x7grKySmz4aQD09TjJd2/ejcCGv5/U2NhZa+PSUY5zIbFd9kUX2tZ3H0Vh3pKLYtfvalQ6tBQjsKKjDfSVFejCz+c3A/nuQ277cTkenIxl7a3hqqtGd5ySfG1zr7/BgybmrGkqC2cNwi/fTap5732NM3Zbsmo/dh68+U6+02zCJMSfPI7Q39eiqrwMqvb2sPliIV+SW9ov8/TNcUcPI/3hg5rt+gFfcsK32SxYCHVnF+SFhCA/LJTeWK+/5tddHFf+ACVj8cNgMxgtBakqwX1ILQDitRweHk5FW09PTxqPvj6IB/ybN29oDHMitnbs2JGKpqtWraJ/5/7Ngkdw4YWEySFJau3t7fnioBPR+cmTJ2jVqhXf5ySGPjkW2V3AhdSXJM0lIi6xI7sGBEOskN9FwrGQEEIkAe/Tp09pIlkiVotTn/oQVlcinD979gweHh418eZJGBxSfy8vLz4xnYQJIl7oZGHExcUFMsQziIdHjx7R5LbckDji2BCIoB0XF0eF9zZt2ohtx607aRtSRhASw56EYCJtScoSr3/iHU9+v6jzxlvvoKAg+r2kjXlj6qelpdHQPryhnPLz8+mxyXXFrStpx+DgYPqexMMnizDkM8HY+PHx8cjIyKDfU1paSq9FwUTO9dVHHLxTL6OpcASbqhoPNSLyC+sayMCIG2ZJnDKEBfdqrxlJIeIC1/OADHeEaTVUk21CL1ZaWvVO60fq1pROtuB84xOekgmUqFAT4kAd86Wk6t2a+vkqA4mOyRUGudfauyQgU3yPYCpMV4cUai7oQFPYI7bau/Dxb83rGSTsfDflGui4XLxtrrQfqKqq8Zh8X4Sk1X12NGvfUlW7LbwxFBY23/CK1I+z9b75mOUpWfJErrBPPYebmUqehSEufx0pb1Sia9pv1fNcoG0pYXg2QSy7NG/IxOa+HuPj6rbnh7zWBJGXfLMGX/0k7dK4CxWCiPJsnOZV0qhrmBuWrjGIc39xy2y/1fgGJOJlfWEuBNuXcz3W/U3EIUTUUYZ3Ea8NuCJ5U54djWn38kZe3M1R34a4H9L4/pUG+Gmm+0PUvVHkI37M/HfR9zY0plFsw5/j6F33LZJSvLXxiYBlZKTqJMWtD3ItEKcPoc8SET+0aqZnszzH3hXSe/zeW/txbQQhbSfqGpb9TPSuoHdx70ry7I79kROGsanICJm/VTQhJCWX4fs/a7StYN8scv5V/ayg8xaehQDBfF7CONZT9G7h/2cWPeOEhP7Y+LND7e7i/wotUtiXBG6oE+LhTyDhT8aOHYvnz59T8Zrx3+PcuXPUS5/shCCiPol3f/v2bZq3QNgiwX+d5hD23yVNEfbfB00R9t8HTRH23weSCvvvE0mE/Q9Bcwv7zY24wv6HoinC/vugOYX9d4Gkwv77ZtMh8bfiv2+aW9hvbpoi7L8PmiLsvw8aI+y/T7Zdb9l9n7jC/oeiscL++6Apwv77oCnC/vugKcL++6Apwv77oCnC/vugKcL++6Cxwv77oLmE/XdFU4T99wET9oXzlffHKexvbP/fE/ZbXCgeXkh89vHjx4v8O4k/T8LvkLAnxIObeO4nJydj27Zt/zlRn4RwEYzRzoWEADpw4AD+X5CXl6de/OTck2uEhOshuRX+H0V9BoPBYDAYDAaDwWAwGAwGg/H/R4sW9knyVd5Eq8L+TsLJkNAyJLY9Cd1ja2tLE4z+1yAx3E1N+TO+c+GG2fl/geRgIOebhDUieRhIWKTmDJ/BYDAYDAaDwWAwGAwGg8FgMBgtmRYt7BPRXjBOvShIrPX/MiYmJvTF4EDi0Lu6urLmYDAYDAaDwWAwGAwGg8FgMN4T7ze7GqM+2LlgMBgMBoPBYDAYDAaDwWAwGAwG4yOCCfsMBoPBYDAYDAaDwWAwGAwGg8FgfEQwYZ/BYDAYDAaDwWAwGAwGg8FgMBiMj4gWHWOfwWAwGAwGg8FgMBgMBoPBYDAYLQNpqQ9dAwYX5rHPYDAYDAaDwWAwGAwGg8FgMBgMxkcEE/YZDAaDwWAwGAwGg8FgMBgMBoPB+IhgoXgYDAaDwWAwGAwGg8FgMBgMBoPRIFJSVayVWgjMY5/BYDAYDAaDwWAwGAwGg8FgMBiMjwjmsc9gfOScjFJCS2aIXTFaMtZqFWjJXDQxR0tGU74ILZUhZi23boScJXZoyXzulIuWzDV1BbRkkgpl0JLRVmjZXjbDhimjpdLNMA8tmSSrlu23U17ZsrOtaStUoiUzty9aNCpyLbtvWT5wL1oqf9+ahpbMXWtttGRGWhSgJXPZvg1aMp31W/ac7a6rB1oyA0wL0VJ55DwHLZmUopY9LmAwWjote+TPYDAYDAaDwWAwGAwGg8FgMBgMBoMP5rHPYDAYDAaDwWAwGAwGg8FgMBiMBpH+P9poUVFRgeLiYqioqLxTm8bCPPYZDAaDwWAwGAwGg8FgMBgMBoPBAFBSUoLZs2dTcV5LSwtubm7w9vZudpumwoR9BoPBYDAYDAaDwWAwGAwGg8FgMAB88803uH79Ol69eoXc3Fz07NkTgwYNQmZmZrPaNBUm7DMYDAaDwWAwGAwGg8FgMBgMBuP/nuLiYuzatQvLli2Dvb09FBUVsXbtWvr54cOHm82mOWDCPoPBYDAYDAaDwWAwGAwGg8FgMMQSkz/Gl7i8fv0aBQUF6NKlS81nysrK8PLyEhlapzE2zQFLnstgMBgMBoPBYDAYDAaDwWAwGIz/LCUlJfTFi4KCAn3xkpaWRv/V09Pj+5y85/5NkMbYNAfMY5/BYDAYDAaDwWAwGAwGg8FgMBj/WX777TdoaGjwvchngkhJSdF/Kyoq+D4vLy+HtLRwKb0xNs0B89hnMBgMBoPBYDAYDAaDwWAwGAxGg0hLVX2UrfTtt9/iq6++4vtM0FufYGxsTP9NSUmBubl5zefkvZOTk9BjN8amOWDC/v8hZ86cwZw5c5Cenv6hq8JoRhKf+yD07GUUpmVA2UAPjqOGwtCzVZNs8uITEX75BtKDQlFZVgYNK3M4jhkOTcvaTopQkpuL4JMXkOL/GlVVVTDp4AXHscMhK6SDzE9Nx8t9J5EaFA5ZBXlYdm6L1hOHQ1pWRmQ9xbFJD4tC2M2HiPX2g66tJXp//2Wd48S/fIXAc9eRm5gCGTlZ6DnYQHP2UGgb82+V4qWyogL3Dl7G6zsvUFJYDDNna/SfN6Zem7zMHPhffwq/60+Rl5GDLw/8CFUtdYnL1Efu69dIOn8GpampkNPWgcGgwdBq115k+aqKcmT7+SHj/j0URETAeNQo6PXpx1emJCUFqTeuIy/oLSpLSqBkZgbDIcOgYmuL5iTg5lN4n76NvPRs6JgZoMe04bBsZS+yfGlRCd7efwnfq4+QFp2EUd/NhF17t2apS2pMMi5tP424kBgoqSjBs38H9Jzcv94V9ciAMDy//BhBz97AqYMrJnw3TWTZ9PhU/L1wAyrKyrHm4gax6kTutdTzp5Hz8jmqysqgYu8Aw3GTIKelLdKmLDMDWY8fIvvpQ5Tn5cFx4zZIy8kJLVtVWYnYbZtQEBoCk2mzoeHZFo3l+V1/nN17A2lJmTAw1sGoWQPh2dVVZPn4qGRcPnwHQX7hKCsth5WDKcbMHgRLB1OJygjjQ/Ytcc/9ad+Sl5QKeVUV2PbuDK2+A2lbR529gOQnz1BRXAINWxvYTR4PZQP9evsccWxyIqIQde4CciOjIK+uAYtB/WDUtXPN30uyc5D04BESHz5GaVY2Om1YC3kN8fsY0t/d3HUWoU9fobKiEtaeTug/bzRUNNRE2mQmpsH36mO8uuWNiopKfH1iXaPKcCnOykbw4ePICAyGtJwsDNq2gcP40ZCRl28Wm9L8fDxd9StKsrLR9fefoKSrU3MP3ppb9zw7T5sM0261bfyx9C0VZWV4cuACwh75oLy0DCaudug2eyzUdLUabZMRm4Rji9cKte276BPYd/Wi/89Ly0TgzSd4e+spinLyMe/YekBG9PnjEvHEFy9PXEVeagbUDXTRduIQWLVzb7JNUU4evI9cROzLN3S8YtPZE+0nD4WcYt3xSn28uHgfL87fQ0F2HvQtjNF71kiYOlmJLF9cUIQ3d17Qaz8jPgWTfvkcFm52EpcRB9KH+B2/hIgHz1FeXAJ9B2u0mz4W6oZ6TbLJjI7Hq7PXkRIUTt/rWJvDY9wQ6Fib1VufGJ9APDt8ETlJaVDT04bnmP6w7+bVZJvi/EI8P3oJUd6vUVFeDuv27ug4dQQUVJTo30sKChFw4S7Cn/ihMCsHavo6cO1fG/O2sagoK2D8iM6YPaUvXB3NMGHuJly+6YN3AblGn564Af/rT+jvNbQ1Q5/Zo6FvZdIkm9z0LNzffxExr8JQVlwCMzdbWkbTgNMHEvIzc3CPlAkIoc8DTUNdqHbvC71OnZAbGorYU6dQlJQEOQ0NGPXtC4Pu3ev9LQ3ZkOdl0s2bSHtMnllZUNDVhemQIdD29Kwpk+Xvj9AdO+oc2+O33yCvJbo/4+XVA3/cPnQNmcmZ0DHWQd+pg+DSSfSYsqy0DK8f+OPZpceID4nFyEXj0LZ/B5HlX1x/hrN/noBDO2d8umaW0DIJt+8g8eZtlObmQcXEGNbjx0Ld1qbeejdk82TBIlQIhJYgGPfsAZtJ42veF6WkIvrMOWQHB9PnomG3rjAbNABSMqLHSvWRFBaL2/+eQUpUPJQ11NBmcFe0H9mr3uszyjeIjumjfILQql9H9PtsnNjfV1FcjJgTJ+i1QK4ZTRcXWEyYADk1tSbZ5EdHI+7sWRTGxgLS0tBu0wbmo0dDRlGxpkxOcDCSbtxAQVQUpOTkoG5nB9ORI6GoqytW3cl87+qO04j0D4WsnAycu3ig78xhkFMQ/UxMCo/DyyuP8ea+L3RM9DFn81K+v5cVl9K/v7r7EllJ6VDX1YRH/w7oMLw7pMTwDibnI/rSVSTef4SyggKoWVrAftI4qJmZNtkmPy4BEWfOIzssHHLKyjDt0xNmfXvVeDSLAxmPpZw7jRyf56gs5cyJjMZNgry26DlRafWcKOsJZ07kvKn+OVH0Vs6cyGx60+ZEjI8HBSFhd4Th4uICbW1t3Lp1C23bcq6NzMxM+Pj4UD2VS25uLv1XXV1dbJvmhoXi+T+ksrKSbgVh/HdIDw6F7/Y9sOzbA703/gzzrh3xcutOZIVHNskm6MQ56Lk6ocv3S9Hj1++hpKONp2v/RGF6Rk2Z8qJiPP55A4oyMtHpu6/Q6481UDUyQIrf6zrfSSZed3/dRoWNwX+sRLelcxHz5CX8Dp8VWU9xbEry8uGz/xT0ne1g6uWOqsq6q8dZMQl4uPFfmLZtheFbfkS/n75GeUkJjq2qO0Hg5e7+Swi46Y1Ry6dj3o4VkFOUx+GV21BWUirS5s7eCygvK0fXiQPogAFVjSsjisLYGETv2Aatdh3g9Mta6PXpi9h9e5D39q1Im2wfH+T4+sBg8BDIqqoIbaOk82ehbG0N26+WwnHNj1AyNUPEX5tQnJSE5iLkaQCu/30CnScMwGe7V8GmrQtO/fgPMuJSRNr4XHpAJw5k0knaSljdG0NxQTH2fLsN6roaWLxrBcZ8PQVPzz/A/WM3RdqkxaXgzqFrcOroButWtrQ/FUV5aTmO/bYPFi7WVBgVl+STR5EX4Afz+Qth/d1qOqiN3fYnqgS29PHZnDpGJ2U6fQeSTr7e46ffuAIpGVlOuarGt2WwXzi2rzmEvqO6YOOJleg6uB22fr8P4YHRIm1O7LgE17b2+P7vL/Dr/q+hY6CJtYv+RnpypkRlWlLfkugfiEebdsG2dxcM3/oTOn85A+F3HiP2ynVEnTmP5EdP4Tp/Dtr/sgoyCvII2PAXKkpF9x/i2OSER8D/943QsLFGh99+gseyRciNjEZpDmdgSYg4eQaV5eWwHDqYc99I0skAuLjpMOKDovDJ2oWY8ddS5KRm4tQvu+u1ubrtBJQ1VNF2eA8qFDa2DIHU2ffPbSgrLEKnH1fAc8lCpL8KxNv9R5rN5s3uA1A1Ma7ug2vbh/yXfOa1bBH6/Lul5mXStRM+xr7lwa7TiPR+hSEr52HCpm/pYsDFH7fX2/4N2eiYG+GzExv5Xu0nDKKLYhZtnGuO83D3aUjLyMBzVF/OdSjGZZgYGIbbf+6D68BumLx9DRx7d8TN9buQEhrVJJvSomKcW7kJ+WmZGPbTl5i49QdomRggxucNJCHg5jPc3XcBvWeOxGf//gATJysc++Fv5KZlibR5duY20uOT0XvmcJHPfHHKiIPfsYsIv/cMPRbPxPCNK+mi5c2ft6K8nn5HHJtXZ67DumtbDN+wAkN+XQYlDTXc+HkzSvILRR43LSIOV9f9C/tubfHJjtVoNbQnbm8+iDj/4CbZkIWnC6u2ICM6AUN/mI9Ptq+GoaM1Ip7615R5e/Mp5FWUMPi7ufjknx/RZmQfej0qy9SOYRvDZ9P6w7OVDZau3g9ZWRlISyBSScrzs3fgffYOBi2chDk7VlJx/dj326hg31gbci+fWLWDLkpNWbcIc3d+DzVtDVqGLOJxubB+P11gmvDzAny+70d4DumGyP37kfr4MUI2b4aGszNa//ILzEaMQMzx48h4/lxknYpTUxu0Sbh8GYlXr8JywgR4rFsH02HDELF3L3KCgvgERSLQtdu2je8lrqgf+Socx9YeQMdhXfHNgR/g1a89Dv+8F7FBosctvjefI8w3BINmD4O0jHS9Y9DU2GTcOnANpg5mnHtYCMmPHiP61BlYjRuLtr/9TMX5Nxv/Qkmm6DGOODYdN29Elx1ba16uixfScZ6Op0dNmeL0DPj/ug7S8vJos2olPFatpPUkDgKNgSz+kOvGwNYM83b+gL5zRuPR4Svwu/pIpE1iSDReXLgHt97tYWBjhkoJx/TkmsiPiIDT0qVw/e47lGRkIGz79ibZlKSnI2jjRigbG8P9xx/h8s03dAGK2HEhY/HkmzfpglSrX36B85IlKC8sRPCff4pVb9LOR1bvpAu487Z+jSk/fYawl29xedtJkTbk+X7hr2MwsjWDWw9PoeOCgNvPkZuRjWGLJmLR/tV0oeDeoat4eOKWWPWKvXYTsVdvwmn6J+j4249Q0tOF3x9/UcG+KTb58Ql4+esf9G8dfl4FzxXLUJKZhYJEyeaUSSePIjfADxbzF8J2xWpUlZchpqE50UnOnEivX8NzorTrtXMi0r8wGLzIysrim2++wdq1a3Hx4kW8ffsW06ZNg5mZGSZMmFBTbtSoUZg0aZJENs0NE/b/z7h//z7Gjx+PnJwcetGR14oVK+q1MTU1xYkTJ2re9+jRA1paWjWLAxEREZCTk0N8fDx9T1aj+vbtS1esyAU8d+5cZGdn19iTbNDc71ZVVYWHhweOHz/O951Hjhyh37t161Y4OztDTU0N/fv3R0xMDF+5q1evwtPTEyoqKrC0tMSPP/7I99DjHmfnzp1wc3ODpqYmevXqhfBwjrdRQ2zevBmtW7fm++zGjRtQ5Fm9JzsfyI1saGhIX5MnT0ZStRAqzu+oz15cIq/egq6zIyx7dYOCuhqsB/SGlo0VIq7dbpJNu6/mw7RzeyroK2iow23qBFSWlSP1Va2AHHH1FkrzC+D1xRwq6MspKcGyd3fqtS9I/IsA5Kekoe2siVDR1aLer66jByLs1kOUFRULrac4Ngpqquj/89ew6dERsiI8ObOi46ha4zS0D+RVlKGqr0OFOOI9SgZZwiDi/ctLD9FlQn/qqU+8IAYtmEA9zYMe1U4eBRm+5BP0nDoEGvraTSojivTbt6Bkbg79fv0hq6YGnS5doe7iitSb10XaEG9+yznzoOYoegsY+btO5y6Q19WFrKoajMeMhYyiAnJfif6tkvL8zG04dfGASw8v6tnTddIg2gYvL94XadNxbF8M/GIiDG3q9wqUFP87L6gAN2zBOKjraMC6lR06j+6Jx2fv1YmLx0XPzACz/vgCrXp6Qka2/k1vV3edg7GtGZw71e9lyktFQT6ynz6G3tARUDK3hLyODowmfoKSpETkB9ZdLONiNudz6A0aCjkNzXqPXxgZjuxHD2A0aSqaytXj9+HsaYdeIzpBXUsVA8Z1h42LBa4dF30uv1o3C537e0HHQAsa2mqYung09cp/5R0sUZmW1LdEP3oBPSdb6qVP+hZyHOehfRFz9Trib9+DxdCB1OteQUsLDp9ORklWFtJe+Ao9FhHvxbEJO3wc2m4usBoxFPLqajXleD3ynWdPh/Wo4VDUkbyPIV5fwU8C0HfmCOhZGEHbSA8D5o1BXGAk4t6KXjCe/Mvn6DSmD5TVVZpUhpDxNhh5MXFw/nQSnRiqW5jBbswIJD7xprsRmmoTc+M2XZi2HNBHZB2ItxsRpbkvcb3MWlLfUpxXgKDbT9Fh0hDo25hDXV8bPeaNR2ZcEmJ83jbJhrdtyCvorjdsO7WGgopyTZlBy2ej3fiBUNHWgLgEXLgNEzcHuPTvSsVj9yE9oW9vhYALd5pkQ8qU5BWg39ezoGlsAHllJbgM6ArbzrUeweJABPhWfTrAoaM73WnXe+YIyCspwveKaDGrxydDMOCzcfR+akqZhiBCfPD1B3AfNYB63atoa6LD7AkoyMxCzFO/Jtn0+GomzL3coaimSvtM1+F9UVpQhJx40WPWgEt3oWdlBo8Rvel5ce7bCeZtnOF3/naTbN5ce4TspFQMWDYL2uZGkFNSgGPP9nDu07GmDLFvPawXNIz0qBe/XVcv6FoaQ0FatGAlDuv/voDPv/kX/m8aJ4aKCxECiUjvNaw7LFs7QFVbA/3mjaXi+6tbzxptkxIZj/TYJPSeNQqahjp0LNZ37hjkZ+Qg6GHtcyY5PA6uPdtCx9QACspK1LOaeNqnPXpE/yXivJy6OnS8vKDTrh0Sr4seg6bcu9egTbq3N/S7daPiv6yyMrQ9PKi3fuK1a3WOR0Q73pe4PDx9F7at7dFhSGeoaqqiy6geMHe0xMPT90TatB/cGeOXTYGli3W9xyae/Ud+PYDBc0dAXUf0WCz+2k0YdOkM3Tat6TPbatwYyCgpIenu/SbZkOcVb5ukPn4KJQN9aDrU7oiNPnsO8urqsJ8+FQra2pBTVYXF8KHQsGvczlyyK4TsgO4zayRUtNRh284Vrfp3wrPTou9vE0crjF8zHw4dW0kcb7o4LQ1Zfn4wHzuWivCK+vqwnDgReeHhyIuIaLRNVkAAyNOdlCFe/KSM+Zgx1K4oOZmWIQtKDl98AQ0nJ3p9KhoYwLBXL5SkpqKs2lu3PoiXfnJEPIYsGEd3xhjamKL3p0Pw6s4L5GcKtycLSXO3fA2vQZ0hryTcu9hrcBf0nz0SRjamUCT9XFsX6rEfeF/4OLPODt5rt2DWrxe0XZygoKkBh08morKsFEmPnjbJJuz4aaiZm1JPflKG6Ah2E8ZQZwpxKS/IR9aTxzDgmRMZV8+J8uqZE5nP/Rz6g4ZCtqE5UUQ4sh4/gMnkps+JGP9dvv76a/zwww9Yvnw5+vXrRz39b9++DSUlzu5AAjdOvyQ2zQ0T9v/P6NatG44ePUovvOLiYvr66aef6rXp1KkT7t69S/9fVFSEp0+f0gcxEfAJ5G8WFhZUwA4JCaHC+ciRIxEVFYWHDx9SwZ+I1Vzat29f891EwCYLC9OnT8ezZ7WDVCLOJyQk0LqeP38egYGBkJGRwYgRI2pWU8lnw4YNw8SJExEbG4tdu3ZRIf6PP/6ocxyyAEBWzIKDgyEvL49Zs4RvjRRnd4PgZ0uWLEFqaipdsCB1Gj58OPbt2yf276jPXlwyQyOh68QfyoSI9llhkc1qQ0SQqsoKyPJsWU/28YehhztklWoXO0SRFhIJdVMjKGnWik+Grg50sSAzMrbZbIRBbORUlBF24wH19CrOyUPk/Wew8XSiAyFhpEQmUHHf0r12KzwRowysTKgn64eChNJRtXfk+0zV0QmFkcIHtY2lsrQElaWlkFZo+NyKA/ESI5735gKhBSxa2SMh+P23Z+zbKJg6mENesVawtWltj6K8QqTXs4NAHIKevkaYTzAGzxslkV1hdBRx0YEKz/mV19GFnK4eFeWbQkVhIRL27YLR5E8ho6KKphL6OgpObfgng0ToD3sj2vNNkKLCYuqRpKis0KQyH7JvIX25oOBLJtgVhUX0/tFydKj5nEyiVU1NkSNiApofG9+gTXFmJvJiYmHQoR3eFXFBnOeABU/fZ2RnTsWx99X3ZYdFQFFbCyo8IYh0nB3pAm12RFSTbHJjYhF5+Trc50yvV6z33/IPbs37Eo+//wmxt+6J9MBsyX1LSmg0vX9MePpddX0daBjqIik4stlsEgLDadgUl77i7Wqoj+TgSBi78o9PTNzskRIS1SSbaO9XsPBypSJ8YynKL6Q7zHjvDXINkfcfclzAGy6nvKQUhi61bUGEeG0LE6SGRjabDfHSJ4sBqga60LIUHbIhOTgKxgLPfNMGz2XDNlHer2Dm7kiFf3HHH9Ev3yArPgVFFeIvMn1IspLTqVc9bzgmWXk5ukMkQcS1Jo4Nd/4hJc3b93H+H8+zcOvYxYMK/WQnCmm/tw98UFFURF/qDrXPKIKGoyMK4+OFhoIhEBG1QZvKSqHPUuJpzdv3kmekz5Il8Fm8GG/Xr0duSAjEJSYwiu6I4sXGww4xzXDvXv7nHExsTeHejd8hjBfi0Uw8wTV52oL8Zg1He+RGRDabTXlhEdJ9fWHYrTb0FGnDTP9X0GvfVqwQLeJArikzFxu+45HQmjkpGcjPaljslhRyHRF4ryUVCwu6yEGuk8ba0OuL/Aae64/7m7j2gpRmZyP10SOo2thQJ6uGiH0bSR3EeEO5Wre2pztA4oPFHzeLQ1FuIeTrGTPXlEtNQ2luLt+YU0ZOjjqWZIdHNNqmvLgYWW+DmzxOLeLOiRz450Tyes0zJ4rbtwsmzTQn+n+FPEY+xpckkP6W6HVEpyO65smTJ6lDMS+nT5/G4cOHJbJpbliM/f8zyEXGXR0nHvPiQDz0iWBOePz4MaysrNCuXTsq6BOR/t69e7QMgYjqQ4YMwfz58+l7HR0d/P333/RCTkxMrEkmwf1u4sE+ZswYnD17ll7wHTrwxywknvZ2dpzBKRHuSQKKO3fuoHfv3li/fj26dOmCpUs5seb69OmD7777Dj///DPd/sL7m/fs2UN3GRBIogxSR+I1R0T2phIZGUlX4sjiBmHcuLpxAuv7HeLY10dleQUd9BGvTV7k1VVRwhOWoak2hLdHT0NeVRUGrWtjURakpkO/lSte/PUP0gKD6N+NPFvDYfQQyPLsbCAUZ+dAUZ3/4Ul2CxCKRHxvY2yEoayjhW5LZtNwPD77ONsedeysMOGnuSJtuB4Uypr830/CSLyLAau4lJEdNwKDSFlVVRoXn8SR5I0H2RSSzp2jXj8abSTzZhRFYW4BFYuEtWfBB2jPvMxcqGryt6OKBqdueZl5MGjk8zcnLRvnNh/HlFWzoCDCw0YU5Tkcr2KyY4IXcr7LxfAIqo/Ew/ug1soDqk4udEtxUygvr0BBbiHUBdpPXVMVOZl5Yh/n6NYLUNVQQeuOzk0q8yH7FrN2rfH4z93Uc9+sfWvkJach+HKtt5pg3FfynjdkDi+l1ee/PhsyqSKQe/3Fqp9RlJYORV0dmPXtDSMxQ8U0BOn7FJQVqSBU914V//w2BeJhLy/YDqoqdLItqv3EsSETzoDtu+E0eRxdBChMSa1zHDK3N+7cAZYD+0JBUxPprznhfErz8mA7cuhH1bdw+1YlgWudXPuF2bnNZkNi6Gua6MPYuWk5WSrKK6horFTdXlyU6vlucW1yUtKo5/f13/9FfEAw/T0kBn/bCUOox7e4oScIxMtZ8N5IChd/QfBdUcQ9dwJtoaiuhqLsvCbbBF29hxcHzlAhjHjt9/5mXr35CUhse9I2gueFxHUvKyoR2u7i2OQkp8O6Qyvc3LSfCvZk0dHC0wUdpgzl2zFCdp/snf4drS/xfu00bSSObP3wCzDiUDsGFbjW1FWRk5LZaBsSa5/slLy37wIGLJhA43s/OnqVhofk9Rwmnv5nf9uNv2esou9JKEqbGTMRc+xYnWcUHZNWVdF41jJC4haTcaucQNJAQRstDw8qlGq6u1PhlQj2GS9fUiGfPO+IlzQJIUPinmtXxy1OuXuXhlBxXrYMajb1x6gn/URhXmGdvlmVjOklGLcI482jAIT6BOPLv7+ut1zNM15g/kXaMz86ptlsUr2fo6qiEvqdanewlOXl0XYknuevft9AbeU1NaHfuQPMBvRvVIx9MhcyM+KPL8+9d8lzRJLcYeJAriMyxxGMl06uJW47NcZG082NxtePP3cOxgMH0nYi78lggNjzEn3sGL3uyLWrbGoKh4ULxdrNR+4tFYH5j5I6Z3zSnHNKskjw5r4PBi9oWE/gzvnr6AFqaigSkYtRHBsS8okb4vDFT+tQkJgIBW0tmPboRuPsi7v7kTsnkhGYE5H3TZ0TJRzaB/VmmhMxGC0B5rHPaBAi2hNPfOJdT0T8nj170s/I/7nhfbjC/suXL2lYHRKqhmw5Id7xttWJN4mATSgrK8PKlSthb29PQ+gQkZ94tBOve17IVhWSfIILWRQgoX1InCoCWQETXAgguwuysrL4Qtno6enViPoEksyC1CE/P79Zzv7s2bPx22+/0Z0De/fupQsYkvyOhux5KSkpock5eF/lpcK9Y+p/aFZJbBN24SoSvF/Cc8EsyCnXerhXVVUi4upNGLZxR98/f4Pn57OQ5OOP1/uP1fP9Qr5TgrB2jbEhMfbvrdsOpyF9MHrX7xj21xooqCrjyPd/1xtnWOT3t7Q4fJIuPzdA+r27yLh/F+YzZtFt0+8S0p4tpTVrPdgaX6MT6w6g/ZCuMHNswsq84Omk13zj65T16D5K01KhP0yyHQQiEXH9S5IQ68KBW/C+448FP34KZVWlRpf50H2LeXsPeM0cj9enr+DktCV48Mc/cBjUU7SBdCP6D16b6n+jL1yB/dRJ6LT+N5gP6IvgfQeR+uLdJHDkv1c//N3amDpwbYIPn4CmjRUM24lO3kkEALfZ06BmakKTIRt3bAfrIQMQfe1Wo2OwfvC+pY4nrFTDl6GYNqWFRTS2eXN464vuW6SbblNZRcPxWHi5YcrOn9Hnq+mIev4KD3fxh4NsDJxxAVoujXmGCLFxHNAdUw5uwugta2Dk5ogbP22hQnxj7gVJ7mNBGzL2fHPtAQwdLDF15xoM+nYOEgPDcXvzIT47RTUVzD2+ETMOrEOvBVPw9OAFqDQxxv6HhgiBkvaBvDZEyB+7eh51tPh33s/Y+ulKlJeUwaq1A9/Y48yvu+kuo1nbvsWio2tpuJ7wXbto7pY6x6/uKySplaANCdOj17Ejwv75By8XLED8+fMw6t+fz4YkPTUiYQ20tOjLfNQoqNna0oSmDVHVDOMWYeSkZ+Ps5pM0XA9ZEH9f/Ud9NikPH0GndSsadocLNzdA7MVLMB3QD+02rIPN5AmIv3oDcVfqhjtqLDXn9T3OkxpzDnltlAwNYf/558h5+5buBnn900/QdHWFrErdkIEW48ah7bZtcF+zBnKamnRhqanCcHO1VEZCKo79tAvuPb3Qpn/too7ENGaey2PDPfdR5y/DduwIdNm4FrajRyD8xBkk3H3QqOo05zw8s3pOZDC8meZEDEYLgHnsMxqExIY3MDCgQj55LVy4EF5eXvTfoKAgur2EK+wTL/hFixbRZBGCcL3jf/nlFyr+79+/nx6bxNknGaJJtuiGHtLkM94Y+nW2bAoZTIh62Dd2wCFoN3XqVLpz4MKFCzhz5gzdrUCEetIO4vyOhux5IZ+vWbOG77OOM6dSob00j3+hoiQ3r85qOhdpWVmJbCKu3ETo+atot/gz6Djwb49W1NSAsp4uzLpyBhBaNpawGdQHbw4cR8LTF/QzPQcb9Fm1CIoa6shN5A9DQELi0OOI2E7dGBthRNx5DBU9HTgP70ffK6iqwPPTMbi4aA1iXocj1PsNjafP5Yu9q6GixTl+YU4+VHi+qyAnH4bWoreev2uI1055Pr93UXluHqQVFOirqWQ8fIDEUydgMWsO1F1rd2dIQuC9l7i0qXaCPeKb6bBt60Inl6Q9eSHtqSLgQdXcfD9occ3/nTq6YtL3M6nXVr6ANyLZwk4Q9OiShOg3ETQUx93DnIkSmUyTCRWpw8A5I9BpRHeRtrLVk7DyvHw6YeBSkZcHWevGe8IWhoeiJDEBwV99zvd5wr5/kXn3Fqy+/k6k7evnIdiw7N+a959+NRo9h3WkQnteNv+5zM3Op177DXHl6F2c338Di9fNgkMra4nKfDF8FfJyCuiYviX0LQS7Pl3pi0uiXyCflxxvv1qWmwdVc+H5IrgeefXZyFfHcDTt25MmzyUYduqAlGcvkPr8JfTbSrbDhiQQj/Dl5C+QlZfFN6fX0/uxpLAY5WVlkOXxcivMznvn9yoXIkgQD3leyE4z4gWmIGKxURybzJAwussh6elzvmf6w29+gFnv7nCaPF7osdXMTGjIiBKSM8hIuUX2LYnhcQi441MjEsw5/HuN525Rbj6Nt82lKCcfRo7C7z1JbUIf+FCR0LGHZNvu4/yDcOWX2gSGXWePg3O/LjThKfluXopy8+p4lHMhMZ7FsVHW0oCagQ4ce3GcQgzsLNFqWC88O3gePT+fUic8hc/lh7jxz+ma9xN/mg8DaxP6/0KB7yrMeX/3Bpfn+07RcDhcRm/7sSY0TXFuPvW45+3btK2Ej1sksSHjVylZGZqnqOOciTg2ww9RT3ygb2+Nqz9srCnXYfJQeIzsAyVNNXrd8ELGALKK8iI9/cWxUdHSoLkA3AZxnqcKVsrwHN0Pt7ccpCEUiXjNheR/UFRVhn33tkh8G4Gi674oqNBBS0JdNgkasklYN5yT02jan1/XXE90zMTzyKivHxbXRtfMEONWz+Oz3TF7DWzautL/k6S5kT5vMenXL6Brzsn54N6nAx7fCkB+ZCTKBJyjyDOLqG+CnvxciINIQzZkQZXENScvLvEXLlBxlXjri0LZzAy5wfw5eIj3/L6VO2veD/9iDNoP6gRFVSU65uQln45BGx+KIzkqiR7znyVbaj7jPle+G/gVFu/8huZQIcira9T+dh7IM17QI5+LpDb5cXHIj4mF5aiRfJ/LqalCSkYaeu3bQdudM7bXcnGGQZdOSHv+EuZDB4v8jUW5Bdj8SW1OvlZ9O9DdHuSaEjamJ6jwhDlsLsh1RLzpiZDO64FP2qa+a08cG7JoRF68fycJnhX0akPn1OQykJaGkpERrKdOhd+yZXRBQKtVq5oyW+f8gsxEjve6vqUR5m1dRttK8NojC2dkfNKUcQEXkjNu//KtsG5lTxPpigN3nEn0AN4lDM4YVL3RNgrV41Tj7l1qQvbotWlNx6cpz1/CtJfoeRAvsmrC50Rkl49yE+ZEBWGhKE5MwNvF/HOi+L3/IuPOLdgsEz0nYrxTv0JGE2DC/v8hJNGtsKzqDcXmv3LlCvXIJyK+vr4+DbNDQu/Y2NjQ+PoEkmiWJIYgIr4oQZ2E8yEJfIl3PRdfX986cacKCwvpwoFT9dbNlJQUxMXF1bwn/754wRGOuTx//pzmD+CG/GkqxNOfN/EvQVjiXWtrayrEk9eWLVuo+M4V5hv6HQ3Z8/Ltt9/SUEK8/BDwBD65+cgIDoPtYI5oTUh/GwptO9GJnrTsbMSyIclxg09fRNtF86DnWjfxqradTZ3wPVKQooOnATs2QJ04r1RfC7r21gi7+bB68sgZRKcEhtKFBm1rc6H1bIyNUKSErfhzJvFkgNZv9kj0nTWCbyKopKoMWQU5xL4Oh171xIYMwlKiEuA1uFbEe98oW9sgPzSU77P8kGAoW1k32fMo49FDJBw/CvOZs6Hh0abRx3Hu7gmnrh78A2EpKRjamiHuTQRa9a31JIkNCIWpS/1bqJvK6ovra+tS3Ubmzla4sfciTXgmVx1yJNI/jE789MwNG/1dP16uFTcIPte9cXHrSVqHhhKFKVla0TifheEh0PBqTz8ry8pEaXoalJowiDWeOhPGn8yoeV9VXk5FfpNPZ0Hdk7OlXRSube2x+9a6mvcknAHBztUSwf4RGDy5V83f3vqE0c/r4+qxezi96yoW/TYTrl72Epf568wqOmm+nqjQMvoWIcQ89aEeYCWZGcgODYNKdbIwIjLnx8XDRMSkRs3cHNLycvXaKBsa0Lj7dbyRSVM04v4fv2oOz4I1x97MyYr+G/smAtYenNimyRFxNNG4afXf3jWadtaIvHgFhWnpdPGYkPk2hP5GDRurRtt0XbuGb4E+KyQML3//E13WroGSrmixLz8hEVJkUZx68FW1yL6F/K6RiyciuUi65jlmYG9J+9/EwDDYd+XsUshLz6KhTIwchbejpDZvbz+FdXs3seOdczFt5YjZxzbVvOcK64YO1kgKDIfHiL41f0t4HUo/F4U4NoaO1nXD+dRzz7QZ1AUeAzrVeY5pm+jTcYFjJ46QQ9qdOAi49hC9C+Rd0HbqKHh9UivekfNNwtDIyMsh+W04NE054xYSpojsWnTs303ocbStzCS2oVRV1YRb0LOzxJRDnHOpIlfFdy6JJz0v9LyQa0xE24tjQ85lSphAXGox+j9x82S8b3LLDelr6/WpNeeS1JWENYl7Ew5zV87znyy2kpxEncbze7JzIfG7JbUhJIXGIDs5A3btOYJvzbmpO3CmoR/zBMagRFhXNjERGoaHQOKQ5wQGSmRDfn+Gjw+fYCqMooQEmpiXF7s2DvjpUm3ONe7Yy8LZCpGvI9B9XO+av0X4h9HPG4u9lyN+ubKB77Mjv+ynoY2mrp7JF/aVhIZTMjBATkgYdD3b1PQf2SGh0BcRj1xSm+QHj6CgqwNNkl+GBzK2UbWse9/Rdw3cOiRkzNdnan8j9xgkES5JoEvOFfeej3kVShOuq+k0fy4LbrilvLAwmmSZUBAbS+OliwrF1BgbQsaLF5AiseOrbYRRM5YQcPqbv/1bnnFC7bjq4bEbyErOgJYhZ6wRFRBK7ynTpuzwJeOcpHQq6lu42mDEkrqL1KJQNtCnixvkWtKqdtwjCyAkVr7V0EGNtiHiP0ncXKePl3CcqmTFmRMVhIVAsy1nTlSayZkTNUXYN/10Jkyn8s+JiMhvOm0WNBqYEzEYLRUWiuf/EBLfPS8vj4ayERci5h87doyK+ETUJ3Tv3h0HDx6s8dYnLFu2jIbt+fzzz2lImYKCAhqqhyTT5UJC8JBFApJUlojmRKwOCAgQ+r3Ee52I4CS57Ny5c+Hg4EBj6ROIwE3i/JMY/uR7yIIB2Q3AjbnfHJBQPySsz4EDB2gYnCdPntTZjTBlyhRcu3aNtin5PWRxgeQhEPd3iGPPhYQ3UldX53vJysvDekBvpL15i7iHT2mC2+g7D5EVFgGrfrWCW/jlG7gyu3axQBybyGu3EXz6AvXU13cTPrAhx8kICUfCsxc0KW1ubDwirt2GSQcvmkyHTE64A2qztq2grKuFl3uOoTg3j04a35y5CpueHSFfHd6nMDMbRyd9gbjn/mLbiIOpVytkxyUh+MpdmiSuKCsHvgdPQ01XE8Z25nQQROta/eLGE/Uc2BmPjt+gohaJEX9t+0nqdeHUtTY51oHlm3Fm7V68L/R69UFhdDTS796hXihZ3s+Q++YN9Hr35fO6D5g/V6LtoZlPHiPh2BEq6ms2QdSvyefB057cwV27ET0R9MAHIU8DqEfws1M3kZWUBs8htQsld/edx98zV6M5IRMr7ot7Pbbu7UXP8aXtp1GYV4DYoGg8OnsXHYd1g4ws5xpIikyg3rBRr8Ib9V28i5z1LXhyIbH1Ndt1ROrFcyhJSkR5bg6Sjh+GvL4B1Hh2T0T8ugaJRw6IXScqSJHvr37RJGH0D1INTgBInUl7cF/c3zBgfHe8eRGCh1df0AS3d84/oYlz+42rFYIuH76D2f2W17y/duI+FewXr50Jt3b8ifTELUMWFkg9WkrfUpKXj5f7TtLjlxeXIOjybcQ88YH9lAkw7tENMZeu0mS3xFsx9NAx6tHE61Xvt24DArdzdkTIKMg3aEPOl9mAvoi7cZuTbLesjIbgyQoMgn494WVEwd/3cdqTCJd27Vxxe/d5ZCWlIzc9Gzd2noGJgwXMnGuF0r+mfo+7+y/hXaDr6gxVUxMEHThKY+cXJCUj7MwFGLb3gqIWx3OrvKQEN2bMR/yDx2LbCPb13OuZeiJXX09x9x4i9vY9FGdloaKkFCkv/RB58RrMuneBjHytJ3BL61vI98sIPMdIbHLiSe995DIy45KosH1/5wloGuvBwpPjnUs4tngt7m4/JpENIT06AanhsY0KwyPqOeE+tBfiA4IQcvcZSouK8fbGI5oc121IbYgrv3M3sWtyraODODakTFJQBMIfvaTjlYzoBLy6eAc2nTyE9oOi6td+ZE8E3HyKSJ8guth1/+BlFOcVwmNg5xrbK1uPYdcXtQui7wJh4xZZBXk49O2C12evIyMqDsV5+fDecxyKmmqw6FC72H5tzZ+4/+cesW1Igt3n+08jJzGF5mnKT8vEk3+O0GeIRXtOGaHnckh3pIbH4PWV+/S8hN5/gVjfQLQaWjvWDLzxGNvHfEnPibg2boO6ITM2CW9vPkF5aRmy4pPhe/YmLL3carz172w7jIQ3YTQuPwkXFXLvOUIfvEBBecvy1udRvPjOJTm/XsN64MWFe4gLjEBxfiHu7DpLva7denNELm7InKMrtkpk8/jYNcS+Cadx54mof3HjQZoslyQ+JWgZ6ULP0hgPDl1GdnI6beO3932oZ7J+t24oychAwuXLKC8qQlZAANK9vWHYt3YMmunrC+9581CalUXfG/Ts2aBNTnAwku/cQXlhIcpycxF18CDK8/NhMnw4X3zz7NevOWXy8qhHf25oKAx71wr1NeMWIX1l11E9aPJxn5vP6RjU+/JjmlC3y8jaxfb7J27jh+HLxD9zAt/F/T7yjcJyuZn074PkR4+R9SaQJrmNOXse5QUFMOpRO3YKO3AIvqt/ksiGQMYDac+ew7BLZ6F9mtnA/kh95o3s4BAaUiknNAwpj59Cr13DYibf/c191g3oRHNf3Nt/kfaF0QEhCLj+BO2G1/a70QGhWDd8EdJiasPkNhZFAwOagyH21CkUp6XR64t41atYWUG1OvQvwXfZMk6MfAlsog4fpkmKSbtk+vkh7tw5Gh6KOFIQyDWbcOUKitPTaRlSNurAAchraUFdIH8EGUsJjqts2jhS7/0r207SePvp8Sm4e+AKXLt51CyClBaX4Mchi+F345nYbUIWCvYv30JF/ZFLP6n5PnEg59G8Xy/E3riD7NBw6kgSeuwUpKRlYNSl9pn+aus/8P19k0Q2loP6I+HeQ+RERNL2yngdiNSXvjCQYJxK50TtOyL10jkUC86J3GrnROG/rkHCe5oTMRgtFeax/3+Ih4cHFZpJ+BciJpNEs0QQrw8i3peXl/OJ+OT/gsK+m5sbFfJJElsSW5/Ezydhe0hMfS7EG3369Ok0mSwZ8PTt2xdjx45FaWkp33eSBYRBgwbRBQQiihORnSTZ5U6cW7VqhVOnTmHFihX44osvoKuri5kzZ9KFguaCCPA7duyg3/HZZ5+hTZs2WLBgAb7//vuaMiQk0erVq2nSWzKQ69q1K80ZIO7vaMheHPRcHNF61lSEnL0E/10HoaKvizafzYCOQ+2AhWzXr6qskMgm9NxlVJaVw3s9Z8LAxWZwPziN5Qy0NSzM4LVwDoKOn4XfP/to8knjdl5wGFM3wSDxCOv57QK82H0M5z5bQUUsyy5t0eaTUXU8wLheVWLZkFjcX65CQVomx3uiqooKeISJRzhbYw1dHdDpi2l4e/4GAo6dp4sOOnaWmLhmHuTrSZrXa/pwesxD322jAy7irTr5p/mQ59lCTkIQ8O6CIULXk1O3arw1/vr0B/rvyGVT4dy1jdhlRKFsZQXL2XOQdO4sEk4cg7y2NkwnT4E6zyCHxJ8FT53IlsOQn3+srnAlks6eRtK5MzTUjtX8BfTjlMsXqddCzL//gDcdl07XbjCdOBnNgVPXNjSEwe2dZ5CXmQMdE32MWjEbeha1u2yqKqpQxZP3IMz7NZ24cjm7lvxfCl7DuqP3TP6txpJAdmRM/3U+Lmw9id8mrISiihI8+3dAr8kDautSVUXPLa+H70+jl6O0qKTmMyLOKWuo4NujP6M5MBw/GcmnjiFq/W+oKi+Dsp0DzOd/CSkZnkc2uZfJOa6GDHRJHH2u1xA35A6xI4mh3gUuXvaY9e0EnN1zHbvWHoO+sQ4++2EKHNxrhd9K0n4VtfU8t/cGykrLsf7r2tA+hMGTemHsnEFil2lJfYuCmio0TAxx84cNKMrOpd7+Pb/9HJVWjtC0t6Nxvf3X/4mK4hJo2Fqj1ZKFfB6KJMEdrxepzZhRDdqYD+xH79WATZtRll8AJX09OEybAn2v2r4j8vQ5xF69UXOdPl3CeTY6z5kh1gLA8CVTcG37KexcsJY+P6zbOGLg55znFJdKgbqTBc6gx2ShnhMi5pehnMXkGRu/gpGdudhlCGRy1Wbx5zRp7YOlK6i3vGHbNnCcPE6ox7DYNmJAbIiQ7/3zHygl4Vz0dGAzYgjM+9SOdz6mvqX7nLF4uOcMTi3fiPLScpi42mLY95/VLDIIO5fi2HCT5qob6MDUXfhC3f1/TuDNDbLwwvlN/0zkOF8MWvEZzFrX3QVIIMfq8fkUvDx+Bff+Pgw1fV30XjQNRk483pVkTMPTt4hjo2tliv5fz8Kzg+dwZ/MBGnLLtlMbtJ04RKL29BjQGcX5Rbj012EUZOdDz8II41bPhaZBrWBM2pK373t9+zku/XWk5v2RlWRcJYUuE/uj68SBYpcRB89JI+i9ReLfk8VGPQcr9P1uAV/oG8F+pyEbTXNjaJoY4P6mPVTcJzmK9B2sMfDHr6Cqpy2yLiTcUb8lM/Ds8AU82nMaqrpa6D5vAk10W1MXbt9cJb6NprE+Bq+chyd7z+LBzuNQVFOFVXt3mjyXi2v/rnhx/AqSgkiOrypoGOnT4+z7nZPjqrEM7uuJY//UhuA6+s9i+hu27bmG5T/zx/hvKh3H9KHOKCSJLRHpya7H8Wvm8yUXptcaz7kUx8axswdu7jxFxX/yORH9O/N49JO+dPTK2bi39zz2L9mA0sJiaBrqwmL8eBj27AkVMzPEnDyJ+IsXaagTs1GjaHz8mjqRk0nOafV74nHusGBBvTbEe5qI9gErVlAhUMPJCS7ffENj6XMhiwpEzA/fvZv2+0rGxjR5KW8Ilfqw9bDH2CWTcPPgVZzaeBTahjqYsPwTWLpa8/fNPPdudGAkdi7lzINIO5/dfALnNp9E616eGPe15GNjo+7dqDgfumc/DR1Hdue5LPoCirq1SWjp/cBTB3FsCOm+fjRBPAmvIwwdj9awnjAOYfsOoCQzEwpa2jAZ0BdmA2ufTZKgpqOJcas/o9fS83N3oayugvaj+9DxeZ3ndPXVQHaQbBjDSTJMPo8PjkLAjafQMTWg+RwawmbGDEQfPYrXP/5I7TWdnWE5eTK/44zAs1UcG63WrRH+779UsFfU14fl+PHQ61y7WEuux8LERIT89RdK0tPpNUy8+a2mTBG564QXIrhPWj0Hl7edxJ/T19BnqXPX1hg4dzRPW1Wf++p8CISdC9cjOTKhZgxKhH/CsuO/0rEFWQTITcvGmwd+9MWFzG2Xn6wbGlkQi0H9qQMDEe/LCwugZmEBjyULIa/G38fwPi/EsTHu1plei6///helOblQ1NWB7ZiRMO1Zzy4wIRiPn4ykU8cQ+QdnTqRi5wDLz/nnRHTOyFO/xGOHaRx97kOFG3LHYv6XUHN+N3MiBuNDI1X1PjObMFocJCY+18urIYiwz+v1QC4dYk/E+6bCHZBy63Ho0CHqeZ+cnNyk43InzoIeE+S3NLbeoo4pjOb6HfWx1PuOWOW4AwJJVqI5E626XQSvZ2NDqMlLvu2ZJLPlbnkX24ZH3OGF6/kkCms1yRLnCv3uikq6s5DbJiLbjec3iVOGcDFOsoShgpMq6onA/UxYkmCec8krkIkqI8hAkyI0N9wJPtfrhDvpl/Q6VJNrnscb6eekec4LFcAEUl3R8FMivGQ49a+q8/fDEXWTcokLtz0aPHf13Ef0eqjn78vdBUJWNKaeAr+dtp3Q6772OSROGcK1BIUW3bckFTb8jBB2LpuLhvqYzgb8i+nvsu/jbStxyhBepMm/s3PKC7d/aeh88tLNUHjS+pbStyRVh+JpyrlsLjth57u8ktN3NyV0nKjf3hxoK1R+kOeYuM+6hIKm/2bSr1Ef8XfgnUhC8bzre7ApLB/Y9B2WMkKuu0qy2NTEafXft6Y1+Vp7l9xNUmz0GPR9MNKisMnHENa3kL5akPrmz4LzWi6X45Xe6bigqeOJzvrN82xr6P4mz+26iH7WSXz9vaNxVUMMMG36nEhwTMNZZBI9ZhI1phJsz0cpjc/B9j7aM6VItkXPiU73/nAheFsyP/sRJ8WPj5UenMgZ/yWYx/7/OeKI01wEhXDS8TWHqE8QZ2GhMXC3RwrSlHqLOmZLhz6oJJxE04EF3j+Nmdy9q2tIrO8WGIiK027vum3p+RZox4YmWC1l+6Fg21BR4wPec4L3u6STZ079m/dsC56rxpy799Gmgr9dnLZ7l+JES+xb3tV99z7675bQ9zVVDPyQ/UtL6lsae981ZCfsfEs3MRfMu+pXP/Rz7H1eix9yzCTIh37GN4YKHm/qD82HGqtLOgb9WBDWt0g672vq/dXYcUFLGcc3dH+/60W8ltgO4iLYNo15xv6/tefHMidiMN4lLfsuZbwXSCgdInQLe61ateo/fRb+n387g8FgMBgMBoPBYDAYDAaDwfg4YR77DNy+fVvk1tEP5dEzefJkTJw48aP/7e/rdzAYDAaDwWAwGAwGg8FgMBiM/x+YsM9okWFl3le4m3f9HR9r2B4Gg8FgMBgMBoPBYDAYDAZDEGkplq61pcBC8TAYDAaDwWAwGAwGg8FgMBgMBoPxEcGEfQaDwWAwGAwGg8FgMBgMBoPBYDA+IlgoHgaDwWAwGAwGg8FgMBgMBoPBYDSItBRrpJYC89hnMBgMBoPBYDAYDAaDwWAwGAwG4yOCCfsMBoPBYDAYDAaDwWAwGAwGg8FgfEQwYZ/BYDAYDAaDwWAwGAwGg8FgMBiMjwgWY5/BYDAYDAaDwWAwGAwGg8FgMBgNwmLstxyYxz6DwWAwGAwGg8FgMBgMBoPBYDAYHxHMY5/B+MgZb12Elsxnt9XRkikrR4um8HAYWjJe68zQUrkUp4iWzJsf36Ils+UnR7RkQlNl0JLJyalES8ZBo2X7dlw8louWSsRgHbRkohNa9rUnIyuFlswk92K0ZP652bL7vkk9q9CSGXtwHloqax+07Huj9HUWWjLeblpoyZRue4aWzI2p7dCSSdu0Ay2ZR8tbbt8itdsPLRn3dZ4fugoMxkcNE/YZDAaDwWAwGAwGg8FgMBgMBoPRIDItey36/4qW7a7FYDAYDAaDwWAwGAwGg8FgMBgMBoMPJuwzGAwGg8FgMBgMBoPBYDAYDAaD8RHBhH0Gg8FgMBgMBoPBYDAYDAaDwWAwPiJYjH0Gg8FgMBgMBoPBYDAYDAaDwWA0iDSLsd9iYB77DAaDwWAwGAwGg8FgMBgMBoPBYHxEMGGfwWAwGAwGg8FgMBgMBoPBYDAYjI8IJuwzGAwGg8FgMBgMBoPBYDAYDAaD8RHBhP2PhGPHjmHWrFn42Nm2bRuWLl2K/0devnyJHj16fOhqMBgMBoPBYDAYDAaDwWAwGI1CWqrqo3z9F/mok+eGhIRg5syZuHbtGlRVVfFfJj4+Hv7+/vjYiYqKwps3b/D/SHZ2Nh49evRBvtv7jj/O7r2BtKRM6JvoYMysgfDs6iqyfHxkMi4dvoO3fuEoLy2HpYMpxs4ZBCsH05oyedn5uHTkLnwevEFudh4MTHTRf1w3dOnvJVadjFUUsLSNDdroa6C4vBJXY1KxNSAaFVXCO1sVWRmMtTNCfwt9GCkrILGgGKfCk3AmIrmmzGQHE3zRykqIdRX6n/NGTmk5xMVZWxXLvKzhoKWKzOJSHA9NwoGgBJHldZXkMNXJFN1NtaGtIIeo3CLsCYzDvfhMvnIT7I0w1t4IRioKSC4opWUuRaWiqagoyeLb2e3Rt5M5ZKSl8dAnAT/teIbMnGKh5du5GWL/bwOE/m32DzfwyDexUfUIvfMUr87dREFmNjRNDNB2yggYuzk0m82r8zfx8shF2HVvj67zJ9d8/vLIBbw6f4uvrKymFhx+XlfnGFVVVUi7dhlZjx+goqAASuaWMBo7AYqmZiLrWJ6fj+ynj5H56D5KM9Jh+90qKBqb8JUpSU5C2rUryA8JQlVFOT2e/qBhULG1gyTIy0nj63kdMLiXDRQVZPDMLxE//vUYyakFQsvbWmrh4p4xQv/29S93cOl2BP3/mZ2j4GSrw/f3U1eC8f36h2gqmS9fIvHSJZSkp0NBTw8mw4dDq3VrkeUry8qoTer9+yiIjobllCnQ69IFzYGxsgKWeNjAQ0+d9i3XYlOx7XVMvX3LGFsj9DPTo/dlUkEJTkUk4Wxkbd/CZayNEUbZGMJAWQFBmfnY6B+JiNxCiernqquK7zrYwFFHFZlFpTgclIi9r0X3LfIyUpjoaIyR9gYwU1NEamEpLoSn4p+AWFRWCT/+oSGtkFNSju5HvcWuV4xPIJ4fuYCcpDSo6WmjzZj+sOvatkk2wbef4v6Oo3XsZh3ZABk5Ofr/i6s3IzEwvE4ZWXVzlHeYX+fzse3NMa+3HQw1FRGeko+1FwLxNCxdZB2XDnbC3N62fJ8lZxej6483a97rqytifl879HQ2gIayHCJS8rHjdhhuvq57DTTEeCtTDDA1hKqcLMJz8/FPcCSi80VfI2pysuhrbEBtDJUUseCpH2IL6pa3UFXGp7YWcNZSR35ZOS7EJuFCrGT9tJy0FJZ4WWGglT4UZKXxPCkbv3pHILmgRKSNm64aZrubobW+OiqrquCfloctPtGIyOHUkeRN62+lh/EORnDQVqHX3b24DGzzi0F+WQWaSl9zXXzWyhwmqoqIyyvGVv/oOs9VXjQVZDHdxRQ9zXSgrSiPuLwiHApKwOWotEbXIS81Hd57TiE5KByyCvKw7uIFr0nDIS0r02ibqspKRD72QdD1B8iOS4KCmgrMvdzQZvwQyCkp1hwn2tsfr8/dQE5iKhRUVeDQpxOgPBCQqpuxzs1ADat62cFZXxUZhWU44BePf1/GifUbie3JiW2QXVyGDjue1HyuryKP2W3N0dtGB7rK8ojILMTf3jG4Gc5/z0nHvYHsi/OQyk3FzSvacBwxEGad6+8/kv3fIPD4BeSnpEJZR7hNQ2Ue/voX0oPC6hxb29YK3VctqWnr4HPXEPf4OYqyciCrpgYNZ2eU5eYiNyQEUrKy0PH0hPmYMZCWlxdZ39LsbEQfO4bcoCCRNtmvXyPh8mUUJSVBRlERup06wXToUEhJS9eMP7J8fZF85w4K4+Mho6wMTVdXmA4fDrkG5rLzWpthnIMRvcbfpOfjl2cRCMkUPi4gWGsoYW5rc3Q01qT3flBmAbb5xsAnJVdo+R+72GG0vSE2+0TjnwDxrhsu8jLS+GaYM4a2MYGCnAyehaVj9elXSMoWPgYlnF/SHc4m6nyfnXgWixUnAmret7bQwmd97NDGShuVlVXwjc7EhsvBCE/Jk6h+GgqyWNHBBt3NtEEem3diM/Dr04h6+6huplqY7mYKZx1VFJVX4mliFja9jKbPYEGUZKVxcrgHrDSUMe68HwIz8iWqn6mxBlYv6412nmYoKirD+atv8fuWBygvrxRp06e7LebP6AAbK22UlJTjZUAC1v11HzFx2fTvpIsYNsAZn4z3gJ21LrJzinDrfjg2/v0IBUJ+gyTISElhaSdLjHY2hIqcDF4m5uCHu+GIySkSy37HEBf0s9HFyjuhOPI6Ce8LWVkZjBjYFrOn9EVHL3us/O0oNu+68s6/d6yjIeZ6mMFQVQERWYVY+zQSTxM450kY+sry+KyNOXpaaNNrNyK7CP/4xeJmVEZNmQdT2tPjCXIuNAXL7oRIVL/5M9piwig3aKgr4k1QCn784z5CBPp4XvR0lbHsiy7o2NYMqiryiEvIxb6jfjh98W1NGU0NRYwZ5oyJo9zo9T1k4iGERYp+dtc3X0g9fxo5L5+jqqwMKvYOMBw3CXJa2iJtyjIzkPX4IbKfPkR5Xh4cN26DdPWYUxDyfIjdtgkFoSEwmTYbGp71P7cYjJbKRy3s5+Xl4fHjxygvF1+sYzD+3wjyC8ffaw5h6uJRaNvdHU9u+mDzyn34ftsC2LpaCrU5vuMS2vdujbFzBkJWThandl3Fb1/+jV/3LYWuIedBevPMY+gaaGHZxjlQUVPGi/sB2PnrMSirKKFNF5d66yQrLYUt3V0RlVuI8Vd9oKsoj/VdnelAcaNfpFCbUbaGUJaVwcqnwUguLEF7A0382MGB2pwM5wwKD4ck4Fgov0D2d093OpGSRNQnIv0/fdxwMTIFXz0IgquOGn7v6oii8gqcDBMu9Mx0MUNCfjEW3n2LzJIyDLXSx8Zuzph/5w2eJXMGb5McjPFFa0ssexQE39RctNIlx3Wix70dVztYawzrvuoGazMNTF52FaVlFVj/dXdsXdkLk74WPmB9/joZrsP28322bGZbjOhtS//WGKK9A/B45zF0+3wKjN0dqWBx47cdGPHHN9A0MWyyTWpYNIKuP4KWmREdiPFSVVkFQ2dbDFj5ee1vTFcS+p3pt67Tl/nsz6BgZIyUC+cQtXkj7Ff/DBllFaE2qZfPQ0pWDgbDRyFu1w6hZdJuXIWaeysYjh5L65d+8xqit2yC3aqfIK/NL6jXx/dfdkaXtmaY/c1VZOUU46el3bD790EYNvMUKirqKrnh0Vlw7buL77M5k1pj/tQ2uO9dOzmXlZHCxn+fY8+JVzWfkYlyUyHCSOSuXTCfOBFabdogw9sbETt2wHHZMqhaWwu1SX/yBPnh4TAbMwYhGzcStQPNgayUFDZ34/QtE6770r7lj85OtJ/YFBAl1GakNadv+cE7hPYt7Qw0saa9PbUhAj+XL9wtMcTSAD8+D4VPWg7sNFQwzMpA5HGFoackj70D3XEuLAVf3H5LRdNNvZxQVFaJY8HCJ7d9LXShqSiLpXeD6aJDK301bOzpBBlpKWz1jeErSybY63s64mVSDl04EJe0yDhc/30n2k0aCoce7RH94jXubD4IRXVVmLVyarQN6XtVdDQxadsqPltpmVpBdPD3C+jiK5fivAIcmvsDKg3qPkf6uRvh53Gt8PURXzwKTsMnXa2wZ04HDP7jHiJThQsp0tJS8I7IwKfbn9Z8RurFy6yeNgiMz8GOW2EoLqvA6Hbm2D6jHcb99RC+0Vlit+MoSxOMsjTFbwFBiMkvxFRbC/zi5Yq5j3yQXy5cQJpsY46yykocCI/Gt62chOm1VNRf384dNxJSsOVtOG2t0ZYmMFNRQlyBeEIK4bsOtuhsrIXPbr1BVnEZVneyw46+rhh93gdCuhbKvFbmOBqciO8fhUJFXgZLvKyxZ4A7+pz0RlllFdz11NDbXAdb/WIQllUAUzVFrO3mAHM1JXx+OxBNwVNfA2u7OmDt8wjcis3AYCs9bOjuhOnXX+FVunBhb4KDMRLzS/DZ7TfILSlHHwtd/NTJgQp49+tZEBBFRXk5bvyyDZomRhixYQWKsnNw+4+dtI9vP21Mo23SwmOQEhRO35MF7eyEFDzYsh9F2bnosWgGLRPv/xb3Nu5Gx9kTYNnRA7lJqbj3517ImEujojX/wjwR4A+Pa43TgcmYd/413A3VsW2oKwrLKnA4oP4FINJvbB7iAu/4bLoowMv89haIyynGzDOv6GLBKBdD/DPcDVNP+eNRDOfekEqPhdzNHSj3Go4Kuw6wVQrAyx37Ia+uCgM34f1HVlQsnm78By7jh8Giawck+ryqYyNOmS7Lv+C7n0vzC3Bt4UoYe7Wq+Sz08i2EXbmFDovnQtvGAmGhKQj64w/Ia2rC7fvvUV5UhLDt21FRUgKb6dOF1pecu5AtWyCroiLSJi8iAiHbtsF89GjodeqEksxMROzaharycvoZgQj+Wa9ewWzUKCibmKA4LQ2R+/YhYvduOH75pchzNNPNFDPdzLDw9luEZxVikZcl9g50w4CTL5ErYnw7u5UZ7sZmYJ13JH2ezXI3xe6Bbhh48iV9lvAy0EoXLjqqtF+QFtYJNcCq0W7o5qiPGf88Q1ZBKX4Z3wp753XE4N/voULEOIPMBdZfCsKuexznAwJZPORlQT97HHwUhWVH/aCqKItvh7ngyIJO6LL6JkorRIvegvzZywmqcjIYd8GPtsWfvZ3os3LeDeF9lI6SHF3k2OYXi6CMfBiqKGBNZzts7+uC0ef96pT/vpMtEvKKYaelIrQPrw85WWkc+HsswiIz0H/MHujrquCfTaMgIyONn9bfEWrjaKeH7etHYMPfD3H4c3+oqynglxX9sHfLGPQawRkPtnI1Qts2pvjx99uIiM6EjaU2Nv0yBHq6Kli4/CKawrLOVlTUn3vxDeJzi7Gmpx0Oj3ZH7/0vUNLAeZnayhjqCrIor6xq1LXWFEYPbo+h/dvi17/OYN/mBXR88K7pZ6WDn7rbUbH9UVwWPnEzxu7BrhhywgeR2cKf3zNbm+Jtej4V84mDymhHQ/zd3wXjz/nDN5mzMNfjsDddWOfirKuK82M9cSNStCAvjNmfeGL2VC98vuwSvQa/+qwTDm4fhT6j9iM3T/ii/5+/DISiohw++ewM0tILMLCPHdb+0Bep6QV4+JQzLv1ybgeUllZg/bYn2LpuMKQaea6TTx5FQVAgzOcvhIyKGpKOHkDstj9h/e0qSPGMJflsTh2Doqk5dPoORMqpY/UeP/3GFUjJyJIJUbPNRRiMD8FHG4onOTm5JjTNwIED0aVLF6xevbpBu+joaMyePRv9+/fHl19+CW9vb2qblsbx5Hn9+jV9T/6dNm0aDZ3i58d5gIeHh2PBggXo06cPPvnkE1y/fl1omJkLFy7Qvw8ePBh//vknKipqJ3Pnz5+nxycvUu9ly5bVfDcvt2/fxujRozF8+HD8/vvvKCsrq1Omofo05D2+atUqDBo0CBMmTMDJkyfr/I7Dhw9j8uTJtK22bNmCyspKHDp0iNZp2LBhfDZcnj9/jqlTp6JXr16YMWMGXr2qFZCEkZOTg/Hj/8feeYBHVXxt/E3vvffeE0JICL03QUAEpKogIArYG3bsqFhQAQuoYAEFRYr03hNCAiG99957T77nzGazJbvJboL+w+f8nudCdnfm3rlz7507c+bMexayshB0jB9++AFz5sxhZXv99ddZGuk869evx5QpU9g9EBkZialTp+LqVYGHUUNDA6vfixcvsms8adKkrrKWlZXhjTfeYOkfeOAB/PjjjxIDgpMnT7LzI9kcuk/uuecevPrqq6itFRkM4uPju64h1f2aNWuQmJjY7dwoHV0Xus4vv/wyq3NpeivPneDobxfgG+yBSXNGwtBEH/csGAc3Pycc/f2C3DzPf7yKed6bWZnAyNQAy5+bh5bmVkSHic5z7oppmDJvNCxtzaBnoIPxM4fD0sYMKbG9G7nG25nBXl8HH0Skoqi+GXHltdgem4157jbMwCaLnxPzsC0mi3kJ1rW04WxuGU5ml2Cqk4VEOjJOCDcbPW3mtSvL87YnqBwtbe3YdCMd5Y0tuJhXjj9SCrHcV7RiQZqPbqTjl8R8ZNU0oKa5FbuT8hFbVoOpTuZdae51tWTe+ZfyKtg5XC2oxMG0Iqzwl+8prggO1gaYNtoZG7dfR2p2JbILavD2tmsY6m+NIB9LuflosCXcmGfPBDccOJOK5hbFB0vixBw6DZeRQ+A2Zih0jAwwZMG90Lc0RdzRC/3O01zXgPNf7MSYNUugqSfbYE+dRjIYCjehh5z04LzszEmYTZgMfW9faBgZw3bRUnS0NKPi2hW55bRduBQ28xZA00J+fdo/vAJGQ0KgbmDI9msxfSbbb2NONhTFyEAL82Z44fMdEYhLLkV+US3e/PQiPF1NMW64o9x8ZPAX3+6/xxPHzqWjplbSK4sGzeLp7kRzU3TqFAx8fGA5bhw0DAxgPXky9FxdUXhK5BEtDaV1XbkSBu6SntT9hdoW8uzdGJmK4oZmxFfUYkd8Dua6yW9bfknOw9exWczzvq61DefyynAquxRTHUTPrqO+NpZ62mFTVBquFFagsa0dMeU1Shn1iQXe1mhub8cHYWkoa2jB+Zxy7E0sYAYXeRxJL8EXkVlIrRS0fVfzKnE5twJDrCQ9HQkyPJzKLEV4geR7szdi/j4HcxcHDL5vMnsOfSaPhGOQL6IPnrkjecSfS3GjvuA3VYnfUi9Hsu/b7YK77Wf1RHccvZWHQ5F5KK9rxhfHk5BbXo/l42RPIAmh+1y8vZO2M31wMA77wrNRWNWIyvoWfH8+DTUNLcxLVFFoyDrXyQ4Hs/Nxq7wKFc0t2JaYBk1VVUy2s5Kb75vEdHyfnIn8evmerSs9XZBeU4ftSRlsv5XNLSyPMkZ9I0113O9hhS+jMhFfVssMe29fS2GGqLH28s+TjPOX8yrY5DgZzHfG5jLPbUdDQTscXVKDFy8kIrKoihkYad/bbmVjrIOp3GdOUZb52rFVBTShXtHUwt6xMSU1eMhHcrWUON/czmYr7PJqm1DT0oa/UouQW9uAQIvuz4siZF+/jZrCUoxcvQj65iawcHfG4PkzkHTqMloaGvucx9LTBSNXL4aFuxPz0Kf/6T1YlCRybki/FAErX3d4TR4FLT1dtp+A2ZOhHnMKaJecKFo0yBbNbR1452wKSutbcDa9DLtv5+HxUKdez3HjVC8cTylBWHb3fupbZ1PwfWQOMioa2ETJzqhcRBdWY6aX6J5Wjz2LDjMHtA2aAugYwGXCKFgF+iLlb/nvgLTjZ2Hs7ADPe6dAy1B2HkXS0HtevP3IvXaDfe84ZlhXmsr0LJh7ucPSzwvq2tpob2xkRhzykNcyN4eegwMc7r8fpWFhaJYacwipSkhAfU4OXB58UG6esuvXmbHeZsoUNgFAaezuvRdFZ8+ijY4JQNfWlk0EGLi5MY9+SkOTADWp3VctdZ0jwDzHd8Xl4Vp+JUoamvH21RRoq6lirqf8tuWVi8k4nlHK+rGUh55LHXU15oEujr2+NltF9sL5RLkr23qCVjk9MNwRnx5NQGxuFfIqGpjXvZeNIVsF1ROsTyLWNksfftX2cFxIKEZVfQvyyhuw/WwqLAy14WQh2wlDFnS+I+1M8N61NGRVNyK9qgEfhKVjgqMZ3Ix1Zeah9/PTZxNwo7CKvXfTKuvxW2I+/C0M2Co6cWa6WcDbVI+9p/vC1AkecLQ3xmvvnUBBUQ2i4wrxxbdXsHT+YOjpyl5B4uttyfrt3+26jpraJuQVVGP3n9FwcTKFgb4gz62YArz+/kncji9kHvr0/4Fj8QgOlN9+KoKWmiqWDbbDV+FZiCyoRlFdM14+nQQbfS3M9JQck0njba6HJ0Kd8OzxBPwv+P3gVTy49gtcuNq/SWdleDTIAUfTSnAopZg9i19EZCG3phHLB8m/DhuvpmNfYiFb2V3Z1Irvo3PZ2DJIrN9H/RjxMe9cL2sU1TXhXJbijmJ0D618cAh+3H0TV6/noKS0Hhs+OgctLXXmbS8Pfx8rHDiSgIysCtTWNWPfwThm1A8QG3O+/fF5bNx8CZky3iuK0lYnWC1tMWsOW12taWYGm8UPoakgH7VxMXLzOaxeB4sZs9hYrCfq01NRefkibJY83Ocy/tehubG7cfv/yF1r2DcxMcGTTz7J/iaj8IcffojFixf3mKeuro4ZY/Py8lheBwcHZnQlr/+mpqYuozF9JuMupX333Xfh6urKDNTDhg2DoaEhXnzxRYwaNQoPPvggdu7cKSEzs23bNmzZsoUZaclg/d577+Ez8kbshPZBZaWNykB56LvGzg4fcenSJVYub29vrF69mh2byiGOIuXpCaorMnyTUXrBggX466+/sGePYLk8lYnOgQz7lI7qggz9I0eOZAZyKhMZy+k3cWmZsLAwjBkzBhYWFiy9pqYmhg8fjrg42S/PoqIiNnFCKy7IeE6QtBJNIlDdUf0kJyezfYjXDxn9jx07hscff5yd9/33388mQsrLBZ5YNJFC15C+t7W1ZYZzSke/U51lZ2czgz9NnHz00Ud47rnnuvZdXFyMo0ePsgkDOkc6BtXNY4891pWG7hvhNaQJBj09PQQHB7NJIyEFBQXsmGRopPNQVVVlE0XiKFKeO0FKTAZ8h0gaz/yCPZASKypvbzTUN6K9rR06ut2X/BFk9Ce5n4rSKgSN7Nlbnwg0N2QeteTZLuR6USXrLPqYKu5laqylwbzR5DHb1YoZGs7lKOe9MNjCEJHFVWI+pMD1wkrYG+jATFv2Uj5Z0PLJejEvTVUZ3kj02cdEnw3Q+soQX0FHKvy2aAIjLrUM1bVNXb/1xsRhjjA30cG+E8l9KkNbSytK0rJg4ycpO2Pr74ViMUNFX/Nc/nYPnEMDe5T1KU7OwE8Pv4A9q1/DmU92oKm4qFua5tIStFZXQ9/Tu+s7Wp6p6+aO+nSR11h/aWtoQNnZ01A3NGT7VpRAX0toqKsh/KZo5UleYS2ycqswxL/nAbKQoYE2cHEwxt6/uw+cHlsahOgTK3Dy14VYv2Y4dHX6v3CPvBQNvSSvi6G3N2rT7lx9KsogGW1LRGfb4m2ir9SzS0Z+IWNtzZgXGhn9+wMNysibXrwVIEONg6EOWynUG9QZJQ/p4bbGzIAvzjxPK7gY6+CLG8obFwoT02HnL/kc2g3yQlFSRr/z1FdUYefy9dj5yMv4+50tKE7tuXwk3+MSOgjQlLxeGmoqGORo3E1252pyCYKdezbAD3E2QfSHM3Dt7anY9shQOJnLNwppaahiwXBHaKqr4mKi4jJpNrraMNHSxO1y0SCWPNoTKmvgY9w3ozJB74bBpsY4V9B3KRkiwMIAGqqq7F0mhAz12dUNTGZHEUy0NLDQ2wbJFXU9Si6QVEhLW4dSHrWyoHJdL5I0tIYXVrJ3tCKQ/AhJ+ZB01sU+eOsTZGg3sreGjtg1pPcQvb9K07PvSB5y6KjMK0Rm2E04hQ6W+F7ay5EM2SpN9VCpkFzhE2JnhOu5lRJty5WsCjga68BCT768zAJ/G7ia6uLTy7Lf07Iw0lZHXYvIS1ylKA3ttpLvAEs/b5Snym8/ypLTYeHr2WMeRdJIk3n+KmyCB7GJACF2oUEsT3lKBtrb2lBBDlsqKjAfNUrinUVW5dp02fVA7zNNExNoW1rKzcMcc6S9UlVVmZREXVb3do/SNxYXoywiAqZDhsg9J0dDbVjoaiI8X/Ts0iROVFG1hKGvt1UZy/zsUFLfjCixZ4pWuX060RtbbmYjQ0EZFWlILkdDTVWibSYjfGZJba+To2umeCLu43tx9rVJeGW2L3Q15U8GmuppYuloFyTmVyNDzgotWdAkOI0VaBJSSERBJfMYV7T+yGN/jocVzmeXsboXQvJ4Lw8TTIrQyqu+EDzYjnlKl5aLJNiuXs9ixlUyoMriangW86Z+aEEQNDXUYGaii/mz/XH+SneHDiGuzqaYMckTJ872rZ8vxM9SH7oaariWK1rNVtnYioTSOgTbGsnNp62uiq0zfLHhXAqbDPgvQO8g6rOFScnuXM2txBBr+XUlDvVfF/hYM7mrSzmy32Oaqiq4z9MSfyQWyl19Jwsne2O2giNMTLKNvOyjovMxZJCt3HxHTyXj3qmesLHSZytOZk71ZJI8Zy4q5+zSG/WZGWwSW09szKZpZg4NcwtmlO8PbfX1yNu5AzZLl0FN7/+3pDfnv8FdK8WjpaWFwEDBMksy/Bob9zwjR2zfvp0ZfQ8cOMCMzjNnzkR9fX2Xt7g4n3/+OTNoCyHP+kceeQQffPBB13e0j3feeUfCYEta/+SVr6Mj8GRKSUnB/v37mfGdsLa2ZpsQ8gh3dnbG33//jfnzBctzaeUBGenff/999pk8/wdLaRUrWh550OQBGazJ652YO3cuqwshRkZGrNza2gKdz1OnTiEiIoJNBtBxCCqzcAUC8dprrzHj9Keffso+k8d9eno6q98//vhD4vg0eUDHJsP+t99+CzU1NTYxQJMLFE/A3FzgKUme7B4eHti7dy9bCXD+/HlWdqpXFxeBlrqZmZnEtRLy1FNPMcO7EPKap7retUskP+Lp6YmhQ4fizTffZJNFBE00/Pnnn3Bzc2Of6Z4hz3shBgYGXedM0HmQx/6OHTvYRA7xySefsPL99NNPXXVBE0o0WSKEVmIoUp7+0NrahtrqehiaiAY4hKGxPqrKFden/HXLIegb62HwSMnZ+6K8Ury45ENm9NfQVMfy5+fDc5AsjXtJzHU0JQxvBHnjEWba8geg4pBcxmhbU7x8JUGuAYxkM45mFqNZSbkRKl92jeQAh5YnC38r6/y7JxZ62jDP4cPpIsPQ2ZwyLPO1x+nsUtwsqUaAmQFmu1kxSQ1TbQ3k96Bz3BOWprqoqWtmEjzikL4+GesV4YFpnrgRV8Q8/vtCY00tOmjyx0jyXtM20mfSAv3Jk3jqMqryizDuSfkeFbpmRhi9ZgnsArzZfiN+OYj0Tz6ExxvvMD1dIa2dnnVqYt+xz/oGaClTbgJIFpUR4cjd9T3zBiSjvuPqdVDXlzxWT1iaCbzHyqV0aemzhalszzJp5t/rhbSsCtwQm+ghIqILcOx8OpLSy+HrYY73XhwLDxcTrHrpGPpKe2sri1MgXscEfSb94n8bc22Nrme1e9ui2KTcUEsjjLYxxSvXEiU8GqlNWOJpi0UetsxAGl9egy0xmUjt1BpXBNJNvSFVL+TBxcquo4nSBvltS9iDI9iEAy1d/zEmF3sSCiS0lF8Y6oKlf0ejtQ8el2R815Z+Dg310dLYhJaGJmjoaPUpD3kij1oxHy6hgcyoefOvEzj4+ueY9/F6mDradNsnGf3Ls/MxcvlcJFyX/M1ET4sZj8qkloaX1TXDwlD2pDNRVNmA9Xtu4UpyCUz0NPHybF/88cwYTNt4lnn9ixun9j09GupqqqhtbMWzv0QhuUDx9yQZ9YmqZslrSJ+tZNSfoljpaLN3BE39fho6CI76uihtbMbR3AIczlZcm5gMg4T080H3H917PUEa948PdmLSGRlV9Vh7KlbufUbvstWDHHE4rahP96K4wZEm78tllNesl/LS83rovhBWXpqQey88FbdK+tYekSZ7t3eUocAIIO/dpkyeI298hpLkDGbkdQwZhNCH7u/6zSk0EOc//wFplyPgPGwwqgtKEPe3QJpDpb4KHWailT6W+lrIypV8f5d3tidk2C+RYUhzM9XFy+Pc8MCeKGbkVISHg+zgaKTDJH+EsLJoS54vyeW0NjahtbGReclL01hZBS2jnvMokkbifNMyUZ2Tj0EPSkok2Y8IQV1JGc6//UmX1AK9o6wnTOhKo0769qqqct9b5JXf7T0nlcd08GAWN6bowoUuKZ78Y4L3a4vUSoCkL79EJTk+dXTA0McHzkuWQB4WnU41ZY3N3Z4Fkr7qCfIm/2icN3sWyKj/xOk4VDSJJmWeHerM7pPf5UjBKQLFKGHlqZVsm8trm2FhIL/tu55WxlZgJRZUw8/OGB8sCoSnjSEe+TZMIt2T0zzx1DQv1janF9VixXdhCt+vwrZPut0j42d1UwuTx+sJkhWb7W7J2uCbRdV47GSshNGWpPS23cxikioeJor10aSxNNdHmZhRnyivEHwmo6ssCotr8fjzfzE5nrfWT2bfRd3OwyNPSI63ib0/LGFGWpL2OXkuBR98fr5P5ewqb+dEIa0MEqesvrnrPSOLdyd4IKqgGsd60G7//4aJtgbrt0jXFT1zPdUVQbKL++YGsWe3trkVz51OQLLUfSJkmqs5kzfam6DcCnXh/SV9/5VVNMBBKv6FOOTVv23TTFw+KlDPoBUhz79xokdd/r4gHLNJj6OoLSYnrf6Q/+tOGAQGQd/Hj02+cjh3O3etYb8vkLzKuHHjugzTQqOsLMO+uOGWOttkUCbPatoHfWaa3VVVzEubpF+EhvyAgICuvwlHR0cmGySEjMZkyCVDOX1Pn0miJU3Ms5EM6CQDIw4ZuM+ePat0eeRB9UBe9eQdTrI55IWuqyvqkPj7+3cZ9QnyfKeJFPG6o+/Ez43KQp7+4tCkhPiKBSIrK4t5s5OxnDzUhZw7d455ttMEh1COhgWZqqhgsjbCY7i7u3cZ9Qnh5ERP11C4f/Kkp8kEYZ2R0Z7kfygQM00QEaampl1GfeE1pDql+qUJD+GkBk02kLGeVnvQBAYZ/MUlieiaiUOSRuKGfUXLIw4dS7i6REhzUws0teQYrOQMqlWUWIN08KfTCD9zCy999hh09SXvKwqY++OZj1Bf14gbF27jx0/2QVdfm2n5K4uwqIqUzMNYDxtHeuO35HwmySOLUTamsNTVwkElZXjk0d7pB6dI+cbYmeDFYFd8FJGGpApRYDMKlEsdtHdGeLIOHS3t/TEuF88OcZHwsrtT0LhHRYESW5npYnSwHV7dfOeDO5O3YUc/8pDuMAXGvfedZ6GmIf+V5Td9vIQBZcIzj+CX1a+j4tplWEydrtAx74QujVFIKJPjaa2pRunpk8jcshlu61+DlpXsGAPykC4KW+mhwM2nr6uBe8a54osfBHIE4rz7pSggYvjNfLy+6SJ++WIWvFxNmbH/TtJXLc1/AmFVKtS2GOnhgxHe+D0lX8I7n87H3VgP2bUNWH46mrUHTw1ywZax/lh4IkqpGB7dytd5sXurspG/XmMyCsFWhvhwnBfTXd0cmQlSBSDjwpdRWUxi4E5fww4lnmDpPO6jJL1Qx6xexDz9Y46cw7g13Q1ZCaevwsDSjHn+47pi50LxNXqqu12XRB5kpP381K5I5rlPQXi/PSvy+LqVVQHvF/6Gsa4G7h/qgC+XBWP512EI6+dAle4VRdpheQhzLnFzxEe3k1hA3kBTI6wf5M2kK47mFvarbZHlFS7NN9HZ2H47B7b6Wngq2Bk/TA/E3AOR3bS9SXpnyyQ/ZkD86Ho/V+zIKZIid2NubSOG7r7MAhhPdjTHG8PcmWGkv7FsuoomrK+O/ueZ/vYzbIK0PDMXV77ZjfNf/IiJzwuMJc7DgzB81ULc2ncMl7f+Aj1zE/jPnoxr23/rvcEQWyEoKyXpjG+Z5YfPrmSwgLiKMNHVDG9M8MCGM8mI78Vjuqst6EMd9ZSnpzQh5e5QAAEAAElEQVTkra9rYQYLP8nVA6nHzyH571MY9dI6FlT30le7UBUXh5yDB+Eg7RjUl35AZx4KyEsyO2TMz/rtN2gYGsJu1ixkkJOP1PXyfOIJdLS1oT4vD5m//orkbdvg8+yzyvfxerkN/k4rwbH0EjYZtiLAHjvuCcD8AzeRWd3AVn7NdrPE7L+icCeQ1W/pqXxv7xdJaVA7++pv0djz5Ch42xoyr3whW04m4+tTKbAz1WXB0Hc/MQrTPzqH6h4mwhWB/Ot7e4peuZjE4ou4GOng9ZHu+OGeAKbTTxMDFOegtKEZu8Um2e8UQud/eW0zaex//+V8bNlxFb/tv8009snA/9PXCzBv2S8S8ZgWrdoDDQ01+HpZ4sM378GXG2dh7YsH73yZe7gf7/WwwDB7Y9zzS/e+6X8R9mz0kia6uAY+316EsbYG5nha4YupvnjkcAzCxFbuCFnoa8NWAeRUy5f0UwbWL+ihhF9vmglDQ21Mm/8TikrqMHWCG9PdX/n0AYTdyMUdR7oo7Ebr+5it4vIFNJcUs2C5HM7/F/5Thn0KtksyMeKQh70sSF5FCOnbkzGVpGcmiHl3CBE3dmtIRdxmBiqxns7zzz+PI0eOMI97kvgh4znJvJDhmCDDLkkGSZdL3GisTHnkQR7ppGVP0jokFePj48NkfOh/eefR07n1VG5pjXwyXDc3NzMvfenrY2Vl1eX1Lo6dnV1XGuljUB2qq6v3eA2FecmITlJC0gjPW965E8JzJR38Z599lnnVL1myhJVn06ZNXdeQqCapjx6uoTLlEWfjxo14++23Jb5b9cJirH5pKWKuJ2HTi9u7vidd/In3jWDG+JpKyQFYdUUt89rvjSO7z+HAzpN4/qNV8A6UrWGspq4GAyM9TJg9ArE3UlhQ3d4M++Rl5Gyo282rQZZHkjRuRrrYNj6AaWDLC7RL3OdqhejSaqbJryxUBmF5xL0QBb/1PJAYaWOCT8b44PObGd0C7VI/++vb2WwT8qC3LRpb25j+aV8prWyAgZ4mNDVUJfTxzYy0USYnMJM486Z4oL6hBcfEDGA9UhsPleK/2Z8/LFTBxOdWwGGIP5MIaKyWvNcaq2q6eS4K0TbQ7zVPaVoWmmrr8dcLGyV08gtV0pB6KQJLdnzA9iONupYm08OnjpvE94YCD5Q2sbgZRGtNDdPG7y+svVBTg4axCWzmL0T1rShUhl1lQXcVobRccL1MjbVRXCa6d81MdBClQFDjWZPdmVfbgeO9L7VOTBUYuZwdjPps2FdVV4eari6rP3FaamqY3v6/DbUtTgZSbUvnxKe05680boa62DLOH6dzSrtp59PgnQxhn95M71pttOlmGs7OGYFgCyOcVVCihzzyhW2JENNOj0HS9O1t0ExavxdzK/D97VysCXJkhn1DTXUWKPf1Ee5sI+itRR6GcSvGYP2FRGbcYeSnA7sFE+rfqgChS2chaM4UJhki/Rw2VNVAXVsTGtqyPS77koeeD1NHW1TJkJVpaWpG2pVIDJ4zRaYxo6KuCa1t7TDVl9y3mYEWSuUEeJMFBcfNKq2DswyNZjKUl9U2Y8e5NIz3tcLSUc4KG/YrO+8LQ00NQEz73lhTA5XNfW/fy5sEeY/nFiKmQtCfCispx+WiUoyxtlDYsC+8v+jdJv6+ofvvZnHPnm/U+yHv++yaRrx2KRnhD47EREczHEgVyZ3pqqvi26n+TDJp5fEY1Lf2T4aHPHLJo9ZUynGBnp/yXvoJwvctTbj9mVqIYTbGLKhub4b9ps+fA+prsVMFsPJ2xfS3noGOsQFbMSZ9nxPaxrLbOGXykEOLqqYmLD1dEbzkPpz5+Fu2GkbXROBI4j1lNNuE5N4UyFt2GIhigBCldc0wlZLzolgIhCxvfUNtdfhaGuDtSR5sI8iQQ+1G2vPj8dzRBBxMEJ3DOGdTfD3bHxsvpHYLxtuhYwiVRsl3QFN1DdS0tKAupy3QMjJEk1T7IZ1HkTRCWpuamb6+56yp3dqP1ONn4TJxNKwGCVad6jo4ME17igNjP3s2S99aVyfQ3e/sI0hD30u/52TlMR8+nG3iUnXsXKTGn0xSSVUV+s7OcFywgAXzpcC6QHf5i7LO59VUWxPpaJAI8NrTKi/xZ6G4vhkfhqdjirM5MxJuvpGJEGsjdo9cXiIqLzmfPDnECcv87TD8F1HA8Z4Qtr/UNheLGRapbY7MULxvkZAvaN+obRY37NPwi9ofardf2B2FmI/uZYHU/whXLH4R1Z90n578m2hFEL3be2v7SFItuaIeb11JwbH5Q5lEWGRRNau/AHMD9p4V5/fZQWwyheR5FKG0vI4FthXHrHOFZmmZyDlInEVzByE3vwrf7hQsbauqbsQ7m87g/KHVGBbsgKvXRXXT3t6BpqZW3Lydj4+/vIAdX8xjntoU9LQvlNY3d91/wlVBhLmuBuJKZE/4kVHf0UgbsWtHS9xr5MW/IsgeE3dJLdH7fwKtFKF3GdWVODTR1tu9J3x26d1NGvsTnEyxxN+mm2GfVu0MtzPGUyeVj1sglH8yNdFFWqZIWomkncSlocRxcTLB+NEuWPzoPqR2Pt9/Ho5ncjzLFg2+o4Z94ZittaYWGmLqHG00ZnPte4yu+tRkNOXnIfG5dRLf5+3cjvJzp+HyokAimtM7/YukxLmT3NWGfeoMKwN5eUdFSXolkKRLb5Ch3N7engVQlfYCVxYypJNUjTAeABnExYPnkrGbPMTJY5ukgoTQ5ztZHjKGr127lm1kkCY9epKqIWmdviBebnFIokbc+114HX7//XemYU8I5YQoXX5+PlstIE9aifKSjA+tdBAa84Wfe4P2T7r+d+Ia0mSMuBY+Gfkp3oEQmrSRrgvpz30pzyuvvNJNgz+mWrCSw3+oJ/OeFw9ISHgEOCPhVhpmLp3Y9Vt8VAr7vieO7jmPP3Ycw3MfrmT7VgSatFHE2+l2aTULUEvB/ISerkMtjZkeb0JFbY9G/a8nBOBsbik+jJSvrUeGAJLpeT+ib/p70SXVmOdhw4xjHWLSP3m1jT0a4EfaGOPzcT7YEp2FXxMlB77yuMfZAlfyK5RaVixNVLzAeE3Bcq/cFBzX182UBWKNSuhdI3reVA8cOpeOxib58Qok0PNBh4tA73D5RnvBAJX0al0dUBCfAs+JI7qS5scmw9pHsg0QQh74veWhYIKuoySDaB57+ysWYHfMmqXdAnGKD/JJT1/f11/iezL2k+xOXUoS9DwE9zUtwSR9fcvpojb3TkGTEMpc2eiEYrS2tiN0sA3+PiMwCFhb6sHR1hBRsd1jBkgz/15vnLqcyWSYeoMC8hLiEwh9Qd/VFTXJybC5556u72oSE6Ev1fb/G9wuq2aBcsXblhBLo17bFldDXWwd549zuaX4KCpN5n4hFSND+Kcy15eW8lMAXfG2hbwmKZAaGV4UheR4hKYrklXw/f6ixO+rBjngIT87jNsTJqm3ausKPLeN/fnosKauANNWXi7Ij5NsL+k5tPJwkesx2Jc8bBVeTiFMHLqvYEm/GsWeW68JoqCX4pBme2xOFYa5m7FAt0JGeJgjIl1xT2zS0Hc012MBGXuCJnKUWXiSX9/ADPgBJkaIq6jukpPxMTLEb+ki/VplqW5pRV5dQ7f7rHNdo8L7uV1Szd4zQ62NcDSjpEs3mjSiezPsSxvE6O4Trxsy6n89JYCtKFl5/HY3T/6+cqukBsFWRvgxXmQoCLU2ltDKVgRFr6Xm05+wOl0c0NjlekoG98STl9kkllBOpyA2mU1qmrvKDmjelzzC94XgD/nXNf1KJNqNbdBhJBk/JzK/CksG2Uq0LSMdTZBT1YBiGYb9ioYWuH5yTuK7x0MdsTzYHsO/vioRRHWssym+mxOATZfT8UNkd6NNh5UrVAskx1MlcckwdXeW2xaYebiiNKHnPIqkEZIXHoW25mY4je2+2pUhlt7A1RX5R45ARcwRqjoxkaWh95kshHkaS0uh3SkX2lsegvTzSZtfz9GxV/dscUcwcTKrGphxOtTaiAVzFcrAkD78tpuKGbclnoXOv7dGZeHrm5La/2cXDWMB3b++pfh+abUTTbpS23w4ShAfyMZYG05mekoZ9inYLlHcQ/9F+O5TUfK9S5rwAeb6iCkV9AOoHSTDsjJtH9UdIbz3Fh++JVEOdxM9HJobzL6nIN+KEhmdj6Xzg2BirIOKTmeckUMd0dTcilg50irsVpGO2dU5jhD31pdGtXPFdn9WVcYW16KhpY0Z61M6jb8kE+hjoY9dUpN+QkhX/+3zkv2FhCfG4F2aKIxRbLx0N0KTQrElNQi1NWLBcIWMsDdm8ZaUQbzfJ84DPtZsAuFUhvKrCzOzK5gMz7BgO0R0xvaimA1DAm2wZUe4zDwiVQV0u/96uvf6go6zC5M7q09NglGIoG/YUlHOxnc6/TDs2z68ErYPrej63NHayoz8dstWwTB46B0pO4fzb3PXBs8lLDsDGJEciiKQ9AsFeyUZHIIM46Rzrgjr1q3D1q1bWVBWIXRcaemZ3iDjb0yMaOkhBcUVN+wTy5YtY8cSytzQZIS0Rn1/ykOGfJLAEQakJSM/eZdLe9Ary4oVK1jw4JwcwQCWAt9+//337HtpKGbA6dOnmb4+afMTJMFDBn0K6CssG708aBIgOjqafSYtfTLib968ucuQTDEJFIEmMU6ePCkRYJi85sXjFCh6DSkgMDNiAyxWAcnqSF9DWhVx69Yt9pkkd6SvTV/KQ7El6Pjim1CGhzpp5D0v3ISdtukLxyH2ehIuHotgQXDPHLiK5JhM3PPA2K79/v3rWayc8nLX5+N7LwiM+h+tRECo7GCl2975Bcm3M9DU2Iy6mga238hLsRg9vfcXIhnPCusb8XKIO/OYcTfSxSo/ByabQx6pBOlehi0YjQn2Zuyzs6EOtpFRP6cMG2/0bLCf6WLFZCpOZfct2OCfKYXQUVPF00HO0NdQY4aEeR7W+CVB1NaEWhkhcsloNtnAPlsb47NxvvjqViZ+Fksnjr+ZPh7xtWdGR9peCnGFk6EuNt/sX7ChrPxqnAnLxvqVQ+FgbcCkdV57bBhuJRYjMk5kDL7080I8u0zSSD480AaONobYe1xy4qlH6N5SUWUbGdaF95r/rElIvxKFzPBoNDc0IvrAKVQXlsLnnnFdWa//cgC/r31TVCe95KF90zHEN+rZUvdW3KhPwXKLUzLR2tyCmuIyXNjyM1vmbjJCFByP7U9VFWYTJ6Ps3GnUpaagrb4OhX/uhYqaKoxHjOxKl/3dNmR8QUYexWguL0Per7vQmJ/HZBVaKitRsHcP2mprYBwSqvB+KqoaceBEMp5ZORTuTsbMU/+tZ0cjI6cS56+JBtqHvp+Hd1+Q9BIjSZ1BPpbYe7i7106QvxVeenwYnOwN2cqOwb6WLH90fBFuid0jfcF6yhRUx8ej9OpVtDU2Mo1h8lK06py8JQqOH0dkZ8D7f5LzeWUoqm/C+iFuMNZUZ23LSl8HHMoo6gqGa6GtiavzRmG8XWfbYqAjMOrnleFDGUZ9YXDvhPIaPDPYlT27BhpqeHawC1vdE1Wi+OCMdIxJruSFUBfWtpBRf6GXNXbGioxlw22MmQegu7GgbXkx1AUTHE2ZZz4FUh1rb4KVg+zxV4routFYSnwTzhPKHGOpqrFN/NkNuHc8SlIzEXv0AnsOky9GIDsqDoNmiU0In7yMbx94Cm2dWqSK5Dm/7Vfk3k5imvv1ldW4+uOfKM8pgN900ftHPGiu4xB/6JnKj5m041wqZg6xY96aelrqeGySO5ws9PCT2Gqj9bN8celNkUQfBcsl/XwKhmtnqoPPlg6BupqKxOTAthVDWWBeSkNSPI9NdEeomxn2RyhukKeqPpiVj/scbeFrbAh9dTU86uXCDKSnxby3Xw30xvvBkhOOvfFHZi6m21vDy8iATRYEmxljlJUZLhQqPpCvbGrFodQiPDnEmb23KObE68PdmdHwolhAvj9nD8GGkR5dQSefHuIMRwNtJvlEkwDvj/ZisjbnswV56J7cOtkfuhqqzKjfH1kqaehdOoLkQlwt2XMz38OaBc4VfxfTOzVskajt/mCUF0tD5aLn9AEPa0xwMMOhtN7bOYEXdee7pnPSyzE0kEngXNvxOxqra1CelYfoP4+zyWhNXYE8YV15JXYuegqZ4bcUzhP79xmknA9jzwU9UxRwN3LPIRZkV7fzGaB4MWE//sH2T7ErKE8GTYCNWNCt7Luj86CjocY08w001TDK0QSLA22xQywoIhn6yRvfw0ywWoXuTfFN1G6IGg7aDxn1P76ULrEvcVr9JkKlJBNqceeB5kZkX7mOwuhYeMwQvQMyzl7GXw890dV+uN0zARXpmUg7eR4tDbLzKJJGXIbHerA/dEy6tx+2IYORee4KShNT2fuZtPFV1NSgrqPD9PEbCguRe/AgzIYOhWanY1FbUxPCH38cxZcFEoVGfn7QsbNjsjmkty8rD03MpO/ahaayMrQ3N6P44kW2OS1a1DWJSvr7tDVXVLCy1GZmImvvXug5O0PXVnawSroau2Lz8LC/HZNio3fBK8Pd2ETdX8kiY+GXk3ywc3oA+9tGTwvvjvZguu80CUDxXV4b7sY8h4+kFXftV/rdQdB9oIyvCUmcUVv53AxvuFsZwNxAC+/MH4SMklqcE+tfHHlxPN5fENgV0JzinZB3PgUFpTaafruVWYGbWQLP4aGupnhhpg8Ldk4rSRzNdPHJkiDUNLbiTKziEmRkzI8oqMKrI9zYZKadvhZeGuaKy7kVSOnUsieilo3CqkGCuBX3ulqwVQtUj9Tmepnq4a1RHsiorEd052RAe7f3rqDS6H9lzJsUzDa/sBrvvjIFpiY6TGbniUdHYu+BmK5AuFYW+kiJeAHTJgraZtLK93S3wPLFwdDWVmce+K8/P5HtJyZeUDcrHwzB3Fl+MDfT6zTW2uKlJ8fhclgmiuV41isCja9+uZ2PJ0Od4GehD2Ntdbw70YN58h9JFk2Y/z5/MAuWK7rXJNsb4ff98Gu6K9hxKxczPSwx1cWMBbF+LMgBTkY6+ClG9B5bP9wFFx8UOTVsm+aLQZYG0FSjWDPqWD3YgU0O/JUk+R6jHtw8byvsTypikwjKQpfhx903sXxxEIIH2zJJp1efG8scjMgLX8jWj+/Fz18LVh5n5VQiMaUEz60dwXT4NTXVMGuaF0YPd8Kp8/2U4JOCtPWNQ0eg+PABNBXko7W6CgW//wpNSysY+AvaOiLtg7eRv1sQ11AR2Lue+sCdG00eCH5Q6WqrOZy7jbvaY5+81ufMmcPkTCjoKGmt92TkDQ4OZjIm5AlPAVnJcE5BTcPDw2VKuYhD0jmkhU/HID16MjiTnIyiEwNCKKgqybdQAF+SriFpIC8vScMpBdq9evUq05Inz+/y8nKmzy4+gdGf8pBxmHTrSd6GvOxLS0uZTAwZqPsDafbTpIW3tzcrOxn26VzJu10WQUFBzLhPnvtkXCAJnhMnTrAguRRgmLzzSY+fAgwLvfspoCwFQabJgu+++47VIV1/ks/p7RpOnz6d5SWPd/KwJ7188piX9oDvjTfeeIOVicpHcQko6PCYMZIGNrovKbjxsGHD2PWllQiU57fffrvj5ekN/xBPPPrqIuz//gS2b/wNlrZmWPvmg/ASk9ahWXYKgCtk/w8n0dLcik0viKR9iJlLJmLBYzPY35PuG4n9P55AalwW8wCxc7bGug0PYthEyUDPsqBgtk+ej2WG/WOzQ9HY1o5jWcX4XExahzor5E2j2umf8IC7LQusO8fNmm1CihuaMPtwhMT+Z7ta4XhWCdtvXyhuaMa6c3FYH+KKh3zsmcQCaeHvThJ5ldA9S+UTek+s8LNn3orPDnFlm5DrhZVYc1YQbCuxvA5j7czw16xg5j10o6gKy09EM3mD/vLiJxexYe1wHN42hw2ALkXmYcMWkaY6Qd/TJh0093ZyCRLugMa668ghzIhBhgiSEjCytcTklx5l0htCaOArfq8pkkcRvKeMQsTPB1Cans2W6Ft6usD1xVehaS65/J0gzf2O5mZmvG+rr4eOoxOcn3hWIjgT87QXek/SctUzJ1H4l2iCNfUDgSSWzQOLYTZuAjRMTKHn4YXcn35gyztVdXSg6+QCl2dfgradKMChIrz1+WW8/tRI7P16DrQ01RF+K58FuCWPOCEUBE3oeSXkgZneyMmvxtXI7hNLMQkl8Pc0x9fvT4OTnRGKyupw6mImtuyK7HdoARb4b/ly5B86hIxdu5jkgNuqVTDwEAxAGTTQbROtCCEZhMRPOidO2tuR+csvzGBiNmwYXB95pM9lobblqUuxWD/EHUdmhbIB6PHsYmwWk9YhW7agbREw382GtS33uVizTbxtmXNUoAdLVfTclXi8GOSGw/cOZbIAsWU1ePJirFKGTPLKf/RELF4b4Ybl/vbMy2r77Vz8HJffrXxChzryniRj7PtjPFkbQ9795E35S5xiTg2KYOXhjCnPr0T4L4dw5cc/oG9uirGPLYZTsF9XGhYLhp6LDsXz+E0bg4jfj6AwIY0Nlsxd7DH73adh4y25mqMyvxgFCWmY/urjPZbzyK18Jvfw5lx/WBlqI724Fo/tuC4R5Ja1c2SF7mT31UxmQApwMEZ9cyuiMiow97NLyBVbZr7nSiZevc+PpWlubWcyEBSgsTevfmn2ZeRCS00Vrw32hr66OtPDfyMqjnndi3vd0SZkjpMtVniI4gZ9NTyI/f9tUhqO5AgMNCfziqCjpoaXB3mxIL10b+5MyWTyPMrwXlgqXh7mhl9nDmbljCisxONSgXBZoN7O4t0mjV9TfXw1yQ+Ohjrsfo0orMLSI7e6glKH2hizjWSMLi4Wrbwi5h6MZLFk+kp4YSXevJqMtYFOeHuEJ9POf/lyIgs+L4Spn4m1hfuSC/D4IEcMMjdgGtrplfVYfymRyff1BXVNDUx9bR0z0v/+2OtM5s11dAhCl4nJq3U+G12BWRXI4zYmFNF/HkPUb3+jqaYWumYmcBkRhID7RJNSJDNnbGeFo298hvqKapi7OWLqa2txIE+wYk6cotpmLP8zGm9N9MCqEAcmkfFNeDZ2RuVKtS20wk7x818zzIlNGLw63o1tQq5kVeDhPwRONx2WzmiZ9CjUIw5APWwv4s1NEbRiCTO0d3undt5qpm7OCH1yFeJ+P4jon/ZBV0YeRdIQtYXFKEtKxYgX1sg8B78Fs6GqroYbX+9kgY0p8KJZaCgz0N965RWoqKvDLDgYTgsXijLRtRS7ptR+eT3xBHtPyctDaQw8PZHw2WdoLi9nkj+ea9bAOEBkgDILCWEa/HEffcQmFWhSwCQoCHYzBH1reXwXncPa/68m+7IgmXGltVh1PEYiEC55lAv7BQV1TbheUMWCv3qY6KGmpZV5kVOAdZKVudO8+cdtvHl/AP58dgxbFRWeUobl30gGuaV2Wfis3s6uZO3ttytD2eQseemfuF2AL08kdb1jbmZWwM/eGN+tEqSpqG1m0mjzN1+SCHyuCE+diceGUe44/kAI2/+57DK8c1XSSYgM+MJe/fmcchaT4Kd7B7HJAIobQt99fTO7TwbUnmhubsOydfvw7qtTcO3EWjQ2tuDg0Xi89+k5yWdXXdTvuxaRjadfPYw1jwzDS0+OZd79N2PyWfBcktYkDhyJx5OrR+CFdWNhaqyD/KIaHD2ZiG92yvbEVoaNl9PZe2zP/EA2nonMr8aD+2+jQUyCTV3GmON/jY+nPa4f/5D9ra6uhvdeWYJ3X16MY2dvYsGqT/+RYx5NoxgXGnhztDss9bTYO+nxY3ESgXBVpepqd3wBXhnhigBLA7biNLGsDiuPxOJC54S6kLGOprDV18bv8X2P8/DNzgjoaKt36uZrITahGI888RcqKhulxhyCXjM9P6ufPYz1T4/Gn7sWQV9PEzl51Xj3k/M4eEwkP7ViaRDWPyWyjxzevZT9/84n5/HrvtsKl8964VIU/vEbMj7ZiI7WFuh6eMFx7dNQUROz+7S30Uum6yMZ/0lHX9iYCCV3KB8Fy+XcOQbYI/6fRqVD3rq/uwgKXEpGejMzs25GclmQ8ZSM5GTcJ4/32bNnMwMxGYdJG/327dssuKusZWqUjuRlyLOcjO7iaShwLf3u5+cncSzyYA8JCZHwyiajN3lbUxnIGE5GXTKyS0u3kFQPTVrQfsgATwFsFS1Pb5BXPOUlo754XlnnQXVMuv7i2u8kY0S3D5VPHDJi5+bmwsnJiWnmiyNr3xQEmLahQ4eySQfh8WjygfYtrU1P0HWiOnR2dmYe/DY2Nsw7nuqHPOlpYoT+lpWXYhQkJCSwFQq0f3FNfVo9QdI+oaEiD1ta2UH7HjFiRNeqBqoLqjv6THVCExDS9UNQPZSVlbHj0MRLbGwsu7cULY8iRJQcQX8RGGw6uuR7yPAqq2mgoLvKSmCtOdN33XKyzQg9iFTkvDxkeXuI5+sNMXvLP1I+Klt/Gtn6X3uXC1MU6jgKAjX3/J0yPPqRg1LphYZB4b3WF+j+FCwa6Hkf4SWiAODKIjTqC4/BngcxQ38XnTJEfeH22yJvmIFwLaUZ9m5341FfYfVHwbjErpm4ob8LJbxlkovV/tm2hS55n48AVFX1PbcybZg86Jx6sj88PlxxXXpxw748+av+IGvfX27vm/GJtQ3/sBeg772C1R59QSgjIX53yHt39JXMvL7fe1QW9rgqkUdsHkUCefewmvqdHQn2dq8ry5JByk+2t7e1dcnS/dN8fbI/bZ+KhEd+b3UpaB+7n1NPXslLJnT8z9uPnsirU8yvjb2j/qVrKiQ8pX+BtlX6+Sz09iw1x1T0a98yFGTuKJoBJv/TdqS3d3fz1rC+71tN5Y5LnUjT9rDiK0z7+y7pS32XfP5tv8tFRmppyMHtTpjEbF7u2Tnhn3h2Fa1Hle9v9qlsXf0qFZUuuSdFEXeyECLvHh70keSK8n6N2eRJA/fQnvfW3v81WdKJkyPgm4STd2VVPO4zFf/f+H9h2FeG3bt344EHHmDGUwrqSt77ZBQ/fPjw/7poHAWh1Q7kpU/XjYz6tCLgzJkzSEtL67ec0N3InTDs/5P0x7D/b9Afw/6/wZ007P8TKGvY/zfpj2H/36A/hv1/gztp2P8n6I9h/9+gP4b9fwNlDfv/Nn017P8b9Mew/2/QH8P+v8GdNuzfafpi2P836Y9h/99AlmF/IKGoYf9/QX8M+/8G/THs/xv0x7D/b9Afw/6/wZ007P8T3AnD/j9JXw37/wb9Mez/G/THsP9vwA37suGG/YHDwO3Z9AHSqycpF1mQlA1JqJBHNknQ0EaGYJKD+frrr/H/jalTpzKJGFmQ5A0Zxu9WKHgwefyTjBGtvDA1NWUxCP6LRn0Oh8PhcDgcDofD4XA4HA6H89/j/5Vhf+HChZgwYUKPgXaffvppPProo0zGhWRiSMLl/yMUS4BkfGQhLRdzt0FxEUjSh6SKKOgvSf78m8tkORwOh8PhcDgcDofD4XA4nP8iqioDe4Xef4n/V4Z90ieX1nuXBQU8HTy49wCfdzOkB///GZJS8veXDJ7F4XA4HA6Hw+FwOBwOh8PhcDj/BfoevZDD4XA4HA6Hw+FwOBwOh8PhcDgczr8ON+xzOBwOh8PhcDgcDofD4XA4HA6Hcxfx/0qKh8PhcDgcDofD4XA4HA6Hw+FwOP8MajzM5YCBe+xzOBwOh8PhcDgcDofD4XA4HA6HcxfBDfscDofD4XA4HA6Hw+FwOBwOh8Ph3EVwKR4Oh8PhcDgcDofD4XA4HA6Hw+H0iiqX4hkwcI99DofD4XA4HA6Hw+FwOBwOh8PhcO4iuGGfw+FwOBwOh8PhcDgcDofD4XA4nLsILsXD4dzlOBm0YSBT+WsaBjI+T7lhIBM9fWCX70j2wH2NBJg1YyCT9pgvBjLmWi0YyFzbn4eBTP6VwxjINBxagYGM6z3mGKgMMWvAQMZGd+C2y4SmagcGMk3tA3ttecgQDQxkfr3SjoHMjJCBWz4Li4F9bdWmDNx2mdAc2E0fMh8fjoGMreXA9vnUeXMNBjIWFgO3/qqeDv5fF4HD4fyDDPDXH4fD4XA4HA6Hw+FwOBwOh8PhcAYCXGN/4DBwpxU5HA6Hw+FwOBwOh8PhcDgcDofD4XSDG/Y5HA6Hw+FwOBwOh8PhcDgcDofDuYvghn0Oh8PhcDgcDofD4XA4HA6Hw+Fw7iK4xj6Hw+FwOBwOh8PhcDgcDofD4XB6hWvsDxy4xz6Hw+FwOBwOh8PhcDgcDofD4XA4dxHcsM/hcDgcDofD4XA4HA6Hw+FwOBzOXQSX4uFwOBwOh8PhcDgcDofD4XA4HE6vqKl08FoaIHCPfc4d5dKlS5g6dep/8lz/S+fO4XA4HA6Hw+FwOBwOh8PhcP53cI/9/zDnz5/HRx99hGPHjt2xfZaVleHq1av4LyB9rnfTuVdX1eOLjw7i2qUEqKioYNQ4Xzz10mzoG+j0mrehvhmrl36JnKwSfPvLk/Dyte9TGeyt9PHm6mEI9bdCfWMrDl1Ixye7ItHa1vPM70P3emPxdC/YWujhdkop3tt+HclZlV2/Twp1wJoHBsHV3ghNLW2ITCjCpp2RyCqoUap87ob6eNTTFS4G+qhqbsaR3AIcyMrrMc8QMxNMt7dBsLkJTuYV4pvENInf/U2M8N6QgG75ngqLQnZdvVLlm+BkipdGusDFWAd5NY348no2DiYXy01vqKmGVUEOuNfDHFZ6WsipbsQvMfn4NbZAIgDOk0OdcL+XJUtT1tCMo6ml+DQsA029XJfeMNPSxFP+Lgg2N0ZLezvOF5Th6/hMNLe3y0yvpaqK+5ysMcXeAra62ihpbMaR7CL8kZEPZUtSm5ePpF/3oio9A+q6urAbMxKu990LFVXVPueh38PeeK9bviEvPQNTb0/2d3trG/IvX0XehcuoKyiCppEBrIYGw+2+e6GqoSH32BqqKngmyAXTnCygpaaKG0WV+PBGGorqm2WmVwEwxckcCzxs4Gmsh6rmVlzILcc3MVmobWnrSudsqIOnBzsjwNwQmqoqyKhuwPbYbFzOr+ix/gquRyL1wBE0lJRB19ICHvNmwWpIYL/yNNfUIv3oSRRFRqO5poalcZ46EXajhnWlaW1qQuqBoyiMiGJpDOxs4bXwfph6efR4bHsLPby5LAShPpZoaGrFoSuZ2PTbrd7blqmeWDLZHTZmeohJL8O7P0UiOadKUMcqwKyRznhoqgc87I1QUdOMM1G5+HzvbdQ1tuKfYsq4QDz60GRMHReIH387h2ff+BH/Fi0NjYj4aT+yI26jo70NtoG+GPbIfGgbGvQrT0FsMuIOn0ZpaiZ7Dqx83DFk8SwYWJorVK577C2xyMUe5tqayK5rwHeJmbhVLrhOstBWU8UkWwvMcrSGq74eNtxMxLXicok0eupqeMTDESMsTWGoqYHkqlp8nZiB1Oo6hcrU0dGBhIMnkH72Mppr62Hi6oighx+AsaNdv/IokqY6r5ClKY5LYm2OsZM9rGbOgpGHO6qSkpG+70/U5xdA08gI9tMmw2b8uB7Ppbc8Cd98h9LIm93y6VhZIuS9t7t9X5mYhNjPv4SOpSWC390g8VtRRCQyDlI7UcraANf7Z8FiyOAey9dbnuwTp5G6d79EHrrPxn/zhcR3zdXVSPvzIEqjY1g9W4WGwH3+HKhpaXU7Zm1xKW7s3IfihFSoa2nCedRQDF58H1TV1eSWU5E8pSkZSDl1CdnhN2Hu7oxJbzwtsY+EI2dw69cD3fbdARV4f/gJ1PUln8WGrEwU/PEbGnNzoKZvALNxE2A+eZrcMtJ518bHofzSedTGx8Jk5BjYLloqkaa9pYX9Xhl2Fc2lJVA3MoZx6HBYTJvR4ztUEfQ01PDmWDdMdTWDuqoKzmdVYMOFVJQ3tPSal96Tfz4wGP6WBnjwr9u4mivqBypCU0UlUnb/jor4RKhoqMMyZAjcFsyDmqZmv/MUXLqC3NPn2D2q72AP90XzYeji3PV7TVY28s5dRPH1G9Cl52bDqwqV+WFPe8x2soahpjoSK2rxRWw60qrl9x1DLIyx0M0WPsYGaGprQ2RpFb5NyEJZY7NSafrKIFNDrPV1hpO+LsqbmvF7Wj4OZRfKTW+prYnFbnYItTSBoYY6MmvqsSslBzdK5bfvimKgoY613i4ItTBhn+kdsC0xA/Wtoj6SNI56OpjpaI1JNhaob2vDQxciu6XxNTbAcg9HuBnosb5panUtfkzJRlJVrVLlszfQxhuj3BBqY4T6ljYcSi3Gp9cz0dreIbffN8vdEkv9bOBhoofKphacySrD5ogs1HX2+8x0NHD5weHd8j59Oh4nM8qUKp+3sT6eGuQCDyN9VDS1YH96AX5LlT8mMtPSwCIPO4y2NoWxliaya+vxc3IuLheI3r3UD53jYoMZjpaw1dNGaWMzTuSU4OekHMgeGchnrL0JnhvqAidDHeTXNuLrW9n4O61EbnoDTTU84m+Pe1wtYKmridyaRuxJKMDviaIxkThTnc3x2URvRBVV4+Ejt5UsHXCvoxWWetjBQlsLWbX12BaXiage7msvY30scbdDoJkhu9rxFTX4Nj4TWbUNSqXpK65Gungl1BUB5gaoaW7D/tRCfBOdLXf8pa6igvvcrTDfw5qNNcoaW3AyqwRfR2ejRc49rCj0Dio++CeqblxHR0sL9Dy9YL1gCTRMTOXmaSkvQ8WVS6i8dgmtNTXw/myr3HFXR3s7srd+jrrkJNgtfxRGwUP7VV4O538FN+z/hyktLcW1a9f+18X4f8PYsWNx6tQp3A28+eLPqK9tYob59vYO9vmdV/bg4y0res37+ca/YG1ngsz0IjYo7Asa6qr48e0pSM2pwvR1B2BhoouvX5sINVVVvL/jutx86x8JwbxJ7nhp82WExxTC29kED0zx7Mrj5WyCra9OwOe/3MTut5JgqKeJd9eOwI4NkzHl8b8ULp+JpgbeHRKAswVF2Hg7AR6GBnhpkDca29pwPFf2oMTLyACzHW3Z78aaGlAlS6AM1FRVcP/py2gX6x4p2+fxt9DH9pl++PhqBv5ILGKD4s+nerPB8KUc2UbaRX42qG5uxYrDsSzdBGdTfDrFmw0g/koSTAg8NsQBjwbZY/WRONwqqoaXmR623+vHyvzuJclJCmWgmvgw1Icdf9XFW9DXUMe7wd7QCVDFh9GpMvNMs7dkhruPo1ORX98If1NDbAjyZIbuX1JzFT52a0MDIjd9ATM/H4zc+DbqC4sQveVbqKipwXX2jL7n6ehgncExn2+EpqGh6FzFDB3l8QmoKyiE7/IHoWNpgZqcXMR8vQNtDQ3wfmix3DKvD3HDSBsTPHk+jg3WXg91x9YJ/lh4NAqybNPU8Z5ob45tt7OQUlkPe31tvDfSCw4G2nj6QnxXum0T/ZFUXoelx2+ivrUdD3rZ4vOxvph/JApZNbIHAmWJyYj+5gf4PrgQViFByL92HTe3fIdhrz4PE3fXPufJOn0eOmamGPrik9DQ1UXhjZuI2fET1HV1YBU0iKWJ27kblakZCFq3ihnwCsJv4MYnWzDynVegb2Mt89gaaqrY+coEpOZWYfqLR2BhooNvnh8DNTUVvPdTlNw6f3nJYMwd54qXvglDeHwRvB1NsGCCW1eeQDczDPW2wDu7IpGeXw1XW0N8tm4kLIx08PRXV/BPMGyIB55YOR3f/3oalmaGUFP7dxdZXt72M6ryizBtw9NQ01DH5S27cO7THZj+9rN9ztPW0oL4I2fgN3MSzFwd0Fhdi/Af9+HU+1sx9wtJo68sRlmZ4hk/N3x8OwWRZZW4z9EGH4T44LEr0cipk30Pz3GyYZODW+MzsHl4gMylqq8P9mKTj69HJqCksYlNAnwS6o8Vl6JQ3tS7kTHp79NsG/n0Khja2yB232Fc+OBLTP9kAzT1dfucR5E0iYdPwnZIAAKXzmVtUuLfpxD72Wb4Pfs04jZ/Bbspk+D31DpmsE/a8SPUdHRhOUz2wLWhuASxveTxXr2KtX/ig+7wF16GWVB3g3xLTS2Sf9gFI08PNJVLTqZUJCYj7tvv4bl0ESyDg1AYdh0x275D8CsvwMhNdtuiSB6qAz07Wwzd8EpXPnJgEKe1oRGRGz+FtpkZgl9+HppGhii8dh2lt2JgNSxEIm1bayvOfbAVRvbWuHfT62iorMKlT79jxwleNl9mORXJ01RTi8hdf8B98mi0t7WhQcbklPeMifC6Z7zEd2ff+xJ1rWrdjPotVZXI+PJTmAwbCcdH16A+KxM5338LVU0tmI6V3IeQhox0lJ07BdPR49BaU83KJ031rSi01dXBfvkqaJiaoSEjDTk/fIeOtjZYzbwP/eHTqV5wN9HFwj+j0dTWjs3TfPDdvX6Y/8etXvO+MtoVZQ0tbEKAnBKUgc7z9hdboaGnh5C3X0NrfQNit3yLtqYm+Kxc3q88mYePIvv4SXg/8jBM/X3RUFSM/AuXugz77Bn98WfYjh8DFVUVVKdlKFTmxe52WOxujzciEpBRXY9HfZyweaQ/lpyJRI3YBL4QEy0NZvT7PS0PiZW1zFD+0mAPfDbCDyvO32R9CUXS9BVqcz8O9cW+jHy8fD0BQWaGeHWwJ2pbW3E2v1RmnuWejmxS9Y+MAlS3tDLnjo9CffHk1RjEVypnKJfm9UAv6Kqr4amw26x/Tm3+K4M88UZUgtw8z/m740JhKY7lFmGirUW336m+Nob44XReMd6/lcTamZWeTvgwxA9Lz99gkwGKQJNUP8zwR2pFPabvjWSG5m3TfNm9/f7VdJl5Ai0NEGJjiPeupiG9sgGuxjr4ZKI3zHU08eyZRJaGHgvax6x9kUipEE1SK3tdyUi/eZQ/jmUX47XwRPiY6OOdod5obG3DgUzZY6KHvBxQUNeIl8LiWT92moMlPhjmg+evxCGiRDAJN9bWDEaa6nj7RjKKGprgZ2KAt0O9oKaigh8SsxUun6+ZPrZO8cPnNzLxV0oRJjmZ4aNx3ihvbMHVPNkTfg94CcZEj58UjInGOZjiw3FeaGgVTKqIY6uvhVeHu+FmUTUzYCvLWBszvBDohg+iUhBRUoG5LrbYNNwXj5y/hWw5RniaEDuQWYAvY9LZ/brOzwVfjgrAQ2ej2LOhaJq+Trhun+KPawWVePVAJDPUfz7eB23tHfguJkdmnuE2xnA10sFb11KQU9MIL1M9bBrjzcZ7H1zv+/iRKNy3B3UJcXBc+xTU9AxQsOcnZG/dDNdXNrBxmcw8f/wGbXtHmE2ZjqI/futx/6Unj0JFTR2g918f7RoczkCAS/H8R7l58ybeeOMN1NbWYvTo0Wz75ptvesxz8uRJ3HfffcwrfcWKFZgyZQo2bNiAhgb5M8Px8fFd+588eTLWrFmDxERBh4OoqanBuHHjcOPGDYl80dHRGDNmDJt8EHrDU3lJ6uaBBx7Ajz/+KGFUFpYtPDwcS5YsYfssLCxES0sLvvzyS8yZM4dtX331FVpbe3/Z9VZuWcTFxbH6IKqrq5mhPypK0pAUExPDzqu4uFih8/onSErIRWR4Kp5++T44OFnAycUST744m3nvZ6QV9Zj31NGbSE0uwKp18j3AFGHKcEc4Whvg9a1XUVBazzzvv/rtFpZM94Kejuz5RmdbQ6yc44e3vgnD+Ru5zBP3ZlKJxESAr6sp68hu3x+Lmrpm5BXXYs/xJLjYGUFfV76HtDTT7G2YV/mOpHRUNrcgorQcJ3ILMc9J/uqEpKoavHUzDmElZWjv5RqSUZ+M+cJNWVYOtkNsSS2+u5nLOqS/xRXifFY5HguWXz5Ku+NmLrKqGpn3xaHkEiSU1CLExqgrzSBLA0TkVzHPt/qWdtwsrMHZzHI2gOgP5KXvaaSPz2LSUNjQxLxgv0vMwlR7S5hqyb4u5NW1LSETKdV1qGttQ3hxBY7kFGOSrWJevUIKroazAbjPw0ugbWIMUx8vOE2bjOwTZ5gxpb95VFRUoaqm1rWJG5DMB/nDa/EDMHBygLqONkw83ZkxqjJF9kCNIE+8+9yssCU6EwnltSioa8L711PhbqyH0bayvVNul9bg5SuJiCquRk1zK8v3bUwWxtiZsoEsYaqtwTyi9qUUMM9/SvdDfC4zhFMHXB6Zx07DzNcbjhPHQsvQAC7TJsHYzQWZx8/0K4/H/TPhNHk8dC3MoaGnC4dxo6BjYd5VN+R5XHA9Cq4zp8HIxYmlof0ZOjkg88RZuceeMtQejlb6eO37CBSU1+N2Whm++jMWSyZ5QE9bTttibYCV9/rgrR9v4PzNfDQ0teEmrQYSmwi4lVqGN76PQEx6OfPQp/8PXslEsJdy96MyhEel4L6HP8ShEzfQJmdlyz9FTVEpsq9HY+hDc2HiYANDawsMW7EAxYlpKE5K73MeNQ0NTFq/BjYBXtDU04WhjSV8po9HTWEJGiqrey3XQhc7XCgoxdmCUrYy5afUHNam3O9kIzfPb+l5+Cw2DSnVsg1DZJwhL86dKdnIqK1HbWsb9qTnoaKpGbMd5e9XCBnqko+egcc9E2AV4A0dEyMMWb4Qbc0tyLhwrc95FN1v6OMPw2H4EGgbGbA0vvdPR3tzC/KOn4SmsRGc585hk48WQ0OYcT73+Am551Jw9lyveWjykgbTwq3sVjTaGhthPXqU5Dl2dCDp+x9hM34s9J0cux2LPOtNfLxhP2EsNA0N4Dh1EgxdXdn38lAmj0S7LOVZnn3iFFpqaxGwbjV0ra2grqMD+4njuhn1idyIaNQWlWDoqsXQMzdhnvX+86Yj5fQltkJFFork0TLQx7T3XoTb+BFQl+MlTu8T8fOoKyljz5PJ6LHd0pJ3ooq6OqznL4S6oREMAwJhOnosSk4dl1ufuq5ucH7iWRgOHiLX+9546DBYzZoDbVs7qGlrQ9/Hj2316bIn5RXF0VAbM9wtmNNAcnk965u8eT4FoXZGCLYRTZbLYqKzKcY7meLdi30zGJHHfW12DjwfXgIdc3MYODrAdf4cFF4NR1NVVZ/zkEd/5qEjcH9gHvPmV9fWhoGTI7weFq2CoHoe+tZrsBs/VubqEFlQr2KRmx32peXjRkkVyppa8NntNOboMMPRSnZ5m1rw3LU4hBdXsrYyp64RW2LT4WaoBxcDPYXT9JX7na1R1tSM75OyWT/6XEEZzuSXMI98eXx8OxUHsgqRV9+ImpZW5sSRV9eIsTb9e8+6G+phiLkxtiWks33TJPA3CRkYbmnKvPLl8Ux4DP7KKmBlkYWDng7rY/2ZlY+qllZ2nn9m5jNjpp2etsLlm+xsBkdDHbxxKQWFdU24XVKDLZHZWOxjy4yssrhVXIM3L6UipqSWeejT/4dTihFsLerTC2nr6GDGfOGmLLOdrdmYiAzIdM9cLazAocxCLPGUP+bYfDudrdDIqaVr2YY/0guQUFHDnFCEnM4txfaEbGTU1LOVE2TwDy+qwCAz5cYcy/ztEF9Wix9iclHR2II/kgpxMbccKwMc5OahtDtj85Bd3chWth5JL0FiWS2GWEm2PWoqwKcTvLHlZhayqvvmCU+TcmfzSnEqrwSV1PdOykZBfRMecLWVm+fpq7E4l1/GnnVasbwpOhVm2poIMjdSKk1fmOlqye7h98JSUdLQjIiiKuyKz8NDvnasPmRBK3833chAUkUdu5Y3i6txOrsUgy17bst7o62uFpXXrsBi1hzoODpD08wMNosfQlNBPmrjYuTmc1i9DhYzZkHDyLjH/dN7rPLyRdgsebhf5fwvo3qXbv8f+f96XpxecHV1xdKlS6Gjo4MPP/yQbdOnT+8xDxmjjx49ygznEyZMwKOPPoo9e/bgwQcflJvHwcGha//r16+Hnp4egoODkZmZyX43MDCAvr4+vv32W4l827dvZ/+bm5ujvLwcw4YNQ3Z2Np5++mnMmzePSQg999xz3cpGEw4zZ87E+++/DxMTE7z55pvYunUrFi9ezMqbkZGBt956q9f7o7dyy0JcisfQ0BCqqqrYuXOnRJqffvoJjY2NsLS0VOi8/glibmZCW1sDvgGigfbgYFfmDRobLf/88nJKseWTQ3hz42JoqPdvsc8QH0vmrV9WKRoUX4sugJamGvzdZHfgJw9zQGNzK05ey5K7X9pHdV0zls7wgqa6KkyNtJmH/4XIXNTW9+51KcTHyBBxlVUSSw6jyythravDvPH7y84xw7B7/HB8FDIIwWaCZcHKEGJrhGtSy86v5FRgiLWhwt5BNDB2N9XFyQyR59TR1BK2jyBrA9Z58zbTw1hHExxKkS/xowgBpgYobmhiAyohUaWVzCuHljErCnn29LRsWhaVKWkwcnWGmpbIcGLq642WujrUFxT2O0/Ym+/j3JpnEf7Ohyi63n2ZthAy0tVk5zLZB8tg+VIT5H2voUryOyLjQn5dE7JrGhBooXgHme5TWv7a3CYwCJPnEu2TJg2MtdSZQWCxly1KqdNeKH85cEVKOkx9BNJCQshoX5GafsfykBc3SfeQUcRysFCqqoMqrZunLVRVUZEi35gT7GmB1NxqlFWJ7rWrsYWCtsVF9sTIlBB7NDa34WSEbE8kWZDH/vRhDjhxXfHVI3cTQkO8ta9I9sjM1REaujooTk6/Y3nqK6qQcuYqLL1cmWG6J8hTzstIv5vszs2yKubp11eEt1iH1CJzmnQNMOn9mastKkVjVQ0s/UT3vJqmBsw9XVEmZxJPkTx92W9LfQNSjp2DhqEhe56Mvbwkfjf28UZdTi7ammTLbFSnpimdp/DSFRh5eTIpHnHyTp5m3sz290yV286aeEsey9TXC1U9tC2K5qFVVheeeA6Xnn4JtzZvRW2OpGRESdQtmA8exCZce6MkKZ2tltAxFt0L1v5eaG9pRXl69h3Lowhp564JJsQGD+n2W31aKvTcPSUM9HpePmgpK0WLHGO1stB7rD4jHXXJiTLLoAwhtoK6CRPry8QU16KqqbVHw76VniY+muSJZ04kKN0fEFKVmgYtExMmgyOEJozIW1OeB70iecpux7A6spQxQdQfyEhMBjvqNwlpbu9ATFkNW82oKCQzRtT34OCkSBpF8DcxxK0yyfuOZH7IyE5Si4pAqfQ01JkXdX/wMzZk+0gQk8eJrqhiHsh+CrTx8iAnlby6Bsx0sGaSb7pqapjlYI2MmjomI6QoZIwnb31agSLkal4FtNRV4Weur9A+yGN/mqu5RJ9eyE8zB+HmIyNxYG4Q5nnJngjqiQAzupbVEm/IGyWV7L6U55wjbxJd3jNL19rXRB/BFsY4n6+cTBAZ48PzJcdEYXmVCFLQKYnGROSx72asy+SMxHkq2JldF5os6AvUb/Ex1u8muxNZUgl/U+XGP0RPbZ4iaRQhyMIQsWU1aOwcPxDhBZUw1tKAi5HsFYjiUJfK00QP4+xNcSZL9uocRanPzADa26Dn6d31naaZOTTMLfo9udxWX4+8nTtgs3QZ1PQUe844nIEMl+L5j2JkZARvb2+oqakxr3RFIW/3H374ARMnTmSfaR+DBw9mKwCCgoK6pSfDvfj+ycufPN937NiB994T6FOvWrUKy5YtwxdffAFdXV00NTVh9+7d+PTTT9nvH3/8MZydnbFr166u/Xh6emLo0KHMcE8GfGHZKF9gYKBEQFva/8KFC9nne++9F/X1vXe2FCl3b9DEyeuvv47PPvsM6urqaG9vZxMhL774olLndacpK62BkYmehMFMXV0N+oY6KC+VrUPf0tKKt9b/iuWPTYGzqxXSU/rWwRFiaaqDsipJzwehIc7cRLb3jKONITLzqvHIfX5YPtsXGhqqiEkpxcc7I5GUKZCfKSyrx9oPzjE5ng2PCXQlbyYWY+Vb8j0AZWGipYmCSsnyVTULOtwmmprMK6cvtLa34+fUTJwrKGYeNJNtrfBmkB/evhmHqLKedc7FoWW6pVJ669Tx1NdUh66GKvO2l4WxtjqiVo1kS3Nb2trx3uV0XMgSHfdwSgkcDLWxf34Qk98hvovKwU+389EfSOKCvHzEIe8m0g41FTOe9wR1jMlb/9MY5Tz0mqqqmWenOMLP9Ju+vV3f8qiqwnnGNNiNG8WMQ4XhNxDzzfdob22FzUiRTjxx5eUNqC8qZgYAm1HD4SJHAogg+SFCWv6DvJCEv/UGLa9f5e+AIxlFaBVbPfLipQRsmeCH8/NHsM8UQ4GkeqSvjRDymqfJDC0Dqbow0EdzVXW/89QVleDSy28xQ4iqhjr8Hl4ME0839puqujosAgOQfvQUjNxcoGtpjsLrUahITmXe+/KwMNFGWbWkF21ZdZPgN2M5bYuVPjIKq7FihjeWT/diUmExaeX4aM8tJGVLDhZ/3zAZQZ7mTDbsVEQONv4qX97nbqahogoaOtrMkCyOtqG+XM96ZfJc3/kHEo9fYF7dJk52mPzK2u6TOFIYaWpAXVW1W/tLnxVtR2RBnqox5dV40M0BmbUNTFv6XgcrOOrrQFUBif3GSsGAXTr2gJahPuqKy/qcR5n9Zl2JwPWvf2LPEk2Q+K5bg8Rvt0NDKq8GPZcdHWipqYaaVvdJ9OaqKhj7+iicp6GkhMn1eD26UuL7msws5uU/+PVXZXqCUzvRWlfXrZ2lY/XUtiiSR0NfH14PLYZ5oD9aG5uQ8ddh3PjgYwx79w3mac3KXVwKswB/3P7qGyaZRnlIp5/0+snLWhy6DnQPi0Mrkdh+5JS1L3l6g/qQGRfD4TImVKZWMEnx6FlITqgK5Xpaq6ugYdQ/L86EF59BW30duxfMJk6B2dgJ/dqfpZ4Wqptau8XvKW9ohoWunBUM5Ak8zRs7b+fjdnEtrPX79tw3VVZ1fzb09di9Ku/+UyQP3Vck71R0LRxZR0+wiS19Rwe4zZsDQ1cX9BUy6hPS7+qK5hbY6irm9U965o/7OjODYn59U5/TKFPmSLGJCGFbTZIh1EehlVa9scTdHvoaajidV9K/smhpoLqlpdvEbU1rK5Pe7CtN7e1Myuf9YF884CLoS9JqgFdvxKFFidXXdL9LxzQgZwzhbz2xZ3YggqwMWZ/9VEYpPgwTTXJSCX6Jy8fPsXmsDznJyRxvjXaHgaY6dsb0HDNM+lrmSk3SkLyO8DdF5OrmutiwVaPHs7s7Ch2ZMYytVqV7g3T7D2QoN8YU1F9Lt/rTozGRuiqTnpQFOblcWTpCMCZqb8dH4em4lCsaEw23NcYcDyvM2S/faac3jLQ6+y1N/eu3POnvipzahm6TZcqmUQSScxLef0KEn+m31Er5dpRD9wWzOAd0LQ+mFuFbOdI9itLaOSktLT2nbmCA1uq+vUuF5P+6EwaBQWwFGkkKcjh3O9ywz1EKDQpANl6k1Tlo0CBYW1sjIiJCpmGf+Pvvv7F3717k5eUxo316ejoznAuZNWsWM+jv27ePGfj/+usvJqGzYMEC9vu5c+dQUFDAjktGANra2trYICcpKQnDhwsMuORVL27UJ0iSZ/PmzdDU1GRyNz4+PuxYitBbuXuDpHWefPJJprtPqyEoWDHJAy1atEip8xKHykGbxHcdLdBSwmNCHvQSlicDtH3LcZiYGuD+hSPxTyE8tjzbDtmZSUM/I78ac5/7m8ndvPxICH56dyqmrT2Aypom9vv2Nydj295o/H4imWnsv/nYMOx8dyoeeOEI887pc/k6fVX6IK/YRWJVDduE7M3IYfr9c53tlTLsy0Io/6PChr6yqWxshcfWi2wCgDT2P57kidrmVuxLEEgwPRJoh8eDHfDwoRjcLKxmHvtb7vFBQ2s7Pg2Tv5qjPyhSn+QV9H6ID1vGejSn+A4cU+ii29HnPPq2NvBYcH/X745TJjKP/MyjJ7sZ9kd+sIF1GqvSMxG3YxcSdu2G73LJAIXd6Oh+/ylSV7Qs/Ivxvmz57KbIdAmPpO8mByCnpgEvXEpgsRUWetri64n+WHr8FlsR0GshhPRYEMXz6FlZYOqOL1lMg6IbtxC7aw/T2LcOEbxLAlY+hKTf9+P6h5+jpa4eZr5ecJo8DvlhktJt/W9bVODtaIzMghrc/9oJQduyNAg/vzoRU1/4G5W1ogH34nfOMMO/r7MJPlgdis1PjsITmy/jvwJ7Djr6n2fow3MR/OAc1BSW4sbP+3HynS8x66OXu00KKHp9+9EsM965lYjHvV2wdcQg6KipIaykHCfzillAyT6joqp0oG+F8shI4zgyhMnxkId/8pEziN38JdNW72F5ghJlkp+n6NIVqOvpwlwseC159id+uwOuixZA20xecDs5ASH70LZI57EdI+qnaBoCPquWoXL9G8g9ewEeC+YJ9tTRzuR4vJc/CN+Vy1BXWIjYr3egpbYOfo/K1liXeUwl6rEvecTJvxnHJs/cJo6Ewj7//emwSOH90Wdob25GfVoKcnf9AFVNTVjNFr0D7xTUTZNX7LVDHZkRbltE31c99E5Hn/PQfdVYVobS27EY8vILUNXUQMaBv3Hr0y8Q+u4GaJveWYcdwbut92tMKzA3BHsx4+n68Pg+p+kvykiNTrQ1xyOeDth4K0ViteedLk9/HhFaGblpqD+uFJXh57QcNk5Z5u7I4rM8fuUWk3Xre9kE//dWvKWHo9kqTx9zPXww1hOfT/LGk6cSuhx+3r4s8momr3MXYx2sCrRXyrAvC2WGUyOsTFjg3c+j05FS1X22fNbRcGirq2GQmSFeD/Zg2v0k0dO/8gn7fT2MiZpaMejHS2xVCHnsvzfGA3UUKDalCEZa6tg0zgsvX0xGRVP/Vq/ILp8iT66AJ/xcMNjcCE9ejpEbiFaRNP2hawzcS7o5hyLZimB/MwO8O8oTbwxzx7vh/fOsl3lgdl37fp4Vly+guaSYBcvl9A9lY9xw/jm4YZ+jFGQUJ4kZccjYTZrysiDN+GeffZZ5oJOED8nubNq0SUKXn7zZly9fzlYCkGGf/icPezLUC3X4yfi9evXqbvsnQ70Q2rc05F1Pkw80WbBx40a2T4olQB74PaFIuXvD2NgYM2bMwK+//soM+/Q/HdfKykqp8xKHzuHtt9+W+O6F1xbhxddlB+Jcu2wr4mMEnSNTMwPsP/U6TM30UVVR19mhFbTGbW3tqK6qZ2lkER2ZgcS4HIwfsl7i+8cf2oKJ0wLx5sYlUIbSika42kl6j5kZCbxpS6U85YWUVDQwuaB3vgvvkvB569tw3PxtCYYHWOP41SwsnOrJdPW/+zOW/V5V24z3tl/Hme/mIdTfGtduFyhUvsrmZhhJecSRt6jgtzs7q59ZW4dJtsotjS2pb4aZjqSnB3lRUIeUtDZ7gpzjaKn7gaRiDLM1wvJAuy7DPmn3/xpbgEvZgkmGGwXV2H4zFy+OcOmXYZ+8eWgJp/SSXBqgl8uRdxBip6uNzcP9EVVWhY/kBNrtCfLubK6WXIki/EwBE+9UHsLA0R6FYRHdviePPtLSJa1+1zkzEf/Dz/BaskDmK7i802vLRFuDGeeFkGfPrZKevVN01FWxdYIfG+StPhPDJmSEDLc2hoexHtadjUVx536/i81mWprz3K3x+c3u8gPkNU+G9uaa2m51Ie0129c8pButqa8Ph/GjURqXiOwzF7oM+5r6esy4L86tb35guvzyoJU/bp0SD0LMDAUeuKVi8jziFFc2MA/8t3dFdq0cIr39W98/gOG+Vjh+PUdisNjUItDg37QnGttfHAcLY22UiMmK3W2c/vBr5EcLDAEU8HbpT59B29iAaYGTTBLp4gtprK6BjhzJHGXysGdCVRXG9tYY+fhS7Hv8NeTHJMIhWCjF1B1aNUWTs8K2WNy4Im/ViTJt1AfRyRLffRjiiwIpg1L18UOoPnkI+zo/T3l/PbQ624QmaiPsREGd6bM8eSFF8iizX3qXk969rqkxBj80D1nXo9nqoZYayXaMfVZR6eZ5LIQkfBTNQ6sDiq5eg9WI4RIe5M2VlWgsLmZBd2kTJCZprQ5cenQN/J5cC+vB/lDX1e12LGonNHpsW5TLw/KpqUHP1oYFMRWiZWTEYnrYjhZMAhi5usDxnilI/uU3FIUJ4vZYeLlh8oZnoG1kiOp8yfhDNIFCyLu+fcnTG+nnrsLcwwXGDrbIloxDzFA3NERrrWTdtHXWlbpB/3SOu55Z0oz3C4D5lGkoOXakX4Z9WnVoqKUOTTUVNIt57ZvraKBUjnQi9VkoLlDak5IxBn6eM4jFGXrkkKDv1xsUP0L6PqLVIHRP0299zaNlbMTuc4+F86Ftbsa+81iyAAWXr6I8Ng62YxVfIS2O0COa+lFZYsE2yfOdYoH0BI3Y3hjiBW9jfTxxJQalUp7hiqZRFiqXtHSlsZYme4f21o8eZ2OGVwI98MntNJyWE2hXqbI0t8BQqk9P50zf9efdMcHGnDlTbE1Ih7C39VV8Og5OGY5xNuY4ktNz3DIhJIlIUjrimOkIylsqJs8jC7LhUuDpW0U1+OR6Br69xx8WumlsnCAL0pFfPdiB9RfF+4jKXku69wS/9Vy+YZbGeH+YN7bFZsoNtNveKR8TVlSB3Sl5WObloJRhnyYwKI6UdP3ReEiRMREF0T2cVowQayMs9bNlhn0KGEurir6b5i9hwCRHkLgVY7D48C0WC6E3qppa2Opk6TEQ1Z8iKx0e93XCTCcrPHs1lkk/9TWNMtDqEeEqISGmnZ9ppW9v9yPdV6TL/010Nt4a4YGPItKYdFhfoPca0VpTCw1jY4l3m7qrO/pKfWoymvLzkPjcOonv83ZuR/m503B58dU+75vD+V/BDfv/YaQN9IpQVVWFoqKiLuM0ydrk5OTAxUX2ElPywn/sscckdOPJWE4a9OKQXA5J05w9exZnzpxhEjpC3Nzc2DGVkQwSH+zSJAFt5An//PPPs2NlZcnXaVem3L1B8Qcefvhhpqf/559/Mr3//pzXK6+80k2Dv6rjlNz0X/2wRsxbVWDE9w90RmNjCxLjcuHjLwgsFB2Zzoz79Jsstu1aJ+Fpk5lWhEcWfI6tO9fBx19+8CR5RCUWY8kML5gYaqGiUyZjxCBrNLe0ITZVtnRBZIJgUN4h1jkQ/t0h5lEg7RHU3plG+L8iJFZWswC64v4Ag0yNUdTQ2KshWlkc9XR7HZhJE1lQjeFSEyMjHYyZl70yqMqYZpd2qBIE+O2f90dcRTWWeTrAWkera/n1EHMjtt/4CtkBLQlbMuqP8Mft8mp8cDO5T74Zxu5uSP3jAAs4KfQGLk9IYgYiPRubO5aHqM3NFwzse4CMAIJnUfbZkGYuDQKCLY1wPEuw/NxKVxP2BtqI7mEQQYO0LRP8oaOuxoz6FBxX4rhS/3d9z4Kqya9ZEw83lCelwPVekU52WUIyjN1d72geVpb29h49+loaGlASHQvXGfInZqOSS7FksgdMDLRQUdPZtvhbMWM8BbyVRWRSSfe2pbMcPd1zQrmq3iRkBjqTXnpMVO+d52LpKbhWRQmpsB0kmGguy8hBc10DLLxkX8e+5BFed/Z/L+0MyUolV9ci0MQIx3NFRtogM2PEVPRvSbY0JM8QaGqELQmS2u0GU2fCYMq9GGHZ0GU0pvKTPE5xQiosfATxBWhyoyw5HT73y45dZGBt0WseRdLIg5XJxBhVSSkS31cmJEHP3k5u0E5DNzdUxMYplKciJg7NFZWwkjJWktb+6O+2SXyXuf8AC7Ib/M4GJmNGGLm7oiIpBU4zpon2mZjEvpdHX/JQXdQVFsHM31e0Hw83NIutnhPe+ioaGhi39TNY64ncximmQcqpS2isru2S1ymKS2YTDaau3YMC9zVPT5CUVd7NWIQ+Kt+JQtfVHRWXLwreMZ11XJucAA1TMwmDyB2BPbP96xeQ4wAxzM64y5nAz0IfRtoaiCyQLSWx7GCMhHegtb4Wrq0YjocPxuBy5z4Uge6XrL+PoqGklE3wEBUJSeyay5PMUSSPkbubDBdG4TrKvr8ncmsbWD9xsJkRosuqu1bhkb7+riT5Uhd0F7wZ7MXiHD15JYYF7OxLmr4QW1HDgpKLM8TMCGnVdRLa3dKMtTbD64M98XlsGo6JtfP9Ia6ihvWNPA312TuECDA1Yu/w+MrejbPy6BDbxL+jf5SxY1LffYmvLUy01VHR2NolA0MxkmLlSKTKgozOvd1pHqZ6TAJLUaO+sF8628VaYkxEWvgFdY09TgKFWhpj43AffBufhb1p+Qqfg7JPys2iaoTaSPa9qf6ii5XrF6ipiuqO+tu+31+U+P2d0Z5wMdLBQ0eiFQ5CzPotVbUYbGaII9miiZ5gc9GzLI/VPk6Y42yD56/FIaGyts9plIXO/ekgZybNJTTIh1obobqpBelSMro9Qc0gW1PUD+96HWcX1meoT02CUYhgNXRLRTmaS0ug0w/Dvu3DK2H70Iquzx2trczIb7dsFQyDh/Z5vxzO/xIePPc/DAVwJa9x2pSBDNzCATh5xJOxW17gXfotLi6OGdUJ8pwnCRpp3N3dmWwOecd7eHhg5EjRUuq1a9fi5MmTEoFoqcwffPBBr2X95JNPUFpa2jWRQbr1FFegNxQtd2+Qpj/JF1HgXooBMGfOnH6dl5aWFiub+NaTDA95uJN+Pm30N0HG/MAhLvhy00EUFVaiIK8cWz/7G0OHe8DVQ+QVOHX4a/j1x3Pd9kObcFJITU2lTxNEJ69lI7+kDm+vGQ5TQy0mobNuUSD2nUrpCnJrZaqLxAMPY+oIwSD4yq18pqn/6qpQGBtoMZmd11YNZZ784TECL5BTYdnwdDLBslk+0NZSg4WJDktfUFKHmFTFvX6O5xWyQFjLPJyZNw4Z9afZWeNglmjp6iATIxyYNJoZ5hVlmbszhluYQU9dnW33O9lhpJU5DmUrtyT2h+g8BFoZYNkgW+hpqGGOlyUmOJky73ohS/xskP7EWOYNR5DsDk0GkAa/vqYa5npb4n4vK/zZ6a3PzjutFIv9rRFqa8QGjQEW+lgVZI+T6coFspImoqSSDeaeC3BjgbYc9HSw0ssJZ/NLUdY5qUH1fWbGSMxwEASmo0kAMupHl1fj/ZvJXd5QykKyOBQEN+nX35nMQmVqOrJOnIbD5PFQVRe0BSShc3rFWpQnJiucJ/3gERSERTBvUZKSyT1/CfmXr8Jxikh3OO3A3yi8foN5r7c1N6M8PhEZB4/AcugQqGlqyl0a/HdGEdYGOsHVUJd5Ib0y1B1Z1Q24lCcyTP8+PQivh7p31d1X4/3YvUpGffI+kia6pJp5hb0Q7Mr2SXmW+9rDXl8HF8X2K43ztEkojY1H7uVraG1oRPa5SyyApfM0QZwVVhdHTuLk6meUyhP9zY+oSE5jsh0ks5N99iKKo6JhN1okQZZ3JRz51yJYmvqSMtzauoNNnDhNFe1HmhMROcgvrcfbK0JgaqAFL0djPHG/P/adT0Ntp+eblYkOkn5ZhKlDBZOSV2ILEZNehlcfGgJjfU0Y6mngtYeCUVLZgPB4wfNB+vtzx7rA3EibBeYe4mGOFxYF4nJMAYorFB/oDETICEgGarZ1tueGNpawD/bHjV8OoKaoFHXllYjY9SfM3Z1ZoFsh+9a8jqjfDimcJycyBrf/OoGa4lKmmV6ZW4ir3+6GrpkxbPwlg6LKYl9GHsbbmmOUlSmTzFnkasd0ew9kiVZjPerlhF/HBStVB7McrTHC0pS1e/Z6OtgQ5I3UmjqJCQRhXZFnvLC+hN953DMBKcfPoSQxFc219bj1y36WzmWs6H6+8vl2nH//C4XzKJKmrrQcEdt/RVVOPtpaW9FQUYmbu/Yxz2LHWTOZLEj24SOsjSLDenFYOOymTu4qU2lkFPOib6oQGEVtJo7vNY+QwsuXYejuBj1b226/URnFN6GRnH3u/Ntx2mSUx8WjoLOdyD13EVUpaXCcMqlrP6RTfv7xp7s+K5In7jtqW1JYu9FYUYGEnb+wCQi78WMl9lOZnILC8Agmk1aTk4vsE2dgPSyErTYRfxYchgZC19wEN374ja0+qcjKQ+z+Y3CbMAKaugIv2/rySuxZ8iRyrt9SOI8ykLa+uqYmnIbLD1hrOnos2pubUHTgT7Q11KM2MYEZ+s0niSZCa5MSEPvEajTmK97nKNy/D9W3bzF9fdp/TextlJ4+CeNh/ZNmzKxswKn0Urw62hWOhtosKO6GsW6IKqhGRL7I4BW+YjheHCFwOqGRBxnURJtQAkc69HXPmPr7ssmq5J/3oKmqCvUFhUjffwiWoSFsQowgffzzq9ai4NIVhfOQgd/Y2xNpe/cz3X16htL2/slki8wCRBNLykLnRobRBW62GGRqCAMNNTzp78LO/6iYV/h7Q72xeaTAw5iesteGeHYZ7GVp5iuSpq/8lVkAKx0tPORuz/omIy1NMNnOAvvSRQbeMdamrN8njB802soUbwQJjPp3QnZRCBnzb5dXYa2PCyy0NVm5Vns5I7K0Apm1Ir3wQ5OHY0GnVr4iRJRUMAkj2hedo766GtZ4C65LVJlkfIGeOJlZioLaRrw12oP1z7xM9bBuiCP2JRaitrmty8Ej4dExmOoiWAnySIAd7ve0YitcyABLOvsvhLrgSm4Fiju99ZcH2GG+lzVLQ1rzs9wtsNzfjmnuK8PBzELoqKlijZ8z9NTVEGxhhPucrZgevhBy1rlw3yi4GAjGRJRm4zAffBuXhd9TZRv11/o5Y5S1KbufSb6FJHsWu9vhmAwd/p7YFZeHAAsDLPUVjIlmuVlirIMpfowRjYkWeFkzT3t6xxPvj/FkkwFUL5TnPndLzHazwoEU0fMk2daI5GgUNeoLoXqaZGeBsTZmbIJpqbsd7PR08GeGqN+yxtcZ+6aIgm6v8nZkcQmeuxbLJqZkoUiavnA4rYgFm34l1I1Jcw0yN8DDvvbYnVjQFbeLguPefHA0QqwEEyqPD3LENCdzFreAriVNBDwe6IiTWSVsRUlfIW1949ARKD58AE0F+SxeTMHvv0LT0goG/qIVnmkfvI383T8pvF9hX66rjyK0ZdDEUh/sGhzOQIB77P+HIQ334OBgFgCXPO7Ju/zxxx/vMQ8ZxslQ7ujoyAzWFRUVLGCtPN36N954A/fccw/bP6UhD/8xY8bITEue9FQGaY90mjTYvn07+54mFSjwL3m6S6eTBRm+SYqHyk369SQZJG5Il4cy5e4JMsTPnz+fBd2lcxPKC/X3vPrLu588jE8/2I+l933MOvYjx/niuVcll1STB7+4B+udhDzzH3nzJN5ZOwKXdy5AY1MbDl1Iwwc7RDImNPZXV1Pt8kChvsTqd89gw+PDcOnHB9Da2o7o5BIsf/Mk09cnwm4X4tlPLuKx+QF4YVkwmprbcCupBCveOoX6Ti8YRSCv/Lei4rDa2xVzHO1Z0K0/M3NxOEfUOSXjhNBjl1BXUcEfE0exv+l7b2NDTLW1Rm59PZ64JgiweTKvEEvcnPCErwfrXGbX1uO9W3EIL5FvWJVFdFEN1h6Lx/qRrnhrrDvyahrxyrkUnMsS7YeKRlI3whL+EpOPZ4c5I8TWiH2XUVmPV84m489EUSd207UMps34+VRvNsim5a1k7Kfv+wN16V6+Hs8M+79NDGFBhM8XlOGrOJE3LJVJUF5BiWc4WLHBFy11pk1IQ1sbZp4IV/jYFGh1yAtPI+Gn3bjw9ItQ19GB3dhRcJszU5SIjAI0idfZYVUkD+k4k+E+efc+ZgDQtbaE38plEvr6dp1pkn7di9b6BqY3bTd+DJzu6W4kE2djRBpeDHbFrmmBrIN8o6gK687FSgTCpXtM+GwMtTJCiJUxkyk5O08yNsfCo1FIq6pHTUsb28fTg11wYGYwNNRUkVndgJcuJ/Qo8WPu542AlQ8j5a+/EbPjZxbENvDxFTD1FHnKMKNKe5tSeRwmjkHKgSOoTMuAiqoKi1kQuGYFbEJFBlmLQH8k7vkDsTt3Q1VNFZZBgRi0ehnU5XgbE80t7Xjkw3N4Z8VQXNk2h7UtB69k4oOfo3psWx7ddAFvLQ/B5S1z0ErL2lPLsGzjuS59/YOXM/DEXH88tyCQTRgUlNXjaFgWvj38z2gRE5qa6ihLFLyvaEJ1eLAnHlk0AUlp+QiZ8hL+acasexjhP+zDoRc/YO8C20AfDF+1UGKFQntbm8R7orc8NgFeqMzOx+kPtqG2uIzJ95B3/4hHF0NDu/cgkBcKy2CsmYF1Pq4w19JkgQo3RCZIGGdUIdk2k8H+7SDvrs8bhngzS9n+rHx8kyiQGCONZNrna4GeaGxrw8XCMnyfnNXjahZxfGZPZSt8rm7ejua6epi4OGLsy+uYx73EihSxulIkT29pdM1MYOnjgevf/Iyq3Hxo6OjA1M0Jg9a/wIyMfk8/gfTf9yHr0N9MRsxl/v2wGjlCVCY6P9b2CT7rWlv1modorq5GeXQMPJZJSmUpg6mvN3xWLEPGgcOI/+En5gXt99gKGEu3LW1tSuWxmzCGTaBWpaVDVV0Dhi5OCH7tRSaVJsTA0QEBTzyG1L37Eb/9R2gaGDADrdvc2d3KSau2JrzyBCK+/w0H1rzGJn2dRw/FkIfmdnuHCFefKJSHDIlPb0BdSbngOnR0sMkBYvHuryTSpZ+/BqdRIVDv4RnRMDaB07qnUbB3D0rPnoK6vj7Mp06H2QSx943wendCkxrxz3ZKEbS3oz49DRVXL0HLyhoeb7zDvjYZPRbFfx9E3i+7mGFf08wcFvfMgNl40WRKX3n2RCLemeCBE0tDmLfshawKvHZWcpUJPcviz/OdgAw3g55eh6SfdyPsxdegoq4Oy5AhcGcSeeLvNdEqMkXyEP5rVyP5198R9vIb7LOBsyMCn3sSWiYi7/Ubb3+A2pzcrutOEwjE6C8/pSsps8y/pOSyCfn3Q71hoKGOpMpaPHctjgX/FkLvNLXOtpYm7ac5WLKVkbsniQyGxGsRCbhSWK5Qmr6SW9eIVyLisc7XBcs9HVm/+rvETJwQC4RL5RXvpz7kYQ9NNVU8H+DONiHUHr8dlYT+8O6tJDzp64ofxgxhjwHFUSHZHHGo7sTvtI0hvggyNWYdVPrt+FTBZNYjl6JQ0NDItP9fj0zAcg9H7Bk/lBl+06vr8FpkPIoUCA4shKSoHjkSg7fHeODSg8PQ2NqOQ6nF2Hg1TbLfQv2+zhIeTCnGumBHPDfUmUk3FtQ14WhaCbbfEq3gONSZ5ukQJ6YZTw4iH1xLx+8JismSCiGv/OevxuGZQW5Y6G7HZPF+ScnDH+kF3cYcQh7ytGe6+Wv9XdgmhAIzP3dVsCrsUGYhVvo44pUh7tBWU2PSdz8l5eAPsckfRYgpqcEzZxLw/FAXvDbcjdXFhsspuCgWCLfrXuss4p6EfDwxxAnBVkbsu8yqBrxxORkHU+/chJIQcmIiKZ6n/V1gpq2FnNp6vHo9ARk1Yv2WznuM/U2TMl6O7LncNloyduDX8Rn4PS1foTR9hcYLq0/H4vVhbji/YBibXPortRBf387qNmYTQr+TQ9L6oW4w0FRj12BfciF+iu9fLAfCeuFSFP7xGzI+2YiO1hboenjBce3TUFETM2PSGKRD9G4j4z/p6AvHdULJHcpHwXI5dw6usT9wUOlQJpIN5/8d5JGekpLCjPX29vZwcnKSm/aXX37BCy+8wALAkgGaJHhIC17cWE2SMxT4dcQI0SCQgr0mJiYyT3lKTzI49J20jjxJ8JAxnfZLAXmloYC6CQkJbD+enp5sYkFISUkJMjIyEBoa2i0fGfTp+OR9RSsDxPP1RG/llj5XWedOUF1RHdOxlT0vRShuFHhL3mnIsE9SLfJkJlpb25ixqTdGPtD3gQEN5voT8FYRfJ7qXDrdx5eZePFkvtxoHN3nIwDRCX3PTZ5EynqWKIuL652bHxYvr4qc+mR2CQX3F2CmuMQRGSiZB8e/KKtyOb3vdceWuDIPIgGdCzO60Z/rP95NMe1ZoXHi3/ZyOfyFbL3WgdK25F853O99CFdaSde3MtJi8njjkGgZcn/6EHTr/RPX/kyB4quhxGHl6ZxQFCKvLelrLY7slOJRBmYk7KMMorIUNCjWtggN+8xjTQmUzSc0eAvvE03VjgHdtljpKB/0si/vELYqVMYwTLgaRN6+b5f3Pgmm6HUTnzjpop9eixGx/en19PyuuxP9nBkh7f/TfoE82Tm67rGlygcQl9f2KdIvULbvQCt17yTi14/uOFlVTa87RS+xZj+6pHT8dun6lFOevpKZ1Tag+/SWlqr/aB9emWspi9Ky/92YSHg/9HT9LSxUB+yzW1V1Z2+eO30/undf/Kcw0n0McWctCXpoz9m7sIff/5qsvIPnf4E/Mo7jbmS+yz34/wb32P+PQwNMLy8vtikDaewLdfbFMTU17WbYJq/1wEDRbLKrq2wt1C+++IJ5t8syfhNk8Cbve1lYWFiwTRZkMPfzU352trdyS5+rrHPvqa4UOa//JbIMSuIoYtTvL/+04a2/SBdvoBX3nx4A/JPlFS65/7eQNqQMdKTvtf/ltWad4LtMZ36gty3iE6wDmX/DSK0s0prHxEC43MxAiIEFe3b70PYpm6+vRuK7qW3pyztE0efnTr2fZF03ZSd1/m16e3b/yXffP9Uv+KfaAum2T5G6+V/3E8WPz952/8PytMuqzwHw7hgo12og9+EVob/l+Sfvh7v92f1fI93H6EufY6C/Czmc3uCGfY6E5vvt27dl1siyZcugo6O8LqgikBzN5s2bmWd7WFjYgDhX0sTncDgcDofD4XA4HA6Hw+FwOJyBCDfsc7pYs2YNqqqqZNYIaeqTYZ+kYu4006ZNYx7rvr6+MDAwGBDnyuFwOBwOh8PhcDgcDofD4XAkUVMZQEs3/uNwwz6ni4AAUXRxeciTu+kPZEj/t43pipwrh8PhcDgcDofD4XA4HA6Hw+EMRAaeOCqHw+FwOBwOh8PhcDgcDofD4XA4HLlwj30Oh8PhcDgcDofD4XA4HA6Hw+H0iuo/EQme0ye4xz6Hw+FwOBwOh8PhcDgcDofD4XA4dxHcsM/hcDgcDofD4XA4HA6Hw+FwOBzOXQQ37HM4HA6Hw+FwOBwOh8PhcDgcDodzF8E19jkcDofD4XA4HA6Hw+FwOBwOh9MrXGN/4MA99jkcDofD4XA4HA6Hw+FwOBwOh8O5i+CGfQ6Hw+FwOBwOh8PhcDgcDofD4XDuIrgUD4dzl/N5rB4GMsvf0cVAprCxFQOZVD01DGTG2jRgoKKn3oGBTHkRBjZuGNCsXG+BgUx67WMYyKirtmAg09AwcJ/fsBIdDGQur96Cgczwb57AQMZRrx4DmfKmge2X9fi4RgxkDDQHbtsy0Mmo1cBAxkanDQOZrAGuW+FlPrDHRJWVA7vt8zQduPV3sWBg111rx8B+NjiyGeBN2n+Kgf2EczgcDofD4XA4HA6Hw+FwOBwOh8ORgBv2ORwOh8PhcDgcDofD4XA4HA6Hw7mL4IZ9DofD4XA4HA6Hw+FwOBwOh8PhcO4iuMY+h8PhcDgcDofD4XA4HA6Hw+FwekWNa+wPGLjHPofD4XA4HA6Hw+FwOBwOh8PhcDh3Edywz+FwOBwOh8PhcDgcDofD4XA4HM5dBDfsczgcDofD4XA4HA6Hw+FwOBwOh3MXwTX2ORwOh8PhcDgcDofD4XA4HA6H0yuqKh28lgYI3GOfw+FwOBwOh8PhcDgcDofD4XA4nLsI7rH/H2f//v1YvXo1SktL/9dF4fST3PAoJO4/grqSMuhZmsN3/izYhgT2O09vadpbWxH/x9/IC49CY1UNtI0NYT8iBD5zZ0BVTa3bMWuKSxGxcx+KE1KhrqUJl1FDEbT4Pqiqd0+raJ7m+gYknbyIzKs3UFdSDn0LM3hOHQPPyWMk9pNz4zZiD5xAVX4R1DTUYenlBod5c6Ftboa0/YdQcCUMrY1NMPZwg/eDC6FrZSm3TO1tbb3mifzoc1QkpUjkswwJwqC1j4rOLSsHGX8fQ0VSKoAOGDo7wW3ubBg6O6Inxtqb4JkQFzgZ6iC/thHf3MrGkfQSuekNNNWw3N8e01wsYKmridyaRvyWUIC9SQVKpRGno6YSVzf/iqLYJKiqq8NhWBAGLb0f6pqacsvRUFGJm7v29ZhHkTRCmmpqceq1D9FQXoUZn78FPQszQdk6OpAZdhPxx86jIisfmno6sA30gbaBHtIuRaCpth7mbo4Ytnw+TJ3s5JaX9nP7rxNIOn1Fbp7KvELc/uskCmKS0NbaBjNnOwx+YAasvN260pz7/Adkhd+S2LemtQeaZz3b7Ziaaip4ZYwbZnlZQltdDddyKvDm2RQU1DahN1QA/DIvECMcjPHU0Xj8nSy6J2wNtLA4wBaL/G1gpqsB768uormtd0+LguuRSD1wBA0lZdC1tIDHvFmwGhLYrzzNNbVIP3oSRZHRaK6pYWmcp06E3ahhXWmubNiImuzcbvs2D/BFyHPrZLYT13/8A0XCdmJ0CIIVaFt6ytPR3o6MK5FIPHkRlTkF0DLQg0NwAIIWzoSGjnaPaYzvvR9qmhrIPHAIxVfD0NbYCEN3N7gtWQSdHtqWjra2HvM0VVTg+kuvysxL6WwnjOv2ffzWb1B2KxruDy6GzbixMvPmRMXhxu5DqC4sgb65KQbPmwa3MUPlllPZPBlhN3Fu84+w8nLFvW8/0/V9U109Yg+fZb/Xl1fBwNIMPtPGAC5TZO7nXicrPOhpB0sdLWTW1GNbbCYiS6rkltHbWB9LPO0QaGYIFRUVxJXX4Ju4TGTVNMhMv9TDDo/5OeNYdhE2RlG7rBxLXO0xw8EKBhoaSK6qxbaEdGTU1stNb6Chjml2lpjpYA1rHW08dvUmsmoly2aqpYGVns4IMjOCrroaCusbsT+rACfzivFP4WBrhkcWT8TyRRNgYWYIM+/laG5uxT8J1cUTvi4YbmECapmuFpdja3wG6lrb5OZx0tfBbEdrTLa1QH1rGxafj+yWZpCJIR7xdISrgS6a2ztwrqAE25Oy0NKumKcZtRPhP/yBws52wnV0CEKW9N629JSH2o30K5FIOCFqNxxDAjBErG0hsm/cZu+Xqjxhv8UVrZMXQd3CqtsxPY308YSPC9wN9VDR3IIDWQXYl5Hf47kNNTfGfU7WGGpugqO5RfgiLr1bmjlO1qyOrXS0UdzQhD3puTiZJ7+/IU3uzThE7TmEmsIS6JmbYtDcaXAdPbRfeZrr6hF35Cyyrt1EfUUV9C3N4DVlDLymjO41Tci9oySOFX/6GqL+Oo3askqY2Fth1MP3wX6QV4/l6y3PrtUb2G/SeI0LweSnHhKUr6EJyZduIPb4ZZRl5WPG+lWAxxD2W11ePlJ270V1egbUdXVhM2YknGffCxVV+T55iuSh+y772EkUXL6G5uoqGHt4wH3xAzL7u/WFRbjx7kZ0tLRi3HdbeqwPDRUVrPRyxjhrC2ipqiK6vArbEtNQ0tgsN4+Ftham21vhHjsrGGtq4r7TV9HSIXomnfR08fXIIJl5P45JwvlC+WPXshs3kHP4bzSVlkLbwgIOc+6D6eDBPZ5Db3nampqQe/hvlEVFoqW6Brp2dnCaNw+Gnh5daerz8pF3/Diqk5LY+EjfyREOc+ZA38mpx2PbG2jj9RFuCLUxYu3Y4dRifHo9E61i9SHd15vpZomlfjbwMNFDZWMLzmSV4YvILNS1dG8vrXQ18df9Q2CsrYHQn66iVkYa8T5I1oFDKLkm6IMYuLvBdXHv/Zbe8jSWlCLzz/2oTklBe3MzdKysYDf9HpgHC+55ojY7B7lHj6E6OYU64NB3doLjnPtYPfbEGBoTDXGBo6EOCuoa8W1072OiZX72mOosGO/k0XgnqQD7xMY7jobaWOnvgBG2JtDVUENieS223szCzeLqHsvSVFmJjD2/ozI+gY1hzEKC4fzAPKj1MDZSJE9lXDxy/j7CnnMaX+u7OMNp3v3QsxOMR9qamlF44QJKwsLRWFICTRMTWI0eDdvJE3tsN4g1QxywyMcGRlrqiC2pxbtX0pBUXic3vauxDtYEOWKEvTE0VVUQX1aHr25kIbJQsm48TXXxfKgLgq0NUd3Uil2xedgV0/M7SRp1FRUs93DBWGsLaKqqIqaiEt8mpqG0qee2ZaqdNaZQ26Khiflnr3R7lj4bNhiu+voS353KL8TWBOX7fRzOQIAb9v/jtLe3o7X1nx2ocf55ShKSEbH1BwQ+vAB2oUHIuRKB8C+3Y9wbz8PUw6XPeRRJQ0b9rAtXMfzZx2DkaIeK9GyEbf6OdTrIuC9OW2srznywFUb21pi16XU0VFbh/Kffsftw6LL5MsupSJ6UM5eZcX30E8vZALAgJhFXtuxCR1s7vKYJjFwVWXm48Nl2DF44C5Mmj0ZzXQPCv9+Dm59tgWXwYORfDkPgk49B29wUSb/uReSmLzDygw1yO2Jpfx7sNQ8NoJxnTIXr/bO68pFhSZz0w8dgM2oYvJctQXtTM9IO/I3Ijzdj9KZ3oaGnJ/PYvmb6+GqyHzbfyMSB1CJMcjTDh+O8UdHYgqv53QeSxHxPG1Q3t2LtyViUN7ZgrIMpNo71QgMNINKKFU4jpKOjHR1/foVmCx1M3fgKG0Bf/Xw7WpuaEPr4wzLLQPVxadM30NTTlZtHkTTiRHz3C4zsbVFfWiHxfXVeIXIiYxGy9D6YONiiuqgUpzZ+zSafpr66FsYONoj67W+cePcrzN38JrT0dWWWOfbwGcQeOoMJz62UmyfmwCk4BPtj6EP3s/LHHDyNE+9twdzNbzBjp/C8PCaOwIiVC7r2/c1ZDZnHfHuCB8Y6meKRv26jvKEFG6d44ae5g3DPzzfQJmeQJ2RdqBMz1qurqna71zaM90B8SS22RWTh7QmeUGFDw573V5aYjOhvfoDvgwthFRKE/GvXcXPLdxj26vMwcXftc56s0+ehY2aKoS8+CQ1dXRTeuImYHT9BXVcHVkGDWJqRG9aziRUhjWXluPDSBlgFD5bZTpymdsLOBvd98hrqK6tw7pPvWBsQulx+29JbntLULBQlpiJ02XwY2Vkx49qlLbvQUFmNcc+s6DFNSWkttM1MUXQ1DL7rHoeWmSnS9uxFzGebEfzuW3Lblsz9B3rMo2VigtHfSBpZCi5cZOnMBgvqTpz8s+fQ1tAgGNjJuX9K03Nw+uPvELJkFtzHDUP2jRhc2PIztA31YRfo0+88NSXlCN/5J6w8XVn9ipN85iqbeJu6/nGWN+dmHC5u/QW6c/WhFTRCIu1YWzO8FOSG926kIKK4AvPcbPHJSF8sP3OrmzFcyDp/Z+zPKMAXt9OhqqKCJ/1dsGVMAJaejmLtnTi+Jvq439UGGdX1LK2yPOBshwdc7PDOrURk1dZjuYcTPhrqhxWXolArxzi9zN0Rze3t+CE5C28EeXc+l5K8MsgLWmqqeCkiDuWNzRhrbYbn/d1R3tSMG6Wy2/z+smnDw4iOz8In2w7i07eXd2tP/gk2BHmxiYu1V2+z+qfPrw32xKs3EuTmecHfnRn4juQUMeO+NM76Ovg41Be/p+fjzahEGGtq4JVADzzj54ZNMb0P4KmdOPn+Vhjb2WDOp6+xPsiZTd+xNp0mefuap4TajYRU9tnYzgqVeUW4+JWgbRnf2baUZ+Xh7Cc7MGTRTHi9Kui3XNv+G8q2b4bVqxu7Tf58GuqHE7nF2BCVCC9jfbw52AuNbe04nF0os5y+xgaY72LLfqd6kXXPz3O2wUpPJ7xzMwm3K6rhZ2yAN4ME/YJLReW91l9Zeg7ObfoOQYs724mIGFzeKmgnbAf59DlP8tmr0NTVwcTOdoMmAq5s+wXq2ppwGxPaYxoDPU14jRdMEqSFReP8t79j0pMPwjHQC7ePXcLh97/Fok9fgom9tczyKZLnoa83SLxfSzLysO/FTXAdJprgjjl6AVWFpRizch7+ev0L1q8iWhsacOuTL2Dq54NhH7zNDOyx276FipoanGdJ9quFKJon8cefUZGQCJ+Vy2Ho6swmAQouX4XbvDkS+2tvaUHcNztg5OGOitj4Xq/zGh9XBJub4I2oONauPu3njveG+GHNtZuQN3/2uLcL0mvq8HtGLtb6uAms1WJps+rqMfP0FYk8C13ssdjVARFSfT5xqpKSkLJjB5wXLYZZ8BCUhocj+Ztv4PfiSzBwc+1znvRffkFNWjo8V6+GtqUlSq9HIOGLLzDojdehYy247tn798NsaAgc758DFXV15Bw8iPhPP0PghjehZSZwPJFGQ1UF30/3R1pFPWb8EckMzVun+EJNRQUfhHWfaCMGWRogxMYQ719NQ3pVA1yNdLBpgjcsdDXx7NlEibRUrfRbbGkNxjuaobemPOuvA8yxwHutoA+S8dtexH2+GUHvyO+3KJInYctWaBgaYtDL66Gup4eiS5eR9M130Hr1ZRi4OLM0uUeOwmLEcLg9uIQZ/7MPHkbsp58jZON7LI8sfGhMNNEPm6MycTClCBMdzfDBGG82jrkmZ0w0z9MGNc2teOJ053jH3hTvj/FCQ0sb/k4XjHeeHuKCy3nl+PZ2Nupb2rBqkAO2TwvAgkNRrM5lQW18wpdbWFkHb3gdrfUNSNz2DdqbmuCxYnmf85ChPv6rrbCbOgXe69ayusn47XfEffYFhm76kPXviq9dQ3NFJdyXPwxtcwtUp6Yi+bsdLK3DTNntBrEq0B6PBjrgiZPxSKmox3NDnfHTrABM2XOjWx9JyGNBDjibVYaN19KhpqqC1YPtsfPeAEz9/UaXE5KHiS72zhmMfYmFeP1iCto7OvBooD3cTXSRWiHf2aHbsbzdEGRmgrdvxqG6pQXrfNzx1hB/PBUWJbdtWeXpiozaOvyZkYvV3m6Ce14qLT1fP6Vm4oCYA1EvQyyODLj8y8CBX4v/MBcuXMDChQtRVVUFdXV1tr322ms95rG3t8fevXu7Po8fPx4mJiZdkwNpaWnQ0NBAbq6gkYyMjMSUKVNgaGgIBwcHPPbYY6isFL1kw8PDu46tr6+PoKAg/P777xLH3L17Nzvuli1b4OvrCwMDA0ybNg1ZWVkS6Y4dO4bg4GDo6enB2dkZ77zzDjP+Su/nu+++Q0BAAIyNjTFx4kSkpio2M6tIWZWpH0XPSxFSjp6BhZ83XCePhZahAdynT4SpuwtSjp3pVx5F0lRmZMMywAdmnm5Q19aGha8n2yrSM7sdMyciGrVFJRi+ajH0zE1g7u6MQfOmI/n0JbQ0NMospyJ5/GZNQdCi2TBxtGODOKdhQXAeGYzMqyLPvfLMHPbG9p01mRmNyXPLY9Jo1BcVI/v0ebjMns687rVNTOCzfCnzii2KiJJZpjbqbCqaR0WFTXIIN2mvicAnVsMyKBCa+vrMCOh87zS01tejLl+2lzzxsJ8dEspq8WNsLjPm/5FciEu55VgxyEFuHkpLnhLZNY3MU+doegnzPhliZahUmi4yE4DibASvXMS85E2cHRCw6D5kXbrOjBiyIA/8ysycHvMokkZI8rFz7B7wvHdSt2MZ2dtgzLqHYOnpyrwfTR1t0Ur3i4oKbAd5Q9fECMNXLkBrcwtSzl+T29mOO3wGvjPG95iHjuMyYgh0jAxYmsHz70FbcwvKMiS9zalfKX4vQIYHjZG2Ohb42eCTqxmIKa5FXk0TXj2dDC9zfUx0EUwSyCPYxhBLBtni5dOSgzohjx2OxRdhmSiqle/lIk3msdMw8/WG40RBG+AybRKM3VyQefxMv/J43D8TTpPHQ9fCHBp6unAYNwo6FuaoTBENYulZEa+vvCvhUNPSgs2w4G7HzIm4jZrCUox4dBFrJyzcnRE4bwaSTl/uoW3pPY+FpwtGPLoY5u5O7D6i/8lrtDhZVE55aapSUpF/9jwcZ85gXvdkkPd4eCmaKypQeqO7V7GwbVEkDxlrxLeiK9dgGuDP0otTl5OLnCPH4blS9oBSSNyRczBzdUDA7MnsPvaaNBL2Qb64fehMv/PQ6qbzm39E0AMzYGjT3fBK+QNmTWK/kYHfbXQIWxHTkpUq05v+bG4pTuWWoLK5Fd8nZKOgrgkPuNvKLeeTl2NxLq8MZY0tKGloxkc3U2GmrYkh5kYS6fTU1fD2UC9sjEpBTYvyDg/0fJORdH9WPm6WVaG8qQVfxadBS00N0+y7e1cL2ZKQju+SMpFXL/s+FXpin8kvRm5dA+rb2nA8r5gZ9T0NJb3N7iSLHvscG7/Yj4Ii+Qa0O4mHoR6CzY3xVXw6q4ucugZsS8jACEtT5pUvjyfDYvBnZoHcazbW2hxVza34MSWbpaH97kjKwjR7S2YM743s64J2YuTqRdDvbCcGz5+BpFPy2xZF8lh6umDk6sWw6Gw36H9a7VKUJGpbyjIE/Rb/2ZOhpafLVrOQR3pbSRHaGyQNI7TigyaItiZkMG/9sOIK/J1ThEWu8lekxVfWYH1EPC4Xlcs1kNBkCa0MCSupYJ7EEaWVOJ5bjCVu9r3WHTvG0XMwdXGA/6zJ0DY0gOekkbAb7MsmzfuTh37zmzkJhuTFqasD11EhMHGyQ7FY/clLU5AoSnPzwBm4jwyC19gQ1o4NWzQDhhamiD5yQW75FMmjqib5/ko8Fw5dE0M4h/h1pQmeNxUT1y2Bhatk363oWjgz1Hs+tARaJsYw8fGCw9TJyDl5hrWnslAkT1VaOgqvXIPPymXsd3qfmvh4dzPqE6l7/4SBkyMshvTs5S5adWSFXSlZSK2uQ3FjE76MS4WzgR5CO50bZPHurUT8mpaDsh48b+m+FN8m21rhUmFZj6t4Ck6dgqG3D6zHj4OGgQFsJk+Gvosr+76vecj7vuxGJOymT4e+szNbEUFp9RwdUHBadF96P/kELIYPh5apKTQNDeG6ZAnLWxkbK/fYk53N4Giggzcup6Cwrgm3S2qwJSobi31soache1VQdHENNlxORUxpLfPQp//Jy3+IleR7jVg3xBG1za3Ymyh7gk+6D1Jw9jwzBAv7IG4PCfogdP59zUMTRbSawWrMaGhbmDMnDrtpU1j/pS47W1R/ax6D2eBAaOjrszq0n34P2urrUd/DmOghXzvEl9ViJ42JmlrwZ0ohLueW4xF/+WMiSrsrTmy8k1GCpPJaBImNd54/n4C/UoqQX9uEyqZWfBKRwQz8ExxlT9AQlQmJqMvOgduDS6Ftbg59Rwc4zZ2D4mthaK6q6nOeutw8dLS2wu6eqdDQ14OWqQmsJ05AS1UVG3sSNuPHwWXhA9B3dGT1azooAJajR6E0IqLHfsvKQHvsjMnD1bxKlNQ3Y8PlFOZIMN9bfr9l/blkHEsvZZMilGdLZDZ0NNTgZy7qk7wywhXxpXV4/2o6S1PW0IIPwzKUMurrq6uzZ/6X1Eyk1dSipLEJ2xJS4aSvh5Ae2paNtxPwW3p2j20L0YEOifaF2/U5dzPcsP8fZuzYsdizZw+MjIzQ2NjItnfffbfHPCNHjsS5c+fY3w0NDbh27RpUVVWZAZ+g35ycnJjBOikpiRnO77//fmRkZODSpUvMoL106dKu/Q0bNqzr2AUFBWxi4ZFHHkFYWFhXGjLO5+XlsbIePHgQcXFxUFNTw5w5c7q8Oem72bNnY/HixcjOzsaOHTvw5ZdfYtOmTd32QxMAhw8fRmJiIjQ1NbFq1SqF6kuRsipTP4qcl6KUJ6czY7o4Fn5eKBMzPvUljyJp7IYNQUlcMjPG0uChPC0TZUmpsB/e3fhWkpTODK46xqKOk7W/F9pbWlGWLurY9TcP0VRbB3UdLYk8ZEBMPnkRbS0taKiqQdqFMBi6ujBvBlMf0RJqMrLrO9ijMiVN5r5JHkTRPDlnLuDM6qdw+aU3kPjzb2ipk7+0kX7LIU9mC3MYOMrvkFLHM7xA0gslLL8Sgy0MoAjkHTTOwRRuRrps6W5f0nTkpQIGJjCwFi2ztfLzYvdueUr3SR2iNDkNOqbGPeZRJA1RkZmDxMMnMWzNsl49SSlvfmwyWhqbYOMnWjKtrqnBZEGKkzJk5iMvf7pPbPw9Fc5DslBk8KTBPqUTJ+NqFH5+6DnsW7cBl7/+Fajvvpw3yNoQGmqquJojMqblVjcis7IewbbdB2xCDLXU8cUMX6w/mYjKhju3CqsiJR2mPpJtABntK1LT71geeh5JuqepohKWgwPkXsO8y9dgOzyETSBKQ8YcWtUj3k7YBPTcTiibh8pQlVeIrPCbcAqVbewQT2Pk7sbaCSNvUTtBg1U9e3tUy6kLGtwpnScnF7WZWbAeK5KgEC7LTvh2O9wWL+xm8JeGjInizwZhG+CFkuSMfueJ+v1Il+G/N9paWpEdGcvqUNNncLfl2D4m+t1kd26UVMLfVLG2jzDSFCxWJSOlOOuHuON8flmPsj49YaurDVMtTUSXifKT1EtcRTXzjO4PFwpLMc7aHBbamqwexlubQ1ddHdeKe/eYvlvwNzFkXuAJlbVd390qr0Jbewf8xJ5RZVFVAdqlhus0gCdvPTpmbxTJaCfoPqd7tVRO26JsHmo3SNKN5OPE2xbKQ5NdiSdE/ZaU82HQ8vaHqo7kKjN/EwPcLq+WONOoskp2X5po9j6BIQ/y4pfukZLnpYeRPpNc6Q2aBLWWaids/HtuW5TNQyskcqME7YZjyKBe07iECt4zdD2KU7NgHyD5vrIf5InCRDnH6lOeFiRfvAGficNlSlRKU5Waxrzp1bRE3tEmvt5oratDfUFhn/OURt2CppERM+b3ROnNaJTHxjOJHkXwMjJgqwRJfkdIUWMT8usb+t32iUPPq72eDo7l9mygrklNg5GXpJSSkY83atLS+penox0q1KCIo6qK6lRJyU1xWhsbaXYbqjL6LULIGJ9aWc8Mn0Ku5VdAS11VwlDaE+SxP83FHCczJeWJQqwN8YCXDV67mKzQfupyOvsgXpJ9EF3qg6Sl9zmPqoYGTAcHMqme5qpqZugvOH8BqpqaMPbzlblfuncLzp5jEwE0gSKPIEtDXC+UGhMVVGKwpeJjIvLYdzXWxbls2WMiQltNFdrqqjKljoTUpKYyCRxxCSJjHx82QVuTntHnPIYe7tAwMmITKDSR0lJbx1Y8GLgJJlLkQXWo1sO952ikzVZ5hOWJ6o9W/UYVVst26pKBvoYalgfYMeN9ZKGgDdBVV8VIexMcSumfXKCwbbldIWpbijvbFh+jvvcLhMx3dsC+iSPxzchgPOLhwq4xh3O3wqV4/sOQMYyMzgR5oSsCeaCTwZy4cuUKXFxcEBoaygzWZPg+f/48S0OQUX3mzJlYu3Yt+2xmZoZt27Yxb/r8/HzY2tpKHJs81ufPn4+//voL+/btw/DhwyWOTZ72Hh6Cjj4Z7h0dHXH27FlMmjQJn3zyCUaPHo0XXniB/T558mS8+uqreO+997B+/XqJc/7hhx+YFz3x3HPPsTK2tbUxo3pv9FZWZepHkfNShPbWNjTX1jHPWHG0DAzQVFXd5zyK7tdl4mjUFZfi7Gudy8LJ+DL3XjiOFmllCyGPa1oOLQ55Y7Hf5JS1L3lIiocGcGOfWdn1nZ6ZCcY//yiT4yG9fsLcwwUOk8YiLj0DmgaS50mfqfMpi6ZOz/He8hi6OMP1vnuZ11Ndfj4Sdu3Gzc+3YuirL0h47mefOovkPX8wD3Hy2h/8zDrmSSUP6oSRRIs45DWhp6nOOlP1rZJSF0KMtdRxackIqKuqoKW9HR+Hp+NyXoXSaRi1VYCu9PnrsfOSd10aK6q7rp28PIqkaWlsRNhXPyDo4fnQNTNhKzrkQfI7ebfiuybLSONYHLq3aopld+QbKgTHky6PrDxplyNwacvP7BqSEXPii49K3LcmDjbwmDCCeWmS9nLY93uhdWszmua9AqiJjC6WeoJBeXm95PUtq2+BRedvsvh4qhdOpJbiUnYF87S5E1AbQJNN9MyLo2mgL/fZUCZPXVEJLr38FqszVQ11+D28GCaeorgE4pTFJaKhtBwO4yWN10JIP1nbqPt1Ikjaor95jr35GTMq0X3kEDIIIQ/e321/0mn0aDn/jUhoSN0/5A1IXlayEHpnKZOn8NIVaBobM+8scdJ+3QNDV1eYh4j0a+VBdUH3rXRd0GRYS0MTNMQmSZXJkx+ThJQL13H/ppd7PH5jTS12r3qV3QsqaqoY9vBcpHhKno+xlgYb4JFHnjiVTS3MA19Rnh7kipzaBtwsFdXnfc5WcPw/9s4DPKqi6+P/9N57752Q0HvvRQERBUERBVSa2Duo2BEsKKJgQRGQphTpPfQAIZDee++953vObDbZ3exu7ibRN/jN7332ld3M3J2de++5M2fO/I+hHt4PFeb8kAc59Vl76qTbV1pXD1t9xRNrIVDk/9pgX+xskQ8hB/gnd+OUavffb1joaKG0vr6dA56i7IVE1iviSm4RFng4YZ67Aw6kZDPJmUXeTsw5LeS41Qquc2W2RZU6f7/TZjfIKT3w8VlS45axLy/F2Q1bcf3nfewzKy9XmD35ktzrL1Nmsbi05V6hv1EUf2eg6OhH3R1wIbsQEcVl8DM1YrsdaGGE7sncauW5X6oV2FmSTyRboaWr0+k6ZDf2LG2zGwMef4hF9ksir4xLX1GZ6vJKNDXSM1t6jEnvqxSd207UIeme2spq+I+XlhZTRG1JmdzxJcGeo44OnapTnZcPfXtbJB04hKwLISxc18TDHR6PPAR9G1F0bk1RMWK3/45eK5+Tu4iuzPaRrZOEbKGZxEJDVyEt/vSKKkQo6GeCouPJmSnvGVpXVtbpOqR7bhoYiKzjJ2Do5sY0+EmTvzw+AZoG8qUcidQ9e6FpaAizQPlBC21jeunIYvEY30pPef/teiAIwdbGTA7ldEoBPpWQ7iG99PWjffFWSByKa4UFfNSXqD4GEVrH66lFiN70DUJfeoW9p10PFKFPUeqSZJ0+i+Q9eykij0Xt+61a0bk5kZbyORH1z8W5bfOd9TcUzHdaeL6fKxqamnFKZvFEdgxHv1sSTUMDtgCkKGJfSB1aKPFbuQzRmzYj7a+D7DMDJ0f4r16lUD+fFgUKbtxgOwEUYa0v6tdC2euvpp7lfVDGA55WWD/Wl/UfOfWXnYhEcY3oOnMw0mWf0zrYvlnBTH4np6IWO6Oy8WuEcI19sf0ok7EtZd1gWyKLS3EptwCpFZVwNzLECn8vuBjq492wyC4dl8P5X8Ed+xyVIKf08uXLWcQ6OanHjBmDAQMGMPmZ119/ncn7fPyxyMF78+ZNREREYP/+/WzCIn4RSUlJzLFfX1+P9957j9Wn6PXa2loWyT579myp79XT00NAQNv2VapL0j5RUVHMAU7R7rKOcIqeLy4uZm21s7Njn1lZWbU69Qlzc3PWhoqKCrZzQRlC2qpK/wj5XbLQd9JLkoY6+ZOqdlElUjQLqCOkDBB94CjSQq5jxNurYerqjOKkVIR+8yNLFuc1bbySNrQcTxxtrcImBWV1ilIzcPHLH+E7eTST5BFDGvtnP/0OgbMmw2vcMKZVG/rLHiQdPKrkO1TbOSFbx3tu27Vh6uWJgCVP4vraj1hUv5lPWzSa0/gxcBwzim2nTDp0lGnsD1n3NnRMlV+TkpCToqURCsvQVtLgX0LYYJf089cN92KRJ7TVVJUyHdIZkUIhdVrKUGJdkoRykrMrRJbxrz3DEtomXryOKz/sxtUf/8D0D0ULgIzO6EbLqUNb/EmOhyIqSb7n5Ieb8cDHr8DEXjRZ7vPItNayOobOGP3C09i77B2op0agyb19crhmOT9dUUsfC7SDq6k+S5bbvSg4J0r7THgdAxsrTNz2NZMPyL15BxHbd7Htw7b92/dH+oXLMHJ2hImb8gR00l8p+k5VrkZFdSa9u5pN/otSMnDl+524+PXPGP3iYqVlik+dVvQlqm/3VVCHot7yrl2H3ZhRUpO7/NBbKI2PR9+1b6v6TXL6orlTdWrLK3Fh03YmVSW7OCuLrpEhntz5BVsQII39S9/thO6DRtDtL38hRxKasgu9i1cFuiHY0gTLQ+61Jk4lh/6zAa547uJdhYkKu4Iq7VPEu318YailiadDbrOt5cNtLPBmkDfeuhUtFSX7X4Si7bsi7x9XVsk055/0dsKTXs5soYDyGfjKOI5VoTvHLVPea7Mbl7fsxPmvfsbYlxa3auyf+uQ7BD00GT7jh7Jxy7Wf9iJ3ywZYPv8Wk7FQhtid1ZX+25mUwRw0rwV5wlJHGykVVdidlIlnfF07r1sgHkeqcr/JqUN2Y8EOkd3IDItkdldTV4ctoisrY2ioA/9x0sFDkqipqas8jFFWJ+rUVTj19oaJrbQDU8UvQCcuOqk6bGdITBz0rKwwcN07bHda3O+7Eb7hawxct5ZF+0f98CMcxo6CiYf8/FzKUGXcoir6GhoYYWOJXxMU79RVSjeM9TyefBKpe/ch6vMNTDKTIvptx4xhzlN5kF584c1Q+D2/mjmxO9WXHTR7/pFwaKmrw8/CAB+O9MbGsb5YdUaUk+TDEd44nVqISxldl1PrTI4VyTq0sBb15Vcserzvh+uYnEzelauI/mYzAl99WSo5rt24MUzmqK6kGOlHjiJywxcIXvsOtFWYE7XtfFfc7tLaBvT9VTTfGeFojveHieY7lLtMlrm+dpjna48VZyKZrF+naO58HVqUi9r4FWzHjIL9+PFoqq9D8p59iPj8CwSveYvtiJCkOjeX9a3VoEFM/khVaHjU0Sk/nJCPo4n5sNTXxtO9HfHT1EDMOhCGlNLq1nwtK/u7YPXpaETmV2Cwgym+Gu/HFkfIwa8K7XaNdYNozg8Ssm33ikvxTVQ8PurfG66G+uw5xxGGUncT51+FO/Y5KkFa8DY2NsxpTa9Vq1ahf//+7L/R0dFMakcckU5R8KtXr8Ynn3zS7jji6PgPP/yQ6dRv376dHZu065cuXYqioqIOBxT0maSGvmyZVgeDxEhb0cBEiPSNkLaq0j9Cf5cktChAiwuSDFvyOLT09VBXVi71eW1pebtoezEUedJRHSFliIRjZ+D94CRYtUhvWAf4wG38SNzb9Scidv8l+szHAxPXroauiTFKs6QHTJTMlJCNzhKjSp3itEyc/mATXIf0Rf8npBeH4s9ehqGVBXrNmMje65A+38KHcXC1qD/rysuhLfG76L0iORydlu9VpQ5hSFFWamqoysmTcuzTOVfT1GASPP6LFuD88heRcz0ULpPkL4zQdl0zXelBnIWeFhuQkv6jMhqbKdKhAUcS89Df1gTz/e3bOe1ly1xbtxJN6emiP1rZQ/3JtYCBMVDVJpnAfn9FpWjXgYLtkdRvteXK6wgpUxCTgMq8QqRdbtGNbLl/j77wLjwnjGQR/lEHjrXWf/CTV2HrL+rv/PgUlGTkwLQlwV1NWYXCa0+vZQs5lZFEXh12DjU0YGBuioELZyP1RjgSLlxHv3kPyj026S436+hDvSyv1fnCfltLpD6dz9zKtggaC30t3MySP5kY6GgKHwsDxKwcKfX511P88XRfR8zcJT9XREeQDSBHe53s+SiTvu67UodkCUjGiiLxCyJjkHbmQjvHfl1FBfLCwuE7T5R08syq11DfcnxrX3dMXruaRceWydgJWmQhZCNnxahSh3a30ZZxytnQ77EZOPvZ9yxinXIqKCtD1JOdkIjGovekoSoPSiynSh2SViAHg+2IYVKfl8bFoSa/AFdWvtD2YVMTEn7byV4UCXaJ7WB5AL1nTICeiXG765z6ghZo5UXUivpIeR2616qKy3Dyo++kn7XNzfjp0VWY/sGLsPZybes/DQ2WkNpzxADkRCUg4epZKcc+RebThJCihCUx09FievYd8VyAC6a72uD5SxFIKG2TRCN5HxMdLfw6rm1nAzkym5qNMcnJGtOPXleYQE4ScUS0CcmeVLYl1qMIcdldBqpA0hMDrczx0vV7SGs57onMPCbNM9PF7j/j2Kf+M5FxTtBSFX3Wlf4jLucVsZcYB31daGuoI0smr0HOmtVoqqzALyQB5+uOKe+uZs+BUgV2QleBzIgqdWTtxhkJ2xJ3hsYt5ug9c0LruGXQooexf9V7qEuMhY63v1T/0bUmiViCpyv9Rw6eX+LT2UvMw672qG1slNIvrktNRMHXosCVX0leZO4D6NViW2pln6EtdoKc8PJQpY7YbrhTfoLoBCZbJOnYF5fJvBOJS5t/Ywlqz37zO3T0deHav1fLbkDp76oqLYe+onPbuoNQWB2S9MuIiMekl5TnOZGEnpWyY2/xe9Jt72wdHRMTFpzjPf/RVkeg19w5uPb6OyhLToaZrw9K4xKY7FvKob+lbPb5xcvg+ejDwJBJ7b67pOU6INtHuT/E0PUYqSS6XhXG2FlBnaLSZe4reWMQDX199syUpL6svF1UtKp1tAwM4PnkQqky8Vu3MakYWbJOnETG33/Dd8VyGHtLy0rJQtHSJKUjiXnLGF9SnkfR/Vnb2IQ7eeX4/EYyvp/UC1ZXElk+mQF2JjDS1mTjfEI847z++FBsCUvD17dTlY5BJH87vVckhyOkDiUdpgjyPu+tbZWcsZ8wngUm5Jy/AM+Fj7ebE1Ekv+cTC3D9+ReQfyMUDhOFz4nM9bTYfEhWck8W8Xzn7yTRfOcxP/t2jv2HvW3x2kAPprmvLKJf3Bf1FdLXEe0GofGXonGzkDp5V65AXUcHLrPa8mF4zH8MN154CUV378GyX9v4pTo3DxGfb2QLT5RIVxkFVaL71VxPG4klbeMWmoOI5yMd9R/NVT66moSJ7pZ4yMcGG2+ksAh+4o/obFzPEo1RTqcU4lhSPqZ5Wgl27JfUKbIt2ojuJtsiJrlcNC6019fjjn3OfQl37P8/hxK5KnIiK9PmP3r0KIvIJye1tbU1k9kh6R0PDw+mH08EBwfjzJkzzImvyKFOcjWUwJei68Xcvn2byfVIUlVVxRzjfqQ5R9qNublIT09vfU//DZVJDnPjxg0WhS+W/OkqQtsqtH+E/C5Z3njjDSYfJMm6iMu4XlqB/Jh4eD8gcloT+VGxMPeS1viWxNzbvcM6QsrQBEdNTuSSjokxpnz9AQw02pb9rbzdEXcqROQYbYngzImMY4NqC/e2aA1JhNYpTs/CqQ++hvOgYAx8em77a47eyn6kJopwVdPURHFsPAwdRNcKSYmQjr7T2FFy22Tk7Ax1ctaoUIdgCXGbm6GtRC+Y7WzpIINPWG4ZG7BLMsjOFHfzVBvkaKh1HE1FZb7asQdzDoW1fCKqoebggearf6MirwCG1qJJTV5kHDvXFhLOOkksvd0R/ddxpXWElJm8Ya1U1F5+dDwufLQJUzasYQl3Cb8Zk2Cg2dw6qaeFAUqeRxr44s4l3VvSWQ+aPVlueynhHl1zOVHxsPX3FFRHDLOrSs5hZVEJ1Gqr0KwvfR7DssvQ0NSEQY6mOBQr0qa0M9SBi6kebrYMjmV54Vg0XjreljBXW0MNsatGYfXxKBxuOUZnMfPyQFFsPNyntdmAwug4mHq6d2sdgs6RvEXWrCs3REmPhwxk78d++TErZ63b2GZbfNxZYkqVbEsn6ojbKfpHc4dlyHlSGhsPA/s2O0Ga+PZjpCXZxBi22BahdXIvXYapv1+7reykq+8x9xGpzy4vWwX3R+ewRQCy217G9a1R/jY+bsiOkk5Wmx0Rx6SjFD2/O6pD/120+yvpNny/mzk8p773vFKtaVH/SfcvRdPHllSgj6Ux/k5tm3j3szJBeIFy2/dMgAtmudvhhUuRiC6WdsidSM/H6QxpOa+vhwciu6qGJdKliasQMiurUVxbh97mxrjXIuOlpaaGAFMj7Ehsc4qqivieaJab9O2/k+otorgcepoa8DExRGyL07S3uQmTmKC/dSdj7S1Z5D5p0kti8+5Gdl9PdKhqtS3kcI85KW0n6DonO2GpwE50po5826KmMGhFNvk65XKgBLqS+wb7WJggp6qmwwSCnem/6/klUjtctF08YNeymDnevqrVtlBycVqok4TsrJWXYtvSmTqt/afgnnAb1p8FfVz9fhdK0tLhPjhIZKc8nJAZmSAlk5N5Lw52fvJl4TS0NFWqE3XmGnSNDOA+UL72vzwoP0vS/r/YeEOjxQFfEhPLor71W3Ygd6aOiZcHsi5ekh+K2/LZqK3fSn2cE3IFcTt2YeT3m9g5TZGTJiqmtJzlwuhtZozzOSKZEsoHYqevi6hucr5RAvKruYUoFZDY3MjDHWVxcXCY3DZOK42NYXrk3VmHdhsWR0TAfmLbWIfIOnkSaQcPwnf5Mpj6y9ePl+R2bhlLlGumo9kqmTPE3hR1jU2IyBdu+0geixCf3iE7rkqN8ce5WOCbCQEYuuMqi1aXh4HEGES/ZQzS0DIGsVUwbhFUR9woefMwZbk6xHMiJQPqO3llGGDbfk4Unl+mcuSx7K0x28sWbw32xCsXonFGif6+GGMPD2QcOcoCK8QLPqUxsezAhh7uXajT/lkgbqzkbk2K7CenvrGXF7yfXqRQpkcMRdfTwtJAOxOEZrfIzKqrMX19SuCsCnT9qUlI+SSXVLUzxy3rhIKJa7EtvcxMcDFHNE6jnWO2erqIKenecQHJ8LC2d/PzkvP/k4aGBpaHs7S0FP369YOtrSigUBn5+fm4c+cOCx4OCgqCvoo7vXiGiP/nkJ57eXk5k7IRCjmrd+/ezZzU5LQmRo0ahd9++00qGv3VV19lCXRJmoY09SsrK5kUDSXTFePt7c2c4CRtU1JSwhzX4eHhcr+XtPrJ6Z2Xl4dnnnkGPj4+TEufIGc36diThj99DznhKcJerLnfHQhtq9D+EfK7ZNHR0YGxsbHUS1NbG15TxiHvXjRSL15DfXUNks6EsAS3nlPGtNaNO3wSB59qi94UUkdIGbt+vZF48jxLmkv62oVxiUg6fREOA4KZ44a9WgYWzgOCYGBphhs/7UZNWTmTx7l74Bg8xwxhTleiqqgEOx5bibQbdwTXoWRopz/4miWdGyTPqU9yN/2DUJKejeij59BQW8ci4m7+th865mZwGDUMyYePoSw1jUUHU5JbbRNj2Axsk3q5+fEG3N28lf2btis7jhmptE5FRib7rDIrh22zL0tJQ+S27TCwt4NFS6IoWgiI3bkHldlUphHVBYWI+uk3NvC1liNHIua3yEwEWhmxyBJ9LQ1M97Bmsjm/RGS0lpnjY4t7i0awpFDEB8O92cCX9CYNtDTwoKc1HvCwkYpMUVTmcHIB1NQ1Wl4tjw3XAMDKAbd//oPlQaDI54i9h+E8pB/0zExZEdLD3btgJZLOX2HvbXr7wcTJXmkdIWVYhKP42tJoaxNbZGp5JZ+/grizV1mUJCXNK0xOh4aOFvtbbUUVe93YfgDqGurwGt22Jf/s51tx/H1Rngwq6z91DKKOnkNuTKLcOhUFRUw+oTgti30PXb/Xft6HmtIKuA0XXQv0Wcjm31CUlskm3cXp2bjw1c9oMjRDo6t0gtDimnrsj8rFy8Pc4GWuD0t9LXwwzhtJxVU4m9w2qTi2oD8+Hi/aJUNj5Mbm5tZXi8II+6/4353FddI4FEREIePSVTRU1yDtXAiTknKdNLa1TNLfJ3Fy6WqV6oRv+RnFcYkswWt9ZRXSzl5E3u1wOAxvL4+QcfEK7Ab2YzuIxOdF1ra4tNiJaz/+wewESVjc3X+cRW6K7QQtpvw6bxVSr98RXCfyyBkkXLjGdLHFizq3dx5iSXb1zU2VliGHu93okUj/+ygqUtNQX1HBdO8pOstyQJttufvZBkRv+aHVttiN6rgOUVNQiJLomHZJc8V9RDtIJF+iz9XYYoOa+N5psZUBU0cjPyEFUccuoK66BgkhoUwSp9f0tnMWc+oSi7Sn3yi0juR9yhz5Yp+khFM/ZPPvyI6MY1IZtPBGmvyJIaHQ6ds+2e6u+EyMd7TCSHsL6GtqYIG3A4to35/UFv21LMAV+yf1b32/xN8Zs93tsPpSBCIVOIjJeS/5ErsRhDr1CSr6Z2o2HnKxZ858Q00NPOvrxu7Jk5ltC2xrgn3w2YA2Cb6OoKjypPJKJiNjq6fDbPoYO0v0szBj+vH/FciZT7sPVvi5MaegjZ4OnvNzRWh+sVT03NGJgzHXvb3GuDJe6uXBkshS342zt8RjHo74LjoZNY1Ncu8bqXHLQJGduLqtzU6E7z8O77HStuWXuauQ0mJbhNSJOHKGJcIlXXa6pyjh7q1dh1jCXLFtcR7Ymz0vIiXGLewZZGoGLSfpBfQjabks8d9SHxcYaGqgr4UJpjvbYm9ym54xOfpPTx4KV0Pp6GBl+JoYsv421tJkLzo/TgZ6+CGmLZl9a/+12BpJ2+I3dTQKElMQffyCaBwZEoqMsEj4S9iJuNOXmG0W2xYhdS5v+Z05+0lzn+xG4sUbLNeNx6hBcsvQcZIv30LSlZvwGzO4tX3BM8Yi/tJtpoNPduzWgVMoyS5A76ltO+Cu/HoQ25eubX0vpI54oSHm7HX4jhnIFgSEYjN0ENR1tBH/+x8sSWZpYhLST5yGw7jRUNcU2c6KtAwWRV8cGye4jmW/PiyPU8LuvWioqmb68Yl7DzBnonFLkFI7m90yhpQ8p7KU1TfgdFYeFnq5wNlAj0XqL/fzYIudN/LbIpw3DwnGKn/FjnJFuBnqsySaHSXNFWM/YQJKo6JYlHNjTQ1yL1xgyXHtxrdJnGYeP47rK1aqVCf/2jUUXL+Bxtpa1BQUIP6HH1gyYsky2adPI52c+iuWw1RCalUZpNmeXVGDtcO9WOS5j7kBlvV1xt7YHFS07MK10ddG1NMjMNFVFLyyKNABs7xsYKmnxewaJZB9aYAbLmcUI68lWrpJ5rkmHg/SM0nRo43GILajRiLj6FFUpMmMQfq3jUHufbYBMRLjlo7qUOS+jqUFUvbuZ7KjlACWkudWpKbCIjioNQlv0u49qMoRzYlqCguRsP039jcLiYh0WXZEZaKXeE6kqYFp7tZMWmd7pMScyNsW4Qvb5kTvD5Oe7zzgYY0H3G1wUGJONNPTBm8PETn1SdJICJQIWN/BAYm/72T6+PRbUv88CMsB/aFjKrLrdP1cXvocC8wQWsc8uDdqS0qQfvhvdn3SbojkXbuZFr+xpyjwiBYGIj7fwHaIeC9+qkOnPkHXwS93M/Fkbwf0szWGsbYm3hzqwRZtD8S23W/fTPTDr9MDW4ONPhzlBS8zfdaf1vraeHuYB4vyP5zQNtb54U4G5vrbsSTGLEGxkxkmu1viSILivGjybMvZ7FzM93CBk4E+sy3P+nmy5LmhBW3jn68G98FyP1E/CIGk+J70cmWLj5pqasy+LPfzQmxpGWJbdtdxhEEBgPfj65+EFDp69+6Nxx9/HJ9++inc3d2xZcsWheXJF0tlKSiaAoHJH0h1jh8/rtL38oj9/+f06dOHOZYp8SxdVJRolhziyiDnNK1CSTqp6d+yjuvAwEDmyKcktp6enizxLMnSvP12m+YvycosWrSIJY+lyP4JEyZgzpw5qGvZeiWGHORTp05lDnJyglOyWkpcK5540arWvn378NZbb2HlypWwtLTE008/zZzv3YXQtgrtHyG/SyjWvXzRb+njiN7/N2798BsMrC0xYPkiWPp4ykSCN6pUR0iZoCfmIGrvEVz/8ge2VVrX1BjOwwfCb/b0du3U0NbCuDdW4PqPu7H/ubfYYNBt+AD0f/whmXY2tUauCakTe/Ii++74s1fYSwxNjh/6Zh37t10vHwxf+SQiD55E2O6DLKLJ0ssVfV9cAX1bW6irazBt+8aaWhZZ3PflVVLJmiTbRHjNeYiNkhXVIQe+obMj7n63FVXZuczpb9m7FzxmPdA6yTJ0tGflaMGgMjsXWjRo8fLAgLdegZ6laPAuj3sF5XjxbDRe6O+GNwd7ILuyFu9eisdFCR1N0jckOQnxPGxXTBZW9HFBXxsT9hlFaay9HIeDEoMwIWXEsAHj7JVQu/Yb/n5+DYtEdBrUB8FPPNzWZ/Q/iQg6uq6Hv/Icbv+0W2EdIWWE4DioLxIPn8Dfb29AdUk59M1N4Da0H5uYkvO+trKKRU5OfGu5lP43RQZJnmeSQGioq1NYh5Ib2gZ4Mcc9LRyR89nSwxlT31sNc2eR80nPzAQOvf1wafMOVkbHyJA5b1L6Lga02yenevtsHN4d7Yk/5/VjiXCvZZRg4YG7TIZEjCgplWojk3VjvTC/tz3F/bD30StHsP8u/PMuQlLlby+2DPBF4NNPIP7PI7i37TfoW1si6NmnYO6t2LYIqeM0dgTi//obJYnJzNlsaG+HoOeeYg58SUqSUlCenskS6yqD7MSEN5fj2rY/sPfZt5mdcB/eHwOeeEgm8qttV4CQOu4jBuLu/mMI232EJWGk8+06pA96PTihwzKG46ZCXUubfd+9z79gkzljTw8Evvi8tG1pbGyJSBPh+vDsDusQNCmkhGoWwdKLQ52BEnKOfeFp3Nx5iC1MGViaY/jSeXDqG9DeNjcLryME30nDEbbnKHJjkth3mNhbY9gz8xDp0H4h/GxmAZPiWd3bDZa6OkirqMLr16KRVNbm+KV5O0V5s3+TA8TXmUW2bxklch6I2RyRjN0JwpO4CWF3Uga7Z9f28YWRlibiyyrxxs0oNjlta5/0vTvb1R5LvNuctFuGis7nt9FJOJyewybea25HszKbhgQxxwVFYW+OScKZbOETZFX5Yt0iLJ4/rtWZVxD9M/vvzIWf4kzIvX/kO9+9HYvVAe74dVRfdp1dzSvCV5FtOriyUYHEZwP80dfClC0a0d/IcU08cfF2q9TOtfxifNjPDw4GukirqMan4fE4nyPMUaOprcVsPjnp/3jmbSYHQ3Zi4ML2tkV8cwip4zFiIML3H8Pt3UeY/Bwlgncb0geBM9psi30vH4xatRD3/jqFWzsPsXGLlbcrLJa+AHUd6WdHQW0dXr8ZhZX+7pjj5sASEe9KzMCB1LZFL+o30b0h6kFythybKIo6p88DzIwwzdEGaZVVeCpEtEhB1/AQa3P8PLIP9DQ02OLLyqt3kSkjY6QIK09XjFr9NG7vOoTQX0R2YsiSeXDs0962iL2NQur4TBiO8H0iu0H9bmxnjaFL5kk59hWVCZog2v1FeA2j3DgVCPlxPyqLSmHmYI1pry+GhYu91A48yd3NQuoQaWHRqCgsQYCCpLnJN+7h6KfbWt8f++xHAD/BcfxYBL/4PGJ37MTlF16Bpp4e7EYMg9uM6QrHV1r6+h3Woesn6MVViNuxG5dXv8IkoEy9Pdln9PzrCt9EJ7KFzC8HBUFLQx13i0rx1u1I5kRu/X4Z27fczx3THO1aF3wPjhPdu2/fjsTtwpLWcpMcbdkOqjCBsmMmfn5MDz/90CEk/rKdJbr1WryYRTG3QtdcY6NKdSh5buofe5C0YweLMjcPDobnokVSz+b0w0dY7oLor0QBImIcJk+Cs0RgmyR1jc146tg9vDfcCyGPDUJNQxNzkH50LbGtkJpo3Ce2xwfj87C8rzNe6O/KFgNoHkAyJz+Ed353mBiX2aIxSGTLGIR2LQS8IDNuoTGfxLntqI6Gtjb8n1+FlH37cefddWiqq4OutTW8nnoSZoG9WBlybuvb2SJ2y1ZU5+SwpMTktO79+qvQtVA+J3rpXDRW93PD6wNFc6L3rsQjRGJOpCYzJ/ojJgvLaL5jLZrvpJZW490rcTiU2DbfeS7YhUm2bRgtvetib2w2PrgmvaOo9XsoIGjVCiTu+B03X3tTtAO0fz+4S+6gpH5jc8pmwXWMXF3hu+xZdn1lHDvOFlANXV1Y8lwtI9F8JPfyZdQVFaMg9CZ7iaFzMHjTlwr7b0tYOnQ1NbB5kj9z7EcWVGDR3/daE+GyY6iptY6rsitqmbzOZ2N84G1ugPK6BtzNL8e8g+GIK2obi+2NyWGLJl9P8GM6/JnlNVh/PRm7o1XT198Sk4glPu5YPyCInY97RaV493aEUtvyrK8HJju02Za9Y0Ryle+FRSCsqATxZRXwNDbC20H+sNPXY1H6V/MKsDsprRvU+zn/31m2bBnL6UnR99ra2vj111+ZX3Ls2LEsUFiW6upqTJo0Cb/88kurXPkrr7zClEIo8JcCe4Wg1ixEXJzz/wLSxKcHnxCnMjmuJSV26DKi+uS87yriwbO4HTt27GCR9zk5wiI1FEFtpGOLbxjJ39LZdsu2VZX+6a7f9cbNM4LKiXUyhazgdydMikdFmuhapMg5gY5LRRIeEHA959RoCP4Odsh/uf/ORAlrnzxoRVqViNPO8HBwXbeey84gdgrISnuIpXhUOhZdS3Lu6X+CzWc6by9pfM1uaQV/p0Gu5KCXoDMgbzFAtpyYh4Y2/89ti6JzSzApHhX5N65HMUkV0pqv3W1bVK3HypNTtOW3exvXd9u5EHyMloUB2vXSEX8lCI8qlkTsthS74RRF5iizjdQ6ZZv/DfQ7f/3Ito99n5zDdWW3zaWl36CriMaDcuyFTJR7Zxi8ZUWn69K5aZLtz27uv0kkxdODbcuJTNW2Z0tCp1Syb+QmviO/U6e/AZhgX/Wv2xZVMNJu/sfsllS9FhkRRfVaFzQkiCzWEmmMC7Dr/9b4SvL5nizwuSbErgq9dzuyx5LY6TX+T8ctCuWgWs7plVi1/+mYvqNjDPdt6tFzousJ6v9a/zFpHjmfK1NLHeQpsP9oUamb792Orr2LkZ3vO/G92tVdwMrw81D7122LKhyeIAqG4khzIfvofdklo+ym/iPHpdybVlZW+P333zF37txWfyFJg5PDf82aNYKOQ5LigwYNYqoqlMNTCDxin9OKrMNb6YUj4whnK+Hd4NTHP+hQozbK+41dabeitv6T/dNZ2ODhX5hwdgeqTu7k6fx3N//24LU7+Ked+kL4Nybq4sS13XKsf+Fa6g46GpzKc9aLJXvuJ9vSneeW+LccR/+GbVG1XldtWHeci3/j/pJ1AHXGDnbdda0YeQ6qf3Ky3FlEAQk9r2FN8vqzBzSzJ9oWecheaz3h2utuO9/ddNZuiX6Xmkq/W5Xr6N8aX3XX872z9+4/YY//qXHLPzlX6A5z3F0m/f/DnOiftI3/hL37J89JT3nO3m/jAk7Ppba2lr0koch4odHxiiBHPDnySYpH0l/Yq1cv3L17V/Bxzp49Cz09PSbJIxTu2OdIQVIxly5dktsrJHNDcjT/Vf4//3YOh8PhcDgcDofD4XA4HA6nI9TV7s8VlI8//ridb2/t2rV4991325UlaXFKbKuM2bNnswVjSpZLkBSPJBYWFigsFCYBefv2baxbt45F9+vqtpfsVQR37HOkOHPmjHxJk39JmkIe8+fPx7x5yvWV78ff/m/9Lg6Hw+FwOBwOh8PhcDgcDuf/M2+88QZefPFFqc8URetT9DxF4ivjoYceYo598TGqqqQlCSsqKgQ56aOjozFlyhTmI6Tcp6rAHfucTsvx/FsoktC533/7v/W7OBwOh8PhcDgcDofD4XA4nP/P6Kggu6OKaodYOictLQ0eHh6tn9P7MWPGKK0bExPDEuxOmzYNW7duVTkHx/0nkMbhcDgcDofD4XA4HA6Hw+FwOBzO/xhy5nt7e2Pfvn1SUfgREREsEl9MSEgILl++3Po+NjaWOf6pzLZt2zqVWJtH7HM4HA6Hw+FwOBwOh8PhcDgcDqdD1Ls/B/l9z8aNGzFz5kwmvUOO/i+++AKTJ0+WcuzTLgD6+5EjR5CXl8ci9Y2NjVm5AwcOtJYbOXIkrK2tBX0vd+xzOBwOh8PhcDgcDofD4XA4HA6H0wlISoei8bdv344rV65g9erVWLJkiVQZcthra2u36u8PGTKE/XvPnj1S5Xx8fLhjn8PhcDgcDofD4XA4HA6Hw+FwOJx/moEDB7KXItasWSOlyy8p3dNZeMQ+h8PhcDgcDofD4XA4HA6Hw+FwOoRL8fQcePJcDofD4XA4HA6Hw+FwOBwOh8PhcO4juGOfw+FwOBwOh8PhcDgcDofD4XA4nPsI7tjncDgcDofD4XA4HA6Hw+FwOBwO5z6Ca+xzOPc5BhrN6MmUN6ihJ5NXo4GeTFV2LXoyOn177vnNqOrZjziN9DL0ZCoaTNCT0Wvo2bavuLZn2xZXwxr0ZGZ7Vf+vm6CQ6wU66MkM3rICPZnq6p5970aVaKMnc+3Zb9CTmXlsEXoy5XU9d9xSVt+zY+7KK9GjUVfr2c/d2oKe/dzNrdZFT6aioA49mWInLfRU6svr0ZNpaO7Zz12OfHr2E+v/F/xccDgcDofD4XA4HA6Hw+FwOBwOh3MfwR37HA6Hw+FwOBwOh8PhcDgcDofD4dxHcMc+h8PhcDgcDofD4XA4HA6Hw+FwOPcRPVuAmMPhcDgcDofD4XA4HA6Hw+FwOD0CtZ6bsub/HTxin8PhcDgcDofD4XA4HA6Hw+FwOJz7CO7Y53A4HA6Hw+FwOBwOh8PhcDgcDuc+gkvxcDgcDofD4XA4HA6Hw+FwOBwOp0O4Ek/PgUfsczgcDofD4XA4HA6Hw+FwOBwOh3MfwR37HA6Hw+FwOBwOh8PhcDgcDofD4dxHcCme/wgHDhzA0qVLUVBQ8L9uyn+ezMxMuLi4yD0HDz74oOAyjY2N2LlzJzZv3oyoqChYWFhg5syZeP/992FoaNjp9tVWVCL0l73IDItkqcqd+gWi/8KHoa2v1+k6jfX1SDx/DQnnr6IsKxd6ZiZwGz4AvR6cCHVNjXbHq6+pxbG3P0NZVh5GvfcqTN2ckXjyHBKPn0NtaTmMnewROH82zL3clf6WjurUV1Uj/dJ1JJ8NQUVWLoa9sQqWft5SxyiMTUDC0TMoik+CmoY6LLw94P/oTBhYWyr97qrUFGTu+QM1GenQNDKCxagxsJ4wUWH55uZmlEdFovDCefZf82HD4ThvvnS/lJYg/9QplN0LR0N5OXRsbGE9aQpMgoOhKmM8LPHKGE+4mesjs7Qamy4l42BkjqC6U3ytsWlWIG5mlGDub7c6XUYeCeeuIPLQKVQWlcDEwRZ9H5sJu14+XapTmJiKyMOnkRebSB0NSy839Jn7ICsrSWVBMcJ2H0TW3Wioa2jAZtgQeMyYhqq8fMT+vgelScnQ1NeHw4ihcJ8xDWrqite2KzKzlNahv19754N29fq+uhrmvtLXIGtbTi5uvPcxmuobMG7bN0r7Q1tDHa894I8H+jhAR0sD1xIK8O6Bu8guqVFY5+CLo+Bvbyz12Z7raXhrbzj7t5oaMDXIHguGucHP3hglVfU4HZmDr47HoLymQWl78kNvIe3QEdQUFEDX2gquMx+ERZ/gLtUJe/9DVKRntKtn3isAAc+vaPd5TshlxP+6AyY+3tDQ1kJpbDw0dbXhPHQAAh6dKdcOianMK0D4r3tQEJ0ADQV1hJTJunUXsQePoyI7F+paWrDwdkevuTNhaGvd7juri0oQ/ebHaKyshN/6L6Ghp9/2t9QUZO/bzWyLhqHItliOn6TUtlRERaIo5DwqoiJgNnQE7OdK25am+nr295JrV1BXkA9NE1OYDhwMq0lTlV7nyqitqsGxrX8h+spdNDU1waufH6Y9OxsGpoqfT4VZ+Qg9egVhp66jqbEJb+37pF2ZpPB4XNp3BhmxqdDU1oJrLw9MeHI6zGwtVGof2YsrP+1D5t1YaGhpwm1IHwx+fCY0dbQ7XYeedT/Nf6ldvRHPzIPvuCEdtonOVd6xoyi8dJGdez0XVzg8Mhd6jo4K6zRUVKDoymUUhlxEXWEBfN5eA117B6ky5VFRyD9zClUpKVDX1oahjw/sZj4ELVNTqIKWuhqW+rhirL0ltNXVcaewFF9HJSG/pk5hHWtdbUxzssVUJxuYaWthysmrqG9qlirjaqiPJT4u8DM1hLqaGq7mFWFzdArK65XbFlmMtTTxQpA7htqZAc1ASHYRvghPQmVDo8LfM93FBtNdbeBipIeCmjqcSMvHr7EZaGyWbiOhq6GOn8YGwdlQH0+fu4PYkkql7ck5exY5p0+jvrwc+g4OcJ4zB0YeHl2qQ5+n//knSiMj0VBdDUM3N7jOnQs9O7vWMsk7dyLv4kWp4+rb2yNwzRp0FxNGBWHJ4+MxcVQQft59Di+88zP+Leqqa3D55wNIvnEPzY2NcOrjhxFPPww9E6Mu10m5GYFb+0+iKC0bJraWGPTYdLj0C1CpfVVFJbjxyz5k3RPZCZfBfdB/wUxoamt3ug7ZhrQbdxB19DyK07KgbaAHh2B/9Hl0OnSNOh7zU/2cY0dREBKChspK6Lu4wOnRudDvwLYUXLmCgpCLqC0ogP87a6Bnb69yGSHQvbislyvGO1pBR0Mdt/NLsTE8EXnVdQrlG8Y6WGKWux08TQxQXlfP7vcfo9Ok7nd/M0M84eOEQHMj0KcRhWX4ISoVKeXVKrXPUFMTz/q6Y6CVGQ0jcS2/CN/HJqFKgW0hnA30MNXRDmPtrVi5J0NuSv19kJU53gnyk1v3xRvhiCurENw+JxNdvDvBB4OczVBd34i/InPw6fkENMjYWnnYGOrg76cGwkxPC8FfXkB5bWNrH88IsMUTfR3hZWmIkuo6nEoowIaLiaisU/y75eFpbIilPm5wNzJEaV09jqRn48/UTKV1+lmYYqqTHfpZmuFERg6+i0lqV0ZTTQ1z3Z0w2s4KRlpauFNYgh9ik1BYq/iZJI8xbhZ4ZaQ73Ez1kVlWg03XUnAwOldQ3SneVtg0PQA3M0sx94+w1s9dTfXw7EAXDHc1g4GWBqLyK/DF5WRWTlWKr1xC4aljqC8pho6tHWxmPgwDH/nXDtFUU4PSm9dRHHIeNVmZcFqyDEa9FY+7C04dQ96hP2E6aCjsFzypcvtWDHbBvN4OMNXTxL2ccrx7Ng4x+cqfj2I+nuiDRwLtsPFSMr69ntr6+XAXMyzu74QgW2NUNzThSloxPruYiLxK1c6tlpoanvJ2xUhbK+ioqyO8uBTfRSeiQMk1YqWrg8kONpjkYANTbW3MOnMF9XLGBIFmxljg4QIPIwMU1NZiZ2I6LuZyXxrn/oRH7P9HoEl3Q4NqEyhO56DBNTnlw8PDUVNT0/p64IEHVCpz9epV9vrmm2+QkZGB3bt34/Dhw3j22We7dGoufvkjyrLzMOWDVzD5vRdRmJyGS5t+6VKdtNBwFKdlYuBTj+Khbz/AoKfnIvbEBdz6/U+5xwv9eQ8MrSzQ3NTE+iL1wlVE7f4LvR57CBM2vsec81c+3YSqwiKFbRJSJ/7vUyjPykGveQ+1fJf0MeizqD8OwnnkYIz9+G2MWPMyGurqcPnjr9BQo9hJSg74xC83Qt/ZBb7rPmKOmdzDB1Fw8bzCOlXJSSg4cxrmQ4ZCz8mZbsp2ZfKOH2OOGLdlK9hxTQcMRMr3m5nTRhV62Rrhh0eC8FdENoZ9E4Jt19OwcUYAhruZd1jXwUQXayb64FZGKTTV1TpdRh40ab2+bTd6z56Khza9D8c+vXDu0+9QmpnTpTq3dv7FJslTP3oNUz58Feqamjj5/peoLW+bNFWXluP4ms/RWN/AruMHP38bWvr6yL8TgVvrv4KOmSmGfvweei15EmmnzyH5yHGFbSJnS4d1mpvZ9TV8w4cYu3VT68vMx6vd8cjpem/zNph6ebI6HbH2oUBMDLTDU1uvYer6c9DRVMfPS4dAQ8m5oPP0+dFo+L56pPX19j6RU58IdjbDpEA7fHEsBiM/OI0V20Mx0scaXyzop7QtJbFxiN26DfbjRmPAZx/BdvhQRH/3PcoSk7pUJ/jtNzB8yzetr/4fvc/61KJv+4lLVVY2Ug8ehqGLM8oTk6Cmpo5+69ZiyIvPIv3KTdzbdUBhW5oaGnD502+Yc3v8Z2/LrSOkTGlaBq5/+QPs+wdh0pfrMPq9V9BQW4cr6ze3+046x6Gbf4aes6vIDjRL25bkrzdAz8kF3u99BLtH5iH3yEEUKbEt1clJKDx3CmaDh0LXyVnuNVR25zZzJDs+uRg+H30O+0fmofDsKeQdPYzOcmDj70iLSsZTn63Ec1+9jJLcIuz64EeldQ5/swcGJoYYMnMUmhrbOw4a6hpw+cBZDHtoDF746R08/ekKVFdU4Ze32vejMqgPjn+8BXWV1Zj9+euY8vZypN+OxKWtf3SpDj1DqNy0tSvx9K4vWl8+YwcLalf+qZPIO3UCTo8vhO/7H0DH0hJJX21kjjhF5Bw+hIayMtjNnMWuF9nnWH1ZGTum5dhx8H3vfXisfgF1hYVI+vpL5uBUhZX+7hhuY443QqOwOOQOW0T8dIA/lJn55f7uzEm+KzFDrg0y0dbExkEBKKuvx9Mhd7D00h3moHm/ry9U5cPBvnA20sPic+FYeuEufM0M8d5AxQvDo+wtmFPw8zuJmHE0FJ+FJWKOhx1WBLrKLf9ysAeyK2uZvaT/KSP/8mWk79/PHPPBH34IQw8PxHz5JWqLijpdh8ZDcd9+i+qsLPi+8AKCP/oIhq6uiN6wQfoaaWqCWVAQBn77beur19tvo7sY1NcLK56egh17L+BORDI0NP7d6eDZTTuQE5uMme+vwsPrX0F5XhGOf7aty3XiL93C8c9+hM/ogXjih/cx8aVFiLsYqlLb6P4/86nITjz46euY8OZyFvRybdsfXapDY5r0WxHoN38G5mxeh7EvL0VBfApCNm0X1K7ckyfZy+WJJ9Br3TroWFkh/ssvlNqWLGZbSuEwc2bLs6i5U2WEQAtyI+0s8PKVSDxxOozZlo1DA6Ch4DYLMDdi9++2qFTMORGKt2/EYJCNGdYOkL7fn/R1xl/JOZh76jaWng9ntmjTiEC2kKAKbwb5wtFAD6uvh+OlG3fhZWyIVwOVB52s8vdCdnUNjmfkQoOiI2S4nl+EB89clnqdyc5jDsJ4FZz69Ft+nduH/bYJW69i8b5wzPC3xZtj248nZaFWffVgAO5ll0NTXV3KrgXbG2OAoynePR2Hwd+GYPnBCIx2t8AnUxQ7lOVBC7of9uuFhLJKLL50E1tiErHA0xlTHKWDayTxNTHCDBcHnM7KQ0JZBVvwVXRehtta4vN7cXji4g2czsrFOPv2wRLK6GVtiB9mBeKvqBwM++EKtt1Mw8apfsyx3BEOxrpYM8YLt7Laz3deGeGO21mlmLs7DGN/uo6ovArsmBMMT/O2QA0h0Pgse/dvsJr6ILze/xSGvXoj7buvUZuTrbBO0cWzLAjE5uF5LeOCZqXzz+KQC9Cxsxc0x5DlmQHOWDrAGa+eiMaYbdeQVlKN3+f0gbFOx/G/03ys0cvGCEXV9VLn2FJfC88MdMHPtzJY383fEwZHY138NidY7r2kDFqQG2JtgbW3I7HsahgLSljXL0DpuOUZHzd2P+1JaRm3yCnb18KUXdehBUVs0W7N7SgMtjZnCwkc4VB33Y+v/yLcsf8f4MKFC3j00UdRWloKTU1N9nrrrbeU1nF0dMSePXta348ePRpmZmatiwOJiYnQ0tJiDmfi1q1bmDBhAoyNjeHk5IRnnnkGJSUlrfWvX7/e+t0Ubd6nTx/88Yf0IJii0+l7yZHt7+8PIyMjTJo0Campbau7xLFjx9CvXz8YGBjA1dWVRbDTwoXscX744QcEBgbC1NQUY8eORUJCgqD+ol0Njz32GGxtbdlr/vz5yM7Oljq+p6cn6w/qlw0bNkBXV7fdcTQ0NFp/M73U5FgJZWWGDx/OovXpt1JfDBw4EPPmzcPly5fRWcghnxMRiwFPzoGxnTWLaO7/+GxkhkWgJCO703XchvZnznxLDxcWxW8b4A3fyaORerV9JHfypVAUp2Yi6JHprZ8l/H0KzqOGwr5/MHRNjJmzXlNPFymnQxT+FiF1/Oc8iKAn58LYUX50ETnqRqx5CXb9gqBjYgQDKwsELXwUVfmFKE5IUdyPl0KY89h+ziPQMjaGce8gmA8fifyTJxTWMXD3gPuq1TDp0xdQECHr8Og8WI2fwCL1NQ0MYDV2HPRd3VBySzoKqCOeGuiMiOwy/HAtFUVV9dh9JxPnEwrxzBD5zgwxNJjaNDMQX11MQkpRVafLKCLq8Gm4DOnLdnPoGhshaM40GFiZI+b4+S7VmfjO83AZ3Bf6ZiYwsDDD4CXzUFNajpyo+NYy9/YfZed7+MonYWRtCW0DfbhOnYi6slI0VFXD74nHoGtmCnM/H7hMGo+0E2fkOh6J7CvXBdchJzPtDhC/5NmBuN37YeTqDKt+He/MMNHXwpxBzthwNBoRGaXILK5mUfc+dsYY42ejtG5TUzMaJV6S84Cw1GKs+u0WbiQVoqy6nh37qxMx7JgGSgbvmSdOwdTPF3ajR0HbyAgOE8bDyN0dmSdPd6kOnSs16q+WV96Va9DQ0YHVgP7Sv6m+HjHfb4P7o3PYFLaprg6eTyyAjrk5zD1c4fvQVCSfCUF9tfyFuqyb4ajIzUefpx+DvoX8OkLKlKRksImV9/QJ7NoiW+I2djgqcvLYziFJYv48Bi19PZgPG9muPcWXQ6CmqQnbhx+FprEJjANbbMspxQtN+u4ecF3xAoyD+yqMvjcdMAg2D8xkkd4aurow9Atgr6okYc9FWYqyCxB1+S6mLJkJGxc7mNtbYtqyh5EamcSc/Yp48qPlGDFnHPSNDeT+XVNbE4+/9ww8+vhAz1AfFg7WGPLgSBRlFaCiuExw+yjivjA5A8OXzoWRtQUs3RwxcP6DiL94A1UKjqNKHXX1ju9rWWhCnX/6JKzGjoeRnz+0TEzhMG8+u2aLryp+rjvOewz2D8+BjrV8ZwY9gzyefwHGAb2gaWjEnh/2Dz/CovhqsrMgFCMtTUxxtMaPcWmIK6tEbk0tNkYkws3IAIOtFC8Kr70dg18T0hVGxw2wNIOxtha+jEhCcV098mrqsCkqCcEWJuhlpjgCWxYfUwMMsDbFxvAkpFfUILW8Gl+GJ2G4nTlcjeTvODydUYD1dxIRXVzBonwpSnhPQhYmOFq1KzvRyQpeJgYs2lcIWSdPwmrYMJj36cPOgfPDD0NDTw95Fy50uk5tfj4qkpLgNGsW9GxtoWVoCEdyqLYsCkihpiZlIzu780Ye12/HY8YTn+DQiZto7IQjqCuU5hQg6Vo4hi2cBXNnO5jYWmHEkjnIjk5CtpyIXqF1aFH/0o/70Xv6KPSaNBw6BnowtbfGhBdUi16liPuilAwMXjIXhtYWMHd1RN95DyLp4g1Ul5R1uo6pox2GL3sc1t7u0NLTZWU8Rw9GXqziRXJJ25J7+hSsx42DcYttcZ73GLMthVeuKKxHZRyZbbHpUhkhtoV2ztC9Rbtgcqpr8VlYAtxNDDDEVr5tiSgqx5rQWNwpLEN5fSOr91N0GobamkFfYqfcq1ejcC23mO3+yamqxc74TFjoajMnvVAoGrePhSm2xCQhq6oGGVXVLCqcIu4pKl8RL4fexcG0LKU7jyigXvzSojGojQVOZuZKrud3yERvK7iY6uPNYzHILq9FeHYZvryUhPl9HGCgrXg3IrFquBvKahuw60776PmwrDK8dSIGd7PLWIQ+/Zd2AvR3VG2n12RHW9Q3NWFrbBJK6uoRWlCM4xk5eNhV8W6RmNJyrLkdiat5hWhS4JSmZ8cgawt8Gh7Lytc2NrFj70luv5tTGU/1d0JEbjl+CE1nDubd97JxPqmIOZaVweY70wPw1ZVkpBS33wGy/HAk9kRkI6OsBsXV9fjwfALrx/Geynd8y1J4+gRM+vaHyYBBbAe49bQZ0DK3QNH5MwrrWE6cCvv5C0WBYkporK5C5i9bWVkNfdUWHAga2Swe4ISfbmXgcmoxi6Z/53QcdLXUWRR+R7tM1o71wvNHoticQ5KCqno8vvcOLqQUsb5LLq7GB+cT4GtlCG9L+eNDRTttJjrYYHtCKhLKK5FXU4tNUQlwNTTAAEvF45YPwmOwMykdRUqi+pf5euBcdj72pWSioqGBHfuze3FyI/s5nPsB7tj/DzBy5Ejs2rULJiYmrZHh69atU1pn6NChOHfuHPt3dXU1ixyniSw58An6G0nJkAM9NjaWOc5nzZqF5ORkhISEMIc/OcTFDBo0qPW7yUlOCwuLFi3CtWvXWsuQc54kaqitBw8eRGRkJHN8k/yMeCWaPiOpGnJwp6WlYdu2bfj666+xfv36dsehBQCKcI+JiYG2tjYWL14sqL9eeukl5OXlscUI+r4ZM2bgl19E0en0+x9//HG8+OKLbHGDyr733ntyd0OMGjWKLXSQY377dvkRN0LKEPT7o6OjmVTPQw89hM6SH5sEDR1tWHq2OXht/L3YhDA/Lqnb6hC15ZXQlFnwKM/Jx83f9jPnKjnGCXKMUVS9pEQOOUnofWG8/OPXVVapXEcodeWi6CZaJFBEZWICDLy8pSbSRr6+qCsoQH2p6lswldFYVQl1XR2V6vR3MsXV1GKpzy6nFKGvg4nSei+O9kB+ZR1bCOhKGXk0NjSgICkVNv7SMjQkqaPoOupMHfG1R2hJXH+0q4Si+jVarjsxJfGJMHF3Zde4GHN/X9RXVqIqW/5OAlXqXFvzIc499wKuv/8Jcm+0X+jKCwtHYUQUfB4jx3THUGS9loY6ria0bQUl535KfgX6drAj47nx3oj8ZBrOvjEObzzgD/0OJoRmBtqob2xCbb3iqN+yhESY+EhHtZHTviwxsdvqkP3LvXwVVoMGMKe0JEm798LQxQlWA/qhobICGro60DZpkxyyDvBh8kYlyWlyj10YlwhjBzu2OKiojpAyVgE+zKGfdOoCk2uhhaW0i9dg09ufOfHFFMQkIOX8ZfRdskBue6rItnhK2xbajl1f2H22hZxAFMFVGRfDFgM6g9h579rbs/UzBy8n6BroKXXsq0p5USlunrgGZ383GJgKdwLnxCbBwMIUJnZtDlyHQB80NzUjLz65y3VOrt+Knxe8hH0vfYzIYxcERcHV5eezyHuSyRFDkk0GHp6oVHK/dIbGSlE0qLqO4ueYLCSTo9kivyMmt7oWmZXVCFDBAS8LW/Igx5aEK0vsyOllJi0PpozeFrRdvxGRReWtn4UVlDIpCvqbUEx0tNrJazgY6OL53m5YGxrbTkZIHhQFXZOdDWOJc0ljEHpfruBcCqojdhhI3P9s0UhdHeUywSkk1RO6ahXCXnsNCdu2obawEP8Fcloc8fa92qKRrT2cWdBITkxyp+tQGdq55z1CenFYVWhMrG9hCmNbK6kxCT2n8hXYFlXr0OdlOflIvnwTLgODOmxTbYttMZKxLYYenqhI6l7b0hko+p5sy638NttCTviMimomoSMUEx1Ndn+Sg1ceptqaeMjdDomllUirEC7FE2BqjJrGRuY8FnO3uJQ5I/1MhduWjhhlQzJEGsyxrwrkaI8vqERBVZsT8nJqEXQ1NRBoq7h9FI0/N8gBrx2NFvQ9Hub6mOprjeOxeSq1j/oosrhMarGCJHNs9XVZNH9nGWptgZTySiRXCJN8UUR/B1NcTZOZE6UVoa+MPKUsLw53Q35VHVsIEIKupjpzeKsiY9Tc0MAi7/W9pXew0bivqhvu3eydv8I4qK9SWR9luJjqwdpAR6r/6P4juaF+9ornlLS74evpAfjySjKSioUFgZFUFFFRJ1xhwtdUZFvuFrXZFnLAZ1VVw1+FMaMsJB/oYKCHc9mq3QscTk+Ga+z/B6BJATnlCYoKFwJFopPDnKAIcTc3NxYxTg59ctKfP3+elSHIqT59+nQsW7aMvScteIo0p2j6rKws2LdoMYq/m6LPH374Yfz555/Yu3cvBg+W3sJOkfZeXqLBOTnunZ2dcfbsWYwbNw6ff/45i2R/+eWX2d/Hjx+PN998Ex988AFee+01qd/8008/sah6ghzx1EaSv6HFAmUkJSVh4sSJrRr4jzzySOvfvvrqK/Y38W8l6Zzly5dLLSzQd69YsYK9qC/++usvlt+gvLycfSa0jBjqn9DQULZgQU79Tz/9FJ2lurgUukYGUtGFFG1IDilFkUadqUNbiuPOXELvh6ZIOWlDvv6JSapQ1H9xuiiSsK5FLkXHWPoBTO8VOeNqSkpVriMEcs5E7NwPY2dHpvuviIbSUuhYSUdPahiJ2kJblrVMlDvQhVJw/hxq8wvgMrhj7WZJrA11UCijUVhUVQdDHU3oa2mgSo6jdpirOWYH2mHK1rbFts6UUURtWQWaG5ugK6N3q2NsqPA66kwdmhDf/HUfjGytYOMvcjqSo5WuY7pmz3z8DYuAI91dy4EDUVtcIuUIJrRbrqva0jIYOkprWYs/F5dRWEddHa5TJ8Fh1DC2SJRz/SbubfmRybrYDR3EytYUFSP6598R/Pxz7RbBFGFtLCpXVFEr9XlRZR2sjBQvAN1ILMTR8EzEZJUhwNEUHz0SBG87Yyz6Qf65tDDUxvLx3vjzZrpCDdemhkbmrNKS6QstI0PUlZZ1W52SqGjmtLIdOVzq84Jbt1EcGYk+a98RHbu+nkW7SyK2ETUKjl1TUsZ26yirI6SMPu0UWb0U1778AeG/7mWfmXu6Yeiry1vr1FGuks2/MKe+jgLNZJLiMbCSXsiiKOzusi3Rr6xmi4VM1mjsBFiMHNOp45QXlUFHXxdaMpN2fRMDlSLrFfH3lv24fjiEOdVt3R3wxLpnBEXFi6kqLm2nra1DzzF1dYUR+0LqUBO8Rw9C7wfHsR1CGXeiEPLDH6guq0D/R6cpbVN9mei5pWkkbW8oQo+0q7sLug+y/zzAFhBIjkMoFi0LlRRxKQm9N9fpvHPmdmEpqhsbsdzPDVspb4K6Gp7zdWPOffF3CmqfrjZKaqXb1tgMFi1LfxMCRfbPdLNlkb+SGs7rBvqwz0iX292446jGupZFNk1j6XNJEfaVKSmdrkPnS8/BARkHD8Jj0SJoGhqK9PhLS1vrs3KWlvB46ikYe3ujrrgYqXv2IGr9egSuXQtNPeGRyj0Rute09XVZfg1J9EwMUaXguS+kDkX10w1ckpWHE5//xJz8ZrQD9ZHJKmnsV5WUst2D8uyEonGJKnVOf/Idsu5EsbGMXaAPBi+e22GbxIu+7W2LYbfals4ivj9l7196by7w3jXT0cJCHyecSMtrlx9jka8TFvk6M9uSWl6Fl69Eyc2hofjY2kwXXhIa9lCUrpmSvAmqQpHFNwuKlWp/KxzTSzj1xWN6wspAfvtMdDXx5YMBeO1oFIuIVsa+Bf1Z4A9JkpyIy8OHZ9t2uwrBXEcbkVXSgQckvSbuW9qp1Rls9fWQWVWNJT5uGGtnzXYFRBSX4ae4ZJX60NpAG4VV0m2g3cyG2krmRM5mmB1giynbhUt1kTQPLQYdixPuDKYcFmhqZOMA2XEfjfm6QvGlC6jNzYXDQmGBjYquPaL99VfPIvKV9QXV2RkubNcg5d14faQHrqQWI71UsRSuLOYt96fs/Uvvu3Lv2rUE95H+/pahfZgmP+3m2ZucwTX2VYRHifcc+Ln4fwo57SkSn6LryYk/ZswY9hn9WyzvI3bs37x5k8nqkByNjo4Oi44nqRqxk5yor6/H22+/DW9vbyahQ05+isynqHtJ9PT0EBDQNsCmRQGS9qHksQRF0MsuBNDuguLiYim5HCsrq1anPmFubs7aUEEP0A5YsmQJPv74Y7Yr4Oeff2aLE2Ioan7AgAFS5fv3l47+cXBwwKZNm+Dj4wNLS0u2U4Cc9Z999plKZcTQwgo5/C9dusR+/4IF8iM9idraWpSVlUm9SDO+I9RIX07FnWWK6lCCsLOfbmbRSAEPjG/9/M4fh1nEq8/EkQK/gI6vaqM6UacFmkTd+XEnyjKzMXDl0ypvaxc7nbprh17ZvbvI2vsHHChpXgdbLYUg9s3K843RBOCLGb3wypFIhRMAIWU6A0nVdGedW78dQG50Aka+sBgaWqJJPjkHiXt/HofXuOGYvflDDHl2ATIvhKAqv/2kt9WBqMLJlK1jaG8Hr0dmQd/GGtokuTBhLOyGDUbK0ZOtdcjR7zRuFEw83KAqsk0jJ5kyv+d7f95DaFIRS4RLyXbf3BOO0X428LVrH7FE0jtbnx6E3LIarPsrQlkr5H+s9JyqXicn5BIMnBxh5Nq2a6i2uBgJv+2Ez+KnlO6uae0UVW5MIXVkypDG/pXPN8Nr+gRM/349Jn3xPrQM9XHpk69b5Zlub90B+/69WRS/SnSj2KPvpxvht2ETXJY/j5Ib15B7SH4elM5C92azqg8TOUxZOgtr/vocK79/A0bmxvjp9W9R30nnQHdCNmX0isdh7mzPFrw9RwxA8KwJuHv4jFKN23/quSULaeqn/biVLZ45P/l0544h+76Zou47fw1SksPXQ6PgbmSAvWMH4NeRfVkEZkZldTdcKaJntxCsdLWxYVgAQvNKsCOubcfZswEuKKqtx/4kYcnlldIZORzJ6Hx1dfgsX86k+O6tW4dbL76ImtxcmPftK7WwZT95MpPzoXKUHNXrmWdYroWiUNX04u8n1Dpxn0jWEV8nYX+dwZTXl2DBlvfgOjAQRz/+AbnxwuSXOqIzt7FsnXGvPoP5v27EtI9eYbtTz67/vvMN6sT46p9Etnso7l6IZSHpnU+H+LHk11/dbb9b85eYdIw5eAVzTtxEfGklvhkRCCMt5UFcQuhoXKUKTgZ6CDAzZhI13YGyMT3x2VR/nIzLx8VkxTk/xDzy+034bTiHWb+Gws1MH5tm9Oq29nUFmmYOtrZgwSVLL9/Ca6H32ELB2r4BXXZQtfafovnONH+8cjxG8Hzn8WAHPNHHEauORDKZmS7DBOI734m1uTnIO3QAjouWtAt46Q6U3Ru0KDLL3xavHo8RdCySPPp6uj/r99VHI7vHttC4pQv3rvh5O8/dCetZfodQdu++1tuHae9zOPcjPGL//ymkcW9jY8Mc+fRatWoVc2DTf8m5TVI7Ysc+RcGvXr0an3zySbvjiKPjP/zwQ+b8J7kZOjbp7FOEepFMkjF5EXn0maSGvmyZNodqm1lXFNknZAL4xBNPsF0Bhw4dYtI3FJ1Pjn76jcq+XxlBQUHYuHEjc7zT4ocqZagP9fX1MWzYMNYOitqnRQFavJCF/k7SQAQtiNBiC7VP39yUOTPJsV5TXsn6Qdxu6luSLpGNihajSh1y6p98/ysWkU+OVUnneF50AgoTU7HjsZVSdW5++xP7b1152xZYorasHDoykdSSbVK1jjLot4X/shvZt+9i+JurYWinXEuUoqIaZL5b/J50c7tKWWQEUn7YAruHHoblSNF9pgoFlXUwl4nisdDXQmVdg9wtou4WBrAx0sEvc/u0fkZJjuiV+OY4zP4llJ37jsrcyRJFnqknh0Lr8m/s3zt+B0aufhoOfXux64HOkSQ1ZeUKrz2KzFelzu2dfyHh3BWMf2slzF3atD01dbSZZq1DnwA4DxTp2Nv4ecFx7Cik/H2iddeImLqW75ON5JeMzheXEVqHMHJ2RM61NsdLSVwCShOSkHTw7zb71NyM008tg/e8h2nZsN0xCloi9c0NdZBX1hbVYmGog1sCJnBiorNEkUCuVgaIyW6LGCR5np+XDoaWpjrmb76CKiVbiklOS1NfH/Uy/UcOJtLO74469RUVKAwLb9HQb6MyIxP15eUI//Tztg9bnhMhS55jyXPhatV67SiyCxSJT7JeksjWEVIm+dxl6FtawOeBiey9tqEBgh6fg5MvvYuC6HhY9/JlMjykt5906qLU8yj61dWwmjwNNtNnsGjehgrpa6uxxbbIRmN2BrqfSM7IKCAQlhMmIf/Y37B5cJbSOr+t+R4Jt2JaNfDf+XM9DE2NUFtVw5Ld0mdiKkvLYdgN0gVMw15dHdbOtpj1wjx8Nn8NEm/Hwndwe4fDH6vWMdkKgvS1Z3/+BvRMjFkUvSS1FZVsV5ZsVL6YztQhLFwd0VBTh6qiUiblowjx+ROdXzupZ0d3PDeonWk//4iq1FR4vPQytCSCG4RQ3BJNa6qtxZzxYkx1tFiUZFeILCnHqmv3pJJBLvBwQnaV8Mg8cr5TWyShEQbp9yvTySUsdbXx7chApJRV4c1rMVJOgGBLY/iZG+HSrGFSdbaNCcKZjHysDY1rdzztlp0zsuMAsmOKzqXQOhSN7/3cc1JlIj76CLoKciwQ5OCnvCI1efeXbMCRD75D+h2RbdHQ1sTSnRugZ2qEuqoattNOvDhPUIS9vgLbIqSOAck+NTdjwKNTYO4kuv/6zZ6ImLPXkHTtDmy82utt//nCOiYhSZg62eHBz0S2hXYTqmpbhNZhNlpbneWsGvDEbBx/90uUZOTAVEkiUvH1w64tO0nbUgatbnhudJXilvuTou7JOS+G3t8tVG5b9DTUsWGoP9OnXxVyD9VyZHjofqYI/czKGnxwMx6nHxyMkfYW+DtV2P1Au5JMtOXYFi0tlKgYXa+ISQ42KKypxY0C4eM0yTG9h4X0TiILfe3Wv8ljoJMpjHU18UQ/0VhYvDgbtnokvrmSgi9CkqScoLUNTbidWYpPzifgpznBsDaIZXrqQiipq2vXf/QcEf2t805usuuV9Q34OV60o4l2Z/0Sn4yNg4LhZKiP1Ioq4XMifS35cyI50fruZvqwMdTBL7N7t5/vvDQas3fexh2JcfPc3vZ4Z4wXVhyOwHkVxuEE7cqixd0GmTEx3ctdGfNVpyajsbISiR+9Kz1GVotHaeg1eH+8UfTdHSC+vszpepPIq2apr63w2iMJKNpJErqs7ZlKcjkvDHPDU/0c0efbS1LrF19O82eSUo/uvo3cCtXut+KW4EUTmXEAXX9RCnZQCUF8rL0pGUhskXf9OyOHJW4eYWOJ24VteSQ5nPsF7tj/j0CJbiWd40K1+Y8ePcoi8smJb21tzWRjSHbGw8OD6esTwcHBOHPmDHNAK3JyU9Q5JfCl6Hoxt2/fZnI9klRVVbGFAz8/kRZcbm4u0tPTW9/Tf0mWRpIbN26w/AFiyZ/uwN3dnTny6UVOdHKW078pwp7aLYnse3lERESwXQOKnPpCy9AiirIFijfeeIPJDonLUrkvYy5Ds+WYVj7uaKytQ2FSGps0iB3uNLmgv8lDaB2SMTi17isY2Vpi1ItL2mmZT3r/JanQJEq8+/drH2Pk2pdxa8t25vyyHyByGlO7C6Pj4Dh0oNw2kdPM0NZapTrKuLv9D2TdCMOwN1bB2Knj68jAwwOFISQT0dS6eFERG8OSHWmZmnbdqb9lM+xmzITVuLYdD6pwK6MEg52lnTpDXc0Rlil/Wyd97v6hdLLTj6f5wd1CH4/+eqt1S7OQMkSTa3/Uuoi0u5+eqiZKhKqmBnM3JxZN7zmmzQ7kRMbC2rdNp1sSuoaE1gnbdRBxp0Iw7s0VUvkgxLBrVdY+0UBdSxNlSSlorKuHRstEpCg6ljmfDSQmyJKYenogYd9fKtUhKjKyoGPaJqUy7sdvpf6eFXIFMb/uwtitm1if3dsr7QAi7qQWo6GxCYM8LHA4TBR1ameqCxcLA5Uc+z4tuqySiwNipz79d/53V1gS3Y4w8nBHaWwcnKZMav2sJCYWRp4e3VIn7+p1tjvIerBIvkiMWa8ADP9hs9Rndz76FBUpqRi0/hNos35uRH5kLFtMMFMgrWXh5Y7k0yGoLa9olceRrSOkDJs0y15eLbZB/N9p330qZbtvhUQhfet38P1kY2tSM313TxRfuihtW+Kiu8W2tIONCTpe7J7/7pLWdoudA87+onssJSIBnn1F2rBZCRmoqaiGs59r9zaT9FZYS+W3dc4Xb0n8DlH7bHzcELb/OMpyC2BsI0pkl3Uvjt3z1t7y29eZOkRRWha7FnQMlUu4UPJb2m5fGRcHQy/vVtkc0te3mapcxkeQU/+nbexYHi++DB1L4RI8YqJKypmMQJC5Mc5mF7RGudvr6yKipL0t6gojbCyY7MPVPOE2615hGfQ0NeBnZsiS4RLBViZMfuNeoeL2Wehq4duRvdgOgdevRaNBZvy09PxdqUeDm7EBdozvg2fOhyOq5XvkOdJ1bWxQFhfHoukJukfK4+JgMWhQt9UhyFlfmZoKu4miRUN5NFRXs11M3SUD+G8x7c1nJGyi6CTY+oh2sGVFJsIpWGRb8pPSUVtZDVtf+bvbhNSx8XYVjUVYJGwbbN6iYO4yY8NbbePWljJW3m64e+A4yvMKYGQtshM5ESI7YeUl3050pg7RlrujWZBtqYiPg5F3m22pSEyE3bTp+F9DeTEo6rqPpTFOZYhsi7WeNsttEaHEsc+c+sMC2H2/MiSCJdHtCNHppf8THq4bXVIGXQ0NeBsbIq5lAaaXmUiaJkpCd7+zUEQyScmcyMztVCT7rcwSLOjrwDTIxRHkQ13MmDP+Xo78/uv39UWpnVYTvCzx/ewg9Ps6BKVKxnbUVoYK0c7RJeUsga5kjHlvc1PkVtd0uOiqDHLMBplLj3vEt6Mqu2NuZZVisJP0cYY6myFMwjkvCX3u/rkoz6CYjyf6wN1cH4/uDpOa78wNtMP747xZpP6JeNVlryiaXtfJBVUJsTAb2iY3WRUbA33PtpwhqmIyYDBM+knPh1O/3gAtCwvYz3+SJVwXQnJxFXPgU//dyBA5s7U11Jh006ar8nOKULLhTVelJekuPzOEyfJ8cy1FxqkfgH4OJsypr4oEj5jYUtG4JdDMGBdIbo0WHXS0Yaevy+7rzpJUXsly+sgmdqZ3PHUu536lZ+3h43Qa0qknOReSchEKOfN3797NnPjk1Bcne/3tt99ao/WJV199lcn2kNY8ydZUVlYyqR5KpiuGJHhokYCS2paUlDAHdHh4uNzvpQh5cuZTAttnnnmGOdNJS58gpzXp/JOGP30PLRjQbgCx5n53QFI3x48fZ/1FbaWFA8oxQKxcuZIl5aVkurQIQQsa334r7ZzbsGEDk/DJyclh0j87duxgiwO020GVMrSAQn1NixsUxU+/lfIJTJ48ufV8yEKLApSMl14kRUQLBdp6eq05Fsgxb+3rgZvb96GyoBgVeYW4teMA7AJ9YSbh0N618AVEHjoluA5phIqc+lYY/dJSqYgpqQhMDY3Wl6TTy2vqeKReuIrcu1EsojV67yG2Bdl13IjW+iSTc/bNj1rfewqoI4S7v+5B5vXbGPbm8zBxbovyVobF8JFoqqtlOsaN1VUoj4lGYchFWI2f0FqGPgtf9gxqsoQnmaU6KVu+g92MWbAar3gS3xE/3UhDkL0xFvZ3goG2Bmb2ssUYT0tsu9YmfTWvjwOLtKcBGkEDVcmXeCwjOYAVUobBkv1psBc71y0TBf/p45By5RbSbtxhSZMjDp5EeU4BfCaNaq16+/e/cGCFSDNdaB2SeYo9eZE59a285E/8Ax6YgPSbd5EZFiFKypuQgoxzF2E3ZBBLghv7+x+or6hESUISUk+chtP40VDXFA18y9MyWBR9UYwocpM08juqQ1H42ddCWSQ/OV0yzocg69IVOE9o0zSXvB/oJXYcSPaZLMWVdThwMx0vTvGFp40RLA118P7s3kjOr8C5qLaEbH+/PBofzhEl3evraobXH/CHq6UBtDXUEexihg8fCWKLBGEtSZZ1tTTw45LB0NfRZE79UoFbiR0mjkdJVBRLbttQXYPs8xdZclyHCeNay6QfO4HLy1apVEdM7qUrsOzfH5oSCWgJ6h+amEi+tM3MoK6thcRdf7DdPCSPE/PXMbiMHtKawLa6qAR/Pr4CmaFh7L19/2DoWZjhzs+7WRS+vDpCytj1D0JZRjYSjp9FQ20dy+lwd8c+6Jmbwsy9xfkvawNbZBJEzibRv81bbEvuX/uZbamIiWaOfstxbbalIjYaESuWqmRbcg7sRdndO0xfn45fHnEXBadPwnRQ24KZIsh208I9vdQ1RO20cLCGz6BeOPHjIRRlF6CsoATHvj8ARx8XOAe0LfiuX7AGp345IridMdcicGH3SRTnFKKxoRF5aTn466tdMLY0hUewt/z2aUj2q6h9jkF+TC7n8tY9TH+7JDMXobuPwGNYXxi0OArqa2qx9ZFViDlzVXCd6FOXEXn8IioLS9h5Tr52B3cOnITfhKFsZ5Ay6Bxbjh2H/LOnUZEQz+RysvbtgZqGOswlgh5Svv8OiV9sUM2p/8tPbU59FXT1JSmrb8DJzDw85e0MF0M9lvTw+QAP5hC/lteWOG/r8CC82Evxwp08lvm5wsPIgDmN+luaYoW/G3YlZiCnWjpXiDLImR+WX4oXerszp6Cdvg5WBbrhem4xksraIgnPzhiCBd6i3CiUG+DbEYHIqKjBa1ej5SbGJdcprR2JX+KJPP1b2QTebsIE5F++jJLISDRUVSHjr7/YObUZ2SY3mPzbb7j3/vsq1ck+dYr9nRyzVRkZiP/+exj7+rYuBtDn8Vu2MGc//ZukehK2boWGtjYslSwQ9ESkbaLo3jW1t4Zr/1648utfTBe/orAEl386ABsvV9j6ttmW7YvfwbXfDwuuQ5HxvmMH4eYfx1kZun/vHDrLFvI8Bot28ikdt7bYaPsgP5g52+P6j3vY2Lc0KxdhfxyB29C+bHes2Lb8Om8V4s9dFVwn9tQl9qosKhGNURJTEfrrAVh4uMDU0a7DfrQeOw65Z8602paMvXvZc9FyaFuOpsQt3yFu40b825TWNeB4Wh6W+LuwPBcUqf9SsAfSK6pxOafNtvwyNhiv9vFo1dxeP1TSqd8+oWaQhTGeCXBhCwQ0lKX/vt3Pm0V5X84WnkyanPn3ikux1MedOQWtdXWw2McNtwuKpaLCD4wdgodd2+dd6ohBVuYsovhEZudkeE7E5iOrtAYfTPKFuZ4WfK0MsWqYO/bczUR5rWixgyLME18bi8neIvtPpk5yvC42fU1NbUvkiwc4s7xZFF1NcwFysL422hMhyYXIUyFymiRKdDXU8aSXK5NNCjI3wWRHG/yZ2jZG6W1ugkPjh8HZoOMcJmLOZInyKSzwcGbXA+VkecLLBUllFcioFBatT/x0Kx1BtsZY2McBBloamOlngzHuFtgWmt5aZl5vexaNr3BOhPbznTm97PD+eB/m1D8eL9rZ0xksxk1E2a1QlN25jcaaGhScPIa6gjyYjRrbWib3r32If6ctl2BHyBsjixZrRJ8Lpbml/57q54T+DiYw1tFkuxPImb43ou163jKjF3Y+Eiy1g0byxT5nz9eW9gHYONWfHbOzTn3xuOVMdh6e8HSBs4Eei9Rf7ufBcjPcKGizLd8MDsZKP+HjlrqmJhxKy8IcN0cmo0W7DCfYW8PHxAghLQsIHGGoqTXfl6//Ijxi/z9Cnz59mMOcJGbIYU2JZskhrgxy3jc0NEg58enfso79wMBA5sgnpzNp65N+Psn2kKa+GIp4X7RoEUuKS86BCRMmYM6cOaiT0X8nh/XUqVPZAgI590lPn5LsigfUJFezb98+vPXWW8zJTvr0Tz/9NFso6C7Iuf7uu++ypLn0YBwxYgTLB0BQ4mBK7ku/7bnnnkPv3r1ZO8hRL7kw8P7777MytDBAfUKJiElHX5Uyjz/+ONatW8d+W0FBAVucoV0PtJDSFUa+sAQ3ftyNgy++xxyJjn0DMeiptgTBBO3ukIws7agOOV7LsnLZluVdC0U7BsTM275RrqNfEtexw1FfVYWwH35jjjMjR3sMeXkZDKwspJwXzU2NKtVJC7mGsK07Wt9f/vgrdk59Zk2F76ypTIIl6eR59pvOv/WxVJuCn34MLqPkO71I4sB9xfPI3LML+WdOse2M1pMmw2qshGOSjWCoH1v6tL4e955vSYzc1ISqpEQUXr4EXVtb+KwRySflHT+G5vo6ZB3Yx15iDH194bHqBQglPKsMyw/cxatjvLB2og+yymrw5tFonEtsG4zQllLaGimK6Pl3HmCuQ/qx7eih2/cxx6exvTVbCJJcVBKd5ybBdehaJe18Oocn1kpPWPvOnwn/aaJzYhvgjaHPLsDNX/ejPLcA+uYmcBgxDO4zpsJh1HBE/7oTF55/hSUedBg5DB4zJaLcaFBPbWo5mVoG+uj78vNK69iPGIrEv44gbudeNNbWQt/WGgFPL2xNnNsV1uy/izUzA7H/+RHQ0VLH9YRCPPnDNakktxRpRi/ibloJAp1M8f3TA+FiaYC80hqcuJeNr0/Etl6fQ7wsMdjTkg3Wb74/Wer7pn5+HvE58qPWzPz94L1oIVIPHkbcz9uha2UF36VPw8TLU7r/WnYbCa5DCyrJKajMyIDHgnmC+oWcG0ZubsypceOl15ij1WnoAAQumC3RlJZz2dJXtONi+OsrEfbTLhxd8abcOkLKWAf4YMDyRYg7dBKRfxyEupZWS/LcFYITIxNapmZM/z57zy4UnBXZFsuJU2AxZnw72yKGbEvUC8ulbEvxlRDo2NjC6x2RU9Fs+EjkHTmIzB3bmWNf28ISVpOnwmJ0+8UUocx+eT6ObN6Hb5d9ypwFnv188eCKOVKLUuxZItHWPz7+BVGXwln0PeW+WDtNZNeWfvkiHLyc4NHHG7mp2dj+9haU5BbCwNQInn19MGPlo9DWVbybTRZyEk5+41mEbP0Du5atYRH17kP6YOiihyX6sSUituUmEFKH3ocdOIGDb21gsj1G1hbo98hUBEwRljvGetIUNNXVIfX779BQWQV9F2e4r1rdmiCZNUumz/JPn0TWgf2t7+M+FJ1Th0fmwnL0GNTm5aHkxnVmA2PWto27CNdnnoNJkHynpTy+jExiTvdvhvSGtro6wotK8VqodCJKcs7T80PM8wHueMDJtjXC89hEkSPx9ZtRuFkgivC7kF2IlwI94GlsgNzqWuxIyMCB1La8SEJ583oMXgn2wJ6J/dhT61J2EdbfSZQqQ+0TR6qOd7KCq7E+HA31cG6G9POcNLnlOfqFYj1yJHPOJ/3yC5MF07e3h8/KlUxKp529UaEOOfBTdu1C/ObN0NDTg8XAgXCcMaN18Y9si8XgwaxMVXo62+1j5OWFgDfe6BZJJ0JbWxOFMb+wf2tqamBwP28smjsGsYlZ6D+ha2NQIYx7/nFc3LoXf7z4MbMTzsG+GLn0URnb0ijVt0LqjFg8B1e3/4V9r65HAyWFdrLFlDeWwtpTeB4jmo+Mfe1ZXNv2B/atWMN2FroM7oOBT0rYltb7uFlwHdchfXDvr5M49s4GVJeUszGK88Ag9J7VtrNNGbaTJzPbkrhlC3sGGri4wGvV8+1tS3Nbn+WeOoUMCdsS9cE69l+nR+fCumWeJ6SMED6/k4jVQW74YXQQsy1hBaV48XKklG3RlLAt/axM0NfKhP3972nS46aFZ8KQXF6FiKJyeJsaMP19RwM9ljDzdn4pnrlwFyV17RcClPFheAxW+Hlg6/B+7P31vCJ8G6PYthAf9A1AMC3MqIn+dmS8SHpkyeVbyK6ukZLhuVNUotJCpiS1jU14/I8wfDjJF9dXjkB1fSMORuZg3Zk2mTDqNhrTS9rmjvgzMpstELwy0gNm+lrILqvF39G5+E4iqloIJN225nYknvX1wCwXB5Y4d19yJg6ntdl4sl40JhU3j871gXEim0yf+5kaY5KDLdKrqrD8iijwoqqhEW/djMBzfu542G0we3+nsARfRMSzBVmhhOeUY/nhCLw6wgNrx3qL5kQnY3EuuW3xh4bLqs6Jnh/iCh1NdXz7oHTybYpMf+d0ewk3RZj0G4DGinLk7NuNhtIS6FjbwnHpcujaOygcF5TfvYP0rW27VjPo37QzevQ42M5+FN3J5uup0NPSwPczApkO/r3ccjy+945U/gGxVJFQKJcD6fDTQvr5xdK5E5/5KwKnJearHfFtdCKe8XHDxoFBLHjpblEp3rktbVvoGpNs3zJfd0ylBdOWj/4cK7oW14RFtsrs/Jogyr3y2YBA6GtoIqOqGp/ejUVYEZfh4dyfqDV3OhsYp6dCEi000BU7y5VBjn1JiR26HKg+Oe+7ilgaSNwOilqnyHuKYu8K1EY6tljfX/K3dEe75enaU9tV2Q3xb/JBmLR8ihCo/9hqv8CHtOzkVRIWiazoexobUdks7JyIj69KUltF7WK/reU44sSW7cq0yMckVyhflFD23eRkk4yMkHRuSjSmtS2STiZFZWS5frlzEwWCJiLtou3FX9kyUVDm9xBSZsk01bIXscGrhF0QQkfnUBE5NRoqfUdHx+sqYo198bn+e0/nt5GKc1ur8gQXLwTIQs5+eYybbdKp39WVe0gVG2Gl2/g/O5dCiC3RUun3dcW2dIZH3US6ol2hifSQaQOP2N7KLBqLkX1eCyG9shuSI/5D18L1AuGLEB1dx+Lz3Q6Jdss9/zJlJMmv7nzfqctsRxc/B7ozeWJ1dXOX2ifZWy0BmO1oUXiSC9VR9ndvy44lQTo7bukO9j6+pcvH0GiJoG8/vu76tPCTY4u6xbYwFZ1/oG8r69V6rG2JKFG+M6jbbYuAMpKkFHWfbenMvdsRJh3Ligu2LUJtn+zvUkbUrc5FLnc0phfydyH06ic8UEEWGmJK9o3cISddbp3+BiAivO5fmxNR++UllidntaJe7ttH65+9d5WM+zp6Ht0O63w+hI7mg7LnXkr2SQZF58A/SLtHj1uOTWyTUuK0EVYofNduT6KPxf9exq674RH7/0FUmUDLOsJpENddznFVHHeqQG2U9xu7q90U0b9ixQoWQU+JhSlav6tR9D0NVc+NeMufyt9DdQQG1XRm8iakXcoWHroCm/DIHLujtvzbk39lA3whTmFVHcdCYBNhFev8U+fw3/4OZVq/qtKZwakiB/7/6nfJu4e64/75N87lP/H7lNXrjP39NxDLa7S+/5dt3P1yLSi7joVcJ//m+W/6F54D3dm+zjgBu+I4/F8+07uTRjlJSnuybelp/GdsSyefU/+re7cn2r5/607qyGnfVad+V5EdYv5DQ85/bU4kan9zj713OzpGd9LRvSDvXP+b12NPH7dwOP8GPXvUxOk0JKVDjm55r7Vr1/6ne7arv33YsGF46KGHYGpqyhz85NR/6aWX/pW2czgcDofD4XA4HA6Hw+FwOD0Vtfv09V+ER+z/R6Gkr4pUlv5XEXXz58/HvHnCtJT/l7+ddO7pxeFwOBwOh8PhcDgcDofD4XA4PRHu2P+P0hk9238aRRI6/x9+O4fD4XA4HA6Hw+FwOBwOh8PhdBfcsc/hcDgcDofD4XA4HA6Hw+FwOJwO6eb88ZwuwDX2ORwOh8PhcDgcDofD4XA4HA6Hw7mP4I59DofD4XA4HA6Hw+FwOBwOh8PhcO4juGOfw+FwOBwOh8PhcDgcDofD4XA4nPsIrrHP4XA4HA6Hw+FwOBwOh8PhcDicDuES+z0HHrHP4XA4HA6Hw+FwOBwOh8PhcDgczn0Ed+xzOBwOh8PhcDgcDofD4XA4HA6Hcx/BpXg4HA6Hw+FwOBwOh8PhcDgcDofTIepci6fHwB37HM59zgjbOvRkXr1sjJ5MfV0zejLaZ1LQkzF5yAU9lZgyLfRktK5noydTN9MUPZkLaTroyeTnN6EnM9K2Z2/a/Pb7CvRUAmfqoieTmdmInoyubs+eCQaZ9+xx1YXnnkZPJquqZ5/fwloN9FTupvRsu1x5rwg9mZKAnj1uyfvuR/RkYt54Dj0Z9Z030ZOJMR+MnorGrjvo0QQN/F+3gMO5r+nZowcOh8PhcDgcDofD4XA4HA6Hw+FwOFJwxz6Hw+FwOBwOh8PhcDgcDofD4XA49xFciofD4XA4HA6Hw+FwOBwOh8PhcDgd0rOF9/5/wSP2ORwOh8PhcDgcDofD4XA4HA6Hw7mP4I59DofD4XA4HA6Hw+FwOBwOh8PhcO4juGOfw+FwOBwOh8PhcDgcDofD4XA4nPsIrrHP4XA4HA6Hw+FwOBwOh8PhcDicDlHjIvs9Bh6xz+FwOBwOh8PhcDgcDofD4XA4HM59BHfsczrFzZs3MXr06G7tvaioKCxcuBCjRo3C8OHD2WvOnDnddvyTJ09ixowZXS7D4XA4HA6Hw+FwOBwOh8PhcDj/S7gUTzfS2NjInNKbNm1Cnz598F+mpKQEly5d6tZjkkN9woQJWLduHdTV1XHkyBHs3r27246fl5eH69evd7nMf4HKskrs3vQn7l2LZluogob2wiMrZkLfUE9u+fq6Blw5dh2Xj91ATnoeTMyNMXhCP0x6bBw0NTU61QY7fR28FOyBvlbGqG5owon0PHx7LxWNzc1yyxtoauBhDztMcLKCnYEOsitrsT8xG38m57SWMdfRwuFpA9vVfetaDM5nFarUPj9zQ7zc1x0+ZoYoqq3Dnrhs7IjJVFjeUlcLC/wcMdLBHOa6Wkgpq8bPkem4kFkkVW62py0e8baHvYEOyusacTW7GJvCk1FS26BS+0YNcsJLiwfAxcEYWbkV2LwjDIfPJCosv33DVAwMsmv3+Z2oPMxbdbjd55NGuuKLd8bhdkQOFrzwt+B2RZ2+itt/nkZFYQnMHG0w7IkZcOzt0+U65QXFuPrbIaTfiYG6pgZ8xwzEwEenQENLi/29trIK13b+jZTQCNSUV8LKwwmms+bCwNlZULurs7KQtns3KpKToaGvD6thw2A/fTrU1BWvf5fFxiLvwgWUhIfDNCgInkuXojvQ0lLHK8sGY9p4T+jqaOD67Sy8v/EScvIq5Zb3dDPDoe3yF0FfWXcWf59KEFRGEYU3byLzyBHUFhRAx8oKjjNmwDw4WOlv6KhOY20t+3vRrVuoLy+HvoMDnGbPhrGXl9zj5V26hOQdO2AaGAif5cs7tC0vBHmgj6UxqhubcDItD99FKrYt+poamO0usi22+iLbciA5GwclbAvtNKW/Uzk3Y32U1jXgUnYhtkaloaqhUWl7ZOllaYi3hnrAz9IQRdV12BGZhZ/uKrYt2hpqeMzfHg9528DJWBe5lXU4FJ+HLXfS0NTykwbamWD79N7t6j6w7xYSiqsEtSv1ViSu/X4Ypdn5MLIyR7+HJ8F7ZP8u16mpqMKNXUeQfP0eGhsa4D6oN4Y8MRM6BnpKy6B+IqCl2+475wx2wTMTvGBrqofE3HJ88lcErsYVKGzjSw/445nx0tdVTkk1Rq49Kbf80vFeePkBfxy4nobXd4ZBFZqbm1F44m+UXLqIpqpK6Dq7wnrOXOg6OCms01hRgZJrl1Fy6QLqCwvg9sZa6Ng7SJXJ/HELyu/clvpM39Mbzs+/rFL7tNTV8MpAN0zzsIaupjquZ5Xg/SuJyKmsVVint5URng12Qh8bY3YP3cktxxc3U5BY0v66ovvkpymBGGRvipfOReNYkuLzIpQJzpZ4LsgZDoa6SC+vwTd3UnA+Q/q5KompjiYWBThijJMFzHW1kV5ejR3Rmfg7Ob/TbSjLLcCVn/YhOyoBmjra8BrRHwPnz2DPou6oU1lYggOvfsqeXQt//hTaEvdGSuhd3DlwEiWZudDQ1oSNjzvUHGai2di63XECbY2wdrwP/G0MUVhZh19vZ2DrjTRBv5Hq7l3QHyXV9Rj8bdv4XkdDHfP7OuDhQHs4m+oht6IWf0Xk4Nurya32Ryg5dyIQtecQKnPzoG9hDu+ZU+A0dECX6jQ1NCLrZhiST4egKD4RAY/OgufUcR22pba4BIm7/kBJVDTUtTRh2b8f3ObMhoa2dqfr5IRcRvyvO+TW7f/R+9Czsmr7XSGXkXXmLGryC2Dg5Aj3R+fAyM1VaZuX9nbCw962MNHRRGRBBT65kYg4JfbdzUQPSwKdMMjOFFoa6ogtqsDmO2kIyytrLUPHmuVpgzk+duwem33ottx7uyO0NdTx2nQ/PBDsAB0tDVxLLMC7f95DdkmNwjoHV4+Ev72x1Gd7bqThrX132b9pfjI1yB4LhrjCz94YJVX1OB2Zg69OxqK8RrUxs4m2Jt4Y7IFRTuagocC5tEJ8fD0RFfWKn98jHM3wZC9H+FsYoqq+Cdeyi/HVrRTkVdW12tOHvGwxy9sGbib6yK+qw+HEPPx4Nx0NCsYb3cHAPp5Y8vgEzJo6EDfCEjB13of4pwm0MsTbw0TjlkIat0Rk4cfwDsYtAfaY7SMat+RV1uFgXB6+C2sbt7By6mpY1s8ZD3pZw1RXE5czSvDB5UQ2zlGF0cPd8cqKEXB1NkNmdhm+2XoVh45HKyyvrq6G5YuHYNZUf1hbGaKwqArHz8Rhw+YQ1NWJrgkjQx0sfnwApo73YWUyskqwY+8d7NofDlV5yMsGi3s7wcZAB0klVfg8NBnXs0sUlrfS12b3O12vxjqaSC6txra76TiTWqhSGaEse3og5j3UGybGurgXlYt1n59DTLzi57eVpQFeXTkcQwc6w9BAG+mZpfh5523sPxzVWmb4IGc8Nb8fegfYoqamHldC0/H5N5eQVyB/LqMILTU1POXtipG2VtBRV0d4cSm+i05EQa3ia8RKVweTHWwwycEGptramHXmCuol7klnA31sHiLfV7c+IhYXcro+dvn/Alfi6Tlwx343QhO6y5cvo7S0tDsP+/8C6rOEhATs27cPQUFB7LOYmJj/dbP+s3z/7nbUVNXije9Wo6mxCT+8tx0/frADKz9ZIrd8WMhdZCRl47HVs2HtaIW0+Axsff9XVJRV4tEVs1T+fk01NXw9oheSy6ow9+RtWOpq47OhftBQU8MX4cly68xyt4WepgbW3ohFTlUtBtqY4t2B3tBQV8O+xOy2Y6urYcGpMCSVtQ0cGlUcX5OTfsvYQBxOzsUrl6IRYG6ET4b7orqhEfsT2px9kiwKcEJWRQ1WX4hCcU09prlZ4/MR/lhxPgLXc0SDt7FOFnitvyfevhqLS5lFbIHio6G+WDvIGy9cbBsMdYS/lwU2r5uAjdtC8eeJeIwb5oLP3hiNopIaXL4lf6C96JVjUg9fUxNdXNw9D6dCUtqVtbcxxFvLhyAsMhcaGsI3diVeC8f57//AuJUL4Bzkg7vHQnD4w+8xd8OrMHO07XSdqpJy7Ht9I2y9XTHns5egY6iPyJNXkHEvHi59/VmZExt+QWVxKaa99QwMLUwRceISQjduRK9334W2qanSdjdWVyP2iy9g7O+PwKeeQk1ODhK2bIGahgbsp02TW6c6JwdZhw/DauRINNXUAE1N6C7eeWE4hg9ywtKXj6K4tAbvvzoS2zZOw4yFe9Eo52JOSC5G4JitUp8tWdAHy57si4tX0wSXUbR4kbhtG1zmzYN5374ouH6d9Y3fq6/CyN2903XISV+RlMQWQ3StrVEYGorYr75Cr7ffhp6t9LVSnZ2NzMOHYejq2mE/k235YlgvpJRXYf7p27DQ1cYng/2YnfjqrnzbMtPNljn33w2NRW5VLQZYm2LtAG9mjw4kiWyLv5kRgiyM8eXdJKSWV8PFSA9r+nvDQkcba0JjIRSaiJED/s+4XKw4FcWcpl+O90N1fRN2RbfZMUkmuloyZ+WLZ2OQXVGLYGsjfDFe9Js23Upt++3qagjYGiK1gCHU9uUnpuPYp1sx6LEH2KJZ8o17OPP1b9AzNoRTsG+n6zTW1+PQ2k3Q0tXGA2uWMed/4rU7SLx6B/7jhygtc/fMPTQ7SDv9Jva2w7q5wXh1xy1cisnD4yPd8eOzQzH907NIyq2Q2046jzcSCrDw2ytS4zV5BLmYYf5wN8Rnl7OJv6oUnT7BXg6Ln4OOnT3yD/+F9K83wn3tB9DQN5Dfj38fhLqWFqwefAhZP26Rf+CmZpgOGQ6bR+d3Sdj0naGeGO5ohqUnIthz6v3hXtg2uRdmHLil8FpZ1scZO6Oy8MbFOBhqaeCVQe74dVpvjN51HfUynt1ngp1Q39TErkX1bpju9bM2wScjfJgD83RaIaa5WWHDKD8sOnEXdwvK5daZ62OPrIpaPHcmAmW1DRjvYol1Q32YA++CkgUBRTTWN+DYB9/C1NEOc7547cGfYAABAABJREFUC1XFpTj52Q9oamrC0EUPd7lOc1MTzn69HZYezki/HYlmtPVpYUomTq3fhgHzpsNv4nDUVVYj5Ifd0Dr9HeoeWit1HGsDbfw+ry/238vGswfuore9Mb6dEYiq+kb8HqbYAUcYaGvg6xm9cD29GP7WRlJ/m+RjBTM9LTx/KAJZZTXoY2+Cb2b2Yvbny0tJgvuxJDkN17/4Hv6PPAjnEYORfesubm/ZDh0jQ1gH+nW6TuaN28i+GQ7fWVNxc/PPaG7u+FlMfR759TfQMjBA33ffRkNVNaK/3YLGmlr4PP1kp+vYDB8Km6GDpepFfbsFtUVFUk79tCNHkXH8BLyfXAizXv6ozs1D9oUQpY79RQEOzMn84rloJJRUYlUfV2ydGIgH/ryFsjr5Tu6nA51wPr0In99MBpmzp3o54YeJvVgd8WLe8mAX1DU2MYf1xjF+nb5r187qhZE+1njqx+sorqzDhw8H4efFgzFt4wU0KlgBIjvx+bEYbLvQFpTSJGGbg53NMKmXHb44EYOY7HI4W+hj47y+cLU0wOKfbqjUvo1j/WCgpYG5h8OgrqbGfutno3yx7HSk3PIWulrMab85LA0xRRWwNdDB2qFe+GZ8AB45JFrwJdvibW6AD64mIKW0mi0AfD7Gjz2vP70h/N5QBXNTQ6xf+wS2/X4GmhrqsLc1xz8NjVt+faA3DsTmYtmJKPS2NsLXE/zYYseuKPnjlkluljCjccvpGGaPg22M8NUE0bjl65tt45ZNk/zZAtRLZ2IQU1iJwQ6meMjHBt/dThfcvgBfG3y/cRY+33QR+49EYMJoL2xYNw1FxVW4dL3tuyRZ8sQALF4wAM++9CfCI7Lh7WmJ7zfMgrqGGj7ccI6VmTurN8rKa7B49X52rNHDPbD+vSmorq7HX0eFz9nGu1hg7TAvvHkxFleySvCYnz22TAzAQ3/dZs54eTzZywHRhRXMUV/b2IQZXjb4aqw/FvwdjjstC3NCyghhyRP9sfSJ/lj+yhHEJRXipWXD8Ot3D2P8rJ9RVi5/0f/LD6dCT1cTC57dh/yCSkwd741P105iTvuQq6mwNNdnx/3x99u4F5UDU2NdfPj2BGz/djamP/ab3LmMIp71dUc/SzOsvR2JsvoGrPT3xLp+AVh+NUzh4vIzPm5IKq/EnpQMPOfrIfI+S5RNq6zCg2cuS9V5xNURc92dcLOgWHDbOJyeBJfi6UamT5/O/rty5UomIzN/vsQETIlD+7XXXmOR6osXL8atW7cwceJEXLkimoBWV1ezY128eBHPP/88xo0bh71797K/FRYW4p133mHlSbLm559pQNvcTlaGZHOWLFmCyZMn480330RFRYWU/I1Y9mb8+PF47rnn5DrUqdzjjz+OKVOm4PXXX2cR+7J01B5F0G+lOsSiRYtYW+7duye3DR21tb6+Hl9//TVmzpzJXrR7oqFBesCrrD8k2/TUU0+x87J27Vp2HlRph5C+J+7evYsFCxa09uvp06e7XeJIltS4dMTcjsfclbNg42gFOxcbPLJ8Ju5di0JWinyn9cBxfTH/hYfh6uvMovp9+3hh7EMjEXpWtWhGMaMcLFhk0Ce3E5BXXYeo4gr8GJWOWe52zMEmjx1xmdgSmYrEsipUNjTiXGYhTqcXYIKTZbuy5NiiMYP4pSqzPO3YRGfDrSQU1dQjJKsIBxJysNDfUWGd9beS8HtsFtLKq1Fe34DdcVmILCpn0YZi/M2NWPTgydR8FuWbWFqF46l58LeQnkh3xJMPByIqvgA/7rmHotIa7D0ai4vX07H40fYRu2KamprZ5Er8mj5W5GD962S8VDkadH/x9lhs2n4bKRmqLVKG/XUGnkP7wGdkf+iZGGHQ3KkwtjJH+N8XulQndM8xtotn4gsLYWxjCR0DffSdNb7VqV9dVom0sGgMmjsNli720DXUR//ZE6FlZMQi6jui8Pp15tx3eewxtghg7OsL2wkTkHP6NJob5UdzkfPZ9+WXYTFwIFsA6C5MjHQwe7oPvvzhBiJjC5CVU4G1n4XA290co4Yo3n1Ag2TJ16wp3jh+NhHlFXUqlZEl+9QpGPv5wWbUKNafduPHw9DdHTmnTnW6TlNDA4vUt58yhTnrNfX1WVl9JyfknDkjdaym+nokbN0K5zlzoNXBAg0x0l5kWz4NE9mW6OIK/BSTjpluim3LzvhMfB+ViqQW20K7e05nFGC8Y9u9G1lcjvV3Etnx6N6l/55Mz0eghXS0YUc86mvLbMuHVxJRWF2Pc2lF+CM6G0uCFduWI4n5+PJmKou8r6xvxOXMElxKL0Y/W+Nus33hR87Bys0JfWaOY/eh/4ShcO7rj7CDZ7pUJ+L4JZRk52Hyq4th7mwHLT0d+I4Z1OrUV1ZG1qlPLBnvhaO3M3DoZgaKKurw1dEYZBRV4clRHkp/Hw1FJO2fvMmgka4mvnyyP17feRul1apFC7LvaGpC0ZmTMBszHga+/tA0MWWO+Ob6OpRelZ5ASmL76HxYP/QItK3aR2BLoabGbE3rS8luInlQZC5FT355M4VF+5KzZe2leOaYoog/RTx7MhIXM4pRWtuAzIpa/HQ3gzl6XEykd/j1sTbGo752eCdE+pnSFRb6O+BGdgn2xueguLYeO2KycC+/HI/7Se9okGTL3TT8EZfN2lpe34g/E3KRUVGNICvV7lXJiHmKvh/xzFwYWprB2ssVfedMRfTJS6irrulyndv7jkNbXw9+44e2O05hCjm3mtH7wfHsuWdkbQG/CcOhXpYH1Ek7hOYGOzDb8v7pOBRU1eFsQgF23snEs4NcOvyNH0/2w/HYfFxLbe/QOBSViw0XkxBfUInKukZcSinCxaQi9Hc0gSoknjgLU1cneE2bAB1jI7iOGQbrIH/E/32qS3Uoen/gqsWwClC+M1CSkqgYVKalw/Px+dC1tIShsxNcZ89E3tVrqFMQnCWkjprMPVpfUYniiAjYjhwhFfWfdugIi/S37N8XGrq6MHRxhtcTiueM5JNa2MsRO6IycS27BAXV9fjgegLbdTPT00ZhvbcvxeFkagEbx1KdLeFpLEDGz7xtkfGj64nM8U8L1p3FRE8LcwY6Y8OxGERklCKzuBpv7QuHj50xxvgpt2vkyJe0zZLTxrDUYqzacQs3kopQVl3Pjv3VqViM8bOBgY7wcZefhSGG2Jvho2uJSC2rYc7UT68nYbSzBTxM9eXWKaypxwvnonErt5Q9d2kXwx8xWehlacSi0YljyflYdzUBEQUVbOHwRk4pfo/KxFT3tkWc7qaopAKjZq7Bb3svoLpG9edUZ3jUTzRuoUh6Nm5JLcLuqGws7aN43HI4IR9fhKYiXjxuyShBiMy4ZYyLOca7WmDVqWiE5Zaz3dt0bFWc+gRFhUdG52Lrb6EoKq7GH3/exYXLSVi6sP0ObjG9/W0RGpaBq6FpqKqux5172Th3OQlB/m2BJXS8H3fcRGp6CRsnHz4ejZi4fPQLdlCtfYGOOJ6cj7+T8tmC+rdhqez59HiA4uOsv5GMA/G5yK2qYzu6t0dksrklBXaoUqYjKD5g8YJ++HlnGC7fSGNO+jWfnIGujibmPBigsF6gvw3+PBqN5NRiVFTWYc/BCObUp8+JgqIqLFx+ABevpKC4pAbJaSX46IsL8PGyhJe7heD2GWpqYqKDDbYnpCKhvBJ5NbXYFJUAV0MDDLBUPG75IDwGO5PS2Y57RdA4UPI1zt4GIbmFbB7A4dyPcMd+N0JObWLp0qX45JNP8NJLL3VYh5zPx44dw7PPPothw4Zh1qxZOHPmDIqKilrlfWgXAH1ub2/PvoPK0d8HDRqEtLQ05vCfPXs2Pv30U7z44otSsjJHjx5lCwa0IEDf8eeff+KZZ55pLePk5MTaSi9aYDAwMEC/fv2QktIWxZudnc2+kwastGhBDrYnn5SOaBHSHkX4+fnhrbfeYv9+4YUXWFtcXNpPRIS0dc2aNfj2228xb9485lBPTk7Gu+++K7X4oKw/xGUee+wxjBkzhh1j165dzPmuSjuE9H16ejpbHKD+pH7V0NBg10N3SxzJkhiRDG1dbbj5t/Wxd5AHa0diRPvobWVyPrr67WUShECRrxStX1Rb3/pZaF4J2/Lta2Yo+DjG2posGk2Wb0f2wtkZg7F9XDCmu3TgLJFDsJUxbueXSi7u40ZOCRwN9VgUj0rtkxggXMwshI2+DpProchiBwNdjHe2ZI5+VegXYINrYdJRMldvZyE4QPhvfXiKD05dTmELA5I8/1Q/FBRXs8UCVaAIxbyEVDgGekt97tjbGzkxyV2qk3T9LnP+a2gp2GTWEqGnJhthq66OiviOnUzlCQkwcHWFho5O62fkmG6srGSR+f8mQQHW0NLUwPVbWa2fZeaUIzWjFH0D5e96kKV/sB3cnE2x51B0l8oQFYmJMPaRdpjQwkd5YmLX6jQ1sWeKJOSoLJc5X6l79kDf2RkW/ZVLwojpbWGMlLIq5gQUc7PFtviYqmhblAzuXQz1MMbBEhdUlPjqa2uM0Gxp23I1swROxnqw1OvYttAlHmRtxKLaTia33yocsmAwQhcOwa4HgzDSyUxwu+h+sw+Ulquh+zI3NrlLdZKv34VTb1/m+FeEkDKEloYaeruY4ZrM9vArsXno6648arGPmznC10/HlQ8m49unB8LFsn30/Ifz+uBEeLZSWR9l1Bfko7G8DAbebTscKBJfz90T1UmK7xehlN26gdgXliNxzevI3vELGspUW3gNsjKClrpIfkcMORdSS6vR10aY09tMVwvz/OwQW1SJlJI2R6CRtgY+H+uLt0LipO69rhJsbYwbudK/k3bA0TNaCCSVQYvr9Ny92IlofSI3JgmmjrbQN237TodAH/b8KkhM61IdkumJOXMFo557TO5x7AN9mNM/6sRFtrOlurQcceeuodHBH9CWXljp72iKG2klUrblcmoRnM30YWWgWF7mkd72cDfXx4aLiYLsT7C9MYa4mOF4bB5UoTAuCZb+0s96qwBfFCckd2sdIZQlJEDbzAx6Nm1jJlM/P7YCWJaY3G11cq9cZQ5+68GDWj8runuPLQJaDVQuQSSJk5EuLPW0cSO77V6oa2xmkjp0jwiBotUf93dAQXWdlBRPdxDsYsakfq4mtNlOcu6nFFSir6ty2/zcWC9EfjQVZ18fizem+0NfW7nD3kxfG/WNTaitF75Lsq+1MZsr3M1v2+UTmlOChqZmwf1HEfszPG1wIb2Q9b0iTHS0mCP7vwQ54+nak/zVVzJL4KziuGWIgylOSIxbJrpZIrawkkXqd6l9QQ64elPaFl++kYo+vRU7zo+ejkPf3vYIDrSDhoYafL2sMGKwKw6fkK8WoKWpjjHD3eHhZoFT5xVLV7arp66GXlZGUvcucS2rWPC1R+PX2d620FZXx+XM4k6XkYeLoymT1bka2raYQlJEt8Kz0DfIXmG9o6fiMH2iN+xsDFnfTJ/owyR5zl5UvFPFtCUYoLJK+BjB19QImurquFvU1n/k3M+qqoa/qWpBccroZWYMRwM9HM/4d+d8HE53wqV4uhFybBOBgYHMYdsR58+fR0hICOLj4+Hm5sY+s7CwkJu8ddWqVcyJLIaiu11dXbF9+/bWz7y9vTFgwADm3DYzE03oKVp9//798PDwaF0ooMh7MUZGRlJtpQh1ij7ftm0bPvjgA/bZ559/ztr366+/svdTp05FZmYmfv/999Z6n332maD2yIP+NnCgaFWdchP06tVLbjkhbaX+JGf6o48+yt5PmzYNVVVtWpEd9Ye4zE8//YSxY8ey976+vggODkZYWBhrn5B2CPmuDRs2wNPTU6pfaRFF/P6forSwHIYmBlJONQ1NDegb6aGsSNhgPzs1FxcPX8X0J0Q7LVSFnOOyk3/xewsdYY7zAdYmGG5njjevSQ/C9iVkYU9iNkpq61n07it9PWCopYndCW2O0o4gaSCKrJfbPl1tFsnTEY94ifRKjyS1TX5JOuDD0Hh8MswXui3Rw6fTCvBlmGpbdq0s9FEo4VQhikqrYaivDX1dTVR1oD0a6GMJXw8LfLz5mtTnQ/raY9ZELzy4+ABUpbq8ksk66ZlIO0/pfVVJWafrkDOjsqiU6XEfen8zsqOToG9qBK8R/TBgziSmsU8OQTtfd4TuOQ5zR1sYmJsg8tQVJqkjRCKnvrQUmsbSA2xNQ8PWv8FBteicrkDnliiSPb8l1WxrqxDmTPdFUmoxbobndKkMRdY3VFayqHtJNI2MUF9W1uk66pqaTCs/68QJGLi5QdfKimny0wILRe+3/ubbt1EaGYnANWvQFdtCtkD8NyH0tzLBMFtzvH29/QTvu5GB6GVhzORdLmYVYtM91RxMFOmcKtN3FE0p/htFVSri+hNDmAYtyQhQ1PROiS3wJInyxY0U/BWfy5wVD/vYYuuUXlh8LIJFyXUESYXoy96Hxoaor6lFfXUti6LvTJ3SnAK4Dw7CqS+2I+VmBLuPXfoFYPCCB1gEMqGoDOrHAVptzkszQx3mPCqQ2RpOkftWxooXmXNKq1kU/uWYfJgZauO1Gb2w96VRmPzhaVaXmDvMFe42hnjp15voLA0tUbsaMte+hqER087vCtp29jAZMgx67h5sASHnj51I+3oDXF97hy0eCIGuL8nrTQy9t2z5myKW93HG8r4uTDqD9IFJykdSQ/qjkd44nVLAnD3iaNauQovfpjpacttroae8vY6Gujg0oz9rL0kUUHTznfzOOTPpOtczNmp3nbO/KXi2CalDevrnvt6Okc89Bt2Wv8liaGGGia8uxcn1W5leP2Ht7Yr6ke3zjFgbaiNVRm+9qEULnBz7+XI0qz3M9fH6GE/M+e0msxvKCHt+JEz1tJj92XYjFTs6kPeRpbaklEXdS0KSOg01tWioqYGmrm631BECRdhryxxX09CABQPUK1gw60yd3EuXYdm/PzT1JfKJ5OdD18ICeVevI+PYcSblY+DsBLfZs2DkLpoLKr53pc8hRf/SOFMZU92s8NEIH3YvkFP/+bNRKud06ghrY9HzoUgmV0dRRS2sjNo/O8TcSCrE0fAsxGSXIcDBBB/NCYK3rREWbZOf68zCUBvLx3vhz1sZHV6vsv0nHgeIId98WW09WzBRBvXdAx7WbDcryZs8dzJCYVl3Ez084mOH7+7Il3+5XyGZr9RsmXFLtbBxS+iTbeOWH8MzsDOybdzibKyL5NIqvD3MHTO9bdiugBtZpfj0ajLLdSS4faT7XyRj+4qrmaNZX0+LReTL8vfJGDg5mGDfz/NbJUe3/RaK3/ZI70Qn2dLQ0yugqamO+vpGfPTFOVy8InzcZ6qrxRbUKS9Bu+duB9ceSTX+Pj2Y3buVdQ147UIM2wGhahllWFmJghwKZZ8dxVVwsle8K4ui+r9b/wAuHxPlFqusqsOLbx9TqMuvra3BNPmvhKYxPX6hmLfkLymtkz6H9N5MST4UVSEt/vTKKkQqeKZzFMM19nsO3LH/P4RkWsixK3bqi53E8pBdKDh37hxzApNsC8nd0Iscx6TfGRsbi8GDRRqP5ubmrY5lwtnZmcnKkASQiYnIYFOS2j179jBnfW1tLZKSkpjzWsyNGzdapXLETJo0ScqxL7Q9XaWjtlLy4i+//BLa2tqszbQbQF/CUSSkP7S0tKTkcHr37g1bW1uEhoa2JkXuqB1CvovOv2y/0vlX5tin76KXJHW19dAW6AxXBkU7CxkmF+eXYNPrP8Cvnzcmzh2D7qJZBdlgLxMDfDjYF38kZDFJHjG0A2D9nTYn+eGUXDgb6WGBj4NKjn15iHU/hbRvuL0ZXurrjs9uJiKupC0ShSL13xnohfeux+Nylkhj/73BPlg3xAdvXontWvta/NeyUdDyeGSaL9KzynDldpaUBMz6N0bj9U8voLhM+IC6I9TU1KW2VqtahySEiJv7TmDC6icw+eVFyE/OxPH1P6Ghtg7DFz3E/j75ladw6Zc/sfe1Dcyx6Nq/FyyHDGE67p1qt4oyF92NbJ/R+RVy7Rnoa2HSGHd8vS20S2WUIeQa66iO+5NPIm3fPkRv2IDGqioW0W8zZgwKb4h0c+uKi5H8++/wXraMSRV0i20RUNbTxAAfDPLFnoQsuQm3l1+8x6KHvE0N8HpfT7w/0AdvyVkA6JRt6aDckN+uMhkFip77bIwPahqa8EWoaJdVWG4Ze4khXeBAa0MsCXIS5NiXh3gHjKTmt6p1SO864vhFDH9qNkYunYPyvCLmwD/z9Q5MfWOp0jIatYVoDF4oqP+U9d2vF9psAGk/P/9zKIvcf3iwC344HQ83a0O88mAAHv3iIuo7o93WEaxPunZcq+ltAR8azgZwWPwsEt9+FZWRd2EU3K9rtoX1n/Krj66n7++kw95IBy/2d8Vv04LwwH6RtjfJS7kY67EcEN2KgiYJ6cmMihoM2HmJLerTrrh3Bnmioq4BZ9JVTyool07kX5Ctc/G7nXAdGASnYJGknDxIY//4R9+hz+zJ8B0/FHVV1bj8415on9yEumkvA+rKo5vFvk95NpsWJ7+ZGYiNFxORKOMUk0e/ry9CT0sDAxxNsWF6AMsL8rmAKH9liNulyhihM3VUoTPHlVenNC4e1Tm58HryiXaFawoLWeR+79degbq2FlIPHsa9jV+i3/u0w7i9vKSy89vRlXg0OR8nUvLZYtjCAAdsmdALc4/cQWpZ56V3hNsWap/iFr73V5uT/FpiId7cG45dy4bB186YOfslIemdrU8NRG5pDdYdVOxcVwUaNnfUf2+FxGLt5Ti4mujhrcGe2Do5EPMOh7WTurPW18Z3E3uxSOyf7mXgv47QOdGg7aJxS387Y6wfKxq3bLwhGreQs3+CqyVz+I/fGcrk4j4a7Y2tUwPw4L7bKifnlt8++Q18cl5fPLNwIBat2Iewe1nw8bTC1588gOqaemzc3LZzvqS0Br6DN8BAXxtjhnvgkzWTmPTMvkNduwapeR31He0wCf4lhO0CedDTGhvG+GHJiXtS0f9CynQGNvdS0r4tnz8IY2MdTJz9C3LzKzFprCe++mganlr1J67dlJZSoh0RX300lS2SLFl9sFPtkb0UWPO6yaOsr6GB4TaW+C1BWKJ5Dqenwh37/0PKy8th2BIVKkZXVxeamu1PC8m9yNYl5zPJ/shCzmwx5KSWPyAWmUjSwSf5G4qqJ/kZas/69eulNOXLysratVPWiS20PV1BSFspYp4c8SR78/HHH7N+27JlS+uCSUf9QdBCAMnSyP5e6geh7RDyXfLOv+x7Weg3vffee1KfLXzxMSx6Wb4256crvkZylChyxNjcCJ/texfGZoaoKK1k7RC3iaKmK0ur2N+UUVJQig0vbIatiw2eeXdhu34SCjngXYyko4/NWhYnOoqG9zDWx6aRvZi+/pcKEu1KEl9SiSd8HKGroY6aRmFbdwtr6likoCTmusLaN9TODJ8N98NXd5LbJdqd623P9PpPtEjvxJdU4bu7qfhqdAC+CEtGvkBdZ5LKMTeVdnZamOmisrqevZShq6OBaWM88MPucKnP3RxNYGNpgK2fTG79jAbdlEAy+vTTmLvyEMKjWySDqqKhVnKU/fPb2WrM2U6OdHKGV5dK55GoKi1nEfby0DMy6LCOlo42tPV1mZ6+x5Bg9plDgCcCpwxH5KmrrY59itKf9KK0RNivb30HHcuOJ8laxsbtItDF7+lv/yaFxSI7Ym6mi7yCNmeLhZkebt/teIvoAxO9WEK1P4/FdamMOLJeQ18f9eXSSSrpvWxEvqp1NA0M4L5Q2mmbsG1b6/mqysxEQ3k5otavbyvQYjuvP/ssepPEmpaDSrZFUvpLHu7G+vhqeC+mr/+1gkh8siB1TU2IKCrHdxGpWD/Un+0EELKLhyB9WrEtESOOPlYW9ca+uxlsi//F9GJsC89gSU3Fjn150Pb22T4y8k3ZSVDb/Sn753dfAoPnP4A+s8ZDz9RIzn1YAU1dbWjpyo+4FFLHwMwEBuamCJw6ir3XcdNHv9kTcWbTb6ivrWP3t6Iyp776DWisAzRE/VNcUYuGxiZYGEq3x8JIp10UvzJq6huRml8BVyvR847kfcwMtHH0DdEuPYLujyaPZswc4ISBbx5DiZxoZ1k0WmxFY3kFINHtjeXl0DDqXjuiZWYOdX191OUJl0Oha48w19NCXkskt/j6uy2xKCQPuvMoQj+trAavXYjD7SeHMm3kA3G56G9rAi8zA4Qvkg5A+XyMLxb2csAjh+6gM1BELkXUmst5FstGLsuDHG+ldQ3sOTzIzpQl1e3IsV/7xYtAVQW2qgG2vu544P3V7DovycyVKldTKrJviuSjhNTJjk5gyXAjj19sKSGyb78ueh19Zk9C/0enIebMZRhamyN4lmgMq2tkwJLvpq98D+o58Wiyb5N9Kqisg7nMzgtLfVHf5cuJfjXW1YS/jRHem+jDXgQ5YSkyOfG1sXjxcCQORuVK25+6RpxPKsQP11OxYqibSo59HRNj1JVJ24vasnImgaepwMZ0po4Q2DNf5hlFO81oBV02Kr+zdXJCLkPPzg4mXp5Sn2tTEFFzM9znzoGupUhr2mPeo8i9dAXFEZGAvcgOyrt3SQoLEsk2zQU+e+heoHt+fWgyS/g6w8MaX4d1X1S52P6aG2ojTyIwhCLsb6UIl8CKzhLZIUqOK+nYJ3men5cMZju25m+5iqo61aRuaKeC7Jie1tnoMxrvK6O5ZUccRUG/fyUeR2YPYBIqtyRsppWeNn6Z0pvtZiJd/n9ozel/RkGVknFLlbBxy4W0Ymy9k4HlfZ1bHfv5VbVscfjTa6LxFu0k+exaMg7M7gNPM33ECVhwZG0oqoS5mbQ0mYWZPosip5c8nprfHzv3hyPkmqgtt8IzWcT+yytGSDn2CcpJRUlkDx6LwsC+jnji0b6CHfslNfXsWSbbf+zeFTDno3uXovt/icjESCdzzPW1a+e0F1JGEQWFoj6m/pNUFLMw12+3C0KMm4sZRg93w9zFe5CQLLq/9x2KxLSJPlg4t4+UY5/mkBs/mIJAPxvMXbIHufnt8xoqo7hO1Ecm2lpSevmm2lqI6qbo+tF2Vmyh+0yW9DObw7nf4I79bkRVJydF6pMGPEm2iJ354vcdQZHgubm5giR/lEGJeEn3XVILnxzWxhLOLHd3dxZ1L4ns++5qT1fbSo5qkuGhF+0WoDwHJM2Tmip8AEsR9fRbbGxECWBIyof08MU7K4S0Qwh0vLg4accayTIp44033miXt+B60TmF5V/5akXrQoLYie/Ryw11NXVIjU1nyXCJuLuJrL88AuRvAyZKCsmp/y2sHSzx3PtPQVOR3rkA7hWW4SF3O5hoa7KJN9Hf2oRtw4wprlDqePtmZC+cyyjAZ2HCJpQeJvoor2sQ7NQn7haUsQS6knGWA2xMkVVRwyYIihhiZ4rPR/jh27up2BnbfodAs7yIppZvEEeXCCEsMhcDg+ykPhvcxx53ojp28kwZ7Q5dXU0cOC597d2JzoPvuG1Sn617aQTcnEywYPURltSsFT1fNOuJHAHPbXJmznm6vqw9nJAZmSCVGDPzXhzs/OQntiTNfCF1SGanfQS98hjTyuIylMfFwXnuXOUdQgtqHh7I+PNPlqhVLGtRFhvLHNS6tsJ07buL8Mg8NDQ0YUAfe/x9SqTjaWttAGcHY4RFdOzYn/OAL05fTGbJqrpSRoyRuzvrR0xuW/Api4lhfdaddRqqq1ESEQG7ll1MJgEBGPjdd1Jl4n/4Ac319fBevlyUsFiOjy6isAyz3KRtSz8rYbZl0/BeOJ9ZgM/vCLMtYsWRjqKdJSEHKkU4S9qWwfamyCivkXK2dgQtunUUrURJUfOqZBx6du5ofn4z++czw2pa7ytbH3dkRUrrxtJ9aOvtqjDiTUgdco7mxsssPsgcT0gZgqLpI9JLMNDLEnuvtT3Th3hb4Wai8EhsHS11OFsZ4kK0yF4eDE3HkVvSEZY7Vg5nSXlf//22tO1TAiW/JdmdqoRY6HuJdMHJppC+vsWU6ehO6kuK0VRVBc2WnYZCCM8rYw6GAXYm+Dsxv1U3muQQJHd7dAQ5xOiaF5+hV8/H4vULbWNCbQ115uR/5XwMjiaplj9Gljv55ehnY4Kfo9rOz0BbU4RLaGULgSbtQqL7tJ//nN2ZTwSRbRRVsPFxR9SJS6gpq2iVzKHrnBYxrdzlJzQXUufxbR9LxSGmht7Dqc+34fEfP2qVqZLu6ZZPxJH/atLPxFuZpXgs2EHKtgx1MUd6STXy5CRIL66uh/sn0smxnx3sgif7O2HwN5dYIm6l14CK0ZLmXu4oiJEe4+ZHxcHcU7GN6UwdIRh7eiD9yFHU5BdA10q0mFwSHct+FD2/ulqnoboGBTdvwXXWDLnHIaTb3/owkQtF15MTsL+NSatDmbS7ycH8fXjaP3IvqMKdtBK26DrIwxKHWySa7Ex14WJhoJJj38dOtECSV14j7dRfPBj62pqY/90VlkRX5fbllUFfSwO9LA1ZoluC+pLkS1SzfeKOa+tAklP5ZWpvtui56kwUWwT4r0Hjlrl+0uMW0svPKFN93CJ5jd/KKcMQB2mp3uZO7JwhPfhB/ZykPhs60JlF4iuCji8Z1Cf6rLlD9U6S7VHF9tD1EFlQjgF2pizRLSTGfTdzSlW+dzvaYyKkjCQpacUs0e2gvo4IvS26d7W1NNCvtz02bZOWa22lpd9kd3M2N1H/NUk59b/4YAo71ryle5HRsnCnCrGl5WwMFmhmjAs5IpkfSx1t2OnrIrqbHPskw3MlrxCl9d0rUfb/hc5sYOT8M/Dkud3ZmerqTCOf5FmEQFr65MQn6RiCjKFkoldlLFu2DCdPnsQvv/zS+hlFgH/00UcqtZmc0ZGRka2GmCLdSVZHkoULFzKt+Dt3RFFXJLnzzTff/CPt6WpbKR9AQUFB6/kg/X5KSqsq5KgXP/BpFwB995QpUwS3Qwikt0/9evfuXfaeFhMo8a8ydHR02PdLvpTJ8KhrqDP9fHrRvwly5nv1dscf3/yJorxiFGQXYd93h+DX3xsO7m3O4pWTX8PxXaJJX1lROTa+sBlW5NRf9xS0tLu2Jng+sxC5VbV4tY8HTLU14Wmij6f8nHAoJbc1G72VrjYuPzQMo+1FEU2uRnosKS5J73yqwKk/19MeD7jasAg/PQ11THSyYp/tTVRNhoci/PQ01bEq2BWGWhoYaGOKhzxtsSOm7d4eYGOCG3OHw91ENAGnMp+P8Me34Sn4XaKcJOfSCzHa0QJjHC3YpIxkgp4NdMG9gjLBEb/E9v0R6O1rjQUz/WGgp/V/7J0FdFRHF8f/xN3d3QWSENzdXUuBUlyKt3gLpaUtLcVairsVd3cIGizu7u4O35nZbFaym+wm0C+08ztnT7K7b96bnffefTN37vwvBna3RadWFth7IqBmm1H9nWikvYK8oJkf0ccRd5/EI706SoMf0nnif3HvgVqOLdo5l6EvGVnZmk5u80FdEfHwJaKevEF5SSn8T99AbkomPPp2rCnqd+Ac9k/9tua9JGVaDO5GE+jG+gfR5INp4bEIvPYQ9u15EhQBVx8g5lkAKssrkJOUhqu/7IaKuTn02rattz11W7eGjIIC4o8doxF4RL4n7cYNKg1DHDGE4oQEPJ8+nTr8PyY5eaU4ezUc86a0hK2VNo3U/3ZhB8Qm5OLuI94A/ty+4VjzNa+NCI62OnB3NqgzIa4k2/Bj1KMH8oKDkeHnh6rSUqTdu0eT4Bp361azTfLVq3g+Z45UZTKfPEHms2eoIvJimZmI3LED8pqaMKrehlxTxHnP/+Jcd5zPxUGS2aaVlGFRc45tISt8JjqZ42Icz7aQHBr3B7dDp2rbYqmujM3t3XAnOQvrxTj1iR3pY2FAbQtNhqajjumuVniWloNMCaKHuRwPTqEOhsWtrKGmIEsHx6OcjbD3baLAgC9kSgcasUb4upU1ulrq0IS+SnIy6GSujS89zGi0NBeyPxJBTbYhL/I9SUx3QNTAlsh3yMgK3Lse/TvRRNYBl+/R+zD83nPEvwyC5wBeFHvQ9UfYNnwuzXshaRn3vh2RHZ+C4Bt+nHszMRUvz9yAlY87jdava5v3+s410fpcdt2KRH8vU/T0MIaqohymdbeniXAP3OOdt28GueL+ap7MHUmW62mpDQU5GZiSJKGfc7TXT/JNDtSyfaiWFJTCSUMmSbS7dEf2nZsojoxAVXER0k//DcjKQLM1zw4l7vwT8ZuIA1kyKnJzkXJwD0qTEulEQVlyEpL37ICctg7UPL0k3k9OWSXNwTDP2wq2WirQVZbHt+3sEJtXgrvxPOfbuaFeWNOekxTZx0iDSu8Q5z+ZyCJJPH/q5Egny29Xl3lfHTHIe3FlmHhSMA3lYEgS2phoYaCNAVTkZDHc3ogmzj0UwnvGfuFihiejee37YztHug1ZpacuL4sR9kboYq6L81H1R+PRSerqe6Omz+TrCTU9bTzceZwmryXyOC9PXYVTtzZQUK1OBpiVi50jv0LMk9cSlyH75xyn+l6sHh1z6sA9tgeyE1IQcOkOlZ4j2v2P953GexUtvNMTnFQ48iqJSuUQzXx1RVm0s9TGmOam2PWc99xoa6lNo/Htq5NHk3PF/+IGGPA79Zd2sUM3Oz0a4U/sTxdbXUxtZYlTATytbEmw7dUFOdGxiL5+FxUlpUh49AxpbwJh24f3XIi9/RDnxs+usTGSlGkI2q4uUDEzReShI1Q7vzg1FXFnzkHf1weK2lqcNigrw4MpM2jkvaRluGQ8e0YT5Bq0rS1DSnT0NR0dEHPiNMrz8umkdsyJk5BVUIC2m6vI+pKzcSg4mSa/bWGgQW381y1tqH06G8m7rn/v7IxdPd1rJu2+a2sHOy0Vau9IVPkSXxsaKdzYCTdhiMTZaf9ELOjtBDtDNeipKWLNUA/EZBbiDt+qj0sLOuGH4R70fy8rbSzp70Kj88lkYHMLbfww3BOv43LwKo4jH6ckL4vdX7aCiqIsdernNcCpTyDOfOJEXdLKlraLiZoiFvva0CSjkbm8vvCLz9thkrsZ/b+vjT7Gu5pSyUyS78NBWxXftrWnmvBvq/N1kNV6e/sQp34J5twK+lc69QnHglOobfm6Naff0tZUC6NdjGiuHy6kLxM2rQPsq/st37QW7Ld0ttDGZE8znAnjXQ+nwkhOoHeY39KSjrkMVRWw0NcKwZmFiOI7L/Wx78gLeLoaY/yoFlQyZ1AfF3RuZ4Pdh3g5c8YM9UTE80XUaU24ficCo4d6omULM5r81c3ZEF+Oa4mbd3kTiT9925tOGBCdfqLXP6SfCwb3dcGpC9LJ8OwLTKTXU3dLXdr/m+xhRhMPHwnh9c8WtrTGzZGcfIOETV2d6UQUyVmjpSiHL93N6Oq483z3uyTb1Acx9fuOvMQXY73g09wEGuqKWL6wE52oO3UhqGa7P9cPwKG/htP/yXgkNCIDC2e2o3kKiH7+gF6OaN/aEtfvcvpipGv565re8PbkOPWl0dXnJ7+iErdS0jHezhIWqso0Un+Wsy2SikvwLJMnM7m1dXPMERNEVhdWaipw0FRnSXMZ/wpYxP4HZvbs2Zg+fTq2bNlCo9j5deiFIU7nnTt3YtKkSdixYweKioqonA2RcBElx8MPcTKTsiR6mzihiWY7cQwLR3PXx8qVK9G7d28aPU4kaEh0eocOHQS2GTx4ML744guaHNjR0RHJycm0zLFjxz54fRpbV+LoJlI8pG2Jxj+Rz+GfbJAEUpZMDhBNfHIucnJycOTIkRqtfknqIQlDhw6lkyEkwTBJ0EvatXv37hJPDDWGaasn4sjvJ7Hy83XUsePRxhVj5w0T2Kaq6h2dfSc8v/0SqfHpSE/MpA5/frZc/VlqR3/5u/eY+zAQX7eww8X+vlRz8Vp8Oja+5a0DJJ0CMhjhzgQPszWGjpICBlkb0ReX9JIyDLnC6bxdS8igEwRTXSxoZzKhsJTK9ZyNkS7LPZHEmXMnCF/72OAzJzOadGtfcCKOhfM6YSSKjtSPO1E90cWM6kjObWFDX1yep+Vi1h1OJ/BkZAoUqycM1rVzos6Rp2m52PRKugScb0MzMHf1LSyc0hIrZrdBcnoRVm14gHtP+ZY/NmtG5ST4IzcsTTXQ0tMYU5ZexcfAvp0XleZ4sPsUTXirbWqAfksmQ9fSpGYbMiHGH9EhSRkzdwd0m/MZHu45TRNtqulq0gj/liN4EeE2vh50H9c27KMOQ7u2LWDUe1iNY74uSMJWx3nzEHfkCF4vXAhZZWXotW8PkwEDBDcUCuV5OXcuHfxzo1eI458k3W3xq+ROO1Gs/vUBls9rh793DIaighyevkrG5AWXaUebi6ycDJVM4Gf4AGckJOXD74V4GyLJNvxoOjtTPfzE8+cRvX8/FPX1YTd5MtTtOY4/CsmrUlUlVRmSPDfu778Rc+gQxyHavDlsv/iCSiw0BmJb5j8MxOIWdjjXl2NbriekC0jr1NiW6vdk9RCxLQOtjOiLS0ZJGYZd49kWMkEwzdWSLgkmdud2YiYOhkunpZtWXI4vLwdiZTtbfOFhRpMfkuXpBwJ5toWcVn7bcjwkBXN9rPBjJwdqY0h0/7ZX8TgYyDuH3G1+6GhPnSSROcWYfjUIt+Iki2Q3tLdCz4WT8OTweTzcc4o6JTtNH81JYlsNzZ/z7l1NJJ0kZbRMDNBvxXT47T2D+zuOQ0ldDdatPGjy3Pq2SSur7by7/CoJuuoKWDXcAwaaSohOL8T0nU8RnlIgECUmy7fC5+ijGCwZ7AZ3Cy0Ul1XiZUw2hv12D4kiJjcbi27PPnhfUY6knX/S3BFKFpYwnzUfcmp8Uh20DXn3cvat60g/y0mMSohZx5HbMxwxBtodu9CofFVnV6Qe2kud+mRVgIqjM0y+mCJ1/onVjyKxvI0t/h7UHIqyMniakovJVwUT4ZKIP+5qlNdpBXDWVcO2nq5UR59cr09T8jD6/Gv6/8fmaWouVvmFY6anJVa3caDa+UsehuIVXyJccj/z28IT4SmY7mEBDz11Kp1F5DG+eRCKG/ENS2AspyCPvitn4eGO4zg8bQXkFBRg18EHbSZyZOAIdCKIntf3EpeRBFN3R3SdOwFvzt7As8Pn6X4N7K1Q3mMWIC947tMKyzDx+Ct818MRk30tkF1cgb+exGEfnywC6e+RHCHSBNgdfZ2EBR1s8UtfF+qUSsgrwVa/WIH9SoK2rRVazp6M4L/P4e3BE1DR00HzSWNh1NytZhtyX5B25AaCSlImPzEZd5ZxgohI2aBjZxF8/BwMm7uh9YLpIutCnjmuX81G5MHDePb1MtpP0PPxhu2YkbyN6MwUqct7yctUk/bgEfS8WkBejLym88xpiDpyDM+XrqDnQs3KEm7zv4KitrbIlWiEXQEJ1EG6sYszNBTlEJxViGk3AgUS4RKzVz0fhdSiMjxPzcMP7R1gp61Kc0wEZhZgwpW3VAaSy3gXUyzw4a3WPTmQM1m47mkUjodJPnmz6tRbrBrshlNzOtBVUU+jsjBx51OBJLfkPuXeq2/jc+FupoXtX7Skk7PpeaW4FpiKzdfDap4xbex00dpOj05gvFjdS+B4fX+7h4g0yVfuzLsdjJVt7HBpmA/d/92ELHz/WHDFGbF93Kh8MtlJnPzEcU8mA8hq3bsJ2fjrTnyNA7+PjT6dJCW20X+84Ip17wMPP5qjP/D+77A006fPOhLEVhB9iH6ubjPuoxwvragcX14KxKr2tpjkyem37HiViP18gQOk1Wi/pRlfn6SlFX7qwuu3/PkyHgcCeP2WwvIqjL8QgO862MG/hTmV+vJLzME3d8KkmhR+E5SK2d+cx9dzOmLV4m5ITs3H8h+u4+5DXn4dMnFKEuByjd/6rfdRXlGFDWv7wUCfk3z3+p1w/Lr1QU2ZwydeY+60dmjZgsg+NkNMfDaWrb2G0xd5Dm9JuBqTCR2lKCxtbQsDFUXE5hVj9s0ggSS3MkLPseNhqfja1waueupU/jE8uwjTrwfiQWKOVNtIwra9z+hK7j9/HUjzrQWGpmPirNMCq3o51xovvwnRyl8ytwNOHxhDJz2I4/77X+/g3GVO0JCVhTYG93WmWv23znwhcLwZi87j1n3J85/9ERKFaY7W2ODrSfu3b7PzsPJlkMAENGk73ooaYKaTDfqaGdec7zNdOZP/q14F4WVWbs12vUyNkFJcitfZjctJwGA0BZq9F16HxGg0xEFLpFuIU9jLq/5oKuJ8JpIsVlZWNILf2NiYRsd7enpSB5ifnx/9X1jXnlBRUYGQkBAale7g4CCg656RkUGlfXx9eTPAhYWFdN9t2rSpiWQnyVhDQ0Ppe6KHT2RryGfC2viJiYnIysqixykvL0dgYCDatWsncX3qgpR7+vQpbS+uA51MDJB29PHxqdlOkroShz7ZhnR2SHJibh0kaQ/+bbjHJ/sWznFQXz0kbXsCOUZ2djZtr/379+Pnn3+mZSXlXgpH6/xDQ3T3SUeIDATp8kQxUjZkNUBdfP2o4drCxLnATVDVTMxyLzr2avARgIry9x+1fqRz2hgjW7Cz4QkJSUdHOOJU1Gfi4ARIN6tJYCuKr/6wlKpOXMcgNxryY/I0o3FOYmGo87padoi+5xv010AiyiWUZXu2VLKoeVGQDjYnUXndn0lSThyt10mWH4V2I0jOjo+ccJi2N19i48gs6Vdj/ZO2JSPj3Qep38didvv6pZj44Tr2SXTxP8GmbbzE49LAXe3fGJ8KuR5ESadxcR8seYLL+q5jev+IWv/PZ2ukJSTmfeN+eyOfW/Vdv0pKH3btNqnzh/ShTWgu3b1BeFdVVSNL1xCkub82X2x4+xHnZX0yO41ty9HdP34bCLe9MHTVl5hnUlaZbIOe+VLd41TyRvp2eBLZcPtKRTgacP64E3n8iLt/iwIaloxdErsqsm5idB7E9WOVXQVXT0iDzAdqu7raL+Y7QYnBhkD6caKuLRKQ1VhMls5o0v2WdzvEyMNIAEniSvTyPyZKs2uv0vmY9640lP3BW8EgLZKMB0W1tyjEnQPHn3g+E2khlp7aFqH2FEa4+sLl6uJKz48nN/0pE553EZ8iDpofViKzKcAi9j8CJiYm9CUJZ8+epVH6xHlNnPpEu93S0hJubpxoFOKcrku3njitSYS6KPT19elLODmr8P6IvAuZOODX1BeFmZkZfRGUlZVrOfXrq09dkHLC9SIa91yde2nqSpzmrq6uDWoP/m1EHV/Sekja9gcOHKCSPObm5lTiiMgy9e/fNAwNv+OVPMzrc+B/DPif/dzl/k2JJl8/ER0waaQlRGlQNhbq+MCnibAEzMd2ZNeFqM61JB1uaTrlkkIHmB9atFfUcT5gezf5e7eJ1YdfHqmp01inNOFjDq6Fr2N6/zShdv0Qv/2fvn6bggJGYye8/qn7qy6n/v+7LRvaBh9rsrGh5+P/1TeQ1mn+T9+vDbm2pOmz/l/q93+4Xzj9uCZg9D6Bfgs/H9up//+4d/8pGjIe/Cfb+10D27PxU2GMT3VM/2+EOfY/IkSqRliLnt8RTJy6CgoK1AlNnMBEgkVHRwcnT55skC58U4Zo8HO15IUhGv5TpkzBf5GAgAAYGRnRVRpk1QaR+CGa/gwGg8FgMBgMBoPBYDAYDAaDIQ7m2P+IEN11boS7MFxpl759+yI2NhZhYWE0optE6zd0GW9TZsaMGcjLE61fRrTs/6usX78eK1asQHR0NG0HknyZwWAwGAwGg8FgMBgMBoPBYDDqgjn2PyKmpqb0JYkMDVd659+Ku7v7/7sKTRaSaLhFixb/72owGAwGg8FgMBgMBoPBYDAYjE8E5thnMBgMBoPBYDAYDAaDwWAwGAxGvTRr1oSTQ/zH+P9l/mMwGAwGg8FgMBgMBoPBYDAYDAaDITXMsc9gMBgMBoPBYDAYDAaDwWAwGAzGJwST4mEwGAwGg8FgMBgMBoPBYDAYDEa9NGNt1GRgEfsMBoPBYDAYDAaDwWAwGAwGg8FgfEIwxz6DwWAwGAwGg8FgMBgMBoPBYDAYnxDMsc9gMBgMBoPBYDAYDAaDwWAwGAzGJwTT2GcwGAwGg8FgMBgMBoPBYDAYDEa9NGMi+02GZu/fv3///64Eg8FoOMoWY5p085XEr0ZTJjAnHE2Zp+nyaMpYq1ehqVLZxJ9ur7Oa9rl11KxEU2aQpS2aMumloWjK7AlTRlOm8l3THS0YKDddu0eIKWzacTut9cvRlAnLa9rtV17VdO8NwpeOJWjK/BagiqbKAItSNGWSipv2Yn87jaZtm0sqm/a9qyDbtDvOck27+VBQ0XQrqCrXtM/tMn8tNGVu9Wn3/65CkyS64AI+RWzUB+DfRtN+OjMYDAaDwWAwGAwGg8FgMBgMBoPBEKBph6QwGAwGg8FgMBgMBoPBYDAYDAajScCixJsO7FwwGAwGg8FgMBgMBoPBYDAYDAaD8QnBHPsMBoPBYDAYDAaDwWAwGAwGg8FgfEIwxz6DwWAwGAwGg8FgMBgMBoPBYDAYnxBMY5/BYDAYDAaDwWAwGAwGg8FgMBj10qwZa6SmAovYZzAYDAaDwWAwGAwGg8FgMBgMBuMTgjn2GQwGg8FgMBgMBoPBYDAYDAaDwfiEYI59BuX69esYNGjQv/r4Dx48QM+ePdGUefHiBTp37vz/rgaDwWAwGAwGg8FgMBgMBoPBaMIwjX0GJT09HU+fPv1XHz8rKwt+fn4ffL93797Fzz//jCtXrjR6X7m5uXj48CH+aZzsTTFlXHeMHtIeBQUlcGr31T96/IMHL2D//vPIysqFg4MlliyZjBYtnOosExeXjA0bDuDJk7dQVlbEyJG9MXXqcMjJydLvX7wIwt69Z/HqVShkZWXg7e2ChQsnwNzcSKq6VVVW4eiOq7h35QVKisrg5GGNLxcOgbG5nsjt379/j+cPgnD574eICU+EsooSWrRxwphpvaGhpVazXVpyFg7+cQkhr6NRXloBYwt9DPm8K9p09aizPlEvgvDg4EXkJGdAw0AbbUb2gksnn0aVqayowJMTNxBy3x+F2XnQMTNEx8/7w7qFc802x1ZsRUJgZK1927hYYtEW8ddLbmYe/t5yGqH+4ZBTkINXp+YYOn0AFBQVxJaJD0/Ag/N+eHHnFQzM9LF0+8Ja2wQ+DcGVg9eREpsKJVUltO7VEv0n9IaMrHTz1aT8ya2nERcSD2U1ZbTp0wq9x/eEjIz4/US8jsSD848Q6BcEt7aumLRqQq1tnt/0x50Td5GRnAlFJUXYN7fFwKkDoK2vJXa/VRUVeH7oHKIf+aOyvALGbg5oM2kE1PS0P0iZ9+/e4eraP5ASFIHOcyfCpq2XYFsEReDl35eQFZMIVV0t9B3fE807C24jzNv7r3Hj4FVkp2ZD10QXvSb0hWtbd7HbV5RX0DJPLj5CQmg8hs0biZa9Wwtsc//kHVzedUHgM3LtrD3/C/5NtkUUoUEJ2PzLeUSGJUNLRw1DR7XF6AmdJC47e9I2aGio4PSNFVIfO+FlEF4ePY/81Ayo6unAc1gv2LZv2agyZUXFCLp4G7FPXqE4Ow/qBrpw7NkBTj3a12xzdc1mpAbXti169lbovZp370fe8UPQ+Rsoys6FpqkRvMYOhrGbY531q69MVlQcgi7cRHpYFDHe0LO3RovRA+m2XE7PXoni7Nxa+3bp7IPec8fV2P2nJ67j7XU/lBYUw8jOHF2mDIO+lanYuklShtjjBwcuIP5tGMqLS6FppAevAZ3h1q1Vje3ePHKxyP07De0LfSd7BBw5jfykVChpacC+bzfYdO9YZ5tlhkTUW0aSbSIu30LsnUcoyc6BvKoqjFu4wXXUIMirKEMSmpJtKUjLxOO9J+l1KqeoANsOPmg5dhBkqm1CY8sUZeXi7Dc/o6ygCOP2/AwFVeUamx31yB8hV+8jJyEFSuqqsPBxh/uIAZBXVhK4jp8fOImc2EQoaarDsWdHuPTvXudvqq9McU4eQi7eQuLLAJTmF0DDxAhug3rA3MdT7P6urf4dimqq+PJW3fanuKgUf/x2AQ/vBKKq6h182zriq68HU5snjlfPI3Fs/12EBMZDQUEOHl42mDKnD4xNdWu2CQ9JxK6tVxAWnIh3797Bxt4Yk2b0gqe3ba39lebkIvDQ38gICoWMnBxMfL3gMnooZBXE903qK1NZVo642/eR6PcMxekZUNLRhkWndrDp2QXNhPoU8ff8EHPjDorTM6FhYQrXscOhZWOFhkBsyeVDN/Hg4mMU5RfD0tEco+cMgZmtidgywS/CcPPEPcSExENBSR5OLewxZEo/aOlporHEBUTi+u5zyIhPhbqOBtoM6QKffjybL8y7qncIfxaEF5cfIuZNBNoO64puE/oLbJOXno1Hp24j8kUISgqKYWBphI5jesHWq+7nuSie3n6NM3uvIyMlG4amuhg2uQ+8O7iJ3T4xOhUXD99GyKtIVJRXwtrRDCOm9oWVo1nNNgW5hbh45A5e3g9Efm4BDE310GtkR7TrVXf/XBIyU7JwbPNphL+JgoKSAlp198aQqf1r+iTClBSV4u7Zh3h26yWy0nKgZ6SDToPaodPAto2uCxkTnd51BY+uvUBpcRns3a0xbt4QGJrpi702Xz0Mws1TDxAXnkj76x6tnDF0cm+oV4+JJNlGUkqKS3F4yzn4PwhEVeU7eLRywvh5Q6ChrSb29zy67o87558gKTaVHq911+YYNKG7wDgl+GUELh25g6jgeMgryMHR0xYjp/WFgQnP/khCUx8TJcWk4uCmM4gOjoeKujI69vPF4Inix0QFeUW4duI+/O+9RU5mHgxM9NB7VCe07ekt1TZ1IS/TDFMcrdDFWA8KMjJ4k52HLcHRyCgtF1vGQEkBfc2N0MfMENoK8uh34zEq3r0X2MZKTQWTHS3hpKkGmWbN8CQ9G9tCY1FQUSlxezEAJrHfdGCOfQajkWRmZuLx48efdDv++fMUnLr4BHuP3sbowe3+0WOfOnUTv/66D7/9tgjNmzth167TmDRpFS5f/gPGxqI7iklJ6Rg1ajE6d/bB2bOboKSkiMOHL+HNmzDqZKuqqqKOuUmThmDNmlkoKSnD2rXbMWHCcly4sBWq1YNmSTjy1xXcvfwCi3+aAH0jHez5/SzWfLUdG49+DUUl+VrbJ8am4fbFZxg2sRus7E2RnZGHrd8fwy/f7MPa7bNrtvtp8R5oaqvjx51zoKahglvnnmLDigNYt+sr2LlYiKxLalQCzvy4izrd3br6IuJpAC79fggqGmqwEuOslKTM3T3nEP7kDQYu/gL6lsaIfBaAMz/swtif5sLIjlOXkatnku53zX5L8ovw15ffonkH8Y4WMrD+c9lOqKirYPmuxSguLMH2VXtQVlKGCUvGii5T9Q6Hfj2ODv3boplMM8QEx9XaJiowBtuW7cKQaQMwc90UZKflYO/ag6iqqKKfSQoZ/GxdvA1O3g4Yv38p0hLSsfu7fbQj3Ptz0at70uLTcXn/VbTr3xZlxWXU8SLK8X9w3WGMmj8cXl28UJBTgEM/HcHe1fuxYOtcsfV5vOckkt6EoOfSGVDUUMOj7Udx7cc/MWT9EsjIyja6zJsz16kzgtT5/XvBeie+DsaNn7bDe0x/dF80BeUlpQg+eR5ubT3o4EMUUW8jcWTdAQyaNQzu7T3x6vYLHPx+L2Zs+AqWzqIdFP7XnyE2KAb9pgzEjq//xLv3gh1t7iDP0MoIX/3BG7w0a0DXsanbFmEyM/Ixf9oO9B7gg7UbxiMkMAHffX0ISsoKGDyyTb2OstVLjsDTy5pOCkh97OgE3F6/A15jBsC+cyvEPw/Ag60HoaShBlMP5waXCb/lR52U3b+ZTj9PfBmEB38egjxxdHb0pdv0XDGbOtW5EOfm3zNXwaIlz4EY/+w1nu46hrYzPoexhxPCrt3HnZ+3od9PSwSc8PxIUsb/yFk49ugAnwnD6X3hf+gMrq/ZiIG/roCiOscRMHjzaoH6EUfo5eW/wK41bxL2xZnbeHH2NgZ8PQm6FkZ4dPgSTqz6A5P+XAElNRWR9ZOkzOXf9qOirAIj1syGqo4Gwh+9xrUtR6CmrQErL2fIyctj3snfBPYb7vcGl37dBy1rCzxa/wfsendFm0UzkRkSjhfb9kNeVQXmbUQ7mwrTMuotI8k2sXcfIejvc/CdMxn6Lg4oSsvA0827UFlaCp8ZE1EfTcm2VFVU4uoPf0DLzBhDNyxHSU4ebq7fgfdV79D6i+GNLkOuu3tb9kPPxgKJr4Lwnu85mxEZh7SQSFpG09QQeUlpuLd5PwpzCtBx7qQaB/yNH7bAtmMrdJo/hTrY72/eAzlFRTj06CCyfpKUCTx3DWr6euiyeDoUNdQR/eAp7v22E12XzoKJkE2oKCnFgy37YOhsh+zYRNTHT6uOIz42HRt3zaBO+h+WH8XKRfuxZc8skduXl1fi+MF7GPl5Jzi4mCEvuxCbfjmLRTN24vD5JXSbstIKLJqxA63bO2PPiYWQlZXFwZ038M3sXTh8fil09TUE2vzphj+hoKqCTt8vR0VxMV5s3o7K0jK0mFp7ol7SMomPnlDnf/MvP4eKgR6yI6Lw8o/dqCovh8PAPjX7Cj9/BVGXrsNz8ucwcHdBYWo64u88bLBj//rxO7h+7DamrZ4IE2sjnNt9Bb8v3IY1B5dCVb22/cnPLsC1Y7fRY0RnOglQXFCMg7/+jY2Lt2PlzoWQrWPCqj6yUzJxeNV2tB7cGWO/m4rYt5E48+tBatPcOokOFIh+FYZX15+g1cCOyEvPEdmvunvkKkzszNF6UCcoq6vg+aVHOPLtdnyxfi7MnCRvN+Kc37b6ED6fPxQtO3nA74Y/tqzYhxV/zIadm+j9HP/rIlp3a44RU/tATl4Op3Zdwbq5f+KHfYuo05xw4/Qj6BlqY/GGqbTNn997gx0/HoOyqjK82ruioVRWVNLzYmxpiO/2fYO8rHz8uWIP7SuPmjNEZJn7F/xQWlKGL1eMg46BNkL8w7Hnh8O0TJch4idYJOHUzst4ePUF5qydCD0jbRzedBbrF2zHjwe/FumcTo5Nw4PLz9D/8+6wsDdBTkYedv14DFuW78WyP+ZIvI2k7PjhKJLj0rF88yzIK8ph25rD2Lh8L1b9KXo/b5+FIfR1NMbOHghTK0MkxqTij+8OITszD9OWjaHbkMmcy8fuoc/ozrB2NKeTOPt/P42fF2zHb8eWSVy3T2FM9PP8v+Da0gE/H1mClPh0em8QWzpoQg+RZa79fQ8KivKYvWYCtPU14X8/ADt+PErHUa27tZB4m7qY7WwDHz0tLHsRjLzySsx3s8U6HxdMffQaQr76GmY62yCqoAjHohMx28Wm1veaCnL41dcVzzJyMOXhazp5MMfFFt+1cMLCZ4EStxmD0ZRgUjwMAUhE+6RJk9CjRw98++23KCkpEfg+JiYGs2bNQrdu3TB69Ghcu3ZNpKTO7du3MXnyZLqf5cuXo7i4mH5GyvTp0we///47fcBJe3xxcI8rTfng4GC0b9+evrp3744ZM2YgNDRU5H6JRM6UKVPQu3dvLFu2DIWFhfT7V69eYeXKlfQ9d19//fVXzQoB8h2R/xkxYgT27t1LB5XCdfj8889pmyxZsoRG7P8/6Dr0O/yx5ypy84r+8WPv3n0aw4b1QPfubaCnp41vvpkENTVlHD0qfgXE778fhJ6eFn78cS510Glra2D27DHU8UYgnZAjR35G9+6toaurBTMzQ6xcOZ067d6+DZe4bmSgeOXkIwz7ojuN1Nc10MT0JcORlZ6Lx7ffiCxjbm2EJb9MgruPPdQ1VWBpZ4yxM/og5E0MstLz6DblZRWIj0pF90GtYGiiC1U1ZQz8rDMdTEWHJYmtj//5uzC0MYPvkG5Q0VSHZ8+2sPF2xrMztxpVJvjeC/gM7AIzFxsoqirDtYsvLDwc8OzM7ZptSAeMOIq5r5AHL+nnrXqKj+glESkJEUkYu2AEdI10YG5nisGT++PpjRfIy84XWYYcZ9mORegwsC0UlRVFbvPi9kuY2Bij+8jOdABF9ttnfE/cOfOADmYk5fnNFygtKsWoeSOgqacJhxb26DqiM+6cukcduKIwtDDA3N9nw6ebF2TlZcVG15COO3H+K6sq0QibVr196efCNoDfmRl+5zG8R/eHnq0F1PV10G7qaOQmpNCo6MaWSQuLRujNR2g/jTNQ4YfU6fHuE7Dt2BIeg3pAUV2VRlaPXfq5WKc+N/rVroUD2vRvBzUtNXQY2hkWzlZ4cOqu2DKt+7fD6G/Gwcq1dkdbGHIfc1/SRh01ddsiigunnkJeXg5zFg+Ajq462nVywYDhrXFkn/j25LL++1Po1M0dLXxqR6ZKQvDlO9C1MYf7wO5Q0lCHQ7e2MGvugsDztxpVhnzn1r8bNIz0oaCiDJv2PtCxNKXXIxcSCcZvW6L9/OnnNh1b8Y514SYs23jBun1LeizPEf2gqq+D0Kvi20aSMj1XzoVlay+oaGtCVVcbraeMQWleAVKDI8TWL+r+U6hqa8DGh+OsIU6oF+duw2tAJ1g2d4Sajia6TRuByrIKBN56IrJukpZJjUyAS5eWdBWVoooy3Hu0oU791Mh4Xv346kZeQbefQsfeGhmBoVDS0oTryIE0ItustTfM2vog/OINsW0WfeNevWUk2SYnOh6aFmYw8fagkeVaVuYwbdkcOdG1nRJN3bbEPX+LgtRMtJ86mq6E0re3QosRfRF64yGdAG1smVenrtJVDI7da0fTGjhYo93UMdC3s4SCshL9a9uhJTLICpNqIm8/gqy8HHzGD4OylgbMvN1h37Udgi6IP8+SlPGdOBIu/bpCw8QQimoqcO7TBbp2Voh7wnn28/Nk51FY+HrC0MUB9ZGcmIX7twMwY8EAWNsawdRcD3OXDEHAqxgEvo4VWYY4/3/a/CW8W9lDXV0ZZpb6GDa6PZISMpGdVcDZb1IW8vOKMXRMe+jqaUBLWxWjxndGaWkFYqPTBPZHIu7z4xLg8cVYqOjrQtPSHE4jBiPx0VOU5nL6acJIUsaqa0caea9pZU7PqaGnG8w7tUXykxc1+yGO//Azl2ikv0lLL8gpkfvDgu63IZCx1I3jd9FteCe4+DhCS1cTY+cOo/1MvyvPRJbR0FHH/F9nwK2VM41QNjQ3wIiZg5Ack4Lk2FQ0hucXHkBNRwNdJ/SDqpY6XDu2gHtnbzw6Kf5ZYufjjDHfToF9S1fqvBTFoHlj0bJ/B+iY6ENZXRUdR/ek/wc/Et0XF8eVY/fg4m2PboPb0iju3iM7wdbVEleO3xNbZuEvk2nkva6hNjR11DFhwTDq7H37hDdmHDqpF3oMa08juFXVldG5f2sYGOsiIjAGjeH1wwC68vPzhSOpk97a2RL9J/TCvfN+KC0WbX96je6KIZP7wczGBCpqyvDu5ImWXVvg+e3a9640lJeV4+bpRxg4vjuN1CcrUCcuHoGc9Fw8vyP6PJhaG2Huukm0zdU0VGFua4LhU/si/G0McjJyJd5GEtKTsvD8XgB10pvZGNFVExPmD6X7CQ8QfR5atHXB1GWjYe9mRdvKwd0a/cd2wbPbb2r8FCRCf9Evk+Hm40DPrZG5PnqN6IC0xEzkZXPsjyQ09TERWblAgkQmLhwObT1NuHjZ08mMq3/foysbRDF8Sl8MHN+DnkPSfh36+tJVEk9vvZJqG3Goy8uht5kB9kbEIyK/COmlZdgYFAVrdVW00udMqoniu1ehOBiZgKwy0VH9Pnra0FCQx6agaOSUVyC9tBxbQ6LRXFcTrlrqErUXg9HUYI59Rg3EET127Fh06dKFOrGPHj2KceM4y8y5cjktW7ZERkYGFixYADc3NwwYMACnTp0S2Oby5ctYuHAhevXqRZ37u3fvprrxX3/9NXVwk32uXbsWf/75p1THrwvucaUpb25ujp9++om+vvnmG6iqqsLb2xuxsbG19kt+B5nMmD59Os6cOYNp06bR721sbPDZZ59BWVm5Zl/ESZ+dnY1WrVohPj4ec+fOxbBhw6hcD2k3LikpKWjXrh2aNWuGOXPmUOfBxIn1R7L9m8jLK0RUVAJateJFfZP2aNXKA69ehYgsQzpat28/Rb9+HeuUSxEmN5fTaZImojY2IgllpeVw87ar+UxdUxVW9iYICxA9ABVFYV4x/V1KKpxoFhK50LKDK+5d8UduVgEdIFw77Uc/b95KvLREUkg0LNztBT6z9HBAUlhso8qQNhUeTMnINKNlxRFw4wmNWK1rmSyJIiEdfwNTXnS0k7c93r97LzLqRFLevXtP21OwvjI0sjU+LEHi/cQExsDSyYIubebi4GWP4vxiGpnfUFxbudAoq+c3XtC/ZGDif/slvDo3r1VvLumRsTSa09iV5xghznV1Qz2kh8U0qgyRQ7m7aT86TB9LnfbC5MQnUykVuw51y64IExcUA1tP3r1BsGtuj7jgxg1kCRmJGfh22DKsGbUSe1buQEpM8r/Ktogi4HUsPL1tBI7t7WuHlKRsZGWKHvQRLp15hvjYDEye1avBx04Pi4aRq6CdMHZ3RHp4zAcrU1VZiYSXgchNSoVFS/GSYxG3H9PviTOeWy4zOq6W05BI6mSEi7ZRDSnDnSwjyCspid5vRQViHj6HW7fWNSticlMzUZxbAHM+OyunIA9TZ2skh4huC0nLOLZvgbCHL5GfkUOjwEPv+6OsuBS2vqJlIwoycxD3OhRWXdojKzwa+s6Cv9/A1RF5cYlUMkQUkpSRZBsTH08UJKUiPTAU76qqUJCchmT/tzBr7fPJ2Za00GhomRlRBzgXEzdHej6youMbVSY1JJKuaiG2uT7IBCy5d2KevIKFb3OB+9DAyU5A6oVc54XpWSgR46RuSBlCeWFRrXuDyF3lp6Sh+UjJIkO5zvvmfJOQji5mUFVTQuBbyfpVWRn5uHT2KdyaW0G7Wr7H3EIftg4muHj6CQoLSuhqqjPHH8HYVAfObuYC5XMioqhMjqqhQc1neq5OdGVOTpToa6whZQgVhUXUec8l7XUgndgzkfBeqI+M5Czk5xTAsQXvfpFXlIedmzXtg0lKYT7H9impiHYeSkpCcAysPASfC9bNHZAanYSKOqQzpIVEMpcWlYh1doojIiAGLl6CtsXV2x4RgbFSyb2Q44trK9KnJ3I/JOq7RduGR+sTIgNjYGxpRCdjuDh72dO+ZVxY/atj+M+vkoro55qkxEcko7y0nB6fi5qmKsztTKRqP1IX0h+r69xJso0wXOe9M9+9YO1kDhU1JURIM2bLL4KikoLYfmBOZj7uXnhKJwHESfx8imMicm/YOpvT386FTLYQeS8SvS8phfnF9V5rkmxDcNZUg5yMDF5n8Z5LaSVlSCoqaZQDnrbWe+Ad3wo57io/dx3ec5shQVs2+zRf/0aYFA+jhsrKSuzZswddu3al752cnNC8eXMald6iRQvqmDY0NMTx48fpA6Rfv37Iz8+nTnHiuObfD3H2E6c3gUTBr169mjq5zcw4eoSvX7/GuXPnMHv2bImPXx/SlldXV6cR9lxIlD+p665du+jEg/DvsbXlDEJIJC+JsidoamrS45CoL/59keh7Kysr7N+/v+YzBwcHOjGyatUqaGtr49dff4W1tTUOHDhAv+/bty+SkpJw+PDh/8xVmZGRTf+SyFd+dHQ0EBjIi5jkJysrD0VFJVQi4/PPlyIwMAoGBjoYMqQrJk8eJlJzkpyzn37aAycna7i6Sh7RSjpvBE2hjhvRys/JEu9o46eosATHdl5D6y7uNDKfy5xvx2Ddoj34st939L2ahjIWr5sAAxPxEQiF2flQEerIKGuqoaKkDOUlZVAQ0QGWpIxDaw/4X7gHCzc7KgkR/TwYMS9DaGdRFCkRcciIS0aXL0UvA+aSn5VPoy35UdVQpR3OfDHRKZJA5H+I3iR5te7dki47vXbkJv1OXNSLKPJE1E9Nk/Oe1M/E2rhB9TOyNMTEFZ9j/w8HcWAd534mcj+jF4wUW6Ykh1NvIlfCD3lfUu04bmiZh9sOw9LXA6aezlSHX5j8tEzO/nLzcWr+Wqr3rGGkh15juorV2CfRO2T5vprQtUXas0CKCCZRqGioYsic4XD2daFLlK8fuII/5m3Cgu1fQ8dI919hW0QePzMfZhaCv0+r2vZkZxbQKFRh4mLSsW3TZfyxdybkxKwgkQQizaFc7Ujnv46IzERFaRnklRQbXKa0oBDHpiyjzqxmsjLw/XwojewXBZEeIRNNvuOH1nxWll9IJ7BIdDg/inXcGw0pQxynLw6chLqRPgxdBB0/XOKfvUF5cQncevC024uq70OyIkrYzualca5DYSQtQ6L4z/+0Gzsnf0vfyyspoM/88WK1+wNvPaXtbtbKCyGnLsJAKAcBkVQhjsiy/ALI6de+l0jkcX1lJNnG0MMFbmOH4NEvf+B99eoni46tqe5/fTQ120Ic3cLXEdfmFouzzRKUIZNIdzfvR/vpY2vZcGEurthAJ8zINUomvbzHDRU4Frlmha9zznf5UNaqrZnekDJh1+6hID0TNtUSWoS8pFS8PHIOvb6bX2e+AWE7R5z4iorytWwdsXN1seWXs9RZT/omxIn/y9bJNQ4tYv9++H0ild7p33El/czQSAs/bZ0MFVVBB1Jpbn7N7+WioKZKJzrK8kSf04aUIQ7/pCfP4TGRN3FDtPdV9HSQ9OgZIi5eQ1VZWU30v7at9FI8pB9D0NAWvl9UkZki2v4IQxyAp3dcpJMD+iai80dJSkFOHqxbCE78qZB+1fv3KMorgJaSdJrk4nh44iZ17JPVAJJSWVlFHYrCbUUCVKSJvD6y9Txt3+ZtBZ9jaUmZ+HrsT9TpT6K8SeSzg4c1GgM5v8LOY26/VdL+LsmnEPA4GFO/Ey0zJSm51deacEAPbb/qlTP1QeRnzu65Bu9OHjR6u6HbiKsfWSVLgqQaen5J3oUrx++j2+Da8ocHNp7BjdMPqSPews4Ei3+dKjZY51McE5H2Uxdxb9DvsgtgZlP/mOjJrVeICoqjOSgasw0XnepJhlyhsUteeQV0hM6zNLzKykNJVRVmOltjV1gc5GSaYZqTNXXu69SR74DBaMqwiH1GDfLy8jSynouHhweMjIzw/Plz+p78JVI0/A8x4tyPiopCTk5OzWc6Ojo1Tn2CiYkJjI2Na5z63M9SU1OlOn59NKT8xYsXMX78eBqNTxzzL1++pL+HH/J7uE59goWFBZX4ycsTH9V0584dhIeH0/p06tQJHTt2pFH5JCI0LCyMbvPs2TMq08MPWeVQF2VlZXQyhf/1/r3o5XGfMqSTI0axpGZp5NatR6mz7cGDfVi1ahrVz96+/USt7clAeNWqPxAZGY+NG7+hkzCNhUa3i6kfP2Qp9Pol++iAc9o3I2o+J8ni1s7bSZeXbz2xFPuvf48Rk3pS3f3o0ETp6tKMY8bFSbxIUqbb1OGw9XHByTXbsXHkYvhfugefQSTZm+gO69vrj6FpqAtLz/qX3YtDiurWwtnbEROWjsWd0/cxv99SbFq0DZ0Hd2iwFjs/Nb+5EfWLfBuFPav3Y9C0gfj5/I9Yvm8JjeDauXJ3/ccXGiQQpwG/5rK0ZYj0Q35qJlp+NrDek/H61DV0mjMBo7atgWO3dji67iDCX4SKKSK6TtIMcsTRslcr+lLTVoeuiR5GLh4LVQ0VPL7w6F9vW2rXl9OeoupMnBTffn0IX87sCUtrXhTph6LmXEplW2qXUVJXw/jDv2Psnl/QYebneHH4HCLuiM5LE37bD2oGujTyv/5jSd+FrauM/8HTVM+84/zJkJWXFythYuTmCC0jPcmOJaWhEy5DnPqlhcWYuGUpZh3+CV2nDKe6+/EiZJ/I9Rh06ymcO3pDTsRETPUBuBtLU6n6ywhtE//wKQIOn0arr77EgJ2/ocv33yA7Igav9x6r93CfhG1pyHNCqMyDv47AsqWn2EkufvqunofxB39D/7ULkJ+Sjgeb99S5fU0kvjSnuY4yiS8D8eLAKSrPo2PFiX4nKzEebNpDJa7E5bmQ2tbVc13OWjQQ156sw/5Ti6Gnr4H50/5CWRnH4UOi9OdN2QYnV3OcvLYSZ299h47dPLBg6l80wv+jdk5ElCG6+c83/gWztq1oAl3epu9RnJmNtDeBaLdiIbr8/C1UjQzw5JdNNNH0h312vJdoIm3X2oN0peKkZZ/hY8C9dxvT7+Mn8N5L3Dt8BQO/Gk3leCRGnG0R09cVxfkDN/H01mvMWTOhltOZSL/sufUz/rz0PT6fNwR7fz2B5/fe4mP1CyQhITIJO1YfQNdhHagkz8c7v+8lGhNtWb6Pyo5OXDy8wds0pH6SjJGIdv6vi3fSpMjDvuxd6/txXw3C3ls/4+dD30BbT4PmWSD1/RA01TERSSoraQXD3kZj10/HMWxyb4FVE9JuIwrho79r5G8jEj1LXwTDWk0Vx7u0xL4OXogtKEJiUckHs1MMxj8Ni9hn1KCiolJr2RmJaifOYwL5q6YmNEuvzpnZJU5uEoXOdbALP1BFfSb8kK3v+PUhbXmieT9//nwaQU8kfMhvW79+fS1dflF1J9TVSSgoKKBO/alTp9b6ztnZud72FMe6devo6gd+ZDVcIa8pPoFpU4EkoPzhhx0173fvXgNnZ84EUHa24CRJVlYu1bkWBdG8JpGz/ft3QqdOnKXM7dq1wNCh3XDlygPMmjW6Zltyjr77bhtu3XqKAwd+hLW16ChHWp8NZ3H1FG9w/9eZFdCqXvqan1soELWfl1MIawfx+yKQzt7P3+xFVkYe1vw5k+rtc4kIjEN4YBx+P7IYxuYcB1H/0R1x/6o/leTpNrAVlk/bWv0bQBPfthrWneqVluRx8jtwKc4roFGcoqL1CZKUUVRRQo/pI+mLy42//qbOe2Eqysqpvn6rod0FHC3fjV+HjCRO5DdJ3kYSQ6nrqKNQ6NhkSSdxoApHTElLqx4+9MUlOoizzFbPRHQ02NzuvGSJ7u3cMHn1FzQyhSS25acwl1Nf4agVaXhw7hGsXCzRfgBHM5kM/gZNG4ANszchMSoJd07cxYubL2s6qp/vX18T3VmSXwBVHd61T/S+DR1Fa0ZLUiY1JIpGQO8fx/v9hHub9iPo0l0M/HFRjVyEx+Du0LPhOG2ce3WgiQDf3H9N3+9Zwbt3B88Zjtb92kJZTRlFQueXtJ9qHfJMDYE4zA0sjJCZlPFJ2hZh8nKLMKjrmpr3/Qa3xOJVw6mufm6OYJ6TnGxO++ro1m7TwoJSRIWnYNNP5+iLWy8SzdrZ6xssXzsaPfoKrlYLehuPWRP/oP+TBTneYwbAfVAPKGtqoDRf8FyS60pOUUGsk1iaMkS2huh0E31wIj8ScvU+7LsIRsQRGZeoe8+ojM7+MZwk0x3nfQlTLzdOVGy+4L1aml9QKyqaC4mslabMyyNnqaRI9+VzoGPJC0Lgh0QrpwaFUyf2hiHz6GfjNiyuibovya9tZ4VXS3GRpEx2Yhpi/IMx8oc50LXgRMq5dW9NpXleXbxP86DwE/8mHHlpWXDv2RbFpA001enKBX5oexB5Aw0x7SZBGUm2ibx2hybSNfHhSMZo21jCaXBvvPjrANzHkpVenL4amThsSraFyPMU5xVR22zoZIN+q+dBWVMduUmCGu3ExhLId6KQpAy5D8qLShBy7T5ng+o+5eFJS9B8aC94jeonoLNcXlyOS6s20u1yE1PxcOs+tJ89EUqaGrWv8+pjibs/pCmT9DoI93/fBa/PBgsk4yV1z4lPwvN9J+iL+xuIDerq8zWWfT8aNy+/xLPHnEkoEshw1e9HKp1TVFhKE+KSz/htnbZu3c9dmu9CRgaWNob4+tuRGNZzDfyfhKNtJ1c8vBOI9NRczF82jCYcJ8xY0B9XLzzHtYsvMPYLzmpeArlOha9hIplDVhUpaoiWYZCmTFFaOh6v+x16Lo7w/FJQElSRrIZ4/x6uY4dBRY/TX3EbNxIJ9x8j/W0Q0SgS+/sv7r+Gi/uv17xfvmNBTV+KOCVJglUu+TmF9fazSGT57h8OIzYsAYs3zaYyIdIkyd065Yea922GdkGPSQPpSpti4X4fed+s2Qe5f4MfvsbZ3w6h/5xRcO8ivq0CnoVRJy0XoovfdVAb2h8jfXp+aFtJULdLR+7g7L7rWPDzZDh6iu6XEYc0kezsMrANgl5EUE16kqRXEhYNWYXC6lxndu7WWLRpNj2HqUIyKKS+hPrOb2J0Mk2i3LJLc4ycNRjScHjTGdw6wxsT/XZiBTT5rjX+VQTkvYV9/WOizcv2ICczD0s2z6Ra+g3Zhsv6xTvpOSaQ1RG7b/xE5YpIAlgSSEM+428vkhuhLgryirBu7jaoa6li4c9f0iTJ4uwPSbI7eclozBn8HQKfh4tMjtzUx0QTOy+q+Z/U/6u1X9Dzmyc0JpL0WiNSTL8t3oneIztSPf2GbiNMTvXEiZaCvIBePnkfVL3ysaEE5xZg3tOAmvckge5nduZIEZM7h8Fo6jDHPqMG4pxPS0ujcjsEkvA2ISGBysUQSBQ+N9qcC5GuIY5volf/sY//ocufOHGCauXz694TJ7+GmE69OERp8JEIf1IXfnkeYUS1p/B7YZYuXSpQX4KB62R8Cowd2xejRvEiIGRlZahj2MrKFM+eBaJnT44TlAwKyfsBAzqJ3I+Cgjzc3e1rRayQfQlH9K1Z8xeuXXuEffvWwsHBss76fTFvICZ+NUCgc07kcciSzqCX0TQpLoEs442LSEbvYbUT3XEhncpfvtmH9JRsrPljJrR1ha6p6moKByCSqCEygHdws8Sxez/Rz55lyNdE0pk4WyMhMFKgDIncNHG0EhvN2JAyRK4l4kkAXKqdm/yEPXxFHXBu3XiJLQmr9n5TEyXOjaKwcbXGlYM3kJmSBT1jTucy7FUEPa61S93nQ1pI8ihtAy1Y2It2ym249kvN/9zfbeNqhfO7LqGivALyCpwJvPBXEdSpROR0GoqoduV+RuzFZ1+PwdjFo/EmS77G6Wlgb0XPc2pwJGzbc9q9MDOHyuQYOIq2YZKU6TT7c3ScxXMsEI3nA58vRKc542HdlrOEXdfKjDpjIRTNzLmnAHtvR/xwaX0tm2fpYo3ot1HoPLJbzXeRryNg5dK4peeinA8ZCelw9HH6JG2LMJpaqrj57Mea99zjuXla4vypp3SQx23jl88iYWSiDT2D2tIYJDnkHX+OneBCEu2eOvIQp66voO0gjKuHRc2x94Ur19gWcr0QRyM/KYHh0HewFmsnGlKGQBxholahxDx+iaqqSozY+h1UdLRQ+a4ZrR/Zl461OY2mt+vCs7upQWFUJ1wUsnJyEpd5dfQcwm88QLdls6FnJ14KI+rOYyq9MfSP72GsXn0/y8rS30MkdIidNXPl7LuyogLJoTFoPVL0KjxtkgCyvjLVbShqRY6oCNOAm49hYGsGQ1tzxBQCuvY2SHsbLLBNRlAYNM1NxU7WSFJGkm3oM0D4+VZjX3hfNDXbsuLIanpthufJ1dTTwNEGIdcf0kksrmROcmA4ZOTkoGdjIXK/kpQZu3OdQBxi/IsA3Pp1F8bu+hGKqrxAAP6VLxOP/I7Y529x57fdaDF6YE2C3YhbjzhSV9XtR65zVT0deh+JrJ+EZZLfBOPebzvRfPQAOPftKlgfDTV8dnizwGdB52/Q5NQXb3LsT9dezWsk/bjXsZsn5x57+zIaPq05k1MRoUk04t61+jtJqKpeYcWNsaH7F7LVXNstnCBZ294GEeevoDgjEyr6nOCKzJAwWl7bTvQ1JmmZorQM+K3bCF1HO7SYNlEgjwFBx756BbDA85Zzv9S3KqXv5z3Q57PuAv1U8rwgchnhb6Lg4Glb0weNCopF/88FVwXXduofQlRQDBZtnFXTR5MUHWM9rDj3K+8XVNfdzNkaUf6CK/1i3oTD0NoECuJWEknh1D/9ywH0nTUCLXry5NBE4dbSgUbPc+FeA/buVgh9HYX+n/Gu5+CXEfTzurh89C5O7bqC+T99SfctCeTcSLOa9peT39Vsz21PWzdr3DvnR53nXFkU0o8mK4EtHET3dwlJ0Sn4fcE2eHX0wNj5w6Ve8TRmziCMnjVQ4FpTUVehY6Kw11E0ESqhqKAY8ZHJ6DqEtypFGHI9blm+F5mpOViyaSa0hMdEEm7DD3G+E0kcAve3ObhxzmHomyi4t+Ss+osNT6TSPiQ5bl2a+j/N+wsq6spYtH6KgMa8OLgrPMWtqG3qY6LdN3n3Brc/YedmhRM7LtEJFq6cEbk3yGSYiZX4MRFx2K9fuB09hnWgiXIbuo0oQnILUPXuPTx0NHAnhTNRoq+kABMVJQQJTUI0lvaGupBt1gxP0iWTMGNw+JfK1X+SMCkehgDEsc3tVBCdeeLkJslgCZMmTaKJY4mEDIHI7xDd/QkTJnwwCYK6jv+hy5PvgoKCah7O5LcRCR1pMTAwoBH65MVl5syZuH79Ovbt21fzGfn+xx95zhzSbkS7n+Qb4CbT3bqVE6UtDkVFRVpv/lezZh9e/uFjQDouJBqW++J2xCZNGoxTp27gwYOXKCgowsaNB5GXV4DRo3nnbdWqrRg06Kua90Qm4/z5u3jy5C3Kyyvw4kUQzpy5hb59edFka9dux5UrD7F//w9U/7o+iEOBdFy5LwLp3PUc0gan9t1EdFgijejY+etpaOqooW033pLWVTP+xG/LObkSKioqsX5ptVP/z5l0uaYw1g5mMDDWwYEtF5GVnoey0goaqR8VkkiT6hK49SCOI25b+QzoTPXtX168TxMoBt19jmj/YCqbw+X11UdYP3gedRJJWib88RsE3HxCNfcLsnJxccMBqpfbekTtiIq3N57AxscV6rqCjkYyaCJ2gLy4AygXH0eY2hjj6MaTVOeRRB2d33MZ3l1aQEuPU57oHM/qthB+l59CUsiA9OAvx5CVmo3ysnI8uOBHXyPnDK01gOfCrRutX/VAu2XPllBUVsDJLadRlF+EmOBYGk3faUiHmmuARNiTaP+IN4IOzLrwbO9O5XieXntGJw2yU7NxfudFGFoY0Be91mg7cV5cJ4ldJ1/4H7uInIQUqnP8eNdxaBobwMKLlyjzzKJ1eLj9qMRliFOBexx6LXGdDNWTSATi1Hfp0wlvz92gkaBkYif8zmPEh8bBo2MLev3xtx/3euwwrDPC/UPx4vozlBaX4snFRzTpZfuhPMf53b9vYcXAryENx34+hJiAKJqkLS8zF6c2Hkd+Vh5a9Wv7SdoWUfDXl3s9DhzWGqUl5fhr02Xq5HrxNII6+keN61hTzv9pJI3Gj4lMrbUfui+u3jRfO4g7Nr9tcenbGRmRsQi+eg8VJaWIevAcia+C4NqP5/wIu/kQ+0Z/RRPISlqG5HdICQqnmvtEmz7y/jNEP3gOu06CE4MEIs9j4eUONX1d3vXKrV//boj180f8s9f0WIHnrqMgNROOvXjX2svDZ3F6NkdbW9Iyr49fQNj1+9Spr28v/lwSB2jUvSew69Qa8oqKAvcuuae8B3TGywt3kRgcRaVz7u4+Q+8vV74JUCKrc2LlVonLaBvrQc/SBA8PXaLJdsl9GXLfH7GvQmnicn5I+cgnb+HRg3eP2PToRCU/Qs9cQUVxCVJevkWC33PY8Tlok56/wpnPZ6MkO1fiMpJsQ5LnJvi9oBMA7yorkZ+YjLDzV2Hg7iwwqdDUbAv3OcY5v9WTDL6eUNPTht+u4yjJK0B2XBJen7wKh65toFCdMJvkJdkz6ivEPn0tcRmyf37bXDORQyduOMcOuHALEXefoDgnn953JAfFy2MXYeTuRJ3wBPtu7elk+8uj5+g9lhIQSp32znz3YUpgGA6NnYPchGTJywSE4u5vO9B89EC49ONNsPDDX3/+5wvX/hDbxrU33IlGM0t9tO3ogm2/X0RyYhYy0vOwdf05OLtbwL05z/k2vNf32LX1Cv3f714QDu2+hZSkLFRWVCEuOg3r15yAvqEmvFpxJsa8WtlDWVkBW9afRX5eMV0VsHPLFZQUl6FVO8FJYQN3F6ibm+Lt/qM0Z0RhSipCT56HaStvKGlzJjYqy8pwceIsxN/zk7gMcfr7rfud49Sf/kUtpz6B6OjrOjsg5Phpqs1fUVKC4GOnqPyXvrur1P1U8lm34R1x6+Q9RLyNpo7WE3+epe3dpnfLmrLbVu3FhgV/0v/JuGfvuiM0MStx6jdUV1/Uufft3wG56dm4f/Qa7XOGPQ1EwB1/tBnCk0oNefQGa/rPR34mx+5IQqjfW+rU7zd7JLx61dY/F4baFr624tqWPqM6IfBZGB5ceU6T4N4+60cTq/YawXvOXjx8G5N7LKl5f/Xvexyn/s9fwt1XtEzctjWHEP42BmWl5SgqKMGts37wfxCIDn1456A+qP2p6ftz2rNFe3foGGrhyMaT1LmfGJWMiweuo13f1jVSQDkZuZjedSFe3ufI/qTEpWHDwj+pU/+zBSMaJGMmbkzUZVBbXDh4E3HhiXR1wcENp2g0vG8X3pho3Zw/8Oe3nDERSfK7dcU+ZKZm0yh8LRFjIkm2qat+3LYyMtdHi3auOPrHBaQnZSE7IxeHNp+FrYuFQK6DOUNW4+/tl2si5tfN+wvKqorUqa8kYvXzy4dBOHfgJtKTs6gEYlJsGpWT0THQoomXRdavqY+J+M4ttw/arpcPPceHNp2hkx2RQbG4evweegxrX5NjKj4yiUb7h77ijImiguPw66Id6Dm8g1jNfEm2EUd+RSWuJ6fjC3sLWKopQ1tBHl+52FLJnCcZPOmy7e08MV/KPFcznKxgq65KnfneelqY5WyNY1GJSC0pk2o/DEZTgUXsM2ogUjqZmZlUQ55E4RPH/ZEjR6jEDaF///5YtGgR1Yy3s7OjyXC9vb2pc/+fOP6HLr9y5UqaM4BE9JNtSIR/hw48542ktG7dmrYDSaJL9jVu3DhMnz4dO3fupNH1ZLKBJNklEfz80faDBw/GF198gVatWsHR0RHJycm0PseO1a9B+6E5f3AJurRzqx5ky6Ag+hD93KPLAsTECS4B/dCQSNv8/CIsXboJ2dm5sLe3xI4d38LMzFBAk54kqeTSvXtrLFs2BStWbEFKSgaMjPQwadIQTJnCSeKck5OPgwcv0t8zZAhH0oHLmjWzMXy4ZEsACZ/P7k8ni1bP+QulxeVwdLfCqk3TBDp/pG7cqLSo4AT4PwqhUbhTB34vsK/v/5wJJ09rKCrJY/nvk3Fw60UsGPcr7YgZmelj9qrR8GrLkWoShbGDJQZ+/QXuH7iAW7tOQUNPGz1njoKtD28wSOpKnFDcIBJJylh6OuLevnO4vfsMfW/j7YLPfpoLJTXBeyc7OZ06oYatnCZR25EO5cwfp+Do7yewYvT3NMLIq1NzjJjDS7r7vnqQSRIWcVk37TckRiZzfsv797STS/j13FoaTU9XNTS3xaaF25CdngMzO1NMWzMJbq3r1yrmhwyKZv4yHX9vPIllw1ZBWVUZbfq2Qp/xfFG276sjc/iCcr4esBRlJeU1k4jE8a+qqYofT3HkVVp0bk4jhG4eu41jG05AUVkRdp62GLNwVJ2ToG2/HIkn+07hwvLfaGS9sasdei2bIZCUkEaAVU9GSlpGErxHc1arXPp2I3WCapoYYuzS8XDwFq91bt/CASMXjsX1g1dwYsNRmnxyzNLPYe3GW6JOIqrIoINLbFA0/lq4tea3nN70N85sOoEWXb0x6muOvm/r/m1x4+A1xIXE0uXUZvbmmPn7XJjYSi538ynYFmGIk2r9H19i08/n8PfBB9DUUsFnX3TG8M94K7/evyf1JVGADT6M6GPbWaHL/C/hf+Q8nu09SZ2GbaeMgbkXn215x7Et3GNLUsapZ3u8OnEZ6aHR9H7RNDFA26ljajn281LSkRYShe5Lpousn1UbbyqD8Xz/SZTk5EHDxACdF06FtrkJX/0E7436ypDrL+DMVepQvfbtBoHjEdkRfmdm8psQFGfnwq6r6Mkl32HdqUwZcd6XFRbTqPlh386ECl+yTWrn+OpXXxniKBu8fAru7z+HI4t/Q3lxKTSN9NBl8lC4dBZ0FgXffU63d+rESySpbmyItotm4u2hkwg5fQmKmhpwGzUYlh34Il1rzul7ictIso3DgJ50n0RTvzgrBwrqqjDydIXbaMnkIJqSbZFTkEevFbPgt/M4jk1bQSdCbTv4oNUEXgJbGrdJr7/3UpSpH7uOvnh98gpeHL2IsoJCqOpqw7pNCzgN4EVikwj7rktmUjmckEu3oaiuCteBPeDchzd5TyVy6EoZycsEnr+BqvIKvDx8hr64kBwT3ZfNRmNYunYMNv10BpNG/kbPVcs2jliwbJiAA5Jjmznn17uVA53M/HrWLqQkZUNbVw0tWzti4fJhUK7ui+kbaOLnLZOxc+tljOn/Iy1rbWeEHzZOgrWtYA4Acq/4zp+JgP1HcWvhCsjIysHY1wtu40bUbrP37yQuQyYBSrNzkPzUn764yCopos9fPBvjM3sKAg8ex61Fq2i4o5aVBVot/grKdLWE9DIQvcd2Q3lpBf5atZc69i0dzDH3l2kCSU7JvcK9X9ITM/H0pj9t75XjyMoRHtO//wLN2/GCCaRF18wAY7+bims7z+Du4as0n0X3LwbAs5tv7X5qNYU5+djwOSdBOPk8Iz4NfqfuwMzJEpN+5ciePTh+neYDuLD5OH1xcWnnieFLJ0pcP1cfB0xZNhqnd1/DznXHYGCii5mrxglI6wjbljN7rtOI8l8X8aR9CP3HdsWIaRxHZddBbXFm7zVEBsXR/r+JlRFmfjsOrbpy5MgairyiPOaun47DG07g6+Hf0UjqVj28MWLm4Fr9VG6b3j37EAU5hXhw8Ql9cdHW18S646saVZ8RM/rj3ft3+GXeNhqIQKK8F/02jfZzRV1rMSEJePM4mEaFLxjGkyAkLN08C/Ye1hJtIynTV4zF/t9PYenE9bQOHq2caBJjfttC61fdVv6PghAXnkSPPbX3coF9bTnzLZ20cGtpj8SYFPyycAdNrks+c2/pgC+/HiFyIuBTHROpqivj6w3TsP+3U5gz8Fu67079fDHkC96YiFSLtB+3dtf+vk/lj8hkGHlxIcmF1+xaIPE2dbE5KJo63Te39oCCrAzeZOVRffwqvjYizvmafAAAvnKxQX9zo5pw8ks9OJOBS/2D4V89oXgvNQsL3Gxhq6GKtJIyHI5KxJm4FKnajMFoSjR7L80aMca/loyMDMTExMDX15c6oImEDdGCV1WtrW9HHOYRERHQ19evJXPDvx8u3P35+PBkPYgTm3zeokULqY8vikOHDtFJB5KQV1z57OxsKnXTpk0bgWS0RE6IONvI9nFxcfQzrg6+qN9TWFhIo+zJfrhOOvIQJm1CJhZIkmBLS86SuoqKCoSEhNDtHBwcaun1ExITE5GVlUW/Ly8vR2BgINq1E7+kURhlizFoLJzIqtoRHdxBVWMoiRfMCdAQuMtZpVkZQqIqRMGV6eASmFM7CaG0kHYiu+RGPVSJO7aUzlbC03TRSRzFwR0wcaNJPySi9m2t3vjkzcSxStqOe144ncbajyZpVwZVfqCnmyT1I0ttxUXGiON1tRSPNJBjU8UBEZGAku+jqkbipC4cNSvxYa6Z9wJtw+9I58KN7pSGQZbSRef807YlvVR04uHGQOpK7A03ekrU92SSUZQMjzB7wpSbtG0hUjxS7aPacSnNdUTuBVEI3x/cATV33wbK0ts97qSItHaiIfuOKZT7v5/Tumitz9PKbYq2JYxK8Xwcu/ohzkV5lZT3RgPOM/+EDz9U3kZMe3LvkynOZR+8X/Uh+S1A9aOc04a0mTADLD6OvjM3QIF7v4jrp5Lv6/q9ScWNPx+irkfRtpjXr+I4iutvWzuNqg9uW2ifT9R5JSsfpbw+Syo/jnAFOZ/cc8dx8otuq/qePwqyje8403Ml5ZhI0nGT3AdoPv760T6TmLFuQ8ZsBRXNmuyYSFXu/f/lWpP0elzmL3mOD2HIXsgRuEchLSfKjImohsTc6iO5b+a/RGLRBXyKmKny5Jf/LTDHPuNfAb9j/7/Gh3Dsf0w+hGP/Y/IhHPsfE2kd+/80H8Kx/7H4UI79j0VDHPv/JB/Csf8x+RCO/Y/Jx3Dsf0ikdez/00jr2P8naYhj/59EUsf+/4sP4dj/mDTEsf9PIq1j/5/mS8cSNGUkdez/P/hYjv0PxYdw7H9MPoRj/2PysRz7H4oP4dj/mHwIx/7H5EM49j8WH8qx/7FojGP/n4A59kWTXPxpOvZNVP59jv2m3XNlMPg069++5WgHCkO06pWVm7aDgsFgMBgMBoPBYDAYDAaDwWAwPhTMsc/4JJgxYwby8vJEfkc09Yljn0jZMBgMBoPBYDAYDAaDwWAwGAzGvx3m2Gd8Eri7u9e7DdH8ZzAYDAaDwWAwGAwGg8FgMBgfh6YrPvXfo2kL5TEYDAaDwWAwGAwGg8FgMBgMBoPBEIA59hkMBoPBYDAYDAaDwWAwGAwGg8H4hGCOfQaDwWAwGAwGg8FgMBgMBoPBYDA+IZjGPoPBYDAYDAaDwWAwGAwGg8FgMOqlWbP3rJWaCCxin8FgMBgMBoPBYDAYDAaDwWAwGIxPCObYZzAYDAaDwWAwGAwGg8FgMBgMBuMTgknxMBgMBoPBYDAYDAaDwWAwGAwGo16asTZqMjDHPoPBYDAYDAaDwWAwGAwGg8FgMBgNJDIyEseOHUNeXh5at26NoUOHolkzyaZBLl26hCtXrmD48OHo3LmzxMdkjn0G4xNn+bkv0ZRZ8DQRTZmQPC00ZcIPJ6EpM2WBDpoqL7MV0ZR5+XsMmjK+C6zQlHmQ2rRty6tsXTRlXLXK0ZTZOWoHmipdd81CUyanEE2a+wlN2zZPdy9CUya9pGkrqV6IV0BTpvxd040xXPtGE02ZlNR3aMpoaTfdc0tIvpGBpoxZLwM0ZRJuZqIpY9VLD02VmAe5aMpYtf9/14DB+DA8fPgQPXr0wJAhQ2BnZ4c5c+bg7NmzOHjwoEQTAtOnT0dGRgYtK41jv2n3DBkMBoPBYDAYDAaDwWAwGAwGg8FoosyZMwcjRozAkSNHsGbNGurUP3ToEO7fv19nufLycowePRo///wzlJSUpD4uc+wzGAwGg8FgMBgMBoPBYDAYDAajXoi6zKf4+ljEx8fj9evXGDduXM1nvr6+cHBwwLlz5+osu2TJEjg6OmLs2LENOjaT4mEwGAwGg8FgMBgMBoPBYDAYDMa/lrKyMvriR1FRkb4aQ0REBP1rbW0t8LmNjU3Nd6K4fPkyzpw5QycFGgpz7DMYDAaDwWAwGAwGg8FgMBgMBuNfy7p167B69WqBz7799lt89913tbbdtGlTnU55wubNmyEjI4OSkhL6Xl1dXeB7DQ0NZGVliSybkpKCSZMm4cSJE9DUbHieHebYZzAYDAaDwWAwGAwGg8FgMBgMxr+WpUuXYsGCBQKfiYvWt7CwgKysbJ37a1at76Ompkb/5ubmwsjIqOb7nJwc6twXNymgoKCA48eP0xeBTBCcOnWKTgZ8//33Ev0m5thnMBgMBoPBYDAYDAaDwWAwGAxGvXxEufqPiqIUsjtDhgyReL8uLi7UyR8SEgInJ6eaz0NDQzF+/HiRZfr37w9TU1OBz8hEgqGhIWxtbSU+NnPsMxgMBoPBYDAYDAaDwWAwGAwGgyElBgYG6Ny5M7Zv345BgwZReZ7z588jMTERI0aMqNlu69atkJOTw/Tp09GuXTv64mfFihVo3749Jk6cKPGxmWOfwWAwGAwGg8FgMBgMBoPBYDAYjAawbds2dOnSBa1ataJJdEliXKLd7+npWbPN2bNnoaSkRB37Hwrm2GcwGsD169fxxx9/4Ny5c6z9GAwGg8FgMBgMBoPBYDAY/wlk/t8VaII4OjoiLCwMV69eRV5eHlatWgU3NzeBbebMmUMj9sWxfv16+Pr6SnVc5tj/j3D37l38/PPPuHLlyv+7Kv8K0tPT8fTpU4m2LS4uxujRo5GdnY0bN25AWVmZfn748GE6oyeKixcvQktLq0F1KysswvN9J5D0Kohk8oC5tzt8JgyHgopyg8tUVVQg6u4TRN59jPzkNChra8K6fUu4DewJGTlOMpGEF29xb8NOkftvu3IxtGytBNslIxNBB/9GdmgEZBUVYNLWF04jBtfsTxT1lXn/7h2Sn7xA7M17KExKhryaKgxbeMBx2EDIKStJ1Y72GmqY5mgNG3U15JZX4GJCCk7HJdVZxltXC/3MjeGjp42rian4MzRa4Ps5zrboZcpLpMIlr6ICn917JlX9OnsYY+Fwd1gZqSMpswh/nA/GhSfxYrcf3sEaP3zhU+tz96mnUF75jv6vICeD6f2dMbCNJQy0lBCdUoBfT77Fw8A0iesVfvsxAs7dQHF2LjRNDeEzbjBM3BwbVYZ8/vbsdSS8DEJ5UTE0TQzhPqgHLH15M98VpWXwP3qBXoel+YVQ1dWGSqt20O/ZuyahDZf3798j/cplZD28j6qiIihbWsF05Ggom5mJrWNlYSGy/R4h68F9lGdlwnHFKiiZCOrhFQQHI+PWDRTHxkJGQQFqjo4wHjwU8lLey+Q8LJ7ojf4drKGkIIsnAalYs+MpUjKL6yzn62qIuWObw8VGB6lZxdhy7A0uP4yl39mba+HC5gEiyy36/SEu3o+RuH6VRUVIOH4MeQEB1F5oeXjCfNRIyCqriC1TkpyMjPv3kf30CWSVleH+47oGbSOKmOt3EH39NsryCqBhZgKXscOhY2/TqDJZYZGIvnoT2RHRkJGVgY69HZxGDoKqgb7AforS0hFy4hwyg8Mgq6AAyy7tYde/F2TqSLLkoKGGmc7WsNNQRU55Bc7HpeBEbHKd9W2pp4UBFkZoqa+NKwlp2BwsaFsIgyyM6DaGykrIKC3D0ahE3EjOqHO/lSWlCD16Aumv3uB91TvoubvA+bNRUNBQ/yBl3lVW4ukP61EQlwDvRV9B18WpQds0BFUVRYwa3A5TxvWAm5M5Rk/7HZdu+ONjM8raDL3NjKAmL4fI/EJsD41GbKH4e1ddXg49TAxpGSNlJcx+/ArxRbW3t1RTwQQ7S7hoa6CwohLn41NwPr7u60YYeZlmmOlmhe5m+lCUlcHLjDxseBOF9JJykdsTy9nVVA9DbIxhp6mKgvIKPEjJxu6QeBRVVonc/vd2rvDS18J3z8NwOylTqvqRtpjnYYO2Rtp4D+BhSjY2vY0WeSzu7+lnYYh+loawUFdGVmk5riVk4FB4Iqrekz1waG2ojdF2pnDUUkVJ5Tv4Z+Rie3AcMktF/25JeH7+Hp6eu4ui3AIYWJmgx+QhMHO2Frt9aVEJAm4/x8srj5CZkIZxP86Cpbu9wDbxQVF4euYOEkNiqN0xd7FBlwkDoG2sJ1Xd3lVV4cWxiwi/9wwVJWUwcrRB2y9HQNNYv1FlLn63GSnBkQLlrFs3R/cFk+qsT/SLIDw8dBE5yRnQ0NdG65G94NzJp1Fl3lW9w9MT1xBy7wUKs/OgoqkO+zaeaPdZP8gpyNNtKisq8OzkDYTe96fb6Jgaov3n/QErT5Tm5CL08HFkBYVCRl4Ohi294DhqGLXj4pCmTHlhIR5/+yPKcnLR4Zfvoayny6l3RQVuTptba3uXiZ/BrKPgUnxhxtqYoa+5IdTl5RGeV4g/Q6IRU49t6WVqgP7mHNsyze8V4gpLBLbRUZTHlw5WaKGrCRU5WaQWl+J0XAquJ6VDGsi9uMDHGn2sObblWWou1j2JQmqx+HvMXU8dkz3M0FxfA+/eA28y8rH5ZRyi83i/yUhVEcMdjDDU3hA6SgpoeegRKsjGUkLaYq67DdoaVtuW1GxsDqjbtvQltsXCEBZqHNtyPTEDhyJ4tkWSbSTFTFcF3w73gK+dHkrKq3DuRQLWnwtCpZjfqqeuCL+1vWt9PmfPc1x7U/u5YKiphAvfdIGWqgK8v7mEgtJKqernqKmGOW6cfktuWQXOxKbgeLT454+uojxG2ZqiraEOtBXkEVdYjCORSXiYli2wna++FgZbGcFXXxuX4tPwe2Dtvo0kdHYxwOJ+LrDSV0VSTgm2Xg/DeX/xY7YRrSzw4yjeGIKL6+JLKK/ijInUleUwubMd+jY3gYGmEhKzinHoUQyO+sVJXb8+ZgYYbWMGfSUFxBeVYHtoLF5l5YndXklWBt1N9DHQwgg26qpY9TIUfumCbacqJ4tJDhZoa6ADDQWOTfgjJAaR+UVS129WZzuM8TWHlooCAhLzsPpiEEJTCyQq++MQd4z0NsOGm+H4825UzedGGkr4prcT2tjoQkVRFk+js7D6QjAScwVtUH2Q+2yakxW6GutBQVYGr7PysCkoGhl1PL8NlBTQ38KI2kty/fW+9riW3bBWU8EUJ0s4a6lBplkzPE7Lxh8hsSiokO7eYDBEoa6uLiC9IwyR6amLKVOmQFqYY/8/QmZmJh4/fvz/rsZ/ktmzZyMqKgrBwcGoquJ1IDt16gRLS0uBbUl27tLS0gY79Qn3N+5GRUkp+qxdTB3d9zftxsMt+9D1mxkNLhP//A1y4pPgO2kUNIwNkB2TgAeb96CsoAgtJwyn25h5u2PswY0C+32y8wiSA8KhaW1Ry5Hz7JctUDM1Rsd1K1GWm48Xm/7C+6oquI4bKbKOkpTJjY5FdlgkXMeNgJqxEQpTUvH6r70oy8uH16zJErch6QT86O2Gm8npWPsmlDrilno6obSqCpcTU0WWcdJUx2BLU1xJTIWWgjztJAizNSQKf4TyOj2yaIY9HXzwNEOws1Yfrpba2Da3HX47GYDTD2PR3csUv05theyCMjwKEu2El5FphtTsEnT9+pLA51V8HZ2lo5ujh7cp5v75GGGJeejewgTb57bHqB9uIzA2p956xT17g8c7j6H9rHEw9XBCyNX7uLnuLwz85RtoiZjQkLRM4MXb0LEyp858WQV5RN57iju/7UKfNfNh6Mhxxj7bdwrJAWHosnAydfynBkfg1m97IKOoBL3OXQSOmXHjOtJvXIPV1OlQMjFB6rmziN60AY7ffQ85VVWR9Uy9cB4y8vIwHjwEcTu3Q3jMVpGfT/ep370HVCwt6YRBwqGDiN68EQ7LV6JZHY5eYVZO9UWHFqaYvOYmcvLLsHZWG+z+tjsGzL0gcL746UDO1Yqu+P3wK8xcdwdqKvJYMM4LN5/E04mbiIRcuA47JFBm6jA3zBrpgXv+iZCG6B3bUVVaCqclS8lsGqJ37EDM7t2wmz1HbJm4Qweh7e0DvfYdkP3saYO3ESb+vh+C/z4DrxmToGNng8hL1/Fk/WZ0WbcKyro6DSpDbGDoibOw7dMDHl+MRVVZBQIPHsfjnzah848rIKfEmSQszszCwzXrYeDphk7fL6POnZibd5EbFQsdB9HJjogTZb2vK3WarH4VSgfLK5o7oqTqHS4miLYtLlrqGGplQr/XFmNbhloaY5KDJb5/HYaAnHxaZiXdb1WtgTQ/Abv2oyglFb5LFtDr++32PXi1dTtaLVv0QcqE/30aCurqtE1r3TRSbNMQZkzsBWtLQyz6bj9unvxWZLt9aIZamWKolRnWvQmhjozxdpb4wccN0x76o1CMA+kzWwtUvHuHA5GxWOrpTObKRDr1f/X1wPWkNGwJjqSOqWFWpjBXVUZCkeSD5PmeNmhloI1FfkHIK6vE11522NDWFRNuv0KViKZ31VFHJxNd7AqOQ1R+EUxUlbDKxxFmasr4+nFwre3HO5qh8v17yMk0g0wDmvt7XyfqrJhy9w09X+T9ty0dRR6L0NFYF7aaqvjtTRQSCkvgqKWG1S0doakgh80BnMlKbUV56vjfGxqPiLwiGCgrYHFzO/zc2hlf3n0jfSUBvL7xBLf2nceQxRNg6mSFx6du4cjKPzFt2zJo6muLLEO2KS0sRvdJg3D0279qXerEUX1n/wW0HtIVfWePQkVZOa79dRKHlm/FtD+WQkFZsiRvhOdHLiD83lP0WDQF6vo68Nt7Epe/34oRvy+HnKJCg8uQe9RzcHf4jOpXU0544lyYtKgEnFu3C+3H9YdrV19EPg3AlY2HoKypBqvmTg0u8+LsLbw4dweDlk6Gkb0lMuOTcX7dblrHzl8Opdvc23sOkU/eoP/iL6BnYYyoZwE49+MueC9ZiOD9hyGvqoq2a5ajorgEr7eS51oZ3KeI1rMl+3258Q+JywTuPgA1UxOUZmUL2DXyL9lXyyULoGXPe040k6k77nGElSlGWJtizetQalsm2lvi55aumPTgpVjbMsHOAuXv3mFPeBxWtnBCMxFpDpd6OFJH/NfPg5BdWo6ORrpY6GaH7LJyvMjMrbNOAvtpZYt2ptqYeTMIOaUV+LatHbb1cMPw8y9F2hbCNE9zHAtNwcqHEVBTkMUCb2vs6e2OHiee1TjhlvjaIDS7CLsDEukxGmrGv2/pRCcupt7n2JY1Pk5Y5e2Ib56Kty3Eib3hLce2OBDb4u0IDQU5bAmMkXgbSZCXbYb9s9ohIiUfvX+4BX1NJWyf2ora0e9PBYgtJycrg77rbiEiheeAFdVHJG22YYIPAuJz0cXNiPOBFBAn/YY2rriWkI6Vz0PhpKWG77w5/ZbzcaL7LZ/ZmSGlpAxLngXT4KheZgb0HCx+EoQXmRyHtqu2OkbYmNB9iBs3SYKrmSa2f9kKv14MxqlnCejhboTfPvNCdmE5HoaJDm4gz6fU3FJ0XntT4HP+9hvd2hL5JRWYvPMpsgvL0NnFEOs/a0EnXs6+kLzf3N5QB/PdbPHz2wh6Tw22NMY6H2dMefhG7PN7iKUxTFSUsDU4BpvauIs8ZaSPp6ekgGX+IcgoKcNASyNsaOWGifdfIrusQuL6Te1gg2kdbTDjsD/C0wuxqIcjDn3ZCl1/u4v8eiaA+rkbw81EAznF5QLnj17TX/giraAUw7f7obi8Cgu6O+DQ5FbotfE+yqoDyiRhjosNnQBa8jwYeRWVWOhmi198XTD5wWuxtmW2qw0i84pwJCoRX7na1LJ8pI+wobUrnqbn4MsHryHXrBnmutnie28nzHsSKHHdGIymBFs98R/g1atXWLlyJQoLC2kSBvL666+/6izTp08fPHjwoOb9vHnz6Gf8EetkP8nJnNn6rKwseoyePXvS2am9e/fSqFguxKnNPXb37t0xY8YMmh1aWN6GzF75+flh0qRJ6NGjB7799luUlAg+9GJiYjBr1ix069aNRsJfu3ZN5H5evHhBZ7t69+6NZcuW0d8vCZLUVRjisJ87dy6tN3HMczl69Chev36N5cuX1ypjZmZWcxzycnBwoNs2ZIaOS1ZMPFIDw9By4gjqgNc0NYLP58OQ9CoQuYkpDS5j3dYHrb4cDT1bSxrFb+TqAKfenRH32F9gcEeiVLmvqopKxD15BfNObWsNWFL936AoPQPuX4yljjQSzW8/uB/i7zyg0aCikKSMtp0N/V7LxopG6JO/pm19kRMhXQRIHzMj6mjZERZNO6TPMnOow364lfiI7tC8Aqx8GQS/9Cy8E+OcIp+SPiP31VxXC3pKinTf0vBFLwcExeZi15Uw6sz/+1407r1NwZS+9Ue6kk4r/4ufgW0ssPdaOPwjMlFYUoGzfnHwC07H5D51R9xzCTh/E1ZtvGDbviWUNNTRYmQ/qBnoIOTKvUaV8R0/FA5d29AofCV1Nbj17wZ5FWVkhPMGTpnR8bBo6Q49GwvIKynC3MsNKlZWKI7jRKxzIQPqjJvXod+1O9SdXSCvqQXTMZ/hXXk5ch4/EltPszFjYTJ8BBQNDER+L6+hAdu586Hh6gY5NXUoGhrBZPhIlCYnoTRF8qhaTTUFDO9uTx30QVHZSM4owso/H8PBUhudfcRff99Oa4Vz96Kx83QQ8grLkZRehIUbHtSsxhB17od2tcWVR3EoKJJ8AFAcH4eC0FCYjxoNJUNDKBkZw2zESBq9TyLuxeH09Tcw7NYNcqoqjdpGmKjLN2DRsS2MvZtDUVMDLmOGQV5ZCbG37ze4DLFX7VYsgpG3JxQ1NKCirwu38aNQkpmFnCje9RR68jwUNdTRfPLn1CYpqKvBcUh/sU59Qj9zjm35MySGRus/ycjBpYQ0jLIRXP3BT3BuAZa+CMajtGyxg5huJvq4kZyOpxk5KK6sooPHa0npGGMj/popTs9Auv8rOI4eTh1QKgb6cP58NHLDI5ETEdXoMhmvA5D5NghOYziTv6KQZJuG8uuf5zHrm514LYWDpTGQgeNQS1Oci0/G6+w8en7/DI2CgowMupsaii33V2g0dofHIrlY9LOP8KWDNaILirAzjHPdkOcSKSONU59ErPa3NMSO4DiE5RYhtaQMv7yKhI2mKtoYiZ4EC8wuwKrnYXidlY+Ciipabk9IPI2oJ04yftx01DHI2gg/vRSM6JYUB01VtDTQwsa30UgsKkV8YQmNqG1npAMrddErDm8lZVKnfmhuIY28fZmZhxNRyehmxosyzymrwMpnoXiTlU/vjdiCEpyNSYWTtjoUGjL7QIIWTt1C8x6t4djGA2raGuj+5WAoqijB//JDsWW6jO+PPjNHQt/KWOT3JEJ/wi/z6D5VtdShZaiLXjNGIC8tG0lhgs+xuqgsK0fQ1ftoMaw3jJxsoKqrhQ7TRqMwKwfRj181uoxwX68+h/TL83dhaGOGlkO60ah6j55tYe3tjBdnbjWqTFpkAsxcbGHh4UAnPUwcrWHt7YLUSN7KxdB7L+A9sAtMnW2gqKoMly6+dPvw46foCiGXCWOhrK8HDUtz2A8fjGS/pyjLFR1FmxUcKnGZuOu3aL/Uqnd3sb+RtJtAO9bh1CTfDLc2wem4ZBrlS5x2W4KjoCgri15m4m3L1pBo7AiLRVIdtsVBUw23ktORWFSC4qoqXE1Kp059EtAiKcSRPcTeEFtexiI4qxApRWVY8zgS9tqq6GAm2rYQZt8KxsOkHOSXVyK5sAz7ghKhp6wACw3e/T7vTgj+ehOP9Doi/yWxLT76WtgUwLMtWwIlsC1vebblVWYeTsYko5upvlTbSEIPDxNY6KlixbHXSMktwdu4HGy+HIqx7a2hqlh3DOa7Ovr0XOb0dkJBSQWO+UluR/ghkc+k37IliPP8eZyegwvxaRhrK77fsjkoBieik2l7F1ZU4VRMCkJyCtDFhLf6KCinAIufBuNBajYkd/PWZlInWwQl5mLnnShkF5Xj+JN43AtJx9Sudo0aE5H97b4bhbjMIrrC4cLLJIQm5cPbWvw1LQrSv7ubkolbyZnIK6/E/ogE+vwdJuZZQDganYTfAqMQni/ad6EhL0cn6feGxyOmoJhO7h2JSkJOWTkGWYjfrzDE7EzpYI09j2LwKCoLGQVlWHU+EEryshjhY15nWTNtZazq74J5x1/XWlniaqIJRyN1fH8xGIk5JfS8fHshiEbx9/cwlqrf0tfcALvD4xGeX4S0kjJsCIyCtboqWumLPw+r/ENxIDKBrqIRRUs9bbrKgawQIfY0vbQcW4Ki0VxXE27a4lesMhhNGebY/w9gY2ODzz77jErA/PTTT/TF76QXBXHKEzkYrtOaOOpv376NkJCQGmmfiIgImJiYUIkZkhwiPj6eOreHDRtGZX9I9DkXc3PzmmN/8803UFVVhbe3N2JjYwUmC0hyibFjx9KEE8TBTRzj48aNE9imZcuWyMjIoPsnelUDBgzAqVOnau1n8uTJ1PlPklKcOXMG06ZNk6i9JKkrP2VlZRg+fDidCCFlSCIMAonSnz9/PpXcUahjaS+X/fv3Q1ZWFp9//jkaSkZYNJWo0bPjyd4YutjTAURGePQHK0Mg0frcyFVREKd/VVk5zDq2rfUdcQCpmxpTpxoXPVdHvKuoRF6saDkZacuQa7gwORUpz1/ByKc5pMFFSwOBOfnUEc/ldVYujFWUaMTsh6KXqSGVaogqkG7ZpJe9Hp6ECkbmEwd8C1vOUm9xGGgr4fnWwXi2ZRD2Le4Ed6HOqaxMs1qTEqSj62Nf/yClqrISmVFxMHYTlBYwdnNEelj0BytTWV6O8Nt+dAWHqadzzefWbVpQqZ7cpFQqKZASFI6SxARoeQku9y/PyEBlfj6VyeFCoo5Vbe1QFCXamdlQqoo4HXKyakBSPB30IS8ngydveZM9xEkfl5IPLyfR58HBUgtWJho4f1fyCayWroawNtXE8evhkIbCyCgqM6RqzZOcUHdwIEtCUBT9YduvPsqLiug9ruvsUPMZcY7oOjuKncxrSBlarpBzj3JtHpkgSnv5FiZtWtbr2OLHVUsdb7MFbcurrFwamdUY20IipWpFAL9/D3tNNSiKqV9utSNex4nXFppWlpBTVkZuZFSjyhDJiqB9h+A+bRJkFUVHGkuyzacEfT4oKuBtNi/KlUSdhuQWwFmL99ySFrIkv7mOFu6k1C2rVB8k+l5ORgb+GTwnZGpxGRILS+CuI/lAVlNRjv6usmq5AoKavCy+a+mIdS8jkV8u+UQhPx66GiiprKLOHi6vM/Oow8BdR/L201SUpw58cZCI/T4WBvBLzUZ5AyQ9SgqKqZSOpYe9gA0hsjpEQudDUlItqaAghZRgVmwiddSbuPHuUTIhrmtpijQxz1VpypAJgD2fLcCx2d/h0e6/UVaHFAwhKTQa5kKSQ8S5nhwa26gy9m2bIzkshr7IMz8jNgmxZBVUe6+abd69e1fLYd5Mphny4xKgpKMNVUPeRD2VAHv/HrlRMWJtnyRl8uPiEX3pGjymflGns/71lu24OX0uHq38HvE373JWLImBPB90FBXwhk+6g9yDQdWrsxrDvdRMdDLSoxIhJGq1s5EeVOTk8FhI9qMuPPTVIS9D5Hd49SOO+vj8EjQ3kOzeJStrRjkaIzynCHF50kl11AexH7VsSxbHtrhpS25bNOTl6X4au40w3jY6iEzNR2ZBWc1nfmEZUJSXhZtF3Su4D81pjzfr++P8N10wvLXg6mhCS1tdjGxriaVHRE/qSQJ5PpCJUX5rSeTMyAousgpRUogjtS7b3FBI+z2OEJR9exSegRZWdTvgibyO/w+98XxtbxyY0QYedbQ1iUDv4mIIW0N13AiQPBiL3FNOmmr0euOHTEKTFQsNhWta3gmcFU7QmDTPS0sdFeirK+FxdFbNZySa3j8uG94WolefEchqki2jW2DTrQhEZ9Yew3LnzPkfsWRcTvqpPpaST4wQmRzSbyGTZlzIpEhSUUmjHPC0/cjqKb7248pnuUthExictvwUX/9GmBTPfwBNTU04OTlRpzGJDJeEzp074/Tp0/R/f39/6twmzvY7d+7A2dmZOvaJlAzhl19+gZWVFXVMcyHR58QBT5JFaGtrU50p/mOTaHwSBb9r1y6sXbu25vPKykrs2bMHXbt2pe9JvZs3b05XHbRo0YJOGBgaGuL48eO0w9yvXz/k5+dTBzyZUODfD3H229ra1kxOSOowl7SuBHJssjqAPKxIm2hocB4GFRUVGDNmDF1xQNorKCio3uOS301WOzRGhqckJw9K6qoCgwkSCaSgqoKS3PwPViYvKRXhtx7CY6j4CaLIO49h0twVyjq1OwYkuklYj5lIMdDv8kRHS0lTxu/7X5ETGU0HXIZennAezbs2JIEMoJKL82rp4BOI44ZErDQWTQV5+Orr0GhNaSGd0ax83gCAQCL31ZTloaIoh+Ky2ksnSQT+mkOvcONlEtVwn9HfGceXdcWg764jIolznq/7J2FiDwc8DUlHZHI+Onsao6O7EXX410dZfiHV2yZR9/woaaiJvY6kKZMREYtLKzfQwa+ckiI6zpkAbQuTmu/dB/dEQUY2zszn3KPNZGVgMnwUNNzdBfZTkc85r3Lqgh03OXV1lGVKpwddF0RHN+XMaTqBoKgvefSWgQ4neiw7XzDCLjuvFHraoiPLLIw47aenpYwrWwfBWE8FcSkF2H4qsEZjX5gRPewQlZiHF8HS6ehW5OVBTk1NwF4QmSE5FRVU5Ik+zx8LIsdFIFHz/JDI+dyYuA9WhlxzwUdPQcPCDFrVsmJl+QWoLC2FrLw8/H7cgNzYeChpacK8fWvY9uspVmNfl9oWwXYi0ddcu9NQ2/IwLQsjrU1xPzWLTko6a6lTbWVZkgNBUZ5GOdVqC3IulZWovJVAW2iooVzMuZSkDGmvgB17YdGtMzStLVGaXVvGS5JtPjXIs4GQJ3QOyXtDKWRUhCE5E4gNJtMzv/l6wEJNhWrDX05MwYV40SvxRKGrxKkf0Ufmh7wn2tWSOt8mOJrjWny6gIb0Ui973E/OwvP03AZHwZP6CbcdWaFC9G4lrZ+lujIGWRlR2R1hVnjbo6c5554IzM7HIj/REhz1UZjDeYaoagraEBVNNaTwRYs3FiLNc3PXGRham8LYru7ISX6Kczj3obJQxLWyhjqKcwsaVUbfzhJeI/pAz8YcOQkpeLDjGK4S6bzv54md4CzKzqdR9/yoaKjRvDjlJWUiJYYkKePUwQt5aVk4tmQj3ld7j7wHdUGLfh1ryti39sDLi/dg5m4HXXMjxLwIRuzLEFRVVgk46AkkJxP5DWJtH+mDVvc5xZUhz4Q323bD+bORdBKgOK3285U8Ok3atYZVnx5Q1NJCZkAQgvcfQXlBAeyGiM6DQ54N/M8KLuR+MVKRLn+UMCTy/9vmTjjSuSV9T5zSP70Nr1O7Xxh9ZU79sksF60fekwj8upjuaUFfxFEYk1dMpXyInNeHpC7bwrWL9WGpxrEt+8LiG7WN2D49n1OfQKRfCPoa4p8dB+9HY/+9KOQUlqOHhzHWjGwOdSV57K3WOddUkacSPMSpn1PU8BUPpI2SssT3WySRfRliZQQTFUVcS2zcBLUoDDQUkVXdXlyIDI+akhxUFGSpDIwwhaWVWH06ANffpkCRjIl62OPvr9pjwK/3EMGnLa+lIk8d/0T2qKLqHX48G4T7oelSjfWIY1q4b0faj3tfNwQS+R+QnY/P7cxp3gzSJ+hvzsn1ICNFrJiBOsd+ZBYKXh9ZReUw1xa/enZRT0da5sgz0dd6UHI+ErKLsaS3E5adCUBxeSXmdXegbU3Ol6SQPrMo28dpv4YHw5CJFSJVOcvFGjtC46j9meFsTQNiJLUJDEZTgzn2GWId+ytWrKCOa+KwJk594qgn/8+cOZP+JdrxBOLsT0lJoWU4s7HvqSOdRMqQjNCtW7em25EVAH///TeSkpJolHt0dDR1ovMjLy9P98PFw8MDRkZGeP78OXXsk79EWoffqUSc+yRzdE5ODp1EIOjo6NQ49QkWFhZU0odkpiYTHfUhSV2J5A5pFyKpQyYauJH6hCVLlsDAwIDK+EjCo0eP6OTBjh076tyO1IW8hCOY5epZEUAilIQm9etFXBmSyPT2z3/SqGrXAd3FOv5JtH/nRVMlP171OZWmPy+uTJvlC2hEd35cIt7uOYRX23bDe47kdREF9xgfapK3u4kBdYzcbWQUJheu9JW4WejLzxIE3q/c7w9vez1M7OmA5Xtf0M9WH3qJxSM8sHthR2irKcI/IoNK80zqzYvikxZyjt5/gDL69lYYf/h3Gh0Ydf8Z7m7ci57LZsK4OsLw6d6TSAkIQ/8fFtEEvGmhUbi9cR9kFBWh07adJAf9YPreJO9D/O6dNMms9czZDduHUF2I30LcuSX5EwhEL5/I78SnFWBARxtsXNQR+URj9LWgRA6ZAOrd1hKbjrzGB4Mb/tIEaNaAcymuDDkPb/ceRkFiMpXnqXFeVW8bfvYSvGZ+SeV3iLb+i607qZPJYXBfiY/NPWpjIkiORifSyLCvPezoQIgkaz0WnYSpTlbSX9bNZBpVJubSNXoPWPfrJXZzSbb5t0Ai6kRpW0sKt+RYWwv8/DaMrvLy1NHENx5OdEWVuLwv4hA+tSROWJLaEemdn9s4UwcCSWjLZZCVIcxUlWmy3I8BuQclqR/RGl7fxhUvMnJxJKJ20sQf/CPw86tImKspY76HLTa2c8XUe2/EyltJC+kzfSh/JPnNl7ceQ0Z8Kiasn0dlehoN7dO9b1SZ1uOH1Pxv5GSLLrPH4/TXPyM1NBrGLnZS7FfQjjakzMsL96g0z9BV02HsaIXMuBRcXL8P8ooKNIEuoeuU4bh/4DzOrNmOkvxCKsnjNbALntchA8QfwSkp3DKhh/+Glq01jHzFJwYmKwT5NflN2vhSHf7oC1dgO7i/VMeV9N6ti+9aONFE318+eImssnK0N9TFMk8HLPcPwZts8ck9RfJe+nt3+5t47HqbAGM1Rcz1ssLe3h4Yeu4llef52Aj3s+q0La2rbUtkUoO3kQZupLO4ZweJ7v/2b16OkL8fx8HaQA1Te9jXOPZ/GuuF62+S8SBEugCODz0mam2gjdmu1tgY2LDErg2Bu/pY3KqZS0L94hV/v4W3tS6+6GSDZcd57ZpbXAGnRRepJBKJ2P9pdHMUllXi5NPGTeI2xM4I892rUMx0tsafbT2gLCtLV9kQCcbGruKh9SNjDjHftbXVxZAWpuizmSfZLAyJ+p+47zlW9HPGrYWd6IT66VdJeBiZ2aDnZK1+y/vGdZqJrfvmeTCmO1nhZLeWVGrqREwy7DRUPmSqJwbjH4U59hki8fHxodI9RF6GOPGHDh1KP/vxxx+RlpZGJXm4DviCggL6/9SptR2nJFqdQKR8iCwNieAnUjtqamrUGS+sn6+iogIZoagf4lAnEwwE8peUFf6eQJz2XMc+mSAQ7QCu31pLWleyL66ePlkNIRx9b2pqWhP5T3IQEEgOAiIRRLT4+dm9ezccHR3RoUOHOuu2bt06rF69ukYyiEwmkN+moqOFYX/+ACVNDZQWFHE609W/mUywENkcJaHoJy7SlCFO/etrNlEd/o7zJ4uN0Iq87QdlbU2YtnBDrohVlwoaGihMEZSSIRGwBEUx9ZSmDKkXSWSpbW8Dp5GD8eL3bSjNzaMRtZKQU15Ooyz44b7/ENH6hJ4mhrifmkk1TaUlM78UOuqCEQ+66oooKq1AUT2JjvghCXKtDHn3Eyn73cGX9MXlu8+9kJgh2BFvlvkWMpGcFT37RgOd50+CubcbbfdSIT3I0vwCKIs5p4oaalKVIVHQ5HO3Ad2Q+CoIoTceUsd+VUUFQq8/QNupo+kEAIFo7Gu3aoOMmzcEHPvcSP3KQnLt8HQeKwsKqE5+YyGRyPF7d6M4Lg62CxdBvtomSUpmLsfO6GgqIT2bZ3N0yZJhMYOzjOrttp8ORFA0Z/n8kSthGNLFBn3bW9Zy7A/oZE2jj87cll46R05DA5WFhQL2gvxmMokhvAriY8OV5SoXun6IXeCX7GpoGfIbA/YfRerLt2izdB7UjA0FIvzJqhDTNi1h6OlGP9N3c4Z5hzZIfvpCrGOf2A9h20KSxtHvpEh2BhGDnP2RCfTFZZiVCcqqqqhesjibSnSgyeoS4mziUk7bQr3BZbLDIqh02o3JgpNaL37dDD13V3gvmC3RNp8a3Eh4IjcAPu17cn5zyxseLck9f1cTUxFQHS3+JCMbD9My0cFIX2LHPtHe5UbdE+c8F/L+rVA0pjDKsjL4ra0Lldv46kEATZrIpbmeJmw0VXBroKDsHkl6O9LOBFPvvpXwd9a+N2Sq21PcNczvVNvSwR1xBcVY8TRUpMvkfbV8SXR+MX59E4kj3b3hpqNBJSbE8eLiA1zbzpN6HLt2Joyq82EU5wnakKLcAqqN31iI3bny598IfxKAcevmQNdUdF4Xgt+ek1Qap6Z+29ZAuboO5LnK/xwtzSuArrXonBsNKUPQsTShzpW8lHTIysnh3IoNNd+1/7w/fId2h4qWOoqF7G1JXgHklRQgL2YliyRl/M/fgUevtrBqwRlnEKe9z+AueHjoEtqO7UufTwoqSug+fSR9cbn51990hRmJkOenoqiIPstIXhVxtq++MsSulWRkIuXxM4Exx4NvVsG8Wyc4fzZK5L7VzU1RRQJ3cnMBmdqa+dx+p6YI29KY5waZkCMrRxc+DUB89X6JY5BI85AEn5I69rk61tpK8sgo4d2rOsoKeJVet20hLUQi9BMKSrH8YTiefNYGXS10cTZSsL/fGHLqsC1cuygOEr27pZ07YguKaeLY9w3cpi6Ik55IvAjss7qPn1kgPj+CMCFJeZim6QBlBVma4LWlnS40lI3weUcb+j23z+b/U1/8eS0MGy/XnT9OwDYLRUeTlYDc7+qCJD393scRfwXHik2021hI++moiRgTlVXSl6SEpeTDSl+11udkAp0k0T3nnwhfW12M72AtsWOfrBQh5bn9vA9173Lbfu1rQTnNX1q6IKWOnBrCZFavdNBVVUAUX5wZeS8cxc/F10oH+mqKeLa0W81nZEwxv7sDJrWzhtfaG/SzqIxCfLHvuUDZe4s647YUKx64to+0F3HG8/dbyOrUxkCkueY85iWnlpdpRldAJIvJ9ccQx79U1+YThDn2/yMIO8vrQ05ODu3atcPNmzdpNPnmzZtpBDyJxP/jjz9oNLqLiwvdlnxOnP11yfycOHGCatzz6+4TxzlXuoYLcc6TfRG5HUJxcTESEhJgXa3nTPIFkFUA/JBId+LIJ47uD4GkdSUTHyTvAInaJxI6pBx3QuHKlStUDogLmRwhyYWJU5448PkhSX3J6gCuw74uli5dWlMvci7IoGFj6CPIVesT6zvaUF37rOh4muiWkB4SSQce5DtRSFqmOCcPN77fBHUjPXRaMIUO4kTxrrIK0Q+fwa5LW44chQi/NXG4x9++j/KCQuogI2QFh0FGTo7qNYuiIWUINbqlUkzBE01kkkCXPwbZU0cLaSWl9ToYJIFEUxA5hd+DIhpU/mVEFlo5CQ72W7sY4nUUTyNREuxNNRGRJH7gpiAvgx7epjjvJyhR8l7XHVW6rvT/ifN0qHOe6pTbmCM1OIImuuWSEhgOQyfRyUTJNSRtGYHzWhMyRATz6D+1V50IyUKQ5LdEdqcoPBxq9pxof+KkJPr6hn05UX6Ncurv2UX3ZbtgERT1pEugRngTlonKqnfwdTPCxfsc3V4irUPkdl6KcewHx2SjqKSiRo6gpj7vRV/2I3rY48aTeGQLyTlJgpqtLU00TCYuVK04kyiFEeFkNpB+90+ioKYKVSMDZIVFwLhlC/oZsYlZoREwa9Oy0WUCDxyjOTrafDMXGmaCSeKI3dGytqqt30yuwTqiiIgmMkmgy29bWuhqIrW4VGDQ8iHoYqyHZxm5YmUNtOxsapxRem4uNfrQlcXF0LKzbXAZ4pTnn0Qvy8nF/UXL4b1wDkeTWsJtPjWSi0uoA99dW5OeZwJZReGsqYFj0YIrpqQhv6KS6skKn0XOe8mfa0HZBVRTuoWeBm4kZtbozZuqKiGwDuc2deq3c4WynCzmPAikSXT5WfMiHGv9ec4F4vy/Pagt1jwPxy0pZBcCsvLpMZy01GgySu6kAVkiH5AlWkKGoKsojy3t3ZBUWIJlT0MkkvGQkXAg6t2vPbz68CYsuM86HVMDxAVGwqmdJ/2cXMtxAZFw7yw+UltSrm47gdCHr/HZj7NhYMWTmxNFm4lD0XoCL4qe9LkU1VSoVFZKcCS0zTmT12SlW1ZcElx682Rq+NGzNpe6DCEnIZU+ZFS0NWHgYIUvj/5OP1eXf1cT+GHiZI3EQMGEyvFvw2HsUNt+cpGkDP0rbH+bcc6POCrLKxD1NAA6zg5If/EKxRmZUNHnJPPMDg6j+9O05eWP4UfL3gbRFy7XWabDT6sF7FpOWARe/LIR7X9aDWU98TmQCpOS0UxODvKqqoAIeXly/xMHtIeOBgKqbYt8s2Y0Z8uhqIbbFm5da9uW97XyLdXF2wyObfEx0sSVGM49b6SiAHN1JbzOEH/vCkO6a+QZ+qHdRESyRNi2eHJtS3Y9tqWdG00svPy5aNsiyTb18TI6G5+1t4a2qkKNZE4bB32UVVQhMJ6Xs6U+HIw1kF9cTp36BN+llwXuh+7uxtg2pRV8l11BnhTJiIl02UBLwX6Lt54mdSDX1W9pqa+FH1o6YWdoPE7ESC4bJy3+MdloJZRjrK29Hl7FSifx52CkjnA+GR5REFk8aa5Pcj2EV6+yu5LI68N76WrhbSMd08KQPE3kOFuCJZd4jckqos79VjY6eBbLCQxSkJWBl6U2ttwWtMFcNt2OwJY7gt89/LoLjj6Lx1ahz/nxMNOEpa4qrgdLPmlHEi6TiRFPXQ3cTub0W0g+EJJ3hD9nxoegg5EuPb+P0yTPL8JgNCVY8tz/CMQRTyLryUtSSBQ+iV4nDm07OzvaOSAR5cTJz9XXJxBpnuvXr2Pfvn01n5HjkOh+LmQfRGeeRIITSDJbIuEjCuJE53Y2iaY9KctN9ksi3UnZZ8840TBEfofo7k+YMKFW1HxDkaauZAKCfBceHo6RI0dSbX0CkR8iEx3cF8kVQGjTpg2VBeLn2LFjtNz48ePrrZuioiKtH3mR1QlEckhBWblm4oY45g2cbPFi/0kUZeagMD0L/odOw9jdCdrmvAHi0QnzEXT+hsRliN45x6mvj84Lp1JdaXEkvgygUV/EsS8OI+/mUNLVRuD+ozRSNj8+ERHnLtNEu/IqHB3x0uxcXJ44izrWJC0TfeUmEh88ptH5JIo7OyIKoX+fg56rE5S0Jc9dcCUxlSYs/MLeisoPNNfRRB8zQ5yO4y2vJZ2ni93bwUJVvAahOHqZGiG2sAiheQ3rlOy7EQ4Pax183t0OqkpyGNjGEp09jLHnKs+5MrqzDcL2jKB6+oQfJ7VEWxcDqsGvq6GIFWNbwN5UA4du8TphPb1NMayDNd3GUFsZG6a1RmXle2y7yEmaXQMdUMvSF3EkcAcObv27IcbvJeKevUFFSSnenr2B/NRMOPfm2Yvnh87ixKxVNe8lKXP7153IiIyjg/LSgkIEnLuB1JAo2HXyrZkgMGvhisDzN5Edl0ST8iYHhCH78WNoerYQrLqMDPS6dkPG7ZsojIygkebJJ/+m0dc6bXnXbOz2bYj6/TfpnPr79vCc+lLo6vOTU1CGM3eiMP+z5rAz14SulhK+m9YaMcn5uPsisWa78xsH4PuZHJmzsvIqHLwUiqnD3GBrpkknZIZ1s4Ongx4uPxLU2He00oaHvR6OX2/YpBJx5qvZ2SPh7+Moz86meQkST56EurMzlE15zu9XX81B6rWr+NjY9umO+Ht+SH8bjIriEoSePI+KwiJYduU5o97sPYx7K36QqkzgweNIfvYSbZbMo9r6orDr1xOJfs+QGRxGpb/IZEHCw8cwaS3euXcpIQ1KcjKY4mgJVTlZ6tQnjv6TsbxVFcTeXO/Vlur1SoqjphpG25hCQ16OvmY5W8NcVRk7w8QnqFQ1MoR+cw+EHz+N4vQMlObkIPTICWjaWEPLnufYvzt/CSJOnpW4DLnHiF3gvrgOPmInav6XYJtPDdJrOReXjEEWJjQBu5qcLKY4WlPJtZvJvIHsMk8n/ODNWeUhKSdjE+lks6OmOp0s8NbVQjtDXZr4UhpN3qvx6ZjiYgkrdWUa8bawuS0SCkvwKJXnANnXtTm+bsE5l4qyMljflt+pXynydxM5G+6L6xCkzkEpfiNxuJFkuXM9bOiEg5GKIma5WeNZWg5iCnh63zcGtMFYe46tIb9hcwd3JBaVYunTEBqRL0x3Mz2MsjWheQ6IHICthgoWN7dFfEExneyoC3I9Clyn1c+61kO64PX1x4jyD0FpUQnuHriE0oJiePXlrQ67tOUYds7+WYoWAK79dRIhD17RSH2irV8fwvcRQU5RAS492+PVqWvIjE6gz8yHu47TSHybNrzn4YVVG3Fzwx6Jy2THJ9NkuSRBfVVFJd3u7h8HoW1mBDMPTh9XVFt5DeiM1Ig4vLp0H+XFpQi5+xwx/sFUD5/L22uPsGHIPFRW96ElKWPX2gMB1/2QGBRF65MWGU+j+O1audccO+LxGwTefEJ1+QuycnF5wwHIyMnCdcJYqJmZIuTAUaqdX5SSiojT52HUyqemr1hZVobrk2Yi8f4j+p5MZNZXppZd45uE4Nq1hLsPEH/rLrWdJKgm7cUrRF+4CvNO7elKU1GQq/pMXAqGWppQZz6xLdOdOLblehLPWbiquSN+ackJupCE5OJSRBcUYaK9BYyUFWnEKpkQ9tbVhp8Uzq3cskpciErDnBaWdPWOrpI8lre2Q2x+Ce4n8PZzcmALrGrDkWzyMtDAXC9L6vyXbQb6d217BxSWV+IuX5kPAde2fOVmAwMlBfpbiTTMs3RB23K9XxuMtePZlk3t3JFYWIplz0TbFkm2kYRrb5KRnFOCNaM8oaOmACcTDczp7YgTj+NQUL0K11BTCeGbBqGXJ2dc9kVnW4xsYwk9dUWqIz/QxwxfdLHF/ns8py6pDnGKcl9c20z+l2b+4UJ8Gh0TTXPm9Fu89DQxwNIIf0fz+i1eupq43a8trKr7LWQb4tTfERovsN3HYN+9aHhaatNIeiKZM8jbDJ1dDLH7Lm98M6aNJSI2DKBOawKR1GnroEfbjrThyiFusDdSx8EHvETYP41pjlZ2unQbNUU5DPExw2AfM5x6Lt1k2t/RSfS+am+oQyVzSF+NOKbPxPImO6Y6WuJoZ2+p9jvQwghtDXTofUv6e6u9nKjUEf8EQn2Q62DPoxgaae9jqQ0NJTkqn0OukZP+vN+57TMvHP6yVU0Z/uuKvAjkD/8tMKerHY3uJxNoxKn/+8jmuPg2WSBRb33kVVTSVURfOljQPjGZvJjnZksn0h6n8/otu9p7YoGbdMFFM52tYKehSvsFPnpamONijSNRiTQ5L4PxKcIi9v8jEEezt7c3dTCT6Pdx48Zh+vTp9Tr2SYT4gAEDBD47d+6cgA4+cbrv3LmTRpITpzzRsCdR9/wR7yRanWjjk2MTuR0SiS9KdoY4qzMzM6nzm0S/E8f9kSNHaBlC//79sWjRIjqxQCYb4uPj6e8izv0PhaR15XfucyP3iXOfRN8LSwHVBZHhGTx4MPQb6AgUpuP8KXi2+xjOLVhNHbBmXu5oNYm3DJlAJi34o4rqKxPr54/85DQUpGbg6ATeeSWM2b9BwNEfeccPRm6OUDfkRDSJgkSGtVr8FQL2HcGtuUshq6hAJS2cxw6v2YY4BfijsiUpY9rOFxFnryDs5HmUFxbS5GXGvt6w7d9TqjYkESgrXwZhupMthlqa0sS5J2KScJ4vUWEzbuRGdegGiZ46043jGCafO2tpoLepERKKizHDjzM5QSCd4w6GetgfKTpRpyS8jc7GV3/6YdFwD6z8rAWSs4qxYt8L3H3LV79mzejSSG5oyZHbkZg7xA0tHfRoJyw4Lhdj192BfwTPMfQoKA3fjPLEijHN6Xuyv1E/3EJ+sWTLRa3belEZHaJ3T1Z4aJoYoNviKQJJbsk5JUkBpSnj2KM9nh88Q1eVkEhpsvy/x5Lp1JnPpcPMcXhx5Dyu//gHyvKLqDyVfrduIqPwDXr1oVHncdu3obKoGCqWFrD5ah7k1NQF6lmz2oPI3dy8juTTPEmG8B/W0L+mI0dDr3MXlKWnI/fZU3r/hH67QuB4VtNmQNOT06aS8N1fT7Fickuc+KUvFBVk8TQgFV+uvolKPjFoOdlmAkmNNxziXGNHfuwFNRUFRCflYf6v9/HotWCU1IjudohPLYDfm4ZHT9lMm4b4o0cQuGolvc40PTxgMWaswDZEP51/5BixaSPyQ0NrlhH4z+A8f9zWrIGivoHE2whj2aUDdc6/3nWAysGom5nAd9EsqOjzRW+Rc8kneVVfGbIqKObGXXou76/kTVATPL74DBadOPe5kbcnXD8bgTd7DqEkK5smCieTBnb9xevGZ5aVY+nzYMx2scFwK45tORadSJ02XMhp5USFcc4vGbRd6sFZ0UI+d9VWR18zQ8QXFWPyQ06eBDKYa2Oggz0dWkBJVhZvs/Pw1ZO3SKpnSTbReg45dAx+K9fS6504r1wmjBWI8iMJrvlXg0hSpinQr4c3jm2fX/P+6Pb59Ln3x56rWLL20Ec55omYROoMX97cCWpyclQPnzxLSNQ9FxniLOZrq8GWJphkz4sS3tKa40jdHhaFSyQimjicktKoQ2CJhyNN0pteUoZ9EbFUnkcafn0dhXme1tjR2RMKMjJ4lZmHBY+CBBLhyvHVz1tfE176mvT7S/04g3ouE269EnCKfQiWPw3Foua2ONrdmzozH6Vm47c3gpJhsnz1626mDyt1FSopcnOAYDBB9wt+1NHml5qDMXamNKpfX1mRrroj+90fmtDgJJ1efdpRh/6FjYdRlFsIA0tjjF49DVqGuoLPOr5nyNtbz3Bh45Ga94eXb6U9iQ5jeqHj2D4ozi/C8wv3qd3ZOecXgeP1mzMKzXvyVrXVR6txg+m1fmnNFppw1tDBGn1Xzoa8kqJgP5CvfvWV0TI1gq6lGW5u2Iu85DTq9Df3coXPqH7UWS4OYwdL9F/8BR4cvIA7u05BXU8b3WeOgo0P7/lN7Aunvyd5mfbj+kNWThZXNh5EYXYeTV5s19oT7cfxnvmWno64v/8c7u45Q99be7tg9Lq5iJNVh9f8WTRpLVkpRKLljVp6wekzvr7ye8E+KHHM11tGAkgZ4sh/unY9ff4o6+tSbX2L7rxxlSjIc4LYlm9bOEFdXg4R+UVY+iK4TttC5NimOHBW1hH+asvph/wREo0LCam0uVe9DKHbbGnjSQNZyOqxP0OjcUvK/E9rn0ThG18bHO7nSev5PDUPM24ECtxj5N4lL26Uv7OuGrZ0dYGFhjJySitomXGX3whIlCxvZYsRjsY1UdLPPuNMns28GQi/ZMmj2Vc8D8VCT1sc6cazLRve1mFbTHm25UY/QdvS4xLHtkiyjSSUEz3yPx7h+1HN4be2D0rLK3HuRSJ+OM2TCSHVIn16brfv/IsEzO7jhLl9naGlKo+4jCKsPRWAY37iJ/MbCpFuW/w0GHPdbDDSxpTKyxyJTMQpvih8Wj86JuJU8DM7M9oXmeFsRV9cXmbmYtHT4Jq+zbU+HLtGyrppq6OfhSHiC4sx8Z7kOaDexOdi9r4X+Lq/M1YNdUdyTjGWH3+Du8HpAit4+cdEhx7GYF5fJ7S00cW7d+8RlJSH0Vse0eh/LocfxmJuH0e0tNal5WIyCqn+/mkpHft3U7OgpRhD+316igpIKCrBypchNBcSFxm+e4NAHPZrvHirF4nTnly4p2KTsS2Uc44fpmVhjosNVjR3QGlVFe6lZmFXWJzA81wStt2LgrK8LP4a5w1NZXkEJuVh/J5nyOEb+9F7V2gFdH1cDkjBdwNcqXOfJOM9+TIRW25LH1S0MTAas12s8UdbD9pveZ2dh6+fBQv8TlI3/vab62pDJz645/tKL851RnT1X2Ry7AZpr4XuttS5n1ZShoORiTjNN9nCYHxqNHsvaeYYxicP6cRHRERQxzlJ+GppKV6+hCv18vjxY1hZWdHtCbm5uQgMDIS7u3utJLQk6pxo75PIeQcHh1rObZL0lcjmkO+J9n5cXBz9jKvDf+jQIeq0T01NpRMDRIKHfKdKlqYKQRz+5LcQZzhXpodLRkYGYmJi4OvLieblyt28fv2aRsxLEtlfX11FHYPo6JPfT7bR1RVcEsj9rm3btgKySOT28/PzoxJDxsY8vW9pWPvqZoOuBRpBJKEjhiZF5hsA8sONEhPYd/UgiJBd3vDoy3dVVTVL3z8WIXl1Jx6uC9LH4e+3i+zzkAiGesrVRfjhhifhIh0dbiTFx2LKAh2ptqcO8wbIgzWEl9mi9XslgXu9c69j+qgUdQ/wXZ/8zmNx2wjU73deZJC0kIS5nGTlH6+c7wLeYKxB7cdnYwSkk/hoxmc/JNmGHxPlqgadS0ltjyiksUevGnH9NdS2SIOrVvlHa7+G2HLhbXaOqjuZvCTIikg6Sgbxje36dt01q8Fl6fOxOvFlXee3MaY7R1CeXCpIi5FDcw9PomlFUVfSWVKmru8b80iXEWq7htSvPqa7Nz7BI33WvSd9JJk6+1H80dyS2p30ksY/P8kEO1189xGexUSKRxq4bSPcn/xYBOYo/CN90Ib8rsb0Sf8J25KS2vCnDjcXszSHbybmN4i7v7W0m/2jtkVa+5N8Q7oJkw/dp69vH2a9DBq+bz67L+68kUPzH11U+9HutphjJNzMbNJjIqte4oPaPsS9K+39w0/MA8knwoThKp6Kaz5pxrXisGov+cr6+vot0toNSbjbj7cij8Ejp+ziJ9kc2orSJav/FGAR+/8hiCON6LsLa7yLgzi1hXXztbS0xGrpE0e+h4dHnTIynp4cPVICcWbXFQXP1dkXBYns53eq80Oc/cLR7yQBbl05AKStq6hjEGe+uGOI+44MFkgug38aaZ2qdOAp4cDkQzps/6lBXkMR7sRI2qn5yP3KGj52B7Yh0EEymj7Czg46sK/nepT0HvkQEOfkP1mu0e0ngV34WBIsDdnv/9v2NNS2fAw+xHmRpD0/RptX8a0OairwDz65NCVT/e4DDIQbM3j+J+r3/3jWSdKP+iftDnfCoSkgTR/zn6Sx5+Of/l1N3rY0oC5cqa9/gobYln/S/nyIPv3HHBfwt8X7f2n7/Vfv3fomFP7fdX33f7QbDEZTgTn2/6MQXfy3b9+K/I7o1U+ZMgX/Vv7Lv53BYDAYDAaDwWAwGAwGg8FgfPowx/5/lBkzZiAvL0/kd8LJXf8pevXqRSV8/ou/ncFgMBgMBoPBYDAYDAaDwWAwJIU59v+jEI38poYoeZv/ym9nMBgMBoPBYDAYDAaDwWAwmjrNmjUdWb//OuxMMBgMBoPBYDAYDAaDwWAwGAwGg/EJwRz7DAaDwWAwGAwGg8FgMBgMBoPBYHxCMCkeBoPBYDAYDAaDwWAwGAwGg8FgSEAz1kpNBBaxz2AwGAwGg8FgMBgMBoPBYDAYDMYnBHPsMxgMBoPBYDAYDAaDwWAwGAwGg/EJwRz7DAaDwWAwGAwGg8FgMBgMBoPBYHxCMI19BoPBYDAYDAaDwWAwGAwGg8Fg1EszprHfZGAR+wwGg8FgMBgMBoPBYDAYDAaDwWB8QrCIfQbjE8dYpQpNGf8sRTRlktLeoymTfOcsmjLNfxiPpsqLJn7tFUdHoinz7r01mjIxhfJoykREVKIpM6x7BZoyJl0Go6miKv8OTZnoHDRpVFWboSmTXda067f5HJo0C4Y07fZLLZVFU6WgqGn3SfOzm/ZzTUmpabs2ZIMy0ZQpbq+HpoxceBaaMkXtddFUkYts2h2DHFeN/3cVGIxPmqb99GMwGAwGg8FgMBgMBoPBYDAYDEYToWlP5P+XYFI8DAaDwWAwGAwGg8FgMBgMBoPBYHxCMMc+g8FgMBgMBoPBYDAYDAaDwWAwGJ8QzLHPYDAYDAaDwWAwGAwGg8FgMBgMxicE09hnMBgMBoPBYDAYDAaDwWAwGAxGvTRrxuLEmwrsTDAYDAaDwWAwGAwGg8FgMBgMBoPxCcEc+wwGg8FgMBgMBoPBYDAYDAaDwWB8QjDHPoPBYDAYDAaDwWAwGAwGg8FgMBifEExjn8FgMBgMBoPBYDAYDAaDwWAwGBLQjLVSE4E59hmfNFlZWTA0NIS/vz88PT3/39VpEpQUFOHWzlOIfhFMba2drzu6TR4KRdX/sXcW0FEeXR//x93dXYkACe7u7g6lFCmUIi0FSqkiLS3FXUop7u4WCAFCgDhxd3fPd2Y2m91NdpPdJPQN/ebH2UP22ZlnZ+eZuTNz5869Ss3KEx8YgacnbiA9Jgky8rJw6tEePWeOgKycHP08/EUALm08VO/e1QCsVnwDJUtrgetlGelIOXsSRRFhkJZXgHqHTjAYNRZSMqLFUlF0JLK9HiHvzWt6P8ulK0WmLc/JRtSmH1FZWAiHX/+EjJIymoOMFPB5W0uMsNaHiqwM3qbnYeOrSMTnl4jM46Clgk9dzOBhoEGHvaDMAux6F4OQrEL8W8jKymD0kA6YN30Aunja49uNJ7H94I1/7ftzM3JxfucFhPm9h4ycLNr1aouR80dCXkFeZJ74sHg8u+YNv4d+0DfVx8o9KwQ+97npg9N/nBGad+1fa6BrrNtgmaqrq5F+6waynj6h7UPJwhJGEyZDydRUZJ6KggJkP3+GLK8nKMvMgN3a76BobCKQpiQlmd63IDQU1ZUVUDI1g/6wEVCxtYMkyMvJYO3ygRg1xBWKCnLwfhmNbzdeQ1JKnsg8tla6WPPlQLR3N4W8nCwiotPx575HeOAVLlEacagoLETCmVPIC/AnUZOg4eYO04mTGuxjxUlJyPR6jKwXPpBRVEKbDZsEPq8qL0fW82fIfPYMJampkNPQgHanTjAYNLhBmSAMIluSz5xEYY1s0ejQCYajG5ctWU8eIffNayhbWcOqjmyprqpCru9LZD5+iNLkJMioqEDdrS30R4yGjKKiROVz01fF+p62cNZVRWZxGf7yT8KBNwki0yvISGG6qzHGOxnCXEMRqQVluPg+Fbt841BFBGwN012MMNPNGKbqisgrrcDjuGxsehaF7JIKscoV/ToI3sevIic5Hep62ugwYRAce3o2K8/573YgMSiiXj5De0tM3LiM/l1VWYVX524j9PErFGbnQklDDbad2wJVXQFpzvjCz/geVpg/zBGG2sqITMrD5tPv8DwkTWQZV4xzxWfDHAWupWQVo9fKa7Xv1ZTlsHysK/q2NYamqjwCY7Lxy4k3CI7LgSQQ2ZJ28wYy+WSLycTGZUuW9zNk1sgWh2/ry5b84GCk37+LopgYSMvLQ9XBAUajx0JOU1Oi8slJS2FlBysMsdaHoqw0XiTn4JfnkUgpLBWZx01PDfPczdBOXx1V1dV4m5aPba9jEJlTJFEacVCXl8XK9tboZqRN5w5PEjPx+5soFJZXivw9I60MMNLaEBZqSsgoKcPNmDQcDUlAZTWnc/Q01sav3Z2F5p977y2CsgoaLVdeaga8Dp1HUlAE5BTkYdfTE52nj4SMrEyz8wTceIyg28+Qn5ENfVtzdP9kHHQsjGvlTrjXawTc8kJ2fDIU1VRg6ekKqA0H5OrLHVcjdawfaA9nAzVkFpbhmG8CDryIbfT3cfOenemJnOJydN7u1eQ0woh9HQSff64iNzkdanra8Bg/CPaNyBZx8pQUFOHlyWuIfhGAyooKWHdyQ5eZo2vnriH3n+PRnlP17u2ybSeka+au/GQ+e4r0O7dQnp0NBUMjGI0dBzVHJ5FlrCwpQc6rl8h48ggliYmwnL8QGu5tBdIUxcYg7c5tFEaE0wmxspUV7buKRkaQlNkOphhlZUj7SWh2Aba+i0JEnug+1lFfE5PtjOGspYaSykq8TsvFnqBY2k+E8U17Wwy3MMCB4Fj89V70mCQMeRkprO5hgxEORLbI4Hl8Nr57EI7kAtGyhQuZHx8f544uZpr44kYwroWl135mrKaAKa7GmOxiBB1lOTjueIKySr6B7yOXLVxMDdWwbkkXdHQ3QlFJBa7ci8DvB16iQsRvnTPBFV/P71jvOilat3HHkZ0nWO8Gusq4tG8MNDUU0WHUMRQUlkMSnLRUsaK9New1VZFVWoaz4cn4532iyPQ6inKY7mhK60hLUQ4xecX4KyQejxOzatOs8bSl9VuXnNJyDL78As1BRUkWq+d1woCu5pCRlobX60T8tNcHWbnC12wdXQ3x18bBQj+b990dPPVLalZ5RlgaYKajCfSVFBCTV4Qd/jHwTc9tsL6nO5igra46pCCFwKx87A6IQUx+cW2ahS4WmOEgOLdIKy7F6Bu+Epdv4VgXTO5vCw1VBQRGZuKnI75438j8x95cEyumtIWHox7yCsrw181Q/HXjvdC0P8/vhPF9bLDt9DvsuRgkUdlIX/yynRUGW+pBQUYar1JzsOlVJFKLykTKk4EWuphgbwQHTRXkllXgUUIW9vrHooCvv9tqKOOLdpZw1VWjdfwkMQtbXkchr0y8OTOD0dpgin3GRw1ZRFdWVtL/GRyu/HoEpUUlmL5lOaqrqnFl8xFc/f0Yxn83v8l5MuKScfb7Pegwui9Gr56LotwCXN/6N+7uOYshX0ylaWw7umDF+d8F7nt75ymE+oVD0dxS8LlVVCBu15904WTz7Y+oyM1B/P5dQFUVDMdPFqn8SD13Glrde6K6sgoVudmi20VVFRKPHoSSuSUKggI4uwvNZElbS4y0McCyx8F0obKqgw3293fF6CuvUVpZJTTPPFczXItKw88vIqgSZZG7BQ70d8WQS77I/5cmDuOGdcKIQR2wYdsFHN2+GNLS/97OelVVFfavPQBldWV8tf8rFBcU4/D3R1BaXIppq6YJz1NZhVO/n0bX4V0gJSWF2JCYemk6De6EDgM7CFw7vP4wstOyG1XqEzLu3kH63duwmLcACsbGSL1yCdHb/oDD9z9Rha0wUq9doYoAw1FjEHdwn9A2lX77JtRd3WE0dgKqq6uQfuc2orf/Cfvvf4S8tg7E5afVw9C7my1mLDqOrOxC/Lp+FI7vmYkB43ejUkRbO7FvFoJCkzF08l4UFpVh3oyuOLx9KvqN2YXImAyx04hD9IF9qCouhv2qNaSz0fcxhw/C5vMvROaJP34Mmh6e0OneA9kv6y/Yct++QXFCIkynTIWigQGK4uIQc3A/KgoK6aaBuFRVVCBmJ0e22BLZkpeDuH0c2UI2b0TJluSzp6HdoyeVHWRTsC7FsdFUMUPuQe5dmpKMhKMHUZGXC7O5omVrXfSV5fHPGHecD0nB/OtBcDdQw64hzigur8TxwGSheQbZ6EJTUQ5Lb4cgMb8U7Q3VsXOwE2SlpbC1Rmk3xEYXP/a2o2kexGTBRE0BOwY7YUt/B8y91vgiKi0yHtc2HUDXaSPg1Kcjol4G4M62v6GkrgqLto5NzjNm/ec127s19ZhfiCPz1sO2s1vtNb/L9+F35QGGr/oUBnaWyIxLwrXNByGtUIQqyyEC3znQwwQ/zfbA1wde4llQKqb3s8XB5T0w4rs7iErOF1pOIvNehqZj9pbHtdfqThm2LewCfU0lzPvTCylZRZja1xbHv+mDQatvIl2EIkAY6XfvIO3ubVh+tgCKxsZIuXwJUTWyRVaEbEm5ypEtRqPHIPbAvnplK8/Lo/fU6z8AyhYWdMMg/vjfiCKyZe06SMmIVi7XZW0XW3Q30cKCO4FUefJ9NzvsH+SCMRdfQ5SubEFbc5wMScK3T8KgIi+DlR2scWSIG/qdfoHymp0lcdKIw8aujlCVk8Xsu28hLSWFTd2c8HNnByzzChaavo+pLmw1VbD5dQRi84vhqKWKDV0doaEghz/eRNE0T5Ky0O3sU8F66GCHDgaaCBZD8VZZXoGrP+6GtpkhJm9bg6LsXNzcdIDOQ7rPHdesPN5/XULowxfou3gaTFzskBmbhJB7z2vTpIbHIikkkir7tUwNkJ2QivvbjkFOLg/lPT8R+E59VXn8M609zvsnY8E5f7gZqWPXWDcUlVfiH7+GlbTkmW0f7YIXcdl0U6CpaYSRHhmPm5sPoNPUEXDs0xHRLwNwfztHTpiJkC3i5KksL8eV9TsgpyiPEd8tosr/SJ+3iHz+Fs79u9A0ZD6rqqOJabu/E7j/88z6Sv3cN35IPHEcZrPmQM3JGRmPHiJ61w7axxQNhSvhSZqy9HS6eRf5+2/1BQuA5AvnoNOzN0wmTqLjS/L5c4jcugUO3/0AWVVVsetxmr0JptmbYs2LEETlFWG+swW293DBpDuvkS9EOa2tIEfznAxPREh2AVVsr25vhz+7t8Gs+2/q9fd+Jro1SrBy2vck5Yc+duhpoY05F/2RVVyOjQMccGysGwb/7VurCBfF5x0tqLJeVlqazvv4Wd/bDsHpBdj9KhY/9LGnCrimTOpbo2zhIicrjcO/DkFEbDaGzDkHfR1l7P5pAGRlpPDLLh+heY6cDcCx84EC1479MQxVVdX1lPqkSres6YPAsAz07mxeU4fiQ5T0u/u44lp0Kr5+GgJnHTVanyUVlTgfmSI0zxxnMyQVlND6zS4px1BLffzazRlfPA7Ei1SOwnijbwStXy7kuVwe3oEqWJvL5uU9YW2mgWlf30RZeSW2fNULO7/ti6lfCTdsehmQApeRfwlc+3puB4zuZ0s/aw69jXXwjYcNfnwZjhep2Zhga4w/ejhjxt23tG0JY4mbJc5HJuOPt1GQkZLCF25W2NPbFZNv+1FFNYEs596k5+ILL147aIo65NORzpg30hmLf3+C8PhcLJ/ijmPf9ceApVeQVyhceW5npoEzPw3E2YeR+HbfC7qhT+5ha6qBiATBDYuhXczRxkob2fml9fq3OHzTwQZdjLSw+GEQnbes62SL3X1dMPG6n9B5C1HU9zXTxe53sQjPKYKpqiJ+6eoAczVFfPGI09+1FORwYIArvBKzMf6aH+SkpbG6ow3+6OWET+8GSFxGBqM1wHzsMz5qDA05O/2enp6QlZVF9+7dG82jqqpK0yooKMDOzg7r169HeTnPciExMZF+vn//frRv3x7Kyso4duwYTpw4ARMTE/z6669wcXGBmpoa+vXrh5iYGGzatAmWlpbQ0tLCpEmTkJcnaFl78+ZNeHh4QEVFhab78ccfqdKTC7m3qakpdu/eDWtra1q22FjxrKz4SYmMR+y7MPSfNw7axvrQMTVA30/HIMo3iCrnm5rn/bO3UFJXQY/pw6CkpkLT9JwxAoEPXqAgizOAk8FaWkam9lVRXoFQ77fQ7NoDUtKCoibv3RuUpafBaMoMyGlpU+t7vSEjqTU+sYASBlkAWX21BppdukNavv6ijJ+Mm9cgo6QEzW490RIQC4EpjsbYHxCHd+n5SCsuw48+4VRJR6wCRLHySSi1EiCTMGJZcDgwAeoKcrDRaN7pAUk4fdkb0xdtw2NvySwkWoIwvzAkRCRg4pcToGOoA1NbUwyfOwyv7vkiL0u49bm0jDS+2rcS3UZ0g4KSgtA0pK3JyMjUvgpzixD8MgRdhnVttExkYZ1x/w50+/aHqpMz5DQ0YTx5GqrLy6jFuChMJk+F0bgJkNfXF5nGbNYn0PDwhKy6Or2v/tDh9L4l8fEQF011JUwe0x6bd9xHQHASEpNzserHK3C0M0C/HvZC8+jqqMDUWBN/nX5Jrfpz80qw8+ATapXfxtFQ7DTiUBQXi4LQEJhOmkwV8EThYTphEvICAqhVvijsv/4G+v36Q0ZZuHJTq0NHmE2dBhVLK2r5r+bgCL0+/ZDt+wqSkF8jW0ymzoC8tjaULa2hP3QkshqRLTZfr4EWkS1CrDgJylY29J7KpHyKivR/zY6dURhZ3xq9Iaa4GKGssgo/PIlERnE57sdk4URgMhZ4mInMcyUsHb/7xCAsq4haF3rFZ+NxXBY8jTRq07gZqCEmpxhXw9NpGpL2Slga3TgQhzfXHkLf2gweo/tBWUMNLgO6wrK9M/wu3W9WHtKf+ceFMC8/et2xN8/SMDUiDsZONjBzc4C8kgKMHKzofaQK61sDzhviiBsv43HVJw5Z+aXYfikICRlFmDVAeN/gUo1qVFbxXmQRykVTRR693Izw54UAhCXkIq+oHHuvhSAjt4RuHIgLkS3p9+5Ar29/qhgkMsBkyjRUlZXR0z6iIJtZxuMnQEGEbJFTV4fN0mVQb+MCWVU1KBgYwnj8RJQkJaIkWXwrQg15WYy1N6CW9MGZBUgqKMX3z8Jhp6WCnmbaIvMtuhsEr4RsOo6RPEcCEqCnLA8LdSWJ0jQGOeHWyVALv/lFIr6ghCo8iAKth4kOrNSFj5l34tKx+XUkVaKRdk8skk+HJWGguZ5AOrL4577IAr6fmS4uR6WKpR6MfulPre97LZgMNV0tuvnUYeIQBN15irLikibnyUlKw9srD9Bz3gRYerpATlEBhg5WAop/8r73gskwsLOAvJIi/Z9Y/kuncxSL/ExuZ4qyiir8eOc9MgrL8CAiAyfeJGBBF4tGf+PGoU64FZoGn9jsZqURxrtrD6FnZYZ2o/vR0zjOA7rCvL0z3ly+36w8gbeeIic5DYO//hTa5kaQU1KAY59OtUp9fvhlEHkJI+3uHTp+a3XsBFk1NRiOGAl5HR1kPHggspwGg4fAbMZMKJmZi0xjs2wl3dQm8kBeSxum06ajIi8PBWHCLVuFQVRhU+1McDoiCa/ScpFZUo4tbyPp3HS4pYHQPFml5Vj6NAg+qTm0X5I+tc0/CrYaKrBSFxyHjZQV8KW7Fda/DKPyUVI0FGUxsY0RtnhHIyCtgG5Ar7kXBgddVfS1Ei1bCB5G6pjqZoxv7oUK/Xz+1UBs84mhJ9WaSmuVLVz6d7eEubEa1v3xFCnphfAPTcfOY36YMtIJKkqi1zv8Y5qJoRo8XQ1x5nr9evx8RnvkF5bh9DXhddwYY2yMUFZVReuMtKunSVm4GJmCGU6iT6Jt8YvCibAkxOUXI7+8AqfDkxCUlY/+5rw1U3Wd+utooAl9ZQVcjBK+XhUXM0M1DOpuiY0HXiIiLgdxyfn4YfdzdHAxRDsnfbHqk+ifR/axwaX7ESgrF25QIy7E8v5efAZux6cjp6wCB4LjkFxYikm2nJNZwlj0OBD3EzJpXydrzo2vI6CjKI/2erx5n7A6lLSk5HfOHeGEo9dD4R2QgvScYqw/+BIK8jLUwl4Uq2d6IJicbDz6mubJzC3Bpr/96in1TfVVsHa2J5ZvfybSMKkhyIbkKBuDmtPuBbTeiLEc2XTrbiJctvhn5GPV01D4peVRIzqSb19ALHqYaEO55sRcFyNNaMjLYcPLCFrHKUWl2Pwqkm7KtdVTl7ic/5+R+kj//Rdhin3GRw1RwhNevHiBkpISPH7Ms8gTRW5uLk1L/j9+/DhVqv/xxx/1TgFs3ryZKtqzs7MxY8YMqohPSkrCy5cvqaI+MDAQ6enpdFPB398fz549g6+vL968eUMV/VyCgoIwcuRITJkyBXFxcTh48CC2b9+O3377rTYNuTf5LVevXsW9e/dQWFgICwsLyesjJIoe+TZy4FnIm7nYUsV6Ymh0k/OQOqm7yy4lLUWtoUTdN9TLDxWlZdDs0q3eZ8VREVAwMoasOm+CouLgSC35S+LqW2hLAnG/ke3tBaNps9BSkEWBkqwMXqXwJixkoRSWXQh3MScAavKymOxohPj8YrzPFt+S52MmKjAamnqa0DPhLYTs29vTdhMT3LznzM+rOy+pewPPfh5iuWkhi2oVe4faa0SZq2xji6KoyBYrU2VxMTIf3KNKfmUb0ZPjurRzM4WcnAy8X/IUNwlJOYiOy4RnW+HK34zMQjx/FU03BLQ1laGoIIu507ogLSMfz15Gi51GHIgim7gCIe5quKja2ROzaBS2YP0RKgoLIKMofHNHFEWRomVLcTNlCxciD4nFfq6fLzTatpcor6eROl4m5gos+p/FZ8NcQwl6yg1vWHIttNoaqKGrqRZuRvLcFdyNyoSRqgL6W+lQS35zdUUMtdWjmwLikBwSDVMXQZdRZm72SH4f3aJ5gu4/h3UnV7oRwMWua1ukvI+m+aoqK5Eek4i4t6Go0nEVyCsnIw1XK2341HG74x2civa2DZ+IaWeri7d7xuDZnyOwc3FXWOjzLGW5p5jq6rOIlamnfeMngLgQy10iW4ibnNp7y8lBxcYWhZEt2zcqCzljiLSC+G6g3PTVqOKJuN/hQpTwsXnF1IWOOBCXCpOdjBCWVYiY3OImpxFGW10NFFdUIjCTd/LCLy0HFVXVcNNVk2gDo7Bc9Ik4ophTlJHBlSjxrDCTQ6OgZWoIZU1eHZm6OVCrfGJZ3tQ8RPkvKy8Hq07uYsud7IQUapFeaS7o7oXgaaqBl3E5grIlOgvmWsrQUxHt+m6iuzGsdVTw++PIZqURRUpoNIxdBeWEqas9UhuQE+LkiX7hDzM3R6r4bwji3uvwrG9wZPZqXPlhFz0FIeykF3Fzxd93CaoOTiiMkmzztjHIKTSCJC7ciMUpUeq9Tuf13bKqavhn5sNVW3wlFFFkEYoqeP2DWAP/1NEBh0LiEVcgfn/lp52hOpXP3vG8TZ+EvBLE5BTBw1hQEcmPuoIstg11xqo7ocgp/nCnWFurbOHi4WKAiNgcZGbz6t/7dRIU5GXRRswxaPxQe+Tml+L2E8F5DlH2TxzmgLW/PUFTcddVx5s0wXnLy9QcmKoqUWt+SeqPnCASxShrQ4RmFeB9dvPclbZ35ijvX/jznkNQRCbyCkprP2uMvp3MoaulhLO3w5pVFlkpKThrq9KNIX5epeVI1vYUOE42iioE689VRw0PRnfGteEdsLGLI0xVJHMNaW6gBj1NJfgE8eqKbGT4vU9HewfhbU9ZURZdXQ1xxavhOTU5cfLn0u7YcTYA0UmiXYk2BLG+J/MW/vV3UmEp3TAiborEhZy0IacHiWEN93QIac/8Rh7ck0XtmGKf8ZHCXPEwPmqItS73f2JlL0kekr5Tp074+uuvsXfvXqxatUog3U8//YTOnTsLXCPKbaKY16zxaztz5kysWbMGBw4coNb4hKlTp+IBn4XPli1b6EmClSs5Ppv79+9P8/z888/1vvPQoUMwNha9g98YhVl51LKeXwlPrJMUVZVQmJ3f5DzEzc7zM3fgc+4uPEb0oq54np24Qbf6SX5hBNz1gVV7Z2qRX5eK3FzIqApOaGTUOO+JYqQ5yo6kowdhPG0WtWxsKfSUOIvirBJBn5TkeKluA9Y0hKmOxljpYU2VbeRY6pKHQSiuaJ71x8cCscpX1RQ8aq6irgJpaWnkiWiPTcHn1gvqu19JtXHrUNL2CLJqghNC0l6If+vmkvPyBeL/OkxdvxClvvn8RRK1RQM9TtrMLEG/uZlZhdDTFX1s/7MVp3F8zwwEPl1N36dnFmD24n9oPknSNEY57buqAvKCuAKRVVambmlaipLkZOpz3HDYcInyleflUmtLfmRbQLZwifp9E2cDqLoaam5tYThugkT59VXkEVPTBrlkFnPkCrFwTi8S7ff27byu0FSUpQsS4pP/eADPqs0vJQ+rH4Zh9xAn6t+YcCMiHb88FU8Rx/FtL9i+iNuL8pJSlBWXUkv65uZJCY+lrkZ6zhkjcN2+uwdyUzNxds1WuulHaDeyD16ldRJIp6UmT10WZNZxM0As9/U0RC9mU7OLsfoQx3WPlpoCvp7ojrPr+mHwmls0L3n5hqVj8ShnRCTlIjW7BJN7W8PWSB3REhwbJ21PqGxRU0NpRvNlC388iuSLF6gSUkFP0HpUnHGMjFv1xzHRil/CorbmWNjOgo5j0TlF1JVPRZ0z/+KkaQgdJTl6zJ4fYoFILO50FRsuHxdLdSWMtTXCwcA4kWlGWRvAOzmLWkGKQ1F2Xj3lsaI6p90X5eQ1OU9uSgY0jfXhf/UhtVCvqqikPvY7zxgFXUvBGAsX1mxFalg07R9WHV2RZCfYhwj6qgqIzRb0iZxV44NYT1Ue6UJcKtjoKOObvnaYcOwVVXIKQ5w0DUHcECmLkBPlxaXU0r4peUj9WXd2x92tfyHGN5D61bfwaIPO00dAQYVjhS2vrIjuc8fDuqMb3VR5feEOLn37J2xXfysQx6KyoID4ARTSd1VbZNzg35xJOnsa8nr6UCEb4mJClPqE7Dr9g/QXYxXxNr/lpaXwuYslfNNyqGKMy8I2FvS+l6Kb7m6EjGuErDrjV2ZReYObSr8OdMDtiAx4xWXT0wcfitYqW7jo6SgjM0dwUyWrZlNUT7vxeS3ZnB4z0B6X74ZTtzNcNNQUsGVNb6zZ4lXPPY8kkPEhvs6mD3ccIW2TWDw3xgRbI5ioKuJ6jPB4OMQ1Sg9jbWzxa/4muL62Mj2hwF8XBOJfnyjrxWHCIHv4BqVSi//moKkgR11MkZMO/JD2yO3X4rDM3Zoqs/34/PKnF5fhZ99wuslCvmexqyUO9nWj7nrIyQBx0K+pD2Jxz09WXglM+Qwg+DHRU4EsOY0pDZz7ZRBszTSRklmIE3fCcewm7yTS8ilt6Xzt1D3J4ngJXX/XqT/S/nQambfwuyWb50Jc46bWzklepOTQzb6vPG2w7U003TwgMSSIor+x+RCD0VphFvuM/3f8/fff1C0OUc4T5f6CBQuoJX1diLuduujp6dUq9QnkbyMjo1qlPvcasfLnt9ivu0HQtWtXmiY5maeYIW58GlPql5aWUjc//K/yssYnkFJS0hI73uPPY2hrjtHffEKt8LdP/QbHv/oDLn07QYZMxIXoPTLjU6glv/vALhJ8IVccNd0hftI/f0HNvR1Unes/uw8BWeM2dpzrRGgSOp54hsEXXsInJYf69GtsM+D/BS0UFyPSPxJp8WnoMkyCtiYMosBrgTJpdOgIl+274bjxV/p3zI5tKE2VfMFMXIcIvBdyaoY/2O6Zg3OQlJKLDgO2oE23DTh68gVO7JsJawsdsdM0v/7QIpRlZyNy13YatFB/wKDm35ArW1rg+Vot+xrOW3fBeuVq6vIn/vCBZt+TqytrzPdo+4PeaLP3GWZeDsAYB32s7Mw7ZTXASge/9rPHynvv0WbvUwz8x5cG2v1zoOjAj41BTmVxqG6RPEH3nkPdQIe63OHn7bVHeH3pPkatW4gF//yKCRuWIfzZG0jHi3bVwQ9RdjZUd8fuhePai3hkF5RRP/xf7nkOGWkpGoSXy+Kd3ohNLcD57wbgze4x6OCghwvPYlqmSbeQbCFUV1Yi7tABGsDafPbcpt2jTlHIQraxtrfnbRzaHX2KQWdeUovKo0Pd6RF5SdM0hSryFKTEUwD82bMNXqbk4FhogkjlnLueBi5JaFErsp1L8Fzr5amuRmZsItIi4zD+168waetqKKip4Or3O1GSL7jZOvqnpZj3zxaM3bicBqqW8zoimWwRUoHEUnvnWDf88TgSkZnCA7CKk6YpcOui7jgnSR4Sxybw1hMYOlhi5v4fMHT1ZzRQ8f3tx2vz2HZrD5dB3aGspQ41fW30mj8JGkZ6SH8gnmxpyty5IZLOnUFBeBgs5y8Q6fZN0r4hjksBGSngx44OtD/+8Ipngeypp4FB5nr45XXTFW/81K0pUnWiSjfV1QiWmsrYLObm84egNcoWLtU19j/i+CTv3ckMhnoqOHNd0L3ThpU9cO9ZLJ6+kiwQsljlq3na4mx9dzPSwvJ21vjtdSTCcoQbkgy30qdK11ux4p0y/FBrNoKBjjK6e5jgzK33H7Qs4kLcZBEXPKufhwrErDkTkYy78Rn0BDlxL/Xti/d0bjPCyqD55SPzKhGfcWNwLJnght/+eYvu8y9gy4m3WDW9PaYO5Jy06uJiiFE9rLB6z3O0CMLmLWJkI653tvV2ppttv72OEtgU+fxBIOw0lXFvXCdcHumBiNxCeoKRRW1kfKwwi33G/ysePnyI+fPn48iRI+jbty9VwhNXPEuXLq2XVl6+/o6tsAmWsGt1g/nWc2NT854/nbDvq8vGjRvxww8/0L/NzMygqKhIFzuq2hpYdOQnKGuqoTivUEAJSIKRkoCF5DNhiJvHrrMbfXHJSkpDZUUlNA3rH9Xzv+sDFW112HRog/dCXADLqKujNFXQh2JlPscqqq7FlCQURYSjsrgIWU8eci7U1O/7VV9Cd9Aw6A8f1aT7cq1RiIsBfospbUU5hIrhVodMVomFFPHLP2BiFwy21MPxEPF9I38M/DJ7AzISOVapRlaG+Hr/11DTUkNBrmD9FOUVUddT5LOWwOemDwzMDWDtwnMN0xDc9lVZQE4M8ALiVeTnUwv75kL7kIwM5DS1YDx+EvLevEG2z3MadFcciBU9QUdLBanpvFMNOtqqePVGuKVYjy42cLI3wLQFfyEljZNn695HGD+yLaaN98RPv98WK4249UesG/nlBfEtThSNLVF/5Tk5iNi6hfruJwFI68bnEKd8xE0OPxVc2dISz5cE9yOuiKxtYDBqLOL27kR5bg71nywOGUXEykhQoaNb44Ino8a6VhRkPUd8/T6KzcI+v3gs6WCBLT6co9Cz3U1wPzqr1vVOaGYhfn8egyMjXfHz00ik1ljrSqVEQeHCr/Tv7XuArtNHwHNM/5pxoE5fzSmggSmJ729hSJKnvLQMYU9fw2NM/3rj4ZurD+E6sBss2nI2IYydrNF+VF88PnoVMO3LUYwTC638MlRUVkFbTfDeOuqKyMgTP8BtSVklYtMKYGnAs0YjAXKX7RUMUnhkZU/Ep4nvNo0rWyqEyBbiJ7+5kH4Wd+QQimJjYbNiJeS0tJo2jinJIZ2vrRGrwTepDVslV9eMY3H5JVj9JAyvZnZFPwsdXAxPlShNQ5ATccTqkB+iyyXuQ+qelqsLsbrd28cVMXnFWOUdInJhTlw9pBeXUh/Rwij4bTmqiwqwhzxBJ2uqUCduo7ITBX9DcS5HhiqJmleJkYcom8mmVI9Px9e67CH+9g/NWIXEgDDYdG1XJ1aFPPW532X6SNzYuB/lRbnki2rTEL/62nXceenWWEsLs9ZXV5SlQXB/GOxAXwSi9CKKocg1/bD8chCeRGU2muZyjQsHqfQYyN/muLXc8w/QedoItBvTn/7e4rrzgNwCyDYgW8TJo6KlARVtTbgO7UXfK1gpw2PcQNzf8TeVN8TFZF2I7NGxMEZSqqDVMDmFRsxPSV/lpzw/r0XGDULSxfPIevaMxstQMhUdU0UY3PZP+wdfsE1iiZpV2vC4QUbQ7zs40OCvCx8HIL2kTMBFDen/14bxTkeREzefOptT/9+Dr9UPdC9qXCOQsY071tD3ynLwTRLedzuaasJBRwWhSwRjYW0f4oy57U0x+iQnHktL0BpkS0NkZBXTQK/86GhxTqFl8LnnEcX4oQ7wC0xFeIxg/AtPdyOoq8pj2mhn+p478r68PAN7jr/B9qPi1XFWSRm1qOeH+74xa/0uhlrY3M0J299Fiwy0SxhpZYi7cekorONqpilk5BRDTUUe8nLSAv7xdTQU652MEMa4AXYoKi7HTS/x3VSKgljmk5NOdeuP9l0xTjp87mqBkVYGWPw4EOG5DZ+uLa2sQkJBCczEOL3MhcQSouVRV0RkYp5AXXE/qwvxqU84fS8CL4I549y9Vwm46ROLYV0tqeV+Byd96uLHez8vZgyx8l86yQ2zhzmi49xzYpUvs0ZekfU3UcZz0VaUx9v0huctSrLS2N23DXUTNu9eQL3T8u8y8jH7jn/tezlpKcxzMUdigfjzSYZ4m2WMfwem2Gd81MjVWLzwB6JtCG9vbxoQlwS45eLn13KTR2E4OTnh1SvBAJDET7+GhobEbndWr16N5cuX079JHACiXDsV9wRy8pyFjomTFV3QpETEwciO46M/PiiCKgTIZ8JoSh5C6BM/evTZtI2g/3Ci7A96+ApuA7uIDFJGlGIkUC5RgHDdlBSGhUJKVhaK5pLHFuBiv+kPAeuq/IC3SDiwB/Yb/4CMctMD1hLrQ3Jkz9NAA1G5RbU+84nv/VPCdi5EIFVjAfdfHARXH/qGz4KH8/usnC1x5/gdZCZnQseIYxUe9jacLq4tnXjWxk2lpKgEb5+8w9A5Q8XOQ4LfEjdQxGqOexSeuLYg7lX0hw5Di0PMriSw+Hvjn4CKikp08bTEpZsB9JqxoToszbTg+1a4P2fuBmE9S1wSBKzGn6Q4acRBxcaGBgMtio2hgW4JpC6J6yEVa/FjCQiDKMjDt26Bgp4+rOYvhLSY7tXqypasurLlPUe2KDVDtgiFazklgXnP6+RcTHUxoj2Em62rqSbi80oEFCKNQeUInxghfa+u9Su3ePw+RKsNrVGyYBf9++t+pbUbJ0aO1kgMEvQlnRAQBkN7S5EWg5LkIRb4JOaKc1/B02sUkrbOV3DyC14sr6xCYEw2Ojnq4Rzfgruzsz5834tv5acgJwNzfVU84fO/WxddDUV0ctDHj/+IPz8gwW+J253CsDBO3Ika2UL86xs0U7ZQpf7hg/ReNstXQkFXfBc8XN6l5VEFQwdDDdyI4tSXoYoCzNQV8SZNfHcjRCHW2CgmTpq6+Gfk0Vg2xB8xCVhJaKenQRWN5DNREB/P+/q6UoXGV0+DRbqLIX1mqKU+LkemUDccwlBZsYUKyemuRHHBKb2hoxUCbz+lm1jEHQyBKN6JfNKzFh40VZw8ho6czWgBt2bcvxuw0uXNdwV/xOuEHExtZyooWyy1EZ9TjLSC+m44sovLYf3LPYFrC7paYHYHc3Te5lXrb1icNLQ0epYonbqV/v3l6Opa2WLoYE0t6flJbES2iJOH1F9qeB0fz41YN5NxMCsuGXL6gkE/yXNRNrdAYXgYdLp1r71e8D4UqraCvv6bQvKlC8h8/BjWXyyFspXoebUoiBsUolxtp6uBtzV9gSihXHXUcThE+LygVqnf0YH64V70JADJRYLt4FBIHI6EChoMXBzcgbrlORoq+r51eZNMZEsVOplq4sp7zqYJiflioakE3yThLvqW3QzBilu8YK7yMlJ4/0UvfHkrGFdr7tFStAbZ0hBvglIxdZQTtNQVal3mdG5njLKySgQ2EidHR0sJvTubY93vXvU+6zL2uIAM7tfNArt+HECvE3/84vIuIw9jbQTnLSTIaFJhCTL4Norq0tlQE791d8KegFicDEtq0Ie/lYYyfnzZPH/2XPyCOe2HBMt99obzvc422tQ1kV+dGD3CGDfQDlceRqGktPmbDGSzmxiAtddTx9UY3mavpz6vL4tioYsFxtkY4YsnQQgWw4hMQVqa+th/niJ+gPOY5DzqhqejswFe1dSNvKw02jvoYec5zhqkLll5pdRnfl0jRvKWe23HOX/sOi+Y//Hu0Th1LwK7LwSKXb6AjHza7zwMNHArhtMXDJTlYaamiHd8xk/ClPq7+rjQfk+U+sTtVmP0M9elfflJQqbY5WMwWhPMFQ/jo4Yox8mLBM+tO8AIw97enga6JYp1EkD39OnT2Ldv3wctI1HEk5MCJBAvCYpLguz+8ssvtT73JUFBQQHq6ur0RVz3aGtrQ0FJiVpzEYhi3tTZBg8OXEBeejb1W/zoyCVYtnWAngVvE2HrxJV4cf6eRHlu7TyJ7KR0VJSXI/ixL/W33+eT0ZCvY3EV+TIQRXkFcBsgRIFTg5p7e+p7P+XUP9RCqiQxHuk3r0GzS3fIKHEU8OU52Qhe8hny3r6WzJpWRqb2xXXBQa9LaPnLT0llFc6GJVMffY5aKjQA1NqONtRS5W4cz3fyoQGu+LWHI/3bXksFX3la0+O5JHiSkYoCvu9iT5Vv9/jy/FcgbZDEryAvbnt09HSEkZURzm47S/3tp8an4sbh62jXpx00dDnWSaXFpVg2YDm1vJcUvwd+dIOr44AOYuch7UC3bz8a2LaQnPAoLETyuTOQkpGGVpeuteli9+1B1J+/i33fsqxMJBw/hpLERBqIj1ieJ505Rdu3ZoeOYt8nK6cI566+xVdL+sHOWg+6OirYsHYEomIyce8J71jw3XOLsPm7kfRvovAnQXB/WDWEpldSksOiOd1haaaNu4/fi51GHIgyX8XWDolnTqMsK4v6Dk88fxZqTs5QMuH5LH63dDFSb98S+77leXkI3/o7R6m/YFGT3RSot+XIliSubEngyBatOrIlcPFnyH0jvmzJuH8H2T7PaIwBuhEUGYHUy+eh4ugEOT73bI3xT2AylORksLqbNdTkZdDNVJMq+g++4R2VJ4r+qMU9Ya/NKe+abtY0KC4JnqYoK40+Ftr4rL0ZzoXwFom3IjMwwFoXg6x1qILESlMJyztbUN/79fz2S8vQF9l45SrJ2g7rRYNKvrvxGGXFJQh9/AoxfkFoN7JvbbaAO8+wfdxSOgaIm4c/aK6lRxt6uqwuNp3cEHT3ORKDI6gf7NTIOPhdeYhqbcd6irpDt95jWCdzDPQwgYqiLD4b6kgD4f59j6cE/HqiGx5v4cVmIMFy3a21qfWeia4ytnzWiVqO8W8OTO1rg37tjGkaK0M17FrcFcFxOQJpxJUt6Q/uoSAinJ5iSaqRLdpdebIlZt8eRG79XTKl/tHDPKW+BH71+ckprcDliFR84WEJG01lqrRa18WWBrh9HMezMr0wuj2+78ZRZnoYqONLD0uYqylSdx5kMb2hpwMKyirwMD5L7DTiQBRufmm5WNHOmi7cyZj5ZVsr+KRkI7JmQ53wZFxXzHQ0rbV63NvHDfEFJVj5NFjATUFdeppoU6vJy1Gpjc4hpPnGMRLcVk1PC0/2n6FW9xkxifA9extO/TpT4wZCQWYO9oxfikift2LnIS6p9GzM8OzoRep6p6SgCE+PXKDW6iY1QanfXnmA0IcvqM/+ShJbITQKPsevotLQgRyZESj7iTeJUJKXof7w1RRk0c1SG1PameDgC16w2K6WWtTS3k6X4z6SKOb5X9zq41fYi5OmIdniNrwX0iJiEVAjJ8Iev0KcXxDcR/DkRNCdZ7T+yG8UN4/r0J5USR981xsVZeU0sLDfxbuw9HSttdZ/uOsEEvzfU7/8JLbBs8MXkBWfAt1efeoVXa//AOT4vkLuGz9UlpQg7fZNGhBbpzfvO5MunEPwmm8gCclXLiHj0SNYL/2yyZvfpKZPRyRhsq0x3HXUoSYng6VuVqisqsb1WF573tjZETt6cFxRktr/roM93GqU+vx+9fnvSxTR/C/udUkiQRF/1+eDU7GymxXstJXpKbSf+9kjKrsID6J5SrKb0z2xsb8933fXb1fk/yaEcmj1sqUh7njFIDm1AN8v6w5tDUU4WGvj8xntcfbmexQUcvqEga4yQu7NxcAegkYxYwfZoaSkAjce8dyMCBhu8L3Ie0JlZbVEHqYuRqbQuccSd0uoyMlQpf4YG0P8E5oooKj2mdgd1uqceQtJs6W7M3b7x+Cf97x0omITROYUIoAvuHFziE3Kw32fOKya2wFmhmrUtc7a+Z3wNjQNr4N4z8jr70lYNstDIG9ndyOYG6m3qBueE2GJGGCmh97GOtQ1zEwHExp4mLjS4UL8418a6ln7fn4bcxqXYMmTQARmCa8XEiy3jbYqjZ9hpKyA7zva09NU16LFb4ekHRy9HkKt6D0c9aCuIo81szxQUVmNC3xtaueKnjj2Xb/a9/svB2HyADu0tdOlsY96tjXC4M7muPYstva+/G2PvAhVfO1Q3HkL8Y3/ubsFrDWU6Un5NR1tqcscr0Te/OL00HZY18mW/q0oI40dfdpAWY6j1M8TodRf6WFFDfTIGr2LkSZWeVrjcFA8EoXISgbjY4BZ7DM+erZu3Ypvv/0WS5Ysob7snz59KjLt+PHj8fz5cwwePBgFBQXUev+LL77A/v37P1j53N3dce7cOaxdu5aWUVdXF3PnzqXW9x+CUd98grt7z+Dgwp/pwsqmowsGzBcM8Ehc7fBvhIiTx8azDS78sh/ZyRnQMTXA0C+nwbF7+3rf73/vOSzc7IW66OFClHbmi5ch5dRxhK1dCWl5eWh06AyDsRN5iUj5iGUa3wQgfP1qlGdlcs0CqOKf4Lzjwz0/Ln/6xXCCVg5wpRMzcgRwfp2jfWRCRXb7CRE5hdS6/7cejtQSJbe0guaZeesdkv/FSYOTvSle3tpE/5aVlcHPq6fip2+m4OaDN5j4qfjKpaZAFCOf/fIZVez/MPVHyMjJoG3Pthi3eGw9C0Ru0EzCloW/IzEikbZR8iKKf8KGi78IBMglmwHu3d2gosGLcSEOeoOGUKtzoryvLCqCkrk5LJd8KRjkllja850ESr93BykXz9e+D9/wI/3feOJk6PTqQ5XJKvb2SDh2BCVJiZBRUoKShSWsl38FRRNB68DGWPPzNfywaiiu/vMZFBRk8fxVNKYtPIYK/rZGN1I4bS0vvwTT5h/D2uUD8eTKUijIyyAiOgPzV5yudd8jThpxsfpsARJO/oPg9euo3lXdzR1mk6fW8wXOL2Mitm9Ffmhobd99s2g+ve78w09UmZ/96iVKU1JQmpZGNwX4cd+2U2xFP0lnuWQZkk4ex/s1K6nbHM0OnWE4Tohs4Stf2PrVKMvkyRai+Ce47OTIFs2OnZF24xpSr1yibpzI89Zo7wndgUMkqjtilT/rcgB+6GWLT9uZIqu4HHtex+PIu0QBa2diScjlRGAyVdITH/pkoUKs+3e8isVRvjwkkC4Jmks2DHYOcUZeaQWexmVjw7P6i31hEEvYISs/gffxK3h86DzUdLXQd8FkWHm04VVbVTVVMnNN9sTJQ8hOSkNScCRGruU887p0nTYCMrKyuP3n3zQgL3FjQpT9r9N4lrNcbryMp6541k1rB31NJUSl5GPh9qcIS8wVlMM1fYNw8mEkVk1yh6uVNopKKvAmIgPjf7yHhAzesfa7rxOxbno7/LmwC4pLK+j3bDkXQBe3kqDPJ1sqCougbGEO6y8EZQupQ1qPfLIl6QJPtoT9wpEtJhMnQ7d3H9onSFBu0tlC138r8H2W8xdCw72t2OX7yTsCqzvb4OSItjRY5cvkHHx2WzDILWl73Op7l5YPJx1V7BrQBubqSlSB9zI5F1Ouva0NnihOGnFZ9SwE33ja4vxQT9rMyMJ982tBy23yfLn7PQMt9Oj4aqamBK/x3QTS9Tj3TEAZR1xlvErNQWKhZMfsZeXlMHzdIjzZfxp/zVsHWXl52Pf0RDf+INBkrOKbr4iTh2wiDF09H14Hz+LYvHWQlpWBvp0lRnz3ORTVOGOafc8OeH32Fl78cw0l+QVQ1dWiLnpSZAfUK2dqfilmn/TD94Mc8WlncxrIdO/zGBx9xbO8JvM7EshRgpjQzcbAzhIDV3wCn3+u4Onh8/Q39FowmQa65ULHetIvqsXPQwIPD/t2AbyPXKT1rKimCqtObjR4Lpc2g7vj1akbSA6J5Gy8WZlS90pROhwFED+anh1QUVBAN63J6TEFAwNYLlgksGHN0TrzrHhz372lG3Vc6N9SUtDr2w/G4yfS35R24zq9FvHbZoHvMx47Dnr9B4pdj8feJ1CFFVHeE1/5xAp46dMg6l+bC5mjcuegxB3HYHN9emLrzECewpCw2icEXsmSu4xpiG8fhOH73ra4OMWDyhafhBzMuuAvYOVOZAvXP7e4/NTXDtPcjGvP/4Qs6UH/n3XRH16x2R+1bOFCgrzO+fomfljWHV5np6KktAJX7kVg426ewQv5/TRgaZ36GzfEAVfvR6C4RLxgqU2BuEBZ+jgIK9tbY6qDKXUvcywkAafDeVb4pFzk+XKLN9vJlM5JvmhrTV9cSD0teRwoYFnd30wPuwPqnL5pJl9teYL1izrj6u7R9Ll6vU7E+p3eAmnoXIFvrsUNmusflo6QqJbrH/cSMqgrqOXtrKCrqIC4/CJ85R2CqDzephIpBrfvki3luc7mtO8e6OMucK+d/tE4UVPvZMNliZsVnLRUUVRRicDMfMy9/w5JdU7mNMbeS0FQVJDF7pW9qGI/KCoTc36+j2y+Ux2cuuIZyJ19EAkVRTlsX94DupqKSEwrxG//vGlWoFxRbHgVia89rXFskDuVLb6pudQ/ft15C7dvkFOJZGOJbCY8HC9oZDjxul/tZt7d2Ax818mOKvfJxueBgHickOAUPoMLsxNvLUhVi2PmzGB8BFDlYHU1tRiWBJKH5OXPV1FRQQPrNpZO3GtNKYO4HHovnm/suop94ptfnKBMTQlnIS4AAQAASURBVLo3OYZfMwG4Ft90FzhEOUj8nvL78hZmZkKt84Xlr1HgifqcECnojrtZkIkZ1Qu23C0RvnZ3s+9BgxzXgVhMtIT4v+g9s9n3IFb30nzPmW48CanFuv2jsXZ8MEwyhT8/XMUbtx1z21I9+MotKd5f3G1y+aSlpQSOvX4Ieu6orzySqP6ouxjx+q7I+m2gf5dXSf3PZIs4vAqSxOZREKJYbcqRfkn4pr9kiz+u8k2Ui7WWzvf7ZvGVNvU9+0gJuCBqaTxm83zo/xuyhbZXYYiQPwHi7ef8a+NYXVRUpJpVPn6DP749HAHq9h9JftcM6opHMqoqKzkW//+C1vyPi03/DqI8EmptL6J+m5Jm+Zjqf0VGNJVnaRz/5R+s75Kxj5tGwr4bl9V0JQnXgVmVhH1DoFiNWOsnxUq2WSdJHxTWNslPELYZIKoN6xvLtmrZkruP535IUohylWv53NA1Se/Bj8Z8xxaZt3CeW/005KurmzHfyf2r5RTHnHm04OkFYdckQesTzomU/0XfbYysM03fPKHzKikpiazt60LqtqH8KqOa7i6zbh/8EPX3bjpnU5EhSEH5o4+ySlTleuO/BrPYZ/xnIIrBpkAGqroKw7pKfVHpxL3WlDJ8SLhHzFv7vesq1SR1p8MNZPpv0dLHh1sKSXyo/y+o2/bFbUMfsh3XbWv/dltqjOZMrv8N6tVfI333367f5sqWD82HVuo3BfKMmrLR0dR8TYWzuGuFFdgM2fJv1l8rFy31yiduX/nQv+vfUko3l4aU+uLWU0vX5b8tI/6rfZc8lupmjiMfcrbYWLsR1ja5Lnv+P8sWLsIU8pIo9ZuSXqJ7V9d38yRpvv/1PPp/Nbduib77IWkJQ6IPWbdN7bsMxn+J1rWSZTCayfr166lSXtird+//3s4cg8FgMBgMBoPBYDAYDAaDwfj/B7PYZ/znFPvr1q0T+tm/cTyawWAwGAwGg8FgMBgMBoPB+K/C9GutB6bYZ/ynIO54muqSh8FgMBgMBoPBYDAYDAaDwWAwPgaYBpTBYDAYDAaDwWAwGAwGg8FgMBiMjwim2GcwGAwGg8FgMBgMBoPBYDAYDAbjI4K54mEwGAwGg8FgMBgMBoPBYDAYDIYYsBiWrQVmsc9gMBgMBoPBYDAYDAaDwWAwGAzGRwRT7DMYDAaDwWAwGAwGg8FgMBgMBoPxEcFc8TAYDAaDwWAwGAwGg8FgMBgMBqNRpJgrnlYDs9hnMBgMBoPBYDAYDAaDwWAwGAwG4yOCKfYZDAaDwWAwGAwGg8FgMBgMBoPB+IhgrngYjI+czNLWvT9nolKB1ky6uhxaM1rqtmjNJBa23vZXXiWF1kx2XgRaM4oy/dGa0VesRGsmUqd1T7HKq9GqkcouQWslu1QGrZnC3DK0ZqSkWnf9VbTysUM6switGUtVebRmHiW33ucr27qHDUgXlqM1U13duitQqrR1z1uqW/m8AK28/Um33iURpApa97yg1bc9BqOV07pHPwaDwWAwGAwGg8FgMBgMBoPBYLQSWvFu1v8z2JNgMBgMBoPBYDAYDAaDwWAwGAwG4yOCKfYZDAaDwWAwGAwGg8FgMBgMBoPB+Ihgin0Gg8FgMBgMBoPBYDAYDAaDwWAwPiKYj30Gg8FgMBgMBoPBYDAYDAaDwWA0ihRabzD6/28wi30Gg8FgMBgMBoPBYDAYDAaDwWAwPiKYYp/BYDAYDAaDwWAwGAwGg8FgMBiMjwjmiofBYDAYDAaDwWAwGAwGg8FgMBiNIiXFXPG0FpjFPuOj5c6dOxg1alSz7+Pl5YWBAwe2SJkYDAaDwWAwGAwGg8FgMBgMBuNDwyz2Ga2GR48eYfPmzbh586ZY6dPS0vDixYtmf29mZia8vb3RGn9jUyjNL8TzI+cQ7xcEsolq7umKzrPHQ15FqUXylJeU4srq35CblIaRG1ZC18Zc4PPo537wv3wPuYmp0DAxhMX4sdB2ckDc3YeIu3MfZXn5UDU1hv2UCdC0tW7wtzSWJ+TYSSQ+8hLIo2JijC4/fcsrb1Exkr19kPDQC0XJKWj/9ZfQdrRvtB4dNFWx1MUKthqqyCktx4XoZJyKTBSZXkdBDpNtTdDNUBta8vKILSjC8fAEPE3JkiiNuPTtYYfVSwfAylwHCck52LbvMS7e8BeZXlpaCks/64Wxw91hqK+GjMxC3LgXjF933EdpWYXYaRrD/+5zvLhwHwUZOdA2M0DvWaNg4W7frDwlBUV4+s8NRLwKRHF+IQxtzNB37hgY2JjVpsnPzMWLc3cR6RtE0+uYGkCq1zCouber933V1dXIvH0dOU+foKqoEIrmltCfMBmKJrz71aWyoAA5Ps+Q8/QxyjMzYLV6PRSMTQTSJB7ai/y3fgLXlG3tYb50JT4UZsY6mDOlL2ZP7gM9HXXoOM5GmZjPSlwyfX0Rf/UaSjMyoKinB7PRo6Ddtm2z8lSWliLh6jVk+r1GeV4+lE1MYDFuHNTt7WrTFCUmIfHWLeS9f4+qigqoWpjDbPRoqFpYCHxX7N2HiOWTE45iyBZJ8oSdu4SYG3dgPqAvHKeMr71+f+Ey+jvqYtavF5ymTRL53c7aqvjKwxoOWqrIKinD6bBk/B0qWrboKsphhpMpeplqQ1tBDtF5xTgSHI9HCYJyY7ydISbZG8NYRQH5ZZXwTs7G9rfRyCkVrz3Evg6Czz9XkZucDjU9bXiMHwT7np7NzkP648uT1xD9IgCVFRWw7uSGLjNHQ6FmfAm5/xyP9pyqf3OblYB0/Wnq+P62+GysCwx1lREZn4vNf72Gj39Kg+U00lXGypnt0b2tMcorq3HxQQR2nHyHsooq+vmjA+Po/epy+VEUVm17BkmYam2KoWYGUJOTQ1huAXaHRCG6oEhkejU5WQwy0cdwM0MYKilivvcbxBYUC6Qhz32uvSXa6WhAWVYGKUUluBCbjDuJaRKVTV5GCqt72GCEgz4UZWXwPD4b3z0IR3JB/XZcF2KTdXycO7qYaeKLG8G4FpZe+5mxmgKmuBpjsosRdJTl4LjjCcoqqyEpGvKyWN3ZBr3MtFFdDTyMy8TGF5EoKK8UmaeHqRZmu5jCWUcVReVV8EnOxrbXMUgrKqOfy0lLYaydIcbYG8BKQxnpRWW4GpmGQ/7xqCBfIgZ5qRnwPnwOycERkFWQh10PT3ScNgrSsjJNzlOUk4d/PuPNVbj0X/4JrDrzZGVqWDSCb3sh2uct9O0sMfz7L0R+p6uZJtaNdoGziQYyC0rx99NoHHwUKTJ9JxsdHF/Ytd71oVseITwln/6tp66Aeb1t0a+NIXRU5RGZVoC998NxN7DhPtcQJUUluLbvEoK8A1BVWQV7T0eMWjQOqpqqIvNkJKbD57o3Xt99icqKSvx4cVO9NBtn/IDc9FyBa70n9cPgOcPEKle291Nk3r2J8pxsKBgawWD0eKg4OIlMX1VSglzfF8j2eoSSpESYzVsENTfRY2PG3ZtIu3IRmp26wnj6bEjKTHtTjLQwhLq8LEKzC7AtMAqReaJli6eeJibZGMNJUw2llZV4nZGLfSGxyCwpkyiNuLLlm372GNHGEAqy0vCJzcb6WyFIzhNPthyb5oEullpYejEA14NTaz+7MrcTnA3VBNKfeZOINTdCJCofqbOV7a3R3VgbpNc/SczE735RImULkRsjrQ0w0toQlmpKyCgpw42YNPwVnCAgN9x01bDQ1RL2Wir0fWhWAXb7xyAoq0Ci8pkaqeHbZd3QsZ0xiorLcfVOOH7f8xIVlZwxqi5zJrvhq0Wd612vRjW6j/gb2bklYqURFydtVVp/9pqqyCotw9nwZBxvZN4y3dEUPUy0oa0oh5i8YhwNjsfjRMF5yzhbQ0y0M4ZRzbzFJyUbO8i8RcJ5bK/OZlgxryMsTDWQlFqA3cf8cPVehMj0f20djo7uRvWuvw1OxZTFV+jf44c64KeVPeulcR90CGXlwp+LKIZbGGCGgwn0lRQQk1+EnQExeF1HVvHjqKmK6fYmcNdVp8FJA7PysTcoBjH5gnMDLiTtgjaWuBGbig1+on+3uNiaa+LbBZ3h7qCH/MJSnL0djp0n3tBxWRjLZnngswmu9a6Xl1eh3bi/UVkl+XygITz01bHcwxo2ZEwvLsOx4AScDRc9JinJSmOKgzGGWenTtpZSVIpLEak4FiK6DTMYHxtMsc9oNWRkZOD58+f4L/Nv/MYHWw+jrKgEIzeuRHVVFR78cRiPth/FwNULWyTP80NnoKqng5yEFDo55Cf07lP4HDmPbvMnw6KjOwozsvH8/H0UZ2Qi/OwFuM6fCw1ba6ok89uyDV1/WQ9FHW2hZUr08m40DymrXjt3uC76VOSRsNibd1BeWAT7SePw5o8dRKvbaB0SBfyfXV1wKy4Na1+FwklTFT94OqK4shKXY4RPHKbbmyG5sASrfIKRU1aOQWb6+KWjE1Y8D4Jveo7YacTB1ckIh7dPxaZt93Dm8hsM7uuEbRvGIjO7EE+eC1/EL5zdHfNndcPcpSfwJiARjnb6OLRtKlXm//DbLbHTNETY83e4s+cMhi6dBsu2DvC77oXzP+3DrD+/por2pua5uuUvFGTlYty3n0FNVxNvbz7FqW93Yu6uNVDV1qBpfC8/hL61CTqN6wdZeXkEPniBhwd2w3z5Kihb2wp8Z9a92/Rl8ulCKBgZI/3qJcRv/wPW63+GjDJncVaX9OuXIS0nB72RY5F0aK/wCqiqhmaX7jCYNI137QMfUfxt/Uy8C47Flt2X8fsPs1v8SGTu+/cIP3gQlpOnQMejPTJevEDY3r1o89XXULOxbnKeqOPHkR8ZBfvPPoOivj4yXr5CyLZtcFv3LZQMDWmauAsXoNPBE+ZjRkNKVhbxly8j+Pc/4L7+Oyjo6NTKibCzF+A2fy5VzEffuAPfLdvQ7Zf1UGpAtoibJzMoBGm+b6Ckp0vlDT99d/1ON4m4ZL8Ph++vf8LAo/5mEv9id18/V1yNSsUKrxC00VHDr905suWciEXJJ23MkFRYgi8eBSO7tBzDrfTxew9nfP4wED4pHLnRz0wH33jaYq33e3glZtGFy8Zujvi+sz2+fByMxkiPjMfNzQfQaeoIOPbpiOiXAbi//W8oqavCrK1jk/NUlpfjyvodkFOUx4jvFlHlf6TPW0Q+fwvn/l1omuqqaqjqaGLa7u8E7r/r2/pK6wGdzfHjws5U2f7sbRKmD3PEwXX9MPLLq4hKzBNaTm0NRZz9dSjevE/HuK9uILegFJMG2qOzmyGe+CXRNH3nX6DKJS5O1tq49Mdw3PWJgyRMsDTBBCsT/Pg2lG7czrazwOYObfCJlx8KKoQrkGbZmqOsqgqHw2Kxrp0jXcTXZbWbAxRkpPH1qyC6GdTTUAcrXGypgsU3Q/yx44c+duhpoY05F/2RVVyOjQMccGysGwb/7YvKRsbGzztaUGW9rLR0PTmzvrcdgtMLsPtVLH7oY1/zGyRfyP/R1wkqcjKYfPUNpKWk8EcfJ/zayxGL7gUJTa+jKEeV9rvfxFGFmqGKAtZ3tcPO/m0w8cobmqa/hS7stVXw8/MIxOQW0w2ALX2coKkgi80voxotU2V5BW7+vAuapkaYsHUtirJzcefX/aiqqkLXOeObnqeaM4cZt+UbaJnxFExS0rzD1CX5BXh+5DycBnRDdWUVCrNEP2uigCdK+vOv4rDoyCu4mWtix0xPFJVV4oR3jMh8sjLScFh5VeD58ythFvWzR3xWIT496IPMgjKM9TTDnjkdMXvfczzl29yRhDNbTiA9Pg3zf1sMOXk5nNz8N479eBiL/hC9aXFx+1nYeTig++heeHj6ntA0ZJNg3JcT0X5Ah9pr4o6JeW/9kHzqb5jM+AQqjs7IevIAcXu2w/qb76iSXxgkTVl6OgzGT0Hsn78KjAd1KYqOQrbXYzrnqDuWiMMUWxNMsTXFulchiM4rwjwnCzpPnXr/NfKFKKe1FOQw1dYEpyMTEZpTAHU5WXzd1g5/dGmDTx69Adl3EyeNuHw/yBE9bXQx56QfsovLsWGoM45O8cDQ/c8blS2LuluhvLKKyhbS7/mRlZbCbw8icNAntvZalZgbcvxs6uYIFTlZzLrzFjJSUtjU3Qk/d3HAl0+Ej499THVhp6mCzb4RiMsvhqOWKjZ0c4SGvBz+eMORG5rystjZ2wXXo9Ow2jsU0lLAYndL7OrjimGXX6JQhMyvi5ysNA5tHYbImGwMnXYa+joq2LVpEGRkpLFhm3DDsyOn/HHsbIDAtb+2j0BVVXWtwl6cNOJA5i17+rjiWnQqvnoagjbaanR+UVxRifMRwuctc5zNkFhYgmVPgpFdUk4Vqr91d8aSx4F4UTNv6Wuqg689bLHu+Xs8TeLMW37p6ojvOtljuVfj8xYuzva62P3LIPxx4CUu3gxDv+6W+HVNH2TlFOOZr3DF7ZwV1wWm55rqinhydhruesUIyI6U9EL0n3pSIG+lhJvWvYx1sKq9DX7yDcfL1GyMtzHG792cMev+W8SKUNQvdrXEhahkbPWPon3iC1cr7Orpiil3/ZBXZ9OjjZYqxlobUblA2nZzUVWWw9ENg/HsTSIGzXsMK1MN7Py2Hyorq7D71Duhef489hrb/xY0bLq6ezQi4nJaXKlvqqqI3f1c8HdIIpY8DIKngQY2dHOgcvBWjPAxaailPq2bFU9C6EYAybOpuwOVJcdDOfNABuNjh7niYbQK3rx5g3Xr1qGgoADdu3enr717RSjP6uDr64t58+Zh8ODBWLNmDb1HXYt8cm/ibmfChAk4cuRIg5NvroufBw8e4NNPP8WAAQOwdu1aFBUV0WuTJ0/GkCFDsHXrVrpIE/e7RP3G8vJybN++HaNHj6avHTt2oKKiaRa3GVHxSAp4jy6fjIeGkT40TQzRefY4aomfHZ/c7DyRXq+QFZsIj8nD692nrLgEL/++hLbjB8OuVyfIKynSxarLvNlUuW7Soyv0PdpCQUMd9pPHQVZREfEPn4j8LWLnkQKkZWRqX/yLYoLtuFFwmjmFWuWKywgLQ7rI2B4YRRVp3qnZuBKbgmm2piLzbAuIwpmoJMQXltDJxbmoZIRk56Ovsa5EacTh0xldERCcjL1HnyEruwgnzr/Gw6fhWDinu8g8bm2M8cIvFs9eRqOouAx+/gl44BWGti4mEqVpiJcX78OxWzs49/KEsoYauk8dCnV9bby++rjJeYrzChHtF0Kv61kaQ1FVGZ0nDISKphre3Hhae58+n4yG24AuUNPVgpK6CjqM7gtpJSUURwludJAFddb9O9Dq058u3mU1NKkivrq8DLnPRVvmGk6aBv2xEyGvp99wJUhJQYq0Q+6rTntsaSbP34qN2y4gOTX7g9w/+e5dqDs6wbB3L8ipqcGof3+oWlnT603NQ6zvM31fw2TIEKhaWkJWWZmmVTE3Q/K9+7X3cVyyGHqdO0NBWxvy6uqwnjqV5s0JDKxNE10jJwxq5ISDGLJF3DyleXkIPHQMrp/NgYy8fL37kGfLL3sSvZ5D2UC/wRNBY22NUFZZhd9eRyGrpJwq4c+Hp2C2k2jZ8uvrKLrwIMqF/LIKnHyfhMDMfAy04MkNZx01xOcX43ZsOooqKhGZW4RbsWl040Ac3l17CD0rM7Qb3Q9KGmpwHtAV5u2d8eby/WblCbz1FDnJaRj89afQNjeCnJICHPt0qlXq88Nfl+QljHlj2uDmsxhcfRKNrLxSbD/5DglpBZg1QrRV7ZLJ7qiorMby372QkFqA/MJyHLwYVKvUJxBFRyXfa2xfG6RmFuHhqwSIC1lOj7cyxoXYJLzJzEVWaTl2BEdCQUYGg0RsbBJ2hkRh//sYJBaJVrTYa6jiflIaEgqLUVRZiVuJaVSpb68u2sK5LhqKspjYxghbvKMRkFaAxPxSrLkXBgddVfS1Er4JxsXDSB1T3Yzxzb1QoZ/PvxqIbT4xSC2QzMqXHycdVXQx1sIGn0jE5pUgOrcYm19Eobe5Dmw065+mIGSWlGPZwxC8Ts1FYXklInOKcDo0CS66atSCmHAzOh0/PY9AYEYBtc59mZKLf4ITMdRaT6xyxbzyp9b3PeZPhqquFrWabz9hKELuPKXznubmqStH+BXRimqqGL1xJRz6doGMglyD5Zzc2QJlFZX46VIgMgpK8SA4FSd9YjG/r+DGtjCI4pW//fPzw8UAHH4chej0QuQVl+OoVxTexWVjWFvx51T8ZCZnIPCpP4Z/NgqGlkbQMdbF6M/HIyYwCjFB0SLzzdu8CL0n9oOyhvDNdy5S0lKQkZGpfUmLOQZn3rsNjfae0OjQCbJqatAfNgpy2jrIeiRaBuoOHArjabOgZCZ4crUulcVFSDx6gKaVURbelhv8TeT52pjgbGQSfNNzkVlajj/8iWyRxlBz4bKFzF2XPw/Ci7Qc5JZV0HnnzsAo2KirwEpNRew04sqWCW1NsOVRBAJT8pGYW4K1N4LhoK+KPnYNz2/bm2pgSjtTrL4uWpFLlG+VfC9J1YSOWiroZKiF315HIr6ghFo9E2v9HiY6sFIX/jzuxKVjk28kgrM4csM3LRen3idhkAVPblioK9PNgn/eJ9KTvWRMPxGaCDV5WZipiT4lXZf+Pa1gbqKOdZufICWtEP4hadh52BdTRjtDRVl0vycKZu7LxFANnu5GOHMlROI0jTHGxohuPpM6o/OWpCxciEjBzAbmLb/5ReHE+5p5S3kFToUlISgrHwPMBOctCQXFtK6585bbEsxbuMwe74rgsAwcOuWPrNwSnL0eiicv4vHpFHeReeiYz1c3w/vZ0OuXboXVS8ufTlKlPmGavQnuJ2TgTnw6PYlwMCQOKYWlmGgjWoYu9grEg8RMOsYRRfQmvwjoKMrDQ5djzMRFRVYGP3R0wC+vw2k9twQj+9hATUUO63d6Iy2rGC/8U3D4QiBmj2kDGbJ7JQSi6uAfQ1zsdGFnoYUzt96jpZnsYERP3u18G0vb453YDNyITsMcZ9HtkWxAHQiMpydHyFzhcUIW/NLy0FZfvcXL9/8PqY/09d+DKfYZrQJra2tMmzYNSkpK2LRpE30R5XljEEU6Ub7369cPCxYswMWLFzF//vzaz7OystCpUyfExcVh6dKlGDduHHWFs3z58gZd/Ny4cQMrVqzAoEGD6P0PHTqE3r174+uvv6YK++nTp+Pnn3/G7t27xf4uUb/xu+++w65duzBlyhS6QREdHY3vv/++SfWY+j6SHvnWs7OsvWbobEsXjmlh0c3Kk5eSjhd/XUDvL2YLPX6e5P8e5cUlsOnBs5QilBcWojApBVqODrXXyMJVy8kBueHCrcslyZMZGIwHC77Ek+WrEbD3EIozJXdrUxdXHXW8y8wTWDy8Ts+BsYoidYkgydFfMlltbpq6dGhnDu9Xgs/TyycKHu6iXclcvxtEP2/vZkqtgJzsDdCrqy0u3wyQKI0oiIViSngczF15rlQIFm72SAqNbnKe6uqq2sU6P6R9JoSIaD+lZfC/8xzVxH2Ls4vgZxnpqMzPg4o9zwqZWOIrWdvW2wRoCnmvX+L9ss8R+d03SD5+FBV5oo/afgzkR0RCw4HXDwkaTo7Ij4xsXp7qqnrPFNLSyIsIF3nfipISYpIJaUVFATmhXUdOENdfOY3IlsbykA3ZgP1HYdq7BzSsebJRFMTlV+prP5j2Er25Rmirpw6/tFwB2fIiNQemakrU+lhcNBRk6eKEy+OETBgoK6CniTZkpaRgoqqI/ma6uC3CeqkuKaHRMK7TD01d7ZH6PrpZeaJf+MPMzZEq/huiMDsXh2d9gyOzV+PKD7uQGs6zzuS3anS104VPgKCF4HP/FLRzFL3hRqz8yWZAeY3bncaQl5XGyF7WOH8/QiJLM2NlMj7I410mr8+XV1UjKDsPzpqSKSrq8jglA70MdaGnKE+fb29DXSjLyuJ5mvjjXTtDdcjJSMM7nrcJmJBXgpicIngYCyoL+FFXkMW2oc5YdScUOcUt6+aLn/b66igqr4R/OscFDOFVSg4qqqrFXnwTi/1RtgZ4HJ/ZoCsgDQU5gf7TEKmhUdA0NYSyJq8MJq4OdPzKiIxrdp7rP+zAkRkrcf6rTXj/4HmDhicN4Wmlg5eRmQKHEr3D0mGuowJdNYUG8z5fPxBvfh6CM0u6o1cDfYmLprI8CsR08VUXrvLe2p234WBqbwZFFUXEBouWN+Jybf8VfDvya2yZuwF3jt1EeVl5o3nIXKE4NgbKfPMCAnHDU9QC84LkE8eg7t6+Qbc+DWGiokiVen58p3PKqqoRkJkPF23xFVPq8pwxpqgB4yFx0tSlnYkmlS3PY3jyKCG3BDFZRfAw1WxYtox2wzfXgpBTJPo5LepmheBV/fBgUTes7mcPZTnRLrCE4a6rQa3LyYY4F780jmxx11WTcNzl1cv77AKquB5va0RdfRAl6zhbI0TkFCIyt1Ds+7Z3M0RETDYys3nW2899E6GgIIs2DuJtQI4f7ojc/FLceRzdrDTCcNdTx5s685ZXZN6iKtm8hax3+E8xkHkLcU3Tw7hm3qKiiH5k3hIr2UkgD1cD+LwRtLp+7peItm1Eb6jXZfwwR9x9GkM3BvjR11XGiysz4XN5Jo78PhSujuI9Dy7kdzlrqdZzu/MqPQeuOpK1PULdUyCr29viUVJmg259JMWjjQH832egpJT3Xc/fJkFLXRHWZqLnCvyMH2SP+JR8PKvzXFqCdvrqtP3xQ06BOGirQFGmcdUm2fP30NegbrQexGW2ePkYjP8VzBUPo1WgoaEBR0dHal1DLNnFhVi2nz9/HjY2nJ32yspKzJgxo/bzX3/9FZaWlvjrr79qr9nb26NDhw5Uoa6lpdXgfYkynhAaGooffviBKu1NTTk7wm/fvsXly5exePFisb9L2G8kwXvJ5sGkSRyfzMOGDaOnA5pCcXYeFNRUBCy+iAWYgooy9efa1DxkMfrwzyNoO2EIXaxmxdUfqPNT0yGnpIjUkAjc/P46SguKqMW+Ts1vlVcXnMDIq6kiL7q+8oZQmpMrVh7iHoOcCNBysEdJdg7CTp6F74Yt6PLzOsgqiW8tUxcdBXkkFgpOkojrHAJR3BBLzMYYa2VEFT234tOalUYYBrqqyMisczIluxCqKgpQVpKn1vZ1uXIrEOYmWrj89zyqtCcQi/+jp15KlEYURXmF9Bi8ch0fucoaqijMzmtyHmLFb+JkDe/Tt6FjZkhd7xClfWZCar3j7EnvY/DPqj/pdWIZbDzr03p+8CtyOc9VRk2wbcmoqlHf+c1B3sgYGl26Qcnahm4gpJw+gbjtv8Ny1Tq6efCxQazjKwoLIVenHxIr/LK8vCbnkZaVhaarK5Ju3YaqlRX1wU988ueHR0BWRbQ1Y+yZs5BVVYWWq6uAnFBogmxpLE/MzTuoLCuD9fDBEIdkn5fUVYZx9/r+bPnRVZJHfB3/6eSYOvczYp3VGJPsjaji/lo0T274Z+Tj55fh1K0P8Z1OuBuXga01LgMag7gKIf2OH+JSh8RUKS8upf2pKXlyUzJg3dkdd7f+hRjfQOpX38KjDTpPH0HHGIK8siK6zx0P645udKx5feEOLn37J2AyB1DgLaK11BWocj8zR3DhTRbielpKIpX0hjrKyCssw+Hv+6O9oz4yc4upxf/u0/61Pvb5GdjVAuoq8jh7V/QmkzDI2MA/VnDJLSuHoTJnM6qpEMv/9W0dcaI3Z+OcKKk2+Yc16Lu/LvoqnPJl1VGgZRaVQ6/mM2H8OtABtyMy4BWXTS2EPxR6yvLU6pUfopvPKy2nfaMhNvRwwAgbfWpN+DYtDwvv8E711MVaQwkTHYyw561wGSGsnSvVkRekndPPRMyrxMojBTgP6gGXob2oZT6x8n968Ax1i+g6vA8kRV9dAbEZgsrErJoTFHpqCsjIr+/rnJxM/P1GCC76xtPYExM7mePwvM6Yc8AHT0KFz0tmdLOCmY4yLryKR1PIz8qDorIidcHDj4qGKvKzeYrXpuDQwQkdB3eGnpk+YoNjcP7P08hMysCUb3jrAmFUkJO+VZXUUp8fWVW1Zm/OZz99jNLUVJjM4rmMlBSi1Kf3qtM/ssvKYazc8KYNF3lpKSxwtqRGKklFpU1OIww91RrZUig498wqKqv9TBibR7TB7fepeBqdJVK2vIjLxo3gVLxPK0AbQzVsGOZMTwLMPino9qMhdJXkhMoWcgJOpxHZwsVSXYkq7Q8E8jbmSiqrsOxxELb1bkPj4BBi8orwxaMguqkrLno6ysjiU+oTsmrGOT2dxtcyxF3mmKEOuHwrDGVllU1O0+C8pY7LGG5bJG1TnHnLBDsj6kKFuC3iQjamNrwKp26SuPOWe3EZ2PZWvHkLf/3xb4oQiBseVWV5KCvJoqiRDWmirHe00cHGnYKucguKyvDztmdU4S8vJ4MF09vh5I6RGD3vAt2IEQdNBTnqYqpu3yXtkduvxeFLN2s6d3yTwZNHo6wMYK6mhO9f1T9l0BzIfCozp0591mx46GkpIzy2Yfd/SgqyGN7LCgfOBojj/VZiSHv0Sc6pN48mLotIPIekQtGyy29ad+oyi7DXPw43xDR+YTA+Bphin/FRo62tXavUJ5ibm6O4uBi5ubl0s+Dhw4dITk6m1vbECoq8iPKfuNB5//49OnfuLPK+XKU+wdjYGEZGRrVKfe61W7d4fseb+l29evXCn3/+CXl5eerCx8nJCcoijuqWlpbSFz8VZWXUp3iDkFFM0tGVL8/rU9c4rhYG1Q8ixIX83oqSUoTee4ZB334OBVVlBFy5j4Cj/wjPINUEBUGdPFbDBtX+LaeqArfP5+HJ0lVIeeFLLW1bEu4cXRz3hV0MtLDExQpbA6IQkVfY5DSSQPxUN1S+udM6Y+En3TF94TG8fpdArfH3/DYRxSXl+G3nfbHTSArZMJK06dXNM2rVHDw8fAnHV/6BspJS2HRoA5e+Hakinx9jB0usOP87ivOLEPTwFR4f2Q/Tz78UsM4XCZ3pNW8Gqjd8VO3fMuYqMPl0ASK//RqFQf5Qa+uB/wxN8eFZJ4/N7NmIPXsOwVt+R0VREbXoN+zTBxkvhW8iJVy/gUzfV3Ba+iV13dPwd0lL/iT58uTGxCL65l10+e4bsV0pJT5+Bv127lBQl/xYryQWuj2MtWgAu82+kXifzZMbvUy08V0nO3zvE17rq/anLg74uasDVj9r2lFo7okKSRwf1M1DTtwE3nqC7p+MQ8/PJiA/LYsq+e9vP46hqz+jaWy7tRe4R6/5k5ASGoWM7FeoNhza6HcSNw2imiRRZhAWTXTDij+88MXmx9R//o5Vvejic+Nh33p5Jg6wg7d/MuJTJQt+KLJ8LXDo9/t2jlCVk8VcLz9klpahu4EO1rjbY+3rELzLkkzxWPdpkuYnqnxTXY1gqalMg+X+rxCn/tZ6vcf6Z2Gw1FDC2s62ODDYFVOu1vcRrq8sjz0DXeCTlI3DAeK7WaqHCHcEkuQh1vzd5/GCbDv264rcpDS8u3y3SYp9YXB9kYvyM+8Xk01fXHbeDYObmSZ13yNMsd/H2QDfjnbB9xcCEJIkfFOjqRDZ0dTTClzGL5tc+7djR2eMWDAGx38+iqHzRkJDRzxL05acF5SmpiDtygVYLltF48S0NJz6khLLQnW9hwO1mF71IrjJaRotjzDZLKJ8U9qZwFJbGUsv+jd4zx9u88av57HZWHM9GCdndoCjvipC05ono6tAytc4ekry2NarDbUKPhbCkxtEibivnxsexGfgQNA76gZhgasFjaMz5eabZrlGqe0LYsy3enUxh4GeCs5eDWlWmqbJlsbTdjfWwop21vj1dSTCcnjzFnLC8NuOdvjxZTieJWXRE1c/dHbAj10caLygZpWPuyYS4wlPHO6I+KQ8eL8W9Md/86HgBsN3v3uhvasBZo13xbotT5pXvgbG3bosdbNCO10NLHwSULthZK6qhEVtLDH/sb/YQeBbpD7FKPTQnlZQVJDFeQmNI5oD10SjsfJ1OPGUbiJ1NNTEz13tqZHEX8EsgG5zkGIOYFoNTLHP+KiRq2MFy128cCdE+fn5VNH+2WccBQI/RIEuyX2FXeNfhDT1u4hLHzc3N+pGaOPGjVBRUaG+94lv/7qQz8nJAYKZmRkUFRXpYkhZSxNT9v0MRQ01lOYX0nJx64JYRJNrolwhiJMnJSQCGRGxODxJMLDZ1TW/w7pre/ReOhtKmhr0Hh2mjqS++gmeU0ci+N5zag1Vli84AS/Py69nkc9FQYOzAJMkD0FORYUG1i1KkcwCvi7ZpWXQrGNNRoKMERqz1u+kr4mfOzhid1CMyEC74qRpiPTMQuhoC/o/1dFRQWFRKQqLhPs4njejK46f9cVjb86x8ldv4qg1/jdf9K9V2ouThiBbFQvFSo4C9rfR5zDq6zlU2U6UoEW5gs+MvCf+8IWhrK4iVh5ipT9i5SyBNGfW74amISeAKj/ktAnJ23FMX7x8Foocr0cCin2ZGsVrJWlbnBitNe/zIaPWsr4W5bS0Ia2sjLK05rXH/xXEsp74Ay7Pz6/XD4kFfnPykL5qO1vwmYYfOAhFvfr+eJNu30HC9etwXPw51O15rl/kRciJsrz8ehb5kuTJiYhCeUEhvFbxgrmSUyD5CYmIv/8IAw7uFFD458UlIC82DnYTRqMxSOBTrizhVw5wPmtYtnQ10sJvPZzw55voeoF2pzgYU3/9t2qOsIfnFGGXfyx29G6DrX7RSKs5xVOVEIXSI5vo33ukgM7TRqDdmP5Q0lRDsZB+KKsoDzlF4Rah4uRR0dKAirYmXIf2ou8VrJThMW4g7u/4m7rMkquxcueHjEM6FsbI9E0XUBRl55WiorKKBsPlR0dDERl1rMu4lJRVIr+wDI9eJ+LOc46l5augVPxz4z0mDbSrp9g3NVBFZ1dDfPmb6LggoiDWswQSXBGFvPKQsaSutZ4kmKoooaOeNla8CEBczX1vJ6ZR1zyjLYzEVuxn1Fjq6yjJIZXPslZHWQ6+ScLL19FUEw46KghdIripv32IM+a2N8VoCaxmGy1fcRm1bqyrVyXXMksa9t1P2glReIRnF+FH73BcG9eBuu95nZonoJw7OsQNUTlF1C+/MFVI5c4VQFEBDkgBho7WGPHjl7Sd5ySmCqQryeXIN1HzqqbkIehYmuDd5Tx66kVUvxMFscjXrmMdrVPjgkeYtb4oiMJ+fMf6PuN7Oupj96wO2HQ1qMFgvPwc/nY/wnw5cRlk5WXx85VfoaqphpKiElSUVdBrXApzCqCm1TyXVXUxsub4sM5ITG9QsU9OghFXcBV1xoWK/HzINmNeUBwbjcrCQkRu4HOvSU4aSoUj95UP7Df+wfnuRuDOO0lfiOU78UXGEjJfbQgyUq1r7wBHTVUsfhaADCF9SZw0DZFRI0+0leWRVsBrazrK8vCNF27d29FCC/Z6qgj+pp/A9T9Hu+KTjuYYc0T4Jn9wKqcfkU0BcRX7ZGwVJluIrG7M2pxYB+/r60oDb696Jig3BpnrQVlWBr/5RdYa/2x6HQmv8V0xwFwXFyLFm99nZhXB2kLQZZF2zSk08lljTBjhCL+AFIRHZTcrjcjykXmLovB5S2P118VIC5u7OWHbu+h6gXYn2RtTQwSu652InCLs9Y/Fn73a0HkO8S0vDhnZxdDWFDzZoKOlhMKichQWN1w+ooAe1tcG+08IDwpbl7DILFiaii8TiGU+cflUd01J3LqKc/p7kYsFRlga4AuvQETwuXci7n2IS7nj/dsLBJqu0lXHIHN9DL3+ol6QXXEhpyJ1NOvMs2rql9S1OG54Hr9KoHGKPgSZxeVC2yPZbGpsHk02+4kbvofxmTRmBpk7M8U+478CU+wzWg3iBriSBGLNn5qaKpF7nw/5XcJ+I1FiEDc85EWs+4lvf+KaJza2/jHx1atX1/rsJ6cBiCJ9d/hTyMpzFm8GDtaoKC2jPlz1bC1qlfJEKaXvwDuBwI84eYb/RL6TN50lQXUvfbUJw39eDj0bzgLQwNG63nY5+W3EYlJOVRXZ78Ng4NmOXiflznofBqPOHYWWiVjfKxsaSJSHUFFcjJKsbChoNsEyi4+ArHyMtDSk1hTcX+2hq4nkopIGFQwd9TSxoaMT9ofE4mxUUpPTNIbv2zh08RT0+929ozX83om2QOSeIql7jWt1I24aQoWUOQpkOf78fzjbjSo5ybM2tDVDfGAkDWLLJdY/DKZteKdq+JGRk5U4D6EgOw/xgRHoN2+cyDScslfVMyEjwW+J252iiPdQtuMEOa0qL6f+9XWG1A8K3RzKc7JRVVQE2Rpl8seImo018sLCYDKY55Im930o1PhOSrVEHtJ3swMDYTxwoMD1pDt3EHf5Mhw/XwRNZ2eBz+Rr5ERWHTmR3YCcECePeb/eMOsjqMj0+X4jtBzt4DB5fD0r/sTHT6GkqwMd58ZPhrxLz8NYOyMB2dLBUBNJBSUNLmK7GGnij55O2PkuFv+8ry83qoVaYnOukGCDXKRNraG4dg/9e7Z7ce1vMXSwRlJQhODvCgiDob2lSGtfcfIQ5WhqeB0lYCMmVVTWxyWjWlZQ0UF85AdGZKKjiwHO3eN9bxc3I6qsF8XrkLRaazP+7xBm5Da+vy1y8ktx94XkbkYSC4upks1NWx0BNa7E5KSk0EZTDccjm+a2hFtW+n/d66gvmxviTXIeKqqq0MlUE1feczYbjVQVYKGpBN8k4ZsDy26GYMUtXsBcEpD2/Re98OWtYFytuUdLQVzoEN/ZLrqqNNAtwdNAgyor3vAp6BuDHMfnICWgnDs61A1xeSX44n6wSDcZ0ot+ozX7iWdpbX4yRwq+/RQleQVQrHGnQ9o52cTUsxYeNLUpeQik3csrK9G4R5LyOiYLU7qQvsc7oNnVVhcJWUVIyxMdmLku9kZqSK+TvqeDHvbN6YgtN0Jw5In4bjJm//hpbfvlWs5atLGi/0cFRMLegxPrJDEiAcUFxbBw5nzWUqTEJNP/1RvxQ0+s6RXNLOi8QKsrbw5f9D4UyraCcUQkQaNDZ2h4CI5Fsdt/h5yODoynzYaUiCDhdSEBRolsaaujQeM/EeSkpah//b/ei5YtRLp/5+EAV201LHkWgGQh7nXESdMYbxNzqWzpbKGFK0Ec5a2RugIstJXhlyBcsb/8UiBWXg4ScJsW8k0/LLscgGs19xCGgz5n84d/A6Ex3mXkQUlWBs7aqjQYLqG9Hke2+GeIli26xCK/rysNuLvyaTBV0NYfd+uMJdWc65LIZr/AVEwZ0wZaGorIrnF50sXDhLrMCQxt2EUkUWD36mpOA+82J01D+JN5i22deYsBZ95CNmRF0cVQE1u6O2G3fyxOCpm3cOuKH269STS2BaaiY1sjgWud25vgbbDoeQGXIX2soagoiws3xTshYGuljYho8WPbEGv69zkFaKenjmuxvPIQH+9vG2h7hAVtLDDW2ghLnwYhOFtwE+tWfDruJgi6kdnRw5WuUze8Dq93Wk0S/EJSsWK2J3U/VFYTi6azuxGNzxApYqOOi5WJOjzbGGD+93fxoSDz6G7Ggq6UiQV+WHYhisWMpcSdKzTlADKD0VphwXMZrQZ9fX1q9U5eLcWiRYtw584dHD16tPYauf+GDRta7Dsk+S5hv3HLli3IyMioVfwTX/zED78wFBQUoK6uTl8kHXEZJK+kBOka35REMW/gZAOfo+dRkJGN/LRMvDx2EcZujtA251guEf6avhz+l++KnYfcn1hCc19cZRA5LcD9W91AF5ad2+H1yWv0PsTizO/MdVQUl8Csf28kPfFGRkAQDTIZef4ytYg17cNzlxN89B88X/dz7XuLwf0bzEOUse927kdeTBz9uzAlFf57DkJGQR6GXUQr/8XhSmwKlGSkqa9REgzLQ1cDIy0NcDqSd1yvva4GHo3oBis1jlsQkoYo7PcFx+J0pHCFvThpxOHg8edo62KKOVM6QUVZHmOGuaFvD3vsO/asNs208Z6Ie/s9nZgRbt4PwbRxnujY3gJysjJwdTbGZzO74vZDntJGnDQUMhMibpGkOO2Cq8TrMLoPQr1eI+z5O5QWleDF+bvISU5H+2G85/zo6GXs/ZRnvSZOnjc3vBD+IgAVZeXUt/7lTYehb2UC1/4891aXNh5CcngsTVOcV4gX5++hOCIcGp26CBad9LE+/ZH18B6KIsJRWVSItAtnABlpaHTuWpsu4cBuxG3bIvYzKc/JQfLfh1GSmEDbY2lSIpIO74esljZU3QVdjXxMGA8YgNzgYKR5e6OypASpjx/T4LhG/XlWdom3buHF4iUS5Un38UHGi5eoLC1FSUYGwvfvp9b0/GmS791DPFHqL/4cmm3aCC2f5eD+SOSTExE1csKMT7YEHf0H3nyypbE8dEOST96RFwfOdX7Is07yeQmTnpwNrsYgFmskyN7SdpZQlZNBRwNNjLM1xN+hPNnSwUADvlO6w0aDI1tImj96OmPnuxgc50vHz4P4TPQ21UEfUx2q8CF+Vxe6WVClRV2LOilpIsNlBPqu2/BeSIuIRcCNxygrLkHY41eI8wuC+4i+vHq88wx7xi9FZXm52Hlch/akysrgu960b2YnpMDv4l1YerrWWus/3HUCCTT4ein1P/7s8AVkxaegWrO++6rDl4IwvIcVDYiroiSLz8a6wMJIDX9f58mor2a1x6MDvE2/gxeDMKCzGXp5mFAf/W52upgy2AHXvAQDCJKqGNfXFhceRIodaJcfso6+GJuMsRbGVJmvKiuDBY5WdGPlTiJPCf5dWwf82kF4exZGUlEJovILMdvOHIZKCvT59jHShYeOFrxTxVcwEB+054NTsbKbFey0laGrLIef+9kjKrsID6J5AeRuTvfExv72tb+JlJ/74uq1yP8SuJAWC6LM903JxTedbKhLBmNVBXzV0RrPErOpJScX3xnd8Ikrxy3iUGs9zGxjQl1PkSCF9loqWN/VDtG5RVQZRSDBHY8MIUr9Yiy537Dva7pJXdM3uPMqy47uUNXVwtMDp1Gcm4/MmET4nb8Fx35dIK/CsWIszMzBgYlfINrnrdh5Aq49ROh9bxRl59G2H+H1CgHXH6LNkF4iN9Ma4uTzWCjJy2DVcGeoKcqiq50uJnexxKFHvOCvXWx1Eb5lBOwMOcpRknaAiyHUleToa15vGwx2MxZQ3pP77P2kI367EYxDjyULJEvmtGQ+S16181QTPTh1boPrBy4jMzkDuRk5uLLnIswdLWBZo/Qn/DJ1PW4duS72d4W8DMbd47foPclpgIi34bi27xIcPB2hZ9p4QGCdfgOR9/oV8t760XEr485NlGWkQasXT56lXjqH8HWrxC4TeY5Eec//4uxvcK6LC2mxZyKTMNHGmG4cqsnJUFeOpE/eiOcpC8lJ0D+7unC+m7ioam9fq7AX5jNfnDTikF1cjgv+yVje2xa2uirQVZHHj0OcEJ1ZhAfhPMX0jXmdsWGok3DZUtMviT6X20Xbm2rgm3521DqfbCq2M9HAhmFOdCPhTYL4LsiIMp8ErV/R3hoGyvJUXnzZzgo+ydmIzOXJFmJpP7PGVz6xAN7b1w0J+SVY6SV8M5C4j5GRkqL3IuO5mpwslre3pkHXX9QJ7tkQdx9HIzm1AOtX9oCWpiIcbLSxaHZ7nL0WioKa0xAGuioIfvIZBvYS3PwifvNLSipw877ovilOmoYgJw8UZaWxpC1n3kKU+mNsDPHPe8F5y4tJ3WGtzpm3kDRbejhjl3+MQDp+HiZkopeJ4LxlvqsFAoTMWxrir3MBcHPSw/SxbaCiJIeRA2zRq7MZjpzhuXmaNMIJIQ/mQV5OcJ42YZgjHj2PQ5oQ6/Jfvu6JLu1NqJ9+sjmydklX2Flq4fhF3oaUOJwIS8QAUz30MtahJzxm2JvQk3jnIjkbj4TPXSxxcbBn7fvPnM0x3sYIS70CEZQlXC9ClPf8Lwr/303k0v1IFJdUYN3CztBQlUdbRz18MrYN/r4ajIqamztaaSPk2mx0dDUUrM9B9kjJKMSjV81wddcIp94nw0hVEfNczaAiJ4NeptoYZqWPv0N47ayfmQ71p69fE0Pjmw426GqkSdsvmYOTz0m8qiuRH+eJagZDGMxin9FqID7oPTw8aIBZKysrTJ8+HQsWLGjWPYcMGYIDBw5QK3cSwJb43SdW9Vyr95ZEnO8S9huJkp644iGKemKFn5eXJ7A5ICn9VnwK7wOncW7pj3RRYebhgq6fThRIQ6zxuT7Zxc0jDj0XTYfP0XO4sPwX+h1a5sZot3wxtJ0cICMvh6BDx6hLDhUTznUS/FawTDxlCvGRT3xvi8pDgpEade2I0OOnkB8XT/1ua9nbouN330BBg2edlfTMB8GHjtW+f/3rn/Q3Wo0aBptRw4T+DnIMeaVPEL50tcEkGxMa/PCf8ESci0oWOMZLrH24TLczpX77FrWxoq/a78vIwYrnQWKnEYe3gYmYv/I01iwdgB+/GYrE5Fys+vEKHnjx/BmSkxKysjzF3ebt91BeUYkdG8fBQF8NGZmFuHU/BJu236vNI06ahnDs3p664rh/4AIKsnKhbaKPMWvnQc+Ct6lE2l1VZaVEeew6u+H+gfO4tuUvyCnKw6FbW/ScMQIyNcG2CO6Du1I//KkR8fQkgJ6lMUwXfgHVNpxAq/zoDByC6vIyJB7YjcqiIiiaW8Ds82U0UF4tpD0Si/8asu7fQdqlc7XvozdyXGIZTJgCrZ59qFW+ilMbpBw/QpX65FSAsoMTjOfMg4xi8wJnNsTWn+bg02n9ap9zRsgR+v/oWZtx3yug2ffXcHKi/vDjr1xB5NG/aKBbu08/hbodnxUjsX7me6bi5CHBc2NPn0HU8ePUBYJ227awnTMHMgo89xPxV6+hqrwCIdu2C5TJZPAgmI8ZQ/82q5ETgYeOUXc6qibGaN+IbBEnj7ikvn6LyuISmPQQ3EASBXGJs/hhEL72sMZ0R1N6TPtIcIKANRuxIuKXLZ+0MaXWhl+2s6YvLi9TcrDoISdI6NnwZBp88Mt2ltjc3ZEGBST+gMlxdnEwsLPEwBWfwOefK3h6+DxVSvZaMJkGuhWwcqf9Qvw8msb6GPbtAngfuYgn+0/TIKFWndxo8FwubQZ3x6tTN5AcEkkVq7pWphj901KcO1Y/aOCNZ7HUFc+6eR2gr62M6MQ8LPjlIcLjeEoUGWlpyBKH0TX4BKTgm23PsHZuB5gbqdHj4WfvhWPXaUHfzj3amcBITwVn7jQ9EN2pqAT6HNa3c6RKnvC8Qqz2DUYen69l8nx5VuXAOEtjzLPnncDa27Ut/X9XSBSukg0OshngF0LT7OjiThUDKUUl2B0ahfvJkgV++/ZBGL7vbYuLUzxoOX0ScjDrgr+AJSppe/zlE4ef+tphmptxrVV2yBLOJtmsi/7wihXf9cOXD4Kxrostro/zpO3sUXwmfnoueCqEKNK45XsUl0WV/ERxTzYDiPXoo/gs7H0YV6uIG2KtBxtNZVioK+H1TMETlR7HnjYa5FJWXg5D132Op/tP45/539KYRrY9PNFl9tjaNNRql/aNarHzkPd+527i9ekbKCkohIahHrrMHgen/rwNZsKpxd/T2BT03tXVdANBrhooH8JxqcUlNbcEc/b7YP0YV8ztZYPswjLsexCOo15RgvMCGenaswynfGLx5WAHbJzUllpMR6Tk47NDL3A3kGcxvbCfHZTkZbF6RBv64uIdnoFZ+wSDTYrL5K+n4dLO89g6/1eq0HXwcMCYLyYIbGhUVlbRU6xcjv9yFIFe/jV1XY1vBnPm1ou3L4OpvRls29ohJSoJh9bsQ3ZaFjT1tNC+Xwf0mSTo6kUUGh4dUFmQj5Rzp1CRmwMFfUOYfvY5FI1NRI4l+f5vEX9gt4BBANkh1O7dD4bjePETWoLj4QlQlJHGLx05soVYAS9/HoRcPncbpF+Q/kEwVVXCIDN9avl8oh9PYUhY+yoEz1KyxEojLutuhmD9QAdcmNMRCrIyeBGbhVkn/QRkC5HN3Lgn4uCflAdXI3Xsn9gWFlrKSMsvpcF2tz+JkjjywddPQ/CNpy0uDPOkeZ8kZmGzbx3ZQmQfn5sdKw1lmKkp4emEbgLpup99RuUGseT/8kkQFrpa4saojvS+4dmF+OJxIJIbCOBZF2KZ/8my6/jhqx7wujyDKuGv3gnHhu3evERkzSFLNh4F62/8cAdcvRNBFbGiECdNQ5DThCQg8Fce1pjmwJm3/BWSgFNhvHkLkf1k7OB24dnOnPXO0rbW9MXlVWoOFj/izFvORXDmLUvcLbGhqyONSUDmNdveijdv4eIfko6l6+9hxWcd8e2SrkhKK8B3W7zw2CdecM0mSwySePVnYaqODu5GmPf1TaH3PXEpGEs/8YSn2wC6WRMcnolpX1yhJywk4X5iBnUFtczdCrqKCogrKMKq5yGIyisSKB+375I2+ImTOe2X+3q7C9xrV2A0ToY33TBMHIj7wjlrb+P7z7vg+cmp9P25O2HY8Q9n45pAikrGkrr9Z3Q/W5y5FVbvlGRLEptfjMUPgvCVpzU1YMksLsM2v2hcjUqrN4/mPu6zYclY5G6BTT0c6SZSfH4J/nwTQ68zmgs79tBakKpubqQiBqMFIZP48PBwasFOAtVaWHBcwwgjPT0d0dHR6NiRZ51dUFCAt2/fokuXLgJW7+Xl5QgJCaHX7O3tBfzlZ2Vl0eC2JI+o+xIFfXx8PDw9eRPfpKQker1dO447B3G+S9RvJAr90NBQat1ka2tbL09D/Oov+XE34kOfWts34wwaUc7WtV4VRlyhePuH3MWSuMEqxYWrjBJqSSUtjbdp4td1XYjuiGsZQWpS2HqFzG24QlacNHWJWnu76eWTkaaL4w/JT+clc3PFVQxyrfc+JFfiGwmyKkF7pEOlkHZElNJN7Ud3PuEpBJoK191VXVriuU86Lt7GKlfh1OJ9l9S3sClKTd9Vl6v612SLqHs0dO8nUbL/mmxpCnPaNu4rVZgsFUfutwQ7VjdtwUUPFElJNWlhSfqSOPmsFtmiqUjVvPhbr6jn21TeB0vmIxt1ykK7tIjPifKB370Tr40KkUMilhiqmjLNKp+kdcO3zyOAKMvGuR1KmzQn4rqla0nIfK3u09h9pIgMok26H1G+ECWVuJCfI+zZEqWTqBXkn19K7kpI6O8m310jW8ncWdiSVdQJ14Y4Gt64X/tmzQtqxihx7lGX9EKpFpMt4rR7SftGgn/RvypbJEXHtv6GcFNli6R1Iw75OySz9uZHRkYKlXW+XNg1cfKJQm2J+KfJ/hfzlrw9wf96/UmCztI2/1rfrYu0CLeMXDIPNt1oQdyxRNLxhR+lqU13e9ZYe2wJ/GfwTgIzeJRWvvooq0NBpgP+azCLfUargkzgHRwc6Ksx9PT06IsfVVVVoT7uiaKcWMULg7iz4Sr1Rd3XwMCAvvgxNjamL0m+S9RvJAuTNiJcTXwIWkKp2tLKnZZWCtY9Cv0h4J8wcI4VN5xenDQtyYdW6jcFqvhA66due6TKmn9JoSkJRLHQkouSpkDr5gM4qmwpmdAS9xF1jw8ltySVLf8GH1KWtiScfaamVdiHtDLjImzh/S98rdg0VhZhijeuW41/g6bU1b/Rfz7UhpfQ+VoTlfoESZUu5LH+W8+2od/9IeJw/S/mBR9qzBAmW8Rp9//m2NIU2fJvUrd8rWHc5UfYXE+c+d+/NUdsjfOWlqi/f4Om9N26/NsrPmFjSVOV+h+CVvJoGYx/DabYZ7RaiM96f3/Bo/JcZs2ahXnz5v3rZWIwGAwGg8FgMBgMBoPBYDAYjP81TLHPaLUsXLgQubnCgyOZm5v/6+VhMBgMBoPBYDAYDAaDwWAw/j/T0u4HGU2HKfYZrRZX1/pBLxkMBoPBYDAYDAaDwWAwGAwG4/87/3uHhQwGg8FgMBgMBoPBYDAYDAaDwWAwxIZZ7DMYDAaDwWAwGAwGg8FgMBgMBkMMmCue1gKz2GcwGAwGg8FgMBgMBoPBYDAYDAbjI4Ip9hkMBoPBYDAYDAaDwWAwGAwGg8H4iGCKfQaDwWAwGAwGg8FgMBgMBoPBYDA+IpiPfQaDwWAwGAwGg8FgMBgMBoPBYDSKFLMTbzUwi30Gg8FgMBgMBoPBYDAYDAaDwWAwPiKYYp/BYDAYDAaDwWAwGAwGg8FgMBiMjwjmiofB+Mi5naiM1sxa91y0Zpa7FKA1M+abgWjNqMgWorXyW4fW3faif1mE1szXbq27/nLLpNCaiS9UR2vGVr0SrRnT+bZorXzbyse1LTJqaM0scc5Ha6a8qnXLFschOmjdtO7nK92KH++3Hq277o6qq6A1s6qVz1vW6LigNdPqxzY9Z7Rm5ju23v57VLv1zqkIl2ftRqtmRo//dQlaKa14QP1/BrPYZzAYDAaDwWAwGAwGg8FgMBgMBuMjgin2GQwGg8FgMBgMBoPBYDAYDAaDwfiIYIp9BoPBYDAYDAaDwWAwGAwGg8FgMD4imI99BoPBYDAYDAaDwWAwGAwGg8FgNIoU87HfamAW+wwGg8FgMBgMBoPBYDAYDAaDwWB8RDDFPoPBYDAYDAaDwWAwGAwGg8FgMBgfEUyxz2AwGAwGg8FgMBgMBoPBYDAYDMZHBPOxz2AwGAwGg8FgMBgMBoPBYDAYjEaRkpJitdRKYBb7DIYYhISEYObMmejZsye2bt2Ka9euYcKECS1ed15eXhg4cCB7JgwGg8FgMBgMBoPBYDAYDAZDJMxin8EQg9GjR6Nfv374+eefYWpqigcPHuDVq1ctXneZmZnw9vZukXupycnicycrdNLTou+907KwOyQahRWVIvNYqCphuJkh+hvroaiiEtMev66XxlVLHbPtzGGtpoyyqmo8Sk7HwbBYlFdVN7msDy88wYPzT5CflQ9jayOMXzQa1m0sRaYvLijGi7u+eHLFG6lxaVj6+0LYt7UVSJOfU4DLB68j+FUoTW/lbIEJi8fCyMJAorJVVlTi2N7buH/dF0VFpWjjbomFX42BsZmuyDyR7xNx+sh9BL6JRnV1NeycTDFz4WDYOpoKTf/zV3/h+ZMgfL5qLIaO7SxWuboba2FJW0tYqCshqaAUBwLjcDMmXWR6czVFzHY2RWcjLSjLyuB9dgH2+MfhbXpebRo5aSnMdTHDUEs96CkpIDqvCDvexuB5cg6aSl5mLm7uPY+ot2GQkZNBm+7tMHDuSMgpyIvMkxQRD98bzxD42A86JvqYv32lwOekTkOevYPP5SdIjUmCoooSbD2d0G/mMCirq0hUvrioFOz//RLCguKgoqqIASM7YfKnAyAtLXzfu6KiEvevvcKtiz5IjEuDprYauvdzx5RPB0JOnjekBryOwI3z3njpFYSOPdpg1YaZaA4e+upY7mENGw1lpBeX4VhwAs6Gp4hMryQrjSkOxhhmpQ8jFQWkFJXiUkQqjoUkoiV5dv8dTh+8g7SkLBia6mDKZ4PRqZdLg/V98dgDWj/l5ZWwcTTFtAVD6P9cSopLcebQXXg/8EdudgHMrQ0xa/FwOLezlrh8d8954c7ZJ8jLzoeptRGmLB4FWxfRsiXMPwq3Tj1CRGAMpGWkYe9mjfHzh0HfWEeiNA1RHBuDlHOnUJIQD1k1NWj37AOd/oNEpiftvTA4CFlej1AYEgjNrj1gNGlag/eP2forZFRUYP/Lb2gqJUUluLznEgK9A1BVWQXHDo4Y8/k4qGqqisyTnpiO59e88erOSyo7N1zeJPB5xLsI7P1qt9C88zZ8BgdPx0bLNcRUH1NsTKGnKI+4gmLsDY3Bm8xckemVZKTRz1gPoywMYa2mgnWvQ+l4yI+qrAw+cTBHV31tqMvLISy3ADuDoxGRV4iWgjzHG8fvwevacxTmFcHCwQyTl4yBqY2xyDzBvu9x7+xjRIfEQV5RDo7t7DBm3jBo6mo0qQyyUlKYbWeFnoZ6kJeWRkB2DvaFRiKjtExkHj1FBQw0McQAEwNoyslj/INnqKgWHO//6NQW1qqC7eJuUgp2hUSgqbx59BY3j91GZkoWdI11MGzOELh1cxWZvrysnOZ5esUbsaFxmLxiIroM6SSQpqK8AjeO3qLpiExQ11aHR992GDJrEGRkZCQqn9fFJ3hy4THys/NhZGWEUQtHw9LZSmR6Mg/xveeL59eeIS0uDQt+WwRbd8F5C7cP3Th0HRFvwyGnIIcuw7qi75R+YpXPTl0V8x2sYK2mipyyclyLT8aF2IZlvoeOJoaZGcFTVwu3ElKwOzSqXho5KSlMtjZDHyM9qMrJ4W1mDva9j0JmA+2mMdlybd8lBNXIFntPR4xa1LBsyUhMh891b7y+y5EtP14UlC2EjTN+QG66oCzoPakfBs8ZJla5sr2fIv3OTZTnZEPB0AiGY8ZD1cFJZPrKkhLk+r5A1pNHKElKhPlni6Du1lZkenLv1CsXodmpK0xnzEZTSYlJwfldFxAbEgslVSV0HtIJg2YMEjlvIYS/Dcezq88Q6B0El65tMHud4PdfO3Qd90/dr5ePzNs2X93UaPurKi9H0sXzyPZ9haqycqg5OMB00hTIa2uLzFOWlYmMp17IfPYUFfn5cN+2E9JycrWfV1dVIcfvNTIeP0JxYgJklFWg4eYOoxEjIKOkjKbi8+Adzh26g/TkLBiY6GDivMHw7Cl63hIflYIrxx8gyC8CFWWVsHI0xeT5Q2DlwJu3XD/5GP/sviaQj8wJ/7q/UawyGSgpYKGjDV1jlVRW4WFyGo6Ex6Kyjqzlx1FDDcPMDNHdQBehOflY/TpQ4PMxFsaYa1dfJlWjGtMev0ReeQUkhfS9i4duwvu2L0qKSmHraoVpS8fAwFRP5Lj39mkQ7l3wQlxYAhRVFOHayQlj5g6GGl9/z8nIw5PrPvC69gLZGbn4/fx6aGiriV2u/EB/pF25iLL0VMhp60Bv8HBodBCU/wLlqqxA3ls/ZHs9QlFkJAxGj4NOP9GGdXlvXiPh8H4o29jA8suv0VRyM3JxfucFhPm9h4ycLNr1aouR80dCvoE1UXxYPJ5d84bfQz/om+pj5Z4VAp/73PTB6T/OCM279q810DUWvV7lPqPUmzeQ8fQJKgsLoWxhCdOJk6FkKnzNSqgoKECm9zNkej1BaWYGHL/9DkrGJgJp8oKDkX7/LgpjYiAtL09lgvHosZDT1MSHwsxYB3Om9MXsyX2gp6MOHcfZKCuTvJ0zGB8jzGKfwWiE3NxchIWFYf78+dRi39raGiNGjMC5c+dadd2ta+sAMxUlLH7ujy98AmCvroo17vYN5lnuYovkohLcSEiFjLSUUMX/5g7OCMjOw8wnflj5MhAuWupY6mzT5HJ633yBi/uvYdzCUfjxn29h3cYK27/ai6y0bJF57px+iOTYVIxbOBJVVVVCJyl7vz2EpOhkfPHbQvx0Yh3M7c3x57JdKMwvkqh8R3ffxN2rr7Bm00zsP/s1FBTlsebzfSgtKReZ59Th++g7xAN7T6/E9mNfUuXv6oX7kJ9X/7uvnnmGwsISyMhI00WMODhqq+DPXs64Hp2GIRdf4e/QBPzc1QGdDUVPlha3tYR/Rj4+veuP0Vd98T67EHv7ucBKXak2zYr21hhrY4jvnodjwIUXOBGaRL/HSVv0YrshyLM58f1+lBQWY8HOrzDjp4UI9w3GtV1nReeprMKVbadgZGsG194eQp9venwq3r8MwoBPRmD5X99j8rq5SAiNwfnfjklUvqKCEqxbvA86ehrYc3YVln0/FVfPeOHskfqLWy5vX4YhPjoVS9ZOwNFr32Hpukl4cMMXh7ZdqU2TEJuGEwfuoHMvF7h62KKqGZteBFNVRezu54LnydkYeukV3WxZ1cEGgy2FL6QIQy31ISMlhRVPQjDgwktsexODhe7mmO4oWokoKYF+kdi67jiGTuiGfZfWot/wjvht9V94HxArMs/fu67DvZMDNh1cgm3/rISegSa++3wP0pJ5ita9m87j+UN/rPxlBg5eWYc+wzzx45cHkBibJlH5vK6/xNm91zD585H49dRaqtDfsnwfMlOzRba9c/uuo/uQjvj5r6+wdvcXKC0pw69f7qELWHHTNER5bg5id/wOJXML2P2wAYYTpiDt+mWqtBdFcXQUMh/ehWbnrlA0Mycdq0FFU+LRA1C2tUN1pehNXHE4+esJxATH4PPfF2PZ7hXISsnCke8PN5jn/LazVDnXc0wvWld1sXGzweabvwm8eozpQZVTNm71lZx16W6gjeWuNvgrPA6TH/rieVoWNnVwgrkKT47VZYylERw1VbEjOJqOa0KGNqxr5wA3bQ2s9g3BxPuv8DI9G1s7u0Bbgadkai5k3Lpz6gFmfjUJPx1fDT1jHWxdsUfkmJSXlY/bpx6g79ge+PHYN1j++0Kq5P7zq31UudIU5jvaoLO+Dn54E4QlPn6Qk5bG9+1dhNYJl0/traly6Xx0Aq0/Yaeuiaw5FhGDsQ+e1r52N0OpH/4uAkd/+Rs9R3fH9yfWofPgTjj0/VFEB8eIzPPi9iuE+r7H6AUj6IZbtRC5S5T6z2++wIzV07Dh/I+YunISvC4/w51/7klUvpe3XuDawasYuWAU1v69DpZtrLBv1V5kNzBveXjmAdLiUjHis1GccU2Iwi4rNQvbv/iTbuIs37sSK/Z+RdPGhYiWqVy05OWwwcMF4XmF+OSpL/aGRmKGrTmGmho2qBgcbWGCe0lpiMgrgLSII/Vr3B3Rw1AXvwaEYcaTl7iXlIr+xvpoKme2nEBscAzm/7YYX+xcgezULBz7sWHZcnE7R7Z0Hy1cthDI9XFfTsQv13+rfQ2cNUSsMhElX9LJv6E/bCQcftoMNRc3xO7ejtKUZJF5sh4/oBupRhOmcORyA2N9UXQUsrweQ9HImGis0VRKCkuw6+vd0NDRwNqjazB91TQ8ueiFuydEt+HU+FTcOnYLrt1cYdvWFlWV9cs5dM4QbLn1m8BL30wfbTo5i7WplHDmFHLfvoXN51/Aad16quiP3LmtwXEo4cxpSMnIwGDQEKHjWlFMNHLevoHRyFFw/mkDrObNR35wEGIOH0JTCX4TiR3rj2PQ+G7YcX4teg/viK1r/0J4oOg+dnLPdbh1dMCP+5bg1+MroWugiZ+W7KEbA1yqqqtgZm2Ivx9uqn0dufOL2BuuP7d3oXJ2/jM//PAmGL0N9fCpvWWDxltkE88/KxfP0zKF9t2LsUkYef+ZwCs4Jw9BOXlNUuoTLhy4gac3X2HRD7Pwy9+r6Jro9+X7UCZiky8pNhVPb77E8On9sfHkGizdOJduVO/89ohAurP7rqGirAIjZg3g9O8GNjTqUhwXi/j9u6DRsTPsftwMnb4DkHjsEApCgkTmyfN7jfw3ftAbMoIaQZB1oyjKMjOpMYaytQ2qRcgecSCyfP/aAyguLMZX+7/Cws0LEPwyBGf/bHhNdOr30zCzM4VHXw9UCelPnQZ3qtd3nTs5wcjKsFGlPiHt7h2k3b0N8xmz4Pzjz5DX1UXEtj9QUSjasCH56hVU5OXBaPQYTt+tU33leXn0nnp9+8H5hx9h++UyWo8R2/9s9ty0IX5bPxPlFZXYsvsyZGVlmJuYf02d/DG+/nv8N38Vg9FCEOt5rmucOXPmoHv37vQ1btw4bN68uTbdnTt3MGrUKPj6+mLevHkYPHgw1qxZg4KCgto0wcHBtfn79++PhQsXIjQ09IM8Kzt1FXjoamJnSBQSi0oQX1iM3aHR6KyvTZXzoljqE4ALscnIFzHh62moi9yyChwNj6NpyH0PhcVioKl+kxUgd08/RNchndC2uyvUtdWosp5YdDy5LPrkwqi5QzHly/EwtjIS+nlGUiaigmIwet5wGJrrQ1VDBaM+HQpIgS7qxYUo74niffIn/eDsbgldfQ18sWY8MlJz4XXvnch8azfPROdebaCuqQI9Q01MnN0XBfnFiItKFUgXHZ5ELftXfD8ZkjDd0QQhWQXU+jq7tBwXI1LxLCmLWuSL4muvUFyKTEVSYSlySivwh180iisq0duUZ2U81EoPx0MTqRV/QXklrkWn4WVKDmY5C1phiAux0k+OTMDwxROhZaADIxtT9J81HO8evEJ+Fu+kAD9EIbNgx1foMLQb5JUUhKbRNzfEmOXTYOZkBQVlRXrfdgM6IS44WqLyPbz1GoUFJVj4zTiq3HfztMXoqb1x+eQTkYozz65O+HTZKFjbm0BZRRFt2lqjS29XhLzjfbephT427l2EXoPa04llc5nsYIT0ojLsfBuLrJJy3InNwI3oNMxp4Hmfj0jBgcB4xOQVo7C8Eo8TsuCXloe2+upoKa6ceAS3DnYYPLYrNLRUMWJKT9i7WODKycci86z9fS56D/GAroEWNHXU8NlXY6kV7Zvn72tPRDy7/xbjZvWDrZMZVNSU6P2t7I1x9dQTicp38+RD9BjWER49XanlF1HwK6oo4OElb5Ftb82uJWjfwwXqWmrQM9LGjGVjkZGchajgWLHTNETOMy9IycrCYNwkyKprQM3VHVrdeiLz7i2Rechi0mLxMqi3bQ9INTxtSz55DGru7aBi74DmkJmUgYCn/hg5fxQMLY3o4nDskvGIDoxCdJDofrbg10XoO6kfVDRURPriJEoi7ossBl/ffw3PAZ6Q5TvxIorJNiZ4lJyB+0kZNWNRPFKKSzFWxFhAOBGZiC0BkQjP5Y3H/KjLyaKTvhaOhMUhOr8IBRWVNE92aRlGW4i+r6QL+runH6Hf+F5w9nSApo4Gpi4dh7LScnjffCm8XNpqWLZlIVw6OVHLRgMzfUxYNIpuWCfFiD6tIwpVWVn0NzbA8YgYROYXIL2klCrfLVRV4Kkr2qp2o38ITkXFNWqdTaxAiV6T+2rOduaDM4/g0N4e3Ud2o7+9z/hesHS2wMOzojfAuo/oiplrpsPaRfTJnriweDh6OsDaxQoKSgqwa2tLX3GhcRKVjyjpOw7uBNdublDTUqP9hIxF3leficwz9JNhGPfFeGrdL4obh6/T+01aOQVa+lq0Hw2aORhWDfwmLkNMDVFOFEfvo6i1/suMbNxMSMF4S9HjRGhuPtb5BcE7LRNVIhRbHXW16GbQxnfvafrSyip679PRCWgKmckZCHzqj+GfcWSLjrEuRn8+HjGBUYhpQLbM27wIvSf2g7II2cJFSlpQxjRkxc5P+t3bUG/vCc0OnehJKoPhoyCvrYPMh6I3+vUGDYXJtFmcDdcGqCwuQvyRAzCZPgvSyk23NCe8uudLlfsTv5wADV0N2LWzQ58JvfHo3CNUilCYGZgZYMkfS6hiUNSchNQTf70RK+GU2BR0Gdal0TIRBWDms2cwGjUayhYWkNfRgdnU6ShJSkJeYIDIfNYLFsFo2AjIaQg3SlGxtoHVp59B1c4esioq9N6Gw0fQe5JN7KZw/eQjuHjaYcCYrlDXUsXQST1h62KB66dEz1u+/m0uegyumbdoq+GTFWNRXlaBdy848xZ+ZGRlal9kviAOXfR1YKSsiB3BEfT0VFheAf6JisdQUyMoidhUIWuwZS/9cTcpjfZJUfDLZANFRbTRUsetBMG1iLgQ5f39i88wYkZ/aqmvpaeJWSsnIDs9B68eCl8TmVgaYsmGT+DkYQdVdRV6Qm3cZ0MR7h9N83GZt3Yqxs4bCh1D0WORKLIe3oOSmQV0+w+ifZfMqVSdXZB577bIPMSa3/TTBVBp4EQOgSihE4/sh97QEZDXb/pmJiHMLwwJEQm07+oY6sDU1hTD5w6jfTqvgTXRV/tWotuIbnTMEmdeVZhbRDcMyGmvxiAGZWn37kCvb3+oOznTvmg2ZRqqysqQ9Vz0eGY2ZSpMxk+Agog6kVNXh+3SZVBv4wJZVTUoGhjCZPxEerKpODkJH4rJ87di47YLSBZhwMNg/Jdhin0GowGcnJywdu1a+veyZcuwadMm+iLKeX5XPGlpabhx4wY+/fRT6rJnwYIFuHjxIrXy52JmZlabf9WqVVBRUYGHh8f/sXcmcDaW7R+/kSVkiySyRrYIFSElUqmUIiQiCtEmkbIkWUJJRKRUWizZSqRQqLwla8q+b9nX7Hnez/fSM545c2Yc27nvMdf3/zn/mTlnMtf7PM+9XcvvMmvXxp+BdrawccNpu2TPKUfGwl17zb8nPFMsU4ZzmjA4vAdhs0i23tn8u2Qq/r1uaywZHTYo115/jVmdwAHvdPiZFxzwgv9usuTJzco/Iv93Vy/fJM79kjecsg9nff5CV5kliyK7b2TpExy4MuflpsC1p7KlDx8+anq0/9Q0b/OABAzOhOuzZTBztsYuN//17z2mRLbIy1bTpEhuUqdIHkuaiYyf0MM9iV2lsp2d7MP6v1abDFkzmcuvOpVZnu/6QpJJSYb9+YB7vWvLDvPHjHmmSPkSZ/Tf4owvVOxqkybNqRLYkjcWlHtG1n0kzro1yzebOT/9ZW6ufGZ/+0wodQX3e0+c+31tlnRyH09HimRI+WQ0JbJeZqav33ne7FqycK0pXiZ2ljWO/mURjg04+M8R8+/xE+bSdKlj7ifPR2gzJg43weBJJHMLmWKFS8WeW4qUusasWBy5fQf2nsymTpM29Tn9js/B1StN2msKyVzkw8Hy2M4d5vje+OVkIpWROLr1b3PFfQ+Yc8V33pPd6XN1oasl6JqQ8+1M+XP2YnNgzwFTtvrNEWU1Fs6YPo7szrwde03xTJHPfaH4j1q4ue+6LOcnELZ9806Rfrk28Dwis3JN8Xxm1eLIr+eB/6SBInnWQrk242XmkuTJzaLdp67ftsNHzOaDh0yRjOf+v7NW3qvN6NvLm/fKlzGNC+aLaG6KD9Z/HO5Bri1VyKz589zWjVK3ljTL568wG1duEifo2iXrzKo/VpvSlUtF/G8c3H/QbF2/1VxTsmCsuYWxci5jg/WE8VCqcumIndFBimbKYBbv3hdrh4ZkDg5DsvnPlvJXXG7W7P/HrDlwfmSp/GuUPyBDlOu/uWXdGQbnwzFxyFemQ422pk+T7ua7TyaLRNPpOHH8uGTepysUWwosXeEi5uCaVeds06bPPjEZry+doKzPmczNua/NbVIF9i2FShX677k8s6q2hPjf5F9NliuzmEKlE670hX/WrCa12KQPBJRTZ81qUmXLZg6sPvfrFyoBQpZ/8pRnpybM/qRo6dhzS/EyBc3yPyKfWw4dPCKZ1KHz8OZ120yTOzuYZve+at5oM9SsXxV/tUfo2F1/4KAE5IJjN1WK5OaaDGdXMRuOajmzmwPHjpuft+04q/9+/YrN5ujho6Zw6VNzH4lTua65yqw6o33VPzJnxueoPlNkXxWSzMC+6tB5GLvbJk4wl2TIIMGCc2X14jUmU7ZMJlvOU2cixhd7XqojzxdIIRJYuqFKmdP+7pHt2yXzPv21p64fcljpClxj/ll1nsfuPyd9EilSpzmv/66iKCdRjX1FSYDMmTObm266Sb4vVaqUKV78pAZjuEz748ePmzFjxpgCBU7K0nBobNCgQcznl112mQQEfO644w75d4YOHSra/eeTy1OnNPuOHYvjgN9//LjJfA7SAugS1y9wtambP6cZt3aLyZgqpWlU8GpxiJxNxv6+nSczFC7LHHvjmj5TOrNu2YazthM93pz5c5ivP5xsGrWvLxvPqaN/lL+397+/GQm7duyXrxlDpGgyZk5ndu88+Vl8TBgxywzp+7Vs/q+4MpN59e0mJk1gEzvwjXGm8HV5TMUqZ+4QznppKrPrcOzsyd2Hj5l0KS8RbfVDx09fKoo0D4GeqetPbfCnb9hpHimc0/y+da9ZtfegqZQzi6lwVeZ4y/NPx4Fd+0y6EM1cNPBxWhzYHfl9iI9POw82K+cukU1x/usLmXtbPnxG//3uHfsl0zz03spnO/ebPAXiz6xsXrun2bx+hziib7/nBlPn8armQsH9/l9InwPuN/clS5qUUoURH/PqV4yR2Xhv0XozKYE+DGcCmfUH9h2Mc/0IfO3elfDYCPJRv6/kvylT4aTDI2XKS+T7cZ/+YAoVy22uzJXV/Dx1gVm6cI1k70cKeq1iT+bYDl+yf9csiyzTVEqg351grr7mKpP32qvP+neCHN+7x6TNFttRkiL9SRuP79trLsl4dkE05CK2TfjS5H2+nUmW4ty3dmSPpUmbxqQMcQgihYGm+Pni18m/mnzF8pkr88QvF+LDeoNjmiqlIDhDsiSgT3s6yPz/Y9c+07Dg1WbdgUNmx+Gj5t7c2aW6LcV5ktj3153Q55G1jmqPSDh25JgZO2SiBAeyRVBeH0rm/67RvhBHJz/7n50tf+7ea37ausOsO/CP6Lu3KlrQ5Emf1rw6P34phPigWurgvoOx9Jf9Zw95onOhwr3lzc4tu8wbT/aRn3Euoa9/4x03RPxv+JmVcfYtGdObjcvPft9CgOvIwSMS8Hm3dX+zccVGk+HyDObGajeZynVuP60cCmNg88HYQa+9/+0Dub+7I3BwhyNH2kvNpoOHRPbj9hxXSFUAcowfLF+TYG+G+Ngfz9ySLuO5zy3X3ljE3HRXOZPt6itE6mfM2yOl+qjeS6f24uH4l+raE/9Ktm8QMk2Zl8+FXT/NMEe3bTW5GjU15wP2sXH3zCd/lkrIBCpCIuXIoSOi512lTpWIgkx+UDrs9TvHgHWovAda4FnK3nxW61y8+5bM6cyeM5hbhr/zlbksUzpTuvypQM1lGdKZJm0eMqXKFxHH/5dDp5hOzfqbXsPbmCtyJJyFzvlpb8j49Mfu+ZKD4y4inzV9y7az7onmr2Ohzx9z9d4Ir9/BA4fM+A+nmDK3ljBpE6ggPxN4xsI9eyeOHDEnDh82ydOcnSP5wNIlZu9vs03+9p3Oi52sHaF9RNL9dyYi8H+++N+3v4p2PxKHp8Of31JeFju4z/U8uuPsAkDx9+AYKwGE1NnilxFVlIuFjz/+2AwbNkwkvcuVK2dee+01k+00z/7GjRtFEWT27Nnyuy+88IKofESKZuwrynkiS5YsMU59yJ07tzl06JAMaJ+JEyeahg0bSlY/Tv558+aZVWcQET9y5IjZt29frNeJY5EfrHBCnp2L9iRot746f6mpnCOrGV+1rBlwcwkzZdO2OI30zpVkyZLHqQw4E9gktejW1KTLkNZ0e6KPaXN/B7Ntw3ZTqlKJsPrAZ25fstPKP9aoU9FM+LmH+eirl831NxUy7Vu8Z3b952xExmfx/NWmxYvnnlnr47vyk0Vwhx8ulMPULZTDvPTzMpF28ek5Z5WZtWmXGXB7cTO7bnlpvvrpkk0Jak+eLefjX3yk8xPmlbG9zZNvv2AOHzhkvnjt/XP+N/1M8dP9b353RFsz6sduptvAFqI1P7DnGBNNYu73aW73jZ//ZCqMnG2en7HENCiS86xllUKJ7/qcSabplx9NNT9NXWDadGto0gUOIK061DWFr8trOj41yDxS+WUz87v55u5aFc6LVqZU8UTwPPO/76Peo82mNVtNyy4Nw5bUR/I7Edt0DmOCUvGNHw422e65Xxo+XkhOzn3nZz7Ys2OPWfb7UlMuAqmHhJBM+3N8NDrPW2o2/XPYDKpQwkysVtaUzJLBTNm47bzMUwmRXO69F5Gze+jrw8Xh/fjL8TdOjoTQv3biPPyvRP4F7WYqwP7YvdcM+GuFKZM1i8mb/sxlR+J7vs7H+KcZ76/fzTHP9G1pek/sYVr2bmFmjv/JTBv1wzn/24zjcxkbfk+A7z6ZYirXqWI6j+gi8lfTR04z076Yenb/5n/mnMuV47IjFYIzsOnPc82Lc/6QIEKX0sXO6yHyXK8f1Hq+rsldJK+5NH1aU/imoua+5jXN/Olzzd4EmmsnbNS52XRk699m64SxJlfjJ0zySy5cLt2pfcv5+ffmz1hgjh0+ZsreedMZ2hHervMB0jurBw0wKTNlNDlrn1kSRwzxzi2RP8njPp5qZk9bYJ7t2jCWYxqtfl7I/pGQ0KJDXZM+Q1rz/difz6epZ82N2bKYrGlSm283nZ0MT0Ikj3CcEJx+t8NH5pKUKUzDNrXOux0hRsmXs72M//5zwGz+ZKi56tFGEiS44JynG75q0SqzbcO2iCS0EkLG7nmyiT3q2g/el8a8eRo1OS//puIOyRLp/11I3nvvPVHvwOfXr18/s2TJEvH9HQtJug2CPxAlD1RA3nnnHZH0fuutt8TZHymasa8o54mUKVMm6CAkaoecT6dOncwjjzxi0qdPb3r37i3O/0jp0aOH6dKlS6z38j7S2OR/NPZCSUZjhhB72Lby3tlmbQWz9nn55EybxqRKnlya7ibEjPE/mVH9x8X8/HTv5pLlCgf2xE6L3L9nf5zMxjMFncZmrz0e6703WvQ12XKGz3R8r8948/XoU9rbH3/9isn0X6b+3t0HTKZAhsqe3QdMgWsTdpBy/9EyzX5VFvPMK7XMw7cvMjOmLDA161cyf8xdZf7etMvUqtwxVubvwF7jzPgvZpn3x7RL8N/GGZ85Tez7S1bPwWP/moOnaar44DVXmhfL5Ddtf1pqft4cW4OQ/7bHnFXy8ml/YwFxeJ2O/k92M7s2n8zuuCJvDtNiQFuTLtNl5mCIrvWh/QdFciD9OUhnBJ3IyVMlNzkL5TZ3PVnTfNj2HbN9/d8mW+642b/33/xizPdlKxUzL7/RyGS6PL3ZszO2fdxrQEc1IWh2nOLS1KLLX69pNdO/2yjzROv7Tarz2GzTZ+ehMPc7TUpxaAYDM+FATgSN/R827DQjlm2WYM3Hf206o7+/4H/LTNfWQ2N+bvbig6ZazZslg35fQO4L9u6OWwURjvGf/mBGf/i9eblPE1O0VGz96MsypjWtOtSJ9V7fTp+a7DlP9YMIgubrZ/1OzS1t3mxmchc8ObfsD7Fv3+4Dol2eEMzZn7w5xsz/abFp985T0qfjbH4nHJR0/7s/dmbW8f9+viQkYypS0G8+smmj+Xv0F/L6z0B5/fX0kyZnwyaiJxsf7788RBzsYkOqS0zPib1E5/vwwcPSzC6ofU9WMZ+dD+Z8+5tJdWkqU7LS9RH9PhmNVBllCsn0pQotNIv/TNl15Jh5fcHyWO/1uqmo2XyadS0cEz+eYiZ+/F3Mz68MaR2znvE85siTPfbzeJrrydrwQbfPzNplG8yL/VqJrvHZsOfo0ZjKh12BTOtMqVKZJXvOvYIqCNItcFXaS83aA/E3rF8yZ6l5r/2pgOzDzz0kWfVkGh4IWTvYF6SPYG5JiB++nGHuqFfFFPxPBuba0gXNLTUqmOmjfjBVHq4c5/d/nvCTGffu2Jifm73R3FxVIGfMWAhyrmMDPX2Cg6VvL22Kli160r4y15qb7ixrFvw431R79M4E//vdR4/KvQ3i/3wu+z6elX+OHTcfrlgbo+394Yo15u2y15ur06c16xK4vx92GGKWB+aW17/qJWt/uLnln/M4t/jkyH9yHdixabs0m42PFOnTs6E4mbkfMjef7bwMh9auEWfWym6vnnrzxAlzcOUKs+e3/5nCPd8yl/C34+H5O1rHfF+8QnHT5NXHJVt6/3/7lODYgPN1/f43+X/SfBPZkEjXNTi+/4BJmSlTrOuHpMf5cOqv6t/PeMf/Ndc819qkiCADG/175HB80MWv+kD4fcu+CPctX3/+gxk77HvR3C9yfcJ9L5BCyZUvu/l7w+mznqk6uzpd7CCov84F5XnOVYaH4GtC4zXI5/3GmenjTwUleo/qELN3Yh3LEMg853rmLpjztE79/q98aHbv2Gva9XtK9PbPFzx/PHtBePaSpUptkqc+O7mfI9u2SiXA+oH9Tr0Z2Fflbf2SSZsv/megW6PuZsemk/eeJrZth7SV8Rm6rhGs50x0Psdu9tzZE+w1E8Sf344fYA45lRhyjLnvv3F9LqDhv3bYB+bgunWm4AttTKrMmc/531QUlzlx4oT46tq2bWsef/ykH2rEiBEmZ86c5ssvvzT16tUL+989++yzJn/+/OaLL76ISZK75ZZb4u2dEw517CtKlBg9erRo7rdufWqjjpM/wxksnO3bt4/138P9P86N83t/7tlvLr0khbk2Y3qz7L9NRIksGU2K5MnMn+ex3A/I3uegt+g00iqV7q9gKt53KoOASQvn9xVXZzMrFq6UbHrfYbZiwSpzY9XS59XO7Zt2mHXLN5iqD98W9vMnW9cwTzx3X6xNefoMl5rUqVOaP+atNnnyn3QWo7++ZvkWc1/tCpH/cc+Tid4P8jR/8QHT7IX7Y/1KzUqvmCefv8/c/eDpsyxobotmepCbrsxkFv1XERAfNa/JLo76l35aKrI7pyNV8mTm9qsvN9+sOb1ua8tB7WOqLPxIeO4i+czMEd+Z3X/vNJmvPOmUXbNwudz3XIXzmvMJ1xfiyy8ZO6tnnAxpssI/GThJGlj6DvlFv6+Ug9/V+U45307HvzQui6CK42xZuH2fSCKF3u/lu/+JSHYpmFV1Ngl0JcsWMqNmnrp+fmY61+/PeatNzQa3x3zG9eP9hJjw2Y/miyHfmva9Hjclbzq9fu/BA4fN3F+WmgceDT92b3+gvLntvnKx7OMZu/LqbGbZglXmhltPzS38XO6OhOeW4X3Hmt9/XGjavt3C5Mqf46x/JxyX5rvG7Pl5phx2fJ39f5YtMSmzXB7LIXImkE1W5J3Bsd7b8f23ZteP00yhbr3FYZUQTV5veqovyX9jN2/RfPJ11R+rxLkISIMcOnAo5rNzgb/325RfTZkqN8TSik4IKsNYz0pentFM3nhqTip1eSaR0jmfECi9PktG886fq8/4v63e4A5zd/2qsdYSObhnSm+WL1xlCpU8WdlH88VVf6419zaodhqn/qdm1Z9rTJu3W5qsOcIHtyJh+d79Ehgpnjmjmfn3SUmurKlTmSsvTWOW/ucYPF8gwwPBAEI4aGT71pReMT/7h5l8xfKaFQtXmap1q8R8tmL+SpO/2LmtG8z9cbKKkyeLt9KofI0Kpty9cfct2XJlk8zIEreUjHmeVy1caUrffnpN4/i4JOUlop0e7L8h9om9p5+4l+zZLw10gzUgJbNkMlsPHT7tfUgInIHXZ4k9N/lr3enWvEavxZ1b8hQ7OX+s/mOVKfTf3LJp5cm5Jc95mFuC/L32pMZ5htP0yiCb/tLcecw/K5aZzOVPSWb+s3ypSVvglJ74mZLxpnIm4w2xs97X9HtTmsvmfLSRaMUnRJ9ve8dJFkK67OuhE6V3gC9ntGL+CgmGXRkIGp4tWzdsNWsWrzFPdI1cOihdvnyyzuxfscxk+S+IfHTXLnNkx3aTLlDJfNZO/QHvmBNHj5hrnntBmuhGQombCpnhP8TdtxQqntcsmb/a1Hj01L7lz7krTcHiCc8tEz//0Ywa8q1p88bj5robT79vYe5Gc79k2dh9G+IbY/fkyiGN3PcdOy7vlcySUWSvVuwL3/T9TCBIQBPsd/5aGfF/U/fp+02dljVirWNpL0sre+XlC1ZJU1y/n9GGlZvN7TXjPxOx1vXvMMzs+Hu3afv2Uybj5eend43PpfkKmIMrYzcy/mfZUnG8n23VSNp8BeLsq7Z8MVyqcPI+9+Jpx277D16KcybKVzSv+e7T78zOLTvN5f+t5csXrBAb8xY59zMRAdMFMxea6o2rR/zf0PwW2Z0Dy5dLk2pfNgd9/Sur33PuTv0Ph8q/VbB1G5M6q0rwKBc/S5YsMX///be56667Yt678sorJRv/hx9+COvYR91j8uTJZvDgwXH2o6eTYQyiUjyKEiVw4P/5558xDkia6zLAz4TUqVPLvxN8JU8Z1ymC82PRrr3mqSL5TLY0qUz2S1ObZoXzmt937I6VPTfxjnKmTr4zk+ZoXbyAuQp9VJy+ObKaegVymfeWrjGHcW4mABsXJif/5W+2qta+zfwy6Vfz129L5WD31QeTZKNYqcapTeJnb44y3ZqeOuBEwtRRP8q/yYZy0+rN5v0uH5lrSxWU5nnhYCJl4+q/gKaq1R+62Yz4cJpZuXST2bfnH9HGJ9P7lqqntPHbPjnQ9Gg/XL5fvXyzGfzmBLNx7TbR89y6ZZd5+/XR8r+3QpXrwv4t/++dvEann5Y/X7rJFM96mcjppL0khameN5upmDOzGb7kVCb2Q9dcaeY+UlHuE9yfP7t5+cZrxKk/LR6nPk58fg+d/isuTWW6Vygs5fcf/Hn6MjAOTf699Q9QBUoXNtnz5jAT3x0t2q87Nm410z6ZZIpXKmUy/Jc9d/TwEdPl3ufNvO/+ZyJlzqSf5bVvxx5z/Nhxs2n5ejPl/fGSuX9FmGx9CF5rf9GsXP0Gk+bSVGbIm+PN/r0HzdI/1pnxn88w9z5cUaotgOa4ZPv/Me9kFcMX708xs75fYPaih3z4mFk4Z4UZ8cH30ishdUhW/flixLItJkf6NOaJ66426VKmMLfmymLuyXdFrPtd5erLRU+f+wYv3VjAlM+RyaRPmULuJ5/XKZTDfLXqzJvryXMZuH7+2K3xSCWz4NdlZvo3c8yhfw6bKWNnm6WL1pp7694S89+OGz7d1LutfczPX38x03w++FvTvvfj5vpysZud+fw4ea6ZNWWeOXL4qNm2eZfp02G4yXx5BnNvnVvOyL676t5mZn7zq/nj16Wi6zrm/cmir1v5/vIx/+1HvUeZjo1P6m3Dp2+PNXOmLzBt+7WIqSgKJZLfiY/MFSuJg2LbhDGSaY+GK47+LLffEfM7OPrJCDu8OfLKCg6ZsV7/jXv5/jQHW5mPQsYujstiNxczXw+ZIBrVyOaMHzTO5C6cx+Qrfsr51qVuZzPpw2/MmbJywQo53JarfiogEwmj1mySdadi9izm0hQpTL38OaVqjJ4vPk8WzmNGVD4zB2uN3Fea8ldkkWDm1ekuNV1KFzYr9/0TK4AQKeHWEt6rUquSmfblDLNi0WpZ40YPHC/z/c133Rjz3w7qNMy81XqgfM9eYViPz83KxSed+mejqx8Eh9H0LVtN/QJ5JDsUZ0/zItdI89w5O05V4fUrV8q0LBJ5lm3hjJeZRgXzSpNWGhzTpLdlkYJm2d59Ztne/We1L7i99m1m6e/LzK9TfhNnxU9f/yKNB2976NaY/3bqiGnmheoJV7eFUqLCdWbGuJ+kaS7yRvybsyb8bK6rcN0Z2Xdbrcrm18n/M0vnLJF9y+Rhk6R5afn7Ts0to/qONH2anQpaRMLtdaqYuVN/NysWrJC1Def3b9/NMaUiaO47eePf0rC4ccG8si8gMHV3ruxm7LpT8wjOwolVK5jcIdnBCTF18zbzr+eZBgVym9QpkpvLU6cyjxXMY1btO2A2/nPwzOeWnNlMkXLFzDfvTzA7t+wwe3fsMV/9N7fk/c/pD90e6Wy+HRb53LLkt7/M959+K/8m1QDMMRMHjzfX3lDYZMt1+oqqrFWqmb1z55h9C+aJM3n7d5PN0e3bzOW3nXIA/z3uS7OsQ+TPHM9L6NwsPr7/3j8dwWfP37fccMfJYOiYAWPNP/v+kaabP3z5o6n0wC0x882mVZsk23/lwsgduT7/m/SrVDf4VSORBpezlLvZbPlqgjm0ebNo4W8Y8blJfUV2k/G6U2Nr6etdzPrPTu6VI4G1cvW7/UUr/Uyc+gntC6rXq2QW/bbMzJh0ct8ydfxss+yPtaZ6YH/x1afTzWNVTu1bJo2caUa9/615kWSEsuH3LQO6fG6WLFgt+5Zd2/eaIW+MNrt37JMqgdPxy7ad0si8ZZECJmPKS0TCrF7+q82UTVtjqnAZd4xdmlmfKWjrH/n3hJm5dcc5rWOp06Qyt91f3kwcPtWsW75RmuB+2neMVJ3deNups9Ubz7xrBnX+RL5nHnu340fS24RM/UxZz69THy6vXMUcWrvW7Ppxuozdvb/9zxz4849Y+6rdP82QfRVO60gJO3b/e/9szkSFbyhscuTLYUb3Gy16+wTR2D8xv2fMmjGmvwVjl8z7M2Xe9HmS3XvTHTdG/r8xeXKT7fYqZtv0qebAyhXm+D//mE1fjjLJmOvLn1rPVg8eZFb0ffOMnPrrPvrwlFNfdfUvYpIlyteRMPLWvHeubNhwstdSjhyxk75w7scnq7N8+XLZ819++eWmTp060tezZs2aZurUM5Nh1Ix9RYkSHTt2lOhdvnz5TNq0ac3BgwelxOZC0WX+MvNssfzmo0qlJavqf9t2mXf+ip19mCIkg7fnDUVN6cszyZzHZ9/deXJRf2zWvBipnV+37Tavlykizv31Bw6ZXotWmBl/nz77Oz5uua+8HIw/6fWFlBdfle9K07LHEyKl48NkJ1nR//G/KXPM8F4jYn5++4WBkhFRvWE1c89jJ8vVqQAY2X+sea/Th+bSdGnMjbeXNjWaVD8jDXB4/Jl7RPKk/VPvmcMHj5oiJfKYbgOejNUIF9t8+/IUuFKyvbu3H242rN1mLsuQ1hQtmde8+UFLk/00DbQiZfHOA6btrKXmmevzmrY3FDB/HzxiXv91pfkpIK2DX+8SshP/+7lZidwmVYrkptctp5p9wZcrtsRI79Cg9fnS+UybG06WcP60aZdp9N1Cs//oyQyiM4XN7COvPimO/bcbd5HDQdFbrjd3N3so5nd4NqWaIdDMa/Azfczfq09q+/PC8Q/tRnY3adJdaordcr2ZNWqqGfrC29KEN0PWTKZI+RLmljqnNvGRkP6yS81r7zQzg3qNMQ3u6mzSpk9jqtUoK9I6MfYZTzKv/NTEqjVuMl8M+c68/9Z488+BQybblZnNXTVvNg88csrhBHVvf8UcOnQ05n8XwYEMGdOZ4d8GSvIjZN3+Q6bV9D/NizfkNy1K5DE7Dx01/eatMV+v3hYrG1/u9383fPTyLeapknlMz1sKS3Bnw/7D5u35a+X980WJGwuZVh3rmhHvTzHvvj7SZL/qctP6tfqmaKBE/cQJL9bYHfnBdxJs6/r8qRJ5qNmgsnm0xcksozLli5hh/SaYQT2/NCkuSW5uuqW4eaZT3VhjLhJuq3GzObj/kPmgxwgpFc+Z70rTuldTky0wDk/8+9/9RUpj7z9m6pifxAHQuclbsf6tRi/WNpXuKRvR7yREykyZTe6nnhXJnJ3Tvzcp0qU3l99xt7m88qkMb8lw/S8ALDYeO2aWtm753w8nzKE1q8zuX2aZ1NmvNAU6vGYuFI+0q2/G9B9jej/ZS+4j2dW1nq0dK1DAtfOvH3zS9SOzaNYiGTc8+22qnawwe3bA8+bqQlfHaprLzzmvyXVGNv24ZafJlGqNebpYfsk2X//PIdNh7hKzJhCwZu3i5YPDvmuZU1mTXfjeM+bLtZvNoCUn5UV+2rrTPF00v+lYqpA5/O+/8neGLlsnDs3zxV2PVDFHDx8z73UaJo79PIWuNs/2aharSWzwem7buMP8OnWuXO+Oj/aI9W8179rYXF+h+Bnb8N7SVeaJa/Ob3jeWlPXgj117zavzFsf638m1CzZMb164gLkrZ44Yp8boyieD7l3mLzbzd+2RjNJrMlxmOpQsKo1WyQ6fvW2HGbF6/VnrG19bupB5tG09M+mjb81nvUdIpcJjHR41Ba6LPbcEn73Vi1ebfs+9+99nJ8yIvqPMyL6jzQ1VS5sGL53sS1CrVU0z8cNJ5oNXP5JGrUhL3HTHDaZ6o1OZVZFw873lzaF/DpkRfb4wB3YfMFfmu9I07fakyfJfZRp4IfbN+e43M7LPqX3LoLYn9y13NKhm7mxw8u8Xr3CdeeCpmmbUmyPM7m27TaZsmU3l2pXF4X86dh45ajrO+1Pu14N5ckrzzdFrNpmv1p+a87mFVG76tzdlsmRmXJWT+zzeL5Ipg7kr55Vmw8GDpsUv8+V9HIvtf19sniqS34zOV05+XrBzj+m7eEVMr5czpW7b+mb8gDGmb7OTcwtVQTWfiT23sG74iTDwabePzOLA3PLSXSfnllbvPG9yFbraXHN9QfP36s3mg5cHm93bdsm1K13lRulXEAkZy9wochRbRo8wx/bukfk1d7OWJs1VOWM5q3j57Fu0wKwfcjIQB+vfHyhOexyNOR6KLSd3vkibPq15qlcLM7rfl6ZDrY7m0nSXmnJ3lzV3Nbwr1hoSrBKFl2q8ZI6wJ/nvPZyHyD+9/mXXmN/BKTjn+zmiz32mPWOurvuI2Th6pFneq6fxjh8z6QsVMgWefiZWk1u5doHrt+GLz8yOWTNj9lcLn20lXwu0esZkKFrM7F+2zBxYsVyu6R8vxq5WLtyhk7k0cG8i5bobCpnmr9Q1o4dOMe91P7lvefrV+qZwyfzxjt0xH57ct/R8Ifa+pUb9yqZu85P7ljtq3my+/OA7s+LPdSZlqktM/sJXmy7vtTJ5/pMFTAgSaDrMW2xaFbnGDL/1JnHC/7Blm3l/2Zo4Y/e/mL3wQcUyJnuaNDKembNx/MO9U3+OI8Pz49/b5d89V2o1v1eerT7PDzKHDx011xTPa154s5lJHXIm8sfumqUbzKLZf0mywQu1Yu9X2r3T0hS87mQwb8z7k8zkz6fHPJ/+7z7Z8VFz0+0Jy/Vdmje/ydWkmdn21Vjz95dfSAVkjnoNzGXFS8S7ryJxYnWP/6RtT5ww28Z/KQkX6YuVMLmbn3wOzzeMqSe7PSmO/S6PvGZSpExhrq90vXmo1YOxfi/0TNSnxZtm08pTZyJfoqv7uG6xGuQSDChZsYSM6zMh+513mxNHj5o1gweZ4/8cNGnz5DbXPPNc7N4CIWN329TvzKaxp3qLLe128n7leriuyXZbZXNk2zaz+7dfZez+1blDrL+Xv1kLk7FkZBKMZ0rfro1N0/pVYtaSHUuGydcHHnvDTJv1xwX5m0ripEcYeevOnTubV1+Ne05HK/+3335L8N8jeZdAni+dkypVqjjJuYcPh5f3PPqfVGbLli3FLmR8yOCvVq2amTRpUqzs/4RI5l2IroiKchFBo4tff/3VlC5dWhzysHXrVonI3XDDDfLz9u3bzZo1a8xNN50quT1w4IBZsGCBufnmm2PKaIgELl26VH4uUqSIWbdunbzH97Br1y6zbNky+W8ipcrks2vMBGzbg9u8/xKJ4hDYX5wxr5Q8y6Zlwb/PZsI7VUrrH1pCYSE/U8d93ssi1y6LDzaxsrE+w78d+m/QSDE0u7bmhLOT54AUyU5qrAOHgXB5u9zbc1kE2t8cuz/C2cAi6EsciE3/0s4xrlVnUo4GpbOeXUAijn3H/42Rd4n4vwkEA2IIqcio9VX8mr/ner/PByPuO/exe3KsejH/u8Nel9PIYcTH3qPJzvvcwr0OR/D+R/I70GvR2WWm+QfQYFYYzcfiQGA2nmsm/wbN0hO4pk2vPfcSf3F+BOa+UGdSfGM39L8LR++zvH5+Ps6JkLFxPue+LqXPfWycr+cxHH0Wn71eL3+Z6+JdwH3B00XPXfpHnBwnvFgOyHBapGezLzh2IpnT+5YBS87h/iaLfe+CjsIYmILO+i8Y0/za/dbmlkj4ZOXZ9Wrwnfr+vBrqLIxobg75N0J54jzMy5HuqwgshTrwQ/+7UD5acfba6HGun4yRMBPJf38/3uubQNZ0uxL7zvvcItfuPO1bXp6b6fyNXTHi9HNz6H+XEB3Ox5ktZOzGt475lQDhnNnxrXV9/rC4rzrN2IVmhQ9YOxPxeyfl5qIwduMbmwG7w17fkN8JMuGxQeZcObmexv23gwlGZ8uh9f/1sFJiccL7K1FekWNHC8TJ0Mf5ziuU9evXS0JuQhQufDKB6H//+5/48RYuXGhKlDgVXKxcubJk7aOhHy5j/9prrxWJ7mCwAcf+ZZddZsaMORVESwjN2FeUCJriVqx4SnMTsmfPLi+fbNmyySsIzXFD/zsmi5IlT5Ur0iQjSJYsWc7IqX+uhC5zcph3MNQXunH2y+NdIRL5nGj8G6EEnbzn4oS50ITeyzPNFLvQBA8fEf83Fv43nE+n/vni5FhNZvW6JEQcLcMI7vXZPA9nghx4QsZEJKXfcf6Ns9SWPRNCx2qkTo4LOcaDTmmXx8b5eh6T6r7gpMxJ7GfcpX2Bq/uW0L2Aq3uDs51bLiRx+x/EnavP9N9wbV91IZ/RONfvNNfibK7vhZhbXNmTxhm7/L8Ixm+0x3jo9YpkHZPxndzxfZXjY/dCPqdnM/ed6fU9HxBw+DexbPgUq6SOx4kfjty5c0f871533XXy7+Lg9x37BBDmzZsnjvtwFChQQHyAvIIgzUPSb6S4sVIpiqIoiqIoiqIoiqIoiqIoTiOB0ET4ulCkS5fOPProo+bNN98027Ztk2BT9+7dpSqpfv2TspDw+OOPmxYtWsQE85o3b24++OADUQGBRYsWmW+++cZUrx55M2zN2FcURVEURVEURVEURVEURVGUs+Dtt982DRo0MFdffbUoeFx66aVm7NixIsUTlPdJkyZNzM9o+yP1TS9OMvd37twpmvtPP/10xH9XHfuKoiiKoiiKoiiKoiiKoiiKchbgzB83bpzI6Ozbt0+kfELlBIcNGxarcgDp76FDh5q+ffvKf5czZ05zySVn5qpXx76iKIqiKIqiKIqiKIqiKIqinAPhdPN9yOYPB81yeZ0N6thXFEVRFEVRFEVRFEVRFEVRIkBbtrqC3glFURRFURRFURRFURRFURRFSUSoY19RFEVRFEVRFEVRFEVRFEVREhEqxaMoiqIoiqIoiqIoiqIoiqKclmTmVANYxS6asa8oiqIoiqIoiqIoiqIoiqIoiQh17CuKoiiKoiiKoiiKoiiKoihKIkId+4qiKIqiKIqiKIqiKIqiKIqSmPAURVH+4/Dhw17nzp3lq4uofRfv9XPZNlD79Prp85c4x4fLtoHap9dPn7/EOT5ctg3UPr1++vwlzvHhsm2g9imKeyTj/9kOLiiK4gb79u0zGTNmNHv37jUZMmQwrqH2XbzXz2XbQO3T66fPX+IcHy7bBmqfXj99/hLn+HDZNlD79Prp85c4x4fLtoHapyjuoVI8iqIoiqIoiqIoiqIoiqIoipKIUMe+oiiKoiiKoiiKoiiKoiiKoiQi1LGvKIqiKIqiKIqiKIqiKIqiKIkIdewrihJD6tSpTefOneWri6h9F+/1c9k2UPv0+unzlzjHh8u2gdqn10+fv8Q5Ply2DdQ+vX76/CXO8eGybaD2KYp7aPNcRVEURVEURVEURVEURVEURUlEaMa+oiiKoiiKoiiKoiiKoiiKoiQi1LGvKIqiKIqiKIqiKIqiKIqiKIkIdewriqKcJdu3bzf//POPs9dvy5Yt5ujRo8ZVduzYYdsERVEURVEURVEURVGURIk69hVFSRQcPnzY7NmzJ9bLtlP9ueeeM19//bVxlQceeMAsWrTIuEqRIkVMjRo1zPjx483x48eNa/B8dejQwfz777/y86hRo0yOHDnE7t9//922eWbjxo3mjTfeiPm5T58+JmvWrKZs2bJm7dq1xjbz5883w4YNi/n5mWeeMVmyZDF333232b17t3EB7u2yZcvMH3/8YVyEwNzixYvNihUrjAvs37/fnDhxwrgK60JigTlv0qRJpm/fvubdd981M2fONC5x8OBBM3bsWJlXhgwZYubOnWtchDUOO12FMcx8fOzYMeMi3Od169Y5M665Xq+88oo5cuSI/MwYyZUrl7nmmmvMjBkzbJtndu7caV577bWYnxkb2bJlM6VKlTJLliwxLiWe/PLLL+bQoUPGRTZt2mRmzZpl2wwlCexbws158+bNk2fQRfbu3Wt+++03s2vXLuMirs8tipJUUce+oiQxVq1aZS655JKIXoULF7ZtrhzYCxQoYNKmTWsyZ84c6/XQQw9Zte2qq64yGzZsMK7iun04yi+77DLzyCOPmJw5c5o2bdqYv/76y7hCt27dTIYMGUyKFCnkoNKsWTPTokULc9ddd5kmTZrYNs+0bt3aFCxYUL7nPnfs2NF06dLF5M+f37zwwgu2zZNrVKZMGfkep+Vnn30mTkIcNj179rRtnhycrr32WpnnXnzxRXmPg9R1111nPWgIEydOFIcW9vTr10/ew3FUvnx543meFZvat29v8ubNK8+Zi3NL3bp1TdGiRcVZjgPOVRYuXCgBQoKvAwYMML169TKVK1eWe/v333/bNs989913su7Wr19fHJfc7xtvvFHstTU2CIQcOHAgzmvOnDmylvg/+w5hG4wcOdIUL17clCtXThxbU6ZMkWBwvnz55Ctj2iaMC+a8O+64Qyr6PvzwQwkGM6ZZN1wIWL/99tsyv6VOnVq+Pvnkk/IcMrYbNWpkPQDBHMi8DASoSfDgvdKlS5tWrVoZ27BX4TpdccUVpkKFCjHOy2effdZ8/PHHts2TNZY9FNewUqVKMe/fe++91h39CZ2PUqZMKfvVm266yXz66afW5uWE7MuUKZO57bbbZN6xhYv7liCDBg2SQBx703Hjxsl7JGg1aNDAuMDLL79sLr/8cknQYY8KXMfOnTvbNs3JuSV9+vQRvxTlosdTFCVJcfDgQe/777+PeT399NNe9uzZva5du3rjx4/3RowY4bVo0cJLkyaNN3LkSKu2bt++3bv00ku9Dh06eLNmzfLmzJkT67V8+XKr9q1cudIrWLCg9/PPP3suMnPmTK9kyZLen3/+6bnMnj17vIEDB3o33HADu36vXLly3pAhQ7x9+/ZZteumm27y5s6dK99Pnz7du+666+T7f//918uUKZPYbZMsWbJ4e/fule+5XjVr1pTvt27d6mXNmtWqbbt27fKyZcsW83Pbtm29F198Ub5nvHCPbXLkyBEvb968Xo8ePWTOu/POO2M+a9OmjffWW29Zte/vv//2MmfO7H3yySfeG2+84bVs2TLmswceeMD76quvrNiFHYyD9OnTe8mTJ/eqV6/ujRs3zjt27JjnAtzHMmXKeKlSpfJSp07t1a1b15s2bZp34sQJzyWKFy/uPfLII96OHTti3lu7dq1XqVIl7+GHH7a+R2BuYcz+888/Me//8ccf3rXXXuu9/PLLVuwaPny4rA+ne9WvX9+KfevXr/dSpkzpPfHEE17Tpk1ljsuVK5fsX/y9FvPygQMHrNg3Y8YMGRcvvPCC9+CDD3r33nuvlzFjRq9v377elClTvNq1a8vYtj1WqlWrJmMW5s2b5+XOnTvGJr5nnNgkf/783rp16+T7UaNGeZUrV44ZN+ybbV+/bt26yd7lr7/+EltXrFgh73Pd+Nk2jRs39u677z65hkE3xNSpU+Xe24R7yDi96qqrvO7du8eciVj3OIv079/fa9eundzniRMnRt0+1gvGLvPw22+/7X399dfeZ599JnNehgwZvGHDhnnNmjUT+xYtWhR1+1zdt/hwbrz88stlvmOe5n76FC1a1Fu6dKlV+zhz58uXz/v111+9qlWrepMnT5b3Dx065F155ZXWz0Quzi3sPyN9KcrFziW2AwuKokSXSy+91FStWjXmZ7IUKG8mi8unTp06ksVKCfTDDz9s7RaR5VGsWDHTtWtX4yK9e/c269evl8wFrivZ3UEGDx5s7r//fqsZ53/++adcQ7IV0qVLF+tzsnpKlixpbJMxY0bJhOf10Ucfmaeeekqy9MiEe/zxxyVbFAmXaEN2EdIjfqbU7bffLt8nT55csvh9iR5b+Pbx3AXtI3PLBduQzyLLlmwy7OvRo4cz9lEZwph46aWX4mS33XDDDZJ5+/zzz1uzj8zFW2+9VeZnZFpC7ePz++67z4ptjE2ytsiS/uCDD0zNmjXNlVdeaRo3bixVGmR62+T111+X7PLhw4eLfVWqVBGbmjZtKnZjq03IKl+6dKn59ddfpRLNJ0+ePKZ///7mnnvusWof0lSsFUGZLyATvXv37mKjDajSI4sbebRgNSFjAdkvpL7862gD9kuMSSoc/AxkMkP9/Qv7LjLimQsZM9GGrFTWVP++UjHC+sp7QMUIlQVUGpBta4vQdRe7kiVLJj+zltheO+LbF7An8PcF2GkLnkMqgLi/7FV8GBf79u0zmzdvlmpOm/Yh60WVZrh1zSbs40ePHi12IP0UPBNxRmL8skfNnj277O+jPVczDn7++WfZvwT3xFS9kjWNPON7770nzyEyiG+99VZU7XN53wLffvuteeKJJ0y1atXiyKhSccO1DZ6FbYwNZMioCgnOIWnSpJGqKta5YJWLDftcm1uoIlQU5STq2FeUJMzWrVvlEBJuI1OxYsVY+tg2uPrqq8U56CqPPvqoXKf4uP76641N2OhjY0LX15UmupQ2IwtAMIeNP5tvdDAJntx5550itxBtcCggd0Nwi8PSV199Je9jIwc7G8GGUPtwpvIMfvPNN+bNN9+U93/66Sdzyy23WLWNa4O0Q8OGDcWRSgCMEnFX7EPDlLJ68J1Gwc9sOmYSg30ERXAK8sJJjQOdFxJLOLoYt2hO24JSdhyWvChnxzYCS8hVMb8g95E7d25r1w7JhHCa68jI2J6XuS4E5HBghj57Nu3Difb555+LAx/nfvPmzeV99gjILtWqVcvYBBtweAQDIalSpYr1Ozhs0LO3ZV/QKUTAP7hHIeDKz9hn07HPuoYcxfLly8UxOXToUHkfpxFSEAQfbIJ9JCGwL/niiy8kQAd8Rd7D1bWDvT5BRVftc2Fd40yE1FPQqe9DAg97VGAvY0N6hL0nQepwe0/sIyjh20dyQrRxfd+CDQSIXbbP9evn8tyiKEkd1dhXlCQMDgai7NOmTYvzGQcWtFdtQoYCr3feecd6llY4cKjiOI/vZStz0IcmpQnZZ9sxTfYMzhgytwYOHCg6umQc0VcB2+mh8OOPP0pjUxu6zp06dTI333yzZPbgSPKd0WRskRVsG64ZAYapU6eK49J3VKJl74IeJwdLmmstWLBADpxkHeEw5Hr6mva2oFKFapbVq1fHcS6gZ8oh2SY4AL///nuxJ2gfARKeP9v2BSF7Gkc+Y/fLL7+UrGrf2eUCXEuyK9EUf//996Xxm+0ml1Q9kGWJprMP2Xh+Hw+bsC4w1xFc9fX+cfIzF5NNyPu2ePDBB6VpH4FgAjTcS1egt8PKlStjfmb9Z/8S2tyZLH4X7COhI7R6xaZ9PgTTWf8nTJhgWrZsGZPhi1OVdTfU4RVt6BNTqFAhM3nyZOlZQIAEPvnkE6kudGG+8x28wWvFHI32OfrYrtmHM517a3td40zEGGCuC2XEiBESMAaaYdvI7Obvs58i6BWE+Zn9lm37XN+3YN/48eNlHxq0j+A/1Tf0AbBtH3soCNrHeKGnke0Ka9fnFmB/wBrC2ZJs/uBLUS56bGsBKYpil9dee010YdFX7dKli9e+fXuvQoUKotH4008/Rd0etMzR/PdfaJkzVaENG3yfV7169TxXQPtw9erV3tGjRz0X2blzp+ggHj9+3HMFNCPRBv3hhx8S/L3XX39dNCajTe/evWNpYEf6WbRA6/VsPosGXBuu0Zl+Fk169uwpfQDuuece6ZWBNu0VV1zhFStWzJoOdhB0utFbvfXWW72yZct6jRo1Em37O+64w5qOM5q5QV3a+LBlHxr7vi5tQtjUwWYu5rnzNeHTpUsn6y3fJ0uWLNYah+5vtKGvCDrJvn1oN7NH4PtLLrkkln30WLABPR3Q+s+RI4eMC1u6+qF9O7hf6EzH9zlzy5YtWzwbbNu2Tew7fPhwvJ8XKVJE7FQSL+xDmV9q1KghvTJY1ypWrOilSJFCNONtM3v2bO+yyy6TMct8R0+bEiVKeGnTpo3paWSTTp06yXxHrxP/TMT14wzy448/yh6aHgHotduA3izsA5j36I1G7yJ6YzBnL1u2zNu/f7/sGTZs2GDFPhf3LT7cO+y6/vrrvVKlSnl333233GfWtWA/AFvQt4v+CfTaQbOeHkGssYwTeqHYxvW5hT48zCPMLexXmjRpInML39epU8e2eYpywUnG/7MdXFAUxS5k/KILu2LFCtGYpBy7TZs2IqURbciqDFdBEA60/Hx9U1tgK9kBixYtkp/RtiSrkBJyMhhsQ5ZR+/btJYMHkAZABxutZOQgbIJOrV/W6SKUY1NVEK4sm/fQZrepJ07GTHxLeEKfRQMyQ++6665YGaLBz8jIZL6xDfJKSD1gE7ri6GC3a9fO+tgA7h9ZblRPka1FJjXZq2j/kxVvA7LxyApEj9ZF0P1HX9+2XEdCIGfjZ72dDqpcoi0xgzQa814kkN2NJIktpk+fLr0T7rjjDqnKsA1zGvNIOJ3hefPmSaUIlWm2YJ+ClJIvRxGEjFXmHJv3Mwjye1wz9gmss6wZjAdXIKubfgRk/5IdjW43+z8XoHqKLFpk75CqQiIKWbKEpCOjCRrxSAeiWU+2PutJ27ZtY6ofbMPejuouKqp45siUZp9PpYZtuF5k57MvYF/PXqVs2bJiX44cOWyb5+S+JXT9pQqcylEqvrhmVM9xLrJdDQRUjFAVxLmcygf2MlT4uZJx7vLc4vcBqlGjRswZiBdzC3bzTCrKxYw69hVFcRbKTWlmhI74mXwWLRYuXChOJGyguRYbWA4CHFjYkPG5Tc1BnJa1a9c2L7zwgjhZKTNGfgStaSQCaITkAsg9cN04nLik0RifY3/btm0S9ELzN7RhcjSJz3nPc4ejCztddOyPGTNGxggls7ZAgoxDpgsHzXDs2rVLmkq74ihSzh84AtE7R0bLRZA9Q//fheCWkvTAkV+3bl2RusHpRqDEdxAiwYTGvU02bdokzY8JOiAjyJ4F+9gTICOI5JGiKIoSXQjC7d69WxIUSWIjSJI2bVpJViAox75aUS5mVGNfURRxDq5Zs8ZKg9KEQP86vgZQfEZ2pk3QVKV5JJmCVA5Q6YAuPFn8NH793//+Z9U+dNe7desmGQw0zSMrqkGDBpJpxovqCJvQbInqBg7vZHvwM6D/P3HiRGt2cZ1wajEm/O/9F85WdIm537ac+r4twe/9FzbxHCbUNPlCwjXDDq6b/33wRXCJYJPNrFUgqIBjlWxfxoJrxYs0CaXihyy3uXPnGtchSIhOravgEAwXZLIBh03mEByATz31lKxjNG10BXqaUJVBI1CCwmQ2YrNydtAfKFyjZFfANpd6GL322muSiUw2PE50kjhwzODsJ4GCjFubtGrVStYx1jcaDZOUQJCfdZf9lXJxQKNm5r3gi6CTK5AtHWqfjV5UiuIKrA049YGgK5VBwNnS5TVYUc4X7qRGKopiBRzkDz/8sDiPKHfG+UFGIdnTZB/ZbgAbH2Ql227yRhlnuGZQbCxoKGm7sR9/v1SpUnHep8ERmx4OyzZLd2lOS2UDB+OgtAfOTBxe9957rxW7CISwEXz66aelWWSwwSAZ1Mgs0ETKFkjHAA5y/3uflClTSjWGrSZbPFvYRBUGQaX+/fvHyaghc8Z2STuBLprk0mgY+R3uKeXYOGZckAPAFsC+t99+W8qdCYbwCm3IGW1nB69QaHSJHIWfrUolBM+iDWcH61coH3/8sTjj/LHMHG2rGoKm9FRO+QHWZ555Rhz73OPbbrtNnkcCnrZgbiNI49tH01IqXHBcYt8999xjXQKPe4zcA3ayzgUDc9iINEC0QWaHZ8qf23BI0/wVyQLGDA2J33vvPdkb2ADZE9Z71n4g8YBnj2acUL16dUlSsF1JMnv2bKkqDGa+k7VPlRdSCuxRbc7R2IcEFJJGwTGNdAvBa5xL0a4E45mKNHC5bNmyqEsInkklZrj5O5owXhm3BJaQvQnin5FswrnsxRdflMBSaEICMlqRyqidLwYOHCjzSCRwXdl3RZNnn33WvPvuuxH9LhI9nD2iCesp0k+RJlAg+RVNXJ9b4gMJQxKc+EqiGFXMinKxo1I8ipLEwcGFLh0OLV7+As4BDz3WSDdE5xMy3sl8Z4PPIYlDXRAOyUTfcSZxGLUFh0820WghBp1YHOiR6OHgjLPGFmhGcg1xagWZOXOmyKQg1YKzyxZsGJFl4ZCO5q+f4Y3N3HNfLsUWv/76qylRokRMBohr4HTDieUihw4dkvmDAKHrEISgFwVZ3QQ4CYrQY8RW1UMoBL7I4MfJz9xCMLFjx45RP+D52aqRrAkEdPjdaMO8FskhGZkPftcVkPXo2bOn+fLLL0VyhKCrK+DcYs14/fXXZW1mTNuuRmPdZ6+Ck9rPlmYdhr59+5qGDRtG3aYOHTpI4JKvzH+s/QSSGjVqJNeQAAkZtVRE2JCcI+MdnWa+cs0IxLH/I4jEWjtgwABTsGDBqDsGQ6lXr57s/0J7S3ANuZ7cd5sSeOztcOLzzAVB5oFgBOuJjQo0qkQBLekWLVqIROStt94qzyTPHPeXigfmmWjvafyxCayxBP1xoHItgWphHMQkUtDjxhY4yplTmF9Y/0P3x1xLzku2YF3InTu3VFLhxA/tOUFFKWM4mvC8LV26NOZn9iac0Z544gkJfhF4Zf+C05d1N9pSVfxdpLL8OQS7mPtIniA5jM8Yz5w1qJ6Ldm80Aqv+ek9C0WOPPSbPH4lNzHPMd+y5mG84l1NNF01cn1uCsC8pV66cfM8z2LVrVwnEctbs0qVL1K+dokSdC9+fV1EUV9m/f7932WWXeceOHfNWrlzpFShQIOaz33//3bvuuuus2PX33397o0eP9l555RXv+uuvl++Dr0mTJnnr16/3bLNz504vb968Xr58+bxnnnnGe/XVV72GDRt6adOmla+2WbZsmZcpUyavRIkS3gsvvOB16tTJq1WrlnfJJZd4r732mm3zvCuvvNJbt26dfJ85c2Zv9+7d8v2ePXu8FClSyHPpAtu2bfN+/vln7+DBg56LbNy40Zs5c6ZtMy4KRo0aJc/inXfe6bnGiRMnvH79+nlp0qTxWrZsacWGjh07eldccYU3ePDgWHPyXXfd5TVp0iTmZ9YTG9SrV88rWLCgN3z48Fj2sY6wnvg/s8bY5MiRIzJmu3Tp4lWuXFnuKesv13DkyJGebQ4cOOBNmTLFe+mll7yyZct6KVOm9IoXL+61atXK++6776zaNn/+fC9HjhxiI/e5fv368v6uXbu8IkWKeBMnTrRiF89X165d5XvuYeHChb2jR4/GfH7o0CG5x+xfbFCnTh3viy++kO+7desWZ45jTGTIkMFbvny5Z5MffvjBy58/v/fjjz/KnAebN2/2GjVqFHOvbfLhhx96ZcqUkefQZ8WKFXI9O3fu7NmGvSfrRChLliyRPZdtypcv733zzTdx3ue9ChUqeDbhOcuSJUvMc+carBk33HCD5yrz5s2TOfjw4cNxPqtevXrM/GOLzz//XOwIhbkZu7HfJpzLnn766Tjvb9++XfZdrHE2cX1uUZSkjkrxKEoSBhkUIu5kj9GIM/QzW41MKQUnW4tyf2SCyJp2ETIrkQTq06ePlO+SdYF00ZAhQ2KkNGyCJAAag2+88YZkn6MPSiNYyhLJ9rENkg+jR4+W7KPg80dmKJlcthvpkvHx5JNPxlQ8rFixQq4fpb1IB5FZYxMyBHnO/AxlvyybTB+y3pB+iLasF2OVUtxx48YlOG75HcaOC6xfv14kHsjYp/yejGR6Z7gCEh9k65P1hq00j7Ql1fLqq69KZhtyVfTwqFKlSkz1CFlRoVm20YasaOYT5jyumf8MIg9FHw8XsvRp7kZWKNmVlOHzrA0fPjxGIsU2ZBAyN5NtSeYgsg9UBrmS7UYG48033yxVXTTI87MJqfpivmY+5LrahLUCG4KVfOy1eP747O6777ZuH9n7ofsuriufRTvrNwhZn2jX88xxf7luVBT4e66gNB73Otqyc9hH5RQyh9iG/JKvbU7lDXJLPvxetKsLqJRjjxIK8zO9FJD9sim3hH3MxaHwnu09AVKC7EXJ7HaxcT1zMnJzrsK9veGGG8JW2lJpyP2lYsimfeHkUxnH2M3n4eRLowV/H4nNUJD6Ys/MeS6c/dHC9bnFB1vC9cPwe5MpysWKOvYVJQmDrjQbsBkzZkh5pw/yNzhGbG4g/EMcL5fh0EazN1dBUxeNbhfBgY/zmaAD0gU4DX/++WfZPH7//fe2zZOADU5VNtNBvf/WrVtL0Mm2Yx+5GBwfOEGCvTDoUcC1jVS383zBhh7nOOXr/vfxYVMCyi959uVtZs2aJY4sgiTjx493QiOU8nWac2Pf/PnzJZCEtA0yFTb7YiRPnlzkEgguoPePIx9ZBVfggE7ZOoElgpc4pRkPLkHAEjkHHOhIUOCgZjxQwu7Ceoc9SBUgYRC0D8dbqCyeDXBshWuQ549rmxrdBPeRpcDRG67hMM4Gm/cYCR7sQ8rQRfsAx1GkMmhBnftogbxDuD4j4bAhS0EAjkB1sG8RIKNFgOSyyy4zNvHta9asWaz3eQ8Hpk1w5iNJyrpB8DpU6sY29NfhhRY8evWuBR+4t/SfQOaTIEnwTMmabNOp79s3cuRICf4Hgw/Yi93IybhgH879YLITczb7Bdvjw/W5heuE1BKSPOH2AaE9KRTlYkM19hUliYOmIAshThA2NjhryOjmYIoWZrSzCNHiDM0kiw+a4bBZVBIv6If37t1bnPs4G8iwxWEdunG0AY4snJZoSeL4RR+UjH1Am5PsI4JjtiBz0R+jHAL8TSvVNjh//UxWG+D4IFs1tBLIBx3iYOZltEFHGu1rDnLMeTabIYcDzdK33npLgg04uWw13Dxd5nnTpk1lDHMvCX7Z0NWPD6obuH4417CVYJcLGftBJypBJb9JLQd3+o2QzY1TyTY4qQn6+/aRfVymTBlxzkTaLPFCQMCQ8ctX1gwCcWRb8pVsaSqsbFS0oK0fDHKRHUjQwQ/EEZBgXWOfZWPu477hNPJhTeOZS5s2bcycTMCOdY2AsZI4YU5hb8yYoIcCzmkq0Qhakzzx8ssvW7WPYDVJEfSIoTKTfQuNnb/77jsZ0zadv2vXrpXrtnPnTgnAhlYp5cuXTzS7bfHDDz/I3EZQjjFKlVIQEk5IWLCZKc0cQkIMQX/06llHOKdhL83ObWZNsw/gmcPpW7NmTXGUoxtP5fB1110n50+bwRKqpdCI51zBPoDEMfZX2Ef1OhWJNnF9buHMxphgfxI6NgC7FeViRh37iqJIc5x+/fqZP//8U5xx5cuXN+3bt496EyE/cwJ7IsEvHbcJBxEOKmTCsakNzfi26UjCHppYEqihRDI0W4FDPo4kJTxs9HES4YxB1mjSpEni2Oe6ksFKpnwwKyna4LDkvrL5J5Oa8nHfoUlGsC9fYEsuA+d0uGoR5hkkM6jOsAWBS19GwUVw+vKMxRcYcYlBgwaZTp06iTOaILFLMFY7d+4sWfw0pvWlg1yCeQTH+VdffSUOENea55IFh304F3BK226ey7UheOlX1lBZQNCGvQPOmubNm1uxi/k2dM7FieVXJ1F5g622HJdIt4UGe3Fs+VnJVMkx31StWtXYhKAwjQ6ZU8iqZd1lvcBO5L8ItNsEpy/zCfYB0otUMbFfxqlK02Tb4Ax88803JXBDNSRBHDLkXZn/kCzizOFX29BQlcqq0IbE0YaKH5yU8UFG8v33329ssWXLFsmOjg8cwjj3bY9fHNATJkwQpznnNGzC2Wq7UtPfW1Hx4FcWMG5JJkMSLyidZgv29CR1sMYSiCCYRHIHgRIX9oMuzy1UFHL+cUU2UFGijTr2FSUJwwFl2LBhkiF9Jp8pp6od2Aw2btxYNl84V4OgT4xD2BY42nCskpkcLrOcbFabjmkfHNJstkMDD2hQ29zINmnSRK5Pjx49RJrim2++Ecc+Grs4GMiusQkOjoYNG4qdOKhxYnItcWxt2LBBKgxsgeONwAiO3o4dO8Zy6vsyRr169bJmn3JukH2ckEyB7YqMhOxjnODkpOrGBmQLUs6Os5wMzDVr1ohzgfGMrjgZjzbloJiLg/ZxL5n3sM23z2alknJxw7pARjL7F/YEyO1QVYXTjSQKHEuhe61oQpCBrFr2fn6/DAJLrG1kfCfkeFUURVEuDFS2Mv+60q9IUaKO7e69iqLYY8WKFV6BAgXi/eyaa66Juk2JiSZNmng9e/b0XOXWW2/1vvzyS89V1qxZ41WpUsVLmTIlHv04r927d1u1b/Xq1V62bNm8GjVqeFmyZPGaNWvmVaxY0UuRIoU3fvx4zzazZ8/2LrvsMq9+/fpesmTJvDZt2nglSpTw0qZN682dO9e2ed6SJUu8rFmzegMHDpSf//jjD7mezz//vBV7vv/+ey9dunTeAw88EPN9fC9+J9oMHjxY/nbr1q1jvo/vxe/Y5JVXXvHmz58f72f9+/f3bMJY3bhxY5z3jx8/7j3yyCPe5MmTPVts377dy5Url4zb999/X9Zal/jf//4na3/Tpk29Tz/91Nu0aZPnMgsXLvTGjBnjuQrP3NGjRz1XwTZsdIVq1ap506ZNk+/nzZvn5c6d2ztx4oT8zPdr1661al/+/Pm9devWyfejRo3yKleuLN8fPHjQS5MmTYytiqIoSvQYMGCAV7t2bW/Pnj162ZUkiTbPVRQlLOis2spoDEIGMuXNyHaQORjM6kYixS+HtgEZ5Taao10s9qHHzf1EgiJcUyjbjZiowqBknB4AlBSjL0mZPZmsaDnahqxBynUpiyU7Ht1pxgTjxQWJJbJnkFFAk5PMRqpHaEyHvTagfwPSWZSGkxGdUHNffifacJ2wKX/+/PLsJ2Qfv2MTrh8yYz/99FNM3wlAkgKdc7TZbUIpNhrOM2fOjCnLJlOf549+HraeQWCuo6LGVZDasV2NFF+lA5UYodDgFxkZ7jeQ2R1sjBgt0JWmcsqv0qMnAQ0uGSPIU9AonrFhq18GOubo/fvZjKwdyGMgqeBXGA4ePNjK3BeEPQFVI4DuOhUifuUeuuehkoe27fOlT7j3fuUcdtqEZxEZQaqBaFwahGfQZjUVILeJNBqyGaFNLhOSwokWyFdyb9n3+RKHwPhBes4mLp+JgOeNilb2zlR+B+275557rMv1UQ3EGEAGKlQ6jWoc5kGbbNq0SeS9kLNEMjIIsoKlSpUyNnF5bqGxOVJGY8aMkX1WaMU340VRLmpsRxYURbGTiZwxY0bJ9k2ePLl8H3yREUoGMNFv2zz66KNenjx5JIuLbC2yLa+88kqx0bZ9ZCAXKVLEW758ueci33zzjVe+fHlvy5YtnotcddVV3sqVK22boVxgpk6d6qVKlcp74YUX9FpfRJCZny9fvpiM7vbt20tlC1m2tiFrlrWibNmy3oEDByQjuW7dupJtu379etvmKWfB8OHDw1Z2hb6ohLA1Hrp27RqTvc2+hedv0KBB3rvvvuuVKVPGK1y4sHfs2DEr9tWpU8f74osv5HvGbPr06b3q1at7Q4cO9d566y0ZG3feeadnm+7du3tFixb1evXq5V1xxRXeV199FWMzlS62M+Iff/xx75ZbbvFef/112YcuXrxY3p85c6ZXqVIlzzY//fSTlzp1au+hhx6S/Tz3/eabb5axcdddd0nFkO1xTFUh4xSbqHyl0pDvsdU2r776qlQa1qxZU67fY489JmODs9JLL71k2zynz0T//vuvV6FCBa9YsWJy9rjuuuu8WrVqeRkyZBAbqXCxyb59++SaMR6KFy8u4/i+++6T/SlnuRkzZlivYuZczjyMnczPVatWlfN4uXLlvL/++suqfYlhbknopSgXO5qxryhJEHTDhw4dKtHrbt26SYPVIGgTk3VmUx8eyJYhmxtNVbLNyPhFY5UsBhr12G7ERPZ23rx55TqRpRCaJch1ve+++6zZR4Yg14oMPTKNQrPIvv76a8n0tgXaueEyMJXECfrCVBHEBxnofhY61RCzZ8+OonXK+QZdaTLy7rzzTmm4+cknn4i+qe0GiECm1kcffSSNDmmoSkNa1hCqbZh3XGDq1KmSGUr2fjALmWbYXEvb0CyX60XDxmDWKv1G3njjjajbkzlzZlljO3ToECvrnUoqGtOSfQ558uQxtmFtpVoO2/yGjGiy09Sc6oK7777bqn2MjQoVKkjfmGDPHfYyVGvQENEWrVu3lnmF5ptUPPh7KBpyMufYbiDZp08f8+KLL0oPm759+8ZUxzFmafprGyoM6U/AdaSaimvGV7JpyaQOVx0ZTehRRA+PGjVqyH6eswhZ3W3btpU9v02Y57h+VLNwDqIqjbFy7NgxGR+2KzFcPxNRIUfGNP0myDqn8fqAAQPkPSrBbPdm4VqxdkyZMkUqhvmer3/88YdUBgWrD23AtaJR7sCBA+XZe/rpp+Ur9rJ+2Oy9kxjmFq6doiRl1LGvKEkQOsfXqlVLOtrfeOONsuFyEUoRcRKxWUiVKpU5ePBgjP1sxpD5oAmnLSjJpRz2pZdeCts817YcCk1Lt2/fLuWb4TbUOPttwqaVAzIHYtsbwnAgn8DBnU011zG0uS8He5tOQhrUckhG6gGpm1AWL14cVXu4hzg9IsGGzBKO1AceeCBiWZxx48aZaMJBmANTJDRr1syqnIzPu+++a+rWrWuGDx/ujFPfB4cqcg/cy9WrV4uTmia1LtCvXz+ZlwmKsI5xDQl0LVu2zDz11FO2zZO5eezYsVL2jwOJa4i8Eg6am2++2YpNyDggQYEDH+c+TcKB4DCOYPY0roBzHHt9pz74jkI+s+3Yx4bQuRAJHu6tbcc+wZtw6wj33AUIMOGMDuX99983ruybkaSA4L6Z8dK+fXsZK748mS37mE+A8YF9adOmNe3atbOeTLR582aRsCQAh0yQf+2w84UXXpC5x7+2NnD9TIR9lSpVEtuC9jG3NGrUSPbSBBRt2kcSAgTtI8GJ99kjEMCxaZ9//4L2sU/gvIZs2k033WTVPpfnFkVJ6qhjX1GSMGSU+U59nJj+Iu2DXqhNnXMO7L5GPFnnOD3IqMGBfuDAgTjanNFm7ty5omfJht9FsI/MD7JWXeS1114THWIqSDiohAZG+IzsVVvgSCDDh402NoZi0zZo3Lix6ITiFMyUKZOxDdliZ5oxs2vXLtHrfP755020NPYjwabGfiTY0Nh/+eWXJWs2FOZhgsQ4LX169Oghz2c04UA+ffr0OO+jA0tG/A033BDzHhmjZOjZguuDEwZHFgGRYcOGiY1o/IbOg9EG3WGy71hv0ft97rnnJHDDekz2NA5qWzz44INyH7nXZJuHex5tsmPHDslSJYMWLedQ0GanesSm4xL7yEB20T7l/O+b2SOw9vE+c7XtHgXMx6H2MabZ0/NcunLtqMJlPPvOShfOHK6ficI9ez7YhzPYJfvI1Pdx9fqx5gFriov22Z5bqJr3K4b97+OD31GUixl17CtKEodsX8qdye4NltsDZX9E6F2A8n+cqzQ2IouQ7FacIzZhY0Pww1Vct4/MDwJK8WG78S/N06gmoMTZRbCPkmcXpCfOFhz7ZH1Hw7HP/BFpxr4NqPrh5SpI2hQtWjSi37WRVda0adOIG9/ZamDqO09xIpQvX16c6H5AnbmaCiYkKmxCdQMZ21QjUank24dD/9lnn5VgMV9tkTt3bsmspOKhZMmSks3oClRi8AKCraxxfmUczg8a/dqQMfIhCcFPROAek4FMtjQgzch8TANOmyDnRpVIfND8ukyZMsYWJCGEC4r44Agm69sFqGJhbV20aJFUBLG+hEtSsGkfyQB8RZbMz+R3AZzQVN3wIpuboPvDDz9sXMHFM1EQAuckxRCEJVECuZag9Jdt2Au+8sorUu3A3EyAPVSW1iaMCWykMhfpJeYVnOiu4MrcEqzkWrdunTOVU4piA3XsK0oSBmkRNqpsDDmMhuoz2szMA8rCgxF4Mhw5zHPoI3sUR45N6tSpI5ub22+/3akNl0/Dhg3lelFm6qLz16VDUjg4nNsOLiRm+5SLC2TbeLkK83BigIxVf22l6geHvp9t5mLWII5+3sNmF+wDtK7pD0TQlX2ACw5BNMJ9eSCfYMXjkiVLpMKPTGAbEJAJlbgJVoeQvYpjy3ZWLfJ3OLOC0OeB96kwtSkTBCNHjoyVWc4+es2aNRKwQW4ER6FNkB/zJQKpACIw9+2330pAjMog2wR76zCGmVd4r2LFitZ7FHDduH4+XC/GLJr79evXty4H5fqZqHbt2lK9BwQMcZYjF0mV0ODBg62v0W3atIlZ20hco18BkqpkmvO97cQKeib4gWDWNoIhyM9xLZExst1DwcW5BXkiehP42B4DimKTZHTQtWqBoijW4LCEliTlprYbkiVGOMTTWAtHDU7W0EAIG1mbMjhIY6ArzqaVUuLQxl9sFMl4dAG0m3EY0QvAlWcRuQ6kHkaMGOGkbiSNq6iowWHjZ10mNrCf59RGZRAVShya6JNBtmpwO0TWKgd6mzCvIIkyb948ydYK2kcW4RNPPGHVPuXsYL2lYoCvgGQRh9N7773XjBo1ytx6662SfWkLnFjI7/DVb8LO2kGFAfMhWXo4sRUlmmzbtk30xWk27WIlImOYBpfM14qiKMqFh+A0Z0e+cnZUt6aSlFHHvqIkYXD4UiKJU9XFg1LQThx/yLaQfe6SjFFCmn04RGxmytPcFYdgfFCpYVNPlw0YlSJkbflOLjJScByRGWU7c5BrR4YUGatk0YSOEZpJ2ry/lOeSQU0WLfaFBkQSg56kTcd+gwYNzKxZs6SknYwyMgbRaEcqhexLJMpsQdCBJnRIPhA0xCbsRH6JIM4777wj2XE2wTYyytBZRVImCNlckcriXCjQh8dBzrOFPm0QshyRL7ABh9CFCxfGyIlgG/bgGMRxyfc2e9swn1DS7jd/Z39AkIv5BnkFnPrBprCKEi2KFCkimbW2s/bjg7mZagMXxgf7K+blUEcXVUK2+3j4+3rWtVBc6BfEWYNMZNYOMryZl201DQ8H1w372OPRzJlKFpfORkA2d6jUJo2xXakyDVd9xvi1fe7wYeyGyuNyPgpN0ErqcwtVFi+99JIkHlA5H+ybEAqJjIpyMWN/dlAUxRo4KnFuoevbvXt369I74fjtt99EoxGnAlq6bGbRgSWrkYxCm2XPHEwqVKhgvXwzIfvuvvtuJ7PNgdJ6GuhSQuxnhVJSTCY6TkOclzZBi5iSe2wMpx3Jgcom6JdSAotGrQuH4cTExo0bxUHEvMIzRwk0jZJxslICbbvkeebMmeJQpYcCzmkCD5Qb8x6HeCpbbDsWqPZBqoXvGQs8g1QBUeJu+/rh8OCQx2EPCRQOdDgZkAbg+tlc65jnghrhrGGhEim+Y4TAZ/v27aNqH/fOd+oDwX+qz8KNITSTmzVrZqINWdG9e/c2K1asEJmgIDT4tak1TWCGYHU4cHowVljv+B2CddEGSRHkZOLbE2bLls1Uq1ZN9oS2ZIPCQX8CguyuNvcdM2aMOC5tO/VZI5Cj+P7776XqKxTGzDXXXGNswVpGtRn793CyXrYzbpEFqlu3rlxHZEeYXwgSE9REisT2vm/cuHFy/ZC8yZUrlzhYsbVevXoih2LbMU3PJPbwVEGGQrJEUDYl2vBs4QRmPQuV+wKkyFq1amVswT2lEpy5JDQZwU/WIhHGFi7OLeydGA+7d++WnxMKcNmeWxTlQqOOfUVJwuD8GD58uGQms6EJdQDjsA7qYUYbHDHo2OM4wBZfw4+DHZsbHF7RaLoZH0OHDhXHuauOfRwHOIZcdewjM4KUUbChKRnoZEbh+MB2m7I8c+fOFfsIIrkI9pFRhnNVOTO4bmTh0QiRg7DfIBQnKwc7tGsJnNi0j4x9bAvah5MVHWcc6AQVbUEQBDkZ7OB68T1fyZbCAWLTcQQ4Dwh4IVPFWvH000/LV+xFLoPgg+twz998882oO/YjBcc+a3K0Hfs4jHjGCMDRpyXUkUVWt+3eNkgqEXzDic6YxWbWEgLW7dq1k30XzyPjJdpBMPZMVP7cdtttIhWIo5K9INU3VH7h1GTc0D+IAGO0MzD5+4sXL44zFrCROcb2fsZveh0EJyHOX5IVbMPzRdNrnNDhAjO+RrYtcAzyzLHG2naSh0KGNA5yxmavXr1imiCzHvO+7xS2BUlNJGNhB3rxfoCaveCDDz4oe2YStWwxf/58SYih4vGmm26KE+Sy3biZgCZyciRysE6EVuHa7kXGev/LL7/IukovhdDzT6FChYxNXJxbHnroIXlR9U1QGolhRUmqqGNfUZIwOLXY4MSHTTkAQOKBQy+bWBwyQW644QbZpNl07OPMYiNLRYGL+PaxwXYRNojh5DDIriVLKtjE0QY4zF2WqHLdvkjg/jKWbTcIXbZsmRzqcWK50CA01D7mQh/ss52Vh6OjatWq8n0w8EC2FO//+OOPVudF7PMDM0H7qPrCeYnsjavzopIwZPpSjTF27FgnL9Xy5cvFgUV/m6BjhoAcTmGybMm8ZN7DwY5DLpqQrEE/h9BmhwQkWHsZvwRMCM7hYOdaRxOCDeXKlYv1HgFXKoRcGLM4VkOzVXEAUwlUtGhRYxv2fCS9YI+LYN/69eutB2jCsXnzZkl0IrAV3FsxFnC62pTng0WLFpn8+fPHaeJLBRiVQl9//bWxCetqzZo1zbPPPmtcffYIDjIXu2rfq6++al1mMTHOLfgzeP5dqjJTlGijjn1FScLgNCer0VXIgvKDC6GZC3xmW2uQjLbbb79dso7IHvSze3xwyNkMjpCZygEdSR4OyqENVskIsSlJQcbMxx9/HKdJ6aeffirZjra1OHEMIhNE81zb0ifx2ccBio126LPnCpS+kmlJNg3VGKEwRri+NkEOA0ccmvAEmrieNqU8QiE7mXuNo5zsXzJrkUBxKfAQ1DV1NTDiO1Apcbdtn3L2MNfZXhtO53xjvQ3ds+AoxCFCfwWy5alKw8Fpwz4CDKGwV0GCibFMc26chdgXbcc+mdGRMGjQIAkCRHttbtGiRcQVk2QvR3uP5cvHuIrL9uEU5FzBnjk0aYJgju1qB/5+qG69a/a5em99+6iscZXEcP1cto+AtaIkaTxFURRH2b17t5chQwZv1apV3nfffefdeeed8v6ePXu8okWLem+//bZV++rUqYNgX7yvL774wqp9ZcqUSdC+OXPmWLXvxx9/9FKlSuXdcsstXrt27bxXXnnFu+uuu7zkyZN7n332mWebcuXKiS1cq6xZs3rZs2eP9VqzZo1V+6644gqxDRv5PtQ+2zBu/WewQIEC8t6xY8e80qVLe2vXrrVqG3PIkiVLYn5ev36917hxY69q1apenz59vH///deqfX///Xes52vBggVe3bp1ZXx8/PHHnm1atmzp9e/fX75fuXKllzp1au+pp57yHn/8cS9NmjTe6tWrrdrHWjF58mT5furUqV769Om9Nm3aeDVr1vSyZcvm7d+/33Od7du3e5dffrnnKrNnz/bKli0b9b979OhRr2TJkt6YMWM8F3n//fe9YsWKeQcOHIgz51xzzTXe8OHD5ecaNWp4kyZNirp9rLXVqlWLM8cxJ2fOnFnuK7DHWr58uecqrC229zAJkTFjRtnDRhvmPfZUmzZt8lxkwIABXu3atWU8uAjrGPatW7dOfj5x4oSMicKFC3sTJkywahu2sLY9++yzsj4A45h7fvXVV3vz5s2zat+RI0dkXI4dO1ZsdY2tW7d6RYoUiZnjXGPx4sVeiRIlvKVLl3ou4vrcoihJnWT8P9vBBUVR7DJx4kQpCUczFzkKHyQLyIqyCVqNlMBSgk2JOxnyNI9CS+/XX3+12jwXvUtf4iEclBrbzCzctm1bvNk9QPav7UZv3FOaIJIlSBYt2dPIK9mQZwllwoQJ0hg0PugNYLNJKJUNCWG7GgeNeKQdkC7ghTwKoFFL1ihN1lyHZmBIVyBT4SLMgUhaRTtTiSoM5jbmEGD9YK0g0xE9+zvuuMPYhAaNrF9+JQs9AT7//HOpWqIKJ5wEmGtQ5YKcGl9dlcR57rnn5Gu0obE61Urc39DG4UhB2NQ6p2KFzHyqCtH+ZYyg+4suMd9zvdasWSPjBInBaMupbdiwQaqnqDRk3uD6MZ5Hjx5tKlasGLMfpA8AL1dhj0D1kgt7hXD41zXaje3pUfTbb7/JXh55itDni+ePak2bWfE04UT2DvtCK1vCNV29kHCPgtJP7EOR4/GrWMiEZx+NvZxDot137IcffohVxYI99OoA5j+/Ao29PFXErHO2QN6LeZl9M2ttaMUyVcQ05bYFaz/yszSpxbbQKmYqNRs3bmzNPqoyWSeOHTsm83Oo5OIXX3whFZy2cH1uUZSkjjr2FSWJ06VLF2k0SFn49OnTxVk5a9Ys2ey2bdvWCUmKr776ShrV4hjEkY8GLE18on1gUhQlcucWEgkEv9atWyfa5r5jn0ZvHJ5w7rvOt99+KwdBvroIczdObL66CE5CgrAuarLCJ598IofVggULGtdQx354Vq1aJQFgnCA45EKdH+hhI3VjE5xtBC6///57cVQSZKIhJzIuLsgI8WwR/Pj5559ljkZiAacgAVjbEoeRoo798GhCwpnvVcaPHx/R7+IMRv4pmhAUnDZtWkS/y56L5CebiTo4fuODeTucJGO0mDNnjvRTig8CNzYb1HIGp89DfCD5ylpiC9fnFkVJ6iSO3ZuiKBcEou5kSxNlRweUg+dHH30k2QIcml054NWoUUNeiqIkDshWZU5hDnGxP4YSHQiIkHXuqmOf7Eb6K7jo2CcDM7SJqEtkyJBBGpraaNBIIgJBGVchAYHECF4uQrYlGvDKxUe0Hc9niu1KwsTWawxHrsv2BcEpbtMxfjoIKtgMLJwOm0GZi2FuUZSkTnLbBiiKYg8yAyjlRC6DrDdfVgaHAk2/KBN3BRr2UH4afJEVZxsy8siiyJUrl1QQBF9jx461bZ5IUFSoUEEOB6H24SCxCffv1VdflezK6667Tp7D4CuhzJALBY4qDnqRvFavXh11+yK1zXbWDJljqVOnNjNmzIjl2KeMHHktnklFSeoQRA9d1/bt2yefsTZT8WATX/Yh+CLDFYoWLSqyWtGGZsjRlq85U5CIoWEz0IAWZxIBpF69ehkXeP3110WqD+bNmyfBN/YwSD0oFwdItP3yyy8iO+IiNDGlOthVkN8Jnfts7EkT25nIv3aLFy8WKUMX4azLvOdqI12SX6h8oJrKRVyfWxQlqaIpc4qShGFj6JeFo3tJeTbakmjDc3hHt9E2OMdffPFF0aQNbQmCvIdNiYz58+ebBx980LRp00auJaXhOB0GDhxocufOLWWdNqEXwTPPPGM6dOggUh2tWrWSDTcSAQQj8uTJY9U+NIZ//PFHyUbKkiVLnM9DJRaiAf0cfMfV6fD1xV3S1XcFnPlcSyptGKccOtFexVHJ4fPDDz+0baKiWAM5qubNm8saQl+CIKy/tnX1WVfRz0daIXTdpfrChq6+T5kyZcSxwFxYv379OBVBtiFgzvzWrFkz+ZngNfeUn9kroGtPUMTmngoZNAINwH3mnl5//fUiFUTlJvrOSuKEPd6TTz5pPv74Y/kZ5yryVKy/pUuXNo899phV+3BWUhHsJw758wvjAolNqnFs8tNPP5mWLVuKYzrYcwwKFCgQIyloC5fPRMAeDy195miuI2ePJUuWmCZNmoj0l+35ml5AzMM49/v37y/noq+//tqMGjXKiZ4i9AEgAMy+YPLkyTIf9+vXT8YN0rk2cX1uUZSkjjr2FUWJcaLec8898kLDnkPzww8/bPXq4NzA6Uv1ABtWpD2C+I0RbTFy5Ejz1FNPmc6dO8vGFdkEmlw1atRIMtBdyNbv2bOneeKJJ+T7atWqSeYg95UsedtZ3TTo++abb6zIOcQHz77LoGGZWOA5IwuUQwmBw5kzZ8ohpX379hIAU5SkCg5pDsJUryBpE8R2Q3McHnXq1BFHzJAhQ+I0GLS9btBzhx5A6MGztoXuA9CKt9lzgmaXrK++A4s1jvdw5pOFSZayTcc+et3+OkfAFd1pbEQ/HAcXmawE/l2HQERoc06XoHeHDcm5Pn36yH6UipFgU/XWrVuL1Idt5xtOVc4b9N4JJpc8//zzUklis1IYRzn7lurVq8u+JXSuCz2DRBvXz0Q0RW7YsKFcO3oDUK0ERYoUkUQYnP733XefNft+//1307FjR0l6okmtDza99NJLor9PHwBbEFwYMWKEZMO/8sorMe8TFM6XL5+MHZtznutzi6IkddSxryhJGBqmBTc3w4YNM506dZJsPBwPZHrbhA1EsWLFTNeuXY2LbNiwwdx9990xmrqUTwKZcDjQOSDj2LRpnx9gCNrHNSWDkGwLmw4GDiEuNBI8HWQeca1KlSrlpL2UEyMLZDvTLRzly5eXl6IoJ6F8nWzLP/74w7oTP77mtKxhb731lnERAiJUncVH/vz5jU1wDvqSHQsXLjTJkyePWWe536EVGjbtw8lPBYTvLHLBvqCdyFKFZiVjK1JMQ4cONTYhm5vrGGof+xqCOmTb2mDSpEmS8YszlWfPByc61xMJTqTybIF9VCyFBvepeLUtzUOja+TH3n//feuZ5YnxTMT9u/XWWyXoGjpH+/fXpmOfagaCwSQ5EcQMXVeoKLDp2Gds4NCn2jsYFCSAkzdvXqnwq1SpklX7XJ5bFCWpo459RUnCoIFN1pMPuuvvvPOOcSnwgMSNq3AA9jdflOgiK4O8AuWKS5cutZ7ZGM4+MvWQW2IDZts+yv7J3qZhs4uZd66Xnbpe0s4hBFmKxo0by8/IQlEJhOwDTUsTg9wDNtLvwVU4RKHTriQeCA6y1nLfXHTss+7i3HIVMhd5nQ6c6szZZPBHk8qVK0sVH0F9HCHI9fmQielL9NgC+1jDcEx/8MEHMfaw1yITEwecTagiwPlGFUE4DWcqDGzaSLVI06ZNpQIt3Ny7e/duGd+2IIHD308FndPsB5EZtN24Pj77eN+2bQQ0sYmx4WIfD9fPRPHdW1fuLzb4+05X7XP9+rk8tyhKUkeb5yqK4ixkKPAi2OBKFlkQyon9zT/lp2SDkNVNxgfZDLYbhBK48bMqOLyjeU55OBk/VBTQB8AmNWvWNLNnzxZ9fWzx77f/8ptIulB2GswCpez0tddeM7YJlrQH8UvabYOUB9mggBMEOSiuKU5DJKJcaHztZ23hoHnooYfkWWQsE9QBghDYbAOCH76WOUEc5D2yZs1q2rZtG/M7OA25365CQNHlDC6CNjYCTEi44Vx1pdlhEJySZF2SFZqYg0ZURIwfPz7qf5c9AHsW5heefz+7ljmQgHCJEiWMTQh00N8GSaM77rhDdLBh9OjR4lAP1+8mmtCTgN4OzH9IF+HID75sVhkCmtwE0bl+obbxsp2kQLYv9zLU+da7d28JNvm9FVyyD0c6exbbe2b282Sbo2HvogPd9TMR95Z5Dwdw8NlDkocEHtv3F/tYE+gfF7SPeQZpUNvVpdjnV9EH7WO8UIFtW7bU9blFUZI6ybzQGkJFUS5qkOyI9GDJoZSst2iCFi069T44AcngwoEZ6oBB04/Dnysgr4B2I1kLHA5cy0imYR7l4Rzcsc+2XiiZ5cg+kNEdzpmAg9VGA12fihUrmm7duomTq2DBgnLtyNiHbNmyydiw6bREt94vaWeT7S/nHKpy5MghWtm2IGuRANe2bdvkZyoIsI8yXrJW0YglqGMLDu2UE+OIwVlOs8vu3buLFBmHFKoLcHLZgkMcTjfGLGOAhmrMjQToqHJh3iPz1gXCNVpHl93m2A1CgDC0CSLVSrayywjSFC5cWCS+cCTx/AVBJu3PP/80tkBCjsouxjAVBaFzM8E6sqldh+oggu2uNhyfOnWqXF/WFxdB75k5Mtr9grgezHdoiLsI6y1BGvbHLsI+lIA0SRw0gq1du7bMJ6y3Y8aMMffff79V+whWI4VSo0YNWcfYC+BUpSktUi0Ev6JdgUF/LB/WMqpaWR+Yi4NQKRTtfUtiOxOxb0Lii2Qdf5+Fs5rnkepSmxJHBEPoH+IHHthDE4ijITF7K5u9WQC7GLv0I9i4caM40tm/cPZAGo9G5zZxfW5RlKSO1swoShKDDUOkB10bUi04PCLNkHUtE5RNP9nc4TLQkEWx3ZCOgE64oA5l5WQoRVtbEqc0zg0qCFzE9bJTl0vaceJzqOOQjC0c3Hv06OGMjjPZvAQ/fKcqjkoy4QkmAVlnNh37HJo41PnOcezr27evHNwJzuJYsunY5/7SbG7w4MExvTuC9O/fXzJbbYGEB7JoHDbDZcVzUKaRsw3od5KQfr3tgCsOmYQk+QhqKucO0njca1cd+zi7aNYebce+63IjrtvHPpTgHAFqnIM4y3Gu8ryRrGAbnOg496kg5dki+IYzH4e0jb0ge4BIzxw2qjES25mIhutk53/xxRfSTJfqG3TjqSy03beAQDrBBdY3qjVxVLMPHDRokFSY2ob+HIwN7jdnI6QsGc/+XGwb1+cWRUnqqGNfUZIYOBVc2CDEB5usRx999Iz+G3R0yRjwnXKugd4+m3NXYfPoN9OLduabizqmoWWnHDpdLDv17eNA4lpJO1m+yBcxJsmKohQbKRnfaW1b/z/YQBK5EbLi3njjDWcCD0H7tmzZInOcXyaOfbYlUkaOHClVDm+//bYc7ELHMc3UbILTiMoQGsIjXRDqUChUqJBVibQzXeOi7ehy2T7l4gaZKjJT6f9kex4JBzJGJEJ88skncaptXACnKjKH/fr1My7CmsH9pb+DC5DA5PJ8dzZnIltQxUqlF1W4fm8ll8ABTZUD45eXayAThJQbe3gX5DQT29yiKEkddewriiJ60mTNUApLYz8OVJTaJRaQcyHbx1XHvhIems9Shk1zWhcPyB06dJCyUzT2d+zYIVk0wbJT2+CIpqQdpzSOYA4qwZJ22+D8JaubwBEBCLJTyeAnU8p2uTiZgn///bfonCONQhamL7PkQuCBv0/FANJA6L/efffdMZnc2Ed/BduNkcnIb9SokXER7KNSilJxl2Hcbtq0SYI1VNO5BgEuMvPIVLWtva4kDehfw/glIIcUSmj1GRm3NrWmsY8sZAL77Fv8PkY+fJYhQwZr9iF3R1Y82b8uQmUca4ftqsL44Nljz+I7ppHlo8qZvSD7FtsSm1QTUjFy3333SYC/bt26sgdE2nLo0KFWJfB+/fVXyej2kzhcg74YVKTZ1qqPD5IlnnzySScDmolhblGUpI6bq6qiKFEDJyUbQ0om/RJjHA3IPKCLaHsTq1y8oGfp8gHZ9bJT10raQ0HaiZ4TQTjMT58+3diGACbZUWRFkc3ty5Ph5F+8eHGCUinRgLl4+PDhMkZwbiHDAzRzxsGP/r5t+1gnXMV1uQx0knEQIgmArJcvcdOxY0fJCLYNWZcE5cjQ8xtJMx/S9Prhhx+2bZ5yEUOwNaEMZca2TWiG7I+J+NYWm7AXoHeMq1Wivn2uJg9RAcm8B0jeffbZZ5LUwR6B+c+v7LMBaxqOX64fsEcgS54qCPapJMnYlBDk3iLFQ8DatuxOfPYhceMq/tggSOMirs8tipLUUce+oiRhkO2gKRNaw0Ti/Sg8Gb+872soK0pSPCADkjsul50WLVrUmZL2xAQa7GToo7UehKzkGTNmGNsgw0PW24MPPhjrfQJLEyZMMLbBuYt9NEsLNh50BZwbjzzyiATgot07JBLQG6ZyhXX3hhtukMxLAodUCeEUIUvUJnXq1JHADRmgBAkJPjBWcLgiXVG9enXjOgSMXbz3SsJQneQyrge22Lsz/5GsQ6Pr0H0UUh/IgdmCOYT5hQbJ7F9CM8xtricENEni8HtR0dsGR//jjz8uzkwqTG3iem8g5Bd5tmrVqiUVuaFylcgyUoljsxKyS5cuUjFyzz33xElcI3hts3KO/RROfc7mlSpVitPTgf2fzWx51+cWRUnqqGNfUZIwmzdvNjt37jQDBw6MpZGMw4ss4JYtW1q1T7m4ifSA3K1bNzlQ2WoqiaNt37598jUI1QShVQa25DLC9UjIlCmTsSmPlZBTjTmG3hO2QM6GOY4qBxchOxDZJzL2XYQMwbVr10rWJYfPtGnTxvqcRsk2NXaZM6huwBnD4T3UeURjP1vNhxmvZFYiW1C8ePGY9+mhUKBAAakisenY37Bhg/Qn4P4GJdJwNOBUQC7AFcc+ATqqH4Jwr3kekSnjpSReqKBibSPA7mI/HhxcyMvRtNSVDGVk2pAOjE9DnH4tvuycDVq3bi1fW7RoEfbz0H1WNOFvkxXPPaW6EGlD1jIXe++42BsIyUXWDqDhayicKW3uadi3rFu3zrz77rvyCqV///7i9LcF0ovbt2+PV1+f4DqJeLZwfW5RlKSOOvYVJQlD9gSbVzaDoYcmDsu2S54VBSgx5kAQbcc+h/amTZtKFlSo88iFTSyOcbJnkOPhIOrSAZmssokTJ8Z6DxvRrsWhTrWGTXDE4MB0FexzNegANFAj2zKhxs42Ydwm5Hy2WUr+zz//yJob7vqR8cbB3ib8fdb+cH1PsC+0ysUGSGUgW0R2bSj169ePkdZyGXSUXc5uZIwgD2WDESNGmPbt20twyQ/WkDndvXt3qwFrf12lio/gIU4uoIqFKhwqbmxqnMOiRYsk4zc+0qVLZ2wSLgnBFajYI+ucDHjOR+vXr4/Ri3eh947rvYGaNWuWYN8d22MDiUXklOLD9nyMPGS4vbwrVcyuzy2KktRRx76iJGFw6iNXQGksGpI0FeLQQiYhGYM2tSQTAzgWbr31VlO1atUEP0NOgxLKaEPGbLt27cI6sYKfcWDGkajEhuuDk4teExzyQrEd+MJ5STbypEmTnOuFQcZsuMwiyoy5lgRLbDY2RV4ExxqOIjIIbVWDxMedd94pzV8/+eQTcVS6lq164403ystVbr/9duMqVPrgNMd5yfobZNiwYdb1a6kaIKuR/jtBHWz2BjjUbdtHNRAOJHoUILUU6oyhJ4UrIGEU6qjBOYPNNiUzgk7W0CxfnDNk/zL/2GpwiYwHVXqsITjNqV4ic5rxwnpnE/qd8OwhJYMjlX3077//Llm2e/bskb4ZNgmtnnIN7qfLjBw5UmRISUIgA529AWMY6TT6F9nE9d5AzBu8XIXAgu3gQkK4tg9NbHOLoiR1knk2U/oURbEK2VDo+yLHA0gqkJmM7jkSI8FsLbQHOehHE5qn/vzzz2ElHYKfEYhgA05wIprQdPiBBx6Qr6HgtERHFK1JW3Bv33vvPfkaLvMSJ9L1119vXIcMPZ7VaGfqlSxZUhqBlS1b1rgIDhgyylxyZEUCzd7QrJ07d641GyhhJ+CGI4a5DkdrUEoBx7DNQzxjk6w8HG84jkLvMdePoIQLkIFOdUvOnDmtZ7yFY+/evTHZjVxLFyBgw9qF45K5GFkFMi4ZEzT3s92cu3PnzuJIZX0jKI2DesqUKWbLli3mt99+E+e/LWjIzbzsQuVAfPiNkf29VRCqlcjstgVOSho04xT0GzeHylSF29NEU2cahzmyD0G2bdsm+twEdqgIsxkUJlufsREERzB2I9tnU5aHtY05ORw4XZE1olrIlo2hlXxBWD/ImLc5vyhnD/t0Agzh4HlDyo19dah2fLQgQLhmzZqwn/n7QPor2HKws7Yyz4WDvQv6/1Rt2NrHuD63KEpSx40TjqIoVmATg+ZgJNjYiK1evVqyZ8I59vls1KhR8hmO12g6X8lmpMkWTkG+50AXhPJsgg2hB9NoQdDj4MGDoj/M98FNILFcJEiQkWETpsQPjkD0Vl3FdfvCwfPHuKXk3SZkHaOnGh+2K1gqVKggvU/iw4WmoEhAPffcczLX+QdjHNWsKQULFrRtnjiQmIOXLVsmPzMP0vyNAHBoU79og9QDzyCSHkguYRsOBbT3XdCopcEgayqNuWnWzPqPXj1VTDabCyaGeY/7idwYEnLc01AnDAEwmxDsp+nm4MGD5VkL7RNj26lKlRzBrlAYs1w79lc2Hfvx2Ue/DJ5LXjYlM5iTabLq5+1R7eVXZeB8I4iI841xbWOuIWjkOwf9Z8+X9/DtI0BCZrrtfYJyZtCTgKAhyWHB+wnMgwQVGRtUHPi9FqIJ/WEIuPpVVEH7/O+pfqVpPIkf0YZgOpUh/nj1r1nQPs5t7KGD1XTRwvW5RVGSPGTsK4qiJMTOnTu9t956K+oXafLkyd6dd94Z9rOePXt6DRo08GxQp04ddjXxvlKkSOHVq1fPO3HihBX7ypQpk6B9qVOn9tq3b+8lFjJmzOjt3r3byvN3yy23eJs2bfJcZMCAAV7t2rW9PXv2eK6xfv16r1ixYnFeWbNm9S699FJv6tSptk1UzoE1a9Z4adOm9R555BFvypQp3oIFC7wJEyZ4VatW9a6++mrvn3/+sXp9Z82a5V1yySXes88+6/3www/evHnzvBEjRnilS5f2ypYta21uVs6d48ePy7z8ySefOHkfu3fv7j333HOeqzz++OOydrhsX8OGDeO8P2PGDFk79u/f79mkUqVKXpcuXeK8P2zYMC937tyebZiPCxUqJGvs4cOHvX///df7888/vXvuucdr3bq1t3btWq9mzZryv8MGAwcOlDn4t99+844dOyavX3/91bvxxhu9d99911u8eLF38803y3Ngi6+//tp7+umn5Trdf//9Ma/mzZt7tuF+Dh8+XGx54IEHYtkX7rmMJjxvrLGdO3f2/v77b3mP/emgQYO8vHnzyr7h008/9dKkSePNmTMn6vZt3bpV7Bg8eHDMvhk7O3bsKOembdu2eT169PAyZMgQY380YZyyfxozZozsoVjfuGZNmjSRe439LVu2lP8NR48ejbp9rs8tipLUUSkeRVFOy8qVKyUTk6/RYNq0aeahhx6STAWkgUIb8pANQmYAWQEJNUi8UCCPgV1PPvmkufvuu6WRpA8liEjG2NTERnqC64O+P5mDlL76kCFFRkpiKpWMphQPWTDBUl2y88gmo7ol9J6SsYw0QDQJ1fpHAiWclAzQZM3mM4icTBDsw06a0dnOWvUh24h5jTmFEmfXwC6qbpAocCELPphZhmwM/SeCMGcj/YU+d6hURTShbwhScqGN8pDJIJNs+vTpkmFrE64VGY5UFKD7y/NXqVIl4wpUfZF9TnUcfQHKlCkjL9uMGTNGpKi4l0gmIO8QBPm7AQMGWLMPiRtkAgcNGmRchLFJBQYa9i7CfEe1CD2f7rjjDtn//fXXX5LB3alTJ+lfZJMZM2ZI9Qo2li9fXrJqmQsZy8OHD4/TNyPa3HPPPdIjiz4tQajgpNIBeSi+R96NqtNoVxdQbUafhNDKEKpI77vvPrN06VKRc2FvbaPBPdVKzB9UDbBOsI7NmjVL9qBt27aVtc8mDRo0EHu4jlwrZNuwk3MJfdFatmxpzTZ6J6HzH04mDWlB9jCtWrUyzZs3F3lXKsCiCVWarGf0yQiF8fLiiy/KuYk9Ks2wkQWLJlyjQoUKxbmHZMjzPteXeZFrxxhCFiyauD63KEpSR6V4FEVxDhwulEIicUPJ+CuvvBLrcw56/I6t5qUcinmNHTvWuIjv6AiVCHINDlBs7MPpWQY/w9keLU1JmqZxQIoEnNTRJtp9JM4EJBLeffdd0efGEc3B2AVHYEJ6pjhh0GzmoIITEy12ml4TtAkNKNqQksGBSXCJgx7OhiVLlojTGsehzeBcfHIUjFMc1HxuE/4+zrdQcFDjULJtH30mcEAj5cY6hpN/48aN4ixkXQnXrDua4KSkgSmHc+xDOgN9/Ro1apjPPvvM6tgoXbq0zDPxgUa3TXBOElBCj7hy5crGNbivNDHHaY5UkGvgwMKRj5MSmS/WY4JxzIehDiUbsD4gR4HU0o8//ihjFycrtobrZxRtmFPCSWeyJuMgJOBPYJ1ECYJ30Xa+xWcfczOfAY3tw/V/uNCQxMF9Zf1n70lCEw3DSZRhr2C7RwtrBM2l2bPQsBnZO+Zj5ucqVapYb0wc37115f4mBvvC7ZnZ63Fv+ZxkIuR4XLp+rswtipLksV0yoCiK+6xYscIrUKCAFQmghQsXei7z8ccfS/lmkJ9//tn75ZdfrNl0MUjs8JmLEjPhQKaKZ9VVXnnllaj8ndWrV3v58uWzOmdEypEjR6ScmbJrJFqCkl9t2rSxIj0WhDLwzJkzi9zIG2+8IeXXPpRkf/XVV1btGzJkiHf99dfHkdzZuHGjlz17dm/mzJmeTdq2bevde++9UioeZP78+SLngZ02KV68uMgY7dixI+Y9ytgpYX/44Yet2nbw4EEvS5Yscg2D9/ePP/7wrr32Wu/ll1+2ap/rIBOITBWJlunTp5fxEHz17dvXqn3NmjUTOT7sY50NtW/8+PFW7VPODSQqb7vtNm/Lli0x7x06dMh78cUXvRw5coiUFetz4cKFrVxqZHaY+4L7O76vW7euV758+RgZRObvaLNhwwbvqquuku/XrVsn18tn9uzZIhdkE2TlfJmTadOmiSSZD/I8SIDa5Pfff5e5JXQO4dohw/j555/Lz0i3fPvtt1G377PPPvOyZcsm0k9Bxo4dK3bPnTtX9gyMjVWrVkXdPqSU+NuMTx/Ga//+/UW+iLMmUmQ8o6zT0cb1uUVRkjqasa8oirP4jbOQRqH00IdMgPXr10vzQZtQbjpixAhphBiEclOaX1KebaPpcGKH+43UERk0iQGyR8nSdLXRW7du3aRZ2YWGUlykgZDIopEWMjKU1IfDtrQMGaFkQFGhMWXKlFifkXVJ025KsW1BqT2ZoZTdh2YnYx+f88zZ4tFHH5UmdNxDpMiQvUE2AWke5j6qNWxCSTuZb0WKFBG5NqqYkHpAxqVZs2ZWpaDItENCgQzftGnTxrxPliBSAZS72wRpIDLyyZgOQpVc9+7dE2w6HW2YY8hi5flzZa0lyzeh5+v66683NmHsIt/hqn0+c+bMMfPmzZOMfapskGYJV91nC7L1Wd/8jH0qhGxKMPowbqnIQLKD+Zm1lgxv5L7Yr2IjFV9UxdpgyJAhUvlDg3oqMcj0RQ6PKiWy0YGKCGQko02w8TH2UIWIvAjSIszbfiNTWwTtY45hrqbKADlGF+xjze3QoYOpXbu2VLT6za7J9G7cuLGpU6eO2bRpk7npppusVN/QuHnq1KmmXLlykvmOjawf2IjMF9VgnNtoEmuj8gt5tF9++UXmE8YGaxoSUNx3mp2zznHupHrTRja863OLoiR11LGvKIqzsOHHaYRTMujYZzPRtGlT0XJkg2gLDiEcUEJh88WmEMcNeo1KbHBgocHOi++DkiIcUjigNGrUKFH1AVCMOF0YqxyeOIhAfLr1OGqi1bMjHDx7viMw9DnjM9sl967bx6GS+a1fv36i74vuNJItvXr1Eqkg2+A0wunmyyrs3r1b1hBkC+jfYhMCSpSqI+8QCgFNWxJzPhzacRCx/oY+ey7YBwT2CbwRRKRPBtBLBmkK9JFtgh3BvjaugVM/Ice+bXDks4bgwCJYTJCJoCGBc/oX2JY3wjHJvpSgA45L5mLsY8+HjFbRokWt2sc1Q6aFgDWSX6zFrLfY7EtoIcdkCwKEOKTZPyMtxxxDkg57aRICAK1z2+CsJMjKi308z97DDz9sXAHn7xVXXCGBa2TxCJjY1v8HHPsED5E2ZKxgI0kKvuwXYwYnug0IgHz44YfiuGfPsm3bNrGHYKzfL8tmLxnGJ9cN5zj7K6TwsIux4Ut/EuC0hetzi6IkdbR5rqIozjXP9cExg67lokWL4nz2wQcfSEbXe++9Z2yB7jWbVjTFw2W94eQKp/Oc1OFAR6YlG0AaDAazVjkk4zhyWZs9FDJr2Izz1UU4OOOkixYEZxibOGdmz54d9ne4zzZ6FPhwYCKoNH/+fMk4IjuQe4jTnIaINMYm69sWZCwyd5DZ/fnnn5s///xTsrRwaJKxT2Ni25ndytlD3xh6oFD14DeR5FkkKILDmkoNm5BZSWCJQB2Zq8wfOELo+fDxxx9brcggIELAkPmjdevW4lClN8YXX3xhPvnkE8m4tN0Y2YdGgvQmINDkUrZ50IlOtiqazb5T1TY4dWkMOXr06BgnORriOANpTosTneQOW+DEYp1gHPhBLq4hTUGpCuL5UxInBC4JNvhVK+wTeO6o8KP/CU5rm7rhPHfMJ361MmOB8wdfOaOxduC8VhInX3/9tVQN2KwoTAjWWHo5cO5VFMVBbGsBKYriPugR16lTJ+p/F53mEiVKhP3sgw8+8GrWrOnZZMKECaKDPWfOnJj30Gfs06ePlyFDBm/v3r1W7XOdWbNmeceOHfMSO2jJBzUxXcPGUo829/Tp0z3XtbDRW0XvtWDBgqI9fcUVV3jFihXzDhw4YNs8r2nTptKz4NZbb/XKli3rNWrUSDS777jjDu/EiROeC6xcudL76KOP5FqiX7t161bPJejR8v7773u9evXyRo0a5e3bt8+2SdKPg+eOcckrXbp0op/L98mSJYuld160aNGo24fOMOuabx9rWcqUKeX7Sy65JJZ91atXj7p9zCvMuYcPH47zWZMmTbx27dp5tqGXA1rY3E//vt5+++3Sp8AFpk6dKnsr/x6nSJHCq127tmiM26ZChQqytwoHOs6LFy/2bMJz/+eff8Z5n7klefLkYZ/LaIMtzHtoX2/fvl3e++2335zZk6LRPWDAAK9169Yx7/34448XxX5QOTlHd+3a1fvyyy9jegbZHrc+7J3GjRvndezYUc4ggJ4+PaJswzmbuZh1lWt39OhRzyXoMcEe4O6775b9XmiPJUVR7KJSPIqiSPY02apk5JONQrbKzTffHHNlyB5APy/aUM6OTdOmTZMsAR/K/8j0ti2pQHkk2VvIAZFZhg4n9qLJSWZZYtGItwVa3GSpkqESmq1MmTGl0ErihCoMJBOIK6ARSkbjjTfeaFyiXbt2osGOHigVBGRakpHM+35ZsU0orf/oo48kS4reBcuXL5dMb7LyXJCpQg+WjHP065Ef27x5s8iiUKn01FNPWc+8RM6LXgnZs2eXuRgtXZ5L7vf9999vzTaeLWTkIsFGljdSPNzXSOC+R5vt27fLuA2XtY0kBRIpNkHegbmP1zfffCPZ+khSUF3Ie8iQ2OzHgoQCcg5oXvft21ds8auW0L3mc5tSX2TBsycNVwmGRJRtKaj47KOShPFgs5oAmOfYv/sSRlSfUd2CvMeoUaNEnswmSHnccccdIo3G/s/X0qdCgwo1eqAoiRf2J8jdMBaoIuCcxl6esxL33ua5iP0oGvHYQYUSmefIklFZRa803re5t2Kv17x5c7l+2MN+BVkjKvlcqEJD1hDpRSQNW7RoIWOV+4qNnNFd6DGiKEkay4EFRVEs88svv3i5c+f2UqdO7V1zzTVerly5YrLLdu3aZds8r0ePHpLBUKtWLa9Tp07e008/7V199dVeoUKFvD179niuZKeQJdi8eXOve/fukv2hnJ7+/ft7rVq1ivM+FQ9kmiUWNGM/PIyDMmXKSEYo1wjIyCtdurS3du1azyZkLh45csRzFbK6qf5xFbLJMmbM6H377bcx7x0/ftz78MMPvVSpUlnPTKaCgHXt999/j3nv0KFDkkVIhrwLa5tydixatMjLlClTnHWW8Vy+fHmpzrDJwIEDvapVq4bNFK1YsaI3bNgwzyZkSVOdFMrBgwe9vHnzxmSx2uKHH37w8ufPLxncfmXS5s2bpWKpfv36nm2Y41jXqMrwoWLvzjvv9Dp37uzZhqqVp556Sq4de3q/mpAqNKpvbGcBU4H21ltvyfdBNwTVVTYqlNiL5MmTJ6IXtkcbxkGk9jVs2NCzPTdTjUaVNxUZLVu2jPnsiSeekLnRJqNHj5ZKpf3798vZgzOIzy233OJUlSl71MGDB3vlypWTcUK2/KBBg5yoOvT3U1RB3n///bLnu/LKK2WeVhTFHpqxryhJGDKg6tWrJ1kVZFmSeQlknfP+Sy+9ZAYPHmzVRmwoVKiQaOqT0UPjQXT327ZtG2OvbfzsPOXMIIORDLJQnnjiCdFNfuONN6xmz/Tp00eyGqnESOgzNM9t6K4yNnr27Hnaz6iMsAEZ01Sz9O/fP0YznExQMgiZb959911ji19++UWyjHiRpe83dnMFdPW5RvSh4DljPLgEvRPIyifD14dsLWxFJ5bGbzYzzLAP7eFgrw6y39FIZs6hbwsN/ZTEB/r6NIykoo9sUDJ/0dinkS7PIOuHTago8DW6g7CW8T6f24S/H25NYA1Du9u2faxb69atkybIZPoybvft2yefUV1AzwcfmjhGu1Ex9lE9RXUItvHM0QMAqBYJ9n3i96KdoUzlGRm/PG/B/RMZ/GQAo9FOVY4tsI81AoL20WuEHgXRhkqLgwcPmu7du5/2d22cOcgm59xDw9fTYbuaheefaiCqvEP37rbub+izR+8nrmd89rlylmPeYH2jQp3KG3rydOzY0bRv395069bNelUkcx/VD/gRWH9nzZoVMw8qimIHdewrShIG6QRkYwYOHBirhI4moJTH0hzWBR588EF5KRcXu3fvDuu451nkM8rdbZa1c0B/4IEHwjr2+YwSVD6zIVMFBD7ic+wHP/vpp5+ibJkxBw4ckIMIZbs4aYIQCLHp1IdKlSqZHj16iAOkX79+4gDGwU/QMFOmTMY22IGcDE1yOcTh5MI+Dno2m/cFHQgcNsOB3a7KZQDv27YPpk6daiZOnCjXEQmj4IGeJrC2IZD+448/iiOQw7sPMmnMLzb59NNP5YXUEs4kpEYIJCIPRdNfmyAThGQWQaSgIxA5MhyaBDpt20fgkPkk2DAXJzQBz0hloi4UNC0n4BoJNsZx165d451bQrExVxMMoclqKDx/vGzKQAXtCx2nNIhnHNuAObdp06biRMWOYEDYBWhujX0080XWq1ixYsZF4nv2gOvqN/110T4aJN93333GNuwFvvvuO9mbfvXVV5LUQVCHJA/2pqx5SOAgZ1WwYEEr9iGPixzPuHHj5AzHvpRECtcSUBQlyWGxWkBRFMsgi0FJezhJiu+++04a5CjKhYLmx+EkAZDLKFmypLMSOzQIRc7DdiO6+JbwBQsWSDm0TSjF9m1AMsOX4oFp06Z5pUqV8lzhr7/+8tq0aSNNEWli+sgjj0hzSVca1P7vf/+TcYL0DS++pxGiTTZt2iSSaMOHD49ZPyhvRwbluuuuE1kPm8ybN0/smzx5coykEU0kn3nmGe+uu+6yfm/ffvttuZcPP/ywPHPIjFx77bUyppHRsA0yBVdddZU0lmZcPProo3I9KblH5khJeF9Fs2sacSPP9+qrr3pPPvmklyVLFpF7sC2xhcwXkjs05mY8YB8SHmnTprUu5aGcO+yfqlSpIvMxkpXsYWheilQQjddt89hjj8l8xzih2TAsX75c9gQtWrSIuj00TWUsANcquFdxAc5i/n1jPeM+ugp7Y9a1GTNmiOwOUjystchX0XTVtkTfnDlzZD1bunSpzH1I8SBNhczrZZdd5u3evduqfVyznDlzepdeeqnMxTNnzgz7e4zvoAxitOA6cf3YB9SoUUOkeJDkURTFDZLx/2wHFxRFsQdZ+ZReIy1CeS5TAjIKSCqQlUeDWEW5ENAkt3z58pIBSvkr2YNkDNLkbdKkSZKRYgOa+a5evdrs379fyteTJ08e8xnjg/cpQSWbxgZ+RjmZR6Gl4WTWYh8NzGxmXnKdmE/IquUr9xKJL7K5mVOQ17KduRoKZcRkQpGJBGQfIWlkW9oj2JSTTH6ypQBZj1dffdVKI9iXX37ZvP322yITwPgg45HnkftOJiaSDz5URrCeRBOu05dffikyC8g/MY79TD3GT7ASiIZ10S6/R05k7NixMg7IYiSTlkw4ZKq4djbHBrIn2EeTV7JDyRakaR7ZrGQ0MveRVW0byv/JbFy/fr1k+iIvw1riAsxzVEJ+++23UhmJNAXyQTQcDGbJ27zH7Pmo5uLZy5Mnj0hUMG5caMytnNuzR4Ur1TY0GybbmzHCV9YO21m1PG9IuLHHIgOdCmG+R9qIKqZoV8wxr1F5SRULY5Pmpew/w8G6Ee0saebhW265RTK1qaoZPnx4LLmnIKy9jGWbIHVH9RTXiuvJM8h9pmkzFVW24VxLNRXrLNeLfQE2cv+pwrUJErN58+Y19evXT1D2Ccmg7NmzR13mi4a+nNkefvhh65U/iqLERR37ipLEWLt2rSlXrlzMz2xokOMBNjkcCigzxlmDPjZaxYpyIQ8tbPjnzJkjTmn0k1u3bi1yLbbAGYOUzNNPPy2SCkFNX2SCrrrqKhkbthwgOCyhdu3aIpcRhIMUB4No6w7Hd8DDKc4hHkkeDitIj+BAR+sUZ5cLIBlE2TMOfe4vhxcOLj/88IM4v3Du2zyQzpw5UyR5uNfZsmUTJzk6tpRBIyPEgTTazn3GK2M3EhgrOLCjCc8bDtVIqFKlismRI4eJFgTe+HvMMb4THY1nWLp0qQS+cODYHA+U/S9cuFBkbnDS0JMAGL84rONzfEWLjz76SIILrBnMIzj52ccQmBswYECsYKyS+Fi1apXMyTh8CR4GQSYSPWybMC4ICCIzFyrLwzpCINE2rF8EbnBcI7+Esz8YcLUJQUzmkN9//13GMMkUBA0Jwtqgb9++EqzmWiUEzx0JCtGGuY55l/NaQrDXYv9qG9be8ePHi8wcDmCCwTyDXF+02W3D3uWbb76RpDbWYsYGgS/b9nHNCHDZDs4oipI4Uce+oiQxcCaweYgEHP02skEVxQWoXKGpqgua5uEgIw/tdZehAgPnM/qqBB3I9qH5F4co2/MgzkGcRzgwq1atKkEI5rtgRi3OGxznfrO/aGbnDx06VBz6ZFvi7MU+Kh+CASX0nnFq4phQEgdkrPpZ+v46S/USATkcXQSVcGjagux8P0t/69at4szCVhwe9BNhzJApbzM5gcx8gm5k1/rjlQBYrVq1JCMz2hUi4WDczpgxIyZjn6oQV4KZsGTJEknc8DP2q1WrZjJnzmzbLAkilS1bVuzBvooVK0rQhvvLNWTettn8lbWAMUr/Hapu6tSpI/rcBKt5j/XC1T2DEj8EGJh/uZ/xJTQReLDVBwCnPkkdQ4YMkWcsHGTJ2xzD2EVyDq9we0ECw5E0Kb5QUGVYr149yXYPhbWNygLWFFuwz6Nqj0CIy5DYwb6ZhAQqWNiX2grKKYpyCh2FipLESJ8+fcSNyRQlKYNzgexVDinB5tI4RHCE2M5+c92pDzjyebkGmYy9evWSbGQy3+PLkKJyBDkXG9UOlNxzyCR7Oj5nAs4vss6UxAvOaCQAOMxz3wkyuQIOEDIIqRBhHOPUR+bLJjjfkN2hoiq0IXabNm3EgWTbsY9EFhJUZG5zDf/++2/JBuX9du3aWc+WprKBe0kFEI5AJJdY42hqTmWVTZCQe/HFF81rr70m8y+Z1HzF8UaDRptOfSBwNHjwYKnsQjbmnXfeka80OWddccGpT7CXwBxrQ6jiLo0ubTeY5nkjEIKdobAm24Aqn+LFi8u4CFZphgO7P/7446jOhThOqS5DSul09iHVQnCCZzSaEPylagA5zXz58sW8z8+sIbYbc+OIxj4CrkGpGxI4aCY+efJkq/Zx1sBp7qpjn2pbJNsI0CBfxVxH4Bo5LYJO4QI6iqJED83YVxRFUZQw4EiYMGGCbPqDcKDjYMABUEmccMDjEOqqZAdyLbadL6eD7Da0fslWRVImCFlnaIrbdh6R3Yh0AgfSIDgIcVjbyrwk261MmTLyM7ZhD0FE+ibwvc17TzULEiPFihWTn8na79Spk8ijkDGNDrBNnXgqqZDGwokaChUsODKx1xbomCMrgiQLGdxU2GATUlo42pCJshnsxCnNMzZmzBgJkADBS5z6yI4xXmxWVJGhT1AEXXGuU8+ePSVoA1S1cP1s6sTjVJ0/f75IeFxxxRUybpHnI3BDkITxE0wEiDaMi3vuuUckgnC+hfuc62gL9lME3nBUh9PT37hxo3Edxshdd91lRZYnEpDjITvdhiwPcwhOXp4zxorv1EfW0oWgJhUZrGlUneGYpsqgSZMm0g+KoJdNfvvtN3HqE3hlzgvdByBnlJD2fjTuLRVLXDNf7pPgIVJRJDwxLyqKYhHb3XsVRbHLxo0bvaeeesorV66cV6xYsVivu+++W2+PkmS59dZbvRkzZsR5//jx417mzJm9gwcPWrHLVVauXOmlSJEiote1115r21zlHNi3b5+XO3du7+abb/aKFy/u3XLLLd59993npUqVyitSpEjYcRNN1qxZ41122WXenXfeKXZWr17dq1q1qpcsWTJZ6/766y+r9ilnz7Fjx7yyZct6nTp18vbs2RPz3pgxY7xcuXLJPGSTrl27ei1atAj7WePGjb0+ffp4Nnnssce8N998M+xnjOPx48d7NilTpow3Z84c+b5WrVreoEGDYu5x9uzZvWXLllm1L2PGjN7u3bvl++uvv977/vvv5fudO3d6l1xyiXfkyBGr9lWrVs177rnn5Hq5SP78+b23337bO3HihJdYWbFihVegQAHPVSZPnixrny2aNm3qlSxZ0vv666+99OnTe2+88YbnCocPH/Zuv/1275577vE++ugjL02aNLJ2uAD3DNdcfC/uq01KlSrlTZ06Nc77zHlp06b1tm7dasUuRVFOolI8ipLEIZMCSREyzEIbfoXLplGUpMLu3bvDNsgly5tsOD53oezeFchaDGaIffXVVyIt0qpVKynRJaORSgd045FZcEFTl8wjMsqQyghKFtDMz2bWL9DI/IMPPpBGjWhMB+0jIxPNfVuQjYxO/JQpU+T+8j1f0Yonq5vSbJvQQBXJORoOklmJbAtfsZeSe9vNN4M67FQW3HzzzcY1GB9o/VM9Yqu6IRxUUZEdSOY+8wj7FGwkGxONaT8L3Zc54lmIJldffbX0AYhvTPO5Tfj7oQ1fXbIvCPfvqaeekgoSskHZq9rM1g9nH/Mw8kXff/+9VBmkSpXKqk30Z+ndu7ezmtfYxzULt7dSLg6o5KMPBedKJA+R1nIF1gj6zN1+++3yHFKZS/NcF8CuhBok2z5vsCcIV61HhRLnIj5XFMUebq76iqJEBZxZHEDZaCNLoShKbI39/v37i6MoKNmClAEl9ziyldiHjqA+eIMGDcSRT6NLH8qgcQJPmjRJDn42Qbt+1qxZYt/SpUtFAgKZB5yENAOzCQck9HSRu6H0Gpuwk/JxnFu2dbCRIPDvNY4spI2AAA7v09j5kUcesWof9zfUPvR1kdBAPuOmm26yZh/OfLRqkStALoMGpgRucAwyv/gyOLYgQIN9yCwxD6LXjWQQAYgffvghrMRHtCDohmxMJNhwAuPM6tKli8hAIb3D3urQoUMi3YZ+MsEmmzAv49RiHkb2AQcw8wzNiPnel1iwBc1xfX1u1gtsw+HFM4cGtm2H9dSpU2MkMnxpEeY7rpsLAWvsWLx4sSlRooRxEd8+m/Ovcv5AmiXcfEyQkHGCNBkvvzksgbpownxLkkkoBJaYU5ineUHLli1l/raF6+dwX4qPhB2/1wnSY7xHQPh0vR8URbmwqGNfUZJ4I12i72QwKIoSm/bt28vhkwMyDkEc1zRuJDOPTG8lftAwJYM26NT3wYFO1r5N0PHlsIduOPcUPVqy0HFe4lBnbrTJzJkz5Rr++eefcugk8EDmMe/haLUdVKL6ws8ey5kzpziCfTjoJZR1ZsM+HNR+Vh732LZ9LVq0EJ3rQYMGxTTBxtFAZUH37t3lWbQFAYZ69eqZ+++/X5y9OKmBij4crTQL9d+zAU7fYGNG13j//fdlnKKTTPNrKgpwTnNduYbBORHN4ueeey6q9tHAkkASDZvJtMT5hn2QIUMGGS8+BJl4DqIJDUyDcB15uQKNfH0IMqAdzssmBCqZ8/wqXJ6pvXv3yt4lVO+f6pto7/kJDPrUrl1bgr70eaCyKzRzv1y5clG1TTk3smTJEmfM+vh9ZHxs7Fv4m/HZF+5/ixI/jFmCMwTMeTGPrFmzRtY4erYoimIXdewrShIG5xXZCUhOsGDbzoRSFJfAeYTTl1JiMrmRL6B5FRmrfjM/Jf65hYaqZGrhKA9tnpc1a1arl46MbhqVYkcwoxvHG5IyVBT4Gd+27OMZw7agfdmzZzeNGjUSSZmg5IhNaBCKY4trh3OJe06li0tyGdiIo4tADrJGNrNZCXgxn+D89Z1xQaehbdkCZE8Yu926dZNmfqH2vfHGG9ZsSwwg+RR0jicEc1C0QaKK4Kqr9vns2rXLLFu2TJxyLjYSp+KV+YRAq+29M3MctgSJLzN6xYoVUZdKCyc1RjVLOIKSc4r7sA9xZS8SDtZ+XomFTz75xLz55puyB/T3fT5ULLG+2AIHPkknJDcRTKQSrWDBghL8pZJUURS7qBdPUZI4Tz75pLn11lslMw+nURAyCikxVpSkSp48eSKWfVBOgZMXZy/ZgxyqcM7gxORQMHfuXJEzcCmjGwcS8jdILrmace6DfbZ1nNu0aRNjH3r1VD+QfY7Tmu9tZ1RTgYHkDhBYQvOXKhsOnwRFbFZkcG95vrAFuYIgBB9sOwmxgetDJm1oNq0L9gHXD8kWAg/bt2+P5QykAiLaWfChUiO25WwSAqd+pI59GzAPU7nCfMJ9Rb6IgBIJKARin3nmGav2Mf/ilB43bpz8TK8dHF4ETJCvQt4o2ixatChifevQXlrRACm5iwnWvmDlhmsgFRlplrriDkgtsnZRtUd/JRzm3Mu+ffuKdNqNN95o1T4ql6lIQ6oyVK7S/8yX6FEUJfrY350rimINnDDoXHPIw/kWuuFH21lRFOVs6Nixo2TqISWDE4TDMBmgZCS51AARaYwrrrjCVK9eXWQKsLdHjx7GJV1TqgeQLyD4ipP6m2++sWoTQd8g1apVk5crcAgOQk8C230JfFhn/WBIsPIHZzUHetvZj1w7spFxFgYd+0gYkUnIOLENcgBkNBL02rx5s8wrfrDQlUaIytnB/EYAmGa5NLr2weFF7wl0sEPlZaIJAQa/+od+Dz7PP/+8ZMnbcOyfTbYs4xippWg0Sz6bQCqOabT4bUBAif5jSFaFc6Yy79B01ebZjfmPKlL62oRCFQkvGxDE6dmzp/TF2LBhg+iu09ODqnA/2G6TLVu2iJQcVXOsczz/nH+RRbNdGfTll1+al19+WSTcuH5IGZEc89BDDzkRqGHOi6/xOskxWm2jKHZRx76iJGHY1LAJI4PRdgaooigXHzRSDTbUDSe38PHHH4tTJJoQcAg6p5He4eCJ9BIHq6ZNmxqboENMmbPvtEHehsMyWvs4YzgouwCl4thEwCFS+ZFoggOOAyfSEy7p5+Ig5x4jhcd9bt26tTyDNLL/9ddfrdqGbi4BBjLfqebDcY4zdcKECbJPIJvaJkgA8EJShIDht99+az799FPJnCYoYrOxbzCrm3GKYxK7ghBg4t7bhOcM+xgb2BpajWMzo59x8Prrr0vVQ7BpPeMXBzE2Fy1a1Kp96EkTnA4GvrCX55IqnMTQt2r58uVxKoZcgv4yNli9erUkPFFZSAAWBzpBVxzlY8eOlSpOm1ClRJAfJytZ0sx/7KOYq+llYKMiIxhwoEKOKqrGjRvLtWKuoWcMARJ68ZCBbgvmYuyg8oc9HvsWpOfo+YS8zOzZs60GDen95Pc0CfY+ITiCDCjXz+/JE03Yk7BP4cX32OmDM59nkXvuQuBGUZIyp3ZMiqIkOchSpXmuC6X1iqIkPTiQ2pA6ohopmNVN1taHH34oh7sXXnghlkPJBhw4g4EHnEb0JkBjNT5t4miDVEa2bNkkq8yXpfj6669NgwYNjAsQoMHJi0PG14rv16+fBHBsg04uMncJPEGtAABRSUlEQVQ8ZzgICdyQvU9gCc1a25AxOHLkSHFc4mDAYUTjXA71tgMkONoIzOHACvafwGGEtCCJCjbBWYrziPFK1QMZolxHZBZw9NOg1ib0diDznV4x3E8qMcjCnDhxomTY2tZKJhjnZ866KAUVn31cRxybNh2DyrlDD5ubbrrJ/PLLLzHv8cwxt9BvySaMU+ZhnNLBigHmZNYUqg1twjqGg5dqGypbqDSkZwzrGvJ8w4cPt2off9/vndW2bVuxDzuxlwAE9tsEOS1//mAfQNDGD0gQ0ETyywYEo6kcJciP5Bjf+y8COVTnMjY0QVBR7KKOfUVJwuDUv+eee2TjFdrET1EURVHCwcGYwxwO/SeeeCLmfTLQ582bJ4dQm4waNUocHzhnghUjzZo1E+eHTc1nsj9xrhIQIdOcLLyFCxeKXS5IVOGgJFMQDV0yVMmcJXuVKgMCObY5Xf8J2/0xGBM4YAgmIaPAmOA97jPPne3+E75GMlIUPINNmjSRLHTkb3DsR7uxaig4VUePHh3HcY5ED4EwV+2jyoCAju3Ag3L2MH9QdfHOO+9I4lMQNPVnzZpl9fIy11G1gmxMqJPXBfuQL7rlllvi2MaYYC7kc5vw97EjdIxiL4F12/YRoPGrfQjekCiBg585r1ChQtZ6t7CfO3bsmEhVUunD9/6L9ZbgNVV9iqLYRXcfipKEYRODVvPOnTtlIxtaws4BlNJERVGUxA4a3GipRgKOLz8LPVrg2EWSJRJwUONotQWZZDj00dXn8BkE3emff/5ZehfYAkclzZtxwgUP8WnSpJFKCDL0gvr20QSnuV9aT1m9L3kT2rzeFjigkbRBt963L5yzxgW4v2Tso6uPbAbOX6S9bFcUIJWFEzpYUYB9NWvWlHkIJ41N+/zGh0H7ypcvb0qUKCHVGTb7ZZBown0lAxnJh4EDB5r169dLIIJ7a7uaCgc+48GXp3j11VdlvqM6g4ovJfFCNQZrBGtGYqoWccW+XLlyxYwLP/jqy7VQIYRT3bZ97F3at28f6/oxBzLv0fzaJp9//nnM9wT5CfhjL1VUtWrVinPPo4VfRUDShqIo7qKOfUVJwqAz+Pbbb8f7ue1GQoqiKOcLnFZkSEeCDScrzrZI7bOd2Y0TwdfKddHB4LIDhHWXLHikeHg988wzksHvO/qpMLDZABanKtnmvn1IVO3bty/G0U+Vn80eDzh8keLxqw5xpuJsxbHau3dv681zQysKgoEvFyoKXK94IOhFRRD3kqQTHG5krSJllFC/lmhBs1IqWLCP4CWBGtYWehYEm+kqiY+rrrpKMqZnzJhhcufOHUte64033rDe2JyMbdYO+gAE1zXWNGTmkAuyCXsY5mTmZzLOkThEmgeNfezmq02QkenRo4fIx9CngAo0qpSoYuK++wFPV+D6BSsiXWHOnDkSdCAgwtzMdbO951MUxZhknrawVhRFURTFUvYojjq+KokHpChwbCF1Q08CDnetWrUShzCZ8GiJ25TM4PBOVj6SPDiiafjKc4bdjz/+uDSOt9lkMBQy4WiO/OWXX0o5/o4dO4wroPuLTAvOczSI6VmAs1UJT4cOHSTrl68EbKh8RKMYh9cHH3wgTmucw7YgK5XKJb5iC9nn9FTAVio4aYRI8MkWmzZtcrIRtw9VBGT+JnaYn8kGti1tFB84rm24KFgzcKbSmJYqEcYu/SeQKCOgY/vZJMBAtR4BWGRRCLJSXYiTmmx52+saaxe69ePHj5d1lkpwbGRNtp2QAARFyNjn3hI4JJDIfPjaa69ZmfeQfuKaIXNDkCGhtZ/fsdkcnjHAusF4wA4CxOjuM4ewd7G5rimKoo59RVECh3cOLJT82TzUKYqSdLDt2EfzGucGsmRkoOO0dOlwgrMNZyoHPg6gOOFcOBzTJJKsNzIFccBgGxnyaLIjEzRgwACr9mEX95LKC9Y1nCBkndN8+K233jLPPfec9SaIOGGQJyArHsk7HEZkxCOD8vDDD1u1jwM88iLYx4vAA9nJ2FejRg0nMhvR912xYoVIswQbYduG540x4TsAf/rpJ6mMJFMeh+H9999v1T7mWuY630GEU3DYsGGScdmmTRuR5LEJWuE495Gn8qWgXLq/SFJx/XzbeOXJk8ckNggyPfTQQ05KbEGfPn3kebQBAWsy4MkyJyDHmMAZ7EpA56uvvjJDhw6VsYwjn0qWdu3aOXcvqf5xOZPbBfvY/9Lwnb0J60RCzd/5HX7fFvR2oAINySBf75+KjGeffdYsWbJEkikURbGHZuwrShKHAzxNENGmRRfRL0cle4GmaoqiKGcKmVA4i8IdzIOf4cAh45tGp9HGb/zKvMeBHccvjvR69eqJfehP26R///5ykOLgyZzMAQqHNU5psuVtgzwBvVk46GFbjhw5pLyddcOWFmyolj3OIaQyuG5kTiNVEGmfhQvF7t27xembMWNGqSbAMVi5cmXrmaDBDEICIcgAVK9ePcZ5aTNTMBSyLRs2bCjzBxm1SFihw869pWrEtrNGOXuYg/2AFy8aNxI49B393G+bMpEEgX37kGxZt26d9O3wxwlyH74mtS0IWI8cOVIyutu2bSvJOshnEJzLkCGDsQ2Z3GPGjJHsab9XDNcSqRsdu4riJkiNsfckqSM0UYFg55o1a+I0nVYUJXqoY19Rkjg4YsgcfPnll0VDFycXh2bKPQcNGiSHZ0VRlPOVic97NFEj29YWu3btEg1dHOcEGJDOAErt0ehGVoYmjrZAyoZDFLrNzMG+o4hsLrK5P/roI2nEadP5hrPItgMrPigPJ9DgQoAhnNONgzEOdF9XH8c+EkbI8NiGcckYwKHKM+jbV7FiResyD0CQhuazXbp0icmI93tTNG7cWGQfGjRoYNlKI3spriEODxczunkOly5dKra57IzZsmWLBDmpAsJmHNRk9bsC6xkBRILBOLgI3NnMnKZiBI1zxiqVXmhhI5VBNjCBMNtBYeSfqPgh0EqGry+3w5pLtRwVX0rihaAN1SBIejEeglCNZvtMyZw8fPhwGRtUHgZ57LHHnKhGcxV8BCRzsFcJwnVkzmPvQBBWURQ7JLf0dxVFcQAapZE1Q/YRWZZlypSRwzv6iJSh0jRPURTlfIKjAT1Ym9BoE0kbXw/bhzmwc+fOUopvEzTM7733XnFUBp3n6P62bNnSun1du3YVhyUZ3b169ZIs6dBDsk3Q0cdZidQETsE//vjDil5zOMg25npRuYLuME37unfvLgdiNHQJstuEfglcL4IjBLeo6iP4RdABSQoO9jbBsVu0aFHRhQ/N3MbhO2vWLGMbHEf+/eR7wK777rvPuADPHYE5KjNI5ACChbYlqnyQQMGRX6tWLXH20twSuRH2pQR1bMI85zf3peKG4Bea09g6ZMgQkz59eqv2vfrqqyKXhTQGlV4+VKdhHxJWNmEu4YyBvFcQqqlszC04oKkSiORlQxKKptGR2sfzaBMq93D+ItVCRV+ofcim2YR1Dfuo4kN+1gX72Aswf0Ty4vrahAA/FUBUxwX9CPQwosJPnfqKYhetVVWUJAxZq5TlkrkaCgfSvn37WrFLUZTECaW4zB0cmnAIhmYuoml68OBBcbbahENIaDaZDwdSPnfZPtsZwDgGceojR0HTNJzR9GchMEyGNw3Wwq0r0eKzzz6LkfLAmYUGLFIyZJphH5VqtqVlcEpzDYsVKyYvdJORhyKTEEe/bXD8Ehjx7aPxME5gxvYzzzxjNWPfd+iHVmTwmW0pDzJCuT449INBBvpj4BTBkYND3RY4Cpl/cW7hKPdBQoagJlri9Kaw6TwisElVF2MVJzDOfVeqb5DdoXIAJyrST2TCEwxzBarOSMrhegWvGRn8zNFUQNicm7EP+TYI2legQAErVXwEP+iz48M8TANdv4KA3hjIBFGRgURptCHZIGgfldQkRxDgpxIDCUGcvpMmTZKxa5Pvv/9eEiboz+LKeA3CGsbehHvpCuyluG4+PG9+kgRrGXtmIAGFcUOA0xasDwQNuce8SEpgz88+nyRBRVHsoo59RUnCsKEle4cNIQ6GIGx8XGpYpiiK+5AlTVM3yrG7desWx4HP4YSMS9tZlxxKcNCQoUrWPk5MHJY4vThoTZgwwap9OKDJbCQrtEWLFpIFiqMfhwOHUxxfLjil/XUDRxeOTK4dDd5wIOEYsQVOe5zSvGDHjh3SR4ZgNQ1+ccTZakLHQf2LL76QoAPVchyM6fGAhjhBCBybNuFeBu1jLONAwslKJYRt+8jKJ/t427ZtsZxH/Mw95hm0CU4anEc4QKguCLUd6SCbjn32e/Q1IdAwcODAmPdxIlEJgTQjttuCfScZ8QRAcEYz9/FyoWk44OwlwBVqH9JfLkBvGAJcoTAH8rIt9+XbF1ptQ5UG63C0ufTSS2M5S5HxwpFPPwKfOnXqyHPJ2Il2Y3PWMt8+krGYP0KvFX0nCCYyZxNctwlrhYtOfR+XgnBAMN/nr7/+Eimgt956Syo22UfR04OAEj2DbDr1AQf+zJkzZY1DShC5Oa4nDeGxVVEUu6hjX1GSMBzk2DBwiGMTwYGFbFAOLWivsoFUFEWJFBwdSBKw4b/xxhtN2bJlnbx4HIBxYKFZT9YqjUypMMDpmjJlSskW9UGzm9LyaEIzYQ50ZPi2a9dO7CMzj+ADjgjkH3xwttrI8Caz3G8iyVccq2g7o5HMemIbGkcGm3CSCedXFNjU6OaAToUDjny+Yg/OEFfAuUBACbvQDseRH5T0sA3VKr50ID0KeO5w9FM5ws/RdrwltooCbMCB76p9ZCQTEMa5z7glmEnFDQ5pnkn6P9mUfMC5y1wye/ZssY9gEnMwzyX2sZ6QyWoL5J5ef/11meP8+0tPFPTDGcu2pYKwr2PHjiKv5NtHpj7SWrYbm3OdyJYOOvV9WDtsZ3ojr0RlQ7gACPYR9LcJGvrcW/YuJE64Br1j0Ph/6qmnrPbBiA96KtH3iUCSD70oqMDBge7KdSX4oL0IFMU91LGvKEkcsjxKliwpmVvffPONOI1wxpHVaLNcV1GUxAvzSPHixSVAGKz84dDs65zaBJsilQOy4dSsUKGCOLgiIZwT4kJDyT8ONhxZBD5wbLFuEBRxgdq1a0vVBT0JsJHqBySibGv8Ak4ZgiKnA8kqnITRllfgPkYiiUGTTvYMNppdEngguPXJJ59IMgJzCtU39ASw/QySjc++ij4UQcc5Wbbsq3BS27YP2QSCIUH7qFZC95yArG0ILpQrV04CNchAUeFFDwDuN3rOtrWcqTzDSe7LVFFNQPAXGaaePXtadewzBgg6IKdEoJo5EE1sqoKmTZtmbEOwEJvos0OgGofl6tWrZX62LUFG0IMAOteJBudBqGKyUVEQmr2PDE/ovor+MSNHjrTeBJvrg9OcMcE6gtRrEOSrmHdswb4TuxivzHPsU0P7PIRWr0eTdevWxUra8GGeJljM59F27NPPib1IpPvW0GuqKEr0UMe+oiiSOchLURTlfIEGLM6P4AGURrA4hGvWrGk1sxbZBDSlTwcOTpwk0SZSuSKqDsgyjDYEfXEizJ8/Xw6cviRFiRIlnHCe4yzCJqRQgnIZNoIgZwuH6TfffNO6bnJCjn0yWKPt2N+9e7c4TuvVqycv1yCTkecPB40/Nsi0xZlONROVBjZp1KiRZEvTCJkMfbLPqRBCv5lsW9uNzcmCJ2BEtQ3zC+MWhxGSaQTpcADbZPz48dKfAPuobsGZiXQaDmvss50JzNjwrx9Vt9xfAiRkK7sgl8H1QsaIygeqMnDuM1bI5LddLcJYfeWVV6Rij+oBkhO4fsiP0BuA+24T9lLMIVRjELwme59ABPJ3SKbZluhDVo5xSvU3c2BokNX22GBMkP3OvhTnPvvRILarWbivBLeofPT3fyTDkHhH0JCASbShwoEG05HAftml6kNFSWok8wjzKoqSpEG7meZMK1eulGg7WQ1sLBRFUc4GZG3IEETTN/TwhH4t2ay2D8mRwLxIc8Rg8zqXGDBggGTv8dUGOLZ8qRucH/RswcmFM9qmjjjgMMIx6NuHk4v1Decbzx9OEZdh7ODI4auL4EQiSz7azqRPP/1UKkR4vriXvHBSu+C09CFTmoobMvRxuBEEwxFHNqsLgS/mZ3ScmdfQ7aZZd+PGjZ0IlJApjXPav7cEQkLXEJvgsCSA6duH89dlTXHlzGFvQr8THJX+mYiqLxf6POC2QXKHBvE40jNnzizVLfTNsJ2xj9P8448/lj2eiyBtSDATyRtXg/kkvBD0wkFOxQPyO+yrkPxyYX5WFMVdNGNfUZI4ZKPS6I2sTw53ZKds2rRJMhrQrGXTqCiKciaQRYZzK5wTCycIuthK4getbhzkZJLx/QcffCAZrTjnbDv2efZwCpKlj304yXE8IKlAFprrjn0lPOgPI0fgB2xoiIzjw3f0Uw1kMyueuY19E5IxvFxjy5YtItNCdj4v14hULobACU0boy2VFolMFdA8Hmcrsj3RyEQmWBMJSKdFOwg2ceLEiH/Xhf4sNCm13ag0Ptg/4fy13UskHPTB4BzpKtgXKg/kEoxLxgryN1RjUp2Gxj598JBhUhRFSQjN2FeUJAwZjWSgoHfZq1cvadAIZO6TGUB5rKuZDYqiuA3NBCnLDmqqMueQuYrja+jQocZ1NGM/PL5eOA4lgsPcVzRr/SxW21qrZJF/9dVXYh9yD2QAU4Xm24fOrm3Zh9OhGfuRgVOfDMfXXntNNOLr168vWf22YM6jyocqAv95Y2zY1v73IZGDpuFU1vj20WfJhUqCM5WtIIvVZiPshEB2hGzbaMiPcP+Cchlk/voF+VQ7IOfhOw7pRxHtzPNQiRM/COE/c6wfvhTOgQMHomqbcv7Yvn27jEf6dbgoe0cF3yOPPCLVhbYlxxIzSBzS64F5hgom5Odc308pSlJAR6GiJGE2b95sdu7cKfp9wVJnSgDR9kUjW1EU5WwbXBIgxAFMBu2hQ4fEUU5GKxq7SuKFzHcyynAKvvzyy9Yd+aGgd03JPdmffE82tyuOVeXcIZMR5wyBG15o2COXQeNQNKhtwnjAaU41AdmXr776qkmVKlWMo59MW5s6xDjDkcrAPprRIjFCFqvv6GfOtt2cVjkzcLL54FRt0aKFVDRwT5n3GB+MDb/Rb7QJOuuxi8ou5OP8yhr061u1amWaNm1qXIBxy3Wkj4gfdPB780Ta1P5CgT00av75559F5iuoqEwyVqdOnawG1AnWIFXFvQ3NjifzHDkyW5CEQMCcaj16dYTuWTjz0uvBJjjLubdUVgWfPcCBnjNnTmu2EZAjMMz4oIKA64cfgfWMCn96KyiKYg917CtKEobDG1F2snlCNUyPHDnidEmloihugwMLyYf+/fubcePGxegmt2vXTpqvKokXAr+RwAGQzDgy5KMJh8xIwLFJJj9ZZ0rigLkE5z3a5lQb9ujRw9xyyy3SqNYFyIquVq2avHxnyMiRI0Wa5fvvvxdnDVn9tiCDHAkbXn6QhEAdVZtUueC8xHmjJE769esnTnP/+QPGCgF27i3N6232LKCahXUhOOeiEY9EGk7V5s2bG5t06dJFrh9zyvTp06WJ7qxZs6T6Ao122zz22GNiDxnx9NepWLGi2Ll//35x/NqWunnooYfi/Tzaslnh/j7XLyH7bULVDc8dwQf2TaG9O5Cgs+nYpzfR6tWrzYIFC6RKyK/SePbZZ0XekIoIRVHsoY59RUnC4NSnLPHRRx+VrEacbWR/kIn5zDPPyAFAURTlbLn11lvlpSRNqNBA2z7ajv1IIfORhoPRdOyTkUdgJJy+efAzMm1xeEWbDRs2mK+//jpsZmXwM7Ix/cN9NMH5gu7w8uXL5XvkO5D64BkjeOgCZFuSEe9XFJD5i30NGjRwQhsb5wx2+TbSE4WqGwKvOl8nbtatWxdWR5yxwf6eLG+bzsH47OM9PrMJQTcqDck8pzfCXXfdZT766COR++KsZFtuhHmE4BsOYCTmCJLQRJfgIX1tQiWPog1zCC9XIUjDy1VoaE6iwfDhw51qBu9DFcs777wTa90nAMEYQV6TalzbDZwVJSmTuAQVFUU5Z8g6IVPff5FJNnr0aNHDZmPNZpaNBYdmMuEURVEuNpjfhg0bdtrPOKyQ7WijZJwD/Ok+IwNNm8AmLnDek1Ub32ccnIGeN2fSdPJ8sWnTJqlkiO8znA5As2QbPXhwPNMHiFeTJk0k2PD444/LWMW5xZ7GJiREMC6ROOTr+++/b/bs2SNOdGR5ChUqZNU+ep5gA/s+5rYxY8ZI1v7kyZOlmoqsbiXxgsZ5+/btJbjkc/jwYbm3VLXYllnCvueee06k0nwYH2T92u6XgKwI8y7jAvks5mMgyEojZHpT2IQ5D8mxrFmzxrKP4CZSRrbtAxLDFi9eHFP5zb2mETFBdBfwJZaAzHgy+O+++24JcNoGxzg9Ylx06vuBr3CShlQAIcEUKh2kKEp00Yx9RUlisCEkOz8SXCltVxRFOd8ZqzgAGzduHPazUaNGyWdk2drINkdzmDJ7NGnDfbZs2TL57MEHH4y6bcqF1cp2uamfS/aRdUwWLbrhvGiYiyQFjuk6depYswtnPi+uFckSfkUBms42JVB80FhHjod5BLt8+7iGSuKHwBKSNiTroH1Nsg4Z3uhhs+bZfgaHDBkiaxfjlEopqghwWOfKlSveYHa0IADi664TAMHxSx8y9MTpE3D8+HFn7GP+Yx+AMxWnqgv27dq1yzRq1Eg04uHdd98148ePlyx5AoposNvUYf/zzz+lBwoVGcD3NJOmuTk2UjESjWbX8UEmPEERV/d1lStXFjkq9se+nCbPHe8h3Ws7aKgoSR117CtKEoMDHNI7iqIoitvOy1BwgixatEicIEriAqcHTlXuIXrIoQ4Eet1wSEaL3QZIO5BZiR00ug61DzkKHEtIP9gEKREclGTA0wSWbF8ybJGReeWVV+SrTZDb4UXlDzbyQjIDuQz0k2lWa1Puhmzul156SeYRbJs2bZpUEuDwxa7OnTtbdfKjb16zZs2wcjHBz8hctpF8ggY72e84zBP6jMpXG9It9LUha5p+DmhhM5ZxoOO4dCETmEofgtZIev31118x7xGMsC11E4SM+HvuuUdezIsEDl2Q0fJBYx/Zk+rVq0vQkICJ7Spr5mMa5/pa9VQDIS2H7j7BB+65Tcc+gSPuJ88Z6zB9J7CZ+Y7KLwLDNp3q9Iwh6IVsFvMHz2CQIkWKSEWJLVgbsI99DC/sXLNmjewVuNeKotjFnRVUURRFURTlAoITi0MmmW2UiYc6L48ePSoOzAkTJli5D8h2vPjii2IHDlacCUFw0nAoxXGjJL6g+tChQ8Wpj+wE3wfhEE+GLU4uG3BQx6YVK1aIFFX37t1jfU6mKLahb28TnC8ffPCBOPCxF2c0GbWugdwN18qvKKBBLdJKVADZ1rGnKSPZoTh8sQ2HEdcUeR4cWzYd++g101sinGOfz3B48Vno+IkWffv2lfEbzrHPZ0iP8BnSRjbvb7CBs2uwhhGg4eUSZB0HG68zD3bq1EkyvJFIsdn0Gnj2aaDqM2nSJHG2EpQl+7xp06ZW7aNXhx+cYZ2bN2+eBEWAIERQfsmWfX5wi8Am49Sf67APm21Cbx0C1wRew8GcQt8HW7BfnjlzZpygIY3YXQgaKkpSRx37iqIoiqIkCcjsxSHEoeSbb76RDN8gyFLwOxzwbXD77beLfTSdRc8cDfEgSHuQnedqRYESPzjua9WqJYEjnL6uNRkkyxL7cH6QOVi6dGnjIsjv8Iqk8oYgBf+bogl/E+cg2fA//fSTBOnKlCljateuLcEIsvZtgrMNeQw0pX/77TfJpMWRTkNk7MN56CJUZiCV4ercR+YqweJwjWGVxAEZyFSCBB2Zfs8TFyBbO5ixzT6FgKErMM8R9CITnvnlpptuirGXPZdtiRnso6IG6Z333ntPsvd9sK9169ZW7WNeTkhOyZdhss0dd9whL0VR3EId+4qiKIqiJAmQKcDRhwOdsnrKxl2CRri8cK7RGI9sKBehpB0HWzgnYPAz/regNx5tyLTEcR6uUWnwM4I4NF2NJjSfo0kkWrrBxsxIA8yfP9+6Qx3HpG0bzgdoJxMgi7ZjnyaRZFbiJH/++edNxYoVpVrDFfr372/Wrl0rmbSvv/66OPVxaLqg38zzjxwV3we14BkbvM+19LWdow2a9WQc8+J7suJ90DnHPvTFg+/bAFuQP6ECg2ADgaXQwBOVQbZgXUOyiOqQ9evXS2VaEO61C3Dd/Oa0PjyTrvQeQxaNV+jaQnKCLVjPaDJM5QBVVH4jc3ooMB+HViBGGypEWBPY+7H+0wMAeI+eD7b3g+GqgFyE4P/27dvjjFXmxXDNdRVFiQ7JPFdWUEVRFEVRlChqnuOkCUqLcJDH2VC4cGG9DwnQqlUruUZ8DfcZ0h4tW7a0dg0pV0cSI1zZOu/hcCUr3RbYxnNHdmOQFi1aSEXGk08+ac22iwWcSDhsbDuT4mPq1KniBLEtyxMfNEhkHEdLExuJJeZkMmqbNWsm0lA+VBXQmBEZI1vNXwlY4ux97LHHzKBBg2JJTyA/QvY0GcG2wVnZu3dvCdpwLXGic11x9iOZ0a9fP6uZv8y96JojHUMVC7rrVI4gY0T/CaRlbEKVDWsXgVeCJEEIVOOktgnZ8Mj1EbQJdeGwpjHnuQZZ6ARw/AAizZyRvQlWR9iEcU1AzndKk71PAIf7bcMWgl40+m3YsKHss/g+a9askphiEwLC9erVi2k+HIrtoKGiJHU0Y19RFEVRlCQFB2Kyt3B+BB37HDzJNnvrrbekjFw5M5CZoSFipUqVnLx0BG5wzNiU88DBgVY4UkuhNG/eXOSX1LF/8YNUDxmarjr2cSDScDVajn2qqID5mGxaVzKjfWgaCQQYqHJwqdFrEJpY0qyZe0dTZJ4vKr9w+vIaPHiwdfvQhverlZBDuffee8XJ26dPH6uOffYFZHPTkJYASGilje2M6h07dphHH31UsuK5l6H22GysmhCMleB4oaKJBso0w3aB0Ca1yCHGl7hwIaHBOtWEu3fvlu/ZR2EHQRz6O4wbN87YhOeOSgx6PNGTIJRcuXJZsUtRlJO4uStRFEVRFEW5QJABxeEpVO+abNDGjRuLbq069uPStm1bM2TIEGmaxrUKbSbIYZTMMr9hXrRBdoVMaOwg8zLU+UaWIA4lmyX36HAj2xEu85iswW3btlmxS1FcgKx3ZDuuvfbaWA43skHJWLWtYY8ckMts3LhRKi2A4Ai9CYBmycwvBBRtOuCC9uE4xz7k0HBU06DWJjQuZX6mib1tSaVwLFmyRJq9du3a1bYpygWSSWNszpkzJyaQCAS+qGKimtSWFBksW7bMDB8+XKoKFUVxD3XsK4qiKIqSpMCpH9/BXZ2r8UPTUgIe6DfnzJkzltQN1w0HDY3pbGXbInFTt25d061bN8kEDZb6I+dBpj5yHjazbZHwKFq0qBzikVQIZouSaasBJSUpg5QIDUvJ6g5CJRDSMlQSKPGDfIwfNCRTn+vJnEyl0s6dO61ndTPPhdqHvMfs2bOt20YWMutY8Bq6BHJPobr6ysXD3LlzReoLSaDQ/SnSZIxhm459qgdWr16tjn1FcRR17CuKoiiKkqQge5FD0rRp00yVKlVi3ufQjH7yQw89ZNU+V8FRzovrh4Ma575L+Nm0SHmQ+ZYlSxbjIm+88YZk5OHUItCAhBHPItrDv/zyi23zFMUa7733nkhShYI+PD0okCOhKkgJD8FLP3D5zDPPmGrVqpkvvvjCbNiwQRzotiWOgjrhaOrTcJhALBUZoRVg0QZnfoMGDSTg2r17d+vSO6HkzZtXXgS+6APgYvBBOXuoUKLvUyjsDxgftuc9pL2YQ6h8RKIt9PkjYSFU1khRlOihjn1FURRFUZIUyDl07NhRyv/R2udAQhb/+PHjpbEgDiQlfsi0RPeVpoxB5/nmzZsl0yxHjhxWL59NqZ1IuPvuu0UqiAx9mnLiiCNTn4al2nwuYWhsiCRAnTp1EvyM7FvkXJSLp5oKkKqy7eByGTLfg4HOefPmmR9++EF6AxAcsQ1yN8EKMNYSNLvJBrYl4RZsDorUCJUNVFShJx6E/g/B6xttuI+sGxMnTpTgAxVyoX0qPv/8c2v2KefGfffdJ32f+OrPgTjRn332WZGt8vtS2AJbtmzZInKV4dDmuYpiF3XsK4qiKIqS5HjppZcksxtZmdGjR8vBCUcDOvK2JQFcB61XnEQ4GYKgvU+1A1nnZPQr8UOmPpm0ypmxePFiqXQI59jnsylTpshnZCrzUhLfuKCpJsGvoGTWyJEjpaIKR7ASOTjMebkKMkG8XICAEcHW+LBd7cB9JOAQH1dddVVU7VHOL6xbNFVHconMd3pBEQhLly6dSJMhJ2iTTp06mdtuu8289tprYZvnulohqShJBXXsK4qiKIqSJHnwwQflpZwZZAXS0C20kWWBAgUkq3H69OnyuaJEk0WLFokUiU2+++470UiuXLlygp/lyZPHpE6dOur2EUyiAWe4qpbgZzgRbVzL1q1bmxtuuEGqqOiTQcB14cKF5ptvvhGZHq6fkjBUUv3vf/8z27dvF037IARebTuoaeCLnjh2hoI0jy141h599FHjKlTCuWxfpOC0tjH3RQq22ZCUIUufOa5p06Zm6tSpIsvDfqpWrVpOOM2plurdu7fMzYqiuIc69hVFURRFUZQzksuIrwGtNh9WLgTIFD355JOiN8wLOYogflNJ5CpsMnPmTNHmDufY5zOkvvjsiSeesGLfhAkTZIyGc+zzGVmhfIaesg2QjJkzZ47p1auXXC+qgKismjx5slZgRABVVAREjh49GkdKBsi4tenYJ3iElAfrR6ZMmZxy7PsQDEGWh34OrlQTBPn333+lRxD3GK1zF8FG7GO+C4V53DbYBuEc+G+++aaxCYFNXq6BQx+pO/osKYriHnZrehRFURRFUZREJ5fx6aefihZxkL/++kuyktGLV5TzSbly5czQoUMlm9H/PviiPwZ9H26++WZnHV1IK9iuKIgPAiM4bVywj8zkvn37SlY3cwr3VmWVIqNr167m8ccfFz3sjRs3xnnRfNUmNMileXh89tlm9erV4szPnz+/NAqF48ePmzJlyph169bZNs/89ttv0juEihp09oHKBxz8//zzj23zRA6N4BFSfL59S5YsCdsQ2wYEfnFMY9+QIUPkPeTbaJbsQkCpT58+Zt++fbHex75Q2UMbEDB87rnnzKBBg8zPP/8sVUHB15EjR2ybqChJmmReaI2eoiiKoiiKoiTgpKRRH45KysTJssWpSlY1fQroW6AoF4KtW7eKI6tIkSJOXeBu3bqJTAEOcjLiQ6UmDh48KFIff/zxh8mZM2fU7WvSpIkZM2aM2IGcTaikzYEDB8Tpi33hsmyVxEGpUqXMxx9/7GwDcaoFGMOu9mCpVKmSNClt0KCBvMiMh8GDB4vU17vvvms1yxynfrNmzUSiZdiwYdJvBHCio7H//PPPW7OPOYSs7tq1a0tV1YkTJ6RfBtx6662mR48epnz58tbsY49SunRp0759e3FEs4dp1aqV2Mp6MmvWLAkq2oL7iQ0ffvhhrPfR2a9QoYLst6gGs8U111xjVq1aFe/n2jxXUeyiUjyKoiiKoihKxKRIkUKyyMiqRft6xowZ4lR45513JKNaUS4U2bNnF8f5hg0bpMmgD3rEaIrjfLDBAw88IE63ESNGiNMcLXMf7KUhN/IK4eRHogHyFzSkpTkoGck4ioL2IdtCJY469RM3JUuWlKxpVx37vn0uVnXhmF6wYIH0iAnNzmfs2nTqA9UrBAdfeuklWX9D7aPBtE3HPo1f6bODlA3Xikz9YMAEOS2bjn0Cm3Xr1jVt27Y1Tz/9dMz7zNcExLjv9evXt2YfQZpwPZ9InKBZLb1GmKNtQWCLYE180ORXURR7qGNfURRFURRFOSPIHCPzjZeiRDNrtXr16pLdGHTskyFfp04daeyMgz3a0HSWF5Id6NTTHNclcAjxIqM2a9as4ihSLj5q1qwpsicEunDuE4QNggPTZuNSsrmp6urcubME4QgqBUFmyxZcM9Y19P9D7eKz+PrKRAts8PsjuGjfli1bpJIgnH3BPii2cN0+xiUB61BwpiNTFTqWo02kVTasz1S4BNdnRVEuPKqxryiKoiiKoiiK8/zyyy8mQ4YMcZpG4pB7+OGHzfDhw41NcBzhNKJ6IAjyIy5oiOPYV6f+xcsLL7wg0h1PPfWUqVixovScCL7COQ6jCRrdyHk0bNhQsrdD7bMJVWc4V6lACzp+0Q6nL0CwysVWtQNyLPQBCNqHU79fv37W7UP3H+11pPqC9tEAe/To0dabrmIfDbkhaN/mzZvN1KlTrdt37733mp49e5rZs2fHCja0bt1avrdtX6QsX75c9fYVxQKasa8oiqIoiqIoivPs3r07bLYl8P62bduMTXDok7FIk8bQrEukcHAsZc6c2Zp9ysWN63IZNM11FeYPZGRq1Khh7rzzTrNnzx7z7LPPmokTJ0pj2lDt82iDjNfLL78sVQ1IGeHgpzpj3Lhx0vTatgzeLbfcIsER5jkk05irCTjQbBXox2MTKkUI0Dz66KMyTxNwQPcfCUGqmai2sgnVLN99950EvMh2Z53gHlOJ8dVXX1mvyFAUxW20ea6iKIqiKIqiKM5DxjFSOzQZDDpi9u3bJ9rxNJG06eB67733xLk6cODAOJ81btzYVKlSRRxLiqK4WxWEQ5rsePTXcbQiOZcrVy7jAjh5hw4dKo19CdRUrVrVtGvXzlr/jtDATYcOHcwXX3whznPsIxOdfjw2G9MGs/NZI77++muxNUuWLKZevXri8Lcd9PKhsS96//R8oAIM3X16oCQWkNiiX4CtfjeKklTR0J+iKIqiKIqiKM5DJiMyIzjbyAAtWLCgOJBojJgzZ07ToEEDq/a5XlGgXPzgsKSRKpIYNAqlp8KcOXMkIIaMlW2QCmK8ko1Mhjwgf4OUjM2sZCRtyH5v1KiR1Sav8UFD3/nz50ujbqoKXIQeAARFeCFhZLOfQzioKPjss8/kexftAyoybPaaUBQlcaIZ+4qiKIqiKIqiJBrQ0udFBj+ZqnfddZfoi6dPn96qXWRa1q1b1yxYsECcSD5r1qyRCgOybdE+V5QLAX0c0Kon+5ixsXDhQsmcffvtt82mTZtM7969rV7433//3dxxxx2SiYyT2vM8eb9Vq1bSN6NZs2bWbFu/fr2pVKmSWbt2rXER5pbu3buLHrxycXLw4EHpU0Cj31BJLcYNwWvX0Yx9RbGDOvYVRVEURVEURVHOA0hPIBVEM18cMThYR40aJbrdX375pV5j5YKBDBVZyAMGDDCFChUykydPFkcbGvEEmnbs2CHyMra47bbbzP3332+ef/55qWDxHfvIVyGJgvyNLWj6ilwMwRAXZGNCwdmL3NiKFSvMpZdealyAea1Xr14R/W6dOnVEBieaEMiieiUSqG5hzrYFTaXpU8AYpWdCaOXXp59+KuPHddSxryh2UCkeRVEURVEURVGU8wByHv379zfjx483P/30kzSS7Natm2nZsqVeX+WCMnfuXGnyilMw6Bgkgz9t2rTiHM6dO7dV+9A3h6B9BQoUEIe1TY4fPy5SXgTgcEDnz5/fpEiRIubzNGnSmOuvv96afdw/5IqoTqLCAc3/4DWkcqlw4cJRtYngERVKPiNGjJDqJBrBIpuGTNrYsWPFztKlS5tow98MXiPmZYJJ6NbjPCfoOnr0aLnX/G+xyVtvvSXVNlSica8TK/SjQP5LUZTooo59RVEURVEURVGU8wAZ0a1bt5aXokSTVKlSiVZ8KGQB86JZqAv2ocUehEx9285AnLw4V6Fhw4ZxPif4QMNaW8yePVsy5GHmzJlxPicgQdPSaEKgww92kHFOpchff/0lwUyfnj17ivwYFRHRhmblvOC7774zV1xxhVRTEaTx6dKliylbtmycZzLa0H+lfv36Tjv1I+nf0aRJE9tmKkqSJLltAxRFURRFURRFURRFOXvuu+8+8/rrr5sDBw7EZCpv3brVPProo6Zy5crWe1BgX8eOHSU73rePTP3mzZtLU1ib4LjHcRnfC7kgm6CxnpB9VAjZ5Ndff5UeBUGnPuBEr1mzpmjH24TqKewIOvUBe5HA+e2334xNSpYsaRYvXmxc7t9RtGhR06dPH/Puu++aPXv2yPvc165du9o2T1GSPJqxryiKoiiKoiiKoiiJGCRkyOzGWYnznCxumsIi2zJt2jTb5olTEJuQQaE5aMGCBc3q1atNqVKlpDGsTQg02A58JASyQK7bh9TSkSNHpM9DkF9++cWUK1fO2LYPO0LBXuyuUaOGsQnjAhu4dkjyUN0SpEiRIiZjxozW7Hv11VfFPr9/h88TTzwh/TsYvzb7dyhKUkeb5yqKoiiKoiiKoijKRcAPP/wgGcqHDx8WhyCa4q5IfCDJMmnSJPP777+Lcx8ddDL5L7nEbr7hwYMHzfTp0+N1+pPpjYY9DbFtgF49WfHx2UcfheLFi1uTNKJKpESJEvL3kWMhmIT8E7r7BJsWLFhg8ubNa2yB9j+yQeXLl5e+ANhJFvoHH3xgdu3aJU2TuYa2oHfClClT4v2cRtj8ji0IvtG/g6849hnDNMoFmk3zbNrs36EoSR117CuKoiiKoiiKoihKIgbJnTfeeMOa8/l0IAdE0MFFqBz4f3v3HVx1mf1x/NCCIL0XSeigwKIgoQmLLARYuijFghpBQcwiAiJIUURWRNYgIm0FAQFFEVikSNGsiosi0iFKkSC9C0no4TfnzNz8UtGw6/e5l7xfM3dyc7+Zuc98b5I/znOez6lRo4YV+H0d3r5ceC2c6/d6CqJTp04yb948z7uTV69ebRsgulmjdCNE1+Nbqw6F1XXqYN3IyEhxQYcza2b94sWL5ciRI1K4cGFp2rSpRbVoDrtr0dHRFgWlGzhazC9RooRFQI0YMcKeu6Sfq+/zTEuuXLmSDXP2ms4h0P8tTZo0SVbY180bLeyfPn3ar0+UADc7CvsAAAAAAAQw7X7XrlrfQFN/owM2Dx065LcFwP79+1vmvxYwtRB9+fJlG7bau3dvGT9+vA0f1sG6jz/+uAwePNjz9WkRWmOWhgwZYt3R58+ftwJrv3795F//+pfNU3jkkUfknXfekQceeEBc0iK161MYgbw+f6OzO6KiomyWQ+3atWXp0qU2cPjRRx+1kzc6nBiAOxT2AQAAAAAIYC+++KIVLLUw7Y+6dOkizZo1s1xuf6Pd+dphrp3mKQesagyKDgz99NNPrbCpRX6vTx5olIzmsP/000+prk2YMEH27Nljnfo6x+DHH3+UadOmebo+ZJzGE2nHu8bb/PLLL/Y8Pfoz+vvpis4i0EgvLe7r/xiNWko6v6N8+fLO1gZAGJ4LAAAAAEAg07gOHXK5du1a66rV71MO13VZHNQO36eeekoWLFggd9xxR6oBoa+99pqztWmMjMbZpBz86jtpEBMTY89DQkIsT95r+v7pnXRIuT4dBuuK/u5t3LhRTp48affTJzQ0VP7617+KS7oezbHfsWOHnD17Ntk1XZuu0UsvvPCCrUc3jnRTxp8z9vXvQrv0/Xl+B5CZcf4IAAAAAIAApjEymoGttm/fnmbXrUv79u2zzHXt+N2yZYv4E80J1+GpGrGjmyO+rn0t+Gv0jcYc+QrX99xzj+frq1atmuzcudNidnRzxJe3rh38o0ePtvkKLtenevToIfPnz5eCBQtajJHez927d1uE0dChQ50W9vV3Tk+L6N+F3jstRutr2imvXec6+Nfrwv6KFSsSn7ss2md0ToY+APgXongAAAAAAECmpQNqu3XrJpcuXZIKFSpYV7IWpnWorsbwFCtWzAataqa9i5MPM2fOlD59+thJDO3M165zjeAJCwuTjz/+2DrSNQt95MiRqU5D/NH0lECrVq1sw0bXooNq3377bfnmm2+kffv2NqugatWq4sqcOXMspki7zQcNGmRr0UHDH374ofTt29c2SPTkg7/r1auXzVFo2LDhH/5e2p0fFxf3u35WN+zo3AfcoWMfAAAAAABkWtrRrVn2mqOvBXPt2q9Zs6Zl22fJksV+RgvnruigUu16X7JkiZ1+KFCggNStWzdZkddVnNHWrVut67xEiRLWEe87HdKgQQMbNqzxSzoDwhVdnw4U1lMESdencx+06L9q1Srp1KmT+Dv93M+dO+fJez377LP2d+ATHx+fGK+k91DnUigt6Ov9JWcfcIfCPgAAAAAAAUwjY44dO5bmtRw5cljkiGZiaza2q6iW62V4a2Gwc+fOUqZMGXFFc+x9sTZp0egWjcJZtmyZuFC0aFEJDw9P97pG8cyePVsmT57s6bp07oCv47148eJWgE56T8+cOSMu6fr0999f1+ePNm/enPh85cqV0rt3b5k0aZI0btzY/p9oNJTO7dCYKIr6gFsU9gEAAAAACGDnz5+X9957zzLNdXiudpzroFCNGWnUqJGsW7fOomQ0KqVDhw6er+/EiROyePFiK9xrJ7x2wW/atMmK5doVr1E4w4cPly+//NLW74+001vvp7/Sbu6kRWsXNONfTxe8/vrrVkSfOHGivPXWW+Ivmjdvbg/N1de4pYULF8rzzz/vell+bfz48RatpLFPPtWrV5dPPvnE5lOMGTMmce4DAO9R2AcAAAAAIIBpDIp25M+aNStx+KvSgpwW4DZu3GhF1oiICCeFfe02HzBggMXF+IqAGuehRVUtSC9fvtyu60PzvRE4dKDqnXfeac91FsGMGTNk2LBh1gn/0EMP2UkMl7p27WpDfVWdOnVsA0mHDuvvn/4++taOtMXExKQ5g0BP2mg8z5EjR6R06dLcPsARhucCAAAAABDANCJDB4SGhoamulakSBErzmnGeN68ea2r2+sBsKVKlZJt27bZiYKkTp48ad3TBw8etALhHXfcIadOnRJ/pMN0NUtev/qjFStWSGRkpH3FzUd/9zT7Xr966bHHHpO9e/fasGHt0Fd62kE3b+bNm2f/W+jYB9zJ6vC9AQAAAADAf0m73vfv35/qdS2S//rrr5YznpCQIEFBQTbw0l/Wp6+dPXvWnms8j248APAfGrWjhfyQkBDbeKtVq5bFLOk8Bx0+TFEfcIsoHgAAAAAAAlinTp3k6aeftiK+du9rTMb27dstV79evXpWiNO4mwYNGkiuXLmcrE8jWV599VW5++67LcJjw4YNMnjwYLn//vvtZ+bPn29xQgg8n3/+uYwdO1b27Nkjly5dSnZNM/dffvllcWnu3Lkybdo0m+lw5cqVZNdGjhwp3bt3F3/Xs2dPqVq1qufvq/87vv32W1m1apXNxdB5HpUqVbJILxebhACSo7APAAAAAEAAGzJkiGWGP/fcc4kd8FmzZrViusazKM0Z1wG7LrzzzjuWp//www8nFn719IAWK3XQqqpWrZr06NHDyfpw4zSmpXXr1pZl365dO8mRI0ey6/q5ut50CA8Pl169etkaU3aY60aTa/o38dFHH9lmnG4yaAFfn2uMlhbWfZtjruhpGh2em3SALgD/QMY+AAAAAAA3AS0Q/vzzzxadUa5cuVRDLzX6RnOyUxZfvRIfH2+FYC0U6vpSdvxqfn3FihU9XZPeE91g+Oyzz657TYfBLliwQJ544glP1/f111/LzJkzreP8etd0doKegvC6AKyRLB988IEsXbpU/NGLL75ofw/jxo0TfxQXFycNGzaU06dP2/P333/fcvQ//fRTeffdd2XhwoWulwjAj5GxDwAAAADATUC74KtUqSI1a9ZMVdRXTZs2tWGXrmghv3r16tbFnVaMh0Z8uNgM0QiZ9DYifLMBChQo4HlRX+l8BI2QSYtuNujQYVW2bFknXd16EkTvjb/y9/VNmDBBbrvtNtvUqlOnTuLrbdq0ke+++y7N2RRe0tkcGrOkpwg04ks35ZI+/HWYNJBZEMUDAAAAAAAyFc0K1+zwgwcP2vOoqKhk1zWLfdGiRVYwd0EHH2/ZssUe+jzl+nRDYsaMGc7W53PPPffIwIEDJTo62kkG/G9p27atzXHQKJ6iRYuKv9FTFjqHQE/RaKE8qfLly1vhPDg42Nn6Jk2aJBMnTpRRo0bJoEGDbF6C/j7qCYj27dtL6dKlna0NAIV9AAAAAACQyWhB/9577038PulzlT17dqlcubLFobig3dqtWrVKd33aPa0nM3wzClz56quvbJNB11KrVi3Jnz9/suuau6+DnV1Zs2aNHD9+XCpUqCB33nlnqpMiffr0seK/y1M2OvQ6pcuXL8uuXbssZ98ljZ/SOR06LPell16SP//5z3aypkWLFvaYMmWK0/UBmR0d+wAAAAAAIFPRLH8tnupMgi5dulghPSkdspqyg9pLWjTV9enwVy2efvjhh6k2HvxBoUKFpGPHjuleL1WqlKfrSev9dWjz9dbvkm4qaDe8fvX9vp07d0769u0refLksegqlw4cOCC33367Pc+bN6/FPyndyNH16gaZRgkBcIPhuQAAAAAAZJJi9ooVKzwfUPt7aaHw2rVrrpcBeEZ/33v37i3Tp0+37n3t0Ne5CbfeeqssW7ZM6tat6zf/Mzp37iw1atSQYcOGWUTQHXfcISdPnrS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      "text/plain": [
       "<Figure size 1800x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,12))\n",
    "sns.heatmap(df_selected.corr().sort_values(by='final'), annot=True, cmap='YlGnBu')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = df_selected.drop([\"final\"], axis=1)\n",
    "y = df_selected[\"final\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.sk-global {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       ".sk-global.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       ".sk-global.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       ".sk-global {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".sk-global pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip-path: inset(100%);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       ".sk-global div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       ".sk-global label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       ".sk-global div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label label.sk-toggleable__label,\n",
       ".sk-global div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       ".sk-global div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       ".sk-global div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       ".sk-global div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       ".sk-global a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       ".sk-global a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-top-container.sk-global {\n",
       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
       "   to allow keyboard-only users to navigate the display. */\n",
       "  overflow-x: scroll !important;\n",
       "  max-width: 100%;\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover td {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table :is(td, th) {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td, .estimator-table th {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "td.fitted-att-type {\n",
       "    white-space: preserve nowrap;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "@supports(anchor-name: --doc-link) {\n",
       "    a.param-doc-link,\n",
       "    a.param-doc-link:link,\n",
       "    a.param-doc-link:visited {\n",
       "    anchor-name: --doc-link;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "@supports(position-area: center right) {\n",
       "    .param-doc-description {\n",
       "    position-area: center right;\n",
       "    position: fixed;\n",
       "    margin-left: 0;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".features {\n",
       "  font-family: monospace;\n",
       "  cursor: pointer;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: .20em;\n",
       "  margin-bottom: 0.5em;\n",
       "  font-size: inherit; /* Needed for jupyter */\n",
       "}\n",
       "\n",
       ".features.fitted {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features summary {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  margin-bottom: 0;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "  padding: .25em;\n",
       "}\n",
       "\n",
       ".features details[open] > summary {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features.fitted details[open] > summary {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features details > summary .arrow::before {\n",
       "  content: \"▸\";\n",
       "  color: grey;\n",
       "}\n",
       "\n",
       ".features details[open] > summary .arrow::before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       ".features details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".features.fitted details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".features .features-container {\n",
       "  max-width: 15em;\n",
       "  max-height: 10em;\n",
       "  overflow: auto;\n",
       "  scrollbar-width: thin;\n",
       "  padding: .25em 0.1rem;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 0 0 .5em .5em;\n",
       "}\n",
       "\n",
       ".features.fitted .features-container {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features .image-container {\n",
       "  block-size: 1em;\n",
       "  inline-size: 1em;\n",
       "  padding: 0;\n",
       "  margin: 0%;\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  align-items: center;\n",
       "}\n",
       "\n",
       ".features .copy-paste-icon {\n",
       "  background-size: 1em 1em;\n",
       "  width: 1em;\n",
       "  height: 1em;\n",
       "  filter: grayscale(100%) opacity(60%);\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  width: 100%;\n",
       "  margin: 0.01em;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(odd) {\n",
       "  background-color: #fff;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(even) {\n",
       "  background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:hover {\n",
       "  background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  table-layout: inherit;\n",
       "}\n",
       "\n",
       ".features .features-container table td {\n",
       "  text-align: left;\n",
       "  padding: 0 0.5em;\n",
       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "  white-space: nowrap;\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".total_features {\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  margin-top: 0.5em;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-3\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_neighbors',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_neighbors;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_neighbors,-int%2C%20default%3D5\">\n",
       "            n_neighbors\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_neighbors;\">\n",
       "            n_neighbors: int, default=5<br><br>Number of neighbors to use by default for :meth:`kneighbors` queries.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('weights',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-weights;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=weights,-%7B%27uniform%27%2C%20%27distance%27%7D%2C%20callable%20or%20None%2C%20default%3D%27uniform%27\">\n",
       "            weights\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-weights;\">\n",
       "            weights: {&#x27;uniform&#x27;, &#x27;distance&#x27;}, callable or None, default=&#x27;uniform&#x27;<br><br>Weight function used in prediction.  Possible values:<br><br>- &#x27;uniform&#x27; : uniform weights.  All points in each neighborhood<br>  are weighted equally.<br>- &#x27;distance&#x27; : weight points by the inverse of their distance.<br>  in this case, closer neighbors of a query point will have a<br>  greater influence than neighbors which are further away.<br>- [callable] : a user-defined function which accepts an<br>  array of distances, and returns an array of the same shape<br>  containing the weights.<br><br>Refer to the example entitled<br>:ref:`sphx_glr_auto_examples_neighbors_plot_classification.py`<br>showing the impact of the `weights` parameter on the decision<br>boundary.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;uniform&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('algorithm',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-algorithm;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=algorithm,-%7B%27auto%27%2C%20%27ball_tree%27%2C%20%27kd_tree%27%2C%20%27brute%27%7D%2C%20default%3D%27auto%27\">\n",
       "            algorithm\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-algorithm;\">\n",
       "            algorithm: {&#x27;auto&#x27;, &#x27;ball_tree&#x27;, &#x27;kd_tree&#x27;, &#x27;brute&#x27;}, default=&#x27;auto&#x27;<br><br>Algorithm used to compute the nearest neighbors:<br><br>- &#x27;ball_tree&#x27; will use :class:`BallTree`<br>- &#x27;kd_tree&#x27; will use :class:`KDTree`<br>- &#x27;brute&#x27; will use a brute-force search.<br>- &#x27;auto&#x27; will attempt to decide the most appropriate algorithm<br>  based on the values passed to :meth:`fit` method.<br><br>Note: fitting on sparse input will override the setting of<br>this parameter, using brute force.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('leaf_size',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-leaf_size;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=leaf_size,-int%2C%20default%3D30\">\n",
       "            leaf_size\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-leaf_size;\">\n",
       "            leaf_size: int, default=30<br><br>Leaf size passed to BallTree or KDTree.  This can affect the<br>speed of the construction and query, as well as the memory<br>required to store the tree.  The optimal value depends on the<br>nature of the problem.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">30</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('p',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-p;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=p,-float%2C%20default%3D2\">\n",
       "            p\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-p;\">\n",
       "            p: float, default=2<br><br>Power parameter for the Minkowski metric. When p = 1, this is equivalent<br>to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2.<br>For arbitrary p, minkowski_distance (l_p) is used. This parameter is expected<br>to be positive.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">2</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('metric',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-metric;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric,-str%20or%20callable%2C%20default%3D%27minkowski%27\">\n",
       "            metric\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-metric;\">\n",
       "            metric: str or callable, default=&#x27;minkowski&#x27;<br><br>Metric to use for distance computation. Default is &quot;minkowski&quot;, which<br>results in the standard Euclidean distance when p = 2. See the<br>documentation of `scipy.spatial.distance<br>&lt;https://docs.scipy.org/doc/scipy/reference/spatial.distance.html&gt;`_ and<br>the metrics listed in<br>:class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric<br>values.<br><br>If metric is &quot;precomputed&quot;, X is assumed to be a distance matrix and<br>must be square during fit. X may be a :term:`sparse graph`, in which<br>case only &quot;nonzero&quot; elements may be considered neighbors.<br><br>If metric is a callable function, it takes two arrays representing 1D<br>vectors as inputs and must return one value indicating the distance<br>between those vectors. This works for Scipy&#x27;s metrics, but is less<br>efficient than passing the metric name as a string.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;minkowski&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('metric_params',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-metric_params;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=metric_params,-dict%2C%20default%3DNone\">\n",
       "            metric_params\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-metric_params;\">\n",
       "            metric_params: dict, default=None<br><br>Additional keyword arguments for the metric function.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
       "            n_jobs: int, default=None<br><br>The number of parallel jobs to run for neighbors search.<br>``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.<br>``-1`` means using all processors. See :term:`Glossary &lt;n_jobs&gt;`<br>for more details.<br>Doesn&#x27;t affect :meth:`fit` method.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-classes_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=classes_,-array%20of%20shape%20%28n_classes%2C%29\">\n",
       "            classes_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-classes_;\">\n",
       "            classes_: array of shape (n_classes,)<br><br>Class labels known to the classifier</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int8](2,)</td>\n",
       "           <td>[0,1]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-effective_metric_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=effective_metric_,-str%20or%20callble\">\n",
       "            effective_metric_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-effective_metric_;\">\n",
       "            effective_metric_: str or callble<br><br>The distance metric used. It will be same as the `metric` parameter<br>or a synonym of it, e.g. &#x27;euclidean&#x27; if the `metric` parameter set to<br>&#x27;minkowski&#x27; and `p` parameter set to 2.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">str</td>\n",
       "           <td>&#x27;eu...an&#x27;</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-effective_metric_params_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=effective_metric_params_,-dict\">\n",
       "            effective_metric_params_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-effective_metric_params_;\">\n",
       "            effective_metric_params_: dict<br><br>Additional keyword arguments for the metric function. For most metrics<br>will be same with `metric_params` parameter, but may also contain the<br>`p` parameter value if the `effective_metric_` attribute is set to<br>&#x27;minkowski&#x27;.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">dict</td>\n",
       "           <td>{}</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>27</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_samples_fit_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=n_samples_fit_,-int\">\n",
       "            n_samples_fit_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_samples_fit_;\">\n",
       "            n_samples_fit_: int<br><br>Number of samples in the fitted data.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>97928</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-outputs_2d_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#:~:text=outputs_2d_,-bool\">\n",
       "            outputs_2d_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-outputs_2d_;\">\n",
       "            outputs_2d_: bool<br><br>False when `y`&#x27;s shape is (n_samples, ) or (n_samples, 1) during fit<br>otherwise True.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">bool</td>\n",
       "           <td>False</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
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       "\n",
       "        if (luma > 180) {\n",
       "            // If the text is very bright we have a dark theme\n",
       "            return 'dark';\n",
       "        }\n",
       "        if (luma < 75) {\n",
       "            // If the text is very dark we have a light theme\n",
       "            return 'light';\n",
       "        }\n",
       "        // Otherwise fall back to the next heuristic.\n",
       "    }\n",
       "\n",
       "    // Fallback to system preference\n",
       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
       "}\n",
       "\n",
       "\n",
       "function forceTheme(elementId) {\n",
       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
       "    if (estimatorElement === null) {\n",
       "        console.error(`Element with id ${elementId} not found.`);\n",
       "    } else {\n",
       "        const theme = detectTheme(estimatorElement);\n",
       "        estimatorElement.classList.add(theme);\n",
       "    }\n",
       "}\n",
       "\n",
       "forceTheme('sk-container-id-3');</script></body>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7873948206845846"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.sk-global {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       ".sk-global.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       ".sk-global.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       ".sk-global {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".sk-global pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip-path: inset(100%);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       ".sk-global div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       ".sk-global label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       ".sk-global div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label label.sk-toggleable__label,\n",
       ".sk-global div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       ".sk-global div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       ".sk-global div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       ".sk-global div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       ".sk-global a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       ".sk-global a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-top-container.sk-global {\n",
       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
       "   to allow keyboard-only users to navigate the display. */\n",
       "  overflow-x: scroll !important;\n",
       "  max-width: 100%;\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover td {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table :is(td, th) {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td, .estimator-table th {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "td.fitted-att-type {\n",
       "    white-space: preserve nowrap;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
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       "    width: 14px;\n",
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       "}\n",
       "\n",
       ".features {\n",
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       "\n",
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       "\n",
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       ".features.fitted details[open] > summary {\n",
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       "\n",
       ".features details > summary .arrow::before {\n",
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       ".features details:hover > summary {\n",
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       "  align-items: center;\n",
       "}\n",
       "\n",
       ".features .copy-paste-icon {\n",
       "  background-size: 1em 1em;\n",
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       "  height: 1em;\n",
       "  filter: grayscale(100%) opacity(60%);\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  width: 100%;\n",
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       "\n",
       ".features .features-container table tr:nth-child(odd) {\n",
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       "\n",
       ".features .features-container table tr:nth-child(even) {\n",
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       "\n",
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       "\n",
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       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".total_features {\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  margin-top: 0.5em;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-4\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=500, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('solver',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-solver;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
       "            solver\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-solver;\">\n",
       "            solver: {&#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;newton-cg&#x27;, &#x27;newton-cholesky&#x27;, &#x27;sag&#x27;, &#x27;saga&#x27;},             default=&#x27;lbfgs&#x27;<br><br>Algorithm to use in the optimization problem. Default is &#x27;lbfgs&#x27;.<br>To choose a solver, you might want to consider the following aspects:<br><br>- &#x27;lbfgs&#x27; is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes &gt;= 3`), all solvers except<br>  &#x27;liblinear&#x27; minimize the full multinomial loss, &#x27;liblinear&#x27; will raise an<br>  error.<br>- &#x27;newton-cholesky&#x27; is a good choice for<br>  `n_samples` &gt;&gt; `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, &#x27;liblinear&#x27; is a good choice, whereas &#x27;sag&#x27;<br>  and &#x27;saga&#x27; are faster for large ones;<br>- &#x27;liblinear&#x27; can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 &lt; l1_ratio &lt; 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   &#x27;lbfgs&#x27;           l1_ratio=0               yes<br>   &#x27;liblinear&#x27;       l1_ratio=1 or l1_ratio=0 no<br>   &#x27;newton-cg&#x27;       l1_ratio=0               yes<br>   &#x27;newton-cholesky&#x27; l1_ratio=0               yes<br>   &#x27;sag&#x27;             l1_ratio=0               yes<br>   &#x27;saga&#x27;            0&lt;=l1_ratio&lt;=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   &#x27;sag&#x27; and &#x27;saga&#x27; fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide &lt;Logistic_regression&gt;` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table &lt;logistic_regression_solvers&gt;`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from &#x27;liblinear&#x27; to &#x27;lbfgs&#x27; in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;liblinear&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
       "            max_iter\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
       "            max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">500</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-penalty;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
       "            penalty\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-penalty;\">\n",
       "            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;, None}, default=&#x27;l2&#x27;<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `&#x27;l2&#x27;`: add an L2 penalty term and it is the default choice;<br>- `&#x27;l1&#x27;`: add an L1 penalty term;<br>- `&#x27;elasticnet&#x27;`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing &#x27;multinomial&#x27; + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` and `C` instead. `l1_ratio=0` for `penalty=&#x27;l2&#x27;`,<br>   `l1_ratio=1` for `penalty=&#x27;l1&#x27;`, `l1_ratio` set to any float between 0 and 1<br>   for `penalty=&#x27;elasticnet&#x27;`, and `C=np.inf` for `penalty=None`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('C',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-C;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
       "            C\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-C;\">\n",
       "            C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('l1_ratio',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-l1_ratio;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
       "            l1_ratio\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-l1_ratio;\">\n",
       "            l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 &lt;= l1_ratio &lt;= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('dual',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-dual;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
       "            dual\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-dual;\">\n",
       "            dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation &lt;regularized-logistic-loss&gt;`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples &gt; n_features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-tol;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-tol;\">\n",
       "            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
       "            fit_intercept\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
       "            fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
       "            intercept_scaling\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
       "            intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a &quot;synthetic&quot; feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-class_weight;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
       "            class_weight\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-class_weight;\">\n",
       "            class_weight: dict or &#x27;balanced&#x27;, default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The &quot;balanced&quot; mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight=&#x27;balanced&#x27;*</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-random_state;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-random_state;\">\n",
       "            random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == &#x27;sag&#x27;, &#x27;saga&#x27; or &#x27;liblinear&#x27; to shuffle the<br>data. See :term:`Glossary &lt;random_state&gt;` for details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-verbose;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose;\">\n",
       "            verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-warm_start;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
       "            warm_start\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-warm_start;\">\n",
       "            warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary &lt;warm_start&gt;`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
       "            n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-classes_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%20%29\">\n",
       "            classes_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-classes_;\">\n",
       "            classes_: ndarray of shape (n_classes, )<br><br>A list of class labels known to the classifier.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int8](2,)</td>\n",
       "           <td>[0,1]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-coef_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=coef_,-ndarray%20or%20CSR%20matrix%20of%20shape%20%281%2C%20n_features%29%20or%20%28n_classes%2C%20n_features%29\">\n",
       "            coef_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-coef_;\">\n",
       "            coef_: ndarray or CSR matrix of shape (1, n_features) or (n_classes, n_features)<br><br>Coefficients of the features in the decision function.<br><br>`coef_` is of shape (1, n_features) when the given problem is binary.<br><br>By default, it will be created as a dense array, but can be turned to<br>sparse (CSR format) through :meth:`sparsify` (which can be beneficial<br>under L1 regularization when many coefficients are zero), and back to<br>dense through :meth:`densify`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](1, 27)</td>\n",
       "           <td>[[ 0.34,-0.48,-0.74,...,-0.18, 0.17,-0.09]]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20or%20%28n_classes%2C%29\">\n",
       "            intercept_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
       "            intercept_: ndarray of shape (1,) or (n_classes,)<br><br>Intercept (a.k.a. bias) added to the decision function.<br><br>If `fit_intercept` is set to False, the intercept is set to zero.<br>`intercept_` is of shape (1,) when the given problem is binary.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
       "           <td>[0.11]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>27</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_iter_,-ndarray%20of%20shape%20%281%2C%20%29\">\n",
       "            n_iter_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
       "            n_iter_: ndarray of shape (1, )<br><br>Actual number of iterations for all classes.<br><br>.. versionchanged:: 0.20<br><br>    In SciPy &lt;= 1.0.0 the number of lbfgs iterations may exceed<br>    ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int32](1,)</td>\n",
       "           <td>[5]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
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      "text/plain": [
       "LogisticRegression(max_iter=500, solver='liblinear')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "forest = RandomForestClassifier()\n",
    "forest.fit(X_train_scaled, y_train)\n",
    "\n",
    "logreg = LogisticRegression(max_iter=500, solver='liblinear')\n",
    "logreg.fit(X_train_scaled, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN accuracy: 0.7873948206845846\n",
      "Random Forest accuracy: 0.844947308226452\n",
      "Logistic Regression accuracy: 0.748345723388612\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\prave\\AppData\\Local\\Temp\\ipykernel_12832\\1055371209.py:15: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x=model_scores, y=model_names, palette=['#264653', '#2a9d8f', '#e76f51'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forest_score = forest.score(X_test_scaled, y_test)\n",
    "logreg_score = logreg.score(X_test_scaled, y_test)\n",
    "knn_score = knn.score(X_test_scaled, y_test)\n",
    "\n",
    "print('KNN accuracy:', knn_score)\n",
    "print('Random Forest accuracy:', forest_score)\n",
    "print('Logistic Regression accuracy:', logreg_score)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "model_names = ['KNN', 'Random Forest', 'Logistic Regression']\n",
    "model_scores = [knn_score, forest_score, logreg_score]\n",
    "plt.figure(figsize=(7,4))\n",
    "sns.barplot(x=model_scores, y=model_names, palette=['#264653', '#2a9d8f', '#e76f51'])\n",
    "plt.xlim(0.5, 1.0)\n",
    "plt.xlabel('Test accuracy')\n",
    "plt.title('Model comparison: prediction accuracy')\n",
    "for i, score in enumerate(model_scores):\n",
    "    plt.text(score + 0.005, i, f'{score:.3f}', va='center')\n",
    "plt.tight_layout()\n",
    "plt.savefig('../model_comparison.png', dpi=150)\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (csgo-venv)",
   "language": "python",
   "name": "csgo-venv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.1"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}