Vanilla_LSTM.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ho7fVx11dom7"
      },
      "source": [
        "# Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "nnJLZJ9geBIR"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd # 1.1\n",
        "from sklearn.preprocessing import StandardScaler # 1.2\n",
        "from sklearn.model_selection import train_test_split # 1.4\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "__ixv25tcLKC"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import DataLoader, Dataset, TensorDataset, random_split\n",
        "from tqdm import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# FOR SVR Baseline model\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.metrics import mean_squared_error"
      ],
      "metadata": {
        "id": "MdW4F84wkXuQ"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lzk-MC-UdrsZ"
      },
      "source": [
        "# 1. Data Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S-_0wKafeMMV"
      },
      "source": [
        "## 1.1 Get Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b7GAbzMh_Cfg"
      },
      "source": [
        "### Player Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "h_-NVKFzu6YP",
        "outputId": "ca6309a2-7573-4278-e323-4a5f050d7f83"
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      "outputs": [
        {
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              "  web-scraper-order                        web-scraper-start-url  newpage  \\\n",
              "0      1710822472-1  https://www.nba.com/stats/players/boxscores      NaN   \n",
              "1      1710822472-2  https://www.nba.com/stats/players/boxscores      NaN   \n",
              "2      1710822472-3  https://www.nba.com/stats/players/boxscores      NaN   \n",
              "3      1710822472-4  https://www.nba.com/stats/players/boxscores      NaN   \n",
              "4      1710822472-5  https://www.nba.com/stats/players/boxscores      NaN   \n",
              "\n",
              "  team      mathcup    gamedate winloss  minuters  pts  fgm  ...  dreb reb  \\\n",
              "0  ATL    ATL @ LAL  03/18/2024     NaN        13    8    4  ...     4   8   \n",
              "1  PHI  PHI vs. MIA  03/18/2024       W        19    8    3  ...     3   4   \n",
              "2  MIA    MIA @ PHI  03/18/2024       L        16   11    4  ...     1   2   \n",
              "3  MIA    MIA @ PHI  03/18/2024       L        32   20    8  ...    10  13   \n",
              "4  PHI  PHI vs. MIA  03/18/2024       W        24   12    4  ...     1   2   \n",
              "\n",
              "   ast  stl blk  tov  pfo plusminus   fp1         player  \n",
              "0    0    1   1    1    1        -7  22.6   Clint Capela  \n",
              "1    3    2   2    1    2        -1  28.3       Mo Bamba  \n",
              "2    1    1   1    0    1         1  20.9  Thomas Bryant  \n",
              "3    6    0   0    4    1       -19  40.6    Bam Adebayo  \n",
              "4    1    0   1    1    1        11  17.9    Buddy Hield  \n",
              "\n",
              "[5 rows x 29 columns]"
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              "      <th>web-scraper-order</th>\n",
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              "      <th>...</th>\n",
              "      <th>dreb</th>\n",
              "      <th>reb</th>\n",
              "      <th>ast</th>\n",
              "      <th>stl</th>\n",
              "      <th>blk</th>\n",
              "      <th>tov</th>\n",
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              "      <th>0</th>\n",
              "      <td>1710822472-1</td>\n",
              "      <td>https://www.nba.com/stats/players/boxscores</td>\n",
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              "      <td>ATL</td>\n",
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              "      <td>22.6</td>\n",
              "      <td>Clint Capela</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1710822472-2</td>\n",
              "      <td>https://www.nba.com/stats/players/boxscores</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>19</td>\n",
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              "      <td>28.3</td>\n",
              "      <td>Mo Bamba</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1710822472-3</td>\n",
              "      <td>https://www.nba.com/stats/players/boxscores</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MIA</td>\n",
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              "      <td>03/18/2024</td>\n",
              "      <td>L</td>\n",
              "      <td>16</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>20.9</td>\n",
              "      <td>Thomas Bryant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1710822472-4</td>\n",
              "      <td>https://www.nba.com/stats/players/boxscores</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MIA</td>\n",
              "      <td>MIA @ PHI</td>\n",
              "      <td>03/18/2024</td>\n",
              "      <td>L</td>\n",
              "      <td>32</td>\n",
              "      <td>20</td>\n",
              "      <td>8</td>\n",
              "      <td>...</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "      <td>6</td>\n",
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              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>-19</td>\n",
              "      <td>40.6</td>\n",
              "      <td>Bam Adebayo</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1710822472-5</td>\n",
              "      <td>https://www.nba.com/stats/players/boxscores</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PHI</td>\n",
              "      <td>PHI vs. MIA</td>\n",
              "      <td>03/18/2024</td>\n",
              "      <td>W</td>\n",
              "      <td>24</td>\n",
              "      <td>12</td>\n",
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              "      <td>11</td>\n",
              "      <td>17.9</td>\n",
              "      <td>Buddy Hield</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "player_df"
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          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "player_data_url = 'https://raw.githubusercontent.com/BerkeAltiparmak/SportSense.AI/main/data/nba_players.csv'\n",
        "player_df = pd.read_csv(player_data_url)\n",
        "player_df.head()"
      ]
    },
    {
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      "execution_count": 5,
      "metadata": {
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        "id": "qUSk6owfva-n",
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        {
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          "data": {
            "text/plain": [
              "Index(['web-scraper-order', 'web-scraper-start-url', 'newpage', 'team',\n",
              "       'mathcup', 'gamedate', 'winloss', 'minuters', 'pts', 'fgm', 'fga',\n",
              "       'fgpercent', '3pm', '3pa', '3ppercent', 'ftm', 'fta', 'ftpercent',\n",
              "       'oreb', 'dreb', 'reb', 'ast', 'stl', 'blk', 'tov', 'pfo', 'plusminus',\n",
              "       'fp1', 'player'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "player_df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
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          "name": "stderr",
          "text": [
            "<ipython-input-6-2dcc550019b0>:10: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  player_df['matchup'] = player_df['mathcup']\n",
            "<ipython-input-6-2dcc550019b0>:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  player_df.drop(columns=['mathcup'], inplace=True)\n"
          ]
        }
      ],
      "source": [
        "# remove unnecessary columns\n",
        "columns_to_remove = ['web-scraper-order', 'web-scraper-start-url', 'newpage', 'fp1']\n",
        "player_df.drop(columns=columns_to_remove, inplace=True)\n",
        "\n",
        "# only consider games before 2024-03-18, the time we got the data (there were incompelete games)\n",
        "player_df['gamedate'] = pd.to_datetime(player_df['gamedate'])\n",
        "player_df = player_df[player_df['gamedate'] != '2024-03-18']\n",
        "\n",
        "# NBA made a typo so we gotta fix that\n",
        "player_df['matchup'] = player_df['mathcup']\n",
        "player_df.drop(columns=['mathcup'], inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "FB7YDWmS1T15"
      },
      "outputs": [],
      "source": [
        "def calculate_fantasy_points(row):\n",
        "    # Points scored\n",
        "    points_scored = row['pts'] * 0.5\n",
        "    # Rebounds, Assists, Steals, and Blocks\n",
        "    rebound_points = (row['oreb'] + row['dreb']) * 1\n",
        "    assist_points = row['ast'] * 1\n",
        "    steal_points = row['stl'] * 2\n",
        "    block_points = row['blk'] * 2\n",
        "    # Turnovers\n",
        "    turnover_points = row['tov'] * -1\n",
        "    # Three-points made\n",
        "    three_points = row['3pm'] * 0.5\n",
        "    # Double-Double and Triple-Double\n",
        "    double_digits = sum(i >= 10 for i in [row['pts'], row['oreb'] + row['dreb'], row['ast'], row['stl'], row['blk']])\n",
        "    double_double_points = 1.0 if double_digits >= 2 else 0\n",
        "    triple_double_points = 2.0 if double_digits >= 3 else 0\n",
        "    # 40+ and 50+ bonuses\n",
        "    fourty_pt_bonus = 2.0 * (row['pts'] >= 40)\n",
        "    fifty_pt_bonus = 2.0 * (row['pts'] >= 50)\n",
        "    # Total fantasy points\n",
        "    total_points = (points_scored + rebound_points + assist_points +\n",
        "                    steal_points + block_points + turnover_points + three_points +\n",
        "                    double_double_points + triple_double_points +\n",
        "                    fourty_pt_bonus + fifty_pt_bonus)\n",
        "    return total_points"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "8x3z6TWG-hm9"
      },
      "outputs": [],
      "source": [
        "def get_opp_team(matchup):\n",
        "    if '@' in matchup:\n",
        "        return matchup.split(' @ ')[1]\n",
        "    elif 'vs.' in matchup:\n",
        "        return matchup.split(' vs. ')[1]\n",
        "    else:\n",
        "        return None"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "6WQHyz79DilE"
      },
      "outputs": [],
      "source": [
        "# Helper function to determine home and away teams\n",
        "def extract_teams(matchup):\n",
        "    teams_a = matchup.split(' @ ')\n",
        "    teams_h = matchup.split(' vs. ')\n",
        "    if '@' in matchup:\n",
        "        return teams_a[1], teams_a[0]\n",
        "    else:\n",
        "        return teams_h[0], teams_h[1]\n",
        "\n",
        "# Function to create game_id\n",
        "def create_game_id(row):\n",
        "    home_team, away_team = extract_teams(row['matchup'])\n",
        "    game_date = row['gamedate'].strftime('%Y-%m-%d')\n",
        "    return f\"{home_team}.{away_team}.{game_date}\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "7p8hNFhbyhjy",
        "outputId": "fbb6f6e7-c11a-4b24-e2d0-15e187a80692"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    team   gamedate  winloss  minuters  pts  fgm  fga fgpercent  3pm  3pa  \\\n",
              "152  BOS 2024-03-17        1        35   14    6   15      40.0    2    5   \n",
              "153  BOS 2024-03-17        1        23    9    3    7      42.9    3    6   \n",
              "154  BOS 2024-03-17        1        20   14    5    9      55.6    0    0   \n",
              "155  WAS 2024-03-17        0        22    8    4    7      57.1    0    2   \n",
              "156  WAS 2024-03-17        0        15    0    0    1       0.0    0    1   \n",
              "\n",
              "     ... tov  pfo  plusminus            player      matchup  is_home    fp  \\\n",
              "152  ...   1    0         16  Payton Pritchard    BOS @ WAS        0  28.0   \n",
              "153  ...   0    0          7    Svi Mykhailiuk    BOS @ WAS        0  13.0   \n",
              "154  ...   1    1          9       Luke Kornet    BOS @ WAS        0  17.0   \n",
              "155  ...   0    3        -17      Johnny Davis  WAS vs. BOS        1  13.0   \n",
              "156  ...   1    2        -14    Eugene Omoruyi  WAS vs. BOS        1   3.0   \n",
              "\n",
              "     opp_team             game_id  days_since  \n",
              "152       WAS  WAS.BOS.2024-03-17         145  \n",
              "153       WAS  WAS.BOS.2024-03-17         145  \n",
              "154       WAS  WAS.BOS.2024-03-17         145  \n",
              "155       BOS  WAS.BOS.2024-03-17         145  \n",
              "156       BOS  WAS.BOS.2024-03-17         145  \n",
              "\n",
              "[5 rows x 30 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-221b9ab6-dc79-46c1-8aff-595c4375b915\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>team</th>\n",
              "      <th>gamedate</th>\n",
              "      <th>winloss</th>\n",
              "      <th>minuters</th>\n",
              "      <th>pts</th>\n",
              "      <th>fgm</th>\n",
              "      <th>fga</th>\n",
              "      <th>fgpercent</th>\n",
              "      <th>3pm</th>\n",
              "      <th>3pa</th>\n",
              "      <th>...</th>\n",
              "      <th>tov</th>\n",
              "      <th>pfo</th>\n",
              "      <th>plusminus</th>\n",
              "      <th>player</th>\n",
              "      <th>matchup</th>\n",
              "      <th>is_home</th>\n",
              "      <th>fp</th>\n",
              "      <th>opp_team</th>\n",
              "      <th>game_id</th>\n",
              "      <th>days_since</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>BOS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "      <td>14</td>\n",
              "      <td>6</td>\n",
              "      <td>15</td>\n",
              "      <td>40.0</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>Payton Pritchard</td>\n",
              "      <td>BOS @ WAS</td>\n",
              "      <td>0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>WAS</td>\n",
              "      <td>WAS.BOS.2024-03-17</td>\n",
              "      <td>145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>BOS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>42.9</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>Svi Mykhailiuk</td>\n",
              "      <td>BOS @ WAS</td>\n",
              "      <td>0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>WAS</td>\n",
              "      <td>WAS.BOS.2024-03-17</td>\n",
              "      <td>145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>BOS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>55.6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>Luke Kornet</td>\n",
              "      <td>BOS @ WAS</td>\n",
              "      <td>0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>WAS</td>\n",
              "      <td>WAS.BOS.2024-03-17</td>\n",
              "      <td>145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>WAS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>57.1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>-17</td>\n",
              "      <td>Johnny Davis</td>\n",
              "      <td>WAS vs. BOS</td>\n",
              "      <td>1</td>\n",
              "      <td>13.0</td>\n",
              "      <td>BOS</td>\n",
              "      <td>WAS.BOS.2024-03-17</td>\n",
              "      <td>145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>WAS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>-14</td>\n",
              "      <td>Eugene Omoruyi</td>\n",
              "      <td>WAS vs. BOS</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>BOS</td>\n",
              "      <td>WAS.BOS.2024-03-17</td>\n",
              "      <td>145</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 30 columns</p>\n",
              "</div>\n",
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
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              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-45149db8-0e13-4e60-af5a-37d1d7b25467 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "player_df"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "player_df['is_home'] = player_df['matchup'].apply(lambda x: 1 if 'vs.' in x else 0) # 1 if home game, 0 if away\n",
        "player_df['winloss'] = player_df['winloss'].apply(lambda x: 1 if 'W' in x else 0) # 1 if W, 0 if L\n",
        "player_df['fp'] = player_df.apply(calculate_fantasy_points, axis=1) # calculate fantasy points\n",
        "player_df['opp_team'] = player_df['matchup'].apply(get_opp_team) # extract the opponent team name\n",
        "player_df['game_id'] = player_df.apply(create_game_id, axis=1) # calculate the game id\n",
        "\n",
        "# calculate how many days has passed since the start of the season till this game\n",
        "season_start_date = pd.Timestamp('2023-10-24')\n",
        "player_df['days_since'] = (player_df['gamedate'] - season_start_date).dt.days\n",
        "\n",
        "player_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yqSatUWH_uZ6",
        "outputId": "1d1db7dc-f5b9-4a79-9274-a7f05910a7bb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['team', 'gamedate', 'winloss', 'minuters', 'pts', 'fgm', 'fga',\n",
              "       'fgpercent', '3pm', '3pa', '3ppercent', 'ftm', 'fta', 'ftpercent',\n",
              "       'oreb', 'dreb', 'reb', 'ast', 'stl', 'blk', 'tov', 'pfo', 'plusminus',\n",
              "       'player', 'matchup', 'is_home', 'fp', 'opp_team', 'game_id',\n",
              "       'days_since'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "player_df.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4jD-JDd2_IAL"
      },
      "source": [
        "### Team Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "emaHdwTM_Njm"
      },
      "outputs": [],
      "source": [
        "team_data_url = 'https://raw.githubusercontent.com/BerkeAltiparmak/SportSense.AI/main/data/teamscores.csv'\n",
        "team_df = pd.read_csv(team_data_url)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "eZMUMqM4_ZMl",
        "outputId": "8b2a7c85-b79b-4b08-f5d1-e0967f5cf969"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  web-scraper-order                      web-scraper-start-url  newpage team  \\\n",
              "0      1710880555-1  https://www.nba.com/stats/teams/boxscores      NaN  CLE   \n",
              "1      1710880555-2  https://www.nba.com/stats/teams/boxscores      NaN  IND   \n",
              "2      1710880555-3  https://www.nba.com/stats/teams/boxscores      NaN  DET   \n",
              "3      1710880555-4  https://www.nba.com/stats/teams/boxscores      NaN  BOS   \n",
              "4      1710880555-5  https://www.nba.com/stats/teams/boxscores      NaN  PHI   \n",
              "\n",
              "       matchup        date winloss  min  pts  fgm  ...  ftpercent  oreb  dreb  \\\n",
              "0    CLE @ IND  03/18/2024       W  240  108   38  ...       71.4     9    30   \n",
              "1  IND vs. CLE  03/18/2024       L  240  103   42  ...       62.5    14    31   \n",
              "2    DET @ BOS  03/18/2024       L  240   94   36  ...       86.7     8    35   \n",
              "3  BOS vs. DET  03/18/2024       W  240  119   43  ...       84.6    15    38   \n",
              "4  PHI vs. MIA  03/18/2024       W  240   98   37  ...       91.7     7    39   \n",
              "\n",
              "   reb  ast  stl  blk  tov  pfidk  plusminus  \n",
              "0   39   27    8    4   14     18          5  \n",
              "1   45   28    8    3   11     24         -5  \n",
              "2   43   21    6    3   13     15        -25  \n",
              "3   53   30    8    8   11      9         25  \n",
              "4   46   23    7   12   12     16          7  \n",
              "\n",
              "[5 rows x 27 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-92fa9a34-3e1a-4f81-a802-3ced70ce79f4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "        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>web-scraper-order</th>\n",
              "      <th>web-scraper-start-url</th>\n",
              "      <th>newpage</th>\n",
              "      <th>team</th>\n",
              "      <th>matchup</th>\n",
              "      <th>date</th>\n",
              "      <th>winloss</th>\n",
              "      <th>min</th>\n",
              "      <th>pts</th>\n",
              "      <th>fgm</th>\n",
              "      <th>...</th>\n",
              "      <th>ftpercent</th>\n",
              "      <th>oreb</th>\n",
              "      <th>dreb</th>\n",
              "      <th>reb</th>\n",
              "      <th>ast</th>\n",
              "      <th>stl</th>\n",
              "      <th>blk</th>\n",
              "      <th>tov</th>\n",
              "      <th>pfidk</th>\n",
              "      <th>plusminus</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1710880555-1</td>\n",
              "      <td>https://www.nba.com/stats/teams/boxscores</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CLE</td>\n",
              "      <td>CLE @ IND</td>\n",
              "      <td>03/18/2024</td>\n",
              "      <td>W</td>\n",
              "      <td>240</td>\n",
              "      <td>108</td>\n",
              "      <td>38</td>\n",
              "      <td>...</td>\n",
              "      <td>71.4</td>\n",
              "      <td>9</td>\n",
              "      <td>30</td>\n",
              "      <td>39</td>\n",
              "      <td>27</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>14</td>\n",
              "      <td>18</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1710880555-2</td>\n",
              "      <td>https://www.nba.com/stats/teams/boxscores</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IND</td>\n",
              "      <td>IND vs. CLE</td>\n",
              "      <td>03/18/2024</td>\n",
              "      <td>L</td>\n",
              "      <td>240</td>\n",
              "      <td>103</td>\n",
              "      <td>42</td>\n",
              "      <td>...</td>\n",
              "      <td>62.5</td>\n",
              "      <td>14</td>\n",
              "      <td>31</td>\n",
              "      <td>45</td>\n",
              "      <td>28</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>24</td>\n",
              "      <td>-5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1710880555-3</td>\n",
              "      <td>https://www.nba.com/stats/teams/boxscores</td>\n",
              "      <td>NaN</td>\n",
              "      <td>DET</td>\n",
              "      <td>DET @ BOS</td>\n",
              "      <td>03/18/2024</td>\n",
              "      <td>L</td>\n",
              "      <td>240</td>\n",
              "      <td>94</td>\n",
              "      <td>36</td>\n",
              "      <td>...</td>\n",
              "      <td>86.7</td>\n",
              "      <td>8</td>\n",
              "      <td>35</td>\n",
              "      <td>43</td>\n",
              "      <td>21</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>-25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1710880555-4</td>\n",
              "      <td>https://www.nba.com/stats/teams/boxscores</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BOS</td>\n",
              "      <td>BOS vs. DET</td>\n",
              "      <td>03/18/2024</td>\n",
              "      <td>W</td>\n",
              "      <td>240</td>\n",
              "      <td>119</td>\n",
              "      <td>43</td>\n",
              "      <td>...</td>\n",
              "      <td>84.6</td>\n",
              "      <td>15</td>\n",
              "      <td>38</td>\n",
              "      <td>53</td>\n",
              "      <td>30</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>11</td>\n",
              "      <td>9</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1710880555-5</td>\n",
              "      <td>https://www.nba.com/stats/teams/boxscores</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PHI</td>\n",
              "      <td>PHI vs. MIA</td>\n",
              "      <td>03/18/2024</td>\n",
              "      <td>W</td>\n",
              "      <td>240</td>\n",
              "      <td>98</td>\n",
              "      <td>37</td>\n",
              "      <td>...</td>\n",
              "      <td>91.7</td>\n",
              "      <td>7</td>\n",
              "      <td>39</td>\n",
              "      <td>46</td>\n",
              "      <td>23</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>16</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 27 columns</p>\n",
              "</div>\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
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              "\n",
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              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "team_df"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "team_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NyXPc7cA_bgl",
        "outputId": "80c61a99-3fae-4f8a-fc84-a7b6d3b0ca48"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['web-scraper-order', 'web-scraper-start-url', 'newpage', 'team',\n",
              "       'matchup', 'date', 'winloss', 'min', 'pts', 'fgm', 'fga', 'fgpercent',\n",
              "       '3pm', '3pa', '3ppercent', 'ftm', 'fta', 'ftpercent', 'oreb', 'dreb',\n",
              "       'reb', 'ast', 'stl', 'blk', 'tov', 'pfidk', 'plusminus'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "team_df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "ak_AizVQ_85D"
      },
      "outputs": [],
      "source": [
        "# remove unnecessary columns\n",
        "columns_to_remove = ['web-scraper-order', 'web-scraper-start-url', 'newpage']\n",
        "team_df.drop(columns=columns_to_remove, inplace=True)\n",
        "\n",
        "# only consider games before 2024-03-18, the time we got the data (there were incompelete games)\n",
        "team_df['gamedate'] = pd.to_datetime(team_df['date'])\n",
        "team_df = team_df[team_df['gamedate'] != '2024-03-18']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673
        },
        "id": "cQbVdRnQAjRc",
        "outputId": "ec16b346-99aa-4f4c-8751-64aa96ba7898"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-16-a5d1a32ab59e>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  team_df['is_home'] = team_df['matchup'].apply(lambda x: 1 if 'vs.' in x else 0)\n",
            "<ipython-input-16-a5d1a32ab59e>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  team_df['opp_team'] = team_df['matchup'].apply(get_opp_team)\n",
            "<ipython-input-16-a5d1a32ab59e>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  team_df['game_id'] = team_df.apply(create_game_id, axis=1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   team      matchup        date winloss  min  pts  fgm  fga  fgpercent  3pm  \\\n",
              "16  LAC  LAC vs. ATL  03/17/2024       L  240   93   28   74       37.8   11   \n",
              "17  ATL    ATL @ LAC  03/17/2024       W  240  110   41   84       48.8   17   \n",
              "18  BKN    BKN @ SAS  03/17/2024       L  265  115   47  102       46.1    8   \n",
              "19  SAS  SAS vs. BKN  03/17/2024       W  265  122   46   99       46.5    9   \n",
              "20  ORL  ORL vs. TOR  03/17/2024       W  240  111   43   85       50.6   11   \n",
              "\n",
              "    ...  ast  stl  blk  tov  pfidk  plusminus   gamedate  is_home  opp_team  \\\n",
              "16  ...   17   10    5   16     13        -17 2024-03-17        1       ATL   \n",
              "17  ...   32   10    7   16     22         17 2024-03-17        0       LAC   \n",
              "18  ...   29   13    5   12     20         -7 2024-03-17        0       SAS   \n",
              "19  ...   30    6   10   18     17          7 2024-03-17        1       BKN   \n",
              "20  ...   22    7    4   15     16         15 2024-03-17        1       TOR   \n",
              "\n",
              "               game_id  \n",
              "16  LAC.ATL.2024-03-17  \n",
              "17  LAC.ATL.2024-03-17  \n",
              "18  SAS.BKN.2024-03-17  \n",
              "19  SAS.BKN.2024-03-17  \n",
              "20  ORL.TOR.2024-03-17  \n",
              "\n",
              "[5 rows x 28 columns]"
            ],
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>team</th>\n",
              "      <th>matchup</th>\n",
              "      <th>date</th>\n",
              "      <th>winloss</th>\n",
              "      <th>min</th>\n",
              "      <th>pts</th>\n",
              "      <th>fgm</th>\n",
              "      <th>fga</th>\n",
              "      <th>fgpercent</th>\n",
              "      <th>3pm</th>\n",
              "      <th>...</th>\n",
              "      <th>ast</th>\n",
              "      <th>stl</th>\n",
              "      <th>blk</th>\n",
              "      <th>tov</th>\n",
              "      <th>pfidk</th>\n",
              "      <th>plusminus</th>\n",
              "      <th>gamedate</th>\n",
              "      <th>is_home</th>\n",
              "      <th>opp_team</th>\n",
              "      <th>game_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>LAC</td>\n",
              "      <td>LAC vs. ATL</td>\n",
              "      <td>03/17/2024</td>\n",
              "      <td>L</td>\n",
              "      <td>240</td>\n",
              "      <td>93</td>\n",
              "      <td>28</td>\n",
              "      <td>74</td>\n",
              "      <td>37.8</td>\n",
              "      <td>11</td>\n",
              "      <td>...</td>\n",
              "      <td>17</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>13</td>\n",
              "      <td>-17</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>1</td>\n",
              "      <td>ATL</td>\n",
              "      <td>LAC.ATL.2024-03-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>ATL</td>\n",
              "      <td>ATL @ LAC</td>\n",
              "      <td>03/17/2024</td>\n",
              "      <td>W</td>\n",
              "      <td>240</td>\n",
              "      <td>110</td>\n",
              "      <td>41</td>\n",
              "      <td>84</td>\n",
              "      <td>48.8</td>\n",
              "      <td>17</td>\n",
              "      <td>...</td>\n",
              "      <td>32</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>16</td>\n",
              "      <td>22</td>\n",
              "      <td>17</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>0</td>\n",
              "      <td>LAC</td>\n",
              "      <td>LAC.ATL.2024-03-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>BKN</td>\n",
              "      <td>BKN @ SAS</td>\n",
              "      <td>03/17/2024</td>\n",
              "      <td>L</td>\n",
              "      <td>265</td>\n",
              "      <td>115</td>\n",
              "      <td>47</td>\n",
              "      <td>102</td>\n",
              "      <td>46.1</td>\n",
              "      <td>8</td>\n",
              "      <td>...</td>\n",
              "      <td>29</td>\n",
              "      <td>13</td>\n",
              "      <td>5</td>\n",
              "      <td>12</td>\n",
              "      <td>20</td>\n",
              "      <td>-7</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>0</td>\n",
              "      <td>SAS</td>\n",
              "      <td>SAS.BKN.2024-03-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>SAS</td>\n",
              "      <td>SAS vs. BKN</td>\n",
              "      <td>03/17/2024</td>\n",
              "      <td>W</td>\n",
              "      <td>265</td>\n",
              "      <td>122</td>\n",
              "      <td>46</td>\n",
              "      <td>99</td>\n",
              "      <td>46.5</td>\n",
              "      <td>9</td>\n",
              "      <td>...</td>\n",
              "      <td>30</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>18</td>\n",
              "      <td>17</td>\n",
              "      <td>7</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>1</td>\n",
              "      <td>BKN</td>\n",
              "      <td>SAS.BKN.2024-03-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>ORL</td>\n",
              "      <td>ORL vs. TOR</td>\n",
              "      <td>03/17/2024</td>\n",
              "      <td>W</td>\n",
              "      <td>240</td>\n",
              "      <td>111</td>\n",
              "      <td>43</td>\n",
              "      <td>85</td>\n",
              "      <td>50.6</td>\n",
              "      <td>11</td>\n",
              "      <td>...</td>\n",
              "      <td>22</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>15</td>\n",
              "      <td>16</td>\n",
              "      <td>15</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>1</td>\n",
              "      <td>TOR</td>\n",
              "      <td>ORL.TOR.2024-03-17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 28 columns</p>\n",
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              "    border-bottom-color: var(--fill-color);\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "team_df"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "team_df['is_home'] = team_df['matchup'].apply(lambda x: 1 if 'vs.' in x else 0)\n",
        "team_df['opp_team'] = team_df['matchup'].apply(get_opp_team)\n",
        "team_df['game_id'] = team_df.apply(create_game_id, axis=1)\n",
        "team_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7bJqVyuxHo21"
      },
      "source": [
        "### Merging"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "cToVxX64HHyZ"
      },
      "outputs": [],
      "source": [
        "own_team_df = team_df.copy()\n",
        "opp_team_df = team_df.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "iaYWRZUdH20J"
      },
      "outputs": [],
      "source": [
        "# renaming columns for the team data\n",
        "team_rename_columns = {\n",
        "    'pts': 'team_pts',\n",
        "    'fgm': 'team_fgm',\n",
        "    'fga': 'team_fga',\n",
        "    'fgpercent': 'team_fgpercent',\n",
        "    '3pm': 'team_3pm',\n",
        "    '3pa': 'team_3pa',\n",
        "    '3ppercent': 'team_3ppercent',\n",
        "    'ftm': 'team_ftm',\n",
        "    'fta': 'team_fta',\n",
        "    'ftpercent': 'team_ftpercent',\n",
        "    'oreb': 'team_oreb',\n",
        "    'dreb': 'team_dreb',\n",
        "    'reb': 'team_reb',\n",
        "    'ast': 'team_ast',\n",
        "    'stl': 'team_stl',\n",
        "    'blk': 'team_blk',\n",
        "    'tov': 'team_tov',\n",
        "    'pfidk': 'team_pfidk',\n",
        "    'plusminus': 'team_plusminus'\n",
        "}\n",
        "\n",
        "# renaming columns for the opponent team data\n",
        "opp_rename_columns = {\n",
        "    'pts': 'opp_pts',\n",
        "    'fgm': 'opp_fgm',\n",
        "    'fga': 'opp_fga',\n",
        "    'fgpercent': 'opp_fgpercent',\n",
        "    '3pm': 'opp_3pm',\n",
        "    '3pa': 'opp_3pa',\n",
        "    '3ppercent': 'opp_3ppercent',\n",
        "    'ftm': 'opp_ftm',\n",
        "    'fta': 'opp_fta',\n",
        "    'ftpercent': 'opp_ftpercent',\n",
        "    'oreb': 'opp_oreb',\n",
        "    'dreb': 'opp_dreb',\n",
        "    'reb': 'opp_reb',\n",
        "    'ast': 'opp_ast',\n",
        "    'stl': 'opp_stl',\n",
        "    'blk': 'opp_blk',\n",
        "    'tov': 'opp_tov',\n",
        "    'pfidk': 'opp_pfidk',\n",
        "    'plusminus': 'opp_plusminus'\n",
        "}\n",
        "\n",
        "own_team_df.rename(columns=team_rename_columns, inplace=True)\n",
        "opp_team_df.rename(columns=opp_rename_columns, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S1K3T_OyLAYd",
        "outputId": "025952c0-5235-48e3-b5a0-63cc384c625c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['team', 'matchup', 'date', 'winloss', 'min', 'opp_pts', 'opp_fgm',\n",
              "       'opp_fga', 'opp_fgpercent', 'opp_3pm', 'opp_3pa', 'opp_3ppercent',\n",
              "       'opp_ftm', 'opp_fta', 'opp_ftpercent', 'opp_oreb', 'opp_dreb',\n",
              "       'opp_reb', 'opp_ast', 'opp_stl', 'opp_blk', 'opp_tov', 'opp_pfidk',\n",
              "       'opp_plusminus', 'gamedate', 'is_home', 'opp_team', 'game_id'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "opp_team_df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "pxFkDlePI_nM"
      },
      "outputs": [],
      "source": [
        "# merging player data with own team data\n",
        "initial_merged_df = pd.merge(player_df, own_team_df, left_on=['game_id', 'team'], right_on=['game_id', 'team'], suffixes=('', '_drop'))\n",
        "# drop the columns from team_df that are redundant\n",
        "initial_merged_df.drop([col for col in initial_merged_df.columns if 'drop' in col], axis=1, inplace=True)\n",
        "\n",
        "# merging the last df with opponent team data\n",
        "df = pd.merge(initial_merged_df, opp_team_df, left_on=['game_id', 'opp_team'], right_on=['game_id', 'team'], suffixes=('', '_drop'))\n",
        "# drop the columns from opp_team_df that are redundant\n",
        "df.drop([col for col in df.columns if 'drop' in col], axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "iG-w-RMA7DWx",
        "outputId": "57438326-bd14-4691-f4f2-c70434005ab9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             player team opp_team   gamedate  days_since  winloss  minuters  \\\n",
              "0  Payton Pritchard  BOS      WAS 2024-03-17         145        1        35   \n",
              "1    Svi Mykhailiuk  BOS      WAS 2024-03-17         145        1        23   \n",
              "2       Luke Kornet  BOS      WAS 2024-03-17         145        1        20   \n",
              "3        Sam Hauser  BOS      WAS 2024-03-17         145        1        23   \n",
              "4     Neemias Queta  BOS      WAS 2024-03-17         145        1         8   \n",
              "\n",
              "   pts  fgm  fga  ... opp_oreb  opp_dreb  opp_reb opp_ast  opp_stl  opp_blk  \\\n",
              "0   14    6   15  ...       10        30       40      24        3        5   \n",
              "1    9    3    7  ...       10        30       40      24        3        5   \n",
              "2   14    5    9  ...       10        30       40      24        3        5   \n",
              "3   30   10   13  ...       10        30       40      24        3        5   \n",
              "4    0    0    0  ...       10        30       40      24        3        5   \n",
              "\n",
              "  opp_tov  opp_pfidk  opp_plusminus    fp  \n",
              "0      16         20            -26  28.0  \n",
              "1      16         20            -26  13.0  \n",
              "2      16         20            -26  17.0  \n",
              "3      16         20            -26  29.0  \n",
              "4      16         20            -26   1.0  \n",
              "\n",
              "[5 rows x 70 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-980c9fae-fc21-47f3-9066-864e1bc2a1e4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>player</th>\n",
              "      <th>team</th>\n",
              "      <th>opp_team</th>\n",
              "      <th>gamedate</th>\n",
              "      <th>days_since</th>\n",
              "      <th>winloss</th>\n",
              "      <th>minuters</th>\n",
              "      <th>pts</th>\n",
              "      <th>fgm</th>\n",
              "      <th>fga</th>\n",
              "      <th>...</th>\n",
              "      <th>opp_oreb</th>\n",
              "      <th>opp_dreb</th>\n",
              "      <th>opp_reb</th>\n",
              "      <th>opp_ast</th>\n",
              "      <th>opp_stl</th>\n",
              "      <th>opp_blk</th>\n",
              "      <th>opp_tov</th>\n",
              "      <th>opp_pfidk</th>\n",
              "      <th>opp_plusminus</th>\n",
              "      <th>fp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Payton Pritchard</td>\n",
              "      <td>BOS</td>\n",
              "      <td>WAS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "      <td>14</td>\n",
              "      <td>6</td>\n",
              "      <td>15</td>\n",
              "      <td>...</td>\n",
              "      <td>10</td>\n",
              "      <td>30</td>\n",
              "      <td>40</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>20</td>\n",
              "      <td>-26</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Svi Mykhailiuk</td>\n",
              "      <td>BOS</td>\n",
              "      <td>WAS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>...</td>\n",
              "      <td>10</td>\n",
              "      <td>30</td>\n",
              "      <td>40</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>20</td>\n",
              "      <td>-26</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Luke Kornet</td>\n",
              "      <td>BOS</td>\n",
              "      <td>WAS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>...</td>\n",
              "      <td>10</td>\n",
              "      <td>30</td>\n",
              "      <td>40</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>20</td>\n",
              "      <td>-26</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sam Hauser</td>\n",
              "      <td>BOS</td>\n",
              "      <td>WAS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "      <td>30</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "      <td>...</td>\n",
              "      <td>10</td>\n",
              "      <td>30</td>\n",
              "      <td>40</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>20</td>\n",
              "      <td>-26</td>\n",
              "      <td>29.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Neemias Queta</td>\n",
              "      <td>BOS</td>\n",
              "      <td>WAS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>10</td>\n",
              "      <td>30</td>\n",
              "      <td>40</td>\n",
              "      <td>24</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "      <td>20</td>\n",
              "      <td>-26</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 70 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-980c9fae-fc21-47f3-9066-864e1bc2a1e4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
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              "      --hover-fill-color: #174EA6;\n",
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              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5e1e4ad2-edb4-49f0-8183-194c3d3a6ea4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "# reorder the columns for visual purposes\n",
        "columns_order = ['player', 'team', 'opp_team', 'gamedate', 'days_since'] + [col for col in df.columns if col not in ['player', 'team', 'opp_team', 'gamedate', 'days_since', 'fp']] + ['fp']\n",
        "df = df[columns_order]\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5qxKM4CjKAbE",
        "outputId": "72b98898-0cc9-4771-a32c-e631717f4144"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['player', 'team', 'opp_team', 'gamedate', 'days_since', 'winloss',\n",
              "       'minuters', 'pts', 'fgm', 'fga', 'fgpercent', '3pm', '3pa', '3ppercent',\n",
              "       'ftm', 'fta', 'ftpercent', 'oreb', 'dreb', 'reb', 'ast', 'stl', 'blk',\n",
              "       'tov', 'pfo', 'plusminus', 'matchup', 'is_home', 'game_id', 'date',\n",
              "       'min', 'team_pts', 'team_fgm', 'team_fga', 'team_fgpercent', 'team_3pm',\n",
              "       'team_3pa', 'team_3ppercent', 'team_ftm', 'team_fta', 'team_ftpercent',\n",
              "       'team_oreb', 'team_dreb', 'team_reb', 'team_ast', 'team_stl',\n",
              "       'team_blk', 'team_tov', 'team_pfidk', 'team_plusminus', 'opp_pts',\n",
              "       'opp_fgm', 'opp_fga', 'opp_fgpercent', 'opp_3pm', 'opp_3pa',\n",
              "       'opp_3ppercent', 'opp_ftm', 'opp_fta', 'opp_ftpercent', 'opp_oreb',\n",
              "       'opp_dreb', 'opp_reb', 'opp_ast', 'opp_stl', 'opp_blk', 'opp_tov',\n",
              "       'opp_pfidk', 'opp_plusminus', 'fp'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ob3lrIAtdtXO"
      },
      "source": [
        "## 1.2 Z-Score Normalization / Standardization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "w0NPMa0sdyZ1",
        "outputId": "d5217295-f667-4a90-e018-b72afb5f9429"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\n# Select the columns with player stats\\nstats_columns = ['points', 'rebounds', 'assists', 'steals', 'blocks']  # Make sure we got all the columns here\\n\\n# Initialize the scaler\\nscaler = StandardScaler()\\n\\n# Normalize the stats using Z-score normalization\\ndf[stats_columns] = scaler.fit_transform(df[stats_columns])\\n\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "\"\"\"\n",
        "# Select the columns with player stats\n",
        "stats_columns = ['points', 'rebounds', 'assists', 'steals', 'blocks']  # Make sure we got all the columns here\n",
        "\n",
        "# Initialize the scaler\n",
        "scaler = StandardScaler()\n",
        "\n",
        "# Normalize the stats using Z-score normalization\n",
        "df[stats_columns] = scaler.fit_transform(df[stats_columns])\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v3m-nQTec3aR"
      },
      "source": [
        "## 1.25: Dashes Cleanup"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a boolean mask\n",
        "mask = (df == '-')\n",
        "\n",
        "# Use `any` to check each column\n",
        "columns_with_dash = mask.any(axis=0)\n",
        "\n",
        "# Print the columns that contain '-'\n",
        "print(columns_with_dash[columns_with_dash].index.tolist())\n",
        "\n",
        "# Convert specific columns to strings\n",
        "df[['fgpercent', '3ppercent', 'ftpercent']] = df[['fgpercent', '3ppercent', 'ftpercent']].astype(str)\n",
        "\n",
        "# Replace '-' with '0'\n",
        "df[['fgpercent', '3ppercent', 'ftpercent']] = df[['fgpercent', '3ppercent', 'ftpercent']].replace('-', '0')\n",
        "\n",
        "# Convert these columns back to floats\n",
        "df[['fgpercent', '3ppercent', 'ftpercent']] = df[['fgpercent', '3ppercent', 'ftpercent']].astype(float)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_aZcyqavOKkf",
        "outputId": "24659419-7d27-41bb-8f14-71e74216fffe"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['fgpercent', '3ppercent', 'ftpercent']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x3kuJsMZiTha"
      },
      "source": [
        "## 1.275: EXPERIMENTAL REMOVING OF PLAYERS THAT HAVENT PLAYED 10 GAMES AND THAT ARE NOT IN TOP 150"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8v98YDscKUV_"
      },
      "source": [
        "1.29. ADDING MORE FEATURES FOR DEBUGGING"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "Wlc4a5S_I9-6"
      },
      "outputs": [],
      "source": [
        "players_list = list(df.groupby('player')['fp'].mean().reset_index().sort_values(by='fp', ascending=False)['player'])\n",
        "players_dict = {players_list[i]: i for i in range(len(players_list))}\n",
        "players_inv_dict = {i: players_list[i] for i in range(len(players_list))}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GTVskQl_LJnR",
        "outputId": "e03db442-50b6-488a-bda6-4de9ab69d8d9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3017"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "int(str(df.iloc[132]['gamedate'])[-14:-12]) * 1000 + int(str(df.iloc[132]['gamedate'])[-11:-9])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "30RO0MWKKzQ4"
      },
      "outputs": [],
      "source": [
        "def intify_player(player):\n",
        "    return players_dict[player] / 1000\n",
        "\n",
        "def intify_gamedate(gamedate):\n",
        "    return (int(str(gamedate)[-14:-12]) * 1000 + int(str(gamedate)[-11:-9])) / 100000"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "atlH9ZfCKv1k"
      },
      "outputs": [],
      "source": [
        "df['int_player'] = df['player'].apply(intify_player)\n",
        "df['int_gamedate'] = df['gamedate'].apply(intify_gamedate)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q9AzA7_sdyp3"
      },
      "source": [
        "## 1.3 One-Hot Encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "9WjkfPqod29d"
      },
      "outputs": [],
      "source": [
        "# one-hot encode the categorical columns\n",
        "df = pd.get_dummies(df, columns=['opp_team'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "V0PoMHUe8GLi"
      },
      "outputs": [],
      "source": [
        "## reorder the columns for visual purposes\n",
        "columns_order = ['player', 'team', 'gamedate', 'days_since'] + [col for col in df.columns if col not in ['player', 'team', 'gamedate', 'days_since', 'fp']] + ['fp']\n",
        "df = df[columns_order]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "-rFgNHIx-w6i",
        "outputId": "05422cf6-5a1c-4f28-ba2c-953d7fb61d0c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             player team   gamedate  days_since  winloss  minuters  pts  fgm  \\\n",
              "0  Payton Pritchard  BOS 2024-03-17         145        1        35   14    6   \n",
              "1    Svi Mykhailiuk  BOS 2024-03-17         145        1        23    9    3   \n",
              "2       Luke Kornet  BOS 2024-03-17         145        1        20   14    5   \n",
              "3        Sam Hauser  BOS 2024-03-17         145        1        23   30   10   \n",
              "4     Neemias Queta  BOS 2024-03-17         145        1         8    0    0   \n",
              "\n",
              "   fga  fgpercent  ...  opp_team_ORL  opp_team_PHI  opp_team_PHX  \\\n",
              "0   15       40.0  ...         False         False         False   \n",
              "1    7       42.9  ...         False         False         False   \n",
              "2    9       55.6  ...         False         False         False   \n",
              "3   13       76.9  ...         False         False         False   \n",
              "4    0        0.0  ...         False         False         False   \n",
              "\n",
              "   opp_team_POR  opp_team_SAC  opp_team_SAS  opp_team_TOR  opp_team_UTA  \\\n",
              "0         False         False         False         False         False   \n",
              "1         False         False         False         False         False   \n",
              "2         False         False         False         False         False   \n",
              "3         False         False         False         False         False   \n",
              "4         False         False         False         False         False   \n",
              "\n",
              "   opp_team_WAS    fp  \n",
              "0          True  28.0  \n",
              "1          True  13.0  \n",
              "2          True  17.0  \n",
              "3          True  29.0  \n",
              "4          True   1.0  \n",
              "\n",
              "[5 rows x 101 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>player</th>\n",
              "      <th>team</th>\n",
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              "      <th>days_since</th>\n",
              "      <th>winloss</th>\n",
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              "      <th>fgpercent</th>\n",
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              "      <th>opp_team_SAC</th>\n",
              "      <th>opp_team_SAS</th>\n",
              "      <th>opp_team_TOR</th>\n",
              "      <th>opp_team_UTA</th>\n",
              "      <th>opp_team_WAS</th>\n",
              "      <th>fp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Payton Pritchard</td>\n",
              "      <td>BOS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
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              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>Svi Mykhailiuk</td>\n",
              "      <td>BOS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>42.9</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>True</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Luke Kornet</td>\n",
              "      <td>BOS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>55.6</td>\n",
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              "      <td>False</td>\n",
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              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sam Hauser</td>\n",
              "      <td>BOS</td>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "      <td>30</td>\n",
              "      <td>10</td>\n",
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              "      <td>76.9</td>\n",
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              "      <td>29.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Neemias Queta</td>\n",
              "      <td>BOS</td>\n",
              "      <td>2024-03-17</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 101 columns</p>\n",
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              "\n",
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              "        document.querySelector('#df-2242b1a9-ceb4-487b-bf74-15e1683c1958 button.colab-df-convert');\n",
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              "\n",
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              "                                                    [key], {});\n",
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              "\n",
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              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4f89360d-1f2f-4093-93a2-98cade715c09 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kpufSpxwL3OE",
        "outputId": "a94eb080-06ab-43e7-e2b4-1840de6df78e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['player', 'team', 'gamedate', 'days_since', 'winloss', 'minuters',\n",
              "       'pts', 'fgm', 'fga', 'fgpercent',\n",
              "       ...\n",
              "       'opp_team_ORL', 'opp_team_PHI', 'opp_team_PHX', 'opp_team_POR',\n",
              "       'opp_team_SAC', 'opp_team_SAS', 'opp_team_TOR', 'opp_team_UTA',\n",
              "       'opp_team_WAS', 'fp'],\n",
              "      dtype='object', length=101)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OrcKBV9PL56t"
      },
      "source": [
        "## 1.4 Split per player\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "hxu0-uMqTcJQ"
      },
      "outputs": [],
      "source": [
        "class NBAFantasyDataset(Dataset):\n",
        "    def __init__(self, dataframe, look_back=10):\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            dataframe (pandas.DataFrame): The dataframe containing the data.\n",
        "        \"\"\"\n",
        "        self.dataframe = dataframe\n",
        "        # player + game_date logic:\n",
        "        self.player_game_indices = self._get_player_game_indices()\n",
        "        # for normalization:\n",
        "        self.scaler = StandardScaler()\n",
        "        self._normalize_features()\n",
        "        # for \"last 10 game\" approach:\n",
        "        self.look_back = look_back\n",
        "\n",
        "    def _normalize_features(self):\n",
        "        features = self.dataframe.drop(columns=['player', 'team', 'gamedate', 'game_id', 'fp', 'int_player', 'int_gamedate', 'date', 'matchup'])\n",
        "        self.scaler.fit(features)\n",
        "        self.dataframe.loc[:, features.columns] = self.scaler.transform(features)\n",
        "\n",
        "    def _get_player_game_indices(self):\n",
        "        \"\"\"Get a list of tuples indicating the start and end indices of each player's games.\"\"\"\n",
        "        player_game_indices = []\n",
        "        players = self.dataframe['player'].unique()\n",
        "        count = 0\n",
        "        for player in players:\n",
        "            count += 1\n",
        "            player_data = self.dataframe[self.dataframe['player'] == player]\n",
        "            #player_data = player_data.sort_values(by='days_since').reset_index(drop=True) # im not sure\n",
        "            for i in range(10, len(player_data)):\n",
        "                start_index = player_data.index[i-10]\n",
        "                end_index = player_data.index[i]\n",
        "                player_game_indices.append((start_index, end_index))\n",
        "        return player_game_indices\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.player_game_indices)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        start_index, end_index = self.player_game_indices[idx]\n",
        "        player_data = self.dataframe.iloc[start_index:end_index]\n",
        "        features = player_data.drop(columns=['player', 'team', 'gamedate', 'game_id', 'fp', 'matchup', 'date']).values\n",
        "        # features = features.replace('-', 0).astype(float)\n",
        "        target = self.dataframe.iloc[end_index]['fp']\n",
        "        return torch.tensor(features, dtype=torch.float32), torch.tensor(target, dtype=torch.float32)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sdf = df.sort_values(by=['player', 'days_since'], ascending=True, inplace=False).reset_index(drop=True)\n",
        "print(sdf.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zewZPbMIaZoY",
        "outputId": "bddad7c7-3032-481b-a628-b7318b0d0187"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        player team   gamedate  days_since  winloss  minuters  pts  fgm  fga  \\\n",
            "0  A.J. Lawson  DAL 2023-12-01          38        0         4    1    0    1   \n",
            "1  A.J. Lawson  DAL 2023-12-02          39        0        19   12    4   10   \n",
            "2  A.J. Lawson  DAL 2023-12-06          43        1         7    4    2    2   \n",
            "3  A.J. Lawson  DAL 2023-12-08          45        1         7    2    1    3   \n",
            "4  A.J. Lawson  DAL 2023-12-11          48        1        14    4    2    7   \n",
            "\n",
            "   fgpercent  ...  opp_team_ORL  opp_team_PHI  opp_team_PHX  opp_team_POR  \\\n",
            "0        0.0  ...         False         False         False         False   \n",
            "1       40.0  ...         False         False         False         False   \n",
            "2      100.0  ...         False         False         False         False   \n",
            "3       33.3  ...         False         False         False          True   \n",
            "4       28.6  ...         False         False         False         False   \n",
            "\n",
            "   opp_team_SAC  opp_team_SAS  opp_team_TOR  opp_team_UTA  opp_team_WAS    fp  \n",
            "0         False         False         False         False         False   1.5  \n",
            "1         False         False         False         False         False  11.5  \n",
            "2         False         False         False          True         False   3.0  \n",
            "3         False         False         False         False         False   2.0  \n",
            "4         False         False         False         False         False   3.0  \n",
            "\n",
            "[5 rows x 101 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "k0sLIpFEjme6"
      },
      "outputs": [],
      "source": [
        "def collate_fn(batch):\n",
        "    \"\"\"\n",
        "    Custom collate function for handling variable-length sequences.\n",
        "    \"\"\"\n",
        "    features, targets = zip(*batch)\n",
        "    # Pad the sequences to the maximum length in the batch\n",
        "    features_padded = nn.utils.rnn.pad_sequence(features, batch_first=True)\n",
        "    targets = torch.stack(targets)\n",
        "    return features_padded, targets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "8eVheY7GT35b"
      },
      "outputs": [],
      "source": [
        "# Create a DataLoader\n",
        "batch_size = 32 # Adjust the batch size as needed # 2 was bad # idt 32 changed anything too\n",
        "dataset = NBAFantasyDataset(df.sort_values(by=['player', 'days_since'], ascending=True, inplace=False).reset_index(drop=True))\n",
        "dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aGHvPhDJd3_2"
      },
      "source": [
        "## 1.5 Train-Test Split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "sl56Nonqbm97"
      },
      "outputs": [],
      "source": [
        "# Define the split sizes\n",
        "TRAIN_RATIO = 0.8\n",
        "train_size = int(TRAIN_RATIO * len(dataset))\n",
        "test_size = len(dataset) - train_size\n",
        "\n",
        "# Split the dataset into training and test sets\n",
        "train_dataset, test_dataset = random_split(dataset, [train_size, test_size])\n",
        "\n",
        "# Create DataLoaders for the training and test sets\n",
        "train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn, num_workers=2)\n",
        "test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, collate_fn=collate_fn, num_workers=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Ga3wMBksMgi"
      },
      "source": [
        "## 1.6 Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X7Dqfj6vM1OM",
        "outputId": "8d191991-928f-415b-f36e-ffb791487af3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ],
      "source": [
        "counter = 0\n",
        "C = 200\n",
        "epochs = 0\n",
        "players_in_dataloader = []\n",
        "for epoch in range(epochs):\n",
        "    for features, targets in tqdm(dataloader):\n",
        "        for idx in range(len(features)):\n",
        "            for idx2 in range(len(features[0])):\n",
        "                player_idx = int(np.round(features[idx][idx2][11].item() * 1000))\n",
        "                players_in_dataloader.append((players_inv_dict[player_idx]))\n",
        "        \"\"\"\n",
        "        for i in range(len(features[0])):\n",
        "            date = int(np.round(features[idx][i][12].item() * 100000))\n",
        "            month = date // 1000\n",
        "            day = date % 1000\n",
        "            print(\"Month: \", month, \" . Day: \", day)\n",
        "        print(\"Actual fantasy points:\", targets[0].item())\n",
        "        \"\"\"\n",
        "        counter += 1\n",
        "print(len(set(players_in_dataloader)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "ZMx4pmTrzjzj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "1f59de8e-9038-4685-f612-73514141af65"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming 'df' is your DataFrame and 'fp' is the column with fantasy points\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.hist(df['fp'], bins=50, alpha=0.7, color='blue')\n",
        "plt.xlabel('Fantasy Points (fp)')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of Fantasy Points')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "n4hQQbBmz82p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "5abf736a-4e08-47c7-a165-34c64238f879"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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26MUXX1RoaKgiIiLUvn17Pfvss9q8ebPat2+vESNGKDIyUsePH9eePXu0YcMGe5iPiYlRSEiIOnbsqODgYH333Xd6+eWXFRcXV2Y/s7+74447tGzZMgUEBCgyMlLbt2/Xhg0bLjpl+pAhQzR37lxt3rxZzz33XJHtJb3WESNG6OWXX9aQIUO0e/du1a1bV8uWLZO3t3e5XCeASshFswUCQIVQONV14Zenp6cREhJidO3a1XjppZccptku9Pdpqzdu3Gj06tXLCA0NNTw9PY3Q0FBjwIABxg8//OCw34cffmhERkYaVatWdZhu/LbbbrvolNUXm/58+fLlxoQJE4ygoCCjevXqRlxcnHHkyJEi+7/wwgvGNddcY3h5eRkdO3Y0vvrqqyLHvFRtxU3dferUKWPcuHFGaGioYbVajcaNGxvPP/+8UVBQ4NBOkpGQkFCkpotNy/53WVlZxrBhw4zatWsbnp6eRosWLYqdot2Z6c8vxcz05yW9vpSUFOP66683PD09jaZNmxpvvvlmscf8/vvvjVtvvdWoXr26IcnhOFlZWUZCQoIRFhZmWK1WIyQkxOjSpYvx6quv2tu88sorxq233mrUqlXL8PLyMho2bGg89thjxsmTJy95zYXTnz///POXbFd43X+d/vyPP/6wv06+vr5GbGys8f3331/ydW7evLlRpUoV4+jRo8VuL8m1GoZhHDlyxLjzzjsNb29vo3bt2sYjjzxirF+/nunPAZQJi2G4wRO9AAAA/1+bNm1Us2ZNbdy40dWlAMBF8YwUAABwG1999ZX27t2rIUOGuLoUALgkRqQAAIDL7du3T7t379YLL7yg3377TT/99FO5zCIIAGWFESkAAOBy7777roYNGyabzably5cTogC4PUakAAAAAMAkRqQAAAAAwCSCFAAAAACYxAfySiooKNCxY8fk5+cni8Xi6nIAAAAAuIhhGDp16pRCQ0NVpcrFx50IUpKOHTumsLAwV5cBAAAAwE388ssvqlev3kW3E6Qk+fn5Sbrww/L393dpLTabTSkpKYqJiZHVanVpLagY6DMwiz4Ds+gzMIs+AzPcrb/k5OQoLCzMnhEuhiAl2W/n8/f3d4sg5e3tLX9/f7foSHB/9BmYRZ+BWfQZmEWfgRnu2l8u98gPk00AAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJlV1dQEAAAB/17Onc/utXl22dQDAxbh0RKpBgwayWCxFvhISEiRJ586dU0JCgmrVqiVfX1/17dtXWVlZDsdIT09XXFycvL29FRQUpMcee0znz593xeUAAAAAqCRcGqR27dqljIwM+1dqaqok6Z577pEkjRs3TqtXr9bKlSu1detWHTt2TH369LHvn5+fr7i4OOXl5Wnbtm1aunSplixZokmTJrnkegAAAABUDi4NUnXq1FFISIj9a82aNWrYsKFuu+02nTx5UosXL9aLL76ozp07q23btkpKStK2bdu0Y8cOSVJKSooOHDigN998U61bt1b37t01ffp0zZ8/X3l5ea68NAAAAABXMbd5RiovL09vvvmmEhMTZbFYtHv3btlsNkVHR9vbXHfddapfv762b9+uDh06aPv27WrRooWCg4PtbWJjYzVy5Ejt379fbdq0KfZcubm5ys3NtS/n5ORIkmw2m2w2WzldYckUnt/VdaDioM/ALPoMzHJFn7FanduPbu0e+D0DM9ytv5S0DrcJUh988IFOnDihoUOHSpIyMzPl6empwMBAh3bBwcHKzMy0t/lriCrcXrjtYmbOnKmpU6cWWZ+SkiJvb+9SXEXZKbzNESgp+gzMos/ArCvZZ+Ljndtv7dqyrQOlw+8ZmOEu/eXs2bMlauc2QWrx4sXq3r27QkNDy/1cEyZMUGJion05JydHYWFhiomJkb+/f7mf/1JsNptSU1PVtWtXWZ395zhUKvQZmEWfgVmu6DP9+jm339tvl20dcA6/Z2CGu/WXwrvVLsctgtSRI0e0YcMGrVq1yr4uJCREeXl5OnHihMOoVFZWlkJCQuxtvvzyS4djFc7qV9imOF5eXvLy8iqy3mq1usWLJ7lXLagY6DMwiz5TcTk7NbhUuunBBw+2ymYz12ecPZ+zd/jQpd0Lv2dghrv0l5LW4BYfyJuUlKSgoCDFxcXZ17Vt21ZWq1UbN260r0tLS1N6erqioqIkSVFRUfr222+VnZ1tb5Oamip/f39FRkZeuQsAAAAAUKm4fESqoKBASUlJio+PV9Wq/1dOQECAhg8frsTERNWsWVP+/v4aPXq0oqKi1KFDB0lSTEyMIiMjdd9992nWrFnKzMzUxIkTlZCQUOyIEwAAAACUBZcHqQ0bNig9PV33339/kW2zZ89WlSpV1LdvX+Xm5io2NlYLFiywb/fw8NCaNWs0cuRIRUVFycfHR/Hx8Zo2bdqVvAQAAAAAlYzLg1RMTIwMwyh2W7Vq1TR//nzNnz//ovuHh4drLVP0AAAAALiCXB6kAADA1as0E2MAgDtzi8kmAAAAAKAiIUgBAAAAgEnc2gcAAK4azt5KWJrP1wJQOTEiBQAAAAAmEaQAAAAAwCRu7QMAAJUetwQCMIsgBQBAKfEmHAAqH27tAwAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiVn7AAAAnMSMjUDlRZACAKASceaNv9UqxceXfS2VGQEMqPi4tQ8AAAAATCJIAQAAAIBJBCkAAAAAMIlnpAAAcBFnn5MBALgeI1IAAAAAYBJBCgAAAABMIkgBAAAAgEk8IwUAwP/HM0sAgJJiRAoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwqaqrCwAAuEa/fpLNZm6f1avLpxYAACoaRqQAAAAAwCSCFAAAAACYxK19AAC31bOnqysAAKB4jEgBAAAAgEmMSAFABebMiI3VKsXHl30tAABUJgQpAEC54xY9AMDVhlv7AAAAAMAkRqQAACXGyBIAABe4fETqv//9rwYPHqxatWqpevXqatGihb766iv7dsMwNGnSJNWtW1fVq1dXdHS0fvzxR4djHD9+XIMGDZK/v78CAwM1fPhwnT59+kpfCgAAAIBKwqVB6o8//lDHjh1ltVq1bt06HThwQC+88IJq1KhhbzNr1izNnTtXixYt0s6dO+Xj46PY2FidO3fO3mbQoEHav3+/UlNTtWbNGn366ad68MEHXXFJAAAAACoBl97a99xzzyksLExJSUn2dREREfbvDcPQnDlzNHHiRPXq1UuS9MYbbyg4OFgffPCB+vfvr++++07r16/Xrl271K5dO0nSvHnz1KNHD/3rX/9SaGjolb0oAAAAAFc9lwapjz76SLGxsbrnnnu0detWXXPNNXr44Yc1YsQISdLhw4eVmZmp6Oho+z4BAQFq3769tm/frv79+2v79u0KDAy0hyhJio6OVpUqVbRz507dddddRc6bm5ur3Nxc+3JOTo4kyWazyWazldfllkjh+V1dByoO+kzlZrU6s4/N4b/A5dBn3EdF+VXP3yaY4W79paR1uDRI/fTTT1q4cKESExP1z3/+U7t27dKYMWPk6emp+Ph4ZWZmSpKCg4Md9gsODrZvy8zMVFBQkMP2qlWrqmbNmvY2fzdz5kxNnTq1yPqUlBR5e3uXxaWVWmpqqqtLQAVDn6mcSvN5UAMH0mdgDn3G9daudXUF5vC3CWa4S385e/Zsidq5NEgVFBSoXbt2mjFjhiSpTZs22rdvnxYtWqT4cvy0yAkTJigxMdG+nJOTo7CwMMXExMjf37/czlsSNptNqamp6tq1q6zO/FMzKh36TOXWr5/5faxWmwYOTFVyclfZbPQZXB59xn28/barKygZ/jbBDHfrL4V3q12OS4NU3bp1FRkZ6bCuWbNmeu+99yRJISEhkqSsrCzVrVvX3iYrK0utW7e2t8nOznY4xvnz53X8+HH7/n/n5eUlLy+vIuutVqtbvHiSe9WCioE+UzmV5i4Im83Km2KYQp9xvYr2a56/TTDDXfpLSWtw6ax9HTt2VFpamsO6H374QeHh4ZIuTDwREhKijRs32rfn5ORo586dioqKkiRFRUXpxIkT2r17t73Npk2bVFBQoPbt21+BqwAAAABQ2bh0RGrcuHG66aabNGPGDN1777368ssv9eqrr+rVV1+VJFksFo0dO1ZPP/20GjdurIiICD311FMKDQ1V7969JV0YwerWrZtGjBihRYsWyWazadSoUerfvz8z9gEAAAAoFy4NUv/4xz/0/vvva8KECZo2bZoiIiI0Z84cDRo0yN7m8ccf15kzZ/Tggw/qxIkTuvnmm7V+/XpVq1bN3uatt97SqFGj1KVLF1WpUkV9+/bV3LlzXXFJAAAAACoBlwYpSbrjjjt0xx13XHS7xWLRtGnTNG3atIu2qVmzppKTk8ujPAAAAAAowqXPSAEAAABARUSQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwyeXTnwPA1aJnT+f3Xb267OoAAADljxEpAAAAADCJIAUAAAAAJhGkAAAAAMAknpECAACoIJx9FpPnMIGyx4gUAAAAAJhEkAIAAAAAk7i1DwD+pjTTmAMAgMqBESkAAAAAMIkgBQAAAAAmEaQAAAAAwCSekQIAN8BzWQAAVCyMSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhU1dUFAEB56dnT1RUAAICrFSNSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmMWsfALfH7HsAAMDdMCIFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAExyaZCaMmWKLBaLw9d1111n337u3DklJCSoVq1a8vX1Vd++fZWVleVwjPT0dMXFxcnb21tBQUF67LHHdP78+St9KQAAAAAqkaquLqB58+basGGDfblq1f8rady4cfr444+1cuVKBQQEaNSoUerTp4+++OILSVJ+fr7i4uIUEhKibdu2KSMjQ0OGDJHVatWMGTOu+LUAAAAAqBxcHqSqVq2qkJCQIutPnjypxYsXKzk5WZ07d5YkJSUlqVmzZtqxY4c6dOiglJQUHThwQBs2bFBwcLBat26t6dOn64knntCUKVPk6elZ7Dlzc3OVm5trX87JyZEk2Ww22Wy2crjKkis8v6vrQMVRGfqM1erqCq4uVqvN4b/A5dBnKr4r/SeiMvxtQtlxt/5S0joshmEY5VzLRU2ZMkXPP/+8AgICVK1aNUVFRWnmzJmqX7++Nm3apC5duuiPP/5QYGCgfZ/w8HCNHTtW48aN06RJk/TRRx9p79699u2HDx/Wtddeqz179qhNmzYXPe/UqVOLrE9OTpa3t3dZXyYAAACACuLs2bMaOHCgTp48KX9//4u2c+mIVPv27bVkyRI1bdpUGRkZmjp1qm655Rbt27dPmZmZ8vT0dAhRkhQcHKzMzExJUmZmpoKDg4tsL9x2MRMmTFBiYqJ9OScnR2FhYYqJibnkD+tKsNlsSk1NVdeuXWXln+FRApWhz/Tr5+oKri5Wq00DB6YqObmrbLars8+gbNFnKr63376y56sMf5tQdtytvxTerXY5Lg1S3bt3t3/fsmVLtW/fXuHh4XrnnXdUvXr1cjuvl5eXvLy8iqy3Wq1u8eJJ7lULKoaruc+4yUj/Vcdms/KmGKbQZyouV/15uJr/NqHsuUt/KWkNbjX9eWBgoJo0aaKDBw8qJCREeXl5OnHihEObrKws+zNVISEhRWbxK1wu7rkrAAAAACgLbhWkTp8+rUOHDqlu3bpq27atrFarNm7caN+elpam9PR0RUVFSZKioqL07bffKjs7294mNTVV/v7+ioyMvOL1AwAAAKgcXHpr3/jx49WzZ0+Fh4fr2LFjmjx5sjw8PDRgwAAFBARo+PDhSkxMVM2aNeXv76/Ro0crKipKHTp0kCTFxMQoMjJS9913n2bNmqXMzExNnDhRCQkJxd66BwAAAABlwaVB6ujRoxowYIB+//131alTRzfffLN27NihOnXqSJJmz56tKlWqqG/fvsrNzVVsbKwWLFhg39/Dw0Nr1qzRyJEjFRUVJR8fH8XHx2vatGmuuiQAAAAAlYBLg9SKFSsuub1atWqaP3++5s+ff9E24eHhWrt2bVmXBgAAAAAX5VbPSAEAAABARUCQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJJd+IC8AAADKX8+ezu23enXZ1gFcTRiRAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJjEB/ICMI0PdgQAAJUdI1IAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwickmAFwxzk5SAQAA4G4YkQIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmORUkPrpp5/Kug4AAAAAqDCcClKNGjXS7bffrjfffFPnzp0r65oAAAAAwK05FaT27Nmjli1bKjExUSEhIfqf//kfffnll2VdGwAAAAC4JaeCVOvWrfXSSy/p2LFjev3115WRkaGbb75Z119/vV588UX9+uuvZV0nAAAAALiNUk02UbVqVfXp00crV67Uc889p4MHD2r8+PEKCwvTkCFDlJGRUVZ1AgAAAIDbKFWQ+uqrr/Twww+rbt26evHFFzV+/HgdOnRIqampOnbsmHr16lVWdQIAAACA26jqzE4vvviikpKSlJaWph49euiNN95Qjx49VKXKhVwWERGhJUuWqEGDBmVZKwAAAAC4BaeC1MKFC3X//fdr6NChqlu3brFtgoKCtHjx4lIVhyujZ0/n9lu9umKcDwAAAChrTgWpH3/88bJtPD09FR8f78zhAQAAAMCtOfWMVFJSklauXFlk/cqVK7V06dJSFwUAAAAA7sypIDVz5kzVrl27yPqgoCDNmDGj1EUBAAAAgDtzKkilp6crIiKiyPrw8HClp6eXuigAAAAAcGdOBamgoCB98803RdZ//fXXqlWrllOFPPvss7JYLBo7dqx93blz55SQkKBatWrJ19dXffv2VVZWlsN+6enpiouLk7e3t4KCgvTYY4/p/PnzTtUAAAAAACXhVJAaMGCAxowZo82bNys/P1/5+fnatGmTHnnkEfXv39/08Xbt2qVXXnlFLVu2dFg/btw4rV69WitXrtTWrVt17Ngx9enTx749Pz9fcXFxysvL07Zt27R06VItWbJEkyZNcuayAAAAAKBEnJq1b/r06fr555/VpUsXVa164RAFBQUaMmSI6WekTp8+rUGDBum1117T008/bV9/8uRJLV68WMnJyercubOkC5NcNGvWTDt27FCHDh2UkpKiAwcOaMOGDQoODlbr1q01ffp0PfHEE5oyZYo8PT2LPWdubq5yc3Ptyzk5OZIkm80mm81mqv6yVnj+K1mH1ercfn/JtFfkfC5+adxWReozcA9Wq83hv8Dl0GcqL2f/tLjibxMqLnfrLyWtw2IYhuHsSX744Qd9/fXXql69ulq0aKHw8HDTx4iPj1fNmjU1e/ZsderUSa1bt9acOXO0adMmdenSRX/88YcCAwPt7cPDwzV27FiNGzdOkyZN0kcffaS9e/fatx8+fFjXXnut9uzZozZt2hR7zilTpmjq1KlF1icnJ8vb29v0NQAAAAC4Opw9e1YDBw7UyZMn5e/vf9F2To1IFWrSpImaNGni9P4rVqzQnj17tGvXriLbMjMz5enp6RCiJCk4OFiZmZn2NsHBwUW2F267mAkTJigxMdG+nJOTo7CwMMXExFzyh3Ul2Gw2paamqmvXrrJeoX/279fvipym1N5+29UVuCf6DMyyWm0aODBVycldZbMxvIjLo89UXs7+7XXF3yZUXO7WXwrvVrscp4JUfn6+lixZoo0bNyo7O1sFBQUO2zdt2nTZY/zyyy965JFHlJqaqmrVqjlThtO8vLzk5eVVZL3VanWLF0+6srW4ySjqZTl7K+Hq1WVbh7uiz8Asm83Km2KYQp+pfEr7Z8Wd3lvB/blLfylpDU4FqUceeURLlixRXFycrr/+elksFtPH2L17t7Kzs3XDDTfY1+Xn5+vTTz/Vyy+/rE8++UR5eXk6ceKEw6hUVlaWQkJCJEkhISH68ssvHY5bOKtfYRsAAAA4p2dP5/azWqX4+LKtBXA3TgWpFStW6J133lGPHj2cPnGXLl307bffOqwbNmyYrrvuOj3xxBMKCwuT1WrVxo0b1bdvX0lSWlqa0tPTFRUVJUmKiorSM888o+zsbAUFBUmSUlNT5e/vr8jISKdrAwAAAIBLcSpIeXp6qlGjRqU6sZ+fn66//nqHdT4+PqpVq5Z9/fDhw5WYmKiaNWvK399fo0ePVlRUlDp06CBJiomJUWRkpO677z7NmjVLmZmZmjhxohISEoq9dQ8AAAAAyoJTnyP16KOP6qWXXlIpJvwrkdmzZ+uOO+5Q3759deuttyokJESrVq2yb/fw8NCaNWvk4eGhqKgoDR48WEOGDNG0adPKtS4AAAAAlZtTI1Kff/65Nm/erHXr1ql58+ZFHsj6a9gxY8uWLQ7L1apV0/z58zV//vyL7hMeHq61a9c6dT4AAAAAcIZTQSowMFB33XVXWdcCAAAAABWCU0EqKSmprOsAAAAAgArDqWekJOn8+fPasGGDXnnlFZ06dUqSdOzYMZ0+fbrMigMAAAAAd+TUiNSRI0fUrVs3paenKzc3V127dpWfn5+ee+455ebmatGiRWVdJwAAAAC4Dac/kLddu3b6+uuvVatWLfv6u+66SyNGjCiz4oCy4OyHCUrS6tVlVwcAAACuHk4Fqc8++0zbtm2Tp6enw/oGDRrov//9b5kUBgAAAADuyqlnpAoKCpSfn19k/dGjR+Xn51fqogAAAADAnTkVpGJiYjRnzhz7ssVi0enTpzV58mT16NGjrGoDAAAAALfk1K19L7zwgmJjYxUZGalz585p4MCB+vHHH1W7dm0tX768rGsEAAAAALfiVJCqV6+evv76a61YsULffPONTp8+reHDh2vQoEGqXr16WdcIAAAAAG7FqSAlSVWrVtXgwYPLshYAAAAAqBCcClJvvPHGJbcPGTLEqWIAAAAAoCJw+nOk/spms+ns2bPy9PSUt7c3QcpFSvN5SQAAAABKzqlZ+/744w+Hr9OnTystLU0333wzk00AAAAAuOo5FaSK07hxYz377LNFRqsAAAAA4GpTZkFKujABxbFjx8rykAAAAADgdpx6Ruqjjz5yWDYMQxkZGXr55ZfVsWPHMikMAAAAANyVU0Gqd+/eDssWi0V16tRR586d9cILL5RFXQCuACYoAQAAcI5TQaqgoKCs6wAAAACACqNMn5ECAAAAgMrAqRGpxMTEErd98cUXnTkFAAAAALgtp4LUf/7zH/3nP/+RzWZT06ZNJUk//PCDPDw8dMMNN9jbWSyWsqkSAAAAANyIU0GqZ8+e8vPz09KlS1WjRg1JFz6kd9iwYbrlllv06KOPlmmRAAAAAOBOnHpG6oUXXtDMmTPtIUqSatSooaeffppZ+wAAAABc9ZwKUjk5Ofr111+LrP/111916tSpUhcFAAAAAO7MqSB11113adiwYVq1apWOHj2qo0eP6r333tPw4cPVp0+fsq4RAAAAANyKU89ILVq0SOPHj9fAgQNls9kuHKhqVQ0fPlzPP/98mRYIAAAAAO7GqSDl7e2tBQsW6Pnnn9ehQ4ckSQ0bNpSPj0+ZFgcAAAAA7qhUH8ibkZGhjIwMNW7cWD4+PjIMo6zqAgAAAAC35VSQ+v3339WlSxc1adJEPXr0UEZGhiRp+PDhTH0OAAAA4KrnVJAaN26crFar0tPT5e3tbV/fr18/rV+/vsyKAwAAAAB35NQzUikpKfrkk09Ur149h/WNGzfWkSNHyqQwAAAAAHBXTo1InTlzxmEkqtDx48fl5eVV6qIAAAAAwJ05FaRuueUWvfHGG/Zli8WigoICzZo1S7fffnuZFQcAAAAA7sipW/tmzZqlLl266KuvvlJeXp4ef/xx7d+/X8ePH9cXX3xR1jUCAAAAgFtxakTq+uuv1w8//KCbb75ZvXr10pkzZ9SnTx/95z//UcOGDcu6RgAAAABwK6ZHpGw2m7p166ZFixbpySefLI+aAAAAAMCtmR6Rslqt+uabb8qjFgAAAACoEJy6tW/w4MFavHhxWdcCAAAAABWCU5NNnD9/Xq+//ro2bNigtm3bysfHx2H7iy++WCbFAQAAAIA7MhWkfvrpJzVo0ED79u3TDTfcIEn64YcfHNpYLJayqw4AAAAA3JCpINW4cWNlZGRo8+bNkqR+/fpp7ty5Cg4OLpfiAAAAAMAdmQpShmE4LK9bt05nzpwp04IAOKdfP8lmc3UVAAAAlYNTk00U+nuwAgAAAIDKwFSQslgsRZ6B4pkoAAAAAJWN6Vv7hg4dKi8vL0nSuXPn9NBDDxWZtW/VqlVlVyHgQj17Orff6tVlWwcAAADci6kgFR8f77A8ePDgMi0GAAAAACoCU0EqKSmpvOoAAAAAgAqjVJNNAAAAAEBlZGpECkDJ8GwVAADA1Y0RKQAAAAAwyaVBauHChWrZsqX8/f3l7++vqKgorVu3zr793LlzSkhIUK1ateTr66u+ffsqKyvL4Rjp6emKi4uTt7e3goKC9Nhjj+n8+fNX+lIAAAAAVCIuDVL16tXTs88+q927d+urr75S586d1atXL+3fv1+SNG7cOK1evVorV67U1q1bdezYMfXp08e+f35+vuLi4pSXl6dt27Zp6dKlWrJkiSZNmuSqSwIAAABQCbj0Gamef3uQ5JlnntHChQu1Y8cO1atXT4sXL1ZycrI6d+4s6cKsgc2aNdOOHTvUoUMHpaSk6MCBA9qwYYOCg4PVunVrTZ8+XU888YSmTJkiT09PV1wWAAAAgKuc20w2kZ+fr5UrV+rMmTOKiorS7t27ZbPZFB0dbW9z3XXXqX79+tq+fbs6dOig7du3q0WLFgoODra3iY2N1ciRI7V//361adOm2HPl5uYqNzfXvpyTkyNJstlsstls5XSFJVN4fmfqsFrLuhpcac50v8K+YrW6tu+i4ijsK/QZlBR9BmYV9hVXv69CxVCa97/loaR1uDxIffvtt4qKitK5c+fk6+ur999/X5GRkdq7d688PT0VGBjo0D44OFiZmZmSpMzMTIcQVbi9cNvFzJw5U1OnTi2yPiUlRd7e3qW8orKRmppqep+/fV4yKqC1a53fd+BA830GlRt9BmbRZ2CWM+9nUHm5S385e/Zsidq5PEg1bdpUe/fu1cmTJ/Xuu+8qPj5eW7duLddzTpgwQYmJifblnJwchYWFKSYmRv7+/uV67sux2WxKTU1V165dZTU5xNSvXzkVhSvm7bfN71PYZ5KTu8pmY1gSl2e12jRwIH0GJUefgVmFfcaZ9zOofErz/rc8FN6tdjkuD1Kenp5q1KiRJKlt27batWuXXnrpJfXr1095eXk6ceKEw6hUVlaWQkJCJEkhISH68ssvHY5XOKtfYZvieHl5ycvLq8h6q9XqFi+e5FwtbjIailIoTfez2ay8wYEp9BmYRZ+BWe703gruz136S0lrcLvPkSooKFBubq7atm0rq9WqjRs32relpaUpPT1dUVFRkqSoqCh9++23ys7OtrdJTU2Vv7+/IiMjr3jtAAAAACoHl45ITZgwQd27d1f9+vV16tQpJScna8uWLfrkk08UEBCg4cOHKzExUTVr1pS/v79Gjx6tqKgodejQQZIUExOjyMhI3XfffZo1a5YyMzM1ceJEJSQkFDviBAAAAABlwaVBKjs7W0OGDFFGRoYCAgLUsmVLffLJJ+rataskafbs2apSpYr69u2r3NxcxcbGasGCBfb9PTw8tGbNGo0cOVJRUVHy8fFRfHy8pk2b5qpLAkrlb58IUCJWKxONAAAAXGkuDVKLFy++5PZq1app/vz5mj9//kXbhIeHa21ppjoDAAAAAJPc7hkpAAAAAHB3BCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJOquroAAAAAXJ369ZNsNnP7rF5dPrUAZY0RKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmOTSIDVz5kz94x//kJ+fn4KCgtS7d2+lpaU5tDl37pwSEhJUq1Yt+fr6qm/fvsrKynJok56erri4OHl7eysoKEiPPfaYzp8/fyUvBQAAAEAl4tIgtXXrViUkJGjHjh1KTU2VzWZTTEyMzpw5Y28zbtw4rV69WitXrtTWrVt17Ngx9enTx749Pz9fcXFxysvL07Zt27R06VItWbJEkyZNcsUlAQAAAKgEqrry5OvXr3dYXrJkiYKCgrR7927deuutOnnypBYvXqzk5GR17txZkpSUlKRmzZppx44d6tChg1JSUnTgwAFt2LBBwcHBat26taZPn64nnnhCU6ZMkaenpysuDQAAAMBVzKVB6u9OnjwpSapZs6Ykaffu3bLZbIqOjra3ue6661S/fn1t375dHTp00Pbt29WiRQsFBwfb28TGxmrkyJHav3+/2rRpU+Q8ubm5ys3NtS/n5ORIkmw2m2w2W7lcW0kVnt+ZOqzWsq4GFYHVanP4L3A59BmYRZ+BWaXpMy5+KwYXKM373/JQ0jrcJkgVFBRo7Nix6tixo66//npJUmZmpjw9PRUYGOjQNjg4WJmZmfY2fw1RhdsLtxVn5syZmjp1apH1KSkp8vb2Lu2llInU1FTT+8THl0MhqDAGDjTfZ1C50WdgFn0GZjnTZ9auLYdCUCE48/63PJw9e7ZE7dwmSCUkJGjfvn36/PPPy/1cEyZMUGJion05JydHYWFhiomJkb+/f7mf/1JsNptSU1PVtWtXWU0OMfXrV05Fwa1ZrTYNHJiq5OSustkYlsTl0WdgFn0GZpWmz7z9djkVBbdVmve/5aHwbrXLcYsgNWrUKK1Zs0affvqp6tWrZ18fEhKivLw8nThxwmFUKisrSyEhIfY2X375pcPxCmf1K2zzd15eXvLy8iqy3mq1usWLJzlXi5uMhsJFbDYrb3BgCn0GZtFnYJYzfcZN3orBBdzlvXhJa3DprH2GYWjUqFF6//33tWnTJkVERDhsb9u2raxWqzZu3Ghfl5aWpvT0dEVFRUmSoqKi9O233yo7O9veJjU1Vf7+/oqMjLwyFwIAAACgUnHpiFRCQoKSk5P14Ycfys/Pz/5MU0BAgKpXr66AgAANHz5ciYmJqlmzpvz9/TV69GhFRUWpQ4cOkqSYmBhFRkbqvvvu06xZs5SZmamJEycqISGh2FEnAAAAACgtlwaphQsXSpI6derksD4pKUlDhw6VJM2ePVtVqlRR3759lZubq9jYWC1YsMDe1sPDQ2vWrNHIkSMVFRUlHx8fxcfHa9q0aVfqMgAAAABUMi4NUoZhXLZNtWrVNH/+fM2fP/+ibcLDw7WWKV4AAAAAXCEufUYKAAAAACoighQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJOquroAAAAAoFDPns7tt3p12dYBXA4jUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmFTVlSf/9NNP9fzzz2v37t3KyMjQ+++/r969e9u3G4ahyZMn67XXXtOJEyfUsWNHLVy4UI0bN7a3OX78uEaPHq3Vq1erSpUq6tu3r1566SX5+vq64IrKTr9+ks3m6ioAAAAAFMelI1JnzpxRq1atNH/+/GK3z5o1S3PnztWiRYu0c+dO+fj4KDY2VufOnbO3GTRokPbv36/U1FStWbNGn376qR588MErdQkAAAAAKiGXjkh1795d3bt3L3abYRiaM2eOJk6cqF69ekmS3njjDQUHB+uDDz5Q//799d1332n9+vXatWuX2rVrJ0maN2+eevTooX/9618KDQ29YtcCAAAAoPJwaZC6lMOHDyszM1PR0dH2dQEBAWrfvr22b9+u/v37a/v27QoMDLSHKEmKjo5WlSpVtHPnTt11113FHjs3N1e5ubn25ZycHEmSzWaTzcX30xWe32rlvj6UTGFfoc+gpOgzMIs+A7Nc0Wd4JKLiKnz/6+r34YVKWofbBqnMzExJUnBwsMP64OBg+7bMzEwFBQU5bK9atapq1qxpb1OcmTNnaurUqUXWp6SkyNvbu7Sll4mBA1NdXQIqGPoMzKLPwCz6DMy6kn1m7dordiqUk9RU9/gdc/bs2RK1c9sgVZ4mTJigxMRE+3JOTo7CwsIUExMjf39/F1Z2IQGnpqYqObmrbDarS2tBxWC12jRwIH0GJUefgVn0GZjlij7z9ttX5DQoB4Xvf7t27Sqr1fW/YwrvVrsctw1SISEhkqSsrCzVrVvXvj4rK0utW7e2t8nOznbY7/z58zp+/Lh9/+J4eXnJy8uryHqr1eoWL54k2WxW/ljBFPoMzKLPwCz6DMy6kn3GTd7CoRTc5b14SWtw28+RioiIUEhIiDZu3Ghfl5OTo507dyoqKkqSFBUVpRMnTmj37t32Nps2bVJBQYHat29/xWsGAAAAUDm4dETq9OnTOnjwoH358OHD2rt3r2rWrKn69etr7Nixevrpp9W4cWNFREToqaeeUmhoqP2zppo1a6Zu3bppxIgRWrRokWw2m0aNGqX+/fszYx8AAACAcuPSIPXVV1/p9ttvty8XPrcUHx+vJUuW6PHHH9eZM2f04IMP6sSJE7r55pu1fv16VatWzb7PW2+9pVGjRqlLly72D+SdO3fuFb8WAAAAAJWHS4NUp06dZBjGRbdbLBZNmzZN06ZNu2ibmjVrKjk5uTzKAwAAAIBiue0zUgAAAADgrghSAAAAAGASQQoAAAAATHLbz5ECAAAASqpnT+f2W726bOtA5cGIFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmFTV1QUAAAAArtKzp3P7rV5dtnWg4mFECgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJOquroAAAAAoKLp2dO5/VavLts64DqMSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwqaqrCwAAAAAqi549ndtv9eqyrQOlx4gUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMInpzwEAAAA3x7Tp7ocRKQAAAAAwiSAFAAAAACYRpAAAAADApKvmGan58+fr+eefV2Zmplq1aqV58+bpxhtvdHVZAAAAgMs4+2yVsyrTM1lXRZB6++23lZiYqEWLFql9+/aaM2eOYmNjlZaWpqCgIFeXBwAAAFQKzgQ3q1WKjy/7WsrbVXFr34svvqgRI0Zo2LBhioyM1KJFi+Tt7a3XX3/d1aUBAAAAuApV+BGpvLw87d69WxMmTLCvq1KliqKjo7V9+/Zi98nNzVVubq59+eTJk5Kk48ePy2azlW/Bl2Gz2XT27FlJv0uyurQWVBT0GZhFn4FZ9BmYRZ+BGRf6y++//y6r1fX95dSpU5IkwzAu2a7CB6nffvtN+fn5Cg4OdlgfHBys77//vth9Zs6cqalTpxZZHxERUS41AuXt/fddXQEqGvoMzKLPwCz6DMxwx/5y6tQpBQQEXHR7hQ9SzpgwYYISExPtywUFBTp+/Lhq1aoli8XiwsqknJwchYWF6ZdffpG/v79La0HFQJ+BWfQZmEWfgVn0GZjhbv3FMAydOnVKoaGhl2xX4YNU7dq15eHhoaysLIf1WVlZCgkJKXYfLy8veXl5OawLDAwsrxKd4u/v7xYdCRUHfQZm0WdgFn0GZtFnYIY79ZdLjUQVqvCTTXh6eqpt27bauHGjfV1BQYE2btyoqKgoF1YGAAAA4GpV4UekJCkxMVHx8fFq166dbrzxRs2ZM0dnzpzRsGHDXF0aAAAAgKvQVRGk+vXrp19//VWTJk1SZmamWrdurfXr1xeZgKIi8PLy0uTJk4vceghcDH0GZtFnYBZ9BmbRZ2BGRe0vFuNy8/oBAAAAABxU+GekAAAAAOBKI0gBAAAAgEkEKQAAAAAwiSAFAAAAACYRpNzI/Pnz1aBBA1WrVk3t27fXl19+6eqS4EY+/fRT9ezZU6GhobJYLPrggw8cthuGoUmTJqlu3bqqXr26oqOj9eOPP7qmWLjczJkz9Y9//EN+fn4KCgpS7969lZaW5tDm3LlzSkhIUK1ateTr66u+ffsW+XBzVB4LFy5Uy5Yt7R+IGRUVpXXr1tm3019wKc8++6wsFovGjh1rX0efwd9NmTJFFovF4eu6666zb69ofYYg5SbefvttJSYmavLkydqzZ49atWql2NhYZWdnu7o0uIkzZ86oVatWmj9/frHbZ82apblz52rRokXauXOnfHx8FBsbq3Pnzl3hSuEOtm7dqoSEBO3YsUOpqamy2WyKiYnRmTNn7G3GjRun1atXa+XKldq6dauOHTumPn36uLBquFK9evX07LPPavfu3frqq6/UuXNn9erVS/v375dEf8HF7dq1S6+88opatmzpsJ4+g+I0b95cGRkZ9q/PP//cvq3C9RkDbuHGG280EhIS7Mv5+flGaGioMXPmTBdWBXclyXj//fftywUFBUZISIjx/PPP29edOHHC8PLyMpYvX+6CCuFusrOzDUnG1q1bDcO40D+sVquxcuVKe5vvvvvOkGRs377dVWXCzdSoUcP497//TX/BRZ06dcpo3LixkZqaatx2223GI488YhgGv2NQvMmTJxutWrUqdltF7DOMSLmBvLw87d69W9HR0fZ1VapUUXR0tLZv3+7CylBRHD58WJmZmQ59KCAgQO3bt6cPQZJ08uRJSVLNmjUlSbt375bNZnPoM9ddd53q169Pn4Hy8/O1YsUKnTlzRlFRUfQXXFRCQoLi4uIc+obE7xhc3I8//qjQ0FBde+21GjRokNLT0yVVzD5T1dUFQPrtt9+Un5+v4OBgh/XBwcH6/vvvXVQVKpLMzExJKrYPFW5D5VVQUKCxY8eqY8eOuv766yVd6DOenp4KDAx0aEufqdy+/fZbRUVF6dy5c/L19dX777+vyMhI7d27l/6CIlasWKE9e/Zo165dRbbxOwbFad++vZYsWaKmTZsqIyNDU6dO1S233KJ9+/ZVyD5DkAKAq1xCQoL27dvncB86UJymTZtq7969OnnypN59913Fx8dr69atri4LbuiXX37RI488otTUVFWrVs3V5aCC6N69u/37li1bqn379goPD9c777yj6tWru7Ay53BrnxuoXbu2PDw8isxKkpWVpZCQEBdVhYqksJ/Qh/B3o0aN0po1a7R582bVq1fPvj4kJER5eXk6ceKEQ3v6TOXm6empRo0aqW3btpo5c6ZatWqll156if6CInbv3q3s7GzdcMMNqlq1qqpWraqtW7dq7ty5qlq1qoKDg+kzuKzAwEA1adJEBw8erJC/ZwhSbsDT01Nt27bVxo0b7esKCgq0ceNGRUVFubAyVBQREREKCQlx6EM5OTnauXMnfaiSMgxDo0aN0vvvv69NmzYpIiLCYXvbtm1ltVod+kxaWprS09PpM7ArKChQbm4u/QVFdOnSRd9++6327t1r/2rXrp0GDRpk/54+g8s5ffq0Dh06pLp161bI3zPc2ucmEhMTFR8fr3bt2unGG2/UnDlzdObMGQ0bNszVpcFNnD59WgcPHrQvHz58WHv37lXNmjVVv359jR07Vk8//bQaN26siIgIPfXUUwoNDVXv3r1dVzRcJiEhQcnJyfrwww/l5+dnv788ICBA1atXV0BAgIYPH67ExETVrFlT/v7+Gj16tKKiotShQwcXVw9XmDBhgrp376769evr1KlTSk5O1pYtW/TJJ5/QX1CEn5+f/ZnLQj4+PqpVq5Z9PX0Gfzd+/Hj17NlT4eHhOnbsmCZPniwPDw8NGDCgYv6ecfW0gfg/8+bNM+rXr294enoaN954o7Fjxw5XlwQ3snnzZkNSka/4+HjDMC5Mgf7UU08ZwcHBhpeXl9GlSxcjLS3NtUXDZYrrK5KMpKQke5s///zTePjhh40aNWoY3t7exl133WVkZGS4rmi41P3332+Eh4cbnp6eRp06dYwuXboYKSkp9u30F1zOX6c/Nwz6DIrq16+fUbduXcPT09O45pprjH79+hkHDx60b69ofcZiGIbhogwHAAAAABUSz0gBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAcBVqFOnTho7dqyryygXU6ZMUevWrV1dhrZs2SKLxaITJ05cst3GjRvVrFkz5efnl+p8FotFH3zwQamO4Ur9+/fXCy+84OoyAKDMEKQAoAIYOnSoLBaLHnrooSLbEhISZLFYNHToUPu6VatWafr06WVaw5IlSxQYGFimxyxOp06dZLFYZLFYVK1aNUVGRmrBggXlft7y8vjjj2vixIny8PAo1XEyMjLUvXv3MqrqgqFDh6p3795lesyLmThxop555hmdPHnyipwPAMobQQoAKoiwsDCtWLFCf/75p33duXPnlJycrPr16zu0rVmzpvz8/K50iWVmxIgRysjI0IEDB3TvvfcqISFBy5cvd3VZpn3++ec6dOiQ+vbtW+pjhYSEyMvLqwyqKnt5eXmXbXP99derYcOGevPNN69ARQBQ/ghSAFBB3HDDDQoLC9OqVavs61atWqX69eurTZs2Dm3/fmtfgwYNNGPGDN1///3y8/NT/fr19eqrr9q3F3eb2t69e2WxWPTzzz9ry5YtGjZsmE6ePGkfLZoyZYokKTc3V+PHj9c111wjHx8ftW/fXlu2bLEf58iRI+rZs6dq1KghHx8fNW/eXGvXrr3ktXp7eyskJETXXnutpkyZosaNG+ujjz4qtu2uXbvUtWtX1a5dWwEBAbrtttu0Z88e+/b7779fd9xxh8M+NptNQUFBWrx4sSSpoKBAM2fOVEREhKpXr65WrVrp3Xffddhn7dq1atKkiapXr67bb79dP//88yWvQZJWrFihrl27qlq1avZ1hbcmvv7666pfv758fX318MMPKz8/X7NmzVJISIiCgoL0zDPPOBzrr7f2/fzzz7JYLFq1apVuv/12eXt7q1WrVtq+fXuR8/zVnDlz1KBBA/v2pUuX6sMPP7S/poWv2y+//KJ7771XgYGBqlmzpnr16uVwvYUjWc8884xCQ0PVtGlTSdKCBQvUuHFjVatWTcHBwbr77rsdzt+zZ0+tWLHisj83AKgICFIAUIHcf//9SkpKsi+//vrrGjZsWIn2feGFF9SuXTv95z//0cMPP6yRI0cqLS2tRPvedNNNmjNnjvz9/ZWRkaGMjAyNHz9ekjRq1Cht375dK1as0DfffKN77rlH3bp1048//ijpwq2Hubm5+vTTT/Xtt9/queeek6+vr6nrrl69+kVHPU6dOqX4+Hh9/vnn2rFjhxo3bqwePXro1KlTkqQHHnhA69evV0ZGhn2fNWvW6OzZs+rXr58kaebMmXrjjTe0aNEi7d+/X+PGjdPgwYO1detWSReCRZ8+fdSzZ0/t3btXDzzwgP73f//3snV/9tlnateuXZH1hw4d0rp167R+/XotX75cixcvVlxcnI4ePaqtW7fqueee08SJE7Vz585LHv/JJ5/U+PHjtXfvXjVp0kQDBgzQ+fPnL1uXJI0fP1733nuvunXrZn9Nb7rpJtlsNsXGxsrPz0+fffaZvvjiC/n6+qpbt24Or8HGjRuVlpam1NRUrVmzRl999ZXGjBmjadOmKS0tTevXr9ett97qcM4bb7xRX375pXJzc0tUIwC4s6quLgAAUHKDBw/WhAkTdOTIEUnSF198oRUrVjiMAF1Mjx499PDDD0uSnnjiCc2ePVubN2+2jyZciqenpwICAmSxWBQSEmJfn56erqSkJKWnpys0NFTShTfo69evV1JSkmbMmKH09HT17dtXLVq0kCRde+21Jb7e/Px8LV++XN98840efPDBYtt07tzZYfnVV19VYGCgtm7dqjvuuEM33XSTmjZtqmXLlunxxx+XJCUlJemee+6Rr6+vcnNzNWPGDG3YsEFRUVH2Gj///HO98soruu2227Rw4UI1bNjQPllC06ZN7aHwUo4cOWL/ufxVQUGBXn/9dfn5+SkyMlK333670tLStHbtWlWpUkVNmzbVc889p82bN6t9+/YXPf748eMVFxcnSZo6daqaN2+ugwcP6rrrrrtkXZLk6+ur6tWrKzc31+E1ffPNN1VQUKB///vfslgs9p9XYGCgtmzZopiYGEmSj4+P/v3vf8vT01PShdFRHx8f3XHHHfLz81N4eHiRkdLQ0FDl5eUpMzNT4eHhl60RANwZQQoAKpA6deooLi5OS5YskWEYiouLU+3atUu0b8uWLe3fFwai7OzsUtXz7bffKj8/X02aNHFYn5ubq1q1akmSxowZo5EjRyolJUXR0dHq27evQy3FWbBggf79738rLy9PHh4eGjdunEaOHFls26ysLE2cOFFbtmxRdna28vPzdfbsWaWnp9vbPPDAA3r11Vf1+OOPKysrS+vWrdOmTZskSQcPHtTZs2fVtWtXh+Pm5eXZg8B3331XJNAUhq5L+fPPPx1u6yvUoEEDh2fYgoOD5eHhoSpVqjisu9zr89efY926dSVJ2dnZJQpSF/P111/r4MGDRZ6xO3funA4dOmRfbtGihT1ESVLXrl0VHh6ua6+9Vt26dVO3bt101113ydvb296mevXqkqSzZ886XR8AuAuCFABUMPfff79GjRolSZo/f36J97NarQ7LFotFBQUFkmR/A28Yhn27zWa77DFPnz4tDw8P7d69u8isdIW37z3wwAOKjY3Vxx9/rJSUFM2cOVMvvPCCRo8efdHjDho0SE8++aSqV6+uunXrOgSMv4uPj9fvv/+ul156SeHh4fLy8lJUVJTDbWhDhgzR//7v/2r79u3atm2bIiIidMstt9ivQZI+/vhjXXPNNQ7HLu3kDrVr19Yff/xRZH1xr8WlXp+L+es+haNHf31N//p6SiV/Tdu2bau33nqryLY6derYv/fx8XHY5ufnpz179mjLli1KSUnRpEmTNGXKFO3atcs+2+Px48eLHAcAKiqCFABUMIXPqlgsFsXGxpbJMQvf2GZkZKhGjRqSLkw28Veenp5FPgupTZs2ys/PV3Z2tj2YFCcsLEwPPfSQHnroIU2YMEGvvfbaJYNUQECAGjVqVKLav/jiCy1YsEA9evSQdOF5pt9++82hTa1atdS7d28lJSVp+/btDs+VRUZGysvLS+np6brtttuKPUezZs2KTHaxY8eOy9bWpk0bHThwoETXUdbq1KmjzMxMGYZhD1kleU1vuOEGvf322woKCpK/v7+pc1atWlXR0dGKjo7W5MmTFRgYqE2bNqlPnz6SpH379qlevXolHkUFAHfGZBMAUMF4eHjou+++04EDB0r92USFGjVqpLCwME2ZMkU//vijPv744yIfntqgQQOdPn1aGzdu1G+//aazZ8+qSZMmGjRokIYMGaJVq1bp8OHD+vLLLzVz5kx9/PHHkqSxY8fqk08+0eHDh7Vnzx5t3rxZzZo1K5O6Jalx48ZatmyZvvvuO+3cuVODBg2y30L2Vw888ICWLl2q7777TvHx8fb1fn5+Gj9+vMaNG6elS5fq0KFD2rNnj+bNm6elS5dKkh566CH9+OOPeuyxx5SWlqbk5GQtWbLksrXFxsbq888/L7NrNaNTp0769ddfNWvWLB06dEjz58/XunXrHNo0aNBA33zzjdLS0vTbb7/JZrNp0KBBql27tnr16qXPPvtMhw8f1pYtWzRmzBgdPXr0oudbs2aN5s6dq7179+rIkSN64403VFBQ4PAM3meffWZ/xgoAKjqCFABUQP7+/qZHCy7FarVq+fLl+v7779WyZUs999xzevrppx3a3HTTTXrooYfUr18/1alTR7NmzZJ0YSKCIUOG6NFHH1XTpk3Vu3dv7dq1y/7ZVvn5+UpISFCzZs3UrVs3NWnSpEw/YHfx4sX6448/dMMNN+i+++7TmDFjFBQUVKRddHS06tatq9jY2CITQEyfPl1PPfWUZs6caa/z448/VkREhCSpfv36eu+99/TBBx+oVatWWrRokWbMmHHZ2gYNGqT9+/eXeHbEstSsWTMtWLBA8+fPV6tWrfTll1/aZ1osNGLECDVt2lTt2rVTnTp19MUXX8jb21uffvqp6tevrz59+qhZs2YaPny4zp07d8k+FxgYqFWrVqlz585q1qyZFi1apOXLl6t58+aSLjxj9cEHH2jEiBHlet0AcKVYjL/fQA0AwFXo9OnTuuaaa5SUlGS/1exKeOyxx5STk6NXXnnlip3THS1cuFDvv/++UlJSXF0KAJQJRqQAAFe1goICZWdna/r06QoMDNSdd955Rc//5JNPKjw8/LITR1ztrFar5s2b5+oyAKDMMCIFALiq/fzzz4qIiFC9evW0ZMkSdenSxdUlAQCuAgQpAAAAADCJW/sAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJv0/QNJM4jYqLbIAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.hist(df['minuters'], bins=50, alpha=0.7, color='blue')\n",
        "plt.xlabel('Minutes Played (minuters)')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of Minutes Played')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "ue9agB2Rzn3N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee89a8fa-b48d-4573-e336-01803a408c45"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The players mean fp: 13.74199124222171  and median fp: 12.5\n"
          ]
        }
      ],
      "source": [
        "print(\"The players mean fp:\", np.mean(df['fp']), \" and median fp:\", np.median(df['fp']))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.1 Basic LSTM"
      ],
      "metadata": {
        "id": "2kSmBvA3c3zJ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "jpgjT71RBStG"
      },
      "outputs": [],
      "source": [
        "class BaselineLSTM(nn.Module):\n",
        "    def __init__(self, input_size, hidden_size, output_size, num_layers=1):\n",
        "        super(BaselineLSTM, self).__init__()\n",
        "        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)\n",
        "        self.fc = nn.Linear(hidden_size, output_size)\n",
        "\n",
        "    def forward(self, x):\n",
        "        # x: (batch_size, seq_length, input_size)\n",
        "        output, (h_n, c_n) = self.lstm(x)\n",
        "        # Take the output of the last time step\n",
        "        output = self.fc(h_n[-1])\n",
        "        return output\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "5wOU8aL6dYnd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "67b16dc2-f7ed-45e7-e40c-51fdbe2772a4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13271"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "len(train_dataset)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nGVBeeMIsazl"
      },
      "source": [
        "## 2.2 Training the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "zM2xxpkjE9xM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04d5e75e-d1df-4217-c7a5-c89e708f20c8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  0%|          | 0/415 [00:00<?, ?it/s]/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n",
            "100%|██████████| 415/415 [00:38<00:00, 10.83it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20, Loss: 97.42247059833572\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 415/415 [00:39<00:00, 10.44it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 2/20, Loss: 46.422968613957785\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 415/415 [00:39<00:00, 10.60it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 3/20, Loss: 43.991927048097175\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 415/415 [00:40<00:00, 10.27it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 4/20, Loss: 42.3280706704381\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 415/415 [00:46<00:00,  8.89it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 5/20, Loss: 40.76441294199013\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 415/415 [00:39<00:00, 10.64it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 6/20, Loss: 39.296754515314674\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 415/415 [00:38<00:00, 10.76it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 7/20, Loss: 37.81199268892587\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 415/415 [00:38<00:00, 10.78it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 8/20, Loss: 36.3295481072851\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 415/415 [00:39<00:00, 10.45it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 9/20, Loss: 34.54615431406412\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 415/415 [00:38<00:00, 10.66it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 10/20, Loss: 32.907549265206576\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 415/415 [00:38<00:00, 10.78it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 11/20, Loss: 31.13475422227239\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 415/415 [00:38<00:00, 10.82it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 12/20, Loss: 29.635057483810975\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 415/415 [00:38<00:00, 10.68it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 13/20, Loss: 27.910996340556316\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 415/415 [00:39<00:00, 10.63it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 14/20, Loss: 26.25673875349114\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 415/415 [00:41<00:00, 10.10it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 15/20, Loss: 24.878888217512383\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 415/415 [00:38<00:00, 10.71it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 16/20, Loss: 23.40291166994945\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 415/415 [00:38<00:00, 10.72it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 17/20, Loss: 22.03585655832865\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 415/415 [00:39<00:00, 10.57it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 18/20, Loss: 20.85615811750113\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 415/415 [00:39<00:00, 10.64it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 19/20, Loss: 19.475222573797385\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 415/415 [00:41<00:00,  9.93it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 20/20, Loss: 18.41221365296697\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "def train(model, dataloader, epochs, learning_rate, device, grad_clip=None):\n",
        "    model.to(device)\n",
        "    criterion = nn.MSELoss()\n",
        "    optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
        "\n",
        "    for epoch in range(epochs):\n",
        "        model.train()\n",
        "        total_loss = 0\n",
        "        for features, targets in tqdm(dataloader):\n",
        "            features, targets = features.to(device), targets.to(device)\n",
        "            optimizer.zero_grad()\n",
        "            outputs = model(features)\n",
        "            loss = criterion(outputs.squeeze(), targets)\n",
        "            loss.backward()\n",
        "            if grad_clip is not None:\n",
        "                torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)\n",
        "            optimizer.step()\n",
        "            total_loss += loss.item()\n",
        "\n",
        "        print(f'Epoch {epoch + 1}/{epochs}, Loss: {total_loss / len(dataloader)}')\n",
        "\n",
        "# Example usage\n",
        "input_size = dataset[0][0].shape[1]  # Number of features\n",
        "hidden_size = 64  # Adjust the size of the hidden layer as needed\n",
        "output_size = 1  # We're predicting a single value (fantasy points)\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(device)\n",
        "lstm_model = BaselineLSTM(input_size, hidden_size, output_size)\n",
        "train(lstm_model, train_dataloader, epochs=20, learning_rate=0.001, device=device, grad_clip=1.0)\n",
        "torch.save(lstm_model.state_dict(), \"lstm_model.pth\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QQSrl1xjsewy"
      },
      "source": [
        "## Testing the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "_2JX6q7-plNK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 982
        },
        "outputId": "f7d5599a-6f16-4938-ec8e-e42085bb5a2f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r  0%|          | 0/104 [00:00<?, ?it/s]/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n",
            " 19%|█▉        | 20/104 [00:02<00:08, 10.47it/s]/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fde495901f0>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fde495901f0>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "100%|██████████| 104/104 [00:11<00:00,  9.39it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy (within ±5 FP): 53.86%\n",
            "Mean Squared Error Loss: 53.5041\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 960x480 with 2 Axes>"
            ],
            "image/png": 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DBqCmpgY//fQTnn76aQQEBNDPluDgYNa/b6jfBlVVVTh8+DB+++03DB48GF5eXrRNu3btsGDBAnppsRTP3wMHDuDixYsmzzPjOqA6cMavvljykcuzydqz2hrmroNBgwbh888/Z3xnro0BQGVlJb799lvGO+WffPIJQkNDsWjRItrX3r17Y9q0adi6dSvmzJlD25r7Pbd7925ERUUxtFGTJQT7g3RmWylyuRzh4eHo3bs3AMMP3aCgIMjlcvpH+MWLF1FdXS36rnH19fXYsmUL9u/fjzt37qCuro7+261btzh1Zjt06IC+ffviwIEDtB+nTp1CeXk53XH39/dHWFgYVq1ahdLSUvTr18/qpg7GbNy4ERs3bqQ/d+vWDZ9//jmCgoLo70JCQhg3PsCwhPahhx6Cn58fY9RwwIABWLlyJbRaLby8vHD58mVMnjyZ0XG777776OVjlrClvi5evAitVouEhASGtg4dOiAiIgLnz5/Hk08+iaysLDQ0NGDEiBGM9CNHjuTUmV2wYAFj5JMaxb179y7S0tJw+vRpFBcXM7SUlZUxHka9e/dmzHx27doVAFBYWMhKw44dO7Bz507cvn0bNTU19Pe3bt0CwK6d9OvXD126dMGuXbvozuyuXbvQrVs31j9+EhMT6f93d3fHk08+iRdeeAG3bt1CaWmpyRLzPn36oGPHjjh37pzVzmzTh2u3bt1Y7Rrds2dPVFZW0iPdffr0YTWCTiAQhKPpfRIwdCSMf2ibw5br9/z58xg8eDDD3tfXFw8//DBOnDgBACgqKkJRURE9UEYRHx9vsRM0ZMgQk++0Wi1++OEHHDt2DIWFhdDpdPTf8vLyGJ3Z8PBwxqovailo00G2zp074/fff0djY6PF91DPnTsHV1dXjB49mvH9mDFjsHfvXly8eBEPP/yw2bTWMO7cUGUYDyoAhjgZa5Pi+atUKunVRLbC9dlk67Pa3HVgrj2ba2OA4feT8W+H6upqXL16FVOnTmW019DQUMTGxtJL8CnM/Z7r2bMnduzYgWXLliE+Ph69e/dmDFgQ7AvSmW2FXL58GTdu3MDkyZMZS4KGDh2K7du349atW+jcuTPKy8sBwOQmIjSrVq3C9u3bMX36dPTu3Rs+Pj4oKirChx9+yOjYsuWRRx7BokWLkJ+fj9DQUOzfvx+dOnWiO+4uLi744osvkJaWhlWrVqGiogKhoaF45plnkJKSYjX/pKQkpKSkQCaTITg4mPHApTC3q2RpaSn279+P/fv3m823oqICtbW10Ol0aNeuncnfzX1njC31VVpaCoD57qQx1IOjpKTErBYuu2gChodZ0w0dGhoa8N577+Hu3buYMWMGIiIi4OnpiYyMDPzwww8mbaHpEl5qaTCbNkMtx37qqaeQmpqKNm3aoKGhAXPmzKHTs20nKSkp+Oabb/Dqq6+iuroap0+fxmuvvcY6FsuXL4dMJoOfnx86duwINzfDbbeyshKA+dgGBgbSf28Of39/xmd3d3fU19dbTdevXz/861//wi+//IIPPvgAgGEFxSuvvIKoqCir6QkEgu2Yu0+yWWZsy/VbUVHBeOeRwviec/fuXQAw2XRKJpOZfR4CMJvnokWL8Mcff2DmzJmIjo6Gt7c3srKysHTpUqv3e2rJr7nv9Xp9s68MUa8hNV02TN1rbVkqSnW8vL29ERISYnajoKaxkOL5W1FRwRhwtwWuzyZbntWA+evAHObamDmdGo0GjY2NFvXfuXOn2fSAYRC6rq4O6enp2LFjB2QyGQYNGoRXXnnF6sQDQXpIZ7YVIpfLARjeg9m0aZPJ3/fv34/Zs2fTD6WioiJ6JI0LHh4eJj+cqQ6XMYcPH8YjjzzC2A6+urqac3kUw4YNw9KlS3HgwAE8/vjjOHHiBL0khyIsLAzvvvsuGhsbkZ2dje3bt+Orr75CSEgIHnrooWbzb9++PXr27NmsjbkRYX9/f9x///30Jkzm8pXJZHBzc6MfbsaUlpYyNoJoii31RaWdN2+e2bTUzCl1Uy8tLWW8E0M9ZG3h9u3buHz5Mt59912MGTOG/v7333+3Oe+mHDp0CA8++CBeeeUV+jvq3Wdj2LSTRx55BKtWrYJcLkdlZSU8PT1ZL1kHDCO85n50UT8AzMW2pKRE9KOVhg8fjuHDh6O6uhpnz57Ft99+i3nz5mHr1q0m7yoRCAT7gu/16+/vb/GeQ92TqE5DWVkZw0av15t9xgOmz8S6ujocP34cM2fOZOw5cO3aNVb+2UKbNm1QWVmJ+vp6RoeW8rvpICAX2HS8msZCiudv27Ztcf36dat2bGjpZ5MlLM3EN/3ez88PLi4uZn9nGbfz5vJ1cXHBhAkTMGHCBFRWVuL06dP45ptv8PHHH+Obb76xwQuCGJBfLK2M+vp6HDx4EDExMViyZInJv+joaBw4cACNjY2IjY2Ft7c3du/ebTE/6kFAbWxgTMeOHZGTk8P4zniXVoqamhp6NooiPT2dh3cGfHx8EB8fD4VCgaNHj6K+vp7ROTLGxcUF0dHRdKdGqJu9OQYOHIhr164hMjISPXv2NPnn4eEBmUyGnj174tixY4yDy//66y8UFBQ0m78t9UXNiN++fdusNmpJV0xMDFxdXenziSkOHz7MJRRmoZb6GnfsdDodFAoF7zzd3d3Nts3a2lpOba65duLr64uEhATs2rUL6enpGDVqFHx9fXlrpujcuTPatWtnEluVSoU7d+4wdrDmS3PXL4W3tzcGDx6M8ePH4+7du2SDCwLBgbB0/Vq6N/bt2xcnT55EVVUV/V1VVRVOnDhB720RHByM4OBgHD16lJE2IyOD1cwxYOjMNjQ0mNyH9+3bx8k/PvTt2xcNDQ0mzzGFQgF3d3d6FZdUSPH8jYuLQ0lJSbODw2yeB4A0zyYx8fb2Ro8ePXDkyBFGey0oKMDFixc562/Tpg1GjRqFESNGmPzmJdgHZGa2lZGZmYmKigq8/PLLZi/Y8ePHY8mSJTh79iweeOABvPDCC1i2bBk+/PBDJCQkwMfHB2q1Gh4eHnj88cfRrl07+Pv749ChQ+jWrRu9tKZt27YYNWoUFi9ejOXLl2PQoEHIzs6mZ4WNGThwIORyOT2i+dtvv+HixYs2+fnII4/g4MGDSEtLQ2xsLONdm+zsbPzvf//DyJEj0alTJ+j1esjlcshkMjzwwAM2ldscs2bNwssvv4zXX38djz32GEJCQqDRaHD9+nXcvn2b3vhh1qxZeOutt/DBBx8gOTkZ5eXlSEtLs7qUyMfHx6b6eumll7B06VKUlZVh4MCB8PPzQ1FREc6dO4d+/fohISEBERERGD16NNauXYuGhgb06tULSqUSJ0+etDk+Xbp0QceOHbFmzRrIZDLIZDJs27bNpjwjIyOxY8cOHDp0CJ06dYK3tzciIiIwcOBAbNq0CT/88ANiYmLwxx9/4NixY4y0XNpJSkoKdu3aBQCCHV0jk8kwa9Ys/Pe//8Vnn32GMWPGoKioCGvWrEF4eDiSkpJsLiM8PBwymQzp6elo06YNPDw80LlzZ2zevBmlpaV44IEH0L59exQVFeGXX35BdHS0Q51nSSA4I99//73V69fSvXHatGk4ceIE3nzzTUyaNAkuLi7YtGkTampqMH36dACGd0FnzJiBL7/8El988QWGDx+O/Px8bNq0Cb6+vqzOTPXz88N9992HrVu3IjAwEG3btkV6ejqKi4vFDA0A4KGHHkKfPn2wZMkSlJeXIzIyEidPnsSePXswZcoUi0ulxcLX11f05++YMWOwZ88eLFiwAFOnTkVMTAyqqqpw+vRpPPnkk4iIiECXLl0AAFu3bsXAgQPpwfWmSPFsMkatVpud8be0qokNzz33HObPn493330XKSkpqK6uRlpaGnx9ffH0009bTf/ll1/Cx8cHvXv3RkBAAHJzc3HgwAHExcXx0kMQF9KZbWXI5XL4+PiYbCBAMWrUKKxYsQJyuRwPPPAAJk6ciMDAQGzevBmfffYZ3NzcEBERwXiovfXWW1i9ejX++c9/0lvkjx07FomJiSgsLER6ejp27dqFPn364JNPPsGzzz7LKPO1115DY2Mj1qxZA8DwoPnggw/w8ssv8/azf//+CAwMRHFxMa2VIjAwEB06dMBPP/2EoqIieHh4oGvXrli4cKHV5cO20LFjR3z77bdIS0vDmjVrUFZWBn9/f3Tt2pWxCVD//v3x3nvvYd26dfjoo48QFhaGOXPm4Oeff7Zahi31NWHCBHTo0AFbtmzBwYMHodfrERQUhD59+jA2P3jjjTfg7e2NrVu3or6+Hg8++CDee+89Tu+JmsPd3R2ffvopli5dis8//xxt2rRBUlISOnbsSJ+Jy5XJkyfj1q1b+PLLL1FdXU2fpTh9+nRoNBps27YNdXV16Nu3LxYvXsxYjs6lnURFRaFz587w8fERdInV+PHj4eXlhc2bN+P9999nnOVn7VgeNrRt2xavvfYaNm/ejLlz56KhoQFLlixBTEwMfvnlFyxfvhyVlZUICAhAXFwcnnvuOQG8IhAIYsLm+rV0b4yKisKSJUuwZs0a/Pvf/6bz++qrrxjPgUcffRTV1dXYtm0bDhw4gK5du+Ldd9/Fe++9x3qzqQ8++ABLlizB0qVL4enpiREjRuDVV1/Fu+++K2xAmuDq6orPP/8cq1evxqZNm+hzZl955RWTM8OlQuznr5ubG7744gusW7cOu3fvxrp16+Dv74/Y2Fh6We3gwYORkpKCHTt2YP369WhsbLQ46yv2s8mYf/3rX2a///XXX3kPPAwcOBCff/451q1bh48//hju7u7o27cvXnrpJVbvFsfGxmLfvn04cOAAtFot2rdvjzFjxmDmzJm89BDExeXw4cONLS2CQCAQ7JmbN29i5syZePPNN/Hoo4+2tByCk7Ju3Trs378f5eXlkMlk6NGjB1566SXGj+GRI0fCw8OD8d7k8uXL0a1bNwCGI1LS0tKwZ88eaLVa9OjRA3PnzuW1bwLBubh8+TJSU1Mxf/58+vQAAoFAaGnIzCyBQCBYoKioCHl5eVi7di3at2/PaeMnAkFoRo4ciccffxxt2rRBfX09tm/fjrfffhs//fQTYznewoUL0b9/f7N5bNmyBenp6Vi8eDE6deqE9evX4+2338b69esFn3EhOC75+fn49ddf6bNhb9y4gY0bNyI0NBTDhg1raXkEAoFAQzqzBAKBYIE9e/Zg/fr1CA8Px3vvvQdPT8+WlkRwYqiNYgDDDKurqytKS0vppaZs2LFjB55++ml6pva5557Dnj178Ntvv5HZNgKNp6cnrl+/jv3799NH3Tz44IN48cUXyXmbBALBriCdWQKBQLDAzJkzyTsyBLvixIkT+Oyzz6DVauHi4oInn3zSpCP72WefQa/Xo2PHjpgwYQKSk5MBGM5fLCgoQExMDG0rk8nQvXt3XL16lXRmCTSBgYFYvHhxS8sgEAgEqzh0Z7ahoQF3796Ft7c3q931CAQCgUDgQmNjI6qrq9G+fXu7OH938ODB2L17NyoqKiCXyxEcHMz4+5dffonY2Fi4urrizJkzdMc2JSWFPo6l6QY+fn5+jKNaKMgzlkAgEAhiIsQz1qE7s3fv3mW1xTaBQCAQCLawdetWk45jS+Lv748nnngCEyZMQHh4OL0JlPG7soMGDcITTzyBAwcOICUlBT4+PgAMM7TGaDQaszt83r17F6+//jo8PDzo74KCgmjb48ePm+QFGDrH8fHxZnWr1WpkZ2eb/Vt8fLzZnXI1Gg2OHz9uNk1UVBRjAyxjiD6ij+gj+og++9NXXFyM8+fPo76+nv7OlmesQ3dmqc0qbt26BX9/f1Zp3nrrLXzxxReC2/Kxf/fdd7Fw4cIW1+IsfnK1J36ah4ufYmshftpuT/xsPu+Kigp07tzZLjdHamxshE6nQ15ensUfNC4uLmhsNBxa4Ofnh5CQEFy6dAm9e/cGAOj1eqjVaowZM8Ykrbe3N/Lz8zFw4EC4ud37uVBZWQkAuP/++03SBAcHY/Xq1Xjvvfc41SXAvf5bIh3XdmRLWXzTSR1HPjEh8RCmLFvSbd26VfQ0gCEee/fu5ZyOb13/8ccfkpTFNx25hzAR4hnr0J1ZatmTv78/685sZGSkKLZ87D08POxCi7P4ydWe+GkeLn6KrYX4abu93ftZqQb8ogAzy1xF8fPOHaChwSRve1hmu23bNowaNQqBgYEoKyvD6tWr4ebmhtjYWADAlStX0NjYiG7dusHV1RV//PEHfv75Z8yYMYPOIyUlBVu3bsWDDz6IsLAwbNiwAW5ubhg6dKhJeZTPkydPZrXBVG1tLX788Uf4+PhwrnuAe3tpiXRcrxdbyuKbTuo48okJiYcwZTlCHKW8ZhwhjuQeYh5bnrEt/wKQjYSGhiI1NRVyuRwAoNPpIJfLodPpaBuVSgW1Wg0AmD9/PjQaDRQKBSMfpVKJ3Nxc+nNxcbHJwz0jIwPFxcX059zcXCiVSvrz/PnzoVAoGNP6arUaKpWK/mxNHwCz+saMGWOiLyMjw6K++fPnm+gDYFbf+PHjOen7xz/+wSp+zekDTOMHAAMGDOAUv/nz57OKH6Vv/vz5rPVNmzaNVfz41O+AAQPM6qNoqm/+/Pms4kfpo/xko2/8+PGs42dJH2C5fiktxvoomup76623OF0fVN5s9A0dOpR1/Czpa65+KS1s4vfCCy9wuj6ovNnqe+utt0z0Udhav5QWNvF76qmnOF0fJvfnO4eB9AegPPqTWX3GbYvL9UFhEr+bNyGfOxe6MWMwf/ZsyOVypKamwl44c+YMnn/+eSQlJWH27NkoLS3Ff/7zH7Rv3x6AIS4LFy7EhAkTkJKSgpUrV+L555/HE088QefxzDPPIDExEW+++SZSUlJw4cIFLFq0qNlRcU9PT9b/KIzrhi180rREOinLcoQ4SlkWiUfLpZMyHnzLc4Q48sUR6lrqNkLhcvjw4cYWKVkAtFotkpOTUV5eznskoCWRy+VITExsaRmiQ/xsXRA/Wxd26+dtOZDxBBD3P6DbTJuzs+rnzZvAqFFAdjbg7Q3I5cDQoaioqEDbtm2xe/du+Pr62qzDkaCesStWrGA9M7t27Vqkp6fT7+e2Nuz2emlBSEyYkHgwIfFgQuLBRIhnrEMvM3Z0nOWHEfGzdUH8bF3YpZ95u4GMZ4CHVgGRUwTJslk/r183dGRzcgA/P2DPHsDMsltnxNJGIZZoaGgwuzOyJdzc3BgbTNk7dnm9tDAkJkxIPJiQeDAh8RAeh19mzJWmy86EsuVjr9Vq7UKLs/jJ1Z74aR4ufoqthfhpu73d+XnrF0NHdvB6qx1ZQfy8cgUYNszQkfX3B/bvB4YN46y9tWJu90pL6HQ6XLt2DRMnTsTs2bORlJRk9d/UqVNRV1cHgHt7oZAyHdfrxZay+KaTOo58YkLiIUxZjhBHKa8ZR4gjuYcIj9PNzIaEhIhiy8dezLyJn7bbEz+FwV7aorP4ycderLw56+7YAbj8JhC/GQgfb91eCD937wZyc4F27Qwd2bg44fJ2MvR6PRobGzFlyhS4uroy3qE1B7VhlE6ng4eHB++YS51OyrL4pCPxaLmy+OIIbV/qe6Ij1HVrbSOOEA9jnK4zGx4eLootH3sx8yZ+2m5P/BQGe2mLzuInH3ux8uasu3MEEH4YcGG3aEgQP//v/4CaGmDcOKBfP2HzdlI8PDx4vTPLN+ZSp5OyLD7pSDxariy+OELbl/qe6Ah13VrbiCPEw5gW68xevHgRa9aswaVLl+Dq6orIyEgsW7YMrq6uyM7OxrJly3DlyhX4+voiOTkZM2bMsIujEQgEAoEgIiw7ssKV5wK8+660ZRIIBAKBQBCEFnln9uLFi5g3bx4SExPxyy+/YMeOHXjllVfg4uKCqqoqvP3224iNjcWvv/6KxYsXY8+ePdi2bZsgZRsf3SCkLR97MfMmftpuT/wUBntpi87iJx97sfJ2Fj8JplDvwXKBb8ylTidlWXzSkXi0XFl8cYS2L/U90RHqurW2EUeIhzEt0pn99ttvMW7cOCQmJsLLywsymQz33XcfXFxccOzYMTQ0NOC5556Dp6cnunXrhmeeeQa//vqrIGVfunRJFFs+9lx2NBNTi7P4ydWe+Gkerjvx2UtbdBY/udq3qJ8Net55c7X39fUFOHSwuGpprRifyyt2Or4xlzId351IpdQodRz5xITEQ5iyHCGOUl4zjhBHcg8RHsk7szU1Nbh48SJcXV3x8ssvIyUlBS+++CKOHj0KAMjOzkZ0dDRkMhmdplevXrh9+zbvHcCMGTJkiCi2Yts7at5Ei/R5Ey3S50208LBVfQacel46LbW1QK9eQFaW4Hm3Zq5du4YtW7bg3LlzAAxH7xQWFqKhoYG2qaysZDyf9Xo99HrmQEVZWRmqq6vpz3V1dSgvLzcpz3hkPzc312R3TIVCwegoq9Vqxhm4Op0OcrkcOp2O/k6lUkGtVtOfNRoNFAoFo46VSiVyc3MZOjIyMhhlZ2RkoFevXpz1qVQquiwu+oB77ZCtvuLiYjoNF31UWVz1Uem46KPScNUHAIMGDZJEHwBERkZy1qfT6aDVajnpAwAvLy/O+nJzc+Hl5cVZn1wux6BBgzjpUyqViIyM5KxPqVQyrjO2+nQ6HZ2Orb7c3Fw6DRd9wL3rjIs+Kh0XfVQarvqodFz1AUBAQAAnfYCh38VVH2Do43HRJ5fLkZqaCltxOXz4cKPNuXCgqKgITz/9NNq1a4eFCxeie/fuOH78OBYsWICvvvoKe/bsQXV1NT766CM6zY0bNzBz5kxs3boVwcHB9PfUge7l5eXw9/eX0g0CgUAg8KGxETj/IaD+FhilANrdL36Z+/YBEycaNnoaORI4dIh1UiEOdHdUqGfsihUrGB1GS1RUVODjjz/GggULWG0AVVtbi7Vr1yI9PZ3XhlEEAoFAcGyEeMZKvgGUt7c3ACAxMZEe4Rw2bBj69euHjIwM+Pr6oqioiJGmsrISAMw+7EJDQzF9+nS4uhommfv374+4v49WiI+PN3tGnkajwfHjx83qi4qKQnR0tNm/ZWRkmJ0d9vX1tTiKr1arkZ2dbfZvRB/RR/QRfU6lr7ERKMoAKrIQP3Ef/Mx0ZAXXd/Mmhrz6qmGJce/ewKZNlvX9jVKpRH5+Pjw8PHi9+0kgEAgEAkEaJJ+ZBYCpU6di+PDhePHFF+nv3nrrLURHR6NLly749ttvsW3bNnqp8c8//4xffvkFGzduZOTDZ2Y2IyOD9fIxLrZ87LkgphZn8ZOrPfFTGOylLTqLn3zsucDbz8ZG4MxcIPcXYNQhwL+7TXmzst+2DZg8GdDpDMfuHDgABAVxypvMzPKbma2pqUFgYGCz9k1nZvm2W6nT8UFKjSQeLVcWXxyh7UsZD77lOUIc+eIIdc0nnRDP2BbZAGrixInYt28f1Go1GhoacPz4cZw7dw5Dhw7FsGHD4OrqirVr16K2thbXr1/H1q1bkZKSIkjZxu+7CGnLx17MvImfttsTP4XBXtqis/jJx16svBm26u+AvJ1AwjGzHVmueVu1v3ULmDLF0JGNiwMOHmTdkeWjhcDE3KoAa/CNudTppCyLTzoSj5Yriy+O0Palvic6Ql231jbiCPEwpkVmZgFg48aN2LFjBzQaDcLDwzF9+nS6N5+dnY2lS5fiypUr8PHxwYQJE8yeM0vemSUQCAQHQV8L1N4FfMKkKzMtDVi9GtizB2jbllcWZGaWvDNLIBAIBHFwyHdmKaZOnYqpU6ea/VtUVBSWLVsmsSLpUavVFt//ak0QP1sXxM/WhWR+yjyl7cgCwMyZwLRpgEzmNPUpNFFRUS0twa4g7cgUEhMmJB5MSDyYkHgIT4ssM25JjLeeFtKWj72ljVuk1uIsfnK1J36ah4ufYmshftpu3+r9/HvvBTH9bM3w/dFlfAwPW/jGXMp0XNuRLWXxTSd1HPnEhMRDmLIcIY5SXjOOEEdyDxEep+vMFhQUiGLLx17MvImfttsTP4XBXtqis/jJx16svJ3FT4IptbW1nNPwjbnU6aQsi086Eo+WK4svjtD2pb4nOkJdt9Y24gjxMMbpOrPUsT1C2/KxFzNv4qft9sRPYbCXtugsfvKxFzRvXRVw5g2grkw6Pz//HGhyCLytiN1eWjts3rFtCt+YS51OyrL4pCPxaLmy+OIIbV/qe6Ij1HVrbSOOEA9jWuydWQKBQCC0MnRa4Oh4oKEecJFgrLSxEfjgA+CzzwBvb+DUKSA2VvxyCQQCgUAg2AVONzNLIBAIBBGorwAOjzX8/8h9gLvIO8w3NgJvvWXoyALAk08CMTHilumEVFdXY8uWLTh37hwAoKGhAYWFhWhoaKBtKisrodVq6c96vR5FRUWMfMrKyhjv0dbV1aG8vJxhk5GRgeLiYvpzbm4ulEolw0ahUECj0dCf1Wo1VCoV/Vmn00Eul0On09HfqVQqqNVq+rNGo4GiyUy+UqlkvO9VXFyMjIwME33GPtijPnuPnzPoy8nJsWt99h4/os959MnlcqSmpsJWWuxoHiHgczSPQqFAQkKC4LZ87OVyORITE1tci7P4ydWe+GkeLn6KrYX4abu9IH7WlRo6sh7tgKHbATdvcXU3NACvvQbFpUtIOHgQeP55YOVKerMnc/D109mP5vn666+RkpLC+WgerVaL4ODgZu1ra2uxZs0abN++HT4+Pjh27BiGDRvWbBo3Nzd4eHgwvuPazmxJx7Ud2VIW33RSlgXwiwmJhzBlOUIcpbxmHCGO5B7CRIhnrNN1ZjUaDevD3LnYim3vqHkTLdLnTbRIn7dTa6mvBBTDAZ9wYMhWQOYlvu7XXweWLYPGzw9+M2YAy5YBrs0vNOKrxdk7s5MmTcLixYs5d2Y9PDzg5tb8m0xarRYfffQRunXrBldXV3h4eKCurq7ZNB06dMDGjRsZHVqudWtLOinL4puOaGy5svimIxqFSUc0CpNOyrKEeMY6/DLj0NBQpKamQi6XA7A+xe7n58d6iv3s2bMMG2tT7H5+fpym2KkKZ7ME4NKlS5yWAPj5+bFeApCTk2NWH0VTfVQ+xjS3RIHyk80ShczMTE5LFPz8/DgtoaC0sFlCUVZWxmkJhZ+fH+slHpmZmWb1UTTV5+fnx2mJB+UnmyUoOTk5nJag+Pn5cVqCYnxjs3Z9eHl5cVoiQ+XNZonM2bNnOS2R8fPz47SEh9LC5v5SU1PDaQkPlTfb+4uX170OpbXrw8/Pj9MSI0oLrc/ND+jxKjBkGxSHMxj6CgoKOC2BYn1/TkxExvDh8HvxReDrrwFXV1Huz0ItgXJkjOPFBWsdWcCwHLmxsRFTpkzBrFmzMHXqVMyaNcvivylTpqCwsJDRfgDw+tHFN52UZfFNRzS2XFl80xGNwqQjGoVJJ7VGW3G6mVkCgUAgtAKuXAG6dwdcXEQtxtlnZpOTk7FixQrOM7M+Pj6C29fW1mLt2rVIT09nZU8gEAgE+4bMzPKg6QyjULZ87MXMm/hpuz3xUxjspS06i5987MXKW1TdPXpAzfPwecG1EEww3hBKzDQA/7qSso6l1Eji0XJl8UVqjVLGkS+OUNettY04QjyMcbrObE1NjSi2fOzFzJv4abs98VMY7KUtOouffOzFyttZ/CSYYrzbsZhpAP51JWUdS6mRxKPlyuKL1BqljCNfHKGuW2sbcYR4GEOWGRMIBAKBHRVXAVd3wC9SmvKqqoAjR4Bx46QpzwxkmTFZZkwgEAgEcSDLjB0cvhtrOBrEz9YF8bN1wdrP8izg4HDg5hZxBVFoNIZObHIysGGDANmx9LPqNqB8DdA3v6uus9BSG3rYK85yX+ACiQkTEg8mJB5MSDyEx+k6s013QRTKlo/98ePH7UKLs/jJ1Z74aR4ufoqthfhpuz0rP0vPG47f6TYbuu5viKKDYV9eDiQmAkePUn+wOX9WfmpvGfzUVUKnd9hFS4ISHx/PK52Uy4y5tjNb0nG9L9hSFt90UpYF8IsJiYcwZTlCHKW8ZhwhjuQeIjxO15k9ePCgKLZ87MXMm/hpuz3xUxjspS06i5987Jul5A/g4Eig52tA3wU4eOiQaDoOHjwIlJQACQnA778bzo7dsAGYNUuQ/JtFcx1QDANCRgEPrcHBw0eEy9sJMT4mScw0AP92IPY9QYiy+KQj8Wi5svgitUYp48gXR6jr1tpGHCEexjhdZ3b06NGi2PKxFzNv4qft9sRPYbCXtugsfvKxt0jxSeDgaKD3fCD2fc55c9Z9//3A6NGAUgm4uQGbNwNTpwqWv0UqrhpmZDtNAAasBFxcRW8vjkJ1dTW2bNmCc+fOATDMnhYWFjJmUSsrKxk7Eev1epN8ysrKUF1dTX+uq6szWW4nk8lQV3dveXd1dTXKysoYNt26dTM5Kzg0NJT+zPasb4VCwahjtmctG/vA9ix3lUpFl8VFH3CvjXM5C5pKw0UfVRZXfRRc9FFlcdUHAMOHD+esb/To0Zz1AUCvXr0469PpdMjJyeEcv3bt2nHWl5ubi3bt2nHWJ5fLMXz4cE76lEolevXqxVkfn/hR9Uu1Yy7tj0rDVR+Vjos+Kp0Y10dTfVRZXPUBQGhoKGd9ffv25VW/rq6unPQJdZY72QCqBZHL5UhMTGxpGaJD/GxdED9bF836eWUF0KgHev5DGjFXrgDDhhlmZ3/6CUhJESxri35qcoADDwORzwL9FpmcW+vsG0B9/fXXSElJIRtA/Y2z3Be4QGLChMSDCYkHExIPJkI8Y90E1kQgEAiE1kKPVyQurwdw8CBw6xYwdqw0ZXqHAf2+ACKnmHRkCQQCgUAg2DcOv8w4NDQUqampkMvlAKwv4VGpVKyXAPz8888MG2tT7CqVitcSADZLFHbu3MlpiYJKpWK9xCM9PZ2TPqVSKcoSCgDYvHkzp/ipVCpOSzyovNjoO3r0qE1LZCjM6du8ebNZfRRN9alUKk5LeChNbPSlp6eLukTGuHxr8Tt37hyn64NKy0bfzz//LOgSqKb6KDs28fv99985XR9U3mz1UUtCjfVR2Fq/VHls4qdQKDhdHyqVCpouXaBwY46zWtJnnDfvJW5ZV4CuUwEXF4Y+lUol2BIoZ6SyslKSNAAY7UCKdFKWxScdiUfLlcUXqTVKGUe+OEJdt9Y24gjxMMbplhmr1WpER0cLbiu2vaPmTbRInzfRIn3eRIv0eUulxdmXGb/++ut48803OS8zbmxstBqvpsuMtVpts2ksLTPmWre2pJOyLL7piMaWK4tvOqJRmHREozDppCxLiGes03VmCQQCgWCG+grAXcL7aFEREBRk90t7nb0zm5ycjBUrVpB3ZgkEAoEgOEI8Yx1+mTGBQCAQbCRnE7D7PkCntW4rBH/9BfTpA3z0kTTlURT9DtSVSlsmgUAgEAgE0XC6zmzTowCEsuVjL2bexE/b7YmfwmAvbdFZ/ORsfy0NOPUCMOh7wM36qKjNfp47BwwfDty5A6xcCRQW8sqbs/1tOXD4EaDwN+HzJphg/F60mGkA/nUlZR1LqZHEo+XK4ovUGqWMI18coa5baxtxhHgY43Sd2czMTFFs+diLmTfx03Z74qcw2EtbdBY/OdmrvwOUrwHDdwOhjwiuxcRWqQRGjgSKi4GQEODIEaBDB155c7LP2w1kPAEMXAWETxA2b4JZSku5z4DzSQPwrysp61hKjSQeLVcWX6TWKGUc+eIIdd1a24gjxMMY8s4sgUAgOCOXlwHnPwBG7AWC48Uv78QJw3E7FRVAp07AoUOGo3jE5ubPwInpwMMbgM6Pc05O3pkl78wSCAQCQRzIObMEAoFA4I7mGnBxITDyABA0UPzyGhuBN980dGS7dDF0ZLt1E7/cnE2GJdRDtgCdksUvj0AgEAgEgqQ43TJje6LpOY6tFeJn64L42Qrw6wZMyAaCBkrjp4sLsH078OijwLFj0nRkAaDsAjDkZ6BTcuuuTxGJj5dg1t6BIO3IFBITJiQeTEg8mJB4CI/TdWaVSqUotnzstVr2O4eKqcVZ/ORqT/w0Dxc/xdZC/LTB/u/NniTzs2NHYPduICJCkLxZ2fdbCIQlAhDXz9aMTCbDli1bcO7cOQBAQ0MDCgsL0dDQQNtUVlYy4qvX65Gbm8vIp6ysDNXV1fTnuro6k81C8vLyUFdXR3+urq5GWVkZw6Zbt26MdGq1Gjt37qQ/63Q6yOVyxmZSKpUKarWa/qzRaKBQKBh1rFQqGZqLi4tNfnRmZGSguLiY/pybm2vSThQKhYk+lUpF23HRR+niqo9Kw0UfVQZXfYDh2uIaP8qeiz4AOHnyJGd9SqWSV/0eOHCAsz6dToeLFy9y0gcAP//8M6/29/PPP3PWJ5fLcfLkSU76lEolDhw4wFmfUqlk3BvY6tPpdHTs2eozbk9cr1/qv1z0Uem46AMM1wuf+4tSqeSsDwB27tzJSR8AHDlyhNf974cffuCkTy6XIzU1FbbidO/M5ubmIjw8XHBbPvZyuRyJiYktrsVZ/ORqT/w0Dxc/xdZC/LTdnvjZfN7O/s7s119/jZSUFM7vzLq4uMDb25u1vY+PD6qrq5tNY+mdWa7txpZ0XNuRLWXxTSdlWQC/mJB4CFOWI8RRymvGEeJI7iFMyDmzAEJDQ5Gamgq5XA7A+qhneHg4q1GJ4uJi5OTkMGysjUqEh4fzGjVhM6pTUFDAadQuPDyc9ain8cg3G30BAQGs4sdnVOfSpUuc4hceHs5pVIy6yNjoo9IZI1T9Xrp0yaw+iqb6wsPDOc0KUH6y0VdWVsZ51I5L/Rrf2KzFLyQkhNP1QeXNRl9OTg7vWRWK5uqX0sImfl5eXpyuDypvtvpCQkJM9FHYWr+UFjbxq6mp4XR9cLk/Z2RkMNqWLbNm5vSFh4cLNmrsjFjryAqVBgCvH122pJOyLD7pSDxariy+SK1RyjjyxRHqurW2EUeIhzFONzNrT/AdnXE0iJ+tC+KnA9HYAChfBcKSgU7jzJoI7ucPPwByOZCWBshkwuVrjbpSwKOdxT/z9ZPMzPKbmRVrN+M1a9Zg+/btrHczdnNzg4eHBytbNrSK+4LAkJgwIfFgQuLBhMSDCZmZ5UHTmTehbPnYi5k38dN2e+KnMNhLW3QWP2n7Bj2QOQsoUADt7ueUnreW778Hpk83dGi/+EIaPxsbgXMfAIceMfy/QIjdXlo7xu++CpVGp9Ph2rVrmDhxIpKSkuh/TzzxBOOz8b+pU6dazFfKOuZbFp90UpbFFxIPYcpyhDjyxRHqurW2EUeIhzFO15lturxTKFs+9lxGIMTU4ix+crUnfpqH68iZvbRFZ/ETAC5l/QWceBa4expIOAr4WF76I5if33wDzJ5t6FCOHAm8+qo07fzsPODaGmDwesOuyRYQsz5bM003aRIznbU0er0ejY2NmDJlCmbNmkX/Gzt2LOMz9W/KlCkoLCxkLGs3hk8d85054Nue+KSTsiyAX0xIPIQpyxHiKOU14whxJPcQ4SHLjAkEAqE1oa8Dfp8MVKqBUQcArw7il7lkCfDGG4b/f+QRwzE8LJeB8qaxETjzOpD7KzD6ENAmWpRi7GmZ8bp167B//36Ul5dDJpOhR48eeOmllxAdfc/37OxsLFu2DFeuXIGvry+Sk5MxY8YMuPzd0W9sbERaWhr27NkDrVaLHj16YO7cuejatatJedQzdsWKFXaxzJjPsmRzG0YRCAQCwT4gy4wJBAKBwOTk84D2hqGDJ0VHdsuWex3Z5GRgxw4JOrINwOlUIG83kHBMtI6svTFy5EisXLkSu3fvxrZt2zBgwAC8/fbb0Ov1AICqqiq8/fbbiI2Nxa+//orFixdjz5492LZtG53Hli1bkJ6ejsWLF+PXX39FbGws3n77bcaxOQQCgUAgOAqkM0sgEAitiZg3gVEKwLO9NOWNHw+MGAE8/jjw88+Al5f4Zd45DNw5YlhC7Rcpfnl2QkREBNq0aQPAMMPq6uqK0tJSVFZWAgCOHTuGhoYGPPfcc/D09ES3bt3wzDPP4Ndff6Xz2LFjB55++ml069YNnp6eeO6551BfX4/ffvutJVwiEAgEAsEmnK4z2/S4CaFs+diLmTfx03Z74qcw2EtbdBY/0a4vMk6prNvxxESLjw+wezeweTPQZNdY0fwMGQ0knUXGnzc45S+KFok5ceIEkpOTkZiYiBUrVuDJJ5+klwBnZ2cjOjoaMqNdpHv16oXbt29Dq9VCo9GgoKAAMTEx9N9lMhm6d++Oq1evCqqzpKREkjS2pJOyjvmWxSedlGXxhcRDmLIcIY58cYS6bq1txBHiYYxbi5TagvTq1UsUWz72YuZN/LTdnvgpDPbSFp3FTz72Nudt4T0XUXW7edvVNSoVgwcPxu7du1FRUQG5XI7g4GD6b1qtFn5+fgx7aia3qqoKjX/v9tzUxs/PD1VVVYLqbFqGWGlsSSdlHfMti086KcviC4mHMGU5Qhz54gh13VrbiCPEwxin68wGBQWJYsvHXsy8iZ+22xM/hcFe2qKz+MnHXqy8ncXPlsDf3x9PPPEEJkyYgPDwcERHR8PX1xdFRUUMO2oJso+PD92ZbbprsEajsehvaGgofvzxR3q2t1evXvTMbmBgINzcTH9G6PV6izsT+/r6WtzkQ6PRmN152M3NDYGBgWbTaLVaaLVas3+z1MnVaDQ4fvy42b9FRUUxNtQyJiMjw2xZvr6+GDJkiNk0arUa2dnZZv8WHx9vViPRR/QRfQZ95rCmz9K9TAx95soSK37mymJTv03TsdFnriw28WuazpI+pVKJ/Px8eHh48DrGrSlO15m1J9RqtcVG3ZogfrYuiJ92Qk0xkJ8OdJ1mUzas/dTrgf/8B3j5ZeDv2T5Hwu7rkweNjY3Q6XTIy8tDdHQ0oqKioFAooNfr6c7n5cuXERYWRncgQ0JCcOnSJfTu3RuAoeOpVqsxZswYs2X4+PhgypQprHYzppDJZOjQgfvmY5Y6rM3RXOe4trbW7Pd+fn5ITEzkXNaQIUM4t6Po6GjO7c4WfVwRQh/bmLSUPrYIpc9aPFpanzWE1qdWqy2msQd9zSGGPkvtw170WUJIfcblV1RUYPny5ZzzNsbp3pnNzc0VxZaPvaXRFKm1OIufXO2Jn+bh4qfYWpzWz+o7wMGRQO5OwxE1NuTPyk+dDpg5E5g3D7lTpgAWOgm26DBrX68Bjj0GlJ6zOX8x61Mqtm3bRr8fWlZWhiVLlsDNzQ2xsbEAgGHDhsHV1RVr165FbW0trl+/jq1btyIlJYXOIyUlBVu3bsX169fpo2vc3NwwdOhQs2XyHQDgszsy3x2V+abjU8dc25EtZfFNJ2VZAL+YkHgIU5YjxFHKa8YR4kjuIcLj8J3Z0NBQpKamQi6XAwB0Oh3kcjljqZJKpaJHhgoKCqDRaKBQKBj5KJVKRiUUFxebTMNnZGSguLiY/pybmwulUkl/LigogEKhYCyvUqvVUKlU9Gdr+gCY1Xfy5EkTfU1ftDbWV1BQYKIPgFl9f/zxByd9OTk5rOLXnD7ANH4AcOTIEU7xKygoYBU/Sl9BQQFrfVlZWazix6d+jxw5YlYfRVN9BQUFrOJH6aP8ZKPvjz/+YB0/S/oAy/VLaTHWR9FUX15eHqfrg8qbjb7jx4+zjp8lfc3VL6WFTfzUajWn64PKW6FQQFN4FTg4HAi4H+qQT6G6eNFEX15enok+Cs71W18PTJ2KjOvXURwUhIIHHgA8PFjF78KFC5yuD8b9ub4CODIWqK+E8qrWrD7jtsXl+qBorn4LCgogl8uRmpoKe+HMmTN4/vnnkZSUhNmzZ6O0tBT/+c9/0L69YedqHx8fLF68GOfPn0dKSgrefPNNJCUl4amnnqLzeOaZZ5CYmIg333wTKSkpuHDhAhYtWgRvb29BtVqaFRU6jS3pjNuP2PAti086KcviC4mHMGU5Qhz54gh13VrbiCPEwxiXw4cPmw7rOwjUge7l5eXw9/dvaTmckcvlvKb6HQ3iZ+uC+NmCaG8CB0cBHYYBA1cBrjLraazQrJ+1tcCkSQB1tMuCBcD779tcplXqSoHDYwGPQGDoL4Cb7R0tvvUpxIHujopWq8XXX3+NlJQUVsuMKyoq8PHHH2PBggXwYXHWsNj21Mxzeno6K3s22OV9oYUhMWFC4sGExIMJiQcTIZ6x5J1ZAoFAcAQ01wwd2dAkYMBywEXkhTW1tYazY/fuNXxevBh46y1xywQM7wIffgTwCQeG/ATIPMUvk0AgEAgEgkNCOrMEAoHgCLjIgKjngd7vAS4u4pfn7g5Qm/gsXQq89pr4ZTY2GJYW+0UBD28EZB7W0xAIBAKBQHBaHP6dWa40fVdMKFs+9mLmTfy03Z74KQz20hYd3k/fLkDs+6w6soL46uoKrF4NHDjA6MiK6ufBQ8CAb4D4Taw6svZ0jRKYND0iSKw0tqSTso75lsUnnZRl8YXEQ5iyHCGOfHGEum6tbcQR4mGM070zq9FoWB+wzsVWbHtHzZtokT5vokX6vIkW6fOWSouzvzM7adIkLF68mPM7sx4eHmbPoLVk7+PjA51O12waS+/MWkpn7Z1Zrm2Cbxqp0xGNLVcW33REozDpiEZh0klZlkO+M5uWloYNGzbAw+PeqPvDDz+MDz74AIBhy+ply5bhypUr8PX1RXJyMmbMmAEXgZbVcQky1woR095R8yZapM+baJE+b6JF+rztTUtrxXj3Zy5Y68gKlcaWdHzqmG+7kDId0dhyZfFNRzQKk45oFCad1BptpUXemb3vvvvw9ddfm3xfVVWFt99+G2PHjsXixYuRl5eHefPmwdfXl3G0AIFAILRqKtWG90aleDcWAIqKgLo6oFMnacojEAgEAoFAEAC7emf22LFjaGhowHPPPQdPT09069YNzzzzDH6ljoUQAOPzFoW05WMvZt7ET9vtiZ/CYC9t0WH8LPwN2NcfKMpgZ2+jFhQUACNGAKNGAfn5gubdrG35X0DuDt5587G3l7ydAa1WK0kaa+kaGhpQVVVl9t/FixdNvqurq+OlwRp82xOfdFKWxRcSD2HKcoQ48sUR6rq1thFHiIcxLdKZVavVeOyxxzBp0iQsWLAA+X//gMrOzkZ0dDRksntnJ/bq1Qu3b9/m/ZBrSk1NjSi2fOzFzJv4abs98VMY7KUtOoSfBQeBI+OAB/4DdBjKK29O9nl5wPDhwF9/ATdvApcvC5d3c7al5wHFCKD0LO+8+djbS96ORGhoKLZs2YJz584BMHQOCwsL0dDQQNtUVlYyntF6vR5lZWWMfMrKylBdXU1/rqurM1nCXFFRwehMVldXm+QzcOBA6PV6+rNWq2WUbaxPp9Ph2rVrSE1NxdSpU5GUlISkpCSkpKRgzpw5+Ne//kV/N3v2bDzzzDOYOnUq6urqUFxcjIyMDEbZGRkZKC4upj/n5uZCqVQybBQKBcMvtVoNlUpFtyedTge5XA6dTkfbqFQqxo9AjUZDb6RCpVMqlcjNzaVtmtNHpeGijyqLqz4KLvqosrjqAwz1zVVfTU0NZ30AkJeXx1kf3/j99ddfnPXl5ubir7/+4qXP+JphW795eXmc9dkSP6odc2l/VBqu+qh0XOu3pqZGlOujqT6qLD7t7+rVq5z1FRYW8qpfpVLJSZ9cLkdqaipsRfINoK5fvw5vb2907NgRxcXF+Pbbb5GVlYXVq1fj66+/RnV1NT766CPa/saNG5g5cya2bt2K4OBgRl58NoAiEAgEu+R2OpDxlGE3367TxC/vxg3DbOy1a4CPD7B7NzBypPjllpwBDj0CxLwJ9H5X/PJsxNk3gEpOTsaKFSs4bwBlbsOllrL/4IMP4O3tbdW+trYWP/74o8UNowgEAoEgLA65AVTXrl3p/w8ODsbbb7+N5ORkqFQq+Pr6mmyxX1lZCQAWHyyhoaGYPn06XF0Nk8z9+/dHXFwcACA+Pt7sy8gajQbHjx83m19UVBSio6PN/i0jI8PsDLGvry+GDBliNo1arUZ2drbZv/Xr1w8dO3a0W31CxY/a3cxe9VHYqq++vh7u7u52q68pfPWFhISYTWcv+oSKn0ajQUFBgTT6cncgY+dSaIM/B650AK7IreoDbIjfnj3Atm1A9+6o79sX7snJQF0dotRqcdtfdT5wazsQ9Cbiu7wGc1tFiFW/5nZZtBQ/pVKJ/Px8eHh4iLbs1FFoDRtheXh4wNPTU5C8+O7y2ZohMWFC4sGExIMJiYfwtMgGUMa4uLjAxcUFjY2NiIqKgkKhgF6vp5caX758GWFhYRZ76/n5+Vi/fj3rmVmdTgc/Pz8kJiaysjXeJdHSDzZL9gAQHR1t8ceXXC43q8OcPmtHFjTVx8ae0hcZGclpN0gvLy9W8aM4fvw4EhMTrcYPMNXdXPzM2QPm42dsb4mm+tjE0Fifpfpsip+fH0aPHs0p5jqdjlX8jO3d3Nysxs/Y1lhfc35Y8tOSvuZi3lQfm5gb62PbzgFg0KBBnGJOtVtr8aOgtLC5vzB0NzYAl5dhyPjXgM6PWbc3wlL9NhcXPz8/JBYWGjqzbdtC/tVXSJw5s1m9wL365RLzyMjIe/oKfwOOvAYkfg70mGPWnsv9uakWa9cHVZ/GWIpfYmIinXdFRQWWL1/OSk9rJD4+nle6hoYGepBZzDRSpzPXjtjA5bqxNZ2UZQH8YkLiIUxZjhBHKa8ZR4gjuYcIj+TvzB4+fBjl5eUAgJKSEnzxxRdo164dYmNjMWzYMLi6umLt2rWora3F9evXsXXrVqSkpAhW/sGDB0Wx5WMvZt7ET9vtiZ/CYC9t0W79dHEFRiksdmS55s3K/h//ABYtAg4dAkJDhc3bkq2bL9B/qcWOLNe8+djbS97OgPG7VWKmaYl0fODbnvikk7IsvpB4CFOWI8SRL45Q1621jThCPIyRvPusUCiwdOlS1NTUoE2bNrj//vvxn//8h15GvHjxYixduhQpKSnw8fHBhAkTBD2WZ/To0aLY8rEXM2/ip+32xE9hsJe2aNd+WjmCR5S4vP224b9yefN2Nmhh2AY+aPgnUN587O0lb2cgKChIkjQtkY4PfNsTn3RSlsUXEg9hynKEOPLFEeq6tbYRR4iHMZJ3Zj/77LNm/x4VFYVly5aJVj6X6W+uU+ViTq2LqcVZ/ORqT/wUBntpi87iJx97sfJ2Fj8JpvBZ9ssnTUuk4wPf9sQnnZRl8YXEQ5iyHCGOfHGEum6tbcQR4mGMXZ0zSyAQCK2eBr11GyFx8g2MCAQCgUAgtF6crjNrfN6RkLZ87MXMm/hpuz3xUxjspS3ahZ9/LQYyZ0pX/0eOAD16AAL6zkpLeRZ7W65522BvL3k7A9RJBGKnaYl0fODbnvikk7IsvpB4CFOWI8SRL45Q1621jThCPIxxus6sl5eXKLZ87KOiouxCi7P4ydWe+GkeLn6KrcVh/GxsBC58AmR9AcS8KU39HzgAjBtnOE/25ZcNGswgeH1mfw/IBwLaW3bVzsVst60ZtVrNK11rXWbMtR1R8G1PfNJJWRbALyYkHsKU5QhxlPKacYQ4knuI8LgcPnzY/C8cB4A60L28vJz10TwEAoEgKY2NwPn3gew1hl2LA2LFL3PvXuDxx4HaWqBXL167FvPi6krgz7eB4TuBjiPEL08ChDjQ3VGhnrErVqxAQECAVfuKigp8/PHHWLBggcWz4e3Zvra2FmvXrkV6ejorewKBQCDYhhDPWKebmSUQCATJaGwE/vwncC0NGH1Emo7sr78Cjz1m6Mj26QMcPSpNR/bSV8DZd4CR+1pNR5YAhIaGYsuWLTh37hwAw9mshYWFaGhooG0qKyuh1Wrpz3q9HkVFRYx8ysrKUF1dTX+uq6uDRqNh2JSUlKDO6B3v6upqlJWVMWwGDhwIvf7ee+darZaxXNicvqqqKoY+nU5nVl9tbS39ubi4GBkZGQybjIwMxnE+ubm5UCqVDBuFQsHwS61WM5be6XQ6yOVyxvnbKpWKMQOu0WigUCgY+SqVSuTm5hJ9RB/RR/S1Gn1yuRypqamwFaebmdVoNPDz8xPclo89F8TU4ix+crUnfgqDvbTFFvEz+3vgwsfA6ENAm6jmbbnmbY7bt4Fu3aBxd4dfjx7A/v1A+/asy+Gt5a9Fhn8j5UD7Adx1S2TPBSpvMjPLb2bWw8PD6s6WTWdOdTpds2kszbRaSmdtZrZpOjFnZvm2VT7ppCyLLyQewpTlCHHkiyPUdWttI1KWRWZmYRg1Tk1NhfzvsxKtjUpkZmayHpXYtm0bw8baqERmZqZooya//vorp1GTzMxM1qMmu3bt4qTv2LFjoo3qbN68mVP8MjMzOY06ZWZmstZ34MAB0UbFNm/ebFYfRVN9mZmZnEbFKD/Z6Nu1a5eoo3aUFmN9FE31/f7775yuDypvNvq2bdsm6qgnpYURv8hngUd+h/JyKUPfoUOHOF0fVN5W9XXoAPm//43fn3gCOHgQaN9e8FFZSgutr74SuPULMPoQFH+WM/Slp6dzuj643J8zMjIYbUvoUePMzEzBRo2dkdLSUknStEQ6Phi3VbHTSVkWX0g8hCnLEeLIF0eo69baRhwhHsY43cwsgUAgtHr0ekAmk668xkbAxUW68iSEzMySd2YJBAKBIA5kZpZAIBAIpkjZkQVabUeWQCAQCASCfUM6sy1I02V8rRXiZ+uC+Nm6IH4SmiM+Pr6lJdgVpB2ZQmLChMSDCYkHExIP4XG6zmzTd6iEsuVjb7y7YktqcRY/udoTP83DxU+xtdiNn/oaKH95B6gtEUWLWfvFi4F9+2zOn5OfjQ1QKk+zNrendi5mu23N8N3cpOkuxGKlkTod13ZEwbc98UknZVkAv5iQeAhTliPEUcprxhHiSO4hwtP8VoOtkJCQEFFs+diLmTfx03Z74qcw2EtbFM1PXRVwLAUhjW6AC7vlvTb52dgI/OtfwCefAF5ewIkTQL9+NuXPigYdkPkcQuqjAAywas5HhyPfiwhMPD09JUnTEun4wLc98UknZVl8IfEQpixHiCNfHKGuW2sbcYR4GON0ndnw8HBRbPnYi5k38dN2e+KnMNhLWxTFz/pK4GiyIf8JvwDubUTRQts3NgLz5wOLFhk+P/44EBtrc/5WaagHfp8GlF9A+KhFrJPZUzvnitjXRWvH29tbkjQtkY4PfNsTn3RSlsUXEg9hynKEOPLFEeq6tbYRR4iHMU7XmSUQCARBqCsHjiQBbr7AsB2Am8i7nzY2Av/3f8DSpYbPM2cCq1eLv9mTvhY4PgnQXAdGHwG8gsUtj0AgEAgEAoElTvfOrPE5hELa8rEXM2/ip+32xE9hsJe2KKifOi1wKAHwaAcM3wW4+Yjv51tv3evIvvQSsGaNxY6sYL7qa4DfHgeqbgGjDwFewXZTn3zs7SVvZ6Curk6SNC2Rjg982xOfdFKWxRcSD2HKcoQ48sUR6rq1thFHiIcxTteZvXTpkii2fOy5nKckphZn8ZOrPfHTPFzPAbOXtiionzIfoPsrwNBfAJmX6FouXboEpKQAPj7Aa68B33wDuFq+fXPJv1k/z38I1JUCow4CnoGc87andi5mu23NlJeXY8uWLTh37hwAoKGhAYWFhWhoaKBtKisrGZua6PV63Llzh5FPWVkZqqur6c91dXXQaDQMm8LCQkYHs7q62mSTpoEDB0Kv19OftVot7t69S382p6+qqoqhT6fToaioiFF+WVkZamtr6c/FxcUmu442/Zybm2uy4YlCoWDkq1aroVKp6Pak0+kgl8uh0+loG5VKBbVaTX/WaDRQKBQA7rVDpVKJ3Nxcq/qKi4vpNFz0UWVx1QcYri0u+qiyuOoDgIsXL3LWd+nSJc76AODkyZOc9el0OuTl5XHSR+XLVV9ubq7ZfKzpk8vluHjxIid9SqUSJ0+e5KxPqVQy7r1s9el0Orods9WXm5tLp+GiD7h3nXHRR6Xjog8wXC9c9VFlcdUHAEePHuWkDwDOnDnDWR8A7N69m5M+uVyO1NRU2IrL4cOHG23OpYWgDnQvLy+Hv79/S8shEAgEcbl6FYiOlu5c17pywMWV9bvArREhDnR3VKhn7IoVKxAQEGDVvqKiAh9//DEWLFgAHx/ry+7tzb62thZr165Feno6K3sCgUAg2IYQz1iHn5kNDQ1Famoq5HI5AO6jnhR8Ru34jCoSfc6p78qVK1CpVFAoFFAqlThw4IBd6bP3+BF9f+u7cQMaoxkm0fV5tKU7sq0ifhz1CTVqTCAQCAQCQRzIzCyBIDJ6vR5r163H1YK7CAyPREluDrqHtMesGdMhE3vzHgKBYBNkZpbMzBIIBAJBHMjMLA+ajuYLZcvHXsy8iZ+22wvlZ1ZWFq4W3EXi1Nl4eEwSEqfOxvW8fGRlZYmmpSX8FCJ/u/SzMhuoVFs1E0xLdTWwezfvvPnYi5W3PV3PXBH7umjtlJSUSJKmJdLxgW974pNOyrL4QuIhTFmOEEe+OEJdt9Y24gjxMMbpOrO9evUSxZaPvZh5Ez9ttxfKz4KCAgSGR8LdwwMA4O7hAb/QLigoKBBNS0v4KUT+dudnxWVAMRy4sVkaLVotkJwMjB8PfP89r7z52KP6DqD8h+EYHgHztqfrmStiXxetHT8/P0nStEQ6PvBtT3zSSVkWX0g8hCnLEeLIF0eo69baRhwhHsY4XWc2KChIFFs+9mLmTfy03V4oP0NCQlCSm4P6v3fprK+rQ1lhPkJCQkTT0hJ+CpG/XflZdtHQke06Dej9nvhaKiuBpCTg0CHDBk9G75WKel1U5QEHhwN1ZYCL9WXv9lKffOztJW9nwOPvwTux07REOj7wbU980klZFl9IPIQpyxHiyBdHqOvW2kYcIR7GOF1n1p4w3vSkNePsfsbExKB7SHvIN67B7wfSId+4Bt1D2iMmJkZihcLgFPVZeg7qX6cD3V8G+i4Uf/fgsjLgkUeA334zHLmzdi3w4ovilgkA2htQ/zIJCIoHBqUBrm7il9lCOEW7FYGoqKiWlmBXkHZkCokJExIPJiQeTEg8hMfpOrPGO2IKacvHPjs72y60OIufXO2F8lMmk2HWjOmYNmEs+ocFYNqEsRgzehSnzZ8cwc+W0CJK3neVwMGRyPaYCPT5iHVHlreWu3eB0aOBzExAJgM2bgRmzOCdN2v7ymzgwDBk6wcDD60CXNm1R3upT672Yrbb1kx0dDSvdMZnyoqZRup0XNsRBd/2xCedlGUB/GJC4iFMWY4QRymvGUeII7mHCI/TdWa5vKfIxZaPvZh5Ez9ttxfST5lMhtjYWCQkJCA2NhZFRUWc0juKn7bmbxd+lv4JxH4AtB8gjZayMiA/H3B3B7ZuBSZNsilvVvbam4Yl1J0fBzqOMpwlK1TePG2lsLeXvJ2B2lrr718LkaYl0vGBb3vik07KsvhC4iFMWY4QR744Ql231jbiCPEwpvWuKbNAXFycKLZ87MXMm/hpuz3xUxjspS2yto1+wfDfG3JptERFGd6TvXYNGDfO5rxZ2XuHAv3+DUROBfbvFzZvnrZS2NtL3s4Am6N8hEjTEun4wLc98UknZVl8IfEQpixHiCNfHKGuW2sbcYR4GON0nVkCgUCwO3r1MvwTAb1ej6ysLBQUFCAkJAQxMTGQydyBrs+KUh6BQCAQCASCVDjdMmMCgeA46PV6qFQqKBQKqFQq6PX6lpbkUOj1eqxdtx4bdu7Dmdtl2LBzH9auW0/iSGBNdXU1tmzZgnPnzgEAGhoaUFhYiIaGBtqmsrISWq2W/qzX601epSgrK2O8o1pXVweNRsOwKSkpQd3fu75TZZeVlTFsBg4cyGi/Wq0WlZWV9Gdz+qqqqhj6dDqdWX3Gy46Li4tNzkzMyMhg+JCbmwulUsmwUSgUDL/UajVUKhWjbLlcDp3RbuUqlYqxKYxGo4FCoWDkq1QqGe+jWdJXXFxM9LWwvpycHLvWZ+/xI/qcR59cLkdqaipsxek6s00rUChbPvZi5k38tN2e+CkMfLWw6YjZ7GddOev0vPJvSmEh0NgoWf1nZWXhasFdJE6djYfHJCFx6mxcLbiLrKwsTvnx1WJP7ZwrYl8XjoK3tzeeeeYZ9O3bFwDg6uqKDh06wNX13s+HNm3awNfXl/5sbnO7gIAAeHt70589PDxMznnV6/WM43K8vb1NlgKfOnWKkb+vry9qamroz+b0+fj4MPS5ubkhODiY0aENCAiAp6cn/TkoKAhDhgxhlD1kyBCGD+Hh4SbL6hISEhh+RUdHIzY2lm5Pbm5uSExMhJvbvYVxsbGxjI22/Pz8kJCQAOBeO4yLi0N4eLhVfUFBQXQaLvqosrjqo+CijyqLqz4AOHLkCGd9CoWCsz4AuHTpEmd9bm5uiIyM5By/srIyzvrCw8NNBnvY6EtMTMSRI0c46YuLi8OlS5c46+MTP6p+qXbMpf1Rabjqo9Jx0UelE+P6aKqPKourPsDwHitXfWfPnuVVv1T+bPUlJiZi5cqVJnlwxeE7s6GhoUhNTYVcbni/zdqoxKBBg1iPShg/DAHroxKDBg3iNCoRHx9vog8wP2ri7e3NadRk0KBBrEdNjH8ssBnV6dOnD6dRHcpPNqM69fX1nEZ1Bg0axGnUadCgQSb6KJrqi4iI4DTqFB8fz3pUrL6+3qw+iqb6Bg0axGlUjPKTzahdQEAAp1G7+Ph4TqN2lBZjfRRN9cXFxdH6qI7Ygw8PRc9eveiO2J9//knro/JmM6ro6urK1Hd6HZTrJgL192Z1jPXFx8dzGvWktFiMX0YG0K8fMH8+unfvzmnUk8qb7f2FesAUFBQgMqAa2jxD59XdwwPtQjohMzOTThMfH89pVJbSwub+EhISwmnUmMv9OSMjg9G2rF0f8fHxnO7PgwYNEmzU2JE5fvw4r3Tt2rWTJI3U6ajnGVeM26rY6aQsC+AXExIPYcpyhDhKec04QhzJPUR4XA4fPtzYIiULgFarRXJyMsrLy+Hv79/ScggEgoAoFAqcuV2Gh8ck0d/9fiAd/cMCTEYVOXP9B+B0KjDkJyAsybq9rahUhuN3CguB9u2BCxeA0FDRi71+YhXCsv+BK/d9h7LQJ1BfVwf5xjWYNmEsY7SUYJmKigq0bdsWu3fvZszuOQPUM3bFihWsNkuqqKjAxx9/jAULFsDHx8fh7Gtra7F27Vqkp6ezsicQCASCbQjxjCUbQBEIBLskJCQEJafOor6uDu4eHqivq0NJbg5CHhxrW8bZa4Azc4FhO4CQ0YJobZY//wTGjDGcJ9uhA6BQSNKRRd5eRN76PxzVTUf6vkIEhqejJDcH3UPaIyYmRvzyCQQCgUAgEETG4ZcZc8V4OaWQtnzsxcyb+Gm7PfFTGPhqiYmJQfeQ9pBvXIPfD6RDvnGNSUesubybbh515coV4MoK4Mz/ASP2CN6RNavl1Clg1ChDRzY0FDh6FOjTR/z6z9wAZDwFl4e+x9Bp32DahLHoHxaAaRPGYtaM6WbfaRRDiz21c66IfV20dow3XBIzTUuk4wPf9sQnnZRl8YXEQ5iyHCGOfHGEum6tbcQR4mGM083MGm8UIaQtH3sx8yZ+2m5P/BQGvlpkMhlmzZh+71iZB8f+fayMzKy9MdTmUVcL7iIwPBIlp84iyl+H7qFL4DJSDgQP5u8QC+0AgMZG4K23gLIyoHNnw1myf2/CIGr939iKmgtfAWM3Ap0fgwyGDTSEWlZsT/cWe7pGpeC7775DZmYm7ty5Ay8vL/Tr1w8vvfQSOnToQNtMmjQJJSUljOvkww8/xODB99r89u3bsWXLFpSVlSEiIgJz5syhN3gSCuPdhMVM0xLp+MC3PfFJJ2VZfCHxEKYsR4gjXxyhrltrG3GEeBhD3pklEAitDpVKhQ079yFx6mx6ibJ84xpMTx6G3vcPlE5IURHw3HPA118DkZEWznzlP0tqlvMfAu0HAZ3GCZuvk2JP78yuWrUKw4YNQ7du3VBbW4slS5bgxo0bWL16NW0zadIkTJs2DY8++qjZPI4cOYIvv/wSn376KXr37o3du3fju+++w7p16xidYoC8M0sgEAgEcRHiGet0y4ztiaZn7LVWiJ+tC0fws6CgAIHhkXD/+5gPdw8PBIZHIr+wgnUegvgZHAzs2kV3ZCU58/X+Tzh1ZB2hPoWgNfj5wgsvoGfPnnB3d4efnx8mT56M7Oxsxjmr1tixYweSkpLQr18/uLu7Y+LEiQgPD8e+ffvM2jc9PsfZaQ3tSGhITJiQeDAh8WBC4iE8TteZNT4SQkhbPvZcjjwQU4uz+MnVnvhpHq5HdbREWwwJCUFJbg7q6+oAwLB51K3rCAkJYZ230H4an/n60MgxnM58FbP+HaE+hbAX08+W4vTp0+jYsSPatGnD+H716tWYMGECZs2ahU2bNjF8UavV6NWrF8O+Z8+eFt914nuMRGtdZsz3qCK+7YlPOinLAvjFhMRDmLIcIY5SXjOOEEdyDxEep+vMHjx4UBRbPvZi5k38tN2e+CkMLdEWzW0eFewlE3UX34NNzkZtivFs8bXTv9GzxQUFBdbzdtBrVEjdTTf00uv1duNnS3DmzBmsX78eb7zxBuP7d955Bxs3bsT27dvxxhtvYMeOHVizZg3996qqKpPZ1jZt2gi+GZLxmb9ipmmJdHzg2574pJOyLL6QeAhTliPEkS+OUNettY04QjyMcboNoEaPZr+DKRdbPvZi5k38tN2e+CkMkrfFxgbI/pyLWQmjkVUxgN48qnv37sK/n0qxaRNG79oFJCQAbuZvq8ZHDXUbMJTTUUMW41JbAngGsrcXgJa4t5jb0Kv7qdOYNnUKp/yF0GIPnDhxAp999hneffddDBzIfAe8X79+9P/36dMHM2fOxOrVq/HSSy8BAHx8fEyWuVVWVlp8V6m6uho//vgjfe306tWLHhQKDAyEm5n2rtfr0djYiMLCQpO/+fr6WixLJpOZTePm5obAQNN2Dhje67VUlru7u9k0Op0OJSUlJun0er3FcgAgIyMDOTk5kMvlJj4NGTLEbBq1Wo3s7Gw0NDSYpIuPjze7jFuj0dCzN03TRUVFIfrvjeTM6dNqtSZp2OgzVxYbfQAYMWGjr2lZbPU1TcdWn3EatvqM03HRB9yLB1t9xmVx0Uely8jI4KSPSqfRaFjro9Ko1WpO+gAgLy/PrH1z+gDgoYceMvu9JX0AEBkZabEsS/p8fX0t3u+b0xcfH282XXP6oqKieOkbMmSI2bKs6fPz8zNJZ01fdHS02bKs6QNMn5uW9CmVSuTn58PDwwN1f6+gswWn68yae/AKYcvHXsy8iZ+22/Pxk+0GP47uJxckbYsNeuDUi0DhUchi3kJsRIRgu/haZN064Lnn4NbQAMTEAB98YNYsJiYG3U+dhnzjGkOHjMOZryZ+NjYCqk+A3F+BsX8ALi7N2wtIS9xbjJdoG2/odfXqVdHqV+zrgi8HDhzA0qVL8eGHH5p0ZM3h4uKCxsZ7+zxGR0fj0qVLjB8dV65csfhj2NvbGykpKaw2gKKQyWTo2LEja3uK9u3bc07TXOe4osL8O/Jubm4mm10Bhg2gSkpKLJY1ZMgQaLVaJCYmstYXHR1t8ce/Jfz8/DiVQWGpDptDCH1yuZyV3pbSxxah9FmLR0vrs4bQ+poO4hhjD/qaQwx9ljqf1vSZeyax0dc0HZv4mSuLTfyaprOkz7j8iooKLF++3GrezeHwy4xDQ0ORmppKXyw6nQ5yuZyxblulUjHeB9JoNFA0WRKoVCqRm5tLfy4uLkZGRgbDJiMjg7EsKTc3F0qlkmGjUCgYo95qtRoqlYr+TPS1Xn2NjY34Pm0dNm/fAWVuCdb9uheffrYQGzduhEKhoDf6ccb4UUtEV69eDaVSScdCUH03bwCZM4Hi4yh+YAcy/rzJWh8F5/h99x1UX34JddeuwNChwNy5FvXl5+dj1ozpmDZhLGICPDCwZ1fGma+s9VVWAufeA65+A3XYl1BdvChM/Oy4/Z09exYBQR3oDb3qqirh7+fLWKIthj65XI7U1FTYC9u3b8eyZcuwcOFCsx3Z3NxcnD9/HnV1dWhoaMBff/2FdevWYdSoUbRNSkoK0tPTcf78edTX12PHjh24desWxo61vkKAQCAQCAR7w+mO5lGpVKxH8rnY8rFnO5opthZn8ZOrPde8N27ciPM38pE4dTZcZTLs2bwB5//8A/0fGgy9pgzdQ9rTnRdH9pNLfQLA+fPncUp55t4S0b9nJI07cny10PYN9cDvU4Hyv4BRBwFv01khwf38+mvgtdcM/z96NFSLFiG2f39uullC2zc2An+8CdzcCow+BPj3sDl/rvXZEvcWS0ctjXigN5KSkljlzddPezqaZ+TIkZDJZCbLZxctWoT7778fWVlZ+PLLL5Gfnw8XFxcEBQVhzJgxmDRpEmPEnDpntrS0FF26dMErr7zCWJ5ModVq8fXXX7OemTU+Ckev15tsTNWcvY+PDyorK5tNY+moHUvprB3N0zQdm6N5uLYjCq7Xgi3ppCwL4BcTEg9hynKEOEp5zThCHMk9hIkQz1j7XEclIl5eXqLY8rFvbu28lFqcxU+u9lzz9vh7Qx93Dw9cu3QRd8srMXTS8+gc2AadwztBvnENsrKyEBsby0s3lzNKxfSTS30CQGFhodklolQsbNFC259KBSqvAqOPAF5BzduypFk/f/nlXkc2KQn4+Wd4NfNekK1avLy8gMYGQPkqkLcHSDgKtLGsj0v+XOuzJe4tlpZoc9Eupp9Scfjw4Wb/HhMTw9jsyRITJ07ExIkTWZVpaZdja7i6cl/4xSeN1Om4tiMKvu2JTzopywL4xYTEQ5iyHCGOUl4zjhBHcg8RHtad2QsXLiA4OBghISEoLS3FN998A5lMhtTUVLRt21ZMjYLCZe0713XyYto7at7OpCUmJgbKnfsMG/sUFiIgrDPqq7Xw8wtl7FwbGxvLOe+uXbua3QDH3OwmV+1ixxyA2TNfqVgIoqXXXMC7k9nNkGzO2xzjxhk2e/L1BbZsATw9xY/5naNA/n5gzDHAt4tg+dvTNWTJXiaTYdaM6fcGcx4c2+xgjphanA1L73dZg88IO99ReSnT8W0XUqYjGluuLL7piEZh0hGNwqSTWqOtsB6W/Oqrr+j3iFatWoXi4mKUlJRg2bJlookjEBwJ4+NgbmZfwcWMQ/D3dENwUNC9nWs5nHNqjPEGOA+PSeJ0RmlLY/bMVxtiYZaAPs12ZAXHywvYsQP46SfA01OaMjsOB8ZdsNqRba3IZDLExsYiISEBsbGx4u1MTXBqGhoaUFVVxfqfEDtxEggEAoE/rGdm79y5g/DwcABAZmYmVq1aBS8vL0ybNk00cWJgaStyW2352HNBTC3O4idXe655G88e5eXloV1DDe6oTuNEebHJzrVc87558ybr2U1z+Te3RFnM+gSAzp070518a7v42lN9WqXJO3WSXHNu3qLkzwV7urfYi58EU3Q6HecdofmkETKdTqfDtWvXMHHiRNZLkDt06ICNGzfC4+97syX4tic+6aQsiy8kHsKU5Qhx5Isj1HVrbSOOEA9jWM/MymQy6HQ65OTkwM/PD+3bt4ePjw9qamp4F/7BBx9g5MiROHPmDP3d2bNn8eKLL2Ls2LGYPHkyduzYwTt/c2RmZopiy8dezLyJn7bb8/GTmj1KTEzEe/PnYXpKEvqHBWDahLGMJcGZmZn0Dr8KhQIqlYre4dccFRUVnGY3jbVTZ3Ru2LkPZ26XYcPOfVi7bj1dnpj1CQCnT5+md/E1FwtLutkgdn1ywZ6uOXu5Rp3FT4IppaWlkqQRMh11Pu6UKVMwa9Ysq/+mTJmCwsJCxu7cluDbnvikk7IsvpB4CFOWI8SRL45Q1621jThCPIxhvZvxO++8g+DgYJSUlKBTp0545ZVXcPv2bbzxxhvYvHkz54LlcjkUCgWUSiW+/PJL9O/fHwUFBZg1axZefPFFJCcn4+LFi3j//fcxb948DB061CQPPrsZEwj2ANXBZLPDLx97YyztBDttwljxz2AVmtoSIG8n0G2mNOU1NAD/+Q/w0ktAK7vHcNlQzJmxp92MpYZ6xq5YsYLzbsaWdgNuTfZsdj8mEAgEgmWEeMaynpl94403UFNTA39/f3ppcdOD19lSVFSE77//Hv/85z8Z38vlcoSHh2PixIlwd3dHv379kJSUhO3bt3Mug0CwZ7i+A0stYWYzu9mUgoICi0uUHYqaIuDgSCB3p2FnX7HR64HZs4G33zZs+GTDKhRO6LTAsYlAyRnrtjyxNltPIBAIBAKB4Aiw7sy6uLjgvffew7x58+hz2UaNGoXHHnuMU4GNjY1YvHgxnn32WXTsyDwLUq1Wo1evXozvevbsyftoAHsnIyOjpSVIAvHTFD4dTL4b4Ai9AVOL1Gd1AXBwBOAfAwzZArjwO4qDNTodMl5/HUhLM3yOj5dmo6f6SuDIOKCuDGjT06IZlyXq5jAeTOkUHOhQG4rxxVnuQ0IzaNAgbNmyBefOnQNg2CCpsLAQDQ33BpQqKyuh1Wrpz3q9HkVFRYx8ysrKUF1dTX+uq6uDRqNh2JSUlDA2VKqurkZZWRnDZuDAgYz2rtVqUVlZSX82p6+qqoqhT6fTmdVnXHZdXR1KSkpM9BUWFtKfa2trERYWxrBRKBQMv9RqNVQqFaNsuVzOWJqsUqkYv3M0Gg0UCgUjX6VSidzcXPpzcXGxSZvOyMhAcXEx/Tk3NxdKpVJ0fRkZGXatD5A2ft9//71d65M6fsZ29qjPGCn0UTrsVR8gXfzkcjlSU1NhK6x/Ec6cOdPs97Nnz+ZU4I4dO9DY2Ijx48eb/E2r1Zq8ONymTRvGQ8hWmgZeKFs+9lz8ElOLs/jJ1V5MP6uqqjh1MG3x03iX5d8PpEO+cQ1jAyYx/eSav1nbqlxAMRwIjAMe3gi4uguXtznq6oBJk6Clbv4ffgj8+9+Ai4sw+VuyrSsDDj0CuHoCI/YA7n5m7dnMqlrTYjyYUlddxWm23p7uLVzsxWy3rZm2bdvimWeeQd++fQEYzmbt0KEDY4OkNm3aMJaHyWQyuLu7M/IJCAiAt/e9Dcw8PDxMnveurq6MjZS8vb1NljefOnWKMZDn6+vLaPvm9Pn4+DD0ubm5ITg4mNFRDggIYJTt4eGBwEDmDulNP3t6euL27duM7xISEhh+RUdHIzY2lm5Pbm5uSExMZGw81fS4Nj8/PyQkJAC41w7j4uLoTTgBICgoCEOGDGGUPWTIEAQFBdFpwsPDERcXx0ofVRZXfYDh2uKijyqLqz7AsK8KV31KpZKzPsBwn+Sqz83NDZ06deKkj4KrPmN7LvoSExNx9uxZTvri4uIYzwe2+uLi4hj3Xrb63Nzc6HbMVl94eDidhos+4N51xkUflY6LPsBwvXDVR5XFVR8A1NTUcNIHADk5OZz1AYb7KBd9iYmJWLlyJWyF9fZ/jY2mr9Yaj3yyIS8vDxs2bMDy5cvN/t3X19dkpLaysrLZNdShoaGYPn06/eDq378/Hdz4+HiTh2VISAg0Gg2OHz9uNr+oqCi60pt2LDIyMsz+IPL19cWQIUPMdkTUarXFc/osbelvTl9lZSXu3r3L0NcUY32UvbE+c6jVauTk5NC2xpiLHwD4+/tDLpebzY+tPmOM9TWNYXPxi4+PNxvz5uq3uTORm+qjYmgtfpS+nJwcRlwsxU+j0aC2thbBnq44tH4FvPzaokZTjmAfb4s7YoaEhLCKn7G9sb7wsFB4e3qgqqoU4V1C0b59e1RXV8PPz88khtauD0s01dfY2Ii7d+9Co9HAy8vL7DuZTeu3srISxcXFuHv3Ltq1a4eoEDf0uPUyXEJGAQO/BVxcGfqM2zmlz1L7u3Pnjtl2y4hfXR3w5JPArl1AYiLUCxYge8AAYP9+k3RN65eKI5v7i0m7rS1BxsaXoW3oB4QnAwd/Y+iLjIykPxvPqlbcyUOxtztyLqmwefNm+kFTWVmJXr16WWx/d+7cQeWNHFzOcEf5ndu4nKFA5Y3LQNhDZnUD9+q3acybuz6oUVhz2Hp/puyb6msK2+vXGKVSCbVajXbt2pGjV3jiyWMlA580LZHOGOooH2sEBASgqqoKbm5uVnc+NobPChq+q24EPS5NpLJIPFounZTx4FueI8SRL45Q11K3EQqrndmFCxcCMPwoof6f4vbt24iIiGBd2IULF1BRUYGXXnqJ8f1HH32EESNGIDo62uRHzOXLl5s9hDc/Px/r169nvQEUNfKQmJjI2pbC0g8iS/aAYVTCkn5LP/L8/PxY6WuKNX3maE6fJXr06IEePXpwLouNvqYxtKbP3I/1loqfXC5nVa6fnx+eeuopThvwhIeHm21flggPD4der0dNTQ1kMhlCQkIwZswYs/k3zdda/CwNLhjHr+mGVRt27kP3U6dN3vNtWr+MdHpPnL1wFokh96Pfoysg+3tpMd/6ffTRR60bubsDoaGG/x82DNHvvgu2VwcVRzb6GDFvbACOjMOQ6DAgfikga/6HrvGsavvOXdG+c1fcra5Hx44BZkf7m+Ln54dJkyYZ4nz1Ktp4e6Hw6lV07xSGkSNHWkzH5/oYNGgQJ3su92dje0DY+59x+RUVFRYHYAmWMZ6FFTNNS6SjEPMoHwou931b0tiSTsqySDxaLp2U8eBbniPEkS+OUNdStxEKq51Z6odnY2Mj40eoi4sL+vXrh+TkZNaFjRgxAv3792d89/TTT+ONN95AXFwcqqqqsGnTJuzYsQPjxo1DVlYW0tPT8fbbb7Mug0BwFKh3YMXYUdhk9+NTZ812JsXCePbQeAflrKysZv01TTcaWzeugeely9LsvOziAnzzDfDUU4YNoKTAxRWI+x/Qri9jCbUlQkJCUHLqLOrr6uDu4YGa6mpcPpOJwNqeUKlUrHYlNj4T+cSJE3hszAiymzGBwBHjo3zYdIxra2vx448/QqfTcZqdJRAIBIJlrA4lzps3D/PmzcPMmTPp/583bx7efvttPP/885ymlL28vBAcHMz4BxiWfvr7+yMkJAT//ve/sXv3biQnJ2PhwoV4/vnnMWzYMP4eNsH45WUhbfnYi5k38dN2e0f28+TJk6x3Sy4uLrZ5Q6GmFBQUIDAsAqVl5bh04RxKy8oRGBZh9Z3Ma9eucdoYS5T6dHUFWMxwCqqlfVyzHVlje+N3oDPku/HV/Lmo0FShxL0t/f7snTt3rGqgBlMiIiI4bShmT/cWe7pGCUz4LM/mu6Rb6nRN8fDwgKenZ7P/XFxceC1r5tMO+bZdKdu8lBpJPIRJJ/U90RHqurW2EUeIhzGsN4CaOnUq/f8NDQ2Mf7Zw+PBhxmxtv379sGrVKsjlcmzevJnzbsnWuHTpkii2fOy5nKckphZn8ZOrvSP7eeXKFdadwqysLE7HtLDxMzg4GKd/O4I/L2bhTu4t/HkxC6d/O0IPYFmivr7e4sZY5jrc9lKfXPO3pf6Nj2kKqq9AeGQkXn7/Uzyc+Cg9aMFl514x/KTqavfu3ZwGR8S8LsSsz9ZM030sxEwnZVl80xlvrCJ2WQC/dsi37fJNx+dsSCk1kngIk07KePAtzxHiKGU8+KaTOo624nL48GHTnZ3McPPmTSxZsgSXLl0yGc08ePCgKOKsQR3oXl5ezvqdWQLBGVCpVNiwc5/JMt9pE8aaLNflYsuWc+fO4d2FXyAo+j4ER3RF0c3rKFb/hYXvvkXvimpCpRp67y5Yk7YemRey4Orlg4aaKgzqE4OZ06dh/Q8b7y2bzs1B95D2ti2bvnsXqKoCOnfml94OUCgUOHO7DA+PSaK/+/1AOvqHsXt/VgxMlrgLUVctiBAHujsq1DN2xYoVJrsKm6OiogIff/wxFixYAB8fH2LfhNraWqxduxbp6ems7AkEAqG1I8QzlvUQ4+LFixEUFISPPvoIXl5evAojENjAZWMkgnliYmLQ/dRpyDeuYXQoqON4jGnuzFu+ndmioiIMSRqPsC5dUVJYiD49EnC7e5TJ2Y73Ehw3nK8avw1wAdy9vOATGIyqkjuAC/93cC1SWGhYSlxVBRw9CnTqxMtPruhL/0LeX3Jcqe4jSNtu+v4sPZP94FgBVXND8LoiEAgEAoFAsADrzuz169exZMkSk7PjCAQhaemNi1oLxhv8FBQUIOTBsRY7TmJ0iEJCQlB26iweGvkIuvXqjfq6Ovx14hhC4nqbGt85AhydADzwBbJKQpF95xyeeeVNRkfI5dQp4Trct28Do0cDly4Bnp5AVpYknVn93XOo3TcMV8sH4IxfZ0HaNpdBC0CagSIxBkcIBAKBQCAQzMH6ndnQ0FDe73zYE1zeJeNiy8dezLwd1c9ff/2V9cZFfLSIGRcxdFDvHqalpXF69zAjI4Pe4CchIaHZDX5KS0vpDYV+P5AO+cY1zXaI2GC8SdGxn9ZbzjN/P3A0GYhbBnR/CUql0mxHCIDZd2nLy8tZa8rIyABu3QKGDzd0ZL29DefJCrQct9k6LT2LRsUIZJQNgsu4regUHGi1bbPJ2/j92f5hAZg2YSxmzZiOEydOmNhSA0Vs343mqoUiJCSErqsbZ08y3nu2NW9b7e0lb2egpKREkjQtkU7Ksvi0Q75tV8o2L6VGEg9h0kl9T3SEum6tbcQR4mEM65nZp556CgsXLsT06dPRvn17xt/CwsIEFyYWvXr1EsWWj72YeTuqn+7u7pxmdewpLkLrMJ6lDugQavGsVr75U8TExODhhx9mNYvLFuOZ4WvXrqFbt26meebtAY4/Awz8DoicAsBwhnHWkd9NZonHPjoGLmf+MJmB5HK2aC8/P2DYMCAnB/D1BfbsMXRsBcJizO+eBg4n4rrXE/izzVg87OmJoMhoTjOWzdWnuSOezNkLtfzXWtsyni0O6BCKC3/+wXpwxJ7uRWLm7QyYOwNcjDQtkY5vWY2NrLYoYcCnHfJtu1K2eSk1kngIk07qe6Ij1HVrbSOOEA9jWM/MLlq0CGfOnMHrr7+OadOmYdq0aXj22Wcxbdo0MfUJTlBQkCi2fOzFzNtR/ezWrZvF3WyF0CJmXITWYdz5GPLoY5xm8rj6yXYWlwtUnhMmTDDNs7ERuLwUGJRGd2QB4KGHHjI7SxwbG2t2BrJjx47s/Tx50tCR9fcH9u8XtCMLWIh50Qng0Bgg9iPURs2l27ZvQHtOM5ZCtPPmlv/amrcxxrPFAyI70nXFpk3Z071IzLwdidDQUGzZsgXnzp0DYDjNoLCwkHGSQWVlJbRaLf1Zr9ebrJooKytDdXU1/bmurs5ktZdGo2FsMFldXY2ysjKGzcCBAxmrCbRaLWpra+nP5vRVVVUx9Ol0OhQVFTHOei0rK2OUXVdXZzKbWlJSYmLTdJCmqKgIOp2Ooa+yspIuy9XVFYcOHWLYqFQqqNVqRhwUCgWAe+1QqVQiNzeXtikuLjaZBcnIyEBxcTGdJjc3F0qlkmGjUCgYcVer1VCpVHRZOp0OcrmctT4KLvqosrjqA4CAgADO+oKCgjjrA4CamhrO+vjGLycnh7O+3Nxc5OTk8NJnvKEb2/qtqanhrM+W+FHtmEv7o9Jw1Uel41q/QUFBolwfTfVRZfFpfwUFBZz1UXq46AOAs2fPctInl8uRmpoKW2G9m3FzP3i4nDUrJI6+m7FarUZ0dHRLyxAdLn468k6oQtenPe5UCwjoZ2Mj4OJi8rWo73X+97/A0KHAgAFWTQXxs/ScYWY2+vkWb9uWdq0eM7Bfi7YnqeBbn86+m/Hrr7+ON998k+xm/DdarZZuB2Q3YwPO8luGLSQeTEg8mJB4MBHiGct6ZjYkJMTiv5YkNDQUqampkMvlAKyPiuXm5rIeNdmzZw/DxtqoRG5uLqdRk+zsbBN9gPlRE4VCwWlUJzc3l/Wo2LFjx8zqo2iq78qVK5xGnSg/2Yzq7Ny5E0898Tg9AzdmYD8MjOtP/9hvqi83N5fTqBilkc2o2B9//MFpVDE7O5v1qOz27dutxq+8vJyeySu/cxvXzvyO0lvX6Guuufql8mIzanfs2DFOo3bZ2dmcRhWN/WLEz8XFRF9OTg4UCgV69epFzxJnZWVZ1EflzWbUc8+ePSiePp3uyFq7PrKzszmNulPlM+LXri8Q/TyUSiXy8/PpGcsI93oM7NmV0ZFt7vqg8mZ7fzEeraf00e8x/7AKR7ek0bPeOp2O06gxZcfm/pKZmclp1oLL/TkjI4PxnbX7S3Z2Nqf7c25urmCjxo4M3x9dxrOwYqaROp3xDK/YZQFgtHEx09iSjnrGS1EWiUfLpZMyHnzLc4Q4ShkPvumkjqOtNPvO7P79+/HII48AAPbu3WvRbty4ccKq4kB+fj5WrlxJz8y6ubkhMTGRYWP8PlhBQQHCw8NNZiLi4uIYn4OCgkyWMDZ9Py88PBzh4eGMvJvm2/SHgDV9gOHdmqb5BAQEMMoKCgoy0WP8uaCgAHFxcYw0AMzqM17GxUZfRUUFq/g1pw8wjR8AdO7cGW3btkXbtm3NvsfXVB/lpzHm4kfZKJVKhIeHs9LX0NBgkrdQ9du5ydmm5uL32GOPoXSdYfMkf39/VFRUoHtoML2szVz8KH2XLl1CeHg4K30+Pj4Mu+biZ6yPbf1SMTfWR9FUX3FxMafrg7qe2ejr2LEjY+loc/GzpK+5+qX8bBo/vV4PLy8vXLp0CWVlZYiJiUFNTY2J5uauD0v3LUv6lEolIiMjGfoAMHe4/nvWW6FQcKpfyk828XNzc2OUL+T9eciQIYy2xeb+wqV+CwoKkJiYiMGDB2PTpk0gcKO2thbe3t6ip2mJdHzgWxZ1TYidxpZ0fJBSI4mHMOmkjAff8hwhjnxxhLqWuo1QNNuZ3bRpE92Z3bBhg1kbFxeXFu3McqXpjyKhbPnYi5k38dN2+5b20+R4HQ5LbqX009qy4Li4OKBBD7jK2NlyzdscdXWA0ftwQvhpDXP5N3fUlK1587E3t1kUV+zp3tLS1yjBMmyWJQuRpiXS8S3L+N1etvBph3zbrpRtXkqNJB7CpJP6nugIdd1a24gjxMOYZjuza9eupf+fjEwTCMLC5t1QITofYsLqXOCs/wJ3T0I/+EdOZwjzPnP4t9+AadOAX38F+vUTxW+zlP8FtL2P8ZVQOwgTCAQCgUAgEExptjNrifLycrRt21ZoLQSC08C7o2ZnWO2sXVwIZP0HGLWfc8eOV0fw0CFg/HigqgpITQVOnDC7yZTgXFsPKF8Bxl0A/LrSXze3gzDpzBIIBAKBQCDYBusNoOrr67F8+XIkJSXh8ccfR1JSEpYvX476+nox9QlO041FhLLlYy9m3sRP2+3F9POnn36iO2oPj0myeuyOvfppsbOWnw+c/wi49BUUXv8DAvuzOhrGWAtXe+zbBzz6qKEj26MH8PPPjI6saPWpXg0o50DhuZTRkQUMG+eZO2rKeKMiNjjqNWpP1zNXxMzbGSgqKpIkTUukk7IsPu2Qb9uVss1LqZHEQ5h0Ut8THaGuW2sbcYR4GMO6M5uWlgalUon33nsPq1evxnvvvYc//vgDaWlpIsoTnkGDBoliy8c+Pj7eLrQ4i59c7cX009/fn9OZn2y16PV6qFQq1NXVQaVSMc5jtIQtfprvrF1HX2wG1N8BCUcwaMT4ZmyZ56waa+Fkv3MnkJIC1NQA990HHD0KdOokmJ8WubIc+PNNYMReDBr9jMmf6R2Em5ybm5SUZCYzy4jZzrm0W65529P1LKafrZnjx4/zSteuXTtJ0kidLjAwULKyAH7tkG/b5ZuO67VlS1kkHi2XTsp48C3PEeIoZTz4ppM6jrbCepnxkSNHsHjxYnT6+wdi165d0bVrV/zzn//ECy+8IJpAofHz8xPFVmx7R82baDFPREQEjp7dh/q6OnoJbUluDkIeHMs776ZLl4+e3cdq6bItfsbExKD7qdOQb1xDn52aFHYFQZr9QMIxwL87/Jqx7R7Snt6dmU3eZu3v3AEmTTJs+tS3L3DgABAcLKifZsn6L6D6BBgpB4IGwZy1ySZeD47ldW6uo7ZzZ7meWzPGRxlxwc2N+1tMfNJInc6WstgMLjaFTzvk23alTNday+KbjmgUJh3RKEw6qTXaCuu7cmVlpcmZsh07duR95hqB4Myw6ahxpSU2GzLbWev5Klzq7wI+nazbNtOxY23fsSOQlgYsWQLs2QPwnDnhhE4L3NwKjD4IBPZv1tTeN/EiEAgEAoFAcFRYLzPu2rUrduzYwfhu165d9FmGjoJarRbFlo+9mHkTP223F9PP69evY9aM6Zg2YSz6hwVg2oSxzc6gstFi/I7p3VvXrS5dppYk//TTT6yXJJuD6qwlJCQgNjYWMncvRkfWWLuJbRN/m/rJ2v7pp4GMjGY7soLWp5sv8MgJRkfWnq45e7lGncXP1kxoaCi2bNmCc+fOATCcv11YWIiGhgbaprKykjGwrdfrcfv2bUY+ZWVlqK6upj/X1dWZzPoWFBSg7u/XCgCgurqacQ46AAwcOJBxr9JqtYz3Uc3pq6qqYujT6XQoKipifFdWVsYou66uDiUlJYyyS0pKTGyaDkAWFRVBp9Mx9BnHx9XVFYcOHWLYqFQqRnvTaDT0u2fU90qlErm5ubRNcXExMjIyGGVnZGSguLiYTpObmwulUsmwUSgUjLir1WqoVCr6/3U6HeRyOWt9FFz0UWVx1QcAly9f5qxPrVZz1gcAmZmZnPXxjV96ejpnfbm5uUhPT+el7/Lly5z0KZVKZGZmctZnS/woTVzaH2XHVR+Vjmv9Un8X+vpoqo8qi0/7UygUnPWdOXOGV/3+9NNPnPTJ5XKkpqbCVljPzL744ov45z//iX379iEsLAz5+fm4desWvvjiC5tFSElNTY0otnzsxcyb+MnN3twxOWL7yWXGjo2WkJAQlJw6i/q6Oujqaptdumy8JLltQDsor7JbkswHY+3WjiOyqT6t6Ba8PpvskmxP15y9XKPO4mdrJj8/Hx988AF9Jqurqys6dOjAsGnTpg0AoKKiAoBhEIr6jqLpma4eHh4mS9J8fX3hYXQ+tLe3N7y9vRk2p06dwvjx4xlpjDuu5vT5+PjAx8eH/uzm5obg4GBUVlYy9FH6KX1N349t+tnDw8Nk077gJq84+Pr6AgBdVkNDA0aNGsVYrtz0GeDn54eEhAQA99ph0/Mbg4KCMGTIEMZ31GdqADM8PBzh4eEMGypfiujoaPr/a2pq4ObmhsTERIZNc/oouOijyoqOjuakDzBsRspVX01NDWd9VD5N87amj2/8OnfuzIgFG33h4eEmgz1s9Rl3MNjWr3Eatvq41q9x/Ki2z6X9URq56qPK4lq/NTU1olwfTfVRZfFpfyEhIQw7Nvo8PT3Rvz9z1Rmb+o2JiWHc163pS0xMxODBg20+/pX1zGzv3r2xbt06jBgxAgEBARg+fDjWrl2L3r172yTAVkJDQ5Gamgq5XA7A+qhYbGws61GTpjcJa6MSsbGxoo3a1dTUcBrViY2NZT0qZgwbfZGRkaKMOgGGhzCX+MXGxnIaFaNuOsb6qI7dpu07cTrnDjbs3Ie169ajTZs2oo3KNp0ttRa/2NhYq/GLiYlBBy8Z5D+sQva165BvXIPObX0YP/QofSqVil6S3OP+fhg4IoHeTdmWUcU7d+5ApVJBoVDg6NGjOHXqFB1zvV6Pr5Yuw4Yde3Hmdhk27NyHb1Z+i/Pnz9N59OrVi9P1QeXNRl9ZWZmgo8ZN9VFa2MQvJCSE0/VB5c1WX69evUz0Udg6akxpYRM/Ly8vTtdHbGwsysvLsXnzZigUCnrFgCV9xj8ihJ4ViI2NFWzU2Blp2pkVK01LpJOyLD6vJ/B9pUHKVyGk1EjiIUw6qV+VcYS6bq1txBHiYYzL4cOHG60ZyeVyXLlyBTExMSa9+ZZEq9UiOTkZ5eXl8Pf3b2k5nNFoNE6xyYi9+6lSqbBh5z6Td02nTRjL6cK0Bz+tzXxSKBQKnLldhofH3NtV9/cD6egfFmD1Gqf91NcCZ+cDvd8FvIJMz879+z1garZXkDj/97+GY3eSkwWPiUU/GxuAxkbA1XHO/+WCPbRbCmttyBb4+llRUYG2bdti9+7d9Cybs6DVajFp0iQsXrzYZGbVHBUVFfj444+xYMECxkxoa7LX6XT0rCrX/Gtra7F27Vqkp6ezsncU7OkeYg+QeDAh8WBC4sFEiGes1ZnZTZs2YcmSJbh48SK+/PJLbNu2jVdB9oLxjICQtnzsuRx5IKYWZ/HTkr2l80ybvvNlDXvwk1q6PGLECLPvmFIYH3uj1+nMHntjiePHjwO6auBYClB8HHA1/LAz3oDq4TFJSJg0E7+fu4g1a9ZApVIhLy/P6nFEzfr56afAm28CTzwB/D3jZi0uVOdow859UOaW0LPubN4PPn78ONCgB04+D1z4l1V7e7rmuNhzPXpFTD+NVwywOX/ZXvxszfA9RsJ4RYiYaaRO1/Q9WjHLAvi1Q75tl286Psc3SamRxEOYdFLGg295jhBHKePBN53UcbQVq53Z9PR0fPbZZ1i5ciUWLFhg8rK5o3Hw4EFRbPnYi5k38ZO9vaXzTPkecs9Xh5T2xuefZmzbQJ9/2txuytSGUbduXINmzwg01lcCow4AHgEAmIMCer0eB3f+jKKqOlwvNHQiT54+g+Kb15o9N/bgwYN0OfQyU50OeP994IMPDEaPPWY4goeFn8Yd7A7+PlY7RwwaG4AT04HiE0D3l62a29M1Zy/XKFcdp06d4nT+sr34STDFeMm3mGlaIp2UZfFph3zbrpRtXkqNJB7CpJP6nugIdd1a24gjxMMYqxtAlZSU0C8Bx8XF4e7du6KLEpPRo0eLYsvHXsy8iZ/s7S0dk/Pkk08y7PguVxVLty32xsfe3L59G2FhYWb9oXzOy8tD5slTqJc1op9vFgqK7yLD5U1Mc/UFlcJ4A6pb164iv+gu+o5MQt9ePdC+fSD2/bAafvqaZo8jGjFiBHOZ6ck/MfWfb+F++T6DwbPPAmvXAn8v82vOT71ejxMnTqBO5onSsnJ07T8Ebh7udOeo2aXNDfXA7b1Ax/PA6COAd8dm42lNiy22UtiLlTdXHQMHDsSPew6wPn/ZXvwkmBIUFCRJmpZIx7es+vp6zun4tEO+bVfKNi+lRhIPYdJJfU90hLpurW3EEeJhjNXObGPjvVdqXVxceC+VsRe4HHjO9XB0voepi5E38ZO9PZvzTE3e5Tt11qbdf23Vba1jzSZ/a7spG/ss8wvAhatq/G/0MZytSUDuyGPI2vwj4wxb40GBu+UVqG10Q3CAPzp27AgXVxe079wV/Tq2QadOnSzG+erVq4yzcjsveB+RVEd29mzg228ZuxZb8pPSfkJ1GUVVdfC9mIXgAH/ExsQ02zkyJK4FMp4G6vyB0YcBL3Y/eO3pmrOXa5SrjtjYWHQ/8wfr85ftxU+CKa6urPeXtClNS6STsiw+7ZBv25WyzUupkcRDmHRS3xMdoa5baxtxhHgwyrVmUFdXh4ULF9Kfa2pqGJ8B4N133xVeGYEgIdY6dsbLVY03LzLuzEmF0B3rptTV1WHPnj347bffUFCtx6y3PsDt/AL4RN4HxZUqVAYMRn/vtiYznMaDAidOnIBSfQOxMTFwcXWhZ9g6PTi22Tg3fX+5LGk8ateuROGYMej83XfA3z8IKY1Xr15F9+7d8eijjzKO9KDqa/Lr7+Dgzp9x6+KfUNfr8NfRdAzoGdXscmqoPgGq84GI5xgdWSFn5gnmYTOwRCAQCAQCgUBhdagwISEBMpmM/jd69GjGZ0f7kWF8DIOQtnzsxcyb+GnZ3uSdTL3e7HfGeVvaJMrSu3xi6KZoutGSufdA+dZ/XV0dXprzKlZu240LxVW4q3eDXHEQPj7eqK+pwkXfx1BdVWtxwyhqUOC5557DgJ5R2L/pe/y2fTOrd3IBwxmCxu8vl9z/AJa+8jrKP/uM0ZGlNOZUNWLltt14ac6rqPs7DXCvvry8vTH2yckYlZCAYDc94qK7WO/03/eO4V1gmRf9lfFGUtSxQk03krKna85erlE+uqk2lJCQ0OwmZnzy56qFwB/jM1zFTNMS6aQsi0875Nt2pWzzUmok8RAmndT3REeo69baRhwhHsZYnZmdN2+eFDokw8vLy7oRD1s+9lFRUXahxVn8BAyH3Ded1Yw6eRJoBLILSxkzncOG3NvF0/h9UEvv8knlZ3Mda2rGk2/979mzB7er9Hjm7U9ReOs6lMcOoqy6DiUlpfD3dENmxiHcH9PTaufUeIYtKysLMcMeYjXDFhUVhbz8AuYy0949EXPffWY16soKETdxOrYsfh979uzBxIkTAZjWV+du3XHJxxuDBw+2PgDn3obWQsFmZt6erjku9lzaLde8ncXP1kzTs8jZ0lqXGfM+OoKnRj7tkG/b5ZuO67VlS1kkHi2XTsp48C3PEeIoZTz4ppM6jrYi3QsjdkJ0dLQotmLbO2re9qalrq7OZFbzzJXryLyQZTLTaTzTZ7z77+8H0s125thooWZ8c3Jy6Blgrn5a2n3ZeJaUb8yvXr2KkO694entjU5RPREY2B5XMo/g9/17UHDhFLoHtUF7twYMionC9GenNtsxpGbYnnrqKaszbBQ9evTArBnTMW3CWPQPC8C0CWNNZlKNNfqGdoGntzdCuvfG1atXaRvGbs3y3fjhv5+htqyYnoUHYHY23lJc2MzM21M7txctzuJna6aqqgpbtmzBuXPnABiOmCksLGTsn1FZWQmtVkt/1uv1qKqqYuRTVlaG6upq+nNdXR00Gg3Dpra2lnHfra6uRllZGcNm4MCBjGtVq9UytJjTV1VVxdCn0+lQVFTE6JiWlZUxyq6rqzM5hqekpATu7u4Mm6YDekVFRYzjKbRaLSorK+myXF1dcejQIYaNSqViDBpoNBooFAoA99qhUqlEbm4ubVNcXIyMjAxG2RkZGSguLqbT5ObmQvn3MWYUCoWCEXe1Wk3PpkRHR0On00Eul7PWR6Xjoo9Kw1UfAERGRkqiDzD8MOeqT6fTITs7m5M+wND+uOrLzc01uT7Y6JPL5YiMjOSkT6lUMjoqbPUplUrGvZStPp1OR6djqy83N5dOw0UfcO8646KPSsdFH5WGqz4qHVd9gOH1UC76ACAgIICzPgDIycnhpE8ulyM1NRW24nL48OFG62b2iVarRXJyMsrLy+Hv79/ScggOgEKhwJnbZXh4TBL93S/rv0dlSRFmzL23CuH3A+noHxaAhIQE+jtb35k0edf1781tuL7r2lw+AFhrNOfPzp07sXLbbsye+yK8PF1RUNsBK96cjdiIEPi3DYB3x3B0iIzmrd1S+aHt2qHXrVuQPfYYa43PvP0pPL29UVtdjS2L30fqk8n0zCyVVqVSYf2Pm6CBG7rF3I+S2zfRPaQ9pj87Fet/2Mi6LlQqFTbs3GcyMzttwljJ35kmSIsQB7o7KtQzdsWKFQgICLBqX1FRgY8//hgLFiyAj48PsW9CbW0t1q5di/T0dFb2BAKB0NoR4hnr8DOzoaGhSE1NhVwuB2B+VMJ41ESj0bAalSguLsahQ4cYNtZGJah8uY6asBnVOX78OKdRO41Gw3rU09iGjb47d+6wih+fUZ309HRO8dNoNJxGxQICAlCSm4PywgLcOHsS9XV1qCq5g4aaKlw78zu0ZXfpmc7GxkaGPplMhoKCAgwaNIieaeRSv9RS1ZETJ6G9jwfjXVdr8dNoNHT8qCW84+IHoJNrDT17CQBfLV2GDTv2QnmzCBt27sM3K7/F+fPn6XyqqqqwfPlyLFy4EP947XWs/WU3Tl66ho2/7sbadesxduxYPBBUDa/f/gX9kXex/I3n0LZdO3Tp0x93avRw9WmDh0Y9gqGPTsT1vHzGe7qW6te4Pqn2R3XIN/66G8q/1HCfNAWyiRNR8r//0fGjbH78dTdOqXPp91SHDBmCmI4BWP3uHGz8/F2sfncOugf5oV+/foz4nThxAjKZDG5tg/DMK2+iY2BbDH10Iq4W3MWePXuQV3ADY7rcwpBRI+i6+Omnn1BeXk7P2CoUCpw/f/7eTO8Pq3F081rIf1hNz8xT9Uv5yeb+cuPGDU7XB5U32/uL8Wg911FjKn6W9FHls7m/qFQqTvc/LvfnjIwMRlls7i9c7s8ajUawUWNnxLiOxUzTEumkLKvp7LVYaWxJJ2VZJB4tl07KePAtzxHiyBdHqGup2wiFw3dm8/PzsXLlSiQmJgIwbAudmJjI2B46NjaWXkKQmZkJPz8/xowbYDhDNzw8nP4cFBRkcgzRkCFDGOfShYeHIy4ujv6cmZmJhIQE+Pn50d9FR0czZm6s6QNgVl91dbWJviFDhljUl5mZaaIPgFl9xj982ei7cOECq/g1pw8wjR8AuLu7c4pfZmYmq/hR+kpLS9E9pD1+27MdeUUlkG9cg/49umJQnxj8df4czp0+RS8hptJZix/b+qWWqvoGtIObuztjqaq1+GVmZjLiJ5PJMHz4cDz77LN0xzorKwuFNXokPvsCQtoHIHHqbNwqr6Lf1aqrq8Oc1+di5+9/4ILGBcobBci6dBkhYWEYM+0lXC24i9yLB/D58COICffAbzmx6B0bi/eXfIMeQ8bggcem4255JW5cvYS2HULg36UHCgoK6BnQmpoa+rNx/WZmZprEj+rYj318KlK3/Ige6isAgGNFRXT8KJuEaS9haMpTdIfz9u3b6BUTg8iIzujRqSMiIzojNrYPIiIiTOLHWB7c2IC2HUIQGB6J29lnMKfLatzfMR8NLvfqol27dvjp51/ojZ4OnDqLU8ozAGBY/pyShEF9emFaShI9i0vVL+Unm/vL1atXOV0fVN5s259xJ47L9WGp/Rnro7Swub80Pc9XyPvzkCFDGG2Lzf2Fy/WbmZmJxMRErFy5EgTulJaWSpKmJdJJWZZxGxczjS3ppCyLxKPl0kkZD77lOUIc+eIIdS11G6Fws25i4Pjx43j44Yfh4uIiph7RafojSShbPvZi5u0IfppbQiq2ljFjxjDL/fvoDwCiHgei1+tRWVmJP47sR1BIKKL6x5vdRMoSbPw07rhFDRwGAIyNoYw3TqqqrUXMqGQcXL0EBWWV6OHhgR7hLuhydQZc7puD7vcvwLjAgzhzuwxe3t7w8/NDfV4BAsI6o6SwEGFduuHymUwEVHfH8d9PQOPqiaCIbmaPCTKnvaCgACGBweg342m0VWai0cUFmx97Crd8faFSqRATE2PxPdVTp04h+04pRk98GhWlpejTrh0unTpu9pgk442gogYOQ31dHfT5ZzEj4ntcqgxDRfQKuLu60XVR1SbK4kZP1nZitqdrzl7uRc7iJ8GU4OBgSdK0RDq+ZdXW1nJOx6cd8m27UrZ5KTWSeAiTTup7oiPUdWttI44QD2NYd2a/+OILeHp64tFHH8W4ceMYI+AEAlfEPiu1OSydKdvc+afNYe1dWsrXK/nFaBseha3rvoe/ny8iOoXBH/XIy8sDAJs70NZ2XDbeOEnX0AgXuKBjdAxyr2XDs+Q0Jrl8iNIOz6Fj309N8gsOCoJ/Tg4yMw5BHxMDxS+b4e3nj5waV/xxOQf3P/AgHhr1CBr0elbn74Z5e6Pnpx+gbd4tNLq6YtMz07DORYb+XoHYsHMfup86jQH9HzTrT0D7Nsi5lYu7tb8hMLwLLqhUqCy8jby8PJMyY2Ji0P3UaXp35MaCP/Bqp1XwjpyAs1kP4+qPaYx3Zn18fMx2oPPy8pB56nSLtFcCgUAgEAgEgnlYd2Z/+uknHDlyBLt378aGDRswaNAgjB8/HgMHDhRTX6smIyPDZClfa8Scn2yOOnEEjDvl/n6+qDDTyaGX1D77PNzc3HE7/zb2pK1E7d0CaDt0wtk7lTh0Zh/vzhHVmc7Ly4NvQx32/bAa7Tt3pTto1Ixi9+7dcXDbbtRWV8PHxxuyYh1yzp6EZ1RX5Bz8Bo1tH0XciK/ofJt2BO/euo74mCj4erugJjIST6e+gdzbt+HbLRbXTh3FjauX0K1Xb5NjgszRMyQE1VUa6F1dseHpZ7HJRYZHnp6GAXFx0OnqId+4BgMAekdi4w5nu7b+qNBokfTKDMYGUE13TwWYRwSV5qnwkPdauEVOgmvcMswa2GAyG5+VlYVDZ/ahvq4Ot//6E2H3PWB1xtaR2qs5nPk+5Gh89913yMzMxJ07d+Dl5YV+/frhpZdeQocOHWibO3fu4KuvvsK5c+fg7u6OUaNG4ZVXXmHswrt9+3Zs2bIFZWVliIiIwJw5c9C3b1+zZcbHx5v93lkpKSlBYGBgS8uwK1rDtSUkJB5MSDyYkHgID+t3Zt3d3TFmzBgsXboUq1evRkhICD777DNMmTIFP/74IyoqKsTUKRhNNwQRypaPvfFRAS2ppSX8tLSE9OzZs6Jq4RoXa8e3GHfKQ8M7MzZ1ojD21cXVBZ06dUJYxw6obHTD2GefZxwHZJyOjW6qM71h5z6cvVOJSrjBV1+DUFSZHGvz6KOPIsxHhi2L38eBTWtxZO3XCPd2w+gHeuO+UR8g7tlNjI60TCbD9GenYlBMFFwKrmHwfdF45+1/wt/fH91694OHl6dh+XG1ll5+bO6YIHPaZVFR8D5+HLnLlqHwgd7o/9BgDIiLw+1L5+m2UFRUZPaYHj8/P3SJ6oEbly8i58RB3Lh8EV2ieljcHZSaifcO7AaPuC/gGrcMcHGhv09ISKDfOzY+0ifn0l/0+9OWZmyNj+axp2uOiz2X+xDXvJ3FT6lwcXHBvHnz8Ouvv2LdunUAgHfffZf+e0NDA9599120adMGP/30E7791rABnPF7v0eOHMGaNWvwzjvvYNeuXUhKSsI777yDwsJCs2Uav2PMhaZHhoiVRup0fDdy4quRTzvk23b5puN6bdlSFolHy6WTMh58y3OEOEoZD77ppI6jrfDaAKpt27YIDAw0/JCtr0dGRgYmTZpksgOlPWL8I1tIWz72YuZt735aOiu1c+fOFtOY61iKqT04OBhr0tLw9YbN2HMmC19v2Iw1aWn0WaUqlQp79+5FTQNw4+pl3Mm9hVvXriIgrDOjk2PO1zt5t9ApuhfdOXJzc4fMLwB79+4122m2pNu4M/3wmCQkTXsBVW7e6NKli8nZrh4eHvh2+f+Q+mQyevvo8PJTydi8cT2jM2eMXq/H+h82IjMrG40h3ZCZlY31P2yEt7c37U9wUBD8Pd1wMeMQbmZfMXv+riXtspgYdJkzB+PGjYNeUwadrh5+7YMZHWJzHc5OnTohwNMVMd0iEBQchJhuEQjwdEWnTp2arc+Q0DCg67NAM+/9UzO50yaMRUc/T0x5dAwG9H8QN2/exOUzmaj5+5xMc512e7rm7OVe5Cx+SsULL7yAnj170hvlTZ48GdnZ2aisrAQAnD9/Hjdu3MCcOXPg6+uLkJAQzJo1C3v37qXPUN2xYweSkpLQr18/uLu7Y+LEiQgPD8e+ffsE1erp6SlJmpZIJ2VZfNoh37YrZZuXUiOJhzDppL4nOkJdt9Y24gjxMIb1MmMAOHPmDHbt2oUTJ04gNjYWL774IoYMGQKZTIbMzEwsWrTI7jfNMN4RU0hbPvZi5m3vfjZdwkotIbW09KK5d2yb2jX3/ioX7WVlZdj/+xn0Gj0efoHB0JQUYf/BXRjw4IM48+dZXC24CxefABzZtQVZl68grk9vHDpwADl/ZiLwsWS6fHO+dg0LgbayFPV1dXBzc8cp5Wmc+O0owsPDkb1hM/r36IrZM2fS2i3pbjrD7SqToQ6uOHnyJNq3b2/iv4eHB+Ms1ubytrQUvE9ECGP5L7X8+KH+vdGpUye6TOO6KCsrs/hOsKW2YGnDpZiYGPQ4dRp/7t+JwPBIXDt7Gj1Cg6xu0MS27qkOdG5uLk6f+cPQ5sIiUKGpwlfz52JI0niU3b5lotGerjl7uRc5i59s+fDDDzFhwgSTXZb5cvr0aXTs2BFt2rQBYDhqKCwsDG3btqVtevXqhZqaGty6dQtRUVFQq9VITk5m5NOzZ0/G8U1C4O3tLUmalkjHtyw+G0DxaYd8266Y15NQZZF4tFw6KePBtzxHiCNfHKGupW4jFKw7s1OnTkVlZSUeeeQRrFq1inEMBgAMGjQIHn//oCYQrGH8LiObHYTZvGNry6ZS5jrBp06dglv7EPR4YKChc9alK9RnT2PHjh2o9vBD4tTZuKm+iqwragT37AM3lxp4BrdFvYcPLhVV4trOe+/BNvW1R48eWP/DRsg3roGrb1scPaRAUFgEYhIeQ1V5CfYf3IWH4uIsvsdGYbxJk6tMhj2bN+Cvixfh/dBgeiMlE//rygGPtpYz/RtLS8Grq0vx3HPPGfmTZNKBzcvLw8nTZ6BxcUd420AcOPmnxbpo2haC+44BABw+fNjsgERzbcfaYAYX7t69y2hzccMTsHXlfxFUX4HxE4Tf8VpohIwFQRjat2+Pjz/+GP7+/khOTkZSUhICAgJ45XXmzBms/3/2zjyszTLr/x+SEAKELWxh3ym0dKd0VbuvtnW3jrbu4zK7vuM4o7P4c5xR51VfHXWcsS5t1draalu70dLNQmkptKWFsu87hBAgIRCS8PuDJiYsBVLA1uF7Xb1mgve573Ofe3me85xt82Zeeukl89/a2tp6FZ03KbqmmPK2trZersMuLi7U1NTYxMcYxjCGMYxhDD8khqTMLlq06KquMtu2bRsWpkYSCoVi0JmYh9LWlvZDwUjy8kPNs6+swv313Z9iVVxcbKbvqfC2a7VsfftVuj7+mNmzZxMbG0tTU5O5f0vF61TqacoamxE5STG2tzFrYiydOh2aJgVdBgMIhXQZDGiaFLToRfhOicNeLKZJUU/kpKm4yoMwFF/Aw9uPWcvWEOTpxuRZc60Ubsu5KhQKs0L20Ucf4Srz4o6f/RZ7sRiDwUDhhbOkpaWZldn+5GJp1dQhIDsrizkrbyPA2xMXT2/OJe62TlJUvhPSfwa35pkV2v767i87cmCIX59rZ/kxQYeAyzlF3Dt3PsvffRX93Pm8d2WNLD8+9FS2vL29+Xbf/gE/SJjGl8vlVutpGj8uUMepdB3+aSFm2sHuWxNfJSUlCF3liETdiXPEEgfCJ0whzN+9z6RP19OZq6urG5QcbcH1NM/r6c4dDH71q1/x5JNPcuTIEfbu3csnn3zCvHnzWLNmDVOmTBl0P6mpqbzyyiv84Q9/sErC6OTk1Csey+SCbIopd3Jy6lXYvrW1tZcSfK3Q6XRD/sBtC80PQWcLTG7eRqOxz2R1/aG5uRk/P78hjWXr3h3J8zRcY9lCNyaP4aEbTXnYOt6NIEdbcSOs9WjvERMGrcyuXLnS6ndGRgZCoXBID+DrAbm5uYPOIjaUtra0H8rLw0jycj3P01KxKC4sI/6mhYglDmbFqtM5zNzWUuE1GAwc2bOThjYdl5XtFF6xUo6LiuSmm26yUnzsnFw5cOQ7fEMjiBs/g8bKEnYfPsaqW+ZgbFFydOtGvIPDaCgvwdiiZM6y2zib3x0zKvPx4cLFizi6uuPi6o7Y3ZPi/Cwmx0aZXX73798PWJfeMc0zLi6O8ePHU5h2CVMkp0lphu9fYPqTS21tLTOmT2MGsH//fur8AqmrraW5NI92iTudWv33JWtKt8KZx2Dul1aW2f7Wsz/3355eGSZYfkzIPJ2CvF3P3S/8mswJscz8aisezzxvlkV0dDSfbtlCRn4JTjJf2pTHmR4dhoerq9UHCV17B9s/eNPqg4SlMmbJu2n8e1dFMyn3fnIX/x9bDjWaFejB7FurfdEl5PyZVDxDIs1Zlq9WF/h6OnPJycmDzr48VCXmeprnUNqP5DyHAgcHB1auXMnKlSspLCzkH//4B88++ywBAQHcfffdrFq1CoGg/3QWhw8f5u233+ZPf/pTr2oCkZGR1NTU0NzcbHY1zsvLQyKRmPMRREZGkpuby6JFi8x0+fn5/c61ubmZL774wnzuYmJizO71MpkMkaj3a4TBYEChUCCRSHr9N2dn537Xor6+vk/lUiQS9ZtBWKPR0NjY2OdYlhmcLaHX61EqlbS3t1vR6XS6q7rJmWhMybJ0Oh2zZ8+mpaWl3wR0Go0GjUZDe3s7QqEQHx8fnnvuOezs7CgvLzcruZYQi8Xme9bT05P58+eb5R8REUFkZGSfYyUnJ6PRaKiqqrLKIeDs7Nzv+hYWFlJUVATQi27u3Ll9JgBTq9WkpKSYf1dVVZGYmDho/nqONVj+etINhT8TzWD5s6QbCn8musTExEHzZznWUPgz0UVHRw+JPxPdPffcM2j+TDQ333zzkPgDaGho6LP91fiD7nNveU8NxB9Ae3s7a9euHRJ/pvuoLxlejb+5c+f2+Zy4Gn8RERH93n9X42/evHl9jjUQf1KptBfdQPyZnhE9xxqIP+j93OyPv/T0dGpqahCLxX3egUOF3bFjx7oG0/C3v/0t999/P1OmTGHnzp385z//QSAQ8Oijj3LXXXddMyO2QKPR8PjjjzN//nwefPBBli1bhl6v58iRIyxatMj8kM3KykIikZgPoFqt5vTp01bxvenp6cjlcvODTKFQ9FqU5ORkYmJizF8dKisrqa2ttYp/SkpKYtasWeYLorCwkPb2dvML5Bh/g+fPYDDwrw/+TXVTCy6BYSQf+BYnqSuTJ8TSqm0nys/LbN2USCS0t7ezZc9BFty+jtzUY5wvqiBowlSmToilva6crLRU7r9tFXFxcZw6dYp9R46zaMNTpH53nFOnzzBxXCR+46fgLg9mx9t/ZemkKPRGI5eKKxBInDC2tzE+2J9HHn6Ir3ft7i7H4+FB7rmzaI0CoiZN5fzZ00yPjWb+ukc49M12Lp4/x4ypkzG0txHh78vDD25Aq9VayS8zM5O33v83Ym9/XIMiaCgvQV1ewLo1K7n77rt7yc/Dw4NPNm2mqLoeqbsHLS0tRMk9cZU6s2t/IkbfEDyCImkoL6I5L5N1a1bw6AIhpP8c/ayvOJIjGPT6mpTmCxcuEBQUZI6Rt1xfU5vdu3ejRMzqB5+g5eAexr36Eq3ubsQVFrP9L6/z+dlUpsVEYhQIkejbySqvYfysm5E4OaPpEnD26y1MCPbDUeZN1KxbUCmbqFeqUJQXIbEz4uziSpTck7vvvIOzZ8/22n8VFRV0qjO4U/x/nJO/Rl5bLFUNSqb7u7N48WKSk5OJioqioaGB2tpa7O3tcXR0tFIIvvzyS84XlbPsgccRCIUc/OwjykuKCZ0wGYNaRaSvjKAAfxYvXvyDn4+rnV87OzvO17YwZ8kK6opyEYkdyMvNZbq/O7NmzfrB+bse7pfB8JeYmMimTZvYunUre/fuHTbrZWtrK4mJiezduxeVSsXy5cvx9/dn586dRERE8Kc//alPum+++YaPP/6YV155hUmTJvX670ajkccff5yoqCh++ctf0traygsvvMCkSZP45S9/CXRnM/7f//1f/va3vxEbG8v+/fv54IMP2LRpk1WJH+h+xt566628//77g3KHbmlp4aWXXuLll1/uV7kba/8Sf/zjHwcVr9vR0cEXX3zBgQMHBtX/GMYwhjHcaGhpacHNze2anrGDVmZvu+02vvrqK+zt7Xn00Uf5xS9+gVQq5aWXXmLLli02DX6tMD1om5ubcXV1/UF4GMPIISsriy17DvZyG54dN65PK52lVa2xuQV1l4jZ8xcxdfJk7AR2nDp8wKzYJCUlkVGtYs6SFSTt+YbCuibCp87A1VGCb0AQ+z95j0VRftaxoRZxh5aWUW9vb6DbMnz6TBpqgQN6gYjL2dnm2qmdOh3bP3iT+MiQXrwbDAY++uRTTl/KMSvNsybG8ujDD/XpEtpTLiarm0N7C0kX85l1x3qkMi/USgWnv97Cq2tE3CzdDbfsAd8Ffcra1vhKS5kLpe6knz7FXKEdz32zHUeNGqXEkX899nO+UzYyaeo0Vq1bj9Fg4PVf/xTnkHGsfeI32AEnvtlKzvmzhAYHU5iTjVgsZuItSynOyUJTX80jP/8VIVExJH7+EevXLO/Tzbfs1Hv4Ff+Ggpj3UAb8xGzVNcncFKdsdr29Ymm2dL213Bem+e3evBE/kZ6VK1feMHGn/e2R/mQ3hv4xHA9aEy5evMjevXv57rvviIyMZM2aNcyfP99sjWxpaeHee+/lwIEDfdIvWLAAoVDYy+L42muvmZXb2tpac51ZsVjMwoULeeqpp6wsnqY6s01NTYSEhPD000/36WU1psz+sO07Ojr45JNPxpTZMYxhDD9aDMczdtBuxnq9Hnt7e5qbm1EoFOYHn1KptGngMYyhP5gUq/3796NDhOCK8iBxdGTc9Fn9xixaJgZKTU0lvbCMuNhY7AR235dSueIiahkPGhYZzYXMHdQUu9Pl509zfQ36xloS7l/bZ2yoaayef588eTKLFy828+54pXYqQFZODs1dYs4UVlDY0GwVvygUCnn04YeYM0hlsr/44drzKQRHxRIaHkFHezte4RFoxwcQL94I8w+Az9AzRQ+kuFm6FguEQrQiR1b86Tc4atQ0ODrx53nzCPRzZ7yfnFXr1pvn6+ThSbOiDnVrK5VFeTQqFETNnI/M0R6JXyhVeVnouyBkSgKlp48Bdla1XXutf8XXBFc8xxH94xw+1IzMfx9nTx5Hq24hNHYiW/YcxNm4h1ZErFj/eL+utz3jhI0GA2KMrFy58oZSAoeaIXoMo4Pnn3+exYsX8+677/bpqufq6sodd9zRL/2xY8cGHEMul/Pqq69etc3tt9/eK6v5GMYwhjGMYQw3IgZdZ9bb25sLFy5w/Phxc1IatVrdZ7zM9Yzk5OQRaWtL+5Hs+0ad58mTJ/lk02a27DmISiLjcnY2+77cgsFg6LO2Z09eTErmI488wvToMLZ/8CZfb/6Ybe+/QYSvB01NTcCVl/0r5WVqKkrQK6qoOXMUdXE2DZkpLJ0znZaWliHzbxrfsnZqg0JBg6oFB3sRNy2/lWX3P0pJVQ05OTm96Pqr+Wo5T7lcTmNFCVVVVRQXd/9vY0UJ8+bNQ6+so768hI7yPOrLS2io01I0IbFfRRZg165d5NcomLp0DfKIGKYuXUN+jcKKv/5gqVg3Nipx9fLhq1+/yOUJkzjzxW6ktyzG09MTMUaMV2rndup0SB0lNJYVsWfjO6Qd3o9S0YDeoMexS4eDiztSNw+E6iZcHSWMn3kzqkbF1ddflYXd3C9ZsP4d1q9ZjqyzGVepE7/++/8xb1m3zBUtavQCUa8yRpa1fS33xanDB/qsm9sfrqczl5qaaq6XO93fnfVrlg9L8qeh8jLS87ye7tzBYMeOHTzzzDP9xpxBdy3ZHwNs+dBt68fx0aYbzbH6y1FwNdi6d0fyPA3XWLbQjcljeOhGUx62jncjyNFW3AhrPdp7xIRBa6IbNmzgueeeQyQS8dprrwHdSaCu9lC+HhETEzMibW1pP5J936jzFAqFZkufSGSPZ0gkh7ZvQa/rQIyxl2JxVV66oLO9nVZlA8b2dujqTjxkGseyvMvKF54DuhMVyOULzZmPbXW/tcoyLHSgsLCACRMmEBIVg1AoROoX0reFsR9YzjM6OpqG0nf45K3XkEdNoLYgG38nISuf+TmNKhUZmSlI3b1Qqy6xdM50xk+acdW+hUIhqg4jOcXlOLq6o62qRdVh/D5x1FVgaclUq9XYO0pp7Gjj7D8/JDxmAp4tKnxdRdgJRXz25itmN+oQTzcUcl9EXQZa6itp6zQyY9XdyGQuCHVdZB3dy5QlS3GUuXMo6VuYMIHqgstIjR1UVVWZZWyWy8TuGEMhEBcXR21tLUoHDyRX4tLsxWJc/IIovHR+wDJGQykZ1d8aDWdbW9v351Vwrbje5jlSGIm+k5OTCQ8Pt3puFhQUUFpaypIlS4Z9vB8SfSWWGQmaH4LO1rHa29uHTKdQKIZMY+veHcnzNFxj2UI3Jo/hoRtNedg63o0gR1txI6z1aO8REwatzC5YsIA5c+YAmMvzTJw4kYkTJ44MZyOEoaSMHmp66ZFMRz2SvFxP82xra7NyoZ0RH09jWSHu7UqWLVsGWNcf7Y+XnJwciuqbeOCZF6xcShsaGvDy8rJSUE1xaD1hSrRkcr9tPHMe6a49zJwxnYCAgKsqOT1dnpudxCxacydCoZBOnQ5VfQ3yWVevIWsJy3nm5+fjFRLJvIS5tDQ14TpnJrlpKRQVFfHoQ327K19NKe/q6qKsKJ9Zd27AwdGRDq2W07u30jY1ekC+LJV2odSdjDOpTJo6jZCoGLMlNWzVEuobG7GXSHCSedOmrEOj1TJ3xRoCwyI4dfggBSWl5J5MpDU0nLrSQjrb1BTnZeNVV8nc2Ajip8SQfu4CaqGEC3WtHM34XvnsCz3r7xZdvkTBxfN4OYk5sOXDXjHNen1nv2WUbFmj4Ww7Gu1Hqu//lnkOFps2beKf//yn1d88PT35y1/+8qNTZm0peWNrmZzRprN1LFuU2aGU8THB1r07miU1RpPHMXkMD91ol1y5Edb6x7pHbgR5WGJIPsI9a8z2lyr/ati0aROHDh2iubkZoVBIdHQ0TzzxhNWX6qKiIt555x3y8/Nxdnbm1ltv5cEHH8TOzu4qPd94KCwsvOEs27ZgKPPsq7ZpY1UpIeFB7Nq9B7XAAa/gcBSnzyH9+htmzUzoU7HsL660qqqK02lnBxUfahkPKhLZczY9nUPbt9AkkCDOuNSLruc8TZax2NhY7DZtJunLT4clfrG2thav4HCiJ04x/01RW2O29Jr+DVRH15RgysnJCVepM8k7NiELDEFZWYar1LnfhCOW8xQKBGalvaqqCgdVDep2DWeOHjLPE6Corol7n37WvKbb3n8DVWYGsxctZ84SaDt0CEd3T+Qujsy4dQ1njuwjIfr7ZFk5OTmcuHCZ5YMoNQPfK9kHtnxIaUUlLWoNIRHROIvtcEGPvZ3BHNNsJ+g7HnfsfP64cD3MU6VS9XpuymQyc/jD9YiIiIgfmoXrChqNZthr8t7ouB7O1vWEMXlYY0we1hiTx/Bj0DGzOp2OjRs38uSTT7Ju3Truvfde87+hYMGCBXzwwQfs3buXHTt2MGPGDJ577jkMV+Lp2traeO6554iLi2PXrl28/vrr7Nu3jx07dgxtZv2gsrJyRNra0r6/2lCjzctozNNgMJCVlUVSUpI5PrEvuLi4fB+zmLiPf/31RSpLSynR2pGSW4KiuRWBUICiuZWU3BK+yy5iy56DfLJps1Wfcrm8uw7tlfpVJithQ0ODWUGds2QFy+5/lILaRrKysnrxV1BQYFaIGxQKWjr0TJi3kOCIaDOdZVxpf+tpstJaxi8uWbRwSPGLljLve24lTDFugvKdAJSVlZljj4/llLHryHdUq9S4hsZikMrYffgYWVlZAEgkEoID/AkNCkBTUURoUADBAf5W9QYtYZ7nV1/BvfciNBqJi4tj2bJlvPjCH3jwtpVWcZrFxcW9PiyEx07C3R4SP/+I6rISlMW5KPMvIcFAwblUZsZG8cgjj5jjhy0/Tog6lWbls6Cg4KoynzMhCicHMQ//5nesvv12Vqx/HI1ATExMjDmm+XsZWsfjDuV89lyj4Ww70u2vdZ5XO9s/pnkOBzw9PamoqLD6W0VFBR4eHsM+1nDB1pcurVY7KjSjTddXncWRGguwqVKDrXvXVrqhnq1rGcsWujF5DA/daMrD1vFuBDmOpjxspRttOV4rBq3MfvDBBxw/fpzFixfT3NzM3XffjUgk4tZbbx3SgMHBwbi4uADd7o0CgYCmpiZaW1sB+O677zAajTzyyCM4ODgQHh7Ovffey65du4Y0Tn+ora0dkba2tB/Jvq+HeZpecktLS3nl76+xefcBMqpVfSqfJjQ0NJgVP8skPjHxs4i6aSnFFVUkn0ymuKKKqJuWIvcP6FOx7JnI58CWD3HSayktLUUodUck6i5tYS8WI/MPZtOWLWzZc9CKPzs7O7PS2B0P6oyqugKZj4+VJW8w6JngqWcR8f4UAtPfjx8/bpWkKMLHg8/efIVN//can7/5Vx4K2IunNhlk04Hu2rUmpd3F05vASTOIW7KW8CnxLLzvMQSuMk6fPk1WVhYlJSUUXzxH8vFjtImcSDlxHGVFsTm+uE98/jmsW9et0L78cp/zNFlUy8rKyMs4TfuVl7lOnQ5ldTkPrl/P+jXLmRHoyd/+8Fv++MsnCHIW9pmsyKTABxT9jUnnl9PZ0Y6ysvSqHwSEQiEuLi6Mj59FQEAAGqXCvG5OTk42J3rqib7WaCDU1tYO+gOPqf1QcC130UB8WfZtyoTd8+yYaEaa7+vpzh0MbrnlFv7+979TUFCAVquloKCA1157jfnz5w/7WD80Ojo6RoXmh6AbzbFsieu1de+O5HkarrFsoRuTx/DQjaY8bB3vRpCjrbgR1nq094gJg3YzPnXqFK+99hohISFs2rSJu+66i0mTJvHxxx8PedDU1FReeeUVNBoNdnZ23HXXXeYadkVFRURGRlq9pMbExFBdXT0s7j3xV0qlDHdbW9qPZN8jPc+BEiNZlnsRGHScyy3qNz6xL156JvHp7OxEb+xi3OwFtFYW4xs7Fb2xC4lvYJ8uopYxq1VVVZwp16MWSui0E3L5TCqeIZFmXopzLtLZCQ/0cGGdMX0aCmXTVeNB5dOWm2VRXl5OZmYmYEok1X/CKEuZ91caZ8MD91vVRc3ctR/p198wI3461dU1iBwkSGWe3OW8G39hLcaFZxBKQwFwcnIyWzJ1bRrcfQNw9vBEq9Hg6i7D2d2T5FOpnLlcQHOrmro2PUvWriUsLAzJrbdyLnE3+fn5fceNZmXBb38LXV0wZw78z//0amIwGPjo00/JyC/B0cOH2noFb/3ul9y0ai2q6gqz4pifn29er9jYWHOmdMt+cnJyqKqsZJXrXnxK0tnW9RJ5X3xClNyTVatWXXWvWrqtB4yfbF63gGnLzWWUhproqSd/lmt3ySKR1NX6mjp16qDKIZnmr1KpyMrKGjSPtt5FgynTZNm3pSt+X+7fI32HXk937mCwfv16amtreeKJJ8xhMwsXLmTDhr5jv29kDKYu7XDQ/BB0to5lS4b86urqIdPYundH8jwN11i20I3JY3joRlMeto53I8jRVtwIaz3ae8SEQVtmW1tbCQkJ6SYSCDAYDERHR5OdnT3kQWfPns3evXvZvXs3Tz31FBMmTDD/N41G0+tLpMmSa0sihDEMPwayxoD1S66rmwcT5i2kpUNPg0IxaKumpTutvb09Li6uVFzORChxouJyJi4urtjb2/fpIgrfWwkDAgLQCMQsf+Ax1m54jElTp3Fo+xYO7vySxM8/QoqeqKkzesXXWlqJbxkXxJxxoXAlHtRkyYuOjjbLoq61nZff+Td/+Ns/OFvZeFULtCUsZWXp+rxv3z7z32cuXAoSZ07llfLNd2dJzSshds58np+WzOSAdv5Z9QQ5Zeo+ZRcWGY26toKa4gKalY3kn0+jubyIsnoFBqkM19AYnH39yTyTipOTMwEBAXgGhfW9Ph98AIcPdyuy8+dDYiL04QaXlZXFoVMZeE+ei3zKHKatvgc7ewdkHSrWr1luVtSvtoe+32cHcFf8iymu59hc/RgBvnGDLjVztVI7A5VDGgz6W7uByhoNhm4w52y4MdT59Fvz+Af6Onu9QywW88ILL/DNN9/w/vvv8/XXX/PCCy/0ykdxPUGr1bJt2zbzhzqj0Uh9fT1Go9HcprW11cr91mAw9PI+UalUVq62uiseL5ZQKpXoroRPmMZWqVRWbRISEqzOgEajMXt29cdfW1ubFX96vb5P/izH1ul0vcrpKJVKq7F1Ol0vb46Ghgb0en2//AmFQlQq1VXl1xd/6enpVi58CoWiVxmM5ORkq+zHlZWVpKenW7VJSkqyknthYaE55MQ0dmJiotUcsrKyKCwsNP9Wq9UkJSWN8dcPf6Wlpdc1f9e7/Mb4++/hLzExkSeffJJrxaAts56enjQ0NODt7Y2vry9ZWVm4ubldU91CV1dX7rzzTtasWUNgYCCRkZE4Ozv3usRND4L+EtKMYXQxkDUGrF9yJU5OqMoqkAWGo1ar8XB3M1s1r4aemXIvnk4lLDqahFsWkXbsEGd3fU5z3ERSOzRMjw7r10W05wv3qnXr0es6cG9XsnLNSgwGA599m0j+pQvd2YE9PFCUFyOfttKqvElfljxLWVw+mUSMPJKK7PP4h4Qxc8HSqyYp6o8/y3hQWWA49mIxxbnZNDa3ctO6x2gpysIlJJKbtG/hYCjiUvxB7FszqK2tNfNUVVWFk17LgS0fIgsM6a6jq2pAOmkq2qZ6vB2FCF3CWHjfY7S2NuNRkE/emZMUZF/Ey1PW9/rs2QNPPQXLlsGSJbBrF/Q4kyZL4qeffkqXi4zoKTMQikQYQsIovJCOQCAgLi6OrKysAfdQt2wV/Hx2IT7KDM5NOErZ18e5OSBg0JmGe5ZgsrTADqbs0kBtrqbM2bLmlnSDOWfDjaHOp6+EbYM52//tcHNzw83N7YdmY1BwdHRk7dq1ZiumQCDAx8fHqo3pg7PJ8igUCvH29rZq09MKKhaLe3247pkcy9HREccr5bVMSEtLY/Xq1ebfPb21+uLPycnJ6v1BJBL1yZ+l5VQsFveZrKu+vt6qTc8PPT377cmfwWDA3d0dgeB7W4JJfj35s3RN7mnx8PLyYt486/rhPX8HBgYSGBho9bfFixdb/e4ZEy0SicyVA0zoefalUmmvfsb4+56/0NBQRKLvX6+vN/5GW36JiYnXNX+WGONvdPlbtmwZs2fPZuvWrVwLBq3MLliwgAsXLrBkyRJuvfVW/ud//gehUGj1ULEFXV1d6PV6qqqqiIyMJCIigqSkJAwGg/mlMS8vD39//z5djP38/NiwYYP5wTB9+nTzosydO7fXwzIpKYlZs2aRkpLSJz8RERFm4SclJVktTHJycp/JH5ydnZk3b16v9tD9VaK/YG/Lr8CWUKvVvfgrLy8nODjYir+esOTP1N6Sv75QWFjIsWPH+izM3pf8AFJSUnBxlFCa8T2PLo4SUlNTkUgkREZGWr3kurjL8FSpKU7cRkdgIAWdHXg7OVJZWUlVVZUVf5YyFAqF3DxvLo5nz6JWq3EYF4zOrpPq88nYNdXgLAJXg5amKzVk+5OfQqGgtayUvGR7OtvbGHfzcsQYWblyJXFxceh0OhTvvkdKbTVuXj5omhTYGzo5ePAgmZmZBAcHc9NNN/VZtzMnJ8csi9b6KiTOMgLdnam+lEFAcGifioBarWbr1q0EBQXR2NhITU0NClULuRJ7vEMjcPUNQFlZyqzYKFIvF1JVVUX+mZPInCXoSy/hIeqioaaEJP1CDrMC19OZNFaU4D15qdlF1MXJEY1QgtTYgaxDxX1rVgDdlg6noFiqq924oGjDTijEUJGH1FmKvrOTrNMnaSkvwFsiorKyksrKShobG2lra8NZImHGPfeATAZvvQUSidW+OHnyJGfSzqJq09IpEOGq01CechD7znbkN69B06QA/IDvlaaWuiqUlaXmPXTw4EFzHVmDwcDEQB1eTYfJnJqI3jEUWWAp5eXlZr5qamrw8/PD09MTOzu7Ps+Had3S09MxGAxUVVXR1dVFVvZlVG1aHN1ktPThTtvV1cW/Pvg3tcomJFI32lPP4J54iLgJ47Gzs2Pu3LlW+7z8wmmCp8xCWVFMnTDA6gFugok/hUKBsrLSSglsKcunThjC/v37KSkpoaSkBIO9AxWX0uky6IlIuLnPjMt93S/l5eXcd999fZ7fnuejtLTUzKtQKPzeG6KHcmq6Xyzvlq6uLuRO9iR+/lGfmbq/+uqrfhPYXOv9bGpvui8Gup/7Qn/yS09PJz09nbCwsH7vaVvQ2NjIxo0bycnJ6ZUQaNu2bcM2zvUA08fvkab5IehsQUNDg00W+PDw8CHT9PUuMpJ0tmA0eRyTx/DQjaY8bB3vRpCjrbgR1nq094gJg1ZmH374YfP/X7NmDZGRkajVahISEoY04I4dO1i4cCEymQyVSsXGjRsRiUTml7Obb76ZDz/8kE8++YT169dTXV3N9u3bufPOO/vsr6amhs2bNw8649+sWbOQSqW9vlz019YS/b0Q9dceur9K9Kd89nSxMqEv/tRq9YCJICz5G0x7E39yuXxISSamT5/OV4eOEzp9rvmFNy83l9uWzDfP1dKq6iEPgHYNIT6ezJo68ao1WnvKMCIiAp1OR21trfm/paWlUenkwrOv/Zkuox6ByN7KYmWSn8mqBiAuKSM/Px+ZX2CvZD/5+fl4h0YxbdlatG1aTh89SEF+Pm6RLjSU1aDt0DFnzpw++Y2NjSW9oIzQ6XNxCoggq6iUyvJKFi6eTZedgLyM08g6xpnjHaFbeZDL5aSmpVHd2o6TzJdLF7Mormkg4aYF1B/ci7aukmxjOxknTnL+UjYuXj5UlhYzd+XtrFqxgo1v/y/J5y4QOn4SzafP4+8kxGBYZLbkGfWdZrnMnTu315e5zMxMzrzzb/LPp+EsdcfQ2oKwtZH582czd+5cM6+W8ZOFFaV0LFjIHXfe0UuRhe66vA0dRpZteJqyglw+fvdtajrOERU7gctbN2JsUZrvC5MS6HrLIjyDwsx7aPnS+eaPUVlZWZy4cJnAdckIHd2+j3edvISzGecoqG3EQx7AxbIqojp0A7od33XXXeZ9npWVxbFzWSzb8HS/Vk8vL68+2wQGBprb9NznOZ9/RJSfN+vWrbsqLytWrEC982trJTDAjzvuuIOnf/krqtsMyEIiKbqYQV5ZNb/448t9Wj37u1+udv57ng+DwWA+kwCl5RV9Kqem+fTse8mSJf3GHq9YsWJId8tQ7mdTexMGup/nzp3b62/9yW/ZsmXmeba0tPDee+8Nip+B8Oqrr9Le3s5tt92GpI8zdD0iJSWFtWvXDpnOlgzNtmZ1Hk06W0oSmsayJVTKluygfb2LjCRdX2drpMayhW5MHsNDN5rysHW8G0GOoykPW+lGW47XikErs1988QU/+clPzL/Hjx8PwNatW7nvvvsGPWBGRgZffPFFt4XIyYmYmBjeeOMNPD2761E6OTnx+uuv8/bbb7N27VqcnJxYs2YNd99996DHuBqG8mI11CyCI9n+eup76tSpXLh4qV9rDPTh3pkwcVDJayx56TMZjdyT4OBglA4eiCUOQPeX7p4Wq560rYiQGtqZGiAj4AovBoOBPXv2sH//ftqd3PH29qayuIBOQxc33/cYQTIXggIDBlXPNPHzj5D5B5N78jhadQuVEWFs/+AdHKWuKO3d2LLnIBGnz4Bdd81VgbMbJzKy8fIPZtb8WTgHRXD26y1INQ2cuXQBtb0zTW5CWsQuSNQtLF61CjdHB7KTj6BX1qGsq2F8zDgmTp2M++JF5KalkJGR8b2L6BU30f5cROPi4lg6ZzoZmSloZL60Keu4bfFNPPrQQ+Y16s8VuKKuDrc+rBmWLqphMROYffMtnElJpjpbh6uTAzMmxFBbW2uuLx0lP9trD02dOtWqPq6TXsu+Lz7DKzjcqm6tLe63lntrMO60KpXKqo1AKESHgP3795vX3tZ97ubm1qf78549e6huM3Dvc39FLJFQXl7Grrf/yhcfvI2/r6/5nA3k/jzQme55Pk5c+D5xVX9u2f313ZfHwmD5GM32I83LYJCTk8P27dtvqJCZ/j66DgRLN8uRpBltutHm0RbPAFv37mjS/VjHspVujMfhoRvjcXjoRpvHa8Wgb9fPP//cSpk1YajK7N///vcB20RERPDOO+8Mus8xjC6uFofYs11/L7lXg+lFPTU1lfTCMu558hnEEgez0uLp4d6vK6QJ/cUbBlyJt9TpdDzxs59bWcDe+P0zLFi1Gnf/IDq1GqRSvwFjBnvKYvkvnwC6rceBoaFWvH/25ivYSyTc+/SzXMrKYrqLL3X5lxB0GYiZPpvii+fIzs5GI3Zm3XN/pa2jg9iFt3Jk41voO/U8+KvfsnvzRtQlOUyYOJG1Gx4zy1xRWwMdTQPKxZLvRx96iDlmhWjBgPGg/SlzJvSMn1xy290oyoqYOT6K2rp61AIx52pUbNt/GHd7WH///cwQCLozP1/ZQ2BtDdYIJbh0dTLF14WAK22OHTuGu38QFcUFKOvrkfn44O4fNGCcqiV68qpr76A4+wKyjhCzFd2yjUAoZN+XW7icnY3jzNls6ZG1+Gr7vD/Fsy+6goIC5FETcLgSJxgcHELEpOlIGopZv+bBPmXUXybkq2GgeFxbzu0YBoZMJjNnMR7DGMYwhjGMYQzXjgGzGRuNRgwGA11dXXR1dWE0Gs3/SktLbf7a+EPBMqPXcLa1pf1I9n0t8xyozmRhYWGf9UQHUy9zIOTn55szuJ4prKC5S0xWTg5dxi6rGqERvh5se/8N9nzyAdvef4MIXw8ry3B/ljeT2/G+ffvMFrBVDz7Jbb96EUVLK98d2EN28lFcHUQI2tX9ZkruT0YmuYSFhRE+YcoV63H3+AKJE04y327lobkRg74TD/9gmhX1GHQ6aoryuHTpErKQSMQSCWJ7MXZAUJg/DgVfUnT5EiKjnpUrVyLGiPHKmCYeExISzJl7T+7+ioOfbcTZqKOqqqrPdTHxGhoaar2GFy9ifO01/J2czMqxwWAwK3MqiYzNuw/wyt9fIzEx0ar+rWXm4KQvP2XO5AmEhYXRJnJk6U8eoVmlwiCVUS1w5YMvd3I24xwLFiwwZxM+duyYVTbdJfc9TGmDkvLycrPMi4qKOPz1No4mJVFbV8vRpCSSD3w7YOyb5T634jVxH++//AK5FzNJzS/jzQ8/5aNPPr1iPe5us3vzRi6eP8fSe9az/M51fWb57evM9ZeR2FSSqCeioqKoLcim40o8pa69HWVZITfddJNZRoPJODzQ+b+WLMTX0x16Pd25g8Gjjz7KW2+9RUNDg9Wz1DKz7Y8FfcUvjwTND0E3mmPZ4tZs694dyfM0XGPZQjcmj+GhG0152DrejSBHW3EjrPVo7xETBtREFy9ebP6S3FdQ7+233z78XI0g2tvbR6StLe1Hsm9b5zmYOpNDbT8YmKxXR48e5UJROff96nmqy4o5mpREg6qFBoXi+yzIk5dQVlFJZ3s77cIWOnskgIL+s6xOmtpdBqo/C5hTXSFBIWHkpSRR5+5Oc7OqV6Zkqzn7B3N269do1S3MW7Ea1ZX5z5g+rdf4xvY21Ioa8i9doLW+hvrqRupKCgiLiSV515fodDoCoidSdDGD8vIygoKCQVXGC/7vclkzmY2bNiJ3smf5s7+gsenLXi66JmtaTk4OmZmZdNa30ypw4EJdK0cz+q9/2tbWZjWfBb9/AUF6GjEJCcQ89DCJn3+EDgGXs7NZes96pk+fRkbGOQ5t30KTQII449JVXVS3b9+OLDCU6rJi6hRNzFz7E6rLS/AQdZGfndFtDYyJgNQH0NTOMStZBoOBw7u+oqiyBpVGy/YDSdgLhUROnIJGD1L/MFzdnLD3CkBT239cmWlvXbp0ifb29l7uwSkpKTRUVxAydRay0AgaykvYffgYM2fEm9vs378fx5mzmREfj53Ark+LfV9nrl8PgaIioqOje7VftWoVe/YfYNvrLyKPmkBtQTb+TkImTZpkbjMYF+mBzv+1ZCG+nu7Q6+nOHQxeeuklAI4cOdLrv/X1txsZtijotir1o003mmPZYjCwde+O5HkarrFsoRuTx/DQjaY8bB3vRpCjrbgR1nq094gJA96Sb775JgDPP/88r776qvnvAoEAmUzWKy3z9Y6huM4N1c1uJN3yRpKXntl5B4pHHGr7gWCpHOqEDjS06TiyZydLbr8HP+/zZB47gKaiELGhwxwzWVTfxAPPvNDvmFaxrIGhNFaUIO3qRCAQkJWVRUREBEe+3k+HVouDo6PZAnbXHStpaFSS+V0KFWWlSOxFGCNCrPjNyckhr7qBmIS5lJeUIIueiFbVaFWOZwaYrXomhTNhwjhS09LYvulj5JHjqc69iKqqFE9HewQCAXf/9BdMmTyZN194ll1v/5VJE8P5XeC/qda60ZLwOg8u9uBc4m6Kioqu6uZtksHFshpWDGJdBAIBBbWNLL/3QcY//yt809MAUMbEsOHxx8jJzWX//v1IEmYREhxMZmYmtUoV4+csJNjLjcmz5l7VRXXixImc27WfuqpKWjs6KSsvo7WxkXZ7IbUqDQf37iS0aD/OjvZETZxL8rffkXXpEjkXL5CalIhncDiekeNoK8yjtb4asUTCnFV3InJyROohIyYwCBdda6+SXj33liwwlAt9uAenpqbiFhTO4vt/ikgsRq/TsePtv5KWlsbkyZPNc9my5yB6fWe/yl9f+70/xdPe3r7PsyAWi/n3e+921xguKOC2u25l1apViK/Qw+AU0YHOXs/z0Vfce3+4nu7Q6+nOHQxMz9P/BvQsNzNSND8Ena1jWZb+GSwsSwENFrbu3dEMLRhNHsfkMTx0ox16ciOs9Y91j9wI8rDEgMrslClTANi8eXOv2m1juDYMNuPwaOJqVp/o6GjzS3ZUVBSrVq0alJVooHn2VA79ImOpqmugsriARWvupL6kgPEyCbNnLzDHTPY3psldtra2lhnTpzHjypxOl1pbKcO83JA7CnpZwIKDg/l4+zd4RY4nIjiMhvISvj1ygtkzE8xWz71795KTV4Si3YjY3Yuaigr0bc3UV5YTHjMBWWAoDQ0NvRROg8FAcYOKFcvW0tGhY/mqVRzb8RlSfRuS+FkkzJiBncCOZ//+Jrv++Qd+G/w+lZ3BGO45QaRDd8KYsqAws2xN/3Q6HXv27KGgoICIiAjCwsJISkpChwjBFQX3arG/tbW1eMkDiXvmKbz37ATg1OrbaXvgARZfyTRuMBh4+Z1/I/QOBDsBLdp2Ss98x6QNDw8YVxwdHY2i7B2yymoQuHgg9Q/BUWyPe0g4GXs+5Y8RGdQqHEm2e5a75sRx+uiLHE1ORafTIXZ2YfyCVYSGhRI0dQ4H3v8bl9JO4xoQwqRFt+Lu7tZtsa8uRx4/vs+9NZiPLc4eXthdkZWdUIizh5eVW7atyp8tFlCxWHxVj5drUURNsLRMl5eXE9xP3PuPCdfDfWt6nt5I+KFldr1Br9ffcOFVI43r4WxdTxiThzXG5GGNMXkMPwZ9I/v4+FBdXU1+fn6v9PIrV64cdsZGCkN5EA31oTXU9ikpKYMuQTGSvFi27e/lWxa30JwwyS9qPEd27GXP/gP84qknB3xZH2ieVVVVlFZU0thxEg//IOorSqkrK8Ze3463hztzJk+wco+1SswjEGA0GlFWluI9eUmf2Y9nTJ9Gm8iRFfc/am6f+PlH/PyJn5KSkkJ2djbL4+OYN28eW7duxSBxYcG6RxGKhBgNRna8/VdOnz5tLgdTXddIfVMTczYsx9PLG9/qGo5vfg9tfZXV/C0T/BgMBj7++GM6RRLs7cX4evsgEtsTETcFWUcThQ0qs9VP0lHJnyfuQCNJYE/RcpbaiTDo9eZ5WsrWMpGVPHI8uzZvR6uoZckd67icmY7oyy2sWrceo8HQi9bkfluSn8/i/3sH74I8AIqf+T27PWWs9/OzWqe2FhW5qccJlLmQV1SBvlUFdA2ooOXk5OAdGsU9y+/m+KGDqMoKaMSOqvTD/GvZacSugZTN+pacL7/gP//5Dx0OLsTOW0zh2WSkPgEgEqNRq7lw6Fu6hGLcwiOoyb9M+QdvsHDpci6faOlXmbP82GLQ6/tUvBMSEkg83V2mSCrzRq1sQN9Yi3TS9/UdB5P0rK8z15/iGRUV1e956AuWfdvKS0+Y9mdVVdWQvqZeT3foUNoP5b61hZfBIisri4MHD6JUKvnb3/5Gfn4+HR0dTJw4cdjHGg7YWkbCaDSa67+PJM1o0ymVSps+7NvqZmzLvGzdu7bSDfVsXctYttCNyWN46EZTHraOdyPIcTTlYSvdaMvxWjHoW3LPnj2sX7+et99+my1btpj/ffbZZyPJ37BjKHFJQ41hGsmYp5HkxbJtzyQ+ppqsJSUl5oRJU2KiuPe5v1LdZqCkpKTP9kOxErW1tdGi1jDvrgcJ8nBhzWO/QiKREOwsYv2a5b3iPC15TN6xxTwm0GdinLS0NLNCU3z2JPZiMe7+QXy5Yyc1bXoi5y4hJauAv/zv25Qq1XQ5OFHf2EhjZgoIBIgkzqSkpHAqM5vF6x5iyqx5hMdNpSw/h5qyYlSVJTi5utGsbOxz/iZX19SsPAoLCzifncOZA9+ga+/olbjp1OEDHP16O1mtk8iWPoOjvoMDWz60mqdl35aJrObedh+Lf/pbXALDkXbpWHrPei6eP8fuzRt70VomJqpt11N15UXr2N0/4QOZR6/EUbW1tTi5yzDoOjDoOhAYOtEbDJxM3DfgmqelpeEZFEZ4ZBTTFiwlYeEyjG2tvDrzEFo7GcnytxE6uiELDKW4uBjPsGhm3XoPC9c/SUdrC/VlRWSf+g5VQy2R8fOImj6Hm+56AFeJPbLO5j73iAlyudycxKr47Mk+E3qZyhQ1ZKZQl5lKQ2YKS+dMx8vLy6ovy6RnpmRMluh55kwfC4IC/JkVG8EUXxczr8ePH+/3PPSFnn0PlZfhxPV0h14v8xwsjh49yvPPPw9013oG6Orq4pNPPhn2sYYLWq2Wbdu2mfk1Go3U19dbKWetra1WCY4MBgMVFRVW/ahUKrRXEptB94e4nmV/KisrrUrRaLVaVCqVVZuEhAQrrwmNRkN1dbX5d1/8tbW1WfGn1+tpaGhAoVBY8Wc5tk6nQ6lUWo2tVCqtxtbpdL3uvYaGBvR6vRV/ra2t5rGEQiEqleqq8jPxBxAe3v1RLT093armrEKhIDk52Wrs5ORkFAqFee9WVlaSnp5u1SYpKclK7oWFhWRlZQHde16v15OYmGg1h6ysLKvELmq1mqSkJKt+h8Kfaayh8gdw+PDhIfN35MiRIfMHsGvXriHzp9frKS0tHbL8vvjiiyHzV1lZyRdffDFk/hITEzl8+PCQ+EtPT2fXrl1D5s8W+ZnW17SPh7L/TDRD5c9ENxT+THQjcT568mcaa6j8AWzbtm3I/O3du9em9f3kk0+GxF9iYiJPPvkk1wq7Y8eOdQ3cDNatW8fTTz/NzTfffM2DDhc0Gg233norzc3NuLq6DormerIqJCYmXneWWei7lMgbb7xBdpuIpT95BKNBj0Ao4tAXHzPBSc+zzz571ZqXPefZs/+Kigq+Sb2IX9x0JFIX2tWt1GRlcOfcKf3Kx9RHdXU1/v7+ZvfjjGoVc5asAKDL2MXBnV+iLcmm2Sjkvl89j729PZ2dnbz/p/9B19nJHY8+jYuXnMycPPJOn0Bqb0dxeRXR85YQGBRMdXkpWccPEB4WRrOmjQkTJjAubjLfHT+GfPxU3MRCQoID2fPJvwjzkDJjxoxeMY5ZWVls2XOQxese4siendQ0NKLr7MTVrpMZ4yJ4+MENAObaqqfPpKEWOOAVHE59aSHV+Vk4SyTExcXxxBNP4HglaRXA66+/bl6XJpUKdYee84e+QdbVzn1P/YaDO7/EvV3JypUrrdYlKyuLTbv2E5Mwl+bGRlxcXWn68J+4rVh6pYxO9/iK8mKkxg6MBj2H0y/xyMvvoM4/h2tMPFte+R23TIjgrrvuMvfd197Jysrii32HWXrfI2Tl5NCgaiE9cQ9+hlyE/rOJnToTd08vctNS0FSXktmg4e7n/45IJCL568/ITjmGi4MYeVQs02bNIzDQHycnZ1L3f4Pc3sCqVav6dZG1jJn18A+mqbqcKLlnL+W3L76TkpKuum+vZg3tlRjtikXWNO71ZLEcyj001L5/DPNsaWnBzc2NvXv34uzsPGj6vvDII4/wzDPPEBcXx+rVq/n222/R6XTce++9fPPNN9fU90hAo9Hwz3/+k7Vr1+Lu7j5g+5aWFl566SVefvllJBLJgFZFy/ZOTk4DWkp7tjehP7r+2vdHN1B76I5hNVlmB9Peciy1Wj3o9gAdHR1s2rSJffv2Dak28WhbVYZ6tq5lrBvBynS9y8NWutGUh63j3QhyHE152Eo3mmMNxzN20JZZtVp9XSmyJvj5+fHkk0+SmJgI9P1VwvKriUgkGvRXk9OnT1u1GeirhEgksumryWC+6ly4cGFIX3VEItGgv4rl5uZa8ZeUlERMTIzZ6pOTk4NcLjeXDDHqOqhLP0FtQTZRUVEIhULa29uJiYkxW4n646+urs5sETxbXM3Wb74l7Ww6bmI7YsODcWwoIcrfG3cHAQEBAf3Kr6urq9uitnQpACUlJWYrnEbVROGZ7zibnk7GmVTwDECIkXdf+DU7N3/M33/9U/RdEBETR8rJkyTu3EqzWo2nsyO6LgFd+g5O79xMxs5PaTx3gllz5rFmw+NMXrCCdmU9Bl07Xu6unNv3Fa2luRze/B+qS0vosBPx7fEU3nnnn3R0dJh5vnTpEm4yLySOjiy5/R6iQoNw0SrxdRAwfeoUjh07xr59+3BxcSEgIIA2kSO3rL4TP5k7lY3N5Cs01Nh7kJFTyC9+84zZelBZWYlMJjOvi9hejPbyaRqL8ggMj0Sv76SrRcHEiRPN62KSX0VFBaUVlaScPEl9XS3nz5zirG8AAG1CB2LGj2fmwqUgceZUXimqLnu8vH344n//QkX+Zc4d2sWU2HHMnj3b3PfJkyf5z8aPzGVoPt+1j3ffe4/Y2FgifDz4/P/+RnlKIkVHdzNO5oSyTUaLsomivFy2b/oYRVkh999/P1HuEhL//b+cPfANTVXlBDrbs3j6BLwdYPbMBPx9vMk8uJPLOTk0O3qyZc9B3n3vPcrKynqdD5NL7vo1y5HTxtr5c8wKpeX5MFk6AUJDQ81Kqmn/mS3Zuw9w+lIuW3Yf4JNNm83lggoLC80XuFqt5quvvrLyEpgUn0BRdb25fI5KpRrSV09T34O9Xywx3F+NTbwM5n4pLS0d0v03lPs5OTnZ6qE53FYBkUg0bF+NTaivrzfvM1OVAHt7+2sqZ3a9whb3WFtofgi60RzLFvdkW938RtM9cDR5HJPH8NCNtvvojbDWP9Y9ciPIw2rcwTacNWsWFy5cuO4SWNTU1PDBBx+YLbMikajXF4+e8WBSqbRXmaH4+Hir315eXsybN8/qbz1/BwYG9srm3LPfyMhIq9+jyZ+fnx8qlYqkpCSzJclW/qKjozl2MrlXwqRVq1YNib+srCxzsqeWpiZ8QiO4fPokbug5f2gPssBQLn13mGg/rz6tbVeTn8FgICrtLMe++RKh1J2MM6lMmjqNlfc8QMa5c+Rv/hBNeioGkYRpK+4AsSNTxsWx7+N/0lRShMAoZNr8FTi4p3Mh5TgCFw+a1S2I7EV4e3nh7V5LYXsX6hNH8XCRMmdcKD7OIk4WFhAydRYSdxcMSDh29jxLcnPNHwLUajVlJWXEa9o4smcnZdU1tBklZJ1OJ7uqgYSbFqCsLqde0UhQgD+ywFDcfOTk5+fToGphxl0PIRYKEAjsSN32Md9++y133nkngYGBbNiwgZQzad3rEjmeggtpaBW1wKJu919/X5YuXWplVQwICCA3N5cWtYYVTz+Ig6MjHW1tbHnld6SkpBAYfzNRM+dTkn+ZxuZWblr3GFK7TrTuRWQlH0Gr0VDboKK9tpyH7lxtXgcPDw9KG1v6TLaEHdhLJBhdvXHVG/FwlmAv8yF+xe10dOi4+dbbOZe4G6lUyqyEeI6knkVdeAlXo45Ft67gkQcfZPNnn3No68dXygTlsfSe9cyIj0ev7yTx849obW3tc/9Zxi5bYijnNysrq1s5feCxAbNDS6VSvLy8kOlE5iRlwROnU1lbb47V/THcL/8N/C1btozZs2ezdetWhgNyudycQM+E/Px8/HrEp49hDGMYwxjGMIbBYdCfCt3d3fnjH//IP/7xDz7++GOrfzcSLL/CD2dbW9qPZN8XL140W0AzqlVs2XPQbEmypW9TyZAn77qVceJ2nrzrVv793rtW7rSA2VKVlJRktmhZwpTsKeXkSapUraScPEl5VTXx06awfs1ygu07rxoDeTXeLa1w7u1Kxk+YwKp161E2NdGqMzB9ySomxsUxfckquuwdEXcZKcvLRihxouj0CdpUjeRnXaCqvIyFD/yUuXPnMGvVXdQ1NlFelEdcbCxudjpmRgbx4G0refGFPyASicxlXbyDwlh432MIXGWcPn3aLH+FvSuVpaW8/tufcyYtDWlQJH7enriGxhAXP46ISVPN8b1tbW3mGM/C/FzcA0Pp0nfirG/DJzgcWUiUlcXMcl0mOBv4zQN38fwvn8autoRZsRFseOB+ADMv2bnlfPHuB2z7agee8gDK8rIoTTvBhTMpOLh70iqUkHEmlbPp6TTW1eHuH0SnVoNE4ojBzg55zCSEYgdkkRPQ6DqtrAb9ZbY+ceIERXVN3Pv0s9z+wMMsuHsDGfnF1CkUyOVywsPDCAgIwDMojMuXL/P4I4/w52d+yVPrbuPPz/ySxx95BLFYbF5bP5Ge6VdqvtaX5FkldRrsXhkqrpa1u6/+LWN1gV6xuiN9t1wvd9F/yzwHi7vvvps//vGP7N27t7uO8uHDvPLKK9xzzz3DPtYPDcuPSyNJ80PQjeZYtiSbsnXvjuR5Gq6xbKEbk8fw0I2mPGwd70aQo624EdZ6tPeICYO2zBYVFREZGUl1dbVVsgWTq9SNAolEMiJtbWkfERExYn3X19cPuv7rYPs2lQyZOHFiL4sG9BEnmHaBqLSz3Dzv+2yYpmRPZougVsu211+ko6ODuLg4JBJJn333h568W7qLbtlzEKPBgFqtRiRxpCI3Cy+JEEVuFpMW3UqAzAWJRMKlg1/hKIKYmfOoKyvFIzAYnVaDV4gfDkYBhZ16Th7ci6eba68Y18uXL9OpN9AFOPmFmMu61NTUoBVLzfKPv3kRf3n8J7iE+zFp+gxqLp4h3FfLL11f5GC5AZHvEwil7uTm5uKk13Ngy4fUlleSm5+P2+3rsZO5UVGcT2FGKjoXe3bu3ElYWBhKpRK5XM6aNWsAzPJ3k/lzKruAi5deQ+7rw7niCu676wHGr1uDfWsLP5tzC6U5l4ieGo/YzohIAL7e3sxfuIjLFzI4tH0LwUHBlFeUs/Se9bS3a3Fy86CxvBjH8HCips5AXVlMeno6U6dOBXpnwm7XasnLOE2Ys4YpwUYEQiHnMzNRqrVIg6PIOXOCfT2yLU9PmNKvJbXn2ur1nYjEDgNmUzZZpU0uvoMtQWN5PgdTYsdyLw5UPmek75ahtB/KPTTUvv9b5jlYLFu2jK6uLr766isMBgOffvopd9xxB4sWLRr2sYYLli7qQ8GP1c3Y5pgugcAmd3JLl/zBwta9ayvdUM/WtYxlC92YPIaHbjTlYet4N4IcR1MettKNthyvFYNWZt96662R5GPUMBRFaShtR7r9UPsGBqz/amvf/bXvr6bnLIsMkU5OToRERFOWl42jqzvaFhUhEdHm5BbDxYulMiFwduNo4gHEYjEBS1ZRe3gvxf98lakzZ9PWWIe0qxN7oZCGwlzs7cVU5Gbj5i7D1TeAEHd3Lp84QHxkCLNnzzYrIyalURI6npajSXz93j+YdetdtFWloW+sxS9iMipHT7P8JY6O+IaE06Jtw81dhsxfwx2tf2eX9ifUuc2gKP0sqSdPEBgYiKO9EH+pHTFeUi6mq6jNz6YzOJzzB3ciFDvgMnEmb1wpwXPXo0+iPJWOtu4DxPYiSuoambXqDhrr6ymrrkGlVOFV14Rrl5DxdyzHp7IMg72YW4LC0HdoSD2wm+CISGprapgwYQJhMeMJixmPXteBr0BHkDSCuqyzNHXouZiZSfSUGQTG34RIKETT1Aj49ylzd/8gkg98S6SPgV/G7OdAaQj79+3DYC8hdPwkyi+eZf7ae7l4Jhm9rgMxRqLknixYsMBqHftKutRLUUw62G825Z4fWNL3HCQq7eygLP+We2swtV0t2w9UPudGvluul76vN14Gi+XLl7N8ef91hq83FBUV2URni9Jnq6I4mnTXMlZLS8uQ6XpmVB4MbN27o0n3Yx3LVroxHoeHbozH4aEbbR6vFT9MpO4YRhyDsSRdC/pSMq7mimlSoAMCAnB3uEhseDAdHTocvILRFpwnICBgWPgywVKZSElJwcXBnrjlt2Pv6EzYjFu4eGgX508k4eDqTkDEONwCnXDo0jNu3HhEujaqszNI0bUiNnSYrbGWmYBNSrtIZI9naCT7PvuYnKRdONoLiQvpVvDyMk4zbd58JI6OdOp0uDo70dRQQsGmJ3lh0j7eSI3m25Im4mef5fzZMzg4S7H38geRiEu5mSyaMYlbVq6hy9mN8pzzuHnLSVh7H56uLnQK7DnyyTtUVVWRm3mR1nYdgVHjySpIo/TTT4ieOY+y6jo8ff1IiIzi3r/+ER+lAp29PV/9/m/sr6kgfsZMFHW1CBqr8HYSs2jNnQiFQjp1OsQYufXWW4mOjmbfvn0cP34cZVEudZ4+ZJxIoq44j6biPKY//VCfMk9NTWVmjCPPT9hFlfynbDpQR/mRTwmeFM+llBN4uUlZsfY2xBj6zLZs2mN9WfoffnDDgHVWTejvA0tfHgqW4/aVtXiwY1rKoy8L8xj+e3G1ZD6jmYxoDGMYwxjGMIYfC4akzO7du5f09HSampqs/v72228PK1MjCbVajVQqHfa2trQfCobad1BQkLl2aX+WJFv7bm5uZtuOHWTkl+Ak86VNeZzp0WHMjI8fUIGOjY0lOu2sOdmTsrLUnOzJxIujo+NVS6AMlneTMlFVVUXYxKk4ODlDVxdiF1dCpybQVl/Nut+9Qt7FDHTaNmrys9EZ7Vh4+70c/nwj49zsmTdvQa/xeyrtCfEzUJYV4aJpQNepRy0Qo3LwoEXdxv/9/tfMW7EaVXUFsybGMkFymZ9NPMK/S9aQWGOPc6eSyozvaKxVMHH+MhzcPGmsLKFa0UhtbS2eji5MW7KEo9pWNPZSRIDQ3h69wAHP0GhOHz+KyMWdpU/+jtaaMtTt7fhFx+Hu4kzIlFlUbtvIg3/7DJlSgVYo5IUFqzh+NhWjVo3U3YPKS+ncvWoZjcomDn3xMV7B4ea9Eh0dzebPPqegthGn8Dhk+eU0lhXiKnVC3GXA09Oz17qYZK6pOMkDTl9SHfQMR5oWoGn/FxJHZ6pzLjJxwXKcpS7U19VhUKtYuWalWeGzXM+BFNG4uDhCQ0Ovul8s16qjTYODk3O/HgpwdQV6IOX0erpbrpe76L9lnoPF4sWL+w3NGcmauT8ErvdyENdCZwtscRcGeuWlGAxs3bsjeZ6Gayxb6MbkMTx0oykPW8e7EeRoK26EtR7tPWLCoD8Ff/rpp3z44Yd4eXmRl5dHVFQUpaWlREdHjyR/w46e5XaGq60t7Uey77Nnz5oT5kz3d79qUqWh9r1v3z4OncrAe/JcfCfPxnvyXA6dysBoNJoV6FOHD3Rn0+2hQFsmaeqLr9TU1H4TV1kml8rMzCQzM5Mvv/zSKtFUXwmo2traKCssIDAqFpdONf7RcdSWFuHo6o6DoyPuXr60tTTT1KLm+OFENr3zv6BtJTw83Fx2xrLvkpISirMvoGvvLr+j13diUKuIiYmhTeTIivWPM2fZKp568a8Ehobi1dnC+jXLmZ0wg9XjSjkf9A+6gu9i0Z33Y/TwpcEgRCLzJmDSDCJm3kz82vsxCrrHDPfxYM9H79HVXM/l5CTEAnBycaOlSUH5xbPotG24esvR6TtpVSrwDonA2cMTQV0pTbVV3NzejkxRT4fEkRcXryLT24eEVXcREDuZDoR0ip2oUragEUpwQc8UXxfzmuTn55uVyZj42dz61P8QHjeZ8OAg7vzJ/dy0ai0NDQ29N4jiDPEtv+NI03yK/X9NyYU0HNy9WfnzPzB53gJUlWXkn09j81t/J8LXw2p/WO7FwSRdutp+AetETJVZGb0SMfWEpQI9Z8kKc2IuU7zt1XA93S3Xy1303zLPweLNN9/kjTfeMP/7/e9/T0REBL/61a+GfawfGj0/eo8UzQ9BN5pj9czIPRjYundH8jwN11i20I3JY3joRlMeto53I8jRVtwIaz3ae8SEQX+SPHz4MK+++iqxsbEcOnSIn//858ydO5e9e/eOJH/Djp6lGYarrS3tR7JvU/vBuDkOtW+NRoPIU0701ASEQiGGkDAKL5wlPT2dRx55ZEBXzKtZuPz8/DicdoFpy9bS0aEjZPwkziXuJisrizNn0zl9KQc7sYSCSxfw9gsk4ZaFbLkSB7nhgfvNlkRLq5rcx5vOdi37P/kXHgEh1JzcCNihbVbRodWiUtTh5OqGi4OI8OiJjJs0hdy0FPz9v48HtbTYufsHUVlayr/++iIzbpqPsrqcKLknTk5OuPsHUVFcgLK+HpmPD6GxEwkL9CQuLo6kpCQyxC8zJ2YlQe51lKRl4B8XT23eJWR+ISjr6+noyMCo1yORuqJUKsnMukxZQxN2Ygfqy0vZ9+H/ETV1JueOHcDRxY3gKTOpKszl8rEDRE+eRu65s4jsHXAOjMLVaOCTLiOSOTdz2cuX5olTWDRrIV4BwehaGtGompi1bA0+nm5MnjWXxM8/IiAgwLwulsqkVCpFX1VLUEwcfjI3gsKjuJz6HfL4Cb03iEiK3dTXqf7OntNvvkJBXi4GqSeOYhEikRBlXRVN9bW4OdhjNFi7XVruRblcTuOZ81RVVXW7pDuIaawoQT5thdV+STqb2a/1tmesa04fH1gsMRhX+f5wPd0t18td9N8yz8Gir9J248eP5+9//7s5iduPBd7e3qNC80PQ2TqWLTGzxcXFQ6axde+O5HkarrFsoRuTx/DQjaY8bB3vRpCjrbgR1nq094gJg1Zmm5qarF4Au7q6mDJlCn/5y19Ggq8x2Ij+4v2GG5omBV0GAwiFdBkMaJoUgN+g4gSvxmNVVRWqDiM5xeXdCaKqalF1GDl16hTfHj2JyN0bQ1snGkS4iZ0JmzSdaTcvZOvbr1JZ/jfKlK0suf8xOvUGsyKsbmnG3sGB2UtXoWlpwtnRgfPHEukwtLPlr88h9fBEq25lWnw8K++5H6FQiKK2xkqB6enyGn/zIra+/SqyzmZWrelW2rOyskj+eAtekePxDg7jwsWLVFxKR7ZqGTKZjEuXLpGccQlXeRDtHR102Tug16qJmjaTvEsX8QyOoLm+Bn1nJ+XZF1C7utCgbCR29kLCJk+noaSQ84f30JB+FB8/f+76zZ8wGA3knD/H+SN7MWiaKck6R1X2OSbfvIjawhzcDG1cmD6ZGnU7t61aS35FDSKBHa31NcgjYlDVVDA5NqpPpc0y7trbywvX0lJOJx+la8IEqgsuIzV2UFVVBfTIEOw+gS6XGDj5KfYSCeNmzOXSuQwObXyLZlUz8qjxBEyYhrCtmW+PnGD2zAQmT57ca59ER0fTUPoOn7z1mlVt4+jo761YAymfg411Ne3JkpISigvLiL9pIWLJwJmSxzCGa4Wvr69NCssYxjCGMYxhDGMYgpuxm5sbzc3NAHh6elJUVIRCobhqQosxXB2W9UKHAybr4WBqy14LEhISMLYoObp1I+ePHeDo1o0YW5QkJCT02d5yngPx2NbWRllRPiHjJhAUHklI9HgKcrLYt28fpRVVOHh44RU5Ae+wcVSVFpOTmc7hXV9Rrmgis66V3LJK/vPW65w4fowTx0+g1OrRaDS4ubpQeuEMBl0HnapGpCI79M0K7PVanITQ1tyE4UpMU1+uqLW1tcj8g2lSNVNcXEJLq5pxUxMICwsjOjqaPXv28MUXX9DWoUMsdUPVqKQDEerOLi7VNPHoL37D9mOnUGl1fPLO/7Jv+1bSvvkcV6krYVMScHJz52Li1zQU5ZJ1eDeO7jKkwVG4+4cgcpLiN24SCXc+QOD4KchkMiZNn0mrUkGXsYugkBCiomMIQMPLz/6SF59+hPGOeqI8nJg852b8ps2lTdPG1x++S3VWOttefxGtoo7ck4cxaltR1NaQf+kCivJiqznHxsaa3cZTjxykOvM0MrsOuurLqcy9SAv2XKhr7dMV/OOPP+b0xRzueuLXbHjyF9z905/RWFOJvUTC9BW3k7BkFcsf+hkCVxlpaWl97pv8/HxkQeHMvWU+Tvo25t4yH1lQOPn5+eY2A9Vzhe89ARYvXmzlNt7XnjTVBP7XX1/ku+2b+nSV/7FhuO+h6xXXwzxNpe1M/4qLi3n//ff7dXu/HjBr1iy2bdtGZmYm0J3Eqr6+3urZ39raikajMf82GAy9QhBUKhVardb8W6fToVarrdoolUp0FtnvtVotKpXKqk1CQoLVM02j0VjVcO2Lv7a2Niv+9Hp9n/xZjq3T6XplElYqldTX11u16Xk3NDQ0WMXH9uRPKBSiUqmuKr+++EtPT6eystL8W6FQ9NrTycnJKBQK8+/KykrS09Ot2iQlJVnJvbCw0Ko2pF6vJzEx0WoOWVlZViWa1Go1SUlJVuNez/zB6Mrv448/vq75G235Wba7HvmzxGjwZ+LjeuUPRk9+iYmJPPnkk1wrBq3MxsfHk5KSAsCiRYv47W9/y9NPP83s2bOvmYlrgZ+fH08++SSJiYnAwAuZnp4+6IXctm2bVZuBFjI9PX1IC2l6eA1mo+3cuXPAjbZr1y4KaxpYdv+jhAT4M3vJSkqqaq3i/frib8+ePX3y15f8AFpaWliQMA2hWomqOAehWsniOTNwd3fvkz/TPJOTkzlz5ozZwjlh0mQmxSdYxSSWlZUhc5WSvGMT544e4Nimf+LuKEZlFOHg4UXwpASifWVMW3knTjIvqi5mUFmQS8zUBMJnL0Dg6IquvR1Xo5aG+lrOnkjC0dGRCH9fQnw9MVYWgEGPg6OE8PGTSLj7UWavvos7Hv8FTeVFfLvp3xzY8iFOei2nTp1iz549GAwGvL29OXvyOJknDlHdqOJ8dg5nTx5Hp9Pxm//5LR/s2Iuyw4hKo4X6coxdXahqK/Hw8cOobSVk3HiWPPE77v/931n1wONEeTjQWlFEh6aZ/LPJeHS14yYC2jU4e3gyZ/U9xE+dysQlt6GoKkOZmYIm9xwuLi7I5XLcHQSEejghba4mNiwIsaEdmVSKQqFg8eLFTJgwAWe/YMJjxiPs7GDtQ0/g4uzIVLkr8+MiSYgKwtBUR172ZQrTT7Hr0/9QX5JPdHS0ef9Zxjf7GtXUFuTQaBRjkHpQ295Fu0ZDwvwl5rjSr7/+mv9s/Igtew5yWdlOm97I8a+2UJlzgYT4GURERjE5MhS5hyt+vr4IRCJ8vDytXnDPnDlj3n9VVVWUV1VTX1mOq4+c0oru3yUlJRw+fJisrCzOnTuHk17L4U3/IvnbnWbl09vbu9f52LZtW5/n19Lq7ufhytN/egVXqRMOnW0sSZhCQvx0swJ8tfNhugsGc78cP358SA8K0/8O9n45c+ZMv+e3J38ajWZIDzITL4N5kB04cGBID9qh3M+mF+ie/JnQkz+NRjOk+zk9PX3YHrQmPPDAA6xfv97877HHHuP06dP85je/GbYxhhtubm7ce++9Zu8JgUCAj4+PVfZlFxcXq5I1QqEQe3t7q37c3d1xdHQ0/xZfCV+whEAgsEp45OjoaPVcAUhLS7P6IOXs7Gyl3PbFn5OTkxV/IpEIb29vK0XZ3d3damyxWIxMJrMau+dvsVjcK57e29vbKqmUs7MzLi4u5rEMBgPu7u5XlZ+JP8Ac7hIfH28VP+vl5cW8efOsxp43bx5eXl7mfR8YGEh8fLxVm8WLF1vJPTIy0uyNk56ejkgkYtmyZVZziIuLsyq5IZVKrdwJNRrNkPgzjTVU/gAuXLgwZP7S09OHzB90f8QeKn8ikYiAgIAh8WfCUPnrK556MPwtW7aMCxcuDIm/+Ph4q3wVg+UvPj7e6kPNYPkTiUTmfTxY/gIDA800Q+EPvn/GDoU/E91Q+IPu8zJU/kxjDZU/gPb29iHxB1BaWjpk/qD7Hh0Kf8uWLeODDz7gWjGgm3F5eTnBwcE8++yz5r898MAD+Pn5odFofvB6eTU1NXzwwQe4uroC3wvKEpaClMvl/S6kJby8vJg7d67V33oubGBgoNXiy+XyXv30rLk0EH/Q90abOXPmgBedm5sbHkHh3TGOnt64+frjGhJl5Tras9/IyEirIseD4S8sLIw5c+Zc1Z25v4OalJRkdgt18/XHzdefytp6M49Tp06lSq0jJmEu5SUltMqDQeKMb0gECqM9mrY2JH5BGPR6xI5OFOVeRhQ0jinxN6GsKkPq6UXghCVou4wEh7hQnZ+NWCymrjKX6vxi/EPDuZx1HoFOS+zNy4iemkDRpfNMjApHGR2Ha5uCdp2eUoWKOm0n7dn51NY3ED9tKlp1C2V1CrwdKmgoL0GrbiEvL4/CRjX3PvdXCg5uw7GqAdc5q1gm3kdQzGWe/CqYoqIS3GOn4uTqjt5oIHLaTKpyMhkXEUJ7XTmK8hIqKsrxD49G19mJu18wntETkTo7o29ooCjtOy6dScFj3GQqi/JZ/9MNqNu05J49hbt/MNv/9if883L4n4oynp8az5mMc6xYspjSikoaO4zIAkPIPZWCpqOTgpJSxB4+tAvsUWh1+EVFYXDzxV5nR0NtOTk5OUyePNm8T0xWzYKCAhRGEet+91dq0o7gMm0h5xN3U35mG+Hz7kcWGIpK1URpYwvL7n+UJlUz57MvU3juDI4lpQjcfXCXOlFVU4tvWTHq1lbUygaa6+u4Zd1t5j0SEBBAfHy8ubxPVWUly554DkeplA6tlm2vv0hHRwd1DQqSzmbi7uWDRihB2tXJtBBfAuZMM+9FX19fq/03d+7cXhdxYGCg1Z6MSLgZgHHTZ+HW0dTrvJjOh8kCbdr/YWFh1NTUAP0/KHqeu54vIle7X0yWu77Ob1/8WSp+g7lf+rr/+jq/0P1g7MmfCT37nThxolWb4byf582bZzXPge7nvvi72v1sus9nz57N1q1bGQ588cUXVr+dnJzMz64fGxwcHEaF5oegs3Wszs7OIdP1tF4PBrZa+kfTQ2A0eRyTx/DQjbYHyY2w1j/WPXIjyMMSA1pmn3rqKavfzz//PNBtnV2zZo1NaeN/SAwlM+BQswjaknVwOPu2dLl08/UfMHPrUPru2X4g183B8Ai93ULnzZvHOH9v8tNTaVE1UVOYQ4CvNwk3L0DfrEBgBy3VFRia6nDQNuPn6Y5dRxs+np50tDbjFRiKXVcXEnsRoeERBEfFolAo8AyOYPHq2xEKRUy6aTGB0RMou5hBl8GAo6s7zc3NGNQqIiMjOZudi9HFE6fQWAxSGV/u2cfmzZuJmDCJyLAQNBVFREeEMWfZKi5fvoxPRCz1FSVoWlTI/AOZqfqAuer32VV/Cx5yf7q6uqjIyqRZUU97uxalspHSglzkvnJcXFyR+/nS2aJEVV+LWlFPU00F7S0qOjs7yTlxgLYWFXqJC+WZaThgxNXV1Wwx1RZnEXw5i0+K8/FtU/NCYR6nz13k66+/pr5Bwdw7NzB1wQrm3rmByuoaMi5m4xUeg6q5GdfACCauvJfAOUuZuPJe2oSSfjPRFRQUmOfZ0lhPdUEOt8YouE/3C0TNuSgrSwHMSqGnp4x2dStNLWpOnEzmk7deo6uthdXz59CQmUJdZioNmSksnTPdSpnx8/Mzu/yqJDLELu7s/fhdygryKMvLJiQimsbGRrMldd7qO1n2k0epam2jvLx8wH07lD3p5OTUZ/v+XOX9/PyuOv5gePmh2o9U3/8t8xws5HK51b8fqyILWFlhR5Lmh6AbzbFsSRpl694dyfM0XGPZQjcmj+GhG0152DrejSBHW3EjrPVo7xETBrTMdnV1Wf2+fPnyiDEzhmtDz8ytV6st+0MhNjaWiNNn+OzNVxBInDC2tzFrYizR0dFmS9eM6dOYQbc7WbOTmEVr7sReLCbywjkyDn1NZOx42rWtJMSGI/f2ZuvufRz54kMEIjFFWZmETp2F77hY6stL0Cvr8A2fSPaFHJS6Lpy85BRdzsLY3oabRMTRrRvp6NTjZqdjxrgIGhsbEbjKWHjfYwjt7amsrKAwP4+8mkaU+eWIxWJmLFtNfmExpedPMy7Ij4xDe9BqtfhLRKxx2cVyhwL+1fo8BUoPqvKP4R0QRPmJw+x992+ET0mgJu8iXco6JEtXcPfDT1FRXIDe3glHd0/sWpVcOJvG5aN7cXCQoCgrJnrmLUhc3HBwcuT8nq1oNBrzx4STf/4zfygpQGIwUO8t5+WlaxGUFJJbWY88djLp3x0hKDKaiqICXOSBtKmUHD12nNqiXDwjxiN2dkbs6IiTpzcOrh5m66IJpsRIarWac0kn0Gq1hHm5MFHzNQ/5HubvF2+l+uI3TI8OIyE+ni/2HaZTp6NJ1YyDs0uvDNFzZs3kprlzrlg0e9fwtXT5bVI1Iw2OpPDcGbqU1cTGTkBbcB6BQGBWmruMXWTl5NDcJeZMYQWFDc1WdWEHuyf7Ojeenp59th+o/u0YxtAfNm3aNKh2Dz744AhzMoYxjGEMYxjDjwMDKrP9FXi/UaFQKKxcDYerrS3th4LB9G2ZubW4uJjwfjK32tL3YNv3lam4F+zAXiLBSeZNm7IOY1cXH2/axPnCMqTuXqhVCqZHh/HQgw9i99nnJH35KbLAUOw6NMyfGM2M+MmkpZ+jWt1Oq6gdZw9PSs+fJmLCJMTtzTTnZKARd6FtqmfpnOl4urvTok5nxdMPgq4N//FTCxWqyAABAABJREFU2PX2X/EMCaalLB9Bh4al69axevVqNm3ahLOHF3ZCIS11NegMXXgFheHnLCZcHkZR5lkMCLD39KO5vZOiWiV6YxdtOh1Rshru8LrMg3tmIwztoiznOC6ODixauZrohNlcOPgtDq313LpmLcWXzoNYgr1YjLK+Ht/QCLxCovB1ssdNJuNMSjIN9bU4+ociFIvx8pTRrNbg4CYjMzOTu+++G/bv57Hdu7E3GKiQefHnux4k9cIZvEOj8ff0oK6ujpbmZkovnUfoKMVJ4oDbuDhmr72Pve/+neaackovZuDv641RIKK1vgq/6QvQ6XTs27eP3NxcKiqrcPQNpFMkRSh2xMnHn2BxIY8FnODpvdNQOfjg7qaluqqGKr8qnI06Dn62kU6RhIL8PBwEXWiaFNRWluPuH0RDQ4PZmt/XXikuLjYrqt5eXni7u1EoEFKSdZ6WiiKi/byYMX2aWWmurSinQdWCg72ImxYvJyg8ql/Fsr9921/G455xmyb0l0G5uLh40MrsSN8tP/RdZCsfN+o8B4uMjAxycnJwcXHBx8eH+vp61Go1sbGx5g/HdnZ2PwplVqfTDdlzyxaaH4LOFlgmlxoK+vMQuRps3bsjeZ6Gayxb6MbkMTx0oykPW8e7EeRoK26EtR7tPWLCoBNA/ViQm5s7Im2H0t4Ub1dTU0NWVtagsg0Ptm+TxU4mkw3a/Xe45tmf+6XlwzgnJ4eiuibuffpZ7tjwCPc+/SznCkrZeSAJ78lz8fD1w3vyXA6d6n7pM7nTTvd358HbVvLiC38gICCArLJqfKbMRSb3Z/rqe3GVeTM7OoR/vvoKrz7/G26NH88v1q/j0YceQiqVEhwRRd7FDErTU2iuqSQqZjwdDZX4BASSsOpu0vJK2PzZ58THx9PZWMPF099RkZlKRV427YoaAiLGERQeSeSESXQoqqnJz8Y7NBL38FgcnJyZoj/MOKcSnv5uKa2O43GV2DNrxW0Ex8TRrm5BHhRB/NJVzF60nAWr7yAoKIjqwlw6dTpkPj40lJegVjbg6ubGktvuJiLQD0d9GxqlgvFzFyIT2xE2aTodmtZud1qFAu65B3uDgUKpCxtf/ZA6BwneoVHEr7mPqQkJuPkFkpVyhJzMc2Qc3U9LkxLfoBBqCnPw8A+iqaqcnGN7Ueeeo/zsd7jbC5g2bRpP/OznfLBjLxdbIb2slpzcPAIjolh49/2s8TjBROFpPtD+HrebfsVNK9Ywfv5KDp05x0c79lBcp8BRryVSKqAw4zT1KjVdQnsSEw+x46MPzAlUDAYDH336Kf/c8iX7MnL455Yv+ejTT+no6DC7/NoJ7IiLjcXNTsfMyCDWr1nOww9uIC4uzpxl+eKJRDKPHcDP25OQqBir0jxD2ed9uc1bJmSxRH9uyZ2dneaznZSUdNWzPVJ3iy3t+5vncPT93zLPwWLixIncd9997Nixgw8++IAdO3Zw3333MXHiRN555x3eeecd3n777WEf91pgS8ymrXSjOZatdJaJVUZ6LMCmF0Nb966tdEM9W9cyli10Y/IYHrrRlIet490IchxNedhKN9pyvFYMeCu3t7dz7733mn9rNBqr30CvrL/XM3omCRmutoNtb1L4CmobkQWGsmXPwUG5RY4EL8Pdd0/3S117B9s/eJMugx4PDw9iY2P7tGp1SZzpFEmInpqAUCjEYDBQeOEsaWlpfVq60tLSEMnkBIzrtu4JhUIKz6cjEAiIi4vrlV1SLpfTWF5Ch8Cedm07DpJ6qgtzkDo5seDuDfj7+aPXd5L4+UdM6+pCaqfn7LdfIfUNoLoon+iYWKLjJnEpv5DW+mrcpE4gsmfi3IUYWptpammmfvwsjti3Il8WSuPRfQSFhYGLF/ZCO3RtGgzKBhrKS5gYvZhOnY6OTj3jQ4NI/PyjbotlfhYNxfmoy6ehbapn1qRY/F0k7Dl1nlPbPsbVR05LcjJ6rZbQ0PHg5QWbN6P6wx9499a7sLMzoGtR4uzmgau7O11Cd8bd7ENLYwMGtQo3Dy/8Yqci6OrCz09O/ol65ixaRuGl8zTU1+HmImXpskWUl5dT3Wbg3uf+SltHB7ELb+XIxreoLCqgQ9/BzPHnOer6UzSO8bSkncDo7UnK0cM4+/jjGhqJsaON9MuXWZgwFVlIBAse+gVOru54zVSR9J9/UFJSwrRp08jKyuLQqQxiFq1GKvNGrWzg0JFveeHnj9PcqrZy+Z0xLqLX+TBZUlNTU6mtrSV83HjOp3yHq4dHd4mhaStHbJ/355a8evVqq7OtTLvQ79keyfM81PY3at/XGy+DwYEDB9i+fbs5k61AIOCBBx7g3nvv5bHHHhv28YYDKSkp3H///UOm65n5d6RoRpvuWsayJf51oFwAfcHWvTuadD/WsWylG+NxeOjGeBweutHm8VoxoDL729/+djT4+K/BjznezlJR7S+Wccb0aSjTLtCp0yES2VNdU01J9kXUzS0YdDqEjo50GQxomhQYjb589OmnZOSX4CTzpU15nOnRYRj0emorSqmtV+AodUGrbqK2ohR9uLeVMtF45jzSXXvw9HAj//JFZFod8qjx5GacoSr3InPveICc4nLq6huYOnkyssBQMjIy8AmN4uZ75qJqVHDJWUx1VTUXUo5TcPE8WnULHTJv9AYB9vb2tGo1eASEIhA5otAaoaMRN98ADDodHg4iTicfZXxsLBVpp9CqW6iMCOPEru2428Oa++9HIBBQW1uLdkIMFwuKyDufhsRehDEihNWrV/PVwSRKL6YjEAoxGgyga2PNmjXdLrrR0Zz+zW9ouphDoL4NqdCO8qLLVF3OxMXDg+bmZuwwsuLBpyjLyyb33ClcXaTkJR/GVeqMj4uEyLkJzJwxnYCAAGJjY3njjTeQR03AwdERvbELO+zwjYylo12Lq8yTx76dQXRsJ7lZ/8ZZ4kBiXhYt7Xpm3X4fUTHjcXFxY8fbf+XUqVNETJqFXVsLlSW5ePr6EzEpnqKiIuDKBwlP+fcfMELCKLxwlvT0dB555BGzy6/35CUAHDt2zCprtsmSGh0dzamf/Zydn32KPGoCtQXZ+DsJiY6O7nOP9uXaPNjYWhP6c0v+MZ/tMQwP7OzsqK6uJjg42Py3nnHqYxiDJYxGI21tbYNuLxKJbrjEnGMYwxjGcC0YUJn9oUvv/NjQX7ydZfmcGxVyudysqDapmvuMZZwBRMk9OfjZRlQdRsqK8pE4OSNoa+fjP/+GGctW01hZhrFFiYeHB9sTj/ey3s0eH4GyspT0PV/g6uNPS301yspStNo4ihtbiUmYS7NSidHFi+TU7zC2a3ALjuaWex7GrsuIZ0AwLp4+NNdUEDLuMcrysqmu6e5D5u2GV3A40ROnADD9pgXs3rwRj44m/vjLJwA4deoUm/YcpLFuMu2dBupLCnBwdCY8MhKtRk3W5fM4t6voaKwlyssFX5GeeSsXERwczJc7dmIvkeAbO4kvDxwhSu7JtCmTOZt1Gb3YCYHECXVnB7sPHcFV6oxAKMQ7Igaf8Gjqi/NpyM+0su67ywO5lJvPqbQ0YuctRafTk/z5+0y7aRF5l7OZMnc+wTFx+IZEkHUyienjApkyZT5OTk7mDNINDQ3mNYyKiuLIjr10aLU4OTkiVOgpvXAGh4gwXJ0kzBkXxtRJkZzau4NGd1/c/EPQa2u5dDyRqTNmYycU4uzhhZugg9PHD6LtaMc7OILssykUpB5n7a++z46uaVLQZTCAUGj+gAF+ZkU1NjaWjz75lNOXcqyShT368ENmBTQ/Px/v0CiWLVtLR4cOh1tv5VzibvLz83udp55eEVeznA4EE4+WY/Q82wKhEB0C9u/fD2CT4jyGHxeWLFnC888/zz333INcLqe2tpavvvqKpUuX/tCsjeE6hF6vp7i4mNtvv92qLu3V4OPjw+effz6m0I5hDGP4r8F/XcxscnLyiLQdbPuBStOMJi/D3XdsbKw5ljH54Ld9xjI2NDTw8IMbmD0+kubKIu558BGefeUN7nnilwgEAjR55xCqlaxdsoCmpiaz9S4oPJLISdNRG+26xzfqqSspwL6xgrqSQiQOYtra2iguLuGjd95gy4f/4uDObXQJRHh4+eAVHIa9ixsiVTVegSH4RYxDIrAjeccmSi9f5MDm/xAl9yQhIQFFeTH5ly6Q8s1Wii5fQmTUs3LlSnMspUAgwL7LQFNhNga1CmV5EbknDlBfkk9D/iWMTXV4GDSoq0tp0UNhUxv7vktl7/4DCFxkLLh7A472QqYuXUN+jYKvv/6a4ooqcHbHK2YSOLtTXFHF559/jiw4ktU//x3Tx0ez+ue/QxYcyaZNm8wWQK+wKKatvo/xcxYSNT6Oh/70KvKQCKJdRQRLReSdPsHmv7/Ax3/8JVFyGS+88AK33347ixcv5mzGOb7Yd5gzlwvM8c3Lly/H30nIttdf5PDWTzj+yT8JdBRx37IF5pjlzs5OHHwDWfH070i49W4mLVlLp8FIysHd5J9PQ99Yy4IFC3CXyXAPCMXdrrP7f2UyQkNDycrKwmg00lJZwtEvPuT8sQMc3boRY4sSd3d3c9zpxo0b+WzHN3Q6ueMe3l0maffhY+ZY1KysLHbv3o3IxQN/P3/Cw8MICAjAMyisz5jZXbt2meU2Z8kKlt3/KAW1jb3c0m1Fc3Oz+WwbDAb2fbmFy9nZqCQys3xNMbQjeZ5taT9Sff+3zHOwePzxx1m5ciU7d+7kL3/5Czt37mTFihU8/vjjwz7WDw2lUjkqND8E3WiNZTAYmDRpEj/5yU94+OGHB/z3k5/8hPr6elJSUmzicSTP03CNZQvdaI5lK0abx9GUo624Edb6x7pHbgR5WOK/TpmNiYkZkbaDbW+p8J06fIDEzz8aVPmckeBluPs2uV+uX7Oc8TIJ3lfK6giFQiulXSgU4uLiwrT5S4meOAWRvYiEGTOYO38hYXJvnnn8IR59+KFu5faK9c5oMJC8axtNykZ0br44ePhw090bGL9kLT/53Uu4+/qjVCo5k/wdnUIHAqfNxcHNg+y0FDx9fNAqaii+dI7aljbKLl+ipaac2x54iLk33UR7eS63LZjLww9uIDY2FkVZIds3fUxhfRPbN32MoqyQiIgIPtm0mS/27CWabQSH+uDu7MD0CbGsuGsdEgcHzu/fQfZ3hwkKi8IxbAKnLuVR29KG65V6tQeOHKdapSGnuBy1kyc5xeWoOoxkZmbi5hfEosefYfrKu1n0+DO4ygO7s556+jBz5xbGVVUjcXDExdOHS5cuIXB2Q6lsoqCgAAepC36RsYjtRcgDQwibFI9EImHFihWEhobg7+dHaHAQs2fNMlsGLV1ipy9ZZVbsioqK+Pd77/LknSuJc9LxxJ0refH3z1l95U9OTsYzLAa/0EiCI8fhLfdD5CDh4pF9FJ88yOJZ0/Dw8GD57Xczc9J4XNzdSZgYy8xbFvL6G2/yzy1fonTwwEseQMm5VJoKs80fMObNm2dOIpZepaTDQYqDsxuTb17KwvseQ+Aq4/Tp0+Y2SsRknEnlbHo6Xcauq34csre379crYjgwb94889nevXkjF8+fY+k961l+57peivNInmdb2o9U3/8t8xwshEIhDzzwAFu2bOHgwYNs2bKFBx544Lq22Pv5+bFt2zYyMzOBbrfX+vp6jEajuU1raysajcb822Aw0NnZadWPSqVCq9Waf+t0ul5JkQwGg1XWX61Wi0qlsmqTkJBglVhNo9FYVV3oi7+2tjYr/vR6PQ0NDUilUiv+LMfW6XS9FFClUtmrTc9nd0NDA3q93oq/1tZW81hCoRCVSnVV+Zn4AygrK0MsFqPVajEajTg4OODg4ICdnR0ajcb828HBgfb2dpycnIiMjASgsrKS9PR0K/6SkpKs5F5YWEhWVhbQvef1ej2JiYlWc8jKyqKwsND8W61W98r2np6eTmVlpfm3QqHo9WKbnJyMQqEwjzVU/gAiIyOHzF9MTMyQ+QNwd3cfMn+2yk+v1w+Zv8rKSqtxhsKfaY8Mlr/09HTc3d2HzN+1yM90Bw9l/5lohsqfiW6o6xsTEzMi56Mnf6axbNl/QqFwyPz5+vratL5NTU1D4i8xMZEnn3ySa4XdsWPHugZudn1Co9Fw66230tzcfEMVnx+OuL3rCf2V47Fy6bySJMfk0pmVlcWWPQd7JYuKjwwhISEBgNOnT/P5N98SMnUWQnsxxXk5xMy6BR+JgEplKw7uMry9fRDSRU1WBlUZ31FlEHPrM/8PgcieTl0H+9/6C0FebugNXdQ1NRMYPZ6aghz0LY3c/dhTtNRW9eJr8+4DTLvitmovEnL4842EyFyoVql4ZellRO21vN/+IvnZhcxfsIDMs2kcT9yHk7MLWm0b85etQCb353RaOuNm3kR4VDQuLm7863dPgp2An77yTxwcHenQatn2+ovYN9fR7hfNLRt+hr1YTHtbGye2/IvqjO94slXNb0oK0AuF/Hn9z9hdkkew3IfGFjXxq+7GzcuH8tISKi6cYfl9D+IVEMK/f/cEfo5CnP1DWXjbPWhamnH18CA3LYUHb+u2MCclJZFRrWLOkhUAdBm7OLjzS9zblaxcsZzxmn+C2ION58b1cvOtLC8jMTOf+/74BvYOEpSKera/8hwibQtT42cwa2IsM2d015xddv+jiET2nE1PZ+d/3sHJ3YNZq+7C292V8THj2PHv/yM+MoTZs2eb405N++JSVhaFdU3U5V9i2rwF+EeMY/8n7xHjZEQrlrLs/kcRCIXs+3ILF8+fY/rM2RjUKqv1tETPPWeKaV2/ZvmwufibzsL+/ftRSWQsv3MddoLuF+1Thw8w3d+dxYsXD8tYYxgdtLS04Obmxt69e23OQmkJjUZDamoqCoWCdevWmRUmWxMLjSRMz9j333/f6kW2P7S0tPDSSy/x8ssvD6qkzFj74W3f0dHBJ598woEDB2wq6TOGMYxhDKON4XjG3vCWWT8/P5588kkSExMB27+KDcdXExjcV52kpCRiYmIIDQ01Z9+9nvgbivxMcYhbv/mWs8XVZndKhULBuKhI1q9ZTrhjF+vXLGdcVCRNTU1At4U6xEPKkc0fcCpxH//664tUlpZS06rlz6+9ycvv/Bulgwdh4RG0V+RTm3MBmY+cQB8vxO0t6FQN+Pj4IhUYCXAw4tCpprq6Gq/gCIzaNuxKLtJlNOIVEkHuuXRcnRyImzodHz9/Jt+8EHcPGeVnjuPnJOL++9YhFApJTk6muLgYz4AQ7O3FaJUNZBzcjaKtk6r2Tp6M/JrjOc5sUj9Lq6oDbYeOY199Tvap48hCo4mdMQvPsHE0FudSkZ+Lu28Azh6etJbm01ZbjthJSmt9NYc/eYes3ZtJ3rEJV6kzU6dOxb2zBeWl0zSWF1GRfR6DspoX4uP5TUkBAKkhkRQY1Dga2omcOBmMBuoun6OjNJva3AtUF2aT/O0ODr/1IkKRGK2LLzlFJRz+9htqq6tJOXmS0opKKioqSExMxNvb2+wSW1uQQ+rRQ2ScSaVWL6A5+SkOnKknu3M+uw8fwyCVdVtfpe58vnMXBoMBQYuCjI9f59jWjXz79svoW1VsePRx7njkKYrqu9fYtL4Hd37Joe1bCAwIYMaUyYTHTECp1tLSqiYkLBKpVGp24a6ursbFUYJAICDAPwC7zg48HMVUZp4xuzD7+Pjg4uSIvViMUChk1br1TImJwrVNYS7f09TU1Ot8NDU1EerpavaKOLzlP/hLxVaWFcvzUVhY2O/56OjoMJfgOXDgAPn5+UD3F9DQ0FCCg4MxqFXo9d3WqfJLGajKi8wW4+vl/Jr6uF7vFxOGg7/CwsIh8zdcX40tx1u/fj2bNm1i06ZN5r9db+V4LBEREfFDs3BdwdKCOoZuWJ7fMYzJoyfG5GGNMXkMP2wrmHYdoaamhg8++MBsmRWJRCxbtsyqjaXVpbKyksDAwF7Wkfj4eKvfXl5ehIaGWv2tZ8rpwMBAAgMDrfru2a+lK0dP/oqKioiMjOxlFZJKpb36kcvlVmN5eXn14sfyt2meljRAn/xZviQOJD/odscy9WN2V93wpJW1q6GhgZtuugmAqqqqXn0IhUKe+Onj5vIqrlInnnjh/1F8KQO3RjVBE6YSPSGW+JsX8sk/XsaLRsqLcmifOQdp9AzU+Ts48tm/ueWmeZy9fBlFTSVdjq7UFOWi6wK7wFi6dDrqi/Jwd5bQ6ezJqg1P0d5Yw/6vttJm70S1vYzSs5fIzP4V/37vXebNm0dmZiabd/+bVrELWkUt2VX1jJ86lf/xeRe1xomNqc74RdRh1HdSlpGCnbaVLnk4637+e7TZqfjOWMh3n32APw2026nIblPjILSjpvAr2jUaImfcRJNKhb61ifhFK2kQ23H7qiWU/PNdTnyzFWdvf5SVpfxRUc26nGwAMhLm8tWau1EVF+HoH0ZRaTl+MZNxcPdA4CDh/vsfIP1UKHnfHaTJyYPHX32PnOMHSE1JJnjBbfgH+jPOyZltr79Ie3s7t99+OwaDgaiMcyR+/hEiiRPpGRlMmTqZZyZ/h1St4K38RZw9kIbAVdbt3msvIj/zPJlZWWiMAtw9vSjMz0NoX4Kx08Cqu9cxdekahEKhOTb6sUcfYd++fSQlJeHjJeOWVbdx6uRJYsQSHF3dUTU10axUMHHe90nmHBwcUKk1nE05SX1tNXqtjtxLFwgOCqazpZGlc6YzM77b6tup09HWpMDB1YMGhYLIuHFW52P27NlkZWWZPQaCg4OZM2eORRbiVb28IizPR1FRUa+zIBKJWLx4cS+vg6qaWhwcHAgJCUEqlXL33Xej3rTZunyPv49ZcW5vb7/q+QXr+6W/e6u/+8XUvq/z2/N+6WueV7tfLO+Wge4XiURixeNw3s/z5s2zmudA93Nf87za/VxZWcmyZcuYPXs2W7duZTjw7rvvsmHDBm677TZWr14NdM//H//4x7D0PxLoKaPBQqvV4ujoOOI0o02n0Whssh5YulgPBT4+PkOmqa6utmndLM/TUGB6lxmNsWyhG82x4PqXh610oykPW8e7EeQ4mvKwlW605XituKoye99991nFovSHL774YtgYGmnU1tYOWtBDaWtL+6FgJHm5lr5tyc7c0y05ODgYpb0bLa1qynJzcPHxR+rhRWtrC5VVVRhdPOmyE9DRqOHIZxuJmXUL9gIQaluxb2mgobqCkKmzaFWpOH14H4fe/zv+MZOozr1IY1khaxbfQlW7BoxGKi5m0KZtZ8qKOwkNDUUu92fb6y+yb98+br/9dgC06hYqss/j0NmGk7MDP7X/M01N9rxW+iBO/nbUVpYj9ZIjcHHHKBTiJPNG2VCHg8GAUGCHvaMTiroamurrEHt4I48YR2tLMz6eXoiEAoTyYIRN1ez+7FPkzmJKxkeSczmXNoEYxEqeLMplXXUFAGmzbmL/r15AVl+CUCRm3r2P0NFYR1l+DvZSd9x8fXF0l+Es7GLSpEk0ugRg7LKDzna8QyJQ1VZBuxqpgz3e/kHk5uaSlZVFbGysubzMnj17mDhhHP8z8TCObaVcnHKQtooj5KUdQBwUi51AQLOykbZOPRHTZuPu1MUzr/+KrW+/SrCHlPImNcvuuNcqNtp78hI2f/Y5BbWNeIRGU3jhIjkXzuHl4cbRrRvp6NTjZqdjxrgIYmNjzXuiqKiIEwf2wumz+MdOojrnIihruefBdYSFhZmVQZMi7uIiJTU5GUepK0p7N3Pd5g0P3G8e35S52F8q5uc/+5k5C7Gt7v79leDxycwkJCQE6L98j6n/kb5brpe76L9lnoNFSUkJb775JoD52erk5GSzonM9o6OjY8iKoi00PwSdLejo6Bh0RmJLeHp6Dpmmvr7eppflkTxPwzWWLXSjOZatGG0eR1OOtuJGWOsf6x65EeRhiasqsw888ID5/zc0NLB7926WLl1qLilw+PBh1q5dO+JMDid6fuEfrra2tB8MTC/cKpXKrIQM5oV7tOZpWY7H9GKvrCxFPs26pJNpHlVVVZw+k4Za4IBXcDjKtAtIOtvIKq+hVexCm5ucwnNpdIkckMVGmcv7RM2ahywugXOH9lBTmIOzszO+IeE0NzfjFhTO4vt/SmriHgKrKpFHT6C5rprIGXOx6zIilUoxVtRydOtGaisrsHNyRSyyR+bti4OjI/KoCeTl5ZGVldWdGGHiFKbMmsul9DMk7/mGbyTRZIkWU15eRl1BNuMT5tLa1IijqzueAUEUlpaRnriHMGcBFZcLqSvI4qbxkXRFR7PgrgcoKyunKHwc6ft3YN/cDIBW1YjcP4BW7PjX59vRu3lz96//jDwoGIeUY6h//hOOBgTwflgkSxwdwNsP78BOCjJSEXYZqcjLpjPrArUBQRz+6jM8BAbGR0eQm55KSMItGHzC6Sg5SVtzEyGeLhh1WuqryvH2iDMrfA8/uMH8waHmu2dwaG/g4uS9pGaWkHEmFW//MPLzsjn8zXbsBHaIHF3Q1FXhOnUCEkdHxk2fxRRfFwKrqkn68lOr2GjArPCJRPZ4RKdzaPsWoseNo6UsH0GHhqXr1pmtUyZLZ3VdHXj4svShn+Pm7oZo1W0c/PD/0Ov1Vh9HTIpiamoq/sHBzFy0itbmJqLjZ5OblsK+ffuuWvP1Wsr09PcBp2eMWl/le/o6Q4PB9XAX2dL3f8s8BwupVEpTU5OVglJXV3ddxsteKwYTYzscND8Ena1jtbS0DJnOlmzrU6ZMGTINjOx5Gq6xbKEbzbFsxWjzOJpytBU3wlr/WPfIjSAPS1xVmV21apX5/z/77LP89a9/ZcKECea/3XLLLXz00Ucjx91/OYazLuZIITY2lqi0s9bulD2yM3d1dZnnIZS6k5FXyqSp05i5cClGg4HN//syTQ11VGSfxzMwBE19Nen7vqKjdgZ1NTUEB4dgFIrRtDUTMW0OhlYlsbNuJnnHZrxFEpw9/LATCtEbu/AKiUQWEIp/9EQ629uQevrQ0dHB2iULOH0pB0NLI4rqKmKmxFOaeRYnV3eqczJx8nblb+99iN7ekfKifFTKRrqMRkSOLhQHrcfb1Z0uZy8629sIm5KAk08ghz96C0/fQNpam6nMPk/Q1ElUXr5Ia30tcffcRpubH4FBQbS0tFC0dzcu3n5o1a0IRCJcA8NpVjfjIfNEq2kldMpM7ET2tLdrYe5CfrPhKSqq8pgVE07WkW9RtbaSnZlJxLRZyMOiMNp1oaqpQO4fgMDegeKqMhy85dSXFXJo41sExU6iLPMM1ZfP47FwMUV5OUyeNp1V69ZjNBisFLvY2FjS0+7gjZwGOssOk3EmlUlTp7Fw7V38/pF1ZB4/iP+4OKrzsnES2eEyf675o0XAtOUsXry4lwXy2LFjVgrfjPh4GkryKT5zHJ/gcMJjJ5GWV0JT8+fMmD7NrHh+9eF7hE2dicTDC3dPGc7OTvhFx1FQUGC170yKYlVVFYfSL3Eq5SSywBAuZWXRWl+NTGhAFhjVr8dAf9ZVk0yuhsF+wLle8GNLOHcjY/78+bz66qv86le/Aro/Er/zzjssWrRoUPRHjx5l165dFBUV0dbWRlJSktVaLliwALFYbGUBfO+99wgPDwe67+JPP/2Uffv2odFoiI6O5te//jVhYWHDOMsxjGEMYxjDGEYPg46Zzc3N7VWqYNy4ceTm5g47U2PoxrW8cI8WBnKnBGhsbDTPo6KyCqewCRSnnaCsIJfwmAmInKTEzbqZsHGx1FSUc9tPNlCem42dsooyZR1u85fSYe9Em6aU/NRjBI2bQOqhfXR2dDBhwnSOn79M/vk0OnQ6Whsb8PAPxtXTG5lfAOf2bccg8+fRhx9iTk4OJSUl/L+/v07qnm34x06iJvcinXWVaNsCiVm8FjcfP0R+4ZzZ+yUSsQNeETGI7EDcpcfVw4OAmEmU513GWdWMd1AYNWWF+IaPw2/cRNzsO7n1qdUc3fQuOTk5OPtryb90gdNHDqFVtyIfF0erooGEO9ZTmZNJe2sz2Ue+pUutokHVStwty9DrOunoaiOzvpp1y5YhdXOjRtOOp5sXHvVK7O3tcfMLYNy8ZTTKLzN90XIELp6c2v4RGIysfuI31JWVIG5r4icPP0pDdRXaijzGT5jAqnXrAagoLqCxuYXU1FTzWq1/8HFzFl5T25RD+3D1C2HikjXYaTUEhYRy4XgieZcuUFhYYP5o0ZcFUi6Xozh9jvxLF2hpasLVw4Paolw8gsO59+lnrfZzV1qaWfENDI/g8qHDxM1fjq5Th7ANSi+mEzYpuk/PhLa2NlrUGlY8/aBVhmhHR0dqriS46kvhtMU93oTBfMAZDEZDybwRPoj9N+HBBx/kzTffZMOGDQCsW7eOuXPnct999w2KXiqVsnbtWjo6OvqNs/3b3/7G9OnT+/xv27Zt48CBA7z++usEBASwefNmnnvuOTZv3jxq7rZjGMMYxjCGMQwnBh3AIZfLOXjwoNXfEhMT8fX1HXamRhI9s2QOV1tb2g8EyxfuorTvBlUX02AwkJWVxcaNG8nKyrKqx9cfrnWeJmVm8eLF5qy0lmhrazPPQyqV0qFpRtOhY9/2rZw+cQyduoXCSxdo7DDi6uRIk96OqrIS5syZg9jenqqci1TnZ1F6PhXvwGBmLlvN8g0/paO9nYaGBiaEyKk7f5L26lKqcy6Ql5JEXXE+qTs20dmmsfImyM/PZ8L0GTz0898wa+ZMbt/wOCJnKfYyXybdvJSgmInEzl1IQOxkJkeHIuwy4uDkjLunFxIHMU0VxYgx0NakoKYwF01rKwKRPUKBHU6urvgGh+Hq44dIJDLXq1VodBgMeiozu5U2RxdXfIxq9LoOvEKj8IgYj51QyObfPc532z7my//3GyTaZm6++WaK6pq49+lniYsMY/UTzyB1caGtugx3Ty+CJ0ylqb4WBycnpB5eOLm60FJbhU9oJMFyb/wCQ9G2qggICKC5rpp2rZb92z/n2693UtfSxrFzWXz06accPnzYvIYrV65EjBGjwUBlcRHy6AnIw6KZdNMibl5zF2ETJoO21Zw1uD+FKDo62jz/spxLbN/0MbUlBYSOi+ulQALmzMpydxecBV3s/b+XSP5mK//5w89pVzXiEBxjzpRtuacVCgUhEdGU5WVTUVxIWV42IRHRREdH96rn7CMRmhVOuVxuHhO4an3anrCspzzd390si2PHjg1Ia8Lhw4fNtXIzqlV9zs0Stp5Ryw9ic5as6FXv1hZcT3focN+5I9m3wWCgqKiIZ599ll27dvHee+/xxRdf8P/+3/+zquN8NSQkJLBo0SL8/f1t4mH37t3cc889hIeH4+DgwCOPPEJnZycnT560qb+rwVQndaRpfgi60RzLVKpuKPjuu+9sGmskz9NwjWUL3WiOZStGm8fRlKOtuBHW+se6R24EeVhi0JbZJ554ghdffJFvv/0WuVxOXV0dxcXFvPzyyyPJ37Bj1qxZI9LWlvZz58696n+3dGcMjJs+oDujpRXGQx5gFRt5NSvMSM9z6tSpfHXoOJ06He7ubpzZv4uywjzcvOVc/tfbSDra8AoIpCL7PD4BQdRnn0erbqGpqYl5K1bjHxJGalIiASHhBE2dhaqhlrN7d9Bp7CKnRomzREyIlwdySQf5Lm7EzVtEu0ZN2ITJ6Brr8Pb2NsulU2BPo1ZP9tlTeIREohfYI5bJaaqrwU6Vi5u4C7UgmHZ1CzoXR7zdXTm79yu8/QMpzsygQ6vGzX0+xRcykIeEETFhKrmXLoCdACWONF84g7K0gLAlN1HTpmfZsrXU1tZyubCUiycSqc3PInxyPA06kEocmVRfQ91t9+EVEMyet16iPTuVn6xezRNPPEFKSor5I4A8ZjI5/5+99w6L67rW/z8whWnAMMzA0HtHvVdLVi+W7dixHdfYiRPfOL+bclOdcpPcJDftmzjFcYl770VWQ0LFEmoIgRAgem8DMwwzw/TG7w+FuYCQBFjCls37PHqkGe2193vW2XvPWWetvVZrJ9qUdBRBfvocNlorT5OaN5umM8XY9F1suvMe3n/pWc6Vl5I5cy4HH/8bDoOO5Pu/jqW9iN88fD8+WRi5q7cQqYlG4Hfx1rsvc/Pa64iJiSEnJ4ecnBzSTpzk5T//hra2NvpsLmatWItMdt7r2dNQQ+7MzAvm3WgPY11dHZrkDDZsuBGbxcLirbdw4M0XqTtzipSZc3G53ISEiOlrb2bj1vUE/TuhU4Q2jpzsLBw9HYgdPfi0ah585EmkctmYkQlz5syhvvcgOamJ5/tUJ+KoLyMxMZENGzaMiBhISEgIrIPxeFcvtT7H8kZPZF1ERERQeKp83FEXk12j4/FAX24f+jhcrvbeMpH2V/M6xwOBQMB3v/tddu3aRVhY2FWrjf6b3/wGn89HdHQ027ZtY+vWrcD5kkc6nW7EHBcIBGRkZFBfX8/69evH7O/o0aOTyo0RERExJTJTLTfZ880RERHY7fYJy1VUVARyC4wXc+fOnfA4MPk5P9G19XHGmozcVI4Fn359TFZuKvUx2fGuBT1OpT4mKzfVevy4GLcxu3DhQl544QX279+PXq8nPT2dn//858TExFxNflccCoXiqrS9Gu0nGs442bDkq32dc+bM4czZCgpeeQaDaYCmc2dJnrWAuMxcTF3t1J04TKY2juvXrsXY20tuZgZnpGJqa2uxiWQsvG4dS9bCgcJCxIMeggdM9LY1EpmYQlTeTAZ6uzhcUkJ+cjza6Cg8hm4itPEM9HYSH6ulv7+fBr2Z7IXLaG1qQh7toq6plUivgIScmbhdDsLsDdzWvI1izwrePDsTY3szG+//CvNXrOEvP/omtnPFRIWFcv0DD3LmeBEJ6dnEZM9EqVEjKPdSf7QQY3QMDmMv2bGRZGdn4+4ZQCQSo1QqSdRG0a2NpbbkGPv/9SdCJTJ+XVTI0p4udiWlUpc/i6y5iwg3tbN69Wrq6upobm6mqaGV+SuuJzY+gU5dDyeKDpCbk4OuuRG3vgdhciItFWcQet1UnT6JlyA233EvSqWSqJQM+prriE9LZ/GajfzyP+4hKSGSb2cUUp74Pxzcvh27QEKZzkzrsCzABIFIIiF3wVKO7N7O9r/+isz5S6krOQ5OW8BLerGswRnFp0iIiyUyIYW4uDiIiwMgJXcmh958gef+8nu0GXno6quIlQnIyflW4ByrTqdDu3BG4Nzt6S4TUvn5xEpjGWJDc6ts7/bAGsmMUV80/HkI4wmPv5rrwmQyTSjMebJcxnO+92pe5ye9h04ll/EgLi6Ovr4+1Gr1Fe8b4E9/+hP5+fkEBwdz+vTpgGF74403Bgyp0delUCguaWQNr8s7EQiFE6/8NxmZqZabao6TyXQ92bk7lXKf1bEmKzfN8crITXO8MnJTzfHjYkK7a0xMzIgMx9O4uhAIBNx7913nM7PW17M4J4MtW7Zc1Mv6cc4BXk0MNxz++7//m+iUDG54+IeIxCF4PW76ujupP1PCV3/wM5Iystn5+kvUVFczZ8Eiao8f5vFf/5R5y1ZiaDiHTdeB3+clWKpg1e1fJi4+ns6ODhrqaqnvNTEoFBGCh8w4DWF5mRzd9T7nzp2j0Wijw2xHqtKga2vB2N1Gn66TxtITRAXreObOanbXaHmuRY1ZX0rIoBeAf/z8e4SpoohfuIIz5eUUF58iKX8BdWdPI5aE4LEY2Xbrbex8/nHk/e1sXLmCr3/969TV1fH0W0/Q4wlCKJHhcdjwGHvwm/twej38raWRJSbjef2IQ6ipOEP92VLmzJrJI7/5AwjFpOXPor76HP/89U9YuGI1ho4WMtShRAu9LN+8lpSUFIxGI5GbrqOxsZGCggKycvNZtWoVra1t2INCMLY3c2L/XhavgdTECP47/zV83iVUn62gr6+P/FWbyE2OY0Z+PgWvPMPOnTsDoc0isZitt93Fv377E4JaKkgcw0t6sazBkRHKQBjv0Pct1WeJSctixQ23nD9Hu3QRNcVHqaurCxido8/dXswQG+4NXjBvLgs4H8Y3llF6qXl5MWP3amOqkkhdqfO907gy+MIXvsCvfvUr7rvvPrRa7YjSd5MNHR6O4WdlFy9ezC233BKoOjCUbXu0cWq1Wq+acT2NaUxjGtOYxtXGhIzZ/fv3s2fPHoxGI8888wxnz57FYrFcUKz+04yGhoZx11+bSNvJtL8cfD5fwOsVroriRHUjff2vXDRseHjSHWN7C6qEZAxtTWjnbr6ivCdznUOGw4wZMxjoMNPdWIfdZESmVCELU6JyRlDwyjP4/X7OVtey/rZ7WDB/Pmu2fYHX/vo71L4BfvvI9wF4/vnnQalBLA6hqfQkDqkSdXwymTEatFn57H3zJcIb66kvL8Xj89Hu99PZ28eGjbch87mQJ2Ry4NlHaT97mi1bFvKPFZXs61nAL3fZWTbby39+80FSUlI4evQocUnJrL71bhobm0gUyGk6fRSH/TgDFjOH33yB+NRMDm9/B1d/L1/86n/Q3tHKf33v+wQHQXnxMfRWBzFZeXTXnaOr8gxp2TN4prUZZ2QEfpOR/8meyTGXE/32N1Bqosmeu5A+qxNN1gzCYmJZnJhG0avPoK8sxuHyII2KJSg2jZM1TVRV1zJ/7mw+3Lkba5AIRUoOVZVVfPTRYaR+F0eOHMFmNpMzbxFlB9/hF7Pe5Wy7jD8UhGG3P01EfDJhA2ZCBXGBFx/19fUoY5Nob6rH2NuLMlJNbGom9tZa4mcsRPLvJDEXa6+KikIZm4BMJgucWQ1XqTEbDSjwkjxvEZkzZgfmhUHXPeJly/C5dTFDLDMzMxA2Hq5SYy4+Q4Y28rLh9Fd6fX6c/sVicUA/4zEyJ7tGx+OBnig+TXvo1bynV6PvP/3pTwD84Ac/CHw3ODhIUFAQ+/fvv6JjwflatoODg8D5N+ZarZaamppAHgGfz0dDQwPr1q27aB8xMTG8+uqrgTmTnZ0dmKcqlWpMj6PP56OzsxORSHTB/8nlcuRy+Zhj6XS6MWuxCoXCi4b32mw2TCbTmGON9R2A1+vFaDTi8XhGtHG73ZeskWg0GvF6vSPaL1myBIvFckFpruH8bDYbHo+HwcFBlixZgslkwmq1XlR/Q/zcbjfr1q0LtIdL689sNpOens77778/ovyTXC6/6HNaQ0MDjY2NwPmIkeHlipYtWzamp8VqtXL06NEx+0tLS7vouikqKsJms10w1nj5jZYbL7/hMuPlN1xuIvyGYyL6GxprIvyG5OLi4ibMz2QysWXLlgndX5PJxLx58ybEDyZ2f4cjNjaWGTNmjJsfgFQqZeXKlRPmp9Vqx7yuy91fnU53gdyVWB+j+S1fvnzM36TxzL/RcuPhN9ZY47m/o+Uuxq+kpITu7m7EYjHuf+cu+TgYtzH77rvv8uabb7J161Zef/11AEJDQ3nqqac+UWM2JiaGhx56iPvuu48NGzbg9XrZv38/a9asCfxIVFZWIpFISE9Px+l0YrVaOXHiBGvXrg30U1JSglarDfyQGQwGysvLR9yUoqIisrOzA2+xOzo60Ol0gbpKTqeTwsJCFi9eHNggGhoacDqdgYf1y/EDAvy0Wm3A69Vw/CC58xZyfN8uqqurAw8lw3Xf19eHubOFN1+oJS0jkxPHiogNDSEz8//OOI7Fr76+PjC21+ulsLCQmJiY854urRa/349MJgu0MZlMFBYWXlZ/o/kVFRWxatUq3vzhT3B73CSnpmMoP0pHRTHPPPondDodJ0+eJDkhkblz5hAUHITNoCM9K4eUlCRmzZqF1+tlzpw5HHnxDUpjUtDIQ7CZ6rC31pKxfi3ZM+fQ11yLX9eM0+UkYcZ8PAhQRrbQsH876bPnYXT6iYrWsiRVzqOr93JcfDul6V9m2YrX0UgEZGVlkZOTQ1lZGTKplJqWDlwIqT56gPQoJb0uPya9Hl3DOdKTEsmdOYuaijMYurspPXmS5MQ4uhx+ZDHJCCUyRKYe8vPz8LQ28NdTx1FazXy4dSvnbv0KH3W30118iBXrN6NMzeHY4UOEqGPRxsQgtfUhSslHGp9GR58JmVxBcuYMFixbzunTpRzb/gY9lgEaOntZf9s9zJ49C+/jf6bwrZdJiIulu7mZhStXkRkt4R7t85xoieK72yNYvDiCpn47fe0tRMUnY+3ppLc5FGNHCwuz0njpne0kp6cTHJPO7h3b6WlpZNXK5dSUleBGQGyUGpk0BHNLLfPysnn2zfdQp+cSpwqn6mw53Q3VbH7k+2RnZxNVXo7NZmPG8vPe1Le278Rm6keujMDjdmNqa8CjmRWYIzabjT179gTm34J5c4np6MBut7Nl28bAfWnp7GbDvf+Bsb0JVUIq+198gqKiIq677rqLzr/y8nKUSuVF1+/F1sfQ+h1KrlZcXMzChQsDyc6G1q/T6Ryxfi+3PrIy0lm8cEHAyOzv76e/v39MfkN9j3d/GZ7Iqrq6GolEEuAzXn5jrd/s7OwAl/Hor6WlZUL730T255qamhEP3pfbnyeivzVr1uB0OikoKOCFF17g4+Lb3/42jz76KK+++ipwPgHYpQzIi8Hn8+Hz+fB4PMB5Y0ogECAUCmloaGBwcJDU1FSCg4MpLS3lnXfe4b777gvI33jjjbz55pvMnTuX2NhYXnrpJYRCIStWrLjomN3d3fzsZz+bUE1WgUBAWFgYoaGhE7o+uVw+KRm/3z+m3MXqugqFQqKiohgYGBghZ7FY6OjouOhYow1qi+V8VvjNmy/+wnjI+BwYGGBwcDDQ/mLG73B+FouFpqYmVq5cecn2QwgPD6ehoYGHH3543Odm09PTA7/tlZWV44pUUSgUbNiwYVz9D8fw/WS8Yw3nN1650fzGO9boZ9nxyI3mNx6Mpb/xjDXWs/bl5C7Gr7Ky8qIhoRe7v5WVlZe81snYApfSX2Vl5YT4XUrmcvwuJne5+zv0ezhefpfC5fQ31ljjmX+j5cbDb6yxxnN/R8tdjN/w8S0WC4899thl+74Uxm3Mvv/++/zud78jOTmZN954A4DExETa29s/FoGPi+7ubp544olAMg2hUHjBTRq+0If+PfxBCS4s9KtWq7nllltGfDf6RsbHx494iztWyOLom3g5fnB+oq1du5bCwsJA2HDOdedlhocNj+YTGRlJWGwSa6/LQ6/rJiU7j576qkAY51jXPXqiBQUF0dHVzcHSyv87A/lvr9fFdDXWd2q1+gJ+y5cvp7y8nOy8GUTPXoJQpsAboSHLZmXP3n04RTIEsek0nzjGi3//E/lz56OMVNNv7EOrXYTP56OmpoaGhgYs/X0Ym2pwa7ToO1rR6Xro6WwjPTcfn30AqUSCPDqe67/0VZqbGmhtS6S/7CTt3b1EJKTQ2drMsvlOdlnW8WpbOpqoRno9wThcDl54fxeKd9/D5/VQXt3MPTffz+miA+czzabPJV0dTYLdxok3n8XidCNADBIFde1dWAcF9Es1RMVEMGC14fYPYlVo6GpuR2R3EG4yIvO4iZq1ko9mLMD74mNcv+0WyioqUbkFmIwmXF3dyNXRZOfmY3M48Tgd9LvcqGbOxBokor6+HovLS9L85Tg6m8lbfj0Wl5empiYU+QsJbmym7Nhh8lZvISYmlK+Ifki1fx6vOpYRFn+a5K33IW1toK+xmqay4yj8s+js7CBDG0lKSgpCqRxF9nwQikmcvwJpqJIZy9dR/9IzHN67k7SZ8zC2NhArE5C4aT1SRRgJeXNQqDQEG/WYdJ0AJCUlkZSUFLj/Pp+PU6dLOfje6//njYyNHpF4Jj8//3ybMebfkGfIZDIRmpSJSCwmOu18ubCwpIzAA/7F5t/o9Tx6/V5sfQxxH17i5tWd+8g4Xcr999170fV7qfWxdOnSEdc8Fobzu9T6HY6J7C+X4nex9Tv0f6P5DWF0v6M/X8n9+WL8hjCR+zsWv6H9fMmSJbz22mt8HAzVSR56yfDWW29xzz33TLifffv28fvf/z7weciI+stf/oLdbufJJ5+kt7cXgUBAdHQ0X/3qV9m2bVug/e23347dbue//uu/sNvtZGVl8fvf//6qlOWZqFE6WZlPQm6yY13MuL4ULuZ1uRRGl1EcL6byyMVkx5qM3FSONVlMNcep1ONkcS3c68/qHLkW9DEc4zZmTSYTycnJACPO+Uxj8rBarZc8LD3Rc3WdnZ20dnRidA+iik+iuqqSgd4uOjs7xz3BrkZt2+HXef5cbxIisYhBnxeRWIQsMprqlg4e+sXvCRYIcAilHN31Hn02Jy6zkViZgLS0tIAxoXMJiErNwm42IhQEE5ecikwRyrF9u7H2dpOhjcTnCUVuDyZIIEAWFo5AIGJwELrqztHb3oJYIuPVYy6OWuOIiBVSfaoIY3MtG7ZuQ2/so7yziwhlOE6Hk3ce/xNdTQ2ExSURnzsbuVJFUFAQrWUn6KgoQRwRRe6qjQTbB4ifv5yW0pM4O9twOx1kLluH09JPwowFlDTX87BokBkKFS0OO/VP/RGHQYclVktL5RkcDheapDQMlaV89OI/sa7bSn9vD8audvIWL6e55hyyyGgMBgMiaRimrnbSUtOob2zG4xuk9cxJSooO4RsEaXQC50qOERJxA4fS76PIv5L2hrfRJGcSJJEij1CTsv4G7G+9iGKgl3tu+0og6dKCFatIz8uhvr6ehKQUwkOE1FWVE52ayazcOYQF+Uj5wi2UFnzAmTNnAu2tViuKmBxC3QNjlqEYT8jreObfxzlvOpHarsPn7bVQ83myuNw+9FnBp+k6h0J/J4qNGzeycePF5/nwlyRjISgoiAceeIAHHnhg3GN+WnT2aYHX6510MqfPKj5Na+vTgGl9jMS0PkZiWh9XHuOuMxsfH8+ZM2dGfFdeXk5iYuKV5nRVMfy8y5VsO5n2F4tZH0JOTk7gXN3Rgp0UvPLMJc/V2e12LFYby2+9j1kr17H81vuwWG2XLQcwnPelkkiN1X48GH6dVquVc+VnUCakEZc7G2VCGtXlZYRFxyISi9H36glTRzF/zWayMzO5/zs/RJOcwZ49ewLGxJK1GxkUiLAMDOAXS+lsaaLu+CHmZSRz55Z1LJh3PrxqoKOZurKT9Pd0M2A0YO7pRKJQEJc9A7FcjlIbR1zubKThEUSnZCKUyikpKaGy8hyKqDhy5y9GER5OSIQGl92Gtc+ASByCUChicHAQs16HzdQHfh/9XR2ILTos+l40SWl47DY8bhfNZccZZJCOc6UM+n3ELF7M7G9/g1yZl/+69zae+ftfsLXWEpWczsKb7iB35Xrm3nA7whAJZw/sob26nLTZ81l7691oY+PY//JTnD5UyL7n/k5EmIIFq9bR21zPyT3v09zUyIDFjDYjh9kbbiIiNpFj777C84fcfPTG84Qqw8HvZ9A/SL++h9baanxmA1lZWYH7o9VqMXa1EaEMJyMjA499AEN7MzBIRFwiYqGQUJ+duLg4IhNSAOjrbMXj+Xe9Vo+bvs7WEWGuw+fL5WoSd3V1jZh/wQIBboLZtWtXoG5yTk4OaVERvPzn3/DCo7/n5T//hrSoiMsmNXK5XBOq7Tp83l7pdXG195aJtL/cPvRx+v68XOdEcS29EJ5sGQm/3z8lMlMtZzQap2wsYFLn2yc7dycrN9G19XHGmozctD6ujNxU6mOy410LepxKfUxWbqr1+HExbmP23nvv5Wc/+xlPP/00Ho+Hl156iV//+tfce++9lxf+FGEiSTYmmpDjSifwGPJk3bNtI5EeM/ds23jJJDcymYyktExaa6toPbyL1toqktIyL3vWZjhvrVZLX3sznZ2dNDWd/7uvvRmtVhs4M/jcc88FDAsg8H1hYeGI78eCwWBg0O+n5kgBreXF1BwpwO/1YNR143G76Sg/gUiqwGnsJW/WrIDRVF9fHzAmBvr02C0mclZtJic+ipjsmQSJRPh8Pt774EMee+Utzhmd2C0mTn/4Bo1F+6gq/ACnuZ/UcAkVhdux9fcRrtEiEAgQhYQgCJEQmZhKSIQGdUomTVXluLpbcTtsuAbMJM+YS29LPbv++ksKHvstH/z+x/Q2VqNUhtN05gTBIjFiWShWo4Hj77xIQ9lxfG43czZ/kbyVG1h519cJkSkYHBzk5ptvZvbs2dx0002IxWK8Xi/hMQmoYpMhOIiolEyikjNwmw1IJSFY+/up3v06jWdOMOh14XPaEPo9HHr/LX750H309OhYsflmJEIBcVkzmHfDnSSGilnz4H+hjk+m5+xJ7P16HA4XZkMPux/7Lc0lRzn9wSsIQ6S0Gkz8/aXXeeb558nMzAwYirtffpqTbz+Pvq4Sl9NJVdEBwkKESKXSgDd0/vz56Fvqee4vv+fAvr08+//+l7rSk7S3t1NWVsbbb7/N7373O9577z3cbveIuVJeXk55efmIeaPX6zG0NVFXcYbiQ4W88Nc/UlVZiUmiGml8/rt8kCZUhkgigXHYBm+//XbghcjSdZvYcNdXqNf1UV1dfVlZrVYbyM4M/J83eJjR/mnaW65GMqHJ9P15uc7LwePx8Oyzzwb+uFyuEZ+fffbZKzbWpwUGg2FKZD4Juakca+HChROWOXz48KTGuprr6UqNNRm5qRxrsphqjlOpx8niWrjXn9U5ci3oYzjGHSuzePFifvWrX/H2228THR1NWVkZ3/3ud1mwYMHV5HfFsWbNmqvSdjLtx4MhT1Z2dvZlQ5vi4uJQhpwlJzURh12DVCbFUV92vtbnJTCcd2ZmJvqWv11QCzQt7Zs889zznKioJlgi43hlLYtn5PDle+8Zs87oxYzu4OBgIqOimbVoORZjHyJtLC0hYnwWA68/9v8YlMho2LWHeQsXk5SRHTAaFudkcKK6EY/bzbmyEqJTM4nPykXIIEkE0V03m4PFZQQrwslftQltdi7yhDROvfsSwr52ZD4TmhnLsSujCOIcFr0OeUQkBAsIIojotByaThWRNmcRCTPmYzMZKa2sImfZGoIG/bhMfdRJQvA47AQFBeN12gny+9CGKzjX2kVHVSlR2elUHy4kKAjSItR4YxPpaaph0OvGOWBBqY4KZOZbtWpVQJ96u5t+Yw1djbWEabTo6isw6TrIXLwKh8OO3+ui12gmKj6ZFbdex8ysDN59+VlMLh/y2ATc+h7OlpxAIg9HRjDBQiGe2CxEwQIU6iisuiYGQ2SEqaPoa20kLCKShtPHiImNI3v1ZnwhUuRuJwVHDzB/zpyAoShTRZMSBLEKCQvn5hKJm57KUzhkUupqa8jQRhIcHIw6KZ3lC5dh6jMQbOuns7OTj2rbOP7Es5iMRmat3siRt3fw/o6dLFm8mGa9CVVsIqdeexeH1cLyTTdg+ve8uf2Lt7L329/h6PETDAYLcbpcXHfjF1l/4zb8Pt8F5YOCg4Px+/2XDPkdCi3u6OhAoIhEKDyfvfRipauG2re1tVFZWUlOTs64Stx8mvaWq7EXTabvz8t1Xg65ublUVFRc9PO15KkdLyZT7meyJYKmWm6yY02mZm9xcfElE0yNhYtlc70cruZ6ulJjTUZuKseaLKaa41TqcbK4Fu71Z3WOXAv6GI4JHfyYM2cOc+bM+VgDPvXUU5w4cYKenh4kEgmzZ8/m61//OlFRUYE2PT09PProo5SXlyMSibj++uv5xje+cdE0+xPBRM66TPRczNU8RzOevnNycsgsPkXZ3u2BB+7MGPVlwy+H911XVxcwTobXAt25cycf7DuIOj0XVWIK+rZmPth3kMgI5YTOEi5cuJD3Cz+ipvgIhp5ubDY7qvhUnGYDp48eQR0bi6Wri6ZKKScLNRi72sjQRrJlyxb6+l+h4JVn0LU20efwEuQfRCCR4Lbb8TjsKCIiiZqxkNCoGEKVEcQlp9F0tpQVomP8Y20pv3Z9BZtfwZwtX6Th5GF8Xg+Gphp8Ph+2U4cRioRIRAKEwRAsElN3poweo5nrbvwi0RFhKLXxpC5YgTw8Apd9Fmf3vENzcwNxMxYxf+vt+BtKSMibzRxNDD/65//yka6Lt7Jn0tfRitflxNzZwoIFXwGgpqaG9wv2I9MmEJGaS9fJImqK9pGQO5u2itOEaWKYs24rAoGQPS88jrOvh6j0XDQRYXQ2N2BxeNjw4HdwD5gwGPvpaahhfrKHE6ebaKs4jTI6hgFDL911VcQlZ6OrOUdjWTHJcxbRXVuB0zqASKkmWB6OJFyJzWzCJgjh/fffxyUJC9SZHbqfiYmJbNiwgerqao4fP85N61YFztiqE1PJnDGbppoqgiRyVn7pQbD2k3H9NkydLeQuXMaqW+7m6UcexlNawVd/9Av6TWYGxKG0V5URm5TCotXrKXjlGfbu3YthwEFizgwGLAMEy+S4fIMY9AaitdGBckCq+NRAyK8ALlpPeXjiJjcizp08TmRSOgvmz8fr9Vxw1nZ4+1CphJe27wm8nLnced9P097ySe9Fk+VxrV7n5fDoo49esb6uFYxVYudqyHwSclM51qUinS6Gyc7dqTwLPJUcp/VxZeSm+qz4tXCvP6tz5FrQx3CMe3fdsmXLmN8Pz5Q4HgQFBfHDH/6Q999/P1Dy4JFHHgn8v9/v55FHHiE0NJS33nqLJ598krNnz/LEE09MaJzPI4aHJc+LVV42LHks6HS6gHEyf+Vq0nJn4A0W8tprr2H1DrLqtvuZs3oT13/pqwSHqSgqKhpxllAoFCFQKEeccRyO/Px8bly3Gmt7A46BAeav3cyS1WsIUWuRx6eSvOA6cleso6GmkpPbXydGJuSuL92BWCwOXNvmZQvQN1VT9NZzVBzZz/F3X6ansYaMGbOw6HUEBQvpaGrEqNcxS3CcB7L380TTRsyuEDweD7IwJdrUDKITU8heuAyP1YLf7UIaqsTn89FRdw5zdxt5S1chChrk8Puvs/v1l5Crosicv4SseYtInTUPTWomSBRIFOFIwlWEyBTMDY/kZ0/+gSirhU2tjQyWFDGga8ema8PrdtHS0gLAiRMn6Hd7kcUkEiSVMXvTLbjtNppOFxEerSVn5TpMBj0WUz8RUTEMul3oWxvoqK/jxIG9KNRRuB12ZHIFEqmU/PB2vhf9V2aHN1N9cCcNRwqoPbSL7HmLScyZSeLM+Sy5/SvkrFjPhm/+FHVSOj0tjSRk5aNNTichK5/+Xh0tLS2XPRs6HMPDb429vShjE/A4bDhdTsKjYtEkpmE29BIilSKPjEIUpkIkFp9PgKDSoElMwdjbGxinqKiI4PBINn35YVZsuxVJSAh+oYjOrs6Alz4jI+OyIb9DGJ646cZ7v8rMOXPZ++ZL7Hnn9THPoA9vH5uUPCIU+XLnfacxjWlMYxrTmMY0pjG1GLcxO9nsi6Px4IMPkpWVhUgkQqFQ8KUvfYnGxkYGBgYAOHv2LK2trTz88MOBQsb3338/u3btuiKFdS9Vg+rjtJ1M+6vR99ADt1arHfHAfalzrcP7Hm6c+Hw+dr7+EueqqpCk5OEGXvnjzzn0zF8oPbgHSaiS8PDwQPtB/yCnSko4ffI43V5h4Izj8LkjEAj4yv1fZvN1y5gzfwHLFi5A4HMzKBCz4o6vkBQfR0RGHgJVDBaZht2nKvjGf34rUE8xPz+f6OhoBEIxVn0Pvo5azLoOhGIJyqhYbCYjB156nJrSkwx88B98K3svO0z3cqhJzezFS0nVKBELg7D0dOFyOOjt6sTpsGPWddBY/BHmtkaqP9rN3BVr2XTzF1h3/zfxeL2YDb3Y+/XYLWYs/UbsVhs+l4vcOfPo72pD39aEfGCAnzzxOyLM/QxIZfzpWz/DrNYQrY5k2cZtxGbl8eyzz/Lee+/R0dGB3WbH0t+PMiYRU08nwSIRoREa8A/iGjCjlEkQCYKIi4vlti8/gEIq5fC+XZgcbupOHcXQ0cbg4CA5wrP8MvsNXutchz75ARKTkkgOl5A7fyEzll6H3+MiOjkTmVKFx+WEwUFiM/OwG/Uce/t56gve5tjbzxPs95GcnDymoajRaALJk3qsrsC9HX7G9kjBDk7v20moWEBiYiLm3i70bY0ogv24HA5sfb14LEY8bjcKhQKrUY++rRlVVBROh4Pa0ydwOp14vD4Ggbi0LMLDlVQe3E3lyaKA8blly5ZAYrQj741tlA5heOImQ0s9W+64h9y8PJRO45gve8aT6OnjrtGJtp2K9ler78/LdU7jQgz9pl9tmU9CbirHSktLm7BMTU3NpMaayjk/2bEmIzeVY00WU81xKvU4WVwL9/qzOkeuBX0Mx2X9wUOJKbxe7wVJKjo6OoiOjv5YBE6dOkV0dHSg3ltDQwOxsbGEh4cH2mRnZ+N0Omlvb5/Uxj4cEonkqrSdTPuJXMtkuAyd/evs7OTEyWKswSGoE1MvONc6vO/hZwPdBFNVUcHMJSsRBw1y6vBB7BFqomPjKd+/B1NTLf/11GNUnqum4JVnECiUnD55nJlz5rLljnvwuN289tffMcdmJT4+PhCWKRAIWLJkCQ3b9xChDMfjcBCqicZptyH0eXH7BkmduwSZMIg5q9ax51+PsnPnTm6++WYAioqKEIeGExIajlAWhlhqxuN2cWLfTpxeL2GhCrYl1nJnZDG/L9tEVPp8whXnOP7uy0RGRHDy8CH69TpS5i2j8sAOnGYjS7Z8ge6zJYS7+vFoY1n5hS9hbK7G6RtkxqpNyN0DnCz6iNO73kaTkIKxuwN7Xw/zV63CLQhBuOcdvthSjcrpoE8k5kebv8ix08fx2q1Ep2aw7+1XMeq6SM6exRNv74B+HQKxhFmbbkEUIkGTms3+f/0/rC4n9gETPXWVSJavpKdHR15eHkKROFAaRx7s48CH71O2fweJAyE8mPEuv9yfQtiKuzAdL8JvtaBMyqeipp5Z8xfjjo+n7VQJmqQUIjRa5HIplp4OwkJDUUao8DjtKCNU+NSR3HTTTZSeKb/gbCgQ8FhaejqRqaL5129/QuXZclweD0GSUBIycjAd/4jjH77J/GXXUX9gOyajkUiJiMMFO8nQqlgyd8b5vmMTqTlyCIfVQkdaCm8+8TekijAys3MoLzrKu4/9kcVbbyUqMZnWkiPMj1OxbNmywBy69+672LlzJzU1NSzOyWbLli1jekqHl/ERikPw+3yI8bN58+Yxw+CHt1fFJ0+o7M+naW+ZSPuJ7qnT1/n5QkNDw6TkPqthxnK5fNJjTSZk2OVyTVgmJCRkwjIw+Tk/meeyyY41GbmpHAs+/fqYrNxU6mOy410LepxKfUxWbqr1+HFx2Z28oqKCiooKfD5f4N8VFRVUVlYSHBzM97///UkPfvr0aV588UW++93vBr6z2+0X/FgMGbqXKzEzHqSnp1+Vtle7/UT7TklJCXjSPqpt51htC0jkLLp+Peu/9ACnaht59tlnqaysJCUlJSA3PFQ5OtiNWCikr7eH+qZmZCoN87bdQeKKTax74D9Rp6TR2dkZaK90GsnNy2PLHfcAsH/7O+jtbprsXFAGZXjZIYteR0vpMQZ6u+mze3A6XRi7WpFpYujo6MDqcvPUU08FMuL29PQgCpGy9Xu/YvHXfsRNP/49QpGYvoYqFi5cxAMP3M5tmj3sjfwrjoRt1NTUYDWbkas0dOp6EEll5Cy5npiYWLbc+3XyFixlRnYWqzZtZcWKFcjxcWznO5woKqK7sQ5bTwcJSSkoVSoG3S5M7Y2Yu1qJSU4nNjWDEKmch2srmVVTTa84hJ986Wvo0zKJy54FAiFnjxxA39VB2sLl3PDV/2TprffRbnIgCBZgam+kv6MFh6mPsEgN7r5uVl+/lpTEBGo/2o29u5UVG2/A5Q8KlMaJjtay7IZbuHulnEey3mOf6LvoQrcSNWjjZ//5dX77yPdZt3AOS7JSqNz/IS0VZTSVHOXUOy/SdPoo+574I96+Hm7ZtpkgYycioZAgYycbli9g1qxZY4aq6/X6gMcyXJvA84//ja4BF41eKadbe+ju7mHbnffy40efIlwhR+0b4NFfPMLPvvUfpCmCeejWrfzr8X/y4AP3n+87XhXgGuWzEp+czH/89NdsvPMBvvDAQwwYdFQXvk9fxQm+uGU9X/3qVwORBj6fjxdffoUT1Y0Ex51PDPbiy6+M+aA4fJ7V1tRctrzVRNtPdo1ey3vLp6XvTxuXzyrsdjtvvPEG5eXlwPmjQL29vSNKzQwMDGCz2QKffT7fBb/XJpMJh8MR+Ox2uy9IiORyuUZEYDkcDkwm04g2CxcuHLHWbTbbCC5j8bPb7SP4eb1e9Hr9iGcNk8k0Ymy3231BGR6j0Tgid4fb7b5gb9Dr9SPKU9hsNgYGBgJjCQQCTCbTJfU3xA/OOw2G+I3W32h+ZrMZmUwW+E3v6OigpKRkRJvCwsIRem9oaAh4U9LT0/F6vRQUFIy4hsrKyhEvNaxWK4WFhYHP6enplJSUBLjC+ezNRUVFI8YuKioKZHVOT0+fMD+A5OTkKeEH5x/MJ8rP6/XS2Ng4IX5w/v5OlF9HR8cF62M8/AoKCkhOTp4Qv5KSkhGGynj5lZSUjNhLx8vP6/UG5MbLr6OjIyAzEX7wf/v9RPgNyU2E35DMRPkNyU2UH4DT6ZwQPwClUjlhfgAtLS0T4ldQUMBDDz3Ex0XQwYMHxxU//Je//IXvfOc7H3vAIRw/fpzf/OY3/PCHP2TFihWB799++222b9/Oiy++GPjOYDDwxS9+kaeffnrEGw2bzcaDDz7IwoULA29Y582bx/z584Hz9fHGKkxstVovWucpLS3tog8xRUVFI35whiCXy1m+fPmYMg0NDTQ2No75f1eTn8FgoLqpheTsfOxuL5awWJqKP2LlqusxuzwcP7QfRZAXTaiMSKmY/LzcCzJqms1mnvmggNt/8GtOFHzAgNtHWFQMMZERKOxGOhtriRT6AxmtDQYDlc3trLvn67Q31XOgsJCEvDnMycshQhnO/hcfJzc1KZBBcnBwkL6+PlwuF20dXXRZnRjtbupqa8hduool2+7g1Bv/YrBfhzxcid/lJDREgFggwOAexKFMwO33IRCI6K6vJM5rwiOLIPu6DUjtBkz9ZgztLahUEfR0dREiVzDg8WMXyYlKSEGjjSFvwRLOHt5LtEKCqama7OQESsvLGXD5kISGYzEaCGKQyPR8bAjxuN2EeJ3093RjdThJiovG3NmGXCRiga6LirhkZDkzEIrE2MJjKT+4m9azp8hbtZnIxBQ8ZiMhIRKCuusxdbUTm5WHTCbHOmDB0tuNRB1D5blqetsamb1yPZ0N1TgMOlZs2IK1t4v03HwkEglWlxeV6TiSiEQkSUvo1BuZF6tk7dq1wPmHyWeef57KhhbkcjnG3h7sAxZkilCEgmCS42P5ygMP0NXVdT6hkVYb8HqONf8MBgPnGpvRZM+muq6BhsZGZly3nqiYWLwCMfuf/gtrF88jLjGJrtYWohUhgRrUl1sfBw8epMfqIjYp+d/zAlrqa1AE+diyZcsFiZZKSkp4f3cBydn5BAsE+H0+WmoqyUlNZtGiRResj6EIhZKSEkQiEZGRkSPm+mh+Q+2HQou9Xu+Y2WY/z/vLZ51fSUkJ3d3diMVi3G43jz32GDt27Ji0V+5ahc1mY+vWrfzzn/9EqVRetr3FYuGXv/wl//M//3PZknDT7a98e5fLxXPPPcfu3bvH1X4a05jGND5pWCwWwsPDP9Zv7LjTTt19991YLBbCwsJGEHC73RNOb79v3z7++te/8vOf//yCGmrp6el0d3djNpsDoca1tbVIJBISEhIu6Ku7u5sXX3xxBK9LwWq1olAo2LBhw7jbDuFiD0QXaw/nr2eib/jH4jdW36MxnN+uXbsITQoha/laenv19NU2oIxNoLmhDl9oJCEiIctWr8Pv97P/zZdIS01h27ZtAc9XdXU1paWlhIaG0Vx9FoKF6Boq0aZmIpVIUKUvZd+eXTx069YA16FMsAWvPEOf2YJrUIhGGYZGrcY/6McukNDZ2cmsWbMuMFDcbjc7d+5k586dBLvtmLo6KHj27+ham8i9biPhiclIFaEUPvVHFqfFcaToBJq0HFRRMfTpOuk8V0bWkgXUtrchra5AFqGm+kwF8vBwtMm59OutdFbXopBKcA0GkZiVj9DvoeSjfZzZvwdFsI9ta1cxc+ZMKtp0rN1wI1ZTP6fLKyg5sAdZvw2T0Uj2ktVEpmUi0sTS8vYLiPo76XF4iM3I4x82N4L2btZuuptBiRSR243NZCRco8XU3U5EXCIuj4ek+cs501yHZTCEmkOHyczJoaOlhajEZLLSZ5CiiCJUHc3cVevYcM+DPPvzb2NqqEQRrqSluZlBqYK6ygrmL1nKDVvvxe/zUfHKMyNCYcvKyqjv7mPW6o1Y+vtJmbeMA++/iVwUzIoVK9iyZQtisRiVSkVycvKIuSWVSomLi0On06HRaAL3Z++eAjh5Bj/BhEXH4nS6UKmj0PebiU7PocvQz7Kb76S2piaQ7Xh0iZvRocDp6ek4nU5e2r6H5HnL8Hs9BAtF1NXWsGXbljFDgU0mE6EJGURkzsLUZyAiUk2fzUV0tGrMtTZ01nr0dV4MQ+3HGvtSGFqj49lfxlrPl9pfLrb+L7a/XGq/GO/+NxpD/MazFw1Bq9VOaP+byP48msvl9uexcDH9bdiwIdC3xWLhsccem3Dfn3d4vd4JZ7acjMwnITcZDPeUTARSqXTCMlardVLG7ETW9sfFZMeajNxUjjVZTDXHqdTjZHEt3OvP6hy5FvQxHOM+MPLLX/6S7u7uEd91d3fzi1/8YkIDvvfee/ztb3/jt7/97ZjFwGfOnEliYiKPP/44drudnp4ennvuOTZt2oT430lZPg5OnDhxVdpOpv3V7NtisQQS+WjUakJDBJQd2EPZyWOc2Pk2mohwqs+e4eiRI0TFJ/H+waM898KLuN3uQHjygFyDxWKht+YMC+fORikNoeiVJ2k/tpc3/vBTYmWCEVmuh4coL0pPIDzITX5ODv5BfyCRlEmiuiDkeHjYqCYpDU1MAqlpaTh7O4jLyidUGUEQg4hEYty+wfNFmYMExM9YQFZSHINBwSjUMfg0SYTFpeC1WTA2VhOm0qBKTKf48CG6OtqYe/M9LF29BlVsIkc/eJ3iA3s58u5rmHq76TcPsHv37vPGtDwcs9lCW8VpgkOkzFq+CnWolIGeLqqO7OXE7vfZ8eRfsHkHEcem4RVKyFq2lpW33oPLZuXE289TfWQvx998BmtfLzPX3kBopIbid16kt7WRotf+RSROslauJ1QTTU/5CRYvWsh1q9fQduYE+Dwk5s3CZOiho7mR8PhUFGotzd29lJeV0lxzDoeln6qTRRwv3M2el59G7nfT2dkZSO5VVlZGS1s7+wsLqWnv5oP336O1U4dJILsgLHf43Bp6IfHS9j2c7jDyq78+wX/+6Kc8+syLuGVKMuctQSIMwtTVijRCjdPlItjnob64iPqqs7z859+QFhVBZmZmoJ/SuuYL7vlwDA/tPfHhW5cN7dVoNJw6coiyqmq6G2ooq6rm1JFDAcP7Sq2hieLTtLd8Wvaiz8t1TuNC9Pf3T4nMJyE3lWMN1SafCEpLSyc11lTO+cmONRm5qRxrsphqjlOpx8niWrjXn9U5ci3oYzjG/UqypaWFzMzMEd9lZGTQ1tY2oQH/9re/IRAI+OEPfzji+9///vfMnDmT4OBgfvOb3/Doo49yyy23IBaLuf76669ITDUQCMO80m0n0/5q9v3FL34R67+9pKrYRIoPH8RvNSFVR2Lo0aHv6iBYFsaKL36Z1toqFqcmUrZ3Ozt37gwk+hEKRUQmpbP3zZeQiUWkxUYRK/QSpxCz5tatAe/ecAx5tXJycgh64UX2vvYsboI5V1XF+tvuCdT3HF6Ldng5lGCBAO/rL3G2rBSpTEbLmZOER8ciC1fx5h8eYXAQpInZKFUaJBIJ9rBwImPiybtuA2oxSOPSaCw/RZhSQ3NxERq7HWV0HA6nm4r9H5KcN5fUuYswdXdQX3KUxVtvxe/x0NfdRaeln/JOIw0NxWRYXEQlp2Pu6ab+6CEy0lKRqyKJSs1CFhaOy2GHwUFsVgsxqZl067oRC4VExsRj6e3GYzVj7O2hv6uNk+++RDBB9He10dtcT+biVbizFqM7W0pXbRXz0xNoqCynx2IjJCKK1uoKXP0GVt92NyaLhUGvB1lcBp5+OxkL1hIeqcZlHaCq4B3CbAacHjtNfWbaDhzFa7eSpFbi93qor69n63fuRhIajjwxi4PP/oU+vYGceYupqa8K6H/t2rUBb/zx48c5XlnLl771I8xmC439dvQ2J3a7Ff8gnDt1HIVSSX9LOQX//B3ps+fTU18FjgFyV63GZTZA0MgSN5erPzz0EiRQw3VYyPPF4LBaaK8qQ5OYgr6qDIfVcsXX0ETxadpbPi170eflOqdxIS73culKyXwScpMdy2K5/D41GsXFxdxwww0Tklm5cuWEx4GpnfOTHWsyclM51mQx1RynUo+TxbVwrz+rc+Ra0MdwjNuYDQkJwel0jgh5cblcE84GePDgwcu20Wq1/O53v5tQv9MYieEGwvHjxwkPlfPQT58jJETCqZISXvvb70idt5TW2ipUCimxMbG0xidTX1+PKj41UJpkwfz59LU2oHQa2bDlfBirXq9Hq9Ve0tgYPv6uXbuQLlrCgvnzCQoOGlHuJD8/H51OhzI2gfameoy9veTOnovb6UB/rhRLTyfdtRW0nS0mWCBi+V1fx2M20FpzjsGgILa4/05bVg4lPh9ulw+/b5BgoYjq44fweLxoM/MJV0UilCpoOXMSp9WCrr4ao66dwWAB7S3NqJMzMFrM+N1eojLy0PUPUHV4L521Ffg8Hsx9BjqD/cjVWr6WlEb+rnf48dwldHe2IoqMoaupnu72VjTaOMJUavraGpHixdLdRkx6NnHZM9E312HqbEYWFUPawpUoo2MQCsU0nzmB3+/H6nETk5BOuCYGT7CYM7vfZs8Lj4NARHbeDIQ+M+GxScRl5SENCUGljaP93BnOnj1Lu3EAmTYBuVRJTXk51Q3BpOXkIQ6L4PTud8hfvZm+znZkERr6XT4OHDyEU9/F9fNmkJ+fH/DG1uv6cAtC0Nvd7N/+DtGpWQhlCtLnL6Ph5CEsra0kz1lEdHo2QqmcmiOFmAbtJKZn8+DfnkIqlwWM1qDi4jFL3LS1tVFfX099fT0ZGRmBFyLDQ3uHn1kdbtgOfV9QUMCMJSvJnjGbfkMvMzLX0pWRFkiYMo1pTGMa05jGNKYxjc8Hxm2JZmdn89prr4347vXXXyc7O/uKk/q8YHSmsMniYjVkhwyElJQUsuYtRiKVEhQcxNw5c0hKTqW3tgKtUsGsmTPwej3ny7BkZNDX3kxnZydNTc10dXfhHehnw4YNnDpdyqs793G6y3TJsNHhvOrr6zEajdSfKcb570yMbqeLpqozNDc3U1lZiUqlomj3hxwoLKTTNMChgwdpqDjDihUruH7bLSikEjoqThOuiSZcpUETn4JIKORG+/8yL6SYfUc7GDD0Mjg4iG8QjF3tKGPjUcUn0XH2FO1nTuIYMBOfNwepIpS43Jm47HYUai3XP/hdZm+4iY3f/ClCqYzmyjIEUinSsAikYSoEYgle3yCN5ypZ3VTHrb/4NnknP+ILhdtZcNOdZC9agSxchUnXRbjPTvPZU/TrusDjQZ2czvoHv8eKW+9h49e/R2RyBqERkSiUSuwmIwqlkpSZCzGbzcRk5jNz3gKiNSpiYmNQJ6TQ39mBo9/AnWnlfDv6r1h0HbRVlNJxrpyuhnPYzUYaGxsxefwkLliJVKMlLCGNrOtvQJuWzZz12wgJCaG3vgqv14MsLJy8lWtZcdeDmGx2Pvroo4CBOORFXb7xBmat3kS3vg9dewsen4/2+mqsVjvqpFSWfulBcpMTWHr7V4hOy0QkEhGfno1Ycr4UxJDRClxQr1bf2si/nn2eJ97eQZVdyBNv7+DrD39zRAbREWHOw+bZ8PD3bq+QslMn6bPambd8FQmpGZi62tFqtVdkPQ3hSq3PTzumr3Mal8KyZcs+aQqfKozOIPxpg9/vx263j+vP8L3342B6bY3EtD5GYlofIzGtjyuPcXtmv/a1r/Gtb32Lo0ePkpCQQEdHB3q9nr/+9a9Xk98VR0lJSSDb8ZVsO5n2Y2XenGjfw71qqvjkQA3Zmfl5gTPJw2tnBgsE7HrzZQbsDhRhSt5/4V/UpqciFovJjFGzceNG3t/5nzz+218QmZhGX1sjySo5Pt8a6roNzN1wI4bGGpLWb6O04IMxw0bhfMKgrz/8TbrsPnIzUmnT1fGzr93Ntrvvp7ToIwzdHXj8cLyyljilAokilIS8OUg9diR5c7DpOoiMjES3/zCERZK+YDm61iZO736HxVu+wA/nHSfJ2sq3D22h+HQd0vp3sM9bQmvdORJzZzFj1Saaqs7QcOIQYrkCt16HvrWBFdetQt/ehlgqIyZrBlazmcFBP0FBwWjTstBXlyJUxzJn8y1EhwRRWVtPf3cHXxHAD04cRgBUqtS8PX854oYaTCIB+rZG4nNmEhkTTZQnCFGIFIUylPiMWUjkMoSDXuJiY0ieMZeit14gv09PgioMh0yBvrmWBenpdJn6CFeqAOhobcGk1xGZnM5X86tYLinmj+ce5FxRIVpdF8mzF3LuxEd0V59hfk4GgohYtBm51J0sIjZrBiJFKO7eJtySCAaDg2mtLMXtB1GIBB9BNJaXEpmQzOmaBp574UXEQkHAi6pRq9Eow2jweKkpOUFbl47EvDnYQ8OQRKgJDg4myGXDpu8mIjYRBvo4V1WF8PWX2HLHPfh9PowdLWzcso6g06UUvPIMYWFhWCwWHL1d2MQK7vjBrwmRSnE5HLzxh5+OqCE8PMxdJBbjdrp484k/0/Hb39Lt8HLbQ99FIBIifP0l9r75Ev115Xg8nnGVz7ma63Oi/V/tvWUi7aev88pw+axissk8TCbTuDIgf1yZqZabbCInk8k0qbq24ykLNgSv10tTUxPf/OY3L8hxMhaioqJ45ZVXAseFJjvnJ7q2Ps5Yk5GbyrHg06+PycpNpT4mO961oMep1Mdk5aZajx8X4zZmk5KSeO6559i7dy86nY6cnBzWr19PRETE1eR3xTER781EPT1X2jM0nr5Hn010Ohy89tff4XLYkclk5OTknE+wU3yKgleeCZxf3XDHfcydM4emmkqOvPsqN25Yy7Zt26isrERvthEeqUGAj/BIDXqTnpMnT2Jy+aluaiMkWIq+qQ2Ty09nZ2cga+3wsNCdO3fSZfdx+w9+jaX6FPk33c+Lv/oeHac+Qt/ZhjZvLqLoJMy6dvYeOsLKG25F6LTQ2dJEVHIqc5atpLe3l55+M7kL1hAWpUUWVU3Tif0sS36HBEUn93wwj5QF87g+fx0VB3ZgrC9HFhqJOiULfXszwcEC4nLngN9LVGQ0Vn03IYpwQlRaZOER9Hc0o9REYTeb8DodtFeV4dDriFbHYdJ14gny09fezHfV0Xz5rZ0EAxUx8fzhnm/gsZgIV4TRWVpEmCaGxV+4lxBdLcuW38DeJ/9Ec10VES4veas2EBIioc+gp6PmfDboqoM76VWGYXV7cRi6iJyXS31lDQdeexqT1UZdWQkhchm/uMnNCkkXd76aj9ffgDY+iYyFKxFJpSTNmI/AaSUjI4MD1S30dnUikMlpOnkESVgEYpcF+2Af3TVnCQsRYLG7WH7rfQz6fMSmZtBadoIlW75AXXcXM5NiMLad96KKxGLyc3I499FuosNCcAzIkAb7kUmldNZV0l1XQZTHjGnAw6DXw6Yv3kVQiJS9b76E1+1CjJ8MbWQgZLi6uvp8SPGqpXz44YdYI5MI+fdRhRCpFG1GHvX19YH57PP5Aob1oH+QyupqzINi2qrrCE/KpLK6mjmzZrHljnvwul0ofA62btt62TO2l1pDVwqfpr3lk9iLrgSPa/U6p3EhQkJCpkTmk5Cb7Fgej2fCcn19feNu6/P5GBwcZOnSpZet8OByuXj11Vfxer0BY3Yq5/xkx5qM3FSONVlMNcep1ONkcS3c68/qHLkW9DEcE8pJHxERwe233361uEwJ4uPjr0rbybS/En3rdLrAw7/P52P/9nfQ2900WP20bd9DRvEp7r/v3oueX82cMRuDrpvQ0FAEAgHFxcUIlGpu+dZPEIrFeN1u3v7rr6moqKDV7GLxLfcGvGonPngN66z0MT3D+h4d2oy8gOEikclInjmfnrNFBEdEk7PuC0gVoTisAzRUnGHHGy+RNGcx0alZnDhxkp7qM8SGSVGlZJI6YzZWs5m0eUu5L/QFkoTN/H9Hb2LdD36BEB+DohAseh2Nh/cgDJbg83pwOuzIVJEYWhuIT81AmZiKoSmVsmNHkKs1xCWno+toZe9j/4sqPoWOc2X0d7SgjYunv7OFyNhE2ppqWeNy8MDOtwCozZnFX25/AL9QiMBho774MDZ9N+q0HPq621FazBib6pGEhhMWn0J3Uz1v/uaHpM2aR2fdOeQiATd+9+cQFMTBV/6FzO8mYuZ8bKHRSEJa6Koup6nmHFJtIo99PZyZrgKeU7+LQfoifbVnyFy2ltDwcMJUavxuB/I58wj227Abemg4WkiIMpL64iOERWlJmjEfY3Mtva2NbLr1JuqbWji77wMiUnOoKzmKSBDMYGgkpo4OVCoVGe7zSblU8ckYO1pYkJXGgnlzefnDArIXLsOo72H7qy9T9sFrLJiZS21zBynJycxbvBSAmuMfYW2uZvPmzWzZsiVgWA4vcVNfX8+ht3fgcjgCc0hXX8VNt24NzOeMjAxObN+Dx+2m32Smt9+M12FDm5BIX38fPcZ+9AYDEcpwxPjZunXruEvoXM31OdH+r/be8knsRVeCx7V6ndO4EJMpKTMZmU9CbrJjTcaY7e3tnbCMXC6flKE+lXN+smNNRm4qx5ospprjVOpxsrgW7vVndY5cC/oYjksas3v37mX9+vXA+bqlF8PmzZuvLKtpjBvDQ4jbm+rp1vcxa/Um5uTlEKEMH5FBduih/6Xte/B6PYEss8aOlhE1SuURaoL+bYwECQTII9SI3UbCFEKK3n4BVXwSxo5WwhRyDAYDDXoz2QuXYenvJ33OQop2vY/M56RFZ8J50x0AuOx22qrK8Pf14dGEEqGJRiwJQaYIJShEikAqJ3vZ9YSqNISEKWksK+ZsZxtKVzDdba2EqtSY9d28eEaLyxaFVS7H7bSjUIZTU3GGtuoKonNmUVdWQnnB+0Qlp9N3/ACa5AwSc2fhGwSvw0qoOJgQgYDU+UvJXrqc7tpqjm9/E6U6mpu//WPiIyN55k+/obH0OJrkdHY01bFeG0dctJb/vfV+utqaCddoiE7NpvlMMRL89DZUE5M1gzDxIGZDL/rmeuZvvY0Mt5ujrz5F5b7txGTmsuaLd+F1OUmfOZfM+UtoOlPMnHU3MCcvh62333Xeo27oJjtXSq6rkOej36DXF4UkNJzIqGjsA2ZiMnKJj41FGBzEGwd3EZsWR2ZWNvb+XprPnkIZHcvcrbcRER3LnPU3se+pP9Ld3c2rL73Aww8/zP5977H6jge47uYv4XG62PH4n3C2VLF161bmpctpaWlicU5GwCDNOl1KXclxVPHJLFy0EEdPB+HiIOKVMr78jW8RFBzEztdfoq29jXmLlnCiupG+/le4/757L/CUbtmyhe27dvPGH36KNiMPXX3VBeWdhkcRuILFFJ84hig4iPg1W+k6eZg9zz2Gbe1GJIPjCy2exjSmMY1pTGMa05jGZxeXPMAxPOHTSy+9NOafl19++aqTvJIwGAxXpe1k2l+JvofX6DyyZwcujxeNMgyZcGTW4LHaH9u3m4JXniE5MixgFCxcuBBvn466smLamxqoKyvG26djxYoVJCfEs2zFCrRSEcv+/Rmgpb2Do0eO0G408/K/HqeuvQtBXAZOUx9PPfJNWqsrePq/v4Opq42UpWuxWUw8/8hDvPPHn7Hv+b/R195M6qwFxCUk4jHqaD1bQogiDIFCib6tmabTx+lpaaCtvITSajM+qRZLewM7Hv0VB1/8J0VvPItMHkpc7mwWffHLhEaqGfS4sZv7cVjMVBcXcfy9l+mqqSA7fwY+Sx9txw9wZue7NJ07S8aSVUjU0ZR/dIDTZWVEJqWTu2oT6kgVyQuX8d3cmfxo7kKK97xHY+kxbAMDlOx4E1NXB6qEVFxOB72NNdjNRnoazhEUFITbakEVrWXRLfeQMW8RCWmZDLg8WPQ6dr7wJKW730Hf3YVo0MvAgIV+k5lwjRahUMjhMiPfbfsZ1S1WmkuPoW+uQyoWIwgKonj76xzd8TaFrzxFmFxGTEwMgz4f8dkzEIlCUCWmEJmQSkSEEmmECrlKg8/n45XXXscrj2DG6s00n6vg9T/9kr988y6CZKH4YzJ49JV3+N+/PoZHnRCoQwsE6gbPi1Vy/xe28ve//ZXrrruODcuXsP/NF/jgxac5W1bK+tvuYeMtd7Dhrq9Qr+ujurr6grkrFot58rF/8NCtW8mTeXno1q08+dg/RpR36u/vD4ypdBrB4+aen/6RBeu2cNt3/5vQ8AgiXP3cuWUdC+bNZefOnSOSno2FoQRp27dvv2zb4e3b2trG1X70dV7ptlPR/mr1/Xm5zs8yHA4Hb7zxBuXl5cD5BEO9vb34/f5Am4GBgRHnwHw+Hz09PSP6MZlMOP6dBBDO51WwWq0j2uj1+hFJiRwOByaTaUSbhQsXjliTNpttRA3XsfjZ7fYR/Lxe7wVjmUymEZ/dbvcFyZ6MRuOIsd1u9wUv1PR6/YhztTabjYGBgUDfAoEAk8l0Sf0N8QMCZ3rH0t9oflarFaVSGfAAj6W/0fyGEjHC+Tnv9XopKCgY0aayspKGhoYR4xQWFo7ot6SkhI6OjsBng8FwQaKboqKiwLoyGAx0dHRQUlIyok1hYeGIedHQ0BDgB+ej0SbKz2AwTJjfUL8T5ef1emlpaZmw/g4dOjRhfh0dHRw6dGjC/AoKCkY8F473/g7vZ7z8JqO/ofs71NdE5t/w+TURfkPfT4TfkNzVWB+j+Q3JTJQfwPHjxyfMr76+flL398MPP5wQv4KCgitSevWSntnnnnsu8O/RmYw/LYiJieGhhx7ivvvuY8OGDXi9Xvbv38+aNWsQCs9fXmVlJRKJhPT0dGpqapg9ezYnTpwYUQ+ppKQErVYbcJEbDAb27NnD3XffHWhTVFREdnY2arUaOH8jdTpd4LBzTU0NTqeTxYsXB5JmNDQ04HQ6A17R4fzkcvkF/OD8RBvNr7CwkOXLl4/gV1NTw/LlywMhxHv37qVe14c8GMo/2odCo6WjogSVK43KykpycnI4ePAgX7zlC7S3t5/fzGJnYzKZAl607OxsFuWkcubIHoRhKjwWI8tnZZOXl0e/2UJdyXFCZRKaW+vIiotBGRaK2TJA/vwEenQ6FNHxxGTNICcjidl5ObzyzL/orC7HbzZy8223ozNaGOjtRq6OJiJChbevm4HeLtqqytEtXY2wpYrOqjLCE9OJzZ5BkLmXoIFemk+bCFVFkpw7k9lJamr9HtrNNqReO/FqFTJVGGHqaIJtNmISk0hSylGoImmpLEPoc5ESrUYQG43EYeaem7dSWlpKi8dG5oxZRKdl0tDRi1jfgl7fTUhoOEKxmPjoSBw+0EVoaKs6Tbgqlg1fupMeDygio7Fk5KLw2ohfvwmiU/AaWlDHJTIjQUuXfQCBREZz6XEy4rUM9LSzp7QYm7GPUKWK6xcv4GBpFR+88BQ3/ccPaDnyGt3d3UTPWkJH0Uc0nzxMemoqjc0d+Lxu4hNT8Q4O4pcrGPT7UERG4bAYCA4Oxmm34rL0k5iVh9jSi+5cKUGKEAxIGdS3I41Scqq2kU33PEh1YysSm559BXsRK8JYeMPtRJg7UK1aT9mxwwCsvvkOit5+cYRH3+l0olQqEQgEKBQKbtiymaKiIhobGwNh621ni1EnpwdeoCiVSnQ6HU6nk+XLlwNw+PBh1q1bF0j4NHp9VFVV4XQ6WbNmDUuWLKHLL6W99AghCiWuYBFZ+bOYMyebwv0H6HX6CFPIOVpZR+yBA9x0440kJSWNWB9LliwJhMErhHCsoob0f4fed3d3j1i/APv27aOhqZlT5+oIV8gpq32JrMR4vvnwNxAIBJfcX3Q6HcuXLx9z/Y7eX06fPo1cLg/o5XL7y9BaLywsHNf+IpFIuO6668a1v8jl8jH3v6ExR/Mb+n70/je0Tw3nd/ToUdLS0sbc/z7u/lxTUwMQ4Hi5/Vkul49bf2vWrKGmpgabzcYLL7zA5xk+n4+77rorYFQFBwcTFRU1ok1oaChAoI6qQCBAJBKNaDM60ZJYLL4guZRAIBjxcksqlV4QCjy67qpcLsflcgU+j8VPJpMhk8kCn4VCIRqNBqPRiEqlCvAbXgdWLBYH/m8IKpVqhAEpFotHvLiDC2vXDv3OG41GhEIhPp8PpVI5IhnUkP5G87NYLIE9bSz9jeanUCgCRm94ePiY+hviN2SUp6SkBHQztLY3bNgwQmb0UQ6FQjFifcrl8guSvqjV6hH7BzDi89BYo8MSR9epHNq3htDQ0DBhfqP3svHwg/MvEEZ/dzl+QqGQvLy8wP42Hn5DcsN1MR5+8fHxtLS0TJjfhg0bKCoqCpxvHA+/+fPnjzBuxssvPj5+hNx4+cH/3bfx8oPzvwPLly+fEL/hY02E33C58fKD8+tlovyGxpooPzi/1oe3uxw/gJ6engnzg/PHUYfv65fjt2HDBpYsWfKxbcyggwcPDn6sHj5B2Gw2tm7ditlsvmyyg88DhmcQjk7PofbUMXDauOnu+zHpOsjQRo4Z/jkcPp+PZ557nhMV1QRLZPiddhbPyOEr938Z4IJETwUFBfzx6ZeRaGLx+rwIQqTEZ+SyaFYeNXV1lBefwNzVht3hAAZJm72I1uqzxGTmkb10NUFBAg698DfaK88Qro3FaR0gKjWTdQ/9CEe/AV1rA80lR1HFJCAWCeisKmNRZhKN/Q4yF19H49lSnINBuG1WEtLSkUUlUL7/QxRyOVHpOXSdLWbx8hVYTCZ0hj5SU9M4Wbgbj0BMwswFmDpb8AyYSFh8PbraKlorShDLQ1n38I+JTkonSBDMWz//TyLcJjKv24QyJZuBgQFcg0EM6HUY6qsYsDtY/cC3EImE9LY2cfLdlwmRSjG0t6CMjiV9zgKqjh7E7/cTk56DVBGKQCQiee4Sjrz0BMEeOxKlhpnXrWfuzDxKyisp27ed0HAldqcLl9VC7qLlVBcfRRWfTM7seVh6OmktO8Hc7HQG1fHMXryMijNnOLR/H77BQcIitehb63ENmJk1dz4StZa8eQtxOZ24BoM4e/QgjeWnSZk1n/DoWEy9Ogb0OmbnZnP3//dfHNu3m3mxyosWwB4q6XP8+HFKGlq57aHvIpaEBOrM3rNt47jPsl4MlZWVPP/eTqIz8tDrutFoY+ipr2JZfiYnqhsDSc8uNWZlZSUvbd9zQXbk+elJLFmy5ILEUeXl5Tzy2z+iTs9Fk5iCvq0ZQ8M5fvvI95k1a9bHup5pXJuwWCyEh4ezY8eOgGHyecHQb+w///nPcWX9tVgs/PKXv+R//ud/RhiP0+0/fe1dLhfPPfccu3fvHlff05jGNKZxNXAlfmMv6Zn9/e9/P65OfvjDH05q8GlcWdTV1aFJzmDDhhvp6ekhKjkNfUM1sSmpLFqzYcT52Yuhurqaxt5+7v7uT0YYCsO9dMPlXS4XIomUzff/B93NDZw7dRyA9vY2evoHIDiI9CWrGRSF0F1zFpE8lCW3f4X6E4fo62wjdfYCYjPz0bc0ooyKwR4SQphGi9hrYlbwHnaK84nNnonfZSc+Mxd9QzVms5n21i6svkHUSRm0l53AajQQqlLTdK6CvvYWhMkp1J86SpDdQmtDLWabi7zr1uO3GAlLySZ13lIEQjFz129j1z9/T+3hvXy5vYWC3Fm0mYzsf/JPpM5dgnPAQhDnPQYd9TWEJ2cRpo5Cr+umr+EcObNnc+DDDyh47H+Jy5lJd10V+sZqlCECpOo4Zm/8Am6HDXVyJprkVDSJ6cRk5HDynRfprj+HOjkNfe1Z1ImpBAlF9Bv7SY6LxZCRQ1/DOaRSBfNWrEIeFkbEli8glsgQOsy4Br2ExadgC4umrboasURK1uwFmIJlNJceRRg8SExyKilzlxEfKub44YM0dfWg0cbgsvRjaK5jcNBP5tLrkUdo6O/t5thr/0IUMvY56uEY/sIjSCyhvuIM//zVIyy87nqMXW2Bs6xDBq9Opwt4A3Q6HXb7+UzbcXFxl8xCnJmZSV/b36ioqUObkUf5qRPEygSEzJsRSHoGjAinHz23hydIG54d+WRDOw16cyBB2hCH4uJigsNUXP+lr45IgFZcXDzCmB1+bUMvdi6XTXka05jGNKYxjWlMYxpXFpc0ZoefDfF4PBQVFZGamkpMTAw6nY7GxkZWrFhx1Ul+nuF2u8/X3qyvJyPjfGIe8b8zF49+mNbpdEQmpBAXF4fL5cYhkILbibG3l9TsvMAD/1Apnc7OzgsMi+EP/3BpQwHOh28lpKZxet8ObBYLfe1N9He20q1QoDf2kzlnEXF5c7DabdhNRvq62hEpwoiITcA+YMbSb6KjtpIQRSjRqVmo4+LpLN3H/eavYh0MZ594NsbOFuIy8/C4PWiT03D3NiNShHH9g9/D7XQQlZZN7bEDyCNUpC1eSfG7rxCVkEzjmWJE0jDMSOm39nOqcDdKTRSySC1BYiket5Ou1iYGg4L4Qfkp7tTruNnt5EdffIDykx/RXnkaoUSGUddJbHYmXR3tiEuOkzZjDn0NVfS2NSLwexGIxYRFqAgRBDFnzWYqBUGo/Xb8iblkzFnAuaMHiM+egVgRikWvI3n2ApSxidh7W/l57nZ2a5ZyuteK1eGk3+VDGDSIpaOZpMhQbAi58a576elopXDvXqTKSAQ2Eyab43yir9xsWtva2PvmS3R3tNOh60WpjkIVE4fLBxptDAKXCWOPDsepYxiiYxgw6DD36oifuRC5SkOIPJQwv4/IhGQ6mhopeOWZSyZXqqys5IN9BwPeyxSRlJayEyhd/WzZtjEgNxTeq4xNoOhfzyORKQgWibDY7CSlZaIMOUvmKGNyOIa/nHG53IRs3UppwQe4XC6MHf9XSuhSxvfwBGn9JjN6k4UQkZAVazeSkJox5guesRKgDcfFajtfLuphGtOYxjSmMY1pTGMaVxaXTAD1yCOPBP7I5XK+9a1v8eSTT/KLX/yCJ554gm9/+9vXXHjK6APNV6rtZNpfDkNhw0+8vYMOu48n3t7B1x/+Jg6Hg+deeJGXtu/hdJeJl7bv4bkXXjx/BujfD/mOrmasRj36tmZUUVG4nS6aqs7Q2NjIb/7397zw/i7eOXqGJ97ewft79/PC+7su6AP4P0NhWO2o4dep0WioOPYRnS1NiMIiCFGE09dcC7om3DYLczfehFgiwWbqx6LXESKXYzf1UXv0AF01lRx74194XS5WPvD/oe9oIdTYxD9XHaRV5+I7++ZQuusdVJFqZi5eTvasOTjMRhwOB1HJGTjtNmirIiQ0DHViKmZDLxaDAWVMHMb2JhSRUaz/xo+YuWoDOSvXI5Qp0MrFGDpbcTpsRManEOT38Z39O7lTfz4ZQlv2TMSxCaQtWMGCObPJXLKaEKkMlTaW1Xd8mdjUDBRBXnLyZpAxZzHGnm4EAiFhKg0zVm9CE5+ENiUDiUSCRa/DZOxDERVDb2sDHqcTx4CZykMFCNvK+VHsU6hEAzgSr8fldNJw8iN6GmsoKfiAPoOeuHkrEITI+PtP/4vWM8VUHt5H5bFDNDS3MODyYukzYBkYICkxkbkLF6MJcpESFcFt99xHSpgYn1mPz2mjqbYWoVyB1+3EYTUjFAoRKkLx2MzIgvz4a0+hEAYhCfKTGhnKPds2XtIwG+69nLN6E9ff+SBh8SkEBweTn5+PQCAYUf9YHuxHnZ5LZPYsREo1t//g18Tkz2PuhhsvSBY1fG4NfzmTmnr+78iE8+e7hpKYHX7rxUsa38MTnp3a8SblB3cTo4kkKSN7zARpF0uAtnDhwkCb4de2dN2mQNKr999/f9xr+2rvLVd6L5ps35+X65zGhRidnOhqyXwSclM51mSONww/+zsRTOWcn+xYk5GbyrEmi6nmOJV6nCyuhXv9WZ0j14I+hmPcdWYPHz7Md77znRHfbdy4kSeffJLvfe97V5zY1UJ2dvZVaTuZ9pfDzp076bL7uP0HvybI42C2SMobf/gpTz75JN127wVnBhdA4MFdqYmm5sRxHFYLXWmpfPjy8zisFiShSmqqG1my6UZkYimLb7mXE28/R/bCZdSVHGcBkKJR8vTvfoEoTIXLZCAjRk1nZydw3jgYfp3Nzc0ESeTkrVxHWKSGqMRkTF1txMZG0VndwK4n/khoVCwtFaUM6LuJy5mJqbsTj8uBsbOF1HlLyFi2jpqDBSj8er6ScJRWs5yv/EtEqOQkHrEclzWFutJiepvr8TidiIKC6GtvRhEWjkOdgN1gQN/SQLgmGpvZiL61CUFwEJFJ6Zh7u1GGhxMEyELDaevqRt/Sgt3UR51YzM9PHWWj9fwP/6HNt/DOzXcjFQixm424YmJJzJ+Lrr6K5ppKhKERiMIjaWxsQCAPIzRUwbb7vkqHxY2+uRZHbxcxKWkcba4jTi7C3NZO9YGdyCKj6Kmvoru2gsj4JIzVx/jvBacRBnl5K+Z11HIN1yXMYPuff075vu1ExCfxhe/8N8nRKtbf9EXefOLP+M0G5i9Zzpx1W6mtPEt1ZQUDbi9l52oRCYJoLS/lp//f1zhddoby/TsJj9TQXHaS5upKBoODcDg9rPnqfxGmVuNzezA8+gvaK8s48OrTJGVm03rkMAMdTdz97b+M66zr5byXwz38bkRoElMwGY2ERsURIpUiDVPicrkv8PoPn1vDvarDPbBxczeydu1aqquraWpqIjU19aJhvgKBIJAg7fDhw/T2ilmz7RYEAsGYHt38/HzWL53H6fKj2FTR2I09rF86b4ROLha9MDrhzaVwtfeWK70XTbbvz8t1TuNCjE7udLVkPgm5yY7ldDonLNfa2jphmcnWz53KOT/ZsSYjN5VjTRZTzXEq9ThZXAv3+rM6R64FfQzHuI1ZmUxGbW0tubm5ge9qa2untOj4lcBQpssr3XYy7S+H+vp6otNzsNutOGw2pPLziZ2qqqpIX7HhgodpvV4feHDX6XRsXvh14LwnLUwh4+s/+RWVp04giIyh3+pAqQmnt72Z/gErNeVlCGRh7Nixg1OlZ+h1eJGpHLTXVNAVF48sOYfCU+U4/vU08TFasrKy2LJlC42NjWQtWEp6ZhZ2q5XgiEhicmbR2lHPoNdDd20VZn0P1r5e5KGhBLldWI29zN92J4aWematuYGy3e8iMtfxwi2VNHrSePjdEOQxMhLzZtPVVIfdakEukTBrxSo+ev15bHo9/XYP+574A5FJaTSeOorbaUMdG0/b2VPII9So4xIx9XbjdbvxuRwotbG0lp8kyOdHHZdEuFrD97a/wZquNgCeyZ7JkwIhylNFGLo6CI3UED1jEQQFMWDoReD1Un5kP4PSUOJzZtF5toggh4U7v/I1rAcP0e5wUP7RXgpfbESpUhE9awnlre/SfKYYZUw8LpsNt9OO1G/hmS/U4BXI+cqb+YTFvMqMpddRVVpMsFBMsFiMWBVDbWU5YaKZKBQKUnNm4u9uJMgvpcdsIzwpi4ETJyjfv4vI2Hishh6c+k4GBwcD9//48ePk5OWxdPPNvPWvf4BYTn93Oz6vC6vRgEQRRoQmiqjYeAxdHUTFxqOWiMYVJrtw4UIKTjxJXVkxCpUGq1F/3nt5142BNsMNUU1CIhWFhYSERTDQ24nL4cBhMRGiTrzAmFSr1YEQ+s7OTuR+N3tefprIhBSMHS0BD6xAILjg/PbFMNQ2JycH8QsvUvj68+fDg4f1N7ztV778ZZYGQvhXX2AoX8zITr3IGeOxcLX3liu9F02278/LdU7jQgzPSnw1ZT4JucmONRljdnRpnfFgIi/WhmMq5/xkx5qM3FSONVlMNcep1ONkcS3c68/qHLkW9DEc4zZmb7rpJn784x+zadMmtFotOp2O3bt3c8cdd1xNfp9pNDQ0XJC2ejjS0tJ455lXUKVmExapobOzg9pTx7h97XJ6LnJmcKyHfL1ejzEkAolUiioqiorKSjRZMyje8x7yiEi8wWLOVJ6jt6mWtPQMulyDrLv3GyjkMk4fOYAyLpmI+BR6bG6OFx0nos/B7uKzvL9zF1vWr2P/BwXIZHchlsixdnVh7m5nZv4s/CIpyEIJHXQjloficQwQk5RMw+njlO54A7FUys6/ncVmNJCRrqVe9UXe1K0iJucYqfOXE6GJRps3l9M73qC+6gz9uk4sej3pc5bjamrA0tuFw6gnNFJNXOYqvGYDkdoEZJEazN3tWHq6qNj3AdFx8XQ21eP3+1DFJmHv7cbQ3ogpUgNdbbyx/iZ2LVyJp/QYTaXHsVnMBGXl0dtch6Gtkb72ZkReJ8qkDOas2YrHPoBmzWYaTh2h9MgBNqxdR1fZMULlQahzc7n+ptuorqogb9UmBno7EQsE+BOTic3K5xcJjyOLns3vGu9g/k1Cmk4cov7YfoIkSlRxCai0sbRVVxARn0ybcYBOfRlV+3aRFC6hpsdE/MwFNNdW4vE4cTus+DwuwiLVuAb6OXXqFHPnziU/Px+dTodRFE5kbAKysAh6OrsRSSQ4LCbEUgmWvh7kMjlikZDInFkM9HbiCQlBp9NdNqxNKpVe1nuZk5NDRvGp81ECsQkYGs4Fzsy+8YefkpSWiaO+jMwY9QhjcvR51AGEKHxOZkeHEjd348dKtDTcS6vT6dBepL+hNTS8nM1wDL+2ixnF1xIutw99VvB5uc4rjbS0tE+awqcKNpvtc5fV+nKYXlsjMa2PkZjWx0hM6+PKY9zG7B133IFaraagoIATJ06g0Wj45je/ybp1664mvyuOjo6OC+oiXYm2k2nf2Nh4yQmdkpICThs1Rw+QmJxCW0szOG2sXLmSM2crLvkwPZzLcE9SUkY2kadPceTVpxHIw4jLn4ccL7bEVIL8XsLUUWSkzaCztZn+tnqU8amEaWKoq62lz+ZCnZqJ1O9CkTOT6qJCNvn9xMoEvPGHnxIen0JrTSWaUDlCsRizxUJ0dAKpUeE427tp7+mk4fgh4lLSUGrUOEwmlFFaznU04gtbwrn0H+Esf5aolAyU2jiC8ZM4Yy4dVWW0lR0lIjGNFff8B5nZOVgMvex54XESNSq6+kyoo6LxqSI4sfM9NKkZJGTnE6ZUUl74ITJzF+31jWhz5qCKUNLb1kR3fS2PZOfT/l//g+76LWQGBVN7dD+hcjlKbQJ2i4m+qlP0WyyERagIdVsYFInQd7YQHh1Hb0sjdquNI7s/xKLr5PoFs/G6XewvO8eJY0cRhUdiNxmxW20IFHLis2fgdjh51XQrYel3oopopKb4KJ5gAWKlhoGudkQMEpeSht3cj67hHF6nDUNrI91NDUQvXkpHUwk7XngCVUIqbruD6PRs1t7zH8gUobz1ux/T3d0d8GpWV1dTeLAIs1BGkDycAcMZygveJTYzn57GGizdHYRn5bL2rq8xaDMRJFfyxh9+it1uv+y8bW5uvqj3cnhisgXz5rIAaGpqYvMj3wcuns14SO6jjz7idF0zX/rWj5BIpYEQ+qG2o5OedXd3T3g9j9eje7H1eTGjeDJcxour2f5y+9DH6fvzcp2fZUz2ocvhcEw4cmsyMlMtN1lj1uFwTFgGuKBm7njgcrkmlc9ksnN+omvr44w1GbmpHAs+/fqYrNxU6mOy410LepxKfUxWbqr1+HExbmMWzhc7vljdyWsFOp1u3IqeSNvJtL8cjEYjN919P5LwCPQN1eRs2IBz8UJMJtNlPUzDuYz2JOG0ka4JR5U9h7yZufTWn8OhjCUsZDW2zibOHNqDNiMXsURGfelJXB4vSlkIHdWVOK0DpM7IR2c24fb5OX78OP/821958sknOXLkCOHBQTz8s9/w5r/+DsFC5m27A0lnDevX3carv/w24U4TIWoNIlkoikgtA329hCjC6G04R/XJwwi9Hno62lBq44lJTsXv9aBvrcfSZyAyNRupXI5MEYo2JpaT6iiCbXpSlBJ8HXX0Gk0EBwcTk5qNSCIlVJuAMjaJrs5morNmsvLebyC39yGJS8XtsGMzGXmzs5XYEx9hMxsRSaXERESTsmQt504VIXYPoInSYvc5UYkU1Jts5KzeiiwsnNjcOez6669IjtRwz78z+D7xxBOYB2ysvP+LuNxu1BkzOPD8PxAxiM9iJEKjpabDxprV4Rj0HchCw8hcch1Cn4fmrjQMrQ3ouzpJXbgSn8uF12pBm5JBdHI6co+JzIUrSJ6ziEGPm7j8ubRXlNJZd47opFQi1NFotdqAV1MkFGJxejjy9suEa6JRqDSkL1pFsN9L1Mr1mDuaiUtOpfbsaUIcZlzScBLTMpDJZBctOzP0fVtbG9XV1eTk5IwwCsfM8quNZGZ+XsDbO5bXd7gcg4Po7W72b3+Hjbd+KRBC39nZyYniUxdkEJ6Zn3fV1vOlMFYExKdpb7nSe9Fk+/68XOc0LoTL5ZqwoTgZmU9CbjJwuVwEB18y5+aYiIyMnLCMx+MZVzu/3z/iBWZbWxsqleqi7YVC4RULzZ7s+pqM3FSONVlMNcep1ONkcS3c68/qHLkW9DEcEzJmu7u72b9/P319fXzrW9+is7MTr9dLUlLS1eJ3xTF//vyr0nYy7S8HrVaLqfgMG9ZsQLR0ecBLpV2Yf9kzg8O5XOhJ2ozPt4FXd+4jLTUVlSqSsqpq+jqaSUtKQlZVRbBYgn8QXA47dUcLUUVE0NfXz6r7vkFS3lwS3U5aKkpxuVy88trrdNu9zL/hNop2f8gzf/gVPq8HWZgSQ1Mdao8ZQ10VyqgYxD0OWpobyFm2lpBwFWa7A49QjLmjlcOvP4M6MZWW0uMYO1tJn7uElorT9LU2Mfv6zXS1tVB5cDdBVjPVp0/Q1daCJz4Rl8lAlKOOxBgt/VFaRDI54lAlbo8BhUpDV+VpZq7YRGhkFGafD19/Jwkz5mHsbCU4OIj64sNkLLmOoCCI0Mbid9qx9HQj1sbR11xPsMdJaFw8CmEQho5WwtRR2Pr7UMXEM2vWzMA9EAgESMMjKC8+jkwZSV9XO8EhEuxuD31nThEeEUG0NoYdf/8twqAgQmMSSYiOQhsdRbfhAKIQKYa2Rpz11eSt3kKkNhb7gIWaI3txq1VoEtKQKMLx2iw4bVYUyghMbY1IhRDiHkCj0VBc28yGu75Ce0cnIdkLOLt/B36bmeik8wmLQlVxOEx9RMTEoWuuJ2HGPAalSrxOO8aOVqKiNoxZdubeu+/ixZdfoV7XR6hUwkvb91xQjmZ4lt/hickWL7y0h2C4XL/JjKiqmvaqMlrra0hIzcDY0YI9NG1SfV9sTVwNfJr2lqt5rdPXOY3xQKlUTonMJyE32bEmk2V4eLb38WI8ia28Xi9NTU3cfPPN4zayo6KieOWVV66IQTvZ9TUZuakca7KYao5TqcfJ4lq415/VOXIt6GM4xv2asLS0lAceeIDy8nIKCgoA6Ovr4/HHH79q5D7vGF5W5Ni+3Zet/3kpDBm/a9euDch7zQbe+Of/o66smJr9H2JoOEdTdSUO6wCW3i6EEimhShUCnwu5ox+lWoPLZqOroZrW8hKU6ijUajW1XXoy5y9BIpVz45e/jkImYUZSDDK/E7Hfhc/pwNTdgaW7A4dQQmZyKCnzl5EwayFzNt2KLFxFZFwic1etIysliWhtFA6jgaoDOzH3dLDo5rvIWrISqUKBsauD00UH6GppJHPZ9WSu2EDq8o0091kxm82YDb3E5sxGm5FPXO4cTHodyqAgBkuK0Lc2AhAsFGI19JKQN4fUeSsQhoRw6r1X6KqpxGJzUnJgN9qYGOYuX8HSm79EzuKVqKKjkYSIEQQH4ezToQpT4Bow8+qrr/LnP/+ZkpISdDodrecqCIvUnC/XExWD09xPojaKh3/032hUEcRIgpgdryZNJSPY2kdudhZOp4v0nHxCBt1EKiR4HDZ6m2rpbW2ksfQ4vW1N2AeD6W5pwGbsJS1eS4pWg7GhkmBrHwM1p9mwdD4KhSKQZVehUOB1Ov4dbh2Bc8BMkM9Db91Z/B4XTpebzpZmms6U4LNZ6Gupx2kboLGxccyyMzt37gx8H5uUHPi+uroan89HZWUlu3btwk0wwf82bscqfTMWhmcH1qjVaJRhuDxejuzZEZjzMpnsovWPh2OIS2FhIZWVlSNqZU9jGtOYOBwOB2+88Qbl5eXAeW9eb28vfr8/0GZgYACbzRb47PP50Ov1I/oxmUwjQm3dbjdWq3VEG6PRiPvfZeGGxh6dAGnhwoUj1rXNZmNgYCDweSx+drt9BD+v1zsmv+Fju93uC8rpGI3GEWO73e4Lfo/1ej1er/ei/AQCASaT6ZL6uxi/0fobzc9qtY4w0MfS3xA/n8/H4OAgt9xyC7fddhv3338/999/P1/+8pe58cYb+fKXvxz47rbbbuOWW26ht7cXr9eL1WqlsLBwRL8lJSV0dHQEPhsMhgvKdBQVFWEwGAKfOzo6KCkpGdGmsLBwxLxoaGigsrJyhG4KCgpG6LiyspKGhoYRevgk+bW0tHyq+X3a9TfN7/PDr6CggIceeoiPi3F7Zp966il+8pOfsHz5cm644QYAsrKyqK+v/9gkPg5iYmJ46KGHuO+++9iwYQNer5f9+/ezZs0ahMLzl1dZWTkimYvVauXEiRMjQqZLSkrQarUB97jBYKCmpobly5cH2hQVFZGdnR3I1tXR0YFOpxvxJqKwsJDFixcH3ow2NDTgdDoD3rvh/IZwKX5DHtUzZ86weNkCli9fjkAgmDS/ffv20dTSSpPehDpnDo7KUkw1pfzkmw8SHBzM448/Tl5yPAlb7kUWEYndYubsq4+RnRhNvd6C0OPAo+8kbKCXCBHExsZysLwGl/sQEk0MhspKbG4vs2bNQhkRQW3jWQSSEBoqqrl+xVI04l4ejPoHLzvnc7o1BrkwCE1qFp2VpZw7XsTs9AQU4ZGkrdiErrqcuIQElOFyjF2dhEWqmbd+G+7aU+gSUohMSCVUo0UlDqIvLpZ9+3YRnpZL9d73SI5SUqvrR2Yx8UpvD/r4WF4+eQBdfBqmrnaEXidakR+n34c8XEWXrZw5edkovDYitTFE5c3HNmAlJDgIQWgE4cg5ceQIsTIRdkUkReWl9DTVct2mG9h1qIiXdxayetliBFIZxqM7iUxWk2Qt4YwsnPDwMHp03Yg08TR0t+Nx9xOvCqW9pY0nfv0zMmfNpbO2kuzkFEQzZqJo1yEVCwkP8hCSPxcGBzF0tpEYoeDc3veIXL8Jv83C6plZpCQlBkJ3i4uL6WlsxjJvARq1mrCWFmpKjyETBtPb0ojH40WbnoOivRqXpZ9ZK64nJjUDhdNMxtxZ9Gek0dTURER0PNVHD6BIyjpvIMcmUl1djVIdHzAm3fYBwhTyEeG/YqGI5roadr7+ElvuuOf8C4zWerRzb7jk+vB4PPS1N9PZ2YnD7gB9G2G4WZCexJIlSwIPp0P1j/1eD63lxSMyIZeUlKDRaNi3/wD1uj6UUTFYjxeTMsx7fKXX76d9f5nm9/H5FRQU8MILL/B5hlQq5cYbbwwYScHBwRec4wwNDQX+r76pQCBAo9GMaDPaCyr+90u34Rgd4iqVSi8IAy4uLg48gwAXnF8di59MJhtxhlQoFI7Jb7jnVCwWX8BHpVLR29s7os1oz+nofkfz8/l8KJXKEd7QIf2N5jecz1j6G81PoVCMMF7H0t9ofqGhoRecr42JiRnxOSQkBJfLNWKc0UfORntk1Gr1iPUJXPA5Pj7+gpDE0f2OPlsoFArZsGHDiO9GR6h90vySk5MD+9unkd9U62/IAfZp5Tcc0/ymlt+GDRtYsmQJr732Gh8H4/bMdnR0BC4kKCgIOL/BDX+T+Umgu7ubJ554IqCcIUWN3kiGFFpYWHjRGzn8pqjV6gvS6C9fvnxE2un4+PgLHpTWrl074gc6PT19xES6HD8YOdGGPKparZbrrrsuENIZERGBUqkc4YFavnw5ERERVFZW8vTTT2MymZgzZ86Ia4iJiaFJb2LDXV9h2botrLzjK7gIpqWlJZDBtc0+SFt1OVUfFdBWeZqBYAkOh4ONy+bjba9GbmzDbNSzcdl8oqKiMFsGUM9aiiBURfLsRZgtAwQHB/PNhx9m4+J5YDOTmBDHLTfM4OG4J/hXeSYHWiJxi+QQm4XDbEIkU2CxmLC7PRjMFrrrzjFj+fXoLTaE8RkEBw2SnD8Pr89Hh93PQJ8BhSoKSU8j9c0tdPeZiJ+/khB5KHaXC0tYDAqTgZdrzjLL6+G6khIS+k1EOAxIlRFEZMykUW/G5/Ni7Gwjc/k6Omw+uru6Mfb2EJmUTphYgEkop6X2HEFCERFxifRLVNgcTmKyZ5C9fA1hqbnIM2aTu/4LiGWhzL/hNkIkdh6J+n/kRvYTogijXyCn1erGbDIhj0shetZi3P5BgmWhtNSew95USagAfG4XrfU1dJwrIzYjl6iF1xOdkgl+H/Oy0ph7830IJVJcrTXce+MmfvrIj7nrrrs4WVLCP199i6o+B7Vt3fzzl4/w0ZvP0VN5irykOBI0EcQmp7Lp/ofJWrSClBvuJTw6jgFDD3FZ+UREKMmcvxRTVztpaWmUHCuiGxndA07Kqqo5deQQWVlZmAy9eP693sWyUCxWG3a7PeCxXXfXg6y4+UucLSvlgxef5vi+XaTEaenu7g7Mv7HWx5o1a9C31PPcX35P+YFd7CzYh76pjri4OADy8vJYvXp1IErh9NHD1NXUkKGNDPQ9f/58BgYGAlyWb7mJNff+R8B7DOB0Oi+5fi/Gb7zrd+hN53j2lzNnzoz5Q3ExfkN9j5ffoUOHLuA3hPHufxfjN8RlPPob2ldG87sS+/Py5ctHvF2+3P48Fr9L3d/CwkI2bNjAE088wTQmjtGexasl80nITeVYCxcunLCM2Wye1FhTqY/RnqGrKTeVY00WU81xKvU4WVwL9/qzOkeuBX0Mx7g9s2q1ms7OzsADJpxPFjD6Ld+nHYsXL74qbSfTftmyZZPqe8xEO6PONUZo40aca/T5fOzcuZNdu3bhlCnx+wcpOX2as+VnaGvq4NSpEnr0BhQKBT0nywmNVKOMScDU3Y6uoZo+4pk/Zw6RSiUNDQ0sXDyPLVu2UFhYiNNu5d0n/oJIIsXjdBAmC0EkEvH8iy9xoqIat3+QMGsx+eV/pyT0/+Od+hbM5p1II9QEC4U4bQM4rBbicmbhjYoj0hWMobURp8OOSChi16O/YtDnQxgSwqo7v0pEVAzmhjqOv/k0mpg49N2dZCy6Dqe5D0GIlNazpwhuquGfR/eTZrPiDhbwo6Wr6Vq2ltBQBSEdrdQdO4jX7cLc00FMZh4AXo+bnr5epDEpFL/zAsqICLpbGjEbDQwO9BOqjiZ79lz6DQZEERoGervpaqpDm5FHSKgSfaeNGE8d3531Ovs6s/nd4Uj83lZi4hPobqzF6/VhaWlCLJUS7BvEYndi9fgwIEGricCua0clFODr66a7sgRRuIrWhno8VjNJX7iNhLxZxKTnkJsRE3gALy8vZ++x02SvuQGFSoM8IZ2yHW8SGSLg5nWbyMnJobCwEB1yHJZ+3IMCzG4XiVm5eHo72PXEH0lNTeFsaQmLZ+SQkpKCw2qhvaoMTWIK+rZmHFYLKSkp9JstFLzyDBHaOGovEv67YP58+lobUDqNbN68mZycnMtm8ayrq0OdlM7yhcsw9nRjslrp6uyiqKEL35lzgTk8VtKz4X0PD1eGkaHI+fn5V3V9wqdrb5lI++nrvDJcPqs4evQoN9544+UbjkJERMSUyEy13KUSJF1urPFkix+NioqKEZ7o8WA8Z2bHwmT1ONG1BZNfX5ORm8qx4NOvj8nKTaU+JjvetaDHqdTHZOWmWo8fF+M2Zjdv3swvf/lLvva1r+H3+6moqODJJ59k69atV5PfFcdENvmJ/iBczfbD245OtON0OHjtr7+jo+23dDu83PbQdxFLQgJJcsrLy/n740/QZfehSkqn8expfvNf32BQEopQrkAYGYPd5eHJF17mjm2bkcpkxGbPJEyjRSwPo7n8FIPKaH7zj39h6O4gLX8WtV1H0fcZUciktDY1kTR7EVFp2ejqz1F38hD/+Mc/6HN40KbnMj/WwPcXHOc3h9I56+pjwGJFEBKCQCzG43Bg7GxDGR3HzPU3owgPI9Fu56MXH6OscCddTQ2oElNIyJ1NZ3U5O//xv8xZfj361gZ8HjcOi5nIhBT8fh+SsAhcTidJQhGPFrxHqteLSyjij3d8leMeN4Nlx5GGKenvbMNpHUCqjGDlPd8kWCggWCCip7EGvbEfvyQUlTYemTqaKL8fa18PAnMPPomEvLkLaGmoo7GhEWNnK5GxCXTVVhEWk0C23Mj3Iv/MK+WxvFQRhTpaS1x8AtbedszdXWhz51JXUkTKnEXIwlUESRU4rAPI5AqCZaE0mhzMXbiYpAE3beXFaJNS6WuqJSEzjyBpKHVlxXj7dCy86/8eLIuLixFGasmcs/B8xuGkFBrLSxCJRAGDNy4uDmXIWWZnpXOuugab3cNATyeWrjaUEWrUcUk4+nsh6Pyb+eWbbiA2KQVjby8zMtfSlZGG0Wjk3rvvOn92tr6exTkZbNmyhbq6Og6c3hOoeez1evBZTWzetjkw/uXmuU6nQ52YSuaM2TSJRFTXNbDyzq+RoAolIT6Ogleeobq6OpDwbLgnbXjfw0tQja6/PNH1drXbf164fF6u87OM0edax4vh3verKTPVclPNcTIlfSZbi3uyHCezVia7vj7tY01WbprjlZGb5nhl5Kaa48fFuHeuW2+9Fbvdzi9+8Qvsdjs/+MEPuOGGG7j55puvJr/PPdxud8CAyMg4b0AM90D5fD72b38Hvd2N2WZApIqisrqaObNmBTxT27dvp8vu4/Yf/BqxREJbWytv/OHn2Dq72fDQ91AowtAmpfL6b75PRUUF85YsA6cNQ2sDXo+H/EUrSE2Kp9c1iM5iY2BQhEAk4IO9B5D6XaiTM1j11e8gEkvwOB1Y9DpMijBsjl7qayr58cxW/lK5gg/qBIQP1uMZlLBg2514PS78/kF8Xg8uu5W6Y/tZsO0OZOEhSELDaTpznMjUHG78wW8JjVBhH7Dwwe9/TF9NKaYuPdLwCEy6Tlx2K6HqaOIyc7EY+0j8sJZUrxe7QMBfvvZ9KmPiiPMPcmbHm/h9HkQyBbIINeFRWvq72pCGR2Az6pGGhSPGj0QqI23+EkKkcpTRsRhaGti0djkHiss49PqzqOKSqNr7HiZDD1mLrqOqqJBFWSF8/8ZyXqnN4R+HxWxbO5sTlTX4ExLR5M6jqVvP8XdeIGnOInJWbiRYIECqULD3n7+jt6MNn0DEiju+SoIqlLXbbuHNJ/7M3NQE0qPCKS6vZPc/fotUIuKmjevIz88PlMk5d+4c3W29+NxuBFIpgz4ftn4D8H9nnnJycsgsPsWZwh2oYhPpbTiLGiex+bO4/rZ7cHu8hISIKS34AI1KhamrnUWr15OanYfT4eCj999E5crgyLFjdA04UahjaDxxGr3RyJfvuYcM7alL1jy+HIYbocbeXpSxCXgcNhSKmAu8q5fC6BJUk+EyjWlMYxrTmMY0pjGNawfjOjPr8/n46KOPuPPOO9mxYwfvvPMO27dv5xvf+Ebg/Oy1guEZva5k28m0vxzcbjdff/ibPPH2Dprs8MTbO/j6w99EpVJhaGuiruIMu15/iYbGJmau2sjyTTcQIhKiN1loqioPeKbMZvP5UFiplKCgIBITk4jLzAW/H01MPOHiIHyDg0SlZAJQV16K1WrDYbUSJBRj1Otw+QfxCsQo41IQeJxEpOVjRURVVRVh0TFIZPLzxq/bxbwbv4Q6IY2c6zYSqonjt633ccS2GIfLidFoJDIpDUFICCKJjKRZ84nPm4MmOR2LoYf2ot20lJ3E2FpPVEQ4KbMXoImORiQQoIpUkzxzPv39/ciUKmLSspm7eCmOAQvtFafpqjtHw/GDvB0ezmMr1/Pwout4qmgvZwre4+grj6NKSOS6G29Dm56N1aDD1NOJz+OmvfI0TtsAAwY9kvAIVHHJWC0WgmwmHHY7srBwnE4n83MzCbIYGGitZUZGCguy0rBWHiM6No7Vt3+NQu7FMO/PJM+Yh1AoJEITjSY9B1G4mqxla4mIjkEaGoZAEIzI2ofNZCRcE404RDzMgFMgloSQmjebxMREOrq68YrlpM+ej0co5eSpEtxuN8+98CIvbd+DJDkXp8PBs//9HU7v38mB157GbzGOKJclEAi49+67WJyTRlBvCxsXz+O2W75AkCyMmpYOOtvbqW5qw+TyI5FIAmdTiwp28OiPv43FaqfZEcS+01X0WD1o/n/23js6rus6+/5N74PpGPTewd6LKFIiRXVatizZVrMlJ3aqnddf7DhO8sax3yR24rg7UlzUrGp1iZJIsYgk2EESIHrvfXrv8/1BAwHYMSIhUcKzFtfiDPY+5zn73n3m7nvO2bt6JeZF69h56AQtLS186aEHeeDOm1mWqeOBO2+eUa7ncvxietbu4Y5mmmr2oJWJMZtM/7u6arWeV3d625MlqC7E5Ur758W4XEnZuZC/Wm1/UsY5j3MxPTvv1dT5MPTmsq9Uajaene/jcjGX9kjVv1LRm8u+UsVcc5xLO6aKa+Faf1zvkWvBHtNxWSuzIpGI//iP/2DTpk1A6ucqPgqYzSQ/2x+Es+UnV89GR0exWq1UVFTMavvP9u3bp1ZUI+MDrLzrAV744T/Q2dmJra+TF1vaSIgkCOUqwn4vC9ffxNhAL/V73yFSkEtL7WFKrEb0ZQU89vLbOBx2BEIRyUQc91AviWgYr9eLKubH3t/LcMtpiiryOVj3PnG5GmNOAR1H9yNXqfBH43TVH8bvsGGqqqS7/jhO2wRKpZKxjmb66o5hG+hFqlQTdDkRiiUodQaK12xEJBKRv3gltoFuxoZ7YWSAgMuBzppFNBjEPTaEMk2PzzHB0OljDNtdKIWwdOVKTrU3kohGUSiVhAIBRtpOo1AoSKq0FK7cgFEKsoIqTr35PE173kJjzmDTl/8PdfEofSePou5oQaHVIZbK0VqySM/JR5Ffjmt0kOGW0zTvewdLYTmdR/fhnRhh4YKF2Lxu9Fl5SAMORpubcDidNDtCqERJ8s0G1qxaQVZWFhUVFfzud7/jvbYh3PISBgN6NCIRSr0Jj8eFPjMXr9uNzxcgnkySVbkQv92OZ3QQeciDIziAu78LS3ERDfveo2TRMo4Md2OxZjLR10WbQ8aIP8amzz1MeLSXguXrqXnpKR577DFGAjG23vcIYrEEY14xbz71a3qPH0CnUbNty6YZwV88Hp86S23ILuRISxe+kQF67V5Wf+ZBIuMDSC05HHn9OUJLSqfOph4+fJjs/Hzu+er/obG5iWVaK2PtDQiTcYoXLqP2vbd44okneOSRR6ioqLjgyuml/Gh6HeT6+no0xBhrPM5ht+2Sq6tnt32++suTftjQ0EAoFLqkH6bqt3M5t1xp+avV9idlnPM4F9NLz1xNnQ9Dby77kslkc9bXXNojVf9KRW8u+0oVc81xLu2YKq6Fa/1xvUeuBXtMx2VvMy4sLKS/v5/c3Nyryeeq41JbFVOVPVv+Qkmazl61mi5/9gN0R0fH1IqqLO/Mqqm1pIrDhw9jzi9h69ZtdLU00VBfh1ytweVyc+Odn2G8p4MScxpr1qyhoqKCSCTCT3753zzxz/8f1tIqRtubwDWGSibh8HP/g7mwnInuVuJeB+PjSrIrl7D8znvwjI8R8roYaG1g74tPEReJWfmp+zHkFSG2jTHQXEe6wUBdSyv7nvgFck0akVCA0jWbMGZmEUVAT+0hdJnZIBAiU6lJJBJ4xkcZaDiBc6iPRDJBLBJBKBZjH+wloFIjlsqYGOpl/SMP0vT4kzz9j39FenElY53NqKI+KhcupCMixVpcjlgmR9nbQUZJFWPdreizcnEM9BIJBQh53Kz4zAP47RMo0gx0Hnmf/Tu3Yy2uRKk1oDKYWHHX/fhdTixF5Zx683lCoRA+l5fT772BTK3B3t9DydrNGDMz0KpVNO5+k7u2Waeu9bJly/jNcy/h8XhILyxj7NB+nJ1NLNt6A8/u3M/yTz+AqSgX1/goY309aDUaGt57A7FESpokydKibDLMWuoOvs/I8BDa9Ew848PoRUm0GzcQjERpOn4EQ3YejuNHCEaiNDQ0UHr9LVOJjlauWIGjv2tG0qXp91hLSwttwxOUr1yHx+mkdPka9rw2iCAeoealJ8+0vX8PWrUKpVI5FRCOjo7ikOmRys88TMVjUfSZuTjHR+isO4HTYceXs2hGorHz3duX40fTg9AZvvDHRE8XCiYv1fbZflh3Ca6z9dvZjjMV2bmQv1ptf1LGOY9zcXa5maul82HopdrX9FI7l4uurq5Z65xdaudyMZf2SNW/UtGby75SxVxznEs7popr4Vp/XO+Ra8Ee03HZwezGjRv5p3/6Jz772c9itVpnbC9eunTpVSF3LePsJE2TyZgmE9nAmcQaarWaeDzOr3/7O3bsP0goGkMuEbN1wzoKCwvZ/eo7hINBZAoF4WCQ0Y4mCkpyUOQUkJWVhdVqxT0xypHtLzNQfxxJPIw8FsZms9HR0UFpaSnt7e2oDSZESTHewS40KhWRhI7snAJQaHANdGFMt6LgTBAhyi4jmhQg05tAKEam1BDzOjHllyEUChnvaUeiUGHMKcQ33odEqSIaCSNNxvCMDtBa8x76jBwGW05jzitEm5ZGf/1x+k+fwOPxsOC6W8hftob2g7tJxGMotQY6j+xDZ80if/Eq9NYsjrz0BG+//Tb6tDQmBkbodbtIxiNUVJYikUgI2tyIxBJCE6NnDC6A9PwiIgEfQpGYiZ4OjDmFSKRylDoDnvERFFodkaCPse42ek4dJr2wjJGOZuKxKCSSaEwWRroaEeutpBeW0nvyTBApUSgQSSSULllJZ91xjh07NlXbVSQSkZmTS0IqYazlFDJNGpk5uXR2duL3umnY/RYSmZKw30vQ4yLmtpNmsqA1m3GPjyEV5+AZcxESSiksX4Q5twDX8AAdR/ZQU1ODM5xk2//3fZQaDaFAgOf/5W8wmbKmaq5eKOnSdAwNDdE7MIg9fABDdh4NjY24vX40MinrrrsOj9NJed51HN29nf7+fhobG6moqJhxljUrM4umtk6aDu1Fl6bF5faw6Pqt3LTpegwG/Tn39gfB+VZXU8Xl+OGF5BOxKELxjVd0bB9FTM5DH3d8UsZ5pTFvs5mIxWIpJ0r6uGLet2Zi3h4zMW+PmZi3x5XHZc/Iv/rVrwD40Y9+NON7gUDA7t27ryyrq4jZ/BDN9kdruvylyoTAmZIHW7dupb6+nsee+D3KzDysxVWMdrbw2BO/59Ef/5BMpYgXfvgPZJRUMtLRTKZSxJ133slzb++ivaEOl93G2GA/fqeDkEbNqboTKPRm/KZc3n/pLd54+x1WLFmMWG/hS1/7B8RSKbFIhKf+9ducPnqIbX/7PZQqNdsf/U8SCMhadSN9LQ3UPPs/pJkzcdvHKV65AYlrhPq6U8jVGkw5+Qx21zHW1YJCkCS9qIJFW+/ietF2FiiEfOYXbgabTmEtqaJk7Y3kSGIIKxYy1tWK3z5OwO1Cl5GFJa8Y+1Af7vERYuEwXscEAZeD4bZG4rEYLQOjCFRp3PqN76HRG4mFArz/u5+gi/mY6BvG8bPv8r2TR/iHxauImK2kZ+cy3NNF98mDCIUi+k4fQ6pUIhCKEEvlxKIR8heuQKpJY6yrlbGuNtILy9FZs3AM9THUepqYx4k0Bv2nT2ApLGGorZGAy4ll8y3EIhGGOttoGj/Aq0Yjt91+O0NDQwwPDSLRR1hcXkxdaydR5zjOWJBIKEw8GkOfaSbk8xD2edFnZHDPw19hvK+H1o529EXlNO56E6XBzLK77kcqk1P37isIlVrcQgVCUZQ3f/FvLNx8O0GPC1N6BlqtlkjEz1P/+T3ESjWJUIDVCypmbMWdfi8GAgE8Pj+3/PlDUy9Fnv/BP5CrV9Jeexh9RjYHd75N0OfBVvW/K60P3n/fVHInXWYurfvexjY0SOGSxQQ1aWhUCkxmEyKR6KJJmubK586HGcnSYrFLJpSaLt9x4iAlazZddgKqD3OcH0R+ch66Gm1/Usb5cUaqZSQSiQRC4WWXsk9ZZ671HA4HFoslpb5SQSqZiZPJZEp9pWrH2foWpO5fqejNZV/w0bdHqnpzaY9U+7sW7DiX9khVb67t+EFx2bPWnj17zvvvWgpkgVnxne3Ypstbrdap1TPgoolsXnvtNYR6M5v//NusuOsBNv/5txHqzWzfvp3HfvkLvnr37RSLg3z17tt57Je/oLq6mvHeDn7/60fZf/gYjS1t5JaWs2LTFjIXrmTN5x6hsiCHe7/5fYYDcRoaGlDpTATCYZwuF4FwGHmanlg0QvO+HQzteokEAjZ+6a+pWHcjZes3E4/G8DltCEgy2FxHQiBEoVKTU72EHKWQzPIFSOQK4iIJkXCIhSM/5rPaV/iv3VqkMiW51cvIKK1CplAikkgx5xVhzi9BlZaGZ2KEE689h3tilAVb7qRo5XqW3fl5LAWllJg0lKzdRFIgIBaPYymqwDk+Slf9cWyjw2gy8vH5fGyUyvjFoT00rl7Jt08dIadyMSK5kvxla5GrNKQXlKE1Wek8eoC+uqP01h1BplKTLgiiNphJLypHIldgKSpDn5VLRlk1qjQDGqkIrcnC6s8+SHVuJgs2b8NrG6Pr2AEe+/u/YoniOP++eR/b332Gr/zFX9LS0kJSruLTf/t9cssX8Om//T5JuQqXy4XWnM4tf/2PXHffV7nlr/8RXUY2Ab+PlqZG4iEfY+PjnHx/F2kF5UjkSpr37WC0p42+hhNYisrJXboWU04h9qE+6DpF0O3ENTHGeFxCbXM7E3Ynar0JiVwOZ+Vhm34vKpVK8opK6WtrYqC7k762JvKLS7nj9tt44M6bEdsH0aqVfP3ffsL6rbez+XNf5FB9E08++SQrli3lC7dtwRR1U5ibw//3vR+Ql5+PXqUgIRBSX1/P0NAQ9oGeCyZpmiufOx+m+2H38QOXTCg1G7/9INyv9Dg/qPzVavuTMs6PM4LBIC+88AL19fXAmYBnfHx8RnDm9XpnJA+Kx+MMDAzMaMflcs0oMxOJRM4p+zM4OEjkj7432bfL5Zohs3LlSuLx+NRnv9/P8PDw1Ofz8QsEAjP4xWIxJiYmsNlsM/hN7zsSieBwOGb07XA4ZvQdiUTOOc8/MTFBLBabwc/r9U71JRKJcLlcF7XfJL/J8U7yO9t+Z/Pz+XzodDrcbjdwfvudzS8UCuH1egGw2WwXvL7T+/b5fOzatWtGu7W1tQwODk59ttls1NTUzJCpqamZssPu3bsZHByktrZ2hsyuXbtm3BednZ00NjZOfX7vvffYsWPHjDE0NjbOSDxzNr/du3fPmh+ceTabLb9YLEZvb++s+AE8++yzs+Y3ODjIs88+O2t+O3bs4L333psVv9raWl577bVZ80vFfpPXd3IOvlx+g4ODUzqz5TepNxt+k3qz4TeJVOy3e/fuWfMDeOGFF2bN76233krp+j7++OOz4rdjxw6++tWv8kEh2Lt37yVf4dXX19Pe3k55eTkLFiz4wJ1eKfj9fv7kT/6EjRs38tBDD7F169YpB7jxxhun3g40NjYil8spLi4mFosRCoU4cuQImzdvnmqrtrYWq9U6lTnQZrPR3NzMhg0bpmRqamooLy/HZDIBZy7k6Ogoy5cvB85cpPfff5/Vq1ejUCh4/Mmn6BoeQ2Mw43Y5KbEaeeC+L/D+++9z4403snv3brZu3cqf/dmf4ZAZWPHQ1xAKhcQCPjpe/S3h0T6eeuop4Ew90czMTLKzs6mvr+fff/lrsopKGU9IkKcZGWs6Qb5Ojl9lIXPhctRSMUa1klM7X0foGKa+b4SSG+7EIohgiws5/uYLxGNxqpcuJ+Rx4RGrKFq+nmTIh9I7Tl8gRjQcQZeejafxEHKplNFgHGtZNSq5DFXAQX17D+J4mFsNe1hTmcFb7g0cavcz0HQKs8lEcWEBsso1WBM+YvmL6Hvjd7Q2N5FQaAkHfFQvXUlWbi7DvjAaUzo++wTmuAdx4WIaanYxUncUk8VMZmEpQaUer22MwYYT3LeglG+98ALqcJhhSwb/sPVWRhMQEEjwe71o0rSU5+fgVJrpqT1E097trFm/gazVNyDQGAgHQzRtf5aivFxUi69HIpMTj0aItB7D2dOGV5NO9uI16FVyEh47J2tPEBnr4ZZqDzcszCax5EGGtZt44Yf/QJ5KRFSfgX7JBqSjnUQshYQbD1K/fxfyytUsvuUzyHx2YskkR3bvZKz5JN/4xVOM1NXQHRAw1tlKVmERIr+bwf5exu0ORBIpq2+9i7SYj4AumwPP/wY5CXIsBkJCGas2bKKh/hS5hSXkGrUULlrBi4/+F8uL89BoNNxyyy2oVCrEYjGdnZ10dHSw92QjS7duIxgI4u1tYbC/n/vu2IpIJOKNN97AmRBz2xfPTCjvvvh7XEP9KCxZSONhSqxGpGIRQyFYf8dnaK3ZxY6330YiERPXZ+Ae7CFTKeKL999HVVXVOf6xePHiKV/ctWsXq1evntpi09nZSSgUmlr1DIVC7Nu377z+W1BQQEtLC/39/Xg8Hj772c+STCYRi8Xn9d/W1lbWrFkzdQZWo1Li8/kozjDzpYceZGRkZIb/wpkHpe7ePronXGgUcvx+Hxk6NX/xZ3+GSCS66PySn5+PWCzG5/Ndcn4ZHR2ls7OT9evXX9b8Mvm281L2m+R3/fXXI5fLz5n/gHP47dixA6PReF77nY+fTqdDLBafM/+d7/q2tbURjUbP4Xcl5ufW1lZWr1491c6l5ucdO3YgEokuy3433ngjcObh4cknn+S5557jrbfeQqVS8UmC3+/n5z//Odu2bUOn011S3uPx8N3vfpfvfe97yOXyS67yTZdXKpWXXBk8W34SF9K7kPyF9C4lDzA+Pj61Mns58tP78vl8ly0/2f73v/99/vmf//my5b/73e/yL//yL5e8V8/H/WL2D4fDPP7447zzzjvncNmxY8dHeiVyrleZPur2SFVvLu2Ran/Xgh3n0h6p6s1lXx6Ph7S0tA/0G3vJHt9++23+8z//E61Wi9fr5e/+7u/YsmVLSp1dDYyMjPDoo4+i1WqBM0W/z75Jpm8PFIvFqNXqGQ9KwIwHMgCTyTQjkAVmPNjBmZT509Pmi8XiGe1OZmg9k9Rp1VQim7P5mc1mjhw9TUZLPUKRmEQ8Rm19A3esWjglM/l2FuDIkSM4k2Iys8uwCIQ47ROItAYcIQ8TE12os/KwlJYhUKrp6Ohg67IqOkZsjDWfxGew4HeMI45FECu1FG/5DKM9HThOHCIcCpAIBommZRIab6F4xXpUeiOHGo4zdPQgCnM2C2++C6FITJJC/MeP83BxLfctHOExxT8QzqikUNuPZ3wEkTqNk40tSLv6WLt0EU21J+mpPUQsGibNKqNs3Wbs48OUVK5CeOIQAZcT+0A3srIFSAN+XMMDiKUixpwesjOLybBYUelMFB5+n3/4/e+RJRLYM7L4+cN/jchoQdR8knB/D0O9vZRftwWbWIujp4OxrhYs2fmcPHWSiaiAzPIFDLc2MNDZgcvrZ8Oi6wm4nYilMupbO1D57Lg9EXKXX0dYrCAoTcM1OsQ9Bd38w/VOasq+h123CRlnknF5245zuq2XFZllWBHimJjg+MFD3LRiBUc6+wm57AQSCSDJWNtp0vR6Th7Yg9JkQS6IozYY8djGESi1BOJg7+0gd/EqElIlAaUGqVyOPj2DiaZaYsVlLFm/mfHOBkw5BYiNGdiifkaPHKFvzEl/by8ZVisen59HvvRF4vE4oVAIoVCIMh5mz4tPoTKmE3CMsaQ4n6PHa+kadyJSWzh99DA8/zSVi5cyYneRs+x6llRVoNWoee6n/06uXk2v3YMpJ5++9lbCQin6gnIyDWnk3riJ1mMHMRqNU4HE+fwDOMfvJgOsScjl8vP67zmJmQYH8T35FF966MEL+u+kv870w//NTnw+flu2bJlKQHX48GHWbNk4IwHVpeYX4LLmF6vVes5q78Xml8kfh0vZ70rxm26/y+E3ibPbLSsrmxW/2czPV4Lfpey3detW1qxZw3PPPcc8ZodUtqumovNh6M1lX9NXgi8XqZZMnEt7pLoVMRW9uewrVcw1x7m0Y6q4Fq71x/UeuRbsMaPfSwm8/PLLfPvb32bLli3s3LmTl19++SMVzH6UcbmJbO644w6efulV9j3132SWVzPc2ohroIs7vv+d82Y5HhkZwTk+Sk5ZNRKpjGTDSU689QcKcnNwdrVxeLAXx4q1jHW2kKkUUVJSQrMjjERnIhqNoDeaiPrcaI3phB2jGExGIl4PjbveQK7S4LFPYC0uR603Egn4CLvtJH1uMGfTuOtNRBIpPoeNzfmj3FM5yNdO3kvW7SuI2ScQikTkL1lNIh5j0W2fYe+v/wvPxCjDrc0o9UZMOYW4x4fpOLwXnTWDXY/9EIlcgXNkAJFIgtpgwdVQi2t0iITfTd6qTeRUL0UiV5AuU/LtnjZkiQT9Gg1fW7SMmMvJ4LGDWI06CqsWYfP4sJZUobVYEYolDLeeJhn0kbtoBVWbbiPgslN94+3E4zGGmutp3vcO6UXljHW24Bzqw+GxnwkSezrQZ+Ux2tXCvZV9/N31Nv5q11rSJWrKlySIBkP0N51C7LLhdTjorTuCJj+L3t4GfA4HZrMZcX0Dh1/8HRklVbQc3IVKrSFv4XLG+rqItTeRs3glooCLvMIibMEYY5EQuaWVDLc1YsotRq034HM66KmvZdH6G3A6bAz19VCQmc2+vbvRu31IJWJajx0kGgqweM0GQh4Hr7+3l+VLl3Cyrp6OUTu6zByOnm7E5XKhMxiRSyUMK6QExXJueeBPEIslGHKLePOpX3N41zuYSxZgStNg0OvZ8crzTAQiaK16evqbOPWv32X9unU4PB40tlE23nc/YokY2+jIZZ0rTRWzTeT0QTDpt0NDQx/bpE/zmMc85jGPecxjHh8HXPI13NjY2NTb7BtvvJGxsbGrTupqYvre7Sspm4r8JKRSKYtXrqVs6UokQNnSlSxeuRaxWMzjTz7F02+8y7H2Pp5+410ef/Ip0tPTESbiHHrpCRr376T/9DGIBMlVCvjlD/8f925ej87Zzy0rFvCrn/2UWCxGX2c7uoxs5Jo00qyZTIwO4x0fIj/dhCHiZsVNtyKJhgkOdhCcGCbscdF37H169r+NUSpk6dKleGxjuMaGSSNCwO3ghf1Btj1exskWF6OdLfhdDpKJBOM9HcQiEdwjQ1jLqhEIBJhyi1h775fJX7yCpXd8Dq0lA8dAL4lQkInudmKhEFKlCpnPRtDjQpumIxnyE3A78IwP0Vt3lJp92/lWxSJa9Ea+uHgVfeEIZpkQbVoabc2N7H31OQxZeWgt6cTCQcR/rHU71tsJSTDlFFJeWoqlsAyxRIoxO5/FN38aY04+RSuuw5hTiLV6OabcQtwTo8h8E+hNRu5ZFuIvd6+n3p7O9t/9gjd/8wt+83//BtdwP14kFK5YjyG7gEjAjymnkOJVGzh27Bgrrr+R9TduJeocpaByIbc+8leYsvNQZOSTDPnY8duf03XyKFqFHFfLCYReG0rieCdG6T11CHt/N90nDuIcHSI82M54ZzPH33qRzuYGGg/souXATrpPn8DrtFO8aiOLN9/GDZ//MkKtgTfeeGMq+LNm5+KLQVphJUXrtqDNK2Xv0eNEECKRShnrakUkkZCWkUMcIeP93Qx0tvP280/T3NTMgutvomr1eq6790tUrFqPQa9nza2fQiJX0N/VdslzpVfC5y6UUK2hoeGi7U2u6J7tQ6msdqTK/YPKzoX81Wr7kzLOeZyLyXOYV1vnw9Cby76KiopmrRMIBFLq61IcE4kEgUDgnH/RaPS8308/g3w2UvWvVPTmsq9UMdcc59KOqeJauNYf13vkWrDHdFxyZTaZTE5tWRGJRFflIXAuMXmO7ErLzkZ+crXV4/HQ2NjI8PAwptwCdAXlCMQSkrEoLvGZc7Lddi9b73sEz9gQ2vQsdjzzW1aWFZBpMqLTG4h6negNRory8/niF7/IiVN1jIUSWCuXMuKw8cxzz2My6Bnq6WD8qccw5RQy2tWKo78XYjGe+tl/kp+bQ2PDaTIKiqm+825O1+yl+3QtMdcEaSoFt2/eiIgkJ7qfoGTtjaRrVYhyK6h99WnEYgXu0SFa9u9EmWZgvKcNtcGCpbCUoNeLY7APkVqGuXABIonkTB3VsRGkSiUIRZTfcDuDTSfxu5xcd/9X0RAlV6Rg9//8Jwq9icHmeuKxKEqtHufIAOa7HuCnlnQyAwESp45QsHAZXrECj9eHpagSv8vOYOMpltz6WU69/QdCXjeV123BPjHOgd//ioqqavxCGePdbVhLqwl6XEhkCsRSKXkLl+EbHcRSsQTncD8SjR5PXz8PvbuerJIqMg0x2o8fpGPXq5jzi/jH373Af//bP9He0UzJ6o2kyYshkqBxz3ay5Um6x+ykV+WjySkhKRDgCoQZHRlFrtUj0egIBUMMOSbQBe185y/+hJ6eHn7yk5+QXbGADfc+jGtiDHNBKUKRgGAoSmHlQobbm2l473WySiq46f4/5cSet9EsWo7WnI7TNo4qNx+V3oTf78VUWI1EKqWl7iRyg4n1n/syOrUSjVLJ2OAArbVHGLr5TgYGBuiY8BKJRll+2z0cfP15dr7+Mtb8QnwuJ70nj1Cc8xnUBgvZJeVoxAlM6RYGWxo48O5bGNO0lFiN5yRCScWPLiQ7vUzQ5MqsY7CXZSsXX7S96Su6033oclZ0Z/sQ+VGYW1KRnx/nleHyccX0xCGzwcd1m3GqZ7qEQmFKz0/hcDilvlLBxfRisRjd3d3cdddd58gZDAb+4z/+4xwdi8XCM888g/SPLyGnI1X/SkVvLvuC1F5AzDXHubRjKvZItb9rwY5zaY9U9ebajh8Ulwxmo9Eov/vd76Y+h8PhGZ8BHn744SvP7Crh7DNSV0r2cuXPPvtX+8a7+EYG6LN7WfOZ/y2d8sKuN8kS5qDNLePw7ncZ7O4iu7AIrTULtVrNlrXL2X+yAYnWQNTjYMva5QiFQtqGJyhfuQ6P00l6YQmtxw6eSbokllJ9452odHoMOYU07nmLhEyG2+Xi6PAQSm0aS2/5NOWLluENxRgZGiIQjhCJRDh89Cgj/X1kVSyiYPEqpEolykCAkdbThNpPQDJByO8l7PdhH+hFpTfhHO6n99RRPOMjqPULmKg/jlyjw5SVg0KtxjM2jMacTkZpFZFgAL/ThnNkEJ9URiw8gjGvkM7BbtQGEyWrb2CwpZ7MikVklC1AEI+Sk1+Ce6iPY4cOkpAqWHX3Q4jCfgYH+mmr2U3tmy+gs2ZSvu4GCnOyObj7PQJuJ4Oj49gHegh5PIQ8LvRZeQTcTlQqNb11x9AY00EoIBL0s/ulZ1DqjazYdh/mnDwEAiE9TadJEkNXVM2+3e/hGBkm6HbRcfh9LIWljPe0E/Z7CQskuPt6WXP/n6MxptN2bD+20WFkGh2l627g5Bvj3PY3/5fDL/6O+vp6ZArlmXuiuApXQkwoGiOrehkBvxd9Zh46rQqVKo3+3m4MxhKSGgMev5/CRctpPH6EaCTM+GA/ftsYnoFuFCW5tJ04wtL1G4EkSp2JRCyKVCJFIBKhs1jp727k8R//AKnewthgHxm5+ViystFYMslZvAqVSkkkHMbe2YRrfBRfFCb6e1iweTPZ+cU073uH5cV5rFmzZsa50g/iRxeSraiooOTYmTJBZ87M9lJiNbJp06aLtjd9RdeYUwBw2aV2rob/z0XbHyUun5RxzhX27NnDa6+9RldXF4FAgF27ds3wu66uLn72s5/R3t6OSqXi9ttv56GHHpp6IZ1MJnniiSfYvn07fr+f0tJSvv71r1NQUHDBPru6ulLimkrQl2qgOJd6H6Qvj8cza73pGUYvF6k+UF5sbPF4nGQyyRe+8AUUCsUl2wqHwzz77LPEYrHzBrOp+lcqenPZV6p68xyvjN48xyujN9ccPyguGcxWVlbO2Mp39udUEw18UnG+s3+P/8f3IBrhnd/9DLUxHZ99DK1Kidls5te/+W9kliwsBWWcfOMNwuND3PTTH9E3MIhcKkFIHJFUglAgYGhoiJ6+fjoHhhErlMSCAUSJGI6uZtKLqyhddR0hvw+NOYOh5jocQ72s/PSDOIb6EIrEDPT24bJNEIzGqLrhNmQSMVmlVbz78+/TV3+C3FValHojUrkCiUJJ0Oemu7OTFZ95iKqNt9B4YBd5C1cST0QZbDxBErjugb/AOz5CJOijZd87mLJyCXrdhL0eFDo9kVCQkM+DzzGB1pKJ0zOAXKnCNTKISKEmu3Ix+UtXEfZ5cI4NIZErCHlCBL1e7MMDhDwO8pauJRkJoTUaSfT2YM4vIRIOYc4rJh6LM9rfg0giZdmnvkDI48ZaXEnQ6yYWDdN5dB8ikZjRrlY0RjOmvCJCXi+u0UEUmjSMWfl47WOo03REoxHEMhmypIrOk0dwu1yIDenkpedhyMqFWISKVdchiEZxNhxCoDSy//nfYcjKZ7DlNEGfl8yKRbj+MAiCJLr0LDJKq2k4uRd9filb73sE89EjvPryS/TUHaer9iBimZyJ3k701YsZGRpgwz1fQpsIsn/f+4yOjKLR64kGQ5x49fcsXbmSjp4uBPEoivxKPAf385Nvf52iqoWMtp5mrKgMabwYn9OGd6iXkoXL2LDts9Tsfg+3XEHB0tU0HzuIxpJBVvkCTFo1Xvs4w60NHN+7k2g4TNDnYbikiObD+1lRVsSXHnowpTqIs4VIJJqZyGnpzRcNoCdxoRVd69KbrzrneczjSkOtVrNt2zbC4fA5K2GBQIBvfvOb3Hzzzfzwhz9kaGiIb33rW6hUKj772c8CZ0o0vPPOO/zwhz8kKyuLp556im9+85s89dRTlxWgzGMeUqkUmUz2YdOYxzzmMY8pXDKY/clPfjIHNOYOPp9vqizDlZS9XPnR0VF0mTkMdHfgGB/HYLGQUVhKW0M9SYkUj8tFPBwkZB/jVCJIXK5h1b1/ikqlJLDyevb+5ke8//772KIC7v8/35mRDMfncdPe0kzOgmWo0owEwlEGmk6jF8UJed0k4zFikTDJRBzHcB+G7HwqNmxhuP4YPY2nkGvTcI30ocoqYKj1NBqdkaGOZhR6A7/4Kws/2dPLOz/5Z4qXrsI2PIhzeACZTEbI76WgaiFJkrQdP4w5v5iwy45IpsTW30XI58GUU8hQaz3BthPEtSYWXLeZ4zte5/grT2PKLWSotYGBplNYMrJxu5z8pVDEuFzO7nAIEkkyKhYy0HSKY68+jc5sJeiyM9zeiMWaSdexA5gs6dj/uIVX5vGw5NbP4B4dQa7VM9h8AkN2AUKhiPTcfIRyNemFZdgGe8ipWsJYdxsCoRDnyCAiIYz2dKO1ZFC5ZgEDna0IEDDS0YJzuI9EPI4mv4zI8ACBQIDMwjIGu9qQqTToTWYcI4N4xoexli9m2O7AmJFDml7PDfd/hT/829/Tum8HKz/zILkVCxjv76K37ijaWAyhKg2Hw0koniDgshH0uTHlFjHQUIswEUcjEWAqr0Qll5JuMKNSqhhsOI4hPRNdmgb/QIBwXytKuZI/+fsfIhQkWbnpJl589L+oSk/DsHopw31NjPlsBBxjlGWZMVcto3TBYhTqNJzP/p6mfTvx2ydISOUkImG0aVmYjAYOvfI0GelpPPAnf0JXVxfd3d2srijhtttuu6xA9kr53PkSql2q7ekrunprFs7RoYtuif4g+LDnlg8iPxvMj/PDw2RW+7q6unP+tn//fhKJBA8//DAikYjCwkLuvfdeXn311alg9vXXX+eee+6hsLAQOLOravv27Rw4cICbbrrpinL9qJeD+CB6qWB63cfZIJWXDKkeB7vS9pg8Y3s+nM+/xGLxeVdxL6V3KaTqy3M5B8w1x7m0Y6q4Fq71x/UeuRbsMR1zl4f9I4IjR45cFdnzycfjcRobG9m1axeNjY3E43HMZjM177zJnl27GHJ52bNrF4d3vIlQoSZv1SYqb7qLmERBUCjjeHM71rJqTFk5qNzDZBSXklG2gJ07d04FxLX79zLQ3YEuM4fe3l4k6jRMJQuwahSYSqoRyJTI5XJGOxrZ8/jP6Txew5GXnsQ1PEDQ52agqQ7haBeRgI8TbzxPX3M9NU8/ykhHM+MDPbiGuvjp1tOsyfNSuv5m3OPDJHsbGetsxe9ysHTpUpz93bz079+m9s0XaTu4i5pnHmWwpZ5T77yMra8ThUaLe2IU99gw1dXViJIJ6ve9g1JvJHfBMlQmC5b8YjRGCwsqSvmq28HXDu/hu8cOUNhymhNvvYC9r4tYNMJAw0kGGmoJuOzojSaWFOeRppRzfPvLHN/5Bj21NQhEAjRGKwKRgJ7aGsb6++hvOIFILEbtGycRjxENBYkGg/Q3nCDk8yKTK/g/Vfu5W/Y0Kr2Jpbd/lhydEoVaS8/JQ3TW1mAb6CF34XIsJVVUbf4U8WQSoUSKSqmi79g++o7sYfDkITZu3szdf/qXmDOyOPbWHzi2/SV2P/5zhIkYQpEI+0APsY5TdBzdj99lR6I3s2fHO+w9dJThcRva9CwqrruJBauvY/2nv4BYJkce8tBVexCtTIJEKqNoxTpi4TDEIqiM6RiKK7FFkohM2bS0tzPQcAKpXEZh1WKKior4x+98m79+8HPctqyCv3rgczx0/324hgeIRiKExwdYd8MW/OPDKIQJPIM9tO15g/aj+3n/+d8hi4W5//77OXGqjuPtvSSthRxp6eKp3z9zWQ9Nc+lzZ2NyRfeBO2/GlPDzwJ03X7XV5A9znB9U/mq1/UkZ50cBXV1dFBcXz7i3y8vLGR4exu/34/P5GB0dnfEiRyQ6k+2+o6PjivNxOp1zovNh6M1lXwsWLJi1js/nS6mvK2mP6Wdsb7nllnP+fetb3zrnu/vuu++iSaMgNb9M1Zfncg6Ya45zacdUcS1c64/rPXIt2GM6PpyCQB8izq4zeKVkz5Y/py7msTpKjh1n2ZLFKNRacqqWoDaYkWt09Jw+ic5oYdn6jQy0N2NIz2DBpluof/0Z7AM9RKNRNJVnyt1M9HWQKZNR8/YbaHKLSbPm4D5+HG9/J1k6FSqjBblaQ0SZgdjtQGW24rEPEwkF6T55iN66YyQSMcKhIEPNpznyhydILypjpLuDkdYGMvPycI+PUbhsLQaLgb9f8x6qeJhvdzxMSKrDnFfMiZZ2RCIxyUQCiURC1DuMbUyGOa+YaCiIRCZHpbeQRIhUoSJv0Ur0mXnY+7tpbW3FjxSBQER6YSlCkYjh1gZkai2r73mELa88yR2tZ7axH87MpTe/iKHaGgZkCmLRCEGPE51eRxIQyhS4VGYEUgfB4SEGO9vQZ+ZgLighkYghlsoZ7WhmtKOFjLIqek4cxpNbwOD+ZxntasGQlUdbzW6iQQ8/udvBikwfn/pNJgmLGxAgKF6KNak4E/B6XeQuWI5UqUJjycDndKDU6kEgZMW2z/PKv/8dob4W8pesZvmGzYyMjhAOh5EqlQhlMpI+L9Ggj5J1N2HIKWAgEMGYlYdYKMQgijJud9F0+H2EyTgSpfbMlt5IlHgsjiUjgwyDlnSDjrHG4/QmRRw5fJDs/BJySqsIIaSz7hgZBi2xoJ9xp5vs6soZW2onVzUrKiqmtuoqYiGe/+WPUBnT8dvHuGnVUlYuX8qRY7U09w3SW3sQYSLCHZuvRyQSpVwaZ6587kK43BJZHxQf9jg/iPzVavuTMs6PAvx+/zlvxDUaDXBmC3IymQQ4R0atVqec/fZiMJvNc6LzYeil2lcqZ2aPHTvGHXfcMSudtLS0WfcDV9Yel3PG9vrrr5/6/6XO2E4iFb9M1Zfncg6Ya45zacdUcS1c64/rPXIt2GM6PnHB7FzhQnUxk7W1LLtuI6YMC3a7g7wMC6WLltJ48hhKmYxkOIg5t4B4LErVqnW8++Lvefdn38NaUslEdxuOvk7ufeDzbD94HJlYjt3hICGW4w2FSSQUOMfGCAX8+Jx2hBIpXts4ykQMqVyJMa+I9KJybN3tDLbUIVFpMeYUEPJ7sRZXoNBqURFHYrCyZttd/EnyHxHGVPzV+zcTiLrJXlCCc6ifohXXYc4t4vR7r3HsWA3ZS9aw8Ka7GO3poHDpGrIqFuEdG8K45Q7G/rhNt3T1RoZa6une9QoSSw7mglKioRDRSBjHYC+51cu46elfcfvbfwBgT14RX8/Ow9nTgeqP9Wm9tnEiwQDhgJ9YUsDS2+7BaLWisOYiVGpIMxhwuM8kTFLrTcQiEeSaNDIKixFLJNiH+nAM9+Ozj6NLzyYWiZBbvYDvLN5PidLH556vQlm6hMHW0xx89jGyyhcy0t6EITuPjMItOEYGURssxCNhNCYL9TteJegcp6f+OLFEEvOS9XT19fLT//fPGKxZeJwOqm64HUtuAQG3kwO//2/Getq55c//liQCAsEg23/0T6SZ0lDrDQTDMcLhAEGHi5yKRWgUctKLi+g5tIuqqiruvPNOWlpaeP311wk7bdjVWgQDffTU1+Ic6qN44T0MNp9i+A9P4R9YiywRmbGldvoLFl1mDscamonG4+QlIREJkVlawObNm+kfHKKhs5tQIIBMJGRocIi3bW8TQYbwj6s+k6VxLpZI6Xw1kufifO085vFJhEqlYmJiYsZ3k6VWlErlVDB79qqdz+fDZDJdsN2MjAyeffbZKd8tLy+fmlMMBsN5t6XG43HGx8cvyPNCiYYcDsd5t+OKxWIMBsN5dfx+P36//7x/k0gk5/0+FovhcDjO+T4SiZCdnX1enfPxi0QirFmzBo/Hg1KpvCS/SXmXy4XP57ug/Sb5nS0PF7efz+c7Rx4ubr9QKHTBldxL8TsfzuY3/Yztxa7vhcbU2dl5wSRk69atO++WRp/Px8GDB8+rU1RUdMEkNTU1Nee9l1QqFevXr5/nN89vnt8V4ldbW8vIyAhSqfSSuzEuB9d8MJuRkcFXv/pVHnroIbZu3UosFmP37t3ceOONU5NwY2Mjcrl86gL5fD6OHDky4w1CbW0tVqt16ofMZrPR2to64wLV1NRQXl4+9cM/ODjI6Ogoy5cvn5LZtWsXq1evnsqi6hkbIhYJk15UjiE7n3jAxlBrLfaECGXYj83rIzQ+SLpawZ7nfoNAJGaws53FC6pYesPNnNi3i7HeDiwySDjtZJoMKJVKJFIZuogHj8KA32EjkjiTqVIU8tFW8x7pJZWEBrvIUEvp7exDl1XILV/7v0iH24jd/Cn+8K9/R9DrRitKoDGY6Ru1EYtGGRjsZtOmTfy5+F8QiqT81PsdNLIaAmPdtB3YSf6S1Sy9aRvx7noahCJcHi8al4twbzP4/cg1aQglEtRSMRrvONGsPKS2HpJFZVjFMQalUrTWLKo230bbW8/DYDtqUzq3bX+B23s72XXjjYhCSf4ikWCiq42KJcspW309WPLw2Mbwv2mj2JLGhFCNpaCEaNCHWSklmZtLf8ANaiNRtx1BYJxAVIzWkoE3GibHaiZMFH9ChFgiRSERUJyXzzfW1WOOBfnnge9QtHCChC6D9KJy6na8QtI5RnHlAkQFCzCbjIz3dRGqf5+WCRdetxu/y0GWTo1cp6F6y19iTfg5EAqRo5bQ0NmKPrsQc14RZmkSaUEePZWL6D6yn4NP/zcFGWZOd/UjSCYI+Hzo9AZWbPssmvRMjrz7Ot0Hd6KurqKxrZ40jRqlUsnhw4cpLy+nsrISa1MXW+59ENdwP2pzJkPNp8grr2JpRQlv/uE5dCEHd999N1KpdGr1tKWlhY4RGyWlpYgMFsyl1eRULaEwTU6aXs+x93fxxhtv8O77NeQXFyPMKOTYu2+w/VAtq5ctZWCwgzefe4pFlRVn7u2+dqxL7zyvf8TjcR77n18zZHOizSuZ2pmQl5PNmjVrpibYzs5OQqHQVED8UfFftVpNTU0NVqv1I8vvStmvpqYGuVz+keV3pexXU1NDKBSaFb8dO3bw5JNPci2gqKiIXbt2EY/HpwLPtrY2MjMzpwIFq9VKa2srVVVVwJmgs7Ozky1btlyw3cLCQr7whS+g0+kum4tIJMJiscx6DBcKuC6GiwV3F1oJFYvF5+Xn8Xgumj3YYDDgcDimeHo8Hg4fPsytt956Wfymy18o+J3O73LlJ6FWq2clD2cyH1+u7Nn8JjHdJhfDxWQuVIKouLh41hlS1Wo1W7dunZUOcMEH8ovhfPxqamou2taHze9SuNL8ampqLqjzUeB3MVwNfhe6Pz4q/C6EK8lvev8ej4df/vKXs257Oq75M7MjIyM8+uijU4YRi8Vs3bp1xtvE6urqKWPW1taiVqvPWQpfvnz5jDeyJpPpnPT269evn/EGOzs7e8aDXG1tLZs3b0atVp/JojrYizY9i/Si8qktnwaDgeOn6mk8chCvfYzGwwdo7+iiqrSYwEgvnsFevCN9dHe0c3TnW0QSkFlahUKjQ5FdhFCjp66ujnGnG/WKLRSuup6yTbfhD4Zoa2tDpDVy3Re+QmVhPpV33M9EOEksFsNcUEosHMajsRJLClAbTMiUKvJv/DRZpZUs3LKNZDxOPJHkWGM779mX8mPPNxkaGKOppYWe3j6cI4NIVVpcDYfZtXsP4YCPhVs/hUSuoGfCSVylJ+BxEw4Gkag1DHjDtO7fQdA2yt7f/Jh9e3czNjaGa2SQ2lefw+72YEuIkcrk7NWbcYvE9Itl/EVSgM/tQiyXk9Bb0S1cizm/mPTCMjJKq2m3eQkGgxx+6UkY7WHY7ad3YBBfMIzHNoJQpcWbloVAACGfB5XZykREQNGNn2bjlptZdOvdePwhtuSdxpQc4O/avkhQaiWeVUFMKMHvdmDOK6a4cgFdw6Mk4zGCXg/RYJCGnkHkBguKND3xaITB0TH8Mi1yqZyoz41SZ0C39nb0GVnEImGkcgVJQyZSSw5+h43MzHSuu/56lFIJJqOJoM/D4PAIY6Ek/b09+F0OjNn5KHRGItEYq9euQyUV09LSgk6nQ6/Xo1QqyS8uwxeOEVUZUKWlkWbJoLetmbG4mKzi8qn7v6ioaOpBfXR0FENOAWXrN+Oy2THnFqA2mAn4vFgKSjBk53Po0CHiqjSW3PMVzFl5iFVpVN70adQL15G7Zgs1e/fQcuIIB7a/SkFWxozzd9P9o6WlhT6njxsf/Cp5WZlsve8ROkbtZGRkzHhTWFxcTHV19dT58ueff56srKwZWdKn+y8ww39ra2sv6L9nT7xyufyi/gtM+S+cWVGZ5DeJi80vk1wuZ37p7e09h9/F5pfJtqfzm26/s/lNTxB0MftNjvNy7DfJb5LLpewH4HK5Ltt+k+O83Pl5/fr1U1ym85vE2fz8fv9l208sFlNbW8vWrVt59NFH+aggHo8TiUSIRqPAmZW+SCRCIpFgw4YNCIVCHn/8ccLhMD09Pbz44ots27ZtSn/btm28+OKL9PT0EA6HefzxxxGLxVx33XUX7DPVZB4ul2tOdOZaL9VETqlyTCVZXapnZlPlmIpNUu1rus9fTZ0PonehnQJXo6+51JtLe6Ta37Vgx7m0R6p6c23HD4prfmV2trBarVdF9mz5C9XFdDqd5C1ZTeGiZcT721i8fiNdag19vd3EEBGKBsjIyKAqJ53utjpi0QhaUzqqdAvesXFGhvs5efIkyqKFeMZHSBoteO3jKPVGBGNuzJnZRL1OxHIVQZcNjcFEYEjCQMMJ0gvLkGu0hHxeRjuasZZWM97ThpI47tAYMpUGkVDESEcLv3o1iT5rH47BXtRGE4tu2kZP3XHq33mJUbMJt8dH2dobESbiIJHRc+IQKq0e+2A3tv4uDGYzY309+N1OytL1tB/Zj0pvIqt8IRMDvUCSjLIFBFx2YtEoo+UL2BaLEghFCUcjCIQCPLZxgh4nCm0a8XgciUxONBzGnF9KRmk1bYd20dvZTn9/P2NdrShUagRiKZ7RQVQGM0PNp9Fl5CASitBn5QJJQkkRsbAPoUjI/3vezfYb7kGWVUI04KfnxEHSC8sI+TxU3XAreqUMOtppq9mFQiLC4/Oz7M4vYMorxGsf48gLv2OsvYGkUETZomV47RPEEwm8DhsFVUs4tfddjr/xHAZrJr7xEWLOCW64bjWdp44glMppOrSH9PxijFm5jPR1E49FiTkn0EuF9AZ8yJW5vPa7/waxlIGMbH765LNkauRkpqdD0INFq2LY7ybscuEZG0RbXM748BA97a1krFjG02+8S8mx41MJj6aXqTFYLNSdPo1coyMrI2PqZYtWq0UlFiIQieg6fQJNega5VUswqOVYFi9jtKMJgj4euG/bRbcNT6/vqjaaL7otecb2Z5OFhrN4Xykfna0/zxZzNbdcDfmr1fYnZZxzhffee48f/OAHU58nVwR//OMfs3jxYn74wx/y05/+lG3btqFUKrnzzjunMhkD3HvvvQQCAb7xjW8QCAQoKyvjBz/4wVUpy5NK+ZZUS77MtV6qfU2+hJgN7Hb7rHUutL36Uphre6SCVPwyVV+eyzlgrjnOpR1TxbVwrT+u98i1YI/p+MQFsxc7D/NBZM+Wv1BdzN/85jcMdLYgN1owJOKMDQ7Q19ZEcHyQpTfdSVFuARP9PZxsbSbutpNAiDYjF1fAizYjl4GWBjxOJ3q/F0tBKSQTKLQ66t99GZNWiywRJuyyExIKESTiqBIhKosKqKlr4tT2F9FaMvCMj+B1TCAd7CUSDCI3WyE8jr2/G99wL6rsIjLLF+Ic6sOQlY+loASJRIpjsAeBQMjg4CCWglLisSgIBChUakrX3gACKN2whTf+/VtM9LSh0OpJJJNEgj70GTks2HwnroFulHozmeULEUulFK5Yx5EXHye9sIwWnwetwcziles59NKTRMMhXMODHH7hcXQZWTgGe/FOjFKwfC0as4XsqqUMdjSRt2A5JGLYO1soWH8TBcvW0XF035nV3JJKhhpqcY8Pk1Fahcd+ZpwkwVi6lK6OXgSd/YjEYmwDPYS8HvTZefgdNrS6ErLLKrF1t9F96D0KNt1J+fobEIpEGLPy6Dy8D99wL/7xYd5/8UlWL1nIYEsDHtsES2/5FEqdicGmOiIuG7GAj7L8LDIyrDQfPEpfbx+IJazZdi+lC5bwyqP/ReOB3XjLKgnaRki6xkiMqQjHk6z+1D2odEY63nqJpvYu1l9vof5ELfVNLRQsWk7TkQMIEjHyS8ppP3UUU1YeK2+7G5PRMCNJ0/QXLLrMHCbaGxluqWe0qIRYKECeSYfVauXgzr20njiMY2Ic5/g4iXAQTYaFZCJBxO/BmJ17yWRK0wPntPTMi9Z3vdD58stJLnU1/Xm2mKu55WrIX622PynjnCvcfPPN3HzzhWskFxUV8bOf/eyCfxcIBDz88MM8/PDDV4PeDKQSIKcaVM+1Xqp9pRLMXujs8cWQaqA41/ZIBan4Zaq+PJdzwFxznEs7popr4Vp/XO+Ra8Ee0/GJC2bnEufLoqrT6RjtakcgVbKsooTOhuMMd7aQWVjCDZ//MmKplEg4xDP//h16OjoQmnMY62rBUlDKWFcL0VAAuVxOIhqhbd876P+46hsNBtFY0wh5HLTUvIelsIyJ7jZy0+So9Bnohm2YcgtJS89EJJHito0S9nvpqzuKTKUhGgwSCvgJh8MUllSiMaUTcNkJel0odQZ6Tx5CpTdRft0W7P3deCZGkSrVhFw2VCIxw62nSS+ppH77S5jzS9AuW4dzZBCfYwKtyUqOPpd4LEo0FECXkYNMpUIskRL2+VAbzIz1tJOWnkUsHMLn9ZC/ZDUCgQCv047XPk7A48Q9NoTWbMWQlYdUpWag4QTRYADXyAC69EzSxALGutoILFuHJa+YgaZTKNMMZBaV0nRwD4ee/w0KTRp+t4OcqiXIVRpkGi1Ne94mHo1gyS9ltKOJSCgEQNjnRq3TE/A4SUtLw+cYxz02glytJeh147WPYc3KIqnQkr9yEypRiLJ1N9B59ACHXngcBEIkIgHrVq+hYuky9r3+B1555z1yFiynoqCSvs4Oave8SygUJHfRSiKxOAmvneKFy+ioq6VlcAyxORuRKg3bUD9CmYLFt99LPBpk7Re+TOPud3B2t1K16jqCE8N4elooLKugdNX1BINBhCIREYS8/fbbwJndApMvWIaGhggvqGTYF0KhN9NweB/eQDryvHI0OgO7f/tTVDodo719NO18heiiZdgGekh4HFitKy95719oZ8L5tsxNX8WFy0suNY95zGMe85jHPOYxj3lc82dmZwubzXZVZC9X3uVykVO5mKoNWxHL5FRt2EpW+QKSiTgCkYhEMkFPeysilY5gLIFco+X6B/+SBWuv5/qH/hK5Jg2j0YhWIcPvstN17AD2oX4kEgliczZ+sYLCqkUYpELWb7kZU24RNpsNrSWL9fd/lcW33s11D/w5+oxcZGotOSYhC7OEiKQStEYL4XCY0c4WfPZxrKXV+B0T1L7+DEOtDSRiUSZ6O8nIzoFkklNvPc9IWwPHXn0aW18XHYf2Egn5qdiwlfwlq1h8693kVC9FolCSjMeQKlXo03RUHdyF1zaKzzGBZ2LkzNbVRIKA24HBqEeqUOG1jaFPt2LIziMei5wJcp0Oxns7Ofnm87z2/W8Qj8epXnc9XpeD5gO7iEejeMeHOf7q0wy11NNXd5S6t1/E0d5AzGXD1d+Fs+M00T9me45Ho4y2NyOWSBAIhbjHhihdtpZEPIqtr4uE30P9nnfobqxDa81ioreTo688SfO+d6bGHI/H0WbmsWjzbah1BrSWDEz5xYhlCuRaHQmhiJam03i8Prw+P0KtkVXbvkDFkpWUrr2BSCxOx+k6AuEIfo8ba+VS4hoDWQuWozOacI4M0tfdxUB7MwKFGo/TicPnx+nxIzVZkRksuJ0OwokEucVlJOMxAm4HwliE7c8/TXNTEy65gaffeJfHn3wKOHNGMSsri6BYzuf+4htUL1xwJsC+8U5KFi1n7bZ7UJqsZOYVUVpWgbu7BVdXEyKfg21bNl008+kkptd3rdBJL1rfdfJ8eTQSwe+y/+8q7mVsV7ma/jxbfNhzyweRv1ptf1LGOY9zkUqGylSzWs613lz2NZvEW5NIZQUYrg17pOKXqfryXM4Bc81xLu2YKq6Fa/1xvUeuBXtMx5wHs3v27OGv//qvue2229i0aRPxeHzG37u6uvja177GLbfcwt13380TTzwxVVLgSqC1tfWqyF6ufCKRQCCWEI6EESo0hCNhpAo1EY+TXS88wcu/+i/qDx9AKoS84jJ01hwm+joJdjcy0deFzppFbm4uBRYDIfs4OpOFoMuO1WxgwcpV6IsqiSj1yJRqBj1hWvuHGRkZQa5NI+D1EHC7CHo9iKVSCszw2Mb3WJ0vYv39fwbCM0l3pAoF1tJKjFl5LLn9c8RjMUIuO8lkkqW33UOOxcSGL/4VErmSjgM7mejpIBzw4x4bIhYOE/b7EEukqHR6tJZMIvEE8XiCnvff4W/+8CT/fmgPK555jOHWRg48/d+M93bSfeIQ491tGCQCTr71AmM97aQXlKJKM5BRWo3GaMGQmYtCo0Gh1mLMLSJ34TIqK6vY9KWvY84vpb+1AWtuPtffdhfpRj1FlQtIRiOEhpv4/uYePn3TWlYtqkamVLNy230UrbyOlZ9+kGQyiVKrQ200I9Mb0Fuzqb7xTvKyMliwZRvZlYuQabToDEZG2xrpPLKP0bYG0vR6FAoVQY+bWDRKFAGxaAyJVM7CrdtYsOVOZEoNrijs2LGDuqOHESvU2Fxu4m4bQqEIgUCIo7+Lhp2voVSrseYVYczIZqy/B4VWx0h3Oy0H3kMRDdB96gg+px2t3ggCIYkELL7hFq67+34cE+OMDvQx0d7IyTdf4Njbr1B79Ahb7rmfmz/zuakETC0tLcDM1VB7XzemnAIkSg119XVMePws2XQzy9as5xv/9mMqFi9hdWke/+dPvsgjX/riZSeEmdyZYDAYqK6uvuD514qKCkqsRnY881tOvLedHc/89oKruKn4XCqywAUzpH4YXK6m/Pw4rwyXjytSTSaUit5c9pWq3vlK1VytvgDy8vJmrRMMBlPqK1WOqdgk1b5S8ctUfTlVvdnONR+kr7nUm0t7pNrftWDHubRHqnpzbccPijnfZqxWq9m2bRvhcJj/+I//mPG3QCDAN7/5TW6++WZ++MMfMjQ0xLe+9S1UKtWMJBYfBLNJLT3bNNSXI+/z+bCNDrE0rxh5mh6j20ntWy8QcthoOnoQc2E5Y+2nUAhhxQ1b2btnD4lEAq/SBLEozqE+Vi2tQKCzsObeDfT09lK6dCX2zmZ6unsZ7u2iamMpUaEZcSRMd3sr7r4hVHEJHYf3IhAIiUUjaPxtPPNAD7WeNeyNryV4uhaN0YJQIkNjTGess5Ww34tEriB/8UoGTh1GrtYw2FKH1pSOp7kOpVaHWCwmLT0Ta3E5Sp2Rsa4WBptOIRQtJzExxlh3K8mCEqxmPV957jGu852pe2gpKEOflUMkFGCsqxWBSMSmL38DbyxC8aoNtB7cTW/jSdyjw9j7uzBm5RIJ+ihZfT2xSARrcQWDzXUMBRNkZGvIrFjIieaTWERiWo4fIs1sYXxkiMWrF/HjVW8zFtTSe9pLrtmESahltLMJRZqBSDCALiObie52tJZMRtob0WcVIFUqsYdAb81Gn5FLYKiLNL2BaFKIQqVCKZfhs4+j1aXR3tXC8VeeRCRTMNLVjkSuQCAUEfZ5yV+ymmjAS+ny1bQf3U/78RoMOfmY8kuIx5y4R/qxmE0Ew2Lco0MceOX3+FwucsuqEAiSZJRWUbr2BkKhAOoJJz0nDxN1TeCy25CrNcQSScLOCUorqqnQSYiadfTZnHT29qKz5iAWn0kEcvbW3elnWq1lCzn88kuYwzEUMhlIxQw0n2bhXZ9BKpdRWLWYgkzd1JbfK+0X55wvn0VN2g/bn+e5fHhtf9S4fFxx8OBB7rvvvlnrpVJmJxWdudb7IH1dqFTQxVBfX8/dd989Kx2tVjvrfuDasGMqfpmqL8+l3jzHK6M3z/HK6M01xw+KOQ9mV648c95ueumISezfv59EIsHDDz+MSCSisLCQe++9l1dfffWKBbMfNurr6xEgpPPwHgxZ+TiGeon4/ShMmdz5p1/HMTFORmEpw611SFUaZFIxR1/4LZllVTgGulEK4qSlpTEQSTLq8iHVmRkbHWF0fJyR1tOEBWIOvvAbTFn5RMNhkokksWQc7/gow20NpBeUIhs7yuOfb+Gt1ize1dyJTOkhmUzgHh1CKpcx3tN+ZiU0O5/RrlYGTtcSDweIC8VEggEGm+uQa9Lwu53E43E05nSW3HYPco2WkfYyTm1/gfqdrxIJ+ImGgshDQb6zfyer/hjI/mT1Rlzf/RnSna9jzitCa7HSV3ecocYTVN14G0G3E7XORNu+t1m8Zj1NJ48zePo4ZRu2olQo8YZDuMdH0JqshLwe/E4HQ831ZJcvQihX0N18GpFESnGBkV+s34lPu4zGnJ/j3vMPHD9+HIyZSATgGh0ip2opY12tLLz505ze8RqeiVEcI0N/DJjLaT+8l8GWesqKComKJOQuv46cvAKiAQ+n3voDt6xdhjQWYayzhVAshsftZtFNnyIZT6DQpNE9cojC0jLsnS3oswuQKJQ0HdyL4OBeYtEo+oxsKjffzomdbxF2OcgrryYRa6fzxCEMRiMKXTrW4nLkKjUFS1az57c/xtnZhCm/mMLl60hXy0hPTyfYUUdlZTFHWrp44Bv/wEB3B7t27qS5sQHnYC+5BQXY+ruxLj2T/XT6mVaBUktvwwmCXhf51UtoPXkMncGEUm+8aOKmK4nznS+fxzzmMY95zGMe85jHPC6Gj1QCqK6uLoqLi2esyJSXlzM8PIzf7095af6jBIFAgEKrpXj5OoIeF8XL19F2aA/ajBzsLhdSrR5hKEwoFKHx+EHSVEo89jEcTccpKK8k3VyNRqOm73gDqz/zIEKRiINvvMj48CD6nGIcDbUoNGnIlGqikQhSuQKVUo08I5fKjbdSpHXyjZXHeLYul28/G6FqUyOWglIm+rvw2cdRaPWoDRbyl65Gozejz8jC0d/NyOkOBBI5A6drsZZWMXC6Fr/TfubMqDkDXUYOEqkM6SIVnUf30bh3OxqDmaLcYn518jCVLjsJ4K+UKo5YrCzvbCbk97Di0w/SdewAcpWG8e52al99hpyFy/E6JoiHgzSePI6lpBpVmgG/20k4EiG7YhETA7007H0bc24RrQd24BzpZ8O9X2S4uxOtNRu9yMFvbj5IZ6iag5b/S9+Ro/jDUfxCOf7ebmzjT5K7eA22vi7EMjkylQZ9Zja+8WHkKjU5C5ahMZiQyBUMNpxgqKeT/OXrybMYyTFp0Wiy0URuoTzbwCOPPMJjjz1GfX09+/va6T72Psa8Esa62jBlZFJcuZDdLz9H6brNKNP0xCMhTrzxHO6xEQwZ2fTVHSfs91G2bjNFZRUULVzKkZefxj/cg2NkDP+GzYiFAhwjg4Q8LvQGPXGPg8Fj+8iQbeRU/VFKM0wolcqprcPZhSU4Bx6jvbkRV3k1R/bvJVMporS0FJi5Gvr222+zcestLF69HsfEGHFHDmM2B4ffextpPDy15Tcej0+tnprNZgAmJiZmtZI6j3nMYx7zmMc85jGPeVwpfKQSQPn9/nPO42k0GuDMFuQrgZqamqsie7nyd9xxB+6Bbk7vehPXxBind72JZ2wEr30CicaAWK0jLSMHW3832EcY62wFpY5Fa9bTPTDEqYP7KCgoQKtWUfPSk7z1258zMTxEduUSJHI5meULWbj105QXZFO8eiOxaAS92YwpO5/cBcuoyhZwVP0gz45tRaHRobVkYIh50BjMpJdWkl1SgaWgmLDfz2hnC2GfD3NOPvF4HH1GNhsf/hrVRblsfPhr6DOyCYfDOIf7GW6txzbQxXBbA56JEaQyOTnVS1mx8SbSo1ESQiH/tGglr5gt2Hq7OPTsrxEIRAw0nMBrnwCBEJlKQ3GGkc6j+7D1tqPOyMVcWM6aex5m6bbPocvMofvkYXrqjzPe004k6MeQ8KIxZ7Ds9s/R11hPNBJh47ZbeOHeZupsFn42dC91x49xavc7JBIJNm65hYrrb8Y9PkrrgZ1oTVaKV65DodEQCfjIKCgmr2oJYa8Lek8T87nILinHaM3CPTqIQqmgsKAQvS4Nx3A/ZrOZZ557npFAjPJNt3HX/V+iwqwlKzyBMuhEFA3zzlOPIpTLyS4pRywSIVVpWLZsGQptGrFQAKHfzaLV11G+YDEaYQKVRIyluBJDcTUKjYYjL/wOz4m9DNcfIxkOUJll5l///m/5x7/+CsuyDVPJlbKysrAP9DA0NMShN15ElZ7J2ts+zboN1/Olv/kW5vwS2tvbp+7FydXQoqIi5ELIKylj5cbNfO4rf41FLafSIJ9qG+DxJ5/i6Tfe5cSgg+/97DH+/l//g+OD9qnkUmeff4/H4zQ2NvLEE0/Q2Nh4zt/nyudSbXu2+LDnlg8if7Xa/qSMcx7nwuFwzInOh6E3l30tWrRo1jqpbGeGa8Meqfhlqr48l3PAXHOcSzumimvhWn9c75FrwR7T8ZFamVWpVExMTMz4zus9szVVqVSeVycjI4MHH3wQofBMXL5s2TKWL18OwLp1684JjsvLy/H5fBw8ePC87RUVFVFcXDwlOx01NTX4/f7z8l6/fv058gCdnZ10dXXNkF23pJpg1EPXoV24JsZRiCAWjVD75vOY8ooZPn2MqrwMDAYj6QVhrIWlyKUSsm/Pp6H2KD09PeTnZFO+ch3/8+//QiIhRCgSkadXI7PokItCCEMxMuVy1i5bxHhPJ4MuO36HjVrlWiLhxXjtPweBgIqqKmQ+OyaBkMHAOMmYnETchzFoI2otwjYyzEh3G1K1FqXOgH2gB61UgbD5MEvLisiTbUWuM5JoPUpYoWJsZBT3+ChqgwmZUs2YRMZ/feN7FI6P0BO0s9FtIxQKgUCI3NmPfBgM0QheTTpBtwNd1UI2bfw0u//nP5nobqdq8TJyZUkiQTsbVq/E3t9NwO0gPzsdg/UWYj43JlMmXbU1hLwe0osqCKHlD8Gv0x4UIptoIx6JUKRXkLFqGdqcYuQCBeP93Yz3dNDfeByPbQSRREKGXEheaSnBUBitMIxcpkAWjiDVSFn/hb+i7XQd7734exx9XcR9LkqsRsbGxugdGqakvBqhSIShrBxB2E+6TstXvvIVRkZGePbZZ3GFA+jUKrwuJ9mFJQx31rL5hhuIR8OYDXr8/gkS/U2osvMYi4JMKmbLp+6mpX+Ek7veounUMfKysslZUs3y5csZGRnBbrcTCARwu91UVFRQWlrKRO/PePzHP0AmlxMSSDEMdeMP2hgZ6UajkHH48GGGhoZm+Mf69etxuj3/W0ZnoJvK3ExycnIYGhpiaGgIm81G79AwK2+8DVQ6vFINA02nyMwrYNWmm9jxzG957bXXUKlU2O12fD4fIyOjeEJhBGIJ+081snpBBY986YtTK7jxeJy9e/fS09ODUqnEaDQSCoXYsWPHBf0XmOG/wWBwSv5s/z0bgUBghuzZ/ns+nO2/03E2v0n/v5z55XxzxcXml/PJX4xfMBjE5/Nd0n4X4nc+TPI72+YXs59SqTyvzeGDz8+T8mfzOxupXN/a2lp6enp45ZVX5jTD60cRGRkZvPDCC6xevZpFixaRSCSw2WyYTKap312v1zv1fzjj22dn1HW5XMhksqn6opFI5JwkQPF4nEgkgvSPZbqCwSDhcHhGRt+VK1fOeDHm9/sRCARTn6fzm0QgECCZTE7t7orFYjidZ0quTecXi8WmPk/ym3620+FwoFarp/hFIpFzEtVNTEyg1+unkiL5/X4SiQRqtZpQKIRIJMLlciGXy8+x39n8ZDIZfX19F7XfdH4+nw+dTjclcz77TfKbRCgUOnNcSKNBrVZf8PqGw+EpnUl+kzt0zscvFoudE4hPt59arT4vv/3797N+/fqpuaGzs5NQKDR1BKW4uJgdO3Zw4403Ttm4sbERuVw+NTf4fD6OHDnC5s2bgTPzRG1tLVardaoOps1mo7W1dcbcUFNTQ3l5+dS9o9PpqK2tnXqmBNi1axerV6++IL9YLMbu3btnxW9Sb3BwcFb8BgcHZ9yzs+E3/dpcDr/a2toZ1+ly+Y2OjqZsv8n5/XL5Wa3WKZ3Z8pvUm+31LS8vnxW/2V7f6fYrLy9P6f4TiUR0dnbOil96ejo1NTWzvr5Op3PGs8el+O3YsYMnn3ySDwrB3r17r1yq4Fmgrq6Ov/mbv2HXrl1TD7fvvvsujz32GC+99NLUdy+//DKvvPIKzzzzzDlt+P1+br/9dtxud8oJD+Ya9fX1/O0/f4++vgH8Hg8qrRaf046uoIziFesZ7Wwh5PdRsuYG3N3NaDLz0GfmoDeaEZCk5eBu1mRqSc/IpGPUzjtvvAZpZj79nf/ENTxA25G9pKVnIQAyiso58PQvCHQ34RHIKFy2FmNuIba+LloO7EQgFFGweCV5i1Yx0dfJcFsDwbFBxFo9Jas3obdmMdjawEh7I0IBCCRSltz2OeQqFZFAgBNvPofIOYI9nEBrsoJQcOa8qymdvAXLsA/1klWxCLFUhlAopvG91zAJwvSMOzEXlBCNRhGKxIhlMkQiMTkLlrHwprsQioQceOpXHH/lKYrXXE/hig1MdLcjlsoI+TzIVRo0FiuDjafQWqy0H96LxmghvbgcW183hsxcrnvwLxCIhLz6vW/QX3eYRbfew3UP/BmRQIB4LErjnu3Eo1GyKxdx6Plfk4jFQCDAoJThD0fJKK4gLT0T5/gIcZ+Lh//y6xSUV/H6U78hQxzj1ltvpaKigr1793Ji2MXaLbeQTCQ5VV/P4fd3oxbE0GvUqBNhLGYTf3jnPVTpOYj1FiLBICG/h9yyaiZ6O0lXSRgd6EOh1pJdUk5XVydVVVVsuese3n53B/XHjhByTiAiSb5BxaM//xnPPPc8HaP2GTVcVyxbyu/f3EH5ynW01J3kwJ5d6HOLKK5aiHd8GFtnM//693879SM2m23Du3btmhpnd3cPI94Qtr4OsnQalm/YxKH33mFxuoaBoWE6Ru0IlFreefkF0vOLqF67Cftgz4z+4/E4jz/51DljuFD5nnnM48OCx+MhLS2Nt95662Nx1GU2mPyN/dWvfnVZJWI8Hg/f/e53+d73vnfBF9Dz8h8N+Y8SF4BwOMzjjz/OO++8c1ny85jHPD4euBK/sXO+zXjyzevkW9tIJEIkEiGRSLBhwwaEQiGPP/444XCYnp4eXnzxRbZt2zbXNK8IJrdZ7tq1a2qbZU1NDafrG5AaLKy+5U6kBgv+YBDnyCD9DSeQqTSIpTKGmk8hSTNgG+xDpTeh0hvIKK5gvK8LlUrFg/ffx+qKIswqGUKBgMGmk8TjMbx2G6d3vsZg40mOvPQ4Y309+JMi4rEojqF+BhtP0Vt/DKlCSXpBKYlEgpG2RopWrEOqUBHweShesQGdNYv+xpPIlGqW3v451CYrPoedhp2v0nF4L/U7XsEzMYbRaEQgFCCWSdFn5pCWnkV21RKqN95CNBikp/YgAscoLfvfZaK3g5hMjbmwlJv+/O+59ev/zPI7P89YRwsTfZ2UrduMUCgk7Pcx3tuBSqen99Rx2vbvRK7RMtrVQvO+HQScE/TVH0MgFDDQeApjdgE3/On/R96C5ZSu2YTfZefYy09y9IXHcQz1kVVQzGh7E53HDuIcHmCsux17fxcZZVXkLVhG8coNJGNR5GoNcbEcoVyB3+/H47Tj93qIxhPYxkYI+gM0HT/E0aNH2bVrF5FIZEaN1O6meiZcHmQSMeu23ApyFYfaeukLi5DL5Nh62hhpOI7ZYmL19ZvYsvE6MjVSVpflT20brjIqMCul3HjnZ3C53Cg0Wkz6NG65627+7O//mfSCUt599106Ru1sve8R1m65ZarszrFjxzBl56NLzyLNkolKbyJv0Up0WXnkVC1BodbOuDentg0Pu3h2+3scP3GSTZs2nbeMzvRxJv1ufI4JJvp7MFgsU0miAoHAFC+N0Uz2wkeNcT8AALiiSURBVBVUb9lG4eLl3PD5LyPUGjh27BgALS0t5x3DZOmgjwI6Ozs/bApzgvlxzuNiKCoq+rApfKRwvtX/TzrmbTIT83PNTMzbYybm7XHlMefB7HvvvcfWrVv55je/CcCtt97K1q1bOX36NEqlkh/+8IecPn2abdu28Y1vfINbbrnlimYyHhwcvCqyZ8ufHSxMnivcs2cPuqxcVt/7CKVl5ay+9xH0GTlIFUqyq5ahTDNgKSyDZBKVRotCreHgC7+lfc9bvPvYj1BJJRQVFfHE00/z7pETFKy5EeJRmve+zVhPB6PtDYQDfmSJMKGxDvxuB1GEyNRaRFIZkXAQkVBEdtVS8pesRiKTERjq5Pirz0Asgk6lpP3I+/idNrIrFxOPR2mteQ+FUoXkjyujeO1E/D5kCiU2mw1dejZLbv8c1QVlrNj2ebwToxx64Tco0tIoWn09RpWUxTd/hoJlawgGA1gKSkkkkwhFYow5BeQvWY17bJg9v/5PWrc/x3v//W8MNtfhczow5xex/oE/p3LjrWx48C+xFJbgHRsi4vOw7M7PkZudSZo1E7lSTVbFQlQGI1Klms5jB2jc8xZKrY7sxWsJBfy0vP829qZjNLz3BnKtHoVWh9s2znB7E9F4jOob7kCfmYtYoWLLn32LW+7/Mnd8/f8SCIV56de/5Ct33EDfqI0RgZrfvLqdm267g/z8fIrS9bzwqx9Rv38XR7a/RLpRDySxu71c97kvU7FiLX/zg59RVlFJeaYJmc+GOuShfvd2VpYX8fDDD7No0SIWLVrEww8/zNpFVex6/glq3n2T+r3vkp+dyYKqSrKysjDmFNDR0TGV6An+t+xOIpHg+IH3OdXUwsjQAOllC0hGw5jkIpZUVbDiuo1T2/inB5NVCxddMpicXgt2sPEkrbvfxNbZzHBfD+88/WuUsSCtra1EOLPlPRLwo0vPQqU34hsdQCASodL/77a/6XVup4+ho6MjZZ+7krLABbcXfxhcrqb8/DivDJePKy607fxSSKXWaar1UedSL9XALVWOFotl1jrTtwTPBqlyTMUmqfaVil+m6sup6s12rvkgfc2l3lzaI9X+rgU7zqU9UtWbazt+UMx5MHvzzTezd+/ec/4tXrwYOPMW+Gc/+xnvvvsur7zyCl/84hdnnIf5oBgdHb0qsmfLX2jlqb29nYySKnIXLEehSSN3wXI0lgwEAgET/Z3EYzFsvR04hgfpPnUM10g/0WCAuGMMhUKGTnPmnMm7B48jzCpHnldB5YabCHtc1L/+e2QqDbd+/Z+5c3MF+77SSGV1MaacAtR6I5b8EtILysgoX8jybV/AlFtI0crrycnNwzM+QtDtoHDJaiRyOWpzBvqsXCyF5YjEEgI+NwgElKzeyOJ1myhefT3wx/M3mTlk+Nz84PGfsu2X/4oAUKQZACG23k7S0jPJqlyEKa8Yt93OcHsTIqkMmVpDMplkoq8LlcGMfbAPR+sphppPk15YhtZiJbtyCRqDGbXeiDmvmOyKxTiGB/FOjPDer35AjsxBuLMG22APIa8bkVjCaEczY51NyJQqbnzka+QvWMINf/INRGIJ4pCXZDxK0O2gvWYXh5//Da6RAcrXbianchEKjRpLXgnjfV3Yu5oY727DmJWH0+1BqNSQUb6QvCWryalewYgnwGOPPUYikcAxMU7Q58E9MU4sGsMxMY4uM4do8ExSM7lCQcWKtXz+ns/yVw98jhyViC/ctoUVy5ayd+/eqZX7ySzDD9x5M5UG+dQqbcBpn1oBLSkpmVolBaa+N5lMBH0eBppOIYoEGGqph2SS7KxsdFot3S2n6enpobGxkaGhoalg0mefmFGH9nyYzitdI+cf//or/Ovf/y1LM3RoiOEXyfGqzDQ3NbH9+afJKyjCNzrASHcHgfFh2k8dI2YfnSrNNX2ld/oYZrvF+Gr682wxV3PL1ZC/Wm1/UsY5j3ORSkCVahA213pz2ZfRaJy1ztnnlS8X14I9UvHLVH15LueAueY4l3ZMFdfCtf643iPXgj2m4yOVAGouMP2g8pWUPVv+QitPAO7xEaLhEOJkklg4hGdiFLFEStHy60hLz8A9NsKxV57E1tOGQqcnKZDQ7/IS6usn6XdzMsOIPSok15qLTKVGpDUw0HyascbjlCxaxWLBQe6Xf5+n+Ts0pQJCPe2Ub7yV1n3v4JkYJatiMfFIhIDHhdacjs2QjVw7gCgRI7e0HF35IiJeN6P2cQQiMWnmdFoP7CB74UqW3PZZpAoVGr8P+0APfscYad3tfGfXGxhdDm7yudl7x+cY1RkQCMWMdjYRj8YI2sfoPnGImECEbaCXHb/4f5jzixnvasVrH0eqUGLKKyYgl2MtqaRg6Wr6Tx3BOTJALBpB/scgZ6Kvk6DbQXn+UirTXfxk83v8a005Lz/7PxQsW8twWyMikYjVn36AoM/HUFsjeeUVtB88gMpgIpSWQUaZlvaaXYx3NLHopk+hUGsJuB0oFAoy8otpPn4YsVSKLyFCLJURdjsQCgQYc4tYfMtnUekNRAIBxvu72b59O4aCcso334XMM04ibwFH3n+HosEcxh1ObrrnAcwm01SwlrX0Zqqrq6moqJh5ZvRYHSXHjk+dGZ2UETz5FLuefwJDdj6njh6ixGrktttuw+58Ziphk32gB3UySkdHB8ULFrN49bozwXU4wnDTCWrCHgY7Wgn6PORXLODpN95FGQviF8mJRiJkVS66rHqyk7yGhoamzt2KRCL8Jxq4+b5HEIslGPOK2fni00RCQWK2IUZcE6gXLiHY3cpNa5dNJQGYXud2+pnZ2267LWWfu5KyqWCu5parIX+12v6kjHMe5+JyztheCZ0PQy/VvlLJMpzK0YvzJX67HMy1PVJBKn6Zqi/P5Rww1xzn0o6p4lq41h/Xe+RasMd0fOKC2bmC1WrFfvQUQ0NDhMMRZDIp9oEeLBYLbd1t7Pn1j1hcnEvdrl3YejtZcuvd5C9ZhUQmQ5+ZS//pY3Qe2IFIriKzohxrUTm2/m5GWuvYuXMnZJagNhiRKlRIZDL8Xg+xWIwl8V3cF2vjJ2Nfoi6aRW/9axiz8+g9cQiJTIEpt5CR9gbUegNpGTkYswvoOXWEpbfdS8u+d+g8fZJRu4vqLZ9CbbYScDkYaKknEYuhUGmw9XcTcDlR6vQo0vQslil4uqEWYzSCWyLlW7d9lnG1hpBtDIlKg1AkwtbXyb5nn0alN1K95U5GO5uxDXQz2tFI2brNGLMLsA/1YszJxzncT0ZpFWKpHGNuEd11x9jzmx+RUVLFRG8nI+1NWIx6VhWG+bfr63gt8iV6Cpaisx2g70QNMq2B5Z9+AIlQQDwBE30dTHR3EY9FyFu8ErXOSCQUZLStEalYiNZsYbyvC8/EGDUv/Jac4jJGOpvxPPUrsisWMtzWRMA+hiJNj0pnQG00IVepUabpUemNjHU3YFl2PZbcArwtdqqWL2O8uw2DMEBBaR4n3n2V04f2EfHYKc0wTWUSjsfjUyv3EqmUUDDIcz/9d5K/+x1r1qyZSsA0WQt2dHQU86ItwJlsjyuWLWUFZ16aHOkN4RXKkCgNtBw5hMftpnrpclZdfwMH334NXciBR63kK9/5F+QKBdFIhHee/jWaZPScYPLszJyXwtkvbVYsX469rxNdyMHD3zlzlOBMUqkbZiSVOnts1qU3z9eqncc85jGPecxjHvOYx6wwH8xeJUwvk2ItqWK0o4lMpQij0YjQ5mWitxObJMZEby/RaAS/x8FYZzMhnxe5WoPfbSfospNZvYyNX/prRGIJiViMd37+PYaO70MpUnHizRcw5RZh6+/CPTbMZ5b4+JcN4/zN7tX0pqUz1PI2o+3NiMVilDoj2ZWLScSiREMhmve9S96iVdh6O9Ga0slfspr+hhP01B8hkUjQdmg3hUvXMNLRRNDtRKPV0X3iEEkgvaiMvvpjKI++z/P9PRhjURwSKV9Zto4Wj5viePxMNkKFivHudnRmLQptGsvu/Dx6kwW1KZ3wnu2IRGICHjeusSFU2jTsAz0ISDLYXEfJ6k3INWkUr1xP4+7t2Pt7SAoEBL1uli2T8v/WH+C3I5+hJrwQ1/gQMrUWbBLURgthnwdFeia+8RECbjcjDSfQ5RcTC/hApSYe8qMyWWje8zZer4+CJWuwDfbSfeoYIZcdhUaLViEjPtpDLOjjnn/6Ma01O+lubmCg8RQaoxmf04Z9sBez2Ux/RwuZ3V1oYjH6eroZH+hh8+b1HD9VR++oA6U+yEBzPUPZOcizS3llzwGc/d1kVC1FKBIRj8fZ/cbLTAQiNDtCdL7x7nlXac9Zyf1jBuOAWMEt9z2CUCQiKFZw8O1XsftDhN0OMpUiVq7cjMIWQP7HkgkSqRRTbiGL0zVkZWV9oGDSarXiOFZHNBJBIpUSi0WJ+1zceuetU6uwF8Lk2C4lN495zGMe85jHPOYxj3mcD3N+ZvbDxq5du66K7Nny7e3tmPKKueehh1lcUco9Dz2MKa/4zNaaeJx4NIJILCYejZBMJhlqPs3pna/jsY1xeufrDDWfRiAQoNKbCQeDJLvrCAUDKNOMiMVikskEnvERBppP4RkfQS8P8493Bvj/Dl6P+KbvU5WbyYYH/5z8JasYqD9OMhFHJJGiz8qjZM0mQn4fXccPoDaaKbWkMdx2Gs/ECNr0LNY/+FdoDGaCHhfVN95OTvVS7GNDqPRGsioXk6+RklmxiL9z2jHGotikMp78yTNU/Ntj5C9ZRcfRfQx3tND43muIpVKM2flkVy0lEgoS8XuRSGVkli1Am56BITOXkNdD0OejbN2NXLd2DRqTleb332ait4O++lqkCiXW4jJioSAP3pbDr78wwt+9m8cztSqMMR8Bj4uJ/m7MOfn4HBOE/D7sg70ko1HGulpIOkdwD/Zi6+0g2HCArhOHcY0OUbr2BgqXrEYqk3DX3/0ArcFE3DnB0g2bWXD9VipLiilfuR7X+DAaUzphn5e2g7toPbCL9gM7CTnP1OKz9/fQd7qWoMdNf+NJ/PYJ+vr6GA0l+fK//pLNn/siCzbfTukN23AnRWjKl6E2pXPw/T28+dxT9LQ2MzJhZ9GmW1h/8x3nTcb0hz/8gbbhCUqXr0Eqk1O6fA1twxMcO3ZsamXUbnegNVlYfuOtLMi18qW/+Rbm/BLC4fB5z6dmZWVNBZLny2B8OZieGOrQe++w45nfnrPCO1c+d6Xbni0+KuNMRf5qtf1JGec8zsXZNeOvls6HoTeXfU3mGZgN3G53Sn1dC/ZIxS9T9eW5nAPmmuNc2jFVXAvX+uN6j1wL9piOT9zK7OrVq1OWnV6X83y1OKfLj46OYsotpHTB4qnvbKMjCDw24rEolsIyQqY8LIVxnCODaIwWCpavQ6lNQ6HV4Robwum2YevvxDM2RFJjxjs2hK2/k9zcXHptbtxjI2jM6XgnxnCOB9j4oyzSr1vAWr2JuFSKRqVBY7IQ8jjx2ifQGs147eOI5UrEMiXRoJe2ml30yWUE/AFi0QiFC5chUyhIL6kkHosgEIkRSWQIAHNBKYasPCKJGEahiH/fdDuBN5/jxdvvJRn0Ez24G11GDoquVtr3v0PGotXc9JVv4h0bgLoTiMUSxgd7kWsNRMMhtGYr1qIK+htqkSlUGDLzEMmkLM0sZ/9Tv6D72D60mXl85h9/TPfJQyAQsuUmJb91ruZoxEv+0pVE0tLI1+WcSWDl8WAf7CURj2PMLcQ52IvXNoo2TUV3awNJkQRFxQI8tj7co8Os/fwjlC2/jkMv/JZELEre4pVEBtsZaDmNOiMPrVLBxFAPWksm+VkZtBhNOAZ6iIUCOEcGiUWjaEoWkSHS4BwewKHTYbZY0V2/GcdIJ+lFFdjtEzTVHkVhsBAKBjh26ggV624gkV5EJgpq9u6hu/k0MnM2Zp0Ws8mEQCiYSsY0GWyqVCp6Bwaxhw9gyM6jrr6ejpOH6VJKEelMLFl7PT6fD4lCTcgxTuH6dWRlZdGXU4BSqZwKOM+3pXg2PgGwbt26qf9fznbhD+JzV1L+g4zzw+ZyNeXnx3lluHxccfDgwZTK4+n1+jnRmWs9g8GQcl+BQGDWeg0NDdxxxx2z0kn1zGyqdkzFJqn2lYpfpurLqerNdq75IH3Npd5c2iPV/q4FO86lPVLVm2s7flB84oLZ2Uzy02UnS+1cKGHP2fIXOjNbV1eHpbCUtfd+GblGS77HhWOwB6FYikQmJx6LoUzTY8orQuYZB4GAwy/8FolCSTQYQCQQUFFRQXhgAl1uEQG3E2tRKXKJCGXQyWDTKep3vIZYIiEWjTLYdAqxTI7XNs7J7X/AWlLJQONJgm4HsUiYaDiEPisXr7sd78QoxYuWMDI6gWd8mLxFq1BodDhHBohHY4S8bqyllUhlciLhEMfCT3GXQEBGexOlRgvG3EJG2hoZ72rDYrFgzS/G3t+JfbCfSMBD24GdyGQSnDY7IrEEa2kVA02niAT9REIBlGlpCERiVDIFIpEYEGLOLaL90B7aDu0iu3IJNeq7GOo9SfEqKWKpnOHuDuRqLVkVi+g+uh+V3kjFxlvQmiy484oJeT34IgEyKxaz4aG/RKpUUXLdzex74qd0HtxHQdUytJZMhlobGO9qYcWypXR0diHTpCGSyRABde++yoDZRMBlp7CyGpUmDa02jaJVG8gz6cmsXsrp/buIe52UFhXQPtqDSCTi+IHdyKy5oErj5Nt/QGfNwZRbQMuhvYy0NbL53ocQC0DlHEApiFBdUYFAKDhvMqZYLIbH5+eWP38IkUTMS6f/E39CRDK3iv72Jv7xT+9n4x2fpq72GAuXLKWoatGMpFObN2++YMA52wefs+UvtV04VZ+70vJXs+15LnPf9keNy8cVPp8vJT2xePaPF6nozLXeXHNMpYRNqrkHrgU7puKXqfryXOrNc7wyevMcr4zeXHP8oPjEbTNOFRcqtXOhTINnzsx28PiPf8Ce93by+I9/wERvBxMTE2gtGWhNZsRSKSGvC2NOASGPk0goSDQUxO+0M9rRjF6vJ/jHjML6jBwEIjFB+zhVVVUk4zG8DjtacwZeh51kPIZSqcRrG2OkvYFYJMxIewNe2xg6gwmN0cyCrXeRlp5F+XU3YczJI7O0ik2PfJ2i5etZc+/D6DNz2fP739K8dzujHS04h/tp3rsdgUBAOOjHMzHK0T88Tsv+nRz9w+N4JkZR6YzIlSqMuYXoM7Ix55cgVShYsmQJzTW7adz3HqFQkIDXS3/DCVwtJwn5PWx8+GvkL1xO5aZbkUjl+Ozj1L/7Cv2na6l792WcIwMISNBTd4SeuiOIxFJ6644y3NaMMk3PaEczIZ8HjSkdgUhM17H9OId6MecVU7xyA+lFFeQuXEZW1WIiwSB5i1agNaWj1htRaLRklFXjHumju7aG7uMHaH3/bTyD3XQ01jM+2E/IPoZCpSIc8CJRKpGnGVBqdGQVlFK8YCmlqzagSjOgUqsI+byE4tDd28fLv3+K2kM1uARy3PYJOo/XYOvvQiAUkVFahXt8BEQSshYsp7utmd6mU6xdu5YVZUXsfO53F9yqq1QqySsqpa+tiYNvv47P7+eGL/4la277NH/6r79ArjMicw6xtiwfQn6O7tk5o53JgHPz5s0pbymexzzm8clDRkYGL7zwAvX19QAkEgnGx8dJJBJTMl6vd0at0Xg8fs42UpfLNSMwi0Qi5wTKDoeDyB+PQ8CZQM7lcs2QWblyJfF4fOqz3+/H6/VOfT4fv0AgMINfLBY7L7/pfUciERwOx0X5RSKRc5LmTUxMEIvFLshPJBLhcrkuar8L8Tvbfmfz8/l8M7IEn89+Z/MLhUKXtJ/X651RSudy+MVisams95O4nOu7f//+GfdFZ2cnjY2NM9rdsWPHjDE0NjbS2dk5ww5nb3esra2dUQPTZrNRU1MzQ6ampgabzTb1eXBwkNra2hkyu3btmuc3z2+e3xXit2PHDr761a/yQXHNB7MZGRl89atfZceOHcClL2RnZ+dlX8jXX3996vPo6ChatYpI4MykL5FK0Zks1NXVTcl0dnZOXcj29naMuUVsuuEGMpViNt9xF8bcIkwmE1niCF3HDjDQcALHyCBmpYzCwiIc/d2Md7cz0nSCRcUF2Gw2tJm53PDw11m7dBFbPv8ltJm5nDx5EpFYxPIli0kvqSR/2TqcTgdSqZT8skrW3PMlCgsL2bDtHtZffwNJQRKV3kg06CdDFEapkANC1CYLQtcY5rgPgUCIzppF16nDVOWkU7Z2E0KxGH12HrnpJgoLCwkF/Ay3NmCv3Ys+MEEo4CceDVOwdA1pUQ/B/jZEQrDkFdPR0cGyJYsoWrWBwmVrKV69kdWrVxGJRDDnFTPR14m7vZ5oVx1aaybxWJzRrjY0Iy24+zoIup3o5BKyzEZKC/IoW38jyjQDh599FGHXKfpOHKJh52sI7EOEOuswqRXINWmEvB4IBxD21CORygl6XMjVGuSuUfDaEThHiEXCuDqbqMrPJtxxikxhiHULy9l8443orVkIZQqEIhEi9xiF+fks2PIpNt7+KVbc/ClErmFGezvpbThB1OtCFPaT8LkhESenahHl193EypWrIBFn5W13U7hwGfpEkFXXXY8gESfi97Fi6zZWluZjLVuIRKbAYrGwsLqKW9etYFmmjgfuvJk7bruVw4cPT91bIpEIsyROoUVHwmMns6gcSdCD0DmCXKkkf+Fy1Go1a9es5t6bb6BQkeSBO29mw/p1M164nM8/du3adVUnuultX2qia2trm9VEPPn95fB7/fXXr+oPxSSXy7HfiRMnZvVDMdn25fJra2s7h98kPuj1nWzrcuy3f//+Wf3QzmZ+rqmpmTGuK/1D29nZecV+aK9ljIyMcO+9904FJkKhEIvFglD4v48PGo0GlUo19VkkEp1JAjgNOp0OxR+T0AFIpdJz3uLLZDKkf8yMDqBQKM4p4XLs2LEZL+NUKtUMLufjp1QqZ/ATi8WYzeYZAaROp5vRt1QqPWf7rMFgOEfm7BfaZrN5xqqjSqVCo9FM9RWPx9HpdBe13yQ/gOzs7Cl+Z9vvbH5qtRqXy0UoFALOb7+z+cnlcjQaDXAm8L7Q9ZXJZOflN4mz+YnF4qkXIJOYbj+/339efhs2bJhxXxQXF8/Y9dPb28vWrVtnjKG6upri4uIZdti8efPU587OTpYvXz5lSwCTycT69etn9L1+/XpMJtPU51AodE6pkc2bN1+Un1gsnjU/OGO/2fLLzs4+x36Xy6+3t3dW/JYvXz51X82G3wex3+T8frn8srOzp3Rmy29Sb7bXt7Ozc1b8JpGK/To7O1O6/+Ry+az5uVyulK5vfn7+rPht3bqVRx99lA+Ka36b8cjICI8++iharRb4X0NNx3RDhkKhC17I6TCZTBQVFU19tlqteI7VIVWemfSjkQgu2zh33Pm/W0FDodBUu0NDQ7ijSUTSNCSFmdiCftzRJJmZmby9532yhu2kWTLwOe2QhMobbiPNlE7z+2+jy8rH43bh8XgoLKtGKEjiicQQGbRkllVxXexXFJqq6ddnodbp6dn5BlpLJqMCCVFhnK7jB1m6YjUBRRpj4QQCoYiQ10NW+SLi8Ri6SISQ10UyEUdZUA0TfeiN2ThHB0nGohw73Uy5NhNTXjETvZ1kHj7IXw718UWDBb9SiTgjnWP1jXjGhlGbrdgHutGa10EiBj4PQbeD4ZFx5OUrWbB8PQKBAGNOIQda6hkaG0cw3E925WJsIQEak5VYMIhaZyARjTDqcGMfGcaQkU22ScaYX4x2wRpMOf8/e2ce3lZ15v+PLFm25X2Xd8e7E2cP2ReyQBKSECg7YW8LtEynnbZDO21pfwwtTGlnpqXLQFsatkChFAgkEENCIHF2h2x2vO+7Lcuydmv9/WGsWl4l2VJi+36eJw9Ift9zvu97zz33Ht1zz5nFrAXL+OT5Z3n/zdeITM8luWARxl4lzUo113zlQaIvnKH0dBGn971FdFIaHcf/Sm97C9ds3Mqpzw9R0/o75s2fT1lZOU0l51l/9+3cddddmM1mfvH7P5O3aj1BZgsieQZlRz9BpI+ltUdNuN7MyoUF+AcEcLSqFHtLPSZFO+1WA32JqZSVl5O/8Brmrd+K1mThzN5W7CYVPXWVzN24DaNcTuW50+g0WlLz5iANDkaFhCB/MfOWLCUkJGTERT7i4+Md/x8XF8espAQuH/8MkclI1RdnSL/nEeLnL6bPYKC9qpSbbt3Oddf1b98TGBg44rTfkc4PuVzuUkc39PwYqaMbTHJyMsnJyU6DhaHlDq4XwGw2j3n+DtU3cHF1RV9mZuawjnhw5+2KvrH6l4E4XclfQEAAixcvdvputPzBP+N0Vd/gnI+Vv9H0jXV8BzZFdyV/UVFRI17IBuNp/7x69WqnOMfK32j6xjq+RqORzZs3s2LFCt544w0E3GPwkz1v+lwJP1/WNXgQ6e26pkI+Bg+ovOkzET9f1uVLP1/mw9P6pkIePWUqHGtft5EBpvxg1l3c2QZksG1+fj7Zp8+MuS/nYHutVkvphXNsWrYJWVgEerWK0guvIla2EpeeTWBIGM2XzxMUEk7ums3olV0k5szBP1BGaFwCNcXHSElJoersCaIzZxMel0ZPWwu3y15hQ3Ivj34WwOKvLKGnpYHgyCgSc+ei6WpDFhFNZ205TV0KAmQGuhvr0GvUhJj6OPPea8SmZ9FaWUqfQU+fQcehP/2KhNwC2ireorejlfDoWFJmLyAkOhatspNFxUf5z5YG/O12/ksi4eDOXUTKk5BlL+TMe68SFh6B1dRH6ecfEZM8i+7mOkRWM/5BMgxqFQEhIfgHBGE29WFQq7DbbKgVnZQd/ZioxFQqig5iMuhIzp1La2UJp2srCE9I5ke3hXJt4EG+Wf5dwA+jTkdgSCjhcQmYwsOQBMm49p5HaLhwBnF7K9puBSGJqeDnR9nRj/H3D8Ck1yP2lxAXGUZsZDj1FWV8oekFm5lN61bzk5/8hJqaGl555RXsoVHkLLiGqrISRH5i0hcux2Y1k5Mvp/zIARqqKrj2ui2UfX6AJSsWOgafL730EhKbhVXbbsFss9Ot6kDbo2Be/hzKLl5g32+eZFbBQqpOfIa/RIzYlIuhM4OI+ERm5+Zw7pMWkpKSRm2DJpOJ/fv3U1VVRWZmJncsmE97ezuK6lLOvP8GzeWXHNs+bdu2bcLt3Bt4U4sQ5+TYe6vsmRKnwHAGnvZ52+dK+Hlal1qtdtuvpqbGbZ+hT8Vdxdf58ARPzktPz2Vf9gG+1ujLPHrKVDjW07WNTIV8DGbGDWY9xZVVWwejUCiw22yUHy0k+stBnt1mo6+vD01vO7KwCOau3kTJsU+5dHAvwRFRlBzah7qzDUlAIHazkZj4aPTdShrOnyI+M5td0hdYkNrJpqf9EWcHYH/3Vfr0eiTSAER+IvwkEoJCw0Ek4sKBdwkMDkFktyKymOkz6IlMSKantZG4tCyqTx0hMiGZudffhF7VzZwN2zHsfR2j3UJnbQUhMfEsP3OUn5w60r8IUkAA767dzPJrtyD2l5KYN4/W8gtUnzxMSGwCAcGh6FTdiKUBGLV9SCQSOuoq+eg3TxIWHom6V0VHbSU2qxURdqxmM1pVN1aLGYNGTWPFJQICg5AFBvGva3u4MfQov+78Fzrqa4lMzaKntYk+g56W8ktEBgdjCwzkzHuvIRKJaSq/RNqCZYhsVnRKBeHyJOIz8+israCj/BKGust8+747sdlsNDQ0kJ2dzZYtW3j19dc5W1lHTUM7epGElrZWlJ0dBIaF09vRSsaSlQRHRtNSdp7De/+OSdHKNbmZTot+PfDAA1Q+/Ss+e2s3sYkpVJRcoPHSWULtJmIjw0kNDyIt3I/bvv0N0tPTeePvb6MpP0ts/jzOH9xHTkLMsHeuBjCZTDzy2L/Qqrciz57DoXc+JFEm5oU//J7169fzwgsvUFpaytZr5vLII484TX/zJlqtdkYsjiPEOb2YKXFONkLOnLFYLB4vXjRdEXLijNDXOCPkwxkhH5PPlH9n1l0Gv6vlru14i+gMtvfz8yM6Lp75y1YTESJj/rLVRMfFExYWRmhMPBsf/j4LCvL5yk/+B6vJRNnnH2Ixm5m94QbC4hIwGY10dnaSt/Z6spet4uHo11mWoOCHlQ/RrPLH1GckIDgUu82KurMVs6kPWVgkakU7isZaRCJQd3XQ097Gkq23YDYaqDj+KdKgYOovnMLcZyAoPJI+vZbwuATMJiPBUdH4BQbT29XGgv1v8cSJw4iBCxFR3JeVT5epD7PBgNXUh81mxaBRo1MqCAwOZfGOO1i161EW33gnUlkwdrEEnVKBuqudlDB/1F1taLo7EYlEhMuTWHrL/SzZcSdLbrqH4MhoetuaSJ23mJ/eDLelX+CnDY9yuScGP39/Ln78Hm1VpbSWnQeRHZvNhp/dRtaCZURExSAW+1F14lNKD+0jOmUWN/77L7j2vm+y7r5vkliwiHqdlTOV9VRU1TB37lyys7MpLy/n4+NniZ2/itmbdqDv7aHw5RcoO3WU8x++jX9QEPGpmYRHx2JUqwjs03DvjVucBrLQ/yvUzuvWI9YqsbdW0VtbRmZWDql5BQSGhLBoyWIef/xxbr31VpYsWcKzT/+Cb917Jwu/fDd2aHmD2b9/P616K3c8/nM23XEfdzz+c1r1Vj744AP2vPE32vQWstZspk1vYc8bf3NaFGUi7Xw8jh075pa9N7UIcU7cXohzcrRMVzzdRmK6TjMeuuiSN+sCz1YmttvtHtXlqUZPcuJpXZ6cl56ey576udvXTKQuX/r5Mh+e1jcV8ujLfHjq5+s8TpQZN5g9dOiQV2yH2i9duhS7VkVl8THMfQYqi49h16qIi4sjIXs2VnMfVpMJu81C6rzFhMUlsfmbPyCtYBHr7vsmcVn5GI1GOqrL+WrSR2isqexJeZeS0lYyMzNR1Fdx+fCH6NUqGi+d5cKBd+hpbaD61BEScuey7cabuO6x/0CeMwexH6TMWciCrbcSnZLB/M03E5uSQXdjHYbeHgJaK9AqOjBq1MSlZ/GV6HieqSnHDzgXl8Af/v1pIhYup7u5nhN/30334bc58be/0FFTjv3LwWnKnEXEzcomde4SohLT0Gs0RCQkk7lkNYHBYWQsWUlMyixk4VFEJaYhCwsnNEZOcHgEUYmpSKQB/GxrD+GBETzR8A0OH6+n7uJZjGoVUYmpJOfPY+HWW0jMmUd8fDxBRjWf73keSWctiqZ6rH1GtN2dJHw5VVvXq8Jmh+zl1xIeE098wTUcK6vhSPE5Xn3/AP/7m98iiZKTs3ApS669nuxFK1A019FYcpaO2gp03V30HHufoj1/oretgYcffpj8/HzKyso4ePAgJSUlWK1WxGIxD9x3LzuuXYXUZiY+KYnv/PzX3HL/V7njm9+jpqPHaYGQgR9E7Hb7uKsKV1VVIc+eQ0BQEN0XjhMQFIQ8ew5FRUXjrqztzXbuLr465ya7bHe5WuL0xN5bZc+UOAWGM3gxLm/6XAk/X9Y10noK49Hb2+tRXVMhH56cl56ey77sA3yt0Zd59JSpcKynaxuZCvkYzIwbzG7cuNErtkPtBz+xU9WWIdYq2XndetLT0+msq8JiNmOzWrCYzbRXlxEcHklN8TH0GjU1xceQSKTEx8fTXVvO068081Z9Ii8/80u6a8tZs2YNwRFRmPoMKJvrEfmJUXe0canwPYLCo5hz7VaM8ixk4ZEk5M6hqvQSEQnJSAOD0HR3oO7qICY9C6NGRVPpF1ysaaT+3ClCY+Uk5xbQtX4bZwoWcTY5nV/c+TUs4ZEkzl6ISOxHw/lTHDt5htqzx9Equ/Az96Hp6kCj6ETf20NvRxua7g6sfQbkmXkEhoXjHxhEcEQMybMXEBkbi7ZHQdysbKKSUolNz0Kr7GJhuh9z+wqpnf1NwlY8hJ9EQt3pz+hpbiQuI5+g8Ci6WxrprKtk4cKFfLj3XdbPyeSLzz4mPCKSW7/5feTpGbRWXMJqMWMx9+EfGERnfRVhkVH0Gs2kzVuMqs9KzpIVdPRqUHW2YbdaMfaZyFu7mdCwcBbMW0BGRhZ1xcc49HEhTeeOsSQ/mx07drD75Vd49f0DnG1V8er7B9j98iuYTCZeeuVVPvjsGBqzjc5eHQfffxur1Yq/VEpUcrpjwZyhbcVqtVJSUuI0OB5MdnY2bZWlKJXdiNPmoFR201ZZSnh4OFHJ6fh/Oa14pHq82c7dxVfn3GSX7S5XS5ye2Hur7JkSp8BwBi+25k2fK+Hny7pOnz7ttk94eLhHdU2FfHhyXnp6LvuyD/C1Rl/m0VOmwrGerm1kKuRjMDPuJQd33utw9x2QwfYDT+xiv1y8Jzt7Idu2bePxxx+npewCh/70a1YsnMeJve/RVPoFKQWLyVt9HSKxmPiMXJovn2duTg6pGVY+KzqO6fMSpAEBXLt6JTabDVl4JPNvuI3QqBjUXR2c+sfLdFVfJio9m4ikVMR+fqg722mvKkMSHEJ3cz1hcQlEyJMwaNS0VZViNvWhV/Vgt9iw22yIsGO2WFF2tLL7X39Cd00FnW3NyEwmZGHhpM1fSvWpz1F1tSMNDCI2LYtEqZXqlgZOvr2b0Oh41F3tdDc3kjArB42yk/iMXAK66khKmUPViU9JTElDqTdx8u+7iU7NQNlYh8mg43wjfKfhCYL7rChbT9JVX02yPJ5ug5bqk4eIkCfT29GKRa9h7ty5/O2tv0N4LHPXXc+58xc4euB9CAigo+wLPvzNz4iblUNHdTmBwaGEx8npVnZTca6YWTl5HDt6FJtEiqGjiU/f+AuBkXE011Uj9YOtd+yi7NIFTh0/Tqw8gUC7mUx5NK+88gpna5pYf+s9mMwW0mbP44vCvbz//vvs/eQwMVmzCQ4NJEht5sSRz8mft4C07DyUzfXIF/1zxWur1UpZWRktLS2cOnMWrcif6JRZKE+fJ/v0Gadpx1u2bOF/f/9HXv35D0jIm0db+UUCDb1s+7dHeavwMJWXzqPu6SEsMhJFYy3yRTf4pJ27i6/Oucku212uljg9sfdW2TMlToHhDN7axZs+V8LPl3UN/ZHTFUQikUd1TYV8eHJeenou+7IP8LVGX+bRU6bCsZ6ubWQq5MOp3itS6wzAarXyymt7qGrvJio5g5NlNXT37OHChQvIImOwWS3oVN3YrBYCg8MJCg1H0VRLaIwcjaKdoLAIRCIRZ85fJGZWDtHJ6XQ313Pm/EUyEuKInZVNYm4BYrGE5ssXiEmdRUxKOu015bz31HdJyp9P46ViFA21zFq4lJaKUswGHUlzFtFS+gX6nm7S5i1h4bbbMBkMiEQiaoqPoelRYrPZOb3vLWLSsuisq0KnVBAWHUtPVzsFm25EIhYTFpfIF/veJCQkCF1lLZ11lRjUKjTdnehVCvIXL0OtVnP2/b8xPyOZi6dO4ycSEZecRm9NLWJpANquDgJkMqwmE0FhUZz+8F1EIj/UinbCYuTEpaQSrFah7e6AkBDCZQGkLFpMb28vtd0aNu/6KicOHaCmqY1lN95J1bGDRCako9P20n75PBJ/f8R+UHO+mMaGOgqWrmH9bfdgNpn423/9iHnZsRi1StQqBT319axYtx4/sYSunl7W3XoPC/JzaWhs5OO3XiW4rhlTUARHThWTmpWDUdOOqs/G0aNH8QuLYsNdX6On5ASR12zig+f/mw9ef5nsrCynFa+tViu7X36FqvZuTPhxuayG62+/l2uWLMFiMVO450XKysocq8HV1NSweM16THYRjdWVXLN4MVKRHbFYjKKhmrfKKpBnz3GsZpyTk3Mlm7yAgICAgICAgICAT5lx04wH70s4mbZD7cvKyqho7SJnyQqkAYHkLFlBRWsXnZ2dxKSks/LOh4lJzWTlnQ8TkZCEvldJZGIKYCcyMQWzQc+xY8fwD49h8Y27WLr2WhbfuAv/8BgqKyvR9/YgDQhyDIjl2QUEyoIIi0+m8dIXdJ76hOayC0QkpaJuriUoPIKA4FDaqy5jMujJWLyKpPwFRCWlE44Ji6kPu81KU8kZVO3NBEdEI5b4E5mQQnz2bHpaG8FqRd3RSqTYRuPF04iAy5cvEzcrh3X3/wur7n6Etfc9RmxqJmWnjtLb1U54fCIAYbEJqBVdKLva0KsUGJWdhISGYNNr6NOpUXe1Y+4zkp4YT2BwGIGhYWTMW8S1t91HSlYukUFSVq9dR1pqCn5+fkQkptBUW0VnbSXRicmoOloJi42ntbKU5DmLWXXvY+Su346qpZ44u47U5BSScvJo/OIYDRWlpGfnsX3rFr779Qd44MbNbF62ED+TgWOffIixz4zVqOfCsc9oaOsgf+V64tNmYbFaCAiLIDQikrTcOTTUVGIymQiOjEEkFgMiEhMSkSelkBYeNGyBp7KyMse7rgmxMcxZvQF1n4UuhWLEqcItLS00t7bRYzCRMXchPQYTza1tnDlzhtj0bB78tx+w4brrefDffkBsejaVlZU+aefu4qtzbrLLdperJU5P7L1V9kyJU2A4Go3GJz5Xws+XdQ3e795V9Hq9R3VNhXx4cl56ei77sg/wtUZf5tFTpsKxnq5tZCrkYzAzbjAbGBjoFduh9i0tLdQ3NXPs6FFaVBqOHT1KfVMzgYGB6FRKLhS+Q0NTMxcK38FmtdLd3MDlD3YzT/FXzrz7GsrWRtra2kjMnUv+uuuJmZVH/rrrScydS3d3N6q2ZkoO76f61Od01lbQ296MVBaGor6KlIKFZK7YSGrBYrrqKomKjiMgSMa1D36bnJXrKdh0I6Ex8Sib61i/701CRH4EhoShamumt66C9qpSpIHBhMUlolMpqT19FJu5D7OpjzkbbiAmK5+CjTuw03/RjM/KJygsAkNvD7KwSOKz8tGpujFoeunTa7GERGM19aHr6ULXXMf8pavInbeIYKmUiJh4IuITCY2KJi49B1mMnKjkdHQ93dgMOvp0GvpE/rQolBw68BGKxhoWLFjAkX3v8darL9PapaSlphKdsgupxYTFbKK55Au66irpqC7FarGQkZFBepKcMMxoWxsIsRkJ8we5XE5VVRUXLlygYM5s7rphE4vS5DSUnqNNqabXLqZHa+DEgffRaXXYTEZKDn/I2U/2U/T2y4SFBDN37lws3e1UnjuNHn8qz59BpFHywAMPDFvgqb293fGua1hUNKrWJvyDgtFqtZhNpv4pyXK5w16r1dLS0UnWum3E5y0ga902Wjo6aW1tJTplFklJSWRk9P83OmWW00DYm+3c3ZsrX51zk132VI3TXXshzsnRMl1pbm7mzTff5MKFC0D/KrSdnZ1Oq9FqNBp0Op3js9VqHTZYUalUGAwGx2eTyYRWq3Wy0el0mEwmx2eDwYBKpXKyWbp0qdPUW51Oh9FodHweSZ9er3fSZ7FY6OrqcprqqlKpnOo2mUzDVulVKpVOW6CZTKZhW6t1dXU5reip0+nQaDSOusRiMSqVasz8DegD6Ovrc+gbmr+h+rRaLREREY66RsrfUH1Go9FxrPz8/EY9vgM6huoDCA4OHqbPYrEwf/58p7qVSqUjx35+fiPqO3LkiFO7qK6udrpB9vf3p7Cw0CmGkpISqqurnfJw8OBBx+fAwECKi4tpbm52fKdQKCgqKnKqu6ioyGlhKq1WS3FxsZPNwYMHx9RnsVhQqVRu6QNoampyW19zczNNTU1u6yssLMTf398tfcXFxU7luqqvuLjYqe91VZ/FYnH0wa7qa25udvi4ow/+2d+7o2/Azx190H8tclffQF3u6oP+e0939EF/v+OuPuh/aOOOvsLCQh599FEmypQfzCYkJPDoo49SWFgIjHwgBze0rKwslw6kQqEYtnDPeAcyKyvL0dD0ej1qrY7FazaQlZHJ6lvvR63VERERwbz0JEKjYghIyyMkKoakqDDm583iT5tPsSqyjLwlS1m5ZDEBoeHoe5WYDHqsWhWm9gb0vUoCgsOIiYsnyqymtaIEm9XK0lvuRy61k7dwCTu+/wvybrqfG7/xPVatXUf5udOERMXQeLGYSF0XXZUXiUxI5jtnjpJ7eC/X/f1FLv/jFTTdnSQXLGHj+vVUn/qM2rNFmI16oqTgp1YQGBKGVtGFwS7Gv7kMWXgUdrudptIv0JWeRGxUU3fuJE2l54iOjGbj9ZtJnbMQoyQIm8XM6jVrSS2Yz79mvM38yGbENjORQf4omhsIjowhY8kqMjIyyV26Gj+xhIbKMqx6DbEyf2av3sg122+jS2/h/fffp13ZQ0TuQmKWX0eyPJ7Ws0epOHuC+Mw88uYvJisyiBXbbyPjmjU0NTXR193GyU/206ozc/CDd1E21bL7lVd57vV3KO428fu/vcdLr76GTCaju7Odmi9O0NFQh778NF3tLWit4OcfgFxsJSYAlq9cRbBUgsVioSBVTuf5Y3Q1VNN14RjLZ2eSl5c3rP3J5XKUzfXoVD1I/UREh4dy9G9/oeLsSQ698jxpkSGOGyOFQkF9ff97zjpVN0ZJAOa6i8QlJmG321E219Pd3EDL5QtOA+GB9peVlQW41tEZjUa3OrqsrCy3OuIBLTD+hSI9Pd2tG5WBsl25UWlvbx+3Ix6sLysry6X8Degb0OJK/xIREeHWhWKgbFcvZOnp6cP0DTBUX1ZWlls3egNaxsvf4PqH6puM/rmoqMipbbnTPw8w1vHNysqatAvtVKa0tJQ77rjDMTDx8/MjLi7OaSAYGhpKcHCw47NYLCYxMdGpnIiICIKCghyfpVLpsL0W5XK502AxKCiIiIgIJ5vTp087/UgYHBxMbGys4/NI+mQymZM+iURCbGys03cRERFOdUulUqKiopzqjoqKctIjlUqdVpEHiI2NdXpvLDg42Ck/VqvVacAJw/M3oA9wtPuR8jdUX0hICCqVynFjPlL+huoLDAwkNDTUoXW04xsQEDCivgG/ofokEonjB5ABoqKiHDkODg4eUd/atWud2kVWVpbj1RuA3NxcNm/e7BRDQUGBU18QEhLCpk2bnMpYsmQJycnJju9iYmJYvXq1U92rV692Wphq+fLlLFmyxMlm06ZNY+qTSCTccccdbukD2Lp1q9v6kpOT2bp1q9v6Nm/eTG5urlv6lixZwvLly93Wt2TJEqeyXdUnkUgcfq7qS05Odti5o29Ah7v6Bv/dVX0DPu7qG/BzV99ATO7oA1i8eLHb+gBuu+02t/Rt3ryZ559/noky5d+ZbWtr4/nnnycsLAz4Z6IGMziRMPqBHMxoDW0wycnJTgcfcJQrk8lIy8yhU6Xuf2pZUUpaZg5nLn9BtzSSdTfuwD8gkPiMXM69Xsb/rjuK2pbFC5Yf0mfX0GS0k7NwGe311RS99n/EpGWhaKims76aReuvo61TQbekf5/ZyMRMejtaOHX2C3JWrEcslfZfiKISMYTJ0Wk19Ha109vRgkEWgkFn4ObfPcXt7S0AnLNLyP76Q/QdOUBwaBii3KUkmyX4Adk3fIULB/dRW36c9IgEAsPCUKtUBESlYtB8Sm9vL2EZUkjMRRYXT5w0lJozRzEbRFwsr2LTmhtRNFQTIJMRlJzNjxP/hF3Xx4/ePoEtLIVAWTA2s4n4zFxyV29C4u9PiNFAfcl5qs8dQ9dnIrNgPnOWr0Wt7sUoDeHl199k1urN5C1fhyxYRkb+XN799U/RVp0jMC2fORtuICAwEFNfH5ruTsyxwYQkzeK2e3bS12ciYPt23v79r7jUWMfWb/0EWVgEerWKg3/6FdUV5cii4pAGBKJUa1B1qoiQJyMODCIzew7F+/9OVFome196gaCQsP78qo0khYhYviCHpKT15OfnD9uLFvpvYrJPn+Hwu38jKjkdjDpW5qazPDeFpKTlTn4xMTGEhYUhsVtJSUnGbDLjP28Zl86fIykpCbG/lBOffEhUcjrnTh13vJs7tK0P7sCupvND0Cfomwr6Nm/ezIoVK3jjjTcQEBC4ujCZTG7taymRSJx+sBAQEJgeTPnBrLtotdphvwZPhu1Q+6SkJCICLpKfkdo/gIpJxVB1jp6eHlIK4vEPDMI/IJAQeti99SRljSI+lf8asb+VyKAIKo8dJCEzF4VSSePFYrQdLXS3t2LpM5BUsBiaGumur0QSEEhvRwv+AYEEyEJoqbiEzWrF3w8sFiutFZcIiYyit72ZhotiUvPm8sOiT7jxy4Hsu4tXceCeRxBZbcSkZCIx6fAT0T/tuLWR2uLjWM0mbF8uOlT83uuk58/jUtlFFI01BEX3Tws2aHqwWUxYLWaiU2Zhi4qmu62FPT96BJvFjDwxnv+aU4PMP4TvXroNnaUWP50GWXAIsrBI9Ooe9ColAWIxJpsNo6aHULEdk0ZN2uK1qI0m6qoq6elsQxISjlHTi9FowB8LkqAQJBIJBQUF1LR3U/in/yY+I5eO2gos3e3M3XAT6uBYkpKS6NPrCJAFo7fYCE/OIC4lHT8/ESHhEUTPyqXx8F78ErPY9Oi/Y9H0YrRYOfCHZ7Dr1KxfuYwwixZj/WWS09O5/dHvIg0MwGwyUbjnRZKSkobdmA9GLBZz3z272L9/P5WVlayck8O2bdtGvbguXbqU9w5+zrnCvcQlpdDZ0oRd08Py5cspKCigrKyM9vZ25Iu2DBtAe7Odu4uvzrnJLttdrpY4PbF3ByFOAVewWCxur2zpic+V8PMEdwZdgxn8tNNVPFkBGXyfD0/qGjgvTSYTu3btorOzc1wfqVSKyWQiLi6OPXv2uDyg9WUf4GldvvTzdZ/oS42+9vOEqXCsr9R1c8pPM3aXkydPesV2qH1+fj45CTGc+/h92mvKOffx++QkxBAZGUlnbQU9bU34q2t5WPM1Lrf789CeGC4cPkBL2UVKDn6ANCiYoNBwlt3xVQwaFXF+JgKCgshatAqTVk18Zh7+/gHExskx67WcePMvmPuMdNVV8cGz/0HTh3t4/1c/oquuCrPRSHTKLJZsvZUff17IjW3971j8QZ7M327aRahRiUGroauhkojYOPq0ahQNNQSGhWO12fHDjlQqIzo1gxV3fI0MeTTLb3uQ2LQsgiMi8PMTY7eD3W7HDqjaW7AYDagV7QRHxrBg7VpeuKEYnUrJc61fQ2MNZMENt5J5zRqW3XI/kUmpqFqbOPWPl+k+vp8LH7xBX3c7mzZtwqRTU3bkIy4XHaT08IeERMWyZPNOFI01nHrnNdqOfsihl/+PjroKbrzxRh6861ZCLVq01ZcItWh58K5bWblyJd1NdbS0tFBS9CktLS2YdGp6WuqwmPrfA7KY+uisqyQ5OZmImDgaLhSjKz1Jc9lFopNSycvLJyoqEpuul9mzZ5MxZwHSwP5pV2PtJzuYgRWuT5bVYA4I4XhpFc/81y8pLCwccZ/ZwXsV21qrHXsVD7yLW1BQwKZNm4a9m+tu23W3nbuLr865yS7bXa6WOD2x91bZMyVOgeH09PT4xOdK+Pmyrrlz57rtM3Sqv6tMhXwMnJcWi4XOzk7uvvtuHnzwwTH/3XDDDdx99910dna69aOCL/sAT+vypZ+v+0RfavS1ny/rmgp5nCgz7sns0Glmk2U71F4sFvPg/fcNe3p2vOgon5dUU/Tq/xElMxNXIOW3x9Ox0EZQWASdtRVIAoIwaNW01lTQuPdNolIzsKbPQ2oqoeb8SWS2+TSeOIJepyVvzXVIOtvpqS7BoFGTuHgtkUnpnL5QQnhcAohEtJZdJG5WLmarma6w/s3UX8iby9N2O7Ob6/FLy6Sz9iKqthbqAwMw6PRIAwKIjInDzz+Ajj4tVquJ+Fm5yMIj8I9fjchgQJ6Vj91uw46dhnMnCYuJR69RoVV00N3dSWz+AtZ/7ds8Zv8hNls6D78qp0/zFtFZ84gwmwiPT6Sh7AKyiBiaL5+jvfQcYmM2uu4uMuP7B/7JqelYVN20ll9CHBSCPC0Ds1qJ2aCn7uwx2spDsFvMBPqJyMnJoaCggPiYmC/39s1m27ZtAHT9/g/s/t9f9m9l8/EnBIvsKHoVvPOrn5CQM5u2ysuYFG3c+61v8NbHnyM26dEGhmPt7EDbUo8uOY7CPS+SLY/mmsWLeH3/J5hNJvyl0n++szpoP9mhWK1W3n//fY6eL2XrfQ8THxfPh2+9xvFzFfRFJGA9e2nYPrNisZivPvgAKwfakFw+7AnsSPUMvMNVUlIyrv3QdusNfHXOTXbZ7nK1xOmJvbfKnilxCgxn8DuV3vS5En6e1qVWq932O336NDt27HDLJzw83O16wPf58ISh52VAQIDT+7wjIZfLnRaw8rQub+JpXb7083Wf6EuNvvbzZV1TIY8TZcYNZn3JwNOzoVNPdapu4jNyCZmVw5/LotF0l2NQdVN//jSx6Vl01JSj6+lGjZ2IpDS2ffdJZOFRmPuM/OPJ79B6+RzXrNlA9urr6FWrWbt6Dd0V5/nHX59H191JdHIqKXMXo2xpwKTXkZw/j7riY9isFl7ffBPHI6J4tbKUjIUrEEskVJ8+QmRCCunzr0FZdpbg+GQMej1NpefobmshJDKGmJQMOusqMBuN2K02TCYjypYGEnMKiM8p4Mw7L2OzWAgMC8OOCL1OS3icHF13N++GPUS9Joyg2GPUn/079oBgVtx2H7KwCHo62jm//+9ct2o5Cxcu5PLly2jDAkjMm0u3XyBmm438gvlkZKRz8MBHdAUEERoVQ1B4JNIgGbNmz8OqU2NWK/noo4/Yu28frRojITEJ1Jw8S5dSybIlS4hJy2L10lWoe3oIW7mMyyePMt9m4lJ1LerKC0T7S7j7/nv4yle+glqn42xlGbKoeGSaDq6/Zh4rrplLUlKSY4Gm7LNfULjnRaKS01E21zveWR0YTA4efALsfvkVPik6QWBqHmW1jZw/fQqFSsOaO79GSlQoKclJw/aZHasNjcTgfWyjktNRnj4/bIAsICAgICAgICAgMF0QBrNeZKSBTVtbG5HyZJbecj/hhm5UC5fz+Yu/wU8iIXfVJoIjo4hOyeDCgX/QWnGJxQuW0dvZjlapwE8sJiF7NtW1JeQuWU5ifAwykY2o2EjQpxMZGUltayOSgABCYzrRdndiMZtJycrFZjYRNyuHsNh4GldtIrSrnaqzx8hdtZH0hctpry6n8vinJMbF0Fh+mcTZ84hNz8ZktaHubGPhdTv5ovBdDv3p1yTmz6Ot4hLdzfUkZObRdOE0QaFhhEbFYLVaiUvPJCI2jt7OdvATYSSR0ORElC1vYBf7ExAUzIUD7xKXloG6o4WIqEj6zBY6++zEz19B86kTpIaEs+W2u5H8/XUunioiOTmZIJmMnEXLMGp6kWflkb9yPbML5tHR3Mgnr71ASbuKhsZGYhJTmXXtUvS9Sj4+9AF2i4WY1Axy5i5wHBtFexsL4kO5/davDHvq+dUH/vk0NDZ2LdC/nYHVaqWkpISuri6uWbyIa7783vHU/fhxKqqqhw0mr1m8iKr2bq6/836OHT1KWu4cDl/8gtDoOMwGHSEhCU5TlV0ZuI7E4H1sB54YjzRAnihFRUXDFtuZjghxTi9mSpyTzapVq660hKsKpVI5bBXhmc5k5cRms7m8R65er8dut0+4Tm8g9DXOCPlwRsjH5DPj3pkdupXEZNkOtR94Svbq+wc426ri1fcPsPvlV+jt7SUmNQORSIQEG2a9jsiUNGLSMwmLkyMNkpGQM4e4zFwik9Jory6j/txJDGWnabp0jqaSL0hISKDoow/49OBBWlQaPj14kKKPPsDf3x959mwW33gXC+bOJWf1dcSlZ2PoUZC+aAUAup5uQiKiCIqMJjQqlsxr1pAeF0VCzhyik9Pp1ZuJTJ3FyrseoWDTjWz4+veJm5VDQGAAO//jvzBqezGUnkDZ0sDC63YS6mclIDgYWXg04sAgrFYLmUtWM3/tJqKS0ig5uA9lbTkXP34Pu91G8qwMZCHBxKdnIjIbySqYh82gxz8qns27vkpifBxr7vwa3b0ammur2HLb3cTFRNF26QzXrFzDprWriA8NIDUrj0BZCE2nP0PRrWD2ivVExCeweOcuAsMj8LNbyVm4FEm0nLa2NpTN9ZhNJqetbAYWbBr63unA09CwsDBOnSnmt399lTcKP+NbP/gx//mb/+Nss5LX93/CmbNfsH79P99hbWxsdAwmV163lc27vkpVezenT58mKjmdzNlziYuOpOjtl/E39FBy9CBhARJiY2JG3GfW3bY4eB/blssXXH6X1912Png/RFfw1Tk32WVP1TjdtRfinBwt0xVPF/MYun+ot3x87efpQk6eahy6j60rePrOrKcaPcnJ0LosFgu1tbXcfPPNbN26ddR/X/3qV9m6dSs33XQT9fX1TvvhulqXq3jaB7jb10ykLl/6+TIfntY3FfLoy3x46ufrPE6UGfdkdrTBwkRth9qXlZVR2aZg0eb+7WDmZEVQdLiY9vZ2ZLJo4jNy8KszEB2ZilHdi8VsIiYtk/aqy1jMZowaNRExsagUXbRcPk9g7mxqKk6jaKrjrofu5VJTB7FZ+QSGRhArDUTb3oTdbic5fz6pBYuRdjQQ39LKseAQWi9dRq3Ts+KOr5KYW4DFbKLnhV+RvmApfmIx7e0t+AWEkL1qA6fffomsvPWEy5MwGw2ERsUgz55NZ30NqYtWkbNsLSFmHXHzgqi6eAZDTAwtTY1Ig4KxGfWExifih53AgECSCxZSfvQTOusrsUqjCAoLZfb8AkrPn+P8oQ9JyZ/H5ZNHMLQ3MvuW26mpraVDpaZHoSVMnoSivY2Ss2foVChJnbeMqrJSZs1dxJy5C+j89CASq5HA4GACo+Q0tzUQnpCA2mImMjGVXkUnCelZ6HoUJGTOQewvpXDPi0TExDltZTMWOp2OvZ8cJiZrNoHhURDegjkwlFnzFnPN+uuGPfXU6/WOwST8c2Eo+npQNtdjs1rZcutd1Fy+xOf/2MPc5Bgqjh2kufwSemUHi3NmjarJlbYol8tRnj6P2WQiJDrWpXd5XS17IvjqnJvsst3laonTE3tvlT1T4hQYznjvMU6Wz5Xw87Qus9nstl93d7fbPv7+/m77gO/zMRir1Yrdbufuu+8ecwXnvr4+AgIC0Gg0PPPMMy49nfU0Ll/2AZ7W5Us/X/eJvtToaz9f1jUV8jhRZtxgdui+g5NlO9S+paUFVZ+NstpG0mXtbFB/k0b/m9BqtUj0eor2/Ik5ybGUHj+Jn8QfU6+Kk3/fTXB4JA0Xi1E0VhMUFk1UUhoFG/v3pJ2dOAuTQc/JkyeRL1xF6fEj9HS2ERmXQHxcIjExDTRVlyHp7uSRZ39KSFc7d2Tlo22pp89koe6LE2iUCrobqtGpumm5fIGk2QsJS8ult6ONqmOfEhQUSmddFTazGbFYgk6lpK3qMuEx8bRWl9Fw4QyJyckoOyqR+EsRSYMIi5WTnp3LrFgRoYrPqA1/BLFYzNnX/0RASDiR8lzKz12ko/oy27ZsxR4YilqrQdTbyZp162mvSWDf22+SuGA5fn5i+vqM1BcfY27BHLpVvVx/+70sWriQD996jY/fepWF1yyjq7KErtpKErNyqT5YSO7sOcQlp9JWWk75qc8JDw2l9NhhrCrF8K1sVi5yaWGk6upq/MKi2HDX1+hsbyUmu4CLhz6kqvQiCZuuHzYtWCaTUd1QP2xhqC3brkM05B3b1dcsxma3cbqkAo2yC5vRCGNck11pi/n5+WSfPjPiu7yutltv4KtzbrLLdperJU5P7L1V9kyJU2A4nmwp44nPlfDztC5PBrOubD0zFE8Hb77Ox0hIpdIx9Q/8zZ0FnYKCgjxaAMqXfYCndfnSz9d9oi81+trPl3VNhTxOlBk3mPUVer2ehppKdmwt4PrGf+FC/HfYu79/b1dJQCAh0bGYDHoCQyMI0qiJTcskZe4SLha+y6zFK7Hb7fS0NhCZsJTgyGhMOi0xyWnEpGZQdbyQizUNxGbmEZuWTWtDFRc+O8DN12+gfn8h9/zL7SRrNZhFIuIba4mJjaHHZqP+3CmqTn6Gf0AQBo2GoFA9HdVlWEx9dDfWYtD0EhgRSXdzHR/97j+RZ+bTfPk8XfXVxMTLqTxyALNBj1gWik3cTYQ8kUXX76C9rR1b5QH+Y/GnfGyK4vC5U0hsFgJCwli16xGidB2sKVjF57ufo+jAPoyIiU/LIHPZWnpaGmhqbqGpthq/0EgSc+bQ3VhLb1cbdmUUi1ds5JolSxD5idh2571YTH1EGJUsnTubBkUPVoOG9qoy+vqMGOwSSk4V0dVYQ8LG7fS0NpAeFTzqYG6kd5rFYrHj+8uXL2O2WLEDQcHBtHd0EBobj0mvG/GpZ3R0NNl9pmGDyYEFnAavbG21Wnl9/yfc890fT9r7raOtoC0s/iQgICAgICAgIDAdmfLvzCYkJPDoo49SWFgI9L93UVhY6PQOR0lJCdXV1QAoFAq0Wi0HDx50Kqe4uJjm5mbHZ4VCwccff+xkU1RUhEKhcHxubm52mh+uUCg4ePAgWq0WmUzG0sReMi5+n9e7dvH8ZzLCQoIJCgpidmI0mo5mxP7+dFSX4q9uJ0xso6nkLFHJaeQtXcOqpUvQdStoKvmC2jNFhGraaTn9KS3lFzEajSSmZ7Ju/UZy12xi/pZbCY6Op7e5mZfNBvK1GtpiY/nBtdfRvXYtFosFdVc7ZpOR5YsXIhbZ6NOpybpmDYmx0Ui76ggKC2PWguWYdRqWzs1HUVNBbXERWmUneTlZxPjbsJr6WH7X18mZv5hr12/AYjIRGRrK3FQ/HpnXwmtnE/jvC4uJiIxErFWyeP5cQiNjEIslhEVGc82iBYh0KqQBASzYsA2pv4S0tDTUegOxaZnc8Mj3KEiKYeuDj5E27xokEgmWnk4Ov/0qr//hfzny4V78zH2Ehoai95Oy+b5HyMyfy+rNN7Bk+Sqiw2QsXn8di9duZFZEANtvv5vYtCzKysr4/R/+wJ739nOmvoNX9n7EUz9/mh8/8VN+9+rfONus5NX3D/Dc735PW1ub411nWWI6YX4W3vnDr+jtVqDpbMO/vRpNZ6tjmx6pVEpJSQkAIpGIe3fdzby0BBbKw7j3xi2OwWVdXZ3j/dz09HROnDjhNCW5s7qMiJg4x/utCoWCoqIip7Y1XvsDOHz4MOnp6SxYsICCggLq6uoc+kY7P06cOOE4PwCXz4/B+sY6PwZ/N3B+DFBdXe2kr729fczzd6i+gbJd0ffxxx+Pm7/x9I3VvwyU7Ur+qqqqXM7f4Dhd1Tf4Pemx8jeavrGO74AWV/L3xRdfuJy/gXrd0Tc4X66cH+4cX4VCQWFhIY8++igzGYPBwJtvvsmFCxeA/kV6Ojs7nd5V1Gg0Tu+BWa1WOjo6nMpRqVQYDAbHZ5PJNOzdzq6uLkwmk1PdQ993XLp0qdOe3DqdzmnP0pH06fV6J30Wi2VYXSqVyumzyWRCqVQ61a1UKp3qNplMw34s7erqcmrfOp0OjUbjKFssFqNSqcbM34A+gIiICIe+ofkbqk+r1RIREeF4AjxS/obqMxqNaDQaR5mjHd/BTzYH6xtgqD6LxcL8+fOdbJRKpSMPJpNpRH29vb0j5m+w3s7OTqfpxWPlb6AujUZDWFiY4ztXrmElJSUe9SH19fUuX8MG+Oyzzzy6xn722Wdu6/P0GjG4HHfuATzRN9AHu6OvubnZ4eOuPnevsQPHV6FQTPo1diR9Az6e3KN4co/n7j3KAB988IFb+ibrGjvln8y2tbXx/PPPOzopiUTC5s2bnWwGP+kqLy9n9erVw/ZCWrJkidPnmJgYZDKZ03dDVx9LTk52eqReXl7uKDcnvJl18z+gOOzbqAxrWZUTSfnpYxwzmznxxQXC4hJICJKg16iouHSOiMYWUuYswE8awMXDH9JYeg6rUYdYKiU4Opa+PgPW4EjE/lLMZjPBSbMIWXgtfhIJAbJQ5ian8+S+N8nsM9InFvPS/Q/TGZ9Fzbuv0aNUEh6bhDwrn56gGCJSMgmtq6Hq9BHMC64hKz6anrZmGi99QaA0gEYjZK5Yj1GjIig8EqO6lw61AXnuPJIy8whQ1GFMX0lklwptyXv8MPXPvNWWxN/bVmDr68Vi1GOXSDlXUsZNNz5IgCkeomPZd+4CppYW4mLTqKmtJjQ6nubuToIiYjAZ9YRFx2GKT0KakIosLILU2ECOHjmMMSichLx5nH1/L4GGXtZ9+1tc6CijrLYRdVszhuBYotNzCZfYsPbqEHV20aw10VtagqazlZMnT9KqNbH53odpKT2HNDaJf/zpOWQRkSzfdiuBEWFcv24TH7/xV06ePOl417mj/CL+CRl8tvctyg+9T5C/mFkJ8axYOt+xTc/gp57BwcEEBASwa9euUdsf9C+oEhsbS11FnWNKclxWPudOHUe+cpGj/Q1ubwPtdqz2B//c4+v8+fOsXr2arKwsp7+PdH5YrVYnu5CQkDHPj+Dg4GH6YPTzY/DKfUPLHaqvurp6zPN3qL6BvIx0/g7VI5PJiImJGaZvMIP1BQcHu5S/AX0DcY6XP4COjg6X8zc4zvHyN6CvqKjI8e7KWPkbiNOV/A18HohzvPxB/yBi0aJFw/QNxtP+efXq1U5ta7zzIzg4eJjNWMe3vLyczZs3s2LFCt544w1mKlarlV27djkGVX5+fsTFxTnZhIaGAjj2URWLxcPe2xzwH0AqlQ5bXEosFiP98gc+6J8eOnQ66tB9V4ODg50GWiPpk8lkTtdziURCbGys0yq8ERERTvvASqXSYSv0RkVFOQ0gpVKpY1/vAYbunxocHAz0D+QkEglWq5WIiAj8/P75LGEgf0P1qdVq0tLSHPoGM5K+kJAQx6AyPDx8xPwN1RcYGOjIjVarJSoqasTjO3jwOKBv8Oeh+iQSCRcuXODWW291fDdY70BdQ/WFh4cjkfzz1nQgfwPo9Xri4uKcjtVo+RtcV2hoqJOPK9cwlUo17DtX+uA5c+Y4xTBeHzzgN7i/cvUaW19f77Y+d68R0N8HDx7cuHsP4K4++Of1wFV98M/rkzv6Btfljr7Bfq7qA/fvoQbX5a4+cP8eD9y/RxkgMjLSqV8fT99kXWOn/GDWXdxZDtvdpbMH26eb9nPEci8flsQRlWyksvgEuYmxiMViguMTiZuVw+WGZuIz8ulqrEffq0Sn6iEhZw5tlaV01VUBYLPaUNRXQ1omioZqbGYzPT09aBQd+Pn5IRL5IQLmNNX2D2T9pfxg3Raag+MIlkiISEqlrvgoWbOXkLv6OsJi4lErOuiqq6K3sw3wQxsSh0jUAzYbcSmp+IdFIouIIigsArvdjk7VTXdLB4F9JmxWC5Gzr6G1rZ0ozVl+OO9j9pRm8XnwN1h521yUbS2c3vs6ATYz6h4VB377FMl5BVT9+Y8omupIzJiNSqEgIiGV2NR0opNSOf/h29h0ai4e+ZjwuER6j3yMsaORyJxlBManctu//giRnxi7zcoHzz3NsWPHaGhWsPyW++iUyWg6cojurg4Co8LQ6/WIJWIWr91OXMos3nz2J7S1tRGVUYC/VIosKQNVWQXBEVGExycRlzoLRUsTKrWaqOR0ysvLUfX5UVbbSFBMOrbeHpLTZjE7OogdO3aMOW3XnfZy00030fPyKy6/3zqRtjiZtlNZy0yJ82rSMlPinM4cO3Zs2A90ruDJVi2ebu/iS7+J1DV4MOUqQweErjD4CaQ7TJU8euLjyTuznvYBnvj5si5P/QSNk+MnaJx8Ztxg1leI1vyD1atsRA95f/Hn//kkOkUn+sgYUuctob3yMn3qXlLnLWb9Q/9GYEgoeauv59Cff8Xlwx8SFBrGiju/hkgkIm3eUj79y3/jFxBEV0MNRa+/4BgItUkDCFm0AuPiVbQmp+Pv70+EPAV1ZzsWi4WolHQylqxC4i8lJj2L0sP7sVmtrL3jQfpMJlLnLsGs19DT1oihoY6clRuISctC2VxPe1UZMaEh2HW9XPz4XTpS0ulqrMXe3MHvjiXycd86Nt63jtiERJIyc6kuPoax8guS5HLiU5KpPfU5eo2au/7zd3TXlVN5rpiLH79HUu4cDColkoAAYqQh6MuLMbdGYVYruWv7ZlTdChJy5hAVFe3Ia0LOHLqbLxMSGMCen/87JouV7tZGZJGxKGZl0NnWRkxSCjaRmIaKUtIyc0hIiKD2y6151L29XD51jJ7ubswiP8ouXcCk1xEm9aO7qY7E4CAaSi6x/Jb7CAgKos9g4NT7fyNv1fYJ7dU60vu5k/V+62jv/goICAgICAgICAhMZ4TBrLcQiRz7lQ4eBAUGBuKfkMzqXY9it9vIumYtioYaQqPjiEhMRizxJzAsHHnWbC4f/pCI+GQ0Xe1Ig4Lp7WwlLiOX5vILBEfGoFMp+//1dBObmsWn4eGEJaTSeOEUcRl5qLs+x263IRaL0Sq7ULU0ERgWhlGtRtPZTkh0LAajkYDgEPTqXvyDgulsakCeN5fkOQsJi5UjC4+g+fJ5RCYdkeHhNF6+iE6poE/dQ2JoOh1By6BXjd1qQd2jpLHsEpruTvKXXYtB20tPXTnB/mISl68jITERkUFLW3QjkUmpGHuV5CxaTkv5BZbNW8zixYupqqoie/m1bNu2jf3793P47X30GQyOgWV7VSnXLczntXfeRyZPJS5zNjpjH+015STGxoCpj/joSBJCAwmKj8RQdY7ly5djP32a/3vyB7R3KlAbzWy+/xFO73ubc5/sIyw+kdLPPiI3PoI1O28krKyWordfJio5DWVzA2EhwcOmnLvDwJ7DVe3d/T8+nD5P9ukzPHj/fcPax2SWLQxoBQQEBAQEBAQEpjNTfgEodxn6QvNk2bpqbzAYCI6MprezFYvJRG9nKxEJyXTVVWK3WhFLJNitVrrqqxBL/FE0VNNn0GOrPY/VYqFPpyFj/jLMRgPRKRnEpWfjHxBIV2MN2s4WKo99giwsgvQIGYm5BYRGRpGSkoKioZbiD96g7PNCij94g562ZtRd7XTWV2GuPEtPayOddRVYdb1EypOJTEhBJPIjJCoO/8BAmutqaGtvI3/5WpbMn8v89VvQWUVERERgVys4+87LnHrnNb746G0SU9O48f6v882f/ILs/NnMy0hG39ZAdFgYWXMXIBb7UfZ5IUatlhPvvoZJ0Y6fRMrJshrMgaGcLKvhldf2sGXLFhJlYt589id8/Ppf+dt//YggY2//Yh/SINbueoS5udmsu/9fSZ6zkGvWrueWR/6VxppqLhYd4tzH75OTEENOTg7HT5yiuVNJUnws4QnJlBWfwi8ohLw11yELDWPW3MVorWJMJhPpKcmsWrOGGKuOVWvWkJ6STFJSklttYTBlZWVUtXezeddXWXndVjbv+ip1LW3D3rXypG2NVHZVezfvvffehMueLK70Oedp2e5ytcTpib23yp4pcQoMZ+jiRN7yuRJ+vqxr6CJKruDJdGaYGvnwZbvyZR/gaV2+9PN1n+hLjb7282VdUyGPE2XGPZnNy8ubdFur2URZRSVKpZKSkpIxp3nqdDoUDTWoO1oxqHswGQx0N9ej7+nmo98+iTx7Nu1Vl+lqqCZtwTJ0qm5O/u0vZMyZS2P1YcLliWQULOTsoX101JQTEZ+IWCJB2VKPVNWG2i4mJnkWPWZoP3scVUsDDzzwAL/9019pvnweTVc7qo4WTEY9UUmplBzahyo1nR6NBj8/MXK5nLbKUkoOf4TIz05r2SX69Dqyl6/FYrESGRXFnGuv49Rnn2DwD6IdGX5BwVSdO93/nm1EDNnzlxAX1/9+cMacBcyLDcbwzru8+asnkGfNRtVQhbalBku4jOjAANZdtwGdnxR5Rh6KlkZiMvIoryqlpqaGF/7we/bv309FRQWhceEExebTpNYji5FTfvIz8pesJCgsgoTs2bTU13L3DTvpbqgmwqjkhhtuID8/n/fee4+KdiUr7ngIs0qB4tIFNL29RCWkMHf1BtoqL5Oalsrx999CoVCQmxhLZfEJIuISHO86j7dX61i0t7c7rVzsL5USkpDmtEfteIzWFkcqO+rLaeau4s454QneOOd8Uba7XC1xemLvrbJnSpxXEy+99BKvvvqq00JKK1eu5IknngCgpqaG5557jsrKSoKDg9m+fTv3338/IpFoUnUMXdzJWz5Xws/TuoxGo9t+DQ0Nbvt4ul+sr/PhK7+QkBCnBaxcxZd9gKd1+dLP132iLzX62s+XdU2FPE6UGTeYHbya6WTY2iqfp/XUH3i16T6ikmdx7P0DY07zFIlEaJUKKk9+RtysHFrKztOn0xIUEYVG2YXu9FFsViv+gTJmLVhGRFIapYf3U1N6CYm/P6l1Vfz3O6/y9PobKI1PwqDuIWXOIqIS0ug4eQC9Wk3T5fOolV30trfQ09FGSkoKeQsWIYmS09PWQqAshHX3PIo4KARtdyeXDu/v3wonSIY0KAilooOmS2eIScvEYjYRGhXDDeuyqRdlomyoofTcGXp7VRSs30pKZBjy2Quo/uIUcdGRdHb3IAsLp7tbSWREOMrmelIWbXEMSquqqrjxgTuZNWsWSqUSuVxOfX09v/7ra0jq24hOzeDsx59gUXWwYfFc8vPzyc7ORqFQUNej5eZdX+OL8+exRSej6WzFbLfjbzHTUllK9rp1WCxmrFoVN9x4g9Mqs1Hp2WQtWAbY6e3V8MWh/ehVClrKCggLDSU0PAJdjwI/vwTnd1mXz5/wO6hyuRzl6fOOlYvNJhOqzjbky13/xX20tjhS2crmejIG7X/radmTxWSfc74q212uljg9sfdW2TMlzquN2bNn87vf/W7Y93q9nscff5wtW7bw7LPP0tLSwg9+8AOCg4O57bbbJlXD4MG0N32uhJ+ndXkymB26dY0ruPNj5mB8nQ9f+UmlUo8WgPJlH+BpXb7083Wf6EuNvvbzZV1TIY8TZcYNZieViuewn/sx+zrvY/OurzkGE4V7XqSsrGzEp24JCQkYQmJIKVhMgEFFd1gEK1ddT/3Fk+h7ejAa1FjMZqwWM316HV0NVUQlpREhTyat4iK/PPoJoVYL/3r4Qx6+5X7is/LRKNqpOVOEtaeHlAUryF6+HlV7CxmLVlB18jC7d+8meelG1t35IKUnPsdotmLzl3LktecJj09k9rU30HL5PF11lWTGRxGdHMPi7XdgtVrIXLKahJo/892It3lW9f9o0espeu9N5Hlz8bOYCQkNQSKRkZydR0KYDFlQEBcOH0DXVIPU2ufYi3Uwfn5+FBQUOL4vLCxEqdaz/eGHCAwNx6jpZd+vn6C0tJTW9g6q2rvp7lWjtUspKSsjKioSMTZ0ahVnPngLg1qForEWwzXXOPZ/HfwkNTw8HHVpHRaLGWlAAIu23MS5g/tQVJXQeOYIqbMX8FlxETa1kqVLl474rrMrVFdXD1uGHOgfkJ8+4/LKxe7gzbJHY7Q4pxtCnNOLmRLnWBw5cgSbzcZDDz3UP3MmI4M77riDd999d9TBbGZmpo9VXt3odLph28XMdIScOCP0Nc4I+XBGyMfkM+PemR28KfCEbC//Ci7+jLPhz6COXY+/VEpvR6tjmufgDakHExMTgzwzj4JN20mWx5O7ciPBsfFoFJ0ER8Uwa/EqIuRJiBDRUlWK1Wwmb811bI6J5H8+/ZBQq4Vmfylfy55NXP58ErJnE5uei9hfikgkIjwuiYIN21mz4zYKNmwnPC6Jjo4OdCoFsoAAktMz6G1rorb4GCGRsVz/6A+Zu3Q5Gx/+PnHp2RiNRuKzZpOUP4/Y9CxWBJ7giUWf85e2XXSTQktFKSGBUloqSgkQi0hKSkKr7KKrsY4YeQIbb7yFWJmU2VGB3HvjFh68/z6qq6t55LF/4fm391Gql/D82/t45LF/cWyefu7cOSITU1B3tqGvL0fd2UZkYgqfffaZ433QNVu2E+AvoUulxmazkZSURIC1j1kRwWy9YRurV69mXlyIo87BT1JvvPFGLN3tHHjh15x//3U+/sv/Eiy2c/ctXyHMz4q6vgyxVsnO69Y7DWDdaSvQP31vJMRiMQ/efx/33riFxYkR3HvjFq7buMGtp72jaRmp7Afvv4+2trYJlz0ao8U5GeW7q8WbZU/VON21F+KcHC1XE9XV1dx0003ceeedPPXUU47+oKamhqysLKe+Jy8vj9bWVnQ63YhleXrTZTAYfOLja7/R8uSNuoBhe766gidPIcFzjZ7kxJfHzNO6PO0D3O1rJlKXL/18mQ9P65sKefRlPjz183UeJ8qMG8yONsh0y/bSU1D2S9h4CFnKtXTWV3P4g3f49J03OPzBO3TWVzs2ox5KR0cHWmUXZr0eqUxGd2MtdWePERodx40/eIalt9zL9u8/RXRqOj2NtWC3M+v0UZa9/xqBRj1NwaHsjIrBMnshcWmZ+AfJiE3PJCFnDv7+/rRVltJY+gXapkoaS7+grbKU5ORkbGoln77xF9ob6qg49TnlRz4hblY2zWUXUFZcQNlcjzwjB7FYTG9nK1azmTWBR7k38P/4zsElvFGk5/xnhSxdsYKb79zF0pUruXzsMF8c+ZTyQx+gqL5Ma0MdB//2Eivnz+Ghhx4iPz+fsrIyTp8+TUWrgtu+9yTX3/0Qdzz+c1r1Vvbv3w/0P6nVKDoIj4lH3KclPCYejaKDvr4+x/ugadl5JMRGc+HwR5SeOkbl4f2g6yUrLxdjTxdrF8/noYceoqCgYNggcf78+Tx4162EWrTYOxoItWh56O7b+OlPfsR3v/4Ad163lu9+/QG++uADTr7utJXxGHjau2nTJgoKCujq6nLLfywtQ8sWi8WT084nCW9qEeKcHHtvlT1T4ryaWLduHbt37+bdd9/ld7/7HSKRiO9///sYDAZ0Ot2wdw5DQ0OB/inIk4knAypPB2G+9vNlXdHR0eMbDcFsNntU11TIhy/blS/7AE/r8qWfr/tEX2r0tZ8v65oKeZwoM26a8ZIlSyZm2/w+VP0eNh6GiLlkBhk48/khjEHhJOTNo+39vQQaevn5v397xDLVajV+zQ2UHS0kNzaCrvoqmi9fYOENt6FTKhCJxUgDZcSkZdF18RT+bfV858QRQowGWqJjebRgAX66Hvp0GkIio/ETi7FZbfRpepHJZHS1NXJsz/NEp2TS3VRDT1sjN912M8lp6by5dx/dH3+AFT9yFiym+tRn+InFRCSm0FtXSd25E8yKjKC6pYHoY99l65xTfO/URj442UyAqISc5evIW7KShYsWMX/dJva+8hci+3p44l8fAaCrqwv5kjmOKa4DW8aYLCIkYZGc/vh91uy8A//AAMKT0vjwww/Jzs5my5YtfPqfT/PJn/+HhNw5tH2wl87acu766v0ovtwf1l8qZeONt9BZV8Wc6CDu3/x1ampqqK2tZXl+Ntu2bRv1SadYLObrDz3E6hUrhu3FOtZ0Ynfairu4W7Y37b0Zp7vlC3FeGXtvlT1T4ryamDVrluP/Y2Njefzxx9m+fTslJSUEBwcP+yFNo9EAjLr9mMFg4PXXX3f0r3l5eY4+PioqColk+G2E1WrFZDLR2dk57G/BwcGjTkm12Wwj+kgkEqKiokb00el0o9Y12rujFovFscLtYD+TyURycvKIPtC/Kq7BYHD4mEwmVqxYgVqtHjV/Op3O8eRywF6lUqHVakfN34A+k8lERESEwx7Gzp9Wq2XFihVYLBanuMbKn9FodJQ9NB/j6RtgcE7G0qdUKrFYLMPqkkgkI9YDzvkb7GcymUZd6GqovgGNWVlZ1NXVMWfOnBH9ioqKhtVVWFhIcHAwq1evHtGnurp62JO2+vp6CgsLWbVq1YgLVmm1Wo4dOzbs+8LCQjIzM0edDTGSvoHv3dE3WIc7+gbKc1dfS0vLiPbj6Vu1atWI34+lb6zXIkbT5+7xHaxvpOuEt/SNVNd4+kJCQob5jacvKytrxLpcyd9Qv9H0FRcX09bWhlQqdczSnAhTfjCbkJDAo48+yv3338/mzZuxWCwcOnSIjRs3OjrHkpISAgMDHSegVqvl5MmTbNq0yVFOcXExcrnccSFTKBSUl5c7NfCioiLycpcTs+UsyJJpbm7mwIEDBManctu//giRnxhdeTFHPinkwIED3HzzzVRXV2M0Gh0DpqCgIBYvWUhNQw1qEhFLpWRnzMLcWo1OpSQ4Iore5lpizWrUSUk0dXXwg7z5bAsP4oXQKCrqa1izZAHnqhtoKXwDXXgC3Y21dDfVsX7pUi7UNmGS+KNXKYiNjCA9ej5yuZw3//42Wkkwy1atoaK+GWWPkuDIGFKTkggOkeGfmkHb5XNER4cSm5jEBvkBflx6G8fr+sjNziI7Nw+9fzBll0tRNdUS7i9CYrNwww39Cy0VFxeTl5fnyN/x48epa2lj833f4ItPPkATm4FfZx1NJWfRWUXUX77INXPn8sa7HyC2mYkMD0dRV0GqTIS2tZ6oiAiuvfZaTp0+w8FXXyA8NQtlcz0r5s0mKUFO8RfnqO5QEpWcwZnSClpa2/jGo4/076k7wvE9d+4ccrnccRxGPb55eY4X2Jubm2lvb3c6OQ8ePMjy5csdF4Chx9fr7U/QJ+gT9PlMX2FhIS+//DJTGZFIhEgkwm63k5mZycGDB7FarY7BaUVFBYmJiaMOQIKCgti5cycREREu1ykWiz2aHjvagGssxho8jbZFjUQiGVGfWq0ec5pcVFQUFovF4atWqzlx4gQ33HCDS/oG24+1d/mAPlftBwgJCXHLHiAwMNDtfdSH5q+zs9Ol4z3W8R3tWI12fNVq9ajTh0c6vn19fVRWVhIfHz/qLIRFixYNK2e8BaeysrKGDe4KCwvZvHnzqD4hISFj/n00RhtwuatvPCZbX2Fh4ag+V4O+sfCGvtEGn1eLvtGYTH2D61er1fzhD39wu+zBTPnBbFtbG88//zxhYWFAf+cz9CANffIWEhLidKMEw39NiImJGXbghn5OTk5GqVSSkDOHqKj+qUCRyzcRU9tIVVUVMPx9o9TUVL4oryZv3RYCxX0kz1lEyaf7UZSdQGm0EJ+RR0dtOW2VJYSKrAQkZdK4fD1/FouwWe34q3pRqVR0dXZwrrScsNge1F0dmLQaVCoVNomU2SvXY7NaEUvE1J46wvnz5zEGhbPpnm/Q1VRPQlgivUWfkD5vCWFZBTSVXSQ5NZXchdcQqevgQnM3b8a/CvEiVs42cfrvu5Fn5nL22FEqL5egyitA1VRHiFlH46L+3C5cuNDpyaherycsLQd/qZSIiEgibHoudfVS9vc9GPQ6MrJyuPUb38Fut7H7V0+RkJKGNDwKwqPJCAwjwM+OUqnkG48+8s+VhRdtcUxd/vz8ZTbv+uqwRbfy8/Opr68HcNomydPjO/RX+qHtZujx9XX7E/QJ+gR93tO3efNmVqxYwRtvvMFU4fDhwyxatIjw8HCUSiXPP/88kZGRjjz/+c9/Zvfu3dx77720trby1ltvccstt1xh1QIC3sVisVBbW8vNN9+Mn59rb9jFxcWxZ88en67yLCAg4D4z7p3ZgwcPTqptdnY27VWl9BkMKM4fo89goL2qlOzsbKxWKyUlJRw8eJCSkhLH1Ct1dydNl4oxajU0XSpGp+wiICQM/4AAuptr8Q8IICxGjlarJTY9h4zFK8iIDiNj8Qpi03Ooqakhc95i8pavJTg0jMxFK0jIm0OHxoAdEU2XLxBt1dFaVUZQUCCtra3Y8KP0+GH0BgPdTbWY9DoaLhajaKonyd+CxWjA2tPJ/PnzmbNwEWJrH/5YMfZ0k1EwH51GTVTKLBZtuIE8eRQL1m1CI5HxyflyXn3/ALtffgWr1erIi1wuR/nlFGE/PxGbN20kWmwhxqplfsFcHvnuD5D4S/CXSgkMCUfXZ2bDPQ+zcHYuG+55GI3OgF6vH/V90IF3aWtOH3EsutXS0sLul1/h1fcPcLZVNaKuyT7+nuJu2d6092ac7pYvxHll7L1V9kyJ82ri4MGD3H///WzZsoVHHnkEq9XKf//3fyOTyZDJZDz77LNcvHiRnTt38r3vfY+tW7dO+rY8gNvrAnjqcyX8fFnX0qVL3fbp7e31qK6pkA9P25XVasVut3P33Xfz4IMPjvvv7rvvJiQkxGlatDfxtL/xpZ+v+0RfavS1ny/rmgp5nChT/smsuyxfvnxSbbdt28b7H37Em8/+hKTsfFr2vkOiTMyWLVsc74xGJaejPH2e7NNnaGpqIjwlB/8AGR0WP/wDZIRExSCRBpKxeBWq9hYi5El0VJbSrVfSVllCcGQU8cnpdBz/lLbKEiIDAzEbDNj6DCTlzKGjvga7zUZUvBx9Vw/y3ALUfpCcV0D1icOEBQRg1OrIWbOZwNBwEvLm01FbiaWnna5LJ1EFypA11HL9ysUsW7KEmg8KSY+PQdXbCzJ/GhtrMbdL0PvLyEnPxS8qkd7OLqLTMkmelcnClWuHbUc0eMuYSHkS5W/sJi8pnvgFszl1uQqLxYzEX4LZZKK3o5mIeDkVl0sJDYuk8XIpsogoAgMDR8z54L1VkwsWO/ZW1YdmOlY/Hm2bpMk+/oMZ7f2OySjbm/bejNPbWoQ4J24vxDk5Wq4WfvGLX4z598zMTJ577jmXyzt27Bg7d+50W0dkZKRPfHzt58lU6IG6PFlk69KlS+zYscMtn5Heg3QFT/PoSU58ecwG514qlRIQEOCSn6crs7rb14Dn/Y0v/Tyty5N8eFrfVMijL/PhqZ+v8zhRZtxg1uVO3mYmpPL/Qd6/gSxpVDOpVMoLf/g9+/fvp6qqih23bmfbtm1UVlaOOLDy8/ND7O/Pxoe/h9hfitVs4r1nHqej6jLLjAYkSWn8Q9OLouoS12/aROHZyyTmzSM0Jh5xSBh1X5wkd1YqNWoTBetvQBIYRExGHt0vVYMNbFYruh4lsrRMeprrMVtt9Fn7CI1JRN3Vjs1qRavsIixWjl7fi9SkJSNCyn33968EbLVaUfz+D1wsq0CePYe2yhJCTFpmZ+RwrKKBBctWoTeZ8IuMp/zoJxhzMpy2IxoYNA5sGVNWVkZLSwsnm6vR+AUQEBCJWqvnN//xHVZv3YGqtYnYEBk93RoKFi9F5CfGbrNSd+zgqBvMj7a3qkwmczyxBUbU5c5F3t0bgqulbEGL78sWtPi+7KtNy3Rl8OJA7jDagj6T7eNrP19r9GRbGXe2fRvMdM2jp3V5ujCNJ32Hp/2NL/0EjZPjJ2icfGbcYNYlrCY4fhdoqmH24+OaS6VSbr75ZqfvBk+FhX8OrLKzs+mShlJ39jjBUTHolAoCQ0L5js3K988cxXr2GLqvfpdCeSIymYz41Fn0aXuxWy2YDDriU2cRERHAnFlJ9CnbMYr96ensZNacefRUlSANknHNzrsIkskQYWfvr5+gobyC0NxoYlMzsJj7iE5Ox9JnRKdV861rLhMXGUb+l1N4y8rKiEnLYvXSVah7eli7chnlp4+REhtOTJeaon+8gixGTs3lEoKCAgkMkjmejMoXbXHKwcAUYQC9JIitXw7sl6zbxFvP/w8xZjU7btxCY2MjnXsPcG7/34lKTkPZ3EB4aPCoi1IMHigPfZf207MHHKsfj6ZLQEBAQEBAQEBAQGDqc1W+M2u329m9eze33norW7du5dvf/jZ1dXWTUnZ1dfXYBlYjHP0K6BqonrUbAl1fkXFw2YPfGQUcA6uFCxdi7O3fWkfV1kyfTsNd58/wRFv/dJb6a1YRccc9JOYW0NzcTGiQlJzcPOQBInJy8wgNkhIdHU2E1I/0+Bgi/SE6SIKyoRaL2Yw0OJSO2kp6qy/RWFaCLCyS8PBwejtaufzZRygaayn97EPUna38bpeRFcldvN+ynLKyMqB/EB6TmkHO3AUsWbuenLkLiEnNwM/Pj/SUZFatWUNqiD9z5szBZjTQXFdD4Z4XyZZHO7ZrGMrQgb00MICMOQuYNWsWBQUFpKamOsqOl9hYtWYN6SnJJCWN/kR8YKCcnp7ueJc2Pz+fbHk0hXte5PgnH42oa9zjP8rxnGzcLdub9t6M093yhTivjL23yp4pcQoMZ6QtHLzhcyX8fFnXWFsFjcZos5rGYyrkw5ftytMp5Z7gaX/jSz9f94m+1OhrP1/WNRXyOFGuyiezb775Jh999BHPPvssSUlJvPLKKzz++OO88soro+4r5ipjdvIWPRy5Gcxq2HAQY6V770sMLnu0qbBKiY2etiZayi4gz57Ntvf/xsPN/QP1ho03cPo3uwkBdKpuFs6ahbKilprTR0iIjaHm9BHsWhXbtt3Pc3/8Pz75+GP8AgKxGY2kx0UyZ24eB4ovIbZbsOl04BeAXtHOmjVreP/wMbrqK9EouzCoFDyzsYxlaWL2hf+ZNk0xJ06cID8/3+l91MFPN7dsuw7R2S+oLD5BeEQkfholq+dks2zxHJKSkhyrBo/EaGUOPDHNz88n9/QZR9mVxSfITYwlPz8fq9X6zyewg/aHHSnnoz2xHc3eneM52bhbtjftvRmnu+ULcV4Ze2+VPVPiFBiOzWbzic+V8PNlXa6+3zkZdU2FfPiyXYnFYrfec3ZlK5/R8LS/8aWfr/tEX2r0tZ8v65oKeZwoV+Vgdu/evdx+++1kZGQA8NBDD7F//36OHj3K9ddfP6Gyh24D4cBmhs+2gd0KGz4G/1AKCiI8Lnu0gdV3v/tdkmfPZ96G7Vy//y1uK7sAwL7wCN5ZspyoY4foaqzDplZy0zceILH4LIVHjtFZfZlAfwk7Nq4D4FJFNTJ5KnEZuXTWVlDf1sh1G64lI01Nd8UF+qLj0XZ3kJqSzLXXXktnj4qyxjbMVhNPLPiMuXITf+j6AbXll+iziDlT1UDbM79kyaIFyCwGPnr1z8SkZjgG4QUFBRQUFAwaWC5zDBTHG3CONrAfeGI6LFdflg2MuIjWg/ff5yh/6PEceGI72nEe9fhP0NZd3C3bm/bejNPd8oU4r4y9t8qeKXEKDCc0NNQnPlfCz9O6RttLdSxG25NyLNzdN3YAX+fDV36e5N5isXDixAmfbeXjaX/jSz9f94m+1OhrP1/WNRXyOFGuusGsVqulvb3daWqoWCwmOzubqqqqCQ9mR8XPH7K+Dsk7QTLyBuzuMtLAKjIykgiDmLTZc+nAiv7YQYrik/hzXhbRhl5UtWWIjXp2XreegoICTp89i0QqRRYYgp/NBCL44IMPCIhNZMe//RQ/Pwk2m4X3fv1TSktLSU9NJ+/L913DIq+h/PQxZs2axYvP/x/79+8nvPVP5EqtPFN6Bw26duav30pMRCgWk5lP/v4aPX6B+IsDCbWbWRAfStKQp5tD47FareMOOA0Gw7hPTEfKVUlJybirE19NaLXaGbFojBDn9EKIU2AshJw5Y7FYPF5QaLoyHXMyeCsfV2YE9vX18frrr2OxWDCZTMJ5Mwih73VGyMfkc9W9MzswpWPogQ4JCfFoWfuhjLlnWPrdTgNZd/cXc8V++/btdFWXcfL9t7jcVs23brmfHyal8MP/+CE7rl1FTnQIO65dxQP33UtZWRkfHPwcWUI6s5asQJaQzgcHP6empobIpHT0BiMmiwW9wUhkUjr+/v5ky6M5tv9dSr84zbH97zqegA4sUrXh0XeR7ypj/pxryMrKZuGcfBLj4tGYrMxZvYHUzBy23PM1dH5SkpKSHO+jjhZnWVmZY8C58rqtbN71Varaux3v4EL/1g4j7Rc7Xg5HW0Srvb3drZyPVv5k2kJ/nN4qe6rG6W0tQpwTtxfinBwt0xVPt5GYrtOMlUqlz+oCz1YmttvtHtXlqUZPcjJVphkPbOXjyr8B3O1rwPP+xpd+ntblST48rW8q5NGX+fDUz9d5nChX3U9pA9Njhm4HoNVqiYmJGWafkJDAfffd55gGsnjxYpYsWQL0X4SHDooPHTrEqlWrRm1MmZmZZGVlOWw3b97s+FtRUdGICwkEBwezevXqYfbQ/zL04GlCnZ2dFKQlIjYpEQVGUqLsxGrU8dHHn9DnLyMqOYOTl6uoqf09dpuV1JRkcmbnY+xuI2l2PsW93Zg7m+ioKcdPLME/UIbFYqGjppy165ai7lURHSojUCrF6A/qXhXHjx9nzZo1/QLEgYhlgaxYsYLq9w+ATkXNyc8QB0Ugam8G/xTqzx4jNCiQxsbGEZ+AfvTRR45pNI2NjYQGBVJ/tj+fUcnpw7bDcTV/Q3Mul8tRnSymouggfmIxNqsVTUMFHeJ0CgsLHcdxaM61Wu2ox1elUnHHHXeM+Leh+urr60lPT3fSN5TBx7e+vp7CwkLH30ZqfwP69uzZQ3p6+rC/DW5/gzl06BDBwcHj5m+w/ebNm4e1v8EM6BvabsfKX2Zm5ojfw+jHt6WlhYceemhEn6H6BnI+WN9QBusbbD+gb6T8AbzyyisjLirm6vEdylB9A3kcL39ZWVkj9hVjnR86nW6Y/Vj66uvr2bVr17j5G7AdaLdj5W9A39Ccj5W/f/zjH0RERIz4t4n2zwP2A3lxpX8Zymj5Ky4u5syZM2RkZHi8Pcd0wWAw8Oabb7J8+XLmz5+PzWZDoVAQExPjuO5qNBqnqZhWq5WmpibS0tIc36lUKgICAhxPuUwm07DrfHNzM3K53HF9MRgM9PX1ObWhpUuXYrVaHZ91Oh09PT2OhZIG6xtAr9djt9sJDu7/sdpisdDT04PdbicuLs6hb/CN2IC+wYsAKZVKp7pNJtOwhQ+7urqIjIx0PKnU6XTYbDYMBgOBgYGIxWJUKhWBgYHD8jdUX0BAAEuXLh0zf4P1abVaIiIi6O3tJTg4eMT8DegbwGg0YrVaCQ0NdeRtpOPb19fn8BnQFxsb6/huqD6LxcL8+fOdcqNUKgkJCUEqlaJQKAgNDR2mr7e3F6lUOix/A9OLu7q6EIlEToPH0fI3oE+hUGCz2RzHeqzjO6AP4Nprr0Wr1TpN2x7t+A74WCwW6uvrnZ5Wl5SUEBgY6Oi7tFotJ0+eZNOmTY5yX3/9dTZs2OBoxwqFgvLycqe+q6ioiLy8PEfbbm5u5tNPP+W+++5z2Bw8eJDly5c7+tbq6mqMRqPjfsxisXDo0CFsNhtbt251WV9xcTH19fXceuutbukb/PDBHX0bN2509O+u6pPL5ZSWlrJ582a39C1ZssRRlzv6JBIJhw4dIikpyWV97h7fAX3Qf70Ti8Vu6YP+dYiWLVvmlr59+/YRExPjlj6A3bt3c9ddd7msr7CwkJdffpmJIjp8+LBnP+F5kbvuuotbb72VW265Bei/MN5666184xvfcJpmrNPp2L59O729vYSFhblUtjvTYdydOuOK/Z///Gf2nrrIpvseRVdxjuDchbz7m18QExHOYz972mk6bZBJS7lOxMqdd2DU6wiUBXN875vEGhWcLqvGPzKO+Mw8OmrKMfd08sAtN1Lfo2Pzrq/i5+eHzWajcM+L3HvjlmEDy8HTg8XB4Zw9fZJ5Cxex7c57sVmto/oNjbOkpIRX3z8wbCrwYN/CwsIRb8THy+GwKcxfvms7eAqzN46RJ7bgeZxX2t6bcXpbixDnxO2FOMcuW61WEx4ezr59+xw3yzMFnU7H7373O3bu3DnqjxKDUavVPPnkkzz11FNOgzVX7GUyGTabbUyfofYDjOY3mv1ofuPZQ/8P0gODIlfsB9el1Wpdth8o/+c//zn/7//9P5ftn3zySf7zP/9z3LY6kvax8j9WrINz4or9SHW5mssBv6st9319fezevZuPPvqIo0ePutXXgOdTtX3p52ld7va9E6lvKuTRl/nw1M+XdU3GNfaqezILsHPnTt566y0WLVpEYmIir776KhKJ5J9PFyeARCIBfSv0KSBy3vi27pY9EmYzvP8+fDk4D4mKJSpWDq1hRMXKEUn8iUxKpUfV65hLH5WYSpihm8MHPuXi2QzC4xLpLbtMW2UJt9xzO9LYJMLlSbQ1NjD/uk30trcgkUicpuWKYdSnpIMXXWppaSGgtx2tUcepTz8etkDTWHGOt7iTuwwu25XViSftGE3Q1l28qdtde2+/6+RNLUKck2PvrbJnSpwCw3F10ZyJ+lwJP1/WNfhJsKuIRCKP6poK+fBlu/Ik957iaX/jSz9f94m+1OhrP1/WNRXyOFGuyqv1HXfcgV6v53vf+x56vZ7c3Fx++ctfTnhbHgB0jXBoAyTdCIv/Z+LljUdfH9xxB+zdC7/8JUs3b6bw5AtUnjtNoEaF4txpJCYDLdUVnCstIyQqFm1DE+VHP+P2zdcSEiBF21SN2NyHtr2JkAApmZmZ9Gq0VLV3kJ6Th7K5nvzkeK5ZvIjX93+C2WQiSKQjpfqnfNySgXzRTSNKG7zo0qZNm8YcNI6GKwPOiTDe6sQCAgICAgICAgICAjOTq24BKOj/VfGhhx7inXfe4cCBA/z2t791bNMzIbR1lPz9fpBfB4t+Pa55SUmJW8UPszcY4Oab+weyACIRBQUFXL9yMV0XjqHraqXrwjFWLJiNn9VMU+k5FA1VNJWew6BV09PTw+obbmTrtm1khkvZum0bq2+4EaVSyYP338e9N25hcWIE9964hQfvv4+CggKy5dEcfeM5rB/eh7r2FLPi48Z9SlpSUuLyAk0jxemO73hMOOeTaO9u2d7S4W17b8bpbvlCnFfG3ltlz5Q4BYaj0Wh84nMl/HxZ11jrFIyGp4tlToV8+LJdeZJ7T/G0v/Gln6/7RF9q9LWfL+uaCnmcKFflk1mvoK6ETzcSGH8HXPMrcGEaTmBgoFtVONnr9bBzJxw82P/5uefgW99CDHz1gQdYWVZGWVkZ+fn5tLS0IEvPJzFtFsrOTubmbKI1OxM/sxpVaxPL1l9PeLCMsPgkLp84gnzJnBHrF4vFPHjHZsyF11Kino190X9z/5x54w4uJxSnC7hzQfC2FnfshThHxt0LvDe1CHFO3F6Ic3K0TFeqq6s98puu04w9fafLz8/Po2mrgxdecqcuT/DUz5Oc+Hqasa9yD54Ngj3tb3zp52ldnv4o4EuNvvTzZT489fN1HifKzBjM9l6GQxsh436y5j/j0kAWGHVVz3HtNRrYvh2OHOmv6/nn4eGHHXYjTZ399Owllq2/noy8OZhNJi6fOMIN265DdPaLf76PevAA2fJocnJyRt7b9fZNiD+7HnH8NSy56WXwc+3wehynF+xnipaZEufVpGWmxHk1aZkpcU5nRlvNezw8GeB4OlD0pd9E6lKr1W77NTc3u+3j6Q3lVMmjJz6+yj141nd42t/40k/QODl+gsbJZ2YMZtXlkP0oFPzU5YHshNBqobW1v66//hUeeGBM89EWURoY8A59H3Xw3q4DKwgffeM5LAdWI07eCEv/DH6T886qgICAgICAgICAgIDA1chV+c7spJPyFZj7MxCJhu1rNxbu2DrZJyTAp5/C22+PO5CFfy6iNPQdWLFY7HiKu3z5csf7qO3t7U6rFvtLpQQm5lMZuAuW/QX8xL6J0wt4W4s38+ItHd6292ac7pYvxHll7L1V9kyJU2A4g/ds9abPlfDzZV2eLHzp6Sq8UyEfvmxXk7LoqIt42t/40s/XfaIvNfraz5d1TYU8TpSZMZgdxMmTJ71iO8w+JQW+8hWXfcdbRGlw2XK5HGVzPWaTCQCzyYSyuRFR+t0g8nNb+4TinGS8rcWbefGWDm/bezNOd8sX4rwy9t4qe6bEKTCcnp4en/hcCT9f1jV37ly3fTy9oZwK+fBlu/Ik957iaX/jSz9f94m+1OhrP1/WNRXyOFFm3GB206ZNXrH1xN7TsvPz88mWR1O450WOf/IRhXteHLa363SI80rbC3FODldLW5wpcXpi762yZ0qcAsOJjY31ic+V8PNlXadPn3bbJzw83KO6pkI+fNmu3M29zWZDr9e7/M/05QMJ8Ly/8aWfr/tEX2r0tZ8v65oKeZwoU34wm5CQwKOPPkphYSEAFr2CwsJCp2klJSUlTisyarVaDg6sMvwlxcXFTi/7KxQKioqKnGyKiopQKBSOz83NzRR/9hkMmtJz8OBBp19Fq6urnZaqtlgsE9YnFovZse0GlubOcpqWfOLEieH6ioudyvGFPnAjf4I+QZ+gT9B3leorLCzk0UcfZSaTkJDAm2++yYULF4D+G/bOzk5sNpvDRqPRoNPpHJ+tVitdXV1O5ahUKgwGg+OzyWQa9gRRqVQ63eAbDAZUKpWTzdKlS52m0ep0OqetV0bSp9frnfRZLJYR9Q2u22QyoVQqx9RnMpmGbX3X1dXl1L6H6hOLxahUqjHzN5q+ofkbqk+r1RIREeH4PFL+huozGo3j5k+j0Tit7OuKPovFwvz5851sXDm+vb29Y+ZvQJ/dbnfSN17+9Ho9cXFxjs+uHN+4uLhhbXS042uxWKitreWWW27hW9/6Ftu2bWPr1q1s3bqV+++/n127djk+79y5k8cee4y77roLlUqFXq/n+PHjVFdXOwa6TU1NHD582Gnw+/nnn8+4PljQN731TdY1VnT48GH7+GZXJzqdju3bt9Pb20tYWBi0HoAT98ANpRAU730BDQ2wYQOsXw9/+hO4uWR8UVERq1evdr9e5RcQEAvBKe77XgE8jnOKIcQ5vRDinF54GqdarSY8PJx9+/Z5vALrVEWn0/HMM8+wa9cup0HSaKjVap588kmeeuopZDLZtLRXKpVERUVdNXo8tZ/MsgfnxBfar2b7J554ApPJNO7TcZ1Ox9NPP01GRobL2w3FxMTw4osvIv1yvZTxsNlsbm1lJJFIXC7bHWbKNcZVhHw4MxnXWMkka7pyNL8Px++GZS+OOZAtLi5myZIlLhU5pm1NTf9AtrERurrg8ccpVqtdLhtw+iXRZS2Kk3B4Cyz6b8j8qmfaJ2Drib1HcV4F9kKcI+NOnN7WIsQ5cXshzsnRMl0JCQnxyE+lUrk0AJ6oj6/9PF1MSKVSebRH6tAnv66g1WpdGoANxdM8epITXx4zX+ZeKpViMpkICAgY066vrw+73c7dd99NUFAQGo2G0NDQUe0HBr87duxwiiUxMZHW1tZh9jabjZaWFpKTkxGNsIvHSH5xcXHs2bNn1AGtp32iu33vROrzVKMv/XyZD0/9fJ3HiTI9BrNN70HJN2Dla5By85imcrnc5WJHta2o6B/ItrZCeDgUFkJODnIP9yRzWUvnUfhsGyx4ZsyBrMPenbLd1eIlvK3Fm3nxlg5v23szTnfLF+K8MvbeKnumxCkwnPFu5ifL50r4eVqX2Wx226+7u9ttH39/f7d9wPf58JWfL3PvLlKplICAAGw225ixDR38Dv5+JD+NRsMzzzzDXXfdNeKqzEP9+vr6eP3117FYLKMOZn3dJ3pSn6cafe3ny7qmQh4nyvQYzJ75Blz3FiRtG9c0OTnZ5WJHtC0thY0boaMDoqLgk09g0SK3y3aXZEkFfHYTLPofyPr6+PYTjXMS7b1ZtjfthTgnh6ulLc6UOD2x91bZMyVOgeF4sq2Jp1uh+NrP07o8GVB1dna67ePpQNHX+fCVny9z7ymuxjUw+B1gtGM98I7zUPvx/MbC132iJ/V5qtHXfr6sayrkcaJMj8Hsyj0uDWQnhZ/+tH8gGxsLBw/CvHner7P1Iyi6DZb8ETLu8359AgICAgICAgICAgICVzlTfjVjAOQbXDYdvBKXR7YvvQQ33wyffz5sIOtO2W6hLkeR695AdsJxTqK9N8v2pr0Q5+RwtbTFmRKnJ/beKnumxCkwnMGrwnrT50r4+bIuT94r9eQpJEyNfPiyXXmSe0+ZCm3f132iJ/V5qtHXfr6sayrkcaJMj8GsG5SXl0/MNjQU3nkHRlgYwJ2yAddX7cr7N8o1GW6VPeE4J9HendXJvK3Fm3kR4vS9FiHOidsLcU6OlunK0K1JvOnny7o89ZNIPJvQ5qnGtLQ0t30Gb5HjDp5q9CQnU6FdeZJ7uPrz4amfp32ipyvUelKfpxp96efLfHjq5+s8TpQZN5h1Zzlsd5fO9qb9VC1b0OL7sgUtvi9b0OL7sq82LdOVY8eOeeQ3dKsWb/n42s/XGgf293WHsLAwj+qarnn0Ze49rW8qtH1P+0Rf+gkaJ8fP1xonyowbzLqFfcpuwSsgICAgICAgICAgIDCtEQazo/Hhh7BjB3g4Xcdj+ry/FLyAgICAgICAgICAgMBUZ8oPZhMSEnj00UcpLCwE+jfvLiwsdNrEu6SkhOrqagCKiorQarUcPHjQqZzi4mKaB/aJfe89FF/9Kq9FRMAPf+iwKSoqcnq5ubm5meLiYqe/Hzx40OldhOrqakpKShyfx9RX/ls4cA3aXuUwfX/729/+qY/+l6yLioqcbAbrKyoqGqYPGFHfP/7xD9f0fcmhQ4fGzp8L+mB4/gBeeuklt/JXVFQ0TN9Yx3dAkyv69u/f71L+XD6+g/S99NJLI+obYKi+oqIil/I3oG/A1xV9//jHP1zO32j6BjSOpG+w3Xj5+/zzz13K34C+gbJd0ffaa6+5nL/R9I11fAfqcyV/hYWFbp0fA7au6vv888+H6Rtgosd34HtX8rd37163zg+X+udB+oaeI5N5fIuKiigsLOTRRx9lJpOQkMCbb77pmHJps9no7OzEZrM5bDQaDTqdzvHZarXS1NTkVI5KpXJ6l9NkMg17b6+5udlpYRqDwYBKpXKyWbp0KVar1fFZp9PR1tbm+DySPr1e76TPYrHQ1dWFUql00je4bpPJ5PR3AKVSOcwmf8j6GV1dXU7tW6fTodFoHGWJxWJUKtWY+RvQBzB//nyHvqH5G6pPq9USERGBWq0GRs7fUH1GoxGNRuOIb7TjO7DVy1B9AwzVZ7FYHNoHGJw/pVI5or7e3t4R8zdAd3c3nZ2d2AfNnBsrfwN16fV64uLiHN+5cnxXrVo1rI2OdnwHsNvt454fFovFyQegpaVl3OM7f/58p8W9DAYDLS0tY+obfHzhn+fH4D10NRqNU92j9cH79+93fPb0Hs/dPhjcu4a5c483kWusp/egA3iSP0/HGJ7c47l7jzKAu/fwk3WNFR0+fHjKzqXV6XRs376d3t5el98RUSgUxMTEjG7w5puwaxdYrSjWriXmnXcgOnpyyh6Ly8/C5f+Caw9AzNIJl+2OvTfLdhdva/FmXtxhpsTpbS1CnJNj7w4zLU61Wk14eDj79u3zeOGOqcrANfaPf/yjSyu7qtVqnnzySZ566ikkEglSqdRle5lMhslkGtNnqP0Ao/mNZj+a33j27pY/tC6j0eh2+b/97W/53ve+55aen/70p4SHh7utfaz8T3ZuPM39gN/VmntP83Ol235fXx+7d+/mo48+GlW/t6+hk1Gfpxp97ecJvtToy7om4xo75Z/MusuYSX71Vbj7brBaYdkyYvbudXkgO27Zo2G3w6X/hLJnYcOhEQeynpTtjr03y3YXb2vxZl68pcPb9t7uiK+WtjhT4vTE3ltlz5Q4BYYz3kB2snyuhJ8v6xr65NIV/P39PaprKuTDl+3Kk9x7ylRo+77uEz2pz1ONvvbzZV1TIY8TZcYNZkfl8GG4/36w2WD1avj4Y/DyHmPVVVVw8SdQ9QfY+BlELfRqfVeKwdMbpjNCnNMLIc7pxUyJc7LJzMy80hKuKgZPFxXoR8iJM0I+nBH6XmeEfEw+M24wO3hOuBNr1sBtt8GGDXDgAISFjW7rbtmjUFN2Gur3wMbPIaJgUst2x97rcdbUXDVavJkXIU7faxHinLi9EOfkaJmuZGVleeTnyV6nnu6P6ks/Twcqnmoc/J6nqwx+v9UdPNXoSU58ecx8mXu4+vPhqZ+nfaK7fe9E6vNUoy/9fJkPT/18nceJMuMGs+3t7SP/QSKB116Dffvgyznbo9q6W/ZoBMTA9nIIz5v0st2x93qcXiz7asqLt3R4296bcbpbvhDnlbH3VtkzJU6B4XgyoPJ0EOZrP1/WFe3Gq04DDF4kyB2mQj582a48yb2nTIW27+s+0ZP6PNXoaz9f1jUV8jhRZtxgdsmSJaP/0d8fgoJcs3W37NEQB3qlbHfsfRKnl8q+mvLiLR3etvdmnO6WL8R5Zey9VfZMiVNgOK4sGDUZPlfCz5d1lZWVue0TEhLiUV1TIR++bFee5N5TpkLb93Wf6El9nmr0tZ8v65oKeZwoM24wKyAgICAgICAgICAgIDD1kVxpAT7Hbof//V+45x7w8H0IAQEBAQEBAQEBAYGrA5PJ5LS/6ni4sn2YwNRgZj2Ztds5+B//Ad/7HmzaBL29Y5oP3VR4PEa1t5qg6E5o/9St8ryixcdlu4u3tXgzL97S4W17b8bpbvlCnFfG3ltlz5Q4BYbT1dXlE58r4efLupYuHXm7vrHoHefeZjSmQj582a48yb2nTIW27+s+0Z36TCYTu3bt4rHHHmPr1q0u/du1axcmk8ntujzVOFF8qXEq5GMwM+fJrM0Gjz3G8tde6/+8YgWEho7psnz5creqGNHeaoSjt4KxHSIXOP1p1apVEyt7kuy9WTZcPXG6ay/EOTLuxOltLUKcE7cX4pwcLdOVY8eOsXPnTrf9IiMjfeLja7+oqCiP69Lr9W77Xbp0iR07drjl4+k7s57m0ZOc+PKY+TL3cPXnw1M/T/vEgb7X3SenixYtctnWYrHQ2dnJ+vXrWbdu3bj2fX19vP7661gsFqRSqcexeeLn7rVoInV56ufLuiaDmTGYtVrh61+H3bsJAfjXf4Xf/AZEojHd3L0gDLO36OHITWDRwoZDIA33uPwJa7lCZQtafF+2oMX3ZQtafF/21aZluqLVaj3yk0jcv73wxMfXfr7W6MkWKmKx2KO6pmsefZl7T+ubCm3f0z4xJCTE8eS0s7PTZb+4uDj27Nnj1lRgmUxGQECARxo9wRM/X9blqZ+vNU6UKT/NOCEhgUcffZTCwkKg/9eZwsLCf/76Y7NR8thjVB850v/53/8d7c9/zsFDh5zKKS4udtofSaFQUFRU5GRTVFSEQqFwfG5ubqa4uNjJ5uDBg/0Xf7MWPruB6u5gSuKfcwxkh+kDSkpKnDZR1mq1wx7VT7q+L6murqakpMTxWdAn6BP0CfoEff36CgsLefTRR5nJJCQk8Oabb3LhwgUAbDYbnZ2d2Gw2h41Go3HaW9NqtQ6bxqhSqZwGByaTadhAWalUOqb9Qf9gQqVSOdksXboUq9Xq+KzT6dBoNI7PI+nT6/VO+iwWy4j6BtdtMplQKpVj6jOZTOTn5zvZdHV1ObXvofrEYjEqlWrM/I2mb2j+hurTarVOq9SOlL+h+oxG47j502g0Tlu5uKLPYrEwf/58JxtXjm9vb++Y+RvQZ7fbnfSNlz+9Xu+0b6wrxzcuLm5YGx3v+Nrt9nHPD4vF4uQDrh3f+fPnO227NJnH12AwYLPZ0Ov1dHZ2cuDAAfR6vePf8ePHqa6udnxuamri888/d6p7rD544Mnpjh07uO+++3jwwQd58MEHufPOO7n99tsdnx944AF27tzJXXfdRWdnJxaLxaVrxPnz5wkLCxszf0OPb1hYGOfPn3eymW7XsKtd32RdY0WHDx+2j292daLT6di+fTu9vb1OjdgJux1+8hN4+ml44gmq772XrOxsl8qvrq52a8N4J/vDN4DdDGv3gkTmchle0XIFy3YXb2vxZl7cYabE6W0tQpyTY+8OMy1OtVpNeHg4+/btI/jLPchnCgPX2D/+8Y8ubeWhVqt58skneeqpp7Db7ePma7C9TCZDp9ON6TPUfrDOkfxGsx/Nbzx7d8sfWpfVanW7/BdffJFHHnnELT0//vGPx53qOpL2sfI/2bnxNPcDfldr7j3Nz5Vu+zqdjp/97GdkZGTg5zfyc66oqCinAWJMTAwvvviiS09O9Xo9X/nKV3jooYdcenLa19fH3r172bNnj0v51Ov1bN26lTvvvNOlvqqvr4/du3fz0UcfIZPJPL6WePu+YjLq8sTPl3VNxjV2+k8zFong5z+HDRtg40aMg34hGA+j0ehWVU72C/4LwnJc3kfWq1quYNnu4m0t3syLt3R4296bcbpbvhDnlbH3VtkzJU6B4Qx+8uNNnyvh58u6PJkyOZ3z4ct25UnuPeVKt32r1Yrdbufuu+8mKChoRJ/BA2CdTsfTTz/Njh07Rh38Di2/sbHRLb1isdjld531ej12u93jfHja3/vyOuFLjVMhH4OZ/oNZ6B/QbtwIQEFBgctu7tgOs4+c55avV7VcwbLdxdtavJkXb+nwtr0343S3fCHOK2PvrbJnSpwCwwkdZ4HFyfK5En6e1qVWq932q6mpcdvHlSdZI+HrfPjKz5e595Srpe1LpdJRB/GDv+/r6xt38DsYjUbDM8884zQ9fCwsFgsnTpzg5ptvdmuw7IqWkfC0v/fldcKXGqdCPgYzMwazVylarXZGLDIixDm9EOKcXghxCoyFkDNnLBaLx4vuTFeEnDgzk/Ix1uB3gIEVg93BlSfFg3F3sHwlEa5Fk8+UXwDKCY0GKivHNHFnWXB3bD2xP3bs2FWhZabE6a69EOfIuBOnt7UIcU7cXohzcrRMVzzdRmK6TjMeuqiMN+sCz1Ym9vSG3lONnuRkKkwz9nRV6Ks9H576+bJ9QH/+BwbL4/0bGCx72vZd6e9NJpPTolh6vR61Wj3su4F/gxebGoy71yJ3NE6Wny/rmgymz2BWpYLrr4d168Yc0B4asorxWLhkq2uA2pfdLttd3C170uOcgL03y76a8uItHd6292ac7pYvxHll7L1V9kyJc6pht9vZvXs3t956K1u3buXb3/42dXV1k1rH4FUvvelzJfx8WdfSpUvd9unt7fWorqmQD1+2K09y7ylToe37sn2Ab9v+eP39wNZCW7dudfr3b//2b8O+G/i3a9euUQe03tA4mX6+rGsymB6DWaUSNm2CkyehqwsuXRrVdOOX7866wri2mhr4ZC10nwa73a2ygWFLWk9IywTsvVk2XD1xumsvxDky7sTpbS1CnBO3F+KcHC1TiTfffJOPPvqIZ599lvfee4+CggIef/zxEffV9PQGNiYmxic+vvYrKyvzWV0Ap0+fdtsnPDx8fKMR8FSjJznx5THzZe7h6s+Hp36+bB/g27Y/Xn8/sLXQ3Xff7dhG6MEHH2T16tVOnwf+3X333Y6thYYy2rVopCe/g/+tWLHCpSe/7sY2WT4T8Zso02NS//btUFoK/v7wt7/BV74yqqk77zGMaauugEMbIfU2WPQ/IBK5/Y7E2bNnJ0fLBO29WTZcPXG6ay/EOTLuxOltLUKcE7cX4pwcLVOJvXv3cvvtt5ORkQHAQw89xP79+zl69CjXX3+9k62ng1lXFm2ZDB9f+5WXl7Nu3Tqf1AU47anrKiKRyKO6PNXoSU58ecx8mXu4+vPhqd9E2sfChQvd9vNl23e1vx+Y1uwKA/v2DuXMmTPDvjeZTHzta18btj/yWMTFxbFnz55x30f25Frm6fXvSl03p8eT2dJSkErhnXfGHMhC/wa9rjKqraoUDq6DWfc6BrLulu0u7pY9KXFOkr03y76a8uItHd6292ac7pYvxHll7L1V9kyJcyqh1Wppb28nPz/f8Z1YLCY7O5uqqqpJq+fChQs+8bkSfr6sq6+vz22fEje2IBzMVMiHL9uVJ7n3lKnQ9n3ZPsD7bX9gsKnX69m7d++YT0UHtv4Zymg5sVgs1NbWcvPNNw+bflxdXT3su+3bt3P69GnuuuuuEZ/0PvjggyxZssSlJ79D8eRa5un170pdN6f0T88DDUstlfY/kV27FsZZfv3ll19mxYoVLpU/om1PCRzZAVkPw6wf9i865UHZ0H8iubpcvLtlTzjOSbS/WuJ0116Ic2TcidPbWoQ4J24vxDl22QM+U2GVTFcYeCIwdDXNkJCQYU8LBmJ29T00jUaDzWajt7eXkydPkpaW5rK9yWQa12eo/QCj+Y1mP5rfePbQ/7RIpVK5bD+4rqioKJftB8oPCAhwy95ms1FcXExOTo5LtoPLHiv/Y8U6OCeu2I9Ul6u5HPC7WnPf29s7Yj7Gsp8qbX+wjzv5t1qt9Pb2XjVtH/r3ya2urnbsk9vR0UF8fPyYMTQ3N9PT00NgYKDj+7Hyb7Va2b59+7AnuW+//TY333yz03d6vZ7nnntu1EEz9M8uysrKAvoH+haLBbVaPe6A1t3rqqc+nvpNxjVWdPjw4Sl7he7q6uL222+/0jIEBAQEBKY5b731FrGxsVdaxoTRarXs2LGD3//+98yZM8fx/b//+7+Tnp7OY4895vhOuMYKCAgICPiCiVxjp/ST2ejoaN566y2CgoI8nicvICAgICAwGna7HYPBQHR09JWWMimEhIQgl8spLy93DGatVivV1dVcd911TrbCNVZAQEBAwJtMxjV2Sg9m/fz8psUv5QICAgICVy/TbYP7nTt38tZbb7Fo0SISExN59dVXkUgkrFmzxslOuMYKCAgICHibiV5jp/RgVkBAQEBAQMA97rjjDvR6Pd/73vfQ6/Xk5ubyy1/+kqCgoCstTUBAQEBAwC2m9DuzAgICAgICAgICAgICAjOT6bE1j4CAgICAgICAgICAgMCMQphm7GPsdjsvvfQS+/fvR6fTkZOTw3e+8x1mzZp1paVNiE8//ZT33nuPmpoa9Ho9Bw8eRCwWO/5eU1PDc889R2VlJcHBwWzfvp37779/Si0q8qc//YmTJ0/S0dFBYGAgCxYs4JFHHiEuLs5h09HRwW9+8xsuXLiAv78/GzZs4Jvf/Cb+/v5XULl7vPzyy3z88cf09vYiFovJycnhkUcecSwJD9PjeA7liSeeoKioiF//+tcsXrwYgPPnz/PHP/6RxsZGIiMjufPOO9m5c+cVVuo+L730Eq+++qrT5uorV67kiSeeAKbf8SwtLeXFF1+kvLwcPz8/0tPTee655/Dz85t2sXqL6Xqt8oTxzp/pzky4vrvLeDlZv349UqkUP79/PjP6wx/+QEZGxpWQ63Vmyv2Rq7iSjzvvvBOlUunUbn760596tCXO1Y637yuFwayPefPNN/noo4949tlnSUpK4pVXXuHxxx/nlVdemdLvK4WEhLBz5076+vr41a9+5fQ3vV7P448/zpYtW3j22WdpaWnhBz/4AcHBwdx2221XSLH7iEQifvCDH5CRkUFfXx//+7//y49+9CP+8pe/AP37WP7oRz8iMzOTv//972g0Gn784x/z/PPP861vfesKq3ed9evX85WvfIXQ0FDMZjPvvvsujz/+OH//+98Ri8XT5ngOprCwEKPR6PRde3s7//Ef/8HDDz/M9u3bKS0t5Sc/+QlRUVHDFsqZCsyePZvf/e53w76fbseztLSUH/zgB3zrW9/i6aefxt/fn4qKCkQi0bSL1ZtM12uVp4x2/swEZsL13V3GyskATz/9tOOH0enOTLk/cpXx8jHAt7/9bbZt23aFVPoOb99XCtOMfczevXu5/fbbycjIICAggIceegiz2czRo0evtLQJsXTpUjZu3EhiYuKwvx05cgSbzcZDDz1EQEAAGRkZ3HHHHbz33nu+FzoBvv71r5Obm4u/vz8hISHcdddd1NTUoNFoALh48SINDQ089thjBAcHI5fLefDBB/nwww9d2vT7aiE1NZXQ0FCg/+mMn58fPT09jjiny/EcoKuri7/+9a98//vfd/q+sLCQ5ORkbr75Zvz9/VmwYAFbt27l3XffvUJKvcN0O54vvPACN9xwA5s3byYwMBCxWMzs2bMRiUTTLlZvMl2vVQLuMxOu7+4yVk5mIjPl/shVxsvHTMPb95XCk1kfotVqaW9vJz8/3/GdWCwmOzubqqoqrr/++iuoznvU1NSQlZXlNJUiLy+P1tZWdDodwcHBV1Cd55w5c4b4+HjHCVpdXU1iYiLh4eEOm7y8PIxGI01NTWRmZl4pqW5z4sQJfvGLX6DT6RCJRNx6661EREQA0+t42u12nn32We655x7i4+Od/lZdXU1eXp7Td7m5uRQWFvpS4qRRXV3NTTfdRGBgIHPmzOFrX/saCQkJ0+p4Go1GSktLmT17Nt/4xjdobW0lPj6eXbt2sW7dumkVqzeZqdeqsRjt/JnpCOfU6PziF7/AarUSHx/PjTfeyPbt26+0JJ8xne+PPGFoPgb4y1/+wgsvvEB0dDTXX389t912GxLJ9ByaefO+cnpm7CpFr9cDw/dTCgkJcfxtOqLT6YbFPHBC6/X6KXmxO3v2LK+88gpPPvmk47uRYhkc51RixYoV7Nu3D7VaTWFhodNek9PpeO7duxe73c6OHTuG/U2n05GcnOz0XWhoKDqdzlfyJo1169axZcsW4uPjUSgUvPDCC3z/+9/nL3/5y7Q6nhqNBpvNxscff8zTTz9NdnY2x44d46mnniImJmZaxepNZuq1ajTGOn9m4pTrwQjn1Mj8+te/pqCgAD8/P86ePesY2E7FNRfcZbrfH7nLSPkA+OEPf0hOTg4BAQFcvnyZX/ziF6jVah555JErpNS7ePO+Uphm7ENkMhnQ/6v3YLRareNv05Hg4OBhMQ9MLZiKcZ84cYKf/exn/OhHP2Lp0qWO72Uy2bCBzlSOEyAsLIxbbrmFX//611RXVwPT53i2tLTw6quvDptePMBocU7Fm7NZs2Yhl8sRiUTExsby+OOP09XVRUlJybQ5noBjYLF582by8vIQi8WsXbuWBQsWUFRUNK1i9SYz9Vo1GmOdPzMd4ZwamcWLFxMQEIC/vz/Lly/nlltu4ZNPPrnSsrzOTLo/coXR8gGwYMECZDIZYrGYuXPn8sADD8yINuKN+0phMOtDQkJCkMvllJeXO76zWq1UV1eTnZ19BZV5l8zMTKqrq7FarY7vKioqSExMnHIDg08++YRf/OIX/PSnPx22CFBWVhZtbW309vY6vquoqCAwMJCUlBRfS5007HY7FouFlpYWYPocz0uXLjl+Bd25c6fjF/Of/exn/PrXvyYrK4uKigonn4qKCqfV96YqIpEIkUiE3W6fNscT+vvYxMTEUVc/nE6xepOZeq1ylcHnz0xHOKdcYya0l5l4fzQWY+VjJGZCGxlgsu8rhcGsj9m5cydvvfUWdXV19PX1sXv37v/f3t0HRXWdDxz/LiwrrKCyIBghRFsyJEaZqIylU5KIUYKAoPElmChJ8SU62CrWt4htU0vUBZQYTZBERTJK20gTIVhoy4hMk1Ebd4IxQCQ6+MKLYKQiiO6uu/v7w2F/ISAgaMji85nZP7h777nn3AWefc4591yUSqVNro76fSaTCYPBgNFoBMBgMGAwGDCbzTz77LPY2dmRkZGBXq+nsrKSjz76yOam23zyySe88847bNq0qV0PG4C/vz8+Pj6kpaXR0tJCXV0dGRkZTJ06tc0jHX7qsrOzaWhoAODatWukpqaiVCoZPXo0QL/5PCdOnEhWVha7d++2vgBWrlzJ4sWLeeGFF7h48SI5OTkYjUa++uor8vPzmT59et9WvAeKioqsXyIaGhpITk7G1dWV0aNH95vPs9WMGTMoKCjg7NmzmM1mPv/8c06dOsUzzzzT79r6IPXXWNUTnf39PAwehvh+rzq7JhUVFZw5cwaj0YjJZOKLL77g73//O5MmTerjWj84D8v3o+7q6npUVVXx1VdfWX9nysrKyMzM7Le/Iw/6e6WiqKjo4egG+ImwWCxkZGSQl5dHS0sLfn5+LF++3OafPVZQUIBWq223PTU1laeffppz586xfft2KioqUKvVREZG2txz6IKDg7G3t2/3TDStVou/vz9w53Eurc9RU6lUTJo0iaVLl9rUP+s33niDM2fOcPPmTdRqNU888QQxMTH4+flZ9+kPn2dHgoOD2z1n9t13323znFlbTGYTEhIoLS3l1q1buLi44O/vT2xsLF5eXkD/+zwPHDhATk4Ozc3NeHt7ExMTQ1BQEND/2vqg9NdY1RNd/f30dw9DfL9XnV2TlpYW0tPTqa+vx97eHk9PT6KiooiMjOyDmv44HpbvR93V1fUoLy8nJSWF2tpaFAoF7u7uTJkyhejo6H65ANSD/l4pyawQQgghhBBCCJsj04yFEEIIIYQQQtgcSWaFEEIIIYQQQtgcSWaFEEIIIYQQQtgcSWaFEEIIIYQQQtgcSWaFEEIIIYQQQtgcSWaFEEIIIYQQQtgcSWaFEEIIIYQQQtgcSWaF+JEVFBQwe/bsvq6GzXrttdcoKCjo62oIIYT4EW3ZsoW33nrrgZUfHByMTqd7YOULIR4MZV9XQIgHLT8/n6SkJObOncvixYu7fdy+ffvQ6XTs2LHjAdauvYKCApKSkhgwYECb7bt27eKxxx7rVdklJSXEx8dTWFiIvb19r8rqqS1btlBYWIiDgwMKhQKNRkNYWBgvv/xyt47ft2/fPZ/PZDKRkJDQg9oKIYT4sfQ0XveFFStWMGbMGBYsWNCrcqKjo2loaGgXk1NSUnjqqad6VbYQDwNJZkW/l5OTw6BBg8jPz+e1115DpVL1dZW65ObmxsGDB/u6Gg9McHAwCQkJWCwWdDodCQkJaDQaQkND+7pqQggh+ogtxuv7Yfny5YSHh3drX6PRiIODQ5ttFosFs9l8z53UHZUlhK2RZFb0a9988w1nzpxBq9WyYcMGiouLmTJlivX969evs2fPHv773//S2NiIu7s7y5Yto7GxkQMHDmA2m5k6dSoAWq0Ws9ncbmSzoKCAPXv2WJPPo0ePkpWVRW1tLXZ2dowePZply5bxyCOP9Lo9KSkpnDx5ksbGRoYMGUJISAivvvoqdnZ37hhYsWIFP//5z7l27RonTpxArVbzyiuvEBUVRV1dHWvXrgUgIiICgFdeeYV58+axd+9eioqKuHr1Ks7OzgQFBbF48WIcHR0BKCoqIjMzkytXrqBUKvH19WXr1q0cPnyYrKws9u/fj0KhAMBgMDB79mxWr15NUFBQp+1RKBQEBAQwYsQIKioqCA0NpbS0lPT0dCorK3F2diY4OLjNl5ro6Gjmz59PeHg4ly9fZu7cuaxdu5aDBw9SW1vLiBEjWLNmDSNGjGD//v0UFhYC8NlnnwH/P7KbmppKWVkZZrOZoUOHEh8fj7+/f68/IyGEEPeuq3gNd5Kv5ORkiouLGThwINOnT2fu3LkANDc3s23bNnQ6Hbdv30aj0bBw4UKee+45AI4dO0ZGRgY1NTW4uroSERHB7NmzrfHz+34Y16HtbK1t27Zx+vRpSktLyc7OBu6MKgMcP36czMxMqqqqGDx4MDNmzGDmzJk9vi6t5x07diz/+Mc/cHZ2ZsuWLcydO5dVq1bx8ccfU1VVRWpqKj4+PuzatYsTJ05gMBh44okniIuLw8fH565lZWRkkJGRQX5+Pjdu3GDgwIGEhISwaNGiHtdZiB+TJLOiX8vJycHX15cJEyYQFBRETk6ONThaLBZ+//vf4+TkxNtvv42Hhwe1tbUYDAYmTJhAdXV1u2nGJSUlXZ5TrVazZs0aRo4cSXNzM1qtlsTERN59991et+fJJ58kNjYWV1dXysvLeeONN3BzcyMyMtK6zz//+U82btxIQkIC//nPf9i4cSMBAQF4eXmh1WqJj48nLy+vTQ+ut7c3KSkpeHh4cP78eTZs2ICTkxOLFi3i1q1bbNq0Ca1Wy7hx4zAYDHz99dcAPP/886SlpaHT6QgICACguLgYlUrFL3/5yy7bYzab0el0VFZWMnPmTOrq6li1ahULFixg69at1NXVsWHDBgwGA8uWLbtrOf/+979JSkrCxcWFt956i9TUVLZv3868efOoqqpqN804MTERd3d3srOzUSqVVFVVSe+0EEL0oc7idavPPvuM3/72t8THx1NRUcG6detwd3dnypQp/PWvf6WlpYW//OUvODk5UVdXx61bt4A7ifIf/vAH1q9fz7PPPsvZs2dJSEjA3t6eWbNm3XNdV65cycWLF9tNM/7yyy9JTEzkT3/6E2PHjuXChQusXbuWQYMGtWvLvSgrKyMgIICsrCzMZjPXrl0D7iTQmzdvxs3NDZPJxJtvvsnNmzdJT09HrVaze/duVq1aRWZmJk5OTh2WpdPpyM/PZ+fOnXh6enL9+nUuXbrU47oK8WOTBaBEv9XU1ERRUZF1FDIiIoLS0lLOnj0LQEVFBadPn2bdunV4enqiUCgYPnw4I0aM6NV5J0yYgK+vL/b29gwePJhf//rXlJWV0dLS0u0yrl69SkREhPXVOqIaHh6ORqNBoVAwatQoJk+ezMmTJ9sc+8wzzzBu3Djs7Ox47rnncHFx4cyZM52eLyQkxHoNRo4cyfTp0/niiy+s7yuVSi5cuEBjYyMqlYpx48YB4OjoyAsvvEBeXp5137y8PMLDwzud7nT06FEiIiKYMWMG7733HgsXLiQkJITCwkK8vb2ZNWsWDg4OeHt7s2DBAvLy8rBYLHctLyYmBjc3N1QqFaGhoV2218HBgYaGBqqrq1EoFPj4+NyXkXMhhBD3rqt43epnP/sZkZGRKJVKRo0aRXh4uHVE1MHBgevXr3Px4kUsFgvDhg2zxvPDhw8TGBhIcHAw9vb2+Pn58dJLL5Gbm3tf25GdnU1UVBTjx4/Hzs6OkSNHEhkZ2eWihTt27GgT8yMiIjAYDNb3NRoNMTExqFQq64wpuBP7PDw8sLe3p6mpiWPHjhEXF4dGo8HR0ZElS5ag1+s5duzYXctSKpUYDAbOnz+PXq9n0KBBcq+usCkyMiv6rfz8fBQKBZMnTwZg7NixeHl5kZOTw+9+9ztqa2txcXFhyJAh9/W8JSUlfPjhh1y4cMHaKwzwv//9D7Va3a0yOrpn1mKxcODAAQoLC7l69SoWi8U6jej73N3d2/zs6OjIzZs3Oz1fbm4ueXl51NXVYTKZuH37NoMGDbIer9VqOXjwIPv27cPNzY2wsDBrb3ZUVBQLFy6koaGBpqYmvv76azZs2NDp+SZOnNjhgkz19fUMHz68zTYvLy/0ej3Xrl3D1dW1w/K+32YnJyf0ej0mk+muCfWSJUvYv38/f/zjH2lqaiIwMJBFixah0Wg6rbcQQoj7r6t43eqHnY6PPPKI9RaSl156CZPJRHJyMvX19YwfP55Fixbh5eXFlStX2i2g6OXlRX19/X1tR1VVFTqdjpycHOs2s9mMh4dHp8f95je/6fSe2dbO5h/6/vVobYuXl5d1m1KpxNPTk7q6uruW9fTTT/P666+TlZXFxo0b8fX1Zf78+dbZVkL81EkyK/oli8XCp59+itFoZP78+dbtN27coLCwkCVLljBs2DCamppobGxk8ODB7croKHC0TtO5efMmzs7OAHz33XfW941GI+vXrycmJobExETUajXffvvtfVmV8ciRI2RnZ6PVaq0jvzt27KCioqLbZXTUptLSUt555x2Sk5MZM2YMSqWSgwcP8tFHH1n38ff3x9/fH4vFwqlTp6z3pAYEBODj48Po0aMpKCigoaGBwMBAhg4d2qM2enh4UF5e3mZbTU0NAwYM6HGnQ0dtHjx4MHFxccTFxXHlyhU2bdrEe++912USLoQQ4v7qTrweOHAgAJcvX25z7OXLl63xxtHRkdjYWGJjY7l+/Trbt29n8+bN7Ny5k6FDh1JTU9Pm2JqamrsmmU5OTm06o6FtrAc6vNdWo9EwadIkXn311W62vns6imM/rENrW6qrq/H19QXAZDJRX1+Pp6dnp2WFhYURFhaGwWDg0KFDrF+/nkOHDnW7A16IviTTjEW/dPLkSaqqqkhOTmb37t3W1549e4A795X6+fnx1FNPodVquXLlCgC1tbVcuHABuBOU6uvr20z1efTRR1Gr1Xz66aeYzWbOnj3bZort7du30ev1uLi4oFar+e6776zn7K0bN25gb2+Pq6srCoWCL7/80rq4UXe1jjxevHixTbl2dnYMGTIEpVJJRUUFhw4dsr7f0NDA0aNHaW5uRqFQ4OzsjEKhaDPqGRUVRV5eHv/617+YNm1aj9v4/PPPc+nSJT7++GOMRiPV1dXs3buXsLCwuwbzrmg0GmpqajCZTNZtR44cobq6GrPZjFqtxsHBoc8eVSSEEA+z7sTrVufOnePw4cOYTCbKy8s5fPiwdRX8zz//nMrKSkwmE46OjqhUKuv/9alTp3L8+HGKi4sxmUx8++23/O1vf7NOa/6hxx9/nJaWFo4cOYLZbKakpITi4uI2+7i6ura7t3TmzJl88skn6HQ6TCYTJpOJyspKTp06dd+u1924ubnxi1/8grS0NBoaGtDr9aSnp+Pg4EBgYOBdjysvL+fUqVPo9XocHBxQq9UoFIoOk3UhfopkZFb0S7m5uYwfP56xY8e22a7RaAgPDyc3N5cXX3yRxMREPvjgA+Li4mhubmbo0KEsW7aMxx57jODgYIqLi5k1axZms5nNmzczZswY1q1bx65du/jwww/x9/cnIiLCOqXIycmJ1atXs2/fPnbu3Mnw4cOZPXs2J06c6HWbQkNDOX36NLGxsdjZ2TF+/HgmT558TyOzjz76KDNmzGDlypUYjUZefvlloqOjiYyMZMWKFZhMJkaNGkVISIg1SbdYLOTm5rJt2zaMRiMajYYFCxa0ubZBQUHs2LEDtVrNhAkTetzGYcOGkZSUxPvvv8/evXtxdnZm4sSJxMbG9rjMadOmUVJSwvTp07FYLOzZs4dz587x/vvv09jYyIABAxg3bhxLly7t8TmEEEL0THfjNdyJNWVlZaSlpaFWq5kzZ451YaXa2lrS0tK4evUqKpWKJ598kjVr1gAwatQo3nzzTTIzM0lKSmLIkCG8+OKLd11lePjw4Sxfvpxdu3axdetWAgMDCQ0N5ZtvvrHuM2fOHJKSkpg2bRoWi4W8vDyCgoJQqVRkZGRYO429vb2Jjo7u9Bps376dnTt3ttm2evVqJk2adA9XEtavX096ejqvv/46BoMBPz8/UlJSOh1hbV0w6tKlS9jZ2eHt7c2f//znNvfmCvFTpigqKrr7qipCCNFNS5cu5Ve/+hXz5s3r66oIIYQQQoiHgMwhEEL02vHjxzl//nyvphgLIYQQQghxL2SasRCiV+bMmYNeryc+Pr7DhbSEEEIIIYR4EGSasRBCCCGEEEIImyPTjIUQQgghhBBC2BxJZoUQQgghhBBC2BxJZoUQQgghhBBC2BxJZoUQQgghhBBC2BxJZoUQQgghhBBC2BxJZoUQQgghhBBC2BxJZoUQQgghhBBC2Jz/A3qBllmzyJCEAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.5385774562989752, 53.50412610860971)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "# Enable LaTeX rendering in plots\n",
        "matplotlib.rcParams['text.usetex'] = True\n",
        "matplotlib.rcParams['text.latex.preamble'] = r'\\usepackage{amsmath}'\n",
        "\n",
        "# Set classic style for plots\n",
        "plt.style.use('classic')\n",
        "\n",
        "def evaluate_model(model, dataloader, threshold, device):\n",
        "    model.to(device)\n",
        "    model.eval()\n",
        "    total_hits = 0\n",
        "    total_samples = 0\n",
        "    total_mse_loss = 0\n",
        "    criterion = nn.MSELoss()\n",
        "    actual_fps = []\n",
        "    predicted_fps = []\n",
        "    errors = []\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for features, targets in tqdm(dataloader):\n",
        "            features, targets = features.to(device), targets.to(device)\n",
        "            outputs = model(features).squeeze()\n",
        "            mse_loss = criterion(outputs, targets)\n",
        "            total_mse_loss += mse_loss.item()\n",
        "            hit_mask = torch.abs(outputs - targets) <= threshold\n",
        "            total_hits += hit_mask.sum().item()\n",
        "            total_samples += targets.size(0)\n",
        "            actual_fps.extend(targets.cpu().numpy())\n",
        "            predicted_fps.extend(outputs.cpu().numpy())\n",
        "            errors.extend((outputs - targets).abs().cpu().numpy())\n",
        "\n",
        "    accuracy_rate = total_hits / total_samples\n",
        "    average_mse_loss = total_mse_loss / len(dataloader)\n",
        "    print(f'Accuracy (within ±{threshold} FP): {accuracy_rate * 100:.2f}%')\n",
        "    print(f'Mean Squared Error Loss: {average_mse_loss:.4f}')\n",
        "\n",
        "    plt.figure(figsize=(12, 6))\n",
        "\n",
        "    # Actual vs Predicted plot\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.scatter(actual_fps, predicted_fps, alpha=0.5, edgecolor='k', facecolor='skyblue')\n",
        "    plt.xlabel('Actual Fantasy Points')\n",
        "    plt.ylabel('Predicted Fantasy Points')\n",
        "    plt.title('Actual vs Predicted Fantasy Points')\n",
        "    min_val, max_val = min(actual_fps) - 5, max(actual_fps) + 5\n",
        "    plt.plot([min_val, max_val], [min_val, max_val], 'r--', lw=2)  # Diagonal line for reference\n",
        "    plt.plot([min_val, max_val], [min_val + 5, max_val + 5], '--', lw=1, color='orange')  # Upper threshold line\n",
        "    plt.plot([min_val, max_val], [min_val - 5, max_val - 5], '--', lw=1, color='orange')  # Lower threshold line\n",
        "    plt.xlim(min_val, max_val)\n",
        "    plt.ylim(min_val, max_val)\n",
        "    plt.grid(True, which='major', linestyle='--', linewidth='0.5', color='gray')\n",
        "    plt.minorticks_on()\n",
        "    plt.grid(True, which='minor', linestyle=':', linewidth='0.5', color='gray')\n",
        "\n",
        "    # Error Histogram\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.hist(errors, bins=30, color='gray', alpha=0.7)\n",
        "    plt.xlabel('Absolute Errors')\n",
        "    plt.ylabel('Frequency')\n",
        "    plt.title('Histogram of Prediction Errors')\n",
        "    plt.grid(True, which='major', linestyle='--', linewidth='0.5', color='gray')\n",
        "    plt.minorticks_on()\n",
        "    plt.grid(True, which='minor', linestyle=':', linewidth='0.5', color='gray')\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "    return accuracy_rate, average_mse_loss\n",
        "\n",
        "threshold = 5  # Define your threshold value\n",
        "evaluate_model(lstm_model, test_dataloader, threshold, device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "u975AEFjivjL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 888
        },
        "outputId": "b19ab401-be3e-48b1-9b31-230c8bc36e30"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  2%|▏         | 9/415 [00:00<00:35, 11.38it/s]Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fde495901f0>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7fde495901f0>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1479, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\", line 1462, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.10/multiprocessing/process.py\", line 160, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "100%|██████████| 415/415 [00:41<00:00, 10.05it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy (within ±5 FP): 81.86%\n",
            "Mean Squared Error Loss: 15.6937\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 960x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.8185517293346395, 15.69372421862131)"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "evaluate_model(lstm_model, train_dataloader, threshold, device)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": [],
      "collapsed_sections": [
        "b7GAbzMh_Cfg",
        "4jD-JDd2_IAL",
        "7bJqVyuxHo21",
        "Ob3lrIAtdtXO",
        "v3m-nQTec3aR",
        "x3kuJsMZiTha",
        "q9AzA7_sdyp3",
        "A36yG3lckDLd"
      ]
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}