mobile-price-classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trying different Classification models on Mobile Price Dataset\n",
    "\n",
    "\n",
    "Dataset: https://www.kaggle.com/iabhishekofficial/mobile-price-classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report,plot_confusion_matrix\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.svm import SVC\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading the Mobile Phones dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>battery_power</th>\n",
       "      <th>blue</th>\n",
       "      <th>clock_speed</th>\n",
       "      <th>dual_sim</th>\n",
       "      <th>fc</th>\n",
       "      <th>four_g</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>m_dep</th>\n",
       "      <th>mobile_wt</th>\n",
       "      <th>n_cores</th>\n",
       "      <th>...</th>\n",
       "      <th>px_height</th>\n",
       "      <th>px_width</th>\n",
       "      <th>ram</th>\n",
       "      <th>sc_h</th>\n",
       "      <th>sc_w</th>\n",
       "      <th>talk_time</th>\n",
       "      <th>three_g</th>\n",
       "      <th>touch_screen</th>\n",
       "      <th>wifi</th>\n",
       "      <th>price_range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>188</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>756</td>\n",
       "      <td>2549</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1021</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>0.7</td>\n",
       "      <td>136</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>905</td>\n",
       "      <td>1988</td>\n",
       "      <td>2631</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>563</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>0.9</td>\n",
       "      <td>145</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1263</td>\n",
       "      <td>1716</td>\n",
       "      <td>2603</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>615</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.8</td>\n",
       "      <td>131</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1216</td>\n",
       "      <td>1786</td>\n",
       "      <td>2769</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1821</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>0.6</td>\n",
       "      <td>141</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1208</td>\n",
       "      <td>1212</td>\n",
       "      <td>1411</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>794</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>106</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1222</td>\n",
       "      <td>1890</td>\n",
       "      <td>668</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>1965</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0.2</td>\n",
       "      <td>187</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>915</td>\n",
       "      <td>1965</td>\n",
       "      <td>2032</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>1911</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>0.7</td>\n",
       "      <td>108</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>868</td>\n",
       "      <td>1632</td>\n",
       "      <td>3057</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>1512</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>0.1</td>\n",
       "      <td>145</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>336</td>\n",
       "      <td>670</td>\n",
       "      <td>869</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>510</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>0.9</td>\n",
       "      <td>168</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>483</td>\n",
       "      <td>754</td>\n",
       "      <td>3919</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  \\\n",
       "0               842     0          2.2         0   1       0           7   \n",
       "1              1021     1          0.5         1   0       1          53   \n",
       "2               563     1          0.5         1   2       1          41   \n",
       "3               615     1          2.5         0   0       0          10   \n",
       "4              1821     1          1.2         0  13       1          44   \n",
       "...             ...   ...          ...       ...  ..     ...         ...   \n",
       "1995            794     1          0.5         1   0       1           2   \n",
       "1996           1965     1          2.6         1   0       0          39   \n",
       "1997           1911     0          0.9         1   1       1          36   \n",
       "1998           1512     0          0.9         0   4       1          46   \n",
       "1999            510     1          2.0         1   5       1          45   \n",
       "\n",
       "      m_dep  mobile_wt  n_cores  ...  px_height  px_width   ram  sc_h  sc_w  \\\n",
       "0       0.6        188        2  ...         20       756  2549     9     7   \n",
       "1       0.7        136        3  ...        905      1988  2631    17     3   \n",
       "2       0.9        145        5  ...       1263      1716  2603    11     2   \n",
       "3       0.8        131        6  ...       1216      1786  2769    16     8   \n",
       "4       0.6        141        2  ...       1208      1212  1411     8     2   \n",
       "...     ...        ...      ...  ...        ...       ...   ...   ...   ...   \n",
       "1995    0.8        106        6  ...       1222      1890   668    13     4   \n",
       "1996    0.2        187        4  ...        915      1965  2032    11    10   \n",
       "1997    0.7        108        8  ...        868      1632  3057     9     1   \n",
       "1998    0.1        145        5  ...        336       670   869    18    10   \n",
       "1999    0.9        168        6  ...        483       754  3919    19     4   \n",
       "\n",
       "      talk_time  three_g  touch_screen  wifi  price_range  \n",
       "0            19        0             0     1            1  \n",
       "1             7        1             1     0            2  \n",
       "2             9        1             1     0            2  \n",
       "3            11        1             0     0            2  \n",
       "4            15        1             1     0            1  \n",
       "...         ...      ...           ...   ...          ...  \n",
       "1995         19        1             1     0            0  \n",
       "1996         16        1             1     1            2  \n",
       "1997          5        1             1     0            3  \n",
       "1998         19        1             1     1            0  \n",
       "1999          2        1             1     1            3  \n",
       "\n",
       "[2000 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"MobilePhones.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking the type and fullness of the data. There are no empty examples or String features that would require executing pre-processing steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Data columns (total 21 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   battery_power  2000 non-null   int64  \n",
      " 1   blue           2000 non-null   int64  \n",
      " 2   clock_speed    2000 non-null   float64\n",
      " 3   dual_sim       2000 non-null   int64  \n",
      " 4   fc             2000 non-null   int64  \n",
      " 5   four_g         2000 non-null   int64  \n",
      " 6   int_memory     2000 non-null   int64  \n",
      " 7   m_dep          2000 non-null   float64\n",
      " 8   mobile_wt      2000 non-null   int64  \n",
      " 9   n_cores        2000 non-null   int64  \n",
      " 10  pc             2000 non-null   int64  \n",
      " 11  px_height      2000 non-null   int64  \n",
      " 12  px_width       2000 non-null   int64  \n",
      " 13  ram            2000 non-null   int64  \n",
      " 14  sc_h           2000 non-null   int64  \n",
      " 15  sc_w           2000 non-null   int64  \n",
      " 16  talk_time      2000 non-null   int64  \n",
      " 17  three_g        2000 non-null   int64  \n",
      " 18  touch_screen   2000 non-null   int64  \n",
      " 19  wifi           2000 non-null   int64  \n",
      " 20  price_range    2000 non-null   int64  \n",
      "dtypes: float64(2), int64(19)\n",
      "memory usage: 328.2 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>battery_power</th>\n",
       "      <th>blue</th>\n",
       "      <th>clock_speed</th>\n",
       "      <th>dual_sim</th>\n",
       "      <th>fc</th>\n",
       "      <th>four_g</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>m_dep</th>\n",
       "      <th>mobile_wt</th>\n",
       "      <th>n_cores</th>\n",
       "      <th>...</th>\n",
       "      <th>px_height</th>\n",
       "      <th>px_width</th>\n",
       "      <th>ram</th>\n",
       "      <th>sc_h</th>\n",
       "      <th>sc_w</th>\n",
       "      <th>talk_time</th>\n",
       "      <th>three_g</th>\n",
       "      <th>touch_screen</th>\n",
       "      <th>wifi</th>\n",
       "      <th>price_range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.0000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1238.518500</td>\n",
       "      <td>0.4950</td>\n",
       "      <td>1.522250</td>\n",
       "      <td>0.509500</td>\n",
       "      <td>4.309500</td>\n",
       "      <td>0.521500</td>\n",
       "      <td>32.046500</td>\n",
       "      <td>0.501750</td>\n",
       "      <td>140.249000</td>\n",
       "      <td>4.520500</td>\n",
       "      <td>...</td>\n",
       "      <td>645.108000</td>\n",
       "      <td>1251.515500</td>\n",
       "      <td>2124.213000</td>\n",
       "      <td>12.306500</td>\n",
       "      <td>5.767000</td>\n",
       "      <td>11.011000</td>\n",
       "      <td>0.761500</td>\n",
       "      <td>0.503000</td>\n",
       "      <td>0.507000</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>439.418206</td>\n",
       "      <td>0.5001</td>\n",
       "      <td>0.816004</td>\n",
       "      <td>0.500035</td>\n",
       "      <td>4.341444</td>\n",
       "      <td>0.499662</td>\n",
       "      <td>18.145715</td>\n",
       "      <td>0.288416</td>\n",
       "      <td>35.399655</td>\n",
       "      <td>2.287837</td>\n",
       "      <td>...</td>\n",
       "      <td>443.780811</td>\n",
       "      <td>432.199447</td>\n",
       "      <td>1084.732044</td>\n",
       "      <td>4.213245</td>\n",
       "      <td>4.356398</td>\n",
       "      <td>5.463955</td>\n",
       "      <td>0.426273</td>\n",
       "      <td>0.500116</td>\n",
       "      <td>0.500076</td>\n",
       "      <td>1.118314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>501.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>851.750000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>282.750000</td>\n",
       "      <td>874.750000</td>\n",
       "      <td>1207.500000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1226.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1247.000000</td>\n",
       "      <td>2146.500000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1615.250000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>947.250000</td>\n",
       "      <td>1633.000000</td>\n",
       "      <td>3064.500000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1998.000000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>1998.000000</td>\n",
       "      <td>3998.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       battery_power       blue  clock_speed     dual_sim           fc  \\\n",
       "count    2000.000000  2000.0000  2000.000000  2000.000000  2000.000000   \n",
       "mean     1238.518500     0.4950     1.522250     0.509500     4.309500   \n",
       "std       439.418206     0.5001     0.816004     0.500035     4.341444   \n",
       "min       501.000000     0.0000     0.500000     0.000000     0.000000   \n",
       "25%       851.750000     0.0000     0.700000     0.000000     1.000000   \n",
       "50%      1226.000000     0.0000     1.500000     1.000000     3.000000   \n",
       "75%      1615.250000     1.0000     2.200000     1.000000     7.000000   \n",
       "max      1998.000000     1.0000     3.000000     1.000000    19.000000   \n",
       "\n",
       "            four_g   int_memory        m_dep    mobile_wt      n_cores  ...  \\\n",
       "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000  ...   \n",
       "mean      0.521500    32.046500     0.501750   140.249000     4.520500  ...   \n",
       "std       0.499662    18.145715     0.288416    35.399655     2.287837  ...   \n",
       "min       0.000000     2.000000     0.100000    80.000000     1.000000  ...   \n",
       "25%       0.000000    16.000000     0.200000   109.000000     3.000000  ...   \n",
       "50%       1.000000    32.000000     0.500000   141.000000     4.000000  ...   \n",
       "75%       1.000000    48.000000     0.800000   170.000000     7.000000  ...   \n",
       "max       1.000000    64.000000     1.000000   200.000000     8.000000  ...   \n",
       "\n",
       "         px_height     px_width          ram         sc_h         sc_w  \\\n",
       "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000   \n",
       "mean    645.108000  1251.515500  2124.213000    12.306500     5.767000   \n",
       "std     443.780811   432.199447  1084.732044     4.213245     4.356398   \n",
       "min       0.000000   500.000000   256.000000     5.000000     0.000000   \n",
       "25%     282.750000   874.750000  1207.500000     9.000000     2.000000   \n",
       "50%     564.000000  1247.000000  2146.500000    12.000000     5.000000   \n",
       "75%     947.250000  1633.000000  3064.500000    16.000000     9.000000   \n",
       "max    1960.000000  1998.000000  3998.000000    19.000000    18.000000   \n",
       "\n",
       "         talk_time      three_g  touch_screen         wifi  price_range  \n",
       "count  2000.000000  2000.000000   2000.000000  2000.000000  2000.000000  \n",
       "mean     11.011000     0.761500      0.503000     0.507000     1.500000  \n",
       "std       5.463955     0.426273      0.500116     0.500076     1.118314  \n",
       "min       2.000000     0.000000      0.000000     0.000000     0.000000  \n",
       "25%       6.000000     1.000000      0.000000     0.000000     0.750000  \n",
       "50%      11.000000     1.000000      1.000000     1.000000     1.500000  \n",
       "75%      16.000000     1.000000      1.000000     1.000000     2.250000  \n",
       "max      20.000000     1.000000      1.000000     1.000000     3.000000  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ram_0=data[data['price_range']==0]['ram']\n",
    "battery_power_0=data[data['price_range']==0]['battery_power']\n",
    "plt.scatter(ram_0, battery_power_0, color='blue', label='Range 0')\n",
    "\n",
    "ram_1=data[data['price_range']==1]['ram']\n",
    "battery_power_1=data[data['price_range']==1]['battery_power']\n",
    "plt.scatter(ram_1, battery_power_1, color='red', label='Range 1')\n",
    "\n",
    "ram_2=data[data['price_range']==2]['ram']\n",
    "battery_power_2=data[data['price_range']==2]['battery_power']\n",
    "plt.scatter(ram_2, battery_power_2, color='green', label='Range 2')\n",
    "\n",
    "ram_3=data[data['price_range']==3]['ram']\n",
    "battery_power_3=data[data['price_range']==3]['battery_power']\n",
    "plt.scatter(ram_3, battery_power_3, color='pink', label='Range 3')\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('Ram')\n",
    "plt.ylabel('Battery_power')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After looking at the scatter plot above, we can say that if the ram increases, the price will increase too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot([data[data['price_range']==0]['n_cores'], data[data['price_range']==1]['n_cores'], data[data['price_range']==2]['n_cores'], data[data['price_range']==3]['n_cores']], labels=['range 0','range 1','range 2','range 3'])\n",
    "plt.ylabel('number of cores')\n",
    "plt.xlabel('Price range')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this graph, we see the box plot of each price range according to their number of cores. \n",
    "\n",
    "We can see that, min and max values of each range is the same (1 and 8 respectively). Although there are some differences in quartile and median values, we can say that price range doesn't really change according to number of cores of the phone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['sc_w'], data['sc_h'], color='red')\n",
    "plt.xlabel('screen weight')\n",
    "plt.ylabel('screen height')\n",
    "plt.title(\"Screen weight-height\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot above shows the screen weight and screen height of phones.\n",
    "\n",
    "It shows that if the screen weight increases, then the height is also increases; but not vice versa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After looking at the data, I decided to develop a classification model to predict the price range of the phones.\n",
    "Since the other features are properties of phones, which determines the price. The dataset doesn't include the exact values of prices but it has their range, divided in 4 groups, from the cheapest to most expensive. The y value that I want to predict is discrete and can only take values 0, 1, 2 and 3. To achive this aim, classification is the best technique.\n",
    "\n",
    "For that, I took out the price_range column of the data and save it as y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       2\n",
       "2       2\n",
       "3       2\n",
       "4       1\n",
       "       ..\n",
       "1995    0\n",
       "1996    2\n",
       "1997    3\n",
       "1998    0\n",
       "1999    3\n",
       "Name: price_range, Length: 2000, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data['price_range']\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating the X. It includes all the features except price_range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>battery_power</th>\n",
       "      <th>blue</th>\n",
       "      <th>clock_speed</th>\n",
       "      <th>dual_sim</th>\n",
       "      <th>fc</th>\n",
       "      <th>four_g</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>m_dep</th>\n",
       "      <th>mobile_wt</th>\n",
       "      <th>n_cores</th>\n",
       "      <th>pc</th>\n",
       "      <th>px_height</th>\n",
       "      <th>px_width</th>\n",
       "      <th>ram</th>\n",
       "      <th>sc_h</th>\n",
       "      <th>sc_w</th>\n",
       "      <th>talk_time</th>\n",
       "      <th>three_g</th>\n",
       "      <th>touch_screen</th>\n",
       "      <th>wifi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>188</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>756</td>\n",
       "      <td>2549</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1021</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>0.7</td>\n",
       "      <td>136</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>905</td>\n",
       "      <td>1988</td>\n",
       "      <td>2631</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>563</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>0.9</td>\n",
       "      <td>145</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1263</td>\n",
       "      <td>1716</td>\n",
       "      <td>2603</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>615</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.8</td>\n",
       "      <td>131</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>1216</td>\n",
       "      <td>1786</td>\n",
       "      <td>2769</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1821</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>0.6</td>\n",
       "      <td>141</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>1208</td>\n",
       "      <td>1212</td>\n",
       "      <td>1411</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>794</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>106</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>1222</td>\n",
       "      <td>1890</td>\n",
       "      <td>668</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>1965</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0.2</td>\n",
       "      <td>187</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>915</td>\n",
       "      <td>1965</td>\n",
       "      <td>2032</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>1911</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>0.7</td>\n",
       "      <td>108</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>868</td>\n",
       "      <td>1632</td>\n",
       "      <td>3057</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>1512</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>0.1</td>\n",
       "      <td>145</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>336</td>\n",
       "      <td>670</td>\n",
       "      <td>869</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>510</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>0.9</td>\n",
       "      <td>168</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>483</td>\n",
       "      <td>754</td>\n",
       "      <td>3919</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  \\\n",
       "0               842     0          2.2         0   1       0           7   \n",
       "1              1021     1          0.5         1   0       1          53   \n",
       "2               563     1          0.5         1   2       1          41   \n",
       "3               615     1          2.5         0   0       0          10   \n",
       "4              1821     1          1.2         0  13       1          44   \n",
       "...             ...   ...          ...       ...  ..     ...         ...   \n",
       "1995            794     1          0.5         1   0       1           2   \n",
       "1996           1965     1          2.6         1   0       0          39   \n",
       "1997           1911     0          0.9         1   1       1          36   \n",
       "1998           1512     0          0.9         0   4       1          46   \n",
       "1999            510     1          2.0         1   5       1          45   \n",
       "\n",
       "      m_dep  mobile_wt  n_cores  pc  px_height  px_width   ram  sc_h  sc_w  \\\n",
       "0       0.6        188        2   2         20       756  2549     9     7   \n",
       "1       0.7        136        3   6        905      1988  2631    17     3   \n",
       "2       0.9        145        5   6       1263      1716  2603    11     2   \n",
       "3       0.8        131        6   9       1216      1786  2769    16     8   \n",
       "4       0.6        141        2  14       1208      1212  1411     8     2   \n",
       "...     ...        ...      ...  ..        ...       ...   ...   ...   ...   \n",
       "1995    0.8        106        6  14       1222      1890   668    13     4   \n",
       "1996    0.2        187        4   3        915      1965  2032    11    10   \n",
       "1997    0.7        108        8   3        868      1632  3057     9     1   \n",
       "1998    0.1        145        5   5        336       670   869    18    10   \n",
       "1999    0.9        168        6  16        483       754  3919    19     4   \n",
       "\n",
       "      talk_time  three_g  touch_screen  wifi  \n",
       "0            19        0             0     1  \n",
       "1             7        1             1     0  \n",
       "2             9        1             1     0  \n",
       "3            11        1             0     0  \n",
       "4            15        1             1     0  \n",
       "...         ...      ...           ...   ...  \n",
       "1995         19        1             1     0  \n",
       "1996         16        1             1     1  \n",
       "1997          5        1             1     0  \n",
       "1998         19        1             1     1  \n",
       "1999          2        1             1     1  \n",
       "\n",
       "[2000 rows x 20 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data.drop(labels='price_range', axis=1)\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Heatmap of the correlations between features of Mobile Phone. Even though there are some features with correlations 0.64, 0.58 and 0.51, I usually include all of the features unless there are some with correlation bigger than 0.75 or smaller than -0.75. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eceVmNOcvR7B6xUaNN4mFwcTEiLs58ioxMSXPzWT16lV5/PiJKq8TE5PVbNq3dyEhIRQfny0MHjy+WDqyKckYfh2UVAwDDBjUm9CogyxdNRcDg8pF1GVI4t2X9XJSUt581BSzxkqb/NInJ6eycsUGLl45zvVfTvLkyVNCQ08A4OrmQFJSKhcvXtWsKZ8yX7AmQ63ptcWMWb06tLZsTkjoAfwCd9KkaUOVXc1aNQg/cQi/wJ20adNCqz9zUtZiRKHJkLs58ytHXmrXlLdu6devh9qstVdFEZ8vH9ykJKViaKzecTM0fp/kpJw291QdTlmW2bx/NT5Hd9Cjbye1dGMnDyMy3p/2XdqxfOGa4ul7A/dZn/fpUuTZLrnJU+6SUjAx1hDTanlcsveDpVHPAEyb8S2Xrp6gW48OfD93WYl9P0HJUdhOuZ4kSVslSTovSdIBSZIqSpI0XZKkWEmSLkqStF5S0BVoDuyUJClekqTRKDroYZIkhQFIkuQsSdJJSZLiJEnaL0nSO8rjt5XnPAFMkiQpLvvikiR9KElSvvPplGkXSpJ0WvlTT3m8liRJx5S6j0mSVFOSJF1Jkm4p9VaRJClLkiRrpf1xSZLqSZL0tiRJm5Tf75wkSR2Un/dXavYDjmjQMUiSpDOSJJ3ZsG13IV1b9pAkDQdzP3XTYPOmnsyVdX0AkgaRua+vySb7eyxZMovJk+eRlc80mrffrsi+vT/z7bgZRR79K4w2TU7ONsnKysLVphstGzjSuGkDPvr05RTkxd+vpFVDJw7u96f/173ynKPY+ijYdwXlr56uHgZVKuPm2JPZ0xazfsvSYukrigZtNvl95tLOjgf3/yAh/lKJaitqHr9V4S1GfPs1S+YV72GL5uJeOH8VJm1umjVvxIsXWXz2YRuaNLBj2MgB1Kr9QZE0v9RV8PUL8rmvbzCNG9vTvftAZsx4tf0MSjKGXwclFcNbNu6hpbkzDladSE25z8zvJ7y6rtcQs1WqVMbd3ZFGDWz5uJ4lFStWpHuPDlSo8Bbjxg9j3tz865qS0qStzOrp6WJQxQAn+67MmLqQTVuXA5Cacp9Gn9lga9WBqd/NY+vWFVSq9E6+2tU15n+9wnwPeL0xUpjrFdbG2ro1X/TrwbSpC15ZU44Lv5K2nu4D6OjQm696jqT3gO5YtG6islk67yeszd3x9Qqiz1c9Xpe813qfNWbcYDIzM/Ha51d0cTklFNuPr3RZrbzpeiabObOWUP8TK/bvPcSgwX3znONfj5z15n5KicJ2yj8G1suy3Ah4AgwDVsmybCHLcgOgAuAhy/IB4AzQW5Zlc1mWlwNJgJ0sy3aSJL0LTAUcZVluqrT9Jsd1/pZl2UqW5e+Bx5IkmSuPfwlsKUDjE1mWWwCrgGXKY6uAbUrdO4EVsiy/AK4DnwFWwFmgrSRJ5YEasizfBKYAobIsWwB2wGJJkt5WnrM10E+WZfW5eIAsy+tlWW4uy3LzgV8Ur0NSFkhOTMXE1Fj1v4mpESkp99RtklIxzWFjbGJESrK6zX9N39Ah/TgTe4QzsUdITk6hxgcvJ4yY1jAmKcd0RlBMGaxSxQBdXcXEkBqmxiQpn4o3a9qIHTt+4sb1GDp3dmflinmqaX56enrs2/szu3f7cPBg0TcgSU5KVY3Ig2Kk5V4u/6UkpWKcw8bIxJDUXDZPnjwlJioWW4c2ea5x8EAArp6ORdYGkKRBX+68S0pKyWVjRErK/QLOm0KAXwgA5+IukJWVRXUNmy5pY+CgPkRG+xIZ7Uty8j1Ma+QohxrK2B8PHmJQpZIqj01MjUhW2iQlpWhM37JVM9q5OZBwKZyNW5bR1qY16zYsKZLOksjjWrU/4IOapgQdP0BUfBDGJoYEhO/jvferF0pTUlKKWkxqitukRA02yfcKlTY3Xbp5cuxoJJmZmTx48JDTMXE0KcLsiMGDv1Bt8pScfI8aOfLK1FQxapGTBw8eYmBQWZXXpqbGeWwATpw4Td26NYtc9nLyJmL4VSipGH5w/w+ysrKQZZmd2/bTpGkjrfagiNnj0X4cj/YjJfkepjVe1svZo0850RSz2VNEFTGbN72tXRt+u32HPx48JDMzEz/fYFq2akqdujWpVfsDTpz05/ylCExNjYg84cvYb4dwPNqPiChfUpJTNZb5gjUp65FixExSYgqHfRUblMWdPU9Wlkz1d6vx/Plz1XTdhPhL3Lr1Gx9+qL5XQU7KYowMGtyXkzEBnIwJIDk5lRo58ytHXmrX9NL/DRp8wuqfFtCj+9c8LMZU5vxQxOfL0VojE0PupTzIa2OS0+Z97qUqbLJ/P3zwiJCAMBppqNv8vAJx8chzq1ooSvI+q3uvjji52DHs6+LNhvh6UF9OnDzMiZOHlXmco9yZGJGcoiGm1fI4bzl4VUqznsnN/n2+tO/Q7rV+P8GbobCd8juyLEcp/96BojNrJ0nSKUmSLgD2gObtBdVphaIzHCVJUjzQD6iV4/O9Of7eAHypnMreA9hVwLl35/jdWvl36xzptit1AxwHrJU/85XHLYDsnYKcUYzWxwPhwFtATeVnIbIsv/oCrDLMubgL1DWrRc1apujr69OxsxvBAaFqNsEBoXTrpXhC16x5Y54+ecq9VO03VP/v+tas3UpzC2eaWzhzyDeYPr27AtCyRVOePH6isRMRHhFNly6KndX79u2Gn59iAsZHH7fmw49a8eFHrfD29mfkqMn4Km+ifl6/hKtXb7JsefF25U6Iu0idurX4oKYp+vp6eHZ2JSQoXM0mJDCMLj3bA9CkeSOePvmTe6kPqFa9KpWVexyUf6s8Vjat+OW6Yj1v7bo1VemdXO34RcM638IQnzt/u7hxJDBMzeZIYBjdeyryt2kh8zfI/xhW1q0AqGtWG319ff7441GRtG1YvwNry/ZYW7Yn4HAIPXsppg02tzDnyZOnpGrQcDzyFB06KRrIXr07Eeh/FIBA/2Ma08+e+QMNPraicX1bvuo/huMRJxk88Nsi6SyJPL525QZNP7aljXk72pi3IzkpFTfb7ty/90fuy2sk7uwF6prVpmatGujr69O5iztB/sfUbAIDjtGzV8eXPnms8Elh0ubm7t1krG0UTUHFihVobmHO9eu3CqUVYN26bbRs6UrLlq74+gbTu3cXAFq0aMLjx081xnNExEk6d3YDoE+frqp4rlv3ZTNnbt4Aff1yRS57OSmpGH5dlFQMv59jXaarhxNXr9woUMuG9Ttoa+lJW0tPDh8+Qq9CxWwMHTu5AvB5784EKGM2wP+oxvR37iTRvIU5FSq8BYCNrSXXrv3C5UvXqVenBY3q29Covg2JiSlYW7Vn6ZK1tLX0xKZNe/wPH9VY5nNzIvIUHToq6pGen7/UVJyY8T98VBUbZvVqU66cPn88eEj1d6uho6O4LaxV+wPMzOrw66+/5evbshgj69dtp3UrN1q3csPP7wif9+4MgIVFE548earxwU9k5Ek6dVJo6t2nC4f9FZpq1DBh1+61DPxqLDdf034z2Vw4d5nadT6gRk0T9PX1cO/ozLGgCDWbY8GRdOyhuD8wb9aAp0/+5H7qAypUfIu331asCq1Q8S2sbFtx/apiU9BadV/OBnJoZ1PsPSJK6j7LzsGKEWMG8kXPoaSn/10sbT+v345Vaw+sWnvg7xdCr88VMWmRHZMa8/hlTPfq3QX/w0eLde38KM16BhT3NNm4ujty4/ovr/X7lQmyXry5n1KisOuic0/0kIGfgOayLN+RJGkmio5rQUgoOrX5DSP/leNvL2AGEAqclWW5oDs/OZ+/NdkcB4agmFo/HRgP2ALZi1skoIssy9fUxEtSy1waS5zxMxYQe+48aWlPcOjYh2Ff9aWLp0vBCV+BFy9e8N24Oezx3oiurg67d3hx7epNvhigmAa1bdNejh6JwMHZmlPxR0h/9jejh7/cpXXtxiVYWllQrXpVzl0OZ/H8leza7oWrhyPzFk2l+rvV2LlvLRcvXKVnZ82vyvk36wMIDDyGazt7rl6JIj09nYEDX04I8T20jcFDxpOcnMrkyd+zc8dPzJo5gfiES2zarH3ZQxtLC/r06cqFC5c5E6u4cZg6bQFBQaFa0+XkxYsXTJswj+0H1qKrq8venT5cv/oLffordv7csWU/oSHHsXOy5vjZANLT/2bcCMXOze8bvsePP81FV1cXHR2JwwePcOyIImwmzRiDWb3aZGXJJN5J4rtvNe+CWhh9k8fPZbfXBmX+eivy90tl/m5W5q+TNTHngkl/9jdjcuTvmg0/YGnVgmrVqxB3KYzFC1axe7sXu3d4s3TVXMKjfXmekcGoYd/lJ6FQHAkOx8nFlrjzoaSnpzN8yETVZ/u8NjBq+GRSUu4xc9oiNm5ZxpRp33D+/GW2b91fYPpXpaTy+FU1TRg3iwMHN6Gro8vO7Qe4evUm/QcomoMtm3YTEhyOk7MNZxOOkZ6ezoihk7SmBXD3dGLh4ulUf7caew78zMXzV+jaaQAb1+9g1ZoFRJ8OQJIkdu3w4vKla/nq00ZQUCjt2tlx+fJxnj1LZ9Cgl1NrDx7cwtChE0lOTmXq1Pls27aKmTPHEx9/iS1bFM+ZO3Vyo3fvLmRkZJCe/jd9+w5/FVeWWP66uNsze+FkqlWvyuY9P3H54lX6di36WxRKKoanzR5HgwafICNz5/dExo+ZWSRdR4LDcXaxJf58KM/S/1aLuf1eGxk5/DtSUu4xY9oiNm1ZztRp33D+/CW25YhZTenPnkng0MEgIqN8ycx8wfmES2zZtKdQmvIr8wB7D/zM6BFTFPXI9MVs2LyUydPGcuH8ZXZsO6A1vbaY2bn9ACt/mk/UKX+eP89g2GDFMgBLSwu+mzqazMxMXrzIYuTIyTwq5D4MZS1GAIKDwnBxsePCxQjSn6UzeMjLkVlvn80MGzaRlOR7TJu6gK3bVjJ9xrckJFxi6xbFBpvfTR5FtWpVWbZc8QaczMxM2lopHnRt2bKCttatqF69KtdvnGTu3KVF2pjzxYsXzPpuEZv2rUJXR5cDuw9x89otevVTPNjYvdWL8JAT2Di24djpQ6Sn/82kUTMBePe96qze8gOgWIrg5x3E8dCTAIyfNpI6ZrXIypJJupvM9HFFe61dTn0lcZ81/4dplCtXjn0HFW8/OXsmgQljZxZLI0BwcBjOLrYkXAjjWfrfqrIMcMB7EyOGTVLG9EI2b13BtOnfkJBwWZVX7xu+S8TxQ1Sq9A5ZWTLDhn9Ji2YuPH36J5u2LMeqbUuqV6/KletRzJu7nO3bCs7j0qhnZs0eT70P65KVlcWd3xMZO3pasX0qKD2kgtZ/SJJUG/gVsJRl+aQkST8DV4EJQG1AF4gBDsiyPFO53vpHWZaz15BfANrLsvyrJEnvoZgubi/L8k3lruw1ZFm+LknSbRSd/Ac5rr0S6AJ8JctyvvN0lWnXyrK8QJKkPkAPWZY9JUnyBfbLsrxdkqT+QAdZljspp6pfA27JsmwvSdIawAPFFPwESZLmAZWBkbIsy5IkNZFl+ZzyHM1lWR5RkGMzHtwq01sf1jBzK20J/2oepj8tbQlaMX6nWmlL0MrzrLw7nZY1/snMKG0JWqlUrkJpS9DKnxnFGwV5UzzL+Ke0JWjl/YoGpS2hQMp6HKdnPi9tCVrRlcr2W2nLeozo6pRt/5m8XbilPaXJk+dvdJypyJT1GNa4N1AZ4/Gfv5R9kYXg+e0zb6xfVa5281LxWWFrtCtAP0mSzgPVgDXAz8AF4CAvp32DYu33WuVGbxWA9UCgJElhsizfR7E7+27luWKAT7RcdyeK0e08m6ppoLwkSaeA0cBY5bFRKKbAnwf6Kj9DluV/gDvK64Ni5LyS8vsAzAH0gfOS4nVwxRvyEwgEAoFAIBAIBAKBQAsFjpSXJpIkjQMMZFnWOg9D0yh7aSNGyv+/ESPlr0ZZH2EDMVL+qoiR8ldDjJS/OmV9lE2MlL8aYqT81REj5a+GGCl/czy/dfrNjZTXbVEqPiuz79qWJMkHMEOxiZxAIBAIBAKBQCAQCAT/d5TZTrksy51yH1N21OvkOjxRluXab0SUQCAQCAQCgUAgEAjeGHIpvj/8TVFmO+Wa0NRRFwgEAoFAIBAIBAKB4N/Kv6pTLhAIBAKBQCAQCASC/xBZ//8j5WV7lwyBQCAQCAQCgUAgEAj+jxEj5QKBQCAQCAQCgUAgKJuINeWC4lLWXzl295eA0paglZr1PEpbglZMKpXtV538VcZfR1XWXwUEUKX826UtQStPM9JLW4JWXpTxBtTw7SqlLUErj/7+s7QlFEhZfx3Qs+dlux58v4yXwbL8ylwo++WvrL9uDODxP89KW4JWKpUv26/+LOuvDRT8uxCdcoFAIBAIBAKBQCAQlE2yXpS2ghKn7A9XCQQCgUAgEAgEAoFA8H+K6JQLBAKBQCAQCAQCgUBQSojp6wKBQCAQCAQCgUAgKJuU8X1qXgdipFwgEAgEAoFAIBAIBIJSQoyUCwQCgUAgEAgEAoGgbJIlRsoFAoFAIBAIBAKBQCAQlBBipFwgEAgEAoFAIBAIBGUTsaZcIBAIBAKBQCAQCAQCQUkhOuWlgJ2DFVFnAok5F8zIsV9rtPl+4RRizgUTFnWIho0/Ux1ftup7Lt2MIuKkr5q9Z0cXImL8SH50mcZNGpSo/pxMnfcj1u496dhnyBu7Jih8eDzWn+i4IEaMGajRZs7CyUTHBXEsyoeGjT8FwMTUiAN+m4k85Uf4SV8GDumjsvfo4EL4SV8SH16ksXn9V9JnY9+G0FO+RMQeZujoARptZs6fSETsYYIiD9CgkUJf+fLlOBSyk8CI/YREeTN24jCV/ZgJQzl1MYSA8H0EhO/DztGqSJrsHdsSczaI0/EhjBo7SKPNvEVTOR0fQkS0L41ylLv80k6aOpqIaF/CThxi/8FNGBm9r/rss/ofE3h0LydO+RN50o/y5csVWmtJxMjEKaMIizrEseM+7PXZiGEOrUXF2t6SY6cOERbrx5B88nfG/ImExfoRGLmf+o0+UftMR0eHw2F72bBrpeqYW3sngqO8+eX+ORqaf5b7dEWmJPI7m+EjB/DgyXWqVataZF0LF0/nXEIoUTH+NG6sOc5q1arBsTAv4uKPsXnrCvT19QtMP2z4l8TEBnLydCAbNy9TK2+DhnzBmbgQYmIDmT1nYqF0lkQMA/T/uhehp3wJifLmuxljC6WlMCxaPJ3486FEnwrIt/6qVasGoeHenEsIVfPrhx/V5WjoAe4/vMLI0Zrr08JSUvlrYFCJbTtWERt3hNNng7Fo0QSABg0+IeTYfqJPBbBn33oqVXqnSHqX/jibK5dPEHc2hCbmmtvO2rU/IOqEH5cvnWDnzjVqegGaN2vM3+m/07mzOwA1apgQcmQ/58+HEx8fysgRXxVJUza2DlZEnj7MibOBDM+nnZu94DtOnA0k5IS3qgyamBqx33cz4TG+hEYf4qvBfdTSfPn150SePkxo9CGmzPq2WNqyWbB4GmcTjnEi5jCN8snvmrVqEBJ2gDPxR9m4dbma/zSlL1++HEfDvTh+0o/o2EAmTRmtVYODY1tOxQVzJv4oo7/RXNfNXzSNM/FHOX7ST62uyy9tlaoGeB/aQuy5ELwPbcGgSmXVZ5/V/5jgY/uIPh3AiZjDqrpmyvSxXLgSye/J8dqdpqQk2rlsho4cQOrjq1SrVqVQWgrLkiWzuHQpktjYYMy1xEtk5CEuXoxg+/bVqvz28HAiNjaYU6cCiYo6jKWlxWvVZu/QlpNngjh97gij8vHnvIVTOH3uCOFR6m3e8lXzuHwzmsiTfq9VE8DiH2Zw/kI4p04FYq6lbg6POEjC+TC2blul8lmPHh04dSqQU6cCORbqRcOGihg3NTUmIHA3Z+OOEnvmCMOGffnadZc5srLe3E8p8Z/plEuSVFuSpIsajodLktT8TenQ0dFhwZLpfN71a9q28KBTF3c++thMzcbByZo6ZrVo1cSFcaOns+jHGarP9uzyoWeXvJXN1cs3GNBnFCejzpT4d8hJRzcn1v44941eU0dHh3k/TKV318HYtPSkY1e3PD60d7Kmbt1aWDZtx/jRM1iwROHDzMxMZk1dhHVLT9ydetJ/4OeqtNeu3OCrvqOIiX41H+ro6DBn0WT6dR+Ko2VH2nd25cOP66rZ2DlaUaduLWwsPPjum9nM/WEqAP/885xeHQfiatMNV5vu2Di0oUnzRqp0G9fswM22O2623Qk7eqJImhYumUGPLl/TxsKNzl098vjM0dmGuma1aWHuxDejp7F46awC065avgEby/bYWXXgSFAY4yYOB0BXV5c1Py9m3JgZWLV0p4N7XzIyMguttSRiZPWKjdi16YBD206EBIXzba7OUmHR0dFh9qLJ9O8+DGfLTrTv3I56ufLX1tGK2nVrYmfhqZa/2Xw5uDc3r99SO3bt6k2G9hvL6eizxdKVW2NJ5Dcobvht7Ntw5/fEIutycrbFzKw2TRrbM3rkFH5cNluj3aw5E/hp9WaamjuQlvaYL/p105re2NiQIUP7Ydu2I61buKKrq0OXrp4AtLVuhbu7I5at3Gll4cqKFRsK5b+SiOHWVhY4udrRrm0XnNp0Zv3qrUX2oSacXWwxq1cb80b2jB4xmaXL5mi0mzVnIqtXbaJJY3vS0p7wRb/uADx69JgJ42azYnnBvtFGSeUvwIJF0zkaEolFU2fatPLg+rWbAKxcPZ+ZMxZj2dKNw35HGDVG8824Jtq1s6devTp8+pkVQ4dOZNWq+Rrt5s2bwvIVP/NZfSvSHj1mwJe9VJ/p6Ogwb94UjhwJVx3LzMxkwoRZNGpki5WVJ0OG9ufTTz8stK7s836/eAp9ug3BrlV7OnZx48M87Vxb6pjVwqqZKxPHzGT+kumq68+augjbVu3xdO5F/4G9VGktrVrg4maPo1Un7C07sHbl5iLpyomTsw1mZrVp1tiBMSOnsmTZLI12M+dMYM3qzTQ3d+Rx2mP6qvJbc/p//nlOB/e+tG3tiXVrTxwc29LcwjxfPy1aMpPunQfS2sKVLl09+Pjjemo2js42mJnVorm5I2NHTWPJ0tkFph3zzWAiIqKxaOJEREQ0Y74ZDCjatnUbfuCb0dOxbOGGp1sfVdsWHBiGo22XQvmupNo5UNbRdpbFqqO14eJiR716talf35rhwyexYsX3Gu3mzv2OlSs30KCBDWlpj+nfvwcAYWFRWFi40LKlK4MHj2PNmoWvTVu2P3t2HUibFu506qKhzXOyVrR5TZz5dvQ0Fv04U/XZnl3e9Ozyag8kNeHiYku9enVo1NCWESMms2y5Zp/NmTuJVSs30riRHWlpj+mn9Nnt23dwcelBy5auLFywkpXKOurFi0wmfzeXZk0dsbPtxKDBffnkk3oazy349/Cf6ZSXFZo2a8Svt37nt9t3ycjI4KB3AO3cHdRs2rk7sH/3IQDOnkmgskFl3jd8D4CY6DOkPXqc57w3rt/il5u/lvwXyEVz84YYVK70Rq/ZpFlDbt/6nd9/U/jwkFcgLm72ajbt3OzZv0fhw7gz56lsUIn3Dd/lXuoDLiRcAeCvP59x4/otjIwVI6YKH95+ZX3mTRtw+9ffufNbIhkZmfj5BOHkaqdm4+Rqh9dexRPZczn0ATz7Kx0APX099PX0kGX5lTU1bd6IX2/9xm+375CRkYGPlz+u7o5qNq5uDuzb7QPA2dgEDAwqYWj4nta0fz79S5W+YsWKKq12DlZcvnSNSxevAvDoYRpZhXz6WFIxoqb17QrF9mvjpg347dc7ufLXVs3GydUOb2X+xp+5QGWDSrynzF8jk/exc27L3h0+aml+uf4rt27+VixNuSmp/AaYO38ys6YtLpb/3D0c2a285pnYeAwMKmOozLecWNu05qBPIAC7dnrj7uFUYHpdPT0qVHgLXV1dKlSoQEpyKgBfDfycpUvW8vz5cwAe3P+jQJ0lFcN9vuzOT8s38vx5BgB/PHhYKL8VhJu7I7t3KfwSm+0Xo7x+tcnh1907vfDwVPj1wf0/iIs7T2YhH5zlR0nlb6VK79CmjQXbtu4DICMjg8ePnwJQ78M6RJ04DUBYaBTtO7gUWm97Txd27DwAwKnTcRhUMVCb7ZONnW0bvLz8Adi+fT/t27+8xojhA/Dx8ed+jnKVknKPc/GKMYA///yLq1dvYGJiVGhdkN3O3XnZznkH4OKmXgZd3Ow5sEcxUhp35jwGOdq5i+c1t3NfDOjB6mUbXksZdPNwZE+h8rsVh3yCANi90wc3ZX5rS//XX88A0NfXQ19fP9/6plmu+srbyx9XD/X2ws3dkT27D6quU7mKoq7TltbV3YE9OxXa9uz0wc1DUQfaOVhx6aLmtu1MbDypqfcL5buSaucAZs//jtnTF/Mabh3U8PR0ZudOLwBOnz5HlSqVNcaLra0l3t4BAOzYcUAVL9l5CvD22xVfy71NNk2bNeL2rd9y+NMfVw3+3KssB2fPJKiVt5PRZ3iUjz9fBXcPZ3bt9AYgNvYcBgaVMNJYN1vi46Pw2c4dXnh6OANw6lQcaWlPADh9Og5TU0U9kpJyn/j4S4Cijrl27Zci1zH/NmT5xRv7KS3+a51yPUmStkqSdF6SpAOSJFXM+aEkSX/m+LurJElblH+/J0mSlyRJscqfNsUVYGRiSFJisur/pMQUjIwN1WyMjQ1JzGGTnJSCsYm6zX8ZI2NDEhNTVP8nJ6Wobjhe2rxPkppNKsa5/FyjpgkNG35K3Nnzr11fcmKq2rUL0peSlIqh0kZHR4eA8H3EXQ3neMRJ4s9eUNl9MbAnQZEHWLxiFpUNCv8wxNjYkKS7L6+XpKFMGZsYkpjTJjEVYxPDAtNOnjaWhMsRdO3uyYLvlwNgVq82sgz7fDYSGulTpCmxJRkj300bQ9ylMLp082DR9ysKrUlNn/H7JKvl3b08+gyN38+3DEz/fgILZi4t9EOK4lBS+d3O1Z7k5FTVDWlxdCXeTVI7d+4biWrVq/I47SkvXrxQ6krBWGmTX/rk5FRWrtjAxSvHuf7LSZ48eUpoqGImiVm9OrRuY8GxMC/8g3bRtGnDAnWWVAzXMatFi1bNOHhkJ3t9N9Goyastk8nGxMSIu3dfxkNiUgomxhr8+viJyq+Jia+/XSmp/K1d+wMePHjIT2sXcTzKl5Wr5lGxYgUArly+gZvyoVHHTq6YmhoXWq+JiRF377y8XuLdZExz6a1evSppaY9Veu8mJmOivDE2MTGiQ4d2rFu/Pd9r1KpVA/PGDTh9+lyhdYGiDCap1XGpeeoZTe1cbpsaH5jQoNGnnFO2c3Xr1aZF62b4hezmwOEtr7TcTZFfOepqDfWMpvw2UdpoS6+jo0NktC/Xfz1FeOgJzp5JyEeDkVpbkJSYkqetNzYxzGtjYqg17fvvvavqYKem3ue9d6sDUK9eHWRZ5oDPJsKOH2RkEWZm5KSk2jkXVztSklK5fPFasXRpI089k5g3vqvnqWeS1Wzat3chISEUH58tDB48/rVpU+RxrvZMgz9zxktSUgpGJXxvbWJiyN2cdVqO+i4bzT7Lq6tfvx5qM3KyqVmzBo0bf0ZsbPxr1S548/zXOuUfA+tlWW4EPAEKO391ObBUlmULoAugcY6fJEmDJEk6I0nSmfTnaRpPJEkaDuZ+WqjB5nU+Ufy3I2lwYm7vaLTJ4cOKb1dk47blTJ88X20E9fUIzHsod/5p05eVlYWbbXdaNXTCvEkDPlJOSdqxeS/WzdxxtenGvdQHTJszrvCSCvCHNpuC0s6bs5TGn9lwYJ8fAwf3BUBPV5eWrZoy5KtxuLv0ws3TibY2rQupVcPB1xQj8+cso2l9O7z2H2bAoD4F2mvWVxhfatZn72zNgwcPuaicrVFSlER+V6jwFmPHD1U9eHnTurR9VqVKZdzdHWnUwJaP61lSsWJFuvfoAICenh5VqhjgYNeFaVMWsGXbyjznyCs076HXEcN6enoYVKlER+fezJv5Iz9t/KFgLYWg+H59LZd/DTq056+enh6NzeuzccNO2rZpz1/P0hn7rWIfk+HDJvL1oD5EHD/EO5XeJkM5AlySerMdt2TJLCZPnpfvA7a3367Ivr0/8+24GTx9+qdGm/y1abps4X0Jinbu523LmPHdAlU7p6uni0GVyng69WLu9CWs3bykSLqKcv2CbAqKIWvL9tT/2IqmzRvz6Weap/+/ip8KkzY3enq6tGrdjEEDv8XNuScenk5YF7JtU9ek4eArtnMVKrzFmHFDWDiveA+cC+J1lElf32AaN7ane/eBzJhR+HuYgrW9WlksKV6XLmvr1nzRrwfTpi5QO/722xXZtXsNEybMLnId869DznpzP6XEf61TfkeW5Sjl3zuAwu6U5QiskiQpHvAFKkuSlGeYUpbl9bIsN5dluXmFclU0nig5MRWTHE/yTUyNSEm5p26TlKr2tN/YxIiUZHWb/zLJSSmqKTyg8E9qcl4fmqjZGKr8rKenx8Zty/Def5gAv6OvXV9KUirGpi+fchqbGJKaoj6lLbc+IxND7uWyefLkKSejzmDroJiY8eD+Q7KyspBlmd3bvGhciBG/bJKSUjCp8fJ6JhrKVFJiCqY5bUwNSUm+V6i0AF77/fBo76y8XirRUbE8fPiI9PS/OXokgsaNC7d52ZuIEe/9h/Fo71Ro+9zXNlbLu/dJzaUvJemexjLQrKU5ju1sOX4ugJU/L8SyrQVL184rlg5tlER+165Tk5q1ahAR5UvchVBMTI0IPe7D+++/q1XLwEF9OB7tx/FoP1KS72Faw0Tt3MnJqWr2fzx4iEGVSujq6ip1GammoiclpWhMb2vXht9u3+GPBw/JzMzEzzeYlq2aqr6nn28wAHFnz5OVlUX1d6tp1VxSMZyclErQ4WMAJMRdJCsri2rVi75ZHsDXg/py4uRhTpw8THJyKjVqvIwHUxMjklM0+NWgssqvpjn8+iq8ifxNTEwmMTFFNVp66GCgahO4G9dv0alDf2zaduDAfj9+/fV3rXqHDunHmdgjnIk9QnJyCjU+eHk90xrGJOXS++DBQ6pUMVDprWFqTFKSwqZZ00bs2PETN67H0LmzOytXzFNN1dXT02Pf3p/ZvduHgwcDC+nNlyjKV846zjBPPaOpnUvN0c79vHUZPvv9CTz8sp1LTkwlUNnuxcddKHIZHDioD5HRvkRG+5KcfA/THOVOUz2jKb+TlTaK/Nae/snjp5w4fgoHR2uNepKSUtTaAk3tRVKiBhtlXZdf2nv3H6imNhsavsf9B3+ozhUVFcvDPxRtW0hwRLE2hi2Jdi67jg49cYjY88cwMTUkJNKb9wqoo7UxePAXqo3GkpPvqdczpnnj+0GeesY4jw3AiROnqVu3JtWLWf/lRpHHudqz3OUgKUUtXkw03Du+DgYN7svJmABOxgQo6+YcdZqGelezz17qatDgE1b/tIAe3b/m4cM01XE9PT127VrL3j0H8T0U/Nq/h+DN81/rlOd+JKbt/7dy/K0DtJZl2Vz5YyrL8tPiCDgXd4G6ZrWoWcsUfX19OnZ2IzggVM0mOCCUbr0UozzNmjfm6ZOn3CvkOqX/AvFxF6ljVosPlD7s0MWV4MAwNZvgwFC69VT4sGnzRkofPgDgx1VzuHH9Fute0yZLuUk4d4k6dWvxQU1T9PX18OzUjpDAcDWbo0HhdOmh2IiqSQ591apXpbJyjX75t8pjZdOKmzcUewVkr1cFcHG359qVG4XWdO7sBerWrU3NWjXQ19enUxd3ggKOqdkEBYbSvVcnAJpZNObJkz9JTb2vNW1ds1qq9O3cHLih3Lws9Nhx6tf/WLXG17JNC65d+6VwWksoRurUfanVxdWeGzeKtwfD+XOXqF23JjVy5O/RwAg1m6NB4XRW5q9584Y8ffIn91MfsHjOCiwbOtO2iRsjv55I9PFYxg6ZXCwd2iiJ/L5y+TqfmrWmaUN7mja0JykxBfu2nbh374FWLRvW76CtpSdtLT05fPgIvZTXbG5hzpMnTzWuwTweGUPHTq4AfN67MwH+ik5EgP9Rjenv3EmieQtzKlRQVNs2tpaq8uZ/+IhqJMusXm30y5UrcB1tScXwkYBQLNu2ABRT2fXL6fPwj0dateTHz+u3Y9XaA6vWHvj7hdDrc4VfLLL9kpLXr5E5/Nqrdxf8D7/6Q8k3kb/37j0gMTGZeh/WAZT5e1Wx0du77ymmFUuSxPgJI9i0cZdWvWvWbqW5hTPNLZw55BtMn95dAWjZoilPHj/JcyMPEB4RTZcuip3V+/bthp/fEQA++rg1H37Uig8/aoW3tz8jR03GV/kA6Of1S7h69SbLlq8vrCvVULRzNZVlUJ8Ond04kqudOxIYRtee7QFFO/fkyZ+qdm7JytncvH6L9T+pt3PBAcdoY90SUNTf5YpYBjes34G1ZXusLdsTcDiEnoXK71N06NQOgF69OxGozO9A/2Ma01d/t5pqedZbb5XH1s5S1bbkJu7sBeqavayvOndxJ8hfva4LDDhGz14dX17nseI62tIGBYTSs7dCW8/enQhUHj+Wu22zsuCqsiwWhZJo565cvk79em2waOSARSMHkhJTcbLuzP0C6mhtrFu3jZYtXWnZ0hVf32B691ZsZNeiRRMeP36qMV4iIk7SubMbAH36dFXFS90cbbC5eQP09cvxRzHrv9yci7tAnRx52bGzO0Ea/NlDWQ6aNW+cb3l9Vdav207rVm60buWGn98RPu/dGQALiyY8efKUFI1180k6dVL4rHefLhz2V/isRg0Tdu1ey8CvxnIz175Ra9Ys5Nq1m6xcufG1f4cyyX9g93W9Urty6VBTkqTWsiyfBHoBJwDPHJ+nSpL0KXAN6ARkd7yPACOAxQCSJJnLshxfHAEvXrzgu3Fz2OO9EV1dHXbv8OLa1Zt8MUCx0+K2TXs5eiQCB2drTsUfIf3Z34we/vKmfe3GJVhaWVCtelXOXQ5n8fyV7NruhauHI/MWTaX6u9XYuW8tFy9cpWfn17+TZG7Gz1hA7LnzpKU9waFjH4Z91ZcunoXfaKc4vHjxgsnjv2e318/o6uqwZ4cP16/e5IsvlT7cvJdjRyJxcLLm5Lkg0p/9zdjhUwBo0aop3Xp24PKla4QcV2y+MX/2MkJDInH1cGDuwilUf7ca2/et4dKFq/Tqovn1KgXpmz5xHtv2r0FXV5d9uw5y49ov9O6v2HF255b9hIYcx86pLZFn/ElP/5txI6cBio73j6vnoqOrq3ht1sFgQo9EAvDdzLF81uATZFnm7u9JTP5W887G+WmaNH42+302oqOry67tB7h29Sb9B/QEYMumPYQEh+PobENswlHSn6Uzath3WtMCTJs5jnof1iErK4u7d5L4doxid9jHaU9Ys3ozIeFeyLLM0SMRhASHF1prScTI1FnfUq9ebbKyZO7eSWL82Bn5SShQ34yJ89m2fw06ujrsV+bv58r83bVlP2Ehx7FzsiL8zGHS0/9mwsjpBZ7X2d2emQsmUa16VTbtXsXli9fo121osTWWRH6/KkeCw3F2sSX+fCjP0v9m+JCXryfb77WRkcO/IyXlHjOmLWLTluVMnfYN589fYtvW/VrTnz2TwKGDQURG+ZKZ+YLzCZfYsmkPANu3HWD1mgWcPB1IxvPnDC3EOsaSiuF9O31YvHI2R054k/E8g2+HT81XQ1EIDg7D2cWWhAthPEv/m2GDJ6g+O+C9iRHDJin9upDNW1cwbfo3JCRcVm2c9r7hu0QcP0SlSu+QlSUzbPiXtGjmUuTpkCWVvwATvp3Fho1L0S+nz+1f7zB8qOI7du3myddfK5ai+PkGs2P7gULrDQw8hms7e65eiSI9PZ2BA79RfeZ7aBuDh4wnOTmVyZO/Z+eOn5g1cwLxCZfYtHm31vO2sbSgT5+uXLhwmTOxipvrqdMWEBQUqjVdTl68eMHUCd+zy2s9Oro67N3pw/Wrv9D3S8WO+ds37+PYkUjsnayJigskPf1vvlGWJ4tWTemqbOeORCo25lowZxmhIcfZs8OHJavmcCz6IBnPMxgzdEqhNeXmSHA4Ti62xJ0PJT09XS2/9nltYNTwyaSk3GPmtEVs3LKMKdO+4fz5y2zPkd+a0hsZvsdP6xejq6uDjo4OPt4BBAeFadTw4sULJoybxYGDm9DV0WXn9gNcvXqT/gMUO+Rv2bSbkOBwnJxtOJtwjPT0dEYMnaQ1LcCyH9exaety+vTtxt27SXz5xShA0bb9tGoTxyK8kWWZkBxt28w5E+jazZOKFStw8epxdm47wA8LVuWruyTauZIkKCiUdu3suHz5OM+epTNo0Mvp5wcPbmHo0IkkJ6cydep8tm1bxcyZ44mPv8SWLXsB6NTJjd69u5CRkUF6+t/07Tv8tWlT+HM2+7w3oKOrq/JnP2Wbt3XTHkKORODobMPp+BBFm5fDn+s2LqGNVQuqVa9KwuUIFs1fyc4i1CX5ERwUhouLHRcuRpD+LJ3BQ162P94+mxk2bCIpyfeYNnUBW7etZPqMb0lIuMTWLYq6+bvJo6hWrSrLlivecpSZmUlbq/a0bt2cz3t34eKFK5yMUWwQN3PGIoILeZ8lKJtI/5W1ypIk1QYCgEjAErgB9FUeGyfL8hlJkroCC4E7wEXgHVmW+0uS9C6wGvgUxYOMSFmWtb6Y29DgkzLt2Lu/BJS2BK3UrOdR2hK0Uk63bD/P+ivj79KWoBVdqexP0qmoV760JWjlaUZ6aUvQSkZW6e1gWhiqlH+7tCVo5dHfZX99oMb11mWIZ8/Ldj34/ttVSluCVp5l/FPaErRS1sufvo5uaUsokMf/PCvYqBSpVL5CaUvQSlmPEYC/nt0u24FSSP4+e/CN9aveataxVHxWtnsWrxFZlm8Dmha12uawOQDkeTQmy/IDoEdJaRMIBAKBQCAQCAQCwX+Tsj9cJRAIBAKBQCAQCAQCwf8p/5mRcoFAIBAIBAKBQCAQ/Mso40viXgdipFwgEAgEAoFAIBAIBIJSQoyUCwQCgUAgEAgEAoGgbCKX3qvK3hRipFwgEAgEAoFAIBAIBIJSQoyUCwQCgUAgEAgEAoGgbJIlRsoFAoFAIBAIBAKBQCAQlBBipPw/Ss16HqUtQSu/3zxc2hK0YljHpbQl/KtJ+/tpaUsokKwKcmlL0MrfmRmlLUErb+uXL20JWnnyz7PSlqAVXansPzPPKOO74VYs91ZpS9DKPy/KdgynZz4vbQlaKa+nX9oStJKZWbbjAyCzjMewnqRb2hK0kiWX7fuE/yvEmnKBQCAQCAQCgUAgEAgEJYUYKRcIBAKBQCAQCAQCQdlErCkXCAQCgUAgEAgEAoFAUFKIkXKBQCAQCAQCgUAgEJRNxEi5QCAQCAQCgUAgEAgEgpJCjJQLBAKBQCAQCAQCgaBMIstl+00BrwMxUi4QCAQCgUAgEAgEAkEpIUbKBQKBQCAQCAQCgUBQNhFrygUCgUAgEAgEAoFAIBCUFKJTXgrYOVgRdSaQmHPBjBz7tUab7xdOIeZcMGFRh2jY+DPV8WWrvufSzSgiTvqq2Xt2dCEixo/kR5dp3KTBa9F4PNaf6LggRowZqNFmzsLJRMcFcSzKh4aNPwXAxNSIA36biTzlR/hJXwYO6aOy9+jgQvhJXxIfXqSxef1X1lgYps77EWv3nnTsM+SNXG/+ommciT/K8ZN+NMqRbzmpWasGIaEHiD0XwsYty9DX1y8w/cqf5nPtVgxRp/zVzjV56hiOn/QjIsoXr4ObMTJ6/41pcnBsy6m4YM7EH2X0N4PynHPEqK94+PQG1apXBaBps0ZERPkSEeXL2TMhdOjQTqOWbJb+OJurl08QdzaEJuaay3Tt2h8QfcKPK5dOsGvnGjXdAM2bNeaf9N/p3NkdgPLly3My6jBnz4SQEB/KjOnfatWQE3uHtpw8E8Tpc0cYlU/czls4hdPnjhAe5avmK21pBw7qw8kzQRyPOcz02eMB+KCmKb+nJBB2/CBhxw+yeOmsQuvMyeIfZpBwIYyYU4H5xlytWjUIi/Ah/nwoW7etVPnwo4/qcizMiz8eXWXU6LzfV0dHh6iTh9nvtaFY2kqiHpw+ZzwnYgMIizrE5h0rqWxQqci6FiyextmEY5yIOUyjxpp9VrNWDULCDnAm/igbty5XK3f5pU+4FE7UKX8io30JjfRRHd+4dTmR0b5ERvuScCmcyGj171Ta+mbPncipuGBOxBxm++6fiuTTkip/l64c59TpQKJj/Ik8cajQegAWLp7OuYRQomL8aZyP/2rVqsGxMC/i4o+xeesKNf/ll37IsP6cPB1ITGwgQ4f1Vx2fNHkUV65HcTzaj+PRfjg522rVZ+/YlpizQZyOD2HU2Lz1KsC8RVM5HR9CRHSueiaftJOmjiYi2pewE4fYf3BTnnbCtIYxt5POMXzkAK3aNLFkySwuXYokNjYYcy31dGTkIS5ejGD79tUqf/bs2ZHY2GBiY4MJC/OmYcNPVWmGDx/A2bMhxMUdZcSIr4qkqSTyuN6HdVR5eDzajztJ8Wr5DDBy1EAe//mLqs0rDIsWTyf+fCjRpwK0xkhouDfnEkLVtH74UV2Ohh7g/sMrjBytfp+2es1Cfrl9mpjYwEJr0URJtMMABgaV2btnPRcvRHDhfDitWjZ7JZ1QMvetr4MlS2Zy8WIEp08H5RsjtWp9QGTkQS5cCGf79lVqMXL6dBCnTwfliRFQtMMnTwbg5bXptWoWlA6iU/6G0dHRYcGS6Xze9WvatvCgUxd3PvrYTM3GwcmaOma1aNXEhXGjp7Poxxmqz/bs8qFnl7w3sFcv32BAn1GcjDrzWjTO+2EqvbsOxqalJx27uuXRaO9kTd26tbBs2o7xo2ewYIlCY2ZmJrOmLsK6pSfuTj3pP/BzVdprV27wVd9RxES/usbC0tHNibU/zn0j13J0tsHMrBbNzR0ZO2oaS5bO1mg3c/Z41qzejEUTJ9LSntDni24Fpt+105tunfLeMK1cvoG2rT2xadOe4KAwxk8a8UY06ejosGjJTLp3HkhrC1e6dPXg44/rqc5namqErV0b7vyeqDp25fJ17K07YdOmPe4evVmzeiG6uroa9bi2s+fDenX45DMrhg6dyOpV8zXazZ83hWUrfubT+lY8evSYAV/2Un2mo6PD/HlTOHIkXHXsn3/+wdG5O82aO9GsuTMuzra0bNFU47lzkh23PbsOpE0Ldzp18cgTE45O1tQ1q02LJs58O3oai36cWWDaNm1b0s7dARtLT9q28uCnFRtV57v96+/Yte2IXduOjB87g6Li7GKLWb3aNG5ox8gR37FsueY4mDN3EqtXbsS8kT1paY/p1787AI8ePWb8uFmsWK650z1s+Jdcu3qzyLqg5OrBiLBobFp5YtemA7/8cptRGh4WacPJ2QYzs9o0a+zAmJFTWbJM88OQmXMmsGb1ZpqbO/I47TF9+3UrVHpPtz5YW7bH3rqT6thX/UZjbdkea8v2+B4Kxs/3SJnSFxYahaWFG1atPPjlxq98823hHnCWdPlzc/0cy1buWFt1KJQeACdnW8zMatOksT2jR07hx2Wa68NZcybw0+rNNDV3IC3tMV+o/Kc5/aeffUS//j2wt+lEm1YetHO1p65ZbdX5flq1mbaWnrS19CQkR32UGx0dHRYumUGPLl/TxsKNzl011DPONop6xtyJb0ZPUz2w05Z21fIN2Fi2x86qA0eCwhg3cbjaOefOn8yxkMhC+zEbFxc76tWrTf361gwfPokVK77XaDd37nesXLmBBg1sSEt7TP/+PQC4ffsOTk7dsbBwYf78FaxevQCAzz77iAEDemFl5YmFhQtubg6Y5fCnNkoqj2/e+FWVhzZWHUhP/5vDfi9j1dTUGDv7Nvyeo80riOwYMW9kz+gRk1m6bE4+WieyetUmmjS2Jy3tCV/0exkjE8bN1hgjO3ccoHPHLwutRRMl1Q6DorMfHBxGg4Y2NG3mxJWrN15Ja0ndt74qLi52mJnVoUEDG0aM+I4VKzTXg99/P4mVKzfSsKEtjx6px4izc3datGinjBH1PBgxYgDXrhWvHf7XIWe9uZ9SosQ75ZIkzZQkaVwx0tlKknS4JDS9CpIk1ZYk6WJx0zdt1ohfb/3Ob7fvkpGRwUHvANq5O6jZtHN3YP9uxdP/s2cSqGxQmfcN3wMgJvoMaY8e5znvjeu3+OXmr8WVpUaTZg25fet3fv9NofGQVyAubvbqGt3s2b9HoTHuzHkqG1TifcN3uZf6gAsJVwD4689n3Lh+CyPj93NovP1aNBaW5uYNMahc9NGy4uDm7sie3QcBOBMbT+UqlTBU5ltO2tq04tDBIAD27PLG3cOxwPQno2J5pCHfnz79U/V3xbcrIMvyG9HUrHkjfr31G7/dvkNGRgbeXv64erwsx98vmMKMaYvU9KSn/82LF4rdM996q3werTnx9HRh+84DAJw6HYdBFQONswDsbNvg5aWYPbB9+346tHdRfTZi+AC8ffy5d/8PtTR//fUMAH19PfT09bXqyKZps0bcvvVbjrj1x1VD3O5V+ursmQQMDCpjaPie1rRfftWLFUvX8/x5BgAPHjwsUEth8fBwYvdObwBiY+MVeozy5r2NTWt8fBSjKTt3eOHh4QzA/ft/EHf2PBkZGXnSmJga0a6dHVu37C2WtpKqByNCo1Rl7GxsAiYmRkXS5ebhyJ7dilHiM9k+0xAv1jatOOSjiJfdO31w83AqUvr86NTZDa/9fmVKX1joCZVPY2PjMTEtnE9LsvwVF3cPR3YXyn+tOajUtGunN+5K/+WX/uOPzThz+pyqjjtx4jSens5F1tc0V73q4+WPq7ujmo2rmwP7lBrOxiZgYKCok7Wl/fPpX6r0FStWVKvzXN0d+e32nWI9YPP0dGbnTi8ATp8+R5UqlTXW07a2lnh7BwCwY8cB2ivr6ZiYs6SlPValNzU1BuCTTz7k9Ok4lT+PH48pcGZVNiWVx7m/z6+3fufOnSTVsfkLpzB96sJCtSfZuLk7snuX4loFxUi21t07vfDwVGh9cP8P4uLOk5mRmSdNdFQsjx6mFVqLJkqqHa5U6R3aWrVk0+bdAGRkZPD48ZNX0lpS962vioeHE7t2vYwRAwPNMWJj8zJGdu70UtUfihh5okwfp4oRUAx+tGtnz+bNe16LVkHpI0bK3zBGJoYkJSar/k9KTMHI2FDNxtjYkMQcNslJKRibqNuUqEZjQxITU9Sun7uCMjJ+nyQ1m1SMc32PGjVNaNjwU+LOni9ZwWUEYxP1fEtKzJtv1apX5XHaU9VNbk6bwqTXxJTpY7lwJZJu3dsz//vlb0STsbFR3uPK/G/nZk9yUiqXLl7No7VZ88ZEnw4gPu4Yw0ZMUl0zN6YmRtzNccOTeDcZ01wdrOrVq5KW9lh1jruJyaoOg4mJER07tGPd+u15zq2jo8OZ2CMkJ57n2LFITsee06ghJwo/vCzvSYl5y7uxsaFaTCQlpWBkYqg1rZlZbVq1bk7QsX0c8t+OedOGKruatWoQetyHQ/7badW66FP7jE0MuXs3Zx4l5+mkVq9elbTHT1Q+TExMwaQQZW7RoulMnbqArGJuvPIm6sHP+3Qp8uifsbEhiTl9puGamuIl22fa0suyjPehLYQdP0i/L3vkubZlGwvu3XvArV9+K5P6APr07cbRI4XzaUmWP1mWOeS3jeNRvnw5oFeB9ipNxoYk3n1ZryQlpeTRpLk+NNKa/vLl61i2aUHValWoUOEtnJ1tMK3x8ub568F9iYrxZ9VPC6hSpbJWfUl31euQ3PlrbGJI4t1c9YmJYYFpJ08bS8LlCLp292SBsp2oWLECo8Z+zeIFq7R4LX9MTIzU8liRf3nz+LFaHuctBwD9+/fgyJEwAC5duoaVVUuqKf3p4mJHjRz+1EZJ5XFOOnf14MCBlw/PXN0cSEpK5aKGNk8befyXlIKJsQatuWLkTd0PllQ7XLduLR48+IONG5YSezqYdWsXU7FihVfSWlbvWxV5nMOHGuq4wsdIT4KDw1X/L148gylT5hW7Hf7XkZX15n5KidfeKZck6QtJks5LkpQgSdL2XJ+ZS5IUo/zcR5Kkqsrj9SRJOqpMEydJklmudBaSJJ2TJKluPte0kSQpXvlzTpKkSsqR9kjldS5LkrRWkiQdpb2zJEknldfaL0nSO8rjzSRJipAk6awkScGSJBnnOJ4gSdJJYLgmDUq7QZIknZEk6Uz687R8bDQczP1kVYNNUZ6+viqSBpG5r67RJofGim9XZOO25UyfPF/tKf3/MwX5pCCbwqTXxPezl9LwU2v27/Pl60Hqa6FKSpOmcizLMhUqvMW344Yx7/tlGrWePZOAZQs3Wlm6MWnCCMqXL6/R7lV1/7hkFt9N1txYZWVl0dzCmVp1mmPRvAn163+sUcPr0qMtra6eLlWqVKadQ3dmTlvEhi3LAEhNuUeT+nbYt+3EtCkLWLthCe9UertAna9Lszbaudpz//4D4s8Ve8JQideDY8YNJjMzE699+Y86a9ZVcjHczrEHtlYd6NZ5AAMH9cGyjYWaXZduHnjt1z45rDT1fTt+KJkvMtm3t3BruEuq/AE4OnTFytKTzh2/ZNCgvrRp0+KNaMrvs+vXfmHZ0nUc8t2K18HNXLx4lcxMxejlxg07MW9oh1VrD1JT7zN33uQS0VdQ2nlzltL4MxsO7PNj4OC+AEycPIq1q7eoZg8VlfzagcLozYmNTWv69+/BlCmKqbnXrt1kyZI1+PvvxM9vOxcuXCEzs3DvKC7pdlhfXx83dwcO+ihGNStUeItx44cxb+7SQul7PVqLfKliUVLtsJ6uLk2aNGTdum1YtHDhr7+eMXHCiDzneWWthbEp4fvW11UPWlu3pl+/HkydqogRV1d77t37g3Ov0A4Lyh6vtVMuSVJ9YApgL8tyY2B0LpNtwERZlhsBF4DsRYI7gdXKNJaA6tGhJEmWwFqggyzLt/K59DhguCzL5kBbIF15vAXwLdAQMAM6S5L0LjAVcJRluSlwBvhGkiR9YCXQVZblZsAmIHuB1GZglCzLrbV9f1mW18uy3FyW5eYVylXRaJOcmIpJjuknJqZGpKTcU7dJSlWbomJsYkRKsrpNSZKclIJpjimKxiZGpCbn1WiiZmOo+h56enps3LYM7/2HCfA7+mZElxJffd1btXlZSrJ6vpmY5s23Px48xKBKJdVa6pw2SYkpBabXxoF9fnh2cHkjmpKSNBxPuUftOjWpWbsGx6P9iL8YhompEeHHD/L++++qXfPq1Zv89Vc6DXJ0iIcO6ceZ2COciT1CUnIKNT4wUX1mWsOYpORUtXM8ePCQKlUMVLprmBqTnKSwada0ETt3/MTN6zF06ezOqhXzVFMms3n8+AkRkdG4FLDx0ks/vCzvJqaGeeI2KSlFLSZMlHGjLW1yUiqH/UIAOBd3gaysLKpXr8rz5xk8epQGwPn4S9z+9XfM6tUpUOegwX2JjvEnOsaf5OR7aqNLJqbGJGvyoUFllQ9NTY1ILqDMtWrVDDd3Ry5dOc6WbSuxsbFkw8ai3ZCWZD3YvVdHnFzsGPb1+EJpGTioj2qjteTke2ojnCYarqkpXrJ9lpSUkm/67O/34P5DDvuF0LRZI5Wdrq4uHu1d8PFS38ixrOjr+XknnNvZM2jAN1o8+WbKH6DSfP/+H/j5BdOseeN8bQcO6qPanCsl+R6mNV7WKyYmRnk0aa4PFTYK/2lOv33bfqytOuDm0otHD9O49ctthcZ7f5CVlYUsy2zdvEer1qSkFExqqNchufM3KTEF0xq56hNlnVxQWgCv/X54tFdMjW3avDEzZo8n7kIog4f2Y8y4IXw1SPsmV4MHf8GpU4GcOhWYJ48V+Zc3jw3U8li9HDRo8Alr1iyia9eBPMwx3XrLlr20bu2Oo2M3Hj1K46aW5XlvKo9BsS9DQvwl7t9TTMeuU7cmtWp/wImT/py/FIGpqRGRJ3zztHnZfD2oLydOHubEycMkJ6eq+8/EiOQUDVpzxUhKru/zOnkT7fDdxGTu3k1WzVLz9vaniXlDXoWydN86ePAXxMQEEBMToMzjHD7UUMcVLkYW0q3byxhp3bo5Hh6OXL16gm3bVmJra8mmTcteSXeZR6wpLzL2wAFZlh8AyLKsWiApSZIBUEWW5Qjloa2AtSRJlQBTWZZ9lGn+lmU5+7Htp8B6wFOW5d+1XDcK+FGSpFHKa2QvsDkty/ItWZZfALsBK6AV8BkQJUlSPNAPqAV8DDQAQpTHpwI1NOjOOx+2CJyLu0Bds1rUrGWKvr4+HTu7ERwQqmYTHBBKt16KzWuaNW/M0ydPuZd6/1UuWyTi4y5Sx6wWHyg1dujiSnBgmLrGwFC69VRobNq8kVLjAwB+XDWHG9dvsW711jemubTY+PNObNq0x6ZNe/wPH6Vnr44ANLcw58njp6RqyLcTkafo0FGxPq7n550J8Fc0AIEBxwqVPid1zWqp/nZ1c+DG9VtvRFPc2QvUNatNzVo10NfXp3MXd4L8j3Hl8nU+rtsK8wZ2mDewIykxBdu2Hbl37wE1a9VQNTo1a5ry0Ud1uf3bHZWGNWu30tzCmeYWzvj6BtO3d1cAWrZoypPHT/J02gDCI6Lp0kWxo2vfvt3wVW688+HHran3USvqfdQKL29/RoyajK9vMO++Ww0DA8X00bfeegsH+7Zcu/aLVh+DIm7r5Pi+HTu7E6QhbnsofdWseWOePFH4SlvaAP+jtLVupczL2pTT1+ePPx5RvXpVdHQU1XOt2jWoa1ab327foSDWr9uOZSt3LFu5c9jvCL16dwbAwsJcoSclb95HRsbQqZMrAL37dMHfP0TrNWbOWMzHH1pS/9O29P9iJBER0Qz8amyB2nJSUvWgnYMVI8YM5IueQ0lP/7tQWjas36HaaC3gcAg9eyk2OWue7TMN1zweeYoOnRTx0qt3JwKz48X/mMb0FStW4J13FDMdKlasgL29FVcuv9zYyNauDTeu3yIpKYXclLY+B0drRn8zmM97DC7Qp2+i/OXR6tCWy5ev5Wu/Yf0O1QZdhw8foVeh/BdDR6Wmz3u/rA8D/I/mm/7d96oDUKOGMZ4dXDig3Bsg53pkD09nrly+nq/Wc2cvULfuy7qiUxd3ggKOqdkEBYbSXamhmUVjnjz5U1HPaEmbs51op2wnADzbfU7ThvY0bWjPujVbWfbDWjau35GvPoB167bRsqUrLVu64usbTO/eXQBo0aIJjx8/1VhPR0ScpHNnNwD69OmKn7Ke/uADE/buXc+AAWPydLrfU/rzgw9M6NChHfv25f9WgjeVxwBdu3mq8hbg8qXr1KvTgkb1bWhU34bExBSsrdpz794DjVp/Xr8dq9YeWLX2wN8vhF6fK65VUIxka+3Vuwv+h0tusONNtMOpqfe5ezeJjz5STIq1t7fiypX846IwlKX71nXrttGqlRutWrnh53eEzz9/GSNPnmiOkcjIlzHSu3cXDh9W1IMffGDCnj3r+OqrsWoxMn36IurVa8Unn1jxxRcjCQ+PZsCAMa+sXVC66L3m80nknTFSmDT5kQy8BTQBkvIzkmV5gSRJ/oAbECNJUvbOKLm1yMrrhciyrLYQTZKkhsCl3KPhkiRV0XCeYvPixQu+GzeHPd4b0dXVYfcOL65dvckXAxRr+LZt2svRIxE4OFtzKv4I6c/+ZvTwl9Pd1m5cgqWVBdWqV+Xc5XAWz1/Jru1euHo4Mm/RVKq/W42d+9Zy8cJVenbW/EqIwmicPP57dnv9jK6uDnt2+HD96k2+UK4z3LZ5L8eOROLgZM3Jc0GkP/ubscOnANCiVVO69ezA5UvXCDmu2ORn/uxlhIZE4urhwNyFU6j+bjW271vDpQtX6dWlaLsjF5XxMxYQe+48aWlPcOjYh2Ff9aWLp0vBCYtBSHA4Ts42nE04Rnp6OiOGTlJ9tvfAz4weMYWUlHvMnL6YDZuXMnnaWC6cv8yObQcKTP/zpqW0aduC6tWrcvHqcRbMW86ObQeYMWs89T6sQ1ZWFnfuJPHt6OlvRNOLFy+YMG4WBw5uQldHl53bD3C1gI2CWrVuxphvBpORkUnGi0xGjJrMH3880mgbEHiMdu3suXYlimfp6Qwc+HKEzu/QNgYNGU9ycirfTf6eXTt+YvbMCcQnXFJtHJMfxsaGbNq4DF1dHXR0dDhwwA//gIJvcBRxO5t93hvQ0dVVxW2/AT0B2LppDyFHInB0tuF0fAjpz9IZpYzb/NIC7NruxfLV84g86UdGRobKv63bWDBx8igyM1+QlfWCcWNnaNzYTBvBQWG4uNhx/mI46c/SGTJkguozL59NDB82iZTke0ybuoAt21Yybca3nE+4zNYt+wB43/Bdjp/wpVKld8jKkhk+4kuaN3VW21ywuJRUPTj/h2mUK1eOfQcVr4c5eyaBCWNnFlrXkeBwnFxsiTsfSnp6OsOHTFR9ts9rA6OGT1bEy7RFbNyyjCnTvuH8+cts37pfa/r33n+XHbt/AkBXTw+vfb4cO/pybXbnru5aN3grTX2LlsygfPly+PhuARSbX32Tq57RREmVv+rVq7J7zzoA9PR02bfPl6OF3DvgSHA4zi62xJ8P5Vn632r+2++1kZHDvyMl5R4zpi1i05blTJ32DefPX2JbDv/ll377ztVUq1aFjIxMxn0zU7U50+y5E2nY6DNkWeb33+4yZtTUfPW9ePGCSeNns99nIzq6uuzafoBrV2/SX1nPbNm0h5DgcBydbYhNOKqoZ4Z9pzUtwLSZ41TtxN07SXw7puhvc9BEUFAo7drZcfnycZ49S2fQoJd7+h48uIWhQyeSnJzK1Knz2bZtFTNnjic+/hJblBtETp48mmrVqrJcuTN/ZuYL2rTxAGDPnnVUq1aVjIwMxoyZptoQriBKMo8rVHgLO7s2jBk15dUcpyQ4OAxnF1sSLoTxLP1vhg1+GSMHvDcxYtgkpdaFbN66gmnTvyEh4TLbtr6MkYjjh1QxMmz4l7Ro5sLTp3+yactyrNq2pHr1qly5HsW8ucvZvm1fkfSVVDsMMHrsNLZtXUm5cvr8+uvvfDVQ+yycgiip+9ZXJSgoFBcXOy5diuTZs3QGD34ZIz4+Wxg2bALJyfeYMmU+27evYsaMcSQkvIyR775TxMgy5c78mZkvsLLyfGVd/0r+A2vnpde5Vlk5fd0HaC3L8h+SJFUDRgF/yrL8gyRJCcAIWZaPS5I0EzCQZXmsJEkxwAJZlg9KklQe0EUx9Xwc8BVwBBgty3J4Ptc1k2X5F+XfB4EtQBoQiGJU/Dfl3+uBSOAsiin2NyVJqgjUAG4Dl4G+siyfVE5n/0iW5UuSJJ0HhsmyfEKSpIWAuyzLWl8GbmjwyZtbBF4MdKSyvcff7zfL3Mb7ahjWKZmO/X+FJ/8Ubw3jm6RqhXdKW4JW0jOel7YErbytr3m/gLLC8xd5dywWFI2MrMKt8y0tdHXKdjunr6P5lZBlhT+fF262SWlRXk+/YKNS5E3uBVRcnmX8U9oStPJeRYPSlqCVtH/K/p5J6em/aRv8/NeQfuSnNxZQFZyHlYrPXmuLJcvyJRTrsCOUHfAfc5n0AxYrO7nmQPYLJPsCo5THowHVog9ZllMBT2C1JEkt87n0GEmSLiqvmY6iAw5wElgAXAR+BXxkWb4P9Ad2K68XA3wiy/JzoCuwUHmeeBTr2wG+VF7/JC/XqwsEAoFAIBAIBAKBoCT5D6wpf93T15FleSuK9eKaPotHsaY79/EbKNaj5+QWEK78/HegvpZrjsx9TLmb4TNZlvO820WW5VDAQsPxeMBaw/GzQM7dWWbmp0UgEAgEAoFAIBAIBILC8to75QKBQCAQCAQCgUAgELwW/gNryv9VnXJJkr4k72vWomRZzvPucOX68/A3IEsgEAgEAoFAIBAIBIJi8a/qlMuyvBnFO8MFAoFAIBAIBAKBQPD/zn9gpLxsb00qEAgEAoFAIBAIBALB/zH/qpFygUAgEAgEAoFAIBD8hyjFXdHfFGKkXCAQCAQCgUAgEAgEglJCjJSXEA/Tn5a2BK2YVKpe2hK0YljHpbQlaCX11+DSlqCVSjVsS1uCVqq89XZpSyiQ9IznpS1BKy/K+FPjv19klLYErTx/kVnaErSiK5X9Z+YycmlL0MpbuvqlLUErFfXKl7YErZT1GHlRxteY/pNZttsQgLf0ypW2BK08fZ5e2hK0UqV82b+XEfx7EJ1ygUAgEAgEAoFAIBCUTcr4Q7jXQdl/FC8QCAQCgUAgEAgEAsH/KWKkXCAQCAQCgUAgEAgEZZMyvmTvdSBGygUCgUAgEAgEAoFAICglxEi5QCAQCAQCgUAgEAjKJmJNuUAgEAgEAoFAIBAIBIKSQoyUCwQCgUAgEAgEAoGgbCLWlAsEAoFAIBAIBAKBQCAoKcRIuUAgEAgEAoFAIBAIyiZiTbmgpFj642yuXD5B3NkQmpg30GhTu/YHRJ3w4/KlE+zcuQZ9fX21z5s3a8zf6b/TubM7ADVqmBByZD/nz4cTHx/KyBFfFVufjX0bQk/5EhF7mKGjB2i0mTl/IhGxhwmKPECDRp8CUL58OQ6F7CQwYj8hUd6MnThMZT9mwlBOXQwhIHwfAeH7sHO0KrKu+YumcSb+KMdP+tGo8WcabWrWqkFI6AFiz4WwccsyNb/ll37lT/O5diuGqFP+aueaPHUMx0/6ERHli9fBzRgZvV9kzQUxdd6PWLv3pGOfIa/93NpYsmQWly5FEhsbjLmWMhgZeYiLFyPYvn21ypc9e3YkNjaY2NhgwsK8adjwU1Wa4cMHcPZsCHFxRxlRhDJo79iWmLNBnI4PYdTYQRpt5i2ayun4ECKifdXyL7+0k6aOJiLal7ATh9h/cJMq/2zsLDkW4U3kST+ORXjT1rpVoXXmZPEPM0i4EEbMqUAam9fXaFOrVg3CInyIPx/K1m0rVT786KO6HAvz4o9HVxk1+mu1NAYGldix8yfizh3lbFwILVo0KZa+JUtmcvFiBKdPB+Wbx7VqfUBk5EEuXAhn+/ZVanl8+nQQp08HqeVxjRrGBAXt4dy5Y5w9G8Lw4V8WSxvAwsXTOZcQSlSMP40b5++/Y2FexMUfY/PWFWrxnF/6IcP6c/J0IDGxgQwd1r/Y+krCfwBXr54gNjaYmJgATpzwK7a+kih/H35Yl+gYf9VPUsp5hhUzjxf/MIPzF8I5dSoQcy36wiMOknA+jK3bXvqvR48OnDoVyKlTgRwL9VLz35q1i7h9+wyxscHF0pWNnYMVUWcCiTkXzMixX2u0+X7hFGLOBRMWdYiGOeqcZau+59LNKCJO+mpMN3TkAFIfX6VatSrF1mdtb8mxU4cIi/VjSD7t8Iz5EwmL9SMwcj/1G32i9pmOjg6Hw/ayYddK1TG39k4ER3nzy/1zNDTX3IYWhUWLpxN/PpToUwFay2BouDfnEkLVYvjDj+pyNPQA9x9eYeTogSp7U1NjDgfsJPbsEU7FBr1SDJf1GHmVe0FPT2fizoZwJvYIMScDaGNpoUozcsRXnDt3jPj4UEaNHKjxvIWhpNo4UJTPqJOH2e+1odj6Skrj68pjOwcrjsf6Ex0XxIgxmvNhzsLJRMcFcSzKh4aNFfWciakRB/w2E3nKj/CTvgwc0kdlP2HKSI5F+RBy3Js93j9jaPRekXUJyiaiU14KtGtnT716dfj0MyuGDp3IqlXzNdrNmzeF5St+5rP6VqQ9esyAL3upPtPR0WHevCkcORKuOpaZmcmECbNo1MgWKytPhgztz6efflhkfTo6OsxZNJl+3YfiaNmR9p1d+fDjumo2do5W1KlbCxsLD777ZjZzf5gKwD//PKdXx4G42nTD1aY7Ng5taNK8kSrdxjU7cLPtjpttd8KOniiSLkdnG8zMatHc3JGxo6axZOlsjXYzZ49nzerNWDRxIi3tCX2+6FZg+l07venWKe9Nz8rlG2jb2hObNu0JDgpj/KQRRdJcGDq6ObH2x7mv/bzacHGxo1692tSvb83w4ZNYseJ7jXZz537HypUbaNDAhrS0x/Tv3wOA27fv4OTUHQsLF+bPX8Hq1QsA+OyzjxgwoBdWVp5YWLjg5uaAmVntAvXo6OiwcMkMenT5mjYWbnTu6sFHH5up2Tg621DXrDYtzJ34ZvQ0Fi+dVWDaVcs3YGPZHjurDhwJCmPcxOEAPPzjEb17DMG6tSfDh0zkp/WLi+xDZxdbzOrVpnFDO0aO+I5lyzXn4Zy5k1i9ciPmjexJS3tMv/7dAXj06DHjx81ixfK8NySLFs8gJCSCpk0cadXSjWvXbhZZn4uLHWZmdWjQwIYRI75jxQrN+r7/fhIrV26kYUNbHj1Sz2Nn5+60aNFOmceKeioz8wWTJs2lSRMHbGw6MnjwF3zySdHrGSdnW8zMatOksT2jR07hx2Wa43nWnAn8tHozTc0dSEt7zBf9umlN/+lnH9Gvfw/sbTrRppUH7VztqVuIMpibkvJfNu3a9aRVKzesrDyLrA1KrvzduHELy1buWLZyx8rSk/T0v/HzPVJkfS4uttSrV4dGDW0ZMWIyy5ZrrmPmzJ3EqpUbadzITqnvpf9cXHrQsqUrCxesZGWOdnLH9gN07NivyJpyoqOjw4Il0/m869e0beFBpy7ueeocBydr6pjVolUTF8aNns6iH2eoPtuzy4eeXTR35E1MjbCxs+TO74mvpG/2osn07z4MZ8tOtO/cjnq52mFbRytq162JnYWnWjuczZeDe3Pz+i21Y9eu3mRov7Gcjj5bbG3ZZJdB80b2jB4xmaXL5mi0mzVnIqtXbaJJY3vS0p7wRb+XZXDCuNl5ymDmi0ymTJ6HRTNnHOy68PWgvnz8Sb1i6yurMfKq94KhoSdo2syJ5hbOfD3oW9au+wGA+vU/ZsBXn2Np6U6zZk64uTlSr16dIusryTYOYNjwL7l2teht25vQ+DryWEdHh3k/TKV318HYtPSkY1e3PHWMvZM1devWwrJpO8aPnsGCJYo6JjMzk1lTF2Hd0hN3p570H/i5Ku1PKzbh0KYTTm07ExIcwTcThuW59v8lWVlv7qeUKNOdckmSZkqSNK4Y6WpLknSxGOmii5qmOLT3dGHHzgMAnDodh0EVA40jsHa2bfDyUozcbt++n/btXVSfjRg+AB8ff+7f/0N1LCXlHufiFV/7zz//4urVG5iYGBVZn3nTBtz+9Xfu/JZIRkYmfj5BOLnaqdk4udrhtVcxwnPuzHkqG1TifcN3AXj2VzoAevp66OvpIctykTVows3dkT27DwJwJjaeylUqYWiY9wlhW5tWHDoYBMCeXd64ezgWmP5kVCyPHj3Oc66nT/9U/V3x7Qqv7bvkpLl5QwwqV3rt59WGp6czO3d6AXD69DmqVKmssQza2lri7R0AwI4dB1RlMCbmLGlpj1XpTU2NAfjkkw85fTqO9PS/efHiBcePx9ChQ7sC9TRt3ohfb/3Gb7fvkJGRgY+XP67ujmo2rm4O7NvtA8DZ2AQMDBT5py3tn0//UqWvWLGiKv8unL9CSso9AK5euUH5t8pRrpz6TJSC8PBwYvdObwBiY+MxMKis8Ym1jU1rfHwCAdi5wwsPD2cA7t//g7iz58nIyFCzr1TpHdpYtWDrlr0AZGRk8Pjx0yJpy9a3a9fLPDYw0JzHNjYv83jnTi88PRX6FHn8RJk+TpXHKSn3iFerZ25iYmJYZH3uHo7sVubnmWz/aYhna5vWHFT6b9dOb9w9nLSm//hjM86cPqcqgydOnFZ9p6JQUv57XZRU+cuJrV0bbt36jTt3it65dPdwZpdK3zkMDCphpFGfJT4+ASp9nkp9p07F5fLfy7YsKuo0Dx/mra+LQtNmjfj11u/8dvsuGRkZHPQOoJ27g5pNO3cH9u8+BMDZMwlUNqjM+8oyGhN9hjQNbQbA7PnfMXv6Yl6luWjctAG//XonVztsq2bj5GqHt7Idjj9zgcoGlXhP2Q4bmbyPnXNb9u7wUUvzy/VfuXXzt+ILy4GbuyO7dynOX1AZzI7h3Tu98PBUxPCD+38QF3eezIxMNfvUlPskxF8CFHXMtWs3i3UvU9Zj5FXvBf/665nK5u0c7dsnn3zI6VMv2+HIQrbDuSlJ/5mYGtGunZ2qnSsuZTmPmzRryO1bv/P7b4o65pBXIC5u9mo27dzs2b9HUcfE5biXvpf6gAsJVwD4689n3Lh+CyNjRdlQv68pmftSQelQpjvlbxpZli3fxHVMTIy4eydJ9X/i3WRMczU41atXJS3tMS9evADgbmIyJsqbEhMTIzp0aMe69dvzvUatWjUwb9yA06fPFVmfkbEhyYmpqv+Tk1JVlcFLm/dJSkxR/Z+SlIqh0kZHR4eA8H3EXQ3neMRJ4s9eUNl9MbAnQZEHWLxiFpUNitYRNTYxJDExWfV/UmIKxrk6A9WqV+Vx2lOV33LaFCa9JqZMH8uFK5F0696e+d8vL5LmsoqJiRF37770RWJiSp6bnurVq/L48ROVLxMTkzXeGPXv34MjR8IAuHTpGlZWLalWrQoVKryFi4sdNWoU3BkxNjYk6e7L8pSUlDdvjE0MScxpk5iKsYlhgWknTxtLwuUIunb3ZIGG/PPs4MKFhCs8f55/o6xRs4mhmg+TNPinevWqpKn5MKXADmztOh/w4MFD1q5bTNTJw6z6aQEVK1YokjbIzuMc9YyGaxc+j3sSHBye53jNmjUwN69PbGx8kfUZGxuSmENfUlLeMqg5no20pr98+TqWbVpQVVkGnZ1tMC1EGcxNSfpPlsHPbwdRUYcZMKBXHvvCUFLlLyddu3lwYH/xptebmBiq+S9n3uXUl9d/efX169dDbVbY68DIxJCkXO2BkXGuOsdYvc1I1lAv5cbF1Y6UpFQuX7z2avqM3ydZrY29l0efofH7+bbV07+fwIKZS8kqwVGfPO1IUgomxhpiOFcZLEy7m03NmqY0alyfM8WpY8p8jLzavSBAhw7tuHAhgkOHtjLo628BuHTpKlZtW1GtWlUqVHgL13b2fFDDpMj6StJ/ixZNZ+rUBa9cPstyHhsZG5KYI4aTk1IKvJdOTkrFOFec16hpQsOGnxJ39rzq2KSpozlz8Ridu3mweN5K/hPI8pv7KSXKXKdckqQpkiRdkyTpKPCx8li4JEnNlX+/K0nSbeXftSVJOi5JUpzyp1CdakmS6kuSdFqSpHhJks5LkvSh8vifyt+2kiRFSJK0T5Kk65IkLZAkqbcyzQVJkszyOe8gSZLOSJJ0JivrL00m2XZ5juV+0qXJJrugLFkyi8mT5+Vbmb39dkX27f2Zb8fNUBvpLTQaL12wvmybrKws3Gy706qhE+ZNGvCRctrZjs17sW7mjqtNN+6lPmDanKJNgiiu37JtCpNeE9/PXkrDT63Zv8+Xrwf1KdD+34Dm4lV4X2ZjY9Oa/v17MGWKYtrdtWs3WbJkDf7+O/Hz286FC1fIzHxRCD3Fz9uC0s6bs5TGn9lwYJ8fAwf3VbP7+JN6TJ89nm/HTCtQ4+vUrA09PT3MzeuzYcNO2rT24Nlfz/h23NBS02dt3Zp+/Xowdar61Mq3367I7t1rGT9+drHqmZKK5+vXfmHZ0nUc8t2K18HNXLx4lczMzDy2Ja0vG03+s7fvjKWlOx079mPw4C9o06ZFqenLD319fdzdHPFRzgIoLX3W1q35ol8Ppk1dUCwd+evTcDC3bwpRT+akQoW3GDNuCAvnrXg1cRTWf5r12Ttb8+DBQy4qR9pKiuLnceHO//bbFdm+6ycmTZhTKnVMQZRWjOR04KFDQTRsaEOXrl8xc+Z4AK5evckPi1cTFLgb/8M7OX/+cqHa4delryD/tXO15/79B8SfK/KE1jyU5TzWeN3C2OTQVvHtimzctpzpk+erjZAvmLuc5g0c8N5/mC8H9S6yNkHZpEx1yiVJagb0BJoAnQEL7Sm4BzjJstwU6AEUtiUcAiyXZdkcaA7c1WDTGBgNNAT6Ah/JstwC2ACM1HRSWZbXy7LcXJbl5jo6b6t9NnRIP87EHuFM7BGSk1Oo8cHLp5amNYxJSk5Vs3/w4CFVqhigq6sLQA1TY5KSFDbNmjZix46fuHE9hs6d3Vm5Yp5qOpOenh779v7M7t0+HDwYWEh3qJOSlIqx6csndcYmhqSm3FezSU5KVXtaa2RiyL1cNk+ePOVk1BlsHdoovtP9h2RlZSHLMru3edG4acMCtXz1dW8ionyJiPIlJTlVbQqoiakRKcn31Oz/ePAQgyqVVH7LaZOUmFJgem0c2OeHZweXgg3LKIMHf6HaOCk5+Z7aCLapqRHJGsqggUFllS9NTY3VbBo0+IQ1axbRtetAHj5MUx3fsmUvrVu74+jYjUeP0rh589cCtSUlpWBS42V5MjHJmzdJiSmY5rQxNSQl+V6h0gJ47ffDo/3LaczGJoZs27Wa4YMmcPvXOwVqBBg0uK9q45fcPjTJ5R9QxrGaD41ILqDMJSYmk5iYohoZOuiT/+Y1uRk8+AtiYgKIiQkgOTmVGjlGRzRdu3B5vJBu3dTzWE9Pj92717J370EOHQoqlDaAgYP6cDzaj+PRfqQk38M0hz4Tk7xlUHM8K2ySklLyTb99236srTrg5tKLRw/TuPXL7ULpe1P+yz7P/ft/4OsbjIWFeaH0vYnyl42ziy3x8Ze4d+9Boeyz9Z2MCeCkBv/lzLuc+vL676W+Bg0+YfVPC+jR/Ws1/70OkhNTMcndHqSo+yY5Sb3NMc6nbsmmdp2a1KxVg9ATh4g9fwwTU0NCIr157/13i64vKRVjtTb2fVJz6UtJuqexrW7W0hzHdrYcPxfAyp8XYtnWgqVr5xVZgya+HtSXEycPc+LkYWUe52hHTIxITtEQw7nKYO5yoAk9PT127PqJfXt98fMt/IZ+ZT1GXue9YE5OnDhF3bq1qF69KgCbt+yhRct22Dt04WEh22F4M/5r1aoZbu6OXLpynC3bVmJjY8mGjUsLpe9NacymOHmcTXJSitqyG2MTI1KT89YxJmo2hqp6SE9Pj43bluG9/zABfkc1XsPngD/uyuUg//eINeVvnLaAjyzLz2RZfgJo3tb0JfrAz5IkXQD2A4XdSvQkMFmSpIlALVmW0zXYxMqynCzL8j/AL0D2Dg8XgNqFvI6KNWu30tzCmeYWzhzyDaZP764AtGzRlCePn+S5GQAIj4imSxfFzup9+3bDz08h4aOPW/PhR6348KNWeHv7M3LUZHyVjdbP65dw9epNli1fX1SJKhLOXaJO3Vp8UNMUfX09PDu1IyQwXM3maFA4XXooNihq0rwRT5885V7qA6pVr0pl5fro8m+Vx8qmFTdvKBqD7DXnAC7u9ly7cqNALRt/3olNm/bYtGmP/+Gj9OzVEYDmFuY8efyU1NT7edKciDxFh46K9VM9P+9MgL+iMgsMOFao9Dmpa1ZL9bermwM3cm2a829i3bpttGzpSsuWrvj6BtO7dxcAWrRowuPHTzWWwYiIk3Tu7AZAnz5dVWXwgw9M2Lt3PQMGjMnT2L/3XnWVTYcO7di3r6AwhnNnL1C3bm1q1qqBvr4+nbq4ExRwTM0mKDCU7r06AdDMojFPnvxJaup9rWlz5l+7HPlX2aASu/f/zJyZSzh9Kq5g5ylZv267avOXw35H6NW7MwAWFuY8efI0z8MrgMjIGDp1cgWgd58u+PuHaL3GvdQHJN5N5sMPFZs62dpZcvVK4TbDWbduG61audGqlRt+fkf4/POXefzkieY8jox8mce9e3fh8GGFvg8+MGHPnnV89dXYPHm8du0irl27yYoVRds1d8P6HbS19KStpSeHDx+hlzI/m2f7T0M8Ho+MoaPSf5/3fhnPAf5H803/rrIM1qhhjGcHl0JPPXwT/qtYsQLvvPO26m9HR2suXSrcVOc3Uf6y6dbNk/37C47d3Ppat3Kjdbb/VPqy/adJ30k6dXJT6Tvsr6hjatQwYdfutQzUUP5eB+fiLlDXrBY1a5mir69Px85uBAeEqtkEB4TSrVcHAJo1b6xs5/JvM65cvk79em2waOSARSMHkhJTcbLuzP1i3NCfP3eJ2nVrUiNHO3w0MELN5mhQOJ2V7bB584Y8ffIn91MfsHjOCiwbOtO2iRsjv55I9PFYxg6ZXGQNmvh5/XasWntg1doDf78Qen2uiMGCymB2DPfq3QX/w5o7GDlZvWYB1679wuqVG4ukr6zHyOu8F8y5iWoT8waUK6fPH388AtTb4Y4dXdmz92Ch9L0J/82csZiPP7Sk/qdt6f/FSCIiohn41dhC6XtTGrMpTh5nEx93kTpmtfhAWcd06OJKcGCYmk1wYCjdeirqmKY57qUBflw1hxvXb7Fu9Va1NHXqvryvcXa14+aNf+99qUAdqSxtECBJ0higqizLM5T//wgkAe2AybIsn5YkqQZwQpbl2pIkzQTeASageMDwtyzLepIk1QYOy7Ks+f0SinObAe7AGGCgLMuhkiT9KcvyO5Ik2QLjZFn2UNqGK/8/k/uz/NAvZ6rVsSuWf4+zsy3p6ekMHPgNZ+MUa0V8D21j8JDxJCenUqdOTXbu+ImqVasQn3CJfv1G8vz5c7XzbNywFP+Ao3h7+9PG0oLw8INcuHCZrCzF5adOW0BQUGie65tUqq5NHnaOVkz/fgK6urrs23WQVT/+TO/+il2Pd27ZD8CcRZOxsW9DevrfjBs5jQvxl/nksw/5cfVcdHR1Fa9jORjMih/WAbB0zfd81uATZFnm7u9JTP52tqryyc3T55qek8CiJTNwcLQmPT2dEUMnqaY/7T3wM6NHTCEl5R61an/Ahs1LqVq1ChfOX2bwwHEqv+WX/udNS2nTtgXVq1fl/r0/WDBvOTu2HWDrjlXU+7AOWVlZ3LmTxLejp5OcnErqr6/2Kp6cjJ+xgNhz50lLe0L1alUY9lVfuni+2oh8pRq2BdosWzYHZ2dbnj1LZ9CgccQpy+DBg1sYOnSiqgxu27aKatWqEB9/iS+/HM3z589Zs2YhHTu68fvvikkmmZkvaNNGERLHjh2gWrWqZGRkMHHiHMLCovJc+51yb+U55uhsw/cLJqOjq8uu7QdY+sNa+g/oCcCWTXsAWLhkBvaObUl/ls6oYd+p8k9TWoDN21eq8u/unSS+HTODlORUvhk/lNHfDObWLy83POrW8UsePHio+v/vzILXmP+4dDaOTtakP0tnyJAJnItT7J/g5bOJ4cMmkZJ8j9q1P2DLtpVUrWrA+YTLfDVgLM+fP+d9w3c5fsKXSpXeIStL5q+//qJ5U2eePv2Tho0+ZfVPCyinX45fb//O0MHjVZteZfNCLvhp7tKlc3B2tuHZs3QGDx5HnFKfj88Whg2bQLJS3/btq6hatQoJCZf48ssxPH/+nJ9+WkjHjq5qeWxl5YmlZXOOHfPiwoUrqiU0M2YsJjhY/WajnK5egfp++HEmjo7WPEv/m+FDJnLunELffq+NjBz+HSkpCn2btiynatUqnD9/ia+/+lYVz/mlDzyyh2rVqpCRkcmU7+YREZ53D8/nLwqe0l4S/qtd+wP27lU8NNXT02Pv3kMsWrQqz7V1pYKfmZdU+atQ4S2uXo+mYX0bnjzJf5NBOc9kzLz6nJxsSH+WzuAh41X6vH02M2zYRJW+rdtWqvyXrW/1Twvo0MFVtblSZmYmba3aA7BlywraWreievWq3Lv3gLlzl7Jt6748139HP289kxMHJ2vmLJiMrq4Ou3d4seyHdXwxQLH7+7ZNig2o5v8wTVnn/M3o4ZNJUNY5azcuwdLKgmrKNmPx/JXs2u6ldv7Y88dwse2S7yh/Rb3yWvXZKtthHV0d9u86yOofN/C5sh3epWyHZy/6DmtlOzxh5HQuxF9WO0fLNs35eng/Bn6umODn7G7PzAWTqFa9Kk8fP+XyxWv066Z5ecwffxe8weSSH2fh6KSIwWGDJ6hi8ID3JkYMm6SK4c1bV1C1qgEJCZf5+qtvVGUw4vghtTLYopkL9Rt8wpGj+7h48aqqjpk98weO5NrXIqsQ96+lGSP/ZD7XeDwnr3IvOG7cMPr06UpmRibp6X8zadIcoqJjAQgL9aZa9apkZmQybvwswsI0v+2mvF65UvFfNm3btmTUmK/p1qX4r20rzTyuVE77fi/2TtbMnj8JXV0d9uzwYfmSdXzxpbKO2ayoY+YtnoqdoxXpz/5m7PApJMRfokWrphwK2sHlS9dU9/PzZy8jNCSSDduWYVavDlmy4r5m4thZWmfwJKdd1rRY519H+u4Zb6zDWqHXrFLxWVnrlDcFtgAtAT0gDlgHfAKclWV5jbLjPkbZKV8K3JVleYkkSV8Cm2RZlgrqlEuSVBf4VVYYLwNuy7K87E12ykubgjrlpU1+nfKywuvslJcEhemUlyaaOuVljcJ0ykuTwnTKS5PCdMpLk8J0ykuTwnTKS5uCOuWlTUGd8tKmoE55aVOYTnlpUphOeWlSmE55aVNQp1ygnYI65WWB/5tO+c5pb65T3ntOqfisTLX6sizHAXuBeMALOK786AdgqPKVZTkXZ/0E9JMkKQb4CMh/dzV1egAXJUmKR9Hh3/bK4gUCgUAgEAgEAoFAICgiZWqk/P8JMVL+aoiR8ldDjJS/OmKk/NUQI+Wvhhgpf3XESPmrIUbKXw0xUv7/jxgpf3Ok75jy5kbK+3xfoM8kSWoHLAd0gQ2yLC/I9bkBsAOoiWL29w+yLG/Wds6y3+oLBAKBQCAQCAQCgUBQykiSpAusBlxRbDLeS5Kk3JuNDwcuy7LcGLAFlkiSpPUpWNkeynhFJElyARbmOvyrLMudSkOPQCAQCAQCgUAgEAiKQCm+qkwDLYCbsizfApAkaQ/QAci506YMVJIUL6N/B3gIaJ2i93/dKZdlORgo2/OMBQKBQCAQCAQCgUBQ6kiSNAgYlOPQelmWc75r2hS4k+P/uyg2Kc/JKhSv9k4CKgE9ZFn7usP/6065QCAQCAQCgUAgEAj+xbzBPSSUHfD1Wkw0rTnPLdAFxcbl9oAZECJJ0nFZlp/kTpiNWFMuEAgEAoFAIBAIBAJBwdwFPsjxfw0UI+I5+RLwlhXcBH5F8cavfBEj5QKBQCAQCAQCgUAgKJuUrTXlscCHkiTVARKBnsDnuWx+BxyA45IkGQIfA7e0nVR0ygUCgUAgEAgEAoFAICgAWZYzJUkagWLfMl1gkyzLlyRJGqL8fC0wB9giSdIFFNPdJ8qy/EDbeUWnvIQwfqdaaUvQyl8Zf5e2hH81Zf094E/vhpe2BK1UrelQ2hIKJDPrRWlL0EqFMv5+2Yr6ZfsdzE/l9NKWoJU7XeuUtoQC+eDAr6UtQStl/V30ik15BcVFp4z7r2K5t0pbQoHoSmV7FWtZj2HBG6RsjZQjy3IAEJDr2NocfycBzkU5Z9mORoFAIBAIBAKBQCAQCP6PESPlAoFAIBAIBAKBQCAom2h/m9j/BWKkXCAQCAQCgUAgEAgEglJCjJQLBAKBQCAQCAQCgaBMIme9ufeUlxZipFwgEAgEAoFAIBAIBIJSQoyUCwQCgUAgEAgEAoGgbFLGdl8vCcRIuUAgEAgEAoFAIBAIBKWE6JQLBAKBQCAQCAQCgUBQSojp6wKBQCAQCAQCgUAgKJuIV6IJSgIbhzaEnfIl8ow/w0Z/pdFm1vxJRJ7xJ/i4Fw0afQpA+fLl8A3ZRVDkAY5G+/DNpGEq+28njyD4uBeBEfvZ4bUOQ6P3iqTJ3rEtMWeDOB0fwqixgzTazFs0ldPxIURE+9Ko8WcFpp00dTQR0b6EnTjE/oObMDJ6X/XZZ/U/JvDoXk6c8ifypB/ly5fTeM35i6ZxJv4ox0/6qV0zJzVr1SAk9ACx50LYuGUZ+vr6BaZ3cGzLqbhgzsQfZfQ3eb/viFFf8fDpDapVrwpA02aNiIjyJSLKl8hoX9q3d9GoJSdLlszi0qVIYmODMTdvoNGmdu0PiIw8xMWLEWzfvlqlvWfPjsTGBhMbG0xYmDcNG36qSjN8+ADOng0hLu4oI0ZoLj+vk6nzfsTavScd+wwp8WvlZPEPM0i4EEbMqUAam9fXaFOrVg3CInyIPx/K1m0rVf776KO6HAvz4o9HVxk1+muV/Ycf1iU6xl/1k5RynmHDvyy2xrKYxwsWT+NswjFOxBymUWPNfqtZqwYhYQc4E3+UjVuXq8VMfulX/jSf67+eIvp0gNq5GjT8lCOhB4iM9iU00oemzRoVSqetgxWRpw9z4mwgw8cM1Ggze8F3nDgbSMgJb1U9aGJqxH7fzYTH+BIafYivBvfJk27wiP4kPrpE1WpVCqUlPxYtnk78+VCiTwVoLYOh4d6cSwhl89YVKl9++FFdjoYe4P7DK4wc/fL7lS9fjrAIH6Ji/DkVG8TkKWNeSSOAXkML3lmwhXcWbaO8e0+NNrqfNOad2et4Z95G3v7uR/UPJR3emb2WimO/f2Ut2igJfxaVkoqPhEvhRJ3yV8VBNrPnTuRUXDAnYg6zffdPVDaoVGitdg5WnIgN4GRcECPyiZG5CydzMi6I0KiDNMzRxi1dNZeLN04QHu2rZj9u0nDOXQ7n6HFvjh73xsHJutB6NFFSebp6zUJ+uX2amNjAMqfP1NSYwwE7iT17hFOxQQwd1r9ImhYuns65hFCiYvxpnE8ZrFWrBsfCvIiLP6amSVt6A4NKbNuxiti4I5w+G4xFiyYANGz4KUdDD3A82o/wyINa6+iC7o2g6PdVHTq2I/p0AA8eX8O8yct2Uk9Pj9XrFnIi5jAxZ4IY8+3gAjynmZK4VwC4dOU4p04HEh3jT+SJQ8XSlhM7ByuOx/oTrSWe5yycTHRcEMeifGjY+GWbd8BvM5Gn/Ag/6cvAIXnbPMH/B6JTrgVJkkZJknRFkqSdr+ucOjo6zF00hX7dh+HQugPtu7jy4cd11WzsHNtS26wW1s3dmTR2Ft8vmQrAP/88p2fHr2hn3ZV21t2wcWhDk+aKynXdys24tO2Cq003jgVHMHp84TtQOjo6LFwygx5dvqaNhRudu3rw0cdmajaOzjbUNatNC3Mnvhk9jcVLZxWYdtXyDdhYtsfOqgNHgsIYN3E4ALq6uqz5eTHjxszAqqU7Hdz7kpGRmUeXo7MNZma1aG7uyNhR01iydLZG/TNnj2fN6s1YNHEiLe0Jfb7opjW9jo4Oi5bMpHvngbS2cKVLVw8+/rie6nympkbY2rXhzu+JqmNXLl/H3roTNm3a063TV6xaNR9dXd18feriYke9erWpX9+a4cMnsWKF5pvduXO/Y+XKDTRoYENa2mP69+8BwO3bd3By6o6FhQvz569g9eoFAHz22UcMGNALKytPLCxccHNzwMysdr46Xgcd3ZxY++PcEr1GbpxdbDGrV5vGDe0YOeI7li3XfP05cyexeuVGzBvZk5b2mH79uwPw6NFjxo+bxYrlG9Tsb9y4hWUrdyxbuWNl6Ul6+t/4+R4plsaymMdOzjaYmdWmWWMHxoycypJlszTazZwzgTWrN9Pc3JHHaY/p269bgel37/Sma8cBec41a+5EFs1fgbVle+bPXcasuRML1Pk/9s47LIqr7cP3UGyJPQnN2DDV3pHeO3aNRpOoMZpYokk09t41GmsSjcbeBRQQEAsCiiioiL3GRGlWLBGVMt8fCyvLLsuCIvh+574uLt3Z58z57XP6nDJ6enrMmDeO3t2+xcGiPR27ePJBvjrH0cWGeuZ1sG7pwajhk5k1fyIAmZmZTBk/F3uL9vi49qRP/54qYU3NjLG1t+TmjaTCHaaF3DzYrIkjw4aM5deF0zTaTZk2imVL/6J5U0fS0h7y5Vcv8uDPI6aq5cFnz57j7dkLKwsvrNp54+xiS+vWzYovVNKjwpff89/8MTwe0w9DC0f0TOuo2lR6i4pfDuO/hRN4PPZrnixVrUvLuXYmK+nf4mvQgZLyZ1EoyfIB4OPZG1vL9jjadlJeCz9wGMvWnlhbeHP18t/8+JNubbOenh6zfpnA510HYNvWh05dvdTaZScXW+rXr0O7Fu6MGDaJOTllBGDrpp307Kp5ULXit7U423TG2aYz+/dG6qRHEyWZphs37KBzx+I/MC1JfZlZmYwbO5PWLV1xcujCNwO+4KOPG2i6tRourvaYm9eleVNHhg0dx4KFmvs1U6b9zG/LVtOimRNpaQ/4UpkHCw4/e+5E9u2NpHULV6wsvLl08QqgeDA0e9YSbCx9mDF9IVMLqKML6xtB8fpV589f5steg4k+HKtyrw6dPChfrhzWFt442HSkT98evF/bTCc/5lJSfYVcPD0+x9LCC1vrDkXSlR89PT1m/jKeXl0HYtfWh45dPdXKs2NOebZs4c7IYZOYPX8S8KLNs23rg5dLD/r0/1wt7P8LsuXX91dKiEG5dgYBnrIs93pVN2zWsjHX//6Xf/+5SUZGJoF+Ibh6OKjYuHo64LtF8XT7ZFwCVapU5j2jdwB48l86AAaGBhgYGCDLiszz+NF/yvCVKlVUXteFFq2a8Pe1f/jn+g0yMjLw992Nh5ezio2HpxPbNiue/h+PPUXVqpUxMnpXa1hVTZWUmhycrDl39iJnz1wA4P69NLI1nKro6eXMls07AYiLjadKNUWc+bGxs2DXzlAAtmzyw8vbWWv4lvk0+/nuxsPbSXm/GbPHMWnCXBUfpqc/JSsrC4DyFcoX6l8fH1c2bvQF4Nixk1SrVkVlpUAu9vaW+PkpZh43bNihnIGPiTlOWtoDZXgzMxMAPv74A44dO6HUExUVQ4cO7lq1vCytmjWmahXdZ3deBd7eLmze6AdAbGw8VatW0bj6w86uHf7+ipmUjRt88fZ2BeD27bucOJ5ARkZGgXHYO1hx7do/3LiRWKCNNspiGnt6O7Mlp5zG5fpNQ5mxtbNgl7+izGze6I+nt0uh4aMPx3L/fpravWRZpnKVtwGoUrUyKcmpheps3rIx16/dyKkHM9jlF4ybp2o96ObpyI6cevBEXAJVqyrqwVupdziTcB6A/x4/4fKlaxibvPD75BmjmDF5fpHqQE14ejmzeZPCF4XlwZ05eXDzRl+8fRS+vHP7LidOJJCp4YHjf/89AcDQ0AADQ4OX0qpf/2OyUxORbydDViYZR8MxbGGpYlPOwomM41HI924BID9KU34nVX8Hw6ZteR6hugLiVVOS/tRZQwmWj4IIP3BI2XbExsZjamask9bmLZvw97V/lWVkp28wbp6OKjZuno5s26KYwTsRd4oqVavwXo6emOg40jSU11dJSaZp9OFY7t9LK5P6UlNucyr+LACPH//HxYtXMDXVLV29vJ3ZrFMefKFp00Y/vHLyYEHhK1d+Gyur1qxbuw2AjIwMHjx4BCjq6CoqdfQtjdoK6xtB8fpVly5e5crlv9Xik2WZSm9VQl9fnwoVK/A8I4NHjx7r5MdcXkdf4VWgaPNelOddviFq5dnd05HtyvKcQJU8bd7pUwW3eYL/HcSgvAAkSfoDqA8ESJI0QZKk1ZIknZYkKUGSpC7Fva+xyXskJaYoPycnpWJkYqRmk5zHJiUpVVkA9fT0CInYzsmLERw6GEP88dNKu5HjhhJzei8du3kxf9YynTWZmBiRdPNFfElJKZiYqmoyMTUiMa9NYiompkaFhh074QdOnYuga3cfZs9YBIB5g7rIMmzzX8WBSP8ClyKamBqRmJicJ051XTVqVudB2iNlpyevTUHhTUyM1a/npIG7pyPJSanKBwZ5admqKdHHgjkUE8TQoWOVcWrC1NSYmzdfxJGYmKLWaNesWZ0HDx4q75OYmKyxYe/T5zPCwsIBOHv2ItbWbalRoxoVK1bAzc2BWrVMCtTxpmJiaqTivyQNvqlZszppKv5LwTRf/tBG127e7NgeWGyNZTGNTUyMSMzrNw1lWVOZyfWbLuHzM3bUdKZOH82ZC1FMnTGaqZN+KVSnsYkRSXnKYHJSKsYa6sH8dWV+m1rvm9KoySecPJ4AgIuHA8nJqZw7c7FQDYWhlr5JKZiaqKZdDbX0LdxfoKjHDx0J4ur1WMIPHCYu7lSxdUrV30G+d1v5OfvebaTq76jGZ1wLqVJl3ho9n7en/I6hlYvyu4q9BpO+bQW85EOMwihJf+pKSZYPWZbx27WG8KidfNX3M43x9/6iG/vCdJuZNtGQ/03y5X8TE6N8NimY6NBZ7zegFwcO7+TXpdOpWrWKTno0URbStLT11a5tRpOmDYmLjdfJXpGHXqziSUpSbzc092uMtYavW/d97ty5x29/zCXqcABLls6kUqWKAIzOqaPPXjjE9BmjmTJpXgHaCu4bKW2K0a8qiICdoTz57wnnr0STcC6CZYtXkXb/gdYwappLsK8gyzK7AtcRdTiAvv16FklXfoxNjEjMV1bzD6w1tXn5fVirtimNG3/CiZw27/8V2dmv76+UEIPyApBl+VsgCXAA3gYeyLLcWJblJsABTWEkSRogSVKcJElxj5/d03hfSZI0xZXfSION4t/s7Gw87LrRtpEzTVs04sNPXiwtmjdjCRaNXdi5fTd9vtG9AtFFU0E2hYWdOe1Xmn5qx45tgfQf+AUABvr6tLVowbdfj8DLrSeePi7Y2LV7pbq0a1a7jCzLVKxYgZ9GDGLmjIXqBsDxuFNYtvHE2b4LI0cOpnz58hrtFHFrjkNX7bnY2bWjT5/PGDduFgAXL15h/vzf2b17I4GB6zl9+jyZmQU/HHhTedm0LwxDQ0O8PJ3x9yv+7GBZTOOSKjPa6Nf/c8aOnkGjj20YN3omi3+bpYNO9WtF9V2ltyrx57qFTBozm8eP/qNCxQp8/+MAfpm1tND4daH4viz83tnZ2Vi38+aTDy1p2bIJn3z6YbF1osGXaiL09dGv+wH/LRjHf/NGUb59b/SMamHQ1ILsh/fJvn65+PHrKrME/VnyGgovH+7On2Fv3YFunfvRf0BvLK1aq9j9NPI7MrMy2bZVt72pGuPi5evANau20LaZK07WnUhNuc3kGT/rpEdnja85TbVR0vreeqsS6zf9xuifp+k8w1tSedDAwICmzRqyauVGbKza89+TdH7I2Srxdf9ejB09nYYfWzN29AyW/ja7AG3q13TvCxYeNj8tWzUhKyubTz+wonkjBwYN7Ueduu9rDaOuueT6Cs5OXbG29KFzx74MGPAFVlZtiqStUA262ORr81atW8TEsbNUVqIK/ncQg3LdcAaUU8+yLN/XZCTL8gpZllvJstzq7fI1NN4oOSlVZfmaiakRt1JUlxKlJKViksfG2NSI1Hw2Dx8+IuZwLPZOVmpx7NwRjIePs9r1gkhKSsG01ov4TE2N1ZY3JSWmYJbXxsyIlORbOoUF8N0eiHd715z4Uok+HMu9e/dJT3/KvrAImuYcFtLvm16EH9pFxOEAUpJTlUt6FXGq3/vunXtUrVZZub87r01SYorG8ElJGq6n3KJuvdrUrluLqOhA4s+EY2pmzMGonbz3nuqs06WLV3ny5AkNG36kcn3gwC85ejSEo0dDSE6+pTK7aWZmTHK+Zb137tyjatUqSu1mZiYqNo0afczvv8+la9f+3MuzjG/Nmq20a+eFs3M37t9P48oV9WVhbyIDBn6hPIAtv/9M8/kGFP6rpuI/Y5ILWJaXH1c3e+Ljz3Lr1p0iaSyLadx/QG8ioxUHECYn38Isr980lEdNZSbXb0lJKYWGz0/PzzsTuGsPADv9gmnRsqlWe8itB1/EY6KhjtNUV+baGBgY8Ofahfhv301I0D4A6tZ7n9p1zNgb5UfMqTBMTI3YE7GDd/OVX218M+ALDh0J4tCRIJKTU1XT19SY5BTV9L2rlr7GOi3fz+XBg0ccijqK80sctiXfu4NU48VyTb0a7yKn3c1nc5vM07Hw/Cny44dkXTyNXu366H/YEMPmllT+ZSOVvhuPwSfNqDhwTLG15Od1+1MTr6t8pOTkzTu37xEUuFflMK0en3fC1d2RAf1+1Fl3kob8r9YuJ6XkszEmJeU22rhz+y7Z2dnIsszGddtp3kK3gxlzKQtpWhb0GRgYsGHTb2zbGkBgwB6ttv0H9CYqOpCo6EBSkm9hVstU+Z2pqXq7oblfo7BR5EH18ImJySQmpnA8Z9XNrp0hykPgen7emYCcOtrfL7jAg94K6hup2BSxX6WNLt182L8vkszMTO7cucexmBM0b675wNS8vK6+Qm55u337LoGBe2jZqvC2rSCSk1Iwy1dWU5MLb/NS8rR5q9YtxG97EMGB+4qt441GzJQLcpBQf6hVLE6dOEO9+nV4v7YZhoYG+HT2YG/oQRWbvSHhdOnRHoDmrZrw6OFjbqXeoUbN6lTJ2dtbvkJ5rO0suHpJ0VmvW7+2MryLhwNXNezfKYiTx09Tv35datephaGhIZ26eBEavF/FJjTkAN17Kg6vadm6KQ8fPiY19bbWsPXNXxw25O7pxOVL1wA4sD+Khg0/omLFCujr62Np1YaLF68C8NefG3Gw7oCdVXt2B+2jR8+OALRq3YyHDx6Rmqre4TgUeZQOHRV7bnt83png3YoKKyR4v8bwJ46fpr75C82du3gRuns/589d4qP6FjRr5ECzRg4kJaZgb9ORW7fuULtOLWWFXut9Uz74wJx//rmhomP58nW0betB27YeBATsoVcvxS6HNm2a8+DBI40NVETEETp39gSgd++uBAYqDh17/31Ttm5dQb9+w9UGZO++W1Np06GDO9u2qZ6u+6ayYvl65SFsQYFh9OzVGYDWrZvx8OEjUjV0NiMjY+jUyQOAXr27sHv3Xp3i6tbNh+3bi+63spjGK1dswNayPbaW7QkO2kuPnHLaKtdvGspMVORROnRSlJmevToRkltmdu/XKXxeklNSsbJpC4CtfTuuXb2u1R4g/sQZ6pnXzqkHDenQ2ZOwkHAVm7CQcLrm1IMtWjXhYU49CDB/yVSuXLrGit/WKu0vnLtM0w9tsWjqikVTV5KTUnGz68rtIjx4+XPFeqzbeWPdzpvdgXvp+bnCF4XlwY45ebBnry7sDtLeYar5Tg2q5pzAXaFCeewdrLh88ZrOGvOT9fcF9I3MkN4xBn0DDNs6kHEyWsUm40Q0Bh82Bj09KFceffOPyU76l2fbV/Hohx48GtGLJ79PJ/N8POnLC1/poCuvw5+F8TrKR6VKFXn77bcAxZkujo7WnD+nWH3g5GzLsB8H8vlnA0lPf6qz7vgTp6lvXofadRRlpGMXzWWkew/FAVQtWjXl0cNH3CqkvL6XZ/+yh7cLF84XbZVEWUjTsqBv2e+zuXjxKsuWrCrUduWKDdhY+mBj6UNQUBg9dcqDLzR93utFvyZ49z6N4W/dukNiYjINPqgHgJ29JRcvKA56S0lJxTqnjrazt+Ta1X806iyob5SXovartHHzZjK2OaskK1WqSKvWzbh0qfC68HX0FdTKtJMN584Vf1uUos2rw/s55blDFw/25CvPe0IO0E1ZnpvklGdF+7Vg6TQuX7rG8mVr1e4t+N9BetnDcP6XkSTpOtAKGAFUkGV5eM716gXNludSu0bjAh3r4GzDpJk/o6+vz9aN/ixd8Ce9+yhO1tywZjsA0+aOw97JivT0p4wYMp6E+HN8/OmHLPhtOvr6+ujpSQTtDGPRvD8A+GPtAswb1CU7WybxRhJjfpqm9hQuL08yn6l8dna1Y8bssejp67Np/Q5+/eUP+vRTvFpnzV9bAJgzfxKOzjakP0nn+0FjiD95psCwAKvXL6HBB/XIzs7m5o0kfho+Sfm0t9tn7Rn240BkWWZfWARTJqruccrOyZdz50/CydmW9PR0hnw3Whnn1h1/MmzIOFJSblGn7vusXP0r1atX43TCOQb2H8Hz58+1hnd2tWPmnHHo6+mzcf0OFvzyu5qP4s+E42jXmXt379O9RweG/ziQjIxMsrOzmT7jV+XgqiAWLpyGq6s9T56kM2DACE6cUOwB2rlzDd99N4rk5FTq1avNunVLqVGjGvHxZ+nbdxjPnz/n99/n0LGjJ//+exOAzMwsrKy8Adi/fwc1alQnIyODUaOmER5+WC3uRzcPatVWFEZOmk3syQTS0h5Ss0Y1Bn39BV18Cn8lnDaq13Yq1GbBr1NxdrEl/Uk63377MydPKM5P8PX/i8GDRpOSfIu6dd9nzbolVK9elYRT5/i63w88f/6c94zeIepQAJUrv012tsx///1HqxauPHr0mIoVK3DhUjSNG9rx8OGjAuPPzC58yXhppnFFA82vEZy3YLIyzw/+dpQyz2/zXcn3g8cqy8yqNQupXr0aCQnnGPj1T8oyU1D4lat/xcqmLTVrVufWrbvMnrGIDeu2Y9GuJbPmTsDAQJ+nT58x4odJnIo/SyXDgrd3gOJ09SkzR6Onr8fWjf4snr+CL/oqTsRdv1pxWNGMeeOV9eCPg8eTEH+W1hYt2BmynnNnLyLnnJI6e9pCDuyNUrl/zKkwPBy6F3hY1KPn6Vr1AcxfMAVnF1uepD9l0MCfOXlSkQd3+P3FkEGjFSts6r7P6rWLqV69KqdOneObr39U5sGIqF0qebBNSzdq16nFHyvmKetxf99g5sxeohb3ja71CtWXi0GTNlToNRj09MiIDOFZ4CbKOSjy0vPwIADKeXSnnI07yNk8jwjmeZifyj30P25KeY/uPPl1nM7xvr+jaKt0SsKf2pYN60vq8w4lUT7q1H2fDZt/U8RpYIDvtgDmz1O0KcdP7ad8+XLcu6foMsTFxvPjMMUp6eUNDPPLU8HJxZaps8agr6/H5g1+LJq/nC9z9quvW70VgFnzJuDgbE36k6cMHzxWeQDZ7yt/wdK6DTVqVuP2rbvMm72Uzet9WbJ8Do0afYyMzI1/Exk5fHKBA/knGc80Xs9LSaXpX2sWYa2sb+4wc/oi1q/bVqie16GvYaOPCdu3jTNnLigPqZ06+RfC9hxUiVvTcmSAXxZMxtlZoWnwt6OUmrb7rmLo4DFKTX+tWZSTB8/yTZ48WFD4xo0/YcmyWRiWM+T63zcY/N3PpKU9xKJdS+bMnYi+gT7Pnj7jpx8mER+vyPf5y4imvlGfnP3Ua/7aDBS9X+Xl48KceROp+U4NHjx4yJmE83Tt1I+33qrE0t9n89HHDZAkiU0bfFmS7xT051mFH+xYEn2FmjWrs3nLcgAMDPTZti2AeXPVz2qqXK5iofpycXSxZeqs0ejr67Flg7/G8jxz3nhlef5h8DhOxZ+ljUULdoVu4NzZi2TntHmzpi7kgI5vTkhOO6c5I75hPFk48LUNWCsNX14qPhODci3kGZQ/RbF8vSWQBUyRZdlPS1Ctg/KyQP5BeVkju4znS106K6XJqxyUlwS6DMpLG10G5aVJQYPyskJhg/LSRpdBeWlSlEF5aVHUQfnrRtOgvCxR2KC8tCnr7VxZp6BBeVmirJcRXQblpUlRBuWlhRiUF53SGpQblEakbwqyLNfN8/Gr0tIhEAgEAoFAIBAIBP8vKcW93q+Lsv2ITCAQCAQCgUAgEAgEgv9hxEy5QCAQCAQCgUAgEAjKJtlle1vrq0DMlAsEAoFAIBAIBAKBQFBKiJlygUAgEAgEAoFAIBCUTWSxp1wgEAgEAoFAIBAIBAJBCSEG5QKBQCAQCAQCgUAgEJQSYvm6QCAQCAQCgUAgEAjKJv8PDnoTg/IS4nl2ZmlL0Iq+VLYXSaQ9fVTaErRSrcJbpS1BK9VrO5W2BK3c/3d/aUsoFFNzj9KWoJXMrKzSlqCVB8+elLYErWRml23/1fX9p7QlFEpWGd/jV97AsLQlaKWs+69K+UqlLUErZb0fk/jobmlLKJREqwalLUErTU/eKm0JWnn0PL20JQj+hxCDcoFAIBAIBAKBQCAQlEnk7LL9EPNVULYfMwoEAoFAIBAIBAKBQPA/jJgpFwgEAoFAIBAIBAJB2eT/wZ5yMVMuEAgEAoFAIBAIBAJBKSFmygUCgUAgEAgEAoFAUDYp4wdjvgrETLlAIBAIBAKBQCAQCASlhJgpFwgEAoFAIBAIBAJB2UTsKRcIBAKBQCAQCAQCgUBQUoiZcoFAIBAIBAKBQCAQlE3Ee8oFJYGDkzWHYoM5ciKUIcP7a7SZPmcsR06EcuDwTho3/VR5/del0zlz+RAHowPUwnw9oBeHYoOJOBLIhCkjXlrj4bgQYk7uYegP32i0mTFnHDEn9xB+eJeKxoVLZ3D2ymEijqhqHDXue8IP72J/lD9b/VdhZPzeS2n8dcFULpw7xInje2nerJFGm7p13yf6UCDnzx5i08bfMTQ0VPm+VcumPEv/l86dvQAoX748Rw4HcTxuL6fiDzBp4k8663F0tiHmeCjH4vfy/Q8DNNrMnDueY/F7iYgOoEkenxUUdvT4YUREBxB+aBfbd/6FcY7P7Bws2R/hR+SRQPZH+GFja6Gzzlzm/TKJU6fDiTkaQtNmDTXa1KlTi/AIf+ITDrB23RKl/z78sD77w325e/8C3w97kT8++KA+0TG7lX9JKQkMGty3yNqKwviZC7D16kHH3t+WaDyOTjYciQvl2Mkwvi+gTMycM45jJ8M4eDhf+hYQtlHjjwnZt5XwqJ3sPehL8xaNAejSzYfwqJ3Kv9T752nU+GOddM6eN4Hjp/ZzKCaIJk01p2vtOrXYG76DuPh9rFq7SKVcFBR+yW+zuPT3UaKPBavcq1HjTwg7sIPI6AAORPrTomUTnXTCm5UH58+fwtmzkcTG7qGZlvomMnIXZ85EsH79MqVWb28XYmP3cPRoCIcPB2Fp2brYOkoqfU+dPcjho7uV6ZiXb779gmMnwoiODWHKtJ911jrvl0kknD7I0aMhNNOSvgcjdnIqIZy165bmSV9zDoT7ce/+RYYNUy1vgwb1JTZ2D7FxYQwe3E9nPSVRhv9c/auynB5P2E941E6V+5nVMuF64gkGDdVN5+vU2LxF4xf1zKFdeHo766QxF3snKyKOBnIoLpjBw77WaDN11hgOxQWzN8qPRk0+AcDEzJhtu/4iPCaA/dE7+Xpgb6X9b6t+YU/EDvZE7OBI/B72ROwokqa82DlaceBoABGxQXw3TLP/J88aRURsEKGRO5T6ypcvx669GwmJ2M7ew378MGqQ0n7pyrkEH9xG8MFtHDoZQvDBbcXWByXTj/nwQ3PiYsOUf/fuXOD7oZr7mkWhXOs21Fy7npobNlKp5+dq3xs2bca7gbup8edKavy5kre+/Er5XaWu3ai5eg01/1pN1fETwbDcS+uBkulPL/9rAfui/NgX5Udswj72Rfm9lMaSaOcABg/pR2zcHo7FhrJ6zSLKl381PhWUHmJQ/prR09Nj1i8T+LzrAGzb+tCpqxcffmSuYuPkYkv9+nVo18KdEcMmMWf+ROV3WzftpGdX9QGflU0b3DydcLTqgF07H35f8tdLaZw9fyKfd/0GmzbedOqiWWM98zpYNHdjxLCJzF0wSfndlk3+9Oii3plYtngVDlYdcLLpxN7Qg/yUp6ErKh7ujnzQoB4ff2rNd9+NYtnSWRrtZs0cx8LFf/JJQ2vu339Av749VX7nrJnjCAs7qLz27NkznF2707KVCy1bueLmak/bNi0K1aOnp8ec+ZP4rMs3WLX2pHNXbzWfObvaUd+8Lm2aufDjsAnM+3VKoWGXLlqJnWV7HKw7EBYazohRgwG4d/c+vT77Ftt2Pgz+dhS/rZhXJP+5utlj3qAuTRs7MHTIGBYumq7Rbtr00SxbsopmTRxJS3vAV326A3D//gNGjpjC4kUrVewvX76GpYUXlhZeWFv6kJ7+lMCAsCJpKyodPV34Y4Fm/a+K3DLRo2t/rNp40amLhvR1sVWkb3NXfho2gbkLJhcaduLUkfwyexkONh2ZM2MRk6aOBMB3eyAONh1xsOnIoIE/8++/iZw5faFQnS6udpib16VlUyeGDx3P/IVTNNpNnvYzvy9bTatmzjxIe8AXX3UrNPzmjX507ajesZ0yfRRzZy3G1rI9s6YvZMr0UYU7lDcrD7q5OdCgQV0aNrRl8ODRLF48Q6Pd9OljWLJkJY0a2ZGW9oA+fT4DIDz8MK1bu9G2rQcDB47g99/nFEtHSaYvgI9nb2wt2+No20l5zdrWAk8vZ6wtvLFs7cGSxar+Lgg3N3saNKhHk8b2DBkyloWLNPts2vTRLF2yiqZNHHLSV+Gz+/fTGDFiMosW/ali/+mnH9K3bw9sbTtg0dYDDw9HzM3rFqqnpMrwN31/UJbVoIAwggL3qtxz+qwx7N8XpYvLXrvGC+cv42zfBQebjvTo0p9fFk5FX19fZ63T547ni+7f4dCuPR26ePLBR/VVbBydbahnXhvrVp6M+mEys+ZPACArM5OpE+bhYNGe9q6f89XXPZRhB309Aje7rrjZdSU4cC8hQft00qNJ37S5Y/mq+3c4W3akfWcPNX0OztbUq18Hu9bejPlxKtN/GQ/As2fP6dmxPx523fCw646dkxXNWykeNg7p/zOe9t3xtO9OaOA+QoP2F0sflFw/5tKlq7Rq7Uqr1q60aevOkyfp7NwVUmydORFRedhw0kb/zN0+X1HByQn9OnXUzDJOJ3Dvm/7c+6Y//61bqwj6zjtU6tyFuwMHcLdfX9DXo4Kj48vpoeT60wP7/YizTWecbTqzOyCM4MDi5UEouXbOxNSI7wb1wca6PW1au6Ovr0/Xbj7F1vlGkC2/vr9S4n9iUC5J0veSJJ2XJGljaWspjOYtm/D3tX/595+bZGRksNM3GDdP1crJzdORbVt2AXAi7hRVqlbhPaN3AYiJjiPtfprafb/q14Mlv/7J8+cZANy5c6/YGlvkaPzneo5Gv2DcvZxUbNy9nNi+WaHxuEaND9Tu+/jRf8r/V3qrIrJc/Izv4+PG+o2KJ+hHj52garWqylnkvDjYW+HruxuA9eu306G9m/K7IYP74ee/m1u376qE+e+/JwAYGhpgYGiok84WrZrw97V/+Of6DTIyMvD33Y2Hl+qsg4enE9s2K2agjseeomrVyhgZvas1rIrPKlVSajmdcJ6UlFuAomNVvkI5ypVTfXquDW9vFzZvVDz9jY2Np2rVKhgZv6tmZ2fXDn9/RWO+cYMv3t6uANy+fZcTxxPIyMgoMA57ByuuXfuHGzcSddZVHFo1a0zVKpVLNI4WLZtw/do/ecrEbjw0lImtm3cCijJRtWoVRfpqCyvLVK7yFgCVq1RWpmleOnf1wn9HkE46Pb2d2ZKTx+Jy09VIPV1t7SzY5R8KwOaN/nh6uxQaPvpwLPc11D2yLFO5ytsAVKlamZTkVJ20vkl50MfHlY0bfQE4duwk1apV0Vjf2Ntb4uenWEmwYcMO2ufUN7l1CsBbb1Uqdt1XkulbEP36f87C+ct5/vw5AHdu69a2eHm7skmZviepWrUyxhrT1xJ/f4XPNm7wxUctfTNV7D/6qAHHYk+Snv6UrKwsog4dVfpZGyVWhvPQoZOHSln18HLi+vWbXDh/uVB9paEx14cA5SuUL1K+bNayMdf/zu3LZLLLLwRXD9W+jKunAzu2KGYhT8QlUKVKZd4zeodbqXc4k3AegP8eP+HypWsYmxipxeHT0Z1dvsFq13XS16IR1//+lxv/JJKRkUmgfyguHg4qNi4eDvhuDQTgZFwCVaoq9AE8+S8dAANDAwwNDDT6xqujGwF+xR/slmQ/JhcnR2uuXfuHf/99uTrQ8ONPyEpKJCs5GTIzeXrgAOWtrHW/gb4+UvnyoKf4N/vunZfSAyXXn86LT0d3/HfsLrbGkmznDAz0qVixAvr6+lSsVIHkZPX+g+DN4n9iUA4MAjxlWe5V3BtICkrcHyYm75GUmKL8nJyUikm+xsjExCifTQomJtqXetdvUBcLy5YE79uC/+51NGuueRmULhibGpGUmKz8nJSYotZgmpgYkZjHJjkpBRNT9UY1P2MmDOfE2XC6dPNm7ozFxdZoZmrMzRtJys+JN5MxMzVWsalZszppaQ+UnY6bicmYmilsTE2N6djBneUr1qvdW09Pj7jYMJITE9i/P5JjsScL1WNiYkTSzRdplqTBHyamRiTmtUlMxcTUqNCwYyf8wKlzEXTt7sPsGYvU4vbp4MbpU+eVD2R0wcTUiJs386ZxMqaa/PfgodJ/iYkpmOqQxrl07ebNju2BOtuXZUxMjUhMzJd2hZTbpKQUjE2NtIYdN3omk6b+TPzZg0yZPorpUxaoxd2hsyd+OnYKTEyMSMybrhryYY2a1XmQ9kiZrkl50lWX8PkZO2o6U6eP5syFKKbOGM3USb/opvUNyoOmpsYqWhU61LU+UNGq+nvat3fj1KkD+PuvYeDAkcXSUZLpK8syfrvWEB61k6/6fqa0adCgLu2sWrM3fAdBoZuUWywKw9TUiJs3X9TRSYkpmOjkM+3pe+7cRays2lCjRjUqVqyAm5sDZrVMCtVTUmU4l3aWrbh9+y7Xrv0DQKVKFRk6/Bt+mb20UG2lpREUDwKiYoKIjA5g5A+TlGlRqFaT90jOE19KUqpaP8VYrS+TqtaXqPW+KY2afMLJ4wkq19u2a8ntW3f5+9q/OunJj7GJEcmJLx4QKuLOr0+1P5aSlIpRjo2enh7BB7dx4sJBoiKOEH/8tErYNu1acuf2Xa4XUx+UbD8ml+7dO7Bl685ia8xF7513yL71YtCXffs2+u+8o2Zn+GlDaqxcRbXZc9GvW1dhe+cO/23bwjtbt/Gurx/Z//3H87i4l9ZUUv3pXCwsW3Hn9l3+zlNeiqyxhNq55KRUFi/8k/MXD3P12lEePnjEgf26rch5Y5GzX99fKfHGD8olSfoDqA8ESJL0kyRJOyVJSpAkKUaSpCY5NpMlSRqRJ8wZSZLq5vydlyTpN+AE8H4BcXwtSdIlSZIOSpL0pyRJGltZSZIGSJIUJ0lS3JPnaQXpVbsmIxduU8gTbAN9A6pWq4Kncw+mTpjHijW/arXXhoboIX/8Gmx0eco+a9pCWjR0wHd7EP0G9C7UviB08ZE2mwXzpzBm7EyyNRwckZ2dTavWrtSp14rWrZrTsOFHJaqnsLAzp/1K00/t2LEtkP4Dv1Cx++jjBkycOpKfhk8oVOOr0qsLhoaGeHk64+9XvFmOskZJpW/fr3syYewsmjW0Z8LYWSxcqrrEt0XLJqQ/Sdd5pu1l07U4ad6v/+eMHT2DRh/bMG70TBb/pnkJ5qvWWhivMg9qqhOLqjUgYA9NmzrSvXt/Jk0q3pkfJZm+7s6fYW/dgW6d+9F/QG8srRT73g0MDKhWrQouDl2ZOG42q9fp9jC1pNL34sWrLFjwB4FBG9i5ay2nT58nK7PwgWRJ1tEAnbp645dnlvznsUNZ/ttalVUSZU0jwInjCdhYeOPi0JVhPw7UfV/qK0jfSm9VZMXaX5k8do7KqjCADl082fUyZfcly2x2djae9t2xaOxCs+aN+PDjBip27bt4EOD7ckvCS7IfA4o60MfblR2+uq20KkSs+rV8RTXz8iXu9PiMe/2/5om/L9WmKdoz6e23qWBpzZ2ePbjdtTNShQpUcHZ5BZJKpj+dS6cuXvj7Fn+WXNf4i6OxWrUqeHm70OhTWxqYW1DprUp81qPjS2kVlD5v/KBcluVvgSTAAagLnJRluQkwFlinwy0+AtbJstxclmW1x2GSJJkCEwALwAUo8LQlWZZXyLLcSpblVpXKVdNok5SUqnzKCYqnaCn5lpwkJaXkszEmJeW21h+RlJRCcM4+sZMnTpOdnU3NmtW1himI5MRUTM1ezDyYmhmrLatNTkrFLI+Niamx2u/Qht/2ILzbF61S/u7br5QHlyQlp1DrfVPld2a1TEjKt2z2zp17VKtWVblHrpaZCclJCpuWLZqwccNvXLkUQ5fOXixdPFNtCeSDBw+JiIzGzdW+UG1JSSmY1nqRZqYa/JGUmIJZXhszRdrrEhYU+4y927sqP5uYGrFu0zIGD/iZ63/fKFTjgIFfKA+/Sk6+Ra1aedPYhGRN/qtaRek/MzNjnZdHubrZEx9/llu3Xn6JWlkgKTEFM7N8aZeivdyamhqTmnxLa9jPenYiKGe/8y7/EFq0UD0kTZdOQf8BvYmMDiAyOoDk5Fsqs4aa8tLdO/eoWq2yMl1N86RrUlJKoeHz0/PzzgTu2gPATr9gWrRsWqDtm5QHBw78kqNHQzh6NERNq0KHutaqKlrVfw/AoUPHqF+/ts718+tK39w8eef2PYIC9yoP7EtMTFHuyT9xPIHsbJma79TQqHXAwC84EhPMkZhgkpNTqVXrRR1tamastrVBs88KT991a7dhZemNm+tn3L+fxpWrfxcapqTKMIC+vj5ePi7szDOIbNmyKROnjOB4wn4GfvcVw38ayNffaF/M97o15uXypWs8+S+djz/9UKvGXJKTUjHJE5+xqZFaPyVZrS9jRGqOJgMDA1asXYj/jt1q+8b19fXx8HYmMGcLRnFISUrFxOzFbKMi7vz6VPtjxqZG3Mpn8/DhI44cjsPeyUpFn7uXE4E79xRZ1+vsx7i7O3Dy5OlX0g5n376N3nsvZpj13n2XrHxL0OUnT5CfKpb9Pz96FMlAH6lKVcq1bEVWSjLygweQlcWzqCgMGxV/NWcuJdWfBkUae/o4s6sY2xNeRzvn4GDN9X9ucOfOPTIzMwnYtQcLi8LPPxKUbd74QXk+rIH1ALIsHwBqSpJUtZAw/8iyHKPl+zZAhCzL92RZzgC2v4zA+BOnqW9eh9p1zDA0NKRjF0/CQsJVbMJCwuneowMALVo15dHDR9xK1V6JhO7ej3XOCdz1zetiaGjI3bv3i6XxZH6NnT3ZE3xAxWZP8AG69VRobKmjxnr1XxwK4ubhyOXLhXek8vL7H2uVh5cEBOzhi15dAWjbpgUPHzzUuB/3YEQ0XbooTiT94otuBAQqOpcffNSOBh9a0OBDC3z9djPk+7EEBOzhnXdqULVqFQAqVKiAk6MNFy9eLVTbyeOnqV+/LrXr1MLQ0JBOXbwIDVY9ACY05ADdeyoOUGrZuikPHz4mNfW21rD1zV/4zN3TicuXrgGKvbubt//JtMnzOXb0hE7+W7F8vfIArKDAMHr26gxA69bNePjwkVqHBSAyMoZOnTwA6NW7C7t371Wz0US3bj5s367+hoA3lZMnTlPP/EUadezsRaiGMvFZz46Aokw8fPhIkb5awqak3MLSug0ANnYWXLt2XXk/SZJo39G90EH5yhUbsLVsj61le4KD9tIjJ4+1yk1XDeUyKvIoHTq5A9CzVydCdis6xSG79+sUPi/JKalY2bQFwNa+HdeuXi/Q9k3Kg8uXr6NtWw/atvUgIGAPvXp1AaBNm+Y8ePBIY30TEXGEzp09AejduyuBOfVN/Tx1X7NmjTA0LKdz/fw60rdSpYq8/bbibINKlSri6GjN+XOK1RnBQXuxtVO0LeYN6lKunCF3CzizZMXy9bSz8KSdhSeBgWF8rkzf5jx8+EhjZzgy8gidOil81qt3F4J2F34o37vv1gSgVi1T2rd3Z/u2wtO5pMowgJ29JVcuXVMOlgB8PHrRsokTLZs4sfz3tSycv5xVf2o/9uZ1a6xdp9aLwd77pjT4oB43/tFt7/GpE2eoV78279c2w9DQgA6dPdgbmr8vc5CuPdoDinNXHj18zK1UxUDul8VTuXLpGn/+pj53YmNvwdXLqlqLyqmTZ6lXv45Sn08nd/aGHFSx2Rd6kC6fKQ7Hat6qSU4/5g41alanSs45JeUrlMfazoIrefor1nYWXL38NynF0Pc6+jG59Pis4ytZug6QceEC+ma10DM2BgMDKjg68iz6sIqNXvUXD+sMPv4YJD3khw/IupWK4aefQvnyAJRr0YLMf4q/JDyXkupPg6Itu3L572LlwdfRzt24mUSb1s2pWLECoDjP5OKFwvuqbzT/Dw56+197T7nGhddAJqoPICrk+b/qmind7llssrKyGDtyOpt9V6Kvr8fmDX5cvHCFL3P28K1bvZV9YRE4udgSc3IP6U+eMnzwWGX431f+gqV1G2rUrMaJs+HMm72Uzet92bzBj1+XTudgdADPMzL4ftCYl9I4ZsQ0tvitytHoq9DYL0fjXzkaXW05Gh9G+pOnDMuj8Y9V87G0bk2NmtU5ee4g82YtYdN6X8ZP+YkGDeqSnS1z80YSI3+YVJCEQgkO2Y+7uyMXzx/mSXo6/fv/qPwucNc6Bnw7kuTkVMaMncGmDb8xdfLPxJ86y1+rN2u9r4mJEX+tWoi+vh56enrs2BHI7uDCT97Myspi9MipbPdfhZ6+PpvW7+DihSv06dcDgDV/bWHvnoM4u9oRe2of6U/SlWlUUFiACZNH0OCDemRnZ3PzRhI/DVf4rP+A3tSrX5uffh7MTz8rTmTv1rGvzgf87QkNx83NgYQzB0l/ks633754zZGv/18MHjSalORbTBg/mzXrljBh0k8knDrH2jWK17+8Z/QOUYcCqFz5bbKzZQYP6UurFq48evSYihUr4OBozfdDx+mk5WUZOWk2sScTSEt7iFPH3gz6+gu6+BR+8FNRUJSJqWzzW4mevr6yTHyVk75r/9rC3rAInF3tOBa/V5G+OWWioLAAP34/gRlzxqKvb8CzZ8/4cdiLk2HbWbUmKSmFf67f1Fln2J6DuLjZcyLhAOnp6Qz+9sVJ6Nt8V/L94LGkpNxi8oS5rFqzkHETfiQh4Rzr124vNPzK1b9iZdOWmjWrc+biIWbPWMSGddsZPmQcs+ZOwMBAn6dPnzFcx3R/k/JgaOgB3N0dOHcuiidP0hkw4MXy85071/Ddd6NITk5l/PhZrFu3lMmTRxIff5Y1a7YC0KmTJ716dSEjI4P09Kd88cXgYukoqfR997132LD5NwD0DQzw3RbA/n2RAGxYt4Olv88m+lgwz59n8J2O++Fz0/f0mQjSn6Qz8NsX4fz8VzNo0Chl+q5dt4SJk37i1KmzyvQ1Mno3X/r2o2ULFx49eszGTb9To0Z1MjMy+fGHCaSlPSxUT0mVYYBOXTzxe8llrqWhsa1FS77/4RsyMzLJlrP5+afJ3Lun28OirKwsJvw8k407lqOnr8/Wjf5cunCV3jmnRm9Ys40DeyNxdLHh0PEQnqan8+MQxTar1m2b07VHe86fvaR85dmcaYs4kHNKfftOHux8yaXhWVlZTBw1k3Xbf0dfX59tm3Zy+eJVevVRvIlg45rtHNgbhYOLDZFxu0lPf8qIoQp97xm9w4Jl09HT10dPT4+gnXs4EBapvLdPZ/eXOuAtl5LqxwBUrFgBZydbvhuk29swCiU7i0eLF1J97i+gp8fTkGCyrl+noo/ioUt6YADl7eyo1KEDclYW8rNnPJimeLtD5vnzPI2IoOaKPyEri4zLV0gPevmzPkqqPw3QsYvnSx3wlktJtXNxsfHs3BnC4eggMjMzOXXqHH/9VXi+EJRtpJc5AbusIEnSdaAVMBG4LcvyNEmS7IFfZVluLklSb8BbluUekiS1AGKB3PcmBMmyXOA6GkmSzIDDQHPgEbAfOC3L8hBtmoyrfVKmHVvW0/1u+qPSlqCVahXeKm0JWnmaqfuhb6XB/X+L/xqZ14WpuUdpS9BKpo4HMpUWGdllW19mGddX0aDsv3P2eXZm4UalSCXD8qUt4Y2mvL7ub/QoDfRL/mzelyLxkeYT0csSiVYNCjcqRZqeLNsnij9+/rS0JRTK4yd/v9LJxdLi8Zgur23g8vYs31Lx2f/aTPlkYLUkSQnAE+CrnOu+wJeSJMWjGJBf0vWGsiwnSpI0EziKYu/6OUD9fV8CgUAgEAgEAoFAIBAUkf+JQbksy3XzfOyg4ft0wDX/9Rx0OW1ikyzLKyRJMgD8gcI3vQkEAoFAIBAIBAKB4OUoxb3er4uyvfan7DA5Z5b9DPA3sLNU1QgEAoFAIBAIBAKB4H+C/4mZ8leFJElHgfyb0L6QZbl4L5UVCAQCgUAgEAgEAkHx+X8wUy4G5XmQZbltaWsQCAQCgUAgEAgEAsH/H8SgXCAQCAQCgUAgEAgEZRM5u7QVlDhiT7lAIBAIBAKBQCAQCASlhJgpFwgEAoFAIBAIBAJB2UTsKRcUl2eZGaUtQSvVyr9V2hK0kl2xbBe+9IznpS1BK5nZWaUtQSum5h6lLaFQkq6GlLYErZR1Hz7JfFbaErQiIZW2BK2Udf8BVClfqbQlaOV5VmZpS9BK1XJl23+SVLbLyNPMst0OVy5XsbQlFMqHsTdLW4JWynpfunL5sp/GgjcHMSgXCAQCgUAgEAgEAkGZRP5/MFMu9pQLBAKBQCAQCAQCgUBQSoiZcoFAIBAIBAKBQCAQlE3ETLlAIBAIBAKBQCAQCASCkkIMygUCgUAgEAgEAoFAICglxPJ1gUAgEAgEAoFAIBCUTbKzS1tBiSNmygUCgUAgEAgEAoFAICglxEy5QCAQCAQCgUAgEAjKJuKgN4FAIBAIBAKBQCAQCAQlhRiUlxKz503g+Kn9HIoJoknThhptatepxd7wHcTF72PV2kUYGhrqFF5PT4+IwwFs2b6i2PpsHS3Zf3QX4bGBfDusn0abSbNGER4bSEjkdho2+VhNQ1D4VlZuWqK85tnehT2H/bh6+ySNm31aZE2OTjYciQvl2Mkwvv/hG402M+eM49jJMA4eDqBJ0091Ctt/QG+OxIUSFRPExKkjAXi/thn/ppwiPGon4VE7mffrlCLrnffLJE6dDifmaAhNm2lO4zp1ahEe4U98wgHWrluiTOMPP6zP/nBf7t6/wPfDVPVWrVqZDRt/48TJfRw/sZc2bZoXWRvA/PlTOHs2ktjYPTRr1kijTd267xMZuYszZyJYv36ZUl+PHh2Jjd1DbOwewsP9aNz4E2WYwYP7cfz4Xk6c2MeQIV/rrKck0rdR448J2beV8Kid7D3oS/MWjQHo0s1HmbbhUTtJvX+eRo0/VovvVTB+5gJsvXrQsfe3JXL/XF5n+TA0NGTxsplERAcQfmgXltZtiqV5wfwpnDsbRVxsmNY8GBUZwNkzkWxY/5syD/p4uxIXG8axo6FEH96NpWVrlXB6enocjQnB3291sbRByZWRoUO/5sSJfRw/vpd165ZQvnz5YukrCf+VL1+eQ1GBxB7bw8kT+5gw4cdiacvPy+TPRUtncu5KNJFHAl9ax5x5Ezl56gCHY3bTtIC2t06dWuwP9+VE/H5Wr12s0vYWFH7Q4L7ExIZw5FgIq1YvpHz5cgCMm/ADh2N2ExUdiP+uNRgbv6ezVjsnK8KPBhAZt5tBwzTXpVNmjSYybjd7onxp1ESRx8qXL0fA3k2ERu5gX7Q/P44epLT/aewQ9kT5EhKxnQ2+yzEyfldnPWr6HK04cDSAiNggviugnzB51igiYoMIjdyhom/X3o2ERGxn72E/fhj1Qt+njT7Cf88Ggg9uI3D/Zpq20JyvdcHByZrDcSHEnNzD0ALy3Iw544g5uYfww7tonCfPLVw6g7NXDhNxJEDFfuK0kRyKDSb88C5Wb1hClaqVi6yrJPp/ZmYmBARvIOZ4KNGxIQwc9JXSvlHjTwg7sIPI6AAORPrTomUTrfpKqox8O6gPR46FEBMbwneD+qjdc+j3/Xnw+Co1albXqi8/JVFPf/BBfY4eDVH+3bp1tkj9mVxKol3+c/Wvyr7L8YT9hEftLLKuN5Js+fX9lRJiUF4KuLjaYW5el5ZNnRg+dDzzF2oe8E2e9jO/L1tNq2bOPEh7wBdfddMp/LeD+nDp4pVi69PT02Pq3LH06T4IV8tOtO/sToOP6qvY2DtbU7d+bRxa+zDmx6lM/2W8yvd9B/biyqVrKtcuXrjCd1/9wLHo48XSNHv+RHp07Y9VGy86dfHmw4/MVWycXWypb16XNs1d+WnYBOYumFxoWCubtrh7OWFn6YONhTe/LV6lvN/1v//FwaYjDjYdGfnDpCLpdXWzx7xBXZo2dmDokDEsXDRdo9206aNZtmQVzZo4kpb2gK/6dAfg/v0HjBwxhcWLVqqFmTtvEnv3RtCiuTMWbT25WIy0dnNzoEGDujRsaMvgwaNZvHiGRrvp08ewZMlKGjWyIy3tAX36fAbA9es3cHHpTuvWbsyatZhly2YD8OmnH9KvX0+srX1o3doNT08nzM3rFqqnpNJ34tSR/DJ7GQ42HZkzYxGTcgaVvtsDlWk7aODP/PtvImdOXyiyH3Who6cLfyzQnP6vitddPnLrIjvL9nTr2JepM0YhSVKRNLu7OdCgQT0+bWjDoMGjWLJ4pka7GdPHsHjJSho2siUtLY2+fXoAcCD8EK1au9KmrTsDBv7EH7/PVQk3dMjXXHiJerCkyoipqRGDB/fF0tKLli1d0NPTp3t3nyLrKyn/PXv2DDf3z2jdxo3WbdxxdbEv9oO/XF4mfwJs2eRHjy79X0oDgIurPebmdWne1JFhQ8exYOFUjXZTpv3Mb8tW06KZE2lpD/hS2fZqDm9iYsS3332FvU1H2rXxQF9fjy5dFWm6eOGfWFl4YWPpQ2hoOKPGDNVJq56eHtPnjuOr7oNwateB9l08+CBfO+zgbENd8zrYtvJi9A9TmDFf0Q4/e/acHh2/xt22K+623bBzsqJ5K8VAbPmS1bjZdMHDrhv790QwbGTxHhbq6ekxbe5Yvur+Hc6WHWnfWZM+a+rVr4Nda2+VfsKzZ8/p2bE/Hnbd8LDrrqJvzOQfWDT3Dzztu7Ng1jLGTPqh2Ppmz5/I512/waaNN526eKnlOScXW+qZ18GiuRsjhk1k7oIX7fyWTf706KI+iIoIj8bOwgcHqw5cvXqd738cUCRdJdX/y8zMZPyYWVi0dMfVoSv9v+nNRx83AGDK9FHMnbUYW8v2zJq+kCnTR2nRVzJl5JNPP+SrPp/haNcJKwtv3D0cqZ+nb2BmZoKDoxX//ptYJH+WVD19+fI12rb1oG1bD9q18+LJk3QCAkKLpK2k2uVv+v6g7L8EBYQRFLi3SLoEZZcSGZRLkhStg81wSZIqlUT8ZR1Pb2e2bPYHIC42nqpVq2BkpP602tbOgl3+ikpg80Z/PL1dCg1vamqMq7s969ZuK7a+pi0a8c/fN7jxTyIZGZkE+ofi4mGvYuPi4YDfVsWsRXzcaapUrcy7Ru8AYGz6Hg6uNmzd4K8S5uqlv7l25Z9iaWrRsgnXr/3DP9dvkpGRwU6/3Xh4OanYuHs5sXXzTgCOx51S+kVb2L5f92Txryt4/jwDgDt37hVLX368vV3YvNEPgNjcNNIwI2Fn1w5//xAANm7wxdvbFYDbt+9y4ngCGRkZKvaVK7+NlXUb1q7ZCkBGRgYPHjwqsj4fH1c2bvQF4Nixk1SrVkXjDI69vSV+fsEAbNiwg/bt3QCIiTlOWtoDZXgzMxMAPv74A44dO0F6+lOysrKIioqhQwf3QvWUVPoiy1Su8hYAlatUJiXlllrcnbt64b8jqFCNxaVVs8ZUrVL02ZSi8LrLx0cfNyAyIkZ57cGDRzRrXrQZLR8fVzbolAet8PPbDcD6PHnwv/+eKG3eeqsSsvzi6baZmTEeHo6sXr25SJry6yuJMgJgYGBAxYoV0NfXp1KliiQnpxZLX0n5L/c7Q0MDDA0NVL4rDi+TPwGORMdx//6Dl9IA4OXtzGad2t527Myplzdt9MMrp+3VFl4/T5pWrFiRlJw0ffTosfK+b1WqqLMvm7VszPW//+Xff24q2mG/EFw9HFRsXD0d8N2imMk9GZdAlSqVeS+nHX7yXzoABoYGGBi8SMPHj/5Thq9UBD1q+lo04vrf/+brJ6jqc/FwwDenn3AyLoEqVTXrM8yjT5Zl3q78os6+lXK7WPpatGzC39f+zZPngnHXkOe2b94FKPJclapVeC8nPWOi40jTkOciDhwmKytLESb2FKamxkXSVVL9v9TU2yScOgvA48f/ceniVUxMjACFTytXeRuAKlUrK/OmJkqqjHz0kTlxx04q+waHDh3Dx8dVeb9Zc8YxcfycIufHkqync3F0tOLvv/8t8gODEuvX5KFDJ48S7b+UJWRZfm1/pUWJDMplWbbUwWw48EYMyiVJeqUH4pmYGJF4M1n5OSkpBRNTIxWbGjWr8yDtkbLyT0pMwTTHRlv4mXPHM2n8HLJfYvmFscl7JCemKD+nJN3C2ERVn5HJeyQnvqjYk5NSMTZRVIQTZ/zM7Mm/kv0KX19gYmpEYh5NSYmpygZHaWNiRFJem6QUjE2NtIY1N6+LRbtWhO7fxq7d62mWs7wZFMvHDkT5s2v3eizatSyy3pt50ygxWa3xrlmzOmkPHirTODFPGhdE3Xrvc+fOPf5YPo/DR4JY+ttsKlWqWCRtoHh4k1efIm51fQ9U9Kn/BoA+fT4jLCwcgLNnL2Jt3ZYaNapRsWIF3NwcqFVLvZHLT0ml77jRM5k09Wfizx5kyvRRTJ+yQC3uDp098duxu1CNZZnXXT7OnLmAh5cT+vr61K5Ti6ZNG2KmQzrnRZEHk5SfNeWvwvJg+/buJJwKZ6f/WgYMHKG8/su8yYwZO/Ol6qCSKiNJSan8+usKLl+O4fr1OB4+fMi+fVHF1Fcy/tPT0+PY0VBu3ohn//4oYmPji6wvLy+TP18lirbzhc+SktTTVFPba5JjU1D45ORUlixeyZnzUVy6eoSHDx9x4MAhpd2EST9x9sIhun3WgRnTF+qk1djkPRV/JCelYpTPZ+pt9Yt2WE9Pj5CI7Zy8GMGhgzHEHz+ttBs5bigxp/fSsZsX82ct00mPuj6jAvsABf2GlKRUjPLoCz64jRMXDhIVcUSpb+q4uYyd8iNHEsIYN/VH5kxbVDx9pkYkJeZtg1PU+jEmJkYk5rFJ1tAX08bnvbuwf29kkXSVZP8vl/drm9Gk6accjzsFwNhR05k6fTRnLkQxdcZopk76pRB9r76MnDt3CUurNlTP6Ru4utop2wwPTyeSklI5c6boq9VKqp7OS7du7dm6dVeRtZVUu5xLO8tW3L59l2vXijfZJSh7lNRM+eOcf+0lSTooSdIOSZIuSJK0UVLwPWAKhEuSpF4C8txHkqQ5kiQdlyRpnyRJbXLud02SpPY5NvqSJM2TJClWkqQESZIG5ok7QpKkbZIkXZIkabYkSb0kSTomSdJpSZLMc+zqSJK0PyfsfkmSaudcXyNJ0oIcffMkSbosSdK7Od/pSZJ0RZKkd/LpHSBJUpwkSXHPMh5q84/atfxPZrTZFPSdm7sDd27f5VT82QLj1gXd9KmHk2UZR1db7ty5x5lT519KQ/E0abbRFlbfQJ9q1arg7tSdyRPmsnLNQgBSU27RvKEDjjadmDBuNn+snK98el/SerVhYGBAs2YNWblyI1btvHny3xN+GvGdzrpexK1+rTj67Oza0afPZ4wbNwuAixevMH/+7+zevZHAwPWcPn2ezMwsHfSUTPr2/bonE8bOollDeyaMncXCpapL21q0bEL6k3QunL9cqMayzOsuH5vW+5KUmMK+g75MnzWW2GMndUrnV6U5l4CAUJo0daBb9/5MnqQYVHp6OHH79l1OnjytFrZo+tSvvYoyUq1aVXx8XPj4Yyvq1WtNpUqV6NmzUzH0lYz/ALKzs2nT1p365m1o1boZn376UZH1vWqtr4KSanurVauCl5czTRrZ81EDSypVqkT3zzoobaZNmU/Dj63ZvnUXAwZ+8cq0asqkuSbZ2dl42HWjbSNnmrZoxIefNFDazJuxBIvGLuzcvps+3/TUSY+6QPVLRfFldnY2nvbdsWjsQrPmjfgwZ6l1777dmTZ+Hu2auDJ13DzmLi76eS6KuDVcVPNf4b+hIIaPGEhmZia+24p2zkFJ5cFc3nqrEus2LmPMqOnKVRr9+n/O2NEzaPSxDeNGz2Txb7Neu75LF6+y8Nfl7ApYi+/O1Zw5c4HMzEwqVqzAiJGDmDn91wI1aaOk6ulcDA0N8fJyUa42Kpq2kmmXc+nU1Ru//yez5IDYU/6KaI5iVvxToD5gJcvyYiAJcJBl2UFL2LeAg7IstwQeAdMBF6ATkLvR5WvggSzLrYHWwDeSJNXL+a4pMAxoDHwBfCjLchtgJZC7sWspsE6W5SbARmBxnvg/BJxlWf4B2AD0yrnuDJySZflOXrGyLK+QZbmVLMutyhtWUfkh/Qf0JjI6gMjoAJKTb6nMKpmaGpOSrLqs9u6de1StVhl9fX2FjZkxyTk2SUkpGsO3tWiJu6cTp84eZNWahdjYtWP5yvla3KuZ5KRUTMxePEU0Nn2P1HzLflOSbmFi9uKpnYmpEakpt2nZthnO7vZEnQxmyZ9zsLRpza9/aN7rWBSSElMwy6PJ1MxIbSlyUlIKpnltTI1JTb6lNWxyUqpyP87JE6fJzs6mZs3qPH+ewf37aQAkxJ/l+t//Yt6gHtoYMPALomN2Ex2zm+TkWyozxKZmJmpLVO/cuUe1qlWUaWyWJ40LIjExmcTEFOJyZq52+hd8iFx+Bg78UnloSX59irjV9VVV0af6Gxo1+pjff59L1679uXcvTXl9zZqttGvnhbNzN+7fT+PKlb8L1VZS6ftZz04EBYQBsMs/hBYtVA+46dTFC3/fN3uWHF5/+cjKymLC2Fk42HTky88HUaVqZa5dvV6ozm8HfsWxo6EcOxpKUnIqtWqZKr/Ln7+g8DyYy6FDR6lfvw41a1annWUrvLxcuHgxmvXrlmFvb8Xq1brNtr2OMuLoaM316ze4c+cemZmZ7NoVioWFbitxXof/8vLgwUMiI4/g5mqvk76CeJn8+bL0H9CbqOhAoqIDSUm+hVken+XOcudFU9ubu9xX0faqh7d3sOKf6ze4m5OmgQF7aGvRQk3L9m0BtNdhOw8oyl5ef5iYGnFLrR3O31YbqbXVDx8+IuZwLPZOVmpx7NwRjIePs0568qOIW70PoO03GJsaqS1Hf/jwEUcOxyn1denRnpDAfQDs3hVW7IPekhNTMTXL2wYbq+W55KRUleXKJhr6Ypro3rMjLm4ODPpmpE5aXkf/DxQP7dduXMb2rQHKdg+g5+edCdy1B4CdfsG0aNlUTV9JlxGA9eu2Y2vdAU+3nty/l8a1q9epV782deq+z6Eju0k4G4GZmTGRhwJ47z2VOS8VXldfBsDNzZ74+DPcuqXS3deJkmqXAfT19fHycWFnzpJ8wf8Gr2NQfkyW5ZuyLGcD8UDdIoR9DuSerHAaiJBlOSPn/7n3cQW+lCQpHjgK1AQ+yPkuVpblZFmWnwFXgbA898oN3w7YlPP/9YB1nvi3y7KcO/3zF/Blzv/7AUU60nflig3YWrbH1rI9wUF76ZEzM9KqdTMePnxEaqr6vqmoyKN06KRowHv26kTIbkVDFbJ7v8bwUyf/QqOPrGna0J6v+wwnKuIIA/v/VBSZACScPEvd+rWpVdsMQ0MDfDq5sy8kQsVmX+hBOn+mOMimWavGPHr4mNupd5g3bTGWjV2xae7J0G9GER0Vyw/fji2yhvycPHGaeuZ1qV2nFoaGhnTs7EVo8AEVmz3BB/isZ0cAWrZqqvSLtrDBu/dhY2sBQH3zupQzNOTu3fvUrFkdPT1F8ahTtxb1zevyz/UbWjWuWL4eSwsvLC28CAoMo2evzgC0zk0jDXvjIiNj6NTJA4Bevbuwe7f2Aztupd4h8WYyH3ygOFDH3sGSC+d1O8xq+fJ1yoNLAgL20KtXFwDatGnOgwePNO63jog4QufOngD07t2VwEBFEXr/fVO2bl1Bv37D1Qbd775bU2nToYM727apnl6riZJK35SUW8qTwW3sLLh27bryfpIk0b6j+//EoPx1l4+KFSsot03YOViSlZnFpYtXC9X5x/K1tGnrTpu27gQG7KG3Tnkwms6dvQD4Ik8eNK9fV2nTrFkjDA3LcffufSZMmIN5gzZ89JElX3w5mIMHD9O37zCd/Pg6ysiNG4m0adOCihUrAODgYMWFC7qV4dfhv3feqUHVqoqHyhUqVMDR0aZYh0nm5WXy58uycsUGbCx9sLH0ISgoTLkqQXvbG0PHnHr5816dCc5pe4N379MY/saNJFq1aaZMUzt7Sy7mlIe8h1l5eDlz+VLh5QTg1Ikz1Ktfh/dz2+HOHuwNPahiszcknC492gPQvFUTHj18zK3UO9SoWZ0qOedYlK9QHms7C65eUuTBuvVrK8O7eDhw9XLhD0016jt5VlVfJ3f2hqjq2xd6kC45/QSFvkcF6ruSo+NWym0srFoBYGXblutX/y2WvpMnTlPfvA6165jl5DlP9mjIc916KlY0tGzVNEef9jzn4GTNkOH9+bLHd6SnP9VJy+vo/wEs+W0Wly5e4belf6ncKzklFSubtgDY2rdTe4D6OsoIwDs5fYNatUzw6eDGju2BnDt7iQb12tCkoR1NGtqRmJiCrXV7rYPg19WXAejevQPbthV96TqUXLsMijrmyqVrJCcV/TySN5b/BzPlr3SvdAE8y/P/rCLGmSG/WK+RnXsvWZaz8+zzloChsizvyRtQkiT7fHFn5/mcrUVH3tRQnogiy/INSZJSJUlyBNryYta8yITtOYiLmz0nEg6Qnp7O4G9fnIS5zXcl3w8eS0rKLSZPmMuqNQsZN+FHEhLOsX7t9kLDvwqysrKYNGoW67b/jp6+Hts37eTyxat83kdxuuamNdsJ3xuFg4s1B+OCSE9/ys9DJxZ6X1cvRybPHk2NmtX5a/NSzp25yFfddFt6nZWVxZgRU9nmtxI9fX02b/Dl4oUrfNVPcZLw2r+2sDcsAmdXO47F7yX9STrfDx6rNSwoluEuWjaTyCOBZGRkMOS70QC0s2rNqLHfk5mZRXZ2FiN+mKTx0JeC2BMajpubAwlnDpL+JJ1vv/1Z+Z2v/18MHjSalORbTBg/mzXrljBh0k8knDrH2jWKA/reM3qHqEMBVK78NtnZMoOH9KVVC1cePXrMTz9NYtXqXylnWI6/r//LdwN1e1qfl9DQA7i7O3DuXBRPnqQzYMCLpas7d67hu+9GkZycyvjxs1i3bimTJ48kPv4sa3IOmBs7dhg1alRnUc6p8pmZWVhZeQOwZctyatSoTkZGBsOHT1AeoqKNkkrfH7+fwIw5Y9HXN+DZs2f8OOxFPm1n1ZqkpBT+uX6zyP4rCiMnzSb2ZAJpaQ9x6tibQV9/QRcft1cax+suH++8W5NtfqvIzs4mOTmVQQN/1ixMCyGhB3B3d+T8uUM8eZLONwNePEDctXMt3373M8nJqYwbP4v165YxZfJI4uPPsHrNFgA6dvKgd68uZGRkkp7+lN5fDCooqmJRUmUkNjYef/9gYmKCyczM4tSps6xatUmjBm2UlP+Mjd9j1cpf0dfXR09Pjx2+gQSH7C+2H+Hl8ifA8lXzsbJuQ42a1Tl1LoK5s5awcf2OIusI23MQVzd74hMO8CT9qUrbud13FUMHjyEl5RaTJszlrzWLGD/hRxISzrIuT9urKfzxuFPs2hlK5OEAMjOzSDh1ljV/Kfw8ZepIGnxQn+zsbG78m8gPwybo7LMJP89k/Y4/0NfXZ+tGfy5duErvnHZ4w5rtHNgbhYOLLVHHg0lPf8qIIYrTzd8zepcFv03PSUOJoJ1h7A9T7H0ePWk45g3qkp0tk3gjiTE/TSuyH3P1TRw1k3Xbf0dfX59tOf2EXjn6Nir12RAZt1uhb+iEHH3vsGDZdPRy8ljQzj0cyNE3avgUJs8chb6BPs+ePWf0j8Vbvq7Ic9PY4rcKfX09ZZ77sp/i1O11f21lX1gETq62HI0PI/3JU4blyXN/rJqPpXVratSszslzB5k3awmb1vsy65cJlCtXjm07FQPf43Gn+PmHyTrrKqn+n0W7lvT4vBNnz1wgMlrxIHza5PnsDYtg+JBxzJo7AQMDfZ4+fcbwoeO06iuJMgKwfuMyatSoRkZGJiN+nExaWsHbPHWlJPsyFStWwMnJhiFDxhRLW0m1ywCdunji9z8woSBQRSqJU+YkSXosy/LbOQPjEbIse+dcXwrEybK8RpKk00B7WZYLfEybe5+c/08GHsuy/Eu+OAYAnkA3WZYzJEn6EEhEsZQ9b9wHcz7H5dUlSVIAihnx9ZIk9QE6yLLcSZKkNUCQLMs78ujpAiwB1suyrHUkXP3tBqX3qEUHqpXXfX90afA4U7cn0KVFesbz0paglczsou3vfd1ULl/0w+leN0lXQ0pbglZMzT1KW4JWHj57UrhRKSJp2lBahpAp000IAFXKl+2zWp9nZZa2BK1ULVe2/VfU1xy+bp5mlu12uKznP4DsMl7PPMvMKNyoFHkT+jK3H1ws2wVZRx70dX5tmbXq6n2l4rPSfE/5CiBE20FvOrISOAeckCTpDLCcos3Gfw/0lSQpAcW+c23rHAOAtyni0nWBQCAQCAQCgUAgEAg0USIz5f+rSJLUCvhVlmWbwmzFTPnLIWbKXw4xU/7yiJnyl0PMlL8cYqb85SnrM5VipvzlEDPlL4+YKX853oS+zP/MTPlXTq9vpnzt/lLx2evYU/4/gSRJo4HveIm95AKBQCAQCAQCgUAgEOSlTAzKJUk6CpTPd/kLWZZf7kWzrxBZlmcDs0tbh0AgEAgEAoFAIBAI/ncoE4NyWZbblrYGgUAgEAgEAoFAIBCUMbJLW0DJU5oHvQkEAoFAIBAIBAKBQPD/mjIxUy4QCAQCgUAgEAgEAkF+5OyyfSjhq0DMlAsEAoFAIBAIBAKBQFBKiJnyEqJyubL9moRHGemlLUErT8v4azCy5LK9uaWiQbnSlqCVzKyy/co2KPuvHCvrr2yr3cC7tCVopay/TqmSYf6zTwVFpZxe2e7iPHhetl8bWNZf6aVXxl/ZVtb7gQBPMp6VtgStlPW+zKNnZbsv/T+FmCkXCAQCgUAgEAgEAoFAUFKU7cfIAoFAIBAIBAKBQCD4/0vZXqD6ShAz5QKBQCAQCAQCgUAgEJQSYqZcIBAIBAKBQCAQCARlEnH6ukAgEAgEAoFAIBAIBIISQ8yUCwQCgUAgEAgEAoGgbCL2lAsEAoFAIBAIBAKBQCAoKcRMuUAgEAgEAoFAIBAIyiRiT7lAIBAIBAKBQCAQCASCEkMMyksBOycrwo8GEBm3m0HDvtZoM2XWaCLjdrMnypdGTT4BoHz5cgTs3URo5A72Rfvz4+hBauEGDPmKf++dpnqNai+l0dHZhpjjoRyL38v3PwzQaDNz7niOxe8lIjqAJk0/1Tns4KH9uPPwEjVqVH8pjfN+mcSp0+HEHA2habOGGm3q1KlFeIQ/8QkHWLtuCYaGhgB8+GF99of7cvf+Bb4f9o1aOD09PQ4fCWK778pi65s/fzJnzkRw7FgozZo1KkDf+0RG7uT06YOsX79Uqa9Hj44cOxbKsWOhhIf70bixIg/UqmVCaOgWTp7cz/Hjexk8uG+RNM2eN4Hjp/ZzKCaIJk01+6x2nVrsDd9BXPw+Vq1dpNSkLfyS32Zx6e+jRB8LVrlXo8afEHZgB5HRARyI9KdFyyZvrD5HJxuOxIVy7GQY3/+gnmcAZs4Zx7GTYRw8nK9MaAnbf0BvjsSFEhUTxMSpIwEwNDRk8bKZREQHEH5oF5bWbQrU9bKMn7kAW68edOz9bYnFoQkHJ2uiYncTfSKUIcP7a7SZNmcs0SdC2X/Yn8ZNFWXA1MyYHYGriTwayMEjAfT/trfSfsLUEUQdC2L/YX/+2rCYKlUrF1lXSeTB8uXLse+gL1FHAomODWH0uGFK+1Fjv+fspUNERgcQGR2Ai6udTjrtnayJPBbEoeMhDC7Af1Nnj+HQ8RD2HvJTtiOmZsZsD1jNwZgADkTv4uuBvVXC9P3mcyKPBXEgehfjpvykk5bXpe/HUYOIO3uAsEhfwiJ9cXSxKba+ktKYy8AhfUi8f7ZYbfGceRM5eeoAh2N207SAPFinTi32h/tyIn4/q9cuVsmDBYX/dlAfjhwLISY2hO8G9VG759Dv+/Pg8VVq1NS9bS6Jdg7gwoVDxMbuISYmmEOHAnXWk595v0wi4fRBjh4NoZmWfsLBiJ2cSghn7bqlefoJ5hwI9+Pe/YsMy9dPGDLka2LjwoiN3cOaNYspX758kbU5OFlzKDaYI1rqwOlzxnLkRCgHDu+kcZ425del0zlz+RAHowNU7Jf/tYB9UX7si/IjNmEf+6L8iqwrL3PnTSQ+4QDRR4O19rMOHPTj5KkDKnnxgw/rs+/ADm7fO8/QYS9+n5mZCUHBG4k9HsbR2FCNeVEbJVFHm5mZEBC8gZjjoUTHhjBw0Fcq9/vm2y84diKM6NgQpkz7uUh658+fwtmzkcTG7imwjNSt+z6Rkbs4cyaC9euXqZSR2Ng9xMbuUSsjQ4d+zYkT+zh+fC/r1i0pVh58o8h+jX+lhBiUv2b09PSYPnccX3UfhFO7DrTv4sEHH9VXsXFwtqGueR1sW3kx+ocpzJg/HoBnz57To+PXuNt2xd22G3ZOVjRv9WIQYWJmhI19O27eSHppjXPmT+KzLt9g1dqTzl29+fAjcxUbZ1c76pvXpU0zF34cNoF5v07RKaypmTF2jlbc+DfxpTS6utlj3qAuTRs7MHTIGBYumq7Rbtr00SxbsopmTRxJS3vAV326A3D//gNGjpjC4kWaB92DBvfl4oUrxdbn5uaAuXk9GjWyY8iQMSxerFnfjBmjWbJkFY0b23P//gP69PkMgOvXb+Dq2p02bdyZNWsxy5bNAiAzM4vRo6fTvLkTdnYdGTjwSz7++AOdNLm42mFuXpeWTZ0YPnQ88xdO0Wg3edrP/L5sNa2aOfMg7QFffNWt0PCbN/rRtWM/tXtNmT6KubMWY2vZnlnTFzJl+qg3Up+enh6z50+kR9f+WLXxolMXDWXCxVZRJpq78tOwCcxdMLnQsFY2bXH3csLO0gcbC29+W7wKQPmb7Czb061jX6bOGIUkSQX67mXo6OnCHws058+SQk9Pj5m/jKdX14HYtfWhY1dPNX86uthSv34dLFu4M3LYJGbPnwRAZmYmU8bPxbatD14uPejT/3Nl2MjwaOzbdcDJqhNXr1xnaAEPTwqipPLgs2fP6eD1BTbtfLBt54OTsw2tWjdT3u/3pauxtWyPrWV79oZF6OS/GfPG0bvbtzhYtKdjF08+UPOfDfXM62Dd0oNRwycza/5EFf/ZW7THx7Unffr3VIa1tG6Dm6cjztadcLTswB9LVhfJfyWtD+DP39fhatsFV9suHNgbVSx9Ja3R1MwYW3vLYrXFLq72mJvXpXlTR4YNHceChVM12k2Z9jO/LVtNi2ZOpKU94EtlHtQc/pNPP+SrPp/haNcJKwtv3D0cqW9eV3k/MzMTHByt+LcIbXNJtXO5uLv3wMLCE2trH501qeqzp0GDejRpbM+QIWNZuGiGRrtp00ezdMkqmjZxyOknKPTdv5/GiBGTWbToTxV7E1MjvhvUBxtrH1q3dkNPX49u3YqmUU9Pj1m/TODzrgOwbetDp65eanWgU04d2K6FOyOGTWJOTv4D2LppJz27qk96DOz3I842nXG26czugDCCA/cVSVdecvtZzZo4MmzIWH5dOE2j3ZRpo1i29C+aN3UkLe0hX371op/184ipav2szKxMxo2dSeuWrjg5dOGbAV/w0ccNdNJUUnV0ZmYm48fMwqKlO64OXen/TW+lJmtbCzy9nLG28MaytQdLFus+WePm5kCDBnVp2NCWwYNHs3ix5jw4ffoYlixZSaNGdqSlqZYRF5futG7tllNGZgNgamrE4MF9sbT0omVLF/T09OnevXjlRFB2EINyQJKk65IkvfM64mrWsjHX//6Xf/+5SUZGJoF+Ibh6OKjYuHo64LtF8fTzZFwCVapU5j0jhbwn/6UDYGBogIGBAbL8Yo/FpBk/M3PSApVrxaFFqyb8fe0f/rl+g4yMDPx9d+Ph5axi4+HpxLbN/gAcjz1F1aqVMTJ6t9Cw02eNZcqEeS+t0dvbhc0bFU+AY2PjqVq1CkbG76rZ2dm1w98/BICNG3zx9nYF4Pbtu5w4nkBGRoZaGFMzY9zdHVi7ZutL6du0yReAY8dOUrVqFYyN39OgzxI/P8Xs7caNvvj4KPTFxBwnLe1hTvgTmJmZAJCScov4+DMAPH78HxcuXMHU1EgnTZ7ezmzJSbO4XJ8ZqfvM1s6CXf6hAGze6I+nt0uh4aMPx3L/fpravWRZpnKVtwGoUrUyKcmpb6S+Fi2bcP3aP/xz/SYZGRns9NuNh5eTio27lxNbN+8E4HjcKWX82sL2/boni39dwfPninx45849AD76uAGRETHKaw8ePKJZc81P2F+WVs0aU7VK0WeUX4bmLRtz/VpuPZjBLt8Q3DwdVWzcPR3ZvmUXACfiEqhSVVEP3kq9w+lT5wH47/ETLl+6hrGJomxFhEeTlZWVE+YUpqbGRdJVknnwv/+eAGBoaIChoeFL1YEK/9144T+/YNw8VdsRN09HduS0IyfiEqiax39nEjT778t+n7Fs4Uplfrybkx/Lir5XSUlqnDxjFDMmzy9WGnt5O7NZpzzYjp05bdumjX545eTBgsJ/9JE5ccdOkp7+lKysLA4dOqZsbwBmzRnHxPFziqS5pNq5V4WXtyublP2Ek1StWhljjf0ES/z9c/Rt8MVHrZ+QqRbGwECfihUroK+vT6VKFUnW0rZponnLJvydpw7c6RusVge6eTqyTVkHnqJK1Sq8l5MXYqLjSNPQpuXFp6M7/jt2F0lXXjy9nNm8SZGXCutn5ebFzRt98fZR5MU7t+9y4kQCmfn8l5pym1PxZwFFP+bixSs619UlVUenpt4m4dQLTZcuXsXERNG36tf/cxbOX87z589zfpfu9aKPjysbN74oI9WqaS4j9vYvysiGDTto394NyC0jD5Th85YRAwODl8qDgrKHGJS/ZoxN3iMpMUX5OTkpFSMTIzWb5Dw2KUmpygZfT0+PkIjtnLwYwaGDMcQfPw2Ai7s9Kcm3OH/20ktrNDExIunmi/iTklIwyTfwMzE1IjGvTWIqJqZGWsO6eziSnJzK2TMXXl6jqRE3bybniT9ZrVKvWbM6aQ8eKjvpiYkpOg1g586dyPjxs8nOLv4aFlNTY27efDFLoinumjWr80BFn/pvAOjTpwd79hxUu167di2aNWtIbGy8TppMTIxIzOszDelao2Z1HqQ9UmpKyqNbl/D5GTtqOlOnj+bMhSimzhjN1Em/vJH6TEyNSEzMl9/zlVsTEyOVsp2UlIKxqZHWsObmdbFo14rQ/dvYtXs9zVo0BuDMmQt4eDmhr69P7Tq1aNq0IWa1Xm2HtTQxNlH1SXJSitrAS1Ndmd/ntWqb0rjxJ5w4nqAWR4/enTmwr2gzqSWZB/X09IiMDuDS30c5eOAQx+NOKe2+GfgFh2KCWPLbLKpWq1KoTmMTI5ISX8STnJSKsYZ2JL//8tvUet+URk0+4WSO/+o3qEubdi0J3LuZHUFraFrMB0ElpQ8Uy+v3HvJj/pJpVK1auK9et0YXDweSk1M5d+ZisXQp8tCLtiMpKUWtXdCUB01ybAoKf+7cJSyt2lC9RjUqVqyAq6udsk7x8HQiKSmVM0Vsm0uynZNlCAzcwOHDQfTr17NIul7oM1LRl9dP2vVpbzeSk1JZtPBPLlyM5uq1Yzx88Ij9+4ta1xRev+VvU5KTUjDR8QGVhWUr7ty+y9/X/imSrrwo0vdFGUlMSsHURENezNfPKqzdzUvt2mY0adqQuDLUj3m/thlNmn6qrKMbNKhLO6vW7A3fQVDoJprntNO6oObDRPXyrHsZ+YywsPAc3an8+usKLl+O4fr1OB4+fMi+IrZ3bxpy9uv7Ky3e6EG5JEl1JUm6IEnSSkmSzkiStFGSJGdJkg5LknRZkiSNGzElSaopSVKYJEknJUlaDkh5vustSdIxSZLiJUlaLkmSfs71x5IkzZck6YQkSfslSVJ7NCdJ0gBJkuIkSYp7/EzzkzRNS1DVnkxrtFH8m52djYddN9o2cqZpi0Z8+EkDKlSswJCfvmH+zGUFuapI6KKxIJuCrlesWIEfRn7H7BmLSl2jNtw9HLl9+w7xJ8+UCX22tu346qvPGD9edVnfW29VYvPmPxg5ciqPHj1+LZqK489+/T9n7OgZNPrYhnGjZ7L4t1kF2pZlfSVRJgD0DfSpVq0K7k7dmTxhLivXLARg03pfkhJT2HfQl+mzxhJ77CSZmVlaf8ubhEaf6GKTx+eV3qrEqnWLmDh2Fo8f/adiN+yngWRlZuG7rWh7UUsyD2ZnZ2Nr2Z6GH1nTolVTPvlUse3kr5Ubad7YEZt2PqSm3mb6zDE66FS/VtT6pdJblfhz3UImjZmt9J++gT5Vq1XBx6Un0yfO54/V8wvV8jr1rftrK5bN3XG16cKt1NtMnD6yWPpKSmOFihX4/scB/DJr6UvoKpk8eOniVRb+upxdAWvx3bmaM2cukJmZScWKFRgxchAzp//62rXmoqmdc3TsjKWlFx07fsXAgV9iZVX0czVKqp9QrVoVvL1daPipDQ3M21LprUr06NHx5bXx8tpy6dTFC3/f4s+S6xq/Zhvd7v/WW5VYv+k3Rv88rcz0Y956qxLrNi5jzKjpSk0GBgZUq1YFF4euTBw3m9XrFuukVRGf+rXi5EE7u3b06fMZ48Ypyki1alXx8XHh44+tqFevNZUqVaJnz0466xKUTd7oQXkODYBFQBPgY+BzwBoYAYwtIMwk4JAsy82BAKA2gCRJnwCfAVayLDcDsoBeOWHeAk7IstwCiMi5hwqyLK+QZbmVLMut3i5fQ2PEyUmpmJq9eAJmYmrErZRbKjYpSamY5LExNjUiNZ/Nw4ePiDkci72TFXXqvs/7tc0IjdrB4fhQTEyNCD64jXffq1nAz9dOUlIKprVexG9qakxKsmr8SYkpmOW1MTMiJflWgWHr1qtN7Tq1iDgcwInTBzA1M+ZAlD/vvaf7roEBA78gOmY30TG7SU6+Ra08M4emZiZqS3fu3LlHtapV0NfXB8DMzJjkfL8jPxYWLfH0cubs+SjWrFuCnZ0lK1fp1lkZOPBLYmKCiYkJJjk5lVq1TJXfaYr7zp17VFXRp/obGjX6mN9/n0O3bv25dy9Ned3AwIDNm/9g69ad7NoVqlVT/wG9lQdIJSffUplt1ZSud+/co2q1ykpNpnl0JyWlFBo+Pz0/70zgrj0A7PQLpkXLpm+UvlySElMwM8uX3/OVyaSkFJWybWpqTGryLa1hk5NSCQrcC8DJE6fJzs6mZs3qZGVlMWHsLBxsOvLl54OoUrUy165e1/pb3iSSk1R9YpLjK1Ub9boy128GBgasWrcQv+1Bansmu/XsgLObHYO/0e0wntedBx8+eMShqKM4OdsCcPvWXbKzs5FlmbWrt9KyleY8mBeFb17EY6KhjdDkv9Q8/vtz7UL8t+8mJOiF/5ITUwnJ8Wd8Tn4syqFfJa3vzu0Xvtq4dgfNWuo+Y/U6NNat9z6165ixN8qPmFNhmJgasSdiB+8W0s71H9CbqOhAoqIDSUm+hVmetsPU1FitbdOUB3O33ijyoObw69dtx9a6A55uPbl/L41rV69Tr35t6tR9n0NHdpNwNgIzM2MiDwUU2Da/rnYu9z63b98lIGAPrfOcwaCNAQO/4EhMMEc06MvrJ+36tLcbDg7WXP/nBnfu3CMzM5OAXaG0tWipk75ckjTVb/n7WfnaFBNTY1JSbhd6b319fTx9nNnlF1IkTQDfDPiCQ0eCOHQkKMd/L8qImakxySka8mK+fpa2bWq5GBgYsGHTb2zbGkBgwB6ttq+rjjYwMGDtxmVs3xpAUECY0iYxMYXAnM8njieQnS1T8x3NfXxQlJGjR0M4ejREra+qKCO65MH8ZWQuXbu+KCOOjtZcv/4iD+7aFYpFEfPgG0cZO+hNkiR3SZIuSpJ0RZKk0QXY2OdM8p6VJKnQA2P+Fwblf8uyfFqW5WzgLLBfVjxiOg3ULSCMLbABQJbl3cD9nOtOQEsgVpKk+JzPuaewZQO5m4w3oBj4F5lTJ85Qr34d3q9thqGhAT6dPdgbelDFZm9IOF16tAegeasmPHr4mFupd6hRszpVcvZ+lq9QHms7C65e+puL5y/T4iN7rJq5Y9XMneSkVDztu3P71t3iSOTk8dPUr1+X2nVqYWhoSKcuXoQG71exCQ05QPecp3ItWzfl4cPHpKbeLjDs+XOX+MS8HS0aO9KisSNJiSk42nTi1q07OutasXw9lhZeWFp4ERQYRs9enQFo3boZDx8+IlVDYxUZGUOnTh4A9Ordhd2792qNY/KkeXz0gSUNP7Ghz5dDiYiIpv/XP+ikb/nydVhYeGJh4UlgYBiff94FgDZtmvPw4SO1QZxC3xE6d/ZU6OvVhaAghb733zdly5blfP31D1y58rdKmD/+mMvFi1dYrMNhIytXbFAeIBUctJceOWnWKtdnqeo+i4o8SodO7gD07NWJkN2KDmfI7v06hc9LckoqVjZtAbC1b6c2sCzr+nI5eeI09cxf5OuOnb0IDT6gYrMn+ACf9ewIQMtWTZXxawsbvHsfNrYWANQ3r0s5Q0Pu3r1PxYoVqFSpIgB2DpZkZWZx6eJVrb/lTSL+xBnqmdfh/TpmGBoa0qGLB3tCwlVs9oQcoFuPDoDinItHDx9xK1VRXyxYOo3Ll66xfNlalTAOTtYMGdafPj0Hk57+VCctryMP1nynhvIk+AoVymPvYMnlS9cAVPZDevu4cv5c4VuQFP6rndOOGNKhsydh+fwXFhJO15x2pEWrJjzMaUcA5i+ZypVL11jxm6r/9gTvx8pWUR7qm9ehXDlD7t29T1EpKX25Z6sAeHg7c/H85SJrK0mNF85dpumHtlg0dcWiqSvJSam42XXldiHt3MoVG7Cx9MHG0oegoDDljJf2PBhDx5y27fNenQnOyYPBu/cVGP6ddxUP6mvVMsGngxs7tgdy7uwlGtRrQ5OGdjRpaEdiYgq21u0LbJtfRztXqVJF3n77LeX/nZ1tOXtWt+0AK5avp52FJ+1y9Sn7Cbn6NPUTjtCpU46+3l0I2h2mZpOXGzeTaN26ORUrVgDA3t6qyAfDxp84TX3zOtTOqQM7dtGc/7or68CmOXVg4YNyW/t2XLn8N8lJRd9j/OeK9Vi388a6nTe7A/fS83NFXiqsn5WbF3v26sLuoMIPl1v2+2wuXrzKsiWrCrV9Xf2EJb/N4tLFK/y29C+VewUH7cXWTtFOmzeoS7lyhlrP21i+fB1t23rQtq0HAQF76NXrRRl58EBzGYmIeFFGevfuSmCgIg++/74pW7euoF+/4Spl5MaNRNq0aaHMgw4OVlx4icOJBUUjZxX1MsAD+BToKUnSp/lsqgG/Ae1lWW4IdCvsvgavXupr51me/2fn+ZyN9t+naYGNBKyVZbnw9YOawxdKVlYWE36eyfodf6Cvr8/Wjf5cunCV3n0UabVhzXYO7I3CwcWWqOPBpKc/ZcQQxenr7xm9y4LfpqOvr4+enkTQzjD2h0UWR0ahGkePnMp2/1Xo6euzaf0OLl64Qp9+PQBY89cW9u45iLOrHbGn9pH+JJ3vB43RGvZVsyc0HDc3BxLOHCT9STrffvtiVszX/y8GDxpNSvItJoyfzZp1S5gw6ScSTp1j7ZptgKJzF3UogMqV3yY7W2bwkL60auGq8xKqwggNPYCbmwNnz0by5Ek6AweOUH7n77+GQYN+Jjn5FuPGzWL9+qVMmjSCU6fOsibncLkxY4ZRo0Z1FuacdpqZmYW1tQ+Wlq3o1asLp0+fJyZGcSjIpEnz2LMnXF1EPsL2HMTFzZ4TCQdIT09n8LcvThrf5ruS7wePJSXlFpMnzGXVmoWMm/AjCQnnWL92e6HhV67+FSubttSsWZ0zFw8xe8YiNqzbzvAh45g1dwIGBvo8ffqM4UPHvZH6srKyGDNiKtv8VqKnr8/mDb5cvHCFr3LKxNq/trA3LAJnVzuOxe9VlInBY7WGBcUy9UXLZhJ5JJCMjAyGfKd42PrOuzXZ5reK7OxskpNTGTSwaK9gKQojJ80m9mQCaWkPcerYm0Fff0EXH7cSiw8UPhk7cgabff9EX1+PLRv8uXThCl/2VZw4u271VvaHReLkYsuRk6GkP3nKD4MVadPGogXdenTg3NmL7M153c+sqQs5sDeSGfPGU66cIVt2Kjp6J2JPMepHzafzaqKk8qCx0bv8tmIe+vp66Onp4e8XzJ5QRZmdMn0UjZt8gizL/PtPIj98P14n/43/eQabfFegp6+nbEe+6Ks49Xj96m3sD4vE0cWWwydCSE9/yo+DFfdtbdGCrjn+C4tUHEA0e9pCDuyNYssGf+Yvncb+6J1kPM9g+HcFl9fS0Dd+yk982vhjZFnm5r9JjPphcrH0laTGlyVsz0Fc3eyJTzjAk/SnKnlwu+8qhg4eQ0rKLSZNmMtfaxYxfsKPJCScZV2ePFhQ+PUbl1GjRjUyMjIZ8eNk5SFrxaWk2rn33nuHrVtXAIrZy61bd7F3b+FvJchPbj/h9JkI0p+kM/DbF9sd/PxXM2jQKGU/Ye26JUyc9BOnTp1V9hOMjN7N10/oR8sWLsTFxrNzZwiHo3eTlZnJqVNn+euvzUXSpqgDp7PZdyX6+nps3uDHxXx14L6wCJxcbIk5uYf0J08ZPvjF4s/fV/6CpXUbatSsxomz4cybvZTN6xV5sWMXz5c64C2XPXvCcXWz59TpcJ6kP1Vph3b4/cWQQaNz8uIcVq9dzISJP3Lq1DnWrX3Rz4qI2qX036DBfWnT0o2GjT6m5+edOXPmAoeOBAEwdfIvhGk4Oyc/JVVHW7RrSY/PO3H2zAUic14zN23yfPaGRbBh3Q6W/j6b6GPBPH+ewXcDdd82Exp6AHd3B86di+LJk3QGDHhRRnbuXMN3340iOTmV8eNnsW7dUiZPHkl8/IsyMnasoowsynnDUGZmFlZW3sTGxuPvr1ixkpmZxalTZ1m1apPOut5ESnOvtwbaAFdkWb4GIEnSFqADcC6PzeeAnyzL/wLIsqx9CQ4gvewp2KWJJEl1gSBZlhvlfF6T83lH/u/yhVsM3JJlebokSR5AMPAu8B6wC8Xy9VuSJNUAKsuy/I8kSTLQU5blLZIkjQeMZFkeWpC22jUal2nHPsl8VrhRKfI0U/1U9LJEVhmrHfJTQd+wcCOBVgxylpKVVZKuFn1p4uukdgPv0paglaeZz0tbglYqGf6Pv3NWwOMM3VZzlBbPs9RPHS9L6JXQayJfFZXLVSxtCYXyJKNs9wX1pbK9oDe9jLcjAE+f/lu2C4qO3PGwe23jqndDIwcCed85uEKW5RW5HyRJ6gq4y7LcP+fzF0BbWZaH5LFZCBgCDYHKwCJZltdpi/d/Yaa8OEwBNkuSdALF/vDcpxjncgbcYZIk6QEZwGDgH+A/oKEkSceBByj2ngsEAoFAIBAIBAKBoKR4jXNhOQPwFVpMND3oyP/QwADFlmgnoCJwRJKkGFmWC9yj9kYPymVZvg40yvO5T0Hf5Qt3F3DNc+mHPN9t5cXe8fzhJgATXkKyQCAQCAQCgUAgEAjeTG4C7+f5XAtI0mBzR5bl/4D/JEmKBJoCBQ7Ky/a6EIFAIBAIBAKBQCAQ/L+ljL2nPBb4QJKkepIklQN6oHibV152ATaSJBlIklQJaAuc13bTN3qmvDAkSeoLDMt3+bAsy4OLei9Zlt9+NaoEAoFAIBAIBAKBQPCmIctypiRJQ4A9gD7wlyzLZyVJ+jbn+z9kWT4vSVIokIBi8f1KWZbPaLvv//SgXJbl1cDq0tYhEAgEAoFAIBAIBIKiU9bOV5ZlORjFQeF5r/2R7/M8YJ6u9xTL1wUCgUAgEAgEAoFAICgl/qdnygUCgUAgEAgEAoFA8OZS1mbKSwIxUy4QCAQCgUAgEAgEAkEpIWbKS4jHGU9LW4JWssr4I6e3DMuXtgStPM3KKG0JWqlUxv334NmT0pZQKE8yn5W2BK3UbuBd2hK08u+VoNKWoJVqtR1LW4JWKsjlSltCoTwr4/VgWUdP46tuyw7ZZbyfoCfpl7YErWRmZ5W2hEIx0CvbPnyelVnaErRS1vta/1PIZbu+fBWImXKBQCAQCAQCgUAgEAhKCTEoFwgEAoFAIBAIBAKBoJQQy9cFAoFAIBAIBAKBQFAmKeO7aV4JYqZcIBAIBAKBQCAQCASCUkLMlAsEAoFAIBAIBAKBoEwiZ4uD3gQCgUAgEAgEAoFAIBCUEGKmXCAQCAQCgUAgEAgEZRKxp1wgEAgEAoFAIBAIBAJBiSFmygUCgUAgEAgEAoFAUCaRZbGnXPCKcHK24eiJPcTF72PYjwM02syaO4G4+H1EHQmkSdNPCw3boaM70ceCufPgIs2aN1JeNzAwYNnyORyKCSImLpThPw3USeOceRM5eeoAh2N207RpQ402derUYn+4Lyfi97N67WIMDQ0LDT9ocF9iYkM4ciyEVasXUr58OeV3A779krgTe4mJDWHqtFE66QRwcLLmcFwIMSf3MPSHbzTazJgzjpiTewg/vIvGefy5cOkMzl45TMSRABX7idNGcig2mPDDu1i9YQlVqlbWWY8mSsqf3w7qw5FjIcTEhvDdoD7F0mbvZE3ksSAOHQ9h8PD+Gm2mzh7DoeMh7D3kR6MmnwBgambM9oDVHIwJ4ED0Lr4e2Fst3MAhfUi8f5bqNaoVS1su836ZxKnT4cQcDaFps4L9Fx7hT3zCAdauW6L034cf1md/uC9371/g+2Ev8scHH9QnOma38i8pJYFBg/sWW+OC+VM4dzaKuNgwmjVrpNGmbt33iYoM4OyZSDas/02p0cfblbjYMI4dDSX68G4sLVurhNPT0+Nou8vKggABAABJREFUTAj+fquLpc3ByZqo2N1EnwhlSAFpPG3OWKJPhLL/sD+Nm75I4x2Bq4k8GsjBIwH0//ZFGk+YOoKoY0HsP+zPXxsWv3QZ0ZXxMxdg69WDjr2/fS3x5TLvl0kknD7I0aMhNNOSBw9G7ORUQjhr1y3NkwfNORDux737Fxk2TLWOGjSoL7Gxe4iNC2Pw4H7F0lbc9AVYsHQ6py9HER69S8X+00YfERi2iQOHd7J2yzLervxWoTpKom2rVr0qfrvWEHtyL3671lC1WhUADA0NWfr7bA7FBBEZHYCVdRu1uDZu/YPDR3cXqnvgd19x+Ohuoo8F820R69GmzRpyKCaIuPh9zJo7QXm9Z6/OXPr7KBGHA4g4HMAXX3Ur0n1zmT1vAsdP7edQTBBNCmg7atepxd7wHcTF72PV2kUqbYem8GZmJgQEbyDmeCjRsSEMHPSVyv2++fYLjp0IIzo2hCnTftZZa1muA+HlyvBnn3Xg6NEQjh4NYf8BXxo3flGGfv9jLtevxxEbu6dIehydbYg5Hsqx+L18/4Pm8jJz7niOxe8lIjpApbxoC9t/4BfEHA/l0NHdTJo6EgA7B0v2R/gReSSQ/RF+2NhaFKrvdfZVAT5t+BF79m8j+lgwh2KCVPqHulISfYVc9PT0OHwkiO2+K7VqKMgnealdpxZ7D+wg9uReVq1ZqFJmi+rTXIZ8/zX3Hl2mRs3qAFSvUY1du9fzb3I8c36ZqFWzoOwhBuWvAT09PebOn0z3zv1p19qDLl29+eijBio2zq52mJvXoVUzZ374fgLzf51aaNjz5y/zZa/BRB+OVblXh04elC9XDmsLbxxsOtKnbw/er22mVaOLqz3m5nVp3tSRYUPHsWDhVI12U6b9zG/LVtOimRNpaQ/4MqfTUVB4ExMjvv3uK+xtOtKujQf6+np06eoDgI2tBV5ezlhaeGHR2oPFi7VXenn9OXv+RD7v+g02bbzp1MWLDz8yV7FxcrGlnnkdLJq7MWLYROYumKT8bssmf3p0Ua98I8KjsbPwwcGqA1evXuf7AhokXSgpf37y6Yd81eczHO06YWXhjbuHI/XN6xZJm56eHjPmjaN3t29xsGhPxy6efJDPf44uNtQzr4N1Sw9GDZ/MrPmKyj0zM5Mp4+dib9EeH9ee9OnfUyWsqZkxtvaW3LyRVCRN+XF1s8e8QV2aNnZg6JAxLFw0XaPdtOmjWbZkFc2aOJKW9oCv+nQH4P79B4wcMYXFi1Tz1OXL17C08MLSwgtrSx/S058SGBBWLI3ubg40aFCPTxvaMGjwKJYsnqnRbsb0MSxespKGjWxJS0ujb58eABwIP0Sr1q60aevOgIE/8cfvc1XCDR3yNRcuXimWNj09PWb+Mp5eXQdi19aHjl091cqIo4st9evXwbKFOyOHTWL2fEUZyU1j27Y+eLn0oE//z5VhI8OjsW/XASerTly9cr3AB2Kvmo6eLvyxQHMeKCnc3Oxp0KAeTRrbM2TIWBYumqHRbtr00SxdsoqmTRxy8uBnANy/n8aIEZNZtOhPFftPP/2Qvn17YGvbAYu2Hnh4OGJejDJc3PQF2LbJn8+7qtdv8xdPZeaUBThadSQkaD+Dvtf+wKCk2rbhPw4kIiKa1s1diIiIZviPigfLX+aUb2sLbzq378O0mWOQpBezJ97tXfnv8ZNC/ffJJx/wZZ/uONt3waadD67u9tQ3r1NouFx++XUKP3w/nlbNnDE3r4Ozi63yO3/f3dhZtcfOqj3r127X+Z65uLjaYW5el5ZNnRg+dDzzF07RaDd52s/8vmw1rZo58yDtgfIBQEHhMzMzGT9mFhYt3XF16Er/b3rz0ccKf1vbWuDp5Yy1hTeWrT1YomNbXJbrQHj5Mnz9+g3c3D6jbVsP5sxewpKls5RhNqzfQceOX2m8X0Ho6ekxZ/4kPuvyDVatPenc1Vut3Dq72lHfvC5tmrnw47AJzPt1SqFhrW3a4uHphG07H6zberFs8SoA7t29T6/PvsW2nQ+Dvx3FbyvmFarvdfZV9fX1Wb7yF34cNhHLNp74ePYmIyOzSD4tqb5CLoMG9+XiBe15sCCf5Gfy1JH8vmw1rZu7kJb2kN5fdtMavrD0MDMzxt7Bihv/JiqvPXv6jJnTFzJx3Bytmt9E5OzX91davHGDckmSJkuSNELDdVNJknbk/N9ekqSgEoi7riRJnxc1XMtWTfj72j/8c/0GGRkZ+PnuxsPbScXG08uZLZt3AhAXG0+VapUxMnpXa9hLF69y5fLfavHJskyltyqhr69PhYoVeJ6RwaNHj7Vq9PJ2ZvNmf2X8VatWwcjoXTU7W7t27PQPAWDTRj+8vF0KDa9vYEDFihXQ19enYsWKpCSnAvB1/8/5df4fPH/+HIA7t+8W7kygRcsm/H3tX/65fpOMjAx2+gXj7qXqT3cvJ7ZvVswCHY87RZWqVXgvR09MdBxp9x+o3TfiwGGysrIUYWJPYWpqrJMeTZSUPz/6yJy4YydJT39KVlYWhw4dw8fHtUjamrdszPVrN/j3H4X/dvkF4+bpoGLj5unIji2KlQQn4hKoWrUy7xm9w63UO5xJOA/Af4+fcPnSNYxN3lOGmzxjFDMmz0eW5SJpyo+3twubN/oBEJv7+43V/Wdn1w7/HP9t3OCLt7fCF7dv3+XE8QQyMjIKjMPewYpr1/7hxo3EAm204ePjyoaNvgAcO3aSatWqYGz8npqdvb0Vfn6Kmbv1G3bQvr0bAP/992Lw8NZblVR8ZmZmjIeHI6tXby6WNkUa//sijX1DcPN0VLFx93Rk+xZFGTkRl0CVPGl8+pTmNI4Ij1aWkRNxL1dGikKrZo2pWuX1zMrn4uXtyiZlHjxJ1aqVMdaYBy3x9w8GFHnQRy0PqnYyP/qoAcdiX5ThqENHlXlCV14mfQFioo9zX0MdaN6gHkcOxwGKBzBehdQtJdW2eXg5sWWjov7bstEfT29nAD76uAERB6MBuHPnHg8ePKR5i8aAogwNGtKX+XN/K9R/H35kTlxsvDINog/F4uXjSt16tdnut4oDkf7s3rOJDz6srxbWyOhdKld5m9hj8Qp9m3fimVNvvwo8vZ3ZolPbYcEu/1AANm/0V2ooKHxq6m0STp0F4PHj/7h08SomJkYA9Ov/OQvnL8/TFt/TSWtZrgPh5cvw0aMnSEt7mPP7TmBm9qK+O3z4GPfuqZchbbTIl+f9fXfj4eWsYuPh6cS2nPQ7HnuKqlUV5UVb2D5f92TRryt4/lzR3t25o0i/0wnnSUm5BcCF85cpX6Ec5coZUhCvu6/q4GTN2TMXOXvmAgD376WRnV20EVFJ9hVMzYxxd3dg7ZqtWjUU5JP82NhZsGunosxu2eSHV069VhyfAsyYPY5JE+aqlJsnT9I5euQ4z54906pZUDZ54wblBSHLcpIsy11LOJq6QJEH5SYmxiQmJis/JyWmKBtDpY2pkbqNqZFOYfMTsDOUJ/894fyVaBLORbBs8SqNg1BVjUYk3nwxu5mUlKLW4a5RszoP0h4pO+UKjcZawycnp7Jk8UrOnI/i0tUjPHz4iAMHDgGKDmA7q9bsD/dld+gmWuR0rgrD2NSIpHw+Mc7vTxNVfyYnKfypK5/37sL+vZE62+enpPx57twlLK3aUL1GNSpWrICrqx1mtUyKpM3YRNV/yUmpav4zNnmPpMQUrTa13jelUZNPOHk8AQAXDweSk1M5d+ZikfRowsTUiJs386Zxspr/atasTtqDh0r/JSamYFqENO7azZsd2wOLrdHU1JibedIosQCND1Q0qtq0b+9OwqlwdvqvZcDAF88af5k3mTFjZxa5g5KLsYkRiSrpl6Ly8ERho57G+euWWrVNadz4E07kpHFeevTuzIF9UcXS9yZgamqkkr55y2cumtNXex48d+4iVlZtqJFTht3cHIpVhl9F+ubnwvnLysG9T0c3TM20P3QpqbbtvXffITX1NgCpqbd5952aAJw9cwFPL2f09fWpXacWzZo1wsxM4bux44ezbMlfPElP16oZFDN37axaK+tRFzc7zMyM+XXxNEaNnIqjbScmjpvDvAWT1X+zqZGKX5PytS0+HdyIOhLImvVLVAZxuqKo+/P4RUPbpantyM13uoR/v7YZTZp+yvG4UwA0aFCXdlat2Ru+g6DQTcoHHYVRlutAhb5XV4a/+uozwsIOFlsLKNIm6WbBeQdyyktem8TUnPJScFjzBvVoZ9mKPQe2ExC8QWP6+XRw4/Sp88qBu2Z9r7ev2qBBPWRZZof/X4RH7WTo8KKvvCrJvsLcuRMZP352oXmwIJ/kRXN/z0hreG0+dfd0JDkpVflA4/8Dcrb02v5Ki1IZlOfMOF+QJGmlJElnJEnaKEmSsyRJhyVJuixJUhtJkmpIkrRTkqQESZJiJElqkucWTSVJOpBj+02ee57RENdbkiT9JUlSrCRJJyVJ6qBFV3BuPDm2E3P+P02SpP7AbMBGkqR4SZJ+0BB+gCRJcZIkxT3LeJDnunpc+WcSJQ1GsizrFDY/LVs1ISsrm08/sKJ5IwcGDe1Hnbrvaw1TUPy62hT0XbVqVfDycqZJI3s+amBJpUqV6P6ZIgkMDAyoVq0qTg5dmDBuNmvWLdGq8YUODRfz+6QYfstl+IiBZGZm4rut+AO2kvLnpYtXWfjrcnYFrMV352rOnLlAZmbRlnu9TH7MpdJblfhz3UImjZnN40f/UaFiBb7/cQC/zFpaJC0Fa3w5/xWGoaEhXp7O+PsFF0+gjvEXZhMQEEqTpg50696fyZMUHVJPDydu377LyZOnX602XWzypfGqdYuYOHYWjx/9p2I37KeBZGVmvVQZKeuUVB68ePEqCxb8QWDQBnbuWsvp0+fJysx6eW262BSi7cch4+nbvyd7Dm7nrbff4rmWlSaKONSvlWTbtmHdDpISUzgQ6c/MOeM4dvQEmVmZNGr8CfXM67A7cK/W8LlcuniVxb+uwG/XGrb7/8WZ0xfIysyiTdsWrF63hIjDASxYNE3jrK82v4aGHKBZQwds2vlwMDyaZcvnqtkWRkm1Hbm89VYl1m1cxphR05Ur6BRtcRVcHLoycdxsVq9b/Fq0QsnVga9KH4CtbTu+/OozJoyfXWp6tIU1MNCnarUquDl2Y9KEuaxcs1DF7qOPGzBx6kh+Gj5B7R6qcatfK8nybGCgj0W7lgzo/xOerj3w9nHB1q6d1jDqmkumnnb3cOT27TvEn1QbVrxyDUX1acWKFfhpxCBmzlhYqDbBm0Vpnr7eAOgGDABiUcxAWwPtgbHADeCkLMsdJUlyBNYBzXLCNgEsgLeAk5IkaTvVZRxwQJblfpIkVQOOSZK0T5bl/zTYRqIYdF8HMgGrnOvWwAbgCjBClmVvTRHJsrwCWPF/7J13WBRXF4ffodgSxZbQ7JhuwY5U6VLtiUZN1Nhi78beo9GY2JMYe2+AUkREQUCwoKjYS9TE0OxiIkbKfH/ssrCwLKAiJN99n4eH3dlzZn57bp975y5A9crvqUpkYmKy6m4+KJbEZC8pUtkkaLBJuku5cvqF+ualSzcvDh+KJCMjg/v3H3LyeBzNmjXi99t31Oz6D+ylenbqzOnzmNYyAU4rrqOc5c7Ng/sPMahaGV1dXTIzM5UaU3K+owb/dvZW/H77Dg+Uy6kC/ENoY9GcXTv3kZiQTIC/YpOUuNPxZGVlUaNmdZVtQSQlpGBSSEySElPU4mZsoohnYXzaoyPOrvZ09e5TqG1e3kQ8ATZv2s3mTYpnFafPGEtiYjLFISlRPX7GJoakaIhf7lmy3DZ6enr8unEJfruDCA48BEC9+rWpU9eU0ChflX1IxB48HLtz7+79IukaOKg3ffoqnjU8fTqeWrVyp7Fxvvjdv/+QqgZVVPEzNTUiqQhpDIrn0M6evcjdImrLZvCgL+nXrwcAp06fo1YtE9VnpgVoNFDTmN8G4OjREzRoUJcaNarR1rIlHh7OuLa3p0L58lSpUpn165fSt+/IIutMSkxWm6UzNjEiJanwNE7OlcZrNy3Bd3cg+wMOqfl169EBJ1c7Pu3wchuUlWUGDupN376K9D2dJ31zl89sNKdv4Xlw08ZdbNq4C4CZs8arzYYUhVdN34K4cf0W3TsrZqsamNXFycVWq31JtW13791XLbk2NHyHe/cVjzZlZmYyZVLOc8sHDu3k5o3fsbRuTVPzTzh7IRw9PT1qvlMd//1b8HbPvxFlNls27WHLpj0ATJ0xhrsp93nyJBU7K281Ox0dHcKj9iqut/8w69ZsU4urSa625dHDx6rjmzbsZKZyw63C6D+wl+p5+bjT59VWTphoaLs0tR3Z+U7Rdmj219PTY+PWleze6U9grr00EhKSVXtrKNpimRo1q/MsOX++LOt14Osuw40afcjKVQvo1LEPD3Ol78uQmJiMSS3NeUdlk5CMaW4bU0OSk+6iX06/QN/ExGSClOl35nQ8WbJMjRrVePDgEcYmhmzatpKhAydw+5Z6H1CTvjfZV01MSCY6OpaHDx4BEBoSQVPzT4iMOKbV7030FSwsWuDu4YSLqz0VKpSncuW3WbP2R/p/NVpNg4TEmbh4jTHJjeb+3l1VHIoT03r161CnXi2iYgJUx49E7cWpXZdi92n+TbziU5H/Ckpz+fotWZbPy7KcBVwEDsuK20bnUSwTtwY2A8iyHAbUkCTJQOm7T5blNFmW7wPhQP4tWHNwAb6RJOkscASoANQpwDYKsFVeOwh4W5KkSkA9WZZfek1u3OnzNDCrR526tdDX16dzFw8OBB1Wswnef5juPToC0LKVOalPnpKScq9Ivnn5888k1d3GSpUq0rKVOdeu3cxnt2b1FmwsvbCx9CIw8CA9enTKuX7qU9XywdxERR6nYyc3AD7v2Zn9QYoO+/6gQxr979xJpGVrcypWrACAXTtLrl79DYCgwIMqnWYN66FfrlyhA3KAM3HnaWBWlzp1TdHX16djZ3dC9oep2YTsD6NbD8WMfIuWTXma+pS7Gr5PbuwdrRk2qj9fdP+atLTnherIy5uIJ0DNdxRLOWvVMsarg2uxl2CfjbtAfbM61K6jiF+Hzu4cDA5XszkYHE7X7orOafOWTUhN/Yu7KYrKfvHy2dy4dpPVqzaq7K9cuk7T922xaOqCRVMXkhJTcLXrWuQBOcDqXzarNmELDDhIj56dAWiV/f2T88cvMvI4nZTx69mrC0FBRZsp69bNi927/Qs3zMPPv2ykdZv2tG7TngD/EHr17AJA69bNePLkqcZOSEREDJ07ewDQu1dXAgIUnSezBvVUNubmjdDXL8eDB4+YNu07zBq25oMPLOn9xVCOHIku1oAcstO4LrWVZaRDFzdC8qRxSHAY3borykjzlk2UZUSRXj+smMP1azf5ZeVGNR97R2uGjexPnx5DX6qMlHVW/7KZthbutLVwJyDgIJ+r8mAzUlOfkqwxDx6jUyd3QJEHA4MK3zjwHVUZNsHbuz27dxUvL75q+hZEjZrVAcXMzajxg9m0fpdW+5Jq2w7sD6N7T0X9171nJ4KVxytWrEClShUBxZ4QGRmZXL16g/Vrt/HJ+9aYN7LHzaU7v924rXVADlBT+V1Naxnj6e3Czh17+eP3P+nQsb3K5pNGH5KVlaXauG3+vKWkpNzjr6d/07KVuUJfj46qejv3c6RuHo5cu/abVg3ZrFm9BVtLb2wtvdkfGEr3IrUdJ+jQSaG1R89OBCs1BAcdLtB/+ar5XLt6g1Ur1qmda39gKLZ2ip25zRrWo1w5/QLb4rJeB77OMlyrlgnbtv9M/69Gc+NG/meii8uZ0+dp0CAnz3fq4sGB/erl5UBwGJ8q069Fq6akpv5FSso9rb7BgYewyZ1++vo8ePCIKgaV2b77V+bMXMzJE3GF6nvTfdXDh6P45JMPVHsOWVq34kohm6rBm+krzJyxiA/es+STj2zo88VwIiJiVAPy3BrsrLwJCjykMSZ5ORp5QlW/dP88p79X3JhevnSNDxpYYN7IHvNG9iQmJNPOpuN/ekD+/0JpzpTn3oUgK9f7LBS6NK3JlfP8z3tcExLQpYiD6ligJXATCAVqAgPInq58STIzM5kwbhZ79q5DV0eXrZv3cOXKDfoo7zZvWLed0JAjOLvYcfrcYdLS0hj29TdafQE8vJz5btF0atSszo49v3Ih/jJdO/Vj7eotrPhpATEn9yNJEtu2+HDpovavfzDkiGL2MD6MZ2nPGTo45+fJdvusZfjQSSQn32XGtIWs27CUqdPGEB9/kU3K3WUL8j996hz79h4gMtqfjIxM4s9dZMO6HQBs3rSHlT8t4NjJYNJfvODrQUWbVcjMzGTSuDns8F2Lrq4O27f4cPXKDb7op5il3rRuJ4cORuDoYsuJswdJe/ackUMnq/x/XrsYS+tWVK9RjTOXjrBo/nK2bfZh/vfTKFeuHLv2rlNpnzB6ZpE0val4AmzeupLq1auSnp7BuDEzVRvRFJXMzEymTpjHNp/V6OjqsHOrH9eu/EbvvoqZms3rd3H4YCQOzrZExwWTlvacMUOnAtDKojldu3fg0sWrHIxUbPCzYM4SwkJf77PFIQfCcXW1J/7CEdKepTF4cM5P9Pj4rWPokG9ITrrLtKmKxx6mzRhL/LlLbNygGES8a1iTqKP+VK78NllZMkOH9aVlcxeePv2LihUrYO9gzYjhU15JY/CBMNq3d+DypaM8e5bGgIFjVZ/t27uRwV9PICkphSlT57N500pmzRzP2bMXWL9Bkf87dnKjV88upKdnkJb2nF69h7ySntxkZmYyefw8tvv8iq6uDju2+HHtyg2+6KssI+t3cvhgJI7Othw7c4C0Z88ZPVQRj9YWzemmTOPslQ/zZy8hLDSSeYumUq6cPjv2Knb3jYs9x8QxmneHfp2Mn7GA2DPxPH6cimPHXgz5qjddvIq3OVpxyc6D5y9EkPYsjUGDc+onX7/1DBkyUZUHN25azvQZYzl37qIqDxoavpMnD/ajRXNnnj79i63bfqJ69WpkpGcwZvS0lyrDL5u+AKvWLMLSujXVa1Tl9MUwvl+wgu2bfenU1Z0+/RXbpuwPCGXHFt9CdZRE27bkh19Yt3EpvXp3488/E+n7xQhAcUNyz951yFkyiYnJDB6Qb8/XIrNx6wqqV69Geno6E8bM4snjVAZ+NZbFP85i7IQh6Ovr47snSOMzm+NGz2Dlz99RoUIFDoVGcOhgBAADv/4CN3dHMjIyePToiVq9XVQOhhzB2bUdcfFhpKWlqZ1jl88aRgydTHLyXWZOW8jaDUuYMm0M8fGXVDu9F+Rv0bYF3T/vxMULV4iMUdwEmjNzMaEHI9iyaY+qz/DiRXqR2+KyXAfCq5fhSZNHUL16NdWO3hkZGdhYK25Wb9iwDBtbC2rUqMa168eYO/dH1eqXgsjMzOSb8bPZ7bcWHV1dtm3ew9UrN+jTTzHru2HdDkJDjuDkYkfsuUOkPUtjxJBJWn0Btm72Ydmqb4k6Hkj6i3SGKdO8/8Be1G9Qh7EThjJ2wlAAunXsq9oITpO+N9lXffI4lVUr1nE4whdZlgk9GEFoyJFipHDJ9hWKSkExAdi551dGDpuiKLPTF7Fm/Y9Mnjaa8/GXVCt1Xiam2jh7IZzKld9Gv5x+9ubBHwOXivyFyiil+az3m0J61V2SX+qiklQPCJRluZHy/Qbl+z3ZnwFhwD1ZludIktQO+FGW5WaSJM0EOpJr+brydbnscyrtx8my7ClJ0rdAFWC4LMuyJEnNZFk+o0XbEaA20BjwAr4HvpdleakkSS2AH2RZtivsO+Zevl4WySzNPf+LQAXdgncILQs8z9T+rGVp87Z+hdKWoJUn/xT+s0WlTXpW8Z7Vf9NUr/BmdyMvLn/ceO0/gPFaqVrHoXCjUqRKuUqlLaFQ/inj9WBZpzT6X8XhWUbZ3sFZT0e3tCVopaJe8X9z+02TVcbz4IvMst0Ol9MtzbnNovHw6fX/xGj29+ZObyyz1o07VCoxK8u7r88EWkqSFI9ig7XcPwh5EsXy8uPAHFmWtf0o8hxAH4hXbgQ3p5DrRgEpsiw/U76upfwPEA9kSJJ0TtNGbwKBQCAQCAQCgUAgEBSHUpkp/39AzJS/GmKm/NUQM+WvjpgpfzXETPmrIWbK//uU9f6XmCl/NcRM+asjZspfnf/KTPltc+c3llnrnQ0VM+UCgUAgEAgEAoFAIBD8P1H2b/GUAJIkuQLf5Tl8S5blTqWhRyAQCAQCgUAgEAgE+SnjizpeC/+Xg3JZlkOAkNLWIRAIBAKBQCAQCASC/2/+LwflAoFAIBAIBAKBQCAo+/w//CSaeKZcIBAIBAKBQCAQCASCUkLMlAsEAoFAIBAIBAKBoEwiy//9mXIxKC8hnqWX7Z8SMXyramlL0EpqGf/JrLL+Mx1P5bTSlqAVHUkq8zGUKNsNwPOMF6UtQStl/SfHHv8RVtoStGJY37W0JRRKWf9pzbL+05Bl/Sfl3irj8SvrPzmWkZVZ2hIK5XkZ/9k7g/Jl+6chn74o230twb8LMSgXCARvnLI+IBcIBAKBQCAQlA3K+D3g14J4plwgEAgEAoFAIBAIBIJSQsyUCwQCgUAgEAgEAoGgTJL1f/BMuZgpFwgEAoFAIBAIBAKBoJQQM+UCgUAgEAgEAoFAICiT/D/svi5mygUCgUAgEAgEAoFAICglxEy5QCAQCAQCgUAgEAjKJHKWmCkXCAQCgUAgEAgEAoFAUEKIQblAIBAIBAKBQCAQCASlhBiUlxEWL57FxYuRxMaGYG7eSKNNvXq1iYzcx4ULEWzevBJ9fX0APD2diY0N4cSJYKKjA7G0bPXKeuwcrAg74U9EbCBfj+yn0Wbm/IlExAZyIHIPjZp8BED58uXYF7qV4IjdhEb7MnriEDWfPgN6EHbCn9BoXybNGF1sXQsWTeP0ucMcPR5Ik6afaLSpU7cWoeF7OHX2EGs3LlXFSZv/uYtHiD4RRGSMP2GRfqrjazcuJTLGn8gYf85dPEJkjH+RtS5ePJMLFyI4efJAgWlat25tIiP3cv78ETZvXqHS2r17R06ePMDJkwcID/elceOPVD5XrhwlNjaE48f3c/RoQJH15GXhoumcjQ8j5sR+mpprjmXdurUIO+LLmXNhrN+4TKXvvfcbcChsD/ceXmb4yP4q+/LlyxEe4Uf08SBOxB5g8pRRL60vL2WtjLyqpu7dOxIbG0JsbEi+NB4+/Cvi4g5x+nQomzYtp3z58kXWVBJlpHz5chw64kPUsQBiYoP5ZspIlf3EySO4eO2oqpw4u9gVWeui72cQf/4IJ04EY64lDx6J2Mu5+HA2bsopI++/b0ZYuC8PH11l5MgBaj5DhvRVxPbUQYYO1Vx/vU6mfvsDth7d6dhrcIlfa/7CaZw6e4ioYwE0afqxRps6dWsRGraH2DOhrN2wRC19C/Jfvmo+V28eJ/pEkNq5Zs2dyPHTB4g6FsCmbSupYlBZq77vFk3nzLkwoo8H0bSA/Fe3bi0Oh/sQd/awWr2izd/AoDKbtqwgNu4gJ0+H0Kp1MwAaNfqQ0MO7iTmxnx27VlO58tta9eWmnaM1kScDOXo6mKGj+mu0mb1gEkdPBxN61FfVzpmYGrHbfz1HjvsTFrOPrwb1yuc3aFgfEh5dpFr1qkXWA+DgZMPx0wc4eTaUEaMHarT5duFUTp4NJSLGXy0NC/MdOrwf91OvUb16NQD09fVZtmo+kccCOBLtj5V16wJ1lVS+c3Sy4URcCKfOHmLkmBzNk6eOIupYABHR/vjsXY+R0bsAVKtelX1Bm/kj6SxzF04pUC+UXPr2HfA5kScDCYvZx5RZY7Vq0Ia9ozXRp4I5fiaE4aMHaLSZ990Ujp8JITx6H41zxW3JinlcvBFNxDH1PsnEKSMIj97H4Sg/dvqtxVAZt5dl0fczOHc+nOMngrX2E8Ij/DgbH8bGTctz1dENOBzuw4NHVxiRp46+eDmKEyeDiTkeROTRfS+tryyWYW2URL/r/wVZfnN/pYUYlJcBXF3tadiwHp98YsvQod+wbNk8jXZz505i+fI1NGpkx+PHT+jT5zMAwsOjadXKlTZt3Bg0aBw//fTdK+nR0dFhzsLJfPnp1zhZdsS7sxvvfdBAzcbeyZr6Depi18qTSWNmM/f7qQD8888LenTsj5tdN9zsPsXO0YpmLZsA0Na6Fc5u9rS36YKzVWdWr9xYLF3OLnaYmdWjRVNHRg2fyuIlszTazZwzgZ9WrqeluRNPHj+h95fdiuTv5d4LW0tvHGw7qY599eVIbC29sbX0xn9fCAH+B4uk1dXVHjOz+jRqZMewYZNYtmyuRrt5875h+fK1NG7cjkePctL09u07uLh8SuvW7Zk/fxkrV85X82vfvjsWFu5YW3sVSU9eXFzbYdawHuZNHBg5bDI/Lpmj0W7WnImsXLGOZk0dePw4lS++/BSAR4+eMGHcbJYtXaNm/88/L/B074mVhQdWbT1xcralVSvzl9KYm7JWRl6Hptu37+Ds/CmtWrkq03gBACYmhgwd2hdLSw9atHBGR0eXTz8tWjqXVBn5558XdPDojU1bL2zbeuHoZEPLXOn604r1qnISejCiiPFrR8OG9WnSuB3Dhk1myVLN8Zsz9xtWLF9L0yb2PH78hC+V8Xv06DHjxs1k6dJf1ew//vh9+vbtjq1tByzauOHm5oCZWb0iaXpZOro78/MPmsv468TJxQ4zs7q0NHdi9IhpLP5xtka7mbPH89PK9bRq5szjx6n0+qJbof7btvrSrVP+GxhHwqKxau2BTVsvfrtxm9FjC77x4OzSDjOzejRr6sDI4VP4YYlmfbPmTGDVyvU0N3fk8eMnfKHKfwX7L1g4nUOhkbRq7oKVhSfXrt4AYPnK+cycsQjLNu4EBhxkxCjNg5u86OjoMG/RFHp1G4y9hTcdu7jz3gdmajYOzjbUN6uLdQs3Jo6ayfzF0wHIyMhg1tSFtLPwxsulB33691DzNTE1wradJX/eSSySltyavls8g8+6DMCqlTudu3ryfh5NTi52NDCrR2tzZ8aMnMaiH2cVydfE1Ag7Byvu/JGgOta7j6I+t23rRdcOfZg97xskKf9zmyWV73R0dFi4eCafdu5P21ZudOnqyQcfNARg+dI12LT1ws7Km5AD4Yz/ZhgA/zz/h2/nLmH6FO11eEmlr6V1a1zdHXCy7oSDZQd+Xr5eqw5t+hYsns7nXQdg09qTTl088qW1o7Mt9c3qYtHMlXEjp7Pwhxmqz3Zs86N7l/x5feWytdhbdcDRphOhB44wNs/ESHHI7ic0bWzP8GGTWLJUcx03Z+43rFy+FvMmDso6OqefMH7crHz9hGzc3T7H0sIDW+sOL6WvLJZhbZRUv0vw30EMynMhSZJuaVzXy8uFrVt9ADh58gxVq1ZR3RXOTbt2lvj67gdgy5Y9eHu7AvD3389UNm+9VQn5FW/zmDdvxO1bf3Dn9wTS0zMI8DuAs5u9mo2zmz0+OxWztGdOxVPFoDLvGtYE4NnfaQDo6euhr6en0tOr76esWrqWFy/SAXhw/2GxdLl7OrFju2IW+1TsWQwMqmBo+E4+O1s7C/b5HQBg+1Y/3D2di+VfEJ06u+Ozu2gz056ezmzblpOmBgaa09TOLidNt271wcvLBYDjx0/z+HGq0j8OU1PjIussCu4eTmzfpohFbHYsjPLHws6uLXv9ggHYvtUHTy9FLO/fe0BcXDwZ6Rn5fLLzo76+Hnr6eq+cH6HslZHXoUmRxk9U/rnTWE9Pj4oVK6Crq0ulShVJSkopkqaSLCO501VfX/+VY+jh6cK2rb4AxMaewcCgMkYa86Alfn7KMrLFBy9PRRm5d+8BcafjSc+TBz/4oCEnY8+QlvaczMxMoo6eUMW8pGhp3hiDKtpnkF8H7h5O7Ni+F1CkT5WqlTWmr42dBfv2KtJ3xzZfPDydCvU/Fh3Lo0dP8p0rPOwomZmZKh8TE6MC9Xl4OrG9SPkvp17ZttUXD2X+K8i/cuW3sbJqxaaNuwBIT0/nyZOnADR8rz7RR08qtUbj3aFoad2sRWNu37zDH7//SXp6Ovt89+Pqrt7Oubo7sGeHYiYy7lQ8Bsp27m7KfS7EXwbg77+ecf3aTYyMc8r+zHkTmTdzcbHLSPOWTbh183d+v32H9PR0/HyCcPNwUrNxc3dklzJGp2PPYWCgSMPCfOfOn8ysaYvUNH3wYUOiIo4BcP/+Q548eYp588b5dJVUvmuRR7OvTxBuno4APH36l+q8ld6qqNL97FkaJ46d5p9//tEay5JK3y/6fcbKJWteuh+TTfMWTbh18w9+v63Qt9d3P+09HNVs2ns4snu7Yhb59KlzVDGowrvKuB+POcVjDeX1r6d/q17njtvL4OnpzHZVHa29n+CnLM9bt/jgma+OTn9pDdooi2VYGyXZ7/p/QM6S3thfafGvGZRLklRPkqTLkiT9KknSRUmSDkqSVLEA24aSJB2SJOmcJElxkiSZSQoWSZJ0QZKk85Ikfaa0bSdJUrgkSduA85Ik6SrtYiVJipckaZDSzliSpEhJks4qz2Hzur6biYkRf/6ZpHqfkJCcr+NTo0Y1njxJVXWOEhKS1Gy8vV05dy4MP78NDBo0/pX0GBkbkpSQMwhISkxRq6wUNu+SmJCsep+cmIKh0kZHR4f9R3YRd+UIURHHOHv6PAD1zerS2qIFew9uZaf/Opo007x0pyCMjQ1JyBWnxMRkjE0M1Wyq16jGk8dPVXFKTEjGRGmjzV+WZXz3bSA8ai9f9v0s37UtrVpx9+59bv72e5G0KtI05w5rQi4d2RSWptn06dOdkJAjqveyDAEBW4iODqRfvx5F0qNZX648l5iMibH6tavn05c/3prQ0dHh6LFAfrsdS3hYNKdOnXspjVr1lnIZeV2asunT5zMOHgwHIDExhR9/XM3168e5ffsUqampHDoUVSRNJVlGdHR0iIzx59qtExwJO8rpXOk6YFBvjh4PZPmq+RhUrVIkrSYmhmplJDEhGeMixU97Hrx06SpWVq2pXr0qFStWwNXVHtNar/emVmlhbGJIQkKu9NFQJjWlb7ZNUfy10bN3Vw6FFrwSQpF/cqVpYv4yoVmfkVb/evVqc//+Q1b9vJCoaH+Wr/iWSpUUzf/lS9dxVw4+O3ZyK/INTCNjQxJzxULRzhnmsVFv5zTZ1KptQqMmH3HmdDyguGGdlJTCpQtXi6QjN8bGhiT+mXM9TeXX2MSQhNw2CSkYmxhq9W3v5kBSUgoXL1xRO9fF81do7+6Irq4uderWoqn5J5ia5q+fSirfGRsb5T+eK75Tpo/m/OVIun3qzfx5SwsKm0ZKKn0bNKxH67YtCAjdzp7ADTRtpvmxpUL1majrS0xIzndtY2P1uCVpyA+amDRtFHEXw+nSzZOF85a9lD5QpFvuNi5RQ/tVo0Y1HufpJxRWR4Oiz7UvYBNR0f70fcl+TFksw9ooyX6X4L/Bv2ZQruQ9YKUsy58Aj4EuBdhtVdo1BSyBJKAzYA40BZyARZIkZbferYEpsix/DHwFPJFluRXQChggSVJ94HMgRJbl7HOcfV1fSsNqsXx35zQtKctt4+8fQtOmDnz6aX9mzBj3ioJeTU9WVhbu7T7ForEz5s0a8f6HiuVoenp6GFStTEeXnnw78wdWrf2+eLIKiUFhNto+a+/0Ge2sO9Ctcz/6D+yFpZX6M8ddunniszvwjWnNxta2LV9++RlTp+YsX3dw6IylpQcdO37JoEFfYGVV8HOAr19f4efOysrCuq0nH71vSYsWTfjo4/eLrS8vZa6MvCZNoLgr3qfPZ0yZokjjqlUN8PJy5sMPrahfvxWVKlWiR49O+c6jWVPJlZGsrCxsLb355ANrmrdsykcfvwfAujVbadbYAZu2XqSk3GPut5PeiNaCuHr1N3744WcCArewd99Gzp+/TGZGZpE0lXVKMn0LY8y4r8nIyGD3zoL31SgpfXp6ejQ1/4S1a7ZiY+XN38/SVMvohw6ZyICBvYiI2sfbld8i/UXRZuVeR/mt9FYlft20hBmTFvDX07+pULECI8YM5Pv5K4qkIb+ml49fQccrVqzA6PFfs0DDoHbr5j0kJSZzKMKXeQsmc/LkGY1lpaTStbA0mDf7Rxp/ZMvuXf4MGJj/mV9tlET6Aujq6WJQtQpezj2YO30xP69fXCxd2vTla2CL8B00MX/OEpp/Yo/P7kD6FTNuapcvoToawMmxK9aWXnTu2JeBA3u/ZD8m/7HSLsPa9ZZcv+v/gSxZemN/pcW/bVB+S5bls8rXp4F6eQ0kSaoMmMqy7Acgy/JzWZafAdbAdlmWM2VZTgEiUAy6AU7KsnxL+doF+EKSpLPACaAGipsBsUBfSZJmAo1lWX6q4doDJUk6JUnSqczMv/J+rMagQV9w4kQwJ04Ek5R0l1q5ZnJMTY3yLVe9f/8hBgZV0NXVVdoYa1zSevToSRo0qEONGtW0Xl8byYkpGJvm3JkzNjEkJfmemk1SYgomue6oG5kYcjePTWrqU45Fn6Kdo5XK50DgYQDOxV0gKyuL6oXo7D+wl2oDqaSku2ozXiYmRiQn3VWzf3D/IQZVK6viZGJqRJLSJjExuUD/5GTF//v3HhIYEErzFk1Udrq6unh6u+Lno74BUl4GDfqC48f3c/z4fpKSUqhVy0T1mWkuHdkUlqaNGn3ITz99R7du/Xn48LHqePZ57t17gL9/SJGf2R4wsDdHjwVy9FigUl+uPGdiRFKyen56kE+fEclFXEYN8OTJU45GncDJ2bbIPrkpi2XkdWtSpPFCunbNSWMHB2tu377D/fsPycjIYN++A1hYtChQ05sqI9mkKtPV0UmRrvfuPiArKwtZltm4fictWjYtUOvAQb05dnw/xzSUERMN+Utz/NT1aGLTxl1YWXri6vIZjx495sZvtwr1Kat8NaAnEdH+RET7k5yUojYTrIhZ4embbZOYkFyovya6f94JVzd7Bn2Vf1Or/gN7ERUTQFRMAMlJdzHNnaYm+cuEZn0KG0X+y++fkJBEQkKyanXGvr3Bqk3grl+7SacOfbCz6cCe3QHcuvVHod8HstuwnFgo2rm7GmyMNNro6enx68Yl+O0OIjjwEAD16temTl1TQqN8OX7uIMYmhoRE7OGdd2sWSVNiYjImtXKup6n8JSYkY5rbxtSQ5KS7BfrWq1+HOnVrERHtT9z5MExMjQiL8uPdd2uSmZnJ1EnzsbfuQO8eQzAwqMxvv90GoN8byHeJiRqOJ+fPj3t2BeBVxMcSsimJ9AVISkghOEDx/mzc+SL1YzTqS1DXp+m7JyWqx91YQ37Qhu/uQDy9nYula+Cg3sQcDyLmeFC+Ns5EQ5t6//5DqubpJxSljk7O1Y8JCAjR2m4URFksw3l50/0uwb+bf9ugPPdDRJmAngabgm5xaLv18Xeu1xIwXJZlc+VffVmWD8qyHAnYAgnAZkmSvsh7ElmWV8uy3FKW5Za6utp3gP3ll020aeNGmzZu+PuH0LOnYtK/detmPHnyVGPDFBFxjM6d3QHo1asrAQGKTccaNKirsjE3b4S+fjkePHik9fraOHfmIvUb1KV2HVP09fXw6tSe0OAjajaHDhyhy2eKzaeatWzC09Sn3E25T/Ua1aiifLayfIXyWNtZcOO6okN8cH8YljaKu6H1zeqiX06fh4XoXLN6i2oDqf2BoXRXzhi2bGVOaupTUlLu5fOJijxBh07tAejRsxPBQYrKNjjosEb/SpUq8vbbbwFQqVJFHBysuXzpuup87eytuH7tJomJyWjjl182YWHhjoWFOwEBB/n885w0TU3VnKaRkTlp2rNnFwIDQwGoXduEHTt+4auvRnPjRs6AIq9WJydbLl4s2hKrX1dvxrqtJ9ZtPQkKCKXH54pYtMqORXL+WEZGHqdjJzcAevTsQlCuzokmatSsjoFyd+YKFcorYnf1ZpH05aUslpHXqal2bRN27lxNv36j1NL4zp0EWrduTsWKFQCwt7fiypUbBWp6E2WkRs3qql23FelqyfVrinTN/Wypp5cLly9dK1Dr6l8209bCnbbZZaRnZwBatcouI5ry4DE6dVKWkV5dCAwqfLPFd96pAUCtWiZ4e7dn966i/2pCWWPtr1uxs/LGzsqboMBDdO/REVCmzxPN6Xs08gQdOirSt/vnndmfnb77DxfJPzeOTjaMHD2Qzz8bTFra83yfr1m9BRtLL2wsvQgMPKha1aE9/+XUK5/3zNG3P+iQRv+7d++TkJBEw/fqA2DXzpKryjJRU5nWkiQxfsIw1q3dpvX7ZHM27gL1zeoo2zl9OnR252BwuJrNweBwunb3BhTPe6em/sXdlPsALF4+mxvXbrJ6Vc6GpVcuXafp+7ZYNHXBoqkLSYkpuNp15d7d+0XSdOb0eRo0qEedurXQ19enUxcPDuw/rGZzIDiMT5UxatGqKampf5GScq9A38uXrvGRWVuaN3ageWMHEhOScbDpxN2796lYsYLqMQA7e0syMzK5dvU3ANa9gXwXd/o8DcxyNHfu4sGBIMX3bWCWU2e7uTuq6puiUhLpCxCy/zBWtm1UGssVoR+jiTNx52lgVpc6dRX6OnZ2J2R/WJ5rhdGth2ITtBYtmyr7WdrLa/1cbZ2rmwPXrxfvhuTqXzZjaeGBpYUHgQEH6aGqo7X3Ezopy3PPXl0ICgrVeo18fS5HGy5dKv5S8bJYhvPyJvpd/y/IsvTG/koL6XVuYlCSSJJUDwiUZbmR8v044G1ZlmdqsD0OLJBlea8kSeUBXaA9MAhwB6oDp4A2wIfAOFmWPZW+A5U23WRZTpck6X0UA/GaQIIsyxmSJI0C6smyPKogvRUq1ClWYJcsmYOLSzuePUtj4MBxxMUpnm3Zu3cDX389kaSkFOrXr8OmTSuoXr0qZ89epG/fkbx48YKxY7+mZ88upKenk5b2nMmTvyUmJlbr9Qzfqqr1c3sna6bPm4Curi67tu1lxQ+/0rOPYifVrRt2AzBn4WTsHKxIS3vOuOHTOH/2Eh9+/B4/rJyLjq4uOjo6BO4NYdn3vwCKDaIWLZ/Nx40+JP1FOvNmLCYm6qTG66f+80zj8UU/zMTRyZa0tDSGDp7I2TMXANjls4YRQyeTnHyXuvVqs3bDEqpVq0p8/CUGfTWWFy9eFOhft15ttmxfBYCunh4+u/xZvOgn1TVX/vwdp2LPsn7tdtWx55mFL5H88cc5uLjY8exZGoMGjSMuTvFsvZ/fBoYMmUBS0l3q1avN5s0rqFatKufOXaRv31G8ePGCVau+o2NHN/74408AMjIysbb2ol692uzcuRpQ3OXduXMfCxfmX2alr1P4noWLf5iFk7Mtz9KeM2TQBM6cUejb47uOYUO+ITlZoW/9xmVUq2bAuXOXGPDVGF68eMG7hjWJiNpH5cpvk5Ul8/fff9O6hSt16tbi59WL0NXVRUdHws9nP98tWJ7v2i8yi79RyZsuIyWt6aefvqNjR3e1NLay8gRg2rQxdO3qSUZGJufOXWTw4AmqPJxNRb1yGjWVRBn55JMPWLV6Ebq6Oujo6ODnu59FCxT57udfv6dxk4+QZZk/fk9g9IippKTc40VW4Wn8w4+zcXa2I+1ZGoMGj+eMsoz4+q1nyJCJipm+erXZuGm5qox81W80L168wNDwHaKO+qvlwRbNnXn69C8Ohu6ievVqZKRn8M03czhyJCbftR//EZbv2MsyfsYCYs/E8/hxKjWqV2XIV73p4vVqm8sZ1tfsv3DxDFX6DPv6G1X67tzzKyOHTVGl75r1P1KtWlXOx19iUP9xqvQtyP/XdT9iZdOaGjWqce/uAxZ8u5QtmxQ/m1e+fDnVSo5TsWcZO0qxg3GmnJVP3/c/zMTJSVGvDB08UVWv7PZZy/Chk1T1yroNS5X57yIDcuW/gvwbN/6I5Svno19On9u37jD06wk8fpzK4CF9GDBAsUQ3wD+EmTMWqbS8rV9Ba4wdnG2Y9e036OjqsHOrH8sWr6Z3X8VOx5vXKzaVm7doKu0cFe3cmKFTiT97kVYWzdkbvJlLF68iZyma+gVzlhAWqr73w/FzB3Gz/5RHuVY65eYfDe2Ik4sd8xZMRkdXl22b9/Dj9z/Tp193ADas2wHAd4tn4OBkQ9qzNEYMmaRKQ02+eYk7H4aTXRcePnxE7Tqm7PZbS1aWTFJiCiOHTVbbbTorV/+wpPKdk4sd3343BV0dXbZu3sMP3yva3o1bVtDwvfpkZWVx504iY0dOV83Snr0QTuXKb1OuXDlSn6TSo8tAritvJpR0+urr67N4xRw+aazox8yZ9j3RUSc0pm9GlvbHZhydbZmzYDK6ujps3+LDku9/4Yt+ij1tNq3bCcD876cp0/o5I4dO5pwybj+vXYyldSuqK8vrovnL2bbZh7Wbl9GwYT2ysmT+vJPI+NEztM6u/52ufcO8H36cjZOzLWnP0hg8eIKqjvbxW8fQId+o6ugNm5ZTrZoB8ecuqerodw1r5qujWzZ3oUaNamzfoegX6unpsmuXP4sWrtR4fYPylbTqK+0y/PRFmlZ9eSmJflfuTRE1kfr3zdIbZb5Gztf3emMD1sa3AkolZv/VQfl7wC8oBtLpQDfgFrAQcANkYK4syzslSWqH+qBcB5gLeKGYNb8HdFT+jVee7y/gi1xL3vNR3EH5m6awQXlpU9CgvKxQlEF5aVKUQXlp8jKDcoE6BQ3KywpFGZSXJq9zUF4SFDQoL0toGpSXJQoblJc2mgblZYmsMt4/LOt1YGGD8rJAYYPy0qawQXlpU9xBeWnwXxmUx9d7c4PyJrfFoPw/hRiUvxpiUP5qiEH5f5+y3iEVg/JXQwzKXx0xKH81xKD81RCD8ldHDMpfHTEoLz6lNSjX9Ey2QCAQCAQCgUAgEAgEpU5p7or+pvhXD8olSVoJWOU5vFSW5fWloUcgEAgEAoFAIBAIBILi8K8elMuyPLS0NQgEAoFAIBAIBAKBoGQozV3R3xT/tp9EEwgEAoFAIBAIBAKB4D/Dv3qmXCAQCAQCgUAgEAgE/13K+L6TrwUxUy4QCAQCgUAgEAgEAkEpIWbKBQKBQCAQCAQCgUBQJvl/2H1dzJQLBAKBQCAQCAQCgUBQSoiZ8hLi3UoGpS1BK4+e/1XaErSiK5Xt+0VlXd+drvVLW4JW6vn8XtoSCuVZxj+lLUErlfTLl7YErVSQy5W2BK0Y1nctbQlaSbkVUtoSCqVqHYfSlqCVfzLTS1uCVirole0y8jzjRWlL0MrDMt6PqVKuYmlLKJRyumV7GPBX+vPSlqCVsh4/wb8LkZsEAoFAIBAIBAKBQFAmET+JJhAIBAKBQCAQCAQCgaDEEDPlAoFAIBAIBAKBQCAok4iN3gQCgUAgEAgEAoFAIBCUGGKmXCAQCAQCgUAgEAgEZRK5tAW8AcRMuUAgEAgEAoFAIBAIBKWEmCkXCAQCgUAgEAgEAkGZRDxTLhAIBAKBQCAQCAQCgaDEEDPlAoFAIBAIBAKBQCAok4jfKReUCHaOVoSf8CfyVBBDRn6l0WbW/G+IPBVESJQPjZp8BED58uXwD93Ggcg9HIrxY8w3Q1T2Hh1cOBTjx+3752hi/vFr17xw0XTOxocRc2I/Tc0/0WhTt24two74cuZcGOs3LkNfXx+A995vwKGwPdx7eJnhI/u/ko4Fi6Zx+txhjh4PpElTzTrq1K1FaPgeTp09xNqNS1U6tPmfu3iE6BNBRMb4Exbppzo+e+5ETsSFcPR4IJu3r6KKQeUia130/QzOnQ/n+IlgrTELj/DjbHwYGzctV2l9//0GHA734cGjK4wYOUBl/957DYg5HqT6S0yOZ8jQvkXWpAm9xq14e8EG3l64ifIe3TXa6H7YlLdn/8Lb367lrUk/qH8o6fD27J+pNHreK+mAN5++AAMG9+Zk3EFiYoOZNWdCsfT+sHgWly5GcSr2IObmjTTa1KtXm6hIfy5eiGTL5lUqvV6eLpyKPcjJEweIiQ7C0rIVAOXLl+doVACxJ0M4E3eIadPGFEtTNu0crYk8GcjR08EMHaW53M1eMImjp4MJPeqrqmdMTI3Y7b+eI8f9CYvZx1eDeqn59B3wOZEnAwmL2ceUWWNfShuAvaM1UbFBxMQdYFgB+uZ8N5mYuAMcjvajcdOPVMd/WDGX89ejCI/Zp2b/caMPCDi4jbDovWzcsZK3K79VbF3zF07j1NlDRB0LoElTzXVpnbq1CA3bQ+yZUNZuWKKWBwvyX75qPldvHif6RJDauWbNncjx0weIOhbApm0ri1XHFJWp3/6ArUd3OvYa/NrPrY1F388g/vwRTpwIxlxLHXgkYi/n4sPZuGlFrjrQjLBwXx4+usrIXHUgwLBhXxF76iCxsSFs2LCM8uXLF0mPg5MNx08f4OTZUEaMHqjR5tuFUzl5NpSIGH+19CvMd+jwftxPvUb16tUAsLO35HCEL5HHAjgc4YuNrUWRNOamnaMVEScCOHpqP0ML6CvMnj+Jo6f2ExqVU4aNTY3YtW8d4cf9ORyzV60Mf/TJB+wL2cKho76s37ai2GXkTcZQX1+fZavmE3ksgCPR/lhZty6WVoDFi2dy4UIEJ08eKLCOrlu3NpGRezl//gibN+fkwe7dO3Ly5AFOnjxAeLgvjRt/pOano6PDsWP78fFZV2xdoKgDo08Fc/xMCMNHD9BoM++7KRw/E0J49D4a54rlkhXzuHgjmohj/mr2474ZxtnLERyO8uNwlB+OzrbF1vWm68DJU0cRdSyAiGh/fPaux8joXa36vls0nTPnwog+HkTTAvoJdevW4nC4D3FnD6v1R7X5Dxnal+OxwRw7Gcza9UsoX74cAOs3LiMqJoComADiL0YQFROgVV9JlJEJk4Zz/koU4Uf3EX50H04udmrnM61lzO3EMwwd3k+rNkHZRgzK3zA6OjrMXTiFLz8dgmPbDnh3ceO9Dxqo2dg72VDPrC62LT34ZvQs5i2eCsA//7yge8evaG/blfa23bBztKJZyyYAXL18nYFfjOZEzOnXrtnFtR1mDeth3sSBkcMm8+OSORrtZs2ZyMoV62jW1IHHj1P54stPAXj06AkTxs1m2dI1r6TD2cUOM7N6tGjqyKjhU1m8ZJZGu5lzJvDTyvW0NHfiyeMn9P6yW5H8vdx7YWvpjYNtJ9Wx8LBoLFu5Y23hyW/XbzFmbNE6tdkxa9rYnuHDJrFk6VyNdnPmfsPK5Wsxb+LA48dP+LJPTszGj5uVL2bXr9/E0sIDSwsPrC29SEt7ToD/wSJp0oikQ4UvRvD34kn8Nakf+hYO6JjUVbep9BYVvxjJ30um8dfkr3i2Yrbax+VcOpOZ+MfLa1BSGulrbWuBu4cT1haeWLZyY/myoufR9q72NGxYn48/sWHI0IksX/atRrt5cyexbPkaPmlky+PHj+nbR3HjIyz8KC1budC6TXsGDhrLzz8tBOCff/7Btf1ntGrtSqvW7XFxbkfr1s2KrAsU9cy8RVPo1W0w9hbedOziznsfmKnZODjbUN+sLtYt3Jg4aibzF08HICMjg1lTF9LOwhsvlx706d9D5Wtp3RpXdwecrDvhYNmBn5evL5au3Pq+/X4qPbsOwq6NFx27uvN+Pn22NGhQF8vm7Rk/cgYLFs9QfbZrmx+fd83f2Vm8bDbfzvoBB6uOBAceZsiI4nVQnFzsMDOrS0tzJ0aPmMbiH2drtJs5ezw/rVxPq2bOPH6cSq8vuhXqv22rL9065ddzJCwaq9Ye2LT14rcbtxldxDqmOHR0d+bnHzTXQSWFq2s7GjasT5PG7Rg2bDJLlmq+aTdn7jesWL6Wpk3slXXgZwA8evSYceNmsnTpr2r2xiaGfD2kDzbWXrRq5YqOrg7dunkVqkdHR4fvFs/gsy4DsGrlTueunvnynJOLHQ3M6tHa3JkxI6ex6MdZRfI1MTXCzsGKO38kqI49fPCInp8NxratF0MHT2TV6kVFC1wuvXMXTqX3p19j39abDl3c8/UVHJxsqG9WB+uW7kwcPZP5i6cBkJmRwexpi7C38Mbb5XO+/Kq7ynfR0lnMn7UEJ+vOHAg6zODhRb+p+6Zj2FvZHtq29aJrhz7MnvcNklT02TJXV3vMzOrTqJEdw4ZNYtkyzWVg3rxvWL58LY0bt+PRoyf0UebB27fv4OLyKa1bt2f+/GWsXDlfzW/YsH5cvXqjyHpyo6Ojw4LF0/m86wBsWnvSqYtHvlg6OttS36wuFs1cGTdyOgt/yKkDd2zzo3sXzQP5X1ZtxNGmE442nTgcGlksXaVRBy5fugabtl7YWXkTciCc8d8MK1Cfs0s7zMzq0aypAyOHT+GHJZr1zZozgVUr19Pc3JHHj5/whaqfoNnf2NiQwV9/STubjrRt7Yaurg5duirqlb5fjsDG0gsbSy/89x0gwD+kQH0lWUZ+Xrkee+sO2Ft34NDBCLVzzp0/udhp/W8j6w3+lRZiUP6GMW/RmNu3/uCP3/8kPT2DAN9gXNzs1Wxc3O3x2aG4+3nmVDxVqlTmXcOaADz7Ow0APX099PT0kGXFjwTcuHaLmzdul4hmdw8ntm9TzC7Gxp7FwKAKhkbv5LOzs2vLXr9gALZv9cHTyxmA+/ceEBcXT0Z6xqvp8HRix3aFjlPZOgzz67C1s2Cf3wGlDj/cPZ2L5Z+b8LCjZGZmAorvbmJqVCStnp7ObN/qq/LTFjM/Zcy2bvHB09MFgHv3HhB3Op709PQCr9HO3oqbN3/nzp2EAm0KQ7fBh2SlJCDfS4LMDNJPhKPf3FLNppyFI+mno5Af3gVAfvpY9ZlUrSb6TdvwImL/S2vIpjTSt1//z1my+BdevHgBwP17D4us18vLhS1bfQA4efIMVatW0XiHv107K3x9FTMDm7fswdvbFYC//36msnnrrUqqspz7M319PfT19dQ+KwrNWjTm9s07ynomnX2++3F1V69nXN0d2KOsZ+JOxWNgoKhn7qbc50L8ZYWOv55x/dpNjIwV3+uLfp+xcskaXrxQ5MsH94ser/z6/sjR5xOMq7uDmk17dwd279in0lfFIKcePB5zmkePnuQ7r1nD+hyLPgVAZHgMHl4uxdLl7uHEju17AUUeqlK1ssY8ZGNnwb69ijy4Y5svHp5Ohfofi47VqDl3HXMq9iwmJkWrY4pDS/PGGFR5/TPw2vDwdGGbqg48g4FBZYw01oGW+Pkp6o+tW3zwylcH5m839PR0qVixArq6ulSqVJGkpJRC9TRv2YRbN3/n99t3SE9Px88nCDcPJzUbN3dHdinrkNOx5zAwUKRfYb5z509m1rRFauX0fPxlkpMVdeaVy9cpX6Ec5crpU1Ty9hX2+Qbj4qZeRlzc7dXKcHZfQXMZNgTA7L16HI9RlpEjx3BXttNF4U3H8IMPGxIVcQyA+/cf8uTJU8ybNy6yXk9PZ7Zty6mjDQw019F2dpb4+irz4FYfvJT1xvHjp3n8OFXpH4epqbHKx9TUiPbtHVi/fkeR9eSmeYsm3Lr5B7/fVtSBe333097DUc2mvYcju7cr6sDTp85RxaAK7yrrk+Mxp3isoT55VUqjDnz69C/V60pvVdTa3nl4OrG9SP2EnP7otq2+eCj7Cdr8dfX0VPVKxYoVSdZQr3Tq7MGe3YEF6ivJMlIQbh5O/H77DlevvNwNIkHZQQzK8yBJUj1Jkq5IkrRRkqR4SZL2SJJUSZKkVpIkxUiSdE6SpJOSJL1UD8fI+F0SE5JV75MSUzBUNpa5bZJy2SQnpqg6xTo6OgRH7ObM1QiOHjnO2dPnX0ZGsTAxMeLPP5NU7xMSkzExVu84Vq9RjSdPUlWdy4SEZIxN1L/Xq2JsbEhCLh2JifmvUb1GNZ48fqrSkZiQjInSRpu/LMv47ttAeNRevuz7mcbr9+rdjUMHi3Yn0tjEUC1miQlJ+TrbNWpU43GemJkUI2Zdu3myZ7f2ZVSFIVWrifzwnup91sN7SNVqqtnoGNVCqlSZt75ZzNuzfkLfKqcTV7HnUNJ2rYZiDho1URrp27BhPdpatSI0fA+BB7bRrBgdPkW5SFS9TyggjdXLhbqNt3d74s+Fs9dvIwMHjVMd19HR4eSJA/x55yyHD0cRG3u2yLoAjIwNSUzIiUVSYoqqU55jk78uymtTq7YJjZp8xJnT8QA0aFiP1m1bEBC6nT2BG2jaTPNy0KLoS1C7drKqjtOmz9hYe/m4cvm6anDv1dG1yDfRsjE2MSQhIXe5LVoezLYpir82evbuyqHQiMIN/wWYmBiqlQ9FLIpSPrTHKykxhaVLfuXK1Rh+u3mS1CdPOXw4qlA9xsaGJP6Zk5801S/GJoYk5LZJSMHYxFCrb3s3B5KSUrh44UqB1/bq4Mr5c5dVN7OKgrGGfoBxvjJiWOwyfPXyDdVEgGcHl2LdBHrTMbx4/grt3R3R1dWlTt1aNDX/BNNilOn8dXT+NrawOjqbPn26ExJyRPV+0aIZTJnyLVlZLzevZmSiXkcnJiTnSztjY/X6JElDvDXRb0BPwqP3sWTFPAyqVimWrtKqA6dMH835y5F0+9Sb+fOWFqzP2JCE3PVKYnK+9NKsz0irf1JSCsuXreHC5Siu/XaM1NSnhIUdVTuvpVUr7t29z83fbmvVVxJlBOCrgb2IiPFn6cpvVelaqVJFRowewKIFKwrU9F9BRnpjf6WFGJRr5gNgtSzLTYBUYBiwExgpy3JTwAlIy+skSdJASZJOSZJ06q9/NM8gaVp6le+uoEYbxf+srCzc7LrRppETTZs34v2PGhbne70URdGs2aas6JAL/ay902e0s+5At8796D+wF5ZWrdTsxo7/mozMDHbt3JfvHCWhtTD09fXxcHfCz/cVZ6g11T15NejqolvvPf7+YQp/L5pIee9e6BjWQq+pBVmpj8i6ff3VNGRLKYX01dPTo2rVKjjbd2X6lAWs37TsjekF8Pc/QJOm9nT7tD8zZ+QMyrOysmjdpj0NzFrTspU5H3/8QZF1Ka6b/1hxtVV6qxK/blrCjEkL+Ovp3wDo6uliULUKXs49mDt9MT+vX1wsXVqvXRSbQsrHmGFT6du/ByFHdvPW22/xQstKkyLrek15sDDGjPuajIwMdu/0L9z4X0BJ1YFVq1bB09OZTz62oaFZGyq9VYnu3TuWqJ6CjlesWIHR479mgZZBxAcfNmT67PGMHTWtUI15xLy03mwqvVWR1Rt/ZObk71RleOzwaXzZvwf7w3by9ttvaV2NlV/Sm43h1s17SEpM5lCEL/MWTObkyTNkZmS+Eb25sbVty5dffsbUqYrl625uDty9+4AzZy4UWUt+bRoO5usLajLRXj42rt1OG3NnHKw7kpJyj1lzJxZTV+nUgfNm/0jjj2zZvcufAQN7FWhXUvqqVq2Ch4cTTRq144OGllSqVIlPP+ugZte1m1ehEyElUUYA1q/ZRsumTrSz6kBK8j1mz/sGgImTR/Dzyg1qK+8E/17E7uuauSPLcrTy9RZgCpAky3IsgCzLqZqcZFleDawGqFO9scZaKCkxRW32xtjEkLvKJW7ZJCemYJzLxsjEkJQ8NqmpTzkeHUs7RyuuXX79S1YGDOytmlGMOx1PrVq5lm2ZGJGUrL6s58H9hxgYVEFXV5fMzExMTY00Lv0pLv0H9uIL5XNlcafPY5pLh4mJEclJ6nF5cP8hBlUrq3SYmBqRpLRJTEwu0D97meH9ew8JDAileYsmxETHAtD98064tHego2dvrVoHDupNn76K54VP54mZialxviWW9+8/pGqemCXl+T4F4eLajrNnL3L37v0i2ReE/PA+UvWcpV861d9Bfvwgj809Mp4+gRfPkV88J/PqeXTqNEC33nvoN7NEv0kb0C+HVLESFQdNIu2X+XkvUyClnb4JCcmqZ/LjTseTlSVTo2b1ApdlDx70Jf369QDg1Olz1KplovrMtIA0Vi8X+W0Ajh49QYMGdalRoxoPHjxSHX/yJJXIyGO4urTj0qWr2kKphqKeyYmFsYY6RFNdlG2jp6fHrxuX4Lc7iODAQzk+CSkEByjen407T1ZWFtVrVONhLs1F05esNuNlbGJESlLh+pKTtZePG9dv0b2z4jnLBmZ1cXIpfJOjrwb05AvlM6Rn4uLVlqiamBYtD2bbJCYkF+qvie6fd8LVzZ6Onl8UaluWGTioN337KsrH6Tzlw0RDm6C5fGiPl729Nbd/v8N9ZRn133eANhYt2LFjr1a/xMRkTGrl5CdN9UtiQjKmuW1MDUlOuot+OX2NvvXq16FO3VpERPurvmNYlB8u9l25e/c+xiaGbNq2kqEDJ3D71h2t+vKSpKEfkJx8L49NstYyvHrjEvz2qJfh367fomcXxX4M9c3qFmsjsNKI4dRJOe3J/tAd/KZllhJg0KAv6KvWDueuo/O3sYXV0Y0afchPP31Hhw5f8vDhYwDatm2Jp6cT7du3o3z58lSpUpl165bQr98ordpyk5SgXkebmBrlq9+SElPU6hNjDfHOy717Oe33lo272bLzp0K1lIU6MJs9uwLYuedXFnybc4O8/8Beqr0mzpw+j2ktE0Cxf1L2LHfh+hQ2in5Cfv929lb8fvuOqu0P8A+hjUVz1USMrq4uXt6u2FmrD9TzUhJlBNTTdfPGXWzb9QsAzVs2xauDKzNmj8fAoApZchbP/3nB2tVbtOr8N5L1mif6yiJiplwzeZM+VcOxl+Jc3AXqN6hL7Tqm6Ovr4dXZjdADR9RsQoPD6dLdG4BmLZvwNPUv7qbcp3qNalRRPhdYvkJ5rO0s+O3ardchKx+/rt6MdVtPrNt6EhQQSo/PFZtjtWplTmrqU1LydA4AIiOP07GTGwA9enYhKFdH4GVZs3oLtpbe2Fp6sz8wlO49FDpaZutIya8jKvIEHTq1V+roRHCQQkdw0GGN/pUqVeTttxU70FaqVBEHB2suX1LM/jo62TJyzCA+/2wQaWnPtWpd/ctm1SZsgQEH6dGzM1B4zDopY9azVxeCgkKLFJdu3bzYvfvVZ9Qyb11B19AUqaYR6Oqh38ae9DMxajbpcTHovd8YdHSgXHl0zT4kK/EP/tm9lqeju/N0XE+e/TSXjMtnizUgh9JP3/2BodjaKXZFNmtYj3Ll9LU+J/3zLxtp3aY9rdu0J8A/hF49uwDQunUznjx5qnHQGBERQ+fOHgD07tWVgADFTQCzBvVUNubmjdDXL8eDB4+oWbM6BgaKpWkVKlTAwcGm2JsJnY27QH2zOsp6Rp8Ond05GByuZnMwOJyuynqmecsmpCrrGYDFy2dz49pNVq/aqOYTsv8wVrZtAMWgt1w5/WIPyHP01aV2XaW+Lm6E5NEXEhxGt+4dVPqepj5V6SuIGjWrA4qZiFHjB7Np/a5Ctaz9dSt2Vt7YWXkTFHiI7j06Aso89ERzHjwaeYIOHRV5sPvnndmfnQf3Hy6Sf24cnWwYOXogn382uNA6pqyz+pfNtLVwp62FOwEBB/lcVQc2IzX1ab5BJUBk5DE6dXIHFHVgYJD2jSvv/JlIq1bNqFixAqDYs6Eoz1KeOX2eBg3qUaduLfT19enUxYMD+w+r2RwIDuNTZR3SolVTUlP/IiXlXoG+ly9d4yOztjRv7EDzxg4kJiTjYNOJu3fvU8WgMtt3/8qcmYs5eSKu8ODlQdFXqKPqK3To7Ebogbxl+IhaGX6aqwx/v0xRhn9dtUnNJ3cZGTl2EJs3FF5GsnnTMaxYsQKVKlUEFLvZZ2Zkcu3qb1o1/vLLJiws3LHIzoOf59TRijyYv46OjDxG587KPNizC4GBina4dm0Tduz4ha++Gs2NGzl9renTF9KwoQUffmjNF18M58iRmGINyAHOxJ2ngVld6ijrwI6d3QnZH6ZmE7I/jG49FHVgi5ZNlXWg9vrk3VzPV7t7OnHlcuEr2Uq7DmxglrO5rJu7I9ev3VT7fM3qLaqN1gIDD9KjSP2EnP7o5z1z9O0POqTR/86dRFq2NlfVK3btLLmaK6+1s7fi2rXfSExMRhslUUYAtefmPbycVenq1f5zVdn55aeNLPn+5//kgPz/BTFTrpk6kiS1lWX5GNADOA4MkiSplSzLscrnydNkWS72zmWZmZlMm/Atm/f8jK6uLju3+nHtym/06qPYGXLLht2EhUZh72xL1On9pKU9Z9wwxe7r7xq+ww+r5qKrq4uOjkTg3oMcVj7j7OrhwOzvJlO9RjXW71jFpQtX6N319eziGxISjotrO86dD+dZ2nOGDMr52ag9vusYNuQbkpPvMmPad6zfuIxp08dw7twlNm3cpdRdk4iofVSu/DZZWTJDhvaldQtXtc09isLBkCM4u7YjLj6MtLQ0hg7OWZa1y2cNI4ZOJjn5LjOnLWTthiVMmTaG+PhLbN64W6v/O+/WZMv2VYBiow+fXf4cPqSI68LFMyhfvhx+/hsAxcYgY0ZOLzxmB8JxdbUn/sIR0p6lMXhwTsx8/NYxdMg3JCfdZdrUBWzYtJxpM8YSf+4SGzfkxCzqqL8qZkOH9aVlcxeePv2LihUrYO9gzYjhU4oVP41kZZG2eTlvjf8OdHRIjwwmK+F3ytl7AvAiPJCspD9IPx/L23PXgJzFi4j9ZCXcfvVr56E00nfLpj2s+GkBMSf38+JFOl8PGl9kvcEHwmjf3oHLl47y7FkaAwbm/DzYvr0bGfz1BJKSUpgydT6bN61k1szxnD17gfUbFBsDdezkRq+eXUhPzyAt7Tm9eit+4tDI6F3WrvlRWc512OMTwP7gwxo1FERmZiZTJ8xjm89qdHR1VPVM776KVQmb1+/i8MFIHJxtiY4LJi3tOWOGKuqZVhbN6dq9A5cuXuVgpGKTpAVzlhAWGsWOLX4sXjGHwzF7SX+RzqivXy4PZmZmMnn8PLb7/Iqurg47tvhx7coNvlCuztm0fieHD0bi6GzLsTMHSHv2nNFDc661as0iLK1bU71GVU5fDOP7BSvYvtmXTl3d6dP/cwD2B4SyY4tvsXSFhhzB2cWO0+cOk5aWxrCvv1F9tnPPr4wcNkWRB6cvYs36H5k8bTTn4y+xZdOeQv1/XfcjVjatqVGjGheuRLHg26Vs2bSH775X1DG++zYAijpm7KjC65jiMH7GAmLPxPP4cSqOHXsx5KvedPFyfa3XyEt2HXj+QgRpz9IYNDinbPn6rWfIkImqOnDjpuVMnzGWc+cuqupAQ8N38tSB/WjR3JlTsWfZuzeY6JggMjMyOHfuIuvWbS9UT2ZmJt+Mn81uv7Xo6OqybfMerl65QZ9+ilnVDet2EBpyBCcXO2LPHSLtWRojhkzS6quN/gN7Ub9BHcZOGMrYCUMB6Naxr2qGvyh6p034lq17fkFHra+gKMNbNuwiLDQSB2cbjp4O5nlaGmOGKZbIt2rTjK7dvbl88RohEYq8+d2cpYQdiqJjF3e+/ErxnYMDD7Fzq59mAWUghjXfqcFuv7VkZckkJabw9cCi188ABw6E4epqz8WLkTx7lsagXPt2+PltYMiQCSQl3WXKlPls3ryCGTPGce7cRTZs2AnApEkjqV69GkuUvziTkZGJtXXhO/0XhczMTCaNm8MO37Xo6uqwfYsPV6/c4It+yjpw3U4OHYzA0cWWE2cPkvbsOSOHTlb5/7x2MZbWraheoxpnLh1h0fzlbNvsw/TZ42jU+CNkWebOHwmMGzWjIAkaKY06cMas8TR8rz5ZWVncuZPIWC19rIMhRxQrBePDeJb2XK2fsNtnLcOHTlL2RxeybsNSpk4bQ3z8RTbl6ido8j996hz79h4gMtqfjIxM4s9dZMO6nE38unT1xKcIe/iUVBmZMWcCjRp/qEpXbTES/HuRirur738dSZLqAfuBSMASuA70Bj4BlgMVUTxP7iTLcoGjyoKWr5cVHv/zd2lL0IquVLYXcaRnFf25ttIgoVuDwo1KkXo+v5e2hEJ5lvFPaUvQSs2KxdvA502TVcbbln8yi/e8+Zsm5VbBP7tTVqhax6Fwo1Kkol650paglQplXN/zjBelLUErf6eX7Tq6SrmKpS2hUMp6XyZTLs0fqCocfR3d0pZQKPdTr5XezmWvkTDDT99Yp8IhZVepxEzMlGsmS5blvNPMsYBFaYgRCAQCgUAgEAgEAsF/EzEoFwgEAoFAIBAIBAJBmaQ0f6rsTSEG5XmQZfk28HI/visQCAQCgUAgEAgEAkExEINygUAgEAgEAoFAIBCUScr27gKvh7K9m5ZAIBAIBAKBQCAQCAT/YcRMuUAgEAgEAoFAIBAIyiT/D8+Ui5lygUAgEAgEAoFAIBAISgkxUy4QCAQCgUAgEAgEgjLJ/8Mz5WJQXkK8yMoobQlakaSyvQwkPSuztCVoRUYubQlaqb3nVmlL0EqmXPar1yrlK5W2hH81/2Sml7YErZT1PFi1jkNpSyiUx3+ElbYErdxoO6y0JWilZcKl0pagFdO3apa2BK1U1i/bdcyD509LW0KhlPV6unqFyqUtQStv61csbQmC/xBiUC4QCAQCgUAgEAgEgjJJ2b6N/noQz5QLBAKBQCAQCAQCgUBQSoiZcoFAIBAIBAKBQCAQlEnE7usCgUAgEAgEAoFAIBAISgwxUy4QCAQCgUAgEAgEgjJJ1n9/olzMlAsEAoFAIBAIBAKBQFBaiEG5QCAQCAQCgUAgEAgEpYRYvi4QCAQCgUAgEAgEgjJJltjoTSAQCAQCgUAgEAgEAkFJIQblpYC9ozVHY/dzLO4Aw0b112gz97vJHIs7QFj0Xho3/Vh1/McVc7lw/ShHYvzV7Md9M5Qzl45wKMqXQ1G+ODrbFlvXd4umc+ZcGNHHg2ja9BONNnXr1uJwuA9xZw+zfuMy9PX1C/U3MKjMpi0riI07yMnTIbRq3QyARo0+JPTwbmJO7GfHrtVUrvx2sfQu+n4G586Hc/xEME3NC9YbHuHH2fgwNm5artL7/vsNOBzuw4NHVxgxcoCaz8XLUZw4GUzM8SAij+4rlqa8+uLPH+HEiWDMteg7ErGXc/HhbNy0QqXvs886cOJEMCdOBHM4zIfGjT9S+fz080Ju3z5FbGzIS2vLy8JF0zkbH0bMif1aYxl2xJcz58LU0v699xtwKGwP9x5eZvhIzfn5ZXiV+L3/vhlh4b48fHSVkXnSd8iQvsTGhhB76iBDh/Z7LVodHG04duoAJ88cZMToARptvv1uCifPHORItD9NcpXppSu+5dKNGCKPBbwWLdm0c7Qm8mQgR08HM7SAemb2gkkcPR1M6FFfGjVR5DETUyN2+6/nyHF/wmL28dWgXir7MROHcOpiGAcjfTgY6YODs41WDY5ONpyIC+HU2UOMHDNQo838hdM4dfYQUccC1OJSkG/Vagb47ttA7JlQfPdtwKBqFQD09fVZ8dMCjh4PJDLGHyvr1vmutXXnz0SfCNKq+d9UD5ZUGRk27CtiTx0kNjaEDRuWUb58+SJrehmmfvsDth7d6dhrcIleRxtv2bSgQchqzA6tocbAbvk+r9S6Me/H7aa+/3Lq+y+n5rAeqs+M54/ivePbqB+0qkQ1llSb8jqwcWjLgWM+hJ70Y+CILzXaTP12HKEn/fA/sp2Pm3ygOl65ytssW/cdB2L2EBy9G/OWjV+LJlsHSw6f2Ed4bACDR2qu62fMn0h4bADBkbv5pMmHap/p6OgQGL6TNduWq46NmTSU4MjdBB3ZyaY9P/Ou0TuvpLGk2t6VP33Hb7dPcjw2+JX0/bB4FpcuRnEq9iDm5o002tSrV5uoSH8uXohky+ZVKn1eni6cij3IyRMHiIkOwtKylcrnl1++584fZ4g7feiV9Nk7WhMVG0SMlv70nO8mExN3gMPRfjRumtPO7QlYT+SJAI4c86f/4Jx2btrscUSdDORwtB/rtiyjikHll9Zn49CWkGM+HDq5l4Ej+mi0mfbteA6d3EvAkR18nCsPhp8OIDBiJ/7h2/AN3aw6PnHGSA7E+BBwZAcrN3xP5SrF6zv/W5Hf4F9RkCSpvSRJVyVJuiFJ0jda7FpJkpQpSVLXws4pBuVvGB0dHeZ/P43Puw7Eto0Xnbp68P4HZmo2js62NGhQl7bN2zNu5Ay+Wzxd9dnObXvp0VVz53b1qo042XTGyaYzh0Mji6XL2aUdZmb1aNbUgZHDp/DDktka7WbNmcCqletpbu7I48dP+OLLboX6L1g4nUOhkbRq7oKVhSfXrt4AYPnK+cycsQjLNu4EBhxkxCjNgxlNuLi2w6xhPZo2tmf4sEksWTpXo92cud+wcvlazJs48PjxE77s8ykAjx49Yfy4WSxbukajn7vb51haeGBr3aHImnLj6tqOhg3r06RxO4YNm8ySpfMK1Ldi+VqaNrFX6vsMgNu37+Dq+hlt2rjx3YLlLF8xX+WzZfMeOnbU3Ol5GbJjad7EgZHDJvPjkjka7WbNmcjKFeto1tSBx49T+eLLnFhOGDe7wFi+DK8av0ePHjNu3EyWLv1Vzf7jj9+nb9/u2Np2wKKNG25uDpiZ1XslrTo6OixYPJ3uXftj1dqDTl0885VpJ2dbGpjVo3UzF8aOnMbCH2aqPtuxzZfuXV7fzYxsTfMWTaFXt8HYW3jTsYs77+XR5OBsQ32zuli3cGPiqJnMV9YzGRkZzJq6kHYW3ni59KBP/x5qvr/+tAkX2y642HYhLDRKq4aFi2fyaef+tG3lRpeunnzwQUM1GycXO8zM6tLS3InRI6ax+MfZhfqOGjOIiIgYWjVzJiIihlFjBgHwhbJsW1t40tm7D3O+nYQk5Sx38/R24e+/nmmN27+pHiypMmJsYsjXQ/pgY+1Fq1au6Ojq0K2bV5E0vSwd3Z35+QfNdfgbQUcHo5lDuNN/Or+5DaaKpx3lGtbOZ/bs1EVueQ/nlvdw7q/Yrjr+2PcQd/pNK1GJJdmmvCo6OjrMWDCRAd1H4G7VDc9Orpi9X1/Nxs7JinoNauPcuhPTxs5j1sJJqs+mfjuOqLAY2lt2xbtdD367duu1aJq9cDJ9Ph2Ci2UnvDu3p+EHDdRs2jlZU69BHexbeTFpzGzmfj9V7fO+g3py49pNtWOrV2zAzbYbHu0+I+xgJCPGDXppjSXZ9m7dsofOHfu+tDaA9q72NGxYn48/sWHI0IksX/atRrt5cyexbPkaPmlky+PHj+nbpzsAYeFHadnKhdZt2jNw0Fh+/mmhymfz5t14efd+JX06Ojp8+/1UenYdhF0bLzp2dc/X9joo+9OWzdszfuQMFiyeAeS0c7ZtvPBw7k6f/p+rfCPDY2jXtgOOVp347cZthhdwo70o+mYu+Ib+3UfgZtUVz06uNNRQLuo2qI1T645MGzuX2bnKBUDvToPwtv+czs45sYqOOIGHzad4tevO7d9+Z/DIV0tnQfGRJEkXWAm4AR8DPSRJ+rgAu++AIs2i/esH5ZIk/VVMe29tdzSUNu0kSQos4LNRkiRVKs41c9OsRRNu3fyDP37/k/T0dPb67MfV3UHNxtXdgV07FDO0cafOUcWgCu8aKu7GHo85xeNHj1/28gXi4enE9u1+AJyKPYuBQRUMDfPfAba1a8teP8Wd121bffHwdNbqX7ny21hZtWLTxl0ApKen8+TJUwAavlef6KMnAQgPi8a7g2uR9Xp6OrN9qy8AsdnX03DH2s6uLX5KvVu3+ODp6QLAvXsPiDsdT3p6epGvWRw8PF3YptJ3BgODyhhp1GeJn99+lT4vpb4TJ+J4/DgVgJMn4zA1NVL5REef5OHDJ69Nq7uHE9u3+Sm1ao9ldtpv3+qDp5ci7e/fe0BcXDwZ6RmvTdOrxi8nfdU1ffBBQ07GniEt7TmZmZlEHT2Bt3fR850mmrdowu2bv/P7bWWZ9g3CzcNRzaa9hyM7t+8F4PSpc2rl61jMKR49en3pCdCsRWNu37yjqmf2+e7H1d1ezcbV3YE9OxQrbuJOxWNgUJl3DWtyN+U+F+IvA/D3X8+4fu0mRsbvFltDi5ZNuHXzd36/fYf09HR8fYJw81SPi7uHEzuUcTkVe5YqVStjaPiOVl83D0d2bFXk1x1b/XD3dALggw8bEnEkBoD79x/y5EkqzZorZtzeeqsSQ4b1ZfFC7TOZ/6Z6sKTKCICeni4VK1ZAV1eXSpUqkpSUUiRNL0tL88YYVHn52ahXpWKT93nxeyLpd5IhPYPUoEgqO7Ytsn9a7AUylelZUpRkm/KqNGn+Cb/fvsOd3xNIT88gaO9BnNzs1Gwc29vht1Oh69zpC1Q2qMw7hjV46+23aGnRjN1bFH2e9PQMnqYWq1unkabNG/H7rRxNAX4HcHZrp2bj7GaP707FCqWzp85TxaAy7xjWBMDI5F3sXWzYucVPzeevp3+rXlesVAG5yPNq+SnJtjcmOpZHDx+/tDYALy8Xtmz1AeDkyTNUrVoFI6P8bUG7dlb4+ipWIG3eskfVpv79d85N0LfeqoQs58Tq6NETPHrFvqyincvpT+/zCc7Xn27v7sBuVX86niq52rnz5zS3cxHhMWRmZip9zmFi8nJlRVO5cMyTB53a27F3pyJ2Z09foLLB26o8WBBHjxxX6Tt7+gJGJoYvpe/fRtYb/CsCrYEbsizflGX5BbAD0DSLNxzwAe4W5aT/+kF5cZFl2V+W5QWvcIpRwEsPyo2N3yUxIVn1PikxBWNjwzw2hnlskjEuQqe438CehEXv5ccVczEwqFJMXYYk/Jmoep+YmJyvIqpeoxpPHj9VVQaJCckYK20K8q9Xrzb37z9k1c8LiYr2Z/mKb6lUqSIAly9dx91D0aHu2MkNU1Pjous1MeTPP5NyrpeQlE9vjRrVePwkVaU3ISEZkyJUXrIssy9gE1HR/vTt16NQe02YmBjyZ+545IpVbn1P1PQladT35ZefcfDgkZfSUTStRmqxTEhMxsRYQ9rniaVxCTYErzN+ubl06SpWVq2pXr0qFStWwNXVHtNaRc93mjA2MSQhV3lNTCi8TCcmJpdoQ2pkbEhiQk6aJiWmYJRHk5GGuiivTa3aJjRq8hFnTserjvUd8DmhR31ZvHyO1nrG2NiIhITcZTQ5f1xMDPPbmBhq9X33nZqkpNwDICXlHu/UrAHAxQtXcPdwQldXlzp1a2Fu3khVp0yeOoqVy9fxLC2tQL0Kzf+eerCkykhSYgpLl/zKlasx/HbzJKlPnnL4cMErIv4L6BnVICPpvup9evJ99Axr5LOraP4h9f1XUHvNbMo1rPMmJZbpNsXQ+F2SE3Ju3CQn3sUwT5/F0PgdkhNz6puUxBQMjd6lTj1THj14zILlM9gbtpV5P06lYqUKr6zJyPhdknLVb8mJd/PVb4bG75KUS7eiDlTonj5vAgtm/khWVv4u+rgpw4iOD6FDVw9+nP/yjyyUxbY3v76cPJdQQD8rf57LsfH2bk/8uXD2+m1k4KBxr1WfkbF625uUmJzvBrKmdi5vO1SrjgmNG39EXK52LpvuvToTdujl6j+jPPkrOTEFQ2P1my6Gxu+SlJin7ChvzMiyzPrdK/E7tIXPenfSeI2un3sTcTj6pfQJCkaSpIGSJJ3K9Zd3ibIpcCfX+z+Vx3KfwxToBPxc1OuW6qBckqR6kiRdkSRpoyRJ8ZIk7ZEkyUC5Rv8Dpc12SZK0rh2RJGmeJEnnJEk6LkmSofLYO5Ik+UiSFKv8s1Ie7yNJ0grlazOlT6wkSbPzzLq/rdRzRZKkrZKCEYAJEC5JUrgGHapEfPbicUFa8x3Le6dVo42s/W7shrU7aGPugqN1J1KS7zFz3gSt9kXSJRddV0Gf6enp0dT8E9au2YqNlTd/P0tj9FjFc4NDh0xkwMBeRETt4+3Kb5H+ouiz1q+qVxtOjl2xtvSic8e+DBzYGyur/M+mvil9trZt+eLLz5g29VXuI2nn5bWWmKQSS9+rV3/jhx9+JiBwC3v3beT8+ctkZmSWSa2vpin/seJqqvRWJX7dtIQZkxaoZoc2rduJZbP2uNh04W7KPabPHV8iGorim5ctm/aQmJBMWKSf4vn9E3FkZGbQqPFH1DerS1BAqFZ/bXqKavMm68GSyndVq1bB09OZTz62oaFZGyq9VYnu3TsWSdO/F40ZTu3t80s3uNGuD7e8h/Fwsz+1fyrZ5ep5KcttyquUdV1dXT5u8gHb1u+ho0NPnj1LK/DZ2+JpKkq88vvJsoyDiy337z/kgnImNS/fz1uBVRNX9u0J4ov+3UtY45ttewu/dvHynL//AZo0tafbp/2ZOeP1Dso196eLYJOnnVu7aSnTJ89XWwUBMHLsIDIzMvHZ9ZL7vbxi/Lp79KOjY0++6j6cnv0+pVXbZmp2X4/uR0ZGJv57Xm3fgH8LWZL0xv5kWV4ty3LLXH+r88jRtBV83uy3BJgoy3KRO5llYab8A2C1LMtNgFRgADAM2CBJUnegmizLv2rxfws4LstyUyBS6Q+wFPhRluVWQBdA0wOvS4GlSpvEPJ81QzEr/jHQALCSZXmZ0s5elmX7PPZqiVipXFWNYhMTUzDJtWzM2MSQ5KS7eWyS89gYkZx8T+P5srl/7wFZWVnIsszWTbtp1ryJVnuA/gN7ERUTQFRMAMlJdzGtZaL6zMTEKN+SxQf3H2JQtTK6uroKG1MjkpU2iYnJGv0TEpJISEjm9KlzAOzbG6za/Oj6tZt06tAHO5sO7NkdwK1bf2jVO3BQb2KOBxFzPIikpLvUyjXDaWJqnE/v/fsPqWpQRaXX1NSIpKTCV5Bkp8e9ew8ICAihRcumhfpk6zt2fD/Hju8nKSmFWrnjkStWufUZqOkzVtPXqNGHrFy1gM8+HcDDV1yGlpcBA3tz9FggR48FKrXmxNLUxIikZA1pnyeWeb/Pq/K641cQmzbuwsrSE1eXz3j06DE3fnu15xcTE5LVloKamBqSnKy9TJuYGJFSBK0vS1JiCia5ZlyNTQxJyaMpSUNdlG2jp6fHrxuX4Lc7iODAnI141OqZjXswb1HwhkyJiclqs74mpkb545KgwSbprlbfu/fuq5aUGxq+w737DwDIzMxkyqRvsbPyplf3rzGoWoWbN36nVetmNDX/hLMXwgk+uAOzhvXw379Fde5/Uz34JsqIvb01t3+/w/37D8nIyMB/3wHaWLTQ6vNvJyP5PnrGOUtG9Y1qknH3oZpN1l9pyM+eA/B3xCnQ00O3WvFWpBWXf0ubkpx4FyPTnNlHI5N3uZunz5KceBejXDOohiaG3E25R3LSXZIT7xIfdxGAkIDD+TZcexmSElMwzlW/GZm8m68OTE68i3Eu3Yo68B4t2pjj1L4dUWf2s/zX77C0acWPP+d/ntp/TzDtvZyKpasstr25GTzoS06eOMDJEwdIzJPnTAvoZ+XPc/n1HT16ggYN6lKjRrXXpjUpUb3tNdbQrmpq55JztXNrNy3Bd3cg+wPUN5zr1qMDTq52DB1QvAmu3CQnpqjlLyMTQ+4m389vY5Kn7KQobLL/P7z/iND94TRplrPRXqfPPLF3tmHs1+r7IAjeGH8CuTceqUX+cWRLYIckSbeBrsAqSZI6ajtpWRiU35FlOXvtxRbAWpblUOA8iofoC9sB6QWQ/fz3aaCe8rUTsEKSpLOAP1BFkqS8D621BXYrX2/L89lJWZb/lGU5Czib67yvxNm48zQwq0uduqbo6+vTsYs7B4PVJ90PBofzaXfFownNWzblaepT7qZoH5S/m+u5RzdPZ65cvl6oljWrt2Bj6YWNpReBgQfp0UOxPKZlK3NSU5+qlojmJiryOB07uQHwec/O7A9SVGT7gw5p9L979z4JCUk0fE+xuYVdO0uuXlFscFTzHcXyQEmSGD9hGOvW5k0CdVb/shlLCw8sLTwIDDhIj56dAWiVfT0NNy4iI4/TSam3Z68uBAVpny2rVKkib7/9luq1g6MNly5d1eqTW19bC3faWrgTEHCQz1X6mpGa+lTjjZXIyGN06uSu0hcYdBCAWrVM2Lb9Z/p/NZobN15905u8/Lp6M9ZtPbFu60lQQCg9Pu+k1Ko9ltlp36NnF4ICX23X1Ly8zvhp4x1lvqtVywRv7/bs3uVfiId2zsSdp75ZPerUraUo0509OLA/TM0mZH8Yn/XoCECLlk0LLF+vi7NxF6hvVofadRT1TIfOmuuZrt29AWjesgmpqX+pOgGLl8/mxrWbrF61Uc3n3VzPurl5OnFVSz0Td/o8DXLFpXMXDw4EHVazCd5/mO7KuLRsZU7qE0VctPke2B9G956K/Nq9ZyeClccrVqygWhLezt6KjIxMrl69wfq12/jkfWvMG9nj5tKd327cxts9Z6fdf1M9+CbKyJ0/E2nVqhkVKyqWELdrZ6XS+l8l7fw1ytUzQb+WIejrUcXDlqeHj6vZ6NbMGUxUaPI+ko5E5qPUEtX1b2lTzp+5RL36talVxwR9fT08Orpw+ID6ZrNhIRF0+kyhq2mLRvyV+hf3Uh5w/+4DkhNTqG9WF4C2Nq25cfVmvmsUl/gzF6nXoA616piir6+HV6f2HAqOULM5dOAInT9TbGJo3rIxT1P/4l7KfRbNWYZlYxdsmrkzfMBEYqJiGT14MgD1GuQ8tuDk1o6b14sXy7LY9ubm51820rpNe1q3aU+Afwi9enYBoHXrZjx58jTfjVWAiIgYOnf2AKB3r64EBCjynFmDeiobc/NG6OuX48GDR69Nq6Kdq0ttZX+6Qxc3QvK0cyHBYXRT9aebKPvTinbuhxVzuH7tJr+sVG/n7B2tGTayP316DCUt7flL69NcLtTz4OGQSDp+poideYtGqjxYsVIF3npL8aRsxUoVsG5nwTVlPWzj0JaBw79kcO/RPH8Fff82ytju67HAe5Ik1ZckqRzQHcVYM0evLNeXZbmeLMv1gD3AEFmW92o7qV7Rrl2i5P3+siRJOsBHQBpQHcUdiYJIl3PWg2SS8510gLayLKs9RKhpqUgB/JPrde7zvhKZmZlMHj+X7T5r0NXVYfsWX65eucEXfRU7pG5av5NDByNwdLbl+JkQ0p49Z9TQySr/n9Z8j6V1a6rXqErcxXAWLVjB9s0+TJs9jkaNPkRG5s4fCYwfNbNYug6GHMHFtR1n48N4lvacoYMnqj7b7bOW4UMnkZx8lxnTFrJuw1KmThtDfPxFNm3cXaj/hLGzWLP2R/TL6XP71h2Gfq2489i1mxcDBig6xwH+IWzZvKfIekMOhOPqak/8hSOkPUtj8OCcu5k+fusYOuQbkpPuMm3qAjZsWs60GWOJP3eJjRsUGy29a1iTqKP+VK78NllZMkOH9aVlcxdq1KjG9h2/AIrNjnbt8udQMXeyz63v/IUI0p6lMWhwzjJfX7/1DBkyUaVv46blTJ8xlnPnLqr0TZo8gurVq6l2lc/IyMDGWjGA2rBhGTa2FtSoUY1r148xd+6Pqg2kXoaQkHBcXNtx7nw4z9KeM2RQTiz3+K5j2JBvlGn/Hes3LmPa9DGcO3dJdc13DWsSEbVPFcshQ/vSuoUrT5++/GY9rxo/Q8N38qRvP1o0d+bp07/Yuu0nqlevRkZ6BmNGT1NtfvSyZGZmMmncbHb5rkFHV5ftW3y4euUGX/ZTLGvcuG4HoQcjcHKx4+TZUNKepTEiV5n+Ze1irKxbU71GNc5dimDh/OVsLUZZKEjT1Anz2OazGh1dHXZu9ePald/o3Vexa+/m9bs4fDASB2dbouOCSUt7zpihijvurSya07V7By5dvMrBSMUmPwvmLCEsNIqps8byceMPkWWZP/9IZOLomVo1TBg3iz1716Gro8vWzXu4cuUGfZT7NGxYt53QkCM4u9hx+txh0tLSGPb1N1p9AZb88AvrNi6lV+9u/PlnIn2/GAEoBrd79q5DzpJJTExm8IDiL5X8N9WDJVVGTsWeZe/eYKJjgsjMyODcuYusW7e9IBmvhfEzFhB7Jp7Hj1Nx7NiLIV/1povXq23AWCwys0ie9RO1181F0tXh8Z6DvLjxB1V7KAaRj7fvp0p7K6p97oGckYn8zwsSRn2ncjf5cQJvtW6CbrUqNIzaxL2lW3iyp/AbhMWhJNuUVyUzM5PZkxaxdtdydHV02bPdnxtXb9L9S8WAbsdGH46ERmPnZMWhk3tJS3vOpBGzVP5zJi3i+5/noK+vz5+/J/BNrs9eRdOMifPZtPsndHR12L1tL9ev/sbnfRS/lLBtw27CQ6Owd7bmyKlA0tKeM2H49ELOChOmj6RBw3rIWVkk3EliyriX/9WAkmx7121YirVNG2rUqMbla9F8O3cpmzcVr58QfCCM9u0duHzpKM+epTFg4FjVZ/v2bmTw1xNISkphytT5bN60klkzx3P27AXWb9gBKPbI6NWzC+npGaSlPadX7yEq/02bVmBrY0HNmtX57cZJ5sxdzIYNO4ulT9Gfnsd2n1/R1dVhxxY/ruXpTx8+GImjsy3Hzhwg7dlzRg+dAkBri+Z0U7ZzoVGKDRTnz15CWGgk8xZNpVw5fXbsXQtAXOw5Jo4pfp7MzMxk1qSFrNu1Qlku9nHj6k16KMvF9o0+HAk9ip2TFYdP7iMt7TnfjJgJKNqzlRu+BxR90QDfA0SFHQNgxoKJlCunz4Y9iv0Mzp46z/Txr+/XFASFI8tyhiRJw1Dsqq4LrJNl+aIkSYOVnxf5OfLcSCX5XGOhF5ekesAtwFKW5WOSJP0KXFF+/AGwGfgRxeBa44N2kiT9Jcvy28rXXQFPWZb7SJK0DTgjy/Ii5WfmsiyflSSpD9BSluVhkiQFAZtkWd6pfIj/B1mW35YkqR0wTpZlT6XvCuCULMsbJEk6D3jLsqz19qhR1Y9KL7BFIC3jRWlL0Eqmhs1VyhKvsuPqm0BXKguLYAomUy7b6QtQSb9kf5v5VSmnUxbuqRZMma9jyngezMh6tb0O3gSP/wgr3KgUudF2WGlL0ErLhEulLUErpm9p3wW6tEnPKplfT3ldPHhesjvyvw7+ySzbMaxeofR+laEovK1fsbQlFMr1e6eLPBtZltlp3PONdbw/S9paKjErCz33y8CXkiTFo5gVD0WxZH2sLMtRKJ4Tf5mHJkYALZUbyF0CBmuwGQWMkSTpJGAMFOV3iVYDwZo2ehMIBAKBQCAQCAQCgaA4lIWplixZlvMOmD/KfiHL8hhtztmz5MrXe1Cs20eW5fvAZxrsNwAblG8TAAtZlmXlpnKnlDZHgCO5fIbler0cWF7otxIIBAKBQCAQCAQCwSuR9Z+Y79dOWRiUlyYtUGwGJwGPgX6lK0cgEAgEAoFAIBAIBP9PlOqgXJbl20CjwuwAJEk6AeR9yLO3LMvnX+H6UUDRfu9KIBAIBAKBQCAQCARvlCyNPw3+3+JfM1Muy3Kb0tYgEAgEAoFAIBAIBALB66QsbPQmEAgEAoFAIBAIBALB/yX/mplygUAgEAgEAoFAIBD8f1G2f4j49SBmygUCgUAgEAgEAoFAICglxEx5CZGW8aK0JWjl2YvnpS1BK5XKVShtCVqpoKtf2hK08iIzo7QlaKW8XtmOH5T9GJbTEdX3q/C2ftmuY/7JTC9tCYVyo+2wwo1KkYbHVpS2BK1UbNC+tCVo5Wn6s9KWoBV9Hd3SlqCVf0M7p1fGY1jW60FdHTG3+ab4f/hJNJGbBAKBQCAQCAQCgUAgKCXEVItAIBAIBAKBQCAQCMokWaUt4A0gZsoFAoFAIBAIBAKBQCAoJcRMuUAgEAgEAoFAIBAIyiRi93WBQCAQCAQCgUAgEAgEJYaYKRcIBAKBQCAQCAQCQZlE7L4uEAgEAoFAIBAIBAKBoMQQM+UCgUAgEAgEAoFAICiTiN3XBQKBQCAQCAQCgUAgEJQYYlD+Bvlu0XTOnAsj+ngQTZt+otGmbt1aHA73Ie7sYdZvXIa+vn6h/kOG9uV4bDDHTgazdv0Sypcvp3bO4SP68+Sv36heo1qx9P74w2wuXzpK3OlQmpk30mhTr15too8GcOniUbZu/UlNL0DLFk15nvYHnTt7AFCrlgmhB3cTH3+Es2fDGD7sq2JpKqkYDh7Sh2MngzkeG8zXQ/qojn8zeQSXr0UTFRNAVEwAzi7tiqTT3tGa6FPBHD8TwvDRAzTazPtuCsfPhBAevY/GTT9WHV+yYh4Xb0QTccxfo9/Xw/uR8uQK1atXLZKW3CxYNI3T5w5z9HggTQqIX526tQgN38Ops4dYu3GpWvwK8j938QjRJ4KIjPEnLNJPdXz23ImciAvh6PFANm9fRRWDygVqc3C04dipA5w8c5ARBcTs2++mcPLMQY5E+9MkV8wK8v11/Y+ER+0lPGovp+MPEx61V+18prWMuZ0Qx5Dh/QrUlZs3XYanTBtN9PEgomIC8Nu3ASOjd4ukE6CdozWRJwM5ejqYoaP6a7SZvWASR08HE3rUl0ZNPgLAxNSI3f7rOXLcn7CYfXw1qFc+v0HD+pDw6CLVXiIPqs7x9ZdEnwgi5uR+Bucqc0WhqfknHD0eyKmzh5i/cJrqeI+enbl26wQR0f5ERPvT+8tuL62vLMbPwcmG46cPcPJsKCNGD9Ro8+3CqZw8G0pETJ4yUojv0OH9uJ96jerVFe2Enb0lhyN8iTwWwOEIX2xsLYqlNS9v2bSgQchqzA6tocbA/OlSqXVj3o/bTX3/5dT3X07NYT1UnxnPH8V7x7dRP2jVK2l4WaZ++wO2Ht3p2GtwiV/rTaZxtepV2Ru4iduJZ1jw/fRia7V3tCYqNoiYuAMMK6CMzPluMjFxBzgc7UfjpjllZE/AeiJPBHDkmD/9B+eUkWmzxxF1MpDD0X6s27JMa5tRGHaOVoSf8CfyVBBDRmrua8ya/w2Rp4IIifJRleHy5cvhH7qNA5F7OBTjx5hvhqjsR0/8mpMXDhEcsZvgiN3YO9kUS1NJtHMA/Qf24tipA0QdD2T67PEAdOnmpWr/wqP2kvLoMo0af5jveo5ONpyIC+HU2UOMHKM5z81fOI1TZw8RdSxATVNBvlWrGeC7bwOxZ0Lx3bcBg6pVAGjeoomqfo6M8cfDy1nl479/CyfiQlSf16xZvVhaclOnbi1Cw/YQeyaUtRuWqLXDBfkvXzWfqzePE30iKN/5BgzqzYm4EGJO7mfmnAkar1kY7RytiDgRwNFT+xlaQH6cPX8SR0/tJzTKVy0/BoZu52CkD4dj9jL2m6Evdf1/O1lv8K+0EIPyN4SzSzvMzOrRrKkDI4dP4YclszXazZozgVUr19Pc3JHHj5/whbJTWZC/sbEhg7/+knY2HWnb2g1dXR26dPVSnc/U1Bh7Byv++COhWHrbt3egYcP6fPSxNV9/PZEVK+ZrtPv22yksXfYrH39izeNHT+jXN6cjpaOjw7ffTuHgwSOqYxkZGUyYMIsmTdphbe3F4K/78NFH7xVJU0nF8KOP3+fLPp/hYNcJKwtP2rs50MCsnup8q1asx8bSCxtLL0JzfZeC0NHRYcHi6XzedQA2rT3p1MWD9z8wU7NxdLalvlldLJq5Mm7kdBb+MEP12Y5tfnTvormxNjE1ws7ekjvFTE/F97fDzKweLZo6Mmr4VBYvmaXRbuacCfy0cj0tzZ148viJamBTmL+Xey9sLb1xsO2kOhYeFo1lK3esLTz57fotxozV3KnNjln3rv2xau1Bpy6e+WLm5GxLA7N6tG7mwtiR01j4w8xCfQf0HY29TUfsbToS6H+QwIBQtXPOnT+Jw4eiihi/N1+Gly35FSsLD2wsvThwIJyJk4YXSauOjg7zFk2hV7fB2Ft407GLO+/liaeDsw31zepi3cKNiaNmMn+xolOekZHBrKkLaWfhjZdLD/r076Hma2JqhG07S/68k1gkLZr46KP3+KLPpzi164JNWy9c2rejgVndIvt//+MsRo+YSktzJ8zM6uLkbKv6zM8nCDsrb+ysvNm8cfdL6SuL8dPR0eG7xTP4rMsArFq507mrhjLiYqcoI+bOjBk5jUU/ziqSr4mpEXYOVmr1ysMHj+j52WBs23oxdPBEVq1eVCy9ecRjNHMId/pP5ze3wVTxtKNcw9r5zJ6dusgt7+Hc8h7O/RXbVccf+x7iTr9p+ezfFB3dnfn5h7klfp03ncb/PP+H+XOXMnPqdy+l9dvvp9Kz6yDs2njRsat7Pq0OzrY0aFAXy+btGT9yBgsWK9q57DJi28YLD+fu9On/uco3MjyGdm074GjVid9u3C7wpnZR9M1dOIUvPx2CY9sOeHdx470PGqjZ2DvZUM+sLrYtPfhm9CzmLZ4KwD//vKB7x69ob9uV9rbdsHO0olnLJiq/NT9vxs2uG2523QgvYvuRrakk2jkrmza093DEztILGwtPVi1bC4DP7gBV+zdk0AT++COBC+ev5NO0cPFMPu3cn7at3OjS1ZMPPmiorsnFDjOzurQ0d2L0iGks/nF2ob6jxgwiIiKGVs2ciYiIYdSYQQBcvnQNB9tO2Fl5063TV/ywdA66urqqaw36aqyq/r5//2G+GBakJS8zZ4/np5XradXMmcePU+n1RbdC/bdt9aVbp/w36K1t2uDm4YiNhReWrd1ZsXSNxmtqQ5Efp9L706+xb+tNhy7u+fKjg5MN9c3qYN3SnYmjZzJ/saLO++efF3zasR8utl1wte1KO0crmufKj4L/Dv93g3JJktZIkpTv1pokSX0kSVqhfN0xt40kSUckSWr5Ktf18HRi+3bFDOKp2LMYGFTB0PCdfHa2dm3Z6xcMKCoID0/nQv119fSoWLECurq6VKxYkeSkFNX55n83helTv0OWi/cLf95ermzZugeAEyfjMKhqoHGWzr6dFT4+iruKmzfvxtvbVfXZsKH98PML4t69B6pjycl3OXP2AgB//fU3V65cx8TEqEiaSiqGH3xgxqmTZ0hLe05mZiZHj57Ey8ulSJo00bxFE27d/IPfb/9Jeno6e333097DUc2mvYcju7fvA+D0qXNUMajCu8rvcjzmFI8fPdF47tnzJzF7+iKKmZwAuHs6saNI8bNgn98BALZv9cNdGb+i+ucmPOwomZmZAMTGnsXEVHNaN2/RhNs3f88VsyDcNMRs5/a9gCJm2dcvii9Ah05u+O0JVL1383Dk9u0/uXL5utbvkE1plOGnT/9SnfetShWLXI6btWjM7Zt3+ON3RUz2+e7H1d1ezcbV3YE9OxSrMeJOxWNgUJl3DWtyN+U+F+IvA/D3X8+4fu0mRsY5ZX/mvInMm7m42HVKbt7/wIxTsWdVZS7maCweXi7Uq1+H3b5rCYv0IyhkG++93yCfr6HhO1Su8jaxJ88CsGP7XlUefV2Uxfg1b9mEWzd/5/fbd0hPT8fPJwg3Dyc1Gzd3R3Yp89jp2HMYGFRWlJFCfOfOn8ysaYvUNJ2Pv0xy8l0Arly+TvkK5ShXTn0lVFGp2OR9XvyeSPqdZEjPIDUoksqObYvsnxZ7gcwnT1/q2q+DluaNMajy8jO2ReVNp/GzZ2mcOH6a58//KbZWRRn5I6eM+ATj6u6gZtPe3YHdOxTtXNypeKrkKiPnz2kuIxHhMao2I+7UuSL3D/Ji3qIxt29l68sgwDcYFzf1Muzibo+PsgyfORVPlSoKfQDP/k4DQE9fDz09vVeq77IpqXau71c9WPbjal68SAfQOJjt3NVDrf3LpkWefOPrE4Sbp7omdw8ndig1nYo9S5WqijynzdfNw5EdWxX5dMdWP9w9FXkxu84HKF+hfLHjWpCWvNjYWbBvr6Ifs2ObLx7K62vzPxYdyyMNfa9+/T9n6Q+refHiBaA5voWRNz/u8w3GxU29vLi426u1KSWdH/9tyNKb+yst/u8G5bIs95dl+VIhZh0BzWtiXhJjY0MS/syZGUlMTM7X2FSvUY0nj5+qKqzEhGSMlTYF+SclpbB82RouXI7i2m/HSE19SljYUUDReCcmpnDhgvqd0aJgYmKkNpOT8GcSpnn01qhRjcePn6j0/pmQpBp0mZgY0aFDe35ZvbnAa9StWwvzpo04efJMkTSVVAwvXbqGpVVrqlWvSsWKFXBxscO0lrHKbsCg3kQfD2LFqgVUVS7B0oaRiSGJCUk510lIxsjYMP93yWWTlJiMsYm6TV5c3exJTkzh0oWrhWrQhOL759Kl4Zqa4meitNHmL8syvvs2EB61ly/7fqbx+r16d+PQwUjN2kwMSUhIzjl3QgrGGmKWmNsmMRkjE8Mi+ba1bMm9ew+4efN3ACpVqsjwUQP4fsEKjXo0aiyFMgwwbcZYLl45SrfPOjBv7pIiaTUyVs+DSYkp+fKgkfG7avHUZFOrtgmNmnzEmdPxADi72ZOU9PJ5MJvLl6/T1qqVqsw5u9phamrEj8vmMHH8bBxsOzF9yncsUs4S5cbYJH8+yJ2PvTq4EnUsgA2bl2NawE2gwiiL8TM2NiTxz4K/NyjL0Z95yoKJoVbf9m4OJCWlcFFLO+HVwZXz5y6rOv3FRc+oBhlJ91Xv05Pvo2dYI59dRfMPqe+/gtprZlOuYZ2Xuta/mdJM4+JiZKxe7yYlJqvdfFLY5C8jeevmWnVMaNz4I+KUZSQ33Xt1JqwYM9GFXdtQQxlOymWTnJii+g46OjoER+zmzNUIjh45ztnT51V2X/bvQUiUD4uWz8bAoPA+QTYl1c6ZmdXDom1LDhzexb6gzZg3b5zv2h06u+O7J/+ybGNjI7W+SGJCcn5NJob5bUwMtfq++05NUlLuAZCSco93auaU9xYtmxJzcj9HjwcydtR0VXsJsOKnBURE+zNuguYl2gVpyY3mdtiwyP55MWtYn7aWLQkN20NA8FaaaYhvYRhryGvG+cqLYYFtio6ODiERezh3NZKoI8c4kys/Cv47lKlBuSRJ9SRJuiJJ0kZJkuIlSdojSZKBJElXJUn6QGmzXZIkjeuZJEn6VJKkH5SvR0qSdFP52kySpKPK16pZb0mS+kqSdE2SpAjASnnMEvAGFkmSdFaSpOy1Rd0kSTqptNf4EJEkSQMlSTolSdKpF+mpeT/LZ5/3Tpc2m4I+q1q1Ch4eTjRp1I4PGlpSqVIlPv2sAxUrVmDc+CF8O/dHTVIL5WX1Zk/hLl48i8mTvyUrS/PTGW+9VYldO39l7LgZarOBJaGpsBheu/obS378hX3+G/HZu54LF66QkZEBwNo1WzFvbI91W09SUu4x99vJRdCp4WDeu5oaQ1fwnc+KFSswatxgvvt2WaHXL1hXycQPoL3TZ7Sz7kC3zv3oP7AXllat1OzGjv+ajMwMdu3c99q1FcW3U1dPfHPNEkyYPJxfVm3k77+fadTzujVq+6ygMpzNnFmL+eRDa3bv3MfAQb2LqDX/seJoBaj0ViV+3bSEGZMW8NfTv6lQsQIjxgzk+/lFv5FRENeu/sayH1fju28Du/3WceH8FTIzMmndpjnrNy0nItqfH5bO0bg6R5vuA8FhmH9ij01bL46Ex7Dyl4Uvpa8sxq8kykjFihUYPf5rFsxbWuB1P/iwIdNnj2fsqFdZPl5wW5HN80s3uNGuD7e8h/Fwsz+1fyq95eqlRWml8cug8XpFsclTRtZuWsr0yfP56+nfanYjxw4iMyMTn10Br09fvnZYk43if1ZWFm523WjTyImmzRvx/keKZdmb1+3Cprk77W27cjf5HlPnjnutml6mndPV06Vq1Sq0d/yUmdMWsmbDEjW75i2akPYsTeOqsFep64riq4nTp85h2dodp3ZdGDVmkGoPlUFfjcXawhMP1x60tWzJZz06atBbcv2YgtDT08WgqgHODl2ZMfU71m18ibL0irqzsrJwtetKq0aOmDdvzAcfNcxnK/j3U6YG5Uo+AFbLstwESAUGAMOADZIkdQeqybL8awG+kUD2gNkGeCBJkilgDajdbpUkyRiYhWIw7oxyZlyW5RjAHxgvy7K5LMu/KV30ZFluDYwCZqABWZZXy7LcUpblluX0q9B/YC/VBmHJSXcxrWWiss2eIcvNg/sPMahaWfV8jYmpkWoZa2Jiskb/dvZW/H77Dg/uPyQjI4MA/xDaWDSnfoM61K1Xm6PHgoi/GIGpqRGRR/15992aBYQOvh78JadiD3Iq9iBJScnUqp1zPdNaxiTm0Xv//kOqVjVQ6a1lakxiosKmRfMmbNmyiuvXjtO5swfLl32rWtqup6fHrp2/sn27H3v3BheoB3gjMQTYvGk3ttYdcHftwaOHj7n5220A7t19QFZWFrIss3H9Dlq0bKpVL0BSQgompjkz7SamRqploCqbxBRMc9kYmxiRnKRuk5t69etQp24two7uIzb+MCamhoRG+vKOlvQERfwiYxQbqiQl3VVbAWCi4Zqa4pektFHET7N/9ve7f+8hgQGhNG+R87xT98874dLegYH9xhSoMzEhWW1W08TUMF/MEhOT1Za/m5gYkZJ0t1BfXV1dPLyc2eu7X3WsRYumTJ81jtPxhxn09ZeMGjuIrwb01Bi/0irDedm9yx/vDu0LiKA6SYnqedDYxJAUDXkwdzxz2+jp6fHrxiX47Q4iOPAQAPXq16ZOXVNCo3w5fu4gxiaGhETsKTQPFsSWTXuwt+mIZ/vPefToMb///idPnqSqnie0s/LGomV7dHR0VBv/TJoyUrF6I08+yM6Hjx4+Vi0x3LRhJ+YFbFBZGGUxfomJyZjU0vy9VTYJyZjWylMWku4W6Jtdr0RE+xN3PgwTUyPCovxU7YSxiSGbtq1k6MAJ3L51p0g6NZGRfB8945zvqW9Uk4y76ktAs/5KQ372HIC/I06Bnh661Yo+C/lfoDTS+GVJSlSvd42V9bG6Tf4ykpyrjKzdtATf3YHsDzik5tetRwecXO0YOuDlNtQq6Np385Th5MQUjHPZGGko56mpTzkeHUs7RysA7t/L6RNs3+SDefOi1zEl1c4lJaao9ks5E3eerKwsauTa2LdTFw/8fPLPkmdfz7SQ/ori2nlslHmuIN+79+6rloUbGr7DvfsPyMu1q7/x7FkaH338vuJ7KNvKv/76mz27A1T9iK8G9FS1AclJKRq15EZzO3xX63fRRmJCMoH+IQDEnY4nK0umRgGb0BVEkoa8lpx8L49NcoFtSjapqU85Fh1LO0frYl3/v4DY6K10uCPLcrTy9RbAWpblUOA8sBLQvMUnIMtyMvC2JEmVgdrANsAWxQA97xqoNsARWZbvybL8AthZiC5f5f/TQL2ifJE1q7eoNggLDDxIjx6KDbBatjInNfWpamlPbqIij9OxkxsAn/fszP4gRWO1P+iQRv87dxJp2dqcihUrAGDXzpKrV3/j0sVrNKzfmiaf2NHkEzsSEpKxtfbm7t37+a6ZzU8/b6RlKxdatnJhn38IvXp2VQSqdXNSn6Tmq6gBjkTE0KWLYmf13r27ERBwEID3P2jLe+9b8N77Fvj6BjF8xGT8lZXar6sXc+XKDZYsXV0mYghQ8x3F0qpatYzx6uDKnt2Ku/O5n1Xy9HLh8qVrhWo+E3eeBmZ1qVPXFH19fTp2didkf5iaTcj+MLr1UMyGtmjZlKepT7mr4btkc/nSNT5paEWrJo60auJIYkIKzraduaclPUERP1tLb2wtvdkfGEr3IsXvBB06KQZ/PXp2IlgZv+Cgwxr9K1WqyNtvvwUoloU7OFhz+ZLijryjky0jxwzi888GkZb2XGvM6pvVo07dWsqYeXBAQ8yy75y3aNlUdf3CfO3aWXLj2k2SEnMG0F5uPWnRxJEWTRz55aeNLFn8C2t/3aoxfqVVhgG1DQfdPJy4fu03isLZuAvUN6tD7TqKPNihszsHg8PVbA4Gh9O1uzegeJY1NfUv7qYo8tPi5bO5ce0mq1dtVNlfuXSdpu/bYtHUBYumLiQlpuBq17XQPFgQ2TvrmtYyxtPbhZ079vLH73/SoWPOjYdPGn1IVlaWapA+f95SUlLu8dfTv2nZyhyA7j06qmKcu7y6eThyrYjxyktZjN+Z0+dp0CAnn3fq4sGB/YfVbA4Eh/GpMo+1aNWU1NS/FGWkAN/Ll67xkVlbmjd2oHljBxITknGw6cTdu/epYlCZ7bt/Zc7MxZw8EfdSccwm7fw1ytUzQb+WIejrUcXDlqeHj6vZ6NbMGURUaPI+ko5E5qPUvKf6T/Om0/hVUJSRutRWtnMdurgRkqeMhASH0a27op1r3rKJsp1TXPeHFXO4fu0mv6zcqOZj72jNsJH96dNjqNY2ozDOxV2gfoO6yjKsh1dnN0IPHFGzCQ0Op4uyDDdr2YSnyjJcvUY1qij3EChfoTzWdhb8du0WgOoZXwBXT0euXr5RZE0l1c7tDzqk+nWEBmb1KKevz4MHjwDF7Kt3x/YFDsrjTp+nQa7zdv4fe2cdH8XRxvHvRrC+eEsMhyruIUSIO64FCqUUihcp7i6lOFXcIQJxgSgWAgGCFCul0BgapIQS2fePu1xyyUUhJG3n208+5fae2f3dMzPPzOzMzvZ0JsBXvcz5+x2ln1JT23YtefpEoSm/tAF+IfQboCin/QZ0x195vG692lkTOXUMafx+A+7ciUNbW1v1hiAdHR3sHSxV/YjNP+9WtQG+Pkc0asnJsYgoVVvS79Osdjiv35Ifvj5HMLdQ7IHRqHF9ypXT5WERnytXlMe6qvLYtYcjwQE525QwtTZFU3msoCyPN5XlUfDvQqe0BWgg5zoSWZIkLeBjIAWoAfyZT/qTwOfANRQD8aFAR2BSIa6VH5k7oaRTDL8FBYZhZ9+Z87EhvEh5yeivpqq+O+i+mbGjp5OYeI+5s1ewZdtaZs2eSGzsZXYodw/OK/3ZMxc4fCiAiONepKWlE3vhMtu27CuqvFz4+x/F0cGKq78eJyUlhWHDsmY5vQ7vYMRX35CQkMSMGYvZvWsT8+dN4fyFy2zZujefs0Ink3YMHNiLixevcCZaMYCfNXsZAQEh+aaDkvMhwM7dG6lRoxqpqWlMnjiP5GRFZ3DBoqk0a/4Jsixz548/+XrcrAJ1pqenM33yQvZ5bEZbW4u9u9y5dvUmnw1VPGu9Y8t+jgSFY21nTtT5IFJevGT86Kxl8T9sXoWJaTtq1KzOuSthrFy6nj073Qu8bmH8Z2vfmZjYEFJSUtR+/wH3Xxg3egaJifeYN3sFm7etYebsicTGXlHtYJ1X+vdqvcuuvYpXFWnr6OB+wIujRxTPjq9YNZfy5cvh6bUNUGysMnF87lfvKHy2gAMev6Clra3y2eCh/QDYvmUfwUHh2NhZcPp8MCkvUhin9FleaTPp3tMJjzw6JEX139uuw/MXfEPj9xuSkZHB3TtxTBhfuCW96enpzJqymD3uP6GlrcX+3Z5cv/obgz7vA8DOrQc4GhSBla05x2P8SUl5ycTRirLdzrg1vfp15crlawRFKMrdsoVrCAku3rOdebF99wZq1KhOamoqUybO50nyU4Z/MYlVq+czacoodHV18XDz1fgc7OQJc9n4w3IqVKjAkeBwjgSFAzB85Gc4OlmTlpbG48dP1PKoKJRF/6WnpzPtmwUc9NyMlrY2e3a6ce3qTYYo68i2LfsIDgzDxs6C6AtHFHVk1PR80+bHsOEDadCwLpOmjGaS8vnO3t0+L9YmR6RnkDj/e+psWYSkrUWyWxCvbt6hWn8nAJL3+lHFoRPVP3VGTktH/vsVcV9n7QhuuHoK77Rvjnb1KjSO3MH9tbt44hZUdB3F5Ju5y4g+F0ty8lOsuw1k1BeD6OlqX3DCIvK28xgg5mIIlav8D11dXZycbejV7XOuXyv4ZlZ6ejozvlnMXvef0dbWYt8uT65fvclnyj1Fdmzdz9GgCKxtzTl5LoCUFy+ZMHomAO2NW9NbWUeCIxXzHUsXrCEkOILFK2dRrpwu+w4pdhCPib7A1Ima3xRSkL7ZU5aw0+0HtLW1VXV44BDFLty7th0kJDgSS1tzIs/6kZLyksljFHW4lt57fLdpEdra2mhpSfgcCuKocj+UGfMm8kmzj5BlmT/vxDF9oubdv/PSVBLt3J6d7qzduISIk96kpqYyZuQ01TU7dmpHfHwif9zW3G1OT09nyuT5uB3agraWNrt3unH16k2GDFW8SWfblr0EB4Zha2fB2QtHSUlJUZ0/r7QAa777kS3b1zJwUG/+/DOezz8bB4BxxzZ8PXEEqalpZGRk8M3EeTx6+JhKlSri5rkFXV0dtLW1CQ89wY5tuefK8tICsN/tZ8aPmanox8xZyS9bVzNj9gQuxl5h1w63AtP/vGU1nczaU7NmdS5djWTZkrXs2uHG7p1urN+0lONRvrx6lcqoEUVfwZFZHne7/YiWWnlUtCm7th0gJDgCK1szjp3152VKChPHKNp7Pb33WL1pMdra2khaEj6HAjmqbPP+S5TmDPbbQipLO/hJklQf+B0wkWX5pCRJPwOZPbIPgZ3AaqCjLMsad5yRJGkIsED5txW4BKTIstxa+X0YMBmIA04BrVEskw8BLsiyPEaSpPVAjCzLW7OnkWX5jCRJ7wJnZFmun99vqfq/RmXHsRp48ar4d6DfBpXKVShtCflSQbt4uxC/LV6lp5W2hHzRyfYKlLJKWffh/3TLdh1JSXtV2hLypaJOudKWkC9/pxdvU7W3SaRe2X6usfHJ19//oCQxaFi4x1FKC12tsjhvk4WuVtluR1LSy3YMBEjPY98fQeGopFu+tCUUyJ+PLpXifuJvjg11Br61cdWYu7tKxWdlcfn6r8BgSZJiUcyKB6NYsj5JluVIFM+N5zddGYli6XqELMvpwF3gWE4jWZYTgHkoZtaPANnX5+0DvpEk6Vy2jd4EAoFAIBAIBAKBQPAWkd/iX2lRFm+DZsiy/FWOYx9n/kOW5bx3i1J8/xvZtnqVZdkux/eds/17K4rZ9JznOI76K9Gyp3lAIZ8pFwgEAoFAIBAIBAKBID/K4qBcIBAIBAKBQCAQCAQCMv4Vi/Dzp0wNymVZvg0U6v0SkiRFATkf5hgky/LFN61LIBAIBAKBQCAQCASCkqBMDcqLgizLHUpbg0AgEAgEAoFAIBAISo7/wpaEZXGjN4FAIBAIBAKBQCAQCP4T/GNnygUCgUAgEAgEAoFA8O9GzJQLBAKBQCAQCAQCgUAgKDHETHkJoS2V7fsdtd6pVtoS8uXv9NTSlpAvlXRy7jFYtpCksr1NZbpc9u95Vi1XqbQl5MuTVy9KW0K+aFG2y2BZjzEVdMqVtoQCaRt3pbQl5EvFhg6lLSFfEm4FlLaEfKnT2Lm0JeRLWW/nynoMBNDWLtvDgMcvn5e2hHwpr61b2hL+M5Tm+8PfFmV75CgQCAQCgUAgEAgEAsG/mLJ9i0wgEAgEAoFAIBAIBP9Z/gvvKRcz5QKBQCAQCAQCgUAgEJQSYlAuEAgEAoFAIBAIBAJBKSGWrwsEAoFAIBAIBAKBoExS9rcHfn3ETLlAIBAIBAKBQCAQCASlhJgpFwgEAoFAIBAIBAJBmUS8Ek0gEAgEAoFAIBAIBAJBiSFmygUCgUAgEAgEAoFAUCbJ+A/MlYuZ8hJm6YrZnDl/hMiT3jRv8YlGm7r1ahMc4kb0uWA2b1uDrq5ugemtbcyIignkzPkjjJ84XO18X44YRFRMICdO+zFv4RQA6tQ1Iu7eRcKPexF+3Itl383JV3dna1MiTvtw7Kw/o78eptFmwbLpHDvrT/AxD5o2/xgAQyN9DnptJeyUFyEnDvPFiIFqaT7/8lMiTvsQcuIwM+dPyldDTqxszDh1NoDT54MZN2G4RpslK2Zx+nww4Se81PyVV9pps8YTfsKL0GOHOXhoC/r6tdTOZ1TbgNvx5xg9dmiRtJpbmXA06jCh0d58NV5z2rlLpxIa7Y1/xEGaNP9I7TstLS18Qvfzy571qmNOXWwJPO7Bb/fP0ayl5rJUWCytTTkW7cfJmADG5JG/i5bP4GRMACHHD9Esmy9Xb1jEpRvHCDvhpWY/edpozl0J40ikB0ciPbC2NS+SJitrM06eCeD0uSDGTfhSo82S5TM5fS6IsOM58jePtD9vXU1o5CFCIw9xNvYooZGHAGjVupnqeOixwzi52BRJq4V1J0KjvIg448uo8V9otJm/dBoRZ3wJjHRX1Y/y5cvhFbyHgAg3jpzwZOK0USr7STPGEBjpjn/4QXa5/4ie/ntF0pTJ8pVzOHchhOOnfGnRoolGm3r1anM01J2Y80fZun2dWszJK/1Xo4Zw8rQ/p6L9GTlqSK5zjh03jCfPf6NGzer56lu2cjZnLxzl2Ckfmuehr2692gSHunHm/BE2b1+rpk9TeiMjA7z8dnHqbAAnov0ZMWqw2vm+/GoQp2OCOBHtz3xlTNREScSYTEaPHcqDp9epUUPhH11dXdZtWkrESW/CjnvRybR9nro00dm6E+FR3hw748foPMrggqXTOXbGj+DIrBhtYKTPgcNbCD3lxdETh9Ri9MdNPuRw4C6OHPNg654N/K/yO0XSVBArv51L7MUwoqL8adky77IZFn6IC7GhbN+xQZX3fft2JSrKn6gof46GuNOs2cdFvv7bzN/qNapxyGcHt+PPsezb/Nvb12XWku8wd+5Ht4Ffleh1clIS7QjAF8MHcCzaj/CT3syeP7nY+iysOhES5UV4tA8j82iH5y2dSni0DwERbmpx+nDwbvzDDxJ83IMJU0eppRnyZX9CorwIPu7B9LkTiq3P0tqU42f8OXUukLF5tHmLl8/k1LlAQo8fVvPfmg2LuXzzOOEn1f3n2s2e8FPeJDy+QotWTYutLVNfWc5fTXy3aj5XLkdyJjqIli01//769esQGeHF5UsR7Nq5SRVjXF3sOBMdxOmoAE4c98XEpN1r67G0NiUy2pcT+fhw4fIZnIgJ4OhxT5q1yOpLu3lvJSLKm7CTXgz7KitOT5k5lqPHPQmO9GCfx8/F7isIyh5iUF6C2NhZ0KhRPdq2tGHCuNmsWr1Ao928Bd/w/cattGtlS3LyUwZ+1jvf9FpaWqxYNY8+PYbRsZ0jPXu58OGHjQEwNeuAo7M1ZsaumLR3YsPaX1TXuf37HSw6dcGiUxemTdSsJfP8i1fOZGDvr7A07kK3nk68/2EjNRsrWzMaNKqHaRtHpn49j6WrFJ2OtLQ05s9aQWfjLrja9WfIsP6qtCam7bF3ssLGtDtWJl35Yf3WQvtSS0uL5avm0rfnl3Rq50SPXi58kEOTjZ0FDRvVp31LWyaOn83K1fMLTLth7S9YmHTB0rQrQQGhTJ46Wu2ci5bO4GhwRKF1Zl5vwYoZDOkzCjuT7nTp4UDjDxuq2XS2MaV+w7pYtnNl+sQFLPp2ltr3n48YwM3rt9SOXbt6k5GDJ3D6xNki6dGkb+m3s/m013DMO7jSvZdzLl9a25rTsGE9OrZ2YPL4uSxfldWp3L/nEP17ae7Q/rRpOzZmPbAx61Ekv2lpabFs1Rz69RpGp/bOdO+pIX9tzRX528qOSeNns+K7eQWm/fLzCViadcPSrBs+XkH4eAcDcPXXG9h07omlWTf69RzGt2sWoK2tXWiti1bMZHCfUVh37EqXno68nyN/LW3MqN+oHuZtnZk2YT6LVyny9++/X9Gv2xc4mPfCwbw3FtadaNW2OQA/rt+KvVlPHC16czQwnPHfFL2DbWvXmUaN6tOqhRXjx87kuzWa6/n8hVPYtHErrVtak5z8hM8G9843/ceffMDgIX2xsuhOJ2MXHBytaNiovup8RkYGWFp14s6duAL0WdCoUX3atLDm67GzWLVmvka7eQun8P3GrbRtacOT5CcMUunTnD4tLY1Z05di3MYBO8teDPtyIB9+pIyJ5sY4OdtgauyCSTtH1q/7ReM1SyrGgKKDZWHVibvZ/DNoSB8AzDu60qvrEBYsnoYkSfn6L7vWRStmMajPSCw7dqFrT6dcZdDKxowGjepi2taJqRPmsXTVbADS09JYMHsllsZd6GL3KYO/6KdKu3LtfJbOX4ONaQ8CfI/y1djPC6WnMNjbd6Zx4wY0b9aZMWNmsGbtYo12CxdNY8P6zbRobkly8hMGD+kLwO3bd7G370uHDo4sX7ae9RuWFun6bzt//375N0sXrWXerOVF0lkcujnZ8sN3i0r8OtkpqXakk1l77J2sserUFYuOrny/fkux9S1cMYPBfUZiY9KNLj00xWlTGjSsh0U7F7V2+O+/X9G/2zAcLXrjaNFHLU53NG2HraMlDmY9se3Ug582bi+2vmWr5vBpry8xa+9C956a/degUT2MW9kzefwcVnw3V/Xdvj2e9OuZeyB/9coNhg4cx8njZ4qlK7u+spy/mnCwt6Rx4wZ80sSMUaOnsn7dEo12ixdNZ936X2jS1Jzk5GQ+H9IPgJDQY7RtZ0f7Dg4MHzGJH75f8Vp6tLS0WPLtLAb0GoFFB1e69XLK5UMrpQ9NWjvwzfi5LFulyOPMvrR5B1ecbfsxZNinqrSb1m3BulN3bM16EBwYzsQpo3Jd+99Ixlv8Ky3+04NySUGJ+cDJ2YZ9ew8BcCb6PFWqVUZPL/cdLTMLYw4fCgBg3x4PnJWzdnmlb9O2Ob/f+oM/bt8lNTUVD3dfHF2sARg67FPWfvcTr169AuDBg0dF1t2qTTNu37rLnT/+JDU1lcMeftg7WarZ2DtZ4bZPcQc05kwsVatWppbeu9xLesCl2F8B+Ov5C25cv4W+gWL2+bOhfdm45hdevUoF4GERtLXO8Zs93X1xdFaf3XR0subAXk8AzkZfoGpVhb/yS/v82V+q9JUqVUKWs5bHODrb8Mftu1y7erPQOgFatG7KH7/f5e4fcaSmpuHtGYCtY2c1G1tHSzz2ewNw/sxFqlStzHt67wKgb1gLSzsz9u/yVEvz2/XfuXXzjyJp0USrNs35/dYdVf4ecvfD3slKzcbeyYoD+w4DEHPmAlWqVqGWsuyeOnGG5MfJr60jO63bNOf2rT/447ZSk4cvjs7WajYOztbsV9aHs2cuULVqFUX+FiItQNfujni6+QCQkvKS9PR0AMpXKK+W7wXRsk0zbv+e6b80vD38sXNUrx92Tpa4K+vHuTOxVKmiqB8AL/5KAUBHVwcdHR3VtdXLYsUiacrE2cWGvco6cCb6vMpHOTG36MghT38A9uz2wNnFNt/0H37YiDOnz6n8duzYaVxd7VTnW7p8JnNmLS9Qs5OLDfsKpc+Yw56KmLh3tydOSn15pU9Kuk/shcsAPH/+F9ev/YaBgR6giIlrVv2YFRPva447JRVjQHFzb/7slWr++fCjxkSGn1RoevCIJ0+e0bJ1s3z9l0nOMnjYwx87R/U6bOdkqRajM8ug5hit8FWj9+tz6oSiMx8RdhInV9tC6SkMzi527NntAUB09DmqVq2MvoYZHgsLEzw9/QDYvcsdVxdFOYuKiiE5+SkAp0/HYGSkX6Trv+38ffEihahTZ3n58u8i6SwObVs2o2qVyiV+neyUVDsyeGg/1q/+WdVPKE4fBqBl66bc/v1OjnZYPU7bOlrirmyHz52JpUpVzXFaN1ucHvh5Hzat3Vysfkx2Wiv9l9Vu+eGgoc07uFfhv7Ma/fck13lvXL/Fbzd/L5am7JT1/NWEq6sdu3a7A3D69DmqVauSa/UjQOfOnfDw8AVg5y43unSxB+Cvv16obN55p1Kx2uDsKPrSWT487O6fy4cOTlYcVPkwqwzeS3rAxQua+9Jvoq8gKJv85wblkiTVlyTpV0mSNgExwGZJks5IknRZkqT52exuS5K0RJKkk8rvW0uSFChJ0m+SJBVqCsvAUI+4uATV5/i4RAwM9dRsatSszpPkZ6oBQnabvNIbGOjnPp7ZqWrcgI4mbQkOccPbfzetsnXy6tarTdixw3j776Z9x9Z56tY30CM+2/kT4pNUnbYsm1rExyXma1O7jiFNm3/MubOxADRsXJ/2HdvgHbwXN59tRVpaZWCgR/yfWdeLj8/tSwNDPeKy28QlKf2Vf9oZsydw4Uo4vfq4smzxWkAR6MZN+JKVyzYUWmMm+ga1SMjmm8T4e7l8o2dQi4S4JNVnhf8UAXfO4iksm7eajIySuV9noCHvDHLoMzDQy2GTiIFB7sYtJ0OHDyDk+CFWb1hE1apVCq/JUI+4uBx5V4Cm+PhE9A31CpW2o0lb7t9/yK1bWTc1WrdpTuQpHyJOePHNhLmqOlgQmsq+nob6oV4GsvJXS0sL//CDnLsWzrGwU5w/e1Fl983MsZy6GEy33s6sWrqxUHqyY2CgR9yf8arP8fGJGBqqD140xxz9fNNfuXIdk07tqV6jGhUrVsDOzgKj2gaAYiATH5/EpUtXC6kvW+zSUI816TPMjImFSF+nrhHNW3zC2TMXAGjcuD4dO7UjONQNn4A9ajExp7aSiDEOjlYkJCRxOYd/Ll+8ioOTNdra2tStV5sWLZsUeqBpoKF85ayf+rnqcMEx+tqvN1U3mFy62uUqO6+DoaEef2YvW9nKXSY1a1bnyZOnqryPi0tQ5X12Bg/uS1BQWJGu/7bz999OSbUjDRvXx9ikDX5H9uHpu4OWxVyCrW+gl2cbm2Wj/hsS45PQyxan/cIOEHM1jMjwk6o43aBRPdobt+FQ0G72e22heSvNj2EUqM9QvZ8VH5eYq34aGKj3ARM0lNmSoqznryYMDfXVYowifhQmxmTZdOniQOyFUA55bmf4iNdbWq9voN43SYhPLLAMavJz7bqGNGv2MTHKOA2KRy/PXDpKj94urFyynv8C8lv8Ky3+c4NyJR8CO2RZbgVMkmW5LdAcsJAkqXk2u7uyLHcEIoFtQC/AGNC4JlSSpOHKAfyZv1OfaFyKmPOOVn42eX2naYVjZhodHW2qVquKrVUv5s5azpbtikFmUuJ9mn9iQWfTrsyavoSNP6/I83nB/M5fGN0Ald6pxM871jB3+jLVXT1tHW2qVquCq21/Fs1ZxQ9bV2m8vmZNxfdlQWmXLFxNi08scDvgzbARgwCYOmMcP2zcpnbn9M1qzZ1OlmWs7Mx58OARl5R3SEsCjfooWv5qYtvmfXRoaYe1aXeSEu8zb3Hez+4WStMbyl+A7r1c8FDOkmcSczYWM2MXbC17MX7iCMqXL/fGtGrK4EyTjIwMHC1606GpDS1aN+WDjxurbFYuXo9xM1sOHfRlyJf9C6WnqNqKE3OuX/uNNat/5LDXdtwPbeXSpaukpaVRsWIFJn8ziiWLVpeqvkzeeacSO3ZvZPrURTx79hwAHR0dqlWrgq1lL+bMXMbWHeveuLa8jlesWIEJ34xU3ezLzu6dbiTEJ3Ik3IPFy2Zw+vQ50tMKd2NIc/kqaoyuyE/bVzNvxnJVjJ40djaDh/XHL2Q///vfO6SmphZOT6Ekv75mAHPzjnw2uC+zZy17a9cvTv7+2ympdkRHW4eq1argZNOPBbNX8tO2wsWW3AJzHypKecvIyMCpcx+Mm9nSslVTPlA+DqOjo0PVapXpZjeAJfO+Y9Pmb4snT9OTKrnakYJ/Q0lR5vNXA28ixnh5BdC8hSW9+wxj3tzXG5Rr9mEhbHL0pTfvWMucGUvVZsiXLVpL26bWeBz04fPhA15Lp6Ds8F8dlP8hy/Ip5b/7SJIUA5wDmgDZd9DK3KHiIhAly/IzWZbvAy8lSaqW86SyLP8ky/JWWZZ1Tp0OJzEhCSMjA9X3hkb6JCbcU0vz8MEjqlarrHqeNbtNfFyixvTx8RqOJ2al8fEKBBSDjowMmZrv1uDVq1c8fpQMwIXzl7n9+121Z0KzkxCfhGG28xsY6pGUeE+Djb5GGx0dHX7evgbPg774+xzJShOXhL+34vP5mItkZGQUuClUJvHxiRjWzrqeoWFuX8bHJWKU3cZIT+WvgtICuB/0xqWLYqlk67YtmLvgG2IuhjBi5GC+nvwVXwwfmCuNJhLikzDI5ht9w1q5/JcYfw8Do6y7oQr/3adNh5bYOHQm8pwf639ejolZO1b/oPm5qOISryHvcvkyPjGHjT6JiffzPe+D+w/JyMhAlmV27zhIq9bN87VXu15cotosoaGRnqpM56XJ0FCfpIR7BabV1tbG2dWWQx5+Gq994/otXvyVwkeffFAorZrK/r1c+ZuzDOSuQ0+fPuPU8Wg6W3fKdY1Dbn44uhZu87lhwwcSecKbyBPeJCbcw6i2oeo7Q0N9EhKS1Ow1xxyFTXx8Yp7pd+44iLlpV5zs+/P4UTK3frtNg4Z1qVe/DsdO+hJ7ORwjI30ijnlRq9a7avoiTngRccKLhIR7qhn2zPMXJiYmZMbE+MQ80+vo6LB990YO7vfCxytIZRMXl4i38nP2mJiTkogx9RvUpW692oQf9yLmYgiGRvqERHpSq9a7pKenM2v6UixNuzKo/yiqVq3Mb7/dzqVLE7ljjF6u+pmQqw6rx+iftq/B0009Rv9243cG9ByOk1VfDrn78cfvdwulJy+GjxjEyVN+nDzlR0JCErWzl61s5S6TBw8eUbVqFVXeGxkZqPIeoGnTj9i4aRl9+3zJI2V7Vljedv7+2ympdiQ+PhE/5d4f55T9hJqF7CdkRxGDc7ex2ckZy/UN9biXw+bp02ecPH5GFacT4pMI8DkKwIWYS0Xqx6hdO069n5W9H5ddn5FaX0xz36UkKOv5m8lXIwZzOiqA01EBxOeIMYr4UZgYo24DcOxYFA0b1nstbQnx6n0TA2WfRd1Gg5+zxenNO9bgcdAHP+8jaMLTzRfnN/iYUVlGPFP+7+UvAEmSGgCTAWtZlpsDvkCFbHaZD4NlZPt35ue8Xie3EWhp0akLvj5H6Ne/GwBt27Xk6ZNnJCXlDljHIqLo2s0BgH6f9sDPV1H5/P2Oakwfc/YiDRvVp2692ujq6tKjpzMBvopGwtfnCOYWHQFo1Lg+5crp8vDBI2q+WwMtLUV216tfhwYN63Hn9p8af8D5mEs0aFSXOnWN0NXVpWsPJ4L8Q9VsgvxD6dWvC6B4Vu/p0+fcS3oAwKr1C7h5/RY/bVLfACXQ7yidzDsA0LBRPcqV0+XRw8d5uFGdc2cv0rBh1m/u3tOZAL+jajYB/iH06d8dgDbtWvD06XOSku7nm7Zho3qq9A5O1txQbq7m6vAprZtZ0bqZFT9+v5013/7A5p92FUpr7LnL1G9Yl9p1jdDV1cG1uwNH/MPVbI4EhNGjrysALds249nT59xPesDKheswaWaHWSsnxn45lROR0Uz4akahrltYzsdcpGGjetStp8jfbj0152+ffl0BxQ2KZ0+fcU9D2c1OrWzPBju62HL11xuF1nQu5iINspXpbj2cCfALUbMJ9Auhr7I+tGnbgqdPFfWhoLQWnU24ef0WCfFZDW/derVVjXLtOoY0fr8Bd//If5OyTC7EXKJBw3rK+qGDaw9HggPC1GyC/UPpqawfrdo255myftSoWZ0qymc/y1coj6mFMb9dVzz/V79hXVV6W0dLfrtRuOcCf/lpF2YmrpiZuOLjE0R/ZR1o266lykc5iYw4RbfujgB8OiAr5vj5Hskz/bvv1QSgdm0DXLva43bQmyuXr9O4QXuaN7GgeRML4uISMTftwr17D9T0mZt0wdykC34+wfQrlL4ounZXxMT+A7rjnxkTfY/mmX79pqVcv3aTTRvUNw7y8wnG3MIYUI+JOSmJGPPrlet83KijKpbExyViZdade/ceULFiBSpVqgiAhaUJ6WnpXL/2Wy5dmlCUwbqqMti1hyPBATnrcJhajH6WLUZ/u04Ro3/etEMtTebNCkmSGD9pBDu3HSiUnrz46ceddDR2oqOxE97eQXw6oAcA7dq14unTZxo78BERJ+ne3QmAAQN74uOruKFSu7Yhe/b+wLAvJnCzGM/Mvu38/bdTUu1IgO9RTM0V9bVho/ro6urysJD9hOxcOHdZPU53dyDYP0zN5khAGD2V7bAiTj/LM07fVMbjIL8QTMwUb0po0KgeukXox2TnXE7/9XAiUEOb17u/wn9tCum/N0VZz99MfvhxO+07ONC+gwPeXoEMHNATgPbtW/HkybNcNzoAwsNP0KOHMwCDBvbC21sRYxo1rK+yadmyKbq65V5Lm6IvXY86Sh927elIYA4fBvqH0Fvlw6wyCPDdhoXcuH6LH3NsJtigYVa/1c7Rkps31DcFFvxz+a+/p7wKigH6E0mS9ABHIOxNnTw4MAxbOwvOXjhKSkoKY0ZOU3233+1nxo+ZSWLiPebNWckvW1czY/YELsZeYdcOt3zTp6enM2XyfNwObUFbS5vdO924qtyMbPdON9ZvWsrxKF9evUpl1AjFEmITk3ZMnzWetLQ00tMzmD5pAcnJuTcJyTz/rCmL2eP+E1raWuzf7cn1q78x6HPFbsE7tx7gaFAEVrbmHI/xJyXlJRNHK3YtbWfcml79unLl8jWCIhQbbixbuIaQ4Ej27fJk1YaFHD1xiNRXqXw9cmahfZmens60bxZw0HMzWtra7NnpxrWrNxkyVLFr5rYt+wgODMPGzoLoC0dIeZHCuFHT800LMHveZBq/34CMjAz+vBvPpK/n5qmhKFrnTl3KjoPfo6WtxcE9h7hx7Tc+HaLYQXrPtoOEBkdiaWtK2BkfUlJeMmVswa/MsXO2Yt6yadSoWZ0tezdw5dI1BvceWSx9M75ZxF73X9DW1mLvLg+uXb3JZ58rdjnesXU/R4LCsbY159S5QFJevOTr0Vk3Br7/5VtMTNtTo2Y1Yi6HsnLZBvbudGf2gsk0bfoRMjJ378TxzdfziqRp+uQFHPD4BS1tbfbucufa1ZsMVubv9i37CA4Kx8bOgtPngxX5q9SUV9pMuvd0wsPdV+16HYzbMG7Cl6SlppEhZzBl0jwePSpc45uens7sKUvY6fYD2traqvoxUJm/u7YdJCQ4EktbcyLP+pGS8pLJYxT1o5bee3y3aRHa2tpoaUn4HAriaJBil/ppc7+mUeP6ZGTIxN2NZ/qkhYX2XyZBgWHY2XfmfGwIL1JeMvqrqarvDrpvZuzo6SQm3mPu7BVs2baWWbMnEht7mR3bDxaYfufujdSoUY3U1DQmT5yn2nSrqPps7TsTExtCSkqK2vkPuP/CuNEzFDFx9go2b1vDzNkTiY29ws5s+jSlN+7Yhn6fdufypatEKF/Bs3DeKoKDwtm1w40N3y/jxGk/Xr1KZeSIbzRqK6kYkxfvvleTg56byciQSYhPYuRwzbry0jp7yhJ2u/2IlloZVMToXdsOEBIcgZWtGcfO+vMyJYWJYxS7r7fr0Ipe/brw6+XrBIYr2prlC9cSciSSbj2dGPyF4vf6+xxh/25PzQKKQWBAKPb2lly8FE7KixRGfJX1ez08tzJq1FQSE+4xe9Yytu9Yz5y5k7hw4TLblTcGps8YR40a1VmzVrHLeFpaGmamXQp9/bedvwAxF0OoXOV/6Orq4uRsQ69unxf6xktR+GbuMqLPxZKc/BTrbgMZ9cUgerrav/HrZKek2pG9uzxYvWERYSe8eJWaqsqD4uibM3UJOw5+j7a2NgeU7fAAZZzerYrTZkSc8VXE6bGKOlJL712+27gILW1txetJDwUSoozTB3Z7snL9AoKOeZD6KpVJo2flqaEgfdMnL2Sfx2al/xTt1mdDlf7bovSfnTlR54NIefGS8dn898PmVZiYtqNGzeqcuxLGyqXr2bPTHUcXG5asmEXNd2uw+8APXLp4lX49NL+KqyB9ZTl/NeEfEIKDgxW/XjnGixcpfDk867W7hw9t56uRU0hISGLmrKXs3LGR+fO+4fz5S2zdtg+Abt0dGTigJ6mpaaSkvGTgoNfb1Vzhw8Xsdf8ZbW0t9u3y5HoOHx4NisDa1pyT5wJIefGSCaMV/eL2xq3prexLB0cqNshcumANIcERzJw3gUaNG5AhK/qtUydofovJv42Mwr2c5B+N9F/btU+SpPqAjyzLTZWftwEdgFsoZsO9ZFneJknSbaCtLMsPJEkaovz3GGUa1Xd5XadG5ffLtGMr6hTu+dnS4u/0N/csY0lQWbdiaUvIl5T0V6UtIV/S5dJcIFQ4KmqX7Try5FXR9zt4m2hpeiCyDKGtVbYXilUo4zEa4PHL56UtIV/KejuXcCugtCXkS53GzqUtIV/Ka+uWtoR8eZlWttth0PxMc1mirMeYGhXe7lsPikNC8pWyncmFZE79AW9tXLXg9u5S8dl/bqZcluXbQNNsn4fkYVc/27+3odjoLdd3AoFAIBAIBAKBQCAoGTJKdV/0t0PZnioQCAQCgUAgEAgEAoHgX4wYlAsEAoFAIBAIBAKBQFBK/OeWrwsEAoFAIBAIBAKB4J/Bv3/xupgpFwgEAoFAIBAIBAKBoNQQM+UCgUAgEAgEAoFAICiTlP139rw+YqZcIBAIBAKBQCAQCASCUkLMlAsEAoFAIBAIBAKBoEzyX3glmhiUlxAvUv8ubQn5Istlu3CnpL0qbQn58io9rbQl/KOpUr5SaUsoEEmSSltCvpT1Mpghl+3FZu/oVihtCfnysozHQACjd94tbQn58iz1RWlLyJc6jZ1LW0K+3L3pW9oS8uVGh7GlLSFf2idcKW0JBfKObvnSlpAvOlrapS0hX7TKeD9B8M9CDMoFAoFAIBAIBAKBQFAmKdtTiW8G8Uy5QCAQCAQCgUAgEAgEpYSYKRcIBAKBQCAQCAQCQZmkbD8Q92YQM+UCgUAgEAgEAoFAIBCUEmKmXCAQCAQCgUAgEAgEZZL/wu7rYqZcIBAIBAKBQCAQCASCUkLMlAsEAoFAIBAIBAKBoEzy758nFzPlAoFAIBAIBAKBQCAQlBpiplwgEAgEAoFAIBAIBGUSsfu64K2xatV8Ll+OIDo6kJYtm2q0qV+/DhERh7l0KZydOzeiq6sLgIuLLdHRgURF+XP8uA8mJu3eiKZlK2dz9sJRjp3yoXmLJhpt6tarTXCoG2fOH2Hz9rUqTXmlL1++HEfC3Ik86c2JaH+mzRxfbH2v47N+/boRHR1IdHQgoaEeNGv2sSrN6NFDOXs2mJiYI4wZ80Wx9a1YOYfzsSGciPKjRUvN/qtXrzYhYR6cuxDC1u3rVPre/6AhR0LcuP/oV8aOH6ayNzIywMdvN9Fng4iKDmDkqCFlSh/Axu+X89vt05yK9i+2ts7WnQiP8ubYGT9Gj9ecBwuWTufYGT+CIz1o2lyRfwZG+hw4vIXQU14cPXGIL0YMVNlv2vwtgeFuBIa7cfJ8IIHhbsXWZ2HViZAoL8KjfRg5fqhGm3lLpxIe7UNAhJtKX/ny5TgcvBv/8IMEH/dgwtRRKvtPmn6IZ+Au/MIO4H10Ly1aay7TRWHVqnlcuhTO6dMBedaRevXqEBFxiIsXw9i5c4NaHTl9OoDTpwNy1ZGrV48RHR3IqVN+HDvmXWx9362az5XLkZyJDsq3DkdGeHH5UgS7dm5S6XN1seNMdBCnowI4cdw3V9zT0tIi6pQ/nh5bC9SxdMVszpw/QuRJb5q3+ESjTd16tQkOcSP6XDCbt61Ri3V5pbe2MSMqJpAz548wfuJw1fEZs74m8qQ34ce9cD+0FX39WgBUr1GNw747uZNwnmXfzsmlwcrGjFNnAzh9PphxE4bn+h5gyYpZnD4fTPgJLzUtBaUdPXYoD55ep0aN6gDo6uqybtNSIk56E3bci06m7fNzYYGYWXUk4KQ7wac9GT5usEabWUsmE3zaE6+wvXzS/EPV8cpV/se6LcsJOOGG//GDtGzb7LW0ZGJpbUpktC8nYgIY8/UwjTYLl8/gREwAR4970qyFog4YGunj5r2ViChvwk56MeyrrDgze8FkIk/7cPS4J1t2raNK1cqvpe9YtB8n89G3aPkMTsYEEHL8EM2y5ffqDYu4dOMYYSe8cqX5YvgAjkX7EX7Sm9nzJxdbX1GYteQ7zJ370W3gV2/lejl5x7wNjYJ/pHHIz9Qc0TvX95U6NOPD8wdo6L2eht7reXdMf9V3BsvG88Hp3TT03/jGda38di4XLoZyKso/33Y4NNyT87EhbN+xXhV7PvigIUdD3Xn4+Crjxn+psn///YacOOWr+otPjGXU6M+LrM3S2pTjZ/w5dS6QsRO+1GizePlMTp0LJPT4YbXyt2bDYi7fPE74SfXyN2fhNxyL9iP0+GG27lr/WvVDEyu/nUvsxTCiovxpmY8/w8IPcSE2lO07stq8vn27EhXlT1SUP0dD3NXavOLS2dqUiNM+HDvrz+g86vCCZdM5dtaf4GNZfRlDI30Oem0l7JQXIScOq/VlAD7/8lMiTvsQcuIwM+dPem2dgrKBGJSXAeztLWncuD5NmpgzevQ01q1brNFu0aLprF//C02bWpCc/IQhQ/oCEBp6nHbt7OnQwZERIybz/ffLX1uTrZ0FjRrVp00La74eO4tVa+ZrtJu3cArfb9xK25Y2PEl+wqDBvfNN//ffr+jqPAizjq6Yd3TF2saMtu1aFlnf6/rs9u272Nr2oV07e5YuXcfGjcsA+OSTDxg6tD+mpq60a2ePk5M1jRrVL7I+O/vONGpcn5bNrRg/Zgar1yzUaDd/4VQ2bthCqxZWJCc/5bPBfQB4/PgJUyYvYN3aX9Ts09LTmDljCe3a2GFt2ZMvhw/iw48alxl9ALt3udGjW9E7AJloaWmxaMUsBvUZiWXHLnTt6cT7HzZUs7GyMaNBo7qYtnVi6oR5LF01G4D0tDQWzF6JpXEXuth9yuAv+qnSjvpiMvYWvbC36IWfdzD+PkeKrW/hihkM7jMSG5NudOnhmEufpY0pDRrWw6KdC9MnLmDRt7MARfnv320Yjha9cbTog4V1J1q1bQ7A9HkTWLviB5w69+G7pRuZPndCsfRlYm9vSaNGDWja1IIxY6azbt0ijXaLF09j/frNNGvWmceP1euInV0f2rd3UNaRpWrpHBz6YWzshKmpa7H0Odhb0rhxAz5pYsao0VNZv26JZn2LprNu/S80aWpOcnIynw/pB0BI6DHatrOjfQcHho+YxA/fr1BLN3bMF1y9drNAHTZ2FjRqVI+2LW2YMG42q1Yv0Gg3b8E3fL9xK+1a2ZKc/JSBn/XON72WlhYrVs2jT49hdGznSM9eLnz4oaKurl/7C2YdXbHo1IXAgFC+mTYGgL9f/s2SRWuYMzN3DNfS0mL5qrn07fklndo50aOXCx982CjXb2nYqD7tW9oycfxsVq6eX6i0hkb6WFh14u6dONWxQUMUdd28oyu9ug5hweJpSJJUoD81oaWlxdxlU/my3zicOvXGpbs9jT5ooGZjYdOJ+g3rYNu+O7MnLWb+iumq72YtmUxkyAkcTHrRpXN/frv+e7F05NS05NtZDOg1AosOrnTr5ZTLn1a25jRsWA+T1g58M34uy1bNBSAtLY35s1Zg3sEVZ9t+DBn2qSptROgJOnfsinWn7vx283aeg5nC6Fv67Ww+7TUc8w6udO/lnEuftVJfx9YOTB4/l+Wrsm7k7N9ziP69ct986WTWHnsna6w6dcWioyvfr99SLH1FpZuTLT98pzkGlThaWhjMG8mdoXO5aT+Sqq7mlGtcJ5fZi+jL3HIdyy3XsTzYsFd1/In7Ee58nvsm2euS2Q63aGbJ2DHTWbNWs38WLprGxvWbadnciuTkJwwektUOfzN5fq52+MaNW5gYO2Ni7IypiSspKS/x9goqkjYtLS2WrZrDp72+xKy9C917ai5/DRrVw7iVPZPHz2HFd3NV3+3b40m/nrnLfnjoCSyMXbHs1JXffrvNuImaby4WB3v7zjRu3IDmzTozZswM1qzV3C9cuGgaG9ZvpkVzS6U/s9o8e/u+dOjgyPJl61m/YanG9IVFS0uLxStnMrD3V1gad6FbTyfezxVjzGjQqB6mbRyZ+vU8lirrcGaM6WzcBVe7/gwZ1l+V1sS0PfZOVtiYdsfKpCs/rC/4xrPgn4EYlBcCSZKGSJK0oaTO7+pqx+7d7gCcPn2OatWqqGZOstO5swkeHn4A7NrlRpcu9gD89dcLlc0771RCll9/OwQnFxv27fUE4Ez0eapWrYKe3nu57MwtjDnsGQDA3t2eOLnYFpg+U6+urg66urrF0vu6Pjt16izJyU9U6Y2MDAD46KP3OX06hpSUl6SnpxMZeYquXR2KrM/J2Ya9exS/Pzrz9+vn9p+FRUcOeSpmlPfudsfFVeG/B/cfEhMTS1pqmpp9UuJ9Lpy/DMDz539x7dpNDA31y4w+gBPHo3n8KLnImjJp2aYZt3+/w50//iQ1NY3DHv7YOVqp2dg5WeK2T3EHPuZMLFWqVKaW3rvcS3rApdhfAfjr+QtuXL+FvoFermu4dnPgsLtf8fS1bsrt3+9w9484UlPT8PYMwNbRUs3G1tES9/2KGeRzZ2KpUlWhD+DFXykA6OjqoKujoyr/sizzv8rvAFC5SmXuJd4vlr5MXFxs2bMnq45Uraq5jlhYZNWR3bvdcXW1AzLryFNl+hhVHXlTuLrasatQdbgTHh6+AOwsZNwzMtLH0dGKrVv3UhBOzjbs23sIUMSqKtUqa4x1ZhbGHD6kiHX79njg7GKTb/o2bZvz+60/+OP2XVJTU/Fw98XRxRqAZ8+eq85b6Z2KKu0vXqQQdfIsf//9d67rt85xPk93XxydbdRsHJ2sOaCMu2ejL1C1qkJLQWkXLZ3B/Nkr1Xz44UeNiQw/CcCDB4948uQZLVsXb4a6eesm/HH7rqrO+B4KwsbRQs3G2sECz/2Kcnjh7CUqV63Me3o1eed/79DWuBUHdx0GIDU1jWdPn+e6RlFp1aYZt29lxplUDrv7Y++kHmccnKw4uE9x3Zhs9fhe0gMuXsgZZxRlNzz0BOnp6co0F4oVnxX6mvN7Nn2H3P1y6bN3suKASt8FqlStQi1l2T114gzJj5NznXfw0H6sX/0zr16lAoq8fRu0bdmMqlXe7KxoYanY4gNe/RFP6t1ESE3jiU8ElW2MC53+RfRl0pOfvXFdLi627N3tARTcDnsq2+Hdu9xxcVHE6Pv3HxJzNpbU1NQ8r9HZshO3bv3B3btxedpoorWy/P1xW1n+PPxwcLZWs3FwtubgXkX5O6ux/D3Jdd7wkOOq+nE2uvj1QxPOLnbsUfnzHFWrVkZfoz9N8PRUtnm73HFV+jMqKiZHm/d62hQx5m5WjPHww95Jva9g72Sl1pepWjW/vowixnw2tC8b1/yiqsMP31IdLm3kt/hfaSEG5WUAQ0N9/vwzQfU5Li4xV6CqWbM6T548VQWzuLgENZsuXey5cCEET89tjBjxzWtrMjDQIy6bpvj4RAwM1Qc3NWpW50nyM5Wm+LhEDJU2+aXX0tIi4oQX13+PIizkGGfPXCiyvjfhs0yGDOlLUFAoAJcvX8PUtAM1alSjYsUK2NtbUrt20QcjufTFJ2JooH7tGrn05fZxftSta0TzFk04E32+TOorLgYGtUiIS1R9ToxPwsBAfbCmb6BHfDabhPikXIPv2nUMadr8Y86djVU73qFjG+7fe8jvt+4US5++gR4JcUk5rp1TXy01fYnxSegpbbS0tPALO0DM1TAiw09y/uxFABbMXMGM+RM5GRvEzAUTWb5wbbH0ZaLI43jV57hs9TOTwteRfgQGhqk+yzJ4e+/i+HEfhg7tn8u+ePpyX7vguOdA7IVQDnluZ/iIrGW4366cx/QZS8jIKPgpNANDPeLissUqDeVcU6zLtMkrvYGBfu7j2crozDkTuPhrBL37dGHp4oLz2sBAj/g/s8qUpphsYKhHXHabuCSllrzTOjhakZCQxOVLV9XOdfniVRycrNHW1qZuvdq0aNmk2J1UPYNaJGarM4nx91T1IcvmPRLjszQmxSehp1+LuvWNePwwmWXr53IoZDeLV8+iYqUKxdKRHX0DPeLUYkhigfU4IT5JLQ8Batc1pFmzj4nJEWcA+g3sQciRyGLpMyjEtQ1yxcHEXLEyJw0b18fYpA1+R/bh6buDlq1e/zGZso6OXk1SEx6oPqclPkBXr2Yuu4qtPqKhz3rqbplP+ffrlrguA0M9tXY4Po8YmJyjHc4Zx/OjV28X3A4W/REjfUM94nPEr5xtrIGBeuxL0BCT8uPTgT05GhxRZG15YWiop9amKGJxYdqU3JoHD+5LUFDYa+lR9FOy+yd3P0VTjCmoL9OwcX3ad2yDd/Be3Hy20eI/UIf/K/xnBuWSJL0jSZKvJEkXJEm6JElSX0mS2kmSdEJ57LQkSfndxjWUJClAkqQbkiSt0GQgSdJwSZLOSJJ0Jj298HfyNa0IzDl7rGnZYHYbL69AWrSwok+fYcyd+/rPiBV0vYJs8vsuIyMDc5MuNPnQlNZtW/DxJ+8XQ1/uY0X1GSjuQA8Z0peZMxXLlK5du8mqVd/j67sbb++dXLz4K2lp6cXQV1z/Fe7877xTiZ17NjFtykK1Wbeyou+1eM2yB4rZx5+2r2bejOU8f/aXml3Xnk4c9ijeLLni4rkPFUVfRkYGTp37YNzMlpatmvKB8vGDgZ/3YeGslXRsbseCmStZsU7zIyOFlvkG/Ahgbt6RwYP7MmtW1lI+K6semJg4063bYEaM+IxOnYr+vPGb0OflFUDzFpb07jOMecq45+Rozf37Dzl37uJb0ZHXdwXFqMULVtPsY3MOHvDiy+EDcxu/QZ15Ha9YsQITvhnJMg03BXbvdCMhPpEj4R4sXjaD06fPkV6MWKjQlftYYbVra2vzSfMP2bPVjW5WA3jxIoXh44YUS0eB1yuMjVqcqcTmHWuZM2NprjgzftII0tPScT9QvD0XNOsrev3NiY62DlWrVcHJph8LZq/kp22ri6XvH0UhHrt4efkmN8w/55bLWB7t8Kb2D7Pegqw3E6PzQldXF2cnGzyL0d5pdFnO6xaiXufF15NHkJaWVuz6oYk32eZ9Nrgvs2cte009uY8VvS9TiZ93rGHu9GWqGKOto03ValVwte3Pojmr+GHrqtfS+U8h4y3+lRb/mUE54ADEy7LcQpblpkAAsB8YL8tyC8AGSMknfUugL9AM6CtJUq4HkmRZ/kmW5bayLLfV1v5fvmJGjPhMtaFEQsI9tdlYIyN9EhKS1OwfPHhE1apV0NbWVtoY5LIBOHbsNA0b1qVmzer5Xl8Tw4YPJOKEFxEnvEhIuIdRNk2GhvokJtxTs3/44BFVq1VWaTI00idBaRMfn1hg+qdPnnEsMgprG/NC6XvTPmva9CO+/34FvXoN41G25dbbtu2nY0dnbGx68/hxMjdvFu75xS+HD+LYSR+OnfQhISFJXZ+hPgmJ6voe5tKnT6KGPM2Jjo4Ou/Zs4sB+L7y9Agul7W3qe10S4pMwyDYjp2+oR2KOpdwJ8YkYZrMxMNQjKVFRvnR0dPhp+xo83XxzPTeura2No4sN3spHLopDYnwSBkZZd7IV186pL0lNn76hXq7l6E+fPuPk8TN0tu4EQM9+XfD3Vuj1PRxUrI3eRoz4jFOn/Dh1yk+Zx4aq74yy1c9MCldHltO7t3odyTzP/fsP8fIKpF0h94X4asRgTkcFcDoqgPhc+nLHtMLHvSgaNqxHzZrV6WjSFmdnW65dO8HOHRvp3LkTW7eqDzq/GjGY8ONehB/3IjEhSW1pvqFR4WJdpk18XKLG9PHxGo4nqp8XwO2AN65d7fN3HIqYalg7q0xpiqnxcYkYZbcx0lNp0ZS2foO61K1Xm/DjXsRcDMHQSJ+QSE9q1XqX9PR0Zk1fiqVpVwb1H0XVqpX57bfbBerURGL8PfSz1Rl9w1q56kNi/D30s81q6RnqcS/pPokJ90iMv0dsjOKxnUDvozRp/lGxdGQnIT5RbebfwFCfpBz+zFmPDQz1VHmoo6PD5h1r8Djog5+3epzp3b8rNvYWjP5ySrH1xWu6ds78zhUH9XPFytznTcTPOxiAczEXycjIKFZ/4Z9EWuIDdA3eVX3W0X+X1KSHajYZz1OQX7wE4HnYGSQdHbSrV3njWoaPGKTagC1nP8YwjxhYLUc7nDOO54WdfWfOn7/MvXsPCjbOQUJcEoYFxK+EePXYaaAhJmmiT/9u2NpbMurL11/VOXzEIE6e8uOkhjbPUEOfRXObkqW5adOP2LhpGX37fKnW5hUHRfzI7p+sfoq6Td59mZ+3r8HzoHpfJiEuSdVXOK+swzX+5XX4v8J/aVB+EbCRJGm5JElmQF0gQZblaABZlp/Kspz7Adksjsqy/ESW5ZfAFaDe64j58ccddOjgSIcOjnh5BTJgQE8A2rdvxZMnzzR23sLDT9KjhxMAAwf2wttbsXFHw4ZZUlq2bIqubjkePnxcZE2//LQLc5MumJt0wc8nmH79uwPQtl1Lnj59RlJS7sY+MiKKrt0Vz1z3H9Adf19FoPD3Paoxfc13a6h226xQoTydLU24cf1WofS9SZ/VqWPI/v0/MXTo17kG3e+9V1Nl07WrAwcO5N69VhM//7QT044umHZ0wdc7mP6fKn5/u8zfr6GzFBFxim7dHQHoP6AnvoXYfGzj98u4du03Nq7fXChdb1vf63Ih5hINGtalTl0jdHV16NrDkeCAUDWbIP8wevXrAiietX329Dn3khQdj2/XLeDm9Vv8vGlHrnObdTbmtxu3SIgv/s2FC+cu06BhPZU+1+4OBPuHqdkcCQijZ1/FBmit2jbn2dNn3Et6QI2a1amifK6yfIXymFoYc/OGovzdS7yPcae2AHQy78Dt34q+vP7HH3dgbOyEsbET3t5BfPppVh15+lRzHYmIyKojAwb0xMdH0WGvU8eQfft+5IsvJqjVkUqVKvK//72j+reNjTmXL18rlL4fftxO+w4OtO/ggLdXIAMLVYdP0KOHMwCDstXhRg3rq2yyx73Zs5fTqHF7PvzQhEGfjSYs7Diff67+locfftyORacuWHTqgq/PEfr17wYoY9UTzbHuWEQUXbspYl2/T3vglxnr/I5qTB9z9iING9Wnbr3a6Orq0qOnMwG+RwFo2CgrZjs6WRcqBp47e5GGDbPO172nMwF+R9VsAvxD6KOMu23ateDp0+ckJd3PM+2vV67zcaOOtG5mRetmVsTHJWJl1p179x5QsWIFKlWqCICFpQnpaelcv/ZbgTo1cfHcFeo3qEPtuobo6urg3M2OowHqy1ZDAsPp3ldRDlu0acrzp8+5n/SQB/cekhifRAOlzzqatefmtcK1GflxPuYSDRrVo049I3R1dena05FAf/U4E+gfQu9+XYHMOPNMFWe+27CQG9dv8ePG7WppLK1NGTN+GEP6jyYl5eVr6LtIw0b1qKvU162nE0H+OeNgKH1U+loo9eU/KA/wPYqpueJ56oaN6qOrq1us/sI/iZTY65Srb4RubT3Q1aGqiznPj0ap2Wi/mzWoqdD8AyQtifTHT9+4lp9+3KnahM3HO4j+A3oABbfD3ZXt8ICBPfH1DS7UtXr3duXgwcL1X3JyLmf56+FEoF+Imk2gXwi9+yvKX5tClj9La1PGfD2Mz/qNfK36kclPP+6ko7ETHTPbPJU/M9s8Tf48SffuyjZvYE98fBVtSu3ahuzZ+wPDcrR5xUURYzL7Mrp07aG5DmfvyzzN1pdZtV7Rl/lpk3qMCfQ7SifzDoCiLSlXTpdH//I6DJCB/Nb+SgvpTWwK9k9BkqQagBPwFRAE2MmybFqIdEOAtrIsj1F+9gG+lWU5LK80FSrULZJj16xZiJ1dZ8XSvOGTiYlRPDty6NA2Ro6cSkJCEg0a1GXHjg3UqFGN8+cv8/nn43n16hWTJo1kwICepKamkpLykhkzlnDiRHS+16uoU65ATSu/m4e1jTkpKSmM/moq589dAuCA+y+MGz2DxMR71Ktfh83b1lC9ejViY68w4otJvHr1Ks/0TZp8yKafVqKtrYWWlhaeHn6sXJZ7D72UtFcl6rPvv19Ot25O3LnzJwBpael06uQCwNGjbtSoUZ3U1FSmTl1IaOjxXNcup61ToL5V383HxtacFykvGTViimo5rZvHFsaMmkZi4j3q16/D1u3rqF69KhcuXOHLLyby6tUraum9S3jkYSpX/h8ZGTJ//fUX7dvY06TpRwQdOcClS1dVz8sumPctQdme9y0sJaHv2bPnbNm2FlOzDtSsWZ179x6wZNFadu44oHbtKuUr5avNysaMeUumoqWtzf7dnqz/7icGKnec3bVNca5FK2bS2dqUlykpTBwzm9jzl2nXoRWe/jv59fJ1lX+WL1yreq7zuw2LiDkTqzpHfmhLed+ztLQxZc7iKWhra3NgzyE2fPczA4YoduPeve0gAAtXzMDCqhMpKS+ZPHY2F89f4aNP3ue7jYvQ0tZGS0sLn0OBrPv2RwDadmjFvCVT0dbR5u+/XzHrm0VcUm4mpYl7L3JvopOT1asXYmdnwYsXKYwYMZmYGEUee3puY9SoKSQkKPJ4584NVK9ejQsXLvP551/z6tUrNm1aTrdujmp1xNTUlfr167B//0+A4k7+/v2HWbEidx3OkAteBLZ2zSJVHf5y+CRVHT58aDtfjZyiqsM7d2xU1uFLDMkW9wYO6ElqahopKS+ZPmNxrrhnbm7MhK9H0L1H7rcBvKOb9VzyilVzVbFqzMhpqli33+1nxo+ZqYp1v2xdTfXq1bgYe4URwyarYl1e6W3sLFiyfCbaWtrs3unGd99+D8D2XRto/H4DMjIyuHs3nknj56hmyM5fCqVy5f+hW06Xp0+e0avb56rBsI2dBYuXzUBLW5s9O91Y/e0PDBmq2I1+25Z9ACxfNRcrGzNSXqQwbtR0NS050+Yk5mIINhY9efToMXXqGnHQczMZGTIJ8UmMHzODP+/Gq9nXKF/4mUQLm07MWDQRbS1t3PZ68cPqLfQbrLgps2+7YsO/ucunYGZposjPcfNV5f/jph+waPUsdHV1+fOPOKaNm8/TJwVvvPUs9UW+31vZmrNg6TS0tbXYt8uTtat+5LPPFTsx79i6H4AlK2dhaWNKyouXTBg9kwvnL9PeuDWHA3Zx5fI1MjIUTf3SBWsICY7gREwA5crp8li5yVVM9AWmTtT8KEpBGwpZ25qzYOl0tLW12LvLQ6O+pStnq/R9PXqGaiPQ73/5FhPT9tSoWY379x6yctkG9u50R1dXl9UbFtG02ce8Sk1l/uwVHI+I0nj9uzd989VXFL6Zu4zoc7EkJz+lZo1qjPpiED1dC14hkh83OowttO3/OrdFb9ZwJC0tkt2CebBpP9X7Kwa6j/f6U32QC9U/dYL0dDJeviJpyS+kxCjKn9GaKVTq0Ayd6lVIe5DM/bW7ST5Y8G7m7ROuFGjz3eoF2Niak/Iiha++msI5ZYx299zC6FHTFCta6tdh2471VK9eldgLV/hi6ARVOxx5zEutHW7b2o5nz55TsWIFrl4/QbMmFjx9mnddeUe3fJ7fWduas3DZDGX5c2fNtz/y2VBl+duiLH/fzlbGm5eMHz2DC8p488PmVZiYtqNGzeqK8rd0PXt2unPqXCDlypVTbQZ79swFpkyYl6eG56lFG7h/t3oBtrYWpLxIYcRX36j86eG5lVGjpqr8uX3HelWbl+nPjZuW0bWro2pTvLS0NMxMu+R7vWrl38n3eytbM+YvmYaWthb7d3uybtVPDPpc0ZfZuVXRD1m8chadrRV9hYmjZyn6MsatOeS/kyuXryErY8yyhWsICY5EV1eXVRsW0qTZR6S+SmXh7G85Hqm5DgPEPb5cvNdmlDFG1e/z1gasm24fKBWf/WcG5ZIkGQKPZFl+KUlSN2A48BHQV5blaOXz5CmaZsvfxqD8bVOYQXlpUphBeWlSmEG5IG8KGpSXBfIblJcFCjMoL00KMygvTbIPyssiWsV8/djbpCiD8tKgoEF5aVOau/wWhjc5KC8JijIoLw0KMygvbfIblJcFijoof9sUNCgvC/xbBuUj3+Kg/PtSGpT/l0YWzYCVkiRlAKnASBTbVKyXJKkiiufJbYDXf9eKQCAQCAQCgUAgEAgEheA/MyiXZTkQ0LQrVoEvq5RleRuwLdtnlzcmTCAQCAQCgUAgEAgEGinNZ73fFmV7faZAIBAIBAKBQCAQCAT/Yv4zM+WFQZIke2B5jsO/y7LcvTT0CAQCgUAgEAgEAsF/mbK9S82bQQzKs5HPEneBQCAQCAQCgUAgEAjeOGJQLhAIBAKBQCAQCASCMklZf1vFm0A8Uy4QCAQCgUAgEAgEAkEpIWbKBQKBQCAQCAQCgUBQJvkvPFMuZsoFAoFAIBAIBAKBQCAoJcRMeQmhrVW273dIklTaEvKlvI5uaUvIl/SMsn3PTquM56+2VLbrB8DLtFelLSFfynoea0napS0hXyrqlCttCfny6OXz0pZQIJV1U0tbQr7oapXtMljW2+EbHcaWtoR8eT9qfWlLyJf0OpalLaFAdLXL9jDgf1QobQn5olPGY4zgn0XZro0CgUAgEAgEAoFAIPjPIjZ6EwgEAoFAIBAIBAKBQFBiiJlygUAgEAgEAoFAIBCUScr2Q6NvBjFTLhAIBAKBQCAQCAQCQSkhBuUCgUAgEAgEAoFAICiTZMjyW/srDJIkOUiSdE2SpJuSJE3T8P0ASZJilX8nJElqUdA5xaBcIBAIBAKBQCAQCASCApAkSRvYCDgCnwD9JUn6JIfZ74CFLMvNgYXATwWdVzxTLhAIBAKBQCAQCASCMkkZ23u9PXBTluVbAJIk7QO6AlcyDWRZPpHN/hRQu6CTiplygUAgEAgEAoFAIBD855EkabgkSWey/Q3PYWIE3M32+U/lsbz4AvAv6LpiUF5KrPx2LrEXw4iK8qdlyyYaberVq01Y+CEuxIayfccGdHV1AejbtytRUf5ERflzNMSdZs0+VqX5/ocV3L59hujowAI1WNuYERUTyJnzRxg/MWd5U7B0xWzOnD9C5Elvmrf4pMC01apXxePwNqLPBeNxeBtVq1VRffdJkw8JPHqAE6f9OHbKh/LlywEwc84ELv4awZ2E8wVqXr5yDucuhHD8lC8tWuTtt6Oh7sScP8rW7etUfssrfeP3GxB5wlv1dzf+PCNHDVE759hxw3jy/Ddq1KxeoMZMVn47lwsXQzkV5U+LfPI4NNyT87EhbN+xXqX1gw8acjTUnYePrzJu/Jcq+/ffb8iJU76qv/jEWEaN/rzQmrKzYuUczseGcCLKL199IWEenLsQoubL9z9oyJEQN+4/+pWx44ep7I2MDPDx20302SCiogNy+bGwWFh1IiTKi/BoH0aOH6rRZt7SqYRH+xAQ4UbT5oo6UL58OQ4H78Y//CDBxz2YMHWUyn7DLyvwCzuAX9gBjp3zxy/sQLG0AVham3L8jD+nzgUydsKXGm0WL5/JqXOBhB4/TLNsdWfNhsVcvnmc8JNeavZzFn7DsWg/Qo8fZuuu9VSpWrnY+jJ5nTjzwQeNCAn14NHja4wfr/4bx4z5gugzQURHB7Jt2zrKly//1vW9qTiYF52tTYk47cOxs/6M/nqYRpsFy6Zz7Kw/wcc8VGXQ0Eifg15bCTvlRciJw3wxYqBams+//JSI0z6EnDjMzPmTiq0PYNWqeVy6FM7p0wG0bNlUo029enWIiDjExYth7NyZ5b9+/bpx+nQAp08HEBrqoeY/AC0tLU6e9MPdfUuxtJlbmXA06jCh0d58lUcdnrt0KqHR3vhHHKRJ849yXd8ndD+/7FmvOjZx+mj8Iw7iG7afHW4/UEv/vWJpy8TCuhOhUV5EnPFl1PgvNNrMXzqNiDO+BEa6q8UZr+A9BES4ceSEJxOnZcWZCVNHcvrSEfzDD+IffhBLG7Pi6yuBOAgw5Mv+hER5EXzcg+lzJxRbX3beMW9Do+AfaRzyMzVH9M71faUOzfjw/AEaeq+nofd63h3TX/WdwbLxfHB6Nw39N74RLcVh1pLvMHfuR7eBX73V65ZEHS5fvjyRkYeJivLn7NlgZs0qXh53tu5EeJQ3x874MTqP+rFg6XSOnfEjONJDrfz5BO8lKMKdoycOMWnaaJW9c1c7jp44xJ0HsTTPI+YXhZJoi6fOHEfo8cMcjfRkv+dm9PRrFVtfSdThr6eMJOpSsKo/Y2ljWmx9/yQykN/anyzLP8my3DbbX86l55IGiRon8yVJskQxKJ9a0G8Ug/JSwN6+M40bN6B5s86MGTODNWsXa7RbuGgaG9ZvpkVzS5KTnzB4SF8Abt++i719Xzp0cGT5svWs37BUlWbXTje6dRtcoAYtLS1WrJpHnx7D6NjOkZ69XPjww8ZqNjZ2FjRqVI+2LW2YMG42q1YvKDDt1xNHEB5+gnatbAkPP8HXE0cAoK2tzY+/fMvE8XMwae+Eq9NAUlPTAAj0D8Wmc88CNdvadaZRo/q0amHF+LEz+W7NAo128xdOYdPGrbRuaU1y8hM+G9w73/Q3b/yOmYkrZiauWJh2JSXlJT7eQarzGRkZYGnViTt34grUmImdfWcaNa5Pi2aWjB0znTVrF2m0W7hoGhvXb6ZlcytlHvcB4PHjJ3wzeT7r1v6iZn/jxi1MjJ0xMXbG1MSVlJSXeHsFaTp1ofS1bG7F+DEzWL1moUa7+QunsnHDFlq1sCI5+SmfDc7SN2Xyglz60tLTmDljCe3a2GFt2ZMvhw/iw48aazp1nmhpabFwxQwG9xmJjUk3uvRw5P0PG6rZWNqY0qBhPSzauTB94gIWfTsLgL//fkX/bsNwtOiNo0UfLKw70aptcwDGDJuCU+c+OHXuQ4D3EQJ8jhZJV3Z9y1bN4dNeX2LW3oXuPZ354MNGajbWtuY0aFQP41b2TB4/hxXfzVV9t2+PJ/165u48hIeewMLYFctOXfntt9uMy+NGWWF53Tjz+HEykyfPY+3an9XsDQz1GDlqCGamrrRrZ4+Wtha9e7u+dX1vIg7mhZaWFotXzmRg76+wNO5Ct55OvJ8jj61szWjQqB6mbRyZ+vU8lq6aA0BaWhrzZ62gs3EXXO36M2RYf1VaE9P22DtZYWPaHSuTrvywfmuxNdrbW9KoUQOaNrVgzJjprFunOcYsXjyN9es306xZZx4/fsKQbP6zs+tD+/YOLF26jo0bl6qlGzNmKNeu3SyWNi0tLRasmMGQPqOwM+lOlx4ONM5RhzvbmFK/YV0s27mq1eFMPh8xgJvXb6kd+2nDNhzNe+PcuS8hQRGMmzyiWPoyNS5aMZPBfUZh3bErXXpqijNm1G9UD/O2zkybMJ/Fq7LiTL9uX+Bg3gsH895qcQbglx92KmNQb0KPRBZbX0nEwY6m7bB1tMTBrCe2nXrw08btxdKXQywG80ZyZ+hcbtqPpKqrOeUa18ll9iL6Mrdcx3LLdSwPNuxVHX/ifoQ7n895fR2vQTcnW374TnMdKilKqg7//fffODj0p0MHRzp0cMTOzoL27VsVSZuifsxiUJ+RWHbsQteeTrnKn5WNGQ0a1cW0rRNTJ8xj6arZyuu/ok+3odiZ98TevBedrTvRWln+rv16ky8/+5qoE2eLpCcvjSXRFm9ctxnLTl2xNutOcEAYk3Lc1CqKvpKowwCbv9+l6s+EHjlWLH2C1+JPIHuQqw3E5zSSJKk58AvQVZblhwWdVAzKSwFnFzv27PYAIDr6HFWrVkZfwx1/CwsTPD39ANi9yx1XFzsAoqJiSE5+CsDp0zEYGemr0hw/fppHj54UqKFN2+b8fusP/rh9l9TUVDzcfXF0sVazcXK2Yd/eQwCciT5PlWqV0dN7L9+0js7W7NvtCcC+3Z44udgAiruZly9d4/KlqwA8fpRMRkaG6txJSfcL4Tcb9u71VKWpWrUKenq5/WZu0ZFDnopVInt2e+DsYlvo9J07m/D7rTvcvZtVt5Yun8mcWcuRC7kjI4CLiy17VXmsvJbGPO6Ip1Lr7l3uuCjz+P79h8ScjSU1NTXPa3S27MStW39w927hbxZk4uRsw949noXSl+nLvbvdcXFV+PLB/YfExMSSpryxkklS4n0unL8MwPPnf3Ht2k0MDfUpCi1bN+X273e4+0ccqalpeHsGYOtoqWZj62iJ+35vAM6diaVK1crU0nsXgBd/pQCgo6uDro6Oxnxz7maPl0eBK4k00rpNc36/dYc/bv9Jamoqhzz8cHBWrzsOztYc3HsYgLNnLlClahVqKcvaqRNnSH6cu46GhxwnPT1dkSb6QpH9lpPXjTNZZTAtVxodHW0qVqyAtrY2lSpVJCEh6a3rexNxMC9atWnG7Vt3ufOHIo8Pe/hh76ReBu2drHDbp5hhiTkTS1VlGbyX9IBLsb8C8NfzF9y4fgt9A8VMy2dD+7JxzS+8eqWo1w8fPCq2RhcXW/bscQfg9OlzVK1aBX0NMzoWFiZ4eCj9t9sdV1eF/06dOpvDfwaqNEZG+jg4WLF1675iaWvRuil//H43Rx3urGZj62iJh7IOnz9zkSpVK/Oesg7rG9bC0s6M/bs81dI8f/aX6t8VK1VAfo2nDFu2acbt3+8o8zgNbw9/7HLEGTsnS9yVeXzuTCxVqmiOMzp5xJnXoaTi4MDP+7Bp7eY3UgYzqdjiA179EU/q3URITeOJTwSVbYwLnf5F9GXSk5+9to7XoW3LZlSt8vqrk4pCSdbhv/56AYCurg46OrpFLp8568dhD3/sHK3UbOycLNViYGHqx83rt7h183aRtORFSbXF2eNMpXcqFrtuv42+zH8J+S3+VwiigfclSWogSVI5oB+gtuRCkqS6gAcwSJbl64U5qRiUlwKGhnr8+WfWoC8+LhGDHB3wmjWr8+TJU1UnPS4uAUNDvVznGjy4L0FBYUXWYGCgT1xcgroGA/XzGxjq5bYx1Ms3ba333lUNsJOS7vPeuzUBaNy4AbIs4+a5hdDIQ4z9WvMyo/w16xGX3W/xibkGLjVqVudJ8jOV37L7tjDpe/Rywc3NW/XZ0cma+PgkLilvJhRaq6Eef/6Z3UcJua5Vs2Z1ktXyOFFjHudFr94uuB30LthQA4aG+mr64uITMTTQ4Msc+gyKoK9uXSOat2jCmejzRdKmb6BHQlzWIC8hPkk1qMmyqUV8XKLqc2J8EnpKGy0tLfzCDhBzNYzI8JOcP3tRLW37jm14cP8ht2/dKZIu1bUN9YjPUf71c9YdA/W6kxBfNN99OrAnR4MjiqUvkzcZZ7KTEJ/E2jU/c/XaCX67dZqnT55x9GjRZwPLQhzMC30D9TxWlEG9HDbqZVCTTe06hjRt/jHnzsYC0LBxfdp3bIN38F7cfLbRopXm5aqFQVGHs/ynKX5o9l/umz1DhvQjMDBM9XnlyrnMnLlEdeO0qOgb1CJBrX7ey+UbPYNaedbzOYunsGzeao3XnzxzDMdjA+nay5nVSzcVS1+mxpz5p6chjxNyxBn9bHHGP/wg566FcyzslFqcGTysP4GR7qxcv4CqVatQHEoqDjZoVI/2xm04FLSb/V5baN7q9ZcQ6+jVJDXhgepzWuIDdPVq5rKr2OojGvqsp+6W+ZR/v+5rX/efTknWYS0tLU6d8uPOnRhCQiKJLmI7bKCh7BvkKn96ecZALS0tAsPduHAtgsiwk5zL0Q6/CUqyLZ4++2tiLofSs7cLKxavK56+EuzLfDasHwERbqxcN/+NPOomKBqyLKcBY4BA4FfggCzLlyVJ+kqSpMxnYOYANYFNkiSdlyTpTEHn/dcPyiVJekeSJF9Jki5IknRJkqS+kiS1U74z7oIkSaclSdJYoiVJ8lMuPUCSpHOSJM1R/nuhJEmaHzIsnKZcx3LeASuMjbl5Rz4b3JfZs5YVQ0PuY4XVUJi0OdHR0ca4YxuGD5uEk10/XFxtMbfoWETNr+e3gtLr6uri5GzNIeWsXMWKFZj8zSiWLFpdJJ1vQmtB6Orq4uxkg6fy7vnb01e487/zTiV27tnEtCkLefbseRHF5T5UFN9lZGTg1LkPxs1sadmqKR/kWD7fpacjXu7FmyVXXFvDwZyOKUYdyeTrySNIS0vD/UDxbrioJJRQGaxWrQouLrY0+cSMxo06UOmdSvTr163U9L1OHMxbW+5jRdVW6Z1K/LxjDXOnL1PNvGjraFO1WhVcbfuzaM4qfti66jU0vjn/DR7cl1mzFEtfHR2tuHfvIefOXSphbbnTybKMlZ05Dx484tKFXzWe+9vFG+jU3J7Dbr58NqxfiWrUJDLTJCMjA0eL3nRoakOL1k354GNFnNm55QBmrZ1wMO/FvcT7zFo0uZgCcx96E3FQR0eHqtUq081uAEvmfcemzd8WT5+6kAJNXl6+yQ3zz7nlMpZHO7yp/cOsAtP82ympOgyK/Dc2dqJxY2Patm3JJ598UFRxr6UtIyMDe4tetGtqTcvWzfjw46I9xlZMiW+sLV66cA2tm1jiftCHocMHFmivWWDB1y5OHd61dT/mbZxxtOjNvaQHzF5YzBjzDyPjLf4VBlmW/WRZ/kCW5UayLC9WHvtBluUflP8eJstydVmWWyr/2hZ0zn/9oBxwAOJlWW4hy3JTIADYD4yXZbkFYAOk5JE2AjCTJKkKkAZ0Uh43BXJNDWXfrS8tTX0p1vARgzh5yo+Tp/xISEiidm1D1XeGRvok5lj++eDBI6pWrYK2tjageK45IeGe6vumTT9i46Zl9O3zJY8eJRfWFyri4xPVljoZGumTmHhP3SZOg03CvXzT3rv/QLUkXE/vPe4/eKg61/Hj0Tx6+JiUlJcEB4bnublYdoYNH6jagC0x4R5G2f1mqJ9r2ezDB4+oWq2yym/ZfRsfn5hvels7Cy6cv8z9ewrNDRrWpV79Ohw76Uvs5XCMjPSJOOZFrVrvatQ6fMQg1QZsCQn3qF07u48Mcml98OAR1dTyWF8tj/PDzr4z589f5t69BwUbK/ly+CCOnfTh2EkfZRnMtlzVUJ+ERA2+zKEvZznVhI6ODrv2bOLAfi+8vYq+0VZifBIGRll3sg0M9UhKVH+8ISE+CcNsy5X1DfW4l8Pm6dNnnDx+hs7WnVTHtLW1cXC2xvtQ8TcAS4hLwrCAupMQn6RWRwwMFXWnIPr074atvSWjvvymWNredJzRhKWlKbf/uMuDB49IS0vD63AAHYzblIq+142DeaEoX9nzT48kDXmcvQxmt9HR0eHn7WvwPOiLv8+RrDRxSfh7Kz6fj7lIRkZGkTaPHDHiM06d8uOUBv9pih+a/Zfl46ZNP+L775fTu/cwlf86dmyLi4sNV68eY8eO9XTubMKWLWsKrREUvjFQq5+1cvkvMf6exnrepkNLbBw6E3nOj/U/L8fErB2rf1iS6xpebv44uNoUSVdOjTnz714ujTl/R+5y8PTpM04dj1bFmQf3H5KRkYEsy+zd4U7L1sVbDVFScTAhPkm1n8aFmEtFLoOaSEt8gK5BVruoo/8uqUnqj09mPE9BfvESgOdhZ5B0dNCuXrxVBP9k3kYdzs6TJ0+JiDiJnV3nIunMXYf1SMxV/hLzjIGZKMpfNJ2t3/xmZCXZFmficdAHly62xdJXUnX4wf1HajGmRetmxdInKHv8FwblFwEbSZKWS5JkBtQFEmRZjgaQZfmpchmCJiIBcxSDcF/gf5IkVQLqy7J8Ladx9t36dHTUJ99/+nEnHY2d6GjshLd3EJ8O6AFAu3atePr0Wa5gBxARcZLu3Z0AGDCwJz6+ig29atc2ZM/eHxj2xQRu3vy96B4BYs5epGGj+tStVxtdXV169HQmwFd94yt/v6P0698NgLbtWvL0yTOSku7nmzbAL4R+A7oD0G9Ad/yVx48ejaRJkw9Vz6GamLbj6tWCNxH65addqk3YfHyC6N+/e5aep880PoseGXGKbt0dAfh0QA/8fBWdYD/fI/mm79XbVW05+JXL12ncoD3Nm1jQvIkFcXGJmJt2yXMg/NOPO1WbsPl4B9FflcfKa2nM41N0V2odMLAnvr7BBfoEoHdvVw4e9CrYMBs//7QT044umHZ0wdc7mP6fdi+Uvkxf9h/QE99sA4y82Pj9Mq5d+42N6zcXSV8mF85dpkHDetSpa4Surg6u3R0I9g9TszkSEEbPvorNxVq1bc6zp8+4l/SAGjWrU0X5XGD5CuUxtTDm5o2sOmJqYcxvN34nMb7oz0Bnci7mIg0b1aNuPSN0dXXp1sOJQL8QNZtAvxB69+8KQJu2LZT68t83wdLalDFfD+OzfiNJSXlZLG1vMs7kxd0/42nXrhUVK1YAoHPnTlwrRF1+0/reRBzMi/Mxl2jQqK6yDOrStYcTQf6hajZB/qH06tcFgNZtm/P06XPuJSliw6r1C7h5/RY/bVLfRCvQ7yidzDsA0LBRPcqV0+XRw8eF1vXjjzswNnbCONN/nyo2yGzfPtN/uTubEREn6dFD6b8BPfHxUcSYOnUM2bfvR77I4b85c1bQuLExH31kymefjSUs7ARDh35daI0AsecuU79hXWpnq8NH/MPVbI4EhNFDWYdbtm3Gs6fPuZ/0gJUL12HSzA6zVk6M/XIqJyKjmfDVDADqN8xa8mzj2JlbN4qf7xdiLqnHmR6OBAeEqdkE+4fSU5nHijjzPM8489t1hZbM50EB7F2sufZr8TbLK6k4GOQXgolZe0CxlF23iGVQEymx1ylX3wjd2nqgq0NVF3OeH41Ss9F+N2vgX6H5B0haEumPn77Wdf+JvI06/O67NVSPTVSoUB4rK9Mib9qoqB91VeWvaw9HggNyxsAwtRioqX5UyCx/199sjIaSa4sbNKyn+re9oxU3ihlnSqoOq8UYZyuu/XqjWPr+abzN3ddLC51Su/JbQpbl65IktQGcgKVAEIV/B3000Ba4BQQD7wJfAq+1bWRgQCj29pZcvBROyosURnyVNSvm4bmVUaOmkphwj9mzlrF9x3rmzJ3EhQuX2b5N8Qqn6TPGUaNGddWO3mlpaZiZKgLjtm3rMDM3pmbN6ly/cZJFi1azY3vuVz+lp6czZfJ83A5tQVtLm9073bh69SZDhipeU7Jty16CA8OwtbPg7IWjpKSkMGbktHzTAqz57ke2bF/LwEG9+fPPeD7/bBwAT5KfsmnDFo6GeyDLMsFB4QQrn3+at3AKvXq7UqlSRS5djWTn9gMsWbI2l+agwDDFDHFsCC9SXjL6q6y3Cxx038zY0dNJTLzH3Nkr2LJtLbNmTyQ29jI7th8sMH3FihWwtOzE1+NmFiNHc5OZx7GXwkh5kcJXX01RfefuuYXRo6ap8njbjvXMnjuJ2AtXVHlcS+9dIo95Ubny/8jIkBk95nPatrbj2bPnCq1WpowbW3ytgYGh2Nl35sLFUF6kvGTUiCx9bh5bGDNqmtKXy9m6fR2z50zkwoUrqrJUS+9dwiMPq/SNGv057dvY06TpR/T/tAeXLl3l2EkfABbM+5agbM+6FUR6ejpzpi5hx8Hv0dbW5sCeQ9y49hsDhih20d+97SAhwZFY2poRccaXlJSXTB47W6Xru42L0NLWVrxS6VAgIUFZz2a79nAo9gZv2fVNn7yQfR6b0dbWYu8ud65dvclnQxU74u7Ysp8jQeFY25kTdT6IlBcvGT96hir9D5tXYWLajho1q3PuShgrl65nz053ln47m3LlynHgkOIVVGfPXGDKhHnF1vm6cUZP770cZXAobVrbcib6PIcO+XP8hC/paWlcuHCZLVv25iWjxPS9iTiYF+np6cyaspg97j+hpa3F/t2eXL/6G4M+V7x9YOfWAxwNisDK1pzjMf6kpLxk4mjFctx2xq3p1a8rVy5fIyhCsYnTsoVrCAmOZN8uT1ZtWMjRE4dIfZXK1yOLX4cDAkKwt7fk8uUIXrxIYcSIrCWMnp7bGDVqCgkJ95g5cyk7d25g7tzJXLhwmW3b9iv8N328wn/KNy+kpaVjalr0XfQ1kZ6eztypS9lx8Hu0tAOKpVcAAKjtSURBVLU4qKzDnyrr8J5tBwkNjsTS1pSwMz6kpLxkytiCd9+eMmc8DRvXR87IIO5uAjMnF3+37PT0dGZPWcJOtx/Q1tZW5fFApcZdqjhjTuRZP0WcGaPI41p67/HdpkVoa2ujpSXhcyiIo8o4M2PeRD5p9hGyLPPnnTimT9T8lpDC6CuJOHhgtycr1y8g6JgHqa9SmTT6DSwjT88gcf731N22EElLi2S3YP6+cYfq/RU3dB/v9aeKYyeqf+oE6elkvHzFn+NXqJIbrZlCpQ7N0KlehfePbef+2t0kHyz6W0Veh2/mLiP6XCzJyU+x7jaQUV8MoqerfYles6TqsL5+LX7++Tu0tbXQ0tLC3d0Hf/8QjRryIrN+7Hb7ES21+qGIgbu2HSAkOAIrWzOOnfXnZUoKE8coyp+e3nus3rQYbW1tJC0Jn0OBHA1S3JRzcLZm4fLp1KhZg+37NnH50lUG9ireWxRKqi2eNX8SjRvXJyND5s+78XwzYW5eEgrUVxJ1ePq8CXzSNDPGxDNjUvFijKDsIf3bd/OTJMkQeCTL8ktJkroBw4GPgL6yLEcrnydPyWu2XJKkMBTb3jcDXIFvgW9lWc49aszGO5Xql2nHltfWLdioFEmXi7fB0NsivZgbIL0ttArxjF9pUr3C/0pbQoG8THtV2hLy5Xlq8WbTBQqqlX+ntCXky6OXRdyLoRTQr1SttCXkS1lvRzQ9T1qW8K+W+7VmZYn3o9YXbFSKVKljWbBRKVOzYtneJCw1Pa+FrGWDCjrlSltCgfzxMLZsB5pC0rte17c2rjr4x+FS8dm/fqYcxWB6pSRJGUAqMBLF9gvrJUmqiOJ5chsgrx5QJGAty/ILSZIiUbyLrngvHhUIBAKBQCAQCAQCQaF5nVdg/lP41w/KZVkORLFlfU4K9RJNWZZnA7OV/45H436KAoFAIBAIBAKBQCAQFJ1//aBcIBAIBAKBQCAQCAT/TMr2w0hvBjEoByRJsgeW5zj8uyzL3UtDj0AgEAgEAoFAIBAI/huIQTn5LnEXCAQCgUAgEAgEAkEp8W/fmBz+G+8pFwgEAoFAIBAIBAKBoEwiZsoFAoFAIBAIBAKBQFAmyfgP7L4uZsoFAoFAIBAIBAKBQCAoJcRMuUAgEAgEAoFAIBAIyiRi93VBsTF8p2ZpS8iXp6/+Km0J+ZKWll7aEvLl77RXpS0hXyqVq1DaEvIl7tnD0pZQIJXLVSxtCflS1vWlZZTtOlzW9VUp4/kL8PDls9KWkC/ldXRLW0K+aCGVtoR8aZ9wpbQl5Et6HcvSlpAvT++GlraEAqloaFbaEvLl3UpVSltCviT9lVzaEgT/IsSgXCAQCAQCgUAgEAgEZRJZPFMuEAgEAoFAIBAIBAKBoKQQM+UCgUAgEAgEAoFAICiTiN3XBQKBQCAQCAQCgUAgEJQYYqZcIBAIBAKBQCAQCARlElkWM+UCgUAgEAgEAoFAIBAISggxUy4QCAQCgUAgEAgEgjLJf+E95WKmXCAQCAQCgUAgEAgEglJCDMoFAoFAIBAIBAKBQCAoJcSgvBQws+pI4El3jpw+xPBxQzTazF7yDUdOH8I7bB+fNP9IdTz0rDc+4fvxCt2DR/BO1fGvp43EO2wfXqF72HpgI7X03n0tjZbWphw/48+pc4GMnfClRpvFy2dy6lwgoccP06zFJ6rjazYs5vLN44Sf9NKYbuTYoSQ9uUqNGtVeS+OKlXM4HxvCiSg/WrRsotGmXr3ahIR5cO5CCFu3r0NXVxeA9z9oyJEQN+4/+pWx44eppdn4/XJ+u32aU9H+r6Vv9XcL+PXKMWLOBtOqZVONNvXr1+H4MW+uXD7G7t3fq/S5utoRczaYM9FBnDrpRyeTdqo0Y8d8wblzRzl/PoRxY4dpPG9eLF85h3MXQjh+ypcWLfL22dFQd2LOH1XzWX7pq1atzI5dG4iOCeL02UDatW8FQLNmH3MkxI3IE96ERRyidZvmhda6+rsFXC2E/04c8+bXy8fYk81/mbRt04K/U+7Qo4czAB980Igz0UGqv0cPrhbJh8tWzubshaMcO+VD8zz8V7debYJD3Thz/gibt69V06QpvZGRAV5+uzh1NoAT0f6MGDVYZd+02ccEhbgRccKLkAjPIvnP0tqUY9F+nIwJYMzXmn/jouUzOBkTQMjxQ2p1ePWGRVy6cYywE+p1+Mct33Ek0oMjkR5Exx7hSKRHofUAWNmYcepsAKfPBzNuwnCNNktWzOL0+WDCT3jRPJum/NIOGzGIU2cDOBbly9wF3wBgYWnC0XAPIk56czTcAzNz4yJpLYkYOHXmOEKPH+ZopCf7PTejp1+rSJpKWt/kaWM4/2s4RyM9ORrpibWtebH1QdmL0VbWZpw8E8Dpc0GMy8NnS5bP5PS5IMKO5yh/+aQdNnwgJ88EEHnKhznK8teztyuhkYdUf0mPf6Vps48oCiWRx67d7Ak/5U3C4yu0aKU5rhaFld/O5cLFUE5F+eebx6HhnpyPDWH7jvWqPP7gg4YcDXXn4eOrjBuf9fvef78hJ075qv7iE2MZNfrzYulbtWoely6Fc/p0AC3zaEfq1atDRMQhLl4MY+fODSp9/fp14/TpAE6fDiA01INmzT4GoHz58kRGHiYqyp+zZ4OZNWtCsbQVhVlLvsPcuR/dBn5V4tfKTkm0w+XLl+fkcR/OngnmwvkQ5s6ZVGx9ZbGdy49Vq+Zz+XIE0dGBeZbH+vXrEBFxmEuXwtm5c6PKny4utkRHBxIV5c/x4z6YZOsX/heQ3+J/pYUYlL9ltLS0mLdsGsP6jcOxUy9cutvT+IMGajYWNp2o17AONu27MXvSIhasmK72/aDuI+hi+Sk9bAepjv2yYQeunfvRxfJTQoMjGTNZcwNeWI3LVs3h015fYtbehe49nfngw0ZqNta25jRoVA/jVvZMHj+HFd/NVX23b48n/Xpqvr6hkT4WlibcvRNXbH0AdvadadS4Pi2bWzF+zAxWr1mo0W7+wqls3LCFVi2sSE5+ymeD+wDw+PETpkxewLq1v+RKs3uXGz26Fa8DkImDgxWNGzfg409MGTlyKhs2LNVot2TJTNau+5lPmpiS/PgJQz/vD0BIyDFat7GlbTs7vhw+iR9+/BaAJk0+ZOgXn2Ji4kybNrY4OdnQuHEDjefOia1dZxo1qk+rFlaMHzuT79Ys0Gg3f+EUNm3cSuuW1iQnP+Gzwb0LTL9sxRyOBEfQrrUdnYxduH7tJgALFk1l2dL1mJm4snjRGhYsmloorY4OVrzfuAEfKf23MQ//LV0ykzXrfubjJqY8zuY/UJTjpUtmEhQUpjp2/fpvtG1nR9t2drTv4MCLFykcOly4jr2tnQWNGtWnTQtrvh47i1Vr5mu0m7dwCt9v3ErbljY8SX7CIJX/NKdPS0tj1vSlGLdxwM6yF8O+HMiHHzUGYP6iqaxYug5zky4sXbSG+YX0n5aWFku/nc2nvYZj3sGV7r001+GGDevRsbUDk8fPZfmqOarv9u85RP9euQfNI4ZOxMasBzZmPfD1CsLP+0ih9GRqWr5qLn17fkmndk706OWSS5ONnQUNG9WnfUtbJo6fzcrV8wtMa2rWAUcna8w7umLawZmN6zYD8OjhYwb0/Qrzjq6M/moqm35aWSStJREDN67bjGWnrlibdSc4IIxJU0cVWtPb0Afw46btWJt1x9qsO0eDI4qlD8pejM70Wb9ew+jU3pnuPTWUP1tzRflrZcek8bNZ8d28AtN2MuuAg7M1FiaumBm7sElZ/twPemNp1g1Ls26MGjGFO3fiuHTxapH1vuk8vnrlBkMHjuPk8TOF1pIXmXncopklY8dMZ83aRRrtFi6axsb1m2nZ3Irk5CcMHpKVx99Mnp8rj2/cuIWJsTMmxs6YmriSkvISb6+gIuuzt7ekUaMGNG1qwZgx01m3TrO+xYunsX79Zpo168zjx08YMqQvALdv38XOrg/t2zuwdOk6Nm5UtEN///03Dg796dDBkQ4dHLGzs6C98kZ0SdHNyZYfvtOsv6QoqXb477//xsauD23a2tKmrR32dp3p0L51kfWVxXYuP+ztLWncuD5NmpgzevQ01q1brNFu0aLprF//C02bWpCcnFUeQ0OP066dPR06ODJixGS+/375G9ElKDuUiUG5JEnVJEkqsHciSdJz5f87S5LkU8hzd5YkySTb568kSfqs+Gpfj+atm/DH7bvc/SOO1NQ0fA8FYe3YWc3GxsGCQ/t9ATh/9hKVq/6P9wqY+X7+/C/VvytWqsjrvDmgdZvm/H7rDn/c/pPU1FQOefjh4GytZuPgbM3BvYcBOHvmAlWqVqGW3nsAnDpxhuTHTzSee8HS6SyYs/K19AE4Oduwd48nANHR56latQp6+u/lsrOw6MghT8Wga+9ud1xcbQF4cP8hMTGxpKWm5Upz4ng0jx8lv5a+Lq727NrtBkDU6RiqVquKvoZZMcvOnXB3V+T1zp0H6dLFHoC//nqhsnmnUiXVqyA++uh9TkfFkJLykvT0dCIiT9G1q0OhNDm72LB3r8JnZzJ9ppfbZ+bZfLZntwfOLrb5pq9c+X906tSOHdsPAJCamsqTJ88AxSssqlT5HwBVqlYmMeFeobS6utqzsxj+66r0H8CY0UPx8PTl3v2HGq9hbWXKrVt/cKeQN4icXGzYVyj/GXPYMwCAvbs9cVL6L6/0SUn3ib1wGVDU4+vXfsPAQA9Q+K+ymv+SCqW1lbIO3/lDWYfd/bB3slKzsXey4sA+RR2O0ViHk/O9hms3BzzdfAulB6B12+b8fusP/rh9l9TUVDzdfXF0tlGzcXSy5oDSR2ejL1C1amX09N7LN+2QL/qzdvVPvHqVCsCDB48AuBj7K4mJivJ29dcblK9QjnLl1Gdw8tRaQjHw+bOsOF3pnYrFfsVLScboN0VZi9Gt2zTn9q0/svnMF0cNPtu/9xCg8FlmHc0v7edf9GedhvKXnR69nPF0K1SXRU1vSeTxjeu3+O3m70XSkhcuLrbs3a2YRSwojz2Vebx7lzsuLnYA3L//kJizsaSmpuZ5jc6Wnbh16w/u3i36jXwXF1v27HEH4PTpc1StWkVjO2JhYYKHh59C3253XF0V+k6dOkty8lNl+hiMjAxUaTLbaF1dHXR0dEv8dU1tWzajapXKJXqNnJRkO6zmP93i+a8stnP5nsvVjt27s8pjtWqay2PnzlnlcdcuN839wncq/SdeEZadDOS39ldalIlBOVANKN6UQcF0BlSDclmWf5BleUcJXatA9A1qkRCX1bFOjE9Cz0C9EdMzqEVCfHabe6qGTpZlth7ciOeRXfQd1F0t3YQZo4g470uXng6sXf598TUa6hEfl6D6HB+XiL5ykJCJgYEecdlsEuITMTBUt8mJvaMlifFJXLl0rdjaMjE01OfPP7OuHxefiKGBvppNjZrVefLkKenp6QqbuII1vikMDfX58258lr4/EzAyVNdXs2Z1kpOfqPT9GZeAoVGWTdeuDly8GM7hw9sZ/qViedfly1cxNTOmRo3qVKxYAUcHK+rUNiyUJgMDPeL+zNIUH5+IoaEGnyU/U2mKj0vEQGmTV/r69evw4MEjNv2wgsjjXqzfsIRKlSoCMG3qIhYsmsblq8dYtHga8+cWbrbS6DX9Z2ioT7euDvz4007yok+fruzbf6hQeiDz92erFxrKvCb/GSptCpO+Tl0jmrf4hLNnLgAwQ+m/S1cjWbB4GgvmfltIrbWIj0tUfU6IT1IN9LP/HnWbRAwMCrec2tikLQ/uP+T3W38Uyl51vT+zrqfp9xsY6hGX3SYuCQNDvXzTNmrcgI4mbQkMOYiX3y5atW6W69quXe25eOFX1cCpIEoqBgJMn/01MZdD6dnbhRWL1xVKz9vUN/TLAYQeP8yaDYupWq1KsfRB2YvRBoZ6xMXlKFsF1In4+ET0DfXyTduoUX2MO7Yl4OgBDvvupKWG8te1hxMeRezYl2QevykMDPXU8jg+LiFXm1KzZnWSc+SxYRE09urtgttB72LpU5TBbO2IhmvXzFUGc/8GgCFD+hEYGKb6rKWlxalTfty5E0NISCTR0eeLpbEsU5LtsJaWFmeig0iIi+Xo0QhOR58rsr6y2M7lR66YGJe7D1ZQeezSxZ4LF0Lw9NzGiBHfvBFdgrJDWRmULwMaSZJ0XpKk1ZIkHZUkKUaSpIuSJHXNL6EkSe0kSTonSVJDDd/VB74CJijPbSZJ0jxJkiYrvw9TXi9CkqRflefykCTphiRJi7KdZ6AkSaeV5/hRkiTtPLQMlyTpjCRJZ568fJCX4FyHct7tkvKx6ec8lG7WA/ii31gGDO1Du45ZS6ZWL9mEeUtnvNwDGPhFX83XLwQaLk+uqW0NNvndtatYsQJfT/6K5UuK1wnNSX4+yt/mjVy+QIqrL7vAw4cDaNbMgp69vmDePEXwvXr1Jt+u3EiA/158fXYTG3uFtLT0EtWUaZPXdzo6OrRo2YTNv+zGrFMX/nqRwoRJiufevhg2gBnTFtHkI1NmTFvMhk3L3orW71bNZ/qMJWRkaH6Jhq6uLq4udri5F372qqT8l8k771Rix+6NTJ+6iGfPngMwdNinzJi2mKYfmTFz2hLWbdK8fLBQWim81oLo3tMZT/eiDTJex3/5pdXR0aZqtSrYW/Vm7uwV/LJtjZrdhx81Zs6Cb5j09ewiaNVw8DVjYCZLF66hdRNL3A/6MHT4wEJrehv6tm/eS4eWtliZdiMp6X6hH5fQrLFsxeiSKn/aOtpUq1YFB+s+zNNQ/lq3aU7KixSu/nqjiHo1HHxDZfBN8boxsSB0dXVxdrLBUzlrWFr6zM07MnhwX2bNyoq/GRkZGBs70bixMW3btuSTTz4olsayTEm2wxkZGbRtZ0e9Bm1p17YVTZp8+Gb0lXI7lx+au3xF0+vlFUiLFlb06TOMuXMnvzFt/wRkWX5rf6VFWRmUTwN+k2W5JfAN0F2W5daAJbBK0jh6AeWy9B+ArrIs38r5vSzLt5Xfr5ZluaUsy5EaTvNKlmVzpd1hYDTQFBgiSVJNSZI+BvoCnZT60oEBmvTIsvyTLMttZVluW7WC5uXmifFJGBhl3cnTN9TjXuKD3DaG2W1qcS9JYZP5/0cPHhPsF0pzDRu1eLv7Y+9ilet4YUmIS8Iw2zItQyN91TJQlU18ktpSLgND/XyXJtdvUJe69WoTcuww0bFHMTTSIzjCg/dqFX5Dui+HD+LYSR+OnfQhISGJ2rWzrm9kqE9CovrS3ocPHlG1ahW0tRX3UIyM9Au9/Lc4jPxqsGoDsYSERGrXyZrBNqptQHyOaz948Ihq1aqq9NU2MiA+Pre+Y8eiaNiwHjVrVgdg67Z9tO/ggJV1Tx49TuZmPksRhw0fSOQJbyJPeJOYcA+jbLPqhob6JCRo8Fm1yipNhtl8Fh+fqDF9XFwCcXGJqtndw4f8VZvA9f+0B16HAwHw9PDLd6Oy7P6LL6b/MleYtGndnN27NnHz+il69nBmw7olqiVgAA4Olpw7d5F79/K4eZbNfxEnvIg44UVCwj2MspU5Qw1lXpP/EpQ2Cv9pTq+jo8P23Rs5uN8Ln2zPTvb/tAfeSv8d8vCjdZsW+erNJD4+SW3VhYGhXi6t8fGJOWz0SUy8X+C5tbW1cXK14bBH0TbZio9PxLB21vU0+S8+LhGj7DZGCt35pY2PT8RX6bNzZ2PJkGVVXTEw1GPHno2MHj6F27/fLbTWkoiBOfE46INLF9tC278NfffvPyQjIwNZltm1/SCt2uSe9c2Pshyj4+MSMTLKUbYS868Thob6JCXcyzdtQnwSPt7BAJyLuUhGRoaq/EHxO/ZvowwWh+EjBqk2YEtIuKeWx4ZGBrnalAcPHlEtRx4nFFKjnX1nzp+/XGCczs6IEZ9x6pQfp075KctgtnZEw7Uf5CqD6r+hadOP+P775fTuPYxHGh6ZePLkKRERJ7Gz61xojWWZt9kOg8J/4REnsC+G/8piO5eTESM+IyrKn6go/1z1RVEec/szv/KYybFjp2nYsK5arBH88ykrg/LsSMASSZJigSOAEaBprdPHwE+AqyzLd17jepnbLl4ELsuynCDL8t/ALaAOYA20AaIlSTqv/JxrVr6wXDx3hfoN6lC7riG6ujo4d7PjaEC4ms3RwAi69VXsUtmyTVOePX3O/aQHVKxUgXfeqQRAxUoVMO1szPWrig216jWso0pv7WDBrZu3iyuRczEXadioHnXrGaGrq0u3Hk4E+oWo2QT6hdC7v2IRQ5u2LXj29Bn3kvIOdL9euU6Txp1o19yads2tiY9Lwta8B/eL0Nj+/NNOTDu6YNrRBV/vYPp/qli+365dS54+fUaShkAbEXGKbt0dAeg/oCe+Pm9mww5NfP/DdtUmYoe9Ahk4oBcAHdq35umTp7k6VABh4Sfo2VOR14MG9cbbWzG4aNSovsqmVcumlCuny8OHjwF4772aANSpY0i3bo75LsH+5addmJm4Ymbiio9PEP37K3zWNtNnGvIsMpvPPh3QAz9fhc/8fI9oTH/v3gPi4hJo/L5iwzmLziZcU5bLxMQkTM06qI7f+i3vZWDZ/eflFcigYvjPS+m/9z/sSOMPjGn8gTHuHr6MGTcDL69AVbp+fbsVaun6Lz/twtykC+YmXfDzCaZfofwXRdfuiuf8+w/ojr/Sf/6+R/NMv37TUq5fu8mmDVvUzpWQmEQnpf/MO3fk1m+3C9QMcD5nHe7pRJB/qJpNkH8offop6nDrQtThTMw7d+Tmjd/VHrEpDOfOXqRhw/rUrVcbXV1duvd0JsDvqJpNgH8IfZQ+atOuBU+fPicp6X6+af19jmBmodhZvVHj+pTTVdSVKlUrs/fgzyyct4rTUTFF01oCMRCgQcN6qn/bO1px40bxnu0tKX21su2R4ORiU+TZ3bIco8/FXKRBo6wy1K2HMwEafNa3fzdA4bPMOppfWj/fI6qd/Rs2yip/oJj16tLNoViD8pLK49flpx93qjZh8/EOov+AHkDBedxdmccDBvbE1ze4UNfq3duVgwc1v8UlL378cQfGxk4YGzvh7R3Ep5/2BKB9+1Y8ffpMYzsSEXGSHj2cFPoG9MTHR6GvTh1D9u37kS++mKB28/vdd2tQtari0Y4KFcpjZWXKNeXmpv903kY7rO6/ClhbmXHt2m9F1loW27mc/PjjDtWGgF5egQwYkFUenzzRXB7Dw7PK48CBvVT9wobZ2o+WLZuiq1tOFWv+C/wXninXKbUr580A4D2gjSzLqZIk3QYqaLBLUB5vBcRr+L6w/K38f0a2f2d+1kFxk2C7LMvTcyYsDunp6cyfvoItBzagraWN297D3Lx2i/6DFRV173Z3woKPYWHTiaOnD5OS8pJp4+YB8O57Ndm4TfFMqY6ONt4eAUSGnATgm9ljadCoHhkZMvF/JjBn8pLX0jh98kL2eWxGW1uLvbvcuXb1Jp8NVSyJ37FlP0eCwrG2MyfqfBApL14yfvQMVfofNq/CxLQdNWpW59yVMFYuXc+ene7F1qOJwMBQ7Ow7c+FiKC9SXjJqxBTVd24eWxgzahqJifeYO3s5W7evY/aciVy4cEW1GVktvXcJjzxM5cr/IyNDZtToz2nfxp5nz56zZdtaTM06ULNmdX69fpwli9ayc8eBIunz9z+Ko4MVV389TkpKCsOGTVR953V4ByO++oaEhCRmzFjM7l2bmD9vCucvXGbL1r0AdO/uxMCBvUhLTSMl5SUDBoxUpT+w/2dq1KxOWmoa48bNJDm5cBs2BQWGKWYeYkN4kfKS0V9lLU096L6ZsaOnK322gi3b1jJr9kRiYy+zY/vBAtNPmTSfXzavRrecLrd/v8vokYr8GDdmBstXzEFbR5u/X/7N+LEzC6XVz/8oDg5WXPv1OC9y+M/78A6GK/03fcZi9uzaxIIc/suPihUrYGNtzshRRVuaGxQYhq19Z2JiQ0hJSVH7/Qfcf2Hc6BkkJt5j3uwVbN62hpmzJxIbe4Wd2fynKb1xxzb0+7Q7ly9dJUL5apaF81YRHBTO12NmsnTFbHR0tHn58m++LqT/0tPTmfHNIva6/6Kswx6KOvy5sg5vVdZhW3NOnQsk5cVLvs5Wh7//5VtMTNtTo2Y1Yi6HsnLZBvYq63C3nk7F2vgmPT2dad8s4KDnZrS0tdmz041rV28yZGg/ALZt2UdwYBg2dhZEXzhCyosUxo2anm9agN073Vm3aQmRp3xIfZXKGKVfhw0fSIOGdZk0ZTSTpowGoHe3zzVuxKVJa0nEwFnzJ9G4cX0yMmT+vBvPNxPm5iWhVPTNWTCZps0+RpZl7t6JY/LXxdMHZS9GK3y2gAMev6Clra3y2WBl+du+ZR/BQeHY2Flw+nywovwpfZZXWoA9O91Zu3EJESe9SU1NZczIaaprduzUjvj4RP64/WeR/VdSeezoYsOSFbOo+W4Ndh/4gUsXr9KvR9FerZlJYEAo9vaWxF4KI+VFCl99lZXH7p5bGD1qGokJ95g9axnbdqxn9txJxF64wvZtWXkcecxLlcejx3xO29Z2PHv2nIoVK2BpZcq4QsY8TQQEhGBvb8nlyxG8eJHCiBFZy309PbcxatQUEhLuMXPmUnbu3MDcuZO5cOEy27btB2D69PHUqFGdNco3B6SlpWNq6oq+fi1+/vk7tLW10NLSwt3dB3//EI0a3hTfzF1G9LlYkpOfYt1tIKO+GERPV/uCE74GJdUOGxjosWXzGpX/3Ny88fUr+s24stjO5UdAQAgODpZcuRLJixcpDB+eVR4PHdrGyJFTSUhIYtaspezYsYF5877h/Pms8ti9uxMDBvQkNTWVlJSXDBo0+o3qE5Q+UlnYvU+SpJpAjCzL9SRJGg80lmV5rCRJlkAI0ECW5duSJD2XZfl/kiR1BiYDXwBBwHhZlsPyOPckoIosy3OVn+cBz2VZ/laSpDBgsizLZzLPKcuyi9IuTHmNFyiWtXeSZfmeJEk1gMqyLOe788P777Upfcfmw9NXfxVsVIqkpL0qbQn5kpL6d8FGpUilcpruY5Ud/nr1srQlFEjlchVLW0K+lNcp3E7ipUVaRuH2OigttKWyuFDsn0VZj9NlvY5oaXoovAzxVxlv59JlzXuGlBWe3g0t2KiUqWhoVtoS8uXdSsXfbPJtkPyybPelAV6+vFO2A00h6Vzb5q2Nq8L+PFIqPisTvRJZlh8CxyVJugS0BNpKknQGxax5ni/2lGU5CXAFNkqS1CEPM2+ge+ZGb8XQdgWYBQQpl9QHAwb5pxIIBAKBQCAQCAQCgaBgyszydVmWPy2Ezf+U/w8DwpT/vgM0ySfNdSD77lKR2b7rnO3fqnNq+G4/sL8gfQKBQCAQCAQCgUAgeHNklIGV3SVNmZgpFwgEAoFAIBAIBAKB4L9ImZkpf10kSfocGJ/j8HFZlsVOCAKBQCAQCAQCgUDwD+TfP0/+LxqUy7K8Fdha2joEAoFA8P/27jq+qvqP4/jrs9EqIKiwgbTxU0q6YXS3hIAiIiig2EWKAYIogth0SnfXaBhdArbCNkIllKHE9/fHObu7d7tL2M6Zfp4+eLh77zl37534nvqGUkoppZRKqn/NRblSSimllFJKqX8XJ8cPTyvaplwppZRSSimllHKIXpQrpZRSSimllFIO0errqeTCP385HSFB5/++5HSEBF29fs3pCAnKkiGT0xESFCjuvt92smoxpyMk6t6wE05HSNClK387HSFBGQICnY6QoMtX3b38MgW6//D897UrTkdIkNu3wUCXr+NbMmZ2OkKCMrp8+WUNru50hERFhW9KfCIH5S/a2OkICbo1UxanI/xnaPV1pZRSSimllFJKpRp332ZUSimllFJKKfWfZYw+KVdKKaWUUkoppVQq0SflSimllFJKKaVcSduUK6WUUkoppZRSKtXok3KllFJKKaWUUq5k9Em5UkoppZRSSimlUos+KVdKKaWUUkop5Ura+7pSSimllFJKKaVSjV6UOyCkTjW27FrO9r0reeb5J/1O8857/di+dyXrtyykRKkHPO+P+vgdDn+3hdBti3ymH/jWy2wOW8b6LQuZMHUM2XPcdlMzjxz5JocPbyQsbCWlSxf3O02hQnezceNCDh0KZcqUsWTMmBGApk3rERa2kh07lrNlyxKqVCl/UzJ9+MEQjh7ZzJ7dq3kogUxbNy/mm8ObmT7tU0+maOXKluLvqF9o3bqJ570cObLz9cwvOHQwlIMHNlCpYtkU5Rvx/iD2H1zP9h3LKVX6Qb/TFCyYn/Wh89l3YB2TJo/x5Lv33iKsXT+X3/44yrN9424jAQEBbNm2hNlzv0pynjp1q7Njz0p27VtD3xd6+J1m6PAB7Nq3hk3bFlPSa7uLb94WLRuydecyzp4/RumHYtZBhgwZGPv5e2zevoTtu1bw3Is9k5wztkzlK5B70hRyT51Gto6PxPk8Y6nS3Ll4Kbm+/IpcX37FLY8+5vksW9uHyT1hIrnHTyBH/4GQMVOKcwC8N2Ige/evY8v2pZQqFf86Xbt+Lnv2rWXCpNE+21x88z/Vqyvbdi5ne9hynu7VNc53PvNsd87/+T25ct+erLzDRwxk34F1bN2xLMFtcN2Geezdv84n7z33FmHNujmc+f0bnunb3TN9vnxBLFk2jbDdq9gRtsJv3vik5TYI8MCD97Fy7Sy27lzG5u1LyJw5ees/tfbhw99sYsfO5WzdvpSNmxcmK1N8y8dbgYL5Wb1uDmF7VzNu4iifbTC++cd8MpRjP2xny46lPt/1Rv/n2LRtMaFbFjF3wQTy5r0ryVk/GPkmRw5vYlfYqgSPG5s2LuLwoY1MnfKJJ2uzpvXZFbaKnTtWsHXLUp/jxuefv8+vv+xlz+41iWZIjW0u5+05mLdwImF7VzNv4URy5MwOQJmyJQndsojQLYvYuHURTZrV88yzaNlUduxZ6fn8jjtyJZo9pE41NoctY9ueFfR5rrvfad5+7w227VnBui0LfM4VPvz4bQ59u5kNWxfFmeeJHp3YHLaM0G2LGfDmS4nmSCifm89latWpSuiOxWzetYzefZ/wO82Qoa+zedcyVm+aR/GS/wMgc+ZMLFk9g1Ub57J26wJefK23Z/omLeqzdusCfjl7gJLxlAnJkRrnMZkzZ2bbliXs3rWa/fvWMWjgizecMzH93/2AGk060LLzU6n+u7ylxjYY7elnunHq/FFy5cqZrEy161Zn++4V7Ny3mmef91/mvDu8Pzv3rSZ06yKfMiexeXs/042zF46TK5d1LvBQ2ZKs37yQ9ZsXsmHLIho3rRdnnn+L65g0++cUvShPYwEBAQwbOZBH2j5J9QpNadWmCffeV9Rnmjr1alC4aEEqPdSAl/oOZPgHgzyfzZw+nw5t4hY8oeu3UrNSM0KqtuD773/i2XhOPlKiQYMQihUrxIMP1qB379cYPfodv9O9/fbrjBnzFcWL1+TcufN07doegPXrt1C+fAMqVmxEz54v8emn791wpkYNa3NPscLc/0A1nn76VcZ+PNTvdEPf7ceo0V/yvwer8ccf5+n2eEfPZwEBAQx9tx+rVm3wmefDD4awcuV6ipeoSZmy9fjm6LfJzle/QS2KFitEqRIhPNPndUZ99Lbf6d56+zXGjhlH6ZK1OXfuPI91bQfAH3+c5+WX3mT0R/4vunv1fpxjR79Lcp6AgACGjxxMu9bdqVy+EW3aNuW++4r5TFO3fk2KFi1IudJ1ef7ZAYz8cEii837zzbc82qk3W7eE+XxXi1aNyJwpE9UqNSWkeku6Pt6BuwvkS3Jer+Dc1vc5zr32Cr91fYwsdeoQWLBgnMmuHDzA70925/cnu/PX5EnWrHfcQbbWbfitZw9+6/Y4BAaQpXbt5Gew1atfi6JFC/FQqdr0faYfH4wa4ne6N996hU/GTqBM6TqcO3eeRx97OMH5//fAvTzWtT21a7aiaqWmNGxUmyJFC3m+L1++IEJqV+WXX04mK2/0Nli6ZG369nmDD0e9FU/eVxn78XgeKlWbc+cu8OhjMdvgKy8NibMNXr12lX5vvEv5svWpE9KGJ3t04b77i/n7ah9pvQ0GBgby+Vfv80LfgVSp0JhmjTtz5crVpC08Un8fbtzoEapUakKNai2SnCm+5RPb4CEv8+nYCZR/qB7nzl2g86MPJzr/9GnzeLhVtzjfNeajr6heuRk1qzZn5Yr1vPxanyRlbdgghGLFCvPAg9Xp1ftVxox+1+9077z9OqPHfMWDxWtw7tw5Hu/aAYB16zdTrnx9KlRsSI+eL/LZp8M980yZMptmzbskmiG1trnnXuhJaOhWyj9Uj9DQrTz3gnXT8Zsjx6ldoxU1qzbn4VZP8MFHbxEYGOj5XT2feJGaVZtTs2pzzp79PdHsQ98fwCNte1CjYjNatfV/rlCkSEEql2nIS30H8d7IgZ7Pvp6+gI5t454HVK1egQaN61C7agtqVm7Gp2PGJ7oc48vn5nOZgIAA3h7eny7tniakcnNatGnMPfcV8Zmmdt3qFC5agGrlGvPq84MZOnIAAH///Q/tWnajfo02NKjRllp1qlKmXEkAjn3zHU8++hw7tu5OUS5vqXUe8/fff1O3fjvKlqtH2XL1aVC/FhUrlLnhvAlp2bgen33gv4xMLam1DQIE58tLzZAq/JrM425AQADvjRxE+zZPUrV8Y1q3bRonU936NSlStBAVStfjhb4DGPHhm0maNzhfXmrWruqT6eiR49St2ZqQai1o3/oJRn40xKfMUelLurkoF5GcItLL/rmWiCxxOlNKlClbkh9/+IWffzrBlStXWDBvGQ2b1PGZpmGTOsyeYT092b1rP9lzZOeuPHcCsH3rLs79cT7O94au28K1a9esecL2Exyc96ZlbtasPtOmzQVg58695MyZ3e/Tklq1qjBv3jIApk6dQ/PmDQD4669LnmluuSXbTWkX0qxZA6ZMmwPAjp17yJEzh99MIbWqMneu9eRnypTZtLAzAfTp3Y1585dy+sxvnvduu+1WqleryPgJMwC4cuUK589fSHa+pk3rMWPaPADCwvaRI0d28uS9M850NWtWZv785QBMmzqXpk3rA3DmzG/s2X2AK1euxJknOF9eGjYMYdLEr5Ocp2y5kvz4w8/8/NOvXLlyhXlzl9Koqe9217hJXWbOWADArrB9ZM95G3ny3JngvMePfc933/4Y5/cZY8h2SzYCAwPJkjUL/1y5wsWLfyY5b7SM9/+Pa+EnuRYRAVevcnndOjJXrZb0LwgMRDJnhgDr/9d/O5vsDNGaNK3LjBnzAWv55MiRnTx54q7TGjUrs8Bep9OnzaOJfec6vvnvu68ou3buJSrqMteuXWPz5p00a1bf831D3+vHwP7vJXu/adykLjOmW78vsW0wOu+MaXNpaj/dO3vmN/bsOcDVWBeypyLPsH/fYQD+/PMvjh37LknlTVpvgyF1qnH40DEOHzoKwB+/n+P69euJ5oyWmvtwSsW3fGKrXrMSCxesAGDm9Hk0aVo30fm3bQnjDz/HFu/9NtstWZO8HTZrVp+pSTpuVGXePLuMTuJxY/PmHfzxx7lEM6TWNteoSR1mTrP2rZnT5tPYXr7R+zBA5iyZb+hY95B9rvDLz/a5wtxlNGjse1OxQePazJppnSvs8XuucC7O9z7WrQNjPvySf/6xtsvEbg7Ex+3nMqXLluCnH6OX31UWzltO/Ua+y69+4xDmzLSeku7ZdYDs2W/jrjx3AHDprygAMmTMQIYMGTzr8rvjP/DDdz+lKFNsqXUeAzH7T8aMGciQMWOqt8ctV7oEObLf3BqaiUmtbRCsGhRDBo4guYutTKxyY/7cpTRqUtdnmkaN6zDLPhfYHbafHDmsMiexed8e+gZvDhjhsy5vZpnjdsaYNPvnlHRzUQ7kBHolZwYRcd3torzBeQg/GeF5HX4ykrxBeXymCQrKw0mvaSLCIwkK9p0mIY90bsPa1RtvPKwtODgvJ07E5Dl5MjLOgTJ37ts5f/6Cp3A4eTLCZ5rmzRuwf/865s+fSM+eL99wpnzBeTnxa3hMphMR5POT6dy5855MJ05GEJwvr+dvatmiIZ9/McVnniJFCnL27G+M++pDwnau5PPPRpAtW9Zk5wsKzuOzzMJjLQ9PPp9lFklwEtbz8OED6d9/WLIuMIKC8vpsU+EnIwmKvd0F54k7TXCeJM0b26IFK7j01yW++W4rB46EMnb0uHgPfgkJuOMOrp8+7Xl9/cwZAu+4I850GR94kFxfjSPnsOEEFipkTXv2LH/NmskdX8/izrnzuP7XX/yza1eyM0QLCsrDyRMx21x4eNz9IFfu2zl/7qJnnVrLMG+C8x85cpwqVStwe66cZM2ahfr1a5IvfxBgHbzDw09xyL6wTI44+214JMFBfvLG2gaTU9YUKJCPkqUeZFfYvkSnTettsFixwhhjmDN/POs3LeCZ5/w/EYk3byruw8YYFi6ezKYti3i8W8dEp/fO5G/5ePO/DeZJ8vz+9Bv4PAe/2cjD7Zoz9J2PkpTV2v68yuh4ll/Cx42GHNi/ngXzJ9GjZ/KrWafWNnfXnXdw6tQZAE6dOsOdd+T2TFe2XClPc4kXnxvo+dsAPv50GKFbFvHSKzHVoePPfhfhJyM9ryPCT8XNHpQn1jSRBAUl3LygSLFCVKpSlmVrZjJ/6eQ4TT6Syu3nMkFBdxHhtWwiw0/FWTZ54yy/U56/ISAggJWhc9h/bCObNmxj7+6DKcqRkNQ6j4nOvytsFREnD7B27UZ2hu296fmdllrbYINGIUSGn+LIoWPJzhQUlIfwEzHbVLif3xcUnIeT3tOcPGWXOfHP27BRbSIiTnluMnsrU64km3csZeO2xbz03CCfMkelL+nponwYUFRE9gEjgFtFZI6IHBWRaSIiACLyk4gMFJHNwMMiUl9EtonIHhGZLSK32tOVFZFQEdktIitFJCi+Xywi5UXkgP09I0TkUDzT9RCRXSKyK+qfc/F8l583Y9+V8TNNUu/cPPdST65evcrcWYuTNH1S+MscO4/4mch7mkWLVlKqVG3atevOoEEpb8OW1N+X2DQfjHyT1994N86FbYbAQB56qASffz6Z8hUa8Ndfl3j1laRV17yZ+eLTsFFtzpw5y769fjfBBPLEfS+peZIyb2xly5Xk2rXrPHBPVR4qHkKvZ7pRsNDdycpsh/Lzy31fXv32OGc7tOf37k9waf5ccr5lNa+QW28lS5VqnO3YgTNtWyNZspClbsrbW93oOo3vs+PHvmfUh5+zcNEk5i6YwKFDR7l69SpZs2bhpZd78e7bH6Zx3qR9/y23ZGPK9E947ZW3klQLIq23wQwZAqlUuSw9ur9I4/odaNqsHjVqVk40Z2JZkjuNP3XrtKValWa0bvk4PXp0oWrVCmmSKaV53xnyISX+V4PZsxbxZI/OaZIVYNGiFZQsFcLD7bozOAXHjbTe5sB6GlelQmPq1mrDcy/09PRj0POJF6lWqSlNGnSkcpVytO/YMpHsfnJx49tfhsAM5MiZncZ1OzBkwAi+mJjS8sXPm246l7nB7e/69es0qNmW8sXrULpMCe77X+JNdJIfMXXOY8DKX658fQoWLkf5cg/x4IP33aTU7pEa22DWrFl47qWneO/d0SnMlPJ1Gt/7WbNm4fmXn2ZYPDdE9+w6QLWKTahXqy3Pvdgz2X2npBfaptxdXgO+N8aUBl4GHgKeAx4AigBVvaa9bIypBqwB+gN1jTFlgF3ACyKSERgDtDXGlAXGA/4bSlsmAE8ZYyoD8d6CMsZ8YYwpZ4wplzVTTr/TRJw8RXC+mOv/4Hx5iYw87TtN+CnyeU0TFJyXyAjfafxp17El9RqE0OvJG38S3bPno+zYsZwdO5YTEXGa/Plj8uTLl5eIiFM+0589+zs5cmT3tGXJly8ozjQAmzfvpEiRAuROZodVAE8/9Ri7wlaxK2wV4RGR5L87OCZT/iDC/WTKmTOHJ1P+fEFEhFvTlC1TkmlTP+G749tp07oJH49+l+bNG3DiZAQnTkR47irPm7eUh0qXSFK+Hj27sHX7UrZuXxpnmQX7WR5nz/5OTp9llpeIRNZzpUpladykLoe/2cTEyWOoWbMKX41L/KQqPDzSZ5vyt92Fn/QzTcTpJM0bW5uHm7F2zUauXr3K2bO/s3P7Hh5KwROZ62fOEHBXzNONgDvv5FqsKujm0iXMZauq4T87diAZApHsOchUthzXIiMw58/DtWv8vWkTGYsnL0P3Hp3ZtHUxm7YuJjLiNPnyx2xzwcFx94Pfzv5Ojpy3edaptQytacLDI+Odf8rk2dSo1oLGDTryx+/n+OH7nyhcpAAFC93N5m1LOXA4lHz58rJx8yLuuituTYFoT/bowuZtS9i8bQkREad899vgvERE+skbaxuM9LPfxpYhQwamTv+EWV8vYvGilYlO7/n703AbDD8ZyZYtYfz+2x9ERV1m9crQeDtri5YW+zDgKc/PnPmNxYtXUrZcqXinfeLJTp7OwSIjTvldPt78b4PWNPEt36SaM2sxzVo0iPfzp3o+xs4dK9i5YwXhEafI77W9+zsmJP24sYMiRQom+7iRWtvc6TNnPdX+8+S5kzNnfasOg9Ws4tKlKP73wL0Anr/rzz//Ys7sxZQpWzKR7Kc8T0TBeroWe12Fh0fGmiYvkZFnEvneSJYtXg3A3j0HuX79eoqOx24/l4kIP0WQ17LJG5wnzrKJiLP88nAq1t9w4cJFtm0Jo1adZDSbSkBanMd4O3/+AqEbt9Kgfq2bkt9NUmMbLFS4AAUK5mfd5oWEHVhLcL48rN44jzsTOO56Cw+PJDh/zDYV7Of3hZ+MJJ/3NPnyeMocf/NGZwrdsog9B9cRnC8v6zbNj3Mu8O3x77n01yVPmaPSn/R0UR7bTmPMCWPMdWAfUMjrs+jGtpWwLtq32E/YHwMKAvcBxYHV9vv9gfz+fomI5ARuM8Zstd+afiOh9+45SJGiBSlQMB8ZM2akZevGrFy2zmealcvW8XBHq/OfsuVKcfHCRU6fSvhAG1KnGn2e686jHZ4mKuryjUQE4PPPJ1OxYiMqVmzEokUr6dSpDQAVKjzE+fMX/Z4Qh4Zuo3XrxgB07tyWxYtXAVaV8GilSxcnY8ZM/PbbH8nO9OlnkyhXvj7lytdn0aKVdOnUFoCKFcpw4fwFv5k2hG6lTRurR9IuXR5mkZ3pnvsqU+zeShS7txJz5y2lz7NvsGjRSk6dOsOJE+Hce6/VuUbt2tX45pvjScr3xedTqFKpCVUqNWHJ4lV07NQagPLlS3PhwkVO+TlZ2rhxO61aNQKgU+c2LF26OsHfMXjQCO67pwoP/q86XR99htDQrXR/4vlEs+3ZfZAiRQtRoGB+MmbMSOs2TVixdK3PNMuXraWD/fSmXPnSXDh/kVOnziRp3thOnIjwPJXMli0r5cqX5vjxHxLNGduVo0cJzJefgLx5IUMGstSuzd9bt/hME3B7TC/GGe6/HyQAc+E8106fIuMDD0DmzABkKlOGqz//nKzf/9UXU6lepRnVqzRjyZJVdOzYCrCXz4WLniqs3jZt3E5Le50+0qk1y5ZaPUQvW7om3vnvuNOq/po/fxDNWjRgzuzFHDl8nGKFK1DywZqUfLAmJ09GUqNac06fjr9d/JdfTKFa5aZUq9yUpYtX0/ER6/cltg1G5+3YqQ1LlyTeo/XYT4dx7Nj3jB0zLtFpo6X1Nrh27SYefPA+smbNQmBgIFWqledoIp0jpsU+nC1bVm699RbPz7XrVOfIkfirSY77cpqnc7ClS9b4XT6xbd64gxYtGwLQ4ZGYbTC+5ZuQIkVjyu9GjevwbQL78WefT6JCxYZUqNiQxYtW0jlJx42tnl6ju3gdN4oWKeSZJqXHjdTa5lYsW0eHTta+1aFTK5bb7xcomD/m4unuYIrdU5hffjlJYGCgZ+SEDBky0KBhCN8cSbgD0X2xzxXaNGbV8vU+06xavp52HaxzhTJJPFdYsXQt1WpUAqBI0UJkzJgxRcdjt5/L7N9ziMJFCnB3gXxkzJiBFq0bsXpF7OW3gbYdmgNWFeCLF/7k9Kmz5Mp9O9nt9tFZsmSmWs1KfHc8br8VKZEW5zF33JGLHDmy2/mzUKd2dY4d+/6m5HeT1NgGvzlynAeLVaV8yTqUL1mH8JOnqFejNWcSOO76ZNp9kCJFYsqNVm2asGKZb5mzYvk62tnnAmXLl+LChT85depMvPN+c+Q4/ytamTIlalOmRG3CT0ZSu3orTp8+67/M+Tl5ndMp98jgdIAb8LfXz9fw/Vv+sv8vwGpjjE+jPREpARy2n3wnxl8FmRS7du0ar7/0FjPnjSMwMIAZU+dy7Oh3PNrN6ql88vivWbMqlDr1a7Bj3yqiLl2mb+83PPN/Nm4kVaqVJ1fu29l7ZAMjho5h+pS5DH1/AJkyZWLWAqsn1d279vPK84NvSuYVK9bRsGEIR45s4tKlKHr0iKlGuGDBRJ5++lUiIk7Rv/9QJk/+mMGDX2bfvsNMtDsia9WqMZ06teHKlStERV2mS5fE29MlZtnytTRsWJtj32zhUlQU3bu/4Pls8cLJ9HjqZSIiTvH6G+8wfeonDBn8Cvv2H/Z04JaQvs8PYPKkMWTKlJEff/yFJ7y+O6lWrlhPgwYhHDi0gahLUTz11Cuez+bOH0/vXq8RGXGaAf2HMXHyGAYMepED+48waeIsAO7KcwebNi/itttu5fp1Q+8+j1OuTP0UdZYG1nb3yktvMmfBeAIDApk2ZQ5Hj35HV7s968TxM1i9cgP16tdk9/61REVF0efp1xKcF6BJs3q8N2Igue/Ixcw5X3LowDe0bdWNcV9M5eNPh7F15zJEhOlT53LkcPLbZ3H9GhdHj+L24e9DQACXly/j2k8/kbWZdSIVtXgRmWvWJFuLFphr1zB//835t6yeTK9+8w2XQ0PJ/cWXcO0aV779jqglKW/WsWrlBuo3qMW+A+u4FHWZ3k+96vls9txxPNP7dSIjTzNowHDGT/yI/gNe4MCBw0yeNDvR+adMG0uuXDm5cuUqL70wmHPnkt+5YGwrV66nfoNa7D+4nktRl+nVM2YbnDNvPH16vWbnfY8Jk0YzYOAL7N9/hMmTYrbB0E0LPdtgr96PU6FsAx4sfj8dH2nNoUNH2bzN6m9zyOD3WbVyQ4J50nobPH/uAp98PJ61ofMwxrB6VSirE8nos/xSaR/Onft2Zsz8HLCq2M+atYg1SWw3G9/yAfh6zpf07dOPyMjTDB44gq8mfMgbA57n4IEjTJ08J9H5vxz/IVWrVyB37ts5dHQTw979iKmT5zDozZcpdk9hrl+/zq+/hvNi34FxcvmzfMU6GjaszTdHNnPpUhRP9ogZlmnhgkk89fQrREScol//oUyZPJY3B7/Mvn2HmDBxJgAtWzWic6c2XLlylaioy3TuEtOlzOTJH1OjeiXuuCMX33+3k7feHuk53nhLrW1u1AefM37SR3Tu8jAnToTz+KPPAlCpclmee6EnV65c5fr167z8wmB+/+0PsmXLypz548mYMQOBgYGErt/K5Ilfk0Hify5y7do13nj5bWbM/co+V5hnnSs8bp8rTLDPFerVYPvelURdusxzXucKn371PlWqVSBX7pzsObyeEcM+ZsaUucyYOo8PP36bDVsX8c+VKzzb6/UkrU9/+dx8LnPt2jUGvPIu0+Z8TkBgIF9Pm8/xo9/T2R4dYerEWaxbvZHa9aqzefdyLkdF8UIfq/f1PHnu5MNP3iEwMBAJEJYsWMnaVaGA1XHYW++9Tq7cuZg08xMOHzpK57YpG/Iztc5jgoLyMH7cKAIDAwgICGDOnMUsXZb4zdYb8fKgYYTtPcC5cxeo07IzvZ7oQptm8dequRlSaxu80UyvvTyE2fPHERAYyPQpczh29Du6drNGlZg4fiarV26gbv2ahO1fQ9SlKM8+GN+8CalYuSx9n+/BlStXMdev8/ILb/L778m/yZYexG6+828k6aWnPhHJDewxxhQUkVrAS8aYpvZnHwO7jDETReQnoJwx5qyI3AnsBmobY74TkWxYT8R/Ao4AXYwx2+zq7PcaYw7H87sPAd2NMdtF5F2guTEmwXqweXLc7+oFe/7vS4lP5KCr193dUUWWDO5us5Mp0N33274pF5z4RA67N+yE0xES5PayO0OA6/rZ9PHPtaQPj+YEt+/DAH9dufFaWanploxZnI6QILevY7eXMRldvvwi/3T/xVFU+CanIyQof9HGTkdI0DWT9A53nXL2wvGb+nDRKSXzVk6zAulA5DZHlpm7SzQvxpjfRGSLfYEcBSTa8NEYc0ZEugIzRCSz/XZ/Y8xxEWkLjBaRHFjLYRTg96IceAL4UkT+AjYAye9GWimllFJKKaVUslx3+U3CmyHdXJQDGGMeief9Pl4/F4r12TqgvJ959gE1kvirDxtjSgKIyGtYHcYppZRSSimllFI3JF1dlDuoiYi8jrW8fga6OhtHKaWUUkoppf79/gttyvWi3IuIjMV3aDWAj4wxE4jp0V0ppZRSSimllLop9KLcizHmxrsFV0oppZRSSil1U/wX2pSn53HKlVJKKaWUUkqpdE2flCullFJKKaWUcqX/QptyfVKulFJKKaWUUko5RJ+Up5Koq/84HSFBt2XO6nSEBGWQQKcjJOjiP1FOR0jQP9euOh0hQaX2nnY6QqL+vnrF6QgJypohk9MREuT2bTBH5mxOR0jQn1cuOx0hUbmy3OZ0hAT9fc3d+/Afl/90OkKCMgS4+zh8K1mcjpCgO7JldzpCovIXbex0hASd+H6Z0xES5Pbl92+ibcqVUkoppZRSSimVavRJuVJKKaWUUkopV9I25UoppZRSSimllEo1+qRcKaWUUkoppZQraZtypZRSSimllFJKpRq9KFdKKaWUUkoppRyi1deVUkoppZRSSrmSdvSmlFJKKaWUUkqpVKNPypVSSimllFJKuZIx152OkOr0SblDho8YyL4D69i6YxmlSj/od5qCBfOzbsM89u5fx4RJo8mYMSMA99xbhDXr5nDm9294pm93n3nGfvoe3/+0k+1hy29a1tp1qrNt1wp27l3Fs88/6Xead9/rx869q9iwZRElSz3gef+jj9/lyHdb2bht8U3LE1tInWpsClvK1j0r6PNcd7/TvPXeG2zds4K1W+ZTotT/AAjOl5c5iyewccdiNmxbRPenOt/UXCPeH8T+g+vZvmN5gut4feh89h1Yx6TJYzzr+N57i7B2/Vx+++Moz/aNWeb33FOErduXev6FRx6gV+/HXZMP4PA3m9ixczlbty9l4+aFKcoWUqcam8OWsS2Bdfr2e2+wbc8K1m1ZQAmvbe7Dj9/m0Leb2bB1kc/0n4//gDWb5rFm0zzCDqxhzaZ5KcrmbeTINzl8eCNhYSspXbq432kKFbqbjRsXcuhQKFOmjPUsww4dWhIWtpKwsJWsXz+PEiWs7fKee4qwY8dyz7/Tpw/Tp88TSc40bMQAdu9fy+btSyhZyv96LVAwP6vXz2HXvjWMm/SRJ1N88+fLF8SiZVPZvnsFW8OW07PXYz7f9+RTXdi5ZxVbw5bz5luvJDlram2DAAEBAWzZtoTZc79Kch5vtepUY+POJWzevZze8WyDQ4a9zubdy1m9eR7FS8aUK7MXTWDD9kWs27qQJ3rGLVd69unKyT8Oc3uunMnO9d6Igezdv44t25dSKp71W7Bgftaun8uefWt9jh0Jzd+r9+NsD1vOtp3LGTdhFJkzZwJgwqTRbNq6mE1bF3PgcCibtiatLE+NcnnAkJfYtHMJa7fMZ/zU0WTPcVuSsngbOnwAu/atYdO2xT7HKm8FCuZn9bo5hO1dzbiJo3yWX3zzj/lkKMd+2M6WHUvjfN+TPbuwY89Ktu5cxuBk7B+xfTDyTY4c3sSusFUJljebNi7i8KGNTJ3yiSd7s6b12RW2ip07VrB1y1KqVCmf4hzxGfH+IA4c3MCOHcspncD+vCF0AfsPrGfS5I89+dq3b+Ep89aum+spD29ESJ1qbNm1nO17V/JMPOcu77zXj+17V7J+y0Kf48ioj9/h8HdbCN3mexx5td+zrN+ykLWb5vP1/HHkyXvXDeVz83EuNZZftKef6cap80fJlYIyMCX6v/sBNZp0oGXnp9Lk90VLjWU48K2X2Ry2jPVbFjJh6pgUlYPKnfSi3AH1G9SiaLFClC5Zm7593uDDUW/5ne7Nt15l7MfjeahUbc6du8Cjj7UD4I8/zvPKS0MY/VHck81pU+fQumXKLtL8CQgIYNjIgXRo252qFZrQqk1T7r2vqM80devVoEjRQlR4qD4v9h3A8A8Gez6bOX0eHdr4P9jcrHzvvt+fTm17UrNiM1q2bRwnX+16NShSpCBVyjTk5b6DGDZyEABXr17lzf7DqVGxGU3qdaBr90fizJtS0eu4VIkQnunzOqM+etvvdG+9/Rpjx4yjdMnanDt3nse6xqzjl196M846/vbbH6hSqQlVKjWhWpVmREVdZvGiVa7JF61xo0eoUqkJNaq1SHa2gIAAhr4/gEfa9qBGxWa0atskznqpY6/TymUa8lLfQbw3cqDns6+nL6Bj2x5xvrdntxeoW701dau3ZumiVSxbvCbZ2bw1aBBCsWKFePDBGvTu/RqjR7/jd7q3336dMWO+onjxmpw7d56uXdsD8NNPv1KvXjvKl2/A0KGjGTt2GGCt44oVG1GxYiMqV27CpUtRLFq0IkmZ6tWvSdGihShbqg7PPdOfkaPe9Dvd4Lde4dOxEyhXui7nz52ny2MPJzj/1atX6f/6UCqVbUj9kLZ0f7Iz991fDIBqNSrRuEldqlVqSpXyjRgzOmkXwam9Dfbq/TjHjn6XpCyxBQQE8M6IfnR++ClCKjWnZZvG3BOnXKlO4aIFqVa2Ea8+N5ih9jYYXa7UqtScZvU70rV7R595g/PlpUatKpz4NTzZuerVr0XRooV4qFRt+j7Tjw9GDfE73ZtvvcInYydQpnQdzp07z6Oe9et//qCgPDz19GPUqt6SyhUaERgYQJu2zQB4/LFnqV6lGdWrNGPRwhUsXrQyScsvNcrljeu3UqtyC+pUbcX33/0U70lufOrWr0nRogUpV7ouzz87gJEf+l9+g4e8zKdjJ1D+oXqcO3eBzo8+nOj806fN4+FW3eJ8V7XqFWnUpA7VKzWjSoXGfBzP9pqYhg1CKFasMA88WJ1evV9lzOh3/U73ztuvM3rMVzxYvAbnzp3j8a4dAFi3fjPlytenQsWG9Oj5Ip99OjxFOeLToEEtihUrTMkStejT5w1GfeS/PHzr7df4eMw4SpUMsffnmPKwQYP2VKzYiPeGjWHMx0NvKE/0ucsjbZ+keoWmtGrj/zhSuGhBKj3UgJf6DmT4B4M8n82cPp8ObeJuX2NHjyOkagvqVG/F6hUbePHVXinO5+bjXGotP7DKwJohVfj1l5MpypYSLRvX47MP/B9jUktqLcPQ9VupWakZIVVb8P33P/HsC3G3g3+j65g0++eUFF2Ui0hOEUlZSRT/d3YVkY9v5ne6VeMmdZkxfT4AYWH7yJEjO3ny3hlnupo1K7NgvvXEe8a0uTRtVg+As2d+Y8+eA1y9cjXOPFu3hPHH7+duWtYyZUvy0w8/8/NPJ7hy5QoL5i2lUZM6PtM0bFKHr2csAGD3rv3W35PH+nu2bd3FH3+cv2l5YnuobAl++uEXfvnZyrdw7nIaNK7tm69xbWbPtJ7Y7tl1gOw5buOuPHdw+tRZDu7/BoC//rzEt8d/IG9Qyu96e2vatB4zpll3qBNbx/PtdTxt6lyaNq0PwJkzv7Fn9wGuXLkS7++oFVKVH374mV9/Tf6BLS3ypdRDZUvyo9c6XTB3WZx12qBxbWZ51ul+sufIzl32Nrd96y7O/XEuwd/RrGVD5s+J+0QrOZo1q8+0aXMB2LlzLzlzZievn6cmtWpVYd68ZQBMnTqH5s0bWDm37+bcufOe+fPlC4ozb+3aVfnxx1/4JYknL42b1mXmDKts2RW9XvPEXa81alZi4XzrQn/GtPk0blovwflPnTrDgf2HAfjzz784fux7goLyANCt+yOMGvk5//zzDwBnz/yepKypuQ0G58tLw4YhTJr4dZKyxGaVK7/GlCvzltGgcYjPNA0a12bOTOsJxp5dB8jhVa4cOhB/uTL4nVd5Z/BITArGXG3StC4zkrR+Y44d06fNo4m9fhOaPzBDBrJmzUJgYCBZs2YlMuJUnO9t1boJc2YvSTRnapXLoeu3cu3aNXue/QQH5000i7fGTeoy0z5W7QrbR/act/ldftVrVmLhAmv/mDl9Hk2a1k10/m1bwvwe67p1f4SPPvgiZv84m7T9I7ZmzeozNUnlTVXmzbPKtile5c1ff13yTHPLLdlStP0lpEnT+kz37M97yZHjNvL63Z+rMH++VR5OmzqXZvb+vGPHHs6duwDAzp17yJcvees2tjL2cSTm3GUZDf2cu8yeYW2Du/0eR+Kuzz8v/uX5OdstWVO8HN1+nEut5QcwZOjrDBk4grQcdrpc6RLkyJ62T5RTaxmGrtviKQd3hyW/HFTuldIn5TmBm3pRnhZEJNDpDADBwXk5cSLC8/pkeCTBQb47Va7ct3P+/AXPjnfyZCRBwXnSNCdAUHAeTp6M9LwOP3nKczLumSYoD+He04RHkjeNsuYN8s0XER4Z58I6b9BdPvkiwuP+DfkLBFOixP/Ys/vATckVFJzHZx2Hn4yIU3Dmzn0752Kt4+BkLLe2DzdlzuyUNQtIzXzGGBYunsymLYt4vFvH5GdLwvqKvc1FhEcSlMQbKpWqlOPsmd/48Yefk53NW5z9+GSk32Xoux/HXc4AXbu2Z9Wq9XHef/jh5nz9ddKbAAQF5eGk93oNj1tu5Mp9O+fPXfRkCvdar0mZ/+4C+ShZ6gF279oPQLFihahctTyr189hyYrpPFSmRNKypuI2OHz4QPr3H8b16ylrg5Y3KA/hJ2OyRYSfIm+sbdBfuRJ7mvx3B1O85P/Ya5cr9RqFEBFxiiOHjqUol7V+Yp6wh4fH3eb8rd8ge5r45o+IOMWY0V9x6JtNHP9+GxcuXGTdus0+31ulannOnD7LD9//lGjOtCiXO3Ruzbo1mxLN4s06nnlvc0nbP6KnScr8sRUtVpjKVcqxet0cFi+fluT9IzarvIlZd/7KksTKm+bNG3Jg/3oWzJ9Ej54vpShH/Pny+OTz3u4Szhd3+T32WHtWrdpwQ3nyBvvuw+EnI+Psn0FBvuszwk9558/rA55jz+H1tHm4KcPfGZ2ifG4/zqXW8mvQKITI8JSXgelJam6D0R7p3Ia1qzfeeNh0wBiTZv+cktKL8mFAURHZJyIj7H+HROSgiLQHEJFaIuK5pS4iH4tIV/vn8iKyVUT2i8hOEYm+fRUsIitE5FsRibdulYgEishEr9/5vP1+MRFZY3/vHhEpaudYLyLTgYP2vCNEJExEDohIT6/vfdnr/Tft9wqJyDci8qWIHBaRVSKSNZ5cPURkl4js+ufqhXgXnojEeS/2RuB/mni/MtWkPGvahPX7u5MyjVe+bLdkY9zkjxj4xlCfu+A3PddNXG4ZM2akSeO6zLefwLopX906balWpRmtWz5Ojx5dqFq1wo1n4+Ytu1ZtmjB/7o09JbcyxH0vJcuwZs3KdO3ann79fKtrZsyYkSZN6nmeeiUt042t18Tmv+WWbEyeNpbXX32bixf/BCBDhgzkzJmdeiFtGdhvGBMmJ+0kNbW2wYaNanPmzFn27T2UpBz+s8V9L7nZst2SjS8nj2LQ68P48+JfZMmahWdf6MH7Q1NeISy11m/OnNlp0qQuJYvX4r5iVciWLRvt2vs2PWn7cLMk3wRM7XK574s9uXb1GnNnJe+mZGrvH/5kyBBIjpw5qFe7LYP6v8f4SR8lJ3KSciV1mkWLVlCyVAgPt+vO4EE396L8Zu3PNWpU5tHH2jOg/7AbzOPnzdjrKgn7uT9D3xpFmQdDmDt7Cd16pKwvGrcf51Jj+WXNmoXnXnqK995N2Y2M9CY1t0GA517qydWrV5NdDir3SulF+WvA98aY0sB2oDRQCqgLjBCRuPUwbSKSCfga6GuMiZ4nyv64NNAeKAG0F5G74/ma0kA+Y0xxY0wJYIL9/jRgrP29VYDo208VgH7GmAeAJ4DzxpjyQHngSREpLCL1gXvsaUsDZUWkhj3/Pfb3PgicA9r4C2WM+cIYU84YUy5Thuw+nz3Zowubty1h87YlREScIn/+mEWULzgvEZG+VQV/O/s7OXJkJzDQerifL19ev9UJU1v4yUifamTB+fIQGXnad5rwSIK9pwnOy6kI32lSS0S4b74gP787IvyUT76g4Ji/IUOGDIybPIp5s5fccBvjHj27eDpgi4g47bOOg/MFERFr/Z09+zs5Y63jiCQut/oNarFv32FOnz7runyR9jRnzvzG4sUrKVuuVJIzAoT7W18RCW9zQcF5iYw8k+h3BwYG0rhZXRbOS1lHiD17PurpjCj2MrSWT9xl6Lsf+y7n4sXv59NPh9O2bXd+j9XspEGDWuzbdyjRddy9R2c2bl3Exq2LiIg4TT7v9RqcN86y++3s7+TIeZsnU7DXeg0Pj4x3/gwZMjBp2lhmf72IJV79GJw8Genp12DP7gNcv27IfUcuv1nTYhusVKksjZvU5fA3m5g4eQw1a1bhq3EfJjhPbFaZEZMtKDgPpyITL1dOeZUrX04axfzZS1m+xCpXChW+mwIF87F60zy2719FUHAeVobO4c677kgwS/cenT0drUVGnCZf/mDPZ9FPub35W7/Rxw5r/cadv1ZIVX7+6Vd+O/s7V69eZfGilVSsVMYzXWBgIM2aN2BeEk/yU7NcfrhjC+o2qEnvJ5PWYdoTT3YidMsiQrcsIjLilE8zEWvZJL5/RE9jHQ8Tnj+28JORLLHb4Se2f8T2VM/H2LljBTt3rCA84hT5vdZd7LIEEi9vom3evIMiRQqSO/ftScoRnx49u7Bt+zK2bV9mn9d4bVt+zln854tZfsWL38/YT4bRvt2TccrD5Io46bsPB+fLG+fcJSLcd3sI8lNeJmTe7CU0bV4vRfncfJyD1Fl+hQoXoEDB/KzbvJCwA2sJzpeH1RvnJVoGplepuQ2269iSeg1C6PXkyzcvsMtpm/KkqQbMMMZcM8acAkKxLnbjcx8QYYwJAzDGXDDGRDeOXmuMOW+MuQwcAQrG8x0/AEVEZIyINAQu2E/b8xlj5tvfe9kYE92Iaqcx5kf75/rAoyKyD9gB5Ma66K5v/9sL7AHut98H+NEYs8/+eTdQKLGFEtuXX0yhWuWmVKvclKWLV9PxkVYAlC9fmgsXLnLKT0G7ceN2WrZqBEDHTm1YuuTGLhpTYu+egxQuWogCBfOTMWNGWrZuwopl63ymWblsHe07tgSgbLlS1t9zKvEDx82wb88hChctyN0F85ExY0ZatGnEyuW+1YBXLl/Hwx2spz5lypXk4oWLnD5lXeh88PFbfHv8Bz4fO+mGs3zx+RRPJ2xLFq+iY6fWQOLruJW9jjt1bsPSpauT9LsefrgZs2f779XUyXzZsmXl1ltv8fxcu051jhxJXjW1fXsOUqRoQQrY67Rlm8asirVOVy1fTzvPOi1lr9PEt7katSrz3bc/EhGeshtcn38+2dMJ26JFK+nUybo/V6HCQ5w/fzHOARcgNHQbrVs3BqBz57YsXmxdwN59dzBff/0F3bo9x3ff/RhnvnbtWjBrVuJV17/6Yio1qjSnRpXmLFuymg4drbKlXPR69bNcNm3cQYtWDQHo2KkVy5daZcvypWvjnX/MJ0M5fuw7Pvl4vM93LVuymho1KwFQtFghMmXKyG/xtJtNi21w8KAR3HdPFR78X3W6PvoMoaFb6f7E8wnOE5tVrhTg7gJ2udLa/zbYtkNzwCpXLlz401OujBwzhO+O/8AXn8SUK0ePfEupe2tQqVR9KpWqT0T4KRrUbMuZRG66fPXFVE9Ha0uWrKJjktZvzLHjkU6tWWav32VL1/id/9dfwylXoTRZs2YBoGatKhw79r3n+2qFVOX48e8JD48kKVKrXA6pU40+fbvTtWNvoqIuJynLuC+nUbNqc2pWbc7SJWvoYB+rypUvzYXz/pff5o07aNHS2j86PBKz/JYvW5uk+b0tXbKGGjUrA4nvH7F99vkkKlRsSIWKDVm8aCWdk1TebKV16yYAdPEqb4oWKeSZpnTp4mTMmInffvsjSTni88XnU6hcqTGVKzVm8eJVPOLZnx/iwoWLfi8gN27cRqtWVnnYqXMbliy18uXPH8z0GZ/R/Ynn/ZaHybU39nGkdWNW+jl3ebijtQ2WTeJxpHCRmNPSBo1q8+23Kcvq5uMcpM7y++bIcR4sVpXyJetQvmQdwk+eol6N1omWgelVam2DIXWq0ee57jza4ekkl4MqfbgZ45T7q6ABcBXfi/4sXtPHdxvib6+frxFPPmPMHyJSCmgA9AbaAc8lkNG77psAzxhjfLqQFZEGwFBjzOex3i/kJ5ff6utJtXLleuo3qMX+g+u5FHWZXj1j7vjPmTeePr1eIzLyNIMGvMeESaMZMPAF9u8/wuRJswC4K88dhG5ayG233cr164ZevR+nQtkGXLz4J+MnfkS16hXJnft2vjm+hXff/ogpk2elOOu1a9d4/aUhzJr3FQGBgcyYOpdjR7/jsW5Wj66Txs9k9apQ6tavyc59q4m6FMWzvd/wzP/5uJFUrVaBXLlvZ/+RUIYPHcO0KXNSnMdfvjdefocZc78kMDCAmVPnc/zodzz6uNWj6+QJX7N21Ubq1KvBtr0riLp0med79wOgQqUyPNyhBUcOH2O1PWzI0CGjWHcT2uesXLGeBg1COHBoA1GXonjqqZh1PHf+eHr3eo3IiNMM6D+MiZPHMGDQixzYf4RJE2PW8abNizzruHefxylXpj4XL/5J1qxZCKldjWef6ee6fLlz386MmdYulCFDILNmLWJNMpentU7fZsbcrwgMDGDG1Hkci7VO16wKpU69Gmzfu5KoS5d5zmub+/Sr96lSrQK5cudkz+H1jBj2MTOmWB0ktWzT+IY7eIu2YsU6GjYM4ciRTVy6FEWPHjHVQRcsmMjTT79KRMQp+vcfyuTJHzN48Mvs23eYiXbnY2+80ZdcuW7nI7vX8atXr1G1alPAquZXp051+vR5PVmZVq3cQL0GtdhzYB1RUVH0fupVz2ez5n7Fs73fIDLyNIMHDGfcxFH0G/ACBw4cYcqk2QnOX6lyWTo80orDh46y0R6C563BI1m9KpSpk+fw8afD2LpzGf/8c4Wneybtzn1q7iM36tq1a/R/5R2mz/2CgMAAvp42n+NHv6fL41bP71MmzGLtqo3UrleDLXuWExV1mRd69wegfKUytLXLlVUbre1u2FujWLc6ee2f/Vm1coNVS+bAOi5FXfZZv7PnjuOZ3q/bx47hjJ/4Ef0HvMCBA4eZ7LV+/c2/e9d+Fi5YwcYti7h69RoH9h9m4viZnu9u07Ypc5PRf0VqlcvvjOhPpkwZmblgHAB7wvbz6gv+RxjwZ/XKDdSrX5Pd+9cSFRVFn6df83z29Zwv6dunn7V/DBzBVxM+5I0Bz3PwwBGmTp6T6Pxfjv+QqtUrkDv37Rw6uolh737E1MlzmDZlDmM+GcqWHUv5558rPsf75Fi+Yh0NG9bmmyObuXQpiid7vOj5bOGCSTz19CtERJyiX/+hTJk8ljcHv8y+fYeYMNFajy1bNaJzpzZcuXKVqKjLdO5yc7sFit6fDx4KJepSFD2fiikH5s2fQK9er3r250mTxzBw0Ivs33/Ysz+//saz5Mp1u2cUhqtXr1K9WvMU57HOXd5i5rxx9nHEOnd5tJu9DY63jyP1a7Bj3yqiLl2mr9dx5LNxI6lSrTy5ct/O3iMbGDF0DNOnzKX/my9SrFghrl83nPg1nJefHxRfhETzufk4l1rLzykvDxpG2N4DnDt3gTotO9PriS60adYgVX9nai3Doe8PIFOmTMxaYN0g371rP688PzhV/xY3cLKtd1qRlPyRIpIb2GOMKSgirYGeQGMgF7ALqAhkBDZhPRnPAuwD3gSmA0eB9saYMPsJdxTQGShnjOlj/44lwPvGmA1+fv8dwD/GmAsiUhqYaIwpLSLbgWHGmAUikhkIxKqO/pIxpqk9bw8768PGmCsici9wEqgKvAXUMcb8KSL5gCtANmCJMaa4Pf9LwK3GmMEJLaPstxRx9daTOUPGxCdyUAZ39MkXr4v/RCU+kYrXrZmyJD6Rw85dvjn9C6SWrBkyOR0hQVeuX3M6QoJyZM7mdIQE/XnF/U9AsmXI7HSEBP197eaPDnEz/eXydZwhwN3H4Vszuvs44q9NuNu4/ULnxPcp6zcnreQv2tjpCIk6df6o+zfEJMh3+4NptrGe/OOwI8ssRU/KjTG/icgWETkELAcOAPuxnoC/YoyJBBCRWfZn32JVC8cY84/dGdwYu8O0KKx25cmRD5ggItFP4qMfJ3UBPheRIVgX1A/7mfcrrOrne8QqMc8ALY0xq0Tkf8A2uyD9E+tGgbvPLJVSSimllFLqX+q6y28g3QwpelKuEqdPym+MPin/d9Mn5TdOn5TfGH1SfuP0SfmN0SflN0aflN84t18D6JPyG/dveVIelPOBNNtYI84dST9PypVSSimllFJKqdQWe8jAfyPXX5SLyA4g9u34LsaYg07kUUoppZRSSimlbhbXX5QbYyo6nUEppZRSSimllEoNrr8oV0oppZRSSin13+T2/g9uhoDEJ1FKKaWUUkoppVRq0CflSimllFJKKaVc6bp29KZSyu1DYVy68rfTERLk9vEIc2a+xekI6Vp6GFLutsxZnY6QoIt/u3sZZsvo7uGy3L4NZgp0/+H51ozu3kcCA9xdGTBzoLuHJg1w+XmM24dsO/XXOacjJMrtw5O6fcgxtw/ZptIXdx+xlFJKKaWUUkr9Zxlj0uxfUohIQxE5JiLfichrfj4XERltf35ARMok9p16Ua6UUkoppZRSSiVCRAKBsUAj4AGgo4g8EGuyRsA99r8ewKeJfa/768cppZRSSimllPpPclmz1grAd8aYHwBEZCbQAjjiNU0LYLKxHr1vF5GcIhJkjImI70v1SblSSimllFJKqf88EekhIru8/vWINUk+4Fev1yfs95I7jQ99Uq6UUkoppZRSypXScpxyY8wXwBcJTOKvF8zYAZMyjQ99Uq6UUkoppZRSSiXuBHC31+v8QHgKpvGhF+VKKaWUUkoppVzpOibN/iVBGHCPiBQWkUxAB2BRrGkWAY/avbBXAs4n1J4ctPq6UkoppZRSSimVKGPMVRHpA6wEAoHxxpjDIvKU/flnwDKgMfAdcAl4PLHv1YtypZRSSimllFKulJZtypPCGLMM68Lb+73PvH42QO/kfKdWX09D740YyN7969iyfSmlSj3od5qCBfOzdv1c9uxby4RJo8mYMWOi8/fq/Tjbw5azbedyxk0YRebMmQDoN+B5tmxfyqati5m/cCJ5896VrLwj3h/EgYMb2LFjOaVLx593Q+gC9h9Yz6TJH3vytm/fgh07lrNjx3LWrptLiRL/AyBfviCWLZ/B7j1rCNu1il69Er1xFK+RIwdz6FAoO3euoHTp4vHku5uNGxdw8OAGpkyJydehQ0t27lzBzp0rWL9+nidftICAALZtW8bcueNTlC2kTjU2hS1l654V9Hmuu99p3nrvDbbuWcHaLfMpUcr6/cH58jJn8QQ27ljMhm2L6P5UZ8/0r/R7hrVb5rN60zxmzvuSPHnvTFG2tMh6M4x4fxD7D65n+47llEpg+1sfOp99B9YxafIYz/q9994irF0/l9/+OMqzfZ/0mad3n26E7VrJzrAVTJj4kWd/SY7adaqzbdcKdu5dxbPPP+l3mnff68fOvavYsGURJUs9kOi8X074kPWbFrB+0wJ2H1jL+k0Lkp0rtpEj3+Tw4Y2Eha2Mdx8pVOhuNm5cyKFDoUyZMtZnHwkLW0lY2Mo4+8gzzzzBnj1r2L17NZMnjyFz5szxZhg6fAC79q1h07bFPsvBW4GC+Vm9bg5he1czbuIon3Ivvvnr1K3Ojj0r2bVvDX1fiN0xKvR59gl+v/gtuXLfDsDtuXKycOkUfonYx3vvD0xgqfk3fMRA9h1Yx9YdyxLcHtdtmMfe/et8yu977i3CmnVzOPP7NzzT1/8+lhS161Zn++4V7Ny3mmefj/s3A7w7vD87960mdGus7S6eeV95/RkOHt3E+s0LWb95IXXr1/T5vnz5g/gpfC+9n+mWrKzVa1dm5ba5rNm5gB7PdvU7zYB3X2bNzgUs3jCTB0re73l//e7FLAn9mkXrpzNv9RTP+68O6suKrXNZvGEmYye+z23Zb01WpoTUqlOV0B2L2bxrGb37PuF3miFDX2fzrmWs3jSP4iWt/SFz5kwsWT2DVRvnsnbrAl58LVnnYAly+3GkVp1qbNy5hM27l9M7nnxDhr3O5t3LWb05ZpkF58vL7EUT2LB9Eeu2LuSJnr7HjseffISNO5ewbutC+r35Yorz1axdlXU7FhEatoSn+/rffgcPfZXQsCWs2DjHZ50uXD2N5aGzWb1lHs+/2ssz/XOvPM2OQ6tZtmEWyzbMIqRutRTni+1GyuumTesRFraSHTuWs2XLEqpUKZ+iDKlRxkTr/Uw3zl44Tq5cVpn8UNmSnnJnw5ZFNG5aL9l5Q+pUY8uu5Wzfu5Jn4jkWv/NeP7bvXcn6LQsp4ZV31MfvcPi7LYRu8611PPCtl9kctoz1WxYyYeoYsue4Ldm5kqv/ux9Qo0kHWnZ+KtV/l3IvvSiPh4gsE5Gc9s/Pisg3IjJNRJqLyGvJ/b569WtRtGghHipVm77P9OODUUP8TvfmW6/wydgJlCldh3PnzvPoYw8nOH9QUB6eevoxalVvSeUKjQgMDKBN22YAjB71JVUrNaF6lWasWLGeV19/Jsl5GzSoRbFihSlZohZ9+rzBqI/e8TvdW2+/xsdjxlGqZAjnzp3nsa7tAfjpp19p0KA9FSs24r1hYxjz8VAArl27yhuvv03ZMnUJqdWKHj27cP/9xZKcKyZfCEWLFqZ48Zr06fM6o0e/7Xe6d955jTFjxlGiRC3++OM8Xb3y1a/fjgoVGjJ06GjGjh3qM1+fPt04duy7ZOcC64L+3ff706ltT2pWbEbLto25976iPtPUrleDIkUKUqVMQ17uO4hhIwcBcPXqVd7sP5waFZvRpF4HunZ/xDPvJ6PHU6dqK+pVb83qlaG88EqvOL/bLVlvVP0GtSharBClSoTwTJ/XGfWR//X71tuvMXbMOEqXrG1vf+0A+OOP87z80puM/ugrn+mDgvPwdK+uVK/WnArlGxIYGEjbh5slK1tAQADDRg6kQ9vuVK3QhFZtmsb5u+vWq0GRooWo8FB9Xuw7gOEfDE503icff56Q6i0Jqd6SJYtWsWTx6mTliq1BgxCKFSvEgw/WoHfv1xg92v8+/PbbrzNmzFcUL16Tc+d895F69dpRvnwDex8ZBkBwcB56936cKlWaULZsPQICAmnXzv8yrFu/JkWLFqRc6bo8/+wARn7ov9wbPORlPh07gfIP1ePcuQt0fvThBOcPCAhg+MjBtGvdncrlG9GmbVPuuy+mHMmXLy+1Qqry6y8nPe/9fflv3n17FAP7vZfMJRmzPZYuWZu+fd7gw1Fv+Z3uzbdeZezH43moVG3OnbvAo4/FbI+vvDQkzvaYHAEBAbw3chDt2zxJ1fKNad3Wz3ZXv6a13ZWuxwt9BzDiwzeTNO9nYycQUq0FIdVasGZVqM93vj30Ddau3pjsrIOHvUb3Ds/SqGpbmrZqQLF7C/tMU7NuVQoWuZu6FVoy4MW3GTL8dZ/Pu7TqSfOQR2hdr4vnvS2hO2hSvR3NanXgp+9/5qm+Kb+pGzvv28P706Xd04RUbk6LNo25574iPtPUrludwkULUK1cY159fjBDRw4A4O+//6Fdy27Ur9GGBjXaUqtOVcqUK3lTMrn5OBIQEMA7I/rR+eGnCKnUnJZtGnNPnHzVKVy0INXKNuLV5wYzdORAn3y1KjWnWf2OdO3e0TNvlWoVaNC4NnWrtaJ2lRZ8NmZCivO9NfwNHmv3NHWrtKR560Zx1mlI3WoULlKQmuWb8voLQ3j7/f6AtU47tuxOo5oP06hmO2rWqcpDXut03KdTaVyrHY1rtWP9ms0pyhfbjZbX69dvoXz5BlSs2IiePV/i00+TX86lZhkTnC8vNWv7lslHjxynbs3WhFRrQfvWTzDyoyEEBgYmK++wkQN5pO2TVK/QlFZtmsTJW6deDQoXLUilhxrwUt+BDP9gkOezmdPn06FN3Av50PVbqVmpGSFVW/D99z/xrJ+bvjdby8b1+OwD/+c5ynLdmDT75xS9KI+HMaaxMeac/bIX0NgY08kYs8gYMyy539ekaV1mzJgPwK6wfeTIkZ08eeLeoa5RszIL5i8HYPq0eTSx7xwmNH9ghgxkzZqFwMBAsmbNSmTEKQAuXvzT8723ZMuarKofTZrWZ/q0eQCEhe0lR47byOvnjnrNmlWYP9+qvTFt6lyaNa0PwI4dezh37gIAO3fuIV++vABERp5h377DAPz5518cO/Y9wcF5k5wrWtOm9Zg+fa79/XvJkSO735oANWtWYd48O9+0uTRrZuXbvn13rHxBnnny5ctLw4a1mTBhZrJzATxUtgQ//fALv/x8gitXrrBw7nIaNK7tM03DxrWZPXMhAHt2HSB7jtu4K88dnD51loP7vwHgrz8v8e3xH8gbZP1df178yzN/tmSuz7TOeqOaNq3HDM/2Z2/vfre/ysy395dpU+fS1N7+zpz5jT27D3DlypU482TIEBizv2TLQkTE6WRlK1O2JD/98DM//2QtswXzltKoSR2faRo2qcPXMxYAsHvXfs/+mpR5AVq0asT8OUuSlSu2Zs3qM21azD6SM6f/faRWrZh9ZOrUOTRv3gCI3kfOe+b33kcyeJU52bJlJcIuc2Jr3KQuM+3lsCtsH9lz3ua33KtesxILF6wAYOb0eTRpWjfB+cuWK8mPP/zMzz/9ypUrV5g3dymNmsYsx3eG9WPQgOE++8ilS1Hs2Labv//+O/GF5+fvmDHdKn8T2x6jy+8Z0+bStJlVfp898xt79hzg6pWryf7d0crE+pvnz11KoyZ1faZp1LgOs+zjxO6w/eTIYS2vpMzrT6Mmdfn5p185djR5NyhLlnmQn3/6lV9/PsmVK1dZumAVdRrV8pmmbsOaLPh6KQD7dh/ithy3cmeeOxL83s0btnPt2jXPPHmD8yQrV3xKly3BTz9Gl4NXWThvOfUb+ZaD9RuHMGem9URtz64DZM9ulYMAl/6KAiBDxgxkyJDB1WXzzTqOWPl+jck3bxkNGof4TNOgcW2fZZbDK9+hA/7zPdqtPWNHfcU//1hl929nf09RvtJlivPTj794tsHF81dQr5FvvnqNQpj79WIA9notP/Bdpxlv0jpNyI2W13/9dckzzS23ZEtR3tQsY94e+gZvDhjhkysq6rJnf86cJXOyM5cpW5Iff/jF63i6jIZ+jsWzZ1j7yO5d+8meIzt32ceg7Vt3ce6P83G+N3TdFk+u3WH7U3SOmlzlSpcgR/bUfyKv3O0/e1EuIq+IyLP2zx+KyDr75zoiMlVEfhKRO0TkM6AIsEhEnheRriLycXJ/X1BQHk6eiOkJPzw8Ms6Oniv37Zw/d9FTGISfjCTInia++SMiTjFm9Fcc+mYTx7/fxoULF1m3LubO7YBBL3L46GYebt+Cd94eleS8wcF5OOH9+7yyRMud+3bOn7/gyXvyZATBfk6SHnusPatWbYjzfoEC+SlV6gHCwvYlOVdMvrw++U6ejIzzu/3ni1u4du3agZUrY/KNGDGIfv3e5fr168nOBZA3KA8nT0Z6XkeER8a5WM0bdBfhPtOcIijIN3/+AsGUKPE/9uw+4Hnvtf592XVoLa0fbsqId8ekKF9aZb0RQcF5OHEippPKcD/rLnfu2znns37jbgOxRYSfYvSoL/nm2Ba+/2EHF85fZN3aTcnO5r3Mwk/GXR5BQXl8lll4eCR5g/Mkad7KVcpx5sxv/PDDz8nKFZu1j8QsQ2v5JGUf9rePtGfVqvX233KKDz/8gm+/3c5PP+3iwoULrFnjfxlaf6/3eowkKNY68l/u5Ulw/qCgvHHft5djw8a1iQg/xeFDRxNZQkkXZ1mGRxIc5Kf8jrU9xv5bb0RQUB7CT/huU7G/Pyg4DydPxNq+gvMkOu8TPToTunURH419lxw5swPWBduzzz/JiGHJPtyRN+guIk7G3KiJDD9FniDfmxh5gu4iItx7mtOeGx3GGCbMHsv8NVNp36WV39/R9pHmhK7dkuxs/gQF3UWE134ZGX6KoDjlYJ445WBee5sLCAhgZegc9h/byKYN29i7++ANZ3L7ccRaHjH7hPfySChf7Gny3x1M8ZL/Y6+dr0ixQlSoXJbFq2cwZ8lESj3kvxp3UvJ5b4PW7054+VnbqTVNQEAAyzbMYs/RDWwK3cY+r3X6aPcOrNg4hxGj37xpVZtvRnndvHkD9u9fx/z5E+nZ8+VkZ0itMqZho9pERPgvk8uUK8nmHUvZuG0xLz03yPO3JUXeYN9tMPxkZJztKyjI9xgS4edvSsgjndsku6aQUin1n70oBzYC1e2fywG3ikhGoBrgOcM0xjyFNa5ciDHmw4S+UER6iMguEdn1z5ULsT+LM33su4IJTRPfZzlzZqdJk7qULF6L+4pVIVu2bLRr38IzzVtvjuTB+6sx++uF9OjZJc53JPC33FDeaDVqVObRx9ozoL9v5YJbbsnG9Bmf8sorQ3ye6DuR77HH2tO/v1V9vVGj2pw+/Rt79x5KdqYEf29SpvHKlu2WbIyb/BED3xjq82Rj2NsfUa54HebNXsLjPTqlOGNaZL3puVKwfmPLmTM7TZrWo/gDNShWtBLZbslG+w4t0yxbUuZt1bYp827wKbmVIe57KVmGNWtWpmvX9vTrZ+0jOXPmoFmzetx/f1UKFy5PtmzZ6NjR/4VTapV78f1tWbNm4cWXevHuO6P85kmplP8dbsiQ8HY34avplCtVl1pVW3Aq8gxD3rFaZ736xrN8NnaizxO4ZIRNcVaADk260bJOJ57o8AydurWjfOWHfKZ7+vluXL16jUVzlic/WyrkvX79Og1qtqV88TqULlOC+/6X/CZZcSO5+zhyM8qXbLdk48vJoxj0+jBPvsAMgeTImZ1m9Try9sCRfDZhZIrycYP5rl+/TuNa7ahUoh6lHyrOvXYzu6kTvqZG2SY0qvkwp0+dZcBbL6UsX+y4N2F5Llq0klKlatOuXXcGDUp+rtQoY7JmzcLzLz/NsHc+8vs79+w6QLWKTahXqy3PvdgzWX28+FtmcQrdJCzX+Dz3Uk+uXr3K3FmLk5xJpR6Thv855b98Ub4bKCsitwF/A9uwLs6r43VRnhzGmC+MMeWMMeUyZcxO9x6d2bR1MZu2LiYy4jT58gd7po1+yu3tt7O/kyPnbZ42NcH58nqqooeHR/qdv1ZIVX7+6Vd+O/s7V69eZfGilVSsVCZOttmzFtG8RcME8/fo2YVt25exbfsyIiJOkd/793lliXb27O/kyJHdkzdfviCfqsDFi9/P2E+G0b7dk/z++znP+xkyZGD69M/4euYCFi1cmWAmbz17Psr27cvY7idfvnx541RD9p8v5m8oXvx+Pv30PR5+uLsnX+XK5WjatC5Hj25m8uQx1KpVhfHjRyU5I1h3YqOr6wMEBeflVKxsEeGnCPaZJg+RkdY0GTJkYNzkUcybvYRli9f4/R3z5yylSbPkd4riRNak6tGzC1u3L2Xr9qVERJwmf/6Y6tLBsdYdWOs3p8/6jbsNxBYSUo2ffv6Vs/b+smjhSir52V8SEn7Sd5kF54tZHp5pwiN9llmwvVwTmzcwMJAmzeqxYJ5Ph55J1rPno54OFmMvQ2v5JGUfjr2PDKdt25h9pHbtavz0U8wyXLhwBZUqlY2TIXTLIiIjTvlUe7fKEd9l5b/cs6axllfc+cPD/bwfeZpChQtQoFB+Nm1dzL5D6wnOl5cNmxZw110JV4v258keXdi8bQmbty2xyxuvZRmcl4hIP+V3rO0xdpl5I8LDIwnO77tNxV6W4ScjyZc/1vZlL6/45j1z5jeuX7+OMYYpk2ZRpqzVdrZMuVIMGvIyew6uo+fTj/HcS0/xRI+kdeYYGX6KoHwxT6PyBufhdOTZuNMEe09zF6dPWdNE///3s3+wetl6Sno9LW3Vvikh9arz4tP9k5QlKSLCTxHktV/mDc5DZOSZWNNExikHT8Xa7y9cuMi2LWHUqnPjnX+5/Thi/e6YfcLf8vCX75RXvi8njWL+7KUsXxKTL+LkKZbbefftOcj169c9nTUmR+xt0Prdsdepbz5rO/Wdxlqnu6hVpyoAZ8/87tlfZkyeS6kyJZKdLdrNLq+jbd68kyJFCpA7mcstNcqYQoULUKBgfkK3LGLPwXUE58vLuk3z45TJ3x7/nkt/XeJ/D9yb5LwRJ323wejjgM804b7HoCA/f5M/7Tq2pF6DEHo9mfwaB0ql1H/2otwYcwX4CWvcuK1YF+IhQFHgm5vxO776YirVqzSjepVmLFmyyvM0qVz50ly4cJFTp87EmWfTxu20bNUIgEc6tWbZUuvgtGzpGr/z//prOOUqlCZr1iwA1KxVhWPHvgegSNFCnu9t1KQu3x7/PsG8X3w+hcqVGlO5UmMWL17FI51aA1C+/ENcuHAxzkkKwMaN22jVqjEAnTq3YcnSVQDkzx/M9Bmf0f2J5/nuux995vn00/c4duw7xowZl2Ce2D7/fDKVKjWmUnS+R9oAUKFCdL64Be3Gjdto3drO16kNS5ZYnWfdfXcwM2d+zhOx8g0cOJxixSpx//3VePTRZ9iwYSvduj2XrJz79hyicNGC3F0wHxkzZqRFm0asXL7eZ5qVy9fxcAerRkOZciW5eOGi50T0g4/f4tvjP/D52Ek+8xQuUtDzc/1GIXz37Q/JypWWWVPii8+nUKVSE6pUasKSxavo6Nn+7O3d7/a3nVb2/tKpcxuWLk24c7RfT4RTofxDnv2lVq0qHDua8H4R2949BylctBAFCuYnY8aMtGzdhBXL1vlMs3LZOtp3bAlA2XKlPPtrYvPWrFWF747/4FOtNzk+/3wyFSs2omLFRixatJJOnWL2kfPn/e8joaEx+0jnzm1ZvNjah+++O5ivv/6Cbt2e89lHfv31JBUqlPEsw5CQqhz1anMcnaFm1eYsXbKGDvZyKFe+NBfO+y/3Nm/cQYuW1k3DDo/ElHvLl631O/+e3Qcp4rUcW7dpwoqla/nmyHHuK1KJ0sVDKF08hPCTkdSq3pLTp8/G+Z2J+fKLKVSr3JRqlZuydPFqOj5ilb+JbY/R5XfHTm1YuuTGblR527v7IEWKxPzNrdo0YcWytT7TrFi+jnb2caJs+VJcuPCntd0lMK93G/8mzepx9JtvAWjW8BHKlKhNmRK1+fzTSYx6/zPGfTE1SVkP7j1CocJ3k79AMBkzZqBJy/qsXeHbgdzalRtp2b4JAKXLFufihT85c+osWbNl4ZZbsgGQNVsWqtWqxHF7+6peuzI9nnmMp7o8z+Woy8ldhPHav+cQhYsU4O4C+ciYMQMtWjdi9QrfcnDV8g207dAciC4H/+T0qbPkyn072e22oFmyZKZazUp8d/zHOL8judx+HLHyRS+zjLRo3ZhVy2Mvs/U+y+yCvcwARo4ZwnfHf+CLT3zzrVy2lqo1KgJQpGhBMmXKyO+//ZHsfPv3HqZwkYKeddqsVUNWL9/gM82aFRto097qpPIhr+XnvU4zR6/Tb611epdXvwcNmtTmmL2/pMTNLK+LeK3X0qWLkzFjJn5L5nJLjTLmmyPH+V/Ryp6yJPxkJLWrt+L06bMUKJjfc4Mh/93BFLunML/8fDJOrnjz7jlIkaIFKWDvIy1bN2aln2Pxwx2tfaRsuVL2Oo5bdnsLqVONPs9159EOTxN1E8sZdWP+Cx29/dfHKd8IvAR0Aw4CHwC7jTHGX1WcG7Fq5QbqN6jFvgPruBR1md5Pver5bPbccTzT+3UiI08zaMBwxk/8iP4DXuDAgcNMnjQ7wfl379rPwgUr2LhlEVevXuPA/sNMHG91UPbmkJcpdk8Rrl+/zq+/nOT5vgOSnHflivU0aBDCwUOhRF2KoudTMXcL582fQK9erxIZcZoB/YcxafIYBg56kf37DzNp4iwAXn/jWXLlut3Ta/bVq1epXq05lSuX45FObTh08Bu2bbeeBg4eNNynTXdSrFixjgYNQjh8eCOXLkXRs2dMVa358yfSq9crREScpl+/oUyZ8jGDBr3E/v2HmTjxayvf632tfHYvylevXqNateT1wh2fa9eu8cbL7zBj7pcEBgYwc+p8jh/9jkcft3pJnTzha9au2kidejXYtncFUZcu83zvfgBUqFSGhzu04MjhY6zeZHV0NnTIKNat3ki/wc9TtFhhrpvrnPg1nFeff9O1WW9U9PZ34NAGoi5F8dRTr3g+mzt/PL17vebZ/iZOHsOAQS9yYP8Rz/Z3V5472LR5EbfddivXrxt693mccmXqsytsHwsWLGfL1iVcvXqV/fuPMH78jGQvs9dfGsKseV8REBjIjKlzOXb0Ox7r1gGASeNnsnpVKHXr12TnvtVEXYri2d5vJDhvtFZtGjNv7tIbXXyAtY80bBjCkSObuHQpih49YvaRBQsm8vTTrxIRcYr+/YcyefLHDB78Mvv2xewjb7xh7SMfefbha1St2pSwsH3Mn2/VWLl69Rr79x9m3LjpfjOsXrmBevVrsnv/WqKioujzdMzAFV/P+ZK+ffoRGXmawQNH8NWED3ljwPMcPHCEqZPnJDj/tWvXeOWlN5mzYDyBAYFMmzLH58ZAfPYdWs9tt91KxkwZady0Li2bP5akTsxWrlxP/Qa12H9wPZeiLtOrZ8z2OGfeePr0es0uv99jwqTRDBj4Avv3H2HypJjtMXTTQs/22Kv341Qo2yBZTXeuXbvGay8PYfb8cQQEBjJ9yhyOHf2OrvZ2N3H8TFav3EDd+jUJ27/G2u56vZ7gvACD3nqF4iXuxxjDr7+c5MW+yR8uzl/WN18fzvhZHxMYEMicGQv57tgPdHzMuuiYMWkuG1ZvpmbdqqzduZCoqMu89uxgAO64MzdjJ74PWJ0yLp63gk3rtllZh71KpkwZmTjnEwD27TrIwJeHxg2QgrwDXnmXaXM+JyAwkK+nzef40e/pbI/mMHXiLNat3kjtetXZvHs5l6OieKGPdTzNk+dOPvzkHQIDA5EAYcmClayN1YN9SjO5+Thy7do1+r/yDtPnfkFAYIBnmXV53FpmUybMYu2qjdSuV4Mte5YTFXWZF3pbtRvKVypDWzvfqo1W52bD3hrFutWbmDl1PiM/fou1Wxdw5Z8rPPd0vxTnG/jqu0ye/SmBgYHMmr6Ab499T6eu1sgO0ybOZt3qTYTUq87GXUuJirrMS89Y6/SuPHfwwdi3CQgMJCAggCULVrJulXVce33w8zxQ3NpfTvwSzhsv+h9RIrlutLxu1aoxnTq14cqVK0RFXaZLl+QPzZdaZUx8KlYuS9/ne3DlylXM9eu8/MKb/P570m8kWMfTt5g5bxyBgQGe4+mj3ex9ZPzXrFkVSp36NdixbxVRly7T1z4WA3w2biRVqpUnV+7b2XtkAyOGjmH6lLkMfX8AmTJlYtYCa0jc3bv288rzg5OzKJPt5UHDCNt7gHPnLlCnZWd6PdGFNs0apOrvVO4jbhuMPS2JSB1gBZDTGPOXiBwHPjPGfCAiPwHljDFnY/3c1f65T0LfnePWoq5esFevJ70zDSc4eacqKXJmvsXpCOnaxX+inI6QqKwZkz9+eVq6+Le7l2G2jPGPXe4Gbi8DMwW6/5757Znd3Vtw1LXk97Sflq6lsDPRtBJwkx9O3GwZApI+fJYTTv11zukIibo1UxanIyQoUNxdoffE9ylrapaWMt5RxN07chJlyVIgzS4MLl/+xZFl5v6jfioyxqwFMnq9vtfr50Lx/DwRmJgW+ZRSSimllFJK/bv9py/KlVJKKaWUUkq5l5O9oqcVd9cLUUoppZRSSiml/sX0SblSSimllFJKKVf6L/SBpk/KlVJKKaWUUkoph+iTcqWUUkoppZRSrqRPypVSSimllFJKKZVq9Em5UkoppZRSSilX+vc/J9cn5UoppZRSSimllGPkv1BH/99ARHoYY75wOkd83J4P3J9R890YzXdj3J4P3J9R890YzXdj3J4P3J9R890YzXfj0kNGlTr0SXn60cPpAIlwez5wf0bNd2M0341xez5wf0bNd2M0341xez5wf0bNd2M0341LDxlVKtCLcqWUUkoppZRSyiF6Ua6UUkoppZRSSjlEL8rTD7e3L3F7PnB/Rs13YzTfjXF7PnB/Rs13YzTfjXF7PnB/Rs13YzTfjUsPGVUq0I7elFJKKaWUUkoph+iTcqWUUkoppZRSyiF6Ua6UUkoppZRSSjlEL8qVUkoppZRSSimH6EW5umEicovTGZRyKxHJ5XQGpZRSSinlXtrRm0uJSCCw0hhT1+ks8RGRKsBXwK3GmAIiUgroaYzp5XC0dENECgPPAIWADNHvG2OaO5Upmoi0AtYZY87br3MCtYwxC5zM5U1ERvt5+zywyxizMK3z+CMi3wL7gAnAcuOyQldEcgODgaqAATYDQ4wxvzmZK5qI9DXGfJTYe04QkarAPmPMXyLSGSgDfGSM+dnhaB72TdMoY8x1+3UAkMUYc8nZZBYRWWuMqZPYeyphIpId32PI7w7GiUNE8gEF8c240blEFhHpBmwyxnzrdJb4iIgAnYAixpghIlIAyGuM2elwtHRBRDIDbYh7njXEqUwAIvKKMWa4iIzBOvb6MMY860As5aAMiU+inGCMuSYil0QkR/RFkQt9CDQAFgEYY/aLSA1nI4GIHMRPARfNGFMyDeMkZgEwDlgMXHc2ShyDjDHzo18YY86JyCCszG6RBbgfmG2/bgMcBp4QkRBjzHNOBfNyL1AX6AaMEZGvgYnGmOPOxvKYCWzEWnZgnfx9jZXZDR4DYl+Ad/XznhM+BUrZNyRfwdqXJwM1HU3lay3WuvzTfp0NWAVUcSwRICJZ7Cx3iMjtgNgfZQeCHQvmh33zZTAxF5UCGGNMESdzAYhIT2AIEEXMcc8AjmeLJiLvAe2BI8A1+22DVe44rRDQWUQKAruBTVgX6fucDBXLJ1jnB7Wx1vVFYC5Q3slQ0eI55zoP7ALedsEN3oV2nt3A3w5n8XbE/v8uR1Mo19CLcne7DBwUkdXAX9FvuunumTHmV+smrse1+KZNQ03t//e2/z/F/n8nwBVPh7xcNsb4e9rrBv6at7itzCgG1DbGXAUQkU+xLjjqAQedDBbNfjK+GlgtIiHAVKCXiOwHXjPGbHM0IOQyxrzl9fptEWnpVJhoItIReAQoLCKLvD66DXD6JC/aVWOMEZEWWE/Ix4nIY06HiiWLMSb6ghxjzJ8iks3JQLaewHNYF+C7ibkovwCMdShTfMYBz2PldMMxzttLwIPGmLNOB0lAS+A+Y4ybLogAMMYMBBCRrMCTwMvAKCDQwVixVTTGlBGRvQDGmD9EJJPTobwsx9ovptuvO9j/vwBMBJo5kMlbfmNMQ4cz+NMeWALkdEPNL+U8t51gK19L7X9u9atdhd3YB4hngW8czkR01VERqWqMqer10WsisgXrTrNbfGQ/fV6F1x1cY8we5yJ57BKRD7BOkA1WNfvdzkaKIx9wC9ZdcOyfg+2aJq44AbSrh3cGugCnsJbjIqA01hP+wo6Fs6wXkQ7ALPt1W9xR7mwFIoA7gJFe718EDjiSKK6LIvI61rqtbjc7yuhwptj+EpEy0WWKiJTDeqrqKPsk9CMRecYYM8bpPIk4b4xZ7nSIeHyP+242x/YD1n7hijLZm4j0x2q6cyuwF+smxyZHQ8V1xS5bDICI3Im7atbFPtc6KCJbjDFV7WY9TtsqIiWMMa64Ue+lrF1Do5uITCbmxiTgviYoKvXpRbmLGWMm2XdvCxhjjjmdx4+nsKqQ5gNOYF1Y9k5wjrR1i4hUM8ZsBk8beLd1SlcC64S+NjEHWWO/doSITDHGdME6kboVqyqz4L71CzAc2CciG7Ay1gDetdvRrnEymJdtWLU1WhpjTni9v0tEPnMok7eewAtYT/AN1hOiv0TkBawH/dmdCGXfXPtZRL4Cwl3a5rM91tP8bsaYSLut5wiHM8X2HDBbRMKx1m8wVm5XMMaMscvmQvi295zsWCibiJSxf1wvIiOAebjv5unrWBcdO/DN5niNOq+2spewyum1uCwj0Bq4inUjMhTYboy57GykOEYD84G7ROQdrBun/Z2N5ONWEalojNkBICIVsM4dwFq2TqsGdBWRH7G2v+jmJ043ZfwMWIHV1MS7thC4rAmKShva0ZuLiUgz4H0gkzGmsIiUxuqAyfFOwNIDESkLjAdyYBVw57FOnt1wIgWAiBwFShpj/nE6SzQROQI0wnqaG4J9AIv+3G13b0UkCKiAlXOnMSbc67MHjTGHHcwWCIwwxrzgVIb0TkSGYJ1UubLNp/2k4x5jzBq7WnigMeai07mi2W23n8Hq/+MC1k2iMW658BCRKUBRrM4QPe2N3XDBJiLrE/jYGGMcu3kaTUR2YnXOeBCvp6fGmEmOhbIl1pTDDRkBROQ2rDKmGtAOOGWMqeZsKl8icj9QB+s4t9YY43itxGgiUh7rXCv6Qvwi8ARWm+kmxphZ8c2bFuwyOg63dMgpIp8aY552Oodynl6Uu5iI7MZ6YrrBGPOQ/d5BY0wJZ5NZRGQC/nuM7OZAnHjZvdKKGzvMszv9esYYc9rpLNFE5Fngaay7tCe9P8IlnRsllYjsMcaUSXzKVM3g6p6kvXr2LWyMeUtE7gaC3Nazr1ebz5eAfMYYx9t8isiTQA+sdvlFReQe4DM3rW8RmYV1MT7NfqsjcLsx5mHnUsUQkW+AB9w2KoE3ESlijPkhsfecICJbjTGOdtp3o0RkrjGmTeJTpsrvLg5Ux+qcsRzwK9ZNv4FO5ImP3Rni3fjWJnHNAwYAEcmBda51Ltb7jzl9A0ZEqmHdPJ1gV/+/1Rjzo8OZshtjLkg8w6a67QGISn1afd3drhpjzsfqSM1NJy5LvH7OArQCwuOZNs2JSB7gXaw2xo1E5AGgsjFmnMPRvOUBjopIGL7V+hyrDWF3PDf6X3L3VhKfJNXtszsqm41vh43znIvkw7tn37eweukei3t69nVzm8/eWLU0dgAYY74VkbucjRTHfcaYUl6v19udDLrFISAvVv8BbjUHa7g7b7OBsg5kiW29iPTAGsHD+xiSnk7onbzR+x5WtfXRQJgx5oqDWfwSkbewRpz4Ht8e9h2vqeEtgQcffQHHLsrtfnvKAfdhDU2aEau5VtWE5ksD07E6Jt6NtT61+vp/nF6Uu9shEXkECLSfwDyL1fmRKxhj5nq/FpEZuKcdL1i9fk4A+tmvj2O1j3bTRfkgpwPE519wQQ7uuImVC6u3cO8TKIPVPtUN3N6zr5vbfP5tjPkn+sapiGTAHduct70iUskYsx1ARCoCWxzOhIgsxlpWtwFH7GrYrrgxGc2uMvwgkENEWnt9lB3rRrQbPGL//3Wv99LbCb1j+4wxpklCnzv5FN9LO6Com5q5JZPTN8dbAQ8BewCMMeF2kwVHGWOiRwrajDU84CZjzFEHIymH6UW5uz2DdUH5NzADq0OItx1NlLB7gAJOh/ByhzFmlt07MsaYqyLiquFsjDGhTmdQqcsY87jTGRLh6p597RsG0W0+6wFfiohb2nyGisgbQFYRqQf0wnpi6SYVgUdF5Bf7dQHgG7HHFnaws6P3Hfq9yXEf1pOsnPgO63QRqymF44wxTo/e8G/nhpsbh7C2Qdc0c0smp29U/mOMMSISfYxzW4e/E7COb2NEpAhWjbBNOkzaf49elLtbXmNMP2Ke9LqKiFwkpsqNASKBVx0N5esvsYajii6IKxEzdJajRGSzMaaa1zL0fISDPV6nJ3Zb6PzGmF8TmMzxJwsikh8Yg1VVzmDdFe8bqyd2J7m6Z9/42nw6GirGq0B3rE62egLLgK8cTRSXG8fnTRc3JI0xC4GFIlLZGLPN6TzxsfeRB/B6eu+G3uuTweknqQlx+oISYChWjZdDuKw2SRI5vX5nicjnQE67H5BuwJcOZ/IwxqwTkVCsJmMhWCMbFcca3Uj9h2hHby4mIhuxhhsLI6Zqi9vGWXQtezibMViF2yHgTqCtMcYtYxyrGyQiu40xbmjXGS8RWY3VdmyK/VZnoJMxpp5zqXy5vGff6Grrm3FRm08RCQAOGGOKO50lPfNzYxKsm6e7gBed7EzNa0gvv1zSQ/wgoBbWRfkyrJEzNhtj2jqZy5v9ZDLKGHPdfh0AZDHGXLJf1zfGrHIyY3xc0lnoYeBz4vaw7/obWwAi8rExpo/DGeoB9bGOcSuNMaudzONNrKECb8EaGWMT1v6bXmtFqBugF+UuZ7ftLI910O2J1WOk354a0zBTggcoN/UIarfxvA+rID7mlhP6aCJSFDhhjPlbRGoBJYHJsXsvVf6JyFhgojEmzOks8RGRfcaY0om9l9bi6/E1WnrpKMrhnpunAa8bY35JdGLll4i8idVB6HSscroDVsdvx4CnjTG1HMwWPaRXVayL3q/t1w8Du40xzzsSzIvdDKEUsNcYU8ru4PQrY0yzRGZNMyKyHahrjPnTfn0rsCo99BovInujR79xMEOoMaamkxkSkh461RUXD10pIh9idRr5N1Z/HxuBbcaYKEeDqTSnF+UuZg/hUN3+lxNrHNdNxpgZDsaKPXarv6rXrugR1C54XwAKGmOetDvLu88YsySRWdOMiOzDqpJbCFiJNTb4fcaYxg7GSjfEGlP9XuBnrJ7No7dBp9rJxiEia7A6HYzebzsCjzs9bJaI/EhM85MCwB/2zzmBX9JLW1UnT5pFZB3WTdOd+Pasn16qlTpORHYYYyrGem+7MaaSiOyP1XO8I+xjXv3om7oikhHrojLE2WQgImHGmPJiDaEagtXe/ZAx5kGHo3m49cakncP1T/FF5AOsC7ZF+FZfd8UDEBFZjt2prn1jKAPWTSK3DN/r+qErwXOz6nGsEUbyGmMyOxxJpTFtU+5uoVhV+IYCy9zS82b0iYhY4wb3wuqgwmBVu/nUwWixTcAaaqKy/foE1jA2rrkoB67bHdC1AkYZY8ZE94KtkqSR0wGSoBvwMfAh1n6y1X7PUdEX3SLyGbDIGLPMft0IqOtktmRy8s7ymw7+7n+L6yLSDmvYMbD6NIjmlqcGwVi9xEfXHrnVfs9Rdr8aB0QkJ1Yb2d1YQxrudDKXH3+JSJnoi0gRKQe45SngWqzy7k/7dTZgFVAFwOkLclv0TcdKXu+5aUg0t3eq6+qhK0WkD9bDt7JYDxjG455+U1Qa0otyd8uNVW2uBvCsiFzHqtIywNlYHpOAC1gdRYH1BHAy1vAdblDUGNNeRDoCGGOi7JMYN7li53uMmN59MzqYJ71xy0l7vOyqzW5+clreGPNU9AtjzHKxxsVViTDGhNpVN6PHdN+pbQGTrRNWh0afYO3P24HO9k1fR9uhehmG1dFWdC2xmsBg5+JY7B6lS9vNnT4TkRVAdhf2m9IXmC0i4VjrOBho72wkjyzR1eoBjDF/2rXsXMMNNTIS4dpOdW1uH7oyK/ABVpOYq06HUc7Ri3IXM8acE5EfgLuB/Fh3bt10wXZfrKqF60Vkv2Np4vrHPrGLPlAUxavql0s8jtXT5jvGmB9FpDAw1eFM6clSYqpgZwEKY7VFdVPVzcJYwxsWwqvMdVEV57Mi0h9ruzNYHdH95mykZHHsRpv9hHcEsMHOMUZEXjbGzElwRuVhd+QWX/vnzWmZJT7GmAl2Fd3oavavGWMinczkZbuIlDfGhBljfnI6TDwKYz3tLYA1ZnQl3HNR5Nqn+CLS2RgzVURe8Pe5MeaDtM4UjxewqtYXFZEt2J3qOhvJR6i4eOhKY8wIpzMod9A25S4mIt9jXWBsxqrKssMtVdgBRGQiVruc7fbrisBjxphejgaz2YVvf6wOelZh1TroaozZ4GSu5HCyE6v0yO6EsKcxpqfTWaLZN6rG4dKec+0O3wZh1cgxWJ3MDHFTR2/2zbUCxphjfj5zrM2nvW7rRT8dF2uM9zVuaAftdiLyijFmeHw9nLukZ/P7jTFH4+vc1A1tetNJvxoHjDEl7X5y3gVGAm/E7kvACfZF+NdYnQ16nuIbY3Y7GgwQkZ7GmM/F6mE/NmOMGZLmoeLh5k517RqS3fHqfR2rM0S9AFKuok/K3e2e6M5H3MTu7dVgPbV/VER+sV8XBI44mc2bMWa1iOzBuisvWGNDn3U4VnIVcTpAemKM2SMi5ROfMk1dNsaMTnwyZ9gX333j+1xExhhjnknDSLF/fzPgfSATUFhESmPdNGgOjrf5DIhVXf03IMCpMOlM9LB7uxxNkbAXsDqIGunnM7e06U0P/WpEty9ugnUjf6GIDHYwjzfXPsU3xnxu/7jGGLPF+zMRqepAJL/8daorIq7oVFd8h650zdjkSvmjT8pdTETyY42zXRXrILEZ68LyhMO5Cib0uTHm57TKkhgRaU1MR3SbjTHzHY6ULOKCMVLdLFa1vgCgDJDbGNPAoUhxiMgjwD1YtTVc13NuYpzeBu1epWsDG6J7WY9+8uZUpmgiMgJrGMPonvXbAweNMa84lyp9EpFbjDF/JT6lSm9EZAlwEqtDtbJY1cN3uqFGiZuf4kfzVwY7XS57E5GvsToZfNQYU9yu2bTNDb3rgw5dqdIPfVLubhOwxm592H7d2X6vnmOJcNdFd0JE5BOgGDEnzD1FpK4xpreDsdTNdZvXz1ex2pjPdShLfEoAXbAuLKNrvrjlKVt6cNUYc959fTSCMeZlrxt/AnyR3m78OU1EKmM177gVKCAipbCaoLiiGRSAiGzCataxCdhiXDK+cTrSDmgIvG/3lRMEvOxwpmiufYpv7xtVgDtj3YDODgQ6k8ovt3eqGwQcFhEdulK5ml6Uu9udxpgJXq8nishzToVJh2oCxaPbDYnIJKx2vemJmw5srmOMeRNARG6zXsb0ousirYAibuoPIp05ZNc2CBRrfNlnsYaVc5zdid8yY8w8+3VWESnk4g633GgU0ACroyiMMftFpIajieJ6DOvGSxtghIj8DWwyxjzvbKz0wVhjfs/zeh0BRDiXyMdJEfkc6yn+eyKSGfc0QcmEdbMqA743oC/gro7U3N6prg5dqdIFvSh3t7Mi0pmYJ70dSV+9IjvtGFY7segn+3cDbhsqJjGvOh3AzUSkODAFyGW/PovV2eAhR4P52g/kBNLrUFlO3xh6BuiHdZI3A6uTHrcM2TYbezxj2zX7Pbf1a+BqxphfYz1Yc9MYxxhjfhCRKOAf+18I8D9nU6mbxLVP8e3OQENFZGJCNRSd7vcDq6PQFcDddlXxqkBXB/PE9gsQYYy5DJ6OQ/M4G0mpuLRNuYuJSAHgY6Cy/dYWrDbl6aL6uNNEJBTr5Hin/VZ5YBtwCZytuuTVWV6cj3BZz7luJiJbgX7GmPX261rAu8aYKgnNl5ZEZANWu+MwfNuUu6LqnN37cD+sjhozEGsbFJGuxpiJziV0LxHZF7vdpIjsd0Nb2fRCROZgjdH7MVYnW88C5YwxHRwN5sUeCeUsVnOyTcA+N3bCqv6bnGxfbnek1hZYS0ynutvd1KmuiOwCqkTXVhORTFjNUPTmqXIVfVLuYnanFK44cU+nBjodIAFNnQ7wL3FL9AU5gDFmg4jc4mQgP/wNZ+Mm07CeDPkM2RbNqQtyEVlMAr0gu+SmxhkRaW6MWQQgIi2wLt5U0j0FfATkA05gdYjotn4/RmNVX++I1VN3qIhsNMZ872wspZxljLkuIn2MMbOw+nRxowzezceMMf/YF+ZKuYo+KXcxESmCdbISPUTHNuB5Y8wPjgZLJ+yLsyj7oHEvcD+w3E3jZ6obIyLzgT1YVdjB6gyxnDGmpWOh/LBHLLjHGLPGHj4m0C2dRYnIZmNMNadzxCYiNRP63LhgnHe77eQ0rLGNwbqo7KIXa/9OInIr8DjwEpDfGOOmzrbUf5TTPbGLyACsHvW/xrcjtd+dyuRNRFYDY2LdPH3WGFPH2WRK+dKLchcTke3AWGLalHcAnnHTUB1uZg+lVB24HdiONR7uJWNMJ0eDeRGRSljD3v0Pq1OXQOAvY0x2R4O5nIhMMcZ0sXukLURM79ehwJvGmD+czOdNRJ7EGus4lzGmqN1Z2WduOSEQkTpYTwDX4lu9fl68Mykf9sWaxL7RIiKPGWMmORQrXRCRO4EnsfZjT+09Y0w3pzLFJiIjscqYW7Fujm/C6uhNb5Arx4nI3ujhIh36/T/6edsYY4qkeRg/Yt08FeBXrOHbvnM0mFKxaPV1dxNjzBSv11NFpI9jadIfMcZcEpEnsO6SDheRfU6HiuVjrJsts4FywKNYw7iphJW1nz4/htXpkhBT1dnpjsli6w1UAHYAGGO+FZG7nI3k43GsWiQZ8R2yzdGLchGZZYxp56f/Bdf1u5BAr/99Ab0oT9hCrIvcNbisgzcv24HhxphT/j4UkQeNMYfTOJP6j/A3ooOIlDfGhNkvP0r7VDGMMYWd/P2JsWsuVYrv5qlSbqEX5e62XkReA2ZinZS2B5aKSC5wT9UgFxN7nM9OwBP2e66rbmiM+U5EAo0x14AJdudlKmGfYfX2WgSrBkS06ItzV9yht/1tt2EDQEQykEBbaQeUMsaUcDqEH33t/6fn/hfcdoPIjbIZY1w9yoQxZnYik0wBHKs+rP715olIM2PMSfA07fkYKAHO9fvhTUSqELe2y2THAnkRkb7ABOAi8KWIlAFeM8ascjaZUr70otzd2tv/7xnr/W6478LDjfoCrwPzjTGH7Tb66xOZJ61dsjsc2Sciw7HGbnVbR2WuY4wZDYwWkU+NMU87nScRoSLyBpBVROoBvYDFDmfytl1EHjDGHHE6iDd7LGOMMT+LSF6s2gYGCDPGRDoaLuncdPPFrZaISGNjzDKng9wAvfmiUlNPYIGINMO6+fMu0NjZSDFEZApQFNhHTG0XA7jiohzoZoz5SEQaAHdh1Q6bgNWppFKuoW3K0zERqWeMWe10jvTKBWN7RncAdhqr6vDzQA7gE23r9O9hDxnzBFAf6+R9JfCVcUnhKyLfYJ1Q/YjVptxV1cNFpDvWSArrsLLVBIYYY8Y7GiwJnG7r6WYichHrxF2wbkT+DVwhZvtLN/1qON3Rlvr3s2v9fQ5cBpoYY844HMnDPoY84JZjWmwicsAYU1JEPgI2GGPma9ms3EgvytMxPRG4Mbr8lPLcGIrDGPNzWmfxR0SOYY0x+5v9Ojew1Rhzn7PJQEQyA22IW21ziP35x8YY7QfkBqSH9tp6LFGpwc+wkA9g1ab7A1wzLCQiMhurN/MIp7P4IyITsIZcLAyUwmrGuMEYU9bRYErFotXX0zetMpfO2b2Wxrkz5pZeS9WNE5GmwFtAQawy11VPAt1y8Z2AE1htAaNdxOo91w0WAueB3Xj1XB9NL8hvivTQXvufxCdRKtnedzpAQrxuGtwGHBGRnfiO4OGKmwZYNdVKAz/Ynf/mxqrCDqSPG3/qv0EvytM3reaQ/pXz+jkL8DCQy6EsKnWMAloDB91avc+N7OHuAE4CO0RkIVaZ1wLY6VgwX/mNMQ2dDvEv5/jNZxF5whgzzut1INDfGPMmgDGmkmPh1L+WMSbU6QyJeB9r/3wPaOn1fvR7rmCMuQ7s8Xr9G/Cb1yTp4caf+g/Qi3L1X+b4yV50lVwvo0RkM1YbWvXv8CtwSC/Ik+02+//f2/+iLXQgS3y2ikgJY8xBp4P8i7lhv6kjIm2wnrjlxuokyu0XTCqd8+p3Ic5HuKC2VfRNAxHJGPsGgohkdSZVijh+LqgU6EV5eveT0wHczO1jewLYQ3NEC8B6cn5bPJOr9OkVYJmIhOJbte8D5yK5X/RTyGgicpv1drxjgjuhGtDVbobiuk7y1M1hjHlERNoDB4FLQEdjzBaHY6l/OWOMq88FRORprNFEiojIAa+PbgPS0/7hhht/SmlHb24mIruw7shPN8b84XSe9EZE9gBxxvZ005jMIrKemAPCVawbLe8bY447FkrdVCKyCvgT64T+evT7sS86lX8iUhyremF0s46zwKNuaAPo9k7y/g1EZLvT1cNF5B5gEtY+/D/gCPCCMeaSk7nUf4uI3IXVzA0AY8wvDsZBRHIAtwNDgde8PrpojPndmVTJpx01KrfQi3IXE5FiWJ1RtAeiL9BXaTXYpBGR8sAngPfYns2MMY53EuXVXlaIGRYI+2d9ivovIiK7jDHlEp9S+SMiW4F+xpj19utawLvGmCpO5lI3R2Lttd1ARI4CvY0xa0VEsIavfMIY86DD0dR/gIg0B0YCwVhDqBYEvtHt7+Zww40/pcCqLqtcyhjznTGmH3AvMB0YD/wiIm+KiHYGlgi7mvqzwCpgMFDPDRfkttvsf2WBp4EgrAPuU1jDnqh/jzUiUt/pEOnYLdEX5ADGmA1Y41qrf4c6IrJMRILsWhHbcV8TngpAKRGZB8zBunnawdlI6j/kLaAScNwYUxioQ/qqHu44EcknIlVEpEb0v+jP9IJcuYU+KXc5ESmJ9bS8MbASmIbVjrGLMaa0g9FcK72M7Qmeqs1tjDEX7de3AbO1R+d/D7uznluw2hxfwSWd9KQXIjIfq+fcKfZbnYFyxpiWjoVSN5XdXnssLm2vLSKzsIbim2q/1RHIaYxp51wq9V8RXdtKRPYDDxljrovITmNMBaezpQci8h5WjdMjwDX7beOmc0GlQDt6czUR2Q2cA8YBrxljojuJ2iEiVR0L5n6uHtszlgL4jnH7D1DImSgqNSTWWY+OkZqobsCbwFysGxobga5OBlI3j91euy/W+v0f0EVE9rqsvfZ9xphSXq/X2xdISqWFcyJyK1bZN01ETmPd4FVJ0xJrH/47sQmVcpJelLuUiAQAc40x7/r73BjTOo0jpRtew3QUBiKMMZft11mBPE5m82MKsNN+GmiAVlgdCqn/Dh0jNWFFgbuxmltlwKq6WRvQHs7/HRYTt712GOCm9rJ7RaSSMWY7gIhURKsPq7SzH6sWyfNAJyAHcKujidKXH4CMeI1+opQbafV1FxORjcaYGolPqfyxe6+vYoz5x36dCdhijCnvbDJf9rBo1e2XG40xe53Mo9KW/VTwIadzuJWIHANeAg7h23u99nD+LyAi2YHuWM2yDLAZWOOmsd9F5BvgPiC6t+sCwDdY26MOgadSlb/ewUXkgG53CRORMVhlSj6gFLAW32FJn3UomlJ+6ZNyd1stIi8BXwN/Rb+ZnoaacFiG6AtyAGPMP/aFuasYY/ZgtZlV/016ZzRhZ4wxi50OoVLNV1jttcfYrzsClQE3tdfWPj5UmvMaB7xoOh8H3Cm77P/vBhY5GUSppNAn5S4mIj/6edsYY4qkeZh0SERWA2OMMYvs1y2AZ40xdZxNplQMHSM1YSJSB+tCLfZTjnmOhVI3jYjsj9Ve2+97Sv3X/FvGAXeaiNwCXDbGXLNfBwKZXdZvhVL6pNzN7KEvVMo9hdUpylj79a9AFwfzKOXPP4lP8p/2OHA/VpvA6OrrBtCL8n8Hba+tlB/GmPPAeaybkirl1gJ1gT/t11mxhsqt4lgipfzQJ+UuJiLZgBeAAsaYHnYvtfcZY5Y4HC1dsXstlehhx5RKSyKyNnbtDH/vKf9E5KAxpoTTOVTq0PbaSqnUJCL7Yg8h7O89pZymT8rdbQJWW5jou3kngNmAXpQngV31axBQw34dCgyx7z4rlapEJAuQDbhDRG7HGs4LIDsQ7Fiw9Ge7iDxgjDnidBCVKrS9tlIqNf0lImXs/nsQkbJAlMOZlIpDL8rdragxpr2IdAQwxkTZQ8aopBmP1WNzdIdBXbBudOhwciot9ASew7oA303MRfkFYGw886i4qgGP2X1s/I21HPUJ6r+E9qKvlEplzwGzRSTcfh0EtHcujlL+afV1FxORrVhj8m4xxpQRkaLADGNMBYejpQtaZUm5gYg8Y4wZk/iUyh8RKejvfb2YU0oplRQikhGrmYwAR40xVxyOpFQc+qTc3QYDK4C7RWQaUBWr0yOVNFEiUs0YsxlARKqiVZZUGjPGjBGRKkAhvMpcY8xkx0KlI3rxrZRSKqVE5NFYbz0kInoMVq6jT8pdTkRyA5Ww7u5tN8acdThSuiEipYFJQA6s5fc70NUYs9/JXOq/RUSmAEWBfcA1+21jjHnWsVBKKaXUf4CIeNdUy4JVA3WPMaatQ5GU8ksvyl1Me22+OUQkO4Ax5oLTWdR/j9279ANGC1ullFLKUXYnwFOMMc2dzqKUN62+7kLaa/ONEZEX4nkfAGPMB2kaSP3XHQLyAhFOB1FKKaX+4y4B9zgdQqnY9KLcnbTX5htzm/1/Q8yyw+s9pdLSHcAREdmJ1Xs4AHqXXimllEpdIrKYmHO/QOB/wCznEinln1ZfdzERedYYMzrWe5mNMX/HN4+KISKTgL7GmHP269uBkcaYbo4GU/8pIlLT3/vGmNC0zqKUUkr9l8Q6Bl8FfjbGnHAqj1Lx0YtyFxORPcaYMom9p/wTkb3GmIcSe08ppZRSSv07iUgeoLz9cqcx5rSTeZTyR6uvu5CI5AXyAVlF5CF825RncyxY+hMgIrcbY/4AEJFc6Dav0oiIbDbGVBORi/g2mxCs3tezOxRNKaWU+k8QkXbACGAD1vF3jIi8bIyZ42gwpWLRCxR3agB0BfID3p2SXQTecCJQOjUS2Coic7AuitoB7zgbSf1XGGOq2f+/LbFplVJKKZUq+gHlo5+Oi8idwBpAL8qVq2j1dRcTkTbGmLlO50jPROQBoDbW3dG1xpgjDkdSSimllFJpQEQOGmNKeL0OAPZ7v6eUG+hFucuJSBPgQSBL9HvGmCHOJVJKKaWUUsr9RGQ4UAqYYb/VHjhgjHnVuVRKxRXgdAAVPxH5DKvweAbrSe/DQEFHQymllFJKKZU+GOBzoCTWxfkXzsZRyj99Uu5iInLAGFPS6/+3AvOMMfWdzqaUUkoppZSbxTOS0QFjTEmnMinlj3b05m5R9v8viUgw8BtQ2ME8SimllFJKuZqIPA30AoqIyAGvj24DtjiTSqn46UW5uy0RkZzAcGC3/d5XzsVRSimllFLK9aYDy4GhwGte7180xvzuTCSl4qfV111MRLICTwPVsdrEbAI+NcZcdjSYUkoppZRSSqmbQi/KXUxEZmGNTT7VfqsjkNMY0865VEoppZRSSimlbha9KHcxEdlvjCmV2HtKKaWUUkoppdInHRLN3faKSKXoFyJSEe2cQimllFJKKaX+NfRJuQuJyEGsNuQZgfuAX+zXBYEjxpjiDsZTSimllFJKKXWT6EW5C4lIwYQ+N8b8nFZZlFJKKaWUUkqlHr0oV0oppZRSSimlHKJtypVSSimllFJKKYfoRblSSimllFJKKeUQvShXSimllFJKKaUcohflSimllFJKKaWUQ/4PSfAjkwqr5FwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(18,10)) \n",
    "sns.heatmap(X.corr(),annot=True, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting the train and test sets. Divided into %80 Training set, %20 Test Set. No division for validation set because validation will be done using the cross_val_score function of the sklearn.model_selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape --> (1600, 20)\n",
      "X_test shape --> (400, 20)\n",
      "y_train shape --> (1600,)\n",
      "y_test shape --> (400,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "print(f'X_train shape --> {X_train.shape}')\n",
    "print(f'X_test shape --> {X_test.shape}')\n",
    "print(f'y_train shape --> {y_train.shape}')\n",
    "print(f'y_test shape --> {y_test.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standardization the features using Sklearn. Fitted the train set (since standardization is a part of the model selection, we don't want to fit our scaler to the test set) and transformed both sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.54835969, -0.98142253, -1.23622976, ...,  0.55713671,\n",
       "         0.9900495 ,  1.00250313],\n",
       "       [-1.3795348 ,  1.01892912,  0.83711156, ...,  0.55713671,\n",
       "         0.9900495 ,  1.00250313],\n",
       "       [-0.01091435, -0.98142253, -0.74838475, ...,  0.55713671,\n",
       "        -1.0100505 ,  1.00250313],\n",
       "       ...,\n",
       "       [ 1.33500924,  1.01892912, -1.23622976, ...,  0.55713671,\n",
       "        -1.0100505 ,  1.00250313],\n",
       "       [ 1.55743843, -0.98142253, -0.74838475, ..., -1.79489161,\n",
       "         0.9900495 ,  1.00250313],\n",
       "       [-1.37499543,  1.01892912, -1.11426851, ...,  0.55713671,\n",
       "        -1.0100505 , -0.99750312]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test=scaler.transform(X_test)\n",
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choosing models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " I defined a function to automatize some steps of choosing the best model.\n",
    " The function takes classifier(string  indicates the classifier name) and hyper(hyper-parameters for that model) as parameters, according to classifier it tunes the hyper-parameter that I choose.\n",
    " \n",
    " I trained Logistic Regression, K-Nearest-Neighbors, Support Vector Machine, Decision Tree, Neural Network, Bernoulli Naive Bayes and Random Forest classifiers with different hyper-parameters.\n",
    " \n",
    "I tuned the parameter:\n",
    "   <ul>\n",
    "   <li>Logistic Regression:<br>\n",
    "       <b>C</b>: Inverse of regularization strength; must be a positive float. Like in support vector machines, smaller values specify stronger regularization.\n",
    "    </li><br>\n",
    "   <li>Decision Tree and Random Forest:<br>\n",
    "       <b>max_depth</b>: The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.\n",
    "   </li><br>\n",
    "   <li>K-Nearest-Neighbors:<br>\n",
    "     <b>n_neighbors</b>: Number of neighbors to use by default for kneighbors queries.</li><br>\n",
    "   <li>Support Vector Machine:<br>\n",
    "    <b>kernel</b>: Specifies the kernel type to be used in the algorithm. It must be one of ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’ or a callable. If none is given, ‘rbf’ will be used.</li><br>\n",
    "   <li>Neural Network:<br>\n",
    "       <b>learning_rate_init</b>: The initial learning rate used.\n",
    "    </li><br>\n",
    "   <li>Bernoulli Naive Bayes:<br>\n",
    "       <b>alpha</b>: Additive smoothing parameter</li>\n",
    "   </ul>\n",
    "   \n",
    "   Descriptions of the parameters are taken from https://scikit-learn.org/stable/modules/classes.html.\n",
    "   \n",
    "  \n",
    "   After the if part, it calculates the cross validation score on training set using the classifier chosen and as cv=10 (10-fold). It returns cv-in this case 10- length array, I took its mean and store it in score variable. If that score is better than the best score, it changes the bestvalue variable as tuple (hyperparameter, score with that hyperparameter).\n",
    "   At the end, it returns the bestvalue.\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def choose(classifier, hyper):\n",
    "    bestvalue=(0, 0)\n",
    "    for i in hyper:\n",
    "        if classifier=='SVM':\n",
    "            cf = SVC(kernel=i,random_state=1)\n",
    "        if classifier=='LR':\n",
    "            cf = LogisticRegression(random_state=1, max_iter=100, C=i)\n",
    "        if classifier=='KNN':\n",
    "            cf = KNeighborsClassifier(n_neighbors=i)\n",
    "        if classifier=='DT':\n",
    "            cf = DecisionTreeClassifier(random_state=1, max_depth=i)\n",
    "        if classifier=='NN':\n",
    "            cf = MLPClassifier(learning_rate_init=i, random_state=1)\n",
    "        if classifier=='NB':\n",
    "            cf = BernoulliNB(alpha=i)\n",
    "        if classifier=='RF':\n",
    "            cf = RandomForestClassifier(random_state=1, max_depth=i)\n",
    "            \n",
    "        score = cross_val_score(cf, X_train, y_train, cv=10).mean()\n",
    "        if score > bestvalue[1]:\n",
    "            bestvalue=(i, score)\n",
    "    return bestvalue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this part, it executes the function above with the given classifiers and parameters.\n",
    "\n",
    "\n",
    "For SVM, the hyper-parameter I tune is kernel. So it tries values 'linear', 'poly' and 'rbf'.\n",
    "\n",
    "For Random Forest, Decision Trees and KNN, it tries integers ranging between 1 and 10(excluded) as parameter.\n",
    "For the others, it tries some values for alpha, C, learning_rate_init.\n",
    "\n",
    "After getting the best result from the each classifier, we see that Logistic Regression with C = 10 is the best classifier for this problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best SVM (with kernel = linear) --> 0.95875\n",
      "Best Logistic Regression (with C = 10) --> 0.9637499999999999\n",
      "Best K-Nearest-Neighbors (with K = 9) --> 0.5343749999999999\n",
      "Best Decision Tree (with max_depth = 7) --> 0.84375\n",
      "Best Neural Network (with learning_rate_init = 0.001) --> 0.9125\n",
      "Best Bernoulli Naive Bayes (with alpha = 0.0001) --> 0.565\n",
      "Best Random Forest (with max_depth = 9) --> 0.87375\n"
     ]
    }
   ],
   "source": [
    "i, score =choose('SVM', ['linear','poly','rbf'])\n",
    "print(f'Best SVM (with kernel = {i}) --> {score}')\n",
    "i, score =choose('LR', [0.001, 0.01, 0.1, 1, 10])\n",
    "print(f'Best Logistic Regression (with C = {i}) --> {score}')\n",
    "i, score =choose('KNN', [i for i in range(1,10)])\n",
    "print(f'Best K-Nearest-Neighbors (with K = {i}) --> {score}')\n",
    "i, score =choose('DT',[i for i in range(1, 10)])\n",
    "print(f'Best Decision Tree (with max_depth = {i}) --> {score}')\n",
    "i, score =choose('NN',[0.0001, 0.001, 0.01, 0.1, 1])\n",
    "print(f'Best Neural Network (with learning_rate_init = {i}) --> {score}')\n",
    "i, score =choose('NB',[0.0001, 0.001, 0.01, 0.1, 1, 10])\n",
    "print(f'Best Bernoulli Naive Bayes (with alpha = {i}) --> {score}')\n",
    "i, score =choose('RF',[i for i in range(1,10)])\n",
    "print(f'Best Random Forest (with max_depth = {i}) --> {score}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After that, it trains the Logistic Regression with the best hyper-parameter C for the last time and does predictions on test set. The accuracy of the model is 97.25%.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9725"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LogisticRegression(random_state=1, C=10)\n",
    "lr.fit(X_train, y_train)\n",
    "y_pred=lr.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy score can be misleading so it checks more improved scores.\n",
    "\n",
    "TP=True Positive\n",
    "\n",
    "FP=False Positive\n",
    "\n",
    "FN=False Negative\n",
    "\n",
    "TN=True Negative\n",
    "\n",
    "$$\n",
    "Precision =  \\frac{TP}{TP+FP}\n",
    "$$\n",
    "\n",
    "\n",
    "$$\n",
    "Recall = \\frac{TP}{TP + FN}\n",
    "$$\n",
    "\n",
    "$$\n",
    "F_1  score = 2*\\frac{Recall * Precision}{Recall + Precision}\n",
    "$$\n",
    "\n",
    "\n",
    "Support shows how many examples of the test set are from that class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.95      0.98       105\n",
      "           1       0.94      1.00      0.97        91\n",
      "           2       0.98      0.96      0.97        92\n",
      "           3       0.97      0.98      0.98       112\n",
      "\n",
      "    accuracy                           0.97       400\n",
      "   macro avg       0.97      0.97      0.97       400\n",
      "weighted avg       0.97      0.97      0.97       400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we display the confusion matrix, we can check the scores better.\n",
    "\n",
    "For example, In classification report, it says that there are 105 examples which belong to class 0 and the precision is 1.0 where recall is 0.95.\n",
    "\n",
    "We see that 100 of the examples belong to class 0 are correctly labeled. \n",
    "\n",
    "TP = 100\n",
    "\n",
    "And the remaining 5, predicted as class 1. \n",
    "\n",
    "FN=5\n",
    "\n",
    "$$Precision=\\frac{100}{100+0}=\\frac{100}{100}=1.0$$\n",
    "\n",
    "$$Recall=\\frac{100}{100+5}=\\frac{100}{105}\\approx{0.95}$$\n",
    "\n",
    "$$F_1=2*\\frac{1.0*0.95}{1.0+0.95}=2*\\frac{0.95}{1.95}=\\frac{1.90}{1.95}\\approx{0.97}$$\n",
    "\n",
    "\n",
    "We can see that the model predicted all of the examples of class 1 correctly but made some errors on other classes.\n",
    "\n",
    "It predicts 5 examples from class 0 and 1 example from class 2 as class 1. \n",
    "\n",
    "It also predicts 3 examples from class 2 as class 3, alongside predicting 2 examples from class 3 as class 2.\n",
    "\n",
    "Although, it made some errors, the model seems to work well. It has very high accuracy, precision, recall and $F_1$ score. Even the mistakes it made is logical. What i mean by that is, It mistakenly predicts the examples with lowest price range as the second lowest price range. Since the ranges are sharply seperated, this little mistakes are acceptable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(lr, X_test, y_test, display_labels=lr.classes_)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}