A B tests with Machine Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recently, I was reading through [A/B Testing with Machine Learning - A Step-by-Step Tutorial](https://www.business-science.io/business/2019/03/11/ab-testing-machine-learning.html) written by [Matt Dancho](https://www.linkedin.com/in/mattdancho/) of [Business Science](https://www.business-science.io). I have been always fascinated by the idea of **A/B Testing** and the amount of impact it can bring in businesses. The tutorial is very definitive and Matt has explained each and every step in the tutorial. He has detailed about each and every decision taken while developing the solution. \n",
    "\n",
    "Even though the tutorial is written in `R`, I was able to scram through his code and my knowledge of Data Science helped me to understand the concepts very quickly. I will have to thank Matt for putting together all the key ingredients of the Data Science world and or using them to solve a real problem.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I decided to replicate the solution in `Python` and I was able to do it. However, there are some minor differences in my implementation but those can be ignored, I think. Apart from cell annotations, I have not detailed any of the steps because, for that, you really should follow the article I mentioned. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "control_data = pd.read_csv('data/control_data.csv')\n",
    "experiment_data = pd.read_csv('data/experiment_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Pageviews</th>\n",
       "      <th>Clicks</th>\n",
       "      <th>Enrollments</th>\n",
       "      <th>Payments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sat, Oct 11</td>\n",
       "      <td>7723</td>\n",
       "      <td>687</td>\n",
       "      <td>134.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sun, Oct 12</td>\n",
       "      <td>9102</td>\n",
       "      <td>779</td>\n",
       "      <td>147.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mon, Oct 13</td>\n",
       "      <td>10511</td>\n",
       "      <td>909</td>\n",
       "      <td>167.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tue, Oct 14</td>\n",
       "      <td>9871</td>\n",
       "      <td>836</td>\n",
       "      <td>156.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wed, Oct 15</td>\n",
       "      <td>10014</td>\n",
       "      <td>837</td>\n",
       "      <td>163.0</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "          Date  Pageviews  Clicks  Enrollments  Payments\n",
       "0  Sat, Oct 11       7723     687        134.0      70.0\n",
       "1  Sun, Oct 12       9102     779        147.0      70.0\n",
       "2  Mon, Oct 13      10511     909        167.0      95.0\n",
       "3  Tue, Oct 14       9871     836        156.0     105.0\n",
       "4  Wed, Oct 15      10014     837        163.0      64.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "control_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Date</th>\n",
       "      <th>Pageviews</th>\n",
       "      <th>Clicks</th>\n",
       "      <th>Enrollments</th>\n",
       "      <th>Payments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sat, Oct 11</td>\n",
       "      <td>7716</td>\n",
       "      <td>686</td>\n",
       "      <td>105.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sun, Oct 12</td>\n",
       "      <td>9288</td>\n",
       "      <td>785</td>\n",
       "      <td>116.0</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mon, Oct 13</td>\n",
       "      <td>10480</td>\n",
       "      <td>884</td>\n",
       "      <td>145.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tue, Oct 14</td>\n",
       "      <td>9867</td>\n",
       "      <td>827</td>\n",
       "      <td>138.0</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wed, Oct 15</td>\n",
       "      <td>9793</td>\n",
       "      <td>832</td>\n",
       "      <td>140.0</td>\n",
       "      <td>94.0</td>\n",
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       "          Date  Pageviews  Clicks  Enrollments  Payments\n",
       "0  Sat, Oct 11       7716     686        105.0      34.0\n",
       "1  Sun, Oct 12       9288     785        116.0      91.0\n",
       "2  Mon, Oct 13      10480     884        145.0      79.0\n",
       "3  Tue, Oct 14       9867     827        138.0      92.0\n",
       "4  Wed, Oct 15       9793     832        140.0      94.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiment_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number summaries and basic investigations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 37 entries, 0 to 36\n",
      "Data columns (total 5 columns):\n",
      "Date           37 non-null object\n",
      "Pageviews      37 non-null int64\n",
      "Clicks         37 non-null int64\n",
      "Enrollments    23 non-null float64\n",
      "Payments       23 non-null float64\n",
      "dtypes: float64(2), int64(2), object(1)\n",
      "memory usage: 1.5+ KB\n"
     ]
    }
   ],
   "source": [
    "control_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 37 entries, 0 to 36\n",
      "Data columns (total 5 columns):\n",
      "Date           37 non-null object\n",
      "Pageviews      37 non-null int64\n",
      "Clicks         37 non-null int64\n",
      "Enrollments    23 non-null float64\n",
      "Payments       23 non-null float64\n",
      "dtypes: float64(2), int64(2), object(1)\n",
      "memory usage: 1.5+ KB\n"
     ]
    }
   ],
   "source": [
    "experiment_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date            0\n",
       "Pageviews       0\n",
       "Clicks          0\n",
       "Enrollments    14\n",
       "Payments       14\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "control_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date            0\n",
       "Pageviews       0\n",
       "Clicks          0\n",
       "Enrollments    14\n",
       "Payments       14\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiment_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>23</th>\n",
       "      <td>Mon, Nov 3</td>\n",
       "      <td>9437</td>\n",
       "      <td>788</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>24</th>\n",
       "      <td>Tue, Nov 4</td>\n",
       "      <td>9420</td>\n",
       "      <td>781</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>25</th>\n",
       "      <td>Wed, Nov 5</td>\n",
       "      <td>9570</td>\n",
       "      <td>805</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>26</th>\n",
       "      <td>Thu, Nov 6</td>\n",
       "      <td>9921</td>\n",
       "      <td>830</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>27</th>\n",
       "      <td>Fri, Nov 7</td>\n",
       "      <td>9424</td>\n",
       "      <td>781</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Sat, Nov 8</td>\n",
       "      <td>9010</td>\n",
       "      <td>756</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Sun, Nov 9</td>\n",
       "      <td>9656</td>\n",
       "      <td>825</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Mon, Nov 10</td>\n",
       "      <td>10419</td>\n",
       "      <td>874</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Tue, Nov 11</td>\n",
       "      <td>9880</td>\n",
       "      <td>830</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Wed, Nov 12</td>\n",
       "      <td>10134</td>\n",
       "      <td>801</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Thu, Nov 13</td>\n",
       "      <td>9717</td>\n",
       "      <td>814</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Fri, Nov 14</td>\n",
       "      <td>9192</td>\n",
       "      <td>735</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Sat, Nov 15</td>\n",
       "      <td>8630</td>\n",
       "      <td>743</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Sun, Nov 16</td>\n",
       "      <td>8970</td>\n",
       "      <td>722</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  Pageviews  Clicks  Enrollments  Payments\n",
       "23   Mon, Nov 3       9437     788          NaN       NaN\n",
       "24   Tue, Nov 4       9420     781          NaN       NaN\n",
       "25   Wed, Nov 5       9570     805          NaN       NaN\n",
       "26   Thu, Nov 6       9921     830          NaN       NaN\n",
       "27   Fri, Nov 7       9424     781          NaN       NaN\n",
       "28   Sat, Nov 8       9010     756          NaN       NaN\n",
       "29   Sun, Nov 9       9656     825          NaN       NaN\n",
       "30  Mon, Nov 10      10419     874          NaN       NaN\n",
       "31  Tue, Nov 11       9880     830          NaN       NaN\n",
       "32  Wed, Nov 12      10134     801          NaN       NaN\n",
       "33  Thu, Nov 13       9717     814          NaN       NaN\n",
       "34  Fri, Nov 14       9192     735          NaN       NaN\n",
       "35  Sat, Nov 15       8630     743          NaN       NaN\n",
       "36  Sun, Nov 16       8970     722          NaN       NaN"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "control_data[control_data['Enrollments'].isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Sun, Oct 26</td>\n",
       "      <td>8881</td>\n",
       "      <td>693</td>\n",
       "      <td>153.0</td>\n",
       "      <td>101.0</td>\n",
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       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Sat, Nov 15</td>\n",
       "      <td>8630</td>\n",
       "      <td>743</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>27</th>\n",
       "      <td>Fri, Nov 7</td>\n",
       "      <td>9424</td>\n",
       "      <td>781</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Tue, Nov 11</td>\n",
       "      <td>9880</td>\n",
       "      <td>830</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>28</th>\n",
       "      <td>Sat, Nov 8</td>\n",
       "      <td>9010</td>\n",
       "      <td>756</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Fri, Nov 7</td>\n",
       "      <td>9272</td>\n",
       "      <td>767</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Thu, Oct 16</td>\n",
       "      <td>9670</td>\n",
       "      <td>823</td>\n",
       "      <td>138.0</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Tue, Oct 21</td>\n",
       "      <td>10660</td>\n",
       "      <td>867</td>\n",
       "      <td>196.0</td>\n",
       "      <td>105.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Wed, Nov 5</td>\n",
       "      <td>9570</td>\n",
       "      <td>805</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Thu, Nov 13</td>\n",
       "      <td>9721</td>\n",
       "      <td>829</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "           Date  Pageviews  Clicks  Enrollments  Payments\n",
       "15  Sun, Oct 26       8881     693        153.0     101.0\n",
       "35  Sat, Nov 15       8630     743          NaN       NaN\n",
       "27   Fri, Nov 7       9424     781          NaN       NaN\n",
       "31  Tue, Nov 11       9880     830          NaN       NaN\n",
       "28   Sat, Nov 8       9010     756          NaN       NaN\n",
       "27   Fri, Nov 7       9272     767          NaN       NaN\n",
       "5   Thu, Oct 16       9670     823        138.0      82.0\n",
       "10  Tue, Oct 21      10660     867        196.0     105.0\n",
       "25   Wed, Nov 5       9570     805          NaN       NaN\n",
       "33  Thu, Nov 13       9721     829          NaN       NaN"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine with Experiment data\n",
    "data_total = pd.concat([control_data, experiment_data])\n",
    "data_total.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "np.random.seed(7)\n",
    "import sklearn.utils\n",
    "\n",
    "# Add row id\n",
    "data_total['row_id'] = data_total.index\n",
    "\n",
    "# Create a Day of Week feature\n",
    "data_total['DOW'] = data_total['Date'].str.slice(start=0, stop=3)\n",
    "\n",
    "# Remove missing data\n",
    "data_total.dropna(inplace=True)\n",
    "\n",
    "# Add a binary column Experiment to denote\n",
    "# if the data was part of the experiment or not (Random)\n",
    "data_total['Experiment'] = np.random.randint(2, size=len(data_total))\n",
    "\n",
    "# Remove missing data\n",
    "data_total.dropna(inplace=True)\n",
    "\n",
    "# Remove Date and Payments columns\n",
    "del data_total['Date'], data_total['Payments']\n",
    "\n",
    "# Shuffle the data\n",
    "data_total = sklearn.utils.shuffle(data_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Pageviews</th>\n",
       "      <th>Clicks</th>\n",
       "      <th>Enrollments</th>\n",
       "      <th>row_id</th>\n",
       "      <th>DOW</th>\n",
       "      <th>Experiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>8460</td>\n",
       "      <td>681</td>\n",
       "      <td>156.0</td>\n",
       "      <td>21</td>\n",
       "      <td>Sat</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9867</td>\n",
       "      <td>827</td>\n",
       "      <td>138.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Tue</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>8448</td>\n",
       "      <td>695</td>\n",
       "      <td>142.0</td>\n",
       "      <td>21</td>\n",
       "      <td>Sat</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9434</td>\n",
       "      <td>673</td>\n",
       "      <td>220.0</td>\n",
       "      <td>13</td>\n",
       "      <td>Fri</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9008</td>\n",
       "      <td>748</td>\n",
       "      <td>146.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Fri</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pageviews  Clicks  Enrollments  row_id  DOW  Experiment\n",
       "21       8460     681        156.0      21  Sat           0\n",
       "3        9867     827        138.0       3  Tue           1\n",
       "21       8448     695        142.0      21  Sat           0\n",
       "13       9434     673        220.0      13  Fri           1\n",
       "6        9008     748        146.0       6  Fri           1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the new data\n",
    "data_total.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reorder the columns \n",
    "data_total = data_total[['row_id', 'Experiment', 'Pageviews', 'Clicks', 'DOW', 'Enrollments']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(data_total.loc[:, data_total.columns != 'Enrollments'],\\\n",
    "                                                    data_total['Enrollments'], test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting strings to numbers\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "lb = LabelEncoder()\n",
    "X_train['DOW'] = lb.fit_transform(X_train['DOW'])\n",
    "X_test['DOW'] = lb.transform(X_test['DOW'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>row_id</th>\n",
       "      <th>Experiment</th>\n",
       "      <th>Pageviews</th>\n",
       "      <th>Clicks</th>\n",
       "      <th>DOW</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>9535</td>\n",
       "      <td>759</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>10551</td>\n",
       "      <td>864</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>10480</td>\n",
       "      <td>884</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9867</td>\n",
       "      <td>827</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>9655</td>\n",
       "      <td>771</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    row_id  Experiment  Pageviews  Clicks  DOW\n",
       "16      16           0       9535     759    1\n",
       "10      10           1      10551     864    5\n",
       "2        2           0      10480     884    1\n",
       "3        3           1       9867     827    5\n",
       "16      16           0       9655     771    1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>row_id</th>\n",
       "      <th>Experiment</th>\n",
       "      <th>Pageviews</th>\n",
       "      <th>Clicks</th>\n",
       "      <th>DOW</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>8324</td>\n",
       "      <td>665</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>9262</td>\n",
       "      <td>727</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9288</td>\n",
       "      <td>785</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9102</td>\n",
       "      <td>779</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>9345</td>\n",
       "      <td>734</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    row_id  Experiment  Pageviews  Clicks  DOW\n",
       "12      12           0       8324     665    4\n",
       "18      18           1       9262     727    6\n",
       "1        1           0       9288     785    3\n",
       "1        1           0       9102     779    3\n",
       "19      19           0       9345     734    4"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper functions\n",
    "- Function for printing the evaluation scores related to a _regression_ problem\n",
    "- Function for plotting the original values and values predicted by the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
    "\n",
    "def calculate_metrics(y_test, y_preds):\n",
    "    rmse = np.sqrt(mean_squared_error(y_test, y_preds))\n",
    "    r_sq = r2_score(y_test, y_preds)\n",
    "    mae = mean_absolute_error(y_test, y_preds)\n",
    "\n",
    "    print('RMSE Score: {}'.format(rmse))\n",
    "    print('R2_Squared: {}'.format(r_sq))\n",
    "    print('MAE Score: {}'.format(mae))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "%matplotlib inline\n",
    "\n",
    "def plot_preds(y_test, y_preds, model_name):\n",
    "    N = len(y_test)\n",
    "    plt.figure(figsize=(10,5))\n",
    "    original = plt.scatter(np.arange(1, N+1), y_test, c='blue')\n",
    "    prediction = plt.scatter(np.arange(1, N+1), y_preds, c='red')\n",
    "    plt.xticks(np.arange(1, N+1))\n",
    "    plt.xlabel('# Oberservation')\n",
    "    plt.ylabel('Enrollments')\n",
    "    title = 'True labels vs. Predicted Labels ({})'.format(model_name)\n",
    "    plt.title(title)\n",
    "    plt.legend((original, prediction), ('Original', 'Prediction'))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear regression: A baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "X_train_refined = X_train.drop(columns=['row_id'], axis=1)\n",
    "linear_regression = sm.OLS(y_train, X_train_refined)\n",
    "linear_regression = linear_regression.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_refined = X_test.drop(columns=['row_id'], axis=1)\n",
    "y_preds = linear_regression.predict(X_test_refined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE Score: 23.988871314039848\n",
      "R2_Squared: 0.050950018270393915\n",
      "MAE Score: 19.5860618387751\n"
     ]
    }
   ],
   "source": [
    "calculate_metrics(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_preds(y_test, y_preds, 'Linear Regression')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            Enrollments   R-squared:                       0.980\n",
      "Model:                            OLS   Adj. R-squared:                  0.978\n",
      "Method:                 Least Squares   F-statistic:                     400.1\n",
      "Date:                Sat, 13 Apr 2019   Prob (F-statistic):           7.92e-27\n",
      "Time:                        12:58:39   Log-Likelihood:                -163.65\n",
      "No. Observations:                  36   AIC:                             335.3\n",
      "Df Residuals:                      32   BIC:                             341.6\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Experiment    -1.4515      8.373     -0.173      0.863     -18.506      15.603\n",
      "Pageviews      0.0672      0.010      6.593      0.000       0.046       0.088\n",
      "Clicks        -0.6022      0.126     -4.776      0.000      -0.859      -0.345\n",
      "DOW           -2.6156      2.137     -1.224      0.230      -6.969       1.738\n",
      "==============================================================================\n",
      "Omnibus:                        3.688   Durbin-Watson:                   2.146\n",
      "Prob(Omnibus):                  0.158   Jarque-Bera (JB):                2.538\n",
      "Skew:                           0.475   Prob(JB):                        0.281\n",
      "Kurtosis:                       2.112   Cond. No.                     1.95e+04\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.95e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "print(linear_regression.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>Terms</th>\n",
       "      <th>p_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pageviews</td>\n",
       "      <td>1.974764e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Clicks</td>\n",
       "      <td>3.804647e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DOW</td>\n",
       "      <td>2.299762e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Experiment</td>\n",
       "      <td>8.634578e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Terms       p_value\n",
       "1   Pageviews  1.974764e-07\n",
       "2      Clicks  3.804647e-05\n",
       "3         DOW  2.299762e-01\n",
       "0  Experiment  8.634578e-01"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(linear_regression.pvalues)\\\n",
    "    .reset_index()\\\n",
    "    .rename(columns={'index':'Terms', 0:'p_value'})\\\n",
    "    .sort_values('p_value')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 02: Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE Score: 26.8540034259326\n",
      "R2_Squared: -0.1892893660531696\n",
      "MAE Score: 21.78333333333333\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "dtree = DecisionTreeRegressor(max_depth=5, min_samples_leaf =4, random_state=7)\n",
    "dtree.fit(X_train_refined, y_train)\n",
    "y_preds = dtree.predict(X_test_refined)\n",
    "\n",
    "calculate_metrics(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SsM6vh+faESnrVIXnR99xeQ3/PbBfyjrz8P+XK4aKU4+R+ej7chQREdlEeFmX54HTgiD4U6njkf6MMY8C1wRBcEWpY5HC0AVvRURkQIHvaj8W3zUnEWKM+QC+5fXKUscihaMWNREREZGIUouaiIiISEQpURMRERGJqHK6PIf6cEVERGQkSb9w+ybKKVFj5cqVQ6+URzU1NXR0dAy94ghUznUD1W8kK+e6geo3kpVz3UD1y7cpU6YMvRLq+hQRERGJLCVqIiIiIhGlRE1EREQkospqjJqIiIgMXxAEdHd3s2HDBozpP869vb2ddeuyvdXtyFOI+gVBwJgxYxg/fvwmxzNbStREREQEgO7ubiorK6mo2DQ9qKioIBaLlSCq4ihU/Xp6euju7maLLbYY1vvV9SkiIiIAbNiwIWOSJsNXUVHBhg0bhv1+JWoiIiICMOzuORnc5hxXJWrSTzIZY8GCScydW8GCBZNIJsu3mVtERKJp5cqVnHjiiey///7st99+fP/73+fdd9/dZL22tjZOPvnkIbd37LHH8tZbbw0rlksuuYRf/vKXw3pvPihRk42SyRiNjdXceusEHnxwDLfeOoHGxmolayIiUjRBEHDyySdz2GGH8eijj/Lwww+zevVqLrroon7r9fT0UFdXx5VXXjnkNq+99lq22mqrQoVcUEXpiLbWTgeuAWrxt3pa6Jy73FpbDdwIbAMsBaxz7k1rrQEuBz4FrAFOcM61FCPW0SyRqKK1tbJfWWtrJYlEFU1Nq0oUlYiIRFUyGSORqKKtLUZdXS/xeBf19b2btc1HHnmEcePG8aUvfQmAWCzGueeey7777sv06dN54IEHWL16NRs2bOCyyy7j+OOP57777mPt2rWcccYZvPjii2y//fa0t7dzwQUXsMcee7DPPvvwxz/+kdWrV3PMMccwY8YM/vKXv1BXV8fVV1/NFltswbXXXsu1117Lu+++y7bbbstPf/rTYU8AyKditaj1AN9wzu0C7AucZq3dBTgbuNc5tyNwb/ga4HBgx/AxH/hFkeIc1draMrectberRU1ERPpL7YV5/PFxeeuFeemll9h99937lVVVVTF16lR6e3t57rnnWLhwITfffHO/dRYvXsxWW23FAw88wFlnncWzzz6bcfuvvvoqxx9/PPfffz9bbrkld9xxBwCf/vSnueOOO7jnnnvYYYcduOGGGzarHvlSlETNOfdaX4uYc64L+CcwFTgSWByuthj4XPj8SOAa51zgnHsCmGStnVyMWEezurrMfwXV1m7eX0ciIlJ+BuuFKaSZM2fy/ve/f5PyJ598kiOPPBKAnXfemQ9/+MMZ3z99+nR22203AD7ykY+wbNkyAF544QXmzZvH7NmzufXWW3nxxRcLVIPcFH2MmrV2G2Av4M9ArXPutXBRG75rFHwStyzlbcvDMimgeLyLhob1/coaGtYTj3eVKCIREYmqQvXC7Ljjjjz33HP9yrq6ulixYgWxWIwJEyZs1vbHjRu38XksFqO31zdGnH766Zx//vnce++9nHnmmZG5uG9RL5ZirZ0I3Ayc4Zx721q7cZlzLrDWBjlubz6+axTnHDU1NfkMd0gVFRVF32ch1dTAnXcGnHtuL21tY6ir28C55wZsu+2mf7mMdOX22aUr5/qVc91A9RvJyqFu7e3tg15HLXXZ5MmZrw1WV7d512I7+OCDufDCC7nllluw1tLb28t5551HY2MjEydOZMyYMRu333eB2oqKCvbZZx9uv/12DjzwQF588UVeeOEFYrEYFRUVGGOIxWL91gcYM2bMxu2tXr2aKVOmEAQBt912G5MnT6aioqLfOsM1bty4YZ8bRUvUrLWV+CTtOufcLWFxu7V2snPutbBr8/WwfAUwPeXt08KyfpxzC4GF4cugo6OjMMEPoKamhmLvs9CqquCSS/rXrcyqCJTnZ5eqnOtXznUD1W8kK4e6rVu3bsCr81dUVNDT07Px9Vlnvc3TT1f06/5saFjPWWe9TU/P5g2ZufLKKznnnHO45JJLCIKAWbNmEY/Hue2229iwYcPGOPpaw3p6ejj22GP5+te/zgEHHMAOO+zATjvtxIQJE+jp6SEIAnp7e/utD/4Cv33b+9a3vsXhhx/O1ltvzV577cU777xDT09Pv3WGa926dZucG1OmTMnqvSYIcmrEGpZwFudioNM5d0ZK+Y+BfzvnLrTWng1UO+fi1tpPAwvwsz73AX7qnJsxxG6ClStXFqgGmZXDf8qBlHPdQPUbycq5bqD6jWTlULc1a9YM2LWYnqjBe7M+29tj1NbmZ9bncPX29rJ+/XrGjx/P0qVLaWxs5KGHHmLs2LFZvT9T/fIl03ENE7Uhr4RbrBa1/YFjgeestX8Ly84BLgSctfYkoBXo6wu9A5+kLcFfnuPEIsUpIiIiWaqv743M5ZvWrl3LF7/4Rdav92Otf/SjH2WdpEVZURI159wjDJw1zs6wfgCcVtCgREREpGxMnDiRP/7xj6UOI+90ZwIRERGRiFKiJiIiIhJRStREREREIkqJmoiIiEhEKVETERGRyJg+fTpz5sxh1qxZzJ8/n7Vr1w57W4899hjHHXccAHfddRdNTU0DrvvWW2+xaNGija/b2to4+eSTh73vfFGiJiIiIpExfvx47r77bu677z7Gjh3LNddc0295EARs2JD5rgiDmTt3LgsWLBhw+VtvvdVvX3V1dVx55ZU57yfflKiJiIjIsMSSSSYtWMDWRx3FpAULiCWTed3+jBkzWLp0KcuWLeOTn/wkp59+OrNmzWLlypU8+OCDfOYzn+HQQw9l/vz5rF69GoD777+fmTNncuihh/a7XMeNN97Id77zHQDeeOMNTjrpJA455BAOOeQQnnrqKc4//3xaW1uZM2cO5513HsuWLWPWrFkAdHd3c+aZZzJ79mzmzp3Lo48+unGbX/3qV/nyl7/M/vvvz/nnn5/X+kOR7/UpIiIi5SGWTFLd2Ehla+vGssqWFjqbm+mtr9/s7ff09HD//fdz0EEHAfDqq69y2WWXsffee9PZ2cnll1/OjTfeyIQJE/jZz37GwoULOfXUUznrrLNwzrHttttyyimnZNz29773Pfbdd1+uuuoqent7Wb16Nd/97nd54YUXuPvuuwFYtmzZxvUXLVqEMYZ7772XJUuWcPTRR/Pwww8D8I9//IM777yTsWPHMnPmTE488USmTp262fXvoxY1ERERyVlVItEvSQOobG2lKpHYrO12d3czZ84cDj/8cKZOncrRRx8NwLRp09h7770BePrpp3nppZc48sgjmTNnDjfddBPLly9nyZIl1NfXs91222GM4Qtf+ELGfTz66KMbx67FYjG23HLLQWN66qmn+PznPw/ADjvswLRp03jllVcAOOCAA9hyyy0ZP348O+20EytWbHJr8s2iFjURERHJWaytLXN5e/tmbbdvjFq61HtlBkHAzJkz+fnPf95vnb///e+bte/hSL1N1ZgxY/J+v1C1qImIiEjOeuvqMpfX1hZ833vvvTdPPfUUr776KuBvev7yyy+zww47sGzZMpYuXQrAbbfdlvH9BxxwwMaJA729vbz99ttMnDiRd955J+P6M2bM4NZbbwXg5ZdfZsWKFWy//fZ5rlVmStREREQkZ13xOOsbGvqVrW9ooCseL/i+t956ay699FJOO+00DjnkED772c/y8ssvM378eBKJBMcddxyHHnooNTU1Gd//wx/+kMcee4zZs2dz2GGH8dJLL1FdXc3HP/5xZs2axXnnnddv/eOPP54NGzYwe/ZsTj31VC699FLGjRtX8HoCmCAIirKjIghWrlxZ1B3W1NTQ0dFR1H0WSznXDVS/kayc6waq30hWDnVbs2ZNvy7GVBUVFZt068WSSaoSCWLt7fTW1tIVj+dlIkEpZKpfvmQ6rlOmTAEwQ8ZVkIhERESk7PXW17NqkIvIyuZT16eIiIhIRClRExEREYkoJWoiIiIC+MteSP5tznFVoiYiIiJAYa4DNtr19PQwZszw0y1NJhARERHAX2y2u7ubdevWYUz/CYnjxo1j3bp1JYqs8ApRvyAIGDNmDOPHjx/2NpSoiYiICADGGLbYYouMy8rh8iODiWr91PUpIiIiElFK1EREREQiSomaiIiISEQpURMRERGJKCVqIiIiIhGlRE1EREQkopSoiYiIiESUEjURERGRiFKiJiIiIhJRStREREREIkqJmoiIiEhE6V6fIiJScLFkkqpEgorOTiZVV9MVj9NbX1/qsEQiT4maiIgUVCyZpLqxkcrWVgAmAJUtLXQ2NytZExmCuj5FRKSgqhKJjUlan8rWVqoSiRJFJDJyKFETEZGCirW1ZS5vby9yJCIjjxI1EREpqN66uszltbVFjkRk5FGiJiIiBdUVj7O+oaFf2fqGBrri8RJFJDJyaDKBiIgUVG99PZ3NzVQlEozv7KRbsz5FsqZETURECq63vp5VTU3U1NSwqqOj1OGIjBjq+hQRERGJKCVqIiIiIhGlRE1EREQkopSoiYiIiESUEjURERGRiFKiJiIiIhJRStREREREIkqJmoiIiEhEKVETERERiSglaiIiIiIRVZRbSFlrrwaOAF53zu0Wlu0J/BIYD/QA/+Gce9Jaa4DLgU8Ba4ATnHMtxYhTREREJEqK1aK2CDgsrSwB/Ldzbk/g++FrgMOBHcPHfOAXRYpRgFgyyaQFC6iYO5dJCxYQSyZLHZIIoHNTREanorSoOecestZuk1YcAFuGz7cCVobPjwSucc4FwBPW2knW2snOudeKEetoFksmqW5spLK1FYAJQGVLC53NzfTW15c2OBnVdG6KyGhVlERtAGcAd1prL8a37O0Xlk8FlqWstzws2yRRs9bOx7e64ZyjpqamoAGnq6ioKPo+Cyn2jW8QC38I+1S2tlJz+eX0Ll5coqgKo9w+u3TlVj+dm+WjnOtXznUD1a9USpmonQqc6Zy72VprgauAQ3LZgHNuIbAwfBl0dHTkOcTB1dTUUOx9FtLWra3EMpT3JJP8u4zqCeX32aUrt/rp3Cwf5Vy/cq4bqH75NmXKlKzWK+Wsz+OBW8LnNwEzwucrgOkp600Ly6TAeuvqMpfX1hY5EpH+dG6KyGhVykRtJXBg+HwW8K/w+e+A46y1xlq7L/CWxqcVR1c8zvqGhn5l6xsa6IrHSxSRiKdzU0RGq2JdnuMG4CCgxlq7HPgBcDJwubW2AugmHGsG3IG/NMcS/OU5TixGjAK99fV0NjdTlUgwvrOT7upquuJxDdaWktO5KSKjlQmCoNQx5EuwcuXKodfKo3Lury/nuoHqN5KVc91A9RvJyrluoPrlWzhGzQy1nu5MICIiIhJRStREREREIkqJmoiIiEhEKVETERERiSglaiIiIiIRpURNREREJKKUqImIiIhElBI1ERERkYhSoiYiIiISUUrURERERCJKiZqIiIhIRClRExEREYkoJWoiIiIiEaVETURERCSilKiJiIiIRJQSNREREZGIUqImIiIiElFK1EREREQiSomaiIiISEQpURMRERGJKCVqIiIiIhGlRE1EREQkopSoiYiIiESUEjURERGRiFKiJiIiIhJRw07UrLUHW2sPzGcwIiIiIvKerBM1a+2D1tr9w+ffApqB66215xQqOBEREZHRLJcWtd2AJ8LnJwMHA/sCp+Q7KBERERGBihzWHQME1trtAeOcex7AWvv+gkQmIiIiMsrlkqg9AjQBk4FbAcKkraMAcYmI9JNMxkgkqujsrKC6ehLxeBf19b2lDktEpKBySdROAL4BvAEkwrKdgcvzHJOISD/JZIzGxmpaWyvDkgm0tFTS3NypZE1Eylouidos51y/iQPOuduttUflOSYRkX4SiaqUJM1rba0kkaiiqWlViaISESm8XCYTXDVA+cJ8BCIiMpC2tljG8vb2zOUiIuViyBY1a+124dMx1tptAZOyeDuguxCBiYj0qavL3L1ZW6tuTxEpb9l0fS4BAnyC9nLasjbg3DzHJCLSTzzeRUtLZb/uz4aG9cTjXSWMSkSk8IZM1JxzY8Bf8NY5pzsRiEjR1df30tzcGc76HE91dbdmfYrIqJD1ZAIlaSJSSvX1vTQ1raKmpoaODk1ETu8vAAAbCUlEQVQgEJHRIetELRyfdgGwJzAxdZlzrj7PcYmIiIiMerlcnuN6/Bi1bwBrChOOiIiIiPTJJVHbFdjfObehUMGIiIiIyHtyuY7aQ8BehQpEREREpNiSyRgLFkxi7twKFiyYRDIZresz5tKithT4k7X2VvxlOTZyzn0/n0GJiIiIFNpIuD1dLi1q7wP+AFQC09MeIiIiIiPKYLeni4pcLs9xYiEDERERESmmkXB7uly6PrHW7gx8Eah1zi2w1n4IGOece7Yg0YmIiIgUyEi4PV3WXZ/W2i8CDwNTgePC4irgJwWIS0RERKSg4vEuGhrW9yuL2u3pchmj9kPgEOfcKUBfqvkMsEfeoxIREREpsL7b082bt4YDD9zAvHlrIjWRAHLr+vwg0NfFGaT8G2ReXURERCTaon57ulwStaeBY4FrUsoagSeHeqO19mrgCOB159xuKeX/CZyGb6G73TkXD8u/DZwUlp/unLszhzhFREREykIuXZ+nA+dbax8E3metvRM4Dzgzi/cuAg5LLbDWHgwcCezhnNsVuDgs3wWfAO4avufn1troTL8QERERKZKsEzXn3AvAzsDPgO8CvwZ2d879K4v3PgR0phWfClzonFsXrvN6WH4k0OycW+ecexVYAszINk4RERGRcpHT5Tmcc2sAl6d97wR80lp7AdANfNM59xR+VukTKestD8tERERERpWsEzVrbT3wA/z9PiemLnPO7TTMfVcD+wIfB5y1drtcNmCtnQ/MD2OgpqZmGGEMX0VFRdH3WSzlXDdQ/Uaycq4bqH4jWTnXDVS/UsmlRe0m4AXg+8DaPOx7OXCLcy4AnrTWbgBqgBX0vy3VtLBsE865hcDC8GXQ0dGRh7Cy52eIFHefxVLOdQPVbyQr57qB6jeSlXPdQPXLtylTpmS1Xi6J2s7AJ5xzG4YV0aZuAw4G7rfW7gSMBTqA3wHXW2t/AkwBdiSLmaUiIiIi5SaXRO33wIHA/bnuxFp7A3AQUGOtXY7vQr0auNpa+3fgXeD4sHXtH9ZaBzwP9ACnOeeic+U5ERERkSLJJVE7HXjMWvsy0J66wDn3lcHe6Jw7eoBFxwyw/gXABTnEJiIiIlJ2cknUfo2/AO0/yc8YNREREREZRC6J2ixginMuOncqFRERESljudyZ4Flg60IFIiIiIiL95dKidh9wl7X212w6Ru3qvEYlIiIikZBMxkgkqujsrKC6ehLxeBf19ZrjVyy5JGoH4K9nNjetPMDP4BQREZEykkzGaGysprW1MiyZQEtLJc3NnUrWiiTrRM05d3AhAxEREZFoSSSqUpI0r7W1kkSiiqamVSWKanQZNFGz1mY1hi2PF8EVERGRiGhri2Usb2/PXC75N1SLWg++a3MgJlyuT0xERKTM1NVl7t6srVW3Z7EMlahtW5QoREREJHLi8S5aWir7dX82NKwnHteVuopl0ETNOddarEBEREQkWurre2lu7gxnfY6nurq77GZ9xpJJqhIJKjo7mVRdTVc8Tm99fanD2mioMWrXMnjXJwDOuePyFpGIiIhERn19L01Nq6ipqaGjo7wmEMSSSaobG6ls9e1SE4DKlhY6m5sjk6wN1fW5pChRiIiIiBRZVSKxMUnrU9naSlUiwaqmphJF1d9QXZ//XaxARERERIop1taWuby9PWN5KeRywVustQcBxwFT8Re/vdY5d38B4hIREREpqN66uszltbVFjmRgWd/r01r7VcABbcAtwGvADdbakwsUm4iIiEjBdMXjrG9o6Fe2vqGBrni8RBFtKpcWtTgwxzn3TF+BtfZG4GbgynwHJiIiIlJIvfX1dDY3U5VIML6zk+6RNuszzdbA82llLwLV+QtHREREpHh66+tZ1dRETU0Nqzo6Sh3OJrLu+gQeAX5irZ0AYK19H/Bj4LFCBCYiIiIy2uWSqJ0CfAR4y1rbDqwC9gC+VojAREREREa7rLo+rbUG2AKYDdQBU4CVzrnlBYxNREREZFTLKlFzzgXW2ueAqjA5U4ImIiIiUmC5dH3+FdipUIGIiIiISH+5zPp8APiTtXYRsIyUe4A6567Ob1giIiIikkuitj/wKnBgWnkAKFETERERybOsEzXn3MGFDERERERE+svpXp8A1toPAhNTy5xzr+QtIhEREREBckjUrLWHAVcBk9MWBUAsn0GJiIiISG4taj8DzgMWO+fWFigeEREREQnlkqi9H7jCORcMuaaIiIiIbLZcrqN2FXBioQIRkc2XTMZYsGASc+dWsGDBJJJJjUoQERnJcmlR2xc43Vp7NtCWusA5NzOvUYlIzpLJGI2N1bS2VoYlE2hpqaS5uZP6+t6SxiYiIsOTS6L2q/CRTl2hIhGQSFSlJGlea2sliUQVTU2rShSViIhsjiG7Pq21PwVwzi12zi0GKvqeh6+PLHSQIjK0trbM3Zzt7er+FBEZqbIZo3ZC2usfp72ek59QRGRz1NVl7t6srVW3p4jISJVNomZyfC0iJRCPd9HQsL5fWUPDeuLxrhJFJCIimyubMWrpY9CGei0iJVBf30tzcyeJRBWdneOpru4mHu/SRAIRkREsm0Stwlp7MO+1nKW/1gAYkYior++lqWkVNTU1dHRoAoGIyEiXTaL2OnB1yut/p71+Pa8RiYiIiAiQRaLmnNumCHGIiIiISJpc7kwgIiIiIkWkRE1EREQkopSoiYiIiESUEjURERGRiMrlXp8iIlIgyWQsvAZeBdXVk3QNPBEBlKiJiJRcMhmjsbGa1tbKsGQCLS2VNDd3KlkTGeXU9SkiUmKJRFVKkua1tlaSSFSVKCIRiQolaiIiJdbWlvkGL+3tuvGLyGinRE1EpMTq6jJ3b9bWqttTZLQryhg1a+3VwBHA68653dKWfQO4GPiAc67DWmuAy4FPAWuAE5xzLcWIU0SkFOLxLlpaKvt1fzY0rCce7yphVCISBcVqUVsEHJZeaK2dDswFkinFhwM7ho/5wC+KEJ+ISMnU1/fS3NzJvHlrOPDADcybt0YTCUQEKFKi5px7COjMsOhSIA4EKWVHAtc45wLn3BPAJGvt5CKEKSJSMvX1vTQ1reKuu3poalqlJE1EgBKOUbPWHgmscM49k7ZoKrAs5fXysExERERkVCnJddSstROAc/Ddnpuznfn47lGcc9TU1OQhuuxVVFQUfZ/FUs51A9VvJCvnuoHqN5KVc91A9SuVUl3wdntgW+AZay3ANKDFWjsDWAFMT1l3Wli2CefcQmBh+DLo6OgoWMCZ1NTUUOx9Fks51w1Uv5GsnOsGqt9IVs51A9Uv36ZMmZLVeiVJ1JxzzwEf7HttrV0KfCyc9fk7YIG1thnYB3jLOfdaKeIUERERKaWijFGz1t4APA58yFq73Fp70iCr3wG8AiwBrgT+owghioiIiEROUVrUnHNHD7F8m5TnAXBaoWMSERERiTrdmUBEREQkopSoiYiIiESUEjURERGRiFKiJiIiIhJRStREREREIkqJmoiIiEhEKVETERERiSglaiIiIiIRpURNREREJKKUqImIiIhElBI1ERERkYgqyr0+RaIilkxSlUhQ0dnJpOpquuJxeuvrSx1W3pR7/URERhslajJqxJJJqhsbqWxtBWACUNnSQmdzc1kkM+VePxGR0UhdnzJqVCUSG5OYPpWtrVQlEiWKKL/KvX4iIqOREjUZNWJtbZnL29uLHElhlHv9RERGIyVqMmr01tVlLq+tLXIkhVHu9RMRGY2UqMmo0RWPs76hoV/Z+oYGuuLxEkWUX+VePxGR0UiTCWTU6K2vp7O5mapEgvGdnXSX2azIcq+fiMhopERNRpXe+npWNTVRU1PDqo6OUoeTd+VePxGR0UZdnyIiIiIRpURNREREJKKUqImIiIhElBI1ERERkYhSoiYiIiISUUrURERERCJKiZqIiIhIRClRExEREYkoJWoiIiIiEaVETURERCSidAupYUgmYyQSVXR2VlBdPYl4vIv6+t5ShyUiEln63hQZHiVqOUomYzQ2VtPaWhmWTKClpZLm5k596YiIZKDvTZHhU9dnjhKJqpQvG6+1tZJEoqpEEYmIRJu+N0WGT4lajtraYhnL29szl4uIjHb63hQZPiVqOaqry9xMX1ur5nsRkUz0vSkyfErUchSPd9HQsL5fWUPDeuLxrhJFJCISbfreFBk+TSbIUX19L83NneHspfFUV3dr9pKIyCD0vSkyfErUhqG+vpemplXU1NTQ0bGq1OGIiESevjdFhkddnyIiIiIRpURNREREJKKUqImIiIhElBI1ERERkYhSoiYiIiISUUrURERERCJKl+cQERHZDMlkLLxGXAXV1ZN0jTjJKyVqIiIiw5RMxmhsrE656fwEWloqaW7uVLImeaGuTxERkWFKJKpSkjSvtbWSRKKqRBFJuVGiJiIiMkxtbbGM5e3tmctFcqVETUREZJjq6jJ3b9bWqttT8qMoY9SstVcDRwCvO+d2C8t+DHwGeBd4GTjRObcqXPZt4CSgFzjdOXdnMeIUERHJRTzeRUtLZb/uz4aG9cTjXSWMSspJsVrUFgGHpZXdDezmnPsI8BLwbQBr7S5AI7Br+J6fW2vVhiwiIpFTX99Lc3Mn8+at4cADNzBv3hpNJJC8KkqLmnPuIWvtNmlld6W8fAI4Knx+JNDsnFsHvGqtXQLMAB4vRqwiIiK5qK/vpalpFTU1NXR0rCp1OFJmojJG7SvAH8PnU4FlKcuWh2UiIiIio0rJr6Nmrf0O0ANcN4z3zgfmAzjnqKmpyXN0g6uoqCj6PoulnOsGqt9IVs51A9VvJCvnuoHqVyolTdSstSfgJxnMds4FYfEKYHrKatPCsk045xYCC8OXQUdHR4Eizcw3cxd3n8VSznUD1W8kK+e6geo3kpVz3UD1y7cpU6ZktV7JEjVr7WFAHDjQObcmZdHvgOuttT8BpgA7Ak+WIEQRERGRkirW5TluAA4Caqy1y4Ef4Gd5jgPuttYCPOGcO8U59w9rrQOex3eJnuac0/QZERERGXWKNevz6AzFVw2y/gXABYWLSERERCT6ojLrU0RERETSKFETERERiSglaiIiIiIRpURNREREJKJKfsHbkSiWTFKVSFDR2cmk6mq64nF66+tLHZZkIZmMkUhU0dlZQXX1JOLxLt2TTyJB3ysjV7l/duVev6hTopajWDJJdWMjla2tAEwAKlta6Gxu1okbcclkjMbGalpbK8OSCbS0VOoGylJy+l4Zucr9syv3+o0E6vrMUVUisfGE7VPZ2kpVIlGiiCRbiURVSpLmtbZWkkhUlSgiEU/fKyNXuX925V6/kUCJWo5ibW2Zy9vbixyJ5KqtLZaxvL09c7lIseh7ZeQq98+u3Os3EihRy1FvXV3m8traIkciuaqry9y9WVurbk8pLX2vjFzl/tmVe/1GAiVqOeqKx1nf0NCvbH1DA13xeIkikmzF4100NKzvV9bQsJ54vKtEEYl4+l4Zucr9syv3+o0EJgiCUseQL8HKlSuLsqO+GTDjOzvpLtMZMDU1NXR0dJQ6jLx7b9bneKqru8t21me5fn5QvnUbDd8rUJ6fX7l/duVevz7FPjenTJkCYIZaT4naZijHL5w+5Vw3UP1GsnKuG6h+I1k51w1Uv3zLNlFT16eIiIhIRClRExEREYkoJWoiIiIiEaVETURERCSilKiJiIiIRJQSNREREZGIUqImIiIiElFK1EREREQiSomaiIiISEQpURMRERGJKCVqIiIiIhFVVvf6LHUAIiIiIjkYVff6NMV+WGufLsV+VTfVbzTXr5zrpvqN7Ec51031K9hjSOWUqImIiIiUFSVqIiIiIhGlRG3zLCx1AAVUznUD1W8kK+e6geo3kpVz3UD1K4lymkwgIiIiUlbUoiYiIiISURWlDmCksdZeDRwBvO6c263U8eSbtXY6cA1Qi7/kyULn3OWljSp/rLXjgYeAcfjz//+ccz8obVT5Za2NAX8BVjjnjih1PPlkrV0KdAG9QI9z7mOljSi/rLWTgF8Bu+H//33FOfd4aaPafNbaDwE3phRtB3zfOXdZiULKO2vtmcBX8Z/bc8CJzrnu0kaVP9barwMn42cqXjnSP7tMv+XW2mr8eboNsBSwzrk3SxVjH7Wo5W4RcFipgyigHuAbzrldgH2B06y1u5Q4pnxaB8xyzu0B7AkcZq3dt8Qx5dvXgX+WOogCOtg5t2e5JWmhy4E/Oed2BvagTD5H59yL4We2J7A3sAa4tcRh5Y21dipwOvCx8Ec/BjSWNqr8sdbuhk/SZuDPyyOstTuUNqrNtohNf8vPBu51zu0I3Bu+Ljklajlyzj0EdJY6jkJxzr3mnGsJn3fhfyimljaq/HHOBc65d8KXleGjbAZqWmunAZ/Gt8rICGKt3QqYCVwF4Jx71zm3qrRRFcRs4GXnXGupA8mzCmALa20FMAFYWeJ48unDwJ+dc2uccz3Ag8DnSxzTZhngt/xIYHH4fDHwuaIGNQB1fcqArLXbAHsBfy5xKHkVdg0+DewA/Mw5V071uwyIA1WlDqRAAuAua20AXOGci+QsrWHaFngD+LW1dg/8Ofp159zq0oaVd43ADaUOIp+ccyustRcDSWAtcJdz7q4Sh5VPfwcusNZuja/fp/DDK8pNrXPutfB5G34IUMmpRU0ystZOBG4GznDOvV3qePLJOdcbdsFMA2aEzfojnrW2b7zF06WOpYAOcM59FDgc3y0/s9QB5VEF8FHgF865vYDVRKTrJV+stWOBzwI3lTqWfLLWvh/fGrMtMAV4n7X2mNJGlT/OuX8CFwF3AX8C/oYfJ1q2nHMBEeltUaImm7DWVuKTtOucc7eUOp5CCbuV7qd8xhzuD3w2HHDfDMyy1v6mtCHll3NuRfjv6/gxTjNKG1FeLQeWp7Tw/h8+cSsnhwMtzrn2UgeSZ4cArzrn3nDOrQduAfYrcUx55Zy7yjm3t3NuJvAm8FKpYyqAdmvtZIDw39dLHA+gRE3SWGsNfozMP51zPyl1PPlmrf1AOLMOa+0WwBzghdJGlR/OuW8756Y557bBdy/d55wrm7/qrbXvs9ZW9T0H5uK7ZMqCc64NWBbOkAQ/luv5EoZUCEdTZt2eoSSwr7V2QvgdOpsymQjSx1r7wfDfevz4tOtLG1FB/A44Pnx+PPDbEsaykcao5chaewNwEFBjrV0O/MA5d1Vpo8qr/YFjgeestX8Ly85xzt1RwpjyaTKwOBynNgZwzrk/lDgmyU4tcKu1Fvx31/XOuT+VNqS8+0/gurCL8BXgxBLHkzdhcj0H+FqpY8k359yfrbX/B7TgZ87/lYhe5X4z3ByOUVsPnDbSJ7pk+i0HLgSctfYkoBWwpYvwPbozgYiIiEhEqetTREREJKKUqImIiIhElBI1ERERkYhSoiYiIiISUUrURERERCJKiZqIjEjW2kXW2vNLHUehWWvPsdbq3q0io5SuoyYiRWGtfRI4Bn+dqf8LbwU10LoG+CYwH3+rrzeA64BznXPrihBuSVhrDwJ+45yb1lfmnPtR6SISkVJTi5qIFFx4W7IG4F/A3vgLgw7mp/gk7Tj8DeYPx1/t3RUovlietqM/fkUkr3TBWxEpOGvtXsBPnHMHW2svAlqdcz8fYN0d8bf1+oRz7smU8unAEuBw59x91tpFQDewPbAvPvk7zjnXGq6/M/C/+MTwDeB7zjkXLlsErMUnjwfib6g9FrgYmA68DVzqnLs4XP8I4HxgG/xtnU5xzj0bLlsK/AL4MvAh4PvAx51zR6XEfjlgnHOnW2tPBOK811J4kXPuivDK/R3AOGBN+Nad8AnrDn23A7PWfhb4H2Aq/ubYp4Y3ze6LpQmf4Dbgb6B9vHOue+BPR0SiTC1qIlIw1toTrbWrgEeBT4TPvwFcZK1dZa3dNsPbZuNvTv5kaqFzbhnwBP42RH2+DJwH1OCTluvC/b4PuBt/P8IP4u99+nNr7S4p7/1/wAX4FrtH8Pe4/ZpzrgrYDbgv3NZewNX4Wx9tDVwB/M5aOy5lW0cDnwYmAc3Ap1LuSxrD34qm796IrwNHAFvibxF1qbX2o8651fiWw5XOuYnhY2Xa8dwJf6/MM4APAHcAvw9vObVxNeAwYFvgI8AJmxxhERkx1EwvIgXjnPs18Gtr7cP4+1h24m98vJdzbqDm/BrgtQGWvRYu73O7c+4hAGvtd4C3wpa3/YCl4f4B/mqtvRn4IvDfYdlvnXOPhs+7rbXrgV2stc84594E3gyXzQeucM79OXy92Fp7Dr4V78Gw7KdhIgnQaq1tAeYB1wCzgDXOuSfCY3J7SvwPWmvvAj7J0N3BAF8K63x3WOeLga+H9X0gJZaV4fLfA3tmsV0RiSglaiJSENbaavyNxQ0wEZ9I9LVCvWmtPdc5d1mGt3YAkwfY7GTg1ZTXfckRzrl3rLWdwBR8t98+YQtenwrg2kzvDX0B+C5wobX2WeBs59zj4baOt9b+Z8q6Y8P9DLSt6/GtbNfgW+76WtOw1h6OvwH0TvhejQnAcwPUN90U/M2iAXDObbDWLsN3g/ZpS3m+Ji1OERlhlKiJSEE45zqBSdbaRuBg59zXrLW3Aj9zzt0zyFvvw3dTzsgwRm1ffFdnn+kpyycC1cBKfOL0oHMutZs0Xb8WPefcU8CR4cSHBfiJC9PDbV3gnLsg220BNwGXWGun4VvWPhHGOA64GT+G7LfOufXW2tvwyWym7aRbCeze9yKcHTsdWDHE+0RkhFKiJiKFljrLcy/g6cFWds69ZK39JXCdtfZY4ClgZ+DXwD1pSd6nrLUHAE/iE7gnnHPLrLV/wLeMHYsfMwa+C/CdvoH3qcIxXl8E/uCce8ta+zawIVx8JXCrtfaecD8TgIOAh5xzXQPU4Q1r7QNhzK+m7HMsvlXxDaAnbF2bC/w9XN4ObG2t3co591amTQNnW2tnAw/huz3XAY9likNERj5NJhCRQtsbaLHWbg30huO/hrIA+BXwG+Ad/OzFB/Ddk6mux3cjdob7OQYgTKDm4icRrMR3B17Ee12vmRwLLA2TtFPwExVwzv0FOBk/m/JN/MzTE7Kow/XAIaR0e4ZxnY5PuN7Ed4v+LmX5C/jJAq+Eky36dVs6514M6/i/+C7izwCfcc69m0U8IjIC6fIcIiIiIhGlFjURERGRiFKiJiIiIhJRStREREREIkqJmoiIiEhEKVETERERiSglaiIiIiIRpURNREREJKKUqImIiIhElBI1ERERkYj6/0k635rUSOsoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_preds(y_test, y_preds, 'Decision Tree')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision tree visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6cDy2S2MlOvOr/WwHZKfa74fwUHyw/3DF6G46Cyr6Wdv0vq31/r3lyts3W5XFwAe16iAoy6nyGCVV+Kg9IYt9hRB88N51b1zVPDW8owg3xVq27DeFljor7xk+1oayB8g0evvBE7mmwkwts7euU9k5kZkV+yStPXa475FzV7Q3MkMK0+8aPMK/KSZFVBhu/BKNtQcRnlFb4i1WtKh6KZai11jbU5XkBRbDKbLR5OE3/lHizZsnHvZjBxIgARIgARIITAQo+gLT2+SzkIAnCZiSkHhykCe6j1beH9yjRbXfZJQSXYYh2YelJY5nCv+EULKs/4c+xt41oz/2H0I0wtNoLTxRKxAhnCj6DcM3xBCEDYTPOuVVSqhCTQuqRCz2zDPzF8+dSSevmf3fNj0dvIkI9UQdOSRzQeIRCEPs/4MhVPHKrfsC8TOio2kf3S0VEtln0j86dHOW8nyh9IUtg5iCYMMHde+QBAUCEHsU8UHim9LKY/jPMLfFx2/88BLaqquXLW1yLfoQ5gmDB/Wrym7Zr0UN8z5ItKPu3r3Hz3DoRhTOX7NLt9f/sZdTn/z4gUQ5xZr20Ql67myZ6WpOZNEMqRLKINT0lfLA2hJ9hoi8+iOhi+XcyIwKy6H+AcBRU05p/TvpaH/2IwESIAESIIHAQIAlGwLDW+QzkIA/JYDMjzDLBCPI6Llk815XK86UMok+R6gkwi8NW7J5n5y5css41d9IJgKzTtOPcg2/tegvnUbN1teNH0YYKpK8wBuHkE+Eo9ozz8yPzKGwBWt3aaEHUQWhB48f1o3Q0uIqSYmRdXKnCvus2GGoTmJj3B8i1ChlYCv01ehn+Q1hiYLkJ5aOEZd5w6RTvd+VFy6G3ptn2c84Rn/YRpdjeq+h0Y7vRRt261NDEG47eEqHaq6xKoOweNNe7S1EP+xdNAyePJPADSYVVMiptcVUXjiIb2Q2hfC2tEUb9qj9l+91jT2jRiC8w8fOXxUkhoEhwykS32C/IH4fDIN43rz/uE5kY+xHNK7xmwRIgARIgARIwDUBevpc8+AZCZCANxJACCZqyPVUIZbIPAlvEUIsUaYAhuyeyOCJcgDw2qGkQAFVb69i0dwCzw48dNbWWaX+X6hKHGjvVqiQUkTto7t577EuFYC+PZr8DF/EeRnl/UI4oZE8pradrJ3oC/PM/MVzZVKCLpj2VBXIltYsJrG3DxkqYcZ+PhwXz51RJ2uZtHi9hAkdUnuorqjEKVNVkhQYsnDasz6TFsklFUJpzyDs7qskObYMIhTeRXhN01Vqq8tZIIRzw76jWpzGieGkatgV0UMbVyoubYZOk4Z9/lbvp6SkSuKsxxnZPJF0xtIQuglBh2Q1tpKtoG/HuhWk+7j5UrPrX3pPo1PkCCq09YzsUaGxsO4qiQ0MAjJf/e76+NLqSbqkBk66N64iTftPkvbDZui9gNhriPV8VjUXx3Vt4mofqR7MHyRAAiRAAiRAAq4IUPS5wsETEiAB9wgED2YKDgihvFmOWH8lEE5dviEnL96QjiNn6SEIxds/f7g06P23TtCyUJVggOhDcpC4SnzMXb1Dl13AfjXUskPoHzw8kX94l/AH/7qJvXXhdpRXwAcGITFMZZvEvkFLC6OSqUBQQiSgvl5ilUzF0uD1Q806wzwzP/rmSJdC70mDsDLMqD0IQQihZxi8cbMGtJUBUxar7J0/k9CgX6vqpVWClWJGVzff8KYdUfX93DMwsGXYK7lkZGfpoDJvbnI5LvPX7jR3g+duWp9W5jIMDX4vqrytbwV1Eycu3mDuFz1KJJnYs5mui2huVAd7f5SnQKF0e9ayWilBkfW/5q3Stf+MfqjJ2EMllmmhrlub5TvB3kPs3+urhO/UZSaBjFITSBpjiFXr8TwnARIgARIgARL4SSCY2m/z/ecpj0iABIISgbRpUkvFfOlVRsdqPvbY8N6grt/TF68kS5pkgnA/GPZzXVQevzjRo+q9XKb9hcH0fit4BNEPgqxCuyGCvXqPdsx1Jc4w78WbdwXZNrEXDOIrXNifGSW9+kA+PT+e6cGTFzoZTZokqB8XzatLdmg8PK4XlccQz5dYFWRPndjZFVdjEoThnrt2W4eDogRHUuc4NvsZ/R35fvrytVy8flcePnuhxTeytRrZOR0Z//HTZ13YHv/ogLDP0MrT61nrNWGh7D57W44eO+7ZoexPAiRAAiRAAgGWgOf/ixlgH5ULJwES8AsC8NjgD3Rrwz63NCojJAy14TJV66D3bp1YMsaciAU13VxOnteZIi09PxiDc2SQtMwiiXbvMp+eP1/mNN61VE/Nk0QJPXw8MmQCNfY3etTX0evwFubNnNrR7m76wWubP4vfcHOzGDaQAAmQAAmQQAAiQNEXgF4Wl0oCgZUAPEkIM0TykwxV2uvELyiwvnHfMZ2N83/1KgTWR+dzkQAJkAAJkAAJkICPE6Do83HEvAEJkIAjBFaO6SYo8bDj0Gm9bwtenYwq+Ui72uWkkkrsQiMBEiABEiABEiABEvg1AhR9v8aNo0iABLyZQNyY0WTk/xroWZ+o/X8oCxAqJP8nypsxczoSIAESIAESIIEgSOBnyrog+PB8ZBIgAf9JIEbUyIFe8CGbJbJxvn3/we5LcKQPBqPoO8oxIDmOe+ZoP3tzODIeaz5w6qLsPHJG7jx8Ym8qtpMACZAACZAACfgiAf4zui/C5q1IgARIwCAwau4qVaR9reyYMVCXrDDaLb896vPvxr0ydsFqcwF7lLloXLmEDGxdy3IacbSfq0EWJ46MRzH1XuMXyuSlG3WZDWM46hRO7NFMUAuQRgIkQAIkQAIk4DcEKPr8hjvvSgIkEEQJoDTF1gMnZcaKLXYJONIHRe9bDZ6iBVbN0gUkmipbsXL7QRmj9kWilEHvZn/o+R3tZ28xjo4fOnO5ruuH+nnlC2WXUGoNa3Ye1nUB6/UcJ5un9rN3C7aTAAmQAAmQAAn4MAGKPh8GzOlJgAR+jQDqyaFYOwqLG4aMnqhtl1xl+0TJB2v78PGTXFPjUH00RcK47tZxe/7qjaod+FqQORT1AH3DGvcdL1v2n5Rn6t72zJE+GNt8wCSd2XR8t6bS8EdRd3j58tXrJvAQotg7SiQ42s/eehwdP+e/7XqKFSohj1FWoWeTapKucltVduOC3FQhqInixbJ3G7aTAAmQAAmQAAn4IAG3fzX54M04NQmQAAnYI5CqQmtJWb6VLNqwR5dtQOmGBCWbCETQw6cvVJH2weJcvLFkrf4/iVe8kSxct8vVVPBwJfytqeSs1Vly1e4sMQvXk/+NnKWFkWXHPcfOSZY/Ouq5M6vvxKWayZ9/zRaEJ/q0hVSJadIkjS/5VK06hGLaMkf6YN/csfPXJEbUSFK/QhHzNCkTxZOc6VPowutrdx0WR/uZJ7A6cHQ82L14/VaPTpcsgXkWZ1VwPqZTFH3+DUqcRgIkQAIkQAIk4CcEKPr8BDtvSgIkYE0AwuHe42fSTHmwkJCkTIFs8uHTZ1m8aZ+krdRWtqtSDoWypZPiuTPJm3cfpKUKbbz36Jme5viFa9J74j9KYESWTvV+l74tqkuKBHFl2vLN8te8VeZbbdp3XMq2GSjX7z6SOuUKSZsaZSRalIgyZekmqdxxmLmfTx1M7d1SNk3ppz8ZUya2eRtH+ly5fV+PLZQ9vRuPZ3Ll4YRdvHFPHO2nB9j44eh4ZFktXyiHnmHIjGXy+u17effho4xftF4L9rRK6DpSEN7GEthEAiRAAiRAAiTgDQQY3ukNEDkFCZCA9xFoUrm4/NWpoRYzs1dtk7bDpmtv3dZp/SV3xlT6RuXaDNLZIU9eui7xlDdp7II1ur138+pSo1R+fdy4UglJXq6FrFb7yro3rqo9eV3HztN74OYNaScVi+bS/Xo0rSolmvWVHYfPiMuJC5JXeeFsGUTl2au3bF0yt0UIG1bmD+lgPvepg8s37+mpY0eP6uYWKZS3D/b4+UtxtJ+bSX40eGb8n/Ur6jDOyUs2ysyVWyVkiBBa+GGP37AO9e3dgu0kQAIkQAIkQAK+QICizxcg8xYkQAKOE+hYp4LZe5VBFWeHIWTQEHw4L5gtrRZ9V+88xKmgkDts3MI1gpDCvJlSaw/e3S2z1H49fUlOXryhPV/OsaJLhcImrxSuRI4QXmqVKSg9VebJNSok0p7ogyA8ePqSaTI7PyFwfMNQOgGGtVtbfPV8MHhDHe1nPYdx7pnx6Pvx42c99MvXr+YMnu/VPstdqnxD0ZwZjGn5TQIkQAIkQAIk4MsEKPp8GThvRwIkYJ9AiBDBJX5sk2hBL0PMwZtnaUYSF2S5hLWvXV42uxzXtepKtxogkcKHk8I50ku5gtmlaom8us/lWybv2IOnz1VykXa6zfiBcETYgyfPjSY33+sn9jYLGTcXfbkhYviw+o7w5lnbm3emZ3FS2Twd7Wc9h3Hu6Hjs56vTY4zeS9hTeU4bVSwu4cKGlnW7j0jLQVNViO1/qixFcvU+fopt4x78JgESIAESIAES8HkCFH0+z5h3IAEScJBAWOWxMwSd5ZBgYju7piH64Ak8uWycLFZ16zbvPyH7jp/XXjt47pDwZe2EXmqfmakIOgq/581kChO1vAeOLb2J1tcMAWrd7hfnsaKZwjqxN9HanqokL7AYan+jo/2s5zDOHR2/4/BpLfiSq32UCKU1rGbpgrJh7zFZse2AzlpK0WeQ4TcJkAAJkAAJ+C4Bij7f5c27kQAJ+AABJHkJp0o7NKtaUn+QFAa18FAfbrfK1nn++l1VmiG2vnPUSBFkZv+2rlZxXyWQOXv1ti7z4OqCxUmbodPkxMXrFi1uD+Fh3DCpj9sL3tyCLJ2wqz8SulhOjzBUWI50ySVeTJOH1KN+luMtjx29z5VbpsQy6ZMntByuj7OlTa5FH8I8aSRAAiRAAiRAAn5DgKLPb7jzriRAAt5IoPnASfL42Su5sXGaQNQhm2Tp/Nl0Db4zV27Jx0+fJEOKxKrmXyiV1fKuINQzRUKTcIK3sOXgqVokzhnYzm4tOQibs2ou9yxCOFPYpXt9vONatrTJJGn82HJN7Wlcsnmf/FEyn54Wa9y8/7h+Tuyhw3oc6Wes6aQStV9V5lTspQRDR+8TRhVih210OSaof4jQUsMWbditD20JQqMPv0mABEiABEiABHyWAEWfz/Ll7CRAAr5AoEKhnDJ12SZdy69V9TJqL2BI2aT2+EHwIXGLIWJQzmHw9GVSrs1gaVerrE6EsnSLiyoHcUogSoyyA7aWvHFyX1vNftbWvXEVadp/krQfNkMOnrqkE9cga+bnL19lXNcmWvBhcY72+/r1m+Sr310/z6XVk3RWVEfHF1SlNMD49OWbkk6V1/i9SC7NfcO+o/odxInhJPXK/6wnqG/CHyRAAiRAAiRAAr5GgKLP11DzRiRAAu4RCKbSbIYI7rp0KBK72DLrfn1UXT7UhYPXq0m/CeYh2GM2a0Ab7bVCY+f6ldTes+8yYs4KQfkGwwpmTStTVA09/7Rvz1ibvW/sl8P+vb6TFmnBi37IHorSCZYCy9F+lvex5O7I+LAqtHbJyM7SYcRMLbbnr91png5ielqfVtoDa27kAQmQAAmQAAmQgK8SCKZCm0zp73z1trwZCZCAfyCQNk1qqZgvvfRsWs0/LMfLa3jy4pXsVPX2IN6wny1jSlOYovXEz5RYQo2/9x8+SYpEcc2hntb9AsL5R1XA/vDZK1owIxwz9I9QS+u1O9rPepxx7uj463cf6hBaeA4TO8eW1ImdxVJEGvP51XevCQtl99nbcvTYcb9aAu9LAiRAAiRAAr5OgJ4+X0fOG5IACfgUAWTmNEo0uHePaFEiSpEcgaNuHARu/ixp3Htcfc3RfvYmcnR8EiX08KGRAAmQAAmQAAn4HwK2Y6f8z/q4EhIgARIgARIgARIgARIgARIgAS8QoOjzAjwOJQESIAESIAESIAESIAESIAH/ToDhnf79DXF9JEACAY5Av8n/CvYXTujeLMCt/YOqp3fy0g3Bdm+EacaObioEb+tBHjx5rusboi5ippSJJe6PuoC2+rKNBEiABEiABEjA7whQ9Pkde96ZBEggkBJYvnW/IKFJQBJ9EG7NB0yW5dv2qwyn38xvpnT+rPo5LMXfF1UWotNfs2X2f9vk27efucAqFc2tM3WGCxvaPJ4HJEACJEACJEACfk+A4Z1+/w64AhIgARLwcwKtVIF6lLyIq2rqtalRRupXKCLRo0SSDXuPuSqDgYV2GzdPUBMwTnQn6d3sDxnUppb2CK7cfkBGz//Pz5+FCyABEiABEiABEnBNgJ4+1zx4RgIk4A8JIOTwmvKcocBMioRx7ZYlwNLvP34mnz5/kUTxYnn4JPDGQeSgzpxhr96+kwdPXkjyBHEkuFXdQKMPvl+8fiv3VXhjyoTxPFWS4Nu3b3Ll9gOJEyOqLg5vOadx7JnnNcZ45Rueu1XbD2qRt2vWYHNIZ4tqpSRP3a6yQ5XBAJfIEcLLzXuPZPqKLeIUOYJsntpPEv/gXCh7einQoIf8u3FvoCkB4hWmHEsCJEACJEAC/okARZ9/ehtcCwmQgBsCY+avlqEzl+vi67iImm+NKxaXYR3qmcUfhNTAaUtl7a7Dcv76HT1HHCXm8mZKJUPa1ZH4sWPotlQVWuu9av1b1ZQhM5bpEMxwSvBVKJxD9asrTftP1AIH+9kihg8rf3VqKLXLFtJjh89aIeMXrZO+qhA8PGIHT1/SoY3oN7htHWlcqbjuZ+8HagJ2HTtXFm3YI++ViEUx+kypEut75MqQ0jzMkec1d/amgxMXVc1CtaaSeTObBR+mzpAikd7XB3H86o1J9G0/dFqHf9YrX9Qs+NA3S+qkMmdgO/liERqKdhoJkAAJkAAJkIDfE6Do8/t3wBWQAAnYIXD8wjXpPfEfSRQ3pq6/B4G1WHmSpi3fLDGjRZbujavqkaOVMBw5Z6VECBdG6pQrJLGjRZUtB07Kim0HtNcOHikY9q09evZSmg2YJAnjxJAyBbLp8MXFm/bJqh2HBAXICyuPVciQIWSrGt9y8BRdzy9erGjy5v0H7d3rOHKWLvzepFIJnaxl3Z6j0n74DIF4rFWmoL6P9Q+IyHJtB2mhmD55QiWusmiPGcIhy7QeKEtHdZGiOTOIo89rPb9Xz5PEjy1bpvYXZ/Wclrb/5EW5ef+RFn+GcIaXElZWsdt19KwcUuL3nRK0GVMmkopFcml2lnPwmARIgARIgARIwO8JUPT5/TvgCkiABOwQGLtgjb7Su3l1qVEqvz5urMRW8nItZPXOw2bRt2X/CX3NZd5wSabCMmFtapaVNBVby+Gzl/W55Y8mlYtrDxvCN2ev2iZth03XIaFbp/WX3BlT6a7l2gySnUfOqEyW1wWiz7AoEcPL9hkDzN7DRRt2Kw/hJOk7eZHULF1Ae/CMvsb3P+t3a8EHz97OmYMkVEjT//SWWJtJWgyaIqPn/adFn6PPa8xrfJ9Unrr+Uxcbp3a/G1UsJuUK5nBzHXv38iivKOz12/eC/X3X7z2UM5dvSZhQoaRP8z/MY67evq+Pxy5cI+uV4LW0NEniy4ox3SSBEtQ0EiABEiABEiAB/0OAos//vAuuhARIwIpAmNChdMs4JTDghcqbKbVEixJR7m6ZpcTVz84j/9dAvqoQT0PwYU/c6cs3JYQSdR8+ftYhnQinNKxjnQrm/XoIYYSlS5bALPhwXjBbWi36rt55iFOz1StfxCz40FizdEEZMHWJ3H7wRO4+eurqmjFomcrmCYN30BB8OMfYLmPm6vtgz5yjz4uxlvbkxWvZ7GISvpbt1seFsqWzbnJz/u7DR1mjwmS/fP2qrwHbxRt35TflnYRIvnbH5OmD4KtaIo/ULGXybg6YtlhOXryhvagbJvVxMy8bSIAESIAESIAE/I4ARZ/fseedSYAEPCDQvnZ5JWaOawFXutUAiRQ+nBTOkV55q7LrcE9jeGrlYUI2yW5j58nlW/d1CKdxzfobewLjx45ubjaElqU3DxeNJC4IzbS0FIniWp7q4zRJ42vRd+PeY5ui7/LNe7of6veNUGGolvZKedZgD5++EEef13I8josoJo93zrNudnMeSoWtemQozfB87wI5d+2O7Dt+XoXXLpReE/5R+w+T6FDX1+8+6CngGZwzsL15utRJnCVd5Xay59g5efrytU4KY77IAxIgARIgARIgAT8lQNHnp/h5cxIgAfcIwPt2ctk4vY9vswrhhAiBFwqfhet2ydoJvbQ4q99rnG7Dvrp8WdJIWiXCIFJ6jl+o9vQ9d3WLsMp7aAg6ywvB5Kcn0LLdWvRFixzJ8rI+DhXC9D+l1n2Njm/emYRd9nTJJWqk8Eaz6ftHWGWUiBEkVrQoDj2v6wlMAtUrtfGQkROZSFMldlZZOSPqEFWwx+fF6zfak7lp33Et+hKopDjwaiKU1dKQLTW1Gn9BeQUhcqP/CJO17MNjEiABEiABEiABvyFA0ec33HlXEiABBwggUySEXLOqJfUHiViQYKVez3GyW3mUzl+/q0I4g2nBFzZMKDmxdIwKAzV58VBgvOOImQ7cxXNdLt2862bA5VsmT17KRPHcXEND0vhxVNKX1zrRS5XieVz12XfivN5PCMHnyPNCiFnb4TOXpeOoWdbNbs7b1Sonf5TM56Yd+/OmL9+ispDW1t5Gyw6GQH726o1uThBH8T0p8uT5K8tuKpPpN53YBo0xnaK4usYTEiABEiABEiABvyXA4ux+y593JwEScIdA84GTBGGdqIkHw3640vmzmffuffz0yVyiIUPyRGbBh77Ltrqo2nImDxvOvcuWbnYxl4/AnBv2HpVLyrMFLxlCI22ZUZIBJSUsDWUffmvRXzqNmq2bHXley/HGMTKLnr1yy8MPQkhtWbpkCXXz2t1HdDkGow/q9yFsFpY1TVL9/Vu+rPp7icp4CmFtGEQ4hC3EaxJnj2skGuP4TQIkQAIkQAIk4PME6Onzeca8AwmQwC8SqFAop0xdtkkqtBssraqXUYlOQsomtcfvjBI48OghCUs0lXkS+/QOn72ik6Igg6TLyQu6SLhxW8zRoEJR49RL36gDWFaVWahYNJcSo+9U7b61er5ujarYnbdzg0o6HHXpFhcJFSqk3oN3U+3/G7fQNLZHk6p6rCPPa+smRXJkUPvwFtq65FAbQjVRCxElGkq16q+Tttx5+FRlSD2k90eiZEbtMqZ6hdVK5BVkGT116Yb81rKf4pBb3ihxPeHf9fpeA1QNRMM76NDN2YkESIAESIAESMDHCVD0+Thi3oAESOBXCfRRhdCRTRLF0Jv0m2CeJnmCuDJrQBvt+Uus9pKN6FhfJ3GZtHiD7oN6fYPa1FKhoWF0QfQ//5ojlYvl0XvVkNHT0iAYbZl1P6MPavHBY4f9gjAkgunfsoZUK5nX6KJETzD9MRqQcXTdxN7SuO94QfkGfGBOkSPoIvNVlZCCOfK8uqM3/4gQLqysVKUWGvUZr4UfxJ9hyPg5tmtjiRQhnG5CFlT0RakJlMo4cOqSbgeHsV0aqzqJhY2h/CYBEiABEiABEvAnBIKpxAOuU9P5k4VxGSRAAj5PIG2a1FIxX3rp2bSaz9/MC3d48uKV7Dx8RguseDGj6ULglqUPMDVCF+F9wn4yeAANMYdMkth/hv12lmUbPLscFIkfo4rALxjSUcoXyqELqSN8FEXJY0SN7NB0CIe8qPYEItEJEqbkSJdCbCVgceR5HbqhJzshnPOqKsmAsgyhlUcymRLXENX27M7DJ7qcA5LQpFV7DcOHDWOvq79p7zVhoew+e1uOHjvub9bEhZAACZAACZCATxOgp8+nCXN+EiABLxOAqDK8YfYmw366Enkyu7mMwuP4eKdBUCITp2cN49ImTaA/7o115HndG/+r10Kqkg7Ym4iPIxZfZfLEh0YCJEACJEACJOC/CdiOa/Lfa+bqSIAESIAESIAESIAESIAESIAEHCRA0ecgKHYjARII2gSiRAwvcWM66RISQZsEn54ESIAESIAESCCgEWB4Z0B7Y1wvCZCAnxD4s35FwYdGAiRAAiRAAiRAAgGNAD19Ae2Ncb0kQAKuCKAweeshU2X9nqOu2gPaybOXb+SIKjvxVtXcs2Vv3n0Qex/r/uiHGoDnrt22vuTpc2TyRNF4j8zRfkhSs07VA7x+96FHU/I6CZAACZAACZCANxGgp8+bQHIaEiABvyFw5fYDmbt6h67bV6ZANr9ZhDfcddTcVfL3P2tlx4yBkiN9Clcz3n7wRNJUbOOqzTgJFya0PN41T59+/PRZuo2bJ9OXbzEu64LxTSoXl+6NTbUAzRccONijCq6XUTUJUQLjwfY5dkc42g/JolEWAiJySLs60q5WObtz8gIJkAAJkAAJkID3EaDo8z6WnIkESIAEPE0AQmjrgZMyY8VPoWY9CcoowFCfME6MqK4uoz6eYZ3+mi1z/tsusaJFEdQTvHX/saxVXrXB05dJwjiqwHpZU4F1o7973/A8Nu47QTyq6uNoP9wLotYRr6F76+I1EiABEiABEiABzxOg6PM8M44gARLwZQL3Hz+TT5+/SCJ3asZ5dUnPX72Rpy9eq9p0cbxUz88z60Cx9i37T8ozdW/3DHXzYMM61JVS+bLa7Hrz3iOZt2aHvrZdeQuN+npnr96WXLU7S7vhM6RSsdwO19JrM3Sa3FPcPTJH+x2/cE36TvrXo+l4nQRIgARIgARIwAcIcE+fD0DllCRAArYJLFi7U6Lmqy2/tejnpsOkxRv0tfZKnMC+ffsm/acslhw1/5QU5VtJusrtJHm5llKv51hBUXB7NnXZJj3P+EXrXHU5pPb+4d4tBk121Y7QxCx/dJQEJZtIZvWduFQz+VN5zD5/+eKqn0+chAwZUtIkjS/5MqeWyBHC2b3F1dum/W8oMG/PjilR9e3bd8mdMaVZ8KFvOlU0PX3yhILQz4s37tob7qp99qptsnrnIR1+6d66HO2HfYoNev8tEcOHVXOWdXUvnpAACZAACZAACfg8AYo+n2fMO5AACfwgULZgdgkePJjsO3FB4L2ztKWb98mXr1+lYtFcunn0/NUycs5KufXgsdQpV0g61ftdhy2u2HZA7QubYDnU1TE8gpgHAsjSEKaI9q9fv5mbN+07LmXbDFRJRR7pe7SpUUaiRYkoU5Zuksodh5n7+dTB1N4tZdOUfvqTMWViu7eBpy+UKpwOr13vif9I84GTZfS8/+SG8u4Z9uT5K31oq7B6hhSJ9LVzarxHBmHYZcxcwZh+LWvY7e5oP0yAsNOrau/luK5NJF6s6Hbn5AUSIAESIAESIAGfIcDwTp/hyllJIEAQCBYsmNqz5XtLdYocUUrnzyr/7Tgkq9Sn5R+l9M0RmnhYZa6MHzu6FM6eXrdt2X9Cf7vMG65DLnHSpmZZldCktep7WV/zyg948rqOnafF4bwh7cxis0fTqlKiWV/ZcfiMuChxmld54WwZxNfZq7dsXTK3RQgbVuYP6WA+/9UDiL7PX75Kza5/uZpiyMxlMrZzYyVYC0vyhHH1tW0HT7nq80WNO3b+mm47d+2Oq2vWJ/AGwiP3Tf1SzBrQVkKHsv2fCEf7Yf5lW1xkwdpdUrN0AalSPI9M+He99W199Ry/7/i9p5EACZAACZBAUCJg+7/oQYkAn5UEgjCBiBEj2i0R4FNYapUuaBJ92w+YRR+8dzAkGjH+IB/5vwbyVYV4Yo8d7MPHT3L68k0JETy4Ov6sE4wYfXUHT/44efGGXLl9X2f9rFA4h3l05AjhdRKUnuMXyppdh+2KPghClEVwz8KHDePeZYeuwUN57Y4pvPN/yttZ/bf88uDJcyWkdspSJajaDpsumVMnldwZUkmCODEEmT4RAluvfBEdIjtdJYgxwjrtiThjIX2UkAXjUZ0aSJok8Y1mN9+O9kMimXZqfYnixpTRfzZyM49fNCDUNGLESH5xa96TBEiABEiABPyMAEWfn6HnjUnA7wnEjecsdx/5br20knkzS4yokcRF1X97+PSFLikA8QKrrTJOGpZaiY6ZK7dKN+WNu3zrvjx69tK49Evf1lkoL9+6p+d58PS53i9oOenrt+9N15S4smfrJ/Z2E0Jqr69X29dO6KXDO7OmSaanwj69Yrky6mOwW751v/RtUV2m9WkldXuMEYhoQ0ijE0I+Ifywf9CebXY5LhPVvsoSeTJLi2omD6ytvo72QxhtI5Wo5o0SWSvGdJNI7uxZtHUfn2q7++iZxEuQ0qem57wkQAIkQAIk4C8JUPT5y9fCRZGA7xDImDGjLJwzw3du9uMuoVTykmol88nkJRu1J61Q9nRy6tINncwkaXyTVw9d6/cap6+jDl2+LGkkrRIsmVIlEXjg4OnyrN23GvP6rakIeoyokSVvplQ2p8ud0XY7OluWSrA52Jsa4c3MlcG2SIHwg+i7fNMkYAtkTSuH/hmlk7BA5KF0Q8Fs6eTfjXu06EubNIHdVS3Z7KKvHT9/VbLV6GTu9+qHAEabc6xoak5TyQiP+mH/3oFTJk9o6yHTzPMZew/HLlij6yv2b1VDyhX86Wk1d/Shg5Pqd61NmSo+NDunJQESIAESIAH/SYCiz3++F66KBHyFQNGiRWXgwIEqfPCBWAoun7557TKFtOhbtf2gPH5u8uDVKVvYfNsL1+9owRc2TCg5sXSMDsHERXiPOo6Yae5n68AI+bTOvnlFeQstLVmC2Po0aqQIMrN/W8tLOskMkqak+LFPztXFHycoVXDi4nVbl8xtkcKHkw2T+pjPf+Vg99GzslElnCmWK4P6ZHI1BQQMDHsh4TXFfj6UamhSuYRuxw94OFsPniohQgQX97J/JowbQydvMQ/8cYBxSIqD0NAI4cKKo/3ChAplcz5DLONdYk6Iet8yvNN7j55KsWLFfOuWvA8JkAAJkAAJ+AsCFH3+4jVwESTgNwTy588vMWPEkEUb9kjPptV8bRGZUyfRe8Z2Hzsr567d1rXjUEPOsPNK9MEyJE9kFnw4X7bVRQzPE85tGfaPwVAXzjCEa/6zfrdxqr8zpEgsEJXwiCHUM0VCUzkEiKSWSiShYPqcge3s1gaEiDx7xYNELkokedXeffioi5rvOnpGCmRNZ06ugnYjhBPJZp6+fC3NBkzSHtGDC0ea90ZiX+IF9Yy/5c1iHos1nVSCFXsmkaUT3tc+zavrj/V6k5RuJu/Vfsr984ebL6GvtdnqZznG6D99+WbpOHKWdKr/uy4JYbT7xveiDbslvrOzZM+e3Tdux3uQAAmQAAmQgL8hQNHnb14FF0ICvk8AdeKaNG0qs6ZNkf/V/V3ChfU9r0sttX8PGTCxVw8JXFDDzbAsKjEJPEzI6InyAUgq4nLyggpT3Gt0EdTja1ChqPncOMj2Y98bMoSitEG8mNFk3e7Del+g0QffCH1EGYjB05dJuTaDdf04JHFBuOT2Q6d0bbvyheyHHW6c3NdyOh87Rnhm8gRxlUi7ISWa99UZMHGzhet2ae9ejnTJBeuENy612ruHDJ31eo6TP37LJ5eU2Bs6a7k4RY4gE7o3Na8RXrZ89bvr80urJ6kyCtHM1wLrAYT/vDW7pEOnP1XZkOCB9TH5XCRAAiRAAiRgkwD/y2cTCxtJIOgQ6NChg7xRXqOJi303lX6NUvnN3qg6SvRZGkIUR3Ssr5OXoGg7MlSiWPigNrV0FkjUrPvzrzna64e6fzBk9YRBwEzq2VyPhTBCrb9ISsxN6N5MX4eYNKxz/UrSrVEVuf/kmS7f0HLwFC34Cqq9cUtGdva1fXvGemx9IwPo8tFd9Z7Ho+euSo+/F+gPQhUrFc2tk6QgpBXPNWdQey2QV6rMqCjv0HfyvypzZiyZ3re1xFXi15ZZ8rB13afajPflU/Nbzzt24Rr5qhrbtGljfYnnJEACJEACJBDoCQRToUyqahGNBEggKBMYPHiwDBo4QA4tHOGre/s8Yo59akjyEtMpig5DNAQKQhmREAR71Iw9fNZzoUg7ipHHjBbZVYiodT+cP3v5Rk5eui7vP3ySFInimkM9bfX1yzaElKLIeeSI4SV1EmflwYvoZjmoy3dGhZ1eu/tQeyvd28fnZnAgbcAe0bzKszl06DD53//+F0ifko9FAiRAAiRAAvYJUPTZZ8MrJBBkCHz+/Fly5MguwT+9la1T+/kLD1eQgc8H9VEC2PtYpEkfieAUS1z271ce0RA+ej9OTgIkQAIkQAL+kcDPOCf/uDquiQRIwFcIhFKZFpctWy5X7z5SyUAm66LevnJj3oQEfJCAqVbgBLn/7JUsWbqUgs8HWXNqEiABEiAB/02Aos9/vx+ujgR8jUDy5Mll1ar/ZM3uIzp7Jf5gppFAQCWAkh2N+02QbYdOy5o1ayVRokQB9VG4bhIgARIgARLwMgGGd3oZIScggcBFYOPGjVK5ciXJr8oAzOrfxua+scD1xHyawEbgyYtX0qD3eDly/pr8999qQT1KGgmQAAmQAAkEZQIUfUH57fPZScAOgUOHDmnhF+zrZ5nYvYmbouB2hrGZBPycwIa9R6X9iFkSKmwEWblqlWTJksXP18QFkAAJkAAJkIBfE6Do8+s3wPuTgD8l8OTJE2nVqqUsXbpMSufPpkobVJZsaZP509VyWUGdwMHTl2TozOWy9cBJqVWrlkyYMEGcnJyCOhY+PwmQAAmQAAloAhR9/EUgARJwl8DmzZulR/fucvTYMcmUKqmUL5hN8qrQz1SqEHi0KBFVPbyQ7o7nRRLwbgLYr4cyG+dVKQaXExdk9a4jcubyDcmVM6cMHTZMihQp4t235HwkQAIkQAIkEKAJUPQF6NfHxZOA7xHYt2+fzJ07V9atXSv37t/3vRvzTiTgDoH4zs5Srnx5adCggeTKlcudnrxEAiRAAiRAAkGXAEVf0H33fHIS+GUC95Xou3Tpkrx48UI+ffr0y/P4p4FDhw6VN2/eCArVBzbbsWOHzJgxQ0aNGiVx48YN8I8XOnRoHbqZMmVKiRMnToB/Hj4ACZAACZAACfg0AYo+nybM+UmABPw9gcWLF0vNmjVl165dUqBAAX+/Xs8u8IsKh8yRI4dEjRpVtm/fLsGCBfPsFOxPAiRAAiRAAiQQgAlQ9AXgl8elkwAJeJ0AvJVp0qSR8ipEcNq0aV6f0J/OcPToUR3+OGXKFGnSpIk/XSWXRQIkQAIkQAIk4BMEKPp8girnJAESCDAEmjdvrmq5/Sfnz58P9Nke//zzT5k5c6acO3cuUIR5BphfMi6UBEiABEiABPyYAEWfH78A3p4ESMDvCOzdu1cKFiwo//zzj9SoUcPvFuJLd3737p2kT59esmbNKsuWLfOlu/I2JEACJEACJEACfk2Aos+v3wDvTwIk4CcEkIAmc+bMkjhxYlm/fr2frMEvbrplyxYpWbKkrFy5UipWrOgXS+A9SYAESIAESIAEfJkARZ8vA+ftSIAE/AeBgQMHyjBV0+3s2bNa+PmPVfnOKurXry9bt27VIa2RI0f2nZvyLiRAAiRAAiRAAn5GgKLPz9DzxiRAAn5FAOUmMmbMKIMGDRLscwtq9vTpU528pnLlyoLELh7Zt2/fBHsfPbLff/9dypUr51E38/WDBw/qUhIVKlTQiXTMF3hAAiRAAiRAAiTgrQRCeutsnIwESIAEAgABCBhk7OzQoUMAWK33LzF69Ogybtw4qV27tv54VKbi+/fvWpx5tJKECRN6SvRdvnxZzxs/fnyKPo/g8joJkAAJkAAJeIEARZ8X4HEoCZBAwCMwe/Zs2b17t8DLFDJk0P2fQNQlXLBggTRt2lROnjwpYcKEcehlYk+gPW6pUqVyaA52IgESIAESIAES8F0CQfcvHt/lzLuRAAn4AwKPHz/W4Zxt27aV7Nmz+4MV+e0SJk+eLOnSpdNhrtjj6IgVLlzYruizNf7evXuCpDlImOOoffjwQa5evSrwMKZMmVJChw5tcyjCTuEtjBs3rnBvok1EbCQBEiABEiABTSA4OZAACZBAUCHQsWNHiRAhghY5QeWZ3XtOhGMOGTJEhg8fLmfOnHGvq6euQYz16tVLl4dwdnaWJEmSCL6rV68ut2/fdneuESNGSIwYMfTYDBky6PfVpk0bLRyNge/fv5cWLVpIpEiRJHXq1Lq+IkT8/v37jS78JgESIAESIAESsCBAT58FDB6SAAkEXgIIS1y4cKEuxB4xYsTA+6CefLLWrVvrOoVNmjQRFxcXCR7c6/8WCBE5ePBgAeeGDRtKnDhxZOPGjbJkyRK5f/++Dq+1tcyjR49Kt27dtFcQdRMxHu9s4sSJEitWLOnTp4/2/hUvXlyvFcl4ypQpI9evX9d1B4sWLSpr1qwRXKeRAAmQAAmQAAn8JEDR95MFj0iABAIpAcMzVKVKFUGmSNpPAhB506dP1wXbx48fL+3bt/950cYRvIO2DGUghg4dqi9B4MGOHz8uyZMn18fwsiZKlEjvpdQNNn6MHDlSizqEmiLJDAwevXjx4um6ghB98+bN04IPBeYPHDggoUKF0v3mzJmjBSbKcFD0aST8QQIkQAIkQAJmAl7/J13zVDwgARIgAf9JYMCAAfLkyRP5+++//ecC/XhV6dOn1x42hGTevHnT3dV8/fpVbH0Q0mkYMoMeOXLELPiwRw/JYkKECKHDNLFXz5aFDRtWN48aNUp27dql7xMtWjR5/vy5OXRz8eLFug/EoCH40FC3bl2JEiWKbN++XV69eqX78AcJkAAJkAAJkICJAOv08TeBBEggUBM4deqUZMuWTZcoaNWqVaB+Vq883MePHyVz5sx6/9369etdTQWRZ2Ts/Pz5s/nYVSeLEyRumTp1qg65RE3Ehw8fag+e0QUCMViwYDp7KMRav379pG/fvnpfIUI0kXAHhuQsxYoVE9T/Q7gnMowmS5ZMrl27pvf9YX+mpd26dUvf5+LFizoBjJl8BdkAAEAASURBVOU1HpMACZAACZBAUCbA8M6g/Pb57CRghwDC6/AHtCOG4ub+NVU/xEWzZs10pk54hmj2CUBQIcyzYMGCeo9frVq17Hf24AoE2sqVKyV8+PB6PmQIRThm586dBdk87Rk8jsjGiX18GzZs0Hv/MA8+c+fOla1bt8rr16/18Fy5cknUqFFtTmWv3WZnNpIACZAACZBAECBA0RcEXjIfkQQ8SwB/ZGM/liNWrVo1fyv6UJLg2LFj+uMdCUoc4RGQ++TPn1/voUPR+t9++01QxN2zdv78eVm1apWECxdO4HFD4XUYvIUeeVoh6jAO/fCBV3HTpk066+eOHTvk3LlzOmQUnsB69erJH3/84Wp5e/bs0eGjSPpCIwESIAESIAES+EmAe/p+suARCZDADwLIsnj48GHzB9kYYQj/s2zHce7cuX+M8l9fd+/elR49eui6fPAg0RwjgEQoqIuHxCu/YmfPntUhlpkyZTILPsyDvXgvX750d8oGDRpIkSJF5MWLF7of9uyVK1dOUqRIoc+xNzBPnjz6GMLS0lCuAV5KlHegkQAJkAAJkAAJuCZAT59rHjwjARJQBIyMiwYM7MmCIYW+I0XN4e3BH+rGPjBjHke+EZLpHQW3UYA9ZsyYOs2/I/dlHxMB7KNDiYSKFStKnTp1pGTJkp5Cg/2TeO/IrAmPIUI79+7dq/fvYR8fkrhgfpSIsLZKlSrJhAkT9D2RRRQhp9hfiCQwCRIkEAjJpEmT6lDPRYsWaXGKPX83btwQJH+BYX8gjQRIgARIgARIwIqA+g8wjQRIgATcJbB27VqkW/yuwv9s9lMFsr+rtPrfVY207zhG3zt37nyPHTv2dycnJzdjVG2178qL810l5DBfe/fu3ffmzZt/V/vA9HgVjvldCYjvqnacuY+jB8oLpOdQtfkcHcJ+VgSqVq36PXHixN/fvn37/cuXL9+VYPuOd4Jjj0wJt+/KW6jfAX4X1D8WfFflGL4rsWduV/+Q8F3t3dN91B5SPaXy8H1v1KjRd5XF0zwW41OmTPldeZXNt1Ui8Lvy3rrqo7J8fh8zZoy5Dw9IgARIgARIgAR+EmD2TvUXBY0ESMB9AuvWrdNhdtjzhX1T1gbP4NWrV3XiDiXeBOGUu3fv1kW24bkzkm8Y47BfbPPmzToLY5IkSbT3B3OjOLh1wW2E+Hmm4DbulTZtWh0miJputF8j8ODBA0mTJo0oESZ//fWXpyfB+BMnTuii6vDQoVwD7OnTpzo7J5L/wPNny1BeY9u2bdrT5+zsrMOKLcszYAz2CF64cEHvG0RZByR2wX5AGgmQAAmQAAmQgFsCDO90y4QtJEACv0gAYg/JU5Cp0TPmnQW3e/bsKSjGPnr0aM8sgX2tCMSJE0eHTCrvq9SsWdOhsF7LKTC+VKlSlk36GMlhPEoQEyNGDJ28xc1giwaISISO4kMjARIgARIgARJwnwATubjPh1dJgAQ8QaBbt26eFnyY3rsKbh86dEjvF8P+LggHmtcING7cWCdHwf47Fdbptck4mgRIgARIgARIwM8IUPT5GXremAQCH4GyZcs69FAqwtxVP6T2hyHbptpHZv6gEPerV690+CfCBd0ziBLU5CtUqJAgCyTNewhMmzZNh1CqPXneMyFnIQESIAESIAES8HUCDO/0deS8IQkEXgJI9e+IWRfoNvb8eaXgNsI5IR5PnTrlyBLYx0EC2K/Zt29f6d+/v6jkLubyCQ4OZzcSIAESIAESIAF/QICizx+8BC6BBAIrASTqQIFtePaMpB1I7IKkL5YGYeGVgtvXr1/XogT7+Yyabpbz89hrBP78808dggtP6vbt283v0muzcjQJkAAJkAAJkIBvEWB4p2+R5n1IIAgSQKjmx48fBQW7DZsxY4agyLalebXgdsuWLXVIaNeuXS2n5bE3EUDdPbw3ZG6dOXOmN83KaUiABEiABEiABHyLAD19vkWa9yGBIEgA4ZoorI2i23Xr1tVF15cuXeqGBDx0c+fOlV8puI0xKP8AQWKd1t/NjdjwywRQdB3F1jt37izYuxk3btxfnosDSYAESIAESIAEfJcA6/T5Lm/ejQQCJIGNGzdK6dKldZKUnTt3unkGVTxb19yzzvCImmyqELsgqyYMGTWrVasmqgi3Fng3b96UhAkT6mvYi1e7dm05c+aMPscP1F/r3bu3FhvmRouD58+fiyoGr0XllClTLK7w0CcIGDUYUZJj2bJlPnELzkkCJEACJEACJOADBCj6fAAqpyQBEnBNAIlbbt++LfAWIVTQnnm24DZKCaBwPIp0R4kSxd60bPdGAlu2bJGSJUvKypUrpWLFit44M6ciARIgARIgARLwKQIUfT5FlvOSAAn4KIHdu3dL4cKFdYIReA9pvkegfv36snXrVjl//rxEjhzZ927MO5EACZAACZAACfwSAYq+X8LGQSRAAn5JAMlhMmfOLKjjt3btWr9cSpC8N8J206RJI5UrVxZbYbUPHz6U2LFjB0k2fGgSIAESIAES8I8EmL3TP74VrokESMBdAkOHDtXhopMmTXK3Hy/6DIHo0aPLuHHjBIXbkUDHMIjBOnXqMOzTAMJvEiABEiABEvAnBOjp8ycvgssgARJwjAD272XKlEmGDRsmHTt2dGwQe/kIAWTxRM1FZGhdvny5tGrVSl69eiUhQoSQ169fS9iwYX3kvpyUBEiABEiABEjAcwQo+jzHi71JgAT8kACKvGMf35s3b3RGUIgLmt8RuHXrlg7zdHZ21uU4LFeya9cuKViwoGUTj0mABEiABEiABPyIAMM7/Qg8b0sCJOB5AigMvm/fPpk+fbr2Jnl+Bo7wLgLfvn3TGTw/f/4s169fdzUt6iXu3bvXVRtPSIAESIAESIAE/I4ARZ/fseedSYAEPEHg0aNH0qVLF2nXrp2gThzN7wicPn1acuTIocNrIfqs6zPifMeOHX63QN6ZBEiABEiABEjAFQGGd7rCwRMSIAH/SqBWrVray3fu3DmJECGCf11moF8XErh06tRJPyfqKtqz8OHD6319wYPz3xbtMWI7CZAACZAACfgWAf7X2LdI8z4kQAK/TGDjxo2yaNEiQbZOCr5fxugtA1ETMUuWLB7O9e7dOzl16pSH/diBBEiABEiABEjA5wlQ9Pk8Y96BBEjACwQgHpAVEmID2SJpfksgXrx44uLiIs2aNXN3ISFDhnRVzsHdzrxIAiRAAiRAAiTgowQo+nwULycnARLwKoF+/frJs2fP5O+///bqVBzvTQSQqAVe13nz5kno0KFtJtVBopfdu3d70x05DQmQAAmQAAmQgFcIUPR5hR7HkgAJeBuBKlWqyOrVq13Nh/pvY8aMkeHDh0ucOHFcXeOJ3xOoW7euLp0B7x88e5YG0cdkLpZEeEwCJEACJEACfkeAiVz8jj3vTAIk8IMAQjgjRYokEAoVK1aUiRMnapGXO3du7Unas2ePBAsWjLz8KYHnz59LjRo1ZOvWrfodWi7zypUrkixZMssmHpMACZAACZAACfgyAXr6fBk4b0cCJOCWwKFDh8xiYe3atZIiRQqpXr26HD9+XKZOnUrB5xaZv2pxcnISJNvp3bu3Xpch0JG5k/X6/NWr4mJIgARIgASCKAGKviD64vnYJOCfCKDgOvaJwVDjDZ6/5cuXC8IGv3//7p+WyrXYIQChh/2X69at0xlWjXBP7uuzA4zNJEACJEACJOCLBCj6fBE2b0UCJGCbwK5du9wU+IbYu3fvnmTOnFl69OghHz58sD2Yrf6KQJkyZQR7MVOmTKm9t9u3b/dX6+NiSIAESIAESCAoEuCevqD41vnMJOCPCEDcYT/f27dv7a4qRIgQ4uzsLLNnz5aiRYva7ccLtgl8/PhRNm3aJBs2bJAjhw/JtWvX5MXLV+aQWtuj2OpfCIQNG0aiR4sm6dKlk/wFCkqFChUkU6ZM/mV5XAcJkAAJkEAAIEDRFwBeEpdIAoGZwNmzZyV9+vTuPiJCBaOpP3qR3TNXrlzu9uXFnwSQYGXUqFEydcoUearKXmRLk1RypE4kKRLEligRw0lIJaZ92rYePicxokSUzCkT+vStAu38Hz5+kscvXsuZa3dl98krcv/xM8maJbN07dZd16809lAGWgB8MBIgARIgAS8ToOjzMkJOQAIk4BUC06ZN08XXv379anMaePly5Mghq1atktixY9vsw0bXBOA9RQKcrl27SCiV9LR5xUJSr3ReiRsjquuOvnT2Rb1b3xCYvvQ4fn6bA2euyrT/dsmKnUe1+Jsxc5YOg/bzhXEBJEACJEAC/pYA9/T521fDhZFA0CCAJC7ueSpatGihi3xT8Dn2+/D06VMpVeo3adOmtTQqnUdOzu8vXeuW8TPBh1VT8Dn27hztlTt9MpnVs5G4TOspob+8kezZs8vo0aMdHc5+JEACJEACQZAAPX1B8KXzkUnAPxFImDCh3L5929WS4N1Duv/p06dL/fr1XV3jiX0CV69elRLFi8uXD29kbq9Gki11YvudeSVQEIBXd9ySLdJ/5mqpV6+ezJgxQ/D/PzQSIAESIAESsCRA0WdJg8ckQAK+SuDRo0duQjaxfy9mzJiyZs0ayZYtm6+uJyDfDMlZ8uVVIZxRw8mywS0lRtRIAflxuHZPEthy6KzUHTBDJ3n5Z9G/+h9NPDkFu5MACZAACQRiAgzvDMQvl49GAv6dgIuLi6slwruXJ08eOXXqFAWfKzLunzxTSVpKFC8mcaKElTUj21HwuY8rUF4tkTOdLB3UUu997dixY6B8Rj4UCZAACZDArxMI+etDOZIESIAEvEbAKMr++fNnPVH79u1l5MiRDE/zBFaE99WpXUs+vn0tyyd1kUjhw7o7+sOnzzJp+XY5fP66nLpyWz5++iKJ48WQnGmTyp+1Skm0yBHM44+oPnPX75MyeTNK6TwZZd+py/LvloNSPn9mKZnL/Yyr5knUQZcJSyRalAjSrW5Zy2Z/cfzuwyf59v2bu2sJGzqUKMzy8cfvqb3OwSSYRAgXRl9+895+XcmI4dx/R58+f5FPX77YvE2YUKEkVEjb4ZsFMqeUKV3qScNBf0vu3LmlZs2aNudgIwmQAAmQQNAjQNEX9N45n5gE/A2BHTt2CARfmDBhdA0+/pHq+Vczc+ZM2bR5i2we28lDD9+1e4+lXv/pcvrqHX2jCKr+23f1f4fPXdeffzYfkH8HtBAkCoFdvftI5m1wkXgxnbTou3jrgT5PFDeGp0Qf5o3pFMlfir60tXrK81f2a0SCA4TUq3cftHjFuT1LFCe6nFowUG4/fCbpa/ey2S1cmFDyYN04m9eMxg5jF8nCTfuNU1ffA5pWkvbVS7hqszypXDib7D99RVq1bKlrWjIBkiUdHpMACZBA0CVA0Rd03z2f3BMEUE7g4MGDcvjwYbl06ZK8ePFCixVPTMGuVgTA9NixYxIuXDjJnz+/rFy5Un+suvnpaSjlVXFycpKUKVNKzpw59QchqP7FXr58KV06d5ZWlYtIjrRJ3F3W2/cfpXTH0fLg6UvJkCy+TPizjmRKnkDgVdp94pIuAbD54BmpP3CG7FdZIaOp2nrWll0lhunb+Hcpki2N9aUAe54mUVx59fa9zfWjLh7MKVIECRUqpKRP6myz34NnL+XJize6HzpAXMOSxY8lcaJF0cfGj7ChPf7P7jUltkOr++VI4/adOsdyMqay+92vSUXZePCsdOvWTf9jit2OvEACJEACJBBkCHj8X58gg4IPSgJuCUDgTZgwQRYuXCjYN4UEI2nSpNFCIHTo0G4HsMVhAigcHitWLB2G5l9Zvn37VmcW/ffffwWlEGLEiCF169aV1q1bS7JkJm+Yww/sAx3Hjh0r8u2LdKlTxsPZJ6qQTgi+lAnjyLbxnSWMClmE4Rv7wXKlSypZ6/fTfVbsOiZNKhR0M2dGJRLxsWWoxXfx5gMVlvhVksaLqYu/2+rnURvCT68r0YRwyuRKNEH8+KRtGPM/m9OjBl7DQTOlfpl8UipPBt2napHsbvpCNBdtM0I+fPwsM3o01Nev3TWJvqEtqshvuU1j3Qx0pwGiMV2SeLJ+9K/tzUOIaa8GZaXFiHnSs2dPSZ48uTt34yUSIAESIIGgQMBn/2saFAjyGQMlgcePH+t/JZ87d64kTpxYunTpIpUqVdIen0D5wH7wULdu3ZL48eMHmCyD58+f155IlJEYP368NG7cWIYMGSLRokXzA3oiX9Ser8mTJknjcvkdEliTV2zX6+zZoJxZ8FkuPHKEcDKuYy1ZvfeEIATRlmE/X9eJS6Vz7VLSplpxc5fxS7fKoNlrBIINhj1njcoXkOGtqtmswfjt2zdpNWq+LN12WHsbN6rQVIi7sYs3y/D56wX77GAhlFe1kXq+IS2r/pL4wzyblPdypRJwxXOmVQXq8+l5Pfpx78kL6ahCLFMrL+CINn+42x3PjXDZqV3rS4oEsXXfa/ce6e8USmB71uCRffjslRTKktqzQ131r1Y0hwyas14mT54sf/31l6trPCEBEiABEgh6BCj6gt475xN7QABhhk2bNpUIESLInDlzpFatWgFGmHjwaP7qMurzBSSDhxefrl27yvz587UHBb8rs2bNkrJlfT9Byfbt2+WhKnlRT3miPLJnL9/Isx/71sqohCz2rGy+TIKPPXv38ZO8ePNOPijvlmEQaUPmrhUnlQCmVsnc8lJdX7//lExduVPiq72A7f4oYXTV31+/flMeqLmyRAm+zCkSyrKhbbSgO37plvSdvkqwL66K8qjBW7Vk6yGZvnq3xPDEfkAIT5QvgNDbcOC0WUDmzeCYtwuJcVoqDxmec36/poIkLvZs78lL8rcSvJi7Rolc5m7w9EH4nrt+V+au2yePnr/SHtZKhbJKYrUf0j27ft/kJYyonn/ism1y7sY9JerD61DPigWz2BTRtuZDGHLt33LK7IULZNSoUQ6PszUX20iABEiABAI+AYq+gP8O+QTeSKBXr14yePBgLfpGjx4tESO63dfkjbfjVAGQAApfN2jQQHt+27VrJ+XLl5cBAwYIfnd80zZu3CjpkiXQIsmj+165Y/I8xYke5Zc8Zvbmh5iBdw4CZ9mQ1oI9f7Bdxy9Khc7jZNn2I65EH0JAmw2dI8uVIEPh+BXD2khUJWhgf6sC47CeDcpL9eI59XGjcgUkVfXusnbvSXeTwCDEctuRc4KQzPUup+SN8pbBsKeurMo8WjZvJpWd1O3+ON3J6sf8jS6y89gFlbgmgxTMnMrq6s/TzyqMtdXI+SoM9bsMbVX15wV1hPBMXK/dd5qr9mHz1sno9jWUGMvjqt3yxAgNnbV2j2WzPkaG1aVDWpmZuelg1QCBP3TuOjl58qRkzpzZ6ipPSYAESIAEghIBir6g9Lb5rHYJ4A+3Fi1aaK8NPDcNG5r25tgdwAtBnkCUKFEE4b9Ijd+2bVtBSPC4ce5nZfROaEePHJEcqRM5NOU3bJBTZggshwY50GmPSgCDEMpCWVKZBR+G4bx3owry7ZvpvmiDh6/x4NmyavcxnUhm1fC2gpBSw8L8SHCCUFHnmFElT/rkunzErVXue6lQVqJSt/EqGcsH7c3KniaxFnkQe9i/6BnDGkf/s0lChggug5tXcXcoQlNvPngqNUvm0h5LozP+twR7EmEda5SUP4rlFCR6WbhxvyzbcUTaj/lH909nJynM1R/7ASFYh6mwVuyPhOd02qpdcujcNek07l+Z2bORcTt3v5F4JpzK0IqESRR97qLiRRIgARII9AQo+gL9K+YDOkIAIXuzZ8+WFStWaM+NI2PYhwRAoKVKjY8EPzVq1JCoUaNK//79fQXMtWtXpUTZnyGF7t0UCVFg9548d6+bqtn3We6q/WwILYzlFNndvrh4/f4T3ceWgEHNP0uDQMIH9vjFa52oxfJ6u2olZLPKOIn9cWU7jdX1BiEe4aWrUiSbZVdXx89fv9OCD40p1Z66qoWzS5l8GSVh7Oiu+jlyAkGKZ0LCFoguewZhBw8nrFNN18+JttUj2ktI5f3MmsokytOqpCxFf2Q8hfCDR9IWM4z9o1gOLaBx3aiZ2C5BCflN1UXM1WSQrFRJdiZ2rutu2CnmgSHEM0m8WHL9+nVTA3+SAAmQAAkEWQL+J/d4kH0FfHC/JoDMnCgIDtGHUD0aCXiWQNWqVWXKlCk6zBP/cOAb9vLlK73Xy5F7xYgaSZUTCK/F0fV7JqFma9zsdXslS72+2htl67p1G/bvwaJE/Omxs+5jeV4sexrJnymFzhDaZ/pKy0sCYXR8bn8Z1ba6yniZXl9bu++ktBw5T6p0n6i8hrYLqKMg+bw+TaViwaxy6+FT6TppqWSo3VsKtBgiw+avM9ckdHUzOydj/jUJubql89rpYWpGCClqFqKeoZG8xRgQLFgwyYlMqD8En9GO76Lq+WGXbz/U37Z+OKt9kHgmQ/AZfVKppDLYI/lVcTA8icY1976jqneD0h40EiABEiCBoE2Aoi9ov/8g//TIIImwzo4dO0rt2rWDPA9bAPDH9pkzZwR7yK5cuaLC9L7a6qbb4FHA/iF7f6AbAz9+/Cj79+/XZTCMNke/UTrDxcVFkMjk9u3bdodhnSi5sWnTJjlx4oS8f2+7FhsmePPmjV7P2bNn7c7n0YUmTZro3yVk9bx//75H3b18/eu3rzoM0dGJsqQ0eZ0MD5WtcfPWu+jmQu7sZbMcl0SFHsIOnrlm2SzYu9d8+FxpOnS2uT2u2k+4eFAr+ft/tSWMytQ5RwlMhIcatuPoBTmvkpY0/b2QLFH9rq8YKYsHttSZRPeohCkXVDkIW4ZEK7+rBCdz+zSRq8tGyCwV+lhOJaNB+QjsZ8vffIhkrNNbJ3exNd5oO3XlthaICWNH0+GpRrut7wU/CqfXsyEO8Uy9pq7Qewytx55U94C5V2tvwMz/dEIbhJpa2hPlHUVWUVi8GB7X6jPGBg8eTGd6Nc75TQIkQAIkEDQJUPQFzffOp/5BoE2bNpIgQQIZOnQomdgggKx/kSNHlgwZMkjp0qUlRYoUkipVKi26LLvDW5opUyZJmjSp3juEMgYoDG1tKGpfvXp1HQ6ZN29eXfcO4y5cuGDd1c3558+ftThHbb98+fJJsWLFBBlAy5Ur50ZkQVCmT59er7VUqVKSJUsWXR4C2VgtDeITNffwjFgPxsSLF0977Cz7OXqM1Ph49g4dOjg6xNf6DWlZRSdcmb/BRaas3OHqvhDpKMVwVmWbjKm8gn/8SKTiqpONkzzK0wU7cPaqIMzSsCVbDwvKOzx6/tpoUnvLQuv7J3OOJZ1+hH62G71Q3quMoLCWKqNn2U5jdNZMnCM5DOrjoT/MKAehT+z8QMZPZP5c2L+5XF0+QqZ1ayClVJ08iKUrd+x71zAdRCfsd+UxhLfOnkGMQdhBTJXP7zY5yrsPHwX7EvvP+E8Xvjfmwd7HVSo0E2aZSfTk5dty7OJNnfgF17YfPa9DR+HltDRkO4WXL10SZ4c9q5bjeUwCJEACJBC0CXBPX9B+/0H66eEtWrNmjfYEhQkTJkizsPXwKErfuXNnXboCXiwkLlmyZIlcvXpVIKQuXrwocePGlXPnzumadRAO9erV06Jn2bJlMnz4cEHRdWS2NAzZLjEHxBXKHGAs3kHx4sW1kHSvjAPmQTFylNKoWLGinhslE9atW6eF5O7du/VtHj58qMN0UUw9Y8aMUrJkScG1Q4cOSaNGjbTIh2CEIQEL6u7Fjh1b6tevLzdu3JD//lOelr59JVGiRLpNd3TwR/jw4QVZX1HTEftEs2bN6uBIn++WJnE8XcR98Jw1WuAt2nzQHIK4/8wV5WW7rz1wE/+s4ya00N7qMCcybS5WpRVKdfhL6pTKo4WjIW6aVyxscygSnCzZdkgJsUe63MPAZpW1gJr23y6p1HW8tKhURNUSDKn3+J25dlcldnHSyV9sTXbi8i0ZMX+DrUu6DbX+cqdLpktA2O2kLiBjJ6yACj91z45euCGv332QLCkTukpEY4wpoLyk2A8Irx6YVC5s2o+4cPMBXX8PiWbgiYRBQBZsafoHp/P/DlEevKjSUGUsPa7EcOMhs2T/mYKSKmFcOXPtjhjZPPs3rWjcit8kQAIkQAIk4DABij6HUbFjYCMALxY8RhAFXrVr165pD1HYsGHNU7169Up7oOAdQ0IFW4YwwDt37miPFYSHPXv+/Lk8efJEkidP7q4Xwt74X2lfsGCBHgZhVaKEqdbaiBEjtGBDoXIUfYYQQ/kCeMymTZumS11gEEJmIXjgQW3fvr1Ejx5d1q9fr+vbodbdvn37tHcNfSEoZ86cqQUdBJM9mzFjhr6EeQoWLKiP+/Xrp72Le/bs0YItceLEsmrVKoHgK1KkiA4BNeZr1qyZFniosQfRB4E3a9YszROewSRJkuiuCGWFZxPPUK1aNYGQ84z9/vvv2rOIZzEYema8T/btUqe0rhPXe9oKgVjCx7Bsag/apC71dEFyoy1YMNPvLYQTLJj6P5hxjmOEa8Irt0Blp0RYIwxZQgep7JdlVAZNWHArzxkKsY/pUFPK/zlOJqhadDVL5JZeDSsI6gAiK2azYXP0OPyAgJrRvaG+h7nR4uCpqkG4zsW1V8zisvmwXH779QchviB8YTnSmH4PzAOtDvaeuqxb7PULrzyaSwe3kjajFojL6StyVHnxDMO+w7Eda9n8/+EQynMIq6/qLmKv5MiFG2Tyip8eWezxm9CpjpTImc6Yjt8kQAIkQAIk4DCB/7N3FeBNJV30Lu4UL7JYcXenuLsu7rLIwqKLLrCLu7u7u7u7u7sWKVIouv8/54YJaZq0SVugSe/9vuS9zJuZN3Ne2M3pvffcX5QK2f9s7i0dBQEnQQAECl4qSO6j+Lq9Bo8U/umA1IB4wPsFcgAPD0L86tevTzt27ODctqhRo9K4ceN8eI2QG4c+hw8fNt4aoZEIk0QJAG179uyhli1bslcNbbFjx6batWvzPcKGtV40Wo8P6PH9+/dMyqBK+eDBAx/T9O/fn3r37s17BREDoUO/x48f+yC3IFbIu0Mf5LnBywccxo4dyx42PSkIG0hcokSJrOboIbQTOH78+JEJXYwYhpwmeBcxDuQZzwAYgojCUweyCc+gNgitQGkTuM+dO5fgjQSpA/Hfv3+/7sZHyNsjN/G4KouQPbt15Ugfg0w+gMQibNRDFU9H6Oj3sMiRI9HwNtX9rPlm7b4gObeVSuXVe49ZJTOdChk0Fw6xNtZa+ysvb/byRY0UgaAWGjF8OGtd/WwHiYPXDZ6++LFcKFOKX60SPj8nCgYXbyhP5o0H6jugSlNAiAViOrYavImXbj1UYbNvOcQV+ZOhVSkJe62cItYZ8xaliRMn2jtU+gsCgoAgIAg4EQL2/x/EiTYvWwm5CECUBN63ihUrBggEkBB46BASCOKBeUCUQNrgbdq+fTt7mhAGCZEQkB5T8tSxY0cmfDVr1mTlUHi7ICqDMEctOAKPFogTvIioGwixGRAsEKeyZcsGaN22DoJYCjxjlvLyIIoCA2mGUAqsaNGiPggf2lKlSoWDMV9P99VeQ76o3nQ/4Pn27Vvd7OMIgouQThBtkOw3b97Qu3fvaNSoUUz4EC4KwgcDpjAQehA3GARa9I9eeOJgqKsHg+fR3JBnCIPXLyAG8v/582fats1QcDwgc3zPMSAP8KCVUcW7C2T2rRQZkHtDwRO5ahndEgWY8OG+saJH4by88vmzcAF3eBEd1YBxSVVqASqf9hA+7BfkGSqgpVROYgpViiIghM9RcZN1CwKCgCAgCAQ9AmGCfkqZURAI/gggny9nzpwUJUqUQC0WIYAgYSCQyA0DUYI3Cp4jCIPAQOTg9Tt16hQlTJiQEBqJPDaEai5ZssR4f3ivUDpi69atTOpA8qBAiT7VqlXjfiA8BQoUYFKJe+DckoGsnTt3ztIlYxty45BfZ8kgZoKcPnND/hzCJ2EgNsjrg7m6uvLR9A2CLzB4u2DW+iKsFfmCkJVHXx1myYNM3nr06MG4wlM4ZcoUVQctDJNE7APkT1uaNGmoU6dO7A2Fxw7CKiCxMBA+5BLCNNkE3qYGsnbs2DFuCqiaJ+4J4oiwUf3sTO8h54KAICAICAKCgCAgCPxIBMTT9yPRlnsFGwRAvOAdCqx17drV6OHS3iHkg2nCh/mRWwZDSCcsYsSIfMTnAQMGEIRHYAgVhVcQtQJBEOEZg7IoyJU2hApCLAWGXDtrhpBJeAr9e1kbb94OQta6dWtWygQRhUgJyKwu+gzSZm5YOwxeOXhD4cmE+deXO1l4g8cT3lQYiBnINQyeUYSRakP+o74X2uAR1AaVUISBwvCMINaCvlAURWmHTZs2UdWqVZmYow+EaAJq+H7heyYmCAgCgoAgIAgIAoLAz0ZASN/PfgJy/5+CADw/yI8LjMHTpIkN5tEiLvDmmZoWcdHpswj/RG4ZrFevXuwlA1HEOYgN+muvGHLVELaIMfo1cOBAHutXLTiQIJAdv146vJEn8+MNReshRgPhFoR0wjs4ePBgHqE9pdqbZzoNyB4MXi/sSZNd//qazqHPUeoB+XcQaOnXrx+HyiIvE7l5oUOHZsIM1U0Y8Fm2TBXoVpgCB4SMnjx5kgkrcK1Xrx73w3oQAop8ROwJobgIm12/fr0x5DN9+oCLZuD7hfWKCQKCgCAgCAgCgoAg8LMRkPDOn/0E5P4/BQF4jDRJC+gCMF4TOtM5rNX40qQPfZFfBvIBsoHQT+SO4QVFUXjnNGECIbEWwgkBEmsWFCUokLcIkRsInmC+nj17socPgiradFgnyKq5acKDPcDQF1429DVXKjXvaz4XciTh3UNI5t9//228DFEWkDTgiDxNhG/CWwdDiKv2sqJOH5RH0Rc5ifCuYg2FChVi3FeuXMleObRhDFQ3A+sNxvdDeyaNC5aTH4YAxGpuPXxKtx8/p7gxolJKlRfnl7jMi9dv6abqn1YJrqDenyXz8n5PF28+pKiRIxDKVdhj+Pd/+9Fzuu/xgrKocg/I2bNmnz5/UWUaHtDDp55q7dG4vyPnNlrbp7QLAoKAICAI/DgEhPT9OKzlToIAI4CQSIRuIt9Mq0veuXOHScrixYs5Xw25gTAXFxdfsv8PHz7kfD2dM8cdzd4wHt4tvwzkbdeub5Lw5n2hfgnCly5dOvac4Whueg3Xrhlk7E2vI8QUljt3bj4i1w6kD33z5s3LbXgDCUNYK8JjrZFVTSpRd8/ccuXKxaRPC+DovjrcVvcHYdS5g+gL4oeQTnhSkZupDT/OIawDT67en74mR8dA4OiFm9R6+Dy6ds8QOo1VQ0hlwO/VrKqdjly0hYuq7xjXhXKYlW348PET9Zi8gqav3WsEIF7MaNSkQkHqVt+QI2q8YOHkiCpe32TATEX4PPkqSl6gXt+kLg1YUMd0yCFV5qHV0Ll0S6mrasPaZ/RsQsVy+P43qPvIURAQBAQBQUAQ8AsBCe/0Cx25Jgh8BwTg2UMoIUo7aENuGfLKYPAOgRAi/BDeJq16iWsgJFACxfgjR46gyaJhDIRc/HppZUtLE8DLh9BH5LTBi2aJ8GEcxHAgSAMiu2jRIuNUIHbwuGEPWq2zVq1afB1iNZhfG4RZYFpVE+cgZCiXADIMQ6gmDEIyyNkzNdTdg2lCqPua18iDeA5yE5EXCbwRHgr1VYTamnphIVSDovGlSpUKVE6f6Rrl/Mch4OH5mmr2nsSEL70qRfFHjeKEGoSeb95R62HzjEXY9Yrw7LcdvUAz1n0jdPqaPnYZv5QJXxyXqNS+ZgmqUigbzzdozgZauOWw7mbxiNIY5VXZBBC+Unky0J+/laSkCWLTEUVM6/SZQm+9PxjHoW/NXhOZ8OEew//4jaqp4u5Ye5P+MwneSDFBQBAQBAQBQSAgCIinLyCoyRhBIBAIgEygpt+YMWPYswVPGEiSVsuEkEvcuHHZ84d6cxBMgRolyAo8gVCbhBcLJQys2e7du61dsqn9xIkTnA8IZUzUCbRkIHNYH8ItIS4DbxlUUVFWAuqaEFpBvTrMAYNACkIsQUQLFy7MZS5QpxAkC6Syc+fOxtsAC9QDhMcNqqgIuQQRhlcQ6p5QxER9PoRrgrxCbbRJkyY8HuUwQDCRdwiCDTyRywfSB0OYKkJw4XnEfRFWC8KNUFYIvaDOH/IQUWw+JNith6pmZezoFCHct7qPr98qT+iL16o+XByLIczvlecLoZOKL3FNPhRbt2QQ8Lnx4CnBK4ZadT/C1h84Q56KHBVUpSjWj/jTeMt2IxfQnI0HaPH2I1Q4Wxpubz5oFm07dpH7GzuandxR4aHzNh/k1u3KC5g0viEX+KKqoZe3eX/6c/RCqqwIGoqyWzIUWf/46TPVKZmHJnU1iDD1a16ZGvSbRmv2naIxS7ZRj0bleeiIhZvp9dv31KpqERrcuga3Na9UiLzef6Ath88rcqq+q8UNnnNL95I2QUAQEAQEAUHAGgKW/09trbe0CwKCACMA0gABEVMz/6yvmbdD/AX1/CDcAmKjDf0QUqlDO1GiAEqZUPj8889vP15BgCCuYi0UUs8XmKMuGg8RlAMHDlicSpdWQF4dcvK6d+9uJK4geli/JmKYAEQXdetAekEO8YKBeEGExTRXUOdKauyQH4c+UBCFt2/mzJk8Fm8gwLNnz+ZQWHwGgYPwCwrIo/SCLr+AWn9oA4GGYW6QaBS7h/ALXjDU7YM3EkTSGS1d7Z6GeofNKhE8VQgjjBg+LFUokIXDH1sMmk27T13hPlFUbtuwtooQl8pjhGL0kq00ZN5GevfeoJ6KUMUm5QvQwFbVSZM/7w8fqfvE5UywvD98YpKdWRVZH9a2JteeM072HU7g6YOlT+5TUClzSoOa7H//Kab61cKo7wBy+GDnb95nwvX1kvFw6uodpT77P8qtauZpwoeL6ZIloAzqHsi9u3L3MWVVeXqW7Pil29xcs1guH5drFs/JpO/09bvG9q3K4xheEeiudcsY23DSv0VVqloou6qBaNiDj4vyQRAQBAQBQUAQsAGBX1Roy7f/A9owQLoIAs6AAHK1QFZAvH6mIe8NKpzI3YNSpLnyJ9YGpVGUcIASJ9at68v9zHVbuveHDx845BRkCmGffpU7QNgmauChrEHixJZ/LFu6B9qQs4ewVxBi5OOBpGlyaDoGeJ09e5aVPuF9xL0sKbZCIAb9kG+IENGgyuODRxEEVRezN11bUJxHjhyJhrepbjVHzdo9UtboRiBG+MNF4ngxmbxsVl4k/K8AhOOD8koVypqaIByyXXnBQoX6hS4sHEAJYrvQqat3qXDrwZTENRYXUIfgydLtR5n0dG9YjvPbME/J9iPo6MWbhPDKkrnTE7xlq/eepLDqu7Gkf2sqkt3gabO2xsC0X1UELGeTfyi68ixuGNmBi8Vfuv2Qc+rgnZv7d3Oq5J7V1y3KdhxFB85eI/OcPuTxdRq7mBqUyUfjOhmUX/XglkPm0OJtRzg3z5QY6+s4ulXvSs9eetHlxQOVR9XFeAmhnJnr/83P4NyC/hzmmaBCB8Z+WvdGtOPYJbqo1p0wjgsVz5mehWiMg+04KadCSzPmLcriUXYMk66CgCAgCAgCToaAePqc7IHKdhwLgYIFC/q7YIQaFitWzN9+P7sDPI/u7u42LQM5dXgFxED08PLP4FnMk+ebh8pafwi2ZMuWjV/W+jhje1MlQgLPG7yqszfsp/ajFtJHpRq5dUwn5dVy4y1X7DKG9iiv39lr95j0jV26jdt7NqqgwgwNnqsm5QtS6t+60/r9Z5j0Ldp6hAkfPHs7xndl8ohBxbek45y6UYu3WCV9Z9R9/p21lu/h11vjcgWoXP7MFrukSuxKbasXo/HLd1CBlgNZwAU5cbCy+TJxXp3FgVYaUySKy1d2nrjko8dn9UeHk1cMOaeX7jz0cc30Q4pE8Zj0gUDXV8RR29GLt/j07pMXTPigHAp7qdZaoOUgJubcoN7ChF5JvRpXpA61SuomOQoCgoAgIAgIAnYhIKTPLriksyAgCAgCzoFA+99KGPP1Mrgl4k2lVyGLmvChoWCWVEz6NCEJH87wv4xxy7azBypvhhQUM1pkurt6OHsOMWbF7uM4sLKlaZmBWioXrdvEZTwfcgYt5fg9f+3Foio8gR9v7mpd1gwET6tkoo8OQ8U5vIDIRbSn3ALCOn+NG5PuqVILjf6dTvVK52OvKDyAmA8WTv3hwJrVKJqTDp+/QYPmbmAhlkLZUrOYDHL5YPCkhg4dim6q3EfYmev32PvXt1llypfRTeF1VY1dT32nr6YcaZLyM+GO8iYICAKCgCAgCNiBgPX/U9kxiXQVBAQBQUAQcBwEkIeXKE4M44IjfCVzpuGHuBhKhYDCdBZAuxolaOuRC3Tuxn0q12k015pDKGi5fJlVuGd27nv9vgcf+81YQ8MXbOZz/QaREpiHEomxRPoKq7kebxitu1s9mpJJ804QQ0EoKXLuhrSpQQUypVTrfUAD5qxjMZTmKmdx/5Qe5sOsfkZtv8l/NaAG/0ynVXtO8kt3hlcRxC9NUkNeoG43PSLf8aAKG12x+wT9PW2V8VJaNQZqnMkSxGERHVMVT4SRarEZkPCbDz0IHtS1SvgFRFxMEBAEBAFBQBCwFwEhffYiJv0FgRCCAEpCTJ8+nVU2oYDpaIZcSShzQkUUxdmdVZglIM8FSp1aLMd0PPL8LJkmfSBSp+b0o6U7jirVywuKzFwnqGXitXDrYVo7rB29eWcgdqh15xLFp2JnXjKEjUaPEsnSbXhNfhVQtzjIrBHlF2AdapUi9yyp+RwiLv82r8KkD4QVOY0oem6rFVBKoEem96a1+09x/iLGuqu2JQoHkL50fhRqB84zezWleiq0E3mOnq/fUaYUiSiPInPZGvU1jk0Y10DC48eKznl9pmsrlTsjkz7TuoOm1+VcEBAEBAFBQBDwDwEhff4hJNcFgRCKAMpIgPShNIIjkT4Is/zxxx9c6gFiL9pq1KjBtQdRO1AsYAjsOnGZlT5RRgCvTyoHcIfKVWvUfzrtO3OVLt95TMlVmYfnr7yodoncVFXVmDO1g6rwOMoXxIkR1bTZeH780i0lmrLE+NnaSdsaxQhhk5YMAimwjG4+1TuRWxctcgRW6ISiqK0Ggog9QrmzaYVvOasgwm1HzCd4TVP+Gs/qdMjle6rmqFAwCxXNntbYb6Xy/MHg8YMl+kr6QJohpmNaQuPJi1fcxxpufFHeBAFBQBAQBAQBPxAI5cc1uSQICAKCgMMhgDp9kydPpvjx49O///7LtQFxjpIMQ4YMcbj9BKcFtxo6R4V1jqKXXgZhFIRZls6bUdXzM4idoH5frnQGkZ0NyvtnakdVMfIyHUZS53HWSZ2XKlR+4dYDf18ID7VmyEuELVGqoqa26fBZJnxRI0XgnDnTa36dg8D+PnQudRizyBjmiv7wbqJUQ7Gc6YylKtAOMRoIvIAQw+aq2oAYv+3oRf6Mty9f/qPRi7dyHmSp3Bm4PalrbEK4KDDA3Ka2Zu8p/qixNb0m54KAICAICAKCgC0IiKfPFpSkjyAQBAi8f/+eywLAQ4CyC36VNHj48CGHJSZNmtTfO6OW3tOnT8m0L+5x/fp1LlEQI4YhbMzSROiHguTx4sXjguSW+lhqQ9FteAJBplA03pohxPL+/ftclgH3+N52+/ZtmjRpEu9l7969XMgd9yxatCjlyJGD5s+fT3379v3ey3Da+VHLb+qaPVTlr3H0e5UiBGEX5PihVl1ClSOYUQnCwCOGUM/lu45T2LChqXDWNFyyAeIvsO4NylnFB3lszzaPs3rdlgttlHJnkwEzaZQiVVAeLZ0nI12//4Q2HTrHwzvXLW0UnbFlvlS/ulJqRcYu3X6khFxmUI1iOVRI5xOuVRgjaiQa26GOcRqQOfdWg/jzJVWiAWUu4OFDEfYOqog7CGRslyg0fc1eFmyBGifCYGEQc+nTtCLV7TOV2gybx4qpCPmEpxAeUnhQG5bNz33lTRAQBAQBQUAQsBcBIX32Iib9BYEAIDB06FD6559/CAQNhjIBLVu2pJEjRxrJH4jU33//TatXr+YaduiHPLQCBQrQ8OHDCUXdYdOmTaM2bdrwfEePHqVNmzYRCGWaNGlo1KhRTABRzB31/ZCjhfHr1q2j6NGjE2rpoRYgShSgHQTp8WODAmHhwoVp3rx5HM7JN7Lw5u3tTR06dOB+qIOHfCXky40bN47y5s1rHAHCiTqIusg7LqDMAorS21JGwTiRnSco/o7wzqZNmxoJH6bInj07F2LHtZBu6iuhCIbP3D2EKFoyEBFTQ9mAd6rw+rIdx6jF4NnGS26qrMH07o25PAPUPNcPb0/NBs7iPDQIkMBAkAapAu7ViuQwjvseJ5j/lVIH7T9rHXvcdFmFMGovXeuVoT+qF7frtsBglsrJA5GEQAxeMHjl+reo4qP2nunEoZUqJwxhqKgPCLVO1PuDRQwflqqrdfZoWJ4/67fy+RWp7taQQ1zHfC2PgWt5M7jR9B6NfXgU9Rg5CgKCgCAgCAgCtiAgxdltQUn6OB0CP7I4+4kTJ7hYOTxxtWrVoihRojD5uXjxIvXr14+JHgAeNGgQ9ejRg68j/8zV1ZU2b97MhdlRzw+eK9iUKVPo999/53OQx7Jly3JxcXi5UCsPwiVx48al4sWL0549e9jTBoKJkEeQQ53TBkJYqlQpLm6+ZcsWwnrggYT4SdiwYdkrBuIGz1ifPn04tA1E8eDBg1zEHPe9desWLV++nPuDWOKesIoVKzLRrFmzJu/9ypUrNHv2bF4fvJJ6Ddw5CN+6du1Kw4YNIxS9//TpEx06dIiL2mfJkoUqV67MZDsIb+fnVMG1OLufi7bxIjxWu09eZk9f/FguSpjkV2M9Pj0FvF5X7z0miI/EiBpZebSSKrITTl/+7keUarigPJAPn73kshIQoYkVPUqA74u6fPBo3nr4jDIkT+hnHp+lmzx+/oqLrX9WYZ9Q4PQLC4TJnleCM2/efSC3RHFUOGosS1Pa1CbF2W2CSToJAoKAIOD0CIinz+kfsWzwZyMAEoIwSuSX1a1bl5cD0gYv3qpVq4ykDwQPdurUKUqRIgWfw6uGIuZQ0jS3yJEj0+nTp7kvCE7KlCnpzp07VL58eZ4XhHD//v0Ewnj8+HHz4eyxGzFiBLfDg5crVy4mfLNmzaIWLVr46j937lwmfPASwoMHYggDmWvcuDENHjyYSd+lS5eY8GEPS5Z8y99CmCmw2Lp1K1WqVMnX/Gjo1q0bnTtnCMOz2EE1Yt9Lly61eBkhpzDcZ+1an0W+06dPTxs3buRQU4uDpdFmBECe/PPYwUOGenj21MSzeQE2dIwUIRzlTJfMhp62dQkTOjRlSZmYX7aN8NnLValy4mWLQcRFh33a0l/6CAKCgCAgCAgC/iEgpM8/hOS6IBBIBCJEiMAzIEQTSpjwlsWMGZM8PT195BaNGTNGCTx8MRI+eOXOnDmjQvFCE0gZiKOppD68gZocgoClS5eOSR9CG0H4YPnz52dyhnBLc4MHTxs8b506dWLyBoJpifRpAgfCqgkfxsMbiHDSnTt30uvXr41ePNxzwIAB1KxZM84ZhCcTHkO/vHzw0MGT6JeB9FkzvU8QPnhVsTZY79696eTJk9SwYUPatWuXteHSLggIAoKAICAICAKCgFMi4DNhwym3KJsSBH4uAp07d6Y4ceKwVw55cyB8VatWZW+VKYkDaQPhKVSoEAukRIoUiUqUKEFeXl4WN5AsmU8vhiaXyNnThvnxAmE0NQiwRI3qUzYfnjAYQjYtGUI0YQhBTapCVfXLzc2NyR7ugfxAtLdq1Yr79urVi8NUM2bMSDi/efOmxfpw3Fm9gTgiV9CvF8JDrdmbN2/4Eoj1okWLOPQVYagrVqzg9t27d9Pz58+tDZd2QUAQEAQEAUFAEBAEnBIB8fQ55WOVTQUnBDJkyMBKlxAxgegKcvMQ1onXnDlzaPv27UyE4JlCG8ieu7s7gYQhlLJLly4ENU9zs+bxMiWSeow56YsVy3eOkPbemffVc2hClTt3bnJxcdHNPo66feLEiVSvXj0mtjt27OCwUeQKwtuJEEuQWUuGnMTAWOLEidnbqT18ei4QUZBq5C2CvObLl09fkqMgIAgIAoKAICAICAJOj4CQPqd/xLLBn40ASB1CGlu3bs0v5N9BOOW3337jUEMQEYRwQrUT/UBKEAYKQ7gnxgW1IQwSc+O+2lC6AQaRG0uGUFJ42Ro0aEAQaDE1hGVqARl4Cq9evUoQTxk9ejR3Q64h8vUWL17MQjTWSB/CShGG6ZfBQ2ktRBOkD2buDYQyqm6DyI2Y4yGAwu1zVM27svkyUZm8mRxuA3efPKcb95+qMg7RKVmCOH4qcaLAPIq0o+YgFHLFBAFBQBAQBASBwCIg/zcJLIIyXhDwB4FGjRpRkSJF6OXLl9wTHjWIrUB4BYbcvQsXLnAIZubMmY2ED9eQR/fq1SucBqnhnvA8akMpA+QUwlDTzpLpkgwgp6YGhUx4Jtu2bcvN8OyVLl2atEgMGiFGA5ILw72tGcgihFz8eiHP0ZqVK2eoAbdw4UImtbofwjpB+lArMHny5LpZjg6EwI0HHjR300E6rYqfO5LtP3OVUtXsRhnr9qbKf42lXE3/pXS1e9L8zb5zV1FQPl/zAZS5/t9UoOVASlKlM/WZ5vPfmyPtXdYqCAgCgoAgEHwQEE9f8HkWshInRaBKlSo0fvx4KlmyJLVv357LFiDEEeQFtfdA9BBuCfEVqGJCFAWhnVDeRDFxnZM3YcIEFkUJKpggyAKvGnLyli1bxveGdw7lFiwZShAgHBW5cigsX6xYMUKZCIRswrQwDMpAIEQVJBLhmggHhaomMIBVqFCBj5beQM4CYwiRhXInFFCRG1m9enVCWCrqF8KgMCqek8AgLGPtQQD1+ar3mEDeHz5R+fyZKU2S+LRPkcAjF25S2xELVMH1uJQvYwqe8vKdR6ptPsErXatEbq5ruGbvKRq9ZKvyCoamno2s/7uxZ03SVxAQBAQBQSBkIiCkL2Q+d9n1D0Sgf//+LEwC7xPy3LShJh68bfD8QZQFoZAdO3Y0etxQz2/IkCFMoFC64Y8//uCwSp2zZxqaiTnNP+v7WGqHtytt2rQ0duxYo8gL8gdB/rQgjCZHejwEaCC0grITIH94wdAOUqU9eSCy2BeEW7B3bZgHpNeSMqjuE9gjsEHeJEpI4HjgwAGeEntCIXp4XcW+IYDacQ+eetKv8WJS3BjRvl0wO3ukat19VPXlkrj6zgU160pvvT/QM1XHz7Qv8kRvPnhKMVWpBxRpt2bod/XuY4qj1oIi77YaiNINNX+8mNEoWuSIVofZul+rE9h5YeOhs0z4utQtTShsr63O31Now8EztP7AGSPpazV0Ln38pDzuHepQo3IFuGvTCu5U8PeBNGLhFmpVpQjjp+eQoyAgCAgCgoAgYA8CQvrsQUv6CgIBQCB69Og0Y8YMJnAIfYT3Cwqb8Kpp8RRM26ZNG6pWrRqrfCLvDB5ATbhAqBCeCBVQkCZLxAmEzZJBCdPc4Ilbv349eXh40LFjx3g9uJ8mlOhfp04dfpmOzZQpE68P+X/IPQThgyfPvAwDCqHjhVy/R48esfALvJemyqKm8wblOUI44Um9d+8eoWYgxGUgpoM9ixkQQKhki8Gz6fil20ZIksaPTdO7NzbWtgORGjB7PRMTeKFgqDOXN4Mb9W9ZlRLFjcltszfsp05jF7Mn6sTl27Tt6AX6oMhLyl/j0aBW1QmF3LtNXEaebwzfQ4xf0r81RY8SkT6oIuSpa/WgLKq4e56MbjRj7T7y8HzN8xbInJKmdmtECePE4M+W3rw/fKTuE5fT4u1pJBQKAABAAElEQVRHmFzh+5tZzTWsbU3Klf5bGK8t+7U0f2DbDp0zlEqpWjiHj6kquWdh0vfKy4DJi9dv6eSVO1w8vkGZbyJDwDBn2mS09/RVWq9IYoMy+X3MIx8EAUFAEBAEBAFbERDSZytS0k8QCCQCsWPHNnrDrE3l6urK+XDm1xH+aUlx07yfvZ9BLnUenK1jQURB4PDyz1AY/mcZPI54iflGoMekFUz4qhTKRtlSJ6Fr957Qgi2HqWLXMXRzxVCKGD4cjVq8lYYv3EyRI4SnuqXyshdt+7ELtGrPSYLHbPPoTjzxf8o79/nLf9RvxhoKrURHSuZOTxduPuA56/SZwt6rOC5RqWaxnLT/zDU6dP6GylNbRaOVRwuFRDwV4dl18jK/iudMR6kSu9KOYxe5b4UuY+jI9N4UNsw3wSG9G3gFK3YZS0cv3lSCJwn5vnceP6fVe09S+c6jmVgWyZ6Gu9uyXz1vUB7/blKJutQtowrUxzdO+0l5TFfuPsmfy34VpLlx34M/F8qa2lf4cYpE8Zj0Xb37xDiHnAgCgoAgIAgIAvYiIKTPXsSkvyAgCAgCDozAFeW123z4HCVXCpKzezcz7sRFhV2OXbqddh6/ROVU/tl2Rbxg+6Z0JzeVewZrU60opa/Ti44rj565RYoQjvZP6cF9QWyyNuhD9zxeUKk8GWhhv5YURv2xAJ6v0h1G0qmrd82HU5vqxWjg79W43bvJRyrSZghduv1ICZ4cosblDeGOpoMWbT3ChA+evR3juxqJYfEt6aj1sHmKtG4hkD5b92s6tz6HiMqFWw/0R4tHkOI5f3/D0bRT5pTf/ugwT4nQrNl3is4oIRp4M4tkS0MguTCQbpilEFt4+2BPX77ho7wJAoKAICAICAIBQUBIX0BQkzGCgIMigPA3hFgiBFIsZCIQQXnxYDcfPqVhCzZRw7L5mWz0bVqZutUvp7x8Yfn6kDY16Mt//zMSvvcqFPPcjQfszcM5PG2m4cCV3bMZ+8IzlzqJK5M+hCuC8MHyqNDOMKFDqXsbPFvc+PWtewOD8io+wtP4R43iTN6OX75lkfSt2H2cRzapUNBI+NBQq3huDifdc+oKvX7rTbbulyczezuoSCo8iX4ZyK4tdubaXQ591X2xtrtPXnAY7J3Hz7g5uoV8RB3e6qVKOIgJAoKAICAICAIBRUBIX0CRk3GCgAMigHzC+/fvO+DKZclBhQAEVpoqojRj3T7qP2sdvxB+WDpPRqpRNCelT56QbwWlyZnr91GPScvpugo/1Ll21taRJL5PkZcI4QzkMX4sF+MQkES8FF/0YRBgiRopgo823B9259FzH+36A9YEQ1jp8AWbdTMfX781ECSPF68phfKU2bJfHxN8/bB+eHtC+GpQ2PB2tahXk4p0VCl3AtdNh86xWucWFSYbOWJ4voUlb56Xt2EvMaLaLmwTFOuVOQQBQUAQEAScCwEhfc71PGU3DoIAyh9AmGXq1KkOsmLLy0Q9PZRvsFbQHaP86oOC7nhZMhBUU6EbS33QhmL3Z8+eZTILD2b27NmtjoOozPnz5/meWbNmpQQJEhinxTwfPnwwfrZ0AsISObLj//ge2b42/VY8F+eW7Tl1mcMoEUqJ8M7lg9pQ0expqXH/GSziAs9f/kwpudxA5pSJqffUlZzTZ44Pwhwtmak3UF+Hl9DULCl16jy+/3Hmn2lvwzmKl8NyKKETFyUKY2p5yY0/Ro8SiY+27Nd0vD4P/5W46s/2HL+oPEcI20SKGI4yJE/EQ13UekrmzkCFVWgnavDBiwiBG5BeGIqymxtEXmCxXaKYX5LPgoAgIAgIAoKAzQgI6bMZKukoCAQdAii6fuPGDYcmfZ6enpQjRw4u5o7yCJbMvz6oFThr1ixLQ2no0KHUpUsXi9d0I2oZNlJlGIClNiiKopYg6iJqQ/F5lLyYNm2aj6LtNWrU4NITUB+dMmUK99FjLB1RWuPmTb/D/SyNC05tIBbwkmVKkYgQwgm7++Q59VX5ayt2n6BZyguVMLYLEz54607M7mtU0ASR6TR2UZBvB+UcMHdoFfqp7erXPLeUSsjEkiVPGIeVQWurmnZVC2f30QVhmSh/ECdGVCZS/u0XJNeStRu5gHPwLF3TbVEihacNIzroj8aj55u3VKL9cPZg3lk13Mfe4D1EmOt/KnwWYZ4Qa4FBZdTcsBdYjrRJ+ShvgoAgIAgIAoJAQBAQ0hcQ1GSMIBDCEXj27BmXmLDmpQM8tvS5fv06l7DIkyePL0T9U968desWlS9fnl69esX1C1GMHSUiFi9eTCjSjrlBAGGofzh58mRKlCgRtWzZku+J2oIoc5EuXTouLA91VZStsGTwEKK8hZ7PUh9HaUOuG8hMu5rF6d8WVXnZiePFoqpFsjPpe//xM136WqIhg1tCI+FDR5BCHToZlPtFiYelO45S7ZKG78HnL19o0sqdfAt3pWhpyXKlS07HLt6iDarWnSnpQ/hkGSUWAwGU47P6kC37tTQ/2kAWbRFysTQ+tlIshWopQjahTqpFW9B38bYjjCM8nAi3xQvCOsizXLbzGIfZoh9UPVECA+S7SDbLxBT9xAQBQUAQEAQEAf8QENLnH0JyXRAIAALwLKFGXIoUKXzVsAvAdBaHoI7atWvXKH78+BQtmvXC2hYHB7ARpAo1AkGosEdLZksfPQ7zZMyYkXbv3q2bbD4OHDiQCd+ff/7JxeExsHXr1uTl5cU1CFGrr169enT79m0uzA7CtnfvXoK3Dla0aFH2VM6fP59JH4giXuYGYotahN7e3lx03vy6o30uliMti7VMWrmLwocNQznSJGMP09Q1e3grZfJmpCwqjBPlF1DH768Jy7jkAEotLNl+1Lhd9IcITFDZn6MX0WmlbJksQWxarcpC4N4Z3RJR2XyZLN6ic53StHDrYVq+67gK5w1NhbOmIZRsGLdsO/fXwjC27NfiDVTjxpG+PXjW+lpq71CrJPWYvILq9Z1CTVShdRSm33PyCu0/e427dzMRr/mrfllqOWQOdVA4gLiCEM5cv5+ghDqqU21j3p+l+0ibICAICAKCgCDgHwJC+vxDSK6HeATc3Ny40DdIRPHixY14gPQkTpyYnj9/ziF/UMU8deoUE4hdu3bRmzdvOLcMOWb169dnQmIcbHaC+nwgFy9evPBxBTX0tm3bxoXQNVkB+ejQoQPNmzePUHg9lPpxjvy0cePGUd68eX2MD+oPuBdq++H18uVLzqUzv4ctfTDm7du3XLi9WLFi5lPY9BnPI0KECNSrVy8f/YcNG8aevyxZsnA78MOzatq0qZHw4QKeC7yC1sirnrR3795ckH7u3Ll+5i7q/sH9iKLq03s0pn9nrlPqnZuNyw0V6hf6vUoRalyuAIutDFahnxBxmbxqF/dBzt4/zSuzsmZ31d51/FKqWig7KWkWvg6SaGrmn/U1S+0oDI/6fPpe6ItSDHP+bs5eLnz+5RfD/Ho8SBGEVpoNnEUo34AXDMQKReGrFTEURLdlvzzwO7y1rFKYkJM3UpWPmLB8h/EOEK0BKW1ZubCxrZYKU0XfvtNXkybgUAbtVLsU1Tcp2G4cICeCgCAgCAgCgoAdCAjpswMs6RoyEahWrRqBSCAU0JT07dixg0lLgQIFuAwCSF716tWZAIKEVapUiXPNli9fTocPH6Y0adKwd8kSiiBy8NyZGwgJBEa0QQADazh48CBlypSJypYtSwhzxD3guVq3bp2PNepxQXXMnz8/7dlj8AjBO1ekSBFfU9vSB4N0Hl6UKFHYUweBFRcXF0KoJ3C0JACibwbC+PDhQwJhBD5z5sxhgRaEb5YuXZpJtu4LbyisYsWKBDJ+6NAhJssghXi2YcJY/88g9jp8+HByd3f3Maee21GP5fNnIbyQL4ZC69GVEErapAkogcrl09aiUiGqVDALnb1+n3PjMioxEp1zV03l0D175cXiIqihZ6mO3tw+zfVUPo5PNo7x8RkfIBazbEBreur5hk5euU3x1Trg5TP9DqC4O16mBoGUA1N60tV7j7nWHRQukfuGkg+mZst+TfsH1TlKVfRWip2ob3jl7mN6ourzJXGNrUJP41KUiD7VSnHP1qoflEZRBxHkNlvqJBROeWPFBAFBQBAQBASBwCIg/zcJLIIy3iERwI9JcwVBaxtp0KABk77Vq1fTxIkT1Q9fQ80xeIlgEBKBQVQEIh9169YlhAxqg1dsxIgRTPxAzAJj8DaB8GXLlo3n0+qWs2fPpsaNG9PgwYOtkj54Ic29YpbWgvBNENbvbQjthCHXztzy5ctHGzZsYBJofg2f9VgIxYBgP3782NgNmPz777/0119/cZsmfSDua9euNfbDSfr06QkeQ3hszQ1kEpjie4L8P3sN40xJi73jf0T/fBlT+HmbeDGjU4lc0X31iRk9CuEV1AbhlVKqdIQ9BiIKwoqXf+bffv0bH9DrwCqvP1jruaEYCrVUMUFAEBAEBAFBICgRENIXlGjKXA6DALxLyP2yxTJkyMDEAqQJOWHwbiEUEyQwUqRIHEqIeXLmzMleJB2GiTYIgMAjBfOvHAB38ucNqp8wqF5qwofPCB9FbtvOnTvp9evXFnP8IKwCguOfWfLe+TcmINc1GUuVKhWTKuQ/gpSNHz+eiS3y8xYuXGhxak36Tp48SUmTJmWyW7BgQd5/3759qXv37pyHV7hwYSNBxNzI2QNWMIRtYnzDhg3ZA2h+I9wbXlRcB8m21/D9wvdMTBAQBAQBQUAQEAQEgZ+NgJC+n/0E5P4/BQGIn9hTpBw//EH6EEYJUrR582bOaQOBiBo1Ku8B6o/wOKHMwPHjx1lABGGbgTFzb+SVK1d4uh49etCAAQN8TA2yh/5YgyVhF4RCIgfQPzMlk/71Dcx1eEQhkIIwVa2K2blzZ0IeI4g2wmlnzpzJeXvm9zEl7NOnT+cwT/SBhxCEEOGeK1asIJA+hN3CEIaLUg7aoNoJgo4wVeRlxor1rbg4cETJCBgIZEAM3y98z8SsI4BswPixolPcGD9GiMj6SuSKICAICAKCgCDg3AgI6XPu5yu7s4IAFCNXrVpl5arv5tq1axMICcZAMEWHdiL8TxtIIerWITcPxcrhVcIRHjbkhQXEtJdQj9UEBmQJ+W+WzFo7BFZQjy64GPLv8DK3tGnTEso13Llzh/P+EIJpbrqcA8RzzENmQRpB+jRBRugm5tIePj0XPIQgfhcvXuS+IIza4BVEO4iiX4XndX9LR3gRIR7j7PbPjDWc3ze2Y127t4pQxstLBtk97nsMuHDzAefPodSDNUP+48XbD1Ue6WdV5/BXzj201BeCLNdUniHKX7ipeoIQk7HHMP7s9XsE8ZyUieOp4vOGIvP2zCF9BQFBQBAQBAQBUwSE9JmiIechBgEQhUGDBjEZSJIkib/7Rl5emTJlWChl+/btHIYI0gBPkrYhQ4Yw4UMR8LFjx+pmzuczfrBygtwv5JCZ5oGBPGqxEz0MIZBPnz4l5BnWrFlTN/MRpRIQdoq1WrIjR45wbT1L10zbOnXqRCC539t69uzJhdLhsdR5krgn9vfgwQO+vSVSiAs6Bw/eTYTNQsVTm87v0zjovpjX1ICvbtN99XVdMD6gpO3y5cv83QqoMqlehyMcV6rafbdUwfeAkL7gsj/PN++oUOvBVEjVBFwxqK2vZaFuYJdxS2n2xv1cUF13qOyejSb/1cAoHIPyCr2nrqQpq3f76FcydwYap0ixq/Jq+mUPn72kWr0m0RlF+EwNqqr9lHIq6vWJCQKCgCAgCAgCAUEgVEAGyRhBwNERgBojwjFR9sBWA9GCNWnShMsNIOTTVKgD6pMweJq0wTNnLS9N98ERBBLk5cKFC8ZmhC2+f//e+BknuiQD8glNDYqU2FPbtr5/sOp+CIk8d+6cvy9NmvS473XcunUrgSib72XBggVcRgFhn9GjG34kP3nyhENm4bGDISwTHkHgaz4eIbgw7bnTzwPP4Yv68a4NYZ0gffHixaPkyZPrZu4DlU8Q0apVqxrb7TnB9ypBggQcvmrPOOn74xF4rlRIO4xeyPXwrN29x6QVqmbePnJVwjY9G1VQpSuqULyY0Wj13pM0evFW47Ah8zaoovK7mJxBabRuqbwUPXJE2nrkPDX6d7qxn6WT12+9uag8CF92pdrZpW5pJaKTnlU8Ucqi/6x1loZJmyAgCAgCgoAgYBMC4umzCSbp5GwIhAsXjpUZoRyJsE1TT5G1vVaoUIFixIjBXiiQPZA+U0PIJUgbcvqg4unh4cFhoDrMEOqe8Lahn7mh7cyZM1SlShUOQ4TICXLazA3eMYQuIjcNe4AnCcXHdfgoREysGfoGhZiMtfntbW/ZsiUTuTp16nANQ5A4YDBlyhSeCkqk2iDu0r9/f2rWrBlNmzaNCRmKswMvkHCE1iLkE7mWENtJmTIl98V4hNlCuRN9ChUqxOUgQBa1Iifug9BXbUePHmUxHITqWsqN1P2sHVFOAmts1aqVDw+mtf7BvR1erit3HlNyFaZoXgohqNbOXu0HioArIhVNkaQfYShX0W7kArqp7vvFQrkUvQYUfJ++di/X/9s0qiOhpiDMXXkFCyvvIArWd29YntvmbDzIx+UD2xgVOFGPL1P93oTi9pgrieu33FHu/PXtiCrIflt5TAtkTkmrh7SjsGFCG+ZSxeebDpipyORO6t24AiEkVkwQEAQEAUFAELAXASF99iIm/Z0GgY4dO9KECRM4Rw9EzT8LHz48/fbbb1xiAOTBVKUTYxEuevr0aVaEhPIkDDXn0AbiAWVNEDYQPJAM05BGEBj0A+Ho06cPeyFBZlAAHQRP94XgCeaBCArmwguGdpAYrM9RDAQO+wOZGz16tHHZ8MDCy4lwWm2alGkc0F65cmX21LZp08YouoJ2qHjCWwhSDANB37RpE5N8HA8cOMDtIPqTJk0yltzgRvWm6xDi2QXEUJ4D5Lpdu3YBGf5dxyzYcoiJTq50yQkExtQmKw9VzykrqEGZ/DTqz9p05to9GjR3Pe07fZW8vD9QGFUaIUuqxFSreG5qrmr4WbMU1f9ir9md1cN9dKneYwLtOnGJTszuayRO3h8+UveJy2nx9iPk/eETPysUZR/WtiblSv/N++pjoiD6EEp9L+K4ROXXKy9vunDLEFJsPj3WDFKIAuma8KFPVoXFzJ5N6PMXQ31NhHa+9DIIJaVLltA4TcI4LnyPJy9e+wj5NHb4enL04k0+q1E0p5HwoaFC/sxcxxD4XL/vQemTf5v761A5CAKCgCAgCAgC/iLwi8oh+p+/vaSDIOCkCPTr14+9QAh7NCdxAdkywgch4AHxFniK4sSJw9PAk4E8L6g5wltozSDccu+eCu/Knt3PouG4D+aDFxGED0QyOIm0WNufpXZ43RAa++LFC/bQubm5GUmupf7mbQiBhYcQ88DD51eOJrC9dOkSi+BAIRQlN4LSrl69SpkzZyZ8r7p27RqUU/uaK3LkSDS8TXUOIfR10UoDctdS1exGH5UQyeXFA30IkRT7Yygdv3Sb1gxtR9nTJKUCLQey5ylTikRUNl9muvXwKa3Ze0qJk3yitcPac/4bbpOl/t+c0/dq+0S+a6KKHZncPFw/yscqqvw1jnYqAnVm3j9MnvC/npLtRxDITnpFkkrmTs+eMIRMhlWhtUv6t6Yi2dP4mON7fQCxLd95NBXPmc5XTh9y9MYu3U6bFUn+pP7dHb14i7zff6SMCpfyipChALu2xv1nEHIcW1YprLxyFTk0c8a6vdRrykpVRzA+HZ7eW3f1dZyz8QCdU6GdrasXo+QJDP/dQCcIzORrMYD731g+hGIromqPles8hjLmLco1Ru0ZJ30FAUFAEBAEnAsB8fQ51/OU3diJAOT4Ie0PTxyEULR3yM5pjN3hiUK9PnODpwpKkf4Z8sDw8s9wH6haWlK29G9scLuOkhc6VzEga4PHzlLIrKW5EAKqlT8tXQ9MG8pzwNMKMgkvcnC0GFEjUek8GWjtvtO0Zt8pgkAIDGGHIHyJ4sZgMrf92EUmfMhLm9a9sXEr8IyNX76Djl26ZSR9xot2nizaeoQJHzx7O8Z3NXq3im9JR62HzaNRi7dYJX3wQv47a62/d2xcrgCVU8QsMHbjvkEAaMzSbbTp0DkfU6VJEp8QyvlrvJjc3qlOKTqkwkanrNpNs9bvZ+/oO0UQI0UIR4Na1fAx1vxDw7L5zZsIHkgQRlieDG52Ez5fE0qDICAICAKCQIhFQEhfiH30snEgAJIH4Q945RBOCQEOU3EWQUkQsAUBeHJREgJCMydOnPDTS2vLfLb0iaDCjeF1s9dql8hjIH3Ka6dJ36o9J3iaOiXz8Pc/mxIS2T62CyWJ/y3/DOUK8IJ9VKUIAmsrdh/nKZpUKGgkfGhA+Gi3ictoz6krBHETSzl+z1970bajF/xdgnuWVP728a/DzYce3AWEr1rh7FSrhCEnF8IqEF35fegc2jCiA/dBTt575UWFIRcS3wsYQjP3nr5ilcRyJ7M3eEXbjVhA9zxesOrnxC71zXrY9vGDela25CzbNpv0EgQEAUFAEHBUBIT0OeqTk3UHGQKpUqWilStXsuomfhxBSMQ0dyzIbiQTOSUCnz9/pkaNGtHGjRtpy5YtQRImbAtQCOt99tLLlq4++kARMlb0KCws4uH5mgujr1BiIbDaivTBcB05aPAynbp6l+4qT2BACCZP9vXNPJMA+WmwfqrO3/AFm7/2MhxevzWo1nqoNVgifYWViMrjDd/yQH0MNvmgxVBMmuw+9Xr3gcfA0zazV1Pj+NTKy5epXm/af+YavVAKoKFUzmODftM5/697w3LUpHxBVvHcePAstRk+j0Yu2kI5VNisf55HlG3oNHYxYRysepEcNLh1DYoTw76wTr1QEORYsb6Rd90uR0FAEBAEBIGQhYCQvpD1vGW3VhAoXrw4E78aNWoQShbMnz/favFzK1NIcwhE4Pnz51zTECUzUNAdIjI/ytKqcOFLqlC4vQYiVL1oDg5BXLf/NBXKkloVAr9P+TKmMOaSIXwSdetA1FIkikvVFPFA0XKUNxi3bLu9t+T+j756CfXgN+8MxC5H2mSq+HhE3czHvOTGx+hWipIjXPp7KYn6WIj6gJBXeNvggTQ1qHCmTuxKV+4+pmv3nxD2B8EXN4VXt/rfyrbAM7j58DlateckIWzWL9K3++RlLu2A3MssKRPToNbV+bmY3teec4SW3nrgQWnS/JjcSHvWJn0FAUFAEBAEfiwCQvp+LN5yt2CMAOq5QRmzevXqlDVrVpbdBxkUEwQsIQDPHspOgIDs3r2bxXcs9ftebfnyKZXNYQZiZm9IMsI4kXcGYZanL9/wEuuWMnj58GH0kq1M+FpWLkxDlZKmNlsInxLFZMETEEa9LoQ5QgjG1FACAiSytiJFVVXYpKmhnALEZqx5t46rnMJOY5eYDrF43rZGMYIaZmAM+Xoot/DslQEnPRf29EytHwZxFfSBZTBR7uQG9QZhHJA+748fdZOv4w1Fzur1nUogw6gF2Kl2KRVx8K2UiK8BNjQcOHuNn2NgcmZtuI10EQQEAUFAEHAABAL3fxQH2KAsURCwBwHI9EN9E+qZJUqUoEqVKnGOlj1zSF/nRgC1FsuWLcvhwPDs6e/Lj9416kZ6vHhFIEj2GrxIECHZd+YqTV+zl4VGKhfKZpzm4i2DB7Fk7gzGNpCRZTuOGT9bO0kcLxYTtku3Hxm7oH7dh6+5broRZSNgGw6c0U18PKrq1ZXpMJI6j7NO6lBCAiUW/HshPDSwpjHA3r98Lc+AOfepsE6QVojbJFO1+6BACtuiCrHDU2dqi7cd4Y+mhBDe1JNX7hiLwkP1ExhDPKdrvTKBJny4IUh91iyZbRKHMl2vnAsCgoAgIAg4HwLi6XO+Zyo7CiQCcePGZXEXFPpGMXSIvIAEoi4cfuQjVAr5VGHDSpHkQEId7Id/+vSJEMKJMg9Qd0XuJ8pD5MqVi7Zv304oeP+zDMqtOXNkp2mqcHj+TCntXkbtkrmpz7TV7OmD5y9KxAjGOXKkTUqX7zwilCuAOAm8gcj7u3bvCfc5dP66Uvu8RQjNNDe0nVdlBur2mUK/lchFUL9EGQZz61ynNC3cepiWq3nDhg1NhbOmYRVR7U1EUXNrVjhbGnq2eZy1y0Hajpy6sUq5EyGwZTqOpMru2ZicTVyxk+/Tr3ll9vYWVKIxGd0S0bkb91WuXy+qWDArJYwdg0M7gYdrrOhUT9X6g4E8urcaxOeXVOmMBLFdWPUTDacVGSzZ3meNQ+6o3lAXMFHcmPqjn8cXr9/SckUkh48Y6Wc/uSgICAKCgCAQMhCQOn0h4znLLgOBAH7sz507l4U6UEdPLGQikChRIvbuNWzYMFAlJoISvaVLl3K5kYNTe1K6ZP6X+jC99yMlGJK2dk8O/9sw4k8qkDmV8fJTzzdUrft4VqfUjSCCYzrUocb9Z9JVlcfWVKlujmxfm7I27MPE0HPrBO4KURMUYj+hvFiwmNEiUxXlRUT5ARC88wv6G0scnL95n5oNnKVyE795BVFWomu9stS6WlEe/yPe/KrTh/tD8KbV0Lmck6fXEz5sGM65a1rBXTfRvScvqKMSYdmqvH2mlkEVVJ/UtQFlUuUpYCB9MUu15fOrSwdRvJjR6ddKHZVaqSHPkS9YeDs1t58x79LCZR9Nf09dRQt2nKRbt29TlChRfFyTD4KAICAICAIhDwEhfSHvmcuOA4EASN+1a9fI09OT4AUSc24EUNLDxcWFUqdOTa6ursFus8iby6G80BG+vKWNI9obc+iCYqEgJqev3SV4jLKmSmysEYdctqvK4weiAoJmzUAqHzz1pCxqrGkBc/P+uM/Ve0oMRc0ZI2pk5T1M+sNEWszX4t/n+0rQBcItEJhJlzQBh8VaGnPr4TNFjB8pYZf/cSF6CL4ENj/P0n2stYGU5285iIYOG0bt27e31k3aBQFBQBAQBEIQAkL6QtDDlq0KAoKA8yFw6tQpypkzJ/3TrBK1rSHCQ873hO3bEQRwSnUYRb9EikFHjh6V8jP2wSe9BQFBQBBwWgREyMVpH61sTBAQBEICAlCaHThwIP09fTXtOnE5JGxZ9ugHAh3GLKZrD57SwkWLhPD5gZNcEgQEAUEgpCEgpC+kPXHZryAgCDgdAl26dKGaNWtSXSX5D/VLsZCJAIR3Fiml0EWLFlOqVN9yNEMmGrJrQUAQEAQEAVMEhPSZoiHngoAgIAg4IAKohzdnzlwqXqIkVfxrHG0+dM4BdyFLDigCnz5/oXYjF9D45TvV92AOCw4FdC4ZJwgIAoKAIOCcCEhOn3M+V9mVICAIhEAEPn/+TL///jvNmDGDOqri3ih7EE6pTIo5LwI3VdH7lkPm0gWlgAoPH+o3igkCgoAgIAgIAuYICOkzR0Q+CwKCgCDg4AhMnz6d/vyzPSVS9d/6NatIZfJmcvAdyfLNEUAh9/HLd9DoJdsoZcpUtESV70ibNq15N/ksCAgCgoAgIAgwAkL65IsgCAgCgoATInDnzh3q2LGDKii/itIlT0QNy+SlsvkyUeJ4sZxwtyFjSyiXcezSLVq5+6TK3TtK/wsVinr06Kmec0dV4D5syABBdikICAKCgCAQIASE9AUINhkkCAgCgoBjIICSDuPHjaOly5aSl9dbco0dg1IkiksuUSJQmFChA72J/1StwF/ULMgrFPuGAAhaKEXKgsLef/xEHi+96PLth/Tu/QdK4ZacmjVvQS1atKAYMWIExS1kDkFAEBAEBAEnR0BIn5M/YNmeICAICAJA4OPHj3TgwAE6ceIE3bhxg169ekXIAQysgVR6e3tTvnz5AjuV04x/+/Yt7dq1i/LkyUOxY8cO9L4iRoxIsWLFovTp01P+/PkpTZo0gZ5TJhAEBAFBQBAIWQgI6QtZz1t2KwgIAoJAkCEApchGjRrRkiVLuGREkE3s4BP9T3k/K1WqRMePH2eSHT9+fAffkSxfEBAEBAFBwNERENLn6E9Q1i8ICAKCwE9AAISmYMGC1K5dOxoyZMhPWEHwvuXr168pV65cFDNmTNq9ezeFCxcueC9YVicICAKCgCDg1AgI6XPqxyubEwQEAUEg6BHw8PCgHDlysFrkpk2bgix3LehX+nNnvHz5MhO/WrVq0dSpU3/uYuTugoAgIAgIAiEagaDJMg/REMrmBQFBQBAIOQggD7BmzZqsFrlo0SIhfH48euTezZs3j1BCY8qUKX70lEuCgCAgCAgCgsD3RUBI3/fFV2YXBAQBQcCpEEB5AIR2rl69mkMXnWpz32EzyO3r3bs3h8EePHjwO9xBphQEBAFBQBAQBPxHQMI7/cdIeggCgoAgIAgoBES4JWBfAxF2CRhuMkoQEAQEAUEg6BAQ0hd0WMpMgoAgIAg4LQIi3BK4RyvCLoHDT0YLAoKAICAIBA4BIX2Bw09GCwKCgCDg9AiIcEvQPGIRdgkaHGUWQUAQEAQEAfsRkJw++zGTEYKAICAIhBgERLgl6B61CLsEHZYykyAgCAgCgoB9CAjpsw8v6S0ICAKCQIhCQIRbgvZxi7BL0OIpswkCgoAgIAjYhoCEd9qGk/QSBAQBQSDEISDCLd/nkYuwy/fBVWYVBAQBQUAQsI6AkD7r2MgVQUAQEARCLAIi3PJ9H70Iu3xffGV2QUAQEAQEAZ8ICOnziYd8EgQEAUEgxCMgwi0/5isgwi4/Bme5iyAgCAgCggCR5PTJt0AQEAQEAUHAiIAItxih+O4nIuzy3SGWGwgCgoAgIAh8RUBIn3wVBAFBQBAQBIwIiHCLEYofciLCLj8EZrmJICAICAIhHgEJ7wzxXwEBQBAQBAQBAwIi3PJzvgki7PJzcJe7CgKCgCAQkhAQ0heSnrbsVRAQBAQBKwiIcIsVYH5Qswi7/CCg5TaCgCAgCIRQBIT0hdAHL9sWBAQBQUAjIMItGomfexRhl5+Lv9xdEBAEBAFnRkBy+pz56creBAFBQBDwBwERbvEHoB94WYRdfiDYcitBQBAQBEIYAkL6QtgDl+0KAoKAIGCKgAi3mKLx889F2OXnPwNZgSAgCAgCzoiAhHc641OVPQkCgoAgYAMCItxiA0g/oYsIu/wE0OWWgoAgIAg4OQJC+pz8Acv2BAFBQBCwhIAIt1hCJfi0ibBL8HkWshJBQBAQBJwBASF9zvAUZQ+CgCAgCNiBgAi32AHWT+wqwi4/EXy5tSAgCAgCToaA5PQ52QOV7QgCgoAg4BcCItziFzrB65oIuwSv5yGrEQQEAUHAkREQ0ufIT0/WLggIAoKAnQiIcIudgP3k7iLs8pMfgNxeEBAEBAEnQUDCO53kQco2BAFBQBDwDwERbvEPoeB5XYRdgudzkVUJAoKAIOBICAjpc6SnJWsVBAQBQSCACIhwSwCBCybDRNglmDwIWYYgIAgIAg6KgJA+B31wsmxBQBAQBGxFQIRbbEUqePcTYZfg/XxkdYKAICAIBGcEJKcvOD8dWZsgIAgIAoFEQIRbAglgMBouwi7B6GHIUgQBQUAQcDAEhPQ52AOT5QoCgoAgYA8CItxiD1rBv68IuwT/ZyQrFAQEAUEgOCIg4Z3B8anImgQBQUAQCAIERLglCEAMhlOIsEswfCiyJEFAEBAEgjkCQvqC+QOS5QkCgoAgEBAERLglIKg5zhgRdnGcZyUrFQQEAUEgOCAg4Z3B4SnIGgQBQUAQCAAC586dozt37vgaCeGWqlWrkru7Ow0aNMjXdWlwfASiRYtGq1evpvPnz1Pbtm0tbmjjxo0Er6CYICAICAKCgCAgpE++A4KAICAIOCgC48aNoyxZstCuXbuMOxDhFiMUTn9iTdjlv//+o65du1K5cuXo4MGDTo+DbFAQEAQEAUHAfwSE9PmPkfQQBAQBQSDYIfDp0ydavHgxvXz5kooXL06jR4/mNYpwS7B7VN91QebCLp6enlSiRAkaMWIEhQoVihYuXPhd7y+TCwKCgCAgCDgGApLT5xjPSVYpCAgCgoAPBNatW0cVK1b00ZY/f346cOAALVmyhGrWrOnjmnxwXgS0sMuhQ4cofPjw9OTJE4LHF+bi4kJPnz6lMGHCOC8AsjNBQBAQBAQBfxEQT5+/EEkHQUAQEASCHwLz58/39UP+8OHDlChRIsqdO3fwW7Cs6Lsh8Msvv3AOJ7y+jx8/NhI+3BBt27Zt+273lokFAUFAEBAEHAMBIX2O8ZxklYKAICAIGBHw8vKiNWvW+Phxj4tfvnzhH/2ZM2f2kednHCgnToeAzt9r3Lgxfx/wHTA1ePjwBwIxQUAQEAQEgZCNgJC+kP38ZfeCgCDggAiA8H38+NHiyhHW9+bNGx95fhY7SqPDI/DixQtj/p61zeD7sHLlSvL29rbWRdoFAUFAEBAEQgACQvpCwEOWLQoCgoBzITB37lxCSJ81g/cHeV69e/emo0ePWusm7Q6OwPDhw2nnzp3+lmX48OEDrV271sF3K8sXBAQBQUAQCAwCQvoCg56MFQQEAUHgByPw7Nkz2r59O4HYWTJNBiHycvnyZcqVK5elbtLmBAgMHDiQNm3aRAkTJqTQoUNb3RFUPOfNm2f1ulwQBAQBQUAQcH4EhPQ5/zOWHQoCgoATIbBs2TKrXj7kb7m6urJXB4W7QQbEnBuB0qVL09WrV6lz585cosGSSify/DZv3kwo5yAmCAgCgoAgEDIRENIXMp+77FoQEAQcFIE5c+b48vLhhz48fG3btqVr165RhQoVHHR3suyAIBAxYkQaPHgwnT59miDio729pnMh3Hf58uWmTXIuCAgCgoAgEIIQkDp9Iehhy1YFAUHAsRG4e/cuJUmSxMcmELqXMWNGmjVrFmXNmtXHNfkQ8hAAuZs8eTJ16dKFkMun6/Xhe5I3b17av39/yANFdiwICAKCgCBA4umTL4EgIAgIAg6CwKJFi4y1+eDdg4dn1KhRdPLkSSF8DvIMv/cy4eVr1aoVe3yR1wkD4UMO6MGDB+nBgwffewkyvyAgCAgCgkAwREBIXzB8KLIkQUAQEAQsITB79myj56ZMmTKcy9WuXTv+UW+pv7SFXATix49PK1asoA0bNnCeJ4gfvICLFy8OuaDIzgUBQUAQCMEISHhnCH74snXnRuD169e0e/duzvO5d+8e126zpvjo3Eg4x+5evXpFW7dupfDhw1P27NkdRqQlcuTIFCdOHMqQIQMVKlTIV3iqIz0dCKKgBAZeyJ2EMMqnT5+C/RYQ4nnhwgX+I0H06NGpZMmSwX7NskDrCIQLF45ixIhBqVKlYnXenDlzyh9+rMMlVwQBQeArAkL65KsgCDgZApBwnzhhPG3espW9Qsnjx6bEcaJS1AhhKZQftd2cDAan286le8/osyIdaX+NQ2FCO0aQxv/of+T98Qs9fvWOrtx7Sh8/fabMmTJQi5atqFGjRhQpUiSHeE4geBMmTFBlD+bSixeeFCuGC6VJnphiRI9C4cKGcYg9YJEvX3vRqYvXKEfG1BQ1smNg7zDg/sCF4t/Ri1dv6PKNu/Ti5SuKHTsWNWjQkIWckiVL9gNXIrcSBAQBR0JASJ8jPS1ZqyDgBwKHDx+m9u3+oKPHjpN7xqRUxz09Fc+SjGJEiejHKLnkKAg89vQi1xhRHGW5vtb58fMXOnjpHq08eJlWHrpC0ZTH6d/+A6hZs2YW1SZ9TfATGlATsVu3biySkzihKzWpVoYqFi9AqZL9+hNWEzS3hLf/6YtXFC92jKCZUGb5qQhcun6H1mzfR7NXbKaHHs+oefPmNGDAAPYE/tSFyc0FAUEg2CEgpC/YPRJZkCBgHwIIL+vevTuNHDmSCmZIQn1ru1PmZPHsm0R6CwI/EIFnr9/RyFWHaPq205Q/Xz5auGhxsAtXXbt2LTVp0pgihA1Nfds1ptoViksI3Q/8jsit7EMAocfz12ylfmNn0/9ChabZs+cQajiKCQKCgCCgERDSp5GQoyDggAi8ePGCKlYoT6dOnqDBDYtS3cIZHXAXsuSQisCZW0+o5YRN9PLDf7R23XrKkydPsICiT58+9M8//1CDqqVpWLfWEgoZLJ6KLMIWBBDC23HgeFqyfgd7/PAHQTFBQBAQBICAkD75HggCDoqAh4cHFS7kTl7Pn9DiLlUoza+xHXQnsuyQjIDX+4/UYvwG2nfxgSJ+66hYsWI/FY7WrVvTlClTaOzf7alJjXI/dS1yc0EgoAhMWrCaOg+aQB06dKARI0YEdBoZJwgIAk6EgJA+J3qYspWQg8CbN2/IvWABeuWhfij3qkEJYkYNOZuXnTodAp+//EetJm6kLafv0O49eyhHjhw/ZY89evSgoUOH0oJRf1PFYvl/yhrkpoJAUCGwbOMuatR1IPXr14969+4dVNPKPIKAIOCgCAjpc9AHJ8sO2QjUqF6d9uzYQlv/qaOUOaOHbDBk906BAIhf7WGr6MpTbzp1+gyXefiRG1uyZAnVqlWLpg7oQvUql/qRt5Z7CQLfDYHpS9ZTu39G0+rVq6lSpUrf7T4ysSAgCAR/BIT0Bf9nJCsUBHwgMGPGDGrRogWt6lmDCqRL7OOa6YeNx6/RlpM3TJv4HGUbEsaKpsb+SnnSJPJ1PTg39J6/i959+EQjmoaMOmMIfbx09ylFjRjez/Dd9x8/07nbT1SBBKKkcV0orktki48RxbnveLyi+89fK7EfVzVvOIv90Gjrva1N8EHJyp9VOXspEsS0WUH2pdd7KtJzPmXNW1D9SF1jbeogb79//z6lS5eO6lUsRiN6tLU6/7qdB2jT7sO+rqPweSLXOOSeMzPly+5YebXdhk2md+/e09g+f/ralzM2eL31pgvXblLUKJEpXYqkVrcIYZSb9x7S7fuPKW4sVRNPKbZGjBDeYv9P6rt+7upNevDkKffNli4VhbVQygP//m7ff0T3HnlQ1vSpApQrivGvvd5RhlTJbBYWav33SFqz8yBdunSZ4sUTkS+LD1EaBYEQgICQvhDwkGWLzoMAJORTuCWnugVT07/1ivi5sYFL99GIVb5/oJoOalgsM41s5jgEKu3vE+mFlzc9md/JdBtOdw7C1GveLpqp1C21xVNErlHxLNS1Wj7dRJ9UGYQ2kzbR6sOX6ct/oHwGK5XNjUY1L0nxXL6VeDh69QE1G7uOHjx/w51Ch/qFsqdIQON/L0Nu8b/J99t6b30v8+Ort++pw/SttOP0TUUcDYXL0yWOQzPaVaBUCWOZd/f1ed+Fu1S5/5If6pmoUqUKnT99go6unEIRwlsnwv3GzqIhUxb4WrNpQ9Oa5Whcnw6mTcH6PFmhGvT85Wt6fWZLsF5nYBf34eNH+mvIZJq6eK1xKtfYManZbxWoR+v6xjacHDl9gVr2Gk5Xb90ztseIHpUGd/md6lfx6QU+eOIcNe85lG7de+Sj75xhPal4/m9hyodPXaAGXQbQfUX4YKFVrc2cmdKyZzlFEv//+LZ43XYaOXMJnb96i8dHU6S12W/lqX/H5vzZr7e377wpR+XmlM+9CC1cuNCvrnJNEBAEnBiB0H2VOfH+ZGuCgFMhAFXBcyeP07yOlShcmNB+7g0/ng9dvk+13TPQlDblqGmJrPwqmyMl/RonGh2+cp9O33xCGZLEtenHuJ83+0EXwyv5/EKqBmGOlAl+0B1/zm06z9xGc3aoEMfokahpyawUP2YU5cnzoL3n73A4b8akcXlhf0zeTMsOXOScznpFMlKaRLGVF+8Nnb/joV5P6TdVqxF2x+MlFVcetJeKkJXMmpzK5UxJT1XBdPTbe+EO1VL99PfJ1nvzxBbeOkzbwrX4krvGpDqFMpBLlAh0WH0PNx67RhVzp6ZokSx7S/RUSeJGp2sPPZX64DZqpURVflGe6e9pR44coc6dO9P0QX9RGrckft5qz5HTdED9yK9XuSTNHNKdWtSuyK8KKv8vcYJ4dPDkeTp54SplTONGqVXxdkew8OHCUdG82SlX5rSOsNwAr7H9P2NoxtL1yhPnws8sQdzYdObyDdp1+CQlSRiPMqVJwXM/eeZJxer9SfcfP2VvWu0KJYi9fncf0nrlLcuXPQMlSxSf+8LrVqJhR3ry7AVVLVWI50XR+5Pnr9KmPYepcfWy7B1EvwK/tSFPVVC9TKE8qtZjfvJ49pLOqvvvPnyK6lUqSeHChrW6N9Tiq9KqJz31fEm1yhenQrmysBdy675j6o89/1Gh3FmsjsUFzJ0wXmzqN3wC4Q8crq6ufvaXi4KAIOCcCIRxzm3JrgQB50PAy8uLJk+aRF0r56TIEax7I8x3DsJgquyJ8yKZkioC8IFmbD1FexSRAAkwNeRXXX3wnJK5ulDEcNZ/jLxV4YefPv/HP+xNx1s6R+je8zfelFzNGdAf8s1LZbM0NbcFxfzWJkdh9Icv3lAiFRZrLXTS2lh72+8+fUULdp3jYVv+qUtJVLgm7NK9p1Sg62zqNGMrVcqTmkna2iNXKGbUiLStfz2jV695KQ8q1G0OP9fX7z4wyRqhauKhODr+ADC+VRmer0+dQtR49Bpae+QqjVt3lLrVKEC23jtSeMvfiW2nbtLS/RcplQrp3NSvjpHgtZ+ymebvPkeTNx2n/vWL8v39eutaLS/l7TyTNm3aROXKfV8FzeHDh7PHpbR7br+W5OMaCINpaCDOi+XLTi9fv6Epi9bSrkMnfAnBfFb4X755h9wSJ7QaJqhv8ujpc4ob00V5g/z+ww4KrV+/84Bc48QkeH4CYq3qVrY6LCjmtzY59vhAEatf48f77oXi7zx4THNWbual7F44jpJ+JW0Xrt2inMoD9ke/0UzaIkWMQOt2HKAXr16Te67MtHnWN9XLNn1G0qzlG2nh2u1UJI/hv0NDpy5UoZZvqW39qjRUlfaAtaxdid6qcFmQvi37jnJ9x8GTF9BHVU8VfyyYOqAr94OHrm6HfrRq6z724PVu24jbLb21UJ5EjB/ft4NRURYeyrzVf6dh0xZSm/pVKJaL37ndVUq68x8jRql6rrPnzLF0G2kTBAQBJ0cglJPvT7YnCDgNAsuWLaNPnz4GWS2+WIoswDxevjVihBysusNXkluzsVTwr9mUtMlYKtV7PpNDYyd1AnJQ4Z9FfN2t+Tgq0n2O8u5cooxtJtEfkzeZdqUDF+9R7o7TCf1yqWOqlhOo2+wdHJqIjgv3nKN49UZQ+X6LfIzDhymbTvA1EB1YPkUEUqvxpubf/KsPqTwWNX+jUd9yxECAkjYZw+2bjl83TgePFPrqPdx87Mn7T996EpXoNZ/StppI2dpPpWPXHhrH2Hty+f4zGrL8AJXpYznM6tSNx/Sfyv3JlSqhkfDhHml/jUPpVZjkh09fmJCjxp23yuXLn/ZXI+FDP3hukdcHe+P9gY/HrxtCz6oX8OnNqZ4/HV/HXDBb782dLbztOGMIPWtcIouR8KFbLeXxg605fIWP/r0hDNRdeXRnzpjuX9dAXff09OQw0ha1KgRqHj04VgzDD294i7SdvnSNarTtTQnyVqZcVVpQvNwVqXDtPxQ5/PZ91H3HzV1Bv+avSm6Ff6NfC1Sjxn8NUsW2Z1HKorVUHpoBW/T1fv9BEZVRFCdnBcpSvjHFz1OZ8tdoxWGJeq50pepTtMylaKcioKYG8pm8UE2KnqW0ykF7RtkqNqEkBaubdvF3/hWbd/Pcdf7sZxz38eMniperIrfDI6YN4Y9YR8tew7jphiKp2D/26F6rLSG8FGs9cuaiHmL3EZ6w/hPmUNF67S2OPaE8byCwebKmNxI+dEyfMhllTJ2cPqi1X755l8d6PDc8u4ypkvuYK0s6wx/GMI+2LXuPUnj1R7G/fq+rm/g4sHML9hxnUh5f2LGzl/hYq3wxPuq335TXDnb64rf/Bulr+vhChd6eOH+FYqvvVqNqhj/Y4BpyDOGd/aL+QLduxze89ThLx+Y1K9BiJViEPyCKCQKCQMhDQEhfyHvmsmMHRWDd2rVUKEMSm7xq/m0RggJL9xl+ZOl8rjfeH6mR8vxsPnFDefhicO5Y1bxpOEyw66ztHFqIeT1VTl3xnvPo4KX7iojEpvpFMzERaT5uvfKGedEj9dIGzw/ys24r8RCE+rUqm51iqnC/aVtO0m9DVnC3stlTEsRlEIpqOhYXQSThdURYIMxTeQsRoqjNlvnzK8EazLH/4l3CvmGnbjxShOijsV3PB68n+uZNa8ixQV4dCBM8a/3qFiKEUN5/9pqqDliqCJchX02P9et4/dELGr7yEOXvMotfQ1ccpCsPnlkc8vzNO25PlTCmr+sgdLBL955RsngutLFvbZXbWZjb9NsRFbYLUo6+EOyBPX/tzcfUCWPzUb9lSBKHTzEfzNZ7c2cLb9gnrLAibKbmpr5PMHw/4B22xSoq7/OWLVvpo8rF+l62ZYshj6180XyBvgW+W4tU3hUsRVLD9+fN23dU589/aMOuQ+zh69m6AdUoU4TOXrlBHfqP49A+fePBk+ernLNJyqPzmSqXKMj5YCBXyCEEOfNSc8Fwn7JNu6hQxQ1qzgTUqVkt9lIhVLF0485Gkoc5QPBWbtmrb8FHhDM+VuGIOTKm4ZA/hBx6Kg+lNlvmL6gEazD33qNnjP+mQEywX0P7aT0d7VT3Q1uB7Jm47a+hk+ioIkEIhwQ5ApG59+gJlWvalcmmcaA/J9du3yNglqNSM8peqSkNnDiPLt+4Y3HUMxUWCUtjIeQ2Y2oDMbv4lVRXK12I+85fs5XDL/Hh4vXbNFV5cGH6u4I8OXgr82bNoP4Y94Xmr95C3YdNofHzVnIkQ52KJZhUYswzz1c4+AofzqQIJ8yU0HODydu1O/f5U6HcWX0Jt6T8mgt45SthNRlm8bRSiQL872nHjh0Wr0ujICAIODcCEt7p3M9XdudECBw8eIDalDDkaNmzLXhxZpkIglx7+Jx2K3JzW+V5hVViAnULZ+TpQBag7Fg9f1qa0ra88RaxVV7ZxA3Hmfy4K9I5cvVhDtMEEZrZviL3g+esliJxIE3aIDLSc+5O9lrN+KOCkbh1rZafyvZdyH3hWYOCKIRH1h29ql5XqEXp7DwFiAsIV8JYUck9fWI9rfFoz/xZk7vSqZuPCV42eMwOKMKqbb/yRGrT6y+eObkiZc9Z/RTkSu8T/VwiR6Dx64/RrrO3CfmR1uz2k5cssLLq0BXOnUO/CErRr3R2NyqjiG6pbD49CXoet/gGsof5TQ1kFGQVhn3UViQ6d+qv5EIRWIRQ4pleUGqfyH3sUbOAcTiI/bPX7wieOBBXbdpjeU8RWZAxW++tx5sfrz80kD6IzpgaQmKhFAqijXXYEp5cQD3zt++20blz5yh7dsN3wnTOoDg/ePAgqyhGjxrFrulOX7xG05asM465qn507zx0ksU8oNrYsEppvoYcP+Rz/aY8PLNUDqC2OCqvbOzs5Ux+CufJqnLCPGn49MWce7VlzgjKktbwvUK+FwieqS1QZOTI6YuqTwras2i8USVy3qot7E0bPm0x5+jVrVSCRs9ayuGKY3q3M4aKLtu0i6erb6UshS3zb5w5jLJnSM0eKBAieMz2HT9rXObeY2eM57vUHmAlC+ZiUrZRqZ8mV2R1/shvdeNcokXltW4/eJwqFM1vHGt+ckupaa7YvIeWKzKMfDgYhHfKFcnLZAz5cpYs5VcSvv2gb68nyCrsovIWwpCL2a5RdX4+eaq1JAi4gBjDyhfJxzl5OL+hcvxgCOnNW70lP0NuUG89VL51nz8aMyFHG/4I8PTFS9q2/xg1rPrNW4fnCLv78In6rntT5EgR+bPp27WvYjKusQ1/ODG9llJ5+2CY2xaLo0KG0ysP5qFDh6R8gy2ASR9BwMkQENLnZA9UtuOcCLx8+ZKeeDxlz5q9O8QPfR12Zzo2euTwNLp5KZVjZ/gxAWK0cHiktwAAQABJREFUGTlkJnX/kMuGFwweCNh25b2D9a5VkI94gwhIF6UqqUkT2s4q4ZEbKjwShePL50yFJjYIeUA4pM+CPbReiXuA9P1WMD2TPuSXadKHsEwY8tAs5QDaM38JJV4C0mfwTirSp7x+WAf2jDUjHzBsmFB0QpHMLMnjcd7e+6/7vaXIG3Li6hfJxO1/13ZnL6hfuY5QyVz1df0Io4WXs0yOFOwBs5YPx5tVb7lSJaBEsaOxR7HJmLVUT5FyhHtCyfPqV1KlRVf0mHcfPtIGVaIDxBD2i3ohJ7NEluTKO/ALE/kjVx5wWCk8tSDve87dpnGKvMLgaQ2tyg4E5N48gXr7T6mHohQEzJJYC7yOIKteivjZYvAOhlME6tKlS9+N9F28eIHSufn+g4J/69uqfrzjZW4u0aLQhH4dyS1JQr4EYrRr4VhKmjC+sSu8Q488nvNnKErC9h8/Q++83yuRjmJGwod2EEKIdEBARtvyTbv5FDldpmUB6lQsTl0GT6TdR05xnhmIWGYlTnLm8nU1/zmeByGYyFlD6QHt0dLz6qOt85dyz8Wkb78ie7jX3qOnKXrUyJQtvdqz8u6BKOH5IbQxmypPEE+RlvcfPvBtbirCBA9mo2pluf3fDk2pp1LQtFYSAYMadO5Pem0IdYSKJrxuxZQIDXLx/LI8WdKr3MG4XCqhXsd/FfEqzf+mpiklT+0lw1phWLdW2MRnPBdtV27dZQEV5HDeuPuAm09fus4iPv92aEb5/8/eVcBFlX7Ru3aD3d3d3d31t7u72xVj7e5ObLETW+zuQsVuRVGxc/d/zze8YRhmYOgB7t3fMO9976t3Hrhz5sZhuQ7gP2buSho2fQnnimbh3MA81Kh6OTrNXwCgHeGaZTgn8DB/STCdq3HCIPlhLn/zEecjwuIwtsYGmRCY5gU2vm7qPFuG1HTLWUc2TV2XNkFAEAi7CAjpC7vPVu4sDCHw7p3OgxI/Tgw/3xUIT2MmVZqBQIFUZEkR34vHBXO7un9hMnaEq3q+4hDBj6QRH20sQr8QqgkPobEoPIp3GJoW6vf6w2fKy3lwhqblmuEarEKetARyhIqiyDGEZ2iLB2lqXNpz74Zz+G3+dISQypO3nlJLDkc95/KCiU8qKsKeMpA+tEdj7xhIE4gSDPfXhiUS7A9eoXEbTqgXqmPCKwlvKGQIzBmqZWqGIjk1C2eikqypGNmXiqsYAzI5r0s1VWQFOXCGeXDAGMQP+zA0SDO8WtVXkSoQ25Frj9I//MqVNrEKCW5dPo8ivCCiaNcM87zj4jrwZkaLovvfgV/X1uYCucTeoaOIyqDGBW+g+wezjeXdm6HNYfiO+eLGjkFubjqCZHgtsI7fub2j3Gl0ocN+mRNFX+BJ0wx/UyhIgg/Uht4akBNUdhwyZaGq6omCIt+ZoBub5jVCnpaxZUqT0gvpc3n0THUZMWOpN/kIFBWBYU0UdmnGhUOuTrjHxUKOKdIHour+6YsqLoIqk6bM0vkrlyysQiqPn79GbetXpzPstSrLJLVYvhyK9MHzF41/HxDaCYIIS508CXVgsgovKXIV8QKBqlqmiCJGOYzy6Az3B4+pZrUqlFAhsKhiaUh8tevG7yCTqM7arM8oDnc9ql5aH3j2QPywDxiKsyAkFkR2yt/dqGTBXCocd/TsFao4S1vOszyzeaHyzKkB/GP+6H764i7IG8TzhMd024HjivS1b1iDifc1RVrtpi7Whqk1IZeRLmUys1IhsTy8f2/cPP9N0SaA5iAM3khLLQEXfLnyUEckLR0j/QQBQSBsICCkL2w8R7mLMI7A9++6b5sRtudXy8m5XXU4N883QxGXcnYrOUeHw5E4HPB/xbIoYW3kg8111Hk1fjEpQignQvX+4v8MTftQr7VpHp0ETCaLZNZ5PrRr2nthj3aQoXpMpBbtvUSO7P0ryYQMEgVF2QuYNrH3sCaM98v88OiBVJ5mQgRCC2ICYftiXAQFBs9fJI9KiRXz6nJ80D6lXUVqWDIbwet49MYTRargrZqz6xytH1RfVUFFP2Ob17W68vRh3Eqna+oFzEDAq3NIaHkmlj4JoyMP8eTkNrTr3F0meW4s3RCTSWNKVRkTpA+5lAh/hRc2U7L4Ks8TxAOhq3gh7xFE9cDl+4r0gUAt6VlThfIipPMDe/tysOxD4UwpVHEdzKeZJWtrfY3fE/E+EWIK0mtM+t4zuYQliGMZ6UNfhMP+8PAO4TywDX9XPunymVsPBTrqVSlj7rK+HUVcijfoqvLeEGLYoFpZAolDjtfM5Rv1/TRvUvSoUfVt2sEXA08T2jSvDjTebNizaMq0cNWGvN7fLL6+4+AJmmbXnbTQzpYe4aemxlo6P6qQgtSevHiNCe0d5RGDV6tEAV3u3nH2/EWKpPuIUaWUZ9jlzOG9FOlEviJCPxEeihdCUbctGK+qoJra15LxgxVp2rSX9Ss3OqoXiC0IZS2WzED4qDkii/mQh3hh+xLazljcuf+EErHnsTRX6Fy385CO9GVMo5bdzxU3Yf05V1KTQkC47dh+HRTpQ1gpwnFTJEmk+iVLFJ/KcL6doVUtXViRPk3nD568lVOGqtBOhHS+5+qgkIgomi875arWmglmGsPhXo4Ts5Yg7KEB6dU6wGsIS8Bhm5ZaVK68+93od8rSsdJPEBAEQjcCQvpC9/OT3QsCgYbAzB1nFeGDLMKE1uX18871CAFEA8IKk7EEBApyXHv0mkMhk+j7gaQZGuQeYDYcRmmYI4g2FGyBBEEGA+9gYw7jBOmDDMEbzvuCIW/NnPllfpCecrnT0kaWEwAJg5XknLFsTJDicmEZ5PWhD4ghCCIMxOX+y/eqIMq4Vjo8njLRGrnumCJ00NGD9IUpy8j3BRF1vHCf2zivD162Lad0L3hK4TFc0beOt+HwdDpde6gqcKIKpmbwsvZatI/DMP9SuCFfEiGfKDDTvYbOk6L1RbgmDIVvYE4c4gsvbo1CmbzsWQuh1TyHlq6tJjXxIyMXnwHpQ1hvQa4+qtl1/l35wkQb1UejeoTRadfC8vvUJesV4YMswtQh3fW3OtPek/ChUZMQQC5crzYN9P1wcI3DMw0tHZMtkEZ4Go2JJzQEUdo/UXzdFyV4r8xkCHl0yDlEQRnoCUKOwJz5ZX6IjzvsOkTLWMoABs9bjkxpKZ5NHMK9RPgrgiKG+XPowrvhrbvLnkoQnsnsRYMhn23otMWK0EFHD9IXpgxeUIio44XCJyq3j0NdN+4+rF7w+FXjnL51M//xNhwk7eDJ80pfD55GzfA31WX4VCWUDjIO08gVtBYNDaQdJBPeVISpIlwU9vHzV1X90/DLg1dvdJEZCT2eA3L5UBW0doWSXu4PxBeW1cPLqE6Mfmj5iJDmMDY8bxi+ABATBAQBQcA3BCL41kGuCwKCQPhA4BZ7sGAQ79YMhTc2sfi3oeVMk1idGkodwHM2hfPeDA0eRnhq4JnSQjFxHR+0ei7aSw0mbFI5dNqY3ByKmJlDTkHAlh24TMh9Q7EYc+bX+bWwzbVHbyiiBwICogfyh+InIK3lmRiiDXaMPXvYo+blRFtKDvms6+E1NQ59xXVTBs/b31xU5dz09nRkfEvqXbuwqqp57OYTU91VuGW3+XtYj++AwkrrBA8oisuAvIJ8g7DCdrPkBASaNUOI6nImpDCNlK86fI0w56ErD7VuagyIPvghPJAwhHpasrY2CbzDkHlAUR0Yqr3CZu88r2/DOcg8rBrnNYYn0ypCGmoAosLlesdDXmDIzUVZYMeZKLl/0oU843yDoxPdcPF8ZmgrnCcb3mgH5+YZ2tkrN6liyz6qKqhhe9NaldRpZ5ZMgEcRWnGmcmS1MX6ZX7svFJEB0YP8AbxapQvnput3Hqh8woolCqo2zA/PXu1Of3vxcoKE1ueKpjBToa/qgtEPhF4O79Garu1eTqc3LaD+HZqw5y2hyqcz6qpO3T64U4chk6gnC7Tj3x/NgOFt9vpVLF6QonAoKgykFaZVYlUn/MORiTMIH7yJ2HOaFElU0ZfPXIAFeZKGhnBaWBGPZ7V88261/v4TOi8irkHwfSoX78GzqMKeQc3gHUZxmV8eOcWosorCNwgNxu+DZve4quc+ng9ks3zRfFqzvAsCgoAgYBYB8fSZhUYuCALhC4ECGZLSnWduNHzNEeWtQV4WvFN3mbTBznAhkAscGogCLgevPFAkD16dhFzd8yCTCa2fhhpCEnsxwYEmXd0xG6hbjYIqpHEre7sOX+eKf0y6jEkAcg/hScPaKOASywcRer/OXy53GlWwBEVRENqpffAtlT21EijHvjXyg2P0j855btAKRFht/gzJlOdvyf5LuExV2FNnzkbxPaBKqjnLliqBvkCOcR94yzKzTh3CSNvN3KnCXjHX5M2nVeXQ6e0rqyEIO5285RShQEvNUQ4qF/G52ycVHguvHnIS4T2FoZAOiuT0XbJfyTLE51w5EGsUw+lVqzAVyJhM9bN0bXQG0Sw7ZKUad31uZ13BHvYkzmKhd0hAYE8oXoPfGRBT3FM3I4+kGhyGf8ADc4tlBIZMWaQ8SMjLQoilFvaH6p6QLyjE/eC1g+eneMOuBCFt6NnBQ2dsgzo2pTXb9ivvVpTIkVUeHQjBDA/v4dBuLb0MqV62CKHAzAuP4jHNa+tIoJdOBid+mR+ePpA8aNfBe6j9TaFQCUTHYRoxxHFF7o/8urmrtyh9OxQ6gQdrwdrtuEzVyhRV76Z+oDAKqqSasxxMBFEkx5RlZi9hFi7YgzDSFv1GU8Pq5dVc4+avUvlwc0b20Q/r2ao+F40ZqwiZqp5apgghz3E3e0lhgzo1U/eJwisje7ejxj1HqKqpIGsI+YRXDx44hL+2qVdNjYGHD3hABB5e2gRxbWmRw3ZCERhIbuD5w0AEi9Xvoo7vOjkoSQ2cDOnSgtr/PVGNP8PkPp5tHJbs2KWI4SzOJzTMI1WD5YcgIAgIAiYQENJnAhRpEgTCAgJaiB/CAS2xYY1LKW8XiMCAZQfVEBDBYxNaEapRHmfPlMOxmyrPbcewxjRo+SH2At5S/ZADOLNjFQ4/3OulcmPfOkXUB8KpW88o+QZtHyU4P21O56reQv0alMhOoxyO8bfxpkM74ZUyvB+/zB+XC4jk5/tBThvW1wyVLGHAq1wu3bf8OEe1SYSljt1wnKt3en74Rr9OVfJRq/LmQ+SgCYhKoD4ZpB9MGapoIv+u/eydtJ1DXfGCoYjLyGZlKCmH18Ige7B+UD3qNMdRET+QP83gvUQ+opY3iHxJZw4znc2ETHu2ILTI2xzcoLg2TFXwtGRt/QCPA+2ZwDu7ZUhDqj9+o8JZk4QA4Vvdv65+P8bjQ8u55gWOyOG5ltgorkqJ8Ex8uO89epYagg/4Z7csUtUoUZUTBA5tC8f0J+SHLd+8RxEOhBL+r3IpVWUSHp44sXTPHR/499hPoTYDx6m8MRQMgaGYx8RBXfReM9XIP6JGiUL1mVAuYZKAvDYtlFS7jtxc/M5p5pf50RfEDXlqyOfTDAVdYCCEIIaapeCQSPtJQ2jkzGVeitCgX7fmdaldw+paV2/vxxir89dve2s3bDBX0AQETeXUMZlDkRZNuxBFXKAVmCyRZ04rvI4fPn6hUbPtlcdNk3SIxN71wZ2bUy+Wc9AMuYRLJwxWz3b6sg1asypms5zvU/MeNuTqnTc4JBXVOrXfA5Bf5HgO69ZKP87wwPCZQPMPBV+GM/HVCDIqlg5gD6ehBITheDkWBAQBQcAYgb841MEz1sH4qpwLAoKAVSDgzCW2s2fProp7aPlXQbExeG+g64cwP4QGoggLDOX4UVAkCVeJtOUcOPyzgW/1teIt8Midd3lOVUasVfpw/ep6/cYeMgEgk984DBR5fBk8tOgC6x6Cen4Ix7/moilxWOYC+EOGIqgNYZoIO4XeHyqFIk/QlKHfA86hg7QEZCcgdZA6kS6f0rg/Cr/AC4cxJbKnVNU2jfvg3NK1TY3V2pD/eJs9xygSg2qx/rF8vZdS176DaODAgf4Z7uuYbFmzUt1yBWmomQ/evk5gQQd4by4731Uf2iFdAK00GLxjdx48pSQJ4ynCpv1N4R26a+iHv7GaHQapyo9vL+zyUtYf895hDTd4DeMx4YNX0SfJAwu26qVLUM8Pb9gr9syh6Axy2pIn9iReXjYSiCeoJHrd5QHrKb7gMM50ZKpaqrYcQmHR98XrtxSfyW029iSicI0pQ1jqtTv3VZGdDCyYjvBPUwZPpPPdRxz6/FvlP/r1eUHi4/y124qkQw5EI5Wm1jLXZjd1ER29zCGkl3QRC+b6SbsgIAiEPQTE0xf2nqnckSDgbwTw7XK+9Em9jYeHQyObnebsUh6+uV2q6sMH8UF1oUfeFnLtjA1ettIeHjXja4FxHtTzo4pocFsk9iYhzxEvnwz9MrEnDS/fLEncWISXb2bp2j7Ng/xHvMK7wcuEvCxjg3crK0s8wBDKmbNaK5W7dc1xub4QC8giyBFy2Ix13HAOmQFNasB4/oCeB/X80LQLboO3Lm+2jOrl29rwpBXOnc23buo68uq0EE2fBiRNGJ/w8q/Ba6tVR/XvHDJOEBAEwi8CQvrC77OXOxcE/IVATc7bQlhnb64kiWqUIIPn2MuHUL6CnBtWgkMLxQQBQcByBCDmjiIoKH6SvUpLQsl/VIXcc/QMV+P8TX3bNbZ8MukpCAgCgoAgIAiYQEBInwlQpEkQEATMI4Cy/wu7V6fNTPxOsaj5IZYDQLhjgxJcWKRtRVV10/xouSIICAKmENi6YBxNY4kHpzMXVd5WVK4mmZulDXq2bqBy+0yNkTZBQBAQBAQBQcBSBIT0WYqU9BMEBAE9AvWLZyO8kPvlzkLg8T1y//QdwsiBlgMHSQJIVWhFVHy6vbN3ntG3n7+pTM40Zrs5c64eBOnN5emZG4gcyls8NnZ0zi00EFQ37v+d14c2HhK203B+n7FQunF/nLuxNuI5lxcqBy9NYtM5gabGSVvgIIBiIlOG6LTrUOHRhgu5QHsurBny365ypUtUE0ibMhklZpF0U+ZbP+TcIdfYJ0PYJUI6fTLf1sHYnz9/KY+rqXlAzo2fE/I1UbX1+as3qoon5B2Mw3NNzSVtgoAgIAgEJQJh7/8oQYmWzC0ICAJeEEDuV1gkfCCzg5YfpJWHrqnqidpN1y6cmeZ2rWq2AMpJ1hisPcaBYnIVy8f2vbVhXt4/sGB6OZY6KMk5jhsHe1YC9NLJ6OQHh/gNXXVYibFrlxLbxqTWFfIoAXitDeQUOnvbztxmSQXPD8QQgp/eoRIl5kI8pgw5mR05V/PI9cc0unkZ6lq9oKlu0hZMCJgrGBJMywfJMtCd62A3SclS/OG/L82qsSTCnH/6qoI2aLO0X6byTemd+0dtGpPvi8cNZBF70xIVlq6DiXuMmkHQIjRlY/t1oD5tG+kvzbDfQGPmrlSaiFpj2pRJaen4wVQkb3atSd4FAUFAEAh2BIT0BTvksqAgIAhYOwIgWMsPXuWw1VgszZBH6fTN331BySdAQH5QfU+ZA+1eUEG089xdyoOhtRm/w5s2wP4g/TL40Gvcx9Q55DFWOV1TmojQ3nvClTH3sPYdNBBTcmXMJqVzqGG9OM9y86lbLDcRm2oVzkSfvv1UGnn7Lt2nLnN30xa7hqamVwL0IHxigkBQIdBl+FQlLg7ZhroVSxJE6iFqDi3CdoMnkOPSSWppS/uhCA7E0k0ZciNh5iQccM3SddAXhXagiVgot05PD22aQZtPs/lrtilNRhSBaV2vKsWJHZNJ7lGuFvqSanUcTFdZTD4ghVy0deRdEBAEBAH/ICCkzz+oyRhBQBDQIwBJAVSEjMa6b5p9/PqDXn/4ouQDNG0z7RreEX6Icf/xf+lZviGKDyFY8Iy5sYREuiS6EvaG8wTFMQgVhMuho7drRBO9/AG078rbraINx51Nkj4Utnnx7rPJLUHyoc/ifUpawdADZ7KzUSP2s+bwddW6b1Qz/X5use5eiYHLqd/S/VS7CJdvZwx3sKZfvNjR6cCY5nqvXofKrlR68Ao6euMx4bnEiRHVywpXHryi0Q46IW0vF+QkxBCApEBSDvdEeKJmIDiv3ryjDFz0BZU/TRkkARBSmDJpYrNhkxj33v0TS0i4q9BDTVDd1HyB1QaphK37jynpg2Pr5ui9ep2b1qEi9TrR4TOXFIGLES2aRf2gYXhg5XST24PAfYt+Y6hN/Wpmxd4t3Q/WgT14AomHtLR/xTSTa2qNDjt1+qbrZ42k8sXyq+Zx/TpS/trtWZ7jCS122EnDe7TWusu7ICAICALBioDnp7RgXVYWEwQEgdCKQM5uC3jr/xHE3CduOkWPXD9wuGMkql4wI4cGlqUu8xwVwUDOTqxokWlC6wp6TxTuedaOszR5y2n6ypp9MAh7I0xxTIuyXsgfQiX7LtlH916+V/1AZuoVy6rCD5EPF1R25NojFRrZvGxOPcHCWtAthHA5Qj+NbeWhq7Tr/F3qxmGRK52uKh1Dwz58i0rzELqH7ky8kNNnqV2+/0qFmBbKlNzLfrKmTEjZWb8PWn4uz93UnpFLWCFPOj3hwxo5WEIDeX14Tp++eSV9XzhHsAOLwMfk59SsTE6a53jB0m1Jv0BEIGP5Jvwn9R+N6tOeQwNXKM8QNNxqVShBEwZ0Zk/YeCZGl9XvVawY0WmaXXdqXqeyfgfwRMFbdu7aLX0bRNjtJ/3tRXbg+Pmr1P2f6XT30TPVD/pzEA7HGsZ5afqJAuEA0hPfvv+gyiUL6Qkfps2VJT3n9SVV9/vx0xelW2hJP42MGW/tOWvq9Rg5g7KmT01Th3Q3vqw/t3Q/WOfL12/06u07KuMhOK+fxOgAuYGXOVcRnjyN8KELCHojxnjU7OWs0ffQaJScCgKCgCAQfAgI6Qs+rGUlQSBMIADS4+r+hbrO382hhTZUJX96QvggZBx2nnOhH7/+UCn2iqGAghNX9uy5cC+VzplaVfiEV2nkumOUivXb/lcsC5ONKLTphDMt3X9ZkaKB9YopjA5cfkBNJ2/hD0x/UVMOXbRhUXS0Ld53SREcc2GKgQHwfRY6h1XNn4GO33yipCggKg/yBLkK5DEaGgjXkJVO6vrQxiUV6TO8juPCmVMoryGOTzg/odqj1+PQInP79FX1y5Tcuzg79gTSB8F15O3t/qeJN+F4FJaBtxB9k8f3KpI+mMNGH7z6QEuZzL5k4XaxkEHgN4t1v377ntr/PVEJe1cvW1SFPa7fdYi2sYfsBxcSKVM4ryJmB06cp87DplLZovn1guaDJs1XhO9/lUuzJmBmJdi+ats+qt5uID09sVmJtu89dpbqdxum/qZa1K1MtiyKjjaEJN6+/0QfXhkUCKRLlZQOrZpByZMk9DL96Us36PHz14r8IewzOhcosqSfl0k8TlRe6pBJ9OHjZ1o34x8vXlLj/pbuB+MecGgmDGR71opNSlzdJk4sJtNZqW6lUqR5SpFf2K5BdcqYJqXqb/jj2u376jRJADT6DOeTY0FAEBAE/IOAkD7/oCZjBAFBgNqwd24ie/FAzODp6rNkP/3kMK49I5sSvFKwumPW0zEmTtcfuSoyMnvnOdU+pGEJJfGAkzYVclO2LvPJkT1lIH0oRmLHJOpf9nws7VGTc9MyqzED6xWnav+sVV7EMxwuWcSMYPrItUfJmUMffbKYHDa3rHctk10eeJC+2bvO0d6Lug9rWkfk820YVJ9ScB4dDAVWOszepfYKGQufwlS1Ofz6jvBX2GH2QBoayPfl+7oPpLefvVXeVJBLGHL5ejHZhncPpDBq5IgEzA1t6+nbtPboDWpUMjvVKZqFkLMoFrIIdGhUU3nx4B1attFReeWg0+e0eqa+CEi1tgPoyNnLqgJm8sQJmLA9VgQxXapktHraMP0N2MaJTSgqcvDUBapSsjANnDCfq13+S6umDldkBR3turWk8s17q/DKUywCX8yMYPrQaYvppovPXqqYMaLx+sP16xsexLe1oaL5bFQTcvm6DJuivHvXXR4Qql9qIY+W9jOcWztesWWvug8Q5tKF82jNJt/9ss79J8/VHEs27PIy12w+K5wnG22dP45smQSi+ur0oT299MEJtBZ3cO4irFYF77nA6oL8EAQEAUEgGBAQ0hcMIMsSgkBYRKBnzUKK8OHesrMXCZaNQw41wodzCLWD9GlESsv7m7PrvCKBRbIkp7ixotODpT3oL/4Pdo0JIrxt0P6rUTCTasMP5KI1LpWdRqw5qkIpzZE+5M9BKN4ni8HVNc0ZPF8wEL66TIZAimDjNhxXe+s6bzftGK4Tyx7FXssbj105hLW8EqlXHQP5R6FMyRTJfPb2I7WduYOacxgmCPGyA1fI5cU7tZox2fzKoWaOF+7qQ1GBLDySFTn0EyT9KXv+kGMIj+vENhUCeccynX8R6NuuET8fnSc5Z+b0ahrkkhlWfQShAem7z3lmMISBwpB3NnHhGi4gUk3l843u047surZQ1y/ecKF7j58pT1ttDhnVDOGLzWpXVMVHtjMxMUf6TjIhPHvFWRtm8h3FSyyxL1+/KxKEvDoYPGXId6tSqrD+3tFuaT/0/fPnD01evFZFF4wf0AlNFptv6yB0FpYhdQqaNLgL50EmI8fDp5WHFJj0Gj2TVky287ae+6fPNHz6Ulq8fqe6hmdbjr2zYoKAICAIhBQCQvpCCnlZVxAIxQggD88wVDAae5Jgxjp2EfgDHQz5fbDuNQqqME0QpVqjHVhvLgqVYukChFIi3BN276WOyLz+8Jny9lqk2rQfyEmD4Zo52z6skSJF5q771g4tPFjhzMlVDp/WH16+vD0X0UkWpH/HhWUusZdtwZ6LVD53WupQOZ/WLdDfo7MnZF6XatRmxnbafuaOemmLZEoWTxG/LCkSaE3qHdIMr1b1JXgAT916RvB+/sOvXGkTU4lsKVmewZG+fP9FG1gyAs9ALOQRiMhhwykMwh+1Ii7wIBma59+U7o8qdfIkBA8hyMXIWfbqlS1DGqrKUgjIJcuRKR3n8D1VU7ziQi9ZKzU3nE5V0UQDrpmzvfZTfNXEMzfWuD1JwnjkfmUvOd97RCcuXCN4Ee2mLqY82TJS2SKef0eW9sP8KBKDCpkNqpVV5Mx4TZ/OfVunUY3yVDBXFoVjPM6BhPVuk1KRVBRo2bLvKC0cM8BLOOlK9jrivqC3mCRBPKW/iNBbMUFAEBAEQhIBIX0hib6sLQiEUgSisWi0qaqcWn6L99vSfUBF8ZELMzrQRs7jO3jlAcErh7BOvByO3aCtdo3oM4cmwlD0pAgTL1MGQmbOogZQ0DoFyx3Aq6Z5+LR14BXLnDw+3WGPGYjpZs5hhCFPsWi/pVo3FVqJE7Ql47k2D2mov+bfg+JM1E5ObkO7zt1lkufG0g0xqSS3bWAc4e3LykLtyNuDmHymZPHJNlY05UEB3nh9+PKdPZUnmHDfp9R8H+dcdN4LhIBq9paJLGwWh+BCHmJYk1JUrUBG7bK8BzECIHmal89wKbN/U9o3Kdx55vBe1KRmBaWBh4IvIFR4Ibxz24LxemKXMK6tWW9eUR805KJGCdgXA4+fv6KXrm6UOV0qJaOAe8qeMa16IQcPZHXv0bOUjsXaLelnSA6B1ZQlDgoyyCRYYpbuB+uAiBuScW3+LFwsBu1PX7py3t8LAtGG9l/rgWOZhB5XEg8DOzalfu0bU+yYMbRh8i4ICAKCQIghIKQvxKCXhQWB8IfAkeuPVKXPdpXyEl7I33PiXLV2HLZ4gqt13nn+ltKyNAPMhsM5F3av4QWklyyJAKmCDOzhMme9OWzx2sPX5i6r9lhcQEYL0TTuqPL17jynt6ypZ2j/sti51gZCmjJhHFUcxbAPjuEFRfhlFCafKFQTUHNl6Qunaw9VBc42FT1zlVC4Arp8WA94IA8SIZ8jm5Vmj2ohL8tq3qH3LH8Rhb2yKOpibJq3FpIS2LsWimvcT86tC4FHz16qapy5smSgyX93U5t78uK18jRt2nOElnIuWlsuMAJDARL7iX+rY+3HC9e3Kl8vU9qUWpO3924jptEVrsDpk8ViYrNv+VSTXaYv20CLHHbQuP4d2Uvm9UsQ7XfzHctIWNrPcJGrt+4RCqWkSpZYFbsxvGbu2C/rjJixlCvj/ksje7WliBE9qwa/efeBgB0MuZWw/uPnKsKXJX0qWjNtBEFLUEwQEAQEAWtBQEiftTwJ2YcgEA4Q6D5/D71hMuWysBtX5IxGkF5A1cl0SeKqgiPff/6hnExI4EmEBwsetQwehUxAcnou2qsqgkI6AZ43U3afx/gmiRCDJQrMWaW86VUlUnjyetcuzKRKl2OFqpvQC0xoE0MRsCENS3JxlJLepsncaS7rEP6ioxNaebtmSQMIK4hXjtQJFT4IJe3GuCGE88Sk1vpqgfCOwutYMW86VUAG+ZSw3Sza3qVaAf2+UfBlORfagUF2ArmSpvYGbcIByw5SH77nriw9IRY6EIBnD6QMZAqkCgYCVL9qWQLpg5QApBHgSUTuHEI9tQqT+JvqwpVAURF05RQ7QqioKYPEww1f5AZi+pDTl53zEmE7nU5Rj5b19OQJeX1agZR82bkyrocUi2/9DPfodPqiOjWspGl4HceojPrslSsljGersLF0Pxh76NRFunTThfLnyKwvgIN2B66s+of/tpBzacOVUOHlW719v6qwioI2QviAkpggIAhYEwJC+qzpacheBIEwjgC0/JawPEO9cRupU9X8iqwgzBMVJkFGNKLTi4nHxE0nufrnBurGeYDIO9t66jYdZk8htOmqFchgFqmdw1nzLACG3EJU7kTF0RojHbh6aCZCnp9W3XI4hz2aCm0NwJL6ofAolB2yUp1fn9tZYZKRpRoQVor8vHYzd1K94lnpLod4Tt58WgnIT29fWfVvWDIb6x+eorPspaw5ykEVbXnu9kmFzkJiAyS5cakc+rXkIGwgULF4AVWsZe7qLaoSJvLP7nHxkQVrt6sbrFamKCWKH5f6t2+iNACrsYxDr9YNKA575jbuOaxITc7M6ahm+eJmAfFNlNzsQI8LTTn0dPy8VQSJhkqt+3E10UJMwt5wQZcTipCBbDavU4ng9bOkn+F6TqcvqdNShXIbNns5XrB2m0eRm6o0b1Q/snQ/mKRdwxp0iUl164Hj1P4Ronr9zgM9WR3dt4Na65Kzi9IiREEbaCGasvJcyAUVU8UEAUFAEAgJBIT0hQTqsqYgEIoRQG0WY9KjecOMbwuhh4Zm16gkfWMvGDT9Os911F9Kz54+hHJqout96xRR5eWnbj2jwha1jihCMqdzVZYgCLp/upBvBFmG7gv20CHWGdTy3yB7MKVtRdYNzKltJ0jfNeyALTyb7VlEfftZLuTCLxiKuIxsVkZfPAehpOsH1aNOXKQFxA8vzUpyFdUp7SpaVLTFVF6ZNo+8Bw0CqFwbMYJn6CBWQWEXU2YYYojr0LeznzSERs5cpoiNNgbPsVvzukxadKGdyC/78+8f7rOW5Rvmad0IZGnR2IFMGAMeiqyf1OggJmvcbV0wjtoM0hEnkD/NUI10FuckanlvlvbDeFTtPOUxVyHWzTNn2r9XGqZ+2U+b+tUIlTgnLFhDc1Zt0S8BYft5o/spwXk0nruqy/H9+u07nbl8U9/P8CCNGU+qYR85FgQEAUEgqBD4i8M7dBUWgmoFmVcQEAQCjICzszNlz55dFfMwrtQY4MlDYAI3DvGElAOkBlDxEyGdGuEz3M77z9+UTALE0ZG3poV6GvYJyuPnbh9VCKUNa5ChWIpPUg9BuQ/MjTBNeEQfvf5A2djbmdFMXiP6QSLjIfeLHCkCgVCnTqTLkwzqPQb2/Pl6L6WufQfRwIEDA3tqNV+2rCywXa4gDe3mv1DcINlUACaFtAKqcCLcMCsXFtFyzQynfPfhI129fY++ciXcTGlT6EM9DfsE1THCOaF7h1eUyJG50mZySpMiqbflLO3nbaAfG/yyDvQFnTnEFbmH2DeKzhgTcD8uHyLd7aYuoqOX79LFSzoPaYhsQhYVBASBEEEg6L4uD5HbkUUFAUEgNCAQnwuhQAPPN4OGX2mWdAgpgyyFoTRFSO0D60Ziz09ullzAyydDv0wcDoqXWPhCoLgZcXVDFCA7YFz90vB6UB4jZw/hkXj5ZJb282kOS675ZR14Igvn0Wl2WjK39BEEBAFBwNoQMB0/Ym27lP0IAoKAICAICAKCgCAgCAgCgoAgIAj4CwEhff6CTQYJAoKAICAICAKCgCAgCAgCgoAgEDoQENIXOp6T7FIQEAQEAUFAEBAEBAFBQBAQBAQBfyEgpM9fsMkgQUAQCCsIjHE4RhB0D80GXcK7rGvok716/5kOX3tE+y/dJ4jciwkCQYUABM2hHRia7YbLA3J5+NSiW3j73p12sQbhw6cvLOovnQQBQUAQCAkEhPSFBOqypiAgCFgNAltP36FVTtesZj9+3ciHz9+pHGv7DVnpZHIoqnn2W7qfcnZbQPXHb6Qmk7dQjm7zqe2MHUo+w+QgaRQEAoDApr1HyH7T7gDMELJD33OFzuINu9IAA2kLcztCAfTWA8ZSwx7Daeehk+a6SbsgIAgIAiGOgFTvDPFHIBsQBAQBQcB/CED6YoD9QfrFxM6cDV11mJYfvMpC77GoVfk8rHEYUQnNQ+8vc4r4NKi+eVFuc3NKuyAQVhGA16736Fn069dvi25x5vKNpAnEWzRAOgkCgoAgEEIICOkLIeBlWUEgrCKAMMIX7z5RCpY7SGQb0+xtwgPl8tyN0iaxpehRIpvtp12APl2SuLEoWhTPf7Y+fv1Brz98UVp0mgCz1t/w3f3Ld3rJ+4K2nTkhecP+2jG8aG6fvlE63iNE203Z95+/lXbef/QfpU8aT2kPmuoXmG2nbz+jPhySCj2+P/+al1p98sadlh24TLYxo9GuEU30en0Qay9vt4o2HHcW0heYDyaI5vr+4yc94NBBeJUypk5BUXz4e3nh+lYRltQWCIEjHDFpogQULaqnMPvHz19Y6++d0qKDwLs5+/DxM710dVNaf37Rq/v333/p3uPnlCRhPIoTy/S/D365X3P782s7NA4RkgoNwT8+fIliOO+lmy40nENZxQQBQUAQCA0IeH56Cg27lT0KAoKA1SIAAtJlriNduPdSv8fUiWxoYfcaVDBjMn3btYevaeLmk3SCxdk/f/+l9OfysPZcw5LZqV2lvPp+CEckJlLDGpeiiZtO0SPXD0wOI1H1ghlpdPOy1GWeIx298Zg/CBPFihaZJrSuQE1K51Djp2w5TfN2n6ehjUrSppO36LzLC/qXO6LfyGZlqHWFPPp1TB2cdH5KfZfso3sv36vL8WJHp3rFsvK6ZbyIyM/acZYm81pfWTweFjHCX2ruMS3KBin542UoAWsd4uXOxBc5fabsCOfwgRQ2L5tTT/jQL0+6JLSkZ00l+G5qnLRZDwLTlq6ncfNXsZj6d7WpiKzD2L5hDZo4sIue/IFIjZq9XIUX3rr/WPVLmjA+Fc2XgyYM6EQpkiZSbRnLN+E/qf9oVJ/2NGbuCs5Be0nRo0WlWhVKcL/O1G7weDp85rIil7FiRKdpdt2peZ3KauyEBatp1opN9E+vtrTe0YnOXnEmrIt+4/p3ovaNaqh+5n58+/6DBk6cR2t3HCQc40uUPFkzqDUM9e8suV9zawSkPQLvJ2E8W/Vy//SZbrg89HG6L1+/Uav+Y9X9t/xfFZq1fJOP/eWiICAICAIhjYCQvpB+ArK+IBBGEEAYIQhf7SKZKV/6JKqwyLqjN+h/YzeQy6Juypv36dtPaj1jOz12daecaRJR1fwZlJdsx1kXHntQeeJKeYixwxPo6v6Fus7fTSkT2FCV/OlpHxchAYnbec6Ffvz6Q6XYYwWBZaerD6nnwr1UOmdqDmOMTV++/yT3Lz9owLKDKqyxTcU89JZDIfdevMf5bQeUt7BxKR1BNIb/wOUH1JTz3uA5bMok0iZmVELb4n2XlGdyi11DNeTKg1c0ct0xSpXQhv5XLAvFjBaFNp1wpqX7LysyNrBeMeOpA+28cOYUynOHCU84P6Hao9ebnPs+E3EYcD7OJPv83Rf0jQlqjtSJqGahTIpwmxwojVaBADxJQ6ctJnjtGlQrqwiGw66DtHDdDiYncWlI1xZqn1OXONCkRWspZoxo1KJuZUqcIB4dOH6etuw7yl47Nzq4aobq9/v3b3r99j21/3sipUqWmKqXLUq7j5yh9bsO0bb9x+jHz19UpnBeihw5Eh04cZ46D5tKZYvmp+SJE9BnJjnw7iH0EecdmOS9eedOjodPUc9RM5g8RqFmtSuZxA0eymrtBiiimCNTWqpcqjA9evaKtvKaVdr0p83zxlA5XsfS+zW5SAAbQZAPrJyuZjl27oral09T9h03R3kFV00dSi/Y4ykmCAgCgoC1IyCkz9qfkOxPEAgFCNzhME0QsrSJbWlZr1r6HSOscM6u86pqZLUCGensnWeK8NUvnlV5ALWOCWxi0DzHC4o0aqRPu9aGvXIT2YsHErby0FXqs2Q//fz9h/aMbEqFMiVX3eqOWU/HmNRcf+SqSJ82Nk6MqLR3VDNKzqGmsPXHb1LXebtpjMNxasSeReOQzV88rx0XRIFXcGmPmlSrcGY1bmC94lTtn7XKs3iGQyuLZElBs3eeU9eGNCxBDUpkU8dtKuSmbF3mk+P5u2SO9MHTOXbDcdXfpx+tyucmYBYQg/cVNnvXOSa8971MhXy+DYPqU4oEOmy8XJQTq0Bg+rINah8jerSmxjUrqGN4+dKVaUQ7Dp3Qk779TNBgZzYtpPSpdX8TPVrWo8wVmtL567fVNcMfHRrVVB42hG8u2+hI3f+ZTj85h81p9Uwqkje76lqt7QA6cvYyXb11V5E8bbxN7Jh0eM0svfdw7Y4DikSiYmfTWhW9/U1h3Jrt+xXhg2fv6Lo5ilSifdXWfdRp6GSasthBkT5L7xdjDe0K73HkTHvDJpPHbRtWp5rlAp7DumnPYbV33G+9KmVo9srNJteTRkFAEBAErAkBIX3W9DRkL4JAKEUAYZewh5x3N3XraWpRNpfK5xvepJQiP1rOXl4OKwQJS83eMc2QA4gXDB88ja1nzUKK8KE9O3uoYNlSJtQTPpyXYI8fSJ9GctAGa1Ymp57w4RxEb9yGE/Ts7UeVd6iRQVyDXWPSCO8YvIU1CmbSNfJPkMfGpbLTiDVHaRcTOpA+LbcQpBb9i2RJTnFjRacHS3vQX/yfOUOO4MErPoeOYSzy7gJqD159UFOA8NUtmkXdPxrGMenEvYIA7xjeOKDLyPggQkDLtZvBxUKSJ0lIxdgbFc82Dr08s80LuZrydzcO4/1XT/iQE3f9zn1CKCiO4Wkz/IKjb7tG/Dely9fLmTm92j08cBrhQ0PpwnkU6bv/xKsMQcv/VdUTPvQD8Rk5y56evnSl56/eeLmG67BNe46o9/ZMNuFF1KxprQqqQibIJXIJLb1fbbz27vb+I+07rvsSRmsz9Y57Cqg9efGaSfIM5X2dPrRHQKeT8YKAICAIBBsCnv/6BtuSspAgIAiENQQQ4giPnP3BK4pUgVhlSZGAKudLT/DqZUuVUN1yfM5BQ8jmiDVHCOGRT958pO8miJ6GD3LkDIlZNK48CUvKlSgNDfk4MP5s68VQuMXYsrCHC6QPIaaGc6PfvZfvVPfXHz5T3l6LvAz99O2H/hoOutcoqMI+bzx2pVqjHSh29CgELyVCKRHuac5Kc59nK3qbu6xvjxxRd6/6Bn8cfOYwV1jhzMlVDp82Bbx8eXsuopO3ntI7JqHIWRSzPgR6t2lA+46dpWu371Pl1v0odswYVLZIXqpRrpgK99R2nDV9alqyYRcNmjifi6Q8UyGc2jXjdxDBFEwgNdOIVjIu6GJonn9TXv+oMqVJYdhNHWfLkEaRvkfPX5kkfS6Pnql+8AZOXLjGy3iQPdjrt+/I0vv1MgGfABO3i47Gzd7OI0cK2EeeP3/+UOuB41So67YF49Tz8LaINAgCgoAgYKUIBOxfQCu9KdmWICAIBD8CU9pV5GIs2Wjb6dscBvmEbj97q15zOLRwPYcRls2VhhDaWM5upSJnGZLGVeQoAxMzt4/faK6jLkTNcOfR2CtgqiqnodfCsD8Kvxha3FjRDE/VsUamjAkiLn7mnEMYCqQUYaJkykCgYFnZ23hhRgfayHl8B688IFTURFgnXg7HbtBWO3hTvHv80KZ5Pk3NH5htKeLHVgQXHk5DA0nPnDw+ISwXRLdQbNP3ajhGjoMfgewZ09L1PSvIgXPu9nOO3okL1zis86R6reaQyd1LJyuPXcv+Y1QbirKUKJCLsmZITXmzZqQhUxbRS87pMzSQPM3LZ9hu9m/K6A8FnkZj08gUPIqm7POXr6q5YK6sZBPH6xc2Wn+b2LEoUfy4Ft2vNkZ7x/3g3oPa4OU7c/mmWqbL8Kn65d6+03nUpy1bT/ab93ChnHaBEkaqX0AOBAFBQBAIBASE9AUCiDKFIBDeEXjMlTXvc6VLFAgZ16q8guMpywWg0MlWJoErOBcPpG8mV7vE58IOlfNxtU1dP3SeyyGSQWF3X+g8d4Zza968DMniGjarY8hHwGw4nBNVRw3t5bvPdOvpGwJJhR25/khVE0XFUbyQD+h07RG1m7mDi6s8ZUL1VhFDwzlwfIGLqQxkbT3frFv1glSPvaQBMZWvd+e5KmJjOM+/XNEThW1gILhi1omA0+mLFD1qVOrUpLZ6QTvuwMnz1KLfGDp27iqhUickSEAEQeauOi7Xe/HglerFRVcC21wePvU2pcsjXVvGNCm9XUNDulTJCfp3zWrrcuAMO0Eq4eevX4rwWXK/IMLGdu7aLepjwb32at2AGlYvZzzc4vMoUaJQrizpvfXXvKWQeojKchp4ZmKCgCAgCFgbAkL6rO2JyH4EgVCIwDH27PVm3TiEPEISAZaSvUnIIwPp00I4b7H3D1Ypbzr1jh+o6LnppLP+PDAPNp+6RV2qFaAYUXU6gCg2AyKYiYlbYlvvHoecTFrhXXThPiCHGVh3DwYPRs9Fe1WVUEgdwFPWff4eesPEyWVhN67wGU1JOSCcNV2SuHSTJRS+//xj8la+cPVMcxILhgOgPxhQq5Q3vap2upkrnvauXVivUYiKn8gtTMgFdNIk0hHdgK4l4wMfgY5DJpEre5GeHN9MtuwhQz5ctTJFlYbe9TsPVL7e4+ev1cK5ODfPMGwTeXRa6GRg7mwDyzWgSEyM6Dov+u4jpwlEMHO6VEp7z9RahfNko3NXnRU5ReETzc5euUkVW/Zhrb+UdGWXPVlyv9pYw3fIJ9y4+9CwyeTxKw4hDYihaumZzQu9TbHIYYeqajqgQxPqycRSTBAQBAQBa0RASJ81PhXZkyAQyhAolzuN8not3HORonLeXf4MyZTnb8n+S+pOqjAZghXIkJTuPHOj4WuOKN29N+5fFSnUPHJn2CsFT1gBA10/NdCfP7AWKnvWLJxJSTigQihsgBk5hYQ2MakXk6OJm07yuA3UjUkscvW2nrpNh9mzl51zE6sVyKDmgF7gEpZnqDduI3Wqml/p8iHME4QPhV1ypE6o+hn/QE7fq9X9jJuD5By5hajciaqmNUY6cDXSTKyN+JPm79bhgEI7pkJQg2QzMqmfEYB+3oK126lmh0HUrXld1uWLwgVLznKRlgeqsAuIXnwOt0SeHrxdA8bP5dDONHTq0g1at9PTm4w52tSv5uf1TQ2AdxGVPetUKqkkHKDdBxvSRScfYWrMoI5Nac22/bRx92GKEjmyysF7zPl/M+w3qu5Du7VU75bcr6n5yxbJR+5X9pq6JG2CgCAgCAgCHggI6ZNfBUFAEAgwAiiIgnBISBFM3XpGPx+KQXSqko8gPwCD0DoICCpHQkMPBiJ4bEIraj9rp9KSczh2U5E+1GYxJiQIZTNlKPhiylBxEzl2F7jqJgyEdFjjkpxL6Bk2iSm1ohXo07dOESU6jfuAfINmJbKlpDmdq/Icun827Vj4/RvrmkE3sDOL0muWnj19wCIy6weGtCFPC7IM3RfsoUOsZXjO5bnaEnCY0rYi6xDmDOktyvo+IDCiZxsWZf/BYuiHqO3gCfqeGVKnIPtJfyvPX5oUSWny4G40aNJ8mrt6q+oDvb6x/TqoPLeBXNylH2vK1atSWlWVjRjB6+8lCKMpi2imkBC0+HZyOCnyBWEIZxzVu52XwjIR/orgJW8QeYB77KdQGy6CAvkGvGBxbWLTxEFdqH7VsurckvtVHa30RwQzmFnpdmVbgoAgEM4Q+IvDlkxnXoczIOR2BQFrRsDZ2ZmyZ89OJye3UVUxrXmvKGjymiUY4rCoOSp4wutlaCgtf5ULuqBqZB6WcNByypBn5vLCjZJw2KWtiQIshnP4djxy7VGaxTp69r1rETxyVx685jDSH4TwTVQQtcTef/6myCnEzJHHp4V6Go914xBPyEVEYZKHqqJYwxoIn/E+n7t9VIVbbJgQZE2ZQB/yatzPms7z9V5KXfsOooEDBwbJtrJlzUp1yxWkod1aBcn8gTUp8uGOnLmkPH3JEsWn3FkyeJE+wDqv3rzjKp/3KCEXQ8mVOR17/3Tkzu2DO71lEXWEUJot1mLBRiESP23pelozfTjVKl+chdTv0qcvXzjHLQMliGtjwQxEyDO8w6GgCAeNx4QPhV1MFWCx5H4tWlA6eUPAbuoiOnr5Ll28pIvC8NZBGgQBQSDMIiCevjD7aOXGBIGQQaAoa9j5ZPDW5Uuf1FsXePVAEgPbsF5+9ib61aC5h1BM3wwkErmL1m7wxhpLVFj7nmV/OgRAqjRvmDlMkiSMxzl1hbxdjm9rwyGglpEyb4PNNIBQFszl9995jIO8A14+mSX369N4uSYICAKCgCDgHQHTcR3e+0mLICAICAKCgCAgCAgCgoAgIAgIAoJAKERASF8ofGiyZUFAEPAZgTgsuZA0bqxg08PzeTdyVRAI/QjYxIpJCC01FY4Z+u9O7kAQEAQEgbCPgIR3hv1nLHcoCIQ7BPpwMRa8xAQBQSBwEBjAFTjxEhMEBAFBQBAInQgI6Qudz012LQiESgTGOByjt1zAZUaHyqFy/9qmobOHYi0ZPYTatXbtHcVqHr5mfTVXd6WFh0Iw0bnKoW929s4zrgj6m8rkTONbV3Uddbge8xrPuEhL7rRJlLyEuYHfed7rj14TKndBmy+RbUyTXVHABhIa6J8+aVzJAzSJkvU0Qqph+abdVL1sMX4VtZ6N+XEn7z58pPtPXnC+X2qKGSO6r6NPsyzFtx8/qFzR/N76fv7yjW7efUCx2TvpW/6gt8EeDY+evWSdw6+UI1NaL5VIf/36TT+4aq9PhsrDpu7Br/fo0xpyTRAQBAQBvyIgpM+viEl/QUAQ8DcCW0/fUfp8oZn0ffj8ncoNWUklucjLxsH1vWFxnmUReizcq4iTdtGWxdtHtyjjo0TCSeenVHuMA8VkIfnH9r21oWbfIb8AmYvnbp9UH8hWQB8RshIga5r9+v2HurGQ/LYzt+kPV0jVDELy0ztU0ovUo98/XPV00d5L9K9BUeeKedMpkp6Ew2XFrA+B+4+f0/LNeyh54oShmvRNXryOZi7fSEfWzaZCXNXTJzt+/ipVadOfYrJAvOv5nfquP37+pEETFxDE0jVLkiAetW9Uk4Z0Na8jqPXFuwPrG05btp5uuDxUzXGYOLZvVIPG9O2gzpds2KUkMNSJmR+pkyehW/tXe7vql3v0NlgaBAFBQBAIIAJC+gIIoAwXBASB8IMA5BkG2B+kX3/+NXnTrh++UJPJW+g9E8NsLORelj12p28/pUv3X1GPBXuV18xURVB41zrP3UUGXMvk/FrjY9cPVHv0evrJREqmgosAAEAASURBVK0SkzJUPYUeIYhgi2lb6cDo5hQzWhTVvdeifbT51C1eO7YSZ//07SftvnCP9l26T13m7qYtdg1Vv8lbTtGCPReVlAME6CFBsYvnPHD5AbWbuYMc/5HQPg1/eQ88BOCtPnDiPC1e70nefJod3rK2g8bz34rnFxha/75j55A9ez0Txbcl6Ak+fv6adjmdojFzV1CqZImoeR2fIwxu3XtMnYdN5S89/qWmtSoqWYmt+48pqQqIyg/v0VrJU+RkSQxTBtmMN+8+qHGG1/16j4Zj5VgQEAQEgcBCQEhfYCEp8wgCggD9ZjLk8tyN0iaxtSic0T+QQc/v/qv37KGKSSjYEhwG7cE+i/fRA17X0FtmvPbuC3cV4YOQ+/ZhjfWXe/PYVU7XaMPxmyZlIHozMXvx7rO+v28HU7eeVoSvSakcNKdLVdV9RNPS1GbGdtpx1oVms0bh4AYl1PPYcfYOxYsdnQ6Maa736nWo7EqlB6+gozce08evPxSOq5yuq3nWD6pHxbKmVMeD6henvD0X0Zk7z+nJG3dKlTBwS//7dp9yXYfAC9e3hLBCeJCCyt67fyJo+qVPlTxAen5+2V8bJm8Hjp+nd+4fLR7WbcQ0ev76rbf+j5+/ohVb9qr2I2tnE0TrYTfvPqSCdTpQj5Ez6H+VS1MM9g6as452k+jnr180558+1LZBddUNXsKi9TvT5MVrqVuLukqEvkE1nZi84Tw/OeSzVJPu9O37D7KfNER/yT/3qB8sB4KAICAIBCICUr0zEMGUqQSBsIRAvl6LKHHzqXTk+iMvtwVil63LPErC116804UWXmOx9WZTtlD69rOo5KDllKbtLKo8bDUt3X/Zy1jjkyyd51I6HmNsjSZuUmvDo6XZN/5Q1XfJfkrVZgYV6bdUjUOYJcIpg9o4clKJyBfKlFx58Myt5+r+VV3Kzl4+Q8udJrE6BWE1tpWHriqPWrfqBX3MyTMcd+HeS3Vav4TXMLj6xbOp9qv8PGB4R45gcSZxiVn0XrMcLCCPvD4YROsR2vnhy3d1ni2l596TxYutchJxwdTe1QD5YTECq7buozi5K1PFln28jZm7eou61pPJCexfzgv9Z+Yyyl+rHWUo25iyVmpO6cs0ouZ9R9Ozl67exmsNC9ZuV/PMWrFJa1LvZ686q/ZOQyd7aUeoZO7qrSl5sbqUq1prSlWingpfBMkMakNebFbO4SuePychjNI3W7bRkbYfPEG9Wjfw1v/iDReFWZG82fWED/Nlz5iW4JlDHt7tB0/MLgEP4sUbd5Qnr3U93Rcp6AxR+0K5s7Kw/L+089Aps+NHzrana7fv04xhPdUYraNf71EbJ++CgCAgCAQ2AkL6AhtRmU8QCCMI1CyUSXmKtp+54+WOjrF36DWHMeZjwXOQAoQLtmYP096L99nDF5cG1itG/2Ox8huP39BADoVEf3OGYiG/f3sPlfzz5z+1tjYO4VF1x2ygFUyQsEavWoWpduHMXJjElfPg1nsjptq4wHovnDkF7RrRRL3Gtypndto6RTKra+uO3eT7130wv/30LS09oCO/VTls0tDgFR2y0olAwoY2Lml4ycdjt4/f1PXMyb2K2edIrSNst3hNWNrEtrT7nyY0unkZda79QMEYeO6wLgTb8cEUIZ2wiZtPqmf69ccvmr/7gnrWCB9Nw3OJBQyBGuWKUYS/ItDJi9cJ3jtD2+h4mP8W/lDdyqVU89QlDjRp0Vp68vI1tahbmfp3aMJhi3Fpy76j1HrgOMOhXo7hqcI8/xl/wcB/Q2gHedFs77GzVLXtAHrIRUuwRo+W9SiebRyav2Yb1ens6a3S+gf2+6KxA+nAyunqlStLeh+nv8OEbcCEeYR+I3u19db37XvdF0RZ0qXydi1nZt3czuz1M2d3Hz9Tl0oXzuulcAsaM6ZOoa5hD6YMxHmG/UZFXhEWamh+uUfDcXIsCAgCgkBgIyDhnYGNqMwnCAQBAhEjRlSzGn5gC4JlvEzZuFR2mrPrvMr/mtKuIkWMoPuOaMup26pfk9I51DsIBCpI1i+elRZ2r6GfI4FNDJrneIHglSrFRU8CYg5Mos7ffUG50iSi/ZyvBpICW3v0usqVm7n9rNmKl/BCjt1w3NflW5XPzcQno6/9fOqQKXl86lq9gLpvhE+igIvmQauaPwPn3+k+fGKOH+xJ6TB7lyqasrB7dZVD59PchtdQqOUt5xceuvqQmpfNqb8EjGBP336kL99/qrBOEFYYyHkvLjDziL2nN7n6aNTIEWlIwxLqGn70ZYmLsxzGiUIuyw9epUgRIxCIXwwuLDO2pfdwNv3AIDz4zd4u7Xc/KJaJFCmSFxIUFGsYzhnXJjZVK1OEth04Ttv2H6euzeuqywhNRBXOFEkTURkmHbD9nOcGO7NpIaVPnVwdg5RlrtCUzl/X/Q2qRn/+gCdv4IT5yju2aupwqltJRzbturWk8s170+Ezl+gUk9Ni7IUzZUOnLaabHsVOTF1HW8wY0Wj1tOHmLlvcjgItrQaMVXu1nziEopiohJsxje73/OCpi17mBdGFBw/mzDl75uzuw6fqUpIEnkWQtL4Z2dsHQ76esQHHjnaTVY7hpMFdjS9b3TkqC0eKLB/9rO7ByIYEgWBAQP7ygwFkWUIQCCgCNja6XCp3zr8KLsvKYX45mWTBm3bq1jMqmT2VyiNz5Ly16FEiUZ0iWdRW8qZLQntHNaPUBvler95/JrxgPwMhTGzrad2H3DYV8ugJH+ZuVDI7DVnhRMduPtHnpqHd0NxYIuLgFfPf8Gt9cX8BNVT2fP5WF/KKuRCSqtndF270iGUcsqTUeedGrTumvIETWpdXhVi0fpa8g2CDoE3cdJJzCL8pUn2Uw3BnM0mHReCa8RpJ1+b7+uMn4dkhPBfGEasq/7JinnTs2fiLyaA7ffd4VuijVfDEPRxnfC2VkVCTB9KPjxxyqv3uB9KUXqaxsbUh90+631MvF4LwBJ4gkD4UCNFI3+a9R9WKzbn4yF+o98825e9unD/6r57wfefnd/3OfSbBEQjH8H5rfdUAP/64cuse3WPvVvIkCal2BU/yjzDLZrUr0pApi2j7oZNmSR+8lWevOPu4qk/5cz4ONLo4bNoSFTo5dUh3FQ5qdFmdFsmTnVIyaX7Koa8IgW31vyrqd3gxV/LUPHRRfCA7j5h4w+LE9h5mmoIxgn3+ogvfViceP9Y7HuKCMa9U4Zi82QL2pZHhvEF17P7xM9naiNc+qPCVeQUBa0ZASJ81Px3ZmyDggUDixIm5AEF0VcBEK7IRHOA05kIh1x85cXGQO4r0HWLyhMIfDUtk0+efxY8Tg1zdv9CINUfoyoNXHDb4UU8e/LvH/5SanOfoey/fqZPR64/T1G1nPC/wETxYMOzBVGEXVMt8tqK36uPTj8ge3lSf+vh2bRrvbTtjlZWJ3fhW5VUuHcI8x288Qfu5CmanuY50dEIrJqEPVKXM8rnTUofK+Xyb1tv11uXzKCIOMgyZBc0QhvmOSS7COqMxMTc05PS9WtWXbj97q8aO5HEYmyttYsrDLxSBQZEaFG9pzV5PjN9z8R71ZO/gDPak5udw3oB6Qg3349sxKqF++vqd0qVL51tXf19Ply493bt/y9/j/TOwcslCKm/sFOvMvX7LBYnYs7Rx92E1FUifZlnTpybIAwyaOF+RM/QNiBkXu7z7SOfZevXGTeULGs79yYPc4Jo522s/hT1v/5m7HGjt+46fozmrtlClEgWpS7M6ZueNHi0qLRk/iJr1GaVCYBEGq1lmDvkE8fNJsy+WhzbgGzfv3jzo/sHgqTU0EG/IO8AGcPhtaLC7j19QzgJFQ8NWZY+CgCAQyAh4/VQQyJPLdIKAIBA4COAb/Tx5ctMFDt9rUTZX4ExqwSz1imWh4asPKzmAia0r0JbTug/ITct4hnwhfLKc3UolN5CBww7/x2MgRo68s7mOOs+TBUt56fLSw0uoNX72IHYgHjbGFTsz60LfvLV7DIYXyxJhdG2tgLwfvPpADe9du7AiyTgBqfqnWRlF+kAAQWY2n9ThCJJclIvSaKYRWLQlY4mFzUN0cgrade0d97SkZ01qxs8BIZ0f2NuXg72yhTOloEJ9lyjSib7I24PHNVOy+GQbK5ryDMGDixfCTsdtOMGSDPd5/HdF+NJ75GRq68CTCmkH5HUilDQ4Sd+Fey/UfvPkyaNtJ9Df8+XLRzu3b+MQzz9BGkZquPHI7G1qWK0czVuzlXYcOsHhnHno6u17Kh8sXapk+q4t+4/h6ycJZKZEgVzKw5U3a0blgXvpAxnTT2B08MLVK4HTiF3CuLZmvXlFuSiKOYsaJYq5S4HavsHRSc136aYL5a3pmcv38fMX1Y625IkT0K4lk6hkQf43cvsSVezlzv0nlIgJdelCuWndzkM60pcxjdm9JWY9PxjyG40NRV5gCeLZerm06/Apus3rFM2Xw0vxFi+drOgEFUav3rpLrTp2s6JdyVYEAUEguBAQ0hdcSMs6gkAAEaharTrNmT5ZhXwZh+4FcGqzwxPaxKQKHP6HD/5HbjxSxVpSJohDkCTQbOaOs4rwwWOFUEXN5nqEGmrn5t6heWcYqgbvAcIgDQ3FWxCm2ZhJSB0uEmNokFOAXh32aspAlFFQxjdD9cx6HDYZEHv82l0Nz5EqkZdpQIZjR4+ivJIIoUyZMI4qouKlE59AYB1hlQhD03T2jPvg3IkJGDybNbjYTtlcafRdtnmEwcLjB4N0w7IDV2hks9LUvUYhfT8cIAQUBk1BrUpqdo9CMOqCx4/86ZMq0oeiO8FpKAxUsEB+ihdP92E8KNauUqUK9e7dm+B1A2EILmtWp5IifVv3HSPNs9TCQEPu9v3HivBFixqFrjouJy28EOS01+hZPm5TC/lEQRdDQyinoUGaAWYTJxbZT/zb8JIqMoN8PVSuNGeQTrjifNfcZdUeK2YM2rd8qo99fLuYKlliVbzFuB/CXPFvRVTO74OXDp7QgyfPU1qWaujAMgua4d+WLsOnqrDYTGnM34+WE3iPxe6NDaGssIJGovGrtuxT7a3/51ntUzVY6Y8jZy/Tl6/fCL/3YoKAIBD+EBDSF/6eudxxKEWgSZMmNGzYMNp/6QEZV4EMylvSvD09WVwchT261yjoJZfoFocLwiASrhk8VptOOmunZt+h+4aiIgg5hPcJturwNS5y8sfLmIIZkykvJ3LSDEkf5BpqjFxHGdmzeGZqOy9jtJMvvGdnXsM3Q0XSgFq2VAmUEPuGE840vEkp/XQgMMAExA+keUjDkuql7+BxkLnTXPrO38YjBNTQ4E1F6CWqc6KIDTCCHh88mLU9KoYi/wsEHFyuosez0OQXIMbepVoBfZ4fcvaWcyVUWB7OyUQ4KAy/W/D6wSuo2XrWFoQZy1Bo14PiHSHE2/j+xk2YFBTT6+fMnDkz5cuXl+w37wlW0ofcL4RvHuOqj873HirtOGjIaaYVHMnFVSc1wodrm/YcIc3DpfU1fte0/C4bEDJ49dZs3++lK6pgglQi7BGhnhk9CJEiSSxQDsH0lVPszGoD3n30jG74UA0Ti8X0QRPPy2Z8OBnRsw3hZWypS9ZXmnhnNi9Ul5zvPaIOQyapEM7z2xbr/42CtxTeuCqlCnspAHOFPV4ojAWM4X0tkDMLwdP64MkLgnexYfVyal6Q5X0nzimsyhfNp98GCPjRc1dUpc/aFT1zIvUdrPBgOf+eFy9ejFKlSmWFu5MtCQKCQFAjIKQvqBGW+QWBQEIgffr0VLVKZZqx41ywkr4q+dOTTcyopIVcoqqnoRXgkMs7z9xoOOf0oTLkG9aqQ67Z3Re6PDwIe8PbVoCJm7EhXBOkr8XUbdSQvXgQPzeWiMCYfnWLkMOxG4TKoVGY9KAaKEIXNW8i8tDMGXL6Xq3uZ+5yoLZ3ZW9h+1k7FfmCVEVlxu4e4wBPKQwVMjVPjKULg8yVZT1C2PW5nZVMRo2CmRTpg26h26evFD92DPboXaZrXHQHchYa1g1LZqPJW06poi81RzkQirY8d/ukwnXhKQTpRt5mZPaaQL4B4ad5ei6kWoUzqXWwbzyfJHG5uIdBSK+le/dvv8X7LlGEiJGoZcuW/p3C4nE9evSkDh060DCuWpk2pfffUYsn8mPHZpy/hwqYrpxD1pw9f7FiRtfPkC97RuWZQkXPAePncmhnGuWNXLfT02MNPb429avpx2gH+XPoZENQLAZi48kSceij0ykmdl49fZB/6N++CY2Zu4KqtRuo075jz9zGPYfpEFfAhLZdzfLm/672r5imLWkV75nZK5klfSom0Y+oRb/RTNrKkwsT2nHzV6lcvDkj++j3CcJWrH4XdX7XyUGFh+JkSJcW1P7viUrI/cyVm0q+YinnVaJC56zR/bgaqeczusAVVEGm82XP5E0zUL+QFR1o3uO1a9da0a5kK4KAIBCcCAjpC060ZS1BIIAIjB4zlgoUKEAI4zP0eAVwWh+HR+VvwetySCXK+EPkO7WHqLc2aFjjUqrCJwjHgGW6D6UggsfYWwUChMqPkFwAEYHqgxZWiPEYC6Jx6f4rVYkyXuzoily4cwEPEDwtjDVurOi0fWgj6jjHUc2F+WCQRBjToqzan2oI4R/Ayd0jV+4y5+vhBYP8Qf+6Rakbe0kDYgj/hCEM1fnpGxW+qWGOiqrIpxzcwPODOkJE1w+qR50YN1T7xEszVCuFFAe8j7A1/etS/2UHOMfvAa05ckPrpjx8c7tUZeLv6f3TXwyCA+QgznG8SP0GDCJbW685VEGwHLVo0YLGjRtLdlMX09oZI4JiCZNzNq5ZnoZNX6JCmw1DO9E5DYcoTh7cjQZNmk9zV29V4yF/MLZfB5XjN5CLu/QbN4fqVSmt/3tCuCMM+W3zmaD0HDWTVm/TefcK58lGc0f2JYRkav3Qd2DHpuxB/kMTF65l+YZ5aFJWivPgoC8XXHl72roBeYe0x8opQ6lV/7FcyOWYemE+FHEZ17+jIr+m5tf+jcE1VFZ14/y94fxcQKphqECKIi2tjEI4j5+/pq5DuD002N9cjTVbtqzUoEGD0LBd2aMgIAgEAQJ/cShH0JffCoKNy5SCQHhFAF6J7Zsc6MTEVpSAK2dag8EbdZVDEFE5EuGC2r6Qc+Pywo2ScOVIw5BB4z2/fPeZPVAf1VgQJHOGde4+f0d3uZpnXA5BhKcwuIq0mNuTqXaEwcJD9vLdJ6WVl5Vz7FDlNLANBAlC7AjXLJE9pVkscB1e1IecKxk5UgRCwRZj8q7tDfmUEI1HOGmaxDYE7UHDD8Zav6B6b8ZeXxe3X3TjpjNF54q1wWEHDhygSpUqKU25/3mIowfHur6t8erNO5YquEcJ2SuXiz1vmmah2wd3evvOXeXcmfMco2jHTfZ6JeTiI4YhoqbWRKESFJP5+u0Hz5lCH+ppqq+1t0GX77rLA3r49AXlyJTOx7xEc/cCXcDz13RfOsFzakoX0NxYa2xfu+OA8mAeO3aMSpYsaY1blD0JAoJAMCAgpC8YQJYlBIHARMDd3Z3yciXPFLH+ok2D6ykvUmDOL3MJAiGFAArPjF5/gg4fPhzsH047depEDuvW0nGH2aGa9ITUs5N1rROBm5x3WbpJD+rUuQtNm2ZdIbnWiZjsShAIuwgI6Qu7z1buLAwjcOXKFSperBjVKJCO5nHonblv+8MwBHJrYQwByFh0nLNLfTDt08cz/yq4bvPHjx9UulRJevXiGR1aNd1sOGBw7UfWEQQCisDTFyyn06I3pc+YhQ4eOsQFayIHdEoZLwgIAqEYAfNxVKH4pmTrgkBYRwDaZVu3baOtrJ/Wae5u+sUhTWKCQGhFYPXh69R53m7q378/hQThA25Ro0Ylx917KHqsOFSxZV8VHhha8ZR9CwIuD59SBf49jp8oCW3fsUMIn/xKCAKCAAnpk18CQSCUIoAcpD179tLey4+o1pgN9ILzx8QEgdCEAL6sGLrKiXot2kt2dnY0efLkEN1+/Pjx6dix4xQ3YWIq0ag77T9+LkT3I4sLAv5BYKfTSSrdtAclT52Gjhw5GiwFkfyzTxkjCAgCwYuAhHcGL96ymiAQ6Ag4OzvT/+rWoZfPn9KIxiWpRblcwVp4I9BvSCYMFwicvfOM+i07RI/ffKLFS5ZQ06ZNrea+v379Sp05x2/V6tXUul41Gtm7rSqIYjUblI0IAiYQQOEfyICgcEu7du1ozpw5FC1a8FTdNbEdaRIEBAErQ0BIn5U9ENmOIOAfBL59+0bDhw+n6dOnU1quzNilSl4u659NX47fP3PKGEEgsBFANdcj1x/Rwr2X6eCV+1ShfHlauGgRpUuXLrCXCpT5Nm3aRD16dKdPHz9S+4Y1qEOjmkrAO1Aml0kEgUBCAKGc0BNcssGR4sWPR/PmzadatWoF0uwyjSAgCIQVBIT0hZUnKfchCDAC9+/fpzFjxpCDwzr6lwWIi2ZJSXnTJaLULMIdO0ZUvaaXgCUIBBcCX378pNfvvygJi6M3n9K7j19UwZQhdkOVTEJw7cO/68DrB4/J7Nmz6Nmz55SdZQCK58uh9N/i2cTmXCmRu/UvtjLOfwhAjuOd+yeC4PqJizf4/RGlTp2KevbsRV27dhXvnv9glVGCQJhHQEhfmH/EcoPhEYH379/Tzp076cD+/XT50kV6+uwZffz0OTxCIfccwghEZ3HrRAkSUI5cuahMmbJUu3ZtypgxYwjvyu/L/+EvUSAl4ejoSOfOnSUXFxf6wHp5v3//9vtkMkIQCAACkSJForhxbSlz5sxUuHARql69OpUuXZoiRJAyDQGAVYYKAmEeASF9Yf4Ryw0KAkGDwKNHj6h+/fp07949WrVqFdWsWTNoFrKyWXv06EGQzDh+/LiV7Uy2IwhYLwIg+7FixaI1a9ZY7yaDeGe3b9+mhg0bEv7tnDlzJrVp0yaIV5TpBQFBQBDwREC+FvLEQo4EAUHAQgTg7ciXL5/ycly4cCHcED7AkzhxYnr9+rWFSEk3QUAQAAJv3ryhhAkThmswsmTJQufPn6eOHTuqQiv16tUjNze3cI2J3LwgIAgEHwJC+oIPa1lJEAj1CCDEDaX14dXDN/enT5+mDBkyhPr78ssNCOnzC1rSVxDQIfD27dtwT/qABPQgp0yZQodYLB0EMEeOHLR37175NREEBAFBIMgRENIX5BDLAoJA2EDA1dVVFd6YNm0aLeKKi/b29hQ9evSwcXN+uAuQvo9czfH79+9+GCVdBYHwjQA8fQk4t1NMh0DZsmXp2rVrVK5cOapatSp1796dUIVZTBAQBASBoEJASF9QISvzCgJhCIGTJ09S3rx5VS7KqVOnqH379mHo7vx2KyB9MAnx9Btu0jv8IoBiN+7u7kL6jH4FbG1tVY7junXr1Dv+jUW4vJggIAgIAkGBgJC+oEBV5hQEwhAC0P4rU6YMFShQgC5evKjIXxi6PT/fipA+P0MmA8I5Ashb+++//yS808zvQePGjen69euUIkUKKlasGI0dO5YQSi8mCAgCgkBgIiCkLzDRlLkEgTCEAEIYUZ1zwIABSvtv27ZthG+mw7sJ6QvvvwFy/35FAKGdMAnvNI8cCN+BAwdo4sSJNHr0aCpVqhQ9ePDA/AC5IggIAoKAHxEQ0udHwKS7IBAeEMC3zgULFqQTJ07QwYMHadCgQfTXX3+Fh1v39R6Rxxg7dmwJ7/QVKekgCOgQQBEXWHiv3qlDw/xP/Bvbp08fFeL55csXyp07Ny1btsz8ALkiCAgCgoAfEBDS5wewpKsgEB4QWLlyJRUpUkRJE1y+fFmFdoaH+/bLPcLbJzl9fkFM+oZnBODpg3B4vHjxwjMMFt87KnqeO3eOunTpQh06dKC6deuSRpwtnkQ6CgKCgCBghICQPiNA5FQQCK8I/Pjxgzp16kStWrWirl27kpOTEyVNmjS8wuHjfSdJkkRIn48IyUVBwBMBEJa4ceNSxIgRPRvlyEcEokSJQpMmTVL/Dl+6dElJO+zevdvHMXJREBAEBAGfEBDS5xM6ck0QCCcIPHr0iIoXL07r16+nLVu20OTJkylSpEjh5O79fpvi6fM7ZjIi/CIgcg3+f/alS5dW0g4VK1ak6tWrqy/kvn796v8JZaQgIAiEWwSE9IXbRy83LgjoEHB0dKR8+fIRyqqjXDhCicR8RkBIn8/4yFVBwBABEWY3RMPvxzY2NrRq1Sr1pZyDg4OqoAxhdzFBQBAQBPyCgJA+v6AlfQWBMIQASoLb2dlRzZo1qXbt2nT69GnKkCFDGLrDoLsVIX1Bh63MHPYQEE9f4DzThg0bKmmHVKlSKWkHVPkUaYfAwVZmEQTCAwJC+sLDU5Z7FASMEHB1daVKlSrRtGnTaNGiRWRvb0+oSilmGQJC+izDSXoJAkBAPH2B93uQPHly2r9/P02ZMoXGjRtHJUqUoPv37wfeAjKTICAIhFkEhPSF2UcrNyYImEbg5MmTKjwIeXynTp2i9u3bm+4orWYRAOl7//49/fz502wfuSAICAI6BMTTF7i/CZB26NWrlwrH//79O+XJk4eWLFkSuIvIbIKAIBDmEBDSF+YeqdyQIGAegenTpysJhgIFCtDFixcV+TPfW66YQwCkDyayDeYQknZBwBMB8fR5YhGYR9mzZ6ezZ89St27dVOVlhOmDYIsJAoKAIGAKASF9plCRNkEgjCHw8eNHql+/Pg0YMICQB7Jt2zaytbUNY3cZfLcjpC/4sJaVQj8CIH0JEiQI/TdihXcAaYcJEybQ4cOH6erVq5QzZ07atWuXFe5UtiQICAIhjYCQvpB+ArK+IBDECFy/fp0KFixIJ06coIMHD9LgwYMJ4UFi/kdASJ//sZOR4QuBT58+ETRAEyZMGL5uPJjvtlSpUkraoXLlyqo4V+fOnenLly/BvAtZThAQBKwZASF91vx0ZG+CQAARWLlyJRUpUoRAUi5fvqxCOwM4pQxnBGLGjKleEt4pvw6CgM8IaOGG4unzGafAuBonThxasWIFbdiwgTZu3KjC98+dOxcYU8scgoAgEAYQENIXBh6i3IIgYIwAvlnv1KkTtWrVSon5Ojk5UdKkSY27yXkAEACRFtIXAABlaLhAAKGdMPH0Bd/jbtCggZJ2SJs2LRUvXpxGjhypdFiDbweykiAgCFgjAkL6rPGpyJ4EgQAggKqc+B/9+vXracuWLTR58mSKFClSAGaUoaYQENJnChVpEwS8IiCePq94BNdZsmTJaO/evUqWBzl/kHa4d+9ecC0v6wgCgoAVIiCkzwofimxJEPAvAo6OjpQvXz769euXKuddt25d/04l43xBQEifLwDJZUGAEYCnDxqgCIkWC14EkLvdo0cPunTpkpKXgbQDdFnFBAFBIHwiIKQvfD53ueswhsCfP3/Izs5OJfCjbPeZM2coQ4YMYewuret2hPRZ1/OQ3VgnAqLRF/LPJWvWrEraAQSwS5cuVKtWLXJ1dQ35jckOBAFBIFgRENIXrHDLYoJA4COA/3lXqlRJhfHgW1x7e3v1zXrgryQzGiIgpM8QDTkWBEwjIBp9pnEJ7tbIkSPT+PHj6ejRoyrfD9IOO3fuDO5tyHqCgCAQgggI6QtB8GVpQSCgCJw8eVJVaEMe36lTp6h9+/YBnVLGW4iAkD4LgZJu4RoB8fRZ1+NHbt+1a9eoatWqyuPXsWNHkXawrkckuxEEggwBIX1BBq1MLAgELQLTp09XEgwFChSgixcvKvIXtCvK7IYIgPS9e/dOquIZgiLHgoARAuLpMwLECk5jx45Ny5cvp02bNqliX8j1Q0qAmCAgCIRtBIT0he3nK3cXBhH4+PEj1a9fnwYMGEBjxoyhbdu2ka2tbRi8U+u+JZC+//77T3JjrPsxye5CGAGQPtHoC+GHYGb5evXqqVDP9OnTq+qeI0aMkC+xzGAlzYJAWEBASF9YeIpyD+EGgevXr1PBggXpxIkTdPDgQRo0aBChQptY8CMA0gcTrb7gx15WDD0ISHindT8r6Lfu2bOHZsyYoeR9IPfj4uJi3ZuW3QkCgoC/EBDS5y/YZJAgEPwIrFy5kooUKUIgG5cvX1ahncG/C1lRQ0BIn4aEvAsC5hGQ8E7z2FjLFXxx2L17dyXtgErQefPmpQULFljL9mQfgoAgEEgICOkLJCBlGkEgqBD48eMHderUiVq1akVdu3YlJycnwrezYiGLQJw4cShatGji6QvZxyCrWzECv3//pg8fPkh4pxU/I8OtZcmShU6fPk29e/dWJLBGjRry75shQHIsCIRyBIT0hfIHKNsP2wigKifCbdavX68S7idPnkyRIkUK2zcdiu4O3j4J7wxFD0y2GqwIuLm5qbzXhAkTBuu6spj/EYC0w9ixY5W0g7OzM0HaYfv27f6fUEYKAoKA1SAgpM9qHoVsRBDwioCjoyPly5dPJdZfuHCB6tat67WDnIU4AkL6QvwRyAasGAHk88GkkIsVPyQzW8OXjVevXiV4++rUqaPkgD5//mymtzQLAoJAaEBASF9oeEqyx3CFAHIq7OzsqGbNmlS7dm0VbpMhQ4ZwhUFouVkhfaHlSck+QwIB5PPBxNMXEugHfE1IOyxbtkxFmcDbB2kHhH+KCQKCQOhEQEhf6HxususwioCrqytVqlSJpk2bRosWLSJ7e3uKHj16GL3b0H9bQvpC/zOUOwg6BODpixAhwv/Zuwr4JrKve/4ruDtFC8Xd3d3d3d0p7u66i+viDou7u2uRKu7uy+733fPSCUlI2pR6O5dfOjNvnt6E5J25956LOHHiBN4ges+BrgF6mZA5Om3atChSpAgGDx6sp3YIdK3rA+gaCHgN6KAv4HWq96hrwKoGGPvFp6a25Pjx44o1jXF8J06cUO40turq5SFDA4kSJdJj+kLGW6HPIgRqgJa+2LFj49dffw2Bs9On5BcN8Ltux44dmDFjhnooWaBAAdy6dcvHLvTUDz6qR7+payDINaCDviBXuT5geNUAGTjbtm1r1T1m6tSpKgVD7ty5cf78eQX+wqueQtO6EyRIoIO+0PSG6XMNUg3oOfqCVN1BMhgZpC9cuKDGYsz57NmzrY57//595M2bF5s2bbJ6Xy/UNaBrIOg1oIO+oNe5PmI41MD69esVA9p///2nCFk0goO3b9+idu3acHZ2xqhRo7B582bEihUrHGoodC6ZT79pzWAcpi66BnQNmGtAz9Fnro+wcpUuXTr18LJnz57o0qULKlasiMePHxuX93//939o3Lgx3rx5o1INPXjwwHhPP9E1oGsg+DSgg77g070+cjjRwMuXL1WePSbA5Y8haczr1q2rmNHy5MmDY8eOYd++fejbty9YR5fQowHG9BHIa4QVoWfm+kx1DQS+BnRLX+DrOLhGYOqgkSNH4ujRo6AbJ1M7aFa9adOm4ciRI2pqnz59QoMGDdT3ZHDNVR9X14CuAYMGdNCnfxJ0DQSyBrp27Qpa9Aj4KExYzB9EUmETNFy8eFG5dgbyNPTuA0EDfP8opk+5A2EYvUtdA6FSA7qlL1S+bX6aNGP7Ll26pJima9asifbt26NPnz5mv3eMVx8/fryf+tUr6xrQNRDwGtBBX8DrVO9R14BRAzt37sSKFSt+YDqjdYgxD/xxTJw4sbG+fhK6NKCBPrKu6qJrQNeAuQZ0S5+5PsLqVbRo0bBgwQKsW7cOu3fv/mGZ/L0bNGgQzpw588M9vUDXgK6BoNOADvqCTtf6SOFMA+/evUPLli0VZbm1pdOVs2HDhvDy8rJ2Wy8LBRpg/GWECBF0S18oeK/0KQa9BnRLX9DrPDhHPH36NO7du/fDQ07Oib93derUAX8XddE1oGsgeDSgg77g0bs+ajjQAMlZuOnhU05rQndPxjtUr14dX79+tVZFLwsFGqC1j+k4dNE1oGvAXAP8/osXL555oX4VJjVw+PBhTJ482SapFcmuHj58iA4dOoTJ9euL0jUQGjTwW2iYpD5HXQOhTQOHDh3C3LlzfZ024/sYD9G9e3fMmjXL1/p6heDVAAE8XdYI8rRXpEiRsG3bNpW8mCx13NiQrIdU5oxx0UXXQHjQQOHChdX/DaYxIast8/NFjBgRtP78/vvviB8/vgKAPPI+k7brEjY0QJZOkrVoZGW2VsXfO4Y7kO2TXi666BrQNRC0GvifWBsM7BJBO64+mq6BMKsBWu/Sp0+vYvZsWfm4Cfrnn3/UJog/lnR7KVKkSJjVSVhZGNNqDB482LgcbnL4XlK4oTF9vwkKuQHWRddAeNAALTh80KVtKfj/QgMB/K7TJE6cOOq7MXLkyFqRfgzlGtC+Fwnk+Z77lsImSpQouHbtGhwdHUP5yvXp6xoIXRrQH7WFrvdLn20o0MDAgQNBi48pAOC0NXBAIMAEt0zVQAKQGTNm6IAvFLyvnCI3trReaMINLl1z+TJ9v52cnHTApylJP4YLDZQqVcpsnQR6/H9hCvh+/fVX9X9IB3xmqgr1FyRpoYfDwoULlXdD9OjR1Zq03zzLBfJzwQedfFCmi64BXQNBpwHd0hd0utZHCgcaoCsTKaxNn3Zz00N3JrqzMBF7/vz51dPQcKCOMLnETp06Yf78+WabWdOFcqPTtm1b/PHHH6bF+rmugTCtAbo0+xa/R9BHog+dsThMfxTUA7CzZ89i165d+Pvvv1VaIq6Y778G9GgVHDBggMr1F7a1oa9O10DI0YAO+vzwXrx//x4uLi4qbuHDhw9+aKlXDQ8a4I9Zr1698OjRI7VcujEVKlRIgUBafvwr/JGMESMGkidPjjRp0ugxMf5V6E+2d3NzU/rXgL1lN3RvInV5rVq1LG/p17oGwrQGmKCbbnvWhMm869Wrh+XLl1u7rZeFMA3w9+zWrVvKa4WMm6aeDH6dKvdOV65cUeDvwoULZgyew4YNQ8aMGf3apV5f14BRA3QXZqwwP0dMH6KLbQ3ooM+2btQd5lJbvHgxNm/aiAsXL/lSW7+tayBoNBAlSmSULl1aNlH1FbgwdTkMmhmE71FI0LJ161bjU2tLbej5ySw1ol+HBw0w7+i0adNsWsG54c+RI0d4UEWoXOPHjx+xevVqrF27BocOHcaXL19C5Tr0SYdfDeTInh3Va9RAixYtkCxZsvCrCBsr10GfDcUwd9rQIUOwTJ5Kxo0ZDVUKZESJ7GmQJVViJIgVHVEiRbDRUi8Ojxqg1cfF6wkypkwUaMvnk9a3H7/A8/FLXLh9D7vP3cK+8zcRVyyKAwYOUrEyzBmnS+BrgG68dNO1JunSpcPNmzet3dLLdA2EaQ0wMXf58uV/WCPd+vj/hXHMuoQ8DRDsEaxPnDgBHz98RIVCOVC2QDbkzJAKyRLFQ4yokQMtJOH9x8/4+PkLEsSJGfIUo88oVGiAn58nL97gmutdHDx7FVsOncPz12/RSEJqRowciZQpU4aKdQTFJHXQZ6Flbt6nTp2KQYMGInHs6OjboCRqFsmG33/71aKmfqlrIPg18OjFW8zafBTzd5xCqlSpsHjJUuTNmzf4JxYOZsBNLONWTN2eGM/Xvn17Rc4TDlSgL1HXgJkGCB5ixoxp1QK+ZcsWVK1a1ay+fhH8Gjhw4ABatWyJ58+eokv9CmhbuyziyYNtXXQNhFYN/COuyZsOnMaYhZvw4NkrFTfK0BuGXoR30UGfySeAuWYaNWyA3Xv2oE+9kuhRp4QO9kz0o5+GXA14PXmJbn9swrGr7uqJLclGdAlcDZCgoFq1aj8MsnHjRtQQ9xJddA2ERw0ULFgQJ0+eNFs645A9PDz0OGQzrQT/BVMtDBGPpirF8mBKr2ZIGDdW8E9Kn4GugQDSAMHf1OXbMG7xJpBdeNWq1YgVK3x/xnXQ5/3hYk6tsmVK4+nD+1jWvyHypE8RQB87vRtdA0GjAVqpJ605gNHL5aGFxNaMHz8+aAYOp6NQ36lTp4anp6eRrZWqeP78OeLGjRtOtaIvO7xrYKS4U/GlpWogARXT0ugPokLOJ4PeCW3btMGSpUsxoVtjZd0LObPTZ6JrIGA1cO6GGxoPmI7Y8RNhz9694Zo9WM/TJ5+tV69eoXSpkvjw8in2TGyvA76A/f+m9xZEGqDrgnP9UpjXqz4mT54E5gvUJfA0QH3369fPzGUkQ4YMOuALPJXrPYcCDfCJugb4OF3m5GvevHkomHn4mWL7du2wYsVyrBnfUwd84edtD7crzZ0xNfbNHYov71+hVMmSePnyZbjVRbgHff/++y9qC7X6q6ePsGVUKyRPEDvcfhj0hYcNDdQtkQMzu9TCmDFjFPNs2FhVyFxF06ZNVQwTZ8d4vrJly4bMieqz0jUQRBpgTLGWfJ1pGmjhixo1ahCNrg/jmwYmTJiARcJI/tfILihXMLtv1fX7ugbChAaSJoyLbTP64/2bF6hVswa49w+PEu7dO4cPH46xY0aLha8DsqVOYvUzQFeI7n9stHovauSISJcsAWoWzYYYUSJZrRMSC49fc8eaAxdQuUBmlM2TPiROMUDn9O+//8Hj8Qt4CfNlfCHoSZMkPiJH/N3qGK/efcTt+0/x+es3pHaIh6Txf/QBf//JNpV1NPlMmIpfxjZtx3O/jGPZdtiSnZi99QQuXLio50GyVE4AXtOVjd8j/BHZvHmz1Ti/ABxO70rXQIjXQIUKFVRibrp2kgk7adKkIX7O4WGCJ06cQJEiRTCqUwN0aVDR6pK53+k6fqHVe1EjR0K6lElQu0x+YfSMYrVOSCw8dtEFq3cdU7GL4Qnovnz7Hu73nyCDYxLwvbMlng+f4t2HT8iUOpnVuNuv/3wDX9YkYoTfhPvitx9u+dYnG3z+8hWXb3up8IhUSRPazeD6M/PRJnj1jhdKth2G3s59lBu6Vh5ejuEa9F2/fh3ZJafHqJYV0L5qYZvvOTftcav1t3mfNxLEiob1I1ohayoHH+uFlJuLd55Cjz83YXCTcuglpDVhWc64eKHT9HW4c/+ZcZmxokXG6NaV0ah0bmPZP9/+xZDFOzB363FhZPw/Y3nZ3Okxo2stJIoTQ5Xde/oKWVqOM943PSGQfLRhlLHI3rGNDUxO/DKOSTPjKT+3ZZxnI0LsRDhx8pSZG6Kxkn7ibw28ePECDg4OyqWN57Fj694C/laq3kGo1gAZsHv27Im6detizZo1oXotYWXyX79+RbasWZE0diRsmtLH5rL4uxGrSBOb93mD6RXYR9a0KX2sF1JuLty8H90nLMKQdnXh3OxH8q2QMs+AnsfAP1ZixsrtODB/OPJkcvqh+zW7jwvRyVZcd7un7jE1R6sapTGiY32zuh1Gz8Xy7UfMyrSLkfIAoXujytol7OmTBCvtRs7FxgOn5GHpf8a2TBUys19rXwmF/DIfY+cmJ/PWC+/B9OXyQPwCssr/ifAkP8LzcLT6Pn2ckdkxMdpWLmj3qo/O6IbffjV4xX6RJx+37z3DH5uO4Ir7Q3ScuhbHZna3u6/grJgrXXIMbVYeJXKkCc5pBPrYT1+9Q70RS0DrXSbJoVcyR1ocv+6h8tx1mrYOSeLFQvHshi/D8av2YfaWY4giwK1SgUyIIE+vtp28hj3nbqLF+BXYOb6Dmq/7oxfqSCtgojjm1NYRI3y3HvplbGuKsHcca21Z9qt8Tqd3roGi3WZg3bp1agNmq25QlvNJ8uHDh7F9+3acPnkCt27dxKs3b4XmPfS7W8SRnIlhTRi7GCNaVKQQBsacefKiXLlyqFSpEqJHN//sh7V1+2U9JO8hm+teIQm4dOkSHjx4gHfv3vmlizBZd+3atZLoe22YXJvpoujGSlbANGnSqJQ5FStWVGyBzE8YUmTevHlwd3fHhtUT7Z7SiaVjZL9jWMPnr//gttdDzFi1HVfEOtNu1Fyc/Gus3X0FZ8U8EtM1rH09lMybJTinEWRjk2Rs3+krWLBxn80xXTzuo+OYeeoBd4PyhRE7RjRsPnhGgcAIv/+KQW3qGNvSWhjh99+Q1wpwTJrgO2mZvX12GjMf6/aeAF0uqxXPi/cfP2HbkXPYefwiWg+fja3ihumT2DsfW320qVUGK3cdh7Nzb+zevcdWtTBZHm4tfRcvXkTOnDmxUaxzJXOm9fHNNbX0Pd/y/UtQa/T8zXs4NRqpLt1WDJFk7ubxC8yl9lWebKRI6POG8MPnr+ATkFjRfHebeP3+I168+YhUDnED3ILDTbnbwxdIKIAmoF1W+cPhIaBJvpPglCSe+iLR9BgYR82iWSRLKmwd2844RLeZG7B09xk0KJULs3vUVeVpG4/E09fvsX1cOxTKnEqV0dqWrfV49cV4eWFf9R4u2XVaufuuGdoc5fJkMPZpeeKXsS3b8trecay1NS1rPXE1bj7/gqvXrpsWB/n558+fMWvWLMyYNgVe9x4gbaIYyJc0MtLGj4xYkekiYniYEuQTC4ABn73/iiNub1ArW/wA6C1kdUGr97sv/8Lz5WdcevQJZzxfI1KkSGjarBkGDBgYrl33XFxcQNp7PlThBr9owbzIlS0zkidNghjRowX4d3PI+mTYno3Kdzt7IXp2bG27Uhi68488AH71+jVu3nHHiTPnceW6i7L+d+7cGV27dg32mMZv3H/IQ5vqknN4fHefrXimlr5XR/7CbxY5ip+9eotUlQwPQD12zPkhp98jyYum9juJff4u/PDps7gM/itgw3y/ZO1j8ertB9nvvENqcQEMjFxrgdU/3Rc9HjxV7otOyRMH+n6Hums17E/sO3UFdO3UxJqlr1irwbjg4o4ZfVuhRbWSqipBfeHmA9X757ZtluxlDQ/2nKp0gkP82DiyaJTWpdWjPX3GFLdghzKtESVSBJxePt5o1aPbZcFmA1S/D/bO99F92N75WJ2kd+Ghs9dQpdtYlWs3d+7vHl8+tQkL98KtpW/unDlIlyKxr4DPnjeZMVwR5Ivxq1gqnohliaCPwGn08r3Yfuo6bt59orqhVSh/RkeMalXJLE6MOdZoJTx5w1OBi2ypHdC1ZjEMXrRDWeL+7P79iQvzsHX/YwNcHzxXfcaJEQW1i2ZXropMIJ9dAMr9Z6+xfnhLsWB9t+J9k3ijTM3H4qVYvC4v6IsjV9zQb97f6C2unZ1rFFV9ffryD/rP34o1By+A5/xy5VwmtKuGvBkMKSwKdJqi3CQPTu2CLN6urJMlTcDYlXuV1fTQtK5GlVUftEDyxrnh3FxnpEwUB9PWH8IEsaZ9lL4pv0q8R4sK+TBG3Cz5FMmv8lFAMq1wm45eQalcadG07I9JyQniKJnEomsqWvwm3ycKXTtfv/+kzmkR1CRJvJiIHzOael+5kaG4PzToPm3SBFo1q0d7x7ba2A/j2GqvlXeqXhgleswE4zmYQys4hFaQju3b4eWL52iUMz6aVM8Op3iRg2MqgTZm9SzxECfKd0tvoA0UzB2//vQNGy4/w/w1y7BwwUIMEJbY/v37I0KECME8s6AbnlY8rpkPMTKlT4s/J45E3WqVECVK2PpM+0ejJQoXQLy4Pj/o9E//Ibmtu+ddLFi2GmPHjlF5U6dPn4769c1d5oJy/vSqePT4MTrW6+vvYaMLdwF/rxlX9fTFawX6+Ds6av56bBVrzU2PB2qMROJFUyBrOozp0khZdLSBvR49Q/uRc3Diyi3DfkdcROkeSFfEknkzY/bA7w9nj0osXjeJL7xz95FqHld+i2uXKYixXRupOLLl2w+jy7iFyJclDXbNGqwNoY6z1+7CgJkr0axKcXW/z7Rl6NO8ulkso2/9523cF3e8HgnYGYksaQx7oIlLt2DMgg1yndwMBFUVAHH0ggsurpmElA4JlMVs3KJN+PjZEP9Pz5uW1UthXNfGP7nf+YLdJy5h4/5TKJ0/q6yrhNl6tQvG12VIlVRdEki9lVg9SyEgJOCLFyu60o92P20KB+TJ7IQj528oyxvHIDh/Iu9z8dyZtGpWj/b2mSl1ctlffkXZAtmMgI8dUr+OSRIokPxW9mK2YkbtnY/VSZoUFs+TGZmcUmDO7NlYsHChyZ2wfRp6H637433h5n3jxg2oXzybP3r53pSgg4CPIMkxscHUTYAzee0B0FLEuDEmek8g/8E2H7uCNpNWGRvT7bBkjz9w/JoHMqZIhGbl8ioCkVYTV+HhizfqpVXec/Ymqg6cB08hI2GfHasVRpzoUTBv2wnUGbZIVataMDO+iY80xzGVw5dcFXDJlTYZHATI8D8dQc4XISuhUCfVBs5X1qVUsobutYujeuEsym21yoB5OHTpjqpHCxj7PyEukpocFWDHsstuD6XPj6qYFr2TUschbkwF+C653geJReJL7CN1wVhCWvoWbD+JqaIre4X9bj1xDS0nrBTr6gg0H7cCW45flS/Wr1a7qFHY4K+9av95XBUXXIqL12M1Ls8r5jd8kREw06WTMnbFPrz7+EX1+efmY0pvGVIklHUY3lu6XbL+dc/HsqYdCrDz/eb7Yir2jm3axvTc3nFM21g7z5EmKdImSySf+Y3WbgdqGa17bVq3VuQmBRLK56ZrNgyvkDLMAT4qMTwAPq6TVtlW+RPjSKfM6FdSHgqNHY18eXKr5Nu8H9ZFiwNZvWolFswYj3MHtqJ5g9o64LN448Mr4KMaUqVMjjGD++D2mYOoWr4kGjRooEDfhw8fLLQUNJcbN2xAwezpkcIX65s9s9l44LQCfGq/I5Y3yhSJCyMYuvf4ORpXKopeTaoa4v6kLi1PmhAYFBcL07FLN5ExVTI0r1pC9iD/oMXQP/Dw2Ut5vdKqKoBTuctoBQLYZ6d6FRBHLE9zJR6rZs+Jql6lIrmFeOR/OC790cJoKuv2npR9yb+oXiKvPGiW/c67D7K3MjxwZj0CKN/6L5wjg+qD/WtCQMR+L93yBC2EFFr0Tly+paxhBHwXb3pgyKzVQhoXQ+mCsYRpkiXG/A17MXnZ31pXvh7Z79+Hz6L5kJlwrNgBTQfNUC6YH30gkpszqJ0CwATBWb2BquVArt4gumiuTD8Qt3CelNsCdim0VFKiCRHMH6tlvyMuof1nLMcmeW+1B+G8b2+fBHZ75gxRDwPYTpOT8hCADwQI/uj2aUvsnY+t9qbl9csVVORr2sN/03th9dzv5pUwoAkSuDx7/kKsQ+n8vJrFO08rCxUbvpcnOAQ2Ry67qX4aiqugxgi57/xtVcYYwFQS+0WhxSVzi7E4d8sQNMsyAsMX8sVRXcDJkn6NWKS+UOsOXyxAy1Vd8w8tUbTC0dVqcf9GqFbI4Jver1EZlBeyDtblXOqXzIUZG49ILNp1TO5QQ8V1sf2GI5d5QMPSudTR8g9B0ZmbXsqyt29yZwVqWGfFvnNg7NvUdQeV5ZCkJgRqx8Xi2K5KITWv00KUQuEXAMFrJQFSp254gTGPZXIbdDx9w2FVZ2DjcmBKAUrLivmRrskobBMQ17dBaVVm7Q+fKO6/cBsbj17GztM3hNHSAPAYU1cpf0YF3PKmT26tqYCdBGLJLCJxl0dRpOt0cZ2NbLToVcyXUdwzvzOX9qpbUubtqYhc6JrJ2E1aJRnjN7ZNFWP/7uL6yvej8ei/jGU8GSfWzskdaxjJYfwytllH3hf2jmOtrWVZqZxO2L9vr2VxoF4z/2XF8uVw/eplLGqQDuXSh8+n/oGq5GDsPIK447Yr6KDe17br3JA7Zw7s2LUb+fLlC8ZZBe7QtFiTnKRQvtxYNnsN4sezvTkJ3JnovYcGDcSNExuzJo5CzUrl0bRjTxQqVEhiiHYjYUIDWAqqNezfvw+tqxbx83CLNu/HL94cBh8+flbA5vD566qfRhWLyH7HYN2nOyHluMQApk5q8JTpVL8CMtboirPXv+9jJgkwfP76HWqUzIe/Rhm8gvj7XqvXRBw6d031wT8Mc+k3fZna7ywd3VkBN5YPaF0TZdqNUHUJsgpmSweSf2w5dFZeZ9C+TjlWU+CB4xLxSuU5AABAAElEQVQ8FBMLldsDg7eVuil/7O2/XIHsCqiR/ZN9s93pq4a9ndrvXHJB5aK5cerKbQVeNWbQaSu2qaEGt62DeuUKqfNWNUohjbhJbj18Dv1b1tSm8sOR+mA83oZ9J7Hj2AWJd/us6jjJg9tKRXKhUtFcyJf5uxfXDx3YUaBZThPKQ3lLSSMecJRnL9+oo9v9x+pIMhxz2aksqOsnOSNW9KhGa6xvfdJllBZgChlDO42dr4DlNde7iChpjwa3qW0+jMWVvfOxaGb1soxYTAf/uQpXrlxRpI5WK4WxwnAJ+vgGKxN4cr9/8TrP2WL1I0AAMbp1JeO9ce2qyhfWf0bAxydM1zweKcD4WVi0+IXBJ2V7zt5SbUiqogldJ/rUL20G+q64PZA4u+dCPBITVbwtUqzPmLv6pXJiiLiCEuiRkZIMoiSWIWFJ0aypFYikmykBaY3C1q2bG8VaSWlRIb8R8PG6fomc6D9vKw4LsH0rXz5FpL+IMj/N0nfhzj3lCkoQSosb3U8J+o5cMXzRa+kgInkTnMzceFhZGgtkTInYYqX0WjNc9MCRrMu5W3dRc/BCNTb1lTtdMtU/9U1Q5ZvQ8kh3V03otqoJ0zIwvjCDWFgptNQxTQPlm7x3/8l7RPkkZVw/3WX5vjH1A4XW0LrFc4gl8C2W7z2ngDVTe2R3SiKkMYmV1dPesVWHJn/8Mo5JM5undGddKA8smFYgKMgFXkt8S/GiRfDigQe2tc4YJi17NpUdzm6kjBMJW1qmR3sBfiVLFMf+AweRP3/+MKcFWsrr1KmDlo3r4o/xI354Qh7mFqwvKMA0ULp4YZzYtREV6zVHUUmZcPTYMSRI4PvvV0BMgA/fHjx8hOxpHf3cXa8pS622IfgY06Wx8d4EiRP8Vx5Ia4CPFipu4unSyL2Pcb8j1jUKSVU04X6nX8saZqCPNP6u9x4jSYI4qFost1ZVufw1rFAYg2SjvlUsYAR9DSoUUaCPJCQa6KMLJIXAlPsGS7G3f1roSM52XAAm5fwNd+UlReshx6M7J0GfBoTLCkikaPud6cKc6SBrKCggJ44QpdzfMx8/zkY1UX8IVKt3H6dcMjnvPJlSC9DLrcBeupQO3yv688zr4TPVA+PrLCWJNzGLBjbd7hkAM0HnuG5NwNQKO46ex1yxWp6+egc9Ji3G4uGd4Zc+tTHp+koQTMsphW/VLYkrJHhmuhdrYu98rLW1LEufMqm8vxFw9epVHfRZKicsXT8W3/YEQr9PFz2/yvze9Y3snWwbNVJEBT4Ys2Yq6QWQLBbCjwHzt0n83TNFEGJ6n+f8IvQUAMF5WCaFZ+4/U7njHcP3+OU7ZGs13vQW3nmb+h+/fKvKSU5C0LdFXDwJ+mh1fPPhM+qJhS16lIhmbbULzpEy4q9dYIyeqRDsUchG6ST57Qj89p2/hVvyZUCLH6VbrWI4INa4oxIrSKE7KcFh0axO6rqLxA3SPfWqAN/K/eciusRBFhPWTIK3WsUMX5SqosUfur9q46dNGh+1JB8iXTIt9WXRzHg5ec1BcXW9qlxnCcQLZ3ZUcxizfA92y3zaTlqNo8K4SlfXZmOXyw/Xf+gv1tMW5fMhkoDkHaduoLOke6Clk4CT890yqo16z+g2SckoMYAlhBWUQovqxiNXFOizd2zV0Mofe8ex0vSHIrr0fpUfX24A4sWL98P9gCz4559/ULVKJTy774EtLdIjSSzrn7mAHFPvK3g1EFnY3hbWS4M2a+8o6+6Zc+fh5GT4vx+8MwuY0Y8fP65c9No2a4gZ44YFTKd6L+FKAymTJ8XBLatRvGo9MI/hMQF+WhL7wFQE9zsUAii/ysJhHY3snWxryNPnoGLWTPtK75gUtArS7Y9WpKfeViLTOupBpuSD437H0s2Uuf9M5Y5s/CmPJZYsc+0epreUdUi7xyNjwxibRssfx2U6ifViJaM0rFBUHS3/2Ns/LZlFc2bE3lOXcdPzgbiluqiuGIO4//RVHLlwQ10fOnddgUNaFSndGlbCnpOXwJi6ip1Gyb4rsoqJo5WudukCqo61P6/E/VWLwUsn8XWsS4CdPADcck3HY35pCkl5LIVMmhSyeVLqiQskwWcmp+QKuLKsm6y/nFhY8zbqq9w8Zw9oK58N+/tkH5SEcWPh5ZGlcHG/r1x+h8xapSxv2dOmBGPurIm984nkbYW21odWxocSCSX29NGjR1pRmD+GS0sfg/C1D6hf3+EaRbKafQnaas+YM1reaF0rmMlRrEkJVfL3wYu2g8CNQhdBxgISAFk+jNKAnNa/lqQ7npDE5M+UUis2O+bPYCivLSBq0MLtavyJ7asJEDE8XWtUJo9ZfdMLxrBRcqdNjpjRIpnegvbMPqbkcKHQZZOg74S4ch4V0EeXSVq3CglD5q4zLspidlFi+JgeQXN3JTC6MN8Z6w5dwl4hX2Fb6ocvupZuGd3G6pMdAsyl/RsLWctlBdL6idWRL1ozGYNHEKYRyphN2vtir8yT0qNOcQWAeU6r14iWFVV/BKEEs7RcEvDRZdTU1bR+yZxqTYyR5JppxdRIbdiXqZAFlqBPA9D2jp1AksVbCp/y2TuOZVtr1/yMUd6/fx/ooK9v3744f/YMtrbKpAM+a2+GSZnLkw/4XX54bJHafBDWTJenHxE94q9Il+DHp7JaV3zKTnbNe6+/IF7U35E6XiQQiNkjHMOWRJVx7ZXffv0fZtd2Qs0lN1GjWlWcPX9BsXza2z6k1nv69Clq166NciWLYtqYISF1miFyXi9fvcalqzcQVTa96dKkRqyYMX6YpwIDXvdw78FD5BTm0+jRDJvNHyraKKBHzY1brnggG7fUjkIGkTyZTW8Ge+bDYfzSp41pWS1OmCAetq9ejPxla4DMnguDgEBCSx3yM3uemiXz/8DeaW1hzQfPVCQuBEmFJHaQRCLZ06VU5CyPn79WTdR+R1wXCYAs9zsa0ND6fuf9oDlerBjiCmh4oKrd0475vV0E6bVFcpc563ar+LdiEqfGlBKcB61S1sQv/RNUEvQxro+WPboy5kjviMI50qsUA0xCfuGmO0rlzWp0d80oSc4vrZ2ictapthdvKv2Q6GbFjqPYJikJrFmyCDCXje6myFp2SfoCRT4jBDRZ06ZQFsXKAgA1Qhlr67K3jGCL4iFzt5SXwkZPiee9L6HlT7P+mdZNL0Cd7rOM42Scnb19Mm6P8ZcE+mRu5V6H+uKLcZcj563DLrEI2wJ99s5HI7MxnbO1c8Yqcl8UXiRcgj6+udYNxwHzttMCRjATKcJvwlzZW+WCY8+kQu41a7NxELo1kOiEhC20zGV3MliOWEEjHdEqk1yFQuA1v3cDrVgdmRLihucjOIkljEKyFAIzAjDG+jEOjpYxpi2wJUz9wNjCBuIqWkNonU2FAI3glP1SGNfXd+7f4vLoCiYfZ547foEVy+akxpy4er/yxS/rHc/HNiSCoctD60oF1Is/AIzTY/47Asebd58qixnrmgrb0HWUL6a02C1rYmzf3nO3lM7GrtgraRRiq1i60lZiNGlJpTAfo6k4OcRXrrG0In4SC5hGwmJZj21IfkPQR9dPWjJpsWR+Q8tUH1eEyIZCF1yKvWOryhZ//DKORVOrl9qPLDdYgSlHjx4FEzNPr+GE9Altg5TAnENo6ZssmBXmXkUhx5hY0SSD2bS/fPsPw3d5YulZg2sNbyaI9jua5EmInsWTmdU9d+8dem52hdtzg0WeN0m2MqRcCtTLYe4xYNZQLh4ISMw79YJlsbqO9PsvcBuUz+o9W4VsM69OapSafQ3Dhg3DuHHjbFUNNeVdZHNOduZFMyda3aiFmoUE4UQfPHqMmk3a4eJVQ/yXNnTnNs0wZlAfeRhgeAh18ux5NG7XXQCf4Uk7Xc/z5swmBDkTkCZVSq2ZzeOUWfMxYuIMfPS2TrBiqhTJsfjPiSiQ53v8ur3zYXt7+2TdnxGSvCwUAqBazdqjZs2aKuflz/RjbxvtO9+am6O9ffhUjxYwghn+VpO5UgMI3O/0mLjY2FTtd+LHUYQtdK8kcNLkiljETIWpGShMX7VwWCfTWwowMJm4FnvGm3TjJOijy6VmvWpU0bqVj/X90j9dNp2n/oXDYs1jPF8JsUBxv1M8d2YF+sYv3qT2O1o8H/s/KOkA6CnUVvLB8cVYQMY9NhNwfFSsg2Q4JcixFFqn6DrKF1kqCfw27DuFPQI6r9zeoFhDaSWd6twCZfKb79Ms+/LpOo2kj6C4iQutpdBiSsmd0eCpMXzOGuW6O1RcXWkZ04R6JvkOhS6sGkupb31Ol3jH+ZI/cJQkdafF0FRIykOhxdOW2DsfW+0ty/n/Qvs/YnkvLF5/fwfD4uqCaU0uXoaNWmZHByPg41QIVjRXRW1qWSUlAmW7uBFqQiZKAidToTWLIJJxaJoliff5YaX7Ya2hi3DehCCGFipKJ7lHMhK6fPr0pZ83fQpVn2DVVAjqKvabA+c538EqGUppEaPbJIEYrXEUgj8KyV8oWjwfzztISgq6dWppEejiUT5vBtUP75P0xTeJKnldaop75/KBTeEq+RDn9aqv+ngooPfOfYN7qmUfGSW2jrLm4EWzWzsFPPK9oAWMgFhL07D7rIuKxTOtzBQWFNYhC9jMTUcwfOlOFSup1eN7pjGmFhDLLsXesVmX1saLd+7jrrC9UvwyjmoQAv7w6TjTMpRKGwe1sxseQISAaYXIKbz88A/6bXXHP/9aB+GDdngowEerXYdCDqiSKS4IEicfvI+1F78/nWV+wOYrbirAl0FAdvuCiZE9STRVt+dmNxx1NwTj21KCh1gHKaniRkL+FDHMXvmS/2iBttWPaXlScecdUCoJJk2aCFdXV9Nboe784MGDWLtuHWZNGomYMX5OH6Fu0f6c8FvxpClZrYECfLlzZEX/Hh1RvlQxZX37Y/5SDB03VY3gIda9MjWbKMBXsUwJ9O7cVgBbMpw8ewF1mnfAhw8ffZzJrIV/od/w8apOy0Z10b1DKyR1SAx3r7uoVK8lHj0x/D+xdz7syN4+fZyYHTerlC+NejWqoEuXLqA7fGCKT7/7ATEuXfMotEBpgI/XG/afNLoq8pqSTSxWlO1HDXsEnhMsEDiZClknCSIZ36WRjvA+9ztkj6zRU1hzTQhisqdzRHrHJApQkSEzijxUIFmMLfFL/7QWMp6NbJUfJJSG1jgKwR9l+fYj6qjF8/GinaSkqNhptLJc8ZrWyAqFcyK19EMxZRFVBVb+0JW2lrh3rhzXAx7bJa3A0I6KtIZAi2kk/CO5JFl9qiQJFeHNuj0njF0xjpJuqdQ902dQ9p+5qtJPENibyto9x5UhI5OA15gCzu3tUwO72yUukA8GNPkmhoBFmw+oy5wZvhsoLt/yVOklCJwp9s5HVdb//KCB334o0Qv8rQFa7JiDjiQkzIWXXghjyAppCjzmS5qFppKeYUjT8spqRZBHyxBzwtEt0BLEaKkOaNliagXm1mN8Ht0JD168oyxZWsoBLqCCuD3S7ZJWQApBn0/CfH0rxc2S/fGJHK12dyV/4ExhAqX0b1jGrDkB3ewtx1SZFrdHQpQEYg1kfjqmY9BSHLBS5QKZwTXXGLwAHaoWljF+VesmuQ0tY9YsbGx3yfWBAOB9PLUqv8qTofySQzBq5AhW73euXkSld2BKBVomKwjQvCPxi7tOu6j6vWTd/FEkcM0i1kC6e2ZtOR5VC2VWVliCQM6RORYbi3ssn/gT8DI9Rfm+c1BT3H0pK/edV6kdcqdLLms1+PXbOzbbM+3GJImlbFo2D2Z0rS1W2dR2j8P2IUHWr1+PGzdv4o+OBp34d04fv/6LFwKOksX+7m7MH30CFaZHoDXLmjx59xWP3n5FkpgRED+a9c8F2xFEvfz4DxyFiCSwN0baPE97vYXz327KFdPk9067rY73Xn3G6guGDevWNpLo23v9N598RKlZl9Fvm7sCgZEj/IpdLi/xStZRMGUMrGth+NyxE46x8vxTlU+vSKqYZv2bXtAllDK0fEqUThvb9Ja/zhvnTojF555j+LChWLZ8hb/6Cs7GgwcNQpkSRVC2hG2rgX/nx5xuDokSGq1f7I9A5dGTZ8raZc0N7LNslN08vWQTDKRNndJmjkQ+iLnj7onECcWzIXrQgFaCNgK6ogXzYefaJfhdGPkoazZtQ5P23TFz/hKM6N8DY6f+KXHGX9G0Xk1l2WMdpjmo36ozNm7bhUl/zsPQPt1ZbFVWrN+iytcvng0SpVDGDemL7EUrSJJ0N8xdsgLD+vZQINKe+USMGBH29qkG8+ef0YOckbFAaSxevBht27b1Z2/B15wWO1qASELSZ9pfyCDxfSfFWrR693HjpOYJEGO+PBK40Go1fvFm5RIYP3ZMcZ28ZEwPoDWIL3F5PZtWVZatKl3HoGuDSoguXk6M1TsgICSzxJeRQMVUGgqhC9Mk0AJFK180IbmzJX7tn4DO9d4u1R3dRyl0H2T8IOMIaTljKgJNqgj5DNdcTUhZOtYtr/ZUBFMkt2FsJXP8WZNLtzx+AMCm9ahnurVGtcHNYFrXt3OS57QVcNptwiKcEgsmWTXJ0EkvrGn9W6r4TfbB3IJdxi1AS0mrcfJyGeWWec3VS9Xl/REd6/OgxJ4+G5QvDOYvPCmMpxU6jURZIW158OSFuOaeU7qkJZPvJYWgsHCLger81paZkhIjjp/moxrqf8w0YH3XZFZFv9A0wI0h//kmJHUZ17YKBizYhjl/G774aKUa3qKC0P9HUKkX+oh7JN0oGeu2bWxb9BFWUMa7UQiYZsrGv8uM9eLO+f2Lq1fdEuo/wSRJ88D0DZrQbfPPHsIyJWBNE54z/pApJgrLfUuiGW2Tq5nryaS5dUxblUOQMXZ8UQgcmTydFjZToesmQV9c8clmvKImRQUsrj98SVIhZNCK1HFQk7KK9YprbDt5tfEeAdQC5wYquNtYaHLyUlxOTa2gJrfMTitLfkJrwnm/EVrgUcv2KEsarWkUpmNwri9fZgKeKXyytXJwM+V+y4TvZOPUhIB0luiXuqCsG9ZCrKvrVRzghdv3tGoqr+G0zjWNAMLesdnBL97+l9oGL4p8XuwdxziBYD6ZMW0qymeIizTxo/zUTFace4IB2z3gXDIZLt5/j4Our/DlmzDCSXzacAElLz58w9BdngqscYC8Yola2ig9YkQyfO49XnxC142uuCBtNUkeOyL+qJUGuZJ93/Ce9HyDvn+7w+2FAfDEjvIbmFh9qLhDMr4uMIXeK7Te8fX2s8TrCZCzlMsPPwhzrLjXyJw1wMc6dJfNKK8b0ubO80/I6hANzwQUUzIkMtd5lsRRVbnGQKsurPzRQJ+tmEIrTewq4sOYDgUSoO/atZg8ZWqQMRXaNTk7K128eBHHT5zAvk0BC1pT5SiiLBajBvbGSHFNpGUqsjzVr16xLCYMG4DmnXvhwJETqk60qFExbexQBYy0aU/6Yx5GTZ5pdGmkS2Sbpg0wacQAI/j7JK5hvYaMwop1m8Fzft/nyJJJxSTmz2PwAtH6C+gjQR+lfs0qRsDH6+oVy6h1cj633Txx5sJlFqNBrWrqqP1pUKuqAn0Xr5h7nWj3eSTo5f3ECRMYAR/L+f1Zv2ZVDBs/FddvGqj17Z0P3Unt7ZNj+VeSJ3VAQ1nrzJkzAxX0/azrmtrveP8u+bRW5qWbIKyO/YTEZfba3aoq4wdHChiILBY3pl7oLSygNcXyRivPjj8Gqeu13hYmAqY/+7dR1P20GGni3LQa/pNN/4Slm9FX+tCkiFja5gxsq4hTtDIe65crjKGSRoXrbSR5/UxF27fxYbwmfumfrpuzJNk7CWNMY8UIANftPaHYJrV+eSTrJ3Mhc42th88y3qLFkO6qtPxZkxeSzmLbEcPey9p9rcyU0VQr8+uRrKfMm0idEaBSaCHtLWC7qQB0TZhL8Y3E2k1YslnpQCuPKwaKP+R9M7Vw2tMnLZgbJztL/sZZCvgR/GlSNFdGTHNuqQC+VqYdtffOL/PR2urH7xr4n/wHke1F+JLhw4dj9eK5OPWn7aeIAaGRJ+Kyx9i8ePKfg1YkDWARyDx/8wFpJAaPX6x8C3jUyFqiyRcm3SrLOs8CwVLveqXMpkNGS1qa+KXCPsioGVDCJyt0IaU1LLZ8AdNypZGxBMQYL2TdtLgZ4hljKBKWn2FR9etc6H55XeIe6QrKhPYZxSoZV0hxrAktrrfuPRWA/X8KLJNJVXvvTOu7CaMq02jEEGCePnkCFX9gel8798vYWhvTo73jmLaxPL8kxDrFu8+Eu7s7HB0N7qeWdfxzfffuXaRIkQLLG6dHiTQ/ZzFadvaxWLE81DSIvUpJPwQ49yXuLIKQhHyV94NgqUjqmDjl+VZZ82hRGl8llWpDN8e9t18pK1g2cXF0E2C09tJTafsLrvbNrYhN9sv95itvKpBdK1s8BRgPSBkBIC1iq5tl9I8a/NT2hMcb1FlyA8WdYpnF9C0981iB3wY5E2BStdRmfXYTULv+8jNMrZ4adSVez/XZJxT745Ks41esF0tfpkRRcUuIXzquu4ObcpxXLy0qZTTEA5t15H3RatUt7L/zCnPqpMX5++/w/P0/ilSmSua4ZoDTWlvfyj798y+yTbyIiVOno0OHDr5VD3H3nZ2dsXmjWK9P2vY0+JlJJ8ucH0+ePVff+SmSJUHmDOmwfc8B9TsQUR4KfpHv9RJFCsjG8HfsOXhEARm3C0eQJLGQYV2+JiQg1UEmyLrVKyOaUK6v2vA3XG67YohzNwzq3UX1U6xyPZw6dwFZMqYT18ri8Lx7Hxu27hQQ9hs2L5uPUsUK/czU7WqzcPkaXL7mgm7tWyB1yhTGNldv3EKuEpXU9YPrp5G9WAXJl/sSnpePK0unVpFWuXR5S4C6uXPusFZsdnz4+AnGTZuNdE6O6NS6mdm9ei07YdP23WjXvBFmjh8Oe+dD9zF7+zQb0B8XpwQgF61cV1HGZ85s/cGlP7pXTc+cOaNyZ97YOB3JEsXzb3c22z8Rpk3G5jEheRanFMbfzBdv5HtF9kJpUyQ23+9IeAWFFjlS/5duNwzMa9eneXWzMQhMrtzyVCEPBIhaPJpZJX9cBGb/zEl4WPIPRhBrt0P82IqQxRbg88cS/NX0i3Aa0ErLPU6uDKnV3sxah8ypd0NceRlvRzdVuoda2xexrT190p3T7f4TuEseQO4H2ScfINgrfpmPT33mbzoANeo3wciRI32qFmbuWX/cEGaWF7wLSSjsRwmtkIvEEesYX5Q2k1YpC99ssSRpLpgEgXO2GiyE1pgpaZXT4ucCeoX8T0w3TS13XUD3T6BlaTUM6DGs9UfLWR7vuEVr903L6JZq6ppqes/0PLVYZPnyTfwytrW+7B3HWtugKmPC4agSuE5SEv9KZCED2dshKxzjRpaYt/9QaPpFPHjzVdwPY2Fh/fRipf0fzoibZI1F13H5gcGqd+fZRwX4mDNuTt20xinQBXT28Yc44vYGJdPEwrBdnsqKNqduGiMY6lU8KaovvK7i39hvXoltsyaj93qBLpY+SRRxuZxrMr5PdW3dSxXPYFE+7PbarMo3Ab2XvNd7W8AexSl+ZEmQnhhzTzxC2dlXlMsr3VYpZdPFVsBZXdj4Q0sf4wpbrTYE72vVphy6jzGVHX0lgtHqWzuSPbSoAPSd27eHStC3c+dOVC5n/sDN2jp/tozpH6aLFY/WqQXLVqNj70EqrcrhbWuMJCTlajfBwaMnFQMmQd/kP+er4ei22LC2wULGfpJnLYgtO/Yo0Lds7SYF+GjZO7ZzvdHaVnZ1UbTp1hcTZs6xCfpIvDJ07BRfl9S6SX1UrVDGar1Wjev9UP7m7Tv0HTZWlRfMm0sltU+TylGBvt0HjqBFwzrGNqfOXVTnXvceqLi+qFZyidEd1lrqjB17D2LLToPVgpZTir3zYV17+2TdgJB8uXMoXfCzFligLyDmaU8fZG8s480KaVqfboN8UVoN+1NZv+YObm904eN+Z/Y6g+skY+0shfntbDE5Wtb9mevA7J+WQcblhWRhHsLCOTL4OkW61+bLksbXeqxgT5+/SagMcw/+bP5Bv8zHrkmHk0o66AvmN7pqwSwK9HWduUHF02WQ+L/TYuU7c9MLJFdhbJcuugZCugbOnz+PzOJuGOG37+4zPztnkpYQ8FHobplW3EUJ+hrkTKgAH8vziGvnb+JCqLknRvIel9fTD99Hw1wJVDxf/9LJhe0yKXj/8sP3cBeLnkOMCKiQPg67URJd3ENJPDNqjxd2SoycLdBHQHju3nfXUa296ZGA1b+SO1k0FY/INbdfexv1xeJHN82/zjyBq1gvKb8L8KUQ4LGeJrSuaUJLp5fEB9pK9cDNlqfcp3Qq7ICaWePjqRDDrBaimC1XX6CvEM1kdRD37YTWLeLaOD4dcyaJgsXnz/pUJUTe+/z5M1xcXDCoR/tAmx+JSzR37myZDJuuLGL1M2WdLF4ovwJ9bh5eah5a7qmpsxYIaUkiFMqXG3Fix8Kz2xeUFYWV1m3epuq2aSZu897xdCxoXKc6eg0aqfpjzKC1GL8XL15h137r1jXVqfef4oXzm176eL7v0DG07zUAd+8/VO6YC4S5kkI3zhNnzisXV6ZSoHWTbq0asKVu6LpqjxBUDhw1EfOWrlTVqVtb1kxb87Ecxy99Wra195oePnmE7ObCBYNLrL3tQmu9asJKSZfHzmPnY/3ek8pVkoyYtPQRUNC9TxddA7oGAk8DOugLPN3a1XMViUVjwnfGwR2/5q5ywZHYpG7xHCoNAa1EuugaCOkacHdzhWPs3wNkmskkDs9UInoDukQC1jQxxJsIm5t3Aclemko6g78kxcGEA/fUK12CyIqcpEbWeAq4aCkNngiwyT/NfJP13jtXHe/ZkrXNMynwZet+QJXTQja9phParrmNrddfqJfWN2PvCPzSecdNzjxyH9ukTnrJ4TeiYkoUECvldbFGTjpwF/tuv0aXDXewp0M2rfkPxzXizvq7gGe6w1IYN1g0dSw5u62A39/XXvgL9KUS8P7w8V1F2BEhwvf3Tw0Wgv94eXmpXG1OqVIGyiwJZpIlSWzsmyQiFIfE3+Ojea2BQgJ0So+ObbBTQNnl6y4oXaORymlXsmgBVClfBvWqV1J1brt5qOPgMZPFXfF7PBEL374zPLR4/FTc0q0Qu5QsWhBvPK+p9j79oZuob8I0CV37DcPWXQb32Ho1KmPyyMFIEN/gbsw4xKMnz2Dt5u3oP8IABNlnpvRp8OLlK3ENTW5GbmNrvCWr1mPAyAl4/uIlEiWIjymjB6N21Yo/VPdtPqYN7O3TtM3PnqdJ7YiTF3zX+c/2H5LaVS2WR2LaOkoc3Ekcu+ii8t+R2KReuUKY2ruFiikLSfPV56JrIKxpwPdv7rC24hC4njoC8Pj69u+/ePP+s81YsxA4dX1KugaUBt68egVHPyTy9kltUQT0WBODbcv8jrYZZunYyqmUtWrr9ec4JqkKbj39pF5zTjyUWMMMeC9soJS4wvxpy5qXx4TwRVU2+aOBT5OiQDstkDImDnbKjh0uL3BHXDnjS46+guI6u1Hi+RToS2iwhB64Y3AB7VzEwehaSxKXQWVTKNB3/fFHMK2DNRZTAmeSxViTYgL8aO2jtdA/wlhDytu3bxEvXjz/dBWkbd+8eaPGixXDuquvfycTSUCeBuhM++J7Yk20z3nmDGnhcmq/xPFtURa5oyfPilvnXvVatmYjdq//C+/ef1BdMN+dZTL0QvkMvdtaF+dEUhn/Ci12Ddp0wavXb5Aza2ZMGjkQhfPnMeuWYy2fOx3NG9QRd9SLePn6NbJlzgi6f2YSVstM6dOa1be8YKqDJu17KNIXPlDo170jnLu0tZrc3Z75sH+/9Gk5n5+95nvx1vvz9rN9hKZ2dcsWAl+M6Xr9/qMiRwlN89fnqmsgNGtAB30h6N37TZ7+2iIXCUHT/KmpMC8Nk5f/n/xzlJi5BBLvaE1IJOMhRCpej19KQHh0pBGSGltEMiS0IekMk6aTBTRpfFoo7BfmBrwsKSFIhsM4SVMhbbFvuQO5QSMrqyZsQ7KYB8/fqNQVTN0RFCQ12vjBefwmJAi/RrK+YQ2Ked0VN0W6bmYSFssRFRzVkCSAGSNxeFvEWrVMLIAkfaGQ7ZOMnqbyWFI8kPgkteSrsyVMg3BVmDV9kmgCfEmo4h8hSDvk+hoplPUykbErbvx7b3GTdDCSXNjb/fXuqy/qfkYhcDEVWtiiy1zeiQXz8z//md4ynpNIhsQ2tOwVEzIZU7n2yLBOh5jmVlfTOvack8WTws9HaJJ/5QEchXEnIUn2Hz6uQFn7Fo3BF0HKnoNH0bBtVxw+fgo3brkitWMKZfVqXLcG6lQzWP+0NRw7dVbFDWrWNq1cO545f0lZ57RrW0fmxCM7pzVx9fBEnRYdBXy+VykT+nZrb9VNkyQ1JLSpUamcGQPnui3bVbcZxeLnk/QYOFIBvgxpnbBqwUxkTGe9vr3z4Vj29unTvPx6j3H0Wg4yv7YNzfX5f4sxb+FJNNKYKELWlzaFA2JFN//etqaLk1du4bPkWtbyAprW+SykT0x0z98G5hNkCgm/iD3t33sT7ljr1zItBufh+fAZ7j95DuZOZNydLiFLAzroC1nvR5ibDYEQE7NvOnoF/0rOKE2YmH16l1og2Y0mZCxlMnnTHIVMkzBaUkY0Kp1bq6byyAxZvANzhezmP3Lbe0vZ3Oklx10tyann89N5Jojv/sdGcaW9KYypBnc+Jl5f3LcR0gpTJ2XxzlNgWg2fJEXC2Li8sJ+qcvK6h1qnp4BVTTj3RX0aomROn59Ya/X1489rgJY9Z0nDwETmtHJRmCS8auZ4CvR9+fafYrZkbB8tZbRgpfYmTOEPVS8BUwRas2qnMcsLaDoj9+efFTA0LbM8jxIAMX0vP35D901uEosXGfs7ZjPGau26KUl5xepXSghptNhJumOS3GWDWAAHlDGsm3Pae+uVAnwEodQDhWDyocT/xRUGVJZ9FDA4Rwhgjnm8RQHJ86f1+UksonQZpeRL8f3/pyrQ/wSrBlp17YOnz1/g4Y0zyorHmL1KZUsijWNKXLlxU5g/vyC/kIOcFsvZ30JoYgr6yBTJpOnpnFLh6vE9VtfxXhKiX3UxJ/WxVvHJ02fWilXZOnHXJOBjzN6Anp1s1lu0fK0CbZEjRTK6YxJsT5o5T33mK5YuYWz7RNxR7z98hHhx4yhWT4LdZWs3qpjFlfNn2AR87MDe+filT+PE9BNdA3ZogAnV6zpPFoDmaVa7Q91yGNGhPrRYXbObcnFUXGArdR4tOfMi4tG+hcbbfEjQbuRcbDxwyizBeYVCOTCzX2uQVMcnsbf9vcfPkbFmN6tdRRam4acHFxvvnZLUCy0kl999yblH4cOMPBmdMHtQO5Xc3lhRPwlWDeigL1jVH/YHZ65BxivSCldV4heZlmLbqevYdcYF7SRf3+ZRbZQSngqlc70RS0DrHQFYyRxpcVyAFPPgdZq2ThK4xzIylo5ftU/lCIwibJFMSB9Bct5sO3kNzK/XYvwK7BzfwUfF9p27BZuPXVH5BctJkvmbd5+q+VQbNB97JnZEsgSxVf5BWwnjH798q1JuaNZBAr16w5fgrTwRq144q8qLeELmvlES3becsBIX5/f5wZLo4wRD4U0bXmlBthJaqiIJ4Fpw6pFK75AjaXQwb99iSX9AKSNMlvHERbKTuEFOPngf9ZfeENZLBxAUbbn2XLF7Mgde+QzfCV4sJ7+hZSbLokC5ZtxeGmHlpHtqe0m9UENyCBKoThVGTbKRTqia2jhu2wKJ0XH9Hfx57KFyaS2TLo6kn/ikQB8rdSuaxAgaF59+jOlHHgghjiEVRCEBeqnEskmrHplQq0qaBsrai8+E0OUf5EwaDeVNCG/UTf1PsGqgmjBSzl60DBXqNkOXNs3BFA+79h1SgC+pQ2JkzZQejimSga6eTIZOqvgSEqfndfcBpgj5C2Wwc1eba2BM34f7Ljbv23Pj2ClDjlPmvCsm6QisyfK501BdLHxMwt7JebCK4SOgm7N4Bcgg6tylHfLmym5sOmvRX5LMfRZaNqqLOVPGqNQVzPcXJUpkxXpqrGhyUrpYYbVWe+fz8NETldPQnj5NhtFPdQ34qIG38iClXIeRYgF7KikRUqFUvqxgEvb9kmSeeQ2ZwmF054Y/9EGrYGvJZceHkpbSacx8lR8wacK4qFY8L95//CT5/c5h5/GLkhdwNrbO6G/ZxOza3vbuklKBwvyCiSyAZETZf2nCtVXqMhpfxXuqvADPDI5JsfXwWZX0vUG/KTi0YIQx2bvWRj8GjwZ00Bc8erc66ofPXwVMvEeKhN83nvwP7/7whbh9RrGZB44g5KG4FBJY2XKb5ID/iaXNTfpKGCc6YkhunMAWxihuOX5V0lNEwf4pnY1WvTaVC6JI1+k4dMlVASXOZbsAQQI+JprfOradcWrdhNV06e4zWHPwghH0/SXXlHXDW6JQZkOOtn4NSyNb6/E4ed0TXk9emunQ2Jmc7Dl7U/q6iLRJE2C3ADxND10k2fqyvWcVmBzTpgpqFcuuXqZtec4vtVI9/xCX0n+EgKeBuj1l7QG1jg7VCmOstKW0rlQAHwTg7pbxCEbrlcipyvU/tjWgxTPRfdFULK+1e7+aIM0k4ob4p7hsjt9/VwEbrQ69C1vlT2R07exaJKn8P4DUua8SvWv1CgoAmlrDCUEZt6eNbXmkS+TsOmlUrj1a3DSrG8HgYEkgb0poU00A4dvP3xRxDZO680Uhs2n3YkkUsNX6/8VbX5rLZWRJL/GXJLenlfO01ztjOgjWrywMqsx/qL0nWh/60f8aoE4t81tZXmujWLJXjujfU4DJJ6za+Dead+qlVQMTi/81e6qyfJHNc8+G5WjWsYdYwzapFyvGjhVTErgPVPn9jA0D4eT0eUPKhZt33Gz2/lUsdXQPvSZWRbJ1duk7VNVlPCEJX4b2MbcuaPGPv3h/GZwWN1TKR9nsasnXVYHJH4Jfir3z8UufJsPopwGsAY8HT5FYHvKaWr8InB4/f63Ah/ZZMB2Wbopsx/2Sk+TyY943W/Lq7Qcwd2BqcYcMiu83MpMSFDEtwt/T+xkTs5O9lJaxWWt2YYjkJ2SaA1PpMnYBaCG0FMZCbj54RvaE0RSY0qx67WqXRcFmA3BI8gJSXzGspDphX35pzzx6lLFdGyswpy6s/GHidu6NGlUsijli2aOM6FgfTQZOV3OdtnwbBrapbaWlXhTUGrD9PyOoZxIOx1uy6zR6z96MgY3L4rxYtPaeu6XiyBhjRvDABO795v0twc4GMoUCmVJi9ZDmiOntJ+0uicHbTl6Dc7fuGrWXMlEcxQZqmpPuk/iD95+/VQEnnvOLLltqB0xoVw15M3x3CTN2EkAnjJfjeGUkV6GpGydzD3KetJC9/fBZAa+nr9+rUTNJEntTyZY6ibokYKXQXVTTBy2CmpDxNL58CT4Ri6G1J2NavX3nDa5LrSrlNwI+3mOORIK+zceugqDPloxevgdXPR5hTs96KhaQ9QjqIsqPjHO9kmbNRrashBpFsiGLo4NZuX5hXQOMudPi7kxrzKuXzvTSeO422JuVwruEVjq+TktqhSfvviKmxO6lFRfJxDEiGtswx1/vkskUELwmJCdMccD4OM3V01gxCE5IzPJgeAGrIzFNwl5h3XR58kHSKnwRBs0oKmm6tcpN8iRC7WzxVSL7x7Lu2GINJJtnHHHjNBWumy9TYWqMjS0zSzzkJ7GMyv9FIV5JI8ygtCjqEjgauHv15A8dk7Tk6xPXH8qZeoAvTWLGiI5508ZhzOA+OHD0hGwUI6ik7dkyZzBLz0CL37kD20DgRTbPOAL48ubMHiAkLdpcbB2fuxoAma37puWjBvaW5OpNcd3ltoprY4oKa0QyQ/t0FyDY3di0a7sW4MsesXc+funTnnH1OvZpIH31LvKbDQzvUA9jFm5Q4I2ug1WK5VZgo/XwWQJkrqvfdcaQTerZTIELrfepy7di3KJN+Pj5iyriA5SW1UthnAAVU/BHV8lu4xfizt1Hqh5BU+0yBWWMRkYgpvUZkEempKDULVvQbByuj+v8JIDV9d4jZEqd3Djs4i0H8LdYyro2rIQlcs6UPZpcuu2p2pQtkM3MjTOL5Dh0TJJA6e+t7BltgT6/tGfidArjD30SJnenkIXVVEjYQ4DKMXUJGRrQf9mD8X0gOPkmxCXDl+4ScoZfUDZPOlz3eKxi2hqOXIqvAnDix4omzJ7Zcfyqh7JiDV28E9M611SzHrBgmwJ8dCnMmTYpXO8/w4p951Ft4Hy4rRyqCFA4Bq+Z948gqYzEvdESRgtclQHzsGZoc7GgpQkULTgmjotdEzrAQQCZqZy64Ym7T18JGEpsJF+pIWsYu2IvVu0/r+L3CAxdvB5jwXbDBqlifoNrHYlR6NLJGMGxK/ZhUJNyorv/YeGOUwrwZUiR0MfE6q4ClCklLNbs5J1k/eGLN6DF1ZSgRZv7savumLHxCApmckT9kgbLHes+fvkOxbKlVonEV+47J/N+otZcWsCuVk/rQz8Gvgby2UiubjpybGHwLJLK/HNpej8knBOgZpHch3z5JrTa5bLBxOlbW94n8QtfuoQODdAVsm71yj5OllZCgknfWDB97CQIbiZOmEDl7wuCofQhQqAG+CD36cs3aDtyDpInioeKhXMqN0Xm89ty6Cy+iFdNsdyZJDfpr9h3+go6jpmnSE0c4sfBxZseGDJrNVIkjo/akgQ9qoDCtbuPY/6GvUIEFwP9Wxr2SrtPXELdPpPwy/9+QeNKRREzWlTsOXkJc9fvwS3Ph766Q/pHbUkTxkObWmVQLJd5eACBHgEfJUGcWMYhOJ++05aBIG5ou7oK9BlvygmB3Z45Q5BE1m8qJHzxevRMtaPbpy3xS3u6d3LPdd3tHpb8fVC9TwSANUvlQ0oHA/8Bx3n++p0aLn1Kw0N6bewsaQxA9oa01yVkaEAHfSHgfWBs2rGZ3ZFKGCj5BZizzQTce/YajDdbMaipJKT+VQCfByr0nYNLrvfVjG/de6Li0AislvRrZFwFyUMITA5cvI1KApQIogj4aNnbN7mz+g/MyisEnDBWbuq6gzZB32W3Bxi1bLexb1snzcvnU2NZ3o8TIyryZ4yqit99/IIuM9Yp6941sZTRMjZQAJsmJFDpXKMI/th0VLl+ch2aRa9ivoxKF1rdXnVLgsCRRC4kXPlNnux9FIsi9ai5V2p1LY8ExhRLN1he09WTcXnPxOoYVSyRpsL3pdO0teppo6klkNZWCudapMs0aBZLlg1etF2B0u61i/MyTIvJg8gwvU59cboGdA3oGtA1EPAaaF2jlLLi0X2Tlq6uYpWjy+DeOUORP6uBDK1y1zE4LFa/y7c8QdA3bcU2NZHB4h6pWZlaST9pqnSSmLJzCvSRtKTf9GWK9G3p6M6oLgniKQNa10SZdiOUO+SJy7dQMFs6VW75h6Dyutt3byrL+7yOKm7Jf42yHivbvOp3QiKt7RtJVTFg5kp1WUDWRoBKIcClyycte8xnaGqpVBXkT9yY0VEgq2Gu7z58QidJdE/X1muud2Vf9TsG++JG6Zf2dO/k3qdh/6na8Oo4ViyyU51bCIAupq7TSMzf81dvVd7FplWKG+ueuXZHnd8VQpgPEoNLPekSvBrQQV/w6l+NTksdAR+FT1XSJU+oQF+TsnkV4GN5/owpFbhhfB8lkrf/t8ejF5i0Zj+aSl0Cl6HNKqBvwzKIHMHw1m4UixilRYX8RsDH6/oSY9Z/3lYcvuxmjKtjuam8FN93upz6JkWyfieWsFX3ozzR2nbyurJssg5dTG/de4qyudOpfFXM13NfgK4mdAvVhGkZuM4MKQzunHQLZZoGyjdx+9RcHz5JGddjy3JJF1GCaUrMqD9++SSJHxNvvT4rshlVyeTPukMXxUL6SrmBZnf6/jTLXeZFuSzpKJILAcyw5hVAN9wjMo+xK/di2JKdyJU2GezRkclwoe7UO1ws1M1bn7CuAV0DugZ0DQS/Bro3qmzMXUkrFyVT6mRGwMfrojkzKtDn/sAQa6btg6av3A4HSfJeUMBQnBjRcH/PfEg4txKmNHC99xhMAl9VXCo1oftjwwqFMejPVYp0xBboO3H5JhiX55NEiRTRp9tm9w4IgUvncQtAZsxEErtIdktNCDCv3vFS4JdkKL4JXVoJbsmfQOHv8C2vhyhXMLtRlz714VN77tE8vPXcs0kV1BNXzccvXmPZ9sNgPCJBeY70jsottY7cOynsnXTPJQFNcbHMHjx7DdO9Qfkv4o1FbzZdgl8DOugL/vcAKSysSpG8AZtDXMPTH06R/wH50uLVSPbSsmJ+LBK3xlHL9qgXXRvL5cmg3EEzpUysVub6wGDZGvHXLkxec8BstbRqUcicqRGamFYols0JjzaMMi2yev670OD7Jozpe7Z5jHJ9JLPlUEm5wBdj9opnd5K5HVTxdBkF2I1rVxWFMzuq2LkxEkNHMpS2k1bjqFhDaVFrNna5Sv/Qv1EZtBArYySx8O04dQOdJd0DLZe50yWzannkE0SCYVoFac2ztPa9F2skJXZ0czc36nz6hsPqXs865k/tSNaiyYyutdVaeJ0vQ0oQENLS+veJa2Ee9Gk60I+6BnQN6BrQNaBrwC8aYByeqUuiBuYc4sc264bggaLtg7pJzBvdNAmUKnYahejC5krAUaloLuXuybp3BARRCFgy1+6hzrU/tJRReM+WbJ850Phg2VYde8pJytJz0hJsP3peVa9dpgAmdGuC+N659biOWWt3oUz+bCApiz1CEpeXR5bCxf0+jl26Ka6uqzBYQGz2tClRPE9mX7vwqT1darfOGKCMBTmFdZSSUUB4ybxZ1DmB34Z9pxToa1W9JI5fclHXHF+TDKmSCmnOe6RKktCMmEe7rx+DXgM66At6nf8wYhQJ5rUmBHmWwuTmmkzpWENYIXOo+LbDl10VoGI82Uxx71w/vAVKSNoDulVScqdNLn7s5tat/N4dacQw3pfGgwJJEX0HdMYGFieMHSSzaDpx3YwVLYoCrRklrpAvWvYIVvecdVFAaa83wUqPOsVR1NtySEA4omVFBfpInkJwSsDIfH9Mxt63QWnjiIydYxoIpmIgWQtdW60JgR4thXxZgr6Xwh5KiScB3qay4/QNZZWktZUkO6aSRBhTKYkFoDOuz1TonkvQZ5p30PS+fh52NDBu3128+PgPJpqkUwiNqzt7962Q2/ynEraHxvnrcw5aDTCZ+6IVa1G5XCn1CtrR/T8ac/NdvXFL5QBMGD8ecmbLbEaI4/8R9B7s0QBBnjVWTmt7IPanhRMQhFxaOwVrJI5v76nLOH7xJrZK6gK+Vuw4im0z+sseyPBwO16sGOIWmdbqdPJ7u0tau2nJqmmtjm9lh8Tq1XTQDGEo/6CSlo/r1hiFsqc3a7ZOQBTl4k135G7gbLz31huYsozWTObhe/TsFdJJ/FxsCaGhjqgHvl5L/yPnrcMuiWG0BfoY92dv+3xZrPM9lBLgR9CnkeLwvVsyoguaVi6O0+LS+UqsfVnFWku95qjXCxlTmROIGRennwS5BnTQF+QqD5gBCVrcxIpHwpNxbauqTkmOQndC5odbvPO0An2pHOLihbhpNiiVUzFJmo5+4pqHkSzGtFw7Jyso2UV9k841iqK2pDiwlBliHSPBCoFb15oG32+tjkYf/0osdxTPxwY3ScvceE4O8Y2xdp/E353rpljWYxndKAn6NNdPllkKUzUo3Uksnilz6RX3h4rAhf0y3tBUVuw9py6blM1jWqzOk3mDPv6wfJH4A+0JJW+SSZQSP1ZUddT/hF0N/C25/ryEZTM0g76Tnm9QZ8kNMMH87YHmzKhh953TV+YfDdxx91SgL0niRKEO9B0/fQ6tuvSBu9ddowqY1oI5BMsUL2Is009CrgboQkhPn7ZClMIX4/f2nbqCZoNn4uiFG7jp8UClZuAK+OB54bBOZosh+CFJSZoUic3KTS+6iCvmJYkh9EnIKrrzz0FWq7gJA2bD/tMEfH7CIIm369202g8pW9iQJDaaW6tpR7SC/vff/yGCAONoEhNHl8n5G/dhVKcG6CYusaaiWUIJumyJve2pP4LHkmIxZG5BU6HLLCVJAgNhDHX+9NUbcZ/NY7QE8v7G/ad4QIZU30NiVIH+J9g08PNmnGCbsj4wNUDLXq2hi4T45IhRIYwpqykpAiga8Mmb3uAbz3g6UznjIu4Q/ebAeY5tUPf+01dc93zs60sDN6b981xzMaXr5b/CUqoJ/c8JSik5vOPjMnq7ozKHnqnsFOsd3VCjR46oYua0NA27xUJIa6GpMJcfRatDy+DFO/cVU6hWr1Yxg35mbDysApS18rl/H1enJI0xFc776BU3eQr5P1SR5PKWQjdbAknqaruFjrdI+gcKXT110TUQkjXwSqyUXTa4Gp+gh+S56nPTNeBfDXh43UO1Rq0V4KtdtSJmjBsmbKiV8Or1GzRq2w0vX7327xB6+yDQQDth/KzYabSycHE4JjqvIOyfqYVYhMJ8urQ48WEsY900yxTv0UWUTKA1eo7HOe+UAyy3FFdJ8UAiF59eJFGxJXSBJOBjyoa+LWpYBXxsSzKaE0vH/PCKLTGKUWX/w3srx/VQFj3Wp5uo2b5KCFcWbTaE8GjumKxH0psLLu4KEPOaFkGKb+0ZujJDYiWHzl6jCHVUI/nDOMBNBwz7t4LZ06nipVsPgu8Fra2acG5Tlm1VlshyBXNoxfoxmDVgbtII5snow9uvgVI506qUDLMFrJDhKXe65JJ4/TnmbzuhOqmQL4M69pbccSvFxXCDWP9Yj3F6d8Xtki6glP5C+mJLGGvHOLyflXricjl+1T7FtFmp/1xF2nJfkshvkxg3slymSBhbLJCGwOrO1Yug5YSVmLb+kAK0FfJmwB2xZO467aKG7yXroBsDCVGY6oHunllbjkfVQpnhEDemuIC6gKygiSTxfOMyBovcPNHFJIljbCoWOsbbUaoUyIzpKQ7jhqSDqNRvLirmz4izwm66XYAp3VC7WFgkmT/xnXz5kbzFWtwjn8INaVYejUf/hU4SU0iLIXMG7jt/W7mi0tLK8XXxmwY+i4uh16vPCoSkihsJEWzEjX779/9w5/lHpIwTCZF//9XXQbxefkbC6BEQSaxZmryT5OZP3v+DVNKH9qRUu2d6fPNJ6kkePOb00xKcm963df5a2r0UUOUo/dtyV7J3vbbG8G+589/uePTWQB/u37709v7TwKMnT/Hg4WMkS+KAhAni2ezsm1g1XG67wckxhdXcdqYNP0iy5mcvXiJl8u/kENz0unp4IV6c2Cpxu2l903PWY74/uj8y8btfhCDq+ctXao62Pvv2rtcv4/pWd/z02Xj77j26tG2OySMNFpr2LRrjvehpx96D2LnvEBrVqe5bN/r9YNYAc93Nk/QM1bqPQ8e65dUeh7FxBGEkbmHKAALBnk2rYsyCDagi7J9dG1RCdMl1vH7fSZBUJbNTclQuatiHWFvOzlmDrRXbXUYiGArBV5l2w622Wzyis1lMo9VK3oUNyhdWeQlJnFKh00iUFdKWB09eSF6/cyqlAtNXNKxgsFQTeBVuMVC1vLVlpmI8tbc99zZOAp4v3/ZE2fbDUUtSYlCWbz+CJxIDmSeTE6p4640WPubjYx5Epm+IFyu6skayLUlgWFeXkKEBHfQF4/ug/QhabiBtsRyZ1ksqboXzezdQKRUIbDThprV91UJgGgVK7OhRsHVMW7SZtErFlzHGjMKUCGNaV0bNogbLlyoM4D/MdbdueEu0mbhKAT+mWdCEcXtTOtWQwOuIqojzeCO+5SDZ0QAAQABJREFU64zzo3WOLwrTMTjXL4Uu4kJK4RO7lYObodeszSop+nJv10veo2vmrB511dp4rbmQmsYKRJE5bR7VGjWHLFSpLJjOgkLAt0r61eajCuXP8Wvu6tQ02b12TztWlryBc3vVQ2+Zk0b4wntk8eR7ZI12WWurH3/UwKxjDzDl0H0VW8a78hFAE0ncPrRcSiP4u/roA6YcvIfjHpJX8et/+E0+91kdoqKWJClvntfwlJdt80w+r6Jg+5dOjslSny6YBHwVJIn70HIplHXrmPTBGJGoEX7BqIqOqJsjAZti2uH7mHdCciaVSo5NV5/j/L13EtBvqDe4bAowKbpPQnfJvgKm3CTpOSV2lN9QPUs8Ne7vXJS32LNerW5gHFece4KdLi/RvmBirDj/NEBICwJjnmG9T1cPTzTv1BuMk9PEMUUy/DVrCvLl/v6k/OLV6xg5cQYOHTslIOUDfpNNLWPRGgtIIXChLFi2Gl37DcOwvt1x9uJl7Np/GF+EQTmdUypMGjlQAcBeg0YpyxbrF8qXG5uXzweTv3/58gUpshVCjqyZUChvbsxdsgJPnj1nNRQtmA9L/pyEpA623eFY78iJ0+jYe5BKDM/ruAIs69WogonD+xtj5uxdL9sHtBDURZRY+gE9zN39xg3th9rVKiJbZnOPj4AeX+/vuwa4D7Lc81hea7Uty4dIHjvmumNOPyZx14Rgha6cBHwUZ3Gp/E8A0ISlm9FX0jdoUkTYQOcMbIuAiNvT+rQ8nr3mqopoabQlTE1hrzDtwcbJzmg1bJZizCT406RoroyY5txSgVqtTDtquvNL+/UyTqcx84Wk5SbOi7VQkxol82F6n1bGh5h1xIpJN9lpK7cJWc1iVY2J50lWM7C14YG71lY/Bq8G/idP8WQbE75k+PDhWL14Lk792T1MLJyxeU8kRwoJWdJLugfLZOhcJJ/4MPUBrWexxbedlsHI4gsfFEJ3TqaaIJtlBElJwfQUKS0YS7V5fJRk59c9H+Hhi7eII4CVbJ5xY1qPiWMcINM+/CvWHvZH4ManU/YKYyBvCvENWU+TiWusf4WuJLQ2vhfLINdId9uQIszvWLz7TLi7u8PR0THAp5U7RzbkjfocgwQM+UeuPHyPCnOvIlmsiKgmAIlAbOOV57jz7BN6lUiKnsWT4f2Xf1Fm9mXcFQCXOVEUlE0fR1kFt1+XVB7f/sOaZhlR2DvxevaJ5/BMrHjkREoaMyIyJIyCvbdfKZAX8bf/4cu3/0Nhxxjq4cIh19fyoAA40zMnEseIiNF7vTDrmOGHOnGMCCiXPjZefPiGPbdeqnbTaqRGnewGgFhw2gUFKB8MNzwN3S9jNF95Uz14qJUtHmJE+g0HpIwAkEnhV8scKfas1z/69K2tq+i13NwrSC3W1G1tsiDrhHMK9AVUTN9pr7eoueg6Hj0SK3win0Gyb3MNyvsnT55EwYIF4XHpGBivFhRSo0lbbN9zAHQ3zJ0jC267emDp6g0KnDy6cVZZ8969f4/cJauA7onZBZhUKV8a7p53sWHbTnwWt6vd65ehRJECmL90FTr1MVgomKS9QuniQlhyE173HkhsUAR8/foVCeLHRamihQSgncGDR4/Ruml9zJo4SvUTI0Um45LLliiK9GlTY+/Bo2JZdEWaVClx6chOBd5WrNuMFp17Y3Dvrhjs3FW1IaDiWviwrVHtaogZMwZ2SdltNw+ULFoQu9b9perZs17jJALwhFbP2KmyKj0tFUC9R9Z13eW2ANlEKFuyqALGATicr12NmzYLS9dugev/s3cVYFGlXfjs2ogooojYWCh2d3d3t2t3K+ra3d2xdnd3YmGLiiCCLWJirevu/5/3G+5wGWZggCEGvvM8w7336++9MNxzT7yeGuUg1A5hbHD16lUqXrw43d81jzJw3FhsFFiXzrre4xesCdiaZU35cmTSKnzq/YJO4I67N2fw/knZM6YVH3W9OZ3/YndO8Oh5ccwgXizDpVVNmB7aXsLSH5QXj/ljxcYCx8zpRQIZfeO/9vtA9zmTKLj9oIBC8YvpUqKdMzVs0ZYmTJgQ05dqkvVJS59JYIzeQUoxvUFoAmUIPHcK111o7U1ZD3J5kK/jE5rAEheSVU3dP7OdDSt7mkBidbmx51DKTKmYwQoJZVpK+BFYclGjZA2tlIEasdUOAitfoZnX6Qhbo6D0XeUMk1D4GuZLRQsbZ9dOliqpNy1zeUU3nvtrlT6lsh2PASseLOGwbA3d70U/+WXBns55qGjGZKJZ87VudOHJZ7r36ptQ+pS+Vonj0d4/8lA6VhohO269pX67PWnqiWfUhNeoWOyV9v/wC5axR7yFVXBps+xUO7fmd3RQhfTUYJUbnff6RFdZGSqWyYqM2a8yrvoIS+f0k0/VRXrP2/C+q7NSrE/+ZgW5545HIrYFOBpyodXXV5aZFgEoU1D4smbORJtWzNcOnoIVptmLV9LxM+epXs2qdPHKdaHwtWxcj6CwKJKaFbi5S1bRles3hTKjlFtwCnvXU/vZvTIzIVNlrhKV6enzl1SrakXasXaJsBIioUnFei3oxq17SjftsV/3TmydcxbX35lcuVSNhuT20IP+2rxTKInahgEnmGPw6ImceOI/sY9GdWqImj9ZIaxQtzmdOufCe3AVbqLG7Fd3fFw7T5hO9x6466vSliW1sKDNKxdor9Unnt4+4vLjx89UlBVoxYqJwvjj4tO44QNoSJ9u6i7yPIYjAHdCxf0wpKWCw89QVsuQ+sXEuvjgdM5sLz7hWV9Y+sNyik9oYpfKmrkHY87L7tDWGxfrpdIXF++63LNEIIYikCggdm8pu1WmTZ6QimW0YrfIBHR/RFEm22UzHEsBe0vax0pYRmuNEoYyxNq95g/kJ1vvdKVnmXTaeL3cbB2E5LK10Cp8uC6VJblQ+rzfI6Ns4D+uFuzuqSh8aNekQGqafuopvfj0k15yHJy6DvX3WCHzYouePVsHa6oUrmRs7UPficd8hDsllD5j9osxdQWJV055fNQtDnaNPRmSycc5OcFrpk6plZlyMBZSog+BJIkTi8kfs0IyZc4i6tS6uYjnmzRqiLCiJWGXLkiRAnnp/MHtHJuXQVzjB2LiXr1+I67/ZgueWprUrSkUPpQlYCtIrhwc081KX8dWTYXCh/JSxQqLc7hb6gqUNUWwhv49/qAu/YbR1Ru39Cp9t+7eJ2T0hPtng1rVlK5klYxjrZs1pOHjptGeQ8eoV+d2oi60/WoHUJ1cuOxKl101SbtUxUFOoewaksccxwiBm2ymDOkIGJcpUYROn+dM0zPm0ciJM6hYofxUvnQJQ0PIcomAREAiYJYISKXPLG+bXLREIHYi0L2UPcE1EspIkzX3yTJRPHa/TC5cK+HuCUmZNAH5ssvmBFaebr/4Ss8//i3cOg0hAo9fKGCKKIqWnaoMdXDthOg6vCNxi67kZCUJSt8ztjjqKn2P/X6I5m++/KQS7PapFrimQlAHMWa/oqHOD2DiOaqYTmnwywTsYqdPTnl8oJWXX1HFbCmoY/GQ47P09ZdlpkUACVa6tm9Fy//aRGOmzhEfJ8fsbJGrRC0a1aO8uXOKCVPZpKTXvn6sPE2l67fvkvez58Id09Bq1Moh2iROrHlRonZZhaUaH93fezvb1JTM0jLI0E45NZb1J0+fBylXLtwfa+J+oIjmKFpBKRZHJE6BvOY6Y/crOuj8OL5rvUhhr1Ns9CWStSiybPYU4XKK65JFC9NjVnzXb9tNuw4clUqfApI8SgQkArEGAan0xZpbGf0bAa/fuqNXqSbTHuBjjvKBCdq9OAuqI7vCIhGNPoHr0sOnvhx3+Ikc0tpwFtKUYYol1DemLNMg4Mgxdxf7FRRxfKdZMbns409HHr4Xn+3sVol4Pbc33zju7454SEUsWv28NhyTlkRkyFzK7p26AiVPX1bOAB1Pt7lI/KIuRAIWXUHiGMj/grUm+vJTo9jZsIUS1jx9UjSDxqXUmP3qWzvKkvweerZSfXOjbA8npoHc5hjK8gsCE4f4ByilKEMcoxJ7KBrLH5GKwMLp4zljZH3avvcQW51chBslXCnh3rl/0yqqUqGMsE6VqNpAuOTmyJqFmjeoQzk4OYvfuw80Z8nKYOtLasDipeuSjI664f02KVMEGy8+xw7pa6s0/PLlqzhNzcopksPoE1gWIcbsV1//RIkCLfz66kMryxCQhMbeLk0QV1j0q8lur1D6HgUor6GNJeslAsYgMG7pVpHVEsTq5iaIg3zk/ZL+5pwFiBtMnyb8ITXmtvfYuN7gTzOxcZdyT1GCACgj1h27JhLJmKvSN3vbaVrA3IcnZvXSG58Hfr+pG49zIPg/WkyRRAbZOyUfnxaScJ+ce/yRqRd+Fxk4kYUT8XFnPD9R9+1MgeH9mR5x4hFku4RVomNxOxGnp0y2NCAeULk21RHJTnTl8TtNGZRNXQE1AwTJW9Qxhyh7ze6gD32/icQpuDZmv1AMdQVxi84HnugWB7uGJbEBxz7qCpLaOAW4uarrYBVFhlIkuUmaMPxKpXpMeR46AkjM4uH1hPI75abZEzUJWJB0ZeTE6bRtz0FasW6zUPpmLlgulLOe7B45d/Kf2oHnsGJoagGdw7+chAuJYBRx99BY8nJky6IUBTlmZfoICGIR1TGHKHvJLqiIxcuR1UHEJRqzX/TTlR6DRtLNO266xUGuLS2T0ondG4OUKRcZ0mss27A8IqOpYv1E/Rvft6KZbSr5YKvgJY8RR2Ank5Q/eeFL5qT0geh+1MLNtHTH0SCW9epMEbGQlVcZuxfx34voGEG/7090rETOKRGIRgTwlvvEdXdadeiSwVUs33+R/lx9SNSDe693w7KCk8/79XtqzBQQr99/NthXVhiHwIDdj4VbJzjxIKA2qJrTWnDo4RrJRx6ypQ9SJUdg3B3cJkGrEBkCq9j3AOsdxj/uzlk42YUzG7t92jLnn6442SWlxGxd9PT7zu0CFUb8jg3a+5har3/AyWY0rm7G7Fd3fFyDpgLKY2gf3wA3Ut0xhjINxbEe+YN9UiSJTxasdKNuVcucut3kdSQhcIote3VadApirUO8WdP6tcWMP5hGAeL28JE41qpaQRzxAxk9t+zar7021QkUok0792qHAy/gghVrxXXFMiW15eoTKK1QosDrh2ydiuB3v2v/EWKPiAc0dr9Kf/UR495l5TGkzx23B+ouQc6zcDykY/asgu5i7+HjQep27j8irksWLRSkXF5IBOIaAlNX76bF244wTVZCQSzfpnY5Ss6Z34+63KL2oxfENThizX6lpS+G30ooEi+Z0By8fLbWGpcwfUt+xRQHP/mfMlwNQ5OvTIvg9wnk6IFt8U8ZtAo2yS2Y5y64ZUEZE+1A/WDL2bLAAWiswCXyMY+fhsnT9ZGcK+MYu1+lvSmO4DAEmTpcO0OSradviuqNo9pRxYI5xPn4jrWoRM85ApNVhy7TyDbVQhpC1oWCQA3mz1t79TW1XHef/iiRVmSURPzZfVb0EJeHJCyF0lsKi9+Eoz4EsnW/r//Q3nt+QhHD8MjuCUtYofSG/15CWUaQalgXm/11n2pxFs7PTOS+PMCFdABn49QnqSwTUK+y9swL+JxacL9ubG1DbCLWeO7xJ8rNljvsE2LMfvXNAdoH7z9logl92JhjWbWKZQUlw4IVfwmKhmKFCpCnlzctXr1ebKdOtcriiAQj9909REwfrIO+b9+xJfAAuXtqLHDIjKnm+YsoFr2GjBZWtayZM9KOfYfF2PmdclHd6lX0Dg0aiKF9uouEKDWatKMBPTpTsmSWtG33ATpx9gLly+1I9TkLqa/fO6P2q2+Sk3s26Ss2ugyWywnOg6hpx57Upf8wusUJXTKkS0tHT52jC5evCSL5Tq2bGT2ebBj1CPzgFxKwnOF5JBtTLxjiwgUtgbvPC3JIb2cUfQDGTJsqBSVWUQ185hjQ134fRfZKNeev7q4/+n+lV0xZkCOjfZjCPT58/krvPvlT1vRpgmWCVuYwdr9Ke1Mc1+47LYbZyVx9ZQrmEufOnRtTnib9yeW2O/m8eksggpdiXghIpS+G3i/ElXWdtZUQJ6cI3AhXDG6hpTSAIjVpw3E6eNmNY8zeiGZ2rFSVyJ2FJnauLRRFFK49coUGL9kjFJLrj57RcVd3+pvJQLOnT01TutRlBfArDV++jz5+0VglSjplpi1/dhC8f2jn2G4S5c+aTpCNrzp4iXw/aqwUZfI6CLfGdPwlaUi+sxvkiBX7aevpG0yiCr6033gse5rerT4Vy6VxBUJfY/ZraI6Ilifgh4BczG8IAc/e52+aRBzqccHBd/vxCwK+isKHevwTaFqhAN+HY/TA57W6izwPBwLDKmegH/9orHZ9dnlqR3Dg2D24SsLyN6JKJrrHiV6QJdP5oMaaAEXweI981GO7BxO2fybE/0HpQ+RdPJD0qSSekrFFVYZTQ+VNOeMm6CKQdRMC10eQvYNoXZHfeQ71sH3LpmeXGKJ5557TmCPeSjMqldmK5jTMps3aacx+tZ3lSaxFIEM6e0HCjiQuU+Ys1u4T3y+9u7TXZsqcOHII3b73QMT29Rk2RrQrVrgA0zIcoDbd+gnC9nVbdwkOP1SqXTPFtYE4UH38piCGh0VsISuiihTM6ySoEBSXSOUhWD3P8P496N///hX7GMT0DYogG+bKedNYqU3ESpZx+1X6mvpYnzOLrlk4UxDYz1q0Qjs84hDXLZktuAy1hfIkRiEwZ8N+ghXqG/NSQvC726lBZZrat41W+bvt7k2TV+2kczfu0xf+fw7aqIKOWahFjTLUtXFV7X4cG/QRoQLjejQX7aH0gVuubvkiNIXHA+H7GVc3oVxaWiSmmQPbU+ta5UT/aWt208IthwkE8duPXaQr9zyEGyTaTezdijrzmkKS8zcfUL9pq8jj6SvRzCa5JZOZl+J5WwfhGDRmvyHNE546uHZCiYU4Zc2oHSKdbUpKbZ2c3rz7GMTlU9tAnsR4BKTSF0NvkfPKA0Lha1AmHxXKkZ48n7+ljSeuU/2RK+jxpjGCWH3ujjM0a9spkXCkdZUiwhJ4kl0U91y4I8jaD0/rIXaHt2G/ODZq3F9H+MH2d6pWNCe5PXlNHjxmqwl/sYXwX0qdwlIoLxfvPqFLbt40Zs1hmtu7kfiygwXszC0P8alcKIcgQT954xFduOtF9ZxX0OXFA/lLKjDuQ4EU82K9Vx/6kFNmO6paxJF83rynvRfvUl3n5bR1TAeqUECTDc6Y/Srjmvq4eEDgW93aw5fSRSa715UP/t+pQ43iQlHWrbvr9VIUpQnBEqvbR17rRwBxcLMaZKORVTMJPjskYUGWTcSfQeGDwJJ2qGteAlfde6YuyM8UDjac0RNyuld+4VapuF3eHFJElKt/iMybASTq6nLQOuCjK3AvnVU/qyBSR6KTPOy+iQyiarnAyWfUEj/ebzSYuQY7l7ATCup3VmQR/6ebCdSY/arHjezzO0OLRvYUcnwDCEARwQfWJmS/RFxcbs6Wqc60CUuay9Fdwvrm9/4DFc6fh1IHxJ+BMB1ulWnT2JJ1iuRaRVE93ZZVC9WX2vPPPsFj5CyYomHvxpXCmuh66w7Zp03DMYe5glgjwBeIj1rix49PY4b2pz5dOrAV7T59+/5dJJtB4hm1GLNfdXtTn7du2oAaM6UFXEH9OQFNNofMgsLB1PPI8UyHwM2HT+jPxVuEhalJlZKUlBWsbUcv0oqdx1kZsaIRnRqR/9fv1GbkPPJ+6StI2muXLSysgntOXaFrbp6UM5M9lS/iJBYFEnHf95+o64SllJGJ62uVKUSHL96kbcdcaO+ZayJ5CdrixfCJK3eo5+TlVLFoHiaAT0lfv/8tFKOBM9eI684Nq5Dfh8906MIN6j99tVAeW9Usq3fzcJFsNnQmvyj8nTRuk0np2KVbtGzHMXLnpCn7548Q/YzZr94JIliYgP+G65QrQrs4FhHK859dmwrleuWuE0Lhy+WQnrKks43gLLJ7dCAglb7oQD2UOd2fvaEjVx9QFs4MuXZ4a23rFJZJaP6uc3Tq5iOqXcJJuCSi8vz8fuRgr7E69GpQhvJ0nMIK4zNtP+XEIlECurCgv2iLL7tCXabTs7cfqXpRR4LLIt6GXXJ7QjWHLaVbns+VbtpjrwZladIfdcQ1rHaVBi5g69YbVkZdhUKkbRhwsvnkdaHwwbJ3YlZvrWKI9r3mbqc5208Lpc/Y/eqOj+uxaw+Rm3fIFjZk4Vw7vI2+7kaXpbWxopk9GgRrf/TaAzrAllZI3VJ5gtXLgvAhAKVKoWjQNwKscgXSWQarQlbLyOCcw3wFw+EuCo5BuGKGJqHtN7T+sj72IFCmRMiKN6xqRQrmC7ZhWN2gJJpaoGiCzD2sktI6hZYOIaS+oe03pL4RrYPFEpZSKeaBwNyNB8RCR7MS0rx6aXHeuWFlyl63F+0/6yqUvkt33IXC16xaKVo1tpd2Y1AKF2w+RFdZ8VOUPqXyDx4DVjz8Da3Ze4r6sgXuJ3s5HV86hkrkyyGa1ek7mc6y1Q9WRCh9iiDO7eTysdqslpsPnxdK5NglW6klWxZ1M+XCijZ8noZ25K9JvalBxWJiKOc/GlHVbuPZsnhPuE+Wyp+TjNmvsg71EWscv3ybukjvecf6lYRyp69ySPv6dIndOJduP0qr95wUz4ewrlrw38y0fm31dZFlZoCAVPpi4E1KnFBjRXjy6h3N3HqS2lUrJqx4Y9rXpGGtqlKShJrbNrVbPTax/6dV+OCCCPdEWPN+MEkvLG3qLxxYDRXlEJa5nOzSCKWvLY8PhQ9SIndmPv9dxPfpQjOc51YkCSuQvRuWE8obXFBhBdOVXefviKKONUtoFT4UtKhYiEYs309nbz8WrpTG7ld3fFy73PMWiqW+OqUMyq6p5RO/TYTldDXH8UH6NymvtVqaei45nkRAIiARkAhIBOI6AsqzwrxNB8meXQ1L5ctJKa0s6fmxFcKVH/gUypVVKGGZ7QMtUa851g7xdpCf/JykK/1b1xEKH8rzZs8kqp2yZtAqfCgoVyi3UPq8XmhCaUQj/tG2Tnmtwoeylmzdm7BiBz177UcvfN8HqUP97Uc+5PnsNcFVsh67kSpildSCWtUsQ6MWbWYF9hpB6TNmv0p/9RExgscu3VYX6T3HngwJXF1B0wD5xVl8/0NaZ5bvHE8J5RcWTynmh4BU+mLgPUOClU61SgiFYuL6Y4RPrkxp2CKXS7hgOmVOK1btmMGW1nC8nvOKA+T54q021s7QljJxTKBaEgcoj/ZsxVIESiI+UBjVAtfFZBaJ1EXaODgfzl6pT7AmyPh1R2jW1lNBmihxc74f/ClbutRG7TfIAAEX+6d01X4Z6auPjLINx68J99d3HIANXKax8g2FWkrsQsCKk6/YcXZOZOKUIhGIKwggBBYcdrapNd4jcWXfcp8xH4F+rWoLN8i7Hj5Uq9dEfiZJQhXY/bJ2ucIEd09IKk4y58sxZyMXbqKbD7xEwhG8EDckiAlUc88pipZ96sDs0Oir8KXqPhtl58QtupIrSzqh9MHFVD022nn4aMJBXvMa8zQZEKQrXFMhqIMYs1/RUOcHMPE9vUanNPgl3Fb1CeL52o6ax5Qt/5HzH41FfGJifnl+8Nx1dnFdQbPW76MiTlkNWgn1jSnLYgYCUumLGfch2Cpm92xIzSsWpN1sLTt721O4UcKVcgG7d+4Y11EkE+k0fRMduOQm4vtKOWURiiESroxefZDpA/yDjWmhykilrlRbA5VyXdLplFYWSpX2GD8gji+oeqitJv9vmkDrIjkycqpfDXeZUqvkHUyeNIkoMma/Sl/1MVEAWbC6LLLO4RL7x4zNIiYxIe99ULNKbOGrEEwZjqz5Y/K4Ou8IYvJSjV5bn3LpCR8pEoG4hAASrXjfvhiXtiz3aiYI5Gbr261ts2krx/Edv3ybLt58SPvPuYrPxkPn6QDHwt31eEplO40SL66zc2bPJlVLEhQzWL/ms4VQV6DkKQmJ1HX6notQr/u/LiUnYNEVxMRB9D0b+QckikuVwopKBriO6vYvwRZMiDH71bd2lCEhTXjl9LV7QuHLxmTsiJNUBFZMxDzu5vjIE5fvSKVPAcaMjlLpi4E3C7xvj9lKltfBnqZ2rSdW+NT3A8evHaZd527TmsNXBAE6FD5Y61yXDWa+OE0GTbyZGbR4j8l3BToHjK3O8ubxzFfMA0udPnGwtyFYw1pWLkQNy+YP0sSFk6UoCWSM2a86Y6Z6oH4Ldoqsmuoy3XPLJInowJRuusVhvh7GGU6RhCYnW1j/GtGGHAMyfoZ5oFjYAdaB6BRQNGy67kvVHK2pWs6gFu3oXFdY5v7ASWmQlRQ8eeAATM6ceSHJgzdfRXIbtNWVn8xn+M+/+h45iGkwftMmxdHtp3v99MMPQvKaXLYW2jfdum3ktekRAO3C6o3bqE71yuJj+hmibsS79905G2YCypnNQe+kPzhOCLQJeNHokCkTpbE1bGEEYT2oLJBUBjQSCZlDzFiJjHnCMqax65TtDCMAZQQWJ2TgxAfxcVA+wBt3njN1PnzygmZzdk9Y47o1qSbi9JTR9Cl8Sl1Ejo8CLHfqMTyeaqx5OVjp1BVQM0BAjaWOOUTZq7cfyO3xM8qeSdPPmP1CMdQVJKwZOHOtbnGw674ta1FTjn3UFVgoIXmyZdStoiK5swqlD26eUswPgZCfKsxvP7FixbDsQZnp26gcje9UW+wpo601NWLFCUrfj5+/hOUPFXmy2GsVPlzvOn9bL+UA6iIioG7YduYmK3CFxTDw8V6yT/M2uHz+rHqHLuaYia49fCqskWql7+oDds3gLJmgjLi2dLCwZIa2X70TcCFcSI1J5GKov7HlsPIhMQ1iIZFcRyp8xiIXNe2evPtBm28wxxJn+jQ3pe/V57+pwyZ3QUGhRguZP52ZniIxK4G68pHJ62suu0ulsySnjW1z6VbT8ANetPWmxr1at3Jk1Yx6s5Sq2+26/ZYWX3xJD5gfEZKMXV3bFk0jsqqq28nzyEHAgxUbKH3I3AnFz1zlw8dPVKJaAwKZ+4Etq4Ns459//qHOfYfS9r2H+IXiv9q62tUq0ZJZk8jONvBl4jmXK0xJ0Z9e+wb+TiPBDCgsOrRsou2r7yQy5gnLmPrWJMvCh0A3zrL5ljNkPjm0hFIkS8r/j+NTTc64mZUtUvc8n/Kz0T903+uZGLxGqcCMynCbREbOyJDtx12od4uaIsEJxj/M2Tsf+bwSWULT2Ghexqvnzccxg7AuurOyCLoGWCMhUFSRHRRZQteO7y0ylBqzX/XYyvlXtia6PX6qXBo8vnmvcSPVbYB4RsgRtuqBR9DaKqm2yeYjF8S5PoVQ20iexFgEpNIXA28NaBGQKAVKFUhHi+TMyMTmfrTigOZLq2bxXFQgW3qRsAVJVMCxByXk8n1v5sPTEIhjW2jfrromM5Qptjlg0S5hVUNWUdBCYO68WdJSzeL6g4EHN69Em1hR2smKKvZRPn82esqUDXBRhYwISAxjzH4Nrf/g1O6GqkxajmymyFiKpDD9F+7SO3bFgtlJnexGbyNZKBFQIeDPZO+NVrvR0w9/i2yk5bMmF1QUZx9/pFWXX1MCzho6unpmVQ+i90xGD35CQ5Y8NIYSnJBpI/QR1NsnDxqbG2Rwvnjk+40G7X1MiNtvkj81pWCL48H772jxhZdizCGVgr/91R1DXksE/N69Fzx4UJD0SbcBzrRl135BjN6wTg36wrQJew8z7+yxU9Sh1yA6sn2d6HbvwSOq26ozff/+gyktqlKuHNnp7MXLdOnaDeo2YIQgUw8pA2hkzGPsmPr2LcvCjwD485YzPUP9/lOpZ7Ma4rkCVAdQ+JAYJW/2jFTUKZuw+CGm7wlbrN5++EQ7T1wSihhmduHsnrCEoZ0pBNbFOn0mU/0KRenTl28iQyjGHa5yi1TPkzplchrYrh5NXrmT6nJG0L4ta1MyDnPZwWs8dfWusK6BLgFizH7VYyvnFTjJyvtzmr8fpSwsRyR4QUIbxE7madyP6nOG0XScsRSuncDajj3LkMBGivkhIJW+GHjP0qdOwSTsLTmBy1HO3hmYAAWBxN3rlRaZMuFvPrVrXQK/3dIAixuoCcZ15DdO7MsNQvShy/YJt0rFN12XfBpZPvWJbju0ATF8jvS22rlQBioGuDkqgc8gqIYo41ons6D9k7tSl5mbhZUMljIIqCcmM/VDo3Ial09j9is6RuMPWCwh31jxu8KWSn2CBDxSIg+B159/sqLzH2WwDhofasoZYUED91+WlImDZL415RzqsVyf+QuFrySTtm9ul0vrdrnnrh/12uFBK1nxG1o5oyBzv+LzmYbse0ze738w+bV6lODnaOOYxoJ2dnIKXhlKSf/dnvSTXUOn13Wg1kU0rkiw8lVfeocWnH/B3INpKSVTUUiJGAIvX78RmQQzZ4y8uFFY2sDnly1Lpij5fQYi4BnsMWgkeT7xCWLBU6P1i93ydh44TDYprenikV1aq16Pzm2paKW6dOqcC3329yerZMnowNETQuEbMaAnjRs+UDtMkw49aB8riVAUDSl9kTFPWMbULlaemAQBEKHDrRBWOxCnK4LYM7hKwvI3rntzusMZMm8/8mYXxzWiCRS8S+umUAd2Az13/T5tOnROKH14NlKeV5SxdK9DKwcX3wGOKxy1yFM0TcRWvLG8BsQSKoJnIyURDMqGtKtP//GX+PS/9tAwpm9QpCwrW0tHdiWMATFmv0pfUx4T8zPk1mkDaQDjB07B9QfOaoeHhW/ZqG7C0qotlCdmg4BU+mLorapT0onwQezbG3ZnQMITWPPsUyXXrrhLnVJUrzSTVDM5eCoOJobVTYm5a1Q2H/l9+srlSYWSqI9SAQqbPnm9a1KwYlget43tSG8/fqEbHs8orU1yMZ+iUKJD0woFxUfdOQ+v6cL8/vTouS95sCumNfuxw3KJ8dRizH7V7SPr3JDlsCdzFOIjRT8CusHt+ltpSrfe9KWh+7yocAZL2tUpaNrnVZdf0fijPtSykC1NZaUDaaJnnH5GRx68p0dvNZnN0iRLQMUyWrEFLBOlM2C1WnPlNY094s3uiBmpa6nA7GrXWcmCZa1x/lQ0m0ngFbnk/YmG8Zoes4UMYm0RnxrkTUVjeA6FFF5pa8ojlD5IQ55LPU/NXCmFW+ePf/4jr3ffKVeapEzky5npmL8Qn88//tW6Xuqu59tPJhz+8g+VMYIfULcv4gpvv/zKSl18cQ+UesQNFk5vSReffKajfC9aFtYog0p9XD/mLFaRnr14Rfs3raLK5Utr4YCC4FCwLL378JEeXTstiNPHTpsrlJX77h6iHcjUSxcvQtPGDmerV+DvqnYQPlmyej0NGj2JpoweSv26d9JWXXG9SRXrt6TWTerTinnTtOVwh+w5eBQ9evxElEG5at6wLs0YN4ISJAj63avtZKITJJEAYTw+nz5/JsT06crNO25CkatRqbxW4UMbkL87ZMpIXj5Pua9G6bt4xVV0b1q/TpBhGtauLnD89OlzkHL1RWTME5Yx1WuR5xFHAJx4i5270vieLZk24B5b+hIwZ561IGFXkqfAknZ21QS65f5EJG8p6OggiNsx+9WN04RbpZ2NJjOn5/5FwRYF4nF/l43Byge0qUv46ArcS7Gmmzwf3EhhIUMGUbUg+YxakARvZJcm1KN5Dbrj7s0vk38KN0/F1VNpa8x+lbamPmZgsvodM4cIYnt37xf8ovE/ysI0GDkzp9M+Z5p6Tjle5CMglb7IxzhCM5TKkyXE/qAMSFNYk+lJ3TAl+2DjY2pJncJSUEeEZVwoorky2YlPaP1C229o/WV9zEegumNKGr7fi674+BOsd3Ych6cILFy/WNGr7WQjihZdeEHzz70gi4S/U/OCqSm1ZUI64/GB9ru9ozf+P2l356BKozIOLIIYBy6KasElytWWspOPPnBM3UNWqn4Tc1gljk+nuAyKoycrmlva63dfVo8b3nMorR2KpaHSOgoaFD0ofBBb3jOkKCu6ipLs8uQTNV17X5Tr/oCVD5I0YTxa7vKS3NldE3sqlCEZ1cmdMkSLj1eA0guFUf1mGuM52CQRSp+nn0b5RpkUDQJQQGYvXkk79x8OovSdOu8i4tBKFSss4vOmzVtCU+cupqQWFtS+RWNOWpKajp3ijMz7DtGrN750et8WvZCCWwwKJHhZ1YI4IJQjyZYih0+coYZtu4qMhJgjeXIrOsJli1eto4cenlq3SaW9qY/Y66m9m8WwcMOs2ij4y0UHTsJyZv9WgYl6fper18n72XOh/CkK8HjnQTRiQC9ycsyubQqXUWAGCSnmMTLmCcuY2gXLE5MiAKWqcQBFg76B8cxRmBOO6ApeSOTKYnrLOuZDgpOwCjgG4YoZmoS239D6R6Q+SzpbwkdK7EBAKn2x4z7KXUgEohWB+OxW86+uhmVgRYgRq5LDmg6xxegQx4p1YndByDPOFHnj+ReyT56QymSxEmWnPTWB5se656MsrHRAupZMS8Vm36CbL76I64j8gHIIiyCWvrRZdqqdW6NsDqqQnhqscqPzXp/oKrtVFsukWY/uXJOO+9DDgGQnunXKtQUrX8ua5VAugxxb6bGYfeY4P1g7IUUzJiMbtuyFRRSlb73rm6DdLr2iImxdXdc6l8HMoI8DFLrUlsHnzJpK41br9/VX0HFDuFJ+J/D7YU4SL4C/6hcncDJG2jRrJJS+vYeP0YJp4/hNeDzRbdvuA+LYjpUvyFFW8CDXTu1jl8vMOKV+3TpS1sLl6OqN2+I6Ij+gDA0ePVEoh5tWzKdGHCsH+XNIX6pQt7lwm4TlDJZFfeI8YTrdexDcMqduC4V188oF6qIwn8PyCOUQ4v/lC3XtP4Ke+DyjO/cfUiJ2LRs7fIB2zIJ5A12U12zaTrsPHCFY29689RMKdvVK5bRtdU8iY56wjKm7Hn3XUNjN7e9D3z5kmURAIhDzETCv/8QxH89Yt0K4b6Zl8nZbtvBJkQgYQiC5tTV9fv/cUHWw8iYFUgul74BK6YP1DtKM6xS34fE1s7BC9j+twgfrl9vrr2zFIM7UxkneuU5pG2wSIwruvfrK7pM/yJ6tjTXZAqlIMraMYY0Tj/nQYVZODSl9UAhdn4WsfCbRk31TmUf3eI4TuAzmJCovPv0kuLHOaRD2t8dI4gJxsElMY2tkpswcn3jMHZbLV2KtIw560eIm+pXQZx//Fn2tEgX/16AkgPnCFA7GCtxQIVZW+pVmY8eJ6nbJk2vc6D+yeyI7EYc6fZ5cOahAntx06959On/pKlXgbJU/f/4U8WZJkiSmpvVriTFmTxotYnkUhQ8p/++4PeS4oniE84j+Pt+6e5+Q+TO9fVpqUKuadt2IjWvTrCENHzeN9hw6ZlDpu3DZlS673tD203diwYTYppSv374LnGCxhODv+aHHY6pZpYKwVqrngrJ35GRgfBFcQL2ZxsEQHYS6b2TME5Yx1WtRn+N3TPl9U5fL85iNgBXnJkibyjpCfHgxe4dydbERgeD/2WPjLuWewo0AyM8f/DUy3P1lx7iBgEPWbHTrccgPi2okKmVPIeLGrj71p7dffgq3zb3s2glpWiDQlSRH6iS0gS1WsMZBOXvLsWoREd3Yw8d+GgXpDa+hxNyg61eUG9QZkm0dnIRSaqje2HLQNjgfeCKUM/Spn9eGxtfIQqn0WNxCG7NhvlRUkOPvcnEiF+uAhCs9OCavSo4UVHHRbTrAyvXs+v/ppYKAVRLixxlCdUXBIwXH+xkrcFO1t7MNE5+asWNHZrtMzBcHVzBwwiHOzBhp27yRUPp27j8ilD5Y9aCUtG7SgJJZal6a5c6RjVas20yD/5xEHhxvB2tVRATcdmpxf+wlLuEqmqNoBXUVJ0bRvJx4zXWG5Piu9SKO1lB9ZJSDmuHr8wfk9tCDk8BcpRHjp/NnGsHCV6lcqSBTzp86lsaPGCgydwLHA0dPigyecBUNTSJjnrCMaWh9+D1wcHAwVC3LYygCgzkZCz5SJALmhIDx/73NaVdxYK2gS1h39KqgSzBEmWAuMLh5v2LC6PiCt09ZM3jxwA0YkuCNMDKW6pN3nMQGWTZzZ07DmUc1Lnv62qnLvnz/m+57v6ZkFomMij/84P+NvJhKw5HjFdXriOja1Wsyl/PChQvThrWrCaTgCeOzGS4UQdISJEpZzXFzsKSBbw7E5MUzJROWKaV7j+0edOThe6GglGAXy5y2SShv2qQ0gS1wb/yDKyZKP0NHxAGq5QsnPYHYsHJkyJpXlGPhDEkiI/ZqqK9SDhfS7tseETKHYm/jamZmHMJvGYNFTrHKKXPgmD21hbBoworow660OZlwXVdsA5RM1OvKh2+av0ebMCh9N158o8KFi+sOFeOvEydOTLly5aIrTJLeuG5No9aLRClDx06hvWxJmzdlDG3bo3HtbN9S49qJQVp368f1xwnWv7IlilFujlODcjN83FQR02fURKpGL18HVeBAewBJbZPSoDVPcatUDaM9TZQoZDoPbcMInng/fc77fUOO2bORdYrkwroHayk+yDg6ZuocOnTiNJUvXZyusdurBbuU5nNyFLOm4BhFWAErs0Jom7MwWyaZS4z7YBxdiYx5oMgbs3ZdhVV3bbiGZffazTs0ZOgwfdWyLBQExi3dSn4f/WnB8D9CaRmzq8Gph0Q0OTLZG1zoa78PzEH4nH7yc1H+HJkpLSewMSQgV0dSGfDt4eWVsQJXY68Xb8jn5VuyTWnFJPH2QSyZ/7A1/m/miQ5J+LGMkvL3G+QL8wUaEksLTRtD9bI8chCQSl/k4Brpo4K3b92xayKbpzkrfR+Z16ZC/wVULl9W2jm+sxa3NYcvC8oJbYGek0xprOn2quHBavCPtPOMzXTmlgdN7Fybejc0HPOBzlAunZniYtWhy9qxkCCnU60SNKxlFW2Z7snsbadpwe5zdGJWL5GRVKmPyNqVMcztWL16dfrKdBYXOcFIxeyG/xmp9wU3Tih9B9nypFiWmqmsfB5vv2kUPlaszvcpoFVkECc2gq1iIQn+8UAQs6cWWJ7UAmoGCBKdLGycXV0lksw85CQoWdlN0pCAQuEuZ7sMSSyZ2HxHx8C4JHXbJ7yeP7a4E6xoQyploD5lOTMa0nRGQKaeeEr/8t/AcKZ6UI/1jq13rwOUXpDY65OsbBGEKC6i6jagjIDo4/5Tt1POv//zL517/Ilm9K6tFJnVsWbNmrRn1w6aPnaEUeu2TW1DNSqXFzxzJ89dpP1shcqUIR0rLiVE/wePPIXClzhxInJzOS5cMFEBYvI+w/4McQ7FhfmnDucdrERqycrUDBAoRn8tnq2uIlBEIF4vR1bDViVQLcCFMiSxtExKJ3ZvDKlJqHWzFq2gZWs30tQxw2hgzy5B2isPqR844+l7/pSr00xYSn0fXdfGSqIDMvvG59hJJLeBIqZP6YuMeYwdM8imDFwg++pbv3eE3zUpYUdg58nLIrukOSt9ID8v23EUlS/sRLvnBFf+EVc8ePZftGbfqSBW+IaVitOy0d2DKGVbj16kORv2MzH7MwGmVdIk1LlhFc522iJUcK/c9WBy+GVaPkN0sE6WlCb3bU1tapcX/VfvOSXWEtJgmdKmpns759Kz136Uu1E/vU2TcNyu7+k1eutkYeQiIJW+yMVXjh4CArDGDVqyh2AZ0xUbzjwKugd98vr9Z0FHAR5AfQJFDAqfsTJ06V76i62myEzaqnJh8nnzgQ5ddqMpG49TBuZMbFUlaNIDKJUnbzxiJfGS3ikisna9A5pBYcaMGal0yRK06aan0UpfXntLgvumi/dneuj7nRD7Vjcgaye27M5lkNx2bKFSUTPsu+dH/qHElWVIoVHU7qgUMihWO269FWMqP5zsklJiViqRkRJJTBSlB/cYBOVnOJHM4ibZDXIDerF7KBTDkMQihJi+fffeCYUPLpn9y6cPaRij60Dsjn0XSGepTUyDzrvuvBVZS+H2CSUXcpdjGvHwDIxhfUUfxAAiGcyeO37UgNcFgbKMpDrAqhwTyBsj2NtPHrtx40BLlzH9YkqbVq1a0cyZMwn0B+VKFTdqWYibA7l4l37D6RvHqrXt2Ugbc6pQNORjd1HE3Cmybc9BreulUqZ7hPIIuXH7nrYKCVA2bNutvcZJfqfcBKUSMXGga8iRNYuox+8zkqUcO32ONiybS4a4AdHnbqiJXDQvBoJMHMaLvLlyih77Dp/gRDadtMoc4vrgtgkpXCCvoH2AMu379h1Bka5WMfAF3obtuwVuSKyi7OeNrx89f/mKUrGlE5hFxjzGjik2EcqP1Ru3UZ48ecQnlKbhrsa9lxIzEYCVElyC+p6BlBUPn7+BVu05ydQUKalTg0qCw2/hlsO0+9QVcmT6BOc/NN+vD548Z6Vtufg+b1mjDFlzZtA9p68KJTBhgng0qktTZchgR9/3n6jZkJn0/vMXYR2sXDwfXbz5gK4/8KIek5ZTelsbkWXUJnkyQR4fbAAueP3uI/kxvRjmhXg9fyOO4FC0s0khzpUfiXQou5RyeYx8BKTSF/kYh3uGV+8+s7vcL4pM0m9Y2t59+kYO9jbah5NwL9jIjuAe7LdwJ7tGvhPcL/q6NS5fgPDRFbg2VB64kJN4/CMI7HXrb3k+p/F/HdEtNnjt8+Y9rT9+TdQfn9lLkNDjAm6epXrPoQGLdlODMvnIIsCNFETzJ64/Irh2GpLwrt3QeOZS3rf/AGrZsgV5vE0nXAmNWXdTtvZNOv5UWPpg+UvKVjFF8tknZUsVP+hyRs8/Dz+hnOyeiBjAnay8KLL26mvmk0ujXGqP+dMlFefIEDqAycZBC3GU3UQVHj6lIWLmepW1p1mnn1OLv+5TN+b0g2VuLyuWsFLlZgWpBnPmGZLwkJ+rx1KsZ7AW1l8Z+ECvbrO4aXaDfITqdsp5WyZUH8KcgyB3v1rUn+9FErrPyW+UbJ7OzF0IgcW0BhOuQ1wHFaK0VonE+QBWPvsxZsMOeNE15hEEZx/6/sOE7VPqOzB9RuA9Eh30/MDYSy75UvNmzcjWNjBGU0/TGFtUsGBBKl2qFE2bv9Ropa9OtUrCygarGqQtZ/VUpFC+PEK5ucouowNHTaDcObOTy5XrtHHHHqWJ4OPr1Lq59lo5KVIwnzjdffAo/dF3KNmntaP9R45refiUdlCQhvbpTuNnzKMaTdrRgB6dKVkyS0IW0RNnL1C+3I5Uv2ZVpXmw48k9m4KVRUZB66YNaOKsBQSKhsoNWgl3TfAcwjUWcY5Q4hAjCRnSuxsNGTOZmnbsSV3bt6KU7MZ56vwloYyjfvTgPjgIWbx6HU2Zs5g6tW5GS2dPpsiYJyxjKuvSd3z6/CVt2rmPFixYoK/aZGWKldhkA0bhQLByufu8IIf0dkEsWqZcAizFns9ek12qFGSVVP+LZFPOh7Eu3npIfaetpMesGKnpVnTn8Xn1llbuPiEsbkeXjKbMzJMHgVWwXKdRtOXoBa3S133iMuH6OX9YZ+pYv5Jo17lhZSrTYSTNXLePejSrQVDa9Ml+JpeHwgdy+EMLR2qb9Jm6ktbuO02bj1wQSh8I59Wk80pDPJdV+ONP+sF8g6vG9hTF2BtkSt82VKN0QXEuf0Q/AlLpi6J7UOCPafT87UfaMa4TVSgQ6Eb2i117nDpMofesRNxeOYzsUiajSRuO00G2ND18qvmjQVmJ3FmEq2J6tjzpkxUHXGgEuyiO61iLeqlIxK899KGaw5ZS84qFaFH/wDc9F+56UX9WvDxfaBIJpLSyoCblCtCkP+qwb3noD3X61mBsGfi/Uie3FJ9P7HfuxgqWsTJpwzG6++QVLR3YPEgMIPp//fGTOk3fRJZJElGbqkVo4e7zoQ570+O5eDNWPFcmrcKHTrkz2wlL4z2eC8TyBbJprDAJ2J0oV0aNkoG6zyH4rOtOHtLaddua43WTJk1owjhHjrd7xrQAmjf5oe2jUb7UNJndEfEyuhnz8Kklo3ViGsfJTMYd9aZVlzW/I+DrG1k1k7AKIrnLqEPebB1MJYjL0VdxZ4QCM7N+VnYD9aJtAdY90BXMqOcgFCIok4r0LZuefweI5p17TmN4TEVKZbaiOQ2zkSni9pQxdY9QaCEhcd9B2QqLgAbiE2fNnHf2Oa28/ErbFYTzsxiTSnrcb+Mp/rDcGllLP3z/h6bwfYFSDYEVFq6nLQsZp8Ah+Y6X31c6NHac6G+uPyZMnEiVKlUSFjK1lcnQfhAT17RBbVrx12ahKGbJlEHbFOezJ47iJC6TaeGKv0Q56A+m/DmULJIkEeTr/Z3HU5N6tbVxOAr1QzpW8pbNmUK9h/5J67buEn1LFClES2ZNIrhkgidMkeH9e7BC/69QfgYxfYMicDNdyQTuURW3p8yr75iUH673bV5F7XsMFIoflD9FKpQpQQunT9Amv+n1RztBbj+dle95S1crzUQ9qCh6dm6nLVNcQ38PwCMy5gnLmNqF6TkZOXEGpUuXjjp27KinNvYV5W0ygJ6/eUe7Zg+liipuOih2jg370vtP/sItEBat2+7eNHnVTjp3476IC4Mbb0HHLNSCLVhdGxt+aeFQu4ewmj07ujwIgI0HTadTV+/Rza0ztYrTd1ZQhs9dLxQanEM5RqzczIHtqXjewGe0IAOZ6EI8A1knZ9L45PTR/6vWHVN3+FNX7wqlsF3dCtp1ow2wWDu+N/0KCF+AwnaDrXLg82vPbRVBjGDRPNno3PX7dIAVu/Z1KypVQY6w9EHyZAv8vsJ1/pyZcQg1WdmE5dvprocPLWd3UyUu0eu55n+Hci0Gkj+iHQGp9EXRLahXKg/N33WO9ly4E0TpO3vLk9588GelLrOIz5u97RTN4g8Sg7Rmt0Jbji07ed1d9HvDpvPD03roXfFP/uLEFwBctdSCh2mU/6si9T127SG1mLBWkFFjjuRJOaW760NazoojFJw9E7uohzD5OfZ6aFp3Me75O4+prnPQL2hDE0JRBYalnPjLv1KhYM3gpgnr4ZphrQhWUmMELqaQnBmCP8zCvRSKHax+itK3eEAz7bC1hy+li2y1NEZCW7sxY8T0NnjgWrx0GZUrV064UUJ5CE1ggXs+tqTBZh2L27GLYkpB05CK+epyszumotjVy2ND75kzziZpfGrIyiM+aoGC0pjdE0FODq47xUVUlxsvfrzfaDDH03UuYSeSySAWLStzAiqunuoxTX3+0LlYuIYsxYlvXowzjFuP0vYEix9cT5EgBrGLcNtUsMOkODc0RpeS9tSuqB3dZKUUz8/52RXXmAQ9GPc50z5MPvmCBg8eQtmyZUOR2UrFihWpWdOm1HPwaLp++gAlt9L/ply9wUWssOCjT3p0aksNmTvvNlM7pE5lIzKDKopd0/q16e2791yeklo2ric+6jE6tmrKmUDr072Hj8iW+youop3bBLUMgvNtzND+1KdLBwKFw7fv3ylHNgetq6d6zMg+h6L5842n3mlAcQFMPZ/40GP+JEyYQHAXqhVldMR+kLETfIZwW33t+5ayZMzAe8pClkk1Fn1lAuwbH7VExjzGjqleh/p8/5ETtHX3fjpw4AAlSJBAXWXy85ji3lm/QlGat+mgcEtUK31nXJlzkV0DS+bLIVwYkYCkzch5hGQk+XJkotplC4u4vT3sznjNzZNysiJTvoiTXpx+cFz5f//jN3g6gmcgvGBXBJjU7TuZEMeGZCfVSxXg+d6yO+QVqt1nEm2fMTiIYqr0M9WxZL6cdGTxaDHceVZsa/WepHfox2yBhNRiDM5dd6Mr9zzpO1O75M2eiepXKMZ/G5oX9J5PNS/3yrEFUHnxoQyYPUNaofQ98gl8AajUKcdGlUrQ5JU7adOh89SW4/cw/izmxrMAAEAASURBVANOGrNi5wnRBPfAkJxnN1Dc19IFHKllzbLaZnDvhAEB8YWwFkKxhALYqHLxIAqstoM8iRIE4qzSF/xrIXLxblGpsFBYDlxyo1k9GmrfzO48d1tM3KqK5o8KroOQ8/P7sculJp6mV4MylKfjFHJ1fybqIvIDvuOwCEI5XDOiNdUvnVcMN7x1VaoxZAnHwnnSJbcnVJIVK30ydu2hUC1zUFjXDm+jr3u4y7DuXnO3iWxnk7vUDTbOLsZx4wlXoQw2LJufFu8J3cqHQbKm0ygKp24GjQHEP4gbj56LeRSLa7BJjSwIbe1GDhOhZkpYR2S7+pQtW5YGDBhAIxYvoDycidKR3SMjKrbJEhI+upKSM27iE5JAUUHsoDECeoOyDsbFqxkzXnS3gZtqkRAyj4a2Plg4S7C1MywCHsWu2x9TZoesNHbs2LB0jbFtFyxcSPnz56eOvQfTjrVLgj1UhXXhSPNvV6l8sG6ITcMnJEmYMCHBTdQYSWmdIhjlgTH9orINFDrH7FnFJ7R5gY0hUvnQ+kbGPGEZU70+L++n1LnvMOrUqRPVrl1bXRUp58p3fnQrf61YIYByAFfCOYM7ap+Bdpy4JPbdunY5cbx0x10ofM2qlWJXwV5aTFJbW9GCzYfoKit+hpQ+beNQTjYdPi8UPlj2Tq8cJzJnosuGg/lEDNus9fsMKn2wQo5fvi2UGUi4WNYpVyTUdiE1eBxgLZu38QAdunAjSFPHLOlo16yhlMEuFXkEKH1pbIL//8qeSRM//DbAmhdkkICLnJntqU/LWgLfUu2dhTvpB7ZAQqDw1WClWJ8gm2cPdivF79bUfkGf+eDeiWefViPmBOk6hS24c4Z01CaHCVIZDRdYu/I3Eg3TR/mUcVLpS8ZEtV85PX9UCtwF8znY0x2vl3SRlSpkq4QfNNw4k3BQa8My+cVypnarxwrZf1qFD7FrsDbFYyvKDyb7jegv6J3HLwiZP9OlSk51Swa+LbPi9LktKheiP1cfIiimhpQ+l3vedJVdRkMSi0gI0t1+5qZIsNKSE60UyKZJaqCs4anvB3ZV3cWxj9Y0o3sDpdioI9w6kazlGbvedpi6kdqyWyjIwJHJE1ZPCFIpR0RCWntExg1LX/+A33fLAL6wsPQNa9tp06aR67Ur1GbTLdrb0ZHSpUgU1iFkezNE4Be7ofbY4UlP/f9HV0/v44Qiic1wF8GXjJjEHTt2CDdPuF+CK06KRCA8CCDRTO0WHckha1ZayC8TokLwvAOJ6mce3b3lZosaLHd3HvlwTNsDglUKz0BwO0Q2R1ibIIVyZaWTy8cGsQaBruAVfyA/+ZkoorLzxGUxBGLe1P/fW9YoS8PY5fMsWx8/f/2mN8bvHbuhHrukeVkf0jrKcXxcREWJi4PC17hKCcL6IHCnvP3Im7pOWEqHF40SFAsoT64nJjEdJ2GBhESfgAyicL1V5Bu7uyryyOelsLTmckivFGmP2465EOIOodAXyBloKMBz6hOmfoAMbFuXmlcrLRK9rD94lnYcv8TxjKuEi6pT1ozasaLr5Mv3HxQVz0XRtT/deSP2NKs7mplc29nZsan5s3gLEdnxa2pIoLBA6dvLLp5Q+mDV+/T1B8fbFRTccGjryG6Ga45cYQoBdnt58ZZ8P2piftTjhOVcl7zXIyCG7/V7f8rfeVqQoRTFANkxDcn+KV2DuZAaamuqcnyBzNt5Vgw3sGnFIMMiCLrLjM30hV0eto/rqMUxSKMQLqBwL+H4wPZTNggXWrjfKpIjva1Q/HJlCp4oRGkT2jGktYfW15T1L/0+Cfcpa+uQLQmmmBPuSvv2H6TyZctQw7UPaVObHJQtgArAFOPLMWIeAnCJ7c4WPpenX+nkqdNm79api3Dp0qVp8+bN1JRdPX/9+4sWThsfYYuf7hzyOnYjAM7AWs07ULz4Cenw4cPM1ZgkSjaM5x3IC9/3pO/BPUoWETBJ65rlWOlbL7JKQuk7fvk2feJkcojVS8b0AhDEpfmyu+fIhZvoJsepQanAy++ICP4Pq0WxjIHnb8Zfe9VVrOx9F9dv3n3Sq/RVYNdSY+gGEP8fUVEUNbi+rh3fRzscrHx5GvenC+xaCSU0KecxgLzlECBd+cJZhCFKVk3delzPXLdXuN3mdshAMwa0pTIFc3GM3lOauHIHHbl4kzqPW0wuf00O0hWYzt1wQJQNalcvSB0u9s93ZoU6HivxDqIOSn+lYnnFORQ/KN7RrfTh+fGN30dKm1ZjDRWLi+U/4qTSly9fPlb4ftEDTpQC61tUSRPORjlq1UFhSZvRvT7tPHdLTN26alHtEpCIBJY2KCOIXYPCkT9rOhq9+iBBUQur6Ma2gYAckip5UirhlFmc6/4okUt/OdolShD1vzKHrtwn92e+Iu4xe/qgcVvP3n4QJOxYW595O3EQ4seBzRAoi+uOXqMxHWpQ7RKBlk1RGfCjTF4Hurx4IO13uSfmsWXqhrKslG89fSPCSl9Ia1evIbLPb7OFNzeTTSvxQ5E9X4oUKejMufNUq0Z1qrPyNs1rkIWqO6aM7Gnl+NGAAOgd4NL56ttvdOr0GSpevHg0rCLyp2zUqBHt3r2bmnFGUi/vZ7R+yWwRlxf5M8sZzB2BE2cuULueAyld+gx09OjRKM1oixd96Zgi5NajJ1SlRL5ohbIpZ390XriR9p11FQlTFNfOtgGunVgc3CfLcmZKKBXZM6YV2SKzZ7QXys18dg8NjyhWQqWvoggVccpGyS0tlGJxhIIFSaFTLgr5B2LmYJmMCsmQxkbw3bUIsPApc4ILD3QND71fkAfH6qUJoER4wnGQuvL+k+ZZKBXnhzAkxy5pnkUHtasrLLBohyQuE3q1FEofkrQgJs82ZaD76MHz18X8wEs3WQvcJQ0lw6nMih+UPkXxNrSmqCh/6P2cyeZ/Ut68GmU0KuaM7jmi/gk+unfM8zs5OYl/1kiQEpVKH3jgqhbhAN6rD0Ts3GFWZjLaWlNZVjog7s/eCIUvccL45LpsMLtgphDleBsxaPEecW7ox28BFVBm1fI4wLKnlDmk1Zj6k/NbtRWDWyrF4ggF8b73K8qmo1ipG/VbsJOgQIQkyJ55YEq3kJqEqW7jcVfRvm21QOVYGSAhu17m1cPnlzggOB4JbBIxR00SThKgT3w5iQ449zLbMQcOk7Ergn84febvEG612QPi/pS6sBxDWntYxolo25M3PKlus9YRHSZM/fGwcfrsOerTuzd1WrWKs0La0sgqGfTG54VpYNk4RiDw89d/tIaze84885Jy5HQk1z17KUuWQBefGLFIEy+iXr165OLiIrgH85WtQdPHjeDkKg2k1c/EOMeW4d69/0Cjp8yileu2UPPmzWkVfw8m1Uk+ExV7rVy5Cp244kqD29WPiukMzpGalYZqJfLTYbYenb52jw6dv0EZOSYNVAGKzGZycfz/7dakmlAMlXJjFD4kIf6HvQ7QX4nTQrjMkxdBlSGH9GkIHHlwS2xUOfD/PuYCncI/7HaKteoTJJMZOHOtvqogZX05Rq4pxyVGRNKz0gfx+xjUgoc9KWWpU1oJixraKYlfcK6Iy213cVokdzalKNjROwCfvNkyBalDEhgQvMP6iQynatlw8Jy4RGZRXUFymiMut6gSZ2kF559abrN7L0RxO1XXRfX58ct3OI46JcEQFFeE87LFPcGXQaNGjWnLmdD9sk2NjpJ1ste87fSNM03B5VP5cnrg80ZMlyeLvVbhQ8Gu87dDpQbImEZjRbnlGaiQ+X/7mzafuh5kC3nZsgmlEvFqcB9VBF+SvXlNjcespushJIxBH1AshPRBDKKpBAovMnwixXFdzoCqK/Ycm3h+Qf9gn0HNKoqmcAdFfaVCmrd3UPJA04A4QMg79mXvMWcbDVy8W/yjEIX8A9ZWWBcrc7+E4bRuhrZ2Za7IPmK/jzgLGCwVUS2I61qxciXt3buXLr35nUrNv01jDnuHSFEQ1WuU84UNAWQDXcVUEOUW3aOpp17S0BEj6co111iv8CkoFSpUiO7cuUMtWrYSJOxFKtWltZt3CDJ2pY08xm0EkKzFecJ0ylGsIu07ckq4Bm/ZsiVaFD7ciUaNG5MLKzNwlYxuUTI8gvT7G4dltK5VTvsMhLXd99IkrKtRqqB2qcjoifix0CQjW8AQJ4jMk4qs3XeGrTlB3UOL5ckuqpFURi3I5lmj5wQaNHutujjI+VemaXJ7/DTUz5v3H4P0C8+FgsF23jueJxQ5f+OBUFqR3CYLc/cVzp2VHNKlEfcXbRUB/yCseIn5pXelYoHPT7CmguJBMRIggykEvH9qQSwhFL5kFkmEcq7UYS2ggcBzWb0KwV/GI34USvqYJVvF/VD64X6DVB5SqkBOpTjajluOulCDBnHrpV2ctPThN6xb9+60bPlyOsVWHkUhiIrfvJrFc7PbQBItpQCUPkVAC4CELa7uT2n48n3kyHxwl+97s5vhTaUJgY+vXfXgad4LZdcE2e5jF8WerMSk5SxOh664aXn4lAFgbRzAitCUjcep/sgV1LthOREHhyyipzmDJWgKaqsSvCj9lOPBqd2V0yg5Xn/0jBBriOQtSDYTUQEtxcytp6gdWw3n921CiNsDXQMU7o7TNlKT8gXJgxXiaZtPiPs0r0/jcE9p6rWHdyGL9lygPE65qRQTTUeXwEJSrVo1Wrx4Mc2fO5v5425RDjsrKp4+CeVg8vAUSeLz28o4+Q4qum6J0fMi06//3/8S3DhvvfpOV70/iiQt7dq3J2fnkZQ+ffAAf6MHN9OGSI6BRBy9evWiiczl12vIaOo7fCxz8xWjwvnzUMb0/H3FpOjKCz0z3aZctpEIwDL04eNHppTwEryDd9wekL29PY0Y4Ux9+/aNNmVPWT6yhKbl2L7FW4/QtP5tleJoOdYqU4hSJEuqTczSqpYmOYmymKLscvnwyQsR0wd3xbcfPnH81yVSKAdcOLsnrG1opysou+f5lFoOn0MtapYRli9FyVC3HdKhgaAngJshvIUQp+fzyk8oKmjn3Nnw//0KbL16f26derhIOwcR+txNB0TyGyijDSoVI3/OA7Fo62Ex5/ieLbReBsM7NRSJXfpNX02X7z4SROyr9pwUuSvmjujEcX+a5ycobGU6jhT93fcuEDQZyNzZ4c+FNHv9fgKFRs3Shdj98iUdvqB59hzSoX6Q7zLX+4/Jn2MFwReoj9C+XOHclC2DnUg2U637OE5CU1LMB+sg6Dlwn+pGMLNpREE/w5ZmN08ffmHXI6JDmVX/OKv0FSxYkGrVqknj1x9j3rxs2j+cyL57iIlrWDYfrTl8hRBLBrdCRXA+tWtdcl55gJbuuyiKQX8wrmNNsmAfclAtDF22j/vnFxx7aAAlEQKL1wJWYmCx2nRSY90r5piJoLTAJVNph7awguEPfybzAWJMReBmuoh56KIjbk9Zg+7x4j0vUVSU9xIeUe8b/X+H/weOAbiB1Hg18/p1nr6ZE7ncFR/UQxmc0LkWK89WuAyXRHTt4ZpUp9NdThyE2FG8ZY5ugdVv4MCB1L9/fzp79iwdPHiQrlxyYXfnh/Th02cCSa+UmIcAFBcry6SUKWNGKlSuGvWZWI3q1KlDSlbAmLfiqFtRLo6T3bhxI82dO5f2799Px48fp92HT9GLFy/I398/6hYiZ4pWBEDfgDjm7NmzU8Uq1WjG7LlUuXLlKIuhDm3zWN8IZ2caxN+/vZrXIFjEoksSsdWpMbtUQiFBwpDMbKlSy7juzYWSg+yUA2euEVVQEi6tm0IdRi8QFqZNh84JxQH/z2FtUmRM92bc15uusxULvHM2yS2pXZ3y9JGTxUDBU54HUlpZ0sGFI6nT2EUE+gZ8INasjIJ6QFFSlHGj64jv3t2zh1F3pkVA0hsocxBgqEt7AAsqSNphXVu+87hoZ5E4Ebv01iN9LphooOCB/QKjiZwVFBZAfCDxORnNsI4NqG/L2uJa+YEEMhB9ijfKMe+OWUOo1+QVwl0W90ORhpWK07yhnYMokUpdVB3h2TZ22XZ+EV2VihQpElXTxoh5fuPNB01rFCOWFTWLcHNzowIFCtAEVqp61C8TNZMaMQvI2vGwnoq/sBCvBsUE8p5dEf2YTBzJTAy9QYZrw322WqVOkTSIi6i+aT/4f+P4vJfCVxtjZotA7Jq+8c2pDLx89568Ju/X78gpc1qBsTmtX99aodhXZe7FhNZ25HLpssHfGX19ZZlEQCIgEZAIxB4EfnLCivwcu5TeOjErEkNj9Mbwv+uW+xORvKWgowPBjRGCWDZ3phCws7HmbJRJDe7hFSd4e+77jgoyjYBCYK6vMeYBJcEjtmpBEURil6hK0qJvPSGVgVLBnRO3JGfFNDfTJ0Cx0idwZYUlFM+NhZn+IizhKXC/hKX0JeMHhTkXZ/NENtWICFxMEWtoxR5ujpnTh3jfIjJPWPou33GMhs7bQDdu3IhT8XzAKE4rfQBg3LhxNGXyJDo2o4fIkokyKRKB2IDA2LWHacl+F/5iu0m5cwcGyseGvck9SAQkAhIBiUDYEEASorJly9JEzsoIlz4pEoG4hgAykVbqOpaGDB1G48ePj2vbl0rfv2zhqVa1Krnfu0VHp3en9EzULUUiYO4IbDrhSj3nbqfVq1dTx44dzX07cv0SAYmAREAiYAIEpk+fzrG4zrR5Sn+qyfF1UiQCcQWBl2/fU5Xu4ylrjlx04uSpGON+HZX4x3lLH8D+8OEDlStbhr5/9KM9EzpRBqZRkCIRMFcEtnHinx5zt9GwYcNp0qRJ5roNuW6JgERAIiARiAQEunbpQuvXr6MNk/pR9VIFImEGOaREIGYhAPfYev2nUvwkVnTh4kVKyVQNcVE0wWJxceeqPYNP7PiJk5Q0pS3HQC2law99VLXyVCJgHgggPHfGlpPUddYWGjRosFT4zOO2yVVKBCQCEoEoRWDJ0qXUunUbaj5sNiG+SYpEIDYjgGyjVbqNo0SW1nTy1Kk4q/DhHktLn+o3/dOnT9S6VUs6eoyDPJtXEtQGCeLHU7WQpxKBmImAz5v31G/hbrpw10tkEkQqeSkSAYmAREAiIBEwhMCECRNozJgxVLd8UZo9qD2lsZHhLYawkuXmhwB4COdsOEBT1+wW2XQ3b94isuya305Mt2Kp9OlgCWvJnDlzaNSokZTWOhkNa1mJGjFFglT+dICSlzECgVfvPtPiPedpxaHL5ODgQGvW/kXFigXncYwRi5WLkAhIBCQCEoEYhcAptnx07tSJ/N76Up8WNalrk2oRztgYozYoFxPnEICyB37Gyat20wvORIqXG4MGDZIZzPk3QSp9Bv4cfHx8aMyff9L6DRtE6tq6JXNTxQLZKa9DWrLlFLYWzJ8nRSIQlQggXfXnb38zrcR7usGk9Udd3enE9Ydkw77pziNHUY8ePShhQvl7GZX3RM4lEZAISATMHYFv374JD5EZM6bTt6/fmJy7IFUrmZ8K5XKgDHapmIA7iXxgNvebHIvXD6qJN+8+CbqJ09fu0t4zruT38TN77rWi8azwZc6cORbvPmxbk0pfKHg9f/6c1qxZQ3t276IbN2+F0lpWSwSiBgELiyTCXaFFi5bUuHFjSpRIP2dP1KxGziIRkAhIBCQC5o4AlL8tW7bQtm1b6cyZs/T333+b+5bk+uMYAgWZe7tBw4Yia3mGDBni2O5D365U+kLHSNviy5cv9ODBA3r79i19/fpVW27OJ2fOnKGlHNQ9f/58srW1NeetBFv758+fhfWrZcuWVKdOnWD15lbw+++/k5WVFWXMmJGyZcsWJ9MNm9s9k+uVCEgEJALmiMAvdpFzd3enFy9ekL+/vyBGN8d9GLNmPAPdu3dPPAfh/2xsEDyjwvunUaNG1KBBg9iwJYN7sLCwoNSpUws+YktLS4PtZIV074zTvwNQXnPlysVZvFrTvHnzYiUW48aNI/AS4Qs9S5YssXKPclMSAYmAREAiIBGQCIQdgYucvh+E9Zs3b6bmzZuHfYAY3GPixIk0depUun//vnhZHIOXKpcWRQhIS18UAR0Tp2nbti27cJwRXwjJkiWLiUuM8Jp+/vxJhQoVonTp0tHRo0cjPJ4cQCIgEZAISAQkAhIB80cA1syCBQuK54MjR46Y/4Z0doDnn7x58woL2O7du3Vq5WVcRCB22LHj4p2L4J5PnDhBGzhJzcKFCym2KnyACIlNVqxYQdjvunXrIoia7C4RkAhIBCQCEgGJQGxAYNasWeTp6UmLFi2KDdsJtgc8/2Bve/bsoYMHDwarlwVxDwFp6Yt795y+f/8u3v7kz5+fdu7cGScQ6NOnj3DfQEwmfL+lSAQkAhIBiYBEQCIQNxHw9vYmJycncnZ2ppEjR8ZqEFq0aEFXr14lNzc3SpIkSazeq9xcyAhIpS9kfGJl7YgRI2jx4sUiKY29vX2s3KPuphCIji94+O5v3LhRt1peSwQkAhIBiYBEQCIQRxBAcjcvLy+6detWrKc6evnyJTk6OlK/fv0EZ10cucVym3oQkO6dekCJzUV3794luDRMnjyZ4orCh/sJF9YlS5bQpk2b6PDhw7H5Fsu9SQQkAhIBiYBEQCJgAAF4OMHdEc8EcYHbFs96IChHUrtHjx4ZQEUWxwUEpKUvLtzlgD2C3Lt06dLiChmrYktq4rDcQtA3uLi4CDcHmdo3LMjJthIBiYBEQCIgETBvBOD1g6zlVatWFRzM5r0b41f/77//UuHChUV4y/Hjx43vKFvGKgSkpS9W3c6QNwMuGldXV1q+fHmcVPiADqgpwLcY2334Q/5NkLUSAYmAREAiIBGIewiMHj2afvz4QTNmzIhTm48XL56wbJ48eZK2bNkSp/YuNxuIgLT0BWIRq8/g0423Wz179qQpU6bE6r2Gtrm1a9dS586dhcWvePHioTWX9RIBiYBEQCIgEZAImDkCN27coGLFiokX3506dTLz3YRv+V26dBGurQ8fPiQrK6vwDSJ7mS0CUukz21sXtoU3btyYbt++TYjpk9mbSLh2vH79mvBPIEGCBGEDU7aWCEgEJAISAYmARMBsEEB4C17y4vnn7Nmz9Ntvv5nN2k250Hfv3lHOnDmpTZs2NHfuXFMOLccyAwSke6cZ3KSILnHfvn20a9cuYdqXCp8GzWXLlonMXdOmTYsovLK/REAiIBGQCEgEJAIxGAHw1eHFN8Jc4qrCh9tjY2NDeO4BRzMyl0qJWwhIS18sv9+IX4NbZ4UKFWj9+vWxfLdh297MmTNp1KhR4h8B3nxJkQhIBCQCEgGJgEQgdiGghLf07t2bJk2aFLs2F47d/O9//6MyZcoQrJ9IbBeXleBwwGfWXaTSZ9a3L/TFg5cFvHSSlDw4VshmBf/+pEmTxml3j+DIyBKJgERAIiARkAjEDgSaNWtG169fp3v37snwloBbeufOHZHNE5zNiPOTEjcQkO6dsfg+X7t2TZjwYdFKnTp1LN5p+LaGbFYrV66kS5cuEdw9pUgEJAISAYmAREAiEHsQAC/v9u3bCe6dMrwl8L7my5eP+vTpQ8OHDyc/P7/ACnkWqxGQlr5Yent//fpFRYsWJWtrazp16lQs3aVptjVs2DDh5w9raFwirDcNenIUiYBEQCIgEZAIxDwEvn//Tk5OTuJZaOvWrTFvgdG8InAWOjo6Uo0aNWjVqlXRvBo5fVQgIC19UYFyNMwxZ84c4dKJoGUpISMwduxYYQnt1atXyA1lrURAIiARkAhIBCQCZoHAhAkTCNkqZZZK/bcrWbJkhGfFNWvWiNg+/a1kaWxCQFr6YtPdDNiLt7e3eLsFsz2ISKWEjgCsoZUrV6YdO3YQ6C2kSAQkAhIBiYBEQCJgngjcv3+fChQoQLNnzyYkcJFiGIHq1asTKKwQ9xg/fnzDDWWN2SMglT6zv4XBN1CzZk16+vQp3bx5kxImTBi8gSzRiwAI2w8dOiQspClSpNDbRhZKBCQCEgGJgERAIhBzEUB2yvLly9OPHz/o8uXL9Pvv0qktpLvl4eFBefPmpSlTptCAAQNCairrzBwB+Zdg5jdQd/mbN2+mo0ePisQkUuHTRSfkayS8wT+LIUOGhNxQ1koEJAISAYmAREAiECMRUNwVEd4iFb7Qb1H27NkJuQ3GjBlDL168CL2DbGG2CEhLn9neuuAL//Dhg+Dkq1+/vsxGGRweo0q2bdtGLVq0EMlvwG0oRSIgEZAISAQkAhIB80AAmSiRnKRNmzYyli8MtwxWUSS9KVy4MOE5SErsREAqfbHovoJr5cCBA9I9MYL3tF69evTw4UMCj03ixIkjOJrsLhGQCEgEJAISAYlAVCDQoUMHOnHihHgOQqISKcYjAHqLWrVqCW+xatWqGd9RtjQbBKTSZza3KuSFnj9/Xviwb9myhUBEKiX8CDx//pxy584tgr8nT54c/oFkT4mAREAiIBGQCEgEogSBs2fPEjx0du7cSY0aNYqSOWPbJEhkd/fuXfFJlChRbNtenN+PVPpiwa/Az58/KX/+/OTg4EAHDx6MBTuK/i0sXryY+vXrR66urgLb6F+RXIFEQCIgEZAISAQkAvoQUJ6DsmbNKjye9LWRZaEj8OzZMxEmNHToUPrzzz9D7yBbmBUCMpGLWd0u/YtFxiX8oUJRkWIaBHr06EHFixenP/74g/7991/TDCpHkQhIBCQCEgGJgETA5AhMnz5dZC1ftGiRyceOSwNmyJBBJHTBc+Xjx4/j0tbjxF6lpc/Mb7O7u7uwRMENceDAgWa+m5i1/AcPHgieH3z5SWxj1r2Rq5EISAQkAhIBiQAQgHKSJ08eGjduHMFCJSViCPz69YsKFixIUABBYyUl9iAglT4zvpegF6hYsSL5+/vT1atXKV68eGa8m5i5dPwTwRvEe/fuUZYsWWLmIuWqJAISAYmAREAiEEcRALn4y5cvBTexJBc3zS8B8kSUK1eOduzYQYjzkxI7EJBKnxnfx1WrVlG3bt3oypUrIs2uGW8lxi4dcQKFChWidOnSiYxWMXahcmESAYmAREAiIBGIYwggeV2rVq3owoULVKpUqTi2+8jdLjKhnjx5UmRCtbS0jNzJ5OhRgoBU+qIEZtNP4uvrK7ho8Ec5e/Zs008gR9QicOnSJSpTpgyB8LVdu3bacnkiEZAISAQkAhIBiUD0IPDp0yfxHFS3bl1avnx59CwiFs/69u1bypkzJ3Xu3JlmzJgRi3cad7YmlT4zvdetW7cWb7bc3NxIvoGJ/JvYp08f2rx5s3jjlTp16sifUM4gEZAISAQkAhIBiYBBBHr16iXcD8Gra21tbbCdrAg/AkuXLiU8/9y8eVPETYZ/JNkzJiAglb6YcBfCuIajR49SjRo1aP/+/VSnTp0w9pbNw4MA4iadnJyobNmytHHjxvAMIftIBCQCEgGJgERAImACBJDHoGTJkrR27Vpq27atCUaUQ+hD4L///hM4g7MPPIi//fabvmayzEwQkEqfmdwoZZnfvn0Tb1sKFy5M27dvV4rlMQoQAAcilGxks6pZs2YUzCinkAhIBCQCEgGJgERAjQBolIoUKUIpU6YUMWfqOnluegRu3LhBRYsWJeSRQEiRFPNFQPL0mdm9QzbJ9+/f0/z5881s5ea/3Nq1a1Pz5s2pe/fu9OXLl2AbevPmTbAyWSARkAhIBCQCEgGJgOkQmDdvngi1WLJkiekGlSMZRADJ7Hr27CnoMD58+GCwnayI+QhIpS/m3yPtCm/fvi2StkydOpXSpk2rLZcnUYcAlG0ofCNHjtROCvcHEMJmy5ZNpI3WVsgTiYBEQCIgEZAISARMhsCzZ88Eefjw4cMpR44cJhtXDhQyAhMnTqTff/+dRowYEXJDWRujEZDunTH69gQuTvGrBgcNUhNLv+pAbKL6DDEEyGbl4uJCyZIlo/bt29P169cJvIlbt26lZs2aRfWS5HwSAYmAREAiIBGI9Qg0bNiQkMDu7t27hDgzKVGHAPIZIIM5MpoXK1Ys6iaWM5kMAan0mQzKyB1owYIFNGjQIJFBCQlFpEQvApUrVxbuJaDOgAL+69cvSpAggeBNxL2SIhGQCEgEJAISAYmA6RDYt28f1a9fn06cOEH4Hywl6hGoVKkSgSoDiXTixYsX9QuQM0YIAeneGSH4oqbz8+fPhTvh0KFDRQbJqJlVzmIIAVj4vLy8CDF8CCiHwgf5559/ZFC5IdBkuURAIiARkAhIBMKJwNevXwV1AOiqpMIXThBN0G3x4sXCyirjKU0AZjQMIS190QB6WKds0KCB1p0hceLEYe0u25sIAdA2II4AX3p4wwWFT1dg9UOgc/LkyXWr5LVEQCIgEZAISAQkAuFAYMiQIbRy5Upyd3cnW1vbcIwgu5gKAWdnZ/EcBH5EOzs7Uw0rx4kCBKSlLwpAjsgUu3fvpr1799KyZctIKnwRQTJifUFMmjNnTgJRKUSfwodyxPXBEihFIiARkAhIBCQCEoGII3Dnzh2aO3cuIYmdVPgijmdERxg1ahSlSJFChBxFdCzZP2oRkEpf1OIdptlgWerTp49IFAI/ainRh0C+fPmoTZs2hIQ6IUnChAlFop2Q2sg6iYBEQCIgEZAISARCRwAvUrt16yZ44rp27Rp6B9ki0hGwsLAQtGGbNm2i06dPR/p8cgLTISDdO02HpclHgsK3ZcsWggndxsbG5OPLAcOOwK5du4Tyh/g9JZZPd5QSJUqI7Fa65fJaIiARkAhIBCQCEgHjEYCXU+/evUWGbLx8lRJzEKhXrx55eHgQ6MTwwltKzEdAWvpi6D26cuWK8JmeNWuWVPhi0D1q1KgR3bhxgzJnzkygz9AnoG/4+++/9VXJMomAREAiIBGQCEgEjEAAydIQR9+/f3+SCp8RgEVxE/AW+/j4EJ5T9QmstFJiFgJS6Yvm+zFv3jwaPXo0/fjxQ7sSWJDgxlCxYkXBiaKtkCcxAgFHR0dBnVG3bl29fImwArq6usaItcpFSAQkAhIBiYBEICYjAO5hvFBFpnK1DBw4kKysrGjs2LHqYnkeQxDAy2/E94G4HcqfIniGnTFjBvXo0UMpkscYgoBU+qL5RvyfvauAi2L7wuf/3vPZAQaKioiBIioWdheK3V3Ygd3d3Y2BrdiFYmGLjdiFIhYqdsd773++u8yyu8zCopR6j79lJ+7cuXN23J3vnnO+D3oz+A+TM2dO8vb2FqPBrMnNmze1pCGxPER5ehUPJEmShJDqOWnSJAH8/vgj9L8S9PqOHj2qcpTcJD0gPSA9ID0gPSA9oOsBiH2DtC5btmw0ffp0QZSGZyPUjM2ZM4cSJ06s21wuxyEP9OnTh6ysrMjV1VWMCs8+0JKGxNiuXbvi0EjlUOABWdMXy/eBmZkZvXr1igAaQBICeQYvLy8R/QMtrrS47wEUMtetW5dAvIMZLsg2VKpUSXyOcX/0coTSA9ID0gPSA9IDsecBPPfs2LFDPAPhWQiT4G/evKGCBQuKydXYG5k8sykeQMACzzxVq1YVn6OupFVwcLAsUTLFiTHUJjQ8EUMnlKcJ9QCieQB8MIUVcufOnYL2P126dKEN5VKc9gDScEEpjZoDfNkhjx3pKspnGqcHLwcnPSA9ID0gPSA9EIse0P29xO8myOvu379PSZMmpdevX8fiyOSpI/IAPi88y0JSbPfu3aK5rqTV6dOnI+pC7o9BD0jQF4PONjwV9Nx00wKxH5EikIC0adNG1PT5+/sbHibX46AHMmTIIBg7XVxcxOjev39Ply9fjoMjlUOSHpAekB6QHpAeiBseQC3Y8+fP9QajgAakd2bNmpU8PDz09suVuOEBkNohGtu5c2fCMw+eX3UNjJ4gJZQWdzwgQV8sfhbHjx8PA/p0h4PZL6Q5jB8/3qgYuG77320ZXzCYWYK0RZHCjpQqpblg1ER6ZWy84sePT25ubtqPIW/evLEyjti49tg6Z3Iu8s9ll5OaNmlCy5YtoxcvXmj9LxekB6QHpAekB+K2B1DPh98PNcNvPABho0aNqGLFivTo0SO1ZnJbLHgAJDsAfMhyMsbSCVI7BDekxR0PqHPOx53x/dIjQR604cyI7gUjbI7/TCAGQdqgNI0HUDs3Y8YMmssF3k+ePqU82ayoiF1mqudYkVIkTUTx4oCv7gUF0+mrd6l+uULyY4smD/zL/zfeffhE94Kek+8tP+qwcSN17NiBGjRoKGpiQQogTXpAekB6QHog7noAoA/yRwAIaqYAiixZslDq1KnVmshtseABsK1iohXPY8YMn52M9BnzTuxsl0QuseN3EZEIT3AdX4KJEiUSRczly5ePpVHGvdOuWLGCevXsSd++fKZ2NUpQy6rFycoibgrXf/vnH/orDgDQuPcpRs+I3n38RJsOnqO5mw/R7QdPRAQYzLiS+S16/C17lR6QHpAe+FEP5MuXjy5cuKDaDcpfEAWcP38+tWvXTrWN3Bh7HggMDBTkLTdu3Ag3gAEBd6TpSot9D0jQF0ufgaenJ1WrVk317AB80IIDqUumTJlU2/xuG8Hk1apVS9q6dRu1r1maBrZwJjOO6kmTHjD0wD///Eur9vjQsMXbKFUaC9q8ZSvlzp3bsJlclx6QHpAekB6IRQ9AnxjyR0oNn+5Q8ByUPHly2r59OxUrVkx3l1yOQx748OEDNWvWjJ/NtqqmeQK0r1y5kpo2bRqHRv37DkXW9MXSZ496PhS5qlnDhg0JjEcS8Gm88/DhQ67ZK0zHDx+inZO706Qu9SXgU7tx5DbhgT///ENEgE8tGkxpEv9JRYoUpj179kjvSA9ID0gPSA/EIQ+cO3dOFfChnAUTdX5+fhLwxaHPS20oyEjbtGkTjRgxQuw2rM8EeJcpnmqei51tEvTFjt/p8OHD9OXLF+3ZkcaA18yZM2nVqlWUMGFC7b7feQGF2yWKF6P/Pr2hw3P7Uom8sk7rd74fInPtaVMmpx2TulGtEnlFVF0KxUbGe7Kt9ID0gPRA9HoA9XzgLDC0JkzMhX3p06c33CXX46AHAPSGDRsmypFAaKfLQYFaTQi2S4sbHpDpnbHwOeA/AVIaFNCHmRDo0SA8XqpUqVgYUdw85bt376hokSL09d1L8prWncyTJYmbA5WjitMeQDF5t2lraMOhc3TkyFEqVEiS68TpD0wOTnpAeuC38ADIQLZt26YVZcdFT5s2jbp37/5bXP+veJGXLl0SdX5BQUHaOj8840LSwVh226/oh7h6TRL0xcIng9TNwpyuCMOMCES9kbcOrTdpoR5o3LgR7ffazRG+fpQhjVnojmheWrfvFB2/dDvMWSzMkpFDdisqbGdDqc2ShtmvbDjDrJ07T/jRuesBdOXuI0qZPAnltklPubNmoDbVSlKKJKG1iPM3H6SrAY+oWvG8VLmwvdKFeD9/4x65ex6jP3gWbaprwzCkMJNW76b7T15QowqOVDxP9EVAP335Shdv3+d8faLMlqkoDfvBFPvy9Rt9MdDtUY6Lz7O78f6KOUZa1PnVHzKfbga9pgt+F8nc3FwZinyXHpAekB6QHogFD4CNMzg4WLB3gnBry5YtQp84FoYiTxmFHsBnWrt2bRGtVeo1keLp6OgYhWeRXX2PB6Rkw/d47QePQT2fYs2bN6cFCxYQQuLSQj2wken3163zoC0TusYo4MMITlz2pxW7jWvLJEnIenwDWpJzsbyhAw5ZWul1grpPX0v/sNwGLGmiBHTn4TO6df8JbT58nlbu9qFVI9pRrsyatJXHz1+Jc71+9zEM6FvvfUY7joYM7IrlDmW/AqCastqLPvN7k0qaCYSQIUTZ29dv/1CnySto86Hz2utB505F7GlWzyZkYZ483HP1mLmWVu85qdpmVLta1L1BRdV90bERdX6LB7akoh0mUE9mf12+fHl0nEb2KT0gPSA9ID1gggfA/AhwAMuePTuB3M7a2lqsyz8/twdSpUpFkCRzdXUVz7e4Ggn64sZnGiWgLyAgQNSoXblyhZ6ybhrYfKQZ9wDEKpEDDapihLwB/GLKUDeYjAWtraysxPlLly4tUk1j6vymnAeMXt1du1Ezp6JUrkBOUw6JljZd6paj5jwGGMDVjXtBtP3YBdp53I+aDHejRQNbUQMdHb55m7xp4IJNon1r5xLUs1ElypQ2JX3mSNn5m/cEGLwRGEQVXKfQoTn9yTZTWiqdLwfNXL+fTl7xF8fp/jlw9qp2df+Zq3qgz+/WfTGmRAn+poI5MmvbReVC16mraIP3WQG6a5TIR5BEwLV7nbxM7Scsp22TXMM9HcDu3/H+okI5rcO0S5865iK3ysmRHjy1a31qOsKN9fw6UtGims9W2S/fpQekB6QHfsQD9+7dE89Cly9fpmfPnonf9x/p71c+9v79++LykOFka2tL/fr1i9HLRWTRwsKC7O3tCc9BGTNmjNHzR+XJEE07efIknTlzhiCP8OrVK6O6h1F5XlP6yp8/P/n6+tKUKVNkbZ8pDtNpg3pXMzMzMSmCshRkCOIZ/kfsu0EfwIq7uzu5LZxPly5fpfh//0XZM6Qmi+QJKREvSzPugXevgql4zgxkHu8dfQ70M94wGvYgAhX06Rvt2f6GAjk18O+/41E1Z2fq3KUrxRU9wIULF9LLly9pWOvwQUU0uEevy4xpzCmntaV2mwOLwCPiNtp9O01Zs4dGLdlOtUvlF2mKr99/pPErPUXbcR3rUJe6odqK8dnHRe2z0qG5/alyj6l00f8BTV6zmyNPrXl7FnH8kxdv6O7jYMqcLpXo4/7TF3Qz8AmlT52CHj57RQCAw9rU0I5FAYmI/kVHmiQ0Brcd9eU6ysTkPbuvNqoHuYwSHcfTId8b9IavOVli44RDdx4944imJe2a2lM77theQBptMU6FHTpkCO0/cCC2hyPPLz0gPfCTewCT3MuWLSM3t4XMNnlR/KbmzJKJ0qY0o4QJ4okJ3p/8EqNl+MGPAskuayayzcxlLe+e0rdoOYt6p6jzfvToC53zOcI1hFMZIH2j/PnzUYcOHalFixaUIEEC9QPj2Nbr16/T3LlzaRVLIrx6/ZrLTpJTdisLUUIS768fAwdRdalWXAmTKE9WUery9WnYye2oOs+v2M/7r//Q3csfad3qFfT81VsyZwDYnO/Prl27frfuYaTRGf6z4KF8+LCh9O7tG6pT1JZGDqpHRW3TR8vD56/2QSJi9PLdJ0prFvukJMFvPtA+37u0+sg5qlChgmDJnDV7jogAxpbfcX/NmT2LmnHKYkTpg7E1xr5Nq3Ct3XECMPPk2r1aDPyW8/qb95/I1iotdaxVVnVoiMqN7ViXqvedSRtZRHxSlwYCVBXKmZlOcA3hKU4rVUCf99lroo+mlYrSLp+LdIEje8H8nz5VCk0toQ+3hZXJZyveI/vn+r3HtIXTTQ+ev057Z/QOczjO9/HzV6pYKJfe55A7SwYxRgBUXK8x0Pf+42cCkC39neMLM6Ao3NCrUUWqN2ge4QcTepjSpAekB6QHIusB/FYtXryYWQuH0muOrNSvXIrGdBxNJQvYMyNlpB+tInv6n759wMMgsk6fNtavA5k4R89dIo9dh8m1WzcaNWokjR07jlq2bBnrYzM2AGTU9e3bV+jfZU6fhno2KEfVSzhQlgxpjB0S69sDnzynDJzh86ORqli/kFgawE3OEtvBmWbL16+hOXPmUOtWrWjCxImUMmXKSI0oUt9MCMc3YXINHw4jt6vkQD1rFqaUSY3P9EdqJL9J4/j8YxAXAB/cnSpZImpcOpd4nb31iEasOy6YDQcNGkQjRoyIlf+cSE+47X+H3Ps0iLN3RAKO3FUtmptWevkQ0jVhvpy+CavP6Z6oHzNmpRyyC6D34s17EdlDJA3ACKAPQK5RRU193oEQ0FehkJ0gQ7l85yF5M0BT0klPXb0jTlEmv+mg5fYDTV3hZmaxvBbwWByfIon6/18bJmzZM70XWXKkUddO8hjv8Zc3wF945DoAhTDUP87ddICu3n1MyflcALi1SuWL1dnvCgXtKH2alLRmzRr+gR+le3lyWXpAekB6IEIPQDu2adMmdOzYcWrfoCr1b9uQUkdQ4xxhp79Zg7gA+OByZOJUKJpfvEZ3b0Xj3dZS69ataTVLZ63kF1JA45KB76B9u3aUJMFftGhQa6pXtmCs/p6a6hsri8iBE1P7/V3aZeeAQu8mTqJsyGP/aRq1dItg/F/KGZfVq1c32Q0mgz6Qj9SsUZ1SJ/6LDoxuQvaZ4u6MgslXLxtqPVAwmyXtGFKPlu73o2ETx5MP1x1uZiYtSEnEpHl5eVEGi1SUJ2vczq/PaKFhfwx4/Fy4JyAE5Fini/iLLQvPzL14c5dwTAHbTCJaN36FJ9f1aYAcmCYPMcBLzqmTBXNYi9q9GR776ADX9QH0+T94ylG/dwI82jMraHgG8LXl8DlBxnKJ00phAK1VGLQ6F8tDToVzqx6O+rci9ppo9NsPnwj1fRgvwCcmLoa0qqZ6nLIR9XywpTuPKZu07452mWnD2M56LKbanTGwgHpap8J2tHuXpwR9MeBveQrpgV/JA6idqsHPQmY8iXV45RTKlzPrr3R5v/W1pE1lRjMHdaZm1ctTmyHTOOvJgXbu9OTUz/yx7hdElgcOHEgTObrTpnpJGtOhLiXmSVVpv5cHECltXKkIOXOpyoB5G/i7qAYNHz5cBGpM8YRJoG/v3r1Us2YNKmdvRfM7OVFizlOX9ut5AA/DLhUdqGDWdNRk6nYqXaokHfA+KApJY+pqz587R4VyWMXU6b77PABkMNS+wV5zqiNMSb8UK0b+/PHH/8SeP0PeCzCwS5wgPiHl8uXbD8z0GcT9faSaJfOJqGGRXDZiv/e56+I4JbUTEUJ8ZsaszdiltImjejDIRjStzF8UDPTK5s9JSDU11T58+iwIXL4xGIXhnIhwVnLMZTQa7P/wqWgLgDuhU12ySZ+adp24RG7bDtNplrToPcuDlvAsZWyZI0ccV+5ZJ3SEoCEkTXpAekB6ICIPgJEQs+qlC9mT+9jelDRxqPxORMfK/T+PBwrltqVjq6ZR8/4TmeSlFO3e7UUlSpSItQsA4HNxceF0zhW0oH9L8dAfa4ORJ44THkBpzby+LUT2VK/RoymYiaPmcH1nRGY8Dy3kSNCs1qpZk2oUzELu3Z0l4IvIo7/A/ryZLWjXsAb09EEAOVetQh8/foyxq/L3v01ZGCDEdQO5CixtSEqPUov3gOv8IrLAIE10MLOl5jpBxFKMC51hpzltUze1E9vAgFkibzZ6+vINIVqnkLhEVC+nRB/RR3WeFapbpiADNftIAT4ci9rK4N2zyMdtME3p1pD+ZpA0bNFWOnLhJnarWoPyhWjnlO60f1YfqsT6g1kzWJBrgwq0aVwXARpRTwj9v9gygNEvX75wMf+j2BqCPK/0gPTAT+SBs2fPill151IFad2UQRLw/USf3fcMNXnSxLRp5jAq65iHnJ2r0sWLF7+nmyg5pnfv3oKsZe2ojhLwRYlHf51OWrP284ph7WjBwgU0ePDgCC8sXND35MkTBnw1qETO9DS7QyX68wepQiMcjWwQZzyQKU1y2tC/Nl27fJE6d+oUY+N68+YNpzXG/dlTFNTCILgOQyQLduZagHg39ufK3Yf0+PlrsdsmBPRhRSFkQc3c/hCphvIFc2q7QW0fDIBQSQONqJ5vIc8IDmzhLMhllu06TnUGziGbev3IZZw7p3ueI6RtGrN7DExPcbopIo8wRPfsmImzXY1SIqcc2/acuoI3VYMkQ8m8mvpF3QaQqUAxN1hk7zK7Z2wZ6gthr5nxTJr0gPSA9EB4HoD8Qq1aNaloXltaPLoXi4n/GV5zue8X8QAIeZaP70t5s1uLbDewise0QVN2+vTptJC1gTGBKk16wNAD1Us60OxezWjcuHG0fv16w9166+GCPhcuZk3wv2+0sLOTBHw6bnvJQtrn/R/T+0/GIxX3n72hw5fv0c2Hz5mIQ5MCqNOFdvHff/+ja/eD6YDfXboT9FJPBFvbyGDhA7Mqvvv0JdyXkoqHQ8Nra9C13mp2S3Oa17EyLeMvnYhuJL0Df2Dl27dvnNJoPGXxB7qOskP3nbkiSFgQgXMK+RKuVDiX6H/tvlP0KFgTBVQ74VzW8oPVLp1fCLcrbZSo3e6Tl+j8jXuUI1M6lmswU3aTAgDXHzgthN6tuKZQiS5qGxksZMtoQQOaV6XTS4aKKF0/Zh1NnSIZM4eepdac+pm5bj9qNtLN4CjN6qz1+6gSy0usVBGpV9JTX759r3osNo5aup2GL95KqE/UNTCQKv6x1Lk+3TYxsayQ7UDfSJr0gPSA9EB4HgBxxp///UPLGABIwBeep8LuC3z0lLxPXqDrd+7Tl6/6z0wfWPv13YeP4b6+hfP8hLOhT2N9QIrhRw1ELysn9qcvH95Tl86df7S7SB1/9+5d6tSpI2fJVKS6TNgijUSG0GmekD7F2sZPmSFczaApbOyl1l7Z9vz1Oy5D8YvUhPRXvj9B5OfJGsZnOFML62qGZ6Hb95/Qfn5+vHgb7Ohf1JqJbSD6w/Ud9r1OpmSP4aCmrCndvlYZatvWhUA0ZcyMFrOAIchz927aMbQBJUski0V1HThj+2ma63mO9oxsTAW4/k3Xjl+7T+3n7KInr0IfiFMzS+bQRiWoSWn9WZo5O8/SpM0n6MPn0C8ma46wzetUhRyzh+rD6faP5byui4Tsg+F23fW5DNYalsxFD4LfkEP3xbq7tMsJWU/xvnv4WniV8tlQ87K5qVvXLlSlSpUYJ3bRDjaOLKze40M9Z64ToxnH8guKZAGkDYpziubxi7ep/uB5tHJ4O9KN5OGAyau9aPWekyJqNrC5s94VgZAFTJ4Kq6YC8pRGSEe0ZqH3K3c16YgKSFT2R/SOKB1eg5mABV84SK/E64iveoqmXeb0oktPlouASL0CklDD6B5CzpIve2jtpR/3iS81sHoiXdX73DX+Igyk/ExUg9pExdYfOCMmNqDfp9RFKvvku/SA9ID0QFzzwLZt22grv3a5jSUzJriSZpoHjp69RK0GTqag4NDoWBrzFDTStQW1qFlRdGJbpQ29eP023A7dRvWkptXLGW3jOoZ/b7fvV90/pkcr6tmyruq+yGwEM+vsIV2orutIasXBkEqVKkXm8O9u27lzJ8rEv/tDdTR6v7uzn/xAgKlOE5eLLCVkCinmxKR0s3o11UpL3Wf9afsm6mmOCePHo6Bds5RD9d5F3eTYJXSQuRMgrdW1fgW9/WorPsy6jjEpbOVoY5Y0ES0Z4sIT9ZrsLGwDSO08eYWYsMc6DO3GdqwnwJpmCwnAONRtMy3ccpAQEFIMEd7ZvZux9mdyZZPq++gOdUQ2GKRHNm3erNpGFfRh9nvQwAFUv4QdFc2RQfXA33EjbgrviwHkzgyXaoaIXaNJW+jjl29UtWBWsk1vTsevPaDTNx9R90V7KbNFCq0/F+/1pRFrj1Ci+H9RszL2AlhvPXmDAp6+pgYTN9HJKa2NSjvkyJCS3nxQnyW4EqhJmUsRQjRy54km6pQlbQqySKH/gxU/nmkpKsMbl6RtPZeKFINhw4apXfovuW0+/8fbcuS8uDbMNj549lJoz2EDRMqR6qhrU7o2pIZD5wuGy5IsYF6UhdOzc7QNmnVnrweI7WDOnNWrCSHNUdeQPgk5h61HfMVmJZ1Tt015TvFcsuOo2GQK6BuxZBsLvGskJXT7UZYBAoNCUk2Vbcp7Ixahn7hqFyHdtGqfGYK05SFf/45jfqK2ED9ETZhBCgawV6rTBLF8be1YskyVglo7l2DQt0akkvrU8Bcpppe5HnGp5zHRbmTbWuJd/pEekB6QHoirHviXHy4H8rNQ3UolmbwlT1wdZpwb15Xb96h2t5H0kUnAqpctQjltrOgIg8CTfteo88jZlCWjJRXPn4v0y3rZAABAAElEQVRyZrGiN+9CJ8h1L+TSzQCxasYkZOGZ//1Hou7dMU9Y+aIMFqnDOzRS+5xKFqSqpQvTgAH9YwT0HTlyhLy89tB2ro1HVtHvbl2nrKQN3meEVFQNnkh+x89VO7nMxsvnErUfv4y2Te4uXHSHI8swaBYqnAtiA//B85cxm71hvwB8xvYbbgdnQoPBc4VeMTK3inM5C0DgJs6kajN6CfmuHCUm8hGNbDBkHr3k6F0untwvVyAnS3TdonPX7wkgmD6NGSmlOhOZxX0+Z4OBaK8a8zDEY/4EXOPeU5ep1ejF5KWiqaw7LlzfeCbOa8CBh9OnT5Ojo6PubrGseift2LGDbt32p9Wdvo9dL+DpK0rLACMBR5IUe/vhMwVx9CtLWogzhk3f+8RACccxrqIs6cyYLEIdkAD9+nMaZNoUiTk9LuYikB3n7qIDDPggrG7MvM77C8DXq1ZhGlS/uLZZi2k8S3jOn3adva0FfeuPacS3V/SsSWVyZxJtRzQuRcX7L6dbj14IYDlQpw9tZ7ywY2hD3VXtMkBj29meIjJXOb+N2H43BPSNblaGELX7HkuROAF1qOxAs2bO4C+8AfT336YzP37P+WL7GOXuRF0bXoohpbJZ5aJUKl92qsekKIYGEHVs4SDqOWOtAG/7Tl8hvBTD/vl9m5NDNitlk947gBxAH2ajijNgNDToyymgr0y+sD9whu1BtHKOwWZ4htkmNQMV9EaWVWjLX6YAfngpBnA6vXsjvfRUZZ/CSNqyanF6zWnQk9fspvmbDyq7xZfgnN5NqSIzf0qTHpAekB6Iyx7YzdlO165dp9Vjw8+I+dFruPsgiNKlNqcE8UN/W9+8+8BRsheU1cpSlSX5E6eH3XnwWDwzZctkycBA/YEWwPV24CNKm8qckiVR/77/0fEbHu956JQAfNAvHNalmXZ3w55jaeehk7Tj4EkB+vYu0UwWahuELGzae5Ra9J9ErWtXoqqlwj646ra/c/8x5cpmTXsWj9fdHC3LQzo1oWKNuhNYXMuVKxct51A6nTx5EgMJW9bxjfi3XjlGeUe9fDqefNUFOW+YEfzJi9dMlJdG9X7CsZgExuRuxjTmlMY8mdJdmHeUdrx4/V5wGYTHIB7mwO/cgAyjbTwBj2wo77n9tVE9pDOWaD9WSFzh+pB5pchFje9UjyoXUZekMhwG0jNHcjlKZGzqGi8B+DpxJtSEzvXFoe04GAAwuofLdPYxUGvIuss7Oe0TgK8kPzftnNpTewpXlsFazlwL67gkSAF9WIdtHN+VM8eyieWBLatRnmZDBKDE8ygm3MMzXHMBZiefOnUKeXisD9M0FJXp7FqyeDGVy2tDNgzQTLE83dyIsRoNbViCJm3yEdEqpA46c7RrVLPS1GnebjpyJVB8OSVhuYcJLctRo1KhD32zdpyhKVt8tGmOeHBsVT4PAago4O8jM/0NWXmY1h+7KoAVmOrzWDMdPPdViDXmotv++usPQoQNdvneM3r7MWyk7eSNh2J/7SK24l35U80xmwB9rxn4wgBwLwY8FcBVAXzYDjBcr1gOGr/xBF17EAo2sC8ie/ziLfVeup+jiylpfIuy2uZ3gzSRvmxcn/cj1qJcHpq69RRt376d6tWr9yNdxfljZ/ZsQnh9jyFlcengNgzuvtI1lmCAtEF61v7Jw4QvSiqosX7bMAsTXsasKsstvN4319juMNu9Z/cNsy0yG6CVeMJtkPgSvcM/Iph1ArOqdbpUet0g9VNtXGDrbF2tBKesPhKEMDg2c7rU2lRRvU7kivSA9ID0QBzzwJIlS6hMYQeyzZwxSkeW3ak1Pw/9R6NcW9LYBWsIoC8hy/bU4KjY+N4u1JY14g6e8hNtkiRKSFP7d6BmNcprxzBt2SYav3AtQU4Hhu9gl7pVaGIfFy34Q5St35TFtNbzoABgeDh3yGEj+iqcN5QkTNtpFC74XLgqeqtbWf/3rFaFYgL0vX77zujZHj19Tq5j5oro4BS+7vDsPdduIX20jGPe8JpF2b68tjbkyL5byvdFdII+kCju2rWblvCzhClm12iQuFdGcAbN+OU7RbohJo+rl3AQKYTtJ7gzMLqhuZ94Qncys3A34QlsxSCxhGjZ2Wt3lU3id34xyyoVsrPRbjvmd5O6T1tDtx88EdsAwuqzdvBYBlgo64guu3ATNXBfebLYXgv4cC6Uk4DbAOmVhqAvG4uZm2LIxGozZgklSZiAmlUpRnM44meKIfoGzWJwJejaGE6xrFMmv5bkD6zrMET5dC2vmPw/rk3jRPrqK57ogSFAoFj61CmYiyGpyDLDBI4p1pZ1HLvx5/Tq1StKkSKF3iFhQN+nT59o/4H9NKFZKb2G4a2ANOTp6w/UZYEXZUyVjJw4yrTH9w5tPHGddpy5xeLS/1CpXBnprz+51udiALm67aXSrPmXzjwpXbj7hEatO0pWqZNR7aI5CKBww/FrtGSfH6XiWri+dYqKG7XOuI105tZjssuYiio6ZKZ7z17T9lO3qNbYDbSmTy3uL1N4Q/zhfXM6OGn7qDF6PZ24/kC7riwMblCCetUsrAWH2I4PctvJm6KJU/4s4v0la7q1LJebsqYLC8QuMaCEIZJpquHHo8vCPazt9pmWda+uF2FFemc8/kG4yqmnKw9e4s/pPQEA1ipsS2DoNNXSmSUhR9sMhCjwrw76TPVJeO1Q/I2InrGoXnjHxqV9+D+bnb888foeS5ooATnq/Gh8Tx/yGOkB6QHpgZj2ACRd9u7dQ+O6t4zyU4Ow7MnzV9Ru6HSySpeG0wYdafeRM+Sx+zBtPXCCPvMkN4AMHqT3nThPnUbOonJFHMgyTUryvXqbhs5cRpksLai+U0kW6E7Ixx0it/WehNqzQR0ai2cm545D6JTfdbLnKFjlEgUp4OET2rL/OFVpP5g2shQB+osuG961OfVr24DsOH1TMZCqbNp7TKw6c5qkmuFZpv2w6fSKI0lrpg7Si3yqtUeUD5aYgfHsVVvpKqeVJk/Kzyp5bKl2heKifl7tuB/ZVoeB68Slm7is4R8G29EDdBBhxmfvZGKkCs+ZABcduL4MGUlViuYhL442oYZ+G2cPfWbfI5MIfe4/c5W6cKpkGdbrtWRAARs0f6MAfEhTzJ/DWtSerfbyoRp9Z9KdzZM5++hvkWLYkNMU/+DJg6YMGMGAvZcZvBduPSQmt5X0yh/xrbFjbSxT0Z6ZfbTjVdohA+nek+cC/GXg6CQM6Z24zqt3HtJyLieBX7JnTEu1yxQIM2GN9n1ne4iJbfehbXkCQRMkwfbwDEARUVH49CvfB2uY6+Eq8y2AfK+Cox01qqgpfUEfdfi8AOJrmc+hmVMxMVZMhC/eflicwrm4Jm0cY0ZKJ5jVxy/3FHWcUExYwu2ecIpoTmtLUmS+whsb9jkXd6CuHEncs2cPNWyonxkYBvRduHCBPvEMUQkGaZG11uXzisgbIlYrvC9SryX7BXPlruGNtMQkdcZt4KjffY50cfiZQd+cnWfEaZDKWL+4ZvYJUb5cXdzIk9MhAfo8jl4VgA+RPZCnwDmwtXmuUDe3PTSTiVWMgT5E1Mat14RMxUFG/rRgEIY6vB8xjE+x1Ycu047Tt/g6nwhADJBbPq+12A0ANbFVeaWp9n0vA2WkgMKcC5k+FpzryOVAAbZL5gr9kkU/SO/8yqC85fTtWNXa5E0naXLr8tS4dGjEVbvTyEJJlu7Ycuyokb1ys/SA9ID0gPSA9MCv4YFLly7R+/cfqFQ01vK1rV+FpnE06w9+uFu6eQ91Gz2H2Si/0QH3SVTEQfM85NxhCB067UcXrvsL0Dd9+WbhYKRNNqpaRiyjnywVW9AObx8B+lbv8BaAzyFHFjq0YgpBegAGwpOOw2fSVPeNRkEfzjNq7krRPrw/beo4UTWOTKoZzqvY8q37aNuB43Thmr8AugCbFYsXUHbrvaMtIpwAwabUUCqgb8nG3Xr9YKVw3hy0efZwSsEgMCoN98OAqUvoxo0bZGdnF5Vda/s6ceIEFcxpE2lNXReO8CCKh/tpGQOe7tNWi2fwvayXWziX5jOpwTX6h31vMKFboABRNzgjCXVxIJ5bxnpviqXgVGCweHuzTBSIRAbM2yCiUsuGt6WapfKLZkg9rNx9ikivRD0beAzUbPiiLXSFQVh4hpKS5Trn121rznWdRUJqOyE1hfq+gEfBzJPwQETbhrSurm2O9E6A4KbDF2q3YWEC18tN695YjzgF9Xcg52vEaZgAZ/M2HtA7xtgKMp9giMyVaD9OAEul7V8L/6AhrWtQz8aVxSZMmIMUBhFEpKKipEaRwkL2lm4Kau8mTuRz+bYgcnHfeZQDZX9wNP+LuA/GdzY9ww6A3CG7Nfn4+EQM+q5duyZQfabUpkeBlIvtVq2QSFHEun2m1GKzXcbUWsCHDSXsuKCXQd/dJy/FfoRHYXM9z5KlOX+wtunJjAfsv6gz/Y//wbZwrRoMYFABfFhvUDInDVp5UPSHmkG1Gr8Xbz/SfpZDiMhK2EUe5IbXJ8Ce7nlBvBLIMg5qaZZveOyjOdrpfuCi6NK1eiGjINbwnGAxApsobo6RTUvr7casWUBITV937rMeg2qwiq45fJk2+9xgUL6P8mZOQ3ZWms9K72CVlZwZUnEa7in6yhTJ8YzUD6gcJjdJD0gPSA9ID0gP/FQewLPQ35yxgZq66LJezCyJB3RYnuyZxbt9tkxawIcNpQrlFqBPATgJOG0PNpPBX3qO/BXLZ0fmyZPSoyPrtJEt1MTBXBgMKoAP602cy1K/yYtEf6gZVKvxe86RkT3HzqF5uFaqoCZCEW4j3gmwp9vfa47i3Xv0hLJb65MEInI2ZekGIYkxvqdLRN2K/ahVhOEzmtinHWWxSkeoJ1zgsVOA3h7j5guZDdEoiv7ktMko/Iz7I7pA39Url8nOOvLZNd0bVdLeT/Y2Gv+CKVsBfHBBSQdbAfqU2jeljhRAZvKq3YR6fNTzIVV0AOv8Ik30/I1A8n/wVESykDKqGEpWGjOh29CFm5nk7YJR0HeCWc1Ps5xBeAbyElMMKc0gN1FkyZC2jDIaAFMsK/q/PRtVpgYVCnFE7g2t9johpKq6T1/NYMhKpFoGcoSwB6+jRm6KayNTTq1tc4fTYWF+t+6LyCp8VYzZ2wGmEdUbwfWBBbmuDnV8qH/UlV0AiFPsZuATMV5E8WAgh0H5Fwx1jEo6J1Jbj3DfZZkExlTLyWSBV69cCdNcg7h0Nr948YJSclplZIszUYeXPmVSbU8KmENUS9e4mTDGI8K6Ohek/Rfuijq5mmM2cF7t35wKakVVC2ThdE9b0eb2Yw1AHONxjKZtO6U5MOSvUlv3hNNL1UAf+rrv3k3vGLWVeFEcpkckD2Qup289ouUM5rzO36Eei/aSJ0c9dQ0AbOTao/ScwakFp3SO43q8moWz6zYJdxkprmD8rMO+AkmOoW0eVE8Awnw2mi+QnJweq9QRAvgBUJsK+lImSyjSRpAnnDq1aUDRcDxyXXpAekB6QHpAeiCuewDPQubJk2kfoqN6vKjDy5A2tD4aJQEwpHDq2h//04BCTOLCureowyDqLF28eZec2g2ipPzgjVTQamWKcLqnpiznZsAD0XbE7BU0abGHWFb+AOzBnjx/qQr6yhbOS8E+G5XmRt9R422KTR/YkYZ3bUYnL1ynJZt2067DpwWD5373iXqHI/UUtY24hqxMTGOKIdJZKLetSGEF8IX1aFmHnEoWooL1utDmfcdowYjuEaaJmnIupQ0Ic5IlSUy4P6LLxHN41qyR6h6pgBl0tG8VIkUQuuiaQqQY8gguQI9L9VJMEneExrhvFy+AEKSW1i9fSACk2/y5wJDSmKfpUN3uCJE32BMjTODYt3NqD/pXeejHhh8wC05hDt4zR3AFQCJruNsWGsYv1Mgh3XL75B5i4gByUTDIT5UrqAFL0CjefOis0EFuy1rFIF0BaQrKUCJjSO9UbHaf5loiFoBrgOm1e0/S9qPnBeibutqLtrI8Fur0JnZpQCXyZqNLzGQ+dtlOQfjSbpw7HXMbLKKGLUYuEpJWiKCC3wETPNAN7DJ5JU1bu0cASWdOATXFUiZPTFdvaiKSuu3D/K9FTZ8C2HQbRrScgCN2ys2k25aBt6opNxxAyJlpbUQdHwTKT1x/KFIckea4jtM6Nw+sxx+MBhlDEy+5IWNnCD4Ksz3krBhTwpAvU9WBRNFGRNzO+wexBEM8yhUSOUvOrJcVHWxE1C5r+7kiRfUV1/OBDRPh5/bMCIoUUJDV9KzpSK7VHSkpg97I2Mwdp0XzpmVyhzkMwN0YyU3Z3NYi2qcA6jAHq2yIH5JWi3tEWtzyAERFT7EWTE7+YglPtP3K3Yd8v/1FEG43NIiZGjMUOUfGwjuP0g9mtfCDgZlIZbZb2ae8i2g1M1Zhpgz1kZH9clb6ke/SA9ID0gOR8YB4FopGpuoE3Lfa956S4WQ4VgX05cqaiS5ucyOPXYdo7/FzdOw8l5IwGyZeq3ccIM+FY4RYOY4vZJ+da9wS63cVEqgJsz2kFcYEUpnvNUTszl6+SYn4NyN3SPQSKZaQPChXJC+lL9WYTl28zoyG7/R0D6cu1QDNlrUqmnzq9BapCC9Dy8HROADq+4+fCYZTuywaAGDY7nvXER2Lzucgzb0X5vE83OGCqVP1fjLyEK7cT+h0Wo/GzDTpKOrJDjPhC2rO8EJ6J0ARMulgqZiVv2ju0NRdsTHkT2F79e3YrUxo6LaPzDJYK4O4BhapkmZ8P+PZFmAOr1dvPwigCsZMsGA65golntE9RzlmPwfou8UC6Yjy4XkJ1nXKKm2z4FdvxfIMj72CWRMRPDWQBZkFWLpUyQXQFCshfyoXsRegD+eBKQzuSPcsxaAUBoA6un1tAfoAACHrcILTY4EjIDWBCKtiqA9E+i10lSHsrjYepa3uO+4HtXs0cneVbo9RtHz48j0CYHSp6CBeAEPeF++x9MBOOnb1Pt14+Fzo2yES1rCkHdUyYMb0YUKVL9/+pdTJE6mO6Nztx9RvWcR5up2rFqS6zJz5vQYphyoj1olIpb9bZ8Ksi2KQmcD6v/99Y329zwL0DVpxUAC+7Kzl5+5anWxDmEGVY0x5v8T1imASBXkOiHIMDf7bd+EOlWGSm7J5rPV2X7ynCU/rRmf1GsiVn8YD+PJ24dmig+dZVJSZo7rWK686duSRl+48kUpzesem8V302txnUGVvMIOnNBCCpjtnKKsRvod3HhzsceA0zfTYpxWaT8aTIJjVMtTuw5dym3FLGfBpIv34P1QwpzXN55k1fDFKkx6QHpAe+N084H3yAoOyv6l9Q2fxAkHKPp/zQuIAWnjX7twnG9bBC+Y0zSYsag6NQV07fv4K1w1+5RQ+/QiQ0ubMpRvUc/x8ZdXou2vz2tSgSukw+1/wBGS5Vv1EBPLh4bV6ZCd4FvqLJ4//5RQ96PMpYvd+N+6IyCVIbSLDxDlizgqhEzuCiWN0SVWesTQBWEBh6dOEBYVhBv0bb8DkK9g482TJKCJRcAVA0YhFW4XmHGrLWjELNyw51/ktGtRGLCt/HjP5CSZ5szFZijGDPAFSIcOzJBzQ8ZzWS7XJLAZhi7cfIYiPuzbQnxRQgk147jjKMlUAf2U5sqcrjo5O/W5rzg+ylfgcrQXzp6EpEhfQHkbwS0mlNmynkMa85SAOSHKU49AOpCswMG7C4F+Y4fmyZrBgRvcEQvYB6gRKO3sDlk8cWyCHtQB9SPP8UYt10NdlwR4KfvOBbszvSIiMoWYPGnOQi4DQ+Cd2aKFs6egsgzdE/3RB3xlOnazOTJrZWNfPh8XM1ezdp690NVDjdLX9yranXOv2Iwam0dT8esbXcphJVcrpgCzITCAN1TxJAmYpTS6ifIhiglVzqWu1CAEfyGgwA2DPEUTdmsZDDJhhNQpnU03H/cA3yFzPc3SUayiLc82iIn+B7dtP3RTHFuUaSmk/twdmbzggAF94V4FIYG9mqcKkipop+f3Q8EmbMpleE90vNL0dKisRnec6F413nbpa5KpDAN6MKZ/BLjaDQSAikINbVRO94guwGjOHgdSgMufq57ROJ4ThAQSbjHAjyFGg8Fua9ID0gPTA7+QBsFs+e/ma7nmvEiQlqNmDll1WrmeDoPln1u8rzELlpzmahuifLug7xeLolVwGiHo63y3qwO7dh490+Zbm2SI8vyI9VM3AIApA+fTFK/JmUpaKxTSkH2i7huUjkF6aMkUywT6qHO990lcs1q5onHET53sYFEypzJKTlaVm0m+/j69gMy2QKxvVYrZOxcBmigd3MJcai2gqbX/398M8WezKhC8AUwBVMCuLlILYBEQnnxiQ5GGAhOeAm1w7d5sjWFlDMoUw4QxSFTCCLh3iYlRD7jbXAwIYhmeJw4kuI6IH82TNuy51yzPA1wRVUPfmvuOo2JcveyYhYQKR9SMXblBJFktXRO1RR7f18DnRrljubILABimVhgZGzd4z1xEIVUC+ohgicQ9YvxCRTvjGmqPIiDrCH6gvrMeyFYpt44gczDGEOCeXjaUQYvfYd5pGtKulNKPdPhcF4EP2EhhXkfEE23PysqgDRERTMWj5wdQAodLG1PdYB33Q8luyj502YRO1d8pPSCHcxzV+AHwgdgHQsWZpAZHqyTVoAD2iTi/4Dc1h8hdYv7rFjF4vWDMfr+hhdH9U7gABy9DVhwVTJkhnQEgDfcLj1zQzDP3qFhWn82OZio9crJko/l+C4VRtDIjOoT3AXrnBq0STS7PbCcZTpT3AJax4zrBRPmwHOU2WtCmEJqDzSA9tjeTaI1cEoQvSZX+UsRTniesGDZd0KZPrzcZohErfCN05tZQIfNGhIBhp6Fk5qqR8eahdK2aYMLtpwzp0ka2FVesvMtt8bwbSyKXbjB6ClAHX6WtEnjnuJWOmsFGN71RXgCxj7YxtN/U8nSatEEBuJqeTtHLWzB66cJSvZKfxNJVz1jvVKcMCrEloMufBA/A14SJxCNrDEAlsMWoxbTvqSzM57WQQ571Lkx6QHpAe+J08UKNcUVro4UnVOw2jLk1qiKgFavwA+JDqiJRK6wxpRarnBq8j4rcL0bN7TGU/c8Vm4arBHRsbdVlZ1iV8dWaL0f2m7OjVuq5guGzcexy1rVeFzJh58RADwKPnLovDISuhawdP+onVkgXDlqko7Rau86SJXJ/YigXb5w7rJja71OWH86tzqNXAKdTe9yohrRO1jks2eon90EGUFr4HyheyE2Qt8zd7i+gWsmn8uS7NbeshcSDkH1KbJRNslCApgYxDNwZESTlIs8n7LHmfuybACOQGjNmu6eoRPGPtDbeDXXPiSk+CREPVXtMEactDzgACeQwkGUDG0qRyEZFRhywgRBWdekxh4FpQdAWGTkTgQK5SrYTxcRqeV1mHLyav3k0tmORmdu9mAnQOd6kpGEJRb3eRo4jpU5uL9Es8C+FZEIQ4sC71KggdwOnr9jDRy3VyKppbAOfdnLIJ68M6f3huLMmpn4gGIt0zT9MhVKNkfu4zhZDeuMzMp2n5GRY6gj9qUQL6MGAlxKoMCMQuamb4gD24QXEGQF9p04nrQsRdOQZgZUGXqgLkATxtHVyfOnANHOQb8IKhNm4Mi78rhC/KsbH13q5yPnrx7iNLSJyh+bs1aB9jATlNf5aeaFspnxgaopawD5+/0embj8Sy4R819lRd3+EB/lSIGHxBBm9qhvrCtX1rU3cmkPHhWknfO0HaZjWYLGaaS4UYBynaAUTjgl2TwQKsjeD/lOOZphegTwiVsnbJ2I51qP2E5XSImZAwS5UEQqVdGwhwoQwJUaeJq3YJqlxsQ1phG05vGNexrh74O+Z3i7rPgFCpJlVWCJWWLcjnqKsXkVX6jep3ISrK6Y+4Bui/zFGhG4amDtIM8HrNM6xXWEtGzZRIn1qtn1p7w22mnOfFm/d0nkFqSn4AaKHz5YVzFuIv4yOcmrHz+EWx7+z1AHGKBlxIrmtYB+i7EEGqiO4xcll6QHpAeiAueQDPTEq0QhmX4bqx7cO7NBdi69D0cxk8VWkmGCzdx/URbJ0gNdnlNpbaDJrC4M9bvNAQ6ZQT+7SlepU1hC/ag6N4oVOj6vSC66OmsDQE9PMUA+nM4I5NqGOj0Ak71ACeCBFzd2RSFmOmPGPqPge1rlOZwAYKMDh3zXbtoYgkzmNgCH3C38H4duL7Sf+Z2+j9pFN6BN8gVXExp2yOXrpdABvFX/B3xzplqXVIamefpk4iQwfgB/INioGhcl6/Fj9ct6f0p/aOrB7UFrbl5x0AP7wUK8XlKtN5Elmp998wrgt1ZSAG8HXuemjEuhZrEM7o2cSkZ17d8iycR7n3dHFNNWYxdRvQinrPWifKVZTxQLYCovZKoKAuPxO+ZlwAkpzzN+6JF9qCcb9fs6oCQGMdkdS1oztRr5lrhSbiKmYcVQwRvvn9WxJkNH7U/scPvhzLCLWxY8eS+7wZdGpyi9CNMbCEmj1ozf0d708C42dulnzQTWXEEAB0bj16QbcfveSLT0AFsqaNEZKWyF4+ZCJu8jiRMmrFUcqsnH6axEQ62siey9T2/kEv6Q6/kvF/HtQPAjBH1s5yOq0T1y0GBgZSxozq0cXI9mnYPr1lOupas5gI4RvuM3U9W4OBYvYHP6wIm4M1yYtD5rjVkaetFSplxtb9Z6+K/9BXVo8hS2a5QuSsTJeJYuaoLuu24MsGAqegBB7IxbUDmlcVw9h76jI1HLZACJU25DTF5IkT0d7TlwUALMMzNtsmuZo63O9u15nTKlaz4Kf74DaCVWvggk3h1vQd9btJ1frMpApc0GxY09eUUyb38DUt4xSNM9fusv/esqCpBUGs1Tpd5GoijJ3nzNW7VIE1fepwn+58Hl3ryV90S3ce4y/A8jSmfR3KUr8/Bb96R9fXjuVi6RTapkj7zNtiuPhcL60ard0e2QV/plzO32ok+fr6koODQ2QPl+2lB6QHfnEPTJo0iebNmkFXd7rFySsN5hRP6PeBJMMydUrKY2ujJ8+AQQNQ3WAmz1sBDznallQQu/wISUtkHfH81Ru6cfc+PeG6L+v0FpTNOj0lYSH1qLa3rKcIYfYXTA4D+QYbjnTq1vhF9flsKrakwcNGULdumohjlPef2ZpaVHCgXo2dorrrcPs7cfEWBXFEDDpvOZlBVRFv1z0Ik7eIbH3klElM2CqpnrptomsZ6ZyYoIZkAtKaUY5i7PkEEhP4nYeshC3LF+imS0bl+JAVdpmjcyClQ5QR6Z9qhhTTK6wr+Ij/LyCbCc+lmABXM2SZIXUUmAfXZ8up28ZAvNrx2DZu2Q7acfYOXbl6Ta9JlET69Hr8zpWUSRNGGLED+s7BWnF4xWUz52uB3mBcMsg5qEk6xKUxRvVYXHiGClE8zAwKodIZazVCpTN6s26NjThdjX6zhLYKvsQA+sBWBUNtWcPyjmIZJCO2jQZxFMpPgD7UxQ2Yv0kjVDrMhWqW1ERwB7aoSpV7ThNRRAhsFrXPKo43/DOcNVyMRdyUtol5kmD50LbKapj3TYfOCcCHujghKrrJO0ybyGxAeieuC+BP1yas3EXTXBtS08qa1GTdfZFdvsXF4jBoABlaNi5qhj1jsAlDkTNAH0B5c45iKnaaASks8MkLQqRT1vUpnpHv0gPSA7+TB1DbFlHEDsAHzJVRzV5pqp8RcSuWL5epzb+7XVKedC2cN+d3Hy8P1HigWJ5sEboCGU1gyYwN+4vvZ9TS4RWRAYDhFd2GCB3SRiMy6BAWstM8d0bUNrNlasIrOkwd9OlHiaPjvLJP6YFo90D3hhUF4MOJ7EOYmjRCpaH/8VDsC0FNJb1RoRYGOUr6VGYM3LLwrExiCtwyWZsWABYozCAh37q6Th67ECqtUJiGLtoiSEeMgT6kHZzmqFd4Fp5QqRAV5bRSISrarWF43Zi0DxHQUEHTStSAi5KDmP0MUURQHHdnsCwETUOKqU3qVKURaJdhyXnmzdAUCmTo5sDqlysoUjiQnouZRejvHGIqadTywZBuEdmZL3Gg/CM9ID0gPSA9ID0gPSA98Bt6QB306SV8/oZekZf803sAUWF9odJ44pp0UwWxQcnV/o80N70rFyjvPX1FFNM695kh8sQBOJyL5SGke8LAXgULev6G8jQfLpaVP2+Z+Qym0PYq23Xfd07u/t1CpWAkazt+mUZUdGwXbR67bv/fs7yd01ExixYqaGpJ5QpoZk41gqbnmF3qx6LXCjvXM671MLR3IQKvZkk1OettmOTlBAuvIqI5jKmjFQOLJ0Bg5nSp9Yh5lP3yXXpAekB6QHpAekB6QHpAeiCsB9RB328c6YOu38qDl8ipQBZyyp8lrMd+oi0geoHkRWlmAv3dDCF33YJv5fpR8KxmSmUr8qx9l48QdXwQ1URUDmmdeK3Ze5IAjt6GRKOEUKkRQdLC9qHRRMPzKdFEw+2mrAeynp5WVHTaKu0hSIWEgYRm+e7jBBIb52KmsVSh9tHRSNpBuQI5QgRNNWQ12hN+x4JFSFqnokej2wWAHCxViLYNPrulXKvYzKmoiIq+5GJ90EYXYRrk/K1Hcj68OnmRbp9yWXpAekB6QHrg+zwAvb5lW/aSc2lHqlq68Pd1EgeOQu3jKb/rZMei9pm51k/az+mBs1zasXzXcarKbKJVeBL+ZzXIWp264s8yVJbRlsIZnm/UQd9vHOkD4cmqQ5dZLiLpTw36IBNRa+wGSswMngFLoqfYOLwb62fdB4HzhAwY29UoJV6oczvAdWWtxiyho8zWef1eENmE5Fqj2HnRwFZ6l6oRKn0kCpz1duisQELB71agzpawi0kSJiDPqT3C7AARjaHIJxoB5MJQ+Avh0QR//y3WTfkD4hWQuJTND0FT/boIv9sPRBcQNP1RUwq+QQdtaCcuadi4CuawFrug+4OIYHWmV1YijtixmSN/MBSZS5MekB6QHpAeiB4P3A58JECfZZqUPy3oQ+lCa2YwhaD9+F5tCILy0n5OD4CYZQWDPnAv/KygD/ejy9gldPDcdcHyrqsFGFOfijroMxINialByfP8mAdeMj1sx3m7hWzBj/X0+x0NHTmADf+NEwU9LhhknYrkFixR0ErRFyp9wmydLFQaQkKC/9AQHgf5CKJUqLlTMxxjCpGL2rH4wju2YGCYXYt3HGHqYA/q3bgyda1XPsz+8DaAVQo1jEd8b7KgaTYt1bAQND2ikR4pljs06o2aRqSZAnwaMuyGd54CtpkEYAZpzAbvM1y3p5FjwJf5vjNXBHAtG1IgvmL3CSHNkOBvF8Egin5xTkQyEZmsXDj6yQHCuxa5T3pAekB6QHogbntg5ootAvDF7VHK0f0uHoBwPABfbJo66IviEQW9fEePXryjDKmSUprkiY32/o0f6m4+fE6ZWaMP0Zbw7P2nr/ScRbGtUifXNsND950nrwhMoOFJEqAdpB9SJ08kBNS1HZiw8Or9Jz7vR7KxSKEl9jA8zNTrNTwuqtZ7LN5Hj9nf0iLvgerQXtl2mGr3ny00ahBZQ40fAB+IWxSg07NRJaEBCPbPbiy+CY2YTYcgVHpdI1QaTvrBrqk9Iz+waDwCZDagPgaYc2L2UbCBwlZzOqtG0NSaFOFVAK9SnSaI/ddYTgEgNDLWv1kV6sDAuufMdSJtEyQ5kGpARHV670ZaNk5E+KDHBymH50zDnYqpjRdvPyLGCN+bwpYVmXHJttID0gPSA9HpgU+fv9CdB4/FZGw2zlT4mzMyjNnjp8+ZafobZbLUsBoba4ft7z9+IqQw6rbFM45/4GNKyaLa0OYzZmh34+4DSpMyBUHbz1T7lzNKEAlMm8qckoWjHfb42Qt69CSYMnANtkXKH88WMXV8aOd79TYNn7MiMof8Vm0xgQ0CN9wDmLhWdOXUnIAMpi/8G21sIlv3GLBqB3MKo25bnANkeeb8/B+edALaQaoAYvB4NjDVcD+CXM/CPLmQaDB2XNDz1/Tw2UvKyNqEaizixo6Liu2+N+/RSGZuj22LVtAHXbhO870IdXKKZWLduoWdq1DBbKHpWRcDntKkTSfo2NX79I7BHEQL82a2oAYlcpJLRQdx6Arvi9RvmTcNqFeMzvsH0QG/u6y39g9lYw28Mc3LUPCbjzR45SECKINBMmFNn1qULFF8octm39WN8linEdvd9/vR09cfRLviOTPQ/E5VyDJl+F94SJfsvWQ/3X78UhxnzjqBdYrloNFNS2ujHaZer+ggmv7AT55nblMX5wK0wvuS+A8dTaeKs92ibi+MUCmzPaqZoQjnkNbVhSg7IlEQcVcMoGgxp3Iqka0+TSprhErXeLF8w0almYiUzevbPFqFSrUnU1kwvB6VJmE2gSl0w9hOIkopBE1ZQFSxWqXy0Ywe6oKmukKlSvuI3htVLCyIWEYs2SbANdrj/IhQ6kozIAp4lYXkZ67fLyKYaJeQU5XrsdDpINZLlCY9ID0gPfCzeGDask00fuFa/m3RsBODedilbhUWSnfRgj88uI6et5p2HDxJ1+5o0v/TpjanYg52IjUxQ1oNhfvSzXuo5/j5NLRTUzp7+SbtPX6OPvMDfHbrDEJ4HQCw3+RF9JIny2DF8tnRxpnDKHnSxKJd1kotySFnFirK/S7esIuePH8l2pUsYE9Lxvam9BbGJbE+8vj7TVlMaz0PCoF4ZF045LChqf076Ekm+DMgdBkyjVAXqBh0+iAe75gn+un+AYRbDpxEKJNoUasizVoZ+w/bih/iwvuMdXtp4kpP8ayD8eC5oU31kjSuUz0t+MP9OJa13nYe8+OyFs0zfNqUyQWj+ZiOdYWoO46FHFZvnpwdzM9O564H0L5TV8QzNzT8xneuT89fv6UBczfQSw7SwCBe7jGms9ACxH1r23AgOWTLSEVY4moJT+w+fflGtCvBWUduA1vzZLvxyYKPPJEykIXi1+07RR8/fxWBmLxZM9JkZjV3DJHlQmcAhO3HLyPUBSoG/TuIqJsqo6Ac9z3vAMJtuEQI92OzKsVoDkf8YsvUQV8U1fQNXXVYAL6ahbNTPpu0DJhe0NojV6jO+I10Y0EnEc17+/ELtZ65g+49fS0E2asUyEoBT1/R9lO3qD+DxWyW5lQql5VgO0QkcIzHMb5B/0cVHTLTlcBgusUgrPm07WIWInWyRFSPgdjx6w/oJJOYjFx7lKa6VBAzay/ffaLDLP6OV7k81pQ9vTl5Xwyg49ceUO1xG+jYxJbaB3rDD2PfhTvUdMo2wfTYuFQuppyPT/sv3KXFey8IEfbNA+uJQ0y5XsO+o3IdgvAAvvYsbD+4QQkB+qKy/5+lr5se48MMFUWzr/fNDbO9B8s64KUY5ATm9mlGo9rVEnp7iPSB8RMkIgrgQ1swXQ5qWY061i6rESrlL5xsGdNoUz2V/mLqvW31UoRXeIaInpoPcAxA7e5pPUlf0DQdz8pp2DSVfvGwYqwPpU1450GbznXLkQv/wJzlHwj82IAxVG2WcTiT0XSoVYauBjyibzzLWNIhOwM/02sVlfHId+kB6QHpgdjyACJOQ2cuE5G4+k4lOZshIXnsPkRu6z0pNUcmBnVoLIY21X0TTVqynvcnoOY1KpAF/+7sO36eNu87RkHBL2jf0omi3X/8MI7vw+GzVwjZmsolCtLlmwF0k0XYG/YcQ1+YvC2NeQpqWKU0HT13mU74XqUhfP7ZQ7qISeAX/BCOGje8KhbLT7aZM9J+n/OibdX2g+nsxrlhRN5xYkRhnDsOEaQo9tmsCecNePiEtuw/TlX4OADLckU0k/T9py4RgK9OxRJUIFc2unXvIa3cvp+cOwyhwIOrKboF4ntPWCginSsm9iNEG6WFegARp+EsK4VIXF2eRIXW7foDp2kRZziBSG1AyKTqdAaGU1Z7EVi3odMLIjbU2m85fF4IuHux3jHs33//Izybj1y8TfyeVypsL0TIbzHLeZNhC8T9mNosKTUoX4iOMS+CD5PjDV+0mWb0bCo4018yiRtSHvGqUMhOaPCBRwFtqzOD+qklw/SevZQrwf1Yo89Mzhi6Q7ls0lMlR3u6FxRMW7kkpVrv6eQxtjOVDWEgH8QT8wB8tUvnp/zMG4CxrfbyoRp9Z9KdzZOj/bmi72wPEel0Z+3lII6axqapgz71oEikxok0zT2+dygzp0Euca2mPRZpl3M8z9LBi/eoasGsBIZJAD6AtQVdqmrbpWIAN2/XOQEaAfoUSxT/Lzo0rjnZsNg4UsIK9V5KD4LfUqV8mWlFz5oiSgjAV22UB124G6Qcpn3vVCU/jW5WRqx/5FmGikPX0PUHz2kNg9GW5cIyAuEcQxhI/cs32JKuzlTdMbs4tn+dolSVz3GEQSTOh8ifKderHYjOwqh1R+kqA9jwLFGCeLRUx4+GbT/zF337OZ5inAs7V6W/uRZN2vd7ICWnEyoSDeH1EptCpeGN63v3xZSgKRhMi5sgBIuZRbykSQ9ID0gP/IwemL58sxj2sC7NqFHVMmK5bf0qlKViC9rh7aMFfftOaEiqfNbNpCxWmkyork1rUY6qbRhA3Qxz6Yn4YfykxyzR9iv//ueu2Z7uP35GVUoWonXTBtNf/AxwwvcKVWwzgFMdb4U5vluzWjSht4vYjgheqWa96Kp/oABnbeo6hWm/eoe3AHwOObLQoRVTtMAQYK7j8Jk01X2jAH3X79yn3UdOk03GdLRyUn9tPymSJqEZKzbTAR9fqla2iHa77gLA8ZXbAbqbwiwnYlC8atKAMNuVDRv3HBHX0KRaWapbqSTNWb1N2SXf2QOzuC4eNrh1DWpYwVEst+EJY9sGAziqd0EL+vZzWQvsqNsgMSmM5S7MF5CrMU8M6ETMsB2GjJ1jboNFWzw352s+jO4z23hl5kRYM6qDmCgH4HPqMZV8b4QlskPfiDTCEMEr23kiXeMJ31VeJ6h1tZJiu+6ftVx+AsCXl6OEB+b01wLDCgzmOk9eQdPX7hGg7wZHKb18Lgk+gWXD2mm7SMFpybNY99f77DVy1tFb1jbgBYDjK1zaE54BNC/X6dew7SbWOl69x4eQ5YTSmXkbDxg2idF1ddAXBUNI8Lem67tcYzdt6ylqVtZe1PMNbVSC+jJgShiyP1+WtOQ1ohFZcdqnYqiJwwuGFE5dq8FRQwA+GCIvtulTCtDXtExuAfiwvXB2S7F8Jygsou5XtxiaCEPdYJeqBamb2x4BLtVA36V7T8mf+7E0T0LOBbMph3INV3xqWMKORqw9wumUt6hd5XxiX0TXq+1AZ8GHI5NnboWmwOrs0i4C7IZno9Ydo8v3ntGElmXJNoM6gUh4x8t90gPSA9ID0gPSA9IDUeuBBJyWDpvJ4C89M2Ei3RL1c4+OrNPjBZjcrz39yxETBfChBvDSzbsieoJlRDaQTqkYomhK23ickZLTxkqAvpa1KwrAh3ZI4QT4878f9vlicEdNhBHtEHlzbVFbgDekZKqBvk17j6IpuTBgxfkUa+JcVqSTHjrtR2/efeC+NNkYd/icExd7UKvalUQ93yjXFjSIzxletsZxBqmQVwjPAHaNWeCjp9RtzFwRVZ02oKOxZr/1dkUyajYDHvAUFOW0SkxeB26bqnd/TezaQLCBIwsIhhrAS/4PNPcjLxvej7U4iqa0Fc/mrKkL0NeC0xmRGQUrwhJXKN+6w5+ToQ3kzCnFcI90Y81kgLez1wJUQR80fGEArLpZWABXA+atp8O+N+jN+4+UICQ7CARyk1ftppZVi4t6vhFtawmAi7IRYwatYADL8Axg15gFPnlOPaavFlHVKa6NjDWL0e2h/3N1TxsF6Z0gWGldPg+5H7hI4zYcF68cDEYq57OhuhzVs7PS5KeDdOXpq/c0Ys0R8r3zhO4/eyO05XSHo7ucSYe4BduRfgdLZ5ZEvOMPvhjx1Wh4GRYpElPShPofkAKQAvm8anbrkaaG7wmPMX/PxXpNkJoKwz5Tr1evg5CVrYPrf7dYN7pAqulCr/NUPq81ta2kAZ9q55HbpAekB6QHpAekB6QHYs4D3VvUoT3HztJFBnBO7QZRUi4hKOOYl6qVKUL1nUJT8nPaZKQlG72o/9TFdJvTIZVaO2MjzcQ1crqmPMxbpg6d9NU8C/1PPKDrtk2byozHoZ+6b8egEYaUTTVD+ihsBKeVTmIwp2sAe7Anz19StkzpCZHMxRt206i5q8TLLosVVSnlSA045RSpocZst9s4UStvbH942//55x8hz/DuwyfaMntEmOsL79jfaZ9rgwq0lyWaAOCce00XJHSl89sKXV+keyqWI1M6Jlk7SoPmbWSW8qfaWjtlv+F7prSp9DYpgR+Uxygm7kd+Puf5Cz1D6ijI8HQtB4NGGFI21ew2p2jCQI4yhcGcrr0J4fZ4+uINQSrKhYHhEmY4H+O+XbxQ7gNW9vqccorUUGO2c2qP7342B+ld27FL6R3X820c3zXM9Rk7Z3RvVwd9oZNJP3T+yW0qUH2Ohm09eYOOXAkUaZRIpUR6p0e/OlQmdyYCiUv5IavETZCVSVlqF7WlrJZm9IIZMud6apC87iASGUHlOhNgoc0N7iwzTsE0tHg86wDDrIWavWM6exjSTYtkV785HJk0BmbK9YqGBn8U4Gqw2eTVzT6ambELDJqL9l2mPU4BpdiGSOWmkNpDbQO5ID0gPSA9ID0gPSA9EG0eyMWi4Be3uZHHrkOCdOXY+cuCrAWELat3HCDPhWOYL+APatF/ktiOqFuJ/LkoJwMlpFIOmuFOQSp1aaj9UzPdaKCy3/DxRo2p86+QCXSjz0IfPoruCtlnF6QwSt/i3UGzBrIY2MxBnalx1bK0ad9ROnTKT6SNInUU6Z0AZOWLqk9OK8BV01vk/gZyautJv2vioM6jZmkPDg4hBpm+bLPQHRzVraXR9FLtQb/wgl3m9OS7YpSo49vHKZwnLt4SZC0gbFnDKZPbJ3cX92PrMYvFdkTCUIqRg4ESUimHLtxMYME0tMRGIl7q96P+87a5CsOsEr0zdj++ZXAPA5N3CtZM1rWilEWsQksZNq1HY2pY0VHo/B4+f0OkjSJ1FOmdAGTlDPSJxUH854fuR47ynbqiiRJ2nbJK6ZKCWRIMNsNjrxCbR8TRWHqp9qAoXFAHfVFwAtTpQegcpCLjWpQVPSKKh/q1LQwCl3EEEKBv1o7TAvC1reTAqYnltGeey8Awqg2plxCv1mU4vMXkMrCs6cxVTwdpBhhYQHVrDrHtMaegXrsfzMeaibpEU64XxxlaT5ZYuBigmbUw3KesJ+H/UNuGNFBW9d4zpEom/Ky3kVdAeINaxPjx/uRiXP0Ip2Fbuf5zeWDU0u2CFnlWzyY/18B5tC+4cBuF1EgXQTpIhjRmP901yAFLD0gPSA+Y4gEQpiDlsX1DZ/FC/d0+Jk4ByDty9hIzdd4XzyQAgUhF89u6QMugiehVD2bqjGpDuif6/jMk7Q7932TpBlg2ZgFVM5uMliwN8YaaVC8nauV02xw/f4UJO74KApmAh0FM3PKI8thmpsl924tmSLscOmsZbdxzlJZs8jIK+rqOmk0Xrvvrdh1mOUmihOS1OCxZGx7Q82TPHKa98uCOZ7/4LJOR4Dd/Fjp47ppIsW1XszThhfq7A6yT22r0Yjp64aZg6sQzMkBgAvbpueUjtQyaiF6BqTOq7Q6za6JvEMUpBukGGFhA1cwmfWpmBn1HjSsV0cpMKe0AZCExAemHgMfBQk85T5aMNLGL5hkaaZcjFm0l1Nu5czTTGOhznbqK/G7dV7pVfU/C2MBzWq8w+3CvQeLL0OBTGK4XAR8l/duwXXStq4M+fRD+XedGZA9gpqtzQRrRRJPCkDF1MqrFkTyAPhCPwK7dfy7eK3Hap2KIUG06EX5et9I2Mu+oD9x4/Bo1LJlLHAbGoYVevmK5ZK6Mql3ZZ0pDCfiDga4f5BoA8GCYfejutlcwgC5ighdEBE25XrWT+HO/phC5qB2LbYPqFxcvw/05Os3nB+tvgvjGcJ9c/7k9sJnz2e/yl9nPBPrw4zKUC6MXbmViJGb8UqwSC63PZiYvSdiieES+Sw9ID/wqHmg/bDo9YxmFe96rCGQmqIeryqmOWa3Scc1eAH3mer17ITVOuRm06EomACQpqZNR6Q9Q5a/bdZiaMoCDgQ107prtYrmMY1hCO+wozFILpy9eF9FIEKQodoqja5VcBgjJCN8t8+kgR/a6jp5DPTitdWzP1qKZlWUaqle5pAB9uF5jdoulHi7fumdst9gO4gw1s+R6SR8mtjG0Ret3CeDcp009cm1e23D3b7feadJyvh/fkj+zVoLMBBE1p6J5xAQs9IjxzBgYpHkut2fQoiuZAC1iJXUyKh0HPAAGUQA42DeekJi/+aBYLpUvh3g3/ONoZ0Nnrt4lTyafUbSF0eY0R9eqsN4wwOLZZSPo8Pnr5DptNbk2qEijO9QR3VhZpBTHAPRh8tmYIa31yt2HxnaL7WA3VTNLrpcEsY2hLd5+mIHzOurdxIm6ct1iTJs66Pvfjw8Dsggga0Gt2d8caSqQJR1Bx27xvgui88r5NSCvQNa0dIOZPodzTV8ARweDWT9vC6crQooBBnZPXZ0/sfEH/vRecoD87j4VrKLbTt0UfSMa6ZRfEw427BoC7t1rFKKJm3yoDks7dGEQi7rAzT436NCle5SLaxOrFswidAJNuV7D/rG+fah6BE+trdwmPfCzemDiyl3iixyFz9WK5eWHnz/FbOJe1vVpxRo2XtPDzpb9rNcqxy09ID0gPQAP1ChXlBZ6eFL1TsOoS5MaItqEGj8APgA8AD1zpspHlAMkKn0nu3FqZybyYVKTtZ6HtE5EHyBFiSpzHTuXo2q3ySZDOiELgXMjUuZcurDqKfq1bSDSUTd4HRESO6hLBFidySmbMIUYBjIQSFEFiESUrWDu7Cyf8IgWrNsp2lUt7Sje1f7sUYngqbWT277fA9VLOJDb1sNUu98s6linnIg2ocYPgA8AD9EpELsg2geWzv5z1xNq4MC86bH/tPbE6KOlc3Ht+o8u9Ji+hi7cCqTMlqlpK8tC4NwYS9Vi6pMQfZpWEemoGxm4YSKlDNcl3mOwOpu1fWEKMUx5loFAiur8zd7iWgvmtGbdvmfsg0OiXRUGvMZs1y/4TKIO+ox5IBLb07PYOdIhx60/Ltg7lUP/4OK79sx0qTBlDm1YUrBOoravn/sB0axA1nR0eHxzajvbk46yYPs6llOwZ2F1mKEgtOG6aCTahYaJlW0Qhs/Oun9uezTRPWyHYPvS7tU4jK1xBcYH0+23Z83CnBb6n7iOwSsPif34U8IuI81uX1ncSKZer/ZguRCnPIDZnrvM7oTah6wZ0qjqxmHAmIG6wWkHNvzFFB4LmXJxiMalY8kBJaSP7WCUesIFxlk4PQG1HMbsFRfHI3c+WwYLvbQHY+2V7RBBfcFpD0h/UMunRztTr1fpMyrel+8+IbrZyPo5ilzDQNYEysPUzj6X/cUXNrSDpEkPSA9ID/wqHhjepbkQMvfYfZhcBk/VXlZWK0shVo4HVuv0aTkVsh31n7KE5q3ZIdqgZm9M91ZMhR9fCKL3nriQ6lQqof1O102FwwGG68qJ1LZDKD0H6/Mp50Jb1A+umjxAy3aofRYKSblDHeAut7HUZtAUBn/e4oXjzLgea2KfthzJ02R0QUTefVxvGjlnlWDvRBsYfus6N6muygyqaRG9f//8Q8MgGb1nifu9D2ldk0XZv9IGjqy1H++uHTDkmhYPaiMifxAun9ClPkHfbkFIxA0RrVHta4vnHgii95vjwdGy/Hw/arrQLZvCFsN15URq23G+7FZptedCW9QPQgpBeXb6H5crwZT7GcB055Qe1HacO0G+AS8YtIXHs/SDQkqTIY25uK7RBj6MBgAAQABJREFUXBIzefVu0QZ//uD+OtYpy8ygJbTbYnJBzQ8xcf7/cZpiaJ4Vn3Hs2LHkPo8FESe3iLLzQ5IADJfJOfcVDJ7pzJPq9Y1ca0TfQN7iYGMhSFPQAClgSKu0MEtM0Pf7XkO4OkPrWeLcEGF/xtFE3ztBgvETUT5jD8aG53v57iPX3j0j6PuhBlBJ9TRsF9H1Grb/WdbP3npETiPWUWBgIGXMmDFahp3eMh11rVmMutQtHy39q3U6g3VrJq7axV+EmrQT/Gdsw18E4zrW1YI/v9v3afwKnoTgnHewMYF22CG7FTWqUJja1dD82KFvuyaDBXAcwcLiaA/Qh1mm6sUdaGzHOtR+wnIh+o7/dkk4TWUy0yI3CUlpmMRfSHNZw2Vomxq0wfusoArG/wG0G81ftm10tGocWgwXfesKpUPMtPuMNYJpC2PBl2J9ZuMay9ehFEVjuynXi3ZRaUjttKzRSwi1BnBaiSL6/i//38/Buj8AwRdWjKTM/OUfE+bPNQT5W40kX19fcnAIYSGIiRPLc0gPSA/8FB6YNGkSzZs1g67udIuS8QZziidkDRD9AsNmHlsbPekDnCQo+CVdvHFHiLYj6qbU3D1/9Ybr6V6LFEpTn1fUBg3ph5RF6hLYNM+wCPvTF6/o3JVbhNRInM+UvlELeIOZPG8FPCQzBoIgdjEmto5aPwjLg+DFjqOXOM+vYDYVW9LgYSOoW7du0XI5NpmtqUUFB+rV2Cla+kenqIc7xKmPqCsDw2aerBn1nhPQ5smL13Tx9gNKzZFoRN0UwIVJ5WB+IYXSlHsGfakZJp8tqriKSOLJJUM57fQNnb9xT4wH5zOlb9TG3bzPdaTME2DG9xkiecYm5FHrF8TPGiB4yZnJkv8fajg71Mb2s28bt2wH7Th7h65cvaZ3KeqRvihI79Q9S9EcYYsZdffjITs/6/UZGpC4IqlguO9H1pGyqVtDaGpfZnyjlLa3irB5RNcbYQeyQYx5wPdmIA1nyl9EmCDGjnqB9QfO0KLtRygVf9ENaF6VwBLVYuQiCuDUgTwh6QYAc9uO+FKfax6Unb/4SuezFWP++u1fQW3cYdIKsrIwpypFc5PXycu03vsMbTvqK2pZ0TYeF9DvP3uVunChcJn8OciSv3TfM5h8xRMLvWd5iHUXBnn4Yt114iL15BxwfJE1Zg0aNUN6RsNhCwizs00rF6HkTMe99/RlWrjtsIhMbpvkKg4z5XrV+v/RbQCd1ThNYzOnbQAMD21dXcwEgkYZgC8n0zPHFOD70WuRx0sPSA9ID0TWA6nMkmujYcaOhZRC2lQFwuxOmSIZ4RXVlsY8hRBzj0y/AKIAcHhFZMWZhVRa3PRAyuRJtNEwYyO0ME9OFR2Th9ltzsfiFdUG4hWIuUfGAESRfopXRFaMWUh/d1MHfb+7V+T1/zYeAGUvbHCratSwvKbWABE120aDaOdxPwH6TnLqIQBfg3KFaNHAVqI9/mD2aw5H5s5w7rkC+pSdLhwpRBQPKS3LPI9xBG6tYJPaO6M3Fc6lqWetwTn1EBC9yFFEgD7FkrOO076ZfbSsluv2nSKAyJFLtnFk0THM7BeiaAPmbxKR8WXDXKhmyXyiq4EtqlJlLmg+xOfwuXxbiLCacr3KOHTfEekc7a5JO9LdbrjcmnP8nbleT81QuIw0ThC5uLNPEC1FdBU1fkjHkCY9ID0gPSA9ID0gPSA9ID0QPR5QB316CZ/Rc+KY7BU5x2lZvD0NR/ikSQ/oekChc5694QCl51nWovZZRFpk4JbJWnCV3zYT7WcQlildaGoKau0UrZovIUy0uv12b1hRW68HBixYrsyWWsCH9ZJ5swvQd4eLinWtmVNRLeDD9kYc3RuzbCfdf/qCHj57pbcP+wHIkK6YnlMVqhcPBVzJGDw25vRTMGbuYPrlovZZtboz4V0v+jQ0pIJA0yciK8XXZMxAnYxUaxhqI5HaCfv4+SsdYWBaliOe0qQHpAekB6QHoscDSJdLl9qc0qSUMjnR42HZa2Q8gKTCdKmSUxoz/ZKvyPQh20bOA+qgL3J9xPnWyFm+PEejFxPnBysHGKMecGXK3L0MZi75PyDnPjMoaaIEImrnzKmISPeEIQ0CKYhD3LaQ7817TGf8IlyaX6QrZ2AWLMWUQmTkzesa0pdh//E/XVPTpcnB6Y8AffeCgsOAvtucyw4Lev6GSVGG63bFqakfxTrGDzPlekVDgz9lOCU1aOd0g61hV3VrB3X3gpSmxajFQicT5C2omYRfkLqKFNdp6/aKXHxjUULdvuSy9ID0gPSA9EDkPYBJztt7l0f+QHmE9EA0eAD343WPCdHQs+zSmAfUQZ/mWdTYMdG+fYzHMXrOpC7T21aM9nNF5wmuBj4ThbHZmDFUzUBgc/fJawp89ppSJ0tEWS3NWOYinlpTIaB5hYXgHz1/KyKWeTNbhCm6VTsQApV4qVl8rrMy9pCu1v5X3GbH0Tff5SNEHR8iWSeYlhhpnXitYTao7VwLd4mpjEt3nii0GcHsWbdsAcGo+fzNO0LEzNAAZtRYORWWK8P2+lRKGgIWwzaoAYQZtsW2t1wLCEuVIomIVIoVgz+F7TUppaZcr9rYsc1YcbTBqVRXUTCO+x1i7KiTVAxRTC+uR9zCtX77z1w1mhqqtJfv0gPSA9IDv4MHIKGwbMtellBwpP+zdxXgTWRd9O6/uBV3d4r74u7utrg7LCyLu7u7y6JFihaKu1OkSEtb3G1x/+956YQkTdqUtmlK7+VrM/PmzZs3Z4Z0zlw5lS3IKIQVHC57+KiiaBnNCL+/8X0xae5cIMRuyVDY5qTbNXJMn4rSJPdbE8LSfhCP/49z57NlSGX277Sl/cJTO+QSlu04SpVZzqCSBcmEsIDH8//eEiKpUDPAkr4j0mMue92l+xxFBY9jroypjJ6LkYLy7bsuKsnSOeOZL4LvM5phH4z98bNlHUD0/Y3/aXND1NinL7poKMNxsAyx9+B4XjdP+owdD6bHDvH1zSzeDs2+sEz6Xr79QGUGrqJiWVPSun90gpCGwJ3mSpjd5rno9QixDRVKRzQpQY1KGCc/n2Ctwi5zdylMtDHQd0GXylQqR2qtyexnr0WutJolL8zZkEbFqGvV/OY2hZu2/UxGQLRRgRM/+E+6lwusQDfuMFfDvHbrIU1lLxSqbbavUYLGc56eZjPWu2qLwfp547bOc2c4qMddXVuGFDrpEsNtkI+AoSKVYc4h2h48fcniovdVlS2sW3O+IIamhj8CvWasNW32s96lbhmqx7mPpubz4JlqypY2mekmysvhsyB9CPMUEwQEAUFAECDyZG07kD5UvAzLpO8Fvxwt2rgnQfR986xhRpf2zoPHlLlya6M2bQUVQZ8e36CtGn3i73FLlo7Yd+ICjfmrlVWi62t2HKApS51Y/N1HjRWLhcnb1K1EI1gWQ8wYgZssSr6cSR9qDYRl0jf53138Yt6V9s7sw5FEaYxPktegPdhx3DJVCV3biMriiwa2pjL5HFWTY6P+9ILJo38295/memF5w36Ltx5W0haGbabLKCJ4cdVI1QytwlUux027qHXIZXRvEHSdTvOkz+whpdFaBOClhObgZy4la84ev3pLjSduphdvPpBjivhM3FLRsav3lIxE1/kulJQ1DrUqobeY/DaasIm9OZ+oRsGMVDhLcjpx7R5tYmLcZuZ2OjO5FaGqqCXzfvSSIrFHLx9rH5oatAXDu3XkAilPXr6mmxvGUWz+I4A3KRW5ehQ8UhArRUlhd58HCqbyBbPp4UJFT8gqhIRBbLRzndKqwAnG33XikipHnDFlIkI1LVNDRVG8aQJZ9GRymJ51/WD4w9hl0ipVJXTxgFaqQqk152s6PtYhUwHyGJA95pLL5gz5jDAX9upBR1CTbEDbGl/BV3OEENvFBAFBQBAQBMIeAvDG9Rwzl1+mmvde3Lyj+9sKzcLEnGtoaPibZsmmLd+kCJ+l7abtV2/epo5Dp7HH5js1rlqKtQVj0iZX1pBmEhiJ038GdfrTdBdZD8MI4NkHkUOoDm7JUGOg/oBZrJv8gWqVyENFuB4BSKATP3+1GrGIzq8Yruo7ZEmVRGkrmxsHz4gww+cZw37xHKKTpecaSEc85WdPyExo5sVa0bgf85shqMkMUoa0/j/zGSqk7wt0Ne49ozSJY1sMZ/yZkzHcB/pmNx++oMSxo3OeVmTDTSG2DH2+vxbuIS8mWhBzt2Q7zngqwgdx980D6um79eR9V+y/ROuOuOtJ35QtJxXha18xD41qWlL1bV0uF71lvZ3d571pzwVvql9U90ZCP5DBAkgfiKXzoB8eKoPN4X6xWtFcNJ9lDWr9M0MJdSL/Ezl++M+MwijQismXOTV7/B6ogig+nFP35MVrcjpwVhExAIiKlPCEmXuT9DMA41jV+kyjGkVz06u377hC6D41zD9//giLNBw3AYck9GxYXkkhoCJo17plVW6i04EztO/sNfWlA7kEmDXnazi2tgxZiac7p2urgf4sliujwhK5kzmaDqLqfG7AF3IWwDoxC9ijgI2YICAICALhBYEvHFlyzfsOpUuRxKLWXVCxgKftGb+MS5cyib44WVDHDGh/6PN1GTGTbt65T9BRs2RevqRvLIu7VyrmN0LE3H7n3T1pyMzl5jZZbGs3ZKrSiJ0xqAu1ql1B9WtTrxIVbtSDJixez6Lx1UNEEsPihOxgA6KAkPoDT1NI2YvXb+k5OznSJrNeDzuoc2k7ejHtYcIXkHduEnsBQfg68gv2sZ10z+FtOZoLL7hd+EX7HshgcfrJTq64bs428jNgyxELqXnlIlSRw2DNWV2OesKPqSGMs3TncQTdzIUDWuo3e3FBPrwg3zHlL31bcC8EmfStPniFei7aQ/kzJKGtgxoYzW/+rnM0+N9D9GfJbDSxVVkWNn9M452O0RH3O/Tmw2dVsh25afWLZiEQGUuWpeNc+sTV/m7O72zUpeH4TXTg8i06ObElpUqo84BAOH3gioOKOL3nSoHIo8qROiGNbV6a5+g3bM1owCCuQCMtPlcIxc+rtx/JnXPwzBnE4WEgY4aGecLwlkIzVyZ1kSP+Tr1qGuuzDW1cgj1/mSh7Kr/hftq+bxnjRy/fUnEOMRUzj8BA1otDzPZ61tGDcLpm8PQtZHkGeP4gtA6ygiqZ0NCDQQD06Lz+1HLUIjrEgu3/sqwCSB/ut99/518G9rtvwRaDJrWIgi/mDFp824+5KZKJ7SCiQ1pXp7ostK7ZbzymVggGbb0bV1DVMCfwF1nfORu0blwhNAPN/rupvmqnNeer3zkYF/DWdvXw9vTX9DW0++QVWmkQwoA3YXN4jvC0igkCgoAgEBYRyFq1Dd199JQ2zRhKpf/48TwDYpepUkt6xm/13bcvVOGaF67dpFFz/6VDpy8Rctoi8N+Z3FnSU+MqpahdgyoWTz9NmaZMXj7TvUNrjPrU7jqU9nKoo9vmuZQ6mS6/7fCZS9R15CzyuKXzRkDnr17F4jT2r9Z+ROGNBguGFfxtSsBRKfh5xQ/+Wkil6dA3b+s8fZnM5PqZ9sX62/cfqHm/8RQjahRqVrMcTV+x2Vw3o7bnr17TORagj88acC14H82QX1ggeyY6ePoibd1/glrUCnronDa2LT9X7TpO3SavpAKOaf0QlLkb99GAuU7UjInJlB6N1TPCKBbt3sbVvPFyGYYXrqhaPrJDHS4SZ+xt1c5j/uYD1G/2ehrerjZ15hQOzU67e1HFHpOoAVcJn92nmdZMR9xuUPfJ/6rIIzTGjRVdpX2MYmmm4MhL0x/IzAL+L8E7B0OuHoidOYO2MZ6t+jSpZLR5ZPvaVLtkHsqePrlRu+EK8v96cihmZj7O+K7GvMewn6XlkUuc1TPlvL7NOfVG9/8VOs0ouFcihKuYB5n0VcqXjnotdqXjHHL44MUbSsLSCJo5Hb/Opdm/UbUCGZW3quW0rYRwxeypElClvOk5R+0lOZ/0oH88HxCKnVgiJ++ZFX834zn7woUhML5mIEu1R2+g0x4PFKEqlysN3eIiKThGzVHr6d/eNdmDlkrrHuyfBTMl0xNfEFsc05zV+CMTjXM6TmsOu1Njzt/LxsTt2t2ntNjVTXWvlDed+gRpe6hIWwoVKrqGc/Oucr+kcWNSac7la1DMsocPAwBfWPQoEWnOjrNqXwf2euZj8lu9QAabvfVTk7DTX9DEm9X7TxretqbSs8OXAKpsImRS+3KCJ20/x4RfYNL3nKULcmdMqYTbcUonFwykG1w9Uwu7vLF2jJ8zhWjoqz2z/LT3YFkH/JgaBN1n9fqTznvcVsLwmAsqiBra+aVDDVdVEnH/5lWpQ61SSvcP+XHI/9NCPbXO1pyv1je4P1PwH5T1Izup+Pkbtx+qN8Cpk8SnTCkTM1E2T4CDew4yniAgCAgCIYFAzTJFaOryjbRpzxEj0rf/lBs9fPqCCuVyVITvNUdvNOk9hnzuPaKcmdJSlZIFyfvuQxVu2JOLt2RMk5zz33KaneL7jx+VHqvpxi9f+FmIyaVmuw6foXo9RqgXg02rlyUHDiFzOXKG5q7ZRtfZs7ht7kita4h84lx3L9JVZTzE5LNS2/5mjwNPYMQIEeiKpw8t2ehCj5+9VOdfp3xRPXk13LHX2HkEorh8XB968OS54SaLy56+pLdE/px+CrekT5VMkb4bPnct7m/vGyoXyUE9pv5PFaGD986wSjhSUPCMXKN4HnUaU7g+wcRVu/iZMDI1qVCIn1tiqTBI5NQj3HCXBa8Wah1gHE1mScME/gm0o0ibZiBTDQbOJjhBcAzUGsCLXujzXue/+1smdNe6hsjnnD7N9eNWZp3ioxc99OvaAggWJLegr/yZHUr/8ktod05fQQhl2QKOLJP1h9bVzyd4RsfxywgVyVcMbadSa/x08qcBhHj6OlcqnD290XG8ObQTFiNqZJrF+s+YD7DL75iGavL1g9xKcJgf0oeBf/iZAj4ECoqUz52Wtp32oG2nPKhtBZ0wNCpSnmUyh7yx4llT0F43H0X46hbOTHM7/whTi89VK2czIUFfS6Qv4FnoeqxlEgXCB4+Zy7BG+of21TmuEHLlpjmfskj64IUcve5ogIdqVjo7Vc6XPsB+/nXIyAS3U+W86rxL9l+pCrig8AsMhK9crrRq2fvRC/X5kr2GpfqvoMe+HkI0Qti6X73C1L1aAdXH3C/vhzrSt3TvRePNO88pz+yav2uRA18/a027L4Lr5rP2uLboB1KlSTSYOx5ICQqOmBqqWuJtT3Abjoew0sAa3qghFDMgC+h8A9o/KNvTMNHDj5ggIAgIAvaKgO5ZSPurF/AsG1crrUgfvEZT+3fkF1m6issbdh1SO/9ZXechOXbeXRG+BpVK0OLRvfUDwysGz9Wpi9ctkj59Z38WPvNL8n8mLlAP6CvG9aWaZYuo3gM6NKayLfvQ/pNudOz8FSqcO6vZUQZNW6pImNmNvo3R2NO2cnxf/7pYtQ3hncj3a/jXaKP+o+etVhiCsGq2weUQrXB2VTl5dcoXo5mrtmib/P284aPzdCYykUzCThlTJ1P7Pnmue1bydyCDjXjwD8nnIHXvWXnrIScMdQicD5+nLfyDF7+wWw+fqbST5AnjKHKDNldfrd3D8/urugVog+cua6MBqi/Wg2Igh33ZI4j0qqVD2ujJZj9+IV2h+0RCFW/kzRViwmPOhrCm8BXfPDlz29GGSpfLBre1tNmqduTOwUDcirYbTYa1CCLM+x8NbFmdejbShQGbDrhi5zF1HpU4pLM4k8bAGPDpxHUkcP+M8Q0p1fbX5oTiL6YGL+76MZ0DFQ0FQm7uHvVD+mLEiEFvOdwtMAaPE0if86kbetK3+cQNNURD3oYD506XmHYNbUgpfcMwsfEhewbxA/v4+cdbKtXwE79Q3ATWokwOPeHDev1iWaj/iv106Mod9px8NJvj95yLr7i6eaO7v4Y8vKAaCN5dll7QDCGpmnncf648dJmTx2c5B90XEQhpivixaFDDolQoU3I6fOW28hSOWHOE8qZLQpbmdNOXNKbj3MmRTUtSmkRxaNfZm7Rwz3lFjv/mYjPzu1TRDh3gJzyPMNwjYoKAICAICAKCwK+KgHoW8kdOwPS8s7J0ADx3bte96AjntJXIn0OFYoIEohIlvFewvFkz0P5lEyhVskT6IeC10jxXCN8MiuH4qPyZLFF8ql76R540qlU2rlqaBkxdQs77TlgkfUeZEEIKwT+LxucTVMODrxd7OGG9WtalBpVL0kPGAcRuPRPlriNmqZDXbBlS0+37j1WoaqqkiWhy3w6BOvSt+49Uf4cY0f3sl5wxgr1++97PNv8a4K0Nyecgde+xN8paa1S+oI70scdOI32bOOcM1rh8If3D/ziuPq5JJ2EbCtUhdQWpJlgOKpl187hDqPwJjxnqB2gWiyOqGpX/gwbN20hbj1ywSPqOXfSkUxwy6p9FixLJv81WbUPuHAzzTZkoLg1tU5MK50hPB89fpzHLttHQhZtVug5qERga8lNRERROl1EcDhtYW7/3lCLjwCIXR4wZGnCDpWNZMOQYoir7juMXaf6mAwqTXlNXq6qihvv4t/yGQ6HN3aN+SF/ixInpKYewfeS3RQh1s8bK5kxN8WJGJUgLoDJlQq5Ys+m47kujYXHd2yRsf8yhikM5x++81yO68+Q/+sDHCIrhBjU0zwc6zxh0/iZzARRDQ/VL2CP2lpkr7AIv450lXQ13Mbus6aWZ3WhlI4qzOJ+8QVk4p280k7EiWVLQZdb0G7vhqCrO0mH2TjowuimT7x9f/lPbltcXdymQMakqFgPP5lb2rloiffUKZ1GkMGvK+PoKn12q5mPPbBoq8s8y2sJzmN6uArunrbvO95+/5j9eUcjBwcHKM5VugUEAX4xJOL7eklZjYMaSvoKAICAICAI/jwCehZ6//I/e8cMTPFvWWJNqZRTpQ2VIkL49R8/RqzdvqRHn6sWMHk0NET+OAz3iMMYBU5bQOXcPusWEBgUdgmLfDeKztFDFh0+fkyPnGRraa/ZswLDNku2cP9pPGJ+lvkFt3z53hArvzMNEGAbiXKaQLloMxM/J5TBlSZtCyTO84YrZyJfUcLT22NF9r93j56/87PLal9THdbC+kjkK47z/8JFwf4SUJUmalO490T3PWnOMcgWyqRQQFJV7zGGaCTlsE1UoYY0q/KEfAlFJi7cdpv6zN3C+3WMjD5e+UyAWDO877ObpS+IROpmjySCjkVDxHPaIt1mybZN6qAqrlrYHVzvCOzWb0bupPjqqYNZ0StdvNTSaD58jU9K3+dA5lZ6C+gogZ4Ex8JWpa3erXXpxDQZTq1+2gCKaWbnGASK2YN1SlKMKXDW+YOsRStZqFudN+lfV1nDM+09fUZKkfp1Ufp72c+TIoXJtUIQkd1rrbmrkPtXmsM0FLudp+2lPpU136dYTKpQ5GXuXYqt5wFtVZuBKfpNAlD5JHKpVKJMSI4eHbdZ23RsJwwlbs/zwxVujbm98iV1elidA7pqR+RJ2P+2+nZB4bKuHbdcLPuqo3avlV1hhBSGpQxsVV6TvMmMH8gzpBlhizpNEiKyhIaQWpM/zgeUvb+yvjWG4b8Zk8VTY7d2nr/VeRcPtlpbdvLmykGMW/VsjS/3Ccvvwxc7qpcf0no1tfhq9OJwAP8FhV7zvsVRHBL0+n7kx8cXs7nNfaRMib9AwF8CwP94YIb48ZrQoLHKa1HCT1ctKJJVDKpBgrQmRmu6MfAHoIt7j/Be85UqdOL6fXD/MxZIhuV9MEBAEBIHgQADPQnhQu3jdm/7IlcWqIetVKk79piyirfuO0+R/2tN6DkmENa1RVr8/irhAtw5jZ+CcsnoVilMGDjN8xgQTUgQ/Yw8e/3gOeOPrtUrA5LIw59aZM+TcWbLI/kglWNrnZ9oRAVYwp3lcQfxA+m7cuku3HzyhE25X1SE6DZ+uP9RTX3mgKUs3Kj3D4V2bU9VSPwiO1jFR/Dhq0ceXkGjt+HzOxXVgIOLW2oWrnqor7o+Qshw5ctI2p9VWD49ncFSJnLdpv/KkIVftItcgQN6YpuOLwVqOXKiKuESNHJGK5MhAmfnvec4MKZQHDs8DgbUHTCwMDZF0sPixY7A3T1ebwnA7lgty0RhLZqt7LxmHvMKSxHfQh75qc6rwRzYC6fPgWg2mNmW1i2pqyoVxAms7jl2k6/x88wefv1a8xXAMeEfNyTJk4mem5LztDv8fR96ftc9gbp53qWlrv5F8fkhf5syZKUniRCoHz1rSh4kjjBOkbyuHeD79T/c2qZGvlw/bp289pQhfm/K5VCVNtMFmbde9jdCtmf+N9MVP7FbFl6QWo4qYYa1QibYXCCY08hBuWpOLpRga5BQ+cbJzAq6sac6QU9hn6V5zm4zaOlXOR3WY4AbFUMwGlo0L2hhaOibDMaNGUkVvPnDl0eS+pA8lZBH+auiRg9cUloBzIi0ZPJ7QpRlQv4hy32v9cH0ePNeF1aIojLW27/Idqt/M+M2htfuGlX4ow+vN+i2hQfqCCyPo4JXoNI5K5MpEThwHbmpfOHH575nraCmLr+L/kWY1i+emufwmKWpkXfjERw736M+VvxYaxJgj8btV1WLUt2llbTerPievcWGR1L20d3pv9TbLdCckNo9ZsV1VUtW2Ie9vHoueFsyqy3HFl142k7eHWl/8EXu4baq2Kp+CgCAgCAQJgXTp0lGK5Mlpz7GzVpO+hHFjU/ki+WjnoVO07+QF2n7gJKVMkpCK58uun8vkJU7qWaZDw6o0iYmhZtYQPjz/fP5iHIaHl2Vedx9ow1BalmaAoXiLYc4g2h48fkaXPW/pc9nQZmpdhs8gEFP/LEa0qLRr4Rj/ugS4DQVedh0+rYrelC2kKzSi7eR2TRfilzxRAlV5OkfGNNom/adGEBCuGDliRIpiIewvIxNrGEJeTQ2hrLD82Y3D+Ez7Ga7vOXaOMmbIQEnZGxdSVrp0aZowYYLy9pkjAuaO25hDBkH6trA3CtrDsCYGMkjXuVonqnbCU3R22TA9wUDIYq9pq80NqW/TSohA4sHQoAtsaJBmgDlwKPGC/q0MNxGKzOBltDnCo3XsNmmlCrnU1s19xmCHzvbJf5nbZHWbVqX0NadaIarR0HuGCpqwBLGNn41BohEKi3BQkOrA2spdx9QuzSqZJ4zDF21RobeDW9UwetkNLb/7jB0sqe8LDLXiz6+bHL7qzYWiypTR5REbdvVD+rCxdp26tGHzWiUToJEsw53MLUN6IRN7kI6436Wrd55RtMgRqDqLiWuGNhg8VJoh5NLp2DVt1eJnygQOdIXDH6/dfaZCItFxxYFLfvIAIRtxhskbdPAMSd9pj/tUbcQ6ysCk6jjLO5gzSEi4335qbpNRm0a2jBoDuYKwzvNeD2n9kaucp1dMv/euc16K8MVg4occPhA2VDVFnt+Os55Uu9APson8SVh+DvXUDN5UfAFmS5lA5TQeuHSLLng/Yo9tIlVBVeu34ehV7vddVTiNZeoR1TqZfJ5hDL0fPKPatWubbJFVe0LgGYdm95qxVnnvLM2r/xwnDvE4wl8gsall1aIqjHuW0z7afOi8KkrTr5nu7dDfs9bTMiaG+PJrzDkDtx89ZymJi0oPEF98+CMTkOFFjesZiKT6TU7W9kVVr0GcwI1Y/WaVChPCXDcd1JHv2v1m0tklQ1RZaa97T9QukNNIHC+Wtrv6NPzSNtogK4KAICAI/CQCderWpTUb1tLAjk30L5wDGqoJ582B9HUYMo1fYn0kFHAxfI5yZ9IFq2igS4ccsXU7DwY0NKVKmpAu3fAhdxYbRygkbOmm3YQXdJrl4LzCKPzi7rr3XSXXAG8iDN/FHYdNZxJ7jpaN/ZvHSqTtYvTpweTosodujkYbDFYsRWsYdAlwEfILILoHT11UpDgSEzcYwmlRARVWJE9WVfH0+Nrpat3w14J1O6jHmDnUu1Vd6ta0ln7To2cv6B7r6cJ7l5LxypstI6VlHUQUjQHG9bmADszz1n3afeSswqr0H7n1+/u38JVfmK7fdZiatw5aIRH/joFtpUqVoticRrPW9ST91ahiQN3VduSIIXzzMFeHRAQP/p7WZNFxza766F4MZOOoHkMiCT1fS7IG2r4pfbX8Lty4rTWpyuKrd5/Ur2MBEUP4W4zq3J7sKUufQneP4d7rMnGFqhS6eGBri9qACDkFMfTPUHk0qIYoooxcNRzz3MY5hoZaels4LxJWgEM9DW3/WZ23GZVQDf8/G/ZBaO1dDsuFpzNlonj6TSDWh1naCxGF1Yr9yHXUd+CFffycdJ7xzcMFA7Vqq9i+zvWUeq5H2CeqeVpja1lCLGGCBFS4cGE/3c2Svvbt29OsWbNUYROtkqSfPc00wMM2fM1hesKepIbFHSmGwZuXvOkT03UWZB/COX0+7Ol6yrl1yPvz8M3DO8n5gPC2ITTT1LAvSF+zKc5K08+Lq1IiH83U/mItO8ggbGSpCLi7VZ7e0/9opq83sU8dvwBoY5TIlpIeLO+hrYboJyp3tp25naZtPc3FZW5ThTzpyJOJ3a7zurdbf9UoqG6q3/mt3sAGRak5n3e3ebvpEpM6VENFJVRIZKRlz2bTUro3iCB7pQesVPO+NKMtJWEPXnMuaHOBBd/bzdxBrcrdp4zJ4tIVDh3VqnkONiCcAZ3wnF3nKVfOHFSgQIGAutr1dni5UDYYIQ+aRyu4J4w3rzeZoMArBgJjCzvGFbG6sW4MiBHuBUuGil7w3MWJGY12Tu5JkEyAFWevYEkWC13LXzAgfei3wvfNlCt757R+CPMs1G4Ul4hercoI+5dU3XbMUp1IKus0+Wdr955Wm1dx+ePSebOo5REsoVGw7Uj+Un6kCOOAFlVJq241pmMdFefu35iyTRAQBASBoCLQrl07mjp1qvLYmQsdNDd+5RIFKDZ72bTCLE24qqehwat01es25/QtJoQcPubKkRs4d03LxUN1z9Ms3WDO8mfLpEhfw79GqTzBm0zQNvoSJK0/vI0ojAIdwCrtB1J3JkQx+WFxA5OVvSfOU/aMqalaqUJadz+fLkH04PkZ0EJDCfZ+pk+ZVHkVy7b8h+pW0L0AX+m8V0lc5GcNvWpmwjUtDKdvnrdmO41buFbp7s0a3FW192vXkNoOmkLdRs1SoaLQLFy0YaeqHDptQDdOObAuNcBp9xG+rs+obduQJX2RI0emlq1a0cJVy6lznTJ6nV39SVpYQIEQVMB88uI1NeZcPsOUB5BCFGw5c9Wb/pm1ToUJopIm/uZrNn/zQWpexa8nKk+m1KoLKoRCrgAvjLcfvajX4dP2T8BaiKh6iWIo1f+eRl3rleUczCjkxNIR+5g0QZO3apGcWnc/nyEpSm54MFRHH8L6y02GzKPOE1aoUNhkCeIyKb2ipC/gsYTouqEdOKtzUJnm+Rn2gZ7hhFU7lS7iDJbd0uzsNR9FkiH3ZemZsCVHUJ2fvIpaj1pMyM2ElNVl9iwiBxM2jIvNWGPQnV6y/Si16dCZ9T/9Ujy/LTxq9uzZqXr1ajRq/XGlB2dJRNp0AvVYZH3E2sMqjNMwtBP9BjUoRshVgzeqz5K9alcQvINjmlKbGdvpMOvaQYcObdD3wI9mA+sXZcLzRHnHoG8XN0YUasL6dq84fhgETxO/jsNfbJsH1KP2s3aofDfkvMEgKzHyzxIqj1AbMzQ/kc/4ipNax6w/poraoLANDBWBIMLeuUpe/fSqsDzE7I4VOfR0H83Y9iMU9g/WBJzH0heRmNyaGmQEYCCEEImftPkEzdule3uBduCHwjBlWcfQGnNjb+EWrozq5ORkTXeb9cnZdLB6q7JhdGcqZSBTAGLn2HggIY/s4orh6gsKwupjlvN9xm9bEC4LrPEl2JBFRdtWL25xzunr9VV/GG5tmmjUp+6A2YQ3P/BCaYToPSfj92Mv2hp+OwedPLwNyskCnxO4YhZK7oak4f8LPHL4ecXJ+leYnJkzzBmksGnFwvp5ox++jBYPaMWaO1/Vbudv3FKhnwit1M4PGxzTJFVf3Je5rDLIM/azZEokNbXuJY4lkVRUDHNjPUIIxGqED+PhHq7HOQqjlm5Tby3Rpnn6Mvi+PUSbmCAgCAgCIYVAlixZqE6dOjRs9irlmcN3WkCGsMM6FYorUlEsbzY/enPDujVXeYIIoYSnCgaCc4K9Wc37jle6cSu37tXn4xlqmA7t2kwVijnLYuMgdSAvECmHADpy4LRntT6t66tiLOMWrqM+LN+gGcJM5w3rYTWJ0PYLiU8Ux9k4Y4jyPh7laqc4J81qlStCMwZ2sehR0frh8/f/GV8TeFNg2nMQllG1FCLtg6cvo3lrt6OJPWGR6e/W9ai5gWC72mDhF6qqjmQpiT+bNKE0aax7drIwlFXNvXr1otmzZ9FC50NGguj+7dyAi4Gg8iQ8a9DIMzT8HR/buR71n7OB5m7crzbBaza8XS31Ahzi631mrlXC5L/5Yqjde0kTxKaZrGfck1/2/utyQu2LZ5rpfzVhYfhV+vsOG3o3qajuPZAfyDdoBrIEAXctLFdrD63PqlxddH7fFtRr+hqatnaPfhqQk1jYvyVFMihkCU/d8cu6XM78WSxfe+3e0ziJNuiRizonFTT3LFnzKkX5ef09TVi5g+Zw5JVmKOoyk4vNlOOCLtbYbKe99JafP3v0MO/E+o1vju/mBrp27RrlyJGdBrIWXOcq+cx1CXQbHjZRDATFW3JxyCE0+mDIK0IIY6I40RVBszQwxN/vs9QBQknx0G7JcByM53n/BetaRGEimdhmRVoszclc+zu+MO7swcR5gbAi7BNVTs0Zcvzg7XzDLD4tSy+kSGAc3mZuH60NYbQQf3/x5gOlTRyHi+s4GP0n1fqZ+4TwZvkhayhW0rR0+MhRc12CtS1Z0iTUpQbfc/x2KyAbNH8TTV/vSi34jcw0g8IrrqfdqU7/WSph1mXKX+oNS9H2o8mHvVcIP6hcOIfK29vC4YwgHc7ju+ljtHM1G6K2aWLqyWv0Uvfn/a2TjaZTq+8MfnN1jdyWD1OkCP+NyveYxKV1vSkrE6Py/B/0FucGbuY3Y6j4unZkRyNiajRYMK8gvKNq72lUNp+jn5w+DbNdU3oymf2mSgGDrGZnXPAGLoKvttTCrYf4y3CtCrecwV/shtZ+3DImtqdozt9NrQrxxL6Ve01hkVRPPzl9iPOfxMnRCAPpULOk4WGo6bAF5MyhF22qFaNJ3RpSk6HzyYWFX5dyeMhpflv5mN9mZuT9anEIiyExNRrEwgpi3vO0GEbnz7MHO5f5cAsLu0qzICAIhCMEPDw81Ivw/u0acChhvWA5c4QJXrh6k55y8ZY8jhkIGn0wRIkgLDMxex3ixIph8VjIzbv7iIvtZUnPb/ONSY/hTiA6bkwuUW0yQ+rkqnCM4XZ7WUaoJXLuHDgCJXPalP6ee1DmjDBYeFFBZiCdoYWUWjPmyDmraNpKZ3J3d6dUqVJZs0uQ+wwaNIimTJpIJxcNohScUhEc9oirmF7kIh94QYy/+xqxe86pIajcj5eqlsIXP3H+G6J9EsSJaRQiam5e6qU7v2x/z8+sGFML9TTXNzTb8AwIjxqqi6Iip2FYZmjMC/O4yhi/4Bc5SGVJw1Fp2jUKaD54rincdhQNGjyE+vfvb7a7WU8feqKgy5AhQ2n4sKFUOEtyqyt5mj2KbyPeQuVhvT5TAzvOlPxH/Kvpdm09CVexxE9AhuNA5w4/9mzRuPhEvgxJrZoiiriYC321ZmcUh8lv5XFMx4Pn1uPBSzq/c7HpplBfb1SuoCJ924650WQmBdp/DCdffZomvjlnJ9hVDsJXnz1HC/q10M8bX3ozN+xVBOJnEnP1A/HCao6hBuHLmT4F7Z3xt14nsqzLceo0cSVNWbPbIumDF3LEkq2Gw5ldbslhF1UKWw6NMLuTSSO+FGDTuHDKzuOXjLYiH2DD6E6UImFcSs9fNjCEZBgaPIHn2AsI03IEDLcHdhkVQyd2beBnN5A7XFcY3sjBEN4JcVOQP0Mbu2IHX/8Gft5sGvaRZUFAEBAEfgaBDFy0Y/jw4TRw4ACVe1Ygx4/c+p8ZD/tAuB25ZqYG71SWdJajJ7T+SRLGI/wEZJAiKFXQ/l9qpU+VlPAT0gYvU1H2vgbWDnPRGXhNEeprK8KHOQ4cOJCcNmygVhzyt4PTMZC2FFRLxC8YyhXwW600rkMMwo9/Bu8XIqSsMXioShpEYFmzT2j0QQ5iPn+8d7aeE6qkF/AtXheYY+OFRsuRixV3+/vvvy3uapH0YY9+/frRwQMHqNnUbbRjUP1AeZcsHlE2hBkEVu6/pOQ0li9fTpkyBb5a0c+caBTWAYRX0xpDqCE8dxf5Lc1Rjk0vzuEDeBO1/agbhytEVB4gjIPEWNdpvSlVkh9/JFGeWCtRjH2CahrRbMWFUQy/mBE+2pfDKSD6+R+77s3Fc6P4yp5TVwKcQvGcfh8SAtzJpIOWFwfCV6dkXg5v1eVojuQwSpDPDuOW03bWykFYJ8gfKma2GLmI/uRQEXgz4QFEnh0sUsSg/wEymZ4KbxjGVay0wi89GpRTZBnHRrliWM+G5RWBf8hvLFdxqMkG1iPqzmEn+GOUNU0y0yHNrn/0vcdwv4kJAoKAIOAfAniIOnTwIDXqPYZcF4+jNMn9vrz2b3/ZFnYRQK7ln33GU82aNalrV12OoK3OBrl9Ths3Uv78+agrV7acw+GRlrxwtpqTHMf+EECEACKwvB8+p3Pn9lJE36JI5mbqL+nDW6f1/JaheLGiVGvsRnL6pxalSuj3DYG5gaUtbCOA/Mq/Fu+lwYMHU9OmTW12MnHjxiWQIGsNFSQvMqlCmWKQPoR2Ii4ase14YwKLx2+vUIZ3IIeDIlftNv/HgEs/KGYaFI1KVbBhrPM38V8Xo6G1yliPWVfIHOkryeV/H26bYrSPuRVDMmluuzVtb3x1dKAVgxw+zaAFk4NzJI9c9OBcyDcsDhpDyTc0G7FQiYJu8q1ohf4ZUyZSxA+eweC0lbuO02DOR8D1RxGccZ3q6Yk7joMwXISfgsTDQPq1PEAQP8htWEv6EMYCw/0mJggIAoKAfwjgQXv1mjVUokRxqtJhEG2bM1xVhPRvH9kW9hG47n2HqnUcQun5pfeKFStC5YSQV+rktJGqVq3K+XARaEr3Rkb5iqEyKTmo3SCAfMMuHE22/dglcnFxIUjN+Gf+kj7s6MBlY/e47qUK5ctR5eHraHHXylSQi4iI/ZoIwKMyYeMJGr/xuIoJHjZsmE1P1DFrNrrqfsbqY9YtlY8GzNtIW9m7h4IpmscNninN4MGCbh3OLT3HbNcplZcyJE9Ez5jcQDvuZ+yBiZDpay4OA8uXJTXnpepyVbVxC5HuP6GDSbu2HS9XQqqSqHYM7TM5i5LCe6d5+LT2VFySGdWiUJzF485j9vTFoKI5M9DJBQNVXh3aE3IcP4g1qn3B2wfSFRyGkM3Wo5fQFs5/RPhI78YVqEeD8nrSjmPgoctSMZzSeTMrbx/mba2hpHWc2LEpYUJdGKu1+0k/QUAQCJ8IxIwZk3bv3kMVK1agMlxtcsW4Pj8VKhg+0Qt7Z72fdRab9Z1AmbM40vYdOylaNOO/67Y8owoVKqhCevXr1+MX2K+Vfq215fttOU85lm0RQN5fG352OnrpJjk7O1PJkiUDnECApA8j4MHo4KHD9OefTajGqA3Us0Z+/ilotnJkgEeUDnaLAMTuey7cSydu3KM5c+ZQhw4dbD5X6Ir0Wr9O6Q5ZU+UJCcXlCjjSrhOXaf+567Tj+EUlnlmMCYtmUzmfDoSvfY0SNJ6JoWYzuAhMQMZcgz5zHhv218Iq4ErXQg21/SEBAQ9VIw7nrM1hk4YGOQWEkGKu5gwllKGtF5B1qVtGVbQMqJ9/2xGyeZxQQMDYm4pz0rxf0JiBV3LvmatcICUeteZCKpoBhy6TdNW6QJyDw/7hCl8gfCCdywe3UVpDpuOiOA3y/ErlyUJl8mUx2uzGSekwQ+0how5mVg67eVChwj9eDJjpIk2CgCAgCBghED9+fDpw4KCKfqnMcgi9WRrhn7YN7KYiodFkZeWnEEDRG1RGncoagg3q16dFixeHKuHTTqJ69eq0d+8+qsvVZIt3HMuVMxtxAbrM2mb5DGcIQF6i+5TVXPY/Ch08eIhDgPNbhYDlEpgmu8eKFYu2bOHQtUmTaNZONyrUZwXLIlyhT/yWXixsI/CA3xwNWnmAivyzgl5QdDp+/ESoED6gWKVKFYJoKwS9rTUUdIF1ZvFPaJQg5FMjaGh39xUlRUVNzVAhaT1rxwRkqOQEwmZYtGTZzmP00SQPsIBvKV6IlxvaKXcvqvTXFOo9c51hs9EyJCQgsRDQD4hYUK18waxqiPX7ThPCAjQDCQJpRXGbNFzaGcsdxi+nntPWKMKr9dvGHlV4/crkz2JU0hje1HPXb6lCK1pfaz7h5ft39wmVB7lsUGuzhA/j4LrCK4t8P8McTLRv5tBeWOHs/oc1qE78C7mV+5jQVq9eQ2uST0FAEBAErEIgRowYtGnTJpo2bRrNXL2NctXqSCucXY0E0q0aSDrZFQIfuIr14o0u6nou2rSH5s2bp0J6Q9PDZwpQkSJF6IKbG+XKX4iqc4XuxoPnkaFguml/Wf/1EDhz1YfqD5xDdfrOpMLFS9H5CxesJnxAwypPnwYbHqS7d+9OtWrVoqFDhlC3BSto8OojVC1fOiqZLRVlTZWAEsWOTqhKKWafCEAeA6GI3o9f0bmbD2n3BW/af9GH4seLRxMmTlJkz78k0JA+q+TJk6tQ4oVbj1hdqbLSH9lZmiMqaSGXGgnU5povc2q6dusBDWLRUp+HT5VwKcJAPXzz8CCECW+buQpO2BeadKgYiTxBVL/czFIPpta7cUUmLydVmCFy75Cnd+vRM334KATPLRkqXD3dOd3S5mBtRzjsdK7cieI3lVhGoWax3Hw/fKDZvroww9rUUPkCGVMkVp43kF0UcoFm3o07D2ncip1K2H16j8b6eYE84s0j7OrqUUoXUb8xgAX8wYKmIUTeUYzFnEGDsXv9cqp8MchlxZ6T9d7UVUwYka+JsFr/RF8Nx/2XK61+5++yhg0bGjbLsiAgCAgCViPQuXNnqlGjBg0bOpQ6D59JA6YupRosJl6mUG4WQE+jZBesFf22+qDSMdgQwMvlB5zqcPGGF7keO0fO+0/QG34Z3Lp1a1XLIEmS4M1ZD66JJ0qUiDZt3kw7duyg/lxssUTHMZQ7E/7+5VAyVYiYiR0zulFBueA6toxjWwTwUvwF603jRfuxSx607egluuhxi/Lmyc2h5rupXLlygZ6QRZ0+a0a6d+8eLVmyhDZv2khnz/l9ELZmDOkTeghE5xj1cuXKUoOGjah27doUKVKk0JuMwZH37NlD5cuXJ2jsoeCINdZz2mpavO0IFc2RQVWfNNznCeu5QbcPhEEzkIRpTFxajlqk8tMQwgjZh9wthpIP6+u9cJmpuqKoSd3+s+kse7FgKEMMXbhXb94rgnd51QhV5RLbQA7bjFli5BWMw7pDfZpUok51SqOLTcw/nT5MAB7DjhNWqKI32oQicy7dmI51jUI5r3jf41LRSxRh1vqhiMvIdrWpgoHXFKQvbkVdVbMba0dzERYHrbv+05JOH8hmv7lO+n7mFuqXYamNvi0U4UZoKcJlDa1m8dw0la8lsA7IoEmYq8Vwqt+4qXpTH1B/2S4ICAKCQEAIPHjwwPdZaBOdOXvWKDoioH1le+gioPLFC+RnZ0ZtatGiBYFUhSU7dOgQocL69m1b6eEj6/Paw9I5ylyJkiZJTFWrVadmzZoRPL4/a0EifYYHffv2rRKtfPLkCWFZzD4RgD4QEtKhNZM+fXq7rQJVulQpenbPi/bP6K0XDA8KoiAmF5j0QYA0N5f2j89hjDDkst1gjx+Iin+kASLi9568ULIAmoC5ufngOPCIoahInFjRCJ5CWxVpMTcf/9ruPn6h3iAhIdwxdVLlbTPtD10+kFnv+08pW9pkSmTVtI+t12/efawIICqhouqof9fNdG4ID52/7Sh5et4Mc3/cTc9F1gUBQcD+EMDzz9WrV+nx48f07t07IYD2d4lU+kf06NFVvQpHR0eKGjWqHc4y8FOCI8bDw4NevnxJnz8HrUJ54I8uewQ3AnDExOaCcxkzZqTg8jwHG+kL7pOV8cI3Avjiyp49G3VjD9nAltXCNxhy9sGCwAkO463ceyrNmDGTOnbsGCxjyiCCgCAgCAgCgoAgIAiEBQSsLuQSFk5G5vjrIJAhQwaaPn0GTfh3F209cuHXOTE5k1BBAF7N5hyqWqVylVArUhQqJy4HFQQEAUFAEBAEBAFBgBEQT5/cBnaNQKdOnWjRwoW0dkR7vRC3XU9YJmd3CDxkTcVqfWZQpJhx6cjRo0p71O4mKRMSBAQBQUAQEAQEAUEgBBEQT18IgitDBx2BmTNnUn3Wyqk/cC5LLJwO+oAyQrhC4AZXvarAkhkUOTrt5gJBDg5+i8yEK0DkZAUBQUAQEAQEAUEgXCIQKMmGcImQnHSoIvC///2Plq9YQQkSJuTKmFPotLs3DW1T02zRkVCdqBzc7hBYw9IMvWaup6zZstO27dsJwspigoAgIAgIAoKAICAIhEcEJLwzPF71MHrOq1evpo4dOlCcGJFpCBd3gXSCoQh7GD0tmXYwI3CJNQgHL9zCAuzu1K1bN5owYYLdyJEE86nKcIKAICAICAKCgCAgCFiFgJA+q2CSTvaCAPSQ+vz9N636919KnzwRNa9UiKoUyUlpkyawlynKPEIBgRev37Hu4BVatfsk7T97VYmXTuNCQEHRswmF05BDCgKCgCAgCAgCgoAgECIICOkLEVhl0JBGwN3dnZDv9++qVfTqv/8oIevsZUiRmGJHj0IRI4Ruqurb9x9V+Omv7IX8/OUrfWWNwyiRIob0pbY4/rdv3+n1+090+9Fz8rr3SGlOli9fjjp37kJVqlSxuJ9sEAQEAUFAEBAEBAFBILwhIKQvvF3xX+x8v3z5QsePH6czZ87oRUnRFlr26dMncnFxUcL3WbJkCa1phPhxDx06pI5RvHjxED+WpQMg3zNmzJiUPHlyypUrF5UsWVIKtVgCS9oFAUFAEBAEBAFBIFwjIKQvXF9+OfngRqBVq1a0c+dOunLlCsWNGze4h7eb8U6cOEFFixalOXPmUNu2be1mXjIRQUAQEAQEAUFAEBAEBAG/CAjp84uJtAgCP4XA7t27qUKFCuTk5ES1a9f+qTHC0k59+vShefPm0eXLlylFihRhaeoyV0FAEBAEBAFBQBAQBMIVAkL6wtXllpMNKQTevHlDWbNmpYIFC9K6detC6jB2Ne6HDx8od+7clDJlShXSaleTk8kIAoKAICAICAKCgCAgCOgRCN2KF/ppyIIgELYRgNfr7du3qrhM2D4T62cfJUoUWrJkCbm6utKCBQus31F6CgKCgCAgCAgCgoAgIAjYFAHx9NkUbjnYr4jAwYMHqVSpUrRy5Upq3Ljxr3iK/p6ThHn6C49sFAQEAUFAEBAEBAFBINQRENIX6pdAJhCWEXj//j3lyJGDUKnT2dk5LJ/KT89dwjx/GjrZURAQBAQBQUAQEAQEAZsgIOGdNoFZDvKrIjBw4EB6+vQpzZ0791c9xQDPSwvz3Lt3r4R5BoiWdBAEBAFBQBAQBAQBQcD2CIinz/aYyxF/EQQgW1CkSBFFdCDVEN5NwjzD+x0g5y8ICAKCgCAgCAgC9oqAkD57vTIyL7tG4OPHj6pyJYTBIdUgRiRhnnIXCAKCgCAgCAgCgoAgYJ8ISHinfV4XmZWdIzB8+HC6c+eOhDMaXCcJ8zQAQ0l1sbwAAEAASURBVBYFAUFAEBAEBAFBQBCwIwTE02dHF0OmEjYQOH/+PBUoUICmTZtGnTp1ChuTtuEsJczThmDLoQQBQUAQEAQEAUFAELACASF9VoAkXQQBDYHPnz9T/vz5ycHBgQ4cOEC//fabtkk+fRGQME+5FQQBQUAQEAQEAUFAELAvBCS8076uh8zGzhEYO3Ys3bhxgxYtWiSEz8K10sI8RbTdAkDSLAgIAoKAICAICAKCgI0REE+fjQGXw4VdBK5cuUJ58uSh0aNHU69evcLuidho5hLmaSOg5TCCgCAgCAgCgoAgIAgEgICQvgAAks2CABD4+vUrFS5cWHn3jh07Rv/7nzjJA7ozJMwzIIRkuyAgCAgCgoAgIAgIArZBQJ5cbYOzHCWMIzBlyhRyc3OjxYsXC+Gz8lpqYZ4i2m4lYNJNEBAEBAFBQBAQBASBEEJAPH0hBKwM++sg4OHhQTlz5qQBAwaon1/nzGxzJhLmaRuc5SiCgCAgCAgCgoAgIAhYQkBInyVkpF0QYAS+f/9OJUqUoDdv3tCpU6coQoQIgksgEZAwz0ACJt0FAUFAEBAEBAFBQBAIZgQkvDOYAZXhfi0EZs2aRcePH1dhnUL4fu7aSpjnz+EmewkCgoAgIAgIAoKAIBBcCIinL7iQlHF+OQR8fHwoW7Zs1LNnTxoxYsQvd362PiEJ87Q14nI8QUAQEAQEAUFAEBAEdAgI6ZM7QRCwgEC5cuXowYMHdO7cOYoUKZKFXtJsLQIS5mktUtJPEBAEBAFBQBAQBASB4EVAwjuDF08Z7RdBYOHChbR//34V1imEL3guqoR5Bg+OMoogIAgIAoKAICAICAKBRUA8fYFFTPr/8gjcu3ePsmbNSm3btqUJEyb88udr6xOUME9bIy7HEwQEAUFAEBAEBIHwjoCQvvB+B8j5+0GgWrVqdP36daXLFzVqVD/bpSFoCEiYZ9Dwk70FAUFAEBAEBAFBQBAILAIS3hlYxKT/L43AypUrafv27bRo0SISwhcyl1rCPEMGVxlVEBAEBAFBQBAQBAQBSwiIp88SMtIe7hB49OiRCuts2LAhzZw5M9ydv61PWMI8bY24HE8QEAQEAUFAEBAEwisCQvrC65WX8/aDQN26dens2bN06dIlihEjhp/t0hC8CEiYZ/DiKaMJAoKAICAICAKCgCBgCQEJ77SEjLSHKwScnJwIP/PnzxfCZ6MrL2GeNgJaDiMICAKCgCAgCAgC4R4B8fSF+1tAAHj+/Dk5OjpS1apVCVINYrZFQMI8bYu3HE0QEAQEAUFAEBAEwh8CQvrC3zWXMzZBoGnTpkqT78qVK+Tg4GCyVVZDGgEJ8wxphGV8QUAQEAQEAUFAEAjvCEh4Z3i/A8L5+aNSJyp2zp07VwhfKN0LEuYZSsDLYQUBQUAQEAQEAUEg3CAgnr5wc6nlRE0RePXqlarWWbJkSUX8TLfLum0RkDBP2+ItRxMEBAFBQBAQBASB8IOAkL7wc63D7Zlu3bpVae6VLVvWCIO2bduSs7Mzubu7U7x48Yy2yYrtEfAvzPPatWt08uRJat68ue0nJkcUBAQBQUAQEAQEAUEgjCMg4Z1h/ALK9ANGYPXq1VSuXDlq3bo1wbsHc3V1VUVbZs2aJYQvYAht0sNcmOe3b99owoQJlCNHDho/frxN5iEHEQQEAUFAEBAEBAFB4FdDQDx9v9oVlfPxg0CiRIno8ePHFCFCBIobNy7Nnj2bevfuTXnz5qUNGzb46S8NoYuAFua5efNm+vvvv+n8+fME8gd79uyZuoahO0M5uiAgCAgCgoAgIAgIAmELASF9Yet6yWwDicDNmzcpffr0+r1+++03+v79u2pD2GfmzJn122TBPhB49+4dpUmTRhE8XK8vX77oJwYtxdq1a+vXZUEQEAQEAUFAEBAEBAFBIGAEJLwzYIykRxhG4MCBA/S///24zUH4YD4+PlSkSBElyB6GT++Xmzpy94oXL05Pnjyhr1+/GhG+iBEj0r59+365c5YTEgQEAUFAEBAEBAFBIKQR+PE0HNJHkvEFgVBAYP/+/QRvkanBe/TixQuqW7cu1apVix49emTaRdZtiAAInpa75+bmpryxpof//Pkz7dq1y7RZ1gUBQUAQEAQEAUFAEBAEAkBAwjsDAEg2h20EEidObBWhq1mzJm3atClsn2wYnv2wYcNo6NChVp3BgwcPCNdVTBAQBAQBQUAQEAQEAUHAOgTE02cdTtIrDCLg5eXlL+HTPIC9evWi9evXh8Ez/HWmPHjwYBo3bpwKxf39998tnhiuGby3YoKAICAICAKCgCAgCAgC1iMgpM96rKRnGEPANJ/PcPqo5BktWjRChciJEyeqyp6G22XZtgiAzKFq56FDhyh+/PgWrweu2969e207OTmaICAICAKCgCAgCAgCYRwBIX1h/ALK9C0jYIn0wZOEqp0XL16kGjVqWB5AttgcARTXuXz5MpUsWdJsLiby+lxcXGw+LzmgICAICAKCgCAgCAgCYRkBIX1h+erJ3P1FYM+ePUbVH7XOzZs3p9OnT1PatGm1Jvm0IwTg6du9ezeNHDlSET/D6quY5t27d+n27dt2NGOZiiAgCAgCgoAgIAgIAvaNgJA++74+MrufRMDb25sePnyo3xvevUiRItHSpUtp0aJFFCVKFP02WbA/BBDu2b9/f4K3Nl68eEbhniCBIt1gf9dMZiQICAKCgCAgCAgC9ouAkD77vTYysyAgcPDgQb0+H/LAUqZMSWfOnCF4+cTCDgLQ7EO4Z7FixfTXU0hf2Ll+MlNBQBAQBAQBQUAQsA8EhPTZx3WQWQQzAqjw+O3bNxUeWL16dYL2W/bs2YP5KDKcLRBImDAhubq60pAhQ9T1hMai5PXZAnk5hiAgCAgCgoAgIAj8KgiITl8YuJI+Pj4Ez9WVK1fo8ePH9O7duzAw69Cd4tatW+njx4+UM2dOypAhg80mAy9UrFixlGcxd+7cVKJECYoRI4bNjh/cB/L09KTDhw+re+/p06ehfu/h/j9+/Dh9+vSJKlasSDFjxgzuU/7lx8M9CtxSpEhBuXLlopIlS6p79pc/cTlBQUAQEAQEAUEgHCMgpM9OL/7bt29pyZIlNH/ePLrE4W2ROR8tc5rklDhebIoWNZKdzto+pvXu/Uc6ePYKFciegeI52JYUfP36jV6/+0Be9x7TrXsPVR5h1SpVqFPnzlSmTBn7ACiAWbx+/ZoWLlxIC+bPo6vXrlOUSBEpc8pElNAhGkWNFCGAvUN+84dPX+isx11KFs+BUieOE/IH/MWO8O37d3r94TPdevSSvB88VfmSFStWoM6duygi/YudrpyOICAICAKCgCAgCDACQvrs7Db4zg9k85joDRkymN7ww3e9coWpXvmiVCR3ZorIuWliASNw49Z9iusQg+LHjhVw5xDs8eTFf+Ry9Bwt33aAjp13p6JFi9D06TMIHkB7tK9fv9LMmTNp+LCh9OnDB6pfPBvVLpKNCmROQRF+t69IcJDrczfvU/6Mye0RyjAzpxev35PreQ9aud+NDl/0ogL589H0GTOpYMGCYeYcZKKCgCAgCAgCgoAgEDACQvoCxshmPe7cuUONGzfi8LUT1LF+RerdolaoExebnfwvfqBTl27QgJmrCJ+oSjl06FB9YRJ7OHUvLy9q2KA+XbjgRp2rFaTutYqSQ3SpcGoP18ZWc3DzekBDVuylw5e8qFevXjRmzBijqqm2moccRxAQBAQBQUAQEASCHwEhfcGP6U+NePToURYKr84hdDFo8bAulD1j6p8aR3ayXwTgxV3gtJv6TVtBxYsXI6eNm+wiJ23v3r1Up3YtShU/Js3tVpMyp0hgvyDKzEIcgVX7ztM/i1woT958tMXZmeLGjRvix5QDCAKCgCAgCAgCgkDIIiCkL2TxtWp0CFHXqFGDyhbMQYuY8EWPKh4Wq4ALo50uXPOiur3GU5IUKWnv3n0UJ07o5aVt2bKF6tWrS9X/yEIzOlXn/D0JIQ6jt1WwTvvq7cfUYPQaihU/Ee0/cJBQQVVMEBAEBAFBQBAQBMIuAkL6QvnanTx5kkqVKkU1SuaneYM60e92ljsVyvD8sof34UIvFToOpZRp0yviFzVqVJufK4TPK1QoT01K5qSJ7SorOQSbT0IOaLcI3Hv2H1UfsoJiJ0pGBw8dtguvtN2CJRMTBAQBQUAQEATsHAEhfaF4gR49esQl03NSznTJaN2EPkL4QvFahMahr/vcozJtB1HNWnVoydKlNp3C7du3KU/uXFQ0c1Ja1LMO5xf+ZtPjy8HCBgK3Hr2gcv2XUKmyFWjd+vVhY9IyS0FAEBAEBAFBQBDwg8DvXFBiqJ9WabAJAg0bNKCXTx7SlukDKGoUkWHQQH/+6g1duXmbHGJGo0gRzYcbfvz0mc6636RoUSL7ix3GcrvhTZ63H3JRCtbQixFNO4xVn9j/xKXr9OT5K4oeLQpFiez3Or1hiYZPn7+Y/bE0fxwc1UUd06ag/uNnkaOjI2XNmtWqOQW1E3ILa3L+6Pd3L2lNv4YU2QLGQT1OWN3/xNXb5Hn/mVk5CGAHqYPLPo8oTsyo/mL3+ctXuuT9kM553qOXbz9wvm50+p018gJjqK7pzqGWKKoTKcLvZndFn4t8HK8Hz9WLo1h8n5oza/sZ7hs7RlTKlTYxDZ6zRun62WvlWcM5y7IgIAgIAoKAICAI+EVAPH1+MbFJy4YNGziXqh65zB3GcgxZbHLMsHKQ/tNX0PRVW2n/olGUP5uxsPrL12+p25j5tPv4eQLZgmVLn5KWjepJmVIn05/i5y9faOCMlTRn3S769u2bvr1CkTw0q397Shzf/zy6+4+fU73e48jturd+Xyx0alCZhndurCd/dx4+pSw1Ohn10VaiMkF8cmiltmrxs8uYebTj2EXy9LxpkxC6pexVbN26NbmObU250iW1OK/wuOHoFR8OaVxG0fna3V7VzwiCk9fuUNspG+ju0/9U++/sHc3HkhEzO9egdEnjGfUFcew0YzP5sKdMs9gxotDCnnWpdK50WlOAn4OX7aaZzsdp95jW6liGO4BUDlm+h+bvPMX3+Hf9pvJ5MtDUTtUocRydRqW1/fQDmFkYuNSF/j3kTp43vShePONzNdNdmgQBQUAQEAQEAUHAzhAI3GtnO5t8WJ0O9ND69+tHDSsWF8JncBHhRdlz/AIt5AqXlqz3pCW0ce9xSpk4AfVsWoMqFc1Llz1vU9UuIwgETLMxCzfQrDU7lLB4gwpF6c+qJcmBvXzQzWs2YIrWzeznf2/eUfn2gxXhy+uYnvq0rE3lC+dWXpTZa3fQ8Hlr9fvdvPNQLadPkYSK5nY0+imcyzoyP6JzE/rw7h1NmeL/vPQHDcLCx48fadDAAdS8bB4hfCY4whPWfupG4tvQjyHMsQaTQRC+8nkzUPeaRSh1ojgEIth0/Fp6++GTfh/0bTj6X0X4ahZ2pAltK7PeYVZ6+eYDtZ68gXCcgAz/F1zPe9JilzMWu45ff5Dmbj9JUdhTW7dYdmpcKhfFihaZdp/zoFaTNuj3s7affgczC30blKIIv32ncePGmdkqTYKAICAICAKCgCBg7wiYj52z91mH8flt3bqVPDw9acPYbj91Jt73HlES9lQZhhqCqDx89pLSp0hsVv/tw8dP5MX74WEyQ8qkFsMm4RXzZCKTOF7sQIdC/tTJ+O7UevB0Rfie//fG4jAgbGt2HlIePdcFI/Tz6zRqLi133sckbzuN7dFc7b90yz71uXFqP0XEsDKgbX3KWqsLHbtwjW7df0ypkpqvSHiStfR8eHuxPI7kPGMgRYyg+2+yfvdRajloGs1mMjmkA4dFRopIXnd1pA/HrVg0jzpmYH/FiRWDOjesxMLt06hv374UKZLfENLAjmmp/3rOy3r48BH1GtLAUheL7SA2z/57RykTxtb3wf2EsMJ4saIRQgHN2cMXr+k+FwVJHt+BEsaOYa6LaoO36uaDZ5SIPVQgL7a27nOc6f7z12YPO3HDIfrEnrVGpXLSrC41VZ8hTctSiwnryPnEVZq++Sj1a1hKtU92Okz/vftIHaoUpNGtKqq21hXz0xvGb/dZD9rDpKx+iRxmj4PGdkw89zLhe/HGf3K4wvWcGmPdgCZUOGsqtdy3QUnK1WkawdN4+/FLda2s7acGsPArRtRI1LX6HzRh9mwaPHgwxYhh+TpaGEKaBQFBQBAQBAQBQSAUERDSFwrgL1q0kMoVyk3pmKBZY5mqdaTv/G9Yp8Y0esF6AulD6GD1kgVodPdm1HboTNp/+pIidDE4n2dS71bUpEpJ/dBTlm+hMYs20LsPH1UbKoS2rlVOESQt5+w9P5D+M3Uprd5xiN4zQfztt98oZ6Y0aqyC2TPqxwqphQhMrLJwfhvsksct+u/tOz+HghcQ1qZOeT3hw3qTyiUU6YMHEOQLoZ0vX+vIY9Z0KdFFWbKEcSlBnFj0iMnxN3PuHN9+Jy5eV0v12UOoET40AG/gDnw8bj9QYaU3fUlfxtRBC5NsVbMsjVu8kZxZF61u3bq+Mwn+j0ULF1DlApkoabxYAQ6+bM9Z+nvBDurPZOasxz32PHnQx89fKUOyeDSqZUV69uot9VuyS3mwMNgfWVJyjmAjiuUr6g4y2GHaRjrD+2qWKlFsmt+jDuXnsEjN3n/8TAOWuNDag270/tMXvveIcqRJQuPaVKICmXT3hNY3pD5xrttOXqMu1QvRciZThuGSOCbOH1avmDFZq1c8hyJ9EDbXDKQucsTfqXe94lqT+hzerDzVKpyVsqXx//99RM49zZxSp5V4hXMHQSBNDSGb8BzCHFP9eHmB65ogVnR69PKNOgdr+5mOb279z9K5afSaA4TQ9BYtWpjrIm2CgCAgCAgCgoAgYKcISHinjS/Mhw8fyNV1L9UsVdDqI+PBDTlm7YbNUmSlcrF89IELmax1OUKONTvTvlMXqUTerFT2j1wqz63jyLmqPw5w/qoXDZq1ihLEdaBezWsqDxU8ffM3uNCkZZvVHOCtqdplOC3e5KqI6F/NalLtMoXo4g0fqtxpmBrf6sn+ZMd5gzuRy7xh6ieHBWF6EC1Y6QLGD97pUyZR7cDo7fsPiqhVK1FAtYEkv377XhHeGau3K8KH4ilpkiVS2839SpE4PrWrW4FK5MtmtBnHB+GDJWJPKMyLvaIghlc4xHTQzFXUfvhsmrycc7lYkiEwliRBXPojZ2baxl7gkLJXr17R4SNHlSafNccAMf7y9RsNX7WXdp25TqVypqMUCRzI494z+nPsGurIOWsRf/9dhRYmjRtTeZeGrnDVDz2A88BA+BDiOKxZOWpaJjfdffKKag1dzjh+Vv1w79UatpyWMulKkyQu9ahVhPtnpUs+D1U45QE3L/14IbVw4+5T6r94F2VLnYgGNi5j9jBPmeDCMpkI12MfGHTtYPCGPnzxhgpmTskvH77R6v0XCHl5c7edUGS2ActjOKb8QdLUTia/4EncPqKl+smW2jxBjMhFXaoUzKz2HLv2AL1+/5HeMaaztx5XhC8LzzN14jh8b1rXz2QKZldRuKZ49jS0lV9MiAkCgoAgIAgIAoJA2EJAPH02vl4XLlwgEL/i+QJfqbFN7XLK8/Y/rgC4ZLMrdeWCJqgaiVDHP3JkUmdStfNwOnDmsqpYmZQ9W1NX6h7QBrVvwDmExVSf1rXLU/oq7cj5wCnq16Yu/bvjICGkEZ69A4tH6b1b5bYdoA4jmMQwOTQlWhpsKHQybO4abdXiJzxZVUvkt7jdmg0et++rbhrh0vbBeqzo0ZR38Mnz/yh6sijUu0UtOuZ2jQu57KRFm/ZQBCYn8HSi2ufYnroQUG1/088WNfw++L/i8Nn+05erroWYnMFjCEN4JzyLjf6ZqNa1X6M5p3BqnzYql1BrC+izRF5HWr//SEDdfnr76dOnCfmkxfjBPTAWLXJEOjSpA6VlUoYXEHm7zFDkDbltK/9pyNj+TxG+ygOX0Pmbumt0/e4Tcjlzg9Iw8Vjcq57+cAgBnbHlGO13u8kex8y05oAbnbp+lz17iWnP2DaKpKBzmX0XqMusLTR10xEqmTOtfn/DhYvsXRu1WhfGa9huuty8XF51LNN2rH/k/z9tuDgLCC48kJHYQ2fO0nOhlqcc2oqwyz+ZvGp2mucOu8NkFoTP++Fztf6SQzNL9J5Lj1/qyCIaI3DRlf6NSjGxLar6BPXXX7WLKdzn7zhFS3efVdcBxA/XC55Yzaztp/X377Moh5HOcTnqXxfZJggIAoKAICAICAJ2iICQPhtflKtXr7LEQGRKbSGfzL/poHAJCB8se4bU6jMrV67UCB8airN3CqRPKzCCvDPYtJVbKVnCeFSYCUtchxh0z3Wp8jxg24Y9x/BBIJWG4YyNKhWnPpOXqvGQM2hO7uDpy9e0+9h5tb9/v+CJDIoh11Ar1IKCLKaWPFE8cvd6R6/f6fKgfDgE9qOvVw7eKi1cD566g4yPJRJrOi7W9568SF1Gz1XHR9XPuYN01TpVPptveCe8ow0qFqWHT1/Sym37Cfl/IOW5MqdVYaDmxjVtc0ybkm4ucqLPnz9TxIi662baJyjruPfiO8SkBCwdEBirwZ46ED4YPEeZkydQpK9pmTyKaKC9YOYUalkjPVF97zvvhy9oEufDNeXCMcjnG9ykDPWpX4K07ZuOXsHu1LJCPj3hw3oDznnrz6Gjhy55q/BGczl+z16/4/w4T3T314pms0xyh7FnEvIL41pXoswmXjzDQVEo5QQXbRnHXrXnfNwSOdISvJAztugIEHQOIceAkFYYJBTgFR38ZxkqlCUVHebzGLvuAA1fuZfyZUhORbOlVv2C8guVQT9wOCzsC///0EKW33MUwKHL3nqybG0/a+aShb2UDx89JniNHRwcrNlF+ggCgoAgIAgIAoKAHSAgpM/GF+H58+cUP46DypkLzKGRhwdio5lG5pJyWKCh/Q8JUWwgJLDuf1ZTpOyShw9V6jiUYkaPSiXzZWevWz6qW66I6uNxS+edGTp7NY1fslG1ab+03LpHz1+aJX2l8mejJwdXat0tfoIsBMVAdpFPB2/dY9bMM/X2aWQvLhdFgazDn/0ns1frmyre0qpWWbXv9kNnqOPIOSqsNX/WDAF6HhEu2nPCQsJ+sHrli9C4ni0oIYfKarZt1mBFVvJkSaeakENYpmAOtQzi58SEGpIS1lh89h7iur18+ZISJEhgzS6B6vPs2TOKF0jChwOkSmgsb6Hp+iWJp5MEQB/kgOLW0+47FHtpxUQO1SdHrd6vfhByWCFfRhUO6phKFxbpcf8pdqcRHEI6iQugGJqWy/aY89PMkb4S2dPSvX/7G+5idtnSvYfcO1S/LJs7PbWtrAsHNjsAN7Yon4+Oud+ijUxSDUNYcU7PuRpnGq7kGSVSBKMqntM7VVfkEGOCFHuxFxCeTecT7kEmfa9Y96/FxHX0lYvfoHhLi/J5VaXanaeuUdfZzjR14xEml8moSNbUVvWD19Uai88Fe2D4HhPSZw1i0kcQEAQEAUFAELAPBIT02fg6ILRTI2yBOXQUruioefkM98PDtjnTUT4ikBA3p+m0dtdhRf6OXrhKWw+eUj+rth+kbTMH6fXu8rEmXmxTL1pO3egOMcx7hxQZs5GwPIgeitigsqYp6YOIOgzEacfhs4rwQUYB4auaNapcnHYeOaskH1AUxr9wUxTGadZ/Cr3gaqLw1o3jAjGmeorA3lKRmzIFcypvnxaSqs3Bv0+NTOEeCQmDXAOISWAN4YLmjGmen2bfdw2qfWK7KoRCJ5uPXaFDF73o6p0n6gfhnesGNlE5gm/e63Ik4f3SCsDoB/XlIRAmN2fwrkW1MDdz/U3bnI5cVk0ISf2j2yz9Zo1sog2FUTYOacr/936jhX/VpSYc2omQTlTWzM75diBz+bvOJHjAYMm4QiksCec4Iv/N0BAOC9LnyTmRQbUDjCcIXzr2wMJzqhlyBl3O3mDM3VUo6md+8WFNP2tJX0jfo9p5yKcgIAgIAoKAICAIBC8CgX8CDN7jy2ghjACKvMBDhsIk+EH+mesJN0VoDp29Qle971La5Inp6cv/qDGTojplCxvN6Oj5q1yq/ouRd8uww+nLHuwNW2TYZHa5W+OqhGqYQbGMLL4O0neTC6oYki0UnEEBl2wZUilCjdBOGNZNLW/W9Ir0aQVZTLdjHaGxjTlHDwVgkAvZu3ktpdFn2hf47WIZiTJcWKbMH77s2LeTJupu6J013f9XXodWnef9Z5Sdc/XG+MoW3GEJgWErXZW3DDloKAyTNnFcJQMBslKLtewM7Th71iCTYCkc9cyNu6q6qOE+5pY7c0VOhGeaGsIvtUIshtsguo5QSeT3Rfd9oYFcvidczKVqwSxq3lp/LTw1cwod6YMsBew1V9xEpVNDkg2PJSz+T3hb1Y4Gv3w4bBaW1ddjarCJ8qRPpkgfKqFa289wf1kWBAQBQUAQEAQEgV8PASF9v941NTojVJN8wuGQPi4LKXbM6CpnD4Lm6bji5WWWRvjI+T8gUKcu36BtB08bkT4Ud6nQYQhlTJWUzq2bajSutvKGyRYqVwZkkEkIqtUtV1iJq6M4Tb0KRdS5YEwIpsOqFs+vPpHnCAMhg6cOOniarWadP5ghIQRB+8o5UdmZJCKn0cn1mCJ8EHX/p1UdbVc/n++4YuL0VVtVjmAxzlnU5C8Qggr5CJi1Au1+Bg/jDQc5h63HnK3UtUZhVbkTp5OCQz5rFcmmSN9H31y0/JmS02kmbzs4LNGQ9J26foeqDFqq5CFOTu9iFg0UTnG/rSP4Zjv4Nmpky7TPgEalCT+mlrHlBJUrh+I1mkHrDnp8IHGoLgpD+PA0LjQDZzu8eLBUfI4Zk8WnG/ee0nY+pzpFs6l2/Npy3F0twzsIw7zusdg7QiaBTWBMk2mAEDuKxhhqJK49eFENBUKYMXl8tRxQv8AcW/oKAoKAICAICAKCQNhDQEhf2LtmgZoxtOXmrd9F1buOpE4NK7N+WERyOXZOET4UdgHRSZ00Ea3afkCFI4L0lMqfnW49eEzTmNDA+rOouSVD3xdH/7W0OVjba7DMxdQVznTl5m2q2GEoVSmej8mqhyKrmdMkp+5NqqnjFWcChkI3yGPMWrML1ShdUBWxQWgniC6KsTSrVkr1xYN7kWb/qOUbW+cSKp7Cuwm7wGSwbNtBatn019KR3VUFVoSQgjSWazeY6jApha3adkBJQ+TncNlqQaxYanrcsLJeOlc6LtYSgXPmTiiPGcI3Iby+YOcpdQoV8mdUn73qFGdZAzdCqCVy70pwSOTtJy9pJoeAwv6pX1J9mvuFYiqP1pq/Pub6B6Wt6h9ZFOn7a942es5VPJEbuWjXaVWwBTIT+TImV8Mj93YQF6tpOn4tdeXqo5e8H1CyeA4q1PK4+21VEAcFcGDAYtKGw0rKYhrn/wXGinFxGngpUYQmV8dpVI3nh1BUVEy9cusRJY4TQ1UZRTimNf0Cc2zpKwgIAoKAICAICAJhDwEhfWHgmsGTgMqAhoaHS3Nm2m9wh4aq+Mk61vRrM2SGfheQlcUjuinPFqp5bp89hFoPnq7kGyDhAIOHDGLn8LDZg0FuwXnGQKrRfZSSmIAnEgbCt3ZCH1WkButROJx13cQ+1GP8QuUZXLF1P5qVwcM3f3Bn5fXU2rRPDVOErMKu+9zTNvn5hFQG5uM0pR914uIwyJU86+6p71erzB80vW+7QBfs0Q9gBwtazp6GizYlhD+aM8N7D2GOkECApAKIjWbIjWtfpSC1YBkFGLTfnIc1p3ZTnVS+G3LeYLFjRGHZgQpU28BTpjaE0i+Eh7qzFt+MzUep9wKdZxmkFp68vg10LxC0qUE/b263Whx6up2mb9aRV2yDeP38HrX1shBa0SVD3LQxAvqEx/Hfvo2o9/ztBC/eKpa40Awevtlda5KWC2ltP21/+RQEBAFBQBAQBASBXw+B37janlbz49c7Ozs8o1GjRtHSBXPowropNp0dcvYOnL7Mnr4IypsFAXRDeQZMBl6v67fuEap5gvChwmVUGxVpCSwYtx9wURCvO+TIhWogpm7JkAN43fueCt9MnSwhZU6d3Gx+nqX9rW335DzDm3ceqAqnIKGGIaXWjnGKSWzpNgPp9u3blCKFLgTQ2n2t6devXz/auWEl7R/X2pruwdYHuXkQLAcJgSwCPFKmhnsPIZHIA4zDWn55ufJkUIq0mI4fXOsPX7xWQuwQXi+WLbW/c4ScwmUWmUexGhRcCWwIp7VzRt7ejXtPVMGWVFxFNBOHl5oSdYxlbT//jgsR+iI955C7uztlyZLFv66yTRAQBAQBQUAQEATsCAHx9NnRxQjJqcSPHStAjx0eFB3TplA/ITmX4Bg7ZZIEhJ+ALE2yRISfkLb0nCOJHzG/CBRy9FtQx7QX7j1UwNSqYJput5f1xHFicuhkTKumA2+cFvZp1Q4/2Sl14jiEn4DM2n4BjSPbBQFBQBAQBAQBQSDsIWA+RjDsnYfMWBAQBAQBQUAQEAQEAUFAEBAEBAFBwAwCQvrMgCJNgoAgIAgIAoKAICAICAKCgCAgCPwqCAjp+1WuZBg5DxRJ6TxqLguonwkjM7Y8zeNu1wg6iGJhFwFo/XWf7Uy7Tl8PuyfhO/MTV2/TATevMH8ecgKCgCAgCAgCgoAgEPwICOkLfkxlRH8Q8ORiJ8uc99GFa97+9LL/TYfPuVP59kOUiLv9z1ZmaAkBrwfPacXe8+Tm9cBSlzDRfvSKD+saLqFmLBUhJggIAoKAICAICAKCgCkCQvpMEZF1QSAABJ6/ekOth0wnKXwbAFCy2SYIvHj9ntpP3cj3o00OJwcRBAQBQUAQEAQEgTCIgJC+MHjRTKf88OkLpRH36NlL001G61++fKXLnrfp/YdPRu3mVt6+/0C37j822gSSA2mCF/+9MWo3XUG/a953CeQosIaxcQz/CJW15xvYY1vbv8vouXT/8XNru4e7fpA1OOd5jx6/9P/6f2GZBncWEn//8XOAGL3le/b2Y+P7G/fITZZ4ePnmvb/7o9/1u08I5CiwhrFxDH/vRyvPN7DHtrZ/9znOdP/5a2u7Sz9BQBAQBAQBQUAQCIcIiGRDGL7oN+88pDZDZ5AmJo5TSZ00IS0e3o0KZM+oPzO36940asF6OnT2Mr1594Ei/P475c6SlhpVKk7t6lZQ/ZZsdqWe4xfRoPYN6MwVT9p9/Dx9/PSZMqZKSuN6tqCnL/6jPlOW6glf4VyZaf2kvuQQI5rql6FKe8qVOS0VypmZFjjtpsfPdQ/oxfI40sKhXSlZonj6+ZhbQLhktzHzyeP2fbU5nkNMqle+CI3p0UyvJ2jt+ZobP7jagJPzgVPUrUk1Wrp5L337/i24hg7z4yBUssO0jXTG44eofapEsZVIe/6MyfXnd5FDKceuPUBHLvvQGyZzEViuIVfaJNSgZE5qXTG/6rdsz1kWN99B/RuWorM8nut5D/r4+StlSBaPRdsr0rNXb6nfkl1M+D6o/hA+X9OvEcViLcCPn7+QY5vJlJPHRPtil9NMQN+qfkWypqK53WtTMjNagfoJ8gLCJXvO3aZ0A9Eel0Xk6xTNTiNblOf78XfV1drzVZ1D6Bdw2nbyGnWpXoiWu56jb9/E3RdCUMuwgoAgIAgIAoJAmEZAPH1h+PL1nbpMEb7aZQrRqK5/UvPqpenOo6dUtcsIvTfv9dv31KTvJFU4JW3yxNS/TT2qy2Tqksct+mvCIiXYDgjwsPjl61caMvtf2nHkDJUpmFPp4N1gofYGf4+ntsNmqofdBhWKKnH3Yxeu0aCZKxV68II8Zw8dipqMWrCOIPzepVEVgkg5yFyVzsPp85cvFpF2OXpO9YGQ+p9VS6p94zLpm7t+F9XuMUa/nzXnq+8cAgvXfe5Rn8lLKXuG1DS0Y6MQOELYHnLAUhdF+GoWdqRhzcpR0zK56e6T/7d3HVBRXV13xwYoTQVFEbFhBSv2rtiNNXaNxpKoMYkxJsbeYmLvXaPGEjtq7L33igWsgB3F3jXm+/9z7vDGYZgZBgUEPGetYd67/e53db09pz1Gw0Hz9dq8py9fo+3oZdh07CKyU8LyXk0rolE5b5wljR+TvD1ndL6e/6MzxZrAIYu2U9sLqFwoJzxcnXDp5n20Hr4EXSatRkr68eKL8j7InM4BHMRk0IJtCkDqioekodt1OliRS59sbuhStxTyZHEhMncVDQb+RefxP7Ngbzl+CfVpzaF3HqJl5cKqbzqH1Ji18QiaDluk72fNfvWN4+Di4o176DNnE7yzZUS/llXjYAYZUhAQBAQBQUAQEASSCgKi6UukT5LNJzfuOw4mcvN//1G/C2dHe0xY+A+2Hw5A3YrFwREmQ8lMk8nan6QB1CRDWkdM/HsdkcaLqFTcWytGalsbHFw4Cjk93BRRK9j4e1wPu4eaZYtiycifkYK0HEz4qn8zACeCokYK/K5FXaWd4wHZjLTiV70RGHwdC9buQvuGfvp5tAsmg73G/UWk83+Y/0cPNKhSSlUxOa329QDsPHpGzZfOyd6q/WrjGn73n7wI565cMyyKcp3GzgYLfu8RpVwrYK1nu34TSLP3f0qTmiql/NPRsOFvNp/czESOkoTP+amJvsrZ3g6T1hzAzoArqF0iryJnV+88UmRtZvdG+nauTmkw5Z+D4GiaFXyy68tT26TEnjGdkYMIIhO1Yt0mKSJZvZgXFvZqrrSETPhq95uLk1d0WmJ9Z7ro+nkp0s7ptNkvX1eBX69ZCLoejkU7TqFd9WKGTdU1z9GXNIj8I8icnl+gXun8qrxXs0qo3XcOdp8OUXtIS5o/a/YbZQIqGEzkNPDaHVNV+rLUNqkwt+c7HPUVEReszew4boU6jzO7N0aqlDrto3E7uRcEBAFBQBAQBAQBQYARkDfXRHoO7OilkCX4RhhGzvVXWr6M6Z0xpGtL0uZ9Aa2+aP6c2DH7N3iS2acm7BN3mz4s/PJoKA1Ja8iEjyVlihRKW8ekj7WITPhYShfKo0xEg8m81Fj6dHr3ompnm0qZQXYeOhVHz10ySfoCLoSCI3q6Z0iPepVK6IdzJLPRlrUroO+khVhL5pSdm9ZUddHtVz+AwcWBU0E4fOaiQUnUSya7lmTAlEWkHQ3F6J/aI1+OLJaafpJ1dqlSqn2HhD3EmBV70MavKDI422NAq6r4hbR5Wn3RXO7Y/EcHeGZw1uPEPoBhET5pxuexPmkNmfCxsFll3iyuivS1qVpUET4uL5nXQ12HhEX1s2SypokdEchv65VBtylryGT0hknSdzokDFfITJXNP+uWzKd1hWNqG2V+OnD+VjKnDMLXtUuquuj2qx/A4OJg0FUcuXDDoCTqJZNdS8LE8WzoHYzoUAt5PVwtNZU6QUAQEAQEAUFAEBAEhPQl1jPAJK5jo+qY7b8FQ6YvUZ/8OTxQs1wxNCWtnneurGprLs6O4AAvTJ5OBl3B1dvhePXafCAX9gk0FNtUOnKZOYPuxZvrPvvsM/X5P0T2H2LS6ZDGzrC7niAZB4XRGl0i81GWsPsPUaBhN61YfbNpKgvXWbtf1cHoz4apAz/I12nLgZOYsmQDqpUurCefRlN88rdZicS1r+FL/nPHMGzxTvXJR2Skhm9updXL75lRYZTeMTXuUICXAUSeTl2+hWvhj/DqTeQfHgzB9MyQ1vAWNhEa1kzpHfTluvOIKMFWMhLpdCANrqHkzaojSFeNgsJobS7fvKcumYgW7jpBK1bfT1+8Vt93Hj6DtfuNNEDEzZrBbT/oPG49cQnT1x+GX5Fc6FT73Q8lpuaSMkFAEBAEBAFBQBAQBBgB0fQl4nMwvldHNK9VHv7bDpJv3hllRsmmlBMW/QP/cX3IL68gOIhLuba/qhdir6yZ8UW1Mio4y71HTzFx0doou09t9JKsNeAXa2MxjmjIwVeMhbWFLMZttXZPX+iIHZPTMhQExpSUiii3Zr+m+ttEaKFM1VlTtmzLftXsJJmzFmv2zpT2yfMXqpzLWFP5z6R+1gyXZNuM/roOmlQoiNUHzmEP+dOxGSV/2LxzWb9Wyi+Pg7hU/mUmnQcgV+b0aFTWG7koOMuDJy8wmcw7jcWcxot+djBuqsY0LOTgK8bCfoAs5s4jB5ZhcXFMg1J5dT+cqAKDP6VIs8hizX4NuukvNeKqL4jhxcp9Z1UPNmct9f0Ufe8nEaSUyzKTptJ/YBt9nVwIAoKAICAICAKCwKeNgJC+RPr8Q2/eJbPIWyqoyMge7dQurpEWj80QV2w9gDmrtirSN3b+GvWC27lJTYzu2V6/2wkmCJ++8j0vrpCp6X8UfCM5RWPUhIOfsHhRFFBTkpN8ElmcHdJE8jnkstvhD5QvHpNVa/fL/Yyl2+8zcIrIryVxSG2LjdMGmWyS1c1F4Wxcyftk369UKVMijZ2tcfUndX+Vgp5cptQGPtnd8Ed7nSnuddKmDV64Df77z2HeluOK9E1YvV+Rs061SmBEx1p6jCYTMYxtCSZzT+PzeDFCk+eV2cXkdNnddBptJ4oCauhzyI1vkwkqp5jwcneBtfs1NUn3aWujTQZvb5cKa4e0M9VdBbTh4C3GkjzZZ8rHj/370pBptYggIAgIAoKAICAICAIaAkL6NCQS2feuY2fAZOaH1vVU5E5eftZMrmjsV0aRvlcUeIQlMCKASc1yRdU9/2GzyWWb9+nvY+uCg50s3bQXLetUVENyXsCpSzeo64q+74LFGM7nkzsbbMk/kckhp2tggsfCmpguv03HtkOnMO+37pRq4qVV+zUcW7vmvH/nKD+hJeFALuZkQOfm4I+xZK/ZkaJSvqHANyONqz65+90UdZPJzHf1y6jInQyAB5l8NiRNHpO+1xEmnEHXdLkfORCLJhzRc8XeM9ptrH1zioflNG5zSgXBwtFAZ6w7pK7LGwSLUQURf5i02qZKASaHTGJZG8nC5/F78gXcfuoKZv/YWKWasGa/EcNG+uJxrQnkEqmTwU3fFlXAH2PJ/dUoZSrLgW9EBAFBQBAQBAQBQUAQMERASJ8hGono2q9UIRWsZSr5mrG5WHFvL5XUfAalOWCpTb59LL5UHkSRPvtOXIgQ0g6GP3hMpHA/OBUDC0f3NMzzpwo/4M/3I2YprVr2LBmxavtBNTanOKhT3tfkqBnSOeGnLxuoVA91vx2qAr84kl/gclrjjsOn4e3lic8pCmn4w8dW7dfUJJumDzJVLGWxiECVwjkpWEsK8jU7pCJJ+nploYAo91WaA56mRvHcarZiXu44TyafHBCFtWXhlG/Pf/9ZlYqBG3AkTo7gGVvSY8Y6pVXjqKKrDwSqlBKsJatVPI/JKTiKaPeG5VSqB0418S3lv3OgIC7+ZFK5MyAYBcg3sU7JvGrd1uzX1CTrhrYzVSxlgoAgIAgIAoKAICAIxBkCQvriDNq4HThLRheVOoCDuHD0Tk2SJUuGrs1q6yNlDqZonqcvhirfvh9HzlbNmCByWoZ2/cZj97GzWLR+t8qtx5XJqb+hGJpqRlfOQWDyUG4+TbvH7QvlyY6FlIqBtXksvD4Ww3F/btcQ/1HKBt5HL0oAr0mFYgUwvX9XsE+etfvV+sp3/CKQxcVJJWEftngHRe/cq588GZkcflOnJNpV0/0IMaC1H85QhEyOktmT8vKx+BIR3EvaqQ5jV2Dv2VAs2RVAued0Zr+G54TbsgmjKTE+t9yGE8PncXfFDAp6oklB0uTN69lUafO4LFmEr6ph/x6Ny6vzOGblXvSZu1nrinLe2TDl2/rqRxZr96vvLBeCgCAgCAgCgoAgIAh8RAQ+I7OlyCEYP+JiPoWphw0bhnmzpuHUsnGxtt39J4NUhEtn+zQULdNDJU83HJz9mk5dCMZ9Ct5SJF9OuFKOPhbOjXch9BbcXJyRlvL7va9wNFCXCq3B0UOPLB6Du6RNPBF4Ra3DhzR1poLAmJrrweNnCLgYQvn9XisfQM3U07htdPs1bp9Y7o9QWokqHfvh2rVr8PDQBQuJzbX37t0bG1csxM4RHWJz2ChjHQy8ijCKcMl+cZxOgIOKGAqfx4CQ27hPwVuK5MwMF9KusbB/JJtVuqW1B+f3e1/haKCZWwwDRw/dP76r0sqdvHwTmdI5EpnMaPV5fPj0JZHT2yqxfC7y49NMPY3XFd1+jdsn5ns2zy374zQEBgYiX758iXkrsnZBQBAQBAQBQeCTQkA0fUngcZctYvnli7UlxfLnirJT1rrFRc45Ntk09CGMMrGZAk7AXrm4j5nad8XR7fddS7n6GAiUzu9pcVo+j5yvz1hYKxgXOefYZLN6MZ15qfGclu45AXvFgjksNVF10e032gGkgSAgCAgCgoAgIAgIAnGMQGRbvjieTIYXBAQBQUAQEAQEAUFAEBAEBAFBQBCIXwSE9MUv3klyNjbfzOSaDqzhExEEPjYC7KaXKZ0DXCk5u4ggIAgIAoKAICAICAKCgCRnlzMQCwhwoJVL66bHwkgyhCDw4QhwNNtzs3p8+EAygiAgCAgCgoAgIAgIAkkEAfHpSyIPMibbGDRtMe49fILJfb6JSbcE1/Ys5d5LRS/4uU0kfn/z71vwx5TYUGqBlCkiH30OInPx6k28ev0vcmV1U9FCTfU1LovpPMb9P7X7oYu2qwAu47t8nqi3zknaU6ZIrhK1G27kBZ0fDkhjSTgPYArya2ThOFpX7zzCjXuPUShnJjhYyBdpOGZM5nlD+QrfUM5MU2JDidx5HyKCgCAgCAgCgoAgkLQRiPzmm7T3KruLQGDl1gOUs+9OoiZ9D588Q/m2v4KTvq+e0CfKs/1++EwsXLcrSjkX/NatNbq3qafq/n37Fv0mLcS0ZZtUNFOtQ42yRTGFSLGbS1qtyOS3tfOY7PwJFq6iRO2hlJ8vMZO+R89eovIvM1HBOzuW928d6Sn6fD0OD6nekkz9roFKGH/4/HV0GreCCN8T1ZzTUfjmzoLJlBYiZ0RSeHPjxGQezlX4985TJoca1MYP3zcoa7JOCgUBQUAQEAQEAUEg6SAgpC/pPMtPZif3Hj1Bj5F/ggmbOQm+Eaa0gCW8o0ZtdM+YXt/tj9krMIUS3Ke2tVFJ4FOS5nDtriPYvP8Evuw7DltmDNG3NXVh7Tym+kpZ4kOA00z8PGs9nb3/mVx83qyuePL8tcm6c6QdZElL6Sg4MX39gX8pDVz1Yl6UXiID1h0OAhPBNiOXYuvwjkhjq8ttaWowa+fhvsFhD5CKtHlMKI3FnfIriggCgoAgIAgIAoJA0kdASF8SfMZvyZTrfOhN5MziBjsLL44fsnXO8Xf5ehjc0jvD0T71hwxldV/Oz/fdHzNwhQgd53qzJFdobQVyZsWm6YMsNcO8NTtUvf/43ihXJL+67tupKQo07IYDp87j6q278KSk8+bE2nnM9U9q5W/puVy8EY7sbulgZ5MyTrbH5pNXbt9HxrQOcExtEydzGA/Kufi6T1+L4NsPKHG7efPN9UO/Mu6q7lnDycnnv/Qrihq+ufHdlDWK8LWoXAhTujVQbQaS1q3dqGX451AQJq7ej97NK5sciwutnYfbhtCa83sSqRzajm9FBAFBQBAQBAQBQeATRECidybwh84mis5lWqDGNwOjrHTq0g2q7ofhs1RdwIUQNO05ElmqfYVSrXoiU5W2qNyhL2au2Bylr2FBjlqdkMUv6stqox//UOOH3ryrb/7y1RuwSWPGSl+iaNPucKd+5cjM8jAlFo9r4TxurmmdUMonD7xzZTU73fOXr3Dn/iPkyRY1F5xhJ9YUPnr6TBUxQdTEPUO6dwnsyefKnFg7j7n+Cb28SNcJyNB0KHYFBEdaKhO7fB3GIGOzobh1X2eaeDr4Nlr+sRg5vhyBcj2mw7PNcFT/dTb+3HQ0Ul/jmzztRyM79TGWZsMWqblZI6bJS/KX6zF9HTxa/Y6S30+hfsNR+eeZOHLhutYkzr45Qi0nkS+R10MRqJhMxBixiSXnIBzeoZbqevzSTfXdpHzBSEM1qaC7DyA8Yyqm5nlO/17vPHoWxfcwpmNLe0FAEBAEBAFBQBBI3AiIpi+BP786FXwVydp/Kgi3wx+o1Ajakpdv2Y+3//2HBlVL4enzl2j16xiEkmaqYO5sqFuhOILJb2/1jkM4evZPCnbijkrFvbWukb5fvn5jMvgEj80fTTjoRN1uQxTBY9JVvUxRpQlbRXPU7joYy8f0QpUSkV9itb6x8V26UF5snjFYDbXn+Dk1p6lxg2/ozOjS2Nli0t/rEBh8Hc72aVDcxwsNq5QCv8CzcDCXzyuWwMptB/D7rOUY0Lk5OHH4rJVbFGnMn8MD2d0zmppClVk7j9kBEnhFvVL5MWnNAaw5eA6VCuXQr3b36WBFJEoRAcqc3hFPX75G29HLVEASn+xuqF08D0KIrP1zMBDHZm1QhKOCT3Z9f8OLV28o8IkJYs3Ekj+a8NlrOHg+EbwbinRVL+qFq3cf0doClZnk4t4tI61R6xdb36XyZdVr1/adDUU9Ms20Rnjd305ejcfPX2H+z03BQVxY7j1+rr7zEBE0FO9suvMWdO3dDy2G9eauzc0TGqYjzfak8Z+69iB4XKc0tsrUs37p/Pp/C+bGNS43/xOIcUu5FwQEAUFAEBAEBIGEhICQvnh+Gkw4+AXNWknraI9a5Yphzc7DWE2fLk11mgI2Ozx69pKKMlmJgplsPXhKEb5mNcrhzyHf64fPkNYRE4n4HD170Szp0zeO5uLvDbsV4SuUJzt2zRmmj4BZjbSRnYdOxdi/VpslfayFHDx9STQzAO0b+KFuxeLRtrPUgP3sWP5ctTVys8VASZ/cWDmuN5wd0qi6nu0a4kDAeQrkslG1T5E8OV68eq18/Ib/2DZyf6O7mMxj1NXkrXYqNFJqstEHFsbk7DWrVEiRvvWHz2N0pzqKEPP0/vvPqlW0qFxYfR8KuqYI3xflfTCzeyNVxn9cSTM25Z+DOHbxBsyRPn3jaC6W7ApQhK8gkUr2d9MiTlbdcQrdyFRy/Kp9ZkkfayGHLd4RzQxA22rFULtE3mjbxaTBwu0nsft0CGqSSWd5A+KbiwK13CP/wO0nL6N11SL6IY8SqWW5Hv4YrKWz5Nen70QX5uZhfz6WuVuOq2/DPyXyZMHSvq0UCTQst+Y6Ls+oNfNLG0FAEBAEBAFBQBCIGQJC+mKG1we3tre3x/MXr2I0TsvaFXWkb/shPenz33ZQjdGqTkX1a33R/DmxY/ZvkfzPwu49xG36sLw2k75AVVr5ZwVF/WTp2KianvDxfYtaFfDL2HnYdewsnjx7YdLH796jp9hy4CQ3tygVixWwWG9N5ZXrOtO4XB6ZMIKIWw4PN2zYcwzTl29SpPXHkbMxd+gPaqhQ0oa+Jk0nC2uWtHD7rP3cTfuxpLmMyTxqgmj+PIs4F3xG4kIcHBzwjIiEtZI/awaw5u5MSBgOkE8bkxYO/88k0I40Vg3K6p5V0Vzu2PxHB3hmcNYPHfbwKcIePFX3sXH22CeO5asavnrCx/fNKhZEn7mbsOdMCJ68eG3Sx+/+0xfYeuIyN7co5SgaZ2wK+52O89+n0jMMbVs90tBMkA9R0JYRS3fhAa2vYsEcyox20pr9qh2bMidPZp31vaV52PeRJWemdBj2VU3koO+NRy9g1sYjikT3nLkes35srNpY8+cZaXVZ4uqMWrMGaSMICAKCgCAgCAgCMUdASF/MMfugHm5ubgh/+BivyayNk5pbI9XLFIaLs6PSSLGvWkYKnrJ8q+7lkEkfC9dzXV9KP3Ay6Aqu3g6nnHPWv+CbXIemeoqovHT1lroaNHUxRs71j9TlyfMX6v7Og0cmSV9lMi0N370wUh9TN5oGx1SdtWXNapSHbwEv8vvzRDonHYH6oXVz0PaSAAAOyElEQVQ9cBqG4i1+gv/2g5jWrwvYtLB1n7EqKAwHb2nf0I+Cj6TCeiKIXX6bhjGkuSxO45jTPFo7jy2NaY3cunsfdnZ2cHJysqZ5jNvw2Qsj/zImtkwqrJEWpO1j0sdmlEz6tp+6rMhVUyJbWk659I6plbnngPlbceryLVwLf0TYmo+sas28xsrwS7fuqW6c52/Myr2RhmCyx3KXfNdMBXap6JMDN//uE6mPqZvYOHuG4zJmnJ6icTnvKCkY2lX3VUTan8jsoAXb9N3ykbnng6cvkT1jWr0pqL7SzIWledhH0NcrCwp4ZkRaBzs1wnf1y4AjhpbpPhWrD5zDJEoRoZmdmplCX8x+g8mIjLq6RjZL1TeQC0FAEBAEBAFBQBBIkAgI6Yvnx1KwYEEiGf/h3JVrKJovp1Wzs+9Zk+pllQni2t1HVG660xdDUbZwPuSgCJ0sbD7JAVXYfM8ra2Z8Ua2MSlrOGraJi9ZaNY9xI/YhNBRNE+Xr7UU+ckYROwvpWjqR75wp4RfFuIokajwfp2QwTMug1efNnoXMYdPjetg95e94IeSmInysEezd8QutGVrUroCN+44rcshms+ZIn7XzsG+gNXKKnmGB/DH3s7JmbG7DZ49NVy/fuo/cWVys6taYNFL9/9qitHsjO9aG/z6daWerCNNOHoTNJzlvHRM1NltsVNYbudzT4wGZL04m8873EdYUGsqzl7ofMJjAOJJPWiSJsMhkXzVTwgQ3riKJmppPK2OTUxZD802tjtc0u8cXaEWmnWzSybn9fLK5oST5SRb/bjLykZbVWrE0jzv5XPLHWPJkcQWna7hBZqQhZAJq7Xz8rHPnygkbGxvjIeVeEBAEBAFBQBAQBBIwAkL64vnh5M2bF5lI48KmjtaSPl4ia/TY72w1mXiGP9BFTGxdt5J+9WPnr1GEr3OTmhjds72+fIIVhO8zfEZ5x/5V/TVfHU7JwIFgDIUJJufIa0mkqLFfGcMqcDqFNxQNM0M601oq9j/8cdSfkfqYuvm+ZV00Jb/ED5FB0xYrMjeoSwu9HxqPF/7wCW5FEFn3DOmxed8JNY23l2eU6YoVyKVIH5t5mhNr5zHX37h866EANGn5pXFxrN0XKVIEzk6OSltnLeljvzw/Cpqy+dhF7KIALmwa6OHqhHLe2fTrmkDpBZjwdapVAiM66nxOuXIyBYGJTvi8/Usmo/xjxbuz93+KiBj2zUEpIDhHHvsZNowwK9XqOZ3CG0pTwms1JexT+DMFlIlOvq1XGmx2GRvC2tGzoXcUVqb8GdmXL5yCudQtmQ+VC7378UczY81LefuskejmYc0oa3b7tawS6d8CB5K5HRF51RQpNDf39oAQVK3V0Fy1lAsCgoAgIAgIAoJAAkXAOqeRBLr4xLqsRo0bY+lmflE2sp+0sKHCeXMgH2mq9pwIpOiSm1WgkYZVS+t7BJLmkKVmuaL6Mo7ouWyzTtugLzRxkTWzK/lqvVVRLrVqzl/HJqiGwkFQWNbtPmpYrPzkanQeiJ8skLpnlEbh3OVr0X7YRPVDZTuRp3EL1oC1ooaydNNeRQY58qgTaSoLRKR92ESJ2B8+eWbYFIs37lH3hoSQtaknyHRWSwpv7TyRBjZzc4RSXgSTL2KjRu8CoZhp+t7FySlITf36DbB0j05bZ+1AzYlosXBuuReUNoEDuGgEjcu1SJNsMqgJR/RcsfeMdmv228PVWRG2oOvh+jbzt50gH9R3UWO5ojgFHWHZcOS8+tb+cLqGOv3n4ZfZ5kkdB0QJvHYn2g+bh8aW7Ay4ooYyFyFzAe2x66TV2Hbykn5K9s2bQNpBDi5riCWv6ySZzV6naKXGEt08XM+kfL0Rbsvp2XC+Qc7fF0VzajxJxH0gRf48E3wrTs+omamlWBAQBAQBQUAQEAQ+EAHR9H0ggO/T/ZtvvsGUKVOUto/9zKwVDujSf8oi3H3wmDR/lWCf+p05G5tcBoXcQN+JCxFCefXCqc0K8vu7GOGHd5AiVLK2rTi1Mxb2Wzt76Spa/DJaBWXhACXs92YsP3/VCIvW7wKnimCT08rFfch38C40bWIf8oszJ9z24f6/zVXHankHCjTT7fcZ+Kr/RPC+82TLgjOXQik6p853asi3rdR8FShojI9XNlVXoEE31K9SEqwBZNNOxsPNJS2+/Lyyassv5GW/7KWuL66djsyUy8/aeazZ3OQlG1C4UCGUKFHCmubv3eabzp1RZv58HDh3FWUKRNVwmhqYI0+y6eTtiMAs7OdnKMW83HGeSNtA8unjvHqsweIIn5du6oKIcHRP1rb55tYRN8O+vrndce7qHbQZsYSCshRSSdfXHAg0bKKuf2pcAYt3BmAlmZey711F8i9k30FNm9iraaUofbQCDpJyZ2l/7TZevjmtBYuhRtRw4rql8qkk7Jy/j81g05OWknManiYNYfeGZSNhxUFXxqzYizZkCjqhaz3DYSgyqOV52Hew+7S16DR+Jfg55CazzrOhYZi39bgaZ2Brv0jjWbqZtvYQvMi0s3Jl3b8JS22lThAQBAQBQUAQEAQSFgJC+j7C8/Dx8UH9evUohcFS+JUqHMnsytJymtcqjwFT/1YaQkPTTu4zuGtLsJ8fa6M4OiULE7yDC0ehXb/xKhLlovW7VRn7E7GPnSaDurZQfY8HXsawWcvoBdSByE4VPH72XBE8zl3HwkFR1k8diA4DJoLTN/CHhdNKDO/eVvkRqoKP/Kdd/ap4/PQ5RszxxxQiU5rwvqb07Uz5BXUh8jnAyrLRv6A74bWZtH0L1u7UmoI1fDMHfKtP7aCvoAsND2vnMexr6vrU+WD4U67AlStXmqqO1bLSpUujCr20D/l7Bzb+1i6Sxs7cRDYpU5CfXgEV9r8sEUVPCjJiKAOIOLCZIROWnhFmlL5EBPeO6YwOY1dg79lQcMoFJn26s0eqrAjp36qq6nuCNFnDl+5COgo2wj5wnNeOCZ4WwZKDkPwzuC2+JvLCY/GHxdnelqJS1kAjCpaSUIR/IGCCxWKK6HI5m5Gy5mwSaeE0zDgiKgd9+bVZZFKVLCKvpIYF92exZp4v/YoqLMes2IPp6w/rOtJfxnkiEchqZLprjVy4EY7FhPncuXOtOjPWjCltBAFBQBAQBAQBQSD+EPiMTAyttzGMv3Ul+ZnOnz+vAmsM6tIcP7T6PFb2yy+Bpy4E4z4FbylCQWJcKUcfC/vnXQi9RZorZ0XQzE3GgVtu3LmPImRKmoK0KeaE57lw9SY4micTPtYUxleQFnNrMlXO5q2cmP3h42fImdUNOdzd9ITNuH0I+S+qwC6EVTb3DMhL2kGN3Bm3Nb6PyTzGfd+SL1qlDn1h75IJe/fuM66Ok/uTJ0/C19cXozrVwlekCYoN4TMREHJb+d0VyZkZLhH+dexPdvHmPbiltSeCZmd2KtYi3rz3GIWpb4qIHxlMNeZ5eDwORpOWxmMt48cI0mJqbe9TxgFr2Dz237f/Q3nvbHG2Fza35XkeUcCYHJnSq+ig1p5vfoZ1B87HGxtnHD12PNIPRu+zZ+kjCAgCgoAgIAgIAvGPgJC++MdcP+OwYcMwZMhgbJs5JEZBXfQDyEWiR6DPxAWY5b8NTMTy5MkTb/vp3bs3Jo4fhy1/tAfn4xMRBMwhwLkEx67aj6NHj6EQmSCLCAKCgCAgCAgCgkDiQ0BI30d8ZqyBq1mzBs4FnFTEL2smyX31ER9HvE/9FwXL+fb36ZhPPnZt2rSJ1/nfUqTVShUr4saV89g4rB1p4hzidX6ZLHEgwCa2ncatxNSpU9GlS5fEsWhZpSAgCAgCgoAgIAhEQeCdY1eUKimIawTYr2758hVwccuMOt2GIpQCsIh8GghwQJzvhs/EgAED4p3wMcIpKBDP6jVrYOuUHg0HLwIn3RYRBAwR4MTtXSauRo8ePYTwGQIj14KAICAICAKCQCJEQEjfR35oTk5O2Lp1G5xcMsLv6wEq2uRHXpJMH4cIsAvt77OW45shU/Hrr79i8ODBcTib5aFdXFywfcdOJEvjjJr95qm0AJZ7SO2ngsBECjDTYexKdO3aFaNHj/5Uti37FAQEAUFAEBAEkiwCYt6ZQB7tkydP0KZ1a2zYuAE/t2tIn0ZIRVETRZIOAhwsptvvM3GA0khMnDgRnSl9QkKQBw8eoHmzZti9exd+bVoR3eqXsRhMJSGsWdYQNwjcoGA6PWZsAOf3GzlypNLyxc1MMqogIAgIAoKAICAIxCcCQvriE+1o5mItEJOBPn16I0NaJ/Tp0BhfVC8r5C8a3BJ69a27DzDp73WYuXILvLy8MO+vv1CsWLEEtWz2Lx01ahQGDhwATpjeq0l5cGJxS5E0E9QGZDEfhAAngJ++7hBmbDgKj6xZMXfeXyhTpswHjSmdBQFBQBAQBAQBQSDhICCkL+E8C/1Krl27hkEDB2L+ggUqJUL9SiVQpWRBSiTuqRKGp7a10beVi4SFAJOnJ5QqIuTGHRyjvIeb9p3AtsMBcEmfHn379VPavZQpUyasRRusJjg4GP3798PixUvohwdH1C2RG5UL5UABz4xwdbZHapuEu3aDbcilBQQ4BQOncLh65yE4P+LWE5fpcwlOTo7o3acvunXrBhsb+T/GAoRSJQgIAoKAICAIJDoEhPQl4Ed28+ZNlQx59apVOH7iRAJeqSzNFAJp0qRGNb9qaNa8ORo1InPdVKlMNUuQZfzDw5w5c7B6lT8CTp9JkGuURX04AnZ2tqhSpQqaNWuOJk2awNbW9sMHlREEAUFAEBAEBAFBIMEhIKQvwT0S0wt6/vw5AgMDER4eDr4WSZgIJE+eHA4ODvD09ESuXLmSRCLrp08pgXhQkDp7L168SJjAy6qsRoCjBjs6OsLDw0OZG/OZFREEBAFBQBAQBASBpI2AkL6k/Xxld4KAICAICAKCgCAgCAgCgoAg8IkjICkbPvEDINsXBAQBQUAQEAQEAUFAEBAEBIGkjYCQvqT9fGV3goAgIAgIAoKAICAICAKCgCDwiSMgpO8TPwCyfUFAEBAEBAFBQBAQBAQBQUAQSNoI/D+x0p+SXBf3KQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals.six import StringIO  \n",
    "from IPython.display import Image  \n",
    "from sklearn.tree import export_graphviz\n",
    "import pydotplus\n",
    "\n",
    "dot_data = StringIO()\n",
    "\n",
    "export_graphviz(dtree, out_file=dot_data, \n",
    "                feature_names=X_train_refined.columns,\n",
    "                filled=True, rounded=True,\n",
    "                special_characters=True)\n",
    "\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 03: `XGBoost`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/miniconda3/lib/python3.7/site-packages/xgboost/core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n"
     ]
    }
   ],
   "source": [
    "DM_train = xgb.DMatrix(data=X_train_refined,label=y_train)\n",
    "DM_test = xgb.DMatrix(data=X_test_refined,label=y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {\n",
    "    'max_depth': 6,\n",
    "    'objective': 'reg:linear',\n",
    "    'booster': 'gblinear',\n",
    "    'n_estimators': 1000,\n",
    "    'learning_rate': 0.2,\n",
    "    'gamma': 0.01,\n",
    "    'random_state': 7,\n",
    "    'subsample': 1.\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {\n",
    "    'max_depth': 6,\n",
    "    'objective': 'reg:linear',\n",
    "    'booster': 'gblinear',\n",
    "    'n_estimators': 1000,\n",
    "    'learning_rate': 0.2,\n",
    "    'gamma': 0.01,\n",
    "    'random_state': 7,\n",
    "    'subsample': 1.\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_reg = xgb.train(params = parameters, dtrain=DM_train, num_boost_round=8)\n",
    "y_preds = xg_reg.predict(DM_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE Score: 20.394753742379482\n",
      "R2_Squared: 0.3140280028163105\n",
      "MAE Score: 17.774598693847658\n"
     ]
    }
   ],
   "source": [
    "calculate_metrics(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_preds(y_test, y_preds, 'XGBoost')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> I used a `gblinear` booster for XGBoost and XGBoost currently does support feature importances linear models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model 04: H2O.ai's AutoML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nbuser/anaconda3_420/lib/python3.5/site-packages/h2o/backend/connection.py:317: UserWarning: Proxy is defined in the environment: http_proxy. This may interfere with your H2O Connection.\n",
      "  \"This may interfere with your H2O Connection.\" % name)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321 ..... not found.\n",
      "Attempting to start a local H2O server...\n",
      "  Java Version: openjdk version \"1.8.0_191\"; OpenJDK Runtime Environment (build 1.8.0_191-8u191-b12-2ubuntu0.16.04.1-b12); OpenJDK 64-Bit Server VM (build 25.191-b12, mixed mode)\n",
      "  Starting server from /home/nbuser/anaconda3_420/lib/python3.5/site-packages/h2o/backend/bin/h2o.jar\n",
      "  Ice root: /tmp/tmp0nfcfis4\n",
      "  JVM stdout: /tmp/tmp0nfcfis4/h2o_nbuser_started_from_python.out\n",
      "  JVM stderr: /tmp/tmp0nfcfis4/h2o_nbuser_started_from_python.err\n",
      "  Server is running at http://127.0.0.1:54321\n",
      "Connecting to H2O server at http://127.0.0.1:54321 ... successful.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime:</td>\n",
       "<td>07 secs</td></tr>\n",
       "<tr><td>H2O cluster timezone:</td>\n",
       "<td>Etc/UTC</td></tr>\n",
       "<tr><td>H2O data parsing timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O cluster version:</td>\n",
       "<td>3.24.0.2</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>1 day </td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_nbuser_filk0a</td></tr>\n",
       "<tr><td>H2O cluster total nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O cluster free memory:</td>\n",
       "<td>910 Mb</td></tr>\n",
       "<tr><td>H2O cluster total cores:</td>\n",
       "<td>2</td></tr>\n",
       "<tr><td>H2O cluster allowed cores:</td>\n",
       "<td>2</td></tr>\n",
       "<tr><td>H2O cluster status:</td>\n",
       "<td>accepting new members, healthy</td></tr>\n",
       "<tr><td>H2O connection url:</td>\n",
       "<td>http://127.0.0.1:54321</td></tr>\n",
       "<tr><td>H2O connection proxy:</td>\n",
       "<td>None</td></tr>\n",
       "<tr><td>H2O internal security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>H2O API Extensions:</td>\n",
       "<td>Amazon S3, XGBoost, Algos, AutoML, Core V3, Core V4</td></tr>\n",
       "<tr><td>Python version:</td>\n",
       "<td>3.5.4 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ---------------------------------------------------\n",
       "H2O cluster uptime:         07 secs\n",
       "H2O cluster timezone:       Etc/UTC\n",
       "H2O data parsing timezone:  UTC\n",
       "H2O cluster version:        3.24.0.2\n",
       "H2O cluster version age:    1 day\n",
       "H2O cluster name:           H2O_from_python_nbuser_filk0a\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    910 Mb\n",
       "H2O cluster total cores:    2\n",
       "H2O cluster allowed cores:  2\n",
       "H2O cluster status:         accepting new members, healthy\n",
       "H2O connection url:         http://127.0.0.1:54321\n",
       "H2O connection proxy:\n",
       "H2O internal security:      False\n",
       "H2O API Extensions:         Amazon S3, XGBoost, Algos, AutoML, Core V3, Core V4\n",
       "Python version:             3.5.4 final\n",
       "--------------------------  ---------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import h2o\n",
    "from h2o.automl import H2OAutoML\n",
    "h2o.init()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> To use h2o.ai's utilities on the dataset, the library requires the data to be in **H2OFrame** format. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train['Enrollments'] = y_train\n",
    "X_test['Enrollments'] = y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "X_train_h2o = h2o.H2OFrame(X_train)\n",
    "X_test_h2o = h2o.H2OFrame(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = X_train.columns.values.tolist()\n",
    "target = \"Enrollments\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AutoML progress: |████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "# Construct the AutoML pipeline\n",
    "auto_h2o = H2OAutoML()\n",
    "# Train \n",
    "auto_h2o.train(x=features,\n",
    "               y=target,\n",
    "               training_frame=X_train_h2o)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>model_id                                           </th><th style=\"text-align: right;\">  mean_residual_deviance</th><th style=\"text-align: right;\">   rmse</th><th style=\"text-align: right;\">    mse</th><th style=\"text-align: right;\">    mae</th><th style=\"text-align: right;\">   rmsle</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>DeepLearning_grid_1_AutoML_20190418_095149_model_19</td><td style=\"text-align: right;\">                 394.013</td><td style=\"text-align: right;\">19.8498</td><td style=\"text-align: right;\">394.013</td><td style=\"text-align: right;\">16.6412</td><td style=\"text-align: right;\">0.122494</td></tr>\n",
       "<tr><td>DeepLearning_grid_1_AutoML_20190418_095149_model_17</td><td style=\"text-align: right;\">                 456.295</td><td style=\"text-align: right;\">21.3611</td><td style=\"text-align: right;\">456.295</td><td style=\"text-align: right;\">17.0919</td><td style=\"text-align: right;\">0.130239</td></tr>\n",
       "<tr><td>DeepLearning_grid_1_AutoML_20190418_095149_model_28</td><td style=\"text-align: right;\">                 456.994</td><td style=\"text-align: right;\">21.3774</td><td style=\"text-align: right;\">456.994</td><td style=\"text-align: right;\">17.3409</td><td style=\"text-align: right;\">0.133017</td></tr>\n",
       "<tr><td>GBM_grid_1_AutoML_20190418_093134_model_6          </td><td style=\"text-align: right;\">                 471.103</td><td style=\"text-align: right;\">21.7049</td><td style=\"text-align: right;\">471.103</td><td style=\"text-align: right;\">17.7187</td><td style=\"text-align: right;\">0.135341</td></tr>\n",
       "<tr><td>DeepLearning_grid_1_AutoML_20190418_095149_model_21</td><td style=\"text-align: right;\">                 474.091</td><td style=\"text-align: right;\">21.7736</td><td style=\"text-align: right;\">474.091</td><td style=\"text-align: right;\">17.3858</td><td style=\"text-align: right;\">0.131161</td></tr>\n",
       "<tr><td>DeepLearning_grid_1_AutoML_20190418_095149_model_25</td><td style=\"text-align: right;\">                 482.937</td><td style=\"text-align: right;\">21.9758</td><td style=\"text-align: right;\">482.937</td><td style=\"text-align: right;\">17.4681</td><td style=\"text-align: right;\">0.140002</td></tr>\n",
       "<tr><td>XGBoost_grid_1_AutoML_20190418_093134_model_5      </td><td style=\"text-align: right;\">                 483.69 </td><td style=\"text-align: right;\">21.993 </td><td style=\"text-align: right;\">483.69 </td><td style=\"text-align: right;\">17.729 </td><td style=\"text-align: right;\">0.138482</td></tr>\n",
       "<tr><td>DeepLearning_grid_1_AutoML_20190418_095149_model_8 </td><td style=\"text-align: right;\">                 483.857</td><td style=\"text-align: right;\">21.9967</td><td style=\"text-align: right;\">483.857</td><td style=\"text-align: right;\">18.8936</td><td style=\"text-align: right;\">0.134775</td></tr>\n",
       "<tr><td>GBM_1_AutoML_20190418_095149                       </td><td style=\"text-align: right;\">                 487.348</td><td style=\"text-align: right;\">22.076 </td><td style=\"text-align: right;\">487.348</td><td style=\"text-align: right;\">18.7017</td><td style=\"text-align: right;\">0.138016</td></tr>\n",
       "<tr><td>DeepLearning_grid_1_AutoML_20190418_095149_model_29</td><td style=\"text-align: right;\">                 487.631</td><td style=\"text-align: right;\">22.0824</td><td style=\"text-align: right;\">487.631</td><td style=\"text-align: right;\">18.8202</td><td style=\"text-align: right;\">0.144757</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Overview of how the models performed\n",
    "auto_h2o.leaderboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract the best model from the leaderboard\n",
    "auto_h2o = auto_h2o.leader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_temp = X_test.copy()\n",
    "del X_test_temp['Enrollments']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
      "deeplearning prediction progress: |███████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "# Employing the model to make inference\n",
    "X_test_h2o_copy = h2o.H2OFrame(X_test_temp)\n",
    "y_preds = auto_h2o.predict(X_test_h2o_copy)\n",
    "\n",
    "# Convert the predictions to a native list\n",
    "y_preds = h2o.as_list(y_preds[\"predict\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE Score: 22.073201859707595\n",
      "R2_Squared: 0.19647364545913504\n",
      "MAE Score: 20.267868598386883\n"
     ]
    }
   ],
   "source": [
    "calculate_metrics(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **XGBoost Still Wins!**"
   ]
  }
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