{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#6304640318 jade utaiwan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exam Instruction EE522 Fall 2022:\n",
    "    \n",
    "1. Answer all of the following questions. Each answer should be thorough, complete, and relevant. Points will be deducted for irrelevant details. Use the back of the pages if you need more room for your answer.\n",
    "\n",
    "2. The points are a clue about how much time you should spend on each question. Plan your time accordingly.\n",
    "\n",
    "3. There are 3 questions. The total score is 100 points accounted for 25 percent of the total scores.\n",
    "\n",
    "4. Exam Date and time: October 1, 2022 from 1-4 pm. \n",
    "\n",
    "\n",
    "5. You have to submit (1) the Jupiter Notebook code (.ipynp) with the name: your student id.ipynp i.e 6504610069.ipynp \n",
    "\n",
    "6. You have to submit your code on BE-moodle.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1 [50 points]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Set Information:\n",
    "\n",
    "Bike sharing systems are new generation of traditional bike rentals where whole process from membership, rental and return back has become automatic. Through these systems, user is able to easily rent a bike from a particular position and return back at another position. Currently, there are about over 500 bike-sharing programs around the world which is composed of over 500 thousands bicycles. Today, there exists great interest in these systems due to their important role in traffic, environmental and health issues. \n",
    "\n",
    "Apart from interesting real world applications of bike sharing systems, the characteristics of data being generated by these systems make them attractive for the research. Opposed to other transport services such as bus or subway, the duration of travel, departure and arrival position is explicitly recorded in these systems. This feature turns bike sharing system into a virtual sensor network that can be used for sensing mobility in the city. Hence, it is expected that most of important events in the city could be detected via monitoring these data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset characteristics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\t\n",
    "=========================================\n",
    "Dataset characteristics\n",
    "======================\t\n",
    "hour.csv has the following fields:\n",
    "\t\n",
    "\t- instant: record index\n",
    "\t- dteday : date\n",
    "\t- season : season (1:springer, 2:summer, 3:fall, 4:winter)\n",
    "\t- yr : year (0: 2011, 1:2012)\n",
    "\t- mnth : month ( 1 to 12)\n",
    "\t- hr : hour (0 to 23)\n",
    "\t- holiday : weather day is holiday or not (extracted from http://dchr.dc.gov/page/holiday-schedule)\n",
    "\t- weekday : day of the week\n",
    "\t- workingday : if day is neither weekend nor holiday is 1, otherwise is 0.\n",
    "\t+ weathersit : \n",
    "\t\t- 1: Clear, Few clouds, Partly cloudy, Partly cloudy\n",
    "\t\t- 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
    "\t\t- 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\n",
    "\t\t- 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog\n",
    "\t- temp : Normalized temperature in Celsius. The values are divided to 41 (max)\n",
    "\t- atemp: Normalized feeling temperature in Celsius. The values are divided to 50 (max)\n",
    "\t- hum: Normalized humidity. The values are divided to 100 (max)\n",
    "\t- windspeed: Normalized wind speed. The values are divided to 67 (max)\n",
    "\t- casual: count of casual users\n",
    "\t- registered: count of registered users\n",
    "\t- cnt: count of total rental bikes including both casual and registered"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.1 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the scatter plots to visualize the relationship between temp and target values (cnt). Then, plot the heatmap to represent the correlation matrix between features and target values.\n",
    "\n",
    "Also, calculate the median of cout of total bikes (cnt) separated by weathersit, season ,and holiday. Is there any interesting thing you found? Explain carefully.\n",
    "\n",
    "\n",
    "[20 Points]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 17 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   instant     17379 non-null  int64  \n",
      " 1   dteday      17379 non-null  object \n",
      " 2   season      17379 non-null  int64  \n",
      " 3   yr          17379 non-null  int64  \n",
      " 4   mnth        17379 non-null  int64  \n",
      " 5   hr          17379 non-null  int64  \n",
      " 6   holiday     17379 non-null  int64  \n",
      " 7   weekday     17379 non-null  int64  \n",
      " 8   workingday  17379 non-null  int64  \n",
      " 9   weathersit  17379 non-null  int64  \n",
      " 10  temp        17379 non-null  float64\n",
      " 11  atemp       17379 non-null  float64\n",
      " 12  hum         17379 non-null  float64\n",
      " 13  windspeed   17379 non-null  float64\n",
      " 14  casual      17379 non-null  int64  \n",
      " 15  registered  17379 non-null  int64  \n",
      " 16  cnt         17379 non-null  int64  \n",
      "dtypes: float64(4), int64(12), object(1)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "# activate plot theme\n",
    "import qeds\n",
    "\n",
    "qeds.themes.mpl_style();\n",
    "plotly_template = qeds.themes.plotly_template()\n",
    "colors = qeds.themes.COLOR_CYCLE\n",
    "\n",
    "from sklearn import (linear_model, metrics, model_selection)\n",
    "\n",
    "df = pd.read_csv(\"hour.csv\")\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def var_scatter(df, ax=None, var=\"cnt\"):\n",
    "    if ax is None:\n",
    "        _, ax = plt.subplots(figsize=(8, 6))\n",
    "    df.plot.scatter(x=var , y=\"temp\",alpha=0.1, s=1.5, ax=ax)\n",
    "\n",
    "    return ax\n",
    "\n",
    "var_scatter(df);\n",
    "plt.savefig(\"scatter1.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "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>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17374</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>11</td>\n",
       "      <td>108</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17375</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>8</td>\n",
       "      <td>81</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17376</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>7</td>\n",
       "      <td>83</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17377</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17378</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>12</td>\n",
       "      <td>37</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17379 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       season  yr  mnth  hr  holiday  weekday  workingday  weathersit  temp  \\\n",
       "0           1   0     1   0        0        6           0           1  0.24   \n",
       "1           1   0     1   1        0        6           0           1  0.22   \n",
       "2           1   0     1   2        0        6           0           1  0.22   \n",
       "3           1   0     1   3        0        6           0           1  0.24   \n",
       "4           1   0     1   4        0        6           0           1  0.24   \n",
       "...       ...  ..   ...  ..      ...      ...         ...         ...   ...   \n",
       "17374       1   1    12  19        0        1           1           2  0.26   \n",
       "17375       1   1    12  20        0        1           1           2  0.26   \n",
       "17376       1   1    12  21        0        1           1           1  0.26   \n",
       "17377       1   1    12  22        0        1           1           1  0.26   \n",
       "17378       1   1    12  23        0        1           1           1  0.26   \n",
       "\n",
       "        atemp   hum  windspeed  casual  registered  cnt  \n",
       "0      0.2879  0.81     0.0000       3          13   16  \n",
       "1      0.2727  0.80     0.0000       8          32   40  \n",
       "2      0.2727  0.80     0.0000       5          27   32  \n",
       "3      0.2879  0.75     0.0000       3          10   13  \n",
       "4      0.2879  0.75     0.0000       0           1    1  \n",
       "...       ...   ...        ...     ...         ...  ...  \n",
       "17374  0.2576  0.60     0.1642      11         108  119  \n",
       "17375  0.2576  0.60     0.1642       8          81   89  \n",
       "17376  0.2576  0.60     0.1642       7          83   90  \n",
       "17377  0.2727  0.56     0.1343      13          48   61  \n",
       "17378  0.2727  0.65     0.1343      12          37   49  \n",
       "\n",
       "[17379 rows x 15 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.drop([\"instant\", \"dteday\",], axis=1).copy()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABGwAAAKSCAYAAACHuE0PAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOzddXQU19vA8W/cXYgSB4IFd3eXFmkp0EKh3lLaX4UK1OVtS4UKFUqRUtwtECRAgACB4CHEHeLu2fePhU02gkVbns85nMPO3pm9z1hm7jxzr0Z+fr4CIYQQQgghhBBCCNFkaDZ2BYQQQgghhBBCCCGEOmmwEUIIIYQQQgghhGhipMFGCCGEEEIIIYQQoomRBhshhBBCCCGEEEKIJkYabIQQQgghhBBCCCGaGGmwEUIIIYQQQgghhGhipMFGCCGEEEIIIYQQoonRbuwKCCGEEEIIIYQQQjSGS5cusWXLFsLCwkhLS2PevHkMGTLkjvNERUWxdOlSrl+/jrGxMSNGjOCxxx5DQ0OjTusmGTZCCCGEEEIIIYR4KBUUFODi4sIzzzyDrq7uXcvn5eXx/vvvY25uzuLFi3nmmWfYsmULW7durfO6SYaNEEIIIYQQQgghHkpdunShS5cuAHz33Xd3LX/48GEKCwuZP38+enp6uLi4EBcXx9atW5kwYUKdZtlIho0QQgghhBBCCCHEPQgJCaFNmzbo6emppnXs2JG0tDRu3LhRp78lGTZNVOyQ8Y1dhQbl9/57jV2FBjNq65bGrkKD2jvxkcaugqgnmnX8jm5Tpq31cD3fKCguaewqNJgpPXwauwoN6mE6bi/FJjV2FRrU1jOXG7sKDWa4T4vGrkKDMjMwaOwqiHri42Lf2FWoVw15T3v5f8/j6+t713LDhw9nxIgRtfqt9PR0rK2t1aaZm5sDkJGRgZ2dXa2WX5E02AghhBBCCCGEEOJfa8SIEbVuiLkfdd25cE2kwUYIIYQQQgghhBB1S+O/maFsYWFBenq62rSMjAygPNOmrvw316AQQgghhBBCCCFEHWvVqhWXL1+mqKhINS04OBhLS0uaNWtWp78lDTZCCCGEEEIIIYR4KOXn5xMREUFERARlZWUkJycTERHBzZs3AVixYgXvvvuuqnz//v3R09Pju+++Izo6muPHj7Nx48Y6HyEK5JUoIYQQQgghhBBC1LV/SUf3YWFhvPPOO6rPa9asYc2aNQwaNIj58+eTlpZGUlJ5R/ZGRkZ8/PHHLF26lPnz52NsbMzEiROZMGFCnddNGmyEEEIIIYQQQgjxUGrXrh07duyo8fv58+dXmebq6soXX3xRn9UCpMFGCCGEEEIIIYQQdU3z35Fh05RJHzZCCCGEEEIIIYQQTYxk2AghhBBCCCGEEKJOafxHh/VuSLIGhRBCCCGEEEIIIZoYybARQgghhBBCCCFE3ZI+bGpNMmyEEEIIIYQQQgghmhjJsBHo+7TF4vFJ6Lf0QtvGmqRPvyZ7z/7GrtZdKRQKTu7ZzqXjRyjIz8POxY1Bk5/Ayt7xjvPFXb/GkS3rSE1KwMjMnC6DR9C+zwDV96mJ8ZzYvZ2bcdFkpabQfcRYeo4ar76MsFDOHvTlRmw0uZkZDH1iFm26966PMKtl3K8XZkMHomVmSlFiEukbtlIYFlljeX3vlpiPGY6Ogx2KkhIKw6NI37yDkpvJAOh5eWA+fhQ6zWzR0NWlNC2NnIBAsvwO13ssCoWCE3u2czHAn4L8POxd3Bk05Qms77IdY69fw3/LOlIT4zE2M6fLkJH4VNiOAKHBZzi+ayuZKcmYWdvQe8wjePl0UiuTk5nBse2biLxygaKCAsysbRg8ZQbOXi0BuB4cxIUAf27GRZOfk8PkV97A2avVvzLeooJ8AnZtJez8WfJysrF1as7ARx/HzsVNbTnpN5M4um0TsdevUlpSimUzO0Y+ORcrO4f7jvX47m1cCPCnMD8POxd3hkydfk+xHt68lpRbsXYdMpIOfQeqx3ruDMd2bVHF2nfsI3j5dFZ9H+i7i9DzQaTfTEJLWxt7Vw/6jnsUGwcnVZljOzcTeu4MWelpaGlp08zZhd5jJuLo7nlfcdbkrP8BAvfvISczA2t7R4ZMnqbar6pzMz6W/etWkxgVgb6hER36DqT3qHFoaCifTuVkZnBw41qSYqNIv3mDNt17MebJuTUu78rpk2z/cykebX2Y/GLV4SjrWmOek0/s3kbgXvXhOA1NTHnm08V1Ft+dbNq4gTWrVpOamoKbuzvz5r9Gh44dqy0bGRHBN1/9H5GRkeTm5GBtbc2QYcN4eu4z6OjoAJCSksKS777j2rUQ4mJjGTFyJO8t+qBBYrkXGzds4O/Vq0hNUcY7/7XX7xjvV//3ZYV4bRg6bBhznimP99DBg2zZvInQa9coKirC1c2Np2bNpl///g0ZVrUO7dmF77bNZKSn4eDcnMdmz6VF67bVli0uKmLVrz8RHRFOUlwsHq28efNj9SFfQy5d4OuF71SZ9+MffsHeybleYrhfw3xa0aOFC4a6ukSnpLM58Dw3MrJrLG9ioMe4Lm1xsjLH2sSYoIhY1gacVSvT1aM5j/XpVGXet1Ztp6SsrM5juBdHfPdwYMdWsjLSsXdy5pEnn8bTu3W1ZYuLilj7x1LiIiNIio/DvWUr5i36RK1McOAJAvx8iYuMpLi4CDsnZ4ZPnES7Lt0aIpy78tu9g92bN5KZnoZjcxeemPMcLdtUvy8XFRXx188/EB0eRkJcLF7erXnns6/Uyvz23dccO+hXZV5dPT3+2LCtXmK4V3UdK8Bx/0Ps3ryBpPh4DAwNaePTgcdmz8XcwrK+w/l305AMm9qSBhuBpoEBRRHRZO/1o9l7bzR2de7ZGb+9nD20j2FPzMbC1o7AvTvY/NNinnzvU3T19audJzM1ma2/fk+bHn0YMXMO8RFhHFr/NwbGJnh1UN7sFRcVYWplhadPJ47v2lLtcooLC7Cyd8S7a098V/9ZbzFWx7BzByynTCTtn00UhEdg0q83ti8+Q8JHX1KanlGlvLaVJbbPzybr0FFS/lqDhp4uFhPHYvviXBIWfQaAorCQ7MNHKY5PRFFUjJ6HG5bTJlFWVETOkeP1Gs9pvz0EHfRl+PTZWNracXLvDjb9+A2z3v8UXX2DaufJTElmy9LvaNujDyNnziE+/DoH1/+NgbExLTp0ASAhMoxdy3+l16jxePp0Iuz8WXb++QuPzV+Avas7AAV5eaz79nMc3L2Y8Ow8DI1NyExNxtDERPVbxUWFOLh54N21B3tXLftXx7tvzQpSEuIYMeNpjM0tuHr6JBt//IYn3/0YE3ML1W+t/fZzWnftRfcRb6BnYEjajUR09ao/pu7klN8ezhz0ZeT0p7FoZseJPdvZsORrnl74WY2xZqQks+mXb2nXoy+jnpxLfPh1/NatxtDYhBYdb8UaEcaO5UvpPWo8Xh06cz04iO3LfmHaawuwd/UAIPZ6CB36DlQ2RikgYNcWNiz5mlnvfYKBkTEAlrb2DJ4yHTMra0qKiwk6uI9NPy/m6YWfY2Rqdt/xVnT1TCB+69cw7PEZOHm04OyRA6z/aTFzFn6GmaVVlfKF+fms++ErnD1b8uRbi0i9kcjulcvQ0dOl+5CRAJSUFGNgbEyP4aM5f8z/jr+fkXyTQ5vX4eTZolZx3I/GPCcDWNjaMemV8r9hDdXRod/+fXz3zTf876238PHpwOaNG3n91Xn8vW49dnZ2Vcrr6OgwcvRoWrRoibGJCWHXQ/nis88oLSnlxVdeAZQxm5mbM+PJJ9m2peaYG8P+ffv49puveeOtt/Hp0IFNGzcwf94r/LN+Q7XxauvoMGr0GFq2VMZ7PTSUzz/7lJLSEl5+ZR4A586epUuXrjz7/POYmprhu3cPb7/5Bj8v/bXGhqCGcOrYEdb++RtPPPM8nt5tOLxnF99/8gEfff8zVja2VcqXlZWho6PDoJFjuHj2DHm5OTUu+6Pvf8bIuPxvjYmpab3EcL8GtvWifxsP1h47R3JWNkN9WvHs0F58ueUAhSUl1c6jralFbmERBy9ep0cLlxqXXVhcwueb1R8INlZjTdDxY2xasYwpTz+DR0tvju7byy+ff8y7i3/A0tqmSvnb27bf8FFcPhdEfl5ulTJhVy/j1aYdo6dOw8jYhNNHj/D711/yyqKPa2wIaignj/rz9+9LmfncS7Ro3YYDu3fy9Yfv8flPv2Fdzb6sKCtDR1eXIaPHcT7odLX78vS5zzPlydlq0z556/UaG0YaSn3EGnrlMr9++xWPz5pD5+69yMxIZ8XSH1n6zf/x9idfVCkvRF36VzTYXLp0ib/++ovo6Gg0NTVxcnLilVdewcXFhatXr7JixQquX7+OsbEx3bt356mnnsLQ0BCAoKAg1q9fT3R0NBoaGnh5eTF37lycncufYvzzzz/s37+f9PR0jI2N6dixI6+99hoAxcXF/PXXXxw5coTc3Fzc3d2ZNWsWbdq0AeDixYu88847fPLJJ6xcuZKoqCiaN2/Oiy++iKdn3TylrW95J0+Td/I0AM3e+V8j1+beKBQKzvn70XXISNVF/fDps/n13fmEBAXSvnf1T+UuHPPH2MycgZOmAWBp50BSVARBB31Vy7FzcVNlHJzev6va5bi1aY9bm/YA7Pt7eZ3Gdjemg/uTc+I0OQEnAUhfvwWDNq0w6debjG1V66vb3Am0tMjYugsUCgAyfQ9gN/8FNI2MKMvNpSgmjqKYONU8JalpGHZoh76ne7022CgUCs4d9qPb0FGqhofh059m6TuvEnImUO0pe0XnAw5jbGbOoMlPAGBl50BSdCRBB3xVyzl7yA9nr1Z0Hz5GVSY2NISzh/YzetazAJzx24ORqTkjZ85RLdus0oVa6269AMjPqfnp4r8h3uKiIq6fD2Ls0y+oMoR6jRpPxKXzXDh2iN5jHgHg2M4tuLRqQ/9Hpqp+37yai9d7ifXsof10HzpK1dAycsYcfl4wj6tnAqtkB6liPaaMdfCU8lgToyI4fcBXtZygw/tp7tWKHiPGKsuMcCDmeghBh/YzZpaywWbSS6+rLXfUk3NZ8r8XSYgIw6NdBwBad+upVmbAI49x8cRRbsbF4ta6dg02pw740q5nbzrcinPY1BlEXr7EuSMHGTBhcpXyl0+doLioiNFPzkVHVxcbRydSkxI57edLt8Ej0NDQwNzKhqFTpwNw7eyZGn+7tLSEbX8upd+4R4kOvUp+Ts03jXWlsc/JAJpamrVuaHsQa9esYdSYMYyfMBGA1954g5MnT7Bl00aef/GlKuWdnJ1xqnANYm9vz9mgswQHB5dPc3Dgtf8p/x4fOnCwfgO4T/+s+ZvRY8YyYaIy3v+98SYnT5xg88aNvPBS1XidnZ3Vrrns7e05ezaI8xXivR3rbXPmPsPxY8fwP3y4URts9u/YSq+Bg+k3dAQA0+Y+x6XgIA777ubR6U9VKa+nr8+M55TrIC468o4NNiZmZpg0wv56N/28PTh48ToXYxIA+OdYEB9OHUlHdydOhkZVO096bh5bT10EoL3LnTMxswsK67S+D+rQru107z+Q3oOHATB59lyunj/HsX17GTdtRpXyevr6PDb3eQDiY6KqbbCZ9NQctc+jJk/l8rkzXDgd2OgNNnu3babP4KEMHK58ADDz2Re4ePYMB3fvrNLoAsp4Z72gbECOjap+XzY0MsLQyEj1OfTKZW4mJfLs/MZ9+FsfsYZdu4qllTUjxiuvlWzs7Bg6Zjyrfvu5HiP5j9CUHlhqq8mvwdLSUj755BO8vb354Ycf+Prrrxk7diyamppERUWxcOFCunfvzpIlS3jnnXeIiIjg+++/V81fUFDAuHHjWLx4MZ999hmGhoZ8/PHHFBcXAxAQEMCWLVt4/vnn+fXXX1m4cCEtWpQ/kVy+fDlHjx7llVde4fvvv8fFxYUPPviAtLQ0tXquWLGCJ598ku+++w4TExO++eYbFLdujkXdy0pNIS8rk+at2qimaevq4ujRgsTIsBrnS4oKp3nLNmrTXLzbcjMmmtLS6p8cNSlaWug2d6Lg6jW1yQVXr6Hn7lrtLIXRsVBainHvHqChgYaeHsY9ulAYFUNZbtULDgAdJ0f03F0puB5e1xGoyUxNITcrE5cK21FHVxcnjxYkRNb824mR4WrzALh6t+FGhe2YGFW1jIt3WxIq7B9hF89h5+rGzj+X8suCV1n1xQec8z9Qb8duY8arKCtFUVaG9q1XEG7T1tEhPvx2mTIiLgVjZefApp+/5ZcF8/j7q4+5FnTqAWJNVsbqXf6kTUdXFyfPlsRH1HyMJkaG41ol1rbciIlSxZoQGY6Ld9Uy8RE1r8OiggIUCgV6txrzKystKeFCgD+6+gbY1vK1hNKSEpJionDzVn/K6ObdpsbY4yPDcPZsgY6urmqae+u25GRmkJmacl+/f2TbJsysrGnXs8/9V/4BNYVzcmZKCr+//z/+/OBtdv/1K5kpyfcXxAMoLi7mWkgI3bv3UJverXt3Ll64cE/LiIuNJfDkCTp2aryGiXulireHerzdu/e453hjY2M5eeIEHTtWfT2moty8PExMTe5Ypj6VFBcTHR5Gmw7q9Wzj04nwkJBaL/+TN+bz+uwZfL3oHUIu3tu6q2+WxoaYGuoTmnBTNa2ktIyIG6m42tT+tQ8dLS3efXQY708aztODeuBo2TgNViUlxcRGhOPdvoPa9FbtfYgMrf22ragwPx/DW1mdjaWkuJiosOu0q7Qvt+3YieshV+vsdw7v24Njcxe8GrFxqr5i9fJuTUZ6GudOnUShUJCdlcnJo4fx6dy1ljUW4u6afIZNXl4eubm5dOvWDXt7ewDVk5rFixfTt29fJt56ygPwwgsvMG/ePDIyMjA3N6d3b/V+RV599VWmTp1KaGgobdq0ITk5GUtLSzp27Ii2tja2trZ4eXkBysaePXv28PLLL9O1a1fV8i9cuMCuXbuYMaO8BX769Om0b6/MuHjsscd46623SE1Nxdrauv5WzkMsNysTUPZRUJGhiSk5mRl3mC8L5xZV5ykrK6UgJwcjM/O6rmqd0jI2QkNLi9Is9WyP0qwc9FtVf2FbmpbOjR+WYjPnSSwfewQ0NCiKi+fmj79XKev42UK0jI1BS5PMXfvIOXqiXuK4La+m7WhqSk5GRo3z5WZl0bxl9dsxPycHYzNzcrMyMaq0XCMTU/Kys1SfM1OSOX/0EJ0GDqPb0JHcjI/l0IY1AHTsP7g2oVWrMePV1TfA3s2DwL07sbJ3xMjUjJCgQBIjwzG/lSKcl5NNcWEhgft20Xv0BPqOe5TY0KvsXvk7Onp6uLf1uedYc7OyVHWoXKecjPQ7zJdJ81bqF3uGpvcaa2aNyz24cQ22Ts1xcFPPfAy/GMzO5b9SXFyEsakZk196vdZZGnk52SjKyjCstBxDUzNyQ65UO09uViYm5pZVyt/+7l6znCKvXOJq0Clmv/PRA9T8wTX2OdnO1Z1hT8zCopkd+TnZBPruZN23nzPjnY9Ur8DVh4yMDEpLS7GwVN92lpaWnDl154bOZ56ereqzZdyECTz3wov1Vs+6cjtey2riPX0q8I7zzp09m2vXQigqKmL8hIk8/2LN8W5cv57kmzcZOWp0ndT7QeRkZ1FWVoZppX3Q1NycKxeCH3i55haWTH/2BVw9W1BaUsyJw4f45oN3eeOjz2nRyK+SmBooX12snAWTk1+ImeH9vxZb0c2sbNYdP0tCWhZ6Otr09XbnpZF9+Wb7IVKyq394VF9ys7IpKyvDpNK2NTEz51odNp4d8d1NRloq3fo1bl9M2Vm39uVbrz7fZmZuweXz5+rkN/JyczkVcJTJM2bVyfIeVH3F6tWqNS/8721++eb/KC4qpLS0lLYdOvHMq/+ONxMalfRhU2tNvsHGxMSEwYMHs2jRInx8fPDx8aF3797Y2NgQFhZGYmIiR48eVZW//WQ8KSkJc3NzEhMTWb16NaGhoWRmZqJQKCgrKyM5WfnkrXfv3mzfvp05c+bQqVMnOnXqRPfu3dHR0SExMZGSkhK8vb1Vy9fS0qJVq1bExsaq1dPV1VX1/9sXMhkZGVUabPbu3Yuvr+9d4+6mpaBPqezgt4WcPsmBdatUn8c/q0xd1KhyElBwt7VWdRZFDV80ZZUyQDQoj6MSTVMTrKZPJSfwDLmnz6Gpr4f52BHYzJnJje9+UZvvxjc/oqGnh56bC+YTx1CSkkruqaA6q/XV0yfxW7tS9XnCc8r+C6psRwV33R6V5ynfjBWmV1ms+jpSKBQ0a+5K33GPAmDr7ELGzRucP3qoThpsmlq8I2fMwXfNcn5//39oaGpi6+RCy87duRkXfWuZyr4EPNp1pPOg4QDYOjUnKSaa4CMH79hgc+X0Cfb/Ux7rI8+/eruCleqtuHuslSdUG2v166M6hzatJT78Oo+/tgDNSqm5zi28mbngA/Jzcrhw3J8df/7CtNffxbgOGm+rxqGoZmKF8jWdm+5RXk42u1b+wdjZz6JfIU29PjS1c7Jb63Zqn+1c3Vn+4QKuBh6n06Bh97ycB1Xt8XmX+n/82Wfk5eZx/fp1flryA6tXrmDmU417s3OvqsSLopptr+6Tzz4jLy+P69dDWfLDD6xasYInZ1WN9+DBAyz54Xs+/vQz1YO6RlX5vKq4e6x3YufohJ1jeefnHi29SUm+ge+2zQ3eYNPJzYlJPTuoPv9x4NZDmsqnHo2qk+5XdHI60cnljfVRyam8PnYgfbzdVa9TNbgHOG7vVXDgCbauXsGsea9jWU2/KY2h6nlKgcZdz9D35vjhAyjKyug9sO4fdj2Iuo41Piaa1b/9wvipj9OuY2cy0tNYt/wPlv/8Q6O/Aib++5p8gw0os2LGjx9PUFAQgYGBrFq1infffReFQsGwYcMYP358lXmsrJSdOn788cdYWVnx4osvYmVlhZaWFi+88AIltzpOs7GxYenSpZw/f57g4GCWLVvGP//8wzfffKNa1r38YdbS0qpSvrrXKkaMGMGIESPuurzYIVVjepi5t+uAnWv5SDalt7ZfblYmJhV6Z8/LzsbwDh33GZmaqp78q+bJyUZTU6veb3DqQmlOLorSUrQqxahlYkxpVvXvypv0742iqIiMLTtV01KW/43T54vQc3elMLx8dKmSVOWrfsUJiWiZGmM2ZnidNth4tPPBznVReTw1bsesKhkUFSm3o3o2RX5Oltp2NDI1q7qts7PVMgCMTM2qjHxkaWdPln/VUQ8eRFOL19zGlqnz3qK4sJDCgnyMzczZ+edSzCyVDcsGRiZoamphZad+k2RlZ3/X16I823VQdW5cOVbTirHmZFfJwlCP1axKrHnZ1cVaqUxOFoYmVTNjDm36h5CgU0x55U3MrateNOvq6aFr0wwLm2Y4uHnwx4dvc/H4EXqOHHfHeO/E0NgEDU3NauOoKXunprhvf3cvkhPiyMnMYO335aNb3P479OWLs5nz/qdVtu2DaurnZF09fazsHEhPvvHAy7gX5ubmaGlpkZaaqjY9PT2tShZKZc2aKTvodXN3p6yslC8+/ZRp02egrd10L81ux5taOd60dCyr6Uy7omZ25fGWlpbx+aef8MQM9XgPHjzAhwsXsujDDxt9hChjE1M0NTXJqpT9mJ2ZWSXrprbcvVpy6tiROl3mvbgcm0R0yiHVZ20tZYO2iYEeGXn5qunG+npk59dt3zMKBcSmZmBt0vCvCxmZmqCpqUl2pWzPnKwMTM1q/5pWcOAJVv74HTNenNckRogyMVXuy5np6t05ZGVmVMlEeVCH9+2lS68+GJs03muMUH+x7ti4DvcWLRn9iLIPuuZu7ujp6/Pp2/9j0vSnsLK5/77+Hha1aeAWSk2+D5vb3NzcmDRpEp9//jlt27blwIEDeHh4EBMTg4ODQ5V/enp6ZGVlERsby+TJk+nQoQPOzs7k5eVRWlqqtmxdXV26du3K3LlzWbx4MTExMVy5cgV7e3u0tbW5cqU8hb20tJSQkBCaN2/e0Kvgoaarr4+5TTPVP0s7BwxNzYi5Vr5tSoqLSQi/jr1bzZ0927l6EBuq/kpCzLUr2DZ3QUur6V4kq5SWUhQTh34r9ZFf9Fu1oDAiqtpZNHV1UZRVajy8PSrDnU6iGppo1PGNg66+ARa3bo4tbJphZeeAkakZ0SHq2zE+4joObh41LsfezUNt2wNEh1yhWYXtaO/qQcy1y2plYq5dVnslxsHdi/QbSWpl0m/ewPQuNx73qqnFe5uOnh7GZuYU5OUSHXIJj/bKvjO0tLVp5uJK+s3K6yQJk7usk5pjLa9TSXEx8eGhdxw2297Ng+gqsV6mWXNXVawObh5q6/D2+nB0V1+HBzeu4eqZQKa88sY9N1QoFApV48OD0tLWxq65K5Eh6tsjMuRyjbE7unkSGxZKSXFRefmrlzE2M8fM6t5erbV3cefp9z5h9jsfqf55te+As2cLZr/z0QN1Hl2Tpn5OLikuJu1mEkam5g+8jHuho6NDy1atOFXpdaDTgadod+s16XuhKFNQWlpKWSONmHOvVPEGqsd76lTg/cWrKKsSr9/+/Xy4cCHvL/qAQYOH1FmdH5S2jg4uHp5cqfQaxZXz5/Bo1apOfys2MqJRhgYuLCkhNTtX9e9GRjZZeQW0cChv3NbW1MTd1oqo5LQ7LOnB2FuYkZ1fUOfLvRttbR2c3T0IuXhebXrIxfO4tajdtj17IoCVS75j+guv0LFHr1otq65o6+jg6unFpWD1fflS8Dm8WnnXMNe9Cw+9RkxkBAOG3f2BdH2rr1iLCgurZOje/lw5m1mIutbkG2ySkpL466+/uHr1Kjdv3uTChQuqkZgeffRRQkND+emnnwgPDychIYFTp07x448/AmBsbIypqSm+vr4kJCRw8eJFfv75Z7VsGD8/P3x9fYmKiiIpKQk/Pz+0tbVxcHBAX1+fUaNGsWLFCs6cOUNsbCy//PILGRkZjBo1qrFWSZ3TMNBH19MdXU930NRAp5ktup7uaDdruq3FGhoadOw/hDP79xB2PoiUhHj2/f0nOnp6tOrcXVXOd9UyfCsMxdy+T3+yM9I5vGktaUkJXDp+hCuBAarXP0D5pPhmXAw342IoKS4mLzuLm3ExZFR4UltUWKAqo1AoyE5L42ZcDFlp6k8d60PWAX+Me3bFuHd3tO1ssZg8AS0zM7KPKkdzMh8/Gtt5z6nK51+6gq6zI2ajh6FtY42usyNWMx+nJC1dNTKUyYA+GLRtjbaNNdo21hj36o7pkAF1ml1THQ0NDToOGMJpv91cDw4iJSEO39XL0NHVo1WX8u24Z+Uf7Fn5h+qzT+8BZGekc2jTP6QmJXDx+BEuBwbQeXD5duw0YAgxoSGc2reLtKRETu3bRWzoNToNHKoq03ngUBKjIgj03Ul68g1Cz53mnP8BOvQdpCqTn5vDzbgYUhLjAeVwyTfjYqpkQ/wb4o26eonIyxfJTEkmOuQyG374CgtbO9r0KO/rq+vgEVw7e5oLAf6kJ9/gQoA/14JO06HvwPuOtdPAoZzav5vQ4CCSE+LYs0oZq3eFWHev/J3dK8v7U/Lpo4z14MY1pCYlcOH4ES4FBtBVLdahxIReJdB3F6lJiQT67iI2NITOFWL1W7eKSyePMeapZ9E3NCI3K5PcrEyKCpU3B4X5+RzbsZnEqHCy0lJJioli7+o/yclIp2Wn2nci2G3wcC6eOMb5Y/6kJCawf/3f5GRm0PHWejy8dQP/fPelqnzrbj3Q0dVl14o/SI6P49q5M5zct4uuQ4arPZ26ERvNjdhoCgvyKcjN5UZstGrf1NXTw8bRSe2fnoEhunr62Dg6oVWPmRuNfU4+snU9cdevkZmaTGJUBLv+/IWSwkJad6//G6bHpk1j986dbN+6lajISL795mtSUpKZ8IjyVctffvqRl194XlV+z+7dHPTzIyoqivj4OA7s388vP//EgEGD0K3Q6XRo6DVCQ6+Rm5tLVlYWoaHXiIyIqPd47ubxaU+wa+cOtm3dSmRkJIu//pqU5GQmPqqM9+cff+Sl5yvGu4sDt+ONi8Nv/35++eknBlaId/8+Xxa9/x4vvPgSHTt2JDUlhdSUFDIz7/88W5eGjp1AwKEDHNnvS0JcLP8s+5WM9DQGDFNeA25a/RdfL3pHbZ6E2BhiIiPIycqisKCAmMgIYiLLt9v+Hds4F3iCGwnxxMdEs2n1X5w7dZKBo8Y0aGw1OXI1nEFtvWjX3B47cxMe69OJwpISzkWUjyT5eJ9OPN5HvVNXBwszHCzM0NPVxlBPBwcLM5qZlWdbDPNpSUsHWyyNDXGwMGNqr444WJhyPDSSxjBw9DgCDx/i+IH9JMXFsvGvP8hMS6fPUOW5Z/uaVSz5eKHaPIlxscRFRZKblU1hQQFxUZHERZXXPyjgKCuWfMu4adPx9G5NVkY6WRnp5NbBKJO1NWL8Ixw9uJ/D+/YQHxvD6t9/ISMtlUEjlf1ErV/xJ1+897baPPEx0URHhJOdnUVBQQHREeFEV9O5/2Hf3TRzcKRV23tvtK1P9RFrx27dORt4ggO7d3IzKZHQK5dZ/dsvuHp4VjtUuBB1qcmnFOjp6ZGQkMAXX3xBVlYW5ubmDBgwgEcffRRtbW2++OILVq9ezYIFCygrK8POzo4et0Yv0NTU5M033+S3337jpZdewt7enqeffprPP/9ctXwjIyM2bdrE8uXLKSkpwdnZmQULFmB3K3X3qaeeAuD7778nJycHDw8PPvjgg7umOv+b6LdqgdOS8hR6qzkzsZozk6zd+7jx2Td3mLNxdRkygpLiIg5uWENhXi52Lu5MfOE1dPXLO8bLSldvQDGzsmHCs/Pw37KOi8cOY2RmzoBHH1cNHwuQk5nBmv8r77DzYoo/FwP8cfRsweRX3gTgRkwUm5Z8rSpzcs82Tu7Zhne3XgyfXnXIwLqUFxRMmpEhZiOHYmlqSlFiIjd/+p3SNGVqr5aZCTo25U/kC66FkbJ8NaZDB2E6ZCCK4mIKI6O5ueQ3FEW3nuZramI+cQzaVhZQVkZxcirpW3fWe6fDAF2HjKSkuJiDG/6mIC8XO1d3Hn3xNXT1DVRlsiultppZ2zDxuVfx37yWC8cOY2SqHBb49hDXAA7unox+6lkCdm7h+O5tmFvbMnrWs2qv7di5uDFu7osc27GZk3t3YGJhRa/RE/Cp0DgRcTEY3wpDt+//ZwUAPUaOo9eo+391sTHjVTZSbCInIx19QyM8fTrTZ+xEtUwGT59ODH1sJoH7dnNo0z9Y2DRjxIyn76vD4du6DRlJSVERB9avpiAvF3tXdya99LparFmVRtwzt7bh0efnc2jTP5y/dYwOmjRNNaQ3gKO7J2NmPUfAzs0E7N6KubUtY2Y/h71reYZN8FFlmv/6Cuc2gJ4jx9F79AQ0tTRJSYzn4omjFOTlom9ohJ2LG4+9+hY2jrUbJQrAu0t38nNzCNizndysTKztHZn84muqbJmczAzSk8tHYtE3MGTqK2+wb+0q/vriA/QNjeg2eATdBqs/sVz+2SK1z2EXgzG1tOKFTxv/XN2Y5+ScjHT2rPiN/NwcDIxNsHd1Z+pr79RZttydDBk6jMzMTP5a/iepKSm4e3jw9bffqfpfSU1JIT4+XlVeS0uLlSv+Ii42FoVCgZ2dHY9Omsxjjz+uttynpk9X+3zs6FHs7O3ZvG17vcd0J0OHKeNd/ucyVbyLv/teFW9KSgpx8eU391paWqz4a3mFeO15dPJkHnt8mqrM5k2bKC0t5dvF3/Dt4vJ9uWOnTvzy628NF1wl3fr0Izc7m10b15GZnoZDcxfmvfsBVrbKG7TM9HSSk9QzEr//5ANSKxzbH72u7N/pj83K15JLSopZv+JPMtJS0dHVxdG5Oa+8u4j2TWS0mUOXrqOjpcUj3X0w0NMhJjmd3/Yfp7BC5qG5UdXR9l4fp96o38bZnrScPD7dtA8AfV0dJvXsgKmBHvlFJSSkZfDT3qPEpmTUazw16dyrD7nZ2fhu2UBWejr2zs15/u33VP3NZGakk1IpA3fpFx+Tllw++tyXb70GwJJ1WwA45udLWWkpm1b8yaYVf6rKebZuw7xFn9R3SHfUo29/crKz2L7+HzLS0nFyceH1hR9jbdsMgIz0NG4mJajN881H75Nys3xffv9VZUfhK7fvVU3Lz8vj5FF/Jkx9osm8+lIfsfYdPIz8/Hz8dm3nnz9/x8DIEO92Pjz21NMNFNW/mAzrXWsa+fn5ksfVBD1sfdj4vf9eY1ehwYzauqWxq9Cg9k58pLGrIOqJZhO5OGsIt/t2eFgUFNfutbB/kyk97r8x8t/sYTpuL8Um3b3Qf8jWM5fvXug/YrhPi7sX+g8xMzC4eyHxr+Tj0gQ6V69H8Y/MuHuhOuK4edXdC/0LNfkMGyGEEEIIIYQQQvzLPEQPCerLw/XIUAghhBBCCCGEEOJfQDJshBBCCCGEEEIIUbc0JcOmtiTDRgghhBBCCCGEEKKJkQwbIYQQQgghhBBC1C0NyQ+pLVmDQgghhBBCCCGEEE2MZNgIIYQQQgghhBCiTmlIHza1Jhk2QgghhBBCCCGEEE2MZNgIIYQQQgghhBCibmlIhk1tSYaNEEIIIYQQQgghRBMjGTZCCCGEEEIIIYSoWzJKVK3JGhRCCCGEEEIIIYRoYiTDRgghhBBCCCGEEHVLRomqNWmwaaL83n+vsavQoIZ8/EljV6HB7H7Itq32Q9TZmKbmw5W0WFZW1thVaDAlpQ9PrAAKhaKxq9BgfM9fa+wqiHoSmZzW2FVoUC3srRu7Cg1GT/vhuoWxMTVq7Co0mIfp748Q9+LhursQQgghhBBCCCGE+Bd4uJqnhRBCCCGEEEIIUf8eokz7+iIZNkIIIYQQQgghhBBNjGTYCCGEEEIIIYQQok5pPGT9O9YHWYNCCCGEEEIIIYQQTYxk2AghhBBCCCGEEKJuSR82tSYZNkIIIYQQQgghhBBNjGTYCCGEEEIIIYQQom5JHza1JmtQCCGEEEIIIYQQoomRDBshhBBCCCGEEELULenDptYkw0YIIYQQQgghhBCiiZEMm/8QhULByT3buXT8CAX5edi5uDFo8hNY2Tvecb6469c4smUdqUkJGJmZ02XwCNr3GaD6PjUxnhO7t3MzLpqs1BS6jxhLz1Hj1ZcRFsrZg77ciI0mNzODoU/Mok333vURZq3o+7TF4vFJ6Lf0QtvGmqRPvyZ7z/7GrtYDacztXddxHN+9jQsB/hTm52Hn4s6QqdOxvkscsdevcXjzWlIS4zE2M6frkJF06DtQrUzouTMc27WFzJRkzKxt6Dv2Ebx8Oqu+P+d/gPMB/mSlpQBgZedIjxFj8GjroyqTm5XJkW0bibp6icL8fJw8WzB48hNY2Da771jP+R/glN8ecjIzsLZ3ZNDkaTh7tqyxfHJ8LPvXrSYpOgJ9QyN8+g6k18hxaFR4WhETGsKhTf/cWg8WdBs6ko79BqktpzA/n6M7NnHt3BkKcnMwsbCk37hJtOrcDYCl771OVlpqld93b9OeSS++dt9x3taY27aik747ObZjMx36DWLIlOmq6XtWLeNyYIBaWXtXd57433v/ungDfXcRej6I9JtJaGlrY+/qQd9xj2Lj4ARAaWkJx3ZsIfLKRTJSbqKnb4CzVyv6jZ+EqaXVA8VbXfyBe3eonZMGTpp293NS2DWOblmvOid1HjS8yjnp5J7t3IyLUZ2Teowcp7aMooICTuzeSviFc+TlZGPr2Jx+j0zFzsWtTmKr7PSh/Rz33U12Zga2Do4Mnzodlxataix/Iy6WPf+sID4yHAMjYzr3G0S/MRPUjuXbYq5f46+vP8XazoEXPvxCNf1mfByHt28iMSaKjJRk+o+dyIBxj9ZLfBXVdaxR165yYMt6UpMSKS4qxMzKmk59BtBr+GjVMkpLSji2ZwfnTxwlKz0dazt7hjw6Fc8K5+b61Nfbg45uTujr6pCQlsnec1dIyc694zzNrS0Y0r4lNqbGZBcUcvJaJGcj49TKdPVsTid3Z8wMDcgvLCY08SYHL4ZSXFpaZXm9WroxsG0LzoTH4Bt8tU7iUigUnNiznYsB/hTk52Hv4s6gKU/c0znKf8s6Um+do7oMGYlPhWMUIDT4DMd3bVWdo3qPeQQvn06q78vKyjixextXT58kNysDI1NzvLt2p+fI8WhqaQGw+OWnq/19n74DGVzh3F1XDu/dhe+2zWSmp+Pg3Jyps+bi1bpNtWWLi4pY/etPxEREkBgfi2crb/730ec1Lvv61ct8s/Ad7Byd+OC7n+q87g9i59YtbFz7D2mpqbi4ufLsS6/Qtn31x9SFc+fYsnE9165eIS83F3tHRyZMmsLwUaPVyrw1/5Uq8/62YjXOLi71Fse92Ll1C5vW/UNaahourq4889LLNccafI6tG9ZzLeRqeayPTmZYxViDz/H2/HlV5v11xSqcmzdurE2eZNjUmjTY1KPS0lI0NTWrvSCrD2f89nL20D6GPTEbC1s7AvfuYPNPi3nyvU/R1devdp7M1GS2/vo9bXr0YcTMOcRHhHFo/d8YGJvg1UF5M1BcVISplRWePp04vmtLtcspLizAyt4R76498V39Z73FWFuaBgYURUSTvdePZu+90djVqZXG3N516ZTfHs4c9GXk9KexaGbHiT3b2bDka55e+Bm6+gbVzpORksymX76lXY++jHpyLvHh1/FbtxpDYxNadOwCQEJEGDuWL6X3qPF4dejM9eAgti/7hWmvLcDe1QNA2XAxfhIWts1QlCm4HBjAtt9+ZMZbC7FxdEahULD1tx/R0NRgwjMvo2dgwJmD+1i/5GtmvfcJunp69xzn1TOBHNiwhqGPzcDJowXnjhxg40+Lefr9z6q9YS7Mz2f9kq9w8mzJjLcWkXYjkd0rl6Gjq0u3ISPL18PPi2nXsy9jnnqWuPBQ9q9dhaGJCS07dgWUN+rrl3yFvqER459+ARMLC7LT09HSLj/9z3xrEWVlZarPuVmZrPjiA1WDzoNqzG17W0JkOBeOH8HG0ana33Np2ZpRT85Vfb594/Bvizf2eggd+g5UNlAoIGDXFjbc2k8NjIwpKSriZmw0PYaPwdbJmcL8fA5vWcfGnxfz1IKPahX3bUEHlOekodNmYWFrxynfHWz5+VtmvvvJHc9J2379gTbdezN8xhwSIq5zaMOaquckS2s82nfixO6t1S7Hb+0KUhLiGPbEbIzNLQg5c5ItP3/LjAUfYmxuUevYKrp0+iR7161m1LSnaO7VgtOH/Pj7h6948cMvMbOyrlK+MD+PVd9+gYtXS+a++xEpSYlsW/4bOnp69Bo2Sq1sfm4uW/5cinurNmRlpKt9V1xUiLm1Dd6dunJw64Y6jakm9RGrrr4+3QcNw9bRGR1dXWLDQ9m5ajk6urp0HTgUgINbN3Lh5DHGznwaG3tHwi5fYN3P3zH77UXYN3et15h7tnCju5crO85cIi0nlz7eHkzr24Wl+45RVFK1YQXAzNCAqb07cT4qnm2nL+JsZcGIjt7kFhZzLeEGAG2c7RnUtiW7zl4iNiUdcyNDxnRug7amJrvOXlZbnoOlGR3dnLiRkV2nsZ3220PQQV+GT5+Npa0dJ/fuYNOP3zDr/U9rPEdlpiSzZel3tO3Rh5Ez5xAffp2D6//GwNiYFh1unaMiw9i1/Fd6jRqPp08nws6fZeefv/DY/AXYu7orf3v/HoKPHmTE9KexdnAiJSGOvauWoaWtQ48RYwF49tPFar99IyaKrb/+QItbf8/qdF0EHGXtn7/zxNzn8fRuzeG9u/nh0w/44LufsLKxrVK+rKwMHV1dBo4czcWzZ8jPq7kBLzcnh+U/fEurdj5kVPMwpDH4HzzA0iXf8+L812jTrj07t27h/Tff4NcVq7BtVvUB1JXLF3F1c2fSY49jaWVN0OlAfvj6K3R1dRk4ZKha2aV/rcTExFT12czcvL7DuSP/gwf49ccfePHV12jdrh27tm1l4VtvsvSvldXGevXSJVzc3Xn0sWlYWllx9vQpfvjma+X2rhzr8pUYm5qoPpuZmdd3OELIK1H34+DBg0ybNo3i4mK16V9//TUff/wxa9as4cUXX8TPz4+5c+fyyCOPUFBQ0CB1UygUnPP3o+uQkXh16Iy1gyPDp8+mqLCAkKDAGue7cMwfYzNzBk6ahqWdA+169cO7W0+CDvqqyti5uNFvwhRademOjq5utctxa9Oe3mMfwatjlwZroHoQeSdPk/rbcnIOH4MyRWNX54E19vauyzjOHtpP96GjaNGxCzYOToycMYeiwgKunqk5jvPHDmNsZs7gKU9gZedA+979adO9F6cPlMcRdHg/zb1a0WPEWKzsHOgxYizOXi0JOlSeUeXZviPubdpjYdMMy2Z29B33KLr6+iREhgOQfvMGiVHhDJkyHXtXdyyb2TN06gxKiovuuJ6rc+agL2179sanzwCs7B0YMnUGRqbmnDtysNryV06foLioiFEz52Lj4ETLjl3pPmwUZw74olAo993go4cwMrNgyNQZWNk74NNnAG169Oa0317Vci6eOEZedjaPPDcPJ88WmFnZ4OTZQnURDWBoYoqxmbnqX8Sl8+jp69Oy04NfJDf2tgXlzeOuFb8xYtos9AyMqv09LW1tjEzNVP8MjIz/lfFOeul12vXsi42DEzaOTox6ci75OdkkRIQBoGdgyOSX/0erzt2wbGaPvas7Qx+bSVpSIqlJiQ8Uc+X4z/kfoEuFc9KwJ5TnpGt3OFYuBvhjZGrOgEnTsLSzp+2tc9LZQ/tUZexc3Og7YbLynKRT9ZxUUlRE2Pmz9B77KE5eLTG3saXHyHGYW9twIeBwrWOr7OT+Pfj06kvnfgOxsXdk1LQnMTEz57T/gWrLXwg8TnFRIRNmP4etozOtO3ej94gxnNy/R3Us37Z9xe/49OyLk4dnleU4unkwbPI02nXvhY7uvTcW10Z9xOrg4kbbbj2xdXTCwsaW9j364NGmHTHXr5Uv5+Qxeo8YQ4v2HbGwsaXrgCF4tevAiX276z3mbp4unLgWybWEGyRn5bDj9EV0tbVp42xf4zyd3J3JKShk3/kQUrNzCY6K42J0Aj1auKrKOFmZE5+WwaWYRDLzCohOTuNidAIOlmZqy9LT1mZC1/bsDLpMQaXrzdpQKBScO+xHt6GjaNGhC9YOTgyf/rTyuuFO56gA5Tlq0OTyc1Tr7r0IqnCOOnvID2evVnQfPgYrOwe6Dx+Ds2dLzlY4RyVEhuHRtgMe7TpgZmWNRzvl/xOjIlRlKp6LjUzNCLtwDgvbZjh71ZyJ+qD279hKr4GD6Tt0OPZOzjw+51nMzC3w991TbXk9fX2mP/si/YaNwKKaxsqKVv78Az0HDsa9Zc2ZaA1ty4Z1DB0xkpFjxtHcxZUX5s3H0sqKXduqfzD32PSZPDlnLm3atcfewYEx4yfSu18/Ao74Vylrbm6BpZWV6p9WHTwAqI0tG9YzZMRIRowZS3MXV55/5VUsrSzZtX1rteWnTp/Bk0/PpU27dtg7ODB6/AR69a0+VjMLcywtrVT/GjvWfwMNTc0G+/df9d+NrB707t2bsrIyTp48qZqWm5vLiRMnGDpU2QJ748YN/P39eeutt/jhhx/Qrecb3tuyUlPIy8qkeavyVE5tXV0cPVqQGBlW43xJUeE0b6me/uni3ZabMdGUlpbUW31F7fxXtndmajK5WZm4eLdVTdPR1cXJsyXxETXHkRgZjmsr9ThcvdtyIyZKFUdCZDgu3lXLxEeEV7vMsrIyQs4EUlRYgIOb8maptES5LG0dHVU5DU1NtLW1iQ+/fs9xlpaUkBQThWuFOAHcvNvUGGdCRBhOHi3UGs3cvNuSk5lBZmrKrRjDcKsUo1vrtiRFl6+HsPNncfTwxG/9an56+xWWffQOx3ZuqXF7KxQKLhw/SututbspbArbdt8/K2jRoQvNW3rX+HvxEdf56e15LPtwAb5r/iI3O+ueY6yoKcRbUVFBAQqFAj1DwzuWAdC/Q5l7pTontWytmlZ+Tqq5nolREbi0aq02zaVVm/s6J5WVlaEoK0NbWz1pWEtHV9VgVVdKS0pIiI7Eo3U7tenurdsRV8M5IS48DBevlmrHskebdmRnpJORkqyadvrQfnKyMuk3ZkKd1vlB1WesFSXGRBEbfh2XFuXHaWlJidp5F5Tn4Ziw0AcN556YGxlgbKBHxM0U1bSSsjJiU9JxsjKvcT4nSzMibqhnUoTfSMHewhTNWw+xYlPSaWZuomqgMTXQx8vBlvCkFLX5RnVqzdX4G0Qnp9VRVEqZqSnKc1SF842Ori5OHi1UDymqkxgZrjYPgKt3G25UOEYTo6qWcfFuS0KF6xFHdy9ir4eQdquBODUxgZjQq7hV2r9uKyos4NrZU7Tr1e/+Ar0HJcXFxISH0dqno9r01h06En6tdq+fHd67i8yMdEY/OqVWy6lLxcXFXL8WSqeu6lmznbp25crlS/e8nLzcXIyNqz7UeOXZuUx7ZDxvvzaP8+fO1rq+tVFcXExYaCiduqg/cOrYpStXL917rPl5uRibmFSZPu/ZZ3ji0QkseO3VRo9VPDzklaj7oKenx4ABA/Dz86Nv374A+Pv7Y2hoSNeuXYmIiKCkpITXXnsNC4u6TcG+m9ysTED5pLwiQxNTcjIz7jBfFs4tqs5TVlZKQU4ORpLq1yT9V7Z3bpby5tioUhxGJqbkVHodQH2+TJpXuskzNFXGkZ+Tg7GZOblZmdUuNy87U21acnwca775lJKSYnT19Bg/9yXV6zOWdnaYWlpxdPtmhk17El09fc4c2kd2Rjq5d1jPleXlZKMoK8PIRP1JqqGpGbkhV2qM0cTCUr3+pmaq78ytbapcfCtjNFNbDxkpN8m8lkLrrj159IXXyExNxm/dKooLCxn46GNVfjfq6mUyU5NpX8uL5MbethcC/MlIvsmomXOpiZt3W7x8OmFmZUNWWgrHdm5m/Q9fMePNhVVuFu+mseOt7ODGNdg6NVc1PlZWWlLC4S3r8Gjbocp+9iBys+90Tqo5/rysTAxbqDeo3e85SVdfH3tXD07t24WVvSOGpmaEBp0iKSocs2pebaiN28eysan6sWxsakbk1epvBnKyMjCttI5vz5+TlYmFjS034mLx37GFpxd8gGYTeUpYX7HetviNl8nLyaastJT+Yx+hy4DBqu882rQj0M8X15beWNnaERFymavnzqCo8OpmfTC69ZprbkGR2vTcwkKMDap/rQ/ASF+P3JvqDSy5hUVoaWpiqKdDTkERV+KSMNDVYWZ/5U2zlqYmF6LjOXipvBGqg6sTFsaGbDt9sa5CUsmr6brB1JScjIwa58vNyqJ5y+qvG+5+jipvAO86dCRFhQX89dn7aGpoUlZWSvfho+lQqc+120LOBFJaUkLrbnXfH2JOdhZlZWWYVjq/mJqZczXj/AMvNy46ih3r17Lg86/q5DXTupKVmUlZWSnmle5NzC0sSA+6t4bBwOMBBJ8N4psff1ZNs7Sy4qX5r9OilTclJcUc2OfLgtde5cvvfqCdT4e6DOGe1RSrhYUlwWeD7mkZgSeOE3w2iK+XlPc9ZGlpxYvzX6dFy1aUlBRzcN8+3nl9Pl98+32jxfqv0YTfvPi3kAab+zR8+HBeffVVUlJSsLa2Zv/+/QwePFiVEmdlZdUgjTUhp09yYN0q1efxzyo7/ar6OpKCux0mVWdR1PCFaCz/le195fQJ9v+zUvX5kedfrfa3FQrFXetT5VtVGBW+qbLcqsuxbGbHzAUfUJiXR2hwEHtXLWPKvDexcXBCS0ubcXNexPfv5fz01itoaGri0rJ1jU8E76qadX8/q/32KwVqIdZQpuJnQxNThj8xC01NTeyau5Kfm8uhjWsY8MjUKvvQhYDD2Lm40cz5/jrRa0rbNu1GIkd3bOKxVxeo9dVTWasu3VX/t3F0opmzC78tfJOIyxdo0aH6Doxva0rxVnZo01riw6/z+GsLqr35LystZdeK3yjMy2PiM1U7jLwXIWdOcnDdatXncc++XLXOKOPXuNtZqfI83P85adiM2fitWcGyRW+ioamJrVNzWnTqRnJczD0vozbuvp0rx3h7qgYlxcVs+u1Hhk6eptag0VTVJtaKZr35PkWFhcRFhOG3aS3m1jb49OwDwIjHZrBj5TJ+XvgWaGhgaWNLh179CD5+pA4jUfYrM6pTeYPpuoCzanUup1HdxErUC9yO9vax2tzagj7eHuw9d4X4tEwsjQ0Z6tOKfq09OXIlDEtjQwa09WKV/ynK7nSA36Orp0/it7b8HDXhOWWHqVWuGxTc/RxVw/lH4w5/jBSV1se1s6e4cuo4o56ci5W9I8lxMRza9A+mVja069m3ym9ePH4Ez/YdMawmy6GuVImrFssqLi7m98X/x6SZs7BuZle7itWT6rb9Xc/PwOWLF/jyk4947pV5tPQuP16cmjfHqXlz1WfvNm25kZTEpnVrG70Ro+q2vYe/RcDlixf5v08+4rmX7yHWG4lsXt/4sYr/PmmwuU9ubm64u7tz4MABevToQVhYGK+//rrqe/0aOla8be/evfj6+t6xDICJW0vcahjxBMC9XQfsXMtHv7j96kblp/J52dkYmppWmf82I1NT1ZNh1Tw52WhqaqFvVH2fD6Lh/Ve2t2e7Dmp9p1SMo+JT2byc7CpPASsyMjVTZRmp5snOUouj2jI5WRhWynLR0tbGwkbZCZ2dixtJMZEEHdrHiCdmK6c1d+XJBR9SmJ9HaUkJhiamrP7qY+zuo+NLQ2MTNDQ1q61z5frcLUZANU9NMWpqamFwK23ZyNQcLS0ttRt3Kzt7iouKyK+0nnOzs7h+4RxDp86459hua0rbNiEynPycHP767H3V94qyMuLCQzl/7DDzvvml2gwaY3MLjC0sSE++8a+Kt6JDm/4hJOgUU155E3PrajrOLC1l51+/kpIQx9R5b6n2k/vl3rYDdi4V41f2tVH5nFR5H6vM0NRM9eRfNU/2/Z+TzK1tmfTKGxQXFlJUkI+RmTm7//oV07v0NXG/bh/LOZXqnJudVSUT5TZjU3NyszLUy9+a38hUmRWZnBjPtr9+Y9tfvwG3GkUUCj56diZPvPIGHm0esJG4Fuoj1opuN041c3ImNysT/x2bVQ02RiamPPbifEqKi8jLycHE3AK/TeuwsLKpi9BUrife5A+/8vi0NJU3dMb6umTnl/dBaKSnS25hYY3LyS0oxEhf/RVSQz1dSsvKyC9SHhv923hxOTaR4Kh4AJKzctDR0mJ05zYcvRqOk5U5Rnq6PDOkl2oZmpqaNLe2oJObE/+3zY/S++h7z6OdD3aui1Sfa75uyKqSHVOR8rqh0jGaU905qtK1Rbb6sX9k6wa6DB5Oq87KhnIbByey0lI5tW93lQabm3Ex3IiJos/YR+453vthbGKKpqYmmZWyH7MzMzB9wA5zM9PTSIyLZcVP37Pip+8B5XGsUCh4bvJ4Xn53EW06dLrLUuqHqZkZmppapKepZ9NkZKRjbnnnh8yXLlxg4dtvMGPW04wZP/Guv9XKuzX+B6vv46oh1BhrenqVrJvKLl+8wMK332TGrNmMHj/hrr/V0rs1RxoxVvHwkAabBzB8+HA2b95MVlYW3t7eODlVP/pIdUaMGMGIESPuWm65/+k7fq+rr6826oZCocDQ1IyYa1dUw5iWFBeTEH6dPhMm17gcO1cPIi6eU5sWc+0Kts1d0NKS3aOp+K9sb119A7WRKBQKBUamZkSHXMa+Qhzx4aH0n1Dz+9/2bh6EXVB/dzg65DLNmruq4nBw8yA65IpqRCVlmSs4uquPIlSZQqFQXdhWpGeg7Ocj/eYN5YXkmLtfuNympa2NXXNXoq5eplWn8nfIo0Iuq0bZqMzB3RP/respKS5C+1ZHq1EhlzE2M1eNzuLg5sn18+rrIerqZexcyteDk4cXV06fQFFWpuqQLf1GEjq6uhgYqz+5vHTiGFraOmqZJ/eqKW1bz/adePIdV7Vl7F39JxY2zeg+fHSNWTd5OdnkZKTXeDPaVOO97eDGNYQEnWLqvDexsqvaQWppaQk7//yVlERlY43RPcRZk/s6J42fVONy7F3dCb8YrDatNuckHT09dPT0KMjLJTrkMn3G1fzbD0JLWxsHFzcirlyiTYXjJOLKJbw7V99Jt5OHJ36b1qodyxFXLmFiboG5tQ1lpaU8/4H68MCnD/sRceUSU194FfM6bqS4V/URa00UCgUl1XSwq62ji6mFJaUlJVw9e0qtHnWhqKSUopI8tWk5+YW42VqRmK5sgNDS1MTZ2oIDF69VtwgA4tIyaemg3kDq3ky5jNvZMjpamlUyIJXfKRuJriXcJHF/gNr3Yzq3JS0nj+PXIu6rsQbudI5SP0bjI67Tb3zN1w32bh6EX1C/bogOuUKzCseovasHMdcu03VI+fVtzLXLaq9klhQVoaGhnvGnqakJiqqvuV0M8MfU0lqtT6y6pK2jQ3MPT66eD6ZLrz6q6VfOB9OpR687zFkzc0srFn37o9q0w3t3cfV8MM+/9W61I081FB0dHbxatuDsmdP0HTBQNf3cmdP07te/xvkung9m0dtv8sRTs5k4+d765AkPu46lVdWRLxuKjo4Oni1acO7MGfVYg87cNdYPFrzFE0/NYsKke4s1IiwMi0aM9V9DU97YqK2m8aL0v0y/fv1IT09n9+7dDBs2rLGrAyhT/zr2H8KZ/XsIOx9ESkI8+/7+Ex09PdXTDADfVcvwXbVM9bl9n/5kZ6RzeNNa0pISuHT8CFcCA+g8aLiqTGlJCTfjYrgZF0NJcTF52VncjIsho8JT6KLCAlUZhUJBdloaN+NiyGoiwxnepmGgj66nO7qe7qCpgU4zW3Q93dFu1jgXxA+qsbd3XcbRaeBQTu3fTWhwEMkJcexZtQwdXT28K1yY7175O7tX/q767NNnANkZ6RzcuIbUpAQuHD/CpcAAug4uj6PTgKHEhF4l0HcXqUmJBPruIjY0hM4Dy4doPLJtA3FhoWSmppAcH8eRbRuJvX4N7y49VGWunT1NTOhVMlJuEnbhHBt+/BrP9p2qdCB8N10GDefSyWOcD/AnNTGBA+v/Jiczgw59lRcU/ls3sPb7L1XlW3ftgY6uLrtX/kFyQhyh584QuG8XXQYPV6X6dug7kJyMNA5s+JvUxATOB/hz6eQxtYvmDn0HUpCXqyxzI5HIKxc5tmsrHfoNUksZVnY27I93l27o1TC86/1ozG2rb2ioHC2pwj8dXT30jYywcXBCQ0ODosICDm9eR0JEGJmpKcSEhrBl6Q8Ympji5XP/T0Ebe1/2W7eKSyePMeapZ9E3NCI3K5PcrEyKCpVZAmWlpexY9guJUeGMmfUcGhoaqjLFRer9dTwI5TlpMEF+ewk7f5aUhHj2/70cHT09WlY8J61ehu/q8nNSu979yclIx3/zWtKSErl04ihXTh2n08Dyv62lJSUkx8WQHBdDSUkxuVmZJMfFkJF8U1Um+uoloq5cJDM1meiQK2z68WssbO1o3f3Bbr7upMfQkQQfP8LZo4dIToxnz9qVZGem06W/sg8Wv83rWPnNZ+Ux3urAe+vy37gZH8vVs6c5tncHPYaORENDAy1tbWwdndX+GZmYoqWtg62js6phTNl5eTRJMdGUFBeRk5lJUkw0aTeT6jzG+ooVIPDAPkLPnyP1RhKpN5I4e/Qwx/fton2P8r5K4iLCuHr2NOnJN4kODeHv7/8PhUJB7xFj6i3W206FRdOrpTstHWyxMTVmbJe2FJWUcDm2fDS1sV3aMrZL+d+AsxGxmBjoMbR9K6xMjOjg6kh7F0dOhkapylxPTKajmzOtnewwMzTAzdaK/m28CEtKRqFQUFhcQnJWjtq/4tJSCoqLSc7KqXVcGhoadBwwhNN+u7keHERKQhy+q5XnqIqN9HtW/sGelX+oPvv0Vp6jDm36h9SkBC4eP8LlwAA6q52jhhATGsKpfbtIS0rk1L5dxIZeo1OFc5R7Wx9O++0h4tJ5MlNTuH7+LEGH9uHZXv18W1xUyNUzgbTr1bdeRx0dOnYCxw8f4KifL4lxsaxd9huZ6Wn0H6ZsGN+8egWLP3hXbZ6E2BhiIyPIyc6isKCA2MgIYiOVo1xpa2vj2NxF7Z+JmTnaOjo4NndB36D2f1drY+Lkqfjt3cPenTuIiY5i6ZLvSU1JZdS4CQAs/20pb782T1X+wrlzvP/WG4waN56BQ4aSlppKWmoqGRWykrZsWM/xo0eIj4slOjKS5b8t5cSxo4ydWD+ZUfdq4uQp+PnuYe+unapY01JSGTV2PADLf/+VBa+9qip/IfgcC99+k1HjxjNgyFDS0lJJS0sls0LfTls3ruf4saPlsf7+qzLWCY0bq3g4SArFAzA0NKRPnz4EBATQp0+fu8/QQLoMGUFJcREHN6yhMC8XOxd3Jr7wmtpT0Kx09QYUMysbJjw7D/8t67h47DBGZuYMePRxvCr035CTmcGa//tI9fliij8XA/xx9GzB5FfeBOBGTBSblnytKnNyzzZO7tmGd7deDJ8+u75Cvm/6rVrgtOQr1WerOTOxmjOTrN37uPHZN41Ys/vXmNu7LnUbMpKSoiIOrF9NQV4u9q7uTHrpdbUng1mVUlvNrW149Pn5HNr0D+dvxTFo0jRadCzPVnF092TMrOcI2LmZgN1bMbe2Zczs57B3Lc9KyM3KYteK38nLzkRX3wAbRycefX4+bq3bViiTweHNa8nNzsLI1Jw23XvSc8S4+47Tu0t3CnJzOLFnO7lZmVjbOzLphddU2TK5WRlqN6B6BoZMefkN9q9bxcovPkDf0Iiug0fQdXB5Y4y5tQ2PvvAaBzf9Q/DRQ8rhoSc/QcuO5U+/TS2tmPLy/zi4cS0rPluIkakZ7Xr2pddI9RhiQkNIv3mDMU89e9+x1aQxt+3daGhokpIQx+VTxynMz8PI1JzmLVox9unn1er3b4k3+OghANZXOL8B9Bw5jt6jJ5CdkU7Yrafkq778UK3MiOmzaduj9n/LOg8eQUlxMYc2lp+TJjw/X+2clJ2uHr+ZlQ3jn32FI1vWc/GYP0ZmZvR/5DG1c1JuZgZrvvpY9TkzJZlLx4/g6NmCSS+/AUBhQT7Hd2whJyMdPSMjPH060Wv0hHrJHGzbtQf5Odkc2bWNnMwMbB2ceOKVNzC/dSznZGSQVuFY1jc0ZMb8t9m95i9++2QhBkaG9Bw6ip5DR9b0E9XKzkjn14/LbyCDkg8SdOQgLi1a8dQb79VNcJXUR6wKRRl+m9aSkZqCppYmFja2DHlkqqoRCJSZHwe3biA9ORldfT282nZg4tPPo29Y/6/ungiNRFtLkxEdWqOvq018Wib/HAuiqKRUVcbMUP0ckZmXz7qAswxt3+rWEN8F7Au+yrWE8gcdx0KUN/b923hiYqBPfmER1xOTOXz53kccrK2uQ0Yq1+2GvynIy8XO1Z1HX3xN7RxV5Ri1tmHic6/iv3ktF44dxsjUnIGTpqllhzq4ezL6qWcJ2LmF47u3YW5ty+hZz6q9Njpo8jQCdm3lwPrV5OVkY2xqRrue/ehR6W/RtbOnKS4qpE2Puu9suKKuvfuSm53F7o3ryUxPw6G5Cy+/swgrW2UmTGZ6GslJ6o2hSz79kNQK+/vH/1M2cPy2aUe91rUu9B80mOysLP5ZtZK0tFRc3dz46Mv/o5mdsr+dtNRUEuMTVOX3791NYUEBm9atZdO6tarpts3sWLFuAwAlJcX88cvPpKYko6unh4urGx9+8X9069GzYYOr5Hasa2/H6urGh198qYo1PTWVxITyWP327qkx1r/WrgeguLiEZZVj/fxLujZyrP8KGpIfUlsa+fn5te/Z7CG0aNEirK2tefnll+tl+Xd7Jeq/ZsjHnzR2FRqM3/v1c2HdVGk3kRFPGkJTGd2loZTV84gtovEUl5bevdB/hLlh4z75FvUnso6Hxm7qrE0enr4HWzk0/Q6665LzHYaV/6+p/Orgf52j5YO/nvxvkPTC/xrst+x+/vruhf6FJMPmPmVnZxMcHExwcDA//PBDY1dHCCGEEEIIIYRoeqQPm1qTBpv79Oqrr5Kdnc2MGTNwcbm/YW+FEEIIIYQQQggh7oU02NynZcuW3b2QEEIIIYQQQgjxMKvHzsMfFg9XhwtCCCGEEEIIIYQQ/wKSYSOEEEIIIYQQQog6pSGjRNWarEEhhBBCCCGEEEKIJkYybIQQQgghhBBCCFG3ZJSoWpMMGyGEEEIIIYQQQogmRjJshBBCCCGEEEIIUbdklKhakwwbIYQQQgghhBBCiCZGMmyEEEIIIYQQQghRtzQlP6S2ZA0KIYQQQgghhBBCNDHSYCOEEEIIIYQQQgjRxMgrUU3UqK1bGrsKDWr3++81dhUazJCPP2nsKjSogwvfb+wqNBidh27owoenzb+srKyxq9CgdLUfnsuD1k7NGrsKDUrzIeoA0s3WsrGr0KC2nrnc2FVoMFYmRo1dhQZVXFLa2FVoMGUoGrsKDcrR0qyxq1C/HqK/OfXl4bnaFkIIIYQQQgghhPiXeHgeoQkhhBBCCCGEEKJBaDx02ed1TzJshBBCCCGEEEIIIZoYybARQgghhBBCCCFE3dL49+SH7Nq1i82bN5Oenk7z5s2ZO3cubdq0qbH82bNnWbNmDTExMWhra9O6dWtmzZqFo6Njndbr37MGhRBCCCGEEEIIIerQ0aNH+f3335kyZQrff/893t7efPDBB9y8ebPa8klJSXzyySe0adOG7777jk8++YTCwkI+/PDDOq+bNNgIIYQQQgghhBCibmloNNy/Wti6dSuDBw9m+PDhODs78+yzz2JhYcGePXuqLR8eHk5paSkzZ87EwcEBd3d3Jk+eTGJiIpmZmbWqS2XSYCOEEEIIIYQQQoiHTnFxMWFhYXTs2FFteseOHbl69Wq183h6eqKlpcW+ffsoLS0lLy+PAwcO4OXlhZlZ3Q7VLn3YCCGEEEIIIYQQom414ChRe/fuxdfX967lhg8fzogRI1Sfs7KyKCsrw9zcXK2cubk558+fr3YZzZo14+OPP+aLL75g6dKlKBQK3N3d+eCDD2oTQrWkwUYIIYQQQgghhBD/WiNGjFBriLlfGvfxWlV6ejo//PADgwYNol+/fuTn5/P333/z5Zdf8umnn6KpWXcvMkmDjRBCCCGEEEIIIerWv2CUKFNTUzQ1NUlPT1ebnpGRUSXr5rZdu3ahr6/PrFmzVNNef/11Zs2axdWrV+84utT9avprsIlbsGABS5cubexqCCGEEEIIIYQQ4j7o6Ojg6elJcHCw2vTg4GC8vb2rnaewsLBKFs3tzwqFok7rJxk29+jixYu88847rF69us47Eqovxv16YTZ0IFpmphQlJpG+YSuFYZE1ltf3bon5mOHoONihKCmhMDyK9M07KLmZDICelwfm40eh08wWDV1dStPSyAkIJMvvcANFdGcKhYKTe7Zz6fgRCvLzsHNxY9DkJ7Cyd7zjfHHXr3FkyzpSkxIwMjOny+ARtO8zQPV9amI8J3Zv52ZcNFmpKXQfMZaeo8bXczR1Q9+nLRaPT0K/pRfaNtYkffo12Xv2N3a11CgUCk7s2c7FAH8K8vOwd3Fn0JQnsL7Ldou9fg3/LetITYzH2MycLkNG4lNhuwGEBp/h+K6tZKYkY2ZtQ+8xj+Dl00n1/fHd2zi5Z7vaPIYmpjz32beqz0WFBRzbvomwC+fIz83B1MKS9r0H0HnQsNoHDwQdPkDg/t3kZGZi4+DAkMlP4OzVssbyN+Nj2bd2FYlREegbGtGx30B6jxqvSuPMyczgwMZ/SIqJJv1mEm2792bMU3OrLOf0gX2cPXKQrLQUDIyM8fLpxMCJU9DV16+TuO7VOf8DnPLbQ05mBtb2jgyaPA1nz5rjT46PZf+61SRFK+P36TuQXiPHqcV/aNNabsRGkX7zBm2692LUzKrx1weFQsHx3du4EOBPYX4edi7uDJk6/Z725cOb15Jya1/uOmQkHfoOVCsTeu4Mx3ZtUe3Lfcc+gpdPZ9X35/wPcD7An6y0FACs7BzpMWIMHm19VGX2rFrG5cAAteXau7rzxP/eu+9Yzx05yOmK223SNJw8W9RYPjk+Fr/1f5dvtz4D6FlhuynXQwiHNt1eDxZ0G6q+Hi6dOMae1cuqLHv+d7+hraNTZfrJvTs5umMTHfsNYsjUGfcdY2357tzOjo0byEhLxcnFlSeffR7vtu2qLXv5wnl2b9lE2LVr5OXlYmfvwKgJjzBw+IOndtcn3x3b2FYhtlnPvVBzbOeD2VkpttETH2HQ8JGqMumpqaz4fSmRYWEkJsTTb9AQXvrfmw0Vzh0d2L2TPVs2kZGehmNzF6Y9/Qwt27SttmxRURErfvmR6PAwEuNi8fRuzYJPv6xSrqS4mO3r13L88EEy0lIxNbdg5IRHGDq2aVxbDGnfku6eLhjo6hCTms62Uxe5kZldY3kTAz1Gd2qDo6UZ1ibGnI2MZcOJ4BrL+7g6Mq1PZ67GJfHX4VP1EEH1Th7Yx7E9O8jOyMDW0YnR02bi2rL6mzGApNgYdqxeTlxEGAZGxnQbOISB4x5RO2+VlJRwePtmgo8fJSsjHWNTM/qMHEOvoeX79/F9uwk86EdGajKGxiZ4d+zC8CnT0Gvgv7dHfHfjt2MrmRnp2Ds5M+nJp/H0rj4LoLioiH/++IXYyAiS4uPwaNmKVxd9qlYmOPAER/32EhcZSXFxEXZOzoyYOJn2Xbo1RDh3dMR3Dwd2bCXrVqyPPPk0nt6tqy1bXFTE2j+WEncrVveWrZi36BO1MsGBJwjw81WLdfjESbRrArE2dRoN2IdNbUyYMIHFixfj5eVF69at2bNnD2lpaYwcqTyWV6xYQWhoKJ9+qjwOunTpwrZt2/jnn3/o378/eXl5rFq1Cmtrazw9Peu0btJg8x9l2LkDllMmkvbPJgrCIzDp1xvbF58h4aMvKU3PqFJe28oS2+dnk3XoKCl/rUFDTxeLiWOxfXEuCYs+A0BRWEj24aMUxyeiKCpGz8MNy2mTKCsqIufI8QaOsKozfns5e2gfw56YjYWtHYF7d7D5p8U8+d6nNd6EZqYms/XX72nTow8jZs4hPiKMQ+v/xsDYBK8Oypuh4qIiTK2s8PTpxPFdWxoypFrTNDCgKCKa7L1+NHvvjcauTrVO++0h6KAvw6fPxtLWjpN7d7Dpx2+Y9f6n6OobVDtPZkoyW5Z+R9sefRg5cw7x4dc5uP5vDIyNadGhCwAJkWHsWv4rvUaNx9OnE2Hnz7Lzz194bP4C7F3dVcuysLVjyrzyGwONSqmb/pvXEXPtCiNmzMHMypr4sFD2r12BgbExrbv1qlXsV84E4rf+b4Y/PhMnzxac9T/Auh+/Ye6izzGztKpSvjA/n7Xff4WzZwueevsDUm8ksmvFH+jo6tH91sVhSXExBsYm9Bw+muBjh6v93cunTnBoyzpGTp+Ns2cLMlKS2b1qGSXFxYye+XStYrofV88EcmDDGoY+NgMnjxacO3KAjT8t5un3P8O0hvjXL/kKJ8+WzHhrEWk3Etm9chk6urp0G6KMv7SkGANjY7oPG835Y/4NFgvAKb89nDnoy8jpT2PRzI4Te7azYcnXPL3wsxr35YyUZDb98i3tevRl1JNziQ+/jt+61Rgam9Ci4619OSKMHcuX0nvUeLw6dOZ6cBDbl/3CtNcWYO/qAYCJhSX9xk/CwrYZijIFlwMD2Pbbj8x4ayE2js6q33Np2ZpRT5Y3YGlqad13nCFBgRzcsIYhj83AycOLc0cOsvGnxcx+/9M7bLevcfZsyfQ3F5J2I4k9q5aho6tH1yEjytfDz9/StmdfRj/1DHHh1/FbuwoDYxNa3loPADq6usz54P/Ull9dY01CZDgXjvurxd6QjvsfZsXSn3n6xVdo2aYN+3bu4PP332Hxr8uwtrWtUj70ymWcXd0YO2kKFpZWnA86w28/fIuOri59Bg5qhAhqFuB/iOVLf2bOS6/Qqk1bfHdu59P3FvDtb8uwsW1Wpfy1q1do7urG+MlTsbC0JDjoDL9+r4yt78DBgHKEDlNTMyZMeQy/PbsaOqQaBR71Z80fvzLjuRdp4d2aA3t2sfijhXz241KsbKpuR0VZGTo6OgwZPZbzQafJy82tdrm/fPMlaSkpPPXiyzSzdyQrI52ioqL6Duee9G/tST9vD9YfP0dyVg5D2rdgzuCefLX9AEUlpdXOo62pSV5hEYcvh9Hdy+WOy7c0NmR0x9ZE3Eitj+rX6ELgcXatWcG4GbNxadGKwAP7WLH4C+Z99g3mVtZVyhfk57H8q09xbenNC4s+IzkxgU3LfkFXV48+I8eoyq3/5Qcy0lKZ8NRcrJrZkZOVSXGFbXn+xDH2rl/DxFnP4NqiFWnJN9ny56+UFBfxyNPPNUjsAEHHj7FhxTIee/pZPFp6c2TfHn76/GPeX7wES2ubKuXLysrQ0dGl//BRXD4XRH5e1X35+tXLtGzTnrFTn8DQ2ITTR/357esveHXRxzU2BDWEoOPH2LRiGVOefgaPlt4c3beXXz7/mHcX/3CHWHXod4dYw65exqtNO0ZPnYaRsQmnjx7h96+/5JVFH9fYECT+Xfr27UtWVhbr168nLS0NFxcXFi1ahO2tv9lpaWkkJSWpyvv4+PC///2PTZs2sXnzZnR1dWnZsiUffvgh+nXcGPufbLBZsGABzs7O6Onp4efnh6amJlOnTmXkyJH88ccf+Pv7Y2BgwIwZMxg0aBA3btxgzpw5vP322+zdu5crV67QrFkz5s6dS8eOHblx4wbvvPMOANOnTwdg0KBBzJ8/H1Ae6CtXrmTv3r1oamoycOBAZs2aVaedDd0v08H9yTlxmpyAkwCkr9+CQZtWmPTrTca2qhdDus2dQEuLjK274FYaV6bvAezmv4CmkRFlubkUxcRRFBOnmqckNQ3DDu3Q93Rv9AYbhULBOX8/ug4ZqWpoGT59Nr++O5+QoEDa9+5f7XwXjvljbGbOwEnTALC0cyApKoKgg76q5di5uGHn4gbA6f1N50LyXuSdPE3eydMANHvnf41cm6oUCgXnDvvRbegoVUPL8OlPs/SdVwk5E6iW6VTR+YDDGJuZM2jyEwBY2TmQFB1J0AFf1XLOHvLD2asV3YePUZWJDQ3h7KH9jJ71rGpZmlqaGJnWnDWXEBmGd9eeNG/RCgAzK2sunjxKYlRkrRtsTvntpV3PPnToq4xz2GMziLh8kXP+BxgwcUqV8pdPHae4qJAxTz2Djq4uNo5OpCYlcMpvL92GjEBDQwNzaxuGTVWep0LOna72d+PCr+Pg5kG7Hr0BMLe2oW2P3lw7d6ZW8dyvMwd9aduztyozasjUGURcucS5IwfpP2FylfJXTp+guKiIUTPnKuN3cCI1KZEzB3zpOlgZv5mVDUOmKONvyHgUCgVnD+2n+9BRqoaWkTPm8POCeVw9E1gl++u288eU+/LgKeX7cmJUBKcP+KqWE3R4P829WtFjxFhlmREOxFwPIejQfsbMUjbYeLZXH4qy77hHOX/sMAmR4WqNFlra2nfc3+/FmQP7aNujNz63zqtDpkwn8spFgo8epN/46rdbSXERI2fOUW23tKQEzhz0pcvg4WhoaHD+2CGMzMxV2065HsI5fWCvWoMNaGB8lyzXwvw8dv71K8OfmMWJShl0DWXXlk30HzqMwSNHATD7hZc4H3Safbt2MG1W1UbRiY9NU/s8bMxYLl8IJjDgaJNrsNm5eRMDhg5jyMjRADz9wssEnznDvp07eGL2nCrlH6kU2/Ax47h8PpjAY0dVDTa2dnbMfuElAE4eO1LPEdw7321b6D1oCAOGKRsWZzzzPJfOBnFwzy4mz5xVpbyevj5PvfAyALFRkdU22Fw6d5Yr54P5v1+XYXLrWLRpVrWhq7H08Xbn0OXrXIpNBGDd8XMsnDSCjm5OBF6Prnae9Nx8tp+5BEC75vY1LltTQ4PH+3Rm7/kQPJpZYaSnW/cB1CDAdxedeven6wDlPjd2xiyuXzpP4MH9DJ/8eJXy508co7ioiElzX0BHV5dmTs4kJ8ZzzHcXvUeMRkNDg+uXzhN25SKv/9/3GJmYAmBRqSEvOiwUZw8vOvbup/q+Q+9+XD4TWM8Rqzuwaxs9+g+i92BldvCU2c9w5fw5ju7by/hpVTMQ9fT1eXzu8wDEx0RX24gx+Sn143305Me4fC6I86cDG7XB5tCu7XTvP1AV6+TZc7l6/hzH9u1lXA2xPqaKNaraWCdVinXU5KlcPneGC6cDpcHmbu6jI9/GNnr0aEaPHl3td7fv+yvq168f/fr1q+9q/Xf7sDl8+DAGBgZ88803TJo0id9//51PPvkER0dHFi9ezODBg1myZAmpqeUt/KtWrWLs2LEsWbIELy8vvvrqK/Lz87G2tmbBggUA/PTTT6xcuZJnnnlGNZ+/vz+ampp89dVXPPvss2zfvp2jR482eMwqWlroNnei4Oo1tckFV6+h5+5a7SyF0bFQWopx7x6goYGGnh7GPbpQGBVDWQ1PiHScHNFzd6XgenhdR3DfslJTyMvKpHmr8j8Q2rq6OHq0IDEyrMb5kqLCad5S/Y+Ki3dbbsZEU1paUm/1FUqZqSnkZmXiUmG76ejq4uTRgoTImverxMhwtXkAXL3bcKPCdkuMqlrGxbstCZX2h8yUFH5973X+WPQWu5YvJSMlWe17R3cvIi6dJzs9DVBmOyTHxeLWuvqU+HtVWlJCUkxUleW4tW5LXET1+2x8RBjOni3R0dWtUL4dOZkZZKam3PNvO3u24GZsDPG3ficzLZWwC+fwaONzlznrzu34Xb0rxe/dRlWvyhIiwnDyaKEev3fb+46/PmSmJiv35Qrx6Ojq4uTZssZ4QLkvu1bZl9tyIyZKtS8nRIbj4l21THxE9cdIWVkZIWcCKSoswMFNPS03PuI6P709j2UfLsB3zV/kZmfdV5ylJSUkxVbdbneqT0JkeJXt5tpafbslRIRXsy+040Z0lNq5uKS4iF/f+x+/vPsam375jhuxVW8gfdf8RcuOXXBp2TgX0SXFxURcD6V9p85q09t36kzolcv3vJz8vDyMjI3runq1UnwrNp9OXdSm+3TqzLWrV+55OXl5eRgZm9R19epUSXExUeFhtO3YSW16mw4dCQu5+sDLPRt4AjfPFvhu28L82TN467k5rP5tKQX5+bWtcq1ZGhtiaqDP9cTyv4MlpWVE3EzFxdqy1ssf3sGb9Jw8zkbE1npZ96OkpISEqEg827ZXm+7Zpj0xYaHVzhMTdh2XFq3UzltebX3Izkgn/dZ1wpWzZ3By8yDAdxdfzn+BxW+9ys7Vf1FYUKCax9WrFYkxUcSEXQcgIzWFkHNBtKzUyF6fSkqKiY0Ix7t9B7Xp3u07EBEaUqe/VZCfj6FR4523aoq1VXsfIus41sJGjlU8PP6TGTYAzZs3Z9o05VOdCRMmsHHjRrS1tRk3bhwAjz32GJs2bSIkJET1ntn48ePp1k35LuLMmTM5ePAgERERtGnTBhMT5YWFmZlZlT5snJ2dVZk3jo6O7Nu3j/Pnz9O/f/VZHfVNy9gIDS0tSrPU3zcuzcpBv1X1F0ilaenc+GEpNnOexPKxR0BDg6K4eG7++HuVso6fLUTL2Bi0NMnctY+coyfqJY77kZuVCSj7H6nI0MSUnMyMO8yXhXOLqvOUlZVSkJODkZl5XVdVVJBX03YzNSUnI6PG+XKzsmjesvrtlp+Tg7GZOblZmaonXrcZmZiSV+EG1d7FXfkqVjM78rKzCfTdydrFn/Hkux9jcOuP8MBJ0/Bbt5LfF76Bpqby9ZGBk6fh3rZ2jRt5OdkoysowMq1UR1NTokIyq50nJysTUwvLSuXNVN+ZV5PqW53WXXuQn5vD6m8+AwWUlZXStnsvBj5SNaunvqjiN1E/nxqampEbUv2NX25WJiY1xJ97H/HXh9ws5X5V3T6Xk5Fe3Sy35sukeSv1hgVD03vdl9X3k+T4ONZ88yklJcXo6ukxfu5L2Dg6qb53826Ll08nzKxsyEpL4djOzaz/4StmvLmw2teKqpN/a7tVPmaNTEyJzrrDdjO3qFRefbvlZmfiYlJpPVQ6pi2a2TFi+mxsHJtTXFhA0KH9rPnmM55650MsbO0AOB/gT0byTUY/+QyNJSsrk7KyMswqxWxmbsHF9HP3tIygwJNcCj7HR998Vw81fHDZt2OzqBSbhQUZ587e0zJux/bx4u/ro4p1Jjsr69Z2NFebbmZuwZXzwQ+83JtJSYRevYy2jg4vvfUuebm5rP79FzLSUnnp7XdrV+laMtHXAyAnv1Btek5+IWaGtUvx97K3wcfFge93N+yrqgB52cptWTk7z9jMjPArF6udJyczo8ornrfnz8nMwNLGlvSbN4kOvYaWtg7TXppPQV4eO1YvJysjjWkvvQZA+x69yMvJ5o/PP0ABlJWW0qFXX4ZPmVb5J+tNTlY2ZWVlmFS6pjUxMyfr4vk6+x1/391kpKXQrd+AOlvm/cq9Q6zXLl6os9854rubjLRUuvVrnHs98XD5zzbYuLq6qv6voaGBmZkZLi7l79Vqa2tjbGxMRoWbQjc3N9X/LS2VNwWZmdXfONX0W7fnvZf56l+lHqo1UL3uVJmmqQlW06eSE3iG3NPn0NTXw3zsCGzmzOTGd7+ozXfjmx/R0NNDz80F84ljKElJJfdUUD3GUVXI6ZMcWLdK9Xn8s68AqHUEp6Tgbol4VWdR1PCFqK2rp0/it3al6vOE5+YB1Ww3BXdd/5XnKd9sFaZXWaz6/u/WRr2TTHs3d5Z98DZXAgPoPGg4oOzMNSEijPHPvIyppRVxYaEc2bIeU0sr3FpX38nm/dCgchyKqhWvNEelGaqbekcxoSEE7N7O8Mdn4uDmQfrNG/it/5ujO7bQb9wj97GkOlDN8Xc/h97tnvgb+nC9cvoE+/8p35cfef5VqquIQqG4+75ceUK1+3L1+3tFls3smLngAwrz8ggNDmLvqmVMmfcmNg7KRptWXbqryto4OtHM2YXfFr5JxOULtOjQueoC71TnauK8U5jVla8yvZrz960vAHB098TRvTxjyMHdkxWfL+Ts4QMMnvIEaTcSObp9I4/Pfwct7ca/vKkSczXTqhNy+RJLvvycp557Ec+WreqpdrVT9ZytuOfYvv/yM2Y9/yJeTTS2qipvx7sf03eiUJShoaHBc6+/iaGREQAznnmBrz94j8yM9CoNffWpg6sjj3Qvf/iw/JDyNZ3Kfys1NKpcUd4XQz1dpvTsyD8BQeQXFddiSbVT9c/Nnf/eVl++/BuFogw0YOpzL6NvaAgoX7X66+vPycnMwNjMnMiQKxzavpmxM5/G2d2T1JtJ7Pp7BQe2bGBIAz4kgaq7reIej9t7cS7wOFtW/8Xsef+rtn+nBlfd38w6ijU48ARbV69g1rzXsWwKsTZ1jdhFyH9F41/R1BPtShdrGhoaVaaB+rBbWhU6X7x9AruXYbmqW25ZWVm1Zffu3Yuvr+9dl9kzM5/+Zg+Wflqak4uitBStSk/utUyMKc3KqXYek/69URQVkbFlp2payvK/cfp8EXrurhSGl48uVZKqfDWkOCERLVNjzMYMb/AGG/d2HbBzLW9gKy1RpsxXfgKfl52NYaX1UJGRqanqybhqnpxsNDW10L91ISXqjkc7H+xcF6k+17zdsqpkFFSk3G7qjaL5OVlq283I1Kzqts3OrpIZUJGunj5W9g6kJ98ElB1OH9uxiTGzn8ejXQcAbBydSY6PJeiAb60abAyNTdDQ1CSnUhx52dlVsm5uMzY1qxL37Vda7qdfEv/tm2jdpTsdbvWrYuvoTHFRIbtXLafP6PEP1BHt/bodf+V48rKzMDSpPhajauK/nTFV0zz1xbNdB7XOqyvuyxWzoPJy7rzP1RRT1X25UpmcqutJS1sbCxtlXxh2Lm4kxUQSdGgfI56YXe1vG5tbYGxhQXryjbuFq2JQ03bLyb6/7ZZze7sp142RSXXrQXkuNjCu/lysqamJXXM3Vf0TIsLJz8lh+aflo14pysqIDQsl+NhhXl289J4ziWrD1NQMTU1NMm69RnlbVkZ6lWyNykIuXeKLhe8yecaTDBszth5r+WBMbseWph5bZkZGlaybyq5eusjnC99l6ownGT5mXH1Ws06YmJqiqalJZqUMuayMjLtuxzsxt7DEwtJK1VgDYO+k7GcqNTm5QRtsrsQlEZuSofqsraW8sTIx0Cczr/y1HiN9vSpZN/fDztwEU0N95gzuqZp2+zr7s2ljWLzzEClZ1b9+XxcMTZTbMrvSw9TcrKwa+8QyNjOvtrzyO+U8JuYWmFpYqhprAGxujQqYkZqKsZk5+zevo32PXnTtr+yLys65OcWFhWz58zcGjn9U7d6jvhibmqCpqUlWpczlnKzMKpkoD+Jc4HFW/PgdM198tdFHiDK6FWt2peM2JysD0zoY5Tc48AQrf/yOGS/OkxGiRIP5zzbY1LXbjTI1NcTcqxEjRjBixN2H6bwx/50H/5HSUopi4tBv1YK8s+WpjvqtWpB3rvrUT01dXRRllRqnbsd6x8emmmg0wpNMXX19tZGfFAoFhqZmxFy7ouoguKS4mITw6/SppvPS2+xcPYi4qJ6iHnPtCrbNXdDSksOjrunqG6iNlqNQKDAyNSM6RH27xUdcr7bz0tvs3TwIv6C+3aJDrtCswnazd/Ug5tpl1Sg0ADHXLlfp06OikuJi0m8k4eylfPJbVlpKWWlplQ7ENTQ176kx9060tLWxa+5K1NVLeHcu/6MfefUSrTp2qXYeR3dPDm1ZT0lxEdo6yvfqo65extjMHLNqRrmoSUlRIRrVxFS7Z6j3pzz+y7TqVB5/VMhlVcfRlTm4e+K/tVL8Ifcff12oeV++jH3FfTk8lP4Tan6Kau/mQdgF9VdJokMu06y5q2pfdnDzIDrkimokLGWZKzi6e9yxjgqFQtWQVJ28nGxybg1De6+0tLWxc3YlKuQyLTt1VatzTVk6Dm4eHNm2gZLiYlWDSfTVK2rbzcHdg+vn1Y/pqJDLNHNxrfFcrFAoSI6PVXWq7OnTiadcXNXK7F21DAvbZnQfPqbBsm60dXRw92rBxbNn6dm3PF3+4rmzdOvdt8b5rly8wJeL3mPSEzMYPbGBM93ukc6t2M6fC6JnhVcBLpwLovtdYvt84btMmT6T0RMfbYiq1pq2jg6uHp5cDj6ntt0unz9Hl569H3i5Xt6tOR1wjIL8fPQNlOeQGwnxAFg38NP6opJSUnPUG0qy8gvwsrMhLjUDUI4A5WZjye5z995HUWWxKRks3nFIbdrwDq0w0NVh66mLpOfkPfCy74W2tjYOrm6EXb5Au249VNPDLl+gTYXMw4qae3rhu/4fiouKVP3YhF2+gIm5BRa3Xr9t7tWCS6dPUlhQoBqiO/WGsrNmc2vlua24sKj6a4gG/Hurra2Ds7sHIReD6VRh3w25eJ4O3XreYc67CzpxjFU//cCMF1+hU4/aDcRQF8pjPU/HOo717IkAVv/0A9NffIWOTSDWfw15Y6HWJEfpHtna2qKhocGZM2fIzMwkvwl0DncnWQf8Me7ZFePe3dG2s8Vi8gS0zMzIPqoczcl8/Ghs55UPJ5h/6Qq6zo6YjR6Gto01us6OWM18nJK0dNXIUCYD+mDQtjXaNtZo21hj3Ks7pkMGNHh2TXU0NDTo2H8IZ/bvIex8ECkJ8ez7+0909PRo1bn8j7HvqmX4rlqm+ty+T3+yM9I5vGktaUkJXDp+RO11GFA+Ob8ZF8PNuBhKiovJy87iZlwMGffxVLqxaBjoo+vpjq6nO2hqoNPMFl1Pd7SbNV5fHxVpaGjQccAQTvvt5npwECkJcfiuVg73W/H1jT0r/2DPyj9Un316DyA7I51Dm/4hNSmBi8ePcDkwgM6Dy7dbpwFDiAkN4dS+XaQlJXJq3y5iQ6/RaeBQVRn/LeuIvX6NzJRkEqMi2LHsZ4qLCmnTXfmHWM/AACfPlhzdvpHY6yFkpiRz+eQxrpw6jqePekeUD6LbkBFcOHGM4GOHSUlMYP+61eRkZtCxn/JJ3OEt61nz7Zeq8q279URHV4+dK/4gOT6Oa+fOcMJ3p2qEqNtuxEZzIzaaovx88vNyuBEbTcqtGwIAz3YdCT52mCunT5KRkkzklUsc2b4Zz3YdGiS75rYug4Zz6eQxzgf4k5qYwIH1f5OTmUGHvgMB8N+6gbXfV4i/aw90dHXZvfIPkhPiCD13hsB9u1QjDVWJvyCf/NxcZfyJ8VV+vy5paGjQaeBQTu3fTWhwEMkJcaqhq70r7Mu7V/7O7pXlfYP59FHuywc3riE1KYELx49wKTCArmr78lBiQq8S6LuL1KREAn13ERsaQucK+/KRbRuICwslMzWF5Pg4jmzbSOz1a3h3Ud6cFBUWcHjzOhIiwshMTSEmNIQtS3/A0MQUr/vcl7sMHsalk8e4EOBPalICBzb8TU5GBj59Bqrqsu778qG3W3ftgbaOLntW3dpuwWcI3L+LLoPKt5tPn4HkZKSVr4cAfy6dPEbXweUNrgG7thJ55SIZKTe5ERvD3tV/khwfp9pf9A0NsXFwUvuno6eHvqERNg5OdZb6fy9GT3yUw377OLB3N3Ex0fy19CfSUlMZOko5at2a5cv4+O03VOUvXzjPF++/y9BRY+g7cDAZaWlkpKVVeSLeFIx55FEO79/HgT3K2P78RRnbsNHKjKC///yDDyvGdj6Yz957h6GjxtBn4GDS09JIT0sjs1JskeFhRIaHkZ+XR052NpHhYcRGVz8qUUMZPn4ixw764b9vLwmxMfz9+1Iy0tIYOEI5+teGlcv58v0FavPEx8QQHRFOdlYWhfn5REeEE12hQ+4e/QZgbGLCHz98S3xMNNevXubvP36lS68+mNYic6euHLsawYA2nrRxtqeZmQlTenWkqKSUc5Hlo4RO6dWRKb3UO821tzDF3sIUPR0dDPV0sbcwxdZM2RdccWkpNzKz1f7lFxVTWFzCjcxsSis/MKwHvYeP5twxf077H+RmQjw7//6L7Ix0ug0cAoDvhn9Y9uXHqvI+Pfqgo6vLpj9+4UZcLJfPnOLIru30GT66/LzVow+GxsZs/uMXbsTHEn39Gjv/XkHbLt1VDeGtOnTi9OGDXDh5nLTkm4RduoDf5vW08unUINk1tw0ePZ6Thw8RcGA/SXGxbPjrDzLS0ugzVPm3ZtuaVXz/8ftq8yTGxRIbFUFuVhaFBQXERkUQGxWh+v5MwFH+WvIt46fNwNO7DZkZ6WRmpJObo96HZkMbOHocgYcPcfxWrBv/+oPMtHRVrNvXrGLJxwvV5kmMiyUuKpLcrGwKCwqIi4okLqr87YKggKOsWPIt46ZNx9O7NVkZ6WQ1gVjFw0FSCO6RlZUV06ZNY9WqVSxZsoSBAwdWO7xXU5EXFEyakSFmI4diaWpKUWIiN3/6ndI0ZYqglpkJOjblT6QLroWRsnw1pkMHYTpkIIriYgojo7m55DcURUXKQpqamE8cg7aVBZSVUZycSvrWnU2i02GALkNGUFJcxMENayjMy8XOxZ2JL7ymlomTlZ6qNo+ZlQ0Tnp2H/5Z1XDx2GCMzcwY8+rhqSG9Qdi635v8+Un2+mOLPxQB/HD1bMPmVN+s/sFrQb9UCpyVfqT5bzZmJ1ZyZZO3ex43PvmnEmpXrOmQkJcXFHNzwNwV5udi5uvPoi6+pZS9kV3q1wMzahonPvYr/5rVcOHYYI1Pl0OwVMzMc3D0Z/dSzBOzcwvHd2zC3tmX0rGfVXmPJyUhn91+/kp+bg4GxCfau7jz+2ruYWpYfG6NnPcux7ZvYveJ3CvJyMbWwovfoCXToV/uhdlt36U5+Tg7Hd+8gJysDGwdHprz0mirrICczk4xbr2cB6BsY8ti8N9j3z0qWf/4B+oaGdBsygm5D1LP2/vxU/UIk7EIwZpbWvHBrm/ceNQ404Mj2zWRnpGFgbIJnuw70Hz+p1jHdD+8u3SnIzeHEnu3kZmVibe/IpBfK48/NylCLX8/AkCkvv8H+datY+cUH6Bsa0XXwCLWbeoAVny9S+xx+MRhTSyue+6R+9/luQ0ZSUlTEgfWrKcjLxd7VnUkvva62L2dVepXE3NqGR5+fz6FN/3D+1jlo0KRpqiG9QZlZNWbWcwTs3EzA7q2YW9syZvZz2LuWZ9jkZmWxa8Xv5GVnoqtvgI2jE48+P181CpmGhiYpCXFcPnWcwvw8jEzNad6iFWOffl6tfveiVefu5OfmcmLvDtV2e/SF+er7bUrl7fY//NatZtWXH6JvaESXQcPpUqFRytzahkdfmM/BTf8QfPSQcqjzyU+oDeldmJ/PvjUryM3ORE/fAFvn5jw2/221Y7qp6NV/ANnZWWz5Zw3paWk4u7ry9kefqoZvzkhL5UZioqq8/35fCgsL2LFpAzs2bVBNt7Ftxo8rVjd4/e+kd/+B5GRlsemfv0lPT8PZxZV3Pv5MFVt6Who3EhJU5Q/t31djbD+v/Fv1+c0Xyx8iAQQFnqhSpqF179ufnOxstm9YS2ZaGo4urry28EOsbW9tx/R0biYlqs2z+OOFpN4s3/8XzVcO8/3Xtt0A6BsY8MZHn7H691/48PVXMTQ2plP3HtUOE94Y/K+EoaOtxYRu7TDQ1SE2JZ0/DpygqKRUVcbcqOo549XRA9Q+t3ayIy0njy+3+tV3le9J++69yMvJ4fD2zWRnZtDM0ZmZr72typbJzkgn7Wb5gzh9Q0NmvfEuO1b9yc8fvIO+kRG9R4ym94jyIX/19PWZ9cZ77Fy9nF8+fBd9QyNad+qqNkz4gHHKgTz8tqwnMy0VIxMTWnbozLBHpzZc8EDnXn3Izc5i75b1ZKWnY+/cnBfefl/V30xmRhopN5LU5vn5i49ISy4fMeyLt5QdKf+0bisAx/z2UlZaysYVy9i4ovxhqFfrNry66NN6jqhmyliz8d2yQRXr82+/p+pvJjMjvUqsS7/4WC3WL2/FumTdFgCO+flSVlrKphV/smnFn6pynq3bMG/RJ/Ud0r+bZNjUmkZ+fn7D5eSJe1arV6L+hXZPmNjYVWgwQz5+uE7sBxe+f/dC/xF6Og9XG3hDPBVtKmr7Ouy/TUNmozS2Lu5Ody/0H6L5EG3b3MKixq5Cg9p65t6Hjf+36+rh3NhVaFDmBrUbpevfpKwBXxdrCvq1anoPHepS8kf/d/dCdcRmYdN+kP6gHq67CyGEEEIIIYQQQtS7yn0mivsna1AIIYQQQgghhBCiiZEMGyGEEEIIIYQQQtSth+g13PoiGTZCCCGEEEIIIYQQTYxk2AghhBBCCCGEEKJuaUqGTW1Jho0QQgghhBBCCCFEEyMZNkIIIYQQQgghhKhbGpIfUluyBoUQQgghhBBCCCGaGMmwEUIIIYQQQgghRN2SPmxqTTJshBBCCCGEEEIIIZoYybARQgghhBBCCCFE3dKQDJvakgwbIYQQQgghhBBCiCZGGmyEEEIIIYQQQgghmhh5JaqJ2jvxkcauQoPSfojS5Q4ufL+xq9CgBn30cWNXocH4f7CosavQoBQKRWNXocFoPETnKICikpLGrkKDycjNb+wqNKiyh+ew5cd9AY1dhQb1yvDejV2FBtPGya6xq9Cg9PJyG7sKDUbxEP39eRhoyLDetSZrUAghhBBCCCGEEKKJkQwbIYQQQgghhBBC1C0Z1rvWJMNGCCGEEEIIIYQQoomRDBshhBBCCCGEEELUrYesD8D6IBk2QgghhBBCCCGEEE2MZNgIIYQQQgghhBCibmlKfkhtyRoUQgghhBBCCCGEaGIkw0YIIYQQQgghhBB1S/qwqTXJsBFCCCGEEEIIIYRoYiTDRgghhBBCCCGEEHVKQ1MybGpLMmyEEEIIIYQQQgghmhhpsLkPCxYsYOnSpY1dDSGEEEIIIYQQomnT0Gy4f/9R8krUv5hCoeDEnu1cDPCnID8Pexd3Bk15Amt7xzvOF3v9Gv5b1pGaGI+xmTldhozEp88AtTKhwWc4vmsrmSnJmFnb0HvMI3j5dFIrk5OZwbHtm4i8coGiggLMrG0YPGUGzl4tAbgeHMSFAH9uxkWTn5PD5FfewNmr1QPHenz3Ni4E+FOYn4ediztDpk6/p1gPb15Lyq1Yuw4ZSYe+A9VjPXeGY7u2qGLtO/YRvHw6q74/53+A8wH+ZKWlAGBl50iPEWPwaOujKpOblcmRbRuJunqJwvx8nDxbMHjyE1jYNnvgeBtr2x7fvY2Te7arzWNoYspzn32r+lxUWMCx7ZsIu3CO/NwcTC0sad97AJ0HDXugeOuKvk9bLB6fhH5LL7RtrEn69Guy9+xvtPqcO3KQ0357yMnMwNrekUGTpuHk2aLG8snxsfit/5uk6Aj0DY3w6TOAniPHoVGhw7bY6yEc2nR7n7ag29Cq+/S1c2cI2LmFjJSbmFvb0mfsI7To0LnyzwFwcu9Oju7YRMd+gxgydYZqelFBAUe2b+T6+bMU5OZgYmFFh74D6DJo+D3H39SP2z2rlnE5MEBtufau7jzxv/fuOUbV7zXCtj4f4M/lwABSExNQKMqwdXKhz5iJar971v8A548dLl8P9o70HDFWbT3UF4VCQeDeHVw6foSC/DzsXNwYOGkaVnfZ/nFh1zi6ZT2pSQkYmZnTedBw2lc4j106foSrp0+QmpSAQqHA1rE5PUaNx9HDq54jqtnhvbvZt30zmenpODg3Z8pTc/Bq3abassVFRfz928/ERISTGB+HZ0tvXv/osxqXHXb1Ct8segc7RycWfftjfYVwX/x9d7N/22YyM9Kxd2rO5Flz8PKuOd41v/1MbKQyXo+W3rz2oXq8oZcvsW3NSm4kxFNUWIiljQ29Bw9j6LiJDRHOPZnaqyND27fESE+X60nJ/O53gtjUjBrLd/dyYbhPK9xsLdHV1iY2NYNNJ4M5HR6rVm50p9YM92mFjakxOQWFnAqLYdWR0xQUl9RzRNV72PblyjZv3Mg/f68iNTUVVzd35s2fj0+HjtWWjYyMYPFXXxEVGUlubg5W1tYMGTqM2XPmoqOj08A1v7sN27ayet06UlJTcXd15bUXX6Jj+/bVlo2IiuL/fvieyOhocnJysLa2ZtjAgTzz5FOq2D748gt2+fpWmVdfX5+ju/fUayx3s3H7dlZt2EBqWiruLq7Mf/55OrZrV23ZiOhovvpxiTLW3FysrawYNmAAc2fMVNuOG7ZvY8O2bSTeuEEzW1tmPT6N0UOHNlRI4iEmDTb1qLi4uF5P2Kf99hB00Jfh02djaWvHyb072PTjN8x6/1N09Q2qnSczJZktS7+jbY8+jJw5h/jw6xxc/zcGxsa06NAFgITIMHYt/5Veo8bj6dOJsPNn2fnnLzw2fwH2ru4AFOTlse7bz3Fw92LCs/MwNDYhMzUZQxOT8viLCnFw88C7aw/2rlpWq1hP+e3hzEFfRk5/GotmdpzYs50NS77m6YWf1RhrRkoym375lnY9+jLqybnEh1/Hb91qDI1NaNHxVqwRYexYvpTeo8bj1aEz14OD2L7sF6a9tgB7Vw8ATCws6Td+Eha2zVCUKbgcGMC2335kxlsLsXF0RqFQsPW3H9HQ1GDCMy+jZ2DAmYP7WL/ka2a99wm6enr3HW9jblsAC1s7psx7U/VZo1Krtf/mdcRcu8KIGXMws7ImPiyU/WtXYGBsTOtuve473rqiaWBAUUQ02Xv9aPbeG41WD4CQoEAObljDkMdm4OThxbkjB9n402Jmv/8pppZWVcoX5uezfsnXOHu2ZPqbC0m7kcSeVcvQ0dWj65ARwK19+udvaduzL6Ofeoa48Ov4rV2FgbEJLW/t0/ERYez48xd6j55Aiw6dCQ0OYvuyn5n22js4uHmo/WZCZDgXjvtj4+hcpT6HNq8lOuT/2bvr8CiuvYHj39jG3d2VQPAAwS3BaXGnQN3lVm57K7f29tZdaQvF3SGQkASCJsEhHiDu7rbvH0k22WQTLELL+TxPnic7e2b2/OaMnDlz5sw1pix7FH1jU1ISYjm84U80tXXp5Xt7ZXw/77dN7N29mLzsUdlnZRWV24qtpZ4q65S4GDz6D8ba2RU1iYTIo4fZ9v3nLHvjPQzNLBrWg4Eho2bOwdDUHKm0YT3s+vlblrz+DmYKyr0zRQUf4lzIYSYsfARDMwvOBu5l5w9fsvTND5BoaCicpygvh90/f0MvXz/8l6wiPSmekK0b0NTRxbWx0TE1IRa3foOwdHJBTU3CudAj7PrpKxb+6+27biS/FxEnjrP5j19ZuOoJXDy9CA08wLcfvce7X36Pkalpm/T19fWoqUkYPWkKV85FUVFW1u6yy0pL+ePbL/Ho7UNhfl5XhnHbIk8cZ8sfv7Jg1RM4e3hxLPAA33/4Hm93FK9EwqiAKVw9H0W5gnjVNTQYPXkq1nYOSCQSEmOj2fDLD0jU1RnlP7k7wurQQ4N7M32gN98ePEZ6QRFzhvbjnTkBPLN6W7sNK71sLLicnMGG8ChKK6sY6enMqzPG8fbmg0SnZQEwwsOJpSMH8cPhcK6lZmGur8vTAcNRU1Xhh8Dw7gwRePC25daCjxzh6y8/5+V/vUYfHx92bt/GKy++wF8bN2NhYdEmvZqqGpMmT8HV3Q1dHV0S4uP55OOPqKut5alnn+uBCNp3OOQon3/3Ha89/wJ9e/dm2+7dPP/6a2z5408szNseN9XU1Jgy0R93Vxd0tXWIS0zkoy8+p66ujucefwKAV55+hmcefUxuvlXPPttuI1B3ORIayuc//sBrzz6Hj3cvtu3dywtv/pvNv63GwsysTXo1VVWmTJiAm7MLujo6xCcl8tGXX1JbV89zjzbUD7bt3ct3v/3Gv198EW8PT67GxPDRV1+ip6PDiKFDuzvEvxfxlqh79s/tO9RF6uvrWbt2LQsXLmTx4sWsXr2a+vp6AFauXMmGDRv4+uuvmT9/Pp9//nmX5UMqlXI+NIjBEybj1ncgJlY2+C9eSXVVJTGRZ9qd7+KJUHT0DRg7ZxHGFlb08RuFl+8wooKbW8jPhQRh6+qBr/9UjC2s8PWfiq2LO+dCmnsqRAYdRFvPgElLV2Hp4IS+iSl27l4YW1jJ0ngNHsbQyTNw9FLcon0nsZ4LOYLvhMm49RuIqZUNk5asorqqkuiOYg1viHXc3OZYe/kOI6JFrFGhR7Bz9WBIwDSMLawYEjANW1d3olrE6tKnH069+mBoao6RuQUjps9CoqFB+vVEAAqys8i4kcj4uYuxdHDCyNySCfOWUFtTTUxU+/nrKN6eLFsAZRVltPX0ZX8tG+KgoeHHc9BQ7Nw80Dc2wct3GBYOTmTcuH7H8Xam8tMR5P3yB6Wh4VAv7dG8RAYfxnuIHz5+ozC2sGL83MVo6+tz4fhRhemvRZyitqaaSUtXYWplg3u/gfhOmETk0UCk0oZYLoaHoK1vwPi5izG2sMLHbxS9hgwjIviQbDlRIYexc/NgaOM2PTRgGrauHnLbNEBVRTn7/vwZ/0WPoKGl1SY/6UkJeA0eip2bJ/rGJnj7+mHp4EzGjcTbiv9+32+bqKiqym3rmto6txVfSz1V1lMfeZz+o8djbmvfcNyZvxQ1dQ2uX7siS+Pq079hPZi1Wg9JCXcc552QSqWcDwtm4PhJuPYdgImVNRMXraC6qpLYDo6Ll0+Eoa1nwOjZCzGysMR72Eg8Bw/lXMhhWZqApY/iM3IsZjZ2GJpbMHbuYiTqGtyMudLucrtS0N7dDBs9jhET/LG0sWXBysfRNzAk7PABhenVNTRY9PhTjJwQgKFx2wa9ltb+8A1DRo/Fyc29K7J+V4L37Wbo6HEMH98Q77yVj6NnaMixDuJd+NhTjJgQgIGCBkwAe2cXBvmNxMrWDhNzC3xHjsHLpx8J0Ve7MpTbNrV/L3acucTp+Jsk5xby7cFjaErUGOnp3O48v4ecYefZSyRk5pJZWMKWUxdIysrD19Velsbd2oy4jGzCriWSU1zKlZQMQq8m4GbZtnGkOzxo23JrmzZuYPKUqUyfORMHR0defOVfGBubsGvHdoXpbWxtmTx1Kq6ublhYWjJ85Egm+vtz8eKF7s34bdiwdStT/QN4aOpUHO3t+ddzz2FibMy2PXsUpre1tmZaQABuzi5YWlgwys+PgHHjuHD5siyNjo4OJkZGsr+09DTSMtKZOWVKd4Wl0Ibt25k6cSIzJ0/G0c6efz39DCZGRmzfu1dheltra6ZO9MfN2RlLc3NGDh2G/9hxXLjSHOvB4CBmTJqM/5ixWFtaMnHMGGZOnszaLZu7KyzhASYabO5QWFgYysrKfPrppzz++OPs2bOH48ePy77ftWsXNjY2fPHFFyxdurTL8lGUl0tZcRH2Hs3dVNUkEmyc3dpckLSUcT1Rbh4AB89eZCXfpK6u4S5Rxo22aew9vUm/3lzBT7h8HgsHR/b9/hM/vvECf/3fu5wPC5ZdbHSmorychlg9vWXT1CQSbFzcSevgoiPjeiIObWL1Jiv5hizW9OuJ2Hu2TZOWpHgd1tfXExN5huqqSqwcXQCoq21YlmqL3lRKysqoqqqSlhh/B5E26OmyBSjKzeXnt17mt3deY/8fP1GYmyP3vbWTK0lXLlJSkA80XNznpKbg6OWN0LBNZKbcwMFTfn10tG2lX0/ExtkNNYmkOb2XN6VFhRTlNTzOkp6U2GaZjp69ybopv007eLRO493mAj1ww5+49xuIvbuXwvxYO7uSeOUCxQUNd0LTkuLJTk2+7QbY+32/bZKWFM/3rz/P6vfeIHDDn5SVFN9WfE16sqwV5aWutgZ1BQ1w0LAeohvXg7WTi8I0naU4L5fy4iLsWmxfqhIJ1s5uZHR0HLuRhL2H/DZp79GL7BbHsdbq6mqpra1BQ1O7czJ/B2prakhOSsDLp6/cdE+ffiTGxtzTskMPHaC4sJAps+be03I6U1O8ngriTbrHeFtKuZ5IUmwMrvfBOcVcXxdDHS0u3kyTTauureNaaibu1m3v1ndEU6JGaWWV7HN0ahYOZsayBhoTXW0GOdsRlZTS3iK6zIO2LbdWU1NDXGwMg3x95aYP8vXlyuVLt7WM1JQUzpw+Td9+/W+duBvV1NQQExfHkIED5ab7DhzIpau319CdkpbGqYgI+vVp/3Hanfv34+TggI93z+23NTU1xMTH4TtA/jFw3wEDuHTt9hqAU9LSOB0ZQf8WPYVqqmtQb3HOBlCXqHM1Npba2p55fFF4cIhHou6Qra0tixcvBsDa2prDhw9z8eJFRo0aBYC3tzezZs3q8nyUFxcBDWOLtKSlp0dpYWG785UVF2Pn3moeXT3q6+uoKC1FR9+AsuIitFstV1tXj/IWFzJFuTlcPB5C/zETGTxhEtlpKYRs3QBAv1Hj7iU0hXluykPrPJUWFnQwXxF2rSr+Wnq3G2uR3LSctFQ2fP4htbU1SNTVmfHoM5ha2wBgZGGBnpExx/fsYOLCZUjUNYgMOUxJYQFlRYV3HG9Pl62lvVPDo1jmFpSXlHAmcB+bvviIZW++L+t9MGb2QoI2r+XXt/+FsnLDIyRj5izEqRvGxfg7qCgtQVpf36YMtXX1uFl8TeE8ZcVF6BoYtkqvL/vOwMSUspIi7HVbbdMKylhLr+22U9Zim754IozCnGymLJPvytzSuDmLOLxxDT+/9YqsjMfNXYRz774dBy+L5/7eb6GhIcvVpz/6xqYU5+cSvm8HW775lCWvvi3XANuRnizr1sL37kBNXQOX3vLjLeSkpbD+s+b1MPOxZxU+BteZmra3NscxXT1Ki9ov//LiIrTcPNvMU19fR2VpKdoK4j61fxcSiTqOvbv/+FNaUkx9fT26BvL50jMwIObyxbtebtrNG+zbupHXP/r0rh7T6ypN8eq1Kgc9fQNiCu8+3iZvPP4IpcVF1NXVM2XOfEZOnHTPy7xXBtoNj28WllXITS8sq8BIR3HjqCIBfT0x1tUm7Fpzg/WJ2Ovoamrw/vzJKKGEqooyoVcT+OtYZOdk/g48aNtya0WFhdTV1WFkZCQ33cjIiMiIsx3O+8SjK4mLjaW6upppM2by+JNPdWVW71hhURF19fUYGcqfd4wMDTkbda7DeVc88wyx8XFU19Qwc8oUnl61SmG60tJSgsPCeGrlyk7L990oLG6M1UBBrOfPdzjvyheeJzY+viHWSZN56pEVsu+GDBzAnkOHGDN8OJ5ubkTHx7H70EFqa2spLCrC5BY9zB5o4rXe90w02NwhBwcHuc9GRkYUFTVfJLi6djzo4aFDhwhUMEBXa3pOHji1GEAzOuI0QZvWyj7PfOJ5ALkBKgGQcstnBVvP09QpRm56m8XK95yRSqWY2zkwYnpD45SZrT2F2VlcPB5yzw021yJOcWRjc6wPP/lCUwbb5OGWsbaeoDBWxeujJSNzC5a+8S5V5eXEXYji0F+rmfv8q5ha2aCiosr0VU8TuP4Pvn/tOZSUlbF397rtngj3W9k69pLPt6WjE6vffZ1rZ04woHHA2fNhwaQnJTDjsWfRMzImNSGOYzu3oGdkfM+PwP2TtC0PaYdFqCh9m+ltFtBUfs3TldoUcnMZ52dlcHzPNha8+G9UVNs/BZwLDSItKYGHnni+oYzjYwndsRl9I5M22wj8/fZbAI+BzXdSTa1tMLe155e3XyXp6qV2B2luN889VNZNokIOc/FEKHOf/RfqmvLjAxmZW7Lsjfeoqign7kIkB9f+xrwXXpOth84QE3mao5vXyT5Pf/zZxhAUrBcF+ZfTep7mDaBN0vOhQVw5cYyHnn4J9XbGReoOrWO6l96mNTU1/Prlp8xeugIT87bjZtwXFJRRZwxT8PJ/P6aqspLr8bHsXLcGEzNzfEeNufWMnWikpxOPT/CTff5wR8Ojlq1L9E7iHeJqz7JRg/hiXwg5xc3jvHjZWDBnqA+/Bp0iLiMHSwM9Voz1Zb5fPzad6Pjisqs8cNtyK4qP5R0X9nsffER5eRkJ8fH88O23rP9rLUuWLe/CXN4dRfXKW23HH739NuXl5cQnJvLNzz+xZtNGHlm4qE26A0FHqK+rY/KEnn35RBNF9eFbnXs++veblFVUEJ+UyLe//srazZtZvmABACsWLSavoICVLzwPUilGhoZMmTCBv7ZsQVlZPLAidC3RYHOHVBVc4DSNYQOgfosBZgMCAggICLjl76xpdXfFubcPFg7vyD43PYZTVlyErmHz3YDykuI2d55b0tbTo6xY/i50RWkxysoqaGhrN6bRl90db15uidydUm09fbnxagCMLCwpDgu6ZWy34tK7r9wAuC1j1WsZa2lJm7u3LTXEIR9reYmiWFulKS1Gq/FudxMVVVUMTRsGZbOwdyQz+TpRIYcJWNTQ+m5h5yC7IKqrrUVLV491n76PhZ3DLeO938q2NYm6BsaWVhTkZAMNb4UI37udqSuelPW2MLW2JScthajgQNFgA2jq6KKkrKxg2ypps201aW9bhOZeCtq6irbpEpSVVdDU6WCbLimR9eBIT0qkorSUPz5sfhOStL6elIQ4LoSH8sIXPyGVSjm2ZxvTVz6FS2MZm1nbkp2WzNngQwobbP6O+21rOgaG6BgaUpCT1W7+WuvJsm4SFXKY43t3MvupF+XKoImKqqpsMF4Le0cybt4g6uhhAhYrXg93w8m7Lxb2Lcu/Bmh7HKu4Rflr6enLehnK5mmMu6n8m5wPDeLUgV3MePx5LOwdOyOMO6ajq4eysjLFrXqNlRQVodeqp8LtKirIJyM1hTXff82a778GGi4apVIpT86dybP/fgevdt5a09U6jFdB76c71XRRb23vQHFRIfu2buz2BpuzCcnEZTQ/BqzW2CvEUFuTvJLmxhZ9LU0KyyvazN/aEFd7np88im8OHmvzhqiFw/sTHp1E0OU4AJJzC1BXU+Upfz+2nLxAfRc8Zt6eB21bbk3fwAAVFRXy8uQHRC4oKGjT66Y188ZBex0dnaivq+eTjz9kwaLFCq8ZeoKBvj4qysrk5efLTc8vLGjT66a1pkF6nRwcqKuv58PPPmXJvPmotuottWv/fsaMHIm+XvvH9+5goNcYa4F8rAWFBRgZGnQ4r3lTrPb21NfV8+GXX7B47lxUVVTQUFfnPy+/whvPv0BeQQEmRkbsPHAAbS0tDPQVn+uFRv/g1213F7EG/yYkGpoYmprL/owtrNDW0+dmTHOX+9qaGtKS4tu8CaYlS0dnkmPlu+nfjLmGuZ09KioNJxZLB2eSY+Wf80yOvSo39oOVkysFWZlyaQqysxS+EeVOtR9rc55qa2pIS4zrcBwGS0dnbraJ9Srmdg6yWK0cneXWYUOaa1g7tb8OoaHCUafgmVV1TS20dPUoyM4iK/kGLn1uXRG538q2tdqaGgqyMtHWazgh1dfVUV9X1+aOgpKycpeMYfR3pKKqioWtAzdi5Nf1zZir7W5bVo7OpCbGUVtT05w++ho6+gboG5s0pHFqu73eiLmKub38Nt36d2/EXMWqcV9x8enP8jffZ9kb78n+LOwc8BwwmGVvvIeKqmr7ZaykDNJ6FPk777dNyktLKC0sQEfv9itfPVnWABHBgRzfs4NZT77Q4WvE5Ujrqa2tuXW6OyDR0MDA1Ez2Z2RhhZaevtwxqbamhvTEeCw7Oo45OJEcFy03LTn2GmYtjmMA50IOc2r/LmY89lyPvs5bVU0NOycXrl26IDc9+tIFnN097mqZhkbGvP3Ft7z12deyv5ETAzCzsOStz77G6S6X2xma4o1pNahqzKULnZ4vab1Ubh/pLpU1tWQWlsj+UvIKKSgtx8e++SaVmooKntbmxKZld7isYe6OPD95FN8eOs6puBttvldXVW3TKFN/O73QusCDti23pqamhpu7BxFn5R9/ijh7Bu/et//Wo3ppPXV1dXI3c3uampoaHm5unImSvxl8NiqKPr1uf7wZaVNsdXVy069GRxOfmMhDU6Z2Sn7vhZqaGh6ubm0e9Tpz7hx92nk9vSL1UqnCWFVVVTE3NUVFRYUjoSH4+fqKHjZClxNb2N+UkpIS/UaPJyLoAPEXoshNTyVwXcNrYVt28z+49jcOrv1N9tnHbzQlhQWEbN9IXmY6l08e4+qZEwwY5y9L03/0eJLjYjh7eD/5mRmcPbyflLhY+o+ZIEszYMwEMm4kcSZwHwU5WcSdj+B8WDB9R4yVpakoKyU7NZncjIaB+gpzsslOTW5z1/h2Yu0/ZgJnjxwg7kIUOempslfgeraI9cDaXzmw9tfmWIc3xHp02wbyMtO5dPIYV86cYJBcrBNIjovmTOB+8jIzOBO4n5S4GAa0iPXY7q2kJsRRlJdLTloqx3ZvIyU+Fs+BQ2RpYs9FkBwXTWFuNgmXzrP1u89w6dO/zaChtxtvT5Zt2M7NpMTHUpSbQ8aNJPau/oGa6irZq5zVNTWxcXHn+J5tpMTHUJSbw9XT4Vw7exIXn54daE9JUwOJixMSFydQVkLN3AyJixOq5t3/xo2B4yZy5XQ4l06EkZeZTvDW9ZQWFuIzvOFu8bHdW9n89f9k6b0GDUFVTcLBv34jJz2VuAuRnDmyn4Fj/WVde32Gj6G0ML95mz4RxpXT4Qwa19xrb8CYhm36dOA+8jIzOB24T26b1tDSwtTKRu5PTV0dDS1tTK1sUFJSQl1TE1tXd47t3kZyXAyFuTlcOdVQxq4+t/eo0P2+31ZXVRK6YzPpSQkU5eWSHBfDzp++QUtXD9c73I57qqzPHjnIsd1bCVi8AkMzC0qLiigtKqKqolyWJmxXy/WQwrHdW0mOj8VrUNe+hlRJSYl+o8YRFXSIhIvnyE1P48j6P1BTV8d9QHP5B65bTeC61bLPvf1GUVpYQNiOTeRnZnDl1HGunT1J/zHN3eyjggM5sXcH4xcuw8DMnLLiIsqK5ePuTuOnzeBU6FHCgw6TkZrC5t9/paggXzb+ys71a/ji3bfk5klPSSblehKlxSVUVlaScj2JlOtJQEMjoLWdvdyfrp4+qmpqWNvZo6HZc49+AYyb2hhvcEO8W37/laL8fEY0xrtr/Rq+ek8+3oymeEtKqGoVL0DIwX1cjoogOyOd7Ix0TgQfJmjvTgaPGN2dobVr37mrPDS4D76u9tiZGPDspBFU1tRyLLp5AO3nJo3kuUkjZZ/93B15YfIo1h2P5FpKJgZamhhoaaKj0TxwaWRSChP6uOPn7oiZvg4+9lYs8OtPZFJKt/auafKgbcutzV+wkIP797F39y5uXL/OV198Tl5uLjMfehiAn374nuefaR6f5tDBAxwNDuLmjRukpaURHHSEn3/8gdFjxiJpNUBtT1s4Zw77AgPZtX8/12/e5LPvviUnN5dZ06YB8N2vv/Lkyy/J0h84fJig0FBuJCeTmp7OkdAQvv/1N8aOGtUmtp3792FnY0N/n/tjHMOFs2ax78hhdh08wPXkm3z+w/fk5uXx8NSGBqXvV6/mqVf/JUt/IOgIQcfCuJGcTFpGBkfCwvjh99WMHTFSFuvN1FQOBB0hOS2VqzExvPnhhyTeuCE3zo2gmJKyUrf9/VPdH331hLsyaPwkamtqOLp1PZXlZVg4ODHr6ZeQtHiOv6RVl0B9E1MeeuIFwnZs4lJ4KNp6BoyZvRC3vs0jx1s5uTBl+eOc2LeTkwd2Y2BixpRHHpfram9h78j0R58mfO8OTh/ai66hMcOmzMRnRHPX5aTLFwhc/4fs85GNawAYMmk6wybPuKNYB4+fRG11NcFb1lFZXoalgxOzn3lZLtbiVl09DUxMmfXki4Rs38jF8FC09Q0YO3shbv2aY7V2cmHqI09wYt8OThzYhYGJGVNXPIGlQ/Md4LLiYvav+ZXykiIkGpqYWtsw68kX5d6IVFZcSOiOTZSVFKOtZ0Av36EMDZh+RzG21JNlW1pYwIE/f6airBRNHV0sHZxY8NKb6BmZyNJMeeRxwvds58CaX6ksL0PP0Bi/KTPpO3IsPUnDww2bbz+VfTZetRTjVUspPnCYrI8+79a8eAzwpaKsjFOH9lJWXISJpTWznnpR1oOitKiIwtzmu7PqmlrMffYVgjav469P3kNDS5uBY/0Z2KKhwsDElFlPvcjR7Ru5cDyk4fXXcxbhLrdNuzLtkScI37eDE/sbtulpK5/osHeWIlMfeZLje7ax/8+fG8rYyBi/qQ/d0RhV9/N+q6SkTG56KlfPnqSqohxtPQPs3DyYtvJJufzdjp4q6/PHgqmvq2Pv7z/K5aeXrx+Tl65qXA9F7P/zF8pKilDX0MTE2pbZT73YLY8uDhgXQG1NDSHbNlBVXoaFvRMzn3wRiYaGLE2b45ixKTMef45jO7dwOTwMbX19Rj08H9cWYwpdDA+hvq6Og3/+Ijev5+ChTGzncbeuNMhvBGUlJRzYvoWignys7Ox55t9vY2za0L2+qKCA3FY9Ur/76L/k5TRvEx/86wUAft6m+BW795OBfiMoKy3h4PYtFBfkY2lrz9Ot4s1pHe/H/yW/RbwfvfoCAD9ubYi3vr6Onev+JC8nG2VlFUwtLJi5aBkjJtz6EfLusPPsZSSqqjw2bijaGhLiM3L477ZDVNY099gz0ZN/ZM+/rweqKsqsHDuElWObb/BcScng7c0HAdh66gJSqZQFw/tjrKNNSUUlkUkprD8e1T2BtfKgbcutjZswgaKiItb88Qd5ebk4Ojnz6RdfYmFpCUBebi5pqc1vC1NRUWHdmjWkpKaAVIq5hQUPz5rNvPkLeiqEdk0cM5ai4mJ+X/cXufn5ODs48NXH/4elRcNjiLn5eaSlp8vSq6io8OeGDaSkpSKVSrEwN2fOzBksmD1Hbrll5eUcPnqUVUuX3nKsn+4yYfRoioqL+WPDhoZY7R348oMPsWx8dC03P4+0jAxZehUVFdZs2kRKWpos1tnTp7Pg4eaXyNTX17Fh+3ZupqaiqqLCAJ++rP7qa6ws/h5jMwl/b0oVFRXiGYb7UOsxbP7plO+Tg3x36Im7Zj1p7H/f7+ksdJuwd9+5daJ/kAfpEbj7pSLaXaofoNeUelmb93QWulX9g7Pb8t3hEz2dhW71nL/frRP9Q/SyebAulNXLy26d6B9C+gCdfwDUzc16OgtdquC3tbdO1EkMVy3ttt/qTuKRKEEQBEEQBEEQBEEQhPuMeCRKEARBEARBEARBEITOJQZlvmdiDQqCIAiCIAiCIAiCINxnRA8bQRAEQRAEQRAEQRA6l+hhc8/EGhQEQRAEQRAEQRAEQbjPiB42giAIgiAIgiAIgiB0rgfsLZtdQfSwEQRBEARBEARBEARBuM+IHjaCIAiCIAiCIAiCIHQqJWXRw+ZeiR42giAIgiAIgiAIgiAI9xnRYCMIgiAIgiAIgiAIgnCfEY9ECYIgCIIgCIIgCILQuZRE/5B7JdagIAiCIAiCIAiCIAjCfUb0sBHuC8rKD07bodoDNvhW2Lvv9HQWus2od9/r6Sx0q/XPvtDTWeg2MRnZPZ2FbjXSw7Gns9BtLqdk9nQWhC4yobdrT2ehWyVk5fV0FrpNSl5RT2ehW2UXl/Z0FrqNRFWlp7PQrR41N+vpLHQt8Vrve/bgXCULgiAIgiAIgiAIgiD8TYgeNoIgCIIgCIIgCIIgdK4H7MmCriB62AiCIAiCIAiCIAiCINxnRA8bQRAEQRAEQRAEQRA6l3hL1D0Ta1AQBEEQBEEQBEEQBOE+I3rYCIIgCIIgCIIgCILQqZTEGDb3TPSwEQRBEARBEARBEARBuM+IHjaCIAiCIAiCIAiCIHQuJdHD5l6JHjaCIAiCIAiCIAiCIAj3GdHDRhAEQRAEQRAEQRCEzqUs+ofcK7EGBUEQBEEQBEEQBEEQ7jP3dYPNG2+8wU8//XTX81++fJlp06ZRVFSk8LMiJ06cYNq0aXf9m4IgCIIgCIIgCILwwFNS6r6/f6gH6pEoDw8P1q5di56eXk9npVNIpVJOHdzD5RNhVFaUY2nvxNi5izCxtO5wvpT4WMJ2biYvIw0dfQMGjp+Ez/DRcmniLkRycv8uinJz0DcxxW/qw7j69Jd9X11ZwYn9u0i4eI7y0hLMbOwYM2sBFvaOcsspyM7k+O7tpMRHU1dbh5G5BZOWPYqxhdUdxXo+LJizQQcpLSrExNKasXMWYuvi3m76nLQUjmxeR+bNJDS0tPEZMYZhk6aj1GJnTo6LIWT7RnIz0tDRN2TwhEn0GzlWbjlVFRUc37ud2PORVJaVomtoxMjps/EYMBiAn956meL8vDa/79SrD7OffumOYuxIVGgwZ44coLSoCFMrK8bPWYSta/vxZ6elcHjTX2TcaIi/38gx+E2eIYu/tKiQ4G0byUy+SUF2Jt6+fkxd/mib5UQEH+bcsaMU5+eiqa2Dq09/xjw0F4mGxl3Fcf7YUSJaluPshdi4uLWbPicthaAt65vLcfhohrYqx5T4GEK2b5Irx74jxsgtJ/Z8JCf27aQwNxsDEzOGT3sYt74DFP7m6UP7OL53O/1GjmX8vCWy6dWVlRzbs434i+catwVj+o4YzcCx/ne1Lu6Vho83hgtmo+HuiqqpCZkffkbJwSM9kpfONrqXCwOcbdFQUyMtv5D9UdfIKS5tN72Ohjr+fT2wNNTDSEebSzfT2HX2cjfm+M7MHuLDWG83dDQkJGTm8vvRM6TmF7abfpCzHRP6uONgaoSaqgpp+YXsPHuZqKQUWZpRXs48OXF4m3mXfPsXNXX1XRFGG1KplDOH9nLl5DEqK8qxsHdkzOyFGN/inJSaEMvxnVvIy0xHW9+AAWP96dPinJSXkcbpg3vITk2mOC8X34BpDJk0XW4Zv7/3OiUKjsUOXr2Z8fhznRJfS1KplLOBe7l66jhVFeWY2zkyatZCjC07PrelJcQSvnsr+ZnpaOsZ0H+sP95+o2TfXz11nJiIU+RnpSOtl2JqY4vvpBlYObnK0qz57xuUFLSN1d7Tm2mPdX6s0HXxJlyIJCo4kKLcbOrr6zAwMcNn1Hg8Bw9rXkZiHOdDDpOTmkxZUSHjFiyX+74rYj19cI/cdjx2zqJbb8fxsRzbuVm2HQ8cF9BmOz51YA/ZqTdl2/HQyTPkl5EQx7mjgWSl3KSsqJAJix6hl69fV4QJ9Nw5uUl05Gn2/fEzTt4+zHryhc4Or43I0CBOHd7fWJeyZuLcxdjdoi51aOMa0m8koamtQ78RYxgxZaYs3ptx0YTs3EJeViY11VXoG5nQd/gohk6cIlvGueMhXD4dTk56GlKpFAtbe0bNmIVdB3XYziKVSok6sp+YM+FUlZdjZueA30PzMbpFHTw9MY7Te7dRkJWBlp4+PqMn4jV0pMK0CecjOLrhd+w8vQlY8bRs+oaP3qS0IL9NelsPbyatfLrN9Hv1IB2jhK6zf/9+duzYQUFBAXZ2djz66KP06tWr3fRSqZQ9e/Zw8OBBsrKy0NXVZezYsSxfvrxT8/VANdioqalhaGjY09noNBFBB4k6Goj/4hUYmVlw+tBetn/3OY/850MkGpoK5ynKzWHnT1/hPWQ4k5auIi0xnqNb1qOpo4Nb34EApF9PYP8fPzNs8gxcfPqTcPEc+37/kfkvvoGlgxMAhzesITc9lYAlK9ExMCQ64jTbvvucZW++j66Boey3Nn35MV6DhuEb8C/UNbXIz8pAon5nF/vRkWcI3rqBCfOXYOPsxvljwWz7/gtW/ucj9IyM26Svqqhgy7efYuPizpLX3iE/K4MDa1ejJpEwePwkAApzc9j+wxf0HjqCqcsfJzUxjiOb/kJLVxf3foMAqKurZcu3n6Khpc2MlU+ha2hISUEBKqrNu83S196hvr75YqisuIg1//eurEGnM1yLPEPQlvX4L1iKjYsb58KC2fzd5zz6zsfotxP/pq8/xdbFjeWvv0teVgb71/yGmkQd3wkN8dfW1KCpo8tQ/ylcCA9V+LtXz54iZOdmJi1ega2LG4W5ORz4azW1NTVMWbryjuOIiTrD0a0bGD9/CTbOrpw/dpRt33/Biv982EE5foatizuLX32b/KxMDv61GjWJOoPGBwBN5fgl3kNHMGX5Y6QmxhO06S80dXRx79ewPaclJbD39x/xmzITt74DiLsQxZ7VP7DwpX9j5egs95vp1xO5dDIMU2vbNvkJ2bGJmzHXmLLsUfSNTUlJiOXwhj/R1Nall2/3n5iVNTWpTrpJyaEgzN/6V7f/flfx83BiqLsju85eIq+kjFFeLiwdPYhvDxyjurZO4TyqysqUV1UTHp3EAOe2ZXc/mT7Qmyn9e/Hj4XDSC4qZ5evDvx+ewEtrdlJZU6twHi8bc66kZLD55HlKK6sY7uHEy1NH899tgcSkZ8vSVdbU8PwfO+Tm7a7GGoCo4EOcCznMhIWPYGhmwdnAvez84UuWvvlBu428RXk57P75G3r5+uG/ZBXpSfGEbN2Apo4uro2NqjXV1egZmeDcpz+nDuxSuJz5L7+JtNWxeONnH+DaeBzobOeOBnIh9AjjFizH0MyCiMB97P7pSxa/8X67sRbn5bL312/xHOzHhMUryUhKIGzbejR0dHDxaYg1LSEW134DsXR0QVUi4UJoEHt+/pr5r/wHA1NzAOa+9G+58055cRGbv/gQl75dE2tXxquhrcOgiZMxNLNAWUWFG1cvc3TzWjR1dHHw6g1ATVUVxpbWeAwcStCG37ssxiaRQQ3b8cRFKzA0s+DMob3s+P4Llr31YYfb8a6fv6bXkOEELF1FWlICIVvWt92OjY1x8enPyf07FS6npqoSY0trPAcNJXBd18baU+fkJoW52YTu3IKNc/sNRJ3pasRpDm9eR8DCZdi5uBEZGszGbz/liXf/D30jE4Xxrv/qE+xc3VnxxnvkZWWy989fkKirM2TCZAAk6hoMGjsRM2tbVCUSUhPiObD+d9Qk6gwcPR5oaNTxGjgEW2dX1CTqnAk+xMav/8ejb32IkblFl8Z8MfQwl48FMWruUgzMzDl35AAHfv2Guf96t/39Nj+XQ6u/x33wMMYseITMG4mE79iIhrYOTn36y6fNy+HM/h1YOLq0Wc5Dz70ud0wuLylmx9cf4+zTv03azvAgHaOErnH8+HF+/fVXnnzySby8vDhw4ADvvvsu33//PWZmZgrnWb16NRERETzyyCM4ODhQVlZGQUFBp+ftvn4kCqC+vp61a9eycOFCFi9ezOrVq2UVldLSUr788kvmz5/PrFmzeOutt7h582a7y1L0SNTRo0dZsWIFs2bN4r333qOwsFBunoyMDD744AOWLFnC7Nmzef755zl79qzs+40bN/L0021bil999VV+/vnne4y+fVKplPOhQQyeMBm3vgMxsbLBf/FKqqsqiYk80+58F0+EoqNv0HC3yMKKPn6j8PIdRlRwoCzNuZAgbF098PWfirGFFb7+U7F1cedcSMPd+5rqauIvRjF8+ixsXT0wNDVn2OQZGJiacSk8RLac8H07sffoxaiH52Fua4+BiSlOvfqga2h0R7FGHg3Ee6gfPsNHY2xpxfh5S9DWM+D8saMK01+LOEVNdTWTlz6KqZUN7v0G4TtxMpHBgUilUgAuHA9BW9+Q8fOWYGxphc/w0fQa4kdE0CHZci6fCqe8pISHn3geGxc39I1NsXFxkzVaAWjp6qGjbyD7S7pyEXUNDdz7D7qjGDtyNugQvYcOp++I0ZhYWjFx/hJ09Aw4HxasMP3Vsyepqa5i6vLHMLW2waP/IIb4T+Zs0CFZ/AYmpkyct5g+w0agoa2tcDmpifFYOTrTe4gfBiamOHh44T3Ej/QbiXcVR2TwYbyH+OHjNwpjCyvGz12Mtr4+F463X461NdVMWrqqsRwH4jthEpFHm8vxYngI2voGjJ+7GGMLK3z8RtFryDAigpvLMSrkMHZuHgwNmIaxhRVDA6Zh6+pBVIh8b5SqinL2/fkz/oseQUNLq01+0pMS8Bo8FDs3T/SNTfD29cPSwZmMu1wf96r8dAR5v/xBaWg41Et7JA9dYYibPeHRSUSnZpFdVMrOs5eQqKrS2779u2SF5RUcPB/NhRtpVFTXdGNu79ykfp7sjrjM2YRkUvMK+SEwHE2JGn4eTu3OsyYsgj2RV0jMyiWrqITtZy6SlJ3PQGc7+YRSKCqvlPvrLlKplPNhwQwcPwnXvgMwsbJm4qIVVFdVEhvV/jnp8okwtPUMGD17IUYWlngPG4nn4KGcCzksS2Nh78iImXPwGOiLmppE4XK0dHTR1tOX/d24dhmJhobsYrmzY70YFsSAcQG4+AzA2NKa8Qsfoaaqkrhz7cd65WRDrKNmLcDI3JJeQ0fgMWgY51sciyYuWUWfEWMxtbHD0MyC0XMWIVHX4Gb0VVkazVax3oy+jERdA5cuiLWr47Vx9cCpdz8MzS3RNzHDZ9Q4TCytSU+Kl6Vx8OrN0CkP4dJ3AEpKXVttbdiOgxjUYjv2X9ywHcd0sB1fCg9DR9+AMbMXYmRhRe/G7TjqaHPdysLekZEz5zZsxxLF27Fjrz74TXsY134D5XqtdIWeOidDww2xfb//zIhpD6NvYtqlcTY5E3SQPsNG0H/EGEwsrQlYsBQdfQOi2qlLXTl7gprqKqYvfxwza1s8+w9iqP8UzrSoS1naO9Jr0FBMrWwwNDGj9xA/nLz6kJwQK1vOQyufYtCYCVjYOWBsYcmkhcuRaGiSePVSl8YrlUq5fPwoPmP8cerTHyMLa0bPX0ZNVSUJ5yPanS/61HG09PXxmzkPQ3NLPH2H4zZwCJfCguTS1dfVEbz+dwYFTEdPQYOXpo4uWnr6sr/kmCtI1DVw6vP3Oibfb8eov6W/ySNRu3btYty4cfj7+2Nra8vjjz+OoaEhBw8eVJg+NTWVffv28dZbbzFkyBAsLCxwdnZm4MDOv3ly329VYWFhKCsr8+mnn/L444+zZ88ejh8/DsBXX31FbGwsb731Fp9//jnq6uq8++67VFVV3dayY2Nj+eqrr/D39+ebb75h8ODBrF+/Xi5NZWUlAwYM4P333+ebb75h2LBhfPzxx6SkNHRFnzBhAqmpqcTFxcnmSU1NJTo6mokTJ3bSWmirKC+XsuIi7D2au2mpSSTYOLuRfr39C8iM64ly8wA4ePYiK/kmdXUNd3czbrRNY+/pTfr1BACk9XVI6+tRVVOTS6OqpkZaYlOaepKuXMDYwortP3zJj288z/pP3yc26ix3oq62lszkGzh4estNd/TsRVpSgsJ50pMSsHF2k6sQOXp6U1pUSFFebkOa6wk4esrH6OjlTebNG7L1kHDxHNbOLgRtWcf3rz/H6v/+m/B9O2XftyaVSrl08jheg4ehJlG/ozjb0xS/o1er+L28SW0n/rSkBGxd3OXj9+otF//tsHVxIzslWbaei/LzSLh0HudePncXR0rbcnTw9CYtSfH2mn49sU05Oni1KsekRAXbRm+yWpRj+vVEHDxap/EmvdX6C9zwJ+79BmLv7qUwP9bOriReuUBx46MIaUnxZKcm49h4h0W4d4bamuhqapCY1byd1tbVczMnH1tjg57LWCcx09PBUFuLS8npsmk1dXVEp2XhZnlnFy2aElXKqqrlpklUVfh2xSy+XzmbV6ePxcH0zhrH70VxXi7lxUXYtdh/VCUSrJ3dyOjonHQjCXsP+X3O3qMX2S3OSXdKKpVy9XQ4HgOHdNqxuKXivFzKS4qxdW8+h6hKJFg5uZJxPand+TJvJMmtHwA7Dy9yUm60G2t9XS21NTUKG5GhIdZrp0/gPtC3S2KF7otXKpWSEhdNQU4W1s6ubb7vDrLt2EM+1obtWPE5FyDzRiJ27m3rTfeyHXelnjwnAxzfswM9Y2O8h7R9jLMr1NXWkpF8A6dWdSknT29SE+MVzpOalIBdq7qUc68+lBQWUJiXo3CezOQbpCbFY+/q0WFeGvZpxTfLOktJfi4VJcXYuHnKpqmqSbBwdCXrZvvH5KybSdi4espNs3XzIif1JvV1zb1czx7cja6RMW4Dh94yL1KplNizJ3DpPxjVdhor78WDdIwSukZNTQ0JCQn069dPbnq/fv2Ijo5WOM+ZM2ewsLAgKiqKVatWsXLlSr788ss2nT86w33/SJStrS2LFy8GwNramsOHD3Px4kVcXV05c+YMH3/8Md7eDQfgl156iRUrVhAaGoq//63HldizZw8+Pj7MmzdPtvz4+HiOHGluWXV0dMTRsXlclnnz5hEREcHJkyeZN28eJiYm9O/fnyNHjuDm1tCtMygoCBcXF7n5Olt5cUMvIS1d+fF4tPT0KO1gQykrLsbOvdU8unrU19dRUVqKjr4BZcVFaLdarrauHuUlxQBINDSxdHTmzKF9GFtao62nT0zUGTKuJ2Jg2tBlrLy0hJqqKs4c3o/flJmMmD6LlLhoDqz9FTV1dZy8b++iv7y0BGl9Pdq6+q3i1Kcs5lo7MRa16cWjracv+87AxLRNY1dDjPpy66EwN5ui2Fy8Bg1l1lMvUZSXQ9Dmv6ipqmLMrPltfvdG9FWK8nLoM0zxc753QxZ/q3GXtPX0uBGjePDs0uIi9NqJv7Qx/tvhNWgIFWWlrPv8I5BCfX0d3r7DGPPw3DuOo6Ixjtbbq7auHjeLOyhHA8NW6VuVY0kR9rryJ1tF27OWXtv9pKykef1dPBFGYU42U5Y91m4M4+Ys4vDGNfz81isoK6s0TJu7COfefTsOXrhtOhoNF5xllfKN7mWV1ehqds3FaHcy0G54VLV1z5ei8gqMdBRfkCsysY87RjraHI9urnSnFxTz05GT3MzNR1NNjUn9PHlv7iReW7+HzMKSzgmgA037U5tzkq4epUXtdw8uLy5Cy03+4qBpH64sLUVb3+CO85Ice43ivNwuuxhsOhdq6erKTW+ItbDd+cpKiuQungA0dfWor69vN9bTB3ajpq6OYzvnzJTYaxTn5+I1ZMSdBXEHujreqopy/nz3Nepqa1BSVmbUrIXYe/ZMQ3hZe3WrW8VaXIytm+K61d1ux12pJ8/J16OvEBt1lmX/fq8TI+pYe3VJbT19rsdcVThPaZGiulTD+iorKsLQpPkRia9fe47y0hLq6+oYMfUhBowa125eQndvQ6KujlsXPRrURLbf6siXsaauLuUdbMsVJcVotmpw0tTVQ1pfT2VZKVp6+qTGXiPpYhSzXvz3beUlLS6akvw8PAZ3zXhMD9Ix6u9IqRtf633o0CECAwNvmc7f35+AgADZ5+LiYurr6zEwMJBLZ2BgwMWLFxUuIzMzk+zsbI4fP84LL7yAkpISv//+O++//z6ffvopyp0Y933fYOPg4CD32cjIiKKiIlJSUlBWVsbDo/mgoq2tjb29vaz3y62kpqYyaJD8oyseHh5yDTaVlZVs3LiRiIgI8vPzqauro7q6Wi5f/v7+fPXVV6xatQpVVVVCQkJkjUCt3e6GpOfkgZNPc7fB6IjTBG1aK/s884nnAdp2mZVyyy5hredp7NkpP73NYuUfuZi0ZBWBG/7g1/+8gpKyMmY29rgP8CU79WbjMhseW3Pu3Y8BjYOymtnYkZl8kwvHjt52g017+UEqvaOeb03dVzsIUZam5WctXT38Fz2CsrIyFnYOVJSVEbJtA6MfntdmPV46EYqFvSPmtva3n7HbpETrMpMqiEB+jlYzKJraoeS4GE4c2IP/gqVYOTpTkJ1F0Jb1HN+7k5HTH76DJbXIVZttr+NyVJS+zfQ2C2gqx+bprdcfLco6PyuD43u2seDFf8uNT9TaudAg0pISeOiJ59EzMiY1PpbQHZvRNzLBsZc4cd+N3vZWTBvQ3HC6/ngUIFc8Df6mA//7uTvy6Ljmu4+f7G7set8qQCWU2sbcjsEudiwaMZBvDh4jt6RMNj0+I4f4jOa7vrEZOXyyaBr+Pp6sCbuzno23IybyNEc3r5N9nv74s0A7+/itCrD1PEgVTr9dV04dx9zOAVMbu1snvg2xUWcI3dIc69RHn2n8r3W+FZyTW2l7uGo/1othwVw5eYyZT77Y7rh0V08fx8zOQeG4W3eru+OVqGsw75X/UFNdRWpcNOG7t6BrZIxtqwuprhATcZrgzX/JPjcNUN02LuktD0N3Urb3i+4+J5eXlnDwr9VMXf54l/cwUaRtnfkWx6f2qg6tlrP0X29RU1VFalICR3dsxsDElD4KGozPBgdy7vhRFr3wOuqaivfpuxV/7izHt2+QfQ5Y8VRjXlsllCqaKE9xnbNBZVkpoVvWMnbhCtRvswyjz57A1NYek046Tj1IxyjhzgQEBMg1xNypO3kUVSqVUlNTw0svvYS1dcOg9C+99BJPPPEE8fHxuLt33sDi932DjaqCC6j6+vo2F9ct3e7K7mgZTX7//XeioqJYsWIFVlZWqKur8+WXX1JT0zxOwqBBg1BXV+fkyZNoa2tTWlrKyJGKe1nc7oa05lik3Gfn3j5YOLwj+1xX29BVr3VvkvKS4ja9Y1rS1tOT3UFqUlFajLKyimwsE209fcqKi+XSlJeUyN2JMTA1Y97zr1FTVUVVZQU6+gbs+/0n2cBtmtq6KCurYGxhKbccYwvLO3osSktHFyVl5TZ5Li8pRqvVnZLmGPUVpgdk8yhM07geNHV0GtMYoKKiItdCamxhSU11NRWl8uujrKSY+EvnmdDirUKdoSn+0jbxlLTpddNER0FsZY3xN/W0uR1he7bjNdCXvo1vuTCztqWmuooDf/3B8CkzUFZRue1labZXjqUld1aOpU3l2BC7tq6isi5pLEft9pdTUiK705aelEhFaSl/fPiW7HtpfT0pCXFcCA/lhS9+QiqVcmzPNqavfAqXxh41Zta2ZKclczb4kGiwuUuxaVmk5RXKPqs07ms6muoUVzT3QtFWl1BaeXuPut5PopJSSMhsfrxLrXGf0dfWJK+0XDZdT0uDovKKWy5vsIsdT/uP4IfAcLk3RCkilUpJysrD0lC3w3R3y8m7Lxb2zePu1NU2nBNbn5NaHytb09LTl/UYlc3TuA+3N75WR8pLikm6fIExsxfe8bztcezlg/krzT1mm86/5SXF8rGWFKOp08H5V1ef8lbn1orSEpSVldvEejEsmNMHdzHtsecwb/X2xSblJcVcv3KRUbM6L1bo/niVlJVlvXNNrW0pyMokKuhgt1wMOfXui4VD21jb1q1K2vTUbKmhbtWq3lR699txV+upc3JaYgJlRYVs+fZT2fdNdfHPnl3Jirc+wMhcvt7YGZrrUoVy08tKituvS+nrU1akuC7Zep6m3jZm1raUFRdxbO/ONg02Z4MDCd29jfnPvYJ1qxcedAZ7rz6Y2TnIPrfcb3UM5I/Jmrrtnxc0dfUoL5GPu7K0BCVlZTS0dci8kUh5cRH7f/la9n1TGf762tPMefk/GJg1D6ZcUVrMzasX8Xuobc/0u/UgHaP+Ee7jRusmenp6KCsrtxkwuLCwsE2vmyaGhoaoqKjIGmsArKysUFFRIScn58FqsGmPnZ0d9fX1xMTEyB6JKi8v5+bNm4wfP/62lmFra0tsbKzctNafr127xtixY/Hza+jGV11dTWZmJlZWzQNgqqioMG7cOIKCgtDS0mLYsGHoNF70dxaJhqbcHTapVNow2GDMNdmrtGtrakhLimfkjDntLsfS0ZnES+flpt2MuYa5nT0qKg2bg6WDM8mxV2Wj/gMkx17FSsEo8Grq6qipq1NZXsbNmCuMaPxtFVVVzO0dKMjOlEtfkJ2JroK3D7RHRVUVCzsHbkRfxaN/85uXbsRclb3VqjUrJxfCdm2htqYa1cYBKm/EXEVH3wB944YGJStHF+IvnpOb70b0VSzsHWTrwcbZlWsRp5DW18u68xVkZaImkaCpI3+yu3IqHBVVNTwG+t52bLejOf4reLZ489T16Ct4tPP2E2snF0J2too/Wj7+21FbXdWmG2PD5zsf4FZFVRULWwduxFyVG5D5ZszVdl+vbeXozLHdW6mtqZGNl3Qz+pp8OTo5E39Rfnu+EXMV8xblaOXozI2YqwxufENWUxorp4bt2cWnP8vtHeSWceiv1RiamePrPxUVVVWqKyupr6tr071RSUkZpN33Fp5/muraOvJbNFwAlFRU4mxuTHp+Q4VRVVkZe1MjDl+M6Yks3pPKmloqi+QfRyooK6ePnRVJWQ1jIampKONhZcb68KgOlzXE1Z6n/IfzQ2A4ZxLaH1y/JTsTQ27mtn2tameQaGjIvXlDKpU2DCwZK39OSk+MZ/iM2e0ux9LBicTLF+SmJcdew6zFOelOXDt7EhVVVdxanC/ulcJYdfVIib2GeeMFUm1NDelJCfhNn9XuciwcnEhqE2s0prYOcrGeDz3C2YN7mPrYs3Kv824tpjFW136dN8g9dH+8rUml9bIGwK52R9vxzPbrVhYOziRdlj8X3ct23NV66pxsYe/I8jffl/s+fO8OKsvLGT9vMfrGXTMAsYqqKpZ2Dly/dgWvAc31tOvRV/Hor7guZePkQvCOzXJ1qaRrV9A1MMSgg3xKpdI22+/pIwcJ27ud+c+80mWv81a0LWvq6pEWF42ZrQPQsC1nXk/Ad0r7vaTN7Z24cfWC3LTU+BhMbexRVlHB1Nae2S+/Jfd9xKG9VFeU4/fQPHRbDUAcG3EaFVVVnH06byDWB+kYJXQPNTU1XFxcuHDhAsOHNze2XrhwgWHDFL8J1tPTk7q6OjIyMrC0bGhozszMpK6urt23St2t+37Q4fZYWVnh6+vL999/z9WrV7lx4waff/45WlpajBo16raWMW3aNC5evMjWrVtJT08nMDCQU6dOtfmd06dPk5CQIPuN6urqNsuaOHEiV65cISIiggkTJnRKjB1RUlKi3+jxRAQdIP5CFLnpqQSua3i9YstGg4Nrf+Pg2t9kn338RlNSWEDI9o3kZaZz+eQxrp45wYBxzWP+9B89nuS4GM4e3k9+ZgZnD+8nJS6W/mOa47oRfYXrVy9TlJvDzZirbP3mUwzNLOg1pPn51EHjAog9F8GlE2EU5GRx6UQYsVER9B0x5o5iHTjWnyunw7l4Ioy8jHSCt6yntKhQtpywXVvZ9PUnsvReg4agJpFwYO1v5KSnEnc+kjOH9zNwnL+s91XfEWMoLcwneOt68jLSuXgijCunw+UaqfqOGENleVlDmqwMrl+7TPj+XfQdOVauF1fDYMNheA4cjHo73dbvxeDxAVw6Fc6F8FByM9I5snkdpUWF9Bs5FoDQnVvY8GWL+AcPRU2izr41v5GTlkrs+UhOBe5j8PgAuXxnpdwkK+Um1RUVVJSXkpVyk9z0NNn3Lr37cSE8lGsRpynMzeH6tSsc27MDl95976h3TZOB4yZy5XQ4l06EkZeZTvDW9ZQWFuIzvKEcj+3eyuav/9ccx6AhqKpJOPhXYzleiOTMkf0MHNtcjj7DG8rx6LYN5GWmc6mpHMc1l+OAMRNIjovmdOA+8jIzOB24j5S4GAY0bs8aWlqYWtnI/ampq6OhpY2plQ1KSkqoa2pi6+rOsd3bSI6LoTA3hyunwrl29iSuPl3zZpZbUdLUQOLihMTFCZSVUDM3Q+LihKp597xxo6ucjrvJcE9nPK3NMdPXYaZvb6pra7l8s3mg3od8+/CQbx+5+SwMdLEw0EVdVRVNiRoWBrqY6nVuw3lnOHg+mukDvRnkbIeNsQFPThxOZU0tJ2KaB0Z8auJwnprYXGEY6ubAMwEj2Rh+jui0LPS1NNDX0kBbvXnwxlm+PvSxt8JMTwd7U0MenzAMOxNDgi7F0R2UlJToN2ocUUGHSLh4jtz0NI6s/wM1dXXcW1wgBa5bTeC61bLPvf1GUVpYQNiOTeRnZnDl1HGunT1J/zHNg/bX1daSk5pMTmoytbU1lBUXkZOaTGFOtlwepFIpV08dx63/4HZf49pZsfqMGk9U8CESL50jLyON4I0Nsbr1b471yPrfObK++RWv3sNGUVpUwPGdm8nPyuDq6ePERJykX4tz67mjgZzat4Ox85dhYGpOWXERZcVFVFXIN2xKpVKungnHtd+gLo21q+ONPLKflNhrFOXmkJ+VwfmQw8RGnsZ9wBBZmuqqSnLSUshJS0EqraekIJ+ctBRKGgeA7+xY+40aT+SRgyRcjCI3PY3D639HTV0dj5bb8V+rCfyreTvuM3wUJYUFhG7fRH5mOldOHuPamROyR8KhYTvOTk0mOzWZ2poaykuKyU5NpjAnSy7WpjRSqZSS/HyyU5Mpzu/8WHvinCxRV29zvlXX1EKioYGplU2HjyXfK9/xk7h46jjnw0PJzUgjcPNflBQV0H9kw3gzR3duZt0XH8vS92p8gcSeP38hOy2FmHMRnAzci2+LulTE0cPEXzpPflYm+VmZnA8P5fSRA/T2ba4Lnwrcz9Gdm5m29FGMzS0oLSqktKiQylb7dGdTUlKi94ixXAg5zPXL58nPTCN08xrU1NVxadHIG7LxT0I2/in77Dl0BGWFhZzcvYWCrAxizoQTF3mKPqMaboarSdQxsrCW+1PX0ERNvWF6yzJsGmzY2Wfg3/aYfL8do/6WlJW67+8ezJw5k+DgYAIDA0lJSeGXX34hPz+fSZMabviuWbOGN998U5a+b9++ODs78/XXX5OYmEhiYiJff/017u7uuLi07eRwL+6/Zv878MILL/Drr7/y/vvvU1NTg6enJ++++y7q6rc3OKWHhwfPPfcc69evZ9OmTXh7e7Nw4UK513GvWrWKb775htdffx0dHR2mT5+usMHGwsICb29vsrOz6d27ex6PGDR+ErU1NRzdup7K8jIsHJyY9fRLcj1xSgrk767qm5jy0BMvELZjE5fCQ9HWa3gNZcveKlZOLkxZ/jgn9u3k5IHdGJiYMeWRx+VeZ11VUUH43u2UFhagoaWNi88Ahk97SK5F2sWnPxPmL+XM4QOEbN+Ioak5AUtW3vH4NZ4DfaksK+XUwT2UFRdhYmnN7Kdekt3RKSsulKu4q2tqMffZf3Fk81+s/b930dDSZtC4ALmLeAMTU2Y99RJHt2/kwvEQdPQNGDdnEe4tTmJ6RsbMffYVjm7bxJqP3kZbT5/eQ0cwbNJ0ufwlx8VQkJ3F1OWP31Fct8troC8VpaWcPLCX0uJCTK2smftMc/ylRUVy8WtoajH/+X9xeONa/vj4XTS0tBg8PoDB4+Ufxfv9w7flPidcuoC+kQlPffQ5AH6Tp4MSHNuzg5LCfDR1dHHp3ZdRHdwt74jHAF8qyso4dWivrBxnPfWifBy5rcvxFYI2r+OvT95DQ0ubgWP9GdiicbGhHF9UUI7N27O1kyvTHnmC8H07OLF/FwYmZkxb+QRWd9gleeojT3J8zzb2//kzleVl6BkZ4zf1Ifp1MLhgV9LwcMOmRbdy41VLMV61lOIDh8lqLMO/oxMxSaipKDN5gBeaEjVS84r4KyyC6trmt1Poa7Wt+D3hL9/93N3anMKycr7aF9bleb4TeyKvIFFVYcVYX7TV1UnIzOGjnUeorGl+I4WJnvwjFBP6uKOqosyy0YNZNrq558i11Ez+u61hTDRtdQmPjhuKgZYm5dXV3MjJ571th+TeuNXVBowLoLamhpBtG6gqL8PC3qlx/JXm8mpzTjI2Zcbjz3Fs5xYuh4ehra/PqIfny72Ou6yokA2fNt+VL8rN4crJY1i7uDH72X/JpqcmxFKYk43/klVdGGWD/mP9qa2pJmzbBqoqyjG3d2TGEy90GKuesQnTHn2W8F1bGl5nrq/PyIfm49Ki0fdyeCj1dXUErv1Fbl6PQUMZv/AR2ee0hFiKcrKZuGhlF0Uor6vira6qInTbBkqLClBVU8PQzILxi1bI9ZDKTrnJru+bj2lnD+3h7KE9bdZJZxk4PoDammqObm3ejh966iW5WItbXYjpG5sy8/HnCdu5mcvhoWjrGzB61gK57bi0qJAN//uv7PPl3DAunwjD2sWNOc+9CkBW8g22f/uZLM3pg7s5fXA3noOH4b94RafG2VPn5J7Sq/FFCuEHdlNaVIiplQ3zn3kFA1m8hRTkytelFr3wGgc3rGH1R++gqaXFkPGT8B3f3Fu3vr6e4B2bKcrLQVlZBUNTM8Y+NI8BjTfUACLDgqivq2PHr9/J5afP0OFM76J6YxOf0ROprakhfOcmqivKMbNzZPKjz8pty6WFrfZbIxMCVj7Nqb3buHbqONp6+gybMRenPnc+SHJGYhxFudmMWdD5+2lrD9IxSugaI0aMoLi4mC1btpCfn4+9vT3vvPOOrLdMfn4+mZnNT44oKyvz9ttv88svv/DGG28gkUjo27cvK1eu7NQBhwGUKioq7vz5BkGhp556ilGjRrU74PCdaD2GzT+d6l302Pi7UrnHFuC/m9q6B+eRoVHvvtfTWehW6599oaez0G1iMrJvnegfZKSH4nFT/onq6kU16J9KVeVv25H8rqg9QHUp9S7sjXM/yi4u7eksdBuJ6oOzHQM8OrZzh1O43xTvP9xtv6U3ZeKtE/0NPVhHuy5SWFjIsWPHyMrKuqeRqQVBEARBEARBEARBEEA02HSKJUuWoKenx9NPP42+/u2/hUcQBEEQBEEQBEEQ/pEesCcLuoJosOkEe/fu7eksCIIgCIIgCIIgCILwDyIabARBEARBEARBEARB6FQt31Ar3J0HazQ2QRAEQRAEQRAEQRCEvwHRw0YQBEEQBEEQBEEQhM6lJPqH3CuxBgVBEARBEARBEARBEO4zosFGEARBEARBEARBEAThPiMeiRIEQRAEQRAEQRAEoXOJ13rfM9HDRhAEQRAEQRAEQRAE4T4jetgIgiAIgiAIgiAIgtC5xGu975noYSMIgiAIgiAIgiAIgnCfET1sBEEQBEEQBEEQBEHoXMqif8i9Eg029ynlB6z7WH19fU9noRs9WAcuqVTa01noNuuffaGns9CtFn37VU9noduc+fD9ns5Ct6qsqe3pLHSbB+kYBVD/AMVbUV3T01noVhpqD061/gGrJmOiq93TWeg2D9o1kCDcyoNzZBcEQRAEQRAEQRAEoXuIBrh79mDd6hcEQRAEQRAEQRAEQfgbED1sBEEQBEEQBEEQBEHoVErKoofNvRI9bARBEARBEARBEARBEO4zooeNIAiCIAiCIAiCIAidS0n0D7lXYg0KgiAIgiAIgiAIgiDcZ0QPG0EQBEEQBEEQBEEQOpcYw+aeiR42giAIgiAIgiAIgiAI9xnRw0YQBEEQBEEQBEEQhM6lJHrY3CvRw0YQBEEQBEEQBEEQBOE+849vsJk2bRonTpy4o3mefvppNmzY0EU5EgRBEARBEARBEARB6Jh4JOpvTCqVcvLAbi6dCKOqohwLeyfGz1uMiaV1h/OlxMcSumMTuRlp6OgbMGj8JPqOGCOXJu58JOH7d1KUm4O+iSkjpj2Mq88A2fdnAvcTdzGKguxMVFRVsXRwZsT0WZha2cjShO/bQdz5SIoL8lFRUcXc1h6/qQ9h7eTyt4q1pdOB+wjfu4O+I8cyfu5i2fSDf63m6hn5hkFLBycWvfLWHcd6O86HBXM26CClRYWYWFozds5CbF3c202fk5bCkc3ryLyZhIaWNj4jxjBs0nSUGrsplhYVErJ9E1kpNyjIzqKX7zAmL320S/LeWk+W7fmwYC6eCKM4PxcAYwtrhgRMxdnbR5amu8u2PaN7uTDA2RYNNTXS8gvZH3WNnOLSdtPraKjj39cDS0M9jHS0uXQzjV1nL3djjjuXho83hgtmo+HuiqqpCZkffkbJwSM9na0ORYYGcSpwPyVFRZhaWeM/bzF2ru3vp1mpKRzauIb0G0loauvQf+QYRkyZKdtPb8ZGc3TnFvKyMqmprkLfyIR+I0YxdOIUueVUVVQQsnsr0VERVJSVomdoxJiH5tJroG+nxXb+2FEiWh6DZi/ExsWt3fQ5aSkEbVnffAwaPpqhLY5BACnxMYRsb9qnDRk8QX6fjj0XwZkjByjMyaK+rg4DU3MGjp2I95DhsjTnwoK5GB7avE9bWjM0YJrcPn03evI4lZIQS2RQIFkpNygtKiRg8Qq5mAGqqyo5vns78ZfOUVlWiq6hET7DxzBw7MS7ivXUwT1cPhFGZUU5lvZOjJ276LZiDdu5mbzGWAeOn4TP8NHysV6I5OT+XbJY/aY+jKtPf9n39fX1nDqwm+iI05QVF6KtZ4DnIF+GTpqBsoqKLNbwPdtJuHRetn338RvNgLuItb34Iw/v49rpcKrKyzG3d2DEwwswsrDqcL70xDhO7NlGQWY6WnoG9BszkV7DRsq+T7wYxfmjgRTl5lBfX4e+iRl9Ro7DY9BQWZp1H/ybkoL8Nsu28/RmyqpnOiW+ls6FBXPmSPN+PH7OQmw7OEZlN9YlMm407Md9R4zBb7J8XeLotk1ktqhLTF0mX5e4EB7KldMnyc1IQyqtx9zWnhHTHsa2g+NHZ+mJY/K1yDOcDNxPfnYW9XW1GJlZ4Ds+AJ9hI7o01qjQIE4fOUBpY6zj5yzqMNbstBQCN61tLFsd+o0cw/DJM2SxxpyP4PyxEDJTblJXU4OJpRXDJk3HrcX+e/54CJfPnCA3PQ2pVIq5rT2jpj/cYf20s0SGBnHq8H5ZvBPndly22WnyZdtvhHzZxpyLIOrYUbJSblLbGO/wyTPk4l37+Yckx8W0WbaJpTVPvPt/nR/k35V4rfc9Ew02f2Nngw4SeTSQSYtXYmhuwamDe9j67WesfPsjJBqaCucpzM1h+49f0nvICCYve5S0xHiCNq9DS0cXt34DAUhPSmDvHz/hN3kGrn0HEH8hij2rf2ThS29g6eAMNFSs+44Yg4W9I0jhxP6dbP32Mx556wM0tXUAMDKzZNzcxegbm1BbU0PU0cNs/+ELVr79Mdp6+n+bWJukX0/k0sljmFrbKPo57N29mNyiYtJUuexs0ZFnCN66gQnzl2Dj7Mb5Y8Fs+/4LVv7nI/SMjNukr6qoYMu3n2Lj4s6S194hPyuDA2tXoyaRMHj8JADqamvQ1NHBd+IULoaHdUm+29OTZatraMTIGbMxNDNHWi/l6pkT7P7lO5a89jam1ray3+uusm2Pn4cTQ90d2XX2EnklZYzycmHp6EF8e+AY1bV1CudRVVamvKqa8OgkBjjbKkzzd6KsqUl10k1KDgVh/ta/ejo7t3Q14jSBm9YxadEybF3ciAoNZsM3n/Lku/+HvrFJm/RVFRWs/+oT7FzdWfnv98jLzGTPn7+gJlFn6MTJAEg0NBg0diJmNraoSSSkJMRzYN3vqEnUGTh6PAB1tbWs/+oTNLS0mfXYM+gZGlFckI+qmlqnxRYTdYajWzcwfv4SbJxdOX/sKNu+/4IV//mwg2PQZ9i6uLP41bfJz8rk4F+rUZOoM2h8ANC4T//wJd5DRzBl+WOkJsYTtOkvNHV0cW/cpzW0tRkaMA0jc0uUVVRIunKBQ+v/QEtHF6fGBhldA0NGzZyDoak5UmnDPr3r529Z8vo7mFnf/X7Qk8epmqoqTKys8fIdxsG1vyn8rdDtm7gZe43JS1ehb2xKakIshzeuQVNHh16Dh91RrBFBB4k6Goj/4hUYmVlw+tBetn/3OY/858N2Yy3KzWHnT1/hPWQ4k5auIi0xnqNb1qOpo4Nb38ZYryew/4+fGTZ5Bi4+/Um4eI59v//I/BffwNLBqeG3jxzkwvGjBCxeiYmVDbnpqRz6azUqqmoMCZgGQNiOzSTHXiNgySr0jU1IS4jjyKaGWL3uMFZFLoQc5mJYEGPmL8PA1JyoI/vZ+/PXLHjtPSQaGgrnKc7LZf9v3+ExaBjjFz5CxvUEjm/fiIaODs59Gi7w1LW0GTB+MgZmFiirqHDz2iVCt/yFpo4O9p69AZj1whtI6+tlyy0rLmLbVx/j3M5NpHsRHXmGoC0bmLigoS5x7lgwW77/glVvf4R+O/vx5m8+xdbFnWWvvUNeU11CXYJvY12itrEuMcS//bpEclwMngMHY+3sipqahIijgWz59jMeefO/GJlZdHqcTXrqmKypo8PwydMxsbBCWUWF+MsX2Lv2N7R0dXHt3bdLYr0WeZojW9bjv2BpQ6xhwWz+7jMee+dj9I0Ux7rx6/9h6+LO8tffIz8rg31rfkUiUcd3QkPZJsfFYu/uyajps9DQ1uHq2ZNs/+lrFr30b1nDSHJcDF4DfLGZ64qaRJ2zwYfY9M2nrHzzA4zMu7ZsD29eR8DCZdi5uBEZGszGbz/liXf/r914m8p2xRvvkZeVyd4/f0Girs6QCQ1lezM+BgcPL0bPmI2mtg5Xzpxg649fseTlN2Xxznnieepqa2XLra2t5Zf/voHXgMFdFqvwYOrWJq/IyEjmzp1LXV3DBUZ6ejrTpk3jhx9+kKVZu3Yt//nPfwBITk7mvffeY+7cuSxevJhPP/2UgoICuWUGBQXx1FNP8fDDD/P444+za9cu6luc7Frbtm0bCxcuJDY2FoDCwkI++OADZs2axYoVKzhypO0d2127dvHss88ye/Zsli1bxjfffENpacOd7crKSubOndvmsavz588zc+bMNvntLFKplHMhR/CdMBm3fgMxtbJh0pJVVFdVEh15pt35LoaHoqNvwLi5izC2sKKP3yh6+Q4jIjhQliYq9Ah2rh4MCZiGsYUVQwKmYevqTlRI87qZ/czL9B46AlMrG0ytbZi87FEqSktIT0qQpfEaPBR7dy8MTMwwsbRm9MPzqa6sJDs15W8VK0BVRTn71/xCwMJHUNfUVvh7KqqqaOvpy/6aGq46W+TRQLyH+uEzfDTGllaMn7cEbT0Dzh87qjD9tYhT1FRXM3npo5ha2eDebxC+EycTGRyIVCoFQN/YlPFzF9N76Ag0tBXH1xV6umxd+vTDqVcfDE3NMTK3YMT0WUg0NEi/nij3e91Vtu0Z4mZPeHQS0alZZBeVsvPsJSSqqvS2b/+Ob2F5BQfPR3PhRhoV1TXdmNuuUX46grxf/qA0NBzqpT2dnVs6feQgPsNG0H/EGEwtrQlYsBRdfQMiw4IVpr985gQ11VXMeORxzKxt8RwwiGEBUzgTdEi2n1raO+I9eChmVjYYmpjRZ4gfTr36kBwfK1vOxZPHKCspZt7TL2Ln6o6BiSl2ru5YNV4Qd4bI4MN4D/HDx28UxhZWjJ+7GG19fS4cb/8YVFtTzaSlqxqPQQPxnTCJyKPNx6CL4SFo6xswfu5ijC2s8PEbRa8hw4gIPiRbjr27F64+/TG2sMTQ1IwBYyZiam1DamKcLI2rT/+Gfdqs1T7d4tx0p3r6OOXUqw8jps/Cvd9AuR5JLaVdT8Rr8DDs3DzRNzahl68flg5OZNxIuuNYz4cGMXjCZNz6DsTEygb/xSuprqokpqNYTzTEOnZOc6xevsOIahHruZAgbF098PWfirGFFb7+U7F1cedci1jTryfg7N0X59590Tc2wbl3w/8t40i/noDnoKHYuXmgb2yCl+8wLBycyLhx/Y5ibS/+S8eC6TfWH+c+/TG2tGbsguXUVFUSf/5su/NdPXUMbT19Rjw8H0NzS7yGjMBt4FAuhjbHZuPqgWPvvhiaW6BvYkqfkeMwtrQmo8W2qamji5aevuwvOeYKEnWNLmmwORscSO+hfvQdPhoTSysmzluCTgd1iatnG+oSU5Y9iqm1DR79G+oSEUHN+7GBsSkT5i2mz9ARaGgprktMX/EEA0aPx8LWHmMLS/wXLEOioUHS1a7tAdpTx2RHj1549BuIiaUVRmbm+I7zx9zaVi5NZzsbdIg+Q4fTb8QYTCyt8Z+/FB09A86FKS7bK2dPUlNdxbTlj2HWWLZD/OVjnThvMcMCpmHl6IyRmTkjpj6EhZ0jcRejZMuZsfJJBo6ZgIWdA8YWlgQsXI5EQ5PEa5e6LFaAM0EH6dNYtiaNZaujb0BUO2V75WxD2U5f3li2/QcxtFW8/vOW4BcwDevGeEdOexhLe0diLzTHq6mtg46+gewvJSGWmqoqfPxGdWm8fzdKykrd9vdP1a0NNr169aK6upr4+HgALl++jJ6eHpcuNe/IV65cwdvbm/z8fF5//XXs7e35/PPPef/996moqOD999+XNcgEBgaydu1aFi1axA8//MDKlSvZvn07Bw4caPPbUqmU1atXs2/fPj7++GPc3RtaR7/66ivS09N5//33efPNNzl69CjZ2dly8yopKbFq1Sq+//57XnnlFeLj4/n5558B0NDQYOTIkW0aeoKCghg0aBCGhoadtwJbKMrLoay4CHtPb9k0NYkEGxd30jqomGZcT8TBo5fcNAdPb7KSb1BX19BKnH49EXvPtmnSkuQvYluqrqxEKpWirqWl8Pu62lounQhDoqGJmc2d3eW8H2I9vHENbn0HYufu2e7vpSXF8/3rz7P6vTcI3PAnZSXFtx3j7aqrrSUz+QYOLdYFgKNnr3bXRXpSAjbObqhJJC3Se1NaVEhRXm6n5/FO3A9l26S+vp6YyDNUV1Vi5Sj/2F53lG17DLU10dXUIDGruaxq6+q5mZOPrbFBt+VDuH11tbVkJN/AyUt+P3Xy8iY1MV7hPKlJCdi5uMvtp869+lBSWEBhXo7CeTKSb5CaGI+9m4dsWuyFKGyd3Ti0cS1fvPIMP77zGmF7dsjdBbwXdbW1ZKa0PQZ1tG+lX09scwxy8JI/BqUnJSo4rvUm62bzPt2SVCrlZsw1CrIysWmnu319fT3Rjfv03TyK2+R+Ok61x8bJlcTLFyhufJwmLSmB7NQUHFut01spysttiLVFvtUkEmyc3do0ZLeUcT1Rbh4AB89eZCXflMWacaNtGntPb9KvN69DaydXUuJjyM/MACAvI53kuGgcvXrLpUm6clH26FB6UgI5qSk4et1ZrIqU5OdSXlKMrZuXbJqqmgRLJ1cyO2j8yrqZhE2LeQDsPLzISbkpu0nZklQqJTUuhsKcLCydXBUuUyqVEn3mJG4DBsvtO52hqS7RevvoqC6Rdj0BWxf5/djJ697rEnW1tdTW1LTbwNMZevKY3JJUKuV69FXysjKwd1Wc5l41xdpynwFw9PImNUlxrGlJCdi2itXJqzelRQUdlm11VUWH5dZUtpo9UbaenVu2AFWVlWi0c50DcD48FGdvH4U91AThXnTrI1Gampo4Oztz+fJlPDw8uHz5MlOnTmXbtm3k5+ejpaVFfHw8y5cv58CBAzg6OrJ8+XLZ/C+99BILFiwgISEBNzc3Nm3axPLly/Hz8wPAwsKC2bNnc+DAAaZOnSqbr76+nq+//pro6Gg++eQTzM3NAUhLSyMqKopPPvkEL6+GE+2LL77Io4/KP287Y8YM2f/m5uYsX76cDz74gBdffBFlZWX8/f155ZVXyMvLw9jYmNLSUk6fPs1rr73WVauSsuKGC0ZtXT256dq6epQWtt+rp6y4CDsP+UqFlp4e9fV1VJSWoqNvQFlxkcLllpcUtbvco9s2YGZj1+ZCN/HyBfb98TM1NdXo6Okz55mX7/hxqJ6O9dKJMApzsjsc08XR0xtXn/7oG5tSnJ9L+L4dbPnmU5a8+nanPopQXlqCtL4ebV35dailp09ZzDWF85QVF6FraCQ3rakMyoqLMDAx7bT83ameLluAnLRUNnz+IbW1NUjU1Znx6DNyj711V9m2R0dDHYCyyiq56WWV1ehqqnf57wt3TraftjrWaevpUxp9VeE8ZUUK9tPG7besqAhDEzPZ9K9efY7y0hLq6+oYOe0hBowaJ/uuICeH6zHReA8eyoJnX6YwN5eDG9dQXVXJhDkL7zm2isbYtBTsWzeLOzgGGRi2Si9/DCorKcJet9U+rSu/T0NDb8cf//0SdbW1KCkrMX7eEpx69ZGbLycthfWfNe/TMx97Vu4Rxzt1PxynbmXsnIUc2bSWX/7zCsrKKrJpznf4yEV5ccPvti5fLT09SgsL252vrLgYO/dW8+jebqzNDeCDJkyiuqqSPz/6D8pKytTX1+HrP4W+I8fK0oyZvZCgzWv59e1/yWIdM2eh7LG4e1HeWNaarePX0aOsqLDD+Wxc5W/oaOroUV9fT2VZqexYUFVRwdr/vk59bQ1KysqMeHiBXENgS6lx0ZTk5+LpO1zh9/ei6RilpXeHdQkDozbpm76727rEsT3bkahr4Nqn313Nfzt68pgMUFlezlevPUddTS1KyspMWrgUl973vr0q0hxrq31NT58bMe3EWlyEbqsbzE3zlxYXKizbyNAgSgoK6O3r125ewvZsQ6Kujmuf/u2muVft1Yu19fS53k68pUVF6LWpFysu2yaRIUcoKcin9xDF+2NeVgbJcTHMefKFu4jiH0681vuedfsYNr179+by5cvMmTOHK1euMH36dC5evCjrbaOiooKbmxtbt27l6tWrzJkzp80yMjIyMDc3Jzc3l++//54ff/xR9l1dXZ2sO1uT33//HWVlZT7//HMMDAxk01NSUlBWVsbNrXmgMzMzM4yM5Hfiixcvsm3bNlJSUigvL6euro7a2loKCgowNjbG1dUVBwcHgoODmTt3LmFhYejo6DBgQOd1Yb0WcYojG9fKPj/cdEBotRNIpdJb7hhtvpU2LarFN22W2/7yQrZvIi0xngUvvYGysnynLVs3T5a+8S4VpaVcOhnG3t9/ZOHLb8oq34rcT7HmZ2VwfO925r/wBiqq7e8uHi0G8zS1tsHc1p5f3n6VpKuXcOvb+V2Z2wQmld7R8bBpH+nuY+j9VLZNjMwtWPrGu1SVlxN3IYpDf61m7vOvygbQ7u6y7W1vxbQBzXeh1x+PUpx3cf6777V+fEUqlaLUQcG13uSl7Xyx7NW3qK6sIu16AsHbN2NgbEqfocNlv6Gtq8fUpStRVlbG0t6RirISDm9Zz/jZC9p9pOZOKYytg0UrSt9mepsFNK2B5ukSdQ2WvfEe1VVVJMdeI2T7JvSNTLBv0TBiZG7Jsjfeo6qinLgLkRxc+xvzXnhNblD8jtyPx6lbORcWRFpSPA89/hx6RsakJMQRtnML+sYmbe60txQdcZqgTc2xznzi+bb5a8r3rWJtJw75WFsvVj7Y2HNnuXb2JJOXPYqxpTU5qcmEbN+InrEpvYc2DNJ6PiyY9KQEZjz2LHpGxqQmxHFs5xb0jIw7jFWRuKgzhG1rfivolFVPK8pmQz5vteu0U9gtJ0vU1Zn78pvUVFWRGh/DyT1b0TU0xkZBj4xrp8Mxs7XH5B4aG2+lbZY7jrPtLnpvj6dGHD3MhfBQ5j//KuqaisdH6kw9cUwGUNfQ4LH/fEh1VSXXo69yZMsGDIxNcWzVu65ztc68tO00udSKg1W0fmLORXB0+yZmrnpK4fg/0PDI3fnjISx8/rUeKVtuUbYKV0/DgtokjT4XQdD2TTz86NMYtBPv+eMNj4V21bhEwoOt2xtsvL292b9/P8nJyVRUVODs7CxrxNHT08PT0xNVVVXq6+sZOHAgK1asaLMMAwMDqqoa7jY//fTTeHh03K2wb9++HDt2jMjISMaPHy+b3rphR5Hs7Gz++9//MnHiRBYtWoSuri6JiYl8+umn1LboYj5x4kR2797N3LlzOXLkCOPGjUNFwcCkhw4dIjAwsM301vSdPHBuHKgPwKV3X9mgfICse3tZsXwrcXlpSZs7Yy1p6+lTVix/5668pBhlZRXZ2CUK05QWo9Wq9RogZPtGYqLOMve5VzFQ0CItUVdHYmqOoak5Vo7O/Pbe61w+eYyhk6a3m8f7Kdb064lUlJby50f/kX0vra8nNTGOi+GhPP/5jwp7WegYGKJjaEhBTla7+bsbWjq6KCkrK4xLUflA++sBaHeernI/lW0TFVVVDE0bet1Z2DuSmXydqJDDBCxqe+yBrivbJrFpWaTlFTbnr7ERVEdTneKKStl0bXUJpa163Qj3h6b9tLTVHfnykuI2dz2baOvrU1qkeD9tPU/T3T9zG1vKios4tnen7OJAR18fFRVVucZzE0traqqrKS8tadPD4U5ptncMKi25s2NQadMxqCE/2rqK9ukSlJVV0NRp7k6vpKyMoVnD/mpua0deVjqnA/fJNdioqKrK0ljYO5Jx8wZRRw8TsFjxPt3a/Xic6khNdTXH92xn+sqnZD1qTK1tyUlNJiL4UIeNGM69fbBweEf2uWWsLXsXlJcUd7jtaOvptYmjolRRrPKPk5aXyK/DY7u2MnCcPx4DGhrKTa1sKM7P4+zhA/QeOoKa6mrC925n6oon5WNNSyEqOPCOG2wcevlgbu/YJv7ykmJ0WsRfUVrSptdNS1p6erLeOS3nUVZWRr3FmGdKysroN+6/Jta2FGRlci74YJsGm/KSYm5cvciIh+ffUTy3q6O6RHu9oDuqS9xpz2loaKw5vmcHc555qVPH2FKkJ4/J0FDuRk3HJFt7cjPTCT+4p0sabNor27KOYtXTp1RB+qbvWoo5F8GeP35m2vLH5N6Y1NLZ4ECO7dnOvGdfxsrRWWGaziIr2+JCuekdxaujr0/ZbZZt9LkIdv/+EzMeebzdeOtqa7l0+jj9ho/p9pdS/C20upkv3Llub7Dp1asXNTU1bN++HS8vL1RUVOjduzffffcdBgYGsl4pzs7OhIeHY2ZmhqqCng1aWloYGxuTkZHB2LFj23zf0sCBAxk6dCiffPIJSkpKjBvX0FXR1taW+vp64uPj8fRs6MqanZ1Nfn7zKxXj4+Opra1l1apVsgaYiIiINr8xevRofv/9d/bt20diYiKvvvqqwrwEBAQQEBBwy/X01/Eouc8SDU25tzNIpVK09fS5GXMVy8bKRm1NDWmJcYyaObfd5Vo6OpNw6ZzctJsxVzG3c0BFpWE9Wzk6czPmmuwNQg1prmHtJH/QPbptAzFRZ5n3/KsYW1jeMqamfN9qLIX7KVaXPv1Z9m8HuWUcWvc7hqbm+PpPabfXTXlpCaWFBejcRSWmIyqqqljYOXAj+ioe/ZtHob8Rc1X2Jo7WrJxcCNu1hdqaalTVJLL0OvoG7d4Z6Sr3U9m251bbaFeVbZPq2jryS8vlppVUVOJsbkx6fkMFQ1VZGXtTIw5fbPs6SaHnqaiqYmnnQFL0Fbxa9NBKunYVj/6K91MbJxeCd2yW20+Trl1B18AQA+P2HzWQSqXU1jYPKm3r4saVs6eQ1tej1FhJysvKQE0iQUtHt1Nis7B14EbMVdz7D5JNvxlztd0eZ1aOzhzbvZXamhpZA/fN6GtyxyArJ2fiL56Xm+9GzFXM7Zv3aUWk9bc+pyCtl1tHt/J3OE61VF9XR31dXZs7zErKyre8MdV+rNca3gJJY6xJ8Yyc0bbHcxNLR2cSL8mX382Ya5jb2ctitXRwJjn2quzNYADJsVflHqWura5GqdUrYJWVlUFaLxdr6968txOrIhINDbk3P0mlUrR09UiJi8bMzqEhTzU1ZCQlMHTaw+0ux9zeietXLspNS4mLxtTWXuHNuxY/qHD7jY04hYqqKi59BymY6d411SWux1zFo8Ubba7HXJW9la01a0cXQlvVJa5H311d4mzQIY7v28mcp1/qltd59+QxWWGaeil1NZ0zrlhrTbFej76CZ4uyvRF9Bfd+ircnaycXQnZublW2V9DRN5Qr22uRZ9i35hemLntMbtktnQk6yLG9O5j3zMvd8jpvWbzXruA1oLlsr0ffe9leizzDnj9/Zvryx9uNFxrGjisvLaWvGGxY6CLd3uTVNI5NaGgovXs33Anx8PAgNzeX2NhY2bQpU6ZQXl7O//73P2JjY8nMzOTChQt89913lJc3XMwsWLCAHTt2sGvXLlJTU7l58yZHjx5l69atbX538ODBvPbaa/zwww8cPdowSrqNjQ39+/fn+++/JyYmhqSkJL7++mskLQahsrKyor6+nj179pCZmUlYWBi7d+9us3xtbW2GDx/O6tWr6dWrF1ZW7b+9pTMoKSnRf8wEzh45QNyFKHLSU2WvSfVscTI6sPZXDqz9VfbZZ/hoSgoLOLptA3mZ6Vw6eYwrZ04waJy/LE3/0RNIjovmTOB+8jIzOBO4n5S4GAaMmSBLE7T5L66cDmfq8sfR0NKmrLiIsuIiqqsaegBUVVQQvncHGTcSKc7PIzP5BofW/U5pYYFcJf9+j1VDS6vhTVgt/tQk6mhoa2NqZYOSkhLVVZWE7thMelICRXm5JMfFsPOnb9DS1cO1ndb4ezFwrD9XTodz8UQYeRnpBG9ZT2lRIX1HjAEgbNdWNn39iSy916AhqEkkHFj7GznpqcSdj+TM4f0MHOcvV8HPSrlJVspNqisrqCgrIyvlJrkZaZ2e/5Z6ejs+tnsrqQlxFOXlkpOWyrHd20iJj8Vz4BCAbi/b9pyOu8lwT2c8rc0x09dhpm9vqmtruXwzXZbmId8+POQrP5aHhYEuFga6qKuqoilRw8JAF1O97n3DVWdR0tRA4uKExMUJlJVQMzdD4uKEqnnPjcHUkSETJnHx5HHOHw8lJyONwE1/UVJUIBvbIHjHZv764mNZeu/Bw1CTqLP7j1/ITksh+lwEJw7txXd8gGw/PXv0MHGXzpOXlUleVibnw0M5dfgAvYc0jyEwYNQ4KspKCdy8jtzMDBKvXiJszw4Gjh7faY9DDRw3kSunw7l0Ioy8zHSCt66ntLAQn+ENx6Bju7ey+ev/ydJ7DRqCqpqEg381HoMuRHLmyH4Gjm0+BvkMH0NpYX7zPn0ijCunwxk0rvni/tShvdyIuUphbjZ5melEBB3i2tlTeA0eKksTtqvlPp3Csd1bSY6PxWtQc5o71dPHqeqqSrJTk8lOTUYqlVJckE92ajLF+XkAqGtqYuPizrE920mOi6EwN4crp8O5dvbkHY8doaSkRL/R44kIOkD8hShy01MJXNcQa8vHQw+u/U3uFeM+fg2xhmzfSF5mOpdPHuPqmRMMkIt1PMlxMZw9vJ/8zAzOHt5PSlws/VvE6uTtQ0TQQZKuXKQoL5f4i+eICjmMS9OrsRtjPb5nGynxMRTl5nC1MVaXTjgmKykp0WfkOM4fDSTp0nnyMtII2bQGNXV1XPs1X7AFb/iD4A1/yD73GjqSsqICwndtoSArg2unw4mNOIXP6ObYooIOkBoXTXFeDgVZGVwIPUJc1GncWlxkQtNgwydw6Tuw3deId4bB4/y5fCqci+Fh5Gakc6SxLtGvsS4RumsrG79qUZcY3FCX2L/mN3LSUok9H8npw/sZNF5xXaKqsoJKBXWJM4cPELprK5OXrMTIzJzSokJKiwqprJC/UdHZeuqYfHz/bpKuXaEgJ5ucjDROHT7A5dMn6D3k3l9B357B4wO4dOo4F8JDyc1I4/DmdZQUFdK/cSyokJ1bWP/l/8nS9xo8FDWJOnvX/Ep2Wiox5yM4FbhPLtarEafZ8/tPjJ45FztXd1m5VZSVypZz+vB+QnZuYcqSVRiZWXRb2fqOn8TFU8c53xhv4OaGsu0/sqFsj+7czLoWZdursWz3/NlQtjHnIjgZuLdVvKfYtfpHxj40r914m5w7HoKjhxeGpm2fNBBoeMysu/7+obq9hw00jGMTFxcna5yRSCS4u7sTHx8vG0/G2NiY//3vf6xZs4Z33nmHmpoaTE1N6devH2qNd+j8/f3R0NBgx44drF27FolEgp2dndyAwy01Ndp88knDCWjs2LG88MILfPfdd7z55pvo6ekxf/58ClsMrOfo6Mijjz7K9u3bWbduHR4eHqxYsYL//e9/bZY/YcIEjh49ysSJEztzdbVr8PhJ1FZXE7xlHZXlZVg6ODH7mZfl7pYVt+gtBGBgYsqsJ18kZPtGLoaHoq1vwNjZC3FrcUfF2smFqY88wYl9OzhxYBcGJmZMXfEElg7Nd/wuHA8BYMu3n8otf+ik6fhNmYmyijK5GWlcPnWcyvIyNLS0sbB3ZP4Lr93V4I89GeutKCkpk5ueytWzJ6mqKEdbzwA7Nw+mrXxSLn+dxXOgL5VlpZw6uIey4iJMLK2Z/dRLsrsgZcWFFOY0v+lMXVOLuc/+iyOb/2Lt/72LhpY2g8YFyF0IAaz5+B25z4mXL6BnZMwTH3ze6TG01JNlW1ZczP41v1JeUoREQxNTaxtmPfmi7G0j3V227TkRk4SaijKTB3ihKVEjNa+Iv8IiqK5tfvuIvlbbiv0T/vKD47lbm1NYVs5X+8K6PM+dTcPDDZsWxxvjVUsxXrWU4gOHyfqoa7fRu9Fr0BAqyko5fmA3pUWFmFrZsODZV2TPv5cWFVLQYj/V0NJi0QuvcWjjGn778B00tbQYMmESQyY097yQ1tcTvH0zRXk5KCurYGhqxriH5zGgxYCs+kbGLHrhNY5sWc+v77+Jjp4+ff1GMWJK8+D598pjgC8VZWWcOrRXdgya9dSLsmNQaVERhbmtj0GvELR5HX998h4aWtoMHOvPwBYX8wYmpsx66kWObt/IheMhDa/DnrNI7m5/TVUlRzatpbSwAFU1CUbmFkxetkrWwAoNj/Ls//MXykqKUNfQxMTaltlPvXjHj8q01pPHqcybN9jyTXOd4+T+XZzcv4tevn5MWrISgGkrnuDY7m0cWPMLleVl6BkZ4zflIfq1Gvz0dgwaP4namhqObl1PZXkZFg5OzHr6JblYm97Q1ETfxJSHnniBsB2buBQeiraeAWNmL5Tr+Wnl5MKU5Y9zYt9OTh7YjYGJGVMeeVzu8bOxcxZyYv8ugreso7y0BB09fXoPHcmQFo9RT3nkccL3bOfAml8bYjU0xm/KTLmBie9F3zETqa2p5viOjVRVlGNm58jUx56TazwpLZSPX8/YhCmrnuHE7q1cPXkMbX19hs+ch3OLBrOaqiqObd9AaWEhqmpqGJhZMHbBI7i2uomVnhhHUW424xY90inxtMdzoC8VZaWcaFGXmPP0Sy3241bHKE0t5j33Lw5v+os/G+sSg8cFMLhVXeKPj+TrEgmNdYmnPmw4TkeFBVNfV8fu336QS+c9xI+py9p/ucO96qljcnVVJQc3/ElxQT6qahJMLCyZseJxvAfffSPyrXgNHEJFaSknDuyhtLgh1nnPvCxXtoWtynbB868SuHEtf3z8DhpaWviOn8TgFr3hzh87Sn19HUFb1xO0db1sup2rB4tf/jcAUaENZbvrt+/l8tN7yHCmLX+sy+JtKtvwFmU7/5lWZZsrH++iF17j4IY1rP6osWzHT8K3RU/HqMZ4D29Zx+Et65rjdfNg6ctvyj4X5GRzI/YaDzeOfyUIXUGpoqLi3kYME2SOHz/O999/z59//onGPd4Vaf1IlPDP0bor9z9dfX19T2eh26Tld9/rvu8Hi779qqez0G3OfPh+T2ehW1V2UXf9+9HdPErzd1b/AMVbUX37j8L9EyhquP+nUnvAxgqpq39w9lvlf3BPCUXmDOlz60R/Y2XnLnTbb2n379ttv9WdeqSHzT9NZWUl2dnZbNmyhYkTJ95zY40gCIIgCIIgCIIgCA820WDTCXbs2MGWLVvw8vJi/vyuGdFfEARBEARBEARBEP4uWg8kL9w50WDTCRYuXMjChQt7OhuCIAiCIAiCIAiCIPxDiAYbQRAEQRAEQRAEQRA61wM2JlFXEH2UBEEQBEEQBEEQBEEQ7jOiwUYQBEEQBEEQBEEQBOE+Ix6JEgRBEARBEARBEAShcymLR6LulehhIwiCIAiCIAiCIAiCcJ8RPWwEQRAEQRAEQRAEQehc4rXe90ysQUEQBEEQBEEQBEEQhPuM6GEjCIIgCIIgCIIgCELnEmPY3DPRw0YQBEEQBEEQBEEQBOE+I3rYCIIgCIIgCIIgCILQqZSURA+beyUabO5TqioPVuen2rr6ns5Ct6mvf3BihQfrQB2Tkd3TWehWZz58v6ez0G183/xPT2ehW0V+/GFPZ6HbqIju2v9YElWVns5Ct6qsqe3pLHSbbWcu93QWutXLU0b1dBa6TVp+UU9nQRDuK6LBRhAEQRAEQRAEQRCEzqX8YHVC6ApiDQqCIAiCIAiCIAiCINxnRA8bQRAEQRAEQRAEQRA61wM0NEJXET1sBEEQBEEQBEEQBEEQ7jOih40gCIIgCIIgCIIgCJ1L9LC5Z6KHjSAIgiAIgiAIgiAIwn1G9LARBEEQBEEQBEEQBKFzibdE3TOxBgVBEARBEARBEARBEO4zosFGEARBEARBEARBEAThPiMeiRIEQRAEQRAEQRAEoVMpiUGH79l908Nm2rRpnDhxot3v33jjDX766acuz0dRURHTpk3j8uXLXf5bgiAIgiAIgiAIgiAIivxtetj8+9//RkVFpaezcV87FxbMmSMHKS0qxMTSmvFzFmLr6t5u+uy0FI5sXkfGjSQ0tLTpO2IMfpOny1pCS4sKObptE5kpNyjIzqKX7zCmLnu03eVdizjNnt9/wtnbhzlPv9ipsUmlUk4e2M2lE2FUVZRjYe/E+HmLMbG07nC+lPhYQndsIjcjDR19AwaNn0TfEWPk0sSdjyR8/06KcnPQNzFlxLSHcfUZIPv+TOB+4i5GUZCdiYqqKpYOzoyYPgtTKxsA6upqCd+7k+vXLlOYm426hia2rh6MnDEbPSPjv12858OCuXgijOL8XACMLawZEjAVZ28fWZqDf63m6hn5BlZLBycWvfLWHcd6/thRIoKat9uxsxdi4+LWbvqctBSCtqwn82bDduszfDRDJ02Xa8FPiY8hZHvTejBk8AT59XDxRBhXz5wgLyMdqbQeMxt7hk99SO53z4UFczE8tHk9WFozNGCa3HroSrOH+DDW2w0dDQkJmbn8fvQMqfmF7aYf5GzHhD7uOJgaoaaqQlp+ITvPXiYqKUWWzT4NHQABAABJREFUZpSXM09OHN5m3iXf/kVNXX1XhCEnMjSIU4H7KSkqwtTKGv95i7Hr4BiVlZrCoY1rSL+RhKa2Dv1HjmHElJmysr4ZG83RnVvIy8qkproKfSMT+o0YxdCJU+SWU1VRQcjurURHRVBRVoqeoRFjHppLr4G+XRrv3dLw8cZwwWw03F1RNTUh88PPKDl4pKez1aGzIUc4GbifksJCzKysCZi/BHs3j3bTZ6Umc2DDGtKuJ6KprcOAUWMZNfUhhXfibsbH8uenH2BiYcXT//1ENv38iTB2//FLm/Rv/vgHamqSzgmsHWeOHiH80D5KCwsxs7Zm0oKlOHQQb2ZqMvvX/UlqY7yDRo9j9LTmeK/HXOP3/33QZr7nPvwU08bj/rnwMHb+/nObNG///GeXxvsgxQo9V5eqqqjg2J7txJ6PpKKsFF1DI0bNmI3ngMFdFis01DdOH9zDlZPHqKwox8LekbFzFmF8i/pGanwsx3ZuJi8zHW19AwaOC6DP8NGy7/My0jh1YA/ZqTcpzsvFN2AaQyfPkFvG2cMHSLx0joKshvqVhYMTftNmYWLV8W93tYXDBxDQ1wMdDXVi07P58fAJknML2k3vbWvJ8tGDsDY2QF1VleziUg5fiGHH2UvdmOtbCws8wJHdOygqLMDSxo45j6zC1bOXwrQ11dVs+OUHUq4nkpGWirO7Jy+995FcmrirV9i9YS1Z6WlUV1VhZGqK37iJTJj+UHeE06HI0CBOHd5PaWN9Y+Lcjusb2Wny9Y1+I1rVN+KiCWlV3+g7vG19Q1BAWfSwuVc93mBTU1ODmpraLdPp6up2Q27+vqIjzxC0ZQMTFyzBxtmNc8eC2fL9F6x6+yP0FTQaVFVUsPmbT7F1cWfZa++Ql5XBgbWrUVOX4Dt+EgC1tTVo6ugwxH8KF8PDOvz9wpxsQnZs7vBC+16cDTpI5NFAJi1eiaG5BacO7mHrt5+x8u2PkGhoKs5Tbg7bf/yS3kNGMHnZo6QlxhO0eR1aOrq49RsIQHpSAnv/+Am/yTNw7TuA+AtR7Fn9IwtfegNLB2eg4eK/74gxWNg7ghRO7N/J1m8/45G3PkBTW4fa6mqyU24yxH8qZja2VFVUELpzM9t++ILlb/wX5btoaOzJeHUNjRg5YzaGZuZI66VcPXOC3b98x5LX3sbU2lb2e/buXkxuUem8mzhjos5wdOsGxs9fgo2zK+ePHWXb91+w4j8fKmzsqqqoYMu3n2Hr4s7iV98mPyuTg3+tRk2izqDxAc3r4Ycv8R46ginLHyM1MZ6gTX+hqaOLe+N6SImLwaP/YKydXVGTSIg8epht33/Osjfew9DMomE9GBgyauYcDE3NkUob1sOun79lyevvYNZiPXSF6QO9mdK/Fz8eDie9oJhZvj78++EJvLRmJ5U1tQrn8bIx50pKBptPnqe0sorhHk68PHU0/90WSEx6tixdZU0Nz/+xQ27e7misuRpxmsBN65i0aBm2Lm5EhQaz4ZtPefLd/0Pf2KRN+qqKCtZ/9Ql2ru6s/Pd75GVmsufPX1CTqDN04mQAJBoaDBo7ETMbW9QkElIS4jmw7nfUJOoMHD0egLraWtZ/9QkaWtrMeuwZ9AyNKC7IR/U2zjs9RVlTk+qkm5QcCsL8rX/1dHZu6crZUxza9BdTFi3HzsWdiNAg1n39P57+7/8wUFC2lRXlrP3i/7B38+DRt94nLzODXb//jESizjB/+cpvRVkZO1f/iJNnL4oL2l4wqUnUee7jL+SndfEF/eWzpziwcS3TFj+Cnas7Z0OO8NeXn/DsB5+2G++azz7G3s2DJ/7zAbmZGexY/RMSiTp+AfLxPvv+/9DU0ZF91tbVk/teTaLOi598KT+tC+N9kGKFnqtL1dXVsvmbT9HQ0mbGqqfQNTSkpKAAVdWur6ZHBh3iXMhhJi5agaGZBWcO7WXH91+w7K0PkWhoKJynKC+HXT9/Ta8hwwlYuoq0pARCtqxHU0cX174NN4BqqqvRMzbGxac/J/fvVLic1IRY+gwfjbmdIyDl1IHd7Pj+c5b++79oaOsonKerzR7iw0ODe/Pl/jDS8gpZMLw/H8yfzOO/bKGiukbhPJU1NeyJvMqNnHyqamrxsjHnmYARVNXWsv/ctW6OQLHIE8fZ8sevLFj1BM4eXhwLPMD3H77H219+j5GpaZv09fX1qEkkjAqYwtXzUZSXlbVJo66hwejJU7G2c0AikZAYG82GX35Aoq7OKP/J3RGWQlcjTnN48zoCFi7DzsWNyNBgNn77KU+8+3/oG3Vc31jxxnvkZWWy989fkKirM2RCY31DvbG+YW2LqkRCakI8B9bL1zcEoavc8pGoyMhI5s6dS11dHQDp6elMmzaNH374QZZm7dq1/Oc//wHgypUrvPzyyzz88MMsWbKEX3/9lZqa5gPcG2+8wQ8//MDq1atZtGgRr776qsLf3bZtGwsXLiQ2NlY2X8tHolauXMnmzZv57rvvmDt3LsuXL2fHDvmLkLS0NF5//XUefvhhnnjiCSIjI5kzZw5BQUGyNHFxcbzwwgs8/PDDPP/888TFxckto66ujm+++YaVK1cya9YsHnvsMbZv3059fb0s3pkzZ1LQqiK5du1ann322Vut3k5zNjiQ3kP96Dt8NCaWVkyctwQdPQPOHzuqMP3Vs6eoqa5myrJHMbW2waP/IHwnTiYiKBCpVAqAgbEpE+Ytps/QEWhoabf723V1tez+/SdGTp+FgUnbg/69kkqlnAs5gu+Eybj1G4iplQ2TlqyiuqqS6Mgz7c53MTwUHX0Dxs1dhLGFFX38RtHLdxgRwYGyNFGhR7Bz9WBIwDSMLawYEjANW1d3okKa72bPfuZleg8dgamVDabWNkxe9igVpSWkJyUAoK6pxZxnX8FjwGCMzC2xdHBiwvyl5GdmkJeZ8beL16VPP5x69cHQ1BwjcwtGTJ+FREOD9OuJcr+noqqKtp6+7E/zLipXkcGH8R7ih4/fKIwtrBg/dzHa+vpcOK54u70WcYrammomLV2FqZUN7v0G4jthEpFHm7fbi+EhaOsbMH7uYowtrPDxG0WvIcOICD4kW87URx6n/+jxmNvaY2RuyYT5S1FT1+D6tSuyNK4+/RvWg1mr9dBY7l1pUj9Pdkdc5mxCMql5hfwQGI6mRA0/D6d251kTFsGeyCskZuWSVVTC9jMXScrOZ6CznXxCKRSVV8r9dYfTRw7iM2wE/UeMwdTSmoAFS9HVNyAyLFhh+stnTlBTXcWMRx7HzNoWzwGDGBYwhTNBh2RlbWnviPfgoZhZ2WBoYkafIX449epDcnysbDkXTx6jrKSYeU+/iJ2rOwYmpti5umPl0P667GnlpyPI++UPSkPDoV7a09m5pVNHDtJ32AgGjByLqZU1kxcuayjb0CCF6S+fPklNdRUPrXgCc2tbvAYMxm/SVE4dOSgr2ya7//yFvsNGYOPkqvjHlUBX30Dur6udDDxAP7+RDBw1FjMra6YuWo6OvgFnQxTHe+n0CWqqq5m16knMbWzpNXAwIyZP48ThA23i1dbTk4tFudXrUJW6Od4HKVboubrU5ZPhlJWUMOvJ57F1ccPA2BRbFzcsu/g4JZVKOR8WxKDxk3DtOwATK2v8F6+guqqSmKj26xuXwsPQ0TdgzOyFGFlY0XvYSDwHDyXqaHN9w8LekZEz5+Ix0Bc1ieKGtoefepFeQ4ZjYmWNiZUN/ktWytWvesKMQb3ZdvoiJ2OvczO3gC/2haIpUWOUl0u78yRk5nIsOpHk3AKyikoIuZrAueup9LK16Macdyx4326Gjh7H8PH+WNrYMm/l4+gZGnLs8AGF6dU1NFj42FOMmBCAQTu9xe2dXRjkNxIrWztMzC3wHTkGL59+JERf7cpQbulM0EH6NNY3TBrrGzr6BkS1U9+4crahvjF9eWN9o/8ghvq3rW/0GjQU08b6Ru8hfjh59SE5IVbhMoUWlJS77+8f6paR9erVi+rqauLj4wG4fPkyenp6XLrU3M3vypUreHt7k5eXx7vvvouTkxNff/01zz77LMeOHWPt2rVyywwNDQXg//7v/3jppZfkvpNKpaxevZp9+/bx8ccf4+7efve13bt34+DgwFdffcWsWbP4448/iImJARpahj/88ENUVFT47LPPeOGFF9i4caNc41FlZSX//e9/sbCw4Msvv2TZsmX8/vvvbfJjZGTEa6+9xg8//MCSJUvYunWrrNHH29sbCwsLjh5tPpnX19cTEhLChAkTbrV6O0VdbS2ZyTdw9PSWm+7o2Yu0dk56adcTsHVxkzuJOnl5U1pUSFFe7h39/rHd29E3NqH30LaPWnSGorwcyoqLsG8Rn5pEgo2Le7vxAWRcT8TBQ76rp4OnN1nJN6ira+ilkH49EXvPtmnSkuQbJ1qqrqxEKpWirqXVYRr+n737Do+i+B84/k659N5776H33jtEQYpSFAQURUVB5av+bNgVBXtXitK71NBD75BCSCNAeki7S08uufv9EbjkkksoaYjzeh6eh9ud2czndm92d3Z2BjBoIE19HqR4FQoFMedOU15WipOn+sVKamI8P7zxMn8sepPQ1cspKsi/pzgrKyrISL6OR63jtqHypF27iou3+nHrUeu4TUu8WmebnoFtybxR/T1oKktlhbzefapQKLhy63tw9qr/oq0p2JmZYGlsRERSmmqZvLKSK6mZ+DneW4OooZ4uRWXlasv0dHX4buZ4fpg1gYWPDMLD1qpJyt2QyooK0pOu4xWkvl+8gtqQcjVeY56UxATcfPzV9rV3cDsKpHlIc7I05klPuk7K1Xi1V3FiL53H1duPPWtWsuS1F/npvf8R9s9mKis0HwvCvamoqCDtxjW8g9upLfcObktyPfs2OTEed98AtX3rc3vfZlfv2zOH9lGYL6PfmPq711eUl7N04Ty+ev1FVn27mPSk640L6A5ux+sT3FZtuU9wO5IT4jTmSUqIx91P/Vj2aVM3XoCfP3ibz+fPZdnij0nUcNMjLy/ny9fnsfjVF/nr68Wk3bje+KDq8V+KFVr3Wiou/AIu3j7sW/c33/1vHr8teoujO7bUe85qKvk52RTny3Crce2gq6eHs7cf6dfqv97IuH4VN3/1awn3wDbcTLrRqDLLVddX9T8kbE4OFqZYmRhx4VqKall5RSWXkzMIdLG/6+142VsT6GxPZNK9P7RrDhVyOUmJCQS276C2PLB9RxJjY5rs7yRfu0pibAy+tc71Lane643Apr3eyEi6Tsqtc5kgNLc79rU0NDTE29ubyMhIAgICiIyMZMyYMWzcuJHc3FyMjIyIj49nxowZ7Ny5EysrK55//nm0tbVxdXVl+vTp/PDDD0ydOhWDW10r7e3tmTVrVp2/pVAo+Oabb7hy5Qqff/459vYNV44dO3ZkzJgxADg5ObF9+3bCw8MJCAjg0qVLpKam8uGHH2JtXdUyPHv2bLUePYcPH6aiooKXX34ZQ0ND3N3dmTRpEkuWVHev1tXVZdq0aarP9vb2XL16lSNHjjBs2DAAhg0bxr59+xg/fjwAFy5cQCqVMmDAgDt9vU2iuLAApUKBkZm52nIjM3OKYjR3xSzKl2FqYVUn/e11d9tT5lp0FFfOn2HmWx/cR8nvTlF+VUNA7e7SxqZmFErrf6e4KF+GW0CQ2jIjMzMUikpKCgsxMbegKF+mcbvFBbJ6t3tw42rsXNzqNGDcVllRweEt6/Bu0wFTy3u/GX4Q4s1KTWH1Vx9TUSFHT1+fR595EVtnF9V6z8A2+LbvhLm1Lfm52RzbsZn13y7myYXv3vWrJiW3j1sN5bmR39Bxa1krvfpxW1Qgw9201vdgqv491HZs+2Yk+gb4tO1Y63tIZtWX1d/D2GdfUnstrDlYGFe98la754usuAQrk7tvABzWzh8rE2OOXqlu/ErLy+fnfSe4kZ2LoUTCyI6BLJo0kv+t+ocMaUHTBKDB7TrKuFYdZWxmTmE9T+KKZLI6v5/bx26RTIaljZ1q+dcL51FcWICispJ+IePo3H+wal1eVhbXYq7QpltPJr/0KtLsbHavWUF5WSlDJ05pqhD/s6r3ba3fsZk5hdGa922hTIqZpXWd9ACF+TIsbe3ITEki7J/NzH5rUZ2eF7fZODjx6IxnsXd1o7y0lFMH9vDHZ4t4/r1PsbZvnifbxQUFKBQKTGodyybm5lyt0UOvpsJ8Gea1juXb+QtkUixt7TAxtyDkyZk4e3pRWVFB+MljLP/yE2YufBsP/0AAbBwcGTdzDg6ubpSVlnJy3x5+//R9Xlj0Kdb2jiLWRmrNaylp9k1uxGYT1LUnE+cuQJqTxb51fyEvK2PQ+CfuI5q7U5Rfde6vfR42MjWjUCZtIF8+rn518ygUlZQWFmJ8n72hDm9ai62zK46e3veVv7EsjavOsdKiYrXleUUlWJve+fy74oUpmBsZoq2txZpjF9h98UqzlPNeFRbko1AoMKu1X8zMLYiRhjd6+2/OeZrCfBmVlQpGT3yCfsNGNnqb90t1TjKte71xLaa+c5IMs9rXG2aarze++V/19UbfMerXG0I9xBg2jXZXL8e2bduWyMhIJk6cSFRUFI888gjh4eGq3jY6Ojr4+fmxdetW/P391S6ugoKCqKioID09HU9PTwC8vTVXxH/++Sfa2tp89dVXWFhY3LFcHh4eap+trKyQSqUApKSkYGVlpWqsAfD19VUrW3JyMh4eHhgaVo8JEhBQt6V09+7d7N27l5s3b1JeXk5FRQV2dtU/3sGDB/PXX39x5coVAgMD2b9/Pz169MCs1gVsc6vzc1AqNSyskb72OuW9db0vLixg58rfCZk5BwPjpnsaEn32JPvWVPfKeuz5V6r+U6vASqVSQxDq6n4ntzdVY02d7da/vUOb1pJ6NZ7JC97UeBOhqKxk54pfKSsuZtyz8xos220PYrxW9g489eb7lBUXE3fpPHv++oNJLy9UDbQcUGOwVltnF+xd3fn13YUkXo7Ar0PnuhtsqMwa4mwoTE3p6yyve3DfXlFne+cP7SX8+GEmvfQ6+obq4wNZ2Tsy/c1FlJUUE3fpHLtX/s7jr/xP9T00hd7+njwzuKfq8+fbbnXZrbVjtNC6659oNx83pvbtwre7j5BdUP3eeXx6FvHp1U+LYtOz+HxqCMPbB7Ii7Mz9B3GXNO7rBiqp2rtRWc+K6Qvfpry0jNRrCRzYtA4La1va3erxp1QqMTY1Y8xTs9DW1sbR3ZOSogL2rl/FkAmTxXSTTaTOfrzX80/13qVCLmfjr98zbNIULG3taidUcfX2xdW7+lUpVx8/fl70JqcPhDJqyvR7KP19uMd6q86XUavesnV0wtbRSbXazcePvOwsjoXuVDViuPn44VZjnDg3Hz9+eO9NTu3fy+ipzRjvfynWuqVv9mupqixV9dTIaU+jra2Ng7sHpUVFHNi4moGPPd5k9VTM2VMcWPeX6vOjc6quU+puX9lQyLfy1M6irGfF3QnbvI60xHgmvfJGvY20TW1AsA8vjuir+vz++qpXp2vvwrsNaeHf2zHQ0yXAyZ6nB3YjQ1bAoSjNvTpaRe3fMnf6Ld+dVz/4lLLSUq7Fx7Ll7xXY2NnTvf/AO2dsRnWO6Ttcb9RTbdX5zp56/W3kZWWkJCZwcPM6LGxsadejed4wEITb7qrBpk2bNuzcuZOkpCRKSkrw9vZWNeKYmZkRGBiIrq7urZO45h9DzeUG9Qxi1qFDB44cOcK5c+cYMuTOAzjVnjVKS0tLdfPWUFnuxdGjR/ntt9+YOXMmAQEBGBkZsXPnTk6dOqVKY25uTvfu3dm3bx/Ozs6cPn1aNaZPbXv27CE0NFTjupqsfILwuTVA6p0YmZiipa2telJyW3FBfp0n2rcZm5lrTH973d3ISkuhUCZl7TeLVctuf/+fvzCT2e98jLXDvT8J82nbQe297duvLhTlq7eAFxcW1HkqVFN9MWpr66gamDSmKczHyLTud3Bo0xpizp9h0ryFWNjUvYlQVFayY/kvZKel8PjL/1MbTLEhD2K8Orq6WNpW9XBzcPckI+ka5w/tZcTUmRr/tomFJSaWluRlZd4pXBXD+o7bwgKN339D5YfqJ4TGppq+hwK0tXUwNFFvWDx/aC9Ht29hwtz5GscK0NHVxdKu+ntIv3Gd8wf3MmKa5u/hfpxPTCYho7rrvORWvWZubEhOYfVTPjMjA2TFJXfcXjcfN14Y3pcfQ4+pzRCliVKpJDEzB0fL5h3U/XYdVfuJbVUdpfmYNjY3p1BWXx2lnuf20y97F1eK8mUc2b5F1WBjYm6Ojo6u2g2AjaMz8vJyigsL6vQ4E+6Nat/W+s0VFeTX6Zlxm4m5RZ19e7tnoYmZOQUyKVlpqWxd9itbb80CpVQqQalk0bNPMvXl1/Gp9QoWgLa2Nk7uXuTezGiK0DQyMjVFW8OxXJTfQLxm5hTmq6cvLKiOtz4uXj5EnjlZ73ptbW2cPTzJyWyeeP9LsULrXUtB1W9CW1tHrZ6ydnBEXl5OyR3O/ffCq20HHDw8VZ9rXm/U7NFYXFCAUQMPHY3NzFS/WVWewgK16417EbZ5LbEXzjLhpdcwb4axEOtzOv4GsTUG5b99/rU0MVJ72GFhZEhe0Z3Pv5myqp6qN7LysDA2ZGqfzg9Eg42JqRna2trk1+qlXSCT1el1cz9sbvVodHb3IF8mZceGNa3WYFN9TpKqLS9q4HrDxNyconu83rBzrnG9IRpsGiYejDXaXTXYBAcHI5fL2bRpE0FBQejo6NC2bVu+//57LCws6Ny56om6q6srx44dQ6FQqE460dHR6Orq4uBw5+7JXbp0oWfPnnz++edoaWkxePD9dzNzdXUlJyeHnJwcVS+bhIQE1WDBt9McOHCA0tJSVSPS7UGOb4uOjsbPz0/16hVARkbdC4Zhw4bx2Wef4eDggIWFBR06dNBYrhEjRjBixIg7ln/NiYt3THObjq4uDm4eXIu5TECN6R+vxVxWzYpTm7OnD4e3rqdCXo7urVkXrl25jIm5hcYZWzRxdPdi1tvqU3Me2b6J0uJihj3+5H0PQKxnYKg2E5JSqcTYzJwbMZdxdK+60KiQy0m9Gkf/sZPqL5+nNwkRF9SW3Yi5jL2bBzo6VYe+k6c3N2Ki6TZkZI000Th7qfcCO7hxNTHnz/D4yws1NkJVVlaw489fyE6vaqy5lwu1BzHe2pRKZYNjfhQXFlAozWvwgrw2HV1dHFw9uB5zGf9OXdXKXF8vHSdPb45s20CFXK569erGlWi149bJy5v4cPXfz/WYy9i7V38PAGcPhHJ8xxbGz51/97ObKRVUVGieJeJ+lcorKJWpv46UV1RMOzcnEjNzAJDoaBPgZMeqY+cb3FYPX3fmDu/Dj6HHOJ1w467+vpuNJTeyc++v8HdJR1cXRzcPEq9EEVSjd1Zi9GUCOmmuo1y8fDiweZ1aHZUYHYWphSUW1vXXLUqlUm0fufr4EXXmJEqFAq1b56WczHQkenoYmYjZBxtLV1cXJ3dPrkZHqk2TfjU6iqAav+uaXL182bdpDXJ5uWrWn6u3962NLYrKSp5f9JlanrOH9pMYHcnjL8yvd/8rlUoyU5Kwd3VvoujqUsV7OZI2XXuoll+NjiSonumX3Xx82bthrXq8lyNV8dYnI+lGgwPt3o7XoZni/S/FCq13LQXg4uXL5bPq9VTuzQwkenoYNmE9pWdgoDbzk1KpxMjMnKTY6KqZMKm63ki7Gk+fsRPr3Y6DhzeJkern2aTYaOzc3NXOs3fj8KY1xF04w4SXXseqmV53q09JubzOzE+5hcV09HBW9UaV6OgQ7OrAHwfrH4RZE20tLSQ6D8YgqLoSCW5ePsSEX6JzjfEmYyIu0bF7zwZy3julQkmFvGmvk+7F7euNa9FRBHWuPiddu9I81xuVTXxNKAia3FVNcnscm8OHD9O2bdXgcwEBAWRnZxMbG6taNnr0aHJzc/npp59ITk7m7NmzrFixgjFjxtTbq6a2bt26qQb4rTmQ773q0KEDzs7OfP3111y7do2YmBh+//13dHR0VD1v+vfvj46ODt988w03btzg4sWLrF+/Xm07Tk5OJCYmcu7cOdLS0li7di1RUXXf3e7YsSOmpqasWbOGIUOGtFh3ztu6DR5O5MljhB8LIzs9jX3rV1Eok9Kxb1UL9+GtG1jz9eeq9EHdeiDR02Pnit/JSk0h9uI5Tu3dSdchw9V6JmUm3yAz+QZlpSWUFhWRmXyD7PRUAPT09bF1dlH7p29ohJ6+AbbOLug00XSUWlpadBo4lDP7dhF36TxZaSmqqZwDa9wg7Fr5G7tW/qb63L7PAAqkeRzcuJqcjDQiThwh6vRxug4erkrTacBQkuKucDp0JzkZ6ZwO3UlyXAydB1YPGL1/3V9EnTrGmBlzMDAypihfRlG+jPKyqjFGFJWVbP/jJ9KvX2XM08+hpaWlSiMvVx/w9d8Q75FtG0hJiEOWk01WagpHtm0kOT6WwC5VF+zlZaUc3ryOtMQEZDnZJMXFsOXnbzEyNcO3fad7irXL4GFEnTpGxPEwcjLSOLBhFYVSKe37DFSVZd03X6jSB3Xtga5Ej91//U5WWgpxl85xet9OugyqPm7b9xlIoTS3+ns4HkbUqWN0HVzdUHpm326ObNvAiGlV05gWymQUymSUlVT3aAnbWvN7SObItg0kxccS1LVpL2402X3xCo90aUNXbzdcrC14flgfSuUVHI9JVKWZO6wPc4dVX3j19PPgxRH9WHPsAldSMzE3MsDcyABj/epB9MZ3b087dyfszExwt7VkztBeuNlYsj9C8wCiTanH0JGEnzjKxaOHyUpPJXTtXxTI8lTvfx/YvI6/lnyqSt+mWy8kevpsW/YrN1OTuXLhLMf3bKf7kBGqfX3m4F7iIi6Sk5lBTmYGF48d5uTeXbTt0Vu1nc79B1NSVEjour/Jzkjn6uUIwv7ZTJcBQx7Y16G0DA3Q8/FCz8cLtLWQ2Nuh5+OFrn3LPXm+Fz2HjuTS8SOcP3KIrLRUdq9ZSYE0jy4Dqvbt/k1rWfHlJ6r0bbtX7dutf/5CZmoy0efPcmz3P/QcOhItLS10dHWxd3ZV+2dsaoaOrgR7Z1f0b11PHP5nEwlREeRm3SQ96Trblv9GZmoyXZp5TIFew0dx8fgRzh05xM20VHauXkGBNI9ut+Ldu3EtyxZ/rErfrntvJHp6bP7jZzJTkrl8/gxHd22n97BRqmPwxN7dRF84S05mOpmpKezduJYrF8/RffAw1XYObttEfFQ4uTczSU+6ztZlv5KRkkzXAc0X738pVmidaymAjv0GUlpcxL4Nq8jJSCcxOpJjO7bSsd+gZq2ntLS06Nh/COf27SYh/DzZaansXfUnEn19Amrc7Ib+9Qehf/2h+tyuT38KpHkc3rSW3Iw0ok4cIfr0cToPqr7eqKyo4GZKEjdTkqiQyykuyOdmShLSGj1xD65fRfSp44yc/iz6Gq6vWsO2s5FM7NmBXn4euNtYMn/MAErK5YRFVw/CvGDMABaMGaD6HNI5mK4+bjhZmuFkacawdv481r0dhy633mxXtQ0e8ygnDx/k2IG9pKcks/7P35Dl5tL31ngzW1et4OtFb6vlSU9OIvlaIoUFBZSVlpJ8LZHka9XXIYd27yDy/FlupqdxMz2N4wf2sn/7Frr1HdCSodXRfchIwk8e5eKxw2SnpxK6rup6o1O/qvrj4JZ1/F3jeiP41vXGP8urrjdiLpzlRKj69cbZg3uJj7hIbmYGubeuN07t20Xb7r01lkGo4V80S9TOnTuZNWsWjz32GK+88gqXL9/djGdpaWlMmjSJiRPrb+hujLu+o27bti1xcXGqxhk9PT38/f2Jj4/Hz6/q6bS1tTXvv/8+y5YtY968eZiYmNCvXz+eeuqpeyrU7Uabzz+vOikOGjTonvJDVffZ//u//+O7775jwYIF2NvbM3PmTD799FP0bo0CbmhoyLvvvsuPP/7IK6+8gouLCzNmzODDDz9UbWfEiBFcu3aNL7/8EoBevXoxduxYtanBoeqkN2TIEFWDTUsL7NKdkqJCju/+h6J8GTaOzkx8YYHqCU+hTEpeVnW3TwNDIx6f9zp71/7F8s/ex8DImG6DR9BtsHrvn2WfvKf2OSHyEmZW1sz9+KvmD6qGbkNGUlFezoH1f1NaXISjhxcTXnxVrWdKfq56LwELG1vGPz+fQ5vWEH7sMMbmFgyaMAW/Gk/KnL18GPP0cxzfsZnju7ZiYWPHmJnP4ehR3ePk0tFDAKz/brHa9nuOfITeo8dSIM0jIaLqSdNfny9SSzNi2kza3EdXydaMtyg/n50rfqO4QIaegSG2zi6Mf34+nrdG3NfS0iY7LYXLZ05QVlKMsZkFbn4BhMx6Xq18dyOgc3dKioo4uWe76rgdP3d+jeNWhjS7+rjVNzRi0kuvsX/d3/z1+SIMjIzpMmg4XWo0SlnY2DJ+7nwOblrDpaOHqqY6nzhV7QnpxSMHqhra/vxJrTzB3Xsz6qnZt74HGTuX/0pRgQx9A0NsnF2ZMHc+nkHqM6Y0h3/ORaGnq8PMQd0x1tcnISOLT7bso1Re3cvJxky92/nQdv7o6mgzfUA3pg+ofjocnZLBBxurXsM01tfjmcE9sTAypLi8nOtZuSzauIermfc2M9z9CO7ag5KiQo7u2kahTIqtkwuTX3oNi/rqKCMjpr7yP/asWcHvH7+HoZERPYaOpMfQ6t5hSoWCA5vWIcvJQltbB0tbOwY/9jid+1WfM8ytrJn6yv/Yt34Vv334f5iYmdOhd3/6jn602WO+XwYBfrjUqG+sZz+F9eynyN+1l8xPWrbuvRttuvWkuKiQIzu3UiiTYufkwtSXX1c9mSyQScmtcZNmYGTEUwveYOeq5fz64TsYGhvTa9goeg4bdU9/t7S4mO0rf6cwX4a+oRGObu48vfAdXO7QY7Cx2nbrSXFhIWHbt1Agk2Lv7MKTryxU9SAplEnJvake7/TX3mTH38v5+YO3MTA2ptfwUfQaXh1vZWUFoetXk5+Xi0RPDzsnF5585XX82lUPhF5aXMy2FX9QKJNicCveWf97B5dmnLnuvxQrtN61lJmVNY+/9BoHN61l2SfvYmxmTttefek98pFmjRegy5ARVMjLObhhNWXFRTi4ezFu7gK1njj5eTlqecytbRk752XCtqwj8tb1xoDxk/Gt0Tu2UCZl9RfVE1JEZocReTwMZx8/Js6rmgAk4ljV9dWm79Xrte4jQug5qnXq6I2nwtHT1eX54X0wMdAjNu0m76zdpdYTx9ZM/ZV3bW0tnh7QDXtzUyoVStKl+Sw/fIZdFzQPVt0auvTuS1FhAbs3rSc/LxdHV3deeOtdrG+NEybLyyOr1iuH33/6Abk1jvdPFr4CwE8b/gFAoahky9/Lycm6iba2DrYODoydOp2+Q+/8JkFzun29cazG9cYTL9a63shW/x1PfeV/7F69gj8+uXW9MWQk3Wv0RlcoFBzYrH69MWic+vWG8O92exiU559/nqCgIHbt2sX777/PDz/8oDZ2bW1yuZwvvviC4OBgjZ06moJWSUnJvY+O9i917do15s2bx9KlS/HxafqT/o8//kh6erpag8/9updXoh4GFZWKOycS/pUe1F4MzWH/A/Cueksa0zGwtYvQYrr/n+ZxyR5W5z79+M6JHhI6YgaLh1ZR2b33cv03q9mo/7DbceHBmIGppbw6un9rF6HFpObWP1Prw2hij7pjsj1MytJabnp7faf7f63y1VdfxcPDg5deekm17Nlnn6V3795Mn17/YPe//fYbRUVFtGnThl9++YUNGzbcdxnq82C8XNlMTp48yYULF8jIyCAiIoKvv/4aT0/Pemepul9FRUWEh4dz8OBBHnmk+Z+GCIIgCIIgCIIgCILQOHK5nISEBDp27Ki2vGPHjly5Un/j8NmzZzl79izPPvtss5avaQYZeUCVlJSwfPlysrOzMTExoU2bNsyePbvJn/h/9NFHxMXFMWzYMLp21TzIoiAIgiAIgiAIgiD8Z7RgT/u7nY15+PDhapMA5efno1AosLCwUEtnYWFBeHi4xm3k5uby/fff8+abb2JkZNSoct/JQ91gM2jQoPsa/+Zeffrpp3dOJAiCIAiCIAiCIAhCk7vb2Zjrcy+dOr766itGjhxJQEDAff+9u/VQN9gIgiAIgiAIgiAIgiBoYmZmhra2Nnl5eWrLpVJpnV43t0VERBAVFcWaNWtUyxQKBY8++ijPP/98oxqOahMNNoIgCIIgCIIgCIIgNC3tB3/IXIlEgo+PD5cuXaJPn+rZfS9dukSvXr005vn+++/VPp86dYr169ezZMkSrK2tm7R8osFGEARBEARBEARBEIT/pLFjx7JkyRJ8fX0JCgpi9+7d5ObmMnJk1fTuK1asIC4ujo8/rppF093dXS1/fHw82tradZY3BdFgIwiCIAiCIAiCIAhC02rBQYcbo2/fvuTn57N+/Xpyc3Nxd3fnvffew87ODqgaZDgjI6NVyiYabARBEARBEARBEARB+M8aPXo0o0eP1rhu/vz5DeYdMmQIQ4YMaY5iiQYbQRAEQRAEQRAEQRCamPa/o4fNg+zBHwVIEARBEARBEARBEAThP0b0sBEEQRAEQRAEQRAEoUlpaYn+IY0lvkFBEARBEARBEARBEIQHjOhhIwiCIAiCIAiCIAhC0/qXzBL1INMqKSlRtnYhhLqWhZ1t7SK0KKXyv3MY6un+t9pJyysqWrsIQjP5Lx3Lxvp6rV2EFtXlzf9r7SK0mFUvvdLaRWhRCv4759sFHX1buwgtaltqbmsXocWEJ6W1dhFalIFE0tpFaDHyisrWLkKL+nDS8NYuQrMqz81rsb+lZ2XZYn+rJf13rrYFQRAEQRAEQRAEQWgZYpaoRhNj2AiCIAiCIAiCIAiCIDxgRA8bQRAEQRAEQRAEQRCalpglqtHENygIgiAIgiAIgiAIgvCAET1sBEEQBEEQBEEQBEFoWmIMm0YTPWwEQRAEQRAEQRAEQRAeMKLBRhAEQRAEQRAEQRAE4QEjXokSBEEQBEEQBEEQBKFJaWmJV6IaS/SwEQRBEARBEARBEARBeMCIHjaCIAiCIAiCIAiCIDQtbdE/pLEeim9w9erVvPDCC63yt998801+/vnnVvnbgiAIgiAIgiAIgiA8nP51PWxCQkJ444036N27d2sXBYC33noLHR0d1edZs2YxevRoHnvssRYvi1Kp5NTuf4g6cYTSkmIc3D0ZNHEq1o7ODeZLiY/lyJZ15GSkYWxuQZfBI2jXZ4BqfU56Kid3/cPNlBvk52TTfUQIPUc9qraNk7u2cXrPdrVlRqZmPPvxkiaLrzalUsnpPdvV4h04Ycqd402I5eiW9ap4Ow8aXifeU7v/4WZKkireHiMfUdtGeWkpJ3dt5WrERYoLC7BzdqPfY4/j4O7Z6LguHjnI2f27KZRJsXF0ZtCEKbj4+NWbPis1mf3rV5FxIxEDI2Pa9xlAz5GPqL0zmhwfw6FNa8lOT8XE3JJuQ0fSoe9A1fqok8fY/fcfdbY9/+tf0ZVI6iw/tWcHR7dvomO/QQx5/MlGRnxnzbWvo04c4crZk+RkpKFUKrFzdqPHqEdx9vZt5oiqteZx/OeiNyjIzamzbY+gtjw6Z16jY2uNYzn2wllO79uFNCsTRWUlFrb2dBk0jDY9+qjSXAg7QPixw+TnZgNg7ehMzxEheLdp3+iYazpzaB8nQndSIJVi5+TMiCeexN0voN70mSlJ7Fq9gtRrVzE0NqFz/0H0HzNO4/vfN+JjWb74I2wcnHjhg89Vyy8eD2Pbsl/rpP+/n5Yhkeg1TWBNzKB9GywnT8DA3xddWxsyPv6Sgt37WrtYTWJAsA+dvV0xkEhIzZWy83w0WfmF9aY3MdBneIcAHC3NsDIxJuJGKlvPRLZgie/ewGBfOnu7YiiRkJIrZcf5y3eMbUSHQBwtzbA2MSb8RipbzkSopbE1M2FQG18cLc2xMjHiUFQ8hy7HN3coDdq4cyd/b95MTl4unm5uzH/mGToGt7ljvqS0VKa/8gpKpZLDGzaqlh86cYLNu3cTl3iVcrkcT1dXZkx6nH7duzdnGBqdP7yfU/t2USiTYevkzJCJU3Hz9a83/c3UZELXriT9eiIGRiZ07DeQPqMeVdVRMRfPcvHIITKSb1Apl2Pj6ESvkY/g176T2nbKSkoI+2cjMRfOUlJUiJmlFf0fnUhQl5b/DgCGtw+gh58HRnp63MjOZdPpcDKlBfWmNzXU59EubXG2tsDW1IRziUmsPX5BLU1Xbzcm9+lcJ+/Cv7ZRoVA0eQx3a3BbP7p6u2GoJyE5R8o/5yK5Kav/d2tqoM+oTkE4WZpjbWrMxespbDoVrpami7cbnTydsTM3RUtLi/Q8GfsiYrmRldfc4dzR0Pb+dPf1wEhPQlJ2HltOR5Apa3jfhnRpg7OVOTamJlxITGbdiYv1pu/g4czUfl2ITslg2cHTzRHCv58Yw6bR/nUNNi1JLpcj0XCzWpOpqWkLlebOzu3fw4VDexk2dSaWdg6c3rOdzT8sYfrbH6NnYKAxjywni62/fENwjz6MeGo2qYkJHFq/CkMTU3w7VJ1o5OXlmFlb49O+Eyd2bqn371vaOTBh3uuqz1pazduB6/yBqniHTnkaSzsHzoRuZ8uPS3nq/z5qMN5tv3xLcPfeDH9yNmmJ8RzasLpuvFY2eLfrxMldWzVuZ//aFWSnpTBs6kxMLCyJOXeKLT8u5ck3F2FiYXnfMcWcP83BDasZ8sSTuHj7cvHIQTb+sISZ73yMmZV1nfRlJSWs/+5LXH38mbbwXXIzM9j91x9I9PTpOmQEANLsLDb9uJQ2PfsyesazpFyNZ//avzA0McW/YxfVtiR6esx+/wu17WtqrEm7dpWIE2HYOrved5z3qrn2dUpCLH4du+Lo5YNEoseFw/vY+vPXTHn9XSzt7P/Vsd3NcfzEq/+HssaFY1G+jDVffoRvjePifrXWsWxgbEzPESFY2TuiraNDYtQl9qxahpGJKV63GmRMLSzpP3Yilrb2KJVKLp8+ztZfvuPJN97DromO66gzJ9mz9i9GT52Bm48/Zw/v5+9vvuCFD77AwtqmTvrSkmJWLvkMd78Annn7Q3Iy0tn65y/o6enTa/hotbQlRUVs+eMnvAKDyc+re0Es0dNn3qfqjeUPamMNgLahIeWJNyjYsx/7t1+/c4Z/id4BXvT092TrmQhyCoroH+TDUwO68t2uI5RXVGrMo6utTXFZOceuJNLZu+Xq2HvVJ8CLXv6ebDkTQXZBIQOCfJk+oBvf7gq7Y2xHr1yli7ebxjQSXR2kRSVEp2QyuG39jbstZd/RIyz57VcWPv887YOC2bRrJ/Pff5+1P/yIg51dvfnkcjlvf/EFHYKDuRgVpbbuQlQkXdq147knp2FmYkpo2GH+98nH/PjJJ3fVENRUos+dYt/6VQyf/BSuPn6cDzvAuu+/5Nn3PsXcqm4dVVZSwppvvsDVx58ZbywiNzOdHSt+Q09Pn+5DRwKQFBeLu38g/R8Zj4GxCZfPnGDTz98wdcFbqoagysoK1nz7BQZGxox75gVMLawokOaio9vw9XZzGdTGl/7BPqw9doGb+QUMax/Ac0N789mW/ZRVVGjMo6utQ1FZOQcj4+jh51HvtsvkFXyyea/astZsrOkX6E2fAC82nrpEdn4Rg9r4MnNgD5bsOFTv71ZHR5uisnLCohPo6qP5d+tlb01EUjo3si4jr6ikd4AXTw/szne7j5JTUNScITVoQLAP/YJ8WH/8AjfzCxnazp9nhvZi8dYDDexbbYpKyzkUFU93X48Gt29lYsTozsEkZmY3Q+kFoVqj7qjPnTvHpEmTqKys+pGnpaUREhLCjz/+qEqzcuVK3nnnHQCSkpJYtGgRkyZNYtq0aSxevJi8GhebcXFxvPPOO0yZMoVJkyaxcOFCYmJiVOtnzZoFwGeffUZISIjq821HjhzhmWeeYdKkSXz00UfIZDK19fv372fu3Lk89thjzJkzh61bt6KoUXGGhISwc+dOPvnkEyZMmMDKlSupqKjgl19+Yfr06YwbN46nn36a5cuXq/LUfCXqzTff5ObNmyxbtoyQkBBCQkIa8/XeE6VSycWw/XQdMhLfDp2xcXJm+LSZlJeVEnO+/hbfiGNhmJhbMHDCFKwcnGjbqx+B3Xpy/mCoKo2Duyf9xk4ioEt3JHr1X/Rr62hjbGau+mfUjI1ZVfEeoEuNeIdNrYo3toF4I4+HYWxmwYAJU7BycKTNrXgvHKo+oTq4e9J37MSqeDXc5FSUl5MQfoHeIeNx8fXHwtaOHiMfwcLGlojjhxsV17kDe2nTozfte/fH2sGJIZOmYWxuzqWjBzWmjz57kgp5OSOfmo2tkwv+HbvQfehIzh0MRalUAhB+7BDG5hYMmTQNawcn2vfuT3CPXpw9sKfW1rQwMTdX+1dbWUkxO5b/wvCpT2NgZNSoWO9Wc+7rEU89Q/t+g7BzccPS3oFBk6ahp2/AjZioerfblFrzOAYwMjFV+81ej45Ez8BA1ejTGK11LLv7B+HbvhPWDo5Y2trReeAwbJ1dSLkap0rj274TXsHtsLSzx8regb6PjEfPwIC0xIRGx33byX276dCrL537DcLWyZlRU6Zjam7BucP7NaaPPHUCeXkZ42Y+h72zK0Gdu9F75BhO7tutiv+2bct/pUOvvrh41dMTTAtMzS3U/j3Iik+dJefXZRQePgYK5Z0z/Ev08HPn2JVErqRkclNWyJYzEejp6tLW3anePNLiEnZfvMKl66mUlMtbsLT3pqefB0evXCU6JYObskI2nwlHX1eXdneIbdfF6AZjS8uVERoeQ2RSGvJKzTeQLWnN1q2MGTyYscNH4OnqymtznsPa0pJNu3c1mO/75cvx8fBkcO8+dda9+uwcpk+cSLCfP65OTsyePIUAb2+OnDrVXGFodGb/Htr17EPHvgOxcXRm+BNPYWJmwYUwzXV01JmqOipkxrPYObsQ0KkrPYaP5vT+Pao6atjj0+g1IgQnT2+s7OzpO2YcDm6exIWfV20n4sRRigvymfj8K7j6+GNhY4urjz9OHl4tEndt/QJ9OBgZR0RSGhnSAtYcO4++RJdOXi715skrKmbLmQjOXk2iuKzh32lBaZnav9bUK8CTsOgELidnkCkrYMOpS+hLdOngUX+PXmlRCTvOX+bCtZR6f7frT1zkVNx10vPyyS4oYtvZSMrkFfg52jZXKHelb6A3h6LiiUxKJ1NawNrjF9CX6NLRs/5484pK2HY2knNXkykuL683nbaWFlP7dmHPxSvkFhQ3R/EfHlpaLffvIdWoBpvg4GDKy8uJj6/qrhoZGYmZmRkREdVdXKOiomjTpg25ubm88cYbuLu789VXX/Hhhx9SUlLChx9+qGo0KSkpYeDAgXz++ed89dVXeHl58f7776saXpYsqXpi+OKLL7Jy5UrVZ4CbN29y9OhR3nrrLT744AMSExP566+/VOtDQ0NZuXIlU6dO5ccff2TWrFls2rSJXbvUT7pr1qyhc+fOfP/994wePZrt27dz6tQpXn/9dX755RcWLlyIi4vmSvytt97CxsaGJ554gpUrV7Jy5crGfL33JD8nm+J8GW4Bwaplunp6OHv7kX6t/puQjOtXcfMPVlvmHtiGm0k3qKzU3PpcH1l2Nr+98xp/vv8Gu5b/giw7696CuAeqeP2DVMuq471ab77064m4BwSpLXMPCL6neBUKBUqFAl1d9Q5qOhK9Rt3wVVZUkJF8HY9A9SdsHoFtSE3UHFPatau4ePupNaR5BLWhUCZFllPV4p+WeLXONj0D25J547pazBXycn55+zV++r8FbPrpazKTb9T5e6Grl+PfsQvu/kF11jWXltzXlZUVVFTIMTA0bprC30FrHse1KZVKLp86RkCXHkj09O9rG7e19rFcM6YbMdHkZWbg4qO5m79CoeDKudOUl5Xi7OVzT3HWp6KigrQb1/AObqe23Du4LclXNb/ekZwYj7tvgFr8PsHtKJDmIa1Rl545tI/CfBn9xoyr/++Xl7N04Ty+ev1FVn27mPSk640LSLhnlsaGmBoacLXGk9eKSgU3snJxtbZovYI1gduxJWiM7f57mD5o5HI5MQkJdO+o/jpP946diLwSU08uOHb2LMfOnuHVZ5+9679VXFKCqYnJfZf1XlVWVJCedB3PoLZqyz2D2pCSqLmOSk1MwNXHX62O8gpqS6EsT1VHa1JeVoKBUfU5Ne7SeVy8fQld9xffLHyJX95/gyPbN9/3uasxrEyMMDMyIDbtpmqZvFJBYmYOHrZ1e4LeK4mODm+PH867E0Ywa1BPnK3qPghrKZbGRpgZGhCfXn0+qahUcO1mDm42Tfu71dHWRldHp1UbnW/v27ga+7aiUsG1zGzc7awavf2RHQPJLSrmfGJyo7clCHfSqAYbQ0NDvL29iYyser86MjKSMWPGkJWVRW5uLqWlpcTHx9O2bVt27dqFp6cnM2bMwNXVFU9PTxYsWEB8fDwJCVU3ue3bt2fQoEG4urri6urKnDlz0NPT48KFqvdCzW898TcxMcHS0lL1GaCyspJXXnkFT09PAgICGD58uFrD0dq1a5kxYwa9e/fGwcGBbt26MWHChDoNNn379mX48OE4ODjg4ODAzZs3cXJyIjg4GDs7OwIDAxkyZIjG78PU1BRtbW0MDQ2xtLTE0rLlLlyK8qsatYxMzdSWG5maUZSf30C+fI15FIpKSgvrf6e1NgcPL4ZNfZqxz73MkMlPUZQvY93STykpuvtt3IuiggbiLZBpygJAcb6s0fHqGRjg6OHNmb07KZTmoVAoiDl7iozrV1X74X6UFBagVCjqlM/Y1Kze7RZpiMfY1Fy1Dqq+K+N6Yi65FbOlvQMjps1k7Jx5hDz9HLq6ElZ/9Ql5NzNUecKPhyHNukmfMS07PlNL7uuTO7eip6ePZ9umHcukPq15HNeWFBtNfk622lgv96s1j2Wo6gn29fznWDLvGTb9tJRBE6fiVavxJCs1uSrNy8+wb+0Kxj77UpO95ld8K35js1rxmJlTKNMcf6FMirGZeZ30AIW34s9MSSLsn82Mnz0X7XpmXbBxcOLRGc/yxIsLmPDMi+hKJPzx2SJyMjM0pheah4lBVaNnUa0n6kWl5ap1/1b1xVZYWobpvzy2mqT5+VQqFFhZWKgtt7KwIEeqeWyO7NxcPv3+O95f8CrGd9kLdcPOHdzMyWHkwEGNLfJda6iOaqiOrpu+6nNhvlRjnnOH91OQl0fb7tXjTuZlZ3Hl/FkUlRVMemEB/R8Zz8Wjhzi8ZUMjIro/ZoZVrx3X7vlSUFKGqWHjjuWb+YWsPXGBPw+e4q8jZ6morOSlkf2wMW2ZB0K13Y6nUMPv1qSRsdY2tJ0/5RUVXEnJbNLt3ov64i0oLcPUUPPr5nfLz9GW9h7ObK41lo9QD23tlvv3kGr0GDZt27YlMjKSiRMnEhUVxSOPPEJ4eLiqt42Ojg5+fn5s2LCBy5cvM3HixDrbSE9Px8/PD6lUyt9//01kZCRSqRSFQkF5eTlZWXfuqWFnZ4excXUlaG1tjVQqBUAmk5Gdnc0PP/zATz/9pEpTWVlZp6u5j4/6E9bBgwfz7rvvMmfOHDp27EiXLl3o3LlzvRfLLSXm7CkOrKvuQXR7gNC6g1MquVMHsbpZlPWsqF/tpzQOHl4sW/QmV06foNOgYXe9nfrEnDvFwXV/qz4/MuelW0VUL6NSqUTrThHXzsO9xzvsyZnsX72CP95biJa2NnYubvh16kZWStJdb6P+4mmIqYGiaUpfZ7mG4+LWCgCcvXzUehc4efmw4tN3uXD4AIMnTSU3M52j/2xk8vy30NFt3qGvWmtfXzy8n6jjRxj3wgL0DQzvo+R39qAdxzVFnTyKvZsHti6a31G/H61xLAPo6Rsw/c1FlJeVkRQbzaFNazG3slHrlWRl78j0NxdRVlJM3KVz7F75O4+/8j9snervBn+v6uxDpZKGdmvd76b6/FQhl7Px1+8ZNmkKlrb1j53h6u2La41Bs119/Ph50ZucPhDKqCnT76H0wr1o6+5ESOfq3qqrjla9AqKs/YbXv7DHdjt3J0I6V/dsW3X0HKApNi0enhfaatDwu6yvfn7vqy95bORI2gbUP7h4TQePH+e7P5fx0cKFODYwJk7z0VBHNXCQ1q3T6lkOxFw4y8FNaxk7ey7mNcftUiowNjVl1LRZaGtr4+juSUlRIfs3rGLQ+Cc0DrLeVDp5ujCxZ0fV598PnFCL47amKMKNrFxuZOWqPl/PyuG1kEH0DfSuM9h2c2jv4czYrtXX5ivDzmhMp4VWnfgbo5e/J9183fjz4Ol6x4lpDh09XRjfo/ph258Hq14xrH2fVxXv/QdspK/HpN6dWH303AP92qrwcGn0nVebNm3YuXMnSUlJlJSU4O3trWrEMTMzIzAwEF1dXRQKBV26dGHmzJl1tmFx6+nF0qVLkUqlzJ49Gzs7OyQSCW+//TYVd/GDrzlT0223f6S3X7l64YUXCLjDSdSg1iCfPj4+/P7771y4cIGIiAiWLl2Kp6cnH3744X012uzZs4fQ0NA7pjP19Mezff3jSHi17YCDR/WMRJW3vqOifBmmltVd/YoLCjCq9USkJmOzuj1wigsL0NbWwcD4/p8C6OkbYO3gRF5W07Sue7XpgIN79fvNlRVVlWTteEsKC+o8pa/JyMyc4lpPj0oK7j1eCxs7Jsx7HXlZGeWlJRibW7Br+S+YaRhM9G4Zmpiipa1d5+lWcWEBRqaau9FqehpWXFi1P29/D8amGtLcitnQRHPM2traOLh5qvZfWuJVSgoLWfbx26o0SoWC5IQ4Lh07zCtLftY4QPH9aI19ffHwfk7u2sqjc15ukpm+6vOgHce3FRfkkxh5iYETptxzXk1a+1jW0tZWDRpt7+pGTmYap0J3qDXY6OjqqtI4uHuSfuM65w/uZcS0uueoe2V0K/7CWmUtKsjHxExz/CbmFnV639yum03MzCmQSclKS2Xrsl/ZemsWKKVSCUoli559kqkvv45PrV5EUPVbdnL3Ivem6GHTnGJTM0nNkao+69y6PjAx1Ce/pFS13Fhfr84T3wddTGomKXcRm8m/MLaGWJiZoaOtTW6eVG15rlRWp9fNbeciIrgYFcUfa9YAVffBCoWCXo8+wuvPz2XciBGqtAePH+f9JUt4b8H8Fp8hyqieOrqoIL9OL5rbjM3MNdZpt9fVFHPhLP8s+4WQGc/WmSHK2NwCHR0dtWtoawcn5OXlFBcW1OlF2ZQuJ2eQlF09Ro+OTlUZTA31kRaXqJabGOhTUNK0x7JSCck50hbrYXMlJYPk7OqeYLq3YjUx0EdWXKNOMmi6320vf0+GtvNn+eHTanVGS4hOziCpZrzat/etgVq8JgZ6jRpLyMHCFHMjA54d2ku17HYj42fTQvjqn0MNzpb3X6R4iMeWaSmNbrAJDg5GLpezadMmgoKC0NHRoW3btnz//fdYWFjQuXNVo4O3tzfHjh3Dzs6uztgft125coVnn32Wrl27ApCXl6c2KDGgavy5F5aWllhbW5Oens6gQffe5dTIyIg+ffrQp08fBg8ezGuvvUZ6ejrOznUHrbpT+UaMGMGIGifs+iwLO9vgej0DA7UZZJRKJUZm5iTFRqtuOCvkctKuxtNnbN1eTbc5eHiTGKk+XV1SbDR2bu7o6Nz/4VEhl5N7MwMX37t7ynQn9xTvoxPq3Y6jhxdXIy+pLWtMvBJ9fST6+pQWF3Ej5jJ9Hqn/b9+Jjq4uDq4eXI+5jH+nrqrlN2Iu41fPILBOnt4c2baBCrlc1WBy40o0JuYWqidaTl7exIer7+PrMZexd/eoN2alUklWarLqFRGf9p2Y4e6hlmbPX39gaWdP9+FjmrTXTUvv6wuH9nJq1z88Omdes0/n/aAex9FnTqCjq4tfp273nFeTB+lYBlAqlKpG7QYSUVHRNE/LdHV1cXL35Gp0JME1pqm9Gh1FUI3voyZXL1/2bVqDXF6uGiT6anQUphaWWNjYoqis5PlFn6nlOXtoP4nRkTz+wnwsrDUP7qhUKslMScLe1b1JYhM0K6+oJLdQfeDJgpJSvO2tScutusnV1dbG3daKveH1j3/yIKovNh97G7XY3Gwt/3WxNUQikRDg48PpSxcZ3Kf6VdEzly4ysFcvjXlWf/+92ucjp06zbP16li35CtsaD3T2Hz3KB18v5d1X5mscmLi56ejq4ujmwbUrUQR2rq73r1+Jwr+j5jrK2cuHQ1vWUSEvR/dWHXXtShQm5pZqPWiiz51mx4pfGTP9WbVt3+bq7cflMydRKhRo3bqpzr2ZgURPDyOT5p15tayigrIC9XNBfnEpfk52JN9qYNDV1sbLzprt55t+8gFHSzPScusfpqApafrd5peU4uNgS2qN362HnRV7Ll5p9N/rHeDJkLb+rDh8plWm86533zraqhqPdLW18bSzZuf5y/f9d5JzpHz5j/rA3CM6BGKoJ2HLmQhyC1tvVizh4dXo93puj2Nz+PBh2rat6noXEBBAdnY2sbGxqmWjR4+muLiYL774gtjYWDIyMrh06RLff/89xcVVFYqTkxOHDh0iKSmJuLg4Fi9eXKdxx87OjvDwcPLy8ii8h7EaJk+ezObNm9m6dSspKSncuHGDgwcPsmFDw+/Mbt26lbCwMJKTk0lLSyMsLAwjIyOsrTUPRmZnZ8fly5fJycmpM0tVc9LS0qJj/yGc27ebhPDzZKelsnfVn0j09QnoXH3DEPrXH4T+9Yfqc7s+/SmQ5nF401pyM9KIOnGE6NPH6TxouCpNZUUFN1OSuJmSRIVcTnFBPjdTkpDW6D1zZOt6UuJjkeVkkX49kZ1//kRFWRlB3TVf1DRNvIM5v38PCeEXyE5LZd+qZUj09fGvGe/ffxD6d3W8bXv3p1CaR9jmteRmpBN18ijRZ07QaWD1a1uVFRVkpSSRlZJERYWconwZWSlJSLOqBy67cSWK69GRyHKyuBETzabvv8TSzqHR8XYZPIyoU8eIOB5GTkYaBzasolAqpX2fgQAc2baBdd9UT70d1LUHuhI9dv/1O1lpKcRdOsfpfTvpMmi4qsW/fZ+BFEpzObhxNTkZaUQcDyPq1DG6Dq5uODy+cyvXoiORZt8kMzmJPX//SVZqCh36Vv1dAyMjbJ1c1P5J9PUxMDLG1smlWbswN+e+Pn8glOPbNzNkynQs7OwpypdRlC+jrKRlRvxv7eMYbg02fPIofp261TuN+P1orWP55J7tXI+5jDT7JjkZaZzdv4foMycJ6tZTlSZs6wZSEuKQ5WSTlZrMkW0bSIqPJahrdZrG6jl0JJeOH+H8kUNkpaWye81KCqR5dBkwGID9m9ay4stPVOnbdu+FRE+frX/+QmZqMtHnz3Js9z/0HDoSLS0tdHR1sXd2VftnbGqGjq4Ee2dX9G/tu8P/bCIhKoLcrJukJ11n2/LfyExNpkv/wU0WW1PTMjRAz8cLPR8v0NZCYm+Hno8XuvatO8NIY52Ku0GfQG8Cne2xMzdhbPe2lFdUEHkjTZVmXPd2jOuu3jPKwcIUBwtT9HV1MdST4GBhiq1Zyw1IezdOxl2nT6CXKrZx3dtRXlFJRI3YHuvejsfuMTYdbS1VGl1tbUwM9HGwMMXKpGVmJaxt8tix7DxwgG2hoVxLTuarX38hOzeXx0aOAuCHFct54f/eUqX3dvdQ+2drbY22thbe7h6Y3RpUeO+RMN796kvmTp9OxzZtyMnLIycvD1lBQYvG1m3ICCJOHuXSscNkp6eyd93fFMikdOpX9WDz0Jb1rFpa3Ugc3K0nEj19tq/4jZupKcRcPMvJ0B10HzJCVUdfPnuKf/78mQFjJ+Hm60+hTEqhTKo2nmGnfoMoKS5k7/q/yclIJ/FyBEe3b6ZT/8HNei1RnyNXEhjcxo+2bk44WJgyuU9nyioquJCYokozuU9nJvdRf9jgZGmOk6U5Bnq6GOnr4WRpjr15dYPTsPYB+DvZYWVihJOlOY/36oSTpTkn4661WGy1nYi5Rv9gb4JdHLA3N2VCz/aUyyu5dD1VlWZCzw5M6NlBLZ+jhRmOFmboS3Qx0pPgaGGGXY3fbd9AL4a3D2Tz6XCyC4owMdDHxEAffUnzvkJ/J0evXGVgG1/auDlib2HK4707UlZRycVr1fE+0bsTT/RW7wXmZGmGk6UZBhJdDPUlOFmaYXdr38orKsmUFqj9Ky2XU1ZRQaa0gMqHaKbDpqJQtty/h1WT/JLatm1LXFycqnFGT08Pf39/4uPj8fPzA6rGlPniiy9YsWIF7733HnK5HFtbWzp27Ijk1tPUl19+me+//5758+djZWXF5MmT6zR6zJo1i99//539+/djbW3NH3/8wd0YPnw4BgYGbN68mZUrV6Knp4ebmxtjxoxpMJ+hoSGbN28mPT0dQDVzVe1Xp26bOnUqP/zwA8888wxyuZzt27ffVfmaQpchI6iQl3Nww2rKiotwcPdi3NwFajdh+Xk5annMrW0ZO+dlwrasI/LYYYzNLRgwfrLatL6FMimrv/hA9TkyO4zI42E4+/gxcd7CqjTSPHav+JWSokIMTUxx9PDi8QVvYWbV+FH269N58Agq5HIObayOd+zz89XiLcjLVctjbm3Lo3PmcWTLeiKPhWFsbk7/x55Qi7dIJmX14g9Vn2XZWUSdOIKzjx8TXnodgLLSEk5s30KhNA99Y2N82nei1+ixjeqVBBDQuTslRUWc3LOdonwZNo7OjJ87X/X0qlAmQ5pdfcOtb2jEpJdeY/+6v/nr80UYGBnTZdBwugyubnCzsLFl/Nz5HNy0hktHD2FibsHgiVPx79hFlaaspIS9q1dQVCBD38AQO1c3npj/Bo6tNM1mbc21r8OPHUJRWcnu5b+q5Q3s1pNhUxv/aszdaM3jGCAlIRZp1k2GPzm7SeNqrWNZXlbKvrUrKZTmoSvRw8regVHTZxPYpUf1d5MvY+fyX1XHu42zKxPmzq8zFldjtOnWk+KiQo7s3EqhTIqdkwtTX35d1ROmQCYlt0ajt4GREU8teIOdq5bz64fvYGhsTK9ho+g5bNQ9/d3S4mK2r/ydwnwZ+oZGOLq58/TCd3Dx8m6y2JqaQYAfLt8tVn22nv0U1rOfIn/XXjI/+aoVS9Y4x2MSkehoM6pzEIZ6ElJyZPwVdpbyiurpqs2N6l5LPDdcvceFv7M90qJivt4R1uxlvlvHYhKR6OgwpnMwBnoSUnOkrAw7Uyu2umOBzR3eV+1zgLM9eUXFLN1xGABTAwO1NNamxnT1cePazRyWHTrdPME0YGjffsjyC1i2fh3Zubl4ubuz9L33VePN5OTmkZpxb68bbtm9m8rKSpb+9htLf/tNtbxTmzb89OlnDeRsWkFdelBSWMjxXf9QmC/F1smFx198tUYdLVVr4DcwNGLyywsJXbOSZZ++h4GREd2HjKTbkOoG84tHDqJQVLJ/wyr2b1ilWu7mG8C0V6satsysrJk8byH7N67mj4/fxtjMnHa9+tFn1KMtFLm6g1HxSHR0GN+9PYb6EpKy8vhl33G18Vcsjesey689ot5jv42rI7mFRXy0aS8AhnoSJvbsiJmhPiXlFaTmSvl+z1G113Za2pErV5Ho6hDStU1VnZQtZdmh02q/WwsNv9uXRvVT+xzo4kBeYTGLb/U06eHrga6Odp1GrfOJyWxqxYF5D19OQKKrw7hu7VT79rf9J9T2rYWGfTs/ZKDa52BXR3ILi/l0875mL7MgaKJVUlLyELdH/Xvd6ZWoh03tQcEeZnrNPGjvg6a8BQedE1rWf+lYNtbXu3Oih0iXN/+vtYvQYla99EprF6FFKR7OoYE1WtCxeV9zfdBsS829c6KHRHhS2p0TPUQMmmicwH8DeY0GpP+CDycNv3Oif7GisvIW+1sP67Xawzv/lSAIgiAIgiAIgiAIwr/Uf+fxqCAIgiAIgiAIgiAILeK/9BZFcxE9bARBEARBEARBEARBEB4wosFGEARBEARBEARBEAThASNeiRIEQRAEQRAEQRAEoUmJN6IaT/SwEQRBEARBEARBEARBeMCIHjaCIAiCIAiCIAiCIDQphehi02iih40gCIIgCIIgCIIgCMIDRvSwEQRBEARBEARBEAShSYlpvRtP9LARBEEQBEEQBEEQBEF4wIgeNoIgCIIgCIIgCIIgNCnRw6bxRA8bQRAEQRAEQRAEQRCEB4zoYSMIgiAIgiAIgiAIQpNSiA42jaZVUlIivsYHUFFZeWsXoUWFhse2dhFaTJCLfWsXoUVJi0pauwgtJjI5o7WL0KIMJP+dNn9zI4PWLkKLik652dpFaDFTv/u6tYvQsrR1WrsELWbT/PmtXYQWZWKg39pFaDFT7c1auwgtStfLo7WL0GIUuXmtXYQWpe/k2NpFaFY5BcUt9resTY1a7G+1pP/O1bYgCIIgCIIgCIIgCC1CjGHTeGIMG0EQBEEQBEEQBEEQhAeM6GEjCIIgCIIgCIIgCEKTUiB62DSW6GEjCIIgCIIgCIIgCILwgBE9bARBEARBEARBEARBaFJiDJvGEz1sBEEQBEEQBEEQBEEQHjCiwUYQBEEQBEEQBEEQBOEBI16JEgRBEARBEARBEAShSYk3ohpP9LARBEEQBEEQBEEQBEF4wIgeNoIgCIIgCIIgCIIgNCmF6GLTaKKHjSAIgiAIgiAIgiAIwgNG9LARBEEQBEEQBEEQBKFJiWm9G++hbLB58803cXd357nnnmvtorSqTRs3sPqvv8nJycbTy4uX5y+gQ8eOGtNeS0zkq8VfcO3aNYoKC7GxsWHIsGHMeuZZJBIJANnZ2Xz39dfExsaQkpzMiJEjefu991swompnD+3jROguCmRS7JycGf74NNz9AupNn5mSzO41K0i9dhVDYxM69xtEvzFj0dLSqpM2KT6W5V9+jI2DE3MXfaZafjM1hcP/bCI96TrS7Cz6h4xjwCPjmyW++xG64x+2b9yANDcHF3cPps95nsA2bTWmvRwRzq4tm0iIjaW4uAgHRydGjX2MgcNHtHCp787hPbvY+89mZHl5OLm6MWnGbHyDgjWmlZeXs+rXH0lKvEp6ago+/oG8+sEn9W474Uo0X733Fg7OLry39PvmCqFeSqWSM6HbuXzyKGUlxdi7edJ//BSsHZ0azJeaEMuxbRvIzUjD2MyCToOG06Z3f9X6yyePEnP2JLmZaSgVSmxdXOk+8lGcvHxVaVZ88CYFeTl1tu0e2IaQZ+c1SWwndm0j4ngYZSXFOLh7MeTxadg4OjeYLzk+lsOb15KdnoqJuQVdh4ykQ9+BamniLp7j2M4tyLKzMLexpW/IY/i271y9jYRYzu0PJTP5OoUyKSOmzaRNjz5q2ygvK+Xotk3ER1ygtKgQU0sr2vcZSJdBwxodO8Dpg/s4tmcHhVIpds7OjJz8FB4N1FMZKUns/Hs5Kbfqqa4DBjMgZJyqnroWE82fX3xUJ9+8jxdje+s7vXAsjC1//lInzbu/LEci0WuSuBprQLAPnb1dMZBISM2VsvN8NFn5hfWmNzHQZ3iHABwtzbAyMSbiRipbz0S2YImbjkH7NlhOnoCBvy+6tjZkfPwlBbv3tXax7pn52NFYTh6PjpUV5ddvkPXdr5RGXK43vcnAvlhNm4TE1ZlKaT7SzduRrt2kvs1xY7B4bAy6DvZUZGaR+9daCkIPNncod61PgBftPVww0NMlPVfG3vAYsguKGszjam3J4LZ+2JgZU1haxqm4G1y6nqJaP6VPZ9xsrerky8ov5I8DJ5s8Bk2USiVn9mzn8skjlJYU4+DmSf8JU7C+Qz2dmhDL0a3rq85B5lXnoLa9B6jWR508UnUOyrh9DnKjxyj1c1DE0UNEnQgjP7fqPGTt4ESXYaPxDG7XLLHWtnFvKKt2bCdHKsXTxYX5T02nQ0DgHfMlpacz4603UCqVHFq+Um2dvKKCZVs2s/voEbLz8rAyN2fKmBAeHzGyucK4axs2buSvv/8mOycHL09PXp0/n4713BckJiby+eLFXLt2jcKiImxtbBg2dCjPPvOM6r6gpkuXLjFn7lzc3d1Zv2ZNc4dyXzbu2M5fGzeSk5uLl7s78+c8R8c2be6YLyk1ladeehGlUknYlq3NX1BBqOWhbLARYP++vXz91Ve89r//0b59BzZv3Mirr7zMqnXrcXBwqJNeIpEwcvRo/Pz8MTE1JSE+js8++YTKikpemFd14yYvL8fcwoInp09n25YtLR2SStTZU+xZ9zejpszAzdePs4f2s+rbxbyw6HPMrW3qpC8rKeavpZ/h7uvPM//3AdkZ6Wxb9isSfX16DRullrakqIgtf/6MV0Aw+dI8tXXy8jIsbGwJ7NSVg1s3NGuM9+pE2GFW/Pwjs16Yh39wMHt3bOfTd95iyS9/YGNnVyd9XPRlXD08CZkwCUsra8LPn+PXb5ci0dOjz8BBrRBB/c4eP8q6Zb8xZfZz+AQGcTh0F999soj3l/6Ala1tnfQKhQKJRI8BI0cTdeE8JUX1X1AXFRay7LulBLRtjzS3bsNFS7hwMJRLh/cxePIMLO0cOBu6g20/L2Xamx+iZ2CgMU9+Tjbbf/uOwG69GTptFumJCYRtXIWBiQk+txotUhNi8e3YBUdPH3T19Lh0eD///PINT7z2Dha29gBMWvAWCoVCtd3ifBnrlnyMT4cuTRLbmf27OXcwlJHTZmFp78DJ3f+w4bsvmfXuJ+gZGGrMI83OYtNPS2nboy+jpj9D6tV49q/7GyMTU/w6VpUrLTGB7ct+pveoR/Ht0Jn4S+f554+fmLLgTRw9vAGQl5Vh4+RMUPde7F75u8a/dXjTWm7ERjPqqdmYW9uSkhDL3jUrMDQxIbhbr0bFHnnmJLvWrCRk2tO4+fpz5tA+/lr6OS99tBgLDfVUaUkxK778FHe/AJ575yOyM9LZ/MfP6Onp03vEaLW0L334BYYmJqrPxqZmauslevrM/3yp+rIHpLGmd4AXPf092XomgpyCIvoH+fDUgK58t+sI5RWVGvPoamtTXFbOsSuJdPZ2beESNy1tQ0PKE29QsGc/9m+/3trFuS8mg/phO28ON5f8QElkNBZjR+P8xQfceOo5Km5m1Ulv1L0LDu8sJOvbnyk6fR49d1fsF85DWV6GbPMOAMwfHYXNnKfJXPwtpdGxGAT6Yb9wHoqCQopOnGnpEOvo7utBVx93dl24TE5BEb0DvHi8d2d+23+83uPW3MiAib06Enkjle3no3CxtmBY+wBKysuJTbsJwObT4ehoV49OoKOtzazBPYlJzWyRuAAuHNjDxcN7GTLlaSztHDgTup1tPy1l2lsf1XsOkuVk8c+v3xLUvTfDps0m7Vo8YRtWY2hiWusc1BUnTx90JXpcCtvHtp+/ZvLr76rOQSYWlvQKGY+FrT1KpYKYsyfZ9cePPP7a29g4uTRr3PtOnmDpyhW8/vQs2gf4s2nvXuZ/9ilrvlyCg03dOvo2eUUF73z3DR0CArl4JbrO+ne++4abOTm8MftZXB0dyJXJKCsvb85Q7sreffv4cskS3li4kA7t27Nh0ybmzZ/PhrVr670vGDN6NP5+fpiamhIXH8/Hn3xCRWUlL7/0klra/Px83lu0iK5dunAzq24d8CDYFxbGVz//zP9eeJH2wcFs3LGDV955m3W//IqDhuvk2+RyOW9/9ikd27ThQuS/80FBaxNj2DTeQzeGzdKlS4mKimLnzp2EhIQQEhJCZmYmSUlJLFq0iEmTJjFt2jQWL15MXl6eWr5FixaxceNGnnzySR5//HGWL1+OQqFg9erVTJs2jSeffJKNGzeq/b2QkBB27NjBokWLGD9+PDNnzuTQoUMtHXYda1evZtSYMTw6dhwenp4seP11rG1s2LJpo8b0Lq6ujB4Tgq+fH46OjvTt159hw0dw6dIlVRpHJycWvPYao8eEYGZm3kKR1HVq327a9+pL534DsXV0ZtSU6ZiaW3A27IDG9BGnTyAvL2PszOewc3YlqHM3eo8Yw6l9u+t00/tnxW+079kXF2+fOttx9vRm2MQptO3eC4mefrPEdr92btlE/6HDGDxyFC5u7syc+yKWVlbs3bldY/pxT0zhielPExDcBntHR4aNCaFb7z6cPn60hUt+Z/u3b6PXgMH0HTocRxdXJs+ag7mFJWF7d2lMr29gwNQ5c+k3dASW1tYNbnvlj9/SY8AgvPz8m6Pod6RUKgkP20/nwSPwad8Za0dnhkx5GnlZKXEXTtebL+pEGMZmFvQfPxkre0eCe/YloGsvLh6qflI/7MnZtOs7CFsXNyztHBgwcSp6+gbcuFL9FNzQxBRjM3PVvxtXItHTN8CnQ2dNf/aeY7twaB/dh47Cr2MXbJ1cGPnkbMrLSrlyrv7Ywo8dxsTcgsGTpmLt4ES73v0J7t6LswdCVWnOH96Hm28APUaEYO3gRI8RIbj6+nO+Rvxewe3o+8h4/Dt20diTDiD12lWCuvXCzS8Qc2sbgrv3xtHDi/TriY2O/0ToLjr27keX/oOwc3JmzNQZmJhbcObQfo3pI04dR15ezvjZz2Pv4kpwl270HRXC8b276tRTxmZmmJpbqP5pa6ufyrW0UFtvam7R6HiaSg8/d45dSeRKSiY3ZYVsOROBnq4ubd3r71EmLS5h98UrXLqeSkm5vAVL2/SKT50l59dlFB4+Bop/50Ws5aRx5O/eT/6OUOQ3ksn65mcqcnMxHztaY3qzYYMoOnEa2dadVKRnUHzqLLl/r8dyykRVGtPhg5Dt2EPhgTAq0jMoPHgE2fY9amlaU1cfN07FXSc27SbZBUXsPH8ZPV0dglzq3uje1tHThcLSMvZFxJJTUET49VSiktLp5uuuSlMqr6CorFz1z8XaAomuDhE3UlsiLJRKJZeOHKDz4JGqc9DQKTMpLysl7nwD56Djt89BU7BycKRNz34EdOvJxYN7VWmGP/kM7W+fg+wdGDBx2q1zUJQqjVfbDngEtcXC1g5LOwd6jh6HxECfjGtXmzVugDU7dzK6X3/GDh6Mp7MLrz09E2tLSzbv29tgvh9Wr8LHzY1BPXrUWXc6IpyzkZEsWfgG3du1w8nWjjY+vnSup0dwS1q1Zg0hY8YwbuxYPD09Wfjaa9hYW7Nx0yaN6V1dXQkZMwa/W/cF/fv1Y8QI9fuC2z78+GNGjx5N27aae3U/CFZv2cyYoUMZO3Iknm5uvD53LjZWVmzauaPBfN/9+Sc+np4M7tu3hUoqCHU9dA02zz77LAEBAQwZMoSVK1eycuVKdHV1eeONN3B3d+err77iww8/pKSkhA8//FDt6fLly5fJzMzkk08+Ye7cuWzevJlFixYhl8v5/PPPmTJlCitWrCAhIUHtb65evZpu3brx7bffMnz4cJYuXUp8fHxLh64il8uJjYmhe3f1k0m37t2JjIi4q22kJCdz+tRJOnbS3FWytVRWVJB24xreQeonBa+gtqRc1fydp1xNwN3XH4le9RNm7+C2FEjzkGZXPwk4e2gfhfky+o0Z2yxlby4VcjmJ8XG066R+k92uU2fiouvvol5bSXExxjWe2j8IKuRykhITCGrfQW15YPuOXI2NadS2D+/ZRb5Uyujxkxq1ncbIz8mmuCAfV//qizldPT2cvHxJv1Z/o0HG9UTc/IPUlrkFBJGVfJ3KygqNeRSVFVTI5RgYGWlcr1QqiT51HP8u3ZukQVKWk0VRvgz3wOruxhI9PVx8/ElNTKg3X/q1q3gEqF/cegS2ITOpOra0a1dxD6ybJjXx3i7yXbx8uRp5ify8XABSExO4mZKMZ+Cdu0g3pOJWPeUTrF5P+QS3IzkhTmOepIR43P3U6ymfNu3q1FMAP3/wNp/Pn8uyxR+TeKXub1xeXs6Xr89j8asv8tfXi0m7cb1R8TQVS2NDTA0NuJqZrVpWUangRlYurtYWrVcw4e7p6qLv50Px2Qtqi4vPXsSgjeZXSbT0JChr9TBQlpUjsbNF16HqybaWRIKyVmOcsqwcg0A/0NFpwgDunbmRISYG+ly7Wd0Ls0KhIDknD+cGjltnKwu1PACJmdk4WJihXU8jcgcPZxIzsykoKWuSst9Jfk42xfky3AKqzye6eno4efuRfr3++jTjeqJaHgC3gGBuJt+44zlI38hY83qFgrgLZ5CXleHg6X0f0dw9eUUFsdcS6d5O/dWr7m3bERmnuY4GOH7hAscuXmDB9Kc1rg87e5ZAb2/W7NpJyAvPM2H+y3y1fBnFpaVNWv57JZfLiYmJoUf37mrLe3TvTsRd9hpJTk7m5MmTdOrUSW35ho0bycnJYdbTmr+TB4FcLicmPp7utcrevVMnIqKv1Jvv2JnTHD9zmlefe765i/hQUypb7t/D6qFrsDE2NkZXVxd9fX0sLS2xtLRk9+7deHp6MmPGDFxdXfH09GTBggXEx8erNb4YGxvz3HPP4erqSv/+/fH29iY3N5fp06fj7OzMyJEjsbOzI6JWo0fPnj0ZOXIkzs7OPP7447Rr145//vmnpUNXkUqlVFZWYmml/l60lZUVuTkNv/bx7KyZDOjTm0njH6Nd+/Y8N/eF5izqPSsuLECpUGBSq4ePiZk5hTKpxjyF+VKMNaSvWicDqsa4Cdu+hcdmPV/nafWDLj9fhkKhwNzCUm25uYUl0hq9yBpy/vQpoi5dZMhIzU9IW0thQT4KhQJTCwu15WYWFuRLpfe93dQb19mxYQ2zXl6AdiveDBQX5ANgZGqqttzI1IziAlm9+YoKZBjWeg3G0NQMhUJBaaHmsUBO7dqGRF8fzzbtNa5Pjo0mPzeboB5N8xSpKL8qttqv6xibmlGc30Bs+TKMav1ejczMUCgqKbkVW1G+TPN2G/jONBk0cQp2Lm78+s5rLJn3DOu+/px+j07Au22He9pObcUFBSg01VPm5hTINJexMF+msV4DKLhVt5mYWxDy5EyeeOEVJr/wCjYOjiz/8hOux1ZfcNo4ODJu5hymvrSAiXNeRFci4fdP3ycnM71RMTUFE4OqhsCiUvWb0aLSctU64cGmY26Glq4OFXlSteWVuXnoWllqzFN05jzGfXti1LUjaGkhcXHG8olxAOhaV12nFJ+5gNmooegHVI1vou/vi9noYWhJJOhYmGncbksxMahqRC0uU290Ki4rx1i//lcNjQ30KC6tm0dHWxtDvbpjgFiaGOFma0X49ZbpXQOo6kyjWvWpkUnD9XRxgQwjk1p5TKvq6frOQSd3bkWir49XrXNQdloKPy98kR9fe55D6/9m1My5zf46lDQ/n0qFAitz9TrXytycnHquJbPz8vj0t195f+4LGBtqfqU37eZNImJjib9xg0/nL+C1GU9zKjycD3/6salDuCe37wusNNwXZN/hvmDm7Nn06tuXcRMm0KF9e154vrrxIiEhgd9+/50PP/gAnVZuWG2Ian/Xuk62srAk59YDm9qyc3P45JtveP+11zGu50GXILSU/8QYNlevXuXy5ctMnFi3a216ejp+fn5AVfe/mhWOhYUFxsbqTwIsLCyQ1brgDggIqPP57NmzGsuyZ88eQkNDNa6racDgwQwZ2riBL2u/BqBUUtVXvgEffvIJxUXFxMfH88N33/L3yhU8NePBbTW/TalU3iG2Wt+FaqkWFXI5m379nqETp2BpW/97rA+6OvtbwzJNYi5H8d3nnzLjuRfw8a9/QNTWpFV7/zWiGV0ul/Pb0sVMeGomNvb1d2dvDrHnT3N4/d+qz2OeefHW/+5939VZffs70ZAvPOwAUSeOMPb5+fWOHXP51FHs3Dywdb6/MUKiz55k35rqwRcfe/4VjeW582+19reB6ger9p1oqt/u0YWw/aQmxjNuzjzMrKxJTogjbMt6zK1t8Axqgq7dGmJvOPRaK28FdTtuW0cnbGsMRu3m40dedhbHQnfi4R+oWubm46eW5of33uTU/r2Mnjr9/mO5D23dnQjpXN0TatXR84CGfXXnakp40NTeiVpa9f4I87fvQeLsiOMn76Klo4uiuBjpxm1Yz5yG8lYv59wVa9CxssT1x68ALSrz8sgPPYDVlIlQqdC43eYS5OLAiI7VvYU2nLgEgJJ7r2Tq5qn/YO/g4UxBSRkJGdn1pmms2HOnOFTjHBTy7O2xSOqcUO5YT9dZ38A56FLY/qpz0NwFdc5BlnYOPPH6u5SVFHM1/AL7Vy/jsRdfu+Ogx02h7nWyst5z7/s/fM9jQ4fSxtdP43qoGqtDC/jgpXmY3LrJf+3pp3n500/IkUqxrvXwqaVp2Mt3vNb45OOPKS4qIi4+nm+/+44VK1fy9IwZlJeX89bbb/PyvHk4OzU8ScKDou51cv37+90vFjN+9GjaBt55EGqhYWKWqMb7TzTYKBQKunTpwsyZM+uss6hReWpqHa69TEtLS+01qns1YsQIRoy480w8RWX3P0CZhYUFOjo6dXrT5OXl1mldr83+1g2sp5cXCkUln338MVOmPYmu7oNxqBiZmKKlra3qGXNbUUF+nafTt5mYWVCUL1VPfyu/sZkZhTIpWempbFv+K9uW/wrcqlyUSj6Y8xRT572Od/CD+16umZk52traSGs9JciX5mF+h4uDmKgoPnv3/5j45HSGjQlpxlLeHxNTM7S1tesMAF0gk2F2nxc+srxc0lOSWfHDN6z44Rugan8rlUqenzSWl956j6AOzfMqoGdwe+xf81R9rqyo6jpeXJCPqWX1b7OkIB9Dk/qfKhubmlN8qweLKk9hAdra2hjUamQODzvAqd1bCXl2HvbunmhSXJDPtahw+o+fcs8x3ebTtgOOHl6qz7djK8qXYVYjtuLCgjpPc2syNjNX/T5rlk9bW0cVm8Y0hfkYmd792Fry8nKO/rOJR2bNVfWosXV2JSslibMH9jSqwcbI1BRtbe06vf6K8huqp8wprFVPFd7qgVVfHgAXLx8iz9Q/m4y2tjbOHp7kZGbcXeGbUGxqJqk5UtXn24Ormhjqk19S/YqAsb4ehaUt8wqI0DiVsnyUFZV1etPoWFrU6XVTU87Py8j5dQU6VpZUSmUYde4AQEV61eC6yvJybn7+NTe//A5dKwsqcvIwDxlBZVExlbL8erfbHBIysvjzYHX9onvruDXW11d7VclIX6/Ba7Wi0nKMa/UcM9KXUKlQ1BmLSVtLizZuToRfT2nWmxvPNh2wd69ZT1eVo7hApnYOulM9bWRqXqdHY3FhgVo9fdulsP2c2rWVR+a8jIOGc5COri4Wtx6W2bt5kJl8XTUQf3OxMDNDR1ubnFo9dfPy87Gqp749dzmKi1ei+ePWWJBKpRKFUknvqZN5feYsxg4ego2FBbZWVqrGGgAP56qGp8yc7FZrsLl9X5CTq36dmJebi/Ud7gsc7KsGiPby8kKhUPDRJ5/w5LRpZGdnk3jtGh989BEffFQ1e6FCoUCpVNK9Vy++WbKEHhrG+WkNqv1d6zo5Tyqt0+vmtnPhl7gYGcHvq1YBVY1bCoWCnqNHsfCFFxk3apTGfILQHB6Mu/Ampqurq9ao4u3tzbFjx7Czs2uWhofY2FiGDh2q9tnVtfVmspBIJPgHBHDmzGkGDRmiWn729BkGDBrYQE51SoWSysrKRjVQNTUdXV2c3D1JjI4iuEv1u7iJ0VEEdu6qMY+Ltw/7N62lQl6O7q2ZUhKjozC1sMTCxhZFZSXPv/+pWp6zh/eTGB3F43NfwcK67kxEDxJdiQQvXz8iL1ygZ9/qaZ0jL16gW+/6X2+Jjozg8/feZsLUJxk97rGWKOo905VIcPPyITriEp17VU/JfCXiEp169LyvbVpaWfPuku/UloWF7uJK+CWeW/gW1s3Yy0rPwEBt1g2lUomRqRnJsdHYu3kAVeP2pCUm0LuBKeMdPLxIjLyktiwp9gq2rh7o6FTXcRcP7+PM7n8Y8+xLalOp1hZz5gQ6urr4dtT8G7obegaGak9OlUpl1UDGMZdxvHWRXiGXk3o1jv5j6x83yNHTm4QI9fExbsRcxt6tOjYnT29uxETTbcjIGmmicfa6+3EPFJWVKCor6zxd09LWbvQNk+6teurq5UjadK2+YL0aHUlQ524a87j5+LJ3w1rk8nLVjE5XL0eq6qn6ZCTdaHBQYaVSSWZKEg6u7vWmaS7lFZXkFharLSsoKcXb3pq03KqbPV1tbdxtrdgb3rgxqYQWUlFBWVwCRl06Vg2cfItRl44Uhh1vOK9CQWV21YMk08H9KYmKplJa67WbykoqsqrSmAzuT/GJMy0+MEF5RSXlFSVqywpLy/C0syJDWtV4pKOtjau1JYei6h/vJDVXiq+j+vnE086aDGl+nVlT/JzsMNKTEH49rYmi0EzjOcjMnKTYaOzdquvptKvx9HlkQr3b0XQOSo6Nxs7VXf0cdGgvp3f/Q8iceQ2eg9QoFaoG/+Yi0dXF39OLM5GRDK5xLXEmMpKB3TTX0au+WKz2+ci5cyzfuoU/P/oY21uNXe38/Tlw+hTFpaUY3fqek9KrXkd1aKAeb24SiYSAgABOnz7NkMGDVctPnznDoIF3f1+gUFbfF9jZ2bF29Wq19Rs3beL06dMs/uILnBwdm6z8jSWRSAjw9eXMhYsM6dtPtfz0xYsM6t1bY541P/2s9jns5EmWrVvL8q+/wfYOE1oI6sQsUY33UDbY2NvbExcXR2ZmJgYGBowePZq9e/fyxRdfMH78eMzNzcnIyODYsWPMnDkTo0a+m3jixAl8fX1p27Ytx48fJzw8nC+//LKJork/T0yZwgfvvUdQUDDt2rdny+ZNZGdnMfaxqpvAn374nujLl/nux58A2L1rF/p6enj5+CCR6BITfYWffvyBAYMGoVdjEMy4uFgAioqK0NbWIi4uFomuBE8vr7qFaCY9ho5kyx8/4ezphauPH+fCDlAgy6NL/6qT0P7N60i7dpWnXn0LgLbdehG2fQtbl/1Kv9GPkpOZwbE92+kf8hhaWlro6OpiV+s1EGNTM3R0JWrLKysqyEqrere8Ql5OoUxGRtIN9Az0sbJr2Vdrahs9bjzff/k53v7++AcFs3/XDnJzchg6agwAq5f9wdXYGN75rOqC43JEOJ+/+zbDxoTQd+BgpLeeumhra993z5XmMiTkUZZ9txRPHz+8AwI5sncPsrxc+g2rulnfsmoF1+LjWfD+R6o8aclJVFZUUJhfQGlpKcm3BvB19fRCR1cXZzf1m1dTM3N0JZI6y5ublpYW7fsP4dy+XVjaO2Bha8+5fTuR6Ovj16m6QXLfqj8BGDq1qpdgm179iTh2iKNb1hHcqx/p1xKIOXuCYU/OVuW5cDCUU7u2MnTqLCxs7VU9UnQlEvQNq+s8pVLJ5dPH8O3Ytd4pXO83tk4Dh3I6dAdW9o5Y2tlzas8OJHr6BNZobN218jcARj31DADt+wzg4pEDHNy4mvZ9BpCamEDU6eOMmTFHlafTgKGs/fozTofuxKd9JxLCL5AcF8PkBW+o0pSXlSLNuqmKMT8vl5spSRgYGWNmZY2+oSEuPv4c+WcTEn0DzKysSUmIJfrMCfo92viZaXoNH8Wm337E2csHNx8/zh7eT4E0j24DquqpvRvXknrtKk+//n8AtOvem0PbNrP5j58ZMGYc2ZnpHN21nYGPPKZqVDqxdzcWNjbYO7tQUVFJ+MljXLl4jideeEX1dw9u24Srtw/Wdg6UlZZwan8oGSnJhDxZt4dpazgVd4N+Qd5k5xeRU1hEvyBvyisqiLxRfaM6rnvVQKBbTlePF+dgUTXOk76uLkqlEgcLUyoVSrLyNY+X8aDSMjRA4nzr9QFtLST2duj5eKEoKKAi88GcDre2vPVbcPi/Vym9EkdJVDTmj45C19oK2baqmfusn52BQaAfqfOrzsHa5maYDuhD8aVItCQSzEYNxWRgH1Lm/U+1TYmLMwZB/pRGx6BtaoLlpHHoe7qT9MlXrRJjbWcTkujl70lOYTG5BUX0CvCivKKS6JTqnmtjbr3+t+N81UDgF6+l0MnLjcFt/bh0PRVnKwvaujvxz9m6g7x28HDmelYusuKSOuuak5aWFh36Debsvl1Y2lXV02f37kRPXx+/ztX19N6//wBg2LRZALTpXXUOOrJ5LW169Sf9WgJXzpxg+K16HKrOQSd3bmHYtPrPQce3b8IjqC2mFla3ZqY6Q0pCHCHPqE8b3Rwmjx7Noh++J8jbm3b+/mzZv5/svFzGDal6APvjmtVEX73K92+/A4C3q5ta/iuJiWhraaktH9a7D39u3sxHP//I7PETKSguYumK5Qzq3r3OeDktberkybz7/vsEBwfTvl07Nm3eTFZ2NuMfq3pg9/0PP3A5OpqffvgBgJ27dqGvr4+Ptze6EglXrlzhhx9/ZNDAgar7Ah9v9YcklpaWSPT06ix/EEwZ9xjvfbmYIH8/2gcFs3nXTrJzcnhsVNXYjT8s+5PLsXH8+NlnAHh7eKjlvxIfV7W/ay0XhJbwUDbYjBs3jqVLlzJ37lzKy8v5/fff+eKLL1ixYgXvvfcecrkcW1tbOnbsiERSd+C3ezVlyhROnDjBr7/+ipmZGS+//LJqXJzWMmToMGQyGcuX/UlOdjZe3t58ufRrHG+1eOdkZ5OaWj2wnY6ODitXLCclObnqYtjBgfETJvLE5Mlq250xbZra52NHj+Lg6MjmbS03yHKbrj0oKSzgyM5tFMqk2Dm5MHXe61hY2wBQKJWSe+tGDcDAyIgn57/BrtXL+fWjdzE0NqLn0FH0HDqyvj+hUYE0j18+/D/V5/NZBzl/5CDufgHMeP3tpgnuPvXqP4CCgny2rFlNXm4urh4evPHBx9je6soqzc0hM7160NGwfaGUlZWyfdMGtm/aoFpua2fP9yv+rrP91tS1d1+KCgrYtWk9srxcnNzcefGtd1U9YWR5eWTXet3j+08+IKfGMfDR668A8MvG1hsMvD6dBg2nQl5O2MbVlJUUY+/uyaPPvaLWeFJQqxuvmbUNIc+8xLGt64k8HoaxuTn9xj2BT/vqmcIijx1GUVlJ6Mpf1fIGdO3JkCnV41KlJsQiy7rJsKmzmjy2bkNGUlFezoH1f1NaXISjhxcTXnxVrSdOfq0u2hY2tox/fj6HNq0h/NhhjM0tGDRhCn4du6jSOHv5MObp5zi+YzPHd23FwsaOMTOfw9Gj+iIx48Z11n/7herziZ1bObFzK8HdezPyyapYQ2Y+x5FtG9m14ldKi4sws7Km9+hxdOxf/QTyfrXt1pPiwkLCtm+hQCbF3tmFJ19ZqOotUyiTknszU5XewMiI6a+9yY6/l/PzB29jYGxMr+Gj6DW8utt1ZWUFoetXk5+Xi0RPDzsnF5585XX82lW/wldaXMy2FX9QKJNiYGiEo5s7s/73Di5ePo2OqSkcj0lEoqPNqM5BGOpJSMmR8VfYWcorKlVpzI3qNhw+N7yP2md/Z3ukRcV8vSOs2cvclAwC/HD5rvpJvfXsp7Ce/RT5u/aS+YA0TtxJ4cEjZJmZYvXUE+hYW1F+7Tqp/3uPisyqOlfX2hKJk/rTddPhg7F5fhZoaVF6+Qop896g7EqN3ik62lhMGoeemzPKikpKLkaQPPdVKjJu8iA4HX8diY42w9oHYCDRJS0vn3XHz6sdt2aG6setrLiUDScuMridHx09Xaum+A6PJTZNPSZzI0Pcba3YpqEhpyV0GjyCCrmcsE2rKSsuwt7di0efn692DiqsdQ4yt7blkWfncfTWOcjE3Jx+j6mfgyKOHkJRWcmeFXXPQbcfPhTny9j39x8U5eejb2iItZMLjzw7T212weYytGcvZAUFLNuyhRxpHl6uriz53xs42lbV0dlSKSmZmXfYijojAwO++7+3+Wr5Mp5++y3MjI3p16Urcyff/+vGTWXY0KHIZDL+WLaM7OxsvL28+GbpUtV9QXZODim17guWrVhB8q37AkcHByZOmMCUJ55orRAaZWj//sgK8lm2Zg3ZuXl4e7iz9IMPcbx1nZydm0tqevP2cPuvEmPYNJ5WSUmJ+BYbISQkhDfeeIPe9XSpu1+NGcPm3yg0PLa1i9BiglzsW7sILUpa1LJPDFtTZHLLjxPSmgwkD2Wbv0aaGhEeZtEpD8aNckuY+t3XrV2ElqX94M7m0tQ2zZ/f2kVoUf+l2dam2rfu7GEtTdfLo7WL0GIUuXc3w+nDQt/pwXl9rDkkZjY8E1lT8rJ/OF9X+3fNXywIgiAIgiAIgiAIgvAf8N95PCoIgiAIgiAIgiAIQosQr/I0nmiwaaTt27e3dhEEQRAEQRAEQRAEQXjIiAYbQRAEQRAEQRAEQRCalJjWu/HEGDaCIAiCIAiCIAiCIAgPGNHDRhAEQRAEQRAEQRCEJvVvmtZ7586dbN68mby8PNzc3HjmmWcIDg7WmDYyMpJt27YRFxdHUVERTk5OPPLIIwwdOrTJyyUabARBEARBEARBEARB+E86evQov/32G88//zxBQUHs2rWL999/nx9++AE7O7s66a9cuYK7uzuPPfYYVlZWXLhwge+//x6JRMKAAQOatGyiwUYQBEEQBEEQBEEQhCb1bxnDZuvWrQwePJjhw4cDMGfOHM6fP8/u3buZPn16nfSTJk1S+zxq1CgiIiI4ceJEkzfYiDFsBEEQBEEQBEEQBEH4z5HL5SQkJNCxY0e15R07duTKlSt3vZ2SkhJMTEyauniih40gCIIgCIIgCIIgCE2rJTvY7Nmzh9DQ0DumGz58OCNGjFB9zs/PR6FQYGFhoZbOwsKC8PDwu/rbZ86cITw8nC+++OKeynw3RIONIAiCIAiCIAiCIAj/WiNGjFBriLlXWlpa95UvOjqaL7/8kmeffRY/P7/7/vv1EQ02giAIgiAIgiAIgiA0qX/DLFFmZmZoa2uTl5entlwqldbpdVPb5cuXWbRoEVOnTmXUqFHNUj7RYPOA0r7PFj7hwfdf27eKB7+eFu7Tv2UgOeHeKfgP7VttndYuQctSVLZ2CVqMROe/tW9LystbuwgtR1sMwykIQtOQSCT4+Phw6dIl+vTpo1p+6dIlevXqVW++qKgoPvjgAyZPnsyjjz7abOUTDTaCIAiCIAiCIAiCIDSpf8vDvbFjx7JkyRJ8fX0JCgpi9+7d5ObmMnLkSABWrFhBXFwcH3/8MQCRkZEsWrSIUaNGMWDAAFXvHG1tbczNzZu0bKLBRhAEQRAEQRAEQRCE/6S+ffuSn5/P+vXryc3Nxd3dnffeew87OzsAcnNzycjIUKXfv38/ZWVlbNmyhS1btqiW29nZ8ccffzRp2USDjSAIgiAIgiAIgiAITerfMIbNbaNHj2b06NEa182fP7/O59rLmot4AVQQBEEQBEEQBEEQBOEBIxpsBEEQBEEQBEEQBEEQHjDilShBEARBEARBEARBEJqUmC228UQPG0EQBEEQBEEQBEEQhAeM6GEjCIIgCIIgCIIgCEKTUiK62DSW6GEjCIIgCIIgCIIgCILwgBE9bARBEARBEARBEARBaFL/pmm9H1Sih40gCIIgCIIgCIIgCMIDRvSwEQRBEARBEARBEAShSSlED5tGe+gabDIzM5k9ezZLlizB19e3tYvTqjZu2MCqv/8iJzsbTy8v5i94lQ4dO2pMey0xkcVffM61a9coKizExsaWocOGMfvZZ5FIJAAcOniQLZs3ERcbS3l5OR6ensx4eib9+vdvybAAOHtoHydCd1Egk2Ln5Mzwx6fh7hdQb/rMlGR2r1lB6rWrGBqb0LnfIPqNGYuWlhYA12OvcGDLenIy0pGXl2FubUOnPgPoNXy0ahuVFRUc272d8JNHyc/Lw8bBkSHjH8enTftmj7e20O3b2LZxA9LcHFzcPXj6ubkEtmmrMe3l8Evs2LKJhNhYiouLcHB0YvS4xxg0fKQqTV5ODit++5lrCQmkp6XSb9AQXnxtYUuFc0dhobvYt20zMmkeji5uTHx6Nr6BwRrTysvLWf3rjyRfu0p6agre/oEsWPSJWpq4y1FsW72SzLRUysvKsLK1pffgYQx9ZFxLhKNGqVRyJnQ7l08epaykGHs3T/qPn4K1o1OD+VITYjm2bQO5GWkYm1nQadBw2vSu/i0mXDrH+QOhyLJvolBUYmFjR/v+Qwjs1qt6G1fjuHhoL1kpSRTJpAyePENtfVPEdnL3P0QeD6O0pBhHdy8GTZqKjaNzg/mS42MJ27KOnPRUTMwt6DJkJO37DFBLE3fpHCd2bkWWnYW5jS29xzyGb/tOqvUKhYKTu7Zx5ewpivKlGJtZENi1Oz1HPoq2jg4A5WWlHPtnEwkRFykpKsTM0op2vQfQedCwJon/9MF9HNuzg0KpFDtnZ0ZOfgqPBuqpjJQkdv69nJRb9VTXAYMZEDJOVU9di4nmzy8+qpNv3seLsb31nV44FsaWP3+pk+bdX5Yjkeg1SVz3YmCwL529XTGUSEjJlbLj/GWy8gvrTW9ioM+IDoE4WpphbWJM+I1UtpyJUEtja2bCoDa+OFqaY2VixKGoeA5djm/uUBpkPnY0lpPHo2NlRfn1G2R99yulEZfrTW8ysC9W0yYhcXWmUpqPdPN2pGs3qW9z3BgsHhuDroM9FZlZ5P61loLQg80dSpMyaN8Gy8kTMPD3RdfWhoyPv6Rg977WLlaDlEol5/ftJOb0McqKi7Fz86D3uCewcmi4Tk67Gsep7RvJy0zHyMyc9gOGEdSzn2p97NmThK1fWSffzE++RffWddbFg3u4HnkJaVYmOrq62Ll50m3Uo1g5NFxnNlYvfy/aezijL9ElPS+f/REx5BQUNZjHxdqCgW38sDE1prC0jDMJNwi/nqqWRk9Xhz6B3vg72WMgkVBQUsrRKwnEpt0EoLuvB76OtliZGFOpUJCWJ+NodALZd/jbTWVjaCirtm8jRyrF08WF+dOfpkNg4B3zJaWnM+ONhSiVSg6t/Fu1/Pzly7zwwft10q9d8jUezs27D+/Gho0b+evvv8nOycHL05NX58+nYz33BYmJiXy+eDHXrl2jsKgIWxsbhg0dyrPPPKO6Lzh/4QI//PgjN27coLSsDAcHB8Y+8ghPTpvWkmHdtY07tvPXxo3k5Obi5e7O/DnP0bFNmzvmS0pN5amXXkSpVBK2ZWvzF1QQannoGmyEKvv27mXpV1/y+v/eoH2HDmzauIH5L89jzfoNODg41EmvK5EwavQY/P39MTE1JT4ujk8/+ZiKygpemvcyABcvXKBLl67Mef55zMzMCd2zmzcWvs6PP/9Sb0NQc4g6e4o96/5m1JQZuPn6cfbQflZ9u5gXFn2OubVNnfRlJcX8tfQz3H39eeb/PiA7I51ty35Foq9Pr2GjANAzMKD7oGHYObsi0dMj+WocO/5ahkRPj64DhwJwcOtGIk4dI+SpWdg6OpNwOYJ1P37NzDfew9HNo8XiPx52iGU//8jsF+cRENyG0B3/8PHbb7L01z+wtbOvkz72SjRuHp48OvFxLK2suHT+HL98sxSJnh59Bw4GQC6XY2ZmzthJT7B/984Wi+VunDt+lPXLfmPy7OfwDgjiSOgufvh4Ee8u/QErW9s66RUKBRI9PfqPGM3li+cpLqp74advYMCAUWNwdvNAT0+Pq7FXWP3rj+jp69N/+KiWCEvlwsFQLh3ex+DJM7C0c+Bs6A62/byUaW9+iJ6BgcY8+TnZbP/tOwK79WbotFmkJyYQtnEVBiYm+LTvDICBsQldh43C0s4BbR0drl+O5OC6lRiamOIRVNW4Jy8rw9rRmYAuPdm/+s8mj+3s/t2cPxjK8GkzsbJz4NSe7Wz6/iuefudj9AwMNeaRZWex5eevadOjDyOfmk3q1XgOrl+FoYkJfh26AJB2LYGdy36h16hH8WnfiYTwC+z48yeemP8mjh5eVX97324uHT3IiGmzsHFyITsthT1//YGOroQeI0IACNu8jqTYaEY8ORtzaxtSE+LYt3YFhiYmBDWy4SryzEl2rVlJyLSncfP158yhffy19HNe+mgxFhrqqdKSYlZ8+SnufgE8985HZGeks/mPn9HT06f3iNFqaV/68AsMTUxUn41NzdTWS/T0mf/5UvVlrdBY0yfAi17+nmw5E0F2QSEDgnyZPqAb3+4Ko7yiUmMeXW1tisvKOXrlKl283TSmkejqIC0qITolk8Ft/ZozhLtiMqgftvPmcHPJD5RERmMxdjTOX3zAjaeeo+JmVp30Rt274PDOQrK+/Zmi0+fRc3fFfuE8lOVlyDbvAMD80VHYzHmazMXfUhodi0GgH/YL56EoKKToxJmWDvG+aRsaUp54g4I9+7F/+/XWLs5dCT+8l8gj++k/6Sks7Oy5sG8Xu377lkmvv19/nZybzZ4/fsC/Wy8GTn6ajOtXObZ5DQbGJni1q25I1pXo8cQbH6jlvd1YA5B+NY6gXv2wdXUHJZwL3c7OX75l4uvvYmBk3CzxdvNxp6uPG7svRJNbWERPfy8m9erE7wdOIK/nd2puZMD4Hh2JSkpj5/koXKwtGNIugJIyOXHpVY0x2lpaTOzZiVK5nH/ORlBQUoapoT6Viuqn7a42lly6nkJGXj4AfQK9mdSrE38ePEmpvKJZ4r1t34njLF2xjNdnzaa9fwCb9oYy/9OPWbNkKQ42da8tbpNXyHnnm6V0CAzkYnS0xjRrvlqCWY062sLMTGO6lrR33z6+XLKENxYupEP79mzYtIl58+ezYe1ajfcFEomEMaNH4+/nh6mpKXHx8Xz8ySdUVFby8ksvAWBkaMjjkybh4+ODgYEB4eHhfPLZZxgYGDBxwoSWDrFB+8LC+Ornn/nfCy/SPjiYjTt28Mo7b7Pul19xsLOrN59cLuftzz6lY5s2XIiMbMESPzxEB5vG+1eOYXP+/Hn+97//8cQTTzB58mTeffddkpOTAZg9ezYACxYsICQkhDfffFOVb//+/cydO5fHHnuMOXPmsHXrVhQKhWp9SEgIu3bt4qOPPmL8+PHMmTOHiIgIsrOzeffdd5kwYQLz5s0jISFBbZsTJ07kzJkzzJkzh8cee4y33nqLjIyMFvo2NFuzehWjx4Qwdtw4PD09ee31hVjb2LB540aN6V1dXRkTEoKvnx+Ojo7069+f4SNGEH7pkirNgtde46kZMwgOboOrqyuzn3mWgIAAwg4fbpmgbjm1bzfte/Wlc7+B2Do6M2rKdEzNLTgbdkBj+ojTJ5CXlzF25nPYObsS1LkbvUeM4dS+3aqBsJzcPWnTrSd2zi5Y2trRrkcfvIPbkhQfW72dU8foPWIMfu06YmlrR9cBQ/Bt24GTe3e1SNy37di8iQFDhzFk5Ghc3NyZNfclLK2s2btju8b0jz0xhckzZhIQ3AZ7RyeGj3mE7r37cPrYUVUaOwcHZs59kYHDhmNiatpSodyVAzu20XPAYPoMGY6jiyuPz5qDmaUlR+r53vUNDJjy7Fz6Dh2BhZW1xjTu3j507d0PJ1c3bOwd6N5vIEHtO5Jwpf4n4s1BqVQSHrafzoNH4NO+M9aOzgyZ8jTyslLiLpyuN1/UiTCMzSzoP34yVvaOBPfsS0DXXlw8VP3k2sU3AK+2HbG0d8Tcxo72/Qdj4+hMWmJ1TwSPoLb0HD0Onw6d0dJq2tOBUqnk4uH9dBs6Cr8OXbBxcmH4tFmUl5USc67+2MKPH8bE3IJBE6di7eBEu979Cerei/MHQlVpLhzaj6tvAN2Hj8HawYnuw8fg6uPPhRrxp11LwLtNB7zbdsDc2gbvtlX/T7+eqJYmsGtP3PwCMLe2Iah7Lxw8vEi/fq3R8Z8I3UXH3v3o0n8Qdk7OjJk6AxNzC84c2q8xfcSp48jLyxk/+3nsXVwJ7tKNvqNCOL53V50B+4zNzDA1t1D909ZW33daWqitNzW3aHQ896OnnwdHr1wlOiWDm7JCNp8JR19Xl3bu9fdUkBaXsOtiNJeup1JSLteYJi1XRmh4DJFJacgrNd9QtiTLSePI372f/B2hyG8kk/XNz1Tk5mI+drTG9GbDBlF04jSyrTupSM+g+NRZcv9ej+WUiao0psMHIduxh8IDYVSkZ1B48Aiy7XvU0vwbFJ86S86vyyg8fAwUD/5Vu1KpJPLoQdoPHI5Xu05YOTgz4InpyMtKSbh4tt58V04excjcnN5jH8fS3pHA7n3w69KDiLBav3ctLYzMzNX+1TTqmXn4d+2FlYMzVo7ODJw8g9KiAjKvX22OcAHo7O3G6fjrxKXfJLugiN0XLqOnq0OQc92b+Nvae7hQVFrGgchYcguLibiRxuXkdLr6VDeytnFzwkhfwpbT4aTmysgvKSU1V0aGNF+VZuPJi0QlpZNdUER2QRE7z1/GUF8PZyuLZov3tjU7dzC6/wDGDh6Cp4sLr82chbWlJZv37m0w3w+rVuHj5s6gHj3rTWNpZo61haXqn462TlMX/56tWrOGkDFjGDd2LJ6enix87TVsrK3ZuGmTxvSurq6EjBmD3637gv79+jFixAgu1bgvCAwMZPiwYXh7eeHs5MSokSPp2aMHF2ukeVCs3rKZMUOHMnbkSDzd3Hh97lxsrKzYtHNHg/m++/NPfDw9Gdy3bwuVVBDq+lc22JSWlvLII4+wZMkSPvnkE4yMjPjwww+Ry+V89dVXACxatIiVK1fy1ltvARAaGsrKlSuZOnUqP/74I7NmzWLTpk3s2qV+07du3Tr69u3Ld999h4+PD4sXL+bbb79l1KhRfPPNN1hZWfHNN9+o5ZHL5axZs4aXX36ZxYsXo1Ao+Pjjj1ttVGy5XE5sTAzde/RQW969ew8iIyLqyaUuOTmZUydP0rFjpwbTFRUXY2rWcjf4lRUVpN24hneQ+us/XkFtSbmquUt8ytUE3H39kehVP2H2Dm5LgTQPaXbdp58A6UnXSb4aj7tfddfYyooKtSdhUPVkLCkh7n7DuWdyuZzE+Djad+qitrx9p87EXtH8pEeT4uJijE0erIYZTSrkcpISEwhs30FteWD7jiTGxjTZ30m+dpXE2Bh8g+7cNbYp5edkU1yQj6t/9etdunp6OHn5kn4tsd58GdcTcfMPUlvmFhBEVvJ1KivrPpVUKpUkx10hLysTZ++WeVVUlpNNUb4M94Dq2CR6erh4+5F2rf6bj/RrV9XyAHgEBpOZdEMVW/r1umncA9uQdq26Md3Zy5fk+BhyM9IByElPIynuCp416g5nL18So8IpyMsFIC0xgayUZDwbeRxU3KqnfILV6ymf4HYk11NfJCXE4+6nXk/5tGmnsZ76+YO3+Xz+XJYt/phEDY2M8vJyvnx9HotffZG/vl5M2o3rjYrnflgaG2JqaEBCZrZqWUWlghtZubhaW7Z4eZqNri76fj4Un72gtrj47EUM2mh+tUJLT4KyvFxtmbKsHImdLboOVU96tSQSlLUarJRl5RgE+oFO69/8PawKcrMpKcjHpca5X1eih4OnL5k36q+3Mm8k4uKrvr9d/YLISrmBokajYqW8nNUf/x+rPnqTPX/+QHZqcoPlkZeVoVQq0Tc0us+IGmZuZIiJgT7Xb+aqllUoFCRnS3GyMq83n5OlOdezctSWXbuZg72FGdq3XuH0dbQlNVfGkHb+zB3el6cH9aCXv5dqvSZ6ujpoa2lRKtfcWNtU5BVyYhMT6d5O/ZX27u3aExkXW08uOH7hPMcunGfB0zMb3P6Mt95g9JxnePHDRZyPimqSMjeGXC4nJiaGHt27qy3v0b07EXfZayQ5OZmTJ0/SqVP99wUxsbFEREQ0mKY1yOVyYuLj6V6rXN07dSIi+kq9+Y6dOc3xM6d59bnnm7uIDzWlUtli/x5W/8pXonr37q32+ZVXXuHxxx8nLi4OG5uqruampqZYWlZfFK5du5YZM2ao8jo4ODBhwgR27drFmDFjVOkGDRpE/1tjskyaNIkjR47QqVMnetxq/Bg/fjxvvfUWMpkMc/Oqk1llZSXPPPMMQUFVN1ALFizgmWeeITw8nA4dOjTPl9AAqVRKZWUlVlZWasutrKw4e6b+J9sAz8ycSWxsDOXl5Tw6dhzPv/BCvWk3rl9P1s2bjByl+SlicyguLECpUGBS66mUiZk5165oPikW5ksxs7Sqk75qnQxL2+qukEtef4niwgIUlZX0D3mMLgMGq9Z5B7fl9P5QPPwDsbZzIDHmMlcunkNZo5dWcyvIl6FQKDC3VL/hMbe0RHrxQj251J0/fYqoSxf5cMk3d07cygoL8lEoFJjV6iFgZm5BjDS80dt/c87TFObLqKxUMHriE/QbNvLOmZpQcUHVk0ajWr2ajEzNKJRJ681XVCBTu6EAMDQ1Q6FQUFpYiPGt76uspJjl7/+Pygo5Wtra9B8/BfdAzWMdNbXifBlQFUtNRmZmFEql9eYrys/Hzb9WHlMzFIpKSgoLMTG3oChfVuc1IGNTM9X3CdD1/9m77/Coir2B499sspvee++9EXrvvSiCgCAKUuyvil69lmtX9FrAht2LIEjvHUIJvYZACJBCgPSQ3stusu8fGzbZZENLQ53P8/Donp05O5PZPWXOb2aGjaK6qpLf57+NREdCbW0NPUaMoVP/weo0gx6eRuSqpfzyzqtI6p6ADpo0Da8WzktVXlJCrbbjlLk5Vy42d5wqwryZ41RJUSGWtnaYmFsw7rFZOHt6UaNQcO7YYX7/Yj6zXvsPHv6q74ONgyMPzXoKB1c3qiorObZnJ79+8h7Pvf8J1vaOLarX3TAx0AegrLJKY3tpZRVmhtqHlfwV6ZqboaOni6KgUGN7TX4Bel06ac1TdvIMti88hVG3CMpPxyB1dsLyEdX8WXrWViiyblB+MhqzMcMpPXiEqsuJ6Pv7YjZmODpSKboWZtTkFbRxzf6Z1MdkE83ji6GpKeW3OCZXlBRj6Ks5P5WhqRnK2loqy0oxMjPHwtaeAZMfw8rRBXlVJRcO72fTos95eN5/MLfVPiTj6KbVWDu5YOfu1bKKNcNYX9VBXFal+Tstr6rGxFC/+XwGMq7naHY6lldVoyuRYCiTUlZVjbmRIW42llxKy2bd8RjMjQwZGuaPTE+XA83MOTU41J/swhIy8otaWLNbKywuoaa2FitzzWO0lbk5p2ILtebJLSjgk59/4tOX/4WxofYhvTaWFrw2Zy5B3t7IFQp2HDzI8x99wPfvvEdEUJDWPO3hVvcFJ041HzkGMGvOHC7XzV350IMP8twzTTsvRo8dS0HdZ8ydPZuHJ0xo1fK3VGFxsaq9LTSvna0sLDlZcFZrntz8POZ//TX//c/bGBu1TYepINypv2SHTWZmJsuWLSMhIYGioiKUSiW1tbXk5OSoO2waKioqIjc3l0WLFvHDDz+ot9fU1DTpjfPw8FD/v4WFBQDu7u5NtjXssJFIJPj51Y+jt7Ozw8rKipSUlA7psLlJp9FTDCXKJtsa+2j+fMrLy0lMTODbb77hjyVLmPHEE03S7du3l2+/+ZoPP56Po2P73QQ0R6lUqsYBNKvx3+LmVs3tT7z2NtVVVaQlJxG5biUWNraE9+oLwMhHHmPL0t/4/p1/g44OVrZ2dOrdn5ijB1uxJnemSTsqb9+2AJfjLvD1f+fzxDPP4evf/OSn9x2t3+WW7/aVDz6hqrKSq4nxbFi2BBs7e3oMGNTyHTcj/swJDqyun6Bw7Nzn6/6v6ffzdu3Z5O2bx7IGb8j0DZjyr7eRV1eRlnCJw5tWY2pljavf7SdVvFuXTh0ncmX9hJrjn36xrjiNv6vaCq+pybFLqWV7k91qHsvjo09y8eRRRs+Yi7WjMzlpKexftwIza1tCe6lCm89G7SUjOYkHn/w/zKysSUtK4OCG1ZhZWWtE4tyzJvW43fe26e9atRvVdltHJ2wbTEbt5uNHQW4Oh3dtU3fYuPn44ebjp5Fm0btvcDxyN2MenXHvdbmNMHcnxnWpj0xafuh0wyrU09Hhb/kMrHFFdXSaHbhfvGUnUmdHHOe/g46uHrXl5RSu3YT1rOnqBwD5S1aga2WJ6/dfAjrUFBRQvGsvVtMmQU37PST4u0uMPsmhdX+qX4+c9azqf5ocX7Vt1NT4eqLx9aW9hxf2Hl4NXnuzbuHHXDiynz7jpzTZ37HNa8m6doUHnn2lybDHexXo4sDw8Ppz/7rjMXWFbZRQhxZPPKGjA+VVcnbFXEQJZBeVYCCTMijET2uHzaBgX1ysLfjz0Ol2O0ZoO9c0d+5977tvmDBsOCF+zc+b5e7kjLtT/eTCoX7+ZObksHzr5g7tsLlJ29f6dtca8z/+mPKyMhISE/nm229ZsnQpT8ycqZHml59/pqK8nNgLF/h20SKcnJwYM7p95wO8E3dzX/TOZ58zccwYQu9gEmrh1sQqUS33l+yw+fDDD7G2tua5557D2toaXV1dnn32WRQK7ROU3Zyn5rnnniMg4NY3qXp6Tf8k2rbda9jVzp072bVr123TDR4ylKHD722lEgsLC3R1dcnL0wxXLcgvwKqZOT1usq+beMzTy4uamlo++fgjHn3sMY2/wb59e3n/nXd49/33232FKCMTU3QkEkqLNZ++lJUUN3mafZOJmQVlxYWa6evyGzeaCO5mtI29iytlxUVEbVmv7rAxNjXjkefmoZBXU15aiqmFJZHrVmFp3fzkdK3N1MwciURCYX6+xvaiwsImUTeNXboQyyfvvMWUx2YwYuwDbVnMVmNiaoZEIqG4UPNpcklRUZOom3thY6/6vju7e1BcVMjWNSvatMPGMzgc+395ql/X1B2zykuKMW0QXVFRUoyhSfOTFBqbmlNeXKyxraK0BIlEgoFx/cSUOhIJFnXfaVtnVwqyszgTuaNNOmy8Q8Nx8HhX/fpm3cqKizTqVl5S3CQ6piFjMzP17/OmitJiJBJddd2Mzcwpa1T/8pISjWiegxvX0HXICAK6qELAbZ1cKM7P4+Tu7YT26oe8uprDW9YxdtYzeId2UqVxdiUnPZUze3e1qMPGyNQUiUTSJEqqrPhWxylzShsdp0rrnvY3lwfAxcuH2JPHmn1fIpHg7OFJXnbbzqt2OT2btLxC9WvduhtME0N9iisq1dtN9GWUNoq6+SurKSpGqahBz0rz+KtradEk6qahvB8Xk/fzEnStLKkpLMKoLhpHkZkNgLK6mhv//YobX3yLnpUFirwCzMeNpKasnJqi4mb3K9wd96Aw7BosGtDwmGxi0eCYXFqC4S3mdzM0NaO8RPO4VVlago5EgoGxidY8EokEWxd3inNvNHnv6OY1XIk5zbin52HWitcYSVk5ZBbUl/Pm79TYQJ+SBr9LI5mMsqrqJvlvKqusxthAcyJzI30ZNbW16rmnyiqrqVXWanS+5JWUIdPTxVAm1ZijalCIHwHO9qw6coai8oqWVPGOWJiZoiuRkNco2rOguKhJ1M1Npy9c4OzFi/y2dg2gug+oVSrpM3UKr86ew/ihw7TmC/bxZc/RI61a/rulvi9odO1YkJ+PdaOom8Yc7FWLWXh5eVFbW8tH8+fz2PTpGvcFzk6qBwk+Pj7k5efz86+/3lcdNhZmZqr2LmhU/8LCJlE3N50+F8PZ2PP8unw5oOrcqq2tpdeY0bz23PM8dB/VT/j7+8t12BQXF5OamsrTTz9NWFgYAElJSdTUjRG+eQBpOJmwpaUl1tbWZGZmMnjw4KY7baHa2loSExMJrOuFvXHjBvn5+bi6ujZJO3LkSEaOHHnbfTY32eKdkEql+AcEcPLECYYMHarefvLkCQYNuvP6K5W11NTUaPwtI/fs4cP33+Ptd99j8JCht8jdNnT19HBy9yT54gWCu9aPxU2+eIHALt205nHx9iFy3UoU8mr06lZKSb54AVMLSyxusRKAUqlEoWUctZ5UhpmlFTUKBZeiT2qUo61JpVK8fP04d/YMvfrXd5adP3uGHn2anxDtYux5PnnnLSZPf5wxD01sj6K2Cj2pFDcvHy6fi6FLXccZwOXzMUT0aH7Cv3uhrNXe3q1JZmCgscqIUqnEyNSM1PiL2NfdNCjkcjKSk+jzQPPt5ODhRXJsjMa2lPhL2Lp6oKvb/GFdqaylRtE2dZQZGGqs/KRUKjE2M+f65Ys4uKs6qRRyOenJifR/sPmJUx09vblyXjNE+frli9i7uavr5ujhTUp8HN2G1h9LU+LjcPL0Ub9WVFc3mUhZIpGAUnU8q62pobampumEvRJJi8dB69Udp67ExRLSrX4usSsXYwnq0l1rHjcfX3avWYlcXq1e0elKXOxtj1NZKddvOamwUqkkOy0FB1f3ZtO0hmpFDfml5RrbSioq8bG3UQ9v0JNIcLO1ZPe51pt/qsMpFFQlJGHUNUI1sW4do64RlEbd5iattpaaXNWDFdMhA6i4cJGawkZDQWpqUNTNFWIyZADlR0+KJTdakbZjsqGpGekJl7Bz9QBUx62sq0n0GNP8MA97dy+uxcVobEtLvIytizuSZuYcUiqV5GemYeXkorH96KbVXIk5zdin52Fh1/zEv/dCrqihUKHZIVJaWYW7rZV6MmBdiQQXawuimhm2BJBRUISvo+ZxycPWiuzCYvXT9PT8QgJdNMtvZWJEtaJG4xp3cF1nzcojZ5ocQ9qKVE+Kv5cXJ2PPMaRX/bXEydjzDOqu/Zpu+edfarw+ePoUv29Yz/8+/gTbW3R6JF6/io2lRauU+15JpVICAgI4ceIEQ4fUD/U/cfIkgwfd+UOqWqWyyX1BY8raWuTVzXf2dQSpVEqAry8no88ytF9/9fYTZ88yuNE0Gzet+OFHjddRx46xeNVKfv/qa2ytb/3wW9D0d55bpr385TpsTExMMDMzY9euXdjY2JCXl8fixYvRrTshWlhYIJPJiI6Oxs7ODplMhrGxMVOnTuXnn3/G2NiYrl27UlNTw5UrV8jLy2PSpJatuqCrq8svv/zCk08+iUwm49dff8XNza1Dh0NNnfYo77/7DkHBwYSFh7Nh3Tpyc3J4aKLqJvD7777jYlwc39UNEduxfRsymT7ePj5I9fS4dOkSPyxaxKDBg5HVTYK5Z/cu3nvnHV548SUiIiLIy1VNJqknlaqHh7WHnsNGseG3H3D29MLVx4/TUXspKSqg6wDVSShy/Soyrl7h8VdUE06Hdu9N1JYNbFz8M/3HPEhedhaHd25hwLgJ6lDIE3t3Y2lji7WDanjX9YTLHN29jW4D6zul0pKTKCkswMHVneKCfKK2rEepVNJn5Fja09gJE/n28//i6xeAf3Awu7dtJT8vj+FjVEsVL//fryQlxPPup58DEHcuhk/e+Q/Dx46j76AhFNQ9YZFIJJjXDfEDuHpFNWFrRXk5OjoSrl5JQk9Piqt7297o3c6QsQ/y+7cLcff1w9s/kEO7d1KUn0+/uvlmNi5fwrWkRF569yN1nszUFBQKBaUlJVRVVpJaN4Gvq6cqHH3/jq3Y2NljXxe6nHjxApFbNtB/ePs+MdHR0SF8wFBO79mOpb0DFrb2nN6zDam+Pn6d6y8a9yxXLbk97FHVRIchvQdw/vB+Dm1YRXDv/mReTeLyqaMMf2yOOs/pPduwd/PEzNqWmhoF1y/GEn/6OP0nTFWnqa6qpKhuQlulspaSgnxy0lMxMDLC1LJlFyQ6OjpEDBzKyd3bsLJ3wNLOnhO7tiKV6RPQoJNzx9JfARj1uKrs4X0GEnNwH/vXrSCszwAykpOIO3GE0TOfVOfpPHAoq77+Lyd3b8MnrDNJ56NJTYhnyrzX1Wm8QsI5FbkDc2sbrB2duZGWwpn9uwnqplquW9/QEBcffw5tXotUXx8zS2vSkuK5ePLoLTuU7lTvEaNZ98v3OHv54Objx6kDkZQUFtC9bl6s3WtXkn71Ck+8+hYAYT36sH/Tetb/9iMDxz5EbnYmh7ZvYdAD9cepo7t3YGFjg72zCwpFDeeOHebS2dM88txL6s/dt2kdrt4+WNs5UFVZwfHIXWSlpTLusVtPktkWjiVco3+QNznFpeSVljEgyIdqRQ3nr2eo00zooXrwsv5E/YT4DhaqSAZ9PT2USiUOFqbU1CrJKS4FQFeig62ZKmpBTyLBxEAfBwtTrZ1G7aFg9QYc3nqFyksJVFy4iPmDo9GztqJok2pRA+snZ2IQ6Ef6PNU5SWJuhunAvpTHxKIjlWI2ehgmg/qS9sK/1fuUujhjEORP5cXLSExNsJz8EPqe7qTM/1JrGe5XOoYGSJ3rhvFJdJDa2yHz8aK2pARFtvZJ/zuSjo4Oof0Gc3bvTizsHDC3tSM6cgdSfX18IuofCu1f8TsAg6bOBCCwVz/ijhzg6KbVBPbsR/a1KyScPsbgafW/uzO7t2Ln7oW5jS3VlZVcOLKfvMx0+k6Ypk5zeP0KEqNPMnzmU+gbGqnnApPq6yPVb5u5n85cSaGnnyf5pWUUlJbT088TeU0NF9Pro/JGd1ZN8r49WjXJ+blraUR4ujIoxI9z19JwtrIgxM2Jrafr5+iKuapKMyTUn+irqZgbGtAnwIuYq/UTLQ8N8yfIxZGNJ89RJVeo59SpVtS0+QpwU8eM5f3vviXI25cwf382RO4mNz+fh4apotu//3M5F68k8d3bqshRbzc3jfyXkq8g0dHR2L5y2zYc7WzxdHFFoVCw89BBok6d4pOX/9WmdbkTj06dyjvvvUdwcDDhYWGsW7+enNxcJtbNN/PdokXEXbzID4sWAbBt+3b09fXx8fZGTyrl0qVLLPr+ewYPGqS+L1i5ejXOTk641/0NomNiWLZ8OQ/fZ0t6A0x7aALvfvE5Qf5+hAcFs377NnLz8phQNw/nosX/Iy4+ge8//RQA7wZTZABcSkxQtXej7YLQHv5yHTYSiYTXXnuNn3/+meeffx5HR0dmz57NJ598Aqg6T5588klWrlzJypUrCQoK4pNPPmHEiBEYGBiwfv16li5dikwmw83NTWPC4XsllUqZPHkyCxYsICcnB39/f9544407mlOkrQwbPpyioiIW/+838nJz8fL2ZsFXX6vnm8nNzSUtPU2dXldXlyW/LyYtNVV1gezgyMRJk3hkav2FxPp166ipqWHhgi9ZuKD+ojGic2d++OnndqtbSLeeVJSWcHDbJkqLCrFzcuHRF17Fwlo1f1FpYSH5OfUhxgZGRjw273W2//k7P3/0DobGRvQaNppew+onmFUqa4lct5LCvFwkuhIsbe0YOmGKuhMIVE/Z9m1cQ0FODjIDfXxDOvHQ7GcwMKofgtIe+gwYRGlxMetWLKegIB9Xdw/e/HA+tnVhqwX5+WRn1N8Q7d+zm6qqSrasW8OWdWvU223t7Pl+6XL169eee1rjc86cONYkTUfo2qcfZaUl7Fi3muKCfBxd3XnuzXewrhvqU1RQQE6j4R7fffKBxndg/msvAfDDms0A1NbWsGHZ7+Tl3EAi0cXWwYHxj86g37DbR7+1ts6DR6CQVxO19k+qKsqxd/fkwadf0njqW9IojNfM2oZxc/+PwxtXE3skCmNzc/o/9Ag+4V3Uaaqrqjiw9k9KiwrQk0qxtHNg6KOz8OtcH+FxI/U6GxfV/5ZP7tzMyZ2bCejWi6HTms5ddbe6DR2l+t2sWU5leRkOHl5MfO5ljUicxnUzt7HloadfImr9Ss4fPoCxmQWDHp6GX6f6ldGcvHwYM/MpjmzdwNHtm7CwsWPME0/h2GB+iMGTpnFk20b2rl5GeWkJJmbmhPbqT89R9cMBxzzxFIc3r2P7kl+oLC/DzNKaPmPGa0xMfK9Cu/eivLSUqC0bKCkqxN7Zhcdeek0dLVNaVEj+jWx1egMjI2b86w22LvudHz/4DwbGxvQeMZreI+o7EWtqFOxa/SfFBflIZTLsnFx47KVX8QuLUKepLC9n05LfKC0qxMDQCEc3d2b/+21cvOqjj9rL4cvJSHV1GdslGAOZlPS8QpZGnaRaUX8jZm7UdPLOZ0doRgsGONtTUFbOwq0HADA1MNBIY21qTDcfN67eyGPx/ltPrN8WSvcdJMfMFKvHH0HX2orqq9dI//e7KLJVxyA9a0ukTppzvZmOGILNM7NBR4fKuEukvfA6VZcarCCmK8Fi8kPI3JxRKmqoOHue1GdfQZHVdPjM/cwgwA+Xbz9Xv7ae8zjWcx6nePtusu/TzqfwgcNRyOUc3rCS6opy7Nw8GT33/zSOyaWFjY7JVjaMnP0cx7as5eKxQxibmdP7wcl4hdWvSlNVWcGhtcspLylGZmCAjbMrDzzzisaQrIvHVHPibftJc1GAzsPG0HV42zwcOpl0HT1dXYaGBWAg1SOzoJg1R6ORN/idmjaaKLyovJJ1x88yOMSPTh4ulNYt8Z2QWf/9LKmsYs2xaAaF+DFjYA/KKquJTcngWPxVdZoIT1Uk+pQ+XTT2f+RyMkfjm18psTUM692HopJSFm9YR15BAV6urix4/U0cbVXH6NzCAtKys2+zF01yhYJv/1hKTn4++jIZnq6uLHj9DXrfZsXV9jB82DCKior4bfFicnNz8fby4uuFC+vvC/LySEtPV6fX1dVl8ZIlpNbdFzg6ODDp4YeZ9sgj6jS1NTV8+913ZGRmoquri4uLC88/95y6E+h+MmzAAIpKilm8YgW5+QV4e7iz8IMPcay7ds7Nzyc9M+M2exGEjqFTUVEh4pRaIDIykp9++ok1a9bcPvFdaMmQqL+iHTF/oxD52whxbd0Q5/tdbkn7P/HuKHFpbTtPyP1GpvfPWV7Y0lj7qiB/VxfS7u5G5a/ssUXfdXQR2ldt20Yu3E82v/bv2yf6G6lV/nMmpJ7jrn1lrb8rPQ+32yf6m6jN/2etgqfv1PGLt7SlownX2+2zevt17KiAttI6084LgiAIgiAIgiAIgiAIreYvNyRKEARBEARBEARBEIT7m5h0uOVEh00LDR06lKFD23+1JEEQBEEQBEEQBEEQ/r5Eh40gCIIgCIIgCIIgCK1KRNi0nJjDRhAEQRAEQRAEQRAE4T4jImwEQRAEQRAEQRAEQWhVtYgIm5YSETaCIAiCIAiCIAiCIAj3GRFhIwiCIAiCIAiCIAhCqxJT2LSciLARBEEQBEEQBEEQBEG4z4gIG0EQBEEQBEEQBEEQWpVYJarlRISNIAiCIAiCIAiCIAjCfUZE2AiCIAiCIAiCIAiC0KpqRYRNi4kIG0EQBEEQBEEQBEEQhPuMiLC5T11IzeroIrSrqzn5HV2EduNpZ9XRRWhX3+0+0tFFaDfDQn07ugjtqqJa3tFFaDcyPd2OLkK7ejnin/Nd/nHevI4uQruS6v5zvssPfPbfji5Cu3pl3KSOLkK72Xb2ckcXoV1ZmRh1dBHaTWllVUcXoV1t/tcTHV2ENiXmsGk5EWEjCIIgCIIgCIIgCIJwnxEdNoIgCIIgCIIgCIIgCPcZMSRKEARBEARBEARBEIRWVStGRLWYiLARBEEQBEEQBEEQBEG4z4gIG0EQBEEQBEEQBEEQWpWYdLjlRISNIAiCIAiCIAiCIAjCfUZE2AiCIAiCIAiCIAiC0KpEhE3LiQgbQRAEQRAEQRAEQRCE+4yIsBEEQRAEQRAEQRAEoVXVigibFhMRNoIgCIIgCIIgCIIgCPeZf0yHzRtvvMGPP/7Y0cUQBEEQBEEQBEEQhL89pbL9/v1diSFRf2P7d2xj16b1FBbk4+TqxiOz5uIXFKI1rby6mj9+WsT15CtkpaXiHRDIax9+qpHm8oXzfPHOm03yfvjNDzi6uLZJHW6nX6A3EZ4uGMikZOQXsfPsRXJLym6Zx83GkqFh/tiamVBSWcXx+KtEX03TSNPNx43OXq6YGxlSUSUnIfMG+2ITkNfUNNlfb39PBoX4cfpKCrtiLrVq/Zqzd/tWdmxYR2FBPs5u7kyb/ST+wdrbtrq6miU/fMf1K0lkpqXiExjEGx//t0k6hVzO5tUrOXpgH4X5eZhZWDJq/ASGjXuwratzR6b0jmBYmD/G+jISs3L4JfIYqXmFzabv4evOiPAAPO2skOnpkZpXyLrjMZy6kqqRbkznIEaEB2BrZkJpZRUnk1L44+ApKuWKNq6RilKp5PiOzVw4epDKinIc3D0ZPOlRrB2db5kvLTGegxtWkZeVgbG5BV2HjCSs70D1+3mZ6Rzbvpkbadcpzsulx8hx9Bqt2ZZpSQlE79tFdup1yooKGfboEwT36NMW1VRTKpWc3r2Vi8cPU1Vejr27B/0mTMXKwemW+TKuJHBk81oKsjIwMrMgYtBwgnv3V79/5dwZzu7bRVFuDrW1NZjb2BHWfwgB3Xqp0yz76E1KCvKb7NstMIQxc55vvUrWiY7ay4k9OygtKsTG0Zmhk6bh6uvfbPob6ansWbWMzGvJGBgZ06nfIPqMfgAdHR0ASosK2bd2JVmp1yi4kU1wj96MnTG3yX6qKio4uHkd8WdPU1FWiqmlFQMefJjALt1bvY63snbbNpatX09eQT6ebm7MmzuXiGaOUw2lZKQz46WXUCqVHFizVr19/9GjrN+xg4TkK1TL5Xi6ujJz8hT69+jRltW4K30DvAj3cMFApkdmfhG7z12+7TnJ1dqSIaF+2JgZU1pZxfGE68Rcqz8nTevbBTdbqyb5copL+W3vsVavgzZKpZIze7Zx+YTqd2vn5kGfhx65o9/t8S1rKcjOxMjMnPCBwwnqVf+7jT91jKjVS5vkmzX/G/SkUgDO7tvJtdgYCnOy0dXTw87Nk+6jH8TK4dbHyPZmEB6C5dSHMfD3Rc/WhqyPv6Bkx56OLtY9mdqnM8PD/TEx0CchM4cf9xwhNbew2fS9/DwY2SkAL3trpLq6pOYVsuZYDCeTUtRpdCU6PNyzE4NDfLE2NSI9v4glB041uf7qCDMHdmdsl2BMDfS5lJ7NV9uiuJbT9FxxU7i7E3OH9sLV2hIDqR7ZRSVsi77IqqNn1WlGdgrg9fFDm+Qd/tEPVCuaXku2l7a4lvpgyihCXB2b5E3JLeCl3ze0RTXu2GP9uzI6IggTA30uZ2Tz3Y5DXM8taDZ9qJsjswb1xNXaAn2pHjeKStgRc4m1x8+p04yKCGRoqD/utpZIdHRIysplSdRJ4lKz2qNKwj+Q6LD5mzp5+CAr//czjz75DD6BwRzYsY2vP3qPD77+Hmtbuybpa2trkUqlDB41ltjo05SXlTa77w++/h5jE1P1a1Mzszapw+308vOkh68HW05fIL+0jL6B3kzr15Ufdx9u9mRobmTIlD6dOXctnU2nYnG1tmRkRCBlVXLiM7IBCHZ1ZHCIP9uiL5CaW4CFsRFjuwSjJ5GwLTpOY39OVuZEeLqQXVjS5vW96cShKP789Scee/o5/AKD2LtjGws+eIf53/2otW2VdW07dMw4zp05RXmZ9puHH778L/m5ucx87v+wd3SmuLCA6urqtq7OHXmoeygPdA3h2x0HySgoYlKvCN6dNJLnf1vbbMdKsIsDsSmZ/Hn4DKWVVfQP9Oa1B4fwzqodXEpXtXW/AC8e79+N73cf5mJaNvbmpjw3si9SPV2+33W4Xep2OnIn0ft3M/zRWVjaOXBi5xbWL1rAjP98jMzAQGueorwcNv70NcE9+zLy8TmkJyexf/VyDE1M8e3UBVB1wppZW+MT3pmj27RfMMmrKrF2dCawWy92Lftfm9WxoZj9uzkXFcmgR2ZgYWvPmT3b2PLT10z99/vN1rc4L5dtv35HQLfeDJ32BJlXkzi0bgUGJiZ4h3UGQN/ImC5DR2Nh54BEV5frF89zYPUfGJqY4B4YCsDEl95AWVur3m9ZcRFrv/oE7/AurV7PS6dPELn6T4ZPfQwXbz+iD+5l9aIFzHlnPuZW1k3SV1VUsOqbz3H18WfGv98lLzuT7Ut/Q6ovo8fQUQAoFHIMTUzoOWIM5w5Haf3cmhoFq775HAMjYx6c8yymlpaUFBSgp9e+p/s9hw6y4Jefee2ZZwgPCmbd9m3Me+89Vi76Hge7psepm+RyOf/57DM6BQdz9sIFjfeiL8TSNSyMpx+bjpmJKbuiDvDv+R/z/fz5d9QR1NZ6+HrQzced7dFx5JWU0SfAiyl9uvBL5JFbnJMMmNQ7gtjr6Ww5cwEXawuGhwdQUV1NfMYNANafOIeupD4gWlciYfaQXlyuO461h3MHdhN7MJIBkx/Hws6e6D3b2f7LN0x+9b3mf7f5uez8bRH+3XszaOoTZF27wuH1KzAwNsGr7ncLoCeV8cjrH2jkvdlZA5B5JYGg3v2xdXUHJZzetYVtP33DpFffwcDIuG0qfA8khoZUJ1+nZGck9v95taOLc88m9AjjwW4hfL39IOn5RTzSJ4IPJo/i2V/XUlEt15on2NWB89czWHboDKUVVQwI9uaNh4by1optXExTfU+n9+vKwGAfFu08TGpeIZ09XXjjoaH8e9kWkm/ktWcVNUzt05nJvTrx6ca9pOYV8PiAbnzx+IM89u2yZutbUS1n/YnzJGfnUSmXE+rmyMtjB1Epl7Pp1AWNdI9+84dG3o7srGmra6nPNu1FT6KrziPVk7BwxkMcjb/aLvVqzuRenZjYI5wvtuwnLa+QR/t14dNHxzHrhxXNtm1ltZxNp2K5eiOPKoWCYBcHXhw9gCq5gi1nVPcA4e5ORF1MIi41iyq5ggk9wvhk6lie/mUNGQVF7VnFv4Ra/sahL+3kHzMkClSdEkuXLmXatGlMnz6d3377jdq6i/fZs2ezfv16jfSNh1HNnj2bFStWsHDhQiZPnswTTzzBoUOHKC0t5bPPPmPSpEk8+eSTREdHt2u9tNmzZSO9Bw2h/7CROLm4Mm3u05hbWnJg13at6fUNDHjs6ecZMHwkltZNbyYaMjU3x9zSUv1Poqt7y/RtpbuPO8firxKfkU1OcSlbTsUi09MjWEsv/02dvVwpraxi97nL5JWUEXMtjdjrGfT081CncbG2ID2/kAspmRSVV3I9J5/Y6xk4WZlr7EtfT4/x3cLYeiaOSrn2A39b2LVpA30GD2Xg8JE4ubrx2JPPYGFpxb4d27Sm1zcwYOaz/8fAEaOwsrbRmubC2Wgunovh5XfeJ6RTZ2zt7fH2DyAwNKwtq3LHxnYOZv2J8xxPvE5KbiHf7jiIoUxK/0DvZvP8b/8JNpw8T1JWLlmFJaw+FkNydh49fN3Vafyd7UjIvEHUxSvkFJdyITWTA3FJ+Dnatke1UCqVnI2KpNvQUfh26oKNkzMjps+iuqqSy2dONJvv/OEoTMwtGPTwNKwcnAjt3Z/A7r04s2+XOo2Duyf9x08moGsPpDKZ1v14BofRZ9wEfCO6qqM42pJSqeT8wb1EDB6Bd1hnrB2dGTx1JvKqShLPnmw2X9yxgxibmdNvwiNY2jsS1LMffl17ce5A/dNrF98APEM7YWnvgLmNLWH9h2Dt6ExmcpI6jaGJKUZm5up/KZcvINM3aJMOm5N7dxHaqw+d+g7ExtGJ4VMew8TMgrMH92mv48ljyKurGTNjLrbOLgR07kaP4aM5FblLvSSmhbUtw6ZMJ6xXv2ZvVGOPHqaspISJz7yIq48fFta2uPr44ejh1ep1vJUVGzcydsgQxo8YiaerK/966mmsLS1Zt0P7Oeim737/HR8PT4b06dvkvVeefIoZkyYR7OePq5MTc6ZOI8Dbm4PHj7dVNe5KNx83jidcIz7jBrklZWw7E4dMT5cgF4dm80R4ulBaWcWe8/HklZRx7lo6F1Iy6d7gOFUpV1BWVa3+52JtgVRPl/PX09ujWiiVSmIP7SN80Ai8wjpj5eDMwEdmIK+qJOnsqWbzXTp2CCNzc/qMn4KlvSOBPfri17Un56MiNRPq6Gj8Lo3MNM+1o+e+gH+33lg5OGPl6MygqTOpLCsh+9qVtqjuPSs/foq8nxdTeuAw1P51b1Ae6BrCuhPnOZZwjZTcAr7aFnXb8+2ve4+z7sR5EjNzyCwsZuWRs1zJyqWnr4c6zcBgH9afOM/p5FSy66IWziSnMr57aDvUqnkP9wznz8NnOHjpCldv5PPJhkiMZFKGhvo1mychM4d9FxK5lpNPVmEJe84ncOpKCmFujSPOlOSXlmv860htdS1VWllNYXmF+l+gsz36Uj32Xkhsj2o166HuYaw6epbDl5O5lpPP55v3YSiTMjjEt9k8iVm5HLiYxPXcArIKS9h7IZHTyamEuNXfW3y6cS+bT1/gSnYuafmFfLPjIOXVcrp5d8xoA+Hv7x/VYRMVFYVEIuHzzz/nqaeeYvPmzRw6dOiu9rF582b8/Pz46quv6Nu3LwsXLuSLL76ga9eufP3114SEhLBgwYIOjUxQyOVcv5JEcKfOGtuDwztz5fLlFu//o1fn8cqsx/ji3Te5HHu+xfu7FxbGhpgY6pN8I1e9TVFbS2puAS7WFs3mc7EyJzlb80nOlexcHC3NkNTdsKbmFmBvYaruoDEzNMDXyY4rWbka+UZ3DuJSejbXbxE229oUcjnXriQREtGobTtFkHT53odjRZ84hqePH7s2bWDerMf499NzWPbzj1RWVLS0yC1mb26KpYkR5xrcnFQrariYloW/c/NP6rUxlEkpraxSv76Ulo2HnbW6g8bG1Jhu3m6cSU5tbhetqjgvl/LiItwCgtXb9GQynL39yLya1Gy+rGtXcPMP1tjmHhjCjZTr1NS0z1Cue1GSn0t5STGufkHqbXpSGY5evmRdS242X/b1ZFwa5AFwCwgiJ/U6NVqGKSqVStISLlOYk42jl/YLM6VSyaUTR/Hr0r3ZDq17VaNQkJVyDc9AzagPz8Bg0pO1t2v61SRcffw0yuIVFEJpUSFFebla82iTcC4aF28f9qxaxrf/foFf3n+TQ1s3tOv3Qi6XczkpiR6NjlM9IjoTe6n5c9DhU6c4fOokrzz55B1/VnlFBaYmJvdc1tZibmSIiYE+VxtECihqa0nNK8D5FuckZysLjTwAydm5OFjUn5Ma6+ThTHJ2LiUVVVrfb20l+blUlBTj4heo3qYnleHg6Uv29eY7TbKvJ+PiG6ixzdUviJy069Q2+N3WyKv58+O3WP7RG+z83yJy0299/JVXVaFUKtE3NLrHGgnNsTc3xcrEiLMNhilVK2qIS8si8G7Pt/oyjfOtVE+3SXRJtaKGQBf7lhW6BRwtzbA2NdYY3lOtqOHc9YxbPvxrzMfBhhBXB43rFACZnh4rX3qcNS/P5JNpY/Fx0P7QrD205bVUY0PD/Dl7NY282wwHbUsOFqZYmxprXM9VK2qITcm8ZSd6Y972NgS5qCLImiPVlSDT073l3+SfTKlUttu/v6t/1JAoV1dXpk+fDoCzszO7d+/m3LlzDBgw4I730blzZ8aMGQPAtGnT2LhxI46OjgwePBiAKVOmsGfPHq5fv46vb/M9uG2ptKSY2tpazMwtNLabWVhw8XzMPe/XwtKK6U89i4ePHzUKOccO7OfL997i1Q8+wa+dw9GN9fUBKKvU7Bgrq6rCxFB7eDaAsYE+ZTc0O1jKqqrRlUgw0pdSWlnNxbQsDGVSHh+gmu9BVyLh/PV09l1IUOfp5OGCpYkRm07FtlaV7khJsaptzS0sNLabW1hy8VzMPe/3RlYWCZfi0JNKef7fb1FeVsayX36gMD+P519/q2WFbiELY0MACss0O48KyyqwMrnzC/aRnQKxNjUm6mL9DfOR+KuYGhrw4SOj0UEHPV0JB+KS+OPg6dYp/G2UFatCZ41MNYcVGpmaUVpUeIt8xbj6Nc1TW1tDZWkpxo1++/eL8uJiAAwb19fEjLJb1Le8uLjJjZ+hiRm1tbVUlpViXPdEvqqigqUfvE6tQo6OREK/CVNxD9R+bEpLuERJfi6BPZpGcrRUeWkJytraJpECRmbmlF2+qDVPWXERphZWTdLffM/C5s6ivgpzb3A9Ppegbr2Y9OzLFOblsGfVH8irqhg88ZF7qM3dKywupqa2FqtGxykrCwtONXOcys3P55PvvuXTN97E2OjOftdrtm3lRl4eowYNbmGJW87EQNXRVl6leU4qr6rGxEC/2XzGBjLKbzTNoyuRYCiTUtZof5YmRrjZWrHueEzrFPwOlJeofrdGJpq/W0NTU8pv8butKCnG0DegUR4zlHW/WyMzcyxs7Rkw+TGsHF2QV1Vy4fB+Ni36nIfn/QdzLUN8AY5uWo21kwt27u0bNfZPYGmi/XxbVFaBlemdDz8bHRGItYkR++Pqz7dnr6bxQLcQLqRmkpFfRLiHM738PJrtmGwPN68hCso0I18KysqxMb19R/Cal2dibmSIrkSHJVGn2Hy6fth8Sm4hn23ax5XsXAxlUh7uGc53sycy+4eVpOe3/7CZtryWasjR0owQV0c+2RCp9f32Ut+2mvVVte3tv8vLX3hM3bbLDp1mW7T2czfAzIE9qKiWcyzhWovKLAjN+Ud12Hh4eGi8trKyoqjo7g6aDfdhaGiIvr6+xjaLugvUu91vm2h0DlQqlS0a9uDg7IKDs4v6tbd/ILk52ezatL7NO2yCXR0Z3bn+CfuqI6phZ037UnW0bWxEM8HNv8jNjlk3G0v6Bnqz8+xF0vOLsDIxYlh4AP2DfDh4MQkrEyMGhvjyR9RJajusN1ezHZUooQVtq1TWoqOjw9OvvIaRsepE9tiTz/LFe/+hqLAAcwvLFpX2bvQP9OKpYfUT3368XjXspfFf+m6q29PXnRkDurFg635yiuuf+AS5ODCpVzi/RB4jITMHRwszZg3uwSN9Ilh55Owt9nhvLp86zt5V9ePZH3zqBQAtv0tl459vE02zKJt5o+MknDlB1No/1a/HzHkOaHJoqvv+3mZnTd5XNtks09dn8itvIa+qIi3xMkc3r8HU0hoXv4DGmbl4/DB2ru7YOLddCHPTIt+6ns226V1QKpUYm5oxavoTSCQSHNw9qCwrY+/aPxk0YUq7DH1T09JmOs38Ad798gsmjBpFaEDTttJm35EjfPu/xXz02ms43mJOnLYS5OLAyIj6TsQ1R2OAuu/yXWqap/k26uThTElFFUlZdx51dbcSo09yaF3973bkrGe1F0upbaOmxu3d+AmovYcX9g2G69l7eLNu4cdcOLKfPuOnNNnfsc1rybp2hQeefQWJ5B8VJN4mBgR58+yI+k7rD9bu0p5QR+eOj0e9/Dx4YlAPPt+8j5zi+vkQf4k8zvMj+/Ld7IkAZBYUExmbcMuhR61taKgfr4wbqH79+vKtQNOqqb63t6/v//1vHYYyGUEu9jw1rDeZBcXsOR8PwMW0LC6m1U9CG5eaxa9PP8KEHmF8u+PuovvvRXteSzU0LMyf/NLydotUvmlwiC8vjq5/AP+flTenCWh0za+jc0dH6VeWbsRAKiXQxZ7Zg3uqhkfFJjRJN75bKKM7B/H68i2UNzMvzj/d3znypb38ozpstE26eHMOG20XsdpC7XUbzdeio6Ojse3mfmobTGzZ0M6dO9m1q5kTYgP+nbvTpfe9Pfk1MTVDIpFQXFiosb2kqKhJ1E1Lefn6c/LwwVbdpzaJmTf4NbK+E0xXovo7mxjIKKmoVG831pdRVtV8SGJZZRXGjZ52GunLqKmtVU9ANiDYl7jUTGKuqcJGc4pLkerqMqZLMIcuXcHF2gJjfRlPDu2t3odEIsHNxpLOni58timSmjYav25qpmrbokLNGe6LCwubRN3cDQtLKyytrNWdNYB65a+8nJx27bA5mZRCQmaO+rW07vdlaWyoEV5rbmRIYfnth2z19HXnxdED+GbHwSYrRE3r25nDl5KJrDsJp+QWoC/V49kRfVh9NKbVO+S8Qjvh4OGpfl2jUA1TKSsuwtSyPrqivKQEo1tM5m1sZkZZXbSKOk9pCRKJLgbG988knB7B4di7N61veUkxJg3qW1Fa0iTqpiEjMzN1dE7DPBKJBH3j+qegOhIJ5jaqm3cbZ1cKsrOI3rujSYdNeUkx1+LO0W9C20ScGJmYoiORqCOoGn6ucaOom5uMzcy1pr/53p0yMbdAItHVuJm1dnBEXl1NRWlJk2iutmBhZoauREJ+QaHG9vzCoiZRNzedPn+esxcu8NuKFYDqEru2tpbeDz7Aq888y0MjR6rT7jtyhPcWLODdl+d12ApRSVk5/G9ffXvp1f29jfX1NYYqGenLmkTJNFRWWa3lnCTVOCfdJNHRIcTNiXPX0tr0Atg9KAw7Nw/1a43frUXj361p4+xqhqZmlJdofqcrS0vQkUgwMNYevSCRSLB1cac490aT945uXsOVmNOMe3oeZtbtM8/Y393JpBQSMuonpdfTU32PLYwNNVY3MzcyaBKZoU0vPw9eHjuQhdsOaKwQBVBcUcn8DZFIdXUxNdQnv7ScGQO6kV3Ufos2HIm/qp4oF+qvL6xMjDQ6lyyMDckvvX19s+oWnLh6Iw8rEyNmDuyu7rBprFapJD7jBi5WFi2owZ1rz2upm/QkEgYF+7DnfEK7P9A8lnBNYyL2+voaaXQuWRgZ3tF3+WbbXsvJx9LYkMf6d23SYTO+WygzB3bnrZXb1JPEC0Jb+Ed12NyKubk5+fn1Q2Wqq6tJS0vDy6t1Q25HjhzJyAYXns1p7mB4J/SkUty9fbh47ixdG3T6XDx3ls69et8i591LvZqMhaXV7RO2ULWihmqFZshqaUUVnnbWZBaobmp0JRJcbSzZG6v9ZAmQll+Ev5Pm01gve9U+bp5cpLqSJhfDqvdUnUTxGTfI3HNE4/2xXULILy3naHxym3XWgKptPbx9iIs5S/c+/dTb486dpWuve1+O2TcwiFNHDlNZUYGBoSpsNjtD1WFl00xYeluplCvUJ8qbCkrLCXd3Uj9VlurqEuhsz9Ko5ie8BNWS6/83sh/f7jykNVRVX0+vyUVFrbL5KICWkhkYaKyoolQqVZPfxl/Eoa5jQyGXk3Elkb7jJzW7HwcPb5JjNSOAUuIvYufmjq7u/XNY11pfUzNSEy6pbwgVcjmZyUn0Gjeh2f3Yu3tx9cI5jW2pCZewdXVv0omuQalU32w2FH/qGLp6evh06nZ3FbpDunp6OLh5cPVyHAENltK+ejkO/4iuWvM4e/pwYONqFPJq9KSq4TVXL8VhYm6BeTOThWvj4uVL3KljKGtr0anrRMi/kYVUJsPQpPmb69YklUoJ8PHhRMxZhvStPwedjDnLoN7az0F/fvedxuuDx0+wePVqFi/4EtsG9Y88dIgPvlrIOy/N0zoxcXtRnZM0L/pLK6vwtLMiq7DBOcnakv0Xmj6VvSk9vxBfR81jrKedNVmFxU2OTX5OdhjJpJy71vxcCq1B2+/W0NSM9IRL2Ll6AKrfbdbVJHqMufXv9lpcjMa2tMTL2Lq4N7tYgVKpJD8zDSsnF43tRzet5krMacY+PQ8Luzuff0K4tYpqeZOOwfzSciI8nDXOt8EuDize3/zE8AB9Ajx5afQAvtoexdH4a82mk9fUkF9ajq5Eh97+Hhy+3H4rCVVUy5sMR8orKaOrt6v6hlump0uYuxM/7j6ibRfN0tHRQaZ360U4vOytuZLddtFxDbXntdRN3X3dMTU00BqJ0ta0fZfzSsro7OWq7riS6uoS4ubIr3uP3dW+dXR01B1AN03sEcbjA7rzn5XbxHLeQpu7f67sO1hYWBiRkZH06NEDMzMzVq9ejULLhf5fxbBx4/ntmwV4+PjhExhE1K7tFBbkM3D4aADWLfudq4kJ/Ov9+eo8GakpKBQKSouLqaqsJOWqahJQN09Vp9WeLZuwsbPDydUNhULB8YP7OXvyOM+89mb7VxA4mXSdPgFe5JWUkV9aTp8AL6oVCuJSM9VpxnVVDdXaclq1zGJ0cipdvV0ZFhZA9NVUXK0tCHN3ZsOJ+smTEzNz6OHrQWZBsXpI1IBgX5KyclAqlVTJFeTINZc9l9fUUCmXazyhaSsjHnyIn7/6Ei9fP3wDg9i/czuF+fkMGqlq2zVLF5OcmMC/P/xEnSc9JQWFQk5JcTFVFRVcT1ZNFOnupVoZoGf/gWxetYJfv1nIQ1MfpbyslOW//kTX3n0xa0HkTmvZGh3HxB7hpOUXkVlQxMM9O1EpV3DwUv2Ely+M6g/ANztUEV99/D15cfQAlkSd5GJqFhZGqo4oRW0NpXVzH51OTmVcl2CSsnJJzFINiZrapzOnk1Pb5emQjo4OEQOGcmr3NqzsHbCwdeDk7q1I9fUJ6FIfObDrj98AGPHYbADC+g7g3KF9HFi3krA+/clITuLiiSOMmlE/WWuNQkFelurGTiGXU15SzI20FGT6+ljYqiZ5rK6qpDBHdZGqVCopyc/nRloKBkbGmGlZero16hvWfwhnIndgaeeAua0d0ZE7kOrr4xtR37Gx98/FAAyZ9gQAwb36c+HIAQ5vXE1wr35kXr1C/KljDJ0+W53nTOR27N08MbO2oUah4PqlCyScOU7fhzSjaFSTDR/Bp1PXZpcjbg3dh4xgy+8/4+TuhbO3L2cP7ae0qJCIfoMAOLBxDZnXkpn60r8BCOrekyPbN7Jtya/0HvUA+TeyOL57G33GPKgRAZqdeh2AqsoKdHR0yE69jq6eHjaOzgBE9B/EmahI9qxZTpcBQynKz+Xw1o1E9B/crsOhpo4fz3sLFhDs60dYUBDrd2wnNz+fCaNUx6lFS37nYkICiz5WnYO83T008l9KTEIi0dHYvvtgFO8tWMALs2YRERJCXoEq0lBPTw/zW0R6tJdTSSn09vckr7Sc/JIyegd4qSf1vGlsF9Vk4Vvrloc9ezWNzl5uDAn1I+ZaOs5WFoS6O7FZy/xonTycuZaTT9EdPA1vTTo6OoT2G8zZvTuxaPS79Ymo7/Tcv+J3AAZNnQlAYK9+xB05wNFNqwns2Y/sa1dIOH2MwdNmqfOc2b0VO3cvzG1sqa6s5MKR/eRlptN3wjR1msPrV5AYfZLhM59C39CI8rpINKm+PlL9tvsN3y0dQwOkznWrBEl0kNrbIfPxorakBEV2zq0z30c2n77A5F6dSMsvIj2/iMm9O1FRLdc43740RjX05KttUQD0C/Ri3piBLN5/grjULPV8KYqaWvVErH6OtlibGpOcnYe1qRFT+3RGR0eH9Q2uvzrC2uPnmN6/Kym5BaTlFfJY/65UVMvVkbcAbzw0FEA9L8tD3cPIKiwmJVd1DAp3d2JK7wiNeQ1nDOjGxbRs0vILMdaXMaFHGN721izcdqD9KtdIW11L3TQszJ/Y6xntGjV1KxtOnmdq3y6k5haQnl/EtL6dqayWs6/B6lWvPqCaA+3zzaoVHB/sGkJWYQmpeYUAhLk78nDPTur7CIBJPTsxc1B3/rtxL2l5hVjWfd+rFDVN5jET/tKL5t03RIdNnUmTJnHjxg0++ugjDAwMmDx5skbEzV9N9779KSspYdvaVRQV5OPk5s6Lb72Hdd1Y/6KCAnKyNHuEv/7oPfJy6kP6PnhFNb/Gr+tVY3wVCjmrl/yPwvw8pDIZzq5uvPDWu4R1aZun1LdzLOEqeroSRnYKwkCmR3p+ESsOn9FYhcC87sRyU1F5BauORDMsLKBuie9KdsdcIj6jPozy8GVVR9WAYB9MDQ2oqKomMTOHA3EduzzhTT36DaC0pITNa1ZSlJ+Ps7sHL7/zPjZ2qpvwwoICbmRlauRZ8OE75N2ob9t35/0fAL9vUi2xa2BoyKsfzGfZLz/w/isvYWRiQucePZn0+BPtVKtb23BStWT7k0N6YWwgIzEzhw/W7qRSXt+pamOmORRoRKcA9HQlzB7ck9mDe6q3X0jN5J1VOwBYcywGpVLJ1L6dsTYxpqSiktPJqSw/dKZ9KgZ0HToShbyafWv+pKq8DAd3Lx569mWNzoTiAs1VZMytbRn/1ItEbVhF7OEDGJtbMHDiVHw71S9PXVpUyJ+ffaB+HZsbReyRKJx9/Jj0wmsAZKdcY923X6jTHN+xieM7NhHYvTcjptffVLWmToOGo5BXc2j9CqoqyrFz82Tsky9o1Le0UPPYa2Ztw5g5z3Nk0xrijh7E2NycvuOn4B1WvwqRvKqKg+v+pLSwED2pFAs7BwZPfQLfzprHp4wrCRTl3mDIo2373Q7s2oOKslKO7NhMWXERNo7OTHruZXW0TGlRIQUNjrcGhkZMeeFVdq/8g98/fQ8DI2O6DxlJ9yGaEZmL57+r8TopNgYzK2ue/fhLAMysrJnyf/9i37qVLJ7/DsZm5oT27kefUQ+0aX0bG9avP0XFJSxevYrc/Hy83N1Z+O576vlm8vILSM+6u6eSG3bsoKamhoW//MLCX35Rb+8cEsIPn3zaquW/FycSryHVlTA8PAADqR4ZBcWsOqJ5TjJrNCl+UXkla46eZUiYHxGerqolvs/FNwmvNzcyxN3Wqt0nur8pfOBwFHI5hzespLrudzt67v/d+ndrZcPI2c9xbMtaLh47hLGZOb0fnIxXg99tVWUFh9Yup7ykGJmBATbOrjzwzCsaQ7IuHlPdOG776WuN/XceNoauw8e2QW3vjUGAHy7ffq5+bT3ncaznPE7x9t1kz/+yA0t2d9afOI++nh5PDeuNiYGMhIwc3l29UyN6wdZMc0jbyE6B6OlKmDu0F3OH9lJvj03J5K0VqrlEpHq6PNqvCw4WplRWKzidnMrCbVG3HDLYHlYciUZfqsdLowdgaqjPxbRsXv1jk0Z97c01O4R1JTo8ObQXDhZm1NTWklFQxM+RR9nc4KbexECfV8YNxMrEmLKqKhIzc3lh8QYup3fc0Jm2upYC1d8o1M2RBVsPtHk97tTqYzHoS/V4fmQ/TA31uZx+gzf+3KrRtnbmmt9liUTC7CE9cTA3rWvbYv6377i6kx1gXNdgpLq6/GficI28u89d5ost+9u2UsI/kk5FRYXo97oPtWRI1F/RwcvNL+n7dzM42Keji9CuPt8a1dFFaDfDQjtmZbiOUiX/60Yh3i1zo/vnSX57eMil45afbW8/xl3r6CK0q8ah/X9nD3z2344uQrt6ZVzzQ2n/boobzF/4T3A3Kzn91f3Tlsfe/K/74+FoW9lw6sLtE7WSh7q176rF7UVMsS8IgiAIgiAIgiAIgnCfEUOiBEEQBEEQBEEQBEFoVWJZ75YTETaCIAiCIAiCIAiCIAj3GRFhIwiCIAiCIAiCIAhCq2qPVVf/7kSEjSAIgiAIgiAIgiAIwn1GRNgIgiAIgiAIgiAIgtCqRIBNy4kIG0EQBEEQBEEQBEEQhPuMiLARBEEQBEEQBEEQBKFViTlsWk5E2AiCIAiCIAiCIAiCINxnRISNIAiCIAiCIAiCIAitSomIsGkpEWEjCIIgCIIgCIIgCIJwnxERNoIgCIIgCIIgCIIgtCqlmMOmxUSEjSAIgiAIgiAIgiAIwn1GRNjcpzaejuvoIrQrP0ebji5Cu/mnte0LI/p0dBHaTVJ2XkcXoV0ZSP85p5BKuaKji9CuNqXnd3QR2o2JgX5HF6FdVVRXd3QR2s0r4yZ1dBHa1Zdb1nR0EdrN5YVfdHQR2lVtbW1HF6HdSCQinkAQGvrnXG0LgiAIgiAIgiAIgtAuav9CI6K2bdvG+vXrKSgowM3Njblz5xIcHNxs+mvXrvHjjz+SmJiIiYkJI0eO5JFHHkFHR6dVyyW6MAVBEARBEARBEARB+Ec6dOgQv/zyC5MnT+brr78mMDCQ9957jxs3bmhNX15ezttvv42FhQULFizgySefZMOGDWzcuLHVyyY6bARBEARBEARBEARBaFVKpbLd/rXExo0bGTJkCCNGjMDV1ZWnnnoKS0tLduzYoTX9gQMHqKqqYt68ebi7u9OnTx8mTpzIxo0bW32iZdFhIwiCIAiCIAiCIAjCP45cLicpKYmIiAiN7REREVy6dElrnsuXLxMcHIy+vr5G+vz8fLKzs1u1fGIOG0EQBEEQBEEQBEEQWlV7Luu9c+dOdu3addt0I0aMYOTIkerXxcXF1NbWYmFhoZHOwsKCc+fOad1HQUEBNjY2TdIDFBYW4uDgcHeFvwXRYSMIgiAIgiAIgiAIwl/WyJEjNTpi7tbdThbc2pMLN0d02AiCIAiCIAiCIAiC0Kpq2zHC5l6ZmZkhkUgoKCjQ2F5YWNgk6uYmS0tLremBZvPcKzGHjSAIgiAIgiAIgiAI/zhSqRQfHx9iYmI0tsfExBAYGKg1T0BAAHFxcVRXV2ukt7Kywt7evlXLJzpsBEEQBEEQBEEQBEFoVbVKZbv9a4nx48ezd+9edu3aRWpqKj///DP5+fmMGjUKgCVLlvDWW2+p0w8YMAB9fX2++uorrl+/ztGjR1m7di3jx49v9aFSYkiUIAiCIAiCIAiCIAj/SP369aO4uJjVq1eTn5+Pu7s77777LnZ2dgDk5+eTlZWlTm9sbMyHH37Ijz/+yLx58zAxMeGhhx5i/PjxrV62dumwyc7OZs6cOSxYsABfX9973s+RI0f49NNP2bJlSyuWrm1MmjSJp556iqFDh3Z0UQRBEARBEARBEAShXbXnKlEtNWbMGMaMGaP1vXnz5jXZ5uHhwaefftrWxWqfDhsbGxuWLl2KmZlZe3yc0MDw8AB6+rljJJNxPbeA9SfOkV1Y0mx6U0N9Hugagou1BTamJpxJTmXlkWiNNN283Xikb+cmef/9x2YUtbWtXgdQ/diP7dhM7JEoKivKcXT3YvDkR7FxdL5lvtTEeKI2rCIvMx0Tcwu6Dh1FeN+BGmkSYk5zdNtGinJzMLexpc/YCfiG19evtraWY9s3cenUccqKCzE2syCwWw96jXoQia4uAAv+b7bWzw/vN4ghk6e3rPK3MDTMnx4+7hjKpKTkFbDpZCzZRbdu3zGdg3G2MsfG1IToq6msORbTbPpwD2em9e3CpbQsfj9wsg1qcGcO7NzO7s3rKSoowMnVjckz5+AbFKw1rby6muU/f09K8hUy09Pw8Q/klQ/mN7vvpEsX+fLdN3FwduHdhd+1VRWadfbgPk5F7qC0qBAbR2cGPzwNFx+/ZtPnpKcSuXo5WdeTMTAyJrzvQHqNekAj/DI18TL7160kNzMdE3NLug8bRad+g7Tu79Lp42xd/BNeIeFMfOal1q6ehuiovZzYU1/XoZOm4err32z6G+mp7Fm1jMxrqrp26jeIPqPr61paVMi+tSvJSr1GwY1sgnv0ZuyMuRr7iDl8gAvHj5KbmY5SWYu9qzv9xk3A9RZ/49aiVCo5vmMzF44epLKiHAd3TwZPehTr2xy30hLjObhhFXlZGRibW9B1yEjCGhy38jLTObZ9MzfSrlOcl0uPkePoNfpBjX2c3L2dK+ejKcjOQldPDwcPL/qMm4iN060/+16dORDJ8T3bKS0qwtbJmaGTHsXtNm27a+XSurY1IaL/IPqOflDdtpfPnuLswf1kpV6nRi7HxtGJ3qMewC9c89xTVVFB1Oa1XI4+RUVZKWaWVgx4cBJBXXu0ST1vUiqVnNy5hbhjdW3r5smAh6fdtm3Tk+I5tHE1+XVt23nwCEL7DFS/f+HYQS6fOkZ+VgbKWiW2Lm70HP0gTl71D7vOH9rPhaNRFOfnAWDt4ETX4WPwDA5rk7re1Nvfi3APZ/SlemQWFBN5/jJ5JWW3zONibcGgED9sTI0praziZNJ1zl1L10gj09Olb6A3/k72GEillFRUcuhSEvEZNwDo4euBr6MtVibG1NTWklFQxKGLSeTe5rNb29Q+nRke7o+JgT4JmTn8uOcIqbmFzabv5efByE4BeNlbI9XVJTWvkDXHYjiZlKJOoyvR4eGenRgc4ou1qRHp+UUsOXCK6Ktp7VCjljEID8Fy6sMY+PuiZ2tD1sdfULJjT0cX664c2bOTA9s3U1JYgL2zKw9On4lXQFCz6TNTr7NhyW+kXEnCyMSEnoOHMWz8wxrn3yN7dnBkz07yc3KwtLZhyIMT6NpvYDvURtPRyF1Ebd9MSVEh9s4uPPDoTDz9tc/FAZCZmsLGpb+RmqyqW49Bwxj64ESNup09epgD2zeRm5WJvqEhvsGhjH3kcUzrJlg9sT+SM0cOkp2eilKpxMndkxETpuDpH9DW1e2Qtjx34ij7t24kNzuLmpoabO0d6TdyLN36D2z6gYLQAu3SYaOrq4ulpWV7fJTQwKAQXwYEe7Py8FlyiksYFh7AU8N6898Ne6lSKLTm0ZPoUlZVzb7YRHr6uTe77yq5gk/Wa56Y26qzBuBU5A7O7NvFiOmzsLJz4PjOLaz77kueePtjZAaGWvMU5eaw4cevCOnZl1GPzyH9SiL7Vi/H0MQEv05dAci4msS2xT/Re/SD+IR3JulcNFv/9wOPzHsDRw8v1Wfv2UHMoX2MnD4bGycXcjPS2PnHb+jqSek5chwAT328QOOzs1OusfGnb/CL6NZmf5MBQT70D/Rm9dGz5BSXMjTMjzlDevH55r1UK2q05tGTSCivquZAXBI9fJtvXwArEyPGRASRnJ3XFsW/Y6eOHGLV4l+YNudpfAKDOLBrO9/Of5/3Fi7Cyta2Sfra2lqkUhkDR43hQvQZKsqav6gvKy1l8bcLCQgNpzC//et5+cwJ9q35k6GPPIaLty9nD+5j7aIFzHr7Y8ysrJukr6qoYPW3X+Dq48/0194hPzuLHX/8hlSmT7ehqmUMC3NzWPf9QkJ69WPMzCdJu5JI5Mo/MDQxxT+iq8b+CnNvcGDDaly8277z4tLpE0Su/pPhUx/DxduP6IN7Wb1oAXPemY95M3Vd9c3nuPr4M+Pf75KXncn2pb8h1ZfRY6hqPLFCIcfQxISeI8Zw7nCU1s9NSbhMYNfuOHv7IpXKOLVvF6u//YIn3voAKzuHNq3z6cidRO/fzfBHZ2Fp58CJnVtYv2gBM/7zMTIDA615ivJy2PjT1wT37MvIx+eQnpzE/tXLMTQxxbdTF0DVKWlmbY1PeGeObtugdT9pSfGE9R2IvZsnoOTY9k2sX/Qlj7/5AQbGJq1az4unj7Nn9XJGTH0cVx8/zkTtZdV3X/Dku59gbmXTJH1VRQUrvv4MVx9/Zr7+PvnZmWxd8gsymT49hqnaNiUhHnf/QAY8MBEDYxPiTh5l3Y9f8+jLb6o7gmpqFKz45jMMjIx5aO5zmFpYUVKYj66etFXrp0303p2cPbCbodOewNLOgZO7trDph4VMf/OjW7bt5p+/IahHH4ZPn0PG1USi1vyJoYkpPuGqtk1Pisc3ohtOnj7oSWXERO1h049fMfXVd7CwVU1iaGJhSe9xE7GwtUeprOXyqWNs/+17pvzrP9g4ubRJfbv7uNPNx40d0RfJLy2jl78Xk3t35te9R5E3c74xNzJgYs8ILqRksO3MBVysLRgaFkBFlZyETFVnjERHh0m9OlMpl7P51HlKKqowNdSnprb+iayrjSUx19LIKigGoG+gN5N7d+Z/+45RKdd+LdPaJvQI48FuIXy9/SDp+UU80ieCDyaP4tlf11JRLdeaJ9jVgfPXM1h26AylFVUMCPbmjYeG8taKbVxMywZger+uDAz2YdHOw6TmFdLZ04U3HhrKv5dtIflGx557b0diaEh18nVKdkZi/59XO7o4dy3m+BE2LVvMhJlz8PQL5GjkLn79fD6v/nchljZNry0qy8v5+dMP8fQP5MUPPiUnM4NVP3+HTF+fgaMfAFSdJNtWLmfS7Kdx8/El5Uoia3/7EUNjE4I7d22yz7ar21E2L/+dhx6fjYdfAMf27ua3L+bzyicLsbRpekyurCjnl88+xMs/kBfe/0RVt1++R6avz4BRquvcawmXWfnTt4yZ+hghXbpTUlTIhiW/seLHb3jy9XcAuHL5IuE9euPh649UX59DO7fy6+cf89JHn2Hr4NiG9e2YtjQyMWXogw9j5+SMRFeXS2fPsObX7zExMyOwU9MH2/9UtX+dAJv71j1POnz69GkmT55MTY3qRJ2RkcG4ceP4/vvv1WmWLl3K22+/TXZ2NuPGjSMxMRGA2NhYxo0bx7lz53jllVeYOHEi8+bNIykpSeMz9u3bx6xZs5g4cSLvv/++eqmsm3Jycvjoo4+YOnUqEydO5Omnn+bgwYMA6s88cOAAr732GhMmTODpp58mOlozWiQlJYX333+fyZMnM336dD7//PMmS3RFRkby7LPPMmHCBJ566ik2btxIbYPOiYyMDN544w31Z5w82XGRCA31D/RmX2wisSkZZBWWsOLwGfSlekR4NX9BV1BWzsaTsZy6kkJ5lfaLkJtKKqs0/rUVpVLJ2QORdB82Gr9OXbFxcmHE9NlUV1Vy+fSJZvOdO3IAE3ML1RNtByfC+gwgqEdvzuzdpU4TvT8SV98AeowYi7WDEz1GjMXVx5/o/fWdURlXk/AO6YR3aCfMrW3wDlX9f+a1ZHUaYzNzjX9J589iaWd/y+iBluob6MX+uEQupGaSXVTCqqNnVe3reav2rWDz6QucSU6lvKq62XQSHR2m9u3CznOXyS9t36eYjUVu2UTvgUPoN2wEji6uTJ39FOYWlkTt3q41vb6BAY8+9Sz9h43E0rppR0BDS7//hp4DB+Pl13btdCun9+4mpGcfwvsMwNrBiaGTp2Nsbk7MoX1a0188dQyFvJpRj8/B1skF/4iu9Bg2itP7dqlDTs8d3o+xuQVDJ0/H2sGJ8D4DCO7Zm1N7d2rsq6ZGwdb//US/cRMw13JB09pO7t1FaK8+dOo7EBtHJ4ZPeQwTMwvOHtRe17iTx5BXVzNmxlxsnV0I6NyNHsNHcyqyvq4W1rYMmzKdsF79MDAy1rqfB2Y9TZeBQ3FwdcfawZERU2cgMzAgOS62zeoKdcetqEi6DR2Fb6cu2Dg5M2L6LNVx60zzx63zh6MwMbdg0MPTsHJwIrR3fwK79+LMvvrjloO7J/3HTyagaw+kMpnW/Ux4dh7BPfti4+SsOmY+NpuK0hIykpO0pm+Jk5E7CevVl4h+g7BxdGbEI49jYmZBdJT2tr1w8ijy6irGzXwSu7q27TliDCcid6rbdviU6fQeOQ4nT2+s7OzpN/YhHNw8STh3Rr2f80cPUV5SzKRnXsLVxx8LG1tcffxxqutsbytKpZKYg3vpMmQUPuFdsHZ0Ztg0Vdsm3KJtLxyJwtjMggETp2Hl4EhIr/4EdO/F2X271WlGPDaX8H6DsXVxw9LegYGTpiPTN+D6pQvqNF6hnfAICsXC1g5LOwd6jXkIqYE+WVevtFmdu3i7cSLxGgmZN8gtKWNHdBwyPV2CnJvv9Az3cKGssoq9sfHkl5Zz/noGcamZdPNxU6cJcXPCSF/KhhPnSM8voriikvT8IrIKi9Vp1h47y4WUTHJLysgtKWPbmTgM9WU4W1m0WX0be6BrCOtOnOdYwjVScgv4alsUhjIp/QO9m83z697jrDtxnsTMHDILi1l55CxXsnLp6euhTjMw2If1J85zOjmV7KISdsRc4kxyKuO7h7ZDrVqm/Pgp8n5eTOmBw3/JO7KoHVvo1m8gPQcNw97ZhYdmzMbMwoJje3drTR999BDVVVVMffp5HF3dCOvek0Fjx3Nwx1b1cevMkSh6DBpCRO++WNvZE9GrLz0GDWP/1o3tWDM4tHMrXfsOoMegodg7uzD+8VmYWlhyfJ/2up09ehh5VTVTnnweBxc3Qrv1ZNCYBzm0s75u15MSMLeypv/IsVjZ2uHu40efYSNJuZKo3s+0Z16gz7CROHt4YufoxISZc9E3NCDhfEyb1rej2tI3OJSQrt2xc3LGxt6BfiPH4OjqztX4S21aX+Gf5547bIKDg6murtbohDEzM+P8+fPqNBcuXCAkJKTZfSxZsoQZM2bw1VdfYWpqypdffqn+ocTHx/PVV18xYsQIvvnmG7p3787y5cs18v/www9UVVUxf/58Fi1axNy5czE21rxo//333xk3bhxff/01ERERfPzxx+TlqZ5a5Ofn8/rrr+Pu7s6XX37Jhx9+SEVFBR9++KG6Q2bXrl0sXbqURx99lO+//57Zs2ezbt06tm9X3SzW1tYyf/58lEoln3/+OS+88AIrVqxALr91Z0dbszIxwszIgIS6kGIARU0tydl5eNhatXj/Ul1d3po4nLcfHsHswT1xtjJv8T6bU5SXS1lxEe4B9UNgpDIZLt5+ZNziAjXz6hWNPAAegcFkp1ynpkb1VC7zWtM07oEhZFytv6lx9vIlNfEy+VmZAORlZpCScAnPIO0XVNVVlcRHnyS0d/+7q+hdsDIxwszQgMTMHPU2RU0tyTfycLdpefuO6BRIQWk50cmpLd5XSyjkclKSkwgK76SxPTA8givxl1u07wM7t1NcWMiYiZNbtJ97VaNQkJV6DY9AzWOkR2AI6cnav9cZV6/g4u2ncZPuERRCaVEhRXm5qjTJV5rs0zMwlOzr19Tfe4BDm9djZm1NSM++rVWlZtUoFGSlXMOzSbmCSW+mAyH9ahKuPpp19WpU13sti0Iub7aDp7UU5+VSXlyEW4Pji55MhrO3H5lXm+80ybp2BTf/psekGw2OW/dCXlmJUqlEv5XrXaNQkJlyrcnx0DMohLTkRK150pOTcPXxb9S2oZQWFdyybaurKjTaLSHmDC7evuxa9Qdfv/Z//PTe6xzcsr5Ff6c7Ud+29eH2ejIZTt5+ZF5r/pyUdS1ZIw+AW0AwN1Kbb9vaGtX3tbl2q62tJSH6JPKqKhw8m+88aAlzI0NMDPS5diNfvU1RW0tqbiFOtzj3O1macy1HM0rk6o087C3MkNQNO/B1tCU9v4ihYf48O6IfTwzuSW9/L/X72sj0dJHo6FDZTtdZ9uamWJkYcbbBMKVqRQ1xaVkEOtvd1b4M9WWUNnjAJdXTbRIRW62oIdCldZeEFTQpFHLSrybjFxqusd0vNJxrifFa81xPisfTPxCpTL9B+k4UF+STn6O61lbIFUilmp3oUpmM1CtJ1DQT2d7aFAoF6de01C0k7BZ1S8DTP0DjmOwXGk5xQQEFuarrTA/fAEoKC7h49jRKpZKykmLOHT9KQHhEs2W5eb41NG678+390pZKpZLEC+e5kZWBZ0DzQ8/+iZRKZbv9+7u65w4bQ0NDvL29iY1VPaWMjY1l7Nix5OTkkJ+fT2VlJYmJiYSGNv+UYPr06YSFheHq6sojjzxCWlqaujNl8+bNhIeHM2XKFJydnRk1ahQ9e/bUyJ+Tk0NQUBCenp44ODjQpUsXunTpopFm1KhR9OvXD1dXV+bOnYuNjY26s2X79u14enoyc+ZMXF1d8fT05OWXXyYxMVEd7bNy5UpmzpxJnz59cHBwoHv37jz88MPqfcTExJCamsrLL7+Mt7c3QUFBzJkzRx151FHMDFUh2Y0jX0orqjAz1NeW5Y7dKC5h1dFoFu87wbKDp5HX1PD8qH7YmLbNAbm8uAgAI1PNOZCMzMwoq3tPm7Li4qZ5TM2ora2horS0Lk0Rxo3SGJuaUV5S/3Sv27BRBHbrxe/z3+arF59kyfy3Ce7Rm079B2v93MunT1CjUBDUvc+dV/IumRqo2rC0omn7mrawfX0dbQl3d2LDyfO3T9zGSkuKqa2tVY+PvsnMwoLiRhF3dyP9+jW2rlnB7BdfVs9D1N4qSktQ1tY2+Y4amzb/vS4rLtKS3lz9HkBZSdPvdOPv/dVLF4g/c5LhU2e0Sl1up/xmXc00b+6MzMwpK7pVXZumv/nevTq4eR0yfQN8w5q/yGwNZc0dt0zNKCsu1palLl/zx63Kuva7FwfWrcTW2RXHVr6pv9m2xo3mqDM2M7/l97hpetXr0uJCrXlOH4ikpKCA0B71x9WC3BwunTlFbY2Cyc+9zIAHJnL20H4ObFjTghrdXnlJM21rYqY+XzWXz8jk7tr22LaNSPX18QrRvBnJzUjjx9ee5/t/PcP+1csYPevZNhsOZayvumkpq9I835RXVWNs0Pz5xthARlmlZiRneVU1uhIJhjLVsDVzI0P8neyQ6EhYdzyGI5eS6eThTP8gn2b3OzjUn+zCEjLy7/04cDcsTVTDrgvLKjS2F5VVYGFidMf7GR0RiLWJEfvj6jtsz15N44FuIThbmaMDdPJwppefB1bGd75f4e6VlZRQW1uLibmFxnYTcwtKmrm2KCksxNRc85x083VJkSqPf2g4J6P2kXIlCaVSSWpyEicP7KWmRkFZSfPzC7amsrrrJpNG51sTcwt1ORsrKSpsmt7MQvVe3d/D3dePac++yIofvuWNWdN4/7k5KFEy5cnnmy3LzrUr0dc3IKgNh4N1dFtWlJfx5uzp/HvmI/z25SeMf2wWgeFiOJTQulo0h01oaCixsbFMmjSJCxcu8MADD3Du3Dl1tI2uri5+fn7qTpjGPDw81P9vZaWKCigsLMTGxoa0tDS6ddOc/yMgIIA9e+qHqtwcgnXmzBnCw8Pp1asXPj4+TfLcJJFI8PPzIzVVFTVw5coV4uLimDRpUpOyZWZmYm9vT25uLosWLeKHH35Qv1dTU6PuxUtLS8PKykq95BeAv78/Esk994Xdk86eLjzcq5P69a97j6n+p3Fno07TTXfrek4B13Pqh41dy8njlXGD6BvoxcaTLR9mcOnUcSJXLlW/Hv/0iwBN17RXArdZ575xnpudrxrbm+xW8y8UH32SiyePMnrGXKwdnclJS2H/uhWYWdsS2qtfk8+MPXoQn7AIjExNb1m2u9HJw5kJPeov2BfvP6G1rDotbF8jfRmTe0Ww4siZZsfldwQdGrfjvddSLpfzy8LPefjxWdjYt+0cJnei6XdUecuvtbb0TbY32cHNv5cO5aUl7PjjN8bOfKrNo0waa1ItpVLLxgbpm1SjZUevU/t2E3P4AI+8+Br6htrnvrpXl08dZ++qP9SvH3zqBUDLcQvlrapcl6dxFmUzb9yZqPWryEhOZPJLr7fhuanxgVTZdJtGai3Hc23bgcvRp9i3biXj5zyLuXWD+ReUtRibmjJ6+mwkEgmO7p5UlJUSuWY5gyc+ouVvf2/iTx9n/+pl6tfjnvw/dS2aVOJ2n9nkPNZ828ZERXLh6EHGP/tyk7naLO0ceOTVd6iqKOfKuWgi/1zMhOf/ddtJj+9EoIsDw8Prr53WHY+pK2ujhDq0+DepowPlVXJ2xVxECWQXlWAgkzIoxI8DcU0jtAYF++JibcGfh063+FqmOQOCvHl2RH3k4Qdrd2lPqKNzx/Xv5efBE4N68PnmfeQU13fO/RJ5nOdH9uW72RMBVJM5xyYwNLTt5xUT7uWc1Oj822j7sIcepqSokO8+eAuUSkzMLejSbwAHtm5Cp53vC5peMytveUzUdq5SvaH6T3Z6GpuWLWbIgxPxDw2nuLCAbauWsW7xzzzyVNNOm8O7tnNifyRz//02BoZt3wHZUW2pb2DIyx9/TlVVJYlxsWxZvgQrG1t8Q9p2Enjhn6VFHTYhISFs27aNlJQUKioq8Pb2VnfimJmZERgYiJ5e8x+h2+DJ9s0fyM2bjzu5IRs+fDidO3fm9OnTxMTE8OqrrzJp0iSmTZt2R+Wvra2la9euzJo1q8l7FhYWVNU9TXruuec0On4autsbx507d7JrVzMn/wYqrF2w9tW+Co42calZXM/dr36tp6s6mJga6lNYXv9UyMRAn5KK1p1vRqmE1LxCbExbZyJL79BwHDzeVb++GXpYVlyEqWX9cJ/ykuImkQQNGWuJwKkoLUYi0cWgLjxT9RRY82l3eUmJxpPTgxvX0HXICAK6qFYdsXVyoTg/j5O7tzfpsLmRlkJ2yjX6jptwN1W+rYtpWRqrUdS3rwFF5ZXq7cYG+k2ibu6Gg4UpZkYGzBnSS73t5m9z/rSxLNi6n9zi9pvTxsTUDIlEQnGh5rxSJUVFmDWKurlTRQX5ZKalsmTR1yxZ9DVQH675zOTx/N+b7xLUqW2jLwAMTUzRkUiafEfLS0uaRJbcpC1qobxU9f29+Z01NtWSpqQEiUQXQxNj0q8kUVZUyOpvP1e/f/M49sX/zWbWfz7Cyr51Jwc0aq6uJcUYm91FXesi35rLcyun9u3m0Ob1THr+5TaZ48QrtBMOHp7q180ft0owusWKiarjVqNjUmmJxnHrbkStX0l89Cke/r9/tclcRc21bVlJcZMompuMzcwp1ZL+5nsNXY4+xebFPzFu5pNNVogyNrdAV1dXoxPK2sEJeXU15aUltzw/3A3PkE7Yu9d/Z2oUqs7s8pJGbVta0iTqpiEjU3N1dE7DPNraNiYqkuPbN/LAUy/i4O5JY7p6eljYqh4U2bt5kJ16jZgDexgydeZd16+xpKwcMgvqy6lb9/c1NtDXiNo1kskou8VcaGWV1RgbaA4pMNKXUVNbq34gUFZZTa2yVqPzJa+kDJmeLoYyqcaDg0EhfgQ427PqyBmKyjWjXVrTyaQUEjLqJ/PW01PV38LYUGNlKnMjgyZRN9r08vPg5bEDWbjtgMYKUQDFFZXM3xCJVFcXU0N98kvLmTGg2y1XexRaztjUFIlE0iTipLS4CNNGkRo3mWqJ7C2tixC9GZ0ilekz5cnneHjWU6rrFEsLju+LRN/AEONWfIh3K8Z1103a6tY4iuYmUy3RNzeP0Tf/Hvu3bMDVy4eBY1ST8jq6uSPTN+CHj99h5MOPYNGgM/3wru3sXLeS2a+8iZt389FyraGj21IikWBTN6Gys7snN9LT2bt5veiwaeDvPFSpvbSowyY4OBi5XM66desICgpCV1eX0NBQvvvuOywsLJoMT7obrq6uxMdrjj1s/BpUS4aPHDmSkSNHsnbtWrZs2aLRYRMfH094uCoyQalUkpiYSO/evQHw9vbm8OHD2NnZae1YMjIywtramszMTAYP1j78xdXVlfz8fHJycrCtW7EmISFBY1Lihm6W9XbeWrXztmkaqlIoqCrRHFNZXF6Jn5MdqXmFgGqFIC87a7aciburfd8JR0tzMlspPFlmYKjxNFGpVGJsZs71yxfVF64KuZz05ET6P9g0OkpdJk9vrpw/q7Ht+uWL2Lu5o6uram9HD29S4uPUK+wApMTH4eRZf4JRVFejo6P5ZEQikYCyaRvHHonCzMoGN//mlxK8F9WKGvIaTf5bXFGJr4MtaQ3a19PWiu1nL97z56TmFrJgy36NbSM6BWAok7LxZCwFpeX3vO97oSeV4ublw8XzMXTpXf/E89L5GDr37HWLnM2ztLLmnQXfamyL2rWdS+diePq1N7G2vbs5Ce6Vrp4eDq4eXLsch3/n+mjC65fj8Ouk/djp5OnNwU1rUMjl6ElVQwquX7qIibmFOvLAycubxHOa3/trl+Owd/dAV1cPB3dPZr71ocb7h7esp7K8nKFTpmNu3fo39bp6eji4eXD1chwBXbqrt1+9HNdk5aqbnD19OLBxNQp5NXp148ivXorTqOudOhm5k0NbNzDpuZfbbDlvmYGBxupASqUSIzNzUuI1j1sZVxLpO77545aDhzfJsZrtlxJ/EbsGx607dWDdChKiT/Lw/73a6p1wN+nq6eHo5sHVSxcIbNC21y5dwL+ZVfKcvXzYv2FVo7a9gIm5pUbbXjx9gq1LfmbsjCc19n2Tq7cfcSePoaytVT/xzL+RhVQmw8ik9W6ObtW2qlW4GrTtAw83ux8HDy+SY2M0tqXGX8TOVbNtz+7fzYkdmxn31Asay3nfkrK21ebIkCtqKFRodkSUVlbhbmulngxYVyLBxdqCKC1RMDdlFBTh66h5PPGwtSK7sJjaugv49PxCAl00Ix2tTIyoVtRodNYMruusWXnkDPltfB6qqJY3iTDNLy0nwsOZpCzVHEtSXV2CXRxYvP/Wi0z0CfDkpdED+Gp7FEfjrzWbTl5TQ35pOboSHXr7e3D48tUW10Nonp6eFGdPLxIunCe8R2/19oQL5wnr1lNrHncff7atXIa8ulo910vihXOYWVph1ei6QVdPD4u6RQ9ijh8hKKJLu0Xe6+np4eyhqltY9/rrpMQLsYR266E1j7uPH9tXLW9Ut/OYWVqqV1mqrq5qUoebrxvejx/csZXd61cx65U32mU57/utLZXKWhTtNF+R8M/RoqPHzXlsDhw4oJ6rJiAggNzcXOLj4285f83t3FxFas2aNWRkZLBr1y6OHTumkebnn3/mzJkzZGVlkZycTHR0NK6urhppduzYwZEjR0hLS+OXX37hxo0bjB49GoAxY8ZQXl7OZ599Rnx8PFlZWcTExPDdd99RXq66IJg6dSrr169n48aNpKWlcf36dfbt28eaNaox8p06dcLZ2ZmFCxeSnJzM5cuX+fXXXzWihzrKwUtXGBziS6ibIw4WpjzStzNVCgVnk+snzpvatzNT+2o+tXSyNMfJ0hx9mR5G+lKcLM2xN6+/+B0e7o+/kx1WJkY4WZozpXcETpZmHE1omwsMHR0dIgYO5VTkdhJjzpCbkcauZaqljAO61p98diz9lR1Lf1W/Du8zkJLCAvavW0FeVgaxRw8Sd+IIXYaMUKfpPHAoKQmXObl7G/lZmZzcvY3UhHg6DxqmTuMVEs6pyB0kXzhHUV4uieeiObN/Nz5hmn83eXUVl06fILR3v1YLxb+Vw5eSGRjsQ7CrI/bmpkzuHUG1okZjYsTJvSOY3FszWsTR0gxHSzP0pVKM9GU4WpphZ66KjpLX1JBdVKLxr6JaTpVcQXZRicZSq+1l6LgHOXZgH4cjd5OZlsqq//1CUUE+/Yerlv/dsHwJC977j0aejNQUUq8mU1pcQmVlJalXk0m9qlrVS1dPD2c3d41/pmbm6EmlOLu5Y9DKQ2VupeuQ4Vw4fpjzR6LIy8pg75rllBYWEt53EAAHN61h1defqdMHdeuJnlTGjj9+JScjjYSY05zYs42ug0eov3PhfQdRWpjPvrV/kpeVwfkjUVw4fphuQ1SdkjJ9fWydXDT+6RsaITMwwNbJBd1bREW2RPchI4g9dphzh6PIzcxgz+rllBYVEtFPVdcDG9ew4qv/1te1e0+kMhnblvxKTnoa8WdPc3z3NroNHaHx+8pOvU526nWqKiuoLCsjO/U6uZnp6vdP7N7OgY1rGP3YbKzs7CktKqS0qJDKira96dPR0SFiwFBO79lB0rkz5Gaks3v5/5Dq66uj9QB2/fEbu/74Tf06rO8ASgoLOLBuJflZGVw4epCLJ47QZXD9catGoeBGWgo30lJQyOWUlxRzIy2FwpxsdZp9q5dz8fgRRs14En0jY8qKiygrLqK6qj4ir7V0HzqS88cOEXP4ALmZ6exetYySokI6183ztX/DapYv/FSdPrh7L6QyfbYs+YUb6WlcPnuKY7u20mPoSHXbxp06zub//cjA8ZNx8/VXt1tFWf1wks79B1NRXsru1cvIy8okOe48h7asp/OAIW16DNbR0aFT/yGcidxJ0rlo8jLTifxzMTJ9ffwatO3uZb+xe1l924b0GUBpUQEH168kPyuTuGOHuHTyKBGDh6vTRO/bxdGt6xkydQYWtvbqdqtq8H09smUd6VcSKM7LJTcjjaNb1pOWlKDx2a3tzJUUevh64Otoi42pMaMigpDX1HAxPUudZnTnYEZ3ro8MPnctDRMDAwaF+GFlYkSomxMhbk6cahBlEnM1DQOplCGh/liaGOFha0WfAC9irtZPeD80zJ8QNye2nrlAlVyBsb4MY30Z0na8ztp8+gITe4bTy88DNxtLXhzTn4pqOQcv1U8y/dKYAbw0ZoD6db9AL14ZO4ilUaeIS83CwtgQC2PVBM43+Tna0svPA3tzU4Jc7Hlvkuo3sOhq+CkAAL1cSURBVP5Ex88hdzs6hgbIfLyQ+XiBRAepvR0yHy/07Nt+1cHWMGDUOE4fPMCJ/ZFkp6excen/KC4ooOcQ1e9x+6rl/Dj/PXX6iN59kenrs+rn78hMTSH21HH2bdlI/1Fj1cebnMwMzhyOIicrk5QriSz7bgFZaSmMmnxnkf+tpd/IsZw5dIATB/aqhzIVF+bTc7Dq2nbH6j/5+dMP1Ok79eqLVF/G6l++JysthdhTJ9i/dRP9RtbXLSiiK3HRpzm2dzd5N7K5lnCZTcsW4+zhqV4q/MC2zexYvZxJc57B1sGJksJCSgoLqShv2/NtR7Vl5KZ1JFw4T96NbLLT0ziwfTNnjhykc5+m0yX8k9Uqle327++qxVfmoaGhJCQkqDtnZDIZ/v7+JCYm4ud3708yAwICeOGFF1i+fDkrV64kJCSEadOm8dNPP6nTKJVKfvrpJ3JzczE0NCQ8PJzZs2dr7GfGjBls3LiRK1euYGdnx5tvvolN3YHF2tqazz77jCVLlvDuu+8il8uxtbUlIiICad3T6xEjRmBgYMD69etZunQpMpkMNzc3xo4dC6h6l9966y2+/fZbXnnlFWxtbZk9ezZffPHFPde9tey/kIhUV5cJPcIx1JeSklPAz3uOUtWg59dCy8R2rzwwSON1sKsj+aXlfLxOtTyegUzKw706YWaoT0W1goz8QhbtPKQxZKe1dRs6CoVczr41y6ksL8PBw4uJz2mO6y8pyNfIY25jy0NPv0TU+pWcP3wAYzPVUrl+neqf6Dt5+TBm5lMc2bqBo9s3YWFjx5gnnsKxwZCJwZOmcWTbRvauXkZ5aQkmZuaE9upPz1EPaHxefPQp5NVVBPdsu8mGG4q6mIRUT5fx3UMxlElJzS3g173HNFacsDBu2vnw0piBGq+DXBzILy3nvxsj27rI96Rbn36UlZSwfd1qigrycXJz5/k331FHwhQVFJCbnaWR57v5H5CXU79C2kevvgTAT2s3t1u570RAlx5UlJVxbOcWyoqLsHF0ZuKz89RRBqVFRRTm1tdD39CIyf/3LyJXLeOP/76PgZExXQePoGuDTkgLG1smPjuPfetWEHNoPybmFgyZ9GizkSztJbBrDyrKSjmyY7O6rpOee7lBXQspaNBmBoZGTHnhVXav/IPfP30PAyNjug8ZSfchmhGKi+e/q/E6KTYGMytrnv34SwDORO2ltqaGTb9+r5EupGcfxs6Y2xZVVes6dCQKeTX71vxJVXkZDu5ePPTsyxrRGsUFmnO8mVvbMv6pF4nasIrYwwcwNrdg4MSp+DaIuiotKuTPz+ovtmNzo4g9EoWzjx+TXngNgPOHVZFy6777UmP/PUaOo9foB1u1nkFde1JRWsqR7ZspLS7E1smFKc+/otG2hY3aduqLr7FrxVIWf/IuBkZG9Bg6iu4NIh3PHtxHbW0NkWuWE7mmfoVIN98Apr/yJgBmVtZMfeE1Itf+yW8f/wdjM3PCevenbyvXT5vOQ0aikMuJWqdqW3t3Lx58Zp5G25Y2PidZ2/LAky9waONqYo9EYWJuTv8Jj+ATXt+25w/tp7amhp1LftbIG9CtF8MeVQ3fLi8uYs+y3ygrLkbf0BBrJxceePIF3ButwtaaTiZdR09Xl6FhARhI9cgsKGbN0WjkDc43poYGGnmKyitZd/wsg0P86OThQmndEt8JmfXfhZLKKtYci2ZQiB8zBvagrLKa2JQMjsXXPwCK8FQ9iJvSRzPy8MjlZI7GJ7dFdZtYf+I8+np6PDWsNyYGMhIycnh39U6NSBxbM81h4SM7BaKnK2Hu0F7MHVof6RCbkslbK7YBqlWiHu3XBQcLUyqrFZxOTmXhtqhbDjW7XxgE+OHSYGit9ZzHsZ7zOMXbd5M9/8tb5Lw/dOrZh7KSEiI3raO4sAAHFzdmv/omVnURJcWFBeTdqO8ENzQy5snX32b977/y9Tv/xtDImAGjxzFg1Dh1mtraWqJ2bCEnMwNdXT28g4J5/p2Pm0RttH3delNeWsK+zevr6ubKrFfeUEfLNK2bEXNfe5uNS3/jm3ffwNDImP6jxtJ/5Fh1mq79BlJVUcHRyJ1sXbEUA0MjvAODGf3IdHWaY3t3UVNTw/JFX2mUp0vfAUx58rk2rG/HtGV1ZSXrF/9MYX4+UpkMOycnpj71f0Q0iAgXhNagU1FR8bfsjsrOzmbOnDksWLAAX987DCm+j9ztkKi/Oj/Huxvi8FeWfCP/9on+RkaF+3d0EdpNUrb2Cdb/rnQlbR9Jdr+olP+zQpyNZLLbJ/qbaLya4t9dRfX93xnQWg5dvtbRRWhXX25p2xXS7ieXF3b8g9H21NxUC39H7b1wS0cbFvrXu0+9Gz/sOXb7RK3kmWH3Nl3C/e6f9YsQBEEQBEEQBEEQBEH4C2ibyQoEQRAEQRAEQRAEQfjH+jvPLdNe/rYdNvb29mzZsqWjiyEIgiAIgiAIgiAIgnDX/rYdNoIgCIIgCIIgCIIgdAwlIsKmpcQcNoIgCIIgCIIgCIIgCPcZEWEjCIIgCIIgCIIgCEKrUoo5bFpMRNgIgiAIgiAIgiAIgiDcZ0SEjSAIgiAIgiAIgiAIrapWBNi0mIiwEQRBEARBEARBEARBuM+ICBtBEARBEARBEARBEFqVmMOm5USEjSAIgiAIgiAIgiAIwn1GdNgIgiAIgiAIgiAIgiDcZ8SQKEEQBEEQBEEQBEEQWlWtGBLVYqLD5j41Ityvo4vQrvT1/jlfRWtT444uQrsKdnHo6CK0m9S8oo4uQrvS0enoErSftSdiO7oI7crfybaji9Bu3g717OgitC/JPye4etvZyx1dhHZ1eeEXHV2EdhMw718dXYR2ZfXx2x1dhHZTdeZcRxehfYX6dnQJhPvcP+cuWRAEQRAEQRAEQRCEdiEmHW65f85jFkEQBEEQBEEQBEEQhL8IEWEjCIIgCIIgCIIgCEKrEgE2LScibARBEARBEARBEARBEO4zIsJGEARBEARBEARBEIRWJVaJajkRYSMIgiAIgiAIgiAIgnCfERE2giAIgiAIgiAIgiC0KrFKVMuJCBtBEARBEARBEARBEIT7jIiwEQRBEARBEARBEAShVYkAm5YTETaCIAiCIAiCIAiCIAj3GRFhIwiCIAiCIAiCIAhCq6pFhNi0lIiwaQORkZFMmjSpo4shCIIgCIIgCIIgCMJflIiw+Rs7uGsHe7dspLiwAEcXVybMmI1PYJDWtPLqalb++iNpV5PJSk/Dyz+AF9/9SCNNzIljHIncRdrVq8jl1Ti4uDLioYcJ7dq9PapzWwd2bmPXpvUUFRTg5OrGlCfm4hsUrDWtvLqaZT8tIiU5mcz0VHwCAvnXB580u+/ES3F8+c6bODi78N5Xi9qqCs06vnc3h3dsoaSwEDtnF8ZMexwP/8Bm02elprBl2WLSkpMwNDah+6ChDHpgAjo6Ouo0CoWCA5vXE3P0EMWFBZiYmdN31Fh6DxulTnN093ZO7IukMC8HIxNTAiO6MmLyNPQNDNq0vrezfu1aViz/g7y8PDw8vXhx3jzCO0VoTXv1ajILPv+ca1evUlZWirWNDUOHDWfWnLlIpdJ2LnlTpw9Ecmz3NkqLirB1cmb45Om4+fo3m/5Geio7Vywh41oyhsYmRPQbRL8x49Vtez3hEvs3rCYvOwt5dRXmVjZ06juAXsPHqPcRfWg/sccPk5ORjlKpxMHVnQEPTsTNp/nPbbW67tpGSV1dR0y5dV2z0zTr2rl/o7rGX2Jfo7pG9NOs68XTJzi6axv5N7KprVFgZedAj6EjCe/dr03rejem9e3CyE4BmBjoE59xgx92HyElt6DZ9CGujswc2A1nawv09fS4UVzK7pjLrD95vh1LfWdGhAfQ088DI5mM67n5rDtxjuzCkmbTmxrq82DXUJytLbA1NeF0cgorj0RrpOnm7cbUvl2a5H3tj00oamtbvQ53Yu3uXSzfuoW8wkI8XVyY9/gMOgU0f4y+KSUzk5lvvo5SqWT/70s13pMrFCzesJ4dhw6SW1CAlbk508aOY8rIUc3srf2s3bWL5Vs21dd3xhN0CrzD+r7+mqq+S5ept5+Ji+O5D95rkn7lgq/wcHZuxZLfu5kDuzO2SzCmBvpcSs/mq21RXMvJbzZ9uLsTc4f2wtXaEgOpHtlFJWyLvsiqo2fVaUZ2CuD18UOb5B3+0Q9UK2rapB63c2TPTg5s30xJYQH2zq48OH0mXgHarx0BMlOvs2HJb6RcScLIxISeg4cxbPzDGtcbR/bs4MieneTn5GBpbcOQByfQtd/AdqhN6zAID8Fy6sMY+PuiZ2tD1sdfULJjT0cX666tO7CPP3ftIq+oEE8nZ16c8gidfP20ps3MzWXim/9usn3BCy/RMyQUgNzCQr5du5r469dJu5HNyJ69+M8Ts9u0DnfKKDwEk64R6BobIc/Lp/jAYarTM2+ZxzgiDKPwEPTMzKitrKT84mVKDh/X2Kdxp1D0zM2oKS6h5MQZKi7Ft3VV/vLEKlEtJzps/qbOHD3MuiW/MXn2k3j7B3Jo905++ORD3lrwDVY2tk3S19bWIpVK6T9iNHFnz1BRXtYkTdKlOHyDQxkzZRrGJqacOnSQX774Ly+8+2GzHUHt5dSRQ6z83y88OvcZfAKDOLBzO998/B7vfbUIa1u7Julra2uRymQMGjWG2OjTWut7U1lpKYu/WUhAaDiF+XltWQ2tzp84yrY/l/DAY7Nw9wvgxN7dLFnwKS/O/xILa5sm6Ssryln8+cd4+Afy7LvzycnMYN1vPyCT6dN31Fh1utU/fENhfh7jZ87F2t6B0uIi5NXV6vfPHTvMztV/8tATT+LhF0B+zg02/O8nFPJqJsx+ul3qrs3ePXv4euGXvPLqvwkLD2fDurX8a95L/LFiFQ4ODk3SS/WkjBo9Bl9/P0xNTElKTOS/n8ynRqHg2f97oQNqUC/u1HF2r1rGyGkzcPPx4/SBvaz49nOefu9TzK2atm1VRQXLv/ovbr7+zHrjffKys9jy+8/I9PXpOWw0ADJ9A7oNHo6dsyt6MhlpSYlsX/4/pDJ9ug5U3RhcT7hEUNeeuHr7IpXpc2LvTlZ8/Rlz//MxVvZN/4atVdddK5cx6tEZuPr4cebAXv785nOeee9TzLV8jxvWdfab75OXlcXm339GKtOn1/C6uhrU1dXFFalMRmpSItuXadbV0MSEvqMfwMbBCYmuLomxMWxZ+itGpqb4hnZqk7rejYd7hvNQ91AWbosiPa+QqX0789Ejo3nq59VUVMu15qmUy9l8Oo5rOflUyRUEudjz/Mh+VCkUbIu+2M41aN7gEF8GBPuw8nA0N4pLGB4ewNPD+vDphkiqFAqtefQkupRVVbMvNoGefh7N7rtKrmD++t0a2zqqs2bPsaMsXLqEV5+YTXiAP+t272bep5+w4osFONg0/W7fJFcoePvbr+kUEMjZS03b7e1vv+ZGXh6vz3kSV0cH8ouKqGpwjO4oe44eYeGSxbw6ew7h/gGs272LeZ98zIoFC3HQcn1xk1wh5+2vF9IpMJCzF7V/T1d8uQAzExP1awszs1Yv/72Y2qczk3t14tONe0nNK+DxAd344vEHeezbZc3+Tiuq5aw/cZ7k7Dwq5XJC3Rx5eewgKuVyNp26oJHu0W/+0MjbUZ01McePsGnZYibMnIOnXyBHI3fx6+fzefW/C7HU0raV5eX8/OmHePoH8uIHn5KTmcGqn79Dpq/PwNEPAHA0chfbVi5n0uyncfPxJeVKImt/+xFDYxOCO3dt7yreE4mhIdXJ1ynZGYn9f17t6OLck8hTJ/lq5Ur+9eijhPv4sv7Afl755iuWv/chDtbWzeZb8OI8fF1c1a/NjI3V/y9XKDA3MeGxUaPYdPBgm5b/bhj4+WA+sC9F+w5SnZ6JUXgIVg+NI2fJn9SUlGrNYzagDwZeHhQfPIo8Nw8dmQxdk/q6GoUFY9avN0V79lOdlY3UwQ6LYYOoraqiKvlaO9VM+Kf6S3bYKJVKNm7cyI4dO8jJycHc3JxBgwYxY8YMfv/9d44fP05OTg4WFhb07duXRx99FJlMBkBOTg4//fQTcXFxVFdXY2try7Rp0+jfvz/Z2dnMmTOHBQsW4Ovrq/68cePG8frrr9OnTx+A237G/WD/ts30GDCIPkOGAzBp1lwunTvL4d07eWDaY03S6xsY8MjcZwBIT7mmtQPj4ZlzNF6PnjSFuLOnOX/qRId32OzZspHeg4bQb9gIAKbOeYq4s2eI2rWDCdNnNEmvb2DA9KeeAyDtuvb63rT0+2/oNWgISqWS6GNH2qYCt3Bk1zY69xlAt4FDABj32BMkXjjHiX17GDFpapP0544dRl5dzcNzn0Uqk2Hv4kpOZjqHd22jz8gx6OjokHjhHEkXY3nls68xNlVdEFs26ti6npSAq7cvEX36q9/v1Kc/cadPtHGNb23lij8ZPWYsD4wfD8C8f73KiePH2bh+HU8/+1yT9C6urri41l9sODg6cjb6DOfOxbRTiZt3InIHYb370bnfIABGTn2cKxfPcyZqL4MfmtIk/YWTR5BXV/HAzKeQymTYObuSm5nOicid9Bg6Ch0dHRzdPXF091TnsbSx4/LZ06Qkxas7MR6a/azGfkdNm0l8zBmuxJ1vsw6b43t2EN64rnHnOR21lyETmtY19oSqrg8+0aCuWaq69hx2m7om1tfVM0Azyq7HkBGcP3qIlMT4+6LD5sFuoaw9fo6j8VcBWLD1AMtfeIwBQT7sjLmkNU9SVi5JWbnq19lFJfT29yTY1eG+6rDpH+jDvtgEzqdkALDi8BnenzKazl4uHEu4pjVPQVk5G+oihcLcbx1ZUVJZ1arlvVcrtm1jTP8BjB+iOkb/64lZHD9/jvV7dvPs1GnN5lv053J83NyICAxq0mFz4vw5TsXGsu6rb9SdFk5aHj50hBXbtjJmwEDGD1H9xv41azbHz8Wwfvdunp32aLP5Fi1fjo+bOxFBQc122Fiamd83nTQNPdwznD8Pn+HgpSsAfLIhko2vzmZoqB9bzsRpzZOQmUNCZo76dVZhCf0CvQlzc9LosAEl+aXlbVn8Oxa1Ywvd+g2k56BhADw0Yzbx589ybO9uRk9p2rbRRw9RXVXF1KefRyrTx9HVjRsZaRzcsZUBo8aho6PDmSNR9Bg0hIjefQGwtrMnNfkK+7du/Mt02JQfP0X58VMA2L/5rw4uzb1ZuWc3o3v35sF+AwB4eeqjHI+7wIaoAzwzYWKz+cyNTbA2N9f6nqONDS8/ojrG7T9zpvULfY9MunSi/OJlymNVx5ni/Ycw8HDDKDxEI2LmJl1LC4w7hZLzxyoU+fXRrYqc+vOsUZA/5bFxVMQnAlBTVEy5vT0m3SJEh81t1IoImxb7S85hs3TpUlatWsWkSZNYtGgRr7/+OjZ1T7EMDAx44YUX+P7773nmmWc4ePAgq1evVuf94YcfqKqqYv78+SxatIi5c+di3KC3+E7c7jM6mkIhJzX5CoFhnTS2B4SFczXhcqt+VlVFBUbGJrdP2IYUcjkpV5IICtccEhPUKYIr8dpveO7UgZ3bKCosYMzEyS3az71SKBRkXLuKT0iYxnaf4DBSkhK05klJSsTdLwBpgw5E35BwSgoLKMhVXTxejD6Ni6c3R3Zt47/znmXBv19i67LfqaqsVOfx8A0gM+UaKUmqk1NhXi6Xz57BP0z70KP2IJfLSYi/TLcePTS2d+vRgwuxdzYcJC01lRPHj9MponNbFPGO1SgUZKZcwysoRGO7V2AIaVcSteZJS07Czcdfo229g8MoKSygMC9Ha56slGukJSfi7htwy7Io5HIMjO7uWHinmq1rUOvWNTPlGmlXVN9/bZRKJVcvxZGXnXnLv0d7cbAwxcrEiOiraept1Yoa4lKzCHSxv+P9eNlbE+hsT2zKrcO925OViRFmRgbEZ9xQb5PX1JKcnYeHbfNPc++UVFeX/0wcwTsPj2T24F44W2m/oWhrcoWC+KvJ9AjTPEb3CA0jNkH7MRrgSHQ0h89G8/KMJ7S+H3XqFIHe3qzYvo1xzz3Dw/Ne5MvfF1Pe4BjdEeQKOfHJyfQIC9fY3iMsnNiE5ocGHIk+w+HoM7z8xKxb7n/mm68z5qm5PP/h+5y5cOGWaduLo6UZ1qbGnLqSqt5Wrajh3PUMgl0d73g/Pg42hLg6cO56usZ2mZ4eK196nDUvz+STaWPxcWg+KqstKRRy0q8m4xeq2bZ+oeFcS9TetteT4vH0D0Qq02+QvhPFBfnk56h++wq5AqlU84GmVCYj9UoSNc1E2gmtS65QEJ9ynR6NpgnoHhRM7JWkW+Z984dFjH7lJZ767yfsO3O6LYvZOiQSpPa2VF1L1dhcdT0VmZP2B1KG3p7UFBWj7+GG3azp2M1+DIsRQ5AYGtYn0tVFWaMZ+aZUKJA52IPkL3k7LfyF/OUibCoqKti0aRNz585l2DDVEwAnJycCAlQX34888og6rb29PZMnT2bDhg1Mnz4dUEXY9O7dG09P1VNZbUMobud2n9HRyopLqK2txdTcQmO7qbkF8Xd4U3snDu7aTmF+Ht37D2i1fd6L0pJiamtrMWtUXzNzCy4Vnrvn/aZdv8aW1St545PPkejqtrCU96a8rm4mjZ5umJibc+VirNY8pUWFmFlZN0l/8z0rWzsKbtzgekI8unpSpj0/j8rycrYsW0xxYT7Tnn8ZgLCevSkvLeHXT95DCdTW1NCpdz9GTG7+iXFbKyospKamhv9n767Dm7reAI5/mzZ191Lqhhd3h+LuMmyMCbDBmDEmTNjYb8zQbbgOd4dSirs7FC3SUkubeiO/PwJp07TARttAez7Pw/MsN+em71mSm3vPfc97HB0ddbY7Ojpy8sTxZ+777ojhXL92jezsbDp37cY77418Zvvilp4qR61SYWWj+95a2dpx+2rBd2xTk5OxdXDM115zNzotORkH59w78FM/+4D0VDkqpZImnbpTq1mrQmOJ3LgGUzMzgkOLZxBL21db/b6mXim4r2nJydjk76tNwX3949PcvjbtrN/XzPR0/vjsA5Q5CowkEtoPGExgvosSQ3CwsgRAlqZ7dz0pLQMnG8vn7r9o1ADsLC2QSIxYfvA028+83AB1UbK10NS5yp8FI8/Iws7y5WpgPU5JZcXh0zxMTMZMakLTigG8374pv2yKIF5eeLZkcZClpKBUqXDMd4x2tLPjxMWCj9HxSUlMnjObn8aNwyrvBUEeDx8/5vy1a5iaSJn84ThS09L4deFCzb4fjivyfrwoWYq88P5ekBW4T3xSEpNn/81P4z4utL/ODvZ8+tYIKgUEkKNQsH3/fkZP+o5ZX39DjUqGzeB1tNZ8F5P0vqfpONs8/4bV6nFDsbO0wFhixKJ9J9h0MveYdy9exs8bI7gZG4+FqZRe9UOZMbwnw/9cwYPE5KLtyHOkyeVPzjfsdbZb29kjL+SzLJfJsMt3vmHz5LMhT5bh5OpGSNVQju+LoErtenj5B3D/9k2OR+5BqVSQJpdj6+BQLP0RcslSNd9bh3zZa462tpwsYDomgIWZGaN79aFaYCDGEmMOnjvL17P/InvYcNrVb1ASYf8nEgtzjCQSVOm631dlejpmluUL3MfYzhZjWxssQoKQ7dwDgG3TRjh260j88jUAZN2JxrJKRTJv3CIn9jFSNxcsq1bCyNgYiYU5qrRXI0tOKJ1euwGb6OhocnJyCA0t+GT70KFDbNy4kUePHpGZmYlKpUKVZ157586dmTVrFqdOnSI0NJQGDRoQGBj4r2J43t94lh07drBz587ntvMPrUVo/Yb/Ki49eQq+AajV+tv+q7PHjrBh6SKGjfkIx1ckTdsof39f4rVycnKY89vP9Bo8DOdimiLyb+R/1zQFvAp/Lwtun/uMWq0CI+j77vuYW2pORjsPGsbCXyaTmizD2s6e21cvs3fTOjoPHo6XfyAJj2PYumwRe9avpnUPw2QcPaX3XqvVetvy+3bSj6SnpxF14wazpk9n2ZLFDBoytBijfDF6cavVGD3jvc3/VO5bq/vE4E++JCcri/u3oohYtxJ7Zxeq1W+s93LH9+zk9IEIBo4dj1khF1NFpcD37Vmf4/x9LeSJIZ9+SXZmFg9uR7Fn7UrsnVyo1iC3r2bm5rz91Q9kZ2Vy+8oldq/6B3snF/wqFlyUvLg0rxzI6Ha5xY6/WbUDyPMePvGih+lPl27G3NSECuXcGNaiLjHJcvZeLDhjqbjV9CtP7wa52Xdz9xzW/Md/7Nuz3I1L5G6eYq934hL4uHNLmlQM0E6nKmn/5pj0zcwZ9AgLo0ohxT5Bk0JuBHz3/gdYPzlGfzxsGGMm/0iCTIaTvX1Rhf6f6Pe3gGPZE9/MmEaPsDZUCS68vz7lPPEplzsFrmpwCI/i4li2ZVOJD9i0rhrMR52bax+PX7YFKOB7ihEvcqbx/vy1WJiaUqm8G++ENeRRUgq7z2syVi7fj+Hy/Rht20vRMcx9tx896lVj+vYDL92X/0LvXVSrn3W6Uei519PtYd17IU+WMeO7L0CtxtrOnlpNmhG5ZSNGIjOhRBV0nCrszbW3sWFAm7baxxV9fZGlylm2c8crPWBTOKPCv61GRhiZmJC0fTdKmWagNGn7btzefAOpuxs5MbHIj53A2MoS5349wMgIVXo66ZevYlOnJqjElJ9nETOiXt5rN2DzrErTV69e5eeff6Z///7UrFkTa2trjh07xvz587Vt2rRpQ82aNTl58iRnz57lk08+oXfv3gwYMEB7IMv7NxT50jVf5G88S7t27WjXrt1z2+2/euuFXq8gVrY2SCQS5DLdVUZSU2TYFjIP9d84e+wIi2f8waBRY16JFaKsbWyRSCQk5+uvPFmG7X88qU1OSuTR/WgWzZzKoplTAc3nQq1W827vrrz/xUQqVy/+KTWWT/omT9a905aWkqKXdfOUtZ19ge01z2n2sbF3wNbBUTtYA+DioTlZliUkYG1nz+51K6lWvyF1mrUEwN3Lm5ysLNbPn02Lrj0xNkDWkZ29PcbGxiQk6BZ/TkpK0su6yc/NTTPFxM/PH5VSxf8m/0D/gW9gYmKYw6CltQ1GEgmpKTKd7WnyFG3WTH7Wdnak5Xtv0+Wa9zb/Pk8zUFw9vUhLSWb/5vV6AzbH9+wkcuMa+n3wMZ5+AS/TnWfS9jVZphd7YX21srMj9V/21a18nr7mGbAxkkhwdNW8/+5ePsTHPOTg9k0lPmBz7MZdnSlC0iffIQdrS53MEHtLC5LSMp77erHJmtWW7sYlYW9lwcDGtQw2YHMpOoZ78RHax8bGmgsxGwszZOm5fbE2N0OeUbS1Z9RqiE6Q4WxTPFP6nsXe1hZjiYQEmUxne1JKCo62BR+jT166yJkrl5m3VnPnVq1Wo1KraTSwP5+8OZxurVrjbG+Pi6OjdrAG0K6WFJsQb7ABG3tbm0L6m6yXdfPUyYsXOXP5MvPWrAby9Ld/Xz4Z/hbdWocVuF/lwCB2HzZA3bhrt7nyIFb7+On31NHakriU3IKl9lYWJKY+/3sa82RVtNuPE3C0tmRo87raAZv8VGo11x4+pryj/Uv04L+xsnly7pjvOJ2akqyXsf2Ujb09Kfk+C0+P29ZPPv9SUzP6vj2KXm++gzw5GVsHe45GhGNmboGVjU1Rd0MogL215nubmO83NUkux/Ff1Iyq7OfPVgN8J/8NVUYmapUKiaVulqqxpYVe1o12n7R01EqldrAGQClLRq1UYmxjTU5MLCiUyHZFIAuPRGJpgSotHcuqlVBlZaPKeP5xQBBexms3tO3l5YVUKuXcOf2pLleuXMHJyYl+/foRHBxMuXLlePz4sV47Z2dn2rVrx/jx4xk4cKA248XuyclGUlLuhf+tW7oDJy/6NwzJxESKl38AVy/o/j+6euEcfoXUdnhRp48cYvH0P3hj5AfUeNkMoCJiIpXiHRDIlXxFZC+fO0vAM5a+fhZ7Rycm/j6Dr36dpv3XtE07XN09+OrXaf/5df8tExMTyvn6EXVJ965x1KXzeAcWfLfSOzCIu9ev6qz4FHXpPDb2DtpVHryDgpHLknRq1iTEaupf2D+pB5WTlY0k390vI4kE9UvlLr0cqVRKcEgFThzXnf504vgxqlStVshe+lRqFUql8oUz44qDsYkJHt6+3L6sW6fh9pVLlA8IKnCf8v6B3Iu6hiIn9729dfkiNvYO2DsVvjqLWq1GqdBdyeTo7u3s3biavqM/KvblvJ/29dYV3b7eulw8fVXk66teG5UaZU7J107IyM7hUVKK9t+9+CQSU9Op4ZubWSA1NqaylztX7sc+45X0SYyMkBob7ic9S6EgXp6m/Rcrk5OSnklwudwMTBOJBH9XJ+7EFf1qex4OtqQU8UDQi5CamBDi58/xC7pTRo5fuEDVQjJKlv08hcU//U/7b0TvPpiZmrL4p//Rsl59AKqFhBCXlKRTs+beI80x+lkrMRU3qYmUEH9/juc7vzh+4TxVgws+jiyb8iuL/zdF+29En76a/v5vCi2fcaf+xt3bODvYF2X4LyQjO4cHicnaf3fiEkmQp1E7ILd4vamJMdV8ynEp+t/VjTIyMsLU5Nk3O/zdnEhILdmpfaA5d/T08+f6Rd3zjesXz+MbVPB76xMYwu1rV3TON25cPIetg6Ne9rWxiQn2Tk5IJMacPXqISjVq6Z1jCMVDamJCiLcPx/NNfzpx+TJVA158lsGN6Gici+DGb7FSqciJjcPMx0tns5mPF9kPYwrcJfvhI4yMjTG2yx28MrazxcjYGKVcrvf6qtQ0UKuxqBBE5u07Rd2DUufpDe+S+FdavXYZNpaWlnTp0oVFixYhlUqpXLkycrmcqKgoPD09SUhIIDIykgoVKnD69Gn251tmbvbs2dSqVQtPT0/S09M5ffo0Xk9WkDEzMyMkJIS1a9fi7u5Oeno6ixYt0tn/Rf7Gq6BFxy4smTEVn4Ag/EMqcDB8J8mJSTR+sorSpn+WcPfmDd7/6jvtPo/uR6NUKEhLkZOVmcn9O5pVS8r7aur9nDp0gMUzp9L9jSEEVqxEypOMFmMTE6ysDXuXJKxzN+ZP+w3foCACK1Ri387tJCcl0qxNewDWLV3EnajrjPvmB+0+D6PvoVQoSJWnkJWZSfRtzeCcl58/JiYmeHr76PwNGzt7TKRSve3FrVHbjqyZPZPy/oH4BIVwfO9u5LIk6rbQrNCxc/Vy7t+KYvhnXwEQWr8xERvWsnbun7To0oP4mEfs37qJll17arPIQus3JnLTOtbN/ZOW3XuRmZ7OlmWLqFK7nvauWIXqNTm0cxuevgGUDwgkMTaG8HWrqBBa0yDZNU/16z+A77+dSKVKlahaLZQN69eREB9Pt+49APhr1kyuXL7E1BmzANixfRumpqYEBARiIpVy9cpl/v5zFs1btDT4ym71Wrdn44K/KOcXgFdAEKf2RyBPTqJmU00Nloj1K3l4+xZvjPscgMp1G7J/ywY2LZxN4w5dSYyN4fDOzTTt1F373p6I2IW9swtObppimHdvXOXo7m3UbtZa+3eP7NzK3o2r6fbme5ol3Z/cUTUxNcXc4vm1U/6L+mHt2TD/L83nKTCI0/s0fX1ab2bPupU8vHOLQU/6WuVJXzcumE2Tjl1JiI3h0A7dvh7P19d7N65yZNc27QpRAAe2bsTTLwAHF1cUihyiLpzjwtFDtOuvv1qeIWw8cYG+DWtwP0HGg8Rk+jaqSUZ2Dvsu5xaCHNepOaBZQQqgc63KxCTLeZAgA6CKlwc96lV7pVaIAth/JYrWVUN4nJxKXIqcsGoVyFIoOH0rt8hy/8a1AM0KUk+Vc9Acg8xNTVCjppyDHUqVSptR1Ca0AnfjEolLScVcKqVJxQDKOdix9uh/r1n2Mvp37Mi3M2dQKSCAaiEhrA8PJz4pke5PMkdmLf+HyzdvMuNLzTE6wMtbZ/8rt24hMTLS2d6mUWPmr1vHpL9m8VbP3sjT0/h90UJa1qtXaCZLSenfsRPfzphOpYCgJ/3dRXxiIt3DNKtSzvpnGZdvRjHjq4kABHjn7+9NTX/zbF+xdSseri74lfdCoVCw48B+9p04weRxr8aKPGuOnuONprW5F5/E/QQZg5rWJiM7h/ALuYWlP++uOe5MXh8OQPe61YiRaQZmAUJ9ytG3YQ02nsgd3BvSrA6X78dyP1GGlZkpPepVI8DNid+3RpZc5/Jo1r4zy/+cjrd/IL7BFTiyZxcpSUnUf7Li6LaVy7h38wbvTvgGgBoNG7N7/WpWzp5Bq669iI95SMTmDYT16K09Tsc9esi9mzfwDgwmIy2V/ds3E3P/Hv3eGW2QPv4XRhbmSD3LaR5IjJC6uWIa6I9KLkcRW3AR/FdNv7A2fDd/LpV8/agWGMj6fZHEJ8vo1kxTh/LPdWu5fOcW08dpli3fdvgQJsbGBHt7Y2Qk4dD5s6yNjGBkj146r3s9+h4AaZkZSIyMuB59D6mxCX7lypVsB/NIPXUWh/atyYmJJfthDJbVKiOxsiL9nKZ+lE3j+pi6u5GwZiOgKUicHfsY+7YtSd57EAC7Fo3JfhRDTozmpryxvR2mHm5kP4pFYm6Gdc3qSJ2ckO3YY5hOCmXKazdgAzB48GCsrKxYsWIFCQkJ2Nvb06JFCzp06ECPHj2YM2cO2dnZ1KhRg4EDB/Lnn39q91Wr1fz999/Ex8djYWFBaGgow4cP1z4/ZswYpk+fzrhx4/Dw8OC9995j/Pjx2ufr1q373L/xKqjVsDFpcjk7168mJSkJDy9v3hv/pfaOR7IsifhY3ZHmv376nsS43B+e/32mKWw4feV6AA6G70SlVLJ20XzWLsqdAhZYqTJjJk4q7i49U51GTUiTp7BtzSqSkxIp5+3D+xMm4uT6pL9JicTF6PZ3+g/fkhCXmx31/cdjAJi9dnPJBf4CqtVrSHpqKpGb1iFPluHm6cXgceO12TJyWRKJj3PvxptbWjLsky/YvGQ+s76ZgLmVFY3adaRRu47aNmbm5gz75Eu2LF3An99+gbmlFZVq1tFZJrx5F8083fD1q0hOTMDKxoaQ6rVo01N/CeaS1CosjOTkZBYtWEBCQjx+/gFM+e133D00F+0J8fE8uJ+7CoexsTFLFy0i+n40qNW4ubvTo2cv+vbTXxK9pFWuU5+MtFQObttIarIMl3Ll6Tf6Y+ydNFlOqckykuJzP6PmFpYMHPsZ2/9ZxLwfJ2JhaUn91u2p17q9to1KpWLPupUkJ8QhkRjj4OJKy+59qdW0pbbNyX3hqJRK1s2ZoRNPtQaN6TL0nWLt64E8fe3/fr6+5vk+mltq+rpj+SLm/vCkr2HtqR+W21e1SsWetbp9bdVDt6/ZWZls/2chKUmJmEhNcXb3oOub71Cl7qsxB3/N0XOYmpjwXtvGWJubcu3hY75asY2M7NwsIRdb3cKmEokRw5rXxc3OBqVKzSNZCgsjj7PtFRuwibh4A6mxMT3rhWJhJuVeXBJ/7z5EVp6pxg5W+nWTPu7SUudxFS8PElPTmLR2FwAWplJ6N6iBrYUZGdkKHiTKmLHjgPbCuKSFNWhIslzOgvXrSZAl4e/lxW+fjcfDRXOMjpfJuB/77zKmLM3Nmf7Fl/y6cAHDvpyArZUVTWvXeeYy4SUlrGEjkuWpLFi/loSkJ/0dPyFPf5P+dX9zFAqmL1lMXGIiZqam+Hl58dv4z2lo4NX8nlp+6DRmUhPGdmiGjYUZl+/H8smSjTrfUzc73RtXxhIj3m7dAHd7W5QqFQ+TkpkdfphNJ3MzDa3Nzfioc3Mcra1Iy8rixqN4PliwnqsPDJO5Xb1+I9LkcsI3riVFloR7eW+GfzIBxyfnGymyJBLynG9YWFrx9vivWLdwLlO//gwLSyuadehMs/adtW1UKhX7tm8m7tFDjI1NCKhUmdFf//DK1D98EeYVgik/fYr2sdNbg3F6azAp23YR++OvBozsxbWuU5fktFQWbttCQnIy/uU8+eX9MXg8+Q1OSJbxIE538Gnhti3EJCQgkUjwdnNjwpBhevVrhn7/rc7jg+fP4e7kxLrJPxdvh54h83oUyRbmWNerjbGVFTkJCSSu36zNljG2stTJpgFI3LAVuxZNcO7bHbVCQdbd+6TsO6h93kgiwbpWdYwd7EGlIiv6AXEr1qJMyZeBI+gRy3q/PKOMjAzxf/EV9DI1bF5HZgaqI2IIcSW8iomhNavgb+gQSsyOc4Uva1saFVEN89fC8sNnDR1CiQopZ7hpNyXtq6p+hg6hZJWhaSjdN7x6GdDF6aOOhl21syRV+PDVyL4qKY4/fGXoEEpM1inDZEsaiuPI4c9v9BqbuGZXif2tb3u1KbG/VZLKzlWyIAiCIAiCIAiCIAglojTXlikpZec2iyAIgiAIgiAIgiAIwmtCZNgIgiAIgiAIgiAIglCkRILNyxMZNoIgCIIgCIIgCIIgCK8YkWEjCIIgCIIgCIIgCEKREqtEvTyRYSMIgiAIgiAIgiAIgvCKERk2giAIgiAIgiAIgiAUKTUiw+ZliQwbQRAEQRAEQRAEQRCEV4zIsBEEQRAEQRAEQRAEoUiJGjYvT2TYCIIgCIIgCIIgCIIgvGLEgI0gCIIgCIIgCIIgCMIrRkyJEgRBEARBEARBEAShSIkZUS9PDNi8ouwsLAwdQolysbUydAglJkehNHQIJcosPc3QIZSYxymphg6hRDnblJ3v7Ucdmxk6hBIVfvGGoUMoMSb+voYOQSgmjtaWhg6hRKlUKkOHUGIcf/jK0CGUqMQvvjd0CCXGyMzM0CGUKMeRww0dgvCKEwM2giAIgiAIgiAIgiAUKbVIsXlpooaNIAiCIAiCIAiCIAjCK0Zk2AiCIAiCIAiCIAiCUKTEst4vT2TYCIIgCIIgCIIgCIIgvGJEho0gCIIgCIIgCIIgCEVK1LB5eSLDRhAEQRAEQRAEQRAE4RUjMmwEQRAEQRAEQRAEQShSKpFg89JEho0gCIIgCIIgCIIgCMIrRmTYCIIgCIIgCIIgCIJQpEpjDZucnBzmz5/Pvn37yM7OJjQ0lPfeew9nZ+dC99m5cycRERHcu3cPtVqNv78/AwcOpHLlys/9eyLDRhAEQRAEQRAEQRAE4TnmzJnD4cOH+eSTT/jpp59IT0/nu+++Q6lUFrrPhQsXaNKkCZMmTeKXX37B09OTiRMn8vDhw+f+PTFgIwiCIAiCIAiCIAhCkVKr1SX2rySkpaWxe/duhg0bRo0aNQgMDGTcuHHcuXOHc+fOFbrfxx9/TKdOnQgICKB8+fKMHDkSCwsLTp069dy/+coP2Pz+++98++23hg7jhSUnJ9O5c2cuXLhg6FAEQRAEQRAEQRAEQSgCUVFRKBQKatSood3m4uJC+fLluXLlygu/jkKhICcnB2tr6+e2feVr2Lz99tsvPGIWHh7O33//zerVq4s5qtdD+LbNbFu3huSkRDy9fRj41ruEVK5SYNvs7GwWzprG3ZtRPLwfTVDFSkz4cYpOm9l//MLBiHC9fU3NzJi7emOx9OHf2LJhPWtWLCcxIQEfP1/eGf0BVaqFFtj2/JkzrF+zimtXLpOeloaHpyfdevWhbYeOOm0++/ADvX1nL1qKl49PsfXjRezfuY3wzRtIliXhUd6LXkOGE1ix4DmQOdnZLJ/7J9G3bxHz4D4BIRUYO/EHnTZnjx3hQPgO7t++TU5ONu7lvWjXvTfVatctie481+qNG1i6ciXxCQn4+/oybtRoalSrVmDbW3fu8PO0qdy+e5fU1FScnZ1p06IFbw8ZilQqBeCb//3E1p079fY1NzfnwLbtxdqX/NRqNad2b+XqsYNkpafj6u1Lo+79cHQv98z9Ht68ztHNa0iKfYSlrR2hzdtQqUHTAttGnTlBxD/z8a5YhXZvjtJu/+fHL0hNStRr71WhCu2Hj9Lb/rJORYZzdPc2UpOTcSnnSeveA/EOCim0/eMH0excsZhHd25hbmlNjaYtaNyhK0ZGRgBcPXOCM/v3EhN9F2VODs4e5WjYvgvBoTW1r3HmwF4uHDtE/MMHqNVq3Lx8aNalB16Bhf/d4rJv5zZ2b1z35HvrTe9hbxH0jO/tP7NnEX37Jo8e3CcgpCLjvv1Rp831SxfZ+M9iYh8+IDsrC0cXFxq1akNYl+4l0Z0X0qpqMHUCvLEwlRKdIGPTyQs8Tk4ttL2NuRkdalainIMdTjZWnLlzn7VHde9Y1Q7wpqafJ652NhgZGfEoKZnd569xNy6puLtTqNVr1rBk6VLNMcrPj48+/FDnZC6vW7du8b8pU7h9+zapaWm4ODvTJiyMt0eM0B6j8jp79izvjByJj48Pq5YvL+6uvJCi7u+p06eZOWsWd+/eJTMrC3d3d7p16cKgN94oyW49U9+GNQirFoKVmSk3YuKYE36E6ARZoe3rBfnQNrQCfq6OmJqYEJ0gY+3Rs5y4Ga1t813f9lTx8tDb9158EmMXri+Obug4HL6Tfds2IU+W4eZZni4Dh+IXUrHQ9o+i77Fh8Tyib0VhaW1NvRZhtO7aU3tMBjhz+CCR2zYSH/MIMwsLgipXpVO/wdjY2wNwbG84pw7tJ/ZBNGq1mnI+frTt0Re/kArF3V09ayMj+GfnThKSZfiV82RM335UDwousO2j+Hh6TvhMb/tvH4ylfpWqAMTLZExfs4prd+9y/3Es7eo34Mthw4u1D8XBPLQKDv17YR4ShImLMzE//IJ8+25Dh/Wv2HZpj0OfHhg7OZB95x7xs+aSeeFyoe2tmzXCYUBvpOU9USYnk7xhK7JVud9B10/HYNu2ld5+qoxMbnXqUyx9KC1UpayGTVJSEhKJBFtbW53tDg4OJCW9+HnIkiVLMDc3p169es9tW2wDNjk5OQWeePxbVlZWRRDNv6dQKDAxeeXHswp19MA+ls35i8Hvjia4UmX2bNvCL99+yeSZs3F2cdVrr1apkJqa0rpjF86dOkF6mv4J9Rsj3qPPkDd1tk367KNCB4FK0r6IPfw1fSqjPhxH5arV2LJhPV99+gl/L1qCq5ubXvvLly7g6+dPr379cXRy5tSJY0z7ZQqmpqa0aB2m0/avhYuxscn9Uto9OekwlFOHD7J60Tz6DX+HgJCK7N+1nZmTv+er36bj6Oyi116lUiGVmtKsbQcunTlFRnqaXpsbVy4RUrkanfsOxNLahhMH9jH7l58YO/H7QgeCSsquvRH8OmMGn40ZS/WqVVmzcSNjxn/GqgULcS/gvZVKpXRs05aQoEBsrKy5fvMmP/72K0qlkg/eeReAj0eNZvSIt3X2e+v99wsdBCpO5yJ3cWF/OM36DMbe1Y3Tu7exbc40+nzyDabm5gXuk5IYz455Mwmp25AW/YcRc+cmB9ctx9zKGv9qNXXbJsRxbOs63P0C9V6n+wfjUatU2sfp8hTWTZ1MQGhNvbYv6/LJo+xetYy2/QfjFRjMqX17WDnjF96eOBk7R/0ibVkZGSyf+jNegSEMHf8tibGP2LJoDqamZtQLaw/AvevX8AmpSLMuPTG3subS8cOs/WsqA8dN0A4E3bt+lUq16lG+TxBSUzOO79nBimlTGP7FJBzd3Iu8n4U5eegAqxbMof9b7xJQoRL7d25j5g/f8vXvM3F0KeR7a2pKs3YduXTmFOlp+t9bM3NzmnfohKe3L6ampty8doV/Zs/C1MyMZm07lES3nqlpxQAaV/BnzdGzxKek0bJKEG+2qM9vW/aSrSh4nrexsYS0rGz2XY6iTqB3gW383Zw4f+8Rd+MukaNQ0qiCP8Na1GP69gMkyPX/PxW3Xbt388tvvzH+00+pHhrK6rVr+eDDD1m9YgXu7vqfMalUSqeOHQkJDsbGxobrN27ww48/olAqGfP++zptU1JSmPjtt9SpXZvHcXEl1aVnKo7+WlpY0LdPHwIDAzE3N+fcuXP8+NNPmJub07tXr5Luop7udavSpXYVpm/fz8OkZHo3qMHE3u0YPW8NmTmKAvepXN6dC/ce8c/BU6RmZtG0YgCfdm3F1yu3c+VBLAA/b9yDicRYu4/URMLvQ7pz+NrtYu/T2aOH2bRsId0HD8c3uAJH9uxi3i8/8tHk33EooHBmZkY6c37+Hv+Qinzw7WTiHj1k5Zwnx5v2nQG4c/0qK/6eTsf+g6hSqy7yZBnrF81j+V/TeHv81wDcvHqZ0HoN8Q0KQWpmxoEdW5g75QfGTvoZF3f9waviEn7iOH+sWMHHAwcSGhjEusi9fDTtD5Z98z3uTk6F7vfbmA8JKu+lfWyb5zolR6HAztqaQe3bs3H//mKNvzhJLCzIvnUX+Y5w3L78xNDh/GvWzRvjMmoEcVP/IuPiZey6dKDc5Ince3MUisfxeu0t69bE7YuPiZsxm/QTpzH19sJ13CjUWdkkb9wKQPzMOSTMWaSzX/lp/yPj/KUS6ZPwYnbs2MHOAm7I5te2bVvatWuns23JkiWsWrXqmfv9+OOPhT6nVqt1Bq+fZdOmTezYsYNJkyZhaWn53PZFNiLx+eef4+XlhZmZGREREbi6uvLhhx+yYMECLl26hKmpKaGhobz11ls4ODgAoFQqmT9/Pnv27AGgVatW5OTkEB0dzeTJkwHNlKiUlBQmTpwIwMWLF1m4cCF3795FIpFQvnx5PvjgA1JSUpg6dSoAnTtrfjj69+/PgAEDyMnJYdmyZURGRpKamoqXlxeDBg2iZk3NRcmFCxeYMGECEydO5J9//uH27dt8/vnn1KlTh3Xr1rFjxw4SExPx8PCgZ8+etGjRQtvv69evM2vWLO7du4eXlxdvvCJ3g3ZsXEfjVmG0aKu5sBn8zkgunD5JxLYteoMuoDnxHzZSk00Sfed2gQM2llZWWOb5Ybp++RKPYx7xzoeGP5ivX72SsHbtad+pCwAjx3zIqePH2bpxPcPeflevfb83Bus87tS1O+fPnOHQ/n16Azb29g4GH6TJa8/WjdRv1pJGrdoA0OfNt7l87gwHdu2g64BBeu3NzM3pP+I9AB7cu1vggE3voW/pPO7Yux+Xzpzi3IljBh+w+Wf1ajq1bUf3Tp0A+OSDDzhy4jhrNm1i9IgReu29PD3x8vTUPvZwd+f0ubOczTNN0dramrwJiOcuXuDBo4d8+/nnxdaPgqjVai4ciCC0RVvtQEvzfkNY8u2nRJ05QaUGTQrc78qRA1ja2dGoW18AHNw8eHzvNuf3hesM2KiUSvYsm0+ddl14GHWdzHTd77WFtY3O46snDmNqZo5/tVpF2U0AjofvoFqDxtRoojl+tu03mFuXLnB6XwQtuuvfnbp4/DA52Vl0Hvo2UlNTXD3LEx/zkGPhO6jbuh1GRka06at7vG3SqTtRF85x/dwp7YBN1+Hv6bRpN2Ao18+d5ubl8yU6YLNny0YaNG9F49ZtAeg7/B0unT3N/l3b6DZwiF57M3NzBrw9EoAHd+8UOGDjExCIT0DuQJyzmztnjx0h6sqlV2LApmEFP/ZdjuJSdAwAq4+e5Ysebaju68nxqHsF7iNLy2DLKc0JcBXvgi/eVh0+o/N444kLVCrvRrCHC0cMMGCzbPlyOnfqRPdu3QD49OOPOXLkCGvWrmX0KP1MNS8vL7y8ci/4PDw8OHX6NGfPntVr+/0PP9CxY0fUajV7IiKKqwv/SnH0t2LFilSsmJvZ4VmuHHsjIzlz9uwrMWDTqWZl1h07z9EbdwGYvn0/C0YOoGnFAHadv1bgPvP3HtN5vOrIWWr5e1EvyEc7YJOama3TpmlFf8ykJuy5eKMYeqHrwI4t1G7cjHotWgPQbfCbXLtwlqMRu2jfZ4Be+zOHD5KTlU3ft0cjNTXFvbw3jx8+4MCOLTRt1wkjIyPuRl3HztGJpu00v9eOLq40CmvHxiXzta8z4D3dzOUeQ0dw6fQJrp8/W6IDNit276JDw4Z0bdIMgHH9B3L00kXW74vkvR49C93PzsoaJzu7Ap/zcHZmXD/N/7u9L1CX4lWVfvQE6UdPAOA24WMDR/Pv2ffqSsrOPaRs2wVA/IzZWNapiV3nDiTMW6zX3qZ1C9KOHCdlkybDWvEolqTla7Dv10M7YKNKS4e0dO0+5pUrIi3nQezk30ugR8KLateund5AzIvq0qULzZs3f2YbFxcXVCoVKpWKlJQU7PIcC2Qy2Qut+LRp0yaWLl3KxIkTCQ4uOKMvvyKtYRMZGQnATz/9xDvvvMP48ePx8fHh119/5fvvvycjI4Pvv/8e1ZM7uuvWrWPPnj28//77/PLLL6jVavbt21fo6yuVSiZNmkTFihWZNm0av/zyC507d0YikVChQgVGjBiBmZkZixcvZvHixXTvrkkLnzp1KhcvXuTjjz9mxowZtGrViu+//57bt3XvYCxcuJA33niDP//8k5CQEJYsWcLu3bt59913mTlzJr169WLmzJmcOKE5iGVmZvLdd9/h7u7O77//zpAhQ5g/f75e3CVNkZPDnagbVK2ue5e8So2a3Lj64nPrnidy13Y8vX0IqlipyF7zv8jJyeHGtevUrKM7fadmnTpcvnTxhV8nPS2twHmEH7wzggE9ujJ+3BjOnTn90vG+DIUih+hbN6lYrbrO9orVqnPr+tUi/VuZGRlYWj1/XmVxysnJ4er169SvXVtne73atTn/gu9t9IMHHDlxghqFTI8DWL91K/6+voRWKdlsMXliPBnyFMoH516omEhNcfcLIvbuzUL3i717i/JBumnrXsGViLt/F1WeCvXHt2/ExtGJ4NoNnhuLWq3m2vFDBNasi4mp6X/oTeGUCgWP7t3Br1JVne1+lapw/1bBFyYPbkXhFRiCNE8s/pWqkpqcRHKC/h2yp7KzMjC3LDwzU6lQoMjJweIZbYqaIieHe7eiqBhaXWd7xdAa3LpWdN/b6Ns3uXXtKkGVDJ/16GBlia2FOTce5WaFKJQqbj9OwNvZoUj/lrFEgomxMRnZOUX6ui8iJyeHq1evUj9fSnP9evU4/4K17KKjozly5Ij2JtJTq9esISEhgeHDhhVZvC+rOPub19Vr1zh//vwz25QUNzsbHKwtOXf3gXZbtkLJ5fsxhHjqZyw/i4WplNTMrEKfb10thDO37xd7pphCoeDBnVsEV9X9XQyuUo07NwoegLobdR2/kAo6x+TgqqGkJCWRFK/5nvsGVUAuS+LymZOo1WrS5CmcO3qYCqEFT5eDPMfkEsyoz1EouHbvLvUq6V5c1a1UmQs3o56574Q/Z9Lho7G887/JRJw6WZxhCv+FiQlmwYGknzyrszn91BnMKxc87c5IKkWd7/dDnZ2N1NUFE7eCv+O2HduQdfsumZeL9ty7NFKX4L+XYWdnp73BUNg/c3NzAgMDMTEx4cyZ3JtH8fHx3L9/X+fGQ0E2bNjAkiVL+Prrr19ocOepIp3z4+bmxvDhmrmaS5cuxc/Pj6FDh2qfHzduHP379ycqKorg4GA2b95Mz549adSoEQAjRozg9OnCL4jT09NJS0ujbt26eHhoRuHz3rWxtLTEyMhIm8ED8OjRI/bv38/cuXNxddV86Tp16sTZs2fZvn07I0eO1Lbt37+/9uQgMzOTjRs38t1332n/h7q7u3Pjxg22bt1KnTp1iIyMRKFQMGbMGCwsLPDx8aFPnz789ttvL/O/8aXJU1JQqVTY2uueFNvZO3Dp3JlC9vp30tPSOH7oAL0HGf5EMiU5GZVKib2Dbn/tHRxIOqVfn6Mgxw4f4uzpU/w6Y5Z2m6OTE6M//IjgChVRKHLYs2snn48by//+mEbVfBdeJSU1RY5KpcLGzl5nu42dPSkXCq9M/m/t27kNWWI8dZs2L7LX/C9kyckoVSoc8723jg4OHD/17MGzN0eP5tqN62Tn5NCtY0dGvfVWge1SU1PZs28fI4eX/DzzdHkKAJbWuvNgLWxsSE+WFbpfhjwFiyDdEw8LG1vUKhWZaalY2tpx/9plbp07Rc8PJ7xQLA+uX0GemECFuo3+XSdeQHqqHLVKhVW++b5WtnbcuVpwOnFaSjI2+d73p/unpsiwL2D638nIcORJSVStV3gf9m1ag6mZGUHVSu5CMFX+5Jic73tra2fPVdnLf28/f2cYqSnJKJUqOvbuR9M27V/6NV+WjYUZgN7FaWpmFraWBU/1+6/CqoWQrVBw5X5skb7ui5DJZCiVShwdHXW2Ozo6cuzJzZ3CvPnWW1y9do3s7Gy6d+3KqPdys8GioqKYM3cuC+bPx9jY+BmvUrKKq79PdejUiaQnf2PE8OH06tGjSOP/L+ytLABN9ldesrQMHK2fn8r+VLvqFXGysWLf5YIHBDwcbKni5cHk9fq1Aota2pNjkrWtbqaItZ098ksFD7zJk2XYOei+79a29prnZDIcXVzxCQpmwMgxLP9zOjk52aiUSoKqVKPv26MLjWXHmhWYmZlTqWbtQtsUNVmqHKVKhUO+3yRHW1tOXim4zomFmRmje/WhWmAgxhJjDp47y9ez/yJ72HDa1X/+TRGhZBjb2WJkbIwySaazXZkkw7hmwTfu0k+ewXnUW1jUqk7G6XNIPT2w79UNABMnBxSxj3XaS6wssW7aiIT5S4qjC8IrzsrKirCwMBYsWIC9vT02NjbMmzcPX19fQkNzP2NffPEFwcHBDBmiyaJet24dS5YsYdy4cXh6emrr3Ziamj63BEyRDtgEBARo//vmzZtcunSJ3r1767V79OiRNtC8qUBGRkYEBQURH1/w3VMbGxtatWrFxIkTCQ0NJTQ0lEaNGuFSwPz/vHGo1WpG5UvTzcnJoVq+ehVBQUHa/7537x7Z2dlMnDhRZz6aQqHA7UndjOjoaHx9fbGwsNA+X6FCyRdNK0z+eXRqtRojXmxu3fMcjtyDWqWiUQv9AlyGojdvUM0L9ffShfP8b9J3vPvBGELyZAuV9/amvHduDYWKlasQGxPD2pUrDDZg85ReV//FvMnnOXPsMOuXLuTNMR/jVEC9I0Mo8L19Tnd//Ppr0tPTuXHzJtP+/otFK5YzbMBAvXbbwnejUirpENamCCMu2I3Txzmw9h/t43ZvPhkwzt8XdUEbdeX/bOctzp6ZlkrkqsW0HPAmZi+YSXLl+CFcvHxw9vR6fuP/TO+Dq79Np7X++17gduDq6RNErF1Bt7dGYuekX38B4PienZw5sJcBYz7DLM9xu8TkPyajfu7n+EV89N1ksjIzuX3jGuuXLsLZ1Y16zVo8f8ciFOrrSbc6uRlUi/cdL7CdEUYvfxssj4YhftQN8mZ+xDGyFAXXEikJBX2Fn3dM/vGHH0hPS+P6jRtMmz6dRYsXM2zoULKzs5nw5ZeM+eADPMs9u/i4oRRlf/OaM3s2GenpXLh4kekzZ1KuXDk6dijZ6X1NK/rzTljuoO8P6zTFVvN/bP/Nd7d+kA9DmtXhty17iUspOHsmrFoIianpnLoVXeDzxUH/t/XZ5xL6z2kPygDEPrjPxqULaNW1JyFVQ0mRJbF15VLWLphNv3f0B20O7tzGsb3hjPjsK8wtXnzwq6gUdJ5c2G+SvY0NA9q01T6u6OuLLFXOsp07xIDNK0nvG1vob0/K1p1Iy7nj8f0XGJmYoEpLR7ZuM05DB6BWqvTa27RuDsYS5Lv3FnnUpVFJLbddkt566y2MjY35+eefycrKIjQ0lA8//FDnBktMTAzOeeqBbd26FYVCwc8//6zzWi1btuTDDz985t8r0gEb8zwFMlUqFbVr1+bNN/Xrpdjb22vfvH97kTl27Fi6du3KqVOnOHbsGEuWLOGLL74oNG326YXsb7/9pneXyszMrNDHT+P76quv9AaEXqYY8YsWQ6pcuz51Ghe84svz2NjaIpFISM63+ktKskwv6+a/ity1g9oNG2NtY/P8xsXM1s4OicSYpETd/spkSdg7Pru/F8+f5+vxnzBo2HA6dX3+yioVKlZiX8Sel4r3ZVjb2iCRSEiRyXS2p6Yk62Xd/Bdnjh1m0Yw/GDxq7CuxQpS9nR3GEgkJ+d7bRFmSXtZNfu5PMur8fX1RqlT88MsUBvXth0m+48CGrVtp0bQpdvnutBUHn0rVcPX21T5WPrnATJenYG2fe+cyI1WOxTO+WxY2tqTLk3W2ZabKMZJIMLeyJubOTdJTktk6e6r2+afHtDmfjaL3R19h75pbvyUjNYW7l87RqHu/l+pfYSytbTCSSEhL0Y05TZ6il3XzlJWtHakFtH/6XF5XT59g04K/6Tz0bZ0VovI6vmcn+zetpe/7H1HOL6DANsXF2sb2yfdWd/UAeXKyXtbNf+H8pBaPp48vKckytqxeXuIDNlfuxxAdn9s/E2PNjGtrczOS0zO1263MTZ85JeTfaBjiR1i1EBZGHuP+M1brKU729vYYGxvrHaOSEhNxypeFkt/Toun+/v6oVCom/fgjg954g/j4eG7dvs13kybx3aRJgOacSq1WU69hQ6b+9hv169cvng49R3H0N+851dMBqsDAQBISE5k9d26JD9gcj7rH9TxT+aRPfjMcrCx0pirZWVogS8/Q2z+/+kE+jOnQjGnb9+usEJWXiURCi8qB7D5/vURWVLF6ckyS58vkTE1J1su6ecrGzr7A9k+fA9i7eT1e/oE076ipJ+jh7YOpmTl//vA17Xr1wz7PYPrBndvYsXYFwz+agHeAflH84mRvbYOxREJisu5vTJJcjuO/OBeo7OfP1sOHijo84SUok1NQK5UY5ztHNHaw08u6ySthziIS5i3B2NEepSwFy5qam/r5s2sAbDu0IW3/YVTywlc8FEo3U1NT3nnnHd55551C28ybN++Zj/+NYlsGKSAggIMHD+Lq6lroAIeDgwPXr1/XZrqo1Wpu3LihM6WpIH5+fvj5+dGrVy8mTpzInj17qFmzJiYmJtr6OE/5+/ujVqtJSkrSy6h5Fi8vL6RSKXFxcTrpTfnb7Nmzh8zMTO1g1bVrBc/9fepFiyGdu/vohWPNz0QqxTcwiItnz1A3z6DPxbNnqNPg5ac73Lx+jXu3bzHwrcI/pCVJKpUSFBLM6ZMnaNI89yLlzMkTNGrarND9Lpw7y8TxnzJw6Jt07/1iS/LdjLqB4zNWDyhuJiZSvPwDuHrhLDXzvJdXL5yjet2Xu8Nz6shBlsycxqBRH1CzfsOXDbVISKVSKgQHc+zUSVrnKQR2/NQpWjR58QFNtVqFUqnU1HfJM2Bz6coVbty8yUejCk/XLkqm5uY6Kz+p1WosbGx5cP0Krl6+gKbeScztKOp1LHwqgJuPP3cundXZdv/GVVzK+yAxNsbFy4deH32p8/yJHZvJzkinUfe+2ORblenaiaMYm5gQEFo8KenGJiZ4ePty+8pFKtbKHQi8c+UiITXqFLiPp38ge9evRJGTjYlUUzPh9pWLWNs56GTQXD55jC2LZtNpyNs6r53XsfDt7N+8jr6jPzLIct4mUine/oFcPXeWWg0aa7dfPX+WGvWK9s6sWqVGkVPytVyyFUoSU9N1tqVkZBLo7sKDRM1FkYlEgq+rIzvOvHwttUYV/GhdNYRFkccNupy3VCqlQoUKHDt2jNatcjNOjx0/TssWLz5oplKrNccolQpXV1dW/POPzvNr1q7l2LFjTPn5Z8p5lFxh1vyKo7+FUatU5GRnF/p8ccnMURAjk+tsS0pNJ9SnHFExmgxwqbExFT3dWLzv2dPAGob48X67JkzfcYAj1+8U2q5ukA82FubsuXD9peN/ESYmJnj6+nP94nmq5Tl3uHHxAlXrFLzErE9gMNtWLiMnO1tbx+bGxfPYOjjg8GSKanZ2FhKJbnnMp4/zjkPt376FXetW8uZHnxtkOW+piQkh3j4cv3KZlrVzf4NOXL5M85ovXnT/RnQ0zoUUIBYMRKEg63oUlrWqk7Y/dzBN8/jIs/dVqVDGawajrVs0JePSFZQy3UE9s5AgzAL9iZ81t8hDL61K27LehlBsAzYdO3Zk165d/Pzzz/Ts2RM7OztiYmI4ePAgb775JpaWlnTu3Jl169bh6emJl5cXO3bsICkpSW9u9FMxMTHs2LGDevXq4eTkRExMDHfu3KHDk7svbm5uZGdnc+bMGfz9/TEzM8PT05PmzZvzxx9/MHz4cAICApDL5Vy4cAF3d3caNiz4wtTS0pLu3bszf/581Go1lStXJjMzk2vXrmFkZES7du1o1qwZS5YsYerUqfTr14/ExMTnLgdWUtp17cHfv0/BPziYoIqV2btjK7LEBFq27wjAqkXzuXXjOuMn/aTd58G9uygUCuTyFDIzM7l7S1P01Mdf92505M5tuJXzpEKVkl8CuTDde/fllx8nEVKhIpWqVmXbpo0kxCfQoUs3ABbM/otrV6/w02+ajIPzZ87w9eef0qlrN1q0DiMxIQEAibEE+ydZSOtXr8LN3R0fPz8UOQoidu/kyMEDfPndJIP08alWHbuyaMYf+AQEExBSgQPhO5ElJtI4TJOqu/GfJdy5eZ0xX32v3efR/WgUihzSUlLIyswk+s4tALx8/QHNksOLZv5BjzeGElixMslPMgFMTEywsjZsFtWA3r2ZOHkylStUJLRKFdZu3kRcfDw9n6wGN2POHC5dvcKfv2pqR23btQtTU1MC/f0xMTHhyvVrzJwzl5bNmmGar5ju+q1b8C5fnpqFDMoWNyMjI6o2acmZPTuwd3XHzsWV0+HbkZqZEZhnIGPv8oUAtOg/FICKDZpw6VAkhzeuomL9JsTeucn1k0doOUCT0Sg1NcPR3VPnb5mZW6BWKfW2Py02HBBau9BlxItC3dbt2LTgb8r5+lM+IIjT+/ciT5ZRs2lLTR/Xr+LhnVsM/HA8AJXrNuDg1g1sXjSHRu27kvj4EUd2bqFJx+7azMxLJ46yecHftOzZD++gEFKf3P01NjHB4knB7KO7thK5cQ1dhr2Lo6u7to2JqWmJpuC36tSVhdN/xycomICQihzYtYPkxESaPKk3s2HZIu5E3WDsxNzjy6PoeygUClLlcs339vaT762f5nu7d/sWnF3dcCuneU9vXL5I+Ob1NG1j+BWiAA5fvU3zKoHEp6QSL0+jRZVAsnOUnL2TW7y1V4PqAKw5cla7zcNec4fbTGqCWq3Gw94WpUrF4xTN3cwmFf0Jq1aB1UfOEC9Pw9pckx2bo1SSVcgSy8VpYP/+fP3NN1SuXJnQatVYu26d5hj1pP7KjJkzuXT5Mn/OnAnA1m3bMDMzIzAgABOplCtXrjBz1ixatmihPUYFBuj+7jo4OCA1NdXbbgjF0d8Vq1bhWa4cPk+mIZ8+e5aly5bR6xVYIQpgy+lL9KwXyv3EZB4lJdOrfnUycxTsv5JbHP6D9pqbCNO2a5ZzbhTix5gOzVi07ziXo2Owt9RMw1SolHqrQ4VVC+HC3YfEJusOFBWnJu06sfLv6Xj5B+IbFMLRvbtJkSVSv6Vmpcztq/4h+laUdjnu6g0as3vDalbNmUWrrj2Ie/SIvVs20rp7L+0xuVKN2qyZ/zdH9uwiuGooclkSm5YtwtPXT7tUeOTWTexcs5x+776Pi3s55E8yhk1MTbF4geVti0q/sDZ8N38ulXz9qBYYyPp9kcQny+jWTHOj7891a7l85xbTx2lWQt12+BAmxsYEe3tjZCTh0PmzrI2MYGQP3c/o9WjNCnhpmRlIjIy4Hn0PqbEJfq/o9MaCGFmYI/V8Eq/ECKmbK6aB/qjkchSxcc/e+RUgW7MRt/EfknXtOhkXr2DXuR0mTo4kb9asAuU0fDBmFYJ4+MlXAEhsbbBu1piMcxcwkkqxbdcK62aNeFBADUDbTm3Jvv+AjHMvvqiJILysYhuwcXJy4ueff2bRokVMnDiRnJwcXFxcqFGjBlKpFIAePXogk8m0y3G3bt2a+vXrI8s33eMpMzMzHj58yE8//URKSgr29vY0b96cnj01y+9VrFiR9u3bM2XKFORyuXZZ7zFjxrBq1SoWLFhAQkIC1tbWBAcHPzfj5o033sDe3p7169cza9YsLC0t8ff3p8eTkxILCwu+/vprZs2axdixYylfvjxDhw7l+++/f+brloT6TZqRKk9h06rlyBKTKO/jw0dff4+zqyYlWZaUyOOYhzr7/PrdV8Q/zk39+2qspu7P4k07tNsy0tM5emAf3foOLLKaKUWhWctWyFNSWL5kMYmJCfj6+fHd/37GzV0zVSAxIYFHD3L7u3vHNrIyM1m7cgVrV67Qbnd1c2fRytWAZkWmuX/OIiE+DlMzM3x8/fj2p5+pa+C5yrUaNiZNnsKO9atISUrCw8ubkeO/0tabSZYlEh8bo7PPrJ++IzEu90f2p8/GATBz5QYADobvQKVUsmbRPNYsyk3ZC6pUmbETfyjmHj1bmxYtSU5JYf7SJcQnJhLg68sfk3/C48l7G5+YwIOHue+tsbExC//5h+gH91Gr1bi7udG7W1f699Ktp5WWns6uiAjeGjzYoJ/l0OZtUOTkcHD9CrIz0nH19qPDiPd1Bk9SZbrTD2wdnWk3fBRHNq/h8pEDWNna0bBrH50lvV/Uo5vXSY5/TIv+xVtAvFLt+mSkpnJo2yZSU2S4lCtP39EfabNlUpNlyOJyjz/mFpb0H/MpO5cvZsHkiZhbWlKvdXvqts7NUDyzPwKVSkn46mWEr16m3e4dVIE3PtKcaJ2K3INKqWTD3Jk68VSt35jOQ98uzi7rqN2oCWmpcravXUVKUiIeXj6MmvB17vc2KYm4fN/bGZO/IzHP/5MfPx0LwJ+rNwGgUilZv3QhCXGPkUiMcXF3p9vAITQJ+29LWha1/VduIjUxpnOdKliYSrkfL2PB3mNkK3JXMnt6EZvX+x10s+cqlncnKTWdKZs0y1rXD/LFxFhC/8a6d8JP3Ypm7dGiK77+otqEhZGcnMy8BQuIj48nwN+fqb//rl0cIT4hgfsPcgepjI2NWbBoEdHR0ZoBKXd3evfqxYB+xTMlsagVR39VSiXTZ8zg4aNHGBsbU758eUaPGqUdBDK09ccvYGpiwtutGmBlbsqNR3F8t2YHmXkGCJ1tdeuFta1eARNjCcNb1md4y9wpbBejH/H1yu3ax252NlT19uC3LZHF3o+8qtdvSHqqnIhN60iRJeFe3os3P/pcmy2TIksi4XFuIW8LS0tGfPoVGxbPY9rEz7GwtKJp+07aJbwBajdpTlZGBofDd7Bl+WLMLSwJqFiZDv3e0LY5smcnSqWSZTP/0ImnVuNm9H1bf1n44tK6Tl2S01JZuG0LCcnJ+Jfz5Jf3x+Dx5DcpIVnGgzjdwYmF27YQk5CARCLB282NCUOG6dWvGfr9tzqPD54/h7uTE+sm69ateJWZVwim/PQp2sdObw3G6a3BpGzbReyPvxowsheTGnkQia0NDgP74OLoSNaduzz8/DsUjzXvp7GTA9Jy7jr72LZpgfM7QwEjMi9f5cG4L8i6pruKpZGFBTYtmpC4ZGUJ9aR0KI01bEqaUUZGxiv1f3HMmDFUqlTpmXPCyoKXmRL1OnKxfbHCqKXBrdgEQ4dQouq6lJ104Tmnytbyjs42Zed76+Vkb+gQSlT4xYKXWy+NPu/Q+PmNhNfSkLnrDB1CiRrS5MWn87zuGmeWXDbSqyDxC8PfDC4pRvlqjJZ25be+GrMzisu789aW2N/6a3jPEvtbJanYMmxexOPHjzl9+jRVqlRBqVSyc+dO7ty5w+jRJVNPQhAEQRAEQRAEQRCEoqd6pVJDXk8GHbAxMjIiIiKCBQsWoFar8fLyYuLEiTrLawuCIAiCIAiCIAiCIJQ1Bh2wcXFx0VuLXBAEQRAEQRAEQRCE15uoYfPyJM9vIgiCIAiCIAiCIAiCIJQkg2bYCIIgCIIgCIIgCIJQ+ogMm5cnMmwEQRAEQRAEQRAEQRBeMSLDRhAEQRAEQRAEQRCEIqUSGTYvTWTYCIIgCIIgCIIgCIIgvGLEgI0gCIIgCIIgCIIgCMIrRkyJEgRBEARBEARBEAShSIkZUS9PZNgIgiAIgiAIgiAIgiC8YkSGjSAIgiAIgiAIgiAIRUqNSLF5WSLDRhAEQRAEQRAEQRAE4RUjMmyEV4K6DE1wVJWxkWa1QmHoEEqMqYmxoUMoURIjI0OHUGIeJCYbOoQSlaNQGjqEEqNKTDJ0CEIxSc3MMnQIJUoiKTv3YbNOnTN0CCXKyMzM0CGUGHVW2frelnZiWe+XV3aO7IIgCIIgCIIgCIIgCK8JkWEjCIIgCIIgCIIgCEKRKkuzKIqLyLARBEEQBEEQBEEQBEF4xYgMG0EQBEEQBEEQBEEQipRKJNi8NJFhIwiCIAiCIAiCIAiC8IoRGTaCIAiCIAiCIAiCIBQpUcPm5YkMG0EQBEEQBEEQBEEQhFeMyLARBEEQBEEQBEEQBKFIiQyblycybARBEARBEARBEARBEF4xIsNGEARBEARBEARBEIQipRIZNi9NZNgIgiAIgiAIgiAIgiC8YkSGTRG6cOECEyZMYOnSpdjZ2Rk6HMK3bWbbujUkJyXi6e3DwLfeJaRylQLbZmdns3DWNO7ejOLh/WiCKlZiwo9T9Nod3reXbetWE/PgARaWllQOrU6/N0dg7+BY3N15ri0b1rN25XISExLx8fXl7dHvU6VaaIFtz589w4bVq7h29QrpaWl4eHrSrWdv2nToqNNm/Idj9Pb9e9ESvLx9iq0fL2L/zu3s2byBFFkSHuW96DFkOIEVKxXYNic7mxVz/+L+7VvEPLiPf0gFxkycpNPm7LEjHArfyf3bt8nJyca9vBdtu/eiau26JdGd51qzaRNLVq8mITEBfx9fPnzvPWpUrVpg21t37zJlxnRu371Laloazk5OtGnenBGDBiOVSrXtVm/ayOqNG3kUG4ubqyvD+g+gY1hYSXVJS61Wc3znZi4dOUBWRjpu3n406zkAJ49yz9zvQdQ1Dm5cTWLMQ6xs7anZsi1VGjXTPh919iSn9uwkOf4xKpUSe2dXQpu1pmLdhrmvcfM6Z/buIu7+PdKSZbTqP1Tn+aJ2MjKcI7u2kpqcjEs5T9r0eQPvoJBC2z9+EM2O5Yt4eOcWFlbW1GjSgiYdu2FkZATA1dMnOLU/gtjouyhycnD2KEfjDl0JDq2pfY3Fv/7AvetX9V7b2cOTd7/5qeg7+QxF3f+716+wd/0qEmJjyMnOws7RmeqNm9GgTcdCX7OkhYWGUC/IF0tTKffik1h/7DyxyfJC29tYmNG5dhU8He1wtrHm9K1oVh4+U2j76r6eDGxam8v3Y1gQcaw4uvCfrdmymSVr1pCQmIi/jw8fvvMuNaoU/Buc170HDxj8/mjUajX71m8o/kCLQFno66CmtelQoxLW5mZcfRjLjO0HuBufVGj7qt4evNmiPl5O9phJTXicLGf72SusOXpO26Z9jYq0rhqCj4sDEiMjomLiWbTvOJeiY0qiSxzavYPIbZuQy5Jw8/Si6xtD8a9Q8LkEwKPou6xfNI97N6OwtLamfsswwrr10h6TNK+5nUO7d5AYF4eDkzOtuvagdpPm2ufPHTvM3i0biI+NQalU4uLmQZN2najTtLn+HyxmlqFVsK5dA2MrS3ISEkmJPEj2g0fP3MeqRjUsQ6tgYmuLKjOT9MtXkR88qvOaVtWrYmJnizJFjvzYKTKuXCvurrwQ2y7tcejTA2MnB7Lv3CN+1lwyL1wutL11s0Y4DOiNtLwnyuRkkjdsRbZqvfZ510/HYNu2ld5+qoxMbnXqUyx9KGrmoVVw6N8L85AgTFycifnhF+Tbdxs6LEHQIQZsSqmjB/axbM5fDH53NMGVKrNn2xZ++fZLJs+cjbOLq157tUqF1NSU1h27cO7UCdLTUvXaXL98ib9/n0L/YW9Rq15DkmVJLPprBn/9+jPjJ5XshU9++yL28PeMaYwaO45KVauydeMGvv7sU/5auBhXNze99lcuXsTH35+e/Qbg6OTE6RPHmfbrL0hNTWnRWvei/a8Fi7G2tdE+trOzL+7uPNOpwwdZu2gefYa/TUBIRQ7s2sGfk7/ni9+m4ejsotdepVIhlUpp2rYDl86cIiM9Ta9N1JVLBFWuSse+A7CytuHEgf3M+eV/fDDx+0IHgkrK7shIfv1zFp+9/wGhVSqzZvNmxn4xgZVz5+Huqv9ZlpqY0DEsjOCAQGysrblx6yY//v47CqWKD0aMAGDN5s3MmDuXCR9+SJUKFbl09So//vE7ttbWNGnQoET7dzpiJ2cjd9Oq/1AcXN05sXMLG//6nTc+/x5Tc/MC90lJiGfznOlUrNuIsDeG8+hWFPvWLMPc2prA0FoAmFtZU6dNBxxc3ZEYG3Pn0gUiVi7GwtoG30qawa6crCycPDypULsB4f/ML9Z+XjpxlF0rl9JuwBC8A4M5GbmH5dOn8O43P2Hn6KzXPisjg2V//A/voBDe/PxbEmJj2LxwNqZmZtQP6wDA3RtX8a1QieZde2FhZc3FY4dY/ecfDProC+1ASO93x6BUKLSvq1AomP3d51SqVbKDkcXRf1Mzc+q0bIOrpxcmpqbcj7rBtmXzkZqaUbt56xLtX0GaVw6kaaVAVh06zeOUVMKqhTAirCFTNuwhK897kpeJREJaZjZ7L96gXpDvM1/f0dqSjrUqcys2vhiifzm79+3j17/+4rNRowmtXJk1W7Yw9qsvWfn37AKPW0/l5OTw5U+TqVGlCqcvXCjBiP+7stDXPg2q07NeKL9s3sv9BBkDm9Tip4GdefPP5WRk5xS4T2Z2DhtPXOD24wSyFAoql3dnTIdmZOUo2HzqEgChPuXYdzmKS9ExZOUo6FGvGpP7d+LdOat5mJRcrH06e/QQG5cuoMfQt/ALrsjh8J3MnfIjn/zvdxwKOJfITE9n9k/f4xdSkTHf/UTco4esnD0DUzMzmnfoAsDh8J1sXbGM3sPfxTswiHs3b7Bm3l9YWFlTuWZtACytbWjdtReu5TyRGBtz5cwpVs+dhbWtLRWr19T7u8XFPDgQu+aNSY7YT/aDR1iGVsGxe2fiFv2DUq5/Dgxg26wR5v6+pOw/TE58AkamphhbW2mft6xWGdsmDUnevZfsmFik7q7Yh7VAlZVF1q07JdSzglk3b4zLqBHETf2LjIuXsevSgXKTJ3LvzVEoHusfQy3r1sTti4+JmzGb9BOnMfX2wnXcKNRZ2SRv3ApA/Mw5JMxZpLNf+Wn/I+P8pRLpU1GQWFiQfesu8h3huH35iaHDKZXEjKiXJ6ZE5aNWq1m/fj1vv/023bt3Z+jQoSxatIjY2Fg6d+7MoUOH+Oqrr+jZsycjR47kzBnNnb/Y2FgmTJgAwBtvvEHnzp35/fffDdaPHRvX0bhVGC3atsfTy5vB74zE3sGRiG1bCmxvZm7OsJEf0KJdBxyd9C8eAKKuXcHRyZl2XXvg4u5OYIWKhHXqys0C7l6XtPWrV9G6XXvadeqMt48v730wFkcnR7Zu2lBg+75vDGLI8BFUrloVj3Ll6Ni1Gw2bNOXQ/n16be0c7HF0dNL+MzY2LubePNverZuo16wFjVq1wb28F73fHIGdgwMHd+0osL2ZuTn9RrxHo9ZtsHdyKrBNr6Fv0aZbT3wDg3Fx96BD7754+ftz/oTh71j/s3Ytndq0oVuHDvh5+/DJqNE4OzqydvPmAtt7eXrSqU1bggMC8HBzo2mDhrRt2YqzF3MvCLbvCadr+w60bdESTw8P2rRoQbcOHVi8amVJdQvQHG/O7QunVqt2BIbWwsnDk9YDhpGTlcn104X/v794eB9WtvY069kfRzcPKjdoQoU6DTmzN/euUPmgCvhXrYGDmwd2zq6ENmuFs4cnD2/d0LbxrVSVBh27E1i9FkZGxftzcCx8O9UaNqFmkxY4e3jSrv9grO3sObVvT8F9PH6InOwsugx9B1dPLyrWrEODth05Fr5Du+JA276DaNSuM55+ATi6utG0cw88fPy4dvaU9nUsrKyxtrPX/ouOukZOVhahebKRSkJx9N/Dx4/KdRrgUq48Ds6uVK3fCP9K1bgX9WrczW1SMYC9F29w4d4jYmVyVhw6jZnUhBp+noXuk5SWwcYTFzh5M5r07OxC20mMjBjYpDY7zlwhUZ5eHOG/lH/Wr6NTWBjd2rfHz9ubT0aO1By3thb8G/zU9PnzCfTzo1WTJiUU6csrC33tXrcaKw+f4eDVW9yJS2TKpggsTKW0rBJU6D43YuKJvBzF3fgkYmRy9ly8wclb0VTx9tC2+WnDHjadvMjN2HjuJ8qYtn0/6dk51AnwKvY+7du+mTpNmlO/RRhunuXpPmQ4tvb2HNmzq8D2pw8fIDsri/7vjsbDy5tqdevTolM39m/foj0mnTq0j3otWlGjYWOcXN2o0aAx9VqEsXfLBu3rBFWuSpXadXEt54mzmztN2nXEw8uH29euFHuf87KuVZ30y1dJv3AZRWISKXsPoEpLwzK04MwwYwd7rKpXJXHjNjJv3kaZnIIiLp6s23e1bSwrhZB+4RIZ126gTE4h81oU6ecvY12nRkl1q1D2vbqSsnMPKdt2kXPvPvEzZqNISMKuc4cC29u0bkHakeOkbNqO4lEs6cdOkrR8Dfb9emjbqNLSUSbJtP+k5TyQlvMgZWvBn6FXUfrREyTMXkBq5EFQiZEF4dUkBmzyWbx4MStXrqR3797MnDmT8ePH4+ycO4CxZMkSOnfuzPTp0wkKCmLKlClkZGTg7OzM559/DsDMmTNZvHgxb7/9tkH6oMjJ4U7UDarmu1NRpUZNblz97z+IQRUrIUtK5Mzxo6jVauQpyRw9EElorTovGfHLycnJIer6dWrW1o2jRu06XLl48YVfJyM9DWsbG73tY955m4E9u/H5uLGcO3P6peN9GQpFDtG3blKxWnWd7RWqhXK7iAfOsjIysLSyLtLX/LdycnK4euM69WrV0tler1Ytzl9+sTs40Q8ecPTkCWpWq5b7utk5mJma6rQzMzXj0rVrKAq5818cUhLiSZen4BVSWbvNxNSUcv5BPLp9q9D9Yu7cwjtEN/PJu0Il4qLvoFTqx69Wq4m+foWkuFg8Awq/wCguSoWCR/fu4F9J90TYv2IV7t+8UeA+929F4R0YgjTP+xRQuRpyWRKyhLhC/1ZWZibmlpaFPn/mYCQBVUKxcyx48LI4lFT/Y+7d4f6tG/gEVSi64P8jR2tLbC3Nuf7wsXabQqnidmw8Pq4vP4W2fY2KJKalc+pW9Eu/VlHTHLduUK+m7m9wvZo1OX+58N/gg8ePcej4MT56973iDrHIlIW+utvb4GRjpfNZy1YouXDvEZXKu7/w6wS4OVOpvDvn7z4stI3UWIKpiTGpmVkvFfPzKBQ5PLh9i+CqutPGg6uGcudGwQO+d6Ou4RdSEampWZ721UlJSiQxTvM9V+QokEp1f1ulpqZE34zSyXR8Sq1Wc+PieR7HPMSvQsWX7daLk0iQurmQdUf3+JF1NxrTcgW/pxYBfiiTUzDz9cb1zTdwHT4I+7atkFhY5DYyNkatVOrsp1YoMHV3A4kBL7lMTDALDiT95FmdzemnzmBeueDfCyOpFHW+7DF1djZSVxdM3ArOnLPt2Ias23fJvGz4G7nCq0OlVpfYv9JKTInKIyMjg40bNzJixAjCntSyKFeuHBUqVCA2NhaArl27UreuJpV+8ODBREREcOvWLSpXrozNk4t9Ozs7g9awkaekoFKpsLV30NluZ+/ApXOF1wJ4nqAKlRj58Xj+/PVncrKzUCqVVKlek7fHfvyyIb+UlORkTY0OB93+Ojg4cvb0qUL20nXsyGHOnj7FL9Nnarc5Ojox6sOPCA6pgEKRQ8SuXUz46EN++n0qVUOrF2UXXlhaihyVSoVNvmlZNnb2XLtwvsj+zv6d25AlJlC3aclmIeQnS0lGqVLhmO+z7OjgwPEzz/4sDx87hms3bpCdk0O39h0YOexN7XP1a9di044dtGjcmIrBwVy5cZ2NO7ajUCiQJSfjXEgmUlFLl6cAYJlvoNDSxpbUZFmh+6XJkykfrHtya2Fji0qlIjM1Fasnn4+sjHQWfvMZSkUORhIJzXoOwKdiwbV/ilN6qhy1SoWVje5x0crWjttXCx54S01OxjZfbSwrW1sA0pKTcXDWP2E8uXc38qREqtZvXOBrJsQ+4t71q/R+b+x/6MV/V9z9n/rZB6SnylEplTTp1J1azfRrCpQ0GwvNRV3+C095ZhZ2lhYF7fLCgj1cCPX15PctkS/1OsVFlpJS8HHL3oHjSQUft+ITE/hx6lT+9+VXWD1jwPFVUxb66mitiTEpLUNne1JaOs42VgXtomPZB4Ows7TAWGLE0gMn2Xq68JohQ5vXIyM7hyPX77xUzM+TJtecS1jnO5ewtrNHfrHg6WlymUxvoNvmybmuPFmGk6sbIVVDOb4vgiq16+HlH8D92zc5HrkHpVJBmlyO7ZPztIz0NL5//x0UihwkEgndh7xFxdCSmw4lsTDHSCJBla6bnadMT8fMsnyB+xjb2WJsa4NFSBCynZrMSNumjXDs1pH45WsAyLoTjWWVimTeuEVO7GOkbi5YVq2EkbExEgtzVGmGyQY0trPFyNgYZZJMZ7sySYZxzYJrPaafPIPzqLewqFWdjNPnkHp6YN+rGwAmTg4oYh/rtJdYWWLdtBEJ85cURxcEoUwTAzZ5REdHk5OTQ2howQcvAD8/P+1/OzpqTqaTk4t3nvF/lbcIHGjuZBhhVEjr53tw7y5LZ/9J1779qVqjFrKkRFYumMuCWdN450PDz/vU6y8v1t9LFy7w86TvePf9MYTkqddS3tub8t7e2scVK1chNvYR61atMNiAjZbee6u/7b86e+wIG5YuYtiYj3AsoN6RIeh/lnnue/vjhC9Iy8jgxq2bTJ8zh8UrVzK0f38A3hz4BglJSQwfOwbUahwdHOgYFsaSVauQFONdsGunjhG5aqn2cacRo5/8V/7Prn6f89N7+umdhTxPmJqZ0/fjr8jJzuL+9Ssc3LgKG0cnvIJL8E5mHnp9et4xSb/50xfSa3rl9AnC166gx4hR2BcyrfPMgUis7ewJqlr9hWMuSsXV/8GffElOVhb3b0URsW4l9s4uVCtk0Kq41PArT8/6ub+d8yM0RTjV+e54GWH0UhPaLc1M6dOoJv8cOFlo7ZBXRYG/SYV8r7/+eQo9O3akakXDfDdfVmnqa8sqQYzpkHuz4ssVW5/8V77PspERL/JJ/mjxBsylUiqWd2N4y/qa6VEXruu161anKh1qVmL8ss2kl9BnW+8d0vy4Ft5e733W3R7WvRfyZBkzvvsC1Gqs7eyp1aQZkVs2YpTnt9XM3IJxP0whKyuTG5cusHnZIhydXQiqUg3DesZ7amSEkYkJSdt3o5RpzvuTtu/G7c03kLq7kRMTi/zYCYytLHHu1wOMjFClp5N++So2dWq+ItNt8sdgpL/piZStO5GWc8fj+y8wMjFBlZaObN1mnIYOQK1U6bW3ad0cjCXId+8t8qiF11v+8wDh3xMDNnm8yAcqb/2Spz9Q/+aDuGPHDnbu3PncdpVr16dO46Yv/Lp52djaIpFISE5K1NmekizTy7r5NzavWYl/cAgde/QGwNvPHzNzc34Y/zG93hiKk4t+kbqSYGtnh0RiTFKibn9lSUl6WTf5Xbpwnq/Hf8qgYW/SsWu35/6tkIqV2B9RcM2JkmBla4NEIkEu012ZIjVFhm0RZHWdPXaExTP+YNCoMa/EClH2tnYYSyQk5PssJ8mScHSwf+a+bk+KXfr7+KBSqvjh9994o08fTIyNMTcz46uPPubzMWNJSErC2dGR9du2YWVpiX0xZsf5VQ7F7ePcQd+nKeLp8hRs8mRTZMhTsLC2LfR1rGzsSE9J0dmWkSpHIpFgbpV7x9dIIsH+yaCbi6cXSbExnArfXuIDNpbWNhhJJKSmyHS2p8lTtFkj+Vnb2ZGWbzD8aUZS/n2unD7Bxvl/0XXYOzorROWlVCg4f/QANRq3QFLCdaiKu/9Ps21cPb1IS0lm/+b1JT5gczk6hnt5VswxeXJxZmNhTnJ6pna7tbkp8peY7uFub4OdpTlvh+WuZvb0t/inNzrz66a9xKUUXDC0pNjb2hZy3JLpZaI8dfLcWc5cOM/cZcsAzTWUSqWiQccOfDpqNN07FFxnwtBKY1+PXL/D1Qex2sfSJ8cLBytL4lJyi/bbW1ogy5d1U5AYmWZVtDtxiThYWTCoaW29AZtudaoytHldvlixlWsPHxf0MkXKyubJuUS+TM7UlGS9DN6nbOztSZHla//kGGVtq/ndlJqa0fftUfR68x3kycnYOthzNCIcM3MLrPJkkkokEpzdNbV8PH38ePzgAXs2rSuxARtVRiZqlQpJvgwvY0sLvawb7T5p6aiVSu1gDYBSloxaqcTYxpqcmFhQKJHtikAWHonE0gJVWjqWVSuhyspGlfH8z0pxUSanaOLMd05s7GCnl3WTV8KcRSTMW4Kxoz1KWQqWNTXvT/7sGgDbDm1I238YVSEFmwVB+O/EgE0eXl5eSKVSzp07R7lyz15StyAmJpr/nSqV/sjzU+3ataNdu3bPfa1zd5+9rOAz45BK8Q0M4uLZM9TNM+hz8ewZ6jRo9J9fNzsrSy/74Olj9QvdZyoeUqmUwOBgzpw8SZPmLbTbz5w6SaNnTOm5cO4s33z+GQOHDqNbrxdbfvBWVBQOJTRdpiAmJlK8/AO4euEcNfK8l1cvnKN63Zdb3ej0kUMsnTmNN0Z9QI36xbe0878hlUqpEBTM8VOnaZ3nvTx2+jQtG794oUqVWo1SqUSlVEKei3UTExPcngw07o7cS6N69Yo1w8bU3Fxn5Se1Wo2ljS3R1y7j5u0LaGpQPbwVRaMuPQt9HXdff25dOKuz7d61K7h4+WJsXPhhXa1WoVSUfFaCsYkJHt6+3L58kUq16mm3375yiQpPVg7Jr7x/IHvWrUSRk43Jk5oIty5fxMbeAXun3MHhyyePsWnh33QZ+g4Vn7Hy07Wzp0hPTaV6CRcbhuLtf35qtdog73GWQkGWXLdGRUp6JsEeLtxPkAGaQRw/Vye2nvrvK4hEJ8j4ZVOEzrZ21StiYSpl/fHzJKbqr4JX0jTHrSCOnz5D6ya5v8HHzpyhZaOCf4OX//mXzuN9R46wYOUKFv4xFRcD/uY8T2nsa0Z2jl72VoI8jZr+Xlx/pKkfJTU2poq3B3P3HPlXr21kZKQdAHqqZ71qDG5Wly9XbC2x5bxNTKR4+vlz/eJ5Quvl/t5fv3ieanXqF7iPT2AIW1csJSc7W1tb68bFc9g6OOpl4xqbmGgXOTh79BCVatR65m+rWq0q0fpxqFTkxMZh5uNF5o2b2s1mPl5k5HmcV/bDRxgZG2NsZ4syWTN4rp1qJJfrvb7qybHIokIQmbfvFEs3XphCQdb1KCxrVSdt/yHtZs3j53yGVSqU8ZoBWesWTcm4dEVn0ArALCQIs0B/4mfNLfLQhdefIa8RSwsxYJOHpaUlXbp0YdGiRUilUipXroxcLicqKopa+YqeFsTV1RUjIyNOnjxJ3bp1MTU1xcLi5ebq/1ftuvbg79+n4B8cTFDFyuzdsRVZYgIt23cEYNWi+dy6cV1nOe4H9+6iUCiQy1PIzMzk7i3Nj5aPfwAANerWY/6MqezZtoWqNWshS0xk2dy/8A0ILHCp8JLUvXcffp38A8EVK1KpShW2bdpIYnwCHTp3BWDBnL+5fuUKk3/7A4DzZ88w8fPP6NS1G81bh5GYmACAscQYO3t7ADasWYWruwc+vr4ochREhO/iyMEDfPHt94boolaLjl1YMmMqPgFB+IdU4GD4TpITk2gc1haATf8s4e7NG7z/1XfafR7dj0apUJCWIicrM5P7d24DUN5Xk+1x6tABFs+cSvc3hhBYsRIpTzJ4jE1MsLLWL8Rckgb07MnEn/9HpQohhFauzLotW4hPSKBHp04AzJw3j0vXrjLr5ykAbAvfjampKYG+fkilUi5fv86s+fNo2aQppk9OMu/ev8+lq1eoUrEicnkq/6xdy807d5j4yacl2jcjIyNCm7Xm5O5tOLi5Y+/ixsndW5GamRFcM/fCfvcyzZLbYQM1dXiqNGzG+YN7ObB+JZUbNuXR7SiunjhMm0Fvafc5uXsrbt5+2Dq5oFQquHv5AtdOHqVpj/7aNtlZmSTHay5A1GoV8qRE4h5EY25piY1D0V441Wvdno0L/qKcXwBeAUGc2h+BPDmJmk019VYi1q/k4e1bvDFOU7y9ct2G7N+ygU0LZ9O4Q1cSY2M4vHMzTTt112ZUXDpxhI3z/6Z1r/54B4Vo6/4Ym5hgka9g9ukDe/GrUAkHAx2riqP/JyJ2Ye/sgpOb5k713RtXObp7G7WbGX5Jb4ADV27Sqmowj1NSiUtJpXXVYLIUSs7cfqBt06+RJiNqxaHcgu7lHDQZROZSE9RqNeUcbFGo1DxOlpOjUBIr0704yszOQSIx0ttuSAO692DiL1OoFBJMaKXKrNu2VXPc6qD5DZ65YD6Xrl1n1k+a3+AAX1+d/a/cuI7EyEhv+6uoLPR1/fHz9G9ci+j4JB4kJjOgcU0ys3OIuJhbNPyTLi0BmPJkQLFr7SrEyOREPxmwrObjQa/61dl8MncxhN71qzO0RV3+t2EP9xNkOFhpzhmzFErSswpfJa0oNGvfmeV/TsfbPxDf4Aoc2bOLlKQk6rdqA8C2lcu4d/MG7074BoAaDRuze/1qVs6eQauuvYiPeUjE5g2E9eitPSbFPXrIvZs38A4MJiMtlf3bNxNz/x793hmt/bvhG9fiHRCEk6sbipwcrpw7zalD++k2+E29GItT6qmzOLRvTU5MLNkPY7CsVhmJlRXp5zQDyjaN62Pq7kbCmo2ApiBxduxj7Nu2JHnvQQDsWjQm+1EMOTGajBNjeztMPdzIfhSLxNwM65rVkTo5IdthuMzsp2RrNuI2/kOyrl0n4+IV7Dq3w8TJkeTN2wFwGj4YswpBPPzkKwAktjZYN2tMxrkLGEml2LZrhXWzRjz4cILea9t2akv2/QdknHvxhT5eFUYW5kg9n9yklxghdXPFNNAflVyOIrbwBQ4EoSSJAZt8Bg8ejJWVFStWrCAhIQF7e3tatGjx/B0BJycnBgwYwJIlS5g+fTotWrTgww8/LOaIC1a/STNS5SlsWrUcWWIS5X18+Ojr73F2dQNAlpTI4xjdlQp+/e4r4h/npjl+NXYUAIs3aZaLbtKqDRkZGYRv3cTy+XOwsLKkYtVQ+g0dXkK9Klyzlq2Qp6SwYsliEhMT8PX149uf/oebu6baf1JCAo8e5vY3fMd2sjIzWbtyBWtXrtBud3VzZ+GKVQDk5CiY9+csEuLjMDUzw8fXj28n/4869V8uk+Vl1WrYmDS5nJ3rV5OSlISHlzfvjf9Se4crWZZEfKzuXbq/fvqexLjcH57/fTYOgOkr1wNwMHwnKqWStYvms3bRfG27wEqVGTNxUnF36ZnCmjcnOSWFBf/8Q3xiIgE+vvw+6Qc83DSf5fjEBB48ys1IMzY2ZtGKFUQ/eIBarcbdzY1eXbrQv0duxopKpeSftWu5e/8+JsbG1Aqtzrw/plLO/cVX/CgqNVu2RZGTzb41/5CVkY6bjx9d3x2rk4kjzzfdwNbJmc4j3ufghlVcOLQPKzs7mnbvR2Bo7sBydlYWkWv+ITU5CROpFAdXd1oPfJPgmrlZKI+j77Jh5q/ax8d3bOL4jk1UqNOA1gOGFWk/K9epT0ZaKge3bSQ1WYZLufL0G/2xtt5MarKMpPjc44+5hSUDx37G9n8WMe/HiVhYWlK/dXvqtW6vbXNqfwQqlZJdq5ayK09tIO/gCgz+6Avt46S4x9y5dpkeb40q0j79G8XRf5VKxZ51K0lOiEMiMcbBxZWW3ftSq2nLEu9fQSIvRSE1MaZ73WpYmEm5F5fEnPDDZOW5k25vpX9T48POur+5lb08SExNZ/K63XptX1VhzZqRLE9hwfLlxCcmEeDrw+/ffZ/nuJXIg0eFrxb0OikLfV115CxmUhNGt2uCjYUZVx885vN/tuhk4rja6Q4SSyQShreqj7udDUqViodJKcyPOMqWPBlmnWtXRmpszJc92+jsu+vcVX7ZXLy1QKrXb0SaXE74xrWkyJJwL+/N8E8m4OisyeBLkSWR8Dh3apiFpRVvj/+KdQvnMvXrz7CwtKJZh840a99Z20alUrFv+2biHj3E2NiEgEqVGf31DzoZONmZmaxbMBtZYiJSU1Ncy5Wj/zvvU6NhyU7jzLweRbKFOdb1amNsZUVOQgKJ6zdrs2WMrSwxttOdfpq4YSt2LZrg3Lc7aoWCrLv3Sdl3UPu8kUSCda3qGDvYg0pFVvQD4lasRZli+MHk1MiDSGxtcBjYBxdHR7Lu3OXh59+heKw5NzR2ckCab4Us2zYtcH5nKGBE5uWrPBj3BVnXdFc2NLKwwKZFExKXrCyhnhQt8wrBlJ8+RfvY6a3BOL01mJRtu4j98ddn7Cm8qFeifNNrzigjI0P8b3wFvcyUqNeRs82rv1JEUbn5OMHQIZSoeg6GXRq8JC2+dNvQIZQo+5dc7Ud4dZ2/V3Z+g75sbOhCp0Jx6f3PDkOHUKLeb1uygx6GVOvAfkOHUKLStz6//mVpoc4q3mXtXzVe4RsNHUKx6vbbohL7WxvGDSmxv1WSRIaNIAiCIAiCIAiCIAhFSqwS9fKKr7qmIAiCIAiCIAiCIAiC8J+IDBtBEARBEARBEARBEIqUyLB5eSLDRhAEQRAEQRAEQRAE4RUjMmwEQRAEQRAEQRAEQShSKpFh89JEho0gCIIgCIIgCIIgCMIrRgzYCIIgCIIgCIIgCIIgvGLElChBEARBEARBEARBEIqUmBH18kSGjSAIgiAIgiAIgiAIwitGZNgIgiAIgiAIgiAIglCkRNHhlycybARBEARBEARBEARBEF4xIsNGEARBEARBEARBEIQipRYZNi9NZNgIgiAIgiAIgiAIgiC8YowyMjLEsJegtWPHDtq1a2foMEpEWeorlK3+lqW+Qtnqb1nqK5St/palvoLob2lWlvoKZau/ZamvULb6W5b6KrxeRIaNoGPnzp2GDqHElKW+Qtnqb1nqK5St/palvkLZ6m9Z6iuI/pZmZamvULb6W5b6CmWrv2Wpr8LrRQzYCIIgCIIgCIIgCIIgvGLEgI0gCIIgCIIgCIIgCMIrRgzYCIIgCIIgCIIgCIIgvGLEgI0gCIIgCIIgCIIgCMIrRgzYCIIgCIIgCIIgCIIgvGLEgI0gCIIgCIIgCIIgCMIrRgzYCIIgCIIgCIIgCIIgvGLEgI0gCIIgCIIgCIIgCMIrRgzYCDratm1r6BBKTFnqK5St/palvkLZ6m9Z6iuUrf6Wpb6C6G9pVpb6CmWrv2Wpr1C2+luW+iq8XowyMjLUhg5CEARBEARBEARBEARByCUybARBEARBEARBEARBEF4xYsBGEARBEARBEARBEAThFSMGbARBEARBEARBEARBEF4xYsBGKDMyMzNRqVSGDkMQhH8hOTnZ0CEIgiAIgiAIgkGIosMCAAkJCSQnJ+sNaAQGBhoooqKlVCrp2bMn06ZNw9vb29DhCEVMoVDw66+/MnjwYDw8PAwdjlCEunfvTt26dQkLC6NWrVoYGRkZOqRi9cEHH9CmTRuaN2+OtbW1ocMpVl27dmXRokXY29vrbE9JSWHQoEFs3LjRMIEVk6ioKDZt2kR0dDQA5cuXp2vXrqXmd7Yg586d0/bXy8uL0NBQA0ck/BcREREv3LZly5bFGIkgFJ2pU6cyYsQILC0tdbZnZmby999/M2bMGANFJgi6TAwdgGBYN2/e5Ndff+XBgweo1bpjd0ZGRqXmhNnY2BhXV1cUCoWhQxGKgYmJCWfOnGHIkCGGDqVEzJkzhzZt2uDj42PoUIrd119/TXh4OJMnT8bGxobWrVvTqlWrUjswV7t2bdauXcuCBQuoX78+bdq0KbUXufl/c57KycnBxKR0nZ5ERkby+++/U61aNWrVqgXAtWvX+Oijjxg7diwtWrQwcIRFKyYmhsmTJ3Pnzh0cHR0BSExMxNfXl88//xx3d3cDR1j0cnJyuHv3LsnJyXqf7dq1axsoqqLx119/6TzOyclBqVRqB9DVajXGxsZIpdJSMWDTp0+fF267atWqYoykZFy8ePGF21apUqUYIylZERERDBkyRG/AJisri4iICDFgI7wyStcZkfCvzZgxAxcXF95//30cHR1L9d3rvn37smjRIsaNG4ednZ2hwykRBw4c4Ny5c8hkMr0TyK+++spAURWPhg0bcvjwYXr06GHoUIrdjRs32LJlCwEBAbRp04amTZvqnXCUFjVq1KBGjRqkpqayb98+wsPDWb16NVWqVCEsLIyGDRtiampq6DCLzODBgxk0aBCnTp0iPDycb7/9FgcHB+1Alaurq6FDfGkbNmwANDcFtm/fjoWFhfY5lUrFpUuXKF++vIGiKx5Llixh4MCBeheCq1evZunSpaVuwGb69OlYWFgwZ84c7Wf28ePH/PHHH0yfPp0ffvjBwBEWrTNnzvDbb78VOIWzNNz8yjsoceLECf755x9GjBhBSEgIoBl8nDdvHn379jVUiEXqnXfeMXQIJWrChAkYGRlpzxPzDsTlfQy89p9lALlcjlqtRq1Wk5qairGxsfY5lUrFiRMn9DI/BcGQxJSoMq5Xr15MnToVT09PQ4dS7EaPHk1sbCwKhQJnZ2fMzc11np8+fbqBIise8+fPZ9OmTVStWrXAwbixY8caJrBisnz5cjZu3EiVKlUIDAzUe3+7detmmMCKyf379wkPD2fv3r2kp6fToEEDwsLCqFq1qqFDK3Zbt25l3rx5KBQKrKysaNu2LX379tW58C8t5HI5O3bsYPny5SiVSkJDQ+natas2S+N1NHz4cADi4uJwcnJCIsktpyeVSnF1dWXgwIHai8HSoFevXkybNo1y5crpbH/48CHvv/8+a9euNVBkxaNnz5788ssv+Pn56Wy/desWn3zySanr7zvvvEPlypXp168f9vb2er+3UqnUQJEVvffee48xY8ZQoUIFne1Xr17ljz/+0MvGEV59KSkp2v++fv068+fPp0+fPtr3+OrVq6xatYphw4ZRp04dQ4VZZLp06fLcG9QDBgwoNQOQwutPZNiUcb6+viQlJZWJAZtGjRoZOoQStXfvXj755JMy0+/w8HCsrKy4ffs2t2/f1nnOyMio1A3YlC9fnqFDhzJ48GBOnjxJeHg4X3/9NS4uLoSFhdGuXTtsbGwMHWaRSUxMZM+ePezZs4eEhASaNGlCmzZtSExMZPXq1URFRTFp0iRDh1mkrl69Snh4OAcOHMDR0ZHWrVuTmJjITz/9RJs2bRgxYoShQ/xP5s2bB2ju6k6YMKHU1+oBqFq1KhcuXNAbsLlw4UKpmmLwlIuLC1lZWXrbs7OzcXZ2NkBExSspKYk+ffqUigy453n8+DFmZmZ6283MzIiLizNARMLLsrW11f730qVLGTFiBDVq1NBuc3d3x87OjoULF5aKAZunGX5ffPEFn3/+uc5vkFQqxcXFBScnJ0OFJwh6xIBNGTdo0CAWLlzIG2+8gY+Pj17dgNJ0wXfz5k3atGlD7dq1de7ollYqlUrv7mZp9vQisKxRKpWkp6eTlpaGSqXCxcWFvXv3smrVKkaNGkXz5s0NHeJLOXz4MLt37+bs2bN4e3vTuXNnWrRooTMFzMfHp9TMNZfJZERERBAeHk5MTAx169Zl/PjxOifPjRo1YtKkSa/tgM1TP/74o6FDKDG1a9dm8eLFREVF6UwjOXLkCP379+fw4cPatg0bNjRUmEXmzTffZPbs2bz99tsEBQUBmqmcc+fO1WZYlSZ16tThypUrpbI2T37BwcHMnj2bjz/+WHtRm5CQwNy5c0tVVtxTOTk5rFq1iv379xMXF4dSqdR5vjRMEcorOjq6wEFVJycn7t+/b4CIit7TTOS5c+fi7OxcJq4JhNebmBJVxnXp0kX733nTA9VqdamYd53XL7/8wtGjR7GysqJVq1a0bt1a725nabJ48WJMTEwYMGCAoUMpMYXV7DEyMuLLL780YGRF78aNG+zevZsDBw5gZmZGy5YtadOmjfaCYdOmTaxevZolS5YYONKX07dvX5o2bUrbtm0LXU0nKyuLdevW0b9//xKOruh1794dDw8PwsLCaNmyZYH1ttLT05k0adJrOeDx999/M2TIEMzNzfn777+f2bY01ZHI+1v7LKXld7dPnz7k5OSgUql06mFIJBK96UGloWhrWloav/zyC+XKlSvw5ldpKMT71KNHj/jhhx+4f/++zoCNp6cnX3zxRak7r1q4cCEHDhygV69ezJ07l0GDBhEbG8uBAwcYOHAg7du3N3SIRerDDz/Ew8ODMWPGaDOpsrKymDp1Ko8ePeL33383cIRFKzMzk9u3bxdY67E0DJ4LpYMYsCnjLly48MznS1s9jPT0dCIjIwkPDycqKopKlSrRpk0bGjVqVGCK7+sm7wWQWq0mMjISb29vfH19dYqqQem6GIKyVbNn9OjRPHjwgBo1atC2bVtq166t9/4mJyczaNAgNm3aZKAoi0ZmZqZePaLS7NKlS1SuXNnQYRSbvNOgJkyYUGg7IyOjUleYtizZs2fPC7dt1apVMUZSMg4cOMAff/xBTk4OZmZmer8/pWFQKi+1Ws2ZM2e0GRdeXl5Ur169VC5cMXz4cEaOHEmtWrXo06cPU6dOxcPDg23btnHu3Dk+//xzQ4dYpK5fv87333+PQqHA19cXgLt37yKRSPj6668JDg42bIBF6OzZs0yZMgW5XK73XGkZPBdKBzFgI5RZd+/eZdeuXezYsQMTExOaNGlC165d8fLyMnRo/9mzLoDyex3vzj/LoEGDePfdd8tEzZ4VK1YQFhZW5uZYJyUlkZOTo7OtLNSMEATh1TZs2DCaNGnCgAEDytQAc1nQs2dP/vzzT1xdXRk8eDBff/01gYGBxMTE8MEHH5S6wTjQ3CiJjIzk/v37qNVqvL29adasWan7bI8cOZKgoCAGDx5c5s6nhNeLqGEjkJSUxNatW4mOjsbIyAhvb2/at2+Pg4ODoUMrNgkJCRw7dowTJ05gbGxMo0aNiI+P5/3332fw4MGv7dLQpW0Q5t8oSzV7+vXrZ+gQSkxaWhqzZ8/m4MGDKBQKvedL4x2w8PBw9u3bR1xcnF6f586da6CoSsbDhw9xdnYuVUu1P3Xz5k0uXLhQYOr9sGHDDBRV8ZLL5QX219vb20ARFY+0tDTat29f6i5oC7N161a2bt1KbGwsM2fOxN3dndWrV+Pu7k6TJk0MHV6RcnFxITExEVdXVzw8PDh9+jSBgYFcvXq1VGRmF8Tc3Jx27doZOoxi9/jxY7766isxWCO88sSATRl3+fJlvvnmG+zt7bXF4iIjI9mwYQPfffed3rKNrzOFQsGxY8e0BUz9/f3p2bMnTZs21S4HfODAAWbOnPnaDtjkNXXqVEaMGKFTnBU0d07+/vvvUlOk9am2bdsSGRlZZmr2PHjwgEOHDhV4UV+a3tv58+dz+/ZtvvjiC3788UfGjBlDQkICmzZtKpXFS9etW8fq1atp164dly5dokOHDjx69IhLly7RvXt3Q4dXpBYvXoynpyetWrVCrVbz9ddfc+7cOSwtLfnmm29K1e/P2rVrWbRoES4uLno3Q0rjNJKbN28ydepU7t69C+TWxSuN9fFAU+vi7NmzeHh4GDqUYrdx40bWrVtHz549WbRokXa7k5MTW7duLXUDNg0aNODcuXNUqFCBLl26MGXKFHbu3EliYmKpOyY/dfLkSe2A3LfffouLiws7d+7E3d2d0NBQQ4dXZCpWrMiDBw/KxPdWeL2JAZsybv78+TRt2pSRI0dqq6SrVCpmzZrFvHnzmDJlioEjLDqDBw8GoFmzZgwZMqTAbIwaNWpgZWVV0qEVi4iICIYMGaI3YJOVlUVERESpuKgvqGbP2bNnS33NnhMnTjB58mT8/f25efMmQUFBPHr0iJycnFJX/+TUqVN88sknVK5cGYlEQkBAAE2aNMHBwYEdO3aUuilwO3fuZPTo0TRq1IitW7fSqVMn3N3dWbFiBY8fPzZ0eEUqMjKSTz/9FNC8z7du3eKXX34hMjKSxYsXl6qMwY0bN/Lee++VugKlhZk2bRpOTk6MGDECe3v7UjkolZe7uztLlizh0qVL+Pr66hUd7tatm2ECKwbbt29n9OjR1KlTh6VLl2q3BwQEcO/ePQNGVjyGDBmi/e9GjRrh5OTE1atXKVeuHHXr1jVgZMUjMjKSWbNmERYWxvnz57WrYqlUKtauXVuqBmzat2/P/PnzSUhIKPC8sbCFDgShpIkBmzLu9u3bjB07VmdJO4lEQteuXUtVkVaAt956i8aNGz8z1d7a2vq1Xx5aLpejVqtRq9Wkpqbq/ACpVCpOnDiBvb294QIsQk/v3j7l7+8PoLf0ZGm7WFi2bBn9+/end+/e9OnTh3HjxuHo6Mhvv/1WqrISQDPVwMXFBQArKyttccAKFSowffp0Q4ZWLOLj47XLIJuampKeng5A06ZN+eijj3j//fcNGV6Rkslk2uVjT548SePGjQkODsba2ppx48YZOLqipVarS9WFzvM8fPiQzz77rNStGFSY3bt3Y2FhwZUrV7hy5YrOc0ZGRqVqwCYuLg4fHx+97SYmJmRlZRkgopJVoUKFUvc7m9fatWsZPXo0TZs2Zffu3drtFSpU4J9//jFgZEXvp59+AmDmzJl6z5XGTEDh9SUGbMo4S0tLYmNjKV++vM722NjYUpNp8lRpWlbzWQYOHIiRkRFGRkaMGjWqwDalZdpQaboD/288ePBAm3ZubGxMVlYWpqam9OvXj2+//bZUXRy4u7sTGxuLq6sr5cuXZ//+/QQHB3PkyBFsbGwMHV6Rc3BwICUlBVdXV1xcXLh69Sr+/v48evSo1A082tjY8PjxY5ydnTlz5ow2C1KpVOrVPHndtW/fnvDwcG0fS7tKlSpx//79MjNg87rf6Pk33NzcuHnzpl7B95MnT5a62kQAhw8ffubzpW3p54cPHxY4IGVubq69gVBalPaacELpIQZsyrimTZsybdo0hg0bRoUKFTAyMuLy5cssWrSIpk2bGjo84T94uhTuF198weeff461tbX2OalUiouLiyiw9pqzsLAgOzsb0FzgP3z4EB8fH5RKJWlpaQaOrmi1atWK27dvU7VqVXr16sV3333H1q1bUavVjBgxwtDhFblq1apx/PhxAgMDadOmDXPnzuXgwYPcvHmTxo0bGzq8ItWwYUN++eUXPD09kcvl1KpVC9Bkfpa2mgL9+/fnm2++4YMPPsDHx0dvykxpmKKa1wcffMC0adOIiYnBx8dHb6pBlSpVDBSZ8LJ69OjBX3/9RVZWFmq1mqtXr7J3717Wrl1b6j7HkJuFkd/TAfTSloXh5OTEgwcP9AbkLl26hLu7u4GiKh47duzAxcVFb6rq9u3bSUhI4I033jBQZIKgSwzYlHFDhw5FrVYzdepUVCoVoLlj3759e515u8Lro2rVqoDmzoGzs7POdDehdAgODuby5ct4e3tTp04d5s+fz507dzhy5Ii2eHhpkTdbKDQ0lD///JOoqCjKlSuHr6+vweIqLqNHj9Zml7Rv3x5ra2suX75Mw4YNS92qHW+99RZubm48fvyYoUOHalfYSUxMLHW1XpYsWaItdp+amlrqsqXye/jwIbdv3+bMmTN6z5XGqQZ566kVpDTVUGvdujVKpZLFixeTlZXFb7/9hpOTE2+//XapKzgMsGnTJp3HSqWSmzdvsmDBAgYNGmSgqIpP27ZtmT17tnb6bVxcHJcuXWLBggWlJjv7qb179zJ+/Hi97QEBAaxevVoM2AivDKOMjIzSlXcs/CeZmZnExMSgVqvx8PAoM0tTlnaZmZncvn27wGVVS1sab1kSExNDRkYGfn5+ZGZmMn/+fK5cuUK5cuUYPny43p0xQXjVKBQKlixZQseOHcvE57Vfv36MGjWqVF7QFuTdd98lKCiIXr16FVh02NbW1kCRFY8JEyboPFYoFNy/fx+VSkVAQIA287W0SU5ORq1Wl5q6eP/GlStXmDVrVqmspbZ48WI2btxITk4OoMnO7t69e6kbwOjRowezZs3SyxyKiYlh5MiRrFu3zkCRCYIukWEjAJq5qb6+vmRlZWkv/MrCSXRpdvbsWaZMmaIt0ppXabzDWZbkPbkwNzdn5MiRBoym6C1fvvyF2/bv378YIykZFy9efOG2pWUqiYmJCdu2baNDhw6GDqVEmJqaaouilwXx8fFMnDix1E1tK0xB9dSys7OZNm1aqVu576kbN24QExNDnTp1AM0NIqlUqjf9rbSysrIiJibG0GEUKaVSyZkzZ+jWrRt9+vQhOjoatVqNl5cXFhYWhg6vyLm4uBQ41evixYvagviC8CoQAzZl3O+//05wcDAdO3YkJyeHjz/+mLt372JiYsKECROoXbu2oUMU/qPZs2dTu3ZtBg8eLGrWCK+VQ4cO6Tx+/PgxWVlZODo6ApopM2ZmZri5uZWKAZsJEyZgZGSkzYJ7mo2Q/zGUrnoJNWrU4Pz584SFhRk6lGLXtWtX7dLepX06FED16tWJiooqMwM2BTE1NaVPnz5MnDixVE3xS0pKYtKkSdy4cQMjIyP+/vtv3N3dmTt3Lqamprz99tuGDrFIRUVF6W1LTExk7dq1pW4Q1tjYmB9//JE///wTW1tb7YqFpVW7du2YO3cuCoWCatWqAXDu3DkWL15Mz549DRydIOQSAzZl3JkzZ+jcuTMAx48fJz09ncWLF7N7926WL18uBmxeY48fP+arr74SgzWlRJcuXV74Qu91v6ifMWOG9r/Dw8OJiIhg7Nix2qy/x48fM3XqVJo3b26gCIvW0qVLtf99/fp15s+fT58+fbQrdVy9epVVq1YxbNgwQ4VYLEJDQ1m8eDF37twhICBAbypuaZq2eenSJS5dusTJkyfx8vLSKzr81VdfGSiy4lGrVi1tbS1fX1+9rIvS9N4+S3JyMhkZGYYOo0jNnTsXBwcH/vnnH958803t9saNGz+3ls/raNy4cToD6k+FhISUyiLLfn5+PHr0CDc3N0OHUuy6d+9OSkoKs2fPRqFQAJrsz86dO4sBG+GVIgZsyrjU1FTt3ONTp07RsGFD7O3tadq0KatXrzZscMJLqVixIg8ePCjTdzhLk88++0z73zKZjGXLltGgQQOdi/qjR4+WuqKAy5cv58svv9SZounq6srw4cOZNGlSqcjOyFvPY+nSpYwYMYIaNWpot7m7u2NnZ8fChQu10w9Kg6cXd5s3b9Z7rrRN27S1taVBgwaGDqPE/PnnnwAFnkeUtvcWYMOGDTqP1Wo1iYmJ7Nu3r9Td+Dp//jyTJk3SWYESNMepuLg4A0VVfPIv/WxkZISdnR2mpqYGiqh49e/fn3nz5jFgwAACAwP1BtJtbGwMFFnxGDJkCH379uXevXsApXb6l/B6EwM2ZZyDgwN3797FwcGBM2fOMGrUKEAzF7mszEMurdq3b8/8+fNJSEgo8A5nYGCggSIT/otGjRpp//v7779nyJAhtG3bVrstLCyM4OBgjh49SseOHQ0RYrGQyWRkZWXpbc/OziYlJcUAERWv6OjoAufOOzk5cf/+fQNEVHzyr75Smo0dO9bQIZSosvTegv6go0Qiwc7OjtatW9OrVy8DRVU8srKy9DLEAFJSUkrlIEZZq+f43XffATB58mSdrF61Wl0qB1tBUwswODjY0GEIQqHEgE0Z17p1a37++WccHR2RSCSEhoYCcO3aNcqXL2/g6ISX8dNPPwEwc+ZMvedK649uWXH+/HmGDx+ut71q1arMmTPHABEVnxo1ajBjxgxGjx6tnU9/48YNZs6cSfXq1Q0bXDHw9vZm+fLljBkzBjMzM0BzgbRixQq8vb0NHJ3wsvIWajU3Ny9zhVpLq3nz5hk6hBJTuXJl9uzZw+DBg7XblEola9as0dYBKU0OHDiAlZUVNWvWBDRZnzt37sTb25uxY8dqa6uVFqV1RTNBeJ2JZb0FDh06RFxcHI0bN9be2d2zZw9WVlbUr1/fwNEJ/9Xjx4+f+XxZu2tUmgwfPpx27drRu3dvne2rV69mx44dperiITk5md9//53Tp08jkUgAzZ2+GjVqMHbs2FK3nOz169f5/vvvUSgU+Pr6AnD37l0kEglff/11qboLqFar2bZtG1u3biU2NpaZM2fi7u7O6tWrcXd3L1VLYBdWqHXGjBmlslBrWXpvnzpw4ADnzp1DJpPp1TspTTWK7t27x+eff46/vz8XL16kTp063Lt3j7S0NH7++edSNw175MiRvPXWW9SsWZOoqCg+/fRTBg4cyOnTp7G3t+eTTz4xdIiCIJRyIsNG0Jlq8VSrVq0MEIlQlFxdXUlKSuLKlSskJyejUqm0zxkZGZWZ5XRLo4EDBzJ16lQuXLigrWFz7do1zp49ywcffGDg6IqWnZ0d33zzDQ8fPtRZYtTT09PQoRWL4OBg5syZQ2RkJPfv30etVtO8eXOaNWumV0vgdbdp0ybWrVtHz549WbRokXa7k5MTW7duLVUX9WWtUGtZem8B5s+fz6ZNm6hatSqOjo6leiUwb29vZsyYwdatWzExMSE7O5tGjRrRsWPHUpdtApqbX08zzo8ePUr9+vXp2bMnNWrUYOLEiQaOrnjcuXOHHTt28OjRI8aMGYOjoyNHjhzB1dWVgIAAQ4cnCGWOGLARUCqVXL9+nbi4OG2V9KdatmxpoKiEl7V3716mT5+OWq3G2tpa5wRSDNi83lq2bImnpyebN2/m2LFjgKZQ3s8//0xISIiBoyta169fJzg4mHLlylGuXDmd5/bu3UuLFi0MFFnxMTc3p127doYOo9ht376d0aNHU6dOHZ2VsgICArQFIEuLslaotSy9t6A5Fn3yyScF3gArjRwcHHjjjTcMHUaJMDU11a70de7cOVq3bg2ApaUl6enphgytWJw+fZpJkyZRq1Ytzp8/T3Z2NgAxMTHs2bOHL7/80sARCkLZIwZsyrjo6Gi+//57YmNjAU2hPKVSibGxMVKpVAzYvMaWLFlCjx496N+/v6iPUAqFhISUusGZgnz33XdMnjwZLy8vne0RERHMmjWr1A3YREREFPqcqakpHh4epeYOZ1xcHD4+PnrbTUxMCiw0/Tora4Vay9J7C6BSqfDz8zN0GCUmMTGRbdu2ER0dDWhuGLRv3x4nJycDR1b0KlWqxLx586hUqRJRUVGMHz8egIcPH+Li4mLg6IresmXLGD58OB07dqRPnz7a7VWrVtVbDU0QhJIhBmzKuLlz5xIYGMi0adMYPHgwU6dOJS0tjT///LPM3D0prdLT02ndurUYrCkl5HK5djlNuVz+//buPyrmfP8D+HPSpLUppTShUBNdGnu6tNbas7sXe7PLdv26tkuJInsdx69FflxchF1LJyG/qqUlFtnF8fPuqmU5rJ81UUx2SUKtkuiHzMz3j27zbcTuvXdnek+feT7Occ54z/zxdKbMfF6f9/v1+tXXSmns5uDBg7FgwQKsWLHC8OW4rlgza9YswelMb8OGDaipqYFWqzXsitPr9YbfY61WC29vbyxatAhOTk4io/5u7u7uuHHjRoN+WufPn5dcg2Vra9RqTe8tAAQFBSEjIwMjR44UHcXsLl26hJiYGLi5uRl6av3www/4+uuvMW/ePENzXqn4+OOPkZCQgFOnTmHixImGotSFCxcQEBAgOJ3p5efnv3AUvYODw29+9yAi82DBxsppNBosX74c9vb2kMlk0Gq1UCqVGDNmDDZt2oQ1a9aIjkj/o3fffRfnzp3Dhx9+KDoKmUBoaCi2bt2KVq1aYdSoUS/skSDFsZvDhw9HWVkZ5s+fj88++wznz59HQkICZs+ejcDAQNHxTG7WrFnYuXMnxo0bZzQVKykpCR999BFat26NuLg4JCYm4pNPPhGc9vcZOnQoNmzYgOrqauj1euTm5iI9PR1paWmYMmWK6HgmNXbsWMyZMwcajQY1NTVITk42atQqNdbw3tbvPaTX65GRkYHLly+jY8eODW6UTJgwobHjmc2mTZvw5z//GVFRUUafQ5s2bcLmzZuxfv16gelMz9XVFQsWLGiwPn78eAFpzM/BwQEPHjyAu7u70fqNGzcMg0mIqHGxYGPl9Hq9YXSsk5MTHjx4gPbt28PV1RWFhYWC09HvERkZiaVLlyIzM/OFXyD/9re/CUpG/4uYmBjDzhlrG7sZGRmJ8vJyzJgxA6WlpZgzZ84L7wBKQVJSEqZOnWp03M3Pzw+RkZFYvXo11q9fj8jISMTGxgpMaRr9+/eHVqtFSkoKqqurERsbi9atWyMqKkpyTWnt7e0RHx+PI0eONGjU+nzvOCmwhvf21q1bRn/39vYGABQUFBitS60BcVFREQYNGtTg3zVw4EAcPXpUUCoylXfeeQdffPEFoqOjAdTuBFSr1UhOTjb07yGixsWCjZXr0KEDfv75ZygUCvj6+iItLQ02NjY4duxYgwaf1LQcOXIEFy9ehKOjI+7evdug6TALNk2LSqV64WMpOn36dIO1nj17IjMzE2+//TaePn1qeM2bb77Z2PHMqqioyFBEr6958+YoKioCUHvc5PHjx40dzSyCgoIQFBSEsrIy6PV6yY1przN+/Hhs3boVo0aNMlp/9OgRxo4dK6ldcXWk/t4uW7ZMdAQhlEolbt682WBS382bNyXTX6u+mpoa7Nq1CydOnEBxcTG0Wq3R81L73Q0NDUVcXBwiIyOh1+sxceJEAMDbb79t1NOGiBoPCzZWbsSIEaiqqgIAhIWFYfHixZg3bx4cHR0N1XVqmr766itERERg8ODBoqOQCfw3Z8ebeg+bTz/99KXPffvtt/j2228BQHLHvwDA19cXSUlJmD59OpydnQEApaWlSE5ONvSLKCwslNzW9Kbej+e31B1XfF5VVRXkcrmAROY1b948zJkzBw4ODkbvbUVFBZYuXWp1uwSl5IMPPkBiYiIKCwvh5+cHAMjNzcXhw4cRHh6OvLw8w2uVSqWomCazfft2nDx5EsOHD0diYiLGjh2L+/fv4+TJkw0KsFJga2uLGTNmIDQ0FDdu3IBOp4OPjw9v4hIJxIKNlavfHE6hUCAhIQHl5eUNxkBT06PT6dCrVy/RMchEXta3pj6p9LDZv3+/6AjCTJ48GTExMYiIiICLiwuA2oksbdu2xbx58wDUXuRL4U5neXk5vvzyS2RmZqKsrAw6nc7o+V27dglKZjp1fU5kMhm2bt1qtHtKp9Ph+vXrhqM0UqJWq1941Ovp06e4cuWKgERkKqtWrQJQO4nyZc8B0imonzx5EhMnTkSPHj3wxRdfoFevXvDw8ICnpycuX76M999/X3REk9qxYweGDBkChUIBhUJhWK+ursbevXu5O5tIABZsCABQVlaGe/fuwdvbu8nfnada/fr1Q0ZGBj9cJYJ3pK1D27ZtsW7dOly6dMnQC6N9+/YICAgwFOx69+4tMqLJxMfH46effkJQUBBcXFwkeZOgrs+JXq9HQUGB0WhvW1tb+Pj4YOjQoaLimVz93RU3b96Eg4OD4e86nQ6XLl2S5Ohna5KYmCg6QqN6+PAhPD09AdT2onry5AmA2hueW7ZsEZjMPHbu3In3338f9vb2RuvV1dXYuXMnv1MSCcCCjZWrqKhAfHw8Tp8+DZlMho0bN0KhUGDdunVwdna2ihGVUlVdXY1//etfuHTpkuSnVlgDqfeteRm9Xo9Dhw7h4MGDuH//PtatWweFQoHdu3dDoVBIpoFpfTKZDH/84x8lNx73eZmZmViyZIlRg2WpqetzEhcXh6ioKLRo0UJwIvOaPn06ZDIZZDLZCyfr2NnZ8bOniXt+VLvUubm5oaSkBG3atIGHhwcuXrwIpVKJ3NzcF/Yba+pednzzp59+MirAElHjYcHGym3ZsgUlJSWIi4sz6lkTGBiIL7/8kgWbJqygoMBqplZYo5qaGmRkZCA/Px8ymQxeXl545513JNcPY//+/di7dy+GDRuGrVu3GtZbt26NgwcPSrJgc+3atZceE5LSxW6rVq0a3MWVqqlTp4qO0CgSExOh1+sxfvx4rFq1yqh/ja2tLZycnBrcPKCm5eTJk3j11VcNBeUdO3bg6NGj8PLywtSpUw1HOaWid+/eyMzMhJ+fH4KDg/H555/j6NGjKCkpwZAhQ0THM5m6Y7YymazByHKdToeamhoMGDBARDQiq8eCjZX78ccfMXfuXHh7extdxHt6euL+/fsCk9HvZa0TLKxBfn4+Fi5ciMrKSnTo0AEAcOzYMaSmpmLRokWG7dtScPjwYUyaNAmBgYHYtm2bYd3Hxwf5+fkCk5nH3r17sWXLFnh4eDQ4JiS1QmtYWBi2b9+OadOm4ZVXXhEdh0ygbvfF/v37odVqcf36dRQXFzfoZ9O3b18R8cgEduzYgXHjxgGoPQK3e/dujBo1ChcvXkRSUhJmzpwpOKFphYeHGx736dMHrq6uyMnJQdu2bfH6668LTGZaEyZMgF6vR3x8PEJDQ/Hqq68anrO1tYW7u7uhyTQRNS4WbKzc48eP4ejo2GC9srISNjY2AhIR0W/ZvHkzfHx8MH36dMMRi4qKCqxatQqbN2/G4sWLBSc0neLiYkNRqj5bW1tUV1cLSGReBw4cQFRUFAYNGiQ6illMmjTJqPB0//59hIaGok2bNkb9XQBgzZo1jR2PTKSgoACLFy823PixsbGBVqtFs2bNIJfLWbBpwoqKitC+fXsAwJkzZ/DGG29g2LBhCAgIwMKFCwWnM72UlBS4ubkZmgt36dIFXbp0weHDh7Ft2zaEhoYKTmga/fr1AwC4u7uja9eu3AlHZEFYsLFyvr6+OHv2LP7yl78YrR85coSVdCILdfXqVcTGxhr1w2jRogXCwsIwY8YMgclMz93dHTdu3GjQN+H8+fPw8vISlMp8Kioq0LNnT9ExzKZPnz6iI1Aj2Lx5M5RKJeLj4zF69GisXr0aT548wfr16yVzgWut7OzsUFlZCaC2D1X//v0B1H4GVVRUiIxmFunp6Zg9e3aDdR8fH+zevVtyP89OTk64e/euoSh36dIlHD9+HF5eXhg6dCgLOUQCsGBj5UaPHo2FCxciPz8fWq0W+/btQ35+Pq5du4ZPP/1UdDwiegE7OzvDpIr6njx5Ajs7OwGJzGfo0KHYsGEDqqurodfrkZubi/T0dKSlpWHKlCmi45nc22+/jQsXLmDgwIGio5gFJ4xYB41Gg+XLl8Pe3h4ymQxarRZKpRJjxozBpk2buHuqCevatSuSkpLQtWtX5OXlGYoZhYWFcHNzE5zO9MrKyox6MdVxdHTEw4cPGz+QmcXHxyM4OBjt27fHL7/8gpiYGKhUKhw8eBAVFRVGR8SIqHHwzIuV+8Mf/oDPP/8cz549g0KhQGZmJlxcXLBy5UoolUrR8YjoBV5//XWsXbsWV69ehVarhVarxZUrV7Bu3TpJnakHgP79+2PkyJFISUlBdXU1YmNjcezYMURFRUmy4bCrqytSU1OxcuVK7NmzB998843RHykZN24cHj161GD98ePHhh4Z1DTp9XrDBB0nJyc8ePAAQO3Pd2Fhocho9Dt9/PHHsLW1xalTpzBx4kTDmPYLFy4gICBAcDrTc3Nzw5UrVxqsZ2dnw9XVVUAi8yooKICPjw8A4IcffkCXLl3wz3/+E9OnT8eJEycEpyOyTtxhY+Xy8/PRrFkzTJs2DQBw8eJFpKen49y5c/D09OTWRyILNH78eMTFxWH27NmGXlM6nQ69evWS5IVuUFAQgoKCUFZWBr1ej1atWomOZDbHjh2Dvb09cnJykJOTY/ScTCbD4MGDxQQzg6KiogZTsIDaCWh1F/jUNHXo0AE///wzFAoFfH19kZaWBhsbGxw7dgxt27YVHY9+B1dX1xeObH9+spBUDBgwAImJiXj27Bm6d+8OoPYoWEpKCoYNGyY4nenpdDpDP7HMzEzDEV2FQiHJHUVETQELNlbu+a2Py5Ytg7+/P7c+ElkwBwcH/OMf/0BhYSEKCgqg1+vh6ekp6QshjUaDe/fuITAwEABQVVUFuVwuuaJyUlKS6Ahmd/r0acPj8+fPG/Vi0ul0yMzMhLu7u4hoZCIjRoxAVVUVgNppYIsXL8a8efPg6OiI6OhowemI/nNDhgzBo0ePsGnTJsO0M1tbW3z44YeSLNh4eXnh8OHDCAwMRFZWluE6oKSk5IVDSojI/GSVlZV60SFInJCQEKxatQrt2rXDN998gx9//BHLli1DVlYWVq9ebRUXD0RN0cmTJ5GZmYmHDx9Crzf+b3z+/PmCUpleaWkpYmJioNFoIJPJsHHjRigUCqxduxZ2dnaIiooSHZH+S8HBwQBqdww9/7PbrFkzuLu7IyIiQnLH+6xdeXk5HBwcJDee3hoEBwf/x+/bvn37zJxGjKqqKuTn5wMAPD098corrwhOZB7Z2dlYunQpKioq0LdvX0OvuK1bt+LOnTuYO3eu4IRE1oc7bKwctz4SNT3JycnYv38/VCoVXFxcJH0BlJiYCGdnZ6SmpiIiIsKw/tZbb2Hjxo0Ck5nOxo0bER4eDnt7+9/8N02YMKGRUpnP/v37AQCRkZGIjY19YUNPkp6WLVuKjkD/o/q7oh4+fIjt27ejd+/ehmmiubm5OHPmDEaOHCkqotnZ29ujc+fOomOYnb+/P7Zt24bKyko4ODgY1gcMGGDoS0VEjYsFGyvHrY9ETU96ejpmzpxpFSOSs7KyEBMTY/TFEagtKhcXFwtKZVq3bt0ybLW/deuW4DSNhzs4iZqG+p81S5YsQXh4OIKCggxr7733Hjp37owzZ85IdsKdNWnWrFmDz1weUyUShwUbKzdmzBgsXboUX3/9Nfr27YuOHTsCAM6ePQtfX1+x4YjohXQ6HTp16iQ6RqOorq427AKs79GjR5IZYb5s2bIXPrYG5eXluHDhAoqLiw1FqzocAU5kebKyshAZGdlgXaVSYfPmzQIS0e+1ZMkSfPLJJ2jRogWWLFnyq6+V0pFroqaCBRsrx62PRE1PUFAQMjIyJL39vE63bt3w3XffYfTo0YY1rVaLPXv2GCZ2SMn169dfuu0+PT0df/rTnxo5kfnk5uZi8eLFkMvlKCsrQ+vWrVFSUgK5XA53d3cWbIgskKOjI06dOoW//vWvRuunTp3i8cYmqv5xRR5dJLI8bDpMRNQE1O9totfrkZGRAS8vL3Ts2LHBpCQp9Dmpk5+fjzlz5sDb2xvZ2dkIDAxEfn4+njx5ghUrVsDDw0N0RJMKDQ3F8uXL4enpabR+/PhxJCQkYM+ePYKSmV50dDS8vb0RFRWFjz76CPHx8WjevDlWrlyJ9957D++++67oiET0nOPHj2P16tV47bXXDD1srl27hsuXL2Py5Mno16+f4IRERNLCHTZERE3A871NvL29AQAFBQVG61JrQOzl5YW1a9fi0KFDsLW1xdOnT9GnTx8MHDgQLi4uouOZ3ODBg7FgwQKsWLECbm5uAP6/WDNr1izB6Uzr5s2bmDx5MmQyGWxsbFBTUwOFQoHw8HCsXLmSBRsiC9S3b1+0a9cOBw4cwNmzZwHUTk1asWIFunTpIjgdEZH0sGBDRNQEWFtvkzoLFiyASqVCjx49EBIS0mA3kdQMHz4cZWVlmD9/Pj777DOcP38eCQkJmD17NgIDA0XHM6n6vYlatWqFoqIiw7jckpISgcmI6Nd06dKFxRmJmjt37ktv/NjZ2cHDwwN9+/aFUqls5GRE1osFGyIislhKpRLnzp1Damoq5HI5/Pz8oFKpoFKp4OvrK8kCTmRkJMrLyzFjxgyUlpZizpw56Nmzp+hYJufj4wONRoN27doZ+qk9fPgQGRkZhgb4RGSZHjx4gLKyMuh0OqN1Xsg3be3bt8eJEyfg7OxsGD6i0WhQWlqKN954A1evXsWhQ4ewaNEivPbaa4LTElkH9rAhIiKLV11djZycHKjVaqjVamg0GsjlcuzatUt0tN/t9OnTDdZ0Oh2SkpIQEBBgVKx58803GzOaWWk0GlRWVqJ79+4oKytDbGwscnJy0K5dO0yZMoVFGyILdOPGDaxatQp37tyBXm98CSGTybBv3z5BycgUEhMTodfrMX78eKP1pKQkALU3FDZt2oTr169j5cqVIiISWR0WbIiIyOKVlpYiOzsbmZmZUKvV+OWXX9ClSxdJHBULDg7+j17HiyEiEm3atGlwdHRESEgIXFxcGhyfadOmjaBkZAojR47EypUr0bZtW6P1O3fuYObMmUhNTcWtW7cwa9YsfPXVV4JSElkXHokiIiKLtX79eqjVahQVFaFz587w9/fHpEmT4OfnB7lcLjqeSezfv190BKE0Gg3u3buHwMBA2Nvbo6qqCnK5XJLH3Yiautu3b2P16tVo166d6ChkBnq9Hrdu3WpQsMnPzzfsqLK1tZXcgAMiS8aCDRERWazDhw/DyckJw4cPR48ePaBUKiX7RfHZs2eIjo7GtGnT0L59e9FxzK60tBQxMTHQaDSQyWTYuHEjFAoFEhMTYWdnh6ioKNERieg5HTt2RGlpKQs2EtW3b1+sWbMGd+/eNephs2fPHsPI9uzsbHTo0EFkTCKrwoINERFZrA0bNkCtViM7OxtHjx5FZWUlunbtiu7du8Pf319SDS5tbW1x//59yRaknpeYmAhnZ2ekpqYiIiLCsP7WW29h48aNApMR0cuEhYVhy5YtCA0NRYcOHYymvQFAy5YtBSUjU4iIiECrVq2wb98+lJaWAgCcnZ0xdOhQDBkyBAAQEBCAHj16iIxJZFXYw4aIiJqM27dvIy0tDRkZGdDr9ZLr6ZKcnAwARgUMqQoLC0NMTAw6dOiAESNGID4+HgqFAvfu3cOkSZOwZ88e0RGJ6Dn1e27VLy7r9Xr22ZKYiooKAECLFi0EJyGybtxhQ0REFkun0yEvLw9ZWVlQq9XIyclBTU0NlEolVCqV6HgmV1VVhe+//x6XL1+GUqlE8+bNjZ6fMGGCoGSmV11d3eDuPAA8evQIdnZ2AhIR0W9ZunSp6AjUCOr3FgPA3mJEArFgQ0REFiskJAQ1NTXw8fGBv78/goOD0a1bN9jb24uOZhYFBQXw8fEBANy7d8/oOakdlerWrRu+++47jB492rCm1WqxZ88edO/eXWAyInoZKRbK6f+xtxiR5WHBhoiILFZ0dLSkCzTPk8KY8v/U2LFjMWfOHGg0GtTU1CA5ORn5+fl48uQJVqxYIToeEf1bXl4evL29YWNjg7y8vF99rZT6ilkj9hYjsjws2BARkcWy1saGT58+xd27dwEAHh4ekjwi5OXlhbVr1+LQoUOwtbXF06dP0adPHwwcOBAuLi6i4xHRv02fPh0pKSlo1aoVpk+fDplMZhjxXB972DR9WVlZiImJgYODg9G6QqFAcXGxoFRE1o0FGyIiIgvx7NkzpKSk4ODBg3j27Bn0ej3kcjkGDRqEsLCwF/Z8aaoWLFgAlUqFHj16ICQkhL0RiCxUYmIinJycDI9JuthbjMjySOebHxERURO3ZcsWnDhxAhMnTkTXrl0BAFeuXEFKSgp0Oh0iIyMFJzQdpVKJc+fOITU1FXK5HH5+flCpVFCpVPD19WUBh8hCtGnTxvB47dq1/D2VMPYWI7I8HOtNRERkIcLCwjBlyhT07NnTaP3cuXNYs2YNUlJSBCUzn+rqauTk5ECtVkOtVkOj0UAul2PXrl2ioxHRc1JSUpCdnW34PWWhVVpu376N2bNnw9vbG9nZ2QgMDDTqLebh4SE6IpHV4Q4bIiIiC1FRUQGFQtFgXaFQ4MmTJwISmV9FRQXKy8tRVlaGsrIy2NjYsHEpkYWq23lRv9Baf6ccC61N17NnzxAXF4f58+fjwoUL7C1GZCFYsCEiIrIQnTp1woEDB/D3v//daP3AgQPo1KmToFTmsX79eqjVahQVFaFz587w9/fHpEmT4OfnB7lcLjoeEf0KFlqlx9bWFvfv30fLli0xatQo0XGI6N94JIqIiMhCZGdnY9GiRXBxcYGfnx8A4Nq1aygpKcHChQvRrVs3wQlNJzg4GE5OThg4cCB69OgBpVIJmUwmOhYR/YoXFVpVKhULrRKRnJwMAEYjvYlILBZsiIiILMiDBw9w6NAh3L59GwDg6emJDz74AK1btxaczLQKCwuhVquRnZ2N7OxsVFZWomvXrujevTv8/f15t57IArHQKm0JCQn4/vvv4e7uDqVSiebNmxs9P2HCBEHJiKwXCzZEREQWom7UtTU28Lx9+zbS0tKQkZEBvV6Pffv2iY5ERM9hoVXa5s6d+9LnZDIZli5d2ohpiAhgwYaIiMhiWNMEFp1Oh7y8PGRlZUGtViMnJwc1NTXw8fGBSqVCeHi46IhE9BtYaCUiMi8WbIiIiCyMNYy6HjFihKFAU9cHo1u3brC3txcdjYhegoVWIqLGxSlRREREFsYaJrBER0ezQEPUxISEhBgVWoODg/l7TERkRtxhQ0REZCE4gYWILNmFCxdYoCEiakQs2BAREVkITmAhIiIiojos2BAREVkITmAhIiIiojos2BAREVkoTmAhIiIisl5sOkxERGQhXjaBRalUQqVSiY5HRERERI2IO2yIiIgsBEddExEREVEdFmyIiIgsBCewEBEREVEdFmyIiIiIiIiIiCyMjegARERERERERERkjAUbIiIiIiIiIiILw4INEREREREREZGFYcGGiIiIiIiIiMjCsGBDRERERERERGRh/g8Teyd16d1GOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,ax=plt.subplots(figsize=(20,10))\n",
    "colormap=sns.diverging_palette(220,10,as_cmap=True)\n",
    "sns.heatmap(df2.corr(),annot=True,cmap=colormap)\n",
    "plt.savefig(\"correlation.jpeg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weathersit</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            cnt\n",
       "weathersit     \n",
       "1           159\n",
       "2           133\n",
       "3            63\n",
       "4            36"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(df[[\"cnt\"]],\n",
    "               index = [\"weathersit\"],\n",
    "               aggfunc = np.median)\n",
    "#with the weather be Clear, Few clouds, Partly cloudy, Partly cloudy (1) has a higher median "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>season</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>155.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          cnt\n",
       "season       \n",
       "1        76.0\n",
       "2       165.0\n",
       "3       199.0\n",
       "4       155.5"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(df[[\"cnt\"]],\n",
    "               index = [\"season\"],\n",
    "               aggfunc = np.median)\n",
    "#in the spring people rental care median is the highest "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>holiday</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         cnt\n",
       "holiday     \n",
       "0        144\n",
       "1         97"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(df[[\"cnt\"]],\n",
    "               index = [\"holiday\"],\n",
    "               aggfunc = np.median)\n",
    "# people tend to rent bike more during holiday "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#the conclusion we see that in during the good wether and in the spring with the holiday median for renting is higher that all of the other choice "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.2 :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will start by training our data. Please split the dataset into 80 % train data and 20 % test data. Then, use two features which are atemp and weatherlist to train the Regression algorithm and make predictions. What is the MAE and RMSE of train and test data in this model? Interpret the coefficients of these tow variables carefully.[10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "scrolled": false
   },
   "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>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17374</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>11</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17375</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>8</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17376</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.1642</td>\n",
       "      <td>7</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17377</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>13</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17378</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>12</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17379 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       season  yr  mnth  hr  holiday  weekday  workingday  weathersit  temp  \\\n",
       "0           1   0     1   0        0        6           0           1  0.24   \n",
       "1           1   0     1   1        0        6           0           1  0.22   \n",
       "2           1   0     1   2        0        6           0           1  0.22   \n",
       "3           1   0     1   3        0        6           0           1  0.24   \n",
       "4           1   0     1   4        0        6           0           1  0.24   \n",
       "...       ...  ..   ...  ..      ...      ...         ...         ...   ...   \n",
       "17374       1   1    12  19        0        1           1           2  0.26   \n",
       "17375       1   1    12  20        0        1           1           2  0.26   \n",
       "17376       1   1    12  21        0        1           1           1  0.26   \n",
       "17377       1   1    12  22        0        1           1           1  0.26   \n",
       "17378       1   1    12  23        0        1           1           1  0.26   \n",
       "\n",
       "        atemp   hum  windspeed  casual  registered  \n",
       "0      0.2879  0.81     0.0000       3          13  \n",
       "1      0.2727  0.80     0.0000       8          32  \n",
       "2      0.2727  0.80     0.0000       5          27  \n",
       "3      0.2879  0.75     0.0000       3          10  \n",
       "4      0.2879  0.75     0.0000       0           1  \n",
       "...       ...   ...        ...     ...         ...  \n",
       "17374  0.2576  0.60     0.1642      11         108  \n",
       "17375  0.2576  0.60     0.1642       8          81  \n",
       "17376  0.2576  0.60     0.1642       7          83  \n",
       "17377  0.2727  0.56     0.1343      13          48  \n",
       "17378  0.2727  0.65     0.1343      12          37  \n",
       "\n",
       "[17379 rows x 14 columns]"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop([\"instant\", \"dteday\",\"cnt\"], axis=1).copy()\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 15 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   season      17379 non-null  float64\n",
      " 1   yr          17379 non-null  float64\n",
      " 2   mnth        17379 non-null  float64\n",
      " 3   hr          17379 non-null  float64\n",
      " 4   holiday     17379 non-null  float64\n",
      " 5   weekday     17379 non-null  float64\n",
      " 6   workingday  17379 non-null  float64\n",
      " 7   weathersit  17379 non-null  float64\n",
      " 8   temp        17379 non-null  float64\n",
      " 9   atemp       17379 non-null  float64\n",
      " 10  hum         17379 non-null  float64\n",
      " 11  windspeed   17379 non-null  float64\n",
      " 12  casual      17379 non-null  float64\n",
      " 13  registered  17379 non-null  float64\n",
      " 14  const       17379 non-null  float64\n",
      "dtypes: float64(15)\n",
      "memory usage: 2.0 MB\n"
     ]
    }
   ],
   "source": [
    "X['const'] = 1\n",
    "X\n",
    "for col in list(X):\n",
    "    X[col] = X[col].astype(float)\n",
    "X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "y = df[['cnt']]\n",
    "df['cnt'] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import (linear_model, metrics, model_selection)\n",
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size = 0.2)\n",
    "from sklearn import linear_model\n",
    "from sklearn import (linear_model, metrics, model_selection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import (linear_model, metrics, model_selection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 15 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   season      17379 non-null  float64\n",
      " 1   yr          17379 non-null  float64\n",
      " 2   mnth        17379 non-null  float64\n",
      " 3   hr          17379 non-null  float64\n",
      " 4   holiday     17379 non-null  float64\n",
      " 5   weekday     17379 non-null  float64\n",
      " 6   workingday  17379 non-null  float64\n",
      " 7   weathersit  17379 non-null  float64\n",
      " 8   temp        17379 non-null  float64\n",
      " 9   atemp       17379 non-null  float64\n",
      " 10  hum         17379 non-null  float64\n",
      " 11  windspeed   17379 non-null  float64\n",
      " 12  casual      17379 non-null  float64\n",
      " 13  registered  17379 non-null  float64\n",
      " 14  const       17379 non-null  float64\n",
      "dtypes: float64(15)\n",
      "memory usage: 2.0 MB\n"
     ]
    }
   ],
   "source": [
    "for col in list(X1):\n",
    "    X[col] = X[col].astype(float)\n",
    "X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = df[['atemp','weathersit']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 2 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   atemp       17379 non-null  float64\n",
      " 1   weathersit  17379 non-null  float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 271.7 KB\n"
     ]
    }
   ],
   "source": [
    "X1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 2 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   atemp       17379 non-null  float64\n",
      " 1   weathersit  17379 non-null  float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 271.7 KB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/jl/_6djyjf16jvgzy1f19c5lh040000gn/T/ipykernel_1976/1746356276.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X1[col] = X1[col].astype(float)\n"
     ]
    }
   ],
   "source": [
    "for col in list(X1):\n",
    "    X1[col] = X1[col].astype(float)\n",
    "X1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X2 = X[['atemp','weathersit']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['atemp', 'weathersit']"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X3 = ['atemp','weathersit']\n",
    "X3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg1 = sm.OLS(y_train, X_train[X3], missing='drop').fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Table 1 - OLS Regressions\n",
      "==============================\n",
      "                    Model 1   \n",
      "------------------------------\n",
      "atemp            452.733382***\n",
      "                 (5.149964)   \n",
      "weathersit       -19.350487***\n",
      "                 (1.672534)   \n",
      "R-squared        0.604025     \n",
      "R-squared Adj.   0.603968     \n",
      "Adj-R-squared    0.60         \n",
      "No. observations 13903        \n",
      "==============================\n",
      "Standard errors in\n",
      "parentheses.\n",
      "* p<.1, ** p<.05, ***p<.01\n"
     ]
    }
   ],
   "source": [
    "info_dict={'Adj-R-squared' : lambda x: f\"{x.rsquared_adj:.2f}\",\n",
    "           'No. observations' : lambda x: f\"{int(x.nobs):d}\"}\n",
    "\n",
    "results_table = summary_col(results=[reg1],\n",
    "                            float_format='%0.6f',\n",
    "                            stars = True,\n",
    "                            model_names=['Model 1'],\n",
    "                            info_dict=info_dict,\n",
    "                            regressor_order=X3)\n",
    "\n",
    "results_table.add_title('Table 1 - OLS Regressions')\n",
    "\n",
    "print(results_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MAE Model 1 = 123.4893\n",
      "Train MAE Model 1 = 125.3763\n",
      "Test RMSE Model 1 = 162.5794\n",
      "Train RMSE Model 1 = 165.8013\n"
     ]
    }
   ],
   "source": [
    "cnt_lr_model = linear_model.LinearRegression()\n",
    "model1 = cnt_lr_model.fit(X_train[X3], y_train)\n",
    "\n",
    "mae_test_model = metrics.mean_absolute_error(y_test, model1.predict(X_test[X3]))\n",
    "mae_train_model = metrics.mean_absolute_error(y_train, model1.predict(X_train[X3]))\n",
    "\n",
    "rmse_test_model = metrics.mean_squared_error(y_test, model1.predict(X_test[X3]), squared = False)\n",
    "rmse_train_model = metrics.mean_squared_error(y_train, model1.predict(X_train[X3]), squared = False)\n",
    "\n",
    "print(f'Test MAE Model 1 = {mae_test_model:.4f}')\n",
    "print(f'Train MAE Model 1 = {mae_train_model:.4f}')\n",
    "print(f'Test RMSE Model 1 = {rmse_test_model:.4f}')\n",
    "print(f'Train RMSE Model 1 = {rmse_train_model:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnt_model = linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.iolib.summary2 import summary_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=X.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=y.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.iolib.summary2 import summary_col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.3 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform the cross-varidation by spliting the dataset into 10 folds and train the model with Regression algorithm by using the two features as in question 1.2. What is the MAE and RMSE in model?.[10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on 10-fold CV on the Test: 7.150692429147798e-13\n",
      "RMSE on 10-fold CV on the Train: 6.616564287062563e-13\n"
     ]
    }
   ],
   "source": [
    "kf = KFold(n_splits=10,shuffle=False)\n",
    "err_train = 0\n",
    "err_test =0 #error to the start with 0\n",
    "for train,test in kf.split(X):\n",
    "    lr=linear_model.LinearRegression()\n",
    "    reg=lr.fit(X[train],y[train])\n",
    "    y_pred_train =reg.predict(X[train])\n",
    "    y_pred_test =reg.predict(X[test])\n",
    "    e_train=y[train]-y_pred_train\n",
    "    e_test = y[test]-y_pred_test\n",
    "    err_test += np.sqrt(np.mean(e_test*e_test))  \n",
    "    err_train += np.sqrt(np.mean(e_train*e_train))  \n",
    "rmse_10cv_test = err_test/10  \n",
    "rmse_10cv_train = err_train/10    \n",
    "print('RMSE on 10-fold CV on the Test: {}'.format(rmse_10cv_test))\n",
    "print('RMSE on 10-fold CV on the Train: {}'.format(rmse_10cv_train))\n",
    "#+="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last excercise, using all features which are\n",
    "- holiday : weather day is holiday or not \n",
    "- weekday : day of the week\n",
    "- workingday : if day is neither weekend nor holiday is 1, otherwise is 0.\n",
    "+ weathersit : \n",
    "    - 1: Clear, Few clouds, Partly cloudy, Partly cloudy\n",
    "    - 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
    "    - 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\n",
    "    - 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog\n",
    "- temp : Normalized temperature in Celsius. The values are divided to 41 (max)\n",
    "- atemp: Normalized feeling temperature in Celsius. The values are divided to 50 (max)\n",
    "- hum: Normalized humidity. The values are divided to 100 (max)\n",
    "- windspeed: Normalized wind speed. The values are divided to 67 (max)\n",
    "\n",
    "to train the model with the same process in question 1.2 What is the MAE and RMSE in this model? Interpret the results carefully"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "X4 = ['atemp','weathersit',\"temp\",\"hum\",\"windspeed\",\"workingday\",'weekday','holiday']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MAE Model 2 = 115.4045\n",
      "Train MAE Model 2 = 117.3157\n",
      "Test RMSE Model 2 = 153.8892\n",
      "Train RMSE Model 2 = 157.2681\n"
     ]
    }
   ],
   "source": [
    "cnt_lr_model = linear_model.LinearRegression()\n",
    "model2 = cnt_lr_model.fit(X_train[X4], y_train)\n",
    "\n",
    "mae_test_model = metrics.mean_absolute_error(y_test, model2.predict(X_test[X4]))\n",
    "mae_train_model = metrics.mean_absolute_error(y_train, model2.predict(X_train[X4]))\n",
    "\n",
    "rmse_test_model = metrics.mean_squared_error(y_test, model2.predict(X_test[X4]), squared = False)\n",
    "rmse_train_model = metrics.mean_squared_error(y_train, model2.predict(X_train[X4]), squared = False)\n",
    "\n",
    "print(f'Test MAE Model 2 = {mae_test_model:.4f}')\n",
    "print(f'Train MAE Model 2 = {mae_train_model:.4f}')\n",
    "print(f'Test RMSE Model 2 = {rmse_test_model:.4f}')\n",
    "print(f'Train RMSE Model 2 = {rmse_train_model:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.4 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Should we use all features or only the two features? Why? Is there the problem of overfitting in the model you select? Why? [10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#but with the observation we see that with the bigger set of data result in less of rmse and mae which i think that this data wasnot over crowding "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.5 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the model you selcted in 1.4 to create the dataframe that contains three columns which are : Actual value, Predicted value, and the error/residual. Then fill in these values from the test data.[10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Table 1 - OLS Regressions\n",
      "==========================================================\n",
      "                    Model 1       Model 2        Model3   \n",
      "----------------------------------------------------------\n",
      "atemp            452.214130*** 627.591554***  -0.000000   \n",
      "                 (4.617394)    (44.129176)    (0.000000)  \n",
      "weathersit       -19.623894*** 12.332120***   -0.000000** \n",
      "                 (1.495252)    (2.098162)     (0.000000)  \n",
      "mnth                                          -0.000000***\n",
      "                                              (0.000000)  \n",
      "registered                                    1.000000*** \n",
      "                                              (0.000000)  \n",
      "workingday                     15.958210***   -0.000000   \n",
      "                               (2.610320)     (0.000000)  \n",
      "windspeed                      150.506549***  -0.000000   \n",
      "                               (9.315124)     (0.000000)  \n",
      "weekday                        5.157935***    0.000000*** \n",
      "                               (0.578914)     (0.000000)  \n",
      "temp                           -123.693075*** 0.000000    \n",
      "                               (40.225263)    (0.000000)  \n",
      "season                                        0.000000*** \n",
      "                                              (0.000000)  \n",
      "yr                                            0.000000*** \n",
      "                                              (0.000000)  \n",
      "hum                            -198.659104*** -0.000000***\n",
      "                               (6.065951)     (0.000000)  \n",
      "hr                                            -0.000000***\n",
      "                                              (0.000000)  \n",
      "holiday                        2.432591       0.000000*** \n",
      "                               (7.408139)     (0.000000)  \n",
      "const                                         0.000000*** \n",
      "                                              (0.000000)  \n",
      "casual                                        1.000000*** \n",
      "                                              (0.000000)  \n",
      "R-squared        0.602356      0.634001       1.000000    \n",
      "R-squared Adj.   0.602310      0.633832       1.000000    \n",
      "Adj-R-squared    0.60          0.63           1.00        \n",
      "No. observations 17379         17379          17379       \n",
      "==========================================================\n",
      "Standard errors in parentheses.\n",
      "* p<.1, ** p<.05, ***p<.01\n"
     ]
    }
   ],
   "source": [
    "X3 = ['atemp','weathersit']\n",
    "X4 = ['atemp','weathersit',\"temp\",\"hum\",\"windspeed\",\"workingday\",'weekday','holiday']\n",
    "reg1 = sm.OLS(df['cnt'], df[X3], missing='drop').fit()\n",
    "reg2 = sm.OLS(df['cnt'], df[X4], missing='drop').fit()\n",
    "reg3 = sm.OLS(df['cnt'], X, missing='drop').fit()\n",
    "info_dict={'Adj-R-squared' : lambda x: f\"{x.rsquared_adj:.2f}\",\n",
    "           'No. observations' : lambda x: f\"{int(x.nobs):d}\"}\n",
    "\n",
    "results_table = summary_col(results=[reg1,reg2,reg3],\n",
    "                            float_format='%0.6f',\n",
    "                            stars = True,\n",
    "                            model_names=['Model 1','Model 2','Model3'],\n",
    "                            info_dict=info_dict,\n",
    "                            regressor_order=X3)\n",
    "\n",
    "results_table.add_title('Table 1 - OLS Regressions')\n",
    "\n",
    "print(results_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2.1:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.1 The below information shows the U.S unemployment rate (unemp)in 2010 from January-December \n",
    "\n",
    "\n",
    "Task: write down the python code using control flow  \"if\" to print the message ONLY the months that have the unemployment rate above 8% i.e.\n",
    "\n",
    "\n",
    "- The umemployment rate in Apr is 14.8%\n",
    "\n",
    "- The unemployment rate in May is 13.3%\n",
    "\n",
    "\n",
    "\n",
    "[10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "unemp= [3.5,\n",
    "3.5,\n",
    "4.4,\n",
    "14.8,\n",
    "13.3,\n",
    "11.1,\n",
    "10.2,\n",
    "8.4,\n",
    "7.8,\n",
    "6.9,\n",
    "6.7,\n",
    "6.7]\n",
    "month =['Jan','Feb','Mar','Apr','May','June','July','Aug','Sep','Oct','Nov','Dec']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The umemployment rate in Apr is 14.8 percent\n",
      "The umemployment rate in May is 13.3 percent\n",
      "The umemployment rate in June is 11.1 percent\n",
      "The umemployment rate in July is 10.2 percent\n",
      "The umemployment rate in Aug is 8.4 percent\n"
     ]
    }
   ],
   "source": [
    "unemp= [3.5,\n",
    "3.5,\n",
    "4.4,\n",
    "14.8,\n",
    "13.3,\n",
    "11.1,\n",
    "10.2,\n",
    "8.4,\n",
    "7.8,\n",
    "6.9,\n",
    "6.7,\n",
    "6.7]\n",
    "month =['Jan','Feb','Mar','Apr','May','June','July','Aug','Sep','Oct','Nov','Dec']\n",
    "for c,s in zip(unemp,month): \n",
    "    if c > 8:\n",
    "        print(f\"The umemployment rate in {s} is {c} percent\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2.2 :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.2 write the list [.] of even numbers from 2-500. Then using control flow to find out the summation of the log of even numbers from 2-100. i.e. log(2)+log(4)+....log(100) \n",
    "[10 Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 this is even number \n",
      "4 this is even number \n",
      "6 this is even number \n",
      "8 this is even number \n",
      "10 this is even number \n",
      "12 this is even number \n",
      "14 this is even number \n",
      "16 this is even number \n",
      "18 this is even number \n",
      "20 this is even number \n",
      "22 this is even number \n",
      "24 this is even number \n",
      "26 this is even number \n",
      "28 this is even number \n",
      "30 this is even number \n",
      "32 this is even number \n",
      "34 this is even number \n",
      "36 this is even number \n",
      "38 this is even number \n",
      "40 this is even number \n",
      "42 this is even number \n",
      "44 this is even number \n",
      "46 this is even number \n",
      "48 this is even number \n",
      "50 this is even number \n",
      "52 this is even number \n",
      "54 this is even number \n",
      "56 this is even number \n",
      "58 this is even number \n",
      "60 this is even number \n",
      "62 this is even number \n",
      "64 this is even number \n",
      "66 this is even number \n",
      "68 this is even number \n",
      "70 this is even number \n",
      "72 this is even number \n",
      "74 this is even number \n",
      "76 this is even number \n",
      "78 this is even number \n",
      "80 this is even number \n",
      "82 this is even number \n",
      "84 this is even number \n",
      "86 this is even number \n",
      "88 this is even number \n",
      "90 this is even number \n",
      "92 this is even number \n",
      "94 this is even number \n",
      "96 this is even number \n",
      "98 this is even number \n",
      "100 this is even number \n",
      "102 this is even number \n",
      "104 this is even number \n",
      "106 this is even number \n",
      "108 this is even number \n",
      "110 this is even number \n",
      "112 this is even number \n",
      "114 this is even number \n",
      "116 this is even number \n",
      "118 this is even number \n",
      "120 this is even number \n",
      "122 this is even number \n",
      "124 this is even number \n",
      "126 this is even number \n",
      "128 this is even number \n",
      "130 this is even number \n",
      "132 this is even number \n",
      "134 this is even number \n",
      "136 this is even number \n",
      "138 this is even number \n",
      "140 this is even number \n",
      "142 this is even number \n",
      "144 this is even number \n",
      "146 this is even number \n",
      "148 this is even number \n",
      "150 this is even number \n",
      "152 this is even number \n",
      "154 this is even number \n",
      "156 this is even number \n",
      "158 this is even number \n",
      "160 this is even number \n",
      "162 this is even number \n",
      "164 this is even number \n",
      "166 this is even number \n",
      "168 this is even number \n",
      "170 this is even number \n",
      "172 this is even number \n",
      "174 this is even number \n",
      "176 this is even number \n",
      "178 this is even number \n",
      "180 this is even number \n",
      "182 this is even number \n",
      "184 this is even number \n",
      "186 this is even number \n",
      "188 this is even number \n",
      "190 this is even number \n",
      "192 this is even number \n",
      "194 this is even number \n",
      "196 this is even number \n",
      "198 this is even number \n",
      "200 this is even number \n",
      "202 this is even number \n",
      "204 this is even number \n",
      "206 this is even number \n",
      "208 this is even number \n",
      "210 this is even number \n",
      "212 this is even number \n",
      "214 this is even number \n",
      "216 this is even number \n",
      "218 this is even number \n",
      "220 this is even number \n",
      "222 this is even number \n",
      "224 this is even number \n",
      "226 this is even number \n",
      "228 this is even number \n",
      "230 this is even number \n",
      "232 this is even number \n",
      "234 this is even number \n",
      "236 this is even number \n",
      "238 this is even number \n",
      "240 this is even number \n",
      "242 this is even number \n",
      "244 this is even number \n",
      "246 this is even number \n",
      "248 this is even number \n",
      "250 this is even number \n",
      "252 this is even number \n",
      "254 this is even number \n",
      "256 this is even number \n",
      "258 this is even number \n",
      "260 this is even number \n",
      "262 this is even number \n",
      "264 this is even number \n",
      "266 this is even number \n",
      "268 this is even number \n",
      "270 this is even number \n",
      "272 this is even number \n",
      "274 this is even number \n",
      "276 this is even number \n",
      "278 this is even number \n",
      "280 this is even number \n",
      "282 this is even number \n",
      "284 this is even number \n",
      "286 this is even number \n",
      "288 this is even number \n",
      "290 this is even number \n",
      "292 this is even number \n",
      "294 this is even number \n",
      "296 this is even number \n",
      "298 this is even number \n",
      "300 this is even number \n",
      "302 this is even number \n",
      "304 this is even number \n",
      "306 this is even number \n",
      "308 this is even number \n",
      "310 this is even number \n",
      "312 this is even number \n",
      "314 this is even number \n",
      "316 this is even number \n",
      "318 this is even number \n",
      "320 this is even number \n",
      "322 this is even number \n",
      "324 this is even number \n",
      "326 this is even number \n",
      "328 this is even number \n",
      "330 this is even number \n",
      "332 this is even number \n",
      "334 this is even number \n",
      "336 this is even number \n",
      "338 this is even number \n",
      "340 this is even number \n",
      "342 this is even number \n",
      "344 this is even number \n",
      "346 this is even number \n",
      "348 this is even number \n",
      "350 this is even number \n",
      "352 this is even number \n",
      "354 this is even number \n",
      "356 this is even number \n",
      "358 this is even number \n",
      "360 this is even number \n",
      "362 this is even number \n",
      "364 this is even number \n",
      "366 this is even number \n",
      "368 this is even number \n",
      "370 this is even number \n",
      "372 this is even number \n",
      "374 this is even number \n",
      "376 this is even number \n",
      "378 this is even number \n",
      "380 this is even number \n",
      "382 this is even number \n",
      "384 this is even number \n",
      "386 this is even number \n",
      "388 this is even number \n",
      "390 this is even number \n",
      "392 this is even number \n",
      "394 this is even number \n",
      "396 this is even number \n",
      "398 this is even number \n",
      "400 this is even number \n",
      "402 this is even number \n",
      "404 this is even number \n",
      "406 this is even number \n",
      "408 this is even number \n",
      "410 this is even number \n",
      "412 this is even number \n",
      "414 this is even number \n",
      "416 this is even number \n",
      "418 this is even number \n",
      "420 this is even number \n",
      "422 this is even number \n",
      "424 this is even number \n",
      "426 this is even number \n",
      "428 this is even number \n",
      "430 this is even number \n",
      "432 this is even number \n",
      "434 this is even number \n",
      "436 this is even number \n",
      "438 this is even number \n",
      "440 this is even number \n",
      "442 this is even number \n",
      "444 this is even number \n",
      "446 this is even number \n",
      "448 this is even number \n",
      "450 this is even number \n",
      "452 this is even number \n",
      "454 this is even number \n",
      "456 this is even number \n",
      "458 this is even number \n",
      "460 this is even number \n",
      "462 this is even number \n",
      "464 this is even number \n",
      "466 this is even number \n",
      "468 this is even number \n",
      "470 this is even number \n",
      "472 this is even number \n",
      "474 this is even number \n",
      "476 this is even number \n",
      "478 this is even number \n",
      "480 this is even number \n",
      "482 this is even number \n",
      "484 this is even number \n",
      "486 this is even number \n",
      "488 this is even number \n",
      "490 this is even number \n",
      "492 this is even number \n",
      "494 this is even number \n",
      "496 this is even number \n",
      "498 this is even number \n",
      "500 this is even number \n"
     ]
    }
   ],
   "source": [
    "for i in range(2,501 ):\n",
    "    if i % 2 == 0:  \n",
    "        print(i,\"this is even number \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.605170185988092"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(2,101 ):\n",
    "    if i % 2 == 0: \n",
    "        total = np.log(i)\n",
    "np.sum(total)\n",
    "       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 3 [20 point]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.1 Consider the stock prices: TESLA (TSLA) stock.from June 29, 2010 to Sept 30, 2022. The data are downloadable from Yahoo Finance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot (1) the TESLA prices and (2) TESLA log returns. Then, check the stationarity condition of these two series. [10 points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in /opt/anaconda3/lib/python3.9/site-packages (1.4.2)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /opt/anaconda3/lib/python3.9/site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/anaconda3/lib/python3.9/site-packages (from pandas) (2021.3)\n",
      "Requirement already satisfied: numpy>=1.18.5 in /opt/anaconda3/lib/python3.9/site-packages (from pandas) (1.21.5)\n",
      "Requirement already satisfied: six>=1.5 in /opt/anaconda3/lib/python3.9/site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas-datareader in /opt/anaconda3/lib/python3.9/site-packages (0.10.0)\n",
      "Requirement already satisfied: requests>=2.19.0 in /opt/anaconda3/lib/python3.9/site-packages (from pandas-datareader) (2.27.1)\n",
      "Requirement already satisfied: lxml in /opt/anaconda3/lib/python3.9/site-packages (from pandas-datareader) (4.8.0)\n",
      "Requirement already satisfied: pandas>=0.23 in /opt/anaconda3/lib/python3.9/site-packages (from pandas-datareader) (1.4.2)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /opt/anaconda3/lib/python3.9/site-packages (from pandas>=0.23->pandas-datareader) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/anaconda3/lib/python3.9/site-packages (from pandas>=0.23->pandas-datareader) (2021.3)\n",
      "Requirement already satisfied: numpy>=1.18.5 in /opt/anaconda3/lib/python3.9/site-packages (from pandas>=0.23->pandas-datareader) (1.21.5)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/anaconda3/lib/python3.9/site-packages (from requests>=2.19.0->pandas-datareader) (2021.10.8)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/anaconda3/lib/python3.9/site-packages (from requests>=2.19.0->pandas-datareader) (3.3)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/anaconda3/lib/python3.9/site-packages (from requests>=2.19.0->pandas-datareader) (1.26.9)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in /opt/anaconda3/lib/python3.9/site-packages (from requests>=2.19.0->pandas-datareader) (2.0.4)\n",
      "Requirement already satisfied: six>=1.5 in /opt/anaconda3/lib/python3.9/site-packages (from python-dateutil>=2.8.1->pandas>=0.23->pandas-datareader) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade pandas-datareader\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "scrolled": true
   },
   "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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-09-28</th>\n",
       "      <td>1.432667</td>\n",
       "      <td>1.384000</td>\n",
       "      <td>1.402667</td>\n",
       "      <td>1.426667</td>\n",
       "      <td>18217500.0</td>\n",
       "      <td>1.426667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-29</th>\n",
       "      <td>1.468667</td>\n",
       "      <td>1.408667</td>\n",
       "      <td>1.412667</td>\n",
       "      <td>1.465333</td>\n",
       "      <td>29539500.0</td>\n",
       "      <td>1.465333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-30</th>\n",
       "      <td>1.476667</td>\n",
       "      <td>1.346000</td>\n",
       "      <td>1.466667</td>\n",
       "      <td>1.360667</td>\n",
       "      <td>32937000.0</td>\n",
       "      <td>1.360667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10-01</th>\n",
       "      <td>1.383333</td>\n",
       "      <td>1.354000</td>\n",
       "      <td>1.379333</td>\n",
       "      <td>1.373333</td>\n",
       "      <td>8965500.0</td>\n",
       "      <td>1.373333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10-04</th>\n",
       "      <td>1.411333</td>\n",
       "      <td>1.353333</td>\n",
       "      <td>1.362000</td>\n",
       "      <td>1.399333</td>\n",
       "      <td>9654000.0</td>\n",
       "      <td>1.399333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-26</th>\n",
       "      <td>284.089996</td>\n",
       "      <td>270.309998</td>\n",
       "      <td>271.829987</td>\n",
       "      <td>276.010010</td>\n",
       "      <td>58076900.0</td>\n",
       "      <td>276.010010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-27</th>\n",
       "      <td>288.670013</td>\n",
       "      <td>277.510010</td>\n",
       "      <td>283.839996</td>\n",
       "      <td>282.940002</td>\n",
       "      <td>61925200.0</td>\n",
       "      <td>282.940002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-28</th>\n",
       "      <td>289.000000</td>\n",
       "      <td>277.570007</td>\n",
       "      <td>283.079987</td>\n",
       "      <td>287.809998</td>\n",
       "      <td>54664800.0</td>\n",
       "      <td>287.809998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-29</th>\n",
       "      <td>283.649994</td>\n",
       "      <td>265.779999</td>\n",
       "      <td>282.760010</td>\n",
       "      <td>268.209991</td>\n",
       "      <td>77620600.0</td>\n",
       "      <td>268.209991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-30</th>\n",
       "      <td>275.570007</td>\n",
       "      <td>262.470001</td>\n",
       "      <td>266.149994</td>\n",
       "      <td>265.250000</td>\n",
       "      <td>67517800.0</td>\n",
       "      <td>265.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3024 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  High         Low        Open       Close      Volume  \\\n",
       "Date                                                                     \n",
       "2010-09-28    1.432667    1.384000    1.402667    1.426667  18217500.0   \n",
       "2010-09-29    1.468667    1.408667    1.412667    1.465333  29539500.0   \n",
       "2010-09-30    1.476667    1.346000    1.466667    1.360667  32937000.0   \n",
       "2010-10-01    1.383333    1.354000    1.379333    1.373333   8965500.0   \n",
       "2010-10-04    1.411333    1.353333    1.362000    1.399333   9654000.0   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2022-09-26  284.089996  270.309998  271.829987  276.010010  58076900.0   \n",
       "2022-09-27  288.670013  277.510010  283.839996  282.940002  61925200.0   \n",
       "2022-09-28  289.000000  277.570007  283.079987  287.809998  54664800.0   \n",
       "2022-09-29  283.649994  265.779999  282.760010  268.209991  77620600.0   \n",
       "2022-09-30  275.570007  262.470001  266.149994  265.250000  67517800.0   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2010-09-28    1.426667  \n",
       "2010-09-29    1.465333  \n",
       "2010-09-30    1.360667  \n",
       "2010-10-01    1.373333  \n",
       "2010-10-04    1.399333  \n",
       "...                ...  \n",
       "2022-09-26  276.010010  \n",
       "2022-09-27  282.940002  \n",
       "2022-09-28  287.809998  \n",
       "2022-09-29  268.209991  \n",
       "2022-09-30  265.250000  \n",
       "\n",
       "[3024 rows x 6 columns]"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas_datareader.data as web\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "df7 = web.DataReader('TSLA', 'yahoo', start='2010-09-29', end='2022-09-30')\n",
    "df7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "scrolled": false
   },
   "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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-09-28</th>\n",
       "      <td>1.432667</td>\n",
       "      <td>1.384000</td>\n",
       "      <td>1.402667</td>\n",
       "      <td>1.426667</td>\n",
       "      <td>18217500.0</td>\n",
       "      <td>1.426667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-29</th>\n",
       "      <td>1.468667</td>\n",
       "      <td>1.408667</td>\n",
       "      <td>1.412667</td>\n",
       "      <td>1.465333</td>\n",
       "      <td>29539500.0</td>\n",
       "      <td>1.465333</td>\n",
       "      <td>0.026742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-30</th>\n",
       "      <td>1.476667</td>\n",
       "      <td>1.346000</td>\n",
       "      <td>1.466667</td>\n",
       "      <td>1.360667</td>\n",
       "      <td>32937000.0</td>\n",
       "      <td>1.360667</td>\n",
       "      <td>-0.074107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10-01</th>\n",
       "      <td>1.383333</td>\n",
       "      <td>1.354000</td>\n",
       "      <td>1.379333</td>\n",
       "      <td>1.373333</td>\n",
       "      <td>8965500.0</td>\n",
       "      <td>1.373333</td>\n",
       "      <td>0.009266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10-04</th>\n",
       "      <td>1.411333</td>\n",
       "      <td>1.353333</td>\n",
       "      <td>1.362000</td>\n",
       "      <td>1.399333</td>\n",
       "      <td>9654000.0</td>\n",
       "      <td>1.399333</td>\n",
       "      <td>0.018755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-26</th>\n",
       "      <td>284.089996</td>\n",
       "      <td>270.309998</td>\n",
       "      <td>271.829987</td>\n",
       "      <td>276.010010</td>\n",
       "      <td>58076900.0</td>\n",
       "      <td>276.010010</td>\n",
       "      <td>0.002467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-27</th>\n",
       "      <td>288.670013</td>\n",
       "      <td>277.510010</td>\n",
       "      <td>283.839996</td>\n",
       "      <td>282.940002</td>\n",
       "      <td>61925200.0</td>\n",
       "      <td>282.940002</td>\n",
       "      <td>0.024798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-28</th>\n",
       "      <td>289.000000</td>\n",
       "      <td>277.570007</td>\n",
       "      <td>283.079987</td>\n",
       "      <td>287.809998</td>\n",
       "      <td>54664800.0</td>\n",
       "      <td>287.809998</td>\n",
       "      <td>0.017066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-29</th>\n",
       "      <td>283.649994</td>\n",
       "      <td>265.779999</td>\n",
       "      <td>282.760010</td>\n",
       "      <td>268.209991</td>\n",
       "      <td>77620600.0</td>\n",
       "      <td>268.209991</td>\n",
       "      <td>-0.070530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-30</th>\n",
       "      <td>275.570007</td>\n",
       "      <td>262.470001</td>\n",
       "      <td>266.149994</td>\n",
       "      <td>265.250000</td>\n",
       "      <td>67517800.0</td>\n",
       "      <td>265.250000</td>\n",
       "      <td>-0.011097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3024 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  High         Low        Open       Close      Volume  \\\n",
       "Date                                                                     \n",
       "2010-09-28    1.432667    1.384000    1.402667    1.426667  18217500.0   \n",
       "2010-09-29    1.468667    1.408667    1.412667    1.465333  29539500.0   \n",
       "2010-09-30    1.476667    1.346000    1.466667    1.360667  32937000.0   \n",
       "2010-10-01    1.383333    1.354000    1.379333    1.373333   8965500.0   \n",
       "2010-10-04    1.411333    1.353333    1.362000    1.399333   9654000.0   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2022-09-26  284.089996  270.309998  271.829987  276.010010  58076900.0   \n",
       "2022-09-27  288.670013  277.510010  283.839996  282.940002  61925200.0   \n",
       "2022-09-28  289.000000  277.570007  283.079987  287.809998  54664800.0   \n",
       "2022-09-29  283.649994  265.779999  282.760010  268.209991  77620600.0   \n",
       "2022-09-30  275.570007  262.470001  266.149994  265.250000  67517800.0   \n",
       "\n",
       "             Adj Close    return  \n",
       "Date                              \n",
       "2010-09-28    1.426667       NaN  \n",
       "2010-09-29    1.465333  0.026742  \n",
       "2010-09-30    1.360667 -0.074107  \n",
       "2010-10-01    1.373333  0.009266  \n",
       "2010-10-04    1.399333  0.018755  \n",
       "...                ...       ...  \n",
       "2022-09-26  276.010010  0.002467  \n",
       "2022-09-27  282.940002  0.024798  \n",
       "2022-09-28  287.809998  0.017066  \n",
       "2022-09-29  268.209991 -0.070530  \n",
       "2022-09-30  265.250000 -0.011097  \n",
       "\n",
       "[3024 rows x 7 columns]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7[\"return\"] = np.log(df7[[\"Adj Close\"]]).diff()\n",
    "df7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df7[[\"Adj Close\"]].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAETCAYAAAAlCTHcAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAABO70lEQVR4nO2deWBMV/vHvzPZ9wSJIMS+JiWWoqgQKqq1tD+qqlRbVLXVeLtQVWvx6qqtvn2pqqq+qFqqKhRFLaWWhFpChAQhKdn3zPL7Y3Ins9x7596Ze2fL8/mH3Llz7vPMPec85zznOc9RlJeXa0EQBEEQFlA6WgCCIAjCNSCDQRAEQQiCDAZBEAQhCDIYBEEQhCDIYBAEQRCCcFuDkZubi9zcXEeLQRAE4TZ4OloAuamoqHC0CFZRXFyMoKAgR4shCe6ii7voAZAuzogz6eHr68t63W1nGARBEIS0kMEgCIIgBEEGgyAIghAEGQyCIAhCEGQwCIIgCEGQwSAIgiAEQQaD4KS4vBr3iysdLQZBEE6C2+/DIKyn/7sHUVqhRtrKREeLQhCEE0AzDIKT0gq1o0UgCMKJIINBEARBCIIMBkEQBCEI2dYwdu3aha1btyI/Px/NmjXD5MmT0alTJ9Z7z58/jx07duDKlSsoLS1F48aNMXz4cAwePFgu8QiCIAiRyGIw/vjjD6xevRrTpk1Dx44d8euvv2L+/PlYuXIlIiIizO6/dOkSoqOj8cQTT6BevXo4c+YMvvjiC3h5eSE+Pl4OEQmCIAiRyGIwtm/fjoSEBAwZMgQAMHXqVJw+fRq7d+/GxIkTze4fM2aM0d+PPvoozp07h2PHjpHBIAiCcBIkX8Oorq5Geno64uLijK7HxcXh0qVLgsspLy9HYGCg1OIRBEEQViL5DKOoqAgajQahoaFG10NDQ5GamiqojJMnTyI1NRXLly9n/Tw5ORl79uzhLWP27NkAdDnmXRGtVus0stsqhzPpYgvuogdAujgjzqQH13kYsi16KxQKq7538eJFfPjhh5gyZQratm3Lek9iYiISE/k3kzGn7TnLgSRicabDVGyVw5l0sQV30QMgXZwRV9BDcoMRHBwMpVKJ/Px8o+sFBQVmsw5TLly4gAULFuCZZ57Bo48+KrVoBEEQhA1Ivobh5eWF1q1bIyUlxeh6SkoKOnTowPm9v//+GwsWLMDTTz+NESNGSC0WQRAEYSOybNwbOXIk9u/fjz179uDmzZtYtWoV8vLyMHToUADAunXrMGfOHP3958+fx/z585GYmIj4+Hjk5+cjPz8fhYWFcohHEARBWIEsaxj9+vVDUVERNm/ejLy8PERHR2PevHn6PRh5eXm4e/eu/v59+/ahsrIS27Ztw7Zt2/TXIyIisGbNGjlEJAiCIESiKC8v1zpaCDlgFr2Dg4MdLIl1OMMCWLvpyQBgc7ZaZ9BFCtxFD8D9dFm59zYahvpgUkILR4tjNc70TriipCiXFEEQLs/aAzewbGuaXZ5VpdKg3fRkbDiUaZfnORNkMCyw5+xdXM12jthogiAcT0m5CgDw+a/pDpbE/tABShZ47esUALa7ZQiCIFwdmmEQBEEQgiCDQRCEW/HJzit4+7tzjhbDLSGDQRCEW/FVcga2n8h2tBhuCRkMgiBcguNp95GZW+poMeo0tOhNEITVXL5VhABfTzRt4C/7s5777C8AFIDiSGiGIRF/XrmPqmqNo8UgCLsyYukxDJp32NFiEHaCDIYEXL5djIkr/sKSn4QfEGWJ7LwK5JVUSVYeQRDSonXLHBn8kMGQgIJSXcd+7a50/tXhy08j/t2DkpVnT8qr1Fiw6aJ+gxNBuBXWHfXjFpDBcGIqXdTFtfGPLPxwOAv/2XPN0aIQEvPr6TvIzit3tBiCKC6vlqfgOjizYCCDIQHuNDXNL6nCwk0XbVqPUdd8VaNxox+GAAAkfZOKsR/96WgxBPFnWp6s5Vt5qKhLQwaDMOLDHVew4XAWdp2+I0l5xy/fR/dZR5H1T5kk5RGOQ1szMsopqHSwJMLQyjwVcKeBolDIYEiAO400VDXTA60NrcGwoW4/eRsAcPpaPtfthItQFztIVtyovYuFDAbBisKdrCBRJyEDJz1kMCTAnSqmFLooWIZgtsxYCOeA3mANdfiHIIPBA+OeEYo7Dcr5dNFotLhBKRrqHGT0jXGn9i4UMhg8fPGruLBQd2hPbDpk5JQY/b36twwMWfAH0m6zHyzFtthILi7Xh3mrrvIq5W6P7tDexUIGgwfTjpILV2lAQmA6e8MOvqxSbXTP6WsFAIA7+cLj8bVaLY5dvoeHZh1AaQVt6HNFXK2DlE1cN2rvYiGDwYfAGudqDYkPRhfDNvHkv4+jqEz4JijDNQzDcj7acQX3i6twLYfcWa6Mq/SXX+5OZ43OS7tdLNrdLDdarRa7zuQ6nVymkMGQEHeaaZhyNqPA7Jo7GUpCAC72vq9kl2DcxyeMrl27W4LhS47i051XrS9Yht/h57/uYN7mq/j6t+vSFy4hZDD4EGkArOlAq9UaPLHsGI5dvif+yzJiavzEGEOLG6bI0rgkbO5KVyO3ZtPhucxCvLvhb7SbnuxgiXTk1yQavVfs3AlHyWDwYYd+7W5+BS7cLMK7P1yQ/2EC4IqEUSrNOwlL/YZpx+LKHQ3Bb+f/vfUyqlTO7U4BAHWNEh5KBX48dgsAUFqhwr0iEbvXJajG+1JzWN28zt5EyGBIiLO/bCFwRcKw2AuLEwVD46M1um6VaISTwFbNv9l/A7/85fzHojL5zZQGFbzrv/ahz+zf7TbLz84rx/RVZ/Gvtal2eZ6UkMEgjKhd9OaeHVicWRgueivYF8AJ18OSnVe7wEBAYzDDMOV8ZqFdZCiv0kUd3rpvHmXo7G2EDAZhhN5g8MwwLM4sDLoWNheXC/QrBAvusHFPVWPVlA7s+ZhZjsKoTbnGb0sGgwfXeIX2QWmFv43WLNwLrsGEK8HowDbDsFd9ZfoVtjbl7L+tp1wF79q1C1u3bkV+fj6aNWuGyZMno1OnTqz3VlVVYeXKlcjIyMDNmzfRoUMHLF26VC7RCB64IpwMK7LQSq3Val3WaJRVqlBUpkJkmK+jRXEa9DXDiV/pRzvSeD9X14zuPRxYLzUshtdFJhjyzDD++OMPrF69GmPGjMGKFSvQoUMHzJ8/H7m5uaz3azQaeHt7Y9iwYejevbscIlmFE7cL+dBXZusjnNiSD+rKsFoquzP+05Po76JH5MpNtcp5e7dVe/n3MTAGgy3qz15o9S4plhmGk/c6shiM7du3IyEhAUOGDEHTpk0xdepUhIWFYffu3az3+/r6Yvr06UhMTESDBg3kEMkqnLdZyIc+SsrkOtsUnrsM/l/OFfy1F7KKHC2C8+H8r80izKL37jN3HSZDrUvK/JpKrcGmIzf1hs3ZkNwlVV1djfT0dIwaNcroelxcHC5duiT145wD53y3ViFk0Vso5rMUK4UinAIxJ9j9eeU+GgT5oHWjQBklEo8zdMSM0WKbYaw/lFXzGTCmT1O7yiUEyQ1GUVERNBoNQkNDja6HhoYiNVWauOPk5GTs2bOH957Zs2cDAIqL2TOqCqG6ujZJHl85ZeW640fVKrXo55WWVgAAtBoN63dtkd8aVCrdZqKKigqj62VlZSgu9kD3WUf118rLy1nlq6zUbYKqqqpCdXVteeqaPDmlZWUoLpZt+UxSDPXTarV2fx9yYY0uxeX87aGiokJ/feKKvwAAp5b1sUFKDjlMnn2/mH/TXUp6Dlo19AcAlJZxJ8ysrKzUl/3twVvQaoFJA6LM7ist1dVpa+tDaakul5pGU9tfmLa3ewWlDq1rvr7sa3eytVo5FzsTExORmJjIew+zXhIUFGT1c7y8an8evnL8/XXb+T08PUQ/L6DSA4Du92L7ruG1/JIqBPt7iXIPicXDU6ezn59xhQkMDDCTz9fPj1VmHx8fAICXlxe8vJhrvvDw0HlA/f39bXov9sRQzuLiYpeR2xJCdSkorYK/jye8PZXQetTuTGb7rq+vr9l1OX4vwzK1Wi1+Pcu+NsowbkUKLn4+BADg7cO918LHx0df9hfJmQCA14Z3MLuvWqFr71xt1hJ+/rqBk6dHbX/h42Pc3nwNZOGjSqVB+p0SdGwaLFoOa5B8DSM4OBhKpRL5+cZZIgsKCsxmHW6DHWa5RWXV6PX2AXy4XRcFMuXL09j2523Znme6+ObIqBJDKqvV+PPKfUeLUWfo+dYBvPzfMwCc0/O642Q23tvEn0jQ0A2l4XFJ2auKa1l2m5u6+4TKsmTLJYxadgw375VJJh8fkhsMLy8vtG7dGikpKUbXU1JS0KGDubV2J2yqbxZqSGFN3pk9KTkAgEMX/sGs9edteSIrXOvRCitmNaa7w6WIAFm4+RImrvgL1+4KO6uEsJ0/LupSZlgKVnDEkILrEC8u+NYw7BWhVLuGwX2PJQ/Nn1fu47XVZ5F6QzdjKhRx/IAtyBIlNXLkSOzfvx979uzBzZs3sWrVKuTl5WHo0KEAgHXr1mHOnDlG38nKykJGRgaKiopQUVGBjIwMZGRkyCGebNg0ApM5cqiyWo27+RUW7+Na9FYANmX2NFTPFlXT7+g6CDHncxDSYOm9Hbrwj30EMUBMXTqbkS+oDVjCVrPCtnFPbJuYvPI09qTkoLJabflmCZFlDaNfv34oKirC5s2bkZeXh+joaMybNw8REREAgLy8PNy9axzWtmDBAqN9GjNmzAAA7Ny5Uw4RBSH4JdpxaGXto15ZdRaHL95D2kr+tR+uFNbWLJuYjUgVzHX2+7/YlY7cwgosHBdjuWzx4kiGSq3BL6fuYHiPxg6N57c3ln7zvTWzX2dl7EcnLN8kAFvrnoZJ6ss7w7AkQ007tfO8TrZF72HDhmHYsGGsnyUlJZldW7NmjVyiyI9JDSqrVOH7Q1l4YVAL4QvUFmqIrROQwxcFZuKUsSe+eJN/b8Pnv6YDgCCD4UjW/Z6J5dvSoFJr8X8PmUfR1AUqqtRYfyjT0WIIDvWVa+/PtK/O4MD5XIsDMRNpALAfSsZgqdfQOiiTPOWSspKSchW6JP2GI5dqO2LmJX/881V8tOMKdp+5I7xAJ9vMZmq/rJHOdJZSWS1dLZdyXKXVapEn4uAa5uyEgtI65hYzqAQrd6fjw+1XJCn2r6t5OJWeZ51IAiumkO0X1ix6HzjPH6FlvSz8wjDrIGL2xkgBGQyBmI5Q0rKLUV6lxhc1o2JDSit08epSdpAMckdycFU/IQ3zXGYh2k1PRsr1AilFYpdHwrK+P5SF3rMOICNH3EK6kwSO2Q3DzslwT4atjP/0JJ755KRk5UlNQWkV3vn+PIrLdQMEW1+7hqUxmfYvzDP+tTYV7aYnm+01cdT+QzIYAvl6n3GOGsknBBZdUvbxWdaeh8H+fD4O1yx6si1+ann+cjRMFFDWP8JCE51sMmg3nFFvoSIxnb01bDpyEz8dv411B6RxwbH9jmbLfTUN8JdTOi/FjVz2umnvd0IGQyA7Tkh/mli1unYGcpvlMBVDuE7CkxrGMKhMTsNhG9GYVlbmu9akQif4KSit0p/7LBdarVZwFJEzGg8+xn1secGbq9ZW17QFtd4NZBumg6/yKjU+2Wm8l+SPS/eMohKZJnU3vwJjP/qztiwbZRELGQwZyM7j7/wBXVhozGt7sWqvbaHDJSaugdzCCr2PfW/KXaTfEedmYSrgv7ddNr7O0UOUlKtwtGYdhzEqbJv8DK8wRVWrNaI7QTlmWK6QDLHnWwfQ6+0Dsj5j05Gb6P/uQVzIYt8N7Yw/k1CZrt0ttar8Wd+dk7x+mA6+TNswAPx+nj1EedPRm0aL5fauu2QwBMI1aGa7/OcVywt4TEfJHEQP8L98ro/e+d54816/dw6iz+zfAQCvrk7BsMVHLMrCxs17xkaP7fEKBTBzbSqe/+IUcgsrajcksdQqtu+/sfac1Z2gHO1ErDFSqR0UqiITp68VAACucgwyjE5StIdAQpC5w9x2ItvsKFWb92GYyCzk9D/mmY7OuEAGQyBcUQvWVlfPmrxKYje0mYpxt6BC1CzC0oiE62Mul1R6zY7rymqNPtTP+Exv7mcln3VcimlbYDrOj3/mT0lhT9QaLX4+mc2b+sISXp66l1WtYjeEzjbDuJ5TKqnhsjQolKqrNn1FQly4TP9jOhijNQwnxfCV7kvNwU/Hb3Hey8XFm0XILdT5iD09zCuJNe8+9UYhhi0+gp1/SbXGwi6FlqUj2nLslrGrSb/pz+Aah1LVNo7OhQy0bt4rw7BFR/QuOnfmu99v4M1157BVQH6x8io1vj14yyxNhmfNniGVEKPD8WJv5JbatMAslKOX7iFx4R/YLsPaoin6zrqmzs373wWbyjOfYVh/OJnevcth5KWGDAYPhlNwww5q+qqz+oYpZtQxatkx9HvnIAD2UQVbuB2bLGwIzanD9oiKKjVe+s9p3ightqcfOJ+rn65rtQZrGBYagBZAzGt7BcnLWUbNs77cfQ2/nGLvNNb9non0uyV4dfVZmxIWrtqbgZnfpFj9fXvwT5HOxZlXUoXSChXvOtpnv1zFF8mZnIMMIXWaqzYOWfCHoAVmMSzcdBHfHrhhdI1xm5VUSBfey8W5G8ZrOnts3NFuy6zAtGkx/cKMNSnWFyrm+XZ5Sh1lw+EstJueXHM+tPGoi22EbMvuTVsik45cuoff//4Hy7Ze5nFJWa7lpu6QarVG9qiuFb9cxb/WnmP9jBlBn8ko0J/PYA0f7biCXadr3WfO5poBjOvTsytOYsDcQ5z3FtUsslYZjErLq9QWZxZC9b6SXesiZfYPFJdX49yNAmEFmLDhcBaW/iQsCEMKhi0yXvdj3K4rd19Dx1f5z+ERgqnkQnRRqTXQaLTYcDjL5Lu6f3MK7DOLJoMhEL6dl1yjf+aYz/vFVWYdLmssNks5FVVqQYurbAvNQjHMnslVdTUWRDCs9Iy76bvfM/U7YQ07J7GNXcwubEOEnq7mhP2/aAx/UkvHy2pYzpTukvQbfjrO784yWvQW+KN9sE2Xjv+l/5zB6A/+tNkVySDHxjXm90jnyISs1UpzYp95OLrl70xY8Rf2pOQgt9DYMJguyMsNGQyBSDlQ3nEyG/M2mvtB2epi56TfMOXL0xYr1Yk048gsrsVPtqtM2UqFguc5FkafAL7ZfwMAUFpRm0Ezv0Q3s1q46SLv97nYm3IXvWcdwMmrNfqJeBGiGzdL2e9vMT9W2FKpX/yajofn/C7u2RIhZEbHGGyWZTQA3Dm/rBnU59R0cKmZBboyJHK1O8Msr4pj3WD5tjS8tvos5/cMB0ylFSrkl1oeEKk1WpRVyu9+s4RrnJPpBCgUwNbjt7DWxJdaqdKIDsd8ax27C4Wrkz96+T4erZkmc810zl4vQLd/7dP/rRbRopjH8mlhKW+SEJeVNZysCVG+dKsID7apJ2o6oBY4mtUbIxa++1387t7Pd5mni7GFAgEdihj075ujLm06ekuyBJD6amFl9eDK1SRHDqWlP13GxAHRgu8vq6wdGFVVa+DtpRt/rzHJCmGKYVsZuugPwe4kZ9gOSzMMgSgUCsz+/m8j/yygm/6L6Zz5sLUUwwVAzhkGTx4bhULB6S6avop7xKQrQ6iUwmRiYHbZKhUKrNydjlIRoyxL9uLmvTIs2XJJdM4va4yIIccv30fsjL2sZ3qcMTmz4db9MvR8q3avilarRcr1Ami1WizYdBH/Sb4m+vlCDvBhQ+5R/fJtabiSbRy8Me2rM3aVhWvWYInY1/eiuLza6GCv8io1Zn6TgpwC493zhrKLWXuY/f3fvJ8fT5P/JEoyGBLAhLTZeo65lAt5pqk9TLl2t0Q/EjI8NMlaCeZYqMyGCHnGjpPZaDc9GRuP3AQA/Jaag89+Sdcb7LU17i8+LM16XvrPGayzsfO3hpW701Gl0uAyS2Tb0x+dwCMLDuv/NvVRf3vgBp768E9sP5GNHw5n4dOalBKWUssAurWlbw/cQFWNgRQbKGE4qj8hYHMqYFCnWR7V++0DRtFPa/Zdx+PvHxVXrsT8lmp9BNTGIzf1ngBAl45k1+m7+Phn46y+ciUOfO4z6wM7hEIGQyDnM7kPj2cqgK2VWNDGPYFlmY7U9M+o+Xfsh39i+bY0sxGVtSqctSFD7X+SzdOjmLrtTGdMQho23xrG5VtFKJf5tLJ3N/zNWm+YaCSuDruyWoN7RZVoNz0ZB01SRCzbqltEvp5jnOqCbxOkVqvFj0dvYt2BTCz96bI+LFTIDmNDTqXn6/+fJfYMaZZXkVdSZRb9ZPQVKzIf2MqCjdattQEwS/fOvRbkBAswVkIGQwKYzuxcZiEO/W39MZVC1gEycoTlxPniV35XRXGN+0o3q2A6MMAeMUOmav53j7Gs81kCArw8xVVVjUbLGZHzW0oORiw9ZjYqtya5X0ZOCdpNT8YLX5wy++zHY7cw+Uvz6/pFZwOVpq86g68M3EtMZ/M9xyFFYnz4v6Xm4N0fLuCD7WlG15UKBdQaLVb8YnnH+vWcUrz9nfgz5Esr1UZ7YITIzQxithzj3hwr1yjdWpcUH3nFVfj1dO3ZOC5sL8hg8CH0xTIdU1mlGlP+c9rq50nZCLhCcbVa83+ZkFlbXWrWYqr2//64aXaPl4d5VTXNrHrs8j2s/i0DRy/dQ/zcg9iXyr5gej2X3eha6hAPXTT3EQ9dqHNBGB6kZUh+STX2mcyGmFfDbHDcdOQm9qXmGmUs1RisK7Fh+HrZXFsA8Pmuq+iS9BtW72VfhFUoFHjus7/w5W7L6yB/cyQktMT5zEJMXPGXfi1KCOsPZtY8kzs8uLBMnsy9chiMwxfvIembVP2eFLkCRADdYGsJS2SfVJDBkAApYrMB4Nv9N7A3xXJ+JcO0x1wYph45frm2o4udsdfob8A4dbo9Rj+eJttVhTQgJs+RIaYpKCZ9fgofbr+C5784xbuYyOe7/2TnFc6Zyb++43af8DF91Vkjo8HUFyYlxHssqSZ2/qUbkXKJqjbYGDNiSa3ff9ORWmP7xa/XUF6lxjkedypXhNgT/z6GW/dr3U5vfMse2SeWarVuRsO3Q3v5tjRss5DihAnXlho5DyZi3rscRonhf3/clHVdjsJqOahWa7D/nLDjFy0ZjOJyFQJ9Lf/U/61JdS7ufGB2DBukqb//uc8NFse0tS6SI5fuoXlEgM3PtoRpqGS1SmuxEbHNMKydEZ26xr1g+1VyBmdqaUAX+cIHlzFPvVGAQZ0bAqh1YfIZrvs1mxW57viNIz2FaZZhPvjyj13IKuKcmdjCjpPZ+HL3NYspPWat557tqdQasxQZrgAzo3xHRICIs0EGg4VT6fn4bJfwTKSW8jiNWnYM9YO8bRVLFCnXa0eVd3gOxVm4+aI+xXp+STXySwrkFk2/wc+Qpw0OhWGDbUer2IRrZZUqDFt0BNkWDgnie59dkn4T9UwGwwRzzIyKL+lcRY1h4jKKt/OEHXTEh6VjVjceMXcN2gqTtNNwD4NYRiw5hvrB9m1PUqDRAHtcNEMzAxkMFub97wJnegA2TPO7sHFfRHoLqaMo+A6q/5FnYdGe8PmrAfYotf8aHD6VKiBKa/7GixaNhS3c5Ikc8lAoUF6lxrsbavfyjFhyFNtnPcR6PxN1ZmlGYwtSbwgUApO2hG9B2xLpd0uQ7oL9bt937Lf7/6+reejRpp7k5dIahgSI3fhlifav2J7grC6QZxDVNOZD/hkKwB3mKBV8Rk+pVGDt/hv6M5oZpvOkkJAb002ohPsw/tOTspRLBoMFOdIOEI6H6yQ5qXidJ8X057vSWddpqiQebBCEnJDBYMHa838J++JqG6DY0rX8UwcOdyLcBzIYhMty8mq+5ZucCDnj7wnCHpDBIAg7kclzoiFBuAJkMAjCTuy18WhPgnA0soXV7tq1C1u3bkV+fj6aNWuGyZMno1OnTpz337hxA1999RWuXr2KwMBAJCYmYuzYsQ5LV0EQBEEYI8sM448//sDq1asxZswYrFixAh06dMD8+fORm8u+H6CsrAxz585FaGgoPv74Y0yZMgXbtm3D9u3b5RCPIAiCsAJZDMb27duRkJCAIUOGoGnTppg6dSrCwsKwe/du1vsPHjyIyspKJCUlITo6Gn369MGTTz6J7du3u1wkDEEQhLsiucGorq5Geno64uLijK7HxcXh0iX2LIqXL19Gp06d4OPjY3R/Xl4ecnLI70sQBOEMSL6GUVRUBI1Gg9DQUKProaGhSE1NZf1Ofn4+GjRoYHY/ABQUFCAyMtLos+TkZOzZw78bevbs2QCA4mL+PE8EQRDuiC19n6+vL+t12Ra9xS5Wi7k/MTERiYn8GV2Z9ZKgoCBRchAEQbgDcvR9krukgoODoVQqkZ9vvKmqoKDAbNbBEBYWxno/AM7vEARBEPZFcoPh5eWF1q1bIyUlxeh6SkoKOnTowPqd9u3b48KFC6iqqjK6v169emjYsKHUIhIEQbg9XKdu2oIsUVIjR47E/v37sWfPHty8eROrVq1CXl4ehg4dCgBYt24d5syZo7+/f//+8PHxwaefforMzEwcO3YMW7ZswciRI2kfBlHnmDm8DbxFnmFeF3mqb1NHi+DUSHUSqCGyrGH069cPRUVF2Lx5M/Ly8hAdHY158+YhIiICAJCXl4e7d2sT2gcEBGDRokX46quvkJSUhMDAQIwaNQojR46UQzyiDhHs54mimoOCzn06GA+8bt0BSPYkqr4/zq94RNBRvNYSHe6HzH+En87njAT4eDhaBKdGwXleo/XItug9bNgwDBs2jPWzpKQks2vNmzfHsmXL5BKHcHI+faELa3rw1pGBog6zMsXwVDsfL9foYOwxqW7XOMApDUaXFiFGp0XywXdiISEPNO8lnIJ2TQJZr38+pYtN5RaUVtv0fXfFWfvax7o3Fnyvk6pgka1v97bPg2T4gchg8PDO/7V3tAiSEx5Suzly5ZQ4nF/xiAOlqUUBBQ6/H2923d9b2knws/2b4fmE5pKWacpj3RuZXVs6PkbWZ4olr8Q5DakYQ6Z00fXNlpHsgyOpkePnIYPBQ6Mw9s0rfLSKDLD5uUO6mEeGrXmlu83lAsDIB2tHcIM6N4S3pxKBvs5xtHvDUPPfW+ozJN4d0xFvPyHvQIBN5lG9mlj8Xs+29dC3QwP07xQuh1hGdG0RIvsz5EZMh/iNRO1HLC8ltjS7Zq/ZnRyPIYPBgzUjGH8f2ztf08iwoV0j0bdDA4673Zcf3+rtkocOsYksJNqvaQN/rHmlOwJkNODNI/yx5a3eeGFglGzPkJIhcdxh9WIiKPs4qP1MHmxuMORYjGZDjghTMhgSI3b08PnkLmbXTDtJKUckbHXIGWb2bDI8EB3C2vmKZctbvfHxpM5WfTfYT3znbcnIzZHZ1TlxQDTv57HRIS4Trs43aHPWdRiGQZ0j4ONl3sUqZOp1pzzSQp6CDSCDwUO7JuK31ouN3HikS6TZtdaNjH2cUjbuDlHBZteiw/0lK19qour72fT9B6JDEBsdgmEs6wpC6Nm2PudnhxbH6/+f8EBE7QcWjByb600qRjzYGDOHt+X8fEBMrZwvDJKug/lhZk+by/DzNo9i42tOzmzzLn4+BCundIUHi5BKhQKvP95G8me2bWzcX5FLys40bSC+I7XGjbV84gNGfw8y7HwkIDZa569e8UIXPNrNvOP89IUuZtfiWoZKKoNYUj4ZDEBnLG0xaEobazjfbCHSYI3ry6ld9f9XG3yHbU2Lq0hr1sxMWfh0J/iydLwAcGTJALw5qp3+74facxtDhpeGmLtU2AiyYiZmymPdG5n9NnyDJSlcOx9NeoDzM2s3BrZrEgSPGkvHNoBUoLZNSolp30OL3k7KTwZhcmqN+XZ8Sx3BgBj+RU5bo0GmPtISu97ti8Su5rMZAAj29zK71jBEvlEwG6YaGo42rVG/W6swAECnpsIbpgdL49ZYsVvWsAp8N+NB/PhmL6PPu7fWyfbZi12MrovZiLb7vb7YPush/d+vPNoKq6Z14zQWgC5Cjk1HU5jZ0r8nxGLcw8301yMMIuxMkSJiaTBLsIdhuTMea4P3xtSmF3qguXMu3Fs6w0ehkMedZvoKaA3DSWlmMBNRqc0ry0ED1wUbwf5eSFvJnX2Xee/W+NOZ75u6uaRC6AjUcKT97wmxsshiSIuGAfjxzV6Y9aTw9QKtVotOzYxddu1ZXHgWyzHwSYUEeOGB5qFGnzcI9kHaykQMiYvEzjl99NcD/cwNNxctGwaiQ9Na2UIDvNHfwsDDTM6ajq2PyUzDmn6G7ztengqEBfLrlrYyEf07hZuVYzhD7Ng0CM/0r12febhTOP5YEo9Ty/rAWto3Ef9+rYVZW1IoFC6zhmQKGQwJCPb3gpeHrgIIGZDyTYMBcxc4U7dCAoR3KGIQWnU3vdHL7FoSj7/ckCXja43EyJ6WQ0wNYRu97pj9EMudxjzQPFRUTqZ2TYKw9tUe+r9/ers3Xnm0leDvM4hZqG/bOAjnVzyCuaM74AkLobdsg4oAX4+aZ7I/lG1viyW4XD3W9nGPdm2Ex3sI25Bnqobhu49kWfuJsGImPKhzrctXjpByFicDAGD2k+3171AOe6FQKLB4XCfpCzaADIZExLXUuRlCBXTqYkNvTetWZ5FTcalGM6adEpsBYRhr4v8ND+Z2ZwC1Mi5+JsaoQes+M7/fy8AQ7J3Xz+xzDytUXvtqD4QYuOdimoXA00N8ExEb2eXtqcT4+Ggzd1H9IG8AwPMJzTmNyRM1xtf0kVve6o1f5vSxaoFdy7Fqz+d24qtiCoV1rj3Tcq2Z7bHBFxQgBabrXqEBXpiW2MqoHcqx6VABYHQfeRMyksGwwPvPCNuhO6ybbn1gwdhOWPZs7Wj6W4MRK4PQDqVRmC8iQ33xfE00C/O9aYniR718CK27pmILMY5xLUOxZ04PNBEY7TT6oSisnNLV6BrbiJdpcOHBPoiOMF9YnjlCfKcQFujNen3TG71EGWnDiClbugXmu88ntMDSZ9ndeIoaI2Nap2KjQ9CmMX+UH/MdhcLY+HPVTz5dvCwYVqH2wswlJUPH2rR+rQuZq/jGIgIQDNdVAPN2cmJ5gllUlD3WMOSADIYF/u+hKMS1CLV431N9myLlk8FoFu5vtKu3V7t6Vj+7XqA3Dr0fbx4uZ1Ax0lYm4t8TYgWvJQDAj2/2wv6FD4uWR8zImTEm7ZsE6UfK1sLWEJjRuMpk/r9j9kM4sXwgQgP4n8nm3uCiS4tQo5mHJcb0sd+mOOansbTQaokuBnWcKUqrNZ7JKRTAi4PZQ3ENZ0ddTHaRKxUKwTMMUzVMZ5tS4M2yN8IUoZ3vyJ6NjdZVAKHvQnjv3r0mgMNiiWQwXAeFQsEaR85MQw1H45YqlNi2P7JnE0TwdICmFemB5qGIMhxlWTkO5pOzZWQgNiT1xDtP1o6+NiT1xI532Bco+X4Tvs2GapMgg/ZRwRaNBQAcEunb59pf8+8JsfpZ5JdTu+Knt3sbuR6kcAcauoj+O60rpj5SOzhgipdyQ7yXZ63M9QK98e7oDvqHvTmyHet3PA18gC0bmgdYWGvQ4mOkNRidmgp0aymEtYoXrdzLIqZabBC4x8UeO8jJYAiAy6crhtbWJBwT8/5t6TGEuqRMHsH3u2i1WnRvHWY0muveOgztOTZDipU+wNcTXVqEmO1hsRZLuYa4GvjInk3QuybKKOGBCMQ043Zdje/fTNQiK5uxiY+JMHK3MZ2ElAlUTAc+8QKirwzXekabzLAUilqX1PyxHfXXjy8baIOU4jn/6SPY/Cb3upshbK/by0OBto0tt+PHBWTcFeqSYjaH9mxr2VNBMwwnxt9CzPzBxf1x/N/sDcJS3871Md/XpBph8m3YM911XT+IeyFbyhEvlx970xu9MSC2dgRqaWHdFEP3iqVcQ9b60g2/NXdMR5z+aJDoMvh+SzlmGEwUG7NfRL/WwfMdT6UCLw1piVXTurG4UBX6hWBD1xWb8ZSz0/P2UgoOYpjM4nrTaoFF44zXNE2NeuongzFdQGSd0JlnpIi1FHuE6jpHmlInZ3DnSNZDXX6d2xcVVWrW7zQKsy2lBR9sU0/D/mLy4Bb44+I9XL5dXHO/pfJ0+Ht7YEJ8NM5mFJjd0yjM16jy/v3ZI7wLnbb61I3kM1HAx0tptrN41cvdOGcvXLw5sh2+/u26VTLYBQHPNFy4FkvtGzL+cu929Y1CeJl3yfcMTw+FPsS6tEJl9JkCtceFGhlelvKcIdcko/v8jRfMPrP0M/NtnDSEFr3dmBcGNTe75uvlgVaRgejE44LgQgutzTmS+LAmBxbAP4MxXfRlMxaGexYstfvjywZi5ZQ4fdoP/lG0+SjO9Pn9O4XLmqPJEWcviHminNIxr4bPR244cjdd7zF0SRnOMJxh75oYGSS1ZTKF1coNGQwBsE31uKJFhLLlrd5Gu3xZnyuiPMMRvdhRmpC6y5Z1k2HeUx3RomEABneuTe1gSYZ6Qd4Y1LmhoGeb3uKIXbLWPtIWUZlACb4ypFhfEwrvDMPQEJh+D7V7E6R4dd/N6IE9LHtvxOLlqTAzgpbkU0g0NTAt5T8vdWW9T1SZdmgW5JISSLCfJ4rKdVNtvjQeQgkL9OaM+7dmXq41+b/RzN+KimT6Hb4d0+MeboZxDzfD5VtFrPIIgbfjs7N9UCjMX4G9zjAwZPXL3bH/XA7vbmZbXFIMlr7L9Yx9Cx7GoHmHARjPKsxmYwZhtR48hsUUrhxsfBmEhbL7vb4IZknFYmkmyfXpkvExojYnMj9DRIgPnuwdhXgJDs1ii9KUGpphCOSXd/s6WgReQ2L60XMDm1tRvHEhT/drigGxuorcnGVzHK8MIo0e3+32dgf9vqg/NpvsYrdiwzcA22ZDkWG+ZjH+pujdRRaeIyTKhguujL0BXBkLTO0FuFxS3DI/0auJJLMILlo2DESDYB/zhH2G/6/5cHZNPjKtVsuS4E/375O9o0TtsmbKrhfojdcfbwOlUoFJA8Tv3/nz3wNx+sNBWPh0JzzYRveOTffBSAnNMARiq39cVPdpofHXq9kIN75/M9bPtVotRvZsgl2n7uDwxXtinmzE/LG6vDTJZ+4KCq00RPjOXssdqr3H9o3C/MyCFmY/2QGeCg1u5Vfj9LV8O0vEjRC7fGzZAPZwXtGuS57e1QBTr41SqTBwSQl7m77eHvDxkn/EzDdzHNYtEj8czkLvdroZjRbGKg+IDbc69T5TjuErmD4kGmt/vwVAF634d5Z5oI0pjJfCMBX7N6/0QF5JlVVyWYIMBgufPN/ZaCOS3bHQC4QFeuHkBwkIMugEDOP/TV1dYtwpTGhqi4a1MwqutOh8yBkl5QjCQ3ww9//a4GxWBSZ/edrR4ugZEBOO9Qcz9SGwbPCFPwuBSXX/nIWT/BhMjYICtQn5DI2JE7xWM4z3DdVD2spEfYQXYKzbVy91s/o5tSld2NvJxn8J2y/CRoCvp2zH/JLBYIHtkCFHYNrwDOuWadRS99ZhOLZsAM5mFODhjuLOLzZ8To829fDtaz3Qg6cDEoJQc6EfaQnYa+AMxMhw8I0t9OnQAJc+HyL6pEcxBPp58q7bmabdZxOFbR8G817Z8jaZFvFsvDBjJRbTusWWUNOwjkr1KzM/gzOEEYuBDIYDsLhxz8LnXDOG+kE+GGQYqSRWsBqYKbgtCG0IwqKknMdiiEmXbi9sNRbWfpvrewqFAtMSW+Fufjm2nciGUlH7uxnO3BUKBT57sQs6m5wXYooUQSZcmNY/002HhveEh5iveVj93Jp/yWAQFhFy6hkbMx5vg7e/Oy8+mZ/Ax9lad00jtUR9l3eKoftn7ugOiGog3/4VIfCFF9dV2NYlXn+8Ddb9fgPbTmQDCgXee6ojmjXwR7+OxmthQ+LEuzvtjUKhwPKJD6Bbq1CUVLBv1LWmTMC+YdFSQLXfznRtGWqUzkIMI3s2QdrKRMG7SR2KwKGTkFxITJRU+6ggyZPRicVTRtePvWHrrKTcUGoYjlsv0BszR7TVD5aEZmC1BSF5xoRG4I14sDGi6vtLNtdlHmvlMSEOg2YYdmbm8LaCZxiu3DVJ65ISV6acuOrRmnwwOh1dOkBULL/l/Rs1kVEm13+d21dUenlrGfFgY7y17hzvPWJfJ+NSE5z11sJzpQwOsQeSG4zq6mp88803OHToEKqqqtC5c2dMmzYNDRpwL8RmZmbihx9+wLVr15CTk4Onn34a48aNk1o0l0GqKiS0LjL2K1jEmQ8Wny1ZSQaNS8IyCaBjTaf3dD9dSGYDkckbha7FmXbKrazJ3OwktGwYgOlDW+HJ3radeaKfWQsdWLFc+/71B22SwRokd0mtXr0ax44dw5tvvolly5ahrKwMCxcuhFrN7furrKxEREQExo8fj4YNG3Le58ow2W09RITrSr3AxoWPlwfee6ojfkgSlndfCGJHTkKipLg2kNmCh1JYymp3JCLEF2krE61ykT4gIFpMSA4qRyN2xqhQKPDaY20EnyDJhdgoKbbberSxfjOmtUg6wygtLcVvv/2GGTNmIC4uDgAwc+ZMvPDCC0hNTUXXruz5Utq2bYu2bXWZLn/88UcpRXIalj0bi81Hbwk6vc8RPPMw+yZAMRgaCWuP5GTj5aGtce7GaXSU6ExnQ/5e8Yhow7xqWjeHL747kn0LHka9QG9UqTS890mRtkRuAiwcUyAXjKGSYxAkJ5IajPT0dKhUKr2xAIDw8HBERUXh0qVLnAajLtAg2AcvD5X2LG7LuE5l5JO0d7v6SP30EVmea01Ian+Ru97djaYNdLubq2p2E3MZBGsigOxdYx21JmWtm/XbV3vAz8cDKjW/sZYLSQ1Gfn4+lEolgoONR4JhYWHIz5cunUJycjL27NnDe8/s2bMBAMXFxZI9l0GOMg0pLS0FAKjVapuepVLp3IAVFeWSy8xWXllZmf7/5RUV+nu0Wi3n8zU1W4BLS0oh889qM3x6cCF3XbEWa3QxpaS0GgCg0bCXVVFRCUC3rin0WRUVFQCAquoqwd/hcn8afl9oWfZ6X2WlOj01ao1RO+GSQ13TlsvLyxDTJBSAh6yy+vqyByUIMhjr16/H5s2bee9ZsmQJ52e6pF3SWfLExEQkJvJv5snNzQUABAVZdzYEH3KUCehO6SsoqUZFta5yeHp62PSs5we1wp9XT6Nrm4YIsjE9hClscvn71456fLx99PcUFxdz6uHhoXMJBAT4y/a7SgWfHlw4q07W6GKKSsHMMBSsZfn46Oqcr4+34Gf51XRU3l7Cv8PVcRp+X2hZ9npfgVU1Xa/Bb2eoh6kcHp66duLn59h2IshgDB8+HPHx8bz3hIeHQ6PRQKPRoKioCCEhtYtiBQUF6NSpk02C1gWYpHdnM6SZjfWPCZd1lywfYuPLXcyVSxjA6ZKy4gyMulINlDXhRm65hhESEmJkALho3bo1PD09cfbsWb2BuXfvHm7duoUOHTrYJCjhWgx8QJifny1rJ+EeOENfqFQ45+Y4sWG1zoKkaxgBAQEYPHgw1q5di9DQUAQFBWHNmjVo3rw5OnfurL9vzpw5aNu2LSZOnAhA5+O8efOm/v/5+fnIyMiAr68vGjduLKWILoGL1SEzOjYNRsuGwkJVnTmCxhaWjo/B94eyHC2GQ2HqsZjzTKSuDic/SIBKLaxF7Zj9kMRP50bsorezNBPJN+69+OKL8PDwwPLly1FZWYnOnTsjKSlJ76sGgLt37xpt5MvLy8OMGTP0f9+5cwfJycmIiYnB0qVLpRbRZXDm+HWpcbUdr5Z4oncUnrBxc5ezY+mN6d0tDnRJBbGcqsdFexnCtrnQGwyB0x9naR2SGwxvb29MnToVU6dO5bxnzZo1Rn83bNgQO3fulFoUl8VV+05rxK5Nwka4G7X2QvzAx11nngxKF633lHyQkBwxbZ3ZOOXh7j1EXcSGjXuuOmgSitjsBc7SOij5IOFQPn6+M346fhvto5wz/JSwHmbjnhsl+JUMsYvezmI/yWA4M3WgoUWE+GJaor13wBP2gFxS3HDptyHpQRxPu29fYURABsMJcffpOFE3oGrMDZOSxsvDeFWge+t66N7aPKmgs9hPMhhOjLNUEqGQoSMM0c8wrFgpdfe6FOLvhRmPtcGQOGHZuZlTNn28HbvsTAaDIAjrsHQeBpgDlFxt6GMfxCQjXfB0J/RsW9/h2a7JYBAEIQ82REm5+xqGWIL8vPBU36aOFoPCap0TN5+PE3UCJmTUmr7f3V1SrgoZDCfGHc+PJtwPrmoaH6M7ya9PB+7jmQnXggwGIRn6QSHZuToF12ygW6swpK1MRKyA41xNsXas1KWF+GcRwiGDQRCE20AL7PJCBsMJIf8t4UpI6Tnt30mXFn9kzyZWff/98THSCUOYQVFSTgyNlQhnxsdLN97sK+EaRbNwf5sO/WoVKSytPmEdZDCcEJpgEK5AgK8n9i14GA1D2M9/JtwPMhiEaB7tFsn7Oc2M6g5NG/g7WgTCjpDBIERxfsUj8ORIP9qsgR8AYHz/aHuKRBCEnSCD4cQ44zYMb0/uOInQAG+b/M8EQTg3FCXlhLjbcaUEQbgHNMMQweqXu+FOfoXdnueMMwyCIOouZDBE8HBNjDhBEERdhFxSBEEQhCDIYDghtIJBEIQzQgbDqaFFDIIgnAdawyBcmrKyMhQWFkKtVsv+LI1Gg8LCQtmfYw+s0cXDwwMhISHw96fNenUVMhiEy1JWVoaCggLUr18f3t7esp8folar4eHhIesz7IVYXbRaLaqqqnD//n0AIKNRRyGXlBNC2zCEUVhYiPr168PHx4cOm5IZhUIBHx8f1K9f321mWYR4yGA4MdQH8qNWq+Ht7e1oMeoU3t7ednH/Ec4JGQxnhGYYgqGZhX2h37tuI/kaRnV1Nb755hscOnQIVVVV6Ny5M6ZNm4YGDbhz5u/ZswcHDhxAVlYWtFotWrZsiWeeeQadOnWSWjyCINyQPfP6oaRC5Wgx3B7JZxirV6/GsWPH8Oabb2LZsmUoKyvDwoULeaex58+fR79+/bB48WJ8+OGHaNKkCebNm4fs7GypxSMIwg1pHhGAmGZ0nrfcSGowSktL8dtvv2HSpEmIi4tD69atMXPmTNy4cQOpqamc33vjjTfw2GOPoVWrVoiKisLLL78MPz8/nD59WkrxXA6a/BMMX3zxBZYuXepoMYg6jqQGIz09HSqVCnFxcfpr4eHhiIqKwqVLlwSXo1KpUF1djcDAunncopYWMeoU1dXVdn2eSkWuG8I6JF3DyM/Ph1KpRHBwsNH1sLAw5OfnCy5n/fr18PX1Rc+ePVk/T05Oxp49e3jLmD17NgCguLhY8HOdhbKycgCARqN2SflN0Wq1suih0WjsHrEjxfMWLFiAJk2awMfHB4cPH0Z4eDhefvllbNiwAZcuXYK3tzdiYmIwYcIEhIaG4scff8ShQ4cAAKNHjwYAzJ07F+Hh4Xjttdfw/vvvo1WrVvryx44di9dffx29evVCbm4uXnvtNbz66qs4cOAArly5gmeeeQYZGRkoLi5GbGwsdu7ciaqqKnTv3h3PP/88fHx8eOXXaDROVS+56pdYGR2tk1ztxBp8fdmP3RVkMNavX4/Nmzfz3rNkyRLOz7RareDoip9//hnJyclYvHgx5+agxMREJCbyH9STm5sLAAgKChL0XGfC378SAODh4emS8ptSXFwsix6FhYV23Ugn1cY9hUKBI0eOYNCgQVi0aBFKSkqwYMECDBw4EBMmTIBarcb//vc/fPDBB1iyZAlGjBiB7OxslJSU4NVXXwUABAYG6gdhHh4eZnIplUqj6xs3bsSzzz6LadOmwdPTExkZGbh8+TLq1auH9957D/fv38fHH3+MJk2aYNSoUbzyK5VKp6qXXPVLrIyO1kmudiIlggzG8OHDER8fz3tPeHg4NBoNNBoNioqKEBJSuwBVUFAgKOLp559/xvfff4958+ahbdu2QkQjCCPe33IJl28VyVK2Vsu+N6Z9VDDm/F8HUWVFRERg4sSJAHSdeXR0NMaPH6///JVXXsGkSZNw7do1tGnTBt7e3vDy8kJYWJhVsg8dOhS9e/c2uubv74/JkyfDw8MDUVFR6N27N86fP2/RYLgCvdvVd7QIbokggxESEmJkALho3bo1PD09cfbsWb2BuXfvHm7duoUOHfgb1Pbt27FhwwbMmzevzofT0k5v96dly5b6/2dkZODSpUtGBoMhJycHbdq0kfR5DFFRUUYzk7CwMFy9etXmZzma8ysegQfHufOEbUi6hhEQEIDBgwdj7dq1CA0NRVBQENasWYPmzZujc+fO+vvmzJmDtm3b6kdYW7duxfr16zFz5kw0adJEP9X29vZGQECAlCK6FLRHSjxiR/pikDKXlOE6gVarRdeuXfHss8+a3RcaGspZBuPmNTzSl2tBm80nbaqLQqFwi+OB+c6dJ2xD8o17L774Ijw8PLB8+XJUVlaic+fOSEpKMqqcd+/eNdrIt2vXLqhUKixfvtyorIEDByIpKUlqEQnCqWjRogWOHz+O8PBweHqyN0lPT09oNBqja0xwiWFAyY0bN2STkyAkNxje3t6YOnUqpk6dynnPmjVreP+u6zzYth4e7xaB10e0d7QohB1ITEzE/v378cknn2DkyJEIDg5GTk4Ojh07hokTJ8LPzw8RERFISUnB7du3ERQUBH9/f/j4+KBNmzbYsWMHIiMjUVZWhg0bNjhaHcKNobmbE+LlocS80W0QVZ9SSNcF6tWrh8WLF0OhUOD9999HUlISvv76a3h5eelnHAkJCWjSpAlmzZqFF154AWlpaQCAl19+GQAwa9Ys/Pe//8XTTz/tMD0I90dRXl7u+k5LFpiwWtM9Ia6CK4TYCUUuXW7duoWoqCjJy+WiLp+HYYi9f3dL2Fq/2k1PBgCkreQP1ZcbZ2rzXPswaIZBEARBCIIMBkEQBCEIMhgEQRCEIMhgEARBEIIgg0EQBEEIggwG4dK4w85kV8Idf2/aGS4cyTfuEYS98PHxwf379xEaGgoPDw86b1pGtFot1Go1CgoKLKY/dzWOLh2AarXG8o0EGQzCdWnQoAGKioqQm5trljZDDjQaDZRK9xiNWqOLUqlEQECAy+5t4iLY38vRIrgMZDAIl0WhUAjOpCwFzrSxylbcSRfCfrjHcIkgCIKQHTIYBEEQhCDIYBAEQRCCIINBEARBCMLts9USBEEQ4omIiDC7RjMMgiAIQhBuO8NwdZKSkvDJJ584WgxJcBdd3EUPgHRxRlxBD5phEARBEIIgg0EQBEEIggwGQRAEIQgyGARBEIQgyGAQBEEQgiCDQRAEQQiCDAZBEAQhCDIYBEEQhCDIYDgpQ4YMcbQIkuEuuriLHgDp4oy4gh6005sgCIIQBM0wCIIgCEGQwSAIgiAEQQaDIAiCEAQZDIIgCEIQZDAcgFZLcQYEYQl3aifuogsZDDtTUlIClUql/9tVK1J2djbOnDnjsvIbcuvWLXz00UdISUlxtCg2k5ubi8uXL7v8iZOFhYUoLy/X/+3K9cxd2jwAeDpagLqCSqXC6tWrce3aNXh7e6NVq1Z47rnn4OHhAa1WC4VC4WgRBXP9+nXMmDEDgYGB+PjjjxEZGelokaxCo9Hgm2++we7du9GjRw+jDsrVUKlU+Oqrr3Ds2DGEh4cjJycH//rXv9CtWzcola4zLlSr1Vi5ciVSU1MRHByMJk2aYMqUKQgODna0aKJxpzbP4Do1yYU5e/Yspk+fjqysLIwePRrNmjXD8ePHsXbtWgCuN+Korq5GXFwcvLy88NNPP7mc/AwpKSm4evUq5s+fj1mzZqF3796OFskqysvLsWzZMmRnZ2Px4sWYM2cOYmJisHHjRpczFp9++imysrLw+uuvo1+/fsjIyMD777+PmzdvOlo8Ubhbm2egGYbMlJWV4ciRI4iLi8MLL7wALy8vdO/eHeHh4Th+/DjKysrg7+/vaDFFkZ6ejuDgYMycORPvvfceBg4ciPbt27vciCk5ORnR0dGIjY3F+fPncebMGURERKBz585o3Lixo8UTzM2bN5GVlYWXX34ZLVu2BAD07dsX+/fvh1qthoeHh4MlFEZeXh7S0tIwduxYxMbGIjY2Fv3798frr7+OX3/9FU899RRCQ0MdLaZF3LHNM7jO8MNF0Wq16NixIwYPHgwvLy9otVp4eHigqqoKAODv7w+NRuNgKcWhVCr1HWu7du2wceNGKBQKlJWVOVo0wZSXl6O4uBhdunTBpk2bsHz5cmRnZ+Onn37C3LlzceDAAUeLKBiVSoW7d+/C01M3/issLMS2bdtQv3597N27FxUVFQ6WUBjFxcW4f/8+2rRpA0A3k61fvz7GjBmD06dP48KFCw6WUBgajcbt2jwDGQyJOXHihNHfAQEBSEhIQKtWrQBAX1GKi4vRoEEDAHBKt4GpHoZkZGToK//MmTORkpKCefPmYc6cOU7pOmDTxc/PD9XV1di3bx+ys7Pxzjvv4K233sLXX3+NVq1a4eDBg0hPT3eAtPyw6dKxY0fExsbis88+w7x58zBhwgSEhobC398f33//PT7++GNcv37dAdJy8+uvv2LXrl04ffq0/lrjxo0RGhqKQ4cOAYB+xvr444/Dz88Pf/31F6qrqx0iLx+mugQGBrpkmxeCa0rthJw6dQqTJk3C+++/j1OnTnHex1SUq1evIjY2FoBz+TP59GDkvH//Pnr16gUAOHfuHDw9PXHu3Dk88cQTaNq0qd1l5oJLF6YBDxkyBKdOnUJaWhoaN26sd908+eSTuH37NoqKihwiNxuWdJk7dy7mz5+P8vJyPPfcc1iwYAGmTJmCRYsWISMjw2kM+eHDhzF+/HgcPHgQBw4cwAcffID169cD0OnSt29fHDp0CPn5+fD09ERlZSUAndH4888/naqt8OmiVqv19zl7mxcDGQwJuHDhAnbu3ImHHnoIvXr1wrp164zC6AxRKBT4559/cOfOHcTExOivOUODtqQH0zn5+fnh999/x8yZM/Hdd99hzJgx8PX1RU5OjqNEN4NPF6YBt2/fHjExMVAqlUYugtatW6O4uBgFBQWOEN0MIbr4+vqivLwcRUVFiI+P13dI0dHRKC0txb179xwmP8OhQ4ewZcsWPPPMM1i+fDnee+89vPjii/jxxx9RXFwMf39/xMXFITAwED/88AMAwNvbGwAQHh4OLy8v3L5925Eq6OHTpaSkxGjdyJnbvFjIYNgA0yjDwsIQFxeHxx9/HBMmTEBubi5++eUXzu+dOXMGYWFhiI6OxrVr1/DGG2/g7bffdtiIVqgeHh4eqKiowP3793HkyBG0bdsWK1aswFNPPYUnn3wS3333HbKzsx2iA4MQXZh7GjdujOHDh+Pu3bvYvXs37t+/D0Dn9mnWrBkeeOABxyhRg9D3wtzn4+OD7Oxs3Lt3T+/OOXnyJCIiIhyqCyOfSqVC27ZtMWDAAABASEgIWrZsiYYNGyIjIwOAzr0WHx+PAwcO4OjRo3oX1OXLl9G0aVM0b97cITowCNGFzf3nbG3eWihKygrS09PRqFEjBAQEANB1PI8//rh+VDFixAhs2rQJ/fv3R1hYmP57TOx1VlYWGjVqhK+//ho7d+5EQkICli5dCi8vL6fWQ61Ww9fXF88++yz8/f2NGu+IESOgVCodtidDrC7MQmSvXr0wadIkbNmyBYcPH0azZs2QkpKCUaNG6f3Nzq4LYxxCQkLQr18/zJ49GwMHDkRlZSWOHz+OESNG6P3p9tYjMjISgYGBAIBevXohPj7eaPTt6emJ8vJyREdHA9DNKAYPHoz8/HysWLECLVu2RGhoKP766y88//zzUCgUDtnDIEYXQ7ess7V5W6HzMERw9OhRfP311/Dy8oJKpUJCQgIeffRRfQcE6KaaBQUFeOutt9C5c2dMnz7dqAyNRoPJkyfjn3/+QUxMDF566SU0a9bMJfRwxs1GUrwTAEhLS0NGRgb++ecfJCQkoEmTJvZWRRJdKisr8cMPP6CwsBBarRZjxoyxuy6megwcOBDDhg3TD540Go3elbZr1y4cOHAAH330Eaqrq4060KNHjyIzMxMFBQUYPnw4oqKi7KqHLbqoVCp91JoztHmpoBmGQK5evYrvv/8eo0aNQmxsLC5cuKBvmBMmTEBgYKA+5j00NBRjx47FihUrkJiYiFatWkGlUkGlUkGpVOqvde3a1WX18PX1dbgBkUKXqqoq+Pv7o127dmjXrp1b6DJp0iSH7b/g0qOoqEivBwC9fH///bd+dsEYC6az7dOnD/r06WN3HaTQxdBYVFZWOrTNSwmtYViAGdmlp6ejvLwcgwYNQosWLfDYY49h7NixSE9Px65duwDAqIH26dMHMTExWLduHS5cuICFCxfi+PHj8Pb2xujRo+1ecaTWw5HGQkpdTpw44dCIFbl0sbexEKOHUqnU151r166hR48eAIDbt29j2bJlSEtLs6vspkipy6VLl+Dn5+eQNi8HZDAswFSGnJwcNGrUyOizRx55BK1atcKpU6eQlZUFoDaSyMfHB4mJiUhJScHs2bPh5eXl0NQTUuvhyJkF6eJ8uojVQ6lU4urVq/Dx8UHbtm2xevVqvPLKKygvL0fr1q3tLr8h7qSL1JBLyoQzZ87gxIkTiIiIQLt27fRhcB06dMDPP/+M/Px8/U5NX19f9O7dG9evX8eZM2fQrFkzKJVKVFdXY+/evVi9ejU6deqEl156ST9VJT1IF3fQxVY9AN3ekszMTEybNg0NGjTABx984JAO1p10kRuaYdSQl5eHRYsW4ZNPPkFFRQUOHz6MBQsW6FN4d+3aFQ0bNsTWrVuNvte1a1colUqjcNKSkhJkZ2fjtddew9KlS+3amN1FD9LFOXWRUg8PDw8EBwcjKSkJX375pd07WHfSxV7QDANARUUF1q1bBx8fH3zwwQf60NBZs2Zh79696Nq1Kzw8PDB69GisWLECAwYM0I9CAN2mIsMNRWFhYZg8eTLpYQOki/PpIrUeQ4YMwZgxY+yuB+BeutgTmmFAt0vWy8sLAwcORGRkpH6z0IMPPohbt27pQ+f69euHnj174osvvkBqaiq0Wi3y8/Nx584d9O/f38FauI8eAOnijLpIrUdISIijVHErXewJ7cOowTBumokA+uyzz6BSqTBz5kz9taqqKsyfPx+ZmZlo2bIlsrKyEB4ejrfffhvh4eEO1sJ99ABIF2fUxV30ANxLF3tBLqkamIoDGEdJ9O3bV39drVbD29sbb775Jm7cuIH09HQkJCQgPj7e3uJy4i56AKSLM+riLnoA7qWLvSCDwUFOTg6ysrLQokULALoKxWzQCQsL0+f3cXbcRQ+AdHFG3EUPwL10kQtawzCB2bRz8eJFeHt7o3379gCAjRs34oMPPnB4cj2huIseAOnijLiLHoB76SI3NMMwgZmaXrlyBQ899BDOnDmDlStXorq6Gq+//rrLHN3pLnoApIsz4i56AO6li9zQojcLVVVVeOWVV/THXo4bNw7/93//52ixROMuegCkizPiLnoA7qWLnNAMgwVvb280bNhQf4g7c4iLq+EuegCkizPiLnoA7qWLnNAMgwNHZfuUGnfRAyBdnBF30QNwL13kggwGQRAEIQiKkiIIgiAEQQaDIAiCEAQZDIIgCEIQZDAIgiAIQZDBIAiCIARB+zAIQgT79u3DihUr9H97eXkhKCgI0dHR6N69OwYNGgR/f3/R5WZmZuLo0aNISEhAw4YNpRSZICSDDAZBWMG4ceMQGRkJtVqN/Px8nD9/Hl9//TV27NiBd999V5/ATiiZmZn43//+h9jYWDIYhNNCBoMgrCAuLk6fpA4ARo8ejdTUVCxcuBCLFy/Gl19+CR8fHwdKSBDSQwaDICSic+fOGDt2LL777jscPHgQQ4YMwfXr17F9+3ZcvHgR9+/fh5+fH7p06YLnnntOf/iOoZvrnXfe0Zc3Y8YMDBo0CABw9epV/PDDD7h06RKqq6sRHR2NsWPH4sEHH7S/okSdhRa9CUJCBgwYAAA4e/YsACAlJQW3b99GfHw8pk6disGDB+PUqVOYM2cOKisrAQAxMTEYNmwYAN1MZebMmZg5c6b+DOnz58/j7bffRkFBAcaMGYPnnnsOnp6eWLx4MY4dO+YALYm6Cs0wCEJCGjRogICAANy5cwcAMHToUIwaNcronh49emDWrFk4fvw44uPjERkZiY4dO2LXrl2Ii4tDbGys/l6tVouVK1eiQ4cOWLRoEZRK3Rhv2LBheOutt7B27Vo89NBD9lOQqNPQDIMgJMbX1xfl5eX6/zOUl5ejqKgIUVFRCAgIQHp6usWyrl+/rp+hFBcXo7CwEIWFhSguLka3bt1w9+5d5ObmyqYLQRhCMwyCkJiKigqEhIQAAEpKSvDtt9/i2LFjKC4uNrqvtLTUYlm3b98GAHz22Wec9xQUFCAiIsIGiQlCGGQwCEJC7t27h9LSUjRq1AgAsHz5cly4cAGjRo1Cy5Yt4efnB4VCgeXLl0Oj0Vgsjzk+dOLEiWjdujXrPVFRUdIpQBA8kMEgCAn5/fffAejCbktKSnD27FmMGzcOTz/9tP6eqqoqs9kFc0yoKZGRkQCgj64iCEdCaxgEIRGpqanYuHEjGjZsiAEDBugXqJlZAsOOHTvMZhfMno2SkhKj661bt0bjxo2xbds2VhdWYWGhlCoQBC80wyAIKzh79izu3LkDtVqNgoICnDt3DikpKQgPD8fcuXPh7e0Nb29vxMbG4qeffoJKpUJ4eDguXryIv//+G0FBQUbltWrVCkqlEj/++CNKS0vh7e2Ntm3bIjIyEq+99hrmzZuHl19+GYMHD0Z4eDjy8/Nx+fJl5Obm4ssvv3TQr0DUNejEPYIQgWkuKU9PT30uqR49epjlksrLy8OqVatw7tw5qFQqdOrUCZMnT8bcuXMRExODpKQk/b179+7Fli1bkJOTA41GY7RxLzMzExs3bsT58+dRWlqK0NBQtGjRAgkJCejTp4/9fgCiTkMGgyAIghAErWEQBEEQgiCDQRAEQQiCDAZBEAQhCDIYBEEQhCDIYBAEQRCCIINBEARBCIIMBkEQBCEIMhgEQRCEIMhgEARBEIIgg0EQBEEI4v8Bg8rjZMnNOQsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df7[[\"return\"]].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Adj Close', ylabel='return'>"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df7.plot(x ='Adj Close', y='return', kind = 'scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.2 Using the library seaborn to plot the distribution of TESLA log returns. [10 points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fcf98b57dc0>"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df7[\"return\"]\n",
    "sns.displot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}