{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6204641010 Phatcharida Amornborirak"
   ]
  },
  {
   "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": 1,
   "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",
    "\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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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=\"temp\"):\n",
    "    if ax is None:\n",
    "        _, ax = plt.subplots(figsize=(8, 6))\n",
    "    df.plot.scatter(x=var , y=\"cnt\",alpha=0.9, s=3, ax=ax)\n",
    "\n",
    "    return ax\n",
    "\n",
    "var_scatter(df);\n",
    "plt.savefig(\"hour_scatter1.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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X7qSmpPDD7G/54tNZfD+3/ndI38/e3SHMm/sDM157g2YtAtm0fh3vvvk6fy1eioODhnheuICnlzdjnhqPta0NJ48dY/a9ePYtGWh38fx5vv78U6a9+BKdu3YjNTWFud9/x5effsx3c396xFcoRP1XL/ZQmTNnDp988gkA7777Lr/88guLFi1i7NixjB8/nj///JPi4mJV+sOHD/Pyyy8zcuRInnrqKd555x1SU1MJCQlh+fLlREZGMnToUIYOHUpISAgAGzZs4OWXX2bUqFFMmjSJH3/8kaysLNU5Q0JCGD16NOfOnWP69OmMGjWK9957j/j4eNXzlZ27ock5eoLk3xeQte8gFDeML1Q1bdn6dQzp25fhAwfi2agRb774IrbW1qzduuW++X766y98PD3p3bXrIypp3WQxoDdZB4+QFXqIwrh4UpasoigtA7Pe3TSm1/doBLo6pK7egCIhkYLIaNI3B6PnYI+2aUkHrTI3j6L0DNWfrr0tevZ2ZO079Cgv7ZFbHrydwZ27MqxHTzycXXh9wkRsLC1Zt2f3ffPNW7UCHzc3erVtV+G57YcP8Vj3HvTr0BEXe3v6dujIsO49WbJta01dxiOhVCo5vXcX7fsOwrdlG+ycXRk4YQoF+XlcOXms0nznDu7D1MKS3k+Mw8bRmRaduxPQvhMndger0pzat4tGjf3oMGAoNo7OdBgwFLfGTTi1V322X35uDlsX/s6Asc9gYFRxcEHMzXCatutEI19/LGxsCWjfGScPL+JuRVRfIGrI8d3BNO/YmaAuPbB1cqbfmAmYmltyZv8ejekvHT9CYUEBgydNxc7FFb9WbWnfbxAnQoJVP+4tbezoO2Y8LTp2xdD4PoMxtLQwtbBU+6tvTu7eSbMOnQns3B0bR2f6PDEeEwsLzh7QHL/LJ46gKCxg4MQp2Dm70qRlG9r3HcjJPaXxO3dwLyYWlvR5Yjw2js4Edu5OQIdOnNi9Q3UeJ3cvej7+JE3bdkRPv/JOqPzcHLb8/Rv9xz2DobFx9V58Dajt97tPi5Z4BbTAys4BawdHuj42En1DQ2Jvhqu9XnpKEnvXLGPw08+hraNT/YGoIUqlkjOhIbTtM5DGQa2xdXah//jJFOTncfVU5fE9fzAUUwtLeo4ai7WjM807dcO/XUdO7SmNr6O7J92GP4Ffm/ZV1skdi/6PvmMnYVAP6uQ/sXvLRjr26E2XPv1xcnVjzLPTMLeyYv/ObRrTGxgaMva5F+nadwCWGm58l7Xk1x/p0KMXXr5Vz2RtCEI2b6RTj9507VsSy6eenYaFpRWh94nluGkv0q3vAKyqGOG76JeGFcuTe4Jp1rEzgV16YOPkTJ8xEzC5Tzt++URJOz5o4tS77VBJO35yd2k7dPbAXkwsrOgzZgI2Ts4EdulBQIfOnAgpbYcuHT9M+76D8G4ehKWtPS279cIroIVamuO7tmNqYcHgp5/DycMLS1s73P2aYuPkXLNBqQHtfNw5cv0m12ITSMzIYvPJi+jr6hLg5lRpnlaermTl5bHz3FWSM7M5eyuGC5GxtG/soUqTV6ggO79A9edma4merg7nbseonau4WKmWLqegsKYu9ZEK3riezr360L3fAJzdGjHuuRewsLJm747K3+sTX3iJHv0HYmWreRDaxTOnuXz+LK9++DEBQS2xdXDA29cPv+YtNKZvSFavWE7/QYMZ8tgw3D08eGXm69jY2LBpwzqN6cdNfJpnn5tGsxaBOLu4MGzE43Tt1p39oftUaS5dvIitnT2jxzyFk7MzTZs1Y8TI0Vy5fPkRXVXtWbNyBf0HDmLw3Xi+PPM1bGxs2Lxe88CRcRMnMXnqNJq1aIGzswuP3Y3ngTLxvHzpIrZ2dowa82RJPAOaMWLkKK5cafjxbNC0tev+XwNVL68sNDQUbW1tvv32W6ZNm8amTZs4cOAAAKmpqXz77bf06tWLX375ha+//pqePUuWXejatSvDhw/HxcWFRYsWsWjRIrrevVGtpaXFlClTmDdvHm+88QY3btzgt99+U3vdwsJCVq9ezYwZM/j222/Jzs7ml19+qfLc4r+tsLCQqzdu0L6V+kj99q1acf7ylUrzHTx+jEPHj/H68y/UdBHrNh0d9D0akXtRPVa5F69g6OOlMUvBrdugKMK0e2fQ0kLL0ADTLh3Ij7hFcZbm0URm3btQEB1LfljdvxH9bxUqFFy7dYt25ZaaaxfQjAthlc/MOXT2LIfOnWXmOM3L9RQWKtDX01M7ZqCvx+WIcBQKxcMXvJakJyeSnZGOu39pvPT09XH1aUJMROXLzcXdDMfDT31pEA//ZtyJvEVRUUk8Ym+G4+5fMU1MhPrN053LF+Ib1IZGTfw1vparV2PCL5wlIzUFgJiIMBKio/D0r3o5wdpUpFAQH3mrQjk9/QMqjW3MzTDcfHzVbph6NW1GVnoa6clJ/+j1FQUF/PL+68x7dyar580hPur2P7+IWlSkUBAfdQuPcvHTVIfuib0Zjqu3evw8ysUvNiK8wjk9/Ztz53Zp3X1Qwcv+pknLNrg3afqP8tWWuvB+v6e4uJirJ49RkJ+Hs6dP6fGiIrYu+E3VMVOfZCQnkZORTqMysdLV18fF25e4m5XHN/5WOI2aqMfO3b8ZCZG3/3GdDFmxCJ+g1rj5av48ra8UhYVERoThX24ZEP/AlkSUm1H6T4UGbyMjLY1Bjz/xUOepL+7FsqmGWIY/ZCz37SiJ5eCRDSOW99rxim1G5e14bERYhXbI079cO3QzDM9yn5eeTZsRX6YdKlIUolvue6eunj7R4ddVj2+cO42Thzcb/+8Xfn7rZf7+8kNO7wupd7MrLI2NMDU04GZC6YwmRXExUcmpuFhbVprPxcaSm3fUZ0FF3EnGycoc7UrW2w/ycCU8PonM3Hz1MpgY8fLAbrzYvyvD2zbH0ljzjM36RFFYyO3wMAKCWqodDwhqSdjVyn+fV+XMsSN4+DRm58YNvP7sRN55YSpL/5hPXm7uwxa5TissLOT69Wu0KTfwrk3bdly6+OCrbORkZ2NmZqZ63KxFC1KSkzh88ABKpZL0tDT27g6hfYeKy3Y3JKp4tmuvdrz1v4inqWlpPAOaNyclOZnDhw7+p+IpRE2plx0qbm5ujB8/HhcXF7p27UqLFi04d+4cAMnJySgUCjp37oyDgwPu7u70798fKysrDAwMMDIyQkdHBysrK9UxgGHDhhEYGIiDgwPNmzfn6aef5uDBg2ozX4qKinj++efx9fXF09OTESNGcOHCBYqLi+97bvHflpaRQVFxMdaWVmrHrS2tSL57E7S8pJRkvpw7l4/feBOTBjaK8p/SMTNFS0eHoowMteNFGRnoWFhozKNISiH+2x+xGjEE9z9/otGvs9Fzc+HO7F80ptcyMsS4XSsy9x2s9vLXJWmZmSV1sVzcrC0sSElP15gnKS2Nr//+k1lTn8fESPMPqPbNm7PlwH4uR0SgVCq5cjOCTaGhKIqKSCsz06++yb5b58ovD2FiZk5OhuZ4leRLx9hcPcbG5uYUFxeRezce2Rnpms+bWXre84dCSUtMoMuQEZW+Vq/RY7F3bcTvH77B7FemsvKHb+g2bBTezYMe6BprS05WJsriYg1xsiC7krqYnZGOsVnF9Peee1A2Dk4MmvAsI59/hccmP4+unh5Lvv2ClIT4qjPXEbn34qehDlUWi5L4lU+vHr/szIr10thMve4+iHOquvv4A+epbbX9fgdIjIlm7msvMOfV59i1chHDpr6kttTfoa0bMDQxrbA/S31wr46Vr4PGZuaq2GvOl6ExT3FxEXn/oE5eOLyf9MQEOt1nv5/6Kiszg+LiYszLzbQzt7AkPS3tX5835vYttq5ezjOvvFavZkM9jHuxNLO0VDtubmlJxkPGcsvq5Tw7o+HE8l47bqKhXb5fO2RS7vPSpFw7rukz1cTMQu0z1dO/OSf37CT5ThzK4mJuXbnI9bOn1F43LSmBM/t3Y2lrx+iX36B1z36EblzNmdD7z8iua0wMSzqfsvPUl4zKzivA1LDyGXkmBvpk51fMo6OtjZGBXoX01qbGuNtZc/ZWtNrxmJR0tpy6yIpDp9l2+hImhgZM7NEOI/2K56hPMu99bpb7fW5haUl6asXlpB9U4p14bly5TNStCF58+z3GP/c8F8+c4s8f5zxskeu09PQ0iouKsLJW35PHytqalGTN9zvKO3LoIKdPnWTwY6V7oAU0a84HH3/Kl59+TL8eXRkxZCBKpZJ3PviwOotf56jiaaVeP62srElJedB4HuL0qZMMKRfP92d9wleffkz/nt14fOgglCh5+/2GHU8hakq92EOlPA8PD7XH1tbWpN+9AePp6UlQUBAvvfQSQUFBBAUF0blzZywqufF6z7lz51izZg1RUVHk5ORQVFSEQqEgNTUVm7vTt/X09HB1Lf1ha21tjUKhILtcT/r97Nixg+Dg4CrTtdNR0qVI8+gRUT9plRsNpERZ4dg9H/3vW0YOHkxz/4Y1ivKhlB9QpqUFlYwy07Ewx/bZ8WQdOkb20RNoGRliNWIo9tOnEP/NDxXymXZqj5aWNtmHK192pCHRoly9Uyqh/LG7Pv7tV0b07E0zHx+NzwM889hwktPTee6LT0GpxMrcgkFdurBk21a0tevP59jlE0fYtXyR6vHjL7xa8o/y712lssKx8io8q7x3qjLPVDhv6b9T7sRxYPNannz1XXR0K2+qT4eGEBNxgxHTXsHc2oaosOuErl+FhY0tnk2b37eMdUHFOCkrq4ol6cs/9y9Gmrp4+eDiVVqfXbwbs+CLjzi1N4S+Y8b/4/PVpgrtilJ536qpKX2F4xWDfO+JBypTyp04Dmxaw1Mz37tv3a1tden9fo+1gyMT3/2Y/Jwcrp89xY7Ff/LEjLewc3Yl6sY1Lh07xMR3Pr5vWeqKqyeOsnvlYtXjYdNeASrWQVBWWbMqfd9X8f9yT8qdeA5vXsfoV9+u03XyoWn8nvnvTlVYWMifP3zLyAmTsdWwVntDV/570sPMaigsLOSPOd8yamIDjaWG9+c/qXel7dD9Tqke/96jx7Jj6QL++vQ90NLC0tae5h27cOHIQbU8jo086T58NAAObu6kJtzhdOhuWvXo8+AFfMQC3BwZ2LJ0Zueqw2eAkvezGq2KP43K0/TTSeMTlMxOyczNIyxefcZvxJ3Sx4mUdLC82L8rzRs5czysfs3u1URT81LZ7/MHoSwu+X3/3GtvYXx3H+BxU19g9icfkp6WikW5DpyGpuL3zAdrqi+eP8cXn8zipVdfw79p6Qy1Wzdv8tMPsxn/9DO0bdeBlOQkfvvlZ2b/7xve/XBWdRe/7tH0nemB4nmeLz+dxUszZuLXtPTz5NbNm/w8dw7jJz1Dm3bt78ZzHnO+/YZ3PvioessuHp2H+MwSD6de/qrQ1fBj6N5MEh0dHT799FOuXbvGmTNn2LVrF4sWLeKrr77C09NT4/kSEhL49NNP6devH+PGjcPMzIzw8HC+/fZbteVqdMqNKLrXYJSdxVKVAQMGMGDAgCrTRfUZVmUaUT9Ympujo61dYTZKalpahVkr95w8d5YzF87zf0uXAiXfe4uLi+k4eBBvTX+JEYMG1XSx64yizCyURUXoWKiPUNUxM6swa+Ues97dKc4vIHVV6Rqjib8twO2HrzDw8SL/hvoyK2bdO5N98gzF2TnVfwF1iKWZWUldTE9TO56SkYG1heZNOk9duczZa1f5a2NJLJVKJcVKJV0mT+KNiZMY3qMXhvr6fPDsVN6Z9AwpGRnYWFqycd8ejA0NsTR9sM7musCneRBOHqXLyBXd/fzPzkjH3Kp0xFVOVmaFEdNlmWgYnZmTmYG2tg6Gd39caUyTlaGagRF7M5zcrCz+/rJ0xJCyuJjo8OucO7iPGd//ilKp5MCmtTz27IuqGSl2Lm4kRkdyYveOOt2hYmxqhpa2tsY4lR+9ek9lcb333L+lra2No7sHqQl3/vU5HjWjyuKXlVlhFs89ldU5KJ01YGKmKcaZaGvrYGR6nz1pyoiNKKm7C774QHVMWVxMVNh1zh7cx6uz51dYqqU21KX3+z06urpY2TkAJfuCxEfe5NTenQwYN5nI61fIykjn1/dfU6VXFhezf+NqTu3bxfOff/9PLr/GeTUPwtGj9Ht32fialY1vZibG5veLb8UZLDlZmWrxrUrcrXBys7NY/FXpzRdlcTEx4Tc4fyiU6d/OqxN18t8yNTNHW1ubjDT1UdWZ6ekVZq08qIzUFOKio1j0y1wW/TIXKGn/lUol08cMZ/p7s2ga2LKKs9Q/941luVkrDyr9biwXzpvLwnnqsXzhieG8/N4smgbVv1jerx3/R+1Q5r12yKLyNFkZd9sh07tpzXn8+RkoCgvIzc7G1MKS0A2rsSizr4WphWWF/VJsHJ3ITFVfBquuuRGXSGzKEdXje5vGmxoaqC3FZWKgX2HWSlnZ+QWYGqjPYDE20KeouJjccnugaGtp0byRM2dvRVfZeVhYVERSZhbWpvV7BQWzu+/19HLv9Yz0tH/9XgewsLbGytpG1ZkC4OzqBkBKYmKD7VCxsLBEW0eHlGT191daakqFWSvlXTh3jnfffI2np0xl2Aj1mc3LlizCz78pT44tGfDk7eODoaERM6Y/z7PPPY+9g0P1XkgdcS+eqeVmo6SmpmJlVUU8z5/jvTdf5+lnp/JYuXguvxvPMWPHAXfjaWTEq9NfYPLUaQ02nkLUlHrZoVIVLS0t/Pz88PPz48knn2T69OkcOHAAT09PdHV1K3SA3LhxA4VCwZQpU1SdJidOnPjHr6vp3ELo6enh17gxx0+foU/X0k3Uj505Q6/OnTXmWf7rfLXHoUeOsGDlCv7+YS52VWx42eAUFVFwKxKjZn7knDitOmzUzI/sk2c0ZtHS14fy78V7j8vNmND38kDf3Y2UZaurtdh1kZ6uLk08PDhx6SK9y6zJeuLSJXq0aaMxz5LPv1R7fOD0af7esok/P/oYu3Jf6HR1dbG/+6V517GjdA5qiXY92oRM39AIfcPSZc2USiUm5hbcvnoJJ/eSG4OKwkJiwq/TfXjla6A7eXoTdv602rHbVy/h0MgDHZ2SZtfZ05vbVy/Trs/AMmku4+LlDYBPi1ZMes9D7Rw7lvyFlZ0D7fsPRkdXl4K8PIqLiiqMBtPS1q7za4Tr6Ori2MiDm1cv4de6dL3lm1cv0aSl5rro4unDvg2rUBQWoKtXcpPg5pVLmFpYYmGjeXPQB6FUKkmIjsLetdG/PsejpqOri6ObB7euXqJJq7aq47evXsI3qLXGPM6e3uzfuBpFYena87evXFaLn7OXNzfOqX+u3rp6CQf30rpbFZ/AVjzt7qF2bMfiP7Gyd6B9/yF1ZoZAXXq/V0apVKo6IoK69cK33Htj7bzZ+LVuT4vO3TRlr1X6hoboGxqqHiuVSozNLYi8dhnHMvGNDb9Bl7sjyDVx9PAm4oJ6nYy8dhn7Ru4PXCe9m7fE4R0PtWO7li3A0s6etn0H15k6+W/p6unRyMuHq+fO0rpjF9Xxq+fP0rL9v1sX3dLahg++/0nt2P7gbVw5f5Zpb76HjZ39Q5W5rroXy8vnz9K6U2ksr5w/S6t/uca8lbUNH81Wj2Vo8DaunDvL82/V31jea8dvXbmEX6vSdvzW1Uv4Bmlux529fAgt147fuqrejjt7+nDjnPpn6q0rl3DU0A7p6uljZqlPUZGC62dPqpXDxasxqXfUl/JMSYjH3Prff194FAoURRQo1PfbyMrLx9PehrjUks4nHW1t3Gys2HPxuqZTABCTnIavs3rduneO4nLfEZs422NsoMe5W+qb0Wuio62NjZkJtxMfbNmhukpXTw93bx8unT1D286l+95ePneG1h01/z5/ED5+/pw8dJC83FwM7y6VHB9bEtf6+l5/EHp6evj6NuHUiRP06NVbdfzUiRN07dGj0nznzp7hvTffYNLkZxn1xJMVns/Py6swqFlbp+S3ZV3/rfMwSuN5nO49e6mOnzpxgm7de1Sa7/zZM7z31ptMnDyZkU+MqfB8fn5+hd/m9x5XmAUnhKhS/bnT9YCuXr3KypUruX79OgkJCRw7doykpCTc3EpGBtjb25OQkEBYWBjp6ekUFhbi7OxMcXExmzZtIj4+ntDQUDZu3PiPX1vTuRsiLSND9H280PfxAm0t9Bzs0ffxQtfBrraLVmeNHfE4W0J2sWHHdm5GRvL9/F9JSk7m8UGDAZi34C9efOcdVXpvDw+1P3tbG7S1tPD28MD8AZeXa0jSd+zGtEtHTLt3Rs/JEetxo9GxtCBzzwEALEcPw+GtGar0uecuou/uhsXwweg62KHv7obtlIkoklMouBmpdm6zHl0ojL9D3tXKN2VvSJ7qP5CtBw+wKXQft2JjmLN0MUlpqYzoWfLl95fVK3npm69U6b1d3dT+7KysSuqiqxvmd0dfRcbHsf3QQaLi47kUEc6Hv/xMRHQML4ys/CZZfaClpUWrnn05vmsb18+eIjE2mu2L/0RP3wD/NqUdUtsW/cG2RX+oHgd26UFmWip71iwjOT6W84f3c/HYIdr27q9K06pHXyKvX+FY8FaS4+M4FryVqOtXad2zLwCGxsbYObuq/enpG2BoYoKdsytaWloYGBnh6tOE/ZvWEnn9KmlJiVw8epDLxw/TuEWrRxeof6ld7/5cOHKQcwdDSYqLZdeqpWSlp9Hy7v4Q+zasZvkP36jSN23XAT19fbYu/D8SY6K5duYkR3dupW2f/mqdSneibnMn6jb5ebnkZWdzJ+o2SXGlNwkObtlAxOULpCUmcCfqNtsW/0ViTDQtu9WvfSna9O7HxaMHOX8olOT4WHavXkpWWhqBXUquY//G1ayc+z9V+qZtO6Crp8/2xf9HYmw018+e5NiurbTpVRq/wC49yUpLKa27h0K5ePQgbXuXzqwtUii4ExXJnahIFIWFZGekcycqUjXDR2PdNTDA0Li07tZFtfl+h5L/r+iw66QnJ5EYE83+jWuIunEN/zYdgJI9V8rHVVtHBxNzC6wdnB5BhB6OlpYWLbv34eSu7YSdO0VSbAw7l/6FnoEBfq1L4xu8+E+CF/+petyiS3cy01LZt3YFKfGxXDy8n8vHDtG6V2l8ixQKEqIjSYguqZM5mRkkREeSllhaJ22dXdT+dPX1MTQ2wdbZpc7WyX+i95BhHNm3h4O7dxIXHcWqv/4gPSWFrv1KOvE2LF3ID598oJYnLiqSqJsRZGVmkp+XR9TNCKJuRgAlN8tdGrmr/ZlZWKCrp4dLI3fVjcKGqM/Qu7EMKYnlyr/+ID01hW53Y7l+6UJmf6wey9h7sczIJO9BYmneMGLZpld/Lh49yLlDoSTHxbL7bjt+b5+n0A2rWTG3TDvetqQd37bobjt05iTHdm6lTe/Sdiioa0k7tHv1UpLjYjl3rx3qU9oOxd4M5/qZk6QlJRAVdo3VP3+PslhJu74Dy5StH7E3wzmyfROpCXe4evo4p/aG0LJ76c3J+uJ42G06+nrSxNkeO3NThrYOoECh4FJUnCrN0NbNGNq6merx6ZvRmBkZ0qdFE2zMTAj0cKGFuzPHbtyqcP4gT1duJaSQllNx4/RezXxpZGuFhbERzlYWPN4+ED0dHc7fjq2Ra32U+g8bwaG9u9m/K5jYqEiW/d9vpKWk0KN/yUoQaxb/zbcfvqeWJyYqksiIcLIyMsjLyyUyIpzIiNKVDzp064GJmRl//TSHmMjb3LhymeV//k6bTp0fauZLfTD6yacI3r6VrZs3cfvWLX7+YQ5JyUkMHV6yF+Qf83/h9RkvqdKfPX2ad994jaHDh9OnX39SkpNJSU4mrcweNh07d+HQgf1sXL+O2JgYLp4/x88/zKGxbxMcHBvgEopljBrzJMHbt5XGc+4ckpOTGDp8OAD/N/9X3pjxsir92TOneffN1xk6bDh9+mqOZ4dOnTl88ACb1q8jNjaGi+fPM2/u3Xg2xCUphahh9XtIlgYmJiZcvnyZLVu2kJWVhZ2dHWPGjKFnz5Ivdp07d+bIkSN88MEHZGdnM2PGDPr06cPUqVNZu3YtS5Yswc/Pj8mTJ/O///2vildTV9m5GxpDP19cf/pW9dhmykRspkwkY9tO7nxZt5acqCv6du9OemYGC5YvJyklFW8Pd+Z8+hlOd6dVJqWkEBNX/7+Y1pSc46dIMTXBcuhAdCzNKYiJ487seRTd3eRO18ICPfvSDr28K9dImr8A80F9sRjYB2VBIfnhN7nz3U8oC0qnx2sZGmDSvjVpG7c98muqLX3adyA9K4sFmzaSnJ6Gl4sr37/2Bk53l0lITksjJiHhH52zuLiY5cE7iIyPQ1dHh9Z+/vz+wUc42dX/TtZ2fQaiKChg96ol5OVk4+ThxaiXXlcb2Z5Rbjq2pa0dI1+Yyd61yzl3cB8mFpb0GjVWbXS5i5cPQ555nkNb1nFo2wYsbe0ZMvl5nDzuP2K9vKGTn2f/xjVsW/g7eTnZmFvb0HnwCFp271115lrm36Y9udlZHNq+ieyMdGydXBg9/TXVKNWs9DRSE0vroqGRMWNeeZOdKxbz99cfY2hsQrveA2jXW30ZzQVfqq+pHHbhLObWNrz4RUn7lJebw46lf5OdkY6BoREObu6Me/1dnMss/1Qf+LVuT252Nkd2bFbFb+SLM8vEL520pNL4GRgZ88TLbxCycgmLv/kEQ2MT2vTqT5syN/4tbe0Y+eJM9qxdztkDezG1sKT36HFqs4ay0tNY9HVpjNMOJnDu4D7cGjfhyVdLBwbUR7X5fs/OyGDrwj/IyUxH39AIOxdXRr4wE8+mzWgo2vQZgKKwgD2rl5Gfk42juxcjXnxNbSZLRrnleCxs7Bg+bQah61dy4W58e4x8isZlZmJlpaex7H+fqh5fSArlwqFQXHx8Gf3KWzV/YXVAm85dyc7KZPvaVWSkpuDk5s709z5SjYhOT00lsdxo/Z+/+pSUMp+xX771KgC/rt70yMpdF7Xt3JXszEy2rV1FemoKzo3cealcLJPKx/LLT0kuE8vP33wVgN/WNOxY+rdpT152FkfKtOOjXixtx7Mz0khLLN8OvcmulYtZdLcdb9t7gFqnfUk79JqGdqh0NqaisJADm9eRlpSAvoEhXgEtGDzpOQyNS5dZcvLwYsS0l9m/aS2Ht2/C3NqGrkMfp2W3uv/9qLyj12+hp6ND/yB/DPV0iU1JZ8Wh0xQoilRpzI0N1fKk5+Sy6vBp+rRoQitPN7Ly8tl57irXYtW/41saG+FhZ82G4+c1vra5kQHD2jbH2ECfnPwCYlLSWbjvGBm5edV/oY9Yuy7dyMrIYPOqFaSnpuDSyJ1XP/wEW/u77/WUFBLi49Ty/PDpLLX3+sevlewP9teGrQAYGhnxxqdfsOz3+Xz2xkyMTU1p2b4DoyY+/Wguqhb17N2HjPR0lixcQEpyMh6eXnz17fc4OpYM+khJTiY2pnSAU/D2reTl5bFq+TJWLV+mOu7g6MjyNSVLTQ8YNJicnBw2rF3D/J9/xMTUlKCWrZj24ks0dD179yEjI52li/4ujef/vsPhbjyTk5OJjS0Tz23bSuK5YhmrVqjHc9nqdUBJPHNzctiwbi3z5/2EiYkpQa1a8dwL0x/txYlq1RAGBtVXWrm5uTK3qw6SPVSql/2i+VUnEg8k9ZN/1tEo7s/8+WdquwgNytqshjkzsLbo6jS4iay1pqhYvm5Vp4a81ENtUMiStdWmiVPDXdalNmjLfYJqFZ5Qv5dqqkvupGfWdhEalO7+9WtgS13nYXv/vTbEPyPfO6uPrfmD7YEnHkzM8HG1XYQquWxYWttFqBFyp0QIIYQQQgghhBBCCCGEEKIKDW7JLyGEEEIIIYQQQgghhBCiwdKWeRK1RSIvhBBCCCGEEEIIIYQQQghRBelQEUIIIYQQQgghhBBCCCGEqIIs+SWEEEIIIYQQQgghhBBC1BdaWrVdgv8smaEihBBCCCGEEEIIIYQQQghRBelQEUIIIYQQQgghhBBCCCGEqIIs+SWEEEIIIYQQQgghhBBC1BfasuRXbZEZKkIIIYQQQgghhBBCCCGEEFWQDhUhhBBCCCGEEEIIIYQQQogqyJJfQgghhBBCCCGEEEIIIUR9oSXzJGqLRF4IIYQQQgghhBBCCCGEEKIK0qEihBBCCCGEEEIIIYQQQghRBVnySwghhBBCCCGEEEIIIYSoJ7S0tWq7CP9ZMkNFCCGEEEIIIYQQQgghhBCiCtKhIoQQQgghhBBCCCGEEEIIUQXpUBFCCCGEEEIIIYQQQgghhKiC7KEihBBCCCGEEEIIIYQQQtQXWrKHSm2RGSpCCCGEEEIIIYQQQgghhBBVkA4VIYQQQgghhBBCCCGEEEKIKsiSX3WU/aL5tV2EBiVh4vO1XYQGI+TDD2q7CA3KoEUrarsIDUrxiMdruwgNSoGiqLaL0GBoy3TsaqWrI2OCqpOiuLi2i9BgBLo71XYRGhT57KxeOtry2VldLkffqe0iNCjZ+QW1XYQGJTolvbaLIIRGtuYmtV2EhkVL2vXaIpEXQgghhBBCCCGEEEIIIYSognSoCCGEEEIIIYQQQgghhBBCVEGW/BJCCCGEEEIIIYQQQggh6gttWRq1tsgMFSGEEEIIIYQQQgghhBBCiCpIh4oQQgghhBBCCCGEEEIIIUQVZMkvIYQQQgghhBBCCCGEEKK+0JIlv2qLzFARQgghhBBCCCGEEEIIIYSognSoCCGEEEIIIYQQQgghhBBCVEGW/BJCCCGEEEIIIYQQQggh6gktbZknUVsk8kIIIYQQQgghhBBCCCGEEFWQDhUhhBBCCCGEEEIIIYQQQogqSIeKEEIIIYQQQgghhBBCCCFEFWQPFSGEEEIIIYQQQgghhBCivtDSqu0S/GfJDBUhhBBCCCGEEEIIIYQQQogqSIeKEEIIIYQQQgghhBBCCCFEFWTJLyGEEEIIIYQQQgghhBCivtCWeRK1RSIvhBBCCCGEEEIIIYQQQghRBelQEUIIIYQQQgghhBBCCCGEqIIs+SWEEEIIIYQQQgghhBBC1BdaWrVdgv8s6VAR97Vmy2YWr1lDckoKXu7uzJz2PC2bNasyX2RMDBNffgmlUkno+g01X9B6zDCwGVZPjcKwSWN07WyJ/+I7Mrfvqu1i1TqlUsnR7Zu4eHg/ebk5OLp70mv0OGycXO6bL/rGNfavX0lyfCwmFpa06T2AFl16qJ5PjovhyLZNJETfJiM5ifYDhtJx0DD1c4Rd5/SeYO5E3SY7PY2+454hoH3nmrjMWmHarRMWfXuiY2FOQVw8qas3kB92s9L0hv5NsBzSHz1nR5QKBfnht0hdtxlFQiIABo29sRw2CD0He7T09SlKSSHr0DEyQvY9oiuqOUqlkiPbN3HhUCh5uTk4uXvR64lx2FZRD6NuXCN0/UqS42IwtbCkTZ+BBJaphwDXz57k8NYNpCclYmFrR+chj9M4sJVamqz0NA5uWsvNy+cpyMvDwtaO3k9MwK1xEwBunD3F+UOhJETfJjcri9GvvIlbY79qjUF1qs14FuTlcmjrBsLOnSYnKxN710b0HPkUju6eaudJTYjnwMa1RN24QpGiCGsHRwZOmoqNo3O1xaG6KJVKDm/byPlDoeTn5uDo7kWfMeMfKJ771q0g6W482/YZSFDXnmpprp85ycGt61Xx7Dr0cRoHtlY9fyx4K9fPnSI1IR4dXV2cPLzp+thI7JxdVWkOblnH9TMnyUhNQUdHFwc3dzoPGYGLl0/1BqKGnA7dzbFd28lKT8PWyYU+o8eq3nuaJMREsWvlEuJuRWBobEJQ1550HvQYWnd/6GSlp7FnzQrio26RmnCHgPadGDJpaqXnu3ziKJv+mo93s0BGT59Z7df3qNVmu35k20aO7disdszYzJznvphdbddX29auWc2yxUtITk7C08uLGTNfI6hlS41pb0ZE8P23/+PmzZtkZ2Vha2tLn379eHbqc+jp6QGQlJTETz/8wLVrV4mOimLAwIF8MOvjR3hFtWfN6tUsXbKY5KSSWM587fX7xvLb/31TJpZ29O3XjynPlcZy7549rF+3luvXrlFQUICHpydPPzOZbt27P8rLqjV7t28leOM60lJTcHZrxJOTp+LbVPPvycKCAhb/No/bEeHER0fh7efPW599rZbm6sXzfPfRexXyfvbjrzi5utXINdQ1/QL96ODrjrG+PreTUll37Bx30jIrTW9mZMBjbZrhamOJrZkppyKiWHHotFqatt6NeLJLqwp53168CUVxcbVfQ12wP3g7uzdvICMtFSdXNx6f9Cw+/k01pi0sKGDF/80n+mYE8THReDXxY8asz9XSnD12hEMhwUTfvElhYQGOrm70HzGK5m3aPYrLqXUh2zazbd0a0lNTcGnkzrgpz9MkQPN7vaCggL9/+ZHb4WHERkfR2L8p7335rVqa33/4joN7Qirk1Tcw4P9Wb6yRa6hLqjueAIdD97Jt3WriY2IwMjYmIDCIJydPxdLKuqYvR4gGRzpURKV2hYby/fz5vD39JQIDAlizZQuvfvgBK3/7HUd7+0rzFRYW8sHXX9GyWTNOX7jwCEtcP2kbGVEQcZvMHSE4fPBmbRenzjgZsoPTe3fSb9xkrOwdObZjM+vmzWbSB1+gb2ioMU96ciIbfptLQIcuDJg4hZiIMPauWoqRqRmNg0puBBYWFGBuY4NPYCsOb12v8TyF+XnYOLng37YjwUv+qrFrrA3GrYOwfmIEKcvXkhcegVm3zthPf47YT7+hKDWtQnpdG2vsX5hMxt4DJP29DC0DfaxGDMV++lRiZ30JgDI/n8x9ByiMiUNZUIiBtyfWY0dRXFBA1v7Dj/gKq9eJkO2c2hNM//GTsbZ35OiOzaz9+Xue+fAL9A2NNOZJT0pk/fwfaNahCwMnTiEm/AZ7Vi3FyNQU36A2AMTeDGPrgt/oNGgYPoGtCDt3mi1//cqTM9/FycMLgLycHFbO+Qpnr8YMnzYDY1Mz0pMTMTYzU71WYUE+zp7e+LftwI7Ff9Z8QB5SbcZz57KFJMVGM2DCs5haWnHlxFHW/Pw9k97/DDNLK9VrrZjzFU3bdqL9gDcxMDIm5U4c+gaaP3Nq2/GQ7ZzcE8zA8c9i5eDIke2bWP3Tdzz70ZeVxjMtKZG1v86heYeuDJo0lZjwG4SsXIKxqRm+Le/GMyKMzQvm03nQMBoHtebG2VNs+vNXxr72Lk4e3gBE3bhKUNeeJR1SSji0dT2rf/qOZz74HCMTUwCs7Z3o/cR4LGxsURQWcmrPTtb+MptnP/oKE3OLRxOkf+nKyWOErFpGv6cm4Orty+n9u1k1bzZTPvoSC2ubCunzc3NZ+eO3uPk0YdLbs0i+E8e2RX+iZ6BP+z4DAVAoCjEyNaVD/8GcOxh639dPS0xg77qVuPr41sj11YbabNcBrOwdGfVK6fcsLa2Gs/JxyK6d/PD997zx9tsEBgaxbs0aXn91BktXrsLR0bFCej09PQYOHoyvbxNMzcwIu3Gdr7/8kiJFEdNfeQUoiauFpSUTJk1i4/rK49rQ7Nq5kznff8ebb79DYFAQa9esZuaMV1i+arXGWOrq6TFo8BCaNCmJ5Y3r1/nqyy9QFCl4+ZUZAJw5fZo2bdoy7YUXMDe3IHjHdt55601+mf9bpR01DcXxg/tZ8dfvjHvuBXz8A9i3fStzP/+YT+f+go1dxd+TxcXF6Onp0WvgEC6cPklOdlal5/507i+YmJZ+JzIzN6+Ra6hrejZrTPcAb1YcPENiRiZ9A/2Y1rcT36zfTb5CoTGPrrYO2fkF7Llwgw6+7pWeO79QwVfr1Af2NdTOlFOHD7J24Z888exzeDfx58DOHfz61We8P/tHrG3tKqS/Vze79R/EpTOnyM3JrpAm7MolGgc0Z/CYsZiYmnHiwH7++O4bXpn1WaUdNQ3F0QOhLP1jPhOffwnfpgHs3raF7z75gK/m/Y6thve6srgYPX19+gx+jHOnTmh8r4+f+gJPTJqsduzzt1+vtFOhIamJeF6/fInf5nzLU89MoXX7TqSnpbJw/s/M//5/vPP51xXSCyHur178krh48SJvvPEGo0ePZsyYMbz++uvcvn0bgCtXrvDOO+8wcuRIJk2axC+//EJOTo4q76lTp3j77bd58skneeqpp/joo4+IiopSO//y5cuZPHkyI0aMYMKECcyeXTparbCwkD/++IMJEybw+OOP88Ybb3Dp0iXV8xcuXGDo0KGcO3eO119/nZEjRzJz5kzCwsJqOCo1b9n6dQzp25fhAwfi2agRb774IrbW1qzduuW++X766y98PD3p3bXrIypp/ZZz9ATJvy8ga99BKFbWdnHqBKVSyZnQENr2GUjjoNbYOrvQf/xkCvLzuHrqWKX5zh8MxdTCkp6jxmLt6EzzTt3wb9eRU3uCVWkc3T3pNvwJ/Nq0R09fX+N5PANa0Hno4zRu2UY1srihMO/dnawjJ8g6dBRFfAKpq9ZTlJGBWTfNM3D0G7mCjg5pG7aiSEyiMDqW9ODd6Nnbom1iAkBBZDQ5J89SGHcHRXIK2cdPkXf5GoY+Xo/y0qqdUqnkzL4Q2vUdhG9QG2ydXek//tmSeniy8np47tA+TC0sS0ZeOzrTonN3mrbvxKndpfXw9N4Q3Br70b7/EGwcnWnffwhuPk04vbf0R+zJkO2YmFsycOIUnDy8sLC1o1GTpmozJZq260THQcPwbNq8ZoJQjWoznoUFBdw4d4ouj43ErbEfVnYOdBo0DEs7e84f3Ks6z8Et63H3C6D742NwcHPH0tYOr4AWmNXBUVtKpZLTe3fRvu8gfFu2wc7ZlYETplCQn8eV+8XzYEk8ez9RGs+A9p04USaep/btolFjPzoMGIqNozMdBgzFrXETTpWpn6Neep3mHbti5+yKnYsrgyZNJTcrk9iI0u8/Tdt1xL1JUyxt7bF1cqHH409SkJdHQrT697C66PjuYJp37ExQlx7YOjnTb8wETM0tObN/j8b0l44fobCggMGTpmLn4opfq7a07zeIEyHBKJUlbbuljR19x4ynRceuGBqbVPraRUUKNv41n26PjcRSw02d+qi223UAbR1tTMwtVH9lO6fruxXLljFoyBCGDR+Bh6cnr735Jja2tqxfu0Zjelc3NwYPGUpjX1+cnJzo2q07/foP4OzZs6o0Ts7OvPbGGwweMhTzOt4BWp2WL1vK4CFDGT5iBJ6enrzx5lvY2Nqybo3mWLq5uTFkaGksu3XvTv8BAzhXJpavvfEGE59+moCAZri5uTFl6nP4+fkRum/fo7moWrRr8wY69exNt74DcHZ1Y+zU57GwsmJf8DaN6Q0MDZnw/Et07zcAK5uKnddlmVlYYGFlpfrT1tGpiUuoc7r5e7Pnwg0uRMYSn5bJ8oOnMNDTpaWXa6V5UrNz2HD8AifCI8nJL7zv+TPz8tX+Gqq9WzfRvntPOvfuh6OrG6MnT8XCyoqDO3doTG9gaMiTU1+gc59+WFZSN0c9PYV+w0fi4eOLnaMTg0aPwc3Li/MnKm/nGoodG9fRpXdfevYfiItbIyZOexFLK2v2bNN878jA0JBnXnyFngMGYW1jqzGNsYkJllbWqr+EuDgS4uPo0W9gTV5KnVAT8Qy7dgVrG1sGDHscO0dHfPz86TtkGOHXr9bkpYiapqVV9/8aqDrfoVJUVMTnn3+Ov78/P/74I9999x1Dhw5FW1ubW7du8dFHH9G+fXt++ukn3nvvPSIiIpg7d64qf15eHo899hizZ8/myy+/xNjYmM8++4zCwpIvEocOHWL9+vW88MIL/Pbbb3z00Uf4+paOBlywYAEHDhzglVdeYe7cubi7u/Pxxx+TkpKiVs6FCxcyadIkfvjhB8zMzPj+++9VP6Dro8LCQq7euEH7VurTftu3asX5y1cqzXfw+DEOHT/G68+/UNNFFA1YRnISORnpNPILUB3T1dfHxduXuJuVd1bG3wqnUZMAtWPu/s1IiLxNUZHmEVv/KTo66DdyJe/KNbXDeVeuYeDloTFL/u0oKCrCtHMH0NJCy8AA0w5tyL8VSXF2xZFZAHquLhh4eZB3I7y6r+CRSk9OIjsjHfcy9VBPXx9Xb19ib1Z+bXE3w9XyAHj4B3CnTD2Mu1Uxjbt/M2LL1O+wC2dw9PBky1/z+fXdV1n89cecCd1db9uW2oynsrgIZXExuneXX7lHV0+PmPB7aYqJuHgWG0dn1v4yh1/fncHSbz/j2qnj//6ia1B6cmJJPP1LR+np6evj6tOEmIjKPyfjbobjUSGezbgTeUsVz9ib4bj7V0wTE1H5/1NBXh5KpRIDY2ONzxcpFJw/FIq+oRH2dXxJliKFgvjIW3j6q4+A9PQPqDS2MTfDcPPxVbuh79W0GVnpaaQnJ/2j19+/cS0WNrY079jlnxe+jqoL7Xp6UhJ/fPgGf338Dtv+/o30pMR/dhF1VGFhIdeuXqV9+w5qx9u1b8+F8+cf6BzRUVEcO3qElq0a9myJqqhi2UE9lu3bd3jgWEZFRXH0yBFatqy4dFJZ2Tk5mJk3nE49TRSFhdwODyMgSD0WAYGtCL/68DfwPn9zJq9PnsB3s97j6oUH+/+p76xNjTE3NuR6bILqmKKomIg7yXjYPfzgDz0dHd4f2Y8PR/Xn2V4dcLFumJ2pCkUhURHh+LcIUjvu1yKQm9V8czk/NxfjuzN3GypFYSG3wm7QvNx7vVnLVty4Wvm9o39q387tuDRyp3EDn+1TU/Fs7N+UtNQUzhw/ilKpJDMjnaMH9hHYuu1DlliI/6Y6v+RXTk4O2dnZtGvXDicnJ6BkJBDA7Nmz6dq1KyNGjFClf/HFF5kxYwZpaWlYWlrSubP6qOtXX32VMWPGcP36dQICAkhMTMTa2pqWLVuiq6uLvb09jRs3Bko6Y7Zv387LL79M27ZtVec/f/48W7duZcKECarzjh8/nhYtWgDw5JNP8vbbb5OcnIytrebe4bouLSODouJirO8ug3KPtaUVx1PPaMyTlJLMl3Pn8s0HH2JSyQ0VIR5EdkY6ULK+eVnGZuZkpafdJ18Gbr4V8xQXF5GXlYWJhWV1F7Ve0TE1QUtHh6IM9TWWizKyMPTT/KO+KCWVOz/Ox27KJKyffBy0tCiIjiHh5z8qpHX58iN0TE1BR5v0rTvJOnCkRq7jUcmprB6am5OVllZpvuyMDBo10VwPc7OyMLWwJDsjHZNy5zUxMycnM0P1OD0pkXMH9tKqZz/a9R1IQkwUe1cvA6Bl994Pc2m1ojbjqW9ohJOnN8d2bMHGyQUTcwuunjpG3M1wLO9Om8/JyqQwP59jO7fSefBwuj42kqjrV9i26A/0DAzwahb4sCGoVtkZJdem6bqz0lLvky+dRn7qP0SNzR80numVnnfPmmXYuzbC2VN9f5TwC2fZsuA3CgsLMDW3YPRLr9f55b5ysjJRFhdjXK6cxuYWZF+9rDFPdkY6ZpbWFdLfe+5BZ5rcvHyRK6eOM/m9T/9Fyeuu2m7XHT286DfuGawcHMnNyuRY8BZWzvmKCe99qlqirr5KS0ujqKgIK2v1+mdtbc3J4/fvEH7u2cmqfT0eGz6c51+cXpNFrfPuxdJaQyxPHL//CPOpkydz7dpVCgoKGDZ8BC9MrzyWa1atIjEhgYGDBldLueuqrMwMiouLMS/3PjW3tOTy+bP/+ryWVtaMn/YiHj6+FCkKObJvL99//D5vfvoVvg18KSBzo5LlEcvPHMnKzcfC+OGWJ03IyGTl4dPEpmRgoKdLV38vXhrYle837SUpU/MgqvoqOyOT4uJizMrVTTMLS65VY+fc/uBtpKUk065bw94vKTPj7nu93L0jC0srLp3TfO/on8rJzub4oQOMnvBMtZyvLqupeDb2a8qLb7zDr9//j8KCfIqKimgW1IrnXn3jYYssxH9Sne9QMTMzo3fv3syaNYvAwEACAwPp3LkzdnZ2hIWFERcXx4EDB1Tp743cjY+Px9LSkri4OJYsWcL169dJT09HqVRSXFxMYmLJqLTOnTuzadMmpkyZQqtWrWjVqhXt27dHT0+PuLg4FAoF/v7+qvPr6Ojg5+dXYdkwDw8P1b/vfQlPS0ur0KGyY8cOgoODqUrv9h3o263bPwtWDSi/3JESZaVLIH30v28ZOXgwzcvES4gHcfXEUXavXKx6PGxayfrdFeuakqomDFbMoqzkif+ycjMctCiNUzna5mbYjB9D1rGTZJ84g7ahAZZDB2A3ZSJ3fvhVLd+d739Gy8AAA093LEcMQZGUTPbxUzV4HdXryomjhKxYpHo8/PmStc8r1EMlVdanCp+dSg3HK5xW/f9AqVTi0MiDro+NBMDezZ20hDucO7C3XnSo1LV4DpwwheBlC/jjwzfQ0tbG3tWdJq3bkxB9++45S9YI927ekta9+gNg79qI+MjbnN2/p9Y7VC6fOMKu5aXxfPyFV0v+USE2yqrjWf6Axnhqjrkme9euICb8Bk+99i7a2uqTn918/Zn47sfkZmVx/nAom//6lbGvv49pPejgrhgnpYaDZdJX1v48oJysTLYu+j+GTp6GoUnlS4LVB3WtXS+/LKKjhxcLPnmXK8cO06pXvwc+T12m8XOyihh99uWX5GTncOPGDeb99CNLFi1k4tMN/2ZVVf7J7597Pv/yS3Jycrhx4zo//fgjixcuZNIzFWO5Z89ufvpxLp998aVqsGCDV759VlYdz/txdHHF0aV0eSvvJv4kJd4heOO6Bteh0srTlVEdg1SP/2/33cFK5ZsXrYqH/qnbiancTiwdkHErMZnXh/aki78XG4430H1R/8Xn5oM6e+wIG5Ys5JkZr2OtYc+LhqhiO6REq8pW/sEc3rcbZXExnXvW/d9A1aW64xkTeZslv//KsDFP0bxla9JSU1i54P9Y8MuPTJspe/nWV1radX7hqQarzneoQMmskmHDhnHq1CmOHTvG4sWLef/991EqlfTr149hw4ZVyGNzd13Lzz77DBsbG6ZPn46NjQ06Ojq8+OKLKO5u2GZnZ8f8+fM5d+4cZ8+e5c8//2T58uV8//33qnM9yBc+nTJrtt5Lr2lZlgEDBjBgwIAqz5cfG1dlmppkaW6OjrY2yanqS5ulpqVVmLVyz8lzZzlz4Tz/t3QpUPKlrri4mI6DB/HW9JcYMWhQTRdb1FNezYNw9PBUPS66+/7MzkhX27sgJzMT4/tsOGlibq4ata3Kk5WJtrZOvb85VR2KsrJRFhWhUy6GOmamFGVo3vTTrHtnlAUFpK0vXa81acFSXL+ahYGXB/nhN1XHFcklnxeFsXHomJtiMaR/vepQ8W4eiKPHLNXjyuthRoXR+2WV1EP1kfy5WRlq9dDE3KJiXc3MVBu9bWJuobZfCoC1oxMZoSH/8MpqR12Lp6WdPWNmvE1hfj75ebmYWliy5a/5WFiXDHwwMjFDW1sHG0f1G1w2jk51Ytkvn+ZBOHmU7ktUNp7mZeOZlVlhFkBZJbFSj2dOpqZ4lkuTlYGxWcWZJXvXLufqqeM88cpbWNpWvGGgb2CAvp0DVnYOOHt683+fvMOFw/vpOPCxB7jq2mFsaoaWtrbGOFU2u6ayuN577kEkxkaTlZ7Girnfqo7d+y75zfTJTPnwiwr1s66q6+26voEhNo7OpCbe+dfnqCssLS3R0dEhJTlZ7XhqakqFmRblOTiUbLLu6eVFcXERX3/xBWPHT0BXt178RKx292KZXD6WKalYW99/Pw8Hx9JYFhUV89UXnzNugnos9+zZzScffcSsTz6hW/eGPWIdwNTMHG1tbTLKzULNTE+vMGvlYXk1bsLxg/ur9Zx1waWoeG4nle71pqtTcuPMzMiAtJxc1XFTQwMyc6t3vxOlEqKS07A1q9+z+DQxMTdDW1ubzHIzerMy0jC3ePhZtGePHWHRzz8wYfoMmrdp99Dnq+vMzEve6+nl7h1lpKdVmGXxb+3buYM2nbpg2oD2P6tMTcVz85qVePk2YfDjowFo5OmFgaEhX7zzBqPGP42NXcPYt0+IR6XedGV5enoyatQovvrqK5o1a8bu3bvx9vYmMjISZ2fnCn8GBgZkZGQQFRXF6NGjCQoKws3NjZycHIqKitTOra+vT9u2bZk6dSqzZ88mMjKSy5cv4+TkhK6uLpcvly7vUFRUxNWrV2nUqNGjDsEjpaenh1/jxhw/rT6l8NiZM7RoqnkGyvJf57Nk3i+qv+fGT8DAwIAl836RDerFfekbGmJp56D6s3Z0xtjcgshrpe89RWEhseE3cCq3nExZjh7eRF1XX44l8tpl7Bu5o6Pz37w5oKaoiILIaAz9fNUOG/r5kh9xS2MWbX19lMXlOoeLS0by33cEl5Y2WvXshoy+oRFWd2/8Wtk5YOPojIm5BbevqtfDmIgbOHt6V3oeJ09vtboLcPvqZRzK1EMnD28ir11SSxN57ZLacknOXo1JvROvliY14Q7mVdzUqSvqWjzv0TMwwNTCkrycbG5fvYh3i5I9A3R0dXFw9yA1oXzM4zGrAzGvPJ6l160oLCQm/DouXpV/Tjp5enO7Qjwv4dDIQxVPZ09vtf+nkjSXcfFS/3/as2YZV04e44lX3nzgG/1KpVJ1c72u0tHVxbGRBzevqtepm1cvVRpbF08fosKuoygsKE1/5RKmFpZYVLI5aHlO7l48+8HnTH7vU9Vf4xZBuPn4Mvm9T+vVBvV1vV1XFBaSkhCPibnlvz5HXaGnp0cTPz+Ol1uS6sSx4zS/uxzxg1AWKykqKqL4Xhv/H6SK5TH1WB4/fuyfxVJZXCGWIbt28clHH/HhrI/p1btPtZW5LtPV08Pd24fL5ZaouXzuDN5+ftX6WlE3I7C0un8HYn2Ur1CQnJmt+ruTlklGTh6+zqUDGHS1tfGyt+FWYsp9zvTvOFlZkJmbV+3nrW26unq4eXlz9cI5teNXL5zD0/fh6ubpI4dY9NMPjH/xFVp26PRQ56ovdPX08PBpzMWz6u/1i2fP0Njv4VcvCb92lcibEfToV/XA5IagpuJZkJ9fYSb5vcflZ/YLIapW5+92xcfHs2PHDtq3b4+NjQ3x8fHcunWLQYMG0a5dO9544w3mzZvHgAEDMDIyIjo6muPHj/PSSy9hamqKubk5wcHB2NrakpyczIIFC9Rmk4SEhFBUVESTJk0wNDTkwIED6Orq4uzsjKGhIYMGDWLhwoWYm5vj4ODAxo0bSUtLY9B/YLbF2BGPM+u7b2naxJfApgGs27aVpORkHr+73u+8BX9x6dp1fvn6awC8yyx7BnDlxnW0tbQqHBfqtIwM0XO5OwpdWws9B3v0fbwozsxEcadhbJj6T2lpadGyex9O7NyKtYMjlnaOHN+5BT0DA/xat1elC178JwD9JzwLQIsu3Tl3YA/71q6gReduxEaEcfnYIQZOek6Vp0ihIDk+Fii5oZKTmUFCdCT6BgZY2jkAUJCfR1piyWaPSqWSzJQUEqIjMTQ2qTc3syuTsTsU26fHUnA7krzwm5h17YSOhQWZBw4DYDlsMPoebiTMnQ9A7sXLmPXqhsXgfmQfP12y5NewwShSUimIjAbArEcXFEkpFN4piZlhY2/M+/Qgc/+h2rnIaqKlpUXLHn04frceWtk7cCx4C3r6Bvi1Ka2H2xf9HwADJ04BILBzD87u38Petctp0bk7sRFhXDp2iEFPl9bDVj36sHLuNxzfuRWfFq0IO3+aqOvXGDPzHVWa1j37smL2VxwL3oJvq7YkRkdyJnQ3XYY+rkqTm51FZmoK+bk5AKQlJmBgZIyJuUWd26eituN568pFlMVKrB0cSUtKYP+G1VjZOxLQoXSvtba9B7BlwXxcvH1x8/Uj6vpVrp06wWNT696+AlpaWrTq2ZdjwVuwdnDCyt6BoztK4ulfJp7bFpXsdzRo4lQAArv04Mz+3exZs4zALj2IiQjj4rFDDHl6mipPqx59WfHD1xwL3opPYCvCzp0m6vpVnnqtNJ4hKxdz+cQRhk99GUNjE9XsDD0DA/QNDMnPzeVEyHa8mwdiYm5JTlYmZ/fvISstlSat6v7ml+1692fz37/j7O6Fi3djzhzYS1Z6Gi279gRg34bVxN2K4KlX3wagabsOHNq2ga0L/49OAx8jJSGeozu30nnwMLWZzneiSpaYy8/LRUtLiztRt9HR1cXWyQV9AwPsyixjA2BgZExxUXGF4/VNbbfr+zeswisgEDNra3IyMzkevAVFfj5N2zeMm11Pjh3Lp7Nm0bRpAC0CA1m/bi1JSYkMf7xkychf5/3M5UuX+OmXXwHYvm0bBvr6ePn4oKeny9XLV/j1l3n06NULfX191XmvX78GQHZ2NtraWly/fg09XT08vbwqFqKBeGrsOD6Z9RFNA+7Gcu1akhITGTGyJJa//FwSy59/vRfLrejrG+Dt44Oeri5Xrlzh13nz6Fkmlrt2BvPxRx/xyoxXadmyJclJSUDJTTOLahgNX5f1HTqcP3+cjYePLz7+TQkN3kZaago9+pX8ll675G9u3rjOG598qcoTGxWJQqEgKyOD/Lw8Im9GACWjqQF2bd6Irb09zm6NUCgUHN2/lzPHj/LCW+89+gusBfuvhNOnuS8J6ZkkZmTRp0UT8hUKzkREq9I81aVkM+vlB0+rjjlbldQ1A31dlChxtrKgqLiYO+kleyv2C2zC7cRUEjOyMNTTo6u/F85W5qw9evbRXdwj1HPwYyz+eS7u3o3xauLHwZBg0lNS6dK3ZNnXTcsWczv8Bi9/WLqnWVx0FEUKBdkZmeTn5RF9q2SmvuvdGZmnDh1g0by5jBg/CR//pmTcnQGjo6uLiWnDnlkxYNjj/DbnW7x8fWnsH8DeHVtJS0mm18CSe0erFv5FxI3rvPP516o8MZG3USgUZGZmkJeXx+2IcADcyw3g2bdzOw7OLvg1e/CO7fquJuLZsl17/vp5Lru3baF5q9akpaSw9P/m4+Htg+1/ZFk6IapTne9QMTAwIDY2lq+//pqMjAwsLS3p0aMHI0eORFdXl6+//polS5bw7rvvUlxcjKOjIx06dABKelvfeustfv/9d1566SWcnJx49tln+eqrr1TnNzExYe3atSxYsACFQoGbmxvvvvsujnenbT/99NMAzJ07l6ysLLy9vfn444+rnELfEPTt3p30zAwWLF9OUkoq3h7uzPn0M5wcSn6cJqWkEBMXW8ulrP8M/Xxx/al0eQ+bKROxmTKRjG07ufPl9/fJ2bC16TMARWEBe1YvIz8nG0d3L0a8+Br6hqUbLmakqi/JYGFjx/BpMwhdv5ILB/dhYmFJj5FP0TiotSpNVnoay/5X+sX4QlIoFw6F4uLjy+hX3gLgTuQt1v70nSrN0e0bObp9I/7tOtF//OSauuRHIufUWVJMjLEY2Bdrc3MK4uJImPcHRSl3v/BbmKFnVzqaOu9aGEkLlmDetxfmfXqiLCwk/+ZtEn76HWXB3ZHY2tpYjhiCro0VFBdTmJhM6oYt9X5TeoC2fQaiKCxkz+ql5OVk4+jhxcjpr6FvaKRKk1luOraFrR0jnn+V0HUrOH9wHybmlvQcNRbfoDaqNM5ePgx+ehqHtqzn8LaNWNraM/iZaWpLOjm6e/LY1Okc3LyOozs2Y2ZlQ6fBwwm8e0MXIOLCWYKXLlA93rV8IQAdBj5Gp0EVl8OsbbUZz/zcXA5uXktWWiqGxib4BLamy9ARaqPcfQJb0ffJiRzbuY29a5djZefAgAnP1vr+KZVp12cgioICdq9aQl5ONk4eXox66XW1eGakqMfT0taOkS/MZO/a5Zy7+znZa9RYfFuWxtPFy4chzzzPoS3rOLRtA5a29gyZ/DxOHqU/cM8eKFmCZFWZ9gug48DH6Dx4ONo62iTFxXDhyAHycrIxNDbB0d2TJ199GzsXt5oIR7Xyb9Oe3OwsDm3fRHZGOrZOLoye/ppqtklWehqpdzveAQyNjBnzypvsXLGYv7/+GENjE9r1HkC73uqjKRd8OUvtcdiFs5hb2/DiFw2/va/Ndj0rLZXtC38nNzsLI1MznDy8GPPae/V+kMQ9ffr2Iz09nb8X/EVyUhJe3t58N+cH1R4dyUlJxMTEqNLr6OiwaOHfREdFoVQqcXR0ZOSo0Tz51FNq5316/Hi1xwcPHMDRyYl1GzfV/EXVkr79SmK54K8/VbGc/cNcVSyTkpKIjim9ca2jo8PCvxeUiaUTI0eP5smnxqrSrFu7lqKiIubM/p45s0vf6y1bteLX335/dBdXC9p16UZ2ZiZb16wkPTUF50buzHj/Y2zsS27epaemkhivPjN07ucfk1zm8/XT10v2YPq/dSXLzyoUhaxa+BdpKcno6evj4taIV96fRYvWdb+zvjrsvXgDPR0dHm8fiJGBHpGJqfy+6zD5ZWZ/WpoYV8j3+mM91R4HuDmRkpXDF2t3AmCor8eojkGYGxmQW6AgNiWNeTsOEJWUVqPXU1tad+pCdmYmwetXk5GaipNbI1545wPVfifpaakklZspPv/rz0hJLB3w+M3brwHw08r1ABwMCaa4qIi1C/9i7cK/VOl8mgYwY9bnNX1JtapD1+5kZWawadVy0lJScXV35/WPPsPWvuTeUVpqCgnx6veOvv/0Q5ISSt/rH75aMoBp0aYdqmO5OTkcPRDK8DHjHmrvpfqmJuLZtXc/cnNzCdm6ieV//YGRiTH+zQN58ulnH9FViRrxH3pf1DVaubm5MrerDqrtPVQamoSJz9d2ERqMkA8/qO0iNCiDNqyv7SI0KDtGPF51IiFqgbZ82a1W99aRF9Ujr7BuL8NWnzzRoW52wNZX8tlZvS5GxVedSDyQDScvVZ1IPLD+gb5VJxIPzMLIqOpEQtSCQPf6sR9gfRE/bWZtF6FKjr/Nqe0i1Aj5NSqEEEIIIYQQQgghhBBCCFGFOr/klxBCCCGEEEIIIYQQQggh7tKWmby1RWaoCCGEEEIIIYQQQgghhBBCVEE6VIQQQgghhBBCCCGEEEIIIaogS34JIYQQQgghhBBCCCGEEPWFlsyTqC0SeSGEEEIIIYQQQgghhBBCiCpIh4oQQgghhBBCCCGEEEIIIUQVZMkvIYQQQgghhBBCCCGEEKK+0Naq7RL8Z8kMFSGEEEIIIYQQQgghhBBCiCpIh4oQQgghhBBCCCGEEEIIIUQVZMkvIYQQQgghhBBCCCGEEKK+0JIlv2qLzFARQgghhBBCCCGEEEIIIYSognSoCCGEEEIIIYQQQgghhBBCVEGW/BJCCCGEEEIIIYQQQggh6gktLZknUVsk8kIIIYQQQgghhBBCCCGEEFWQDhUhhBBCCCGEEEIIIYQQQogqSIeKEEIIIYQQQgghhBBCCCFEFWQPFSGEEEIIIYQQQgghhBCivtDWqu0S/GfJDBUhhBBCCCGEEEIIIYQQQogqSIeKEEIIIYQQQgghhBBCCCFEFWTJrzoq9ZP/1XYRGpSQDz+o7SI0GH0++7y2i9CgbJO6Wa10tWTKa3XS1pZxF9WluLi4tovQoCiKJJ7VSalU1nYRGozgc9dquwhCVOpmYkptF6HB8HWyre0iNCgGunJrqjrZmZvUdhEaFPmeJOosuf9Ra+ROiRBCCCGEEEIIIYQQQgghRBWkQ0UIIYQQQgghhBBCCCGEEKIKMq9SCCGEEEIIIYQQQgghhKgvZInuWiORF0IIIYQQQgghhBBCCCGEqIJ0qAghhBBCCCGEEEIIIYQQQlRBlvwSQgghhBBCCCGEEEIIIeoLLa3aLsF/lsxQEUIIIYQQQgghhBBCCCGEqIJ0qAghhBBCCCGEEEIIIYQQQlRBlvwSQgghhBBCCCGEEEIIIeoJLW1Z8qu2yAwVIYQQQgghhBBCCCGEEEKIKkiHihBCCCGEEEIIIYQQQgghRBWkQ0UIIYQQQgghhBBCCCGEEKIKsoeKEEIIIYQQQgghhBBCCFFfaMk8idoikRdCCCGEEEIIIYQQQgghhKiCzFARQgghhBBCCCGEEEIIIUSdsXXrVtatW0dqaiqNGjVi6tSpBAQEVJr+9OnTLFu2jMjISHR1dWnatCnPPPMMLi4u1VoumaEihBBCCCGEEEIIIYQQQtQXWlp1/+8hHDhwgD/++IMnnniCuXPn4u/vz8cff0xCQoLG9PHx8Xz++ecEBATwww8/8Pnnn5Ofn88nn3zyUOXQRDpUhBBCCCGEEEIIIYQQQghRJ2zYsIHevXvTv39/3NzcmDZtGlZWVmzfvl1j+vDwcIqKipg4cSLOzs54eXkxevRo4uLiSE9Pr9aySYeKEEIIIYQQQgghhBBCCCFqXWFhIWFhYbRs2VLteMuWLbly5YrGPD4+Pujo6LBz506KiorIyclh9+7dNG7cGAsLi2otn+yhIoQQQgghhBBCCCGEEELUF9oPt6TWo7Bjxw6Cg4OrTNe/f38GDBigepyRkUFxcTGWlpZq6SwtLTl37pzGczg4OPDZZ5/x9ddfM3/+fJRKJV5eXnz88ccPcwkaSYeKUDHr1Q3zQX3RtbCgIDaOlKWryb8eVml6w2b+WI4Ygr6LM0qFgvwb4aSsWIfiTsladrZTJmLatWOFfMX5+UQ+92pNXUatUSqVHN2+iYuH95OXm4Ojuye9Ro/Dxun+Gx9F37jG/vUrSY6PxcTCkja9B9CiSw/V88lxMRzZtomE6NtkJCfRfsBQOg4apn6OsOuc3hPMnajbZKen0XfcMwS071wTl1mnGQY2w+qpURg2aYyunS3xX3xH5vZdtV2sOqk262t9olQqObxtI+cPhZKfm4Ojuxd9xozHtoo4Rd24xr51K0iKi8HUwpK2fQYS1LWnWprrZ05ycOt60pMSsbC1o+vQx2kc2Fr1/JnQ3Zw7FEpGShIANo4udBgwBO9mgao02Rnp7N+4hltXLpKfm4urjy+9R4/Dyt6hGqNQfc6E7uZ4yHay0tOwdXKh1+ixuPk0qTR9YkwUu1YuIf52BIbGJgR27UmngY+hVWYt1sjrV9m7dvndWFvRru9AWnbrpXae/NxcDmxey7UzJ8nLzsLMyppuj43Cr3U7AOZ/8DoZKckVXt8roAWjpr9WTVdf/WqzfpZ1NHgLBzevI6hbL/o8MV51fPviP7l07JBaWicPL8a98cG/vOKaVZvxPBa8levnTpGaEI+Ori5OHt50fWwkds6uABQVKTi4eT03L18gLSkBA0Mj3Br70W3YKMytbao/GDVAqVRybMdmtXan56ixVbc7Ydc4sH6Vqt1p3at/hXbn6PZNJERHqtqdDgMfUztHQV4eR7ZtIPz8GXKyMrF3aUS3x8fg6O5ZE5da407s3cXh4G1kpqdh7+xC/zHjcff1qzT9negoti9fSMzNcIxMTGndrRfdhgxX+yy9J/LGNf7+7gtsHZ158ZOvVccTYqLZt2ktcZG3SEtKpPvQEfR4bGSNXN+jVt3xvHXtCrvXryI5Po7CgnwsbGxp1aUHnfoPVp2jSKHg4PbNnDtygIzUVGwdnegzcgw+Zdr4+qyrvzctPV0x1NcjNiWdHWcuk5SZfd88jWyt6NOiCXbmpmTm5XP02k1O34xWS9PWpxGtvNywMDYiN7+Q63EJ7LlwncKiogrn69TEk57NfDkZHknwWc0jWesapVLJke2buHAolLzcHJzcvej1xLgHaodC168k+W471KbPQALLfE4CXD97ksNbN6jaoc5DHqdxYCvV88XFxRzZtpErJ46SnZGGibkl/m3b03HgMLR1dACY/fKzGl8/sGtPepdp/+uLfTu2ErxxHempqTi7NWLMM1Np3FTzRseFBQUs+W0ekRERxMVE4ePnzxufflXpuW9cucT3H72Ho4srH/8wr6YuoU7ZsmE9a1YsJyU5GXdPD6a99ArNWmj+TDt/5gzr16zi2pXL5GRn4+TiwvBRT9B/0GC1NG/PfKVC3t8XLsHN3b3GrqOu2LJhPWtXLiclOQV3Dw+ee+nlyuN59gwbVq/i2tUrpfEcOZp+ZeN59gzvzJxRIe9vCxfj1qjhx1PUngEDBqh1lPxTmr6vViY1NZUff/yRXr160a1bN3Jzc1m6dCnffPMNX3zxBdra1bdQl3So1KCioiK0tbX/0X9+bTFu1xrrcU+QvGg5+dfDMevdDYfXpxPz7qcUpaRWSK9ra4PDjBfI2LWXpN/+RtvQAKsxI0ryvDULgOSlq0hdvUEtn+MHb5B37cajuKRH7mTIDk7v3Um/cZOxsnfk2I7NrJs3m0kffIG+oaHGPOnJiWz4bS4BHbowYOIUYiLC2LtqKUamZjQOKrnRUlhQgLmNDT6BrTi8db3G8xTm52Hj5IJ/244EL/mrxq6xrtM2MqIg4jaZO0Jw+ODN2i5OnVab9bU+OR6ynZN7ghk4/lmsHBw5sn0Tq3/6jmc/+hJ9QyONedKSEln76xyad+jKoElTiQm/QcjKJRibmuHbsg0AsRFhbF4wn86DhtE4qDU3zp5i05+/Mva1d3Hy8AYouek/bBRW9g4oi5VcOnaIjb//zIS3P8LOxQ2lUsmG339GS1uL4c+9jIGRESf37GTVT9/xzAefo29g8Mji9CCunDzG7tXL6PvkBFy9fTmzfzdr5s3m2Q+/1HhDOD83l1U/fYurTxMmvD2LlDtxbFv0J3r6+rTrMxC4G+tfZtO8Y1eGPD2N6PDr7FqxGGMzM5q0bAuU3Ihe9dO3GBqbMOzZFzGzsiIzNRUd3dKvQBPfnkVxcbHqcXZGOgu//ljV4VJX1Wb9vCf2ZjjnD+/HzsVV4+u5N2nKoElTVY/v3ZSpi2oznlE3rhLUtWfJDX4lHNq6ntV338tGJqYoCgpIiLpNh/5DsHd1Iz83l33rV7Lml9k8/e6ndTqu95zaXdLu9B37DFb2jhwP3sz6X+Yw8f3P79vubPztRwLad6b/hCnERtxg7+plFdsda1u8W7TiyLYNGs8TsmIhSbHR9Bs3GVNLK66ePMr6X+Yw4d1PMLW0qqlLrhEXTxxlx8olDBr7NI0a+3JibwhLf/yW6Z98g4WNbYX0+bk5LJ7zNe6NmzD1/U9Jio9j44Lf0TMwoFO/QWppc7OzWf/XfLz8AshIU//+X1iQj6WtHf6t2rJnw+oavcZHqSbiqW9oSPte/bB3cUNPX5+o8OtsWbwAPX192vbsC8CeDWs4f/QgQyc+i52TC2GXzrPylx+Y/M4snBp5PMoQVLuOvp60b+zB5pMXScnKpou/N2O7tmH+zoMUKCp2fABYGBsxpnMrzt2KYeOJC7jZWDGgpT/Z+YVci70DQICbE72aNWHr6YtEJaViaWLMkNYB6Gprs/X0JbXzOVtb0NLTlTtpmTV+vdXpRMh2Tu0Jpv/4yVjbO3J0x2bW/vw9z3z4RaXtUHpSIuvn/0CzDl0YOHEKMeE32LNqKUampvgG3W2HboaxdcFvdBo0DJ/AVoSdO82Wv37lyZnv4uThVfLau7Zz9sAeBox/FltnV5Jio9mx+E90dPXoMGAoANO+mK322ncib7Hhtx/xvfudqz45cegAK/76g3FTX8DHvyn7dmzjxy8+5uMf5mFjZ18hfXFxMXr6+vQcOJgLp0+Sm1N5B2F2VhYLfpyDX/NA0jQM2GmIQvfsZv5Pc5k+8zUCmrdgy4b1fPjWm/y2cDH2DhUHel2+dAEPTy9GPfkU1ja2nDpxjB+/+xZ9fX169umrlnb+34swMzNXPbYoN1q9IQrds5vffv6R6a++RtPmzdm6cQMfvf0W8/9epDGeVy5exN3Li5FPjsXaxobTJ47z4/ffldTZ8vFcsAhTczPVYwsLy5q+HCH+FXNzc7S1tUlNVf9OmpaWVmHWyj1bt27F0NCQZ555RnXs9ddf55lnnuHKlSsEBGjuNP83ZA+Vf2DPnj2MHTuWwsJCtePfffcdn332GcuWLWP69OmEhIQwdepUHn/8cfLy8mqptP+MxYDeZB08QlboIQrj4klZsoqitAzMenfTmF7foxHo6pC6egOKhEQKIqNJ3xyMnoM92qYmAChz8yhKz1D96drbomdvR9a+QxrPWZ8plUrOhIbQts9AGge1xtbZhf7jJ1OQn8fVU8cqzXf+YCimFpb0HDUWa0dnmnfqhn+7jpzaUzodztHdk27Dn8CvTXv09PU1nsczoAWdhz5O45Zt6kUHXk3JOXqC5N8XkLXvIBQra7s4dVZt19f6QqlUcnrvLtr3HYRvyzbYObsycMIUCvLzuHKy8jidO7gPUwtLej8xDhtHZ1p07k5A+06c2F0ap1P7dtGosR8dBgzFxtGZDgOG4ta4Caf2ls6o8mnREq+AFljZOWDt4EjXx0aib2hI7M1wAFIT7hB3K5w+T4zHycMLawcn+o6ZgKKw4L7/j7Xl5J5gmnXsTGCXHtg4OdNnzARMzC05s3+PxvSXTxyhsKCAQROnYufsSpOWbWnfbxAndwejVJa8v88e2IuJhRV9xkzAxsmZwC49COjQmRMhO1TnuXDkIDmZmTz+/AxcfXyxsLHD1cdXdQMBwNjMHFMLS9VfxMVzGBga0qRV3b1BUNv1E0puLG5d+DsDxj6DgZGJxtfT0dXFxNxC9WdkYlo9AahmtR3PUS+9TvOOXbFzdsXOxZVBk6aSm5VJbETJTGEDI2NGv/wGfq3bYe3ghJOHF32fnEhKfBzJ8XE1F5hqUtLu7KZNmXan37iSdufafT6vLhwKxcTckh6jxmLt6ESzu+3O6b07VWkc3T3pOnx0SbujV7HdURQUEHbuNJ2HjsS1cRMs7ezpMPAxLG3tOH9oX01cbo06ums7gZ260rpbT+ycXBg0dhJmFpacCN2tMf35Y4cpLMhn+OTnsXdxo2nrdnQeMISju7arPkvv2bTwDwI7dsXV26fCeVw8vek3eizN23dCT79uddg/jJqIp7O7J83adcTexRUrO3tadOiCd0BzIm9cKz3P0YN0HjAE3xYtsbKzp22PPjRuHsSRndseyXXXpHY+7hy5dpNrsXdIzMhi84kL6OvqEuDmVGmeVl5uZOXls/PcVZIzszl7K5oLt2Pp4OuhSuNqY0lMShoXI+NIz8njdmIKF27H4mytvia6ga4uw9u2YMupS+SV+91elymVSs7sC6Fd30H4BrXB1tmV/uOfLfl+fr926FBJO9RrdGk71LR9J06VaYdO7w3BrbEf7fsPwcbRmfb9h+Dm04TTZdqh2JtheDcLwrt5EBY2tng3L/l33K0IVZqy7bmJuQVh589gZe+AW+PKZxvXVbs2b6BTz9507dsfJ1c3npoyDQtLK0KDNW90bGBoyPhp0+nWbwBWGjpby1r0y4907NkbryaVz3RraNavXknfAQMZOOQxGrl78OKMmVjb2LB1o+YBdk+On8ikKVMJaN4CJ2dnhgwbQedu3Ti0P7RCWktLK6xtbFR/OvVgEMnDWr96FX0GDGTAkKE0cvfghVdexdrGmq2bNmhMP2b8BCY9O5WA5s1xcnZm8LDhdOqqOZ4WVpZYW9uo/v4L8WzQtLTr/t+/pKenh4+PD2fPnlU7fvbsWfz9/TXmyc/PrzAL5d7j8t97H5Z0qPwDnTt3pri4mKNHj6qOZWdnc+TIEfr2Len1vXPnDqGhobz99tv8+OOP6NeHG4o6Ouh7NCL3ovpU6NyLVzD08dKYpeDWbVAUYdq9M2hpoWVogGmXDuRH3KI4S/NoDbPuXSiIjiU/LELj8/VZRnISORnpNPIr7e3U1dfHxduXuJuVL5sWfyucRk3Ue0jd/ZuREHmboiJFjZVX/LdJfX0w6cmJZGek4+7fTHVMT18fV58mxERUHqe4m+F4+KnHycO/GXcib6niFHszHHf/imliIsI1nrO4uJirJ49RkJ+Hs2fJja4iRcm5dPX0VOm0tLXR1dUlJrxuzQQsUiiIj7yFR5lYAnj6B1Qay9iIMFy9fdU65jz9m5GVnkZ6cskyaLE3w/AsF0fPps2Iv10a67Bzp3Hx9iFk1RLmvfMKf376Hge3rK+0ziqVSs4fPkDTdnX7pmFdqJ87ly/EN6gNjZpo/kILEBNxg3nvzODPT94leNnfZGdmPPA1Pkp1IZ5lFeTloVQqMTA2vm8aAMP7pKkrVO1Ok6aqY6XtTuVxiLsVgbtfU7Vj7n4B/6jdKS4uRllcjK6u+sR8HT19VYdVfVGkUBB7+ybeTZurHfdq2pzoSj73o8PDcG/cRO2z1DugOZlpqaQlJaqOndi7i6yMdLoNGV4jZa+LajKeZcVF3iIq/AbuvqWflUUKhVr7DSXteWTY9X97OXWCpYkRpkYGRCQkqY4piouJSkrF1cay0nyu1hZE3FEfyR9+JwknK3O07w4Wi0pKxcHSTNWBYm5kSGNne8Ljk9TyDWrVlCsxd7idmFJNV/VopCcnlbRDZdoUPX19XL19VYNpNIm7Ga6WB8DDP4A7ZT4n425VTOPu34zYMt/7XbwaE3XjKil3O+mT42KJvH4Fz3Lvj3sK8nK5dvo4zTtpHoBZlykKC4kMD6NpoPpGx02DWhJ+7eGWh9u3YyvpaakMHvnEQ52nPiksLOTGteu0aqs+s7tV27ZcvnTxgc+Tk52NqWnFgTevTJvK2MeH8c5rMzh35vRDl7euKywsJOz6dVq1UR/Y1bJNW65cfPB45uZkY2pmVuH4jGnPMW7kcN597dX/RDxF/TZ8+HB2795NcHAwUVFR/P7776SkpDBwYMmKFQsXLuT9999XpW/Tpg3h4eEsX76c2NhYwsLCmDt3Lra2tvj4VBww9DBkya9/wMDAgB49ehASEkLXrl0BCA0NxdjYmLZt2xIREYFCoeC1117Dyqr+LB+gY2aKlo4ORRnqNzmKMjLQsdA8qkKRlEL8tz9iP30KNhOfBC0tCiKjufPdzxrTaxkZYtyuFamrN1Z7+euC7Ix0oGSUc1nGZuZkpafdJ18Gbr4V8xQXF5GXlYWJTL8UNUDq64PJvvuZaFIuTiZm5mSlVVwKsTRfOo3K3QA0Ni+JU25WFqYWlmRnpGs8b05mutqxxJholn3/BQpFIfoGBgyb+pJqaSVrR0fMrW04sGkd/cZOQt/AkJN7d5KZlkr2ff4fa0NOVibK4mJMzNRHkRqbW5B99bLGPNkZ6ZhZWasdMzG3UD1naWtX4cYDgImZhVqs05ISSL+WRNO2HRn54mukJycSsnIxhfn59Bz5ZIXXvXXlEunJibSo4zcIart+nj8USlpiAoMmTqUynv7NaBzYCgsbOzJSkji4ZR2rfvyWCW99VOFGYm2r7XiWt2fNMuxdG6k6UMsrUijYt34l3s2CKrxP6qLszPu1O5XHNycjHWNf9Q67f9ru6Bsa4uThzfGdW7FxcsHY3ILrp44TfyscCw3LutRl9z5LTc3VP0tNzS24eUXzTZasjDTMy9WRe/mzMtKxsrPnTnQUoZvX8+y7H1fr2tJ1XU3F857Zb75MTlYmxUVFdB/6OG169FY95x3QnGMhwXg08cfG3pGIq5e4cuYkyjLLT9ZHJneXG83OK1A7np2fj6mR5qX9AEwMDchOUO8Ayc4vQEdbG2MDPbLyCrgcHY+Rvh4Tu5fctNXR1ub87Rj2XCzthArycMXK1JiNJy5U1yU9MjmVfT83NycrLa3SfNkZGTRqovn7edXtUOnv/7Z9B1KQn8ffX36ItpY2xcVFtO8/mKBy+9Ldc/XUcYoUCpq2q3/7dmZllmx0bF6uDTG3sORKmuaNjh9E9O1bbF61gne/+rZeLMVZXTLS0ykuLsKy3D0wSysrUk89WMfmscOHOHv6FN///IvqmLWNDS/NfB1fP38UikJ27wzm3dde5ZsffqR5YFB1XkKdUlk8raysOXv61AOd49iRw5w9fYrvfirdv8fa2obpM1/Ht4kfCkUhe3bu5L3XZ/L1nLkNOp6ifuvatSsZGRmsWrWKlJQU3N3dmTVrFvb2Jd+3UlJSiI+PV6UPDAzkjTfeYO3ataxbtw59fX2aNGnCJ598gmElSwz/W9Kh8g/179+fV199laSkJGxtbdm1axe9e/dWTZOzsbG5b2fKjh07CA4OrvT5ezpmp9PNxKLKdNWq/OwnLS2oZEqUjoU5ts+OJ+vQMbKPnkDLyBCrEUOxnz6F+G9+qJDPtFN7tLS0yT5c95ah+TeunjjK7pWLVY+HTSvZLK3icltKqlqAq2IWZSVPCPHvSH19MJdPHGHX8kWqx4+/8GrJP8pdm1KprPJ6KzyrClOZZyqct+J5rB0cmfjux+Tn5HD97Cl2LP6TJ2a8hZ2zKzo6ujw2ZTrBSxcw7+1X0NLWxr1J00pHEtYJGurPP6k696bpqoWxkjRlHxubmdN/3DNoa2vj2MiD3Oxs9q5ZRo/Hx1R4H5w/tA9Hd08c3OrW5ox1qX6m3InjwOa1PPnqu2p70ZTn16a96t92Lq44uLnz+0dvEXHpPL5Bmje4f1TqUjzL27t2BTHhN3jqtXc13twuLipi68Lfyc/JYcRzFTdrrQuunjzKnpVLVI8fm/YyULHdUSqVaFXV8pTPwz9vd/pNmEzIsoX8OesttLS1sXdthG+rdiRGRz7wOeqyqutp+RjeO6qForCQtb//TN/RY9U6A/7LHiaeZT3z1ocU5OcTHRFGyNoVWNraEdixCwADnpzA5kV/8stHb4OWFtZ29gR16sbZw/ur8UpqXoCbE4NalXYqrzxUMtq54keclqaD5agnuBfNe5+XjWyt6OLvzY4zl4lJScfa1Ji+gX50a+rD/sthWJsa06NZYxaHHqe4mpf1qAlXThwlZEVpOzT8+ZLNoit8P1dSdTtUSRujdZ8vTMpy8b52+jiXjx9m0KSp2Di5kBgdyd61yzG3saN5x64VXvPC4f34tGiJsYYR8PVFhbg9xLkKCwv5Y/b/GDXxGWwdHB+uYPWUprpbZRsPXLpwnm8+/5TnX5lBE//SzxPXRo1wbdRI9dg/oBl34uNZu3LFf6IDoGL9fIDvTMClCxf43+ef8vzLDxDPO3GsW/XfiGdDpaVd/+/DVGXw4MEMHjxY43MzZ86scKxbt25061bzgyOlQ+Uf8vT0xMvLi927d9OhQwfCwsJ4/fXXVc9X1eM1YMAABgwYUOXrxE+rWClqSlFmFsqiInQs1Eet6JiZVZi1co9Z7+4U5xeQuqp0TczE3xbg9sNXGPh4kX9DfVqyWffOZJ88Q3F2TvVfQC3wah6Eo4en6vG9pXfKj6jOyczE2Ny8Qv57TMzNVaNiVXmyMtHW1sHQRPN69EL8U1JfH4xP8yC1fTXKxqnsaNScrMwKowfLMjG3UM0CUuXJzFCLk8Y0WRkYl5vBoaOri5VdycaDju6exEfe5NTenQwYN7nkWCMPJr37Cfm5ORQpFBibmbPk289wrGMb2hqbmqGlra0xLuWv+Z7K4gio8lQWR21tHYzuLhlgYm6Jjo6O2o1pG0cnCgsKyC33f5mdmcGN82foO2bCv7zSmlOX6mfszXBys7L4+8sPVc8ri4uJDr/OuYP7mPH9rxpnoJhaWmFqZUVq4p0HvewaU5fiWdbetcu5euo4T7zyFpa2GjbFLSpiy9+/kRQbzZgZb6vqeV3j1SwIR/ey8S3Zx6B8u1P+PViesbmFatS2Kk/mP293LG3tGfXKmxTm51OQl4uJhSXb/v4N8yrWwa9r7n2WZpWLSXZmRoVZFveYmluSnZGmnv5ufhPzkpmpiXExbPz7dzb+/Ttwt0NBqeTTaRMZ98qbeAfU4Y76h1AT8SzrXgeVg6sb2RnphG5ep+pQMTEz58npM1EUFpCTlYWZpRUha1diZWNXHZf2yNyIS+D/Qkrjp3P3xo6poT6ZuaV7iZoY6JOdn1/pebLz8jExVF9m09hAn6LiYnILSj4/ugc05lJUHGdvxQCQmJGFno4Og1sHcOBKOK42lpgY6PNcn06qc2hra9PI1opWnq78b2MIRXVoj0Xv5oE4esxSPa78+3lGhdklZZV8Py/3OZmlqR0q9x0+U/3zd/+G1bTp3R+/1iWDIeycXclISeb4zm0VOlQSoiO5E3mLLkMf/yeXXGeYmpVsdJxebgZqZnoa5v9yw/P01BTioqNYOG8uC+fNBUo+S5VKJc+PHsbL788iIKjVwxa9TjK3sEBbW4fUFPXZKGlpqVha33/llovnz/PRO28y4ZlnGTJsRJWv5efflNA9mve4aigqjWdqaoVZK+VdunCej955iwnPTGbwsOFVvlYT/6bsb+DxFKKmSIfKv9C/f3/WrVtHRkYG/v7+uLq61naRHk5REQW3IjFq5kfOidI1FI2a+ZF98ozGLFr6+lB+Svq9x+V6SPW9PNB3dyNl2epqLXZt0jc0RL9M55lSqcTY3ILIa5dxdC+5ca0oLCQ2/AZdho+u9DyOHt5EXFCPceS1y9g3ckdHR96eonpIfX0w+oZG6BsaqR4rlUpMzC24ffUSTmXiFBN+ne7DK18X2cnTm7Dz6uvR3r56CYdGHqo4OXt6c/vqZdr1GVgmzWVcvLzvW0alUqn6wV2WgVHJHgqpCXdKfuAOqfoHyaOko6uLYyMPbl25hF+r0vWVb129hG9QG415nL18CN2wCkVhAbp3N5q+dfUSphaWWNy9Cers6cONc+qxvnXlEo7upbF29W7M5RNHUBYXo3W3UyX1Tjx6+voYmaqPqrx45AA6unpqMyvqirpUP31atGLSex5q59ix5C+s7Bxo339wpbNWcrIyyUpLrfRG5aNUl+J5z541y7h66jhjZryFjWPFzZuLihRs+es3kuJKOlNM6kAcK/OP2p1hoyo9j5OHF+EXzqode5h2R8/AAD0DA/Jysrl99RJdHqv8tesiHV1dnN09ibh8kYAyn1MRly/i37qtxjyu3j6ErF2h9lkacfkiZpZWWNraUVxUxAsff6WW58S+ECIuX2TMi69iWc9u8P8TNRHPyiiVShQaNkjX1dPH3MqaIoWCK6ePq5WjPihQFFGgUB8wl5Wbj6e9DXGpJTfwdbS1cbO1YveFa5WeJzolnSbO6p3IXg4l57g320RPR7vCLNSS50p+e16LTSBu1yG154e0bkZKVg6Hr0XUqc4UuF87pP45GRNxg27DKv9+7uTpTfh59e/nt69exqHM56SThzeR1y7Rtk/pwM7Ia5fUlpVUFBSgVW7zYG1tbVBWXIbuwqFQzK1t1fbFqk909fRo5O3DlXNnadOpi+r45XNnadWh031yVs7S2oZZc9SXP9+3YytXzp3lhbffx6YBzwDU09OjcRNfTp88QdcePVXHz5w8Qedu3SvNd+HcWWa98xbjnp7MiNEPtudMeNgNrG1sHrrMdZmenh4+vr6cOXlSPZ6nTlYZz4/ffZtxTz/D8FEPFs+IsDCsGng8hagp/51FcqtRt27dSE1NZdu2bfTr16+2i1Mt0nfsxrRLR0y7d0bPyRHrcaPRsbQgc88BACxHD8PhrRmq9LnnLqLv7obF8MHoOtih7+6G7ZSJKJJTKLipvnyCWY8uFMbfIe9q3dokuTppaWnRsnsfTu7aTti5UyTFxrBz6V/oGRioRvkABC/+k+DFf6oet+jSncy0VPatXUFKfCwXD+/n8rFDtO7VX5WmSKEgITqShOhIFIWF5GRmkBAdSVqZEb4F+XmqNEqlksyUFBKiI8lIUd/csaHTMjJE38cLfR8v0NZCz8EefR8vdB0a7s2Af6O262t9oaWlRauefTm+axvXz54iMTaa7Yv/RE/fAP8yNzy2LfqDbYv+UD0O7NKDzLRU9qxZRnJ8LOcP7+fisUO07V0ap1Y9+hJ5/QrHgreSHB/HseCtRF2/SuuefVVp9m9cTXTYddKTk0iMiWb/xjVE3biGf5sOqjTXTp8g8voV0pISCDt/htU/f4dPi1YVNn+vC9r06s/Fowc5dyiU5LhYdq9aSlZ6GkFdS34ohG5YzYq536jSN23bAT19fbYt+j8SY6O5fuYkx3ZupU3v/qop8EFde5KVlsLu1UtJjovl3KFQLh49qHbDIKhrT/JyskvS3Inj5uULHNy6gaBuvdSm0iuVSs4d2o9/m3YYlLnBUVfVZv00NDbGztlV7U9P3wBDExPsnF3R0tKiID+PfetWEhsRRnpyEpHXr7J+/o8Ym5nTOLDujdCs7fd7yMrFXDx6kCFPT8PQ2ITsjHSyM9IpyC8Z4V1cVMTmP38l7lY4Q555Hi0tLVWawgL1vQrqopJ2pzenQnYQdu40SbEx7Fq6AD0DA5qUbXeW/EnwktJ2p3nn7mSlpRK6bgUp8XFcPHKAy8cP06pn6ffvIoWCxOhIEqMjUSgKyc5IJzE6krTEBFWa21cucuvyBdKTE7l99TJrf/4OK3tHmrb/dzfOalOHvgM5e3g/pw/sJTEuhu0rFpGZnkqb7iX7c4SsW8mi779UpW/erhN6+gZsWPA7CTFRXDl9goM7NtOh70C0tLTQ0dXF3sVN7c/EzBwdXT3sXdxUHWNFCgXxkbeJj7yNorCArPR04iNvk5IQr7Gc9UV1xxPg2O6dXD93huQ78STfief0gX0c3rmVFh1K95qIjgjjyukTpCYmcPv6Vf6fvbuOjupaGzj8i7sLcTc0eIDgFlyKtKVAC9S9vbfu7W17+/VWqbe0xd01ECS4BZdABIlCdOI+3x8Jk0wyIVAIk4T3WStrMWf2ObPPy95zzpxtC7/7P5RKJaHDRt3T828Mh2Ov0CvQh0AXRxwszRndtR0lZWWcTUhRpRndtR2ju1bfqxyLT8DCxIghHYKwszCjo5crHTxdOXjxsipNTEoanbzdaePmhJWpCd6OdvRr609sahpKpZLi0jLScvLU/krLyykqLSUtJ+9ehuAf0dHRoVP/wRyJ2ETMiSjSkxMJX1B5HarZ0WPzvD/YPO8P1evg0Mrr0M6Vi8lITeb0/t2cPbSPLmrXocFcvRjN4a0byUxN4fDWjSRcvEDnGtchn3bBHInYTPyZkygy0ok5eYyonVvx66B+zS4tKeb80UO079VHw/TBzceQ0ePYv2s7eyLCSUlMYMmc31BkZdJvaGXnh1UL5vL1h++o7ZOccJWES/Hk5eZQXFREwqV4Ei7FA6Cvr4+rh6fan4WVNfoGBrh6eGJs0vTvLe/E+EkPErFlM1s2rOfqlcv8Mvs7MtIzGDFmHAB//fYLb75a/Tzp1PHjvPfGa4wYM5YBg4eQmZFBZkYG2TVGDa1evoz9e3aTlJjAlUuX+Ou3Xziwdw+jxzfPkVG3Y/ykyUSEb2bLxg2qeGamZzBi9FgA/vr9V9569WVV+lMnjvP+m68zYsxY+g8eQmZmBpmZGShqrL+0ZsUy9u/dUx3P33+tjOe4lh/PFk1Hp+n/tVDNv0uxFpiamtK7d2/27dtH7969G96hGSg4HEWmuRnWo4ejZ21JSVIK177+kfKMymGG+lZWGDhWP5QuOn+B9F/+wnLEEKyGD0ZZUkpx3CWu/W82yho/7HWMjTAL6UL22k33/Jzuta6Dh1FWWsKO5YsoLsjHydOH8c++qtZDMydLvYHDys6BcU+9ROTqpZzeuwszK2v6T3gY/xpzy+cpsln0fx+rXp9Oj+T0vkhc/QKY9OLrAFy7epmVs/+nSnNw81oObl5L6+69CJs6s7FOuckxDgrAbfaXqtd2j0/H7vHp5GzayrXPvtJizpoebZbX5qT74OGUlZSwfdkCigrycfbyYeLz/1LrUZhTazi2tb0DE555hZ0rF3OyKk4DJ04hoFP1SAxXHz9GzXiafRtWsW/TGqztHRk182mcvap7rOfn5LBx7u8U5CowNDbBwdWNCc+8gnebdjXSZLNr1RLyc3Mws7SmbUhPeg4b04gR+edadw2hKD+PA5vXkZ+jwN7ZlYnPvqoabZKfk632ANTIxJTJL7zGtqXzmfffDzE2NaPboGF0G1TdWGJt78CEZ19lx8rFnNizE3MrawZNeoTATtU9iy1t7Zj8wr/ZsWIJcz97HzNLK9r37EOv4epxunoxmuy0a4ye8VQjR+Lu0Wb5bIiOji7pyYmcPbyf4sICzCyt8QgIYvSsZ9Ty15RoM54n9uwEYFmNaxhAz+FjCB05jtzsLGKreiDP/+IjtTTDps6kXY+mfz/aZdAwykpL2bmi+roz7plX1K47uVnq8bWyc2DsUy+ye/UyTu+NxMzKin4PPKR23clXZLPoy09UrxXpaZzZvxtXvwAmvvAaAMVFhexfv5q87CyMzMzwC+5Mr5HjmuXoynbdelCYl8vujWvJU2Tj6OLGIy++hnXVd2ledjaZNb5LjU1NmfbKm2xa9De//ed9TMxM6TlkBD2HDK/vIzTKzc7i10+qHy5Gpe0gavcOPAOCeOy1d+/OyWlBY8RTqawgYuUSsjPS0dXTxcbBkcEPPKhqpIHKkQc71iwnKy0NQ2Mj/Nt1ZPysZzA2bf5TqB64eAl9PV2GdWyDsaE+SZkKFu+NoqSsXJXGylT9OqAoKGTpvmMM6RBEZx938oqK2HriPBeSqzvk7I2ufHDdr60fFibGFBaXEJOSxq6zLafTXrfBwyvLxvKFFBXk4+Tlw4TnXlW7DtX5nrR3YPzTLxO5agmn9u7CzNKaAROnqI0AdvHxY+RjT7Fvw2r2b1qLtb0jI2c8pTb15cBJU9i3cQ3bly2gIC8Xc0sr2vfsS49a90sXjh2htKSYtj2a32L0NXUL7UN+bg6bVixDkZWJi4cnL7z9AXZVCx0rsjJJS1VvMJ796Udk1Pg++OTflQ0Ev61cf+8y3kT1GziI3JwcFs+fR2ZmBl7e3nz8xf/RyqlyPZnMjAxSkpJV6bdt2URxURErly5h5dIlqu2OrZyYu7RyZpOyslL++PknMtLTMDQywtPLm4/++39079Hz3p6cFtyI55Ib8fTy5qP/fqGKZ1ZGBinJ1fGM2LK53nj+vWQZAKWlZcypHc/Pv6DbfRBPIRqDTmFhYdMa+9pMfPDBB9jb2/PCCy80yvHv5Roq94PNU6ZoOwstxuBP/qPtLLQoEe8134cQTZG+hgWcxT+naUFs8c9U1J4mU4gmpLS8vOFE4pZYmzbNBkMhAC6lZTacSNwSe4vm3/jVlAS5tNwpsbTB3c5a21loUWpPNyj+OVfbpjtlbXOU9v5nDSfSMoeP39Z2FhqFPCm5Tbm5uezZs4cTJ04wZkzT7AUshBBCCCGEEEIIIYQQQoi7q/mNc9eyl19+mdzcXKZNm4anp6e2syOEEEIIIYQQQgghhBDifiIzSmiNNKjcpjlz5jScSAghhBBCCCGEEEIIIYQQLYo0ZQkhhBBCCCGEEEIIIYQQQjRARqgIIYQQQgghhBBCCCGEEM2Fjo62c3DfkhEqQgghhBBCCCGEEEIIIYQQDZAGFSGEEEIIIYQQQgghhBBCiAbIlF9CCCGEEEIIIYQQQgghRHMhU35pjYxQEUIIIYQQQgghhBBCCCGEaIA0qAghhBBCCCGEEEIIIYQQQjRApvwSQgghhBBCCCGEEEIIIZoJHV0ZJ6EtEnkhhBBCCCGEEEIIIYQQQogGSIOKEEIIIYQQQgghhBBCCCFEA2TKLyGEEEIIIYQQQgghhBCiudDR0XYO7lsyQkUIIYQQQgghhBBCCCGEEKIB0qAihBBCCCGEEEIIIYQQQgjRAGlQEUIIIYQQQgghhBBCCCGEaICsoSKEEEIIIYQQQgghhBBCNBe6soaKtsgIFSGEEEIIIYQQQgghhBBCiAZIg4oQQgghhBBCCCGEEEIIIUQDZMqvJsry6RnazkKLMmLeEm1nocXY9N672s5CizL4k/9oOwstyo7339N2FloUAxlCfBdJH5a7qaKiQttZaFEM9eUnwd3Sxq2VtrPQoujqyHXobvJ2tNV2FlqMNUfPajsLLYqdhZm2s9CilJaVazsLLUoFSm1nocVwtbXSdhZaFh35jaktEnkhhBBCCCGEEEIIIYQQQogGSIOKEEIIIYQQQgghhBBCCCFEA2R8vxBCCCGEEEIIIYQQQgjRXMgU3VojI1SEEEIIIYQQQgghhBBCCCEaIA0qQgghhBBCCCGEEEIIIYQQDZApv4QQQgghhBBCCCGEEEKI5kJHpvzSFhmhIoQQQgghhBBCCCGEEEII0QBpUBFCCCGEEEIIIYQQQgghhGiATPklhBBCCCGEEEIIIYQQQjQTOjoyTkJbJPJCCCGEEEIIIYQQQgghhBANkAYVIYQQQgghhBBCCCGEEEKIBkiDihBCCCGEEEIIIYQQQgghRANkDRUhhBBCCCGEEEIIIYQQornQ1dF2Du5bMkJFCCGEEEIIIYQQQgghhBCiAdKgIoQQQgghhBBCCCGEEEII0QCZ8ksIIYQQQgghhBBCCCGEaC50ZMovbZERKkIIIYQQQgghhBBCCCGEEA2QBhUhhBBCCCGEEEIIIYQQQogGyJRfd+itt97C09OTp59+WttZEUIIIYQQQgghhBBCCNHS6co4CW2RBpVbdPr0ad5++20WLFiAlZWVtrPTKFZuj2Dh5o1kZCvwdnXl5SlT6RgY2OB+CampPPbheyiVSnb8+ofaeysitrFiewQp6Wk42dnx6OixjAjt3Vin0KSY9+2F1ZAB6FlZUpKSStbyNRTHXqo3vXHrQKxHhWHg4oSyrIziuMtkrVpP2fU0AIz8fbEeOwKDVo7oGBpSnplJ3r5D5ETsukdnpF1KpZKDm9dxZv9uigoLcPL0ZuCkR7Bzdr3pfokxF9i9eikZqcmYWVnTddAwOvTur3o/IyWJA5vWcT3xCjkZ6YQMG03PEWMb+WyaB+Pgdtg8PBHjQH/0HexJ/fR/5G7epu1s3VNKpZIDm9dxel8kRYUFOHv6MHDyI9g3UO4SYi4QuXopGSlJmFtZ03XwcIJrlDuAiyeOsn/jGhTpaVjZOxA66gH8gzur3t+/aS0HN69T28fUwpKnP/tG9bqkuIi961YSe+o4hfl5WNrY0iG0P10GDr3zk78HonZt59C2TeQpFDi4uDB40iO4+9d/3bmelMDWJfNJuRyPsakZnfoOIHTEWHSq5o7NU2SzfcViUq9eIet6Ku1CQhn12BN1jnNk+1aO7d5BTmY6Jmbm+Ad3ZsD4yRgaGzfauWrD8cjtHI7YTJ4iG3tnVwZOmoK7X/3xTUtKYNvSBaReqYxvcJ8B9Bo+Ri2+O1cu4VrCZbKuX6NtSC9GTK8b3+ZKqVSyf9NaTu2LpLiwACdPHwY/OPWW6vuuVUtIr6rv3QYPp2OfAWppLh4/yt6Nq1X1vc/oB/AP7qJ6/3jkdk7uiyQnMx0AOydXegwbhW+7YFWazfPncPbQPrXjOnv58Mi/373TU7/rju/ewZGaZW/iFNz8AupNn5aUQMSyhdVlr3d/etYoewAJMdHsXHkjzjZ0H6Ie5zMH9rJ5wZw6x37l29/QNzCos/3glg3sWb+STn0HMvjBaXd4xk1P+IZ1rF+xnOzMDNw8vXj0qWdo3a69xrRnT51k0+qVxF64QEFBPk7OLowY9wADwobd41w3DeHr17K2RuxmPP1s/bE7eYINtWI3cvwDDAwbrkqTlZHB3N9/4VJsLCnJSfQdOJjn//36vTodrdu+aQObV68kOysTVw9Ppsx6ksC27TSmLSkpYe7PP3AlLpaUxAT8WrfhrU+/qJOurLSUdcuWsH/XDrIzM7C0tmH4uAcYMvr+uIcf3CGQED9PTAwNuJqRxdrDp7mmyK03vYWJESM7t8XV1gp7C3OOXUpg+YET9aYP9nJlSu8unE9M5e9dhxvhDLTj4Pat7N28ntzsbBxd3Rg5ZTpega3rTZ+acJX1C/4iMT4WEzNzug8YzIAxD6hdm8rKyti1bhUn9u8hJzsLc0sreg8fRa8h1d8B+7du4tCOCLIz0jA1t6B1p66ETZ6CUQu779wdvomI9WtQZGfh7ObOxEdn4de6rca0pSUlLP7jZxIuxZOalIhvYBAvf/CpWpoThw6wJ2ILiZcuUVpagpObO8PGT6JD1+734nS0bnf4ZravX0NOVTwfeHQWfq3baExbWlLCkj9+IbEqnj6BQbz0wX/U0pw4dIB9EeFq8QwbP5H290k8hbjbpEFFABBx6CDfLFrAa9MeJTgggJXbt/Pq11+y6LP/4mRnX+9+pWVlvPfzj3QMCOT4hWi191btiOCn5Ut5c8Ys2vr4ci4+jv/+/ScWpqb06dS5niO2DKZdOmI7eTyZi1dSFBePRd9QHJ97kuSPv6A8K7tOen07WxyfmUnOzj2k/70IHSNDbMaPxvG5J0j+4DMAlMXF5O7aQ2lSCsqSUox8vbGdMpGKkhLydu+/x2d47x2N2MKxnVsZ+shMbBydOLRlPat+/JpH3/203oegiow01vz6HW179GbY9MdJio9l57KFmJhb4N+x8kFWaUkJlnZ2+AV3Zv/G1ffylJo8XRMTSuKvkLslglbvvqbt7GjFkYjNRO0IJ2zqTGwdnTi4ZT0rf/iKGe99iqGxicZ9FOlprP7lW9r16M3w6Y+TFBfDjmULMTE3J6BjVwCSL8Wy8a9f6TViLH7BnYk9eYwNf/7MQ6+8hbOXj+pYNo5OTH6p+qGLjo56D5TIVUu5euEcw6Y9jpWdPUmxF9m2ZC4m5ua06d6rESJy95w7eoiIZQsJe3g6bn4BHIvcztIfvuKJDz7HytauTvriwkKWfPcl7n4BPPbmh2RcS2Hj3D8wMDQipOpHa1lpKSbmFvQMG8mJvbs0fu7ZwwfYuXopw6fOxN0vgOz0NDbNn0NZaSkjp89qzFO+p84fPcT25YsY8tA03HwDOL57Oyt+/JpZ732GZT3xXTb7S9z8Apn2xgdkXkth07w5GBga0n1wZXzLy0oxMTcnZOhITu6NvNen1OgOR2zm6I5whk+dhU0rJw5sXsfy2f9j1vuf1Vvfs9PTWPnzN7Tv0YcRjz5BUlwMEUsXYGpuQUCnqvoeH8v6v34hdMRY/Dt2IeZEFOvm/MyUV9/C2csXAAsbW/qOnYiNYyuUFUrOHtrH2t9+YNob7+Pg6q76PM/ANox4tLoRS1dPrxEj8s9ERx1ix/JFDH5oGm6+/hzfvYMVP37NzPc+vUnZ+x/ufoFMff19Mq+lsnn+HAwMjeg2uPKBfnZ6Git/+oZ2Pfsw8rEnSYyLIWLJfEzMLQisijOAgaEhj3/4f2rH19SYknwpjlP7I9Vi25Lsj9zF3F9+YtZzLxLYti1bN6zn8/fe5utf52Dv6Fgn/cVzZ3H38mb0xMnY2NpxMuoov33/DQaGhvQeMFALZ6A9+yJ38tcvP/H48y8S1LYd4RvW8em7b/HNb3NwcGxVJ/2F8+fw8PJm7KQHsbG15UTUUX79rjJ2fQYMAqC0tBRLSyvGTX6IiM0b7/UpadWhPZEs+uNXpj39HAGt27B980a+/vh9PvvhF+wc6pZFZUUFBgYGDB45mpNRRyjIz9d43J+/+oLM9HQee+4FWjm7kpOdRUlJSWOfTpPQr40ffVv7smz/cdJy8hjcIYDHB/Xky3XbKSkr17iPvq4uBcUl7DobS4i/502Pb2tuyshObYi/ltEY2deaU4f2s3HRXMZMm4lnQBCHtm9l7tf/5aXPvsJaw/OOosIC/vryU7wCW/PsB5+RlpLMyjk/Y2hoRO/ho1Tplv38PdmZGYx77AnsWjmRl6OgtEZZPHlgL1uWLWL8jCfxCggiM+06q//8lbLSEh6Y1XJmOYnav5flc+fw0Kyn8A1sze6tm/nx80947+vZ2No71ElfUVGBgYEh/cJGcPZ4FIUFdet6zPmzBLbtwOgHH8HU3IIjeyL57X//5eUPPqm3oaaliNq/l5Vz5zB51pP4BrZmz9Yt/Pz5J7zz9fc3iacBfW8Sz9jzZ/Fv256RD07BzNyCI3t28/v/vuDFDz6pt6FGCFG/Fjk26K233uKnn35izpw5PPzwwzzyyCOsW7eO0tJSfv75Zx566CFmzJjBjh07ALh27RqjR49m3759vPfee0yYMIFnn32W48ePq95/++23AZg6dSqjR4/mm2+qewhXVFQwb948pkyZwtSpU5kzZw4VFRX3/sTvwOLwzYwM7cPY/gPwcnHlX9OmY2dtzaod22+634/LluDn7s7AbnVbtTfv38eYfv0Z2qMnro6ODOnRk7H9BrBgU8v/IWE5qB95B46Qt+8gZanXyVq2mvKcHCz6hmpMb+jhBnp6ZK/ZSFlaOqWJySjCt2PgaI+umRkAJVcTKTh6gtKUa5RlZJJ/OIqicxcw9vPReMyWRKlUcjwygm6Dh+PfsQv2Lq6ETZ1JSXER0VGH6t3v1N5IzK2sGTBxCrZOLrTv1ZfW3XsStSNclcbJ05u+4yYT1DUEA0PDe3E6zUbBwSNk/PYXebv2QoVS29m555RKJcd3RdB9yAgCOnbF3sWNsKmzKsvd0frL3cl9uzC3sq4cQeXkQofQfrQJ6UXU9upyd2xnBO7+QYSEjcLOyYWQsFG4+wVybKf6CCBdPV3MLK1Uf6YWFmrvJ1+KpXW3nngEBGFlZ0+bkF44efmQcrn+0XBNxeGILbTv2ZuOffpj7+zC0IemYW5pzfFIzdeds4f3U1pSzKjHnsTB1Y2gzt3oETaCwxFbUCory6e1vQNDH5xKh159MK767qwtMS4GF29f2vcIxdreAa+gNrTrEUry5bhGO1dtOLojnHY9Qwnu3R87ZxcGPzgNM0trju/eoTH9uSMHKC0pYcT0J3BwcSOwUzdCho7g6PZwVXyt7BwYPHkq7XvWH9/mSqlUcmznNkKGjCCgU1ccXNwYPu1xSoqLOH+z+r63sr4Pmlxd39uG9OJIjfoetWsbHv5B9Bg2GjsnF3oMG427fyBRNeq7X4dO+LTtgI1DK2xbOdFnzAQMjY1JvqReLvX09dW+E0zMzO9+MO7Q0e1badcjlODQftg5uTB48lTMrKw4saf+sldWWsLw6Y9Xlb2uhAwZztEd1WXv5N6dmFlZM3jyVOycXAgO7UfbHr04sn1LraPpYG5lpfZXW3FhARv+/pWwR2ZgbGp6t0+/Sdi4eiX9hgxl0PARuHl4MvPZ57GxtWXrxvUa049/aAoPPTqDoLbtaOXszNBRo+ke2ptD+/bc45xr34ZVK+k/ZCiDh4/EzcOTWc++gI2tHVs3aI7dAw9N4eHHZlbFzoWwUWMICe3Nob3VsXN0cmLms88zYGgY5rWu4y1d+NrVhA4cTP+hw3Bx92Dak89gbWPLjnoaloyMjXns2RfoHzYc23o69Z05foxzJ0/w6vsf0a5jZxxatcI3MIjW7Ts05qk0Gb1b+7DzbAxnElK4pshl6f7jGBno08nbrd59svILWXf0DFHxCRQU19/wpKujw8O9u7DlZDSZeZobs5qrfeEb6Rzaj279B+Ho4sroaTOwsLbh0A7No+9PHthLaUkJE594llZu7rTrFkLfEWPYG75RdW2KOXOS2HOnefTVN/Br1wEbB0fcff3xqfGw/0rsRdx9/ekU2hcbB0d827SjY2hfEuJj78l53yvbN66lR7+BhA4aipObO5NnPomVjQ17tta+TlcyMjbm4SeeoffgMI0NWgCTHnucoeMm4OUXgKOTMyMnPYSHjy8nj9R/X9ZS7Ny4jpB+A1TxnDTzCaxsbNh7k3g+9MQzhA4eirVd3c4rABNrxNPByZkRkx7E3ceHU/dBPFs0HZ2m/9dCtcgGFYBdu3ZhYmLCV199xcSJE/n999/5z3/+g6urK19//TWDBg1i9uzZZGRU97yYP38+o0ePZvbs2fj7+/Pll19SWFiIvb09b731FgA//vgj8+bN48knn1TtFxkZia6uLl9++SVPPfUU69atY8+e5vMDpLSsjAuXL9O9nfrQ6+5t23E6Nqbe/fadOMG+kyd45RHN0ySUlpZhWKtXoJGhAefi4ygrK7vzjDdVenoYerhRdP6C2uai8xcw8vHSuEvxlQQoL8c8tAfo6KBjZIR5j64UX75KRT09swzcXDHy8aIopmU9BNQkJyOdghwFHkHVN6f6hoa4+gaQcqn+m9HUy3F4BKr3XvFs3Y7rV69QXt6Cy6C4KxQZ6eTnKPCsUe4MDA1x8w2o85CzppRLcWr7AHi1bsu1GuUu5XLdNJ6t25Fcqzwr0tP59d1/8ccHb7Dxr1/ITk9Te9/Vx5/4MyfJzcoEKnvCpyUm4N1G81QaTUV5WRmpVy/Xyad3m3Yk1vMDMyk+Fne/QLWGT+827clTZKPISL/lz3b3C+B6wlWSqj5HkZlB7Knj+LYNbmDP5uNGfL1a14pv67aq864tOT4WN98A9fi2bnfb8W2uFBlplfW9RswMDA1x8wusN2ZQWd+96tT3dly7ellV35MvxeHZum6apHjN3yMVFRVEHz1ESXERLt5+au8lxcfw45svMeejtwhf9Df5uTm3dZ6NrbysjNSEumXvZuebfCmuTtnzaqNe9pLj4zSU5/Zcu3JZ7XpeVlrCr+/+m5/feZWVP3/LtYQrdT4vfNHfBHbqimdgy+yNWVZaSnzMRTp07qK2vUPnLlw8d/aWj1NYUICZedNrsGtMpVWxC+7cVW17cOcuXDh/7paPU1BQgJn5/dVwoklZaSmX42JpV2tmgrYdOxEbff4fH/fYoQN4+wUQvnY1r8ycxhtPP86C336hqLDwTrPc5Nmam2JpYkxMSvX9YFl5BfHXM/C0t73j44d1bE1WXgHH4hPu+FhNSVlZGcmXL+HXTr3Rza9tB67GXtS4z9XYGDwDgtSuTf7tgsnNziKr6n783LGjuHn7si98I1+88ixfv/EyGxb8TXFRkWofL/8gUq5e5mrVc5XsjHSij0cR2KHT3T5NrSkrKyUhPo7WHTqqbW/doSPxF6M17/QPFRUWYtoEO5PcTfXFM6hDMJfucjyL74N4CtFYWuyUXx4eHkyZMgWAcePGsWLFCvT19RkzZgwADz30ECtXriQ6Oho/v8ofq2PHjqV798qRFtOnT2fHjh3Ex8fTtm1bLKp6E1lZWdVZQ8Xd3Z2pU6cC4OrqytatWzl58iT9+vW7J+d6p7JzcymvqMC21nnZWllxtJ4fXunZ2fz37zl8/vxLmJlongYjpH171u+OpH+XbrT29ib68iXWRUZSVl5Odl4e9tbWd/tUmgQ9czN09PQoz1Gfx7Y8Jw/jIM0/rsozs7j2/S84PP4otg89ADo6lCQmcf2H3+ukdf3sffTMzUFPF8XGreTtOdAo59GU5OcogMr1I2oytbAkT5F9k/1ycA+ou09FRTlFeXmYWVnf7ayKFqSgvnJnaUledna9++Xn5OARqLncFeblYW5lTX6OArNaxzWzsKSgxsNRZ0+fyqnGWjlRkJvLofANLPn6Mx595xNVr/QBE6cQsXQev7//Grq6lVP/DJg0BZ92TbtxoCAvF2VFBWaWtWJgacnlaIXGffJyFFja2NZKb6V6z1rD8HdN2nTrQWF+Hgu++gyUUFFRTruQXgx4YPI/OJOmSRVfC/XruqmlFfnRmh8K5ucosKgnvvm3Ed/mKj+nsu5pqpd52Vk32U+BR5D6g3lTy1ut7+plPS0pkUVffUpZWSmGRkaMfeJ5HFyrexx7t26Hf3BnrOwcyMlMZ++GVSz7/kumvf6+xmmttKGwquzV/t40s7DkSs5Nyp61Ta306mUvP1eBp0WtONf6XrVp5cSwqTNxcPWgtLiIqJ3bWPTVZzz29kfYODoBcHJfJNlp1xn56JO0VDk5CioqKrCqFVMraxtOZx2/pWNEHTrImRPH+firbxshh01X7o3Y2dSKnY0N2ceP3dIxbsTuk6+/a4wsNiu5OTlVZdFabbuVtQ3nTp74x8e9nprKxfNn0Tcw4Pk33qEgP58Fv/9MdmYGz7/5zp1luomzMDYCIK+wWG17XmExVqZ3th6Hv7MDwZ4ufLep5U3pWZBbWRZrj1o0t7Ii7txpjfvkKbLrTFN5Y/88RTa2Do5kXb/OlYsX0NM3YMrzr1BUUMD6BX+Rk53JlOdfBaBDj14U5OXyx+cfogQqysvp2KsPYZOn3P0T1ZK8nFwqKiqwqPXb2sLKmpzTJ+/a50SGbyI7M53uffvftWM2Rfk3ieeF06fu2ufsDt9EdmYG3fs2j+eWQjQ1LbZBxcvLS/VvHR0drKys8PSsni9UX18fc3Nzsms8FPP29lb929a28qGCQqH5wU59n3Vj3/r227JlC+Hh4Rrfq2lAm7YMvseLQ+lQayiWUgm1t1X58NefGT9gEO38/DS+DzBjzDgyFAqe/PRjUCqxsbRiRO/eLNi0EV3dljvsq1qtKZJ0qIppXbqWFthNfZC8Q0fJP3IcXWMjrEcPw+Hx6Vz79me1/a599QM6RkYYeXtiPX4UZekZ5B+OasTzuPeijxxk+9L5qtdjn3oRQG0BwErKekpotbq7KOt5Q9zvzh85SMSSearX455+CdBQ7pQ0WH5q71Nd7Gpsr3NY9e8H77bqC+A6e/sw58M3OXdoH10GhgGVC1knx8cy9skXsLS1IzH2IrtXL8PS1g7vNpoX0G1Kal93lDe57tzYo9YOmrbe1NWL0ezbtI6wh6fj4u1L1vVrRCxbyJ71q+k75oHbOFIzoOH773a++m5MadESvy7PHTnAtsXV9f2BZ16u/EeduqtsuL7X3qCxvmv+TqjJtpUT09/6kOKCAi6eiGLL/DlMful1HFwqG1WCuoao0jq4utHK3ZPf3n+d+LOnCOjYpe4Btajud+DNy56m9HW2a7gHqHoDAFcfP1x9qu9LXXz8mPv5+xzbtZ1Bkx8h81oKe9at4OFX3kZPv8X+BFKpE1MN2zSJPnuG2V98zmNPP4dfYFAj5a5pq3vdV95y7L774jNmPPMc/vdp7DSrXRYb/l69GaWyAh0dHZ7+1+uYVk0/Oe3JZ/nfh++iyM6q05jYnHX0cuWBkOpOMn/trJyap/Y9o45OnV+et8XUyJDJPTuxeF8UhSWld3Ckpq3ubdHN7zs1p69+R6msAB148OkXVFNIjp42g7//9zl5imzMray5FH2OnetWMXr6LNx9/Mi4nsrGhXPZvno5g1tQZx6oW62Vt/jdeSuOH9rP6gV/M/Olf2tcf6lF0nTveJfieeLQAdYsmMuMl/6F7f0SzxZK5754tto0tdhfE/q1fijp6OjU2QY1L4qgV2Nhzxtf/Mp6HoDf7LOAetdQGTZsGMOGDWvwmAXH717Lc0OsLSzQ09Ulo1ZP/8ycHGytLDXuE3X+HCcuRPPn2spFvJVKJRVKJb1nPsq/pz/KuP4DMTY05N1ZT/DmozPIzMnBztqatbt2YGpsjHULHgZfnpePsrwcvVo9r/UszCnPydO4j0W/UJQlJWSv3qDalv7XQtw+/wAjHy+K46rXQyjLqJzapzQ5BT1Lc6xGhbW4BhWf9h1x8qpu4CyvmiKudg/qgtxcTC01l1Go7O1+o9exap+8XHR19Vrc/P/izvm2D8bJ6wPV6/rLXU6d3uY1VZY79Ub1wrwctXJnZmlVt2zm5tbp1V2ToZExds4uZKVdB6C0pIS961cyauYz+LbvCICDqztpSQlEbQ9v0g0qpuYW6OjqklcrTgW5uXVGrdxgbmlVJ643pju6MZLiVkSuW0mbriF07N0fAEdXd0pLitk0/y96jxzbJBf5vl034ls7XgW5OZhaaI6VmYb43hgxVd8+zZlf+444e1WvQVazvtccCVWQd/N6WV/c6tb3Wmny6v5f6OnrY+NQuei1k6c3qVcvEbVzK8Memanxs82tbTC3sSEr7VpDp3vPmNRX9vJyb6/s5d0oe5WxN7PQFOfK67mJuebrua6uLk4e3qr4JMfHUZiXx1+fvqtKo6yoICH2Iif27uLlr39pMiN97oSlpRW6urpkV00FeUNOdladkQK1RZ85w3/ff4dJ0x5l6KjRjZjLpsniRuwy1WOnyM6uM2qltvNnTvP5++/w4LRHCRs1pjGz2WxYWFqiq6uLotYov5zs7AbL4s1Y29hiY2unakwBcHZzByAjLa1FNaicS0wlIT1b9Vpfr3LGdgsTYxQF1dNKmRkb1Rm1cjucrC2wNDXm8UE9VdtuPA/5bMoovt6wk/Sc5rumiqlFZVnMrdXpNT8nR+NaWwDmVtYa01e+V7mPhbUNlja2autxOTi7ApCdkYG5lTXbVi2lQ49edOs3EAAndw9Ki4tZ/edvDBg7Qe0ZVHNlbmmBrq4uObVG8OflKOqMsvgnjh/az9wfvmX6cy/T4R53OtYGs6p45tb67szLycaynvJ6O04cOsC8H75l2nMv0f4+iKcQjaXFrqFyt91oNGlui83fCgN9fQK9vDhy9oza9iNnz9Lez1/jPgv+8xlzP/6P6u+J8RMwMjRk7sf/YWC3ELW0+vr6ONraoqery7ZDBwnt2Ald3RZc9MrLKbmaiHFQgNpm46AAiuMva9xF19AQZe1Fv2+UtZt26dRFpwX2sjQ0NsbaoZXqz9bJBVNLK65eqJ4upKy0lOS4GJy96x8l5eTlS8JF9SlGrl44h6OHJ3p6LS9u4s4YGptg49BK9Wfn5IKZpRVXotXLXVJ85aLm9XH29lUrqwBXos/Rqka5c/by5eoF9SkVr144W2e9hJrKSkvJupaqajyoKC+nory8zvepjq7uLXUG0CY9fX2cPLy4fF79unPp/BncfDTHwNXHj4TYC5SVVi+mevn8WcytrLGqZzFLTcpKitHRELM769vZtFTHV72MXY4+q9aDvyYXHz8S4y6qxzf69uPbXNRf36tjVlZaSlLcxXpjBpX1/Uqd+n6WVh5eqvru4u2r9j1SmeYcrj71f49AZWeV8pusOVeQl0tedhbmt9Gg2Nj09PVxcvficrR62bsSfbbe83Xx9q0qe9W9oq+cP6dW9lx86sbwcvRZWnl61Xs9VyqVpCUlqL4z/YI789g7n/DoWx+p/pw8vGjdpTuPvvVRixm1om9ggI9/AKePqU9Rdfr4MQLatK1nLzh3+hSfv/82Ex6ZysjxLWy03i0yqIrdyePqHZVOHY8isHX9a+6cO32Kz957m0mPTGPk+AmNnc1mQ9/AAC9fP86eUJ9q7uzJ4/gFtf7Hx/Vv3YbszEy1NVOuJScBYN/CelqXlJWTkZev+rumyCWnsAh/p+ppOPV1dfF2sOVKeuZNjnRzCenZfL1+J99tjFT9nU9M5fL1DL7bGElWXsHdOB2t0dfXx8XLm9iz6p1WY8+ewsMvQOM+Hn7+XLkYTWlJiVp6C2sbbKqmQfXwDyA3O0ttzZSMaykAWNtXXr9Ki0s036u3oPtOfX0D3H18iT59Qm179OmT+ATc2Wi9qAN7mTv7W6Y9+yKde/S6o2M1F9XxVJ8uLfr0SbzvMJ7HDuxj3uxvmfrsi3S6T+IpRGNpwU+17y5HR0d0dHQ4evQoCoWCwha26N3DYcPZuHcP6yJ3cTk5iW8Wzic9O4vxAwYB8NPypTz/xeeq9L5u7mp/DjY26Oro4OvmjmVVb6GrqSls3reXhNRUzsbH8d5PPxCfmMQzEyZp5RzvpZztkZj37IZ5aAj6To7YTBqHnpUVuXv2A2A9diSOLz2tSl945hyG7q5YjRyKvoM9hu6u2E1/mLLMLEquJgJg0b83Ju3aoO9gj76DPea9QrAc3L/FjU7RREdHh079BnN022ZiT0aRnpzE1oV/YmBkRFCX6ga88PlzCJ8/R/W6Q+9+5GZnsWvlEjJTkzmzf7fadElQ2Sv5euJVridepay0lILcHK4nXiW7CfX41RYdE2MM/Xww9PMBXR0MWjli6OeDfquWvZbCDTo6OnTqP5gjEZuIORFFenIi4QvmYGBopDb1zuZ5f7B53h+q18Gh/cnNzmLnysVkpCZzev9uzh7aR5dB1eWuc//BXL0YzeGtG8lMTeHw1o0kXLxA5wFDVGkiVy8lIeYCivQ0Ui7Hs37OT5SWFNM2pPLm18jEBDe/QPasW0FCTDSK9DTOHtzLucP78QtWXwC2Keo+eBinDuzlxN5dpKcks23pAvIU2XTqW9mDb9fqZSz65gtV+jbde2JgaMSGuX+QlpTIheNHORC+ge6Dh6lNJ3At4QrXEq5QUlhIYUEe1xKukF71oAXAr30nTuzdxbkjB8lOT+PSuTPsXrcKv/YdW8TolBu6DgzjzMG9nNwXSUZKMtuXLSRPkU3HPgMAiFyznCXf1Yhvtx4YGBqyad4fpCUncvH4UQ5t3UjXQWGa41tUSGF+fmV8U5LqfH5zo6OjQ+cBQzi8bRMXT0SRlpzI5vmV9b11jfq+ad7vbJpXvb5ZcO/K+r5jxSIyUpM5tX83Zw7to5tafR/C1YvnORS+kYzUFA6FbyThYjRdatT33WuXkxh7EUVGOmlJiexeu4KEmAu07toDgJLiInatWkpyfCyKjHSuXoxm9S/fY2phiX8Tq+9dBw3lzMG9nNoXSUZqMtuXLyQvO5vg3pVlb/fa5Sz97v9U6dt064G+gSGb51eVvRNHObRtI10HVpe94N4DyMvOrI7zvkjOHNxLt0HVo773bVzDpXOnyU6/zrWEq2xZ8CdpSYmqMm9saoqDi5van4GREcamZji4uN21aUmagpHjJ7ArYivbt2wi8eoV/v7lRzIzMhgyYhQAi/6awydvvqZKf/bUSf773jsMGTGKPgMGkZ2ZSXZmZp3exveDUQ9MYNe2rWzfXBm7P3+ujN3QkZUjdhb++Qcf1YzdyRN89u7bDBkxit4DBpGVmUlWZiaKWrG7FBfLpbhYCgsKyMvN5VJcLAlXrtzLU9OKsLHj2bsjgsitW0hOuMrC338hOzOTAcNGALB83l988d5bavskXb3Klfg4cnNyKC4s5Ep8HFfi41Tv9+jbH3MLC/74/huSrl4h5vxZFv7xK1179cayha7RWdPe8/H0b+tHW3dnWllZMLlXJ0rKyjl+KVGVZnKvTkzupb7oubONJc42lhgZGGBqZIizjSWOVpVr8pWWl3NNkav2V1hSSnFpGdcUuZTX7vjXDIWGjeT43kiORO7genISGxb+TW52Ft0HDAYgfPli5nzxiSp9cI/eGBgasvKPn7mWmMDZo4fZvXEdvcNGVl+bevTG1NycVX/8zLWkBK7EXGDDwrm06xqi6uwQ1LEzR3bt4NTB/WSmXSf2zCkiVi0jKLhzixidcsOgkWM5uGsn+7ZvIzUxgeV//0F2Zia9h1TeD61dNJ/vPnlPbZ+UxAQSLseTn5NDcVERCZfjSbgcr3r/6L49/D37G8ZOmYZf67YosrNQZGeRn6e+Vm1LNGDkGA7t2sn+qniu+PsPFJlZqniuWzSf2Z+8r7ZPSmICiZcvkZ+TS3FREYmXL5F4uXqmk6h9e5g7+xvGTJmKX+s25GRnkXOfxLNF09Ft+n8tVMvoinUP2NnZMWXKFObPn8/s2bMZMGAAr7zyirazddcMDumBIi+Pv9atJUORjY+rG1+9+m+cq3pWZGRnk3T9+m0ds6KigsXhW7iamoK+nh5dglrz27vv4+zQ8h/GFkSdINPMFKvhQ7C1tKQkJYXrP/5OeWblsE09KwsMHKp7/BZdiCX9rwVYDhmI5eABKEtLKb50heuzf0N5o1eMri7W40ehb2cDFRWUpmWQtWbDfbEoPUDXwcMoKy1hx/JFFBfk4+Tpw/hnX8XQuHoBxpysDLV9rOwcGPfUS0SuXsrpvbsws7Km/4SH8a8x13yeIptF//ex6vXp9EhO74vE1S+ASS++3vgn1oQZBwXgNvtL1Wu7x6dj9/h0cjZt5dpnX2kxZ/dOt8HDKSstZcfyhRQV5OPk5cOE517F0NhElSa31rQqVvYOjH/6ZSJXLeHU3l2YWVozYOIUAjp2VaVx8fFj5GNPsW/DavZvWou1vSMjZzylNgVRXnYWm/7+lcL8PEzMLXD28uHhV9/B0rb6u2PkjKfYu24lm+b+TlFBPpY2doSOHEfHqkaJpqxN1xAK8/LYv2k9eTnZOLi4Mvn5V1U90vMUCrLTqq87xiamPPTSa2xdPI+/Pv8QY1NTug8eRvfB6tNo/vmp+o+L2FMnsLK159mqMhs6YgzowO51q8jNzsTE3AK/9h3pN3ZiI5/xvdW6awhF+Xkc2LyO/BwF9s6uTHy2Or75Odlq8TUyMWXyC6+xbel85v33Q4xNzeg2aJjaA2uAuZ9/oPY67vQJLG3tePo/zf87ofvg4ZSVlLB92QKKCvJx9vJh4vP/UqvvObWmArK2d2DCM6+wc+ViTlZdZwZOnEJAp+r67urjx6gZT7Nvwyr2bVqDtb0jo2Y+jbNX9YiN/JwcNs79nYJcBYbGJji4ujHhmVfwbtMOAB0dXdKTEzl7eD/FhQWYWVrjERDE6FnPqOWvKQjqEkJhfj4HtqxXlb0Jz76iXrfTa5e9fxOxdAHzv/gIY1Mzug4Mo2uNRilrewcmPPsKO1Yu5sSenZhbWTNo0iME1ohzcWEhWxfNJT9XgZGxCY7uHjz0yptq36v3i179+pObm8PqxYvIyszE3cuLNz/+FIdWlVPKZWdmcC0lRZU+cls4xcVFrF+5nPUrl6u2Ozi24oe5C+55/rUptN8A8nJyWLl4IVlZmbh7evH2J5+pYpeVmcm15GRV+p3bttYbu5/mLVS9fv256o5UAFGHDtRJ0xKF9OlHXm4u65YvQZGZiaunF6++/xH2jlVlMSuL66kpavt8/cn7ZNT43fnBKy8A8PfaTQAYm5jw2sefseD3n/noXy9jam5O55AeTJo+4x6dlXZFnovFQF+Pcd3bY2JoQEJ6Fn9sP0BJWbkqjbVZ3evCyyP7q71u4+ZEZl4BX6yJaOwsNwkdQnpRkJfHrnWryFVk08rVnemvvqkabZKbnUXm9eoOdcampsx47R3Wz/+Tnz58G2MzM0KHjSR02EhVGiNjY2a89i4bFvzFzx+9g7GpGW06dyNs0sOqNP3HPAA6OkSsXoYiMwMzCwsCO3Zh6IQH793J3wNdevUmPzeHLauXkZOVhbO7B8+++Z5qvRNFdibp11LV9vnpvx+TmZamev3fN14F4MelawDYG7GFivJyVsydw4q51Z0m/du05eUPPm3kM9KuynjmEr56uSqez7z5rmq9E0V2Vp14/vLfT9Ti+UVVPGcvrZymf29EOBXl5ayc+ycr5/6pSufXpi0vffCfxj4lIVocncLCwubf3aAFupdrqNwPcuct0XYWWoxN48ZrOwstyuBP5Oblbtrx/nsNJxK3zMhA+l3cLS2hd2dT0hKnYNWmljQ6Q9u6+rhpOwstiq6Uzbsqv7ik4UTilqw5erbhROKWdfN113YWWhRrE+OGE4lbVtGCpmjTtr5B919Hl8aU+eMfDSfSMtvnHtd2FhpFyx17I4QQQgghhBBCCCGEEEIIcZdI11MhhBBCCCGEEEIIIYQQormQkbxaIyNUhBBCCCGEEEIIIYQQQgghGiANKkIIIYQQQgghhBBCCCGEEA2QKb+EEEIIIYQQQgghhBBCiOZCV6b80hYZoSKEEEIIIYQQQgghhBBCCNEAaVARQgghhBBCCCGEEEIIIYRogEz5JYQQQgghhBBCCCGEEEI0FzoyTkJbJPJCCCGEEEIIIYQQQgghhBANkAYVIYQQQgghhBBCCCGEEEKIBsiUX0IIIYQQQgghhBBCCCFEM6Gjq6PtLNy3ZISKEEIIIYQQQgghhBBCCCFEA6RBRQghhBBCCCGEEEIIIYQQogEy5ZcQQgghhBBCCCGEEEII0VzoyJRf2iIjVIQQQgghhBBCCCGEEEIIIRogDSpCCCGEEEIIIYQQQgghhBANkAYVIYQQQgghhBBCCCGEEEKIBsgaKkIIIYQQQgghhBBCCCFEc6Er4yS0RSIvhBBCCCGEEEIIIYQQQgjRAGlQEUIIIYQQQgghhBBCCCGEaIBM+dVErcwr1XYWWpSK8Q9oOwsthr6Ojraz0KLseP89bWehRRn48SfazkKLEvnhB9rOQouhVCq1nYUWRUeuRXdVSVmZtrPQYmTnF2o7Cy1KhXx13lU/bN2n7Sy0GC+GhWo7Cy1KWzcnbWehRTEqyNd2FloUpdwniaZKpvzSGom8EEIIIYQQQgghhBBCCCFEA6RBRQghhBBCCCGEEEIIIYQQogEy5ZcQQgghhBBCCCGEEEII0VzINMhaIyNUhBBCCCGEEEIIIYQQQgghGiANKkIIIYQQQgghhBBCCCGEEA2QKb+EEEIIIYQQQgghhBBCiGZCR1em/NIWGaEihBBCCCGEEEIIIYQQQgjRAGlQEUIIIYQQQgghhBBCCCGEaIBM+SWEEEIIIYQQQgghhBBCNBc6Mk5CWyTyQgghhBBCCCGEEEIIIYQQDZAGFSGEEEIIIYQQQgghhBBCiAbIlF9CCCGEEEIIIYQQQgghRHOho6PtHNy3ZISKEEIIIYQQQgghhBBCCCFEA6RBRQghhBBCCCGEEEIIIYQQogHSoCKEEEIIIYQQQgghhBBCCNEAWUNFCCGEEEIIIYQQQgghhGgudGUNFW2RESpCCCGEEEIIIYQQQgghhBANkAYVIYQQQgghhBBCCCGEEEKIBsiUX7fhrbfewtPTk6efflrbWRFCCCGEEEIIIYQQQghxP9KRcRLaIg0q9xGlUsn+TWs5tS+S4sICnDx9GPzgVOydXW+6X0LMBXatWkJ6ShLmVtZ0Gzycjn0GqKW5ePwoezeuRpGehpW9A31GP4B/cBeNxzsYvoG961fRse9ABk+eqtpeUlzEnrUriTl1jKL8PCxsbAnuPYCuA4fe+cnfZUqlkgOb13F6XyRFhQU4e/owcPIjtxTLyNVLyaiKZdfBwwnu3V8tzcUTR9m/cY0qlqGjHsA/uLNamjxFNnvXreTSuVOUFBVhZe/AoMnTcPcPBCDmRBSn9kVyPfEKhXl5THrxNdz9g+5qDO4mbZbN45HbObkvkpzMdADsnFzpMWwUvu2CVWnycxTsXruCy+fPUFxYiJtfAIMmPYKNY6u7GIW7R5vlc/+mtRzcvE5tH1MLS57+7BvV65LiIvauW0nsqeMU5udhaWNLh9D+dGmCdf1uMg5uh83DEzEO9EffwZ7UT/9H7uZt2s5Wozm+ewdHIjaTp8jG3tmVgROn4OYXUG/6tKQEIpYtJPVKPMamZgT37k/P4WPQ0ameFzYhJpqdK2/UeRu6D6lb5y8cP8q+DavJTr+Otb0jvUc/QEDHeq5HWzawZ/1KOvUdyOAHp6m2lxQVsXvdCmJO3rge2dGxT3+6Dgy7w6jcXU39u3Pz/DmcPbRP7bjOXj488u937/TU7zptlNeT+yI5e2gfGSnJKJUVOLp50nvUeLXPPRa5nZN7d1XH2dmVnsNGq8W5uVIqlRzasp4z+3dTVFiAk6c3AyZOwa6B8psYe4E9q5eRkZqMmZU1XQaG0aHGterM/t2cP3KAjNRklEoljq4e9BgxFldf/0Y+I+3atWUTW9etQpGVhYu7B5Mfexz/Nm01pi0tKWHhbz9xNT6OlKRE/AJb86+PP6v32LHnz/HVB2/j5OrGB9/80Fin0GREhm9i29pVKLKzcHbzYNKMx/FvXX8sF/32EwmXKmPpG9iaVz9Sj+XFs2dYu2ge15KTKCkuxtbBgdBBQxkyZvy9OJ0m4cFenRjSIRAzI0NiUtP4PeIACRnZ9aYP8fckLDgIb0dbDPX1ScjIZuXBExyJS1BLN7JzG8KCg3CwNCevqJjDsVeZv/sIRaVljXxG2iN1vXGtWrGCxQvnk5GRgZe3Dy+98grBHTtpTHvpUjxff/klly9dIj8/Dzt7ewYPGcrMx5/AwMDgHudc+5avXcOCpUtJz8jAx8uLV597nk4dOmhMG3/5Mv/3/XdcunKFvLw87O3tGTpgAE8++pgqdh9+8V82hofX2dfY2Jg9mzY36rk0BSvWrWP+8uVkZGbg4+nFK888Q6f27TWmjb9yhS9/mF0Zz/x87O3sGNq/P09Mm65WFpevW8vytWtJuXaNVo6OzHh4CiOHDLlXpyREiyINKo2otLS0SV1ID0ds5uiOcIZPnYVNKycObF7H8tn/Y9b7n2FobKJxn+z0NFb+/A3te/RhxKNPkBQXQ8TSBZiaWxDQqSsAyfGxrP/rF0JHjMW/YxdiTkSxbs7PTHn1LZy9fNWOl3wpjlP7d+Pg6lbns3atXMKVC+cYMf1xrOwcSIy9wNbFczExN6dt9153PyB34EjEZqJ2hBM2dSa2jk4c3LKelT98xYz3Pq03lor0NFb/8i3tevRm+PTHSYqLYceyhZiYmxPQsSqWl2LZ+Nev9BoxFr/gzsSePMaGP3/moVfewtnLB4CiggKWfvM5Lj7+jHvqJUzNLVBkpGFqYaH6rNKSYly8fWndrQdb5s9p/IDcIW2WTQsbW/qOnYiNYyuUFUrOHtrH2t9+YNob7+Pg6o5SqWTNbz+go6vDuCdfwMjEhKM7trJs9v+Y8e5/MDQyumdxulXaLJ8ANo5OTH7pddVrnVq9JiJXLeXqhXMMm/Y4Vnb2JMVeZNuSyrreponV9btJ18SEkvgr5G6JoNW7r2k7O40qOuoQO5YvYvBD03Dz9ef47h2s+PFrZr73KZa2dnXSFxcWsmz2/3D3C2Tq6++TeS2VzfPnYGBoRLfBw4CqOv/TN7Tr2YeRjz1JYlwMEUvmY2JuQWBVnU+Kj2X9nz8TOnIcAR27cPFEFOvm/MSUV9/GxVvT9SgSB1f3OvnZuWoJV6LPMfLRJ7CycyAh9gJbF/2NiZkFbUOaThltyt+dN3gGtmHEo0+oXuvq6TViRP4ZbZXXhIvRBHXujquvPwaGhhzdsZUVP37Fo299hI2jEwAW1jb0GzcJG4dWKJWVcV7z62ymvfkBjhrKbnMStX0Lx3ZuZciUGdg4OnE4fD2rf/qG6e/8B0NjY437KDLSWPvr97QNCSVs2uMkx8ewc/kiTMwt8K9qOE2MvUBAp244+/hhYGDIsV3bWPPLt0x57f0m2xHiTh3Zt4elf/3OlMefxq91G3aFb2L2Zx/x4Tc/YuvgUCd9RUUFBgaG9B8+kjPHoijMz6/32Pl5efw1+xuC2geTnZnRmKfRJBzdt4dlf/3Ow48/jW9QG3aHb+LHTz/i/ZvF0tCQfsNGcvZ4FAUaYmlkbEz/EaNw9fDC0NCQuAvnWfTbTxgaGdEvbMS9OC2tGt+9PWO6tmP25t0kZymY1LMTH0waxvNzVtTb8NHWzYnTV1NYtDeKvKJi+rb25fWxg3h/6WbOJ10DoE+QD9P7duOnrXs5l3iNVlYWPDesNwb6evwUvvdenuI9I3W9cW3fto3vvvmKf732Bh2Cg1m9cgX/fuVl5i9eipOTU530BvoGDB8xEv/AACzMLYiNieGLzz+jvKyMZ194UQtnoD1bd+7gqx9+4I2XXqZj+/asWLuWl958g2V//Y1Tq7rXXgMDA0YODSPQ3w8LM3MuxsXx2ddfUV5ezotPVc4I8+/nnuf5J55U2+/xF16ot5GmJdm2axdf/fwTb7zwIsHt2rJi/Xpefudtlv4xBydHxzrpDfT1GTlkCAG+fliYmxMTH8dn33xDWXkFLz5ReR++Yv16fvjjD95+5RXaBbXmbHQ0n337DZbm5vTp2fNen6IQzZ6MDbpNFRUVzJs3jylTpjB16lTmzJlDRUUFALNmzWLRokV89913PPTQQ3z11Vdazm01pVLJsZ3bCBkygoBOXXFwcWP4tMcpKS7i/NFD9e53cu8uzK2sGTT5EeycXOgQ2o+2Ib04sr26p0DUrm14+AfRY9ho7Jxc6DFsNO7+gUTtVO95XVxYwMa5vzFsygyMTMzqfFbSpTjadO+FR0BrrOzsaRsSirOXDymX4+9eIO4CpVLJ8V0RdB8ygoCOXbF3cSNs6ixKiouIvlks91XGcuCk6li2CelFVI1YHtsZgbt/ECFho7BzciEkbBTufoEcqxHLoxGbMbO0Zvj0x3H28sHK3gGPwDbYObmo0rTp3oueI8bi3UZzD4amRNtl069DJ3zadsDGoRW2rZzoM2YChsbGJF+KAyDr+jVSLscxePJUnL18sG3lzJAHp1FWWkJ0VP350xZtl08AXT1dzCytVH81G/ugsmGmdbeeeAQEYWVnT5uQXjh5+ZBy+dLdDUYTU3DwCBm//UXerr1QodR2dhrV0e1badcjlODQftg5uTB48lTMrKw4sWeHxvTnjhygrLSE4dMfx8HFjcBOXQkZMpyjO8JRKitjdXLvTsysrBk8eSp2Ti4Eh/ajbY9eHNm+RXWcqJ1b8QgIomdVne85bDTu/kEar0cb/v6VsEdmYGxqWic/yfGxtOneU3U9ahcSirOXLymX4+5ilO5MU//uvEFPX1/t+8DEzPzuB+MOaau8jprxFJ37D6aVu2flteWh6RgYGXPp3BlVGv/gzpVxdqwV5/jYxg1KI1MqlRyP3E7XwcPx79gFexdXhj4yk5LiIi7c5Np6el8kZpbW9J84BVsnZ9r16kvr7j05tnOrKs2w6U8Q3Hcgjm4e2LRyYuDkqRgaGXMl+ky9x23uItavpVf/QfQZEoazmzsPz3oKK2sbIrdu0pjeyNiYR556lr5DhmFjV7fRsKZ5P31Pj/4D8QkIbIysNznbN6ylZ/9B9B5cGcsHZz2FpY0Nu28SyylPPkufIcOw1tAAC+Dp60e30L64uHtg38qJkL4DaBPcidjzZxvzVJqMUZ3bsurQKQ7GXOFqejazN+/GxNCAvq19693nz52HWH34FLGp6aRm57LswAnir2UQ4u+pShPo6sjFlOtEnosjLSePMwkp7DobS4Bz3YaFlkLqeuNasngRI0aOYsy4cXh5e/PKv1/Dzs6eNatWakzv5u7OiFGj8PcPwMnZmd59+zI0LIyTJ0/c24w3AYuWL2dU2DDGjxqFt6cnr734IvZ2dqxYt05jendXV0YPG0aArx/OTk70Cw1l2KBBnDh9WpXG3Nwce1tb1V9SchJJKcmMGznyXp2W1ixauZJRQ4cybsQIvD08ee2557G3tWXl+vUa07u7ujJqaBgBvr44t2pF3569CBs4iBNnquO5eXsEY4ePIGzAQFydnRk6YADjRoxg3rKl9+q0RCPQ0dVp8n8tlTSo3KbIyEh0dXX58ssveeqpp1i3bh179uxRvb9mzRrc3Nz4+uuvmT59uhZzqk6RkUZ+jgLP1u1U2wwMDXHzCyTpJj/KUy7F4RWkPoTYq3U7rl29THl5ZY+i5EtxeLaumyYpXv2hytbFcwno2BWPwNYaP8vNx5+40yfIycoEKnsaX09MwLtGnpsCRUZ6ZSxrxMXA0BA334A6D5JqSrkUp7YPgFfrtly7ekUVy5TLddN4tm5H8qXq/6PY08dx8vJmw5+/8PNbLzP/vx9yPHK76iFOc9MUyuYNFRUVRB89RElxES7efgCUl1UeS7/GaDMdXV309fVJiou5jTO9N7RdPgEU6en8+u6/+OODN9j41y9kp6epve/q40/8mZPkVtX15PhY0hIT8G7TtOq6+GfKy8pITbiMV63v7pvVveRLcbj5BmBgaFidvk078hTZKDIqpzpKjo+rc0zv1u25dkW9znsF1U7Trs7D5/BFfxPYqSuegW005sfV15+4MyfIyarsoZkUH8P1xKtNqpG6qX933pAUH8OPb77EnI/eInzR3+Tn5tzWeTY2bZZXTXkpLyvFSEMjH1TG+XxVnF19/DSmaS5yMtIpyFHgUaMO6hsa4uobQMrNrlWX4/EMUq+3nkFtuV7jWlVbeXkZZWWlGGvozNMSlJWWcjU+ljbBHdW2tw7uRNyF6Ds69q4tm8jJzmbkhMl3dJzm4kYsW2uIZfwdxrKmhEtxxF+Ixv8+uO9pZWWBjbkpJ68kqbaVlJVzLjGVQNe6vaxvxsTQgLyiYtXr84nX8HK0UzWg2FuY0c3Xg6j4hPoO0axJXW9cpaWlXLwQTbeQELXt3UJCOHP61C0dIzEhgUMHD9KxU+eGE7cgpaWlRF+8SI+uXdW2h3Ttyqmzt9aZISEpiQNHjtCpQ/1Tmq7euBEfLy+C27Xs787S0lKiYy4S0kV9yuKQLl04de7WGuITkpI4ePQInWuM5iktKcWoxr0rgJGhEWcvXKCsrOVOkyhEY5Epv26Tu7s7U6dWrvvh6urK1q1bOXnyJP369QOgXbt2TJgwQZtZ1Cg/p/IBhpmFpdp2MwtL8rKzbrKfAo9aP1xNLS2pqCinMC8Pcytr8nMUGo9bkKtQvT61L5LstOuMmP4E9Rk4aQrblszjt/f+ja6unmqbb/uOt3SO90pBTuV5mdY6Z1NLS/Kys+vdLz8nB4/AWvtY3Gosqx9AKdLTOLlnJ50HDKX7kOFcT0pg5/JFAHTqN+hOTk0rtF02AdKSEln01aeUlZViaGTE2CeeV01LZ+vkhKWtHXvWrWLolEcxNDLm6M6t5GZnka/I/qen3Wi0XT6dPX0qpxpr5URBbi6Hwjew5OvPePSdT1Q90wdMnELE0nn8/v5rqro+YNIUfFrAmgACCvNyUVZU1CmDZhaWXMk5p3Gf/BwFFtY2tdJbqd6ztncgP1eBp0WtOq+hjJpa1i37+TXq/Mmq69HIR9WnEKhp0KRH2Lp4Lr++W309GjT5kSZ1PWrq351Q2ZjlH9wZKzsHcjLT2bthFcu+/5Jpr7+v1kitTdosr7XtXb8KAyNj/Nqrz9WelpTAwv9Vx3ncky9onKquOblRJ+tcqywsyVPUX34LchSYBqh3zLkR16K8PMw0xPXAxjUYGhrh3b5lXmPycnOoqKjAwtpabbultTXRp0/+4+MmXbnMhuWLefOzL5vkVH2N4UYsLWuVI0sra6Kz/3ksb3jrqRnk5SgoL69g5KSH6Dt0+B0fs6mzNqucfjI7v1Bte3Z+IbbmmhuPNRnWsTV2FmZEnqvuMLDvwiUsTIz55KER6KCDvp4uu87GMn/30buT+SZG6nrjUmRnU15ejq2trdp2W1tbjh45fNN9n35iFhcvXKCkpITRY8fx1DPPNmZWm5xshYLyigpsbdTvjWxtbDgcdeym+858/nkuxFykpLSUcSNH8tzjj2tMl5eXx/bISJ6dNeuu5bupys6piqe1hngeP37TfWe9/BIXYmIq4zl8BM/OmKl6r0fXLqzbsoUBvXvTOiCA8zEXWbtlM2VlZWQrFNg3MIpNCKFOGlRuk5eXl9prW1tbFIrqBwz+/jdf8HLLli2Ea1hYqzYrnyB8O3ZtMF19zh05wLbF81SvH3jm5cp/6KgPt1IqlXW21VbnXeWNQ9V4p85xq/+deS2FPetX8tDLb6GnX3+ROxYZQVJ8DOOfehFLWzsSYi8SuXoZVnb2Wu0VfP7IQSKWVMdy3NMvAbXOHyrj0lAs64mTeixrH1Z95IlSqaSVhxd9xlQ23Dm6e5J9/Ron9+xsFg0qTals3mDbyonpb31IcUEBF09EsWX+HCa/9DoOLm7o6ekz5vHnCF/4Fz++8SI6urp4BrZpMj3Vm1r59G6rHhdnbx/mfPgm5w7to0vVgt7HI7eTHB/L2CdfwNLWjsTYi+xevQxLW7smE1dx5+qWJ+VNi6Cm9HW21znAjfJXvV2nTiGtLqOZ11LYs24FD7/y9s2vR7siSIqPZfzTL1WW0ZgL7Fq1FCtb+zpl/F5pbt+dAEFdq3t5Ori60crdk9/ef534s6cI6Nil7gG1SFvl9YaonVs5uW8Xk194DSMT9fVvbFs58+hbH1FcWMDFE0fZPO8PHnz5DVWcm4PoowfZsXSB6vWYp14A6om7hvioqb1PdQGuk/T4rgjO7NvN+OdexaiedYVaitpxu5ORy6Wlpfz+zZdMnD4T+1Z11w1o8TSUsQa+Vm/Jvz7+nOKiIi7FXGD1grnYO7YipN+AOz9wE9K3tQ9PDQlVvf50VeVUkbVL4+3Es4e/J4/268bXG3aSllO9BkgbNycm9Qzm94gDXExJw9nakpkDQ3gotBNL9t38oWNzJnW9cWm+H7h5gf3oP59RUJBPbEwMP82ezcL585j26GONmMumSdPvz4bq+mfvv09BQQExcXF8/+svzF2ymBlTHqmTblPENirKyxkxZOhdzHHTpum3eUP3SJ+9/Q75hYXExMcx+/ffmbd0KY89/DAAMx+ZSkZWFrNefgmUSmxtbBg5ZAjzly1DV1cmL2q27sYNivhHpEHlNulreABzYw0VAKMGFqgeNmwYw4YNa/Bz5u+Juv3M1eDXvqPaItE3pi3Kz1FgaVPd66IgL7dO78CazCytyM9R75FakJuDrq4exmZm9afJy8G0qrdm8qU4CvPy+Puz91TvKysqSIy7yMm9u3jpq59RKpXsWbeSMbOeVfUAdnB1Jy3xKke2b9HqQ1bf9sE4eX2gel0zlhY1Y5mbU6dHb01mlpZ14lSYpymW6tOhFOSq/x+ZWVqprZcCYOvkTE5kxG2emXY0pbJ5g56+PjYOlYvlOXl6k3r1ElE7tzLskcoeHU4eXqqHWeVlZZhaWLLgy09w8vC6zbO/+5pa+azN0MgYO2cXstKuA1BaUsLe9SsZNfMZ9bqelEDU9nBpUGkBTMwt0NHV1VD3cuvUvRvqq6tQ3YPdzEJTnc9FV1cPE/Ob1PncXNXogeT4yuvRX5++q3pfWVFBQuxFTuzdxctf/4JSqWT3uhWMmfUsflVl1NHVnetJVzm8fYvWGlSa43dnbebWNpjb2JCVdq2h071ntFleb4jauZU961cz8dlX1P6Pb9DT11ctpu7k6U3KlctE7djKsKma49wU+bTriJNnzfJbCtS9VhU2UH5NLa1UIzFV+1TF9Ub5veH4rggObFrD2KdewsnT+26cRpNkbmGJrq4uObVGpuUqFFjW6sl+qxRZmaQkJjD3x++Y++N3QOVDRaVSyTOTx/HC2x/QpmOnBo7S/Nw0lhpGP92uGw+sXT29yFFks2H54hbXoHI49ioXU6qnejWoGvFgY2ZCRm51Y4iVqQnZBYV19q+th78nL43ox/ebd3MkTn0qrym9O7P3fDwRpy8CcDU9CyMDfZ4NC2XZ/hNUNNPpkOsjdb1xWVlbo6enR0ZGhtr2rKysOqNWamtVtei6t7cPFeUVfPH5pzz8yFSNz45aImsrK/R0dcnIzFTbnpmdVWfUSm03Flj38fKivKKCT//3JdMefAj9WqOl1mzcyIC+fbGyrP8eoaWwtqyKZ5Z6PLOys7C1sb7pvq1uxNPTk4ryCj795mumTp6Mvp4exkZGvPevf/PWSy+TkZWFva0tqzdtwszUFGsrzfe8Qoj63R/f8PchQ2MTDGv0xFMqlZhZWnEl+izOVT8qy0pLSYq7SL9x9c+V6uztS+wp9WGaV6LP0srDCz29yuLj4u3LlehzdB88vEaac7j6VC406NehM4++7aV2jC0L/sTGoRUhYSPR09enpKiIivLyOq3wOrq6Wl8bpP5YnlP9QC8rLSUpPoa+YyfVexxnb1/iTqn3lroSfY5WHp6qWDp7+XL1wlm6Da5udLt64azanPQuPv5kXUtVO07W9WtY1rMQZlPTlMpmfZRKpephZU1GJpVTE2Rdv8a1q5fpPWp8A2fb+Jpa+aytrLSUrGupuPsHAVBRXk5FeXmdXjBNoa6Lu0NPXx8ndy8uR58lsHM31fYr0WfrHZXg4u3L7rXLKSstVU0FdeX8OcytrLGys69M4+NLzEn1Mno5+iytPNXr/OXos3QfMlwtjUvVehN+wZ15zNNL7Rhb5s/BxrEVIWGj1K5Hdcqoji4oK9CW5vzdeUNBXi552VmYWzadH23aLK8AR7aHs2/DaiY8+wpufgG3lmllBWVVDRLNhaGxMYbGxqrXSqUSU0srrl5Qv1Ylx8XQe+zEeo/j7OVD3OkTatuuXjiHY41rFcCxnVs5uGkdY596EVffm48eb+70DQzw8PHj3KkTdOnVW7X9/KkTdO7R8x8d08bWjve/nq22LTJ8E+dPnuDp19/GzuH21r5oLm7EMvrkCbr0rI5l9KkTdAr5Z7Gsj7JCSVlp86rHt6KotIzU7Fy1bVl5BQR7uhCbWrnGlIGeHq1dWzEv8shNj9Ur0JsXhvVh9pY9HLh4uc77Rvr6dRpNKm5llFszJXW9cRkYGBAQGMSRw4cZOGiwavuRw4foP2DgLR+nQllBeXm5Wqfbls7AwICggAAORR1lcP/+qu2Ho6IY0KfvLR9HeSN25eVQo0HlzPlzxMTF8a/nnr+b2W6yDAwMCPIP4HDUMQb37afafujYMQb27nPLx6lQKjXGU19fn1YOlWtPbdu1k9CQEBmhIsQ/ILXmPqGjo0PnAUM4vG0TF09EkZacyOb5czAwNKJ1jSk5Ns37nU3zfle9Du7dn9zsLHasWERGajKn9u/mzKF9dBsUpkrTuf8Qrl48z6HwjWSkpnAofCMJF6PpMmAIAMampji4uKn9GRgaYWxmhoOLGzo6OhiZmODmF8judSu5ejGa7PQ0zhzcy7nD+/Hv0LQWddPR0aFT/8EcidhEzIko0pMTCV9QGcua05tsnvcHm+f9oXodHFoZy50rF5ORmszp/bs5e2gfXdRiOZirF6M5vHUjmakpHN66kYSLF+hcFUuALgOGkHI5nkPhG8hKu8bF40c4Hrmdjn2qb/QK8/O4nniV9JTKBSCz065zPfFqnd6yTYE2yybA7rXLSYy9iCIjnbSkRHavXUFCzAVad+2hSnPh2BGuXjxPdvp1Yk8dZ/kP/8OvQ+c6Cw43Bdoun5Grl5IQcwFFehopl+NZP+cnSkuKaRvSC0BV1/esW0FCTDSK9DTOVtV1v+CmVdfvNh0TYwz9fDD08wFdHQxaOWLo54N+KwdtZ+2u6zpoKGcO7uXUvkgyUpPZvnwhednZBPeu7Im7e+1yln73f6r0bbr1QN/AkM3z/yAtOZGLJ45yaNtGug4MUzW0B/ceQF52ZnWd3xfJmYN76TaouoGvy4DKOn8wfAMZqSkcDN/Q8PXIyAhjU/Xrkbt/ILvXrqi+Hh2ouh4FN51pqpr6d2dJcRG7Vi0lOT4WRUY6Vy9Gs/qX7zG1sMS/idV1bZXXw9s2s3vtcoZNnYmNoxN5CgV5CgXFhQWqNJFrasY5gd1rl3M15gJtut3dh7v3mo6ODp36DSIqYguxJ4+RnpzEtoV/YWBkRGCX6vIbvmAO4QvmqF63D+1HXnYWkauWkJmawpkDezh3eD+dB1RPARK1PZx961cxeMqjWDu2Ij9HQX6OelxbmsGjx3Jg1w72RmwlJTGBpX/+jiIrU7VGx+qFc/n6w3fV9klOuErCpXjycnIpKioi4VI8CZfigcqGRlcPT7U/C0sr9A0McPXwxNik5U6fNmhUVSy3V8Zy2Z+/o8jMpE9VLNcsnMu3H6nHMuVGLHNzKa4VS4CdmzdwOuoI11OSuZ6SzL7tW4lYv5ruffrfy1PTmg3HzjK+ewdC/D3xsLfmheF9KCotY/f5OFWaF4f35cXh1Q9eQwO9eXlEPxbsOcq5hFSsTU2wNjXB3Lh6QeWj8QkM6RBIaKA3jlbmBHu68HBoZ47GJ7S40Sk3SF1vXA89PIXNGzewfu0aLl+6xLdff0VGejrjxj8AwC8//chLz1evj7Jl8yZ2bI/gyuXLJCUlsT1iG7/+/BP9BwzEsNbi3y3dlEmT2BAezpqNG7l05Qr/+2E2aenpTBg9GoAffv+dZ/71qir9pq1bidi1i8tXr5KYnMy2XTv58fc/GNivX53Yrdm4EQ83NzoHt8y10DSZMmECG7ZtZc3mTVy6eoWvfvqR9IwMHhg1CoAf58zh2ddfU6XfFLGNiN2RXL56laSUFLZFRvLTn3MY2KevKp5XEhPZFLGNq0mJnI2O5p1PPyXu8mW1dVZEM6Sr2/T/WigZoXIf6T54OGUlJWxftoCignycvXyY+Py/1Hq85tQapmlt78CEZ15h58rFnNy7CzMrawZOnEJAp+r1XVx9/Bg142n2bVjFvk1rsLZ3ZNTMp3H2unlP1tpGz3ya3WtXsGnubxQV5GNpa0foyPFNcl2QboOHU1Zayo7lCykqyMfJy4cJz72qFsvcWkM0rewdGP/0y0SuWsKpvbsws7RmwMQpBNRYK8fFx4+Rjz3Fvg2r2b9pLdb2joyc8ZTaFCBOnt6MeeI59q5fxcEt67GwsaPXyHEE96meMiD+9AnCF/6ler1t8VwAegwfQ68RY+96PO6UNstmfk4OG+f+TkGuAkNjExxc3ZjwzCt4t2lXI002u1YtIT83BzNLa9qG9KTnsDGNGJE7o83ymZedxaa/f6UwPw8TcwucvXx4+NV3sLS1V6UZOeMp9q5byaa5v1fWdRs7QkeOo2PfW+/91RwZBwXgNvtL1Wu7x6dj9/h0cjZt5dpnX2kxZ3dfUJcQCvPzObBlPfk5CuydXZnw7Cuq3vt5CgXZ6ddV6Y1MTJn8wr+JWLqA+V98hLGpGV0HhtG1xkN+a3sHJjz7CjtWLubEnp2YW1kzaNIjBKrVeX9Gz3iavRtWsW9jZZ0fPetpXLxv73o0asYz7Fm3go1//1p9PRrV9K5HTfm7U0dHl/TkRM4e3k9xYQFmltZ4BAQxetYzavlrCrRVXo/v3k5FeTnr//xZLT9tQ0IZMb1yUdb8HAUb//6N/FwFRsYm2Lu6M/HZV1rE9IhdBg2jrLSUnSsWUVyQj5OnD+OeeUVtJEuda5WdA2OfepHdq5dxem8kZlZW9HvgIfxrjCY6uXcnFeXlbP77N7V9W3fvydB6pqNr7rqF9iE/N5dNK5ehyMrExcOT599+X9W7XJGVRXqt0c0/fPYxGWnV5fo/r70MwK8r1t2zfDdFXUP7kJ+Xy+aVy8jJysTZ3ZPnasUyrXYsP/+YzBqx/Oz1lwH4eXllLCsqylm94G8y0q6jq6uHg5MT4x55lD5DGp4GuiVYffg0hvr6PDmoJ2bGhsSkpPHxii0UlVaPaLS3VJ+yL6xjEPp6uswa2INZA6s7OZ1JSOH9pZsBWH7gBEqlkod7d8bO3IzcwiKOxiew8A6nzm7KpK43rkFDhqBQKJj7119kZKTj7ePLl19/g5OzMwAZ6ekkJSap0uvp6bFg7lwSEhNAqaSVkxMPTJjIgw89rK1T0JqhAwaiyMnhzwXzSc/MxNfLi28//y/OTpVTHaZnZpCUnKxKr6enx9+LFpGQlIhSqcSpVSsmjRvLwxPVZ1TILyhg644dPD59eoNr2bQkQ/r3R5GTw1+LFlXG09OLb/7zKc5V08ulZ2aQlJKiSq+np8fcJUtISEpSxXPimDE8/MAEVZqKinIWrVzJlcRE9PX06BLckTnffoeLk6yfJMQ/oVNYWNgyu280c3e6hopQ11J7KWmD7n10I3MvSNm8uwZ+/Im2s9CiRH74QcOJxC2RKe3urvvpR/W9UHKTqdrE7Wnj2krbWWhRKuSr8676Yes+bWehxXgxLFTbWWhR2rrJQ927yaggv+FE4pYp5T7prjFqdf9MJXgvKFY2/cZxqwlNtzPynWi5Y2+EEEIIIYQQQgghhBBCCCHuEpnySwghhBBCCCGEEEIIIYRoLmTUvtbICBUhhBBCCCGEEEIIIYQQQogGyAgVIYQQQgghhBBCCCGEEEI0GRs3bmTVqlVkZWXh4eHBE088Qdu2betNr1QqWbduHZs3b+batWtYWFgwcOBAHnvssbuaL2lQEUIIIYQQQgghhBBCCCGaixY+5deePXv4/fffeeaZZ2jTpg2bNm3iww8/5Mcff8TR0VHjPnPmzOHIkSPMmDEDLy8v8vPzycrKuut5kwYVIYQQQgghhBBCCCGEEEI0CWvWrGHQoEGEhYUB8NRTTxEVFcXmzZt59NFH66RPTExkw4YNzJ49G3d390bNmzSoCCGEEEIIIYQQQgghhBBC60pLS4mNjWX8+PFq2zt16sT58+c17nPo0CGcnJyIiorio48+QqlU0q5dO2bMmIG1tfVdzZ80qAghhBBCCCGEEEIIIYQQzYSOrq62s9CgLVu2EB4e3mC6sLAwhg0bpnqdk5NDRUVFnYYQa2trTp48qfEYqampXL9+nT179vDyyy+jo6PDn3/+ySeffMKXX36J7l2MlzSoCCGEEEIIIYQQQgghhBDirhk2bJhaQ8nt0rmNdWKUSiWlpaW8+uqruLq6AvDqq6/y9NNPExMTQ2Bg4D/OR21NvylLCCGEEEIIIYQQQgghhBAtnqWlJbq6unUWlM/Ozq53+i4bGxv09PRUjSkALi4u6OnpkZaWdlfzJw0qQgghhBBCCCGEEEIIIURzoaPT9P/+IQMDA/z8/Dhx4oTa9hMnTtC6dWuN+7Ru3Zry8nJSUlJU21JTUykvL8fR0fEf50UTaVARQgghhBBCCCGEEEIIIUSTMG7cOLZv3054eDgJCQn89ttvZGZmMnz4cADmzp3LO++8o0rfsWNHfH19+e6774iLiyMuLo7vvvuOwMBA/Pz87mreZA0VIYQQQgghhBBCCCGEEEI0CX369CEnJ4dly5aRmZmJp6cnH3zwgWq0SWZmJqmpqar0urq6vP/++/z222+89dZbGBoa0rFjR2bNmnVXF6QHaVARQgghhBBCCCGEEEIIIZoP3X8+pVZzMXLkSEaOHKnxvVdeeaXONltbW958883GzpZM+SWEEEIIIYQQQgghhBBCCNEQaVARQgghhBBCCCGEEEIIIYRogDSoCCGEEEIIIYQQQgghhBBCNEDWUBFCCCGEEEIIIYQQQgghmgsdGSehLRJ5IYQQQgghhBBCCCGEEEKIBkiDihBCCCGEEEIIIYQQQgghRAN0CgsLldrOhKhr8f7j2s5Ci1JSVq7tLLQY+np62s5Ci6Knq6PtLLQoZeUV2s5Ci9Lvw4+0nYUWY+ELL2s7Cy1KdMp1bWehRekb5K3tLLQY5RXy00o0Xfp60p/ybjGQ30R3lZG+zEZ/N13PydN2FloUQ32p73fLEwNDtJ2FFiVn8zZtZ6FBlsOHaDsLjULuqIQQQgghhBBCCCGEEEIIIRogDSpCCCGEEEIIIYQQQgghhBANkHGVQgghhBBCCCGEEEIIIUQzoaMjU8hri4xQEUIIIYQQQgghhBBCCCGEaIA0qAghhBBCCCGEEEIIIYQQQjRApvwSQgghhBBCCCGEEEIIIZoLHRknoS0SeSGEEEIIIYQQQgghhBBCiAZIg4oQQgghhBBCCCGEEEIIIUQDZMovIYQQQgghhBBCCCGEEKK50NXRdg7uWzJCRQghhBBCCCGEEEIIIYQQogHSoCKEEEIIIYQQQgghhBBCCNEAmfJLCCGEEEIIIYQQQgghhGgudGTKL22RESpCCCGEEEIIIYQQQgghhBANkAYVIYQQQgghhBBCCCGEEEKIBkiDihBCCCGEEEIIIYQQQgghRANkDRUhhBBCCCGEEEIIIYQQornQlXES2iKRF0IIIYQQQgghhBBCCCGEaIA0qAghhBBCCCGEEEIIIYQQQjSgSTeovPXWW/zyyy//eP/Tp08zevRoFAqFxtea7Nu3j9GjR//jzxRCCCGEEEIIIYQQQgghGo2OTtP/a6HuqzVUgoKCmDdvHpaWltrOSpNwLHI7h7ZtJk+Rjb2zK4MnTcHdP7De9NeTEti2dAEpl+MxNjWjY58BhI4Yg05VBclTZLNjxRJSEy6Tdf0abUN6MerRJ9SOcerAHjbNm1Pn2P/+/jf0DQzv7gk2IqVSyYHN6zi9L5KiwgKcPX0YOPkR7J1db7pfQswFIlcvJSMlCXMra7oOHk5w7/5qaS6eOMr+jWtQpKdhZe9A6KgH8A/urHq/pKiQfRvXEHvyGAV5uTi6eTBgwsM4eXqrHSfreip71q4kIeY85WXl2LZyYvijT2Dn5HLX4nC3HI/czuGI6rI4cNIU3P3qL4tpVWUx9UplWQzuM4Bew6vLIsDVi9HsXLmY9JQkzK1s6D5kOJ36DlQ7TnFhIXvWr+TC8aMU5edhYWNL3zETCerSHYBf3v0XOZkZdT7fp20HJj736l06+8YXtWs7h7ZtIk+hwMHFhcGTHmmwrm9dMl9V1zv1HUDoiLFqdX37isWkXr1C1vVU2oWEMuqxJ+oc58j2rRzbvYOczHRMzMzxD+7MgPGTMTQ2brRzvVPHd+/gSM2yOHEKbn4B9aZPS0ogYtnC6rLYuz89a5XFhJhodq5colYWO/YZoHacC8ePsm/DarLTr2Nt70jv0Q8Q0LGLxs88uGUDe9avpFPfgQx+cJpqe0lREbvXrSDm5LGq8mxHxz796Tow7A6j0jQZB7fD5uGJGAf6o+9gT+qn/yN38zZtZ6tZ6N/Wjy6+7hgbGJCUmc3GqHOk5eTVm97c2IiwjkE421hia27GqStJrDl8+h7muOmY2COYge0CMDc2JDY1nT93HCIxM7ve9N18PRjSIRAvB1sM9PVIysxm9eHTRMUnqNL0a+PLM0N719l32uz5lJZXNMZpaIVSqeTQlvWc2b+bosICnDy9GTBxCnYN3Dslxl5gz+plZKQmY2ZlTZeBYXSoce+UkZLEwc3ruJ54lZyMdEKGjabH8DFqx/jzozfJ1XA992rTnrFPvXhXzu9eUiqVHA5fz9kDeyguLKCVhzf9JkzBzvnm93hJsRfYu3Y5manJmFla03lgGO1C+6neP3tgD9FHDpB5LRllhRIHN3dCho/FxcdflWbux2+Rm1U3lp6t2zH6yeYXS2i8eMaeOErU9nAU6depqCjH2t6R4H6Dad29V/Ux4i5yfOdW0hKvkq/IZtDDj6m93xwplUoObl6nVtcHTnqk4boec4Hdq5eq6nrXQcPq1PUDm9ZxPfGKqq73HDFW/RixFzm2I5xrCVfIV2Qz5JEZtA0JbYzTvCe0dV96w/mjB9nw16/4tAtmwjMv3+3Tu+eO7orgwNaNVb+JXBk6eSoeDfwm2rJ4LsmX4zExM6dTnwH0GTlOFc8rF8+zc/UyMq6lUlpSjJWtPR1796Pn0JGqYxzbs5PTB/eSlpyEUqnEyd2TfmMn4HGT37rNhVKpJGrbRqIP7aW4oABHDy9Cxz+EbQPPG5LjLnJw/QqyrqVgamlFcP+htOnZV2Pa2ONH2LHoTzxat2PYzOdU2xd99g55WZl10rsHtWP4rOfqbG8O5FokRPNyXzWoGBgYYGNjo+1sNAnnjx4iYtkihj48DTffAI7t3s6yH7/m8fc/w8rWrk764sJCln7/Je5+gTz6xgdkXEth07w5GBgZEjJ4OABlZaWYmJvTI2wkJ/dG1vvZBoaGPPXx/6lta06NKQBHIjYTtSOcsKkzsXV04uCW9az84StmvPcphsYmGvdRpKex+pdvadejN8OnP05SXAw7li3ExNycgI5dAUi+FMvGv36l14ix+AV3JvbkMTb8+TMPvfIWzl4+AGxdNJf05ESGTZuFubUN548cZMUPX/HoO59gYW2j+qwl33xOm269CBn2GkYmpmReS8HQqOk9yD5/9BDbly9iyEOVZfH47u2s+PFrZr33GZb1lMVls7/EzS+QaW98QOaNsmhoSPeqspidnsbKn76mfc8+jHrsKRLjLrJtyXxMLSwI7NQNgPLyMpbN/hJjUzPGznoWCxsbcrOy0NOv/lqc/sYHVFRUP8zKz1Ew978fqhpcmoNzRw8RsWwhYQ9Px80vgGOR21n6w1c88cHn9db1Jd99ibtfAI+9+SEZ11LYOPcPDAyNCBlSVddLSzExt6Bn2EhO7N2l8XPPHj7AztVLGT51Ju5+AWSnp7Fp/hzKSksZOX1WY57yPxYddYgdyxcx+KFpuPn6c3z3Dlb8+DUz3/v0JmXxf7j7BTL19ffJvJbK5vlzMDA0otvgYcCNsvgN7Xr2YeRjT5IYF0PEkvmYmFsQ2Kmy3ifFx7L+z58JHTmOgI5duHgiinVzfmLKq2/j4u2r9pnJl+I4tT8SB1f3OvnZuWoJV6LPMfLRJ7CycyAh9gJbF/2NiZkFbUNa3g2xrokJJfFXyN0SQat3X9N2dpqN0CAfegZ6s+bwKTJy8+nXxo/p/bsxe9NuSsrKNe6jr6tLQXEJe8/H08W3btm7X4zp2o6Rndvy89a9JGflMCEkmLcfGMKrc1dTVFqmcZ82bq04k5DC0v3HySsqpneQD/8a1Z+PV4QTnXxdla6otJSX/lqltm9LakwBiNq+hWM7tzJkygxsHJ04HL6e1T99w/R3/lNvQ7siI421v35P25BQwqY9TnJ8DDuXL8LE3AL/qkbn0pISLG3t8e3QmQOb1mg8zkP/egdlrev54v/9B/+q7+Hm5tiOcE7s2saghx/DxtGJI+EbWPvLN0x965N6Y5mTkc7632fTunsoQ6bOIiU+lsgVCzE2N8cvuDKWSbEX8O/UFWdvP/QNDTmxK4J1v37HQ/9+D2uHVgBMfvVttXujghwFS7/+FL+OzTOW0HjxNDYzp9vQEdg4OqGrp8fls6fZsXQeJuYWeLVpD0BpcTF2zq4Ede1JxKI/79k5N6ajEZV1fegjM7FxdOLQlvWs+vFrHn3305vW9TW/fkfbHr0ZNv1xkuJj2blsYd26bmeHX3Bn9m9crfE4pcVF2Dm70rpbT8IXNO94auu+9Ibs9OvsWr0MN9/6G3Cak7NHDrJ16QKGTXkUD78Aju7azuLZX/L0h//Fyta+TvriwkIWfvsFHv6BzHzrIzKupbL+798wNDKix5ARABgaGdNt4FAcXd3RNzQkMTaGTQv/xMDQiK79BwOVjS5tuvbA3dcfA0MjDm3fwuLv/o8n3v0U21ZO9zQGd9vJXVs5vTuCfpOnY+3YimPbNrHp9++Z/NqH9X93ZqazZc6PBHbvxYCHZ5B6OY69qxZjbGaOT4fO6mkz0ji0cRVO3n51jjP+xTfVrusFuTms+u5zfIM710nbXMi1SIjmpUlP+QVQUVHBvHnzmDJlClOnTmXOnDmqm/i8vDy++eYbHnroISZMmMC7777LlStX6j2Wpim/duzYwcyZM5kwYQIfffQR2dnZavukpKTwn//8h2nTpjFx4kReeuklDh8+rHp/8eLFPPdc3Rbw119/nV9//fUOz77xHN4eTvueoXTs3R97ZxeGPjgNc0trju/eoTH92cMHKC0pYeSjT+Dg6kZQ526EDB3BkYhwlEolANZ2Dgx5cCodevbB2NSs/g/X0cHcylrtrzlRKpUc3xVB9yEjCOjYFXsXN8KmzqKkuIjoo4fq3e/kvl2YW1lX9tBycqFDaD/ahPQianu4Ks2xnRG4+wcREjYKOycXQsJG4e4XyLGdlb2uS0tKiDkZRe8xE3D3D8LGoRW9RozF2sGRU3t3qo6zd8NqPIPa0u+BB2nl7om1vQM+bTtgYWPbeIH5h47uCKddz1CCe/fHztmFwQ9Ow+wmZfHckcqyOGL6Ezi4uBHYqbIsHt1eXRZP7NmJmZUNgx+chp2zC8G9+9O2RyhHIraojnP6wF4KcnN54OmXcPMLwMrOATe/AFXDFYCphaVaOY0/cxIjY2MCO3dr3KDcRYcjttC+Z2869qmq6w9V1fXI7RrTnz28n9KSYkY99qSqrvcIG8HhiC3Vdd3egaEPTqVDrz4Ym2mu64lxMbh4+9K+RyjW9g54BbWhXY9Qki/HNdq53qmj27fSrkcowaH9sHNyYfDkqZhZWXFiT/1lsay0hOHTH68qi10JGTKcozuqy+LJvTsxs7Jm8OSp2Dm5EBzaj7Y9enFke3VZjNq5FY+AIHoOG42dkws9h43G3T+IqJ3qoy2KCwvY8PevhD0yA2NT0zr5SY6PpU33nngEtMbKzp52IaE4e/mS0oRjficKDh4h47e/yNu1FyqU2s5Os9EjwJO95+M5n3iN64o8Vh8+haG+Pu096+/9ll1QyObj5zlxOYnCktJ7mNumZXin1qw9cprDsVdJzMjmp/C9mBgaEBrkU+8+cyOPsO7oGeKupXNNkcvKQyeJv55JV18P9YRKUBQUqf21JEqlkuOR2+k6eDj+Hbtg7+LK0EdmUlJcxIWo+u+dTu+LxMzSmv4Tp2Dr5Ey7Xn1p3b0nx3ZuVaVx8vSmz7hJBHUNwaCeDjqm5haYWVqp/i6fO42hsbHqQW1zolQqORkZQZdBw/AL7oKdsyuDp8ygtLiIi8fqj+WZ/ZWx7DfhYWxbOdO2Zx+CuvXieI1rzdBpj9Ohz0Ac3DywcXSi/6RHMDQy5sr5s6o0JrVieeX8aQyNjPFrhrGExo2nm38QPu07YdPKGSt7R4L7DcLe2ZXk+BhVGq827ek5cjx+Hbugo9Pkf5o3qLKuR9CtRl0Pm1pZ16NvUtdP7Y3E3MqaAROnYOvkQvuquh61o/p3kpOnN33HTa6s64aa67p32w6Ejn4A/05d1UZlNEfaui+Fyo5nG/78lT6jH8DK3qHRz/VeOBSxmQ69+tC5zwDsnV0Z9vB0zK2siarnN9GZw/soLSlmzGNP4ejqTuvO3egZNpJDNX4TOXt607ZbTxxc3LCxd6R9j1B82nTgauwF1XHGz3qWbgOG4OThhZ2TM8OnPIahsQlxZ0/dk/NuLEqlktN7dhA8IAyfDp2xdXKl/0OPUlpcROzxI/Xud/7AHkytrAgd9yA2rZxpHdKbgK49OBUZoZauoryc7Qv/pNuwMVhqaPAyMbfA1NJK9Xc1+gyGRsb4dJBrkVyL7i86ujpN/q+lavI1JTIyEl1dXb788kueeuop1q1bx549ewD49ttvuXDhAu+++y5fffUVRkZGfPjhhxQXF9/SsS9cuMC3335LWFgY33//Pd27d2fhwoVqaYqKiujSpQuffPIJ33//Pb169eLzzz8nIaFyqoYhQ4aQmJjIxYsXVfskJiZy/vx5hg4depeicHeVl5WRevUy3q3bqW33bt2WpPhYjfskXYrF3S9A7ebVp0078hTZKDLSb+vzy0pK+Omdf/HjW6+w/MdvSE2ovxGsKVJkpJOfo8AzqK1qm4GhIW6+ASRfqv/BZcqlOLV9ALxat+Xa1SuUl1f2bE25XDeNZ+t2JF+q/H9RVpSjrKhA38BALY2+gQFJcTfSVBB/5gR2Ti6s/Okbfn7rJRZ++QkXog7T1Nwoi163URaT42Nx81Uvi96t1cti8qVYvFurx9G7TTtSr1xWxTr25DFcff2IWLaAH998kTkfv83eDatV79emVCo5tX8Pbbr3wsDQ6B+f872kquttasW3TTsS66vr8bG4+wWqx7dN+9uu6+5+AVxPuKr6f1RkZhB76ji+bYP/wZk0vvKyMlIT6pZFr9btSIrXXK+TL8XVKYtetb4Xk+PjNJTv9lyrURaTL8XhFVQ7TTuSa/0fhS/6m8BOXfEMbKMxP66+/sSdOUFO1VQsSfExXE+8indVzyMhbMxMsDAxJu5adV0uK6/gSlom7nbW2stYM+BoaY6NmSmnriartpWWl3M+6RoBzrf3sMnEUJ/84hK1bYb6esyeOYEfZ03k9TED8XJoeh0g7kRORjoFOQo8anx/6Rsa4uobQMrN7p0ux+MZpP6d5xnUlus17p1ul1Kp5OzBvQR17dFsruc15WSkU5Cbg3tg9X2OvqEhLj7+pFyKr3e/1MvxavEH8AhqQ1rC5XpjWVFeRllpqcZGfKiM5bmD+wjsGtIsYwn3Lp5KpZKEi+fJSruGq69/nfdbClVdD1KPZ2Vd13zvCZB6OQ6PwLq/ge6krjdn2rwvBdizbhWWdna061F3OsrmqLysjJSrl/Gp9ZvIp3U7EuNiNO6TGB+LR63fRL5tO5CbnUV2RprGfVKvXiYxPgZP/6Cb5qXye/UmHVCbgdzMdApzc3ALaK3apm9giJO3P9eu1H9dv3YlHjf/1mrb3APakJZ4hYry6pHShzevxcLWjoCuPRvMi1Kp5MLhffh17o5+PY2tTZ1ci4Rofpr8lF/u7u5MnToVAFdXV7Zu3crJkyfx9/fn0KFDfP7557RrV3lhfPXVV5k5cya7du0iLKzhOePXrVtHcHAwDz74oOr4MTExbNtW3Zrr7e2Nt3f12hQPPvggR44cYf/+/Tz44IPY29vTuXNntm3bRkBA5XDYiIgI/Pz81PZrSgryclFWVGBqaaW23dTSivzocxr3yc9RYGFtWyf9jfesb7Hnil0rZ0ZMm4WjmzslRUUc3bmNBV9+ysx3P8bWsXkMeS3IqRzhZGqhvhaPqaUlebVGONWUn5ODR2CtfSwsqagopzAvD3Mra/JzFJjVOq6ZhSUFuTkAGBqb4Ozty6EtG7BzdsXM0oroqEOkXIrD2sGxMn95uZQWF3No60ZCR46jz5gJJFw8z6Z5v2NgZIRPu6bzQPtGWTSzuM2yWGukjVmtsli7wQvAzMJKLdbZ6ddRXEinTbeeTHj2VRQZaUQsnU9pcTEDJjxU53Mvnz+LIiONDr00z+/aFKniW2vdKDNLSy5HKzTuk5ejwLKe+ObdRl1v060Hhfl5LPjqM1BCRUU57UJ6MeCByf/gTBpf4Y3vRQ3170rOzb4XbWqlr1UWcxV4Wqjf5Gqq96aWdb9P8nOr/49O7oskO+06Ix99st5zGDTpEbYunsuv7/4bXV29ym2TH8G3fcebn7y4b5gbVz7wzC9S73iSX1SChUnzfBh6r1ibVU7nWXvkiKKgEFtzzQ+bNRnaIRBbczP2nK9+2JCclcMv2/ZzJT0TEwMDhndqzUeTh/PGwnWkZufenRPQshvfZ3XunSwsyVNk1btfQY4C0wD1By83vkOL8vIw+wejnK9eOEdORnqzfVB4457Q1MJCbXtlLLPr3S8/V6H24AvAxMKSioqKemN5cNNaDIyM8K7n3jHhwjlyMtNp06PP7Z1EE9LY8SwuLODvD9+gvKwUHV1d+k2YgmfrltvRIb++30kNxTMnB/cAzb+T/mldb860eV966fwZLkQd5tG3P7qLZ6Rd9f3mNLO04lL0WY375Ck0/Saq/P/IVyiwsXdUbf/ujRcpyMulorycPqPG06XfoHrzsmvtCgyNjAhoxlNTQY3vTnP1MmpiYUHBTep6QckuQAABAABJREFUYW4OJrUanEwsLFFWVFCUn4eppRWJF84RfzKKCa+8fUt5Sbp4ntzMDIK6N9/1kuRaJETz0+QbVLy8vNRe29raolAoSEhIQFdXl6Cg6i9jMzMzPD09VaNHGpKYmEi3bupT9wQFBak1qBQVFbF48WKOHDlCZmYm5eXllJSUqOUrLCyMb7/9lscffxx9fX127typaqSpbcuWLYSHh2t8T+08/drg18jzOtcZeKVUathYI33t95S3P8WKq48frj7Vc2C6+vrz16fvE7UzgiEPTr3t490L548cJGLJPNXrcU+/BFB3GLkSDUFSV3ufGyFU217nsOpxHj7tccIX/cXv7/0bHV1dHN08CewSwvXEK1XHrJwSz7d9J7pULUbt6OZB6tUrnNi9o0k1qKhoKFu3M0r/xrDrm4RRlabma1MLS8IemYGuri5OHl4U5uezc8Ui+j/wYJ3/q1P7duHk6U0rd89bz1gToUPtcqfkppW9TiFUatp6U1cvRrNv0zrCHp6Oi7cvWdevEbFsIXvWr6bvmAdu40j3Vt06evOyqCl9ne11vzxvvFGdpJ6YA2ReS2HPuhU8/Mrbamv81HZsVwRJ8bGMf/olLG3tSIy5wK5VS7Gytce7rdww34/ae7owukt14/LCPVGAhst3yx2J/Y+FBnrzxKDqXpFfrK2aEqRW8HTQueXboe5+HjzSpyvfb95Nem6+antMShoxKdW9XS+kpPHFI6MJC27N3MimN7r0VkQfPciOpQtUr8c89QJQz3dsQwWw9j4oNW6/VWcO7KGVhxcObh4NJ24CLkQdYtey6liOeuL5qn/VjouGe9Na6r2X17DfycjtnNm/m3HPvFLv+oBnD+7B0cNL47peTdW9jqehkTEP/vs9SkuKSbx4nr1rl2Fha4d7rQdgzVX0kYNsXzpf9XrsUy8CmmKnbPBSczvl835xr+9LC/Jy2Tx/DqMee6rZj6DQpO7v9wauQfXdntc6zvTX3qW0uJjE+Fh2rFqKtb0DHTQ02h/eHs6xPTt45OU3MTLR/L3aVMUcO8yelYtUr4fNfLbyHxqLUwPfnRp/m1Yqys9j17J5DJwyE6NbLIPnD+/Dwd0Te7kWVe10/12L7msyRZvWNPkGFX0ND48qKirqPByt6VbnS73ZMW74888/iYqKYubMmbi4uGBkZMQ333xDaWn1HOLdunXDyMiI/fv3Y2ZmRl5eHn37au7FPmzYMIYNG9bg5y7ef/yWzuGfMDW3QEdXV9WD6IaC3BxVT/TazCytNKa/8d4/pauri5OnF1nXr/3jYzQ23/bBOHl9oHpdXlY5dLL2SImC3Jw6o0tqMrO0rBPDwrwcdHX1VOtQVMY5Ry1NQW6uWu8kawdHHnzpDUqLiykuKsTcypoNf/6iWkzPxMwCXV097Jyc1Y5j5+Tc5Kb9ullZNLW4/bJ4Yx+NaapibWJuXpXGGj09PXR1qy9Adk7OlJaUUJinHvP83BxiTh1nyIPT/uGZaseN+ObViVdunVErN5hriF3+P6jrketW0qZrCB179wfA0dWd0pJiNs3/i94jx6Krp3cbZ9L4TOori3m5t1cW826Uxcr4mlloKq+5VWXRrP7j5OaqetElx8dRmJfHX5++q3pfWVFBQuxFTuzdxctf/4JSqWT3uhWMmfUsflUjUhxd3bmedJXD27dIg8p96kLSNZIyslWv9aq+78xNjMgprB5pYWZkSF7RrU2Xer+Iik8gNrV6ajSDqu8sKzMTMvIKVNstTY1RFBQ2eLzufh48F9aHn8L3EhV/844/SqWS+GsZONtY3DRdU+bTriNOntVry5SXVd431753qn29rc3U0ko1Mli1T9V3aH1reN1MQW4O8adPMGDilNveV1u82wbT6t/Vo95v3IcW5OaoxzI3BxPzm9yHWlhRUOseszAvF11d3TqxPBm5nYOb1zD6yRdp5al5xH1Bbg6Xzpyk34TmE0u49/HU0dVVjSJ3cHUn61oqURGbW8xDLJ/2HXHyqhvPur+TcuuMxq2p8ndSrd9Aef+8rjd32rovTYqLJV+RzbLZX6rev/HM5H8vzGLmu//BtpX6b8zmoPo3Ubba9vzcnPp/E1lZka+o7/mH+j43Rqs4urqTn6Ng9/rVdRpUDm8PZ9faFTz04r9x9fa9k9PRCs82HXD08FK9rvndaW6tfl03saj//sXEwpKCXPW4FuXloqOri7GZOamX4yjIUbDxt+9U798og7+/8RyT/vUe1jVmNinMy+HK2ZOEjq87w0RTJtciIZq/Jt+gUh8PDw8qKiqIjo5WTflVUFDAlStXGDx48C0dw93dnQsXLqhtq/363LlzDBw4kNDQyuGDJSUlpKam4uJSvXirnp4egwYNIiIiAlNTU3r16oV51UPbpkhPXx8nDy8uRZ8lqEt31fZL0WcJrGdUjKu3H7vWLKOstAT9qgU/L50/i7mVNVZ2dRcJu1VKpZLriQk4NuFegobGJmo985RKZeUinNHncKr6kVlWWkpSfAx9x06q9zjO3r7EnVJvKLsSfY5WHp7o6VVWRWcvX65eOEu3wdWNblcvnMXF24/aDIyMMDAyoqggnyvRZ+hT9dl6+vq08vQi63qqWvqs66lY2Nrd5tk3rhtl8fL5swR1ri6Ll6PPEtBRc1l08fEjslZZvBytXhZdvP2IOXlMbb/L58/i5OmlirWbrz/njhxAWVGBTtVDxqxrqRgYGmJirn4TeObAHvT0DQjqGnJ3TvweqY7vGVrXrOvnzxBUX1338WPn6lrx/Qd1vaykWBXXGypfN83Fw/X09XFy9+Jy9FkCO1ePXLwSfZaAehbadfH2Zffa5ZSVlqrWNbpy/px6WfTxJeaker2/HH2WVjXKoou3L5ejz9J9yHC1NC5Vo/n8gjvzmKeX2jG2zJ+DjWMrQsJGoaevT0lRERXl5WoNhEDlooJVo9bE/aekrJzMGg//AXILi/BtZUdyZuWPWX1dXTwdbNl6MlobWWyyikrLKFKoT7eVlV9ABw8X4q9VrlNkoKdLkIsjC/dG3fRYPfw9eTasNz+F7+VQ7K2tG+dhb8OV9Mx/lvkmwNDYGENjY9VrpVJZuWjsBfV7p+S4GHqPnVjvcZy9fIg7fUJt29UL53Csce90O84d2oeevj4BNe45mjqNsbSwJOHCOVpVPdwqKy0lOT6W0DET6j2Ok5cP8XVieR4Hdy+1WB7ftY3Dm9cx6skXcPGpf37184f3o6evj3+nbvWmaYrudTxrUyorVA2MLcFt1fVx9f9OcvLy/X/27jssiqOPA/iXdvTee+9gRbFXVKwpliT2qInpMdWYbuqb3nuMvYsdFUUEREXBCkoVpfdy9H7vH8jBwQEawTvI9/M8PI+3N7s3+3N2d3ZnZwYpMZL1pfs51ns7WdVLzWztsfSdjyW+jzi0F9WVlfB7bCF0DXvnBPVKysowt7HDrRux8Bjccj93K+463AZJvyeycnDCyb07Je6JUm7EQltPH3qdxEEkErU7xiNPHEXYoQA8/sLrsHFy7YY9evCkHevq2jrITIyDibUdgKZjPedWMnyndzwagamtA25fvyKxLCMpHsZWtlBUUoKxtS3mvPauxPdRxw6htqoSIx95DNptJqhPiDoHJWVlOPbv2dFduhuvRUS9X6/tG2RhYQFfX1/88ssvuH79Om7fvo1vvvkGGhoaGDt27F1tY+bMmbh69Sp2796NrKwsBAUF4dy5c+1+JzIyEsnJyeLfqK2tbbetyZMnIzY2FlFRUZg0aVK37GNPGjpxCmLOReBqRBgKsrNwYtdWlAtLMHD0eABA6P7d2P79F+L0HkOHQUUgQODGv5GfmYGEy9GIPB6IIX5TJHoE5aanIjc9FTXVVaiuqEBueioKsjPF30cc3o+UGzEoyc9Dbnoqjmz+B/mZGRg4ZvyD2/n7pKCggIHj/BAVfARJVy6iICsDQVvWQUWgKvHA/eimv3F009/iz/1HjkNZSTFOBWxHYU4WYs6G4/r5Mxg8sWW+n0Hj/JCWGI8LxwNRlJONC8cDkZ6YgEHjW8rU7bhY3LoeA2FBPlLjr2P3j19B38QMnsNaxgwdMtEfCZeicO1MGIrzc3HtTBgSLkZhwGj5i7PPhCmIjYzA1TNhKMzOwsk7ZbE5r2H7d2PHD63K4pCmsnhk09/Iz8pA4uVonD8eCJ+JLWVxwOjxKC8pwsndW1GYnYWrZ8IQGxkh0VA1YPR4VFdWNKXJzcatGzGICNyPAWMmSJRpkUiEq2fC4e4zFKodDHkhz4b6+ePauQhciQhtOtZ3bmk61sdMAACE7tuFbd+1PtaHQ0WgisOtjvVzQYcx1M9f6rFeW1WFqsrypmM9q+VYd/IeiCsRobgRFYmSgnzcuhGL8IN74eQ9QO56pzTzmTgZsZERuHYmDIU5WTi5eyvKS0rQf1RTWQw/sBs7f/hSnN5jyDAoqwhwdPOdsnglGudPBMJnQktZ7D+qqSyG7NmGwpwsXGsuixNbyuLg8ZOQlhiHyKDDKMzJRmTQYaQnxmPwneNeTUMDxhZWEn8qqqpQ09CEsYUVFBQUoKquDmtnV4Qf2IO0xHiUFOQj9lwEblw4C+f+0m+8ezsFdTUInBwgcHIAFBWgYmoCgZMDlE17583+gxKZmIpR7o5wtzSFia4WHvb1Rm19PWJSWyZbf8S3Hx7x7SexnpmeNsz0tKGqrAx1gQrM9LRhrCO/L4/0hKOX4zDLxwtDHG1gZaiHZyePQnVdPc7Et0wW+tzkUXhucstbqcNd7PCC/xhsj7iEuMxc6GqoQVdDDZqqLZOmzvbtj362FjDR0YKtsT5WThoBGyN9BF9LfKD715MUFBQwcOxEXAw+huSrl1CQlYkTW9dDRVUVrq0ebgVtWYegLevEn71HjkV5STHC9u5AUU42Ys+dxo0LZzFo/GRxmob6euRnpCE/Iw319XWoKBUiPyMNJfl5EnkQiUSIPRcBl0FDJR5i9DYKCgroP9YPF08ew81rl1CYnYmT25ti6TKoJZYntv6DE1v/EX/2GjEW5cJinN63E0W52bgeeRrxUWcxsFUd81JIEM4d3osJjy+BnrEpKkqFqCgVoqZKsmG2aTL6CDgPHNKrYwn0bDyjTwQiPeEGhAX5KMrNxuVTx5EQHQnXwcPEaWprqpGfmY78zHSIRI0oKy5CfmY6yooLH0wAulnTse6H6BNHkXz1IgqyMnF86z9QUVWFW+tjffM6BG1uOdb7jRqLspJihAbsQFFOFmLPhuPG+TPi4YuBpmM9LyMNeRlpqK+rQ2VZKfIy0lCS3zLSQW1NtTiNSCRCWVER8jLSUFrU++Ipi3qpQFW1XZ1TVV0DAjU1GFtYdTr0rLzz9ZuKq+dO43JEKAqyMxG0czPKhMUYNKZpvpOQfTux5dvPxek9h46AikAVBzf8ibzMdMRfisLZoEPwbXVPFBVyHEnXLqMoNwdFuTm4HBGKyBNH4O3bcl9+LigQIft2Yubip2BoaoZyYQnKhSWobnNe7W0UFBTgPXoCrpw6jlsxl1GUk4nQnRuhoqoKp1YN7ae2b8Cp7RvEn92Hj0ZFSQnOHtiF4txsxJ+PQGL0OfQb2/RStIpAFQZmlhJ/qmrqUFFtWt66DIpEIsRfOAPH/j68FvFa9N+lqCD/f31U770iAli1ahX++usvfPzxx6irq4O7uzs+/PBDqKre3cSqbm5ueOmll7B161bs2LEDXl5emD9/Pv744w9xmhUrVuDHH3/EW2+9BS0tLcyaNUtqg4qZmRm8vLyQl5cHb2/5H1rF3ccXVRXlOHP0ICpKhTAyt8Tc518Vv71SLixBcasbUTV1DTz20hs4vmMzNvzvQ6hpaGLoRH8MnSg5fNn6zz6Q+JwccwU6BoZ47tNvAADVVZU4tnUDKkqFUFVTh6m1LRa8tgYWdg7oTYb4TUV9XR1Cdm9FdWUFzOwcMPv5VyV6spQVS75ZqmtkjEeeWYWwvTtwLSIUmjp6GD9nvkRPDAsHJ0xfuhJnDu/D2SMHoGdkgulProR5q/jUVFUh4lAAykuKoaahCaf+gzFq5iMSbyA49R+ESY8vxvnjR3AqYDv0jU3hv2i5XM6f4u7ji+qKcpxrVRbnPNdSFitKSyQeiqiqa2Dei2/gxM7N2HSnLA6Z6C/xgFrPyBizn3sVIQHbceX0KWjp6mHi3AVwbVW50zEwxLwXX0fInh3Y+Nn70NTRhffw0RgxdZZE/tIS41GSn4uZT67s4Uj0DA8fX1SVl+PskUMoLy2BsYUl5r3Q+lgXSsRXTV0Dj7/8Bo5v34T1n38INQ0NDPXzx1A/yWP9n0/fl/icfO0KdA2M8NxnTcf6yGmzAAUg/OBelJUUQV1LG07eAzC2kzeRZc1tsC+qKipw7tghcVmc/dwrkrEqaFsWX0fwzi3Y/MVaqGlowmfCFPi0aiRtKouvSCmLLce9pYMzZj75DCIO78WZwP3QMzLBzOXPwOIehwOY8eSzOH1wDwI3/IHqygroGBhi5IxHMLCTiTF7MzU3F1i1GpLCcMViGK5YjNIjx5F7pxxSe2fiU6CipIhpgz2gLlBBRqEQm8OiUFvfIE6jq9H+pvSZKZJDV7hamqKkohLfHw7r8TzLi4PRsRAoK2HZBF9oqqoiOScfn+07geq6enEaIx3JoWkm9XOFspIilowbiiXjWnpF3MjIwUd7mubU01QV4KmJw6GnoY7K2lrczi/C2j3HcDO3AH3J4In+qK+rw6k921BTWQEzW4c783O0lLd2dSdDYzy08iWE79uFmIgwaOrqYuyjj8O51RvaFcISbPuq5Y1qYUE+Ys+Gw9LJBXNefEO8PCM5AcKCPPgvXtGDe/lgDJowBfV1tQjbsw01VZUwtbXHQ8+s6jSWOoZGmPnUi4jYvwsxZ5piOeaRx+HUqtE9JiIUjQ0NCNr0p8S6bkOGw2/+k+LPmXdiOXnh8h7awwerp+JZW1OD0D3bUC4shrKKCvRNzOC3YJlED6m89FTs/6XlmnXh2EFcOHawXcx7Ex8/f9TX1SJkd8ux/shzr0rEs7TNQzpdQ2M8vPJlhO3biZiIUGjq6mHc7CckjvVyYQm2ffmR+HNMQRhizoTB0skFc196EwCQm3YbAT99LU4TefQAIo8egPvQEZiycFlP7XKPkFW9tK/yHDIMVRXliDhyAOXCEhhbWOHxF16HXuvnHwWS90QLVq3G0W0bse6zD6CuoYFhflPh69fSo7yxsREn9+6EsDAfiopK0Dc2wYRHHsPgOy+uAUB0WDAaGxqw96+fJfLTb/gozFraO+8vm/UfNxn1dXWI2LcDtVWVMLGxx7SnXpQ41stL2pw7DYzgv/x5nDu0BzfOnYamji5GPDQPDv0G3fPvZ99MRGlBPiY80buO7Y7wWkTUuyhUVVXJ5/grvdBzzz2HsWPHdjgh/b3oyTlU/otaPyii+6Msp70LeiulPtxiLwv1DRzeqjuN/XCtrLPQZ2x9cZWss9CnxGfndZ2I7toYN+nzZNC9a2jkrRXJL2WlXjtAhdxR4T1Rt1Ltxb1f5FFeabmss9CnCJR5vHeXpyb0rmHU5V15xLmuE8mY1qjhss5Cj+BVqxuUlJQgPDwcubm5dzXhPBERERERERERERER9S5sUOkGixYtgo6ODp5//nno6urKOjtERERERERERERE1FcpcMQTWWGDSjc4dOiQrLNAREREREREREREREQ9iIOoEhERERERERERERERdYE9VIiIiIiIiIiIiIiIegsF9pOQFUaeiIiIiIiIiIiIiIioC2xQISIiIiIiIiIiIiIi6gKH/CIiIiIiIiIiIiIi6iUUFBVknYX/LPZQISIiIiIiIiIiIiIi6gIbVIiIiIiIiIiIiIiIiLrAIb+IiIiIiIiIiIiIiHoLBQ75JSvsoUJERERERERERERERNQFNqgQERERERERERERERF1gUN+ERERERERERERERH1ForsJyErjDwREREREREREREREVEX2KBCRERERERERERERETUBQ75RURERERERERERETUWygoyDoH/1nsoUJERERERERERERERNQFNqgQERERERERERERERF1gQ0qREREREREREREREREXeAcKnKqoVEk6yz0KYocV7DbNDY2yjoLfQzbtbuTSMRzZ3fa+uIqWWehz1jw0/eyzkKfcv7Tj2WdhT6luq5e1lnoM3gd6l6NjGe3qqqtk3UW+gw1FT5K6U68Xe9eRtqass5Cn8LnSSS3FFk2ZYVP8oiIiIiIiIiIiIiIiLrABhUiIiIiIiIiIiIiIqIusJ8qEREREREREREREVEvoaDAfhKywsgTERERERERERERERF1gQ0qREREREREREREREREXeCQX0REREREREREREREvYWCgqxz8J/FHipERERERERERERERERdYIMKERERERERERERERFRFzjkFxERERERERERERFRb6HIIb9khT1UiIiIiIiIiIiIiIiIusAGFSIiIiIiIiIiIiIioi5wyC8iIiIiIiIiIiIiot5Cgf0kZIWRJyIiIiIiIiIiIiIi6gIbVIiIiIiIiIiIiIiIiLrABhUiIiIiIiIiIiIiIqIucA4VIiIiIiIiIiIiIqLeQlFB1jn4z2IPFSIiIiIiIiIiIiIioi6wQYWIiIiIiIiIiIiIiKgLfb5BZebMmThz5sw9rfP8889j27ZtPZQjIiIiIiIiIiIiIqJ/R0FBQe7/+irOofIfcTk8BFHBR1EuLIGRuSUmzJkPKyeXDtPnZ6YjeNdW5KSmQE1DE/1HjcPwqbMkDob0pHicCtiBguxMaOnqY+ikqRgwerz4+4KsTJwJ3I/c9FQIC/MxYtpDGDn94Q5/M/LYYZw+FICBYybA77FF3bLfPUUkEuHskQO4diYMNVWVMLN1gN9jC2FkbtnpeulJCQjd2xwzPQzxk4wZACRejkZE4D4IC/Kha2SM0TMfhXP/weLvzwcFIvHqRRTn5UBJWRnmdo4YPWs2jC2sxGkiDu9F4uVolBYXQUlJGabWthg54xFYOjh1byC6iSzj2Vpk0GFEHNqLAWMmwG/eQvHyo5vX4fp5yYZZczsHLHj93X+5x7J1OewkLrQ+H8ydD2sn1w7T52em48TOLS3ng9HjMaLV+aBcWIJTATuQm34bxXm58PQdgWmLn3pQu9OjZFk2L4edxNUzYSgtKgAAGJpZYpj/DDh69Ren6WtlsyPjPJ0w2NEaaioqyCwqQeDFG8gvLe8wvZaaKqYMcIO5vg4MtDRxLTUT+y/EPMAc9y5q/b2g/8QcqLk6Q9nYCDmffo2yoydknS2Ziw4NxrmgQJQJhTC2sMSUxxbCxrnjc2VuRjqObd+IrNspUNfUwqAx4zF6+sPic2VqQhxC9u1CYW4O6mproGtghIGjx2L45OkS26mpqsKpA7sRdzEKVRXl0NE3wPhH5sHTx7dH97c7yaLemXApCudPHEFJfi4aGxqgZ2wKnwmT4TVslDjNpbCTuBoR2nJeNbfEcP+ZEudVeSTLa1F6cgKig4OQm34b5cIS+C9cJhFTAKitqcbpAwFIunYJ1RXl0NY3QP9R4+EzYXL3BaEbiUQinDt6EDFnwlBdVQlzWwdMmLfgruIZtm8nCu/E08dvKvqPGieRJvFKNM4G7hfHc+SMR+Hcf5D4+8bGRpw7cgBxUZGoKC2Bpo4e3If4YvjUh6CopASgKZ4RBwOQfO2y+BzQb+Q4DJbTeLYlEokQffwwbkRGoKayEqa2dhj96BMwMLPodL2sm4k4c3APinOyoKGjh4HjJ8NzxBjx9zevXsTlkCAIC/LR2NgAXSMT9BszEW5DhovTbPnkbZQVF7Xbto27F6aveKH7dvIBuRR2EudPtJxL/ebOh3Un16G8O3X27NtN59IBo8dj5DTJOnvInh3IaVVnn7FEss5+JSIUsZFnUZCdCZGoEabWthg981FYd3IO7y1kcV2/EX0eZ4MCUZSXi8aGehiYmMHXzx/9R4zu8f3taRdDgxF54gjK78TTb+6CTuOZl5mOoB2b7pRPLQwcMx6jpj0kjmf85ShcDj+FnPRUNNTVwcjcAiOmzoJLq3Po5dOnEHP+DAqyMiESiWBqbYuxsx7t9F62N4gODca544HiWE6e13nZzMuULJsDR0uWzfhLUbgYHoLc9FTU34nlqGkPScRy0zefIi0xvt22jcwt8cyH/+v+nSTqY9ig8h8Qf/E8QnZvg9/ji2Dl6IzL4SHY88u3WPbep9AxMGyXvqaqCrt++hrWTq5Y+Ob7KMrNwdHN66AiUMUQP38AQElBPgJ+/Q5ew0dj+tKnkXEzCcE7NkNdSxuuA30AAHV1NdAxNITzgEGIOLSv0zxm3bqJa2fDYGxp3f0B6AEXgo8iOiQIUxcuh76pGc4dPYjdP32N5e9/BoGautR1SgryEfDbd/AeNhrTljyFzJtJCN65BRpa2nC5E7OslGQcWv87Rk57CM4DBiPpykUcXPcb5r+6BuZ2jgCaHigMGD0eZrb2gAg4E7gPu3/6Gk+++wnUNbUAAAYm5pg4byF0DY1QX1eHiyHHEfDrt1j+/ufQ1NF9MEG6B7KMZ7OmMhgOY0sraT8HW1cPTGt1w9F809vbxEWfx8nd2zDp8UWwcnTB5fCT2PPLt1j+3mednA++gpWTKxat/gBFudk4smkdVAQCDPWbCgBoqK+DupYWfCdPx9WIsAe9Sz1KlmVTW98AYx6aA30TU4gaRbh+/gwO/PkzFq1+X+Jc2VfKZkdGujlguKs99l+4hsKyCoz1cMLicUPw05Fw1NY3SF1HWVERlTW1iIhLwWDH3nFdkSVFdXXUpqSi7FgwTN99Q9bZkQvXoyIRtGMLpi5YAmsnF1wMPYltP36FZz/8H3QNjdqlr6mqwtbvv4CNsyuWv70WhTk5OLjhT6gIVDF88jQAgEBNDUMmTIaJlTVUBAKkJyfhyJZ/oCJQhc84PwBAQ309tn7/BdQ0NDH76Rego2+A0uIiKKuoPND9vx+yqneqaWpiuP9MGJiaQ1FJCSmxV3Bs63poaGnD4U6DibaePsY+PBf6xqYQiZrOq/v/+AmL3voAJnJcB5XltaiupgZGFpbw8B2Bo5v+lvpboQE7kJpwA9MWr4CuoTEykhNwfPtGqGtpwXPoiJ4Jyn2ICj6KiyFBmLJwGQxMzBB57BACfv4GT773aYfxFBbkY9/v38Nr2ChMXbwCmTeTELJrK9S1tOAy4E48byUjcP0fGDHtITj1H4Tkq5dw+J/f8Pgra2Bu59D02yeO4srpEPgvXA4jCysUZGXg2OZ1UFJWwTD/mQCAsL07kZZwA/6LVkDX0AiZyYk4saMpnh5yGM+2rpw6jqthwRj/+BLoGZvi4olAHPrjBzyxei0EampS1yktLEDg3z/DbcgI+M1/Etm3knE6YDvUtLTg2K/pAaCqhiYG+02DnokZFJWUkHrjGkJ3bYa6lhZs3b0BALNXrYGosVG83YpSIfZ8/zkcO3iZSp7FRZ9H8K5tmPxEU539UvhJ7PrlW6x4/zPodnAu3fnjV7B2csWS1R+gsLnOriqA7506e/2dOvuwKR3X2dMS4+HuMxSWjs5QUREgKiQIu376Gk++8xEMTMx6dJ97kqyu6+paWhg1bRaMzCygqKSEpJgrOLTpb2hoa8PZe8CDDEG3uhEdiRO7tmLKE4ub4hl2Ejt//hpPf/A5dA2kx3P7D1/C2skVS99ai6LcbBze+BcEAlX4Tmoqn2mJCbB1dcfYWbOhpqmF6xfOIuD3H7Dg1bfFjQtpifHwGOwLq3nOUBGo4sLJY9jx41dY/s4nMDDtneXzelQkju/cAv/5S2Dj5ILo0JPY/tNXeObD/3UYy+ayuWzNWhTm5uDQhj8hUFXFsElNZTM1KR52bh4Y99AcqGtqIfb8Gez+7Xsseu0dcSznPvMyGurrxdutr6/Hnx+tgcfgoQ9mx4l6uQc65Fd0dDTmzZuHhoamByBZWVmYOXMmfv31V3GaTZs24b333gMApKWlYe3atZg3bx4WLlyIr776CsXFxRLbDA4OxnPPPYdHH30UK1euxP79+9HYqhLV1p49ezB//nwkJCQAAEpKSvDJJ59g9uzZWLZsGU6caP9G5v79+/Hiiy9izpw5WLJkCX788UeUlze9GVtdXY158+a1G1bs8uXLePjhh9vlVxaiTx6H17CR6D9yLAzNLOA3byE0dXVx5XSI1PQ3os6hvq4WUxevgLGFFVwH+sB30lREhwRBJBIBAK5GnIKmrh785i2EoZkF+o8cC89hIxB18ph4O+a2Dhj/6OPwGDIcKgJBh/mrqarE4Q1/YMqCJ6GmodG9O98DRCIRLp06Ad9J0+Ay0AfGFlaYumgFamuqERd9vsP1rkaEQktXDxPnLYChmQX6jRwLT98RiDoZJE5zMfQEbJzdMMx/JgzNLDDMfyasnV1x8VRLuZzzwmvwHj4axhZWMLa0wrQlT6GqvAxZKcniNB5Dh8PW1QN6RiYwMrfEuEcfR211NfIy0nsmKPdB1vEEmspg4MY/4T//Saiqa0r9PSVlZWjq6Ir/mhuvepvokCB4DR+J/qPGwdDcAn6PLYKmjh4uh3d8PqirrcW0xU/dOR8Mge/kaYg+2XI+0DU0ht+8hfAePhpqmtLj1xvJumw69RsIB89+0Dc2hYGpGUbPmg2Bmhqybt2U+L2+UjY7MszFFhFxKYjLyEWesBz7LlyDQFkZ3rYdv+laUlmFo5fjcOV2Jqpq6x5gbnunysgoFP65HuWhEUCjSNbZkQuRJ46i/4jRGDR6PIzNLeH/xGJo6+ohOuyk1PQx58+grrYGDz25EiaW1nAfPAQj/KfjfPAx8bnS3NYeXkOHw8TCCvpGJug3bCQcPPshLSlBvJ2rZ8NRUVaKx55/BTbOrtAzMoaNsyss7jyM7Q1kVe+0dfWAc/9BMDQzh76xCQaPnwxjSytk3EwUp3HuP6jpvGrS5rzaqg4lb2R9LXLw7IfRs2bDdaBPh8M2ZN66CY+hI2Dj4g5dQyN4+o6EuZ0Dsm+ndF8guolIJMLl0GAMnTQNLgN8YGRhhSkLl6O2phrxncXzTFM8J8xtiaeH7whcbBXPS6eCYe3sBt8pM2BoZgHfKTNg7eSKS63imXUrGY5eA+DoPQC6hkZw9G76d+tYZd1KhvuQ4bBxcYOuoRE8fEfAzM4B2bdv9UxQupFIJMK18JMYOGEKHPsNgqG5JSY8sRR1NdVIunyhw/WunwuHpo4uRj/6OPRNzeExbDRcfIbjamhL7Kyc3WDvPQD6pmbQNTJGvzETYWhuiexWx6+6ljY0dHTFf2nxsRCoqvXKBpULJ4PgPXwkBowaByNzC0x+bBG0OqmzX7/QVGefvuQpGFtawW1QU509KrjlXKpnaIxJjy1Ev+GjoaYhvc4+a9kzGDzOD2bWtjA0M8eUJ5ZAoKaGlOu9u6evrK7r9m6ecBvoAyNzCxiYmMJ34hSYWlpLpOmNLgQfQ7/hozBw9HgYmVtiyuOLoaWjh0th0stn7IWzqKutwcylT8PkTvkcNkUynpMfW4gR/jNhYe8IAxNTjJ7xCMxs7JF49aJ4Ow8tfxY+4yfBzMYOhmbm8J+/FAI1ddy8ce2B7HdPOB98FP3ulE2jO2VTS1cPFzsom7EXmsrmrKV3yuagIRjeJpZTHluEkf4zYXknlmNmPgpzW3skXGmJpbqmFrR09cR/6ckJqKupQf+RYx/IflM3UVSU/78+6oHumaenJ2pra5GUlAQAiImJgY6ODq5dazn5xcbGwsvLC0VFRXjrrbdga2uLb775Bh9//DGqqqrw8ccfixtMgoKCsGnTJixYsAC//vorli9fjoCAABw5cqTdb4tEIqxbtw6HDx/G559/DlfXplbZ77//HllZWfj444/xzjvvICQkBHl5eRLrKigoYMWKFfjll1/w+uuvIykpCX/88QcAQE1NDWPGjGnXEBMcHIwhQ4ZAX1+/+wL4LzTU1yMn/Tbs3L0kltu5eyEz5abUdbJu3YSVo4tEI4idhxfKhSUQFjYNkZCVcrPdNu3dvZGbehsNDfW4F0HbNsB1oA9sXT3uaT1ZERbmo6JUCNtW+68iEMDKyRWZndyQZ9+6CTs3T4lldu5eyE1riVnWrZuwdW+fpqP/KwCora6GSCSCageNUQ319bh2JgwCNXWYWMnf25fyEM/j2zfCZYAPbFzdO/y9zJQk/PLWy1i3dg2Ctm1ARVnpXe+jvGior0dOWvvzgb27Z4exzkpJbnc+sHeXPB/0VfJQNps1NjYiPvo8amuqYWEvOXRfXyibHdHXVIe2uhpu5raUtfqGRqTmF8HaUE92GaM+raG+Htlpt+HgIXmudPDwQsbNJKnrZKQkw8bJVeJc6ejZD2UlxSgpzJe6TnbabWTcTIKti5t4WcKVi7B2dMGx7Zvw7esv4LcPViPs4F6JNwjlmbzUO0UiEVLjb6A4NwdWHQwD0tjYiLg751V5HRIVkK9rUUesHJxxM+YKSu8MtZSZkoy8jHTYt/k/kwfCwoKmeLaKjYpAACtHl3YvLLSWfeumxDoAYOfuidy0VHE8s2+3T2Pr7oWsWy3/T5YOzkhPikdRTjYAoDA7C2mJcbD38JZIkxJ7VTx0VVZKMvIz0mHvIX/xbKusqACVZaWwdmm5r1NWEcDcwRk5nTSw5aamwMpF8l7Qxs0D+emp4pcxWxOJRMhIjEdJfi7MHZylblMkEiHu/Fm4DB7a6ct98qi5zt72GOqszp55KxnWTpLnUgeP+6+zN9TXo76ursMGmN5Altf11kQiEW7FXUdhbjZsnaWn6Q2a49n6vAUA9h5eyEiRHs/MlGRYt4mng4c3yoXFnZbP2pqqTstec/lU76Xls8Oy6d69ZRMAaqqrO32B+XJEKBy9+kvtAUdE7T3QIb/U1dXh6OiImJgYuLm5ISYmBjNmzMCePXtQVFQEDQ0NJCUlYenSpThy5Ajs7e2xdOlS8fqvvvoqnnjiCSQnJ8PFxQU7duzA0qVLMXLkSACAmZkZ5syZgyNHjmDGjBni9RobG/HDDz8gLi4OX3zxBUxNTQEAmZmZuHjxIr744gt4eDRV4F555RU89ZTkOKIPPfSQ+N+mpqZYunQpPvnkE7zyyitQVFTElClT8Prrr6OwsBCGhoYoLy9HZGQkVq9e3VOhvGtV5WUQNTZCQ1tHYrmmtg5SS29IXaeiVAhtPf026XXF3+kZGaOiTAhbbclKr4a2DhobG1BVXg4tXb27yt/VM2Eoyc/D9CVP3+UeyV5FadPDSk0pMS0v6bhHUkWpEDZubWKmIxmzilKh1O1Wlgk73G7Inm0wsbJp95D1ZswVHF7/B+rqaqGlo4u5L7wml8N9yTqe1+6Uwc7m/LB394Jz/0HQNTRGaVEBIg7vxa4fv8KiN9/vVcOwVN45HzQfz800dHRREd/J+UDfQGJZczlqPh/0VbIumwCQn5mBbd98ivr6OghUVfHQUy9IDEvXV8pmR7TUVAEAFdU1Essrqmuhra4qiyzRf4D4XNnmmqmpo4vyuOtS16kQSjlX3jnGK4RC6BuZiJd//+ZLqCwvQ2NDA8bMfASDx04Uf1ecn49b8XHwGjocT7z4GkoKCnB0+0bU1lRj0tz53bWLPUbW9c6aqkr89varaKivh4KiAvweWwQHz34S6+VnpmPr1y3n1YefflGuh5yVh2tRVybMnY8TOzbhz/deh6KikniZoxwOZ1NZ2rRvbcuoho4OyktKOlyvorQUNq5t1tG+23i2vOgwZNJU1NZUY8Nn70FRQRGNjQ3wnTIdA8ZMEKcZP2c+gnduwl/vvyGO5/i588VD18mzyjvlVb1tfLV0UCEs6XQ9K2fJF5vUtXTQ2NiI6opy8fm4pqoKmz56C431dVBQVMToR5+QaGxsLSMxDmVFBXD3HSX1e3nWfB3S0LnHOrueQbv0zd/92zp7+MEACFTV4Nxv4L9aXx7I8roOANWVlfh+9UtoqKuHgqIips5fDCdv+T+eO9ISzzbnOx1d3I7vIJ6lQmi3edm4ef3y0hKp5TM6NBhlxcXw9h3ZYV7CDu6BQFUVzv0GdZhGnnV0f66po4tbHcSyXCiETrv7c+lls1n0qRMoKy6C9zDp58PC3GykJcZj7rOr/sVeEP03PfA5VLy9vRETE4O5c+ciNjYWs2bNwtWrV8W9VZSUlODi4oLdu3fj+vXrmDt3brttZGdnw9TUFAUFBfjll1/w22+/ib9raGgQd3Nr9s8//0BRURHffPMN9PT0xMvT09OhqKgIF5eWCdZMTExgYCB5crp69Sr27NmD9PR0VFZWoqGhAfX19SguLoahoSGcnZ1hZ2eHkydPYt68eQgLC4OWlhYGD27ftfjYsWMICgpqt7wtPUd3ON4Zj7c7tO2iLxKJ0EGv/Q7Tt1vebgPNce9kw60U5Wbj9ME9eOKVt6GkLL/T+dyIOocT2zeJPz/afJGRFqPOggopkRE1b6rjuIo6GX3lVMAOZN5MwhOvroFim6501i7uWLzmQ1SVl+Pa2TAc+uc3zH/tnbtu7Oop8hTPotxsnD4UgMdXrem0DLq1mgjY2NIKpta2+PP9N5Fy/RpcBvS+IQTaBa6L80FbLeeD7suSPJCnstnMwNQMi9d8iJrKSiReuYhjm9dh3stvwtiiqVGlr5VNb1sLzBzc8nbv1tNN3dLbxaaPlT2ST1LrTp0UvranBVEHXyx5813UVtcg81YyTgbshJ6hMfoNHyX+DU1tHcxYvByKioowt7VHVUUZju/aCr85T3Q45JK8kVW9U6CqhiVr1qK2pgZpCTdwKmAHdA2MYNuqYcHA1BxL1qxFTVUlEq9E4+imv/HYqtXi86qsyeO1qCuXwoKRmZKER1a+BB0DQ6QnJyJs3y7oGhq1e4P5QYuLikTwjpZ4PvzMywDalzmI0HU8O4iVZDzbblYyoAmXLuDGhbOYtuQpGJpbIj8jDacCtkPH0Bjew5smqb4cdhJZKcl46OkXoWNgiIzkRITv2wUdA0OZx7OtxIvnEbZnm/jz9BXPA5BS1YSo62t3BwW29WKBqirmvfYO6mpqkJEUj7MHd0Nb3xBWUnoE3IiMgIm1LYzkuMG0K+1D0nkc258m728Yz6iQ47gSEYrHX34TqurS5xfqTWRxXQcAVTU1PP3ep6itqcatuOs4sWsb9AyNYd+mh2Dv0zZAovbLJFJLD6i0/4P4S1EICdiBh1c8J3WOG6BpWLzLp09h/sure335bH9N6rxsSg1904baJY27FIXggB149KnnoddBLC+fbhrWsjfP6/Of1UvuDfqiB/4U28vLC4GBgUhLS0NVVRUcHR3FjSw6Ojpwd3eHsrIyGhsb4ePjg2XLlrXbhp6eHmpqmt5Wff755+Hm1nl3yQEDBiA8PBzR0dHw8/MTL2/b8CJNXl4ePvroI0yePBkLFiyAtrY2bt68ia+++gr1rYZfmDx5Mg4cOIB58+bhxIkTmDhxIpSkTAzs7+8Pf3//Ln93S8SlLtPcDXUtbSgoKqKiVPJNs8ryMmi0aQVvpqmjKyV909tGzW9zaWpLSVNWBkVFJahr3V13y6yUm6gqL8f6T98VLxM1NiI9ORFXIkKx6tvf5eINayfvAeKJJAGIh92oKJV8M6Appjrt1m8mNa5lpVBUVBLPO9FR7KX9X50K2I74ixcw76U3oSflLQSBqioExqbQNzaFhb0j/l77FmLOhmP41Fl3sdc9R57imXWrqQxu+Ow98feixkZk3EzE1YhQvPzNb1LLoJaePrT09VGcn3u3uy0XNDo6H5RJL2NAx3EG0OE6vZU8lc1mSsrK0Ddu6lVpZmuPnLRbuHjqOPwXtL82Ar23bDZLyMxFZmGJ+LPSnYZiLXVVlFZVi5drqgpQ3qbXClF3aT5Xlrd5m7qyrLTd25jNNHV1US6Ufq5su07zm4OmVtaoKBUi/NA+8YMXLV1dKCkpS7wkYWRuibraWlSWl7V7+13eyLreqaCoCH2TpnOmqbUNCnOzEBl0WKJBRUlZWZzGzNYe2am3cTHkOPwXSj+vPmjyeC3qTF1tLU4fDMCs5c+Je6QYW1ojPyMNUSePybwBwNG7P8zsPhB/bh3P1m+fV5aVdnp8aerotItVVbm0eEoOu1lZJvn/FL5/N3wmToHb4KYXIowtrFBaVIgLx4/Ae/ho1NXWIuJQAGYse1YynpnpuHgySObxbMvOsz9Mbe3Fn5vjW1lWCq1W8a0qL2vXa6U1DR0dce+W1usoKipCtdXccAqKitC9cw41srRGcW4OLp082q5BpbKsFLevX8XoRx//9zsnQ53V2TsacaCzOvu/GaUgKuQ4Th/ci7kvvNqr5vGSRpbXdaCp3Bo0X3esbVGQk4WIowd7bYNKR+WzorN46uiiXEr65u9ai78UhYPr/8DMpU/Dpb/0nicXTgYh/GAAHnvxNVjYO/7bXZE5cdksLZFY3lkstXR1UXGXZTPuUhQO/PM7HnpyZYexbKivx7XI0xg4ajwUpTzDJCLpHvjsMJ6enqirq0NAQAA8PDygpKQEb29vXLt2TTx/CgA4OjoiLS0NJiYmsLCwkPjT0NCAvr4+DA0NkZ2d3e57CwvJiWp9fHywevVq/Pbbbzh5smViJ2trazQ2NorndAGaGlCKiorEn5OSklBfX48VK1bAzc0NlpaWEt83GzduHAoLC3H48GHcvHlTouFGlpSUlWFmbdeu62Vq/HVYOki/8FjYOyLjZiLq61om8k2NuwEtXT3x2wEWDo5IbdPd+Hb8dZja2kFJ6e7a6Zz6D8LSdz7GkjVrxX9mNnZwHzwUS9aslZteKwI1dejfaZjQNzaFoZkFNHV0kdoqpvV1dci8mdjpGNzm9o5ITZCMWWr8dZjatMTMwr59XFPjb7T7vwrZsw1x0ecx76U3YGhmflf7IRKJ5GIMdnmKp1O/QVjy9kdY/NaH4j9TGzu4DRqKxW992GEZrCwvQ3lJMbTkcAi1zigpK8PMxg6323Rtvx1/vcNYWzg43Tkf1Eqkb30+6CvkqWx2pKvjuLeWzWa19Q0oKq8U/+WXlqOsqhqOpi1j+SorKsLW2ADprRpeiLqTkrIyzG3skBIXK7E85cZ1WDlKH6vfysEJackJEufKlBux0NbTh55hx8OsiEQi1Ne31LesnVxQlJ8L0Z35AoGmYRhUBAJoaGn/2116YOSt3ilqvIu6j6hR4v9A1nrDtai1xoYGNDY0tHu7VkFR8a5eXutpHcezZb/r6+qQmZLU6UM5c3tHpLWL5w2Y2tiK42lu54i0BMmyn5ZwXWJY3vraWigoSN6CKyoqAqKmY745nm17nstLPNsSqKlB18hE/Kdvag4NbR2kJ8aJ09TX1SE7JRlmnTyUN7V1QEZSvMSy9MQ4GFvbSn1JUayDelFC1DkoKSvDacCQe98pOdBcZ2875M+tTurslvZOSE+WrLPfivt3dfYLwccQfjAAc55/BdZOLl2vIOdkeV2XmqZRhIY62d+X/1vN8bzVJp6342Jh1cGcRpYOTkhvE89bcbHQ0tWXKJ83os/j4PrfMWPJU3AfPFTqts4HH0XYwT2Y98KrsO5gnrTeQhzLG5KxvBV3/2XzRvR5HPjnN8xa+nSHsQSA+MvRqCwvxwBORk90Tx54g0rzPCqhoaHw9m56w8bNzQ0FBQVISEgQL5s+fToqKyvx5ZdfIiEhATk5Obhy5Qp+/vlnVFZWAgCeeOIJ7N27F/v370dGRgZSU1MREhKC3bt3t/vdoUOHYvXq1fj1118REhICALCyssKgQYPwyy+/ID4+HikpKfjhhx8gaDW5k4WFBRobG3Hw4EHk5OQgLCwMBw4caLd9TU1NjBo1CuvWrYOnp2e7Rh1Z8pk4GbGREbh2JgyFOVk4uXsryktK0H/UeABA+IHd2PnDl+L0HkOGQVlFgKOb/0Z+VgYSr0Tj/IlA+EyYIr5Z6j9qPMpLihCyZxsKc7Jw7UwYYiMjMGRiS++bhvp65KanITc9DfV1dagoFSI3PQ3FeU1vTqtpaMDYwkriT0VVFWoamjC2sJLbYS0UFBQwaPwkXDhxBIlXLiI/KwNHN6+DikAV7q2G3zmy6S8c2fSX+HP/UeNQVlLcErOz4Yg9fwZDJk4Rpxk0bhLSEuNwPigQhTnZOB8UiPTEeAweP0mcJnjnZsRGRmDG0pVQ09BERakQFaVC1NY0vb1dU1WFiEN7kX37JkqLCpGTdhvHtvyD8pJiuA6Sv5sKWcZTahkUqEJNs6UM1tZUI3TvTmSlJENYWIC0xHjs+/1HaGjrwLmDtzzkmc+EKYiNjMDVM2EozM7CyV1bUS4swYDRTeeDsP27seOHL8TpPYYMg4pAgCOb7pwPLkfj/PFA+EycInGM5qanIjc9FbXVVaiqqEBueioKsjMf+P51J1kf6+EHdiMjORHCwgLkZ2Yg/MAepCclwN1nGAD0ubLZkcjEVIxyd4S7pSlMdLXwsK83auvrEZOaJU7ziG8/POIrOU+CmZ42zPS0oaqsDHWBCsz0tGGso9V28wRAQV0NAicHCJwcAEUFqJiaQODkAGXTvjtHUleGTZqKq2dP4/LpUORnZyJox2aUCYvF46Kf3LsTm7/9XJzea+gIqAhUcWD9n8jLTEfcpSicOXYIvn7+4nPlhZDjSLx2GYW5OSjMzcHliFCcO34E3sNaxgYfPHYiqirKEbRzCwpysnHz+jWEHdwLn3F+clsvaktW9c5zxw7hdvx1lBTkoTAnC1HBx3Djwjl4DB0uThO2v/V5NR3hB3YjLSkBHkNa0sgbWV+LamuqkZeRhryMNIhEIpQWFyEvIw2lRYUAAFV1dVg5uSL8YADSEuNRUpCP2MgI3LhwVi7HtFdQUMDAcX6ICj6CpCsXUZCVgaAtTfFsPYzm0U1/4+imv8Wf+49siuepgO0ozMlCzNlwXD9/BoMl4umHtMR4XDgeiKKcbFw4Hoj0xAQMahVPB6/+iAo+ipTYqxAWFiDp6iVcPHUcTndi1RzP0wf3ID0pHsKCfFy/E0+nXnBtV1BQQL8xE3E5JAgp1y6jMDsTp3ZshIqqKpwHtjzQO7ltPU5uWy/+7Dl8DCqExYjYvwvFudm4ERmBhKhz6D+uJXYXg48gIzEOpYX5KM7NxpXQE0i8GAmXwb4SeWiajD4CTgN8IFBT6/md7iFDJ05BzLkIXI0IQ0F2Fk7cqbMPvFNnD92/G9u/b1VnH9pUZw/c+DfyMzOQcDkakccDMcRPep29proK1VLq7OePH0Ho/t2Ytmg5DExMUS4sQbmwBNVVlQ9u53uArK7rpwMPIOVGLIrz85CfnYlzx48gJvIMvIeNeKD7392G+vnj2rnTuBIRioLsTBzfuQVlwhIMujMf1Kl9u7D1u/+J03sOHQ4VgSoObfwLeZkZiL8chXNBhyXieT0qEgf/+R3jHp4HG2dXcdmrqigXbyfyeCBO7duF6YtWwMDErE+UT1+/qbh67jQu34ll0M6msjloTFPZDNm3E1talU3PO2Xz4Iamshl/KQpngw61ieU57F/3GyY88liHsWx2OSIU9m4e0DduP+oJ9QIKCvL/10fJpAuAt7c3EhMTxY0nAoEArq6uSEpKEs9nYmhoiC+//BIbN27EBx98gLq6OhgbG2PgwIFQuTMEz5QpU6Cmpoa9e/di06ZNEAgEsLGxkZiQvrXmRpUvvmiqeEyYMAGrVq3Czz//jHfeeQc6Ojp4/PHHUdJqQkJ7e3s89dRTCAgIwJYtW+Dm5oZly5bhyy+/bLf9SZMmISQkBJMnT+7OcN03t8G+qKqowLljh1BRKoSRuSVmP/eK+E2AcqEQJQV54vSq6hqY9+LrCN65BZu/WAs1DU34TJgCn1Y3DHpGxpj93CsICdiOK6dPQUtXDxPnLoDrwJZ5X8qFJdj0v5Yu9iURebgaEQprZ1c8vuqtB7DnPWeo31TU19bi5K4tqK6sgLmdA+a88BoEai1jd5a26cmkZ2SM2c++glMB23E1IhSaunqYMGc+XFrFzNLBCTOefAZnDu/FmSP7oWdkghnLnoG5Xcsbc1dOnwIA7PrpK4ntD586CyOnPwxFJUUUZGci5txpVFdWQE1DE2a29nh81Wq5nXRVlvHsioKCIgqyMnD9wlnUVFVCU0cPNi5umLn8WYn89RbuPr6orijHuaMHxeeDOc+9Kj4fVJSWoCS/7fngDZzYuRmb/vch1DQ0MWSiv8RDLADY+PkHEp9vxlyBjoEhnvnkm57fqR4ky7JZUVqKwI1/obJMCIGaOowtrTD72Vdg79HUk7Ovlc2OnIlPgYqSIqYN9oC6QAUZhUJsDotCbX2DOI2uRvsHJs9MkZx00dXSFCUVlfj+cFiP57m3UXNzgVWra4rhisUwXLEYpUeOI/ez3n0M/1ueQ4ahqqIcp48cQLmwBMYWVnjixdfFY0+XC0tQ3OpcqaahgQWrVuPY9o34+9MPoK6hgWGTpmLYpKniNKLGRpwM2AlhYT4UFZWgb2yCiY8+hsGtJqPWNTDEglWrcWLXVvz18TvQ0tHFgJFjMXr6Qw9u5++TrOqddTXVOLFjE8pLiqGsIoCBqRmmLVkhboQGmoZ5CtzwJyrKhFBVU4eRpTXmPPeK3A2j1JYsr0U5qbex68eW+56zgftxNnA/PH1HYuqi5QCAmcueQfiBPTiy8U9UV1ZAx8AQI6c/goFtJmaWF0P8pqK+rg4hu7eiurICZnYOmP38qxLxLCuWjKeukTEeeWYVwvbuwLWIUGjq6GH8nPlwaTXnpYWDE6YvXYkzh/fh7JED0DMywfQnV0oM4TZh7nycCdyPk7u2oLK8DFo6uvAePgbDWg3JO/3JlYg4GIAjG/9qiqe+IUZOf1hi4np5NmD8ZNTX1eL03u2oqaqEiY09Zjz9kkTjRnmJZHx1DI0wfcULOHNgN66fDYemri5GPfwYHFs1ytXV1CA8YBvKS0qgrKICPRMzTHjiSTi3eWEs62YihAX5mNjB8Ki9hbuPL6oqynGmVZ197vOvtjqXtrkOqWvgsZfewPEdm7HhTp196ER/DG1TZ1//mWSdPflOnf25T5uu9xfDTqKxoQEH/v5VIp3XsJGYseSpntjVB0JW1/Xammoc3bYBpcVFUFYRwMjMHA8tWwmvofLbkH83PHyGoaq8HGeOHER5aVM8H3vhNYnyWdKmfD7x8psI2r4J6z//AGoaGvD1m4qhfi3l83J4CBobGxC8eyuCd28VL7dxdsPC194GAFwMbSqf+//+RSI/3sNGYebSp3tyl3tMc9mMaFU2H3+hTdkskIzlglWrcXTbRqz77E7Z9JsKX7+WsnnxTiyP79qC47u2iJfbuLhh8WvviD8X5+fhdsINPHpn/isiunsKVVVV8td3uJc6ffo0fvnlF2zYsAFq9/k2THfNoUJN5LGLPBGAdkM60P1pbDVMDt2/zKLSrhPRXVnw0/eyzkKfcv7Tj2WdhT6luhcPPSJvWOfsXo2MZ7eqqpWf4e16O2kvc9C/p8K5G7pVQyPPnd1JsQ+/Zf+gzR3Wr+tEdNeqEpNlnYUuqbt0PERtbyYfk1T0ctXV1cjLy8OuXbswefLk+25MISIiIiIiIiIiIiIi+cIGlW6wd+9e7Nq1Cx4eHnj88cdlnR0iIiIiIiIiIiIi6qs44onMsEGlG8yfPx/z58+XdTaIiIiIiIiIiIiIiKiHsCmLiIiIiIiIiIiIiIioC+yhQkRERERERERERETUSygoKMg6C/9Z7KFCRERERERERERERETUBTaoEBERERERERERERERdYFDfhERERERERERERER9RaKHPJLVthDhYiIiIiIiIiIiIiIqAtsUCEiIiIiIiIiIiIiIuoCh/wiIiIiIiIiIiIiIuotFNhPQlYYeSIiIiIiIiIiIiIioi6wQYWIiIiIiIiIiIiIiKgLHPKLiIiIiIiIiIiIiKi3UFSQdQ7+s9hDhYiIiIiIiIiIiIiIqAtsUCEiIiIiIiIiIiIiIuoCh/wiIiIiIiIiIiIiIuotFDjkl6ywhwoREREREREREREREVEX2KBCRERERERERERERETUBTaoEBERERERERERERERdYFzqMgpkUgk6yz0KcpKbDvsLvUNjbLOQp/S2Mh4dicFjiHareKz82SdhT7j/KcfyzoLfYrvO+/JOgt9SvTnn8o6C32GkiKvQyS/BMpKss5Cn1FdVy/rLPQpe87HyDoLfcpr08fKOgt9SmaRUNZZIJJOgc86ZYWRJyIiIiIiIiIiIiIi6gJ7qBARERERERERERERkdwIDAzE3r17UVxcDBsbGzz11FPw9PTscr2srCysWrUKIpEIu3fv7vZ8sYcKEREREREREREREVEvoaCoIPd/9+P06dP466+/MG/ePPzwww9wd3fHhx9+iLy8zocmr6urw5dffnlXDS//FhtUiIiIiIiIiIiIiIhILuzfvx8TJ07ElClTYG1tjZUrV0JfXx9Hjx7tdL0NGzbAzs4OI0eO7LG8sUGFiIiIiIiIiIiIiIhkrq6uDsnJyRg4cKDE8oEDByIuLq7D9aKiohAVFYWnn366R/PHOVSIiIiIiIiIiIiIiHoLhfsbUutBOHbsGIKCgrpMN2XKFPj7+4s/l5aWorGxEXp6ehLp9PT0cPXqVanbKCoqws8//4w1a9ZAQ0PjvvLdFTaoEBERERERERERERFRt/H395doKLlXCvfQaPTNN99g6tSpcHNz+9e/d7fYoEJERERERERERERERDKno6MDRUVFFBcXSywvKSlp12ul2bVr1xAbG4vt27eLlzU2NuKhhx7Cs88+e18NO22xQYWIiIiIiIiIiIiIqLdQ7LtTo6uoqMDJyQlXrlzBqFGjxMuvXLmCESNGSF3n559/lvgcGRmJXbt24dtvv4WhoWG35o8NKkREREREREREREREJBcefvhhfPvtt3B2doaHhweOHj2KoqIiTJ06FQCwceNGJCYm4tNPPwUA2NraSqyflJQERUXFdsu7AxtUiIiIiIiIiIiIiIhILowePRqlpaXYtWsXioqKYGtriw8++AAmJiYAmiahz8nJkUne2KBCRERERERERERERNRb3MOE7b3V9OnTMX36dKnfvfLKK52u6+fnBz8/v57IFvruYGtERERERERERERERETdhA0qREREREREREREREREXWCDChERERERERERERERURc4hwoRERERERERERERUW+h2PfnUJFX7KFCRERERERERERERETUBTaoEBERERERERERERERdUFuGlRmzpyJM2fOdPj9mjVr8Pvvv/d4PoRCIWbOnImYmJge/y0iIiIiIiIiIiIionuhoKAo9399Va+ZQ+Xtt9+GkpKSrLPRq4lEIpw9cgDXzoShpqoSZrYO8HtsIYzMLTtdLz0pAaF7d6AgOxNaunoY4jcVA0aPl0iTeDkaEYH7ICzIh66RMUbPfBTO/QeLv78cdhJXz4ShtKgAAGBoZolh/jPg6NVf6m8e37YB186GY+zD8zDEz/8+9/zBuBR2EudPHEW5sARG5pbwmzsf1s6uHabPy0zHiZ1bkH07BWoamhgwejxGTpsFBYWmMRDLhSUI2bMDOem3UZyXC0/fEZix5KkOt3cjKhIH//kdjl79Mff5V7p9/3qSLMvm+aBAJF69iOK8HCgpK8PczhGjZ82GsYUVAKChoR4Rh/bh1o0YlBTkQVVNHdbObhjz0BzoGBh2fzC6gbwf60c3r8P185IN6OZ2Dljw+rv3u+vd7nJ4CKKCW47rCXPmw8rJpcP0+ZnpCN61FTmpTcd1/1HjMHxqy3ENAOlJ8TgV0BxnfQydJBnnq2fCcP38GRRmZ0EkaoSJlS1GzXhE4ncvhZ3E1YjQljibW2K4/8wOz6m9zZxh/THBywVaagIk5xTgn5DzyCgq6TD9EEcbTOrnCjtjA6goKyGzqAT7LsTgYkq6OM1YD0c8O3lUu3UX/bQZdQ2NPbEbD1x0aDDOBQWiTCiEsYUlpjy2EDadXIdyM9JxbPtGZN1OgbqmFgaNGY/R0x8Wl9fUhDiE7NuFwtwc1NXWQNfACANHj8XwydMltlNTVYVTB3Yj7mIUqirKoaNvgPGPzIOnj2+P7q+8UuvvBf0n5kDN1RnKxkbI+fRrlB09IetsydSFUydwNigQZSUlMLGwhP/ji2Dr4tZh+tyMNBzZthGZt25CXVMLg8dOwNgZj0icS5ulJiVgw1efwMjMAs9/9IV4+eUzYTiw/s926d/5bT1UVATds2Mycj7kBCKOHUZ5SQlMLC0x9YnFsOsknjkZaQjcsgEZd+I5ZNxEjJvZEs9b8Tfwz5eftFvvpU+/gvGdusOliDDs++ePdmne/2MD48l4SpDVPVFNVRXCDwYg4XI0qirKoa1vgLEPzYH74KE9tq8PgkgkQuTRg4g9G47qqkqY2dpjwtwFMOyiXp+RlIDwfTtRmJMFTV09+Ez0R79R48TfF2Zn4tyRg8jLSEVpYQF8/Wdi+LSHJLZx4fgR3Lx2CcW5TfdJZnYOGDlzNowsOv/t3m7+qMHwH+AGLTVVJGTl4bfjZ5BWUNxhei9rcywdNwSWhnpQVVZGXmk5jl+Jx94L1x5grmUvLOgIThzYC2FJMcytbDD3yRVwdveUmrauthbb/vwV6bduIjszA46u7nh17WcSaRKvx+LAtk3IzcpEbU0NDIyNMXLiZEya9ciD2B2Ziw4NxrnjgSi/U6+fPK/zen1epmS9fuDoNvX6xDicalOvHzCqfb2eiO6OzBtU6urqoKKi0mU6bW3tB5Cbvu1C8FFEhwRh6sLl0Dc1w7mjB7H7p6+x/P3PIFBTl7pOSUE+An77Dt7DRmPakqeQeTMJwTu3QENLGy4DfQAAWSnJOLT+d4yc9hCcBwxG0pWLOLjuN8x/dQ3M7RwBANr6Bhjz0Bzom5hC1CjC9fNncODPn7Fo9fswtrSW+M2Ey9HISbsNLV29Ho1Hd4qLPo/gXdsw+YlFsHJ0waXwk9j1y7dY8f5n0JXy0L2mqgo7f/wK1k6uWLL6AxTmZuPIpnVQURXA128qAKC+vg7qWloYNmU6rkaEdfr7Jfl5OLV3Z6cPeuWZLMtmelI8BoweDzNbe0AEnAnch90/fY0n3/0E6ppaqK+tRV56KoZNmQETK2vUVFUhdN9O7Pn1Wyxd8xEU5bChtzcc67auHpjW6mZYHuMYf/E8QnZvg9/ji2Dl6IzL4SHY88u3WPbep1Ib02qqqrDrp69h7eSKhW++j6LcHBzdvA4qAlVxw3BJQT4Cfv0OXsNHY/rSp5FxMwnBOzZDXUsbrnfinJ4YD7dBQ2Hp6AwVgQDRIcex55dvsGTNWuibmAEAtPX0MfbhudA3NoVI1BTn/X/8hEVvfQCTNufU3maWjxemD/LEb8cjkFVcitm+/fH2o5Pw6sZ9qK6rl7qOh5UpYtOzsfPsZZRX12CUmwNemzEOH+0JQnxWnjhddV0dXl6/V2LdvtKYcj0qEkE7tmDqgiWwdnLBxdCT2PbjV3j2w/9B19CoXfqaqips/f4L2Di7Yvnba1GYk4ODG/6EikAVwydPAwAI1NQwZMJkmFhZQ0UgQHpyEo5s+QcqAlX4jPMDADTU12Pr919ATUMTs59+ATr6BigtLoLyXdTt+ipFdXXUpqSi7FgwTN99Q9bZkbnYC+dwbMdmTF+wFDZOrogKDcaWH77E8x99CT0pZbO6qhKbvv0fbF3c8NS7H6MwJxv7//kDAoEqRkyRvOmvqqjAvnW/wcHdE6XF7R92qQhU8dLn30ou6+UPq2MunMOR7Zswc+GTsHF2xYVTJ7D5uy/w4idfdRjPjV9/DlsXNzzz3icoyMnG3nW/QyBQxUh/yXi++PGXUNfSEn/W1NaR+F5FoIpXvvhOchnjyXi2Iqt7ooaGeuz88SuoaWjioRXPQVtfH2XFxVBWlvnjjvsWHXwMl04dx+QFy6BvYobzxw5h7y/fYsm7n0KgpiZ1HWFhPvb/8QM8h42C/+IVyExJxqldW6GupQ3nAU0vQtXV1kLH0BBO/QfhbOA+qdvJSE5Av1HjYGpjD0CEc0cOYO8v32Dx2x9BTVNL6jq93Zxh/fHIUG98FxiGzMISPDFqED55fBpW/rkLVbV1UteprqvDwejruJ1fhJq6enhYmeIF/9Goqa9H4KUbD3gPZCP6zGnsWv8XnljxDBzdPBAedAS/fLoW73/3CwyMjdulb2xshIpAgLH+03H98kVUVlS0S6OqpoZx02bA0sYOAoEANxPisO3PXyFQVcXYKdMexG7JzPWoSBzfuQX+85fAxskF0aEnsf2nr/DMh/+DrkHn9fpla9aiMDcHhzb8CYGqKoZNulOvV71Tr7e0hrJAgIzkJBzZKlmvJ6K712Xfm+joaMybNw8NDQ0AgKysLMycORO//vqrOM2mTZvw3nvvAQBiY2Px2muv4dFHH8WiRYvw119/oa6u5cKzZs0a/Prrr1i3bh0WLFiAN998U+rv7tmzB/Pnz0dCQoJ4vdZDfi1fvhw7d+7Ezz//jHnz5mHp0qXYu1fyIUlmZibeeustPProo3jmmWcQHR2NuXPnIjg4WJwmMTERq1atwqOPPoqXX34ZiYmJEttoaGjAjz/+iOXLl2P27Nl4+umnERAQgMbGRvH+PvzwwyhucxO3adMmvPjii12F94ERiUS4dOoEfCdNg8tAHxhbWGHqohWoralGXPT5Dte7GhEKLV09TJy3AIZmFug3ciw8fUcg6mSQOM3F0BOwcXbDMP+ZMDSzwDD/mbB2dsXFUy1vYzr1GwgHz37QNzaFgakZRs+aDYGaGrJu3ZT4PWFRAU7t2YbpS5+WywesHblwMgjew0diwKhxMDK3wOTHFkFLRw+Xw0Okpr9+4RzqamsxfclTMLa0gtugIfCdPA1RwUEQiUQAAD1DY0x6bCH6DR8NNQ3NDn+7oaEeB/75HWNmzYaeUfvKiryTddmc88Jr8B4+GsYWVjC2tMK0JU+hqrwMWSnJAABVdQ3MffF1uA0eCgNTc5jbOWDS44tRlJONwpzsngvMvyTreN7tsa6krAxNHV3xn7oc3pRFnzwOr2Ej0X/kWBiaWcBv3kJo6uriymnpx/WNqHOor6vF1MUrYGxhBdeBPvCdNBXRIS3H9dWIU9DU1YPfvIUwNLNA/5Fj4TlsBKJOHhNvZ8aTKzFonB9MrW1hYGqOSY8vhoqqGm7diBWnce4/qCnOJm3ifKfc9mZTB7rjQFQMLiSnIaOwBL8GRUBdoIKRbg4drrMxLAoHo2NxM7cAucIyBJy/ipS8Ivg42kgmFAHCymqJv74i8sRR9B8xGoNGj4exuSX8n1gMbV09RIedlJo+5vwZ1NXW4KEnV8LE0hrug4dghP90nA8+Ji6v5rb28Bo6HCYWVtA3MkG/YSPh4NkPaUkJ4u1cPRuOirJSPPb8K7BxdoWekTFsnF1hYdfx/1dfVxkZhcI/16M8NAJoFMk6OzJ37sRRDBgxGoPHTICxhSWmzV/SVDZDg6Wmj4k8i7raGjyy7BmYWlrDY/BQjJw6A+dOHBWXzWYHNvyJASNGw8rBWfqPKwDaunoSf73d2aAjGDhyDHzGToCJhSVmLFgKLV09XDglPZ7XIs+grrYWs1c8C1Mra3j6DMXoaTNx5viRdvHU1NGRiJWiouStogLjyXh2QVb3RDFnI1BRVobZz74MaycX6Bkaw9rJBea9/FokEolwOSwYQ/ymwnnAYBhZWGLKwmWoralG/MWO6/XXIsKgpauH8XPmw8DMAt4jxsB96HBcDGmp15vZ2mPMw/Pg5uMLFYH0hrxHn3sFnsNGwcjCEkYWVpiyaLnEfVJf9NAQb+yJvIqzCbeQWlCMbw+HQl2ggrEeTh2uk5xTgPC4m0grKEausAynrifj0q0MeFqbPcCcy9bJwwcwfNxEjPKbAnMrazy2fCV09PURfvyI1PSqamqY//RzGD3JH3odjPpg6+iEISPHwMLaBkamZvAdMx4e/QciOe56T+6KXDgffBT97tTrje7U67V09XCxg3p97IWmev2spXfq9YOGYPiU9vV6zyHDYXynXu89bCQcPPohLTlB6japl1BQkP+/PqrLBhVPT0/U1tYiKSkJABATEwMdHR1cu9bSfTE2NhZeXl4oLCzEhx9+CAcHB/zwww948cUXER4ejk2bNklsMzQ0FADwv//9D6+++qrEdyKRCOvWrcPhw4fx+eefw9W14y5tBw4cgJ2dHb7//nvMnj0b69evR3x8PICmFu9PP/0USkpK+Prrr7Fq1Sps375donGnuroaH330EczMzPDdd99hyZIl+Oeff9rlx8DAAKtXr8avv/6KRYsWYffu3eJGGS8vL5iZmSEkpKWS2NjYiFOnTmHSpEldhfeBERbmo6JUCFt3L/EyFYEAVk6uyOykQpR96ybs3CS7adq5eyE37TYaGpreFM66dRO27u3TZKZIPkBt1tjYiPjo86itqYaFfUvFpLGhAYHr/xA/rO0tGurrkZN2G/atYgsA9u6eHcY281YyrJ1cJCqvDh5eKBeWQFhYcE+/H34gALqGRvAe3n4om95AnsomANRWV0MkEkFVQ6PTNACg1kkaWZGneHZ0rANAZkoSfnnrZaxbuwZB2zagoqz0nvazpzXU1yMn/Tbs2hzXne1v1q2bsHKUPK7t2hzXWSk3223T3t0buaktcZaWl4b6ug7LZGNjI+LuxNnSoeObvd7AREcL+poauJaWJV5W19CAuMxcuJjfW4OxukAZFTW1EssEykr4adls/LJ8Dt6cNQF2xgbdkm9Za6ivR3babTh4SJYtBw8vZNxMkrpORkoybJxcJcqro2c/lJUUo6QwX+o62Wm3kXEzSWKopoQrF2Ht6IJj2zfh29dfwG8frEbYwb1oqJdenum/pb6+Hlmpt+Do2U9iuaOnN9I7KJvpKUmwdXaTKJtOzWWzoKVsXjh1AuWlQoyZ0fHQH/W1tfjuzZfwzRsvYOuPXyE77fb97ZCMNcfTydNbYrmTZz+kJydKXSctOQm2LpLHupNX+3gCwO8fvYsvXnkO67/6FClSHljV1dbi6zdewlevvYDN33+FrNTb979TMsR4di9Z3hMlXr0EK0cnnNi5BT+tfgl/rX0bpw/v67Bu1VuUFhagslQIm1Z1dGWBAJaOLsi+1XG9Puf2Tdi4StbZbd29kJeWel8xqRPfJ3X8sl9vZqanDQMtDVy6lSFeVlvfgOvpOXC3Mr3r7TiYGsLd0hQxafL38l1PqK+rQ1pKMtz7D5BY7t5/IFIS4rvtd9Jv3URKQjyc29R3+5oO6/Xu3Vuvz0m7jYw7dS4iundd9oFVV1eHo6MjYmJi4ObmhpiYGMyYMQN79uxBUVERNDQ0kJSUhKVLlyIwMBAGBgZ49tlnoaioCGtrayxZsgS//PILFixYALU7XVJNTU2xfPnydr/V2NiIH374AXFxcfjiiy9gatr5RWvgwIGYMWMGAMDCwgKHDh3C1atX4ebmhitXriAzMxMff/wxDA2bWrxXrFgh0SMmNDQU9fX1ePnll6Gurg5bW1vMmzcP337bMjSAsrIyFi5cKP5samqKmzdvIjw8HJMnTwYATJ48GSdOnMDs2bMBAJcuXUJJSQnGjRvXVXgfmIrSpoeVbbuaa2rroLyk4/FAK0qFsHHzkFimoaODxsYGVJWXQ0tXDxWlQqnbrSwTSizLz8zAtm8+RX19HQSqqnjoqRdgbGkl/v5M4H6oaWq1m7NB3lWWl0HU2AgNHV2J5Ro6uqiIl97Ft6JUCG09g3bpm7+7254mt27EIu7iBSx7+6N/kXP5IA9ls7WQPdtgYmXTrgGgWUN9PUL37YSj1wBo68vfw1h5iGdXx7q9uxec+w+CrqExSosKEHF4L3b9+BUWvfm+3AwTVNV8XEvZ39TSzo5r/TbpJY/rijIhbLXbxFlbMs5tRRzaCxVVNTh5D5RYnp+Zjq1ft8T54adfbDeEYm+jp9k0JF3bniPCyioYaN19A+bkfq4w0NLE6biWxq+s4lL8fuIsUguKoK6igqkD3bF23lSs3noQOSVl3bMDMtJ8HdJscx3S1NFFeQdv8VUIhe3OYc3Hd4VQCH0jE/Hy7998CZXlZWhsaMCYmY9g8NiJ4u+K8/NxKz4OXkOH44kXX0NJQQGObt+I2ppqTJo7v7t2kXqplrLZ5lyqo4vyG9LLZrmwBDr6hu3SA0B5qRD6xibIzUhD2MG9WPH22nZv/TczMrPAQ0ufhqm1DWqrqxF58hjW/W8tnv3gcxia9s63hivLytDY2AitNse6lq4ubrbqxdhaeakQum2O9eb1y4Ql0Dc2gZauHmYuWgZLewc01Nfj6rkIbPj6Myx7813YuboDAIzMzPHIspUws7ZBTXU1zp04hr8//xDPr/0chqbmPbC3PY/x7F6yvCcqKchDakIBPIYMx9znXkVJYT5O7NyMupoaTJj9+L/YG/lQUdpUx25bH9XQ1kG5sKST9Uph7dJ+ncbGBlSXl0PzX/aGCg3YAWNLa5jbO/6r9eWdvmZTXbOkolJieXFFFQy1u66Hbnx+PnQ11KGoqIDtEZdw9HJcj+RT3pSXlaKxsRE6bcqVjq4e4kuu3vf216x8EuWlQjQ0NGL63McxZvLU+96mPBPXnbTb1+tvxXdUdxJCp229Xkd6vf6H1S31+tEzJOv1RHT37mpQUW9vb8TExGDu3LmIjY3FrFmzcPXqVXFvFSUlJbi4uGD//v1wdXWVuLHx8PBAfX09srOzYW9vDwBwdJR+Af7nn3+gqKiIb775Bnp6el3my87OTuKzgYEBSkpKAAAZGRkwMDAQN6YAgLOzs0Te0tPTYWdnB3X1ljkF3Nzat84ePXoUx48fR15eHmpra1FfXw8Tk5YT0sSJE7F582bExcXB3d0dwcHBGDZsGHTa3DwCwLFjxxAUFNRueVu6Dm5wHODTZbqO3Ig6hxPbW3oGPfrsqqZ/tOluJRKJuuyC1e5bUfOmWn3Tbrvtt2NgaobFaz5ETWUlEq9cxLHN6zDv5TdhbGGF9KQEXD9/Bovf+rDTvMiz9nESSVnYKn3b76QFrROV5WUI3PQ3Zi5bCTXN3vOWkDyWzWanAnYg82YSnnh1jdQHNI0NDQjc+CdqKivxyNMvdZq3B0Ue49nZsQ4Abq0mqja2tIKptS3+fP9NpFy/BpcBg9tvUIbaToAsEok6DaO09O2Wtz/4m79ot72Lp47j6plQzHvxDaiqS85/Y2BqjiVr1qKmqhKJV6JxdNPfeGzVanGce4ORrvZ4auJw8ecvDtzpxt6mYClA4a5PkUOdbLBgtA9+PBqOgrKW8ZiTsvORlN3yhlZCdj6+WDATU/q7Y2PYhX+/E3JEannt5ELUtiiKOvhiyZvvora6Bpm3knEyYCf0DI3R706vSJFIBE1tHcxYvByKioowt7VHVUUZju/aCr85T0idRJz+e9qVw3utI7WUTtTX1WHPnz9j8rz50Dc2aZtQzNrRGdaOLUOBWTu54Pe1a3D+ZBCmzV9yD7mXQ/d4bWoX7DbXJmNzCxibt/QOt3FyQXFBPiKCAsUNADZOLrBpNVefjZMLfvlgDSKDj2P6AsazaTOMJ/Dg74maVmm6Fk1d+CQUFRVhZmuH6ooKnNyzDeMffazXXIvioyJxcudm8eeHVjbdb7TPv6izkN5Zp+0qog6+uDthe3ciKyUJ81a91WFDdm8zztMJL/iPFn/+cFfTELxti+DdhuzNLYegJlCGm4Upnhw/FDnCMpyKld6joE9qey5FV+fSu/PaR5+jproat5ISsG/LRhiZmMJ3bO96CfffaHfcd1Gv7+DS1O7/ZfEb76KupgYZKckI2bsTekbG6Desd452QgAUe8f1rS+6qwYVLy8vBAYGIi0tDVVVVXB0dBQ3sujo6MDd3R3Kysp3Kp/S/zNbL1frYPK0AQMGIDw8HNHR0fDz63pSJKU2c2woKCiIH151lpd7cfr0afz1119YtmwZ3NzcoKGhgcDAQERGRorT6OrqwtfXFydOnIClpSXOnz8vnlOmLX9/f/j7+3f5u5tPX7yvfDt5D5AYM7Z56I2KUsmW68rysnZvvLSmqaMrfjNGvE5ZKRQVlcQP8aWmKS+FRpsWdSVlZegbN/U6MrO1R07aLVw8dRz+C5YhLTEO5aVC/PZOyxBwosZGhB/YjYuhJ/DMJ9/cy+4/UBpa2lBQVJQap7ZvCzfrKK7N392N/KwMlAtLsOOHr8TLmsv/F88vw4r3PoWhmfy94SaPZRMATgVsR/zFC5j30pvQM2r/gKaxoQGHN/yBgqwMPPbyaolJRmVJHuPZ2bEujZaePrT09VGcn9vV7j4w6h0d1+VlUssP0HF8gJY3CzW1pcW5DIqKSlDXkmwYvXjqOE4f2oc5z70idQxwJWVl6Ju0xDk79TYuhhyH/0LpcZZHF1PSkZzTMqSHyp3ruq6mOgrLW94O1NFQg7CyqsvtDXWywfNTRuPXoAhcTEnvNK1IJEJKbiHM9bX/Ze7lR/N1qO3bqk3XIenHvaauLsqFHV2HJNdpfqvN1MoaFaVChB/aJ25Q0dLVhZKSssTDFSNzS9TV1qKyvKxdrzb6bxGXzTbnvYqy0na9Appp6eq1K5vNvS+1dHRRJixBflYm9q//E/vX/wngTv1HJMLapxdhwctvwKnNEGMAoKioCAtbBxTl5XTHrsmEhrY2FKUc6xWlncRTRxflpZLpy8ta4tkRKwcnxFw41+H3ioqKsLSzR2Eu48l4NpHVPRHQdN5QVFSSuBYZmpmjrrYWVV3UgeWJg/cAmNnZiz+3rte37lVaWVYGjQ6u70DTdbz5vClep7xMol5/L8L27kDCpSjMefF16PbCOTs7cj4pFQlZeeLPzfVQfS0NiZdy9DTUUVzRdT00V9jU4zk1vxh6mupYMGrwf6JBRUtbB4qKiihtMxpCmVDYrtfKv2F0p1eppa0dSoUlOLx7e59uUGmpO5VILK/opF6vpauLinus15tYtqrXs0GF6J7d1asFnp6eqKurQ0BAADw8PKCkpARvb29cu3ZNPH8KAFhbWyM+Pl48YTsA3LhxA8rKyjAz67prvY+PD1avXo3ffvsNJ09Kn2zpbllbW6OwsBCFhYXiZcnJyRJ5s7a2xu3bt1Fd3TK0SEKC5IRMN27cgIuLC2bMmAEnJydYWFggJ6d9RXfy5MmIiIjAsWPHoKenhwEDBtxX/u+XQE0d+sam4j9DMwto6ugitVUXwfq6OmTeTOx0zH1ze0ekJkh20U6Nvw5TGzsoKTW1x1nYOyI1vm2aG7B06LwrsEgkElcSB4yZgCVr1mLxWx+K/7R09TB4/GTMe/H1e9r3B01JWRlmNnbtul/eir/eYWwt7Z2QnpyI+rqW8f1vxV2Hlq4edA2N7up3zW0dsPzdT7Ds7Y/Ef879BsDayQXL3v5Ibieol8eyGbJnG+Kiz2PeS29IbYRqaKjHoX9+R35mOua99OY93eD1NHmMZ1utj3VpKsvLUF5S3OmDiAdNSVkZZtZ2uN3muE6Nv97h/lrYOyLjZiLqW83VlRp3Q+K4tnBoH8Pb8ddhatsSZwCIOhmE0wf3Yvazq2DV6g3WTokaUV9f13U6OVJdV49cYZn4L6OoBMUVlehn0/Jmr4qSItwsTJCYLX3832bDnG3xgv9o/HY8AueTU+/q922M9O/qBlneKSkrw9zGDilxkkPUpNy4DitH6ZN1Wzk4IS05QeI6lHIjFtp6+tAz7Pj6IRKJJMqZtZMLivJzIWpVvyrMzYaKQAANrd7fWEX3R1lZGRa29rh5I0Zi+c0bsRK9R1qzdnBGalI86lqVzZvNZdPIGDp6+nh27f/wzAefif98xk6EgYkpnvngM1g7Sj9nikQi5GakQUtXX+r3vYE4ntfbxjMG1h1cK2ycnJGamCAZz+sx4nh2JCcttdNJ0pvjqX0XowrIK8aze8nqnggArBycUdzmWlSUlwMVgQDqvehaJFBTg56xqfjPwMwCGjq6SGtVR6+vq0PWzSSYdzA8MQCY2TkiPVGyvpmWcAMmNrYS9c27ERqwHQkXz2POC6/BoJcOR9eRqto6ZBeXiv/SCopRVF6JgXaW4jQqSkrwtDZDXMa9vfilqKAAFaW+0ZOnK8oqKrBxcEL81SsSy+OvXYGDa/fOzyFqFEnca/VFzfX6W22GnrwV1zP1+oZedv9IJC/u6gzfPI9KaGgovL2bJu1zc3NDQUEBEhISxMumT5+OoqIi/Pbbb0hPT0dUVBQ2btyIGTNmdNgrpa2hQ4eKJ4BvPdH7vRowYAAsLS3x/fff49atW4iPj8fff/8NJSUlcc+VsWPHQklJCT/88ANSU1Nx+fJl7Nq1S2I7FhYWSElJQXR0NLKysrBjxw7ExrYfU3fgwIHQ1tbG9u3b4efnJ3fdYBUUFDBo/CRcOHEEiVcuIj8rA0c3r4OKQBXurYbfObLpLxzZ9Jf4c/9R41BWUoyQPdtQmJOFa2fDEXv+DIZMnCJOM2jcJKQlxuF8UCAKc7JxPigQ6YnxGDx+kjhN+IHdyEhOhLCwAPmZGQg/sAfpSQlw9xkGoGncdmMLK4k/RSUlaOro9oqK29CJUxBzLgJXI8JQkJ2FE7u2olxYgoF35oMJ3b8b27//QpzeY+gwqAgECNz4N/IzM5BwORqRxwMxxG+KRM+q3PRU5Kanoqa6CtUVFchNT0VBdiYAQKCqCmNLK4k/VXUNCFTVYGxpBSXle6ssy4qsy2bwzs2IjYzAjKUroaahiYpSISpKhaitaWpobWxowKF1vyH79k3MePIZKCgoiNPU1UpOeC0PZB3Pro712ppqhO7diayUZAgLC5CWGI99v/8IDW0dOPcf9AAidPd8Jk5GbGQErp0JQ2FOFk7u3orykhL0H9V0XIcf2I2dP3wpTu8xZBiUVQQ4uvlv5GdlIPFKNM6fCITPhJbjuv+o8SgvKWqJ85kwxEZGYMjElp6LF04cRfiB3fBfuAz6JmYoFwpRLhSipqqlx0bY/tZxTkf4gd1IS0qAx5CW4bN6q6OX4zDLxwtDHG1gZaiHZyePQnVdPc7Ep4jTPDd5FJ6b3PIm1XAXO7zgPwbbIy4hLjMXuhpq0NVQg6Zqy8SMs337o5+tBUx0tGBrrI+Vk0bAxkgfwdekTzzc2wybNBVXz57G5dOhyM/ORNCOzSgTFovHRT65dyc2f/u5OL3X0BFQEajiwPo/kZeZjrhLUThz7BB8/fzF5fVCyHEkXruMwtwcFObm4HJEKM4dPwLvYSPF2xk8diKqKsoRtHMLCnKycfP6NYQd3AufcX69ZoiV7qagrgaBkwMETg6AogJUTE0gcHKAsql8vujQ04ZPmoorZ8JxMfwU8rMycXT7JpSVFMNnXFPZDA7YgY1ffyZO7+3bVDb3//MHcjPTceNiFCKOHsTwSVOhoKAAJWVlmFpaS/xpautASVkFppbWUL1zzxF6MADJsddQlJ+H7LTbOLDhL+RmpsOnl48VPmLKNFw+E47o8FPIy8pE4LaNKCspxtA78Ty+ZwfWf/WpOH0/35FQEQiwd93vyM1Ix/WLF3D6yCGMnDxNfIyePX4UNy5FoTA3G7mZGTi+ZwfiLkfDd+Jk8XZCDgQgKfYqivJykZ12G/vX/4mcjHQMGcd4Mp4tZHFPBAADx4xHdWUFTuzeisKcbKTciEHE4f0YOGZCr74WKSgoYOBYP0SfOIrkqxdRkJWJ41v/gYqqKtwGt9TrgzavQ9DmdeLP/UaNRVlJMUIDdqAoJwuxZ8Nx4/wZDJ7QUq9vqK9HXkYa8jLSUF9Xh8qyUuRlpKGkVY/xkF1bcSPyDKYueRqqUu6T+qIDUTGYO3wARrjYwdZIH6/MGIeq2jqE3UgWp3l1xji8OmOc+PPMwZ4Y4mQDC30dWOjrYHI/Vzzq2w+nridL+YW+aeKMh3AuNAQRJ48jOyMdu/75C8KiIoy+M9/J/q0b8f3adyXWyU5PQ/qtFJSXlaGmuhrpt1KQfqulvn/q6GHEXIxCXnYW8rKzcObkcQQf2oeho8c9yF2TCV+/qbh67jQuR4SiIDsTQTub6vWDxjRdI0L27cSWVvV6zzv1+oMbmur18ZeicDZIsl4fFXIcSdcuoyg3B0V36vWRJ47A23ek1DxQL6GgKP9/fdRdP3H19vZGYmKiuPFEIBDA1dUVSUlJcHFpeoPH0NAQH374IdavX4+XXnoJWlpaGDNmDBYvXnxPmWpuVPnii6bK1oQJE+5pfaCp2/Q777yDn376Ca+++ipMTU2xbNkyfP755xAImh6wqKur4/3338evv/6KVatWwcrKCkuXLsXHH38s3o6/vz9u3bqFr7/+GgAwYsQIPPzwwwgODpb4PQUFBfj5+YkbVOTRUL+pqK+txcldW1BdWQFzOwfMeeE1CNRaxuUvLSqSWEfPyBizn30FpwK242pEKDR19TBhzny4DGyZ38XSwQkznnwGZw7vxZkj+6FnZIIZy56BuV3LW9wVpaUI3PgXKsuEEKipw9jSCrOffQX2Hl49v+MPgLuPL6oqynHm6EFUlAphZG6Juc+/Kn6zqlxYguL8lu7EauoaeOylN3B8x2Zs+N+HUNPQxNCJ/hg6UXI4uPWffSDxOTnmCnQMDPHcp/I7BNq/IcuyeeX0KQDArp++ktj+8KmzMHL6wygrKUbytcsAgM1frJVI479wGbzksHusPB/rCgqKKMjKwPULZ1FTVQlNHT3YuLhh5vJnJfInD9wG+6KqogLnjh0SH9ezn3ul1XEtRElBy3Gtqq6BeS++juCdW7D5i7VQ09CEz4Qp8GnVKKVnZIzZz72CkIDtuHL6FLR09TBx7gK4torz5fCTTQ15//wmkR9P35GYtngFgKahHwI3/ImKMiFU1dRhZGmNOc+9AnsP754MyQNxMDoWAmUlLJvgC01VVSTn5OOzfSdQXdfSy8lIR3K4ikn9XKGspIgl44Ziybih4uU3MnLw0Z6mecs0VQV4auJw6Gmoo7K2Frfzi7B2zzHczC1AX+A5ZBiqKspx+sgBlAtLYGxhhSdefB16HV2HNDSwYNVqHNu+EX9/+gHUNTQwbNJUDJvUMtGnqLERJwN2QliYD0VFJegbm2Dio49h8JiWepmugSEWrFqNE7u24q+P34GWji4GjByL0dMfenA7L2fU3Fxg1eqaYrhiMQxXLEbpkePI/axvXb/vhtfQ4aisKEd44H6UC0tgYmGFBS+/IX5jskxYgqJWD/DUNDSw+NW3ELh1A/78+D2oa2pixORpGD552j39bnVlJQ5t+hvlpUKoqmvA3MYWT775Hqy66FUp77yHDkdleTnCDu1DmbAEppZWWLTqTXHviHJhCYryJOO55PU1OLxlA37/6F2oaWpixJRpGDGlJZ4NDfUI2rUNpcVFUBEIYGJhhUWr3oBLv4HiNNWVlTiwcR3KhSVQuxPP5avfg1UnvV97A8aze8nqnkjHwBCPvfg6QgJ2YP1n70NTRxfeI0Zj5NRZPbzHPc/Hzx/1dbUI2b0NNZUVMLN1wCPPvQpBqxdWS4sLJdbRNTTGwytfRti+nYi5U68fN/sJOLeaq7BcWIJtX34k/hxTEIaYM2GwdHLB3JfeBABci2i6Twr4WfLa5es/E8On9c3r/J7IqxAoK+PZKaOgpSZAQlYe3ttxBFW1LW/xG+tIDv2sqKiAJ8cNhamuNhoaRcguKcWG0As4culG2833WT4jR6OivAxHA3ahtLgI5ta2eP7t92F4Z64zYXEx8tsMafjz5x+hqNX54LM3VwEAftt9EADQ2NiAfVs2oDA/D4qKSjA2M8PDC5Zg9KSuh9Dv7Zrr9RGt6vWPv9CmXl8geS5dsGo1jm7biHWf3anX+02Fr19Lvb6xsREn90rW6yc8IlmvJ6K7p1BVVXXvs771Urdu3cJLL72E7777Dk5O3V9Z/fXXX5GdnS3RIPNv3e8cKiRJ+T/S3fZBqG9o7DoRkYz05rcQ5VHwf2Dc5wdlxkB3WWehT/F9R/pcdfTvRH/+adeJ6K4ocXJQkmMVNfLXu7q3av2iB92/w5fiZJ2FPuW16WNlnYU+JbNI2HUiuitzh7Wf547+vVphadeJZEyg2zvmMbtXvWNMoH/p3LlzUFVVhYWFBfLy8rBu3TrY29vD0bF730yrqKhAcnIyQkJCsHr16m7dNhERERERERERERGRGF/kkZk+3aBSVVWFDRs2oKCgAFpaWvDy8sKKFSu6/Q3mTz75BImJiZg8eTKGDBnSrdsmIiIiIiIiIiIiIiLZ69MNKhMmTPhX86/cq88//7zrRERERERERERERERE1GtxYgkiIiIiIiIiIiIiIqIu9OkeKkREREREREREREREfUl3T2lBd489VIiIiIiIiIiIiIiIiLrABhUiIiIiIiIiIiIiIqIucMgvIiIiIiIiIiIiIqLeQpH9JGSFkSciIiIiIiIiIiIiIuoCG1SIiIiIiIiIiIiIiIi6wCG/iIiIiIiIiIiIiIh6CwUFWefgP4s9VIiIiIiIiIiIiIiIiLrABhUiIiIiIiIiIiIiIqIucMgvIiIiIiIiIiIiIqLegkN+yQx7qBAREREREREREREREXWBDSpERERERERERERERERd4JBfRERERERERERERES9hSL7ScgKI09ERERERERERERERNQFNqgQERERERERERERERF1gQ0qREREREREREREREREXVCoqqoSyToT1N76sChZZ4FIKpGIp4zuJFDmVFbdqba+XtZZIJKKx3r30lQVyDoLfYrPmndknYU+Y+uLq2SdhT6lEax3dqdXBzrLOgt9xoHMIllnoU+5mpYl6yz0KWoqKrLOQp9SV98g6yz0GR/PmyLrLPQpNXV1ss5Cl1T76PmIPVSIiIiIiIiIiIiIiIi6wAYVIiIiIiIiIiIiIiKiLnD8CSIiIiIiIiIiIiKiXqKRI6PKDHuoEBERERERERERERERdYENKkRERERERERERERERF3gkF9ERERERERERERERL1Eo4hjfskKe6gQERERERERERERERF1gQ0qREREREREREREREREXeCQX0REREREREREREREvYSIQ37JDHuoEBERERERERERERERdYENKkRERERERERERERERF3gkF9ERERERERERERERL0ER/ySHfZQISIiIiIiIiIiIiIi6gIbVIiIiIiIiIiIiIiIiLrABhUiIiIiIiIiIiIiIqIucA4VIiIiIiIiIiIiIqJeopGTqMgMe6gQERERERERERERERF1gQ0qREREREREREREREREXegTDSrbtm3D888/L5PfXrNmDX7//XeZ/DYRERERERERERER/beIRCK5/+uret0cKjNnzsRbb72FkSNHyjorAIC3334bSkpK4s/Lly/H9OnT8eijj8owV3dHJBIh8uhBxJ4NR3VVJcxs7TFh7gIYmlt2ul5GUgLC9+1EYU4WNHX14DPRH/1GjRN/X5idiXNHDiIvIxWlhQXw9Z+J4dMe6nB7F44H4uzhfeg/ejzGz13QXbv3wMkynueOHMD5Y4cklmlo6+DpT7/ttv170EQiEc4fOyQRz/Fz5ncdz+QEnN63SxzPwROmtItn5NGDyMtIE8dz2NRZEtuora7GuSP7cfPaZVSWl8HE0gZjHn0MZrb2PbGr3epyeAiigo+iXFgCI3NLTJgzH1ZOLh2mz89MR/CurchJTYGahib6jxqH4VNnQUFBQZwmPSkepwJ2oCA7E1q6+hg6aSoGjB4v/j72XASOblnXbtuvfP8nlFVU2i2PPHYYpw8FYOCYCfB7bNF97rHs9VRZjT0bjriocyjMyYJIJIKJpQ2GTXsIlo7OPbxHD5Ysj/V/1r6FsqLCdtu28/DGQytf6pb960myON4TLkXh/IkjKMnPRWNDA/SMTeEzYTK8ho0Sp7kUdhJXI0JRWlQAADA0t8Rw/5lw9OrfA1HoORdOncDZoECUlZTAxMIS/o8vgq2LW4fpczPScGTbRmTeugl1TS0MHjsBY2c8IhHfZqlJCdjw1ScwMrPA8x99IV5++UwYDqz/s136d35bDxUVQffsWC+j1t8L+k/MgZqrM5SNjZDz6dcoO3pC1tnqFcZ5OmGwozXUVFSQWVSCwIs3kF9a3mF6LTVVTBngBnN9HRhoaeJaaib2X4h5gDmWL+M9nTHY0RrqKirIKCrB4YvXu4yf/wB3mOvrwFBLE1dTM7HvwjWJNMY6Wpjg5QxzfV0YaGngVGwSTl1P6uldkak9gYHYsncvCouLYG9jg1eeegoDPb26XC8tKxNLVq2CSCRC6O494uWnzp7F3qNHkZhyE7V1dbC3tsbSeY9hjK9vT+6GTFwMDUbkiSMoFwphbGEJv7kLYOPs2mH6vMx0BO3YhOzbKVDT0MLAMeMxatpD4utQ/OUoXA4/hZz0VDTU1cHI3AIjps6CS/9BEtupqapC2ME9iL8UhaqKcujoG2DsQ3Ph4dP3YgwAU/q7YZiLHTQEAqQWFCHg/FXklpR1mF5bXRUP+XjD0lAPxtpaiE5Jw44zlyTSDHG0wROjBrdb983NB1Df2Njt+yAvJnq7YIijDdQFKkgvLMHB6BjkCTs+b2qrqWLaIA9Y6OvCUFsTl29nICDyqkQaH0cbDLK3hImuNhQUFJBdLMSJawlIzS/u6d2RuUn9XeHrbAcNgQrSCoqx7/w15Ao7L5szfbxgaaALI20tXEpJx86zlztMP8DOEgvG+OBGRg7Wh5zviV0g6rN6XYPKg1RXVwcVKQ8DW9PW1n5Auel+0cHHcOnUcUxesAz6JmY4f+wQ9v7yLZa8+ykEampS1xEW5mP/Hz/Ac9go+C9egcyUZJzatRXqWtpwHtBUYairrYWOoSGc+g/C2cB9neYh+9ZNxJ49DSMLq27fvwdN1vHUNzHDnJfeEH9WUOjdHdAunmyK56T5T0LfxAwXgg5h36/fYfE7n3QazwN//AhP35GYsmgFslKScGr3tvbxNDCCY79BOHdkv9TtBO/YiIKsDExesAxaevqIj47Evl+/w6I1a6Glp99Tu3zf4i+eR8jubfB7fBGsHJ1xOTwEe375Fsve+xQ6Bobt0tdUVWHXT1/D2skVC998H0W5OTi6eR1UBKoY4ucPACgpyEfAr9/Ba/hoTF/6NDJuJiF4x2aoa2nDdaCPeFsqAgFWfPilxPalNaZk3bqJa2fDYGxp3c17Lzs9VVYzkhPgMnAIzB2coKIiwKXQE9j/+/eY/8b70DcxfZC72KNkeaw//to7ELW6qa0oFWL715/AuVXZlleyOt7VNDUx3H8mDEzNoaikhJTYKzi2dT00tLThcKfBRFtPH2Mfngt9Y1OIRCJcP38G+//4CYve+gAmveTYj71wDsd2bMb0BUth4+SKqNBgbPnhSzz/0ZfQMzRql766qhKbvv0fbF3c8NS7H6MwJxv7//kDAoEqRkyZLpG2qqIC+9b9Bgd3T5QWt38YoCJQxUufS74Q8V9tTAEARXV11KakouxYMEzffaPrFQgAMNLNAcNd7bH/wjUUllVgrIcTFo8bgp+OhKO2vkHqOsqKiqisqUVEXAoGO/aOY7WnjHJzwAhXe+y7cA0FZeUY5+GMJeOG4scjYV3G73TcTfg42khNo6KshJKKKtzIyMVE744bwPuKE6fD8e1ff+LNZ59Ffw9PBBwJxCsffogdv/wKMxOTDterq6vDu19+iQGenrgcGyvx3aXYGPj064dnFi2EjpY2gsJCsfqzT/HrZ5/dVUNNb3EjOhIndm3FlCcWw9rJBRfDTmLnz1/j6Q8+h65B++tQTVUVtv/wJaydXLH0rbUoys3G4Y1/QSBQhe+kqQCAtMQE2Lq6Y+ys2VDT1ML1C2cR8PsPWPDq2+KGmoaGemz/8UuoaWjikaeeh7aeAcpKiqCk3Plzkd5qgpczxno6YUfEJeSVlmFyfzc8M2kk/rcvGDX19VLXUVZUQkVNLUJiEjHMxa7DbdfU1eOzvccllvXlxpQx7o4Y5eaAPZFXUFBagQlezlg2fhi+PXyqw/OmkpIiKmpqEXYjGUOcpJ83HUwNcS0tG6n511FX34CRbg54crwvfjp6GoVlFT25SzI1ztMJYzycsOvMJeSVlmNSP1c8NWkEvtp/spOyqYiK6lqcik2Cr7Ndp9s30NLA9MGeSMkt6IHcE/V99/XENTo6GvPmzUNDQ9PJMSsrCzNnzsSvv/4qTrNp0ya89957AIC0tDSsXbsW8+bNw8KFC/HVV1+huNWNZGJiIt577z3Mnz8f8+bNw5tvvon4+Hjx98uXLwcA/O9//8PMmTPFn5uFh4fjqaeewrx58/DJJ59AKBRKfB8cHIznnnsOjz76KFauXIn9+/ejsdUFbebMmQgMDMRnn32GOXPmYNOmTaivr8cff/yBJUuW4JFHHsGTTz6JDRs2iNdpPeTXmjVrkJeXh/Xr12PmzJmYOXPm/YS3R4lEIlwOC8YQv6lwHjAYRhaWmLJwGWprqhF/seOW6WsRYdDS1cP4OfNhYGYB7xFj4D50OC6GBInTmNnaY8zD8+Dm4wsVQccPAGqqKnFs09+YNH8JVDU0unX/HjR5iKeikiI0dXTFfxq9uLGvKZ4n4dMqnpMXNMUzoZN4xpwJg6aOHsbNmQ8DM3N43YnnpVMtFVkzW3uMfnhuUzylPKCqr61F8tVLGDlzNqycXaFnbIJhU2dBz8gY186E9sTudpvok8fhNWwk+o8cC0MzC/jNWwhNXV1cOR0iNf2NqHOor6vF1MUrYGxhBdeBPvCdNBXRIUHirplXI05BU1cPfvMWwtDMAv1HjoXnsBGIOnmszdYUoKWrK/HXVk1VJQ5v+ANTFjwJtV5+zDfrybLqv/gp9B8zASZWNtA3NcOEeQshUFVDanxsh9vtbWR5rAOAhpa2xHnz9o0YCNTUxI0y8kxWx7utqwec+w+CoZk59I1NMHj8ZBhbWiHjZqI4jXP/QXDw7Ad9E1MYmJph9KzZEKipISsluWeD0o3OnTiKASNGY/CYCTC2sMS0+UugrauH6NBgqeljIs+irrYGjyx7BqaW1vAYPBQjp87AuRNH23V1P7DhTwwYMRpWDh30NlMAtHX1JP7+yyojo1D453qUh0YAjX132IDuNszFFhFxKYjLyEWesBz7LlyDQFkZ3rYWHa5TUlmFo5fjcOV2Jqpq6x5gbuXPcBc7nI67iRsZOcgTlmPvhatQVVZGvy7id+TyjU7jl1UkRNDVeMSkZaGuQfoDxr5k+/79mDFxIh6e4g97a2u8vvIZGOrrI+DokU7X+3nDBjjZ2WPiyFHtvnvt6ZVYMncuPF1cYW1hgRVPzIeboyPCIyN7ajdk4kLwMfQbPgoDR4+Hkbklpjy+GFo6ergUJv06H3uh6To0c+nTMLG0gtugIRg2ZTrOBx8TX4cmP7YQI/xnwsLeEQYmphg94xGY2dgj8epF8XaunT2NyrJSzH12FaydXKFnZAxrJ1dY2Dk8kP1+0Ma4OyEkJhHX0rKQU1KG7REXoaqijEEOHb/wWVxRiX0XriHqZhoqazo/V5ZV10j89WUj3OwRdiMZ19NzkCssw+7IK1BVUcYAu457nZdUVOHwxeu4dCujw/PmrrOXEZl4G9nFpSgoq8CBqBjU1NXDxdy4p3ZFLox2d8Sp2CTEpGUjt6QMO85cgqqKMgbadxzP4ooqHIiKQfTNdFTW1naYTlFBAQtG++DY5TgUlVX2RPbpAZH1cF7/5SG/7qtBxdPTE7W1tUhKauqmHBMTAx0dHVy71tK1OTY2Fl5eXigqKsJbb70FW1tbfPPNN/j4449RVVWFjz/+WNyoUVVVhfHjx+OLL77AN998AwcHB3z44YfihpFvv216W++FF17Apk2bxJ8BIC8vD6dPn8bbb7+Njz76CCkpKdi8ebP4+6CgIGzatAkLFizAr7/+iuXLlyMgIABHjkhW5rZv347Bgwfj559/xvTp03Ho0CFERkbijTfewB9//IE333wTVlbSL65vv/02jIyM8Pjjj2PTpk3YtGnT/YS3R5UWFqCyVAgbN0/xMmWBAJaOLsi+1fEDj5zbN2Hj6imxzNbdC3lpqWhokN5K3pHgHZvgNGAwrF3c7y3zckge4iksKMBf772Ofz58C0c2/AFhQf697YQcEcfT1UO8rCWeNztcL/t2CmzdPCSW2bp53lM8GxsbIWpshLKyZAc+JRWBXD8MbKivR076bdi5S76ZZ+fuhcwU6THLunUTVo4uEg11dh5eKBeWQFjY9KZKVsrNdtu0d/dGbuptiZjW19Xij3dfx2/vvIqA375Hbnpqu98L2rYBrgN9YOvq0e673upBltWGhnrU19dBTV2zezIvB2R5rLclEolwPTICbj7DoCJQ/VfbeFBkfbw3E4lESI2/geLcHFg5SR+CpLGxEXHR51FbUw1LB6d72k9Zqa+vR1bqLTh69pNY7ujpjfSb0ofmSU9Jgq2zm0R8nTz7oaykGCWtrscXTp1AeakQY2Y80vHv19biuzdfwjdvvICtP36F7LTb97dD9J+jr6kObXU13Gz11ml9QyNS84tgbagnu4z1Es3xS5YaP/ntqSxv6urqEJ+cDN+BksNJ+Q4chJi4+A7WAiKiohARdQGvPf30Xf9WZVUVtLW0/nVe5U1DfT2y027D3sNbYrm9hxcyUqRfhzJTkmHt5CpxHXLw8Ea5sFh8nZemtqYKahotdcvEKxdh5eiMoJ2b8cObL+KPD99C+KG9/7p+Jc8MtDSgo6GGhKw88bK6hkak5BbCzrh9b997paKkhHdnT8H7c/yxfMJwWBq0f+Gsr9DX1ICOuhqSslvqPPUNjbiVVwgbo+49byopKkJZSalPN/w3l83EVmWzvqERt3ILYGticN/bnzrQHUUVlbiYkn7f2yL6r7qvBhV1dXU4OjoiJqZpbN2YmBjMmDED+fn5KCoqQnV1NZKSkuDt7Y0jR47A3t4eS5cuhbW1Nezt7fHqq68iKSkJyclNDyn79++PCRMmwNraGtbW1li5ciUEAgEuXWoaj1L3zhvPWlpa0NfXF38GgIaGBqxatQr29vZwc3PDlClTJBp2duzYgaVLl2LkyJEwMzPD0KFDMWfOnHYNKqNHj8aUKVNgZmYGMzMz5OXlwcLCAp6enjAxMYG7uzv8/PykxkNbWxuKiopQV1eHvr4+9PXlt8JdUdrUSKWhrSOxXENbBxWlpZ2sVyp1ncbGBlSXdzw2ZlsxZ8MhzM/DiOkP332m5Zis42lm54DJC57Ew8+8DL8nFqOiVIid332Oqoq734Y8qSjrJJ5lQmmrAAAqS4X3HU+BmhrM7Rxx4XggykuK0djYiPioSOTcvin+f5ZHVeVlEDU2ttt/TW2dDvNdISVemtq64u+Apv8LzQ5iWnUnpvqmZvBfuAwPr3wJM598BsrKKtj2zWcozssRr3P1TBhK8vMwaob8zy91Lx5kWT0XuB8CgSrsvXvXPBSdkeWx3lZawg2UFhZIzAUir2R5vANNvc2+f+UZfPvSUwj47TtMmLsADm0aH/Iz05vSvPwUTuzYiIeffrHXDPVXeSe+mjpt4qWji3Kh9PiWC0ugqaPbLj0AlN+Jb25GGsIO7sXsFc9BUVF6FdzIzAIPLX0aj7/wKuY89QKUVVSw7n9rUZibIzU9kTRaak2NwhVt3oauqK4Vf0cd6yh+5dU10Gb87lpJaSkaGhthoKcnsdxATw+FJdLnPigoKsLnP/+ED199DZp32Zt5d+Bh5BUWYur4CfebZbnR2XWos+t8+/RNn8tLS6SuEx0ajLLiYnj7tsxPW1yQj7iLUWhsqMe851/F2Fmzcfn0KYTu230feySfdNSbhpZt23OkrKoG2ur3d6znlZZjx9lL+CckEpvDo1Df0IAXp46BkXbfeTGqteZ4lUs5b2rdZyzbmtTPFbX19YjLyO3W7cqTjuJZVl0DbXXpQyLfLRdzY/S3s8TeNnPVENG9ue85VLy9vRETE4O5c+ciNjYWs2bNwtWrV8W9VZSUlODi4oLdu3fj+vXrmDt3brttZGdnw8XFBSUlJdiyZQtiYmJQUlKCxsZG1NbWIj+/6zftTUxMoKnZcnEyNDRESUkJAEAoFKKgoAC//PILfvvtN3GahoaGdt2PnJwk356cOHEi3n//faxcuRIDBw6Ej48PBg8e3OGNcFeOHTuGoKCgLtNp27vCvn/3DTkSHxWJkztbeuw0T7bbfqJUEdpPnSqp/SqiDr6Qrig3B2cP7cXcVauhpNw7p/GRp3gCaPf2kpmdA9avXYO482cxaMLku96OrMRHRyJk5xbx51krXwTQPp4ikQgKXUW07Tq493hOXrQMwds2Yt0Hb0JBUREmVjZwGTQU+Rlpd70NWZEas052XVr6dsullOs7XwAALB2cJN48t3BwwsbP38el0JOYOG8BinKzcfrgHjzxytu99phvJquyejk0GLFnwvHI869CVU39X+RcPsjbsd5a7LnTMLWxg7GV9PGb5ZEsjncAEKiqYcmataitqUFawg2cCtgBXQMjiV5DBqbmWLJmLWqqKpF4JRpHN/2Nx1athnEvmjOtXRkUidBZsWwf+5Y6Zn1dHfb8+TMmz5sPfeOO5w2wdnSGtWPLUGDWTi74fe0anD8ZhGnzl9xD7um/xNvWAjMHt/R43nq6afiedqMs/LvTY5/Xz9YCMwe39M7bejoagLT4KaDvDlzRg6ScGzu6xn/wzdd4dOpUeLu53dWmQ86cwU//rMcnb74J807mZOm9pFyHOjmQ21+3OlgOIP5SFEICduDhFc9Bt/XcYKJGaGprY9rC5VBUVIS5rT2qKsoRvHsrJsx+XMr9bu8xyN4Kc4cPFH/+++TZpn+0ObC7YxdT84uQml8k/nw7vxCvz5yA0e6O2HfhWidr9g797Szx8JCWZxCbwi5ITacAhXbxvR8jXO0x1NkG/4Sc73Aekd5ooL0VZg9reWnun5CmIQzbPq/8P3t3HR3V8TZw/BvZuGzcXQkS3N21RQot1mKFFmrUfm3fljoVWmhLoS0VCi1S3N2CE9wCCUFDCCHussnu+0fCho2QCiHC8zkn57B3Z+7ODHfn3h0tKs9/X6BmxkYMa9eUxfuO1ekZPo8SWQ23+vznlq0GDRqwceNGbty4QU5ODn5+ftpOFisrK+rVq4ehoSFqtZrmzZszbty4MudQFo9amTVrFqmpqUyYMAFHR0cUCgXvvvsuBX+jojQwMChz7G7lc3dJsSlTphBcycOZSakNcP39/fnll184ceIEZ86cYdasWfj4+PDxxx//q06V3r1707t370rDzQ87+o/PfT++DRvj7O2jfV1YXKZZ6WlY2pRMGczOyMCs1MiWe5lblZ1xkZ2Zgb6+ASbmf2+0Rdy1y+RkZfLHZ+9rj2nUamIvX+LMgTCmzJhT7mbWNUlNKs/yGBmbYOfsSkpC7Ri14dugMc5eJevyFhYU3dxLl2dOZkaZEdb3MrOyJrvUqK2cjH9enkp7R5546Q1UeXnk5+Zgbq1k0+8/YVXORsQ1hamFJXr6+mVGrWVnZmBmWf708vJGuWVnFl2Pd8vZ3LKcMMVlampRfpnq6+vj7Omjvf5uXblMTmYm8z99VxtGo1YTEx3Fqf17eGXmjzX+O39XdVyrJ/fs4NCmNTw+6WWcvXyozWrad/2u7Ix0rpw9RZcnRvzjuNWhur/vevr62Dg6AeDk4UlS/C0Ob92g06FiYGioDePs5UPc9Wsc37WN3qPKPgfWNGbF5ZtZqiyyMtKxsCq/fC2slWVmr9y9v1tYWZORlkrCrVjWzJ/HmvnzgOLnVI2GDyeOZuTLb+BfapYPFNWnrl6+JN+RGSqiYpGx8cQmpWpfGxT/RrEwNSY9J1d73NzYqMxoVwEXY+O5+TfKz0LK7x9RWllhoK9PckqqzvHk1LQys1buOnbmDCfPnePXJUuAonZYtVpN28cf443nJzPont/Ruw4c4IOZM3n/1al0bNWqinJRPcwquM9nZaSXmYVyl7mVdbn3rbvv3eviiaOsm/8TA8ZMJDBUd0k2c2slBgYGOm0dds6uqPLzyc7MKDOTtTY5H3ObG4kle9AYGBTl0dLUmNTsHO1xCxNjMnIe7Hddo4GYpNQ6M0Plws3bxCSWzDQzLC5LCxNj0rLvue+YPLh6s22QDz0aBfH7niM6dXZdEBFzmxv3lqf+3WvTRKc8LUyM/tNePM5KS6zNTJjYo6322N1O0s9HDeDrdbtJSK+dq5wI8bD95w6V+vXro1KpWLlyJSEhIRgYGNCwYUO+//57lEolzZoVzbLw8/Nj//79ODo6ltmb4K4LFy4wceJEWrRoAUBKSorOpvWAtnPmn7CxscHOzo64uDi6dv3nU4HNzMxo37497du3p1u3brz++uvExcXh5lZ2M6h/k76HwcjEBKN7Oos0Gg1mVtbciIzQNtAVqFTcunyJ9gPLziK6y9nbjytnT+ocuxEZgaOnFwYGf+9y8mvYBKe3vHWObV88H6WDIy169KsVI9hrUnmWp0ClIvnObdwD/t7orur2j8rz8ScqPI+Lty+Xz57SOfZfylNhbIzC2Jjc7CyuXzxP+8cq/uzqZmBoiLOHN9cunieoaQvt8esXzxNYwQbbrj5+7F27nAKVStuhcf1CBBbWSu1INVdfPy6d1r1Gr108j5OXd4VlqtFoSIiN0S7v4x/alDFe3jphtvzxKzaOTrTq1b9WfOfvetjX6ond2zi8aR2PT3oJN78KNrCuRWrqdz3iyAEMDA0JbNryH8etDjXp+w6gUWu0AwvuE4iCgtoxEs7Q0BBXLx8uR5ylfvOSRrrLEecIuae87+XhG8D2lUtQqfJRKIy04S2VNijtHVAXFvL8h5/rxDm6ewdXIs7y5JSpKO3K31hVo9EQf/MGTh5eDyh3oi7KLygkOVN3U9mMnFz8nOy4lVzUwGqor4+Xgy3bTle8d8WjqqLy83ey1yk/TwcbKb9/QKFQEOzvz5FTJ+nWvmQ5zfBTJ+nStm25cRZ//73O672HjzB/2TLmz/wah3sGNu3Yt4+PvpnFtFemlrtxfW1nYGiIi6c3Vy+co16zkmeTaxfOEdSk/PuQm68/u1f/RYEqH8Pi+9DVC+ewsLbRmYEScewIGxbMo/8zE3XOfZeHXyDnww+hUavRK27UTb5zG4WREWYWlg8ymw9dXkEBeRm6zyvp2bkEujoSU9xAb6ivj6+jHeuPn3vgn+9iY8Wt5IqXA69Nyqs303Ny8Xd2IPaeetPb0ZYtJy/8589rF+xD94ZBLNgTzvWE8pcMrM0qvDZdHLSdR4b6+vg42rHx+Pl//TkxSal8tW6XzrHejethaqRgdfgZkjOz/vW5hXjU/OdWrLv7qOzZs4dnnilaiiA4OJjExETi4+MZM2YMAP369WPbtm18+eWXDBkyBGtra27fvs3+/fsZN24cZmZmuLq6snv3bgIDA8nNzeX3338v0/ni6OjI6dOnadCgAQqFAou/ufnc8OHDmTdvHubm5jRv3pzCwkIuX75MUlJSucuQ3bVmzRpsbGzw9fXFwMCAsLAwzMzMsLMrf5MyR0dHzp8/T5cuXTA0NNTZ56Um0dPTo0mn7hzdthFbJ2eUDs6Eb9uAwtiY4GYljQdb//gVgF6jxwPQqH0nTu/bxZ6VS2nUriO3rkQTceQAfZ4p2TSwsKCApNu3gKKGseyMdO7cvIGRsTFKBydMzMwwKbUmrqGRESZm5ti7lu2kqg2qszwB9q5Zhm/9UCxtbcnOyCB86wYK8vIIaVX+j5Warqg8u3F02yZsnVxQOjhxdNtGFMbGBN1bnn8Wl+eoovJs2K4Tp/ftJmzVUhq27cStq9FEhB+k99PPauMUFhSQfLc8C1RkpaeRcPMGCmMTlMXLsFy/cA6NRoONkzOpCQnsX7ccG0fnGl+ezbv1ZOOCn3Hx8sHNL4BT+3aTmZpKaPsuAOxdu5y4a1d58uU3AQhp0ZqDm9ay+Y9faN17ACl3bnNk+0ba9nlcO1IltH0XTobtZNeKxYS270zs5UucO7yf/mOf037ugY1rcPXxw8bRibycXE7s2U5C7E16PPU0QLnfeYWxMSZm5rVq+Z/yVOW1enznVg5uXE2v0eNROjppRykaKhQYm/69dcVruur+rkNRg/W5Q/sJbNpSp7Onpquu7/uhLetx8fZFae9AYUEBV86dISL8EN2GjdSGCVuzHL8GoVja2JKfm8OFY4e5cSmSIc+/8vAK6D9q06MPq379ATdvPzz9AzkWtpOM1BSad+4GwI6VS4m9eoVnXn8HgIat2rJn/SrW/PYTHfsPJOn2bfZvXkfnAYPR09PDwNAQp1J7yJhbWmFgqNA5vmfdStx9A7B1ciYvJ5sjO7cRHxtDv1ows6eq6JmaoHBzLXqhr4fCyREjf1/UGRkUxFe+LPCj6nDUdTqG+JGYnkVSZhYdQ/zILyjg7PVb2jCDWhXNilp9pGT5GWdlUYOpsaEhGo0GZ6UlhWrNIzdi9VDUNTqG+JGQnklSZhadQvzJLyjkzD3lN7i4/Fb9g/Iz0NfDwaro96uhvj4WJsY4Ky3LbZysC4YPHMgHM2dSPyCQRiEhrNq8icTkZAb36QvAnAW/ExEVxZxPpwPgV2oAzoVL0ejr6+kc37Y3jA9mzuSlceNo0qABScUDMA0NDbG2rN0N/vdq2b036+b/hKu3L+5+AZzYu5uMtFSadiwaILp79TJuXbvCyKlvAVC/ZRv2b1zD+gU/067P4yTfiePQ1g106DdIe58/f/Qw6+f/RNchT+EZEERmWipQ1IFjal50XTbt2JVje7azbdmfNO/cg7SkBPatX0XTTt1q9XJfFdl7IZruDYO4k5ZJQnoGPRoFk1dQwIkrN7VhhrcvGqyyZP9x7TFXm6J2HhMjQzRocLWxplCtJj4tA4CeocFcT0gmIT0TE4WCDvX8cLWxZmUd3rfi4MWrdG7gT2J6JokZWXRp4E++qpBT12K1YZ5o0xiAFYdOaY+5KItmPRkriupNF6UVhWo1d4rrzQ71fOnRKJjlh06SmJGl3edKVVhInqruLPtV2r4Ll+nWMJA76ZkkpGfSvWEgeQWFnLxaUp5PtSuaYbb0wAntMVebovI0KS5PVxsrCtQa7qRloCooJD41Q+dzcvNV6OvrlTkuaofSy8KJh+eBDAtu2LAhUVFRNGxYtIaikZERQUFBXLp0icDAQKBoT5Mvv/ySBQsW8P7776NSqXBwcKBJkyYoikdKvvzyy3z//fdMnToVW1tbhg8fTlqp5RPGjx/PL7/8wo4dO7Czs+PXX3/9W2ns1asXJiYmrFq1ioULF2JkZISnpyf9+/e/bzxTU1NWrVpFXFwcAL6+vnzwwQdllga7a+TIkcyZM4dnn30WlUrF+vXr/1b6qkPz7r0pUOWza/li8rKzcPbyZdDkV3UalNJTknTiWNs5MHDSy4St/ouz+/dgbq2k85DhBNwzIjYzLZXFX36kfX02MYyzB8Jw8w9k6EtvVn3Gqkl1lmdmagqbF8wjJysTUwtLXLx9efLVd7CyLb/jrzZo1q03BSoVu1eUlOfA56fqlGdGSrJOHGs7Bx6f9BJ7Vy/j7P4wzK2t6TT4KZ3yzEpLZfGMj7Wv0xITOHdwL27+gTzx4hsA5OXmcHD9ajJTUzA2N8c/tClt+w38T7OGHobgZq3Iycri0Jb1ZKWnYe/ixpDJU7Wj0jLT0khNvKMNb2xqxrAXX2fHX3/yxxcfYmJmTvOuvWjerZc2jNLegSGTp7Jr5RJO7duNhbWSbkNHEtSkuTZMXk4O2xYvICsjDWMTUxw9PHlq6lu4eJcs7VSXVdW1enr/btSFhWz+fZ5O3Hot29BzZN1pXK3O7zrAzehI0hLv0PvpCVWYywevur7vqrxcti9dSGZqCoYKI2ydnOn7zATqNW+tDZOVnsbG3+dp6wR7Nw+emDy1zH5fNVmDlm3Izspk78Y1ZKal4ujqzsiX39DOJMlISyX5nmU1TczMePrVt9i46HfmffwepubmtO3ZlzY9+/6jz83Nzmb9wl/ITE/D2NQMF08vxr75Hu6+fg80f7WJSXAg7rNnaF/bTXgauwlPk75pG/HTv67GlNVsBy5eQWGgT99mIZgaKbiZlMYfYUfJLyjUhrE2K/t75rleuqP9g9ycSM3K5psNYVWe5ppk/8UrKAwM6N+sPiZGCmKTUlkYFl6q/MruaTa5Vwed18FuTqRkZTNrwx4ALE1MdMLYWZrTwt+Tq3eSmL/7SNVkphr16NCRtPQM5i/7i8TkZHy9vJj1/gfa/U6SklOIvf3PljRcvXkzhYWFzPr5Z2b9/LP2eNMGDfjhs8/vE7N2CWnempzMTA5sWkdmeioOru48+cJr99znU0lNKLnPm5iaMfzlN9m6ZCHzP3sfEzMzWnXvQ8vuJcukndy7C7W6kB3LF7Fj+SLtcc+AYEa9VjRAwMrWjuEvvcmOFYv59dN3MbeyplHbjrTv+/hDyvnDtevcJRQGBgxpFYqpsYIbCSn8tP2Azv4cNuZlv+uvP6a78kkDDxeSM7P4ZOU2AEyNFAxt0wQrU2Ny8guITU7l+y37dJZ1qmv2XriMwtCAAS0aFN13ElOZv/uITr2pLKfefLFvR53X9dydScnMZkbxTIrWAd4YGuhrO7buOn4lpk53UO05H43C0IBBLRtpr82fdxzUuTaV5VybUwd00Xld38OF5MxsPlu1vcrTLMSjRC8nJ0e6s2qgB72HihAPivSAP1hGtWi5q9ogvw5tTijqFvmuP1jmxkbVnYQ6pfnb/1fdSagzFr34SnUnoU5RyzbwD9SrTWr/8qE1xdrY5MoDib/t9I1blQcSf5tJLdmfsrZQ3dMpJP6bj4f1qjyQ+NsS02v+Mm32VnVj76jS5Ne9EEIIIYQQQgghhBBCCFFLyMCT6qNf3QkQQgghhBBCCCGEEEIIIYSo6aRDRQghhBBCCCGEEEIIIYQQohLSoSKEEEIIIYQQQgghhBBCCFEJ2UNFCCGEEEIIIYQQQgghhKglNBrZQ6W6yAwVIYQQQgghhBBCCCGEEEKISkiHihBCCCGEEEIIIYQQQgghRCVkyS8hhBBCCCGEEEIIIYQQopaQFb+qj8xQEUIIIYQQQgghhBBCCCGEqIR0qAghhBBCCCGEEEIIIYQQQlRClvwSQgghhBBCCCGEEEIIIWoJtaz5VW1khooQQgghhBBCCCGEEEIIIUQlpENFCCGEEEIIIYQQQgghhBCiErLklxBCCCGEEEIIIYQQQghRS2hkya9qIzNUhBBCCCGEEEIIIYQQQgghKiEdKkIIIYQQQgghhBBCCCGEEJWQJb+EEEIIIYQQQgghhBBCiFpCLUt+VRuZoSKEEEIIIYQQQgghhBBCCFEJ6VARQgghhBBCCCGEEEIIIYSohHSoCCGEEEIIIYQQQgghhBBCVEIvJydHFlyrgQ5GXa/uJNQpoV4u1Z2EOmPr6cjqTkKdEuLuVN1JqFNSs3KqOwl1ytmY29WdhDrDRCHb1j1I1mYm1Z2EOiXi5p3qTkKdMXL2N9WdhLpF36C6U1CnrJw6tbqTUGdYmBhXdxLqlJFOVtWdhDrF0Ne7upNQp6iTU6o7CXWGsau0zT1INxJTqzsJlfK0V1Z3EqqEzFARQgghhBBCCCGEEEIIIYSohHSoCCGEEEIIIYQQQgghhBBCVELWnxBCCCGEEEIIIYQQQgghagmNRnbxqC4yQ0UIIYQQQgghhBBCCCGEEKIS0qEihBBCCCGEEEIIIYQQQghRCVnySwghhBBCCCGEEEIIIYSoJdSy5Fe1kRkqQgghhBBCCCGEEEIIIYQQlZAOFSGEEEIIIYQQQgghhBBCiErIkl9CCCGEEEIIIYQQQgghRC2hkSW/qo3MUBFCCCGEEEIIIYQQQgghhKiEdKgIIYQQQgghhBBCCCGEEEJUQpb8EkIIIYQQQgghhBBCCCFqCVnwq/rIDBUhhBBCCCGEEEIIIYQQQohKSIeKEEIIIYQQQgghhBBCCCFEJWTJLyGEEEIIIYQQQgghhBCillBrZNGv6iIzVIQQQgghhBBCCCGEEEIIISohHSpCCCGEEEIIIYQQQgghhBCVkA4VIYQQQgghhBBCCCGEEEKISsgeKkIIIYQQQgghhBBCCCFELaGRPVSqTZ3sUHn77bfx8vLiueeeq+6k1CphWzexfe0q0lJTcHH3ZOjYCQTUq19uWFV+PovnzSXm6mXiYm/iF1SPVz+cXuG5oy9EMOuDd3Byc2fazO+rKgs1ysoVy1n8x58kJSXi4+vLy1NfpXGTJuWGvXrlCl/P+JKrV6+SlZmJvb093Xv2ZPyzE1EoFAAkJiYy+5tviIy8yM2YGHr36cO773/wEHP08BzdvZ2DWzeRkZaKo6sbvZ4chVdgcIXh42/GsHnJAmKvXsbU3IJmHbvSsf9A9PT0yoS9cSmS37/6FHtnVyZ/+Ln2+J3Ym+xZt5K4G9dITUyg04BBdH5sSJXkrybaumEd61csJzU5CXcvb56Z9Dz1GjQsN+z5M6fZtHol0ZGRZGdn4eziSt+Bg+nSq/dDTnXNsGfLJratW0VaSgquHp4MGzOBgJCK685F8+Zy40pR3ekfVI/XPrp/3fn1++/g7ObO+7PqZt2p0WgI37qe84f2kZeTjZOnD52GjMDOxfW+8WKjI9m/djnJt29hbqWkaddeNGjXSfv++UP7uHj0EMnxt9CoNTi4e9Cqz+O4+gZowyz46G0yUpLKnNurXgMGTHzpwWWyimg0Gg5uWsuZA2Hk5WTj7OVL9ydHYe/idt94MZci2bNqKYlxsVhYK2nRvQ+NO3TRCRN18hj7N64mLTEBa3sHOgwYTEBos5JzREdybMdW4mOukZmWSu9R42jQur3OOfLzctm3diWXzpwgNysTSxtbQtt3oXnXng+uEKrQkV3b2b9lA5mpqTi6udFn+NN43+dedPvmDTb++Ts3i+9FLTp3o/OAQdp70dWLEfz25Sdl4r306Qwciv/PTuwPY/VvP5UJM+2n31EojB5Qzmq2zvX9aebngYlCQWxyKhuPR5CQnllheAsTY3o1DsbFxgpbC3POXI9lTfjZh5ji2sMktAE2w5/AJCgAQwd7bn/6FRmbt1d3smoc64H9sBk+BANbW/KvXSdh9jxyz5yvMLxFlw7YjhqGwsONwtR0UletJ3XpSt1zDuqPcnB/DJ2dKIhPIPmPpWRs3VXVWakx2gf7EurtjomRIXHJaWw7fZHEjKz7xvGws6Fbw0DsrczJzM3jcNR1Tl27qX1/RPtmeDrYlomXkJ7JrzsPPfA8VBeNRkP4lvWcP7SX3JxsnD196PTECOwqudfHRkeyb82youck66LnpIbtOmvfP3dob9Fz0u27z0metO6r+5x0Zt9uzh0MIz256FnJztmV5j374VO/UZXk9WFbsW0rizasJyk1FR93d6Y+/QyNg+tVGu9GXBxj3nkLjUbD7t8X6rynKihg/upVbN63l8SUFGytrRnRfwBP9u5TVdmoMZavWMEff/5JYlISvj4+vDZ1Kk0qaP+4cuUKX8yYwdWrV8nMysLB3p6ePXow8dlnte0f9zp16hSTJk/Gy8uLZUuWVHVWaqQVG9bzx4oVJCUn4+vlxdRJz9GkQYNK492IjeXpF19Ao9EQtnpN1SdUiEdEnexQEf/csQP7WDb/Z4ZPeA6/4BD2bt3EnE8/ZNqsOdg6OJQJr1arURgZ0al3P86fPE52VsUPxFmZmfz+/SyCGoaSmly24aou2rF9G998/TWv/+9/hIY2ZtWKFbz2ysss+msZzs7OZcIrFAr69OtHYGAQFpaWRF+K4vPp0yksKGTKS0WNeqr8fKyVSkY/8wxrV69+2Fl6aM4dPcyWv/6k74gxeAYEcnT3DhZ9N4MpH36BtZ19mfB5Odn8MetzvAKCePb/PiLxdhxr589DYWxM2559dcLmZGWx+rcf8Q2uT3pqis57qvw8lPYO1Gvagl1rlldpHmuag2F7WPDjXMZPeYmg+vXZtmE9n733DjN/+hV7R8cy4aMizuPh7cOAJ4ZhY2vH6ePHmPfdLBRGRrTv0rUaclB9jh7Yx1/zf2bEhOfwrxfCnq2bmD39Qz64X92pMKJzn36cO3GcnErqzvmzZxFcx+vOE7u2cmrPdroNH4ONozNHt25g7Y+zGPX2xxiZmJQbJz0pkfU/z6Zey3b0GDWeuCvRhK1YhImFBf7Fjf6x0ZEENGmOi48/hkZGnNqzg3U/fctTr7+H0sEJgGGvvoNardaeNzs9jb9mfop/4+ZVn/EHIHzHZo7t2kqfUeOxcXLm0OZ1LJ/9FeOnTcfIxLTcOKmJCaz8YRYNW3eg7zPPEnv5Ejv++hMzC0sCmxTl+9aVaNbP/5F2fR8noHEzLp06zrpff2DEq2/j4u0HgCovD3tXN0JatWXzwl/K/aw9K5dyPTKCvk9PwNrOgZvRkWxbsgBTCwvqt2xbNYXygJwNP8SmJQsZMGosngFBhO/ezh+zvuDFT2agLOdelJuTzYKvPsMrMJjn3vuExNtxrPr1R4yMjGnXu59O2Bc//hJTCwvta3NLK533FUbGTP1ilu6xR6QzpV2wL22CfFgTfoakjCw6hfjzdOcWzN60l/yCwnLjGOrrk52Xz/4LV2jm5/GQU1y76Juakn/lOhlbduD07hvVnZwayaJrRxxemsSdmXPIORuBcmA/3L78iOtPP0fBnYQy4c1aNcf5vTdJ+O5Hso4cx8jLA6c3X0KTn0faqg0AWD/eF/tJY4mf8R25EZGY1AvE6c2XUGdkknUw/GFn8aFrFeBNC38vNp04T1JGFu2CfXmyXTN+3nGgwu+1tZkJQ9s24ez1WNYfP4e7nZKeocHk5OcTeesOAKuOnMZAv2QFcwN9fcZ3a8PF2PiHkq+H5cTOLZzcs43uI8Zi4+hM+Nb1rP1hFqPe+aTC56S0pATWzfuOkFbt6DlqAreuXiJs+WJMLSxLPSe1wNXHH0OFEafCtrP2x28Y/sY07XOShdKGtgOGoHRwQqNRc/HoITb9OpcnX38Xe1f3h1YGVWH7oYPMWriAN8aOJzQ4iJXbtjH1889Y8tVMnO3L3ufvUhUU8N7sb2kcXI+TFyLKvP/e7G+5k5TEWxMm4uHiTHJaGnn5+VWZlRph2/btfDVzJm+9+SaNQ0NZvnIlL02dyvKlSyts/+jfrx9BgYFYWloSdekSn06fTkFhIS+/+KJO2PT0dN7/8ENaNG/OnYSy9fCjYHtYGF//+CP/m/ICofXrs2LDBl55713++mkezuX8Xr9LpVLx7uef0aRBA06clcEmonbauHEjq1atIiUlBU9PT5599lnq1y9/AOvZs2dZu3YtUVFRZGVl4erqymOPPUaPHj0eeLrq3B4qs2bN4ty5c2zcuJEBAwYwYMAA4uPjuXHjBh9++CHDhg1j1KhRzJgxg5SUFJ14H374IStWrGD06NE8+eST/P7776jVahYvXsyoUaMYPXo0K1as0Pm8AQMGsGHDBj788EOGDBnCuHHj2L1798PO9n+2c8Na2nTuRvvuvXBx9+DJ8ZOwsrFh77ZN5YY3NjFhxMTJdOjRG6Wt3X3P/ecP39G6c1d8A4OqIuk10tLFi+nbvz+PDxyEt48Pr77xBnb29qxeuaLc8O4eHvTrP4CAwEBcXFzo0LETPXv15tSpU9owLq6uvPr66/TrPwArK+uHlJOH7/D2zYS27UCzjl1wcHGj74hnsLRWcjRsZ7nhzxw5iCo/j4HjnsPRzYOQZi1p17s/h7dvLjP9cd2Cnwlt0wF3P/8y53Hz8aPn0BE0bNUWhZFxleStptq4eiWdevSkW5++uHt6MW7yC9jY2rJt4/pyww96agRPPTOW4PoNcHJxoWf/AbRs154jB/Y95JRXvx3r19K2czc69CiqO4ePn4S10oaw+9SdIydNpmOP3tjY3b/uXDi37tedGo2G02E7aNatN/6hzbBzcaP7iLGo8nKJOnGkwnjnDoZhbqWk05Dh2Dq5UL9NB4JbtOXk7pKR1j1HT6BRh644uHti4+hM56EjMTI24fqFklHGphaWmFtZa/+uXziLkbEJ/o2blfexNYpGo+HE7u206tGXwCbNcXB1p8/oCeTn5XLhWMVld3r/HiyslXQbNhI7Z1catetE/VZtObpzqzbM8T3b8QwIpnXvAdg5u9K69wA8AoI4fk/5+tZvRIfHhhDUpHm5swEBYq9eJqRlWzwD62FtZ0/9Vu1w8fYl7tqVB1cQVeTg1k00adeR5p264ujqRv+RY7CwVhK+e0e54c8cPoAqP58hE57Hyd2D+s1b0qHvAA5s21TmXmRuZYWltVL7p6+v+ziup4fO+5bWyqrKZo3TOtCL/ReucOFmPHfSMlkdfgYjQ0MaelU8Yy01O4fNJy9w6losOfmqh5ja2if78FGS5s0nc89+UMsSEeWxGTaI9M07SN+wFdX1GBK+/ZGC5GSsB/YrN7xVz65kHTxC2pqNFMTdJvvwUZL/XIbNiKHaMJa9upK2YQuZO8MoiLtN5q69pK3fohOmLmvh78nhqGtE3rpDYkYWG4+fx8jQgBD3so2sdzXxcSczN4/tZyJJysji9LVYzt2Io2WAlzZMrqqArLx87Z+7nRKFoQFnrsc+jGw9FBqNhlN7d9KsWx/tc1KPEePIz8sl6vh9npMO3H1OGoGtswsN2nQkuGUbTu7apg3Ta/SzhN59TnJypvPQUcXPSee0YXwbNsY7pCFKB0dsHJ1p028QChNjbl+9XKX5fhiWbNxIv46dGNitGz5u7rw+dhx2Njas2r7tvvHmLF6Ev6cnXVu3LvPekTOnOXr2LDPffItWjRrh6uBIA/8AmlUwc70uWbRkCQP692fQwIH4+Pjw5uuvY29nx4qVK8sN7+HhwYD+/Qksbv/o1LEjvXvrtn/c9fGnn9KvXz8aNix/9YRHweLVq+jfowcD+/TBx9OTNyZPxt7WlpUbN9w33uzffsPfx4duHTo8pJSKh02t0dT4v/9i3759/PzzzwwbNoxvv/2WevXq8cEHH3Dnzp1yw1+4cAEvLy/eeust5syZQ58+ffj+++/Zs2fPf0pHeepch8rEiRMJDg6me/fuLFy4kIULF2JoaMhbb72Fl5cXX3/9NR9//DE5OTl8/PHHOiNTz58/T3x8PNOnT2fy5MmsWrWKDz/8EJVKxRdffMGIESNYsGAB0dHROp+5ePFiWrZsyXfffUevXr2YNWsWly5dethZ/9cKVCpuXImmXmhjneP1QptwJfLifzp32NZNpKem0nfwsP90ntpEpVIRefEirVrpPmS1bNWKs2fO/K1z3IyJ4cjhQzRpWv4U2bqqsKCAW9ev4hei+7DkG9KQm5fL/07dvByNV0AQCqOS0bt+9RuSkZpCamLJCJaju7eTmZ5Gx/4DqyTttVWBSsWVS1E0aqrbgNyoaTOiIipe3qK0nOxszO8Zcf0ouFt3hpRTd17+j3Xnni1FdWe/IXW77kxPSiQ7Ix2PoJIfmoZGRrj6BhB3teJG99vXruAZFKJzzDM4hISYaxQWFpQbR11YQIFKhYmZWbnvazQaIg4fIKh5q1rRqZqWlEBWehpe9Uqm+iuMjHD3DyL2SnSF8eKuXsY7WPeHvXe9BsTfKCm7W1cv41WvbJjYK/+sAcXdN4DLZ0+RnpIMQOyVaO7cjMGnXuXLE1SnguJ7kX993XuRf/1GxERHlRvnRvQlvAJ170X+DRqVuRcB/PjRu3wxdTLzZ3zKlQtl61lVfj5fvfESM157gT++mcGt69f+e6ZqARtzUyxNTbgcn6g9VlCo5npCMh52yupLmHh0GBpiHOhP9tETOoezj57EpEH5ywDpGSnQlBp9rsnLR+HogKFz0ahhPYUCTanOPk1ePib1AsHA4AFmoOaxNjPFwsSYq3dKZtoWqNXEJKXgdp/vtZutUicOwJX4RJyVVuhX0Inf2NuNK/GJZOTkPZC01wTpSYlkp6fhGVzyzGNoZISrXyBx1yq+J9++dkUnDoBncH3uxFyv9DnJ2My8/PfVaqJOhKPKy8PZx+9f5KbmUBUUEHn1Cq0a6S5d1qphI85GlX+fBzhw4gT7T57g1WfGlvt+2NGj1PPzY8mmjQyY8jxPTH2Zr3+fT3Zu7gNNf02jUqm4ePEirVu10jneulUrzvzNWRExMTEcOnSIpk2b6hxfvmIFSUlJjB9bfpk/ClQqFRcvXaJVqbJp1bQpZyIuVBhvf/gRDoQf4bXnnq/qJApRZdasWUO3bt3o1asXHh4eTJo0CRsbGzZv3lxu+GHDhjF69GhCQkJwdnamb9++tGnThoMHDz7wtNW5DhVzc3MMDQ0xNjbGxsZGW9A+Pj6MGTMGDw8PfHx8ePXVV7l06ZJO54i5uTnPPfccHh4edOrUCT8/P5KTk3nmmWdwc3OjT58+ODo6cqZUo3ibNm3o06cPbm5uPPnkkzRq1Ih169Y97Kz/a5kZ6ajVaqxKjYC0slaSlpr6r88be/0aG5cvYexLr6Jfx38s3Cs1NZXCwkJsbHXX9LW1tSU56f7L9kwcP47O7dsxbMhgGoWG8tzkKVWZ1BonOzMDjVqNRakZOBZW1mSmpZYbJzM9FfNywhe9lwYU7bEStn41g8c/X2Yk8KMuPT0NtVqNtdJG57i10obUlJQKYuk6fuQw506dpHuf8kdv1lV3605LpVLnuJVSSfp/rDs3LF/C+Jfrft2ZnZEOgJmlpc5xM0srsjPSKoyXlZGGaallkkwtrVCr1eRmlr/XwuFNa1EYG+PTILTc92MiI0hPTiSkde0YwZWVXlR2pZeLMre0Ijv9PmWXnoZZqTrTzMoKtbqQnOKyy0pPK/+89/k/KU/XoSNwdPdk3nuvM/OlZ/nrmy/o+PgT+DVs/I/O87BlZ2SgLu9eZG1NRlr5ZZCZnlbuvQsgo/j+ZWGtZMDocTw15RWGT3kFe2cXfv9qOtciS34M2zu7MGjcJEa++CpDJ72AoULBL599QFJ83APMYc1kYVLUkZmVq9sYmpWbr31PiKpkYG2FnqEBBSmpOscLk1MwtLUpN05W+HHMO7TBrEUT0NND4e6GzVODADC0K/otkB1+Aqu+PTAOLtqbwjgoAKt+PdFTKDBQWpV73rrCwqSokzk7T7fTKTsvH3PjipcyNDcxIju3bBwDfX1Mjcrur2BjYYangy2nr9Wd2SmA9r5rVuqebGZx/3t9dkYaZhal4lgW3esrek46tHENCmNjfEs9JyXeusmPb77A3NefZ/eyP+k7bnKtX+4rNT2dQrUaW2vd+7attTVJFfzmTExJ4bOf5/HB5CmYm5a/rOqtO3c4ExnJpevX+Wzqq7w+ZiyHT5/m4x/mPugs1Ch32z9sy2n/SKyk/WPchAm07dCBQU88QePQUKY8X9L4Hx0dzc+//MLHH32EQR3/TXQ/2uu11O91W6UNScWDlkpLTE5i+rff8sHrb2BewWAyIWo6lUpFdHR0mb2YmjRpwoULFXcmlpaTk4NFFQz+fST2ULl8+TLnz59n6NCy06rj4uIIDAwEiqYd3ltRK5VKzM11R2golUrSSv2YDg4OLvP66NGj5aZly5YtbN26tdz37uXfuDlN2rSvNNwDVWq0jwZN6UN/m0ql4tdvZjBk9DjsnSqezl2XlV4CRaOhTBmX9vH06WRnZXPp0iXmzP6OPxcu4Okxj+5ojLs0Gk0lZVf62r17VI8ClYqV876nx9AR2DhUvL7oo67M9VrOsfJcPH+O2V98xpjnpuAfVPFmzXWZXunr7z9Ma1WpVPw8awZPPF03687I40fYs+xP7ev+z75Q/K9/fv2VeftuuZcT73TYTs4d3MvA56dWuLfI+cP7cPT0xsGtZu7BEHH0ENuXlGx8Ovj5V4r+UeZeU1l9Wbq00VaaOmVe3j3sHzoRtoPYK5cYNOklrGztiImOImz1Mqzt7PEJqQXLNpRTtvcv2lJvFhfa3XJ1cHHFwaVk6SpP/0BSEhPYv3Uj3kH1tMc8/QN1wsx5/20O79hGv5HP/Pu81EANvVwZ0KxkJtSifceBcq61f/ksKsS/Vvoi1NOrsBJMX78FhZsLLtOnoWdgiDo7m9QVa7EbNwpN8UoIyQuWYGBrg8fcrwE9ClNSSN+6E9sRQ6FQXe55a6sQd2d6NymZzbP84Cmg6HflP1U2TsWVQWNvNzJy8oi+nVhhmNog8thhdt/znDRg4t29JMo89FR6ry/z/n2ek06F7Sh6Tpr8apnnJBtHZ556Yxp5OdlcPn2CHYvnM/iF17FzcfsbOarZyv5e11T4/PnBnO8Z3KMHDQICy30fipbe0QM+evElLIobsV8fO5aXP5tOUmoqdqUGYdU15VyllT7PT//0U7Kzsoi6dInvZs9mwcKFjB0zhvz8fN55911efukl3FwrXvbzUVL293rF1+u0L2cwpF8/GtYrf3alqDv+44paD8XfbQfv1asXvXv31r5OTy8awKosVXcqlUpOnz79tz47PDyc06dP8+WXX/6jNP8dj0SHilqtpnnz5owbN67Me/f+x5TX6136mJ6ens4yYf9U7969dS6QihyMuv6vP+OfsrC0Ql9fv8wm3RlpaWVmrfxd6SnJxN2MYeHcb1k491ug6AFFo9Ew5cmBTHnnfUJC6+ZyVkqlEgMDgzKzUVJSksuM2ijNqbgB1cfXF7W6kM8//ZQRo0ZjaPhIfFUxs7BET19fO7PkrqyM9DIjf++ysFKSlZ6qG744vrmVFZlpqSTExbL293ms/X0eUNzgqNHw0aSnGfnSG/jVrwUNe1XEysoafX19UkuNbklPTcG6kof+i+fO8fm0/2Po6Gfo2X9AFaayZrpv3fkvfzClFdedC+Z8y4I5unXn88MG8uI77xPSuPbWnT71Q3F63Uf7urCgaNmJ7Ix0LG1K6secjHRMLSoetWtuaU128QwNbZzMDPT19TEpNRDidNhODm9ew4CJL+Hk5UN5sjPSuXruNJ2GjPjHeXpY/Bs2xsXbV/v6btllpadhdU/ZZWdmlBnJei9zK2ttHamNk5GOvr6BtuzKDZOZjpnl39+/S5Wfz751K3ls/GTtjBQHNw8Sbt7g6M4tNbpDxczSEn19/TIzI7PS73cvsiaz1L0os3gGVkVxANx9/TkbfqjC9/X19XHz9iEp/vbfS3wtEhkbT2xSqvb13c2lLUyNSc8pWR7F3NiIzNy6s4SPqLkK09LRFBSWmY1iYKMsM2vlXkk/zidp3gIMbG0oTE3DrFljAAriijZH1+Tnc+eLb7jz1WwMbZUUJKVgPaA3hVnZFKalV3je2ij6dgK/7Sq5fxgWf6/NjY11luIyMzYiK6/ijbqzcvMxLzUzzcxYQaFaXWavJH09PRp4unL62s3/NKilJvBp0Bgnr3vv9UV5zc5I03lOquxeb2ZpXWZWaXZmhs69/q5TYTs4vGkNj016GedynpMMDA1RFg9Kc/L0Jj7mGqf2bKfb8DH/OH81hdLKCgN9fZJKzShPSU/HtoJ79rHz5zh5IYJfi/dE1RTvDdBu5HDeGDeegd26Y69U4mBrq+1MAfB2K+p4ik9KrLMdKnfbP5KSdX9PpiQnY1dJ+4ezkxMAvr6+qNVqPpk+ndGjRpGYmMiVq1f56JNP+OiTT4Cidj2NRkOrtm35duZMWpezj01dpL1eS/1eT0lNLTNr5a5jp09x8uwZflm0CCjq3FKr1bTp15c3p7zAoL59qzrZQmj93Xbwivydgb7liYiI4KuvvmLixInaiRQPUp1spTU0NNTp9PDz82P//v04OjpWScN0ZGQkPXr00Hnt4VEzR7iWx1ChwNPXn4unT9HsnlkxF8+cokmrNv/qnEpbO979erbOsb1bN3HhzCkmvfEOdnV4poBCoSAoOJjw8CN07d5de/zokXA6d+3yt8+jUWsoLCz8Tx14tY2BoSGuXj5ciThH/eYla7BeiThHvWYtyo3j7ufPjpVLKVDlY6gw0oa3VNqgtHdAXVjI8x98phPn6J4dXIk4x5OTX0Fp51B1GaoFDBUKfAMCOXviBG06dNIeP3vyBC3bVbz0UcTZM3zx/rs8MXI0/QYNfhhJrXHu1p0RZ07RrG1J3XnhzCmatv53daeNrR3TZurWnWFbN3Hh9Cmee7P2151GJiYYmZhoX2s0GswsrYiJjMDJ0xso2pvm1pVo2j02pMLzOHv7cuXsKZ1jNyIv4ODhjYFByX3+5J7thG9eR/+JL+LqG1Dh+S6EH8TA0JCAJuXXMzWBkYmpzqhRjUaDuZU11y+ex6W4AaRApSL2chSdBla8946Ljx/RZ3T3B7h+8TxOniVl5+rjx/WLEbTs3ueeMBG4+f79NdPVhYWoCwvLPADr6evX+AYvw+J70eXzZ2nQouTH+uWIs4Q0a1luHE//ALYtX4pKlY+i+F50+fxZ7b2oIrdvXL/vpvMajYb4mzdw9vCqMExtlV9QSHJmts6xjJxc/JzsuJVc1BBoqK+Pl4Mt207/t32phPhbCgrIi4rGrHkTMvfs1x42a96EzLAD94+rVlOYWDSYyrJbJ3LORVCYWmpJpsJCChKKwlh060T2wfDaMbT0H8gvKCS/IEfnWGZuHj6OttxOLeo8MtDXx8POht3nKt6rIjY5lQAX3WceH0c7bqeml9ngNtDVETMjBaev3XpAuag+5T4nWVlzIzICJ8+Se/2ty5do/9gTFZ6nvOekmMgIHD28dJ+Tdm/jyOZ1DJj00n2fk3Ro1NpBHbWVwtCQIB9fws+epds9z+zhZ8/SpWX59/lFX87Qeb332DF+X7Oa3z75FIfizq5GQUHsPHKY7NxczIr/H2/EFS3Z6XyfZ4HaTqFQEBwczJEjR+jerZv2+JHwcLp2+fvtH2pNSfuHo6MjSxcv1nl/xcqVHDlyhBlffomri8sDS39Np1AoCA4IIPzESbp36Kg9fuTkSbq2a1dunCU//KjzOuzQIeb/tZTfv/kWBzu7Kk2vEA+KlVXRANaUUkvRp6amlpm1Utr58+f58MMPGTlyJH2rqAOxTnaoODk5ERUVRXx8PCYmJvTr149t27bx5ZdfMmTIEKytrbl9+zb79+9n3LhxmP3HNQUPHjxIQEAADRs25MCBA5w+fZqvvvrqAeXm4ejW/3F+nz0Lr4BA/ILqsW/bFtKSk+nQs6gxZc2iBVyLvsQr73+ijRMXc4OCggIyMzLIy80lpngDYQ8fXwwMDXHz1P3xb2ltjaFCUeZ4XfTUiBF89P77hITUp1FoKKtXrSQxMYGBg4saCH+Y8z0R588ze+4PAGzetAljIyN8/f1RKAy5GHGBH+bOoXPXrhjds8FtVFQkAFlZWejr6xEVFYnCUIGPr2/ZRNRSrXv0YfWvP+Dm44uHfyDHwnaSkZZC805FD2c7Vv3FrauXefq1dwBo2LItYetXs2b+PDr2e5yk+Nvs37KeTgMGo6enh4GhIY6llvAxt7TCwFChc7ywoICEW0XrLheo8slMS+P2jesYmRhj61j3ll66V79BQ/j+qy/wCwoiKKQ+OzZtIDkpiR59+wOweP6vXI68yHufF/2QOH/mNF9Me5ee/QfQoUs3UotHI+nr6//rmRm1VfcBjzN/9ix8/APxC67H3m1bSEtJpmNx3bl60QKuXrrEqx+U1J23Ym5QWFBAZnoGuX+n7rSqu3Wnnp4eoZ26c2z7JmycnFE6OHFs+0YUxsYENi3pVN2+6DcAeowsmmnaoG0nzuzfzb7Vf1G/bUfirkZz8ehBeo6eoI1zYtdWDm9aQ4+R41E6OGlnXBgqFBibltz3izaj309AkxY6jRg1nZ6eHk279ODI1g3YOrlg4+jE4S0bUBgZU++eDulNC38GoO/TzwIQ2r4zJ/fuZNeKxYS270zslWjOHTlA/zGTtHGadu7B0m8+58jWjfiHNiX69Alioi4y/NW3tGHy83JJTbgDFJVhekoyd27ewMTMHCtbO4xNTXH3D2LvupUojE2wsrXjZnQkEeEH6fh42SVYa5q2vfqy8ue5uPn64+kfyNE9O8hITaFl56J70bYVS4m9epmxb/wfAI1atWP32lWs+vVHOvcfRGJ8HPs2rafLY4O1nUoHt21GaW+Pk5s7BQWFnD60nwsnj/HUlFe0n7tr7Uo8/Pyxc3QmLzeHwzu2cvtmDANGl51lXRcdjrpOxxA/EtOzSMrMomOIH/kFBZy9XtJQOqhV0SbCq4+U7GnorCzah8nY0BCNRoOz0pJCtYaE9PL3CnhU6ZmaoHArXjpFXw+FkyNG/r6oMzIoiE+o3sTVECnLVuP8f6+ReyGKnHMRWD/eF0M7W9LWbgLAbuIYTOoFEju16DlU39oKy87tyT51Fj2FAqu+PbDo0p6bL/1Pe06FuxsmIUHkRlxE39ICm2GDMPbx4sb0r6sljw/b0egbtA3yISkzm+SMLNoG+5JfUEjEzZKZd/2Ll//bcPw8ACev3qSpryfdGgZy6losbrZKGnq5su5o2Q2uG3u7cS0hmbTsnDLv1XZ6eno07tiNo9s3YeNYdK8/um0jRsbGBDYruddv+/NXAHqOGg9Ag3ZFz0l7Vy2lQdtOxF2N5kL4QXoVPwtA0XPSoY2r6Tmq4uekA+tX4h3SEEulLfl5uUQdD+dmdBQDnn2R2m54v358OOd7Qvz8aBQUxOodO0hMSWZQ96KBsnOXLCbi8mW+f/c9APw8PHXiX7hyBX09PZ3jPdu157dVq/jkx7lMGDKUjOwsZi34na6tWpXZr6WuGTl8ONM++ID69esT2qgRK1etIiExkSGDiwbefT9nDucjIvhhzhwANm7ahLGxMf5+fhgqFFy4cIE5c+fStUsXbfuHv5/uQB4bGxsURkZljj8KRgwazPtfzSAkKJDQkPqs2rSRxKQkBvct2sN0zvzfOB8ZxdzPPwfAz9tbJ/6FS1FF12up46L2q+kD1f4LhUKBv78/p06don37kgGsp06dom3bthXGO3fuHB999BHDhw/n8ccfr7L01ckOlUGDBjFr1iwmT55Mfn4+v/zyC19++SULFizg/fffR6VS4eDgQJMmTVAoym5q90+NGDGCgwcPMm/ePKysrHj55ZerZDpRVWrergNZmRlsXrmM9JRkXDy8mPLONO1o6LSUFBJKLTfx/WcfkVzcmAIw/c1XAPhh+bqHlu6aqnuPnqSlpfH7/N9ISkzE18+Pr2Z9g0vxSIqkxERiY0s2TTQwMGDhgt+5GRNT1BDg7MyQJ4by1PDhOucdM2qUzuv9+/bh7OLCqrV1p8wbtGhNTmYGezeuJTMtFUdXd0a+9AZKO3sAMlNTda47EzMzRk99i02Lf2feJ9MwNTejTY++tOnRp6KPKFdGago/ffx/2tfHE3ZxfO8uvAKDGfPGuw8mczVU206dychIZ/WSxaQkJ+Ph7c1bH32KQ/EU7NTkJOLjSjZEDtu+lby8XNavXM76lcu1xx0cnfh+wZ9lzl+XtWjXgayMDDatXEZaSjKunl68UKruTCxdd07/iKR7ruFP3ngFgJ9W1J3v8T/RtGsvClT5hK1YTF5ONk5ePjz+3Cs6nRsZpaa4W9nZM+DZF9m/ZhlnD4Rhbm1Nx0FP4R/aTBvm7P49qAsL2bpwnk7c4BZt6D6iZG+q2OhI0hLvaBshapOW3ftQkJ/PzmV/kpudhYu3L0+88JrOTJb0UssvKO0dGPL8VHavXMLp/Xswt1bS9YkRBDZprg3j5utP/7HPcWDDKg5sWoPS3pH+457DxbvkB+zt69dY9l3JWrQHN67h4MY11G/Vjj6ji8pywLjn2Lt2BZsWzCM3OwsrWzva9RtEk04loxdrqoYt25CdmUnY+tVkpKXi5ObO6Ffe1M42yUxLJflOvDa8iZkZz7z+Nhv+/J0fP3oXE3Nz2vbqS9teJSOiCgsL2LpsMekpySiMjHB0dWf0K28Q2KhkGb/c7GzWLviVzLRUTEzNcPH0Yvz/3sPd1//hZb4aHbh4BYWBPn2bhWBqpOBmUhp/hB0lv6BQG8barGzH53O9dPcdDHJzIjUrm282hFV5mmsTk+BA3GeXjLK2m/A0dhOeJn3TNuIfkcb9ymTu2kuClSW2Tz+FgZ0t+VevEfu/9ymIL7pvG9rZoHDVHRlt2asb9s+PBz09cs9f4OZLb5F34Z7ZFwb6KIcNwsjTDU1BITknzxAz+TUKbt/hUXDk0jUUBvr0DA3GRGHIrZR0/jpwXOd7bWWq+71Oy85l+cGTdGsUSBMfDzJz89h+OpLIW7plZm1mipeDLWvL6WipK5p2602BSkXYysXkZWfh5OXL489P1XlOyiz1nGRt58BjE19iX/FzkoW1NR0H6z4nndm3G3VhIVsWlH1OujuAJTs9je1//kpWejrGpqbYubrz2MSX8KrXoApz/HD0aNOWtIwM5q9eTVJqCr4eHsz831u4OBTd5xNTU7kZH1/JWXSZmZgw+//e5evf5zP23XewMjenY/MWTB5ec5eUfVB69uhBWloav86fT2JiIn6+vnw7a5a2/SMxKYmbpdo/5i9YQExx+4eLszNDn3iCEU89VV1ZqNF6dOpEWkY685csITE5BT9vL2Z99DEuxb/XE5OTiY2r/bP0hCht4MCBzJw5k4CAAEJCQti8eTPJycn06VPU3rdgwQKioqL49NNPATh79iwffvghffv2pXPnztrZLfr6+lg/4I5tvZycnLrbnfUQDBgwgLfeeot2FUy1+7ce5h4qj4JQr0dnSmhV23o6srqTUKeEuDtVdxLqlNSsujc6sTqdjal7+zZUFxNFnRzDUm3Ka1QX/17EzUejYfdhGDn7m+pOQt2iX3aPS/HvrZw6tbqTUGdYlNrjRfw3I50q3hNG/HOGvt7VnYQ6RZ2cUnkg8bcYu0rb3IMUcfOfdfxWh//a5rVx40ZWrVpFcnIyXl5eTJgwgQYNijr2Z82axblz5/j111+1r3ft2lXmHI6OjtowD4r8uhdCCCGEEEIIIYQQQgghaonS+4rVRf369aNfv37lvje11ECRqVOnljlWVfQfyqcIIYQQQgghhBBCCCGEEELUYjJD5T9av359dSdBCCGEEEIIIYQQQgghhBBVTGaoCCGEEEIIIYQQQgghhBBCVEJmqAghhBBCCCGEEEIIIYQQtYTmEdhDpaaSGSpCCCGEEEIIIYQQQgghhBCVkA4VIYQQQgghhBBCCCGEEEKISsiSX0IIIYQQQgghhBBCCCFELaGWFb+qjcxQEUIIIYQQQgghhBBCCCGEqIR0qAghhBBCCCGEEEIIIYQQQlRClvwSQgghhBBCCCGEEEIIIWoJDbLmV3WRGSpCCCGEEEIIIYQQQgghhBCVkA4VIYQQQgghhBBCCCGEEEKISsiSX0IIIYQQQgghhBBCCCFELaHRyJJf1UVmqAghhBBCCCGEEEIIIYQQQlRCOlSEEEIIIYQQQgghhBBCCCEqIUt+CSGEEEIIIYQQQgghhBC1hFqW/Ko2MkNFCCGEEEIIIYQQQgghhBCiEtKhIoQQQgghhBBCCCGEEEIIUQnpUBFCCCGEEEIIIYQQQgghhKiE7KFSQ+nrVXcK6hZ9PSlQUTPJtflgqWUJUVFDyfq2oiZTI9fnA6NvUN0pqFvUhdWdgjpFYSDX54OSk59f3UmoW/RlrK8QQvxT8hOz+shdSwghhBBCCCGEEEIIIYQQohLSoSKEEEIIIYQQQgghhBBCCFEJWfJLCCGEEEIIIYQQQgghhKglNLLmV7WRGSpCCCGEEEIIIYQQQgghhBCVkA4VIYQQQgghhBBCCCGEEEKISsiSX0IIIYQQQgghhBBCCCFELaGWJb+qjcxQEUIIIYQQQgghhBBCCCGEqIR0qAghhBBCCCGEEEIIIYQQQlRClvwSQgghhBBCCCGEEEIIIWoJjSz5VW1khooQQgghhBBCCCGEEEIIIUQlpENFCCGEEEIIIYQQQgghhBCiErLklxBCCCGEEEIIIYQQQghRS6hlxa9qIzNUhBBCCCGEEEIIIYQQQgghKiEdKkIIIYQQQgghhBBCCCGEEJWQJb+EEEIIIYQQQgghhBBCiFpCo5E1v6qLzFARQgghhBBCCCGEEEIIIYSohHSoCCGEEEIIIYQQQgghhBBCVEI6VIQQQgghhBBCCCGEEEIIISohe6gIIYQQQgghhBBCCCGEELWE7KFSfepch0p8fDwTJkxg5syZBAQEVHdyapU9Wzaxbd0q0lJScPXwZNiYCQSE1C83rCo/n0Xz5nLjymXiYm/iH1SP1z6aXuG5oy9E8PX77+Ds5s77s76vqizUKCuWL2fRn3+QlJiIj68vU199jcZNmpQb9uqVK8z48guuXr1KVmYm9vYO9OjZkwkTJ6JQKADYvWsXq1etJCoykvz8fLx9fBgzdhwdO3V6mNl6KI7u3s7BrZvISEvF0dWNXk+OwiswuMLw8Tdj2LxkAbFXL2NqbkGzjl3p2H8genp6AFyLvMDO1ctIuh2HKj8Pazt7mrbvTNte/bTnKCwoYP/m9Zw+tI/0lBTsnV3oPuRJ/BuEVnl+H7at69eydsVyUpOTcPfyZuxzk6nXoGG5Yc+fPsWG1SuJjowkOzsLZxdX+g0aTNdefbRhUpKSWPDzj1yNjibuViwdu3bnhdfffFjZqXZhWzexfe0q0lJTcHH3ZOjYCQTUq7juXDxvLjFXi+pOv6B6vPqhbt0Zdf4caxcvJP5WLPl5edg6ONCuW096PDboYWTnodNoNIRvXc/5Q/vIy8nGydOHTkNGYOfiet94sdGR7F+7nOTbtzC3UtK0ay8atCupD6NPHeP4zq2kJd5BrS5Eae9IaKfu1GvZtuQcl6M4uXsbCTdvkJWWSrfhY3Ter+k0Gg2HNq/j7IEwcnOycfHypeuwkdi7uN03XsylSMJW/0VSXCwW1kqad+9DaPvOOmGiTh3j4MY1pCUmYG3vQLv+gwkIbap9X61Wc2jTWi4cPUxWeirmVkrqtWhFmz6Po29gAEB+Xi77160k+sxJcrIysbKxpVG7zjTr2vOBl0VVOLJrO/u3bCAzNRVHNzf6DH8a7/vci27fvMHGP3/nZvG9qEXnbnQeMEh7L7p6MYLfvvykTLyXPp2BQ/H/2Yn9Yaz+7acyYab99DsKhdEDylnN0aV+AM38PDBVKLiZnMqG4+dJSM+sMLyFiTG9G9fDxcYKOwtzTl+PZXX4GZ0wDlYWdG0QgIuNNbYWZuw+d4nd5y9VdVaqnfXAftgMH4KBrS35166TMHseuWfOVxjeoksHbEcNQ+HhRmFqOqmr1pO6dKXuOQf1Rzm4P4bOThTEJ5D8x1Iytu6q6qzUGiahDbAZ/gQmQQEYOthz+9OvyNi8vbqTVe00Gg3Ht2/k4pH95GVn4+jpTbtBT2HrfP/7+q3LURxev4KU+DjMrKwJ7dyTkDYdte9HHj1E2LKFZeKNm/4dhsW/l07u2sK1s6dITYjHwNAQR08fWvZ9HFvn+98Xa7q2Qb6EerthrDAkLiWdHWcukpSRdd847nZKujQIxN7SnMzcPMKjr3P6WqxOGCNDA9rX8yPI1QkThYKMnFz2XYgm8tYdAFoFeBPg4oCthTmFajW3UtLYFxFNYiWfXVus2LqVRevXkpSaio+7O1OfGUvjevUqjXcjLo4xb72JRqNh98I/tcePnz/PlI8+KBN+6cxv8Har3dfg37F8xQr++PNPEpOS8PXx4bWpU2lSQfvHlStX+GLGDK5evUpmVhYO9vb07NGDic8+q23/OH7iBHPmzuX69evk5uXh7OzMwMceY/SoUQ8zWzXGig3r+WPFCpKSk/H18mLqpOdo0qBBpfFuxMby9IsvoNFoCFu9puoTKsQjos51qIh/5+iBffw1/2dGTHgO/3oh7Nm6idnTP+SDWXOwdXAoE16tVqNQGNG5Tz/OnThOTlbFD1VZmZnMnz2L4IahpCYnVWU2aozt27Yx6+uveON/bxHauDErVyxn6ssvsWTZcpydncuEN1Qo6NuvP0FBQVhYWnIpKorPpn9KQWEBL770MgAnT5ygefMWTHr+eaysrNm6ZTNvvfkGc3/8qcKOmtro3NHDbPnrT/qOGINnQCBHd+9g0XczmPLhF1jb2ZcJn5eTzR+zPscrIIhn/+8jEm/HsXb+PBTGxrTt2RcAIxMTWnXtiaObBwojI2IuR7Hhj/kojIxo0aUHALvWrODM4f0MeHo8Di5uRJ8/w19zv2HcW+/j4un9MIugSh0I2838H+cy4YWXCK7fgK0b1vHpu28za96vODg6lQkfeSECT28fHh/6JDa2tpw6foyfvp2FwsiIDl26AaBSqbCysmbgsKfYsXnjw85StTp2YB/L5v/M8AnP4Rccwt6tm5jz6YdMu1/daWREp979OH/yONnl1J3GJiZ07tsfN09vjIyMuBx5gcXz5mJkbEynXn0fRrYeqhO7tnJqz3a6DR+DjaMzR7duYO2Psxj19scYmZiUGyc9KZH1P8+mXst29Bg1nrgr0YStWISJhQX+oc0AMDG3oEXPvtg4OqNvYMC182fZ9ddCTC0s8Q4p6kBU5eVh5+JGcPM27Fj820PL84NydMdmju/aSq9R47B1dObwlvWs/P5rxr73KUYmpuXGSUtMYPWP39CgdXv6PD2B2MuX2LVsEaYWFgQ2bg7AravRbJz/E237Po5/aFOiT59gw28/8NTUt3Hx9i367O2bObVvF71Hjcfe1Z3EWzfZ8sevGBgqaN17AABhq/7iRmQEvUdPwNrOntjoKLYvXYCphQUhNbzj6mz4ITYtWciAUWPxDAgifPd2/pj1BS9+MgNlOfei3JxsFnz1GV6BwTz33ick3o5j1a8/YmRkTLve/XTCvvjxl5haWGhfm1ta6byvMDJm6hezdI/Vwc6U9sG+tA3yYXX4GRIzMukcEsAznVvy3aYw8gsKy41jqK9Pdl4++y5cprmfZ7lhFIYGpGblEHEznm4NA6syCzWGRdeOOLw0iTsz55BzNgLlwH64ffkR159+joI7CWXCm7VqjvN7b5Lw3Y9kHTmOkZcHTm++hCY/j7RVGwCwfrwv9pPGEj/jO3IjIjGpF4jTmy+hzsgk62D4w85ijaRvakr+letkbNmB07tvVHdyaozTe7Zxdu8OOg17GqWjEye2b2LTz98x7I0PKr6vJyey5dc5BLVsS5fhY7l97TL7Vy3BxNwC30YlnfmGCiOeeusjnbh3O1MA4i5HEdK2Iw4eXqCBY1vXs/Gn7xj6xjRMzMyrJsNVrKW/Fy38Pdl8IoLkzCzaBPkyrG1Tftl5EFUFdaW1mQlDWjfh3I1bbDx+Dnc7Jd0bBZOTpyIqrqizRF9Pj6FtmpKrUrHu6BkycvKwNDWmUF0y2tnD3oZT125yOyUdgPb1/BjWtim/7TpErqqg6jNfhbYfPMCsBfN5Y/wEQoOCWbltK1M/+5QlM2fhbF/2Gf4uVYGK976dReN69TgZEVFumCVfz8Tqnvu80sqq3HB1ybbt2/lq5kzeevNNGoeGsnzlSl6aOpXlS5eW2/6hUCjo368fQYGBWFpaEnXpEp9On05BYSEvv/giAGampjw5bBj+/v6YmJhw+vRppn/+OSYmJgx94omHncVqtT0sjK9//JH/TXmB0Pr1WbFhA6+89y5//TQPZ0fHCuOpVCre/fwzmjRowImzZx9iioWo+2rlHirHjx/nf//7H0899RTDhw9n2rRpxMTEADBhwgQAXn31VQYMGMDbb7+tjbdjxw4mT57M4MGDmTRpEmvWrEGtVmvfHzBgAJs2beKTTz5hyJAhTJo0iTNnzpCYmMi0adN44okneOmll4iOjtY559ChQwkPD2fSpEkMHjyYd955h9u3bz+k0ngwdqxfS9vO3ejQoxcu7h4MHz8Ja6UNYds2lRve2MSEkZMm07FHb2zs7O577oVzv6N15674BgZVRdJrpCWLF9Gv/wAGDhqEj48Pr7/xJnb29qxasaLc8B4eHvQfMICAwEBcXFzo2KkTvXr35vSpU9owr77+Ok+PGUP9+g3w8PBgwrMTCQ4OJmzPnoeTqYfk8PbNhLbtQLOOXXBwcaPviGewtFZyNGxnueHPHDmIKj+PgeOew9HNg5BmLWnXuz+Ht2/WTn909fKhQcs2OLq5Y+PgSKPW7fGr35AblyJLznN4P+169yewURNsHBxp0bk7AQ0bc6iC70BttWHVSjr36En3Pv1w9/Ri/OQXsbG1Y9uG9eWGH/zUCIaPGUdw/QY4ubjSq/9jtGrXniP792nDODo7M27yC3Tp2QsLS8uHlZUaYeeGtbTp3I323YvqzifHT8LKxoa996k7R0ycTIcevVHall93evn506JdR1w9PLF3cqZVxy6EhDYh+kLFI41rK41Gw+mwHTTr1hv/0GbYubjRfcRYVHm5RJ04UmG8cwfDMLdS0mnIcGydXKjfpgPBLdpycnfJyGD3gGB8GzbBxskFa3tHQjt1w97FjVtXSkaqe4c0pE2/Qfg3boaeXu16JNJoNJzcs4OWPfoS2Lg59q7u9Bo1nvy8XC4eq7jsTh/Yg4W1kq5DR2Ln7Eqjdp0IadWW4zu3asOc2L0Dj4BgWvXqj52zK6169cfDP4gT95TvravR+DVojF/Dxljb2ePXsOjfcdeu6ISp16INnoHBWNvZE9KqLc7evsRdu1o1hfIAHdy6iSbtOtK8U1ccXd3oP3IMFtZKwnfvKDf8mcMHUOXnM2TC8zi5e1C/eUs69B3AgW2bykzFN7eywtJaqf3T19e99vT00Hnf0lpZVdmsVm0Cvdl34TIRN29zJy2TVeGnMTY0pJFXxaPYU7Nz2HQyglPXYsnJV5Ub5lZyGltPX+TsjVuoCstvbKxrbIYNIn3zDtI3bEV1PYaEb3+kIDkZ64H9yg1v1bMrWQePkLZmIwVxt8k+fJTkP5dhM2KoNoxlr66kbdhC5s4wCuJuk7lrL2nrt+iEedRlHz5K0rz5ZO7ZD2pZcgOK7k1n9+0itEsvfBs1xdbZjc5PPYMqL5fok0crjHfh0D7MrK1pN/BJbJxcqNeqPYHNW3MmrFSdq6eHmZW1zt+9+j77EkEt2mLr7Iatixtdho8hNyuD+GuXqyK7D0UzP0+OXLpGVNwdEjOy2HziPEaGBoS4lW2kvivU252s3Dx2no0kOTObM9dvcT4mjhb+JR3RDTxdMTNWsPrIaWKT00jPySU2OY3bqenaMCsOneTcjTgSM7JIzMhi4/HzmBob4WarrMosPxRLNm6gX6fODOzWHR93d14fNx47GxtWbdt233hzFi3C39OLrq3bVBjGxsoaO6WN9s9A3+BBJ7/GWbRkCQP692fQwIH4+Pjw5uuvY29nx4qVK8sN7+HhwYD+/Qksbv/o1LEjvXv35tQ97R/16tWjV8+e+Pn64ubqSt8+fWjTujUn7wnzqFi8ehX9e/RgYJ8++Hh68sbkydjb2rJy44b7xpv922/4+/jQrUOHh5RS8bCp0dT4v7qqdrUeFMvNzeWxxx5j5syZTJ8+HTMzMz7++GNUKhVff/01AB9++CELFy7knXfeAWDr1q0sXLiQkSNHMnfuXMaPH8/KlSvZtEm30euvv/6iQ4cOzJ49G39/f2bMmMF3331H3759+fbbb7G1teXbb7/ViaNSqViyZAkvv/wyM2bMQK1W8+mnn9aatewKVCpuXIkmJLSxzvF6oU24HHnxP517z5ZNpKem0m/IsP90ntpEpVIRefEirVq31jneqlVrzp45U0EsXTExMRw+dIgmTZreN1xWdjaWVnWnAbuwoIBb16/iF6K7/JRvSENuXi5/uY6bl6PxCghCYVQyetevfkMyUlNITSw7KhMg7sY1Yi5fwiuwZEp3YUGBzgg3KBrxdiM66t9mp8ZRqVRcuRRFaNPmOsdDmzYj8kL5I6zKk52djblF3bnu/q27dWe9curOK/+x7rxXzNXLXIm8SEBI5VO6a5v0pESyM9LxCCpZIs3QyAhX3wDirl6pMN7ta1fwDArROeYZHEJCzDUKC8uOmNRoNMREXSAlIR43v7qxHGhaUiJZ6Wl4BZeUncLICHe/QG5drbjhKO7qZZ04AN716hN/47q27OKulQ3jVa8Bt66WDChx8w0g5tJFkm/HAZAUd4sbURfwuaf+dvMN4Mq502SkJANw60o0CTdj8Knh13JB8b3Iv77uvci/fiNiKrgn3Ii+hFeg7r3Iv0Gjcu9FP370Ll9Mncz8GZ9ypZyOUlV+Pl+98RIzXnuBP76Zwa3r1/57pmoYG3NTLE1NiI5P1B4rKFRzPSEZDzubakxZLWRoiHGgP9lHT+gczj56EpMG5S9do2ekQJOfr3NMk5ePwtEBQ+eika56CgWaUp1Wmrx8TOoFgkHdbxwU/05GciI5Gem43/OMbagwwtkngPjrFd+b4q9fwT1A93r1CAwh4eZ11Pd0jBaq8ln86f+x6JO32fLbHBJjY+6bHlVeHhqNBmNTs3+Zo+plbWaKhYkx1+4ka48VqNXEJKbiamtdYTxXG2uuJeiuDHH1ThJOSiv0i5ehDHBxIDY5je6NgpjcqwNju7ambZCv9v3yGBkaoK+nR66q/A7t2kJVoCLyyhVaNdJd2rlVo1DORkVWEAsOnDjO/hPHeXXsuPuef8w7b9Fv0rO88PGHHD937oGkuSZTqVRcvHiR1q1a6Rxv3aoVZ/7mrIiYmBgOHTpE06YVt39cjIzkzJkz9w1TF6lUKi5eukSrUvlu1bQpZyIuVBhvf/gRDoQf4bXnnq/qJArxSKqVS361a9dO5/Urr7zCk08+SVRUFPb2RcswWFpaYmNT8oNs6dKljBkzRhvX2dmZJ554gk2bNtG/f39tuK5du9KpeE+KYcOGsXfvXpo2bUrr4sbxIUOG8M4775CWloa1ddFDTGFhIc8++ywhIUWNO6+++irPPvssp0+fpnHjxlVTCA9QZkY6arUaS6VS57iVUsnFs6f/9Xljr19jw/IlvDV9hnY99UdBamoqhYWF2Nra6hy3tbXlaHjFo4YBnh03jsjIi+Tn5/P4wEE8P2VKhWFXLFtGwp079Olb/ujD2ig7MwONWo1FqdFmFlbWXL1Q/sNoZnoqVja2ZcIXvZeGjUPJFNiZb7xIdmYG6sJCOg0YTPPO3bTv+dVvyJEdW/EOqoedozNXLp7nwsljaO6ZxVbbZaSnoVarsbbRbayytrEh9eSJCmLpOn7kMOdOneTjmd9WHriOu1t3WpUaPW5lreRi6r+vO+96e9JYMtPTKCxU02/oU3Ts2afySLVMdkbRKEizUjObzCytyExLrTBeVkaaTmMNgKmlFWq1mtzMTMyL/0/ycrL5/YP/UVigQk9fn05DRuBVr/z9gmqb7PQ0oKis7mVmZUVmamqF8bLS0/EMKhXH0gq1upCczEwsrJVkpaeVWYbK3NJK+/8F0KJHH/Lzcvl9+nvo6+mjVhfSqlc/Gnfsqg3T5YkR7PhrIT9PewP94tGZXYaOwLeG702VnZGBurx7kbU1lyMquhelYV3BvSgjLRUbB0csrJUMGD0ONx9fCgsKOH1oP79/NZ1xb76Ld1DR9Wzv7MKgcZNw9vAkLzeXQ9u38MtnHzDlw8+wc3KpgtxWDwsTYwCycvN0jmfm5mFlWv6SQKJ8BtZW6BkaUJCSqnO8MDkFw2aNy42TFX4ch5cmYdaiCdnHTqFwc8XmqaJ9ugztbCm4fYfs8BNY9etJ5t4D5F28hHFQAFb9eqKnUGCgtKIwKaWKcyZqI+193UL3HmJqaUn2fe7rORnpmAbo7lFlammFRq0mNysTMytrlA5OdBo2GlsXd1R5uZzbv5u1c2bwxNR3sXYof8mbg2uXYefqjqOX73/LWDUxNy7qpM/K060rs/PysTA1rjieiRHXE3Q7TbPz8jHQ18fUSEFWXj7WZqZ42ttw4WY8Kw+fwtrMlO6NgjAyNGBPBftOdW0YRHxqBreS0/5jzqpXanoGhWo1tta693lba2uOnk0tN05iSgqfzfuJz199HXPT8pdVtbdR8uaEZwnx80NVUMDmvXt54ZOPmDvtA5qEhJQbpy64X/vHkaMVz0wDGDdhAheL94gd9PjjTHm+bON/3/79SSn+jGfHj+eJwYMfaPprutT09KLrVan7G95WaUN4ysly4yQmJzH922/54t33MDernR3KQtR0tbJDJS4ujj///JOoqCjS0tLQaDSo1WoSEhK0HSr3SktLIzExkTlz5vDDDz9ojxcWFpaZReLt7a39t7K4g8HLy6vMsXs7VPT19QkMLFmj2dHREVtbW27cuFGmQ2XLli1s3bqVygQ0aU7TNu0rDfcg6aE7GuW/zLBRqVT8PGsGTzw9Dnuniqcj12V6pUb3aNCUOVbaJ9Onk52dzaVLUcz+7jv+WLCAZ8aOLRNu166dzP7uWz7+dDouLnWngaUiGo2maA2UCpUu67tHdY+PffM98vPyuHklmh0rl6K0dyC0+HvW+6nRrF/4K3On/Q/09LB1cKRx246cOrj3AeakZihzHWoqvzYBLp4/x7dfTGfs81MICKp4Y+ZHTrnf9f9+2tc++oy83FyuXopk9Z8LsHd0olWnLv/9xNUo8vgR9iwr2byz/7MvFP+r7He4smuyzNt371n3vGFkbMKTr7+HKj+Pm1EX2L92GZa2dngEVr7haE1z4ehhdiwt2Yx34HNF+2uV/T5TSX1Zzv1JU87xMqfVfSaIPBFORPhB+j7zLHYubiTcvMHulUuwsnOgYZuiZQVOhu3k1pVoHp/4Ila2dtyMjmLv6mVY2drpzGSpscqUU2Xf7bJ1a9Fpio47uLji4FKynJWnfyApiQns37pR26Hi6R+Ip3+gTpg577/N4R3b6DfymX+fl2rWyMuVAc1KZiYt2ncMKLn2tPT06vDCAFWsdGHq6ZVTwEXS129B4eaCy/Rp6BkYos7OJnXFWuzGjdIOJElesAQDWxs85n4N6FGYkkL61p3YjhgKhXVnsIn4by6dCGffysXa173HTS76R5l7dHkHdVX2W9TJ2xcnb997XvuxctannDuwm3YDnyxzvkPrVnD72mUem/xamaUVa6p67s70DC15xl55+FTRP8rUlVT4/f679PQgO0/F1lMRaID4tAxMjBR0aRBYbodKl/oBuNspWbzvWJ2pp8t7Hqro+fOD779jcI+eNAiseG8uL1c3vFxLNp9vGBhEXEICizasq9MdKneV97Wv7Hl++qefkp2VRdSlS3w3ezYLFi5k7JgxOmF+njePnOxszp47x+w5c3B1daVf37q3r2Rl/kn70rQvZzCkXz8a1qt9v3nEP1NLFkaqk2plh8rHH3+MnZ0dU6ZMwc7ODgMDAyZPnkxBQfkbo93dJ2XKlCkEB9+/EdDQsGyRlHfs33Y29O7dm969e1ca7vCl6//q/P+GhaUV+vr6pKfqjjTLSEvDqtSslb8rLSWZuJsxLJjzLQvmFI1k12g0aDQanh82kBffeZ+QxnVnI/V7KZVKDAwMSErSnWadkpyCbQV7JtzlVLxhm4+vL4WFaj779BNGjh6tcw3u2rWTD6dN4/0PP6Rj8WyqusLMwhI9fX0y03VHPWVlpJcZKXyXhZWSrPRU3fDF8c1LbQB4d7aKk7sHWelphK1fpe1QMbe04qkpUylQ5ZOdmYml0oYdK//Cxq7iTQlrG0sra/T19UlNTtY5npaaWmbWSmkXzp3ls2n/x5Ojn6FX/8eqMpm1xn3rzgew58Hdzmg3L2/S01LZsHxJre9Q8akfitPrPtrXhcX37eyMdCzvGd2fk5GOqUXFG3iaW1qTnZ6ucywnMwN9fX1MzEs2ndXT10dZ/L13cPMgJf42x3dsrpUdKn4NQ3H2fl/7+m7ZZaWn6ZRddkZ6mdkl9zK3stLWkXflZKajr2+gLTtzK2uySpVvdkaGzmyYvWuW07xbL4KbFS3v4ODqTnpyEuHbNtGwTQdU+fnsX7+S/uOex69h46Iwbh4kxMZwfOfWGt2hYmZpib6+fplZUlnp97sXWZNZ6l6UWTxSu6I4AO6+/pwNP1Th+/r6+rh5+5AUX7v25ivtYmw8N5NSta8Nihs3LUyNSc/J1R63MDYis9SsFXF/hWnpaAoKMbTVvY8b2CjLzFq5V9KP80matwADWxsKU9MwK57NUhAXD4AmP587X3zDna9mY2irpCApBesBvSnMyqYwLb3C84pHi1dIIxw9vbWv772vWyjvua9nZmB6n332TC2tyM7QvTflZmagp6+PiblFuXH09fVxcPciPfFOmfcOrlvO5VPHGPDcVKxq0bN89O0E4lJKyuFuXWluYkzGPXWjmZERWXn5ZeLflZWbj7mJkc4xM2MjCtVq7f5TWbn5qDW6q9snZWRhZGiAqZFCZ5+qLg0CCXZz4q8Dx0nLzvkvWawRlFaWGOjrk1RqRm9KelqZWSt3HTt3jpMREfy6YjlQ1Lah1mhoN/xJ3hg/gYHde5Qbr75/ANsPHnig6a9ptO0fpX5jpiQnY1dq1kppzk5OAPj6+qJWq/lk+nRGjxql0/7h5lo0GMXf35+k5GTm/fLLI9WhorSyKrpeU0qVb2pqmVkrdx07fYqTZ8/wy6JFQFHnllqtpk2/vrw55QUGPULlJ0RVqR1DNe6Rnp5OTEwMQ4cOpXHjxnh4eJCdnU1h8dqqdyveezebt7Gxwc7Ojri4OFxdXcv8/VdqtZpLl0pGcdy5c4fk5GQ8PDz+87kfBkOFAk9ffyLOnNI5fuHMKfz+5Sh0G1s7ps2czbtffav969izN47OLrz71bf41uHR7QqFgqDgYMKP6C7vFR5+hIaNGv3t82g0agoLC3Wu5R3bt/PhtGm89/4HdO3W/YGluaYwMDTE1cuHK6WWVLkScQ73CvY9cPfz5/qlSApU+TrhLZU2KO0r/gGl0WgoKGf9X0OFEVY2tqgLC7lwIpygxnVnjVaFQoFvQCCnTx7XOX7m5HGC6lU8airi7Bmmv/cOQ0eOpt+gIVWdzFrjbt158fQpneMXz5x64HWcRl3+9VrbGJmYoHRw1P7ZOrtgZmlFTGTJHj4FKhW3rkTj4lPx8hzO3r7EROmuGXwj8gIOHt4YGFQ8VkSjUVNYUDvL0cjEFBsHJ+2fnbMr5lbWXL+oW3axVy7h6uNX4XlcfPy4Eam7Z9L1ixE4eXppy87F248bkbp7e9yIPI+rj3/JZ+Xno6en+xipr68PmqJ7lrqwEHVhYdkN1/X1a/wec4bF96LL53XX/b4ccRYP//JHpnr6B3A9KhLVPfeiy+fPVnovun3j+n03nddoNMTfvFFmWdbaJr+gkOTMbO1fQnomGTm5+DuVzCw31NfH08GGGFlK6p8pKCAvKhqz5roDlcyaNyH3XMVrqwOgVlOYmAQFBVh260TOuQgKU0st5VNYSEFCEqjVWHTrRPbBcBkOKbSMTEywtnfU/tk4uWBqaUXsPffoApWK21ejcfKq+N7k5OVLbLTu/nM3L13Ewd2rwqWjNRoNyXE3MS3VaX1w7TIunzxK/0mvoHSsXSslqAoKSc3K0f4lZWSRmZuHl0NJo7SBvj7udsr7Lrt1KyVNJw6At4Mt8anpqIu/v7HJqSjNdZcCsrUwI7+gUKczpWuDQOoVd6YkZ2Y/iGxWO4WhgiBfX8JLLW8efvYMDQODyo2zaMbXLPxihvbv2WFPYmxkxMIvZtx3g/pL169ib6N8kMmvcRQKBcHBwRwp1f5xJDycRg3//gAatUZTpv2jNI1ajSq/4s7EukihUBAcEED4Cd3lvY6cPEmjkPIHiS354Uf+nDNX+zdx1GiMjY35c85c2aBeiAek1s1QsbCwwMrKiq1bt2Jvb09SUhLz58/HoPhBS6lUYmRkxIkTJ3B0dMTIyAhzc3OGDx/OvHnzMDc3p3nz5hQWFnL58mWSkpIYOnTof0qTgYEBP//8MxMnTsTIyIhffvkFT0/PWrF/yl3dBzzO/Nmz8PEPxC+4Hnu3bSEtJVm7Zv/qRQu4eukSr37wiTbOrZgbFBYUkJmeQW5uLjHFGwh7+PhiYGiIm6eXzmdYWlljqFCUOV4XDR8xkg/fn0ZI/fo0Cg1l9cqVJCYkMGhIUWP03O+/J+L8eb4vXoJu86aNGBkZ4+fvj8LQkAsXLvDDnDl06doVo+INbrdv28oH06bx0suv0KRJE5ISizZyNVQotMvP1QWte/Rh9a8/4Obji4d/IMfCdpKRlkLzTkX7nexY9Re3rl7m6dfeAaBhy7aErV/Nmvnz6NjvcZLib7N/y3o6DRisnQJ7ZOc2bOwdsHMuWh7tetRFDm7bSIvOJZ1SN69Ek5GagrOHF+kpyYStX4VGo6Fd7/7UJf0HD2H2jC8ICAwmqH59tm3cQHJSEj37DQBg0W+/EB0VyfufzwDg/OlTfDbtXXr2H0D7Lt1IKR55pK+vj/U9DXxXLxdtVp2TnY2enj5XL0djaKjAw6tuf9+79X+c32fPwisgEL+geuzbtoW05GQ6FNedaxYt4Fr0JV55v6TujIu5QUFBAZkZGeSVqjsBdm/egL2jE07FSwZcijjHjvWr6diz7o0k0tPTI7RTd45t34SNkzNKByeObd+IwtiYwKYlG1tuX/QbAD1GFm0C2qBtJ87s382+1X9Rv21H4q5Gc/HoQXqOnqCNc2z7Rpw8fbCyc6CwsIDrEWeJPHaYjoOHa8Pk5+WSVrxhuEajJiMlmYTYGEzMzLC0uf+Mwuqmp6dHk87dCd+2EVsnZ2wcnTiydQMKI2OCm5eU3eaFvwDQ5+misglt15lTe3exe+USGrXrxK0r0Zw/coC+YyZq4zTt3J2/vv2C8G0b8W/UlOgzJ4iJiuTJqW9pw/g2COXojs1Y29lj5+LGnZs3OL57GyEt2gJgbGqKu38Q+9atQGFsjJWNHTejI4kIP0jHx//bc9fD0LZXX1b+PBc3X388/QM5umcHGakptCzee2vbiqXEXr3M2Df+D4BGrdqxe+0qVv36I537DyIxPo59m9bT5bGSe9HBbZtR2tvj5OZOQUEhpw/t58LJYzw15RXt5+5auxIPP3/sHJ3Jy83h8I6t3L4Zw4DR998AtzY6FHWNjiF+JKRnkpSZRacQf/ILCjlz/ZY2zOBWRQNRVh05oz3mrCwa5W5saIhGo8FZaUmhWkNCeiYABvp6OFgVjWg31NfHwsQYZ6WltlOnLkpZthrn/3uN3AtR5JyLwPrxvhja2ZK2dhMAdhPHYFIvkNipRc9O+tZWWHZuT/aps+gpFFj17YFFl/bcfOl/2nMq3N0wCQkiN+Ii+pYW2AwbhLGPFzemf10teayJ9ExNULgVD87T10Ph5IiRvy/qjAwK4hOqN3HVRE9Pj4YdunJy5xaUjs5YOzhyYsdmFMbG+DdpoQ23e8nvAHQZPgaAem06cP7AHg6uXUa91h2Iv3aZqGOH6DqipO47vm0Djl6+WNs7kJ+by7kDu0mKi6X94BHaMPtXLeHSiXB6jpmEsamZdr8xhbExCuPauT/T8cs3aB3oQ3JmFimZ2bQO9EFVWEhEbMnMxb5N6wOw6UTRYIjT127SxMeDLg0COX3tJm62Shp4urLhWMmgtVNXi8J0axjEiasxWJua0C7Yl1NXY7RhujcKIsTdhTXhp8lTFWj3dMkvKERVPKC1threrz8ffj+bEL8AGgUFsXrHNhKTkxnUoycAcxcvIuJyNN+/VzQ72M/TUyf+hSuX0dfT0zm+dONGXBwd8HH3oKCggC379hJ29Cifvfr6w8tYNRk5fDjTPviA+vXrE9qoEStXrSIhMZEhxfudfD9nDucjIvhhzhwANm7ahLGxMf5+fhgqFFy4cIE5c+fStUsXbfvH0mXLcHN1xau4jE+cOsWfixbxxBNPVE8mq9GIQYN5/6sZhAQFEhpSn1WbNpKYlMTg4v1058z/jfORUcz9/HMA/O7ZygDgwqWoouu11HFR+9X0gWp1Wa3rUNHX1+fNN99k3rx5vPDCC7i4uDB+/Hg+++wzoKhzY+LEiSxdupSlS5cSEhLCZ599Rq9evTAxMWHVqlUsXLgQIyMjPD09dTak/7cUCgXDhg1j5syZJCQkEBQUxNtvv/239iSoKVq060BWRgabVi4jLSUZV08vXnhnGnbFS6WkpaSQWGq5ie+nf0RSQskU60/eeAWAn1ase2jprql69OxJWloa83/7laTERHz9/Jj5zbfa/U4SExO5GXtTG97AwIAFv8/nZkxMUeOAswtDhg7lqeElPxBWrVxJYWEhs2Z+zayZJT9mmzRtyg8/zXt4matiDVq0Jiczg70b15KZloqjqzsjX3oDpV3RKNbM1FSS77nuTMzMGD31LTYt/p15n0zD1NyMNj360qZHyQbeGo2aHSuXkpqUiL6BPjYOjnQf/KS2kwaKRs/tWrOclIQEjEyMCWjQmEHjn8fErGT5oLqgXacuZKans3LJIlJSkvHw8uadj6fjUDzdOiU5mfhbJY1Zu7dvIy8vl/Url7N+5XLtcQdHJ+YuXKR9/eaU53Q+5/iRQ2XC1EXN23UgKzODzSuXkZ6SjIuHF1NK1Z0JpevOzz7SuYanv/kKAD8sL6o71epCVv/5O0kJd9DXN8DB2ZmBI5+hQ4/Kl4usjZp27UWBKp+wFYvJy8nGycuHx597BSOTkoaPjFJT3K3s7Bnw7IvsX7OMswfCMLe2puOgp/APbaYNk5+Xx54Vi8lMS8FQocDG0ZnuI8cR2LSlNsydmOusmVNSn4ZvWUf4lnUEt2hD9xFl96+qaVp071NUdy1fRG52Fs7evgyZ8ipGJiWbpZYuO2t7BwY99wphq5ZyZv8ezK2UdHliBIGNm2vDuPr602/MJA5sWM3BTWtR2jvSb+wkXO5Zu77r0BEc2LiGncv+JDszAwsraxq26UjrPiVLAvYbO4n961ayacHP5GZnYWVjR7t+A3U2rq+pGrZsQ3ZmJmHrV5ORloqTmzujX3lTO9skMy2V5Dvx2vAmZmY88/rbbPjzd3786F1MzM1p26svbXuVdIQWFhawddli0lOSURgZ4ejqzuhX3iCwUcnMgtzsbNYu+JXMtFRMTM1w8fRi/P/ew923ZHZQXbH/4hUUBgb0b1YfEyMFsUmpLAwLJ7+gpJHO2qzsxr+Te+mOrAx2cyIlK5tZG/YAYGliohPGztKcFv6eXL2TxPzduqNn64rMXXtJsLLE9umnMLCzJf/qNWL/9z4F8UX3GkM7GxSuunvuWfbqhv3z40FPj9zzF7j50lvkXYgqCWCgj3LYIIw83dAUFJJz8gwxk1+j4HbZ5ZUeVSbBgbjPnqF9bTfhaewmPE36pm3EP8IdT6Gde1KgUrF/9VLyc7Jx9PSh77Mv6tzXM1NL3ddt7ek9fgqH1q8g4tA+zK2safv4MHwblcwUz8vNYd+KRWRnpGNkYoK9mwePPf+azpJjEYeK9j7c+NO3Oudv2qMfzXvWzkFS4dHXMTQwoHujYEwUhsSlpLP84AlU99SVlqa6nUVp2bmsPHySrg0CaeztTmZuHjvPRhIVV/L9zcjNY/mhE3RpEMgznVuRlZvP2Ru3OBR5VRumiU/RihtPtmumc/4DF69wMPJKVWT3oenRth1pGZnMX72SpJQUfD08mPnWO7g4FN3nE1NTuBkfX8lZdKkKCpj9x0ISkpMxNjLCx8ODmW+9TdsmdWfFg4r07NGDtLQ0fp0/n8TERN3Y6qMAAQAASURBVPx8ffl21qyS9o+kJG7GxmrDGxgYMH/BAmKK2z9cnJ0Z+sQTjHjqKW0YdWEhs7//nltxcRgYGODu7s4LU6ZoO2keJT06dSItI535S5aQmJyCn7cXsz76GJfi3/CJycnExt2q5CxCiAdJLycnR7qz/oMdO3bw008/sXz58soD/wMPcw+VR0Go139f2k0U2XzqYuWBxN/WwKN2LUVQ0yVm1M3Rx9Xl/M3avW9DTWJkWP6SJeLfsTEv29Au/r1zN/9Zo5Go2Og531d3EuoWde0eBV/TrHvzf5UHEn+LWlPxskTin5vg5VjdSahTDL09Kw8k/jZ1sixH+qAYlxrgIf6bPRGXqzsJleocUvGSn7VZrZuhIoQQQgghhBBCCCGEEEI8qtSy5Fe1qXWb0gshhBBCCCGEEEIIIYQQQjxsMkPlP+revTvdu3evPKAQQgghhBBCCCGEEEIIIWotmaEihBBCCCGEEEIIIYQQQghRCZmhIoQQQgghhBBCCCGEEELUEhrZQ6XayAwVIYQQQgghhBBCCCGEEEKISkiHihBCCCGEEEIIIYQQQgghRCVkyS8hhBBCCCGEEEIIIYQQopZQy4pf1UZmqAghhBBCCCGEEEIIIYQQQlRCOlSEEEIIIYQQQgghhBBCCCEqIUt+CSGEEEIIIYQQQgghhBC1hEYja35VF5mhIoQQQgghhBBCCCGEEEIIUQnpUBFCCCGEEEIIIYQQQgghhKiELPklhBBCCCGEEEIIIYQQQtQSsuRX9ZEZKkIIIYQQQgghhBBCCCGEEJWQDhUhhBBCCCGEEEIIIYQQQohKyJJfQgghhBBCCCGEEEIIIUQtoZYlv6qNzFARQgghhBBCCCGEEEIIIYSohHSoCCGEEEIIIYQQQgghhBBCVEKW/KqhLt9Jru4k1CkG+tJ3+KBcTZBr80HycbSt7iTUKd9vO1DdSahTejQMqO4k1Bk5+arqTkKdYmRoUN1JqFNebSLf9Qflx6lTqzsJdYrCQL7rD9JjX35R3UmoM14bMLS6k1CnbDx5sbqTUKfYWphVdxLqlMzcvOpOQp2x7vWx1Z2EOkVW/Ko+0soshBBCCCGEEEIIIYQQQghRCelQEUIIIYQQQgghhBBCCCGEqIR0qAghhBBCCCGEEEIIIYQQQlRC9lARQgghhBBCCCGEEEIIIWoJNbKJSnWRGSpCCCGEEEIIIYQQQgghhBCVkA4VIYQQQgghhBBCCCGEEEKISsiSX0IIIYQQQgghhBBCCCFELaHRyJJf1UVmqAghhBBCCCGEEEIIIYQQQlRCOlSEEEIIIYQQQgghhBBCCCEqIUt+CSGEEEIIIYQQQgghhBC1hCz5VX1khooQQgghhBBCCCGEEEIIIUQlpENFCCGEEEIIIYQQQgghhBCiErLklxBCCCGEEEIIIYQQQghRS6hlxa9qIzNUhBBCCCGEEEIIIYQQQgghKiEdKkIIIYQQQgghhBBCCCGEEJWQJb+EEEIIIYQQQgghhBBCiFpCo5E1v6qLzFARQgghhBBCCCGEEEIIIYSohHSoCCGEEEIIIYQQQgghhBBCVEI6VIQQQgghhBBCCCGEEEIIISrxyOyh8vbbb+Pl5cVzzz1X3UkRQgghhBBCCCGEEEIIIf4V2UOl+jwyHSqPupNhOwnfsZnMtFTsXdzoOnQEHv5BFYZPiI1h+19/cvv6FUzMzAnt0IW2fR5DT09PG+ZG1EV2r1xCYlwsFtY2tOzRhyYdu2rfLyws4PDWjZw/vJ+M1BRsnVzoNHAovvUb6XxWZloqYWuWc+X8GfJzc1DaO9LjqafxDAx+8AXxEO3evJGta1eRmpKMq4cnT417lsCQBuWGVeXn88dPc7h+5TK3b8bgF1yPNz/+XCfMxXNn+GraO2XifvzdD7i4e1RJHqpbh3p+NPFxx8RIwa3kNLacjCAxI+u+cTztbejeKAgHKwsycvM4HHmVE1dv6oRp4e9JU18PrM1MyclTERV3h11no1AVFpY5X9sgH7o0COTY5RtsPXXhgeavuuzctIHNq1eSmpKMm6cXI8ZPJKh++ddmfn4+C374nuuXo4m7GYN/vRDe/vSLMuEKVCrWLVvKwT27SE1OwkppQ5+Bg+kx4PGqzk6N8GTbJvRoFIS5sRGXbifw845DxCSlVhi+VYAXvUKD8XG0xcjQkJikVFYePsXRyzE64fo1DaFXaDAOVhZk5uYRHn2DP/YeJVdVUMU5eng0Gg2HN6/j3MG95OZk4+zlQ9ehI7FzcbtvvJuXItm7+i+Sbt/C3FpJ8269adS+s/b9pLhYDm1ax52b10lPSqRV7wG06at7Pd6MjuLErq3Ex1wnKy2VHiPHUr9Vu6rI5kOh0Wg4tm0DEYf3k5edjZOXNx0GD8fW2fW+8W5djuLAuhWk3L6FmZWSJl16Ur9tR+37l08f5+SuraQlJqBWF2Jt70ijjt0IbtFGG+bPT94hIyW5zLk96zWg34QXHlwmH6ITYTs5sr3k2an70BF4BFT87HSn+Nkp7lrRs1PjDl1o17fk2SkzLZVdK5ZyO+YaKXfiqd+qLf2febbMefJycti7biWRJ4+Rk5WJpY0tnR5/gnrNWlZZXqvDio0b+XPVKpJSkvHx9GTqs8/SpIJ70b1u3IrlmVdeQaPRsGf5Cu3x3QcPsmrzZqKuXCZfpcLHw4Mxw56kY6tWVZmNGqV9sC+h3u6YGBkSl5zGttMXK31u8rCzoVvDQOytzMnMzeNw1HVOXSt5bhrRvhmeDrZl4iWkZ/LrzkMPPA/VQaPRcHz7Ri4eKao7HT29aTfoqb9Vdx5ev4KU+DjMrKwJ7dyTkDYldWfk0UOELVtYJt646d9hqFAAcHLXFq6dPUVqQjwGhoY4evrQsu/j2Drf/x5YF5mENsBm+BOYBAVg6GDP7U+/ImPz9upOVo00vF1TeoYGYWFiTFRcAj9uP0BMYmqF4dsEetO7cTC+TnYoDAyISUpl+aFThEff0IYx0NfjidaN6dogADtLM2KT01iw52iZ31F10ZjOLenfrD6WJsZciI3nm41hXEso+0xzV6iXK892b4OHnQ0mCkPi0zLYeCKCvw6e1Ibp3TiYtwZ2LxO35yc/kF9Q9jdnXVEVv4k+erIPDTxcysS9kZjCK7+vrops1BijOzanb5MQLEyMuXgrnu837+N6YkqF4Rt6ujCuS2s87JQYKwy5k5bB5lMXWHH4tDZMnyb16N4wCC8HG/T19Ii+nciCsHDOx9x+GFkSos6QDpVHwIVjR9i5fDE9nhqNu18gJ/fuZMWcmYx/bzpWtnZlwufl5LBs9gzc/YMY/b/3SY6PY9PCX1EYGdGyex8AUhMTWDl3Jg3bdKD/mEncvBzF9qV/YGZpSVCTFgDsW7eK8+EH6T1iDHYurlyNOMeaebMZ+fq7OHl4AZCbncWirz7F3S+AJyZPxdTCkrTEBMwtrR5eAVWB8P17WfrbPEZOfB7/evXZs3kj337yAR99Oxc7B8cy4dVqNQqFgq59+nP2xDGyszIrPPdH387F3MJS+9rSqnaXVUXaBPrQKsCb9cfOkZyZRft6fozo0Jwft+2v8CHU2syUJ9s15fS1WNYePYuHnQ29m9QjK09F5K14AOp7uNC1QRAbT5wjJjEFpbkZ/ZvVx1Bfn40nzuucz9XWmiY+7sSnZlR5fh+WI/vCWPzLT4x+bgqB9ULYuXkjMz+axvTvfyz32tQUX5vd+w3g9PGjZGeV3zDzw9dfkJyYyJgpL+Lk4kZ6agr5+flVnZ0aYVDLhjzWvAGzN+/lVkoaQ9s04f2hvXnh1xUVdnzUd3fm7I04Fu8/TmZuHh3r+fHm492Y9tdmLsQWXasdgn15umML5m7bT8TNeJysLZnSuz0KQwPmbt3/MLNYpY7t2MKJ3dvoOXIcNo7OHNmynlVzZvLMu59iZGJSbpy0pATW/PQt9Vu3p/fTE4i9Es3uZYswtbAkoHEzoKij2srODv/QphzcWP6PLVVeLnYubtRr0Yatf/5WZXl8WE7t3sbpsB10eeoZlA5OHN++kfU/fcvw/31YYVmmJyWy8ZfvCW7Rlu4jxhJ3NZp9K5dgYmGBX6OmABibmdOse1+Ujs7oGxhwPeIMe5b9gamFBV71GgIw5JW30ajV2vNmpaex4pvP8AttVvUZrwIXjh1hx7LF9Bxe9Ox0Yu9Ols2ZyYRp07Gu4Nnpr+9m4OEfxDP/e5+ku89Oxka0Kn52KihQYWphQete/Ti9P6zczy0sLOCv72ZgYmbO4xMmY2ljQ0ZKCoaGdeuRffu+vcz8eR5vPv88oSH1WblpI1M/+IClc+bi7Fj2XnSXSqXi3S+/pHH9+pw8d07nvRPnztK8USOeGz0KKwtLtobt4X/TP2Xu9Ol/q6OmtmsV4E0Lfy82nThPUkYW7YJ9ebJdM37eceA+z00mDG3bhLPXY1l//Bzudkp6hgaTk59P5K07AKw6choD/ZJVog309RnfrQ0Xi+9VdcHpPds4u3cHnYY9jdLRiRPbN7Hp5+8Y9sYHFdedyYls+XUOQS3b0mX4WG5fu8z+VUswMbfAt7juBDBUGPHUWx/pxL3bmQIQdzmKkLYdcfDwAg0c27qejT99x9A3pmFiZl41Ga6h9E1Nyb9ynYwtO3B6943qTk6NNbhVIx5v0YBvN+0lNjmNp9o14aNhfZj8ywpy8lXlxqnv4cyZ67f4c99xMnPy6FTfj7cHdef/lmwk4mbRd3lUh+Z0ru/PnC37iUlKpamPO28P6s7//lzPlTtJDzOLD9Xwdk0Z1qYxn6/ZSUxSCk93asFXTz/O6Nl/VlieOfkqVh05w5X4JHJVKhp6uvBq/y7kqlSsPXpOJ9zI7/7QiVuXO1Oq6jfRl2t3YqhvoI2jMNRn1jODOBh59aHkq7oMa9OYIa1C+Wr9bm4mpTKyQzM+HzmAcT8sqfDazM1XsfboWa7eSSKvoID67s683LcTeaoC1h8vausI9XIlLCKa8zG3yVMVMLhVIz4b3p/nfl7OrZS0h5lFIWq1R2oPFbVazcKFCxkxYgSjRo3i119/RV3843/8+PGsWrVKJ/zbb7/Njz/+qH09fvx4lixZwqxZsxg2bBhjx45l3759ZGZm8uWXXzJ06FAmTpzIiRMnHmq+KnNs11YatGlHaPvO2Lm40v3J0ZhbKTm5d1e54SOOHkKVn0/fp5/FwdWdoCYtaNWzL8d2btVOJzu1bzfm1jZ0f3I0di6uhLbvTP3W7Ti6Y4v2POfDD9KqR1/8GjZGae9Ik45d8a3fSCdM+PbNWFhb02/MRFy8fVHaO+AVHIKdy/1HhNV029evoW2XbnTs0RtXdw9GPPsc1jY27Nm6qdzwxiYmjH7uBTr17I2NXdmGmntZWltjbWOj/dM3MLhv+Nqqpb8XhyKvEnkrnoT0TNYfPYuRoSH1yxmdcldTXw8yc/PYdvoiSRlZnLp2k7PXb9E60Fsbxt1OSWxyKuduxJGWncv1hGTOXr+Fq621zrmMDQ0Z2KIRG46fJ1dV/gNLbbR17Wrade1O5569cfXwZPTE51Ha2LJr88ZywxubmDBm8ot07tUHWzv7csOcO3mCiNOneHXahzRo3BQHJyf8goKp17BRueHrmv5N67PqyBkOX7rOjcRUZm/ei6mRgo71/CqM89vuI6wOP0P07URup2aw7NAprsQn0SrASxsmyM2RqLg7hEVcJiE9k3Mxcew5H02gi8PDyNZDodFoOBm2gxbd+xDQuBn2rm70GjWO/LxcLh4/UmG8M/vDsLBW0uWJEdg6u9KwbUfqtWzD8V1btWGcvXzoOHAYwc1boTAyKvc8PvUb0W7AYAKaNNeZgVkbaTQazuzdSZOuvfBr1BQ7Fze6Dh+DKi+XSyfDK4x3/tBezK2s6TD4KWycXAhp3YHA5m04vadkVLB7QDA+DRtj4+SMtb0DjTp2w87Fjbgr0dowphaWmFlZa/9uXDyHkbFJre1QCd+5lYZt2tG4fWfsXVzp+eRoLO7z7HQ+vOjZqd8zz+Lg5k5w06Jnp6M7Sp6dlHYO9HhyFI3adKiwofTswf1kZWQw5PmX8fAPRGnngId/IC7evlWW1+qwZM0a+nfrxsBevfHx8OD1Sc9hZ2PDyv9n767Do7j2Bo5/IxuXjRF3D4Tg7i6lpVCgUKrUb5Xq297e2q3cCnVX2lKsuENwd7ckhJCECLFNNr6b7PvHwiYbIVAk2fT3eZ487cyeM3vmcHbmzBxb1XA96ZIvf/2VsKBgBvfuU++z5x5+hHsnTKBtRCT+Pj48OHkKUaGhbNm160adRovSNSyAXQkpnM64QK66hBX7j2NlaUGMn1ejcToG+1FcXsG6I6fJU5dwOOU8x1Iz6VbrXlSu0VJSUWn483NTorC04Mi58zfjtG44nU7H0a0biBs4nJD2nXD18mXAnfeiqSgn6eDeRuOd3LkVO2dneo+dhIunN9Hd+xDRpQdHNscbBzQzM7o22jkZ1zdHPfQUkV174erli6u3LwMn30d5iZrslDM34nRbtNJde8n7/heKN22DapnCpDG3dmnHgt1H2JmQQmpuAZ+u2NxkvfPH9btYsPsIiZk5ZKqKmLP9IGeycukRHmQIM6BtGAt3H2FfchrZF3u1709OY2y32JtwVs3njh5x/LltP1tOnuHshXzeWxSPnZWCIbERjcZJyMxhw7FEUnLyyVKpWXckgb1nUmkfUPcdho784lKjv9bsRj0TFZdXoiotM/xF+3pirbBk/bHEm3Fazeb2bu2Zu+Mg204lk5KTz4dLN2BrpWBQu/BG4yRm5bLpRBLncgvIUqlZfyyRfclptAuoeYfy/uL1LN13jDPZuaTnq/h81RZKKzV0DW2ds560dtU6XYv/a63+UQ0qmzdvxtzcnA8//JBHHnmEpUuXsnXr1qs6xtKlS4mIiODTTz+lT58+fPLJJ3z00Ud06dKFzz77jHbt2jFjxowW0zO7SqslKzWFoGjjnnnB0W05X+tFSG0ZyUn4hUYYvYAKjm5HcaGKwrxcfZizSQRHtzU+Zkw7ss6lUFWlvfjdGqMeWKDvpZV+JsGwnXj4AN5BoSz58Wu+fPFJfn33NQ5sijfpeQC1Gg3nziTRtkMno/1t4zpx5tSpaz7+f194luceuJuPXn+FU0ePXPPxWiKlvS0OttYkX8g17NNWV5OWW4Cfm7LReH6uziRnG/egOpOdi7eLE+YXX5am5RbgqXQ0NKA42doQ7tOGM1m5RvFGdYrh5Plszl1muLep0Wo0pJxJol3HOmWzQ0eSTv396cwO7N5JcFgEa5Ys4tkH7ualRx/kj++/pbys7FqT3OJ5Ojvi4mDH4Vovliq1VZxIzyLSt/Fe1g2xtVJQXF5h2D6Znk1QGzdDA4q7oz1dQwPYn5zW2CFMTlFeLqVFhQRE1dxPLK2s8A2NIPNsw/cogKyUMwREGt+DAqPbcSH1nOEe9E+jzs+lVF2Ef0SMYZ+lwgrvkHCyUpIbjZd9Lhm/WnEAAqJiyEk7R1UD0yDqdDrSE06hysnGO6ThBzqdTsfJ3TuI6Nyt0casluxS3Sn4KupO588m4R9mXHcKiTGuO12JhMMH8AsNY93cP/jipaf44c1X2Lp8Uasq1xqNhlNJSXSvcy/q3rETR082Xk/atncv2/bu4bmHH77i7yotK8PRweFvp9VUONvZ4mBjzdlavci11dWk5RXge5l6k6+r0igOQHJ2Ll7KmnpTXR2CfEnOzkVdVtHg56ZGnZ9LmboIv4howz5LhRVeweFkn2u8USP7XDJ+4dFG+/wjYshJP0d1rWtnlaaSP995lVn//T9W//wVuecvfw/XVFSg0+mwtrX7m2ckWjNPZ0dcHew4WGsarkptFcfTs4i+2nqntZVRvVNhaVFv9ESltopoP89rS3QL5u3ihJujvdH0UpXaKg6fy7hsJ766wrzcaefvZfQ8AGBlacmcZ+5h/vT7eG/KLYR5Ndw5rTW4kc9EdQ1pH8nBs+nkNTGlpSnzUjri5mhv9NxXqa3iaGrmZTtK1BXq6U6Mn36EWmMUFuZYWVpcNs+FEPW1rvkDmuDv78/UqVMB8PX1Ze3atRw+fJj+/ftf8TE6derE6NGjAZgyZQqLFy/G29ubQYP0a4dMmjSJdevWce7cOcLDG285vllKi9XoqquxdzTuDWXn5EzJqRMNxikpKsTRxXiuZPuLvalKigpRuntQUlRIYJTxyyx7R2eqq6soKy7GwVlJcHQs+zasxT8iClcPT86dPkHCof3odDVTgqhyL3Bwy3q6DBpOj+GjuZCeSvy8PwDoNKD+nKOmoFhdRHV1NU7OSqP9TkolJ44c+tvHVbq4MvWRxwkKi6BKq2Hnpo18/MarvPDWe0S0sqks7K2tASgpN26YLKmowMG24akXAOxtrCm5YNwAUlJRiYW5OXbWCorLKzmRnoWtlYJ7+uvnorcwN+fIufNsOFbT0NchyA8XBzuW7D16vU6pRVAX6cums1JptN9Z6cKJw4f+9nEvZGWRcPI4lgoFT7z0KqUlJfzxwzeo8vN44uVXry3RLZzS3hYAVYlx45GqpAxXhyt/GTKiQzRujvZsPlHzsnb76bM42trw9p2jMMMMSwtzNh1P4vct+65P4luAkiL9sHK7OtM82jk6UVyouky8Ivwj6seprq6ivLgY+zrX33+C0qIiAGzr5qWDEyWXycvSoqJ6LwVtHZyorq6mvKTYcP+vKCvjt7deplqrwczcnL7jJhMY3fC9Jz3hJOr8XKK71x9FYAou1Z3q9iRvsu6kdK0X/tJnSvcrG1mmyr3AudO5xHTtyYTHp6PKy2Hd3N/RVFQwaPydf+NsWh5VURFV1dW41rkXuSqV7G3kXpSbn897X37B+//3CvZ2V3Ztnb9iORfy8hg5cFDTgU2cg42+Ia+0wrjeVFpRiYONdaPx7G2sKL1QP46FuTm2VgpK6hzPxcGOAA9XFuw6dH0S3gKUqvXXTjsH42unraMjpZe5dpapi7ANN17v0dbRCd3Fa6edkzNKD0/6T7wbV28/NBXlHNu2kSVffcgdz/4b5wamWQXYsWQebj5+tAlsXaPSxPXh4tBwvbOwpAxXxyufIm5Ux2jcHOzYeLym3nnwbDq3dm3HsbRMMvILiQvypWdEUKONq63Bpbp6QYnxyJGCklLcHZtujJ8//T6c7WyxMDdj5ua9LN1XM310aq6KD5Zs4Ex2LrZWCu7oEceX08Yz7Zs5nM9vfdMq3chnotq8XZxo5+/Ne4viG/y8tagpm8b5qS+bTf/WZz11t6Fs/rF1HysONFx/BbhvQHfKKjXsTEi5pjQL8U/zj2pQCQoKMtp2dXWlsPDqbma1j2Fra4u1tbXRPuXFh8PGjrt69WrWrFnT4Ge1KcNiCOvQ5arSdll160E6HVdTN7o0YqR2nPqHNB5VMnjCFFbP+oWf33oFzMxQurchtmcfju7cZhTHKyCY/mMnAODpH0jBhWwObF5vsg0qBnUySKfTXdOUMl6+fnj5+hm2QyOjyc3JZs2ShSbfoNLW35tRnWp6SM/drp82r/44JbOGdtZhHOBSjl8qngHuLvSJDmX1wROczy/E1cGOoXFR9IsJY8uJJFwd7BjQLpzfN+9pxcMTjcuhDh1XdUGoQ6erxszMjEefexE7e30F7+6HH+ejN/5NoaoAZ6XLNaW2JekXHcIjQ2sWLX9noX5apLol5Wqys0d4IPf278qM5RvJKarpaRXj58WEnnH8EL+ThMwcvJVOPDCoO3f27sic7Qcvc8SW69TeXayfWzOX9G2PPAXQwLVRV+8eU1f9KLpGPmidEvbvZvNffxq2Rz/4L6CBezO6+jvrqve5rt5uK2trJj73KpqKCtITT7Fj6XwcXdzwi4iqG5kTu7bRxj8Qd1/TnjqgfrZcPi8bLZNXQafTYe/oxMip92Nubo5XYBDlJSWs/+tPBo6bZPJT0xlpoNyZNZLBr3/8EeNGjiQ2qn55a8iG7dv54udf+O+LL+J9mTVZTFWMnxcjOtY0hM7fcQi4+Hu/SvXjNF7GOgT5oi6rICnrykddtTSJB/awdUHNtXPEA4/r/6f+xbOBncbqlte6z0KeQSF41pquzzMolAWfvMOx7RvpPXZSvePtXPoXWSlnuPXx5zA3/0dNJCEa0T8mlMeH13ROeOuvRt4jmJld8T2nZ0QQ9w/szodLN5BTVLNu5w/xu3hiRB++nDYegMyCIuKPJlx26itTMyQ2gufGDDBsvzxrOVA/6/S/7abz88mfF2BrZUWMnyePDO1FZkER646cBuBEehYn0msW+T6elsWPj97JuO7t+WLV1c2S0hLdzGei2oa2jyS/uLRVjdgHGNQunKdH1XT0/vecS9Nx13m3YWZ2RXf6535bjI1CQbSfJ9MG9dBP/3U0oV64sV1jGdUphpdnLaO0kXVZRMvWal9ZmYB/VINKQwt6XlpDpaEH1IamurCos16FmZmZ0b5Lx6mutTBrbSNGjGDEiBFNpnXWdXpZZufgiJm5uaEX8CWl6iLs6oxaucTeybnB8IAhToNhioswN7fA9uLUCnaOTox79Gm0mkrKSkpwcFayefF8nN1rhro6OCvrrZfi5uWNusB0F75zcHTC3NycIpXKaL+6sLDeqJVrFRIeyZ5tW67rMZtDYuYFfoyvKU8W5vrfkYONFeqycsN+e2srSioaH4paUl6BfZ2emHbWVlRVVxsWbuvfNpzjaZkcStEPR84pKkZhYcHozm3ZevIMfm5K7K2teHhIL8MxzM3NCXB3oVOwHx8siafKROd1dnTSl81CVYHR/iKVqt6olauhdHHFxdXN0JgC4O2nf5Gal5PTqhpU9iSlkpCZY9hWXLz+u9jbGg07d7azRVXa9JRnPcIDeXpUfz5ftcVougGAKX06se1kMvEXK7+puQVYKyx5fHhv5u04ZJINfiGxHfAKCjZsV2n10xjVHRlZqlZj5+RUL/4l9k5OlFwckWGIU6zG3NwCG/t/xiK+QW3j8Aysn5el6iIcauVlWbG63qiV2uycnAyjW2rHMTc3x9q+pnemmbk5zu76F9Puvv4UZGdxYP2qeg0qpeoiUo4fpu840x1Ncbm6k73T1dedGovTEAdnJebmFkYvU928vNFUVlJWrK43mssUKZ2csDA3J79AZbQ/X1VYb9TKJfuOHOHgsWP8NHs2oH+9UF1dTa/bbuWFxx7n9lp16w3bt/PGjBm8Pv1Z+nXvfoPOonklZeXw84aa8mZ5sbzYW1sbTcVlZ21Vb5RJbSXllQ3UmxRG9aZLzM3MaBfgw+GUdJOemjcwpj1tAoIM20bXTmXda6djo8exdXSiVG38my8vVmNmbo6NfcM9283NzfHwC6Qo90K9z3Ysnc+ZQ/sY8+izOLm1nrXSxLXZk5RKQsYiw7alpf63rrS3Jdeo3mlTb2RAQ3pGBDH9lgF8smITe5JSjT4rKivn3UXxKCwscLS1Jr+4lHv7dyW7UH2dzqb5bT991rDQOdTU410d7Iwal5T2tuQXN52fWSp93py9kIergx33DehmaFCpq1qn43TGBfxclddwBi3HzXwmusTS3JyBbcNYdyTBJJ+DLmdnQgqnGiibLvZ2Ro1LSjvbK/qtXyqbKTn5uNjbcne/LvUaVMZ2jeW+Ad14dc4KTmfUvy8JIS5Pur5c5OzsTH5+zVRBlZWVpKenXyaGabCwtMQrIIiUk8eN9qecOo5vSFiDcXxCwkg/k4BWU2kU3sFZifPFRal9gsNIqTPtRcrJ43gFBmFhYdxwZamwwlHpQnV1FQmH9hHevmbObN+QcAqys4zC51/IwsnVdOcXtVQoCAwN48Rh40axE4cPEnqFPSuvVNrZZJR1pmczRZXaKgpKSg1/ueoSissqCG7jZghjYW6Ov7sL6XmqRo+Tnl9oFAcgxNONzIIiQ6VLYWFe70WA/jN9I87pjAt8v247P67fafjLyC/U9ypav9NkG1NAXzaDQsM4fsi4bB4/fJCwqOhGYjUtPDoGVX6+0Zop2Rn6Biv3Rqa0MFXlGi1ZKrXhLy1PRUFxKXGBNQ3DCgsLon09OX3+8hXTXpHBPD2qP1+s3trgEGtrS8t6DwvVusZ7cJsCKxsblB6ehj9XLx/9Auana+4nWo2GjDOJeAc3fI8C8AoKJS3B+B6UevoEbQIC692DWisrGxuc3dsY/lw8vbFzdCItoWY9JK1GQ2ZyEl6XWdDcMzCE9ETjdSvSEk7i4R9YrxOJEZ3O8CKyttN7d2JhaUlYh65Xf1ItxKW609lTxnWns5epO/kGh5GWZFx3OnvSuO50JfxCwinIyUZXq2NO/oUsFFZW2Do0/nLXlCgUCqLCwthd516059BBYqMbrif9+eWX/P7554a/h6fchbWVNb9//jmD+9T03o7fupU3ZnzMf555psGF61uLSm0VqpIyw1+uuoTi8gqC29TUCS3MzfF3c+H8ZepN5/NVBHkY1yOD27iRpSqqd/+J8GmDnZWCwymNz8NuChq6dto6OnG+zrUz62wSnoGNL6TsGRjC+STja2d64ik8/AIxb+TaqdPpyM9Mx7ZOI+uOJfM4c3AvtzzyDMo2Vz43vmj9yio1ZKqKDH9puSryi0vpGORrCKOwsKCtnxcnm6h39o4KZvotA/h05WZ2nE5pNJymqor84lIszM3oFRnE7sRz1+t0ml1ZpYbz+YWGv5ScfPLUJXSptRi3laUF7QN9OJ6WeVXHNjMzw8ryMvUm9M+lecWtY92Pm/lMdEn38EAcbW0aHGlh6soqNWQUFBn+zuUWkKcuoVNITdlUWFjQLsDbaOTTlTAzMzM00Fwyvnt77h/YndfmruR42tUdTwih989463AF2rdvT3x8PN27d8fJyYl58+ahbeBFgSnqMmg4K2Z+j3dQCH4h4RzaupHiQhUd+g4EYPPi+WSeS+bOp18CIKZrD3asXMzK336k58hbKcjOYvfaFfQadZthBE6HvgM5uDme9fNn0aHPQNKTEzm2axtjHnjU8L0ZZ89QrCqgjX8AalUB21csRleto9vQkbXSNoxZH73DzlVLiercnez0c+zfGE+/28bfxBy6/oaOGctPn88gKCyCsOgYNq9ZiaognwHDRgGw4I9fOZuYwPNvvmuIk5GWilarpbioiIryclLP6hcRDgjWvwhbt2wJ7m3a4OMfgFarZdeWjRzcs4vHXnzl5p/gTbAn6Ry9o0LIU5eQX1xK76gQKrVao8rtmC76qc6W7TsGwIHkNLqE+jO0fRQHzqbh76akfaAvi3YfMcRJzMyhe3gQmQVFhim/+rcNJykrB51OR4VGS46m2CgtmqoqyjUao55Lpmr4bbfz/acfExIeQXh0DBtXr0SVn8/AEfqyOf+3X0hOTOClt98zxDmfmopWq0FdVERFWRnnkvWLtAaG6F809Og3gKVzZ/Pj559w++S7KC0pZtaP39GlVx+crmHki6lYfuA447vHkZ5fSGZBIXf06EC5RsuWkzWL2T41sh8An6/SjyjrffHBYebmPZxIy0Jpp593WFtdRfHFtYP2JacxpnNbkrJySczST/k1uXcn9iWntZpeWWZmZnTsP4S9a1fg6umF0sOLPWuXo7C2JqpzTc/yNb//BMDwu6cB0L5Pfw5v3cCmBXNo37sfGclJnNi9nZH31ixWXaXVkpelf/Gn1WgoVRdxIT0VK2trlB76BVYrK8pR5egf8nQ6Her8fC6kp2JjZ4+Tq3HjbEtnZmZG+36D2R+/Cpc2Xjh7tOFA/CoU1taEd+xmCLf+z18AGDzlfgDa9uzHse2b2LZ4Hm179iXz7BlO793JkKnTDHH2x6/EMyAYJzd3qrRazp08RsL+XfS53XgUin4x+m2EdeiClU3j612Zgm6Dh7Ps1+/xCQzBNzScgxfrTh0v1p02LZ5PZkoyk5+5WHfq1oPtKxezYuaP9Bp5K/kXsti1dgW9R99mNAo6O03/YqqivAwzMzOy085hYWmJu7f+5VjHfgPZvzmedfNn0bn/EArzc9m2fDEd+w1qVdN9TR47ljdmzKBteATtY2JYuGolufn5jBupvxd9NfNXTiQk8NU7+npSaGCQUfyTiUmYm5sZ7V+7ZTNvzJjBUw88QMd27cgr0I/GtLS0xPkyIw1ai71JqfSKDCavuJR8dQm9okIMCwJfcktn/fqHy/frGwsPnk2nU0gAg2MjOJRyHl9XJbGBPixtYA25DkG+pOTkU3gFPY1NiZmZGbF9B3Fw/WqUda6dYR1rGoY3zv4VgIGT7wMgumdfjm/fxI4l84ju0ZfslDMk7NvJoCkPGOLsX7ucNoEhOLt7UFlezrHtG8nLPE+fcVMMYbYtnE3igT0Mu+8RrG3tKL040k1hbY3C2rSvo1fLzNYGhe/Fl7HmZig822AVFkK1Wo02O+fykf9Blu47xsSeHUi/2CgwsVcHyio1RvXOZ0brpw76dMVmAPpGh/Ds6AH8snE3x9OyDOtdaKuqDQtRR3h74OZoT3J2Hm6Odkzu3QkzMzMW1nqOao3+2nWYqf26kJpbQHqeirv7daGsUmMYIQ7wf7frpyK/tG7H7d3ak6UqIjVXf5+JC/RhUq+ORutv3tu/KyfSs0nPV2FvbcW47u0J9XTjkxWbbt7J3WQ36pnokiHtIzl6LqNVjZq6nEV7jjC5T2fScgs4n1/IlD6dKK/UsOFYoiHMC7fq14n7cOkGAG7r0s7QwAXQPtCbO3p0MLwvAZjQowP3DezG/xavJz1PhcvF60GFtqreWmyi5Wst7wVMkTSoXDRhwgQuXLjAf//7X2xsbJg4caLRiBVTFt2lO+UlxexctZSSokLcvX254/Hphh6TJUUqwwslAGtbOyY++QLr5v7Ob++/gY2dPV0Hj6Dr4JrpFJTuHox/fDobFszm0NaNODgrGTzhLiJrPXhoNRq2LluIKvcCVtY2hLRtz+h7H8bGrta0QEEh3P7Ik2xZuoAdq5bi5OpG3zHj6Nhv8E3ImRunW59+lKjVrPhrLoUF+fgEBPL0q2/gdnEe78KCAnKyjHsCfPbfN8ir9e/w1nP6tQV+XKif21Wr1TBv5s+o8vNQWFnh6x/AU6++TvvOptsL+HJ2JpzF0sKcER1isLGy5Hx+IbO37adSWzMVn/PFCtclhaVlzN1+gKHto+gU4k9xeTlrD53kdEbN8Nltp/QNVf3bhuFoa0NZRSWJmTlsOp7IP0H3vv0pVqtZOn8Ohfn5+AYGMf0/b+LeRv+CWVVQwIUs4x5ZM97+D3kXasrm688+CcCvS1YCYGNrywtvvcsfP3zDm889g52DA52692DCPfffpLNqXov2HMXK0pKHB/fE3saKxMwc3vprNeWamkZ5dyfjaaiGd4jC0sKcaYN6MG1QD8P+Y2mZ/GfuKgDm7zyETqdjcp9OuDnYoy4rZ19yGrO27r85J3aTdBkyAq2mkg3z/6SitASvwBBuf3y60Qv5ojrTQDq7eTD2kafZvGguR7dtwt5ZyYDxkwnv0NkQprhQxZ8fvGXYPpq7maPbN+MbFsGEp14EIDs1hQVffGQIs2vVEnatWkJ0t14Mn1rzUsxUdBg4DK2mkq0LZ1NRVkqbgGBuefgpo7wsVhnXbZzc3Bn94BNsXzKf4zu2YO/sTJ+xkwitNZpUU1HBlgV/UqxSYalQoGzjxaDJ9xPeyfj+k3EmgcLcHAbfZXp5V1d0l+6UlRSzvVbdacK/aupOxYUqCmrds21s7Zj01AusnfM7v16sO3UbPIJug42nef3l3deNtpOOHsLJ1Y3H3/kYACdXNyY9+TwbFszhl3f/g72TM7G9+tJ75K03+IxvrqF9+1FYpOaXeXPJzc8nJDCQT15/w7DeSV5+Aeezrq7H5KJVq6iqquKTH37gkx9+MOzv1K4d37z3/nVNf0u0OzEFhYU5w+KisFFYklFQxNztxvUmJ1vjF/SFpeXM33GQwe0j6BjsT3F5BesOn6439YeznS2BHq5GLwtbk7gBw9BqNGxbNIfKi9fOUQ89eflrp6s7I6b9i53L/uLEzq3YOznT67aJhNS6dlaUl7H1r1mUqouwsrHB3defWx97zmjKsRM79S8VV3z3mdHxOw0dTZdht9yAs225bKIi8PviQ8O224P34PbgPRStXEv2ux83Y8paloW7j2BtackjQ3vhYGNFQkYOr89bbTRNn4eT8bRzIzpEY2lhzkNDevLQkJ6G/UdTM3l1tn6tBoWlBXf17YyX0pHySi37ktP4ZMXmy04b2BrM3n4Aa4Ulz4zqj6OtNSfSs3nh9yVG+enpbNwob2FuxsNDeuKldKKqupqMgkK+j9/B0lovrR1srHluzABcHewpqaggMTOXp35ZxKkmRmuYshv1TAT6f4PYAG9mLN90w8+jpZi38xDWCkueGNEXR1trTp2/wP/9udyobLZxNv6tm5ubM21wD7ycHS+WzSJ+3rDL0JECYEyXtigsLPj3+GFGcdcePsVHyzbe2JMSohUxKysrk+asFuh6raEi9CK8THcKsZZmy8UGCXF9DGrb+LRG4up9uHxzcyehVRkaG97cSWg1KjStY9RrS+Fs98/quX2j3e4n9aTr5dvjKc2dhFal7jQl4trc+sH/mjsJrcZzYyY0dxJalaJa62aKa+fqYNfcSWhVLo3kEtdu6fP/jE6XN8vcnYebOwlNmtQzrrmTcEPICBUhhBBCCCGEEEIIIYQQwkTokDESzUUWpRdCCCGEEEIIIYQQQgghhGiCNKgIIYQQQgghhBBCCCGEEEI0QRpUhBBCCCGEEEIIIYQQQgghmiBrqAghhBBCCCGEEEIIIYQQJkKnkzVUmouMUBFCCCGEEEIIIYQQQgghhGiCNKgIIYQQQgghhBBCCCGEEEI0Qab8EkIIIYQQQgghhBBCCCFMRLXM+NVsZISKEEIIIYQQQgghhBBCCCFEE6RBRQghhBBCCCGEEEIIIYQQogky5ZcQQgghhBBCCCGEEEIIYSJ0utY/59eKFStYuHAhBQUFBAQE8NBDD9G2bdtGw6ekpPDtt9+SmJiIg4MDI0aM4M4778TMzOy6pktGqAghhBBCCCGEEEIIIYQQokXYunUrP/zwAxMnTuSzzz4jOjqaN954gwsXLjQYvrS0lNdeew2lUsmMGTN4+OGHWbRoEYsXL77uaZMGFSGEEEIIIYQQQgghhBBCtAiLFy9m8ODBDB8+HH9/fx555BFcXFxYtWpVg+E3bdpERUUFzz77LIGBgfTu3Zvx48ezePHi6z6aR6b8EkIIIYQQQgghhBBCCCFMhClM+bV69WrWrFnTZLjhw4czYsQIw7ZGoyEpKYnbb7/dKFzHjh05efJkg8c4deoUbdu2xdra2ij8H3/8QXZ2Nl5eXn/zLOqTBhUhhBBCCCGEEEIIIYQQQlw3I0aMMGoouVJFRUVUV1ejVCqN9iuVSg4fPtxgnIKCAtzd3euFB1CpVNe1QUWm/BJCCCGEEEIIIYQQQgghRItxtYvJX+/F5xsjI1SEEEIIIYQQQgghhBBCCBNRbQJTfv1dTk5OmJubU1BQYLRfpVLVG7VyiYuLS4PhgUbj/F0yQkUIIYQQQgghhBBCCCGEEM1OoVAQFhbGoUOHjPYfOnSI6OjoBuNERUVx/PhxKisrjcK7urri6el5XdMnDSpCCCGEEEIIIYQQQgghhGgRxo4dy/r161mzZg1paWl8//335OfnM3LkSABmzpzJq6++agjfv39/rK2t+fTTTzl37hw7duzgr7/+YuzYsdd9KjCZ8quFyi5UN3cSWpUT6dnNnYRWI8LbvelA4oot3ne8uZPQqjw1vHdzJ6FVScrOa+4ktBo2CqlyXU/lGm1zJ6FVWXI+v7mT0Go42Fg3dxJalbJaPQzFtXtuzITmTkKr8fGy+c2dhFbl1CcfNXcSWpXq6urmTkKrYm4ufdFFy9Sap/wC6Nu3L0VFRcybN4/8/HwCAwN5/fXXadOmDQD5+flkZWUZwtvb2/P222/z7bff8uyzz+Lg4MDtt9/O2LFjr3va5OleCCGEEEIIIYQQQgghhBAtxujRoxk9enSDnz377LP19gUFBfH+++/f6GTJlF9CCCGEEEIIIYQQQgghhBBNkQYVIYQQQgghhBBCCCGEEEKIJsiUX0IIIYQQQgghhBBCCCGEidC18jVUWjIZoSKEEEIIIYQQQgghhBBCCNEEaVARQgghhBBCCCGEEEIIIYRogkz5JYQQQgghhBBCCCGEEEKYiGqZ8avZyAgVIYQQQgghhBBCCCGEEEKIJkiDihBCCCGEEEIIIYQQQgghRBNkyi8hhBBCCCGEEEIIIYQQwkTodDLnV3ORESpCCCGEEEIIIYQQQgghhBBNkAYVIYQQQgghhBBCCCGEEEKIJsiUX0IIIYQQQgghhBBCCCGEiZApv5qPjFARQgghhBBCCCGEEEIIIYRogjSoCCGEEEIIIYQQQgghhBBCNEGm/BJCCCGEEEIIIYQQQgghTES1TPnVbGSEihBCCCGEEEIIIYQQQgghRBOkQUUIIYQQQgghhBBCCCGEEKIJ0qAihBBCCCGEEEIIIYQQQgjRhJvSoJKdnc2YMWNITEy8puNs376dMWPGXKdU3VgTJkwgPj6+uZMhhBBCCCGEEEIIIYQQohXR6Vr+X2t1Uxald3d357fffsPJyelmfJ24Cn2jQ+kQ5IuNlYKM/ELWHDpJrrrksnEC3F0YHBuJh5M96vIKdiWkcPBsuuHzu/p2IdDDtV68nKJifojfYfjevtGhRp8Xl1fw+crN1+GsWo5hcVH0iAjEzsqKc7kFLNx9mGyVutHwjrbW3NqlHX5uStwdHdifnMac7QeMwnQNDeDOPp3qxX3p96Voq6uv+zncDDqdjp2rlnJ0+2bKy0rxDgxh0MS7cPf2vWy8tMTTbF40l7zM8zg4K+kyZCRxfQYYhUk4tI8dKxZTmJuDs7sHvW8ZR3hcTf5VV1ezc+USTu7dRUmRCnsnJdFdu9Nz5G2YW1gAMOPJaQ1+f1zfgQyeOPXaTr4ZDWkfSfewQGytFKTmFbBkz1GyCy9fPkd3aouvqzPujg4cOJvG/J2HGg0fF+TLlD6dOZmexa+b9tyAM2gZNq1eydqlCyksKMDHP4CJ9z1IeEzbBsNqKiuZ9f3XpCafIfN8OmGR0Tz31ruNHjvp5Ak+fv0VvHz9eP2TL2/UKTSbg1s2sDd+FcWFKty9fRl0xxT8wiIaDZ9zPo34ebPIOpeMjZ09cX0G0HPkrZiZmRnCpCWeYuOCOeRmnsfB2YVuQ0fSoe/ABo93ct8ulv/yHSHt4hj/2DPX+/RuugOb17N7XU1+DpkwBf/wyEbDXzifxrq5f5CZos/PDn0H0ntUTX4WF6rY8NccstJSKLiQTdvuvbjl3oeMjnFo2yaO7dpBbuZ5dLpqPP0D6TtmHP6X+Xc0FTqdjl2rlnJsxxbKy0rxCgxm0IS7cGvi3pSeeJoti+aSl5WBvbOSLoNH0L7WvSkv8zw7Vy7lQvo5ivJy6T5iDD1H3WZ0jD1rV3LmyAEKsrOwsLTEKyiE3mPG4+5z+e9uqfZvimfXupUUFxbi4ePLkAl3EdBE2Vwz57eLZdOBjv0G0mfUbYayeergXg5u2UhW2jmqNBrcvX3oNfJWIuKM60cVZWVsXvoXpw7spaykGCcXV/rfNoGYLt1v6PneaDqdjj2rl3F858WyGRBM/zumNFk2zyedZuvieeRfLJudBg0ntvcAw+fHdm7h1N6d5GdloKvW4eEXQI9Rt+ETEm4Ic2TrRo7t2ExRfh4Abl4+dBk2muC27W/Iud4svSJDiAvyxVphSWZBEfFHTpHXxDORn5uSge0icHe0p7i8gj1J5zicct4ojJWlBX2iQ4n08cRGoUBdVs7Wk0mczrgAQPfwIMK9PXB1sKequpqMgkK2nkhq8nnM1Ezu3YlhcZE42FiTkJnDt+u2k5arajR8z4ggRnSIIsTTDYWFBWl5KubvPMSepFRDGAtzM+7o0YFB7cJxc7TjfH4hMzft5UCt59J/Kpu4drhMvgObyHAsPdzJeucj1KvWNXeyWpzt61azaeVS1KoCPH39uW3qfYRExTQaPjPtHItm/kTqmSTsHBzoMWgoQ8feYVQP3b5uFdvXrSY/JwcXN3cG3zaOLn0H3ISzufl2xK9h88qlqAtVePr6cetd9xEcGd1o+My0VBb/9hNpyfr86z5wKENuG2+Ufwd3bGPTyiXkZmVibWtLeNtYbrnzHhyVSgB2b4xn//YtZJ9PQ6fT4RMYzPBxkwiOjLrRp3tDNUdZPLx7BxuXLyY3O4uqqio8PL3pO+IWuvYbUP8LhRD13JQGFQsLC1xcXG7GV4mr0CMiiG5hgSzff4z84lL6RIUwuU9nvlu3nUptVYNxnO1smdirE0fOnWfpvqP4uykZ3iGa0opKw4PBgl2HsDCvGfxkYW7OQ0N6cTI9y+hYeeoS/tiy17Cta2VNlwPbhdO/bShzth0kp0jN0LgoHhnai/8tWk+FVttgHEtzC0oqKtlwNJEeEYGNHrtCo+W9hcaVYlNtTAHYG7+K/RvWMHzqA7i28WLX6mUs+PJj7n/tHaxsbBuMU5ibw6JvP6Vdjz6MvOdBzp9JZMO8Wdg6OBDRoQsAGWeTWPHLd/QadRthcZ1IOnyA5T9/w53P/h/eQSH67163ikNbNzBi6jTcffzIzUhn9e8/YWGpoMcI/Yi4R96ZYfTd2akpLP7ucyI6dr2BuXJj9Y8Jo190KPN2HCSnqJgh7SN4cHBPPly6vtHfv6W5OaUVlWw6nkT38MbLJ4Crgx2jO8aQnJ13I5LfYuzdvpW5v/zAlAcfJSw6hk1rVvLFu2/yxidf4erhUS98dXU1CoUVA0aO5tiB/ZSVNP7CpKS4mF+++ISo2DhU+a0vH0/t382G+X8y5M678QsN5+CWDfz11QweeO0dnFzd6oWvKCtj3hcf4R8WydQX/0N+dharfv8JhZU1XYeMAECVm8OCrz+hXc++jL7vYdLPJBI/53dsHRyJ7NjF6Hiq3AtsWjQPv1DTf/EPcHLfbuLn/cmwyXfjFxrBgS3rmffVDB78z7s4N5Kfcz//EP+wSO596XXysjNZ+dtPKKyt6D5kJABarQZbBwd6DB/N4W0Nd3hITThFdJdu+IaGo1BYsXfDGuZ98RH3v/oWrm28bug532j74ldzYONaht31AC5tvNi9ehkLv5rBvf9+BysbmwbjFOblsPi7z2jbow8j7nmQ88lJbJw3C1sHR8I7dAb0DatObm6ExXVix4pFDR4nPek07fsMwDMgGNCxc+USFn71Mfe88hY29g436pRviBP7drFu3iyGT74H/7AI9m9ez9wvP+Lh19/D2dW9XviKsjJmf/YB/mGR3Pfym+RnZ7J85g9YWVnTfai+bKYmnCYwMpr+t47Hxt6B43t2sODbz7hr+iuGhpqqKi2zP/8AGzt7bn/oXzgqXVGr8rGwVNzU878RDqxfzcFNaxky5X5c2nixZ80ylnzzCVNf+e9ly+bS7z8npntvhk19kIyziWye/ye2Do6ExenL5vmk04R37IpPcBiWCisObV7Hkm8/ZfIL/0Hp4QmAg9KFXmPGo/TwRKer5tTenaz86WsmPf9v3H38bloeXE/dwgLpGhbAqgMnyC8uoWdkCBN7deLH9TvQNPpMZMP4Hh05lprBiv3H8HNTMqR9FGUVGhIy9c9E5mZmTOjZiXKNhqV7j6Auq8DR1pqq6ppnHn93Fw6lpJNVUARAn+hQJvbqxM8bdlKuafh5wdSM696e27q247OVWzifX8idvTvy1sSRPP7jX5RVahqM09bfiyPnMvhj636Kyyro3zaU/7t9CK/OXsGJ9GwApvbtwoC2YXy1ehtpeSo6Bfvxf7cP4aU/lpF8ofXVm66Gua0tlcnnUK+Ox/PfLzR3clqkQ7u2s+SPXxh334MER0SzI34NP374Li/87xNc3OvX4ctLS/n+/bcJjozm6bfeJyczg7nff4mVtTUDRt0K6BsYVsyZxYRpjxIQFk7qmUT++ulbbO0daNupS71jmrJDu3awdNav3H7PNIIioti5fi0/ffQuz733CS7u9e/t5WWl/PDB24RERvPUm+/p8++Hr7Gytqb/SP0zd0rCKeZ89wWjJ99Nu87dUBeqWDTzJ2Z/+zkPv/wfAM6cOkFc914EhUeisLZm6+rl/PjhOzzz3w/w8PK+qXlwvTRXWbRzcGTIbXfQxscXcwsLTh7cz/wfv8bByYnoDvU78AohjP3tKb/27dvHxIkTqarSVzIzMjIYM2YMX3/9tSHMb7/9xmuvvVZvyq+jR48yZswYDh8+zHPPPcf48eN59tlnSUpKMvqODRs28MADDzB+/HjefPNNVCqV0ec5OTn897//ZfLkyYwfP55HH32ULVu2ADXTjG3atIkXX3yRcePG8eijj3LggHFv/9TUVN58800mTpzI1KlT+fDDDykoKDAKEx8fz+OPP864ceN45JFHWLx4MdW1Xl5nZGTwf//3f4bv2LPHNHpidwsLZGfCWU5nXCCnqJhl+45hZWlJW//Gb0Sdgv0oLi9n7WF9r61DKec5mppB9/AgQ5hyjZaSikrDn7+7EoWlBYfPGffYqq7WGYUrbaRCbar6RYey4WgiR1MzyFKpmb1tP9YKSzqGNP6wWVBSyuI9R9l7JpXSisvnh7q8wujPVOl0Og5uiqfb0FFEdOiCu48fw6dOo7KinFP7djca7/D2TTg4K/W9hb18aN+7PzHde7F//RpDmAMb4/EPj6L78Ftw8/Kh+/Bb8A+L5MDGmsaojLNJhLbrQGhsB5zd3AmN1f9/ZkqyIYy9k7PRX9KRg7i08bxsz++Wrk90CBuPJ3IsLZPsQjVzdxzUl8/gy5XPMpbuO8b+5DRKKyobDWduZsbkPp1ZffgU+cWtq4dlXfHLltBrwGD6Dh2Ot58/k6c9grPShc1rVzYY3trGhrseeZx+Q0fg4lb/JXdtv339OT0GDCIkwnTL2eXsW7+Wdj16E9e7P25ePgyZOBV7Z2cObd3QYPgTe3ei1VQy8p4H8fDxI7JjF7oPHcm+DWsMDfKHt23E3lnJkIlTcfPyIa53f9r26MXe9auNjlVVpWX5z9/Rd8w4nBt4UDFFe9avIbZnbzr0GYC7tw/DJt2Ng5OSg1sazs/je3aiqaxk9L0P4eHrR1SnrnQfNoq98TX5qXTzYOikqbTv2RcbO/sGj3PrA4/SecAQvPwDcfPyZvjke7GysSH5+NEbdq43g06n4+DmeLoOGUl4h864+/gyfOoD+nvT/sbvTUe2bcbBWcnAO6bg6uVDbK9+RHfryf4NNfcmr8Bg+o2dSFSX7iisrBo8zrjHn6Vtjz64+/jq74t3T6OsWE1GclKD4VuyPfGrad+zDx37DsTd25fhd96Dg5OSA5sbLpvH9uxAU1nBmPseps3Fstlj+Gh2x682lM1hk6bSa8QYfIJDcW3jSd9bbscrIJiEw/sNxzmyYyul6iImPPYM/mGRKN098A+LxOdihwpTpdPpOLRlPZ0HjyQsrjNu3r4MnaIvmwmXKZvHtm/G3klJ//FTcPXypl3PfkR168nBDWsNYYbf/RBxfQfh4ReAi6cXAyZMxcrahnMnjxnChMR2ICgmFqVHG1zaeNFz9O0obKzJOnvmhp73jdQ5NIDdiSkkZF4gV13CqgPHsbK0IMa38UbhuCA/SsorWH/0NPnFpRw5l8HxtEy6hgUYwrQL8MHOWsGi3Yc5n19IUVk55/MLyVIVGcL8tfMgx1IzyVWXkKsuYcX+49haW+HrqryRp3xT3dqlHQt2H2FnQgqpuQV8umIztlYK+tWZraC2H9fvYsHuIyRm5pCpKmLO9oOcycqlR61nzgFtw1i4+wj7ktPILlSz6tBJ9ienMbZb7E04q5atdNde8r7/heJN26C6dXVavF42r1pG174D6DFwKJ6+ftx+7zSclEp2rl/bYPgDO7ZSWVHB5EefwNs/gPbdejDwlrFsWbXccG/av30z3QcOpmOvPri18aRjzz50HziUjcsX38Qzuzm2rl5Olz796T5wCJ6+foy95wEclS7s2tBw/h3csQ1NRSWTHn4CL78AYrv2YODo29i6uib/ziUl4OzqRr8Rt+Dq0YbAsAh6Dx1B6pmapQOmPPYUvYeOwDcomDbePoy77yGsbW1IOHLoZpz2DdFcZTG8bSztunSjjY8v7p5e9B0xGm//QM6ePnkzTltcJ9U6XYv/a63+doNK27ZtqaysNGokcXJy4siRI4Ywx44do127do0eY+bMmdx77718+umnODo68vHHHxsuAKdPn+bTTz9l+PDhfP7553Tr1o1Zs2YZxf/mm2+oqKjg3Xff5auvvuKhhx7C3t74gf/XX39lzJgxfPbZZ3Ts2JF33nmHvDx9j5X8/HxefvllAgMD+fjjj3n77bcpKyvj7bffNjSYrFmzht9++4277rqLr7/+mmnTprFgwQJWrtS/LKuurubdd99Fp9Px4Ycf8tRTTzF79mw0mpbdOKC0s8XBxpqztXrvaKurScsruGwF3tdNydk6Pc6Ts/PwdnHCvNbwwto6BPlxJisXdZnxS3+lvS1PjuzH48P7MrZrLEq7hkcimCJXBzuc7GxIuDhqB0BbVU1ydh5BDUyHdrUUFha8On4Yr90xnGmDeuDr6nzNx2wuhXm5lBQVEhhVM0WSwsoKv9AIMi7zcJ559oxRHICg6LZkp56jqkrfoy8zpX6YwOh2ZJyteSHlGxJOWuIp8rMyAcjLzCA14STBMQ0/jFWWl3H6wB5ie/W7uhNtQVwd7HCytSExM8ewT1tVTfKFPALdr718Du8QTUFxKQeS0675WC2ZVqMhNTmJmLgORvuj4zpy5vSpazr2ptUrKVKpGD1+4jUdp6Wq0mrJSkshKNq4jhAU3Y7zyQ3/7jPOnsEvNMLoBXRQTDuKC1UU5uXqwySfqXfM4OhYss+lGK4LAFuXLsTJzY12Pfpcr1NqVlVaLVmpKQTXO/e2nG/kBfz5s0n4hxnnZ0id/Py7adFqNI02wJiKorxcSosKCah1D7G0ssI3NILMs403amSlnCEgsv5950Kte9PfoSkvR6fTYW1i+Vql1ZKZmlLvnhoc04705IbXVjyfnIR/WGSdshlLcWHBZctmZUWZUblLOLQfv9Bw1sz9nc9efJLv3niZLcsWXtO/Q0tQUzZrpgKxtLLCJzSCzJTG601ZKclGcQACotpyIa3xslldpf89N1buqqurSTiwB01FBV7Bjb8cb8mcLz4TpVzIN+zTVleTlqvC5zL1ax8XZ1JyjJ+Jzl7Iw1NZ80wU7u3B+fxChrSP5PHhfbl/UA96RYY0+swE+inCzM3MKG/hz5JXytPZEVcHO6PpoSu1VRxPzyLat81VHcvW2oriWp3IFJYW9UZVV2qriPbzvLZEi1ZPq9Vw/mwyEbFxRvsjYuNISTzdYJxzSacJjoxGYWVdK3wHigryyc/RP/NrNVoUCuOOEgorK9LOJFHVyAwVpkir1XI+pYH8a9f+MvmXQHBklNG9PSI2jqKCAgpy9c+kQeFRqFUFnDi4D51OR4m6iMO7dhAV17HRtFyqd9ram1b96JKWUhZ1Oh2Jx45wISuD4KjGp20TQtT42w0qtra2hIaGcvSovgfi0aNHueWWW8jJySE/P5/y8nISExOJjW28h8jUqVNp3749/v7+3HnnnaSnpxsaO5YuXUpcXByTJk3C19eXkSNH0qNHD6P4OTk5xMTEEBwcjJeXF507d6Zz585GYUaOHEnfvn3x9/fnoYcewt3d3dAYsnLlSoKDg7nvvvvw9/cnODiY6dOnk5iYaBgtM2fOHO677z569+6Nl5cX3bp144477jAc49ChQ6SlpTF9+nRCQ0OJiYnhwQcfNIzcaansbfQX15Jy417mJeWVONg03FsSwN7aipKK+nEszM2xta4/hYKrgx2BHq4cSjGey/Z8fiHL9x9jzvYDrDxwHHsba+4Z0A1bK9OfhgHAyVY/3ULdkSPFZRU42Vo3FOWKXShSM3fHAX7ZsJs/tuxDU1XFEyP74u5ompWI0qJCAOwcjddYsnNyouTiZw0pKSqqH8fRierqKsqKiy+GKcS+Thh7RydK1TU9A7sOHUl01578+u5rfPr0w8x89zXadu9Fh36DGvzeU/v3UKXVEtOt95WfZAvjaKMvg8Vl9cun4zWWz3BvD+ICfVi050jTgU1csbqI6upqw5y+lzgplRTVGVF5Nc6fS2H5/NlMe3q6YR2f1qasWI2uurreb9jesfHffUlRYQPhnQ2fAZSo6//m614Xzp48xun9exg2+d7rci4tQeml/HQyfvln5+RMSeHl8rN++Euf/V1bli7AytqG8PaNP/yagpLG7k2OTpQUFTUU5WK8xu9N5RfL4N+xacEcPHz98Taxl9aXyqZ9nXUU7Z2cL/tbrx9ev11cpGowzr5N8agLCojtXnNvLsjN4eT+vVRXaZn4r+n0v3U8B7duZNOi+ddwRs2vVN1I2XRwMtSpGotn53B1ZXPnisUorK0JaWf8oic3I51vX3yCr59/jI3z/mDUA4+b7HRf9tYXn4kqjOtEpRWV2Ns0Xieyt7Gq9xxVWnHxmeji84yznS2RPm0wNzNnwa5DbD+ZTIcgX/rFhDV63EGxkWSr1GTk//3rcEvi4qDvMKcqKTPaX1hShtLB7oqPM6pjNG4Odmw8XtOgffBsOrd2bYevqzNmQIcgX3pGBOFqf+XHFf9MJWo11dXVODgrjfY7OCtRN1KHV6tUODob15subasL9XEiY+PYs3kDqWeS0Ol0pCUnsWfTeqqqtJSoG1+n0tSUXHwGcqhT73RwVhryoi51oap+eCel/rOLeR4YHsGUx59m9jdf8H8PTOHNfz2IDh2THn6i0bSs/msO1tY2xJjolGrNXRbLSkt4ZdpUXrrvTn76+D3G3v0A0XEy3ZcQV+Ka1lCJjY3l6NGjTJgwgWPHjnHrrbdy+PBhw2gVCwsLIiIiDI0kdQUFBRn+39VV3ytapVLh7u5Oeno6Xbsar08QFRXFunU1U/VcmmJs//79xMXF0bNnT8LCwurFucTc3JyIiAjS0vS9ps+cOcPx48eZMGFCvbRlZmbi6elJbm4uX331Fd98843hs6qqKsNImvT0dFxdXWnTpqaHTWRkJObmDbdVrV69mjVr1jT4WW0WviH4xMQ1Ge5KtfX3YmTHml5p83YcBEBHneFXZtTdU0/dzw2drBqI2CHID3VZOUlZxj0Kk7NrtnPQN7A8PrwvsQE+7Ek610QKWp5OwX7c0bODYfvH9Tv1/1Mvs5rO36acyyngXE7NtHQpOXk8N2YgfaJDWLyn5U+xcnLvLuLn/GbYHvvo0wBGC6gB+oy6TA++huJcGk1otL/eYY3/BU4f2MOJPTsYde9DuHn7kpOeysYFs3Fy8yC2Z99633l0xxbC2nfEztHxsmlrSToE+TKue8315JeN+ilB6uaF2TWWTztrKyb27Mjs7fsbnRO7NTKjbjn8+7mo0Wj44ZMPueOeB3D3NO31J65E/d+w7rI/+4bC19tf7wCX/j3MKC1Ws+r3n7jlvkdMfgRFQ+plnU7XwM5a4etl1bXdofZuWMuhbZu48+kXsbY1rVGnp/buYv3c3w3btz3yFNDAvQnd5bL0Ypy6UXSNfHBlNi+cS0ZyIhOfebnR+mXLV/dmrKu/zyh0A3WChvYDpw7sZcOCOYx98HGc3WrN266rxt7RkVFTp2Fubo53YDBlJcXEz5/FoPF3NvBv2zKd3reLjfP+MGyPefjJi//XwLWuqXOqV9dqvGwe2hzPsR1bGPv49Hrr2bm08eLOF/5DRVkpZw4fIP7PXxj3xPO4eftewRk1r2g/L4bF1TwfLth1SP8/DdTZr/WaaGYGpRUa1hw6gQ7ILlRjY6VgYLsINh2vP0JrYNtw/NyU/Ll13zU/LzSX/jGhPD68ZvTnW3818txrZnbF+dszIoj7B3bnw6UbyCmqafz7IX4XT4zow5fTxgOQWVBE/NEEhsS2jrXRxI139fWmOvXQOvuH3n4H6kIVX771Kuh0ODgr6dy3P5uWL8HMZO/fjav//K677L21oTqV/gP9f7LPp7Pkj18YfNt4ImPjKFIVsGLuHyz45XvufKR+o8q2NSvZvTGeh156DRtb025Iba6yaG1jy/R3PqSiopzE40dZNmsmru4ehLdrf+0nJW6Keu90xU1zTQ0q7dq1Y8WKFaSmplJWVkZoaKihkcXJyYno6GgsLRv/CotaPW8v/fAvvRy5khdSw4YNo1OnTuzbt49Dhw7xwgsvMGHCBKZMmXJF6a+urqZLly488MAD9T5TKpVUXOyp9K9//cuoYaa2q31xNmLECEaMGNFkuBkrt1zVcZuSmJlDRv5Ow/alReMdbKyNpuKyt67f26q2kopKHKyNR7DYWVtRVV1d7wWquZkZsQE+HEpJbzKfNFVV5KqLcb2KnkotyfG0LM7lbjRsW1ro89fR1hpVaU2PrLr5fT3odJCWp8Ld0TQWqQ2NjcMr6HXD9qUhpyVFhTi61Ew3VaouqtfTvDb7BkawlBUXYW5ugc3FIb/6HrDGPYlL1WqjXp1bFs+ny+DhRHXuDoCHjx9F+XnsWbuyXoPKhfRUslNT6DNm3NWccrM7kZ5FWq7KsF1TPm0oLC037Le3sa43auVqeCkdcbKz4cHBPQ37Ll3b351yCzOWbyS3qPWsqeLg6IS5uTlFKuN1t9SFhTjVGbVypQoL8slMT2PmV58x86vPAP19RqfT8djEsTz5yuvEdDDtnv8Atg6OmJmb1/sNlxar642auKShHu2lxfrf96XftL1jA2HUaszNLbB1sOf8mSRKClXM++JDw+eX7k8fPTmNB/79X1w9TW9BS7vG8lNdhL3TVeTnxdF7jcW5nL0b1rJ16UImPDHdJNeoCIntgFdQsGG78XuTGjunpu5Nde47xWqje9PV2LxwDqcP7OWOJ583yfV+GiubJeqieqNQLrF3cqa4gfCXPqvt1IG9LP3lO8bc9zARdXpU2jsrsbCwMGqEcvPyQVNZSWmx+rJ1jJYkuF0HPANrflNVWn19u1Rdp2wWq+uNWqnNztHZMLqldpyGyuahzfHsWrmYWx95Gq/AYOqysLRE6aHvTOYZEER2WgqHNq1j8OT7rvr8brakrBwyC2ry4dIzkb2NtdHIcjur+qPyaysprzSM+DfEqfNMVFJeSbWu2uhVR566BCtLC2ytFEbPTgPbRRDl68nc7fspLDUezWFK9iSlkpCxyLBtaanPX6W9Lbnqmjqgs51NvVErDekZEcT0WwbwyYpN7ElKNfqsqKycdxfFo7CwwNHWmvziUu7t35XswtYzEkDcGPaOjpibm9cbTVFcVIhjnZEClzg2MAK9+OIo4EsjLxRW1kx6+F/c8cAj+ucBFyW7NsRjbWOLvQl1xmuK/cVnoIbyr+4olEscGxi9culefynPNy5bhH9IGANG6xdW9w4IxMrahm/e+Q8j7rgTZa1OE9vWrGT1gjlMe+4VAkIbH/XX0jV3WTQ3N8fdS//s4xsYzIXz51m/dKE0qAhxBa6pQaVt27ZoNBoWLFhATEwMFhYWxMbG8uWXX6JUKutNv3U1/P39OX3aeM7AutsA7u7uhkaKv/76i2XLlhk1qJw+fZq4OH3PbJ1OR2JiIr169QIgNDSUbdu20aZNmwYbfuzs7HBzcyMzM5NBgxqe/sff35/8/HxycnLw8NA/6CYkJBgtWt8SVGqrqNQaV1qLyysIbuNGZoH+IdXC3Bx/Nxc2HEto9Djn81RE+BjPd3vpGHUXG4r0aYOdtYLDKcaL0TfEwtwcN0d7zuXkNxm2JarQaqlQG89FWVRaToRPG9LyVABYmpsT0saNZfuPX/fv93ZxJtNEpgawsrE16umo0+mwd3Lm3KkThod2rUbD+eRE+t1Wf/TYJd7BoZw5ctBo37lTJ/AMCMTCQv979g4KJfX0cboOqWnETD19HJ/gmkqXtrISMzPjHkPm5uagq/8bPrp9M06u7gRExtT7rCWr1FaRV2dx+KKycsK9PEivVT6DPVxZefDE3/6etFwVM5ZtNNo3vEMUtlYKFu85SkFx6d8+dktkqVAQEBLGiSOH6NyrpjfmySOH6NSj52ViNs7F1Y3/zPjCaN/mNSs5efgQj774Cm4eVzffeEtlYWmJl38QKaeOE9mpZjTquVPHiejQcN3BJziULUvmo9VosFTop1M5d/IEDs5KQ690n5BQEg8bXxdSTh3HMzAICwtLvAKDue/Vt40+37ZsIeWlpQyZNBVnN9N7YQ0X8zMgiLOnjhPVuZth/9lTx4ns2PAUCL7BYWxaPA+tphLLi3Msnz153Cg/r9Se+NVsXb6ICf+ajn+YafYMtrKxwcrGxrCt0+mwc3Im9bTxvSnjTCJ9xjZ+b/IKCiX5qHEZTD19gja17k1XatOC2SQc2MMdT75gkg19oC+b3gFBnD15jOhaZTPl5DEiO3ZtMI5vSBgbF82tUzaP4eDsYlQ2T+zbzfKZ33PLvQ8bHfsS/9AIju/Zia662tAbM/9CFgorK+wcTOfF1uXKpmdAnbJ56x2NHscrKITko4eM9qWdPkEbf+OyeXDjWnavWsqYR57CJyT8yhKpqzaZ9QE02ipUDTwTBXq4GhaLtzA3x89NyeYGRpFcklFQSLi38T0jyMOVbFXNM9H5fBXRfsajTV0d7KjUVhk1pgy62JgyZ/t+8k28rlRWqanXyS6/uJSOQb6GGQsUFha09fPil417Lnus3lHBPDOqP5+u3MyO0ymNhtNUVZFfXIqFuRm9IoPYdursNZ+HaN0sLRX4BoeQcOwIcd17GfYnHDtC+649GowTGBbJijl/oKmsNKwDknjsME4urrjWqZ9bWFqidHMD4NCu7cR07GzCI0zrs7S0xDdIn3/tu9U88yQeO0ps1+4NxgkMi2Dl3Fl18u8ITi4uuFzsMFJZWVEvny5t137VtGXVctYunMsDz/0fwZENd3w2FS2tLOp01WhN5H4uRHO7pqv6pXVUNm3aZFgrJSoqitzcXE6fPn3Z9VOaMmbMGA4fPsz8+fPJyMhgzZo17Ny50yjM999/z/79+8nKyiI5OZkDBw7g7+9vFGbVqlVs376d9PR0fvjhBy5cuMCoUaMAGD16NKWlpXzwwQecPn2arKwsDh06xJdffklpqb4yO3nyZBYuXMjixYtJT0/n3LlzbNiwgfnz9fMvd+jQAV9fXz755BOSk5M5deoUP/74o9Hom5ZqT9I5ekYEE+nTBg8nB8Z0bkulVsvxtExDmDGd2zGmc80itwfOpuNoa8OQ9pG4OdoTF+RL+0Afdiem1Dt+h2A/Ui7kG43QuGRQuwgC3F1wtrPFx8WZcd3jUFhYcORcxg051+aw5eQZBrULJzbAGy+lI3f26USFVsvB5Jr1ZCb36cTkPsY9Kn1cnPFxccbayhI7awU+Ls54Otc8+A+LiyTSpw2uDnb4uDgzqVdHfFyc2JFgmg8PZmZmdBwwhL3xK0k8tJ/cjHTW/PETCitrorrUVMhW/fYjq3770bAd13sAalUBGxfMJi8rg6M7tnB893Y6Dx5uCNNpwBBSE06xZ+0K8rMy2bN2BWkJp+k0cKghTEi7OPbGryL52GEK83JJPHyA/RvXEtbe+N9FU1nByX27ie3V12SmCbmcbSeTGdA2jLb+3ng6OzKxV0cqtVVGi4ZO7NWRib2MR0N4uzjh7eKEtUKBnbUV3i5OtHHWj47SVFWRXag2+iur1FCh0ZJdqKaquvUNRx0y5jZ2btrAtvi1ZKanMffnHygsyKffsJEALJo1kxlv/NsoTkZaKmlnkykuUlNeXk7a2WTSziYD+kqvb0Cg0Z+jkzOWCgW+AYHYmNg0SpfTZfAwju3axpHtm8nLymD9/FkUq1TE9RkIwJYl85n72QeG8DFde2CpsGLV7z+Sk5FOwqF97F63gi6Dhht+k3F9BlKsymfDX3+Sl5XBke2bObZrG10H6xtVrayt8fDxM/qztrXDysYGDx8/LC4zqral6zZ4OEd3buPwts3kZmawbt4sigtVdOyrz89Ni+cz+9P/GcLHdOuBwsqKFTN/JOd8OqcP7mPX2hV0HTLc6BqXnXaO7LRzVJSXUV5SQnbaOXIzazpK7F67kk2L5zPq7mm4tvGkuFBFcaGK8jLTfiloZmZGx/5D2LduFUmH95ObcZ61s35GYW1tGNEIsOb3n1jz+0+G7fZ9+qNWFbBpwRzyszI4tmMLJ3Zvp/OgmntTlVbLhfRULqSnotVoKFUXcSE9FVVOtiHMhnmzOLFrOyPvfRhrO3tKigopKSqksqJmVKGp6DZkBEd2buXQtk3kZp5n7dw/UBeq6HRxrbKNi+Yx65P3DeHbduuJwsqaZTN/4ML5dE4d3MvONcvpPmSEoWwe37uLpT9/y4CxEwkIjzSUu7KSmumAOvUbRFlpMWvn/UFeVibJx4+wddlCOvUfbNL3cTMzMzr0G8z++NUkHT5AXuZ54v/8BStrayJqlc21f/zE2j9qyma73v0pLixgy8I55GdlcnznVk7u2UHHQcMMYQ5sWMOO5QsZPPlelB6ehnJXUev3vH3ZAs6fSaAoL5fcjHR2LFtIelKC0Xebmv1nUukeHkS4twfujvaM7BiDpqqKE+ezDGFGdWrLqE5tDduHU9JxsLFhYLsIXB3siA3woV2AD3trjaI4dDYdG4WCwbGRuDjYEeThSu+oEA6dTTOEGdI+knYBPizff4wKjRZ7ayvsra1QmMCz5JVauu8Y43vE0TMiiAB3F54e3Y+ySg1bTp4xhHlmdH+eGd3fsN03OoTnbhnIb5v3cjwtC6W9LUp7WxxqrWsT4e1Bz4ggPJ0difHz5I0J+mvEwt2tfx2/ppjZ2mAVFoJVWAiYm6HwbINVWAiWnqbZceRG6D9yDPu2bGL3xniyz6ez+LefKSoooMdg/TVx5dxZfPvuG4bwHXv1wcramrnff0lmWipH9+5iw7LF9Bt5i+GekpOZwf5tm8nJyiT1TCJ/fDmDrPRURk68shlUTEnfEbewf+smdm9ab5iqq0iVT49B+ufsVfP+5Pv33zKE79CzDwprK+b98DVZ6akc3bubjcuX0HdETf7FdOzC8QP72Ll+LXkXsklJOMWSP37BNygYF3d9h4pNK5ayat4sJjz4GB5ePqhVKtQqFWWlplvvbK6yGL9kAQnHjpB3IZvs8+lsWrmU/du30Kl3/WnPRct1aTaLlvzXWl3zm4PY2FgSEhIMjSdWVlZERkaSmJhIRMTf76UYFRXFU089xaxZs5gzZw7t2rVjypQpfPfdd4YwOp2O7777jtzcXGxtbYmLi2PatGlGx7n33ntZvHgxZ86coU2bNrzyyiu4X7wYu7m58cEHHzBz5kxef/11NBoNHh4edOzYEcXF3q/Dhw/HxsaGhQsX8ttvv2FlZUVAQAC33HILoG8xf/XVV/niiy947rnn8PDwYNq0aXz00Ud/+9xvll0JKSgsLBjeIRobhSUZ+YXM2X6ASm2VIYyTnY1RnMLSMubtOMCQ9pF0CvanuLyCtYdPcTrjglE4pZ0tQR6uLG5kYWonW2tu6xqLnbUVpRWVnM8vZOam3RSVmd6LgsZsPJaIwsKCcd3jsLVWkJpTwPfrdlBRq8Vf2cCiic/dOtBou62/N/nFpbyzYC0ANlYK7ujZASdba8oqtWTkq/hq9VajKZ1MTdchI9FqNGyYP4vy0hK8gkIY/y/jObvVBcajl5zdPbj90WfYvHAOR7Ztwt5JycA7phDRoaY3tk9IGKPve4TtyxexY+USlO5tGH3/I3jXmo5m0IQpbF+xmPXz/qC0WI2DkzOxPfvRY+StRt93+sBeNJUVtO1huovR17b5RBIKSwvGdovF1kpBWm4BP67fafT7V9rXf3n/zOgBRtsxfl7kF5fyv8XxNzrJLVLX3n0pUatZuWAehQX5+AQE8sQr/zGMJCksKCA3O8sozpfvvkVeTs01878vPAPAd38tvWnpbgmiOnenrKSEnauXUVJUiLu3L+Mff9bQA724sBBVbk0+WdvaMfHJ54mf+we//+9NbOzs6TJoOF1qNaIq3T0Y//izbFgwm0NbN+LgrGTwhLsaHaXRmkR36U5ZSTHbVy015OeEf02vlZ8qCmqVOxtbOyY99QJr5/zOr++/gY2dPd0Gj6DbYONpSX9593Wj7aSjh3BydePxdz4GYP/m9VRXVbHkx6+NwrXr0Ztb7n3oRpzqTdNlyAi0mko2zP+TitISvAJDuP3x6UajBYoKjNcJdHbzYOwjT7N50VyObtuEvbOSAeMnE15r5FVxoYo/P6h5yXA0dzNHt2/GNyyCCU+9CMCRbfrRfgu+/Njo+N1HjKHnqNuu+7neSDFdelBWXMz2lUspLlLh4ePHpCeeMyqbqjplc/LTL7Jm9m/88t7r2NjZ0X3ISLrVGm16cMsGqquriJ8/i/j5swz7A8KjmPrcKwA4ubox+akXif/rT35659/YOznTvlc/+phY/jWk0+ARaDUaNi/Ql03PwBBue+xZo7JZXLfe5ObBrQ8/xdbF8zi6fTMOzs70G3cnYXE1ZfPI1o1UV1Wxeub3RnGjuvZk6F36KZJLiwpZ98dPlBQVYW1ri5uPH7c+/BSB0e0wVXuSzmFpYcGQ9lHYKCzJLChi/o4DaGrViRxt6z4TlbNg10EGtYugQ5AfxeUVrD96moTMmrKsLq9g/s4DDGwXwb0DulNSXsnR1Ax2nq7pBNUxWN8ZcFJv49GZ208ls+N08o043Ztu4e4jWFta8sjQXjjYWJGQkcPr81YbjWTxcDKeunhEh2gsLcx5aEhPHhpS0wP+aGomr85eAYDC0oK7+nbGS+lIeaWWfclpfLJi82WnavunsImKwK/W9KZuD96D24P3ULRyLdnvfnyZmP8cHXr0pkStJn7JAopUBXj5BTDthVdwvThaokhVQN6Fmo4Otnb2PPzyayz89Uc++89L2NrZ03/UGPqPHGMIU11dzeZVy8jJzMDCwpLQmLY88Z936o0aaA069OhFabGaDUsXXsw/fx547v8Mo03q558dD734Got/+4nPX/8/bO3s6TfyFvqNuMUQpkvfAVSUlbEjfjXLZ/+Gja0dodFtGXXnVEOYnevXUFVVxayvPjVKT+c+/Zn08L9u7EnfIM1VFivLy1n4y/eo8vNRWFnRxseHyY88ScdaMy8IIRpnVlZW1iqbi7Kzs3nwwQeZMWMG4eFXOFy9Bbnea6j80+WpTbfHQksT4X11U8GIy0u+YJrT3LVUI+MimzsJrUpSdl7TgcQVsTA33R7xLVG5RqYjuJ7srKyaDiSuSO11OMS1K6uUl+PX09ZTKc2dhFbj42XzmzsJrcqpT1p+h1RT0tKmoDd1rWnKtuY2NNb03s+2ZF+s2dbcSWjSk8NbZyOd6c5tIYQQQgghhBBCCCGEEEL8w7TCWdVNhjSzCiGEEEIIIYQQQgghhBBCNKHVjlDx9PRk2bJlzZ0MIYQQQgghhBBCCCGEEEK0Aq22QUUIIYQQQgghhBBCCCGEaG10Opnzq7nIlF9CCCGEEEIIIYQQQgghhBBNkAYVIYQQQgghhBBCCCGEEEKIJkiDihBCCCGEEEIIIYQQQgghRBNkDRUhhBBCCCGEEEIIIYQQwkRUyxoqzUZGqAghhBBCCCGEEEIIIYQQQjRBGlSEEEIIIYQQQgghhBBCCCGaIFN+CSGEEEIIIYQQQgghhBAmQidTfjUbGaEihBBCCCGEEEIIIYQQQgjRBGlQEUIIIYQQQgghhBBCCCGEaIJM+SWEEEIIIYQQQgghhBBCmAiZ8av5yAgVIYQQQgghhBBCCCGEEEKIJkiDihBCCCGEEEIIIYQQQgghRBNkyi8hhBBCCCGEEEIIIYQQwkRUy5xfzUZGqAghhBBCCCGEEEIIIYQQQjRBGlSEEEIIIYQQQgghhBBCCCGaIFN+tVD9o0OaOwmtSklFZXMnodWwtpTLxvXk5mjf3EloVdr6eTV3ElqVtLzC5k5Cq2Fm1twpaF3+2n20uZPQqkT6eDR3ElqN12KDmzsJrYu59P+7nlYcPNXcSWg1Tn3yUXMnoVWJevb55k5Cq+L6zmvNnYRWpWL/4eZOQusRG97cKWhVdDLlV7ORGqoQQgghhBBCCCGEEEIIIUQTpEFFCCGEEEIIIYQQQgghhBCiCdKgIoQQQgghhBBCCCGEEEII0QRZDEEIIYQQQgghhBBCCCGEMBGyhErzkREqQgghhBBCCCGEEEIIIYQQTZAGFSGEEEIIIYQQQgghhBBCiCbIlF9CCCGEEEIIIYQQQgghhImoRub8ai4yQkUIIYQQQgghhBBCCCGEEKIJ0qAihBBCCCGEEEIIIYQQQgjRBJnySwghhBBCCCGEEEIIIYQwETqdTPnVXGSEihBCCCGEEEIIIYQQQgghRBOkQUUIIYQQQgghhBBCCCGEEKIJMuWXEEIIIYQQQgghhBBCCGEiqmXKr2YjI1SEEEIIIYQQQgghhBBCCCGaIA0qQgghhBBCCCGEEEIIIYQQTZApv4QQQgghhBBCCCGEEEIIEyEzfjUfGaEihBBCCCGEEEIIIYQQQgjRBGlQEUIIIYQQQgghhBBCCCGEaII0qAghhBBCCCGEEEIIIYQQQjRBGlRugPj4eCZMmNDcyRBCCCGEEEIIIYQQQgjRyuh0uhb/11rJovTCYMPK5axevBBVQT6+/gFMnvYwEW3bNRhWU1nJb998ybnkM2SmpxEWFcNL77xfL5xWo2HZ/Dns3LQRVX4eTkoXho8dx9Bbbr3Rp9PstqxZxfpliylSFeDt58+4e6cRFh3TYFhNZSVzfvyW9LPJZJ1PJyQyiqdf/69RmEO7d7I9fg3pZ8+i0VTi5efP8NvvILZLt5txOs1u0+oVrFmykMKCAnz8A5h0/0OEx7RtMKymspI/vvuK1ORkMs+nERYVzfNvvdfosRNPHufj/7yCl68fb3z61Y06hWaza/1atq1ahlqloo2vH6On3ENQZHSj4bPSUln2xy+kJydha+9At4FDGHjrOMzMzAxhtFotm5Yu5NCOrRSpCnBwcqbPyFvoNXSkIcyOtSvZvSEeVV4Odg6ORHfswvCJU7C2sbmh59vcFv71F7Nn/U5eXh5BwSE8/eyzxHXo2GDYs2eTmfHhh6ScPUtJSTFu7u4MGTqMBx58CIVCcZNTfvPt2xTPzrUrKC4sxMPHl2ETpxIQHtlo+Avn01g9eyYZKcnY2jvQse9A+o4eayib5xJOsnHRPPKys9BUVuDs6k6HPv3pOWy04RgHtm7k6K5t5GScR6fT4eUfSP/bxhMQ1vj3mop9m+LZuWYF6ov5OXzS5fMzO904Pzv1q5Ofp0+yoU5+duxrnJ8n9u1mx5oV5F/IprpKi2sbL7oPGUFcr743/Hxbiil9OjOiQxQONtaczrjAN2u3k5pb0Gj4dv7e3DegK75uSqwtLblQVMzaQ6dYuOfITUx1yzA8LooeEUHYWVlxLjefBbsPk61SNxre0daa27rE4uumxMPRgX3JqczZfsAoTNfQACb36Vwv7ou/L0FbXX3dz6Gl+GvtGmYtX0aeSkWwnx/P3nMvHaIav9dfkpqZyX2vvIxOp2Pjr78ZfabRavll0UJWbd1CbkEBrs7OTLllDJNGjGzkaK3DX2vWMGvZkpq8vPd+OkRfYV6+/KI+L3/7w7B///Hj/OutN+qFnzPjU4J8fa9jyluu+wZ045bObXG0sebk+Ww+XbGZlJz8RsPHBfrw0JCe+Lu5YKOwJLtQzYoDJ5i746AhzIgOUbw8dki9uMP++w2V2qobch4twfZ1q9m0cilqVQGevv7cNvU+QqIafsYEyEw7x6KZP5F6Jgk7Bwd6DBrK0LF3GNXrt69bxfZ1q8nPycHFzZ3Bt42jS98BN+FsTINNXDtcJt+BTWQ4lh7uZL3zEepV65o7WS3Ogk0b+HPNGvIKVQT7+PL0pDvpEB7RYNjM3FzGv/JSvf0znnqGHu1iAchVqfjir3mcPneO9AvZjOjRk3/fP+2GnkNLYhfXDocuHbGwt0OTl0/Rpm1Uns+8bBz7ju2xi2uHpZMT1eXllJ44hXrbLqNj2neIxdLZiaoiNerd+yk7efpGn4oQrZI0qAgA9mzbwuyfvmfqI48THh3DxlUr+OTt1/nvF9/g5tGmXvjq6moUVlYMGnULR/fvo7SkpMHjfvfxB+Tn5XLv40/i6e1DYWEBmorKG306zW7/jm0smPkTE6c9TGhkNFvXruab997m1Rmf4+ruUS98dXU1CoWCfsNHcfzgfspK6+dn0snjhLeNZfSkKdg7OLJ36xZ++Oh/PPX624021LQWe7dvZc7PP3DXQ48RFh3DptUr+fydN3jj068uWz4HjhzN0QP7GszPS0qKi/nl80+Iio1DlZ93I0+jWRzZvYMVf87k1rsfIDAiit3r1zJzxvs8/e7HKN3c64UvLyvllw/fISgymsdff5eczAwW/PQNVlbW9Bl5iyHcvG8+R5Wfx9j7HsLN04viokI0lTW/7cM7t7F63p/cfv/DBEVEkZ9zgUU/f4dWU8m4aY/elHNvDuvXreOzTz7muRdeon1cHIsW/MXzzz7D77Pn4uXlVS+8wlLByFGjCY+MwNHBkaTERP733rtUabU8/uRTzXAGN8/xvbtYO/cPRky5l4CwCPZtWs/sLz7k0Tfex9m1ftmsKCtj1qf/IyA8kgf+703ysrNY9uv3WFlb02PoKACsrG3oOmgYbXz9sbSyIj0pkZWzfkZhZU2XAfqXLucSThLTpQf+oeEorKzZvX41sz/7gIf+/Q6unvX/jUzF8b27WDPnD0bedS/+YRHs37SePz//kMfeeB/nBn7rtfNz2itvkpeVxdJfv0dhZU3PYRfz0+Zifvr5o7CyIi0pkZV/GOenrYMDfUbdiruXD+YWFiQePcSy337EztGR8NgONzMLmsUdPeK4vVssn6zYzPk8FZP7dOK/d47ike/nUVapaTBOuUbD0n3HScnJp0KjJcbPkydG9KVCq2XFgRM3+Qyaz6B24fRvG8acbQe4UKRmWFwUjw7tzfuL4qnQahuMY2luQUlFJRuOJtAjIqjRY1dotLy7cK3RvtbcmLJu5w4++W0mL9w/jbioSBasXcuz77/H7I9m4OVe//d/iUar5bUvPqNDVDQHT9Yve6998RkX8vJ4+cGH8ff2Ir+wkIrK1l2PX7djO5/M/IUXpj1IXGQUC9au4dn33mH2jE/waqAOf4lGq+G1zz6hQ3Q0B080/Due/fEMnBwcDNtKJ6frnv6WaHLvTkzs2YH3F68nLa+Ae/p35aN7buPuL/5o9DpZVqlh4e4jJGfnUa7REBvgzfRbBlKu0bBk7zGjcHd9/rtR3NbcmHJo13aW/PEL4+57kOCIaHbEr+HHD9/lhf99gksD5bO8tJTv33+b4Mhonn7rfXIyM5j7/ZdYWVszYJS+g+OO+DWsmDOLCdMeJSAsnNQzifz107fY2jvQtlOXm32KLZK5rS2VyedQr47H898vNHdyWqT4vXv4dM4cnr/rLuLCwlm4aSPPff4ps954Gy83t0bjzXj6WcL9/A3bTvb2hv/XaLU4Ozhw98iRLNmy5Yamv6WxiQjDeUAfCjdsofJ8JnZx7XC9fQw5M/+kSl3cYByn/r2xCQmiaMsONLl5mFlZYeFQk5927dvi1LcXhes2UpmVjcKrDcqhA6muqKAiOeUmnZkQrYdJNqjodDoWL17MqlWryMnJwdnZmYEDB3Lvvffy66+/smvXLnJyclAqlfTp04e77roLKysrAHJycvjuu+84fvw4lZWVeHh4MGXKFPr160d2djYPPvggM2bMIDw83PB9Y8aM4eWXX6Z3794ATX6HKVqzZBG9Bw2h/7ARANz18GMcPXiAjatXcsfd99ULb21jwz2PPQFA+rmUBhtUjh08wIkjh3j/2x9xdHIGwN3T88adRAuyccVSuvcfSO/BwwCY8MBDnDx8kG1rV3PrlLvrhbe2seHOhx4D4HxqSoMNAHfc96DR9qgJkzh+cB9H9u5u9Q0q65YtptfAwfQdOhyAyQ8+wvGD+9m8ZhXjpt5bL7y1jQ1TH/kXoC+fl2tQ+e3rz+k5cDA6nY4DO7ffmBNoRtvXrKBT7/50HTAYgDF330/iscPs3rCO4RMm1wt/eOc2NJWV3PHQ4yisrPD08ycn8zzb1qyg94jRmJmZkXjsMEknjvLcB59h76h/GeBSp2HrXFIC/qHhdOzdz/B5h979OL5v9w0+4+Y1Z/afjBp9C7eOHQvAs8+/wO5du1i8cAGPPv6veuH9/P3x8695iPDy9ubggf0cPnzoJqW4+eyOX0X7Xn3p1HcgACMm38OZE0fYv3k9g26fVC/8sT3b0VRWcOt9j6CwsqKNrz+5mefZHb+a7kNGYmZmhndgMN6BwYY4Lu5tOHVwH6lJpw0NALdPe9zouCOn3MfpQ/s5c/yISTeo7Fq3iri6+Xn8CPs2r2fwuPr5eXS3Pj9vu79Wfmbp87PH0CbyM7EmP4OjjEcKdh88nCM7tpKaePof0aByW9dY/tp1mB2nzwIwY/kmZj11N/1jwlh96GSDcZKycknKyjVsZxeq6RUZTFt/r39Ug0q/6DA2HE3gSGoGALO37efNSaPoFOLHzoSUBuMUlJSy6OJInvaBl+/Zry6vuK7pbclmr1jB6H79GTtYf69//v4H2HXkMAvXreXxyVMajffVn7MICwigY3RMvQaV3UcOs/foURZ8+rnhxb9PA51YWpvZK5Yzuv8Axg7WX+Oef2Aauw4fYuHatTw+5a5G4301axZhAYF0jIlptEHFxcn5H9OIUtsdPeL4c9t+tpw8A8B7i+JZ/MI0hsRGsGz/8QbjJGTmkJCZY9jOUqnpGx1K+wAfowYV0JFfXHojk9+ibF61jK59B9Bj4FAAbr93GqePHGTn+rWMmlS/fB7YsZXKigomP/oECitrvP0DuJCRzpZVy+k/cgxmZmbs376Z7gMH07FXHwDc2niSlnyGjcsXS4PKRaW79lK6ay8Anq8838ypaZnmrFvLqF69uK1vfwCmT76LXcePsWjzJh4bN77ReM72Drg5Ozf4mbe7O9Pv1N/DNu7ff/0T3YI5dO5A6YlTlB7V30+KNm7FJigAu7h2RiNOLrFwUWLfIZac3+eiza8ZJa3Nqalv2sVEUnr0OGWnEwGoKiyi1NMTh64dpUHFhFW34im1WjqTXEPlt99+Y+7cuUyYMIGvvvqKl19+GfeLva9sbGx46qmn+Prrr3nsscfYsmUL8+bNM8T95ptvqKio4N133+Wrr77ioYcewr5WK/iVaOo7TI1Wo+HcmSTa1pmSpm2HjiSdavhlwJU4uHsnQWHhrF2ymOem3cPLjz3ErB++pbys7FqT3KJptRrSks8Q3b6D0f6o9nGcTTh1Xb+roqwMO3uHpgOaMK1GQ+qZJGLijMtnTIeOnDn998sn6KcRK1QVMHr8xGs6Tkul1WrJSDlLWLv2RvvD2rYnNSmhwTipSYkERkShqNVAHN4uDrWqgIJc/YPtiQP78AsOZfuaFfzv2ceZ8dIzLP/jVyrKyw1xgsKjyExNITVJX2FT5eVy6uB+Its3PPVVa6DRaEg4fYqu3bsb7e/avTvHjl7ZVD7paWns3rWLDh073YgkthhVWi2ZqSmExBhPKxkS3Y70M4kNxklPTiIgLNKobIa2bY9aVYAqL6fBOFmpKaQnJxIYHnXZtGg1Gmzsrq4u0JI0mp8x1zc/M1NTSD+jv0Y0RKfTcfbkcfKyMy+b562Fl9IRVwc7DpxNN+yr1FZxPC2LaL8r70AS4ulGtK8nR1MvP41Da+LqYIeTnQ2nMy4Y9mmqqknOziPIo/GerFdKYWHBv8cP5z93jGDaoJ74ujb8sqY10Gi1nD6bTPf2xvf67rHtOZrQ8L0eYPuBA2w7eIDp997f4Oeb9+4lOjSU2StXMOZfj3HHs0/z8a+/UFrrXt/aaLQaTicn0719nNH+7u3jOJrQ+JQo2w/sZ9uB/Uy//4HLHv++V15m9CMP8cTbb7L/2LHLhm0tvF2ccHO0Z++ZNMO+Sm0Vh89l0Nbf+4qPE+blTjt/Lw6fO2+038rSkjnP3MP86ffx3pRbCPNqfESWqdNqNZw/m0xErHH5jIiNIyWx4fJ5Luk0wZHRKKysa4XvQFFBPvk5+uuvVqNFoTDuGKqwsiLtTBJVjYwWFKI2jVbL6dRzdK8zHXe3mLYcPZN02bivfPMVo557hkf+9x4b9u+7kck0HebmKDw9qEhJM9pdcS4NK5+GO3/ZhgZTVViEdVAAbR6YSptpd6McPhhzW9uaQBYW6KqMR/DptFqsvDzB3CRfDQvRrExuhEpZWRlLlizhoYceYuhQfc8MHx8foqL0D+533nmnIaynpycTJ05k0aJFTJ06FdCPUOnVqxfBwfoelw1NwdKUpr7D1KjVRVRXV+OkdDHa76xUcuIaeknnZGeRePIECoWCx196hbKSEmb98C2q/Hz+9dIr15jqlqukSE11dTWOzkqj/Y7OSk5f4UvVK7FlzUpU+Xl069f/uh2zJSq+VD7r5KeTs5KTqsN/+7jp51JYNm8O//feh5hbWFxjKlum0ot551Cn14+DszNnThxtME5xoQonV7d64S995urRhoILFziXcBoLSwVTnniW8tJSlv3xC0WqfKY8MR2A9j16UVqs5sf33kAHVFdV0aFXX4ZPbLynrKkrVKmoqqrC1dXVaL+rqyv79u65bNxHH5pGwunTVFZWMua2sTzy2OOXDW/qSovV6KqrsXc0Lpv2Ts6cPdVwb9XiwkKcXFzrhNf39C0pLMTFvabn9GcvPUVpsZrqqir63nI7nfsPbjQtm5b8hZW1NRFxptuIZchPp/r5WXyy4fwsKSzEsW5+Ojacn5++WJOf/cbUz8/y0lI+fekpqjRazMzNGTnlHsLqvPBpjVzs7QBQlRj3ji4oKcPN0a7J+DP/NQVnO1vMzc2Yve0Aqw5eWycBU+Jkq19Lq+4oEnVZBc5217bO1oWiYubsOEBGfiHWCkv6RYfy5Mh+fLR0A7nqxkesmipVURFV1dW41rnXuzo7s/dYw/f63IIC3vvhe96fPh372i9basm4cIEjp09jZangvWenU1xSwse//qqP++z0634eLYGqSN14Xh5VNRgnt6CA977/jvenP99oXrq7KHnxwYeICQ1Fo9WyassWnvjvW3z9nzfoGNO6R5m7OuivhQX1rpOluDs23Sls/vT7cLazxcLcjJmb97J0X809LTVXxQdLNnAmOxdbKwV39Ijjy2njmfbNHM7nF17fE2kBStTqi/V6pdF+B2cl6kZ+62qVCuc69XrHi+VbXajCrY0nkbFx7Nm8gXZduuMfEkr62TPs2bSeqiotJWo1Ti4uDR1aCANVsf7a6VJnBJ6rkxP7GphOEsDW2pon7phI+7AwLMwt2Hb4EP/5/lsq75/GiB49b0ayWyxzWxvMzM2pLjW+blaVlmJt59dgHAtnJyycHLGNDEe1Zj0ATv164zp2NLmz/wKgIiUNu3bRlCcmo8m+gMLTA7vYGMwsLDC3taG65J8z2k+I68HkGlTS0tLQaDTExTX8oL59+3aWLFlCZmYm5eXlVFdXU11rzuQxY8bw9ddfs3//fuLi4ujZsydhYWFXlYamvuNyVq9ezZo1a5oMF9W5G11639wFXc3qbOt0GC1Wd7V01TrMzMx4ePqL2F0cBXTXQ48x483XKFQV4Kxs5ZWzOnmn09Xf93cd2r2TxX/M5P6nn8P1HzD9AtQvi9cysFGj0fDDjA+44577cTfhKX6uVP3ftq6BvU2Fr/lEp6sGM5j06JPY2OkflMfcfT+/fvQexYUqHJyVnD11go1LFzLmnmn4h4SRdyGLFbNmsn7RfIaMa50jgi6pV1Z1uiavpW/+911KS0tISkzk6y++YNbvv3H3vffdwFS2DPXyRafD7DJls+5HNUXT+IN7Xvg3mooK0pOT2LBwLkp3D9r36FPvcHvWr+HA1g3c9czLWDfyIsyUNFj2Lvdbr5ufjXxw74v/prK8gvNnk1i/YC5KNw/a96zJT2sbGx5+7R0qK8o5e/I46+b9idLNg+Bo456Kpm5A2zCeGFFTN3tj3mqgVjm86Epv9S/+sQwbK0uifDy5f2A3sgrVbDzW8IgiU9cp2I8JPWtGKP64fof+f/5m3l3OuZx8ztVa7DolJ4/nxwyib3SoYbqw1uhq7j1vfPUl44YOpV0jiwWDfgoJM+CtJ5/C4eK9/vn77+fp994lT6XCTam8XklvcernZePPRG98+Tnjhg6jXUTjeRno40ugT80UdbERkWTm5DBr+dJW16AyJDaC58YMMGy/PGs50MB1EjOupDb/5M8LsLWyIsbPk0eG9iKzoIh1R/SjMU6kZ3EiPcsQ9nhaFj8+eifjurfni1Vbr/lcWqp6JVGnu1y1vtFnqEv7h95+B+pCFV++9SrodDg4K+nctz+bli/BTHqti6vQ0H2oscKpdHRkyrDhhu3ooCBUxWpmrVn9j29QaZxZ41dNMzPMLC0pWLWOKpW+Qblg1To8H5iKwssTTVY26t17sbC3w/3OcWBmRnVpKaUnTuHYtRNUy7RRpkonU341G5NrULlcYTl16hQffPABkydPplOnTjg4OLB7925+/vlnQ5hhw4bRqVMn9u3bx6FDh3jhhReYMGECU6ZMMdwAan+Hts4w1yv5jssZMWIEI0aMaDLc/lrTR9xojo5OmJubU6gqMNpfVKjC6RoelpxdXXFxdTM0pgD4XFxwLD8np9U2qNg7OWJubo66Tn4WF6lwamR+0KtxaPdOfvvyU+7+19PEdul2zcdr6RwaKZ/qayifhQX5ZKanMfOrz5j51WeA/nev0+l4dMJtPPnq67TtYLq91S+xu5h36kLjXnolRUX1Rq1c4uCsbDC8/jN9HEelC04urobGFAAPb/2LAlVeHg7OStYtnEv7Hr3o2n8QAF7+AWgqKlj08/cMvG08Fq1wVJCzUomFhQV5eXlG+wsKCuqNWqnL8+L6UsHBIVRXVfO/995h8l1TsbQ0udv0FbFzcMTM3JziIpXR/hJ1kWHUSV0Ozs6U1CmbpWp92awb59Loija+/pQUFbJl2aJ6DSp71q9h05K/uPOp5/ENDr2W02l2hvwsVBntL71Mfto7O1N8lfnp6VcrP2s1qJiZm+PaRl+GvfwDyc3KYNuqpa2uQWV34jmjKaoUF69jLg52RiMflHa2FJQ0Pb1pdqEagHM5BSjtbbmrT+dW26ByPC2L1NwNhm0LC/1LOkdba1SlNXnlYGONuuz6rn2i00Fangp3R9Od1u9ylE5OWJibk6dSGe0vKCrC1anhe/2+48c4ePIEPy3Q91rV6XRU63T0vmsyLzwwjbGDh+CuVOLh6mpoTAEI8tXf67Pzcltlg4rSybGRvCysN2rlkn3HjnHwxAl++ms+UCsvJ0/ihWkPMnbI0AbjtQ0LZ92OVrh23+mznDyfbdi+dJ10dbAjp6hmIWWlvS35xU1fJ7NU+uvk2Qt5uDrYcd+AboYGlbqqdTpOZ1zAz1V5DWfQctk7XnzGrHOvLy4qrDczwiWOSiVFdcrzpXu/w8Xrg8LKmkkP/4s7HngEdWEhTi5Kdm2Ix9rGFntHx+t9GqIVUjror535deqVBWo1rlexblTb4BBWtMLr4tWqLitHV12NuZ3xaGcLO9t6o1YMcUpK0VVVGRpTAKpUheiqqrBwdECTlQ3aKlRrN6CK34S5nS3VJaXYxcZQXVFJdSufll+IG8Hkuhz4+/ujUCg4fLj+VD8nT57Ezc2NO++8k4iICHx8fLhw4UK9cO7u7owYMYKXX36Zu+66yzBixPliRbmgoObFbXJy8t/6DlNiqVAQGBrG8UMHjfafOHyQsKjov33csKhoVPn5RmumZGXo5711a8WjKiwtFfiHhHLqqHEZPXX0MMGNzDt/pQ7s3M5vX3zK1MefomOPXtd0LFNhqVAQEBrGyTrTz504fIjQyL9XPpWubrz+yZe89vHnhr9+w0bQxsub1z7+/G8ft6WxtLTEJyiYpOPGPXKTjh8hIKzhnpQBYeGcSziFprLSKLyj0gUXdw99mPAI1KoCozVT8rL1c/8rL65npamoxLxOrzYzc3N01zS2qGVTKBREREaxd4/x9F579+ymXWz7RmLVV62rpqqq6opHPpoiC0tLvAOCOHvCeA75syeP4xca3mAcv5AwUpNOo9XUlM3kE8dwVLqgdPNo9Lt0Oh1VWo3Rvl3rVrFxyXwmPfEcAWGR13AmLcOl/Ew+aZyfySduTH5q6+RnvTDVOqo0rW/e9bJKDZkFRYa/1NwC8otL6RhU0/NcYWFBW38vTqZnX+ZI9ZmbmaGwMLlq+RWr0GrJVZcY/rJVaopKy4nwqakPWpqbE9LGjZScvMsc6e/xdnGi6Do31LQUCktLIoND2HPUeMqfPUePEtvIqIlZH3zIb+//z/D30ISJWFtZ8dv7/2NQ9x4AtI+MJKegwGjNlNRM/b3ey73xa4QpU1gqiAwJYU+dOvyeo0eIjWj4XjHrw4/57X8fGv4emjhJn5f/+5BBl+llnXjuLO4uyuuZ/BahrFLD+fxCw19KTj556hK6hPobwlhZWtA+0IfjaVe3bpSZmRlWlpfvkBPi6UZeceub2g/0z5i+wSEkHDOu1yccO0JQeMPlMzAskrOnTxrV6xOPHcbJxbXeLAcWlpYo3dwwN7fg0K7txHTsXK8uL0RDFJaWRAYEsqfO9F57T5wgNvTKZ4NJTEvD/Tp0QDV51dVosnOwDvQ32m0d6E9lRlaDUSozMjGzsMDCuaYBy8LZCTMLC6rU6nrHry4uAZ0O26hwys+mXO8zEOIfweS6vtrZ2XHrrbcyc+ZMFAoFbdu2Ra1Wk5SUhK+vL3l5eWzatImoqCgOHDjAli1bjOJ///33dO7cGV9fX0pLSzlw4AD+/voLlbW1NZGRkSxYsAAvLy9KS0uZOXOmUfwr+Q5TNPy22/nh048JiYgkLCqaTWtWocrPZ8DwUQD89fuvnE1I4IW33zXEOZ+WSpVGQ3FREeXlZaQmnwEgIETf07dHvwEsmzeHn7/4hNvuvIvSkhJm//Q9XXr1vqaRL6Zg4Ohb+f3LzwgMDSckMopt8WsozC+gz1D9sNalf/7OuTOJPPnaW4Y4melpVGm1lBSpqSgvJz3lLAB+Qfr1fvZv38pvX33G7VPvJSw6hqKLIzYsLC2xd2jdvYeGjhnLz5/PICg8nLCoGDavWUVhQT79h40EYOEfM0lJSmD6G+8Y4mSkpVKl1VKsLqKivJy0s/rGUf/gECwtLfENCDT6DkdnJZYKRb39pq738NH89f1X+IWEERgeyZ6N61CrCug2cAgAa+bPJj05iWkvvQZAXI8+bFi8gAU/fsPAW8eRm5XJlhVLGXTbeMMovrgefdi0dCELf/yGQbffQXlpKctnzaRdl+6G3m5RHTqxfc1KfINC8QsNIz87i/iF84iK69QqR6dccufkKbz95uvExMQQ2z6OxYsWkpeby9jbxwHw7ddfcfLEcT778msAVq9aiZWVFaGhYVgqFJw6eYLvvvmaAQMHYWVldbmvMnndh4xkyS/f4hMcin9oOPu3bEBdWECnfvr1OTYsmkvG2WSmTv8/ANp268WW5YtZ+uv39Bl1G/nZWexYs4x+t9xuKJt7N6xF6e6Bm6d+odtziafYtW4lXfoPMXzvzjUr2LhkPmMfeAw3Ty/DqA5LKytsbJte96Kl6jF0JIt//lb/mwsL58BmfX5eWu9k/cK5ZKQkc/fF/Gx3MT+X/PI9fUffRl52FttXG+fnnjr5mZp4ip1rV9JlQE1+bl2xBN/gUFw82qDVakg6epiju7YzYvLdNzkHmseSvUeZ1Ksj6XkqzucXMql3J8oqNWw+UbMI6/RbBgAwY/kmAMZ0bktWoZrzeSoA2vl7M657e1YcaHie8dZqy8kkhsRGcqGwmJwiNUPbR1Gh1XIguWaU9uQ+nQGYvW2/YZ+Pi/4+Y2NliQ4dPi7OVFVXG0b8DIuL4lxOPjlFxdgoFPSNDsXHxZkFu/7+umst3eTRo3nzqy+JCQ2lfWQki+LjyS3I5/aLoyO+nv0nJ86c4ct/6+/1of4BRvFPJidjbmZmtH9Y7z78vHAh//32ax4cPwF1aQmfzPyVQd27NzpaozWYPPoW3vzyC2JCwy/m5Vpy8/O5fegwAL7+cxYnziTx5WuvAxAaUDcvz+jzstb+OStW4N3Gg2A/f7RaLau3bmHz3r28N/35m3dizeivXYeZ2q8LqbkFpOepuLtfF8oqNcQfTTCE+b/b9feV9xbFA3B7t/ZkqfQN1wBxgT5M6tWRJXtrGg7v7d+VE+nZpOersLe2Ylz39oR6uvHJik037+Rusv4jxzD7my8ICAkjKCKKnevXUlRQQI/B+vK5cu4sUs8k8ugrbwDQsVcf1i2az9zvv2TwbXeQm5XBhmWLGTpuguFen5OZQeqZRALCIigrKWbLqmVkpady5yNPNNdptjhmtjYofH30G+ZmKDzbYBUWQrVajTY7p3kT10LcOXQYb/38IzFBwbQPC2PR5k3kFqoY21+/3us3CxdwIiWZL6a/AMDKHduxtLAgIiAAMzNzth85xIJNG3h83B1Gx01ISwWgpLwMczMzEtJSUVhYEuzjc3NP8CYr3n8Il5FD0GRlU5mRhV37tpjb21N6WL+OlGOfHlh5eZL31xJAv2B9ZfYFlMMHUbhxGwDOA/tQmZmFJkvfAdxC6YyVtyeVmdmY21jj0KkDCjc3VKvXN89JiutCZvxqPibXoAJwzz33YG9vz5w5c8jLy0OpVDJw4EBGjRrFuHHj+OGHH6isrKRjx47cddddfPPNN4a4Op2O7777jtzcXGxtbYmLi2PatGmGz59++mm++OILpk+fjre3N4899hgvv/yy4fNu3bo1+R2mqFuffhQXFbFs3hwKC/LxDQjkmdfexL2NvudKYX4+F7KMexF9+tbr5OXUjM55Y/pTAPy8eAUANra2PP/WO/z5/be8/fyz2Dk40LF7D+64576bc1LNqHOvPpSo1axZNJ+iggK8/QN47OV/G3oCFaoKyM027l3w7ftvk59TUyH730v6BT+/mLsIgG3xa6iuqmLBzJ9ZMLNmirmwmLY8/fp/b/QpNauuvftSoi5i5V/zKCzIxycgkCdfeR23S+WzIJ+cLOP8/OKdN43K59vPPw3A9wuW3byEtwDtu/eitLiYTUsXoi5U4enrzz3TXzaMNlGrCsi/UNOT2sbOjvtfeJVlv//M12+8go29Pb1HjKb3iNGGMNY2Ntz/wr9Z/scvfPPmq9jY2RPTqSvDJ0w2hBlwq35u1vhF8yjMz8Pe0ZHIDp0ZNn7SzTv5ZjB46FAKCwuZ+csv5OXlEhwSyoczPsHLW/9COi83l/Pp5w3hLSws+GPmTNLS00Cnw9PLi3Hj72DSnZMb+4pWo23XHpSVFLNt5RKKC1V4+Phx5xPPo3TTj3IqLlRRkFvzG7axteOuZ15i1Z8z+end17G1s6PHkJF0HzLSEKa6upr1C+dSmJeDubkFLh5tGHT7JDr3G2QIs29zPNVVVSz84Uuj9LTv2Ydb73vkBp/1jXMpP7fWys/JT9bJz1rXRBs7fX6unj2TH9+5mJ9DR9JjaE1+6qqrWb/AOD8HjzPOz8qKclb9+StFBflYKqxw9/LmtgceoV23f8b813/tOoyVpSWPDe+Dg40VpzMu8NqclZRV1ozi8XAyXnjZ3NyM+wd0w9PZkapqHZmqIn7dtIeV/7AGlQ3HElFYWDC+exy21gpScwr4bt12KmpNt+tiX39to+dvHWS03c7fm/ziEv67YC0AtlYKJvTsiJOtNWWVWs7nq/hy9VbDi9nWaGjPXhSq1fyyaBF5qgJC/P2Z8dLLeHvo7/W5KhXp2Vc3asrOxoYvXv03H//6C/f/+xWc7O3p16Urj0+eciNOocUY2qs3hepiflm0gLyCi3n58iu18rLgqvNSo9Xyxe+/kZOfj7WVFcH+/sx4+f/o1dH0p5e9ErO3H8BaYckzo/rjaGvNifRsXvh9idF10tPZuHOYhbkZDw/piZfSiarqajIKCvk+fgdL99WMxHSwsea5MQNwdbCnpKKCxMxcnvplEafOm/bsEZfToUdvStRq4pcsoEhVgJdfANNeeAXXi/X6IlUBebXq9bZ29jz88mss/PVHPvvPS9ja2dN/1Bj6jxxjCFNdXc3mVcvIyczAwsKS0Ji2PPGfd/4x63ReCZuoCPy++NCw7fbgPbg9eA9FK9eS/e7HzZiylmNI124UlhTz68rl5BUWEuLjy0dPPo33xXpoXqGK8znGjU+/rlxOVl4e5ubmBHh68sq999dbP+W+t9802t525DBebm4sfO+DG3tCzaw8IYlCWxscunfBwt4eTV4e+YuWGUabWNjbGY1GAchfvALngX1xn3Q7Oq2WinPpFG3eZvjczNwch84dsHBRQnU1FWnnyZmzgKqiOiNYhBBXxKysrEzas1qgm7mGyj9BSUVl04HEFbFupWs6NJccdeuclqC59I8Kae4ktCqrDzc8T7m4etdjsW1RY/aOQ82dhFYl0qd1TuHUHF6LDW7uJLQuMuXQdXX7YtOfWaGleG50/+ZOQqsS9ew/Y8TWzeL6zmvNnYRWpWJ/6x3lerO5Pj6t6UDiir02b01zJ6FJb08c3txJuCHkzagQQgghhBBCCCGEEEIIYSKqZc6vZiNdfoQQQgghhBBCCCGEEEIIIZogDSpCCCGEEEIIIYQQQgghhBBNkCm/hBBCCCGEEEIIIYQQQggToUOm/GouMkJFCCGEEEIIIYQQQgghhBCiCdKgIoQQQgghhBBCCCGEEEII0QRpUBFCCCGEEEIIIYQQQgghhGiCrKEihBBCCCGEEEIIIYQQQpiIap2sodJcZISKEEIIIYQQQgghhBBCCCFEE6RBRQghhBBCCCGEEEIIIYQQogky5ZcQQgghhBBCCCGEEEIIYSJkxq/mIyNUhBBCCCGEEEIIIYQQQgghmiANKkIIIYQQQgghhBBCCCGEEE2QKb+EEEIIIYQQQgghhBBCCBOhkzm/mo2MUBFCCCGEEEIIIYQQQgghhGiCNKgIIYQQQgghhBBCCCGEEEI0Qab8EkIIIYQQQgghhBBCCCFMRLVM+dVsZISKEEIIIYQQQgghhBBCCCFEE6RBRQghhBBCCCGEEEIIIYQQogky5VcLFeTu2txJaFXS8wubOwmthoeTfXMnoVXRaKuaOwmtinVpSXMnoVW5UFTc3EloNdwd5dp5PT03un9zJ6FViT+W2NxJaDUsQ4KaOwlCNMrVwa65k9BqVFdXN3cSWhXXd15r7iS0Kvmvvt3cSWhVzKytmzsJrYbr49OaOwmtik6m/Go2MkJFCCGEEEIIIYQQQgghhBCiCdKgIoQQQgghhBBCCCGEEEII0QRpUBFCCCGEEEIIIYQQQgghhGiCrKEihBBCCCGEEEIIIYQQQpiIallCpdnICBUhhBBCCCGEEEIIIYQQQogmSIOKEEIIIYQQQgghhBBCCCFEE2TKLyH+v727jq7iaAM4/Iu7hyQkhBiEGASH4u7uVkrxAsVqQIV+0JYWWqRIW9zdnRLcJbgmeICEEHe99/sjcOGShKRtQoT3OYdzuHtnd2cne3dnd2beEUIIIYQQQgghhBBCiCJCqZSYXwVFRqgIIYQQQgghhBBCCCGEEELkQBpUhBBCCCGEEEIIIYQQQgghciAhv4QQQgghhBBCCCGEEEKIIkJCfr2SmprK4sWLOXLkCCkpKfj6+vLJJ59gbW2d7Tr79u3j4MGDPHr0CKVSiaurK71798bb2zvH/ckIFSGEEEIIIYQQQgghhBBCFDkLFizg5MmTfPHFF/z8888kJCQwadIk0tPTs13n6tWr1K1blx9++IFff/0VBwcHJk6cyNOnT3PcnzSoCCGEEEIIIYQQQgghhBCiSImPj2f//v18/PHHVKpUiTJlyjB27FgePHjA5cuXs13v888/p02bNri5uVGqVCmGDRuGgYEB/v7+Oe5TQn4JIYQQQgghhBBCCCGEEEWEogiE/Nq7dy/79u3LMV3z5s1p0aLFv9rHnTt3SEtLo1KlSqplJUqUoFSpUty8eZPKlSvnajtpaWmkpqZibGycY1ppUBFCCCGEEEIIIYQQQgghRJ5p0aLFv24oya3IyEg0NTUxNTVVW25hYUFkZGSut7NixQr09fWpUaNGjmmlQUUIIYQQQgghhBBCCCGEEIXCihUrWL9+/VvT/PTTT9l+p1Qq0dDQyNW+tm/fzt69e/nhhx8wNDTMMb00qAghhBBCCCGEEEIIIYQQRUThD/j137Rr144GDRq8NU2JEiVQKBQoFApiYmIwMzNTfRcVFYW3t3eO+9m+fTsrV65k4sSJuLu75ypv0qAihBBCCCGEEEIIIYQQQohCwczMTK2BJDtlypRBW1ubixcvqhpgwsLCePz4MZ6enm9dd+vWraxatYqJEyfmqvHlJWlQEUIIIYQQQgghhBBCCCFEkWJkZETTpk1ZsmQJ5ubmmJiYsGjRIpydnfH19VWl+/rrr3F3d+ejjz4CYPPmzaxYsYKxY8fi4OCgmm9FV1cXIyOjt+5TGlSEEEIIIYQQQgghhBBCiCJCqSzuQb9yb+DAgWhpaTF16lSSk5Px9fVlzJgxaGlpqdKEhIRgbW2t+rxr1y7S0tKYOnWq2rYaNWrEmDFj3rq/Qt+gMmPGDGJiYpg4cWJBZyVXoqOj6dOnDz/99BPly5cv6OwIIYQQQgghhBBCCCGEEMWSrq4uQ4YMYciQIdmmWbRo0Vs//xOFvkFl8ODBuW5x8/Pz46+//mLDhg35nKviadvmTaxbs4rw8HCcnV0YPmo0FXwrZpn20oULbFy/lls3bxAfF4dDqVJ07tqdlm3aqqU78Pc+1q5exeOgRxgaGVGlajWGDv8USyurd3BEBctv9w52b95IdGQEDqWd6D1wKOW8fbJMm5KSwtJ5v/Pw7h2ePg6irKcXE36appZm/sxfOX7QL9O6unp6LNywLV+OoTDZuXULG9euISI8HCcXZ4aMGIlPBd8s0165eJEtG9dz++YNEuLjKengQIcu3WjeqrVamq/GjMy07vxlK3F0csq34ygMju7bjd+OrURHRVKylCNdPhpAGc+sY0WmpqSwZuEfBN2/R8iTx7iV82D0xB/V0lw6c4pjfnt5fP8+qakp2JVypEXHrlSoWv1dHE6B27BtKyvXrSMsPBxXZ2fGDh9BpQoVskx778EDpv4+i/sPHxIXF4e1tTXNGjZk8Ef90NHRAeD7X35m1759mdbV19fn2O49+XosBUGpVOK/fxe3zhwnOSEBm9LO1O7YA0s7+7eu9/RuAKd3bCTyWTCGpmb4NmiG1wf1skx75+I5Dq5eTGlPH1r0H65avvqnr4mLjMiU3tHDh5YDhmdaXtj5H/bj9P7dxEVHU8LegSZde1O6bLls04c+CWLf2uUEP7iHvqExleo1pE6r9mhoaABw6+I5Lh49REjQQ9JTU7EuaU+tlu1w962s2sbFY4e4euYEYU+foFQqsXV0on67TjiWyX6/RdWRfbvZv23zi2tnabp+PJCyb7l2rp4/j6D7dwl+8hi3cp6M/d9PamkCrl9j2+rlPHv6hJTkZCxLlKB242Y0bdfxXRxOodC4vDvV3EpjoKtDUHgU289fJTQ6Ltv0Jvp6tKrshb2FGVYmRlx88JhNpy+rpanqVprKLg7YmJmgoaFBcGQ0+6/c5uHzyPw+nAK1YeNGVqxcmXEvcnHhszFjqFSpUpZp7927xy/TpnH//n3i4uMpYW1Ns6ZNGTxokOpe9LpLly4xZNgwnJycWL9mTX4fSoHL67L0v3CBufPm8fDhQ5KSk7Gzs6NDu3Z82KfPuzysAtW9ViWaViiHkZ4ugSHPWeB3iqDwqGzT1yjrRHNfD1xsLNHV1iYoPIpNpy9x7m6QKs2k7i3xcSyZad1HYZGMXrolPw7jnTvpt48ju7cTGx2FrUMp2vXuh0u57GPBBwc9YuvyRQTdu4OhsTE1GjalSfvOqvs6wMWTxzm8exthIcHoGRhQ1rs8bXr0xcTcHIAzh/zwP3GUZ0+CUCqV2Du50LxTd1zKeeT34b5zmw4fZPW+fYRHR+Fi78Co7j2oWDbryYiDw8LoPOGrTMunjxxNTZ+MjrRhUVHM3rie2w8f8jj0GS1qfsA3Hw/I12MoivR9fbDo2QX9cmXRLmFNyI+/Ertnf0Fnq9AxbdcSi26d0LKyIOXBI8LmLSTp6o1s0xvXr41Fr67olHIgPTqa6K27iFr/6lpo8+UoTJs3zrSeIjGJe2265csxCFGc5VuDSmpqapYV8n8qp5hl+SUtLQ1t7ULf3pRnDh3wY86sGYz67AvKV/Bl25ZNjPt8LEtWrMbWzi5T+uvXruDi6kaPXr2xtLLm3Nkz/DbtF3R1dWncrDkA165cZsoPkxgyfAR16tYnMiKCmdOn8eOkifw2a867PsR36vSxI6xa8Cd9h47A3cubA7t38uv/vmHK3PlYl7DJlF6pUKCjq0uT1u247H+OhPjMLxP6DPqEbh/1V1v2w1efZdtIU5wcOXiAP2fPYviYsXiXr8DOrVv49ssv+GvZCmxsbTOlv3H9Ks4urnTp0RNLK2v8z53h91+noaurS8MmTdXS/rl0OSYmpqrPZi8eJoor/5PH2bBsET0GDMGtnCdH/97D3CmT+Xb6bCytS2RKr1Ao0NHRpX7zVly/6E9iQnymNIE3r1POuwJtu/fG0NiEc8eOMP/Xnxk9cXK2DTXFxd+HDvLbnDl8NWo0FcuXZ+O2bYwa9xXrlyzFLotzU0dHh9bNmlOubBlMjIwJuHuXn6b/Rnp6OiOHDAXg8+EjGDFosNp6Az/9NNtGmqLu8uG/uXrUj/rd+mJuY8uF/bvZveB3un3xPbr6+lmuExMRxt5FcylXvRYNe35MyIO7HN+8Bn0jY1wrVFZPG/6cM7s2Y+dSJtN2Oo4ch1KhUH1OiI1h86wpuPlWzpS2sLtx/jT716+iec++OJZxx//IAdbN+ZXBE6dgZmmdKX1yYiJrZk3FsUw5+o37HxHPgtm5bAG6unrUaNoSgEcBt3Eq50n9dp3RNzLm+tmTbPpzFr3HTlA11DwKuIVXlRqU6lYWHV09zh7Yy9rfpzHg6x+wtM1cfyiqzp84xvolC+g5cChuHl4c3bebuT/+j+9mzMWyRDbXTl1d6rdozfWL/iTEZ7526unr06BVGxxKO6Orq8vd2zdZPX8eunp61G/e6l0cVoGq5+lGHQ9XNp6+RFhMPI18ytK/YU2m7zxESlp6lutoaWkSn5zCkRt3qFamdJZpXG2tuPIomIfPr5Oalk5tD1c+bliD2XuOER6b+e9QHPy9fz+/Tp/OuC+/pKKvLxs2bWLkmDFsWLsWuyzq8To6OrRp3Zpy7u6YmJgQEBjIjz/9RFp6OqM+/VQtbUxMDBP/9z+qVa1K6PPn7+qQCkx+lKWhgQHdu3WjTJky6Ovrc/nyZX76+Wf09fXp2qXLuz7Ed65j9fK0q+rD7D1HeRoZTdcPKjGxawtGLNpIUmpalut4l7Lj6qNgVh/3Jy4pmXqebnzZvjHfrdvDzSfPAJi67QDamq/CeOhoazLjo46cvH3/nRxXfrt0+iTbVy2lY98BOLt7cOrA3yz69Sc+mzIDC+vM9/WkxAQWTJ2MazlPRv5vCs+Dn7JuwYt7SsuMTo8PAm6x9q/ZtO75IT5VqhMbHcWWZYtY8+fvDB73HQB3b93At0YtnMuWQ0dPj2N7d7Jw2o+M/mEqJewyN2AVVX7nzjJz7Vo+790b3zJl2Xz4EJ/9PpNV30/G7i2dP6ePGkPZUo6qz6avva9KTUvDzNiYD1u2ZNvRo/ma/6JM08CAlHsPid3rh+03XxR0dgol4wZ1KDF8EM9n/UnitRuYtWuF/ZSJPOo/nLTQsEzpDatXxvbrz3k+Zz4J5y6gW9oRm7HDUSanEL1tFwBhcxcQvmCZ2nqlfv+FxCvX38kxCVHc5FmLwfjx43F0dERPT4+DBw9iY2PDmDFjWLJkCdevX0dXVxdfX18GDhyIhYUFAOnp6SxevJgDBw4A0LhxY1JTUwkKCmLKlClA5pBf165dY+nSpTx8+BBNTU1KlSrFyJEjiYmJYdasWQC0bZtRYejZsye9evUiNTWVVatWcfjwYeLi4nB0dOTDDz+kcuWMFyZXr15lwoQJTJw4kdWrV3P//n3Gjx9PtWrV2Lx5M3v37iUiIoKSJUvSuXNnGjZsqDrugIAA5s2bx6NHj3B0dKRPEe1ptGHtGpq3ak2bdu0BGDnmM86dOc32rZsZNHRYpvS9+/ZT+9y+YycuXfDn6JHDqgaV69euYV3Chq7dewJQ0t6ejp27Mnvm9Pw9mEJg77bN1GnclIbNM15K9R0yjKsXznNw985MjSKQ8VLl42EZoyWCHtzPskHF0MgIw9cqbAE3rhMaEsyQMcW/ErJlwzqatmhJyzbtABg2agz+Z8+ya9sWPh48NFP6Hn36qn1u074jVy5e5MTRI5kaVMzNLYp9I8rrDuzaRs36jajduBkA3foP5sblixz7ey/te32YKb2evj49B30CwJNHD7NsUOnab6Da59Zde3D9oj+Xz50p9g0qqzdsoE3zFnRs0waAL0aO5NS5s2zcvp0RgwZlSu/o4ICjg4Pqc0k7Oy5cvsSlq1dVy4yNjTF+bZ3L167yJPgp/xs/Pt+Oo6AolUquHjuIb8PmqoaQBj0+YsX/vuTOxXN4fVA3y/VunjqGoZkZtTt0B8DCtiShj+5z5YifWoOKIj2dA6sWU61FO57eCSApQf3aamBsovb51rmT6Orp41qhSl4e5jtx1m8vFT6oQ6W6GXWU5j36cu/6VS4cOUjDjpl7nV07e5LUlGTa9huMjq4uNg6lCAt5yhm/vVRv0gINDQ2adVev09Rt05E7Vy8TcNlf1aDSfsAnamla9OpHwOUL3L1xpVg1qBzYuY0PGjSmTpOMOk73AUO4fukCR//eTYfeH2VKr6evT6/BGfWnJw8fZNmg4uRWBie3Vw191rZ2XDpzijs3r78XDSq1PFw4cuMO14NCANhw+hJfd2pGRWcHzt55lOU6UfGJ7PTPePD3KZ31i731Jy+qfd527ipepWxxL1mCU8W0QWXVmjW0bdOGjh06APDl559z6tQpNm7axIjhmUfbOTo64uj46oVgyZIl8b9wgUuXLmVKO/nHH2ndujVKpZIDBw/m1yEUGvlRlp6ennh6vhpV4GBvz6HDh7l46dJ70aDSprI3m89c4XTgQwBm7znKkmG9qOfpxt9Xbme5zuJDZ9Q+rz91iSqujtQo66RqUIlLSlFLU8/TFT0dbQ5cC8yHo3j3ju3dSdU69anRsAkAHfr25/bVS5w++Dctu/XKlP7iyeOkJqfQffAIdHR1sStVmtCnTzi2dyf1WrRBQ0ODh3cCMLO0ol6LjHqrZQkbajdtwbYVi1Xb6fWJ+gj+Tv0Gcf3COQKuXCpWDSpr9/9Nq1q1aF+3PgBje/bm9PVrbDlymE86dc52PTMjY6zMzLL8rqS1NWN7ZPxtDvn7532mi4mE0+dIOH0OANsJnxdwbgon8y7tidl3gJjdfwMQNmc+htUqY9a2FeGLlmdKb9KkIfGnzhKzPSOaQVrwMyLXbMS8RydVg4oiPgHiE1Tr6Ht7omNfkmdTZryDIxL5RSFzqBQYzbzc2OHDhwH4+eefGTJkCOPGjcPJyYnffvuNyZMnk5iYyOTJk1G86A26efNmDhw4wKeffsqvv/6KUqnkyJEj2W4/PT2dH374AU9PT37//Xd+/fVX2rZti6amJh4eHgwaNAg9PT2WL1/O8uXL6dgxI2TCrFmzuHbtGp9//jlz5syhcePGTJ48mfv31XuvLF26lD59+vDHH39Qrlw5VqxYwf79+xk6dChz586lS5cuzJ07l3PnMi7+SUlJTJo0CTs7O2bMmMFHH33E4sWLM+W7sEtNTSUg4DZVq6mH56larTrXr13NZq3MEuLjMTF59XLKp0IFIsLDOHn8GEqlkuioKA4d8KNGzQ/yLO+FUVpqKg/uBFK+onoPZ59KlQm8dTPP9nP47z04lHairKdXnm2zMEpNTSXwdgCV3zg/K1erxo3r13K9nYT4eIyNjTMtHzlkEL06tWfc2FFcvnjhP+e3MEtLSyXo3l08K1RUW+5ZoSL3Am7l6b6SEhMxNMpc3sVJamoqtwICqFm1qtryGlWrciWX52bQkyecOneOStmErwPYsmsXrs7O+PoUv9FosRFhJMbGUMr91YsmbR1d7FzK8uzh3WzXe/bwHqXKqoe8cHT34vnjhyjSX/VsP7tnGyaWVrhXzfm+o1QquX32BGUqV0dbV/dfHE3BSU9LI/jRA1y81Oduc/Hy4fG9rF8sPbl3B8cy5dB57VhdvcoTFx1JdHjmnm8vpSQnom+Y/ejh9LQ00lJTMXhLmqImLTWVR/fu4PlGGFRP30rcu513186g+3e5d/sWZb2K32/9TRZGhpga6BMY/GrEQ1q6gvuh4ZS2tsjTfWlpaqKtpUViSmqebrewSE1N5datW9SsUUNtec0aNbhyNXf1+KCgIE6dOqXqbPbSho0bCQ8PZ8DHH+dZfguz/CzL1926fZsrV668NU1xYWtmgoWxIZcfPlEtS0lL58bjEMo5ZB61/zYGujrEJSVn+32TCuW4eP9xsRiJlpaWxpMH93Avr14/dPepwIPArBuhHt4JwKWch9p93b28LzGRkUSGZVxrnct6EBsVyY2L51EqlcTHxnD59Ek8fLMOaQev3dcLKHJIfkhNS+P2o4fU8FLv+FXdy5urd++8dd0Jf8yl1WejGfLLFA76n8/PbIr3lbY2eu5lSDh/SW1xgv9F9L2zDr2noaOD8o16jjIlBR2bEmjbZn2tNW3djOT7D0m6kbfvAYR4X+RpTCtbW1sGDMiIEbly5UpcXFzo16+f6vuxY8fSs2dP7ty5g7u7Ozt27KBz587Url0bgEGDBnHhQvYvNBMSEoiPj6d69eqULJnRO+L1HkGGhoZoaGioRsAABAcHc/ToURYuXIiNTcaFpE2bNly6dIk9e/YwbNir0Rc9e/ZUVWyTkpLYtm0bkyZNwts740ZrZ2dHYGAgu3btolq1ahw+fJi0tDRGjRqFgYEBTk5OdOvWjenTi9YIjOjoKBTp6VhYWqott7C0xP987ioJp04c54L/eX7/4y/VMm+f8nzz/SR+mvQ9ycnJpKenU6VadcZ9821eZr/QiY2JQaFQYGqu/kLAzNyC65cvZrPWP5MQH8/ZE8fo+mHxf8CNiY5GoUjH3EK9PM0tLIj0zzz3QVbOnDzBpQv+/DZnnmqZpZUVI8Z8hruHJ2lpqRz4ex/jx47ml5m/Uz6buYOKuriYWBQKBSZm5mrLTczMibl6OeuV/oUj+3YTFRFG9XoN8mybhVFUdDTpCgWWb5yblhYWnPV/e+Nc/xEjuB0YQEpqKh1at2b4wIFZpouLi+PAkSMMG1A84y8nxMYAYGhsqrbcwMSEhOiobNdLjI3BoKz6A4WBiSlKhYKk+DgMTc14fPsG9y7703nMhFzl5UnATWIjwvGoXvufHUQhkBAXi1KhwMhUvRyNTM14cCvrYfzxMdGYvHHuvlw/LiYK8yxCAJ4/7EdsZCTla2RfRke2b0RXT4+yFYrPi8K42Bf39TeunaZm5tyK+u/XzvFDPiYuJpr0dAWtu/agXrOW/3mbhZ2JgR5AppejcUnJmBpmHerv32paoRwpaWncfPwsT7dbWERFRZGeno7lG/V4S0tLzrzoBJad/gMHcuv2bVJSUujYvj3DP3k14uzOnTssWLiQJYsXo6Wl9ZatFB/5VZYvtWrThsgX+xg0YABdOnXK0/wXRuZGBkDG6LLXRcUnYmlsmOvttKjoiZWJEUduZP2yu6SFKT6OJZmyJfN8k0VR/Iv7jrGp+kgIYzNzYq9n3bgXGx2FmYX6uWtsap7xXVQUliVscCrrTq9ho1jzx2xSU1NQpKdT1qcC3QePyDYvezeuRU9PH6/KVbNNU9RExcWSrlBg8Ua9ydLUlPM3s56jwkBPjxFdulGhTBm0NLU4fvkS383/k5SPB9CimHcYFe+WlpkpGlpapEdGqS1Pj4xCq3LWnfASzl/EevhADKpUJPHCZXQcSmLepQMA2lYWpD0LVUuvaWSIcb3ahC9ekR+HIMR7IU8bVNzc3FT/v3v3LtevX6dr166Z0gUHB+Pg4EBkZCTu7q8m/dLQ0KBs2bKEhWXdM9LExITGjRszceJEfH198fX1pXbt2pTIInb16/lQKpUMf2OIdmpqKhXeiEdftmxZ1f8fPXpESkoKEydOVJvELS0tDdsXcfGDgoJwdnbGwMBA9b2Hx9sna9u7dy/7sphs+E116jekQeMmOabLS68fJ4BSCW8sytK1K5f58X8TGTF6LJ6v9fJ4cP8+s2dOp0+/j6lWvSYR4WH8NW8O06f+wvhvJ+Z19gudzOWpRINcFGgunDx8AKVCQe2GmScVK67eLE+U5Ko8r1+9wi8/TGLoyFGUe200T6nSpSlV+lX8dU9vH56FhLBp3dpi26DyUqaiVCozl++/dPHMSbasXEr/UZ9jlcV8QcVRludmDsX503ffkZCQQODdu/z+158sW7uGj3v1zpRut99+FOnptGraLA9zXHACL5zl2KbVqs8t+r/o1PBmeSmzWqjuzd+/8rXhzknxcRxev5xGvfqjl8uREjfPnqCEoxPWDo45Jy60Mv24My9TS5353M1yOXDrwjkOblpLh4HDMLPKHLsd4OyBfVw8doheo75C77W6UbHx5n0dZa7qSTn5bNIUkpOSuB94my0rl2FtY0uN+g1zXrEI8XV2oEO1VyOolh85m2U6DTRU52FeqFXOheplS7P44BmS07Keq6G4yOoymtO9/acffyQhPp6AwEB+nz2bZcuX83G/fqSkpDDhm28YNXIkDvb2+Zbnwiovy/J1C+bPJzEhgavXrjF77lzs7e1p3ap4hfer5+nKkKavGt1/3Jwx0fSbP+t/cu2sWdaJj+pXY/rOQzyPyXr0SdMK5YiIS8D/XlCW3xdVmeuYb6+zZ/5OdWMH4NmTx2xbuYTG7TtTrrwvMVGR7Fq3kk1L5tNjSOZGleP7dnPmkB+DvvoWfYPcN4AVFVk9r2dXbzI3MaHXi9DmAJ7OzkTFxbJq315pUBH5JNOVM9s6UsyufejY21Fy8tdoaGujiE8gavMOrPr1QpmuyJTepEkD0NIkdv+hPM+1eLeUEvKrwORpg4r+a5PHKhQKqlatSv/+meeLMDc3V/3R/+lLvNGjR9O+fXv8/f05c+YMK1as4Ouvv852yPTLF4XTp0/P1LtKT08v288v8/ftt99marD5L5PVt2jRghYtWuSYLjw2Icc0ecXMzBxNLS0iwsPVlkdFRmQatfKmq5cvM/6LsfQbOIj2HdV7Wa1euRwPTy969MqIwe5Wpgz6+gaMGj6UAYOHZjmZeHFgYmqKpqYm0ZHqoydioqMyjVr5tw7/vZeqtepgbGKSc+IiztTMDE1NLSIj1MszKioSc8u3l+e1K1f4btwXfPjxANq075jjvjw8vThy8MB/ym9hZmxqgqamJjFRUWrL42KiM41a+TcunjnJsjkz6Tt8NBWqVs95hSLO3MwMLU1Nwt84NyOiIjONWnmT3YsRk67OzqQrFPz46zQ+7N4D7TfuU1t37aJhvXqYvdGDrqhy8qqATWln1ef0Fy84E2JjMDZ/db9JjIvF4C3XNwMTUxJio9WWJcXFoqGpib6RMSEP7pIQE82u+bNU37+8ry/4ajhdP/sWc5tX83skxsXw8Pplanfs8Z+Or6AYGpugoalJfIx6mcTHxmQatfKSkakZcVmkf/nd625dOMf2JX/Rtt9g3H2zrm+dPbCPo9s30f3Tz7B3ccsyTVFlbGL64toZqbY8Njo606iVf8P6xVwzDk7OxERHsXPDmmLXoHLzcQhBYa/KT1srI+qwsb4e0QlJquVG+rpvDenzT9Qq50LTCuVYevgMj8Oj8mSbhZG5uTlaWlqZ7kWRERFY5VCPt3tRF3d1dUWhUPDDTz/xYZ8+hIWFce/+fSb98AOTfvgByHi2UyqV1KhVi1nTp1OzZs38OaAClB9l+fpz48vGqTJlyhAeEcH8hQuLXYPK2TuPCHgtlJ/Oi3qNhZGBWiguM0MDohISM63/ppplnRjVqj6/7znKubtZN5Zoa2rS0LsM+68EFJtY8kYv7juxb4zWjYuJzjRq5SUTM/Ms07/8DuDQji04upahQeuMeSlLlnZCV0+fP378jhZdemD+WoeJ4/t2s3fTWgZ8NoHSr833VRyYG5ugpalJRLR6PSgyNhbLf1Dn9nZxZdfJE3mdPfGeS4+OQZmejtYbz5NaFmaZRq28LnzBMsIXrUDL0pz0qBgMK2d0IH9zdAqAaatmxB89iSI289y9QojcydM5VF7n5ubGo0ePsLGxwd7eXu2foaEhRkZGWFhYEBAQoFpHqVQSGJjzJHIuLi506dKFKVOm4OPjo5rUXltbWzU/y0uurq4olUoiIyMz5cPKyirbfTg6OqKjo8Pz588zrfcydJijoyMPHjwgKenVg+Dt21nHNC3MdHR0cHcvh/8bQ9n9z53D26d8NmvB5UsXGff5WPp+PIAu3TK/hEpOSsrUiKX54gG6OLeiauvo4FymLNcuqYf3unbpImU9PLNZK/fu3r7Fo/v3aNAs54a54kBHR4ey5dy5cF79/Lx4/hxe3tnHmb96+RLfffU5vT/6mI5dM0/InJW7dwKxfMt1oajT1tbB0dWNW1cvqS2/dfUyru5vH12XE/9Tx1k2eyYfDhtJ5Zq1/tO2igodHR083N0580b85LP+/lR4y7n5JqVSQXp6utrcHwDXbt4g8O5dOrZukyf5LQx09fUxs7ZR/bOwLYmBiSlPAl7NL5WWmkrI/TvYOmX/Ut7WyZUnd9Tj/T4OvEWJUk5oamlRwtGJLp99Q+cxE1T/nLwqUNKlDJ3HTMDEUn2Exe1zp9DS1sbNt2iGs9DS1qZkaWfu31Sfu+fBzWuUci2b5ToOrmUIunObtNRXk/rev3kNYzMLtREoN86fYfuSP2nz0SA8q2TdUHrGbw9Htm+k24ixOJYplwdHVLho6+hQ2rUMty5fUlt+68olXMv9t2vnm5QKJWmpxW+uj5S0dCLiElT/QqPjiElMoozdq05L2pqaONtY8igs8i1byp3aHhmNKcsOn+Xh8/++vcJMR0cHDw8PzpxRn8T7zNmzVCiffT3+TQqlMuNepFBgY2PD2tWrWbVihepf506dcCxVilUrVmQa5V9c5EdZZkepUJCakpLt90VVUmoaIVGxqn9B4VFExiXg6/RqpJOOlhaeDrbcfpL5Jd/rapVzYVSr+szee4xTAQ+yTVejrBMmBvocuBqQbZqiRltbGwdnVwKuXVFbHnjtKs5ls77POpVx5/7tW2rnVeC1K5haWGDxIoxnSkoymprqr4Befn798fzonp3s3biG/mPH4ZLH97nCQEdbm3KlnTj7RnivczduUP4fNB4FBgVhnc0E9UL8a2lpJAfcwbBKRbXFhlUqknQ9h/lOFArSwyIgLQ3jhvVIvH6T9Cj1hkM9D3f0yriqJrwXQvw7eTpC5XWtW7fm77//ZurUqXTu3BkzMzNCQkI4fvw4/fv3x9DQkLZt27J582YcHBxwdHRk7969REZGZopb+1JISAh79+6lRo0aWFlZERISwoMHD2j1omePra0tKSkpXLx4EVdXV/T09HBwcKBBgwbMnDmTAQMG4ObmRmxsLFevXsXOzo5atbJ+8WdoaEjHjh1ZvHgxSqUSb29vkpKSuH37NhoaGrRo0YL69euzYsUKZs2aRY8ePYiIiGD9+vX5VaT5qmuPnkyZ/D88vLzwKV+BHVu3EBYeRtsOGb36F/w5j1s3b/DbrDkAXLpwgQlffka7jp1o0qy5anSLpqamaq6LD2rX4bdfprBty2aqVa9BRHgYc3+fRVn3ctja2WWdkWKiRftO/DVjGq7u7pT19ObQ3l1ERYTTqGVrANYvW8y9wADG/fCzap0njx6SlpZGbGwMSUlJPLyXMSGzk6v6S8XDf+/B1t4BD5/i+TCblY5du/PrTz9QzsMTr/Ll2b19G+Fh4bRq1wGAJfP/5Patm/w8PaM3+pWLF/lu/Je0ad+Bhk2avjo/tTQxfzFKaMuG9dja2eHk4kJaahoH9+/j1PFjfDPphwI5xnelcev2LJszEyc3d9zKeXDMbx9RERHUaZoxhH3b6hU8uBvAqG8nq9YJfhxEWloq8TExJCclEfTgHgCOzq4AnD9xjGVzZ9KpTz/KeHoT/aIXt7a2NkbGxXsUVa+uXZk4ZQreHp74+viwacd2noeF0bltWwDmLFjA9Vs3+eO3jLm1dv/9N7q6upRxdUVbW5ubAbeZu2AhjerXR/eNidC37tpF6VKlqOyb/YT1RZ2Ghgbl6zbi4oG9mNvYYVbChgt+e9DR06NMpWqqdIfWLAWgYc9+AHh+UJfrJw5zctt6PGvW5dmDuwScP0WjXhmjYnV09bC0c1Dbl56+AUpFeqblSqWSW2dP4OZbFV39vJ274V2q3qQF25f8hb2zK6XcynLh6CFio6OoXK8RAIe2rOfpg3v0HjMOAO/qH3B811Z2LFtA7ZbtiQgN5tS+ndRt3VE1evj6udPsWPIXjTr3oHTZcsS96Pmqpa2NgZExAKf/3sXhbRtp9/FQLG3sVGm0dXWLVXiQxm3as3T2DJzKuuNWzpNjf+8lOiKCui/mO9m6ahkP7gQyeuKre0hw0CPS0tKIi43NuHbef3HtdMm4dh7asxNrG1ts7TPOycAb1/DbsYV6zYpXj/XsnLx1nwY+ZQiLiSMsNp6GPmVISU3n0oNXk1d3+aAiABtPXVItK2me0XtYT0cbpVJJSXNT0hUKQmMyelnW9XSlaQUPNpy6SFhsPMb6GSPQU9PTSU4tnmG/evfsyXfff4+3tze+FSqwafPmjHvRizk65sydy/UbN/hj7lwAdu3ejZ6eHmXc3NDW0eHmzZvMnTePRg0bqu5FZdzU658WFhbo6OpmWl7c5EdZrl2/Hgd7e5xehJq9cOkSK1etokuXLgVzkO/YzgvX6VzDl8cR0QRHRtOlZkWSUtM4evOuKs3IlvUA+H3PUQBqv2hMWXbkLDeCQjA3zAgjmaZIJy5JvSGqSYVyXH34lGfRse/oiN6Nui3asO6v2Ti6lsG5bDlOH9pPTFQENRs1BWDP+tUE3bvD4HHfAVDxgzrs37qB9Qvm0bh9J54HB3No5zaadOyiuq97VarKxsV/cerA37iX9yU2KpLtq5bh4OyChXVGZ4rDu7azb+Maegz9lBJ29sS+GNmurauLgWHxua/3aNqMSYsX4uXsQoUyZdhy5DBh0VF0qF8fgD82b+LGg3vMHvsFALtPnkBbSwv30qXR0NDkxJVLbDp8kGGd1H/HAUGPAIhPSkRTQ4OAoEfoaGnj8h6GT8yOhoE+Og4vykNTAx1bG3TLuKKIjSXt2fO3r/yeiNq4DdtxY0i+HUDitZuYtW2BtpUl0Tv2AGA1oC96HmV5+kXG/MSapiYY169D4uWraOjoYNqiMcb1a/Mki7kkTVs3I+XxExIvX8v0nSh6FMW3r3qhl28NKlZWVkydOpVly5YxceJEUlNTKVGiBJUqVUJHRweATp06ERUVxaxZGS9BmzRpQs2aNYl6IxzNS3p6ejx9+pSff/6ZmJgYzM3NadCgAZ07dwbA09OTli1bMm3aNGJjY+nZsye9evVi1KhRrF+/niVLlhAeHo6xsTHu7u459q7q06cP5ubmbNmyhXnz5mFoaIirqyudXlSoDQwM+O6775g3bx6jR4+mVKlS9OvXj8mTJ791u4VRw8ZNiImOZuWyJUSEh+Ps4sqUab9hZ1cSgIjwcJ4+efWAu2/PLpKSkli/ZjXr17yKh29rZ8eajVsAaNGqNQkJCWzdtJE/5/yOkbExFStVZsiw7Ce9Ky5q1q1PXGwM29evISoiklJOTnz23WSsbTLCAURFRhAa8lRtnd8mfUtY6KueWt+Ozpj3Z/n2vapliQkJnD52hA7de+fZnBdFQf1GjYmNiWHNiuVERITj7OLCpF+mqhrmIsLDCX7yqjz3791NclISm9atZdO6tarlNrZ2LFu3AYC0tFQW/jGP8LDn6Orp4eTswv9+nkr1Yh4Dt0qtOsTHxrB3y3piIiMp6ViaYeO+Vc13Eh0VQdizELV15v08iYjnryq3P381FoC567YCcNxvL4r0dDYuW8TGZYtU6cp6eTN64o/5fEQFq1nDRkTHxLB45QrCIiJwc3Zm5pSfKfni3AyLCOfJ01fnppaWFktXryboyWOUSiV2trZ07dCenl3U5xuLT0jg74MHGdi3b7H/rfs2aEZaairHt6wlJTEBm9IutBr0qVrjRlyUevgVU0trWgwYzqkdG7lx6hhGpmbUat8N138xEXrw3QBiwp7TqGfmEKVFiVfVmiTGxXFi93biYqIoYV+K7iM+U402iYuOIur5q3uMvoEhPUd9yb41y1kyZSL6hobUaNKS6k1ejX68ePQgCkU6fhtW4bdhlWp56bIe9Pks4wHN//ABFOnpbF04Vy0/5WvWoW2/wfl5yO9U1dp1iY+LZc+m9cRERlDS0YnhE757de2MjOT5G9fOOVMmEfFamf/05WgA/tiwHQCFIp0tK5cS/jwUTU0tStjZ0aH3R9Rt+n6MQD168y462lq0reaDga4Oj8OiWHLoDClpr0brvXyJ+rpPW9VT++xZyo7IuASmbT8IQM2yzmhradKzThW1dP73gth0+nI+HEnBa9a0KdHR0SxasoSwsDDcXF2ZNWMGJUtm1OPDwsN5/Fo9XktLiyXLlhEUFJTRKGVnR9cuXejVo2iGPcxL+VGWivR0Zs+Zw9PgYLS0tChVqhQjhg9XNdIUd1vOXkVXW5vBjT/ASF+XwODnTNq4l6TXGjitTdXnO2te0QNtLU0GNKrJgEavwstdCwrmu3V7VJ9tzUwoX7ok03cezvfjeNcq1qxFQlwsB7dvJiYqErtSjvT/bLxqtElMVCThoc9U6Q0MDRn05bdsXb6I3yeOx8DQiHot21CvxauRzlXrNiA5MZGTfnvZuWY5+gaGuHl606pHH1WaUwf2kZ6ezqq5M9XyU6VOfboPVp+XtihrUq060fFxLN29k/DoaFztHfj101GUfFFvCo+O4slz9Zf7S3fvJCQ8HE1NTUrb2jLho48zzZ/Sb/L/1D4fv3IZOysrNk+Zmr8HVIToe7hTavY01WergX2xGtiXmN1/8+yn3wowZ4VH3OHjaJqaYNG7GyUsLUl+8JCn4yeRFppxTmpZWaBjr95J2bRZQ6yH9AM0SLpxiydjvyb5tnoEIA0DA0wa1iVixbp3dCRCFF8aiYmJhao9a9SoUXh5eTFkyJCCzkqBepdzqLwPHkdE55xI5EoJ09xN8Cxy596z8JwTiVyrXkKG3eelBf45DCsXuWZtItfOvORoZV7QWShW/K7lHHJX5M74VnUKOgtCZOujhZsLOgvFxkd1q+ScSORanaTiNcKooEV8XfQ6+RZmGm/Mvyz+vVK7imZUn8Jq8MJNBZ2FHM0f2Lmgs5Av8m2ESm6EhoZy4cIFfHx8SE9PZ9++fTx48IARI4r/CAYhhBBCCCGEEEIIIYQQ4p8qzvNTF3YF2qCioaHBwYMHWbJkCUqlEkdHRyZOnEjZsllPoCqEEEIIIYQQQgghhBBCCFEQCrRBpUSJEkydKrEkhRBCCCGEEEIIIYQQQghRuBVog4oQQgghhBBCCCGEEEIIIXJPQn4VHM2CzoAQQgghhBBCCCGEEEIIIURhJw0qQgghhBBCCCGEEEIIIYQQOZAGFSGEEEIIIYQQQgghhBBCiBzIHCpCCCGEEEIIIYQQQgghRBGhkDlUCoyMUBFCCCGEEEIIIYQQQgghhMiBNKgIIYQQQgghhBBCCCGEEELkQEJ+CSGEEEIIIYQQQgghhBBFhET8KjgyQkUIIYQQQgghhBBCCCGEECIH0qAihBBCCCGEEEIIIYQQQgiRAwn5JYQQQgghhBBCCCGEEEIUEUok5ldBkREqQgghhBBCCCGEEEIIIYQQOZAGFSGEEEIIIYQQQgghhBBCiBxIyC8hhBBCCCGEEEIIIYQQoohQKCXkV0GRESpCCCGEEEIIIYQQQgghhBA5kAYVIYQQQgghhBBCCCGEEEKIHEjIr0JKKcO2RCEl52beUiDlmZeUaWkFnYViRVdbq6CzUGxoamgUdBaKlScR0QWdhWIlNS29oLNQbCgiIgs6C0JkKy4puaCzUGxoakrf1LyU7H+5oLNQrGjo6RV0FooVZbJcO0XhJO/nCo7UAoQQQgghhBBCCCGEEEIIIXIgDSpCCCGEEEIIIYQQQgghhBA5kAYVIYQQQgghhBBCCCGEEEKIHMgcKkIIIYQQQgghhBBCCCFEEaGQKVQKjIxQEUIIIYQQQgghhBBCCCGEyIE0qAghhBBCCCGEEEIIIYQQQuRAQn4JIYQQQgghhBBCCCGEEEWEUikxvwqKjFARQgghhBBCCCGEEEIIIYTIgTSoCCGEEEIIIYQQQgghhBBC5EBCfgkhhBBCCCGEEEIIIYQQRYSE/Co4MkJFCCGEEEIIIYQQQgghhBAiB9KgIoQQQgghhBBCCCGEEEIIkQMJ+SWEEEIIIYQQQgghhBBCFBEKCflVYGSEihBCCCGEEEIIIYQQQgghRA6kQUUIIYQQQgghhBBCCCGEECIHEvJLCCGEEEIIIYQQQgghhCgiJOJXwZERKkIIIYQQQgghhBBCCCGEEDmQBhUhhBBCCCGEEEIIIYQQQogcSMgvIYQQQgghhBBCCCGEEKKIUEjMrwIjI1SEEEIIIYQQQgghhBBCCCFyICNU8tDVq1eZMGECK1euxMzMrKCz849t27KJ9WtWEx4ejrOzC8NGjqKCb8Us0166eIFN69dy68ZN4uPjsHcoRedu3WnZuo1augP7/2bd6lU8DnqEoZERlatUZejwT7G0snoHR1Sw/HbvYPfmjURHRuBQ2oneA4dSztsny7QpKSksnfc7D+/e4enjIMp6ejHhp2mZ0p08cojdmzcQ8uQJBoaGePtWpEf/QZhbWOb34RS4nVu3sGndGiLCI3BydmbwiE/xqeCbZdorly6ydcN6bt+6SUJ8PCUdHOjQuSvNWrVWSzNuzKhM6/61bAWOpZ3y7TgKg6P79nBgx1ZioiIpWcqRTh8NoIynV5ZpU1NSWLvwTx7fv0fIk8e4lvNg1MQf1NJcOnOKE377eHz/PqmpKdiVcqR5xy6Ur1r9XRxOgdu4fTsrNmwgPCIcVydnxnzyCZXKl88y7b2HD5k2Zzb3Hz4kLj4eaysrmjVowKAP+6Kjo6NKt2H7NjZs20bws2fY2tjwcc9etG7a9F0d0julVCo5u28H108dIzkxAdvSLtTv3AurkvZvXe/Jndsc37aBiJCnGJmaU7lRc3xq11d9f+fSefwP7CM6LBSFIh1zaxt86zfBs3qtV9u4G8DFQ3/z/PEj4qOjaNyzn9r3Rc35w36c+nsXcdHRlLB3oFm3PpQuWy7b9KFPgti7ZhlPH9zDwMiYSnUbUrd1BzQ0NAC4deEc/kcP8izoIWmpqViXtKdOq/a4+1ZWbWP5bz/yKOBWpm1bl3Rg6Pc/5/1BFqC8Lt+HATc5tGU94c9CSE1JxszSmop16vNBs9bZbrO4aepbjhplnTHU1eFRWCRbzlzhWXRstulNDPRoW9UHB0szrE2MuXAviHUnL2abvqKzA73rVeXG4xCWHDyTH4dQaG3cuYMVGzcSHhGBq5MTY4YMpZJP1vXQ1z168oS+n45AqVRyZMvW/M9oESHl+d98WK8qrSp5Yayvx62nz5iz5xgPwyKzTV++dEn6N6yJo5U5ejrahEbHsufSTTaevqxK07KSJ03Kl8OphAWaGhrcCQlj2ZGzXA8KeReH9E6c2L+Xw7u3ExsVia2DI+379MPVI+s6O0Bw0EO2LFvEo7t3MDQ2pmajpjTt0EV138nY5h5O7N9LxPPnWFhZ07h9J6rWbaD6/vKZkxzauZWwZyGkp6dTwrYkdVu0oVq9Bpl3WMQZ+vpgXLUSWkaGpIZHEHP4OClPgt+6jlGlChj6+qBtaooiKYmEG7eIPX5abZtGFcujbWZKekwssWf8Sbx5O78PpVAwbdcSi26d0LKyIOXBI8LmLSTp6o1s0xvXr41Fr67olHIgPTqa6K27iFq/RfW9zZejMG3eONN6isQk7rXpli/HUBTp+/pg0bML+uXKol3CmpAffyV2z/6CzpYQxZ40qAgADh3wY+6smYwa+zk+FXzZvmUz47/4jMUrVmFra5cp/fWrV3FxdaN7zz5YWltx/swZpk/7BV1dXRo3bQbAtStX+PmHSQwZNoLadesRGRnBrN9+5adJ3/PrrNnv+AjfrdPHjrBqwZ/0HToCdy9vDuzeya//+4Ypc+djXcImU3qlQoGOri5NWrfjsv85EuLjMqUJuHGdv2ZMo+fHA6lSoxbRUZEs+3MOf/42lXE/FK+XVm86cvAAf835neGjx+JVvjy7tm3lu6++5M+ly7Gxtc2U/ua1azi5utK5Ry8sray4cO4sv//2Kzq6ujRsov5S+s8lyzE2NVF9NjMzz+/DKVD+J4+zadkiug0YjFs5T479vZc/pkzm6+m/Y2ldIlN6hUKBjo4O9Zq34vpFfxIT4jOluXPzOmW9y9O6ey+MjE04d+woC379hZETJ2fbUFNc7D98mN/+mMdXn47E18ebjTt2MPrrCaxbuAg7m8y/dR1tbVo3bYq7WxlMjI0JvHeXn2bMIC1dwchBgwDYuGMHcxYuZMKYMfh4eHL91i1+mjkDU2Nj6n7wwbs+xHx34eA+Lh3eT+Oe/bCwsePcvp1s+3MGfcZPRldfP8t1YsLD2LFgNp7Va9O0zwCC793hyMZV6BsbU8a3CgD6RsZUa9YKCxs7NLW0eHD9KgfXLcfA2ARnr4wGr9TkZKxKOuBR9QP8Vi9+Z8ecH66fO83f61bSotdHlC7jzvnDB1gzexpDv/8ZM0vrTOmTExNZNfMXSpctR//x/yP8WQg7ls5HV0+Pmk1bAfAw8BbOHl40aN8FAyNjrp05wYY/ZvLhZ1+rGhK6Dh1FelqaartpaWnMnzQeryrFq0E1P8pXV0+fao2aYePgiLauLo/vBLJ71WJ0dPWo2qDJuz7Ed66BdxnqeZVh/YkLhMbE0bRCOQY1rcW0rQdIfu2cep22pibxSSkcuhZIjbLOb92+pbEhrat4c+9ZWD7kvnDbf+QIv/35J18NH4Gvtzcbd+5k9LffsO6v+Vnem15KTU3lm5+nUMnHhwtXr77DHBduUp7/TbcPKtK5hi+/7jjE4/Aoetetws+929L/jzUkpqRmuU5SSirbzl3lfmg4yWlpeJeyY1Sr+iSnprHD/zoAvk72HLlxh+tBISSnptGpRgWm9GzD0AUbeBoZ/S4PMV9cOn2CbSuX0KnfQFzcPTnpt4+F037ii19mYJFFnT0pIYH5P0/GpZwnoyb9zPPgp6ybPwddPT0atGoHwEm/fexau4quA4ZSukxZHt0NZOOiPzEwMsa7clUADI1NaNK+Czb2DmhqaXHzoj8bFs7D2NQUz4qVM+23qNJ3L4NZgzpEHzxKypNgDH19sOzYlufLVpMem/lZHMC0fm30XZ2JOXqS1LBwNHR10TI2Un1vWMEb07q1iN5/iJSQZ+jY2WDetCGK5GSS7z14R0dWMIwb1KHE8EE8n/UnidduYNauFfZTJvKo/3DSQjPfhw2rV8b26895Pmc+CecuoFvaEZuxw1EmpxC9bRcAYXMXEL5gmdp6pX7/hcQr19/JMRUVmgYGpNx7SOxeP2y/+aKgsyPEe0NCfr1BqVSyZcsWBg8eTMeOHenXrx/Lli3j2bNntG3blhMnTvDtt9/SuXNnhg0bxsWLGb3inj17xoQJEwDo06cPbdu2ZcaMGQV5KP/IxnVrad6yFa3btcfJ2ZlPx4zFysqKHVu2ZJm+d9+P6D9oCD4VKmBv70C7jp2oW68+x44cVqW5cf0a1iVK0KV7D0ra2+Pl7UPHzl24eTP7XgrFxd5tm6nTuCkNm7fEwbE0fYcMw9zCkoO7d2aZXk9fn4+HjaRhi1ZYWmV+MQNw5/ZNLK2sadG+EyXs7Cjj4UnTNu25m0XP4OJmy4b1NGnRkhZt2lLayZlPRo7G0sqSXdu3Zpm+e58P+WjAILzLl6ekvT2t23egVt16nDh6JFNaMwtzLC2tVP+0tLTy+WgK1qFd26lRvyG1GzfDrpQjXfsPwszCguN/780yvZ6+Pj0GfULtJs0wz2ZkWZd+A2nWoTPOZdwpYVeSVl274+jqypVzxb838OpNm2jTrBkdWrXCpbQTXwwfgbWlJZt27MgyvaODA22aNcfdzY2StrbU+6AWzRs15tK1Vy9a9hzwo33LVjRv2AiHkiVp1rAhHVq1Yvn6de/qsN4ZpVLJ5SN+VGncgjK+VbAq6UCTXh+TmpxEwIXsz59rJ49gZGpO/c49sbQtifcHdfGoVouLh171xipV1gPX8pWwsC2JmbUNvvUbY13Sgaf3AlVpnL3K80HrjpSpWAUNjaJdJTrjt4cKtepSuW5DrEs60KJnX4zNzPE/ciDL9NfOniA1JZl2/YZg4+CIZ+VqfNC8NWf89qJ8EYu3efcPqd2iLQ4ublja2FKvbSdKOrlw+5K/ajsGRsYYm5mr/gXduU1qcjK+r40WKg7yo3xLOrngXe0DStiXwsLahvI1a+PqVYFHd96Pnqx1Pd04dC2Qq4+CeRYVy9oTF9DT0aaSi0O260TGJ7Lt3FXO3w0iISUl23SaGhr0rluVvRdvEhGbkB/ZL9RWb9lMm6ZN6dCyJS6lS/PFsGEZ96ZdWddDX5q9eDFlXFxoXLfuO8pp0SDl+d90rF6BdScvcvzWPR48j2Da9oMY6OrQyKdstusEhoRx+MYdHoZFEhIVy4FrgZy/F4RP6ZKqND9vPcD289e4+yyMxxFR/L7nKAkpqVRzc3wXh5XvjuzZQbW6DajZsCm2DqXo+NEATM3NOXXg7yzTXzh5jJTkZHoOHUFJx9JUqF6Thm06cHTPTtV9x//EEWo0bEylWnWwsrGl0gd1qNGwKYd2blVtp6x3eXyqVsfG3gFrWzvqtmhNSUcn7t+++S4O+50xrlKRhBu3SLh6g7SISGIOHUMRH4+hb9Yjz7QszDGqWJ6IbbtJunuf9OgY0p6HkXz/oSqNoVc5Eq5eJ/F2IOnRMSTdvkPClRsYV6v0rg6rwJh3aU/MvgPE7P6b1EePCZszn7TwSMzatsoyvUmThsSfOkvM9j2kBT8j4cx5ItdsxLxHJ1UaRXwC6ZFRqn869iXRsS9JzK6sfwPvq4TT5wifv4S4w8dBIfNpvG+USmWh/1dcFe23B/lg+fLlrFu3jq5duzJ37lzGjRuHtfWrF9wrVqygbdu2zJ49m7JlyzJt2jQSExOxtrZm/PjxAMydO5fly5czePDggjqMfyQ1NZWAgNtUrV5DbXmVatW5fi33vakS4uMxNn7V09+7fHkiwsM5eeI4SqWS6KgoDh3wo0bN4tfD+nVpqak8uBNI+Td68PhUqkzgrX9fES3r6UVUZAQXz55GqVQSGxPN6WOH8a1S7T/muHBLTU3lTkAAlauqH2elqtW4ee1arreTmBCPsYlJpuWjhgymd+cOjB87mssXL/zn/BZmaWmpBN27i2eFimrLPSr4cj+PG+aSExMxNDLO020WNqmpqdwKDKBGlSpqy2tUqcKVG7nrORX05Amnz5+jcoUKr7abkoqerq5aOj1dPa7fvk1aNr22i6qY8DASYmNwLOetWqatq4u9a1mC79/Ldr2QB/coXU599FNpDy+eBz0gPT1zGSmVSoICbhL5/BkObtm/wCmq0tPSCH70AFcv9ZcArp4+PL4bmOU6j+/doXSZcui8dq65eVcgNiqSqPDn2e4rOSkJfUPDbL+/ePwwbj6+mFkWn9Ce76p8Qx494PG9QJzKeuRd5gspS2NDTA31CXgaqlqWlq7g/rMwnGz+exjTlpU8iYhPwP9e0H/eVlGTcW8KpEZl9XpojcqVuXIj+3ro8bNnOHH2DJ8N/SS/s1ikSHn+N3bmJliZGKn9FlPS0rn6KBivUpmjIGTHzdYar1J2XHn4NNs0Olqa6GprEZeU/J/yXBikpaXy5P493Murhzd2L+/Lg8CsG90f3rmNSzlPdHT1XktfkZjICCKeZ1xr01LT0NFRr2Pq6OoSdPeO2mjTl5RKJYHXrhAa8hQXD8//eliFh6YmOrYlSH6gfo9IfhiErn3W56WBmwvp0THoOZfGpn8fbAZ8iHnzxmgaGLxKpKWFMj1dbT1lWhq6dragWYxfvWlro+dehoTzl9QWJ/hfRN876zqNho4OyjdGqClTUtCxKYG2bdYj/0xbNyP5/kOSbhT/DqVCiMJPQn69JjExkW3btjFo0CCavohVb29vj4eHB8+ePQOgffv2VK+eEcaib9++HDx4kHv37uHt7Y3Ji5e1ZmZmRWoOlejoKBTp6VhYWKgtt7Cw5ELE+Vxt49SJE1zwP8/v8/5SLfP2Kc/XE//HlEnfk5ycTHp6OlWqVeOrr7/Ny+wXOrExMSgUCkzN1cvTzNyC65ezj/Odk7IeXgz7fBx//DaV1JSM8vSpWJnBoz//r1ku1GKiozPmP8ji/Lx0wT+btdSdOXWSSxf8+XX2XNUyS0srho/5DPdyHqSlpXLw77+Z8NkYfp4xi/LZzB1U1MXHxKJQKDB5I6yZiZk5t69eybP9HN23m6iIcKrXK1491N8UFRNNukKB5Ru/dUsLC85efPtvfcDoUdwODCQlNZUOLVsx7OP+qu9qVq3C9r17aVinDp7u7twMDGDb3j2kpaURFR2NdTGagyohNgYAwzcaOw1NTImLjsp2vfjYaEq5qz/YG5iYolAoSIqLw+jFOZ6cmMDS778iPS0VDU1N6nfuhZNn1vPbFGUJcbEoFQqMTNTrHkamZty/lXXjXlx0NKZvzL9lZGoKQHx0NBbWmR9mzx/aT2xkBOVr1slym+HPgnkUcIuun4z+F0dReOV3+c76aiQJcbEo0tOp26YjVepnjhde3JgYZLzwe/PFZ2xSMmaGBlmtkmvuJUvg6+zAjJ2H/9N2iqqomJis703mFpyNzPreFBYRzk+zZvHLN99i9JYG0/eRlOd/Y2mccfyR8YlqyyPjE7A2McpqFTWrRn6ImaEBWpoarDx2nl0Xso900K9BDRJTUjkV8OA/5bkwiI/NqLMbv1FnNzYzJzabDo+xUVGZOjOYvHgnERsdhZWNLeXK+3L2yEF8qtbA0dWNx/fvcvbwAdLT04iPjcX0xfNWYkI8kz8dQlpaKpqamnT8aCCevsUn3JemgT4ampooEtRHMKYnJKBnWCrLdbTMTNEyNcGgXFmi9mWMTjWtVxvLDq0JW7MRgOQHQRj6eJIUeI/UZ6Ho2JbAsLwXGlpaaBroo4gvniMmtcxM0dDSIj0ySm15emQUWpWznvM04fxFrIcPxKBKRRIvXEbHoSTmXToAoG1lQdqzULX0mkaGGNerTfjiFflxCEII8Y9Jg8prgoKCSE1Nxdc364s+gIuLi+r/lpYZD8rR0bmP0bp371727duXY7ra9RvQoNE7jp/92mR1GZSZF2Xh2pUr/DRpIiNGjcHD61WP4Qf37zNn1gz6fPQxVavXICI8jL/mzWXGtF8Y9813eZv3QkjjjcJTKpVokIsCzcaTRw9ZOf8P2nfvSflKVYiKjGDdkoUsmfc7Q8YU/1iZmcqT3JXn9atXmfrDJIZ+Oopyr83nUap0aUqVLq367Ontw7NnwWxev7bYNqioZDo3My/7ty6dOcXWlcv4eNRnWGYxX1BxlPm3To7n5k8TviY+MZHAe3eZvWABy9eto1/PngD0792H8MhIBoweBUollhYWtG7alBXr16NZxHu33fY/w+H1K1Wf2wwa8eJ/b/6+M5frmzLfspSZvtDV06f759+SmpLM44CbHN+2HhNLKxzdi1Evy9dkKrOc7juZk7/cUKakNy+cw2/TWjoNGo55NqEpLx47jLGZOWXLV8x1nouS/Crfvl98Q2pyMo/v3eHg5nWYW5egQjaNVkVVJZdSdK75qn69+GDGBL5vhgHQQOO1gvrnDPV06Va7MquPnc92bob3RZb1pmyuq99NnUbn1q0p71k8r415Qcozdxr5lGVUq1cdar5Zu+vF/974rWtokJtf+mfLt6Kvo4NnKVsGNKqZEf7rakCmdB2qladVZS/GrdpBQjH67Wc6wzIqmdmnz3Seqi9v2rELsdFRzJn0NSiVGJuZU6VufQ7v3IbGa3VMPX0Dxv44jeTkJAKvX2XHqmVYWpegrE8Fire3nJcaGmhoaxO5Zz/pURnvfyL37Me2fx907GxJDXlG7JlzaBkZYt2jE2hooEhIIOHGLUyqVX5PQjG9eYwamRe9ELNrHzr2dpSc/DUa2too4hOI2rwDq369UKYrMqU3adIAtDSJ3X8oz3MtRFGmzNXdVOQHaVB5TW5iu70+v8LLisk/iQnXokULWrRokWO6sJjMEz/nFzMzczS1tIiMiFBbHhkZiYXF28MuXL1ymQlffEa/AYNo17GT2ndrVi7Hw9OL7r16A+BWpgz6BgaMHv4J/QcNyXIy8eLAxNQUTU1NoiPVyzMmOirTqJV/YsfGdbi6l6N1p64AlHZxRU9fnx/HfU6XPv2wKpF5csLiwNTMDE3NzOdnVGRkplErb7p+9QrfjfuSDz/uT+v2HXLcVzlPL44ezDoefnFgZGqCpqYmsVGRasvjYqIwzYNRdZfOnGL5nJl8OHwU5asWrwmps2JuaoaWpibhb/zWI6MisbQwf+u6ti8msXV1ckKRruDHGdPp060b2lpa6Ovp8e1nnzN+1GjCIyOxtrRky+7dGBkaYl6ERj9mxcXbF9vPX3VMeBleIiE2BpPX7jeJsTEYGJtmux0jEzMSYmLUliXGxaKpqYm+0aserxqampi/aNgr4eBI5LMQ/P32FLsGFUNjEzQ0NYmLiVJbHh8boxoV8SZjMzPi3+gQ8nLE0Jvr3Lxwjm2L/6T9x0Nwz6aHanpaGldOH6NSnYZoFrO5qPK7fF+OVrFxcCQ+JpqjO7YUuwaVG0EhPAp7de/RfvHizsRAn+iEJNVyY31dYv9DuB47cxPMDPUZ3LSWatnL+vrPfdry2/ZDPI/JerLh4sLc1DSbe1NUplEWL52/fImLV6+wcNUqIOP9l0Kh4IPWrfhy+Ag6tso6Bv77QMrznzkV8IBbT56pPuu8uB9YGBny/LXnW3NDA6LeGLWSlZCoWAAePI/AwsiAD+tVzdSg0qFaefo1qM7Xa3dx+2loVpspcoxMXtTZ3xitGxcTnWmk+Usm5ubERL2R/sV9yNg0o/6oo6tH98HD6dJ/CLHR0ZhamHP6oB96+gYYvTZaWFNTE2u7jPlqHJxcCH3yhAPbNxebBhVFYhJKhQLNN0aQaRkaZBq1olonPgFlerqqMQUgPSoaZXo6WibGpIY8g7R0ov4+SJTfYTQNDVDEJ2BY3gtFcgqKxJzP96IqPTomoxzeeDbXsjDLNGrldeELlhG+aAValuakR8VgWDnj/HpzdAqAaatmxB89iSK2eN/DhRBFR9Hu6prHHB0d0dHR4fLly/9qfW3tjPYphSJzi3phpqOjg7t7OfzPnVVb7n/uHN4+2YdGuXLpIuM//4wPP+5P527dM32fnJycqTf1y8/FuRVVW0cH5zJluXZJPQzAtUsXKfsfYs+mvKflqaOjQxl3dy6eVw8/d9H/PJ4+WU8aCHD18iW+++oLen/Ujw5duuVqX/fu3MGiGIVTepO2tg6Orm7cuqp+jbt19TIu7v8tZv+FUydYPnsmfYaNpFLNWjmvUAzo6OjgUdads/7qc++cuXCBCl7e2ayVmUKpJD09HcUbMZe1tbWxLVECLS0t9h8+RO0aNYr8CBVdfX3MS9io/lnalcTQxJSg269CeKSlpvL03h1Kurhmux07Z1eCAtRj1z+6fZMSjs5oaWXfV0SpVJCeVnx6rr6kpa1NydLO3L+hPq/U/ZvXKZXNnDGlXMvw6M5t0lJfTex978Y1TMwtMLd61UB/4/wZti3+g3b9BuNZJfuG0lsXz5MQF0fFYjYZPeRv+b5JqVQWy3M0OS2N8Nh41b9n0bHEJCThXvJVWWhrauJiY8XD0Ii3bOntgsKj+HX7QWbsPKz6dyMohPvPwpmx8zARce+uw1JBybg3leXsBfV66JmLF6nglXU9dM0ff7Jy7jzVv8F9PkRPT4+Vc+e99xOqS3n+M4kpqTyNjFH9exgWSXhsPJVdX00Ur6OlhU/pktx4HPKPtq2hoaFqoHmpc40KfNywBt+u2831oH+2vcJMW1sHBxdXAq6ph+QNuHYF57LlslzHqUw57t++SWrKq/tO4LXLmFpYZho1rqWtjbmVFZqaWlw6fQKvSlXeWsdUKhXFax4/hYLUZ8/Rc3JUW6zn5EjK06zPo5SnwWhoaaFl9qpThCrUVWxspu0r4uJBqcTAoyxJ9x/k9REULmlpJAfcwbBKRbXFhlUqknQ9h/lOFArSwyIgLQ3jhvVIvH5TrdEKQM/DHb0yrsTslsnohRCFh4xQeY2hoSHt2rVj2bJl6Ojo4O3tTWxsLHfu3KHKG5MOZ8XGxgYNDQ3Onz9P9erV0dXVxcDgv8WBfle6dO/Bzz9MopynFz7lK7Bj2xbCw8No26EDAAv//INbN2/w66zZAFy6eIGvv/ycdh060aRpcyLCw4GMF/wvRw3UrFWb6VN/ZvuWzVStUYOIsHDmzZ5JWfdy2NrmfhLCoqhF+078NWMaru7ulPX05tDeXURFhNOoZWsA1i9bzL3AAMb98LNqnSePHpKWlkZsbAxJSUk8vHcXACdXNwAqVa/B4jmzOLB7J+UrVyEqIoJVC//E2a0M1sU8tFLHrt34bcqPuHt64uXjw+7t24gIC6dV2/YALFnwFwE3bzJl+kwgo7Fv4vivaNO+Aw2aNCUiIuP81NLUwszcHICtG9djY1cSJ2dn0lLTOOj3N6eOH+Pr/00uiEN8Zxq2bseKObNwciuLazkPjvvtIzoikjpNmwOwffUKHt4N5NNvJ6nWCX4cRHpaGvExsSQnJfH4wX0ASjlnjDTwP3GM5XNn0bHPR5Tx9CLmxQgYLW1tjIxNKM56de7MxKm/4OVRDl9vbzbv3ElYeDid2rQBYO6iRVy/fYt5U6cBsNtvP7q6upRxdkFHR4cbAQHMW7yIRnXrofti8uqHjx9z/dZNfDw9iY2NY/WmTdx98ICJX3xZYMeZXzQ0NPCt34Tz+3djYWuHeQlbzu/fhY6eHu6Va6jS7V+1GICmvTPmmvGpVZ8rxw9xbMs6vGvVI/j+HW6dO0mzDweq1jm/fxe2pV0wtSpBenoaD29c5fb509Tr1FOVJiU5ieiwjAnClUoFsZERPH8ShL6hISYWRatxtUaTlmxb8if2Lm44upXF/+hBYqMjqVwvYz6Og1vW8fT+PfqMHQ+Ad/VaHN25le1L51OnVXsinoVwct8O6rXpqOrRf/3cKbYt/osmXXpSumw51bw2WtraGBgZq+3/4vHDuHh4YVFM70f5Ub7nDv6NuXUJrGwzegE/DLzF6f27qVr/HYd8LSDHbt6lcXl3QmPieB4TR5Py7iSnpXPx/hNVmh61M0ZErT3xquHa3iLjZZa+jjZKpRJ7C1PSFEpCo2NJTUvnWZT6i62klFQ0NTUyLS/OenXsxMRfp+FVzh1fL282796VcW9qlVEPnbtkMddvBzDv54x6qJuzs9r6NwMD0NTQyLT8fSXl+d9sOXuFnnWqEBQWyZOIaHrVqUxSSioHrwWq0nzRrhEA07YfBKB9VR9ComIJCo8CoIJTSbrUrMiO868atrvWrEi/htX5ZesBHodHYWGU8dydnJZOQvKrRoWiqn7Ltqz5YzalXcvg7O7BqQN/ExMZSc3GzQDYvW4Vj+4GMnTC9wBUqlWH/Vs2sG7+HBq370JYyFMO7thK005dVfed58FPeXQ3kNJl3EmMj+Ponh2EPH5EjyEjVPv127aJ0m5lsbKxJS01lZuXL+B/4igd+vbPlMeiLM7/EhYtm5Aa8oyUpyEYVvBG08iIhMsZc6OZ1KmJrp0t4Ru3ARkT1qc8C8W8eSOiDx0HwKxhHVKCQ0gNyRhRoWVuhm5JW1KCn6Gpr4dx5YroWFkRtbf4RkB4KWrjNmzHjSH5dgCJ125i1rYF2laWRO/YA4DVgL7oeZTl6RcZ8+lqmppgXL8OiZevoqGjg2mLxhjXr82TMRMybdu0dTNSHj8h8fK1TN8J0DDQR8fBPuODpgY6tjbolnFFERtL2rPnBZs5ke/ei2iChZQ0qLyhb9++GBkZsXbtWsLDwzE3N6dhw4a5WtfKyopevXqxYsUKZs+eTcOGDRkzZkw+5zhvNGzchJiYaFYtX0pEeDjOLq5Mmforti+G+oaHh/P06asH3H27d5OUlMT6tatZv3a1armtnR2rN2wGoEWr1iQmJLB18yb+nDsbIyNjKlauzOBPhr/bgysANevWJy42hu3r1xAVEUkpJyc++24y1jYZYc6iIiMIDXmqts5vk74lLPTV8NZvR2eU0/LtewGo27gZiYmJ+O3azprFCzAwMsSzvC89+g14R0dVcOo3akxsTAxrVywnIiIcZ2cX/vfzL9jaZTTMRYaHE/z0VXn67d1DclISm9atZdO6tarlNrZ2LF27HoDU1DQW/TGP8LDn6Orp4eTswv+m/EK1mh+824N7x6rUqkN8bCz7tmwgJjKSko6l+WTcN6qea9FRkYQ9U++Z9efPk4l4/qoy9stXYwGYvW4LAMf99qFIT2fTssVsWrZYla6MlzejJv6Q34dUoJo2aEB0TAxLVq8mLCICNydnZvzwIyVfhDQMiwjnSXCwKr2WlhbL1q4l6MkTlEoldra2dGnXjp6dOqvSKBTprN60iYePH6OtpUUV34osmjkLe7vi2RBduVFz0lJTOLJxNcmJCdg6udB+6Gh09fVVaWLfCLViamVN20Gfcnzreq6eOIKRmRn1OvagjO+rzg8pyckc3riauOhItHV0sLCxo0nv/rhXfjXKIjToIVvn/qb6fHbvds7u3Y5HtQ9o0uvjfDzqvOddrSaJ8XEc372NuOgoStiXoseIz1XzncRFRxEZ9uoeo29gSO/RX7Fn9TIW/TQRA0NDajZpSY0mLVVp/I8eRKFI5+/1K/n7tblvSrt70Pezr1WfI5+H8uD2DToNLL739/woX4VCwYHN64gOf46mphYWJWxo1LE7Veo1eufHVxAOX7+DjrYWHatXwEBPh0fPI1ngd5Lk13pBmxtl7pg0pq16vdzbsSQRcQlM2bw/3/NcVDStX5/o2BiWrFlDWEQkbs5OzJg0+bV7UwRPgp/msBXxkpTnf7P+1CX0dLQZ0aIuJgZ63HoSyvjVO9XmObIxU2+k19TUZEDjmtiZmZCuUPA0MobFB0+z0/+6Kk3bqt7oaGnxTedmauv+ffkWv+4o+vMsVKxZm/jYWPy2bSImKhK7UqUZ8MUELK0zRvbFREUSHvoqvJqBoRGDx33L5qULmfXdVxgYGlG/VVvqt2yrSqNQKDiyZwfPg5+ipaWNm5c3I777UW0ES0pSEpuXzCcqIgIdXV1s7O3pOeRTKtUqXqEokwLuEG2gj3GNqmgZGZEaHk7Elh2q0SZaRoZqo1EAIrbuwqxhXay7d0SZlkbyw8fEHDmu+l5DUxPjKhXRsjAHhYLkoCc8X7uJ9Jji36Afd/g4mqYmWPTuRglLS5IfPOTp+EmkhWY8Q2pZWaBjr/4sY9qsIdZD+gEaJN24xZOxX5N8O1AtjYaBASYN6xKxYt07OpKiR9/DnVKzp6k+Ww3si9XAvsTs/ptnP/32ljWFEP+FRmJiorRnFULvcg6V98GTyJicE4lcsTYxzDmRyLW7oeEFnYVipYaFcc6JRK4tv36/oLNQbJgbFo0Rq+L9dOVRcM6JRK58U6d4zDEgiqeuq/cWdBaKjU+bF68GhoJW5djRgs5CsZKwa19BZ6FYUSb/+/ndhDpHv20FnYVipcP0ZQWdhRxtHftRQWchX8gIFSGEEEIIIYQQQgghhBCiiFAqZYxEQSnas9sKIYQQQgghhBBCCCGEEEK8A9KgIoQQQgghhBBCCCGEEEIIkQMJ+SWEEEIIIYQQQgghhBBCFBES8qvgyAgVIYQQQgghhBBCCCGEEEKIHEiDihBCCCGEEEIIIYQQQgghRA6kQUUIIYQQQgghhBBCCCGEECIHMoeKEEIIIYQQQgghhBBCCFFEKGQOlQIjI1SEEEIIIYQQQgghhBBCCCFyIA0qQgghhBBCCCGEEEIIIYQQOZCQX0IIIYQQQgghhBBCCCFEESERvwqOjFARQgghhBBCCCGEEEIIIYTIgTSoCCGEEEIIIYQQQgghhBBC5EBCfgkhhBBCCCGEEEIIIYQQRYRCYn4VGBmhIoQQQgghhBBCCCGEEEIIkQNpUBFCCCGEEEIIIYQQQgghhMiBhPwSQgghhBBCCCGEEEIIIYoIpYT8KjAyQkUIIYQQQgghhBBCCCGEECIH0qAihBBCCCGEEEIIIYQQQgiRA43ExEQZHyT+tb1799KiRYuCzkaxIeWZd6Qs85aUZ96S8sw7UpZ5S8ozb0l55i0pz7wjZZm3pDzzlpRn3pGyzFtSnnlLyjPvSFkK8W7JCBXxn+zbt6+gs1CsSHnmHSnLvCXlmbekPPOOlGXekvLMW1KeeUvKM+9IWeYtKc+8JeWZd6Qs85aUZ96S8sw7UpZCvFvSoCKEEEIIIYQQQgghhBBCCJEDaVARQgghhBBCCCGEEEIIIYTIgTSoCCGEEEIIIYQQQgghhBBC5EAaVIQQQgghhBBCCCGEEEIIIXIgDSpCCCGEEEIIIYQQQgghhBA5kAYVIYQQQgghhBBCCCGEEEKIHEiDihBCCCGEEEIIIYQQQgghRA6kQUUIIYQQQgghhBBCCCGEECIH0qAi/pPmzZsXdBaKFSnPvCNlmbekPPOWlGfekbLMW1KeeUvKM29JeeYdKcu8JeWZt6Q8846UZd6S8sxbUp55R8pSiHdLIzExUVnQmRBCCCGEEEIIIYQQQgghhCjMZISKEEIIIYQQQgghhBBCCCFEDqRBRQghhBBCCCGEEEIIIYQQIgfSoCKEEEIIIYQQQgghhBBCCJEDaVB5D8yYMYP//e9/BZ0NIcR7YPz48fz5558FnY1CQ8rjv5fB1atXadu2LdHR0Vl+zsqJEydo27btv97n+6ht27acOHHiH60zfPhwVq9enU85KlxyKp939VuPjo6mbdu2XL16Nd/3VVisXr2a4cOHF8i+5RouxL8nv5+3e/bsGW3btiUwMPA/baco1Xm6du2Kn59fQWej0PDz86Nr164FnY08U9TeO72PdSohRN6RBpX3wODBg/nss8/yZFv5+VBbkA/MQoh/JjcvtYXIDx4eHixfvhxTU9OCzooQKhMmTKBv374FnY0i79807OWnN/+uAwYMYPPmzQWYowzyojr/5NVLbiFyYm1tzfLly3F1dS3orAiRJ/7Je6fi1phUmMlzuxD5Q7ugMyDyn5GRUUFnQYhCIz09HU1NTTQ0NAo6K0Jkkpqaio6OTkFno1DT0dHBwsKioLMh3hO5/U2amJi8g9yIvJSbv638XYUQ+UVLS0vqM6JQyKvnj4J675SWloa2trzaFEK8W3LVeQ/MmDGDmJgYJk6cyPjx43F0dMTY2Ji9e/eiqalJw4YN+fjjj9HUzBiwdPLkSdasWcPTp0/R1dXFycmJr776Cn9/f9asWQOgGlY8atQomjRpwtatWzlw4ADBwcEYGRlRpUoV+vfvj7GxMZDRA+Gvv/7im2++Yf78+Tx79gx3d3dGjhyJnZ0dfn5+2W67sLl27RpLly7l4cOHaGpqUqpUKUaOHImTkxM3b95k2bJlBAYGYmxsTI0aNejXrx+GhoYA+Pv7s379eh4+fIiGhgZly5Zl0KBBODo6qra/Zs0a9u/fT2RkJMbGxlSqVImxY8cCGZWdpUuXcvToUeLj43F1deXjjz/G29sbyOh9MGHCBH744QeWL1/OgwcPKF26NMOHD6dMmTLvvrDegYMHD7Jw4UKWLVumVhH89ddfSUxMxM3NjRMnTtCxY0fWrVtHaGgoa9euxcDAoABz/W69/N3r6enh5+eHpqYm3bt3p2XLlixcuJAjR45gYGDAhx9+SKNGjXj27BkDBw5k3Lhx7N27lxs3bmBra8ugQYOoVKkSz549Y8KECQD06dMHgEaNGjFmzBgAFAoFy5cvz/Ya8755W3kMGDCAxo0b8/z5c06dOkXFihUZN25cQWc5z72tDOLi4liwYAFnzpwhNTUVT09PBg0ahJOTU5bbenmdW7lyJWZmZkDGdWDlypVER0dToUIFqlatqrZOcHAwixYt4vbt2yQmJuLg4EDv3r2pXr06kHHdPX78OHPnzlVb78svv8TNzY0hQ4bkQ6lk7/z580ydOpU1a9agpaXF06dPGTJkCC1btmTYsGEALF++nMDAQCZPnsyjR49YsmQJ169fR1dXF19fXwYOHKj2osbPz4/NmzcTEhJCiRIlaNmyJe3atcv2d7lx40Y2b97MxIkTKVeuHFFRUcyZM4eLFy9iZmZGz549M63ztrpAUlISffv2ZdSoUdSuXVu1zsWLF/nf//7HkiVL/tWLpX9aVteuXWPJkiXcv38fIyMj6tWrR79+/VT3j9evlwcPHsTGxoYZM2bkWD7jx4/HycmJoUOHAhkjGZo1a8bz5885evQohoaGtGvXjk6dOqm28eTJE2bPnk1AQAA2NjYMHDiQX375hSFDhqjqPwEBAcybN49Hjx7h6Oiouua+lJ6ezty5c7l8+TJRUVFYWVnRvHlzOnbsiKamJteuXeObb77JVL7Lly/n3LlzzJ49O8/KNqfzMCAggBUrVnD37l3S0tJwdnamf//+eHh4qMoM4OeffwbAxsaGRYsWqfJz9OhRVqxYofqdf/rpp6prAOR8jrdt25ahQ4dy+fJlLly4QMuWLfnoo49YtGgRJ0+eJCYmBnNzc+rXr0+/fv1U58PLv+v48eMJDQ1lyZIlLFmyBIAdO3ZkW375ZcaMGVy7do1r166xa9cuABYuXEhycvJby//l84C3tzfbtm0jJSWFli1b0rdvX9auXcvu3bvR0NCgffv2dOnSRbW/tm3bMmTIEPz9/bly5QpmZmZ8+OGHNGzY8J0fe155W3184MCBAKq6t4+PD1OmTAFyd4598sknXLhwgYsXL2Jtbc3w4cOxt7fn999/58aNG9jb2zNy5EhVvfzlM9IXX3zBokWLeP78OR4eHqpnpKIupzpQ69at1a6LWV1LmzRpQkhICKdOncLIyIj+/ftTqVIl5s2bx7lz57CwsGDo0KFUrly5oA5T5Z9cN0eMGMHAgQOZPn06ZcuWzfVzXE51nufPn/PXX39x/fp1UlJSKFGiBL169aJevXqqOv5nn33G7t27uXPnDjY2NgwePFit/PKiXvH06VNmz57N7du3sbGxoX///vlZ9FlSKpVs3bqVPXv28Pz5c8zMzGjYsCEfffQRS5cu5fTp0zx//hxzc3Pq1KlD79690dXVBXJXji//di+1bduWcePGqeo5Oe2joGRV1xkzZsxb/+bp6eksXryYAwcOANC4cWNSU1MJCgpSXSNff+8E2b83iYmJYdasWcCrdz89e/akV69epKamsmrVKg4fPkxcXByOjo58+OGHqvPz5e9k4sSJrF69mvv37zN+/HiqVavG5s2b2bt3LxEREZQsWZLOnTur3atyqlMVZtmdyy1atPhPz+1CiH9PGlTeQ0eOHKFt27ZMmzaNe/fu8euvv1KmTBnq169PZGQk06ZNo2/fvtSqVYukpCRu3boFQN26dXn48CHnzp1T3TRfNhRoaGgwcOBA7OzsCA0NZf78+fz1119qQz5TU1PZsGEDo0aNQkdHh5kzZzJv3jwmTZr01m0XJunp6fzwww80bdqUzz77jLS0NO7evYumpiYPHjzgu+++o1evXowcOZLY2FgWLFjArFmzGD9+PABJSUm0a9cOFxcXkpOTWbduHZMnT2bu3Lno6Ohw4sQJtmzZwhdffIGTkxPR0dHcvn1btf8lS5Zw/Phx1UPW1q1b+f777/nrr7+wtLRUpVu2bBn9+vXDwsKCBQsW8NtvvzFv3rxiOSqjdu3azJ8/n9OnT1O3bl0A4uPjOXXqFF988QX37t3j2bNnHDlyhK+++godHZ0Cr8QWhMOHD9OhQwd+++03zpw5w4IFC/D396dKlSpMnz6dgwcPMnv2bHx9fVXrrFixgv79+/PJJ5+wbt06pk2bxqJFi7C2tmb8+PFMmTKFuXPnYmJiolamb7vGvI9yKo+tW7fSvXt3pk+fXsA5zT9vK4OZM2fy+PFjvvnmG4yNjVmxYgXff/89f/75J3p6ejlu+/bt28ycOZPevXtTp04drly5wooVK9TSJCUlUaVKFfr06YOuri7Hjh1jypQp/P777zg6OtK0aVPWrl1LQEAA7u7uADx+/JibN2/yySef5EuZvI23tzcpKSkEBgbi4eHB1atXMTU15cqVK6o0165do0qVKkRERDBu3DiaNWtG//79SUtLY8WKFUyePJlff/0VTU1N9u3bx6pVqxgyZAhubm48evSI2bNno62tTZs2bdT2rVQqWbx4saqMXjZszZw5k9DQUCZPnoyenh4LFy4kNDRUbd231QX09fWpV68e+/fvV2tQ8fPzo1q1av+6l+4/Kavw8HC+//57GjZsyOjRowkODmb27NmqF3svHT58mObNm6te7OemfLKybds2evXqRadOnfD392f+/Pl4eXnh4eGBQqHgxx9/xMLCgl9//ZWUlBQWLFhAamqqav2kpCQmTZqEj48PY8aMITw8nAULFmTKj6WlJV999RVmZmYEBASorsvNmjXDx8cHOzs7Dh48SOfOnYGMF5yHDh2iY8eOeVa2uTkPExMTadiwIYMHDwZg165dqjqMmZkZ06dPp0+fPowYMYLq1aurNfaFhoZy7NgxJkyYQHJyMlOnTmXFihWMGDECINfn+Jo1a/jwww9VL/Z27NjB6dOn+eKLL7CxsSE8PJwnT55kWR4TJkxg5MiRNGnShFatWr217PLT4MGDefr0KaVKlVKFI1MoFIwZM+at5Q9w/fp1rK2t+emnn7h37x6//fYb9+/fx9XVlV9++YUrV64wb948KlasqPYCd/Xq1Xz44YcMHDiQ48ePM2PGDEqVKqX2IrGt1ZFOAAAawUlEQVQoeVt9/LfffuOzzz7jf//7Hy4uLqpez7k9x9atW0f//v3p378/q1atYtq0abi4uNCqVSuGDBmiejZ4vTEzNTWVNWvWMGrUKPT09FiwYAE//vgjv//+e5Gvu+dFnXD79u306dOH7t27s2fPHmbMmEGFChWoV68effr0YePGjUyfPp3FixcXeB3/n1w3s/O257jc1Hn++OMPUlNT+emnnzAwMMjymrZ06VIGDBiAs7Mzu3fv5scff2T+/PlYWVnlSb1CoVDw008/YWxszLRp00hOTs50j3sXli9fzp49exgwYADe3t7ExMRw9+5dAPT19Rk5ciRWVlYEBQWpnsdfvnTOTTnmJKd9FKTX6zrx8fE5/s03b97MgQMH+PTTT3FycmL37t0cOXIk25B1b3tv4uHhwaBBg1i+fLmqXqOvrw/ArFmzCAkJ4fPPP8fa2prz588zefJkpk+fjouLi2r7S5cupX///tjb22NgYMCKFSs4efIkQ4cOxcHBgVu3bjFnzhyMjY2pVq1arupUhdnbzmX498/tQoh/7/3sLvyee9ka7+DgQN26dalQoQKXL18GIDw8nLS0NGrXro2trS1OTk40b94cCwsL9PT0MDAwUA1PfrkMoH379vj6+mJra0v58uXp168fx48fR6FQqPabnp7O0KFDcXd3x8XFhY4dO3L16lUUCsVbt12YJCQkEB8fT/Xq1SlZsiSOjo40aNAAR0dHNm/eTN26denYsSP29vaUK1eOYcOGcfLkSaKiooCMl/+1a9fG3t4eFxcXRo8ezbNnzwgICAAyesJYWlpSqVIlbGxsKFu2rOohLSkpiT179tCvXz+qVauGo6Mjw4YNw9zcXNVD8aU+ffpQoUIFHB0d6dGjB48fPyY8PPydltW7oqenR4MGDdQmODxy5AiGhoZUq1YNyBgGPHbsWMqUKYOTkxNaWloFld0CU7p0aXr16oW9vT0dOnTA1NQUbW1t2rVrh729PT169ABQNaBCxu+6evXq2Nvb07dvX2JjY7l37x5aWlqqMChmZmZYWFioDfF+2zXmfZRTefj4+NC5c2fs7e2xt7cvwJzmn+zK4OnTp5w5c4YRI0bg4+ODs7MzY8eOJSEhgcOHD+dq29u3b8fX15fu3bvj4OBAy5YtqVmzploaFxcXWrZsibOzM/b29nTv3h03NzdOnjwJZMQxr1y5Mvv371et4+fnR5kyZdQe3t4VAwMD3NzcVJNkXr16lTZt2vD8+XMiIiJISkoiMDCQ8uXLs3v3blxcXOjXrx+Ojo64uLgwduxYAgMDuXPnDgBr166lX79+1K5dGzs7O6pXr06XLl3YvXu32n4VCgWzZs3i7Nmz/PLLL6rGgidPnuDv78+IESPw8vLCzc2NMWPGkJKSorZ+TnWB5s2bc/HiRdX9KC4ujtOnT9O0adN3Ula7du3C0tKSTz75BEdHR6pXr85HH33Ezp07SUpKUm3T1taWAQMG4OjoqDaCNLvyyU6lSpVo06YN9vb2tG3blpIlS6p++5cuXeLJkyeMHTsWV1dXPDw8GDhwIOnp6ar1Dx8+TFpaGqNGjcLJyYnKlSvTrVs3tX1oa2vTp08f3N3dsbW1pW7durRo0YKjR4+q0jRr1kztHnnhwgWioqJo0KBBnpVtbs5DX19fGjVqpCrXIUOGoKury4ULFwBUo02MjY2xsLBQG32Snp7O6NGjcXFxwcPDg+bNm6u9oMztOV63bl2aN2+OnZ2dquHP3t4eb29vbGxs8PT0zHZ0tImJCZqamhgYGKjqqgXByMgIbW1t9PT0VPnYs2dPjuX/ct2hQ4fi6OhI/fr1cXNzIyIigo8++kh1/bSxsVErW4APPviAli1b4uDgQPfu3alQoQLbt29/14eeZ95WH3953pmYmGBhYaGq7+T2HGvUqBH169fH3t6ebt26ERUVReXKlalZsyYODg507tyZBw8eqMWyT09PZ9CgQarr69ixY3n06FGxqDvlRZ2wcuXKtG7dGnt7e1UP9pIlS9KoUSPVPT06OpqHDx/m01Hk3j+5bmbnbc9xuanzPH/+HC8vL1xcXLCzs6NKlSqZGnBatmxJ3bp1cXR0ZNCgQVhbW6vO5byoV1y6dImgoCDGjh2Lm5sbXl5eme5x+S0xMZFt27bx0Ucf0bRpU+zt7fHw8KB169YA9OjRAy8vL2xtbalatSrdunVTu3fmphxzktM+CtLrdZ3z58/n+DffsWMHnTt3pnbt2pQqVYpBgwa99T74tvcmOjo6GBoaoqGhobqPGRgYEBwczNGjR/nyyy9VHULatGlDlSpV2LNnj9r2e/bsSeXKlbGzs0NPT49t27bx6aefUqVKFezs7GjQoAHNmzdXvSfJTZ2qsMrpXIZ//9wuhPj3ZITKe8jZ2Vnts6WlpapS7+LiQsWKFRkxYgQVK1akYsWK1K5dW+2hNiuXL19m48aNBAUFkZCQQHp6OmlpaURGRmJlZQVkxL0vVaqU2n7T0tKIj48vMjGqTUxMaNy4MRMnTsTX1xdfX19q165NiRIluHPnDsHBwRw7dkyVXqlUAhASEoK5uTnBwcGsXLmSgIAAoqOjUSqVKBQKnj9/DmQ84G3fvp2BAwdSuXJlKleuTI0aNdDR0SE4OJi0tDQ8PT1V29fS0sLDw4OgoCC1fL7+N345ciUqKgpra+v8KpoC1bx5c0aPHk1YWBjW1tbs37+fxo0bqxpOrKys3vsYxa+fExoaGpiZmam9DNTW1sbY2FjV+AeovUh+eR7lZjK7t11j3kc5lUdR7eH7T2RXBkFBQaqeai8ZGRnh5OSU6bqWncePH6saT1/y8PBQaxxJSkpizZo1nDt3joiICNLT00lJSVHLV/PmzZk5cyYDBw5EW1ubQ4cO0b17939+sHmkfPnyXL16la5du3Lt2jXatWvH5cuXVb1dtbS0cHd3Z8OGDVy/fj3LiT2Dg4OxtbUlLCyMuXPn8scff6i+S09PV92jXlq8eDGampr89ttvmJubq5a//Du9HL0DGeGYXh8ZCTnXBcqWLYuzszMHDhygW7duHDlyBGNj43/8guLfltXWrVspV66c2sgHLy8v0tLSCA4OVl3z3NzcstxPduWTnazO+5fX2MePH2NpaamqI0HGteD1vAUFBeHs7KwWovL138pLe/bs4e+//yY0NJSUlBTS0tKwsbFRfd+4cWNWrFjBzZs38fT0xM/Pj5o1a2JqaprjMeTVeeju7k5UVBQrV67k6tWrREVFoVAoSElJUdWB3sbGxkbtBYCVlZWqLKOjo3N9jr8Z/rRx48Z89913DBkyhEqVKlG1alWqVKlS5EJU3r17N8fyh4yX2693KjE3N8/0YsXc3DzTPfvN887Dw4Nz587lVfbfubfVx7OqK/+Tc+z13/3L68Tr9a2Xy6Kjo1XPV9ldXx89ekTFihX/49EWrLyoE76+DQMDA/T09LIs58JS18ztdTO7zm5ve47LTZ2nbdu2zJs3D39/f3x9ffnggw8yXfte/02/PP9e1rtyup7kpl7x8h73+r3ozftvfgsKCiI1NVVt9P3rTpw4wbZt2wgODiYpKQmFQqHWGTQ35ZiTnPZRkF6v6+T0N3dwcCAyMlLtOvUyXGJYWFiW23/be5Ps3L17F6VSyfDhw9WWp6amUqFCBbVlrz8/PXr0iJSUFCZOnKg2qi8tLQ1bW1sg93Wqwiincxn+/XO7EOLfkwaV91BWE3a9vLFraWkxadIkbt++zcWLF9m/fz/Lly9nypQp2fbSDQ0NZdKkSTRr1ozevXtjYmLC3bt3mTZtGmlpaap0b44KeHmzKyyVitwaPXo07du3x9/fnzNnzrBixQq+/vprlEolzZo1o3379pnWefnCZPLkyVhZWTF8+HCsrKzQ0tJi2LBhqnIqUaIEf/75J5cvX+bSpUssWrSINWvW8Ntvv6m2lZuh/6+X9cv0bz7wFScuLi64urpy4MABatasyZ07d9TCzb0cQvw+e/N3r6GhkeW14PXz5N+eR2+7xryPciqPwjgaL69lVwZvO59yG+YkN+fk4sWL8ff3V4UG0NPTY8aMGWqhJ6pVq4aenh4nT57EyMiIuLg46tWrl6s85AcfHx927drFo0ePVPNBvXxJY2pqiqenJ9ra2igUCqpWrZplbHJzc3OSk5MBGD58eI4PjhUrVuTo0aOcP39erZd+bso4t3WBZs2asW3bNrp165ap8fvfym1ZKZXKbM+r15dnd8/Irnyyk1W952VZvi0v/8SxY8dYsGCBai4SQ0NDdu3axenTp1VpzMzMqFGjBvv378fBwYEzZ87w7bff5mr7eXUeQkZs9aioKAYOHIiNjQ06Ojp88803audHdrI6R16W5cvraW7O8Tf/tmXKlGHhwoVcuHCBK1euMGPGDFxcXJg8eXKRalTJTflD1uWY1Xla3O/ZOdXH3/RPzrGs7nc51beKs7fVgbK6BmY1giGrczSrOmphOW9ze93Mztvq37k5b5o1a0blypU5f/48ly5d4osvvqBr16706tUrV/nPi3pFYTi/35aHW7duMXXqVNUoB2NjY86cOcPixYtVad5Wjlk9F715/cjNPgrS6/fDnP7mL4/zn9Zbsntvkt18Ry/rRtOnT8/0u3/zeen1zy/z9+2332ZqsCkOk9Xn5vf0vr3/EaIwKPpXF5HnNDQ08PDwwMPDgx49ejB8+HCOHTumiiP8ZmU1MDCQtLQ0Bg4cqLqQ/5tea1ltu7BycXHBxcWFLl26MHHiRA4cOKCKH5tdyJ6YmBiCgoIYOnSoqofFnTt3Mj046OrqUq1aNapVq0aXLl3o27cvN27cwMvLC21tbW7cuKGapDI9PZ1bt269t3NTvK558+Zs3ryZmJgYPD091UZDibz3snJaVH6zonAqXbo0CoWCW7du4ePjA2SECHj48GGuXlhDRo/r1+eaAjJ9vnHjBo0aNVLN3ZGSkkJISIja9VpLS4vGjRvj5+eHoaEhtWrVwtjY+L8c3n/i7e1NamoqmzZtwsvLCy0tLcqXL8+cOXMwNzdXjepwc3Pj+PHj2NjYZPnQaGhoiJWVFcHBwTRq1Oit+6xatSoffPABv/zyCxoaGjRu3BjIKGOFQkFgYKBqlGRoaCgRERGqdXNbF2jQoAGLFy9m586d3L17ly+//PLfFdBrcltWjo6OqhBkL1+W37hxA21t7VxN/pxd+fwbjo6OhIeHEx4erup0cefOHbVrqqOjIwcOHCApKUn14iOrc9vd3V1tDoeQkJBM+2vWrBk///wzdnZ2mJub57rXe16dhwA3b95k8ODBqt7VkZGRREZGqqX5N3VBCwuLXJ/jWTE0NKROnTrUqVOHxo0b8/nnn6t65L6psNRV38xHbsr/v7h9+7ZaaL7bt2+rhcMrSnKqj2dVv/mv51hOsru+FtUyzi0zMzO1+0hKSgqPHz/Odk6GoiK3181/Izd1HsgIZdqiRQtatGjBxo0b2bFjh1qDyu3bt1W93ZVKJYGBgdSqVQvIm3qFo6MjERERPH/+XPWCOyAg4J1eP1+Glrp8+XKm5/ObN29iZWWlCnkMZJoXDrIvx5ejy16/h927d+9f7aMwyM09xMLCgoCAANV18+V5k1MUiKzem1SuXDnL+6mrqytKpZLIyMhMI1Le5uXf+vnz59mO4shNnaqwetu5nBvy3C5E/ig6Xa/EO3Hr1i3WrVtHQEAAoaGhnDlzhrCwMFWF3sbGhtDQUO7cuUN0dDSpqanY29ujUCjYvn07ISEhHDlyhG3btv3jfWe17cImJCSEpUuXcvPmTUJDQ7ly5QoPHjygdOnSdO7cWTUZ7N27d3n69Clnz55lzpw5QEZMcFNTU/bt28fTp0+5evUq8+bNU+tN4Ofnx759+3jw4AEhISH4+fmhra2Nvb09+vr6tGrVimXLlnH+/HmCgoL4448/iIqKKtDJUQuLevXqERkZye7du2nWrFlBZ6fYs7GxQUNDg/PnzxMdHU1iYmJBZ0kUQfb29tSoUYO5c+dy/fp1Hjx4wG+//YahoWGuG4rbtm3L5cuX2bBhA0+fPmXfvn2cOnUq035Onz7NnTt3VPt4c/4PyHjpfO3aNc6dO/ef5vXICy/jsB8+fFgVa93Dw4OwsDBu376tWta6dWsSEhKYOnUqt2/fJiQkhEuXLjFnzhwSEhKAjDjTmzdvZuvWrTx+/JiHDx9y8OBBNmzYkGm/1atX56uvvmLevHkcPHgQgFKlSlG5cmXmzp3LrVu3uHfvHrNmzVKb1DK3dQEjIyPq1KnDokWL8Pb2zpN5g/5JWUVERPDHH38QFBTEuXPnWLZsGW3atMn1SMasyuffqFixIg4ODsycOZP79+9z69YtFi5ciJaWlqpnYf369dHS0mLWrFk8fPiQixcvsn79erXt2Nvbc+/ePc6fP8/Tp09Zu3Yt165dy7S/SpUqYWJiwpo1a2jSpEmuR1/k5Xlob2/PoUOHePToEQEBAUybNi3TixsbGxsuX75MZGQkcXFxuS7Pf3KOv27r1q0cOXKEoKAgnj59qpqD7fVQbG/m7/r164SHhxdoKA1bW1sCAgJ49uwZ0dHRuSr//+LkyZOq+uuGDRu4fPky7dq1y4Mjefdyqo+bm5ur5vaJjIwkPj4e+PfnWG5oaWmxYMEC1fV15syZlC5dusiH+8pJhQoVOHLkCFevXuXhw4fMmjUrVyPWCrvcXjf/jdzUeebPn4+/vz8hISHcu3ePCxcuZGqc27NnDydOnODx48csWLCA0NBQ1fNkXtQrXt7jZsyYwb1799Tuce+KoaEh7dq1Y9myZfj5+REcHExAQAC7d+/GwcGB8PBwDh8+TEhICLt37840t8nbylFPT49y5cqxadMmHj58yM2bNzONPMnNPgqL3PzN27Zty+bNmzl16hSPHz9m0aJFREZGZjtq5W3vTSDjPpaSksLFixeJjo4mKSkJBwcHGjRowMyZMzlx4gQhISEEBgayefNm1byHWTE0NKRjx44sXryY/fv38/TpU+7du8eePXvYu3cvkLs6VWH1tnM5N+S5XYj8ISNUhBojIyNu3LjBzp07iYuLo0SJEnTv3p2GDRsCGXN8nDp1im+++Yb4+HhGjRpFkyZNGDRoEJs2bWLlypV4eHjQv39/pk6d+o/2nd22CxM9PT2ePn3Kzz//TExMDObm5jRo0IDOnTujra3Nzz//zMqVKxk/fjwKhQI7OzvVRIGampp8+eWXzJ8/nxEjRlCyZEkGDBjAlClTVNs3MjJi06ZNLFmyhLS0NBwdHRk/fryq52y/fv0AmDVrFnFxcbi5ufH9999nimP/PnrZw/TEiRPUqVOnoLNT7FlZWdGrVy9WrFjB7NmzadiwIWPGjCnobIkiaPTo0SxYsIDJkyeTmpqKp6cn33//fa5DoXl4eDBy5EhWrVrF2rVr8fHxoVevXvz111+qNAMHDuT3339n3LhxGBsb065duywbVOzs7PDx8SE0NPQ/vfDIK+XLlycgIECVF11dXcqVK0dgYKAqjrWVlRVTp05l2bJlTJw4kdTUVEqUKEGlSpXQ0dEBMkbw6evrs3nzZpYvX46uri6lS5dWG9XwupeNBr/88guQMcny6NGjmTNnDl9//TWmpqb06NEj05xLua0LNG3alIMHD+Zp43duy+r7779nyZIljBw5EmNjY+rVq0ffvn3/0b6yKp9/SlNTk6+//prZs2czduxYbG1t6d+/P1OmTFE1VBkYGPDdd98xb948Ro8eTalSpejXrx+TJ09WbadFixbcv3+fX3/9FYBatWrRoUMHtUnoIWP0cZMmTVQNKv9EXp2Ho0aNYs6cOYwZMwZLS0t69uyZqVFiwIABLFy4ED8/P6ysrFi0aFGu8vhPz/GXDAwM2Lx5M8HBwUBG79jvv/8+2wa23r17M3fuXAYNGkRqaio7duzIVf7yWseOHZkxYwbDhg0jJSWFhQsX5lj+/0WvXr04efIk8+fPx9TUlFGjRqnF0i9KcqqPa2lpMXjwYNauXcvatWvx8vJiypQp//ocyw0dHR26devG9OnTef78OeXKlWP8+PF5EhawMOvatSuhoaH88MMP6Ovr061bN7URK0VZbq6b/0Zu6jxKpZK//vqLsLAwDAwM8PX1ZcCAAWrb+eijj9i6dSt3797FxsaGCRMmqOYPyot6xev3uM8++4wSJUowYMAA1b3qXenbty9GRkasXbuW8PBwzM3NadiwIa1ataJTp04sWLCAlJQUKlWqRO/evdXmhMmpHEeNGqW6h5csWZJPPvmEcePGqb6vXr16jvsoLHLzN+/UqRNRUVHMmjULgCZNmlCzZk21uuDr3vbeBMDT05OWLVsybdo0YmNj6dmzJ7169WLUqFGsX7+eJUuWEB4ejrGxMe7u7jmOWOnTpw/m5uZs2bKFefPmYWhoiKurK506dQJyV6cqzLI7l3NDntuFyB8aiYmJElhPCFEsTJw4EWtraz799NOCzooQoogaNmwY9evXL9AJ6Yu7Y8eOMXfuXJYuXSpzXL3m/v37jBw5khkzZvzjiW9zY968eQQHBxeZlweicGjbti3jxo1ThUwUecvPz4+//vorT0a6CJGTZ8+eMXDgQKZPn642qbcQ/8aoUaPw8vJiyJAhBZ0VIYR452SEihCiyIuNjeXSpUtcunSJ33//vaCzI4QogqKiojh69CjPnj2jRYsWBZ2dYikpKYnQ0FDWr19Ps2bN3vvGlFOnTqGnp4e9vT2hoaEsWrQIFxcX3Nzc8nQ/8fHx3Llzh4MHD/LVV1/l6baFEEIIUfyFhoZy4cIFfHx8SE9PV4UpHzFiREFnTQghCoQ0qAghirzRo0cTGxvLhx9+iJOTU0FnRwhRBH344YeYmpoyfPhw1WSjIm9t3ryZ9evX4+XlpTZJ6/sqMTGRpUuXEhYWhrGxMT4+PgwcODDPw/z88MMPBAQE0KxZM9WE8EIIIYQQuaWhocHBgwdZsmQJSqUSR0dHJk6cKCOdhBDvLQn5JYQQQgghhBBCCCGEEEIIkQPNgs6AEEIIIYQQQgghhBBCCCFEYScNKkIIIYQQQgghhBBCCCGEEDmQBhUhhBBCCCGEEEIIIYQQQogcSIOKEEIIIYQQQgghhBBCCCFEDqRBRQghhBBCCCGEEEIIIYQQIgf/B/XfoAlp5AoUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 2160x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,ax=plt.subplots(figsize=(30,15))\n",
    "colormap=sns.diverging_palette(220,10,as_cmap=True)\n",
    "sns.heatmap(df.corr(),annot=True,cmap=colormap)\n",
    "plt.savefig(\"hour_correlation.jpeg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>instant</th>\n",
       "      <th>dteday</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>2011-01-01</td>\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>2</td>\n",
       "      <td>2011-01-01</td>\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>3</td>\n",
       "      <td>2011-01-01</td>\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>4</td>\n",
       "      <td>2011-01-01</td>\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>5</td>\n",
       "      <td>2011-01-01</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17374</th>\n",
       "      <td>17375</td>\n",
       "      <td>2012-12-31</td>\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>17376</td>\n",
       "      <td>2012-12-31</td>\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>17377</td>\n",
       "      <td>2012-12-31</td>\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>17378</td>\n",
       "      <td>2012-12-31</td>\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>17379</td>\n",
       "      <td>2012-12-31</td>\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 × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       instant      dteday  season  yr  mnth  hr  holiday  weekday  \\\n",
       "0            1  2011-01-01       1   0     1   0        0        6   \n",
       "1            2  2011-01-01       1   0     1   1        0        6   \n",
       "2            3  2011-01-01       1   0     1   2        0        6   \n",
       "3            4  2011-01-01       1   0     1   3        0        6   \n",
       "4            5  2011-01-01       1   0     1   4        0        6   \n",
       "...        ...         ...     ...  ..   ...  ..      ...      ...   \n",
       "17374    17375  2012-12-31       1   1    12  19        0        1   \n",
       "17375    17376  2012-12-31       1   1    12  20        0        1   \n",
       "17376    17377  2012-12-31       1   1    12  21        0        1   \n",
       "17377    17378  2012-12-31       1   1    12  22        0        1   \n",
       "17378    17379  2012-12-31       1   1    12  23        0        1   \n",
       "\n",
       "       workingday  weathersit  temp   atemp   hum  windspeed  casual  \\\n",
       "0               0           1  0.24  0.2879  0.81     0.0000       3   \n",
       "1               0           1  0.22  0.2727  0.80     0.0000       8   \n",
       "2               0           1  0.22  0.2727  0.80     0.0000       5   \n",
       "3               0           1  0.24  0.2879  0.75     0.0000       3   \n",
       "4               0           1  0.24  0.2879  0.75     0.0000       0   \n",
       "...           ...         ...   ...     ...   ...        ...     ...   \n",
       "17374           1           2  0.26  0.2576  0.60     0.1642      11   \n",
       "17375           1           2  0.26  0.2576  0.60     0.1642       8   \n",
       "17376           1           1  0.26  0.2576  0.60     0.1642       7   \n",
       "17377           1           1  0.26  0.2727  0.56     0.1343      13   \n",
       "17378           1           1  0.26  0.2727  0.65     0.1343      12   \n",
       "\n",
       "       registered  cnt  \n",
       "0              13   16  \n",
       "1              32   40  \n",
       "2              27   32  \n",
       "3              10   13  \n",
       "4               1    1  \n",
       "...           ...  ...  \n",
       "17374         108  119  \n",
       "17375          81   89  \n",
       "17376          83   90  \n",
       "17377          48   61  \n",
       "17378          37   49  \n",
       "\n",
       "[17379 rows x 17 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weathersit</th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>967</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>968</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>869 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     weathersit  season  holiday\n",
       "cnt                             \n",
       "1           1.0     1.0      0.0\n",
       "2           1.0     1.0      0.0\n",
       "3           1.0     2.0      0.0\n",
       "4           1.0     2.0      0.0\n",
       "5           1.0     2.0      0.0\n",
       "..          ...     ...      ...\n",
       "967         1.0     4.0      0.0\n",
       "968         1.0     3.0      0.0\n",
       "970         1.0     3.0      0.0\n",
       "976         1.0     3.0      0.0\n",
       "977         1.0     3.0      0.0\n",
       "\n",
       "[869 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.groupby('cnt')[['weathersit', 'season' , 'holiday']].median()\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The interest thing that I found is that the total rental bikes including both casual and registered from 1-977,\n",
    "the weather is 1 meaning that cnt will be up to 977 if the weather is clear, few clouds, partly cloudy.\n",
    "The season is from 1-4 means that the rental bikes will be rent every season.\n",
    "Holiday equal to zero is that no holiday but people still rent a bike."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1.2 :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.iolib.summary2 import summary_col\n",
    "from sklearn import (linear_model, metrics, model_selection)\n",
    "linear_model.LinearRegression()\n",
    "from sklearn.model_selection import KFold\n",
    "kf = KFold(n_splits=5,shuffle=True)"
   ]
  },
  {
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</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",
       "      <th>log_cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
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       "      <td>2011-01-01</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>3.688879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
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       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <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>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>2.564949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
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       "      <td>4</td>\n",
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       "      <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>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
       "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
       "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
       "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
       "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \\\n",
       "0           1  0.24  0.2879  0.81        0.0       3          13   16   \n",
       "1           1  0.22  0.2727  0.80        0.0       8          32   40   \n",
       "2           1  0.22  0.2727  0.80        0.0       5          27   32   \n",
       "3           1  0.24  0.2879  0.75        0.0       3          10   13   \n",
       "4           1  0.24  0.2879  0.75        0.0       0           1    1   \n",
       "\n",
       "    log_cnt  \n",
       "0  2.772589  \n",
       "1  3.688879  \n",
       "2  3.465736  \n",
       "3  2.564949  \n",
       "4  0.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.log(df[\"cnt\"]) #ln(price)\n",
    "df[\"log_cnt\"] = y\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['instant',\n",
       " 'dteday',\n",
       " 'season',\n",
       " 'yr',\n",
       " 'mnth',\n",
       " 'hr',\n",
       " 'holiday',\n",
       " 'weekday',\n",
       " 'workingday',\n",
       " 'weathersit',\n",
       " 'temp',\n",
       " 'atemp',\n",
       " 'hum',\n",
       " 'windspeed',\n",
       " 'casual',\n",
       " 'registered',\n",
       " 'cnt',\n",
       " 'log_cnt']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <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",
       "      <td>2.564949</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",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </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",
       "      <td>4.779123</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",
       "      <td>4.488636</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",
       "      <td>4.499810</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",
       "      <td>4.110874</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",
       "      <td>3.891820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17379 rows × 16 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   log_cnt  \n",
       "0      0.2879  0.81     0.0000       3          13   16  2.772589  \n",
       "1      0.2727  0.80     0.0000       8          32   40  3.688879  \n",
       "2      0.2727  0.80     0.0000       5          27   32  3.465736  \n",
       "3      0.2879  0.75     0.0000       3          10   13  2.564949  \n",
       "4      0.2879  0.75     0.0000       0           1    1  0.000000  \n",
       "...       ...   ...        ...     ...         ...  ...       ...  \n",
       "17374  0.2576  0.60     0.1642      11         108  119  4.779123  \n",
       "17375  0.2576  0.60     0.1642       8          81   89  4.488636  \n",
       "17376  0.2576  0.60     0.1642       7          83   90  4.499810  \n",
       "17377  0.2727  0.56     0.1343      13          48   61  4.110874  \n",
       "17378  0.2727  0.65     0.1343      12          37   49  3.891820  \n",
       "\n",
       "[17379 rows x 16 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop(['instant','dteday'], axis = 1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df[['atemp', 'weathersit']]\n",
    "y=df[['log_cnt']]\n",
    "X=X.to_numpy()\n",
    "y=y.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['const'] = 1\n",
    "X1 = ['const', 'atemp', 'weathersit']\n",
    "reg1 = sm.OLS(df['log_cnt'], df[X1], missing='drop').fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                log_cnt   R-squared:                       0.153\n",
      "Model:                            OLS   Adj. R-squared:                  0.153\n",
      "Method:                 Least Squares   F-statistic:                     1567.\n",
      "Date:                Sat, 01 Oct 2022   Prob (F-statistic):               0.00\n",
      "Time:                        13:41:02   Log-Likelihood:                -30104.\n",
      "No. Observations:               17379   AIC:                         6.021e+04\n",
      "Df Residuals:                   17376   BIC:                         6.024e+04\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          3.2629      0.040     80.931      0.000       3.184       3.342\n",
      "atemp          3.2355      0.061     53.281      0.000       3.116       3.355\n",
      "weathersit    -0.1868      0.016    -11.442      0.000      -0.219      -0.155\n",
      "==============================================================================\n",
      "Omnibus:                     1956.749   Durbin-Watson:                   0.259\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2683.933\n",
      "Skew:                          -0.951   Prob(JB):                         0.00\n",
      "Kurtosis:                       3.303   Cond. No.                         12.6\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(reg1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit model: log(cnt) = 3.2629 + 3.2355 atemp + -0.1868 weathersit \n"
     ]
    }
   ],
   "source": [
    "X = df[[\"atemp\",\"weathersit\"]]\n",
    "sqft_lr_model = linear_model.LinearRegression()\n",
    "\n",
    "model1 = sqft_lr_model.fit(X[[\"atemp\",\"weathersit\"]], y) \n",
    "\n",
    "beta_0 = sqft_lr_model.intercept_\n",
    "beta_1 = sqft_lr_model.coef_[0]\n",
    "beta_2 = sqft_lr_model.coef_[1]\n",
    "\n",
    "print(f\"Fit model: log(cnt) = {beta_0:.4f} + {beta_1:.4f} atemp + {beta_2:.4f} weathersit \")\n",
    "#On the average of atemp and weathersit, the estimated is 323.55% in atemp and -18.68% in weathersit to log(cnt)in term of percentage change "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE (Model1:Linear) on 5-fold CV on the train data: 0.9962198874197945\n",
      "MAE (Model1:Linear) on 5-fold CV on the test data: 0.9969339383275226\n"
     ]
    }
   ],
   "source": [
    "err_train = 0\n",
    "err_test =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_train += metrics.mean_absolute_error(y[train], reg.predict(X[train]))      \n",
    "    err_test += metrics.mean_absolute_error(y[test], reg.predict(X[test]))\n",
    "mae_train_5cv_model1 = err_train/5              #average the rmse\n",
    "mae_test_5cv_model1 = err_test/5              #average the rmse\n",
    "print('MAE (Model1:Linear) on 5-fold CV on the train data: {}'.format(mae_train_5cv_model1))\n",
    "print('MAE (Model1:Linear) on 5-fold CV on the test data: {}'.format(mae_test_5cv_model1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (Model1:Linear) on 5-fold CV on the train data: 1.285065010268848\n",
      "RMSE (Model1:Linear) on 5-fold CV on the test data: 1.2860501123657861\n"
     ]
    }
   ],
   "source": [
    "err_train = 0\n",
    "err_test =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_train += np.sqrt(metrics.mean_squared_error(y[train], reg.predict(X[train])))      \n",
    "    err_test += np.sqrt(metrics.mean_squared_error(y[test], reg.predict(X[test])))\n",
    "    \n",
    "rmse_train_5cv_model1 = err_train/5              #average the rmse\n",
    "rmse_test_5cv_model1 = err_test/5              #average the rmse\n",
    "print('RMSE (Model1:Linear) on 5-fold CV on the train data: {}'.format(rmse_train_5cv_model1))\n",
    "print('RMSE (Model1:Linear) on 5-fold CV on the test data: {}'.format(rmse_test_5cv_model1))"
   ]
  },
  {
   "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": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "kf = KFold(n_splits=10,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE (Model2:Linear) on 10-fold CV on the train data: 0.49812739177178383\n",
      "MAE (Model2:Linear) on 10-fold CV on the test data: 0.49836206971345476\n"
     ]
    }
   ],
   "source": [
    "err_train = 0\n",
    "err_test =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_train += metrics.mean_absolute_error(y[train], reg.predict(X[train]))      \n",
    "    err_test += metrics.mean_absolute_error(y[test], reg.predict(X[test]))\n",
    "mae_train_10cv_model2 = err_train/10              #average the rmse\n",
    "mae_test_10cv_model2 = err_test/10              #average the rmse\n",
    "print('MAE (Model2:Linear) on 10-fold CV on the train data: {}'.format(mae_train_10cv_model2))\n",
    "print('MAE (Model2:Linear) on 10-fold CV on the test data: {}'.format(mae_test_10cv_model2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (Model2:Linear) on 10-fold CV on the train data: 1.285112919968708\n",
      "RMSE (Model2:Linear) on 10-fold CV on the test data: 1.2856455477092685\n"
     ]
    }
   ],
   "source": [
    "err_train = 0\n",
    "err_test =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_train += np.sqrt(metrics.mean_squared_error(y[train], reg.predict(X[train])))      \n",
    "    err_test += np.sqrt(metrics.mean_squared_error(y[test], reg.predict(X[test])))\n",
    "    \n",
    "rmse_train_10cv_model2 = err_train/5              #average the rmse\n",
    "rmse_test_10cv_model2 = err_test/5              #average the rmse\n",
    "print('RMSE (Model2:Linear) on 10-fold CV on the train data: {}'.format(rmse_train_10cv_model2))\n",
    "print('RMSE (Model2:Linear) on 10-fold CV on the test data: {}'.format(rmse_test_10cv_model2))"
   ]
  },
  {
   "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": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "kf = KFold(n_splits=5,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df[['holiday', 'weekday', 'workingday', 'weathersit', 'temp', 'atemp', 'hum', 'windspeed']]\n",
    "y=df[['log_cnt']]\n",
    "X=X.to_numpy()\n",
    "y=y.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE (Model3:Linear) on 5-fold CV on the train data: 0.996253284063477\n",
      "MAE (Model3:Linear) on 5-fold CV on the test data: 0.9968033130460089\n"
     ]
    }
   ],
   "source": [
    "err_train = 0\n",
    "err_test =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_train += metrics.mean_absolute_error(y[train], reg.predict(X[train]))      \n",
    "    err_test += metrics.mean_absolute_error(y[test], reg.predict(X[test]))\n",
    "mae_train_5cv_model3 = err_train/5              #average the rmse\n",
    "mae_test_5cv_model3 = err_test/5              #average the rmse\n",
    "print('MAE (Model3:Linear) on 5-fold CV on the train data: {}'.format(mae_train_5cv_model3))\n",
    "print('MAE (Model3:Linear) on 5-fold CV on the test data: {}'.format(mae_test_5cv_model3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE (Model3:Linear) on 5-fold CV on the train data: 1.2851110566946768\n",
      "RMSE (Model3:Linear) on 5-fold CV on the test data: 1.2852699713852715\n"
     ]
    }
   ],
   "source": [
    "err_train = 0\n",
    "err_test =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_train += np.sqrt(metrics.mean_squared_error(y[train], reg.predict(X[train])))      \n",
    "    err_test += np.sqrt(metrics.mean_squared_error(y[test], reg.predict(X[test])))\n",
    "    \n",
    "rmse_train_5cv_model3 = err_train/5              #average the rmse\n",
    "rmse_test_5cv_model3 = err_test/5              #average the rmse\n",
    "print('RMSE (Model3:Linear) on 5-fold CV on the train data: {}'.format(rmse_train_5cv_model3))\n",
    "print('RMSE (Model3:Linear) on 5-fold CV on the test data: {}'.format(rmse_test_5cv_model3))"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "MAE (Model1:Linear) on 5-fold CV on the train data: 0.9962198874197945\n",
    "MAE (Model1:Linear) on 5-fold CV on the test data: 0.9969339383275226\n",
    "RMSE (Model1:Linear) on 5-fold CV on the train data: 1.285065010268848\n",
    "RMSE (Model1:Linear) on 5-fold CV on the test data: 1.2860501123657861\n",
    "\n",
    "MAE (Model3:Linear) on 5-fold CV on the train data: 0.996253284063477\n",
    "MAE (Model3:Linear) on 5-fold CV on the test data: 0.9968033130460089\n",
    "RMSE (Model3:Linear) on 5-fold CV on the train data: 1.2851110566946768\n",
    "RMSE (Model3:Linear) on 5-fold CV on the test data: 1.2852699713852715\n",
    "\n",
    "Compare model1(two features) with model3(all features), the model1 from two features fit teh model better as the error both in \n",
    "MAE and RMSE in train and test data have slightly small error than model3."
   ]
  },
  {
   "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": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.41621978],\n",
       "       [3.39258911],\n",
       "       [3.39258911],\n",
       "       ...,\n",
       "       [3.78433194],\n",
       "       [3.92186807],\n",
       "       [3.69324423]])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = reg.predict(X)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    " model_mse =  metrics.mean_squared_error(y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.772589</td>\n",
       "      <td>3.416220</td>\n",
       "      <td>1.651742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.688879</td>\n",
       "      <td>3.392589</td>\n",
       "      <td>1.651742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.465736</td>\n",
       "      <td>3.392589</td>\n",
       "      <td>1.651742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.564949</td>\n",
       "      <td>3.568636</td>\n",
       "      <td>1.651742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.568636</td>\n",
       "      <td>1.651742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17374</th>\n",
       "      <td>4.779123</td>\n",
       "      <td>3.918025</td>\n",
       "      <td>1.651742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17375</th>\n",
       "      <td>4.488636</td>\n",
       "      <td>3.918025</td>\n",
       "      <td>1.651742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17376</th>\n",
       "      <td>4.499810</td>\n",
       "      <td>3.784332</td>\n",
       "      <td>1.651742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17377</th>\n",
       "      <td>4.110874</td>\n",
       "      <td>3.921868</td>\n",
       "      <td>1.651742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17378</th>\n",
       "      <td>3.891820</td>\n",
       "      <td>3.693244</td>\n",
       "      <td>1.651742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17379 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Actual  Predicted     Error\n",
       "0      2.772589   3.416220  1.651742\n",
       "1      3.688879   3.392589  1.651742\n",
       "2      3.465736   3.392589  1.651742\n",
       "3      2.564949   3.568636  1.651742\n",
       "4      0.000000   3.568636  1.651742\n",
       "...         ...        ...       ...\n",
       "17374  4.779123   3.918025  1.651742\n",
       "17375  4.488636   3.918025  1.651742\n",
       "17376  4.499810   3.784332  1.651742\n",
       "17377  4.110874   3.921868  1.651742\n",
       "17378  3.891820   3.693244  1.651742\n",
       "\n",
       "[17379 rows x 3 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'Actual': y.flatten(), 'Predicted': y_pred.flatten(), 'Error': model_mse})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.9969807573164299\n",
      "Mean Squared Error: 1.6517421287876872\n",
      "Root Mean Squared Error: 1.2852012016753203\n"
     ]
    }
   ],
   "source": [
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y, y_pred))  \n",
    "print('Mean Squared Error:',  metrics.mean_squared_error(y, y_pred))  \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2 [20 points]"
   ]
  },
  {
   "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": 92,
   "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']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The unemployment rate in Jan is 3.5\n",
      "The unemployment rate in Feb is 3.5\n",
      "The unemployment rate in Mar is 4.4\n",
      "The unemployment rate in Apr is 14.8\n",
      "The unemployment rate in May is 13.3\n",
      "The unemployment rate in June is 11.1\n",
      "The unemployment rate in July is 10.2\n",
      "The unemployment rate in Aug is 8.4\n",
      "The unemployment rate in Sep is 7.8\n",
      "The unemployment rate in Oct is 6.9\n",
      "The unemployment rate in Nov is 6.7\n",
      "The unemployment rate in Dec is 6.7\n"
     ]
    }
   ],
   "source": [
    "for u, m in zip(unemp, month):\n",
    "    print(f\"The unemployment rate in {m} is {u}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "if u > 8:\n",
    "    print(f\"The unemployment rate in {m} is {u}\")"
   ]
  },
  {
   "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": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "for i in range(2,501,2):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.214608098422191"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j = np.log(i)\n",
    "j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(j)"
   ]
  },
  {
   "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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "path= 'C:\\\\Users\\\\6204641010\\\\Downloads\\Mid-term_exam_EE522_Fall_2022'\n",
    "os.chdir(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('TSLA.csv')\n",
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      5.554870\n",
       "1      5.554535\n",
       "2      5.562641\n",
       "3      5.561438\n",
       "4      5.564201\n",
       "         ...   \n",
       "248    5.620437\n",
       "249    5.645235\n",
       "250    5.662301\n",
       "251    5.591770\n",
       "252    5.580673\n",
       "Name: Adj Close, Length: 253, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_price = np.log(df['Adj Close'])\n",
    "log_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    .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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>log_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-09-30</td>\n",
       "      <td>260.333344</td>\n",
       "      <td>263.043335</td>\n",
       "      <td>258.333344</td>\n",
       "      <td>258.493347</td>\n",
       "      <td>258.493347</td>\n",
       "      <td>53868000</td>\n",
       "      <td>5.554870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>259.466675</td>\n",
       "      <td>260.260010</td>\n",
       "      <td>254.529999</td>\n",
       "      <td>258.406677</td>\n",
       "      <td>258.406677</td>\n",
       "      <td>51094200</td>\n",
       "      <td>5.554535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-10-04</td>\n",
       "      <td>265.500000</td>\n",
       "      <td>268.989990</td>\n",
       "      <td>258.706665</td>\n",
       "      <td>260.510010</td>\n",
       "      <td>260.510010</td>\n",
       "      <td>91449900</td>\n",
       "      <td>5.562641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-10-05</td>\n",
       "      <td>261.600006</td>\n",
       "      <td>265.769989</td>\n",
       "      <td>258.066681</td>\n",
       "      <td>260.196655</td>\n",
       "      <td>260.196655</td>\n",
       "      <td>55297800</td>\n",
       "      <td>5.561438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-10-06</td>\n",
       "      <td>258.733337</td>\n",
       "      <td>262.220001</td>\n",
       "      <td>257.739990</td>\n",
       "      <td>260.916656</td>\n",
       "      <td>260.916656</td>\n",
       "      <td>43898400</td>\n",
       "      <td>5.564201</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2022-09-26</td>\n",
       "      <td>271.829987</td>\n",
       "      <td>284.089996</td>\n",
       "      <td>270.309998</td>\n",
       "      <td>276.010010</td>\n",
       "      <td>276.010010</td>\n",
       "      <td>58076900</td>\n",
       "      <td>5.620437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>2022-09-27</td>\n",
       "      <td>283.839996</td>\n",
       "      <td>288.670013</td>\n",
       "      <td>277.510010</td>\n",
       "      <td>282.940002</td>\n",
       "      <td>282.940002</td>\n",
       "      <td>61925200</td>\n",
       "      <td>5.645235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>2022-09-28</td>\n",
       "      <td>283.079987</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>277.570007</td>\n",
       "      <td>287.809998</td>\n",
       "      <td>287.809998</td>\n",
       "      <td>54664800</td>\n",
       "      <td>5.662301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>2022-09-29</td>\n",
       "      <td>282.760010</td>\n",
       "      <td>283.649994</td>\n",
       "      <td>265.779999</td>\n",
       "      <td>268.209991</td>\n",
       "      <td>268.209991</td>\n",
       "      <td>77620600</td>\n",
       "      <td>5.591770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>2022-09-30</td>\n",
       "      <td>266.149994</td>\n",
       "      <td>275.570007</td>\n",
       "      <td>262.470001</td>\n",
       "      <td>265.250000</td>\n",
       "      <td>265.250000</td>\n",
       "      <td>67517800</td>\n",
       "      <td>5.580673</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>253 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date        Open        High         Low       Close   Adj Close  \\\n",
       "0    2021-09-30  260.333344  263.043335  258.333344  258.493347  258.493347   \n",
       "1    2021-10-01  259.466675  260.260010  254.529999  258.406677  258.406677   \n",
       "2    2021-10-04  265.500000  268.989990  258.706665  260.510010  260.510010   \n",
       "3    2021-10-05  261.600006  265.769989  258.066681  260.196655  260.196655   \n",
       "4    2021-10-06  258.733337  262.220001  257.739990  260.916656  260.916656   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "248  2022-09-26  271.829987  284.089996  270.309998  276.010010  276.010010   \n",
       "249  2022-09-27  283.839996  288.670013  277.510010  282.940002  282.940002   \n",
       "250  2022-09-28  283.079987  289.000000  277.570007  287.809998  287.809998   \n",
       "251  2022-09-29  282.760010  283.649994  265.779999  268.209991  268.209991   \n",
       "252  2022-09-30  266.149994  275.570007  262.470001  265.250000  265.250000   \n",
       "\n",
       "       Volume  log_price  \n",
       "0    53868000   5.554870  \n",
       "1    51094200   5.554535  \n",
       "2    91449900   5.562641  \n",
       "3    55297800   5.561438  \n",
       "4    43898400   5.564201  \n",
       "..        ...        ...  \n",
       "248  58076900   5.620437  \n",
       "249  61925200   5.645235  \n",
       "250  54664800   5.662301  \n",
       "251  77620600   5.591770  \n",
       "252  67517800   5.580673  \n",
       "\n",
       "[253 rows x 8 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['log_price'] = log_price\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['Adj Close']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['log_price']].plot()"
   ]
  },
  {
   "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": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "  \n",
    "sns.lineplot(x=\"Date\", y=\"log_price\", data=df)\n",
    "plt.show()"
   ]
  },
  {
   "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
}