{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eaa2416a-0267-47ba-9d79-bb261e26a468",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np\n",
    "from skimage import transform, color\n",
    "import cv2 as cv\n",
    "\n",
    "tuple2int = {\n",
    "    (1, 1, 1, 1, 1, 1, 0) : 0,\n",
    "    (0, 1, 1, 0, 0, 0, 0) : 1,\n",
    "    (1, 1, 0, 1, 1, 0, 1) : 2,\n",
    "    (1, 1, 1, 1, 0, 0, 1) : 3,\n",
    "    (0, 1, 1, 0, 0, 1, 1) : 4,\n",
    "    (1, 0, 1, 1, 0, 1, 1) : 5,\n",
    "    (0, 0, 1, 1, 1, 1, 1) : 6,\n",
    "    (1, 1, 1, 0, 0, 0, 0) : 7,\n",
    "    (1, 1, 1, 1, 1, 1, 1) : 8,\n",
    "    (1, 1, 1, 0, 0, 1, 1) : 9\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3d2098e2-ef3d-4a2d-87c4-6be66407bffa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# импортируем видео и берём первый кадр\n",
    "cap = cv.VideoCapture('video5v.mp4')\n",
    "_, frame = cap.read()\n",
    "cap.release()\n",
    "\n",
    "# преобразуем в чб и обрезаем\n",
    "img = color.rgb2gray(frame)[40:160, 60:810]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4137cf71-9e31-4359-a21a-7b54a6354534",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n",
      "1\n",
      "4\n",
      "0\n",
      "0\n",
      "0\n",
      "2\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABKUAAADuCAYAAADobbT3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9aYxtWZYehn3nznPciHhDvpeVWVVdWdVV3cUqkVVFskVCagEEJFOAREDQLw+UJUCUaRsGCKppCKQBGYQoiiKagC21CNtNUBZk6xcBoyFTAH/IjUbTRLtBsgd0V1dlZ2VmZb4xXgx3ns7xj8hvx3dW7HPuvfGGzHy1PiAQ9557zj57r732Wt/eaw9JlmUZHA6Hw+FwOBwOh8PhcDgcjleIyqedAYfD4XA4HA6Hw+FwOBwOx08efFDK4XA4HA6Hw+FwOBwOh8PxyuGDUg6Hw+FwOBwOh8PhcDgcjlcOH5RyOBwOh8PhcDgcDofD4XC8cviglMPhcDgcDofD4XA4HA6H45XDB6UcDofD4XA4HA6Hw+FwOByvHD4o5XA4HA6Hw+FwOBwOh8PheOXwQSmHw+FwOBwOh8PhcDgcDscrR23XG3/lV34lfM6yDGmaolKphO/6WxmSJLn2Wa8pNK2ie/Re3rPrc8x/mqaF91SrVSRJUpqOfV+WZciyDJvNBpVKBZVKZWsaTGez2SBN02syVVnpd+YxVjbep/fa+on9FqsjiyRJgvz0fZRl7Dn9PZYflq0or7H8xfSNZbZ5oGzTNMVms8F6vcZkMsFqtcJyuQy/a71Vq1XU63VUq9Vr9aKoVCqYzWZ4+vQpJpMJlssl0jTF3bt30e/30Ww2Ua/Xr+U/ph/VahW1Wi3IlzrI+/Q7r2katm70PdVqNfdn5ZumKU5PT/Huu+/i8ePHOD8/R6VSwdtvv41bt26h2+2iXq9jvV5jtVohSZIgNwCh3Ov1Gg8fPsTp6SkePnyIk5MTZFmGSqWCVqt1LX9pmuLg4ADtdht3795Fr9dDlmWYTCa4uLjAfD5HlmVoNpvh+fV6jcVigdlslqv3zWaD1WoV/haLBZIkwWq1wtnZGZ48eYKTkxM8efIE6/U6Wp83gdVfyrtSqaDX66HdbqPVaqHVamE+n2M+n4e8NZtNNJtN3LlzB5VKBZ1OJ9TpZrPBs2fP8PTpU6RpikajEeqL9TAcDtHr9dBqtbBYLIJsWO/z+RzNZhNZlmGxWCDLMjx9+hSLxQKLxQKbzSakqeVJkgS1Wi20gUajEeoRuNTN9XqN5XIZ3kk95jN8frVahfupw/P5PNcuG41GqN9GoxH0NE1TtNvtnP5rO1CZNxoN1Gq1a/Wxrc2oXWWa1paU+SybhrVXfJ/VFWurYvaryKdVKpXQ9mL28Sagzmq9bIPNb7PZDHWvf+12G+12G71eD51OJ+hIvV5HrVYLda62V/UgSZKQH7WHKlv+pvmyMiF/od+i7PQzvyt+5md+Zk9pXsE5lHMo+5xzqCs4h3IO5RzKOZRzqEv8pHKonQelFKqM/K4vtw4/9nzssyU4+yiGKm2R49OGF2sksTRjDWCf8ujzuxCqXdIsSi9Gluw9sYZbBtuo9R3a0OiMeU+RwbH5tQbIvmMbispjr9MJMO1KpYJarRaMd5IkwSloPrRMZflK0xTVahW9Xg/L5RLL5RKr1QrPnj3L1RkNfRn52ZXQFqVj0yNoQPgXS7NaraLf7+PevXuh7BcXF/jxj3+M+XyO4+Nj9Pv9nMNK0xSr1aqwQ0CDpwTfGkCr+9VqNefk7d96vc4ROZJgGly9j/nMsizkM8sytNvt8A6F2jH+t3qgum/tH6/x/SQxlANtVJqm4VnK8/T0FEmSBCfCd5H4VCqVHDFneZQc1Ov18AcA6/UalUoF4/E4ODAAGAwGODs7w2w2i+o264uOkA5PO9K1Wg3r9RrNZhO1Wg3L5TKQwHq9HvLIfAGXzpZ11m63AVwScbZJJWTr9Rrz+TzkcbPZoNls5sim6pWSI6ansrGdvqJ2VKvVgu5qnlqtVqgv7fSwrJquEjfbabL/tXNjdVKfpU7ovZSL1csiOxCD+k9LKLZ1bLV9abvTe8p8p9oCtY+sB5Ipyly/U7Yk0bVaLXRead+V1FsbSL1ku9IBlCIy9iLgHGr38jiHiqel6TmHcg7lHMo5lHMoBLkonEN9fjjUjQaldGTRCif2uegei11GE4vyE8ufVqI14DGSEsO+ZaJCahSUsIa6KC1LLMvyXuSU9iE1sTLEnlUHZhVNG9M+5JHGQN+9La9F5bFEIdaY1ekClw1YI38sA50Xf6fRKCoXf2MUajKZIMsuo1Ta8Gk4Y0ZSR555Xe/V0WxLjKzBjrWJXY1Eo9HArVu3Qr2kaYqLiwt89NFHWCwWODw8xGAwQK/XC7KkAWW708gDnTLrgUZR60cJkcpd5UuwvtbrdagblYU17gCCY65Wq2i32xgOhzg+PgaAYHzLZKjl2Ww2IZqp72O+eJ15ZNRxs9nkIsn8zEhdpVIJhIrvjzk0OvtKpRKeS9MUZ2dnuTKkaRocPd9HB8J3M3paBN6zXq/RaDTQbrfRaDRCepoXjWQvFgsACJ0L6n6apqH+N5tNuMa0qCt0gJRFmqaYTqdYrVbhepZloe6Aq8gSZc26iNUV61HJZJF90bbCCKvtCCnpirU1OnW24UajgUajkWvXligomVO5KKmybaTIrm6D5r1sZktZx9IOqKhvIcFVvxizz6vVKpfGNj9iZyooQbOf1QbpvZS32mmNUKu9+va3v12an13hHKr4HudQzqGcQzmH4jPOoZxDOYf6yeFQNxqUKiM+KsgYypySvY+KFhtRB/IjklrR6pSKnrWGU0lY2YikVpTmlVASFDPssTRj72D59Z4iBedvqly7yDiWDnB9lDl2jy1PWSOL/RaTGVCe720NjE7N3sfPdvq43mvJk+a9qNyWUDItNkYlZnREjIYoKG8lBVbXrJGL6S9RRIg132XtkMTw1q1bwRkDwPn5OT7++GPMZrMQET04OEC9XkeWZblpxJvNJjjrLLuMBtGh2/yq/LrdLtrtNg4ODnB6epqTOeuXUaNYVJlRNTp8lQn/c+SfU6npvHXKssoill87E8HK0tot5t0uf8iyy4gI//QaPy8Wi0ASWDZLyoqIrEY1+acRWeCKDBQRC97L6fIxG2+dqLYHe2+sM0bi1u12Q6Sm0+lguVwCQNAlq9tcXgBctqNWqxUipCy/1gMJ1nK5xGKx2NqJt+1/Pp+X3r8NZZ2amM3SZ7rdbvjc7/dD3nu9Xq6DRXuhkV4baY/lh7JlZDym29v8inZqgKvoOz8DCKS6qMOkKPIdaqO35Wmb/7HXYgMZ+/rTbXAO5RzKOZRzKOdQzqFs/TmHKodzqNebQ+08KBVbc68ZUeEURbdiKHIEVGTrbNXhWANQpABl748pOJ+xzlPvKYtAbCuzIqbgtgyx7/bdsfzGUBbR3Aabpsp9l0ZXJJNd8lREpoquxfJkjT1wNS2XRoh5YYOnM4mRlBgRZCSk3++HfRYmkwlGo1GYuksjp+VXx63GTfO1q2GLtSPrwHZJg3sf0ME1Gg08fvwYjx8/DlOLm81mWOfP9fBKBqy8bVkpR3YINNoRi7pp++NfrF6UMBAkdSR4OoLPMjK/MZ1kdMnmSeukqI4skbflKXJgShpth497euhnJVZKVFguldt0Og3XKa8sy0JUzKal5Iy/kzjafNpntdwxO0X/QtL27NmznEy0TkiyVA8AYDqd5iJl7BhQdwEEGZGUa/ovG0VtsqzDrSCZTZIEZ2dnuVkX9h0AclP0tT3pEghtaxrNstEtOxWf79JILJclaIeNHTLbidNO5DZSG4u2l7U7RYxH7ELC9rlvVziHcg7lHMo5lHMo51DOoW4G51CvP4faeVCqLGEVXNHvMYWJjaJRACqEmHF73gZgG1NsWrV1Qqr4RYRK09yFWKhsyhRjn/K8KMTKsKtT3wfb5HSTPGiDVug07yzLclNf1cCwfjUiU9apYF4qlcsNFhuNRjDas9kMFxcXaDQa6HQ6IcqkxEm/a1pKKmIOu4hw7KKHu6BareLw8DBEDarVKj766CM8efIktPfj42N0Oh00m02MRqMQkWIZ6Hi1Tqx8Of2a+0isVqvg/CzR5HO6Ft52amK2iGv3LaGifLhpJh221T+dZm1tkiW+tu5sFIb5pCPTyK86mV07QGX2UG2XkjG1sfq8Tv9VUsyNbPm8ElcbbdRp9UriNDqp72A+lIyrjjAfy+UyV+eWjGw2m0DqkyTJkZAYUbXTkV8W9B32fdvap33WEoSie/eJSMbyoJFxJVGqqzoVn/ZBCRs7kUqcVed1eY21dWoL9VkgT85jHZGYPPT7Lp1KG8l/XjiHcg7F98Q+Pw+cQzmHcg7lHApwDrXLs86hPrscau9BKR2htC+yU2qLCmMV3BIMNpaYIy1STFsJRXnQ60X5A/KbYcYarlZMUVnK3qW/WeK2TwXSURHqXGPl2sV4qILexNhYQ2yvFb0vdn1fsll2H2VidVgdnX7XumA0KaaTdsS5Urk8KeT4+BhZdhmdePbsGcbjMQaDQdjU0DrZImMSI1IsR1kdleniLkZco2jtdhv37t0LZPHBgweYzWY4OzsLkUsaSq5b5/sZKdRp1bYTNpvNMJ1OMRgMApki8aKTrdVqwdnHysc8cxq3dTost04zr1QqYQpypXK5RODo6ChM81WZAYg6eZZJ72UkSQmHvc77lVwoyaYOWKLGaAplrk4KyDtCALk0CS4XsO0zyy5P51E7rLIm+acMdDqxvlMd3jZboPqonR2SOCXdlCUJuI0q694T/K5pxqb+a0Ta5kdhr8XKVXR/LMpkiS7JiMrWOn7ra8rs6j6IpaHkWdvdNliCY+0j/6tOWvLEPFEmJHLcJJX6zj049KQaTdd+j+XL2lAr63198ja5OIfK3+sc6jqcQzmHcg7lHMrKzjlUPn3nUK8fh9prTylbufbl25TSjvTbRht7lxocRcy5WWNdlGYMtkwamYjBOtzYfbs6u10qzCqiPmuNVkxBYsbxVaKMNOl/mzfbGIrSiOlm0XvUmDHqp9E21q1Or43ljYjVX5ZluH//PhqNBs7Pz5EkCcbjMcbjMdrtdjAAalgskSqK8OgzsU3qNA+8Fpu+vg1a3mq1ik6nA+DSGQ8GAzx69AiVSiVEVlqtFnq9HiaTCWazWW5qMp0gHZp1vKvVKhdl434LSo7KHEiSJNemvMfKo1EFPpMkSZie3Ov1rm0iGpOv2i+tA+1Uqp7Z55kfdfIkC0V7HOh3lpVkUEmbrbuyDp/mhzJh5NNO/485SI28AFdOWOVk93XRd+k9sTakRERJLN/FdKy/sIRXSZTKS/XMRiN5Tae6j8fja/nJsiy374IlT5a0WT22OsT77bUip8/fbZ4U9r59idhNfAafsf67zFbv+x6rR1ZPNeLIU4oAoNPphPva7XZOz5XAFdmBm8I5VLwsRWk7h7qCcyjnUM6hnEPZd+k9zqGunnEOtRs+axxq50GpbeRB74t95ncqdyyyUZSGVZ5d8lpUaWXX1QBaJ23vt1M2Y1MBb+q8Ys8Xyb+IlBZ936UOXwRs2Xd9ryW2RenYa7ZRleUBuCLEJFS6iSQblRItwhp5jaiwHvi/2+2i0WiEjQfX6zWm02nYdFE30VOCZDsd1mnEHJstn+bRplOGMnJP+dTrdRwdHaHZbGI+nwfDxb0lBoMBLi4uQll58oie4GHfyd+XyyVms1k4vpfTnZOkeGmLzWMR1HFVq9XgXBmF5AahwPU9JazuxKKwWi9F9izWDiwpLiLS+rwlSTG7qzZK905QR6eElumQrHGavk1DCYq2h1jb1um71J1arYZ+v49Wq5U7YpZlt/lXMqKyVbnZ+uLzStytjKyuJElyjcBpeklytemp/paml8dNMz3bAWBE0spVI6QawVQyp8sAbOTUkuc0TYNMyoiz/T2WlvWHsTRfBGK+3vqzovfa+2wdW5yfn5fmxbbj2GlSzwPnUM6h9oFzKOdQzqGuyqd5sNecQzmHcg71enCovZbvWUdlG7Ii5tCtElIB6ESAK4NoBRXN/CdrWWOEYtu0spjCWefFxq0Nyiqzfd82qNLEUGSI9bs6bvtM7LvKfZd8linlvuRc86h1VVZO+57YZ0t2rVytjGMypyEnIYjJjaSL3y2ox3rMKA3zZrPBwcFBSHs+n+Pi4iJMl03T69PZrWysw7bOXMtSJEPbXrfpgJ0qrR0hzROngTJK0mq1kGWXU5eHwyEeP36M0WgUyBHzYKMg3P+A8uA9agvU+Wld6X/mlc5Ly8m0eW+tVsNyucyR2na7fS0KGNMtlYHmQUmxdQxqMzQvWnfWoMecoM2D6qe9136PES1FEdm1f9QNayNjTlnre7O5On1mNpths9mEDW2BK5KbZVlY1nB2dobxeByORFbipTKzER3dfFXX3uv0bt1wcls0x8rOyi1WfvUXZaSziOxakqXtgmRLI5TssOlzTNO2YxsFjdVbrIwEZW7Lzu8xW6ztweaHz6gO6Lvs57I2EvtcdE/sN/6u0d0XAedQzqFiaW+DcyjnUM6hnEMBzqFiMqdcnEO9HhzqRsv3dkGZw1QDtNlsckcbqvEvIlKx6WbbSEDMGBXleRfSsAshKsr/LmlapbZEwk4/1/8xJ60N1Rrum+TPXtf0ihoDGzLvsURB7y+r06K6K2t4sfu3ESrNo9UfCzpJrZf5fI5Op4NOp4PNZoP79+9Hnao6cc2bfZ+dql5WfisH6sA2udBhqtFVoxd7Z6VSCdEITuFut9sYDAaYzWYhgsdIoIU6DDVmjIZyfbuNpNo0dPpwzLDyGU6tJrFi/fOznX1gn7eyLZuxEJO3bgxpO2xFz/Be2y5ibT0GLUuMUGuEiOTUEj8tr71mEdMVkt35fI7JZBJIwHK5DGR2vV7j2bNnODs7w6NHj3J7a8R03BIWwhIuXrPfeZ+SqyRJwn4l1Wo1nCik9oK/8Tltm1peXlOdK/OH/J3libVDltvaViVclkRph4TT7bUzAyDsL2FtAOtJl46o/PU+7QjZ8sVIF2Wv9auRXlvvsc5kESzJtTbhpnzmeeAcKl6+ouvOoZxDOYdyDmWfdQ7lHMrKRvPnHOrzy6F2HpSym3QVTckqMj58xjZGa7A4cqjPFzXgXQQSIxs2LeaB93CNtOZx1/epsulnnQJZFGUpInxFzrOskmMOpQxF6RUpnt5v81okL5abaVKHbAMtMzpF32PXiz5Tx3hdHSJBHdQI9DZCpRGPJEmC8zg+PgZwSTZqtRouLi5ysoq9Q42LbSfWmMYIVhGK2i2h71DjHTNOWuY0TcOpHoyc9ft9TKdTnJ2dYTqdhshDjAwxXRp43h+L1seio5aUxTocNkLEz9VqFY1GI7e2v4w0xBy01pfeF7Nj1r4V2aii98YcqU2Ln8tItrZTbZtcAkAiy+tKCiirIgfP8mq6SZKg0Wig2WzmHPNyuQyni5yfn2M0GuHi4iIc2Uu9ipF6+z6NsPJa2XdeU3nqdUtsYu+08tQTUYqIOn9Lksvp+K1WK9fe7QassUgw79W0rQ7F2m2MxPN3bUP6p2RK5TCfzwMBs1FH3qcRTO34sF6Bq5kFNkLJ52Jl2QdKcG259Z7Y533fVQTnUM6hiu53DuUcyjmUcyjnUM6hftI51M6DUkWGQivGOl9tqNucbCzz20b0igpadn+Rc9S01IHZ8sTeqde2Rfa0kdrGWEScdi1fWXns89YJb0vT5mWXfMWIkY7gbnv3LoSxiMyVgURAnQGvFXUEdoF19JvNBtPpFO12Oxz322q18PTpU1xcXORIi0WRPthOyS6w5PcmZErvsYSe+dpsro6SpSOgY6A8NpurzQfVmNLAqjG1J4PYDSEVml/Nq+obI1lKmizxitmsGCwJL+uQ8HpZXet/dZS2XPa77ZDEoPeWtVvex+nh3GxViUKj0cjNFFiv12GWBp1xvV4P7YmRs1arFUgTj72lnnGvEE5Nn81mOD8/D/ownU7DlGo7RdsS2rI/2761/kleSIbq9fo1glxEzmzdaD55XcmgtYuaN9vG9R3WbyRJkiNrOrVep9pXKpWwKTDrR/eB0eiu7gOjbcfK23Z8Sbr0Xn5m3nVKOckVf2MkUYmVRgxttFF/VyLG6CX/dJnENjIW81cvEs6hnEM5h9qernMo51BWps6hnEM5h/rJ4FA7D0rZRlsWmdAMlmW0rICxxn8TR6x5UKVWQ1dEBCyp2pfAxUioKm1Z5en9+5S7iDDqPftEemLO095nv29TzF0Vd1sa9p3b7gWuCK2ORLNOarUa6vV6MAxa//V6/drIs5UF06Mx3mw2mEwmIf1Op4NerxfIwXw+x3Q6zU1d5b0xB7CvrGwd70IU1PjQILHd0IBZBxFz7mmahs02iwybPss0df23GkqNyjINLQfzY6e9WtJKx5NlWW65AfViF3nbiM02Im/zug0kGbEyxNrcLm2+7PfY+6krPBGoUqmEDWe56episQjTxefzOVqtFhaLBabTKRqNRpAxyfZ4PEar1QonEKncsiw/LX06nebalJa3qPzPi1h9lvmIIrtI8qJ6ws/8bnXPEijqIm1RbCaEQomXdlxincWYHmi+uH8EN1QlMWY+eZw2//NZ1iuQn04eyzPr1c6K0U6p1qu2a01T07EnR6mdz7Is2KI0TUN00l63EUq1e9a27AvnUM6hbLrOoZxD6budQxXL1DmUcyjnUK8/h9p5UCq2ERwzDWyfiq6C0O8x4mKfs89sgz5bNgKu77bXY9Goonzs8lkVPmYAywxzEQEpkrdVXhsZ2SbDXYjqtnTKiJfFLlGrIj2yMlDHqwZG38O6paGjwVHD0Ww2cw2Uf9rQ1CDqNUZB+P3i4gK1Wi3sFXD79m3U63Wcnp5iMplgPp+j1+vlDG2RsY7Jrkh+ms42Es38q4FSAkUjr/Ioyg8NE0+N4TTmooi+NYA8MYZTWZW8qbNnGXfRL/scI34EIx9F8lQySlKmebBlstdtOpqPGDRKpG3aykz/27RsnoF8R1jfr0sCarVa2BiTETjqznK5RJJcRuqazWbYPHM+n4eNT9M0RaPRAIAQFVytVmg0GqE+eeqQghu1rlarkA7/x8jyi0aRXsfqsUznptNp9Po2O872b6eh86/ZbBYSPbVdNl0lRYQleEyHbXy5XIbIagwa0bOdW+Byw1t2FLm3BL/biKrmUzuT+hv3pLDys5FW3mPvs4Q05oe1Lam+xcjfTeAcqjgfzqHK73UO5RzKOVQ+Hc1HDM6hnEM5h/r8cai9Njon1FnotDVbybynKCNagDKlLSMHsbSKSIvmP/ZbjNCVEaRtsIZWK0XzECNTViF2ITkqx11InqKooVuZlD1vFVPzUuaErQysU7KESRs9HTwdv55CwogDiRFHrg8ODtDtdtHv99FoNHKGnlNpdQoj64xTOTWSxX1CdHRZ801D8uzZszBq3ul0cHBwEMrFUWZdb27rK2ZA1SBaecbkWkaoNHoX+0yiGFunXPROthF9jvZAdUXT1Prk3zbCpO+3n7XDZ3+z03aL5KORGlvWsnzo80X38DfNp7WXatOoj8vlEpPJJJBWdgK5t0On0wn7UmhUM+aQ+H624YODA/R6PSwWixyJ1rpjGXicdb/fR5ZlGAwGgaxmWZabqt5sNsO1WOTOHvOrNrPMfrxsvKj3FqWjZaNd24ZYm2f9q77qf72Pvts+Y+2LftZ7Wq1WzveXlZfl0TIWoVqt5jrASjT5LtphlkXvY/TRlp/gNZ4WBeQ37VW7pPfE7OzzwDmUc6jY886hnEM5h3IO5Rxqv3ScQ13h886hdh6UKjLaipjCFI2k8VoZuVHnWuYMyvJbZPRi6dlGW5a/ffKgsBWjyhBzUDd5X9mzMdK4Ld8xcsdrGoHhb+qkrNOPyZdTWbkemqP8s9ksRHpIlLiB4HK5xHw+D8baOgaSI5IrIO9UW60Wer0ehsMh3nzzTQyHwxCF4zuZZ6Zlp4RzJHw2m2E8HudIGN9Vq9UwGAzQ7/dDenfv3kWr1cJgMAAAnJ+fY7PZoF6vB70v01vmIxZ5V93W6a2xetX6jUXv+J2/sXxKtGIdGKvjsY0Gtb4YMVDDxsiQNb4xEmnzZMum5adeUt7WgVgZ2euxuimSq30uBrYlJTsxXSZ5n8/nOD8/x8OHD/HkyROMRqNAqPhsq9XCG2+8gTfeeAO3bt1Cv98PZIbRTo16KsHld+BqZofqpDriNE3RarVy9acdbXWMlkBNp9NrRF+jKZZEOvKwMtE2wO/bdDWmm0UdebXrbENKxGzUTj9bO0QoYSOKyJZej0UeVZepbzHdsRE7lsceax2LSGqU9CZwDuUcSn93DuUcyjlUuVydQzmHellwDhVP69PmUM/HshB3vqxM/m4r0RrfGGxjjqEoylB03zaypHm3RNDmc5dpaIoyx1bkPPdNdxeoDKzyaln52TptW592/SmvkQzpcbR0khqh4CaMOs1YFZ6Gwjr6XUgjnyW0EU0mE0wmE5ycnOD999/HcDjEnTt3cHh4iE6ng0ajkdugkHmgIanValitVkiSqxNieBKCvp8kodVqYT6f4/T0FABw584dtNtt3L59G91uFxcXF7nTmWK6aknKLtiVUOln/mk9aAQmFnnR6LXNL5/hqLk6YRqvLMvCFFWus1Y5qjHX9zBvMTJly02jWKvVckScadk2biMIvLYNMUdRpq+2M0WZsX1uNhuMRiN8/PHH+Pjjj/HRRx/h2bNn12yUvv/Ro0f4/ve/j9u3b+PLX/4ybt++jX6/j+FwGO5TeWkZnz59Gqb/j0aja2Vi9FCnjrMD0u12g+6wnTSbzRBt1L1gKF/WhR6Zq3YlZr8dccTaMhAf0Ch7NgbrL3fV6aJ0lZgx4m7bpBI5JfOqj0rueE3zoWmSFGmnIta+6ZOA/AyIlwHnULvBOZRzKOdQzqGK8uccyjnUi4BzqE+XQz33oFRMWEUNfZ8Ksc/E3rcLEYk53xjJ089s7HbU3eZBn4uNjtrfmXZRnvReVjYrfNtot+a1qHyaVpqmwbHodF91pvyNxCfLrjbSo8KREC2Xy9yO/urkYsRGZWojDlbmZYqsjVZloLKKRX4oW0YTJ5MJnj17hqOjIxweHuLg4ACNRiMQJ6bPhqunZVAG+m4lgZvN5ekXXOc8nU6xWq1w584dHBwc4OjoCM1mE+fn57m11Koz1umqDtk2ESOUuxCBIh1T0mTJLWEjOaqzdK7UOTvNmnnT6aVFnTI1pprnWPuLXaPxte+zBJXvKKqDMhvG8trfbbvUMliZUW6r1QonJyd48OAB3nvvPTx9+hSTySQQQyX8TIM6uFqt8OjRI6Rpislkgtu3b4fjhPVZrYvN5nJj1NFohOl0GnT94OAgR6Lm83nQhfl8jna7jcePH+Pi4gKtVgvT6TQQ5Erlcn+RXq+HZrOJdrudI7k2gmOnnlv74ShHTOfK/GhsIGEXbPNH9h02j6zrorYaQ5EvV1+pnS9uMKs2oyj6HuusUf81Evii4Rzqer5i73AO5RzKOZRzKOdQzqFeNpxDfXocameWtY3IxAqp9+5CnnZ5dxGKHEYRcdp2zZKebRUeIz1FTk8VuIj4qNO35bSRCXt8JtMn6dFd8tP0ci31YrHAeDzOkSKNxnGzRBpa3ZSNjlUNoSVE+4B5jTlklV+MVKizJ6j4Smj0O9PXqeQAMJvN8Pjx4zD9nVN2NeLIZ2kQSCYpY56YwXtXqxXG43GIcA4GA6zXa5yfn2M0GuH27du4d+8eBoNBeEanEiu5tw63zDmrzIocub2fsBE8yojkRyMw9jfVSaZFHSSZYn5U15gGdSFJkhzxskZT9SJGKGNkLBYNoHxJgNSolpE1la+FGuwyAmbtiyUQy+USp6en+PDDD/Hhhx/io48+CmvLYxvIap5Yzs1mE0jVarVCq9XC8fExut1u1EFsNhv0er2wLwiXeYxGo6DbJGTcQHO1WuXkyvdz+i7rc7PZBFkX2QmN/qqcijaL/EmBtX1F5MfqtJ5iYztGdimJ6tDzoKhd2HvK3lV0fR//kiQJRqNR1I/a9G1+1G6q/XkeOIdyDsW0nUM5h3IO5RzKOdSrg3OoS3zWOdTOg1I3JWTbiMjLRtn7Ysay6D6mVXZ/GUGKQQ170e86LRtAIEA0UPP5PHfSBh0UDSEjdDqVlQqiRIN/dtTVyo8N0EJJxy4yjU0nt9/1v94Tez8jcUoGYnlh46JRp+w5/Xm1WoVGuFqt0O120Wq1wvt1BLnb7eL8/Dx8n06nYY29rqslWdCjjU9PTzEajfDs2TOMx2N87WtfQ7fbDc6D9WBlzzIqyVKoIdilMxMjHtvujV3XKBnTojypm5QNp6BzQ0JGrVqtVoiqAlfT3bXurJEjQduWb/7ZPS1iuqoyUR3Sdf8xI615jKWl68JVv237I/k+Pz/HBx98gA8++ACPHj0K7V7brk2LoFy4L8HJyQnSNEW73Y6WgfluNBpotVro9/shDXZ2NGpP+wIAx8fH2Gw2uHXrVpATnTmJmR6BW6vVQgRR8xgjh7ZcP4nY1nGy1ynnVquVm/av9/CP/oSdau0423er7dSp4rxfOwP2b5vvLCvrrmWPge8vSruIWL0MOIdyDsV7nUM5h7LXnUPl8xhLyzmUc6ibwDnUbmWP4VVzqL0HpVTJiwjEPngeIceu67VdGqT+VlYWKpM2eDXwVgGpRFQy3sNRcZ22yenGJEiTyQTj8Tg3xVudKd8V2yjROgrNKx27juhqnrUsRbKgM2LalpSx7DqqXFQHVHRLxjSKyO9q1Ln3AK/rxp12CrzKhkbmzp07ODo6QqfTQavVQr1ex3q9Rr1eR6VSCZt/UsZAfv8BypJTaPUYUY0k2cgn65JRkslkEmTa6/XwxS9+EZ1OB7PZLDe1V2WvBmwXcr9rm9zW8dj3OdX3NE0DqQeA09PT3Caoy+USjUYj1KnWdww66m4jsJpnKzsb1S0ik1b/2c5IINRhxdKKyY4kyNoL236oI6PRCE+ePMGTJ09wenqK+Xyesy3avq2cmD6jfJVKBbPZDI1GA2dnZ2i1Wuh2u7mp4RodStM0tAng8qhn5p2yaLfb6HQ616bYZ9nV8oyY/rFuNcJYrVaDjWObf9Ud788y9nHy1erlyUHcswK4fqpVrF1wg2DaWOvTaOfq9Xro+LA92baepmmYKaEbF8fA9qhkrciHW1JflqZ+LtIl1bNXQdqdQzmH4mfnUM6htj3nHCovO+dQCHl3DrUfnEN9fjjU3pskxKILakz2zVhMeLvca2GJnl63CmSJED/bEW9GgnStO6NoaggZXeOIqW7YyGsAro2o6sg204oRnrIyK8mLEUhVGHViduRVG4WmXyRzdWYqZxJGyoryU7lx4046TRIY3sNGyOOK7bNMd59piABClO3s7AzD4RC3b9/G3bt3w1pvNlROlx2PxyH/3B9BZcpjM/md8tWp75RrvV7PEel+v4/RaITRaIRms4mzszMcHBzg7t274fSaGJlSPd0V25xTGfm6qaHREX9d1qBGmPet1+sgWyUWqtOaR32W+rFtNF8jE0CelOkJMvouADmd5Dt0TweNFGsaep16oDLRe9mWWN7NZoPJZIKLiwucnZ1hOp3mor42mmLlxDS1g5WmKcbjMS4uLsKmsAcHB8Exatn5LDfUJCFeLBYYjUZYLBZotVpYLBZot9uhrdOZM5LX6XSu6aq1KTrFnbrApQiqD47tqFQqgfTorAW2PcpS9V4JsY1QE0mShHqt1+toNBrXOidA3geo7dOOt023UrncK4PRSGtTmS51YzabhU5zmZ9UH05ZaIff5pffb8Jh9oVzKOdQlIVzKOdQRXAO5RzKOdSrhXOovCw+DQ6196CUNgj78het+JYklTl45sc6HjWcep1Gxu4BwKmyVEZWIKd3K1myUzKpNLFReCqVKpsti5KfIlnYshJMu+h5e73ICGt6ugZZy6iRNf7GaCWnwvN+bu5pI252qqk6miRJQmOnoQYQTmphXmNkuaz8/H2xWODi4iI33Xk4HOYMTpZlYXpso9EI6/lVZsyjNviY4bCNNk3TECXi3gjPnj3DcDjE4eEh2u12MIR0imrsiiIoWnbKcl/yZdMpk+e2563e68aQGnFWgqBLJPhbLHLN75aEF8mAEYRYZF4jtEqO+Pt6vQ4Rd0a4SAK4XMASAG5myXx1u91AHkjsqVs8aph5ns/nODs7w9nZGU5PT0M703ZiO2NabrYNjZiR9IxGIwwGA4xGo2tLUbT92ant0+kUo9EIDx48wHq9DuVpNBrodruYzWbodrshYnt0dBTkqOB72G6042o7SNZmOMrBaB1JBPWEHVPKkcSLHT/tTMc6cbyXJIp75dg6Y11aMmOjhryX5E83jbX7XtjnWC5rU22eOdui1+vliKN2RHSZBzv4Mfv9ouEcyjmUcyjnULs87xzKOZRzqFcH51CfPofaeVCqzHg/L3Q0UK/pNDTrSAHkRu/UmFJAFJwSIR311FNQKExWlk5dBq4iCnwHFZOgwuqU4xhUwWhUYsZRy2w/K1RBLHEhdI2rkkCVkU4NnkwmgUSSJOnUUDoJJbnqVKi8nLLK9e1ZloVGxqmRuq5W809nwzXg1WoV0+k0OCvbqJh+kaxiBHI2m2E6nYYp6JxKTD2oVquBSHE0WhscDRjLrzrC96hDt3rQbreRZRnOz8+DAz07O8OtW7fQbDYDmddOQBmZAuIbWj4vyoyMdnLsZxqsGGEuixJb8slyWfIQM4LaXrWNWvnFiJPqoeoio74k9MvlEr1eLxAP1QEl1+x4cH+AzWYTnNl8Pken0wkRcY30TSYTTKdTTKfTQKaoX2rntAOgzoz5VfJFzGazsLSB0TolXUxb6ydJkhCJ7na7IerIDimP6NbpzvyvoHzZjqkrRYRKO1+OciiJoN+0UXC919qHIuJKfdV0Va81+qx2Xd+ln1XXuE8G/aD6bW2TvDdm26wfisH+pm1fO+bW9rxoOIdyDuUcyjmUhXMo51DOoT59OIf6bHCovQalYi9hxel3S4Ksw7W/A1cb9unINBsSjZOSoc3m8phd3ZiS13XDSgC5qXdF09VsY1aoUea9mncaIDXmKg+FjQTydyVw25ygKp+OtpJopOnlhpAs73Q6DWnzFBPKj0aeBpQNxpIPNlgdHeY6Zv5Ow8uNAOlEuEEjyUGj0bhmJJMkyaWt8m40GrmIkH62TtCS61hda2PVxkvHp7qq+qIj0LzW7XaDI6vVatdGqRU6+k3Ht1gsgoMdj8dYLBZh00qN1igh24VYad3tCkuIrCyVuKsMYjKmkWTb5fKLNE1zBrdWq2E2m0U7BJS9OnbNa9G7bd6tU8iyLJB96yw0De1A6RRtJVKxdpIkSe7o3zRNw3HWtEWMamj7Z7R8MplgNBqFk520TvhO6gMdUKvVwtHRUdANTjO35JBtn3bS2mJL0JjXwWAQ2u1sNssRR0bikyQJG0OqTK1d5DRzgp0Y6kGMWL2OiOnztvuL7tM64322Q0No1NvaTpsm65HTzjlAoXrEcnA/Gdot6keMH9CX0BbSh9OfMS/stOoMAds5jvlM+ofxeBx8i3ZAWH613dQ9G+18UXAO5RzKOZRzKOdQzqGcQ70YOId6/TjUzoNSOkKvmY9F4rTQavjowEmOdG8AjTwpgeJvOr00ZnBVACR5FJ4VfhF5KhJi0SizddxqhC1onDj9VKNfAHInbAAII/EAwrRuVraNVDIPjAgAyE1X1TwqqGganeO6dF0/TifEzyRIlcrl5n+6Vrzb7YZ10EyXZeZ9lUoll1fd48DKj/VFB6D1ygau5aETZzpK5lkmrr9tt9th/e1qtcqROX0364b7FCwWC1SrVSyXy5yMmB9tB/puG/EigeRJGuPxOEQduT6YxJjl1brU9yjp34VsWWy7X8ms6pHqFQkHgJxxIslsNpvo9XrhNJ7lchlIA8mtkmWmTRsTsz/Mg0KjVeoUKBeSBO1MqA2rVK6OYyaJ4elBvFfXhFtSValU0Gq1ct+Zb9pBrSs6FHZmKDPbZqnbrVYrbOjKsg0GA7TbbWw2Gzx+/BgfffQRzs7Ocu1/mz7E7Dj1lm1ms9mg0+mE9shyaGfLdp61LmP2V3XlZQ0IfFYRqxOrT7tACRKAa+1I36cdsrJ7ma6SJI0aWturbayIpGlelSipD+FzSvxsWytLX+VI/6i+tlK52gPFdqT27YTuA+dQxfc7h3IO5RzKOZRzKOdQ+8I51OvFoXYelOr1erlInE4/0wicjuqXRd/UUapQdETf/qZGQUf8LLRB83et/JhxKascmwf7Xx2BKoaOZusGnuPxGMvlErPZLMhNp9Faw6N5U2XdBTF5qZOhEaTy8jSUer0eDDTJEMnIfD5Hs9kMcmY5l8tl7gQCdfJ0BpSPEiqN1sUMwHq9zjXY2DREloXHpur63Url6hhV/q7OzZI9qxu6T8ZisQjTK6vValjLy03sYoRaSYA6DhJaEieSaOoAT/ZQsqn1ybrU9JV8PA/s89bYUZc0OqeEyraLJEnQbrdxfHyMwWAQyDiXFij5ot5TRrHoqG3PCm0rfE47DiofS6aAqxkHVq81L8yr6ri2Md0oUW1akiTodDrhXrYJflZ5a3k0b5PJBI1GA1//+tfxta99DT/7sz+L999/H7/7u7+LDz/8EH/wB38QInyqJzH52PqO6Y3qLu33bDbDZDLBarVCu90OZFkdMN9FsmzrSm0znbXqkzrQ1w3WflnbF+sIF8mCdkRJRxFBVUKlgwCWHAHIESS+X8lXrDzacVD7YNsk27F2pGLljOlTmf+z9tCmae0mYdN9GbrnHCr/2TmUcyjnUM6hnEM5h7oJnEO9nhxq50GpX//1X89NNyMJ0AwpAdJpb1qgbVDDoWlbWIWzFWYrPPYscP2ECxo1jVAxGqnl1lH4+Xwe/mazWe4+axhjDSX2+7a8lzmEIqMY+2M0aTAY5JyOjoRS6bl/BCMkjJJww8JqtYrZbBbyybT4XfPFqAmQPwKYRoHkjYai1WrlpkBydJibvPFdGlVSwq7OWhsvy6JExJIqNU4kgkyb+aS8SJIZMdR3c0o+38vpjiRn3Ajx9u3byLIsyEX3ogAQjppl+UlkaCRVN5TYanks1HAxmshnVTZqhJXgAVdT91U+bCcktEreNc98ju1Go0XMk9Unrd+Yo2b6tm51iYHWtzoFRoCZtm1T1HtG+wiVvXYEtJMX6xTRSZG8K6GwJDrLMvzO7/wOfvjDH4Zjibl3CadvW7Kk9ad6ZB2QlSHzr1FzbrzLqDanmtMelBE1tj1rv7T8fP8+HcfPExgFbjQa6HQ618i3yoJ70dDnxKD1ygiyzoqwUF9tO7pW37R+sizLDYjo+9WGFxEv7eDEIpL2fu0s06froIWVQUyPLbHS9zO/OsvoZeqdcyjnUM6hnEM5h7pK1zmUc6ibwDnU68uhdh6U+vDDD8OLdISQ4PRkzbw6rpiTp+BjsOSD0OiQGlibDt/NqCQJgf3j5p1aWRqZ1P0WeJ3O9SZQIx4rM+/Re/VZm4auE1UjVfRe/UzlouNVhbYOJ03TsEyAjZaoVqth00LrQOhIbURRP5MUWEJlp4KzbLGRX9aN1Qk2RCXqqr+UuZ2CqLAdCU49t6Pq/H0+n+emxdMgaZn5HL9zQ1I9fYcRSRpAGyHkmn6rH2oUbeStDKqXSjwZwWO6avxYDn0HI5ManeSffR/rndO7W61Wboo9iZSN8OkMgiKnoQSJz1Ie1nHYdqb6ZdtVzIHEZKz7PvB9sQ6RklieJMMTjWxbVifEDTd5nfURa+fageLyEJ0eb2UXgxLp8/PzcLLSZDLB8fExqtVqmA2g+VYyxz0YtEOjHU/VGdtJf12gdcRIsUZB9c+Sb9ve+Az9myWufEah96ptsWC6+qfEy6bN+5MkyS3/0fuUpPHdZe24KC9lvtLmKfZ+mwcbjXwZcA7lHMo5lHMo51DOoZxDPR+cQ72+HGqvPaUsUVIDriOQeh9wPfoQQ4w82WssPMG1+Ov1Ojii+XyOi4uLcJ1RF406WiFq2YqUUBU1SZKwbwCfUXJIQxsjNkwrlj7frVEHftc8xAyl/l72Xvs877eytUrbbDZxcHCQO36Sf3SG/EyiYR0ON9tU8L28n3piHbu9VxsFoYaJ95MAWJnYxmOJmKatOsORdC2r5o2ESqMiBB2HkhGtb9Xj2Wx2TQZperWHAh1omqY5XdTN6Hg/81cGrWuVr/7FDLM6PCXCRSRK36XECLg8sWi9XqPT6YToGcmGRgepm9Zo2/SZJ+bdkmbbbtSJ6XWrC0XvUuxKVLRjBACdTgfdbjdEGbkswubTOg17LdbWGV1qt9vhSGZbr2V6Qv1N08tNgBkdZxS6UqmEaH+snGyLJF58hjMo1AapXX6dCBXbLTuntBexOqBMdEaJklOC7YPPlslNSYS9n79rPvhufo8RXO2s8jdLvPRe6py2YztIYX2UEqpYmjEyFSt7rDNelO6LhnMo51DOoZxDKZxDOYdyDrUfnEO93hxq50EprXRthBrJ0MJw2iU/8x5bCODqJAEqFxuobuKpxIkRutFodC3qRkXapSFa5VHHUURWlChYolN0X9n7GX0oylfsOw2wJSd2ZF3fodEE5oukgNEkTuvmH4mTEgcA0eiDhTZC1o8SCtYhj5rW64zMcoprml5Obz08PMTBwQEGg0GI9uimojTY2kBZD7qkQAlSjDhZvSDZsSSBBlENHHVY64G/T6dT1Gq1oNMA0G63MZ1OwzvH4zHG43HYFDFJkrDEg5/plJRocZo0ZcH/Oh0cKD8hibAkKkak1ADTkOq9sVF7JZ/cT4URTbbvg4MDzGazsMxADbDqnBpvW2+aFyVimhbzbtuf7WjECKZ1aMSuBrnIIfFzp9PBYDDA+fk5er1eOO2JsFHqmBOJ2bBGo4GDgwMMh0Pcvn07bPIZsxs2v9pBqNfr6Ha7oc2S4DMv2vZU1mzLKkO2Le6Vo3tMxDrZn3coWaK9KwJlFBtkIGjTaaOzLAt7ecQ6FlwmAFztxaGdO0JnZQDlG3kyX0r8lIhpHtSWqq1QG6LYbDahLEq+mJ6VBeVhdUgHd3R2Bm048/Qydc05lHMowjmUcyjnUM6hnEPtD+dQrzeH2nlQioaTDUJHxuxoP4DcKCadnG7iyQ36lCwxOqLOK0aOrHG1+Yw5gRjpsd9jjsQaBTWw9ndrtLdF2+xnez/JgP6uzpKkiH/aCPgsR4D51263c4QwRg5jeWIda1RGr+lvPGaahpJ1T+KsesE0gOvKr8iyLBz1ul6vcevWLXS73WuEl2XXSJo2XqZFPVZiQoJldQ1AMH6MaiyXy2s6wPJxarqtZ0bglGzUajV0u92g+zxBptvtYjabBT3SKDrbH/Nr20mWZbmIaZHTtfK1uqe6zvXuaZqfhm7TULKp5FOdqNY3jaVGeVQ+2pYsmWE9WmKlZSjrsMQ6REWI2ZQXAWtnkuTySODBYIDDw0OcnZ2FKJiS81jHUevEpsk9T46Pj0PbKSLMFrTPfEe1WkW/3885WE6dJ6FiB5WIkSN1amoHWPevYvbKpwVtm/aafi8Dbby2DRvh084F/QPbbyxCrukqSSvSM9tJV7+t6fG/+hzbfhW2vWvn0eaV6ZJYttvta7qnNga42jdnl2nvLwrOoZxDOYdyDuUc6sXCOZRzKHtNv5fBOdRnj0PtPCg1Go3CyRmTySSM0OvnoqlnFLIKR52AFiZGUvjfGnI2Wjtyr+nEUGR0WNFaSUVpbSMg26Dv4DMcgaUB5BRR5qnVauWmfdNJ6/4DuubbGsyYPLSOqFzz+TyQIY6+6+fFYpFTbiXXSrhYR6qwVl72WTUI2oCr1SpOTk4AXG7wyfXcAK45zBgxte9UQ6OGTaMZNjq2Wq3CBpkkV4wiUTfr9XqQlyXalBujmMDltNFer4fJZII0TTGdTvHw4UNUKpfTeBlNoRyYR0bRK5VKiJ5qW+NId0yvY0RI/1tZWnKlRNSmo8SIBkvTVZLFe7IsC/KkodN6070H+J5YXnnd2g9rI3ivdWj2t11QlgZ/L/vNktY0vdy0dTgcYjab4fT0FOPxOLcHiSXqtv5iJK3b7eLo6Ai3bt3C8fFx2DB2l3LaiJGNMHMmQMypl6XP34oiLLHO9OuEffQmdm+MUMWINnDZDriUoayTpcSLvkXbpCVVeq/Wf1m6bM82Uq/32mgdo6H2Pr2Xs1TU3vEZcgXt0Gt071WQd+dQzqGcQzmHcg6Vh3Mo51A3hXOo149D7Two9Su/8ivBiOsUMzuqqIXlf/5mR9XVYPJ3SwBUWfT5omicfTaWJ37WKcmWFBVFB0heNEoEXE2FVSfLKd1UDBoeRt54agv/+F49sYV/fJ8d0VVnq1HXLMtyUTUSIpIiTpHmb3T2LIcaTHWUWs/acGwUxxJmpk05My90qlahKUvKkzJqtVqYTqeYTqfhtJuYcVBd0LSLCHeRgeFzzL9OmdcNRzXt1WqVmwrOdzGS3Wq1Qj0Al0eFc8rtfD7Hs2fPkKaXmzAeHR3lTiexZJDyZVqUmeqTjepaksVyqnGznRyWkevdWT8asdPOEtfLq37ofVmWXwdNedq6sm1cSXzMCTN6wHpheYuch/4vg6ZhyYLaA0aWtW7U0VkCFrN11PPDw0Pcv38fwGU0+OnTp7m8lBFhTbfX6+HNN9/EF7/4RXzpS19Cv98PzkfrJZYW9TRJrvYyYDtg+dg2rW1g22Q+NLKvdaXRfmsDXlcytQ0qtyIZaIeJiEXD2H6142ftu72XNlfbnCVo1erlce4kana2hrV/tEs2XS2z3sdlRexwxYg6fSmXqWw2G4xGo8Ip9eoji2YRvSw4h3IO5RzKOZRzKOdQzqFePpxDfT451M6DUpPJJDfSTiGxUVgyYwmOXgfyU8s0Lf4WS0cJkt5r32PTKQMNgZIlANecJT+rstFgx47dJRFQ56UOLebYaVyAPFmhE2djYKRNI3AkSHr8ZRG54eeYfCwJstEjbSyxPQLSNA1TZWOjvfvAGmjdL4Mn/pC0UpaKIn2MEWV1gKwrzYeSM0avSApZF1ofzWYz9w51tlxyoR2Nfr8fHM7p6SkuLi6C8dH9H1QemkfmS52e1TklV0q6VNZa9yp/yswaNN6npJh/qi8xQqWRQO2sWaersIaQ9WcJjjoGJVaaDstkibXtWLA9M38kCpo2QblThno/O3AxAkc7xhOHWq0WhsNhkH2lUsntB6P5jjldlr/f7wcydf/+fQwGg1wkRN+vMiAajUawJ9yfRjuSzMP5+TmazWaIMFPGKm/dLJnPxhy6rZOfVJTJQNuz2i9LDop8sSVdhHbaVY9j9aT2RO1nTBftYABQHMXVcpUN3NgOI+/jcqcyoqTvflWk3TmUcyjnUM6hnEM5h3IO9WrgHOrzx6F2HpTqdDrXyBH/U2Bl5KiIdPGzClo/81lNy8ISML5L00mSJLdBmW5sxhFFkiGSp0ajEUYP6YjtdGM1ppaYWYXkHx0qK59T4LhhIUfR6bTVwfA3NUwqI70G5B0h01iv17g1n+Ot+Rw/TBJ8mOUjhNYx0vnZ6/relwE6Nn6u1y+P7yV5YcSMo8ZFsDoKXOkMiUqSXO2bsF6vc6exKNTh8k/JhJItNXZ0pPxsCVWz2US/38dsNkOn08HFxQUmk0k4RYQnfVD2Glm0ZddIIEk471VCxUjMLkZO01Nyou/W6aExo6q/UYYaASZpULlYZ2A7B5quvis2JVbfT+Jp5adRb84A0DY1nU7DqS569HC1Wg0nWfE+6gCfb7VaWK1WYckEcGWH1B4xv4z4dTod9Pt9dLtdPHz4EA8fPgzHGFuwo9Tr9XD79m3cv38fb775Jm7fvo1ut4tWq3XNvlJnKDtLpDudDjabDdrtdi56ojaAOsk8q7zVtlgibO1KzPH+JML62jLSQVnZCLk+r53EbRE+JSnasdD71Bbo+2NpKvmyRClGknRpVVEHT2Vg86p2pQivwn9ZOId6fTjUrfkcX5jN8MMkwY+RX2rpHMo5lHMo51CEc6hPB86hPp8caudBqV6vd40E8b/908ojqQDip8zYKJ3eb6GGXAVJJWBj7vV6oVKAy4gdHa9WmEbfYnmKjXzb6zQKJDmxU1CWyyVms1kuEqTOWI28/qfMbDSHzk0di3Xyun6ao56U65/fbPBLmw2qADYA/n0Av7yrIrxCqNPkKC5lxt/1XoU1xtYoEGrAeR+Ng61/Ps988DQlypzv1ME/NnoSBgAhKqjkgYSfOpplWSCNjNhq5HBX0sJIE4BcPhhx0jYUIydFBocGFbh+iovqdJZluXamum71XQ2ndQj6nWXS+2zEirZCbY4tjyVrsQ6Z7gFSqVzuPdFut9FsNlGpVMISA0bCaMxJDvn94uIC7XYbWZbh4uICzWYTy+Uy7OvRbDZDBM46q2q1ina7jbt37+L8/ByPHj3Cxx9/jJOTE0yn09BG6vU6Dg8PMRwO8YUvfAF37txBq9VCv9+/ZutindNYR4n/mR9dUqHPbevQUEa2Dapzp26yTdn6el2wK1HUDhCAXFux0LrlsfGsTxIldqJim1PqgIclaJoOofoEXHX0Y8uIlBPEOkax+3iv7cxb2ah8tJNWpjcx2/kq4Bzq9eBQ/85mg/9S+NN/kCT4v35G7ZRzKOdQfJdzKOdQrwucQ72+HGqvQSkgvqdBDJakaAMtIl78TEGpg+Hxru12O0z75ikodrRe3833xqJfNm90EIy4Uel4EspyucR0Og0bkrLyNGpHQ6/GwcpJHZ5+1uhP7HcqzGKxCArPPQ9U8cvwJoBfAsDYZBXA3wXwPwD4qPTJTwdaLisTW14lJ/xMJ74NdFxMk1OG1Zip8WfUTp8h9HfNC2F1UdsCIz3VajUX4U3Tq6ObbZtL0/RahC1GIBnZ0nfyXfbIaqZlo2p2tJ9kjFOOSTq0XeveEdoR0PfoNSVBOuJPWWhHhLLo9/tB3ovFItdh2ge0C3QQi8UiF4XTmQJKEpVgpmmai/hlWRamlNuOJ++35FBJHd8LAIPBAG+88Qa++c1v5vaT0HuVUNKOWru9K3ZpO9t8gUZ27HUtg/qI15FIEdsIKEGSbDsu/M8OW5qmuah2jBSrXeOfkim+D8j7eCXSWke6nMN2kGzbtgMutox6XaOG1uYqsbMkzfqHMtl/WnAO9fnnUG8C+C+R50+/lGX4f+OzyZ8A51DOoZxDOYd6veAcKl/G14lD7Two1Ww2rwlDBW9/0yibbuTF9escIe90OgAQGj4/q0Fkpaoz0DxoBIgCVcOra9cZXaGzopKRmOhUcCC//lorV40jlVinSWsUTxsCHUGMBNER68ki22ANVayh6u9fxRWhCnUF4B18NklVLMKp0cyisttGrFPwVbZKitTIMAKm0HeTcAG4lg8l1ABC+oz+WIOnZW21WuFkHB7drNN97Wg5ibUSO0vECeZbnbkaIyWQzJ8+r4RK309SlWVZLirV7XbR7XbDshWVl3Y+YkbOps860Yg68wRc7tdin+WyEe2saXmKHBs7cNws024qqCSU35V409bp1HW+h5+pB6xryl/bM2VGHS5ygFoOPh8jMLasz+tcYs4xdk+sjTKP2pbZZqhDLyKPn1XsUi52lGx71jaz2VwuxSF0cEHvox3h4ECMeNhOE4kU/XLMptBuMk3b6YqBNrfVauV8tnbyWH71mbF0NCJpiVeRzD8tnXIO9fnnUJ83/gQ4h3IO5RzKOdTrB+dQryeH2nlQqt/vXz5gNr4jWYodJ0jDo+uSdcRSG491nDGSxuf4p6egTCYTjEYjzOfz4NAYfdOpdnR+rNTYyKYFlUhJ1nQ6Db9zc0w1eFQGVbBdlO0mUKNVZEyJH+ByyrlShTWAH77wXD0/YgbbktaiZ+jc+ZnfrV7pd46YqyxJggglwdYo0fAkydVmjppn/qakXw1As9nEcDgMegYgTDvnsxoB0nLqd817zAgqtDzq+O1/NVKUE69rp4Lo9/vo9XqYz+cYDAY5AqrpWQOo+eT9tAfAdfKqciLUBsUiAZoHlR2A0Jm5uLjIOSruZTCbzdDr9VCpVDAYDHJHxOr0dOZJ64z2kKSLpMzu22B1Erh+ZPe2DlcZSVX5l91fhJiN3AbWtdVTnS2h9UX/0ul0crMo1MHaDunrhizLcp17XiPS9GqGCXWviFhrWyvyQ+rnqtVqiJZbnaRNy7IsDFDotHOmpW22UqkEnWU0WuvO2i0OLLDzVNQ5UJukyzZsWfV7mX98mXAO9fnnUJ8n/gQ4hwKcQ/GacyjnUM6hnEMRn1UOtfOg1M/93M9dI0rW2DLaECNK6nxYaOAq8rBYLHInnywWC8xms9zGlZvNBtPpNFSMCkWFbo2RRof4HN+r6fC6RiG4Gd5mswmfOdVW3x0bNX0ViL2zLB8f4XIPqb+Ly8pfA/gL+GxG+bQcqjOxxhUjSmzUsd8sdnUSSj6sTltiy/uU0FAnuXyBOkjHy80fOdVZj42NdTJ4jU5bZaPyyrIs6K2SUc1XkdFiGnq/vW7l22630el0wt4B+g4rG+ZfYe0I09eOSqxutO2rY7ZyY170vSS7dOS9Xi9s2Mn6qlar6Pf7oQNJ58L/k8kk7JNxcnISpn1zmnCWZTg6OsJ4PA7vWK/XGAwGufwpkdfoa8y2afm1E0E5qV7yc1k05EXBOknaVFvfmlcllywno6mqM4SdLaGdWPVL9r6blKPs88tE2XuyLAsyBbbbsW3lV3ujbc/OCqBMdQ8Q6wNjfpV2p2zgQo+Mt21d7ZAuD2InqtFoBB2hbEj42MH5tAi4c6jPP4f6PPEnwDmUcyjnUIBzKOdQzqFsmp9VDrXzoNTt27evCVWjZmmaBuOjgtap3yRLvE7jqFE7XR9L6NRRGiadcqbvsZE1TY/3at55T2w9eRE0ovB5xC/jcg+pd3AZ4fusEiog70B0/e0uBHZXI6P3W1JkQR2yBEDTsESLUMfKKaB0xll2OWLOte/tdjtEjrUDYCOVdmRfr9lyx5ZBWGPI/JfJUh2V/q7yYvSKbZWj+nTw6hh5HHij0UCv1wun5GiEgbJXxxgjySTeNsIXI1RJcjW1m2g0GrkjwWknSE71yF5L8LWDqfKgc2KZaD/S9HLfhMFgkMujTY/1rte0Q8f31Wq1qN2M1Wfs+st0NFpvlBv1Q2eOaH3xXtUbjRQzz9aJ26i6rf8iUmR1hL9px1udtZWfJW2xvL0sIltms/ZJI8uuRxaBYhsaK4ttm1rm5XJ5LbKu+Y7NTCiS42azyR3tbf9s+urjPw04h7rC55lDfZ74E+AcyjmUcyjnUM6htsE51GeDQ+08KHV6ehoMMMkRp73p8bJKcLSSrZNRIVnB8bsSo5hT4ei7TkVU8qTv1P/W4L0MBb8pdnH+Ouq6K7mw+AiffTKloEFVEq71bafmAtcbDkkZPzNN63iVtMXS1bwoEdGplUC+nlQn9TQSGi4lN1wfnyQJRqMRsuxqz46iY5b5XuZfSQXRaDRySzC4Zl8jZ3b6KBHTudjoOz/zO5eeqMwoD5LJZrOJLMvQ7XbR6/XQarUC0dE9FHRD2piDZNl1JkIZocqyfJRPZzF0Op3wLqah9ysZY3lJCtrtdpC31r/KT6ec63pzBWXG8seIAGVho7RWr9WuWgIeI/8x7GtjLPnWvAKXdTAYDIIvWS6XGI/HUdJh81qWb7tvTlm59B7bKSA0yqi6F3Pa1q8VESpbFvu8DgBYeVjS+DIQy9tNntPrNs9FJNc+F/uNBImyYmS96PlYFPJVwznUq8Gr4FCfN/4EOIdyDuUcyjmUcyje5xzqs8mhdh6U+tVf/dVco7TRBR3xPp7N8NZ8jg9bLTz5ZHNPOjAadJ0SptNhKXhO9VRBxBSrqOHZz7zffv40iFTMkKkzKDIobMy6AarK9LNCCl8GlOBolHgXZ0D9ZHSOURvqmsoeuHK2vK8ookfZE0xfo8aEEhw+pwaa08yn02nYGLJer+Po6ChMwdROSqy8Wo4YieB7KAuWT403gJxsVQ+Zf6ubsTxZ48W6Yz1SXxndazQauH37NobDITqdTm5T4Jijsb+pLdINfm3UiPVCPVCCpH8x2dr7bJlJHnl9OBxea+sqKxvtiBEqXrc6o3bYRkOBq5M9mI4SWJufXTpxzHOZjbG/2Txp2SkrTrmnXrKjnGVZiKjq9GPqlXXOVr42AhfLK8te5IhtudVG8HrsuSJ5bpNdjIgVkcoiohF7jyVoMYIR+1/2jqLyxHxbUVmKiNIuYN4Yjd/1mU8Tu3KoW/M53pxOA3/i786hruAcan84h3IO5RzKOZRzKOdQmr/PGofaeVBKjSqNO5A/YnGz2eDPPXuGv/74Maq43BDyFw4O8N9Iw1DypEZ8l3fvQoJiFclKtiRQlWab8myDNiK+R0f1dcqiVSxu1keDwil1MZLEabyM9lB+MSPzOiDLriIrjJSog1G5xggEn9eGTkKhBFanSNsGqu9VQkDyRLCToBu26m908o1GI+oY6/U6NpvLzWUPDg5CGSqVSi6CTmJidU7LHZNFjAxQRtQd6hVH0G17slOei+pM86fOkL+rDA8PD3FwcIC7d+9iOBzmZG4dcpGOk7BRjnajTrUdSu60rZY5TEss7e+8znRj9aDyL3JmJIu8Rlu7WCxwcnKCi4uL8Mf14J1OB51OB7du3cLBwUEgqDz9hvKxOmOdX6xMLwpWdvrOwWCAXq+He/fuYTabhT0lOIMj1vGmHtgTS9QeahvUqLSmZes4FgnSeovtrbFr+ZXIxGSjforXiwYIthHGWB6LOlqqd7E0Yj5Sy8N2ySi8likmg6Lf9Fn7niK8CpL0orALh/pzJyf4jx8+DPzpf394iP+23c7NznAO5RxqHziHcg6l6TqHcg7lHMo5lD73WcLOg1I//vGPoyOEut/A3dUKf329DieTVAH8zfNz/HcAHktasRHyfRAzBHqdRlQ3E9URf95Lp6y73u9bQTEl1BNzSJC410MMnIaq6/3V8Nr0dUpxjEi8jrjJyLk+p85T9UQNPcnVtvTVmGh6alDW63UgJ7x/uVyG+qU+aIeEm8wBCA5xOp2GzSCZln1vUX6ts9C8WMJP+TAP6qR26chYxEiUkiGmSwLUbDZDdG+1WuV0XO+3zodl0TYTc3qan5iTsu3K2qhdyl5k1yyBVSKv+Wf+SAxWqxVGoxFOTk7w8OFDfPDBBzg/P8d0Og37zrC+ms0mjo6OcO/ePbzxxhsYDochYsop8EU687KgMrN6qWSPMmg0Guh2uzg+PgZwtTxESYh1ytzAGUBoU+qc0zQNS6OYDqPmtMu8j5swp+nlVGYiFl2PtQntHNmZAfSVureFPhODdiRtWqozNm/8XKS7MaJmOy+KGAGLXY8NABTZIP2dtkFtDT/HfHKRXvE5TauMwL5qbONQ9zYb/MfzeY4//Y3TU/w/Tk9z/AlwDmXhHGo7nEM5h3IO5RzKOZRzqM8yh9p5UOrBgwdbM/Il5I/K5QveQX79vTWCMeziQFWpwvs+mZbNkX7dKFCVTBsXr+nRsbuiSJmZB2tMYwpHB6BRvpgSqzGOjZa/rrDlLoqY7pOekileU1ijBVwZNnudxIARvFarFaJ6NNgkx2zs8/k8ur5fo031eh1pmqLZbAbCbw2lGgs+q4bL6p/mWcuiMgaujmZWBxXT3yJo/cQMopI6dj7oYJkvRszojDT6GmtHfIbfLWG0Nkd1yna2rLys7IrksI2w2Gdj6a7XlydWPXz4EE+ePMEHH3yAk5MTzOdzVCoVdDod9Pv9nGPOsgyTyQTvvfcezs/PcXh4iPv37+PWrVthj4mi/SFu2lnZFRp9o41Tu8z2XESCy5w9daaILGqnIPa5iPCrjdFnNF22X0Zi+fxsNgufueGrEqpY2vruIlJP6P1ldaQd8rLo27Z0YrAdLW2vsfyW2Vptq7vAdg5j71Jipv9jBKzoHazb5/E1im0c6gvYjT8xf86hru51DlUO51DOoZxDOYeKwTmUc6jYuz4tDrX38r0y/ACXU86VWK1xeUIJYY2YPeWBjUf3qOIorV0TzvSyLAuRNU7LZjo6TZHpWcNJg37T4w71GY3u2WmtsQbPa7ruXg26pq2ki/fqev7XEZawUrHtOul9EHOYRe+2htTmy35vNptotVoAEHRST1OiUdUjcvV5tgFGfVutFhqNBmaz2bUoseZBHRZ/21Y+S+KYDnWu0WigWq3mTjrZJgemq/UTkxXzyw4QN+jUTVGZL8qsyKiR0DKt2AalLJMaf8pL7dAu7ajsniL7YfU3FnmgfXv27BkePHiAH/3oR3jw4AEuLi5yus579TtP3UmSBA8ePMBkMglrxY+Pj3F4eIh2u50j/2X5V3mVlb2MmKkj4jIDOl1GrtUR8xn7vygv+s7Y/5ijtx0hrYui6FtRfWkeVHeUXOmztpOhdoxRRe6JonVM/ecf86c6owRC86KkrciWWXnbslswX3yXwrZ3q/fbsM2ebyM3toOi+5MU3WN1zb7PPnNTbCv/LvyJeXEOlX+WzzmHug7nUM6hnEM5h3IO5RxK0/mscqidB6V2wUcA/n0Af/eThNcA/gKuonxKXhqNRhjpJUmwjk4bAP/HRje1YdJQ6v4LWgm8V6Nwz0tIihq6JW4xxMpdBiVTP2lQo5BlV8se9gGje1o3nOJqG1qZIbejxmy4s9ksjPK3Wi30ej0AwGQyQaVSCevYeWoMDWSswSfJ5aaaSrLsvUVy0v8xJ68Ox8pGCQr1UqfoavpF6VI+avD5rpjsOP08Sa6ioUzLkgdbVm3XNpJlbYnakV06PPtim4HW6JJ17ovFAuPxGA8fPsSPf/xjnJyc4Ozs7JpjtWmzLcxmMzSbzRxZbDQaqNfrgZhbYml1PhbNLSMy9nPsHm2zmncl9LzHRmY1ndh79J4i3xEjs7aTautK25p9vojUE4yQA3F/pdgl+sY8a8RKCZX+rv5OBxO0c6OEKEa4bFr23RqtJGhDY8RMl/jre2JkrMi3xfRxV9tfJlsgbgsJ1v+una3nwTb+xPw4h4rf5xxqO5xDOYdyDuUcysqbvzmHcg617dnY9RfJoXYelNrViP8ygP8Bl1POf4j8gBSn0+qmlHbtcuw9JGEUeEwJmT4Lzoq3mw0CCESOjmOX8t0Eu6apBqbMkNnKLWvgrwtoAGz9a2NXB7EvLKEgwVdjo5En5kmNAv9Xq9Uw5TzLsrCBIk+CAS7XbE+n02hnoYj4UN+ZL35WQ6BEW+VRlPY2ndE02U5io/+2DfK9Go1YrVY5GVJ2JE1KqNSAx4ibzYMlB2XRKy2X1uu2Ttw2WBJlf2O+bRmsA5vNZvj444/x4x//GI8ePcLTp09zG0qWRXlZ9uVyiWq1islkgizLAqknoep0OqH8rFstN/fl0A6G1fMYWdkmn6IomU3Lkif7rI3c8rcY6bLXVD9Vd1SOSrJj5dvH3mZZlls+EUvDElqb913lHHuH/d1CdS9WP2oD9R1q/9QOc8NUvTdG+Phu3aOCBE3v47P6TCyfVkesreGzttyxZ60safteBD/YpQ6L+BOfdw4Vh3OoYjiHcg7lHMo5FOEcqvhdzqEu8WlyqBsNSsUqRxvGR7i+BwKQd1jA5TQ7TqlVg5MkSW4KuRq+svwpoVKFtIZXhaRRn5hD2wexRr0L6Yk5O2tkkiQ/HV+VQWX3ukHLSDlpA9SR5qLnLSyJYpqUMSNAOrKtjYrv52aWmuZ6vcZyucw5IAC5SMtisQh5izlja0wJGzFQUqX55GebVlEbLtJP1T22LUtoitoMZcQIn0btlIxq56pWq+WMs66TtzKxsrO2JQa7tCPWobsJsiwLywr4npisVZ94XR3I+fk5Hj9+jKdPn+LZs2fh5BQAhRv8sjxq99I0xXg8Ds90Oh3U63W02+2wV0elUgkOkKcdsRydTicQgTRNc5FmEmDWHzunzIeVi61v2l7+vlwuwykxXI5BqF7onjHMi84QUZlbYmY7G2xHlrTZcsRIjf0rqg/Ws23btr0XLRWxbSr2rjLiv4s/KOqc7PL+WDsseqe1UTGUdTY0fSVd6gNIzqzN4YCKtjN9XvWTz2hbs/XyPNiVQxXxJ8A5VNlzzqHicA51BedQzqFicA7lHKrotzI4h3rxHOqF7im1DdoQmKYWTEeWeX+9Xt+pUPp7GUEpeuZ5UaTwuyicXqfR4NR8ll0NCEfh9e91JVQAcrqhyq9TGreBz1pjyf9KrO0eABpZ0CgjIyIAwqabFxcXmM1mqNfrIaICIOcUYoaCKHof869RGHUiMYOkBjtGfLbpjCXzfKfVN9v2mA81eJVKJThyfZ46zZNEmO9ms4nZbIbVaoXFYoHlchk9ZlrzwbSKRuWVnKkd0t/2bUdMj51D3bdCyT6PsCYJYX6Zh/V6jclkgslkgvF4jOl0mnMM+r5YPSph4R+PwH7y5Al6vR663S4Gg0HY/JVRwel0ivF4HE5Oefr0KdI0DXvEtNvtIO/BYIBarYZ+vx+mWBfJLtbhU4K2XC6xWCwwm83C+w4ODnJtZLVaBbKn77FkSN9pwZN1KH/dDJb1oB14W7+2rizKSIiVi3YqtCyxQYCi99i88bcy0hXr7NvIV+yZovYQI2Ox/FjCor6/CEXl3EYqY0RYN962pMxGIPkb29tisch17LZ13rfBOVQ5nEO9PDiHcg7lHOrqfc6hnENp3vibc6hPn0O9skEpa5zZaFTo9j1WGV5kXq3SlhGefd6p6diRxVj6dDw6mt1ut4PRV+OvRIInEnDz0teZUAF5+arSl0U/Yg1To3qM6OnvMV2j3KmzxGq1yu3pwVNjlstlrvHyBCPey30GmMeYzqsB0nai7YV5t5FKm8+iKLklckXE1JIQzVdMzlomq5tqvPg8NyMl0WDdqDOz9cC0gavTQxgVstE85kcJmc17rEO3K0j2NJLZarUwnU5DpOH09DToS5ZdRreoE+12G/P5POx/MBqNclP3d4UlFyQkFxcXODs7w2AwwHw+j0Y2GR2h7nC5h+5nwXsoX9VjlaV+j11LkgTz+RzT6TQQqWaziW63e02u4/E4pzMkY+xYapm1XfM7bSs7BFyOlCRJ2DyXJI/HabdaLTSbzVwUkbppfZXtFNkOka3D2DMqFy1PGVFU8lBEpvQ++xlAjtwoedK0Y3aKPrzIn6ltsPncBUW+VMlnTA6xsuuR2cyTEuoYIbPpFdm5feEcard3Ood6OXAO5RzKOVQ5nEM5h7JwDvVqOdQL2+h8V4FZgRQ1uOfNC9OKpa+fX3SUjI2YhomNuMgw0ugRPIZZnUjsWRvReN0JlUJHZHW0dhvUwWpEhJ8V+jvfGTOUdAiMFNDwMjq1WCzQaDQA5KNMsY4EDRGjH/V6PecQeJ/VBz7LMlInLKkqGiVXclYE6pvKvqydMp9MX6NQsTaXJEmIhNmpn7E6Zjn5mTJSZ2vzZ/NMQmAdCstry1NUTtURdnRYjyz3ZDJBrVbDfD7Hw4cPMZ/PcXZ2hjRNcXBwEMgqo8Ux26V1y+/bOoLM03g8xnw+x2Qyye3PwbIyarder1Gv10Nnrdfrhe/D4RC9Xg9ZlgViGJOD/cwNXnmd+4Zou6N+W93me2azWYi6zOfzEBHWzTT15CBG7bSebT3qMcOU+Wq1CuSKso5ttKn6p9f0z0bhbZsoIlRFhE2f1fxoW7Lp6m8q2xgRseUrInFF32mH2B53hcoxRj7t9ZtAbaRt7zGbavP3Kvyrc6irNJ1DvVw4h3IO5RzKOZRzKOdQu+JVcagXevrePogp+IvCNiPDeziqr6Rl1/xYx8hnNQJhR22LwHs1L0Xy0ZHnoim2ryOsIWGk7yaE2I5i21FpytUaFUv2aYzVcPNZkmg6VfvusnJyE08A6PV64TQQbdhWp+xvMVJVZERtZChmyGNyK+oMKWlSolbkdFV21H01frE2xPRZTl2zXzb9nI5Xo1WxMjAfWuex9BghYjk5XXu1WoWTWtI0xf3799FsNgNRefbsGYDLad4kzyx/bHqudgRIhlRXqYs6VZb3kFRNJhOcnp4iyzK02+0w1brVaqHT6eSINSPgJCcqK9orvidGHFQ+y+UyRxaSJAl7gvC52BIJvtPaR6ZBG8ClDbyPMya0865lYzrazhn50xN21D7o1HMtS0w/LAHSumQ71Xv1XZqOvaZ1qm1Q36Ppakck1rnRNkpbWtTpUj2MXY9913utLbK/a7nUjvGeIlKzj92PkVj+V78dgx0I+izAOdQVnENth3Mo51AK51DOoZxDOYf6rHGovTY6j2V+H4ceUxprvGP3qrIUkSVbCars1hEysqajtzeZvh2rHOZTHQRwfQquBX/XEXFND0CYotloNH6iSJUaDhpNXWu/S93F6so2EjV2jCLYZ2JOmE4VuNwQkdEbGjFG6vg8/2KNtFqtYrFYhN/b7TYqlQp6vV5uTwBrQMsQIwfW0PM+KzM1wpa4Wh3V9+hmd3yWUUIAOQKiJEg35uQ71BnwupI81pfdwNHmkY6dMlQHWSTLMvlq2twA05JJ61x5jRHc1WqFVqsVbAX3U1DnzzxwBoDmV8tK8rNcLkPUirLh1P7BYBA+p2n+xI75fB7S5HKA6XQaoq98b2y5h91slflk+4wRJdZ/lmW5qJA6UV22wengzCfrkeSJ91jfUeRIbeSZxFjri7/bJSqaTux/rH0BcYetfkqfKeoA2Ws2Lc2n7r1BIquRZJ1FoESUz1BHSFSV5MYIlXZ0lPxq/Wo5YwMIRQMQmi8gH8FU+xN7pqgTqYTW3ldGHm8C51DX4Rzq1cA5lHMo51DOoZxDOYdivoDPJod6rplSz+vIbSYt6dHoGxt9ETabDRaLRVAIfdY6Eyu0mxBO2wg0PXUQ+r6yd8UUyRLCF0GMP6+wzl0b403SsrBOisbURqr4uxpj1s9mswmbq/I3dY58RqdFa164z8V0Og3RwcFgcG1zSpVDbIqnGnTrWGLytAam6L5txJX6zqm8PBEkFrmy0UeNVhGsX07l1rIo4dC9RLZ1MGIEugzWqBbJTO+x/207Hg6HqNfrODo6wte//nVsNhuMRiM8evQIDx48CPqhdUi90XyrHEhoAOQ2weRvJDn9fj+QnUqlgsVigdFohPF4jPPz89BZy7IMx8fHmE6nyLIM3W4X1Wo1bPSpjhVAODmHf2wLKkcSJ757m7zZcaLz55RzdnZ4Co7qgHY2bZ3E2hxw1S5t24zp5E19nr471pHSQYAYUdnmO4reRXDWwWKxwPn5OVarFUajEdI0jZ4E1Ol00O12UalcnTBEW8gNdUnW7LtjPpCw320nTW2MLrexcqDua4SyyBbyv7ZFK88yO1BEjl8EnEM5h3pVcA51XRbOoZxDOYdyDuUc6rPDofYalCoaQdv1ZXqvzWSsgDpSrlGDsnStY7CNiZ/1JA8ahW1rwmPv0zQJNeyxxrNLevaaTed5yeznEaxfRoM0+vM80GmrSpYYlYqRNiUPbMytVgvz+TwYJXUuTFvXbNv2s16vwyh8ll2u9eapKfvoEdMDrndSlDBquYqMhqbD6ci2zaoDYpvl2nXdE6CoTbKtxAgODaa+T9uplWls7bsaUcq9jIgXGVo16CqHModn2yzzy5Nc0jTFZDJBkiRhzwTdp0Gf43tYD5bQ8Tedms6NQUmoYoShXq+HU2Nms1nQe0Yi0zQNkcF6vR5OYUmSJExfz7LLJROr1So4YZZV5a+dTH5n+7DyYtujLl1cXIRIquoe2x6njRft/WF1gv9VF5RUqPOPEfVtndyYn4tBSUIsr0XfYzKz74zlgQSKSzA47b5Wq4UI8cXFRW5DV+pOvV4PpwepbACEk5M4G6HVaqHdbodNp1XWKlvaPtpKbgrbaDRykW0rf+qWRhMtbCTQ1rv1ITbCHpP388A51PX3aZqEc6iXA+dQzqGcQzmHcg519Zz+j73TOdSr5VA7D0pxxNYav13XpdOI6ZRvGzWwiqHrbdWwxsDKppBjBodgo9FRyJtEi5hna/iYJkfLY6PFN32X/ftJhBICbtrX7XYLG5WFEidLBvi7/dO606mVjDhUKpcbHXITRBIqbtSpI9CqI6p3s9kMp6enePr0aYiSkKBwY0VGPIiyMisBVUOmJNF2QthOY7qqxqfIAaRpGoyyXZtP6NprkiB7Cgn3FCizKSxPbO07cJ3wFV3T/Nvv1j7FIq5leQOun3hDe8AyAgiEYDgcYjabhVNVVNZaPus09Z2UCW12u91Gr9fDYDDI6SGd6mAwCHLhJpWxqEmv1wtkjqC9VcdGfaLDVn2zU8yzLAvHItu6ybIstx+DbuKqywxIgDRdTU/rykaXY+Sa71F7YBHTfy2/ksgY4bF53MVu2QhZjPjHSIDW7WKxCPe2Wq3cbJrNZhPIaZIkYTkFSUalUglT+yl7rVuNsvK/ntyi/1WHsizDYrHA2dlZ2KB4Pp+j1+uh3W7j4OAAvV4PtVrtGgfIsixsjMvysdzUDV2+oIMoPHVN7RTrQjtqL8rXOoeKwznUq4dzKOdQKkvnUM6hnEM5h/oscKidB6X6/X4Qoiq8HqtbRkq04rYZM1sgOicaoRg5Yd6U6G3DiyYmZeTwRaT9ItJ5HaCEWY35TbBNB+x16xhp4GlMGo1G2LNCj4GN5c/qyGw2w9nZGU5PTzGfz9Fut3F+fh5Gy7eRC6ZjYUlVmb4Xpb/tOtNUkqDRG96j5I0j+Jyur3aFhs4SSIU61V3ybPOqxDxGyOxz+l8d4q7Qe+1eCQDCkb6UB20e88rnbOeVhF6jISQpg8EAg8EA/X4f/X4/lxaAsA5+OBwGZ0P7q/nW65ZEMS+MFKoucPo72wgjTLyPpM62YZat3W6HZ3QqOyOJTEcdp+a5qA5sB4n/Y52FbfaBDpoRKuvnbDsvSsvODNH79DfNc6xs9I+2XXA/CbUlWoeahi5/sASa8tZ3pGka9lkg4eUU9aJBCyVXWZblIriMJrdarWsy1PIy/9oJWSwWSJIEnU4Hk8kE3W43tKterwfgKpqnJzhpZ4kdPZaR7et54BxqO5xDvRo4hyqGc6jiPDuHcg7lHMo5FPDyONTOg1LtdjtaEI76srKLjB8bIxtwkiS5aV4x6P1lTpNpq3FlunYk1CqsNfj7khZrbF8UOdv2nthvPylQQlVGpGL1ab9Td6gbZbKMEWbqJfWNhqDRaITjUtM0zZ1Uwv+qf9R1GqzpdIof/vCHaLfbYVqmrlvWPMUcvc2zkqoiAmLLH/tcRuzpIFlWO4XTOhY7Aq82ROu4yEYkydUxxpou07Dte1sd73N9nzZHuVsnbnWXkdxer4fRaITlchlspRIFdUR0RrSRvHe9XocIyWAwCHswKNRu8nn+V3vG9gEgR7pYN7PZDJPJJKyvZ5nZyVayrHqh+eA77WfWMQm45qHb7YbnVcdi+m3biNU1S7RiZEZ/t/dQ7znlXvNgZ3lsIxZlRJBQsq2/az6LfFKsg0C7QPlpOuy40N/rwAXTqFQq6Ha7ObJGnS2z0drWySMYHaQ9ZGTRRgazLAtRQeaDJIiRO+UdWiZ2Pmg/OKOA0Uyts9VqFSKXzwPnUHE4h3r1cA7lHIrpOYdyDuUcyjnUZ4VD7TwoNZlMcgqh09XsqHMRdJophWiFS1DhtXJ5T9l7VHGtImkjKmp4LwIxhX1eqLNKkiQoAJ325wk3Ia5q/IGrtf3b0tL6tvqlBEMNmRp/bVjUcU0TwDXDwigN86lRBABhDbld9350dIQsyzCZTDCdTnF+fo7f/d3fDeuMj4+PgxPR99KwaGdFoXmN6XzM4Wvau+ixdcBqgGOgo+U6e8rEyto6PPsuqxdEzAHGiKftqFmbo1HJbWWPvb+o7HzvYrEIRrzVauHo6ChEi+hEqCsAcqfeqIzsu9vtNobDIY6PjzEYDHB8fByOxS5ysDFHaeVlo0HU9U6ng8FgEMrF97B8NqqkZIbvtnWh5aFjU9u92WyukXFtm4ziMNoYO4XGkpgkuTzSuGyQwBIj5jtN0/AO1aGYH+JzZR2YGGEiYnIqIv5ZloUZB+qrrW7rXgS2MxLLk40E63N857YBDOCyE3pwcBAtf6wd6/vYAVksFmg2myHCSF0ZDodBr3u9XkhvMpmETYTH43HulKskudr/QfPzvHzBOdTucA5VDudQzqGcQzmHcg7lHAp4vTjUzoNSo9Ho2rVdjS0FYL+XGZ0i4e+LIocbM9Rl9+8KNRrWMTwvSKhY6fz+otJ/VSjLKw1RkuRHe63BLBp5j6GoTq1+aTRZnbXmW6dTU39opGhImT81Xmz8nU4HAMJUSG6ISOdweHiIo6MjTKdTrNdrjEYjnJ+f4+joKEzFLDpqmfnWKKLew7JZw2DJS0xWRZEJC10TzborIlVAfsmIpq2dNZuPMpITM7xF1+n4Y0RSicW292geY06GsxkWiwVms1luWr2Wh1NjWe5KpYLRaBSW28T2eFEioteazWYgD51Op3TjSuY11umIOVUlRMwXI9Hs6LHuY06Y6+5Zdi5XoFyoOyQnSZKEyDn32iAZpb7bpQtMm7Lgb0ruuNkoy1qpXE3fZz1usy20CSp/JVIxmRfVwz4+riySqd9ZT5whwLLZ9q738lnr2215ytpHLD9aTpv+NvJpy6wdRG7mqfbWElo7MNNsNnNRS7tkINaBuCkHIZxD7QbnUNvhHMo5FOEcyjmUc6jy6zE4h/pscqidB6W2ndqyDUVKVuYU1Ynqvdvu2wVaoTGB34SgKMnhdzbum6ap+dEKt2X4PCBJkmDkdRqsyl1H7Gko6QwoWxpk4Hpk2G4iqL9Zgwcgt3SCRt5G/arVaiCu6gy0gXNTTaurukklGzrXCjOyxd/YwJkO83F+fo5nz56h0+lgsVjkZMd82DYVawuxDoSmsyusIdQ6YNnoyCaTSeHzSqaUSLOeWdf6nlgHxRJI3q91se0Zm7+yJTFFZKronvV6jclkgrOzM5yfn6NarWI+n2Oz2YSlBTz1pFarhRNlKpXLtevPnj0Lm57aaJnNh+odSdt4PA6En9FCpk1dYp1p3cSgeq8ytZEjEukiskH5punlyTnn5+eYTqcYj8dhQ9HBYIBut5ureyVInDUS61yx/dBOsNz8Te/n81Y+WkarU1rP2sbVl6iOxWyP/V7UPne18bG8aZo8GpoRMKbLurDltx2hsrxrPmOdvRjZ0zzae2K8wOq7frd2Nla/qtN8tlK5PLrZkkqr/yTbzwPnUNvhHKoczqGcQ+nzzqGcQzmHcg71OnGonQeligq7DwmxBo7P7KowsYrS9FQoduRXhcqGSIXRzQI5Us3NAW3etpG2NE1zSsu87uOwtpX/0yJRRcSWxo2fKVvrtNRpKmK6RdCxcsSfkYN2ux02X+v1euG0FgA5sqVyL9JbWzb9I5mio1eoc9FIX71eDw6So8c2OmjLzndNp1McHBzgzp07WCwWQZ/G4zGm02nulJyYvHaBGhNbJpZ7myG032mEeLoNTxnhb5pPGnASKY0EqvO0RjP2zl07W1l2Geng1G3Vz1i6u0A7Snx/TEf0nYwuMG+cKsvy855qtYper4fxeIxWq4WTk5OQDvVJbd5iscB6vcZsNgtHyHLTw81mg9FohH6/n+sYTyYTtNvt8P4YyYw5oVhUkxvU8hrLNp/PQ0eBHRHg0nnz2O7VapXrKC2XS8xmMyRJEtpSklxOY59Op9hsNuE6869gfU+n07BpJO0xCRmnl3P/AlvufaCRVqtTVj/LdCvWASi6v8ifFBEy5o+nskwmkyDrNE3RbDaxWq3CpqitViv8rif1dLvdQCx0JsN6vcZ0Og3v1PvKYMvJTV25PE2JcEwmbAe6X4heVzupf9o5U19N22T3U0qSJOTtpnAOlS9DEZxDOYdyDuUcSu9xDuUcyjlUHK8bh9p5UComCP6/qSIqNA1b+F3zwmc5HU2FbEf61Gir0GzEJPYudTgxcvBpEZ59oY2fUy6B/Ci4flYSqqA8YoQlph+7klRLPPi9Vquh1+vh8PAQw+EwHHW5L6G396uOcB06jXAs34rN5vJ4TE69JkEvOlEp5rxJnmi4+v1+OP0gy66mMDNvMejUyW3tpyhfaoCL9Lmoc0P9oEGiA9f86v1FJFvbpYW2P37XdIrIsRLIbTq5DSqbIgKiU8jr9XrY6JgOjHVFO0XZ6ckxjATGHIuVEaOJs9kMnU4nRJFJPqxOz+dzVKtVtNttLJfLEOnbp/OnHQW1nbaO+LtG97gvAfOmBJWRfXaKuTSD+4FwerpGJrXueVQt17xTrq1WC51OJ7d8Q/Mfmz1QBn2n1n2s/RX5SvsuW6+xTswuNlO/2zTtTAbOYqBO8Ah2ypGyY+dW65oEX8k6baH6XAuN5vPUGS6zaLfbIZprI7RKfqg3sX0cYtFW+z/LsjBtnfKK2byX4dOdQzmHIpxDOYcinEM5hwKcQzmH+snkUDcalLLG7KZgYWMF5qjiroSKDY6NEchPL2V0iH+sPBuNo+Oy5WQ6VtifZfJE2XJ6psrzfpriqwDeq9XwQKY7W0OgMtNrCiuPWGQtlrei5wl1fjQIPA3jjTfewJ07d9Dv96OnahShyJGyzNQTnZK+jYgxr3Se3A+A+dJpkbzX6hx1eDqdIssyNBoN9Pt9JEkSoieMGK7X68JTDCj/GPHVerxJR8g6B03DOg/Nq80DkI/wqQ2IkeiyfKrxjNkL21FTZ21RpjuWZMRIDckI38PoFaMrdE66NAVALvql7YIdmGazGSJ/sY6gzReJxGQyCU6QURst+/HxMWq1GtrtdtBZm4eisut11r2WgfmwZMNGCjW6qTrA+mSUlPsgUJ4kiRoF1PZPveFSDtp1dlg0n5pH2sFd7Lrep3Uf+yvSY9WXoneW5WVXx6/vJolerVZhnwQ+p9FQAGHWgtpGfZe2YyU+28gUkNcTRqgpE8qL/ss+R9vCk2PG43Hud+aJHQvdEJkdVxKyGCG3/9VGvQg4h/p8c6j7aYqvpCl+VK/jwSc21TmUc6htcA6Vv9c5lHMo51DOoRQ3nillUVbpNqNKmFqtVriuv6uwSX6KjDOAsFaYI44x6NpcHRnURlhWnl3JUxHxuCmKHAqdLUfny55X+f5P53P87ckEVQAbAL9wcID/p4l+Wtj0Y440RhJiJM0+o8SXUTL9a7Va6Ha7ODg4QL/fD9etE+X7y9ay874io6l5VSOpxoTESeuFZWD0gUa+VquFhkudV2dso8zdbhfPnj0DAPT7faxWq1Be1V+VIfOjxjk2DZb3Fsk+JgOmp+mrfsQMOg2dPa5c9QK4Wn+t0RpOB7b3WofDvNh6iBnHSqUSlg9sa9datpj8VAc4jRe4Wv9P5027NR6PcXFxgbOzM5yenoZNN9M0RaPRwGAwwOHhIfr9fliu0Gw2w/tUNuwIsvPY7/fRbDYDcbm4uAj5SdMUo9EIg8EgRG663W7OOfHUHl7jFONtUb6Y8+c11gd1MOaQqEuWjOgSEyUhdNZ8BzuIujcJpzlTP7k5KeuDv2leKpVKblmA1m2MAMX0jFBbZJ1vGVnS31XXbacrlpddfRTbAqNmGtGyz8TshtqAIpujvhxAjvyrjNT20P7xdB++S084slPKre1jOvV6PdgOAOFI9U6nk7O95AeTySRH0KkzLA/tli5xeJGDUjE4h8qXd5/7d00vdn1fDvU/Wyyu+NNshr/c7+O/k1OJnEM5h3IOla/T2L3OoZxDKZxDOYcCXuCgVBloYHRdJUfhgOsRAsKO8FLJihoz38OKoyLYZ3YlUM9T3jIUkQzdcM42Ti1T0fusE4zl495mg789HoMxoCqAv3l+jl9tt0PET/PJ/0oCrLOyTo3ORR2mGjg6Ej7LKEaz2Qyb8um7CSXLWuYyglSEMsOjEQYadL5byZGtC3UALCfztV6vc46Auso/gqfLzGazEOFZLpfhqGNOa7fvtiRP68oad82rvruMuBe1O6sv2tZ1OrGtR+bLrsVXu6Dl4OcYwbIEr0h3rDNQecQQczz8m81muLi4wMnJSdh4s9fr4datW2Ft+ePHj/H48WOcn5/j6dOnmEwmYd8COihGrfv9PobDIW7duoWDg4PcqTucGktZVKtV3L17F2+99RbefPNNLBYLPHnyBH/4h3+YO41mPp9jOp1iMpmEzS8pF+o3bSv3hCnSAZVvTA9U3kX2lfqhbUf3G9H3a7SdU9S1PlTXY5265XKJ6XQa5MZokR5Tqx1qkjjmrYisWMe+DbuSHpZDf1PbBiDn2+z9MZlbW2AJr9ZJUVmsvbBtKsvyx0xbv7YN3HuBHUb6Qep+rD5YHpIhALn72Eb0OZvvorKy7bCjoml9mnAOlcenxaFi/OlvjUb4tW4XDwoGJ/jfOZRzKOdQzqGcQzmHIpxDRcqz010oNrbMyDbQIbHBW+WwxlIdmD0xoSwfaoB3KdM2R2wb6rb0dBTdOgk1FkVkyTaSmELHSE8szzG8s17DTkquAfjSeo0HhuAyjyRGOvJKI8fN2LRh6LG7jMbxGX6mbLRerWNUQ6BkRR2jLTeNRpkOWOOjeVEyq+nGRu7tu/mMRio5hZ0Nle+zhEoNwcHBQdgQLkku1waTjBbVrTWUzKMS35jDs9eK0td62HZvkROxBEv1n3lhVJcOnsRMnYraBhrWonwkSTwqqXkvImb8b435ZnO56eWjR4/w4MEDPH36FLVaDbdu3QrLAsbjMd5///0Q2VssFiENlj3LMkwmE0wmE9RqNQwGA5yenuLOnTu4desWhsNhrjzUv1arhbfeegs//dM/jfv37+Ps7CxEgzWv3B/h6dOnSNM0bMxI2bLtNhoNtNvtXJvn+1RvNSodiw6zTHo/yYolwErodTPNRqOBTqcTpsoryYqRYKuPNj88Jlk779qW6YdI3LYtS4iRkKL3x4hAUdtherYd0w6wU0ZCaG1U0fv0vTGbWoSySL4F9UTrx9aZllnzACB33LQlyTH/zPS5Z4J2UBjdU38Z6/TTLy2Xy5yNJpFiJF33fogt59kHzqE+/xzqq5vNVv7E551DOYdSOIfKl8M5lHMo51CXcA51hefeU2ofMNOr1SoYmJhiWYenwi9DjPzE0reft5E0pkPjE1ufqVG52LvKonRl5SlyYkWKpzKINfgPWy1sgByx2gCY3r+PLw8GaLVaaDab4WQWbhDIRmxHYO00UcJOs1ZDax2gykzLvousYuSAaW4j1rbRayRIf4+9hwbEGhKmkaZXp8josgimw2vq8CknNlyexFCtVtHv93F0dIRKpRJO3ND8kYxpOko8aXCsrGPtoUjGZfcx38vlMkSX5vN5GIm3HQXNC50mv3P6KOtA11onSZLb+FTrmHURazN2mYkiRkRjdoj1u1qtMBqNcHZ2hqdPn+Li4gKDwQDL5RJPnjzBcrnExcUFHj58GIwzN5ekk2RaqnsXFxdhfTc3K+SUWo1+9vt93L17F9/4xjdw7949PHnyBI8fP86d1ME888ShJ0+e4L333gty5lrxw8NDHB0d4d69e+j3++h2u8HZkFykaRrSUdvNKfKMTLPNt9vtXL0wYse2oB2Z6XSK0WgUjjMeDAYYDodI0xTD4RCLxQKLxSLIgJ23IkIBIOSP+aFcqGfUVRJ1JZDWGbPd6m9KzlTWaodtGnpf7LPt+Eyn01Bvk8kE0+kU1WoVR0dHODg4wK1bt7b6lCL9pb6pXunMBdqSSuVqs2KWTyN7OkBA2bAMZWCbpm2ItUn6F2vLYj5R30cCpHK1fokdfyV2TNfWlZLcbeXaB86hPp8c6kf1epQ/nR4f494ny4ecQzmHKpJx2X3OoZxDOYdyDsUylOF15lA7D0rdlETZNJQkabplxGef9IuuWedZpIhUMI3E8b+OBiti6VjjbYlQUZ6tcWc++Kf50WuabxIfRptIkprNJnq9Hv7rH/wA/4tf/3VUswxpkuD/9Wf/LP749753bTQzRigs1IjEyqBEoqjhq8GKkeJY9K8sT8xDEamKkU7g6mQLK2v7DIBrTk7Ly7Iw4jmbzcKGeNQdEiqmpe2Cpyg8evQIm80Gg8Eg7I3AdbnWECmpjRlrJYGxcinUYFk9LiOps9kM5+fnODs7w/n5OcbjcY48xzoF1Fktg40oZlkWZMQ0LHlQgqtg2Tn6r9dZ11mWhTqio7D6wzyQ0J6cnODJkye4uLjIbcRJgnBxcYHFYpFz8jGiZknxfD7H+fl5iLwdHR2FpQx0Bnfu3MEXvvAFvPPOO7h//z7q9ToGg8E1uwQgHNfLaKjajfF4jJOTE7TbbTx9+hR37tzB7du3cXR0FKLKdHy6yagS2tlshvV6HfYpGQwGaDabyLIsSmIpb0ZvSZh0Y2TWJWXGaAyXNCTJ1b4pNurPuubUc92Ikp1hdnSU0AJXR+gyHyRmPEWF0VEtC8ElHNRp2zHT9sp6VxIC5CPxdPj9fh/A1THX3JSYctml06kyH4/Hwb6wHjUdfuZSIfoSbgrM+tEou3awFapr1t/aa2obrJ2w8mRb5L1KoGxasU6z2kRbB9t8yvPAOdTrwaH+/scf48//438c+NM/+Ff/Vfzcd7+b6+g5h3IO5RzKOZRzKOdQzqHyctyGV7KnlBp0xa4F2ecdQP5oXv3NkqOyCldjZwWp5YiRDqsIdnRQK5d/vG4bOJWZjZlTvOv1epji3Ww2c1NJNRqpys58nfzsz+Lv/kv/EoZPn+Ls1i1MDg/RAa41+CIyYeXN79ZZxJxvjFhpA7Ej1y9SR7bBEtQsy8JIMw0HnadtiEqOaHxZb3b6OYkbj/LUNAGESBlPRGB0ZDqdot1uo1qt5hw9/6se8X9Md8vIFPNoCYyNUvJ5rePRaITxeIyzszM8e/YsOMTYO3QpgpJSrQfVKf5plEifo0NjmfW7dWYqL957cXGBJEkwmUzC76xv1gnz0Gw20Wg0cPv2bfR6vdwpOYxYcWNMlV+MoMbkyRNfJpMJer0eKpVK2GCz3+/j9u3beOONN3Dv3j3cunULP/rRj67tv6FpxuwbZQMA8/kcP/rRj3BycoKnT5/ii1/8Iu7cuYPhcIharZY7ZYuEijrJo7Z5PDAdbZIkOXJqiSPlxXspU8p8Pp8HcsJ2pHnQJUm0bWrjkiQJpEn9Am0n653Ps76Bq30SNOJKfT04OAh1aHWUsPud2A5BmqbBdmt+Nb0kuVyGcefOHRwfHwcyr+1ll6iT6pi1bbVaDfP5HLPZLJwkQ+JG2dg9elj/JNLL5TKUQUkxj2NvtVqFEVlGb0mWWUckdDq7hM8wfbWbVp8VRR1AbY82XzGbFWs/rxLOoT47HOoMwP/lX/lXcPDkCU6PjzEeDjEwfMU5lHMo51DOoZxDOYdyDnV1fRcO9co2Ot/lWhGsA9AGomvridhmc0XKpw5eP1vCpffzPkvIFDribhWao8x0tvV6He12O0z91hMdeK8lVUUjq9ZxaP6UJCxu38bDW7cuyxq510IdeKxx8L8qKeVjHWGRwsbIlI4os8Faw2LzoSiK9G1DGQln47eg8c6y6yPwnKKv0Sg2aps/bv44m81wdnaGg4MDpGkaRuXpdIocM+uI6dJhMD9Mw8KSFyUD9h0qJ9YvI1RnZ2eYzWa56GVMTjaqrHLbZrxU1hrBUr1XBxaTt+pjrVbLOT1GtiqVStBdTq2v1+vo9/s4PDxElmVhE07ufaBr2DUPVqbWmWqZJpMJzs7O0Ov10Ol0UKvVcPfuXdy5cwff/OY38d3vfhdf+cpXMJ1OUalUgoNTp2zrVqcRA7im29PpFI8fP845+n6/H6apU76dTifkt9/vh4g2p/RaEssNalUfkiQJ5CbWRpUcaaTYRtq0Q6rX1E7z9/V6HaKeeh/XvlsSRL1WvdH/PM3MkimSBJIxRm9ZN41GI9Q1bbnWE4AQVSWJ1M4HO9P2mW2IdSp02Qfrgu+x7cn6B37mUcTn5+dBx27duhX8W7vdDu9TGwRcLq+ZzWahzXA/DMrJzkpgfZPMa6eN9oMzcagr1Bd7xDrzyrqmDlieYa9/GnAO9dniULPjY8yOjy/1puBeC+dQzqGcQzmHcg7lHMo5VDFeyqDUmwC+CuAHAD6S6zFHrAJQp2XJii1QUQF5vUwRymCFqVO/CY2i0FmRKOlflmVot9tot9sYyH5NJEgkThzJVuMWy6tVZltu/R97Tp+JGV0rA5t27Df9PSZjJVccOQYQRvg5WktDpNMirXFbLpeo1Wphn4bhcHjtiFZb9zeBEmYbPVIyZKcz81lLWGL6C+SjAQo6H06HfvLkCW7duoVut4vRaBQMlOZ1l/KoM6cBidVXjFSxHPZe/mdk8uLiAufn59dOjlGUESrVVY2wxvSVZFujkjFoBFbzTVQql8eerlarsP/AaDQK6WpnRKc7c90/p59z6rm+N0aoVK7WOfFzmqbBqR4cHODevXv41re+hZ/+6Z/GF7/4RfyJP/En0Gw28cEHH+Dk5AQnJyc4PT3N6aztnKgsVN6ap81mg7Ozs7ABImcZUEZaD5QD7R/vYzrWJtmp6CSr6tgI1n+sDIyk22U5Wp+c5aCRdY0Y6dRpOmjVI0YoWWY+z2eSJAlkR+tbI7tJchlJ5DIMtufBYBBOCyJhViJDAkaikaZpWHKi8i3r6FiwjjqdDtrtdmizMV1l3tXmWRkzyso0O51OON2KRIodAdvZ0zpg+pzarvpofSF1gnVE0sq8aMeRMqWOsG4XiwVmsxmm02lu+rp2cACEQY56vY5er4csu1qK8LJRxJ8oA/vdOZRzKOdQzqE0n86hnEM5h3IO9XnkUDcalLJCVmH/O5sN/otPTnjbAPhf12r4bz/Z6FELqMKzo7/2HXqdlWKvxT5bRdjVwdJpkiRx+hwrnsaGETcqFE88UFLFhm+dBZCfOqtOXGGNzDYSWVRGfUeMdMTkq7DP2EZSlOdKpRLWJl9cXITN5549exZ+19HrXq8X9r7SyCbvm81mYZ09p71yyn0sH2XGZlvZKS+dmmqnkdvICQ0TCTJHm5fLZVgnTqOoZJKNulKp4Pj4OJymkSSX06E/+uij3IapVndi5IflouG1usXjxbVOrfNXB2z1j/eQTHC6vO4toHliWuqg1B6ofqrzsOVTA6iEiqP3Nk19v272qeWgreAJJlmWhTa+Wq0CubBtjUSSRtqSFBsZKtI3bf8sFzsWzWYTf+SP/BF861vfwttvv423334bAPDo0SO8++67+K3f+i28//77IcoSI2nW+aqcqXeU92g0QrfbDTMOzs7OkKYpRqNRIOLr9RrD4RCz2Qy1Wi03Rb7X64U0gXw0hXXF66PRCM+ePcNkMsFsNrumA8w/o1r8jfa11+uFjqrafdUztXn6eblcBgJKmattIyGL6a/KVQmsthXqy7Nnz/DRRx+Fk4SOjo7w1ltv4e2338b9+/evkdEkSUJkisf82k4cO10AQjvTvChUF9UGxOyF/q62j8erM5Lc6XQwn8/DhrzaviqVSvCDWhfqn2hPK5VKmPau+1XQbmp9kCvQ1tBuUtaVytXJMTo9Xwkoba62MxIunlzEema0u2yD35uiiEPF+NPf/0QWzqGcQzmHcg7FdJxD5fXNOZRzKOdQn38OtfOg1GAwCILRiJxWxr3NBv/F48fhZJIqgP/zeo3f7HbxoHq1C7w11iycKjzT1cq1n22D0WtUHkswNKJo9xpgtI0b1HGTRf7G3/mnURIlLHZ02la0JSTqADQtC5umvb+owm3DsfKk0qlx0WdtY9hGTPX+NE0xm80wmUzC9MuDgwPcuXMn6BDlq9MwtREp2u12cOAc3eXUVitXoHxTyW1loC7paLTVt5jM1UGzXPP5/Jp8eC8buZ5wcfv27UAQarUaJpMJRqMR+v0+xuNxmPLLtKzDVuOsdQFcEXmVD9uMJS1lZeR1niaixor51iiIlp9tQaNI/M3alyJYnadDYflstJXltKCzUnkwOtNqtXBxcZE7HYX2gev2dV8LzZeNnGi+i6DkME0vN5a8f/8+vvGNb+Cb3/wmhsMh6vU6Hj16hH/6T/8pfvVXfxW/9mu/hvfeey/q2LVMMfJGGWk7GY/HGI/H6PV6mEwmYWrxyclJcKqso4uLi9xUYUaQ7B4f1D/KhSSAJ/DMZjPM5/NgC9iG+LxuDGoRmyXCuo6RKz6z2WxCJ7her2MymYROsu08sC2TBChZpA3iPfoMO1f1ej3sLzEcDkOHMUmSED1SW2in2Kse0SZRxpSlzopQqK2ybVz1wULLw9/5jtFohKdPn+LBgwfhdJtq9fKEq3v37uHtt98Om73qngqqC5VKBb1eL8jJ+sFYXtmRYlSQRJq6TPJOX6K2lnVEcm7LzftjPilN01xU9CbYxqHup2mUP/3/jo/x8JOOoHMo51DOoZxDOYdyDuUcyjnU68ih9hqUijkSVfKvrFaomudqAL64WuFjo9iEVr4SKnUWlhhZwefe9wl5q9Vq4bSUZrMZpiu3Wq0QFeL0bzYSKo+NPhBF39V52euaZ5bFOrqYPCyJsTKyU+Xsuy2Jio3yq9NnftmQOZoac6xFDdC+N8uyMCK7Xq8DOeXpBza6GyPV1jhQyS1h1v/aiNjQboIYSd8FbNTMC0fE1UCojGikuKkgAHS7XbzxxhtI08tpp1wff3x8HNZJ65pom56VqyWI1tjsQ6Zi7yShaDQa6PV6GI/HqFQqIZpr61Odn47m01iqDrCuNX/ahmL5sShry9qhaDabWCwWwQmQbHH9NY/aZb50+YDKzJKqbZ0QPqdyqlQquHfvHt566y28+eabuH37NgDg8ePH+J3f+R389m//Nt577z2cnJwEe2CdVkzn6CR0WYc67OVyGch7t9tFt9sNjr9Wq2E8HqPVamG5XOam9uomoSoLrUfKibZhNpthNBqFqM1isUCr1doqpyzLrp1+QtulET+14xrhpc178OBBcMCbzSaQH1tfvEfbNe22JY76R1vVarVCe+52u2EpEo/GJqnS99vOm4LEVklULMpn69/67SLyRbBDpG1vPp/j8ePH+PDDD/HBBx/g4cOHGI/HqNVqOD4+DjaLOsNoWaws1hfawYiYj+NnSzpj+dfy0/9k2eXMkFjHh3LhEhP1MbrE6SbYxqG+8skMKUUNwJc3GzwwRN05lHMo51BXMnIO5RyKzzmHcg7lHOrzy6F2Zlk6eqmNRK+/32hgA+SI1RrAh59M22SBYwpiFVKn2FpBkiSRDJEYkTBxHaNOD+f7rYJpxdmGZEmN/o+lYRFTVmtoaIzVkKrjjjmCovxb0qnpaIRRwUbJKFSz2QzrXC3RY3pFeYiRBTsdmEbJOiIdqbfltWkrOQXyR4na+lISyzIU5TcGJdqW2NNhqUFlfmwEi6PSzBeNHyME1vFyBHwwGISpjxcXFxiPx+h2u5hMJsGB8X7KweqQGmbKQ2VWREwU6nBtRJH5JqFi5CTLsjAdukiuGlFRw6XGVfMQ+2/T3YaYs2QdMt908KvVCv1+P9idTqdzTb5WRpagFOU1li+2u263i5/92Z/FN7/5TXzzm9/EF77wBSyXSzx48ADvvvsufud3fge/93u/h6dPn6JevzwRRaduZ1kWCDpnGWTZ5Qab/X4/5G80GmE2m12zH7PZLCwh6HQ66Ha7+MIXvpCTFZdGUL90w02+T/2D6jn/c3NXnrQzGAxweHgYpi7HZDwajTCZTAKhGo1G4VltDwpLdplH5pNEnr4k1olXf5QkSVhGQ1KjM0D4PG3rxcVFbsNNXaqkjps+K6andkkTp2dziYDaA7Vx3JyUZVZY0sBnY+1O6zFJEnS7XQyHQ2RZFiKXb7zxBm7fvo3Dw8NA2vUEIQXzpHVWNlhyE6geWOLLssXasu57QXuuNvwm2MahivjT+59wGMI5lHMo51DOoZxDxfPlHMo5lHOozy+H2nlQSh0YM6SFr1QqOAPwV+/cwV//ZAr6BsBfu30bzzodNICcMtgp4PqXJEluSiBH3Di1UddNquLaiudvNFJZluUMuN6vjtOOPGqZrcLa9xVFgmLkqoxY2s8a1dIIiY6yW4PIBjSdTsPml9axUqn5Po7sc5MzvlvlaUlFWVkt8bIKrf9jzlDrgWmpUScpi90fg62jIkcXI6qUN9Og3C2holx0HwdOVbZyUgPPfCuZpe5vNpenW1xcXITpx5wCHJOXlk0jQCo7RiNtnVg5aR6LyD4dKsvAKFCRfFV/+aeRMjv9XMukado82XwVGUwLGn5ujKn7cCjRswSd33XZxE3ATkKtVkO73cZXvvIVfPOb38R3vvMdfOlLX0Kz2cSjR4/w/e9/H9///vfxB3/wB3j48GEgfmwDWm/VahVvvvkm3nrrrRCt6/f7GI1GGI1G+PDDD3ObilqZcUoz9Yb7IHDaM32ALh1QQsXPVsdJZChv/mkHWU+q0XwBCPUyn88xHo/D5r3AZXT81q1buUih1r9upjoej3F6ehr2seh0OmFKOKNzfJZthde47ODi4iKUjf4pNoWa+SZZyrLLKCf3dKHsSLSsrdTOt9q3VqsVTjEaDAY5p5+ml0djn5+f49mzZxiPx6HtU17Wh/NdrAfrK9lm2em+d+8e7ty5E+wi/TKXGlUqlcKoqbYfXlMfp/9jfrfItsQ61fys7bks0kn9JkgKnwfbONRZq4X/w927+D8+enTFn+7cueRPYoOcQ11Py6YR++wcyjmUcyjnUM6hnEM5h8rrQ0x/Py0OtfOglB4tSGhhWUH/aDDA7731Fr60XuNhr4ezXg9f/0SRdJ8Brjeks6HgSXh0Y08taJEQaZB4n/6ngKko1oHEHLD+pt9ZZjXUKoci407lieXDNhz7GUBwTqxsTvsErqaSqvJz5HwymYQ1zWz0lH+n08nJNU3TYIg0+qb3FMm/DHaPBVtGu17WyjhGfjVvKtsY1BmXkakYYeN/JeQ0tsDVaRh8DkAwuLrJY6PRCIRW38c/TYf6kWUZBoMB5vM5VqtVmKo6n8/DRogqxxiZKpKFJeBF+k9oRDPWGUiSJOxVoWuItY3wPpWpjZwQ1AG2E0uo1NHZyL2FEjVbNtVpOkie2ME8c3PVZrOJarUa9kVQkso2ZWWyS3upVqvhHd1uF1/72tfwx//4H8f3vvc9fOMb38DBwQEeP36M73//+/jBD36ADz74AKPRKOSfp6/Ycq1WK7TbbRweHuKnfuqncO/ePXz00Ud49OgRPv74Y7z33nvhOGR9jvKn3FlORqQvLi5CmWlD1DFrfbATofXEzyREjNix88DolZ0eT5nats9OoZ5Cw5NWLEhkqtVqmNlA2zoYDNDr9XB8fIyjo6NCveF0eZZnPB6HPHL2CQmhtmud2UAifHFxEci8EkldVmLrle2CHRcAIdqnvoj6/OzZM5yfn2MymQR7q7MuYnrKd06n01wdcoaNTnsnSKhIoLQDZ/Vd/Qq5g+qadlK0k2k7nNZn6H8AuSiq/mZnQdiyb/MVN8EuHOq/73Twz+7exZc3Gzzq93Ha7eKd2tXRzM6hnEOpTJxDOYdyDuUcyjmUcygt2+eZQ+08KHX//v2g7JxSq/sN8FQLTvmuVqs4xNXIGgWohWNG1THEph8rigpmBWw/xxyupmV/t78xPY3oxCrWbqwWy4s+GyMBSXJ1IsV0Og2RB448LhaLICtOC63X67k1nmxctVoNg8EgKD+VReXPEXfKgKPMjGrwPjXasXzH6ob1rWt2VdZKBtQo8x6r/NuIwouEvl9JHKMdRaPEem+z2cRsNkOr1QrRBHU2dFIKOoVOp4PDw8NQz5PJBI8ePcptBllEwG2+tB5V5rymHRJrAO177G+8n5E9Lv2gsVXnp1FRNYA27diU2KL3W6If04Uisp0kSc7BqTGvVCrhmFRtP4vFImxGyc4fO4BF7y3STz7PJTNf/epX8af/9J/Gd77zHXz729/G7du3cXJygo8++gg/+tGP8MEHH+D09BRAviMXK1+apvjBD36Ad999NzgnRti0PGX5JaliVIfTcIGrE0s0ykbipXVdq9WubSTJ/SW4V8pqtQp7G/AvRrRj9ck0aSO5hr8o6lqpVEKeadM5LZy6qevhCdpK2vjZbBY+z+fzMP17OBxiOByGZQysC/5tNhtMJhOcnp6GE3O4vwn3E+n3+zl7yDZP+8kjwxltpI+gX2EZGc08OzsLe5RwBg11mPViOyvcnFWXx2jHQcmQ6pySLEaECXIHne2j9Ul9YzSWe2bwN3IMazeKOi/acWN+Wbfqc9Qf24GNMl+9D/blULdFNs6hnEM5h3IO5Rwq/n7nUM6hnEO9Hhxq50GpP/Nn/kwQgBpDGuJYYYoiXkDewBQZ1bKGVGagyp63ziFGtChoa3BY9mq1miNOeg8boE6v5OgziQwbbMyx8TNlx3XJTJN55lRmjmjbqXJpmgaFPDk5CXlRI9Hr9cKUOh2ZtY5O5c08xsiilacaExoujnqroeL79F3asK1+2fvLIqtliJVDG7aSZ+aZjZJRLL6/SB50tPP5PBcd5HN8VtPg50ajgcPDw+AguJEfjZBG6sqMCmHrkIiNxO8KlRcJJKeg0wnzPjo+NXIxQ6UdFdthsQ7WEipbh7uSTO0oMtrNOuA1Eif7TtoDRqYsQd4mv2q1ilarhXfeeQc///M/jz/6R/8ovv3tb+ONN97AkydP8N577+Hdd9/FBx98gPF4nLO96pjsEgzgyhEXLQOIdWw0fc7AIGE5OTkJ5IjOrdvthqURtHusb7YbJRTaWWQEm3lXu6n2VuuraAkHCRVPMbLLAdg+qtVqWMJE/8V8Kdm0U8iBq40a9ej6JEkC+RiPxxgOh0iSJOgN/Yi22yzLMJlM0Gw28ezZM8zn8zBtvNfrYTgcXtuEV3VuMBiE01r4R7mxDrlB8mw2C0fJM4o4GAzCZsn2HcAlEZpOpzg5OcFisQhRTc4u4BHSCpInXcLBDYXZ7tl5oNy0rROUO2ezKJlTuSsPKSJE7HzQ5ul+FaoTVsZqk9n2iwj6rnAO5RxK8+gcyjmUltU5lHMo51DOoX7SOdRep++VIeac7CitNYT6jL3HFrLour5P71VSoM6ZDtHmS40wr7FR0SioU1XjZckhp1VuNhs8efIE5+fnYQoyo3LdbjenlGrcgcuGy+gQR3JVfmw4m80Gs9ksN41c5c8IEZVE953odru5fANXpJGGLcuujia19VxEXJRYUdFjDjtGIAlGAgglgjYayPwp+eF/K1ebTws2mphzsuSBdaBGk7/zT426TqW1BJP/KYdarRYitSTvLCcNl+YxJv+YnC008rsrEbWo1Wro9/sAEDaK1OnQ2gaZZ9sGbf553UZaFJZ422f53RJyS97pYJvNJgaDwTWiq3Lmb9omuIzj4OAA5+fnGI1GhaSK7TLLrqLmlUoFb731Fr73ve/h29/+Nr773e/i1q1bGI1G+Pjjj/H+++/jww8/zDndJEnQ7/cxnU7R7XaxXC4D4db6p90q0wErt1qtFiJNtCvA1fRx5mG1WoVoLtsCdZmyitWV/k691/qhbRoOhzlCxTpZLpfXNqnNsqvlNnTo7CQyH6x3dcaqn4vFAuPxGIvFIkwF17xr/fGzyoUEYD6fB1/BjVJjfqvRaESdNzvKtNOxAQvt1LNcqqu0NVyqwnxpp46dAGsvSCDYoaBss+xyM87BYIBbt27lNkXVPJKcnpycYDwe4/Hjx8Eu9Hq9cIpbr9eLdoxpO2jjptNpiIJqR4wdSn0vQf/FZRKsM0ZS1Q+qjbAdeZJzyvR54BzKOZStZ+dQzqGYV+dQzqGcQzmH4vM/qRzqRqfvEdaxFqHIIBIxZ2uNon4ucpJKpHRKWawcNDCsHFXqLMvCPgKcjtjpdDAYDNBsNoNTjJWdJKzRaGA6naLRaKDb7eYcvspEDZmWiwrBfQ84vVyn3tFZVyqX62d1xJZlS9MUBwcHuQiLll/frcbOyrcoEltUZwSnKmZZFqZeT6dTLJfLEMHUE384bblSub6BKPOkBFednq1rS6z2iWRRd5RIWsPD66wvGxVgPlutFubzeW46NnBliPlf0+QpJUSlUsFsNgv1nqZpdKqzljcmC6avYJ5ikd2itqfXmc8sy3B2dgbg6kQiRn+UpKhRpMxs/vQeez0m49jzMX3UyKz9vUhHqAfaRpTckgAsFgscHBxgtVphOp3mDL6dGUFH3+l08PWvfx0/93M/h3/xX/wX8d3vfhdHR0e4uLjAe++9hz/4gz/AD3/4Qzx79izkjY6eGzrq8dGqr7F6LOtY0Ga02230+/1AqDQqOxgMwns41Zkkh21El0YQ6iApQ16zZIOdRF36orY91s4pT0YzW60WDg4Ogu3lPbrcg3aHZdB6ZWdOCYimkyRXEfxYu6/VamHjTLZTzvgALqNPZ2dnIQ2SFu7FQUJXFF2yAwjM33K5DL8zPU7vp7zVz7Fe1VZqp5JLy2q1WpjG3mw2sVwuw+8xcKkNTyViJ0ttIjcbtuSW7Wo0GuHJkychgsrfuJxKj61W+adpGt6tUc5q9XIvkzRNw7u1LQIIAyH8zGPo1effFM6hnEM5h3IO5RzKOZRzKOdQzqHi2HtQyiZqiYJCC2sLriNpRJHxtM/riBz/KBRrFNWwLhYLbDabMGLJ6NdgMMit8aRDAy6jFjqtktPWipwz7+MGaXrcpTYOTtMtIidcV3t0dBTKqGtL2TD4/Gg0ujZiao2VypxGRBsrR6RjdWrT0Xdbo6cklZFGEh5OOT05OcHJyQnOzs7QarVwfHyMN954A2+88UahM1RUKpUQ1WCdF9XJvmSK6Wt51WnbKbGsizRNrxm/JLlag0ujyfu0oVKH1eGqbDn1lyPT9l3aRvYFnYlGJIsIWOwdfIYGiISZjkuJn+qKytDKLEao9P2UkZ1eTGgUQMk3cBUNUYdt5aFQu2JJPHWD02KHw2GYcj+ZTEIeNLKkUazvfe97+JN/8k/iO9/5Dr7+9a+j1WrhyZMneP/99wOZOjs7C5EfyoPTmg8ODjAej9Hr9YLcLYGKfVZ50qGSJAwGAxwcHOTW5LOTyGnmq9XlMc+r1SrUAQkWy2yjfJQXP9PB2noFLvV9PB4H26kd4DRN8eTJk3BqDO0Zn1O7pzJTYr/ZbKJTkPm+g4MDHB0dhU0w+ftkMglpXVxchPaoesOp2o8fP8aTJ09yHRb+n81muXbCvHNfCN0nxeaR7weQk7nawCzLcuTM7jWhuhzr1AMIp7bp6VWz2SxEQbUja9sp28Pp6WnO1umeE9zQNKaTJL7tdjvInJujttvt4KO17Wv7ZsdzPB6Hjjun6PP459jR0Tqb6ezsDKPRCE+fPsV4PMbzwjmUcyhNxzmUcyjCOZRzKOdQzqEUP6kcaudBKVamLXzRdCxLnJQgAfmoDQ2nVqzeq5VMQsSRS1ZukiQhCsIpkcCls2s2m0GBqBgkTPaEAeZhtVqFXf1pQFutVhhlVAdryV6SXK67vH37dlAklkmVjw1GRzf5vBoGS1iUSFLG+m6VJdNkXfHdvE4jw+c5pdE2Ym6Wtl6vg6Ow09S5TrXf74eGUqvV0Gw2g/Fg9KNer+Pw8BDHx8dh6i6jRUybp2lofmPIsquNAlU/7dRsW1/2s72P7+V9MSddBiX6mpYSKiWDatSpJzy54fz8HPP5PMjIGhPmUY2uhZbVEiaSqth9SsBV/2xZueEgj8slCQAQoqBAXg9jR52yHGoDmJY6aXWO6iSYH+oDnQ/bMjs3Vl9sp4ydEh3hjxF3fuf+Fc1mE4vFAh999FHu5I1ms4lut4t79+7hG9/4Br7zne/ge9/7Hr785S/j1q1bWK/XePDgAd599128++67+OEPf4jz8/NoPXJpAsGO3MnJSW7vA9UJqxuUMYDg4IbDId58803cvn0b3W432IN6vR7Kwg4Ep/FSprSblIftzGodsN6UlFJGrBva2UqlkmvLSmqVPGm75DHBJKNKHKrVKk5PT3FycoKLi4trywSyLMPFxUWoU5aFulGv1wNBsc8B+f1K+N+2r9FohIuLi1xUifrH6fOsEy5bsu3S+lCt49lshvPzc4zH4xDxUtuvxJadXttJ5owS6u1oNAr7HNBG8ehoG+nkfePxGKPRKNcxZJ2TMOneQtQZRmCTJAl+g3txMG2W2+p1lmXBr3c6nRAB59R7zrrgSXbWjjKtfr8fTjRK0zTo8E3hHMo5lHMo51DOoZxDOYdyDuUcKo6dB6VY4fpCJRJFoIAqlUpOcHxOSZolYWocCTYiGu/FYhF+Xy6XGAwG4VqlUsHTp0/DGmeu/8+yLETY1DEpCalULje/ZF50lF7Lps8xfzQu1ljb8tAwWMdvYZWFRkHfrURUoxrWcHGklSPL6jA41ZF7NWjERyN3eooE38EjPZPk6iQNKiLBkxU4AkwnGXOg1mnbRqOERMthG/bzQkmU6mkssm1JButVyScJqEZUaVwoTzVuavgA5DoCMeJjy65tNUa0rIw1mlSks/Z5vlNPB+FUZOqMOhcSHNsxsZ0HG+3UKc1l5NqWifrGThjbN6NzrCslXCQOtm3GlgkwesD0ms1meNfp6SnG4zEajQaOjo7w1a9+FX/qT/0pfPe738U777yDu3fvBuL8/vvv47d+67fwgx/8AD/+8Y+xWCxCJ4XQTpk6Au2AnZ6eXtsbIWar9VqtVsNwOAyRdjobdlD19Col2bQpOmNBHTVtPkkBn7dT0UmglFBxyYramSRJco7NkimdDq77CWid6fRzjVipHnLvCT1RBrj0MSSXo9EokBVL2HRKupI+6jFlSXJi64aOn3vnaMeMafD50WgU5KidI+6BYAk2ZcpId6/Xy5Fz9RPL5RIXFxdhLwI+R7kwystn+B7uw8BTbegTqavcv4O2kLaAeSQ55wwJzpBZLBZhCny32w06qvaY+kl9oi7pXhnT6RS9Xu/app1qO7rdLubzOSaTCTqdTm5/npvCOZRzKOdQzqHs886hnEM5h3IO5Rxqj0Ep3RBNIx/WoVtYJ8gKjRnomPG26VYqlVzD45QwGiUKjWvPb926FaZQcr09y8MGp3ninx7bqPmlIvO6NlQlMVoGGgx1ettIqHWoVh5WbjSudFCc/q5HXTLfdCgxB16r1cJ0Rp12qXlrtVpBuWjcer1eLiIQKx9Hx5l3kgvmQctm60TB8lqibe+xuCnR0vqIjYZvA+VNo8962Gw2uRFrNTq8n9eyLAtyZQRU09Uy2jbGfJaREEuebNtTY16kg3Y6akw+dMSURWzqqJaD91hjVpQ3G+3jb+12G8fHxzg/P8dsNsOjR4/w8OHDQICU0Mf0ykbH1dEzPzTs/X4/2J9Wq4XRaISf+ZmfwXe/+118+9vfxre//W0Mh0NUq9UQmfuN3/gN/MZv/Ab+2T/7Zzg/P8fBwQGOj49z8lEiqFExABgOh6GzCQDPnj2LOgDbBuj8SaTu3LmDo6OjXKeI+k/SzzrmuzgrgARE6482n3mORWW1belUd362dUKyrvXDOqJ/YFoacaNdZP1pxNLqHJ0w7ZJGdzVPqms2D7ElJFl2tR+MdnRt3SwWC1xcXCBJEjx79izYZN7XarUCsVE/obbf2n2bdyVGjNZqOThFW32lyhDAtaOUFePxGOPxOCy3UZump8qwc2jbP8lUt9sN09Tp03gUdrvdzpExIk3TQJY4zXwymQQix30SSLhY76pPHFzodrthqcDzwDmUcyim7xzKOZQtu3Mo51DOoZxDKX4SOdTOg1KWEABxI20JFisbQGEDL3NQ9jtHHlmpOqLa7XZDdI7Egr+rEVLFLYocsAGpMsbWn2tFxogZcBXtoaHgd2s0LNI0xWg0ulbmJEnCEcdUPG6ox/RJbjjVm45MDQCn4PK6KhOdGOWXJEkYlVeDxZF1HW1leVivbAg6Cs0ILcvV6XRQr9fRbrfD2mruJTGfz8M6Vm0A1qlagnsTWD3Qhq5kp1q9XB+uU1DX63UgXKoXrVYrrMflZ6bJxk3DzogOZdVsNjGbzYJhYBplnQ+VixrZGJjPWFRPy13WEeC6Y5J0ykE3bgSuph2rg7VTZtW5qD4pVLfUuTNioMRKwanhw+EwjPRPp9PQKWB71wiUJU6aB5Wb5pfleeutt/Dmm2/inXfewR/7Y38Mb7/9dlhvPZ/P8fTpU/zWb/0W/sk/+Sf4x//4H+P3f//38eTJkxBtY8SVnVl1HContot2u41ut4u7d+/i0aNHePz4cZjebPWkXq+j1+vh6OgIBwcHeOutt9Dr9cLeMEosNCJq7ZuSWUbOlJzajg83fcyyLMy+4Eag2kngu61u0vbRFuuyHjp+jezQxmoUlx04rUfqsUZ8tJOq+Se5pP1aLBahrPV6PZAQLQdlRduskUttE+rYdTbGfD7P6Z6eJKZtmO+wUVQ+p7NDGO1kpJJReu3wnp+f49mzZ2HaOfVNyaaSZWK9Xgcbb6Oc2q74DEmTJaiNRgPz+RyDwSDkQSOY7Lxx803WG2U7Go1Cfknm+DwJFcka2652okjKdDnWTeEcyjmUcyjnUM6hrvKgcnMO5RzKOZRzqJ0HpWy0qQgxw1OWESVTvL8oXRIkHolYq9WuTfOmQDRykqbptSnJlhzad/NZmwdVoLL80gCoctNp8hmelmLzQBmyQXDTt1qtFqbVK2lhlGKxWASjQjmwEWk0jdFLRjjSNA1khmRJo7psRJxKrqP0/F3zTVmpnFWW3Fvh5OQET58+xXw+x+3bt3Hnzh3cunUrnPZA0kYSx3JYsv68nYWbwBo+GhcaTAWNgpJ/yo7tiiPu+rw6EUa1eRx2kd6VIUYUbZQkdm+MZBLUKyXIJMnU+6LTlpSAFMk31j6ZT+ZLib+1B5aA8Lla7fL0iV6vF37TCBY/WxnZMlgw3fv37+OrX/0q3nnnHdy5cweHh4dhyczJyQneffdd/OZv/iZ+/dd/Hd///vfx4YcfBofFtkE56jIZlYu2A5UFABwdHYXpy9zsMsuyQMz1SNp+vx/2PlC5qmO1bUxJZJZlYQo0T/jge5RY67Ptdhu3b9/GcDgMelKtVkPHyk4b1/JqO+j1euF9/X4fw+Ew2CqdbcA2p3aZnQzeQ3mx08L3U6/0fpU5ZUTbavdzILQNsFw2KkonTkfPOmFetG0QMR3Xd9CmkPBpBJSdGhIstTkqp8VigTRNg1z0eZadPoHlYieacotFPTXix3Kqfqs8WU4+Q9vPY8iZl0qlEtIcDocYj8e5qfXsYPKdnDWhSzy0Pc/nc4xGo9xJXjeBcyjnUM6hruAc6hLOofJwDuUcyjnUTy6H2mtQahdYQxMzRrH77HVLbvhfRwmpjLyuFcp7NJrA/JBkWINtSZdGkzRf+t2WkSOMWZaFCMJiscD5+XkuP4zUcFRa0+bzvJcGhI5bG7cqPUkm81OvX54CQTJHOWgkkO/RaF6MBFOmfCdlqEbFElutGzU8dBQcxeZUwvF4fG2zTo1MKqHVRm9162XCOjTr3DnqbIkKo4L6XZ02ybbWC+WtI9HtdjukQ53XPGyDvYff2W50KYaW2eo8r5N8TKdTnJ2d4ezsDOfn54FQ2XdZucQ6OdphiLVTvlvzTuPIDgYjjRaUOSOSSiIoA6vT2hbs7wDCDINut4svf/nL+NrXvoYvfelLuHv3bu7EoKdPn+Lx48f47d/+bfze7/0e/vk//+f4wz/8Qzx69ChE7YGr2RCMSB4dHQXnEpOZ1gOn/ydJgqOjI9y9exfT6TS3LID6s1gswukmGs3T8mokh+vPte6S5HLd+mQyCeSPR3GzUxbrZNPBaTvghpDsFFpboh04EjIercs21+v1ctO+bfSa9rHT6eDo6CjklbrPzjqjPhqtpN3RTaCZLu0qSUtMp1UO9E+xaeGUAaOUlLfqpHa6lAQpadOOb5JcRYn5XwkR/YzaMTsYAFwtM9CIpHYQWWeM+MZII0E9W6/XGI1Gufwzvfl8notWs/PE5+bzediM1NrDNL3cp4cnuMXqg7aD9kLbF8lcp9MJ0/CfB86hnEM5h3IOZWXhHMo5lHMo51DOoT6pw+xVeSKHw+FwOBwOh8PhcDgcDofjE7z6ObsOh8PhcDgcDofD4XA4HI6fePiglMPhcDgcDofD4XA4HA6H45XDB6UcDofD4XA4HA6Hw+FwOByvHD4o5XA4HA6Hw+FwOBwOh8PheOXwQSmHw+FwOBwOh8PhcDgcDscrhw9KORwOh8PhcDgcDofD4XA4Xjl8UMrhcDgcDofD4XA4HA6Hw/HK4YNSDofD4XA4HA6Hw+FwOByOVw4flHI4HA6Hw+FwOBwOh8PhcLxy+KCUw+FwOBwOh8PhcDgcDofjlcMHpRwOh8PhcDgcDofD4XA4HK8cPijlcDgcDofD4XA4HA6Hw+F45fBBKYfD4XA4HA6Hw+FwOBwOxyuHD0o5HA6Hw+FwOBwOh8PhcDheOXxQyuFwOBwOh8PhcDgcDofD8crhg1IOh8PhcDgcDofD4XA4HI5XDh+UcjgcDofD4XA4HA6Hw+FwvHL4oJTD4XA4HA6Hw+FwOBwOh+OVwwelHA6Hw+FwOBwOh8PhcDgcrxw+KOVwOBwOh8PhcDgcDofD4Xjl8EEph8PhcDgcDofD4XA4HA7HK4cPSjkcDofD4XA4HA6Hw+FwOF45fFDK4XA4HA6Hw+FwOBwOh8PxyuGDUg6Hw+FwOBwOh8PhcDgcjlcOH5RyOBwOh8PhcDgcDofD4XC8cviglMPhcDgcDofD4XA4HA6H45XDB6UcDofD4XA4HA6Hw+FwOByvHD4o5XA4HA6Hw+FwOBwOh8PheOXwQSmHw+FwOBwOh8PhcDgcDscrR23XG6vV6k73JUny3PckSZL702eSJEGlUgn/Y/fH0t8lX/tgn/SyLAMApGma+67XsywL3/lblmVI0zQ8F0tz1+v75JP/WUbKm6CMed3KoqweLLbdY/NU9LumV6lUUK1WQ9rr9Ton4yRJgmyzLMNms8l9t+UvKgvvZZp6X1G+y8rLPOhz++iZbSuabuz78+jKZx0vur3vgtdFnmw//LN6zfay2WyQpik2m03uebUP2hatD7H2rayNl7U/AFEbae/d5VrZdcfnE/v4I30mhtVqdeN8NJvNa9eKdI3twnXx5aFerwe7ZO2btWvKK/R+PlOkL5ar7oKitLbpQszPxzhFEe+MlUN1sKgdKQ+yae3D1WN8S3+P8c3YvTEebVEkY5XVLj4jxhtj77Z50nftAivDsvxZuRfVV9F79H/RtV0QK3PZvVZf9XrMFpaVIdbubFrPa1tjMla9tteL8hr7r78XtTn9r9hWPvtskZ3Q33bVbfsOh8Nim27sPCi1C3YZbIqhzLiWOaCYsyxydPvmddv9+w4axAxB7B27NuaiTlrMMe+DmBEqGqSJXdffbT5j13bJZywv+wxWbSMYNMYx8lakS9aJqiOyaem7dx2E0+/PO8CybZDqdcTLLuO+Ovx5guo0gDC4tM/zlgAWPV/Ujrd1lixi5Lssf/tc/zR+c7w47NMBeZmD2ev1eqf7tvk2x4tB2YBBUUeQA1YWRZ1K2r2Y/dtH18r4S4yj2A5njA9Z3sPniwZ5sixDtVqN8syizmtRWW7aziyX4juLuHOZrS/rj2wbUCnjobvKYF+bVBT0jKVF/81A0C4csEwelO8+fZSbYBu/3/XdltvH5LVLWrsMFsVkq/UUk1msHmPvsXnYpS+xaxmtTF4kivqnDsc2vNBBqTLcxAntMjpc1uDtvfrbvg5h13R2LeeLGIB6njT3fVcs7ZsQkF0Na1Ge1DkW5cEShCJ9sI72JvIrG/yKGfsyMnATArUNRQ6zqA7cceyGnwQ5FZGbfTsgZfeXXY+9qyzIsI1ob8tvWfsvs8FlbbOsbe9qb34SdO2zgn3q/0Wl/bz3Om6GmC/cxk32rRcODuggv33/Pvktu15UlqIOfuzaNpt2Uy4S41n7yCPG0WLcMlaWm+b9VbfBMh8R8yPWN8Z0dx9ea/Xo0xhYiL17W3+vCNu49z59tdj9LyOAsWsdvuh3b5NrUT/b/ZTjReOVDUrti6IRaItdr90U2zo62zokuzb0IkdQJIOyQbEyo14GnRnB5/TPplnk9IvKYSMLtvz7RCNseWP5sk67bEaUft5nWrW+F8C1pUz7PHtTFJG7IrnZetIo7r5LRvQ9+wxWOD4/yLIs6DU/71LXtt2VkdyYXWNb3EWfyiKM+zyzz+9ls8e2Pbutg7nvbzd91tvqbngRcnJZf7awzdfH2nfM7sUGTHYZcCnr/N8EL0u/du187mLz7KBS7D3KD5XDFC1L4j03WSJZxLfL9KKIX9oy7TMQtMu71I9uG6ix+dilL1U0CLLvgMi+gWa+k2XU/ofVlzI/VpTmTfOo91qeu28a+7wr9r2sD1Q02LZNt2J2qyg/7rscrwovbFBql2jxrr8Xdait89Hfy9ZQ74ttA0nbHNqug1Z6zy7GMzYwVER+ikbcd0XZ4FhRPVhCUZRuTD6xOk2S5Np+NNsMcGyASgeMtKOrneZ9OtmfZ6hjLWpjwP5l3TZ4avEqZbkLIfu8Y1ebt2/ZYyTbDlbfFGWkft/0izo4z4NtHcYX2aFkemXYxY/u6ke2Xd8Xr2Obcrz+oI3ZZVA7SZLAFbb5UMuDdu1s7pKPXTrFZYMUZenG8lPG62zHtizwYO+JBSI0H7H8xgYEXyQ0/V1k9qqgevoibO22/tq2vszzyqGsf7DPs7G28LLqaFvfxmKbXdlVtkX1UWQHOHhZqVSiurLPQF1ZW9tHD50fOHbFC58pdZNRcgsOLNVq17OnA092UEr/gPJZVGURjl2wz8BU2ft26XjFDNE+Dqoo4rENRYSJ12MbF+t7YgZNBxz1mt1QmdcbjQYqlQoajQaA3TeHtLN/uHFpmqbXNj7nYNVqtbq28XJRB/zzbGTtwFTs911IrabFP1s/Zc70RcuwyJkXvft5B1WeF2WysZ/tM2XXywYWdyUWHLyN3b9tY/IixOwQ299N0tvlXc97zz64CdF+kb9te39ZXRdh32de5Dscn23s20n5LAQo7HvL2gz/NGBG/sA82yCo2riY7+F33WuqiBPZvMTeYcuj//k5NrNV+WMZp9Ly67XY9zJ+GeMTdkZMUbn5F5uFvq+/3MZrivjeNltc9O4XpdtF/pi/7YqiuuNnq/N6z7bBkaJ87wKrm3ags4h3WN/2Mgakdk1z176vtSkxuZa1f8LqvsqC8orZuKKDtfTZsjLFBpbL8uZw7IPPxPI9VXBttEWDT7EBjGq1ikqlglqtFh3MKprau81pWYddRGRihMAObig54HW9Z5dTJ6yj22YcYs/EYMulpMESKNZNrVa75sBs/SgJ09NrLOy7KAsOSrFOVS4xOTHflUolt1wvTVOsVitsNhtUq9UwQMX7N5sN1ut1dMDm82xgY/oaG5AquqcoTftdCVPMsb5sqK5ZO2J/A3Bt8HGX9F9mnvWaJWX75KGoTssGFfchFjcdUCwjQTdJ77MIW4bntR9ldR8j4ds6q8/zfot9B8DKfOauHZwyvA7685OK5+1ov2hsGyDRGQixQaUkuQqyFQ3obBuUKsqTDpjY+2P8ks/a0xzVD26zU+ojnscP2oGGonti74/95+ei33fp8Nt3UEa7nubKe8tO0H5ebh57bxmsXhTJLfZMmcyK0i3jD/uWzw5EWd2L9VNi5XlZKJKb/d32Xy0ftX3YXTnztkFVvWYHpWK6ScT4uz4TqweH42XglQ9KxRq1ftZBDz2yl79Xq9UwGKKDG/V6HbVaDa1WC81mM6Sry79iA112YIUN+Wg6xf3JBB93u3jcaOQGPpgOGzZn3uhMHADh+nK5RJZlWCwWucEpncFjHaF2mmMDU0Bxp2UXg7FLh1PrgQNK/F6tVtFqtcKAEe9tNBrodDqo1+uhHpIkCfXTbDZRr9evLZmz+1hRlqyfW/M57pyf46NOB09brZzMOaDEz8wny7FcLpGmKRaLBdbrNebzeW7G1HK5DGlQ3rvIbBvh+CyjjPzGdEn/J0mC+2mKrwL4gzTFR8nVRq5sk/YoXv3/ohEjUto5iA1ic1Zc7DQlzWfs9KRt7a8IVl9iS1a1TRRFRmPvfDPL8FUAPwTwUQnptvnZ9luZndgVSpL4vtdpMKoIN7XDN8W2Du2u119kWvb6PgPdRdhnEPV5nnEUo6hjXzawcZOO46uon1jeijqJ2qEEroKh9DO1Wu3aZ+2MxgalCJ11pbNH7YAUeY7en2VZ4Jk6o4j8RnmpplPW2dzXvxXdt23QougelRtQfpIhr+szunLCDgrE8hoLZNp3xQLH5I7K2TUNyzGsH9w2uKPvK8O2vpV+Lhqoim2DUjRgtS1f24J+lgfoYKsdfNK8lPWFXuTgSUxmRf1Vm0ftL2nfkzaBfahY/mL9D9s32WXFSNGg1Gq1yvVngaulfuSclhuW1WHZ7w7Hvnilg1K7OH5t6DoIwmsceLKDU7VaLQyE8H41rjpwAlwnEzoo8qd+//fxP/+1X0Mly5AmCf7vf/pP4//zzjvhdxoU5puNm86H39nxnc/nWK/XGI/HWK/XYXBqvV6H7/zMxq2DLWVEociZ23tvUldaD9VqNcxaajabqFaraLfbOQNbq9XQaDTQ7XbRaDTCoFWSXC7DazQaaLfbaDab1/LGuuQfjW6tVsO3f/M38T/5B/8g1Md//2/+m/iNb30rN7BEWavxppFdLpdYr9eYTCbYbDaYTCZYr9dh5tR8PsdqtQJwabBJ7iypsPXweTHI1sGXoYzYEP/LNMUvZRmqADYA/gMAv1wQMbb5iP3f97d9UJQXe08RAd9WJpuOfV6hxKvo/tjv25CrjyzD/6pSwd+LDKLF8lok55c5cLTPAIRjd1g5brNPuwxK7pJWmR/ap253vbdsAGwbebbvK2urjjhuMrhU9PwuHe1XVRdl7UADZxroVO5JjkQexFn7dhALKJ8ppR1GO8BEjkOeo6jVarnlhTb4qf5HO6BFHfnnkX2sY6u/6aCR9beUJYOhVlYqT5WfDgDGBqM0HTtgolxe/yw04Mz71+t1mG2v9cVBv21bGryoQRRNfxe+EstP7E9XOViZ6nNAvj5s2fR3vcb7OLDKALPqrPJ75eKxdhKT4z5y3dZPtYNPOpBH2IFptQ+xQamYrqntsRMXyrgZ74n1HbVfRDnxTydmaP0V+f5XaZsdPzlIsh21KrbkKpfQlg5ZUcco1tDpzDudThiE0kGnTqcTBj042EEjwMZOcLBDZ/vQgXHAS5f8dU9P8Rf+xt9ARcSSJgn+b3/tr2E8HCJJkjCwwjxpR1JJAMnDaDTCbDbD48ePsVgsMJlMsFqtsFgsMJ/PcX5+jvl8jvl8nou8cMCKgyRMP/Y/5LXEaNl8ltUlB5JU/qyPdrud+8/fm80mms0m+v1+mDHFemk2m2i1Wuj3+2i329f2AONAo9YT6+Nf+HN/DolGqCoV/NN/8A8wPTq6NlNqvV5juVwG+a7Xa8xmM6zXa1xcXGA+n+Ps7Ayr1SoMRo3HYywWCzx+/BjL5TIMXjHNxWJxbYDQEryXjV0GQ2Lfdxlk2pa+/v5mluEPlkuoNVgD+Fq9jo8KiAyf10iYQkmy/i/64zOaN0vA+N12CgidKVXWkbWRQ0vg7P1lZLBoQ/0ip78LsXozy/D783lpfQCIEkh+tx0V+9m+e199j8nEtiPH64F97cuududFpqko073Ybwxe3ATPO5jzWcFNyrGtg7zv4OWL6rzrNgSxTjI/qz8hZ6zX64F/kos2m83wnZxHZ+ra2T52qwPOWFD+QTtMTkh+Y+30YrHIzYzSGeKbzSZ8V3662WyCThcFKMrqYRd+EevMkoPrLBL+Rj5pZ+KTj3a73RyX19+V29sBP82fykDzRz5AWelAgJ1Vz+eVS6p9UP6og4isN757V99aFCiwci0aMIrxI8tn7CwzHVjRrTT4Z7flsJMI9L+uSonlNcsyTCYTTCaToOOsJwb2LRdnYJmBfiurstlaZfKMyapoOZ7Kgc9RPrQRah/q9XqwITaIXpRPO8Bsy2AH6VSHi7YxsWnyfuvjLC8sel+ZbB0OYptuvJKZUmUjz9rYadTofGKDHo1GA/1+H81mE+12G41GIzR8HWCyI/Mc7NDZVXwXDSkA3H38ODcgBQCVLMPbyyUeHR8jy7JgWGiMihwLnRLLslwusVgsUK1WwzKyer2O9XodOu9qIIBLEqyRHabPzmSRbJmHohFva3RjU5w58BYbjKrX6+h2u9fqqdFooNfrhVlROihFkkaiVjYoxXz0f//3cwNSAJCkKQaPHwNf+MI1kkVjzBlPq9UKnU4Hq9UKtVoNy+US1WoVi8UCi8UCq9UKjUYjDApS7jrDivWgDi7WcX/VeJ4OXdnvZc+9k2Www9M1AO8AeCCOWfd4U/LDiIwlI8D16e5WxjoTUZ+LtQNCo8GKIgJQZpssISlqe7pvWpJcRUp1yWkMu7ZlxVc3m53rQ/9r21OZxKLr9loRQdmlI6Nlc+Ly+qGI6BfVdWxwtiw9vbfsN8fz43n9y773lT0Xq+/ntR9q62knbeCEv5HDsKNJ7tjpdNDr9dDpdNBqtQIv7PV6aLVa6Ha7YbsCRZE/Iddjp1E7lkWDUrTN8/k8zPzWwRB26OfzOQDkeJJd0mf/LM8sCnrawTZt97EgDH2RLnfkM5Qj+T15NnnmYDAInF2XRJHLx2ZKaZ4AXDv0hnmlr+Mgh/6t12tMp9MgSx3Q43Pq29k3YB52sY272LUi3h/7s/0d/jWbzcC77cCf9qfIybW/w+uxpatJkoTBwdggl8rC9pnSNMXp6SkuLi4wGo2C3q7XazQajdAX4jXKPEmSXHvQNGMyfBF2Q8tEuWibpszYZ+12u2i326HvRF3VwGisjRB2gFTLodf1OwefbLCXQXrdTkZX7FBvNS3dc1ftgsPxovHCBqXKOoZE0UAIGzWdPp0SBz24XIwDGsPhMOfwdcYUndiug1JKMACgUa8jq1SuzczpfOtbuHfrFrIsuzaNVQ2C7YCnaYputxv2MVoul2GQZDqdhlk5fL/ubQRcDoYocSiSp60D/W3bIJY6FTWuJAG9Xi9XL9wzqt/vh8iVDkqxXjgjioSBA1U6KMX/rGPmIeCdd6L1Ufna19Dr9aJOiGSL/2mc+/0+FosF2u12blBqPB5jNpshTVNMp9MwUDifz7FYLIIeqfMgUXwVHSDbbna572Xl4Q+rVWxWq2szc94z9alEUQegAITfdCaVEkIl4pYo6uBjbEYVoQNdMdnEntO2Exu8iU3VtkSRpEyXBGu5YichbZN5Gd5LEmyAwvpQsqn2jiRTCaSSdO0Y0SZxYNbWjx083GWwwA6SO15P7FO3L0oPbjLYfpNnPst4kfned/CpiIPEsM1WFA1y2P2Qbgrlgkyfdoy/cYbDwcFBbpZIrVbD4eEhbt++jW63i263G3zD4eEhOp0OhsMhms1mmO2eZRk6z57h4PFjjN54A/Nbt0I+dFCK9lZ9ng5K6WAK880Z3xw4YSdzOp1itVphMpnkbDw5kHY8Y7Y8NmgS60DrIIjmyw7W6KCULnNk+TnAx+Az/SmDncPhMHTsdXUEB1qq1Sp6Z2c4fPoU53fuYHp0dK3e7X6ihOUadpbU2dkZFotF2IqDQU5N17YXHfBUGdwUMT5oZyXxP2VEfs3BpoODAzSbzSBLHTzpdDo4ODgInN0OStVqNXRPT3Hw+DGmb76J9Rtv5Dif3T6F+dQ2pnxC/z7++GM8fvwYJycnGI1GYaUIP1erVSyXy9yAKushtuSyqN/zPLLXsukSXV1uqv2dRqOBo6Oj3OA1ZWk5LvNZrVZxOJngzvk5nh4e4qzXC7IrGrBi/nTmpA3wchuZ5XIZ7AT7mNPpFMvlEuPxOKRnB8ctX/ZBKseLxqd6+h4bts6m4cwbNmgOSrED1el00Ol00G63w95FJA06e0qNtY7u2ymYdvpw9tZb+MFf/sv46n/+nyNJU2SVCv7wr/wVVL/4RbQ/MXg2AqNRpdigVJZdzg7p9XrB2a1Wq3DfdDoFcDWYUq1Wg4Fl3mxkqkymscErOzClBtAaVxpMDjJxuSQdHEf7B4NBGHzSfRT4n/XKDj7LTXkoiVmtVrloC//mvR7e/4/+I3zxP/lPQn18+Ff/Ki4GAySzWSif/rcbS2pd8x28hwOW6/U6l3fgcvN8bpxOPWGdWsfwKhB7X1ln6nkGs4ru/yhJ8L+p1/F/Wq1Qw+UAyP+20cDHJmqk+4zZGTmUM+8nSDIodyW07IToPg06OAUUL3eLDUwVzXBTwmEHr3WAXAdvCdVptmGWS5dRUP9im6NqvreR2CRJ8HGlgv9dq4W/M59fqw+1r0rgtTytVivcp1O6dVYaSYsSQTv9u2gwSttyrAxOaBwvGjeJkO8yi+GziFcRiNj1XWWDUkTZIFSsDmJpqs15UdD0lXfpoJQuyWNH/vDwEIeHh+j1eiE4V6vVcHBwgHa7HTr3zOub//Af4ht/5+8ELvP9v/SX8PBf/9dzfkdnLzCgpjaZs0QsTk5OwvYEy+UyzIg4Pz/HarUKfpWDW/RTfIdugExfW4ayzr6Vp36nX1W+qYEqBp/7/T5arVYIgvZ6vTA4SPnrMj9yiq/8j/8j/th/9V8hyTJkSYJ/9hf/It7/M38mlz87KKWd8CzLcltpsKyLxQKVSgXz+Tw3OKKDEfoMyxrb55Qc2AY2dw3W2HZhZyLpoBQDyZRVs9nE0dEROp0Obt++HR2UGg6HuSC/8rg3/+E/xNd/8ReDDr/7C7+AJ//Gv3GNF9ly6IAm69sOhPZ6Pcxms9BPon5wRlqj0chxJu7LpH2kmKz28QlFvNnqMOWs3JDlZ/90MBiEPw5cq07b9kPZ/fSv/Rr+xC//Miqf6PD/99/9d/Huz//8tTan/Fh1OE1TzGazsKRUA40nJydYLpe4uLjIbXNCPVKbYwc9Y3zN+ZvjReIzMyily8I4M4f/dSpkp9MJ+xL1er3wG2fgcJqkdio1YmCNQKwje/Fv/9v47X/5X0b7o4+w+uIXsbp7F13kjWssDW3AvE6Cw2nHy+USlUoFi8UiGPrZbBaclxoJDlDpiPSLlD3/U/52c07OeOLgE3/rdruBkFH2OgClHV8dkNhsNph9MpBkZ6LY9eka8Tn/+Z/Hg299C+2PPsL8C1/A6u5dJOPxtXT4X2dxsD7ouHQfMP7WaDTCbDX+JyljmXWwkHX7Kgeltg2ClQ0sWUJPxKIuu6T/92s1/KNqFT+VpvjDSiU3AEJ56SCItkMA15baMn27DJPkjgSSDhPATgMiLGPst6JBXiUbGv3T/dL0ZEmNstu9Q5Tg6jIK6tNsNovuVRZr66pvlij9N80m/lG1ii9vNng3SXKn77Ft2+WxukSabU6j5dqGOKOQ+VdbFXtmF7Kn15zQOF429tWxz4NO7usPnvcd+wxKxQa0YrZhW/uPDWhrR/5568l2vNQn6sA9uaUOlnS7XQyHQwyHw9DpZFCU93B/zWq1isbjx/ipTwakACBJU/z0L/4iev/Wv4X0/v1rHXn1hQqdycV8A8CDBw8wHo/x8OFDzGazMPOBwVAG39hRZfm4HJBp8736XWVV1vlX/6fXlCNrcJB8TGef9Xo9tNttDIfD0LHnYBS3h9D+ge5d2j45wR/7u38XCd+XZfgXfumXkPxr/xpWd++GvHJgLzZ7mQEkO3uMnH02m4WBksViEb5rMJmnI5KPcvCPv5HPUK5WJ4sQ43TajyIPpl6QtwwGgyC7ZrOJu3fvotvt4gtf+EJuL7ROpxP0moNSusKk9vAhfuqTASnq8Ff+s/8M1T/7Z5HeuXNNh+3AHuXLYK8GFQEEnaU+1mq1wD+SJMFkMslNAqjVarlBKds2iga7i/pyRbYr9qcTHjghgnrd7/cxHA5x9+5d3Lp1K2c72u02Dg8PQ9Bf661er6P+6BG+8u/9ezkd/pN/7+/hjT//5y/7PkmSKy/lpYPWnBHF2ZC6B1qn0wkrQrhiR+XNtHS53svqhzocFi9sUKqIsMSMKKEdPw4u6WZw7EjpOmcaWTuKH5uCrcZZO7a7YHnnDtZvvJGbmltWFv3OBqyOLkmSMMiTZVmI8iRJgul0Gjp6jLyEfJiNFe0Jf89jIIocm0Y8lCzYteR8XqegUtYceNP7Yk7DRld0kIl5C1Gge/dQyTIkjx7lr8v9upePdvRpvDUiQKfIsjUajbB+nUY/yy7XYMf2gFDZfZqGep8OyDZSueu7Pk4uZ+nwWsxRcwDTDk5pG9fp/tRt1XmSQTpa6hfbC5DvpLxIkPQwCsYNK2mbtL3ojEvKgvnXmXhpmoZBKV1KYXWV5dK8kHzFCNeDahUf8bt0rCh7ThmnbtOm6qaxRYNS9Xody+USSZIEwqJRYSWeDofjsw/LYewgfdFg1C4DYTF+VDagweuxtGPBg318rc1DLG1ru9RPcfYIO5AcjBoMBmFJDmdJMVDK/+yoklu0f/d3r++Pudng4MkTrH/6p3OyoE21s4GB65sL8xm7tFC5JnC5VxN9J205+Q7lQFnYATo7kKify2Ss8lSOyY68zkCjTxoOh+h0Ojg+Pkav1wsDe1y+x2A0B6M04NL//veje5DeOjvD/Otfz8nQzpTSP7tVAINLWZaFlQ0M1NCnJkkSZpxxEIXXKBud0aK8yba7sqCObSe2bihv7VtRdxm853/d9iTG7fUdSZKg8f77UfnW338f83v3rrVTLbPOFIvNdCeY51arFWbGt9ttAAh1rvxPB15UHsqjYgNTMVhub6/HBqbY3pQTcv8oDk4xkM//g8Eg1IPyxlqthmbMTqQpeg8fYvr22zn5cnBPZcg+EPkm+72Upe4ZlmXZ1dY1n8xCI1dn/dFm6Gy0Iv+h8vLBK8dNsPOg1K4d1th9RWRFBz74X6eS6ql7dlBK15rroJQOTFnjpIakqEzqJHRAqawcLIv+aRr8ncsNaVB1phQ724y+EDS2nOpqj0ZlWZmf2GdbvpgT00iLrh1X2ceW5AFXpI75sQNFSXI1C0aXYuk9+ox91joB63jtAJUOdOkgJN/PdGm8Oa2dRpnTs2n07RI0JWufF2wbPLNl2bVsdnBO27Td9416w8gzyRDB6KWeZMPvbBucLcXlni8TJAl6ego7GSyPdgKsDdJp1HZQarlcYj6fI0mSsF5fYcmaJUvWBnFgTp+xhEnJJ20pBw01ymv/GK1MkuTaSU4k3Z+ntuBwOK44i3Idi7IBiKJ7izrPRSgasIpxMHu9DLH8xga3YnaWs0xarVYYBNFBqeFwiDfeeCP4Mp7IPBwOw94xuhdPp9NB/dvfvr4/ZrWKzre+hazfz+WLXMVuaE5OooFAPqM8jYEQAGGJNvcyZTnpR+mD7cyIfYOeVrYx7qY8385Ao2/VpWUc8GMAhSsp1Afrhty1b3wjKuP2H/kjaB8ehrxQfnb5HstM/6YDJ9wofjqdhmXt8/k8LOmnDMkfCa0j6tvL4pA6UMIZ3uQtXGlCWXMbFJ3ZHltNolh96UvRPV6Xb799rT+iA1Aa5CoalOJzzLdOJGBQjH2larUagpScvcZZaVbOmvY22fG/tXt6XX+3g6zKcTl4fXx8nNuPt9FoYDAYhK1obMC7WmAnal//emirKjduB2HLyEEn8jP2j7gihLPMqJ/s/3AZop3xZ8tOmcTss8NxU7z0mVL6uyUr2qnTzimVWqdPB6cjMyt0sGk2m2E2m+H8/Dz3vqLRbtvIdGBDjbo90UMHPeysBR3U0RFkdq71Pnb21ElzKRN/s0ucmJ5Oqd51hkLZgJWWXQekdC8FGlWSNd2ji3m2A0+WSJFkkSxZ4x6rNzuASNkW1YVOWab8dOYagFz0i++gfm02m+AQNRLB98Yc02cVluzHCHnst1gHoaisscEs1gtlqtE4EiT+cRlckiSBhHADRg7mcKbOZDIBAMxmsxAZBnAjAm3LrN+VQFP/O51OTq840zE2KEXo3h0AcstAiyKSZfnjtaJy6nVtz0UzvRix06i6EkcOgM9mM8znc9RqtXBgA0kL/7P+gN1tksPh+PSwbXBnX/+2a+fE3qMdmyKbHJuBsSti5Yj5N11WpltCcEkeO/HkQxogtVzDvitNU2zu3UP6S7+Eyl/8i0g2G2TVKrJf+iVkb76Zs7scdNLBfzuDVTv5QP60YNp+8kzadvJonTlBvqP+lPJV225lWTTwGAuUKnfXVQ9cKqZLH7ktBAeleJ2+mNtK2JkprLfK22/j4m/9LQx+4ReCjCd/+2+j9qUv5QaKVN4MrNiyA8j5OS0HVzMwmKm6oJwXuDq5OdYHeh5oesq1tO/AUyC5sTk3j2eQyg44kKNPJpNc3yOsQGg0kP2Vv4Kf+pt/M+wp9cP/8D/Eo0oFePw4lz87OBT7rzIHkNsiQOXM2X7tdjvwM131QB6pM89Zj88zSFJkO7R/xz9dicNBN54CTh1l+1wul2g0Gtd0I0kSrG7dQvqLv4jOX/pLQYdP/9P/FKPBAMvxOJSPZeU71G5k2dUKA7u6hrxQD5xiPhgw5SqXRuPyZHLKkf03ylT3eNV6LLLnDsc2vJBBqW0d1iKnxoZKQ2qnB9KJ2SUnVHYdmGHUh7MsbIdPpyXz3bHBDR2MYmcuODxZlqSdSr6HAzU0Ttpw7bRdjRrZgTl2xPV329hjAyN20Ck2+KT3FA3KaX4YXbH1oLOHdJBQT33Rdc66WfJyuQx5KuqY20Ep/rfL/OygVLVaDREIOl7KUuWUpmkYzFBio0vNYrOkYrJ7lbB1uA1lzsHqlL1u3xlrz/ZZ/s4/jYryPzd8ZB2xDulAm83m/7+9Nw2SbbvKA9fJzMqqzJpv1R2fnoanAQGSkEASQkMjgcHdYEy7aYcbkJDAEpIwCHcb2+HGETQdbjsMuN24GUzbQgwC2U3YEWCgG4EbZEkGxGRABkt6T09vuPe+O9WYUw2Zp3/U/XZ958u1zzlZ073vvbMiMrIq8+Q5e6+99xq+tfbatre3F0AcjOv09HRQnGxEY73H+h7ji/ZV1wJndSFKCweEXwxKKUCGfqVpmplvR5076pCxccSkADsDzHAIuEaWBgW4IC5+myQHqd+8tcHMwjgc1wCsqKKKzpaOE3zIA8fz7uPJL/4df68ZpNzmkwCmFDyBs8ZZDQClGIzSQAv0gOohdlrTNLXhO95h6Vd/tSWPPGLpC19o9pznBHnLwTvIXmSmsmPJABU/A/YW23LoU5qmwX7jMguwNWHr4H4c+PT4qAALAxA6NmzL8+FFs7OzYXsTMqJwunar1QoZU1xAGlujcA/uK4Kmw3e8wza+6qus/uijlr7whZY8+KBN3dXPIPBRA8gACHAtdDV4xjszAPhBR+KF8cI4AYzweJg3X/Pmt/If7eKgGeYwn2SILDPeaQIegi/IzmM7m2n9TW+yx176UmtdvWrdy5etv7Jitr4evo8By5rd5xHPYQWlkiQJ4BTGBvMNWT5or2ajTWJrxcbHk2sMsjEpKIU1hrUNnxVrU+87+Kt/1bZf/3qrffaztvPgg9ZfWbH9bjeclskAFJ7BgLbZYS1WlheY8wBTFcCu1WohAAnwDMRzWGUn+lyBURUdl04ElIoZIXnGiZmNKRQ+vU2LITMIhAUB1BnR/H6/b1tbW2HPt9khIAQE3svgYZSbnThOa2XwCIoSxguu4TRjVnb8rgKMecACggEXvu40wBBtA0e0OG2diyTjOhDahjRnPpoYxhW2LLGgY/KMVc2gAl+5mDpHsABkAKRE9ApCNgaAsWKHQdJsNkOU46zBpzzyFGPR90XRZeb3JECOZgtq9pB+z89TMBSgBo9HnmFchh95fY59h2fDqMMRyQDGOPOIHRTMQaxxFOiEck+SJHNyEjsDmtrutc9bH2xQs7xVmQXKy+7EfbgIqd6L1xpnVh4HaKuooorOliZ1GtjZKANI3U9OSQxAAXFwFDIfDjwXK+dMKTjLnNEEh7DX62VsN08+jmZnLb1+3YZPPpnJemBHUg+TAKm9iHtyoWMNrqTpQaYU7gdwan9/P2SGw0HmbTuqs1k/xwJVzHPYaLDDeKseAlMcoNI6sZylroFg1TcZAPDKFRtduXLwBdVBZGBESwN4tZ/4nU8yQ+AScwAFpHGQEdsQfPou84XfuQ9560z7jjmgJxZijp47d85arZatrKxkbGGURgGxzYEtngp8Mu9Go5GlKyuW7uxYevXqmM3FdpuWWUBgTv0dtEPBFdgZAFa5jRgfgFIAcY8LSHmfe3MdaxcF79EXrC0AQDq/b926lQFpkQGG/odT2mdmbHTjhu09+WQADL3xYL+WfSXmL48LssvYpsPn6AfGCc/b3d3NrAn2KTjb637UARU9fejUT99TNBXEQlW3sXE2EgNSnvDCwgDgsb29bZubm5nPh8Ph2PGX+D2KF/LpAwy8cKYU2jY/P5853hORtMXFRZuZmQknXUDh8D3BE3UwPXBKBSquKQL7JiU1YNiZ1awQjkhxdAX34bHodrsBxe/3+7a7u2u9Xi/szTezzD1YkWnEkQEzBqM4HZxrFHEWXZqmGUdaDQPlg2ZQPd2dbcw3D+ABxZSu3iN2f13PDIbkzWd+pq6BPL4fd0y8vmg/ML+QKcVGMkfO2QicmpoKc3lqaioYu/V6PRjGMKwYuPN4HGujbqnga721HFLvyUjjLYQKCJpZppg5ZxEyGAXQ8CjGX0UVVXQydNR1VxTMyPtdnvyJ2XzeM8+SVPewg8aBUa4TxfYdF4eGTFXnEIAQSIMNmr2A/9nhw/dcU4rlrNb5ZBCE7UzVqXA8kfHAJ/CxA8uAlGe3lwk2s57hLeTYts+F4bnIOZeFYDsAwBTbhaortbapBtEAFnC2CkoEAGyKbS3j7DVk5OBZDLohm5uD3L1eL+hLrqeax98ifqutxHYKeAxfBQW3OTjLWUjoB9vM4I9m2fB3Hp/0b7PsCdlou/oZ7Ntoe9gm16w+vjfvtvFsTY+8QB//7dmrOnYoyYBaYmxDgceQHejv2tpaBhxEGQXwttvtZuYlb0tUO15tQvAC98b2Vx4XyBHepYD7IxuNATA+NRo8jtl+FSBV0XGoNChVlGXhoaNFDiVH6fXFKaicmcNgAqO1WMys9LmGEbaNIc1RX6zQsEiRyqhgUafTGTthpdlsWq/Xs1arFY7f5cyier1u8/PzGRAGvIIxwMpY+aSCib/PGwt8r4aiN55sAHBGRF7bYu3AM1iAog/eMcfaN+9e7EzrFsNWqxXGkIEkGAGxCALuzWOgn8dAg/uV1BjT9xjlKXE2FpjYeG00DmsYLC8vB8XIdQ40uwigL/iMaFCSJEEp8rY9NsLzwK6TGCeeb3ipoc+AOYNSPPfxGQwURM84IgwnSdesylM2PrzxYmOZ69ixowVDBcVOIWvZAGcjnA1xbXMFSFVU0b2l4wSpPDBJybMZYsBTmft513s66rR1LWQ49BEyolB/Z2lpyWZmZuzcuXMhgwpb0CHDzQ4DX2yHcqFsgE4ogh0yTQis0mwHb6sT5C4fusEH0GhZBbMseKGkwIZ+pzZeGftBbXvoeegfZB7jlEJknzEwBd2C7GK24czM1tbWzCy7HUx5xrxV8ATjgnHi76E/sWUQDrra6wBFONCsp+5BV2LHAE7ZVuDEW18xfuP34K/W6uIasHyyofow6BPai/91ZwOfhAxfClk84Jm+g9BHbL3DIU+wlfhES80sBPE8UltP7SP253islLf8P797/owHSnE7MN+wrQ6f41RGAJ+6VnX8FhYWAnCEnT/wX9mWU1mv4+ptL+X5invg2Qz8cXYXgDa2u9n2js3NmN9ZUUVl6US37+mCiRlLvLh5gSro4EVx2BHFM1S567vnYOnvWKBqNAOkjvPe3p7NzMzYzs5OEJrYP8wCCIg5F2b0wCXuG/exyOBU0EXHhK/RMdAXA4XsjKvwm8QRZfCQo4SsEDh7TQ1TvDwgE+MMniE7hecLTmpDwU/+ziybespZIzwG2pang7AtC0bp9Xkgo6fc2QCAIYTojG71YtCZAQ4eC6Rqe5mSOgd5LubN9zJUJKu89aLtwt8M4vA6grL3jEQeA4/fMUcQvGdjV8eFM1A5q5BrUGiEV41rHgusRc0MqKiiip5+lAds5YFVMefkKPpxUn01CaluwDtnjcBBBkjCwD1vtYEchd0Chw6Z93t7e5kMHM7I6Xa7GVCKAwH6GbedHU5kXcCWguOJPnJ9QL5HjC+x74sAKc/mZxtKM2w5o51P0Nb6qdCH4C3sM97ahEAzbHEOQHPdHQar+J0z0Nh2Rlva7bY1Gg2bnZ3N9JN3DQDA0r5p4NXL0vf8C35O3hpgW119Jbx7/NVnwjcyO8w0450Ng8EggKj4nGvG8m89OwU2NGxBPuEP2eJpmga7HDxiGwRjD8AG9kfMBvfWDuYkX8M89uYwv/N9vHHTrXNIauD5C5CK7SfMjXq9bt1uN8wnMwsHynCShbbDLFtagUuZcHCdt/MyoR0MyGpGfCwhQfnDdFw7vKJnN53Z9j2z7GRWRcvRdxgKiqQzIIH7YMFiX/zMzEzGGGDBoKCUZlZ5Rgv/DQGJCA4rEQieJEnC6WCaZYP6RN42KgVFVBB4PNU2aNv1NwxYxZ4PBeBt31OQioUoDDN24JW45kEMlIoBH3wPNiI4ioMTIzQyEAMTmLce4OYps9OmPOPwKG04jnMwKdjgGUs8HpqyrevezEIGEYwVMwtAiipNNlS8LW3oS1ke8NyHQYfsTO4XZ0V5hc55W4gCWqrYj0Kx/ujnPM+1NhxnGHKmFGShgvi1Wi0Y+zxm3Gdv3eva9docG7eKKqqomI4iS7x1d5Q15zk7RwGXin4T04vHaTM78JzBq9v3WI+xjaD2CzImULoAp1ghS2Zvb8+2t7cDGMAZJrr1jB1z2FrQN5DP7Jw3Go1MnR21sT0AjPmt29M0UKv88+zSJDnMQldwhE+xhQ7ijHzevgf+ojaptnF7e9t2d3dta2srtBm8x2FHDDgxgQeqo6HnAUYB7GO7hec627AMRrDdjG37bAt5wEbMLo+BfgxEcVCJ5y2AVT4lEr/h+3EtM56/29vb1u12rdPpZMA+ZJfpPInNFWTPo9Yu5iMy0DCHmThIibmLa2F3FI0veOWBSHp9WTmCazgIruuI24bvOCsexHMBIJ3nb3AGJe6vsojXGLKzeFukZuAzzxE8Vf7y83lsGdzUMff80Un4W1FFZicISuVF2jxSgcGLgR1Z3b6ngAMUMAv/RuOwcLAaEJriqULVE2gg3AsGARQosh8UDNOFyGi3946/J82SAhVFV2Kgm/cMFXzgO48VZxSZHSLtHin/NSKo0aw8AE3HTTPiPGHJ/dXPvcwvz2DIAzhOQvCWHfPTpDLRpzziLBrPCFNDm6/Hc7y5x79hY5uz3MwmHwflNd9b5zsbhJoBpf31ACncv2icjzP+Hr+1nezgMPCMdaXgEhvYmrkGWeitIVCZ8dBxqwyZiio6HfIAoDyZMwlQVPZ3MT1Thk5K12pwBPJdAw6efNc2c5ATDjwcfAalUCtGa/KgzhPrWrW94CByBji22sBJ5BfsMpbnbDtpYNDb/hTjfZ7NqLpGA1CatcP2O/MYNjTvetjf37etrS3b2dmxtbW1jM0NXiMoHANKmBREM7MAUGJcWF+Dp3n+Qszm8XjGjjzzV/mtz/ACdMxnBvk4E439LB5vzFvUg+33+9bpdEItXrw4WMXj49mISXJ4IJG3tXRmZsYFXNQvQeCReQBim5H/93iM7/MAkzK2l8pPrDUFrQBW82l2sXZxkI/nmcoJvY6DoMo3BbS8a5JkPJlAfS+2C2M8ismFyo6raFI69UypSUgXhjqjrMz4N3ytphvy9brFTxWemR+t0L+R/osFh1P3QIjuIEWVoz98H8/h1iiX9pP/9gSAOudlhAI74ZwG6vGR/1dUP+Zs8/+6fQ8vKLg8UApCGG3me8Fw0yOb9XQ0gJwcjWGhy5HFWGTrNIVsGeegLGihBo/ex/uMt0PquOE6rx3eGmMllrdG2Rj1jJKi/pXlRxGxscenAHHbOOMI74hScSQbssXsENTBtgou1snbUNEPzhTzANzY+DE/YlskFVjyQDPmrfccrx1Fa6LIsfWMzTL3raiiZwvFnAHvu9i6mfTz2DXqLE9yP++7mLN5kqSyBnYBskUh0zmzR4twQ6Z6+o6d+k6nEw51ARi1t7dnnU4nsy0H5AVG2SbjPqBAOTujsaAAO64KQGg2B8Ad3UoY46N+xjoevMJhQDjRUAESL/uegR9uFwAobCvrdDohc4czpQBKeZm5Sgh4oyYpeI2SD7q1icee7VX8z1vdAEwC0Inx1QNNeH2xPQzAibNjONMMvNZMKc5OS5LDHSZc6wy87fV61ul0bHt72+7cuZPJpuIC6Gqze3NhOByGsiWaza62JP+O+cEv/Z5/y8BVDOSLUZ4vFSN9BtvNajtz7S4mBo7xztdpLSn4wLx+0AbMTwapuY3sPyuA5tny4DnLKfWH9FXW56yoohjdV6AUKM9J4u/aa2s2d/26ra+uWvduWqpeo0CCOnneS9ui/0MY875qCEuOmCG7YHZ93VbX123v+c+3nfPno/fN6+tR+chCIk9Y4DnqrMbAAq+985ubduHJJ22YJNaNtJsFXSxSh/89UnBElZVn5Ohncxsbtnz7tnUuX7beuXNR5zxm8J8GncYzHkhTe7GZfcbMrorRkwdM8d+qoPJ+g/+Lsg7VgG2vrdnCjRu2vrpqnaWlM+N97N6aGcgGAwNpXiQdRgJAOX4td7u2dOuWPdpo2PX6eI0HXW8KmuJvppjT4AGAvLY9sBkGyuz6urWvXrW1lRXbmJvLfc4kVCRbK6qoovuTPLlzUuvXk/Un5dR4jj07sZxZwgA+y3XeZob/zXxbRMEpZO/A+fcy6VW+c4AI7UU2vm7zK+Kf6hi2sxT4j9W1itkL3viz7mFwSgMhamOqfsIzwVPOQuP6UZzBs7u7OwYAgVca4OX+wJEHiIKgEF+jbfbmGdu2nE2kta1ivFTbzAMK2DbR+Rp7aTaV8oB5zHxFoXM+RIrnLnisGWngNcZTr9GMIeVDkVzR748ri8qAKHn3956v/mZen80sAy7pfTB3MO4K2iLIqeCg8j3Pp4zZZjGf2LuXAl6VfVfRUeieglLsEHmAB4Sat6f/RR/5iL3uAx+wWpraKEnsN/7qX7XffdnLxhTqaSG2/Bx2IM0OHdLRaGQv/8Qn7Gt+8RdDO3//Xe+yx7/qq8aUd0zhgQ9Av5HFU6QYPYDBIx4D3U+s16gA4hTTl/3u79pf+IVfCP38wOteZ//uwoXSvMz73/scxhkbH2wEcfSIs0Ze/olP2Js++EGrpamlSWKfeOc77VNvfKOZWdj2iX33aZqOKXc2LMqkh98r+tbRyP55mlrdzIZm9p4ksZ8kpVEGmOL/y6wjNlSwHQEGXgyEfdFHPmKv+Rf/wpK74/E73/Zt9ieveU1GLnAmlVm+UXPc9a6AGW93y9uqx0Y1A6V4fcl/+k/2db/8y2F9/ItXv9p+vtXK9AmgENY47qeOSN6YoB2IkCJiquAZb9tD5DVNU3vwwx+2V/7Yj1lyt53/4Zu/2f7gla90ATOV3Uehk3RuK6ro2UgxcOBeRqvznh9rr/duVs5+YcqzJ9jWQabr7OysTU9P2+LiojWbzUxWD/7HO2cjcNFnzurZ29uzXq9n3W43nLzGW8u8bTWeTEcGLctuBsY8ecxgjt6PwR7mB4NBCkbxeOjvvLFg0ARZaJy9w3oJf8/OzgYeo7YO2oEsKNQMBS+R0cN1uwBK8ZiYWcY+VMJYz87OZrZx4sUZSZgzuKcCiWgntr/1+/2QLYUT6zzAL8bb2Djw/GU9zpndCgLqOOH+sNXAM86iY9CJM+h0t0kZwDpJDrfL8kFQ/BnbKfwcBlLVR/LaoH4Qr4XYHPb+jn2fd50CMpyJmEda2kX7YjaeVQV/TXeLqK3ntRltY+BUi9fzdTzf1U+H3FAAjOfBvdRFFT29KB++vcfkAVVJchDJByBlZlZLU/sLv/ALNr+5aWblQQ7veUoxgZd3b1wzv7kZACm089X/8l9ae20t6oiVcdDupRPnCeq5jY0ASJkd9PMdv/M7ttLvm1k5YKPouzylA1LjTEGDha2tAEiZmSVpaq95//ttbmNjDJRjg4/760UG7jd6IE0DIGVmVjezH09Te84R71cEEpYBtzyaXV8PgJTZwXh86Qc+YHMbG+Gas+ZvzDhRYDY2FzwAd35zMwBSZgfr452///t2ngrze06ZN5eL2o6XRvsVfNZMwiRJrL22FgAptPO/+vmfHxsPjw9l21hRRRWdDT3d16ICIjGHnv/2ZJH+hq9nAIVPI+VAKDvO7Pyrc68OPGedsPPHTj07++z0e1uiYlmuefI3z17x+Fg0DrFrveewflFAwjuFT0svcJDHA0cAACKjx6t9hL8ZMOTPcU/PXtSXBseUlxys9DKlYnZrbAzydK2Zjelynq+wATSAFgM9dA7jf/5c/87rj7aX5wPWltohXjZd3rz0QCdvzR+H8mQMPotdz+1g3nqvPLmgrzyw35MPsbYz32JlIhSQ8vr6dPCJKnr60H2zfc8zFmJbTxZu3AgOHqiWpnZubc0enZ/PoL66sM3GhRmeB7Q5hv5zO82yJ6XwwgatrK2Nt3M0stnr122wujp2/9hzY4CM176jkAoRNZBwjY4R+rx48+ZYP+tpahe3t+3a4mJhjYKyhOdxVskkmUtLt2654zF/44Ztzs+7vAjX5Sh28OI0qcgQZHoxAVKghpm9yMyePOaz86KlrBQ5SsORHTY8Fm/eDAAIqDYa2cLNm7b+ghccoaVHo5ijgs95/sZS5TmLKvQF8ur2bXd9PGcwsCfuFlT1aJI5pWCWyk4Gq9gx4LT+2HicW1uztec8J4wj82g0GmVkc8zwjfWvMmAqqujkSB0O7/tJdVVRUCL2jDJAR9l7xohlEn7PIJbngOEzgCQ4tQw1pZBhrVv/2cFP02xmDNcP0uwDLQztBXOKHE0FdBQg806B9XSR2WEBZH55Dn1esEkdUbxDlwBgQvuT5KCG1/LysrXbbVtYWAgA1MLCQuZUvr29vaBvWQcDgNKaTcg+49pSnGXD7VQwYX9/fwzE4Wwp7SOTAmVeZpHyWMcW73nz3gMWeEspZzzj9L25uTmbnp62drs9lpmEe8GWTtM0ZEoxiMd2tdcftemZjwrmIhsNfyPzEC+sP4wBgEb12zxfxQNwy1LRtR6Y7WWgeaAPfl8mKM/fx+RCDMjUTEqMNe8UYdvb7DBLKgZ8Qa5hvSnI6mXL8avs3K6oIqX7IlNKFUUsMoC/O5cvW6rCKUls7dw5F+ktI/D1s5iCLitgzMzunDtnI21nrWa9K1cKf+uBUUXXHIc8QRqLfihtnD8/1s9hkti1dntsLPD3cahMlMb7zeaFC+54dC5dmrgN99qZznv+w0limiy8b2YPn9Bz86JF+JsjNvhff7918eL4Oq7VbKvkts88OmlFqAC2RqTyItjrq6vu+rjaakWfM4mcAelzPXBQQX6+vvfAA5ZKuveoVrNNqoPnZXbhc/5/UqoMl4oqOh6dtE46SfuC6SSdRlzj6SIl7QvkmMrEer1uy92uveBzn7OFra2xe7Pd4Tns3tanWGCxjJ2nWa+avbPU6dgLPvc5W9zeHnOaOSChffBqznD7ypDqfg5+cOa5BkSmp6dtqdOxBx9+2OY3NzPZUwoKasaGl13ibTnzMn9iWUCsx1SPzq6v2+X/8l9sdn09ygcdQ26Ld+2k/OX26dh62/Kbzaad6/XseZ/9rC1ubwd+xrLtlMfefI0BEDqPPTtEs+bQdnw3t7Fhl/78z21uY2PslD7lmbcNk5+r43Ec8tYO8+3KaGRv2N21K6PxU6A9kCr2DKW8OaI89TL6eCtnvV63uY0Nu/KpT9ns+vqYDadj6GVtxeQX/p+0fxVVFKMjZUrFEP8yVGaBqvOjC7u/smK/+8532mvf/36rjUY2qtXsN/77/962FhYsvXXLXTRFIIaCMrG+6meaLcXCfXNx0X7167/+sKZUrWZ/8O3fbjvnz1tdnLm8ZzGY44Fs99qh6ywt2Ye/4Rvsq//Nv7FamtowSez9r3mN3Z6ZsfRu1IsjLzE6Sj+Y50xeKvz24qJ97G1vszd+8INh3vzBt3+7dZeXzeh0DGRi3S+ka6bo/2tJYu81sx8bDq1hB4DUe5PkoNh5SQBP783GLT9PDU+N2uat9965c/Z773qXvfpf/sswHr/7rd9qnaUls729MeMRdFZj461NNrrV2GLDkK/pLC3ZL33t19pf/pVfyayPW9PTZne3uDKpEQjKM3J0m4SmwuNvRCoRyWbQarC6an/23d9tn//DPxzG42Nvfat1l5ct6ffHnu1FCiuqqKKTpxjokvc5R6xVjsR07VF1cJ5OOk37hO1Fzw5QUucZQAnXkPrST37Svv5XfzXU//vtd7zDHv+qrwryjkGH3d3dUD8I2TsoDq0FzpGdgHfPjtZxM7NMRgwXOm42m/b6P/9z+9pf+qXQ1o9/y7fYY3/hL4TfmJkNBoNMRqvZgQ7lQtbeLoIy4AnaquAUnsUZuZw58/n/8T/a637yJw9qSdZq9pnv+R7b+IZvCPfMy9RhcMKbyzoXebyV1/q/ZptxrdQ0Sez33vUu++xb3uLeA5+xD8DZJHnb3rx17AWYdPsjn7I3MzNjCwsLtry8bK/7z//ZXv9TPxVqdf7pd32XPfkX/2IYZ6wXrkvE9aTUf4qBfLjOa7dXOkADZWZmn/exj9kbf+ZnQlv/5Du/0x7+8i/PjK/yikErBhPZb/TGvQx5wBJ4xmDgN/X79o/W1kLd1r937pz93/PzY7LPA2/yZLB+r4Acbzfmba9crw2ZaFNTU/byT3zC3vAzPxPm8B++5z32yJvfbIPBwAV9sf5YjrE/pWPPbZwEcK2oIo9OZPueGj8xIXAUxBif63efffOb7frLX27ta9dsfWXFrtfrZtvboQ388vbLnhbp/f/oi7/Ybr7qVXZ+c9NGDz1kuxcu2NQEv499Pkk/igRgHpX93Z++9rX25899rs1ev24P291tYltbuf0pAvwmeX7sN6oQPvXGN9rtL/kSW7p927qXL1t/ZcVsdzd6LwUdj9Ous6SfqtXsw2b2wjS1h+3w9L2jRpI0ksifKzihBoMSj/2jX/EVduuLv9jmnnrKNlZXbXtx0azbHVu/+ruzJi+iHIsIeob6H77qVfbHly7Z0u3b9nizadfrdUuvXs0o+rxXEWk7YpE6Nm4ZUMQznvjqr7arL3uZNR9/3O4sL9tau21ptxuu8QD5Mm2739dLRRVVdPpU1kFUp/Kk5EcM2Mf/y91uAKTMDspCfNlP/7Td/pIvMZudNbOsk85gCW/ZY5mO/9Xx80AJ/Rv/a2Yr2gpACm19w8/+rN3+ki+x3QsXcnnN9ozK9ZPgtQZu+DW/uWlfeheQMjNLRiN78Q/9kP2nN77R0gceGNP9HsATA3nKtp91pbYXenFxeztTKzVJD2rCPvVFX2SdpSVXzzNvtZ3M3zK6Xe0KzTz3tpYicw6AFNr98h/5EVt7zWusv7IyVkPLA0C0X55d7oESXvu9z9Cv+c1Ne8NdQCq09Ud/1K69/OW2OT+f68flPdvjpdcP/d5rp45DkhxkSAGQMjuo2/oP19bso+22PdVouPcqeiaojNzzam9ptlStdlBHF4CU2QF/X/UTP2HXX/EK256actca89pLJijL98rmq+goNDEoVTQJPYQ6FpXwiH/DCoIJ+2J3L1yw7vLywcl0GxthETG662XMeE7uaRo93eVl27t40RYWFlyGK+oco7Jt5edPYtjxb4q2PfL1oK2FBXuq0bC1O3fMer2MYMN9TwJ0KkuxVO3O0pINVlcP5pHw1FPEKoSfLsL2anKQHVXWSMv7XKNE/L2nuPlajs56kZbeuXPWX1mx3d1dS52tEHlrt6xB4hlMoDLrKpYyzt+jz9gOoLQ5P2+3Z2as2+2a3Y1e48WOC/ef++r1i5/v1btiR6ZWq4WaE7Ozs5lCsnt7e+FZe4uLtvOSlxycGLWzM1ajjyPWRW2chE7aMaqoomcKwbaahPKAnaMGJ7x2lWlfDASK3a/oeSDI2iJggnUUso7wOxTAbjQadilSB3Tx1i3bevDBMRuTM6VwKlys6DYDK5opxY4l2sv9YfkOWl1fj9bIvLW6mrG/9BQz/jyPYra6jnfMxteTkGu1mi07NRaT0cimn3jCepcvj2VnINNsMBhkCpuD/7u7u5am6RgYGANKdNsTZ0ZxAfZLd+64/F26fdsGq6thjDAH2fbB58x38C3WPl6Tnk3lAVBcOByv5du3x2pDJqODWrb9lRUzy55abXY4/2I8KgOgcNu94CWew7bJijMXUHd3Y24ujL2uKWQlYk6gnluej6dtyfsfIGXs/YWDgVu39aHRyG7d9U+5HZxl6fEvTzarH82ygm1snStJkrhrrTYaWevqVRs++GBYX7rmPFLf1pO3HoBV2XIVTUJnXuhclVeZ/cHed/x7dTBZ+cZSaPnaPNQ9ZszFBLennO8llQGjcJ1HRb8tctb5Pqwo8p5RFvgsC+R54zwajXLbHrv/s0nA5kWPYtfgM43qmo2vVY/ylPZxeF92HfA1XnaUXhtzgjzF7L3yMqW8Z/D853axwWJmGcNV09xhGHqy02t73vYDr++TkCdbK6qooqNRHthUBogqKyeP0qbY/8dpi+cU5d1PHX3VZWsrKzZKkowjx3UnPXnsBRY4w7oM5YF7Cl5Bpt9eXnbbipqMRRm4sR0EebZVbCxjIIpnG6OWJAMnaa1m/QceGAsgcpCGnWfVm94Jdx7F9KU3L2JzoXv5sqs3dcyKgKcY5QX99DrNlDE7qPHq8Zdr2er4e5+xvV52t4n6R7zG2PbAa+vixTEep3d5bGaZ8ffmhNYTY96Wmb9FPNf5i78/NzVlQ7MMMDU0s8cI7EafGTiPjX9M1vHvtF3MV++7JElCvV9PRig/vcLnZja2trT9nj9dUUVHpTMtdK4LR/cb4xpF7VkgK0qse4hZeempAV60X09K8U6dU8HEEQpEgbC3GxE43cLEQIqmcpvd2wU9KSDFY6PXswLjz6Aw1OjwDCTvnp4xGFP6uF/ecct599Y2MqiZx5enMykP80AYj+exOaHKVNd7mfZMyu+8ecttzTPY1RHg9ayGvc4TT7Z4BVhjx/+WAX5ifE2SJCOXOCuAv8MJOJ4Bxv30MtX0MzVgJyWPl5VxU1FFcYqt/dj3+lvv77Noozp4eQG9ovapnvdkqHcPzSjlE6p6587Z//fX/pqNkN1bq9nv/fW/br1z56LP0v+9dio/ysg22E3oD9u9jUbDOktL9v/+lb+Saevvfuu3Wu/cuXBaHWeWqN2rWVx4pqd/YmPJABTzFNlGqCfFv9s5f97+6L3vDe1OazX71P/0P1l/ZSW0i9u2u7trg8HA+v2+9Xq9cOoeZ8+wni0DTOlcwxzkIM724qJ95Ju+KcPfP37ve22wupqxa9U+ZF0ZyzyJ2UteGz1fh9vLc7hWq1l3edl+/93vzvD3z777u23n/PnQXs080hMN9STJSfir9TXRbtgkOHFvbm7ORleu2Cfe+c5MWz/5vveFOYxMRGQhYtzRRs5O9OphTUI8p71DBdgvfarRsO9dXbX9u7/dN7O/f/782Na945AH8PH/XIZBM+cwHwarq/Yf3/72zBz+xF//69ZZWgrjCr4yL7kWngLB3ivmu2lfKqqoiE5sBTEKfFwqI7C9v7kt3t96TdF12id9lpf+y8ID1+Q9V7MxvL6zwxbr5/1K3OY8lJ0pr186Jh5Awp8rCIDtVZy2zmniHmiGfnjAWFF7n47krWUva0idDCb8H1NYmmadJEmuwiuTUXSS/de+aB9jCtgDTrUfsbarE4BtALH+cXs0tV+/9+SQR2x4lQHsKqqootOnGPDkvSt5OrMsMHJcKmPD4f+TluF5z2YnU2Xnn7/+9Xb9Fa+wlfV1616+bPuXLh0UCSZ5rmC86it8hrbou/a3CGQ0809P/dPXvtbWX/taW93YsO7ly9Y7dy7DA9g6DHCpTVRWprNe0HnobR/ynHm8P/5VX2Vrr3mNLdy8abvPfa7tX7pkdUfPaz+Ux/h+Ur3EbWb+6tz489e/3m688pV2pdez3pUrtnP+vJmMufLZa1OeveCtXw/I9QASBSPwm8e+8itt+8u+zBZv3bL+Aw8cbNtLs4HW2HxQPmstr7JzJZYZrzx+9C1vsfXXvCast53z582kjqVnCx6lXV5bmP8ecB5bj7+wsGAfbbfteXt79nizmQGkeK3EnssUk1c6J7SOWKyuGK+/R978Zrv5qleFOrqb8/M2ku283kmWKjO8OZxnw1ZU0VHoVLfveUo4b5GW2d5zku3C3167Yk62FylCZCh27Ko6egrUqCPKTqVGJ1RAlFUQkypsjweTUlmBVaYvzKMYL5jPAJmSJLHd3V2r1Wqhbg4LcTML0TiOFmm6eMwpfyYJYFaM2q/9/f2x65MkCZGwqampAPSxAh8Oh1ar1cIc5iyc/f39zJghmotomBcJ9TKPYmNw2mPjgTWeoarKnueUF9VXoE8NV29dzszMWKPRCNmaZtlitjBa0Y6dnZ2xyJvKKm231iapDJCKKjpd8uyOPCeWSXWm9x3f56hruYxDVfbzGGB2VNDKsxvYbsOpVa1WK2QXJEli/ZUVu3Hp0oFuu/s7lYOQ9V7dG0/Wq+3HfcPLcyw1AwKnbMHe7C8t2a0HHji49q7NaHaoT9Eu3TXANo9mwORlmymQo23UF++IwPjWajUbrK7a8PLlg35ScMrLPsJY5hVeLksxgBf85+wes4NTgu+88IVupjSPvWYVqe2SF5ACL7k9zFfv5D0+dQ0v1O2q1Wq2e+GCbdydF4nU54J9xa+dnR3r9XphbiBbCjaA9l0Jn4Ov6s+xnc78HQ6H1l9ZsTvPec7BdQLs8jrSuYHPjmuLeCCg1mvifuE3N6am7AYy0XPuGVv7ee3hd8grZL7zqYuYC5BjPBcwdwarq3br4sUDnt71bzCuGH+uh8eZZxj7vMCwvs7Cj6/omUknDkp5Sow/x99FAqQsMOIJ9yKHiQ2cIkMo1gZ2QNn5hBDzfjcajTLZD9xWpViGVZ4ynhSgYmOUFQZANi6EzAIJfYxFtbQt2sejGpjczzJAF1+jY4X2o32xzByOdsaMiWcD5QE//OKsM50jyksttMrbbmHMsTHNf+dt1zgJ4qinGj15hlmMR0oa6TQ7jKyDb1h7sUgePjOzMSdAI3y8lmEw63WQX7xO2DDR7Yanyf+KKnq2Uwzo8QACfWe5ofZNns1zmpQHXJ1WOxTMY5nL4IPnjGpgQO0Cz66IZaCUsR08x1hBSAZ+uJ1KsfZ6L21TWVtN28sgigf8cLvUNuPnMqjHGUis99lRnkT3eHMA80D/9k489PirJUF0PngF2GN8jgWfeE4woMOfxYif681LHgfl+aQB8Dzy/BW1qTx7m9eaZ/dz2z3K8yE92eCtPw9oi90XY4H2FK0hD8TxgF98pmVvPHvPS4TQ/3lOqr+jbfB4W2bdVTZhRZPSqWVKeQuNFwciIt6kjRlaec+KCTUWZCpkdLHmGU1MeAYQ5X6/H+q2IFIBR9MzbriNsUwJfrYnRGJGBLdbP/cEIP8Ng2J2dtba7XZA40ejUTjZQh1mM8soXeW9gnZ5dFJGsqd8YSDU6/XgmHP7Yy/PqODn3Gvy5u5ZtUuNW8wTZDqxgtQ5jyw2s0NQENGZXq9nu7u71uv1bG9vL9QUwClH/X4/gFScPcVtKsuHmIzi6Bz6Va/Xx4AYBmrN4kUnmfAZ135SI1zHk8ElNkIgKzhTip0WPlVo6u4RwPv7+9btdjPgM/OCI6WoM8C1BrSeB8bNi25XVFFFk5PnpHiOfsx28QCpswahtC15n3E/TrqN7LzV6/WQYYB3ldFetoyZjQEPmm3gZRpARoLU1lPSjNUYIMVZECC2yziooAEF2D8KTExiSyh4gkwevHjHAG8tU3sB4BLbCIPBIFPrRmsdceZXHsCma4B5iJpXs7OzNj09be12OzMvkIkGParjB15qLSbOPIItxGPgtZf5qHxlEIoBCJ6jrOsxD9Qf0ixz1tX8znOG7fUiUIV5o9eqT+bxUbOwta1aZ0x3MqhvpHIkBt5i7imwqsCUB0YpEKgyDL4G1iX7nN7vYu3i53PbdJset4vbhufDRvaAU/ATPIslVrDv7I17nu19r/RPRU8vOvPT9yalIkCKKYaq8308oy4msPLQazMbM0iwbQbKAsIUDjiEAacBe4aOl6Wj/TwpgiDhNsCYYqGlAAKEIz6DQNMIS9l937GIAX/Pf6uC4LZCQLPiqtfrwQBihW5mblpwUXufbcQGnn4GStM0Y6SBzxpV1nFiQ4SN+thBBbGo40mMl2ec6bYNfIbtcHgm97dWq1mr1bJarWYLCwsBFMLR4wDU9vb2bH5+PrPNg40u5nmaphmjianZbFqtVgsgExwDdhTYkcmLpKkMwvM9kHfSSHVFFVU0GZW1gWI2jAJTZjaWsX3UthSBZbHflKGTkCnsbMa2lulWuTxwJpZ5FLM7y/bDu8bLfuHABMtv3iavgUDVY+rUMyDgvcryl4MkDKRoNi6erc4+2j0YDAKgwyCUtz3Sy2L22ujxVgEfBYHyfAM8S3l9lMyiGLAcu9YDKxQsQJvwGw6uaTa6ZnrxvMnjqbZbZQHz1lsjHjDlgSUMAMaCgmWDYTEZ6fEyD5CahBhwneQ3/DwFnrz2Fc1T/K3rngO8eGfwmtdYLDte/z4KnyqqyOxpAEqB8hYc3lUZeMqBFSecPL2mTDugYPf3963T6YSFOD09HYTwYDCw0WgUMjvMxlFsjq55e9AnFWZHITwHhsBgMAh9QRvh9JplQTezbPYLCzI1giDcWICyACsr+FUYQ9Du7OxYv9+34XCYqauD4uZpmlqj0QhRDI4sqeHGyg7tvF/JM/onbW+eE1H0LDY4B4OBmZltb2+HUykxVwA68dizooMxikwpREz5NJAiIxr3nbTPIBhAaCdqL6FfzWbTzCyTEQnQFplKzWbTRqORTU9P23A4tPn5edvb27NOp2N7e3vW7XZtf3/fnvvc59poNLKdnZ2QKea985xEu9kgZhmh2wIRCUadAbQPJ++BX1jLZtntE/ocXSseUOgBmBVVVNHRyDPyi4I0INgieo880CEvcOT9n/f8MjrdA81OCpAyOywQDtkHWcgnxCFowMWiQZCvsawOz7Eu0zYPvMJnaAMfXAHijBkzC/pHgR/O1tnb28ucYMa2Ep+0NklQjgEyBfbwrhldaFev1wu2MPiFNkDvd7td293dtW63G174DvpRs6XK8F2zjaDL9TvdhujNdy8bTQFMHVNuSx5pf7ytZQqgQZcj+MXZMeAtss85C10z/PQ0O+Uh2uO1z+MT8wN9Q1sBisCWwpzUbEMu4aABwrw5UORfeJlRMdDtLEgBKa1TzPNSD9nC77zMLMxP1MzVEwzhp6pPurOzk/HxVAaalUucqOzBisrQqRc6V+XLgtTsMNWUnSIsgL29vTFEmEGNJBkvijhppGcSigknjkCwYEX74VyyUAHpNhncB0JBFd1Ro255xNlR9Xrd+v1+AHCGw2EGyOFIGPjA0Q0F1NRAU4HPY+q1Py+yoYoYwBRH0GCwQcCifyzYoXy3traCosYYwBBC5hgbHEcBQs6KPNBP5H1v+gAAdbhJREFUvytL3j3wmWbcAMyBwQsAE8XmkbE2NTUVsqnASxjH3W43GNHD4TBz5K/OL13jxx0HNZYARnW73QA0JUkSQFr0h9PzzQ7mNQxyyKl6vW57e3vWbDZDZNssKwO9KCKvIRgg+hue70zInJqdnbWpqamwva/ZbFqappmMSBAbKWy0KjioYNX9tgYqqujpTEW6z/vMk9P8rp8dxXkoCzwVOXPsrHM/8kCxowRbADZwkWgAUAxOsH3jBTo4sOIVt+bt5ApGxJzlGP/52eoMsi6FbkBf2BFlAAJlJiDP8eJsfg2E6tyJ2WjMZ72OgR7mrYITCCx6WV3eHPaAv6L5offJC2h5OpgBP34xcKJgjme7x9Z17J3tLK2D5WVKwR9hnoOn0OUApQBS6mEy4LsGwrw2l6WYjcB8joEbXnC7jJzKm6f6mSdH2b4F8Vh499RyCNw//B+T09wm3AvgEzLhubC9yjMPVMez4YcClNKtpgwCevY2ZGAsQ76y/yo6CTr1TClvour2KUx0EP7HSRBQCOyQ8RYtBW9OEozKE2DoH2dzmFkAo6D0IVQAWjEgMhgMrNPp2GAwCBkUcMzhnLNgwDOL+le2/xxZ6fV6gfc4TQ01F6anpzOngHCEC0pbT/PQvemK9LMi4r/VqPHGQaNDML7MLNQD2NnZsWazaYPBIAqo4bkAP1ggI1oDYEojA/e7Ix4zfryIlplvpOFz/j0bVxy9S9M01IEys3CiEcYUc1v3x4Mw17EOcCoQxoXBKTX6TmrNc9agmdnW1lZY17OzswFQ4m2JnIkEsAkGBH5nlj0NCZ8DIOK5yesMvMX85TpvmPeIemmEUz/XuQvjFNeBh91u1zqdTnjxOuj3+yHCpun0SkdxIiuq6NlOMf0Xc5yKZDq/g/JkZtk1W+SU5oFSec7MpM5uEcHebLfboV4mavA1Go1M1hT0FcszlpewC6CPFOTRDFIPmMrrnwIvbF9yFjvke6PRyGyVxzxAkG1/f9+2t7fDyWr9ft+2tras1+vZ1tZWJqAIQMKbG2i/Zw/Htg+x7uIt4xxMgR0JXabb9NTm4nbprgN9NvPTs9vYdlCwg/lSq9XCdkPmFW8x1NqLPA880CvmV+h3DEohqwuBJc7WBn/RbgRWcS/wEvYM5gZ8j1jbFVzN47U3B0BsZ+vnzG/vefgdB74ZrIrx0Wunyk58rgCfEmchwXblci1mWQAJ/+M3Ov8UQObvmZ8YV4DmkFdzc3NhJwLXJmXAUuc2Z0LBX4JcUP9T66Np1nwsI7Sy9yo6Lp3Z9j0WzFjYrHSxxYdTjzudTlC6jBYzGowFt7m5ad1ud6ywIL+4LUxFggy/8QxEMxtTcp5CS9M0AGqcJeFt3dPTR5SPJ0XoE6IQUGpehgvQePzN72jn/v5+EHCIwKiCU77yfnMF+/jaGMDCRg7AQPwPXnJUNBZFYAceChxGJhtHrAxjBv9xqIxBPomh64F5/FJ+xDLAYk5ErPAirmNDAp8xKMhGJfMdxikivhyNVOPxJEFonjtmFsAcGBrb29thrQAc4gg8F6HlTCmsF9wrSRKbnZ0NEXyWKSznwGPwF+sQNDU1ZaPRKIDI7AyxcYpML3zH/IURgu97vZ51u13b2tqy7e3tMB4wYBF5B8jGcruKoFVU0fGpSOfpFo/Y5xo08BxyprLBLg+gmKQvGoQ6DR2q9oTqNcgs2AiQ9WgT/57BKDhsg8HAtre3rdfrBcAH8pe3luF5eW1VYtvMLFtkHfqHdScy9LWkAetM2JoayFFbaxLy7pPnaOM7ACboIzI4dLsh15bUbUOcweQ9J9YfDgBxm6ATmX9mFvitbeWDV9ju9XY5eP6P18Y8YIrBEy3EH7NpPWCH/S4Gg2LAkLZX2+rNCZ4bef5RkiRjBwFgLBSg5KwetXM0QKx8VEDK4zm3QYEhlqfg/czMzNihCBy016CrzgHtM/MOxPIb9wYYhawpAFZ8wIAHhDGgqmPN84Hta9UVRf5GRRUdl84ElPKEGWdJIXOAIxBYAJxaDSeNBQYWFowC1KNRgEcVJdqFez2QpvbC0cgeqdXsmpOy6Rl/avTwwsezzSzjrOv2Pe9UEU0DPk1iYQTli8wiGGso4s5FlIHYq9IbDAbW6/Uye8L5dK6YMcsGgtnheHy2XrdrjlGrypINCAaitKCpjhkLaPCB01hj++s9g+64xG2LKTM1vPKMMM8ZYPDIO7lDM/JAl/b37aHh0B5OErtGWy4564nvzfdDdhR4GXO4MEeQIcnGNCLFXtbgSWasYY7iWQz4AKBtt9s2HA6t1WplHATMYY6465Y5Hpd2u52JuPH4MCiFz7ElsNFoWO3aNUseftjsxS82e85zMgYaz00ASMjUxJw2s7D1kKNkw+HQtre3bXNz09bX121zczOAUqjxsb29nVkTbOTmrQud0xVVVFExqTOlMlvtEXagOJAEZ48dD80I0ucylXVO1Dn0dBqee1KyO+bMoz3sYLG+T5KD8gr1ej3IOa/t+/v7mW1wDEpBXnY6nSAD8zJItZ1oY8zRQ1s50xiEAKLWdIIOY8eet2eV3QJVhhR4w305y4mvhU3AfWC9z1vLtP15GTVlKAZMgL/IHMauBtiSmCuYQ6iLtb29HTKLeds72/OgvDZrmzwwhTOwdbupWdaGho5Hm3nc2YZSgCLmc3g2Z4y8eaVtYB8QvAGveL57dY8YnIptJ/My6Lyx9+YC2gs7lv0G9n8WFhZCrbokSQIoxCcge/dlfuIztme5H/hcA5MzMzMZn4wDlmx/e4CjAlMMdvNnMeCa+VnZcRWdNJ06KKWILaP4yF6CAOWtP1AEelqKEhYR7432FmDeAvvmwcB+aHvb6mY2NLO/NTdnP0eAi/aHBTSEp5mFLV4QXPgbRg/XAsA9oNy40BxHizTd9aTJiyCoMYcIHHhrdui8Y3zBC4AHnPapzmssogFF8rbdXftnOzthPP5mq2U/NzNjZodCmqMH/M7ACANSqtzRByYFONTJv1+oyCFQw4YVNAN53D9WYt76+Ke9XhiP901P28/ezfrR+/IzocR5viuIxsTKkbMd8+bPSY6LyifwhaOxOEEvTdMAzOC3MFaxHQRFxvE73o7HGZTsYLIjw4Yl/8be/36bft/7zEYjS2s12//RH7W9b/mWMWPScwx4TbORiKwnztj0HFbPeFLQtMyY5M2Diiqq6JDUEWQQCu9wgLhOEgD0qakpm52dDboY6x+Ut23F7HAbioJY+AwODoKLkFvs6JpZxnlkuwlOakwWFMmIGPDFuo4dcJZzSZKEuoGwFfj34A+DJQqaaCa4gnZlZF2eo68ZJhz4ZNtLMzP4FDvO8NLthcexc6AbNasIuxs6nU7I5sWzG42G9Xq9MV2EjGhtN7aQA6Di38Taq06/Nz/4e8xTDpqx3teSE5wpzDaut0aOS9x2LWit449x4C2HDAjBT9ITDnkexwDFMjYc89QsCz5j/Hq9XiajSO0fHl9sNUWgm+u9Mv91/ub5bh4g5fk/CAwC+IGMxYEx586ds2azaXNzczY1NRWC9LgutjODnx3jG8tUzCmMP9oEm5L9HvjImJOQdQD4+AAhBk8Z5OPgpQaAYzZfRRWdFJUGpSDcJkFHPUXAyhWGEYSLmYWI/tTUlM3NzWVqrjAopcqUt4qpkshTuldGowBImZnVzeyHOh37yMyMXXcypswOjbjRaBS2uEEZ8OlcOLkLdZlUuNXrddvZ2bFOpxOENYQxZ4WcxaLPA6Y4DRhtQhQJxIamGmuKzHvPxvaCK6NRAKTMDsbjn/b79rHZWbtWq40Z03D09YQKBjI5i6XmjKnODZ6jHoh22uMRM1C955ZVwp4SZIAvRldGowBImR2Mxw/v7Ni/bzTsWi27z16BQk1r9vrB84EdB82UYoP8NEmjVKPR4emNAGVnZmZsb2/P2u12aCMMrk6nY+1226anp21nZ8dm7oKpAKo489MDpZQ/atxM3bhhS+97nyUYu9HIGt/5ndZ5wxtsdOVK+A0bebxmNCjgFe3VzEAPrI29KqqootMjBqghY5MkCbKFs5mbzabNz8/bzMyMnTt3zswOASPYchrI0YxlMxsDO1Rms7OpGRkM3mCLMBwk/N7TqZ48KQJ1VMexDcNADsAo2C/IjuGAFRO2xgGMYoCEM2onsdVU52I8YuCJ9kHlu/Z/NDrcug3HEwCEB0p5MlzHpQjQSdPDbe79ft9Go1GYh6PRwTZ3XGd2GNiEzkfb+NRdBgFRoLkMCKFAbhGx3YH7QW+qzanBG7ZxNdDJPOSx9eZ2XnvZBmAbyywL/vK9+XvMGwT+GVjVOrAMWsf4G7NX8Dy8cxsQnO90OmM7X7ysrTRNQz00AJMMXMaypLz26md5gJRmpGGLHDKkWq2WtVotO3/+vLXbbTt37pzNzMzYwsJC4AcDSSxTdGw4qIDvOWCI7EuApQw+sS/J4B6DkLDxOOsMcwBjzydyajBYMyorO6+i06aJMqXygKkiBaBGDYSJtycaFPtOHSVORfcyPZRYATw0HFpdvm+Y2QuGQ7te12/ie5S5vfwcs0Ply8YLBIimpkK5MMCmBgOeyf8fRVDgPt42t1qtForocVFQz3DTNpZpi6Ls+N0LRyN3PJ6/v29XaZuTGucw0JE+i/RWTmdlpWNmGQATQBtnyPAcPe2MtZOksuAxXxdbvy/Y34+uj2uOAwMeQ2FiKxuAWXV62BiH8gRYCwMkSZKgVE9SIeb1m9vHoCwcGs3a9LZBYj6hODr6xhF5gLvsQOk6Yjk49yd/EgApUDIcWveP/9j6lN3JMkJlBzuhqGsFQwiOGowZBsd1C6zKv7MAbCuq6NlCHmg0NzdnMzMz4RAJbBVZWFiw6elpW1paCvK20WjY0tKStVotW11dzchftll0Szdn1LDe42AiA9vIYrhz504GVIDjg61NkOOsWz3gg+XyScgTBk4g01ivA8RDlo9moSHrhMEoBk1iWVLoTwxo0rHOA6TA11qtFsopcPuYOFCLdxxkwyevcaZVzOn02qSABAc+AOoga44BP80sVhueD+3g/3UeerybZJ5o8JHbgvm5t7cXdJ0+G21jZx7zijNMPF554KnXH+9vMwvzADzX4taYF3yQFPseODwJ2/X5EBnoes8GybMTvc80y5oDjf1+P7QVv48Fr/VwG/7uuLJB/QH8zSfdNRqNcHIxZGqr1bJ2u20rKys2Oztrq6urNjMzY/Pz827/dX3GwDD8jvk1NzeXscu8+QM5gDnHYBP4vbu7G0ox8OnWmAt8GqdmWKl8OA6/K/uwoiI69va9MtEIT4mZ+VuF+DM4tzHFi3cVaJPQZ+t1G5plHO99M3u0AJDSfnl9NRvfEsbGDoNS7ISzwcf8Oi2CUcZ1mJDhNTU1FQxgHQONUhwFmFJj6OEkccfjs7XD0yS8qAaDalAg09PTmSLSapBDwSEDBsYnjChkuqkSeDoJ1xhApYBUrD+x8XiEAA/9PTs5ONWo3W6HaBPP6TQ93LsPo8jMApiI/2FUq7Hj/X2SpNsTYGwnyWE9OwZEUc+DQanp6emwnrkgJfqObD/0gyPI6qz0FhftBbVaBphKazW7ubBge2trY4YOF01XEBcFM5GlCEMEUc1+vx+cNQDpXEizjIFdUUUV5ROvfQWVWY4CfJqbm7OlpaUATjWbTbtw4YLNzc3Z5cuXbWZmxtrttjWbTVtaWrJ2u20XLlzIZJ1r5gXLiE6nY/1+Pzgvqif5FFFkh/Z6PWu1WrazsxMOg0BU3uxwCyCcK9az6LvyYxJi+4o/4/ujHlOj0cjIZ7QP+h7yGIEH2AW8DYadbA4e4lnan8vDoT00HNpn6/VMsFPtCq/vvPUK+kbrY/JzWX8AkAB4qKChtreI9559izaaZUGp0WgUdj6YWdD9HhiE33KGNBdlL6LLw6E9f2/PHm003GAyt5cdf7Ns3R30gwOR0PX4PYNSDEx5tWDz1jbzzxt7/p+zoRg84FP4GGjW+YG+IZtR63ZiTuRlxlwZjewF+/sZHpdZtzwvYVewbQcQUDMO0zTNgONetjwH345CzCPwDHIQAVXIUtiwOMXz3LlzNjc3Z+fPnw+gFI8rbEcPcGZfBO1vPPWUNR97zAYPPmi7Fy6EcYHMxfUMBPMcxfpBkIBBaNQCHQwGtrW1FTIPORtLs880KYLHs6KKTovO7PS9GMKrW61gOKGYGwwyzbLAwkRaMEfisFC9BQXCZ9dqNfue+Xn7we1ta9iBw/09c3MHWSDkdOM3KvjZIWUHnGvI8GkM4AULfey5Rz8hgMzG6x4dh/8KILDhC+ELYApZFNPT07a4uBgcaeY/jAbU2IHBhj5o9FAz23gc8H69Xre/2WrZP+33w3j8j62WPZGmZpTFxEKe60chs6vVagVgiq9lYxxRAK7dhb+hlDDWnBqLezEPlc5acGu7WCmqgaRtzzNIn7SDGlI/vLMTxuN9zebBYQCWzewxszAGmEcKIGqmjT4XTgFnFMJ4YUPG6/tJ8Y2/YyAWcx3tRH/geMEgAJi7vb1tzWbTOp1OyN5DJl+9Xs/IN8/BgAOS+a5etz96z3vslf/8n1ttNLJRrWZ/8h3fcSCvbt0ai/JjO0+r1Qpt5CxMBnGHw4OiuQxIYSwRlUWb1KnVdTkJ7yuq6NlKMWcU6wrbn2ZnZ21mZsaWlpZsfn4+gFLQ0efPn7e5uTm7ePFiJpNqYWHBWq2WLS4uZgBwBtWZsI75AAbIdwDpAHWQCWt2IPe73W4AHpBpkqZpcHYA5miRZi8K78kRtDlPbnj3QJ8YSDKzkJGD/uI6dgDxzltauK6RylMFp8zMvmkwsB/a2go1Gb9nYcE+dFcee+Pu9Z23lpnFdxGYjQNE3EYOJKKNx5XDfC/oba67CD1aFDRmHjNPdSuR2pX/Q7dr/3hjI/D37ywu2ofa7Vy7Bt8x4Irxh/2KecHPgs2IeYQDljj7jF8gBR9rtZrLC7XNPBCQwVwmLpGgRdC9+cr8js0DPOcbez37gc3NwOO/vbBgP99quf6ERwwE8smEbIvrOjI7tMe97/P8O689DMBjbNkm9TKkms1m2J7HtaPa7bYtLS3Z3Nyczc/PZ+o7eTWQPX+B5VP7Qx+y1e/9XktGB3VCb/yv/6ut/3f/XQho8uFfKof4c85wxzY9ZMX1+/1wEBUD6iobygLBFVV0GnRmoBST56gysIAFDocKwAIDAxBavJcbTqxHvHjV8fy5mRn7942GvWA4tEfvRrISuSYm3Hg7DjJzeOsYHENWcGbZgn5wwgHyILpUJkPqOFECEBxjPmK00WiEehRLS0vBkQY/gN5zu+GUs9HH4CGPnRpuzN+fbTbtN+p1e2g0ss/WDk5DtGF2X78qQgY0p6enbXp6OkSRWREwuKlgB6dum1mmWL0HhvDzme6lQNdna4QO7x7QGmv3Tzca9uEksRemqT2SJBlACr/D37wuPPBCC9MriGZmGacHoC7Wdp4jc5J8089Z8WOOwADBvIbih9zqdrvWbDat3++PgVIATzlzivkB8rbcbLz61fbwD/6gLdy8aZ1Ll6y/smLJxka4nvmN9Yzfc0SYHU0ATo1GI/SBtwZwQU1+MW9YrucFAiqqqKL8TGsOGCGjcW5uzhYXF63dbtvc3Fx4n5mZseXl5RC9n5mZCet+fn7epqenwzZddpS0JovZYWYoZDTLdtTWg/6FnQI50m63g2zEb/f39zNZ2Ngi54HwRfxhnV8kS2LgFIM74AUf4OKBdQxIaAZPLKsAz78yGgVAyuwg4/gHt7bsP8zM5JaH0L7wM1Rfe7zk4J/ayCcBRPGzPB2el1mM+WWW3drE2UB4MRBkZpn2p2lql/b3AyBldsDff7y5ab81PV2KvwyiYfwYlAKB9/yOTBYEcQAWeNk8XhvyPteX+j6cZYQ28zNht+KezFtup2YneXR5OAyAFHj8A1tb9pvN5ljWX54tzG1He2H3KDiia47nQwxMV14qAAR5Cn+BS5XwDhEUEUdtUAb2AUrhf2RNQV5iSzXsPa9N/H+apmZPPmmLdwEpM7NkNLKL3/d9tv+VX2lTq6uZ+cnZcgD/mScMWOmhU8jq4/phPCfKAH3c7pPwPSuqSGkiUMpzBIuiEZ4C5QXMABSctFarFUAFOEQK6uhCxGLCSSpl+4N2XQP4cfBFVMiBWJDBAISBuLi4GAQbp8yjCLrZISgFh5W3ynBqMNJquR2ecTaJgNBrWfGhbwy8cWSTBRLAA/QHAAKEuypW8JwFnyoNtOtJM3siSSwdjcxEAWnWEiK2UBZzc3M2Ozsb0m51Cyj+h5Ljkw45U216ejqMkWa56VwAsbA+SeMvj7Q9/Dm/m41HasoaRtfN7FoJxY81AcWMtQ2wEGucx4SVLRwczDszC2sor70nTR5PYeCbZdcIjAT0SQ0fgE4KyPH3ZjZWD0IjeR6oV79+3Wo3bmSewZlqc3NzoaYV7sOyE/30oqwxQPG0eF5RRc8mynOksN6QeX3hwgVbWVkJ6xmAMQJhkCUKkMBJQQZzo9EIeomv5ewTZBBxEW+zQ5sFn/H2D9TE5JO92Ibh77n+jj4b715w0QuCxHjJ8pttNbY3wTPVY3wPtlcU2OHsj1jbYjUZn7+/X7jNzJOzeZltrE8BAjFwxu30+stUBJjiHTpBdzWAvzisCEFNzeDhucVONddmystGe97ubi5/FQRggMfjOQdP8RteJ8hO0YyjWKaJB0IU2ercTn7xPGUdzX+DdPseP0/nra5vncPP39tzefzQaGQ3aOuglv3gzCLIMrZn1K4BmKLyi+cE7HSP1zGe453bCTspSZIwZ+HHcdAQfijAJwadIHvxe+6TB0yqvxPA/kceceuE1h991NKVlbF7YJ3gNELwBXJ1e3vbOp1OkMucMcWZUuAzZ6OpfxabE7HPKqrouFQalPIcXP0+zzH2nF3ONGJDAYsdiizmkLLCBfo7SV9i/8euYcEGAcQZOgBFkFIPIYwizzgy1OxQCcNhhAMOwYJnlu1TrJ+eUvKIDTjtJysSXAsByVkcDCiwguRxYyWowJRew9fqvdAfnkOIKGPrHpQKK0FW8DAk8B22HgAk2NvbGzNcPaPCMzzutYAuWqexecHzAO86fvy9kqYv8/rm7EfNtMEa4IwozqLkcUM7z5rHPF8R7YPxjO/LkIKjWPts0NdqtZAhylE9ljkMeMGQ4q2BvO2Ot+Vyf8wODTZuH69rldkVVVTRyZHqQF5vWNMLCwu2uro6FmTRmjJae4W3dNRqtUwWgmZKoC186ilvueLMGwBdDD6x4wgnCU6mfs6OJZ7r6dSY7ZDHSyUOrsFO0ECB58h6tgrzLvY8pklqluJ+ecEXzojBGHPtK9gyCqp4W3I8G6Ao4KBBKPCQbV38z/VI8T3ayvzDfGIeYM542Tzclxh/P3cXcOA+5ekyZADi+Z4DzgFwBdHKbn0qw1+QZiTH+sDAFP/Wm9NMeXYqz59HGw2Xx49RWRIOsgGowU6FVquV8eO4bdo/trEUSPO272l7PT7hfy/QxzU3YZ+y34m5i88ZlOIMUH4e2uLJKrarQttf8AJLa+N1QncefDAzRjwHsT68Qvu8pRTBAGRKsUzmLE9O7IiBlN6a8HhfUUXHoSNv3/OEQAyY4usZ3IEgQEYLF9ZG4TjeBsdKFAoKTqEW6VZh57X7KAuLBQOyD5gHrKjZCALyjjax8kI9LGzlAygyGh2clqGkws8DS4r65QGDUCYwfBnYgYPMoEu9flgoFEJNFY62xWsXeKrtLxoTNaZ0C6huPcLcwHziujoo2Gpm4XuMMc8rVTheKnHZ9h+XyoKOMRDWW6t8PwWAYkYtzyUGaHk+IUuq3W6PRf10CwSDUJhn7DyorNF5lvf3SRC3Fdv48mSOEtqttUvYqWDDiY1PNkIZsILxj3fUQxgMBpmMU8465bXABejZCGs2m4HXcDzVeL8fQNiKKnq6kuc0mx0CRIPBwLa3t8fKAmD9A3iGs9FsNm1hYSG81+t1297ezjyTHW119L1tHbC52PZBBB7HtW9sbISCumh3r9cLjhGfpJVX28gDE5hXZWUN5BzsycXFRWu1WqEcAUAP2DDIZOf+AiDBWCBThrchmVkG1OO+eDVL//bCwkFW/ihbCJltMYw1gg2wyRR8wDPRBrTb7ODkOy/LSOdZTGfFQELV941GI+gdZJvwFigAq5ivDOio/mRbQzM2dJ6YHdQg/TuLi/aPNzcDf//u4uJBlpTMBdadDEJocAf/o8+wa+Fr1Gq1zDZU2CmT6EIdgzzewoZC5hGCVjo/vHHEnED2I8ab66IpuKP2+LVazf72woL9wNZW4PHfWViwpxqNTFs5mMZ+HbYOY33xrgMQ23snscVU7VePzxx4Rbt5hw7sH/CcT/PGHNjZ2bFutzs2X3lNmo3XN2Y+j0Yju/g3/oa9/Ed/NNQJ/YN3vcseefJJ2//c5zLZTJCfCAR0u91wCijmZ7fbDVlU2FrKpVVQ4493KPBOBa+N+J/5q59VVNFJ0LG375X9nRf50ZRqzqaAE6tGGC8UzlBQp9UT+nlO6yR94oiIl9ETA6c4osl9QLYUIglmh6ePMeilpA76JMYa/17bqlsreUse8wD3gOBVparPU/JAlaMIOc9p5z7hBf5z4XlOx+drAY4wyKm88wDAMiDRWZLXDm13bI3wWGKNxMYSxFlSPG94nXtRPPCbswDwXF7HXkQqZuCdJFiiY4t5w9l0vF49nupnDGzy92zscn9iAFWtVgvGPwzXvb29YByORiPr9XqZbXy8FrBW0EddNywP4EQwLypwqqKKjkeeXIWcgSPR7XYDmGJ2ICewbRinNMGeMLPMlikcxgBS2cPrV7Os+H+ANAClEMzZ3d0N/wO8RgSfC+pytklMVqhDeRyeAnBHUfjZ2VlbXl62mZmZcBIZMtO1sDvaqWUKuBYlPsfv+Pegn5uZsd9sNu0Fw6F9tlazpxoNs9TPjoOs5QAtZ2+wUwzi3QLKX2ROcDv5mcpv7ofaMjHHHu3imqRcDxYOPnQV5hX4mWdXlNEpH2q37bemp+35+/v2uYZ/+p7qTs08Zj3HWT1oI9YJ10WLZflo+5XfMbCV9T+3kUE+ACdcJoEzoUEKOGDdAhT0QEkvUwzX/HyrFeYweFy37BZOBtK0bAOy6EDqS0DuePYTU1EmWhF5viED/GgzA1FafwptRw3afr+fkYu8nVO3/DJ4zWv1kc//fPvD/+V/sfkbN2x9ZcU25uZs9NhjmcOkoAsgWwFMMSAFecYnQUIe84ETKucVVEMbvXdQZetVdBp0JoXOY8LXbFwA84vRaXWEOEsJf/MrD5iKpWQr6WfsoPP/EMweuMNOOqd5sjLWNp4FsYLWtHYYRJopxQqDhXC9Xg9CU7dtQTHqGHiCzuN33jV5fGPHnpUPGxNQzpNSXsTgfhLUXpsY7Ig5Qto/bwulN29rtVoGUEaGIIOAcJgYNOJ1wLKAx6honIquOcq4oO8xQwbERpXOdW0jkxeBij3DM2j5hQwnAKpYe7u7uwGY8pwmb43wOoGxBtCWDd/7aa5XVNHTkXQdq57c29uzfr9v6+vrNhwOg0PKchK1Qzg7eGNjw6ampmx+ft7N4PS2ZrCjBP3O/+t2D9SUQo0S/I+sDD4FCllS7KDlkQZ6ivSrJ59Z57Tb7fACj5aWljK2m8pnBnswDv1+3zY3N8fKLOCdARemJ5LEnoDevVsygGUv/gbQgPqkqCGGAAM7yHgm+IpxADiFzAfNutW+ltGtSpq1yydP439kUCHzC7/TIuY8Xhqw8rJ4PHv9apLY1bugImegqf2NdiFrDidO8+4ABqXQxl6vZzs7O5YkSWY7KnRvr9fLzFNuM/pXZIcxOMSBINjhqGkE+xztBTDFAWzwmDMTzSzYBQhCcTa6Bw5y+6/X62N1urw5oQAPZ2rrWsNYohbw7u7uGNiLeYC2cFJAbD7oZ5zNhTIrkKNYWysrK9ZqtTI7dOr1g1PIYQNBJuzs7Fin0wnrDFvpkJnU7/et2+2GeYI2xWzuTJBgc3MMtOUsVd7KqFuhed1j3LlmlJdQUVFF9xPdk9P3zHygyjMo4Ah5gBCEqpfFEktx9t49R9z7G05yrD+q+L2MBlZA6nAfB5iaVLgor9B+zmhRoI9BNfCeI6AaTVBlG1N42ib87Y1DGR55hr4af3pd2Xt7dD8Ldk9ZKx+86/BZHggYW7ds/CnorNk/vP4USCwaF/1cHTrPSDwK5c1Ts8Mi/2x0eG325r/XvrL34OuQwcSROkT4OR1e5VkR2AUDUcEr7cP9vAYqquh+Jg8sgCxBlHwwGAR9y4GfTqcTMjf48729PZuamgoFzmPZjd4WKTg4XIgcjhGAMoBSo9EoROC9qD7XMOGsjTIyg2V4Wfni2TWajQ8eAcRHJgfbNhxk4HqlaZpat9sNYJXqHM6q4fZov9Q+ZZ3MIA+COwxE4J31Jhxjs8NsLs8W02ceVW6zftAMGeY3BzuVV55dyOPHIAR+g/eidns2CWfvAJjAFjMuscHb9xiUrdVq1u/3M2PHfFYAjckDJGI6l20m5aueisslErAFH8/GvGC7Sn0RT/aoDNL+aF+03V52PG/v8wBy5iPup75WDEjXIFtsLuDZPA94fvIhSfgM1wPwYxkJsBrgEw5y6Ha71ul0bGtrK1zDbVFZ45HOfQWSGDDH//gd/mZwCnKpAqQqut/pxEEpdWKZPLCmjMOpAvOoiykPTS9C2lnQQyizEGbgRg0gjmCqkGdB7LV1EkU8qeEGxcdGhaYKK7iAfjO4gWNJvRRXBRjyDFLP+c8bFwWczA4BAvQDkWXesoQomEYSuO2e8mTgQef5/S7gef4yCGk2Dg55c1+NGG+ca7VaiOqhdhRvgcQY6TY3Hoc0TcM2M4wljhjPkxenRTrenpHG/0/SPk/2qHHqkWfAcrsYIGZeF91Lx1+N1Yoqquh0SAFt1mdmFrKhdFvUYDDI2Byo38JAC3SfmYWIvwJS/Blv99HC6OwQcV09/pwj+QB1AFCpY1SmQLTqojx5yYFB3pLFJ2ZBrgLwWVxczPCFM8OGw2GozYLnT09P22g0yshnz1ZUUlBGv9NMGc6W0RcyYllWA5SIARExh123j3vtVL3PABky0Vqtli0sLGTayZlSuC/mBIAc9BtZuBhDDzTl7ZMeOMIEPvJJadgOOT8/b7OzszY3N5fJNOJtkmaWKcKObbCYx7CL1DbJm8d5upR5jIwo1JICiKZbOdE3gJYMVAI4gW3ONnq9XnezumMgcGzu1Ov1TO0oZMehJjDmBGxwBWUgA8AfzjjzXjGZxHMY/gn3gX216enpkHXGNXQBSCE7Ef4az0eeE6iZh8xJruHU6/UCWOXNVwWdvLniXaNrgWUyiL/jlxbpZ/tT/+fn6Wfe/xVVdFJ0YqBUkcAFxdB5Js1k4b89gwS/UUdOszJi7ctbcDGDyAPY8l7qaEIZK2ij5O3zzTOAioSF8lMdWy87zRsvFv7IplDlXDQfVEgfxfnV9il4xkaDGhDcfp0/Maf/6QBAlSFvPFm5l/mdNx94/ng85Wt1TSuQEovq3WuKzdUywLFe733GMiL2HAb1YjI0JlvyDM5Y++8FIFhRRc90YlAh9j1nH2nZAmQxIgsBGVLI9IA8QUAGssUDpjgqz9tAPFDKzIKzhes0Ws/AFG/ZigU/tN/eu/6t5Nll0CVqn7DzD6ddtytyhpfWlonJ5qOQgj6cJcM6FS/0DY46xlllfllbDG33QAp+V75yUBMgCQdj+URtvofZYd1ULr+hOi8vOybWZm2/8o5BXAaskKGG9YK2Jslhth22v7FtwzZOTKeXHQO9J2974/nApRH4pWvZs5/UF/EIfNV3bSvGkQPYzFtkpaldjt9zEFkDwbFxL5Ihnn2j/gB4yFlnHLz2thri3pARkA3Ymtzv9zPb+ABIcaaUl00XA6g82acyWoElbaMCd7E1FONdRRWdNZ3q9j0WZCxsWdCqcmfBxYI4SRKb29iw+aeesq2LF21rYeGgA6K48RsYEp7xULQwvf+ZYo44+rO4vW1XnnrKeg88YIPV1ajjGHMOPUGY18YioCrWB09Yw9BhAanADpRU684dW7hxw56an7c7rdYYGOT1JcaDmOIr6gODad5Y1Go1m9/ctJW1NetcvmyD1dWMQcf98xS3vtDGkwZJWDEd1dh9IE3txWb2GTuoscAUA9O4D0WOArcDcyQ2j8BPfYaZ2fzmpp27c8e6V66E8eBU5GazGZQuHC0YrvcrMOLx7IE0tRelqT2cJPZkyd+yoYb/88gD79SRyYviJkliM7dvB7m6t7SUucYD1fOM2Yoqquho5DmvLJM5e5SDKax3e73emJ7me+IzrlniOTqeLvDuxW3UvmhknmtJqe0Se1bM9smjWMa66iemWq0WAD5PVzKgorJUTwuL6VmV8yyrUSAcW8pQlH1xcTGzfY/LKoCvOIACNY4AFqKWoAKRebai2jc85txeZJxxBlK73bb5+fmQpa79AuFwEGxLxdxQgOo4xP3EuLPPwfOAt3RingCUQt9hk+C9KGAWA6fUxlXfB7zV2lHgMbaY8TZELjCPNYesHfA3FhiMtZEzjzyQk9uME+u4XWgTXnnjCZnG9p1mVGr7dC3x395LM/tjvpv6MAqQ8gsZfzgBD5lRKEKOMcB6VP566zAPmPJkTOwenBmlMjYmr3SbrFIFWFV0FjQxKHXcianOjWah8DV87Us++lH7sp/6KaulqY2SxD7+Ld9if/ZlX5a5j+cwnZUTi7a+8VOfsrd+9KOhnf/fX/tr9tm3vMVtxyRtmwQ0K9PWWBti7WQAKEkSe+nHP25f/vM/H/r5K3/5L9vHX/pS9/qi+0/adjUANGrFr1f83u/ZV/+bfxPa+dG3vjWMx2mASydBqnDLjPW3jkb2z9PU6mY2NLP3JIn9pGNY6jMmBQK9Nuq9mZ8auf383/5te8uHPhTG4xPvfKd9+o1vtDRN3bUci/ze7/Sto5H9+GiUGY8PlABzMB78HrvOM8wg/7zIpEfP/fVft1f+2I9ZkqaWJon9wbvfbRuvfnXmnvp3RRVVdHLkyWf+XP8G8MBBN7zg4MUyN9RByYuix2SPymQlyC2vhsmkINNR7Bx2OHnbELc9Znd6fYjxwttymCevPX4xQMJZPNimhXds19STg9lpR19jmTFH5SXe1Xbn2kFeMXHN7mEwD1vMta1edspR24t3BR84gMmlNRScAE+53hHGLGbX5s0Hvib2e/CA28h94LnBWV4oucF13XgbPz+nTFCJ28jyiT/nNaXgniYJcBC4jAxQcCx2jUc6ZxVo0m2EOu5eAJ5JwXYudB7LENU+FYHw+pn3d4wnus1aQScFnyqwqaL7iUqDUicxcWPodB5qXavVbG5jIwBSZma1NLU3/OzP2hNf8AW222qN3QuCkQ00z+CLLfCYAMA9NB0Y157r9QIghXZ+xb/+13bri7/Ydi9cCNeCNNMEAnI4HEYzEjzD8SSEixcV4H7za25jIwBS6OfX/tIv2Z9euWIdJ6vCizrE+sXP1L/1vmo46r7yxe3tAEihnW/6uZ+zm696lfVXVu5rMMr7LNbWB9I0AFJmZnUz+/E0tQ8nyVjGlFlWObGRAn7mtUOzeKDg2fjA9Qr4zW9uBkDK7GA8Xvv+99uTX/AFtr24WMSepw09kKYBkDI7HI9ft4MMtuM4ZBxt5XGLbe/Io9adOwGQMjNL0tS++P/6v+wzDz1ka2U7W1FFFR2bPD3uOUSQ3ajJozoMJ4R5uiLPCfK+K9KNKl8mCZwdFSTJIwVMNMOItxPxVjNcz/3mejy8hU+Pfdej3znzDPcyGy8lARnONZlw6t7c3JzNz8/b3NxcyJTiLCmMO54F+T81NRVsRw/UOCpP2YZW8AmZMHxSHGdz8fVqR3IGmz4LpPaPN7cV0GJfot1uZ2oFAcg5d+6czc7OhhPXvIziNE1D9tnU1FSmDlYMkCwLonA/tP/gJRfbZn7Pzs66gBT6wHW3wJOy2S/MW5VDatNpfxUI9saN7UheR7p+dI15WZxeu2OgpLdtE5lnkAOzs7PhM+4DH/yAdnK9vJ2dnXDiHgAq1JtCLT3mZ6wPRQFI/k2sv3ydd8DNSfmLFVV0WnSi2/di0SAlFsjeuwIPCzdvBkcWVBuNbPHWLVt73vPGBBDfz3sG2uoZYmXJy8y6uL093s40taXbt+3WxYtjbdKX8qaIyrQ5b0w8gylGfI9FbzzS1FY3NuzauXPR3xeBUt7/yhePZ97vkySx1fV1d97M37hhg9XVaFvuJZV1CJhebGaaHN0wsxeZ2VXLKix+17+LnsnXIkofU5Jm2XlVq9Xs3J077ngs3LxpnbvHc5dpx/1OLyKAENQwsxemqV0V42wS8n7Da0EjgbHr8N3CjRsBkAJhPOzChYnbV1FFFU1O0NGqq/OcBziEnh70dG3RvfLaFbu+qP5gDPg6DijPFNP/DFJo1ol+pgWY0/SwKHQs+Ifnxb7z2hMjBtG809W4zg1vi9fAEGceaQDQs4nLtI15qTqGs6Q0K4YDI/w97hfLglbijI7YXNB2MSCnWWdcEBx/z8zMBFCCgRMAIdp3UF6byszbIrvc25avgFts21mapmNzgNumfM3rC66LzR0F1DQ7SjPh+Pm6zQyf8UEKaJvX5hjvPDsyxjs9IZCBNQV6PJ4B/PFAanzHRcjL8lwptt2P+xsDnsrI3Qqgquh+oRPPlFJlrOi6BxYxKcBUq9Wsc+mSjZIk49COajXbuus4efc+TVLQDIL55sKC287u5ctB2PE9YADxS1PyzfINoJgxpNfkgTv4PK+//NvNCxfG+5kkdntpqTwThWJZYfp8Nbq8Yn5QjlsXL0bHIw/cylMcRwUUTpM+YwdbxBgI2Tezh+U6brsqqxg/QDqvkiQJ9SzMDrMH9TlspG5H1nH38uWxtPmy5PWHP78X9HCSuOPxSA5fPeJ5jzHSbEDd9qGZUjFnoV6vW/fKFUuTJANMje7K2yTilGkmQEUVVXRypDLZ+57la0wflbELij6P2QZcRLmoH/y/V7OkrF2jFLNXWCby6Vp4KTCB2kfzm5u2ePNmpl4pO9FeYWHdHpPHC2+88F6r1UIWDLJ6cNIaZ4TjNbu+bos3b9rmhQvWWVqKbjvSbUtl+Mr89e4BPuLENZy0hhN3FxYWAk8BDCXJQU2mmdu3bfbaNVtfXbWNubkxfc92HP4HMKH8UqccdZcYwJuamgpZZ4uLi7awsBBqMC0vL1u73balpaVwUrDZQdH+6Vu3rPHoo7Z54YKtz866dpGXFTcJf7nt+Fu/y9tRwuPJr5nbt6199ardXl62vbm5jJ3s1RjiWm8e0KFtY8BMi8Ujg6vVaoWsOawxnsf7+/vWunPHZp54wm4tLVm31QqFwVEcfDAYhALiyFqM8Zzb7L1YJui2WD7VEFlTACk9GdZeW7PVq1ft1tKSde7avuApMqnQB08+MN/zgDVvTuD3fF3M37s8HNoL9vftM2ahnqnXDu/esc8rqugs6MwKnedR0dau/sqK/e63fZt96Qc+YLXRyEa1mv3OO95h/ZUVqw0GZja+gD0Dp8gBLLP4YgI7SRLbmJuzD735zfaNv/VbBzVzajX72Fvfav2VFavLFqmi+8b6UoY8AIHf9bNJwbzeuXP2H775m+2/+rmfC7WBfvnrvu7AmOt0JrpXHpVtV8yw7S4v20e+6Zvsyz/0oTBvfvvtb7f+ysqJtfF+oKtJYu9JEvvxNLWGHQAg7038rXtm446PKrq83zDxtbFoLBsHvXPn7GNve5u98YMfDOPxB+9618F43I0mFYFiRf3Rz++FQr2aJPbeWs1+bDQK4/EdtdrYeHjtZgBKr/MoBjB7hpmC6Dvnz9ufftd32ct/5EcsuTsef/ze91rv3DmzO3fCs2MGTFmjpqKKKipPRXZTEXgTkx+TOiJ5DjMD5GXoJIMHeQ6zgimayeOVFHjpxz9ub/zZn83UK/3Pr3vdRG2cFIwAMaDEgQUFI/B6yUc/aq//6Z8Obf2Pb397qK3KbfEc39jfRTYpvzS7hE+GAxCEPoD/SZLYQ7/5m/YlP/ETltxt98fe9jb7nS/8wsALr81580SBWQ9EY2BMD//QYDB+/8Cv/Zq97Id/ONRY/Pjb325//vrXj/GGwcrj6jvW3br1kv+OgY/47fN+4zfCdnzM42sveIGZ2RiwqrzWfvC4aBsV6NHMNJ4DGmxMksQ+72Mfy9QH/uWv+zr7nS/8wky79IAEtKXIDvU+U3DKkwHe53ge7vV5H/uYvemDHwzt/nd/6S/Zb73whaFtCvqB8uZxbF57dmAZfyhNU3vrzo798GAQ6pn+jUbDfupuQkSMhxUgVdH9QqcKSjGxUIs5OviO3/GbR978Znvqi77I5m/csO2LFw+2++zvjzmxLBS0ECQ/O9ZGvi7vf+93SZLY73zhF9rGl36pXdzetsFznmOD1VWbEaexiE9lqEhYeIJQHVRVyPpszbTg/emfftOb7NGXvMTmb9ywG/PzttZuW9LrFfYpT8h63x1XKP6XN7zBrr/iFbZ465Z1L18+AAjlGTEQxJuL95OQ5nZ/oFazX7eDLWIP2/jpe0e9b8zA5S0ORfdh5f/pN73JbrzylbZ85471rlyxnfPn3XXMVMYJY+PlNMkzGLw2faBWsw8nib0wTe2RAoAwtlbz2sDkgYP4PM9Qr9Vqdu2/+W9s/bWvtbmnnrLtixdte3HRamtroY9qHKpzWRXNrKiik6UyjgGfFFfmXux8eveNgVIxu4Df8/Q43y8GNuTZg5NkzqqzjOwdZEBozaFarWYLW1sBkDI7rFf62Etfaptis2r/ygQ9PMcSL87m0tpMjUYjZJ2gP7Pr6wGQQltf/zM/Y4+99KW2PjubKbqsmSdat0d5rrYNHHUFQJABoyetIcMEtbGwHa5Wq9n0rVsBkEK73/jBD9on/+7ftTU7dOjzMkti4+1dE9vWpjWs+BloJwApM7PkLn8ffclLrNtqjdU84ldZUmAHfOb5wHMC/OYXMpEY+NH6kJjHv/83/6Z1arVQ/wjzAn1hnmsWtgLTCkxiXWn72u32WM02jMf0rVtj9YH/0r/7d/bJBx6w2zMzY/aGAmkYL7XdFTxVnmP8kSnHJQ5i23vZj5xdXw+AFNr9db/8y/ZH3/ZtZnYI+vFa4754vu9R/Avtm97rymgUACmzg2z9H9nft1+zQ58gTxbrZxVVdNZ0JqDUpGhvjPorK4dZLjmKgPcon9RCK9vuJElse3HR9i9dOjimdcL+5rXzqN+hXXgvC5Dxb70x7C4v29bCgvX7fbO9vcx3XpvKGNB5jr7XDs6y8yItw+HQOktL1jt37kD52PiceCYI4SRJ7KrlF9KOATqeopvkufrbPCfD7CDTbu/ixTAeT1cq4tPVHDDqKPf2jJY8PsfWn8qC3QsXbOPSpQNHd3e3EBCbpA0VVVTR5FRmPU0q54uckaL7sp7ViL5+z/+XuXfe87x7ebLNC7rFtrPhffn27WidwycvXMg4x+rA5/E0r0/aRj6xTjOjOKvHreV5t0bm+kMPZYBHrdUzCcV0T16midc3/D3/1FNu7cLlO3fssaWlDD+PszVcs4e4jQo4eFloc9evR2ss3nrwwcAbBUzKkLcWPKCHwUCtK+b1A/2L8fjc2ppdW111C4XraWxe+/LWGQeruZC9zg/+7cKNG2492nNra3br8uUxuRRrm2e3eu3XtniBdq9Wm9L8U0+57V5ZX3f9Dm5nmfeiPsUAK/48TVN7aDiM1jPVbXyVzVbR/Uj3JFPqOMBU7N4qFGKvmMNcxqBCH/hZQMX19BYFTI7S5zzDMXadR7FograzCKzi/utnsd/F9n/H+oexwTUaldVnckFC7D1HNGh3d9emp6dtd3c3jA/XOfCiMPoMbq+2/ywprw3ed7G5UyZCkqcYvXuy8ufoIadAqzHkFelVul+VZhmDqCxN4rh5zh9kUJ6M07Eys7E1r/N9UuC6oooqOj6VdVLKysYyzlDR7z1ZwG07DTmhsqhM+xg4UcedC4lzbT23PmatZhurq8GG6Pf74R11bvTI97z6Up6u5NPqWq2Wzc/Pj52ux+2t1+u289znujUA11dWMplGfGLZpNlH3jUKmnhjpEAdg1K9Bx5w231nedlGd4tEcy0ez2YsE3zB87TGIrJ2OIsLGTMM/Ow+73mW1mqW0PNHtZqtnTuXsWtQ84gLXDNPYr4F/882E7eB6x0hIwrZSJyZhvmDe8R4vH73lOm8uaA89Ox8tSlqtVrIipqenh7bqueVCzAz61y+PN7OJLFbi4uZsWdbJua36bri70ejUebggHa7nSlsj7bjc95yinvz8zfOn3fr6D45M2PD7e0wFzx/s4xcKPNdnq2I91g904fl+ooqul/pzBMVYkot77pJSJ1fT6jlGWZqBMWctTIo/aTt9v4u+5u83+X1k4GzoucUGTNFgFbRNfqsWNv5nny/WDTImwMxRRcb/zzHPXbNUemk51YZKjvG3m90bBl48gAp/V+Pro2NH57xbAZMmMfMZ00X9+Z2GZpku0xFFVV08nQUW8C7R8w+OMo9i0CBmNMVa0eRbIrxQPWDOvh5ATbvuu7ysn30rW+10V25hzqgnbsZPGmaZgCJvEBWEXl2hefEx2yQweqq/f67351p68ff9jbbXlx0x/YobWRSu4izd/LqG2m9od0LF+w/fcd3ZNr9kW/8xlBQ3stGmyQLiduoz+aaV7rlTYGU/UuX7DPf8z2WUjv/wzd9U5gLMRvSa0deG702m1kmWyeWjca/488Gq6v2J9/5nRke//bb324dOXwo1mavnUVgWgwM1u+53/2VFfvEO9952M4ksX/7F/+irbXbGTswtoXPzM/G974DT725oMBl7KAd3Kt37pz95jd+o43u9mOUJPavv+IrbK3dHpsT4HMeHdXHK7r2Wq1m39VsGmBSrmeap1cqsKqi+4XOLFOKyXPuvXTPMvfwlDgLBxZWkzhrGimIGRReyrW+yvZJhaz2ga+JgQgxoEU/h5GRB0oxIIAaFvycWq2W2S+tfCqiWFQpj+/ePZi/HL3C3+iLWTZDRA1NkBfpwYv5x3NYlWNs7Mrw5H4gb1zyCAYV5sre3e2czWYzY3ByLRSeQ2qI6EvHh9t4v/DsNMkDdUejke3t7YX3RqORieY2Go0QvVOnYVIgtTJaKqro6UGezvHsgTL3wbUMhnvPKXNPzzbx/o/1hUntAm87HPSR2YGsVHACv/nUG99oV1/2Mlu8dcu27p5oN9rZCboI2dfIwFYHuayjz23l02r5OzjIyh/097Gv/Eq79rKXWfvaNbtzt3zCsN/PtMvL1MD9y+hKnTNJkoSaUcg04YwezkjiE9kC2LO/b1f/6//anvqiL7LG5z5nd5aXba3dttHaWtBVu7u7trOzE7LTOPuojM3OoBnahHYiS2p2djZkGWldJtTAWvsrf8V+70u/1Aaf/KTdWV62q0liQ7Inwc99+YzHtgyYxn3R7D7oZ684v/aZ6Ymv/mq7/opXWPvaNbu9tHQA+j35ZMb+Z2LbP28Ln7bTa0utVgvZXajfxnVo0ec0Te2RN7/ZHnnRi8weftiuz87a1SSxnbsZiMiWw3rjjL9YpldeYJhroPHpemjr7OxsBmRFv3T9jEYj++RrX2ufet7zbOqxx+zmwoJdTRLb6/Uy9i2vN+XhUWQw95HlQB79zNSU/Ua9bs/f37eHzeyJiIytbLqK7ke6J6BUjMo6lwpo4W81TOD08n5sPsmhiDwhzim3ntIoq/j1OWroeNccFd32hJmCgbF7KjCFPio4o7yBwtf2a1aM91xV2PyOv3WvvTcnlOCcx4Q7GwYwYnV82UjUlyqPIsCtLOUBc7G+6hjp30dti/ccj+dFgBGDmXmZjN68Z34/G0j5q0ZeDGyfNNJcUUUV3Xs6Csg+qW0Qk9dln8UyKC+QxL9R2e69x56T953aH7otCraf2fi2ZVB3eTnUK02pXqnqJQ06eaROqNqpaAdfrwEDDrwob/srK7a9uGiDwcBGVMAcAI9XhFt1aozn3G8mZJpgO5SeFqjZJhysBa/6KyvWa7Vsd3fXRnQoTpqmma1PHi+LSO0/9gNi9mHMltq9cMFuv+xl1uv1LF1fd7dkFbVlUttEbUmex3nBYwWWusvLtjk/bzs7O7a/u5u7nT9vW6c3hzUbir9nX4v57vkWo9HI+svLduehh6zX69loaysD/qhd4/Hcs2f1O49Pyl9PFsTWSJqmtr24aBvPfa7t7e1Z2u2O8ZJByiI6ipzXPsbs5qtJYo8jiD6hbK+oontJZwpKeYrZA3LKbCnDfVTIIEICpxcRl+np6Yzw5uiRUkwgQejqvu5YMUJVhnj3gB+8H0coeALK66MnmFXJ8P32756OBuEPAw/3wUkVfGpImqbh9JeZmRnb2dmx3d1dm5qaCqd/8HNUCcZADYwDImKIinH0c1JgEH2Ympqy4XAYonxQMmx8QUkqMBDrQ8x49ca5DPjk9euoToyCVfqdd30eefMdf/P/4B/GkgFLNUTYSGGj3XudFpUF8U6qDTouKg95fcMIOmpR2yKqgK2KKrp3VCRTYs5aTFbl3S8GDOVdG3MK80AvtU8mAUdACjLwSXac0cO1ePhEuCI+oA8AVfTFz+Y2eTrVA5w8wISBNH626j/oRWQkI8sINtZgMLB+v38ATFDdq1jwJ8Zb/h91eWZnZzMnrKEej9rEzWYz0zfNmOF3zW73wKm8OaI2AWw5Hnu1WXmc1Vb2Mu/18KQYSFUWkFLwDG3W4tu87YxBHp3H4N9gMLD9/f2QdcZjn7ct0psXygsGIpGJyDYbzwOex94z0A6en3oqIM/ZGKndHQOn1M+JAYDcTgaamHc6X9l+VWBqUtu0SN56clSBuzzAKnaviiq6X+ienb5XdkvbJM9gMAhAie4d11MWPCc6toDZmGAghItRaiqoB7zF0Hm8e4pSFbrX1iIqI4w8I4v55oE+zGuAUqPR6OD0QeJ9KCi5u5vpIzKYitqDtuNezWbTZmdnMwq90WiEY6BhmGrESRXU1NSUpWlqMzMzoS/7+/uhzwsLC1av1zPGCaedwxBhwKpMOnSZMSh7rTc/PAM5z5lg0jmSB2Cxkcz8Ac9qtZrt7e2Fd2SisVEFZa5HVvMznilKtKzRyu/4m50lT4Z4WzcADnO2H36L8cGYTLLFuaKKKjpZygs+5FEZYCcW9DjuOvfaWgRMnSSpLARI5Z2sxbYAf54HVGiwxbPrarVaxo7RoCfbHLHfK2AFeQ+Z7Ml6r8CyOtFlAZ48QEVPhFPnPgb0qWNcBhgBv7wglzevePxi2TrKYx1vDgxDX+7t7YXC9lzkHuCJlhc46vxmHqr/wHPGA0GVf0Vb3RTA8MZIP9dnFmVxxeYB+OvZKLFSGtyOGHE7i4Ap/Vwz+5TXaoNyG2O2Kvrpyd2y5PmA2p8YUMW/P46/WFFF94LO/PQ9z5nia45KmikF8GJvb8+SJLHBYBCu5ahH7ChQb8HjGTjFAS+AUthnD9CKgRxVhLFnHtUZLPoNC22l2JiwwOYsJBbu+JvrIMzNzQWjChEQrvHE0QVtg97XAxAZ4Gq1WmOG3tLSki0vL4d95F5f8TdAJNR32N/ft1arFaJO/X7fGo2G9Xo9m5mZCf1BZHIwGNjOzs6YUs3jNfePx4ffy1AMuOB7xtZZ7DmTPJ+NCfAFkdvBYBDWIPiBcY/VSeCaAl5x2Zgxey8oZjDkGWQxypN7MQPccwYApmIsarWa7ezsWKPRsH6/H/iHbczIAsR6ihnZlSHj01k53RU9O+gkAKnY32WefdLz17vfSYDdnnOugA6ChVrIGLpeM5K4bXwKWJIkLoClNkeersU9IGc1k55tKG4b7qu8ZAeZT65TfalZMp7znMdjJt2+FwPZvGCSBvIUfFDAyuMxj4e2XYEF3cGgIApnbYFPuDd0Z6/Xs263a5ubmzYYDKzb7drOzo5tb29br9dzs9C4P0x5c4NtWS9Typu/PK/4GTzm4LW2hcE3bWOR/6H2txaO577G2of5oPW52Pbw5pBSDGxRIAk7MjDmDBphXaIfvC0x9gy2sRRY4/7wZ3lyvUzf+LMiACoPiKrsk4qeDnRmmVKeIwVQAAqLDQBV2gruwIliQQEh1Gg0bDAY2N7enjWbzZCdAWcZggMOGtrIQkwdYAgXPmYUmTgQzHxkq9YzUDAkJiDUeMD1SpMYogxIqUED4A6CttFoBL6o4vKMBYAM4BvGFH3nyFKZLAxVaFASOgegCJFGzAp7cXExpJk3m83AV+9Z+B3GaDgchuLcSIefnp62fr9vCwsLwQAEYNXpdKzX62WOiR4Oh9a/W3xUjzjOUxDeOBZFoPLIm2dFzkfsGZ5iBe+ggMGbRqMRCp3jezbcwWs1AsBvLiqLlxcNZsX/TFS4eU6ORtnZqMc8BChVr9et2+0GwBDrBoXQeS2PRiPb2toK8xpyFCAtANjjRIafCZQXVJlkfT2beVjR/UWTAFN5838SYqct5rzHnu+B8mi/Bj3wuYJD+lvVz2q3qQ0UA8iYl0W8Ykc5xiPtX8wJ1TZ6PItRUd/NsnxVHqgtAOKsEk93xAIvDCTxNQz+qH8AO5zBJwa0tN2sN80sE0Tl4CPbJLytjEGeo9ghPF+ZvPsVATXeXPQyxcwsUzfVzDK1x3Se4bcIumtAHuCvB7TiHgxIceCMt55yoXsNRnrASwyI8vSwrjf+jsFs5bXyhOdLmYw/fn5szCoboKKKDulUQSlPifM2OhCAJA+U4jRc/W2appltc61Wy1qtVnCeEPHY29uzTqdju7u71rt7WgIyOViYIXVXU6JB6EOr1bJ2u23tdjsTOYKghvAGYMJ9zRNARUCNGg2TKC3+jk9AS5LEdnZ2MnUAOEqnaaj8PZ9wMxqNbHp6OgCBAAChzHGvWK0t5jO3TQEMZGzFMtzMzNrtdiaDzYtmMH9QkH12djajeAB2wjHn6Fi327VOp2Nra2u2tbUV5hz29K+vr9tgMAjzjXnPxkzRHChrqB9F6U3iWOh4qeGB7Y7ISMR8MjvMTIRBx3OADU4AeaiHwXyHYahG4VkDUpM4TkWUN2b8rt+pQ8aOF+q/Qb51u90AtMJ4xDqq1+theyu2raZpatvb27a5uWkbGxvW6XSCLEW0mE96KjISn4mkTqpZuWikJ69ic+kk+PdMHoNnGmH9HFe2HGXtlf1NDDzIk1egWN+8Z5fhA+SeAkvQtRy8go3CAU3O/PayiLRNGgRQcEN5gvtoxjcDZSy3Pf578lWzM7TtOiYKdsQoNv7MQw0Isn3OQT0usM42tWZzcfs0a5pBAg3scf9hQyAIDNCEdyrE7Fe2cZEtBXsVNgcCMwjqwCbR7XvHAaT4f89OzQNUQDzuaiN4QX7o+2azmakZ67UL14Ov8/PzNj09bXNzc6HWGEp1sJ+G8eRgGYK9vNMAPN7Z2bF+vz926h6DkByUjPGAeaW2Pj7DmOn8qdVqbiCZ1xln+3k+2HHtodO+vqKK7mc6k0wpFrYAfMwOi4/DUd3d3Q1CgZFyXtyI8vP/KGq4v79vc3NzmYwVVoij0ch27h71u7OzkxEeEFKc+qqIOATe/Px8AMBQPLNerwdhDbBKwShkc+HeWqPoNB1tCGAV7FpoEHWA2MDAtWyYQUmxIYHr5+fnMxEnVjBe9A7vnoGrIAgbSKqAQJq2je95HvG7R8j+YuMIICj3Z2Zm5uA0GZofKOiOec3PP4nMnpgRXAQo8TNVkek9856tL7PDNHisK2Qygne8lZWzIvG3mYUoGb/nFWpFn+41MHUcZ/Iov/N+ozIW48HblzU7kvnPoFSv1wvAIIOBsazHvDY+W4ylkzYMTwL4PO4YPFvG7ulOReD2Sdzf+6yMHs27h+pjfFYGkGICuI6j31Ebkq/zwBnPxigLhumL2w4ALE0Pt6pzMAB1L2G3chCTtxyyjcMBUs4wGQwGIfin2SacvV0UyIn9D92ANuA7BqQ8kAd6xdsKzs9nEAN9hV7ygD8FTtge1fFTe5c/Z76i3dw+kIKA2oYYkOPZWrpO+fecceWBjfwbD2jhfvF8BlA3NzcXtqmxbzQ9PW3D4dB2d3fd9sOWQ9B9amrKZmdng5+DZAA+cIj9BAVU4SNw8J8z07i8R4yfXr9ZbnDbY1l5sSwsD/T1gtn8bN5+qfLDmzfePNA5EeOBB6DD9zgqCFZRRfcTnSoo5SHUcFyBmI9GBwWxkyQJoNTu7m5w/uEsmR0qMM7oYZDLbDyrCP9DWDIYo4IHUR1NN+Y9z6PRaCxdFc9CxGh6ejoDRug99vb2AjilwA0LsdMYD1UQAAVhxMBwSpIkbMVigJCjjbOzs5n95CA2AJWXnmGE+2laNX6PaxmMYoPD65saI/xdLIrI35kd1snC3MH84XmNyJmZBcWOuckn9nEfJlEaquRi4NEkjkGZZ+cBXDoOZoegFDITzQ4inFNTUyFDLvZ7/M0GNd75JBmOcJ210i0LPHkGUJ5D5lHMCSx6rkZ8R6NR2K7c7XYDgM/R7p2dnTAG4CuyALvdbibrlKPEnqOR15dnu5E06Vo9rWczFY3JUcfu2T7W94LU5sm77qj39+ZwGSCnDHlOZdl2wTGenZ21xcVFa7VaGbtEgSN+95xnbpPXFs4S8rK1+IUgGgKwyChpt9tWr9dDmQEFp9jJhQ0Je4azSnq9XigfgEACinL3+/0AOBTVCozxnLNsuH/QIVoDi4EHM3Ofy2OnW/RwD9wTxKCNghCcqRUDBdhWVLsvb8w1eKzk9assKdABO4mDxmXvr+sRfMQugMXFRdvb27N2u53xc2AfwGZTnZ6mabBjATwBnAL4C4CVAVVdV+Ajn7KnW/i0nqiuyRgfYt/Dx8K4gS/Me+YxgFD1CWNAFe4H3iAxQgFx9XWYNOML18JH1Lmp9apic9cDZyuq6OlApwZKKWrP6C8iKNhigndkQPX7favVakFgYgsWCxV2rDiTBc+OgVIe2IHf5gEZfD0AKa8YJd9T03uxPQlZC9gSs7GxYb1ezzY3NzPbw/r9fhBCCrgo5Qlsz2ACP6EsoBhg5JkdROQQWUGGhfbLizqZZQ03ABPKXx4fAHlafFS3u2lfVHlhPsFw87Lm4LCD2EhRIIGfwXNAFagaEkzHcczLgiFKRY7JpG3xjD2z8cKZMHbMLBjDzWYzgCIwzs0Os6fwDp5ub2+HbXuQE7ptD224FwBVWcpzEovGNA+MZN7DiIKcAMhsZmEdA1hmQB8yjI9J5/WDbClE4dmgPI4h/kygIgf66WIElgE6y1zn3bcCss6GiiLkZwmAxnSEd52uIW17kf7SF2QZHGfemgeKZZ9woA1OImRos9m00Whku7u71m63gxzE1i441biP2o4MurDcZXBqenralpaWwpYoLqXAWUr8bGRJ9fv9sOWJs1rx4qz/mL4sAqc0Gx62I4NK3Ge8eAx5G5lmQWnJDtxveno6BKc9wjMA4s3Ozlq73bb5+fkwF3hLHweT+R7Qa7DROesMtp9n5zGgwbZfnn70wAroZnwWC76p/c++BwdCQbCrwD8kAuCZsPsBSqlNh+dhbYHP2AUCmx189gqFK7DDvoPyyssOwn3KEq85tmvUduSgNoNMeMd4ICliamrK9vb2bGZmJtj/sItgJ7F8AKnvCGI5o34mwEKME98L4+WBU2cd7KqootOgUwGl1GBg4YtFBAUNMCpND7f8tNttq9Vq1uv1wvdwwHg7EEAss3hKLYNSLGw0suA5gV50EMYLK1A8D/WEUEcIoAiUG07t6Ha7QZj1ej1bW1uzXq9n29vbQaixI66AVB4w5RkeKoj5xVv4YFyBx6g1hYwpFvDY5sf3hQJjYAovVjyckYTrkQoMY0xTwdWogvBmhWt2aLyNRqMQNeTi4xgfL7uOo22aCcQ8ZACFQRmNyPH1x1EaRb/NAzC8/2Of8fOKnqOKkI0M8AcHDvC4ekfvYg5grmOcwF9O775XmVJnRQqKesR9ZzAQvOGtI1zri2tI4Z3lKeYugHO8WD7cy3pe9xMV9fsoQOT9RsdprweiFtGzdS4dl2Ig4CTjV3a8jnpPJgWh8kDM2D1YN/NWMq5tA/KAKSboH8hC2DwAj+r1enBCGaBA0MUDTfAcbiM/C4492ruwsGBTU1PhNGHYxpzFwcFDBqQAjsFm5KLcMQCAxyL2N9qv2SXoL4NtoFh2D/Q9AszQS1yTim1qPJd1jW7l44AmeNdqtQIvMXbIpONMHuYrQAbY6Jx1Bv5pABL8YN7GAAj2Q/izWAaO2vmxMWM+wHZl+xnlE4bDoU1PT2dsY7xjDnOmFNvYw+EwE4QHQMVBeS9jkNupgJS3ndTzbVgu6LuS2sgKlnL2HoNVPLZs95sd+kGYhwD4EKhHdhfW3Llz5zI7YcwO66Hx8/lz2FTgA9rS6XQy4CR+gzXGfgb+fibYHBVVdCY1pcyyxgAWEGdC7e8fnvzW6/VsNBrZ7du3bXp62nq9nk1NTYXi4jgRrd1uZ6I17OSCFBTAd82bN2368cdt93nPs71Ll4IAU0CNP1dipeAJcwgcOHdA1AFK4W+kYKPfcMABak2iqPLAKK+taZoG5YsxAJCA6OBgMLCZmRlbWlqyZrNpCwsLGaAQ4CAcYc7CQBq2ApQcnazX6zZz+7bVn3zS0s/7PNt/4IHMbzSKpO3XaBUbb5wWDL53Oh0344ONIVVwOjdw/06nE4xDBhwx9moQFjnyCkowrzzwVMc57//Ys7zPr4xG9qI0tYeTxK5Gnot7o39mh2ubFTGyHr06Hwr8QamzQsZ9sN2XFfVZgiNs+PDzTlLxs8PG930gTcN4XJPszCTJ1u5gBwg1pdiAZ0BfnTeOyDMIBbCKDVldP7FxeKaCDXlAQN7aOq1n59G9NE6PAlA9G+k4gYuTpLwAnRek0zmvQR7Vn55NpfIcf7Ps0UAI2waQV7AVNQjpOf5m2QwelJEAIDQzM+PW3VFnFg4tP0f7wm0G6ITgAINffJALy2O2dfhzDdjEQBKP8tai2lX8TH4G+sR9hU2IcebfouYrg2uLi4t25cqVYKtxCQfut27Rg66DvQnQqdVqjYFdrVYrbDvjkhToDwA9LmjOAZmYDa4vHXcee50L3rU6BjF9qnYqgkrIRIONpBk1ADYUFPFsKvSZQVusD8104/WOe+C+GFfY3bCXu91ueIHfPJ9jpQL0f4+/yBrDYUx83+np6UwbYVNqMJ0PSRiNRgGMgp+KrCXOmIJcYJCJeYHn8vZF/g3AZAa+8HzYXVhjKgsrquiZQGdy+p4KVyhxgCGI6M/MzJiZhUwjCMJ+vx9OeWi32yGNst/vBwECZcN7mrkNcJbr9bqd/6Vfsuf9w39oyWhkaa1mT/z9v28b3/ANZpY9DYSdQ1YCqnA8Yc5CB+AIgAtk6kAw81YZFkAcTfAycfKAKXUWPWca10EB7OzsZKJ4u7u71mw2rdfrBb63Wi0zs0whb6QMg78MSnHxQwYLOZ39yv/z/9iLfvAHw3is/aN/ZLtve1uGxx6xguK0YI5eIKUdIODu7m44idHbGqhGNv/PIArGFieHIMLGJw7q+BUBKEWGvwdKeffLA6PKKK9v2d+3H9ndtbqZDc3sO5tN+5mGLyrwDDbM8RwYdNpunqdeX9iQ4s91vPLuda/Ikw/HpXcMh/Zjw2EYj++o1+2npI4b8wptUMApSZLM9j3+jkFEzwDX67y5HPv72UanbSiWmfOenMijszJqi0D1Z/O8MTtZYKrMvTRgFbumDCiFz3jbPjuvnC3DmQzaZrPDbAA4bf1+fwwI4XfYL91uNxzA4gE0XlAPbeKDW0ajg9OEYZcCwABp0ETbz2319AF4oKcJs43Bz1TwQ21MrRuV11+PYrpYszE0Y9kjABexOk98+NDu7q6trq5mAh34nco5nS+Y3wqaaFYav9A+BHLMLIBSfPobgBS0ie1LBW0UkIrxl+0y9otiwZ0iW1FBXzNzM+z02QyScKBJ24v+6RZUDt5iXMBPrFmen/B/GJSCzcy+j5eVrZlpZXgNUIoD4lzGg/mvwVP0S4FV9jHAY+4nb+fDmmV7SYE/3s3BbcP/tVot7DbAmkM/AF55dobK8yIb4dmubyu6/+hUQKmYo2qWdZg5CmZmIQ05SQ6PNJ+amrLh8KAuDQwPGAwApRiMwr5nFph4T5LEZm7ftlf/b/+bJVi4o5E9+A/+gT3xBV9gO+fPj2WleICECgP0F+2H8IWQ3drassFgYDdv3gyGE0djtre3g5BmBxzCiiM1GiUoK1RwLQOFrFi4bxDQaXp42hwMs3a7bTs7OxkjjrOaNJ1XlRlnsyVJYkudjr3pB34gMx7Lf+/v2a8tL1t/ZSVjFCgxOMG0vb2dKdQMgwc1vcB/z6hUfnhzlw1Gjvhx5JINmLKAVN5zNauI+68ZMqxIywIk+O6BNA2AlJlZ3cz+z91d+/UknjHFc4vvp6AuPi8CpSYx9PK+Pw3Kc+AmBSPKGLAPpGkApMwOxuNHh0P7sIwHfqOOkPc9DB58HzOydRw8gLVsnyo6HSpaG6CiOTnpuJ0GiFXNnXEHY9LfFQFMRff29EUeKMXf428GeWCbMXDAW7n5uepMj0ajoFfZuWPZA/kF55dLBcDOQDAM3+/t7YVMb+4XZ9eYHW5Fg/OpwRDNumKKZSkxwIJnev2HfQHbAqUfYD/CbsSLnWI+kIId4Ty9Gvsb7WDHWf/nrC7wnU8k46x4ZJmAr8g24SwSBVD4O/08Njd5jvLnOnf4nXmkABRn7DBQou3yKAZI4R4AIcAL2Mn9ft+SJAk+T5qmISMMdqYCU9iWr9n+MR3uBfr4N1gjCkR513KNNR5fgE6Ypxyc51N+ebukt8OgiMfcZgRDe72emVkItGM+8pzCdxgXzFNk3oGPaM/09PQY7xhQgowyO6y/62VKQb7xLgDIuyRJMhlTCHjjHjs7O2Nj5M3FIp0as+crquhe0allSvGiYMWgxgRHg7DoIHghRIAMA5FOkoNtKNj3z6AUiu6pI4/nz3zmMwEAASWjkQ0/9SnbJGTcA6XwOSso7idvD2O0v9Pp2GAwsLW1tZBiDkGELXz4G8RGmArnSYAo7zMFV9S4hLKHEgSgg6y10Wg0lgHl8Ztfmuobjpl94gmrSTtro5Hd+u3ftkef97xgEEJQcwFCVZZ4RwQGgBQMNCgN1CzSLLRMG5ysOBgM4JGZjUV2NL3dG7c8YMozlPKcAjYw+DO0N/a8mFPyUJpaXT5rmNlDo5E9GYlsK58UgIqBUvx7/B3jVVkenhXFnP5JAam8fqRpai88wniYjQO2bFixcekZ7JO8Kro3NCn/T2qsVI9P+ruKfIo51d51k3xflu+x6zwbyPudglKw4fg0OTh6+J8zp9VJhs6GczYYDIK94ukC2Ia4jjO18RwuicDbeEDcbpaXeRm6XrAMn8f0mD4ToBq2CLJdjDZzNglsGy79wA5x3tYnT27EQCp1qHFv7GyIBUvNLFMcm2103jqFdmLeqC7CPRmIZHtNeYz5wXOE5RX/Lqav1Z7i+3nB0ZiNrfYQtwfv0MVs65tZ4DFOIcecwFrgLCLW5cjuYTCQM//wrjatR7Vabew7tol1XoFP4CeDl+znwP4GyKLF+Xk+xXwe73+1ITFnwB9sj8VBOwCXpqamQjkYzko0s8yBA3xvzkYDxUAp3b7HckNBqeFwGPxa+BRYJ2gDnhHzs7j9R9X5aqNXVNFZ0pnUlIpNcE6lhLDHfm8I40ajEeoE4OjcXq8XjuBU4wPgFEcLWPFsJYl9eZJkgJBRktinRyPrPv646/xz1CFJkjGgCH1EpGB7ezsYScPh0Lrdru3u7tr6+nqIFPBea2znA/rNgr8IkPKEdZnr8By8e8IN7cffEMgbGxsBAEySJGwF0r3YXARTeYj/O/2+jcyMzd1hktjvb2zYbYr4sfHC79p+Mwvb56DwGNDkKIxHMWOFDXI1dGDAcVSV0+nPypHnZ7BiiRlO2h8zs0+nqQ3NMkDIvpl9ejSykfnOixqT/HkZsMZTpkV88hTnUR3mk6A8Xnt9z5sT3LdPRcbjM2QU6VrII10rnlEe608FRlV01LGP/e7ZZACXkYHedUXr+6iA31HbU6YtAHdmZmas3W4H241tNQalFIxC5oCZBRtQbS+zrA5mm0nbl6YHp4oiaAXCITto62iUPawFfYn119OjaZpa/fp1m378ces/5zm2e+FCpq0aLAIABVAK78iK2t7etvX19RBkg525ublp29vboXQA1wnSLCk8z7MD8/qkYBTsHAbM0vSw7iWXgsDnuB/bghpM9Oxu9IELk4OH9evXrfbIIzZ48EHbv3Qp4+xrwNHMMgAJAzsICIO/KO2wvb2deUfhafwOwIECZmqDMWkAmOcl5j3AKOxG2N/fD0AubGAcXMKnPjJwDNuc1xjbqTrOsXlQu3bNGo8+elB39+LFzLgASOP7AjTtdDqZrZDIiEJWVGxXgdrK3nz0/Bq1dQHWoW1JkoSaptiBA38tSZKw4wO+I+ZLq9Wy0WiUKQejYBSI/R3O1kRGpga+R6ORNW/etOYTT9jmhQu2ubQU5B7AKJyGjowp1PtFZh3Wl2bF5Y1pRRXd73Rmhc5VSLMxwVEDCHmkBCOttdlsBnQd6deaHo5oHJ/kobRWq9kvfs3X2Nf/6q9aLU1tlCT2i1/zNfa5/X1Lbt8eA2r4b44CqNJnUArbwyBwsW1sY2MjRPMAkiDdmvcNM4/KGhJ5v9Hv9W9vrz76CUGKlNckSazb7WZO8mIQ0CtwrrVtQLVaze7UavZ/fMEX2Hf/2Z+Fmjn/9KUvtU9ubFiapiEVmLPIvL4w37ioIfOX+xNzzj0DyQPAYjyH8vEy3MqQZ8yUpUnmSex3T5rZe5LEfjxNrWEHAMh7k8SeNBvLMJzkuXmOFv7X+5Tlg/72rMGpvOfkOd1lPn/SzL7dzH7CLIzHu83siTQ1S8ez1Pi5sfbE/p5EZlRGT0UnQc+meVQGBCoLTnm/iQUHjtK+omfl/R46ErYZikzPzs5m7DUFpeCUw87g7BEAE6x/NYgB/Vur1cLJwXySHmc14/m4HrYGZ4jgWd7WpTw+LP/bf2tXvv/7MzVL7/y3/23oJxxndqDRfgZ7cDhOt9sN2facXcL1LDkTg0GZsrI7FoxgEIozwDmLCrYh3tEHs8MaRwwSmB1mpSmfFcQCSIOxStPUZv/Vv7KV//l/Dvy9/v3fb5vf8A0ZkE/nBG+BAg8ZlEIJDQah9vcPT2rmkxa1rIaCUTEee/wG6ICxY+Cv3+9brVYLtWbNDjJl4Avt7x8W3ebMZ/aFYIPz3PCAHf1s9l/9K1v93u8NPH7q+7/fNu7ymDO7OMjLpSwASsHfwfa8vCCxB/bk8dHjNXjBgBlkAHiHeQYZgXcElRuNRpAHZeQevmcZpe1THl/6lV+xz/vf//cD/iaJ/eF73mOffctbwthhqzESM8Dz6enpUPuX5SG/juoHaH+eTbq5ovuHzgyUMhtfIKyQITiTJAmLbnt7ewyB5rpRDH7wtjDe/qfASK1Ws/8yM2Mf/tqvtUudjj01N2dr7bbZZz4zdp2ZXzeBU4eZEN3jKB+ENfZWQ8GxYteMK7znAUxFDuSkoITn1EPocZowjw+fcAP+MM8BVnlgJH/+gVrNfvnFL7bn7u7a5xoNu5GmZtevhzbEFJa2F6Q1D2L702O80/uWNTBiY6dKomg888YiBlrGQJxJFUuSJPaTSWL/b5rai8zsYTO7anFAKkae4cP9KdOuWJ9YZuRdF2vLadCkwFPRd0w/aWa/ZpYZj7x7aORQvzsOVYZKRc9UYsNeAzV5+ues6bhruMw9JpFnnjzWreyq33hLHP7nTBR2XD0gwMtAVvsMTiae3Wq1AuAAR5kP0sFWHtiaalOyLcjPywAC168HQMrMQs3Szde9zvYuXhzbkoY+oT16Qlmv17NOp5MBpTgbyeOHjlVZme1dB0AHWRvINkMZBz7JFXVe0zS1jY2N4PBzLbFerxcCl7zli2uPxsAlM7PGU08FQAr8vfx932frr3mN7V64kKmphWwt2OT9ft+2t7dDTS7sUuBMKWwxY9AK92Qgkee3Z9PlrZ8yASOAIgCgzCzDR/gU8H+QAYU1gy2ovOXOKyuh6y9ND7L8nncXkAKPL33f99mjL3mJ9VdWAm+w5QzgEmrn3r59OwNKAfjb3NzMZKkh2MwHA+nYe7ID755to/4F+IhtvVzHDjW7ms2mDYfD4Fdiax924yD4rrpBx1F9Fc8PSNPUpm/dss/7J//ksI5umtqrfuIn7JEXvcg67XZmiyPWum7/8+aOFzA/S3DpJPTSUe51FJ+3ovuXzhSUUlKFw9lSfI1ZNi0cRgLevaLaEDCMjDPAdafRsE8liVm3e/C6SwyweIAU2ukJRd4TzsqH9wizwVRm3/SkoNRRqAhA4GwpvNdqtbC3mYW0joUa+Nwf5ulGmtqn09TSvT2zu8oi7zd5UeS8lOAYr7mfyos8oVcWsCpDsfurEczGfx7ocJQ5gt9cTZIM+OHdM6Y4TgKIwf2LgKlYO5hvp0EeuHaaSvGqmTseHp0U/yuq6NlCGm0+jS0RkxrtRWC7d/1RKE+Olrm36ijOpuFsEr0X62dcx7qbaxnxPdWpNjvU3cg8MTu0V1qtljWbzXC/wWAQnFNkRXA9Ks5O4HnAmV0g5VnzsceCMx+uGY2s+fjjtnP+fGgzXmwbcjYJb3Vixx3XMUhXxrY5CrGTj7HhmlUIUKZpajMzM4H3SZJk6nZyrS5sh+K6PJxlj61PMRB46uGHXf7WH33U9paXM3wCQIaso8FgYFtbW9bpdAJggq2Pm5ubIeuMgRaAUVzrS+dhjM/HAQMxjuA57G3mP2ca4TeoP4Q1wOOI7xnE5Ew4/D/36U+7PB59+tO28fmfH8YWOxAAIG9vbweAj4Pw4DEAPs4M4/pYvLVQgR2PN0V+C2S5ZrhxzS5s6UN2FbI4R6NROHjKLHvQFR/cpO3ROaFyKk1TW3j88bEgb200spknn7Sdu3V0+aXgPq+N07TnJrGfY34Z06T6rMzz9P4VPf3pTECpogmkoJQ3wfkajg7hOv0dsnR4Gx8bm56Bwc/Ma7MKBq92lf4Oik23drGCmwSUitFpgFX6zhlUZv72thhyP6mQ09/os7y/8RvvdwomKI/zlF2s7acJEPJnRetIec7GynHJA4g8QCw29h5/ywB6/NyyvztNygNDy1KlPCuq6P4iXZPIDvbA57NsB8v+PKD9pAG0oxDr2Hq9bv1+38yyW++Q2YE2MgDFTjJnnbNz5gXyVKdA78HG63Q6lqZp2Ao1OzsbTnVGxk6z2bTNzc1M9gmCahzgBLEdyYHMvYsXLa3VMk59WqvZU3NzNrhzJ4ACyDJBxg6ydzY3NwMYxfV4kCnFGVKcNaV1lMCHScASnms879l2xXa2NE1DZhn4MjMzE7J4ADbx6XsoucEFzwFS4Vqux8OAANrVXVy05zv8vT47a/3btwO4CFAE8wWZaJubmxle4h21pFDvlbchAqRi4MGz2WNUdA3b09x3LpmBrJ1er5eZKwBcMQe1pAk/g9eXBwbj89n1dfv8ZLzu7r9/7DFbv5sFxaeLY7wBTvV6vfA/v2MbIgeKucxGbMeIx0Pvcwbf8L+ZhbUCQubU7u5uqCM3GAys3W6HrX6oydXr9azdbmcSGzBfWY7xnACIZHa4/ZW/X08Se53D30+PRnbz6tWQocf1hjmTEtl9DJ7Gdu8wb8rMxdjvyl4X87MmuddR6TSD0BWdHZ1ZplSZrAoWNDHnz0OfPdLT2XAPjoR67fCAjiKASIs16hZAs0MwDYKYjYY8R31SUOo0yQPNPCoD7MUoD5D07umNlwKU3j01suu1swwQcpZjcxSFclLtmyTKoWtAASnP+PDGWz+7X9bBcaksH4vWV941Fd1bOm0DrKKTp7y1dC/kfAzUP4pePanAROyerGvgYKM+C9dz0VqccEbh9GtWDm/ZK8osB3HGPbJGsJ0IYAm2nuFaZNOYmfV6vUxQc39/P7yjf5w1wVv70vl5+/Tf+lv2kn/yTw6yS2o1+8Nv/3a7NT1t6dZWBmxjYAlbnLa2tgIYxc4oZ0vplkYveyxmryrPYs6q2tsAR7CVjI+uB7iWJEnoH0AVlHJIkiSAWACl8AwEjlGOQ9vCoFRvbs4++b732cv+2T8L/P2jd7/b7rRaNrpbB0pBKRSQ3tnZCacVgo96+hsAFAYSvPIPefMvjzybjOc9E3jNhd41uxBF0AGacH1dfpauHwYyeP3h3j//5V9u3/iRj1g9TW2YJPb+17zG/vPmpu3fBVa1Fi7zSIEu+Dv8DFBeTakiQEop5ksqyMhtQJ0mzEU+nQ9rkLP5MI8xt/Ec3pmh/ebvRqORrZnZr37919vX/OIvhrrG//dXfqU9PhrZzuZmAPa4phkDgVzfTAEpjzdl/IAyQY2i+3hJCGcR1KnAqGcWJWnJ0TyKkc3KCIqHJxCiRl7xPW+Cm2VP7sjLjokBIzFEN+9vvj+/4++iZ4DYaGBFH3sWO/FFFBMq2qcy43hchyoPlOK2lfkNGwJl7gVAhOeb984Ewc6RnLxnFYECecBZGfLuWWaZ6roo4t0kFAP39BpOxediptwerQ1SxJ+yIEzsupNUWE9HsOGZpLDBf+8Qi7zAwVlRUdasUhHonfe7Z9K43m8UG78yujhGXM8kRpPMB6+Np+UExIJ1MV3XaDRCVgyfuqdZSOwg8xYXzTpg50yDHLoWoIfwLGTwtFotm5qasrm5OWs2m9Zut0N9o3q9bvPz89ZsNm1+fn6sqDl0GdqogRW21UajkbXX1mxlfd2utdsHgMnd62Hz8im9eOesE3X08Ux8x4BUXp0tjyaxJQC6gUeor4OMHGSdLS4uWrPZtMXFxTDm+B3kIU6FA5DCoFSSJCHjJ2/uoV+tO3esfe2ara+s2Ob8fOi/OvAAAwA8YeseZ+ANh8OQmcKgmo4Xj3eZwHje+uT/9QX+g3+Yt9PT0+Ed48InXutnMVCb+Qhb1wPclrtdu7i9bU/OzNgNqgHmbbU1OyxfwmAM7s1znkFpBsbU7s4DVfPkpMdHZI8xgIc6cktLS9Zut212dtamp6et3W6H62N+JrdPAUwF/PQF3qz0+3ap07EnZ2bs5t2trQzgsRwAzxlMZV4WAdEnZS/E7pFnl+X97qTofrD9KipHRWN06plSChJoIcMYlUFqi4RurD3e/3lgVaxtZRF8bScLKLN8R6bsIisyQMoAbmX6rs/yQKEiygM1yjyT/8/jufLUU/5pmj22OO+ek3xfBIqW+a1+9nQUuNwXPTGkjAM2Sb+PCuY90+npOndilAf63Mt+HkWHlF0H/Ixn0ljer3QveVzWuI7piuNSkX3g6UoFquBg4TsALgpyqEOL+/Hn7OhNwhs4bQwooNbO9PS0DYfDTEYUas3gqHi1NfGZl7Wln4WA6507Y1t70I409bdPwWnFvWFDcx/y6mYyuBfjTR6pTOJ2Ma9Go1Eosj01NRUyyhqNRvibt+/xaWKcnaJZPtpOz36+Y2Z7588fOO5raxkgAI47BxmRBYUTC7UuF2f98LgCJIiBkB4/Y8Aw+OqBbJ6diD6ZWWaecM1dLsavNXa1Hd5z2QfT/m2ORvbZ6Wkb7u/bcGcnfMeZWlwiQtcyz0NvjeCaPPCp6LMYsc3MYBrb//gM9c3QdvCze7fWMI8DzwX0k/nB77yW0VcGpa6b2Z+mqRnJJ91Wyc9i4Eu/Zz4/G6myi545dKqgFBvnWt+JwQCeUHnATJqmY8fI8jUsgLx2xNrnXZPnUOQ5RDHB4BlynjI6C4opQQ+08drHgpX7mwcaxu6vbYi1U8e6rBBSxcsFCmPXnwTlgVIeMJZHMUOzyCg6DSpytFlpmmVT79kQYWWaR/ebovHacy/W8LOd8mQYvj8r4jmeB5gxqRNf1oG839aDksq9SQC3s6KYflNn/H5q82nRafQRvEP2D+sCtgVRP0idfrNDh1adtFgWszfvPF2UJMnYycx4sYOv5J0izO30eBC7zgM02HbM+14zMJh3J7XW9F5YFxxUAsC4v78ftmjW63Xb3t4OoBN4zDzkujxq83PQ2psT/JkCfd7/WqcM2/i0eLRmnCgfYrwtkhFlg4sx+c8gBK7DfNbrwTt94T7cBn7nwLh3rQIruEbnnndPb+7qGmf+6lrybN0iiq1RPrGS51u9XrednZ2QITUzM2Nzc3PhVE4AlnngG/eR/46tx7x1qrxQPp/Wmi9LRfbVvdaZFTj19KdTBaXyQBfPeI8ZtHrPMs/le07ym7xnqyMRu0eRML0XiybWvzxwKG/sivoVU75lACmOKHlzQiNOkzh0UKjeyRl5fT4p8nhQ1onVPnsg4L0CSNAWz5jG9/z/M5HK8v6ZzINJqUzAwPs8tn3vJKnMeuL1rLUFtU3e32yIx2Sm/pZlYp4eymurd90keivvGWY2lvFQ9NtJgkJF7cl7lhccwdjxc1lu3Uu5eppUVm8W2T2xe7HjpHNWA5SeU8f3iG2XigGMfB9dY9D/CjShPbqFLE/maN8n1et6j5gNqUCAt90qbz0XtbvoGh1LOOec7ca1ugBacTHoorXE5T28DBr9XGuNebVaNYDKWx89Pnp8mRQc8WxU/S52fzM/Uw2AlK4N5h3ey4JS3u/5euahflYEjHh/e3N1Et57VFZPePdBIXmA45i/Zgfbj7FlGLXJdC0wUJXX5jJjrp/H1rbKtLIy/KwoT0dUVFEZKg1KeQh0GcIC8hawIueqtPIUlNc+NniOuijLLPIiI5WVShEAE7vmNIgFKT9b+xOLdKjBnicYPeEE5eoZkfqZzjd12Fg4e99xyi2eifpFWsdIBT8by3nEYxwzTE+KPGfJ48tpUcyg5AMFzMadCLRZ26hGkGeUFDnd90rxmo1ngbKT4zkYnuNwlPYfZW5NYihMYkAdlxQYwGdm/kEV/D2DoOp8eNmyR2kbakvgdKk8AxpONk5JQju15otXBNZzGjzCd6jPg/oseYY+y+5Wq5U5/Uy3U3BUXDMyOKMglumYJIf1I+fm5jLAg0Z++aWOFP9OgQD+vW6b8LZRaBYE1xhB3RBkdmCs8c61c1qtVm6G7b2iozhtk67no8os1r9FbStjd3nXeDKCr/ccRMxhT7YAtNL2FQEqMZuuSJZq/9T558/Lgiixz4q+i9lebHPAdsI2JxSTR9YZ5DaAPbWltO0KVsZAHG+u5DnuXn9VtpX5bVmK2X9HsQu1LbxVE/M9ti4YZI/ZIt577Pl5PIrZbQrwx/qvIGtR1qFHx7G14TtgKyeA1M3NzTDXPd/Vs+Vi7Sy6pmg8yswfnQte22LtOyr/8tri2QXHeVZFzy6aCJQ6jsBWAzn2DH7H3+qQl2nnUY2xsn1Upeq1o8w9ixbqaYMOHq885cP/e9fG7g2aVCDpOHp/63vZdkEZ8XPyBHaREcf3uR/oLIEqvPNWXP7OU7KeseIpsrIA5b0iBVXUsNZ5NWmbT0qJT8IvXRdnSeqgeNkM2j52IItk8qRt4SKy7Kx68pAL0wLEqtVqmZO7FLjlvpbRW9x3tKt5t0Aq39d7Jcn40fZe7QvIRn7nQEZMh3Mb4aSCf/w7b/uBglGo88PAFDssmhmh7cX9PX7jfvxMgFMo0Izi2Pw+Ozt7X4JSRXQv5eRxHf1Jn1PmGs9RP8r9mFRWFv3Pz4g5lPx5DJAqo1sm6YtnT8X0LT5ncIe393E9Ma9/bIPhuhgwVQTwlZlnHpByv1FMf0GWxQKwSiwP+Rrvd3mf6Twr8gtUL+T9XVY2nIXs4DkLfcd+gddPr21l5l+eHIj5V5OCmZN+V0Rlfdk8GVQBUhWVpdKn71VUUUUVVVRRRRVVVFFFFVVUUUUVVVTRSdHTL/RXUUUVVVRRRRVVVFFFFVVUUUUVVVTR054qUKqiiiqqqKKKKqqooooqqqiiiiqqqKIzpwqUqqiiiiqqqKKKKqqooooqqqiiiiqq6MypAqUqqqiiiiqqqKKKKqqooooqqqiiiio6c6pAqYoqqqiiiiqqqKKKKqqooooqqqiiis6cKlCqoooqqqiiiiqqqKKKKqqooooqqqiiM6cKlKqooooqqqiiiiqqqKKKKqqooooqqujMqQKlKqqooooqqqiiiiqqqKKKKqqooooqOnOqQKmKKqqooooqqqiiiiqqqKKKKqqooorOnP5/ZBgyrEAgjckAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "src = np.array([[0, 0], [0, 55], [230, 55], [230, 0]])\n",
    "dst_1 = np.array([[15, 60], [15, 100], [151, 90], [151, 50]])\n",
    "dst_2 = np.array([[604, 19], [600, 55], [740, 62], [744, 26]])\n",
    "\n",
    "warped = [None, None]\n",
    "# warped = buf\n",
    "tform3_1 = transform.ProjectiveTransform()\n",
    "tform3_1.estimate(src, dst_1)\n",
    "warped[0] = transform.warp(img, tform3_1, output_shape=(50, 300))\n",
    "\n",
    "tform3_2 = transform.ProjectiveTransform()\n",
    "tform3_2.estimate(src, dst_2)\n",
    "warped[1] = transform.warp(img, tform3_2, output_shape=(50, 300))\n",
    "# warped[0] = buf\n",
    "\n",
    "fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(12, 3))\n",
    "\n",
    "\n",
    "segments = np.array([[9,34], [18, 44], [36, 42], [47,30], [36, 20], [18, 22], [28, 32]], dtype=int)\n",
    "\n",
    "shift_x = [0, 0]\n",
    "shift_y = [52, 46]\n",
    "start_x = [-4, -2]\n",
    "start_y = [-3, 34]\n",
    "\n",
    "min_val = 0.019\n",
    "delta = 3\n",
    "for i in range(2):\n",
    "    ax[0][i].imshow(img, cmap=plt.cm.gray)\n",
    "    ax[0][i].plot(dst_1[:, 0], dst_1[:, 1], '.r')\n",
    "    ax[1][i].imshow(warped[i], cmap=plt.cm.gray)\n",
    "\n",
    "    sh_x = shift_x[i]\n",
    "    sh_y = shift_y[i]\n",
    "    numbers = np.array([[0, 0], [sh_x, sh_y], [2*sh_x, 2*sh_y], [3*sh_x, 3*sh_y]])\n",
    "    for number in numbers:\n",
    "        bool_arr = []\n",
    "        for segment in (segments + number + (start_x[i], start_y[i])):\n",
    "            ax[1][i].plot(segment[1], segment[0], '.r')\n",
    "            area = warped[i][(segment[0] - delta):(segment[0] + delta), (segment[1] - delta):(segment[1] + delta)]\n",
    "\n",
    "            bool_arr.append(np.mean(area) > min_val)\n",
    "            # print(np.mean(area))\n",
    "        print(tuple2int[tuple(bool_arr)])\n",
    "        # print(bool_arr)\n",
    "        \n",
    "    for a in ax:\n",
    "        a[i].axis('off')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c7df7aed-0b5e-47c8-b2df-0c7ea1eff160",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "libGL error: MESA-LOADER: failed to open swrast: /usr/lib/dri/swrast_dri.so: cannot open shared object file: No such file or directory (search paths /usr/lib/x86_64-linux-gnu/dri:\\$${ORIGIN}/dri:/usr/lib/dri, suffix _dri)\n",
      "libGL error: failed to load driver: swrast\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 0\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 0\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 0\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 0\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 0\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 0\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 0\n",
      "Error: 1\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 0\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 0\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 1\n",
      "Error: 0\n",
      "Error: 1\n",
      "Error: 0\n",
      "Error: 1\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Error: 0\n",
      "Can't receive frame (stream end?). Exiting ...\n"
     ]
    }
   ],
   "source": [
    "buf = None\n",
    "\n",
    "with open(\"data.csv\", \"w\") as f:\n",
    "    f.write(\"U[V], I[uA]\\n\")\n",
    "\n",
    "cap = cv.VideoCapture('video5v.mp4')\n",
    "prev = (0, 0)\n",
    "\n",
    "while cap.isOpened():\n",
    "    ret, frame = cap.read()\n",
    "    \n",
    "    # if frame is read correctly ret is True\n",
    "    if not ret:\n",
    "        print(\"Can't receive frame (stream end?). Exiting ...\")\n",
    "        break\n",
    "    \n",
    "    img = color.rgb2gray(frame)[40:160, 60:810]\n",
    "    \n",
    "    warped[0] = transform.warp(img, tform3_1, output_shape=(50, 300))\n",
    "    warped[1] = transform.warp(img, tform3_2, output_shape=(50, 300))\n",
    "\n",
    "    cv.imshow('frame', warped[1])\n",
    "    \n",
    "    if cv.waitKey(1) == ord('q'):\n",
    "        break\n",
    "    \n",
    "    v_nums = [[], []]\n",
    "    \n",
    "    for i in range(2):\n",
    "        sh_x = shift_x[i]\n",
    "        sh_y = shift_y[i]\n",
    "        numbers = np.array([[0, 0], [sh_x, sh_y], [2*sh_x, 2*sh_y], [3*sh_x, 3*sh_y]])\n",
    "        for number in numbers:\n",
    "            bool_arr = []\n",
    "            for segment in (segments + number + (start_x[i], start_y[i])):\n",
    "                area = warped[i][(segment[0] - delta):(segment[0] + delta), (segment[1] - delta):(segment[1] + delta)]\n",
    "                bool_arr.append(np.mean(area) > min_val)\n",
    "\n",
    "            try:\n",
    "                v_nums[i].append(tuple2int[tuple(bool_arr)])\n",
    "            except KeyError:\n",
    "                buf = warped[i]\n",
    "                print(f\"Error: {i}\")\n",
    "                break\n",
    "        \n",
    "    if len(v_nums[0]) + len(v_nums[1]) == 8:\n",
    "        U = float(\"\".join(list(map(str, v_nums[0]))))/100\n",
    "        I = float(\"\".join(list(map(str, v_nums[1]))))/10\n",
    "        if prev != (U, I):\n",
    "            prev = (U, I)\n",
    "            with open(\"data.csv\", \"a\") as f:\n",
    "                f.write(f\"{U}, {I}\\n\")\n",
    "        \n",
    "cap.release()\n",
    "cv.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "496a2ecf-5a9e-45a2-a815-cfaa72e31fa2",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}