2025-02-AI-HW2/report.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e815086e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_digits\n",
    "\n",
    "import sklearn\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.font_manager as fm\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from collections import OrderedDict\n",
    "\n",
    "fm.fontManager.addfont('ChosunSm.ttf')\n",
    "prop = fm.FontProperties(fname='ChosunSm.ttf')\n",
    "plt.rcParams[\"font.family\"] = prop.get_name()\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b5dff32",
   "metadata": {},
   "source": [
    "# AI Homework 2025, Homework #2 Report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8ed0fa0",
   "metadata": {},
   "source": [
    "## Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "66214571",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = load_digits()\n",
    "plt.imshow(data.images[0], cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84818428",
   "metadata": {},
   "source": [
    "## Analysis about the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2ef5bfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, ..., 8, 9, 8], shape=(1797,))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e2eec4b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 1. Basic Dataset Info ===\n",
      "Total Samples: 1797\n",
      "Feature Count: 64 (8x8 pixels flattened)\n",
      "Target Classes: [0 1 2 3 4 5 6 7 8 9]\n",
      "Feature Range: 0.0 ~ 16.0\n",
      "============================\n"
     ]
    },
    {
     "data": {
      "image/png": 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7tl+7ds2MHj3alChRwvj6+ppmzZqZk/+PvfuOs7Mu8///uu/79Dpnes9MJjPpCSGEKhC6KAgsWMCOX1RWxB/ooggsrOuyiCurIBbsKKIoKFJcOhFCEtJ7TybT65k5vd/374/P5IQhIQlJIIDX8/GYR5hz7vI5ZwLMvOf6XFdXV/H5wcFBa9asWdasWbOs66+/3srn89Ytt9xi+f1+q76+3rr33nsty7Is0zStH/7wh9aHP/xh69prr7UuueQS64UXXihe57rrrrM0TbNOPvlka9myZZZlWdYvfvELq6yszGppabH+8pe/7PM9sSzLisfj1le+8hXrM5/5jHX11Vdbl19+udXe3m5Zlvq6XX755RZgTZs2zXrwwQff8Do7d+60zj//fAuw2trarKuvvtr6/Oc/b334wx+2vvzlL4/72i1cuNA655xzLMC64oorrI0bN1qXX365ddNNN+113VgsZgFWX1/fPt8by7Ksu+++26qoqLDOOecca+nSpZZlWda1115rXX755dbHP/5x6+Mf/7j1pS99ycpkMm+4fiGEeLfTLMuyjl7UK4QQQgghhBBCHJhpmsyfP5+f/OQn/O///i+zZ8/mmmuuOdrLes+5++67efDBB1mwYAEOh6P4+COPPMItt9zC2rVrZbiVEELshwzIEkIIIYQQQgjxjqfrOtOnT+eKK65g5syZfO5znzvaS3pPuuaaa8jn81xxxRW0tbXh9XoZHR0lnU7zzDPPSNAqhBAHIJWtQgghhBBCCCGEEEIIcQTIr6SEEEIIIYQQQgghhBDiCJA2AkIIIYQQQgghhBBCHGFPPvkkf/3rX6murqajo4P/+Z//oby8/GgvS7zFpI2AEEIIIYQQQgghhBBHUF9fH+eddx7Lli3DbrfzxBNP8Itf/IJHHnnkaC9NvMUkbBVCCCGEEGKMZVn813/9F/F4HKfTyZo1a/j3f/935syZw+23387w8DCFQoHKykq++c1vHu3lCiGEEOId6uc//zlr167lBz/4AQCmaVJTU8OmTZsIhUJHeXXirSRtBIQQQgghhBizaNEi7r33Xnp6etA0jTVr1nDFFVdw1113sX79eh544AEALrvsMp5++mnOPffcg7quaZr09PTg9/vRNO2tfAlCCCHEO5JlWcRiMWpra9H1t2eEUDqdJpvNHtFrWpa11//LnU4nTqdz3GN9fX3jWgbouk5LSwtbt27l+OOPP6JrEu8sErYKIYQQQggxprW1leuuu674eUtLCz09PfzqV7/iiiuuKD5++eWXc//99x902NrT00NDQ8MRX68QQgjxbtPZ2Ul9ff1bfp90Ok15YyOJwcEjel2fz0c8Hh/32K233sptt9027rHGxkZeeOGFcY+ZpolsMH/vkzYCQgghhBBC7INpmtx666243W5+//vf8+ijj9LS0gLA+vXr+fCHP8yGDRsO6lqRSISSkhI6OzsJBAJv5bKFEEKId6RoNEpDQwOjo6MEg8G35X7BYJAvvLgEh893RK6Zjcf56fwT9vr/+b4qWyORCMcddxxPPvkkra2tPPHEE1xyySWsXLmS6dOnH5H1iHcmqWwVQgghhBDide644w6eeuopTjnlFL7xjW9w33334fF4is+XlpaSTCbf8PxMJkMmkyl+HovFAAgEAhK2CiGE+Kf2drfTcfh8OH3+I3rNg/n/eTAY5K9//Svf+c53KCsro7W1lUmTJjFhwoQjuhbxzvP2NMkQQgghhBDiXeQb3/gGzz//PBMnTuTDH/7wm97y99///d8Eg8Hih7QQEEIIIf75TJs2jZ///Od85zvf4ZJLLqGkpATfEaqyFe9cErYKIYQQQgixD5qm8dnPfpZVq1YxNDQ0rpI1HA6Pq3R9vRtvvJFIJFL86OzsfDuWLIQQQoh3iPXr19PY2Fj8/Ac/+AE33HDDUVyReLtIGwEhhBBCCCHG/OIXv6C3t5ebb74ZUIFrSUkJs2bNYsOGDcWerZs2bWLOnDlveJ199W4TQgghxD+PpqYm5s6dyze/+U1yuRwtLS1cfPHFR3tZ4m0gYasQQgghhBBjurq6aG9vL37e3t7O6Ogot99+O7/97W+58MILAXjwwQe56qqrjtIqhRBCCPFO5/V6+etf/3q0lyGOAglbhRBCCCGEGHPDDTfwb//2b9xwww24XC42bNjAww8/zDHHHMPy5cv52te+RqFQYM6cOZx33nlHe7lCCCGEEOIdRsJWIYQQQgghxrjdbn74wx/u87lvfvObb/NqhBBCCCHEu40MyBJCCCGEEEIIIYQQQogjQMJWIYQQQgghhBBCCCGEOAIkbBVCCCGEEEIIIYQQQogjQMJWIYQQQgghhBBCCCGEOAIkbBVCCCGEEEIIIYQQQogjQMJWIYQQQgghhBBCCCGEOAIkbBVCCCGEEEKIo8iyrKO9BCGEEEIcIbajvQAhhBBCCCGE+GdjWRbJV5cSe/ZZ8gP9OCe1EvjA+ThbWo720oQQQghxGKSyVQghhBBCCCHeZsmlSxn+2X2kN23CyuZIvPIKg/fcQ667+2gvTQghhBCHQcJWIYQQQgghhHibxZ57DjOTxdncjK2iAkdLC/n+fuKLFx/tpQkhhBDiMEjYKoQQQgghhBBvs3z/ALrXW/xc03XQDQrDw0dxVUIIIYQ4XNKzVQghhBBCCCHeZs62NhIvvYRVXo6m65jZLJgmjglNR3tp4rWySQhvB5sbSieCfhj1SvEBWPMn2PWSum7dXJh+MdTMPmLLFUIIcfRJ2CqEEEIIIYQQb7PA+e8nu20b2W3bwDDAMnFNn4b3pBOP9tLEbh1LYNkvINoLug2qpsMp14Kv8s1fK7wD/u+b0LkEClnQDRjcAn3r4Ox/h+qZR379QgghjgoJW4UQQgghhBDibeZsbqbyhn8jsWgxhfAw9oYGvCedhOH3H+2lCVBVqIt+CMkwlNRDIaeC0lfvgzNuAk17c9db9zAMbFAhq6cGrAJkYjCyU11z+iVQ3gbBenW8aap7vNn7CCGEOOokbBVCCCGEEEKIo8BeXU3JJRcf7WX8c7EsSAyB3QXO/QTb3StU4FrRBpoONhcEaqB3DcT7wV/95u7Zu1ZdRzPGQlQbWKa61panVJWrOwit74d8BrqWgOGAiWfAhFPAE9r/eoUQQrxjSNgqhBBCCCGEEOK9b2ATrPgNDG8fCzJPh2M+Dg7P3sdaJmABr6ks1TT1uFnY+/jUCGx6ArqWgsMHLWfAxDNVj1dNA2+5akVgFcauram+rZapesFWTIZYD7x8F7iCEKiFVDfseBFcIShvhZYz4ZgrVFAshBDiHUvCViGEEEIIIYR4l7Asi+zOneR6erGVhnC2taHZ5Me6A0oMwz++C5Eu8Fep6tG1f1LPHX/V3sdXzwR3KYx2QEmDaiMQ7YX6eSoIfa18BhbcCV3LwelRx/auhtQozLxMHTPlA9C/HjIRVVmbz4KZA4cXsgkV0hpOdU6gFtwlMLhZ9XdNj0I2Dmv+oALbuZ96868/m4Rdr8Bou3pdTe87tN6zQgghDkj+ryyEEEIIIYQQ7wJWLkf4d78jsfAVzGQSzeHANXMm5V/4PIbPd7SX987WtRSiXapCVDfGHtRgxwKY9VFwBcYfX9IA8z4Hy3+jtvjrBlRMUcHs6/uodq9Q4WppE9jd6rFIF2x8DNreD04ftJyl+rAu+Ql0L1fXMzXIpVQAq+mQTUEhA4ZLtTDIpcBVAvm0+tOww/bnYNaH99znYGQT8OId0LVMVdYCbH4S5t8IZS1v/r0UQgixXxK2CiGEEEIIIcS7QOLVV4k9/wK2UAhbTQ1WKkVq2TKiTRMIXXrp0V7eO1s2rroCFINWwOaEXHIs1AzsfU7rOVA7BwY2qnCzeqY65/USA6odwGsDUFdQVammwips1TR1LYdPhbaeUtj5D3XvfAY8ZZBLq/6uhSxgAJZ6zuZUrQ6svPo8n95zr2wSdi5Qa3QFoelU1Wf2tXb+Q4XNJY3qPMtULRXWPQyn33B476sQQoi9SNgqhBBCCCGEEO8C6fXrwTQxSkoA0DwedK+X1LJlErYeSMVkFTQmhlT/VMuEWB/UzAZvxRuf5y2H5lP3f+1Andren42rMBUgGVbnvvba3cvVQKyKNvW83Q1oKmTNxMEdUoO4cgnImGNBqwZl01Tla3wAGk5QVa6gzvvHd6FjkfrcMmHrM3DqV6Fh3p77Dm8DrD0BraarsLdvnQp3X1+pK4QQ4rBI2CqEEEIIIYQQ7wKaYVPh2GtYhQKa3X6UVvQuUjUDplwAmx6HxKB6LFgPcz+thlgdjppjVAi6a+FYe4A82D0w47Lx1a6F7Ng/aGAbqzAtZMcCT12FraFmmHOFClY7l0KkEzIxFcaWTIA5n9wTjna9Cp2LVcsDu0ddZ2ir6u1aN3fP63KVqBYGrw1Wswkor5WgVQgh3gIStgohhBBCCCHEQbIsi/zgIJrdji0Uelvv7T52DvGFC8n19mKUlmLG41jZLN6TT35b1/GupGmqB2vD8TC0RYWg9ceDbz9VrQDhndD+MqQjUN6mBks5POq51AgU8qqC9dSvQu0x0LMSHH51XP1x469VNV21K4j2qFC2kFPBrGGHfArC22HGh2HqhyA5DMd+CsI7VIDq8Km1e0oh1q9aB2x8XIWywcY9r9FbrvrFZiIqvAVoPg22Pg19a8fulVZrnPLBI/oWCyGEUCRsFUIIIYQQQoiDkG1vJ/zgg2R3tqMZBu45cwh97KMYgX30+3wLuI85hpIPX0bs7/9Hvq8P3eMhcP778Z955tty/3c9TYOaWerjYPSsgn/8j9r6r+nq/I5FcMLnYfUfoXMJmAWonArHfRamXqg+3kjpRDjm47D6QRV8Whb4KsEZAJtDha+ZKDz+/0F8EBxeVY0741/29JqNdMFz/6lC2ExUtULoehXq56kAORsDb9WedgYAoQkwcT4s+hGkhkF3QFVIVcoKIYQ44iRsFUIIIYQQQogDKMQTDP3kJ2R3dWCrqsLK54k9/zxWPk/51V9Eexu2Y2uaRvD88/Gdcgq5vj5spaXYysvf8vv+U7IsWPNHSA6qMFXT1DCtjsUQ64Xh7aoq1uaEXa+oStTz79xT9bovmgbTL1YVr09+HaJdqr2BMfZjed9aFeaWNqsK1XQMlv9KDdiafL46ZtMTqgK2Yoqqii3kVBjct05VvaKpwNd4TWuJaC9se061TWg8QbUvCO+AV++D93/n8NsoCCGEGEf+qyqEEEIIIYQQB5Bet45sVxeOpiaMQABbaSn2ykrSq1eTHxh8W9diBAK42tokaH0rZeMw0q4GXO0O0u0etd2/ZxUEatRznlIoa1EBaO/qg7t2sF6FrjYnmDn1WC6lhncZDlUB6wxAsE5VtG57ds+5AxvB6VeP25xQd6yqji1kVQB7yrUw+QPj79e/ToXBoUYVwtqc6trD22F012G+UUIIIV5PwlYhhBBCCCGEOAArkwbTAtuezYGaw4GZz2GlU0dxZeItYXODK6gGU+1mFtSgKV1XgeVuhkM9nksc/PXbzlM9YyNdMLhJ/emvAU/Z+OMMh6pw3S1QB9mkqry1LNVLNpdW5zUcrwZ1vb5SVRv7/LXD1UxTPa7LZlchhDjS5L+sQgghhBBCCHEAjpZJGH4/+YEBbJWVYFnk+npxNDVhr6092ssTR5phU/1Sl/xEbbm3eyAZhoo2tX0/0gNlEwFNtRVwl0D55IO/visAZ90C3ctV31V/NSSGYfG9KuB1+iCfUWFq2/v3nNd2LnQvU0O+skkY2akCU7MAS3+uWhGceYvqAbtb7RwI1KpK1pIGtf5oD0w4WVXZCiGEOKIkbBVCCCGEEEKIA3DU1xH40IeIPPoo2e3bAbBVVhD66MfQ7PYDnC3elSZ/QG273/ykClrbzoMZl0J8AF65Bwa3qOOcfmh6H2x8DJJDUDkNJp2tAtj9sTlV4LlbLq22/O9aqCpdNQ2qZ8G0i/ccUzMbTr8B1j4MG/4K7lI18MtTqnrK9qyAnpWqN+tunlI4+cuw9GcQ6VYtCBpOgBOv3tMiQQghxBEjYasQQgghhBBCHITAB87HPX0a6c1b0Ox23LNnYSsrO/CJ4p0jGVb9Uf3VKnTcH11XAWvruWoL/u7t+aXNUNKoKkwLOfXc2ocgNaLC2faXoWupqjB1+tSW/UwUHL49w7D2xe6C0/4Nes+B0Q7VGqB+3t5Dt+rmqn6xg5vAHVL3AFV9axYg3rf3teuOharvq+pWmxNCzTIYSwgh3iIStgohhBBCCCHEQdA0DUdTE46mpqO9FPFmZWKw7Few6xW1jT7UBHM/A9UzDnyupu1dARqogcCFKmj9+9chHVUDqjRNbf/vWaXu5fTBmj+pVgOeMjUYa9LZb1xRatig/jj1sT+echW0pkb2hK3ZpAqQ/TX7PsfmhKppB369QgghDov8KksIIYQQQgghxHvbsl+pbf66obb9962Fl+9SfVIPRy6ptvx7SvcEqDYnYEHXq/Dy92Fos6p4jXTContVm4DD5fCo4NYqqHYG4R0wugvq5qkerUIIIY4aqWwVQgghhBBCCPHelQxDxyLwVYC3XD3m8MLQVtUKoO28Q7+2za2uO7x9z7XNvPoz0qMGXFVMVtWumq4qXLc+rXq8Hq4pF6ierTv/Adm4ahXQeq4KdoUQQhw1ErYKIYQQQgghhDiqTNNkx44d7Ny5k1wuR0NDA21tbTidzsO/eD6jeqs6vXse03RMyyQ61Ee+dIjS0lL0Q+lhqusw9UPwyg9VeGt3jbUUmAyuIIwYqrdqtEeFsGYeDKdqP3C4w6k0DZpOUR/7Ypqw/XnY8YJqo9Bw/FhAW3J49xVCCLFfErYKIYQQQgghhDiq1q5dy8qVK7EsC13X6e3tpb+/nzPOOAPDOMAgqwPxVamhVr1r1JAqzSA1tItIJMXi9X1Etz1JVVUVJ598Mn6//81fv+VMsLlUxWpiEFrPUwFsxyLY8hSkR1UFrM0FyWGI9UHPCjXo6q207mFY8Rv1z4UcbH8BFv8YZn0UpnwQylre2vsL8Q7xSeuX+Kwj8IsbIG5luOdNHH///ffzzDPP0NbWxtatW7ntttuYOHHiEVmLeOeSsFUIIYQQQgghxFGTTqfZtGkTdrudQCAAQDabpauri76+Purq6g7vBroOx10JL30PhrdTKOQZHU2x1TkTs3QSHsuis7OTxYsXc/bZZ6O92YrTN6ownXQ2LLgT8jlABy0D/moV+HYsObSwNRmGxBD4q1Tl7BvJxGDTYyrgdfqhewXkEpCJwqrfQ/dyOPtWKJXQR4i3SkdHB//5n//Jhg0bsNvtrFq1ii984Qs888wzR3tp4i0mYasQQgghhBBCiKMmkUiQyWTw+XzFxxwOB6ZpEo/Hj8xNKqfCB74LXcsZ6NzBwq1h7FVTsI9VzQYCAfr7+4lGowSD+wkx3wxXAKqmq9YCdo8KPv1VEN4Jhcybu1Z4Fyz/BfSsBAvVCmD6JepjX+FwMgyZOLhDanBWLgGeMkiNgq9StTXY8hScePUReKFCiH3ZuHEjEydOxG5XfZRnzZrF1q1bj/KqxNtBwlYhhBBCCCGEeAtZlkVmyxbS69ZhmSbu6dNxTp365iso36O8Xi8ul4tkMlkMOjOZDIZhHNq2/jH9/f1s27aNWCxGVVUVra2t+FrPJmZtIbLzJSpe06NV13Usy8I0zcN+PeM0nQqjHRCoVYFrakQNyqqdc3Dnmyas/B0s/yUMbwPDodoi6Dos/7WqlJ1w8p7jI90qXLW5wVUCqWFV5arbVSsBTQenD8wCbP47hHeoELblLKg/7vD7yAohiubNm8eGDRtYs2YNs2bN4k9/+hMXXXTR0V6WeBtI2CqEEEIIIYQQb6HYM88w+qc/YcYTgEXs/54ieMklBC/44NFe2juCy+Vi2rRpLF++nKGhITRNo1AoMHHiRKqrqw/pml1dXfzjH/8glUphs9no6uqis7OTc845h+rqajweD6OjowSDQUzTJBKJUFNTc+SqWnebfjGEt0PPKlXN6vCpfqkT3ndw53cugXV/UiGtzQMOt+oL6y5RQ7Z2vaLCVtNU7QE2/Q1SEbCPha2FAuRSKnA183vaGHQuVZW2mgb5LHS+Cid/GVrOOLKvX4j3qGg0Ou5zp9O510C/0tJS/vznP3P22WczadIkLMviueeeezuXKY4SCVuFEEIIIYQQ4i2SHxkh8rfHAA1naysAuf5+ok8+ifeE47FVVBzdBR4k0zTp7Oyku7sbTdOoq6ujoaHhiFXnTp8+Ha/XS3t7O7lcjvr6eiZNmoT+murTg2VZFuvXryedTlNRUVEMbwcHB9m5cyfTpk1j3rx5LFu2jOHhYTRNo6ysjOOPP/6Q7rdf7hI461boX6uGYwUbobz14CtIe1aoMNRwgD72p5FVQ7Z81ZBPq+O6lsLah8DhgYo2yMZhtAsmnAK1s2HL02CZe4LWQh4aZoO3TJ0f3gHrHlGVuIbEBEIcSENDw7jPb731Vm677bZxjw0PD3PrrbeycuVKamtr+fnPf84nP/lJHn744bdxpeJokP+KCiGEEEIIIcRbJNfVRWF0FEdjY/ExW0UF2Z07yHZ2vmvC1pUrV7Ju3ToKhQKWZbFlyxaOOeYYZs+efUSur2kazc3NNDc3H/a1TNNkZGQEt9tdDIONsd6skUgEgEmTJlFTU8PAwACGYVBdXY3D4Tjse1PIw/bnYdfLqtq08QRoPffg2wbsRQMs1TogNaqqUy1USwDL2jNkq2elCl5Lx94/Z0D1jI10wqU/h7mfVcO6ul5V/VsxIR1Rw7NsDlUFmxiEbEz1eRVC7FdnZ2dxoB+wV1UrwD333MO5555bHPL3//7f/+M73/kOnZ2de4W14r1FwlYhhBBCCCGEeIvoPh+604mZSmGM9R81Uyk0hxP9NQOh3skikQibNm3C4XAUh1jFYjE2bNhAS0vLuMFW7wS6rhMMBunt7cXr9aJpWrEX62vDEa/Xe0TC3XFW3A/r/qxC10xM9UXd/iKcd7tqJ5AagVATBOsO7nr182DrU6AZ4K1QFa2FDATqofVsaDlTha7h7ao3bHIYPOUQmqDO18YqdYe3QaQLSlsgOQh9G1SLgqEtUDpRVdqWTQLHoffILYr2Qu8qVUlbMxuC9Yd/TSHeYQKBwLj/nuxLb28vLS0txc93V9GPjIxI2PoeJ2GrEEIIIYQQQrxFHE1NuGbOJLlkMWYqBUBhdBTPccfhnDTpKK/u4ITDYZLJJJWVlcXHvF4vw8PDjI6O4vV66ezsZOvWrSQSCaqrq5k6dephDbc6HJqmMX36dMLhMIODgzgcDjKZDOXl5TQ1NY0/ePdAKXcIyierwVOHKta3JxhN9kImripR1/1JtQNw+lRLAHcQpl4Esy8/8P3qj4NjPgHrHwFXEBxeFY7OuwrqjlXnb/4/1UYgl1JBbGoUYr3gLYfpl6ggdfvz6k9PKQxtBt2AQlYd27dGtTc4/V/23ULAslSbgcSgGvRV0rj3Mbu1L4TFP1LHMna/ef8PJp116O+rEO9S5513Hn/4wx/4xCc+gaZpDA0NEYlEmDZt2tFemniLSdgqhBBCCCGEEG8RTdMo+9yV2MrLSS59Ve0I/+AHCH7oQ2hHuj/oEWZZFlu3bmXFihWMjIwUA1eXy0VfXx+JRIKXXnqJ8vJyenp6KBQK2O12BgYG6O3t5dxzz8Xtdh+VtTc2NjJ//ny2bdtGLBajqqqKyZMnk81mWbduHeHhIVoTy2hIrMJZSKqBUrXHwvu+okLNQxHrU9WsybAKWndfJ9oNAxuh4XjVTzU+CGv+COVt0DBv/9fUNJj1YZg4HyId4C5VlbG7e74WcrDhr6ptQPUsGNkJ+QykR6HhRBW2WpaqbB1ph8HNkImqQBgAS7US8FWo418vl1bhafvLqv2Aw6/aIhx35d7BbCYGy36h2hNUTAY0FWQv/7Vqo+ApPbT3VYh3qUsuuYRt27Zx5ZVXUl1dTVdXF3/84x+x2SSKe6+Tr7AQQgghhBBCvIUMv5/ST3yc0Ec+DIB2JHqDvg127tzJokWLAPB4PESjUTo6OjAMg2w2i9/vJ5vNsmrVKhwOBxMnTgRUz9ShoSHa29uZOnXqUVt/XV1dsVciwOjoKM8//zwjIyPUmt24h//GkN1LaeNk3HoB2l+CkgY47rOHdkN/tQptk8Ngd6lANJ9R2+l1m6py1XTwV0H/RtVn9UBh626+CvXxepmYak3gLlHVuYFaNRwr0gXNp6lK2G3PwUiHqmK1TLUmNBWWOjyq5UE+o3q+2l3jr7/5Sdj6NPgqVeuDZBg2PAplLXtXqw5vg3i/qnzd3b4g2ADDW1W7gsZ9hLlCvIdpmsYNN9xwtJchjoJ39q9ShRBCCCGEEOI9QnM43jVBK8CWLVswTZPS0lLq6+uprq7Gsiyy2SyVlZU0NjYSDKrqzWw2Sy6XA1TPVIB4PH5I9909hOtI27p1KyMjI1RWVtJgDOGyaUQtN5FITFV3uoIqcD3Ue/urofU8FWimo+ojlwKbW4WPhn3PsRpqK//hcgXBX6MCXstSYanDB3YPlNSrxzb+TVWVBuvV2rAAUw3wMgtqKJZV2HdF765XwHCoIFfTVWsCy4TOV/c+1uYG3a7aE+xWyKrHbK69jxdCiPcoqWwVQgghhBBCCAFAuKeLrUteIdzTRc9QGL28CijHMAwqKipIp9Ok02kqKyvRNA3LsnC5XMTjcXK5HHa7nXw+j6ZpBxwe83pDQ0OsXbuW/v5+3G43U6ZMobW1tRjeAkSjUTZs2EBPTw9ut5vW1lZaWlrQdm+r399rC4ex2+1q3WOPGYZBJpMe+8zaU5F5qI67Ug2qWv+wChl9lZAYUlvrDYfa9h8fALtX9Vw9XLoBMz8MC38Ag5tUoFvIQf3x0HCCqlhNDIG3DMonqarXaI8KVy1ThcGg2gpomqpcTY2o4Njh3dOuYBxr34+Xt0HlVOhaBoEa9VisD6pmQNX0w3+tQgjxLiFhqxBCCCGEEEK8g1mmSXbHDsx4HPuECdhCobfkPqP9fSz842+Jh8M4PR4KI0PEB/rwer24yyrI5XLouo7H4yGZTOL1etE0DZfLRSaTIRqNkkqlyOfz1NTUjBtGZZkmQ10dZBJxSqpq8JWWjbt3LBZjwYIFjI6O4vF4iEQiLFq0CMuymDJlCgDpdJoXX3yRgYEBXC4X0WiU/v5+crncQbUrCIVCdHZ2gpkja1NBsCszjMNbrwLGdAymXfwGAeNBMmxw9q1QNU1tv8+loOYYwFI9U0faVZXojMvGHj8Cmk4BVwB2/kO9jqrpMOls1dLAsiDUDN1LwVOuqldjvaDZ1cAuw6l6sRaysOSnsGMB5JKqEnbmR2DCKdC3VlXOuoIqjNUNFeS+nq7DKV+BV3+mzsFSfWCPv2p8Va8QQrzHSdgqhBBCCCGEEO9Q+ZERhn/xC9IbNmJlMhihEMGLPoT/7LMPqprzoO+Tz7PkxedoH4kSCFXgdjqo9QXYtWkDA5s34J40FU3TaGpqIhAIsGXLFhKJBAAul4uZM2dimiaJRIKqqipaW1txOp0ApONxljz6JwZ2bCefy+Ly+pg8YzbV0STptWsxAkEGGxsYTSapGKuYBRgZGWHTpk20tbWh6zodHR0MDQ1RXq4qbUH1Yd24cSOtra0HHDrT2trKyOZXmLD5IQKFEcjHCGk5XAUnZIHWs2HGvxz+m2nY4ZgrYPq/qODSPRaOj7SrMDTUdOSHRVXPVB+vp2kw8zI1OGtgI+RTe9ao2wALPJWqX+vqh1RfWG8FJAZh6c/g9G/A5A+oIDcxDC6/CoqbT9v3OvzVcObNqnrXMtXnR/DvqRBCvBtI2CqEEEIIIYQQ71CRv/yV1LLl2Orq0F0u8gMDjD70J5xNTThbW4/IPUzT5JVXXmFjewdZ3U42lSWczhF02rFX1WIYBjNmzKCsrIzGxkZsNht1dXX09vai6zoNDQ1UV1e/Yfi74aXn6d64nmBFFXank9TgACM/+Sk2w4GrvIJsZxfWq68SamsjFwoxMjJCKqVCwXw+Tz6fx+FwkEwmsSyrGLSapkk+n6e/v5+1a9fS2tqKz+d7w9cZ8jqZb19NxhknqlVQsNfissLYy5vhzFvU0KcjGQzaXeMHTpU2A81H7voHq/YYOOdbqmq1d5VqGeAMqOFauqFC10xc9WP1V6lzHBNUONu9HE7+Mky7SAWwgbo9LQLeiKbtuY4QQvwTkrBVCCGEEEIIId6BzFSK5IoVGKEQhtcLgK26muy2baTWrT9iYWtfXx87d+7E5/MRS8SxGzqRbI5ELo+tYOH2eAiHw8yaNQvH2ICvCRMmMGHChANeu5DP07VxPW6fH4fbDUAomcExMkq2ZRL+2loA0lu3UrZlM6sqyknlcmiaRqFQoFAo0N3dTXNzM8FgEF3XyeVyGIZBb28vIyMj2Gw2VqxYwY4dO5g/fz5lZWX7XkzfWpzJPpwTZhMwxgaVZceqOK3Cu7YC07IshoeHGRkZwe12U1NTUwyki8pa1EchB49fB5ueUP+MpXrLuktAf93wNk1XlbCaBqEJ6kMIIcQBSdgqhBBCCCGEEP/ERkdHyefzlFbVkI3FiCaTFHQDzbJwOx1U1dTR09PDtm3bmDlzH1vVx1iWxcjICENDQzidThX66fq4gVQARioFFvCabf+eigocO3aQGg6Tdan2A/rYuRs2bKC+vh632015eTkDAwNkMhlGR0eL9/F6vQwODrJ27Vrmz58PQCaTIZ/P4/F4VNWtVWCvIViarvqaFnJH7g090ixL9VQ1HHsFwqZpsnTpUrZs2UI2m8UwDKqqqjjttNPwjgX042gGaDbVKsDmAptDDcKKdEGsRw3SsrnUQC9Nh5rZb9OLFEKI9w4JW4UQQgghhBDiHUh3u/Eceyyxp59Gc7vR3W7yAwPoPh/uGUduurvb7VbBpmFQPamN1I7tFNIZXBo0lFdh93hI53L09va+YdhqWRarVq1i/fr1ZDIZdF2nrKyM008/nYbpM9nw0gvYnU4cLhdRs0CFYeB2uornWqOjZH1eCi4nTqezWJmZyWTo7u7m8ccfJxaLAeDz+bDZbGSzWWpra3GPVcy6XC62bduG1+tlaGiI0dFRLMuivLycuXPnUlY5DXzVMLJLVWlaFox2QGkLlB+ZKuEjyrJg+/Ow8XFIDKh11h8HnjIoaYSSBjo7O9m4cSNut5tgMEg+n6e7u5t169ZxwgmvGWKVS0FiCEwTYt1QOVVVs+6WjqnestFuKOTVcK2286Dp1Lf9ZQshxLudhK1CCCGEEEII8Q4VvORi8uFh0hs2kh/oxygJUXLxRTgmTTpi96ivr6eyspLe3l7cbjeG3UH9th00DwzgKuTJuT2kJ7XgnjjxDa+xu2+q3W6noqKCQqHAwMAAK1as4OT3nUEyMkrv1s0kI6O4a2vwGS6M/gEysTgUCuDx0DehEZvTWQx/LcsiGo0SjUYxDINQKEQ+n2dkZASPx4PX68XlUoFtNpulr68Py7JYuHAhyWQSn89HRUUFnZ2dJBIJzj//fFwnfAGW/ASGtwMaBBvgxC+CzXnE3s/DYpowsEFVlsb7YMVvwcyD3QPr/gSrHoBgHfiqYPIH6c1PoVAo4PF4ALDb7bjdbnbt2sW8efPQNQ22/B+sexiSYVXFmhwGt7UnbDXzamDW3M+Cv1I9X9IIFVNB1994rUIIIfZJwlYhhBBCCCGEeIeyhUJUXn892R07MONx7BMmYAuFjtj1TdOkv7+fUChEOp0mnU7TEo9Tun07eU0jardjHxygMjyMaz8tBPr7+8nlcoTG1maz2fD5fCoA1XVOvPRyRnp7yKVTBCurcGgGiYULSW/ejBHwYztmDrl1a3EMDpLJZFTrAcvCRp4AOSrKJpI3IRwOE4/Hiy0Eent7KSkpob+/n2w2S3V1NZFIBJfLRTabpVAoUFZWRjgcpru7m5aWk6ByCvStU8OhqmeC03/E3s/9sSyLoaEh4vE4Pp+P8vLy8UPFUiPw0l3QuxryGbWt33DChJNhaBvkc2CZYPOooVbrH8ao/sRe9zFNE5vNpq7d+Sos+al6wlOqQtxMDLJJwFIhc3xIDe+acAJ4yt+1vWuFEOKdQsJWIYQQQgghhHgH03Qd5xGsZN3NsiyWLVvGxo0byefzWJaFx+NhcipF1OkgZrejaTqW241vdJTIwoUMn3vuPgdQ6WMVkJZlFQNE0zTRdZ3t27ezfft2UqkUNTU1zAiV4S4NEDjvXALnnVu8RnMiTiKRQNd1zHSMisQmJrGVUDZOrnMNz1knEsto6LqOruvYbDZM0ySfz5PP5ykrKyMQCDA6OophGBQKBbLJKFXswp/oQ+svheZmtV2++a3dHm9ZFjt27GDr1q0kk0kqKytJpVL09fWRzWZxOBw0NTVx4oknYrfb1UlrHoKORVAyARweGGmHTBwSwxDvVf1VzRwUMuCrhIEwDfSx2VnGyMgIPp+PbDZLNptl1qxZ6uvQ/hLk01AxWd3D6YdsQgWqpqkC3NrZakjW49eBMwBt74fJHwBD4gIhhDgU8l9PIYQQQgghhPgnNDQ0xObNm3E6nZSWlhan2vfv3IlRMHEHPNgddnRNw55OEYsn6Onp2WfYWl9fz7p16wiHw/j9fvL5PMlkkoqKCpYuXYqmaRiGwdq1a9m2bRunn346zc3NxZAWYM6cOaRSKXp7epgd+zu1Wju2ikZGYjrZ6CCW1YvDW082b+L1eqmqqiIWi3HyySezZs0aRkdHcTgcOBwOUqkUupmjYfRVWkbWgGVRsmEb0AknXP2WB4lbtmxh8eLFmKaJ3W5n5cqVpNNp6uvrCQaDpNNptm7dSnl5OVOnTlV9UnctVNWnTp+6SKAWhrZAcgjQwCyowWKu4NhdLKoCdo5vPp7Vq1cTi8Ww2+1MmzaNadOmqUOycVXBu1supYZhmQUINanwdWiLqnT1lEG0R7VZyGdg1ofVOX1rYevTEOmBqmkqjA3WHbk3q28ddCxWoXD1DJhwimprIIQQ71IStgohhBBCCPEaP/jBD9i1axc+n49NmzZx2223MW3aNG6//XaGh4cpFApUVlbyzW9+82gvVYjDEg6HyeVyBIMqvDNNk2QySafbTWOii4zdjuX14tY1MC0SVZVYljXuGqZpMjg4SC6XY+7cuaxfv55YLIZhGLS2tjI4OIhhGHi9Xnp6ekilUkQiEZ566ilmzpzJSSedhM2mfiz1eDycddZZjLavwf38Q9i9MzC8IXBH2NIXg2QWIzOKy1tJRUVFsbJV13UmT57MokWLiEQi+P1+kskkntwwjdoWRuyl+IIlaB4nbHkK6udB44mvfRGQHlV9Ue2uw35fC4UCGzduRNM0ysvLAYhGo8UKXE3TcLvd6r3u7FRhK4Cmq6FYu5U2q2Fe8T71XDYO3krwVqhQ1O5Bazietvo2JkyYQDQaxe124/P59lyjdg7sWqSqWR1e6F8PyRHVSqG0WbUsiHTBxPl7erhGumDL32HqhTCwHhbcCalRNTSrbw10LYVzvqWqaw/Xtudg8Y8hE1XVtpufhNZz4eRrpV+sEOJdS8JWIYQQQgghxrz88sssWLCARx55BIDFixdzxRVXcOedd7J+/XoeeOABAC677DKefvppzj333P1dToh3NJfLhaZpFAoFbDYbIyMjxGIx8hMnUh5P4O7vQ4/F0FwuInV1pCZPpqampnh+LBZj4cKFDAwMUCgU8Hq9zJ49m7KyMpxOJ3a7nb/85S84HA7C4TCpVAqnUw2istlsbN2yhVK/j8nTpoOmsX3ZYjrWrsZMjlCf1GhrsVHI5tA0aChzsyNXIOr2UVFfj2EYjI6O4vV6qaiowOVykc/n2bx5M5lMhkktzUztWY4nYyNuGcTicVLpNDXGKJ6BTWi7w9a+tbDyARjZqcLI1vNgxqWHVfmay+VIJpPF1woUe9DmcrniY5Zl7ansNWzQfDqs+r0KfR1e1V+1ahq0nKUqPfvXQawPot2qFcKsj0LdXACcTicVFRV7L2bS2SpQ7XwVcmkV0voqoLxNXdNVotoVpEf3hK0Or2pfkE3Ahr+p5yomj7UeKMDgFtixYE/l6yG/UWlY80coZKFyLHBOj8KOF2DSWaqfrhBCvAtJ2CqEEEIIIcSYzZs3s379evL5PDabjXnz5rFp0yZ+9atfccUVVxSPu/zyy7n//vslbBXvarW1tZSXl9Pf34/H4yEcDmNZFt7qahKtrQytXk1+aAgzVIpz5gzmzJ1brNS0LIvly5fT1dVFKBTCMAyi0SjLly/n/PPPx+/3Y1kWoVCI7u7uYrXr7p6utnSCkc4Ont2+ieWhEEG7TiocxuZwALCm18HQSCd11TbyhQK+wgiTjBrW++YQDocBcLvdzJ07F4/HA8CMGTOYMmUKmUwGt8POyC8fIBFP4/AE0XWdfDZLIhUnl8xQAhDphn98F+L9qmI0HYHlv1Lb7mdedsjvq8PhIBgM0t/fj9vtLlayjo6Oks/nGRkZIRqNYpomJSUle06ceZlaS9dSSAyCuxSO/bSqMNU0VfU62gGpMAQbwfu6dg7hHdC+UIWxvip1nrcc5t8IPatUu4AVv1HtAmzqfcbpV9fOpRj7wkJ8EKqmq/A1vFMFsruHZumG+oh2HfL7UxTrVfd6bYWsqwRi/aqiV8JWIcS7lIStQgghhBBCjPn4xz9Oc3NzcVvzSy+9xOzZs1m7du2eHojAlClTWLFixRteJ5PJkMlkip9Ho9G3btFCHCK73c7pp5/O6tWr6e7uRtd1AoEA5eXlZLNZjNmzGR0epqmpidNOO23c9vRkMklPTw9+vx/HWEBaUlLC4OAgPT09FAoFMpkMU6ZMYXR0tNiCw26348ZkqKePjM2JzWYnMxpjsG8X5RWVlNU3ApDQsnS0r6XaM0SFzyJl9zDoaMHtDzFjxgx0Xae6unp8WImqmLXZbBQKBbZqzUzUOnAUohRw4rcixDQv/VqdCls7FpEZ7mDEqKIwksLj8RIwTIwtT8O0iw+qujUWi7F582b6+/vx+Xy0trZSW1vLrFmzeOmllxgcHETXdQqFAjU1NUQiEYaHh4sB7ObNm6msrKSxsVEFn6d/XVXZpiNqUJandM/NNA1CE9TH63UtU9v9+9epqlQsWPJTOOtWmHI+NMyD+uNUJW/7y2MtE9yQT4G3Cgo5GNiowlZfJcy+XIWqZS2w6xUV3moamHmwChBseFN/1/bJHVKDwDIxVU0Lqm+rpo1/3UII8S4jYasQQgghhBBjXC4XZ555JqAC0i9/+cv86Ec/4pOf/GSxeg6gtLSUZDL5htf57//+b/7jP/7jLV+vEIcrEAhw6qmnksvlWLNmDUuXLqW9vZ18Pl9sL1BfXz++D+h+mKbJmjVryOVyxdYCU6ZMIRAIsGXLFjweD9nBPjKagU0Hv9sJaZOUaRKJRqkdu07eGWREK2OLfxpb9BzdhVLiWR96Vxdz586lpaXlgGvpChyHlk/TkN2CrZAi7qphjTaTeo9qhTDQvRMiEUZtLjQ0YrEYWadJhd2JXsiOC1t3b/+329Xgpmw2y+joKK+88gpDQ0M4HA76+vro6uri1FNPpbGxkXPOOYcdO3aQTqepqKjA4XDw4osvUlZWhsfjwWazMTw8zOrVq6mvr1ctBTQNSie+ia8gamv/ygdgeBvks6oi1bJU5ehL34PSJlXNuuheFcamwipw9VWqQPec28Dhg8HNKvCdcNKeNUy7SIWw/etVIJpLQUWb6vF6uNwl0HoOrP6jGshl2FVv2JrZUHvs4V9fCCGOEglbhRBCCCGEeJ1CocCnP/1pbrjhBk499dQ3ff6NN97I9ddfX/w8Go3S0HAEKsGEeIvsnmK/bNkyUqkUhmFgs9mw2+3s2LGDqVOnFoNGUMOsampq2L59e7GaNBKJkMlkKBQKlJeXY7PZiEajbNiwgXPOOYeamhq2bNlCV9cuNE3H63FjGDYshxNdN8hls5iWha5p5JNJLLuT7YVysqZai2ZZZDIZVq9eTVlZGTt37iQWixEKhWhpaRn3CxHDMKhvamFtPEVv2cl4jAJDaZ28CfNqasjn82waLNCGjt9ukTdcmIU8xDuJ1s+mxO4GVAXvqlWr6OjoAKChoQG73U57ezvhcJhYLEZdXV2xbcLQ0BAbNmygoaGBioqKcX1UV61aha7rxYFkAF6vl2g0SiqVwuv1HtoXLxmGSKcKQm1O9QGqSjQ1rKpewztg10II1quQdWiralNwzregbCxYbTlj/HUtSwWzNqcazpVPw6Rz4MQvqmB0tENVvNqcHLJjPg6uIGx9FnIJ1Z92xqVHZFDZXixrTzsEIYR4C0nYKoQQQgghxOt885vf5Nxzz+WTn/wkAD6fb1wlazgcHhfsvJ7T6Rw3HEeI3cxkksSSJWQ7OrGVhvDMm4e9uvrI3sM06ejooKenB03TqKuro6GhAe0AQVMkEsHpdOKvrWcolydrs1FmtzMQDtPf3099fX3x2HQ6TXl5OT09PYTDYXRdx+1243K58Hq9xb//u1sL9PX1MWfOHKZOncqzusW6devRd6/HsFHwBXGm4oS7O9A0NTTKVVXHQCaHphfIZrNYloXH42F4eJjHH3+cdDqNYRiYpkl7eztnnnnmuMBy9uzZRKNR+vr6GM4VcLsdHDNrBjU1NcTjcTqpIRSYSU1yE65sGLAYsZUyWn0GJZqGaZq8/PLLdHR04PV6i31q8/k8lZWVxYFX/f39OByO4r/3u3bt4rHHHsNms9Hc3ExbWxuGYRT/m1EoFDAMA1AtRzwez+H998LpU9vxzdye4NMyQQM0OySHVPuAQJ0KNgFqZsHwdhjdtSdsfb2ORbDwHnXdskkq1B3YAMt/DX3rIJfc03Jg4umHtnbDDtMvUW0b4MiHoZal2iBselwNB6ucCtP/RVXnCiHEW0TCViGEEEIIIV7j0UcfBeDqq68G1NCshoYGNmzYUNy6vGnTJubMmXPU1ijencxEgsF7fkhqzRqwLCwsYi+8QMU11+Cc+Ca3ju/HypUrWbt2LaZpYlkWW7ZsYc6cOcyaNWu/51mWxXA2T4euYRp2dDRGU1lceYuynn76w2GCHg9er5fFixczMjJSHHjV0NDAvHnz+L//+783DHX7+/vZsWMHlsuDw+0hnk5iy2QwLTAqqpk1fSpeXcM0C9RMmsz6jm76ly+HsXvsDlaj0Si5XI76+no0TaNQKDAwMMD27dvHvUa3283ZZ5/N4OAg6XSa0tJS/H4/oFqGuDw+VlvziYRm4s30kcHJ9lwlJ1VPpVAo0N3dTW9vLyUlJTidTizLor+/n2w2i8PhwOv1EovFyOVyxONx7HY7/f39mKaJy6UqM/v6+ojH48ybN4/GxkbKysoYHBzE7XaTz+exLIvJkycX+0TvU2pUDb1yl0KgZu/n7W6Y/EHoWa0qUc2C6q1qOMBXroLSnf9QPVh303QVRJr5N77vlqehkIbyNuI5je5cNWbfJqr7HiVU06DaAES7VXsCX6UKMg/VW1VxuusV1Uohn1ah9PbnYWATnPstKGl8a+4phPinJ2GrEEIIIYQQY4aGhvjVr37Fww8/XHzs7rvv5mMf+xh//OMfufDCCwF48MEHueqqq47WMsW7VGLJq6TWrsXe0IDudGKZJtlt24g+8SQVX77miNwjEomwadMmnE5nsc9qNBpl/fr1TJw4cb+9V/2lZfQ7nDhiMRyBIFlNZ5vPR5/LxysDcUrNHMdFh6iOhrHZbFRVVaFpGslkkr6+PtLpNLW1tWzduhWHw1FsI7C7avO5554rtihwB0uwu1zkMxkoFPCGQiQdHqbOnUv1WKXvyu3txdDS6XSi6zrxeBxd1/H7/cVQ1zAMDMOgr6+Puro6PB4PbrdqA6BpGqGQDV0vw2bb89rtdjtTp05l6dKlbE2XYrNVkslkqK6pJpFI8OijjzIyMsLIyEixanU3bazq1e/3E41GiUajRCIRkskk+Xye6upqQqEQAPF4nK1btzJ16lR8Ph/z589n/fr1dHd3EwgEaGtro7W1dd9fEMtSFZlrHlKBq90NzafBvP+39zb7GZeqIVev3A2JQVXhWtKktum3vl+FjP0b1MAr3VBtBzylUDX9jf8yJQbB5qYrYfBKn4tYToPUBNx6HccWAkxx5lSv14FN0LFEha27q191O1TPVBW3R9PmJ1TQWj72Hvuq1Hp3LIBjP3l01yaEeM+SsFUIIYQQQogxP/3pT9mxYwcf/ehHARW+btq0iXvvvZfu7m6+9rWvUSgUmDNnDuedd95RXq14t8nu2gWWhT4W3Gm6jhEKkdm6Bcs00XT9sO8xOjpaHMi0m8/nIxwOE4lE9hu2JtAYnDiFim0b0eMxtpRW0+kL4U4nceQzDLu9vBKq4YSBXupcqIFOqL6jiUSCgYEB5s6dy8jICN3d3eRyOXw+HyeccAJdXV3FdWmaRj6fp7OzE5vHS2VpabEq9B//+Afnn39+sQdqMBgkEokQj8cBdc9AIFAcWAUUq10zmQx9fX04HA6mTJlCS4uX3t6HiMU3ABaloffR2PgZDEMFgFOnTsXpdLJt2zbS6TR1dXU4nU6WL1+OYRi4XK7iOpuamnC5XOi6jmVZxT61wWCwGLJqmobD4RjXk9XlchGJREgkEvh8PoLBICeffHKxIni/elbCsl8CGgRrIROHjY+pKtJZHxl/rGGDOWP9T5f/ClIj4A6qzw0bHP8FePku1bvVstTArLmfgUDtvu6s1MwmP7iFpSN2EnmNCkcWUiNE9HJWDjmo9eQJONTyyCVU9eyrP1MhraZDaAKcct3R3bIf7VEVrbtpOug2SAwcvTUJId7zJGwVQgghhBBizE033cRNN920z+e++c1vvs2rEe81RqgETHNcsGomkzgmNh+xbdRutxu73U4mkylWhWYyGex2+377DAOE7Ab+ikp2evzUp+L0Zgq4olHcuQw2w4YzEWPY7aPfHyIUGSoGhpZlFfuX7h6Spes6DocDy7Lo6elhYGCgGLLa7XZ0XSeVSuHz+QgEAoAKJgcHB+ns7GTatGk0NDSotgOWhWEYWJYFQFVVFYlEgp6eHtxudzFo9fv9eL1e0uk0K1YsY+fOjVisx26LYbPnGB3dQiKxnenT/wdN09A0jZaWlmJ7EIDHHnsMTdMoKSkBoKamht7eXnp7e4trLSkpIRqNUigUiEQi2O12KioqSKfTxf60tbUqxEwmk7hcruJr3O2AQStA11LIJvZsz7e5VJ/U7c/DzA+rvzORLtj4uOrJauZhaAs4g1DapKphX71PBa4TToETvwS9q8BbDnVzwX+AXsFTL2SkfT3RoQxBPYZmWeApIVjIMJjzM5g2CJhDKrz018DL31frK5sEVgGGd8CSn8AHvju+hcHbqXKaGr7lq1JBayGr1lbacuBzhRDiEEnYKoQQ4g1t2bKFtjYZICCEEEIcCd4TTiC+YAHZ7dsxQiHMRAJ0Df9ZZxfDt1wux65duxgaGsLlcjFhwoTilvSDUV5eTn19PTt37iSdTgOQzWZpa2srBohvxKHr/EtViJ925tlkBcjEB3FaJpqmYzPGqm4tC1NT1Z3xeBybzcbAwACZTIa1a9eyePFiNE2jvr4eXddJp9Ns2LCBXC5HoVBgdHSUkpKSYuWqw+Eo3n/3e5DNZgGKVbCapqHrejHAjUaj2Gw2kskksVgMwzDwer2Ul5er1+Fw0NHRi2n2UFU9QqHgJpPwkM0m0fV/EA4vpKzsfXu9fss0MSK7aErvwhUJMOptIRQKkUqlKCsro7W1ldraWvx+P11dXXR2drJlyxYqKyux2+0EAgHS6TSjo6PYbLZin9mZM2cW2xq8KWYeVTb6Gpqh2gUAJIawnvs2heHtGE4/2tBmyMah8URwBtTH0BbY9CRsfQb616pz/dXgKjlw2BqowXb69RgjD1PQ8ti9fnAGMHvXoSfS2CMd4M2qQHPl76B7GTj9Kswsb4NgPYR3wki7al9wNEy/BAY2qtYBuk2trXoWTJx/dNYjhPinIGGrEP/kVqxYwTe+8Q2eeeYZjj32WJYvX77P4x544AE+8YlPMHfuXG699dZiz7qj4c477+RHP/oRu3bt4mMf+xh33nknDQ0NR2097xWbN2/mP/7jP9A0jd7eXl566SUsyyISiRQn8P70pz8ln89zzTVHpq+cEEII8c/EXlNDxZe+ROTxJ8hs346juRn/WWfhPeVkAPL5PC+//DI7d+4sVotu2rSJ0047rVgpeSC6rnPKKadQUlJCe3s7mqYxceJEpk6delDVlPNLA5TYbLw8EuXvvZ30uD0YVoFCoUDS4cKWz1Maj1BaWkomkyEWi5FOpykpKcHj8dDf30+hUCCdTuPxeMZVsOZyOTKZDP39/SSTSQKBAJZlYZpm8TibzVYMTTOZDMFgEK/XSz6fL1bKdnR0EAwGaWxsJJ/P09XVVQxod0unM5SV96NpdnTdid0O2ayXbDZDJLJin2GrtuGvnDDyF3KxYWxROxlHiI1l78fhKGHWrFlMnjy5eGxrayu5XI6dO3dit9vV+ZpGZWUl4XCY2tpavF4vjY2NTJgw4aC+dnupmQ2b/w6JIbXtP5eEdAQmfwA0jc5lf2fdDpOIcRzBrMV0PU2jtU6Fm75qldMaDuhaokLaknownKoadvG9EGqCYN1+l1BS1Uj1pFm0t7dj2v3o6EQ9EykrNamedRq4vbDs1xDrGwszLQi3q/v5a1T1rXb47TEOWXkrnPufqkdrfADKJkLz6Wq4lxBCvEUkbBXin9yxxx7LE088QXNzMytWrKC9vZ2mpqa9jvvLX/4CwH333cexxx77Nq9yvBtuuIFQKMQ999zDgw8+eFTX8l6xfft2LrzwQn7zm99w0kknAbB161bOPvtsli5dyvz587nmmmv48Y9/zN13332UVyuEEEK8ezknTaLy//sKVj4PhjEuAO3q6qK9vZ1gMFgMFoeGhli9ejXV1dXFHqkHvIfTybHHHvumvmfLZrPFvqpVoRBX11fiX5/jd4k0oy4vJhb2XI7W/k4qsiksy42madjtdsrKyigvLyeVSlEoFMjn82SzWUzTJJvNomla8TVFIhFisRilpaWcdNJJLF++nKGhIQBsNhttbW3FYNnn82EYBjabDa/XC0BPTw+maVJeXo7dbsfhcBAIBBgeHiaRSOD1eslms+TzGoZhgWUCKnfUKGCaBobNu/cbMLILVv8Bv89Pt+Ulk04RTAzSkH0KbfZ1NDc373VKSUkJNpuNdDqNy+XCsiySySQej4dAIIBhmKRSK+noeBans4JQ6ESczqqD/prQeBJM/RBsfRoGN6k2Ao0nwoxL1C/G13WRznpxuzX6UjrhQhtnWH3UZmLqdeczkI6qCtlQrap0BShthsEtqifsAcJWTdM46aSTsNls9PT0kMvlqKtvYN68eTjKymD5/RDrUcOwOpMqENYNGO1QVbSNJ6pQ92gK1qt+tkII8TaRsFUIgd1uZ9KkSZSWlvKXv/yF6667btzzkUik2CPrtVu9jqbd31yLI+Pqq6/m2muvLQatoCo2fvWrX7Fo0SLmz5/Pvffey8qVK4/iKoUQQoj3Ds22949iIyMjmKZZ/B5H0zS8Xi8jIyNkMplD24p+ECKRCAsWLGBoSPVh3d3fNTc6yhmRCIOU4Er48CULuBzQOHUSLp+dWCxGX19fsT1BLBYrBsK7+6wWCgUMw8Dv92O32/H5fAwMDNDY2EhTUxOVlZV0dnaSzWYpKysbFypXVVXR0NBAe3s7drsd0zTJ5/O43W5sr3n/AoEAqVSKRCJBIpHAMAwqK2tIpxvxeNZTyCQpFOzoRg6XK0QwMJvBwUG2bNnCyMgIZWVlzHB040+P4qiYQkOJSSwew0r7abAStBzTgu1133dmskOY5j9onLCI4eE8o6MTyWQqSafT6LrOxo3rSaW7MfQ4zRNHKC+PMTT8Ii0Tr8Ptbjy4L4xuwPFXwaSzYHQXuEuhagYYNrYte4mUaafClkKz2fHaDAZTHrbSRK21UbUP0A2omaWCz3HVpWMBv1VQlajRbhXKljSOHyY1xuv1cvrpp5NIJDBNE7/fj5YMw4I7YcPfINKpeqGWTVShdWoUsNR2/RP/9Yj1IxZCiHcLCVuFEEWXXnopDz/88F5h62OPPcZFF13EI488cpRWdvAOarKrGGfdunW88sorPProo3s9d+aZZ1JdvaeflwTcQgghxFtnd5i6e1s9qIpTj8dT3Kr+Vli7di0DAwOUl5djGAbxeJwdO3YQCoVoKW8htMkgn4ZCIY8zHyKxU8c+OVusPE0mk8Vq1t0hqMPhQNf1YsVnoVDAZrMRj8exLIuSkhIsy8Lj8Yzbnv9ahUKBtrY2PB4P4XC4WEW7ceNGYrEYfr8f0zSJx+O0trYya9Ys4vE4Xq8Xh8PBY48lGRyM4vMNohtJTNNJNns88XgVL730LCMjI+i6TkdHBxn3IKcUTOxmHpvNTqikBJIm5A1wjQ8gc7lRdmz/HvHEVgIBN3Z7hFxuBLPwfnbu3D0MawBdHyGdDtDX66G+Pk86tZX+gb/TNOELB//F0TTV7/R1PU8TiQR2fymaVgqpMJpmYC/YSLjr4LSLVO9UX5UahLXgO7DrZbC7VVuBSBe4g1A6CV6+C3YtUmGpp0xVgE46ex/L0PD5xt4Hy4JF90D7QrUdP9YLkW5VSVtzLAxugPrj4QP/A8ZRGowlhBBHkYStQoiiSy+9lP/4j/+gt7eXmpqa4uN/+9vf+NnPfrbX8blcjn//939n06ZNhEIh8vk8P/7xj4vbvB588EEeeeQRpk6dWpzg+r3vfa/4w8OqVau4++67ufHGG7nnnnsIh8N0d3fz61//+pB6Ww0NDXH77bdzxRVXcP/997Ny5Uquu+46LrzwQm6//Xa2bNnCpk2buO+++5g7dy4Aw8PDXHvttVRXV+NyuVi7di0333wzxx9/fPG6v/vd73j00Udpa2ujp6eH0tJS1q9fz+rVq1mzZg3l5eXcddddPP/889TX1zMwMMBPf/pTKisrAXj00Ue5//77qaurI5PJYFkW99133z5fw8KFC/nOd77DihUr+NznPseSJUuora2lo6ODG2+8kbPOOgtQofL+7rlt2za+9rWv8fOf/5xLLrmEbdu28fe//51jjjlmr3suWLCAOXPmvGG1zLRp097wPV+5ciXf+ta3mDZtGplMhl27dnHvvfcW17Fw4UK+973vUV9fTyKRIB6P8+CDD45VfGzk5ptvpra2lkQiwcjICPfffz9+v/8AX2khhBDivamxsZENGzYwODiIx+Mhn89TKBSYPHnyuErOI6lQKNDd3Y3H48EYC8ZsNhuFQgHLssgOurBh4AjliUYT4NLIRb1khg3s5Wl8Ph+BQIDR0dGx7ft5ysrs1NbGgTTDw050vZlUKkU4HCaRSGCzGaxYsYKenh5OOOGEsXByvJ07d7JixQpisRg2m42amhpOPvlknE4nsViMtWvX0tfXh9PppLq6mnnz5hV7vQJ0dnaSyxn09c3F44ngcORIp304HBWEh1+mu7sb2POL+rVJO9MCASqHt0GgFrOQIzPcQb9vJt1rttPQmKOhoQFN0xgZWUwiuR2PpwVdt+F2W6SSO8jlN6Jp03C73USjMdB0XG6NTEYjndax2QLE45uPyNetsrKSrq4uCtXHYMR7MVMjZDM2Ko+ZA3M/OP7geZ9T1abDW1VLAU8pzPkkdC6GzU+Bvwq8Zarv6pL7INgIFfsZkDqyE3rXqO35Tr9qFzDSDvE+GNyoqmlPuVaCViHEPy0JW4UQRdOnT6e1tZVHH32UL37xi4DaVqbrOsFgcK/jr7/+enbt2sVf/vIXDMPgnnvu4VOf+hQPP/wwvb29fOITn2DRokXF4PLUU0/lb3/7GxdffDG//OUv+epXv8pJJ53Evffey/e//310XedLX/oS119/PQ8//PCbWvtTTz3FVVddhdPpZM6cOdx9990MDQ0xZcoUbrrpJr70pS9RXl7OHXfcwbXXXsvChQsBuOWWWxgZGeGBBx4A4LnnnuPzn/88q1atAuD555/na1/7Gps3by6+B//6r/9KRUUF//Zv/0YwGOSuu+7iD3/4AwsWLMDj8fDYY49xwQUXsGjRIsLhMNdccw0bN27E5/PR19fHSSedtFegvdspp5zCl770Jd7//vczPDzM3//+dzRNY9u2bZx88sk899xzzJw5c7/3fOKJJ/jSl75EVVUVX/ziF2lqamLx4sW8+OKL+wxbOzo6aGk5tAmxH//4x/nMZz7DDTfcUHw/b7/9dr7//e+Ty+X46Ec/ypIlS6irqyMej3P66aezbNkyjj/+eD7xiU/w4x//mOOPP55cLscll1zC3//+dz7ykY8c0lqEEEKIdzuPx8P8+fOLQeLuqs83qvx8s/L5OJpmxzCcxcd291197YApY6yXbD5fIBfXMOwWFhaGzcA0TQpmnvhIlnS6C7dmURv0Ym9qRPcF2L7zZVzO59D0GABV1Q4aG/14nB/iib8+SjYWR9Mgmk6TiMUwTZNzzjmn+Mv4VCrF2rVrWbx4MZZlEQqFsNvttLe343A4KCkpYdeuXTidTgzDwLIsvF5vsZXBbrt27SIej2MYNjStDsuyARlyuRydnZ3k83l8Ph+6rlMoFIgnc7zqOJULqvqxhrcRicTYnm1gU24a2S1b2LZ9O3PmzGHWrFmkM30A6Lqt+B7a7EFy+TC6rgadGYYLLJNcTmWODgfk8ymczv2EmG9CW1sbXV1dDA4OAj5w+Kisq2Dy3NP2PjhYD+ffAf3rIJeC8sngCsAjn1cVrp5SdVzJBBWWdi/bf9iaS6mA1eZQlbcVk8FXCQObYNZHYO5n1PWzCdj0BOz8h2pp0HQqTLkA7K4j8h4IIcQ71VEcCyiEeKfRNK3YSmC3v/3tb1x00UV7HTs8PMxPfvITvv3tbxerIL74xS/yzDPPsGPHjmIzfZdrzzdTEydOLPb8vPLKK7n66qtZsGAB119/ffEb7NNPP501a9a86bWfd9553HzzzWSzWT7+cdUAv7y8nKqqKvL5fLHS4ZRTTmHDhg3F8yZMmDAuaJw4cSJr164ll8sB8OqrrzJz5sxxYfMpp5zCihUriuu+4447uOWWW/B4PABceOGFRKNRFixYwPbt23E6ncXnqqur+c1vflMMc/fF6VQ/AN1yyy3FlgiTJk3iIx/5CD/72c/I5/P7veeHPvQhbrnlFrZv3860adP47W9/y7Jly7jyyiv3eb9wOLzPMP1gTJs2bVybgdd+jYeHhxkdHaW0VH0D7/P5+MMf/sD69esBWL9+fTFwttvt/OY3v6Gzs/OQ1iGEEEK8V5SWlnL66adz2WWXcdFFFzF16tSDHoz1RtLpHnbsvJv1G77G+g3X09X9AIVCEgBd15k0aRK5XI5YLEYmkyEajRIMBrHbbeRIkknlyeWyhEpClJWVoaGRzUQg3I/ZsZW1zzzB8gd/Se/LzzJl0iChkIXbPZFgcCpVVa2kU8vZsuIh4pEIbh3shk42ESU7Gqa3t4dwOAzAwMAAf/3rX3nuuecYGRkhEonQ3d1NOBzGNE2WLVvGU089xeDgIE6nk/r6empqaujr66Orq6v4ejOZDFu3biWdTpPNZkkkEiST6vXu7vtqs4fxetfi863D7RnBskxGjHI473ZGT/0PnvVdzPbaiwhWTaCiogKbzcaGDRtIJpNjQ64sTDMPqOrYfC5CMNiM3x+kp6eHVMpDKu0lk0lTUREhn9+BrruoKN97i/6h8Pl8nHPOOZx00knMmDGDk046ibPPPnvvHUK7A89F96pqVH8N+CpUKwAzr0LQ3TQ1Royx1/WGQs3gr1btCMYGkJFNQPkkmPVRFbRaFrzyQ1j6c4h2qcrXV38Gr/5UPSeEEO9hUtkqhBjnsssu48477yQcDlNaWsrjjz/OL37xi72OW7RoEX6/n5kzZxYfs9vttLW1sWLFCi677DJefvllCoUCq1evZsmSJSxcuJCKiori8Q6Hg/nz59PYuGdIgMfjKQadb5bD4aCiomLcDyRut3vcPd1u97jrf/3rXwdUMLh48WKeeOIJTNMkk8lgt9s599xzufPOO1m9ejWzZ88mm83y4IMPcvbZ6hvlTZs2MTQ0xOmnnz5uLTNmzGDFihVcc801OJ1OLrroIr7xjW9w0kkncdpp+6g4eJ3dQfFrTZ8+nQcffPCA9zzzzDOLfdJ2v77Zs2e/4b12T/A9FH/+85+xLIudO3eyZMkSfv/735NIJAA11OLEE0/knHPO4ZZbbuGss86itbWV1tZWQFXFnn/++dx2221ccMEFlJWV8dWvfvWQ1iGEEEK81xhHaAt2oZBiZ/sPice34HCUY5oZ2nc+zto1AySTjbhcLlpaWpg1axbbtm0jlUpRUlLC6aefTqFQYP3SHQxv1LAbPgJuP/lMgboJXpIjK8hlUsRiUXUjS6Nz0zKcTSNUTGjB5aksriEW6SM6uhHdmITd4UTTNHSbjXQyQSoWJ5fNFsPUvr7dVaN6seo0Eons9b4MDg6i63qx92ssFises3nzZpLJJDabjXw+X/zeTtd1NE3D5RqmoWEphi2LpoFpOsGaSmXlcaDrjJoeonkHFa9pseT1ehkdHSUSiVBefiLDwwtIJLZhGB5MM42meejoqCIajZHP5wmH05SWNtLYGKGsvBOPexoVFecRDM7d/xcsPgi5BATqwNh/n163273fdk9kk/DiHdD1KqCpgVjbnoVTvwp1x0L9PBXEOgNgc0FiAOweNYRrfxweOO5KFeAObhlbTBDmfEK1JAAY2qraFARqwTX2S/1kGHa+BNMvUcO4hHib7Ng+r1ikcrh2/+JGiP2RsFUIMc6cOXNoaGgoDsXaPTX29XZ/03vjjTeOezwYDOLxeLAsix/96Ef89re/5eKLL+bcc88dN+l+t7dqqu7Bam9v55prriEQCHDZZZdx5ZVX8uMf/7j4fG1tLRdccAEPPfQQDz30ENFolMsuu4xPfepTwJ42C//93/897rqpVIrS0lJcLhevvPIK//u//8tHP/pRPB4PN910E5/+9Kf3u659Bc7ZbBaHw3HAe+7W3Nxc7J+7Pw0NDSxevPgNn9/f0LEXXniBm266iRNPPJELLriASy65hF/+8peAqpR+/PHH+dGPfsSXv/xlEokE119/Pddddx26rnPffffxm9/8httvv53Pf/7zXH311dxyyy3jqqGFEEIIcXii0bUkEjvweJrRdQe5HGzYkCccHkbXs+i6QUdHB6eddhpnnXUWGzduJBKJ0NHRQWtrKxddfi59OyO0rxkiMZqhtM6HNxBh+eMjpOMxNMDh8WIBmZRJOp4lGRsuhq2WZWKZJkbORsHuoFuzk3K68BRyeOP9OCIbWPJAhGBtHbsGR0il0lhjlY+WZWGaJrlcDsuyqKioKPaFNU2Tgf4B0hGLXMIgFzEoFEwMQ6ezsxOfz4fD4WB4eJhcLodpqgpMux0aGzei6ZBOh8CycLpSVNe009qqgsLdA8my2Wxxx1E6ncZut+P1erHbA7RM/CpDQ88RT2zG4aigY5efzo4MXq+7ODRM1z0cc8xHKC0NoWkHqE7OxGDpL6BjkdqiH6xT2/HrDhDOAuTS0LdWhbQVU1TVKahrdS1T7QHsblVROrQF1jwEtXPgmCsg2gMDG9SALFcQZlymnjuQCSdBqAl6VqhK2OpZUNq85/nkkAp7gw17HnMFIDkMicHxYevgZtjxogpjK6dCy5nqWCGEeJeSsFUIMc7uVgKPPPIImqZx8cUX7/O4qqoqPB4Pd9xxxz6ff/jhh7nzzjtZuXJlMQA8UhUaR4plWVx44YV85jOfKVZUtre3jzvmK1/5Ck1NTdx66604HI69rlFVVYVpmnzrW9/a5/OJRIJAIMBtt93GzTffzJ///Gc+97nPUV5ezgc/+MG9jt8tEomwbds2Jk2aVHxs2bJlnHzyyQe855t16qmn8rWvfY329naampr2ev673/0uX/ziF/caXtHV1cUFF1zAiy++yLx58wDV/3W3TCaDzWYrBqxPP/00n/70p3G5XHzhC18gn89z5ZVXcuWVV7J48WI+/elPk81m+e53v3vYr0kIIYQQSqEQB0w0TVVJdndpDA05AROHQ8c0IRqNsnjxYkKhEOFwGKfTycDAAF1dXZx22mkESgN4mhMYmQzBMhd+RxCb3UEuncGwqx8pzXwOXXeQHa6mUBcjne5B113kcsN4fI1EfK0sd5ZSER3BmcuSHx7AjAzjLC0F02Tr0sXEMjkK1RMY25heDF1BDe2qrKwkmUzS19dHKpnClaglkfWhY2P1030kegyO+0AzNpsNy7KoqqqipKSERCLB0NAQ+Xwel6sfmz1KOu1H13TQNfJ5P+XlEAqpX3ZXVFRQV1dX7BNrWRb5fJ6pU6cWvx9yOiuoq/sYoH7hvXjRX9G0LH19feTzeSzLIhqNsnr1as4888wDf6FW/BY2PwneCnB4YWgbLPwBnH/nnvB0X0Y74KXvwfA2KOTBHYI5H4epF6pgNd4HmaiqWPVVgqccRndBNq4+P++/oG8NpKNQOhFCb2JIbaAGAm/w/WywfixcHVKvCSAxpKpoXxvAdi2Df/wPpMKg21To2rkEzrxZvQ9CCPEuJD1bhRB7ufTSS3nqqaf485//zAc+8IF9HnPCCScQDofZsWPHuMcLhQIDAwM8++yzXHDBBeMqLXdXFLxTDAwMsG7dumKVKuy9xptvvpkHHngAr9dLa2srLS0tzJs3jzvuuINsNktLSwsNDQ3FgVuv1dvby29+8xv+8pe/AOqHhI997GN86lOf2ufxr6XrOtdddx3xeByAZ599lgULFvCv//qvB7znmzV37lzOOussrr322r1e/+rVq9m8efM+pwQvXLiQpqamYtAK49+/5557jrvvvhtQIf55553Hddddx8KFC9myZUtxqBbAiSeeyLe+9a0Dvi9CCCGEeHM8nmYMw0sup1oGDQ5qFAoWTqeGw+HC5XKhaRpDQ0MMDAxQUVFBSUkJFRUVZDIZli5dylNPPcXSpUtZvXo1CxYsYP22HdROnYGFRSaRIJnJEMfADJSSHm0i5PsgdlsQKBAMHEtz8zXsnH4WPRU1DJSUMuwNYKSixAIhCrWN2H1+8nYXtlwWWzYN7AladV3HZrNhGAb5fH6sl6wdWzaIK1uOy2PHFdSIp8OsfXU7D//ymWLYGovF0HW9+At/l8uFrnvQsGOzmWiajt1uJxBw4fOVYLP5sCyLbDbLiSeeyNy5c/H7/YRCIY4//vhx3/O8XqFQYGRkBNM0cbvduN1uTNOkffsWMoM7VfXpG0lHYdfL4CkDbzk4/ar/aawfupa+8XmWBct+Bf0bVIBZ0QZmDlbcD2v+BK/eByO71OCqvjWqnUB4O7hLwTa2u8ywq+rZljPeXNB6ICWN0HoepCOqcnVwM+SSKgT2Ve5Z/5qHID2qKnLLW6G0CXpWqapcIYR4l5LKViEEmUxm3ATaE044gbKyMnw+X7G3TSaTASge5/f7+frXv861117Lww8/jNPppFAocPPNN/Mv//IvNDY28vzzzxe3oO/YsYNnnnmGj33sY/T39xerM18f7u3rsYNZ8xud+/rHXvt5SUkJPp+PzZs3U1FRgWma/OxnPwNgaGgIu93Ot7/9bc455xy+973vUVpaimVZbN68mY9+9KOk02luu+02/vM//5MbbriBZ555ptg37J577qGxsZGLLrqI4447jmOOOYaJEycW+5uec845+319oVCIG2+8keuuuw7TNLHb7bz00kvFYVT7u+fFF19MoVB4U+H2fffdx+mnn87555/P17/+dSZMmMBTTz3Fj370I/7+978XjysUCuTzamhCY2MjPT09RKNRAoEAiUSCBx54gEKhQH9/P2eddRaf+9znOO200zjuuOMA2LlzJ8cddxxTp07l2Wef5fHHH+eCCy4Y95wQQgghjhy3u4nKivPoH3iSRHwLhUI1mlaCrvsBDcuyih82m63Y+171NnUVt+RXVFSgaRqZTIadO3dy2qnvQweWvvwP4k4vpmEjDkRMG2VDkzjt9E/jdOoYhg9N0xhw7KTV46IkZkczHbhtNuIuL4mCSc5S37c4HA4Mu0HuNe2LvF5vcQjW8PAwDoeDTCaDV6/F5XLj8FokEglyZg6dHPF+2KGto6QkRMEwSCQS6LqO1+ulpKSEoSGdWLyMgL+XrOXAMOz4fA78/jlks2U899xzDAwMYLPZmDhxIh/84Aex2/fdO9U0TXbt2kV7ezuxWIx0Oo3X6y0GtgE9xZzhlzAffQxCNTD9X2Dy+WODqF6jkFNb8Q3nax4cOyafeeMvbmIIBtarylfb2LmBOtVS4JUfqG35NgeYJpgFSEXUnxNPB+NtiAKO+6wKULuXAxo0HA+Nr2krlk1ApFMFzLvfE5tL9ZaNdO3zkkII8W4gYasQ/+SWLVvGV77yFRYvXsyHP/xhfvWrX+Hz+bj00ks566yzAFi5ciXXXHMNAJ/+9Kf5r//6r+LE++9+97vMnz+f+vp6crkcn//855k3bx5Tpkxh1apVvO9978PnU1UCn/3sZ7n33nt53/veh2EYPPjggwwPD/OjH/2If/3Xf2X16tXcdddddHV18dWvfpXvfe97+1zzt7/9be699176+vq45JJLuPvuu+ns7OSee+5hw4YN3HHHHXzjG9/gjjvuYP369dx9991Mnz6d6upqbrzxRlKpFJ/5zGf49a9/zf3338+Xv/xl6urqGBwc5KKLLqK1tZVPfepTPPPMMzz00ENs3bq1WKGraRpTpkzhqquu4oknnii+J5Zlcf7551NbW4tlWXzoQx8qtmA488wzuemmm7DZbGSzWU444QQuueSSA35tTj75ZE4++eR9Pre/e/7ud7/jrrvuYvPmzVx33XXceuutlJSU7PdeEyZM4OWXX+amm27isssuI5/Pc8YZZ/DYY4/R0NBAMpnkG9/4BkuWLKG3t5dAIMBnP/tZvvKVrzB//nxqa2sZHh7m0ksv5dvf/jbf+973uPPOO/nIRz7CXXfdVfzBraSkhGuuuQZN0/j4xz/O73//ex544IHiFr177rnngO+LEEIIIQ6epmnU1n4Ev3868fhmstkEw8P95PMahUK6GLS+9hfhuq5jWVZxEIzPpwJTVW0aJZXup6PzBWaecTLb4hky3V3k0ykMhxPT7mDN2rVous65555b7PveMNTH4Ka1eK0CmgWaaeEJD+AuK8VmN9BME1OD0uoacvFUcR0NDQ04nU6CwSBTp07F4/GwevVqMj1uGIVUIkE2m0VHg0KMfHgducgQ/ZrGpJmzmHbGeQQqKou/PK6trSManU8stgyXu4tgsISG+jMpLb2Q555byPDwMH6/n1wux+rVqzFNk1mzZrF161Z6e3txuVxMnDiRuro61q1bx8qVKzFNs9hfNpVK4XA48JBkrrmSSj2M5pqottO/eh94SqHxxPFfJE8plE9W2+edfrWdPjGgttFX7mcAlm4AOliv/QW7pSpIs3FVteoqUf+cGxvq4y5V7QLeDrqhgt2Jp+/7ebtbVfOO7lJ/ggqdQbU7EOJdrr+/n5qaGs444wz8fj+gBix/9atf5aqrrjrKqxNvJc16bSMcIYQQ4xx77LFceeWVxbAZVHXnZZddxkknnTRuK/yR8uyzz3LZZZcxOjp6xK8thBDi6IhGowSDQSKRyD5bswjxdslkMjz77LPs3LmddDqNYeiUl1cxf/58Vq5cycDAAHa7fay/qau4UyYYDJJIbCGV6iYWszFx4hDlFVm2bpnA4GAZhmFgs9kwTZNMJkNJSQkf+tCHqKqqIpPJ8NuHH2HLaJSEy4tN17AN9lHWvokqnwePw0EsHifn9OBsaSMaS5BOpwmFQlRVVREOh3G73XzoQx/C7/ezfv16/vHkInKdJZhY5PUcugl6bBmGPYot6CafyxN0O6mfPJUzPnUVS5ctY+3atTidToyxiteqqhLOPvss3O4Q27ZtY8GCBZSXlxd/SRyPx7Esi5KSEnp7e7HZbBQKBex2O8ceeyxr166lUCgQCASwLIvt27eTSCQoLy/nhMxLVCc2kCtppqG+XoXOg5th4nyY/429vzDD2+Gl/4HwTrW93umH6ZeoIVZvMKgUUP1OtzylBmoZToh2QT6rBm6lR1XFq+FQA7CySShvg4vuefsC1wPZ+gws+qHqN2t3qzWXt8G531YhtHhPebv/X7j7fn/4wx+KOzYPVzKZ5GMf+9hBvYa//OUvrF+/nptvvhlQ1fAXXXQRf/rTn2Qo8HucVLYKIcR+PPbYY9xyyy1cddVV1NXVoes63d3dnH322Xzxi1884vdbvnw5N954I5FIhCuuuILf//73R/weQgghhHhrmNkshXAYIxBAP0I/2B9pDoeDWbPy2GzbiUZzuFw2JrWWUFdXSSh0Jlu2bGFgYAC/38+kSZMYGBhg2bJlDA/3kMn0k826CQQM6uprSKe34g+sZWDgZAzDB6heq7quUygUSKVSgAo87LksU8tD9OUhUSgQrK/H47LRVFlOKODH0mDHrqVEEqtx5gO4fTU4nU46OzuLA6defPFF5s6dS0tzE8vMvzLiSaBnarAVnJDrxSwM464oIW9aON0eQlWVhLs6Cfd2c+yxx+JwONi+fTu5XI7Jkycza9Ys3O4gsKdl1u6gFVS//ZGRERKJBGVlZcV2AuFwmDVr1pDJZAgGg1iWxfDwcLFKOBKJYNfCODw+yqqqi9W9GHbIxPf9hSlrUcOwuperULRisgpE9xe0Ahx3papa7V0NhTD462DaRbDq99C3Wt1Pt6lj7B6YeBqEmg/1r8+RN+lsFQhveQoSg9Byplq/BK3iPSAajXL99dcXP3/kkUf4wAc+IEHrPwEJW4UQYj/q6ur45S9/+bbdb+7cuSxdup9BCEIIIYR4x7Esi8TLC4k+9hj5kRF0nw//2WcTOP/9aPqbm0kcj8fZtGkTvb29eDweJk2aRGNj457A7jBFoisYHHqYyiob9Q1l5PNxEolnGBysoarqgxx77LHjji8vL8cwDFaveZFMpkB1jZOmJhO3W8NuqyMa3YTNFiOdtmG328nlcjgcDnw+H6FQCAC3241hGBTicZrcbtx+NTwq6g8y5X3z8drzLHr2q3jLugk6NMycjWRfFfHCWeTzeex2O5Zl0dfXx8svv8y86VNwpeO01bsx6ac/ajIa6cNMpUmm0jicTkpKSsjl84TDYR5/7DF0r5/KykrmzZtHTU3NXn1Yd4epyWQSj8dTHLDlcDiK1ay7eTweUqkUhmGQSqXI5XIMDQ0V+9z6fD7ChYm0GetxaHnArsLOfAbq5uz7C1PIq2FWDh/UHguug6z685TCmbfASDvkUhBqAodHnb/4JzCwQVW5ukNQOVUNy3rqm9B6rqqyPdy/V7kUbH9Bhb0OLzS9D2rnHPx1NQ2aT1Mf70DZdJ7uzSNEh9K4/XbqJofwBp0HPlEIVOu33XbPB3n00UeP4orE20XCViGEEEIIIYQ4DOl16wnffz9WPo8RCmHGYow+9BBGwI/v1FMP/jrpNC+88AL9/f04nU6Ghobo6enhpJNOYtKkSYe9zuHhYRYuXEBfXxn+gI+6OovycgeFQoJweCFVVR/c6xzDMJg2bRrVNXG2bXsZr7cWXVdhk2mlKCurobKyic7OEdLpNHa7HZ/Px7Rp0wgGVdVoMpkklUoVA0nDMPB6vbS0tFBdXc2ql/8bXLuwG2VoBSe6PYmrppvOXWvIZhuwLItMJlPs8T4UHqWQyzG4aweWaeLx+bAHPQwN2yCbomC30dffSz4WJW9zkOkfxGSQLbs6eG7bTubOnMGlp5yEzTAANQA2n89TVVVFT08P8biqPvX5fDQ3N7Nu3Try+Tw2m/rxOZVK4fP5qKysZNmyZcRiMSzLwjAMQqEQdXV1tPcXmGRPURvpUj1VNR2r4XiSdW3oqS5crro9AXqkC17+AQxtVj1LfVWqYrX5IP/uaBqUvq5atflUqJmtWhfk0rD6ARjeBu4C9A1A/3qsfIZo9Umk02lKSkpwOt9kiFjIw8v/Czv/AZquBlvteBFOvBpa9z8M9t0gm8qz5LEdDOyKqQcsi/a1wxx/YTMlle/MynXx9olGo+M+dzqd+/136Mknn+T444+XqtZ/EhK2CiGEEEIIIcRhSL76KmYigXMsEDV8PrLt7cRfevlNha27du1iYGCgWE0Kasv6+vXrmThx4rgt7m9WJBIZC3KTaJpOeFgjMqoxbZqJP6BjWbn9nh8MzCbgn0QsvgG7PYRl5SkUElRXfZC5x36Sbdu20dfXh9PppLa2lrq6OkD1ul+8eDG6rlNdXU0sFiOTyWAYBscffzy6rpPKr8Mq2NC0sRCi4MXUY3i8QxSsScWQNZVKoWkaqVSKZGSUZDSCzeEgMTKCZbdjVNZBbBQtnVQBqm4jV1KBZhgMBEpZW9VIwuHipdEcS1Zt5saZrRTCwyxatKjYK9/hcFBfX09ZWRkNDQ243e5i6L27ytUwDKZOncquXbswDAPDMMYNFrOsAnnDw47mT1Db5IJ4P0kjTXt+BZn274Jmw++fSmPDZ3HYy2DJfdC7CkqbQLdDpBOW/ATKW8Ff/cZflExM9Wf1lO67ktQVgIZ5sPFx1Qu2YrJqKQBkhnax5IVn6fANks8X8Hg8zJkzh9bW1oP/S9W7Cna9AoE6cKo2Eoy0w9o/qVYFyWHwVapq2yNUmf126twUZmBXjGCFG8OuY5kWI70Jti0f4Ljzm4728sRR1tDQMO7zW2+9ldtuu+0Nj7/77rtlGPA/EQlbhRBCCCGEEOIwmMkkvC4I1RwOzHjsTV1nd0Xl7qAVwOVykUgkyGazh1URtWPHDkZHR6msrCQWH0LXM8TjTjo680yenKCq8oL9nm8YTpqbv0xf31+JRFei636qqz5EZeUH0HWdtrY22tra9jpvqKeHWHcPJRXlOJxOysvLMU2T4eFhIpGIqqj0+GBEo5DPYdhUy4ACFsZY79fXznTOmxbbe3vIVFRT4vWRiUUwTYu0041WVkH9MXNJDQ/S399PTjMoAGndYHVNIynDgT+TwrLZWR5L8bNd/UxZt5TRkZFiy4ORkRFGRkY4+eSTi60D5s+fz+bNm+nu7sbtdtPS0oLb7Wbp0qXU1tYyOjpKOBxG1y3i8UG6uzsxTTsunx+r8VLy+SjbN/872ewATmcNpplndGQJGgYTyz+KNrABArWqpyqocHJwC/Ssgsnv3/uLkYnDyt9B+0tg5qB8Msz9jOr7ui/JYfWnvufH/7WZWraO6viCGl5vkFgsxquvvkowGKSysnK/fxeKIl1QyO0JWkG1K+hZCY//f2ptDp9qEXDCF1Vv1neR0f4kmgaGXf27rekaTq+d4a436Lsr/ql0dnaOG5C1v6rWjo4OOjs7mTx58tuxNPEOIGGrEEIIIYQQQhwG17SpJBYvxkwm0T0ezEyGQjyO/9w3t5U6EAigaVpxy7plWSSTSSoqKnA4HOOOtSyLdLqTXG4Up6sWp6N8v9dOJBLouo7TWUE+HyWd6UXX8iQSGiUl86isPA8rnye9cROFSAR7XS2OpqZxvWKdzgomTLgK08yhaQaa9saVtmY2S+RvjxF/7lmaO7soVFWSPPFEstXVxfBU0zQ0TaOm4TzisR2kRuLkEga6I4Nut2PRjNPpJJPJYFkW/Q43u9pmEs7m0GfU0mJmOGtoF/58lo6BQdKmhd3twVbXyHDOxIpEsAp5+nxBEnYXJYkohmZhMy3cmsWKkSi+aJy6YJB0NEImlcTQbQwPDTE4OEh5eTmpVKpY9Tlnzp5+q7sHd9lsNkKhEKlUnFhsiEIBMlmd6upRLOtRhofL0TQbmUw/Hk8zmmagsnSTWHw92eAQbzqCXP4r2PiYqmg1nNC5RA2XOv/Offd6Ddarbf65NNhdFEyLHREdl9OBxxsATaOkpITBwUG6u7sPPmz1VoBuQD4NtrFfBITbVbhbOlFVtaZGYfPfVYA87aI3+0qPKrffgWWqoWe7/z3IpQsEK9xHeWXinSAQCIwLW/fnySef5IQTTniLVyTeSSRsFUIIIYQQQojD4D35ZFJr15FasQKrkAdNxz1jOv5z3lzYOmHCBLZu3Upvb68aKFUo4HK5mDlz5rgWAoVCio7OXzM6uhTTTGGz+amsOJ/q6ovfcJBWKBTCsixM08LrbcPprCGbGaBpQgOTWv4FMxpj4Cd3kd6wASuXQ/d68J1+OqGPfQzNNv7HRl237/MelmUxMqJ6t+ovvMDInx8mjkXWNHFs30EgGmXgwgsJ5/MEg0Gqq9UW+ZqaD5LN9jHQ+xLZTBSbUYav5GxiKR+mlcTlchHJ5dkwYSqUVVIy1EsinmS9vwTKJ3DJ4E4cloXpdJJIJPB4PPh8PkbCYQBMTccCLDSwLOxWDo/bTdoCExjatZNUdFS9PxbgcLJu9Uoi8SSZTAa3282MGTNoa2srvr9lZWV4PB6i0SjBYJDSUotcLoNhOJk1W6eiIkAmM8Lg4DOUl58FlgXs+RpqmoFlmZieUqicBh2LwObY00bAWw61x+z9JieGoX2het5boR5z+iG8A7qXQ8sZe5/TeBLUzYWuV0E3sPImpjEdLVhb3N6vadrY3w9zn1/bfaqbC9UzoXuFWkMhC8kh8JTtaX/gKYX0qFrzuyxsrZ8comPdMCO9SVxeG9l0Ad2m0Ty74mgvTbzLLF++nPr6+qO9DPE2krD1LfD0008f0nlf+MIXDum8pqamQzrvrrvuOqTzgHG/1X0v2rhx4yGd9z//8z+HdN7w8PAhnXf++ecf0nkf/ehHD+m8kpKSQzrvcEQikUM675prrjmk8x577LFDOu/CCy88pPNuuummQzrvULegHKlJxgcrmUwe8rk333zzIZ33y1/+8pDOe+0WxTfjq1/96iGdd9111x3SeX6//5DOe7tlMplDOu/73//+Id/ziSeeOKTzfv3rXx/SeRMmTDik8w7138PD6dUohHhr6W43FV/6V9Lr15Pr7cUoLcM9exb6m9z273A4OOOMM4r9Tz0eDxMnTqSmpmbccYODTzM8/AIORwUORwW53DC9fQ/jdjdQUnLcPq/d3NzMpnXr6Grfia7r2JwuQqE65sw5DU3TiTz5d5IrV+JoaEBzuSiMjhJ7+hmcbW14jz/+gGtPp9MsWrSIrq4uzFSK5sceQ8vmKIRKwOEgarPhHxiETZsoPf54TjjhhOK2W01z0NT0JaqrP0QuP4rbVYfDUU5FZS+bNm1idHSUXGklmivE5KCPrFZFfzyGFR1lq93BjqFhJtbWEJhxLNvb2xkcHCSZTOKwadgLGaoSI7hyOeIuD6FMHJ89x4hlMS8UoDSfoTcSweN0ohs20vkC+UyajWvWUlpbh9vtJpFIsGTJEjweT7FP4+5q12XLljE4OEgmk8LpLDB5skVVlfqeQtc95PKjeL1t2B0lZDI9OJ01WFaBTKYPv386LncdnPB5yKXUgKxCTg3Imnflvvu15hIq1HSU7HlMt6kwN5t4g79YHpj/dTW8amADNncZDaPlbOwYwpXLYbPZSCQSOByOYgB+UOwuOP3rsOFvKsh1+tXaYz0Hf413sEC5m3kXNLN1aT8jfUlKa1xMPKaCuraSo7008S6j6/oh5zbi3UnCViGEEEIIIcS7mmVZ5AcHsbI57DXVaK/pefp20ex23Mccg/uYYw7rOm63m5kzZzJz5sw3PCYcXoiuu7HbSwBwOqtIJLYRiazaZ9hqWRbty5dQ2L4BezpDFh2Xx8Ock0+ksrJSDZ9athQjEEB3qy3StlCIzPAw6Q0bDypsXb16Ndu3bycYDGKzLMx0hoKu47TZsBk2HA4HejxOc3UNUz/4Qex2O4nEDvr6HyWR2IrdVkJFxTmUlp5GPl+gUChQU1NDTU0N0WiUh7a2k05kSMYsHE4Le1kOey5JxvJjtJZx7GmXUVFTS0tbG0NDQ+zYsYPe9iw1mT4mO/J441t5MTiREY+fhA4VgwNMT4Yp8XkYskxyaFAo4LYZ5Ewb2WyGYCCApus4nU4GBwdpb28fNxRn8uTJlJWV0dPTQyy2gVx+A+XllYALyzLJ50coDb0Pj6eB2tqP0tPzR5LJ7YCO291Ifd0nVCuGYD28/79hcCPkM1Detu92AKYJI7vUYKzwDlUR6ymH5KDqm1q+d8/cIqcfpl6oPoDZ8TjRwkv09/cXK6hnzJhBbW3tAb/W43hK4bjPqA+Abc/CS3eptgbuUkhHVIDc9L43d11QAXT3ckiGITQBqmaO640cH0nTvnaYkb4E/lIXE2aUEar2vvn77EdZnY+yOh9mwUTTtbe9cEG8N/z0pz892ksQbzMJW4UQQgghhBDvWvmREUYeeID02nVYhQL2hnpCl1+Oax/Dmt4rLKy9Qh/Lsshk+ujp+ROgEQjMxOtV294j/X1senkBdl2nua4OLItwbxfti16iZdoMNF1Hs9uhUNjrXpr9wD8y5vN5du3ahcfjweVykbYsEsEAwYFB8tkcNrcNWzpDwWbDMbEZu91OJtPPjp0/IJPuwWYPkUp3snzFA/T2rOX/Z++/o+w67/ve/737Pr2f6RW9FxIg2EmxqpCqlinJTbLsa2c5iS07Xomc2Mld146d5JeVe30T2/GNJUuWZMtLCqlCiUXsBUTvfXo7c2ZO77v+/jjggBAAAhhRpEjvl9YscQ72s/dz2gDzOd/n+zQaCrqus3nzZrq7u3nxxRcp1Zs4yX5OVm3S0igIJmWlkx53jnTgCQ4eDXJf52fp6Oigo6MDURSYnR2hLKmIZp51uRJk8uT9EXqSSXpkjXKmjCNCym0RCCcREPArEqfmGkiqurTEHtqVaW9ctdFoNBgfH6darRIOh1m58kFmZycplg4A7cdR13vo7HwYgFTyHsKhjVSrZxBFlVBoI7L8hmBQkttL8q/4pLuw72/gxGNg1tsh5sRL7b6okT7Y8DFIrrrqc/W6YDDI/fffTyaTodlsEo/HlzYJ+4kM3dXu23r2Scida2+QteYDsPqB6ztPNQvP/yfIHm+HzLIGw3fBzb8Fskqt2GL3o6MUF+ooqkR2vMLcuRI7Hx4i0R286umvlyh5q0s8Hs+188JWj8fj8Xg8Ho/H867kui6Fr32N2ksvI6fTCLJM68xZcv/zr+n8w3+HdI2bl7zbxKK7mJ39eyyrjCQFMc08llWmWNpPuXwYgPns9+npfoR0+gFyM5O0GnUSPX3tkFYQCMWTlBayVHILRNKdBG69jcI3voGVzyMGAti5HKLff82Vum/s9amoKnMbNqBW9xDO5VAUGVsUKa1aSbSrkzO7X6LaOIyhzBBOrMJxRE4cFzlzBiwrhyhKCIJANpsllUrhui6rUinuEU2el5rMiTEEFJLUuNc9g19tUa+/wsLCh+jo6ABA0w+hquMsllVEN4jpyKi2w86Qn3A4CoAqCtQqZQKRGK3sLP5wlEqpiY6D6Q9gWRaKomAYBrZtL1V9VioVnn32WRYWFpbuc0dHB3fe+WvEYkdpNKdR5DDR6E407cJmU5rWgaZ1QGEcRl9uL8Pv3tauOr2K4uRRsgdfQpY76O4aRu+ow/xJkBS44/ehf9dF4fC1kCSJnp6e6xpz9ZPKsPPzsOZBKM+1w+Bo/3XPjWPfhrlDkFjZDlqbpXbVbPdWWPE+Jk/kKC3UiXUFEMV2v9nCbI3Rgws/lbDV4/F4rocXtno8Ho/H4/F4PJ53JSu7QPPoMeR0Gul8b3lR1zEmJmgeO0bgllve2Qn+lKTTD9JsTlMqH6DVmkcUdURRQlFi6Hq7v2uzOUtm/lGi0R3IioYggOs4Sy0WbMtCkiVktd03NXT/fdj5HLVXd2PNzyNFI0Qeegh9/fqrzkeWZQYGBjh+/DiapqEoCnZ3N8d23URvtYrmujTicehIcuwHj2EZTQxjAclvsvYek1xVZ3xcxLJAFF0kSWq3hrAs5ubmGBoaQhAEttpN+pynmXQbqEKYVVIexapiCBIuWRqNBgCNxgzl8jNs2CizuBikVNJpNmoUixaBeHxp3pIkIUgyG+95kPmjBylm5ghEoqzdeiPTlTpzmQyu6yKKIgMDA6xcuRKA06dPk81mSSaTSxuZZTIZRkcn2bLljvNzaJDJZBHFKTo6OlBVtV2deuxbcOSb7fBQECA2CHf8K0isuOLje+zYMQ69/AytXBxB8RGpOdzeKZLu3Ail6XZY+7O2vD3S2/5aDteFqdfAF2sHrQB6BCpzkDkGK95HebGJKIuI4oUNvlS/TCnbeIvugMfj8SyfF7Z6PB6Px+PxeDyedyXXNHBtC+Gi5dgSuC7OMjfqezeQ5QBDQ/+Cen0Uw8xjGnkmp/6mXTV5nqqmaTYnaDQm6Vy5mki6k/zcNMFYAse2qZUKrNi+k0C0vXRc1DTiv/zLhN7/fpxSCbmrCyl47RWCmzdvplwuk8lksCyLUCjEmjVrkGUZ27bpFGDsuadQ/T4iqTSNhkR2Ms/Evnmq0UEcx0UQQJLaAZpl2QgCOI5As9lc2iAygZ+oswfLTOK4InXHQtcrVKsxRkdH6evro9mcwrLKBIMrCYVcwKVWk9m7t0WxuEg83q7mLJfLhMNheleuZnj9RgTHRlY1RElijWkyMzNDrVYjEAgQCoWwz7dZyGQyaJqGdD64liQJRVHIZDJs2bKF8fFx9uzZQ7VaRRAEYrEYt956KymhCIf/vh2MptaAY7WX2u/7X3D/H18SmLquy/79+3n++ecRzRrdTglJcCi2Auxd0Hh/YgFR8bVDyfca1Q+N/MW3uS4ofgBCCR37lIPruAjnK1vNhk3HoO8dmKzH4/FczAtbPR6Px+PxeDwez7uS0tmJ0ttH6+wZRN8QiCJWJoMUDr9nera6rsuRxSMczh7Gdm02JDZwQ8cNSKJEILCCACvO9wHVsO06stwOSB2ngSjqyHIIze/npo9+ksNP/YDC7AyiJLFyx81suffBS66npNOQTl9y+9UEAgHuu+8+stkstVoN27bx+Xyk02k0TePEC8/gWC384fZu97qvk0B0jvJ8EVMvI4p+QAVEbKeJ64JtuyiKgGnWKBYVFEWh0eggHu9AVRcwLQFVdbDsIK6zjcnJSWZmZojFo294PNpBvKrWGByAxUWN+fl5Go0Gtm3TaDT45je/iSRJxONxNm3aRH9/P4qiMDg4yOTkJAcOHKBSqSDLMitWrEAXLcxmDdwICMJSFW4wGKRWq/Haa6/RaDRIJBK4rksul2P37t3clSqhVnKI6bUogtBuARDqgsWzUJ2HUOdFj+nIyAh79uzBMAx8epCm7cPXKBDUbPJVhZJcIrbxPohcRyuARgEyRwGh3SPWF73u53pZSjMw+Wp7c6/EynbbA0m5/LGCAKvuh91/CXNHoFUGowq+BHRuBKB/fZzpUwXyszUUXcJqOfhCCsNbU2/P/fF4PJ434YWtHo/H4/F4PB6P511JkGVin/oUuf/5PzHGx8AFKRwm8rGPorzVvSjfIU+MP8G3zn6LltWu1H1x5kXu7b+XT6391NImWYHASkKhDZSKe7GVGOBimiVisZvw+4cBiHf3ctcvfZ5aIY+kKPhCl/azdd32xltG06IwV0dSBOJdgWveHEgURSRJ4ujRoxQKBVzXJRwOs2vXLmStvRzcdRwEUUQUZDR1AEWJExkc5MzZJpZlYhjm+QJPAUmCvv5FUkmLcjmN0TLp69vIypV3sXfv/0IQs5hmDNhELNbHwsICuVyOvr4thEObKBT3IMthwMW2a3R23Y7PN8zx48cxTRPHccjn87huu3XB7Owso6OjrF+/njvuuIN6vc7LL79Ms1knGssjWGOMHf8eyaaEWkmRq86jpwZpmA66rrNixQrm5+ep1WokEglEsf24hcNhxsbGkCZnuKFcoNKYIBqPk4jHERwLBAkkFcd1sBwLVVIBOHPmDIIgIMsykqxQC63AqE6jOxaioiBv/XnY/vFrfzFNvga7/0c72AUIdcMtvwU926/9HMuROQov/GeoZNrfiwoM3wm3/c6VA9c1H4CzT8Gpx8G12+0EHAtOfg+6txOM6ez6yDDjRxYpZOoEYxqDm5Nev1aPx/MzwQtbPR6Px+PxeDwez7uWvmY1nX/0hzSOHsU1DPTVq98zQWupVeKH4z9EFmV6Y+3+l/lmnhenX+T2ntvpC/cBIAgigwP/B3NqimJpPwAdHR+iq/OjS4Fs+ziBYDxxyXWshQVK33+cxuFDNC2ZWd9acqnNiLJMtNPP9gcGCMX1q87Xtm1effVVcrkc8XgcQRAoFArs3r2bu2+9hUA0Tn5uhmAsjmUYGLUG6++4n7V3vA9RepGjRw9gmgauKyDLLitXOqxZrTF79CxyVkB0NDTJJrBuDYJwJ81Gk0gkArSDYgBVVREEkYGB30DXeygUX0MQZCzzLk6cEikWj1MqlRBFEcdx0DSNer2OIAgEAu1NscbGxkilUoiiSL1eo6v7FLJ0FIw8quJgODpbwzXGZpNU8w4dK29i48aNdHZ2MjY2dunzWCpRr9epdAxiGklCxjylhSY+t0HAKeOseT/PLh7k2clnqZpVVkZX8qHhD9FoNPD7/RiGQbPZRFVVKlIaRVHYvm0bwZ13XXuv1mYZ9vwV1BfblaW4kBuF1/4SPvTf2sv2fxpcFw59HSrz7dYJgtiubh17HobuaFe4Xo7ZaPe17doM/jgoPrANmDvY3jirbyfhhI/Nd/f9dObt8Xg8PwEvbPV4PB6Px+PxeDzvalI4TPDWW9/paVyzlt3CcR188pv3l8zUMpSNMl2BrqXbYlqM0eYoM7WZpbAVQFGi9Pd/jl7nMwCIonZNc3EaDRb++/+geeokBEKUp0r4zXNI25vUNt7F4lSVI89OccvHVuICDcfBL4oXhbivW1xcpFAoEIvFkOX2r5rxeJxcLkfVMLnpIz/HkR89QXkxiyQrrLnldtbfcTeKppFIJNA0mVCohaIoOI5IqyUwdbhE9qhBOG6i6X6mTxyjms8xcMMtHD1xgnK5jKIoVCoVQqEQfX195x+PML29n6Gn51NYls13v/tdLKuC3++nXq8DYBjGhcfBcbAsC2j3YJ2amqKnpwdFWUSWj4BTQpDqSJIIro3jy/KgEsRtHEPZ+YsIyX4Aurq6CIfD5PN5IpEIrutSKBRQVRVfoodR30MMLDyDVpujWRMJbLqfF1Ir+PqRv0NUJHTFx+653UxVprg/fT+FQoF0Ok0ul6PZbOK6Ln19fey86abLPgdXtHAaynMQHwJROv9iGmhvsLV4Brq3Xvu5rkezCPkxCKbbQSu0N/QqzUBu5Mphaz0HrSoEkqCe78ksymDbUM3+dObq8Xg8bxEvbPV4PB6Px+PxeDyet0HVqPKdke+wN7MXF5fNyc08vPJhkr7kZY+PaBF8so+aWVtaWl636qiSSky7/KZI1xqyvq5x+Aitc+dQB4eo1xyaPgVdLeEb2Y+54RYCMZX8TI0fjCzypNmgaFkM+DQ+3hFjc+jiasjXw7/Xq0zf+N+CINAxvJJ7BoaoFQsouo4eaC/5NgyDs2fPEg4nCQQXMZsVBNFHqShRXzQIhXWiPQEEW0MP9LI4N4M0P4uiKJRKJXw+H93d3Wzbto1wOPxjcxKpVIpUq1WCwSDNZhNBEJY2t3o9cBUEgXq9fr4ytr10P5VKoWoFYB5BtHEdcAFFblEVbEzVQarZTExNYxUhlUoRiUS45ZZb2LNnD8ViEVEU8fl8+P1+BEGgFBjmqL+fxtxZ+lasodG9mpf+8c/oK1TRIyGslQrxvmFGS2PUB+uk02kWFxfx+Xyoqkp3dzf33XcfmnZ9zzOiBKIIrnPhNtduB6DiTzEWUPztqtlmCYi3b3NswAU9cuVxwXS7n2w9dyFsbVVAViHc/dObr8fj8bwFvLDV4/F4PB6Px+Px/JOXqWU4VzyHJmlsSGzAr7y1y6pd1+UrJ77CK7OvENEiiILIM1PPkG1k+d0bfxdFvLR3ZWegk52dO/nR5I+oW3VERGpmjRs7bmRVbNVbMi+7WATHQVRVqDXbc1V9SK06QrMGSpS8ZfFUJkcrKBOSJA6W60w2WvzRyh4GfRdCv0QiQTweJ5vNEovFltoIRCIROjo6ABAliVDi4nDZMAxM00QURKqLQerFOo5bw1Y1oskyyd4yopoFV8GprGQhE6UwOk4onkBVVSRJYv369XR2XrzB1OsEVeVYKM5pNQBBmRQa/ZnJi0LhdgDr4roGrVaT4eFhent76ekO0GiaWLYGjouEgyyDJVjkCjn2lTZSOHAOhFF0XWf79u2sXbuWD37wg+RyOSRJYmZmhv3799NsNtE0jVq9RVNLo4c6ee3b/4AwX0HUZYRCE2V/BlfqxvW7NKUmD93/EJOTk9TrdSKRCH19fUtVw9clvR5iQ+0q1mh/e3l/aQrSG9rL+39aZA1Wvx/2/Q0Up0DRobYA0YErV7VCO2Dd9AnY89ewcKrd19Z1YOhO6Nz805uvx+PxvAW8sNXj8Xg8Ho/H4/H8k/bUxFM8du4xykYZAYG+UB+f3/R5BiODb9k1ZmuzHF04StqfJqK1K/qCSpAzhTOcyZ9hQ3LDZcc9svYRUr4Uu+d2Yzom9w/cz/2D9yMK17Zp1dUoPT0IqopdqaAE/Biii53LUYsmmbFUQpUmc3GJhl9i+HywGlckTtWavFyoXBS2SpLELbfcwiuvvLK08VQsFmPXrl1vWonp8/kIh8OcO3YUt1FD0WJYtoXumyPamSVX7KDa6MLFQRRrGHKYjkSCYCyO67rkcjkO7H+BVjNDy5hA13tIpe8nGrkBgG8VGuyLd9OqVpBtl0yiiwVZ5cZzx/DrGpIkYNs5BKGBIBrE4lmSyc2I4lrWrLmZkye/i+3YiK6AZDVxXRvZFjnW6iavdpNIJBFFkVKpxIEDB+js7CQajS6Fv5FIhEKhwNTUFOVyGU3TWL9+PXYug1Gtoafi5Fp5lGAId7FG5fAY+dUN5u15csEcq1evvuJjl8/nmZiYoNVqkU6n6e/vv3wYq+hw62/D7v/eXtYP0LkFbv5nV96k6q2y4aPtytrTPwSzCoN3wJZPtVsEvJk1H4BACsZfBrMG3dtgxftAknEdl9JCA8t0iKZ9yKr0070PQK3UYupknmqhRSiu07cujj+s/tSv6/F43n28sPVNlEqlZY377d/+7WWNs237bR33O7/zO8saB/Doo48ua1w0Gl32NZfjjZ9WX4+ZmZlljXv55ZeXNW5hYWFZ44aGhpY17vV+VO8Gjz/++LLGPfHEE8saNzw8vKxx+/fvX9a4vXv3Lmvccp/7615y9hN68cUXlz32Bz/4wbLGPfLII8sad/r06WWN++53v7uscZ/5zGeWNS4UCi1r3HIt9+foct8Tf/EXf7GscQAf/ehHlz12OYrF4rLG/fgS12v1+q7WHo/nrTVVnuLRc4/iuA7DkWEs12KiNME3T3+T39vxe29ZqFk1qrScFnE5vnSbJmlYjkXVrF5xnCZpfGD4A3xg+APXfK1Cs8DJ3ElcXNbE11zSpsBx2kvJRVFEX78O/66bqL30MnONFmbDwFI1JrtvIlM0Cad9TG4I4JMvhFmCICAJAnnz0t9DEokEH/jAB1hYWMB1XZLJJIqiYFkV6vUxJMmP36amTg8AAQAASURBVD+M8IbHVZIkBjrSTL6wiL/ewtR1zFiEjnSRxWIv+UIfgtCes2FHQJTwR6IA2JZJo5ihnJ9B4yD+QBQtNM/8/DiDg7+CmrqB5wsV0j4d12jQbDYxcah1dBNQIFktkkxOg3AAw/BjWxaptMD09JfRtCTB4CqCodUYxiKO0wQngGBb6EI3o74dhP3xpZYEkUiEhYUFMpnMRb/3qKrKXXfdxeLiIrVajUgkQjQa5ZV//BqiLNMfHaCWq1NoFBGtFnZNoE/oQ11Uef7557nnnnuWKoPfaGpqipdeeolarYYgCJw8eZLh4WFuu+22pTldJLkS3v+foTAGCBf3b30z2ZNw9imozEFqLax+AEKXryK+LEmGjR+DdQ+3N7lSfNe2sZcgQN/O9tcb1MsGB5+cIDdTw3EcglGdjXf20Dn8Jm0JfkKVfJPXHhultFBHkAQc22X6VJ5dH1lBIPLm/762TYf58RK1kkEgqtExEEZSvL/TPZ73Mi9s9Xg8Ho/H4/F4PO8Y17axCwVEvx/R/1PaEf1NnCmeodwqsyK6AkEQUASFdCDNeHmchfoCHYFLQ67l6An2ENfiZOtZeoI9CILAQmOBsBpmIDzwllwD4PDCYb5y/CssNNof5if0BJ9a+yl2du3EMAyOHj3K2NgYjuMwMDDA5s2bSX7uc9RXr2XfK/twdJ3a2o3Ugj1UcTgRElkd9jFZrpNWZSRBoGE7uMDqgH7ZOUiSdNGS/lzuRWZn/wHDzCEICj69h47OjxAJb0aSfLiOQ+i1PWzbtx/ZAUeSMDpSlPslZqoJ/EEVWTRwbAvBsKk3ZBqNBn6/n4WJMWr1RYKROhghMjmFQqMLW1Q5ceJp9FUGcwRQq2Ukpx0O+1UVwR8gFVuBfXAvpnUIVbVwLRfR1NGlbkxrmkJxD12dn8bvexDL+hGyXANAVWIkk49wcmpuKbiGdoj9xn6wbyQIAqlUimg0yvz8POVymXBHF86xw4SFAFtSWzgzfRrTLKL19jEUuQMZmYWFBUZGRi4JWx3H4dChQzSbTdLpNIIg0Gq1GBsbY3h4eGmjsEufHBmS19GCYu4wPPen7d6psg7T+2B6D9z7H65emXq5a0s/WQThui5Hnp1ibrREKK4jygKVXJODT01y56fWXHelqeO44LqI0puHn2OHFygu1Il1BRBFAcdxKczVGD+yyIbbe644rlU32fv4ONnxMrggiAIdgyFu/OAQqu7FMR7Pe5X37vZ4PB6Px+PxeDzviMbRYxS//W2s2VkEXSN4551EHnoIQfkpL2t+A0VUEAQBFxeBdrWd7diIgojyFi6vDqpBPrLqI3zj1DcYKY4A4FN8PDz8MJ2B66gSfBMNq8E3Tn6DfDPPUKS9Cma6Ms0/nP4H1sTXcGj3Ic6ePYuu6wiCwNGjRymVStx7772Ud+zk2Vgv/ZpC2M2gujPoYhf1JmwN+SmYNmdrTUBAFODGsJ/bYsGrz6kxyfT0V7GdJqqaplo5Qbl8hHzhVaKR7fT2/gLqmAh79iArCqaioMkKemaewukYTkxGpI7ZsgAbRaqDEyA7P0/A76NYLqP4IBXP4YpBspVuDFMiFLUQxSYLp05idq7A9vlICi6u65JvmSi1Kjet34CjSYyNf596VqZ8VgDDpXp8nmCXTX3dAnv3fIdazUUQbiaZbDE8FEFspGjMBUhEwkxn5jFNk3K5TKVSQdM0Go0GjuNcsiIhl8vx0ksvLbVY8KsqvkSa0vwcrmMRLFo4oU66hm5CEdqvPVmWqVYvrXyu1+uUy2UCgcDSxmSaplEqlSgUClcOWy86SR6mXmtvPJVYAV1bL610Pf5Y+7jU2nalqWPB4lkYfa7dU/VtVi8bLE5VCUQ1VF87zoikfRQydbITZQY3XVsAbBo25/bNM3Uyj9myCcZ0etbE6FkdxRe8NLDNz9VRNAlRbD/WoiigqBKFTP1NrzN+NMf8aIlwqt3qwDRs5kbLTBzNsWrHW/NBjsfj+dnjha0ej8fj8Xg8Ho/nbWfOzLD4P/8ndj6PnErhNBoUv/UtkGWiDz/8ts1jQ2IDKV+KidIEHYEOTMdksbHI7T23E9fjVz/Bdbit5zZ6gj0cXTyK7disja9lbXztW3b+sdIY2UaW7mD3UvuDnmAPE+UJDk8dZmpqinA4jM/nA0DXdebm5shms3TFkyTEGn2lb9HjnEF0TcpCDEt7kO2Rh7gjHmZ3sUretOjTVW6KBvG/STWg67oIgkC5fBTTLOIPrKRSOYpplZAkP45jUm9MMzH5v+g4tRXBsomuWs3i1CQNs4XiukSPNdHeF6DZKqFqBiIidiuC6jjEZFB9PuquTXfMJBzOkV8cwrRUNLmCKEj4/d2YJZeV2WlGh9YwK4gIArhIrC7nGdIUlLVrwbyH3S99H9cQ0cMKtmmROeUwPTuNHQXXsTBsl6kph6P7ivitM0RdA9UXwN/Ry2wmg2maaJqG3+/nwIEDqKrK2rUXnlvHcdi9ezeLi4vE43FEUWy3o4l3sLXDoTl+EFfOI6kuZbufAuuxbRvTNEmn05c8vqqqoigKhmEsPZ+2bSMIArp++Yrji+RG4Pk/g8I4ILQ3slp5L+z6ZxeqT10X8ufAF72w7F+U25tVlaaufo0fZ9SgPAu+2PVXxZ7nOu7519YbbhQuTPdaHXtumtHDC+BCebHBzOkio4cW6BgKs/WePrpXxS46PhTTyM1Ul17XrutiGTaB6Ju3EJgfLyOr0lJPWUWVkCSBmdMFBjYlvOpWj+c9yntnezwej8fj8Xg8nrdd/cAB7MUF1JWr2kuvw2FM26b2wotE3v/+t626NeFL8LmNn+MfTv8Dc7U5FFHhlu5b+Pk1P3/FMTWzRtWoEvfFUcSL5/l6r2vhCj0phyJDS1WnbzVVVJEECcuxUCUV13WZq84xV5vj+2e/T2e1k/6O/qXj271ULRqNBkOqzEPis1SMA+TFJDZhQvYid8rfI+3ehF/t40Pp6Jte33EdXp55meenn6fQLLAhsYFbwgoILo7dwDSLSFI7GHRdB5+vj2Zziro6C65LKJFC8wdoVMq4uTy+jg7KybWcHDmJY8rgiuCqRFWHYK3EPZ/8NM9lpzDzJiSSOFINFwNJtVDUKD5fH9VqhZX5edZ1pJjQA9gIpCoF1gv2UuWpUx7AafnRok0QTGRNxMZHfS6L3QAHQBRwND+OJCKqOqYewW/WqcxO44vGSacVBKGMKLVoNoKcOnWK1atXL12jUCiQz+cJh8OYpkmz2USSJCL5/fTLh/D3xjHENKXZs0SnHmU+alFUOuno6GDVqkuX/auqypo1azhw4ACFQgFZlqnX6ySTyatXtbouHPxau3drcnU7QG0U4eyT0HcT9N/UPk4QIDYEU7sh2HG+stVuf4V7r+UlecHZp+DwN863I/DBirvhhl9ph7zXIRDViHUGmB8rISsSoiRQyTfRAwrJ3ksrrV3XxXVcBFFYek/Wii1mzhTRgyqFuRog4AupWIZNJd/kyLMzJHqCaP4L7+2BTUkyYyWKc3VUv0yrbuELqQxsTLzpfFVdwrYvpMCNskE+U6NSaPGjvz1J//o4a3d1eT1cPZ73GC9s9Xg8Ho/H4/F4PG87p9EAhItCSUHTcBoNXMt6W1sJbEhu4N/F/h0z1Rl0WafD33HZsNR0TL478l1enH6RptUk7U/zkVUfYVt6G6Zt8qPJH/HSzEu07BZb01t5/9D739Lq2MXGIplahqgWXer7+kaDkUGGIkOcyJ2gM9DJTGWGsdIYPtlH1s7itByac0029G7Aci1q1Rq6rhOPx7GsCgPuSQrBDjQ3guNCQokQtsapVI7g9199WfpzU8/xtZNfA0CXdX40+SNmwwkeCPhptjK4bru/qeO00LQuRLEdtGmDA1jBaczZWZRkEknVsWSZyN13s6IjwfypEwj+EC4QVhWkWhlZ0fBHImy86z4OPfl9ckcHcMNFREcGBgj4hyhnS1QKRWRcotlZBuNxTNOkXq+z5oYblnqrCo6OT+8lFPFjOy1EwcfsyBlwHERZwkFAcBwwDRB1RERyTQNfMEgrl8NoLGL5S+0w0nQxTYVi0cayLFS1vST99WrIfD5PrVZb2uTYbrYoxQL4I72oQHQojH/2MNvidRobbmJwsI9AIHDR4+y6LuWFLN3xKNKNN3JuZASzUeXGTpeVPS6+2gzoK668CZVRhYWTEEi3g1ZoV69W5mDxzIWwFWD9w7Bwqv2l+qFVa7ccGL7zqq+HJZlj8NpfgWNCMA2tKhz/NvgT192KQBAENt3Vi9m0KGYbOI6LP6Sy/rZuQvGLK3oXJiuc3TdPKdsgENNYuT1N96oozbqJbTpIiJgtG0WXQAAX8AVV6uUWudka3SujS+dK9gbZ8cEhRg8uUFpokOwLsnJ7mnjXxc/Nj+tbG2d+rEwl10AUBebHKziOS6xLw7YcTr+WQVJE1u7quq7HwePx/GzzwlaPx+PxeDwej8fzttOGhxEUBbtcRgqHcS0LK5cjeMstiOeXRb+dFElhMDL4psc8PfE0j517jKAaJKAGmKxM8qVjXyJxY4JXZ1/l8bHH0WQNWZD5wdgPmCxP8rs3/i6qdH2b9vw4x3X47sh3eXriaSpGBZ/s48bOG/nMus+gyxcCJlmU+dzGz/F3J/6Ok/mTzNZmiWpRNqU2EVSD1Bt1CtMFDo4exHAMEKF7VTeCTzgfhLrEFJUO9fXqU5e6Ba5rX3WOptMOmyVBoifU3jAooSc4XR7j5uR2UuYZGo0JLKuMqqbw+wdpNqdRlCixVfdhPdJL6TvfxZydRdB1gnfdTeT970esVkjtfplmtUAwFsds1WnUa6zasQtZVRnevoNoZzfzI2dwXJdBR+TcxASTZyewjBaq4BKwTRqOhSiK+AIBNm3axIYNG5bmnuwfRNH9WHUVfyRNtVSAVgs3GMGRFLBtXEnCRQCjRUOWcVyX6UoDBxDtJq7rR1FkXBeMqkkoNIsgNIH2cx+NRvH7/czMzKBpGqqqYhgGVXROWH10uu1sVFEV5ECQVDjPpPC3jIxUCQbX0tn5Yfz+QeqlIvu+/ygL46M4jkM4meKW228mOf5NxIWTMG/BsQCseT/c+KsgXqZiUlJB8UGzfOG280E46o+Fhz03wPv+LZx5AspzkF7XPnfw0tYGVzS1B1rl9lgAxQ9WE849BRs/fkko7LoujuUiysJlP/SIpHzc/shqFqeq2KZDvDuAL3Txe6yQqbH3+2M0qiZ6QCY3XaWUrSNKQ8S7A+ghhUqu2W4r4YJjOYiS0K5qNtv//ePSA2HSA+Frv99A9+oomxq9nNufJT9bBdclPRAilGj3TXYcl8lj7f6t0lU26fJ4PO8eXtjq8Xg8Ho/H4/F43na+rVsJ3HE7tZdexspmAdAGBoh85MNv6XXsSgU7l0NKJpGCV9/Q6Uoc1+HF6RfRZZ20vx00BZUg54rneGnmJXbP7SashUn62r0oo1qUM4UzHM8dZ1t62090Hw5mD/Ldke+iyRr94X6qZpXnpp6jI9DBh4Y/dNGxnYFOfvfG3+XlmZf5i8N/wUB4AO38Um2tS+NA/QBpM01CT2AGTF7lVYQTAr+x+TcIBTeQz7+IJPkRRZVWax5ZDhEKrb/qHJtWk3KrjF/xL90mnd9sqSQPcPuKX+b41LcYnfkmVqOA0nyNWKCfTX2fw+frgXt78O/YgTk7ixQOI3d3IwgCEb+fGx/6GMeefZJKLoesKKy5+XbW3X7X0nXi3T3Euy/sCK88+QNa504QTaYIaSqiUqRUPk0qqXDDPR8lFLp46Xeyf5C1t9zB8ed/RH52GgQBUZIQ/QEcScJpN3pFatZwRRHX8aEIAqLZxJYkJNWm3lAQGu3gzueT6OlZxDRzKEo7nBNFke7ubsbHx3Fdd6nHa0iNkG2YtEwTXVWgVcU0C2TcM7RavUiSTi7/Eo3GBCtX/gEHfvB9pk8cI5xMIUoShblZFr77pyRiBUitAllvL9U/+R3o3HxxlerrXu/PeuAr7R6qig+qWQh1ttsI/Lj0BmgUYPJVqGUhe6Idtl7rBnJ269LbRBmsi293XZfZM0XOHchSK7UIJ3ys2tFBx+ClAaesSHQOR654yakTeRoVk1i3H0EQ8EegMFdj/OgincMR1tzUydFnpynnXIxyC1mVCMY1GiWDaKf/si0JlkMQBIa3puhfH+fws1OMHVwknLzwYZIki1img2O7SNKbnMjj8byreGGrx+PxeDwej8fjedsJskzis58lcNNNGOMTSOEQvq1bkSJXDlCuh+s4lL73PapPPY1drSKFQoQeeIDwB95/xX6qb8Z2bRpW46IerYIgICBQaBZoWs2LWgaokort2pRapeu+luM6jBRHWGgskPalOZw9jOEY9J1fyh/RIlTNKnvm9lwStr4+r3WJdcT1OBWzshS2ztXnKKgFBnsHiUg6eslFF+McXTjKXH2Onp5HMMw8tdpZXNdCUaJ0dn4Uv3/FVeccUAJ0B7s5lT9FVIsiCAINq4GERHewm2yrzlcmT1BodjCgdWKYNcpFHc1QeX2rJCkSuezz37NmHZ0rVlHN51B9PnyhN68ubOazxPxNIkET/OcQgmcJJ2qY0gTT02WGhn4LXe9eOt62LObHRsjNTGE2mwiShKL7oFJAAUxEnHoVwbEx0l1IdhOfXkYLObhyCNs1WTHs0Gr5UDWXaHSBYFBBUdqhbqFQYHJykmw2i8/nI5FILG1kVS0GkdwWUvEQYOOKEoWYj1q6G5+v3RdVUWLU66PMjj3H/NgIoXgCzd+uQI13dhLKPEkrksavnA/xAkmoLUDm6OXDVmhXlDp2u7rUbELnJtj2CxDpufTYw38Ph7/ePl4QYPyldruBXf/syq0K3qhjI5z6HtTz7c2xrGY7vF1xz0XjM6NlDjwxgWU5qD6Z7HiZ0kKDmz+ygnj3my/X/3G1soGoXFwZK2sStZIBwOCmJKG4zvjRRSZP5LBa7WtG0j62vK8PWXlrk09ZleheEWXqeJ5mzUQPKDi2Q7Ns0LchgaJ6SavH817iha0ej8fj8Xg8Ho/nHSFIEr5Nm/Bt2vSWn7v2yquUvvUtBN2HnExil0oU//EfkRMJAruuEEC9CUVU2JDcwHNTzxHVoyiiQsWooIgKW9JbmKxMkmvm6FXaAVnZKKNLOj3By4RXb6JhNfjysS9zIHuAlt1Cl84vN359mfd5Apfe9kYJX4I7e+/k+2Pfp2pUEQWRQrNAQPKz+VSL/j17UKsGpiZybK2fytYSPcH1rF71B1Srp7DsGgH/MJrWAYBhG7wy/RyH555FweKGzp3s6P8wothevi0KIg+teIhMLcNIcWQpiN6W3sa29Da+P/J9FhoLrIyubAeNwHx5guemnuOW7lswzRIgLFWCLj0ejQazs7PYtk06nb5q0FqvjyEknscvnQSfDHIVx4hgVv0EYlGqtTPMzP4D6b5/TsV2SKkKx597ilMvPw9AIBrDNAzMVhNfKIxjWyiOjd/vEIlIzCkC/kAOR2oiiwoNXATBIBA8QUdn5/mWCwKJ+MfIZEqMjx/mzJkzOI6D4zhUq1UMw6C3t5d6vU7Tcllz8wdRkvdSK00xTpO8+TQB+UKFsGMrLJxZzfiMRW6yQiAmIOsBZFkEQcRBgDe2enDd9tebVZ7KGmz/Rdj4MTDq7f6pl2s5UF2AU99ttxcIdZ5/kPMw8gysfrDdv/Vq+m+GNR9sB7vV+fa8em64pF/rxLFFTMMh1tW+776QQmGmxuSJLPHu69tULt4VYPZMAdtykGQRx3Ex6hYD6y98IJLoCZLoCbL9/gFKiw1wIZzUEX9Ky/k7hsIMbEoyeTxHo2yAC9FOP2t3dVKcr3Nu/zyLM1UCEY2hLSl6VkeX9cGQx+N553lhq8fj8Xg8Ho/H43lPadktnjj4DxxaMY0WjrKp6XKDL401OkZtz55lha0AHxr+EFPlKcbL47i4qKLKrT23clNX+3xfP/l1zhXOAe1w9o7eO1gRvYYw6g1emH6BV+deJe1PE1SCVMwKY8UxXFxyjRxxPU7VrFI36+wY2nHF8yzUF4hoEbaktlBoFvArfoYjw5x48VH6nzqOKmm0Qip2ucy2fU2Shyfh3vWIoko4vPmic9mOzV8f+nOeG38M22nhui4vzbzExxZf5aPb/gRJaveN3ZjcyBdu/AJ7M3uptCoMR4fZ2bUTTdIotUpIgnRReOSTfVTqM4yO/TmVylEAwqEt9PT8PKqaJJvN8uKLL1IqlXBdF5/Px9atW1m//vJtDWy7wfj4XyAH84ilAKaxgKzUsWwDxw3hj3ahKGFOLx7hP1dPUnb9dKgK6ROnCDoOgUg73JJkGccyUWWZB37jX6I5VeJ7/5SyFOE7iwZFWyMoSpgtB0tU6OoV8fnC+PR+ZCVEOLST48ddJiaeJpfLYZomwWCQnp4efJpGdn6O4sIswWiSDRs2sHnbdvbmD/ONqcMsNhbYKWforE/SG9tBRI8weSDNwkiCYDiNpIQpzmWwLYHOoSiV/CI5dSW90iLUFtuhaHW+veFV386rv+DUwKV9Wt+oMtvutxoduHCbL9a+Rnnm2sJWSYZdvwkr74HCRHt81xaQL+6zWi8ZyOr5oNN1idf3MGD9iOjRCog3wMZPQHrt1a8HDGxIMDdSJDdT5fXdr6Idfoa3XdprVhAFomn/pSd5i4mSyNZ7+uhdE6OYraP5ZDqHIxhNm1cfPUsl30QPKCxOVSnM1cF16V371m2w5/F43j5e2OrxeDwej8fj8XjeM1zX5SvHv8Iz2jHEUAM0h5NankWpwb2SjNtsLvvcnYFOfn/n73M4e5iyWaYv1Mfa2FokUeK2nttI+VIczB7EsA3WxteyvWM7onB9VXIHswdRRZWQGgIgrIaJaBEUUcF2bUZLo/hkHzd338x9A/dd9hxHFo7wpWNfYrGxiCAIBJUgD3c/zINDD/LSY6/RNM6RSVuAhRrz018LY+3eB/c+eNnznS6c5pXppwmLJiFfAkEQyTQqPDm9h1v6XqC74/6lYwfCAwyEBy45x0BkAHfKpWW30CQNx3Uot4q8L2qRyz2PqqYAyOWexbRKrBj+ffbs2UOpVFpadl8ulzl06BBdXV3EYrFLrlGpHKfRmCQUWY3gTlJYzIIrIPtsNL+FJUySa/QyZSg0NZGwLDHeaHG0ew23j54lwKWVwqFEgrCgAjYRTWBtMsPhokbLaC//703XGVwhIhBhePhfoqopXvvRU5z80ROIjoMgiKjRJM1mk0p2grgxh2HbrDeLbNWD+FfeRMGp8fWTX6dslBkMD1F1QpSa+5AK+7DE1RSmVhIMKsTtOfwBndm6S3V+gnl7gXBnB+Fbf5N67nnU+X2ochkhmKa6civZ6hM45ccIh7eQTNyFJC1j47lAuh3GNkvt9gTQDl8Vf/vPrpUgQGpN++sKkn1B8pkajuOSaO6lL/9VbNtA1RIw9gIsnoX7/y+I9l31cnpQ4eaPrGD6dIFqvoU/rNKzJoY//JNtVveTEkSBVH+IVH9o6baxI4tU803i3YGlDyOK83VGDy3SsybmVbd6PO9CXtjq8Xg8Ho/H4/F43jPGSmPsnd9LKtSJmp1BdHUKmsWr2jTbhSQrtmyhZbfI1DIElMDShlbXKqAEuKXnlsv+2Zr4GtbErxwm/bhCs0CmliGmx+gMtJdoq2K71yu0e7cWmgUKzQKrY6v58IoPY7s2veFehsJDlw1hDNvgm6e/SbFVZDg6jIBAtp7le6PfY3NqM2t8gyyGxwhGgyAIJPQEWraE07g0hDYdE1mQma3O0jTLJHQfwvnwOKL6KLWKTBUO0ZG4G8eyUXT9ivd1V9cu9s/v59jisaX2B6sDQbqVPD5fH2VHY6rZomnGiC4cpiztp1AoEA6HEc8vbw+HwywsLJDNZonFYti2jWEYaJqGKIo4TgsXF0GQcMQ8/pgP23ZwXQtV9WFZdcpujnn1QVJ6ux1BUBLZG40zle4lPX4CzR/ANA0c2ybVP0AokcI2dbIhnbMTpyi5Cj3dRZx6gGAQ7HiAxcUystRHqWTTzB7l6A+/g1WptB+PahWqZaS+IdT8KI4CghwjHg7gL5yEV/+c4+s+SjVXpTfeiyiKtMQOJtWbmDPGuTt4C6oySNichXoG3Rehv2cT2VyT1ekZku+7h5OvvsKBxSY6K0gkgnRtH6Bkv4BbtACJUukA1cophob+OaJ4nRFAuKvdW/X4o+3NsYw62AasemApOK0Wmkwcy1HM1gnFdQY2Jomkrj/YHd6aYmGyQnGuxoDxNLZjYoWG8XWG2slF9lQ7dN32mWs6n+ZXWHGZStafNfVyC4SL+8squkSt2AIX8LJWj+ddxwtbPR6Px+PxeDwez3tGtpGlYTbo6hmmVTawczn8DYuFgEXrxvWcXhfg0Vf/TxbqC6iSyo2dN/LJ1Z/Er1x5GbHrukxWJim1SvQEe0j4Luxmbzs2VbOKX/FftHnWm3Fdl++Pfp8nJ56kYlTwK352de7ik2s+yWB4kD2ZPcxWZsnWs2TrWZp2k0w9w+GFw6yOreb+gfsREalbdXpDvYTVC31MZ6ozzNfn6fB3LFXVpv1pRkujjJZG2bJxA77duwmJSaRgEKfZxKzV8G3bunSOkeIIj48+zkhphJgWoy/UhyQqGLaJ7/w+PnXLRBMEmtN5fvDD/4plmqQHh9lw5z1E0h0X3d/5+XnOnDnDqsIqutVunLRDKp5iXUBjYeovKdkyR6p1Wo6Digx2k1fm51jlgmpf6EXqui4AkiRx+vRpTpw4QaPRIBQKsXnzZjo7V6EoUVqtOWy7hSDICIIKiLiuhYvLmLiFcd/9RC2LZrWKYFQJUCUUBLdVptasIyoayb4Bbv/Ur2CaeUbH/h9m1WlaXWU0HBDAjhVo+RwEJwdI1BuzvPzy/4MykwTXQQyGUHUftiTTKhUgP48RdKnJceK6S39UxHUGOD5Z4ERmP/FyHDfj4iZc6IeG4CNDNx0rHqCyP0tj2iTk94OsYdoqwaiAFj7NgScew2xBtKMT13WZXpyiMfYtEv1d+P3tCmPLqlIq7adaPUk4vIz+yDd+Duq59kZZVgO0MORGYOIVqpEbePV/j1BebCApEpmRErNni+z68AqiHde3ND8Y07nl4yuZOVUgtbuOLMfRO8JI8vm0URShkQfHgflj7U261AD07oRA4s1P/jMskmo/Tq/3l3Vdl1bVpGdtDEH0klaP593IC1vfxKFDh5Y1bnp6elnj/uqv/mpZ43buvIZePJfxiU984uoHXcGxY8eWNe62225b9jWXo1AoLGvc/v37lzVuZGRkWeMCgevbXfN18fjyevj4fMtYQvQOCYVCVz/oMj7/+c8va9zNN9+8rHF/9md/tqxxrVZrWePeLYaHh5c9drk/E/3+5fXc+s3f/M1ljdu0zE1d0umf/UoLgFqttqxxy31PDAxcuvT0Wt1+++3LGvfcc88ta9zQ0PVt1vG6t/vvQo/nZ5VrWbitFoLf/5Yuk0370vgVP1WnQXjjRuxikWptnqiqErj5o/z18f9F1ayS8qVo2k1+NPEjAnKAn1vzc5c9X82s8bfH/5ZD2UMsNhZxcdmS3MIvbfgl8s08j48+TraRJapFuaX7lvbGWWaF3mAvW9NbUaVLly0fyB7g0ZFH0SSNnmAPVbPKD8Z/wGuZ1zBtk7pZZ7oyjeEYaKKGKqn4ZB8Nq8FsdZb/99D/S1yPo8s6MT3Gh1d8mDv77gTafVAVUcFwDHy0/81nORYiIrqkE7hlB80TJ6nv3Ys1n0EQJXxbthC+r92SYLI8yX/d91/JN/Mk/UkmK5NMlCdI+zuZKY/itwrYgohlG+xQ/czvKyK7OpIsM37kAJXFLHf/yq+j+tp/H2cyGZ599lnq9TqqqtJqtYjVYty88mb8fofSfJypyjQtO05ElvDbC5hKgnNyL53RecjOIQgCoihSLpeJRCKYpsmePXsQBAFN01hcXOTll1/mnnvuoaf755kY/wrNSpPipENlOobghon1S0T6LcT+jzGVsyhmjuM2azQdg4YvwP3xIhu2CSwUDMJb38fQQ7+BrChMT3+NYv4wzYqOKIQQxSaoZUxRwDYsJDFEwNTpquQxmz/k8MhmRHzYlRK1ahnZH0B0XYRmDSkkMxBqsTnl4JddpooqBypBtIiLX4eKWcef8WPKJnOBOTYmNzKcGkDaXOLYtEO+FgdRZCEoc3C1hF3ZTO/CSYJdfQRECV0UCXf4sawyrjnA+acfWQ7Sas3RMrLLe1O1KpA9CfGh871bXciPwb6/YSqRorzYINYVQBAFXNelMFNj9NAC2x+4/r/TfUGVlTd2QPVGGPkRSOdLO63zVZ6xIdj3v+DU98FstAdFeuGOf3XN/Vx/1vSuiTF9usDCZAVBANdxCUQ0Vt3YcfXBHo/nZ5IXtno8Ho/H4/F4PJ63jWtZVJ56isozz+LUa6jDw0Q/8hG0FVfeaKdhNZivzRNSQxdVlV7OUGSIHZ07eHH6RQqtAq7kQljnQ8MfYqI2RaFZYEV0BYIg4Ff8mI7J7rndPLzyYTRJu+R8Pxj7AS/PvEzdqlNoFjAcgycmnuB4/jiKqKBJGjE9xkR5gpdmXiKmxQhpIURB5MaOG/m1zb92yXkPZQ9h2AZ9oXbvyZgY42TuJDPVGXZ27iTui/PqzKsgQkSPUDfrBNUgVaNKsVWkZtbQZZ2UL0WmluFrJ79GX6iP4egwHf4ONiY38srsK9iujSRILNQXGIwMsj6xvl21+Rv/B8277sTKZJDicXwbNiCoKq/OvspfHP4LzhXOEVJDaLLGQHiA6co0Ef8wm+MrOTC/G01w2RpdQexoEIQ0gfMFAFogSCEzy9zZMwxs3grAqVOnqNfrpFLtnqyO02JhYZGRkRG2b99Od9cnOVb+/0i5U2i2gCmGmAl8AMGJUl0ZZ1s4wMzMDI7jkEql2LFjB4cPH8ZxHJLJdgsIn8/HwsICo6OjbFy9mey+QXKZKoWZOnZLQFJMWiWb+nySBwYi7J07x6zsQw7JSK0qK2ZHkfJz9N0wSG9hFIQjiFK7hLdUPoQk+sCpI6gSSBGQGzi2hdnyk9SK+Nw6yA6hqoOVy9B0fciaH7NRp1kugQAhn8LN1iESZQdiN7GnoHMoo1BxVLrK02ySWmQFmBRErAWXDQMb+OUNv4xo1BkMHieU+DaZYoqqHucHa28g69MZqjqIksRcy8AtQ2+1QK04S2K7RWF+BEUNoug+bLuGKCho5/viXrfFM+0WAolV7d6rCO2AszSF0TyNpPQsVWAKgoDikyll68u71us2fhwWTre/ENpL6Xu2gz8Be/8atBDEBsCx271cD/wtPPAn5+f31mrWTKZO5MnP1fCHVfrWxa+7avfNqD6ZXQ8PM3UqT3G+ji+k0rsmRjj57imQ+Vm3y1xN2Ay+Jecqm9W35Dye9zYvbPV4PB6Px+PxeDxvm/KTT1L8xjcQNB1B12ns2481M0vHv/nXyKlLw6BXZl/hsXOPkW/m0SWdm7pv4udW/9xlg1Fohz2/uP4XGQoPcTB7EFmS2dGxg11du/jOyHeWjnmdLMiYjont2CBdfC7HddgztwdRECm3yvhkH2EpTKlZYr42j4DAnX13IokSM9UZmlYTdFgRXUHdrLMvs4/t6e2X9Hh1XAfhDY0YG1aDulknoAQIqu1AIO6LM1udxbCNpflatkXDbuC4DrPVWQrNAgIClmPxnZHv8Ns3/DaCIPCZdZ9BkzQOLxym5bTYmt56UasEQZLwbdgAGzYszeFM4QxfPfFVco0ckiDh4DBRnkARFfyKn7rV4HO3/mc+j41l1WiUDJ5+8X+g+S70aZVkGdeFVuPCqoh8Po+u69h2jVp9BMss0WzKHN8/T/3sYVQtSDn2cc6oObp0lYq6lqrURbPWZGU0xp3r7qRSqWBZFpFIBEmS2L17N4pyoWVDza0xIU5Qy9do7B2nMFPFqqVQhBJ6RMKoN/D5E7itIMW9r/Kh0RHyg2uoV6ZJZ0/TWa1SMgxmR44SFHO41eMUzvwZ3UOfR5ZDCLKA4vPRqtUQ/QEQBETBIhCsoxjtVUrlkERWUJHP2qg1C0cARxRwXAfdH6Rvw3YyzSRW6RWOj5rkXR9NR6CFzJyp0CEpdDtlFEVHSazkF3b8AqILPPcnCGMvkNRzJLXdvBTazIK0heHKKGq6G3G2SKSYp24YLFaLCK5LI5NGCy4yP72PWFcfuCbR2A6CwfWXfc9clayDqIBtgnj+TeKYICr4YmGsWQfXdRGEdmWr2bIJ/aRBYXJVezOs8ZegvgjxYRi8DU7/EMwmxAYBsFwFW+9DXRxBqOcubOL1FmnVTV77ziiLUxVEWcSxHKZPFdjxoUGSvctbgXc5qk9+V/SX9Xg818YLWz0ej8fj8Xg8Hs/bwjVNqs8+h6DpKN3dAEjRKMboCPX9+wk/+OBFx48UR/jaya/RslskfUnqZp0nxp8grIR5eOXDV7yOJmncM3AP9wzcc9Htq2Or0WWdXCNHXI9jOib5Zp7bem67Ys9Wx3Vo2k1s10aR2gGfIAgICJiOSctuoQs6xVYRVVKxHAsAv+LHdm3GSmOXhK0bkxt5dfZVCs0CUS1KxaxguzZx/UKLqA5/B3O1OQQEmlaTptmkZbewXGtpgynLsZBECcuxeGzkMe4fuJ/1yfVEtAi/uulXKbVKmI5JQk9ctVXDgfkDVIwKfaE+TpunUUUVx3WYq80ttUiQRRmQUVUNKWYRjCcpzs2i+nwIgkCjUkZRVWKd3UvnTSQSnDp5nOL8GLbRQNZU6jUBe3oURziHqnYSkGRYt4PnV2xAM0RaTpNhv8b7EmEEQSAcDtOyWxxaPES+mccNuzQWG/hkicn6CK+IByhTI9QMsbvS4u6gRSTroLsOiqtRUoPYhoiqybRqNXSrxYaR5xDL02BUaNoSri3QsgpEMahoAovT38agQTxxB9XyScIdOsW5BpY7j1sTkHQJRbDQmzaWJKE4DjVZJjpkIE0qVLQA+VwZRdNQ/QEkWaYeHOalkkxdCNCXkKkVMhhWABvIGzqaZuM2m2wcHG73250/BtN7IdgJ1Sz44hh6DFeUkfQQrtukduPNaLtfRB89DbKEPxwhpG1Bqi7QqJwinkjQM3wfycT7rn9zrNel17fDz/njEOoCXCjPQe+NdN5wI6OzYxRm6yi6hNmy8QUVhja/BaFnpAe2/PzFt6l+wMWy4MxsP5MLaWzDJB4ssS4P0eV1Z7uimdNFFqerRDr8S/1Ui3N1zu7NvqVhq8fjeW/xwlaPx+PxeDwej8fztnBaBk6thvCGXesFUQQE7ErlkuMPLxym3CovLfv3yT4M2+CV2Vf40IoPLW0Ada3WJdZx/8D9/GjyR4yWRhEFkVXRVXxk5Ucue7woiOzs2snZ4lkc18F2bAzHQBIl/LKfQrOAi4uAsFQhG9NjQHszJxeXkHZpILOjcwdjpTFenH6RkcYIuqzTH+4HAUzbXApQe4I9NK12yGraJgCSILWvKQjYro1jO8iiTNWo8ueH/pz/dvd/W6r6jWiRS67tOg6OYyPJF2/m1bAaCIJAyp9iob5ArpnDsA1aVou1sbU8OHRxEC7JMpvuvp+93/kWuenJ9m2KwoobbyLZd6FXZ1c8xv7sLA3DQUTDqYiIRoOw3kL1W8RiKWr5KjsnjrPlhhuZdUWGfBp3JyN0aO05FptF/vLIX3IqfwrHdVBNhVULMcqH87zUP0JVM0hKCYYGVjFVfZX9oSwPBuOU8gECUpmoU6UhhnFdh951GzDmz1KcmycSCuPYUGkYJPUaLSPBUS2G7VcJVObJnD7CTN6mWfATlzMMdNsUU33I8iC2dA6rmcVqZrBFEF0d1VVoaBa6JuHr68Nw5qjmc/gCwaXXhOFKmKJGywU/LaKqQ8EQqdsCVVtm0Fdj/arzPe9rC+0qTi0CVhO0AGudIlG7zpyeottYoJUKMb7rPjaV8nQm44TjKURRxG0Fqc/IRDd/nM6Om970fXFVsgq3fQH2/DVkT4AgwtAdsPPXCIcC7PrwMGOHFyjO1wknfQxuTpLofmuWbF+idydEejl1SuN0sRtVbCK5TWabq6k+Nc/tPx9BD1x+ozrXdZkbKTF2eIFa0SDRE2DF9jTR9JVbApRz7b6wktz+WSMIAlpAppCpMXp4gamTecymTedwhBXbU/iCl/Zo9ng8//R4YavH4/F4PB6Px+N5W4gBP+rwEI0DB5CiUQRRxK7VECQJbXDwkuNbVnuJ9hurMiVRwrANHNe57rBVFEQ+sfoT7OjcwUR5goASYENyAz75ykue3z/0fqYr0zw++jiLjUV0WSeuxwkoAboD3czX5nFdF0mU8Mk+JEGi0CyQb+ZJ+VLc0HHDJeeURZlPrf0Ut/fczlxtjrgeRxRE/ubY3zBdncZxHWJajA3JDeyd30t3oJtcM8d0dRpZlGlazaUKWgQIqSEc1yFTy3Ayd5Kt6a2XXNN1HEYP7uXc3t00q1WSff2sve0uEj3tvrFrYmt4fvp5GlaD9cn1zFXnmK5Osy29jd/e/tt0BC7drKd79Vru+qXPM3v6BKZhkOwboHPlqvMBelvu9HHCjRyqbuBIIWhUsRYWEbpEwMF1LHyhMG51lq2Nb2EygmTouPIdOKkHEEWVpyef5tjiMfrD/WiSRiuTo5EdQYoGMMMiSTuGbNpUsrMMYDMiSdQjBlpJY6GuoAg2QmGSjuQqVm7dRmzyexwsipSaAiIhosEFqol+XnJSGK6OuyCgLfTi1iVwigjoTIkr6akvsp4Z8kMCRmMe0y1ghVRE28IVbCTHRa2HWGz6UStG+zUnyUiyQrNWpVosYBkmTdVitmjTZdqEpUUcJUlIsblTP03nyq2Isa72gxfuATUAZgMXiUbLIdma51OFF/l698OcVjsQHI3uziRDA0M4xcWl90qjXELRdWJdPdfy1mjLj8GJx9oVtaEuWPN+6L+53Qc12gf3/QeoZNrfBzuW+qNG03623bf8DS6vSzCFsfN3mDp+DE2qE9BaEO5Gi/dTzDbJjJYY3HT5qtq5c0X2/2AC07BRdJnxw4vkpqvc8vFVBGOXb0vij6jguriOu9SX1mjYiLLA4acnEUQBURY5/eoc+dkat3x8BbIiXfZcHo/nnw4vbPV4PB6Px+PxeC5jYWGBT3ziEzz//PNLt/3Jn/wJuVwO27ZJp9N88YtffAdn+O4jCALRD38Yc3YWY2SkfaMsE7jpJnxbtlxy/Jr4Gp6efHppub3hGJSaJe4bvO/8kvblzWEwMshgZPCajg+pIX77ht/m9p7beWriKWaqM8T0GLf33s4t3bdwbPEYmVqGmB6jYlR4ZfYVqkaVjYmNPLTiIXqClw+7BEGgL9xHX7hv6bY/2PUHnMqfomW3WBVdxX8/9N/xy356Qu0NiHLNHK7r4pf9VMwKIiKqpCIK7f/XJZ2qWaVpNTldOI1hG6yKriKqRxk5sJcD338UQZRQNI3JY4cpZOa465c+TzAWZ0fnDk7kT7B7djdZJ4uIyG09t/EbW36DpO/KS8Ij6Q4i6Svvmr44NU40FMEURoAKtiyTc0xaFZdANIok+6lV8wSGTlOqjaLpcUyzzPT032GZZXp7P8Ph7GECSmCpYjeQc7EtFyckoys6sqIiCy61QoGQ30HVfHRFdLriGQotH6LgEk8I9MfOoI0/SXdPklTzNHkliSg4TM5b7GumCKpVtICLZUMmn8TvNEhRBkHCtJpM2WF6p6cJrurFlEsYxiKOKCOIOq5tIIguG27YQW79LnILOfo3bkGUZeZHz9Gq13A1HTkQI+QL0Gw2mXe6UMwKIUrcGs7S3d0HOz57YZOn5CoYvovCwR9yZDpOvgqiAH3Faf5P5xuc7b0NZeUdbImGaWgfYO93v01upl1lLKsaq3fdeu1hayUDz/4xFCbAF4HSTLttwG2/A0O3v/6ihXDXtZ3vp8iMrMaK2yiyBQENJIV2vG9gNu0rjhs5tIBl2sS62r0G3KhKYbbG9Ok8a3dd/n71rI4xcTRHYa6G6pcxmw6SJOBYDrImEYy1q/StkENupkJ2vEz3qthbfI89Hs+7jRe2ejwej8fj8Xg8b2AYBl/+8pfZv38/r7322tLtTz75JMePH+drX/saAJ/4xCd48sknuf/++9+pqb4raatW0fGv/zWN/fuxKxXUgUH827YiqJcuv92S2sI9/ffwwvQLjDZHkQSJdYl1fHD4g2/rnEVBZEfXDnZ07Vja3Or1CsKbu2++6NgHhx6kZbUIKIGr9kkFqJt1ZFFGlVR8so9t6W1Lf2Y79tI5Ur4Uc+oc2XqWsBqmYTUwHRMBAVVUSflThNQQsijzx6/9MdOVaWzXJqEn+OTqn6O8dy+iJBFJdwLgD0fIzUwyfeIoa2+9E0VS+NzGz3FL9y3MVmeJaBE2JTehy/pl5305pmMyWhzFtCqErQka1eP4B89gTqj46KPRmASliiA7OJaE04hTbM0jBbP4UxaB4Fqk84Fqq7VALv8CHR0fwK/4sWrWRddyXYi7QbpckzExQ1BQEQWBSVlkhSFwc9CHnjJAldtL8XtWYbZa5Pb/EGXLxwkpr9IhL4Ivxt75NCo2tubQFGrYgojjCDiIiIqKqVrYqkGrpTKtqww1FnGcBooSw3UtfL5+ZDmCaZWQum9iW8/FfUYtw8Bo1HnymWexq1VCoRClUolGI4RTzzPYodB/26ehayuu4uNM/jRHFo5gWy1WJ1aRqUxQsqcJBFs4lsWpnMTqzn7uv+3TEDzf53ftekKJJLNnTmJbFsm+ARJ9A2QyGQzDIJFIEAy+ydL+sRfbQWt6bbtNAMDiOTj5nfbGVNfwWl4O13Vp1SwkVURRr60i1B9SCSd95GaqqCEZAWjWTGRFJNpx+ZYArutSzbdQfBciEEEQEESBesm44rUCEY2bHh5m5OACuekqyR6V9ECY4y/Nor7hXLIi4jpQL5vXdsc9Hs97mhe2ejwej8fj8Xg8b6CqKr/+678OwN/+7d8u3f6lL32JT3/600vff+pTn+IrX/mKF7Yug5JOo7z//Vc9ThIlPrX2U+zq3sV0ZZqwGmZ9Yj2q9M71Rbxa6wJFVFDUCz0jG1aDg/MHma/PE9NjbO/Yzlx1jqcnnmbP3B4qZoWoFuXegXv56KqPXtTS4MbOG/nmmW9SMSoElSDdoW4c16E32Evan2a0NIrt2tSsGmE7zEcHPsozE88wXhqnP9yPJEjM1mb52+NfYX1eJOCoqFYDn+xb6pXbqtcuum/rE+tZn7j+XeunKlN86diXmCyPsUkcpVdqkAr04ktamCzSmBlEdoYwKnk6VwToW7+dSraMrKok1/bSUsqI4oXnVZaDmEYO0yxya8+tnCmcIVPLEFJD1IMV0ppEqKlyh7ERBIcpZ46+pM765BD35YpoM2Ng1s9XY3YzVQ1y5GSDulFEyuylO7adbaFRtNoCqpamiY6qO4i4uIKAiwCCiK22MHw2ji3gClAKNTlcOYouSUQk0JUA/sAqBEHGrlWRpEtbUsiq2v5SlHbLCUkiHm+HpAsLKsLAahhob6L2/NRz/P3JbzCUG2dXbobCokI2003n0CakWC+4DkKpyFQDNrg6b7xaOJUmnEqD61KdOclT3/oy2bKBJen4/X62bt3K2rVrL/8hQC3bDlnf+PrWQ1CZA9cB4a1fGl/I1Djx8izFTB1JFulcGSHRHUBSJJK9QVT98nGFIAqsv7Wb/T8cpzBXB9dFViQGNydI9l4+UBYEgXhXgOlTefxhtd3z2HJwXZdI6vJtRFzHpV4xUH0y2+7rX7rdMm3GDi9SK7WW5mg0LURJIBS/9g8nPB7Pe5cXtno8Ho/H4/F4PNfg6NGjrF9/IYRau3YtBw4cuOyxrVaLVqu19H25XP6pz++9ShAEhiPDDEeG3+mpXLeqUeV/HP4fHJw/SK6Zo2E1CMgBAmqA+do8dave7j2LyJniGUZLo/zbXf92afw9/fcwVZni0MIh5mvzhNQQn1n/GXqDvXz5+JdZFVuF5VhUzSqapBHVokxWJukMdC4F0rqkczh7mJYWZHBCZcqeZ2VsJXEpgiCKRDqWvyy8aTV5evJpds/s5vDiYWzHZle8ix4McpZIpV5iW3o7gqChKCXqI2vpXbOd1TffttQrFmB+ei/7nn6eRnEMLaCSWhlGjRZR5DiF6TI9NR8Pxe/lpdo+yq0y0a4OttxzG5zI0MgUuVPvJbDBJpy2UOUGlbCfqcQmeo+fQgx2UlT72L8vh9EwCXb0Ymk+RmdzqDseZvutNxA49jXso2Vabg+qCrYDstBEcBxKokQ4uIDurxAULOq+HD4HJlsSHapNlxzGcZoYrQVUNYlRSLH7lb+nXiqRGhhixQ078UeiAAwPD5PNZqlWq+i6Tq1WQ5Zl+vv7l14v3x35LkOVRT6+OIfowjlXxTEbONmjSKoGgRSSpmPVqpjNJr5Q+OInxXFg/5fY/+peZqp+YoqJEohTlgfZv38/yWSSVCp16ZMZ7QdcsA2Q2n1KqRdg4FYQ3/qgtVk12ff4OOXFBv6IRq3cYt/j42h+mUBEIxTX2XpvP6n+SzeYA0j1h7j9k6uZGylhGTaxzgDpgdBSX9XLWXljmsJcjcJsDUEScW2HZF+I3rWXLvsvZGoce3GG0nwDQRToXhVlw23dqD4ZWZFYvbODwz+aIj9ba+fTLvSuiZHq/yltDObxeN5VvLDV4/F4PB6Px+O5BtVqFb//whLVeDxOvV6/7LH/8T/+R/7Df/gPb9fUPMvkui711/ZQ270bt9HAt3ULwTvvRPRfeXfy6/Hy7MscnD9IoVmgZbdQRZX5+jxCXWhXyLogILSrU40aj48+zkdXfpQNyQ0A+BU/v7nlNxkrjZFv5ukKdtEd6Oa/7PsvuK7LcPRCAH2ucI5jC8cQEHBxgXYbgtHiKJZr4RvuQisamPkak4WTCNEB+tdupnfthmXdN9d1+capb/DM1DMICBRaBQQEzpVt1odcfEqEqlml1CoRj/WgB4vc/L5fwe/vv+g81UKeA4+9yOxZsJ08juWSOT1Lzw0iit3BiZG/x7FtVJ+PR7bdSc8tN5IMpNAkjfptJYqZOSrGc5SbY/h8A4iiD8sqsRDJE9x+P/HR08yNTdCoSySSMYSOFSiqhG2FmD57jk33P0wwnmflKpPMnI9GHQTDIKXNY5ahY+Acql7Hdh0UrUlYdLFch4wlcNKQCLplIr4Cfv8gYn0n+x5/kla9jqKqZEbPMnfuNHd8+rPowSA+nw9JkpiZmVmqbr3hhhvo62sHz3O1OYqtIh+oFpFti1wwjuaIWPMC9YZBpDyL60tQK+SId/cRjCcufWKmdtM8+h1mjVUEfQqqJEJtnkjApYTEzMxhksl7Lq1uHboDRp+HzBEQZXBMCHTAuodgeh80ixAdhMSKt6SlQGa0RCXXJNoVaFeQTreX8ju2SyiuUcm3OPLsFHd+es0VN5wKRDVW3pBe+r5VN5k9W6ReNghENbpXRS+qjk10B7n5YyuZPpWnXjaIdvjpWxdH8ysXnbdVvxAEB2IatuUyejCL67hsf6C9EVj/hgS+kMrMmQJmyybVF6J3XQxRur5N+zwez3uTF7Z6PB6Px+PxeDxvsX/zb/4NX/jCF5a+L5fLS4GK58rsUonq88/TPH4CMRoheNtt+DZt+qldr/yDH1D85j/iWhaCLNM4epTmqdOk/vlvIcg/+a9KZwtnqVt1GnaDsBrGxUVqSViuheVYCAgggHv+f3WrzrfOfmspbIXzlb3RYYa5EKxWjAqKdHFAJIsygiCwMraSg9mDiIJIzahRaBUIq2Gi6U6MOwSEqRKLpSw7tu9k1y0fQb5Mr9xrMVebY29mL0lfEkmQmKnOIAkSmVadrM/FL9cxHRPLsTDNIrIcRtMu3Whr/NB+FibHcQwdUQ6jBGyaZZvxFwQUqUDv2vVIikqjXGJi7x56h9eire4F2n1n/eEIx0/8f8hyCElqh+SKEsUwFin3DBJf9Ss4z/4QyicIDJvIvsdp6CJ60I+ZH8ZxHILBlSSTz5OM+Dn97AKNXB3XbhBKiAQDZSQZqoKIg4zgGmiCgGRFyTREjpUM7otsRdnyz3n+K3+D2WyS6OlDEAQc2yI3PcnUyaNoHT28/PLLS5vr1Wo1VFWlp6dnKfgMq2F0WUdp1bDPV5NqPpNYysJejLC4WAJrikAszqZ7HkCULhNCzh5EOF+d6gKIEnJ6BjFyhJiiUKkeY3JqnL7eX0QUtQvj9Ai879/CyDOweBYCKejaCkf+AeaPgW2CFoTVD8KNvwriTxYqmi0bFxBFgXrVxGo5qLqE6wAIhJI6lVyTQqZOqu/y1a1vVC8bvPadEfLn2woIgsDE8Rw3PTSMHrjwXomkfERSb75p2Px4uR0EdwYQpfZzIwgwN1KkUenCF2q/Z1L9oStW3r6VjIZFrdRCDyr4gu9cCxXP8jSbTf7wD/+QYDDI/Pw86XSaP/qjP3qnp+X5KfPCVo/H4/F4PB6P5xoEg8GLKlnz+fxFla5vpGkamqZd9s88l+fU6yz8+Z/TPH4CQdNwTZPGgYMkPvdZAjfffPUTXINis8gT40/w4syLYDlsODDPA0oM3/ll3HatRm33q2hr1xC+914ERcF2bCYqE9iOzUB44Lr6xUa0CIZttCtYBQFcQADRFbGx2+ErUvs2QcRxHcZKY+3WAm/SG3ZzajOPnnsUwzZQJZWG1cB2bdYl1rEpuQnbsTlXPEfNrKHLOoORQTRJww1BZZWP6RI4g1EkRbniNa76WLaKNKwGcV8cWZAJKkGKzSK263K2brNJX8CHwELpCGKgm3VDP78Uhr5RITOLbZo4lo2uJcACWWzQqFQQAgai3A6R/ZEotXKRhYkxulevvegcoqDiNgsY+QmadhFLFnFlBStSgvRaOra12Db+9yQKU0gFMCWFqaiPypCD6UyTTj9IpXKcc/uOUM0I+GI2ihYl2h3DMaZQFBGdCi3XwgYajsBIy6EmSHTKYfTJw1ibW1QWF9EDwaXwVJRkcF1q+TxjhQqGYSwt4Y9Go2SzWUZHR7nhhhsA6Ah0sKNjB6emj9FTztKSJZquzZr+CB1xm3x6G/LKO+lavZZgrN3z1bYbLOaepVQ6gCAoxJw8CdGiL2hxqqjgi8yjRjOYlg9XTuPzRZjOfJ+jC+PI+SaDjshg13b0FfdCIAkbP3bhgd39FzCzD+LDIOtQz7U3zOrcBP27rvoaMQ2bxakqjuUQ7wlcFBRGO/zIikizar7heAd/UEVWJUyzvTmc+CZtAd5o7NACudkasU4/oiRiWw4LkxUmjuVYc1PnNZ3jdZbhtN+qb3gLSrKIUbcwDZvLd3h967muy9ihRc7szdCqW8iqxMDGOOtu7UbyKmjfNb7whS/wmc98hltvvZXDhw/z8Y9/3Atb/wnwwlaPx+PxeDwej+cabNq0iRMnTrBixQoATp06xbZt264yynOt6vsP0Dx5CmVgAPF8taUxMUH58R/g37EDJAkcB+Fy1XzXoGk1+b8P/N88P/08LbuFbVvs72xxPJji9/NdSPUmrbNnsTJz5P7yr6i9/AqtTz7A3zdeZLQ0imNbdBLhkcGPsXHt7ZffZOjH7OraxeNjj1NoFtqBqGOjSzotWji2g42N7dqI5xf/B+QAkiBhOiaadOWw/p7+ezidP82Zwpl2YCtI3JC+gVu6b8Gv+Pm9Hb/HRHmCptXk22e/zcn8ScpGmVwjx7nCORRR4R9P/yPHFo/x2Y2fJa7Hr/vx7Ap0EVbD5Bt5OgIdrIqt4kj2CPlmnj01AVuMsy6gM+fA6XqYdcHtlz2P5g/SrFWxDANbsxAlCce221WbjsP5jgi4bjupFuVLn/+4k2as+BiGaLRLEE0XxxYpFw9Qr0+QKOxGk7NUJZGGraIbBoP5JrO9FoXCq/T2/iK63kt9YRRJdRFlGdc1sZoWSkjFyRnUQgIFQUQRXHKGwAGjTkoOcKeYAsdGkmVCySQLE+P4wpHzGzBZIAgE4nHKEzMXfQAjCAKSJFGtVi+6L4+sfYRnBJn5V/6CjuoCISVITNBR1myl4+7fAN+F/qKu6zAx+dfk8y8hiiqu61AWK7SiLbZXx2n4+6lq85iOgCBGSKU7sWSXTG6KwfEjBCsOguuyeOr7dJ59Cvl9fwix9jJ5HBsmd4M/Dsr5eDGQbG+klTl61bC1OF9n/w/HKS82cRwXf1hl4x099K1rv9aSvUGGtiQZO7yI2bJxHBfXhWBCwzRsKrkmie4A0c5ra+mxMFVB1aWlZfySLCIrIovTlesOW+NdAVRdol428IdVcKFWbBHvDhCMvn0fos2Plzn2wgwI4I+oGA2LM3vm8YVUVmxLX/0EnndcLpfj9OnT3HrrrQBs2bKFc+fOvcOz8rwdvLD1TczOzi5rXE/Pmy9LuJJdu67+6eDlDAwMLGvczp07lzUO4ODBg8sad9ttty37msuRz+eXNW50dHRZ43y+5X3OqSyzqiCRuEyfpp/i9d4Jd9xxx7LGbdmyZVnj9u/fv6xxV+rZdzWdndf3j7/XScv8RfPtNjg4uOyxodDylmUtt0fi6dOnlzXut37rt5Y1Lhh8d2yg8Mwzzyxr3FNPPbWscf/pP/2nZY0DOHHixLLG7d69e1njPvShDy1r3N13372scZ63n23bOE57t2pBEPiVX/kVvvrVr/LQQw8B8I1vfINf+7Vfe4dn+d5hZbPguktBK4AUiWAtLFD+4RPUdu/GLpdQ+/oJv/9BfBs3Xtf5jy4eZc/cHizHIqJFkFyRYmOWQ4FF9jTm2HZoATubRZAVpFSK5tgof/PUnzK+OkynHcSamOMcp/irY4f53Sc+QN8vfx45dunGOm+0KraKf7HtX/Bf9v4XMvUMsigT1+NEtSh2s8XJ0ilwHCRXQHYddNthZXgYVXzz6tmYHuMLN36BwwuHyTfydAY62ZTctNRaQBREhiJDAPyq/qt8/dTXOZo9ymhpFJ/sY1NyE7IkczB7kNDpEL++5deBdqBZapXQZA2f7MNyLMZL41iuxWB4EF3WL5rD/YP38+i5RxkpjIAAaX8aSZToCnYRDnaTFRVc12W0NMrJ/Ek6Ah0X3Y/ZM6eYOn4EyzAwWg2aZgNXk5B0DU3WkZBp1qqouo9aMY8eCNC1cs3FD4brkpqYYtYRaekSAgISEsGmi13PMjHxV/jnd2N1K2iGD93QEATwNWqEaw0sq0ytdpZq7TSBWAqjZKKqGrbdoJqv4MePlKrQEgUCSDRtm9GaRNp2CYmgtEqw7m4E1ceam++gOJ8hNzOJrGiYrSbJ/gH61m1irmFy5swZQqFQO4g9//Plx/89r8s6H9jwC7grP4w7uRuxOg+hrna4qQYuOrZWO0uxuA9N60SW2/9uaklZFjsFUjMi9zqnOOarUglphDvWIikqRxePkC6U6a0ZzAV7MESBGaNKaP4YkePfhtt+5/zZhfZGWcbFYXD7Bfbmv0s4jsvhZ6cozteJdPgRRIFKrsnR56ZJ9ATxh1UEUWDTnb10DkcoZOo0awbzYxUaFYNG2SDRE2Dbff3XXMGpBxXyc7U3vCxcbMtd1rL7SNrHqh0dnN0zT2GujkC7P+yG23re1p6sc+eK7c2/utvPu6xKmIbD1Im8F7b+DPjxjS8vt6LlpZdeYuXKlfzxH/8x+XweRVH44he/SDj8Yxvbed5zvLDV4/F4PB6Px+P5MV/60pd4/vnnMU2TX/u1X+Puu+/mM5/5DPv37+f3fu/3sG2bbdu28cADD7zTU33PkFPtfp6uYSCcD1ztchkEgcI3vwmmgbWYo777Ncrf+x7hhx8i8bnPXTbwdC2L6osvUtv9Gm6ziW/7NhbXCFStKqqkIovtX4P8eohSq8jh2hk2zberIpV0GrWrk2mxzASjpOsxxKkJ5FaLXn+EGX+V48deJPj3IVK/+RtXvV83d9/Ml9//ZZ6bfI65+hxxPc5NnTfhfvXb/OVChr09TcqSgYVDw2owPnOc0dIoK6IrrnjOUquEYRvs7Nz5pu0GADoDnfzO9t/hm2e+ybfPfpu1sbWI5/ttJnwJjuWOUWqVyNazfOvMoxyu1qkTJESBRv0MhtXuNzsYHuTT6z59UT/ZDwx9gJ5gD0cWjmC7Nn2hPr515lv4FB/KVQI502hx+MnvY5ktOrd2UGgeRLRNqhWVvRGReLqXj/hvpzQ+SbVQwxeKsOHOe0j2/VihiW0gljNoIT8hx0Vx1XZrBrtE3irQyj1HTa1jyyDYLbqqOkFLg0YF13UIBFbTamVwnCada7opz85TXWyh6BKtWgO5OURUqTMr1Qm5OpW6QzJfJ1VUWJQkJrqH6N30CALQs3Y9t37yFxg/fIBasUBqcJgV23egB4OsX7+e+fl5stkskiThOA4dHR0MDw9f9vERtBDCqvuWvi8vZilmRtCDQZL9A4iiRKuVxXGaSNKFD3AVJULL18C451+imjpdxmmMxW9huxVwIrSMPLGGhSUo2JKMBLgIVCWZyNzhCxMQRVhxN+z/MtQW20FvNQtaBPp2vOlzW8k1KM03CCZ0JLn9WgsndQpzdRanKvRvaAfMgiiQHgiTHmgHT7btUJyvIyAQ7fBdV7A5sDFJdqJCMVtH88s0qxaaT6Z/w/VXbQuCwJqbOkkPhMnP1pAUkY7BcLvK9W1kWy78WAG9KApYpvO2zsNzeT/eh/2P/uiP+Pf//t9fdNu5c+f43ve+xyuvvMLAwABf/epX+aVf+iUeffTRt2+inneEF7Z6PB6Px+PxeDw/5rOf/Syf/exn+fKXv3zR7V/84hffmQn9E+DbfgP6mjU0Tp5E9PtxjRaCprX7nYoCZqGIXSjgOg5WoUDp0cfAcUn/3u9esqS/+NhjlB59DEEUQZJonTmDvqsfqVPCcqyl4yxNRsFPPN6N6M8id3aiDQ2CIGKf7xXpVGo4zSZSOIyNiyDJEIvQPHIEq1C4anUrQEgN8dDKh5a+dwyDmZPneFhZwdnOcwguxG2dSMmkrBX4+smv84Ubv8DRxaNMVaYIq2Fu6LgBXdL56omvsjezF1EQWR1bzSfWfILhyOUDu9fNVGc4kTvBYn2RCWmCrmAXuqwjIGC7NrlGjr88/NfsMXopSitwCj9AqB9DxCKk6DStJqZj8uXjX+bf3fzvCKvtcEwQBLamt7I1vRVoVxMeWzzGvvl9qJKKIirMVeeIalHWxddd/Bxl5qgUckT6TBqRPSSEGiIiCbOOLqV52Q/SpmHuv//DtOo1QskUqn6ZVWSSCpE+ArWz1BUbCRnBdWlKFhYOfm0lmlWj1hihocO4UqRzEZISiN07icdvpdGYQhQ1gp0uK25PkzlepFasEu7S2XbbJygdt5mezeL4LRKWizYSxm4opBWdiVYC/YVX2f7+hxAliY7hlXQMr7xoio7j4DgOGzdupFQqYZom8Xic4eHhq66Mcx2H48//iDOvvUyrXkNWFNKDK9j50U+iaWkk0YdtV5Dl9nPS3owsiBZeCUqMuLOOhlAnl3+BZmMcXZSYk0LE3Sa4Lu75t47uOhD4sWrJDR+Feh7GX4RKBvwJ2PIp6NjAmxFEAUHg/GZX5++H2+7wILxJD1ZJEkl0L2/lT+dwmG3393Nuf5Z6ySDRHWDVjg6SvctbKSUIAvGuAPGuwNUP/ilJ94eYOpGnWTPR/DKW4WA0LVbe4FW1/iyYmpq6qEL1cn3aq9Uqv/qrv7q0GvkXfuEX+OIXv8j8/DwdHR2XHO957/DCVo/H4/F4PB6Px/OOk4IBkv/in1N55hmaJ04iRyP4tm8n/3dfw6lWsBYW4HwPTBwHp1ql8tRTRB95BK2vd+k8drFI9dlnEQMBlHQ7lHDqdfoPzrHpo8PsqR0n38gvbUgVCyYZ2PEJpNIBhHOTgIDrOKSmi3SuCzGjtUgLLuCQUerEbI3hZhgcG9c0L39nrkKQJESfj4ybQUJkSzOFiIBtlvDLMSYqE/zXff+VkdIItmMD8MzkM1RaFQ4vHkYURGRRZqY6Q66Z4w92/cFSAApQMSrsntvNRHkCx3E4tHiIhdoCLafFmcIZ5uvzdAW6KBtl7u67m9HSKGeaAlV9Hb76AQxzAlsARwjgij5Mp4HlWGTrWU7lTrGz6/LtyARB4JG1j1A364yWRrEci4QvwSfXfPKSFgKyqiLJAkL4OI7QolH3IwkystMila6ScOuMl8e5q+8ugvE3aZ0lCLDx46ReOUm5OU5NWUR0XJq6iCj70fUuKsYRbFtBsk1c0SEfl5H6b2Lghv+IJPsIBtcQjdxAvvAK/g6VwZSNY6sEAh/k6JkmhdYWuspTLJbrNHKT0BAxYj4GEisJi3FGD+yha+VqetauB6MO556G2YOgBqh13sTL4w0ymQyWZeH3+9m6dStr1qy5pr6/mZGznHzpORRNJ97dR71aZezUCfzPP8ONH3iIaHQnufyLGEYeXAdBEEl3fQxFaX8IIIoyfX2/RCp1L63WPM3CJI+XvkEqf5hweZ6iJNInyIT8UVjz4MUXV3xwy2/Bpk9AswThHtCuHoaG4jqJngBzIyUEUUcUBSr5FoGoRqp/eeHn1QiCQN/aOL2rY5iGjaJKbxrsvhv0rI2xOF1l+lSBRslAlAS6VkRYsS31Tk/NA4TD4au2A5Ak6aL3uSAI9Pf3Mz097YWt73Fe2OrxeDwej8fj8Xh+JsixGLGPfxw+3v7edV2qL7xA9fkXcFutdnsBQUAABJ8Pu1TCGB25KGy18gWcWh05dSGQEP1+JMPi3wQf4a/TA+zJ7KFlt7AcC1mU+c7Y93h1p58POAGGxibAcVETCX55x0P8XesFJgr7cCiQsEN8ODeAf66Aum3bRde4HoIkEbzzToQnTuMaBo5p4jZbiLpONaQyU5khV8+xJrEGn+zDcR0OzB8gU8sQUAIE1SCGbdAwG5zKn+JQ9hA3d92MIilUWhX+bO+fcbZwFk3WyFQz1K06Ozt3kvAlOJE7wVx1joX6AjE9RrFVZLGxSE1M4CIi2rl2NbEgAA6G6xKQVKpmlYASwOHNlzB3Bjr5/Z2/z0hxhKbdZCgydFEQ/LpoRxeJgRQ1p4DdknFtB8G2sDQNTbEJGA2iavTaHtC+Hfjv/L9YdfZRZktHKCIiEcVwM2TrOQS7ge7vwjFKyKKG6hukpoVAaO++JQgikcgjnD0bZG5uGp9PYu3ajYyPK+Tz8yS6h0h0dOOfOUujWkWJqaQ7+0j6kggIVAt5cjOT9KxaDS/+/2DiZRAlcGwOHTrNpLCaaNcQiqJQLpfZt28fiUSC1DW8fubHRjCNFpo/wNipEzRqNWwXXvzRU+g9A6zb+KsEg6solg4gCiqx2E3EYjdfch5d70bXu7k1vBVZ6+CI7+8ZmtrHoOWSTKxB3fIpGL5CT/FQJ26wg3K5TKuUJRqNoqpXXlIvCAKb39eP40ySn63hOA6RpM6mu3rRA8vbO8J1XKrFFqIktHu+XiGoFkQBVX9vxBySJLLt/n4GNiWo5Jr4QiqpvuDb2jfW85NZu3Yt//t//++LbltcXCSd9qqT3+veGz+FPB6Px+PxeDwez3uOIAhEP/xhGkeOYk5N4Zomgigi6DquICBK0lJ/19fJ6RRSKIRdKCB2dQEu5uwcdrmMfHaC37//M5S3/AZ/uudPydQyDEQGEBGZrk7z+F1x/tUHP0PAVdDWrKEvHuePrA9wwPwKpRdeoD9jEXQaqCuGiX3qkWuqTLyS8AP3s90o8fTE3zCjVEnpYc7FTTLFQ+1N2RBo2A1u7LgRRVKwHAvLsdBlHVEQ0WUdwzKYr8/zV4f/ikfPPcqGxAaOLBxh7/+fvf8Os+y+63zf92/FnVPtylVd3VWdc7daUisHywq2bGxjGxjAeIyBYwaGmXMIZ2DOwJ07z33mzj0XmABDGsDYgJATOMiWbOXYLXVQ51RdOe2qneOKv/PHbpfU6qiyLBtYr+fRo6d3r99aa6+1qrrqs7+/72/hVUzVJK63qwh96TNdm2ZVYhW6omNqJtlIlo3pjZwsnEQRCqYQuNJDUWKAikQD6SCQOL6Dqihkw+0x16IpGhsyG666TbM5SXrDAs1Jl5DvgK1ihTTcjILv1YmY2StW0LbqNc68/AKTx15HD4dZs/MGRm64mfkFhyOvCJqlPGW7gWVoZLfNkuxq0fQgqmnE4utQ1Sie38LzmqhqhHq9znPPvUw+D6HQWhoNhwMHiriuSzqdbi9MqkYYXLWVidEpUmqMznA7KJVSIpFohglzh2FqHyT7wYhhezC1JIiqi5jaWlAUkskkuVyO2dnZ6wpbFVXBdz1mzp+j0WiCpiN8F1lc4rkvP0ojfy8btm5n7ch7r+t5FEKwt3cve3v3tuf2O03QQu0erVdgWRb79u1jcnISz/OIRqPs2rWLkZEr9xWOpU1u+9G1lBebeK5PsiuMpq9sgdfifJ2jz8xQzjUu9HmNs+2egRUtfvV2SF+yNF2jWmgHnV2r4qj6uxt0CiHo6IutuL1C4AfroYce4rd+67fI5/N0dHRw6tQpstnsJf1eA//0BGFrIBAIBAKBQCAQ+KEV2ryZ7t/4DWb+j/8dr1hCaBpoGoqqYPT1Ywyuumh7NRYj/tCDlB59FGt0FK9YxF1aQolGqT7zDM1Dhyl+9G7KssxgYhBTbffZG4wPMlmZZGprlBu6b3jj+GqI3UO3UV3l4OrzmOvWkvrIR1DfMn3U8R1O5U9RtIr0RntZm1p71fBL6DqDH/kX/OLcFv7m5N9wuHSc+do8qlAJ6SFqTo2F+gLH88fZ2bUTKSWqotJyW+3FpyTU3Bq+9DFVE8u1+NtTf0vNrqGrOmEtTNWuYns20F5UK9/MLwe22VCWhJnA9m3m6nPcl72Bz+XLFLR16NopcD0UH6RXxlFUhpMb+KnNP0UqlFrxvfS8BvX6OXzfY2b2b3EYI9M/SLMxRcixsFWPJj5SH+JHN/0afbG+S/bhex7f+dM/YOrEUaT0EYrK7JmTFOfnWDh7Cid3jpg3j3Q8qhWV6tEYibsrWJpBTVlFp9FNq3meeHzr8lT78fFxCoUCnZ2dy4uHLSwsUK/XSSaTb7ppAj3bg1ctUCsuIZUm9eISZixG1/AAlE+C54LxpmBMNcBzwG2CGr+wmzeeCyklExMTjI2NYVkW/f39rFu3jlAoBEDPyHoOP/4NmvU6hCIoAnwBeC6N2Un2Pf0dZvY/z8ievWx/zwPtPsWXvfYtisWXqNXPoKlx0umbiUbXghG55n17/fXXOXPmDPF4nEgkQrVaZd++fSSTSbLZ7BXHCUWQ6r72/q/Gbrkc+NYE5VyDaCaE9CVTJwv4vuTmDw5/Tx94XI3n+Bz6zgQzp0t4jo9QBJ2r4ux53+oVV+cG/vmJRqN89rOf5Zd+6ZcYGhpiamqKz3/+8z/o0wq8C4KwNRAIBAKBQCAQCPxQi+zcQfYzv0jpC1/Ar1ZBCNR4nMTDD6P3XxrIxR94AL9Wp/yNb+AWCiipFJHduxG6jjMzQ/XJ78Cd3nUdu/b00xT/5m/xGw0A7MlJpOfR8alPLQc9ZavMfzv43ziYO4jjOySNJO9Z9R5+estPt4PRq9jUu4P/0LWZTz/xaXLNRbrCXXh4OL5Dy20xW5ulI9RBRI/QKTpxfKcdovo2ru/SG+1lVWIVlmfhSW959XJVqMSMGMVWEc/3sH2bsl2m5bXIhDIX9VAVCH5h848ykhvj0fkSuchPojaPk5azDIXC7OrewUNrHiJpJq/wLq6tUjnC5NRfYlnzeG4dy14gkdhFJJJC1+M0m5OEvCbruj/E0KqfJRS6fD/D0y8/z8TRwwhFxQiF8VyXZrnEieeexJAt0t4ETSSWGiWut2gteszlNxLrt5BelXLtLIlwL329H0OIdjBZr9fBbaE0C2DGQTOJRqM0m00qlTKqmkfXa5Qr0LFuI6tjBucOfgOrkceIQ8fWErnynxA17yQsBFQXQA9jmHEG9DInmv148yH0sMBSypimSV9f+7k9ffo0+/fvx3VdVFVlZmaGubk57r33XjRNo3NoDQObt7K0+DS+lCiAoij4HiiAEYmhau3rkl21mv4Nmy65Zr5vMz7+BxRL+wGQ0ieff5ahoZ8nldpz1fvmOA7j4+NEIhEikXZwmk6nyeVyzMzMXDlsdVpw/hmYPwpGFFbfBr07rv2gvMXSVJXKUpNUT2R5+rwQsDhZpVpokei4+gJjKzV1qsDUiQLhpIkZ1nBtj4WxMqMHc2y5o//7cszAP0179+5l7969P+jTCLzLgrA1EAgEAoFAIBAI/NBLfeTDGAMDNA4dBNkOYCM33XTZyrbG889TfeJx/Hwe6TjIRgP73DnMTZvQu7vpmZ2mT2YYq84wGB9ECMFMdYbuaDcxPcbfnfo7cs0cq4xeRr75DHHpk9/Yw4RRRa+1GNn3HLE77iC0fj0Anz3+WR4ffxxFKO0ennaNL5/7MhszG7m1/9Zrvjdd1dEVvT1eEWhopENpFpuLSCTZcJaPb/g409Vpnp95nkKrgO3ZmIrJrq5dCCHwpY+CgiY0DNWgalfRlXb7gc5IJ9uy26g5NRzPIRPKEFJDLNQXmKhMsCG9geP549zfs40PDoRZtFro4m7SRuQdqRx03SqTk3+OZS0QCg9gWzkazXEajVGSyRsIh1eh613Y1hx9fR+5YtAKMHn8dTzXJZZOIIRA1XRarkurWkVTa7iai2+E8KRGSwmj0kKtKLw28mMk/CVu7B+kJ7IVXelE+j6Loyep7f8yVtHFKjUxTQOZXkPTjTI8PIRpvohlH0YIi+5uk2x2Gz09N+Nn6qgMEkkmEIpHvT5K3gozYJVh4RgoKlINk3HuRa/voFo2QEq0aJobHhyis7MTx3E4fvw4QojllgKO4zA9PcWp00/SmXUwjC7W33IrY8ePUm20MKNRmpUKeB5Eo8TDISLREI1qmdzYucuGreXK65TKrxEK9aGqEaSUNJtjzM3/PYnEThTl4ligUbEpLzYxwirxDgPf9y9Z5AfA96/Qv9dz4YXfg7FnAQHSg9Gn4JZfhJF7396zY/tIyUWLXSmqgu+5eM7V+wd/LxanqkgJZrh9bTRDRQ9pzI2Wg7A1EAhcUxC2BgKBQCAQCAQCgR96QlGI7r2Z6N6br7qd32xS/trXkJ6PsXo1XrkMqoqby6H19iI0DV03+enhj/PXxSeYqk7hS5/eWC/3DN7DHx/5YxYbi+iqziv15+jJLLE+3cX+1AR1xUGkBNmox89PHGTb+vW4vsvj448jpSQZSiIQtLwWhWaBF2ZfuK6wFeDm3ps5nj9O3a4T0SJ40kMXOncO3Ml/uv0/oSkaUkruHryb+fo8ilD4/MnPU7JKdGvdhLUwyoVKzU2ZTSw1llhoLJAyU3xm52d4aPVDADw1+RT/MPoPHModotgq4kufV+Zf4dWFVxlODrMxs5FCq4DlWWzMbOQDIx9gMP72+wtKKVlsLqIIBaU1SsuaIxJZjRAahtmJpsWw7SVct4KmxbHtWULhfiLh1VfdrxAKiqriuS6a3q4a9n2JZhqkI5JiWSesAp5H0xL0hD0apuCEP8J9kZ0U9x/mxLnPI32J73vYi6N4tSWs5BBjoTgp10dpThHtXcvadTbV6gSCVVhOiZY9TrnyOI3GSwihksisvhA8ahgiTuTw15BaGtG5AepLuC2bkJunq38QYYLr+FgVn+asiZSSRqNBs9kkHH6jOlNVodmcZuz8PlqtEgKVWGwrG2/Zy9HnXsAql/BtC6mbxLt7yYQv9C2VoKiX74naak4jpYeqRi5cQ4Gud2BZ87huGcPoWL5n5w7kOLN/AavhoGoK2cEIMWBq7Byys5NoZw/1RgPDMK68mvrc4fYiYYl+MC+0VCichyOPwtBtoJnX8QS1ZfpihKI6tYJFLGMiJdQKFqnuMIns96eqFdrhqpQXv+Z7Et1cWd/ZQCDwz0sQtgYCgUAgEAgEAoF/MtylJbxiCS2TwTUUaukQarGCaUncuTnQNaI372Vw4y38pr+H0dIovvQZTg3z2eOfZbGxyEhqBCEEjl7nWHKKs3KCLhljlR3H81ymwiW+ar/GFvlxco0clmehqdpyxV9IDVGWZap29ZrnO1Ob4eujX+fg/EFieoyaU6Pu1FEVlZHUCL+8+5fRLlQeCiEYTg0znBoGYL4+zz+M/gNnCmdQhEJfrA8hBFW7iqEZbMlu4cNrP8x7ht6zfLz3DL2Hwfgg/2nff0IRCuVWGU3RcKXL6cJpzhTPMJwcpiPcwUuzLzFTm+E3bvoNEkbisud/ObO1Wf721N8yWhpFCMEN8TibhbP894piEg6vpl4/Q7M5haqGMc0uBvp/ClUNXXXf/Ru3Mn74AK5l4To2+D6+7zK88xZ2rrI4+K0vU7BDhBWXeEIy2NXi68n13B4TrH3+rzl5foJIKo1v6eTGRwmJGr2JGiH3LAvlLlQRZku8yohWorzQRGh1WoZGqXESz/cR+Gh+CVNRaDSmiEbXAGCWFjEbTejbC1o7AG3O5YnOz9ARmadptu+ZrjkU5xu0ag6RSIRQKESz2cQ02wFkrT6N59WIxVNEo514XotK9TBr9m5m9dZ/w/kTx5gbH6Ocm6czYoLnUsmXMMJhetddfvEyXU8BAt93l6tYPa+GrqfQtDf6y+Znapx8aQ6hQKo7gtVscuK5f0Awi/RrLE6dp5BIkdy8g23bd9Db23v5m1SaBN99I2gFiHZCfRHqS+0FxK5TLG2y6dZeTr44S3Gu3cojljLZdtcAqqZgN10mTxTIz9QIx3T6N6bfkcWk+tammDpRoLLUJBw3sJsu0pcMbsp8z/sOBAL/9AVhayAQCAQCgUAgEPgnQ02lEOEw87nzjBsV7HQLTXPpW/ToChtk3nM/qY98GCEEhmqwqeONaddj5THiRnw5NNXDUbxomFqjxPpSGCmaCMehK9vBjFFjqblEWAuTDWWZrk8vL17VcluoisqmzKVTut8s38zzH178D5wunEYisVwLIQSDsUHuXnU3n9ryKWLmpcGRL30eH3ucF2ZfwPVdFKGwo3MHH1z7QXojvZwonMDyLNal1tEbuzQQK9tlABQUFEUhYkSWe8Hqio6PT0e4g6SZZKIywZHFI9zef/t1XX/bs/mzo3/G2eJZOiOdSCQvLI2TiNVYo04SCg0ipY/0HTo67qKr80EUxSAe34phXDnIshp1Tjz3FBNHD6NqOp7rIhQFVTfo27CJWz72k8RMuK3+KpXzryOkSyjsM58d5P0b08i5v+T4+XH0MLiUsFoRUDxcF2zfIGQqdHpLkLfZFjpPZKmDclhHhi2qCmiKRdhX8BWfpg9SA61xFl1P4ftNIr6DriUvWqBKqCoCHyHdN+6dJ1EUgaop6LpGx+oO9u3fx1hxjIgewXCrpNI2nZ3t+66qIVQ1RLV2hA3rP0z/+o04VotD3/oG0yePUs3XCcXibL7jHjqH1lz22qVSe4guDlOrn0XXk3ieBUg6s/ejKG9UmeYmqjgtl0x/+9jN8jmcxgR6KMXw5mGqlRLVpRyburPs2rXryg9BNIsE6uUiLddHVVTisowWTkI4de2H6C2Gd3YSz4YYO7wIUrBmZwedg3Ecy2P/18dYGC+3Wwu4kqlTBW54cDU9w9fXX1heKF99a7uMrqE42+8e4Myr87SqDrqpMnJLJ6u3dbzt8w8EAv/8BGFrIBAIBAKBQCAQ+CdDjcep3LiewqOvEtMEqm6gNlTOrY9Q+JUP8YFtH7vi2N5oL/P1eaSUy31QRSKGqUiEE0V4PvpAP3Y2hmw08U6Pkt24k/eufi9fOPMFWm4Ly7Pwpc/G9EY+MPyBq57r189/nZOFk4TUEJZr4ePjeR6z9VmOLB7hxbkXeWD1A5eMe2HmBb5w5gsYmkFPtIdCq8D58nk83yNuxrm59+qtFqJ6FE3RsDxrOWTyvPaCYapQcf12OKgpGkio2bWr7u/NzhTPMFYeYzAxiKm2gzxd0XnNkgwkYsjmOACR6DCrh36BSOTyAeGbSSl57WtfYeLYYcKRCOnuLsr5AolsF7seeD8Dm7dihNtT5KM/8ldw7os0lg7QiMbpXnUf4eoJztemEYQwQiae69KqVfE9H18o4EmE7yM9BV06+BrQs4VECKbd4yjYCEMgfYmKT9qCgqniGyF830LXk6TWP4BefAyKE5BeDUgi/jxls5e5QifhTLvHaKvqMHJDJ0ZY49jSMb5c+jKtTItYLUbBKTDSXWF4sIaqrnvT+3fQ1Hi7NcPEGEtTEyQ6O9m76cfRTZNEZxdmJHrF66dpcdYM/wq5hW9QrhwhFOon23EPHR13XfW614uTIBRUzUQIQSqdAdumPDt11XFe7y4W/A60qdexMFHxcDUV89YPkDCufJ5XsjRd5dC3JqgVLSSSxakKm27tQ9MVchMVEp0RNF1BSklpvsGZ/Qt0r05c1Of1rRzL4+xrC0yfKiAlDKxPs/bG7uUerUIIVm/P0r8xTaNsEYrqmJGrL3YXCAQC3xWErYFAIBAIBAKBQOCflMM3pDg/n2XnGGiWx9zuVRzcrBMrH+Zh+dErLvr0nlXv4WzpLGPldpVhza6xJjWMm3DJZWt0R7up18rMjx/n5pkw9pf+iLnubj78Lz6KudnkxekXsTyL9en1/MSmnyBuxq96nqfyp/B8D03TqPpVDNXAxcWTHgLBtye+zR39dxDRIxeNe3HmRYQQ9EZ7kVIS02OMlkd5beG1iyp1r2Rdeh3rUuuYr83TclsIRHvBLdXEkx5pMw3AVGWKxeYiT04+ScNpcPequ8mELq4+9X0L286jaQk0LUbLbbX7zSpvBFO6opPzQ6jmMKJ5pL1QlHr1a+P4DvP1ecJaGLVsM3/uFHGqhErnQfqYZoxG0yTWkV0OWgGEZhLb+JPE+Mnl12bnvkS8K4kZtakutLDqDq4lkdLHBRxNQzY8XMdndVcZt3sAIllG503m/Bax7BiGaOAIBewQplsnplpEOz7I1rWfRlEiqKoJrIJX/gjy5wBQ0wNEdn+K5IkU1aKFqgnW7Miy+bY+pJR8a+xbVJwKa1atQQiB67uMV8fYhEezOYWup3CcMkLopNO3cuLZJzn54rM4rRYSiCSS7PnAh68atH5XyOxh1aqfRUqJ5zUpFl/g3Oj/D0XopNM3k07fQtdQgtEDOaqFFtGkCVLDdz0iSQPlQnDpey66efVWDzO5Ii+KW1ifSdJlTdBSQhxjCGr9vPfCBxnXy/N8jj4zTa1kkeyJIES7Z+vJl+boXZtEStD0djWxEIJQ3KBaaOHYHkbo8nGHlJLD35lk8ngePawhBJx8ZY5KvsXeHxm+KKTVDZVkZ+Sy+wkEAoErCcLWq2g0Gisal81mVzQuk1lZ/xflTVNV3g5dX/knc/V6fcVj303pdHpF49ZfWFn27dq06do/3F7OxMTEisbFYt97P6IfdvH41X8QvxLbtlc07jvf+c6Kxg0Ovv2FI2Dlz5p6hQUQftho2sr/mflu77K3a+/evSsat3///hWN+8u//MsVjbv11utbMOWt1q1bd+2N3kFPPvnkisbJt64qcZ1eeeWVFY0DePnll1c0rlQqrWjcT/7kT157o0Ag8K7yajVkq4UnJGe3Z/DvHFn+O6s+R8h3rzIatnVu4zM7PsPTU08zV5/jpp6buG/VfdScGl848wVmqtOIc+e5eUrlYXUzWp+JMz0Nn3uEn/id3+FDIx/C9m3SZvq6AqXvhrGudJFIFBR8fEzFJGkkqdpVCq3CJWFrrpGjZJVYml+i5bVbFyhCuWaP2FwjR82u0RPt4dPbPk1Mj/HNsW9SdapEtAhxI47t2TScBodzh1lsLBLVoxRbRb587sscyx/j397wb4kb7fMuFF5kbv4r7bBVjZDN3sdw6g4yoQxztTn6Yn1IJHP1OXbrFfLFlzH0NB1mlEr1MGPjRdav+w/o+sX9YE/kT/Do6UeZq8+hKzoj6gCZ0gTd7hLoJggF3crjluvYxXlYPXzV960baSw7R//2DMe+MY1re3iKRNEERsSmKASxRIzO7jrRAY2wOkLJVjlX1tG8DF6lSaWrhhFtIlSfpq4SbQjWpHYyN/f32E6BWHQdHX13oX/g92H+CAgVereTCSW5c6dPvWihGSqRRLufq+u7TNemSZiJ5WdFUzRyMk7BXE2P0cBxShhGmu6u9yHsIU6//Mdomk5ysBspJcX5WY4++QQ9w+vQDOOaz1up9BoLC9+kUHwB161hGllUNUK5fICWNU9v/0fYfHsfp/fPU841MMJDhGKTqGodp6XRqtcQQrBq646rHiefz9MgzGzfQ8xeeK3RaODlC1iWRSh09bD2zapLLap5i1gmtBz4xjImpbkGjtWuxvY9H0Vt/05sN1wS2RCaceWflStLLeZHy0RSJqFo+3diI+SSm6hQmK9ftuer5/p4jo8eUt9WWHzpsZuUc030sErnYBxVW9nv8oFA4IdbELYGAoFAIBAIBAKBH6gr9U28Xn6jQemLX6K+bx+4Lj3rw6gjDoVmgXQoTcNtULNr3LfqvmseY2t2K1uzWy95/Te2/Gsm/ui/Yn/1KKZqonVOwZo1GKtWYU9M0Dp5itjNN13X+UrbpnX6NHfXBnlWiVJy6ri+i+/7INrtDGpujbgRp+E0eGL8CRShsDW7lTPFM4yXxxmvjCOlRFd1TNXE8iwO5Q5xIn+CjZmNKOKNEKfpNnnk1CO8tvAalmuRDqX50NoP8cu7f5mf2vxTnCmeoe7U6Y52k9ATvL74Ol86+yU0obE5uxlFKDi+w7niOQ4sHODuwbupVk8yOfnn+NJC1ztw3Rqzc4+ySovxo+t+lEdPP8r58nkAkqrB+UaRV8oeiqgzGElwf+cgNKeoVA7T0XHn8rnmm3n+19H/xVJzie5oN7Zn82rlEH3peaKzSRLhMEIIarZCSKmT8uaueb2zHfdSr59FiRYw4wp1z6ImPWQkTKrTIWtWSY1orN/4IXrGJjDHjjAnwUg2SSanQbjg6Ti2jmY6CEVBj8WZmP1zJB5CaJSKr1AuH2Rk5FfRhu++6PiqqpDIhpf/7PsutjVPfyTF8eI42XC7WMfzPZCCROZ2Ng3eg+MW0bU0qmoydug1rGaDjv72B/xCCOKZDmrFPJWlRTJ9V190qlI5wvjE/8S2lrDtAkL4OG6BcGQQz2uxtPhtOrP3MrK7i771KcqLTYzQBhbHOzmz7wUa1RJGOMKmO+5maNvOqx7LuBD8yjdVsXpenmh0kVLpaZLJ7YTD17dIlqorCKUdqEI7QJW+BAGdq+LUSxaF2TpGRMOzfRRVMLKrazmYvRyr4eA6PpHUGwG1HlKpFS2s+sUfyPiez7kDOcaP5nEsj1R3mI239L7tRbiklJx6eZ7RgzmspouiCjJ9UfY8tLpdRRwIBP5JCcLWQCAQCAQCgUAg8APhFotUvvEYjQMHUAyDyG23knjgAZS3Obuh+OijVB9/AjWdRoRCrD2wwF7L4/CuBudbRQzN4IbuGy7b//R6SCmp/NXn0V46DJ5AquDMzuK3WoS2bQPeqOafrc1yIn8CgM0dm+mL9V20L2dhgaU/+iPs0fP0ug4fH07w7XU641oZ13eJG3HCWhjXd+mN9PJfD/1XqlYViSSqR6k7dVRFRVM0pJQ4voPt22hC48TSCf7L/v/CXYN38dObf7rdcxV47PxjPDX5FB3hDhKRBEvNJf7m1N/QG+1lODV8SY/XjnAHT0w8QdJMLoe2uqIjkeQaufY1L+3HcSvEYu1ZOpoWo9mcJF94njs2/EdGUiOcLJwECU+OP8bJcosOI4JQVE5V87Rciw+lLObnv4rrVkmnb8EwMhxdOspic5E1yTUoQiGqR5Fui2J2gtYiOGUbBOiawpYeScy89kyKTOZ2fGkz0fgqrjFBxZPo0SQhLYZbTTE1XWM0k+JefxORwQ0QHsaZ+yYxLYdHFMWDcHQRNAenEScuojQiHn79LJnMHWhaBN+3qdZOUCztozP7niueS6VyhJmZR2hZc9ysNFC0KsdKDoYWo+7UWZ1Yzc09N6OqJqraszxOD4VQFAXfdVEvzFB0bRtN1zHC4SsdbtnS0tO4bg3d6MCy51HVJJ5bpdWaIxJZg9Wax7Jy6HqacMwgHGsHkZne21mz6wYapRLhRBIzcu0p9atWreLEiRMsLi4Si8UQylli8RdJJhWmZ46zsJCif+BfkO24+7Lj62WL2TMlWnWHWMakoy/G3GgJKUFRBLWiRSIbYmBDmu6hBKOHcuQmqoTjOqu3Zulbn8JuupQXm+imSrIrfNGHLPGOEGZEo1G2iSRNFEXQqNiYEY1E9uKq23MHchx7bgbNVNEMhfnzFWpFi9s/tu5thaRLUzXOvrqAogvSvRE8x2dxssqpl+e44cHV172fQCDwj0MQtgYCgUAgEAgEAoF3nXQclv7oj2i+fgQ1mcSvVik98nf4xSKZn/mZ696PVy7T2P8qaiaD1tFeKTwUi3H/8THuvvkhSpv7SZkp1qbWoiora8Pjzs7SfP0IxqpBpOPglUqISAS3VKR8/DB+fzfZtUO8OPMij5x6hJJVAiAdSvPjG3+cW/varWOklJQe/QLWyVPoq1YhDIP3Liyws2RT/JlPMWZWyDfzRPUoGzs28tVzX8X1XYZT7Wnyx5aOkWvkWJtaS0SLYGgG5VYZ27dJmSmEEIT1MM9PP8+27Db29OzB8R1enn2ZmBEjHWq32OqP9TNaGuX1xdeX9/1mIS1EV6SL0dLo8hjHdxAIuiJd7evuNVDExddTCB3Xbbcy6Iv1UXNqfG30axxYPEZCMdFxMNUwmiGZqM0xqfsY2hmazQnyhecZGflVbM8GCYI3wjFNj2DGw9y4poHjDSARdEeadIZMyF7aXme5UrpRALeFiPfSmX0PmfSdnD7+/8E8chBTN5GOg2g49KRdtuX3oTw9CmoIGevGWp0hXFcpV3SE6mGqRRThEgmZRLMbsewz4NVxnDyaFkFRDJASq3Vxpa3v+0C79VvLmmd84o9w7AKG2U1SMdgTq9FDlHN+H+vT67l31b3L1/zNuofXku7tZ2l6gmgyje95NGtV1t5wE7H0tdvRWVYOVQ2jqmFARUoXhILvt3CcMqoWwTAu3w5PVU3MWAe6eX3xQTwe54477uDw4cMUi3Mk4weJRg2yHZsQikKrNcPs7N+RiG9H19PLU+uNiIZuqBz41gTVfLN9D4Wgoz9K37o0+Zkari/JDsbYdtcARkjDCGlsv+eNdl7lxQbP/PVppk4WQEIkadC3PsWu967CCGsUZuo4tke8I8TowRwLYxVUXSGWMtl6Vz+x9Bthq+f6jB/No5kq8Uz79VBUpzjXYO5cmbU3dF3X9YD2Il+O5ZLJtitiNUMlHDdYGK/g2B76VdoeBAKBf3yCsDUQCAQCgUAgEAi861qnTmGdPIUxOIhyoTLPLRSov/wKife9D62z87r24zebSNdBib5pWq+iIHxJrxNlw1uqNlfCq9WRto3IZDDXr8c6dQq7UsKuV5lLOTy1I0T14O/QdJvoqs5Iqt0rdro2zZfOfImt2a0kjAR+pULrxAnUzk6UC30rtZ4eOs6dY20uxn3v/7HlY746/yplu8ya5JrlqrxsOMtMbQaJRFM1FBSkkKhCxfVdwnqYslVmpjrDnx79U4QQbO3Yiic91DcFo9/dnysv38NWEQoPrn6QPz/254wWRzE1k6bbZEN6A3u69wAQj28in38W162haTF838Z1K2Sz9wBwYOEAf3HsL5itzVKxKzSFQsP1GKKM7zXxfBfVXE08vhUpPer1cywtPcXa9B6iepTF5iKd4U486ZFrLLKt90bWqzlEdQ6QoIdhw/uge9sb98lrspD7JsWlZ5D5s6SLFt2NJFp6Lez5FGrXJrY+9H5eL59EzbuoaDAQZbtxmg7XQsmsBc1EFsdw84uEkmlEqojjeIBEVUPEMn2IWA9qaRrXrbRDS0BKDwTLgWWz2eTo0aNMTEwghGB4eJjunmlse4lIZAQhFCBGUvqkFY0f3/xrqOqVq0Z1M8TeH/0xjj75BIsTYyiaysZb72Dr3fdd8/l1HYewOUKtfhrD6MYwOrGsOXzfxvcdPK9OT8+PYBgdF42TvuT864ucP7SI1XCJpk023NRD37rUNY/Z09PDAw88wNLSQSYmnyAcHkC58GFHKNRLozFGrXaO2eP9jB1exG55KIrAdXx836dzMI5QBK7jkZ+ts/O9q9h+zwCe61/Uv/XNqoUWzz96lvnRMoh2FWy10GLyWB4k2E2HxckazbqD3XQJRTXMqI7T8lA0he41F/cO9hwfx/KWF+CC737tSOzW1fs/v1X7fMVFrRWkL1E0he+hBWwgEPghFYStgUAgEAgEAoFA4F3nlcpI10W8abEcJRLBzeXwKpXrDlu1ri6MgUFap0+hRKMgBF6hgBKNYo5cfeGk62UM9KN1dOAuLKAPDmLu3sn0keexEiaP/9gaToZLFHMHcXyHLR1bgHYo0xvtZao6xUR5gm2d20BRQBFwodrxzfLUOT7zErqqs7ljM2EtjCpULNfC9m0UFEJaiLAWpm7XSZtpco0cjuegCAVN1XB9l5naDLZvM1ub5U+O/Amf3PJJtndu58mJJ4kbcUzVpNAqYKomG9Ibrvie9/TsIaSGeGT0aY5VFlCjq4l13kXRN4gBmfQtVCpHKRVfwbLmAUE8vpXurofwpc9j5x+j7tTZlNlE023SdJvUpIqldVN1pkkZBmvTGxFCIISGqoap18+wrvdjvH/4/Tw29hjny+cRCFbFV/Hx7T+HUCMwtR/sOnRugJ7t7WtKu5p1auqzLC09hVrNQW2R2bBGU/cYmXsd8fzvwvv+C1v6d9L/nls4OPkqIdWkxy4Qn6iidazDNKLtfaUHceQ4LauOpicRwsfzHIQQ6FoSAF1PY9uLuG6FVmsW160SjQyTSt2E7/u88MILTExMEA6HkVJy8OBB1q5dIhbnQtDapig60nfxfYdrrX+ayHZx68d/EqtRR1U19GssNGU3G5x4/mmmjh/BdRpo6TgdG0bRIzq6nkQIjVTqZrLZu+nI3HHJ+MkTBY4+Pd1eSCykUVpocOiJCcyodl09S4UQRCIpVMXA9+129S/g+w5CaBRnNUYP5NBMlXSviWf7TBzLE04YiAthqqarSAnlXIPhHVf/njB5PE9poYGiCoyIhhACq+Hi2B7nDizgOn47dG25+J5EAAObE+imSnG2zszpEpneN96XHlJJ90SYGy0TiuoIRWA3XRRVIdV17XYKb9a9Jsm5AzkqSy2iKQPX9mnVHTbc1I2mB1WtgcA/NUHYGggEAoFAIBAIBN51+kA/SiSCVyqhpdvTpr18HjWdRuvuucboNwhFIfXxj7H0x3+Cfb69IJMSDhN/8AGMtWvfkXNVolFSH/1RCp/7HPa5czTdJnXV5fQtgxwJLYKEuB5nqbXE+fJ5POkhpaTltVCEgnIhEFTjccI7d1F78kmEriNME3dhgYOrJU/xbSpHLASCvlgfn9j0CTKhDC/NvHjhJAQKCnu695A200xWJzFUg6bTpOE2CGkhCq0CqlBJGAk2ZTaRb+V5YvwJfnHnLzJXn+Ns8Sye9IjpMR5c8+BlFwJ7s4I6xNnQQwhDElIVvlP2OO/M8dsj/WQNk67+T3CsIRnPH8AXETalb2BYTVC1qyw0FkiFUhiawXBqmHPFc5StMvNWg95QlrviPhGt3XtUSonvW5hGF0II3j/8fnZ07mC0PEpIC7E1OkAsdxo8B3q3Q2rVJefaas1SKu0HR0PklvA8H6n5FKIVuju3EV+agpkDaGvv49PbPs3TqREOLRxiqBJlUJ8gKjVwWqCHKClFJAJTS+PhIgQoioFA4DhFXLeCopr09HwIRYRw3DwdHXfT1Xk/hpFhfn6e2dlZ0un08mJRhmEwP19kOKpi23l0PYOUHpa1SDp9E5qWuOQ9vZXnuixNjuPYFpm+gauGrVJKDn7ra4wdfA0zGkNRDCrjCbC72Pq+tUSjg6TTt6DrySvuY+J4Hikg3tGuPDciGsXZBjOni9e9QFQksoZYbCOl8gFMswsQWNYC0h7m2HOSwlyjvS/Znlqvh1RaNRvflyhKuxIUKQlF9Wseq1a0Lql4VVWBa/vYrXZIGorquI6P9H3slkd1qUnHQByhKrRqzkVjhRBs3NtLtdCiOFcHBIomGNyYpnv42vfrzVLdEbbfM8iJl2apFy1UXWFoawcb9va+rf0EAoF/HIKwNRAIBAKBQCAQCLzrjNWrid19F9Vvf5vW0hICUGIxkh/4AGos+rb2Fdq4kZ5//1s0X38dv9nEXLsOc/26ixbF+V5Fb70Vvb+f5pGjtCrTPGs9w/kOF6/lkTATeNJDt3WabpMzxTOE1TCO7xDRIzw/9TybMptQhEL6Yx/Fr9dpHTuGLBYp9cT4zg0OriFYE12DL30mKhP8zZHPUh8/i+o0kAKEpqFFY0S1KL9246+Ra+bQFI2MmeFr57/GI6cewZMeaTPN6uRqokYUy7fIt9o9YH9tz69xsnCSql1lVXwVA/GBq14fKSVfXyzhSlgbbYd63VJytt7ipVKNPZEG//HF/xcn8ycJC5N0KMXB6hfItUr8+MYfJ2kmyTVypMwUXZEuIlqEM8Uz3L/6fh7q205l7rM0m+dR1TiuW0HXU3R03AW0Q67BxCCDiUGYPwpP/DaUpwEJ4TTs/gRsfP9F5+s4BexmgepUmZRjIxUN6fg4doNSfR7TLpCffhQvtEAmczsPDz/Mw5EheOH3wapB9RAURqFjHU5oCSUdIpXZi+2W8T2LWL5AbH4aNVJFJrqJjHyY+JoPXVSl+l3NZhPXddH1NwJCwzAolbJEo3di2/toNM4BgmhkNX29H7/ms1otLLH/779IfnoSz3WJJJJsu/d+hnffePnt80vMnjpJNJUhHI+3zyEcoba0RNi7i66u9Vc9HoBVdy6ZQi9UsJrXP4VeCIVVQz+HOh2mUj0GgFPZSO7YHdSLNq7lsTRdxWq6ZAdihOMGtUKLymIDI6zTqjlEkyb9Gy7tY/tWic4wqiZQNAXX8tFMBdf2EaoASfvvVIGmK3iuj/QldstbDl/TvZd+38n0Rbn9Y+uYO1fGbrmkuiJ0DydQ1Uvv+7UMbs7QM5KkstTECGvE0uY7+j0qEAj88AjC1kAgEAgEAoFAIPCu8G2b2lNPU9+/H6QksucGOn7hF7DOnUNoOpFdOzE3blzRvrWODuL33vsOn/HFjKEhjKEhIp5DeN88pekXkUh86dOwG6RDaYqtIr70Cekhes1euiPdHMgd4GzxLBsyG1BTKTp/5V/jTE3hNxpMaFPUzn6O4WgfQggUodAT6eHk2Vfw6nV22il8RSBbLUQqxGxqhvHqOHO1OV5beA0pJbu6d/GprZ/ir078FWuSa1AVlfn6PFOVKdYk2wGurups79x+3e/V8iWLtktceyNUUi8EQ+cr03z9wO9zcOEAvu9TEVBo5unTunlZf5kHVj/A/UP387mTn2O8PI6pmtScGru6dvGJzZ8gbsQpm3FyuW/SbE2TTN5Ad9eDxGJvaWvgObD/T6EyA9m1IFSoTMPBz9Hs6CFvnabZnMSwPZLTY2jNUSKihaoqCDxcQ0dxBUrhLA1VIa8s0Jr/CoXiSwyv+gyxfX/c3veqvZA7BbUFWDiOuWE3ImHj42OaXYQX50ifPYUqdaJqGjGVg/yXILHtsgt0pdNpQqEQ9XqdWKxdAVqr1QiFwqwe+jiS+2g0zqOpMRKJ7Wha/Kr3QkrJ6088Rm7sHMmuXlRdp5pf5PVvP0amf5DUZSrB7UYd17HRwh6NxiIoGrrWged5WI3GdT0D3asTnHl1gXDcR9UUHMsDyXVXtX6XaWRZs+ZXcJw8tVKLl55fwjAUQgMC167guj61QgsjrKKqgnV7unEdj1bNoW9tivU3dZO4UF17NUNbOpg5XWDhfIVm3aZVkyAg0RlCUcCxPHzPRzNU7JaLBBzLp7rYpHsoyarNl19oLJo039ZiWFejmyod/W/v+n2XlJJGxW4vhBfXg6A2EPghFoStgUAgEAgEAoFA4PtOSknhc5+n9tRTCF0HIbBOnyZ29910/Nynv6fgwM3n25Wivo++agjF0FHTmbddIftdzsIC1unToKqEt25FTV481VpXdT6x5RPM1GY4tnSMqlUlbsbpDHcuV46OpEYwVAMhBKOlURYaC2zItMNEIQTGqvZUeGOuhEDgSx9VqDTdJpO5M7SqJdBUXF0lophIxaBZreHV6zx2/jEO5w6jKAqe7/Hy3Mvs7NzJ5o7NnMyfJNfMUXNqCNrh7e++9rv84q5fJBu+dLX5uuehCYGpXFypZyqC1WGDA5UGWf1C/0vfRwhBvriP80tncaRHlyHZGPJIaC3y1iSVEhRbRe4cuBNTM3lh+gXKVpm7Bu7ivqH7iBvtYDGZ3EkyufOiBYMuUZqE0hQk+9vBa2kUWV/EcpY48dqnqEVVNCWKXq9Qsn1CDQPLaGGFXSQCIX06ljwM16Y6sge1cwsRoF4/y8LkI8TKUxDvgfoSSB/CGUCS3PhJkupxiqVXkdJFmz9LU5MkMtsQZg91JY4zc4SxL/0uxXUfY9OmTfT2vjEdPJVKsWnTJo4ePcrCwkK716uus359N4pSJBRaSyy6jtkzpzjxxN9TLxXoHFrDuptuJd5x6T1qVissTowRTWXQTRNo92/NT0+SGx+9bNgaz3aCVmRpbg4z0b7GTkMnHFpFurfv8tf7LUZu6CI/U6O40EDK9iJPvSMpBq8QSl6NEALDyOI2KzhWjkTCQFEVMv1RSvMNWjUH1/JYs6OTbfcMoOkKvitRNHHd3xsiCYNbPryW8SNLHH9uhnrFJhTVMUIqvuPj2B52y0MI0E0NM6yx7sZueoaT9K1LoZs/vL1TK0tNjj03Q2G2DgK6hhJsvbOfSML4QZ9aIBC4jCBsDQQCgUAgEAgEAt8XXqmEk8uhZbP4jSaNfa+gdXSgplLtvy+Xaby6n8SDDyyHj29X8/Bh8n/+F7hLS7jFIn69jpbtwBgcJH7/AyQeehChXP+U39pzz1F85O/wSkVAoHd3k/n0zxLesuWi7YaTw/zu3b/L7x34PU7mT6IqansafyhNKpTC1NqhWMNpoCkaHaGOS47l+A7lVpmyVeaF6RfIhrOUrBKNWoEdi4LFpM9YaIk1XgeGapAzWwzKCKeLp0mGkni+x2hplJpT41vj3+LGnhtZk1rDVHUKBQVVUak7dZ6dfpZViVV8cusnl4893bJ5ZC7P4eICeGVujlT5WN8A3akdFxatEny4O8140+J0vYWmCFwfdsTDaPl5HNumJ+TzUNIhrkhsCWsMm6o3Q+ZCL9a4HmcoOYSCwtbs1suGvVcN0jQTFBXsBuTPIpslXL9FKebgulVMN4OtVBCyhQjFUL0Q8XMSkW5X/9XdTRSbkxTWCFLDW0AIBKDrSRr2TLu37uyriFYFVANFKgi3hTL1Gqvv/j9wzv1nCoWXKMR8ypEQCbVATzVCbm6JiOWiaUXGxsZYWFjg3nvvpbu7e/nUd+7cSTabZWZmBsdZQFX3A9/h1GmVeHwzWuMWDnz92zitFrppkp+ZYmFslLt+6lNEEheH+0II8D1kowKiAQiozkOxhDj3JGwahsTFvT9tf4rMhhLNgxqtogJIhGqT3twk1nF9YWk0aXLbR9cxN1qmWbOJZ0L0rEmi6m9/Cv2b92mEVFp1l0jCIJYOoSgKVt3hlo+M0DuSWt5W1d/+BzDRpEmmN4oe0ujvjS4HqKWFBqquopsqvucTS5tsvq2f1dsvfSZ/2HiOz8FvTZCfrRFJmSBh6nge13K55SNrgwrXQOCHUBC2BgKBQCAQCAQCgXeU9H3KX/861Se+jV+tokQjGCMj7SC0843puEo8jru0iLuUX1HY6jebFP7mb3CLRZRYDH9qCuk4eIUiXjJF6dFH0TJporfccl37c5eWKP7do/itFsbwCEiJPTFO8W/+htBv/zbCuLiKLG7E+a2bf4vTxdPM1+fpCHdwtnCWb4x9g4nKBJrQaLpNdnfvJm7E+eKZL5Jr5BhKDHFTdAvPPPr/p/T6QW7Wmhxa5XG6bwmEYCg6wG3zdUozVb6z2WU0tERChuj1Yrxn+H7+bv6bJPQErxdex/ZskkaSil1hvDxO3alTd+v40kcTGmjtUPebY99cDlvrnsfvjc9ztDRH1JnEkfDlZojFylN8ZvUYvb0fBmBAb/FAeJTX7RKqsYobs+u5pyPBV5wBXMVnR8gjpvjMOe3wTQBDpo/XOMo3Fk/w1dGv0nSbAHx74tt8dP1HuW/ovuu/wYl+6NsJx78CjQLSCCNbFRrRECAI1S38eBhPaWJIgReWdOrduJNzqHic9LuJrI2T6l5EtmtdAfDcGmZkLTXvKNHqHK6mIX0XzReoRgKRO0G9cIBmY5xYbD26Hccvj1HWizi+h+4omLqCnVpDZ7qTxcVFzp07d1HYmsvlOHXqFMXiNMnUY0SjNoY5gpQuxeI+KpOHca0BOvoH28+y51GYnWbq+BE23HLHRZchXJ+g2znD+HQVzbBR/RZVL0zYMOkuPA9PzcF9/xFinctjGo3zpFZ7JLuGKM00kJ4k2iUJZ5vYzhIh8/oWoTPCGkNbL/2gYKWiKZOhrR2cfS2H3XBBgPQla3Zm6Rm+8mJdb0etZCF9eVGlaiRhoGkKNz68Gs1QSXSEMcL/OOKQxekqxYUGia7Icg9dRRUsTdcp55qkuiM/4DMMBAJv9Y/ju0sgEAgEAoFAIBD4R6Px6muUv/xlhBlC6+nBr1Ro7H8VPA+vWETLtqvJvGIRNRZH7+m+xh4vz56cxF3Ioff1YZ09C1KiptPtgNc08Wo16q/su+6w1TpzBq9YxBgeXq6G1Xv7cGbnsKemMEdGLhmjKiqbOzazuWMzAFs6ttAZ6eSVuVewPIsdnTtYl1rHfzv435hvzKMrOi+NPcPM16fomaiR0nyyHmyY0vnmjSr713jUaPLCBo87X3L5sWd9FlI+8bjJ/R/6ZbT1t/D1wnNM16ZpeS3iehzHdzBUg4gWYaIygStdVKHiSpeaW0MXOhW7Qr6ZpyPcwaFKg7P1Gt3eeTTVQ1XDFD3BEW81Z+efJpO5jfF6kT89+qfkGjmQENEjCPN9xLs+wJ0Dd/LFI39Hp16i5SuICzGmJjXiRpLF8gm+NT2Drur0x/sBmKvN8fXzX2dP9x5SodT13WAh4ObPwOyh9lR/z8HVdYQZRyo2wrYRSgoEuL5FXCbpXrUGR6lSMQbYsvWjdK3rYXbxf1Kvn0XTEnheHU2Noigm+bROOGeiCAXp+zgquJl+Qm6LauEQvm8RNoYgY6I2i+hWkZY2R4cXopIYYTGxfblFQLlcXj7tYrHIM888Q71eJ5WexfeWKBQ6MAyXRCKBofu44hih5ODyGEVtB4P1UvHia+C04JU/ZGdnCcdLszifx3MVoqbL9i0DJHrDsHQWxp6FbR9dHqapMUAQ6dCIdbYrWS1rEUWEUJUfbDi3+fY+4h1hZs8WkRJ616ZYtTnzjlVoRuJG+5lw/OVw0mq4hKIaHQNxdOP73y6gVXOYPJGnON8gHNdZtbljxaGoZ/v4vkRV37g+iqbgez6u471TpxwIBN5BQdgaCAQCgUAgEAgE3lGN115Fui7GqnaIqnR24jcaSCHwazWscrkdpAlB4sEH0fuur4fkWwndQGga0nHAddsBqe+3962qCE3Dv87FgIB25aqigOe1/w9I10Wo6iVVrVeiKRp3Dd7FXYN3Lb/2p0f+lPnGPGtTaxEIKidepHOywnxaYKvtKfvdJY9bTgoODQlKrRJHh6LUsr1kJ0pE1TDjq7vYurOPG6Pd3NF/B4+efpSW2wLZnmbeF+2jalfxpd8+bymRSKSUuLiUrTKvzb/GA2seoOb6uJ6NIluoaruHqik8KiJMxbWoNcZ59PTj5Bo51iTXIBAsNZf45vg32d65nTXJNfzWnf8XTx74ZdJKlYqnoQqVmBElE0lTlQZlu8xI8o1wujPSyXR1mpnazHWHrWWrzDNzL+DGE9wWimKmh5EsELIahC1JIyKwaYKqozvQVXRQW+dQe9cTuuNX6epuB+Dh+P9OLvctavUzmOZmOrPvYWrqs8hML3aijK9qSE3HpkXUrUC0HxFOQf3CiRgxGLgRWTqJW21yJLqVWngzdq6IYjaxHI/OzjeqSsfGxqjVanR1daFqixiqgmtLSqUSiUQCRdXQTAOrVSUcafdT9dx2leebe7ZKKfFzR1FKU0R6hrizz6Rw4jSu65E2WhjJblDiIBQoz1x07RKJnYTDgzSb59H1LFI6uG6F7q4PoOuJ67r+3y+KqjC0teMdrZh9s+41CTpXxcmNV9BDKp4jEYpgeGfnioNW3/PJTVQpLzYxwhq9w0lCMf2y21oNh1e+Okp+utYORV2fmdMlbnx4NdmBqy+Gdjnp3ijhmE6t2CKWCQFQK7SIpUySXUFVayDwwygIWwOBQCAQCAQCgcA7SjpOOwB6M0XBGFpF8v77aRw8CBLCu3YR3Xvzio9jrB7CXL+OxqHDiHAIP2cjHAc1lUIYBrJQILxjx3XvL7R5M8ZAP/bEBFpPD/g+zvw8kRt2ow8MrPg8R0ujxI14u4+oXUcv1FBQcLT21HtFKFRDHrGGT9LSWAq7+L7PZKzF/PYEmzo2UWwWmWvMA/DR9R8laST5w9f/kJbbYigxRCaU4aXZl1CEgiY0HN9BIpfPoe7U+b9f+79JmAnWJHcR1TSqVoyUdBHoFPww3aJEp+ZQdT1majN0R7pRLtzHbDjLaGmUM8UzTFYneXX+NY5puyl6x1kXspBKlIRuIkJrSKVvITR5iobbIOGbdJ3KocwvkQ5JYutduI42mQ2nwR8c/gNO5E+Q0VXShs7Q4gky0Qxxt4Vqw8SaBK6ik+i4gdXRe0nWASMKgzcvT6mXUjJar3C0EcdjJ1viWxhO7EDT4jTiYRpdA0QXJsF1ULwGSigKWz5CsmMTi8XnaDanMYxOPNnAC8dId3+M4wfHqZ7cj/R9hKIQ6+1nzY98cPncm80miqIQckoMLhwj05zAY4zFRB+yJ4vlzJHJbqGipJienqap6ji+T7ajm86R9QBUqyeZn/8K/vzr9DdGUXUIxYfpSIWhNg9CbX+o4LvtDxiS/RddP11PsGb1LzE79wXq9bOoSojOnvvo6fnQip/jfwyk4+PN1dmxKcNMR4j5uTpmWGdgfYqsJqg8PYUwVEIjSfSe61tAz/N8Dn97kqkTBXyv/VWVzIbZ8/7VpC4Tdk6fLpKfqZHsjqBqClJKinMNzr6aW1HYGkkYbL6tj+PPz1CaayAvvLb1zv53pUo3EAi8fUHYehUbNmxY0bjJyckVjatWqysaV6/Xr73RZRw/fnxF4wB27dq14rHvplgstqJxAyv8Ybq/v//aG13G7OzsisY5jrOicf+Ymqi3Wq0VjXviiSdWNO7VV19d0bgPfOADKxqnaSv7NvzmqWpvR+rCgiTvFtu23/WxP/IjP7KicW+uSHk7/v2///crGvf444+vaNy6detWNE5Kee2NLmOl/xbu3bt3ReOOHDmyonGw8u+l999//4rG3XnnnSsaFwj8cxDevoPGq6/hVSoo8Th+vY5stYjuuZHorbcSvfXWd+Q4QlHI/Mt/iTD+mtbx46jxONK2EdEIXj5PeM8eYnffDYBbLNI8cACvVELv7ye8ezfKhZXdv0uJROj4+Z+n+Pm/xp6cBEUhetNNZD7x09/Tz0+9sV7mFubalYr4VGICKSAidRrCwZMe0aakkgA9mSaBRUgL0RnppCfaQ1SPUmqVSJkpoF09e8/QA1hqhu+c/yJ1a4lCq0BPpIep2hQhLUSx9cZ0dAWFzkgnNbvG5058jv96zx7e19nNl6xFxuwaCJWkUuU96stkE1tIxDagKRq2/8a/xb70EUJwYP4AZ0pnqHk+E02HGdlBSY1zazzDKQY5Z9zDb3buZmPmZfaNPst7nlhkYLqFQLDFSKIufR77X6ev2aP3cO4wpwunGUoMYagGB6I9jE++xkbH5YYN74POTnqSXaTS24nFNl7x/jwx/gRfOvslmm4TgeDpqad535r3cWvH3ZypFpgf6mBjIkpocRK0DKkd/xpn+E4036a//18wv/AP2NY8ihoi23kf9sIwZvMYelc3vqJgNpbwc6dYeuTfkb3jXtj4MNlslnOnjrF25qukrClsLYUilxgqjFM55SC33snqof8N6S8y+9JL2LZNzIwR9zo49ZVDrNvZz6z4Eyxm0eNpmhGDUP4YLQHheE+7ilUBWhWoLUJ2PQzffcl7j0RWMzL8q7huFUUxUNXQ2352/zFxCy0qT0/iLjXBh86Ixuo9PYQ2pqk8NUVttNTeUEqsMwVidw4QurAgl5SSylILq+GQyIYJRd+oWl04X2byRIFwwsAMa/i+pDRX59Qrc9z8geFLnr3KUhOEQNUu9DIWAjOqUco1kH67yvbtWr09S6YvytJ0DaEIuobiRJPmtQcGADiU/w7RRvgd2Ve92XxH9hP4py0IWwOBQCAQCAQCgcA7Knr7bVjnztLYtx83l0OYBtG9NxO/9553/Fh6Vxed/+bf4OYWkbaNW8jjLS2hZbOENm9G6DrOzAyL//1/tANUAEUQ3rWbzl/8DEr44l/AzeFhun/rN3FmZxGahtbT8z1/UH3v4L2cLpxmrDxGWAtzbkBlqldj66JBXVGx7QauCqd2Ztjev5stHVt4bvo5JJKm2yTXyLE2tZbdXbsBOFiu8xezS8y1OlCSn6RP5PiJ/m7qrXn+8/7/TMtpIYRASokiFMJqmLAWxnItFhuLlKwSPzOQZVtsG/vnXsVpnGGTvsDm7E30dD+MpsXZ3b2bpyafQkqJrujkGjk6wh1MVCeIGTGkjKN5TUJNlbHFMrHy7dgDe6g4HRRdn+5IN2vOVumdrDGXUVEMg3iyF2d6mso3v0n2F37hqtdssbmIj4+htts3NIwIc4M7OOhYGGIvC08fx7HOkOlvsuWu2PJCU29WapV4bOwxVEVlbXotAEvNJf5+6iDPNX8U39mJ6i7wXKSPB7bvZFvvneTqZymf+j+RvksksoahVT+PohhoepKQ2cNzL/wlmmGQ6e2H/DmozFDyBTMzFhsPfBZyJ1hz529SSrQI5yZZUtP4roZpdhKPufQ0Vfr6/hUitIrJ+WPEOrJ0xTronQxhNhTcikOlNImRWId2YxJCLuUtd8Dx5wiXx5CREcSaOyA5CNKDzk2w4UGIXr5cuN1T9gfbNgCgkm9SWWphhjU6+qMoqnLtQW+DlJLaK3O4Cw3UbJhm1aYxU2Nx8ixyOEmmbKGnQyimipQSL9+icTCHuTqB4/i8/uQU86NlXMcnFNVZf3M3wzs7EUJQnG/guxLzwoJaiiIIxQ0KM3Vcx7+kujSaNMGXFwWrTtMlORhfUdD6XYlsmET2nQkMA4HA91cQtgYCgUAgEAgEAoF3lGIYdPzczxG/5x6chRxatgNz/frlRafeaUII9O4uAIzBS2colR97DHt8HGNkBKGq+M0mzYMHqL/yCvF7Lg2AhapiDF4a3q3Uts5tfGbHZ3hq6inmanPcs/YBxhITNF+foWeyhh/rIHbr7fzUne9nfWYDIS3ESGqE56efp2SVuLXvVh5Y/QAxI8azc6/zO6OLlFxJf0gnG+7htN3D758fJ1z6Io7n4EgHXejY0sZUTRJmAtd3AUiaSaJ6FEUI9qSz7Ek/hJQPXLiOb9yfj6//OAoKB3MHabpNtma3ckPXDfz1qb8mG87SsFwii3OEq0UqRg3Ov0THiUnUW++jtibEK3OvsKsaIxlyEakElttivrlAT3yI1smTSCmvGmJ3hjtRULA9ezlwLVtlVk+HODv5DIYZQjNMZk4dp7q0yD0/83NEkqmL9jFbn6ViV5YX6QJImF0cb6TZUfqfDCiLgE/D0zll38ZA5QSlwjMYRgeKGqVSPYrjlNiw4XfQtPb0b1XXQPrg2VCaBFXDV3W0sAmZXph7HWPxOLu3jODmYqihLjRNJx6PoUsH6ovgetRbLer1OuFwmGRBJ1RXaUU8mi0bPdzCKGfxZsBdO4sTTzKz4ybCjQbrRv4dIrsBtOvrIfyDJn3JyZfnOH9oEavpomqCzsE4Nzy4+oo9T1fCrzm4C3XUhEG9YpOfqSF9ScSXVM8U0RRB5kJQKYRAien4VRu/7nD29SUmj+eJpEzCSYNGxeb487MkOsJ0roqjh1S40P/4u8+sdyGUffOiVd/VvyHNxLE8xbk6eljDtXx0U2VkV9c134fVdKlc6AubyIbesRmJdsulONdA1QXp3ijqOxx2BwKBiwVhayAQCAQCgUAgEHjHCSEw163DvEL7E+k41J5/nvorryBdj8gNu4nfe+8llabfKyklrePHUVIpxIUV35VwGCTY58/DZcLW74dtndvY1rlt+c9lq8yJPSdwfIcN6Q10R7sv2v72/tu5vf/2i157bf41fv/4E8yxg4QoMVdxqVkFUOKMNsp0VnMI38H1XBShENEieNKj7rTbjsWNOO8bfh8R/eI+k+Kt/XWBmBHjk1s/yYetD2N5FtlwlkKrwBfPfpHj+eM0ag2MsktVF6BGcKN9yFyDzScPUtyZompXUdMpFL+IkGBqIap2FatRJrbq0qnXb7WzayfbUqtYKh/EVQ1ybghDJBhcjGKYYRLZdvufcCxOfnaamVMnWHfzm9pTSEnabtLnujTtOvqFRblmXJOdyin6mcHShpFCQ/VKROvPc8ZLMRjuRNfTAKhqhGZzgkrlCOn0rRRmpglFY3ieR3VpgbDj0PLbgeFgTxj0cHtxtfoietdG9GQnYU2BaBqkhOIUxHsh2U9IMYhGo5TLZcL1NL7abjGBEOhmHGkVEPkwrAUpPRyvRKLvTpSebfxjsjBR4eyrC2iGQro3gmv7zI+WObN/nu33vnMfaKC0F9yTPlSW2m3IzIiGannoER23ZFEvWCS62t9f/JaHElKRhsr06SJGRMOMaDSrDq2aQ6Nkcey5GW7/2Dp6R1KcP7hIab5BOGHg2h6u7bF6b89lK3SjSZObPzjM6OFF8tM1ogMGq7Z20KzaPP3Xp/Acn96RJGtv6MKMvBE4jx9d4tTLczRrDpqm0L0mwc77VmGEv7fYZvZciWPPTFMv2wgFUt0Rdt8/FFTJBgLfR0HYGggEAoFAIBAIBN51pS9+kfI3HmsHoIqCdeoU9vkxsv/qF9/RClghBGoqjTc+vvyalBLp+yiJ5Dt2nLcraSa5pe+W695eSskTE09g+S4hI0RURJGapGyVsGQDVypoqklCSyOlZKm1xNrkWjojnUzXpsmEMjy4+kE0ofHbL/02ADf13MS9q+4lrF05dEmab1yjjlAHmqIxV5vDsAWq7mPrGplGLymZpD8bJ9uqErIUwlqY8RFB39EwidkKtahCtuGgp0zi77kXaPeBLbQKhLUwUT26/D4lEqt2jHvC8+Q9i4ZbhHCCnuz9zL0+hWq+8Wts+1mRWI03rWNRXYB9f0zXzCF+tjjKSRye69lAOdND0Y+xy5zH07JI0d6PJ8LE/fMoVgFb7UVVYyiKjhAq4GM1S7z07b9m7twp7FYL17KoWxLX1jAUh83DWVb3RsCqgqpDog86RmDD++DEP7SrWSUQScOunwI9jAps3bqVl19+mbJdp8OOYEmLSCRCPN5Ns+rR0Kao18+AFITDg/R0v7EIl5QSz6uhKCEU5Z2rEH27XNsjN1nFbrqkeyIksuGLgvTFySqu7ZPobD9juqlixnRmz5XYelf/O9ZOQI3q6IMxmicLSNtDVQS67eFqCrVMGL/qECm1cA0FPB/pQ2hXF4quoNgeqYaLVbEoFG0cwHV8pk8VOPCtcW58eA03PLSaky/NUVlqopsa627oZuSGK1eqJrJhdt33Rl/iY89Nc2b/AqqmIBTBqZfnKC002PvhEVRVoTBX5+jT09QrNv6FFgTVQgszorHjPVfvb3w1jYrN609O0ao7xLMhpC/JT9c48vQ0t/3o2u+prUEgELiyIGwNBAKBQCAQCAQC7yp3aYnac8+jJhJo2XavSa9apXHoENa5c4TWr39Hjxe/527yf/7n2NPTKNEoXqGAlu0gcuOed/Q432V7NifyJyi0CvRGe9mQ2YBymerRt8P1XXKNHL1amLywqRIhTgMflSZRTHeMuLAQQiAQ6ELH8i1+9cZfpSvchSIU/uL4X/Ds1LPL4eojpx5htjbLp7d9+rqmK0/XpnE8h83pEez8PH6zRWcjQqzuszXrYjZBMUyGO9ezs7yTF+0XsO/rZOOBRRILNUJDg/T+2GeI3HILpwun+dLZLzFdncZUTW7qvYmIEmLf/Ku0vDrd/gR7YgYDHTcC0GxOYjiHSPVtYP7sBKFIFLVlYbeaqIpKuvdCqwDfh5f/B87Y80y36jgWbLaaRJrHeKSl8MHddyArBSyvgVAlimwRs04RlwVMoVGrlXCcArHYVjyvgqbGmT9dZvLY68Q7siSy3TTKJVr1Krv23EJ/5SUiyiwU1HZ7gTV3Qc92EAJu+JfQuwMWjoNqwOBN7RD2gpGREUzTZPrAKOppn6wIYYYjyKJLJLWK6O4kicQQmp4knboRw2h/rVRrp5if+zKN5jiqGiWbfQ9dnQ+iKO/ur/e1YovXvjFOcaGO9EEPqYzs7mLTrb3Lz5NymTBPygu9TK/xzNkt90K4qZLIhmlWHWZOF2lUbOIdIfo3pJf7qALE9vYhbR+znMNpuTgRnUpHmJYCxahO50gCxfFRQiqh9RlCGzO4S02GbZdGucnpuofmg2koNEyVZGeYudEyixNVeoaTZAditBoOuqGivaVP69U0azYTxwroZnucqgpCUZ3cZJWlyRrdaxIsjJUpzNfxHL99bSR4rs+pV+bZeucAqr6y7x9LU1WaVZtUd2Q5WI1mQhTn6lQLraC6NRD4PgnC1kAgEAgEAoFAIPCucpeW8Bt1tN6+5deUWAx3YQEvn3/Hjxe94w6k41D9zpN4lTL6qkEUM8Ti7/0+aiZD/N57id5+23X3R6w7dY4vHafpNhlODjMQH1geW2qV+OMjf8ypwik86WGoBjf13MQnt3xyuffo5diezcHcQcbL40T1KDd03UDLa3GqcApFUdjasZX+WD/Hlo6xOTbOSX81BRnFVlSSfh61/hRSl4BKy2uhKipxPU5IDaGrOlOVKV6bf43OSOdytWrZKnNg4QAPrn6QwcS1p3TXnTrSr3NHOE+8q0H+XBSnqOCZVeqLsyASbLrzHrRWiw8d1Nn+VJWcW2JmQwfJT36SvTs+TESPsNhY5E+O/AmLjUU6I53Um1X+dP8f4jkuHTJONG5yXiwwb2X5mZiPqWqEQgO0WpMM3/wQzclpjO98i1C+QVQR9GzcSDaVaZ9kcYxq/jXOdZSoi/Z1MFpJNky7fPR8lOFta5nugdm5R6nai0T9PHFZwNCTpOLrqFZP02xOYjemiTc1ev01zJ95nGioGzPSrr6NptI062XyUUF6/W3IhQlCagZ1YC+M3APqhV+zFQUG9rT/uwwhBIODg0QiEY7U9hGa9jEX68ioSt9N6+lduw5GKzB3CPTTsPp2Wh19jI/9Dyw7h2FkcZwiMzN/jRAq3V0PXfMeSilp1aqomoYRjlxz+6s58eIc+dkaya4IiiZoVh3OvrZA52CczlXtHrfdaxKcP7RINd8ikjRwLA+74TKys+uyQex3TZ0qcOKFWZoVG1VTSHSFaVUdakULxBvb3PyBYULRdmWvGtVJPriaZm+UE8/MULY8RNXBc32SnWEie/vI9EWXv1allDQOLhDVFCoxE6vaQAN0V5JKG8Q7whTm6tRLFgBCEYRjb79XbqvmUCu0aDUcfE+iCEEorqNqCs2aDUCzamM3vXbrgwvBqlV3aZRtaqUWyc7v7V4FAoF3VxC2BgKBQCAQCAQCgXeV1t2NEo3hlUooXe2puH6lghIJo3V3X2P02ycUhfh99xG76y7smRmW/uAPaI2dQk2l8M6fJz8+Br5H7K67rrmvycokf3LkT5iqTuFLn7gR56E1D/Hw8MMIIfj2xLc5unSUwfggoQt9Sl+aeYlNmU3cMXDHZffpeA5/dvTP2D+3H096APztqb9FFW9Uz6VDaW7tu5XJyiSl6kusVY9TkHFWx/v5yOAO/st+m3yrgi50FKEQ02Pc0ncLHeEOAApWgabbvKg3bNyIk2/mybfy1wxbLXsJv/htet0zGEqFupoktEfFOeWhL0lCZottd36cdTfczOLv/h6NQ48zEF9iOGLRPJFjYf4btP7D3UT6IhxePEyukWM4NQyepDA7ToM6QqjELB2Rb5DNwnSzymi9xOZQGGmXQbq4nGJwfh+yWUT2hDFFEnNumuL/+lO6/91v4dllJmILNLwWWlNF0VRaEZvFPh/ljM/ixDh3fOijnNU9ZhafRzbH0bU4ycRWFMVAUVQEAtVx8ZEsiHE6FZu428mk342nmCBcokMnaBmvM+6lIQuRSIzh1bsI6W+vUtDzPF566SVyVo70ujSKD4VKianxCg8XTqFNvgBCAd+DsWcpbd1Ny1sgGl2LEAq6nqbZnGZp6Um6Ou+/0Prg8koL8xx98nGWpiZQVIXBzdvZes99Kwpd7ZZLfrJIT+gscbtOy+9FxNdQmHPIz9SWw9aO/hhb7uzn1CtzVJdaaIbC0LYs62688td5ebHJkaemcWyPWCaE6/hMHMkjpWRgcwZFEXiuz9JklakThYv2JYSgd2cXZneU6VMFcuMVykstqoUWL33pHF2r4ux87ypCUR3p+Di5BlrCoL8rwkTdwXF8DEVghDVcx0NRBOHE97YYmWN5NCo2ntdeVEv6kmq+RThuEM+EAC4sFibxfYki220EpJRohorVcFd87OxgnHDcoLzYJJEN4XuSeqFF95rk8rEDgcA7LwhbA4FAIBAIBAKBwLtKS6eJ3/9eyl/5e6zzo3y3VC12z90Ya9Z8344rdB3r5Emc6RnMkZHlBbPsyUkq3/420TvuuGq/WCklj558hNHxQ/Queqi+Tzlb4ev237OlYwvDqWEOLR4ipscIae0gI67HaBWnmTj8PLdlbkCJXBpsvb74Oq/Ov0pXtIuoHqXhNHhx5kWiepS9vXsRQjBVneLgwkF+fvvPcyh3iKXmEuvS67i9/3bSoTQdof8vf370z8k1ZukxdHZ37+XDG35s+Rg9kR7iRpxCq0A23J6OXmgViBtxeqO9V71uvm8xPvY/qFePsT1m0rIEJauKolmYuxKsMpL0JqNs3n4PzUOHcc4+S3pwEk33kVIjlqoQqb3Mqa/+Dbf8wq/SdJsIBIpQqFUKNJpVRFRFQUENhcBSwWrgmk1Ki6dwZA1baSGETvn0PMasjeiN4MsKrlcmYplYLz+GvX8PzbVJLEMSK9gI30c4KroHzbBONaSTiUZRFJMNQ59guO9DnB/7r1SrxzCMLLXaaXzPRvUEhqcR0bpoKDXK/WE6SwtEyycoJbZjcxwjWyAa20I02oPvu9Tr51hY+DpDQz/3tp7JpaUlCoUC6XQaXW9XaGb0DOr867iLL6B1DILZDi4pTuBOvwQ94YsWNVPVEK5bw/cdVPXyYavdarLvy4+Qn50mlsrgex6nXnoO13G46Ud+9G2dM4BildlY/yPi9mm0hsRTTArhmyjKD6Job1SstmoOlaUm0pdouqB/fZqtd/ejm1cOhXMTFVo1h3RfpN1zWVfwpY/vtkNIFIGqKShau9fp5WR6o2i6wtTJIqqmEEub7T6sZ4oYYY3dDwwhVAVhavg1Gz2msLo3ytnpGnXbx2o4SNene02S7qHE274+b1aYq2OEVRwL7Ka73D1BUQXJC71se0dSxNIhWjUHu+EiFIERardPiCTMFR87kjDYce8gx56dprLYQijQMRBj+z0DQb/WQOD7KAhbA4FAIBAIBAKBwLsu+cEPovf00jjwGtJxCe/YTuy265/Kv1JeuYxELgetQLuPa7GEtG1E6MrVXoVWgdOnXiQxX0KVIRCC+GSBGavC6cUTDKeGCWthHN/B8z2Mlsf6J8+y++QknVqJuacLpH70I0RvvfWi/U5Vp3B9d3mRqLrTDpB8fFzpYigG3dFuFuoLGKrBJ7Z84pJz29W1i//3nk9zfvLPce1FDI6wOPO/CA38DGXXQwjBPYP38I2xb1C2ygBoisb71rzvomrXyykWjjB98gReK01H2sU1G5i6gkTSl1pPiAbhcDdCCLxSkWhkGlX1aNlxQCB8g2ikijn1Cq16jeHkMIZqULJKCNdB9RUkoAiBIgWoGtVGlLBRJ+sU8E2DBN00/RKyVEJ1wuDWUaTE1QR2REevNvFf+zxyzc8iAQ0f8EB6qFLgSAMRSTO4edvy+9L1BD3dD9NonKfRGMNxCvjSQvMlIaLtClepQsIkFI2i2wVKC3PEhstEUili6Z7286No6HqSSvVIux/pZZ7hhueTd1wyukr0Tc+elO0KxreKuEVQ3TeCVoBIB5HGJIovcd06mhZFSg/HLpBO70VRrhzKzZ87S3F+jkzvAKrWjgGEojBz6hj1u+4lmkpf9Rl4K+3s1+hST7HkZxFaBJ0aqdLz9MfW0LNmOwCe5/PaN8fJjVcwozoogrHXFxECdt0/dMV9S18utwpYPp6u0rKcN7aREt/1CcevvDjYwniFVs0mfaF1gKoreK7B/PkyVtPFDGuEN2WovTyLW2zRH9dR0iaLlo/oj9G7Ic3wzs4V90t98/sxIxrp3iiNso0vJYpoB6HfleqOsOGmHkYP5/A9f/l81+zIEkuvPGwF6FuXIjsYozBXR9MV0r1R1HdoYbJAIHB5QdgaCAQCgUAgEAgE3nVCUYjuvZno3pvf1ePq/f0IRcVvtVBCofaq7sUC4e078G0bYZpXDHyVUhUKJXxDR1HbFarS0PGbRZidh3XQHenm6cmnGS2NMrjg0zNZJ2RGSHSvxs3nKfzV59D7+jBWr17eb9yIt4NK30NVVDRFQyJRhIJ2YdEjy7PQFZ2Ifvkp37a9xMTEn2Bb80TCA/iOxfzCk+ybe40Xmu0erSOpET6+/uNM16YB2NG5gxu6bwBgpjbD0cWjOL7DhswG1qXWIYTAbjZ49SvfZPp0C0WAGoKuXRrZjIdQJLq3hKInyXa+FwCjvxcZdnFsBb5bOed6+KbADLtous7m6GbuXnU3z049S80vUw416XaToCosUMKXNrpu8kGZ5b21FIo2iCY1jpuHEVkHwg7kHURWB1z8souaSmDoRbSpc5i2pJVOo1dtfKeJFZLEWx7Dtz5IdvDikC+R2MnQ0M+TW3gM162iKHWiRNAdG6lKXOHS4ceId5hs2PwR+jpvpsFzFEqPXxSs+r5FyOy75NmRUvKtpTL/kCtRdj0SmsrD2SQPd6UQQpDNZkmlUhQKBTKZdt/ZYrFIMt6N7prgNOG7rQmsMim9m3RmB8XqISyrPb08FB6gt/cjlz63vgetMhgxHKuJlBLlTUGvZhg0qy2cVuuyz9RVTb5MKJslEkrRqNhYXoyQVmDjmoXlRZeWJmssTddIdIaXF5Rq6jYzZ0qsv6mHaOryIWJ2MI4RUqmXLKIpE8/xUVUFM6pTXWphRDTqJQvDVOlefZWqU8kloa0Q7XXMpN8OuMNb2lXezRN5pOWxaksHm27oRu+Jvv1rcgXt96Pje5JMfxTX8aksNukZTi1fF9fxiXeEiCQNmhWHeMZk3U09DG3teEfOwQhp9KxJviP7CgQC1xaErYFAIBAIBAKBQOAHxm82EaqKML63vojXK7JnD+HtL9M8cgSkRPo+SIk9Nsbcb/yf6IMDJD/wQcLbtl46tumzuRDh5d4aqm9hSJXFUJN0RWOT183RxaMcXDjYbgVgVSm5FR7fLri/0UUChe5IitDYPM0jRy4KW3d37+Y7E9/heP44AoHt2yhCQUWlbJXxpU+hVeDm3psZiA1ccl5SSvaNPUou/xp5V0fOzxBv6Vh6DVefIKLdjDSTHM4dpuk2+Y2bfgNdeaMi8LX51/irE39FsVUEIKpHeXj4YR4eeZixwwfIjy8RSuhoBkg/xuLrXXTtLBDtBEfv47zfySvjh9hUs9g7fDNK73qM2UM0a3WEUJCKwDUMYiM70c125fDH13+cXZ27mKhMMP/6MZTjOSqyxnSoCCGdu3Z+gPtnn0J3ciDDSCRRP85SqoB/i4LxjAcLoABa1CB9Uw+q2US1bQbLSaYyDSyzCoYgasHQQot4+Kuw43Yw3gjShBBk0reQTt1MqzXD2Ngf0igeoO7NgbdIxA7TtVRHZLcQ3fF+opEM9bpKtbGfZmMMTUvSsmbxvAbJxC4cp4KuvxEA7ivX+YuZJRQEaV2l5Hp8di5P2tC4PR1H0zRuueUWXnrpJYrFIlJKEokE62+6G/VEBaZfAzMGngNSomz9KKtHPkK6fJhmcwJNi5NK7cEwshc/FJP74MgjyMos9VCWRHYvRihEo1wimkojpaRWyJPq6SWefctYKaE4DrUcJHohterSLyQ9hIpH52Acx/bwPYksJplpDXLmsTHCMQNFE/ievKgyVDdUGg2bVsO5Ytia7omw8ZZezuxfoDjXQFUFfetT9K9PM3O6yNxoCcfyEMCBb00wsruT9Tf1IIRASkm10KJRtgnFdYyQRq1oEUu3Q9tG2WbV5szyolpCFUS2dxLe0oF0fYShviPV9VJKlqZr5GdqKIpg1ZYMM6eLy++ne3WCjbe023f4ns+Bb40ze7qE0ASKKmjVXXzXDypQA4F/pIKwNRAIBAKBQCAQCHzPpJQ0Dx2ieegw0vcIb9tO5MY9F03XfzNnbo7S3/89rePHEbpB9LbbSD78fpSrTON/JyjhMJ2//EvUX3kFe2wce26W1tFj+K6LEonQOn4CZ3aO7l//NYxVF4dMek8P72+ux104w+lul6bboGfB4f6TBvGuJb4RO0fTbbKnZw+23SQ/9RLHUzX+OnGeWOs8yZbCw6rJrtI0b64xy4Qy7OzayZHFI5TtMqpQSZtpVidW4/ntFgDvWfUePrLuMtWLwPH8cZ6e/A7rhYPX9LEci7oGhuIRdQX20hKZtf0YcYPz5fOcLZ5lc8dmAFpuiy+d+RI1p8ZIagQhBLlGjm+Of5Nd3btYOH8OP2SSS8coOrMkXIV0yaB0pg9/w718deYwDWcMRZnglfl9nMif4Oc/9ps4X/lNQktTOJ5AMRW87k103f8pnhh/gm+NfYuFxgK90V7eu/q9/NQHfoXFzefJT0+gajr9GzaT6unFfdWi/urnCZll1FAC1WrhhH1a2x1CWQdj0iXqx+nvX4vp5yC9HgZvJnXmcaKjZWq6QGgx4tUmqojB0lnk+Evk9GFqhSXC8STdw2tRNQ0hFMLhQdat+3UKhRdpzb+CuTRNuq5hbrkZNj4MkXblaTQ6wtDQLzA78yjF0st4XhNNTVIovoJt5xke/reYZicALxarWL5kfbQdLMY0lXP1Fs8WqtyebrcI6O7u5v3vfz8LCwtIKenu7sY0Tej8dTj5dZjaB0YMRu6FtfehKArp9E2k0zdd/iFfPA0v/B5HZZQvpO5nQkRJlursHezCmFxgaWoCBESSKbbd+wCq9qap+K4F+/4Yxp4DuwpGHNa9F/Z8CtQ3bTdyL7zyR1BbQDfjOI0S++bvYWFxCBEuID2JZqogJc2qQyRhIKWkUWmHoFdbnEkIwbo93fStTVFcaGCEVDoGYqiqglAgP1Mj3hHGjGo0qw4nX54nngnRPZzk2LMzTJ7I47Q8NFMhmgzRrNrLIWfnqjhb7ui/9JiqgniHgk0pJademuPsazkcywMk4bjBxlt7MSM6Zlijoz+KcuF4S9M15s+ViWZMjFA7oqkWWpw7mGPVlo7l1wL/vOzfv5+NGzcSj8d55JFHqFar/PRP/zTh8NtbhC/wgxF81QYCgUAgEAgEAoHvWflrX6P85a8gHRuA+vMvEH/wAdI/8ROXBIR+o8HiH/4h9tlzqJkMfr1O+ctfRjYaZD7x09/3c1UiEeL33ouUkvnf/h2UcBijvx3AqKkU9rlz1Pfvvyhsla6Lm8vRdetdfPybTXInpmgUS2QrEjMdp/LEE0w3Whgj7UBO10PMZKHmOygKOGGFfMjlj2+xCctHuHNfmfcPv5/tndspW2X2ze1jMDHInsgeFKGw2FzEx+dXdv8KvdFeYkbsiu9n39w+5l2NDUaYiKwiFIOWYhPRXGqtJKWqTbhaxYzH8HwPy7OWx87WZllsLdId6V6+T53hTs6XzjNeHscKwZPJExQiNj4Oiu/To8CPdNzO8/lJPOkzkh4B2r1mDywc4NTAnWz9if+OefYJ3KVJRM8W5Ob38Ufnv8JXzn6FhttAUzRma7NMVs6zWDzI+4buZNPwreh6Aun7nH7lBc69PI0120V0dIm1Xeeobi4T0wchNYhiFsmI8ySLVTRy0LkBbvkl6BiBVTej7z9KWghQ6qBq0LkOv1Vl7Nl/4OB8EtexUVSNnuG13PyRHyMUjV24b2m6ux+G7oev+gylkjfQbExSrZ0hmVyDqobxfZda/QyLi08wMPCTANQ8H+0tz7+uCOqef9FrhmEwODh48UHCadj90+3/3o7xF5iwJb/X9zBLSpis32IBlS/19PGJ7XexpbKEahj0rt1AvOMtVa1nn4DTj0G0s13V2ijAiX+A9BpYf/8b2214H9TzcO7bUMsx29xKTmwj0d+JZihIKSnONdAMFafpUqg6gMSIaGzc23tdAWI0ZV5S/Tp9uoiiKUSS7Ur4WNqkOFdn9lwZq+kxejBHKKYT7TZp1R3KuQabb+sjngmhvym0/X4qLzY5dzCHqgviHVGshkNxvsGx52Z477/cstyr1bU9chMVJk8UsJouya43WoSEojqtqkOzagdh6z9DH/7wh9m9ezd79uzh937v93jssce4//77+fVf/3X++3//7z/o0wtch+Cr9ip27NixonFXWgHyWn71V391ReMsy7r2RpcxNja2onEAt91224rHvpuMFU5HM82VNSGv1y+/Gua1uK67onETExMrGjc1NbWicT09PSsaBxC5zMq716NcLq9o3J/92Z+taNzJkydXNG6l9/CVV15Z0biVfg3+8i//8orGxWJX/gXvalb6/QngL/7iL1Y0LplcWT+qc+fOrWjcSp/R0Aorty63iMb1WOmUuB//8R9f0bhbbrllReO+9KUvrWgcwF/+5V+uaNy/+lf/akXj+vsvrYwJBP65cgsFqt96HGGaywGlWyhQe+ZZYnfehTFw8ddL8+gx7LFxjDVrELqO32zi5vOUvvhF1GyW+D13o7xL1TteuXTRolhCCBACv1pdfs0aHaXwuc/jTE2BoqB1dJCtVPAUH2P3MHpnJ75ts2riMOMdi7jxPppuk0XdwnNAd0B4kmYIGiY0lBb75/czXZvmF3f8IkIISlaJ/ng/qtL+XaIz3MloaZSyVWZdet3yuUgpmTt7iomjh2nVanQPr6WqFGkpMWb8VWQ4TjbURFFcqh6ctzR838dzXebqc2RCGYaTw8v7i+gRDMWg5bUIae3r4PgOqqIS1aMc66ywkK+RaUQx9RhNr8VMrMHCUJhia4x06I2FlaJ6lPn6PHP1ObYOvReldzvf/Wl839w+npl6Bk96dEW6ACi1lsjXJ/nO2Cxr3IMkI4OsHvrfWDxX5fXHv4GqqRjRJOX5Aq9Nhujur9GlOQgRg45+asn1VHPHGVz1s8RXfwC0C0e7/d/C9AEonm+HhrFuMJNY+TlmCouE04OYkSiO1WLmzEnO7n+Zbfe899rPiufhed7y7xj1+hk0LYKqtp9VRdFQ1SjV6onlMTsTEQ6UGzQ8j4iq0vR8Gp7P7sTFPxs7TnF5XDy+GV1/ewtWXaRV5uXIWnJKhI1+CQF0+HXGfZPnzCQfvH3rlX8umHipXcF6oYqXaBaaRZh8+eKwVdXhxk/Blg9Bo0D1dQVZLaIZ7SBTCEE4/v+w999Rclz3mT7+3Mqd4+QZTETOgSDAKDCKpHK2LUtykJPkddJ6LX+Pdm2v17srr6W1vV5rLctJli3LClQWKZEUMwmQIIkcBhMwOXYOlX9/NDggiDwMkn6nnnNwDrq77q17q2pqpt967/tR8T3YfGsX+ZkqkiRo6YuTab+yvyt928MaK+JWbJSkgdoexXN9xMu0UiHAczymTuYRkiAUa5yfUEzDrDosTlWWluy/HuRnqth1l2RrmPxsleJcDcf2KOdMfviFY1z3jgFUXWbft4fJTVeoVx0qORMhQVNXHASYVRvNkAlFX594lYDXjuuvv/4cnaCvr4+vfvWrl2yzbds2PvGJTwDwuc99jieffJJ4PM4f/dEfvaZjDXj1CMTWgICAgICAgICAgIBXhD01hVcqoXaezROVUymswUHsiYnzxFavXALPA0XBLZWoHz6EWyyB67Lw939H/egRmn7t1151wdUzTZzZWaRoFCWVQgiBsX49pQcfRMlkELKMV6uBEOh9DUHSq1RY+JvPYo2Noba14Xse1tAp3FwebfVq1DNFjSRd59rTcUYdg5HiCFWrSt0zEZKEoqlYmocnzjycFZA1spSsEg+PP8yb+9+MLuvU7Bqq3liqXXNq6LJOTIudM4eRF/bz7LfvxbEsZEVhevAEySYNt9dmJtIJ1ihhJYeDIOTrbIoWec5zmfLnyShZ3r3q3ST0sw8mW8ItbGraxGMTj2G7Fp5TZr42S1+8g75oE18SMzRnOlBzFo5lEVbDeIkQlYQgXAxTskpE1EYGqumaSEIipZ8vFI4WR7FcC1k0MjFd1wK3RtV3mRMOx8ol1ninGJ/4ZyafawenTtycgsI4unCZNyNUx8M48TzV4WEm/a1UKoJIdIBMbB0RWaXueoQkgdAisPvX4Mm/AtcCpw7lGYp+gjnaiIcb41V1A0XVOPb4w7i2TSSRpHPdBkKxc4suua7L4cOHOXnyJLZt09LSwubNm1HVNL5vn1Moy/dNNO1sUaPb0nEOl2rsK1ZwPRtZgmsSEe7MNs6BbZkMH/4684WvglJBM0LoRgsrun6ORGLb8i705rUUZ55B8hq5pngeOCahWIS8416obtS5XOjB7sXE2XAawmmM5CywiOf5SGcKo1k1h0RTiI6VSTpXX5147FZsig+MYk9VwPNBltB7E7T3JlicrGDVHFRDxqw6gKClN8HE8dxV7ePVwvd9CrM1ivM1jKjaEJwF1EoWxdkaSKDoMr7XEFwPPzqBHlGYHy+TbAkTb4Ip26UwU0MgUDQZIaBjVYoDD42xOFkhmjbo29JEa19Q5OonjY6ODr70pS9dVZsXzV9PPPEEW7ZsIR5v3JOq1eqrPr6A14ZAbA0ICAgICAgICAgIeEUoqRQiFMItl1FSDVHFq1QQoRBK+nyRRevuRgqHcPN5nIkJ3HIFoShIySTaim5q+5+jum8f0ZtuetXGWHnqaQpf/SrOwgJC14lcfx3Jd7+b+N33YJ0awnpx1ZckEd6+nfDORh5m/ehRrIlxtO5uhHoms7KjE2d6Bnd+fkls9R2HtKXy6y3v4egqg6H8EJ8/+nkqdgUL8ADhCXx8VEklaSQpWkWmKlN0RjvZ1LSJJyafoObUEEJQMAtsbd7KQHJgaQ6u43D8iUfxPY9MR2PJuWNbLM5MsqljBSPqSdLxKgt1GSydNitOOGxyZ1uCUN/7Wde0jdbIuSuVhBC8f+37CSshHh3+Mqa1wCpD58bQPKNDn0QXBko0RGfrSlzbRlZVThWHSBpJ9iT28NWTX+V08TSKpFCxK2zIbmBj00YAyrlF8lOTaOEwYTmELutU7Aq2a1O3FvB8GxBkZRfZnmGkmkBTh6kUZOTSOJADfISqI1ke4QWXRTvBsbEOTExkxadQbOb/PT3CXI9GWQh6QjrvbEmxYeA2UIzGsvjqAqy8g8kxCTt3cmnujm2Rm5rA9zwcywQfhp7bx/XveT/R9FnB9MCBAzz33HNomoYsy5w6dYp8Ps+NN+4gl3+6UShLTeI4RSTJIJM5e91GFJn/2NvGC6Uq06ZNs6awJR5GkyRqpSJPfOUfqOtfRdJquPUY4aRGvGWOsfHPE42uQZaXsTqs92b6x0YQtknVrhD2bdxQikWjhVujIaRLrXbpvh4mn2scMyMJtcXG+ysuvWKlY1WSkYPz5CcraBEFx/SQZEHf1maEdPWra2qH5rHHyyiZEEKV8EwH81Setl1t5FYnmRkqUs6ZqLpMx+oUsbROU3eMmZECtbKFEVapVxtFxdoGkuf0PTNc5PSRBeplm2xXlJ5N2WU7SD3X49AjE4weXMAyXWRZkOmMEk8bTI8UsW0XVZdxLZdYxiCaNlicrICAUExdKh7W2p9k+lQBRZdp6YmTboswfGCOasFGCyuUh4osTJa55u7eQHD9CcJxnGWtZpudneWjH/0oDz74IF/4whfwfZ9PfepTPP3006/BKANeCwKxNSAgICAgICAgICDgFaG0tRG+9lrKDzzQEFklCa9SIbLrWrT+/vO21/r7ie65hdL992ONj+O7LiIcRu/pRo5EcDwPc2SU6BVorV6lQu3QYfx6Da2/H+0l7toXMYeGWPyHf8Cr1VCamvCqVYrf/g5SNEryrW+l+ff+E7Vnn8XN51E7uwht24p0Zqm4b9tnnHVno8KEoiBnGmKcOTyEpBt45RLawEparr2Jjngcb4XHYH6wIaDaNVzfxcNDEQqdsU7CSpjJyiQ3pG5ACMEH13+QplATT089jYfHXb13cU/vPUuxAnWnzmMnH+LY+AEUXQczRFJPoqgaEhK3Z26m0tlLaSaHHG8jZClICPSIhi+XWJUdIBq5cCRUVIvy5va1DNQiSEoXCSOF73tUKoOs1toZLruMFEeJaTGq9SoxNcbO1p0MJAeIa3GemHyCil3hthW3cXv37SgIjj7+CMce/yH1chlZVVBWZOlob6NklyiaeUynDgiyimBnFFKyzcFyjqM1wSotjlQtE4mFkJ06tieBkOnQLKYnOrBtnWRGYES6ORXp5z4RIZYv0ZaK80yhwmjN4r8MtNPdeyP03rg0z5bUMU4dH6UwN0s4Hmd+7DRWrUpr/yqSLW14rsvC+GlO7nuSrXc2Mlsty+LkyZPour7kLguHw8zPz7O4uJ6e7l9heuZbmOYU4XAPLc13k0hsP+f4qpJgRyJy3nE/ufdJFmafJ7XWRTjNSDpUcwXC0U5MaZrx8X34fjeJRIJkMnnlkUBamOtu/BBPHz7A3koznqTga3F6ImHe0XIZh+nKOyA3AkM/hMog6DFY/3bo23PJZqGYxs439zL4zCzzYyWSzRo9m7JX7Wh9Eet0EXQJXxYIQNIVXCz8hRrXvqmPhckK1aLJ7GiR2ZESU4N5tJBCLBuiWjDJl2xUXaZ3SxMr1qeX+h0/nuO5+0exTRdZkZgdLTIzUuS6tw+gha5eHpkaLDD03BxaRCGV1nEsj5mRIj0bsriuz9jRRRCCRHOIZEuYesVBUgSSJHCcs7m9kiwIRVXWXtfGml1tHH50gmrBItUeWTrvuakKwy/MBWLrTxAzMzOUSiX+23/7bxQKBSKRCB/72MeIRM6/H7yUT37ykzz00EN85CMfYe3atezfv5+BgQG++93vvk4jD3ilBGJrQEBAQEBAQEBAQMArQghB+v0/g9raQuWJJ8HzCN9zD7Hbb0dIDeeWPTND9emnsefm8et1PMuCcBjf9/EdB1wXa3wCEYmC76MkLy8oWKdPM//Xn8E6fRp8HykaJX7P3STe8pZzhKnac8/j5PPoK1cihEAKh/Ftm8qjj5F405tQUilit912Tt++7+MVi6idncjpNPbEBGpHB3ge9uQkxrq1JN70JipPPImzuIhxyx5it92GfEaQk4TEhzZ8iJpb43TxNFW7StkuE1IaDs9ThVOsiK7glhW3AI2803evfjdvX/n2Jffri9iezd8e/Fv2Te6lkzJKzmXWy7E6tYq02hDhEulmeppbOVl8CF3PLuWIWtYC+CFULc2lqNaG0YRH5EwGqxASmpalw5xFReNkcRTP92gKN/Gra3+V/ngnnlvmps6buLnrZgAqlUGmJz7H4aEjDD5UQTeaSLcP4No2+cFJbo6tpWNFF0+PfY8yZTpVwU0xj5Qq81jJ5VjNRyg2xE+zWS8yW4mjODoSHh1xk55YiYmKTFqPEc9eC0LioBrFcz1SVp6UHCUTMThWqfN4rkR36Nw6DG0rV7Pljjdx7PGHqRby2GadaCpDorkhQkuyjB6KMDcydPYasyxs20ZVz54P6cw1Xa/XSSZ3kEhsw3VryHII8fJA0UswdfI4qhputBE+sqxgC0G1lMd3bY4ff5padRBd11m5ciU7duy4eH2QWh4WBhtu3qY1hPQQv735Gp4pNsTnpCqzKxElo11GAlA02P0RWPtmKM82imQlzn+AcSHimRDb7uy+4vlfDN/3mSpajA2XMKUKiYhCT1uEkOfj5kyK942gAj6CsSOLqIZCKKFRL9tYNYf1N7YTSRpEEhqxjLF0LzCrNnu/NUR+uooeVokkNeJNIcqnS0x9d5hkVEXJGBir08hX6HSdGyvhuf6SM1bVZfSwwsJkmeve1c+jX3SplWyiaQOz6lAvWfRva0YPKRx5YoqqbKHqMtWCiRFVae1t3PcqBRNJls65j6mGTGlx+fUQAl5disXiOa91XT+v9ks+n6e3t5ff+73fQ5ZlPvOZz/C+972Pb37zm5fsW5IkYrEYHR0d+L7P8ePHKZVKy67dEPD6E4itAQEBAQEBAQEBAQGvGEnXSdxzD4l77jnvM2tkhLm//EusyUmc+QW8XA5h6PiOC46DUBSQJJzZWdxikfDWrYSvueaS+7Pn55n50/+FdeoU2sqVeKUS9RMnqB8+TO3gQTIf+MBSsS7ftmjUvTorXAhFwTfNRp7lywQsa3yC/Je/jHn8OCgySjaL7ThLUQNqayvp978fY+1aIpcoCLgus47f3/n7PDf7HFWnSlOoibJVZro6TXO4mV1tu2gKN53TRpHO/4p2eP4w+2f20xprI7TBQn1uBjNXY6xyEox22gZW0b56DbKqEI9tIF94FkWJ4/sunlejufludC17Xr/n7FeJAT6+7y0JhpZTYqQ0he13saNlBzWnRsmcZ3byixwufAVwiUZW097+XoSkMDT8F5j1KYozOla9hhabwLJiGEY74VgCMZTjt+7+HY7ELE5MfZeS65CWbaYth8G6ICxJdGS3oUoqqdl7aVucQ1GjxNUqPaKEIoUQ0Q7qcgcxBL7vsuBUkGwXR8qTz08SCnUh0cKi7Z43RyEEA9fsonvjFsr5RY4/8SjDzz1zzja2VSecSC69DofDJJNJZmZmMIyGcFev15FlmfSZCAkhJBTlfKdarVRkdrgh3Db19BKOn32A4Ps+WsykYhfB00DNgZ1ASCa2bFItNqMqK4g2halWqxw5coRsNkv/BZzinHoInv2Hhjgqq9C0Bm74LYx4GzekYtxwteZSISDV3fj3I2Ds6CLHp2s4dQ9V8ZmuOxRyJltTGjgejq4APtZcjSbAbj2TwZuWyU1VKMzVGNjeck6fvufzzHdGWJyoICSBbTosTjnIVZvOmoN7bBEzG8IczGMOF0m8seeKBFdZls6LuPU9H1mRiCYNdtzdy6FHJigvmsiKoGdTlnXXtyPJAst0GT+Wo16yiSYN1l7XRrKlERuRbAlz+sginushyRK+72NWHdr6k6/CEQ54Nejq6jrn9X/5L/+FP/iDPzjnvfXr1/OpT31q6fUv//Iv88d//MdMTExcstDq29/+drZt28aOHTv49Kc/zXe+8x3uuOMOfvd3f5e//Mu/fFXnEfDaEIitAQEBAQEBAQEBAQGvKYXvfg97YhK1vR1negYRjeJXqw03ajIJ9TpSKIQvSQhNI/med6O2Xbx6ePW555j/f/+P2v7nGiLt3r3geUi6jufYVB57HL9Yovnjv4eSSjUcrZqGm88jJRK4+Ty1Y8dQ4nFm/tf/IrbnFsLX7kQIgVetMv+Zv8YcPIXS1ASOg3n8OOGdOwlfcw1CkTHWrVtysPqOgzU6iu+66D09CO1cgaYt2kZb9OorodedOo9PPs6h+UOcLp4mb+bpjnfj9oTwdRlveJ5KtU7frhvYet3tqHojF7Cn59eYmf02+dxeJEkjnb6B5uY7L7u/ZGIHs7Pfo1o9hapm8LwaJXORQTvEingvsiQT02OslUaRq4OY+gZ0Jcxi7klMa45kYhv1+hSRyACqmkeSLHzfplYfRzfa8H0fScgISaIts51i/in8fI45y2fIFZi+TTLcSVxPUyge4LHWJLdKC6yRy8g41LUI0vZfZ3Xb25h85DHm5ubwvDypSJp8qpNQRMPHJFcZxdGirDTCzM8/iGUvEjI6SSS2IkkN15lqGKRa2xnYcS1TJ49RyB/GiOnUcwqKFibV63P02P+H61SIJ7ayYcN2SqUSc3NzAMiyzMDAAG2XuEYnTxzj2W/fSyW/CD5EUim23fVWOtasazg3p76MseJpwuHTOB5IsofnLyLrMpbdTbVyLfF4Q0SMRCJUq1XGx8fPF1sL47D3b8CqQKYPHKuRubrvs3DLJy5e2OrHFN/3GX5hHhFSSEVV7MU6Xs2hbrrUTBejNYwa0/F9cObqJC2XnOXiaI0HJkIIHMs7r9/FqQpzYyX0sIJje6iGglVzMOZrKIrEIj5mWSHTGsGZrVI/niNyRrA1qzazp0vUihahuEbzihh6uOF0bu1PMHxwntJCnVBcxa67OKbHinVphBA0d8e5+aejlBcb+bLh+Nn7w+Zbulh1TQtmzSGa0lHUsw99VqzNMHE8z+JkGSFL+K5HLGUwsK35tTz8AVfB2NjYUrQIcJ6r9UIIIeju7mZ6evqSYuu2bdv4xCc+AcDnPvc5nnzySeLxOH/0R3/0ygce8LoQiK0BAQEBAQEBAQEBAa8p5onjSMkkvmni2zZSLIZbreK7LrKu41oWcjqNb9v4noeSTF60L69WI/ev/4pXLCHO5N55uRz4PiKRQHgealsb1tgYtWefJXbbbYS2bCF2y62UH3kEe2oKa2ICIQRyKkX9yFHMwUGyvkdk925qhw5hjYyg9/aeLYilKJjHj5N+/880BNgzWGNjLP7932MOj4Dvo3Z2kP7ABzBWrXpFx8v1XP7u0N/x1NRTyEImb+aZqcyQ1JN0J7rx2mNMJ8rE1Ca2XP9GDCW01FZVE3R2/DQd7T915RmfgK4309vzUSanvkIxdwzQ8MLXM5I/Ru8Zp6vqV8mQo+aryGoaTQ2jKDGq1RFkOYKg4fJMtIdRjTx6qUhPaILM/Ci5uo66+R2omk7Ga2fjSJ2Ni9M4uAxGQoyHUrQm1uN7Jo5TYCgUYaY7wW+kO9GFTE4v0NHVR2tLF7feeisnT55keOS73BKe54exFBOkwPPxvRq7pWO0zX+RkXrDiSyQSSS30dvz0aV4BYB4a5TeW8oszJ7AceqEOmPE4huoKd9FqqkIoTIz801isVPcccevMj4+h2VZZLNZurq6Lrqk367Xee6+b1It5km1NZbgF2ameP6+b9HU3UPdGWJm5luE4ilw4xTnJ/HEIsJpp7fvFzg+6eC6hfP6fTG+AKA4N8upZ59GnPwefbXjqB2bCUkKaArEWmH6EFTmIPqTJc75ns/iRJlKwaIiwDFdXNcnDthVh7npKpm6CyULyXbwLA8vX4fmCFbNwfd9mnvi5/VbL9u4jk+yNczCRAWz0ogciGgCRxaohkq1YCKAdFTFWajh+z6Dz87y3P2nyc9W8X0fI9JY6r/j7h4yHVGyXVE23NTBib3TVHImqibTv62Jvq1n7xOyLJFoCp03Jmhk3YZi5ztojajK7rf1M3Z0kfxslUhSp2ttmlj66ostBbw2xOPxc8TWC/HZz36WU6dO8T/+x/9Yem9qaor29vZLtntRuH3iiSfYsmXL0n6q1eorHHXA60UgtgYEBAQEBAQEBAQEvKYozc3UjxxFaWpqLN+3LIQsgyThFgr49XrDHWrbSOEwC//4TzT9+kfRXrZME8AaHcWZmUHt6mq4SsfGGjl2nodbKCDHYo1l/xMTuIVGpp6QZVIf+FnCu3dR+NrX8Oo1jA0bkc6IqdbICMX7v0941y68ahXf83kh3czBRBpPCNYa02waPIZXqcAZsdV3HBY+93eYx48jxWI4s7NYp05hDp6i408/ecGxXynHcsd4duZZWiOtRNQIjudQskqcyJ1AkRRc38X3ffb07SGkXFjEuRqh9UVkv5XpfW3MjRVxLR8raaF0CaYr07RGWsGtY7t1dDWFg0TRNonKKuAhK3Hm/CQVS9CUNVi1SaXl0CBh28H3Y/SHZSLmIzCxE+Xpz5CsStiJ9XhOhW31Mj+npvm7yhQxNUzerOP4cG3zCvzmLmq+h1etIJ3JsW1ubqa5uZlk6l4sa46N6gFesJooehpJZ5Bt8hxWLU8o3I8kKbhujXz+GXK5J8lmGxm5rmsyeOrPqDnPk+7oQVXSmNYU1erjhEUvoVAjgsLzMpTLJ2hpHmXTpl1XdBwXJsYoLy6QaGpZEkgTzS3kpqeYHzsN0WO4bpVIqBO9FeJNzdRrM8iKSv/GPVj6EHNze6lWq+i6TrVaRZblpWXL5cUFHvviP5GfmaZHz1F3yiwMnaSlbyXhy4g/V4XvN/5JV55D+0o5fXiRUt6kXrYRAmzTRdEkHFUGTcIuWOTLNvGQQkSRKFke+ZkqjumjGDJda9N0rT0/NyGWMdAMGVmRaO6OsThZwaw5mJIgYyg4qkBIMvWShaMIwgmdmeEiz3xnhPxMQ2gVQlAtWEwPFTj4w3Fuet8qJFmib0sTnatTlHJ1jIhKJHF5h+PlyM9UGTk0T2m+TqI5ROfqVCC0/gRSLBaZnp5eev3444/T09NzSVc8wOzsLB/96Ed58MEH+cIXvoDv+3zqU5/iqaeeeq2HHPAqEYitAQEBAQEBAQEBAQGvKbE9t2ANnsKZm0MKh7FnZpB0HSWTwZmawnccRDiMmk6jr1qFdfo0hW9/m6Zf+ZXz+hKaBrKC7zjo/X34ros1NHSmjxD6mtUNgUiWULvOFvYRQmCsWkWlrR05GlsSWgGkaBRnfh5cF723l/vXb+VbXQPYugE+PBpOcGcixUdf4kayhoexRkcRkTDmyZP4tt14/+RJpv/bn9D+3/8EJXX1ldh932eiPIvtOUTUhnNXkRQ2ZjcymB/EkA1aIi3c0HEDN7TupvzY41T37wcgvHULkd27Gxm4l9nHZGWS6co0GSNDd7wbIQQHfvA9xo8cIp5tQo6qlBbm6ffCnF7lMVQYQsYnKcc4NWvxrZn9yJrEipDKutQAB2q3cNRZh1svsFKa5IPiSZrSHm5iC3o4i26EEXPH4Nl/hPxpRHYV2ov5tNUFbrAq5Ntu5OniEOFoB6vUAjuTcVy3hmlOo2kZ4vHN58wjldzF5OQXiSnz3GzUse1FHFEEXyCrCSRJwXFKmPUp6uYkk1NfIx7fhO35HD75p1RzDyDwcd0KutGGrnfgukfwPPvstSFpgIdl5674HEpyIy7Bcz3kM1NsZG/KyIqCf0Y0flHAk2QZWZXPiMkya9asoVgsMjQ0tCS4btq0iRVnMohHDz5PfnaaTOcKHD+Jnz9FqL5IcXaSsCFDaRp6b4JI00VGeBl8H0Yeg6PfgNIUZFfBhndCy/rl9XeFuK7H4P5ZwnENAVSLFiBwbR87LGElDIy5hvCJ7yNJglRHlNWmi1iZIr21mWxHFCGd/6AhljHo2Zjl1P5ZPNdHkgWKKmM1hfAdD9V0kQDheEhRFWNViqOPT1JaqAONwlcgcGyXesUmP1uluFAnHNOYHy/h+5DpiGJE1PP2fbXkZ6o89fVTVPImii4zO1pk6lSB3W/rJ5698MOVgB9Pfv3Xf52Pf/zj/N7v/R6qqjI2NsY///M/X7bdJz/5SR566CE+8pGPsHbtWvbv309/fz/f+973XodRB7waBGJrQEBAQEBAQEBAQMBrSvjanYBP6fs/wJ6aQuvrQ6gKciKBmy9gnTqF2tODkoiDJOPX65hHj+J7HuJlrjqtpwd95QD1Fw6gtLSgrejCr9dxyyWUbBPuwiKuLBPZeQ2hLVuW2rmlEtV9z2CPjuAWi7jVKnI4jO/7uPncmTxWhUJrGw/v2I0+O0vH4jwAuXiSJzdt5+0evOhX9T0PfA9ncgbftpHjcXzPw/N97Ilxqk8/TfyNb7yq4/RsocK9szmOFFKMq3uQ7QV61DIApmsykBzgv17/X4lq0ca4vvhFit/5bmMsQHXfPqzRUdLvf/9F9+F4Dl86/iUem3iMkl0irITZ3ryd93S/g8mTx4gkU+jhhsibbGmjd8JmT+xmnJ4Ebr7CF5/8DEPOBGFLQlZgf0rjYe06IpiksLDweN5bTdi4k99V/xZHjOLbC0hqL5oehuJEQ8wTL1mCL2voVHlnz128Qw3h1OcZy99HsXwQyy1hGB10dLwPXT+36FFz853UaqMUi89jmtMocozm7BupLDxNJX8M15+m6s9Tlz1836JYPMjTxz7JwXqcVPUgYVQ0IRMTCvX6JLIcPyPQls8eL6eEJDRCxsXzFV9OpnMFqbYO5kdHiGWbEEBxfo5M1wqyK7oxLRVV+za12ii63oLn1bHtAq2tb0WWG67I6667jnXr1lGpVEgkEggxw+Tkv2I7RXKFaWS1EStgkmQ8eh0t9mOEKhNQ1qF9K1zzi8vPax19Ah77NDgmGLGG8Dp/Em7/I0j3Lq/PK8CqOdTLNtGkTjwbYmaoQK1kISSJaNqgkA2xMF2lRZeQIipyTENO6KTnqoRbwkS7YhftWwjB+ps6SLdFmBkt4pguY8dy1FTBQtogUjDxixYiY5C+pw8lZVApWPief454K0kCz/VxbI/CbJW93xyinDMBiCR0Nt/aRWtfoxCaY7nUyjZGRD0j1l4Zwy/MUcmbRDMGlbyJ5/nMjZU49dwsW2//0RQtC1gemqbxZ3/2Z1fdTpIkbr311qXX27ZtY9u2bfz1X/81v/qrv/pqDjHgNSIQWwMCAgICAgICAgICXlOEEER27SJ87bWNbNWXCKi5r3wFZ2YaOZlcEoe8eh2tpeWCYpGQJDK/8Avk/vkL1I8dA98ncsP1xG69DXtyAq9cQevpIXzNDqQzxaqc+XlG//JT7C0cYCJUI9JSYuPhHB3xLoQkoTQ1Eb/7HgAmTIdqUzNdiRhSoQCyTEc6zUlfYrxu0WU0+tR6e1Hb2qkfO44wjIbQWqkghcNI4Qj21PR5Y78Uh8s1/vfoDCXHJaHFcLVu9loxyuZ+Yu4UAHf13rUktNozM5R/+DBSLIaSyTTmubhI+ZFHie3Zg3qR4it7p/by/dHvE9fj9If7KVklHp18lGY10zg3FzjmaTnBqvbr+dcH/pQJO0eT0oxqeDh1i5o9QMkL0WkeRJcFhiSTtZ9BDw1yuE0l7VnEKgu4xQpJK4bcdwtMH4TyNERbwfegOAmpXjjwJcTEM6ieQ2+qF2vTB3DSnRhG15II+VIUJUpf329SqZzEtnMYRgfG6EEWBk9QDU9Tdy0U10WNRvDCSbTIGoZyR4jj4Sgp6i4Id5aSiBIRLvX6OKHQCiRJo1I5CQgEEun0DcRiV+7qlBWFnW95J89+5+ssTkwAPi19/Wy7663IikpY6aar6+eYnPx3LGsOSdJpyt5KW+vbz+knmUySTCYpFJ5jZPSvsa0cCAkvmifUJfDMNiRJZ0HrY9jxWNnfROaNb4fsapYstcvh+HfBqUN2ZeN1pBlmj8LQw8sSW33fx6w4ILik81MPKYQTGsX5GonmMJnOKDPDRRzLbQjWORNHl2lLqGhdsUZBO8sFSaBkLu/4lCRBx+oUHasbjvPmnnmOPDbJTN4CIYj1Jth2ZzdaS+NhQ7Yzxkl5Bs/1cOyGG9ZzfRCQaglzYt8M5ZxFojkEQlCcq3HgwTFSrWEmBwuc3DeDWbHRQgp9W5sY2NZ8QdftyynM1UAIZoeL2KYLAlzb4/jTM6y7vn2pOFelYFKYq6GHFNJtERCQm6pSnK+hhxWau+PI6usXARHw6vHII4/wB3/wB+dEEPi+j2magdj6E0IgtgYEBAQEBAQEBAQEvC4IIc4TUCM7d1L+4Q+xTp1CTqfxqlXwfWK37Llo7qja3EzTb/0mzswMeB5KayvO7CxesYAUDqP19CC9pDL0zA++y9+LJxhc7SGQ8Lp0nlsw+dlqjA1bbyeye/dSxmpalYnIErVojNSZGICi4xK2HdLq2a9PQlWRfvpnEC+8gD87i+c4SKEQWl8fXrGI2nquC/NyPLRQpGA7rIoYCCFIZbt5JhfCdG2uDaXY1b6L69qvW9remZ7GLZfQunuW3pOTSayhIeyZmYuKrQfmD+D7PmkjDUBcj1O0ihwoHOaW7l5OHz6AqhvIikJpcQE9HKGpu4/i/Bzzs+OIlIIuhZCRUBUf4aqoOICHoiTJOiOs8h5GSDa1kETBrWFKEl0TJaoRDaunn0i6DePI/TB3vDGoeDtoERi8H6ItoIYRMwfR9+bR7/5TuIDQCo1CYtPVaVQpRVNyFcIswoEvkjEjeJH1jIuDeLJPrGRBbAWTfgTTE0RlFxsPU+tBsmxwi7iijq630NvzEQyjg1x+L65bJRZdSyp1LUJcuTMRINHcyp4PfJjC7MyZ1y3nPGRIp3YjiX6OHHmUmZkihtGK40zR399/znXv+x5T0/fi2EXCkZUIIVDkAmb1BfLHD0C1E8eyCCeaaX/DT0PLq+A8LU83zseLCAGyCpXZq+6qUjA5/MgEc2MlQNDcHWP9jR2E4+cXhZJkiZXXtPD890+Tm6yADHpYwQirKJpMKKbRs62J5FgRZ/ZMoSBJoPcm0C9QFOty9GzM0rQixsJEGVmWaOqOoRlnf8ZX727h2N4pFsfLCNfHc3wAUq0RBna08Nx9o8SyBpLcOK/xrEFhtsbxp6cZ3D+LJAlCcQ2z6nD40UmMiErX2vQ5Y6hXbEqLjazXaEpHCEE8G2L00Dyu46OFG+Op2Ra1ksWp/XOsvb6NE3tnGHx2hnrFQVElsp1RjKjKxPE8tuUiSYJ0R4Rr7u694LEO+PHm85//PH/+53/Oc889R1NTE+vWrWNoaIh77733Rz20gCskEFsDAgICAgICAgICAn5kaF1dNP3qr5L/xjewT4+hNjcTu/02IjfeeN62nmVRefxxagcOIjSVyI4dhHbsoLpvH4v/+E+4uRzgIyeSpN73XqI33wzA3pFHOZkyaXdSaL6Mj89ofJ5HM2VufM97ztmH6i6ywh/k2bxLMdIosFR2XG5MxVgZboh+E3WLz0/Oc9gP0f2zv8jdX/oCWcdCSaXwCnm0/n7Cu66smNKLzFkOuiwtCW2qrNIRaaIn1MHHVp9fbEvOZJHCEdxicSkb1iuVkCJhlGz2ovuxXZuKU6FklohqUYQQ+PhIQmLTbXdRK5VYnBzHc11C0Rjr33Ar6fYO8jPTZJwoUT9EXpRJ+zHAR/WGibGdgsgS9h06nAOAR4UYaqiKbGpU5Qqn2zXcpIyf/waqlqLz+neRqYUaQmqqB37wXxpCazhz5sIIw8IpmNgPA7eeN4+h/BD/euxfGSuNIQmJDdkNvD+1mXh1EZHopNnVUQvT1J15cB0mkqcR1igJ36Qsr0f38yhemaLcQ1hM0RQKsWrg90ildgAQja6+qvN3IYQkkWy9cCGcer3OI488xexsDsMwKBRmmJmZwbZt1q5du7RdI3N2ElXLLF0bupEg3tREItRLdaKTWCZLz5btZDq6GrEYPshieUXSgEY267HvQKwVhASuBZ5z1ul6hXiux7PfG2VutEgooYEPpw8tYNUcrnvHwAVdnl1r0ughhbGji5hVh6YVMbrWpPC8hvAqKxJu0cQcLuDVXZSMgd6TQCjLc3BGEvpFC1qpmsKdv7Cefd8eZX68iO9BS0+c7Xd1Y9VchCQa+bEvztdrOHgPPDhOvWJjRFUkRSKa0inM1Bg7urgktvq+z9Dzc5zY23C/yppE+0CSTbd00bs5y4GHxnAdH7vmYNYcQOB5Ds//4DS1ssX4sRyyKpFqC2ObLqOHF/Acn1R7hHBSw/d95k6XOLF3mi23rVjWsQn40XHttdeyceNGWltb+fKXv8xdd91Fd3c3X/7yl3/UQwu4QgKx9RLEYhfPfLkUn/nMZ5bV7oMf/OCy2qnq8kK4/+RP/mRZ7QAGBgaW3fb1ZLl/YKxbt25Z7bq7l5ehMzg4uKx2hUJhWe1yuSsP+H8podDyA9nD4fCy2hnG8qpuZs4sp7talvvztLi4+Lrub7nnfn5+flntotHostpp2vKfpJ84cWJZ7f7lX/5lWe0ikcjlN7oAP/3TP72sdm9+85uX1e71Jp1OX36jC/BK7hfLpbOz8/IbXYCVK6/uy2NAQMCrj7FuHS1r1+JXqwhdP6/Ak1et4jkO+S9+kfIjj55516f69F4Sb3kzlccfxyuX0fr6QAjsyUlyX/4yxqZNKKkUswnwTA/Nb7gTBYKoKTEer2F7Nqqk4vs+3xn+Dt8d/i55s4DuCkqlTlZ0vIe3t3dxT1MSSQjqrsefj85wpFyjRVeZ2LCZf9BC3HniENc5dYzVq4i+4Q1XXRxrXdTg2WIF2/NRJYHj+ZRdlw3RC99P1Y52Irt3UfrBA3ilUuOI2DbRm29G7TpfnAV4eupp9s/uZ7w0vlQcq0NLYJsFdjbvJp7OcOP738fM6GF8O0y2s49oqvF7INHUTFtbLxsmp3mheYYZKYfjWMSEwV2hER71YozaMbZ5FTxUOplEUg0sPUa9XqVmaKTTa9G0Jur1CSbKjxBf899Q1RTkTzfEPO0lfy8KuZHt6tTPm0e5tshn9/0Z47U5WuPduLg8MfkE4co8H1QMhFUBRSccWkGlMs90yqFGHsWXkYWM75ZZMDYTtUcQXgG0Fazq+5kloXW5+L6P4xQQQkZRYue8f7RSZ3+xiuN5bIiFic1MMD8/TzabRZYb12U+n+fo0aOsXLkS5czPgCyHUdQ4pjmPqiYB8DwHWVHo3nwjTS/JddxfrPCN2TynaxZdIY23NCXZnljG3zfr3gozhxvOYyEa56F1M/TtuapuFqcqLE6WiTWFULXGHGVVYn68TG6m2lj6fgGau+M0d1/cqSrHdcKbm69qLMsllg6x5/2rzxTrolG8Swhc1yPRHGJhvEIso4MQLIyXqVdtJFkgJIFtuSxOlJFlgaQI7Lqz1O/8WJnDj06CgGjGwK67jBycJ5zQWbu7jc41aaYG89hmw6Wqh1UcuyHwHn96GkkStJzJhtUMBc/zKefruK6H7zciGbSwwvRQ4UxxtiBO4CeJY8eO8fjjj7Nr1y6efPJJPvzhD6MoCo7jXL5xwI8FgdgaEBAQEBAQEBAQcIXs3buX//N//g99fX0MDg7y6U9/mqamZVb8DjgHIQTiZQ++3HKZwle+Svnxx3EXFnAWF9FXr0JtbizRt6enKXzjm/iWhbpixdIybbW1FWtkGPPoUexIhB6RIVoHxyshazqeaVKPCrpa+lBE4yvRidwJvnnqm8iSzECyH9M1GSud5hrxDO9t27I0psPlGierdfrCOvqZ/RVWr+bLAyu5ZlUnbaELu+Qux62ZBM8Wqxwt1148IqyKGNyRTVz0eKV+5mdQOzqo7tuH7/lErtlBdM+58Qv2xATlRx9lfPIE/9D0AiTCDCQHmCxPYOaHqbqwR0S5uXgvxZEfcLBdp+DbCCVNn/kuVtNwBwtJYttdb8H9ukV2ZpBJJYceiXDH9e+mt8dg09C/cdKO0OLXSHqTKELgeRaOU8X3PQyjDV1rAiFhGO1Uq8OUK4OkktdAvINqtIX6xD5ykSbCeoQm10Ux4tC89tyJTz7PkSf+lMnKcXrQUcwatGyAcDN7q1O8o20j8ZHHoV5Ad0zMqISjh5CNJgwtRc1vQqpNMG1rHNF/gz7d5Jf61pOJXP0ydGgUvhrav5fZ0eO46mninQWizRqJxDY62t+Lqib5/kKRf5iYp+S4+MC35wpsM6s0+f6S0Aqg6zq1Wg3TNJfEVklSaW66k7Gxf6RaHUGWDRy7QDgyQDJxVhw+Uq7xv0dmKDguSUXmhWKVoarJ7/a2sjF2laaHdB/c8ccw/AiUZyHVDb03gXHha/Fi2KaH5/rIL3GdyoqE5/qNLNLXieJ8jalTBVzbI9UWoaU3jnQF2akvIoQ4z/0qyxJbb+/m+QdOk59uRBpIcmM7PaywMFlBUWVs06G0UEfVFZp7zx6/6aECdt0h3dEwNMhRCdtyGT+6yJpdray9ro1K3mR+rISiybiOh2YoZDoizIwUcf2XRk341Ms2ruNj1R1c26dWtBCSwGkN47k+0tWlYAT8iNmwYQPvete72Lt3L/fccw9btmxhxYoVNDe/Pg8ZAl45gdgaEBAQEBAQEBAQcAVYlsUv/MIv8PDDD5NOp/nWt77Fb/zGbyzbUR9waXzfZ/Ef/5HC17+BV6ng1ev49TqeaSLflELStEZG6egoCIGXy+EJ0XDGahq+67L4hX/BKxbps+q8x1Z4YqDOZJuHmVHQs53csfldS8LkscVjVOwKA6nGCi5DMUgbaQ7OHaRqVwmrDcGq6nq4vo/6EkFTlyQWbZeK6y17vllN4f/ra+OJfJkp06ZZU7kuFT0nJ/blSJpG/I47iN9xxwU/t8bGmPv0/8aamuRQR40FMUX3fJrwpk3sdqBnehJVUripYwNeZRRn+HGcejfDHasI1yeYHfwMeZJc27oZgGRLK3t+7pfZNDaK69hkOleghxsC+XWr4qzJPUml4lIuN5zCvm/jOCUkIRGNrGssSQd830EIGVnS8X2f0eIwX1VMdrkV0vPzFIFqKE3ndb+NnnpJBml1ER7/c8zyBL6iIgsVSjMgJNSmNZT9MvnN7yZenIFTD4BnYycMNC1DKLkTJJkosCBKNGkWt3auZGs8TFRZnhJVzi3y+L99ntzUBLY7iVkvo5/S6b8pjm19H9ep0LTiP/Cl6UU8H9accSnPWTZ7TYUbNIO0baOqjeNVqVTIZrPnrezKZm9DSBoL8z/EtvOkUtfR0nwXqnpWvHtwoUj+JZm/TZrCiarJDxaKVy+2QiNCYNN7Lr/dJUi1hAnFNMqLdWKZxpwqOZNQTCXZsrxVb1fL9FCB/feNUis1nKmyItG7OcumPV1XVKzqUiSaQtz47pUU5mr4Phz84Tj5mSrRtEG9bFMtWTi2R71i07kmTd/ms/Eevu+fl18tBI0CXD7EMgbtK5MsTpXxPJ9YyiCeDaPoMqou43tQWqwTSWiU8yau46EoEo7lISSBkBoxDmbFZvTwAv1bA5HuJ4kPfehDfOhDHwLgve99L1u2bGFycpIbLxCvE/DjSSC2BgQEBAQEBAQEBFwBDz30EKtWrVqK17jrrrv4+Z//eSqVyrKjSF5NrNFRqs88i1upYAz0E9qxA+kVxLn8qHEmJyk/8iheuYzQdWRdx7FtvGIB8+RJQuvX45VKKKkUTqFA5dlnQQiELDe2TyRw/Hm0nh5UVaVvTCM2vsj92zsJdfewp/sWrm29dml/0pIQ6C8JsJ7voUrq0mcAKyMGSUVh2rRp0xtxQJOmTauu0L1MV+uLJFSFu5qSr6iPl1J64EHsyUn0gQHk2BQilMObL2NPTrFSWqTXcRnRFE7kThJ2p9AlQXOxSqQnhu9HkK0hnph4jI2pLqql53HcCuFwL8296xDi3GXJqtSNZilEorfT1Vknl3sM05wjHOmnWHyeSvk4QpIBgWlOE42uwnbKnDj5RxydfRJFK7N/43W0LFZIl2YZETZrQwbXvVSQmtwPpSlWplaSqA0y7Xu0qiG8ygLTxii96TW0l+agOAZtmyCSJSoGKdcn8HPDiMwAnuegY9GX3Uh7enmxcZ5nYttFhp/fT256ilhLlFKpjp6IU5m3WTgJvTe2USwdpFA4Tc726DDORkdlVYUFVUNu6yB/egghBJ7nEQqF2LRp0zluVwAhJLKZN5DNvOGiY5q1bIyXZP4KIQhJglnTXtYcXw2MqMra69o4/MgEuakKIDAiKutv6EDVJGolC81QkNWrW+JeKzUyS8v5OrF0iM7VKYzo+dFcrutx5PFJzKpDqj2CEIJ6xWbk0AIdq9NkO5cXk/VSJFki1RqhMFfDtlxy0xVcxyPdESFc0ijO1xjY0cKOu3qQFEFhrkYlbxKKqkiyoFq0CMVUHMvDqjr0bswycmiBI49NUq/YKKqMWXPQoyqqIVFeNInEdbrWp5k6WaA4V8exXYywipAFlbyJABACRZPQoyrjx3OB2PoTzurVq1m9ejWf/vSn+a3f+q0f9XACroBAbA0ICAgICAgICAi4Ag4ePHhOrrssy3R3d3P48GF27tz5IxwZ1J5/nvm/+SxObhEhBCVJJnLddWR/6cPn5Z/+pOBWKri5HD4gh0Lg+4hQCL9Uwp6YQI5GwXPR167F3jeDkCQ802w4xkwTORxG7e1F0hsCaGxFH/opj4+l30181+3n7W9jdiP3jdzHRHmC5nAzdadO3szzxt43YihnnYatusq7W1J8cXqR45U6CMioCu9vzxL+MctFtEZGkCIRhCSx0kwS9zTmIlVaSkXMmEPNrlCXfebr8zRTRnVtPBrinBASiiTjmpMcOv6HSPYUAJJkkM3eQlfnB5YE1+Hnn+Xgg/dTLeSRVZXm7l6uecs7CSeSAGQzNzM2/o+Uy8fA90kktpJM7GDs9N/iejXKVokW2SRbfJ7OGQvDsul06hiPfBrUFPTf0piQ64Dv0yqHeLvexlfNSYaoI3BpMTK8b837UI58F1y7sRQeaBJ9FJ1FqtVTYPjg+0QjK8lm33DZ45efnuL4k48yf3qEaCZL37ad6Nkp5ua+i20XGD3m4vkGoAM+kiSjGh7VRQtZTuPYi0QlE0PSqbjeUuxEzfPRJIkbt20l3NHC7Owsuq7T29tLS0vLss71mohxJhPWR5EEju9TcT3WXSTz9/WiZ2OWVGuYudNlAJq7Y5QW6vzwX45TLVjoYYX+bc30bs5eUb2NSt7kqa+fIjdTbThTPZ/TRxbY9dZ+wvFzHy5VCxaVnEk4oS31bURUagWLwmz1VRFbARYmyuz71jClXCNfeH68TGG2SiwTontTli23rwAJDjw4xukji1h1F1WX0cMKjuWSm7JRFIm2/gRtK5M8de8QruORagsTzxhMnMiTm6ziWh6hqMbqXa30bs6y5to2CvM1fM/n2e+NsjhVRg8pqLqMY3vIioSmK/ief5kZBPw4sHPnTsrl8kU/9zyP0dHRQGz9CeEn8y+vgICAgICAgICAgNeZcrl8XsHHdDpNtVo9b1vTNDFNc+l1sVh8zcblOw75r30Nt1hEH1jZKN5SLlN96ilqu3cR3rr1Ndv3y/EsC6EoS9mprwStsxMpHMbJ5c4sSfeRNA3PMJAiEfTVq4js2k11/36c2VmEqqJEo+D7OIUCztwc+sqXFHX1fUAg5At/BepJ9PBTa36Krw1+janKFJqkcV37dby1/63nbfum5iRrYyEOlWooQrA1HqbD+PFzEavt7ZjHj+P7Pq1OhDcVevm2dJiJdJ0nFY82IdGLhqVEUM0qmm8yEhX4eKheBdcTJMUUnlkkGh1ACBnbzjM//wOSiW3E45vIz0zz/H3fwrVtUq1tOJbNxImj6A/cx653vBcAXW+mv+9jWNYsvu+i620MDf85jlslEumHYo5C2WLX+BSKJTMd7aRgFljvufDM30PbFginoXUDhFJQGOOWRCdrhMHJ/CBqup/11/8xiVAK/O+ccwx032Cg1EZO1jGb78bQW0mldp+zBN+sVhk/epDy4gLhRJLOdRtxLJPH/+3zFOZnMSJRSoPHmTr1PE1bZkmt0FHVOEpkBkfMIKcnSLRO4ZoKlp0inGqlXp9C15voT3Szu1TivvkiNddDEoK87bA9HmFzMo6STrB27ctyaZfB7dlG5u+JSh0hBL7vs/ISmb+vJ4mmMImmxr1zfrzMc98/jW25hGIq1ZLFgYfGUTWZrnWXL8o59MIcuZkqqbYIkiTwXI/cZIWRg/Osu779nG01Q0ZWJRzLRQs1fu491wPB0utXg5PPzlApmGQ6o2Q6olRyJoX5GivWZ9h25woUVWb00AJDz8+jRxVSKR2z6lAtWKy6poVUWwQjopBujzJ+dJF6xSbVFkYIgaLLtK9MUlqos+mWroaLN9Jw8RpRdcnRu2Z3K89+b4RaycbzfBRVIpLQ8Tyf9pXJV22uAa8dt912Gx/+8IeRLvL70/M8/viP//h1HlXAcgnE1oCAgICAgICAgIBXmf/+3/87f/iHf/i67MstFHCmZ1CyZ51hcjSKMz2NPT4Or4PYao2MUPjGN6ifOIkcjRK9ZQ+xW29FvGw5tFuuUHnyCczBQeREgsiuXeh9fRfsUwqHSbztbcx/5jM48/P4tg2ui1AUQhs20PSRjyAnEpQefADfNJGz2SWRV6rX8W0ba3wCTVYQmoYzNYWcyWBsWH/ReVzfcT1bmrcwXhonokboiHYsCVfAOUu0B8IGA2Hjon39OBC7ZQ+1AwewTp1CikTYXC6zsm0z5bvezHPeKE/aX+TWcolULY8rqRxJpZlOCULmKRwpxGl5I5uUUcJGM0I0zqWqJrGsOSqVQeLxTcwMnaReKZPpWIEQAi2kEEmkmB48Qb1Sxog03INCCHT9rGvTMueQJJ1asYi2YEG9hFozWVQ0cvU8ES1CNLMWChMwdwy6r4NEJ+z4OXj2H2HuBO2SRHtqHdzwOw0RFqBjeyOvtTIH4SyYRbS6RUvnz0LXe887RrVyiSf+7Z+ZGx1eOs9D+/fR1N1LYX6ObMfZwmsTQ08yd9ykfXU/AC2rfXz9JMgeeAaSXiG5skYk7qMo3bS3vxdZDvPznQYZVeGRXAnH93lDc4p3tKZQXmFm6Dnn2nP57bYU++sOU5ZNi65yQzJGRvvx+so/fnzxzLL+hpioh1UKM1VGDy9ckdi6OFlB1eWlAleSLCHrMouTlfO21cMqXWvTnNg3g+f5SLJErWgtFcm6WjzXo15xUA0ZVWv8PPi+T26qih5VG/cHAdGMgVt3iCzWKN83gtAVFqYr4PuEoo2HMkZExaw45GeqbHxD59I+JEVCCHBMF9fxUTUZz/PQQjItPfElofXl9G1pIp41eO7+0yxMVpAkgarLtA0k6N0cFHH8SeD3f//3iUYv7LZ+Md4mEFt/cvjxuvMGBAQEBAQEBAQE/JgSjUYpFArnvLe4uHie2xXg4x//OL/927+99LpYLNLV1fWajEsKhxHhMF6lghxvCAi+4wAgJ165q82rVrHGx5EjEZT29vOW+joLC8z9n7/CHh9HTqexZ2dZ/Pw/g+cRf+Mbz/ZTqzH/V39F7YUXQJLAcag89jjZX/llQps2nbtPy8KemCB2xx24hTy5f/lX8DxEKIScSWNPTbL4+c+T/chH0HoaBZS8eh3ZMPCsRiEctaUZY91anOlpfNtBaWkm9VM/hXqZas4RNcLq9GqgUcjoazM5nilWiMgyt6bjvDGbeFVFsovh+z6DVZNp06ZJV1gVNpCuYJn1S9EHBmj+jf9A6cGHsE6fRrvmGtpuuxW9rw9z/FE+19RJuK1Ee20ES7KYVWQmvDimdg+OsZLN2dWsyP1PPK8YGR4dAAEAAElEQVT+knF54PvIcnhpnBccP41CPxdi1rQ5pWynZs4THR8mbErEpBRCymE7EiFbYk3LWiKyBrIC8kuycFfdCW2bYfYoKHrj/9pLMpN7boD5k3DyPpg7Dmq4EUOw9k0XHMvIc88wMzJEuq0dWVHxXJeFiTHqlTKSEOe4tGXVxyq9JNdXmSfc5GIVkggSaCEFJZonluxi1cr/TCjUAUBYlvip9gzvbUvj+byq10+9Xmf//v2Mjo7i+z7Nzc28e/t2UqnUq7aPVxOr6iDJ4pz7iKRK1CtXli0bTmgsjJeXzoHv+7iWRyRxYWf52uvbUDSJ00cWcW2PFeszrNndimZcnRQyNZjn2NPTVHImqiHTuynLwI4WJEkQTevMny5B4sx16ni0Vywi0x626eC7PvFcnajpUZgVSJJEKK4BPrzsUsh2RrFNl+EX5hsRKrKEHpJZubOVaOrimdBCCJq64tz+8+spztcoL5qE4hqp1vAVxTME/OiZnp7m4Ycfpq+vjz179iy977oun/zkJ+no6OADH/jAj3CEAVdDILYGBAQEBAQEBAQEXAEbN27kb/7mb5Zeu67LyMjIOTmuL6LrOrr+yoolXSlSKER0zxso/Pu/Y42PI+k6bi6H1t9H6BW6WitPPUX+y1/GmV9AaCqhTZtIf/CDyLGzhYWqzz6LPTmJ1t+/5GS1JycpPfBgw92qqme220/twAHUzk4kw8D3fayhIfJf/zrIMl65gtbTgz0xQf5LX8KenUVSVeTmZtS2NuR0GjkeQ47HcQtFagcO4kxPE7/7Lorf/hb2zCyu44CiIEejGBs30vKJT+BOTeGZJtqKFUihK8+vrLoe/2t4msPlGklVJm+7fHZ8jrLr8r62zCs6rpfD9Dw+OzbL/XNzLFo1dOFzQzrFfxoYuOpcWH3lSqxWi9K8TdEcpao8QqJQYHUsy46IglY/xYTmUfcEITy26gs0SY+yvmUH8dYOJuSbmJr6CiAhSTqWNYeut5BIbAOgpW8AIxKlOD9LLJ3FsW0qhRy9W7ZjvMyl5fs+987kuHc2z4K1GsdUaYsc5q3GDzCQsRaydBdcyrZKqFoFZwqa1zTiA15KrBVirfi+T2F2BseaJ9nShqJpIMmw8xdh1R1QGG+4W7Mrz6v8/iJzp0dQVBVZaVynkiyjGWHsWg0fH9dxkBUF3/fxLJ1wWxXftxBCp14pYpZ8nApIkokkS4TUZnzfRpbP//mXhODV1Ol93+fpp5/m5MmTRCIRZFlm/NRptDGbTX3r0FJh9P4k8kXckD8Ksp0xxo/lsC0XVZNxHQ+r1igMdSX0bMgyM1QkP11FDyvUqw5GVGXF+gv/TCqqzNrr2ll1bSue6y85Uq+G3HSF/fefxqw5hOMqVs3h8KOTKJpM35Ym+rc0k5+qkpuqohoSWsGkXQhCrVGUqIrvgztfI16xOV20QW6IqOGYRseqc0Xx4QNzlBbqeK6P74PnOHiuRzxjXJFoKoQ4J7Yh4CeDubk5Vq9ezV/8xV+cI7RCIx/+4x//OH/yJ3/C3/7t3/KLv/iLP6JRBlwNgdgaEBAQEBAQEBAQcAXs2bOHj33sYywuLpJOp/nud7/LbbfddtFlf68nibvvRjIMyj98GK9cJnrrLSTuueccUfRqscbGWPzHf8Kr1VBaWvDrdSqPP44ci5P+4Fl3jVcqAf45kQEiFMKrlPFME/mM2GpPToDnIRmNpfdCCEQ0SuWRRzGPn2g4V3Udt1REGCG01lacQoHqD36AX6shlYpozS1IoTBC0/CLRbxaDb2vj6aPfITFf/1X3IVFhKKgdnaQ/pmfQdZ15J6eC86v+txzlB/6Ic7sLPqa1cRvvx21o2Pp8/3FCscrdfrC+lJhoynT5vsLRe5pShJTLi7a1MtlbLNOJJVCkq5e3Hl4scS/T5ymVBsDr0wJjS/X0iQo8turd1y0nT07S+WJJ3Gmp1A7Oohcdx1VdYLh4b/EdgqAzNz8gwCEwz1skKdZlGUqniCiamRUjYRTwC08R/X07xIP76B15y/gtTiMz9xH3Z4jEu6hu/vD6HrDIZxqbWfTbXdx6IffJzc9iawotA2sYtOtbzxvfIfKNf5tOociCVZHY8xZ7RzzJDKOwZ2VEUbVBO3yPsL2GMKuQNd22PlLDQfry6gWCzzzra8xNzKE5zpE01k233437avWNDZIrmj8uwzhRALXsc+6VQHHMmkdWIVrWcyODiFJMp7rkmzupWN7C/XaCJ7vMXeihpaUUI0QApVaaQFHFImmUxw99vtk0m+gvf3dSJKKVa8xc+okVr1Gur2TZOv5LvGrpVgsMj4+TiwWIxwOI9uClZUU6hQUC9MYmk79eI7End3I8dfn4Q+A7/lMDxWYHS0hJGjpSdDcE0MIQde6NNMjBWaGCniN+FSyHVH6t13YcW7VHSZP5Cku1DCiKh2rUuy4u4dT+2cpLtRp600wsKOZTMel78OyLCFf4EextFhn6IU5xo4sYlZtwnGd7g0Z+rc1o5/JdZ08madetki1R5aiD4pzNUYOztO7OUvbQIIdd/cwfGCecs6kI64RK9TRzuSpmlWbuu0R0iQiHlRdcCwPRZdZ8bLohONPTeM6Hoom4Tk+PuC5Pqf2z7L+xo6XDz/g/09oamrizjvv5CMf+chFt/md3/kddu3aFYitPyEEYmtAQEBAQEBAQEDAFaBpGp/73Of4zd/8Tfr7+zl58iR/8Rd/8aMeFgBCUYjfcQex229viJYXUhWuktoLB3DzebSBgYYopOv4lkVl3z6S734X0pn4BLWrq+FMrVSQIhF8z8NdmMfYtAkpcnZ5t5JuiAq+4yCUxtcQa3gYr1Ih1NqCMELUjxzBHhsjvGsXQtdxJibwbRvfc/GrNcyREZx8HiFJqO3tyGciAWK33Ya+Zg3msWOgKIQ2bkTJXNx9Wn3mGeb/39/g1apIoTDW9+7DPHKU5t/9jyjZhsMub7t4+EtCK0BUlig4LnnHvaDYapt1Dj54P6cPvYDrOMSzzWy69U5a+gbO2/ZSPLIwx2J9gQgmIa0RDTHrmHx98hS/0reOsHq+a82enGTuf/855tjpRhEw16Xy5FOU3qdgO0UMqZPi4rO4fhkkgaubSF6JpGTRHMsgyyEozWL7NggV29Bh+iD+43/JN1ub2Ds9juNU8OUc11T+kV+55g8xlIZTuG1tE2pmgHJugWhkLdFMhNncP2PPFYjHNpLJ7EFV4xwo1ai4LmvOOIybEhlm5hY5aPZxc97H8T32VdfR3nodLe94P6S7L+hI9X2f5+/7NuNHDxHPNCHJCsW5WZ751le59ed/lUjyypbQe55LdkUvIy/sZ3FynFAsjlkpo+o6q669jnRHF6MHnic3NU44nqR70xaimQSFwnMsTA4xNPM4ofgYSqSA59moSgWrrOLp3ZAUzMx8A11vQvU28vRXv0huegrf99FCIVbvuoH1b7jtFQmupmWR9wUptbGEPrGoEK4q5NUKsYSMEgtjz1apHV4gurv9Mr29ehx9cooTe6dxnUaUxPCBedZe18aqa1pRdZlr39TH9FCBcq5OKKbR2p+44LJ+q+aw95vDzJ4u4nuNTIqRAwtc+5Zern/XSnzPR7wCq3Bpsc6TXzvF9FABs9qIMSjM1SjO18jPVtn1lj4kWcI2XRDnRh/IqoRVa8S1CCFoG0jSNpAEwBwuULh/BLvqUCtZVPImku0hx1QyTRHijofreAgEVt1FeYnbtlowl1ytkizAF7iOy/RQgYe/eJx0a4SeTVli6QvnRdfKFhPH81QKJtGUTseq1EVzXgN+vNi+ffslP9d1nXw+//oMJuAVE4itAQEBAQEBAQEBAVfIzp07+ad/+qcf9TAuihCCC9q3loPnnu3zDD7gLS5SevAh9P4+9NWrCW/ZQmTntVSefhrfdRH4KM3NJN/2tnPahrZvR3vgAayhIaR4HK9SwSuV0FZ0IYUboqwci2F7Hm4+D66LWyohxWJ4NQVJUXAKBbxiESkSQWgqC3/1f8l+5NeQo1G0zk60zk4uh+/7FO+/H69eR+9rFDrym5uxhoaoPPU0iTfdA0BXSEMXEkXHJa7I+L7PvOXQbqg0X6To0OGHH+D4k48SisbRQ2EWxkfZ+/V/Z8+Hfplo6vLFf16kZOaxPYeQGlo6hqqsYznzjJXGljJlz2nz4IOYp0+jn4lz8F2X+qlBpsZz1MLgzowQChdQHIGv+jiVKYyWFZSdwzhOGcmT8L06SDIKKiE5BekEhcl9jJZcREQnoYSo+Q6Pjj/Miuj/4x0bf5OFhYcZG/88tpXHx6WY+w7kfBQlhiQpFAsvUCwdpL/vY+fpppIik2hppTYzRWlhHgWfTEc3G9/8duTMxc9ltZBnZugk0WQa/cy1k2ptZ2FynOlTJ+nfvvOyx3hudJgXfvBdCrPTWPU6VadKtVwn09LJ1hvvpG3lGoQQrN59w3lt0+nroL4CyT+GO9uJnJrB8o5j1VRKYxnSu5JoWgzHqbCYe5LZZyZZmBwn3daJJMtUC3mOPfEILX0DNHX3XnasF+JQqco/L9TY19yDb1lskT3eV3WxfRdJkzEMAyEJJF3Gnjq/eNRrRWmxztBzc6i6TKK54aYt5+oMPjNL19o0oaiGrEp0rL68ID5+LMfMaJFEUwhZlfA9n9xkhcFnZ9n+xp5XJLQCjBycJz9TxfN8tJCKrAqsakNAnR0psjBRoWlFjHR7hOEX5rDqDpqhnCmUZdO3pemCYrnWFcOL6xQOzWPZPsJviKczNRfZkFGEQqVgIksSinpuLIgR1cjP1JCVhrjruS744NgeC2Ml5kZLzAwX2f2OfiKJc93KlbzJU98YIj9dOfOQwuf0kUV2vaWPUOzCmbYBPz7MzMzgui7yRX6HP/zww69Z9nvAq08gtgYEBAQEBAQEBAQEnIe+eg1SJII9M4PS3IxbKlE/eBChaeT+9V8Qmk5k9y4yH/oQmV/+JcI7tmONjCBFo4R37EBtazunPyWVouk//AeK991H/dDhhiMWkONnow6kRAIkCa9UQug6uC6+66K2tiJpGm61itB1Qtu2Icfj1J5/jvIPH14SSK8I38eZmkZ+SfyDkGUQAmdubum9DdEQN6Si/HCxxJTZWGaeUBTe3ZI+x+36Ina9zumDL2BEYkvOSs0IsTAxxtSJY6y89rorHuL2uMb356DsaYQlFxMVz4dmZggrF85iNAcHkSORs65mWeL+vjK16jzN1CgrHu2aR0hSUYSPv1AFWUKORvF9B8tZQEgeChopN0vKzYAEpfoikqEjVVTqVJElGSF7PDP5Q9685ueZnPx3bLuI69Vw7Dy2vQhCOuNmTWBZeRYWHkNVU6yNv4WoIjNRt2jWVGqeR0XVeduWTdy6qR8hBJnOrqX81IueQs/D97xzilchBPg+3pmHBJeiWizw9L3/TnlhHjkeYcyZxiyVKSQUzJUOWniCLjZeso9EcyuJ5hYWJsZIy33Uy3UKczPo4TCxlhejMiRss8z82CjRZBr5jKM7kkwxPz7K/OmRZYmtM6bNn4/OMGM5tCUTTC0s8pgvswGLnb5MOpVeyoz2bQ/pdRTaSgs1zJpDqu3sdRqOaxTn6pQW6oSiVz6W3EwFIRouUgAhCfSoyvxY+VUZa2G2hiQDno+sSwghkGSBY7kYikTt+VkKJ3OkYipdvQnGRopUFk0A0m0RVl3TcuGOZcHJ2RqW6ZLWJDwhM1e1mc+ZJCbLiKiGWbFZdU0rWuhcSaZnU4bZkWLDFYzbcPQK0MIK4YSBGpLJT1U4fWSRtbvPvccOvTBHbrpCqjWCJAs812NxoszIwXnWXvf6OZsDlse73vUu3v72t/M//+f/ZO3ated89s1vfpOPfvSjfOELX/gRjS7gagnE1oCAgICAgICAgICA89BXrSTxtrdS/Na3sU6dwllYAN/H2LABJZnELRYpP/Ioxrr1RG+4nsju3UR2775kn2pbG5kPfWjp9eI/fZ7id7+D0A2EYeCVS6gd7UixKG4u14gcMAy0jg7M0VGELCOnUiiZhktUaDr1gwevSmwVkoTW20P1mWeRs9lGRXO7sYRY7TgrSMhC8KsrmtkSj3CkXCMsS+xMRFgbvXCRLce2zhTokhgVM+REmYgwiEoutmUubWdPTFB6+GGs4WHUjg6iN9+M3nuu4PbezjXcN76fF+oRTKIovkXKPs6ebITO2IUdn0pLC+bxE0vZo6eVEk/GF2gzs7RIk2QUHw8fRzORyjrOnE3NO4lTS5BM7CHVAdLwwySKDunQADICcsPkhWBYkYgKgRASnudiexZWfZFS6SimNYdtL+I4JSRJw0fC92xKpUOEw/1UKsew7QJTU18hXDjI22If4nuVZkbqJoYkcVMqxns7mogrrVd8DiOpNOnOFUydOIaq6UiyTGlhHiMapbm777LtpwdPUFqYJ9XewdHcUfJKlVg0TFNBMF6zeerQP7OpXqeja3ejGBcNR3R+epJKLkcknSbZ0sa2N76ZvV//MrnpKWwLFA2a18Li9BBWvYAWtWhp2okk5xsOxTP4fmNJvKxdXnh0PJ+HcyX2Fsr4PuxMRqk4LtOmzaqIgSQMMiGD0WKZ51aGue20wHBl3JKFV3eQdJnQmit3Vb9S9IiKokrYdXdJSGwslZcuu5zd83x8118SV42oiu/55+Tp2qZLInvlhe4uRTxrMHXqjMBqn8lJdX10Ieiu28jDBUxDBtenN23QcVsXpbqLHlFp60ucJ5S+iFm0mJ+uIlQFJ9zYxlUkxKKJV7TREgarrmll7XVt57VdvbON4efnKS7U8H1wLBcQGGEVLSQ33MqyRHmxfl7b+fESvutTmq8hqxLhuIasyixOvn7O5oDlc8cdd6BpGh/4wAeYnp6mr69xLzt69Cjd3d184Qtf4IYbznfaB/x4Eoitl2C5eRif+9znltUudBXVUV9KqVRaVrs/+7M/W1Y7gObmCweYX4477rhj2ft8PclcIuPrUgwODi6r3fXXX7+sdh//+MeX1a619cr/mP1RE4/Hl9Xu3e9+97LaPfvss8tqt7CwsKx2AwNXl+H2Ilu2bFlWu2Qyuax2y0W7gi8RF+Ouu+5aVrsTJ04sq12xWFxWuwtVIb8SEonEstq90mIaV4vnectq92//9m/LaveDH/xgWe0AwuHlVd594IEHltXu7rvvXla75d6DpQs46QICXkuEECTe9CbC27djDQ8z/7m/AwHKmd8lcjyOMztL/egRojdcj+95WCMjeJUK2ooVyFdwn0u8/W24hTy1AwfwFxeR0ynSH/gAen8/laefJvdvX8IeG6O6b18jokCWUZqbqR8+jFss4dfryOnU+S7HyxC/6y7MoWGskycbxbYsC33NGiK7dp2znSZJ3JyOcXP68oXGjGiMWEcbX5n9NlNyFU/4eI5LU1OEnc2Nv2fsqSlmP/2/scbHkcJh6ocOU312P82/+Rvo/f1LfemKxp9tfSOfP/4NDuQOERI1drWv4Z2r3nnR3wOxPXuoHziINXQKKRZnRB+jnpZQ0+sZPO2T1haJxB0MrU665iGlQJJ17PnVjB6q0/yWd9K3+c3wxP+BhVEA/Egzx2v91KRpEB4hHyq+hS9cOmomp8c+h+MUcJwCihJHCAlJknFdF8cpUyg8i+fV8X0fx6li1sdZJ3+BPf1/xISjE1ck+kI65fJRhhcewjRniEZWkc3eimGcL0S9iBCCLXfcg1mpkJuexPc8QrEYG2+5g0TzRdyGL8GxTMDH8R3y9TyGbCD5MpLp8OapUdrKpzDGT0KyD9a+BWfdO9l/37cYO/QCVq2GFgrRtWEz2+56C7f94q8xOzyEbdfIlb/D7OwPMC0TIWuUp+IUDs2Qae1j/NhhhCShaBrlxQXCiSRtA+fHQbycf5qc55uz+YZxF9hXqNCmN/6+ks5cC5qmkUnEKcR8ImvSyIcXcXJ11LYo4U1ZtK7LX7++5zE/NkppbA6jbBCNptFaIui9CYRy5T9b6dYILd0xFo4toqoSpiJRtX26N2SIZS6cM+q5HkPPzzNyYB7LdMh2RFm9q5XO1WlOH14kN1nFiKlYdQdJEvRszl7xeC5F94Ysk4MFrKqNVbGxqg6SIsjik1AljI4okiTwPR97tkqsYtN2Bdm3sixQJLBcf+k9SRbIqmDF2jTr3zlwwYxagEhSZ+sd3Rx+bIJKzqRasvBcn1RrGCEJPM/Hcz2iL8tsdSyX3HSV3EwVRW24dFVDRg8phONBhIAS3oMSfnWKWSri1XFWX4g3vOEN7Nu3j9nZWcbGxnBdl66uLtraLn4/DPjxJBBbAwICAgICAgICAgIuitrWhtrWRvG738MaHz/vc8kI4eRyLPzd31E/cgQsCzmVJvGOtxN7wxsu2bcci5H96Eexx8bwymXUFSuQo1E8y6J+4CBCljHWrcMrFrHn5sBxMIeGwHVBksB1sUZGKf/wYWK37LniORlr19L8O79N5fEncBbm0fv6idxwPfIyH/JCQwCsb8ww/myZSEVC9VU8WSWf9njOO8EatlN+9FHsiXH0gQGEJOH7PtbgIKUHHjhHbAVojbTysa0fpmgVkYRETLu0YGasWUP2Ix+h9P37sU6fJtW/Hq19GD8exsr0MnrCJr/o4nkqP33MIxSKUO6/ASPSTpUJxo8com/rz8GbPg3TB8D38JvWo//t/2VL+IccV/Ms4mDIsNFRWWkPnBFaq3iuiSdZCCEQvoRck/AogeXiJxRkuZE9a9k5RFVGt0fYmtgKQLF4gOHhv8R2CkhSiFLpCIXiCwwM/Cd07QKimufC9EGSpWluuXs3sxUDx3HJdK644lzcTGc3WihEvVBA0BAaRdkjHamxpjLKlKxSTXSQwYcDX2RkymHo2UOE4ylimSbq5TJDz+4l3dbBwDW76Fy3AYDZe4cpHp8i2RFFuGEMK8tCcRylW2fgmt1MHDtErVQn2drGxlvuJJa5tGg4Ubd4cKFEUlXInskJXrAcxuoNp/Si7ZBWFVzfZ8a02ZmIkO5JQE8CPB8kcUUPaR3b5plvfpWFgyN01HuoE6IamiaabSK0Mk18T9cVC66+7bHakMmrElbZxpMFykCS7jd0XnQsJ5+Z5fCjEyiahKzKnD66SHG+zg3vXsk19/Ryct8M+ZkKqZYwfVua6LyCvNcrIdEUYtdb+xh+YY6pUwVcxyPZFKKnaqM7HtKZTFghCSRNxp6tXlG/alSjozfOySOLVGoCRZaoVR1CukzPjuaLCq0vkmwJo+kyFR80Q8axPco5E8f2sE2XZHOYFevOvdbHj+cwqzay2nDnygpU8nUkOcSK9cszEgX86Ghubl62wS3gx4NAbA0ICAgICAgICAgIuCyRG27A/Pw/Yc/OIkejOAsLSJEI4e3byH/1q9T2PYPS3o7QdezxcXKf/2e07u7zlsi/HCEE2ooV57xnHj+ONTKC1t2NdCZ7UimXqT/3HH65jBQOI3QdtbMTt1Yj//V7idx4A5J65VW39d7ey47taplgjmRzKy1+Cs910CMRcpQ5OH+Qn+KnsMbGEZq+5MIVQiBFo1ijoxfsTwhBQr/ylRChDesJbVgPQNqu8sTe/8mpwiky6TT13iaKuSm2ngyhzcWo7diOF2le2s/SMvdQEnpvAkACulZtpvpUnjVdiyyKk4RqYZTZJJkdCUI5D1evITQJ160guRramIErXCg4hB9VsDcZuDenEbKCbedwvSqSdNaVNzP7PWynSDg80Ih08F2qlVPkFp+gtfUt507QqsJjn4KxveCYqIpOR9dOuOG3cCSPhcXHcJ0y4XAvkciqi4p76Y5O1uy+iWNPPkqqopM381iJMGuS0xTwcPUE6VAW1DDMHmPi4D4kKUoo1hC8Q7EY9XKRieNHGLjmrBu6vLiI5GQRtdYzxxVUTaNWKnLrz/8KlZtvwa7XiWWzl82lBZg2bUquS79+thBSSpWZs2y2xiMcq9SYsxoFnVaENH66PXN2zvKVr4QZPfAcI8/vZ5WyjbAWpyZVqNQLCFtDGpKx+hPofckr6qt6cA57uEC8LQKGjFe2oWzhT1eg9/xr2bFdRg7Oo+gysTNuTSOqkp+uMjmYp29LE9nOKK7bED9f7RU+yeYwW2/vZuvtZ98rPTxG7fDCUnyB7/v4tof8soJUl2LD2wfw6scYH69gWw7JiMLaG9pJrrq0UOzYLs/dP0o5b5HpiuL5PgtjZVzXI54xaFoRp2dj5rziWPNjZWyzsRrJdTwcG2RFIhzTyHS8Oo7OgICAKycQWwMCAgICAgICAgICLkvslj24pSLlHz6Mm8uhZDMk3vxmtO5uav/3/yKn0+C61A8exCuX8Wo15j/zGdr+4A+QrjIuyzdNfNdBKArg4+YLuAsL+LaNlM0QXr+h4Qo9dQpndhZndJSZ//pfSX/wg+c5RF9PImoEZEEsedaxaBdzRLWG2KF1dlB7Zt9S7IHv+3jlMtqOHRft0/d96seOUTy5n7JtEl29g6a165EuUrH6RcJqmF/e/MvcO3gvxxaPEW3r4tbNb2ftmiSHf/ggkVgY3fcxqxU8x6F99doL9rP2xj0U5maZHjqEVm5C0XX66jUSXzmKb9qEIx7Ru28lPzBOfXoIVy6j+gnUGQ+JCsZeh3pnHWdAw/NMVDVDJLJyaW612ukzEQRnXIRCBiFTNyfPH8zJ+2D4EUh0gh4DswTDj1DLtDCsnqRaGwV8ZDlMNnMrnZ0/gxDnOzKFEKy7+VbaVq1lbOQYj0w/xjF1Anm0iu5ptKVWElHD52zvv6yPl+aIvki2q5uZ4UE810WSZTzXxTLrZLu6AZYKp10pTZpCVJbI2y6ZM87WvOMSU2R+riNDxfUYrJpEFZnt8TApdXlf76dPnUARKiEiOLKDJCtItky1nCemJXEW6uiXj8IFwBoqIHQZ6UyeqRTTsGeqWGMl9AuJraaHY7qo+tnr+UVHqVV3lt6T5cs7a33fBw+ELHBLFuapPG7RQk7pGP1JpPCVPYwx1qQxR4vY42U828U3XaSIitp55aKllgmx/Vc2s2akgF2xia6Io6bOPmSw6g6O5RGKqedcR4uTFQrzNVRdZm6sTLVg4p2JIyinTFZfGyaaOj+OoVowqZdt9IiKHlbwHB+zYiNdhegeEBDw6hGIrQEBAQEBAQEBAQEBl0WoKql3vYv4HXfgFgooTU1IhoFbroAPnm1jnzyJV602xFXfp/b8C+S/+jXSP/PTV7Uvrb8fOZ3BnpzEs6xGzEC1iuDMfvI53Ll5nLk5cF2UpibMwUEW/uaztHziE8jRyGtzEC7DjtYdPDH5BKeLp0kbacp2Gc/3uL69kc8fvekmqvv2YQ0OIsJhvGoVpSlL7NZbLtif7/vM/tvfMDT1rxSSeRx8nPujhB7Yw7Xv+yjJlktnQLdH2/m1Lb9Gxa6gSAq6rOOstKkWSowdOUg5t4Cq6/Rs2U7f1msu2Ec4nuDm9/8800PHGTz+VxhHB4k+UcKPabhRB60gIX3lOXrfcy1TR/P4doao0o9ZHsXKDOKP+UgjJl6fi6LE6er6EJLU+BoqhCAc7iGf34emNZ1xETqAi2F0AFBxXJ4sVJisW9xw4lE6JQ1NPxOpoMfwFY3Jma9TSRuEw/0IIWPbOebm7yeR2Ew8vumC8xJCkG7vIN3ewSb/FqYqU8jPf5Hs0W8jS2ojrqAyC4pO5+adTD15iGohjxGNUS83amZ0rt1wTp/923cydfIYC5NjSJKM57mk2zro377zkufpYnQZGjelY3xnrkDBaTiPHd/nzmycnpCOEIINseXllb8URdVwfQdPeMiejIuLj38mK1wsCaeviJdpfr7rUT+Wo34qR0/NZi7v4hsxkCWsmoMsiysuhOX7PvUTOWqHF/AqNnJSx83Vccs2QhJw5vPqqjQjx3KUc3XS7VEGtjeTbjv/XqG2RAhtzFL8wWn8moM4IwTX9s+itUSQY1eWgSoUiejAuQK7bbkcfXySieN5XMcjnNDo2ZilY1USPazieT5mxaFQqWHVHTynIbQKCSoFk+d/MEYsE1pyAb+I5zdEas/xkISE5/kgQLoCkTogIODVJxBbAwICAgICAgICAgKuGDkePyfbVI5GCG3eROFr9+KVy0iJBH69jhQOo7S0UHnqSeJvfQvu7BzmyZMITSW0aRPKJQqSKqkUqfe+l4W//VuskydBCOREAq2/H+vECczDh8FxQZaREwn0lSuRDANr7DTm0SOEr7mwcPhasya9hp9d97N8a+hbLNQWiKpR3tT3Jm7svBFo5N82/eZvUX7oQczhEdSODmJ73oDed2HbYP3YEUbnv0CxLY9T1ZCFQGktUss9wL5vtHDrz//aZR2ucMZxewZFVdn5tnczcM1uyrkFoqk06Y6uSy7Pdr08Uvwkrauasb59AE9xIK4QrsuEVBt7dgz3B/PEZ8HVO4j0L5CJjuAZi3iyRU7KsBBaSbbpTpqyt53Td0vzXVQqJ6hUTiLLBq5bIxIZIJ26joLt8KfD0xwo1/ABtexh5fOESsNENZVoOoPs25RlE03rWhJxNS1NpTxPpXLyomLrUNXk8VyJvOOyKmJwQ6qFyDW/BI4FY3txSzPMSxGear+L4z0301sJ4Z44SH5mCi0UYtXuG+jZvO2cPqPpDDf9zM8xeugFSvNzxLJNdG/YTDiRvOw5uhBCCH6uI0u3ofNkvoyPz+5klFsy8Yuer3JukYljR7BqVVKt7bStWoOsXPprf+faDZw+dIBZb4w2twfJBtXTiCpp5LSBdgFH6sXQ+5NU9k7jVW2EoeCVLYQizivSVX56itrBeYQQJHUZpWSxMFxgylCQFJmuNSmae68sQ7l+PEf50UaetNBk6kcW8EwXfSCJbCj4rsf0UIETR/P4IRnVUBg7ssDiZJnr3jFA/AKirjNdRY6qKH2JxkMAz8eZrWIO5glvPZulmZuuMD1UxHM9Mp1RWrrjDYH3DK7jgTjrzD3y2CSDz8yihRXKizWmhvIMPTdHNGXQtSbFqt0t2JaLbbrgNwpr+Z4PCEIxjVrZZna0eJ7YGo6pRFMGrttwCsuKQE8ZF5xbwI8/jz/++HlFtPfu3cvQ0BDve9/7fkSjCrgaArE1ICAgICAgICAgIOAVkXznO6k+80yjiFW5jNA0tN6eRrGruknxm9+i/NCDeOUKAEpzM9lf+jDGunUX7TN6w/XY01M4s7Mo7e0o6TSSYSCFw1iDg/iWiZxtQuvsQDJC+J4Hno9nWa/XtC/I7vbdbG/ZzmJ9kbgWJ6ye6zzUOjtI/+zPXlFfheEnqMWLWGUdxVMRCHxHYMSrFCcPsjg5vrRE/WoQQpDp7CLT2XXZbSuVIY4f/8+UysfwfZtExUGWDOLGSvTccRAyaGEwmgh3F6icPIFRN5E0ge9EUfQabVGVVvk29K4PnScSxmLr6e/7GAsLD2NaM0Qiq8hm3oCmZfjubI4XSlV6wzqy63JSdLDS3Yddn8Gdd3Dmh4k3J5Eizbi+vdSn7zfEqZdmw76U54tV/nx0mjnLQRaC7y8UeTpf5j/2thG6+T/hL5ziK8On+I4dpxjK4pXrPN27mXes3cStGkRSKWLpCxe3CieSrL3+5is/GZdBkyTe2JTgjU2XFzwXxk/z5Fe+SGl+DgBJkVmxcQs73/Ku8wRX3/NxFmr4tkdr7yo23XYnx594DK84SEZuI55uIrGhg+j2VuTIlWchhzZmcQsm5kgRv1xDCimEt7WgdZ8VTt2iiXkijxRSkKMaCiBHFPS8RXJNmnh/krb+xJVFB3g+5ednqRQsqj7IqkTE8xGej1+xwVAQssREycZyfZrOCLjhhEZussLY0UXW39hxXr/OYh0ppJyNt5AESAInby5tM3Z0kRceHKNesRGAosr0b29m/Y3t1Cs2x5+eZmqwgBCCjtVJejZmmTiew4ipWFWHWqlxzdqmQ2GuSmmxxsxIEc2QcW1vqV9ZkRrRCI7XWEXgvjzUAtoGkkyezIMA1/Xxz2zTsTp5ZSfu5cfV96nkTRzLI5Y1ruhcBLx6HD58+DyxdefOnXz2s58NxNafEAKxNSAgICAgICAgICDgFaFkMjT9xm8w/V//GKEoqB3tCEXFGh5C6+6h9NCDCElG6+8H38caHib3xX+j9T9/4kwu64XROjqQ0ynUpibEi8WvHAe1owM5HsOenEKoGr7nYU9OIqdSGKtWvU6zvjiarNEaufQS/yvBN2R8ycd3GkIPAK4AGVC8hsD8GuL7PqfH/p5S6QhC0lHVBM6qPNKTVWr5QTTbxCMCko3eEcfUBaHyMJ5lUc0ZSCGZ6KpNaDEXMXYQtpzJ4nQt9k7v5fjicUJKiO0t21nd/eHz9n+sXEeVJHRJIj83y6i/AiVzG3vyT9Luz1Opu4yJTaQ7dzI1fS9CyEiSjmXOoukZEomtF5zTV2dyLNguayIGQghqrsf+YpW9hQo3pSKc0Fv5qiqIhST6z+SgTps29zvwxv4VxLQfv6/Rvu9z6OEHKC3MkenoQkgSZq3K6YPP07VmA53rzkYeuCWL0iPj2NMVfNdHjmv07NpEz69up5xbIBSLE4rEEMsQ2CRNJrani/BiHbdio6SM85bduyUbz3RR0meLPGkJHcl0aV2dwuhPXvH+rKrNwnABq+rgKhK+DzgeYeHjn1mC7/s+NdNDe4loLIRAyIJq6cIPZ5RsCHOogBTzl5ytvuejpBpjti2XI49PUitZaGEFVZPxfZ/h5+doH0hw5Ikppk8VMKIqvg8nnp6hOF/DdTxUXaGcrzeEUdsDIdDCCp7rUy7UkWWJeNZA1WXqZRvVkBtFryyPcEyj6WUuYYCO1SkG988w/Pw8jt0oJhbLGMQyF37gcCnqFZsDD40zO1LEc32iaZ0NN3XQ3H1lTuOA5fHRj36UiYkJTNPk9OnT3Hvvved8blkWMzMzP5rBBVw1P36/JQICAgICAgICAgICfuIw1q0j+da3UvzB97HHxkAI1LZ2jPXrsb7zHdT+/oZLTAiU1lbsyQns6Wm0zs6L97lxE1rXCqyREeRsFhwHN58netttRK/bzcLffg5zcBDfslAyGVLvfQ9KU9NVjdv3vEbOrGFcUvj9UZDadDvGxN9RCxexyz6KEEhhE9s2iEYHSLVf/Ni9GnhejVLxAACqGgUE7u4kzrSJNF7ArAgkwyK6thmygvLwOKk2gUeceiyF5XnUNcEJyWKidJL00HfY2ryVr5/6Ok9NPoWPj+d7PDbxGD+77mfZ3b77nP1nVAXb8xvF0KpVfEnmvuY9TEbW8lPzx8gVTfxynLvb3o7n2yzmnsBxSoQj/bS3vwfDaD9vThXXY7RmklXPuhZDsoTjexya2Utm6ts8U8+waN5Ia7yZF78yp1WZ8brNjGUvFav6ccKu18hNjBOOJxFSQyTVQ2FK83MsTk3QuW4DM6bNYKVG9okZEqMV1JSOkCWcgknp0QlSbx0g03F5t/PlEELw/2Pvv6PkOM8zb/j3VO7cPT05YZAjAwJBgmBOIhVJKlCWnGTLQfbK2vU67He+7931OT7r1/Lu6/VrW/ZaOvbaCla0ZInKIsUcQBIgEpExA0yOnVPF5/ujhwOAyEOQCqzfOTgHmK676qmnq3rQV13PdWvZCFr23EvY1aSBYqkENW9BiA3qHsLU0FLmOWvOx+RQiaIdkNEErqWCELgVh8CDoO7ilx2Cukc6ZTAlTzU2870A6UvS7efOvI1saMWdquJN1RCGinR89LYo5oo0AMXpGtMnSnhOgMw1EEJgxXU0XWX0cJ7ZkTLJtshC46+GoTA3ViGSMCjNNhbEWxk0owJUVUH6PmZER1EEiqpgRDRc28eueeimSiRhsHZ7F6n2M+dVSkl5rkE179DSHUO3VFRdpZa3OfD0OK0fTCw0HbsU9j85xsiBOaJpE0NTKEzV2fWDYW754CqiyUvLqw25fP72b/8W13X567/+a8bHx3n3u999xutCCDZs2HCe6pCfNn76fkuEhISEhISEhISEhLzheHNz1HbuJChXMJb0E7nmmlPu0UUghCD9wYeIbNqIc/w4IhIhsmED5UcfJahWCWo11FgzO1Q6DkLTUawLu67UeIzWj/02+a9+FefYMdA0km+/j/SDDzb3v2ULzvg4BAGB72MPnSB63XXNBl2XQH3PHorfehh3Yhw1lSZxz93Eb7vtgvmlbyZWax9Lr/047oG/paoVCZB4voVmb2XTvQ+hvY7361IQQkcoBnBq2XIQU6i8Vyc22UbfWBuWN4reH+Xg7AlsrYyi9RDzK2iWQcH1+SdvmBEvQBpZOPRFvnX8W5SdMt3xbuJGs7v7cGmYbw9+m80dmzHUU2LOzS0JnsyXOVazUU2TGSuOJQNWNxxqWiuVxgidPW2oaoS+vl+hs/N+fL+KYbQv5LeeNaeKQkbXGLcdsmgo5SLK1Dixeg6vYy9uIkcaHcWbYbJUoCdzFYqiUXB9YqpC20+h0Aqg6ga6ZS0074LmgwQEmNEo354u8NXJHF7F4ZeOl7AMhZV6hKiqoGUtvOk6zmiZyLrzZylfKk7DY3akTOBLsr1xIvEzBTo1YWCty1J7eZqgUZsfLETWZVEv0YlZnJ5ieN9uhvYO4zZiJOIDGA0fBARCULQUuvuT0PAwB5KsvTFB/YVJcuNVFEUgJWR74/StbTnn/o2eOKm3DdA4nMcv2mgdUSJrsqjz5zI7WsGueWiGgmHpBH5ANd9At5oCqe9JNOOUM/hVl+rAhlYGd89QK9jNXFZAMxSCIEAoAs1UicR0rrq9l+kTJWolh3rZwal7WDFtweGqmyrTJ0sc2zlNcaYOQDlXJ9kaoZq38dwAzVDJT9ao5BqXnN1arzhMDRWJpAyseSdwuiNCfrIpLg9cfe74jJArg67rfPzjH+fpp5/m1luvXBxJyJvPT+dvipCQkJCQkJCQkJCQNwx7aIjZT/0d7tgYCIFQVaLbttH6Gx993YKrtXo11urVuFPTzP7v/03j0GG8mRncyUnMFStQMxm86SnMVatpHDyEuXIFeuf5l9wb/f20//7v4xcKSMelceAVCv/+73izs9R2vICaSiFaW7GPHGH27/+e8g9/SPr97yf59vtQDAPpujQOHMCdmkJrbSOyYT3CMLAHB5n9h3/AL5VRMxncyUlyn/0simkSu/HGSzpfGQQ0XjmAMzSIsCyimzahtV5ZMaLjqodILr+J2ZOPUyoWiMXX073iegzrjW98oyg6ba13c7J2HMfJo2lRfL8OmiB13R2k3/URpl74ez43/jgHo1UqkRgpS+ehqsG1pRmOWIJBVWOZ1YXRuYlAN9k5tRPHd1jVciruocVqYa4+R66ROyN+YXXM4j8u6eAb03mGZJruuRnWHtlPt1NlrlFDN0xWbLlhYXtdT6Hr5842DaRkd7nGwUqDlKZwtBaQ2/cy3Xueg2qZddRIZWp4t/ewMqtxjV/lxUaGenkOVU8jpeSBjgxtxhsrcC8WVdNYtnkre374XUqz02iGSa1UINHSit2/gi9OzCGBFZaBpQgKfsDRWoNrEqc5O+XZWaCXy+xohZd/eJJyrtEUUJMGV93WS+/qzBnbxTZ3oKVN7BNFCMBYksRamb6kBx1zoyM8+9XPU8nN4TQk5bkajj/Ost5b0NEo1D2U7jgbHlyJgIWGVdv74owczFErOaTaovStzWBGz/9+6h0x9I7YOV8rztQxo1qzmVXDw7E93EaA5waMHs7j2h7Vgk08YyGlpFZ0iMR0+te30LM6w8jBHIefn2BmuIzn+M3PzoSO9AN61mToW9NC35oW9j85yuxIGaEIqkWHfY+Pkp+osWxjGy9+5wSNmosZ1SjPNijPNaiVXYRonnOt5KAbKsFlxI34riTwJbp+WoTEqwkm/hsbWxLSxDAM7rjjjnO+9oUvfIEPf/jDb/KIQhZDKLaGhISEhISEhISEvIWQUlL85rdwR0cxli9HqCp+pUL1ueeIbt5M7Pqtl7QPZ3AQb2oKra2tuR9FOeP1/Be/SOOVA+h9fajZLI19+3CGhtB9D+n5OMeOMTs4iJpKkX7fe0nceed5jyeEQOg6s3//v2m88goEAe7EBNJxiGzejH3kCEGlghACb3KSwle/ivQ8Um+/j9l/+DT1l19Geh5CVbGuvorWj32M6vPP4+fyGCtXNgWeTAbnxAnKjz9xSWKrDALyn/8C5cd+jLQdQFL63vdJveud2MeON8+1r4/EnXdgrV59Se/N+YhEe+hbu/gv2DIImBw8yszJIRRFpWvlmktqjgXQ1/cRGvY4MzM/wPMqKEqETGYL/f2/RmC08rlknN31LuJuG0b1OBOiwOfSKmraYk8JZDSL0Xc9qBoKkDJSjJRHcHxnwcVadatYqoFb2ctkeQdWpJdk4moURWdTKsbGZJR6IKl1xjmm1JgdOUG2p5cV191Az+rzN1lbOH8p+fz4HA9PF2gEARIwygV6dz+L6nvEujpJN/bjF1xO7phl3X09fDBxjK5AMGzeQyoWYVs6zh0tP92ZlatvuAmAwZ0v4DYadK9ex/pb7uApzaLkVVgdM6kDhYxB22SDOd3FjvioZRcloqF3x1/X8X0/YO9jI5RzDVLtEYQQlGYb7Ht8lGxP7AyHq1AE1soM1srMBfZ4NjKQHPjBIxSHJ8m09iBadAKKzM0dJ7A6iKTWoUcNrr2u86yl8/GMxdobz46WWAyarhBNGghFMDdWwW00hUhFU/CcgMCT1EoOTs0DwIhorN7WtSDurr6+k5Vb2hl+JceRlyaplVysqEbf2hZWX9984FArOZzcn8OI6gvL9526x+RgEbvu0qi6ZLqiCCEwLJXidI2g7hFLm0gJgdd0NhenG6Tbzy0av5ZYyiDdGWV6qIRuqk2Rt2BjRjSyPa/v+gi5MPfffz+f+MQnuP3223nggQewbfuM14MgYOfOnaHY+jNCKLaGhISEhISEhISEvIWQjQb20aOo2SxCbeYJqvE43sQEzokTFxVbA8ch98//TG3HCwT1etPRuWUL2V/7yEIsgJ/PYx86hNbejhKJoEQiRLdvxz54EBGJgmigJBL45TLu6Ci5f/ks1po16D1ndwV/leozz9LYvx+9vx/FMPDrddyREeyjRwmqVZREAlmrISIRlHic6pNPInSd2osvond3o0SjBPU69V0vU/nxj/GLRdC0M5x0wrLwc7lLmkf70CHKjz2Gmkii9qWbLtdDB5n65CfRMi0oiQTOiRM09u+n7RO/tyjBVUpJcWoSu1Yl2d5BJH52Y5xL2ce+x37I4WefwnMcJJIjO55h033vZuCaTRetV1WdNav/hCX9v065chjTaCORWIei6IyURjhWOEZXvAevdgg8HbWuMee57NXjGG0+UokhFfVVcxyqotIWaeNE6QRJI4kbuLhenavjgqnRfwQkitBIp69nyZLfRlVNhBBEVUE0odK6IQNXZaH7Wohdmot4sG7zvdkicU1lwDCRUlIYnMBo1Ni4YgWqkJzw4sxFfGanFZzZGOvSE9xilli5rIVE4vzX5U8TiqqydvutrLp+O57jYESagqcynWchCkII9q5JsKHh0VrykEEDETOIbe1Ey1x+M6XTKU7XKc81iLdYKPPNtZKtFoXJGnNjVXpXv768TykllRcnmNpzGM1V8WdqCE2hrTWG9Ew0vdJ0hK5voWPgygvjvhswdaJEtWhjWBqartKou/ieRAhQFIEQgkrBJhLTaOtP0DGQAgEdA0laus8UPBVVYeDqVpZclcVt+CiaQNPVhdfrZQe34RFvOfW+6JZKJW9TydlohrLw+aWoCqrebNIVeBIUSLREEApUi2eKdhdCKIKrbunlpeoJitN1pAQzqrH6+k5SbW+8m/6tzPbt2+mZ/x3Y09PDe9/7XpTXPMT8X//rf/2khhdymYRia0hISEhISEhISMhbCKHrKLEo/twpUfHVrvZK7OLup+qTT1J54km0tja07m6CSoXqM89gLltG8t63zR+k2QhLnrYs+VVRIMjnkULgjY0i/QApJe7UFIVvf5u23/qtM44lXXdBELUHj4OioBhNwUZvbcUbH8fP55uNrXwfGfhora0okQhBvUZ9z+7m+UabS6WVSARhWdR27yF+4zaqTz1N0GigWBbS8/BLJWI3ntmk6XzYg0NI20bp6iKo1RCmCZ6Pny9gXXMNqhVpOoCPHaP84x9fttjq1Gu89O1/Z+LoITzHwYonWHfL7ay4bhuOM43nVbCsHlT1wgJZcWqSYy88h25FSHd0NQXc6Sn2P/4IPavXoV8kN/dVIpE+IpEz3bCudPGlD/j4QR0zmkIzBaV6mUzHACuMCqNFg+OF46StdNPBqln88rpfZroywe6xZ4hrCVa1ZBmQx4hElqMoOp5XJZd/jlTqWrLZ+dzC44/BC5+B2lxzWXO8k+CG36aSSTSbYkUGsKyuc459sGZT9QN6zKarUAhB2jRwpKTm+eT8gKNBD9LPowYuT1eWsE1X+HD3NcTjr8+V/JNA1TTU05q9XZuIktE1TjYcukydUkRh19Vx3i9Mrs+m0dqjC1mkr4dXxcbT73spJcwLka8XP9fAfiVHLJpmrjKOiGhI20fmGlhRjfW3rGTDbUtf93HOhVP3ePE7Q0yfLCMDOZ+Fq+HMeshAoigKZlRDt1ScuoejCCIJg3U3XdxJK4TAiJwtzcRSJkZEo15xMSIqdtXDtX0URdDaG2f4YI4gkM25FaBqAiOi074kgVAUhAKl6Tqxy2w6lu6IcssHVzFzsozr+LR0x0iep9lZyJXjD//wDxf+/sd//Mf09Z29+mDJkiVv5pBCXgeh2HoBdu3atai6r3/964uqu/rqqxdV9973vndRdcuWLVtUHcDy5csXXfuzwGc+85lF1T333HOLqvvLv/zLRdVFo+funvnzRKFQWFTd5z//+UXVdXR0LKpuYGBgUXWTk5OLqruc7KXTMYw3t4PoyZMnF1373//7f19UXTK5OCfDli1bFlX3uc99blF1iw29X7fu4sslrySHDx9eVN0nP/nJRdVdd911i6oDqNfri6pb7OfFDTfccPGNzkHnBbIpQ0LeDISmkbj9dnJf+FfciQmUSARvdgatvZ3ols0Xra+9vBuhqqjzn7dqIoGfz1PbtXNBbNUyGaz166g8/QyKYSAMA3diAjWdJiiX8EbHUKLRZqaqlPgzM9Se30HwkY+gGAb28eOUvv1tGseOo2UyJO66EzWdaQqq893Etc4OlBNJZKOBbDQIpETv7ETr7sIdHsZatQolm0UeOXLG+KXnocZixLZto7ZzF/V9e8GfbyS0bBnJe++9tHm0LLxcDndqCnwfYZoEtRqoKsq80CWEQInFcMfHL+ctAuDg009wYs8uEtlWYukWqoU8e370Hare0xA7QhA4mEYr3d0fIJM5/+dRYXIcu14j29O/MKZ4JkMln6M0O022t/+C47DtaWbnnqBeG8KyuslmbyESadb0JfroifcwWDhOCpXAd8j5gkwkzvJMK4bn8pHV7+K5/CwnSidYllrGXUvu4nq9FbH323woN45Esr8zj9/Si6I0xVBNi2HbkkrlSFNsreXgpX8Ctwptq0BK7MIRTuz/L1Q6upAE6FqK9o530tnx7rNyP5OaigI4UmLOv1bo6MNKpClPjXPSiGH6ATHbo9rfQ6Sljz36Wn6pbS1CKPyssyRi8hu9bXxhYo6xhouhCLZmE7y9vx1Tv3KSQKotQkt3jKmhIvEWC6EIKnMNElmL1r7XvwTdm60T2B5LBq6leHCWYnkaXTGxK1XSy3vp33DNFTiLczO0d5bJwSLJtgiaoeI5PqWZOlZcR9EVGlW36TRVmmJz4Ad0LD13fvClYsV1lm9qY9cPhqkWbIJAIoBE1qJjWZJyrkFhooZQQAbQ2hcn8CSVnI2iK3iOT7o9Sq1kc+CZcVq6YrQPJC9J+DYsjZ7VlxfxEHLluO6669i1axeRSIRMpvk+TE5O8tGPfhTXdfnHf/xHVq5c+RMeZciFCMXWkJCQkJCQkJCQkLcYibvvRvoBlccfI6jWsK66ivQDD1ywUdWrCF1fcMIuEAQI/cwHepkPfpCgWqVx+Ai4LmprK+n3v4/CV7+GfXwQJRpFeh5BrYaSSEDg403PIDSVmU99Cm9iEjWTwRkeZu6f/g/Jd74DNZvFOX4MNdNCUKlg9PeTfPvbaezdQ+PgQYSm4w6dQGtrI/Xge5GNOvWXduKOjaGm0/ilEkJRiG27ASUWo+33Pk5t1y7c8XG0lhaiW7agps4USKTn4efzKPE4SuSUu0vaDYJyGek4KLEYfrFI0KijptIwL9DJIMCvVIgNXJ7bLvB9Rg4/Q7wvhxV3wW4j2drG2LEXGTl0gCXXdaFpSRr2FMMj/4RpdhGNntvxZMbiKKqK77po8w9dnUYDVdcxoxd2Mtv2DMeP/w9qtSGEYlIovEA+/zzLlv0+sdgydEXnw2s/zD/u/0eG80UadpGkpnF7SxbdHSUaW8Gq/vdw/bIEbuCiCQ3hu/DdP4CZQ5BeghCg1Udx88chNgBKcyk0MkDV5gW66YNQmYbWlc25FTDRKiiJOSJyOUqsA8eZYXLyG8Rjq0gk1p5xHtckoqyKWbxSqdNqaPhSUtRMNt7zLsRj38YaPoEOeF09eNe/g/50lsPVBqNOQN/Pibfh5pYEG5NRTtRt4prKEsu4pGZUl4NQBNfe1cfuRyA3XkVKSaYzytV39GFYr196EJYGiqAjNcA16+5meHQflUqetvZ+rn7gXSRb267AWZyb6ZMlVENFM5rL/DVDRdVVhBJgCNB0Qb3s4jR8Al/StTJN/9qW133c7hVp9j8xhhfT0U0F3WwKvUdfnGLb/csZP1qknGuQaLHoWZ2hnGswfGCORsXDjKrMnKxw4OlxQKBqgoGrW7nmjr6FpmEhP5088MADbN3ajPQZGBjg4Ycf5k//9E956KGH2LRpE3/zN3/DX//1X/+ERxlyIUKxNSQkJCQkJCQkJOQthtA0Uu98B4l77kY2Goh4HPfESco/fgwlFiNy1YaFpfevJbplC/Vdu/BmZlBSKYJSCSklsa1nOtO11lba//APcYaGCGp1jKUDqPE4Qa3ebFjlOCAEaiKBms2iJBKoiTiVJ5/EnZjEPK3pljM8TGPvPlp/52OUvvtdnBMnMZYuJXnP3US3bYP730N97z6cweOISITopk3oXc0l8y0f/hCF73wHb24WJZUi9cD9ROed6UokQnz79vPOU+2ll5rNxKamUGIxEnfeQfK++xCqSn33HvSeHmSjgV8uo8bjKNEoajqNc/x40+nq2OjdXSTuOn/zr3ORyz+H3v0MqPVmREJgERQ24PllFCWCrjedTpHIEmrVo5RKu88rtrYvXUZr3wBTg0eJJlIEQYBdq7J8yw3EW7IXHMfc3JPUakNEYysQoimCVqtHmZ75AUtjHwNgZWYl/9cN/xcH5g6QK+wk7Q0RpUEqeTXt7W9H05o5s/q8a5XZw5AbgswA6E3xupWlDHtHaJSOosa6cd1ZdD1DJj2fH6yZoKgQeKCo+PiUlDyGr6FqURAKptlBtXKEcuWVs8RWS1X4/YFOvjKR4+VyDVMR3NuVYuN4jp2uizQtXFVFrVSIvvwcEze/jaiqkDktP/PngbimsiHxxqjHUkqCmkc0prP9vSsoztaRgSTZFkFVr4w72OiJo3fEcMYrtMV6aVvWQ1D3iKzPklh1aQ3fFn3siNbMQj2NwA9IZC0aZZd62cGKG8hA0j6Q5OYPrETVX/955ydrIASdy5LkxqqU52x8P6BazNG7OsOabd24jt98aKGrRJMGHQNJgkDy1JePYNddMt0xhBA0qi4n98/RsypDW//l5z+HvHmUSiX27t1LS0sLO3fu5NOf/jTj4+N86lOfAuDFF1/8CY8w5GKEYmtISEhISEhISEjIWwTpeTQOH8abnkZrb8daswaRSFD46lcp/+gRgmoVFAVjYAltH/vYORtWxbbdgDs1SeXRH+NNjKNEY6Te9U5i5xAthaJgviZ+Kr79Riq33EJj375mFIGqIhsN4tu3o6ZSeIUCQogFoRVAiUbx8nmslSux/vN/RrouQtdPOfM0jeimjUQ3bTzz+EKgdXSiZTI4hSKKbiw08boY9uAgc//4T/iVClo2S1CpkP/yV1Asi8RddxFUKqjJJPqa1QS2g9BUnJPDWFdtwFyxAvfkMHp3F7GbbsbovfQGS55XYWLyq5hJk/KEwIhEUMwafmQ3ejRFsvPU+BdycKV73v2pms4NDz7EK0/+mImjh9BVgxVbb2Dt9tvO2M736xSLu3CcWUyzg6ToQh5/lES9AqZPoKvN+dQS1GqDZ9TG9BjXdV4HnZcQBRP4gFxw/wK0+h34tSlmWiP4QZ1odBldnQ8Qi81fOx0boGV5U6hN9oD0wKojIykwm3EWr+aECs4tkHaaOr830EHdD9CEANfmh197koiuEl+ynDHbwXBtjBNHKPavYuO6dayKvr6GUW8V3KkqlRcm8ecaCE3BXJUmtbED8RqxMXB83LEKgeOjt0ZQW6zLctcKTSF5Rz/VXVM4w2WEoRBdlyW6sf1Kn9JZ9K3JMHm8SGm2jhXTaZRdqkUb321GkEgg0xll7bZOuldmFhyw5yPwAyaOFZkZKaOoCl3LU7T2xc+aD1VTEAJyE1WqRRvdVBGKgmv7HHhmgtxEjcJ0DSEEPSvTrN7WhRnRKM3WyU9ViSRPfU5aMZ1a0SE/VQ3F1p9ytm3bRktL0xm9efNmnnnmGUzzVPaudYm/x0J+coRia0hISEhISEhISMhbgKBWY+4zn6H28m6k4yAMg+jGa4nddBOl7/8AJRLB6OwEz8M+dpzCv/0brR//+Flf/oWqknnve0ncfjvezAxaayta9sIOydNRIhHafvd3KX7rmzT27kNYJrGbb17ISjX7+ykLTjWuCgL8fJ7o9dcj9PkGRxfIIA8ch8aePbiTk0jXo/SjHxIUS6jZLF4uR+5fPovQNOK33HLBcdZefBG/UMBYsaIp/iaTHCnX2LXvEGtv2E77tddgf+PfUW0HxTSbQrUMiF23lcQdt59zn+7EBI1XXgHAWr8evevshk61+gkcZ4aWtrUE9RHq5RKyoaAnavRvacPM5gmCFoQwcJxZVDVKIr72rP2cTiydYeu734vnugghFponSSlpVMq4lWnGf/RJ7KPHkDGFeI+DbtfIOj5JL48/Nsfcui3YyQy+XyESufaCxzudSuUIheJOAr9OPL6adPYqlFQvfqHpblWFhsifpDO5lrYNf46nga6lUZTTvqrqFtz8n2DHp2HuKCqCTMs6JhMNFK+Eqkaw7Sk0PUUyeeHczsi8y7JYLFIvl4kmUqw1LaKqYNJR0MoFbi9NclMuwXRQoX3pchT158vheiXxKw6lHw/jFxzUhI70fGq7pgGIbz11fXu5BqUfD+PN1SGQKJZG5Jo2ohvbL1lwlYFESegkb+tDzoucQrt092jN8chVHbIxk8hFxNDX0rUizVW39XJs5zR2zcXzAlRVIZ41MaM6japLreggERcVWqWU7HtijKHdM/i+BAkn9s2y4ZYell17ZhRC25IEsbTJyIEcqq4gJfhuQCxtUJiuUSs5ZLpiBH7A0ZemqJZs4mmT4QM58uM1VF2hfSCBGdUJguYDiSsR6RDyxvLSSy+Rz+fJZDI899xz5PN5jhw5spBZvti+HyFvHuFdFhISEhISEhISEvIWoPLEE1R37EDv6m5mjFarVHfswCsWkY0GWm8vAIHnge9TfvTHWFdfQ+zGbSjnEDe1lha0lvNnEkopCao1hKGfVa93tNP6G7+BdF1QFMRpYlb0uuuIvPAC9d27QYKUAXpnF6l3vfPUvoMAP5dDWBHU+Knc0aBWY+bv/zf13S9DEODNzBJUq0RvvBE1EoFMBufkScqPPXZRsTWoVkE0u6vXFZXP9q1gZzSBoxukjo2x9eqtvP/YcdyDB0EGCFUjtnUrsRu3nXN/1eeeI/e5z+MXCoBETWdo+cUPE7vxxjO2U5UIitARakDXilU0qhVcpwpamYGlH2By8ptUq8cQQkfXErR3vIN4/NKaJ2rzYjVAaXaavY98n5mhQWrH9pHITbMESUxzaJ+dwO9QoGcdjq1ilKZJHnqek+tXoBsZ2lrvOvcB7DIMPw/VGUj2kIsHDE98AdctIoRgZvZHpFKbUJZnKY+/jOI+T6YeoSuxBvWGj6FGW8/jSwValsHb/gxKoyAUuqJJ3NF/plh8GdueQgiFdPo6NP3SGmVGEgmMSAS7WiFhRVgetehTAsYbVSL7XmLPoT2omk7H8pXc8MBDmG+BxrSLwTlZwi84aG2R03JAbewjBaLXtqMY8/ETL0zgTdfQWiOgCoKyS+3laYzuOHrnqXtYSomfaxDUPdSMhRrTqRZtjrw4xdRgEc1QWbI+y7KNbaiXKLRKKfn+/km+u2+CYt0lFdF5x9VdvG195yULvUIIll3bRv+6FhpVlxe/e4LiTB0r1vxsi8QN7IrL5GCRJesv/PCpMFVjeP8cRkwnEm/ek+W5BkdenKR3TeYMMdSwNK65o4+poSKeEyADSSxjEkkaVOZsom0G0WRzDJqhMLR7thknkDGIpgyK03UmjhVp7Yvj1HxSbRE6l72+xl0hiyMIAm655Rb+w3/4D3zwgx+84Lb3338/S5cuJZvN8sADD/Dwww/T1tbGr/3ar7Fy5crX1YQ45M0hFFtDQkJCQkJCQkJC3gLUd+9BGCZKrClsqLEYvm7gjo2BlE1xtFym8cor+MUiKApzn/kMjX37yP7Wb55TcD0fzokTFL7xDeyjx1Asq+lcfcfbz9qHOE38exUlGqXtd3+X2osvYp8cRkuniG7dutC8q3HwIIWv/Rvu6CgYBvGbtpO6/34U06TyzDPUd+5E7+lBiUQIGjbe7GyzQdaKFc1jRiL4+fyCQ+h8mCtXUf7xY/jVKk8PrOT5TBvZuRky7W24usZTdZe1v/ob3DY6hJ+bQ+/qwlq//pzn5JdK5L/8ZYJ6HWM+VsEdHSX/la9gbdjQjFOYJxpdSjy+hkJxJ4bRhmoKPEqYRitTU9/BdXOAwDDaWNL/G6RS1152oyPPcdjx9a8wM3IC05fYlTrVSBxpCa5NzeHqCiN2J4ot6IpdjRDHsepzdBgbSA28n6CeZXhkD0ZU0Na3ClW1mg2sHvuzZjOr+WXVgVVFrughlliJEALHyTMx8Q10PU2kYwO+XWTSr+B1bmKgZ/MZY6zVhiiV9gGSRGI90eh8hm+6H2h+kV068HGmph5mbPzLBEGDXO4pKpWD9PV9hEz6wpEGRiTKyq3b2PfoD8hPjKFoGuXZGXzfxYwncKpVqvk8+YlxdNPixvf9wmXN8VuFwGk2yzu94ZLQFKQXNN2nhoqse7iTNZSEseBEVRI63nQdd7K6ILYGjk/5qTGckyWk66NEdMz1Lew8lGd2tIoV13EaPvueGMWuu2y4pfeSxvj8YI7P7ziJKhRSUYN8xeYzjxyjMlzhpjXttC9JoFxirqxmqMQNtSn0nhnhipRc0r1Ynmvg2D6ZllPLwiMJnVrBoZxrkO2On7F9x9Ikq7Z2cnLfHMk2CyOqMTdWBUUQTZ36TBWKoF52yHTHiKVMogkD3VDJT9VoVDz61mZYs60LK3b2Z1TIG8/nPvc5jh49eknb3n///YyOjhIEAclkkg984AOsW7eOz33uc5w8eZK/+Iu/eINHG/J6CcXWkJCQkJCQkJCQkLcAwrKQvn/Gz6TvY/T24U1NYQ8NEeTz+KUSaBrmwAB6ZyfVF3YQ2bQJoWk0Dh5EGDrRjRsx16w5p7Dg5fPM/N3f4Q6PoLa24lcqFL72NaTvk3nfey9prEo0SvzWW4m/5ufu9DSz//APzfiCtnaCRoPiN78Fikrm/e+jcfAQqCpKpNl4SctkcEdG8GdmYMUKpO8TFApE77zjoqJI9PqtxPbsofbCC+zqX41Wq5GKWJj9S4hoKrOOx866wzuu33rR83GGhvBm5zD6+xeOq3d14QwP4wwOErn22oVthVBYsuQ3UMfilEv7CGRAMnk1tdoggVfEsnoIApdGY4y5ucdIpa4990EvwPixvUwNv4KbSnK0JCj3rkT36pRrZex0ivWmy5jRhnSjTNdgndJDd7ydWN9H2PPcXo688GkqxXEUzaNlSYIt73of3RNTiKn90LoSVAO/Poc1eYSYliLf3iDWYiKlh+9XiUYGMCKdEOlEODkKlb3Y9jSm2czenJ19jNGxL+C5RRCgqQl6ej5IW9s9Z5yH55WZmf0RQghisVUA1OsnGRv9Aon4OjQtdta5L7wnzizta3TWcTUzx4u4DRdVNyjPzVCcnMBpNFA1FbfRYP9jP2TFlutpH1h22XP9847eGkFogqDmokR1ZCDxyy5GXwIlOi83qAIUAcFr1Elk87V5antnsI/kUBMGIqHjV1yKz0xgV13SXfGFhlPVos3wgRwrNndcknD4zLFZXF/S3xbBs33UnEO+3OAHxTG0Q2V6VmfYdM+Siza0klKSG68yM1JGAE7dpV5WMaMajYqLogi6ll/cNWrFdVRV4LkB+nzkgGv7aIZCJH72Qy0hBBtu6cWueeQmqtRKLmZEI9UawXMCzFhzbNW8jaIqC3MiFEGmO4aUknU3dbNmW9dlP5gJuTIUCgW+//3vc+98XM6lEI+f+g24dWvz98zAwAAf+9jHrvj4Qq48odgaEhISEhISEhIS8hYgdv1W6rtfxp2cRE2l8ItFhKqSvOtOhGmR/9KXqA4OInQdvacHY2AAoWlI16Pwla803aCeCxIqjz1O5kO/QOL2s7NJ67t3446OYixfvhAP4E5NUX3qKVJvvw/ltOXYUkoaBw5Qf3k30nWx1q0jurkp7J6L+q6X8aanMZY1XY5qMokrJdVnniH1rneiJhNwmqCstbejxGIEts2h2RyvtHXB9tu57va7aZES5TzCg3QcAFp/6zepb7+RxFQZ1YwSaWtBmXeuBlJiKJfmhhOW1ZxLp9lIqxFIsG0UTUPMC8OnYxitLFv6cRwnBwQUirspl/YRjS1HCAWBgiJ05nJP0dZ2N8nkVZc0DoBy+SCDxz5NqTrDkG7gqDqmGuCjM2PFea5nMx/PTbGiMUw+OoBbbVB2ZqmsvZO54TkOPvMjXDmKlfEJXIPpo0X2Pv45srqPZSZBbYpFPia1ItSmRjngJ7ASGj1bXTBAVU9dA6oawXGqeF4Z02zHdYuMT3yVIHCJxlYCYNsTjE98nVRqE4bRulBbrR3FsaeJRAcQ8w23LKuXRmOEWu04yeTV55yD2bnHGRv7Eq6bR0QVOq7rZ2Dgt9n/w5fY8+hJ3EYdK9ZsVhQEAYHncfylHRcUW4PAJZd7inzhRaT0yaS3kM3eiqKY5635eUDviWOtbaFxMI9fbjZqU9MGses6FoQ9xdQwlyWp7Z0FVSA0Bb9ko8QNjL5TDc7sYwWEqaFEm/eYljKpzzaIeQHuaUKoYao0Kh5O3bsksbVie2jzztviTA276mEYKiKiEUkZjB7K07E0Sf+6Cy//P75rmoPPTOA0PKQEzwmo5O3m/iyVVVs76Vmdueh4sr1xWvsTTA4WMaMaMpC4dsCKze0LkQCvJZ4xuekDK5kbreA2fNKdUcYO5zn0/CS5sUpzXiIa7UsSNMouVkxvOl0rDrqp0dafCIXWnyB/+qd/yn/9r/+VT37yk+fd5hOf+AS//Mu/zObNm/m93/s9PM874/UgCHjkkUc4duzYGz3ckCtAKLaGhISEhISEhISEvAWI3nADqbk5Kj/8Ed7sLGoiQeLd72o2nlIUzDWrGf+jP8IvlTAHlgLNbFRp29hDQ5gDA6jpNADO2BjFhx8mtnXrQizBqwSVCiDOyGFVLAvZaBDU62eIreVHHyX/L58lqNcRuk7lscdI3HUXmV/6xXMKA0G9Pt/I/pTwIgwDadtIxyG2dSvVZ5/DOXkSNZMhKJfRe3vZ87Z38K+tPZR0AzUe54mKx7vH5/jF7uwZx/ErVYoPf4vajheQvk9000ZS99/P7f0qrwxPMRNAOgjIuz6KENyYfq339hS+lByv2bhSsmxgKebKFRT37GM4kaTsBSTyc8i160j1L+F8faWrc3UGd71Iofw0amsOTelBNXzK5VfwvCJS+hw7/j/o7fkQ7e0Xd0wFgcvo6OfQEnkakQyi6hOLBqA7WIUGuUSaA20rebRyI3foO0mXZtBQeD6+Eia7WJrbi+sU0RIeup4CHXxbIT9sU1tWwvKawqKUkBsdR3HA1VR0y6FWsBl+sUb/TXGYn3MpJY36JLragWk0mynV6ydx3TyWdcoFbJod1GpD1GonzhBbFcUEoREEHqr6atMvFyG0s0RO25mlUHiBeu0kM7OPoChRotFlSOlRqw8yOvp5etY8yJ5Hvkfg+0gp8VwHAcTSLRSnpy44t2NjX2R65nuAAASl0h5qtRP09//Gz7XIJRRB/MYezIEU7kwdxVAwliRRX+PQjG3pRLoBzskSQcNHS1vEru9CS5/2Pkma03caqi5QHIFddTFjOlJKaiWHeNoilro0IXtjf5p9YwXqjke97OIpEtf16ReglB0Cx2dutEJHXMebriEMFXNJEvW0/ddKDod3TIECme7mZ155roGiCjbe3U+mM3bJ41FVhS33DXDspSnGjxVQVIX+9S0sv7b9onVt/QmK03XmRiskWy2uf89ScmM1FAXaB5L4nmTn906Qn6wBoOkKy65tI9tz/s+qkMVRKpXO+Ldpmpjm2dfA7t27iUajrF174UaG/vznDkA+n+eOO+5AOe13nZSSAwcOXIGRh7wZhGJrSEhISEhISEhIyFsAoSik3/UuErfeijc3h9baippILLyuRqOk3vVucp//PO7YWDPbNDeHEo0ibRsldWp5rJbN4s3M4E5NYS470+1nDAwgDAO/VEJNJpFBgDc7g7V2LWrmlOurceQo0//z/8EvFBCKgjBNtEyG8qOPErvpJsxlS886B3PFcoRp4hcKqOk00vfxZ2eJbN6MkkhgrVtH9tc+QvHhb+PNzqC2ZDDuvpvv9axCuh7rLaPZqMlx+d5skZtbEgxEXhUIJbnPfpbqk0+ipFIIRaH8gx/gTk5yyx/8AVMdLXx/rsh4wyWhKXygM8NNmXMLGKMNh78bnuZYrUEgodvS+aUPfIgjjc9hHT2KqQiGN1zDo3e8jX2TeX5/4OxGPfmJMZ7+0mep5OYwkz6mqONUdxLNGgRBCYTANLsAlfGJr5FIrCMS6b/gNdBojNJojJFu78Ffl0bsmSFSrBEEHkEyzszSq5BWjKPLb+dAYT29SpmKEWPI6OBdB54lqc4ipbfgIl24toRKqaOdluEq5E/gKRG08jC+maYS3Yjh2mgtLo1CgriyDRkMUikfJn8iYO6oiiYFs7v+hTXbbyXdF0NRDIKggaI05zcIbBTFQNXOnO94bBWx6FLKlcNYVmfTHWlPkUxuIBpdvrBdrTbE4NBf06iP4npFbHsKy+rFsrpRFAPT7KJaG6R3VYqlG7dw+NmncBsNVF0j1dGFDAJaes6fD9poTDCXexJNS2EYTXek6xbI5Z+jre0eotGBC74vP+sIRWD0JjB6E+fdRrE0krf345dsAttHy1gL+a3QXCpvLktR3TlFYKkIQyWouOhRnURvgqnhCtWCA0jMqM7qbZ0XXfb/KnesaeeV8RJ7RgrMuS5Bw2MZKms8iVur4foSf7BA6UQR6UtAUt87S+L2Pox5kbIwVaNRdcl0RRfu1VjapDzbwLA0IoZK43gBpETviqNexHFrxXQ23NrL+lt6Fs7/YshA8srT4wztmcFp+CiqINsTZ8t9A2c4Ym9+aBWTg0U8xyfTGaO1N/5zLfj/pOjr6zvj3//tv/03/uRP/uSMnwVBwF/8xV/wmc985qL7+9u//duFv//pn/4pAwMDZ21z7WmRMyE/3YRia0hISEhISEhISMhbCDWZPKMh06u44+Mo0Qjx7duxjx4lqNeJbNqEuXo1xa9+DWnbCKvpwQyqVRTLQk2dnU9orVtH7OabqT75BN70FEjQ2ttIP/jggiM1cBxmP/Up/NnZZmMu14VSCS+XQ43FqO/Zc06x1Vq/nsTtt1N+/HG82VkA9N4e0u998JQAcsMNRDdvxi8WURIJjnqSuSOjdJj6wjatusaRms1QzV4QW92xceovv4za1oaWToMQKPE49qHDeEeP8gvr1nFPa4ppx6XD1GnRz/1VKpCSfxiZZm+5Rn/EQEUwXHf4pBDY73mI9W4dXVEoRqFR2McjQ/u4WruW23o2oiunBJrjO3dQyc2R7V2CEAJpCzzxPLadQzci6HqGWGwVqhqlVjtGpXLoomKrEAYIlSDw6LsqyfOpNbTPjqOLKiy/kUKkl+VCMJ6TqFaSitVOVdVZWyvQK308x0c34tiVMiLh4dvguz6pPh91xbuhuw0OfBOKk5RFminzGrz4OtI0G7Dl6iMkrftoXx5lcM8TzOzbh6rEMBOtzI6c5IVvfpXtD/0iifh68oUXTgmXzhzJ1LXE52MFXkVRDJYs+Rijo5+lUj2KANLpLfT1/jKKcur9mZx6mHp9hFhsJY49hevmcZxZHGcay+pGygAhFFRV54YHP4hdrTI3NoJhWgQyINHayorrtp13Xh1nBt+vYlmn5l/TUjjODLYz83Mvtl4OatJEPc9r0Wva8PMNnNEKsuigWBrRje2su7aNlsESc2MVVF2ha3malu7z5/G+loSl8wf3rGbfWIGnvn6UfK5Mt1QoCZ+qKogrguRcHWVJspk7KyXeTJ3qS5PoXcubYnJERdMUPCdAN5tn4LkBqqagFmzyz47hF5vxI0pcJ7G9B3PpxfNbL0cEnTpR4tjOKYyIRiZj4rsB0ydKHHlhkmvvOnXtRZMGy65tu+T9hiyOkZERkqf9Lj2Xq/Wzn/0s73//+4nFLv16feyxx/je977H8ePHUVWVlStX8s53vpNt27aFYuvPEKHYGhISEhISEhISEvIWRkpJ8eGHKX/3u/ilMkJVMZYto/U//C7GwAB4Ho19+6nv3dsUaX2foNEgee/b0LJnZxwKVSX7K79MdONGnKFBlEiEyKZN6J2dC9vYhw/jTU+DpiFrNRTDACkJPI+gUae+by/pB+4/e9+KQuaXfpHIls04J06gRKNEN25ciDdY2E7X0Vqby81TuJiKQj0IiMx3HG8EEk0IUtop2cc+cgR7cBAZBKiGgdbdjd7TjXRd/FIZgKyhkTUu/BVquOFwtGrTaxnE5qMUlkZNdhWraL5HxG6QVwscmvk21cY0jpT8U+NZhvI38pENH1kQXPOTE+hWZEGMEY1l1KddzMQeYpk+IpE+hFCRsplRK8TFsystq5tEYh2F/POsNgRr2hO8mFpKoESJRHpYFrX4jd42vv7sKM8FHroM2Fae5sbyDI7nkunupWPpNvY9+WWqc0VUXdKyXGXJtWtp73g7GK2w4i60epHhr32NiePHSUddFFWlPDtNJJGkY+lyEslWSiM70ZQMLd1Nx6gVTzA3Nszw/r1c87bfQNfTFIq7mvPeeic93e9HiLNlukikhxUr/gu2PQEITPNMl3AQeFQqh9D1DEIo6EYLqhqbF1xzaFoC256kJbMNw2jHNAW3/tKvM7R7J4XJcRLZVpZeu5lUe+dZx34Vw2xHVWN4XnFBIPa8AqoawzQuvDQ85BSKpZG8ZwBvukZQ81BbrIWYgZ7VmUvKQz0fhqawMhZhJO+TESqBEPgSAl+SjKjEJQtZsUII1ISBn2sQVF3UhEFLV4xsb5zJoSKRhNGMwCi79K5Mw6E5/JKD1trMX/ZzDSrPjqN3xlAiV05ymR2t4LsBkfami1UzVKy4zuRgEd8PUNVLc/qGXBmSyeQZYuu5ePzxxzFNkx/84AcAPPfcc4yOjqJpGu973/vO2Nb3fR566CFqtRoPPPAAd9xxB67rMjg4yJ/8yZ/Q0tLC5z//eVT1fI8rQn6aCMXWkJCQkJCQkJCQkLcw9pEjlL71MGgaxvLlSMfBPnSI0ne/R9vv/g7oOq0f+21K3/setZdeQjFMYjfdROKeu3HHx2kcOgSKQmTDhgWBU2ga0U0biW7aeM5jykYDFIGIRqBSQb7aCMT3UVIpvOkZZBCckc36KkJRiKxfT2T9+ks6v05T54Z0jB/MFmn4AaoQzLke1ySibEg0xREvn6f48LeaebOKgu/7BMeO4heL6O3tGEsu7Bg9HV9KJJLTRy6A1UPHuPGxH5LNz/KjdTOI7gCldR0tVoSuqOS58efY3LGZzR2bAUh3dDF78gRSyqazNQiwiyYR9RqCYALPK6MoBrY9gWl2nLcZ1BlzJwT9fb+KQKFcfoUHjGfZEr2aWvJe2mJdbE3FSOsanasHWP+lz+K7HvFkEqdWQ8qAZRs3s3zz9SzduJmJk8+hmBXS7f2k05vRXl3ir+qIeCvX3nc/zr9/lfzEGFIGRJIprrnzXhLZ5jVSLxXRDOOMsamqRr1cQtczLFnym/R4zcY/mnbhvEkhBJbVfdbPZ2oz5Bt5UOIE3jjQzHmNx9ZQLO0kCBp4Xol0agu9fb+yINLGMy1cdfvdF53PV7HMTrLZ25ie/g6e18xxlDKgrfXOi7qNQ85EKAK989JdgJfDxPEiTgBtUR0sDYmkYfvUnIAgIZCBRMw30pKuj9BVxLyLVVEVNr1tCYeenWByqIiiCFZsbmfF0iS1R4fR0tZCrdpi4c3WcaeqmAMXd7deKop6Kuv41WtVBhJFEWFMwE8p//zP/3zGv3/lV36Fe++99yyhFeDv/u7vuOeee/jN3/zNs177xCc+wde//nX+5//8n/zxH//xGzXckCtIKLZegNHR0UXVWdb5Iu4vzMc//vFF1d12222Lquvo6FhUHUDkHF1TfxqpVCqLqvv85z+/qLoPfOADi6r70Ic+tKi6ROL8uUg/L+j6xV0a5+IjH/nIourWrVu3qLpvfOMbi6p79Snn5bJy5cqLb3QO3ux7N5/PL7o2Hl9ckP9dd921qLpGo7GouoMHDy6qzjDO3W32p42ZmZlF1S32qfudd965qDqAPXv2LKrukUceWVRdrVZbVJ1yid3LQ0LeLOxDhwiqVcz53y3CNFHb2mjs30fp+9+nceRoUzzdvJn0Qw8181WFoPLkk+S/+CX8YhEAtTVL9lc/cl6B9XSMFStQW7Io+QKBaYIQSNdFSSTQe3ubTtVFigeBbeNNTqLE4wvO24/0tpLRVZ7MV3ADyTvaUry/swVj/n6s79yJNzWNtX49zuAg0nUJHAc5PU3mQx9C7+q65OMvsUwGIiYHqw2WKgJVCHJjEzzwvX+ns17jcDrGWGyUzIxLypukff1aUobGTH2KE8UTC2Lrsk1bGT98kNmRkxiWhWs3SGbbWL3hfgqN71Eu7cP3KkQiA/T2fHjBUXkxDKOVZcv+E7YzhQxcNlvdZzlG2weWcd197+bg049TKxbQrQhrtt/C0o1bAEi0dJJoeeCCx0l3dHLHR36LmZNDeK5DtrefaPKU8NS+dDlzYyMEvoeianiug++5tPUPLGxzMZH1fNi+zVcOfYUdkzuoe3VWGnWuMwuAgqpGcb0cqdQWens+RCTSTyTS/7rFqp7uDxKxeigUXkISkE5vIdty809MBHPqNY6/tIPRQwfQDIP+Ddew9NrNKD/DrjgpJUHZBdFcqn+Gg7nu0Tiax52po8Z1rBVptOyZ/+/13QAloiEcH2wfoQkUTxJoCmp7DHe6hpowkF6AbPhEN2dRjFPzFU0abLp3Ca7tgwDdUHGnqtQEVOoubl0QMRV0XxI4Pn7ZOUMYfb10LE1y/OVpynMNokkD1/ZxGh4rNrejKKHY+tPOpz/9aR577DHGxsbo7e3l5ptvPuP1iYkJ/uzP/uy89Q8++CB/9Ed/9EYPM+QKEYqtISEhISEhISEhIW9lFrq4nyYK+B7O6Bhz//JZhKaBDKjt2EHqwQdIvfvdlJ94gpn/5y+RjoO+bBlaSwZneIT8l76ItW4tymvMB36pRH3vPmSjjrF8OcbAAJkPfIC5f/pH/Lk5AtdFTSbROztRNI3EHbefV6CQvk/1mWeovvAC0nWJbtxI/LbbUCyL2osvUvja13BnZ1EMk+j1W8k89BCxSIQPd7fyUGcWH4n5mocefrkCUmL09qImEni5XDOXNholcdflPQTSFMFHe9v42+EpTjYcpITtQ0dYWq/QumoVscDnCWOEmlalt1IkKSCQAUiIGU1H38zwCQ49/QROo958iwyTgY1bWLHlBtIdnbTIVdj2BEFgY1m9KMrlPZwWQuB5ZWZmHqFeH6ai9jJmbkOLLGNzMkq3ZTBwzSZ616zFPvo0hpdDT2ng2WBEL7jvSm6OI88/w+TxI1jxOEs3XsfA1RvPcCnXyyXMSBRNN5k+MYhmmggh6Fi+ioFrN59331JKXLuBqumo2vm/yj5y8hF+NPwjMlaGjlgHQ/VZbK/BXRGLOJBKbqKz634S8TWXNW8XQlE0Wltvp7X19iu2z8US+D47vvFVRg/uRzdNAj9geug4tWKeq+542096eIvCK9hUnhvHm6w2hc6uOPFt3ahJg6DhUfzRCdyxCigC6Uvso3mSdy05wyXb0h1Di2gEMQOt7uLVXBxNoX9DltZ7llB7aRJ3qoZiqphXtRK79twREK9mtgKQMjla8Zgcq1ALIC0DejVB3NSQT4/jTlRJ3Nx7ReIEst1xrr6tj0PPT1ArOKiGwrJr2lixZfEmqpA3j9/8zd88p2v1VTKZi8dktLWFWbw/K4Ria0hISEhISEhISMhbmMg1V1P67ndwR0bQ2tuRto07MYn0PPS2toU8VHdqivKPfoRfLlP8t6/jjo2BaRLs349cOoDe1YU3OYlz8iTW6tUL+7cHB5n5q/8Xe2gIoQjU1jZS73onqfe8B3PlCkrf+z61HTsIXBctmyVx553EL7ByK/fFL1H48peRvo9iWdT37MU+dpzkO9/B3P/5PwSVKlp7O0G9TvkHP0SJxcm8v7lkUw18lCCA16wuMPr7EJqGX62iplIoySTO4HGsdevO2UzsYqyMWfz5qj4OVOq4UrJkOImtaSAEWU3nDrubh6NHyQuboJEj55fpiHawqX0Txekpnvvav1It5InEk8ggwKvXSLkB5vQMQTqDYprnXDZ/qVSrxxgc/Esce5Zp3yBv76fECzxrfYRvRJbwO/3tbImqaM/9v2gnnoZgPuahYz3c+l8gfu4v/HatxrNf/VdmR05ixWJUcjlmR4bxHJuVW28EYHbkJDu+8WVKs7MEvoekGZmw+sZb6Fu7Af08qwTnRkd45clHyY2NYFgRlm3ayqobtp/l1JRS8vzYk/Rrddp1k7rQMGPdHCvUWWHezAdXvw9FsX6ul13PnBxi4vhhkm3tGFbT3VnJ5xjc9SIrrttGJHH51/SVRPoSd7qGtD201ghq/MKrfaQfUH58BHe8gppsbmsfLxA4Pul3LMM+XsAdq6BlIwhNaTa4mq5R2zND6jSxtX0gyZK1GQafm8SrN6/pZIvFmhu70VsjJN82gLR9hKYgtEtbhXJi3xyTDR9HFWi2S4+hgoSCH+Dk6ggvQIk1G2ZdCZZsyNK1IkUlZ2PGNGKps5syhfxs4r0ap3MBpJRvwkhCrgSh2BoSEhISEhISEhLyFiSo16nv2YtfKBC7+RZqL72INz2NMAzMNWtwhoZQUqeWfauZDM7JE5R/8AOUSAQRjaJEo0jHwRkZRYknEJqGclpHZhkETP2P/0F95y5QFFBV/FJTrI1cdRXm8uVkP/KrtPzKLxNUKs39XiBCqHH4MLl//EeCWg2h6wTlMmoiQfWFFxC6hp8vYKxYgRCiOTbPo/rMMyTfdg+lH/6I2jPPEHgekWuuJvXud6O3N51rkauvJnrDDVSffw7P8wGJ1tZO+v73QBBQffElGgcOIAydyLXXYq1bd1GxLqIqbE41hZ7asmVM2g3qL7+MmsmwvSWNqybYtUbHUwVXZ67m3SveTXu0nX07fkQ1nyPbuwQhBGZOMnNgP68cOoKhx9F7esj++q9hrlhx1jGdeg3XtokmU+fMu32V2dkfY9uzSHMZQ6UaQm2hPTjJVmUXP/Z6+Nz4LNcoB9EHn4BkF5iJpqt1Yi8c+CZs/eg59zt2+BVy4yO0dPcuOE+LM1McfeE5lm68DlVV2fOj7zIzfBLDiqBqBroVoVYskGxtO6/QWi3kef7fvkhxdoZYKk29XGL3D78DwJrtt5yxbb0+ygrvJUylgOHq+EJnVhnguABPBqjqz0Yc2uuhVizgu96C0Ao0xe98jlqx+BMVW/2KQ+mxEbzJKtKXKFGN2KYOrPXZ895T7mQNb7qGlrUQ+ry4rip4UzW8mRpergGIBYG0ef/rTUH3tBxWAQz4AdGUTi1jomuCNMDeGWRfAqEKhHV5EsnIoRxqRMWrqrRHwFSgFICuqzQciQ1oQ0XiW7sQ+pWJEWpUXSqFBk5Dw4rqqFdovyE/WT71qU8xPDx83vsgCAJ++MMfhlECPyOEYmtISEhISEhISEjIWwxvdpaZv/0U9rFjEAQIwyCyeROJX/91tHQar1xm+s8/SVCtos5naAflcrNRk+1gLF+On8/jl0ooloVfreIMDxO/+Wb0/lMNgWq7d1N/8SUCx0GoKgiBbzewh4dpHDmCuXw50Gx6dSkO0uK3HsYvl1EzGRRdR3oeXqFAUKlQqtXwczlEIkFQqeDnchAEqK1Zcl/8ItXHn0BJJBCqSvmRR/HGx2n/4z9GMU2ErtP6Gx8lunkzzokhlFiM6ObNaF1d5D/3ecqPPIL0XEBQeexxMh/6BRJ33HHeceZdj6GaTVRTWKEKyo8+QlBtjs+dmEDoOndu2sT73vafCbpaSeiJhS/Y9VIRoagIIQg8F/vIEVTHwYnGUFo7aRw+xMzf/R09f/7niHmHrue6HHjyUU7s3oXnOqTaOthw+910LDtbkAWoN0ZR1Sg5P8CVkpSm4kuLiD9FV1Rn0nYp5faSRTaFVgDNBCsJIy+cV2ytl0pIyRlL/M1IFLtawanX8FyXk3t306iUqStFpJRohoERiTE3Okxr35Jz7nfs0CuUZmdo7elbEJFLs9Mc37mDldffuHA8KSUTE1+hQws4UdeIiDiWcEl7R2hVlrAuu7hs/p814i1ZNMPArlUxo03Bv14uYUZjxC5hqfLl0qi6lOcamFGNRPbCruHqi1M4I2W0FguhKfglh+qLk2jtUWoCZkcrIMGIasTTJqn2aDNDNZCgnhIVhSaQfoB0g6YzVsozhNXA9tA7Yk2FdR5vpoY3WaO1O44yHwUQOD7eVBV3qorRffkZwTJo/gkCidAESECcch9KrxkRIqXk9XqppZQc3jHJsZemseseiiLIdEXZct8A8czZDyqklJRm6xSn6xgRjba+RCjM/hSzfPlyrr/++vNH6EjJ3r173+RRhSyWUGwNCQkJCQkJCQkJeYtR+u53sQ8dQh8YQDEM/HKZ2o4XiG7cRGTdOrQgILppE9Vnn8W3LAgCpO8RveEGGnv2IhsNzLVrsY8dawqcQhDdsoXsr33kDEdl5fEnCOp1UFWEoiCDAOl4BEEZaTuXPW53ZLgpMHoe6DpCVZDVKp6UiEQCv1iktmMHimEgIhGCahXpeVQeeQStoxOtpQUAJZnEPnqMxiuvEN20CQBhGMRuuJ7YDdcvHM8eHKLy5JMoqRTavEjljo1R/NbDRLdejxo/u2v6j2aLfHEyR87x0BXB7ccOcvdLO4letQE8H69UwpueRu/uJta/9Kwv1pnuHuTOHXiuA8USfq2KZxi0+pLaU08hq1Xso8c4OfNRuv/ikxhdXRx6+nEOPPFjzFgM3bSYGR5ix79/ldt/9TdItLSeNcZoZIBy+SCq1hSAAhmgygY1rRc7kBiKgm7GmirS6fjOKfH1HCRb21AUBdduoJsWUkpqpSIt3b1Y8TijB1+hXimjqhpmNIqUkka1ilOfpVYqnreZkF1vNiQ8/drSDBO30cBznAWx1fNKVKqHaUssp8gU+Uaeqh+QVVxuah1gY/vFm7e9EUgZ4LoFVDWGqr7xy75b+5bQt+4qTuzZRbWQR0qJqqmsu+VOrNjimo6dCyklg7tnOPLCFI2qi6YrdC5Pcc0dfRjncIgGtoczUkKN6wuNp9SUgTddZ+yZcfaPVakUbGpFGyklidYIXctSXLO9GyWu4xds1Exz/vy8jZo00NoiqEmT+qEc3nQNJaLh192mYLvk1AOcwPGxh8v4VRclfmpsQlOQvkS6r7nWL5HuVWnyT1VRVYVCw6NDBQtAASElWiAx+xNnNNpaLHNjVY68MIWiNkVW35PMjlQ4+OwE171j6RnbykBy8NkJDr8wSa3koCiC9iUJbrh/eRg98FPKJz/5SW644YYLbrPYJskhbz6h2BoSEhISEhISEhLyFkJKSe3l3SipFMq8M1JNJPCmprAPHyZ+03aEopD99V/DWLqU2s6XEJpG7IYbiN18M3P/8Gmqzz6DEk+gtbcjdI3Y1q20/eEforxm6bo7PNyMDxCimTWnKOA4oGlYV1912WNXW9vQ0mmCchm/VCJwXaTrorS2Etm4kcbLL+MMDRFIiWqa6B0dKJEI7uQUev8p16QwDKTvE5TLFzyeOzZKUKtidHWdGkM2izc9jTc5gfqapfyDNZvPjs/hBJKlURPbD5gdOslM3WZppNlYykgkmmMaHyeo1hYE26PVBk/kykzHOmjt6scbH0apVnFkQFwotB4+TlCugqZBENDYt4+J/+//j+6//ztO7NmFEYkSz2QBMKNNp+j44YOs3nZmx2uAtra7KJb2ENRP0AG4dp2i3s0h5TqmbJe3taZItt8CQ49BbhBibWCXIPBh5V3nna+uVWvpXrWWscOvAAIZBFjxOGtvuR1FUamXipiRCK5t4zkOnutgVysAHHjqcSq5HNe960Gs+JmCYKazG0VTceo1jEgUGQTUSkW6V63BiJxaKq8oOkKoqEJyVetVFO0CDc9G92dY2XsrmvLmf/0tlfYyPvF1GvNu4tbWO+lofwfKGzgWoShsedeDtC0ZYOLoETRDp3fNBnrWrr+ix5kdqXDgqXGkgETWwrV9hvfPEUuarLvpXJnC80L6a2InfS9g7FgBX1Nx6m5zSyFolF3KRwucmKrSnTbxqw6B7SEUBSWmEdvahWJqYELqniVU987QODBHUPVQTJXarmmCoo25Ik3lmXHcuTp+0SYoO+h9cdS4gV+0UWI6asqkcbxAUHZQEgZmf/KSlv0v39hGabbO8Cs5ilWXkQB6dIHlSYyYRmxlmujmK9PAana0jNvwaOlp3h+aLoimDKZPlrFrLvnJGlNDJaSU6KbKvifGmsL1/JwP7ZlF1RVu+9CVawwXcuW4mNAKsH379jdhJCFXglBsDQkJCQkJCQkJCXkLIYRAiUXxC4WFn0kpQUrE6cJVNErqne8g9c53nFHf8pGPoHV1UnvueVAEyfvuJXXvvWcJrQBqPI6IxcB1IQiaf4RA7+3FWrbssseeuPUWnGPH8CMRgnodOTsLlkX0mmtQFAW1pQVlehqhaVhrVqN1dOAXi3gzM3hTUyhLm05SP59HiUbR+/oveDw1nUboOrLRWJiboFpFiVgLjcNOZ2+5RsH1WB1rLqXWNBU9naLiB9i+jznfzCmo1dDb21GspsNsX7nGX56YZNbxMBSFYP2NXOvDqtwE8XyB1vFpjHKVhq6jBgGqZaFHItiHD1PZt4/jWoSJjg6ilsXKRplWzwYBbqNxzvOKRPpZsfwPmZt7Ar1ykp2NDDvlRmytg/ek4/xCVwtoHbDtd2Hvl6E6A0YcNrwfVt5z3vnSdJ0bHnyIk/v3MDs8hBmL07fuqoV4ADMWJ5bO4Ps+ldwcdq2KUARWLEEik2Hklb1Ek0k2v+P+UzuVkq6Va+jfcA3D+/cQzM4AEM+2sv6WO85wwqpqlEz6Bianvo0QGgndxAgK6GYPqdSb72qt10c5cfLvcZ0cutGK55UZH/siiqLT0f72N/TYmq6zfPP1LN98/cU3XiRTJ0o4p4l/qqbgOT4jh3KsubELRTnTpayYKuaSJPVX5hC6gtCbMQIeUPQlqgUpNyBhKrgSHN9niQvKjIurChShoFga0c0dWMtSqMlTDk0tG8FamsI5VkSN6SgxnaDhU39ljvrhHPigZyMohoIzVsU5WUbLRlBjOpGr2yg/NYo71owwQAG9M0bi1l609LlzhF/FsDS2vmMpKzd3MDtaplKwUV2fWFSjbXkaq6eZBXslUFSx8ODq1es+CCSqqjD48gxHXpzCc3wQglrJoVa0MaM6utX83KmXHU7um6NedYnEzp+NHRIS8voJxdaQkJCQkJCQkJCQtxjxW24h/9nP4U5NocRi+HNzqMkk0S2bL1qrxmNk3vc+0u99L8AF8xmj226gtmcPQa2GtG1kECBUlcyHfmFR447dfDN+o0HlRz8iKFeQPT24U1MLubJKtOkeVeJx9M4upBAEpTLm2rUA2MeOIhAI0yRxz90YSwcueDxrzRqsteuo7d7dzJT1fYJ6neS996K1nr08/1yMrl3PiheexTt2DLWlhaDRAN8nfucdiPnl79+cyjPrzIu0vk9kzzN4Q0fJR0wimQyFQgERs8jUXTxdpxyNkxKguS5fq/l8e8VGyvU6mmGyI97K3eNH6dZ0Wnr6zjuuSKSX3t4P0wtslJL3eT66EMS105Y7L78dltzYFFutNJgXX4KuWxYrtlzPii1ni3zdK1fT0tvP3MhJrHgCt9Ecc7qzm0giReD5jB56havvuhe9cBz2fx1mj6Ame9h67dvpW7OB3OQYZjRGz+p1xFuyZx2jq+u9+H6DQvFFXKdGxOqlu+chLOtcTsszOVm3OVRtYCqCjYkoKf31fV0uFF7AtmeJxVYihEDX09Tro8zO/pj2tnsR4qcjP1P6AdKXTQH0Io3fzqiTEs5KIhXNfNVzvgbRLZ34NQ93vIIsB6gxA2VZCvulaXoLNqYAxW/W64DwJK4CaApai46ft1F0FTVp4to+0/OCb7ojinqyhPQCtGzzc0CNKQRlB2+mjrk8jdAUtEwEYep4U1Ws1RliG9uxTxRxRsro2QhIcCYr1HbP4AyXiF7dRuy6TpTo+cVJoQhaumO0dJ8dK3Il6RhIcTQ2TWmmTixt4rkBjYrLwFVZBvfMomgKLW3Nh0K1ko3vBiin3c6aruC7AeXZeii2hoS8wYRia0hISEhISEhISMhbjMQddxBUq1Qee5ygVELv6iL1nndjrVp1yfu4FFEmfuttNA4coP7yboJ6DaHpRDZvJvXOdy5q3EJRsFauRFar+NUa1rp1FL/1LezDh1FiMYJ6vdkEyzBwhoeRrouaTJL96K+jZlup796NdF2s1auwrr76oucgdJ3W3/4tit/7PvWXXkLoOrGbbiJxz93n3P7qRJSUpjHacOm2dBpBwHAkTv4jHyW163nsw4fRe7pJ3HEH9Ru3853xOQ5X6zyZr5DRtaYgN3YSa3SIfCpDORZjIhnguT4VK8bm4Ul008QLJF6pyOTadTwSTdMR8WkZHsLOF5mJJnk81cF/6e2ic8Wl5fvVyyXs6SlkNIbfkaVU2ottT2IYWVKpTaip3st+r86FEYly4/s+xIGnHuPojmdQNYNsbz/p9s75CRdNkS5/AufR/05lboKSiBOfniAxdZjeO/6Y3vX3XvAYmhZnYOC3sJ334nsVLKsLRblwRqWUkm9O5/naZIGS54OQ9FoGv7ekgzWxyAVrL4Tv1xDizHtFUUx8r4qUHkIYi973lUD6AbW9szQO5ZCuj94eJbq5A70tekn1bf0JBl+eoVq0iSYNPCfArrksWd+Bop5bSFZjOqm3DeDN1pG2j5a18DWFroN5ooUGVcD3A0DQpQBegKEJ5HQNt6giDBW/ZFPONXjpO0MUpmpICbqpsiahk3ptRsE5bnHFVFDiOtaKNFo2QvmZsWamqiZwTpYJai4ICOoe9VfmCOoeybcNXJYQ/UaQaotwzZ19HHh6nFrRaYq8XXEUtelkbek+9b7FMxbluQZO3cNUFIJA4rkB0YSBEQlloJCQN5rwLgsJCQkJCQkJCQl5i1DfvZvy40/gzc5grVlD23/8BIploba2LuS3XknUeIz2T3yC+v79eNPTaG1tWFddtehjVZ54gvy/fhG/VAKg+sQTxO+5m8j6dTT270dNp4lu2YJfrWIfOoyaShG7cRvWmmZGoblsKQcrdR6ZKzF2dIyVUYu3taXotc4/HjWdpuUXPoj84EPAhUXmZVGTX+7J8qWJHIM1G10RbEnFeKB/KW03bkZ6HqgqOdfn/x4c50i1gaUIZh2PCdslrat0l3IQBNiawYTt0ggCSGbwhMLxZVfzS498h5Tt4EajFD7wQWqKYGUkRmPlGrxSgajn0TC76b5mJYpy4aY8UkqOvvAsB596nHq5hGYptF41RqyjgVAEAkE8voalS38Pw2i5zHfr3CSyrVx///sZuGYTT/3rPy/ETziNBrVykRXXbcM+/iSzYyc5EvQ2BVhiLK+P0b7nYRL9F881BDCNVjAuzX18suHw1ckCQkhWxUwC4Fi1wd+cnOYXu1rosQz6LOOyxbZodBkCDc+roGlxpPRx3RzZ7K0oypW73zzHYfLYEWqlAolsGx3LVqCoF2/IVNszQ/WFSYShIDSVxmARr2CTftdy1EtwPnYMJFm1tYPjL8+Qn6ih6Qrdy1Os2tp5wTqhCPT2U8KgAixfmaI4V6fhBgS2z6sr7xVAjRsomsCvewjbR4np7H92gtx4lVRnFEUVNCouw9M11kRUlIqDEtORto+UoLVG8fMNyDTjPbx8AzVtonfNO+JNDekFBDWPoN7Mew1sHyWioaZMnLEK/lwDrfX8wruUkqDabMylxPU3TJjtXZ2hY2mSyeNFDj07QWGqysxImUquAUhaexMgwIrrGJaGlODUPRRFoBsqfetaSLUt/gFCSEjIpRGKrSEhISEhISEhISFvAWovvsjspz9DUK+hWBGKx47T2P8K7X/8R2+I0PoqwjCIbtr0uvfjl0oUvv51As/DWLECIQTu5CTVxx+n80/+hMwHPnBmwX33nbWP/eUa/2Nokrzr4UnJo7MFvjI+w39e1sPdrckLCiSXKp7c05riulSMwZpNVFVYHbNQ5mtfjQ14Kl/maLXBqqiFpghiqsKOYpWXSzVUPUp7IMF1EbpOUlNxnQZ2KsNLW2+hNZGgbXaG1H33smTjNRSOjvFMvYwnJVE9SktUJa1pxLSLf9WbOTnEvkd/gBCCTGc3vnWAunMQtbiM1t4VBIFDqbyfmZkf0tPzwbPq7VqVudFhVE2ntX8JqnbpS5PbB5Zx1R33cPDpx5kbH0XTdbpXrmXDbXcx9O3/G+kIkommaCUlFCoGjA2RuOQjNPH9OsXiLmx7CtPsaDp11TPFpoOVOmXfY1W0KcbJQFL2Ax6bK3Gs1qBV17i9Jcmv9rSiKZcuoqXTW2hp2U4u/yy2PQFANDpAV+d7LvMszk+9XOK5f/siMyeGCIIAVVXpWbOerfe/D908f96odAMah3IIQ0VNGvj5BkHNw84VKD82QuptSxD6hQVbIQRrb+ymd00LpZk6ZkyjpTt+VlbrpRBrjSKzFomMiQzAyzdgsgoIhBcQBGI+T1UQxDRmR8pYSQNVa4r1kYRBruhQz0YwHR9vuobQVawVaSIbWqnumGi6aSVoWYv49h682Tr2UBG/4hLYPoHjI/2AwG0eR02aCF1BViWB7Z937H7ZofLcBO54BZDonTFiN3SjpS/sqF4smq4wdjRPabZOoi0CQuLUXQpTNRRVoFsaTt1j5XUdCAXmRioomkLXijQbbun5iTt0Q0LeCoRia0hISEhISEhISMjPOVJKSt//AUGjgblsOQCa7+MMDlLbsYPkvRdemv3TgDMyQqFSI+jtJyt9BKC1t+MMDuKePIne3n7B+qBW4/tjM+Q8H93zOFEs43o+c5rGf9u5n+m4wb2H9+LP5TBXrSJ+6y1omcxZ+3ED2Tz2BQSljK6xOXXur1quY3NkZg4RBAv76DANVkV9Cq5PbGA52RMDaCcHwYygBR4VKdm/bD2zyQzfu/Vutqbj/NHSLvaMT5MvlmgEYPkuZcNk2jD4xd5WssbFv+pNHj+KU68vNLBSEkV8Raear9DaI1EUA1WNUSrtPUtsHT2wn90//A6VfA5FUch09XDde95HuuPCrsZXEUKwetvN9K7dQH6imcOa7etHUVSO+t2sFT4aHj46CgEJUeWQ7Gf5Je29SW3wFU5+58+xZ0eR3Qb++iSJ9qtYtvQT6HpqYTtTUQBBAKjAqO0wYbsYimBlxKQWSL4zW2B51OT2bPKSj68oBkuW/BaZlm3Ua8NoepJ0assZx369HH3hWSaPHyXT0Y1mGDj1OiMH9tKxbAUrrju/CzhwfKQTIHSBO1nFL9igCKQXUD84hxLVSNzed0nCXKLFItFy4UZSF8NYlkI9MIdfsFFjOiKQeKqC2mohJEgvAE1HsTS0hIGiKrjOKQFUStlc+r8iTXplBr/QQInpaG3RppP23cvxZurIQKK3RbGHilSeHiOwfaQAfAmvRhBI0DujiJiGn7dR4zpa67nPTwaS8hMj2MNl1KSBQGAPFgkaPul3LUOcJ07h9WDXPGaHK3h+wNRgkSCQ6JY63whLEInprNjUzopN7eiWSq3kIIQgmmw+VKsWbI6/PM3sSIVI0mDJhixdy1OhCBsScgUJxdaQkJCQkJCQkJCQn3c8r9lIKnHKFyjmlxm7MzM/qVFdMhXP57OeyuO33odvGAw4DR4cP8HA3DTCMFDi52/c5E5OUvj612m8coBVNRt/3VV8Y1UzrzUlA8pIvFqNb5w8wZodz9MWeNR37aK+ezftf/CfF5pvzTgu/zaZ58ViFV0R3JpJ8J6ODNHXiCmB41B59FGqzz+P9HyiW68jeffdKNEoJ/bs4sCTP2bWSDLXtYLpoklrTy9CCIQQvK01yX9JGeTWXsVfGVG8uWli8QRy+VqivcvINjzubUvxH/rbadVU/nLHLjpQ8EyLomGiVyuorkGb2nGJM/uafMvARIgAOd/fqBmh6qDpZwqMtWKBXd/7Fo1KhUxnN4HvMztykl3f+ya3/tJHyfkBEUU5s9nWeYilM8TSZ4ra5d5bGBp/jtX2CCAQBAwrHUz13nOJ5wWNAwcY+1//FX9yCEOLIPa6yOMa5ffsYTb5GF2d9y9suzEZpdvUOVZr0G0anKw7+FLSbRpENJUIkHc9XixWL0tsBVAUnXRqM+nUxZvPLYaJo4cxrSjavDvdiEQQQmFq6NgFxVYlqqFmLZx5Z6fQlfk3HNSUgX2iSGSuDf0CS+fPhV91kW7QFB4vw+GqpUySd/VT3TWNN11DieuoUiIUgRLVkJ5E2j5GbwKzLUrf2gyHnp+kpgp0Q6FWdIgkDDqXp1EzJkHNxR4qYg8VMXoS6L1x9M5mAyvpBlRfnkL6AXpHM84gaHj4FRdrfSvuaBnpBPgzdZSI3myQZZ5bOvFm67iTNbSMhWI2r3ehK7jTNdzJGkbPxZvKXS6KKqgWGpTzNpqhIhRBo+wiA8nqGzpYf9OZ7tVYykRKSW6iysSxAkdenMKpeUSSBoXpOjPDZTbe3U/f2isTFRISEhKKrRfk1fygy2WxT4T+5m/+ZlF1zz///KLqHnzwwUXVAdx2222LqjPewCVq52JsbGxRdbOzs4uq6+y8tCf5r2XXrl2LqluyZMmi6gYGBhZVt9h74vWgXkLe1LmwrMU9XX/00UcXVfeVr3xlUXW+f/4lSRfixIkTi6q77rrrFlW3WBZ7jQJs3ry4LyX/9E//tKi6Zkfdy+d3fud3FlXX23tlmo280fT1nb+T9YVYsWLFour+6q/+alF1APl8ftG1i6Farb6pxwsJeV1oGkZfL/W9e1Gz2eZSabfZBMbo6vpJj+6ifHZ8lu96ColkAm1mlv2pDHOdS/j9A/tpX7sac+XZjaCklFSefobpv/iLZl5sJkMinWX944+Sn5rhsVvuAlVFKiotlRIl3WCyf4CewEG6LvaRI9ReeIHEHXfgBAF/dWKKPeUaLbpGwwv414kcBc/nY/1nOmrzX/gC5UceRZgmCEHhS1/GHR6Bd7+DXd/7Fr7rcm3W4LBd5XDgk5uaRiTTpDSVdw4dYuLfv4Gfz7O2fxlf2rwdvauLWCJOzfO5Ph3j9/o7yBoa9vHj5Go1UrEEnbUyXqOK8FxO6hblmRkYuPj/SzuWruDI889Qzs0RT2fwCu2QOUKsxccPyrh2CSFUsi03n1E3PXScWqlAS1cvQlFQVJVEto2R4WH+5MW9DEVSGIrg1pYED3W2YF2mu2/b+uX89YnfZF/hRZYoM0wFKQbjm/jVazfNv7c+xeLLVKpHUIRBOr2ZaHTpGe998dvfwZudRvZFEVoCHB9xrIh2RKXcvv8MsTWja/zekg7+ZWyW4YaDRJLRVVbFTv1/cn4F+08dRiSK702d8bMg8NGtC4ukQghimzvwJqt4c3WkpiAUgZoyUVsi+LN1gooDrxFbpZT4JQegKajOf/cNbI/q8xPYJ0pIX6JlLGLXd2J0X7rYqHfEiN3QSfnRUdy5GvgSd67W7HGlCJS4jtETQwjBstUZqscLTAxXcFRBoivG+lt6SLZGqO6ZpvbSFNL2QUB9/xyRa1qxVragxnX8ikNQcVHiZ3439XKNpgDbFm1murZFMHoTaNkLZLU6PjKQCPW0i0NVIJBId3H/z78YQmlGa8hAoqgCRRH48ykLAnFOPWLw5RkOPDNOabZBtdDAjOpE0yaZriiF6RrHdk3TuzpzWQL5zxKrjn2NhHllYh3Ktn1F9hPy800otoaEhISEhISEhIT8jCClpL5zJ5WnnsbP57E2rCdx551o2ewF64QQJN/+DpyTwzjHjyF0A2nbmGvWEN269U0a/eLIuR47ClXaDI2W5ctwFIX43CzD8RRDb3s76+69cyEL9XRqL7zI7F//Nd7kJCIaxS+XSSoqc8k0awaP8sT12ykm0sQ9h3i9hq0opBpVMHSErhM4DuUfP4ZfLHFgyTIOEGEgYhKZFw5Nx+PZQoUHOzJ0mM2sUndigtrzO1BbWhYiCPxymdrLL1NIxWhUK7T1DQDwweI4Y8dHyEzP0N3SQvdV65FPPcXxSpWnr72e/d192J5HfXaOaDzK29tSvKs9sxAPENRqrJ2e4MdrWvF9Fz0IqKk6AbAqcC9pbtuXLmf9rXdy6Nknm7mphkFby120tFfxvSKG0UJ7+71kMjfiuiVyuaeoVo8xN+fg+415cadJI/A5UXc4VLNJxwV2IPnaZA4F+KWesxtVvfqQ81zC0Ir2BL95zya+vbeTH8/W6ElH+MWrO7mmL42UkpGRzzI79yiBbyOBmZkf0t//a2QyTSentG2ckycR6TiSZjM1DBUCCdM1NO1sd+q6eIQ/W9XLWMPh6UKFL0/kqHgBqi7IuR6KgK2pK+9SfL0MXL2RmRODlOdmMKMxaqUShhWhf91VF601uuMk71tK4etHkUFTIFXiOkHVRTFV1PSZ5gWvYFN5dhxvqvmwUeuIEr+xBy1tUt0xSe2VuWYzK0vBnaxQfnyE9LuXo8YvzXAjpaT63CTedBWtxcSte80XdBW9Nw6uT23PDErCoPb8BAM1l64WA9cLSGYMWnrj+BWH+p4ZhCLQOmPNBlGjZUo/HKb28gxqyiSyPotiqsiGB/PNsOyTJYKqB1Lilx20pIm1KnNBoRVAa4ugJvRm/EG2OV9+wV6IMHgj8ByfaMok8CWu7eN5AYapoRoKmnG2UaVatDn0/OR84yyNRk0lCCSFySqRhI4Z0aiXHDwvQD9HfUhIyOUTiq0hISEhISEhISEhPyNUn36auX/+Z6Rto5gW9pEjNF45QPsf/sHCcvfzEblqA+2//5+oPP0Mfm4Oc+VKYjfdhJq8vGXRbzZ1P8CRkqSqomgq5qqVGN4y9LqNNtCJlk2fs6786CMEjUYzZiAaRQCJYoFVsRgnGnVi1QrCjNDiNijG4mw6coClMgDAnZ3FHR1BVqs4g4OMDKygccs9mEtOrUqIqgpTtkvR8xfEVi+Xw69VmezUmYpMkvANlssUcnKSoJA/Q1hc8vLLrNx/AKEqtPpLmf3X3djlKg/f9x6eHViFHnikPY9Go06s0eDBjgE6zVMNqIwlS3hbYZqhmUlOtnU2nZe2zcbCLLcv33ZJcyuEYO1Nt9G3/mqKU5OYsRjZnj4kHq5bRNOSqKqJ51UYHPpflMuvIISKp7sEqmB2HLKdSwl8n6npafLZLvo7O1HmVyZJ4Il8mQc7M8Tmf1bxfL45nefpfAUJbE/Hub8jQ+I1kQMbelJs6EkRBPKMhkvV6hHmco+jaUn0SKb5AKJ+kvGJfyOV2oiimAjDQM1k0ItR3GgNz68gpA6BjUi1kG256ZzzoQpBf8TkvaZOyfV5Kl9mqOaR0FQeaM+wPfNTKLZes4lGrcqxF56jUa0Qy2RYs/0WOpaf7fY+F2ZvgtgNXdR2zxDUPYK6B4ogelXbGQ2epN/MJnXHKqip5s+dEyXKbkDyzn7sE0XUuI4ab16jwlDxZmo4I2Uiay/8MOhVgrKDN1Wd379A1j3UmI50A0QgUbMRvOkalR0TBAUbrSOKLpo5s95kDftYASVpENQ8tHlHrl+08csOePOZriWb6guTGP0J7OMF5Fwdv+wQlJ2FzFrhCdypGpVnxjH7L/z5qJgasa1dVJ4Zw5uuAc3Yg/jWTtTYpTeMuxysqE6mM4pre2R748gAZBBg1z3S7WcLvIXJGo2qS6YrCgEoQqDpCp4b4NQ9GhWXtv4Emv7mr2IMCfl5JRRbQ0JCQkJCQkJCQn4GkJ5H6XvfBz9YaHIlXRf76FHqO3cSv/XWi+7DXLECc5ExHz8pOk2dJZbB/qlZlPFRZK1GoSVLtL2dVdHzNK2REm9qGjWTISiXkbaNsCwkEC3kWN/dw4NjJ3jedUDCprET3Hl0P56mEpgmjcOHEZqGddVVKJZFr+Njzc4wk0zQkW0KfNOOS9bQ6LFOCSqivZWHV5R4KXUC21JQURgomly94hpe2nADB6anWeI12F6YZvXxIVxFIda/BLenh6lyjbbZOQY7urGkT6pWRfU82pGMB/08X6hwf8epbFM1mWTlO9/Bf/zqV9llJShYEbqlz82330yy9dLErVeJZ1qIZ07lNQoMTLNt4d+FwguUy68QiSxBUQwiEUn35iPM7CtRnJlCUVS0niWMbdjGwGkRUKbSdLjagSSmNt+X/z0yzRO5MglNRQBfnswxbrv80dLOc7pcX9vZvl4fxvfrmGZPc6xCYBhtOM40tj1FJNKPUBSS99yN+0+jRHIejlIgKJVRujN03PVbJJMbLzgfpqLwW31tvKM9zYzj0m0aZwjdP0mklBSnJ7HrdVq6etBNk7Xbb2X55q00KmWiyfRCfuulEtvSiZaN4JwsISWYSxKYy9JnbONO1/Cma6gtFsqr7kdV4E3XcCeq4EvE6a7I+bdNusFljOTVotM902cTFGyUqLZwvQhNAQHubJ1IZwyhK0jbR0Q0/KINEoShoEZ1lIiGO1VD6AqJm3ppHM3h5e2FmAJFV5FSIqsuzkgJ6QcXbXJlLU+jt0ZwRiuARO+Oo2VeX8OwCyEUwdobu6kWHCr55pJ2VRUs2ZClbUnirO11S0VRBb4bEE0ZWHmdaqk5L+Vcg3jaZOWWjrBBVkjIFSQUW0NCQkJCQkJCQkJ+BgiqVfx8/gwnqtDnHZWzcz+pYS2aqufzWK7M/kqdpKZwUybB1YmzXVmqELy/mmN08DiDpgXxJJFSibuPHqCvMw7Llp1VI4TAXLUK98kn0bq78UZH8QsFpG2jdnXR/ru/w0c7OvjQ/v0ARO/5EH6hQPnJp3BHR1GmpzH6+lDmM+CXGio3Dx7myc4ucmbzZ2lN46HOlgXHJsA+f5gXV0giY9BakTiqx8spj+eXx4inWsGXvFyrMxwo/K6qk22Jke7sIu8HzGZbSU1O4PsBlmNj+B5Ko0HQ1opimlT8s0Wr+M03sXxgCT379yODgMj69RiLzOa/EI3GOEiJohgL89s60EpLt6Q99WEazkkGpw9x9YGv4O1JE/RdRW35WqZ8hS2pKJl51+pQ3WFnqUaXaZDSmz+Lez67SlWO1WxWxi4uUGlaAlCQ0kWI5nh8v4aiWPOvNYndfDOoKpUfP4Y3N4u+fYDUO96DtWTpefZ8JkII+iyDPuvN7TlxIWqlIk9+4f8w8so+XLuOGYuz8Z53sPG+d2NYEYyL5LS+Fme8gj1YRDo+enecxM29zUZZ50C6Z2eTClUg50VWtTWCO1pGmGrTQVpxEaa60JTqUlASOnp3HPt4ASVtIiwNv2CDoeBXXZyJKoquoGUjBLbPq3eelBIC2Vy6n7Uw+pPYR/MEjk9Qd8EPUCIGgT0fSyBAegGR9Vki67Pkv3mM2q5pFK157kIIEAKhCIKqh5q8+DWgpkwiqSuTCXoptPbGufmhlUwcK+LaHpnOGO1LEmc9nADI9sZp6YoxM1ImmjSIt5j4fkAkrrNsUxv9a7Nk34BGXiEhb2VCsTUkJCQkJCQkJCTkZwAlHkdra8M5cQI1nQYgsG0QAq3jUrvP/3RgBwF/eXKKFwsVNEXgBZKn8xU+1tfOzS1nO7O6nniM39q9l71XbUToGlfVy/Tu2U2tK4t1DrEVIHnffdjHjuGOj6O2tBDUaphr19D+B39AZO1aoLkU/3Qi116LX6kw/l/+P3B6E0vP4/7j+7jutpsY7GxBVwSbkzFWv0YcPJw7jMykaE/0483OYUiJHVepqqNsjJkQ66NWKbM/keLZLdvZevQgIzWHrK4RtW1qra1kXZujepp4uUw1ncZetpwJ2+XfJnM8mSsxEDXZnIxxQypGStcw+vowFtnM8FLRjabrVUofIdT5KSkRjS3DM1/kxCs/YHRHnWw9wFFUqicHKQ8eof/u+/mFruyCY67getT9gK7TXKJxVWHClhS8S2smlExeQzy2gnLlELqeQUoP36/S0f4udP2U81cIQXz7duLbt1+pafiJIqXk+X/7EsdefB4hBJphUCsWef4bXyGebWXV9Zd3no0jeSrPjBE0mrEBjSN5nP8/e/8ZZtd5nvfiv3f13dv0BsygAwQIsIC9iZQokZYoq1vFlhzbSU4cJ05s2ZZz2c6Vvx2fc/4n8Ynt5Bw7jhTXuMiSVWyKosQm9o7eMZjedm+rv+fDHg4AAiCAIShS1vpdFz7MzHrWevdaaw9m3+t+7meqTvqukY6IKmXHHaorCFVB74mjpgyC8tnZpGraQO+Nk7yhn1rTw19qd5ykpkp8Rzda96ULwEIIkjcNELoB/nwLYaooSQ1pBwRFG6ErKDGNoNFxpHtLLRRDI2x7qBkTa30WIQSp2wdR00ZHtE0a+KU2ftsjnPE7OcOaQrLntAhsjmZo710iaPudAVGy4x5VCzGE9c7NME1kTNZf23PR7VRV4br3rWX/92ZYnKijagpbbxlg660DWG9R1EFExA87kdgaERERERERERER8QOAUFXS999P8Y/+COfYMYSuI10Xa/t24tde83Yvj9C2O1my6fRF21FfqrV4sdpk5IyBUydbDl+eL3NjNol+hjtr0fV44dhJwrZD4sRxNCHwLB1h6PiLixc8hjk2Su/nf5HmM8/gzS8gW01Cz6fx3e8SVmvEd1+PUM518anJJImbb6L2jW8gwxBhGASLi1hr1nDzzu3c9gYZt4baccBp3d1o3d0gwZk7jqFZsDwlXIknKbZDXt24lVv2vYI2MUETQVbAKzuvp7j7Rtr1Bnt6erFMC8+TGApM2C57G23UcoPvFGtck47z+dF+DCEoegE9hkZSe2uEoVz2BpaWvkuzeQxNSxMGbRTVIJ3ezvz8N1k8JCCMUeg3cPwGiaBFvjzHbbLOxjME6eFYx9G64PorguuC65PVVEYu0UGqqjFGR/8ls3NfoVbbg6Ik6e19P709978lr/2dQqtaYfLAXoQQxNMZAHTTolkpc+Dx716W2Cr9kObLC0g/RO/tiI6h4+OcqOJtaoCA5ksLBGUbYWnEtxWwthZI3NBP48kZ/MU2AEpcJ3FDP4qloVgauQfW407WkW6A1htH64pddmu6mjbI3j+Gv9hCeiF+3aX28CnUhIGa0hGqgl+xkW6I1h0nbPqY63PEr+5ead1XTI3kDf0kru+j8fws9YcnIJQdoVXKTkxBeNopbm3KYW7I4U4sD1NTBIqlEd/Zczoy4QohQ8n8eI2lqQaqJugdzZDvf2P3b63YZvJAiUbZId0dY822AvFLcNueSSJrsvtHRnFaXid6IxZJQRERbyXROywiIiIiIiIiIiLiB4TEDbtRkgmaTz9NUKlgbd5C8vbbUGKX1z58JQldl+rXvk7ze08gHRdjdJTshz+EuW7dBWtmbJcQVoRWgLyusej6VHyfbuO02+rLc2UqfUPsnppEKgJHCGaaLbpbNrl153e1vobe30/mgx+k+N//O82XXgYhQIa0nnuezPwc2QceOG9d9sMfRug6zSefRDou8d27yX7oRy86TOzq7qt5ZPIRpupTdMW6aHpNDOEi41evbDPtuNhSYmzZwoGPf4re/XtpOA7VLVvZcOP1xJ54EmNigtLQCIc3beO4FSerqRxvu3TrKo0gREjBgXqb/3BshkYQ0ggCMprKB3qy/Eh39opnLxpGgbHRn2dx8UHqjQMY+ga6uu7G8yo47TZuU0OPK534Bi2GrjiErg+V0ln76TZ0HujO8r/mShxqdAQ7S1X4aG9uZcjY62k0jrC4+G1a7ZPEY2vp7n43yeQm1q75Z4ShixDqitv2HzNhGBB4bucePgOhKNj1+mXtK2h4hE0X5QxXo2JqBBUXZ6KGc7xK2PJQEjphw6X+1AyogtiWAnp3HHe6czxjMHVWi70S07A25s453uUiFLEiAvt7l1A05axhXUpMI8Qnfecwata84P0uFIE/30briqGYWmf4laEQtH3c6Qbxq3tWXnv2faO09izgTtQRlkZscx5rc/68+10tUkr2PT7N8ZcXCDwJwPGXFlmzvYDb8mnVXbqGkqzd3kUs1TmvlYUWz37tBI2SjaIpTB0qMXO0zE0fXH/ZgiuAGY+crBER3w8isTUiIiIiIiIiIiLiB4jYtm3Etm17u5exQvXvvkb1K19BSaVQTJP2yy/jL8zT+6u/ipY7v/DSZegIwA1DjGV3ac0PyOsq1skTlF95Fem56Bs38aKZw7zpVrZNjZObmiBUFBzPp7hxA1tuu+2cfYdScrhpU/R8Bk2DpfFxvlOxad78Lrb4DjeWFtBmZ6h/+9skb7/9vGtUTJPcRz9K5v3vR7ouSip1SQLmpvwmPrn5k3zt+NdYai9hqRY/Mvpe9oqb2VtvYyoK865HXFGIaxoPj22mvXYjGtCjwCe/9rf4L70Mpok8cYj/6riUtu2gmkyjCdAUBU1K2mEIQvBYuc72VJxuQ6fs+fzxTJEeQ+eG7JXPX4zFBhkZ+Sdnfa9cfg5NVzHiCnYtwIhBGPrIUEEIjUT23HP7o7051sZNXqm1ALg6Feea9LlZvQDN5glOnPzPuO4Sqpqi3ZqgXt/H2Ni/IZncuJIh+8NAMlcg1z9I48A+At9HUVVcxwYkA5u2XNa+lJjWab93AhSrIwlIPwQF/IpD0HDRe+Odez6h4xfb2AdLWJvzqGmDWPrSBrAt1GwWGw69aYuu5OryTLWsiVAVQttfWWvY8FDSBmrSuOj7spMxK84ShcO2f9bQq7Dl4ZfamGszHaeu/taI99WFNuN7ljBiGrEeAyklSxMNXvj7cdJ5C81UWRivMX+yxk0fWo8Z0zjx8iKNkk1uIIEQgjCQVOaanNpfZMtN/W94PBlKijMNaks2VlKnZ00K7S16bREREWcTia0REREREREREREREasibLVofu8JlFQKfTk3VkmlcE+epP3SS6Tuvvu8dddl4mxNWuypt0lpKm4YogjBx04epPy1rxLW651h5A99m2u3X8Nj776f5z/70wzseZlYcYnD2S6ufted5wilDT/g9ybmebHWwglD3FBSrzeID65FtyyeF4IDqSw/5diImRn8hcULCsJAZ0CWdXlTxW8buo1re69lvjVP2kgz7cc4PrGAbbdpBiHrYgYnWg4vVJtIQBfQDCWG3eDIqWmeve0eDhR6SPo+Wq1Ks1xFT6YIZEdI9kLImirTtoshxEo7flw1ONK0earSeEvE1vORTu8gld5Abt0+pl8U1BZsFDVEkTkGN4wxsGnrOTVCCK5JJ7gmffHBScXiI7juEvH4BoTo5Ii2WsdYKj5KMrnxrXhJ71iEENzy8c/wzd/9P2kUi0gpUVSV7pFRrn73+y5rX4qpYm3N03x+Dr/YBlVB2j76YBI1riEUcZaIKXSFoOVf8v79IOQvnpvg0cOLNF2flKnx7q19fOTaofMOcHrDtSZ01IKFN9vsDO8KO3mwiV29FxzmdSbmuizuVJ2g7nYcsS0PhMAc60Qx2McrNJ+ZJai7oAj0LovUHcNohSvfLVBdbOM5AYl8R3gWCFzbx3dD4jkTK6bRqDhMHiyjfu0E2+8aojzXRI9pK9dDUQWKrlCZb73hsYIg5NWHJ5k8WCLwQoQiKAwmuf7+tSuu2YiIiLeOSGyNiIiIiIiIiIiIiFgVoeMgHbcjSi4jVBWQhK0LiwEJVeXfru3nHxYrvFRrkdVV7orpjP3JwwSeh7lhAwB+ucw1e17i+c07mFi7huJt72LB9TCFwk8Mn+vq+uZihe+VGwxZBjFF8Hi5Tl2oXOV75CpNWqbFy+k8+zSTq+NxtK5Lc+hdLnE9zmhmlBnb5b+cmmbR9dmUiNEMAg7VW9jVKmEoMXyPVjpD1rJoCYX/fMMdOMk0Gc+lZuq0uuPorkPb8/EkzLs+aVVFSokIQ/IKyCBYPuegCrDPyKJ8q1FVi9HRf4llfRUz9izFEy5q2MPabXewYfdtmPHzO1YvFdueQVGsFaFJCIGixLDtqSux/B84etaO8bFf+4/sf/Rh6sUlCsMjbLj+pvM6iC9GfGcPiqVhHykTOj7mljyx7V24Uw3sw2VCN0AxVGQoCVs+1pbMJcdTPHp4kW/smSUd0xnKxSk3Xb7y8jQD2Ri3bui6pH3IIKTxzCzO0TKh3RmeJiwNczSNtS6HMXhpDxSsDTmCmoN9sERQcRCWSvyaHsx1WYKaS+PJGaTjo3XFIJB4cy3qT82QvX+sMyzrCmLEOkJ24IdoukoYSjw7QNUFmqZQnGlSL9l4ts/43iVqRRsroeHZQec9v/zAIfQkyZxJbanN+N4lqgttUgWLvtEM8axBMmcxc6TCqX1LxNIGZlzH9wIWJmocfWGeHXe9tQP1IiIiIrE1IiIiIiIiIiIi4iwWFxf5yEc+wmOPPXbW93/rt36LYrFIEAT09PTwhS984W1a4TsHNZNBHxnB3rev02qvKATVKsIwMdaOvmFtl6HxmcEuPjPY+do5eZK5Uhmt67QYo2azdC0tcZ9T52uyMyyrW9f4eH+BTYlzHadPV5okNZWUptIKQiQCoShMpHPop04QttrUcyonpcItd9yOVnhrxNbXeL7WZN7x2JiwUISg6fvsfuI7HOvuR5OSVLtJy4qhbN5CyTCYTme5rlpCX45WmEHBUOBTQ1280PKYaDuoMqD31Emump3hlUIfS+MuubVraGdzBBJ2pS7uGL2SmGYPa9f+DCMjP4kQCkJc3G14qcTjY9Rqe5AyQAgVKUOCoEkifuE84H/sJDJZdj/wkTe9H6EIYlsLxLae/R4wxzK441WcUzWCUAICrStG/OruS973MyeKCCFWogN60hbHFxs8N166ZLHVPlSmvW8JJaajFQzClkdoBxh9yUsWWqETI5Dc3U9sa4Gg7qGmDdTlrFp3tkHY8tC6lwd5KQI1a+IvtgmqzsrArStFz0iK/GCCxVN1rJSODDq5rbqu4nshjbKNogk0QyXdHcO1O25iM6ZRmW2hx1Tclk8ia5IfSPDMV49TL3VqTryyyMvBBJmeGIXBJKouCMPTGa2armLGNWaPVdl+59AVz3WOiIg4m0hsjYiIiIiIiIiIiABc1+VLX/oSL774Is8+++xZP3vooYfYv38/f/ZnfwbARz7yER566CHe8573vB1LfccgFIXshz/M0uIi7okTne8ZBsk778Dadm4L+RuhptMolkXYaq0M/JKOg6pp3Ld+hDs3j1D1A/pNnZiqIKVk1vGoByHDlkFcVdBEp9UewBACTQhaYUiQyVFdsxazWsExTA5fdTXqB++9sifjPLSCjstUWRY2rOPHuPX5p1m4817m+wZIKoJko0H91DjN0fXomobSqBOoKoQhcTOGP7KG9/R38SlDJ5SSqf/5x/jfehC3f5AvmiZ7YylmZ+ZJaia39eS5M59a9XqbfsD3Kg1OthzyhsYt2SSD1qW1HCvKlf9o2dV1F9XqizSbx1EUnTB0icWG6eq655xtPdumVasSS6cxrLdvYNwPOoqhkr5nTUdsLdkocQ1jbWZFoLwU/DDk9aZQQSde4FJxTlYRQqAmO8dVUwah3cI+UcEcyyC0yxP11WQn4/X1a0Iu/3ttvfLMH54fKSUEElRxWaKlqitcf98oh56ZZf5EDdUQbLqxl8XJBuWZJp4TomoCM6GTyJoEXojb8tl+1xBzJ6s0ig7967Ks29XN1OEy1YUWVtqgtmTjeyEylDjtgOJUA98LXz9LjTCQaKYaCa0REd8HIrE1IiIiIiIiIiIiAjAMg5/5mZ8B4H/+z/951s+++MUv8slPfnLl6x/7sR/jj//4j3/oxVYAa9NG+n71C7Reeomw1cIcG8Patg2hXJ4YohUKJG66idqDDyIdB1SVsFrBumo75tatxAyNgtH5+NLwA/5oaonnqk3cMKTL0Phkf4Hb8in+x9QSRdcnpSkYQhBKUFWF+Z5evK5esprCuKZyyPbYbV3a0J5F16MVhAyYBvpltBZvjFsYikLZ88npGr1TE5iOw5bFWSrdPUyns6i6haNpOJ5HO5FibmSUwVoFDJ1GVx8b+rrIactDjJpNxPPPo+fyxJIJ/sXkUQ4lMswXS2xI61y/ewvqKoWUph/wv5+c45V6J/4hlJKHl2r8wmgfG8/jIv5+YFkDrF//SywVH8duT2LFBinkb8eyTkdISCk59tzTHH76CexmAzOeYMMNN7Ppxlsv+x6M6CA0BWtdFlZpIL5+bYF9MzVqbY+UpVFte0jg2jXnjzuQfohfshGagpozV9rlzxQ8pYSw6WEfKuEvttH7EsR39aB3rV5Y1weTKCkdv9RGy1rIQBJUHYy1adTM+X83OONVWnsWCSouas4kfnU35kj6ko8ZTxtc8541BH5HDFVUhYVTNfY8OsXkgRKpgkWmO4aqKdhND81Q6V+XYe32sx3Bz/zdcWolm1rJxrUDFFWgmxqhH5LpS7E00cm9ri22iaUNXNsn8ELWbHtr3fwREREdIrH1Dbj11ltXVffzP//zq6qr1+urqrNte1V1Tz755KrqADZv3ryqupGRkVUfczUsLi6uqq5UKq2q7itf+cqq6h5++OFV1a32fP76r//6qup27ty5qro3Q7VaXVXdV7/61VXV/f3f//2q6ubn51dVd/PNN6+qLpH4/rYIrpZkcvUDOgYHB1dVV6vVVlVXWGUr6Y4dO1ZVZ5qrm8r7/WbNmjWrqlvt75kjR46sqg5YtVPjd37nd1ZVV6lUVlUX8YPJ3r172br1tFNz8+bNvPTSS2/jit5ZaF1dpK+A8Jz9+MdQMmmaTz6JdFySN99H+kfuRzHOdqT99VyJbxer9Jo6OV1nzvX4w6lFfnWsn/d1pfnmYpX9DR9kSL/dottu0rTitJMpmgHU/JAXay2uyyRWXKfno+4HfGm6I+p6oWTQ0vnMQBc705eWRbozHec9hTQPl2osuD7rNB1VwPqlBXIHX+WFrn5aQjBUKXGov58JRWHKD1i/tEDKc0mHIT961RjassArPQ/CEJbFV11KrqqX2ThxnJx346qFVoAnKw1eqbdYGzMwFYVQSg43bb46X+bzY2889fyCeG2QIRir/7vFNHsZHPjoBX8+fXA/rzz0TRRVJZZKYzca7Hn4QWKpNGu271z1cSNWz91beji+2OD5kyXmazZxQ+XuzT3ctuHcKAJ3sk7jmRmCamdAlTGYJHnrIObaDN50p81fxDS8uSZB1UXNWxBK7KNl/KU22fePneNYvVTUpEHqtiEaT8/gVxyEItCHUiRvGTzv3xTuVJ36I5OdPNuYhjfToF60Ee9ZgzFw4b95nbZPea6Jpivk+hOoqoJ6hjO3Z02a2z++ke/99VHKs03ctk+74eHZAZtu6F2JAniN8nyT+ZM1nJaPogmQEPohXuiTypkIAZqhMLQpT22pTbvuoZkKm27oY3TnpcU4REREvDkisTUiIiIiIiIiIiLiIjQaDeJnDPvJ5/O03mAAlOM4OI6z8vVqHwT9sKGYJtkHHiDzgQ+AlOc4E+cdj79frPAHU4toQJ/RiRRYaxkcbtrsa7TpMXSEgKQiUJbK+KUSoyePMtfVw0xPP+18HmEYPLRUZSxucm9X5oLr+bOZIt9aqtJj6CR1heMth9+bmOe3NgzRY168rVoVgp8e7uamXJITLYfU3bcRf+VZ4nMzmE6be48eJluv8tT1t7CoG2w7dYL7vvG39NUq9Jk66ZhFV20O+bnPIVQVNZvF3LiB5nPPoSQSCF3HX1xESSSwNm0EWBFJq37AmpjBguvzzYUKE7bLupjJ/T1ZtibPdQOOtzv3q7l8zhUh6FN8Zpem8I1ZtHgOcms5pzf5fNg1eOXP4dSTIAMYuBZ2fRpSvRevvUwm9u8h8D2yvR1B2LBilGamOLX3lUhsfZuwdJV/+a71HFtosFh36M1YjHUlzhEwg4ZL/fEpgobbcZIGEud4BVRB+o5h/GIb50QFWXMJKi5KSkcfSCKbHkiJO1mn+eI86TtWP/DJXJNG70/gL7YRmkDrjl9wMFb7cInQDtB6OhmvSkLHX2hjHylfUGydOlxm/+PTtKouQoVcX4Jr7l1DKn+2W9ywNK6/f5TDz86xeKqGbmpsvL6XDdee/Z6RUvLc10/itHyEIgh9iQzpuICFxErqNCsuRkxj8019JDImzaqLGdewLiMKIiIi4s0Ria0RERERERERERE/VJw4cYKf+7mfO+f7f/AHf8DAwMAVOcZ//I//kX//7//9FdnXPwaklMy7PgLoMbSLOtGFEOeIeouux388McPhps2S6+OEkinHY9A02JLsCBclz+epSoO0pjJWr2JPnKCYyvDcVbuQYci6mQlagUfvti3U/YC/X6xwTyF9Xkdo1fN5utIgo6nk9U7O4TpV4UjT4cVak/d1Zy/ptStCsCMVZ0cqDr052j/3L1j4X39JfXqGY4rguV038MRtd5FVBPc89SiFWpWZwWHW5FIk7TbNJ75HYvduYjt2IIQg97GP4ZdKuOPjEISoqRSpBz6AsX49Fc/n907N82qjjR2EqELQCALiqkJa03i80uBAs80XxgbY8LpogLyuEUpJKCWF5gzXnvgqY1OP0u2WUBNpyIzAmpvgxv/tjZ2qUsIz/xWOfQfieRAKHPkHaMzBvb8F6pUVfFy7jaKe/bFWUTW8VXb//WNhqeFwYKaGIgRXDabJxlfn/lwtQgg29KbY0HvhDGF3qkFQc84SOGUo8aYahI5P6o4hYld14Zfa1B+fQhgqwWILv+yAlEgvoPXSAnpvgtjm/KrXqhjqGw7dkl6Ic7KKc7RCaPtIJyBo+wQ1l7Dt45yoEt48gGKoZ9U1qw57vjuJa/ukeyzCQLI02WDvI5Pc9KH15/weTOUtrnvfWgIvRKgC5Tyib2W+RWW+haar6JaC74Z4TkDgh6iqoF33iKcNNu7uI93VEYUz3VGGcUTE95tIbI2IiIiIiIiIiPihYmxsjG984xuXVZNMJs9yspZKpbOcrq/nV37lV/g3/+bfrHxdq9UYHl69++qdjAw7Q28ulI85bbt8cWqRg00bgWBr0uInh7rpe50zdNH1ONK0iSkK21KxFYflawOvHi/VOdZyyGoaEodASiQw7bjUg4C1MYMuQ6PqhwxbOkGlAhK6kZw0TfTAJ2VZFFp1EpqKlFD2fB5cqvJitYkrJddnEry7kMFSBA8uVXm13kJKmLI9xuIGBV1DAE4oWS2x7dsZ2bwZd2aWR0pN/qbukdRUUvUauYV56tkcmqLQDkLUdBp/YQF3fJzYcmyNPjhI36/+Kvb+/YStFsboGMZQJ3rnL2aKPFVpsCZmEjMFT1caLLk+t+RSdJs6fYbG4abNI6XaOWLrzdkk3y7WmKws8cCB32OkfICcUyKmCES7AnoC9v8dFE8Q9u/A7tqKNXozyuvF08oETL0A6X6ILWd0milYOAhze2HwmlWfu/PRv34Ts0cP4dptDCuG59j4rs3Ahk1X9Dg/SDx1fIk/fmqcpYbbecCRtviZ28e4ejj7di/tbIJlS+YZmqJQBNIPkYFECIHeFUMrWDhHKzjHK/h1F0VTOiK+AKEKWi/PY45lzhE7rwTSD6l9dwLnZJWg5hA0PPyagyIEUhVIN6A8XWfxLw8zfN8oqcJpYXPxVB274ZHtj3ecsCokcgbFmSbNikMyd/4sZFW/cNaw3fRRNYFuqQReiG5paIZKu+HSuzbDVXcO0jWUXBFaIyIi3h4isTUiIiIiIiIiIiLiImzfvp0DBw6wbl1nYsyhQ4fYtWvXBbc3TfMHJpt5tfjlMrVvfpPWCy8gdJ3EzTeTfu97UWKnxQYvlPzuqXn2Ndr0mzqSTjZoOwz5jfWDK47Sby9V+fPZIkUv6GSaxiw+O1jg+VqLpyp1VAR2ECLoiKuWoiCAdhDiSUkrCBmLmexOJ/jyXIW6H5LQNEBS1juOPl9Vaeom5nJW4pLnEVMU/vvkIp4MqfoB/7BQ4e/my9zXneUvZ0toQtAIQ0qeR6PuszZmkdAUNicvfWDUsePHmDl0gJgM6FkzSk88ReMfHsQ5cph7DYPprgG+u+0ayokUvmGiizZ7emu8lGuSkzo7u0Le87qsdsWyiF977crXJc/nL2aK/OHUIgFgh5JAShbcADeUHG87dC07ik1VYcbxzlnngGXwC2v7eOXl5+hrToGewPJr6IkceC2ozSBDH686zdTUAWyhcXzoTvK3/xy35M+IYnDq4DuQOCMLXbMg8MC58nEaY7uuY/HUCaYPH0QGAUJVGNi0lbFrb7jix/pBoNry+NNnTlG1fdZ1J5HAeLHJ/3x6nN/q246lX1lBUkpJUHMhlKgZ84It+OdD70+ixDSCqtOJEQglQc1BH0qhpk87cYUQxK/pxZ2oIZ1gpW1eTRuoXTHCpk9QdVC6Tz8Ak1IStnwAlPiF3fTSCwjd8ILbOOM1nPEqatZEzZq4EzWCskOoCkJdpeRLTrR93FeXOL5oc9Xdw4xevZxN+xZonemCRSxlIBSFdt3FcwLCQBJPGdzy0Q10Da1+XkJERMSVIxJbIyIiIiIiIiIiIs4gCALCMERKufLh+7Of/Sx/8id/wvvf/34A/uIv/oKf/umffjuX+bYifZ/i//P/0n71VZRMBpotKn/9N/ilMl3/5CcJpWTa8TjabHO05bA2ZhJTOyKnoQgONW2Otmw2J2JMtB3+eKaIF0o2xE08KdnfaPFT++q0whBTEWQ1jZof4IYhCh23qx92nK2aAEVAQlUZjJncmkvy90tV6rkCpZbDZDKDGYYg4aW+QXoMjWzTJqupNMMQTQjm3IC637nmT5Qb7K13xOFrMwn21ls0/JCqH1J0ff63kW42xS8utlY8ny898jilRx9EsVtYqkqv9gTDxTJj1TZ136W5tMR9vs/gwZd55cbbOLHramarf81szqOASTlwOL7Jwuou8n46A7sONW1UAVsSMWJqZ5DV752a59lKEwl4YcixloMqwBCCNjDneEzZHgOWjh2EbIyf/0HAhoTFhoyCjJsgQ4SzrBYJFZw6vmpQVFMcS4ySC+oMTz/BX+zfTXLnbVydilOem2Fu/zh9DY+YfRyzf0vH8dyYg1gWCutXjmU3Gkwd3EerViFV6GZo8zZ069JF7NfQLYubPvJJFk4ep1Eqksjl6B3bgKr9cH7UPTxfZ7HuMNqVXGlDH8rGmK3YnFhssnUgfcWOFdRdGk9O4802kRK0LovkzYPoXRduWw/tZQHU0tDyFonre2m+MI+/0AYFtK4YyZsGzhE+jf4EyduHqD44jmKqqAkdJWV0BmjpCkrs9PUOai6NZ2bxZhoA6INJkjf2o6ZOC7jSD2m9soB9uIz0QtSCRfL6PvS+sx9s+KU2hKy4ZvWeOGHDQwJTIcwGEjOuobZ9fNvnwJOzdA2nSOUtukdSxFIGtcU2qYJF4EuaJYf+DVkS2dU9jEtkTcZ29XDk2TmsuIZmKGiGylW3D0RCa0TEO4gfzv+BIiIiIiIiIiIiIs7DF7/4RR577DE8z+Onf/qnueuuu/jUpz7Fvffey4svvsgv/MIvEAQBu3bt4t577327l/u2YR88hH3oIPrw8IqT1S+XaT/3HMfe817+2JYcbdnUvIAZx6PbOP2xQxcCX0rsoNOKv7/Rpur7bIxbCCEwgLIfMGm7pLWOwDHneuQ0lXbYaeGv+QFCdIY5ZXWFmh9yuGXjhiGfG+qiz9R5pFRj3PHJNRpsm5vGNwxODQwhkkke6M6yPmHx+xML1IOO0JrVFBCCiutT9n2SmsomTWV3JsmS5zPRdri7kOKjffmLtudKKfmjkzPMPv0YqcDH7x1kMQiwaxVkvUKQ68EdHyeMx4m3bYbnFlg6tpfKDWMsdqUZWfJwnQA7nmEmn+D/nXwMrfcO/m6pzazjEroeQ4bKv94wglQE+xptRmIGmiLY32gjlyMWNFXBlIJAwv5Gi7pvsCFhcXfhwkPBKKxDaB2xFUUBrw1uC5C4UlC0ukjrGoGWpcspkqpP8lixTn7yOC984yvY9Tp95Nio7ifTqJDI5hBmEq7+McgMAdAoFXnyr/6U0swU0HEunhxdzy0f+xRm/A3yYC+Aqmn0/xDHBpyJrgoUIQhCiaoICEKcpRZhxab92CTtHb1Ym/MI9cKt6m+ElBJvtok306C1d4mg7qLlLQTgTtWpPzJJ9oF1Z7X0h46PO93APlTCX2oDYIykSVzfR2xbF8ZwCm+uhdAV9MHkOXEAMggJWz7WWBZnYw5vsg5AUHWQbkhsRxdqsiOkhm5I5RvH8WabKEkdoSk0X5qnfWCJ+M4eYlsL6N1xWq8u0nxhHjSBEAL3VJ3KYpvY1jxKXMcYTKIVYigxvZMPG0qEIlBiOkJXaEnJrB3ihYJ62SEMQVNcNDekON0glbdIZEx23DXEvsenqS3aKIqgMJJk3TU9OC3/vAOrfC+gPNtCSkm+P4F2nmiEzTf2keuNs3Cq4xTvWZumd+2VE9EjIiLePJHYGhEREREREREREbHM5z73OT73uc/xpS996ZyffeELX/j+L+gdSlivIT0fcYYTUYnHaTWa/P5MkeOayYBlYArBibbDc9Umd+WSIARTtkevobN+2V2pCAEIJJ2u20YQUnIDEJDWFHRFwQ5Dan5Aj6HTpas8WWmgCYGpCDwJfaZGICUTtsv6uMUHe3NsTFhM2R6xbIpj+RwNBKoi0ELJpmSMq1IxUprCibaDLjqCnxtKNEWQFhol16foeiy5Ps3lXNpd6fgl5SDOuz77Z+ZYYzcRqUxH+PI85jQDL5Pj4bUbcDfu4uZjB9hx7DBxKREhJGtTxAe6OFbopul5WIaBErSYs2v85tEj5LQc8flp7HabV60kvzE1wT/ZvhknlMRUhdGYyaTtshiGBIAmBFen4jT9AFtKPtqXJ66qfHOxQpehc0s2ScF43UfCvqth/d1w5CHQYtAuI4FJM8ZRI0Ux2cOwlOihSygUbDOD126z75GH8do2haERfLGGg5UB0u1Jtl5zO8mtd0DvVSuHOPrcUxSnJigMjqCoKr7nMn/iKOOvvsSmm257czfnDzlb+tOMFOKcWGwykLFwZhrM1my2xU0G6z71J6YJ6h7JG/sve99SSpovztN+dZGw6eGXbNAUpBNAIEGA06riTtWxxrKdAVZ7l2jvL+JO1ZFOgJI20PIW9oEiYcsn8961qGkTNX1+p6d9vELr5QXCuouwNMx1GdSchTdRQ9EUrI05Yld1ddbnBVS+cQL7SBkUQdj2kX4IAgI7oPXKAs6pGonremm8MEtQ9zq5sWFH0JVzAX6xhZo0UWIayVsGMEfTtPdb+AstlISO9EJEXEMLwCm3aLoBqqKgmgpBKHGqLtXF9sr6Bzfl6B5JUZ5rUS/bTB8u89zXT6CoCoMbs2y9dQDD6rwHizMNXvn2BLViZ7hbKm+x855huobOHjImhKBvLEPf2Bs8NImIiHhbicTWiIiIiIiIiIiIiIjLQh8aQkkmCEoltEInmzNYWuL4mjFOqTqjcbMz4EpT2Zq0ONS02dPoDL8qGCqfGSiQXHat7kzF6dI1TrZdhiydJdfHDQNiioInQQcMRaHme6Q0lc8OdVP1AwxFwZeSpKYSEwJHSqwzhnQZiiCQIQfaLr4UWGpHTK0EAc9WGtyeT3FfV5Z9jTZlP8QNJR6SjKpiKgpSkTxZabymIZHSVF6pt7m3KwuAHYYkVeW84ms7CLEtC18zaDcbtIVKGAR4moarajiKwkx3L3+X7yZZKhF3mtiBT1DTsfsEVb9FxkyiCIHn1jG0LAtBnOTiFO1mHcuy6A4cpgKFg997lOyOW5hzPAZMnc1xk7ofYCqCG7NJkqrC4abNLekEh1s2+xv2ivP14WKVXx7tZ8A6Y1K9osCN/wKGdsPSYVynyZ+3jvNMcS+V5hJKeIKr3QQfakumc1vZn9rMx4I2zUqJRC63cj7CzFqOTyvk09ezvm/7Wedn/uRxzHgCRe3cA5puoKoai6dOrlpsrbRcZqs2hYRBT/ry4giklJSmpyhNT6JbFn3rN2IlLtySXZye5Pjzz1CemyHb28/662+kMDSyqnVfaSxd5Z/fsY4vPTXOiakqftNjRybOpwfymKZOUHexj5SIbe9CPY+z8o0ISjb23iWEpqDlY3g1B2yfwPZREwYylMiWR/tQCdnyaTw/hzdZRwIylCimimz5BIaHIgT2oRL6QIL4ju7zZr26s00aj08hvRAloRO2PFqvLJK6Y4jULQMAZ73/7CNl3IkaQhWImNYRW90AsZzHqmRMvNkm1W+cJGi44IegdSIIXhOMhaai9cQIyg7N5+bIfXgDmXvW0HxlAW+2iZozSdzUT9ZQUL54kNALMWIqiqESuAGKKrAb7lmvw4hppLstXvnOBM2yQzxrEvghx19aQAi4+u4RAi/k1e9MUl1ok+6OgYDaYptXvj3BHZ/ajP4WDP+KiIh464jE1oiIiIiIiIiIiIiIy0IfHiZ117uoPfggTqkEgJJMYtx+O4Giop0hgAxaBk4oeX93ltG4yY5UnD7ztMjTa+r8s+FuvjSzxLTt4UtJl6GT0hRKXkDF8/EkqAg+2JNlRypGXFU6GaSmjqUIFl2f23IpBs/Y71jMJKmpVP2AvK4hEARSktQUjixHDny4L0c7DPkvp+ZZ8nwUoBQGWEpIl6HRZ+hkdZW0phFTBC9Wm/zuqTmOtWymbA9TVbgxk+QDPVk2JE4LfEOWQW8my8Gh9Yztf55k4NM248SbNdqxJI4ZJ1depJLt4dktV1NJJuheOkWmFaNHxDnlHaEdlpFINCWGkr6VdiCotG0MRccMIWFo6IqGnJ/mvXrA1z2Vw02HUIZkVYHqe5wsltEti+FEjB5T5+8XK6yLmxiKQiAlh5s2f79Y5aeGu8++wKoGa26CNTfxxMR3eGT/c+S7t1GIlZguneCZ0KbStYvpNZ9lSzbHnV1xntF1PMfBsDqxEqHvI4SCdZ5YgHg6Q2V+duVrKSWB7xNLXX4rtJSSr786wzf2zFK3PWKGxu0buvjkDWswtIu3yksp2ffdhzjy7JO47TZCCDK9fdzwox8nPzB4zvbl2Wme/Ms/oVkuY8TilGammDt+lFs/8eMUhoYve/1vBWPdSX79/ds49sIsrWdmGRlIoiw/iFAslaDuETa9yxZbvaU2oe2j9cRBgqIIwlB2QpMVIAChqbjjNbzJBmHzNdFRgheCpYIEf74FukAE0Hh8irDhkrhpAH+2iXOiSuj4GAPJzvHaPlpvx1GuxDT8pTb24RKxTflz1udONhCGhjACpBt23LaAdEKUlI4IJNIOEIaK0BWkF0IoO0IrgCKQXoAQAjVj4Jcd/KU2xlCKzD1rkIEE5bTAW9iQxT1WRYaSwA8x4zq6qSLluedu/kSNRtkh1xc/S1iePlJh80391Ip2J9u1y0LVO9cq3R2jvmRTnG7QNxq5WCMifpCIxNaIiIiIiIiIiIiIiMtCCEH24x/D3LQJ++ABhKYRu/pqEmPr6D4yxZTtMrzslpyyXUZiBp8Z7CJ+gZzI3dkkV6XinGw7WELw4FKFby3V0IRC3e8M1Lm/O8vd+RT/x8k5an5IOwzZ32yTUVV2puM4Ych/m1jkAz0ZhmImihDckk2xv94mAAIpKRgavYaGJyVuKFEUwY5UjJFlQTiuCtKaRkIVHG46XJWMsfaMYVIHGjZ/O18GISh7Pk4g2Vdv80ylwb9c08vt+U67r64I7u1K84ubr8GWMHzyIBKY6V9LsasXKQVWq46tq7y6YSOOaVLs7uIjs0fJzHYxs3EbjfZJckYCNb6NV5xudM/HMS1016HluJQ0k7FWhUKjxp0G7BwZYE+9zeLECcKXnqTk+xRjKXKWycduvomveQamomAsi26qEKQ1lf2N9usvx1m8PP8yqqKSjeUglmOoMEZp/hAzjYA7vvddBnJZ2tftZmTb1Rx59nuEvoeiajSrZbpHRuldt/6cfY7uuo65E8cZP3kKTzFR3BaFXJo123de5p0IL5wq85fPT6JrCgPZOHXb45t7Z+lNW7xv+8Vb5Zcmxjn8zPfQDJNCoRsZhpRmptj73W9x+6c+d45z+cRLz9Mol+gaWoMQAiklxakJjr/03DtGbAVQFcHYSIbKniI4IcQ61z1oeigxDTVtXGQP56KYakdYDSRCU1BTJmHTBwmhE3RERBW8uSZKXENLGoSKj2pq+K5D2O5sSygRqopiKqgZA/tQGRQF+1AJ6XTe786xKiggxdnuVTSls5/zIAwFoYDWHcdf6OSevpZPonXHCVud2AAlZSFsBd9ZFlu9kM6ELwWxLNBLL+w4ZM3TjlKhnn0vDG3KUV1oEc+YKJoCSFoVl5415z408NxlQfeMXaiawHMCfC8840Vc8uWI+AFASslv/uZv0mg0ME2TPXv28Gu/9mvs2rXr7V5axFtMJLZGRERERERERERERFw2QlGIX7OL+DWnPzRawE8MdPE/phc52rRBCLoNjZ8c7L6g0PoacVVhW7LjivzpWA/r4jGerTZQgBuzSe7Kp/mjqUWONG22JWPsEDHmHZ8Xa00eK3ValUPgSzOL/OJoP+/rynBtOs5o3CShqqRUBVMRHG057EzFcYKAf3togmcrTYq+jyEEA6bFVakYEjjScljyfNbSEVvdIKTi+2Q0ldpym35O1yi7PvOux1/OFbk+kyC2/Dq3JGNsTCdQxjbgNCssFAaYyPeSW5xhw8n9pGsl1lpHWOgd4sT67Sj5LoYXjiLLZX563cf4q2KDih+w5HqoImB70mJprkVDqAhVJb84zfZ9T+NbBtnefvqTMUZ8m28//iC+67C5qwcpbSrjx5m2K+Tf+3HcUCKlXBGvWkFIj/nGHwlfLzYqgY+oFDGqLXLhEPXZaV742t+y4573sf2u93D8peepzs8hZUi7XuPIM0+y8cZb0M3Tzt/Cui2MD1xPce9zaG4Tx0ozkb+WW9K9F7/xXscL4yXcIGSk0HHQFpImTcfne8eWLk1snZrAc2wy3Z1jC1Ulkc1Tmp6kXa8RT5/tKKwtLaIb1sp5EUKgWxa1xfnLXvtbjdYTx1yfxT5c7rTNSxCaQvzqbhTr8qUAYzCJ3h3viKlJA6EpCK3jOFXiOn7V6Qi7QNj2CWTnHMmgI2QShOBL0ASKKtC646gpE3euQXvPIqHjgxcS2kEnQzWUCFNFTRpIN8BbbBG2fIyhJH7VRsucHRdhjmVwTlRBSrT+BEHRJqg6iIQGgSRoeKAqaDmLsO0Ttj3ksnAsFTquV10laLiEDQ9jNINWiF3wfIzt7KY43WRxot6JSVAFgxtzDG8913Wb70+gmyrtukcspSMlNCsuPWtSxFMGVlwn3RWjMt86K0YgXbAoDF440iLinc3TTz/N7//+7zMzM4MQgj179vDJT36Sffv2vd1Li3iLicTWiIiIiIiIiIiIiB9CvOlp6o88gjt+Cn1wgOQdd2KOjb7p/d6eT7ExYbG33kIRHedot3F57cqGovDe7gzv7T5b6Hql3iKrqWjLbbgLrkvZD1CApKqQUARzjs+vHJ7ib+dL3JJNcks2ydOVJlU/AClZGzP5eH+B/3RqnkdKdfTlDuh2KDnQbKMK2JywyGkqoZScaNlYikLZ8xFAyfNpBCG6EDhhp184riosuj6zjsfYshN22DLYlozziltgMwF9R16itWEn64/tIVct0o6niHkOVx3fR9b3YHQjrWqZ3nUbuGugm6sLWQ422jxVbvBspcFoOs62iUM0F+dASnK1El2mgaJYLJ46ychVO1iaOEW7XqMwMIQQnSnrqUI3tcVFdgVtntZVjrYccnpHMI6rCvcU3rg9+bre69i7uJeSXSJjZJhemsB3HbbFd5BKdAYTVRfnOfHy87znZ36W4vQUjVKRWDKJ7zrsefhBGqUiuz/40RWB8ukTRZ53uxm44UMkFIkQKgeXWnz91Rn+6R3rLute8V9rFV/OoVWEQFEEXhC+ceEymmEAAhmGiGXXbxj4KKqGpp9732b7+pk9dnhlexmGuHabXP+5kQOXS7NSZvrQfpx2i1zfAP0bNqNqq//ILhRB6tZBjP4EzlQdRVMwRjMYw6mLF59vf7pK6l0jNF+Yw5tpIJIaxliWsObil9rIUKLGdZS4TlCxCVy/I5TaAUKCkjQI3RA1qaP3xlEsDbk8oCpouYR20HGauh3HKUgkAvtYuRNDsHxJ3ZM1Fn7vFQo/sQ3zjOFRSkJHzRg447WOmzUfI359L0IR+MU2el8cd6pBaPsoSR0R15F1F2GqaEkDBCtOVmtLgcTuvvNmyb6GGde56YPrWDhVo1V3SeYsuoeTKOd5sJQfSLD+mh6OvbRAebbVyYHOW2y9dQChdIb3XX3P8FkDstJdMa6+ezjKa/0BZsOGDfz8z//8ytfr1q1jZmbmbVxRxPeLSGyNiIiIiIiIiIiI+CHDm51l4T//Du7UFEo8jn1gP62XXqbnX/8rzHXnF7v8YpH6ww9j79uPms2SvP02Ytddd94BUX2mTp955TMGc7rGgttpIS66Pkea9mtdwjhS0vRCJOAiqfgBDy7VuDmb4FfH+jnRdkhpKtdlEmRVhe8U6yhCEFcV6kGnzpOwv9Gm6HhsqZW4ef+r7Mn30O7v57bRNXzF81lwfKQEH0nN6zhcdSGwFIXUGRmhTqPOe6cPox8+ykIqi2LEuO3oy1ilBcq5bmQijXRt6kKQnz6BsTBBtVllzY5rkGHIsGUwbBkMWQaHWzazjSab7SZpy6ItJfH0GkZ6u6nMzlCenWLkqh2oemcQUBiGqCvCYYCiKmxKxvjX3Wm+ulBmou2yKW5xf0+W69JxoCNWLpw8ztLkKRRNY2DDJjI9fdw8eDNTjSmemnmKidoEoe+wpdHP5vSalddqxuI4zQZzx4+ycPI42Z5ejFhnv+16nelD+6kt3kGmp+MePTLfQAqImwaSzofSbMxg73T1LOftpbB1IM1XXp5iqtxGVQRpS0dTBA/sHLik+oENm0nmcpRmp0nmCgSeS6teZfPNt6+8hjNZd+1uZo4cZGl6Ak03CDyXdFcP66+74ZLXfD6KU5M88+W/oLa0CICiqYxcdTW7H/jomxNcNQVrUx7rPBmnq0HLmmTuWUNo+4ReQOPpWZyq23GICgGGgkjpKH6IrDjIIETNGKhZk+TNA3izLdp7FzvuVT8kbHhoXXHcqRrSD5avfcf9ig8ioSGrTicOQAFUBaQkbHiUv3KMnp/ajhLT8BZb1L51iqDmoGZNZNtHGArxbV1o+Y4DVkqJfaBE6+UFwrqLnrdQN+Uw12bQchZ6fwJp+7CcD3spqLpC//rsRbcTQrDlln56x9KUZ1tohkLfaAYreVrQLwwkueOTmyjNNIGOG1a7BKG1VXMpzjRQVYWu4STGKlzLEZdPrVY762vTNDFN86zvdXd38/nPfx6AMAz57d/+bX7hF37h+7bGiLeP6F0YERERERERERER8UNG4/HH8aanMNev77jzpMQ9doz6d757XrE1aDRZ/N3fwzl0CCWRwBk/SXv/fgqf/QmSt9/+lq71TMfiuwtpDjdtJtoOs46Hf8YgmkBKApb1GCHIahppTWVvw+aTAxofzpwe1OQEAa6UKEiqfoCCwBASdzni0a3XuevRb3NXeYH3AlXb4fce+ASl7gFcKTsGu+Vjy1DihiG35ZIrDt52vcb3/tcfszRxirWKQqzVphxP42zaQffBl1g3PMi041NSBGG9QqpWoieXI97Tx8S+V8kNDLLlljsA2JKw+ERfnr+dLVJVNSzb7oh7qRjqsjD1mijYO7qeTE8v5blpUoVuZBBSLy8xuHkbmZ5edgnBzlQMV0qMZefra+d43yPf5vBTj+O5HWHr8JOPc92P/ChDW6/iU1s+xd0jd7PUXsKbWuLIgQcJTRfVtJBS0qxV6BpaQxgE+J6Dbp1uvfYTSU6gMlyucl13T2cAVUzrTK4/Q1i1/YCBrHVZQivAqVILL5DYXkAQSiotl7WFBHdt6rmk+kQ2x+4HPsqe7zxIbXEBVdPZeMMtbL/rPefdPt3Vw+2f/BwnX36BysIcme5eRndeS7r70o53PqSU7H/8O9SKixQGhxGKgttuMbHvVYa2XMXw1u2r3vdbhWJptF5dxD1WQc1b+A0H2fAIFtsEdQ9hKoiYSmxjntjV3ZjDKYSmoPfGCb0Ab7IOUmJuyJG4tpfil48QNLzl/FQ6Lla1c4/K10zKmtK5P2QnmiAo2zSenUXvS+CMVwlqzsowLSkl3nwL+1CJ5M0d4V0IQWxbAXNdhqDioMQ11PTZ4piIX54L/2KEocSzfXRTRVEVCgNJCgMXjgXQdPW8ma8XYvJAiX2PT9OuuyAgXbDYde+aNzxGxJVhePjsjOZf//Vf5zd+4zfOu+1v//Zv861vfYtbbrmFX/7lX/4+rC7i7SYSW9+AoaGhVdX97M/+7KrqDh48uKq6xx57bFV19Xp9VXUArVZr1bXfT1Z7DS/3j7zXWL/+3AEAl8InPvGJVdV9/etfX1Xd7/7u766q7g/+4A9WVQegqqtrfzGMyw/vB7jttttWVbdnz55V1W3btm1Vdb/0S7+0qrobbnhz7onvF41GY9W1jz766KrqfvzHf3xVdc1mc1V1Dz300KrqrrvuulXVdXd3X3yjdwBbtmxZVd2mTZtWfcxTp06tqm7t2rWrqistT2CPiPhBxJ2cQpjWStu0EAIlmcS9wPuo/dKLOEePYIyOIpZbq93JSWoPPkji5psRb8J5dyGaQcDX5is8Xq4TILklm+KB7iw/NdTFNxerHGs5qKITARlAR4Cho9EkVZW8rqEKcIKQF6tNnio3WBc32Z1JYCgKWxIWT5cbBHRMdCECTUq2yYCgWkYUCuimwF1c4o+37OIFVFzPQ1VUFLE8x2bZVntNOsFnBrpW1n5qz8ssTZwi3T/Aq02XJcMlU1ygvjDHjFCRxRJX9fVSbpSZrywRz2QY3LgFRVWpLS1w4sXn2HjDLahax6n6QG+OG7NJni/exsKjD5HyGshmQLFaJpErMLi583eIblns/uBHeeXBb1CZn0UIheGtO7jmfe8/K2PUfN3fupX5WY48+ySaYZLp6UNKSWVuhr2PPETf+o1ohkFfoo++RB9+xqN5aILJA8uZg1JipVJsue0uUvkCZjxBq1YhkcnxSjzHd60MzX6L5xqSa0/O8c+Gu7lxrMAjhxY4sdQknzBoOp3BSfdsubzM1nLT5enjS2zqS2HpKk0nQBFQd3yOLzYpJM2L7wToHVvP3Wv+OY1yEd20iKXeWOxKFbrYcc97L2utb4Tn2JSmJ4mnsyvvSSMWJ1xapDw7/Y4UW2UocY5XEDENoYAIQSqi0/5v+8g2YCj4xTbeTANjOEl77yLtPUudAVeGQmxTnsT1nVb9+PYuGlWHsLksuBoCEUjClrvyYAMvRApW4gSk7dN4YQ4tYRBUbISlwRn3uaIr+CX7nLUrlobSd/7fWTKQeAstCEK0njjKm2jhnz5S5sjz87SqDlbSYP21PYxsza/6s+bradVc9j42hecEZPviSCmpzrfZ+8gUt39i43njDCKuHJOTk6TTp39XvN7Veia//Mu/zC/90i/xxS9+kY9+9KP8zd/8zRW7DyLemURia0RERERERERERMQPGcbwEO0XXzidOyklYaOBcf35HwT5S8XOcJ0zMizVVIqgVCZsNlEzVzYyQErJH0wu8p1ijbSmIoC/nisxabt8YayfewoZvnBkkr+dK9OtC8p+SCBPdxpflTSJKYLjLZtTbZdfOzaNE4QoQjBs6fxoT473FNKMtxxOOS5+2HHD9pg6a/2QE0FASlXxpmc4XKmxf9dt9C4ssJDKIEKJoeuEgKkpxBXBWNzEOkPYqMzNoqgqs37ItO2gCoFrxki1G5S37KK4/wUmDi3hNjrmh/zAMMryg2nNMPEcm9D3z2of7zV17rvzLo7EDI6/8Cyu3aZ//Sa23v4u0l2nH8QVBod51+f+KbWlRRRVJZkvnPOhfqLtsKfeBmBHKoY/O43TbFAY6kQDCCFI5gs0yiVqS4vkB07nkZYl7LvuLg4ZOeJLc6zPZ7nrhhvoGx4BYP31N3LoqScYD8r8fW4UqaiMZdOYlskT5To5XeVnhnv453et5ysvTzNTbtOVNLl3Wy93bLy8B4o128N2Q/JJg7ihkbL0juDU9qi2vcval6KqpLtW7059M6iajmHFaJ/RlizDjqJoxhMXKnv7kZ2HDkHDBUBNGQR1F5YHW2EooAra+4pIP8Q5WgFFoCR1ZNun9coiatYktilP/Kou/NkWzqkqQcVBOiFSwkqoctg5Hme42QlB0RW0nhhB0yWoOoQtDzXeuQ9CL0TNm4RugFAF4iLio1+2qT82hb/YQoagpg2SNw9gXobT9DUWJ+q8/NAEnhtgJXXqRZtXH55EN1QGNmTP2b443WD+ZI0wlHQNJeldm37DvFiApak6dsMj2xdHKAKBIJk3qS61qS3ZZHvPjcGIuHKk0+mzxNaLIYTgc5/7HL/5m7/J+Pg4o6NvPiM94p1LJLZGRERERERERERE/JCRvP12Ws8/j3vsGCIeJ2y30Lq6Sd1993m31/t6QRGEjoOy7N4JKhWMtWtRkm+uXbUVhDxdaXC0aZPRVG7Odfb3fLVJn6mT0zsfWdJ+wKv1FoebNluSMTYmLCxNwZdgCoG/rMloQNkLaQQ2Rden4gfEFQVNETT8kP1NhyPjc+R1je3JGL2WxrGmS7ehMWDpTLRg2G6x6dgh3MkJFrv6aRgG2fICuuvgmBah7yNUlYKqkNdVMvrZH6sSuTxN1+VgvUU7lGgKmHaLxVw3YmwziSP78NoNVFXDx6FeXCKRyRL4PvWlBYav2ol2HpeUoqpsvvl2Nuy+Cd91MWLx87qjhKKs5KO+nkeKNb44vUTZCwDobZS55eXHaM3O0qpWyfT0kunuxXMcNF3HiMVolEucfPkF5mem+cue9UymcnSv38b8uq3sC0KkiPFPlve//a73UBgc4c9PzYCWYmsyTjyTQQhBW9d4ptLk0wMh14zk2DmUpWZ7xA0NQ7t8F15v2qIrZTJXs1mTVxFCUGl7xAyVkfwPjtCkahpj1+zmlYe+SW1pAc0wadWqJAtdDG7a+nYv77wIRaAVLJovLSC9EOkFKFIFRSAMBcXovCfUhI7v2NiHSiBB71m+LoZKuNTGOVohtimPmjTI3LeW5rNzNF+Yw6844J8RKXA+DAXphggh0PsTOMereHNNwrRBUHGRfkDjqRlaL8yjFmLENueIX92DOM+9JkNJ/XvTeLMN1JzVGapVsWl8bxqtK4aauLx4gcmDJZy2T66/8x61EjrluRYTB4rniK3je5fY++gUbtsHBMd1hfXX9XSGZ72B+1FRFRCdh1Oi47UnlMtD4tTINflO4I/+6I+YnZ3l3/27fwd0BNdsNvumuowjfjCIxNaIiIiIiIiIiIiIHzL0/n66//XP03j0EdyT4+iDgyTvvBNz7PxOm9iuXVjbtmHv2YswDKTnoSSTpO+/H7HKqCAAOwj5nfE5nq02O5qKlHy7WOO+7gztMKTHOC1wJFSFKTuk4ndEwo2JGN26xrzr0zHAdXIae0yDHx/sIqYI/uvkAponMFSF+vJ2AEjwpORQ0+aWXJL3d+d4stLACyW39eS4fzFJvFbFLldIBxLFsyllcvSUFljI9+DqBoYCeT3GSMzg5uzZgvOa7Tt56OmnsJbmyegmhucSGCZHR7cy9tLTxFp1BjdtxdBUZo8corowR6tWIfA8VE2jMj/D1IG9DG/bcd7zpmo6qnb52ZIVz+fPZovYYcimhIlot1Af/wemSot0WybtRh2n1aRdq6FZJuuvuwkhBE/8+ZcozU4zWRjgQLxJd7VCdmwdVjLFouvxeKnOB3tyFAwNoSgMbt7KSKaPxMwSieTp/NbluE3k8pVQFEE2vrrIKABLV/nItUP80fdOcnS+DkKgqwrv3trDxt4frMzKjTfegkRy4sXn8GybgY2b2Xb7u0jmC2/30s6Lt9TGm3stWk5CIDvxAJaKRCD9EDVrnnaTBhJeJwDWpWSh0mS43GIwG4MQ3Mk6StJAt1SCot3ZJywPzFq20i4j9NOiqWJpaFkTrT+BP9ckDEIIQTY8QuERugFhzUF6kuSN/ee8nqDi4C+2UDPmSnSAlo/hL3ZiENQNucs6P3bTQ9XEWWKppotOtuoZuG2fQ8/MEoaS/GDnnm3VXE68usjwljzprhgXonskRTJnUl1ok8xbyFDSKNn0r8uQKliXtd6It4apqSnGx8dXvh4fH6dSqbB16zvzIUrElSMSWyMiIiIiIiIiIiJ+CDGGBsl/+tOXtK0Si9H9sz9L4/HHO0OyslkSN95I7AKZ5XYQcrLtEFMV1ljGBd1ZL9SaPFdtMmQZxNVOnMGRlsNz1SYZVWHB9Ri0OmLcouuT1TVGlr++Nh1DCIEnJSlN7XQZS9AUwYaExXXpOH84vQRA2fVxpVwRW0PAEgoSmG67/NPhHj7RX+jsS1VgtJ9Wfy/H/sNvYttNNpw6yN5N1+LpGnGnDQKSbovbhnv4SH8XQ9bZgmG6u4fJ2+9D7H0RbWGW+XgfE2NbmSn0s/PlJ+gp5LGWIxn6N27GffUlAs8j3d1DutCN02jywje+QjyTozA0jJSdlb/ZjL/jLYcl12c0ZnayWydPEK+WKBV6WRvTEEvz1JcWaTdqXHfXh9j+rvdw9NmnKM9OUxgYZirdhWLFoFSnujCPlUyRUFUWXY+qH1AwTn+83JGKkdJUZmyXPlPHDiVF1+fergyJNyHQv55b1nfRmzZ58VQZxwvZ3J/mmpHsyrmq2R5tN6A7aaJcpC377URRVbbccgcbb7hl2bUc+75nOjYcn+fHSyzWHfrSFtetzRE3zi8Z2EfKhC0Pc2MOnAC/YuMvLeejhhIR09C6YisRA+b6LM6JKqHtI0yV7yzW+OZ8lWZSJ/GVfVw/mueTQ3mCuovWFSNseQQVl466ekZ2wMo1lOCFiKxK6ASd4ViFGIlreqk/0mnX9xZaCENBBhLZ9pFpA/tomfjObhTrda9LyrMjCs762eWfy+7hFLPHKvheiKYrBH6Iawf0jJzddl4v2dh1j0T+tDgaS+mUZ1tUF9tvKLaaMY1r7l3DnkemqBdthCLoH8uw8+6RKA/0HcLnP/95fvEXf5HPf/7zWJbFgQMH+PKXv4z2FuScR7yziK5wRERERERERERERMRFUVMpMvffD/ff/4bbvVht8qWZJeZsD00RbE1Y/LORHrqNc52Yp9ouIZK4quCEIfOORzMIeL7aZMTU2d9oc7DRJq1pCKDXUPmzmSK35VNsjJv0GBq6ADuUGIpgwDQoux4n2w7XZxK8K5/iQKONI89WS0Kg4vsUdG1FR4mpCmfKGonrrkN+9nPU/+SL7Dj0Kv2lEnOFXtqxGL70uTaw+Rd3XUcseX4xpH9wmCfjWdbHTVq2B55HxnUZSMYpdEZ6dZDgex7xdIa+sQ2dtaQzFKdOceKl5xh/9UVmjh7CMGOs3Xkt66+/cSXLteT5vFBtUvcD1sUtdqRiKG8gssRVBV0IXBmioSLsFiESRVUwYzFyo+tJFroIPJctt96JYcWoLsyjaDqKqtLj2ZhhiG3FMZcHSy64Ht26Rp959vXdnLD4RF+ev50vc6RpYyoKu9JxPtGfv+D6Vsv6nhTre1Jnfa/tBvzVC5M8eWwJNwgZycf5+PXDbBu4svnCVxpV087K6v1+sdRw+J1vH+HwfB0pOy7kqwYz/Ot7NpKJ6UgvwD5awZ1uoJgq3nwTNKUjYMc0jFiy4wjVFPS+ON5si6DioJgq8e3dxK/pRJG4J2scXKjzN6UqiioYMDTsUPLdQwtk3ZB3CyCUKAkdLW91WutD2fmnLd/bgUQkNPSeBEIThC0PrRAjeUM/oRsgvQApQDoBclmslRIoO52MaieAZbG1ttRm8kCJRtmmO4B40cboiYMiCMo2SkpHH7j83NyRbXnmTlZZnKiviLVdg0lGd56dS2zGNTRDxXcCtGWnru+FqKrAjF/8PugaSnHHJzdRXWijaoJ0IXbRrNeI7x+xWIzf+73fe7uXEfE2EImtERERERERERERERFXhEXX479OLlDyfAZNA09Knq40CCV8Yawf/XUDarK6ipRQ83z2N22qXkA7CHFlyILtMhIzKHo+C65LRtPwpcqTlQbP15p8ur9AVtfQhGBg2VnqhyEztssrtRZOGHJDOsGXVAU7DM46rqQj0FaDgOGYyfr4+adIX/XAj/AXp6bIv/gYmUYZocAseQqNCpu2b8dKXLhV/d6uDK/WWzxRbtAKQ7xQktU1ctfciP3QVwnLJcxcgZbbRlFVYmcMGRNCICUce+5ppAArkcJpNHj5wa/jtptsf9e9HG/Z/F/jc0y1XRACUwjeVUjzM8PdqBcQXDfELTYnLV6qtegzdWLpPFkEWd8jo6lIKbHrNbpHRoklO+JlqqubwPeQYciA2+bqZoln9QR2IkWr0SajaXysP0/8dddWCMEDvTluzCY50XZIqQqbEzG075MQ9OUXJ/naqzPk4joJQ+PATI3ff+QYv/GBbfSkohbr1/Pt/fMcmK0x1p1EVxUcL2DPZJVHDy/wge0D1B6dwjle6RhNQ0loBxBKZK4TFSBDifRCYpvzJG8dJCg7BA0XNW2iZTvvr/S7RnBnGhz46gFaUrJO1RBVh3hDIZ7UeHapwb25OH7RRs2ZqBkTte0R1NxODAGAACWhk7ypH3MsS2vfIu5EAxGEBC0PrSuGYml4S62OQKuynF8hkU0PumIoyc7vi/Jck2e/doJmxUHRFBYcn1EFuko2ilgekHXTAGry8qMuzLjOjQ+sY/Z4hUbZIZ42GFifxYidLcEkcxb96zOM7yniewFCCOymR9/aDIWhS4vCUFWFfP87eJBaRMQPIZHYGhERERERERERERFxRXil1mLR8diQsBBA0fFZcH3+er7MvOvxY/0Fbst3RDw/lNS8gAXXZ1+jTSghqSkIAQYKQhHEVJUtps5TlSamojCW6Ihkk22Xf1iqckcuyV/NlZlsu1iq4FjTphGEvFRrsqfeQhcdATYuwJFnz9rRAEMI7synMJTzD2fSFIXPfPbT/IEaYp48jAhDuhsVRnp6uOFd737DVt2d6ThbkzEONtqYiqDP0NE9j38o2dxVaSCaZcTkKfK9/YxdewMTe1+hUS5jmCbxdA7PsZFS0ju6Hm05cqBRLnLipRdYv/tm/utUmb31NuviJgVdo+YHPFyssTuT4NrM+YUXxXP5Z3aVv2s1eFot4PYNs2bLdvomjlKanMD3XEI/oFkt88I3v8rYNdezdscuJva+QnF6AsOKs2t6gt7eEYyb30WhO8/12QSbExdude41dXrNy8+XvRwq83MUpybQTZPC8FomDh9k/z88xZjQMIc3EiQHSFpJji00eOlUhfde1feWrudCyFDizbeQbR+1YKFlzi/yv1kajk+l5dKVNLH0S4ts2DdTJWFqKw9ETF3F0BQOztR4XyGNc7KKmjFQzI6E4C22CGou/kK7k8UaStS8RWxbF0IItLyFlj9b1BZKJ8M0sH0UTUGNde6L0PEJay5BziJ1xzCtp2bwi51IAmtdlvjuPvzJBu5UHW+hhQwlrX1Fao9OdVyumkKw0MKdqJO9dy3W1gLutxudyIEAELKT76oItKyJWM6OPf7SAs2KQ24ggRCCMAg5MtvEHMswsiWP3hM/N27gMtBNlZGt52buum0fu+URTxtousqOu4aJJQ2mDpUIQ8n6a3vYdEMfqnr5Q+MiIiLeGURia0RERERERERERETEFaFjPuvMxZ53fQ422iszbSZsl/82uUBKU9mZjvO1hTJ/NVei19Aoez6tMMQNJVlNxQlDFCEo+wGaIlAAV54eSZ7TVSp+wC3ZFAlN5eGlGmUvQBGCNTGTjcui7JGmjSNBEwJdgIokCMEDNiYsLFWQ1zWklOxrtHm51iKUkh2pODvTcRQhuCqb5v/30z/F06+8QmNuhqFMmi3brybd1X3uCTiD1nJu7bZkjF5TRwC1V19lD3BiaC3XTXo4joO3sMDs/j3Ui4udoT5IVE2nf/1GgsBfEVoBDCtOs9Xg/x6f48GKTSihvpyVelUyxpzjcaRln1dsbe/dS/lP/wx/bo77VZX71q0j/hOfJf/jP8HM4YPMHDnI8ZeeAyRuu83R555i5shBbv34j3PrJz7DseefoTg1wUBXN++59gZ61o69qXvlSiCl5NBTj3PwiUdwWk2EELjtNqFQiFU9FAFKZRx7w014A5vxw5DpSgvHDzC1S8+Nna/ZPHV8iWLDZSQf56Z1BVLW5QnIYcuj9ugk3kwT6YUoMY3Y9gLxa3qvWL5mEEr+7uVpvn1wnqbjU0iafGDnAHdu7L7oMbJxnaPzZzvAvSAkE9fxK06ntd88LR+oaQMExLd3I90ANW1irst2vv9GayzbbNJ0HtMdip5PXlNpC0HN9Xh3XwazN4HxgfX4xU42spq38BfbyFASNDzClo/en8BbbIEbgBAIU0EgCJsejadn6PrcVThHy7gzTYSuIFSBsDSkE6D3xFfWUpptYcS1lXOjqApCUyl7IRtel616JQiDkCPPzTO+ZwnXCYinDDbu7mVkW4Gttw6w+eZ+kJ1Yj4iIiB9sIrE1IiIiIiIiIiIiIuKKsC0VI6urTNkei65HICUCQZehsTlhcaTl8ES5zqaExbeKNWKqwqBlYIeSGdvtDJ7SVFpOSIgkpiiYiiAA4sppcazsB2Q0lW5T54F4jluzSf5mrsSfzTpkVJWaH+CGIYqUBFKi0Blk7gO6gISqkNQUkqrGaNzkH5aq/MlMkYbfEZu+uVTlQz05fmyg40pLWxb33njjWa81kJJnK0321ltoiuC6TIIBQ+fZapOS5xNTFI41bap+wJGWQ48qSFYryFQG3YoT100SuslstUh7bpZYNrvsrgtw223ajTrxdAa72cBKJJFS0qyWGR/ZyFNOiCEECDCVTvvzKdVBwnmHT/nlMsU/+h/4S0voAwPIIMDfuxf5v/4C9d/+G0au2kFpZhIhBL1r13dyLaWkODXB8ReeYfcHP8q193/wrblp3gTl2RkOPv5dEAqFwRGa1TKLp8aJZ7JYhSEWmy65oI42sYcXvC7mGj7f3DPLgZkaH752iJvXdV30GCeXmvznbx9mqtxeycN98vgS//Y9m0hfhuDafHkRd7yGmrMQhkJY92i9vIjek8AYTl18B5fAI4cW+MsXJokbGtm4wULd4UtPjtOdNLlq8I2zau/Y2MOeySqnik3SMZ1KyyVpady2oRt1eU6V9MKOQxQI7QA1YRC/phfFPPeek36It9gGQO+OITQFGYT4JZvNbck9is5jvsdxP8AIYVfC4oFrBgEQqkDviRO6AbWHT9F+eZGg7YG3nNuqdoRVEJ2IgEAiLBU8QVBx8Es2asGCuSZCU1BzJrLtIxI65lh2ZY2pvEXjeMdB3onukIRBSDL71sRMjO8tcvCpWTRTxUxoNKoOr353kljKoHsktTzALcpbjYj4x0AktkZEREREREREREREXBGGLYNPDxT489kiC66PKyXdhsamhIUQAl0IKn5AMwhp+iHJZQfXoKWz5PnU/QCBRFEEbhgS0Jlg32toCCEZbzt4oUQXgvd3Z4irCidaDv9pfI6jLZs512PSdtGFQBOCRhDgLousrxEACaGgKwrv78lQ8wP+y6l52mHIaKzTkr/k+vz9UpXb8ykGl/NgwyBg9uhhynMzGLE4j6V6+QdH4ochEvjaQmVZ1JWEUjLjeFT9AE10nL4nvZBUOosIfGphwDO5HvKejVUtIZGYsfgZqxS06zXGrrme2SOHaJSLICVmIklzy04UobA2bnKs5aBIiQSOtRxuyibZfR5Xq73/AP7CAsboKOI1Mbavj9nxCfYdP4VZKNCanUXXTcRypIIh26StgMr87BW+S8Btt5g8sJfqwgLxTIbhrdtJZHOXvZ+lyXGcdpvC4HCnPd1zUVSVwHNZkzVo+SG1lslkO8Z0uUVfLslQNsZMxeaPnjhJT8pifc8b52J+c88MU+U2G3pTKELg+iH7pms8eXSJ923vv6R1ykDinKyixLUVYVJNG3hzTdyZxhUTWx87soiiCPoyHbEwYWocna/zzIki/RmLB/fN8fJkmZSlc/vGbu7Y0L0s8MH1a3P8k9tG+eaeWUpNl9GuBB+4epCrBjNIL0QfSOJN1hGmCkFn4JS1s2fl9YRtn7DloaQMgopD/YkpglInCkDNWSRvHcA5XKZ9sIQSSu53FK5VDOZiKpmYxvYbh8jkz46jaO8v0t6zROgGiLiGrHvghwRlZzkK4LXxdqcHZ6FA/bsT+BUbpMQv24QtD2NNmsQ1vRiDp6/32M5uitMNKnMtdEvFbfnE0wa+H/Dc109gJXWGt+TJ9V2ZPNTJAyWEIkjmOvERhqVRmm4yc7RM98iVuQciIiLeGURia0RERERERERERETEFeM9XRl2peP8/ql5Hi3X2ZaIEddU7KATE7A9GSOnqwxYOkeaNmlNpcvQGYuFHGvZpDWNXekEvUbHNdhv6lybjnOo5fBStUlSU7ktl2R7Msai4/HnM0ucajtsTViUPZ9p28MXEk0IvGUtxlQ6rjV3OVLgvu4Mnx4o4IUhv3Z0muMtB0MIKl7AsGWwKW5ytO0y3nYYtAwC3+f5r32Zib2vEAQ+dhBy0kjSc8f7SPV33HhPlhuUPJ97Cik8CVOOh64I4opCAEghqCbSbD34AvFKCVvXOWEl6Eum0IOQ7LK7DjrCrm6ZXP/+D7MwfpzFU+PopsHg5m3MSotwqcb6mImUMON4BDKk19T4V2t66TtfPuprA8LOaCV/obufvxxaR2OxgVLz2CRMNjWbDB4/xKD9PCmzgqdIZHYHLN0HXesv/SbwXZjfC24LujdBsmflR06ryZN/+acsnDyOXBaKT770PDd/7NNkenov/RiAqmmA7IhqnovTahF4HoqqkorrXL8mzvS0y145wFhPis0DWUAQNzWOzNd58VTpomLrobk6mbix4mo1NAVFCE4sNS9rrUIBKc/3g8vazRvScv1zhtAJIai1Pf7v7xxl33SVpKUzVbY5NFun6fj8yI6Ble3u3NTDreu7aDoBSUtDXRZiha6QuXuE1r4l3FN1hKVibchhbcwhA0nr5XnaB0tIJ0CJaQRtD+mG6IWO6Osvtqk9dArphKhJHS1n4Rdt+kttBlBI3zpC/OpzYzmc45WOm1ZTUDSVwAyRbR/pBQjT6NzPoUT6AUEz7MQOpE38ioPek0AogtD28Ys2se1dWBvPFvR7R9Nc/yOjnHx1kUbJoXdtmupCm6PPzaNoCqEfMnWozPX3j14RMdS1fRTt7AsuFPDc8AIVERERP6hEYmtERERERERERERExBWl29D5l2v6cKTkQMNGOi4Kgmsyce4upFGF4BN9eX53Yp5DTRsBqELw6f4C/3Skh6SqnJMxuTUV50O9Oep+wJ/NFPnDqUXsoON2XRsz0BQFFUFCVWgHIT6yM4gciKsKCVWl7gckFMHWhMXWRIxfOTJJMwhIqQracgvvlO2SVlVMIcjqnY9Ls0cOcWrvyyQyecx4nOm2jTY+Tt/e52j2fRAJOGGIAOxQEiwP4zKEoNvoDIhqBwGHnBgDjSpWq4qraOhCMtc9yMk1m9kxvp/1tSUCzycMPNZefTOxVIo123eyZvvOlfOwu9rk0VKdOdulX0jipkZF1/jnI91clYpzPszNm1FzObzpafSBASqKyl/2raGZTrMhmwIhODW8kV0PfoMR5SUyXQ28uoquqCRqR+GJ/z/c93+CeQmCU3UKnvi/YOkoBB7EC7Dr07D5PgDGX32Z+ZPHyPYOoOk6YRhSnJrgyLNPcv37P3RZ91n/+k0kcwXmTh7Drtfx7Da+7xE2AxZPnSSezJDUQrLZXqxkjNeUTSE6g5psL3jjAwA9KYv9s9WVr6WUBGFI12VMqBeqwFyXpfXiPMJQEYZCUPcQlooxdOUcjdeM5PjyS1M4noGpqzRsH5AYmsKh2RqjXUkMrSPGTlfafGvfHHdv7iVmnI4B0FSFTPzczFAlrpPc3Q+7z3bztg8Uab44j2KqKEkdv2jjL7UxhlOI5eFcWsHCnWkAAjVvdlzufXG0gklQ9zCGUghVIWx5yECiJPXT73+Fjh0dUCyNwA07qrUAtSsGXogMQxRdxVyfxa84KHSGcb1WIzRBUHbOe85616bpXdvJZz25Z4nJg2UyvXFUrROlUZ5rceT5ObqGk286W7dvLMPh5+bwvRBNV3DbHc999xVyNkdERLxziMTWiIiIiIiIiIiIiIgrTsHQ+HfrBni+2mTB9Rkwda5NJ4gtO++uyST49fWDPFNp0PBDNiYs8rrKH04tcqxpM2gZvK87wzXps1t4vzS9xLeWqhQMjZgqqPoBh5s2faaBEB1hVQc0RaHiB0g6ubGG6Ai6+rKQu+B6TDseI5ax4hDVFWgHIeO2w3sLGTYvD9oqzU4R+gFmvCNmWpqGl0wRW5xFeC4YJq/JVaaioAD5WpnsiQOM2g1Svf0cH94AVpzZ932E8cP7yVSKBJksx/vWomkqBxIJhp/8JqauMXrNddz54z953vN6XTrOA4bkr05MMOWHWGHAzTGVd20ZPGu74tQkp/a+TLtWpTC8hp6PfIjm334Vd/wkR/qHqeTyjPV0U5qcoF2rkKlWyTsNkmtCPJLIeBwN8CoK+uJxlJmXYfT2N77oUsIL/wPm9kFhDFQDqtPw4pegZzPkxyjNTKIo6srgL0VRsBJJFsZPXOqttUI8k+W6H/lR/uH3/xOu3cKwYiS7ugl8H7tep3t4LRtvuo1avcBDB+fJxg0MTaHactFVwaa+iw9BevfWXo4u1Dmx2CBlaZRbHr1p65LyXs9a685ugrqLO1FHNlyUuE5iVw96/5VpUQe4b3s/RxbqHJqtE4QdkfXW9V30Z2OEEhJCMFDxsDxJHsHh0KNme2eJrZeLfbSMEAI13WmNV9MG/kKLsOFC1jy9oSIQiI5Tdfl4oR0gDAVUQe3RSdxTNQglWk+c5A39WOuyuBM1/LpL2Aw6MQFSoqZN0nePYG3NEzZ9gqqDmjJQuywqXz1OUGyvHFbKzpMPJX5x6aNR6kQPqMuCtBACK6FTXWwT+hJVf3Ni6/rreijPtShO1wlDUDXB8JY8Q5suP0Ij4vL44rsFVuLKDB6zmwL+jyuyq4h/xERi6xvQarVWVfeNb3xjVXUHDx5cVd3evXtXVXfLLbesqg4gn8+vuvb7yfDw8Krq7rvvvlXVHTp0aFV1q31KurS0tKq6ZvPy2p5ew/O8VdUBqOcZ1nAprPZe27x586rqVvu+/6mf+qlV1d1++0U+tFyA1Z7P7zdvZp2+7198o/NgmubFNzoPirK6P8CKxeKq6kql0qrqurvfePr1labdbl98o/Pwp3/6p6uqKxQKq6oDqNVqq6prNBqrqkskVvcBWZ63j/TiXKlp1RER308Sqsqd+QsLWmtjJmtjnd/bp9oO/+H4DHOO1xmyVW1ysGHzb0f72JXuiJwLjsdz1Sa9pk5+2XW6Pm5ysGkvRxCoLDg+KU1hNGbyar297DQNKfsSVQj6DJ1d6TgpTcVUBHYo2ZKMEVMVpm0Xf1mcOWk7/PH0Ej/am8OwOlmSMgwRikJOV8nKgCXVYsYLCQKXmKqgKh0RN18tseupB9FrZVTDgKkTjBw/RHjnjzCjpyit305GU/BlxxG7NmEhCgVuv/k6NnTl3zC/1Gk2yX3nq3ywXMHt7idht1BLixzw6yvO0Omjh3nwm1/Hr1fJhT4T+/bQO7aeG7/wy8jJKboVAz00WZg+hdeso2ka1sI8mttCGCqWmUFiQBAStppI1wbPvvgFbxVhfh+k+kBbHjKUGYLFwzD7KuTHiKcyBEGwMpQIwHXaFIZW93d7LJUm1dVNrn8QMx5HN63OcK/pSTbdfDvrrt1NumYzWWlzdL5BKCWWrvKuzT1ct+biIteNY3mkXMeD++dYqDncvK7A+68eYDh/fhfxhVBMjfTdIwQlm9AO0HImSvzSB2xdCrmEwa+8bwt7pqqUmg4D2Rhb+9O8MlkhI2H3ySY9buf/INsP2JA2yKgX//sndAOcI2XcmQaKpWGuy65kn0o3BPX0/09KTEPogqDpIYNOe7xfstG6Y6gJA2+qTmipEHaGaMWu7qb1ygLO4TJK0kDoAneiRq3lkX7vKLFii8azc8jm8ucQTUFYKkpcQ43pqDEdvet01mtsa4HGE1P4xTbCVAmbHmrGPGsw1oWwkp3rEYZyJcvWbfvk+xPntP+vhljS4OYPr2NhvEa77pEqWHQNp1aOFRER8Y+HSGyNiIiIiIiIiIiIiHjbeaxUZ87x2Lw8TKvPkBxp2XxrqboitrbCEDcMSamnP8asj5ssuB4SyGgq6+MmqiLwpGR9wqTk+rTDEEtVGIsZfGagm+3JWCejMp/my/NlAinJ6SqTbYmpCAYtAzeUfHm+zKTt8nObtpF87imKM1MkMlk8x2FQgey1u6mbBoYi+EhfDksRPFysox3ZQ3e7RnZ0lACBDEPMxTlurEzzh0PbmHY8Qk+iKh3x11QEqqqxZs0ICf2NP6LNHj1EfWmRwb5BFBVIxGn5GaYORCLdIgAAgFRJREFU7uOqO+9hHI3fOjjO5NBWLNNknd3gXUsTLJw8zuzMFOt272ZXENL9zCscQKE3mcZTBHO9g9hBDKcOptEgEDmk56IZHiKR72SvXgyhAArI82RQis4D2JHtVzO+52WK0xPEUhncVhNNNxi7dvcl3ilno5kmqqahahq62RF4ZRh0WtWXH8D2pC3+3f1beWWyQs32GMnH2dR7aSKXEIKb13dx07oCUvKmhDEhBFohdvEN3wSWrrJ79GyzwtXDWd5vxsjMNZg0IEAQ0wVXCZXwRA22X9ilK/2Q2ncmcMdrnZb+EOxjFVK3DWJtyGGsTdF6vtXJUdVVpBugpk2EqeIXlwdkZU1Stw2h5kzqj0/hHK2AoJP7uj5D9RsnUdPGivj8Wq2/0CK+swfneBXyFkpCR4nrBBWH1ssLmGMZFPPs94u17BJt71sibPmYoxniO3vQshd/GD+0KcepfUXKs03MmIbndtr91+3qvmIPPDVdZWBD5GSNiPjHTiS2RkRERERERERERES87cw6HoZyOqtVCEFKVZm23ZVtBk2DActgvOUwpioIoOgFrI9b/MpoP92mTkFXOWW7FF2fIcug39QZbztU/ZCRmLHiiAX4RH8eSxE8UqpT8XwMVWFNzMBUFVRgwNLZU28z2Zvjxg99gn2PPkxlbhYzkWDLbXey+ebbCcOA0vQU2BXyg8O8rzvLtx5v08plycRPC2uVZoxMtchvv2uIXz06xbTt0WtopDSVqh/ywZ4suYsIrQC+6yJDiTijI0PVdfx2i1Lb5r/MNziBRg4fIUP2JDrCzq2zE9SLna6oSdvF9BwauklTN0j7HuvTCYxCnuJ4inishabNARIl349y3Y9Dbs3FL2I8D4PXwtGHOhECmtnJcI0XYPAaAHL9g9z44U9w6KnHqczOkBscYuMNtzC05aqL7/88JLI5BjZu5uTLLyClRFFVGuUi2d5++tZtWNkuZqjctG713ROdnNdVl7+t6KrCLck4S10BEtA1QU/KItXy8WYbbyi2upN13Ikaas5EWW7/94ttWq90xM74ti78+VYnkzUEoSrEthVI3DSAN9vEL7bRMiZazsQ5WcObbiA0AULgjNeQfkjoh6jWGfe+IkBKpBcSlBwIQeuJr/xuUFMGQc0hqDgovWe/Z4QQxDbnO8OwQgmquGShNJYyuOEDYxx/aYGlyQaFjMHa7V30r89c3gmPiIj4oScSWyMiIiIiIiIiIiIi3nbGYiZPlOv4oURTBKGUVP2Am7Knp8XriuAzAwV+f2KBo00biSCpKfxoT5ZtqdiKqLIubrHujC7v0bh13mOaisLH+wv8aG+OvfUWv3h4inHbwwtdhICkqpBQFSpewPY1o9z54z+F02yimSaarlOcnuTFb3yVysIsSMj09HLt/R+kq7eP41MTK63yUko81yHT08tVqTj/beta/nquxCv1NqYi+NF8igd6Ls3tlh8axojHaFUrxDNZpJTUS0V61oxyRDGZdisMhh6+a2NqCvhwzEyyw7CIZzIcb9n87ydmmdJiFOwybSEwZcCt9QXKa/qIdefJJSxMbRFrwxj6XR+Fnktwtb7GdT8JbgPm9nQGZCV74drPduIElukdXUfP2jHCwEdRtTflGhRCsPPeH0FRNaYPHyDwPPrWbeTqd9+HEbu8Vv9/zOhJnUJVp6+rc06klPgN76JZpkG1I3YqhtqJwfElwtIIKg7tgyXUtEHqXSO0XlnAm26gxHSM9VmChkvrpXn8kg0SRExD2gFCE+i9nRiesO3jzTRQTJWg5iCW4wDCmosS09B74oRND9TXZb26HRetEtMI2z5h20dNGwjt9AMIoYiOaHuZpPIWO+8Zuey6iIiIiDOJxNaIiIiIiIiIiIiIiLeduwppnqk2ONy00RWBF0qGLIP7u7NnbXdNOsFvbhjipVoTN5RsTlpsiltvSrAzFIWkorDkerihpMvQCIGS5+OGKkPWcnuzEFjJjvjrex4vfP0rlGamyHT3IgSUZqd54et/y673fYDZY0coTk1gxGK47RapfIHRndcBMGAZ/Ku1fdhBiCYE2mWIQvmBITbdeBuHn36C4vQEAKlCNzvueS8vd1ZJrqePpVMncJpNQsPEFgrx3gGGtlzFnxZrLLgeV+WzFJdmUQ6/iBOE7PPa3BqLsfZTn6VvlS5TABIFuOc3oHQC3CYU1oFxbsa1EAJVuzKZpVYiye4HPkK7XiPwfRLZXJRz/TqsTTm8mQZ+yUaxVIKmjxLTMNe9scivJHQQENRd/JKNdDriJkIQPjbVcamGEhkup0jQxptrIoUEN+zEJigCb66BX3Iwx05nOEsF/LKNmjYI2wHuVAPFUFFMldiuHrS8Rc0UzBignKxATCUXMzBDsDbnaR8oYR8pI70ANWUQv7YXa132rTyNEREREZdEJLZGRERERERERERERLztdBkaXxgb4LFynfGWQ5+pcXs+zZBlnLNtn6lz3+tE2DfDwUab/3RqgWYgCZEsuD6GItCEIK4qqJwr3BWnJqguzJHt6UMzOmvM9vRTXVxABgG3fuIzHH/hWSrzs2T7B9lw/Y1k+/rP2od1CcOJXo8Qgm133kP/hs0sTZ5CMwz6N2wins6wqe2Q1lTqWorutWNUi0ssCo0Nusr77/wI8XSG6flpYqqC5tqMHd+HMnkSJwj4/9j77zjJz7PO9/7cv1i5qnOenpylmVGWrGBblpwDtsFgYcDeNc/hgNmFw2HX9oK9u4R9nj3PLvgs7AIHbAxeOHBsDNgGS7aylUdpcuqezrm6cvil+/xRrZZGEzQajWZk63q/Xn693N31u393VbVGqu9c93U5Cpy1w+R6+y/sRTx1k62Q9RKLp88+jO3Nzt2QQ3vRai9TqytOcnc3Tt+5hz06wxmszhiNg3l0pNEAvgYTMFoDsZojRYykjbs+i1IKf6FGMF/DWZsBBcF8jaDooRsB3lil9TgD/PEyYc3HSNitqlQFsW3txHd0YHXGqTQD/uv3j3OiUuYaQ7Ou6DNd99l0XT+JpEX10RmUY2CkHcJCk8pDU5hpB7tbKpqFEJeXhK1CCCGEEEKIN4QOx+LDPZd2eMxUw+O/nJxdrag1aA3XylkWPY6FaxqrlafVMOS+pTLPlWskZ5dI+wEppVfXUmql1ySajsE1dAy+PseRlVJ0DA7RMTh0yveH4y4/0dvG38wuM+0kUf1Jdro2vzzcQy7VOqK9MeGyt1TFHjmAMzOB391PDehyLCrLixx74hH2vPN9r8u+xeWjlCK+vYPYljaiRogRt1pH7V+B4Zi463M0R0ooBTqM0ApwjNZxf9cCQ6GDaPWov5G00RFEzYhwuUpY8lCmQpuKqBHQHCm0Bl1Vfcw2F3sl8A3m6+hmiN3VCksffnaGZ48usibhMNXnMBM3ObZQ5dpyhZ95ZpGo6qMshSr7WF1xoppP82RRwlYhxGUnYasQQgghhBDiTeuRQoWZps+V6ThPFmvUowhTg681jSji6mySPtfGjzS/f3KOx4pVLKWI7BRb3RTe1DTrVkLP4vwM6Y4uOofOY5jU6+QDPW3syiQ4XG3gGord6QS5lwzeur0jwxPFKuWJMQxlUAVSpsmaZJyomWDh5Mhl27t4/SnTwEy+uopqpRRm2sbuSRIsN/BnqmhAhxoUrf9FGt0ICMoekReCAm+yDCv/H6XAADNjEzUidBBhph3cgfRqywcjbhIs1QFojpU49sgkUamJ0dB4BQ+rzSVjGhx6bo5m08GyDAzXQvsRwXytFfL60UV9vYQQ4kJI2CqEEEIIIYR40yr4AYaCuGmyPRXnSLVOwQ+phhHv7szwLwe7UErxXKnKU6UagzGbpGkCLrPX3ob75P3k5mZQQLqzi6vf+yFs98wDuS6V4bjLcNw948/6Yw6f39DPP/Z0U5gdpzfuMhBzSJomS16TRDZ3aTf7JpeveixWmnSlXNqSp7fMeCOwuuIo2ySq+aiE1WonUAvQlkEwX2sFraGmMVZC6ZUQFlCWgdZABKDBgDDfBFNhr8sQFb3VIVZaa6JGiNuXQgcR1SdmyUagHRNtK5Sv8RfqFMKAtQGYpoIgIqwGmEmbqBGiXBO799xtEYQQ4lKQsFUIIYQQQgjxprUuEUOhqIcRnY5F1kqyr1znhlyKL2wcWK26m276hFqvBK0tscFhjrf/OB+Oh3Q5Nl1r1mLHXv+gteAHnKx7pC2D9XH3VQ+D6nVtPnTLzTw8eYJmcREVZliuVrEcd3WIl3h9hZHm63snuefQLNVmSMq1uHN7Dx++ahDjVQxMO186iGicKOBPV1G2gbsuizOQOq9r7f4U7qY2GkfyRBWvVT1qKJSpiOoByjFbX0cawzGJ/BBdDzDiJqEXQLiy0AtFp5HGn61htbl4c1UMQxF5EWbGIb6zk7DkEZaaXNuR4uGxGsdrDdIa6lpjG4pbnDhWzCGs++BrwpqPMhTOYBp3bfaiv3ZCCPFqSdgqhBBCCCGEeNO6KZfi0UKFp4pVQt2qyFsTd3l7e4ZKGJG2WuFqt2NhAPUwIr4y2KoUhKQTCTZuHTolhH093b1Y5P+ezZP3A1zDYHc6zi8MdZO1z/zRLgpDTj7/NBMH9hEGAQNbtrH+qmvpWrOW6z70Exx55AFKiwu09fWz+Yab6d+y7ZI8jze7h44t8I2nJ0m6Fr2ZGIW6z9efnqI3G+fmTZ0X9V460pQfnKRxdHnlG1A8uMjMhgx6fZYrBrLEnTP//oalJtWn5/HGyyizFQJb7S52b3K1jYA/U0WHmtiWNpRSNEeLhIFGNyOwDAhfcrR/JUcOi020ASqCoOajNUSNgNpz8ySv6kZZBu1lj3+hXb4PHNcBgxjcEtlchdlqQ5B0iBoBaI3dnyJz5/DqHoUQ4nKSsFUIIYQQQgjxpuVFET/V284N2SRjdY8DlTozTY//MTHPX82afKi7jfd2ZdmdTnBFOsHTpSpJ08DXGoXiJzuzlyxoPVJt8OdTi4TAcMylHkU8vFyhw7b59FDXGa/Zf//3OPTwfSgUShnMnTjG0uQ4N37kpxjYso3+zVsJmk0sx0EZr66Xp7hwj51YQivozrQqoXtskxMLFR4bWbroYas/U6V5ooCZcVAxi/GlGoWZCoW5En97dIbBrhS/+LaNrOk4dbBU5IWUvj+ON1PFTNqgFFHFx0jarcFYKwLDQAchaECBkbQJKx5YBqF6sa0rRqtnLEEEfkS01Fjp9wrYBtQDqk/OEeQb2IMpvMdmGPA0P6Md4CUtFgKNSppEXogOI6zOONnbh7EyZ26dIYQQl5qErUIIIYQQQog3nXoY8bXpJR4ulGlGmn7XZl3cZbTeJGuZpC2TvB/w1elFel2ba7JJfnVtD99dLLK3VCNtGtzWnuHmtnMfxT5WbXDPUpHphs+GhMsdnVkGYw6lhXlKC/PEMxna+wfPK+h8tlSjFIRsScZQSmEbJnXb4rFihU/0dxAzT12jVioysvcJ3FiCZFs7AM1alakjh1icHKdrzdrWOpeg9YE4lRdGGC9r/2AohR9e/AFP4XIDHWiMmEWx7jO6WMExoUtZbE7FeG6+zNceH+PfvnvrKS0p/MkK/nwNuzOOslq/W2GxQVj0iJoBhmsRea1eqVbSIlioYaYdlG2gTINqGOF5EWlaeWoYgaUjjJVQVhkKbSgIQxRgpGx0PSSYrRLf0o7R5hKV/dZmTFr9XX0NgQZDYSZtzP4Uufeuw+6WXq1CiDcOCVuFEEIIIYQQbzpfn8vzD/MF2hyTrGUyUmvyUL5Cj2vR7doADJgOR6oNHi9WuCabJGdbvKcrx1vbM3Q51mlh2csdrtb5/43MsuD5xE2DZ8o1nixW+fD0EUp7H6VZrWK7Ln2btnLtBz6ME0+ccz3NyuChl9x3ZRD8yk9OVSss4zXqpDterJR04gkq+SWqy3m61qw9r9fqR0HDD3ng6ALPjhdwLYPr1rdzw7qO16U/6vm4erid56eKlBs+Kdei3AgIo4g9a9pe07rlhs/IQpW4Y7KhK4VpKIykDQZoP6JQ83C9iD4MIhWRRNGTiXFsvsJS1aMz9WJ1aNQIWinpytF8HUSomA0Vn2ChjrJNMCC2MUd8ZyfVp+cIl+oo2yC6uptvHpsjF5hcX4pINTWm5sVfVN1qb4DfCpe1H622JcBQBCWP+JZ2yhOVVkWsbbYGcZmtpNZqd4lf0UV8cxvmSkWrDiLCYhPlmpipN+awMSHEm4OErecQRRf2t4pf//rXL+i6e+6554Kuu+uuuy7oug9/+MMXdB1AR0fHBV97KZkXeKTrv//3/35B133qU5+6oOt+5md+5oKuW7du3QVd96UvfemCrnPdS380R+szfXR4ZY899tgFXZdMXtjfit96660XdN2F/o7+sMhkMhd87c///M9f0HW/8Ru/cUHXeZ53Qdf94i/+4gVd98Py52gice4P/mfjOBf2Ied3f/d3L+g6gHw+f0HXvfvd776g66699toLuu7VDrIRQlx8jTDigXyZjG3S7bSC1ZRpcLTWoBqeWh1qAM1IUw8j/npmiQeXy3iRZjjucld/BztS8TPeQ2vNd+aLLHj+aiVqpDXPL+T51sg4bzUtOgbX4NVrjO1/lmxPLzvf+o5z7vvKdIK/twpMNzx6XZt6FLHoB7y3K7vaR/alkm3tuIkEjXKZVHvr3zvNWhXLcVa/fjOIIs0fPzjCQ8cWMY3W10+czLNwTZMP7hm4LHt6+9Zujs2XefLkMrOlBjHL5NZNXbx1y5nbQZyPR44v8rXHx1iseFimwZaeFP/LbRvoGkxj9yXxJsqkig02NMBSEXVbcd1UnXrOYDxhYL+sutrqiKEcg6gWQBDhz9eI6gGYCiNhE9vchjOUxhlKoywDM+cQLNQxsw57l2s8fnKWjQMp6pWQ20arZJoarVsdA5QFeC/57/xQt1oUxC2UaWDlXMzOGJWHp9DNEB1ErWpY00DZJqmbB4lvejGYbo4UqD41R1jyUJaBuyFL8vo+jLP0ohVCiNeThK1CCCGEEEKINxVfazytcV5aIaoUadOgFkVUw5CEYVAMWsebd6cT/M1snm/MFWizTZKmyf5Kjd8/GfDbmwfoWglsAXQUMfLMk4zsfZKH2tcSpnP4ZjdOPN6qhG3UWHTiJLM5ANxEErdaZeLAvlcMW7clY/xkbzvfmFvmWK2JayiuzSb5WO+Zg9N4OsPGa27gwAPfZ2l6AkMZRFHI2l1X0zG45jW/jj8sjsyVeXw0T0/GJR1rvVczxTrf2T/DW7d0k020vjeRr/H4yBLLNY/1XSlu3NBBwnl9PjLHHZNffvsmjsyVWSg36cnE2NSdOmOlrdaaZhDhWsZZ/8JuulDny4+cpNoMGGpP4AURz04U+MvHxviVOzaTuX2YysOT+E/MkTdh0oJG3CDbDNk461PYkubIXJkrB7PE7FZAaXUniG3roP7cPP58HSKNipnY3Ql0MyQoNknd3Aqrq3vnqB9YIio3QSm6+hMkTUXVC5lLmlRtAxWGEEGbqVqtAF54KittBV4YpGX3JoltakPFTGIb22iOlVrl26rVANbuSeAOpVefe7BUp/xQK5Q1Mg7aC6nvX0K5Jqnr+i7aeyaEEOdLwlYhhBBCCCHEm0rKNNiejPPQcpmMZWIpyPshfa7DYMxhuunjR5q4qbi9I8OeTJy/nFkiZ5v0rLQYSFsxjlabPFms8p6u3Orax558lGf/+Vsow6Az3sE+02Fm5BgDm7Zi2Da+MmivV075JKbRqPM4zq6U4oM9bdyYSzFSb5KxTLYkY5jnqJjffuvbSXV0MnlwP2EQ0L95K2t3XfWmqrKfLTVo+iHp2IunNdoSDvOlBguVBtmEzeHZEr//vWPMlRor7SHmeHw0z6+8YzPx16k60jAU2/oybDtLHqi15rGRPN/eN818qclALs77d/dz1RlaDeybKrJc9djYnWr14TUNulMOz44u85WHRjg4V4Fik13xiD2DWZpLNepeSDUIyUSwPFfh/3/3Ebb2pflXt2+mPemglCJ1Q1/reH55BiPrYKUdlGsRLNVpHM5T0GAmberH8uiqT1gPIYhw52v8fMrkD70q2jJJ1kNiIeCsBMY6bG3cUq1+sKaBDiNim3Jk7liLmWmdkEnfOggPTRIsNyACI+eQuXkQI/biP0DNsRJRzcfqTrTWdkx0qGkeL5C8qme136wQQlwqErYKIYQQQggh3lSUUvxkXzvTTY/RehOtIW4qPtCd466+Dg7VGix6AQMxmy2JGHk/xI807ksC0VYg12ov8IIwCDj+5GMYlkW2q4cbwzrTpmI8tKnlC+hUmrWpBDvLi1R0SCKbpVmr4zcbDO/cdV57L8zNcvKxh1k8OUI910bq6usZ2nHFWcNTZRgMX7Gb4St2v5aX7HXT8EPuPjjHoycWiSLNjRs6eeeO3osacHalXBzLoNIMSLmtj8DFuk/StehIumit+eYzU8yXG2zuSaOUouGHPDO+zGOjS7xtS/dpa4aRpuYFJB3rvPu++mFEpRGQjllYZ2j78HJPjxf4Hw+coOGHZOM2B2ZKjOdr/O/v2sLW3lNbNZ3aeUsTLjfxZiuMNposzZbJZmNgKo6W6yy6BncNt3Fyqcb8bIWYa7ClM0lBaZ6fLPLt56f5xI1rgdYQK6cnQSPjYHXEUYbCX6jhz9Ug0njjJcKy1+q3Cq1j+3GLqOyxpuDzcSNiJPTQkYVpGcRcE2UoosZK2BrRqmwNIwzXInFtL1bOJViqUz+UJ1iqY3cncDe3Y+Uc7L4UZtLmFMEZehkbCh1odKR58/y1ghDijULCViGEEEIIIcSbznDc5bc3DfJ0qUY5DFkfd9m60lt1V/rUftXttsnGhMtTxRoZy8RUiiUvIG4abErGVh/nNxo0q1WcWKuP65BX48eXxnkkUKhciuu613BHZ4aK/xaOPvYwhblZbNdlw1XXsfH6m15xz9XCMj/4m7+gODdHLJWmWsizNDmGjiKGr9x9UV+fS0FrzZ/9YJR7D80Ts02Ugr94bIzxfI3PvH3jRau+3daX4erhNh45sUTMMgh1q3foR68epC3p0PBDRheqtCfd1XvGbJNIaybztdP2/MDRBf5p3wz5qkdfLs4Hd/dz9XD7OZ/n/UcW+NbzMxRqHp1plw/u7uemDZ1nvQbg/iPz1LyAjd2tI/PtSYdj8xUeOLpwWti6cyBDLmEzuVyn1zSpzlSY8HxCpeg3TFK1ELs3yWIt4ImlCrfH4xhVn/aw1T/1LeM1IkOx3lI8dyK/GrYC2AMpjJRNkK+jLANvsgyhBstoDc0yFFHVRzkmylStYVd+hAo1u0zFLl4SjgYazamzUbQXglIot/WXA0G+QfHuk4TLrWFX/nQFM+3g3rn29KAVsHoTKMsgrPgYSavV/7XqE9vaLj1bhRCXhYStQgghhBBCiDellGVya3v6FR+nlOLjfR3MeT7Ha01YqYR9d2eWnS8ZkOUmEmS7u5k/OUIs1aqQ7C4vc3t+kZtuuoahoZXhR7e8jbVX7qG0uEA8nSHT1X1eweL4gecpzc/RObgGtTLMaHlmiqNP/IA1V+z6oWsNMLlc5/GRPJ1pl7ZE69h4qe7z1Mk8o4tV1nelLsp9TEPxC2/dwKaeNHtP5nEsg5s2dnLrptb74ZgGuaTDRL5GZ6o1EDaKWtWS2cSpAx8fG8nzpw+NEmpNOmZzeLbM1H0n+PV32WzpPfPv0hOjef704VG01mTiNmNLNf74wRHSrs0Vg9mz7nux0lztnwqt30PHNFiunD7Uc7AtwSduXMtfPTHOickSZhAymHCY8wLScRvdDAnLHu2DGUYniiyHERkgFUAsjPBNRd1WbKtFZGLBqa9fyiF9yyCVh6doHC+sBq1Gwiaq+GAp0KBXqrzDerDSYxWUZYJtoGs+mAoco1WGq0JwDAzbBK2JAo2yFVHdx58qEyw3sXtabQG01gRzNeqHlrC7Tx/c6Qymie/spH5oiWDBb/V17UuSuLrnrK+tEEK8niRsFUIIIYQQQohXsCkZ47c2DbK3WKUaRmxKxti+Ugn7AmUYbL/l7ZSXFlmcHEOpVn/KwW076Nu87ZT1EtkciZUhWeerXioCajVoBXDiCWqFAlEYYlo/XB/vlqoedS+kJ/NidXAqZjFXarBU9VjfdfHulXAsPrCrnw/s6j/tZ4aheNeOXv7koRFOLlZJOCaL1SaWYfDoiUWOzZW5aUMnN6xv54Ej8zTDiA0rQXBbwubYfIWHjy+cNWx98NgC3kuuySUcjs2Veej4wjnD1u19GY7MlgnCCMs08MOIZhCype/M97ltcxe7B3M88/dHsBYbpDvi/OexeZaDkJxSEGryfkh7X4ot79hA7RvHCIGaAktrEk1N04ArtElY8TBTLwbNzlCa+J5u/LkqoalQpsKwDbShiLwQI24S+Zqw7LUGXRmqNVDLNVG2QdgIWlWtjaDVNsBQGLaJEW/9zuqqj0JhZV2axwutStV8g7DoobVGWYpgvnbG560MRfKGPtwNOYLFOkbMwh5MSVWrEOKy+eH6t7EQQgghhBBCXCbttsUdnWcPxwB6N27m1p/+FJMH99OsVcl2dRPP5liemaJjYAjDvPAAKNvdg1KKwPexbButNfVKiYGtO17TupdLfzZGOmaxVG3SnW4FrvmqRyZuM5CLv8LVF9dbt3SxXPP4+tNTzJYahFGEryLGlmqcmK+wd2yZ5doalqoe8ZdVm9qmYrnmn3Xt5ZqP+5IhTeWGT7Hus/fkMiMLlbNW8L5zRy/7poqcWKgCGoViR3+Wt24+vYfsC7IJm+t29FD5wTSGMrgll+LupRJ5P8J0HGJ+yEeuGiRT8NGepuEYaDT1SBMDui2TdMxGe60qVR1G1Pcv0TiSJ1hqEPkRZsYhKntELwSnQURsVxdWe4zGoTxBvkFU98E0WtWsgLJNMMFI2hgxm7DUhEAT1QNAQ6ixB1NY3Qms9hi1fYtoP0SZrf1RDgmSTqsH6xl65CqlsLsTZ6x8PZNyvkGz5pNuj+EmTm9NIIQQr4WErUIIIYQQQghxEbX19tPW28/U4YM8+91vUVnOY5gG7QNruPYDHyHTeWElm0Pbr2Bs37PMjRxHKYMoCknm2th6062vqYWA1vqytCDozsR4545e/u7ZKY7PlwEwDYP37+qj/zzC1kLNQ6HInmdYVm74HJ2rYJuKLb1pXOvF0PTQTJnvHZqj7gX4YcRsscFAW5w17a2j7FPLdb6zb5YrB7KcWKgQhC6WadD0Q/xQs+1lPVRf6or+LIdnSnhBxGKlweHZMpVmQBBpfuvbh7jr+jXcvu30I+/dmRife882HhtZYqHi0ZeJcf36dtKxcz/f2JZ2/OkKzfEyH8BmMJXisKtIbGrj6g0dXDPcRv2ZeZRj4sYsuqBVrRpqlBdh51zMbKudQvXpeWp751C2gbIVuhEShRFmVxxd9onqAc5Aiuy71mG4Ju5whsbRZZonCviFJroWoNEo28QeSJG5fQ1oKN07Tlhoor0QHWqc4SS5965HGQp7INWqjo00mK2OA8QtdBDhz9Vw+pLn9X6fie+F7LtvkuljBQIvJJa02Xx9L+t2df7QteEQQrxxSdgqhBBCCCGEEBdZrVhg73e+SaNcJtvdSxQGzI+e4Jl/+gdu/elPXVCw48QTvOUnPsHYvmfIT02SyGRZs3MXud6+V71WGGm+f2iOew/PU2kG7B7K8f5d/acc6b8UPnr1IGs6EjwzvkykYc+aHNev6zjnNfOlBv/z8TGeOLlMEGquXtvGv7h53Wq/1TN56mSerzxykvlSE8OA4Y4kv3DbBtZ2Jokizd/unWC+1GRTT5qp5ToL5SZLFY+lqkdnyiWXsCnUPK5d186JxSrH5ysoQCnYNZjlls2dPDdR4IGj8yxWPLb1ZnjH9h660i537ujhwHSRA9MlRpeqRJFmIBdnZ3+WhUqTv31qgqvWtNGWdE7bdy7h8K6dr+79NVyTzB3DeJMVwmKTd2Qc3jOYRr2kutbqiKFcA8NwCUtNjGYIQYSRcnA25qjvW0BrqO9fxIiZmJnWa6s1+NNVdDXASNqtAPWtQxiOQeWhSRrHCuggQkdgd8Qwsw7KMrH7U8Q3t2F1tEL07DvX0jy2TFjxsXsTxLZ2rA6/UpaBmYsBGu1FKNfEzDpEJZ+oevYK4vNx7Kk5Rp9dIJFxibXb1Eoe+x+cIt0eo2vNK/dvFkKI8yFhqxBCCCGEEEJcZHOjJ6gVCrT3D6IMA9OySLV3sDg5TnlpgUzn2Y+Cn4ubSLD5+re85v1967lp/ucT41iGwrFMvrNvhuPzFf7d+7aTcs/9MVFrzcmlGuWGz1Bb4owh4fkyDMUN6zu4Yf25A9YXBGHE73//GN8/NIcfakKtOTJX4shsiT/5xDWYpnHaNfmqx58+PMpyzWO4I0EYaY7Pl/mzH4zyhffvoFT3GV+q0ZV2MZQibpsYSlFp+uyfKtKfjWEaiu5MjC29aT73nm08MZpnqdKkLxfn2rVt7Jss8nvfP0ql3hpItX+qyPNTBf7tu7fRkXL5t+/Zxv/95Dh/+dg4Q21xejIxLNOgLxtndLHK6FL1Nb2OL6dMA3f47NW2zlCa2MY2GseWMRI22g8xEjbOQIr63nm0H6IDTVhsYvWleKEG2O6Io5shsY053I1tOIMpjJhF43iB+uFlzJSN8UIV6mIdZyBD+uaB0+/fn8LpP3P7BDPrYmYcdBBh9bVC3rDiY8RMrLazB+qvpDlVZvT7E1D1Uc0A2mKk22PkpyvMjhYlbBVCXDQStgohhBBCCCHExaY1+mXfalWzak77wSVW90LuOTRHzDZXj+t3pBxOLFR4emyZWzefvc1BueHzJw+O8OxEgWYQkUvY/NieAd65o/eSHMM+OlfhsZElmkFEOmZjGYpyM+DpsQLfPTjHe644vQr04HSJxXKTdV0pTENhmdCdjvHMeIG/fWqC69a1E3dMKo2AXAJijoEfRDT8CLRPueHjWibv3tlLwml9hL5j+4vH/os1j9/5p0OMLdaI2SZVL6Q9aXP/kQUWyk3ev6uft2/t5pZNXdx3eIFcwsEyFGEU0fBDHMsg6Vzaj+bKNEjfNogznCGYq4GlMDIO1YenUa6J1R4jCiLCQpNgroqZdTAMRVQPMFyL+BVdOAMvhqX+TBWiaHXglbIMzLiFN1ZCv6X/Vf1umEmb+JWd1J6cw5+rtsqHDUViZydm+9krr4Nik+bxZaKqj9Uex92Yw4i19hPkG5TvnSCoeKAU2mu1JGBlWzq6zP9QCiF+pEjYKoQQQgghhBAXWfe6DSSyWQrzs2S7eojCgNLSAn0bt5Du6HzF671GnUalQiKbw7Iv7gCfcsOn2gxOqWC1TQMNLNe8c177zWemePj4In3ZOD2OyXy5yV89Mc5wR5JtfWevpLxYqs2A5aqHaxnYK1WsKddiwWuwb7JwxrBVKVZDNWg9/31TRUp1n798fIx7Ds4x0BZjrtRgulBnvtQgiCLaEja5hINrGfhhRDOIzrinrz46xsnFGq5p4FgGi5UmU4U6pgEnFqp89dGT7J8q8at3bGJbX5r7Dy9Q90O8IEIruHF9Oxu7z1zl+fpShCWP5lgJ3QyJooiw0MDKugReiJlysHoSBHNVgpkqylRgGsS2tGG/rG+qstVpf4mgI93q83oBPYETV3ZhtcXwxsqgI+yBNO667FnX8RfrlO45SbjcBEOB1jRGCmTvWIsRt2iOFonKHj19ScZmakSWQvkRlbkaZluMriGpahVCXDwStgohhBBCCCHERZbMtXHVuz/As9/9Nsuz0ximSdeadVz17g+gjNOPur8gCkMOPXw/J/Y+gd+sk8i0sf3WtzF8xe6Ltre2pEN3JsbJxSrpmIVSikozwDIUg21nn+buhxGPnlgiG3fIxFsBcH8uzpHZEs9PFi5J2LquK4ltGTSDiITTSvdqXohlGrSf5Rj+jv4M3ekYY0tVBnJxDs2UWK569GVj7OzLMFtqMr5U553be9k3VeTkUpW2pMueNbnVYVRTy3VOLtVOW7tQ83huskAmbtH0o5WKVU0YaRzLZKgtTnvK5fnJAvumSly3tp27D8xRaQbYpsIxDYo1n9HFChu7L23gVz+wSPXxGZRjgq3wJyvoWkBUC1CGInAamFkHqy9FYnsHGo3dk8Rdm0EZp4ae7tosjUN5gqU6RsohqgcEyw3MIGL5r4/gDGdI7O5e7cv6SpRSuGsyuGvO73eq/tw84XITq6c10EwHEf5UlcbxAokrOonqAShY15ekWg9ZLnuEXoRlKTZf3U3v+uyrfv2EOB+///u/z9jYGKlUisOHD/PFL36R7du3X+5tideZhK3nkMvlLui6b33rWxd3I+KS6+3tvaDrvvOd71zknQjjHB9GzuWXfumXLul14swu9P0D+OhHP3pJrxNndqFHQj/5yU9e0uuEEOKNaGj7FXSvXc/S5ASmbdM5NIxpnfsj2MjTT7L/vnuw3RhuIkV5aYG93/4miUyWruF1F2VftmnwkasG+aMHTnB0roxhKAzgxg2dXDl49tBJr3RAONO/GvQlOoXdmXK5c3sv//DsFMs1D2Ml9BvIxbnuLIO1cgmHf3nLOr7yyElOLlVZqnp0pFy292dRhkFvNsax+QrZhM1P37CGzv0O9x6ZZyJfI26bdKVdal7AUPvpQbQXRISRZjAXZ2ypRqHmtypWtSbpWHRnYtimQRhppgt1vv38NJVmQNwxSLk2G7qSLFQ8Hj62eEnDVh1q6ofyKNvAyrlENb8VUiog0uAYRHUf7Yek3zpE6qb+c65n9yZJ3TxA9ek5oopPWPIgAiwD3QypP7dAkG+Qe/e6UwZ1XSz+XA0jYa3+d4uyDFAQLNYBsLrioBSW1uzelGW55FFfqNO+rY3+WwcuSQsM8ebz8MMP88ADD/CNb3wDgMcee4yPf/zjPPvss5d3Y+J1J2GrEEIIIYQQQrxO3ESS/s1bz/vxo888hWGaq60GnFicpclxJg/tv2hhK8B169ppT9o8PpKn6gVs7klzw/qO1aP5Z+JYBteubedbz0+Tci3ijsl8qUnStdg58NoqA8sNn+8emOXpsQKuZfCWjZ28dUsX1hn282vv3EKkNU+dXMYPI3ozMT56zeA5g+I9a9rY2pth79gy/+e9x8jGbZIrbRS01hRqHl97fBxTwcRyjVI9YM5oYhoKc06xoy9zSp/WF3SlXTZ0pXhmYpnt/Rlmi3VOLAQoZXDFYAbbNGj4IYahGM9XeWaiQBRpTMNkueZxYDqkM+2Sr569fcPx+TJPjxXwwohtfWn2DLWthswXSocRuhmg7NbrG9UD0KBcC2UZKBTYBkbMJra1/bzWjG1qw12bxZupUPreWKtva6pVbWwkLPyZKt505byrVV8NI+3gT1dWB3lprSHSGKnWexxbn8UbLdIcK0GkyaBoG06TfYsEreL1c+TIEQ4cOEAQBFiWxbXXXsvhw4cv97bEJSBhqxBCCCGEEEK8QXiNGqb14lFrpRTKUPjN5kW/V38uzvXrO8jGbbrS5zfl/cNXDTBXarB/ukSzEJJL2Hxo9yA7+i8sQJsp1hldqPJ3z0xyYqFKwrEII83BmRJL1SYfu3bNaddk4zb/8YM7Ob5QoVj3WdOeoCdz9sFJL4g7Jjdv6uS5yQLfPzSHaSgcy+DEfGudnnQMQ4EfalzbJB2zcCyDWjOgK+2yrjN52ppKKT5+/RqWax4T+RqubbGhO0XTjyhUPWpeRMML2TWUZWShRjpmUaoH2Cv9XQs1D6tqsLn3zFWtDx5d4CuPnKS40kv3O3ZrUNdP3zD8mkJCZRvYPUkaJwoYCRsM1RoSZSic/iRG3Cas+a3A9DyP/r+wrhGzIALDNV/8gWVAqFuh7usgsaOD0nwNf7GG4VhEdR8z6xLb0LayL5PM7cM0x4oE+QZG3MZdl1kNg4V4Pdx1112sW7cOa+VEw0MPPcSuXbsu867EpSBhqxBCCCGEEEK8QfRv3sahhx8gkcli2jbNeg1QdK1Ze1Hvc9/heb7x9CT5mkfMaoWQP3XdGmK2ec7rcgmHf/OurRxbCSjXdiToPo+g8+W01nx97yTf2T/LTKHOVKFOd9plbWcS1zKZKzX4/qF53rmjl1zi9EDMMBSbe85+7L7uhVS9gLaEg/myKtC7rl+DF0Q8P1mg3AiI2SadKZfhziRHZkuYhiJmGqA1uZhNqe7z8PFFPv93+/jJ69Zw1Zq2U9Zb35Xi339wJ89PFKj7IRu7U0wX6vzg+BKzpQZdKZfr17fztcfHGMwlmNR1yg0fgKYf0dPtcsumrjM+h6/vnaTph2zqSaOUYqnS5HuH5njLxk7Wd134UC2lFImruvGX6vgzVXSoQdEagmUZRI0A3QiI7epqhaevgplzMRIWYdnHam/9PkW1AOUYWG2v/nflfDjrsqQ11A8sEZaauBvbSFzZidX+4v2UbRDb2HaOVYQ4f6VS6ZSvXdfFdU/9S6tYLMbb3/721cd/5jOf4Q//8A8v2R7F5SNhqxBCCCGEEEK8QWy+4WaWJidYnBgDrTEsi+ErdjO08+JVQx2aKfHnj54kCDVd6Ri1ZsC3n58hF7f5sasGX/F6w1BsOUsl5vl6frLIN5+dxrUMcgmb8XyNqUIdP4rYPdRGJm6zUG6yVPXOGLaeTRBG/ONz03zv0Dw1L2SgLcZHrhpkz0sC0lzC4V+/YxOzpQZ1L+SRE4v83dPTALgv6SdabQaM+zWCSJNL2IwtVfmjB07wG+/bftogsZRrcdPGztWv17QnmC02ODhT4uRilWcmlql5raP6uwazLFY9inWfZhDyiZuGOThdYrHSpC8b48rBHI5lMFWos1j16ErHVqtY25MOx+crjOVrrylsBVCOiZm0CRfrgMYZTGMkbXTVB8sgsaubxDWnt054JYZjkriqh+oj0/hzVUCBqYjv6Gj1Tn0dKKWIbcgR25BDay2tAcTrbmho6JSvv/CFL/DFL37xjI8Nw5Cf/dmf5dd//de55ZZbLsHuxOUmYasQQgghhBBCvEEkc23c9olPMXPsKPVyiWxXN93rNmCY5644fTWeHl+mXA9Wj67HbZOGH/HgsUU+tOfS9LDcP12k7oV0p12OzpVphhFomCk00LpAbyZGR8qh+zzbG7zguwfm+Ksnxkk4FgnX4uhchf9+/wn+3Xu3s6bjxYBUKUVfthX81byQuw/MMVOs05VyVwZdtYZvaTQx22RdZ4qejMux+TJ7x5ZPC1tf7tBMmW88M4VtGmzsTqEbIXa+TEcA9UaVYkyRcExu2tDB4yfyHJ4rE2mNaSiuXtPGL719I9m4TcwyqHkBcaf1/ntBhGkqMrHzP9p/JlEQUb5/An+qgtXReh3CYhOVc8l+eBNGzMRwLzwuiG9tx8y6eGNFdKBx+lM4w5lL8rslQau4FCYmJshkXmyf8vKq1pf63Oc+x5133sknPvGJS7E18QYgYasQQgghhBBCvIHYbow1O6983db3gwhelkeZhqIZRGgNZ8qqmkHIycUatqlY25F8zQOajJWbzBQb1LwA1zSoeyEhrT6uYRTxszcNk34VoWIUae49PIdrm/TlWgFiJtYKXJ88mT8lbH2pHf0ZfvyaIf7huSlmSg36cjF6szHG8zXitsmmnjQ9GRdQKBRNP3rFvRycKVHzQjb3JHD9iOtnPbJNg0hDh+tQN0yC6/o51mzyjaenGO5I4tomNS/giZN5Hjy6wLt29nHjhg7+af8szSDCMhT5cpPtmTgbqyFhqYmZOXcYrUONDkKUY6KUYmypyj8+N83+kWXS83Vu60xxvRcQVQN0pPEny4QVDyv72iqXAZy+JE7f6X1uhfhRkMlkTglbz+bv//7vAfiFX/gFoDU0q1arsWfPntd1f+LykrBVCCGEEEKIFb//+7/P2NgYqVSKw4cP88UvfpHt27cD8Du/8zssLS0RhiHd3d187nOfu8y7FeLCbOvL8N0Dc+SrHm0Jm2YQUah73Lq5/7QQtdIMuPvALN/ZN0OlGWAbBpt6Unz61vWrlaEX4srBLP+8f4bRxSpeEOGHEcoA22gd4w9Czc0vOZZ/PkKtqTZDXOvFKmClFApNtXn2wUxKKd6/q58b1ncwnq+Sjdus60jypXuP8dCxRdqTDlrDcs3DsVrP/5WYK90ItNasKQV01kNmHAWGwZbhLHqxTrwc8neLJRKuhbvSKzfhWFiGYt9UiXft7OOnbxgmF7d58NgCjbLPbaHJO8uK5gNT+EmL1A39xDaf3odUR5r6gUXqB/PoRoDVGae2pY3/8tRJJvI1Mkox3/Q4OZXHM1xuMB3QoMOIxuE87sBrD1uFeLNbXFzky1/+Ml//+tdXv/elL32Jz372s5dxV+JSkLBVCCGEEEII4OGHH+aBBx7gG9/4BgCPPfYYH//4x3n22We5++67OXDgAF/72tcA+OhHP8rdd9/NnXfeeTm3LH7EFes+R+fKmIZiW29m9Sj5a3XN2nbeuaOH+47Mc3y+iWUqdg/m+ODugVMet3csz588NMqTo3m8MKIj6bChK8WzEwX+7OGTfO49Wy/4yPb2vgw/dd0wv/e9o0wXGhgKsjGbbMKhVPcxlOLp8QJv29p93mvapsHOgQz3Hp6nLWFjmQaVRkCoNWP5Kv/H3UdY257gti3ddJ2hPUFX2j3l+x+9eojpQoOTS1W0brVbuHN7D7sGc6+4l12DOf4xPsPEcp2rq+BFmjqa4ayLZRkEMQtvtkoyYVJq+MyVGjiWQS5uE0SalNt6r2O2yUevGeJDV/Sz9M1jKN3E6ohTaQYUZ6vU/vEYy7f0c+MVPWTjL1YBNw4uUXlkGmUZKMfEGy/zwMklJqImm/oyGFrTVvAZLzV4wPa5IRkHL0IpA3+iQlTzMRKvrVWBEG92f/RHf8TIyAgf+9jHgFb4evjwYf7gD/7gMu9MvN4kbBVCCCGEEILW0b4DBw4QBAGWZXHttddy+PBhAL785S/z8Y9/fPWxP/VTP8VXv/pVCVvF6+aJ0Tx//sgo8+UmhlIMtSf4X27bwMbu1zYUCVotA372prXcvKmLieUaubjNjv4szkuGQ+WrHn/68CjjS1VA05ZwqDQDJpZrbOpOcXSuxES+ftaj+a9EKcW7dvaSdEx++a+fIYxavVErjQDHMsgmbIp1/5RrWm0C5rnvyDylhs/uoRzvu7KfnsyLE+c/uHuAk0s1RherAASRpu6F7B1bJmZbPHJ8kcdG8/z6u7bQnY6htUZrztgWYag9wRc+sINnxwuUGz7rupJs6UmfV8C8vivFz920lr99aoKZ5Rr9EfTlXNZ3tY7Vay/ETCcJ/YCxpRqjC1Ucy8C1Tda2x7lpw6lVvXq5gVHyMHMuhYbP/qkiDS+kN4D7fjDGo7MFfu3OLaRjdquq9eASyjSw2lqvjYqbHD9aIB/5TKPIKYVlKuLAchjSrAfEbBOrJ0HkhQSFJo6ErUK8Jp///Of5/Oc/f7m3IS4DCVuFEEIIIYQA7rrrLtatW4dltf4T+aGHHmLXrtYE+H379q22EwDYunUrTz/99FnXajabNJvN1a9LpdLrtGvxo2i56vGVR0bJV33WdaYII83JxSp/9oNR/sMHdmCZxisv8gqUUmzsTp01vD04XWK+1KQr7TJfbmIZioRjUaoHNPyISLeO7V+omWKdbz8/w/OTRbLx1rqGoWiP23QmXUIdnRbkfnvfNF97bBzDUDiWwbefn+H4fIXPv3c7qZVhTkPtCX7jfdt5emyZYt3n6bFl9k0V2NybwVCKMNIcny9z3+F5cgmH7x+ao9wI2DmQ5QO7+hlqP/WeKdfi5k2vrp3BC27e2EndD/leOMXoZIU9zYA1JY9EEKEck9GcxeF9Zda0xylUfepBSL0ZsKknzZWD2VMXMxUYCh1pxpZqNPyIXMzGbUZ0ZV0emy7x6Ikl7tzRC6EmaoQop/V7orXmO0tlflBvMB+EFGs+CUOxyTApKs3umEuyP4mVdIi8CGVqzJQErUIIcaEkbBVCCCGEEAKIxWK8/e1vB1rh6Gc+8xn+8A//EIBKpUIi8WII097eTq1WO+tav/u7v8u///f//vXdsPiRdWi2xEK5ybrOFKahMA3FQFuc8aUaJ5dqF6W69Xzl4g4x26TqBTimgUYzX2qwa02OobYL69larPv813uOcny+QiZuk3JtSvUA21CkHJMgirh5YydXDrwYODb8kLsPtoZf9a8Mv+pMuRyfr7B3bJnbNnetPjYbt1fbD/zg+CKZuLM6kMs0FK5l8r1Dc5TqAZZpELMMvn9ojpGFKr/5vu1kz7Ois+GHPDdRYLnmMdiWYHtf5pQK2X98bpq/fnKCCI2Zsfh6sc7x5YBf2tRH5sou9k4u4YcRO/pz1Jo+hbpPzQvIV73T7mV1xLF7kjTHS3hVn7RS5JoRxZhJIWOjF5pMLtcBULaB3ZugcbyAkbAZb/r882KZNmXgGZoCmkIUsQ/YqQzuiCzQEFZ8tB8R39nxioO3hBBCnJ2ErUIIIYQQ4k1lZGSEX/7lXz7t+3/8x39Mf38/YRjysz/7s/z6r/86t9xyywXd47Of/Sy/+qu/uvp1qVRiaGjogvcs3lxeCAa11kDr/0calGqFhZfC9v4M3RmXuXKD9Z1Jjs1XyFc9UjGLzb1pfu6mdRdcYfvkaJ6RxSobulJYpkFfNk7MNtHAe6/oZUtvhmvXtp+yfrkRUGkEqxWs0OrRqjXMFuo8dTJPGGk2daeoBxFx26Q96TDQFmd0qYrWGqUUWmsaQUi16JNLuAysBMbtKYfRhSpPnsyze02OfNWjI+WQr3hEGtZ1Jk9ps7BYafKl7x/j8Gy51QLBMrhpYyefvmU9jmVQbvj884FZXNtYHSRW6wg4WmowtaeD7jVZjKkltNaMLVUZy9fwVwaFVZshdT8k4bz4XJWhSN86AD+A9HN1qs2QxbTD8z0uLzRb6Eg5q49P7OkmWGoQLNQ5WqtT8QLWxW06A4sFE5b8gEjDJ3raGKpEnCg3qGqNuS7DrisvrJL3pcKSh79Qw3BM7L4kynrt1dhCCPHDQsJWIYQQQgjxprJ+/Xq+9a1vnfXnn/vc57jzzjv5xCc+sfq9VCp1SiVrPp8/pdL15VzXxXWlMkxcmB39GfqyccaWqgy2JQi1Zmq5xq7BHMPtF9Yj9dVqTzp86i3r+OqjJ5krNRlsi5Ppz/DhqwZ4+5ae1zSsK1/zQHNKmNqZdvGDiI9cNXTGytK2hE13JsbJxSrpmIVSimozoBmEfGvfDA0/pOaFVBo+bUmHjqTLtevaecuGDvZNFjk+XyHhmswWG1hKEWqNbb4YXFuGgQa+s2+Gv3pinOWax0K5ScIx6Uy5DLYn+Bc3ryPhmOybLHLv4XkOzpTY0pvGtQzmS02+e2CWHf0Z3rqlm+WqT7kREIQRT53M468MGAu1Zr7cRGtNZ8plqeqxWPFIOCaWqfACTdUL+dZz0/zEtWtOeQ3MjEv2Xesw++L842NjzBES9zyKRZ817Qlu3NCx+li7K0HufetpniySPZnHOuFhJGKo+Sr9jkXCUARAserze+UajzoRyZhNcqLJVfeH/Jt3bSXpvvq4QGtN48AS1b1zRLUATIXdnSD9tiGsrPyZKC6Pzy4tk6lfnN+/Uq3Jf7ooK4kfZRK2CiGEEEIIseLv//7vAfiFX/gFoDU0q1arccUVV3Dw4EE2bNgAwOHDh9mzZ89l26f40ZaO2fz8rev58g9OMl2oYxiKnQNZPnXzujMOcnq9XLO2na19GY7NlbFNgy29aeyL0C92IBfHUIq6HxK3TbTW5KseW3rSpGNn/ohqmQYf3jPAHz84wrH5CoaCSGuCUFP3AvqzcZ6eKFCsefihpj3hcPeBWZSCX7ljM/+8f5Z7D89R80IyMYt8xWOh0uSGdR1kEw7VZkCp7nNwpshALsFy1aNY96k0A8JIc2KhwtNjy3SnHep+xOhiFaVaLQtKdZ981aPcDPiv9xylM+WyrjNJzQs4sVDBtUxMQzGy2BqClY1b/N9PTvCdfTOUGz51LyQII5KuRdyxqDQC/tt9x8nEbe7Y3ntKNbNSiut29ULG4Z6DcyyUm1y/rp33XtFPp23hTVUwXBOzI4aZdkhc0cUNG3J8p1pjfKlGm63wq03yaLbbDn+9XGZcRRiGTanhE4Qmz44XeOJknrdt6X7V722wWKf65Cxag9Udh0DjT1eoPj5D5o7h8xouJoQQP+wkbBVCCCGEEAJYXFzky1/+Ml//+tdXv/elL32Jz372s/zcz/0cf/EXf8H73/9+AP7qr/6KT3/605drq+JNYOdAlt/+sZ2MLFSxTMX6zuRFGYz1aqVciz1r2i7qmlcPt7FnTY69Y8utoVVa05aw+dCegXOGydev76At6fD4yBKVZkDSsfj2vhn6c3Hy1SYNP6Qj5VJtBhiGQUfK5anRPD95zRpu2dTJkyfzbOhKk4pZnFys8OxEkR8cX6Q/FyflmsRsg5Rr4VgGNT8kHbNZqDRpBg3ijsnoYpWFcoNbN3WxXPNZrnrsmyxgGYqka2EZiuWax3+//zj/7r3bUYqV1gUQRhpDKQylODpb4Z5Dc8Rsk+GOJJoqYajxwgilQKOpeSF/9vAoDT8k1JrvH5qn7oVcv66DD+7p5/r1HVy//sVK1vrhPMv/PEZY9VGmwhlKk75lECNukU3Y/OLbNvK1x8c4OVOBssdthkUZzUSjjp2wcW0LrTXFho9R91goNc/6PpyLP10hagRY3YlWsGorjLSDP10lqgWYSRm8JYT40SdhqxBCCCGEEMAf/dEfMTIywsc+9jGgFb4ePnyYP/iDP2BwcJC9e/fya7/2a4RhyJ49e3jnO995mXcsftTFbJPt/ZnLvY1XLYw0j48s8cxEAaXgqjVtXLe2fTVIjdkm/+odm3j42CJHZsvkEjY3buhgY3d6dQ2tNU+PL3Pv4XmWKh7b+zK8c2cvm3vSbO5pPe7RkUUKT/h4QUQQaWClw61SKNXq6Vr3AhpByOhilSDSpGIWM4U6Y/kalgHNMKLqBbx1Sxfz5WbriD+gNVS9gCjSxBwTd6XnaBjBXLlJfzbGQrlJtRliGYpG0KpM3dqbYa7c5LHRJdKuxbbeDJWmTxDq1SD42HyZmhcy2JZAa5gq1AmjkEazVXXb9CPWdSZxbZPf+94xCjWPINIoFM9OFNg7nue3f+wKutMxAPyFGtXHptGBxmpz0V5Ec2U4VvrmAQA296T5wvt2MFduELNMcjGL/+3/eQ6adbxQ49qtYNhA4QURXRc6IOtMYbnWYLT6zgohxJuBhK1CCCGEEEIAn//85/n85z9/1p9/7nOfu4S7EeKH1/98fIxv75shXAlAHzq2yIf3DPDj17w4JC7hWNy5o5c7d/SecY1HR5b4owdOUPNarQaOzpXZP13ks+/eRlvS4dhcmb98bIylSpNJL8QyDfywNWCqLeGQck3G83WuHMzSlXJJrbQnaAYhJxYrNLzWY6HV+/Xug3N85KoBTi7WyMRsEo5JoeCh0DiWQcOPsAyFZSrKjYBcwibSUSuYVRBFoFArbRHAtUy60jEmluvs6M8CsFBukq+FuFardUKkNZ0ph+H2BAdnSkRo/FDTnYmxpj3J5HKNmWKDmG3Qk4mhNRTrPvsmizx0dIGPXN16Pb3JClEtwOppVZOquIH2I5oni6Su70XZrf66hqHoy8apNAP2ThRwbZOkY620UPBQSlHzAq4czHLd2vYLeu+dwTRG0iZcamBmXXQQEVV8Yts7MOISPwgh3hzkTzshhBBCCCGEEBfF5HKNew8vkI7ZdKZa1ZHzpQbfPTDLbVu6Vqsxz0VrzXeen6EZRKvVrkEYcXy+wlceOUkubnP/0QWWKk2uX9fBkbkyhZpHM9CYhknCsZhcrjPUFueu69dgGIpr17bzT/tmOTRTpFQPqHsBWkM2YeFaBrPFBhP5Otevb+fZicLKwCqDIGpVzaZck5RjslT10FpzYr5CEIFttFoD5OIWQaQ5OlemNxtnR3+GXMLmTx4c5chcmYVyk1LdJ+la7J8uUvNCjs+X6c8myCYculIudT9ka1+G3kzrNZorNdBoEo4JtKp1Y7ZJ1QsYW6q/+IJFGlZaFqxSQARB0cOfrqC9EKsrwTEj4o8fHmW22KDuhVSbQSv8RVP3Qrb0pvni+3dc0HAsAKstRuotA1SfmCUsNsFSuBtypK47c6guhBA/iiRsFUIIIYQQQghxUUwXGpQbPhu7U6vfa085jC3WmCk0zitsbQYR8+UmmdiL/T0NQzFbbPDNZ6doSziMLrYGT/Xl4lw93Ea5ETA6X+GdO3vZ1p8hbpvsGsqRjbfW6MnE+OXbN/FnD48ykZ9GA21Jh3SsFZI6lsHJpSqffc82xvM18lWP5ZrHXzx6knIjoCPlUmkGxB2ThGMysuhDFOHaJg0/YrHqYSlFZ8rlo1cPMNyRZLgjSVvC4cs/GGW+1GRjd4r1XUnKjZByIyDlWpQaHrZp8LHrhijVA546mefYfAWNJh2zSTVabRISjgYUQRRhAMMdidXXxhlIUXNNwmITI+Og/Yiw6uMMpSl99yRhyQMgMuC5ZoM5O2C4c+X9mQO05qo1bWzuSfP2bd30ZeOv6XcgtiGHM5QmWGpgOAZme0wGYwkh3lQkbBVCCCGEEEIIcVF0pBzijkm5EZBZCTrLjYCEa9KedM5rDdcyGGiLs3+qSHvSQSnFXKlOqRGwrS/D+q4k+UqTYsNndLFKR9LBtQyaUcRUoc7m3jRXDGRXg9Zyw6fcCFjfleR3P7yT+XKDJ0bzWEarPUBjZRhWbKUFwM6B7OpeNnWn+d6hWSbzdTZ0p3jnjl6eGsvzO985RKUBfqgxDUUQarQBn3zLWt57Zf/q9dv6MiRdizUdCdZ2JAFoT5oU6q0+tHfdMEzSsWhLOjT8kMdH85yYr5CKWazrTPB73zvGgakSS5UmCoUfRWzry3Dr5q7Ve1g9CRJX91B/doFgvo6yFM5gGu2FhCUPqzuOUop8vk5/3uOa4QT5lWFrm7vTTC7X+PBVg+wayr3ie6MjTfNEgeaJIpEX4g6niW3twHDNUx5nOCZOX/K83m8hhPhRI2GrEEIIIYQQQoiLYn1nkmuH23ng2ALFug9AIwi5Y1sPg23nVzGplOIDu/oZX6pxbK6CYxnMFOvEbYPhjgSGUgy2JyhPF8lXPcbzNY7OlQkjzdHZMiOLFe4/Ms+/un0TDx1b5L4j8zT8kO50jI9ePcgvvHUjY0vPUW4EOJZBXzaGUrCjP0vmZX1Ft/dnThtS9ujIEuYLlZpatwZLGQqFOuPxe0Mp0C/7pgbTMBhse7FCNWab3La5i9teEqR+/j02f/LQCPsmiwQ64trhNv71HZvpSr84wEopRXJXN7F1WYLFOsq1MDIOha8fw0zbL1aVuiamhlwjIp99YfsapcA8z+FVtecXqD45C5FGGQp/qoI/UyVzx1qUKdWrQggBErYKIYQQQgghhLhIlFJ8+tb1rOlI8OiJRQxDceOGTu7c3nPWo+RRpHlmYpkDUyVsy2D3UI7dQzl+/V1beejYAksVj50DGZ4czfPCCrm4jWub1Joh44tVFHDtujY6kjGCMOLYfIXf//4xRhYqKNWqPJ0uFJgu1PkPH9zBL7x1I//w3HQrcDUN1nUm+di1Q+d13L0nE8OPNLm4hTIUUaTxwgjHMlgoN057/PXr2nl+skC+6pGN2yzXWsf6r1nb9or32t6f4b/8xC5K9YCYY6wO19IrIe9LmRkXM9MKYbUfomwD7YWrP8+4FkuWyVTNo+61qn4nCzU2dKbY1JPilUSNgMb+JZRpYHW07hM1Q5rjZfzpCs5Q+hXXEEKINwMJW4UQQgghhBBCXDRxx+RDewb40J6BV3ys1pq/enKcb68MxELDP++f5WduHOb2bT1s6W0FeMW6z3/81kFOLFQwlGJkoYIXRnSmXKpeiGuZ5OKtANAyDZKOyZOjeUxTUWmEBFGE1prpQp3f+vYhfubGYT777q3MFBskXYsrB7MknPP7eHzDunZ60i7ThToxx0IDScckHXPIxk9vlfDWLd2M52v84PgiJxc9UjGL913Zx80bO097rB9GVBoB6ZiFtXLUXylFOmZRqHncfXyWh44vUmmGXLUmxwd29dOdOb0PrrJN3C1t1J6cQ+tmK3itePQMZSAWrIbCG7tS/Mtb1uNa5mlrvFxU8YkaAUbqJb10XZOwoAnL3nm9dkII8WYgYasQQgghhBBCiMtiPF/jngNzpFyL4ZVqycnlGt94eopdQznitknStcjGbf71Ozbxd09P8TdPTWAYil19OQbbE+ybLDKer7FYadCTiaO1puGHhBqKVZ+4bRKzLZYqTep+yBOjeYp1nysGs/zqO7aQTdjn3GMQRtx7eJ6Hji1SaQZcM9zGJ9+yjj/7wSgNPyTpWBiGYk17guvXt59WdepYBv/ylvW8a2cvC+UmvdnYaUOotNbcd2Sebz8/w3LNpyvt8sHd/dy0oZP9U0W+vneSx0eXmCs16ErH6M3E+M6+GUYWq/y79247Y1Cc3N2NUtA4soz2I5y1Wdqu7uHzHTFGFqoArO9KYq+Euq/ESNsYMYuoFmA4rXA2aoYoU2Fmzq8frxBCvBlI2CqEEEIIIYQQ4rIYW6pRaQZszL54jL0tYbN/qsS//utniDsWV/Rn+fFrBxlsS/DTNwzz1Fge2zToTLkopRjuSDBVqDG6WMU0DIp1n3TMJh2zeGJ0mUzMotLwaQQRpgLHNOhOx9g/VeL+o/N8cPe5K3C/+cwUf7N3ElOBYSj+du8EuwZz/OvbN3PfkXkKdZ/h9gTKgM/8z2coNXy29WX4+VvXc+VgbnWdwbbEKT1aX+qxkTx/+vBJALIxi7GlGn/84Ai1Zsjf7p1godxkqeKhNeSrHl1pl3WdKY7PlXlmvMBbzlAlqyyD5NW9JHZ1o4MI5ZqrIfALFcOvhuFaxK7ooPbELP5CDaUUOoxw1+ew+165DYEQQrxZSNgqhBBCCCGEEOK8NfyQp8eXmS816Uq7XLWmjbhz7mPoIwsV/mn/LCMLFQbbEtyxvYedA1lyCRvLVCzXPByz1ZP0qbFllioekY7oycS5/+g8s6UGv/n+7RyYLjK2VKPUCGhPOgy3J7BMhW0a1JohR2ZLXNGf5ZM3r2Om2GD/VIlSzaPYCIg0GAY0gwgviHBMg0PTpXOGreWGz/cPz2MqRbnhs1zzQcMDRxd4x7Ye/tNHrsQLIv7rPUf4mycnCaIIy1Dcf2SB4/MV/tvHr2Jj9ysHkQ8eXcAPIzZ0tR6bTTgcmyvzd89MsVD2GGyLM1NskLJtmkHE1HKdgVwcDas9YM9GWQbKOr/q1VeSuLILM+XQPFFoVcsOpYltbZfhWEII8RIStgohhBBCCCGEOC/lhs/vf+8Yz08V0VFrkv22/gy/8o7N5BJnPkp+crHKF/5hPzOFBpm4zchChafHlvnVOzfTn4tT90MOTpcwDUUzCGn4rWFTxXpAvloil3B48mSev3pinAePLmCbCq01c6UGi+UmfhRhKEXcNinWfJ6eKHDddJGPXD3E9w7O8sDRRZRSGGgspVAKji9UaE86tCVP3/OhmRL3Hp5nqlCnM+kwX2qwUPGoNgNitkmEZqHi8ffPTXHL5i6Wqk3uPjgHaHoyMUDR8AOmC3X+4bkpfvWOLa/4uhbqHu7LAlHLNCjUPJSCuG0Rt00qzQDLUHhhRKnhY5sGQ2epln09KKWIbcgR25C7ZPcUQogfNhK2CiGEEEIIIYQ4L/cenueZ8WXWdCSJ2QaVRshz40XuOTjHj18zdNrjm0HIb337IM9OFHFMxWKliQa0hs99Yx8be1JEoWYgF2e+3KQZRCgg7dpUvQAviGgGDSxD8ZePjZGN21wz3M50sc5UocH0cg0AxzEwlCIdtynWff704VF2D7XxU9ev4ZmJAkpBzQtxTINUzCRf9WhPOty86dTj9/univze945SqPnEHZOD0yUm81UaQURXKoZhKPwwwjEUh2fK3Hd4HtCUG8FKWNqq8HQtkxIBE0u183pdrxzIcWS23Kq4tQyafogfRly1rp2nTuap+yHrOpMcnClSqPvk4jaLFY9bN3Wyoz/zGt5RIYQQF5uErUIIIYQQQgghzsv+qRKObeKHEYdnS5TqPg0/4v/ZO8n7d/UTs09tJ/DQ0UUOTJewDYVrtULOMNLEbJP5coPZUoOtvWnWdqY4uVjh8GyJajOkWPcwlMIxDUKtiTsmhZqPY5mgFP25BP25BA95Ifmah2EYJN3Wx9uko6k2Q35wYpF37+xjuD1J0jWpeSFThTp1LyBuG7znij529GdP2e93D8xSqPls7E6hlCKKNLOlOoEXUmq07hNFGo1iYrnO7/7TIepeSKUZEEaaSEM6ZtMMQhStqt/zceeOHg5MFzkyW0YDhoI9Qzk+9ZZ1WIbi8dE81WaA1pB2Lbb1pfnI1YO8Y1sv1nkOuBJCCHFpSNgqhBBCCCGEEOK8ZGIWDS9kf7FIpdE6Vh9GmrGlGn/z5AQ/c9PaUx7/7ESB+Eo4W/NCAEyzVR062JZkqlBnqeqxthNitoVlGLhWRCPQRFoTao2pFGs7kiyUm1QaPn4YYZsGfhhhmgoFvBA3aq3xo4ika1Gq+Qy1xRnuSHB4tsy6ziS9mRjjyzXSMZsP7ek/7fmN52ukYtbqICnDUHSnYqAV2biFYSiWKk2qzZD2lEPdCynUfUwFIZqlqkep4WMZBpu6U/zYnnMP33pBR8rl375nG3vHllkoN+nLxrh6uI2YbfJLb9/E4LNTfPWRkyQck7akS6EW8NCxJW7a0Iljnbl9gxBCiMtDwlYhhBBCCCGEEOfl5k2dfPfALEvlJtm4gx9qXNtkoC3Gw8cX+dBVA2Ri9urjHcsgE7fQwEyhTqg1OoK4Y7K+K0m+5lGs+wRhREfSblVpBoq0a1L3Q2zTYF1nkvVdSfwwIh2zOblYRdM6sH/1cBsnFiocn6u01gZilkk6ZrG9P8PIYpUtvWkmluuMLlYBaE863HXDMN3p2GnPb31nkgePLdKb0SilCCONacBbNnZwcLrE1HKNqheilKLSCAi1JuVY+IZBX85mueoRafjgnn7+19s20nWGe5zJfKnB9w7NcWS2TGfKZWtverVK2LEMxpdqxGyTnQNZlFIEYcRzEwV+5zuHGGyLs7knzc2buki58hFfCCEuN/mTWAghhBBCCCHEedk9lOO2zd389ZPjRFoTtw3WtCdJrRzTr3vhKWHrdevaeWxkiYRjEemImWITx1Rs683gWiYdSZeYbXByqYrWsKk7xVB7gvGlGicWKmTiNu0Jh+PzFTZ0pfjM7Rs5MV9lrtygOx3j+vXtLJSafOEf9jO6UMU2FdmkwzVr2hlbqvG3T01S80MMBd0Zl/df2c81a9vpSrtnfH7v2tnHwZkSx+YqxGyDmh+yvivNXdcP8bvfOUxXOo5ZbeKHEVUvQEcQBBEAwx0JtvSkaQQRn3n7JjpTZ77Hyy1XPf6Pu49wdK6CocALIp4eX+aX3r6Jq4fb0FpzYqFCNm6vVtwW6z7ThRqFusdgW4IHjy7y1Mll/rc7txB3zFe4oxBCiNeThK1CCCGEEEIIIc6LUor3XtnHsxPL2JZBdzqGUjCyUGFLT/q0gPH6de385LVDfHvfDD3pGJEGUylCHTGxXOeG9e186uZ1jC3V8IKILb1p+nNxtNY8emKJ7x2aZ7HS5IYNHbznij6G2hOs60yhtWbv2DJ/8uAIy1WPbX0ZNvekcSyD69a1A/Bf7zmKH2iUAUnXYmq5Tr7qnTVoBdjSm+bfvGsr9x2ZZ6bQYENXirdv6+bZiQIauHZtG3vHljm5WMVUCh+NVoCG5ZqHoeCKwRztifM/2v/IiSX2TRWpeyF1PwQNC+Umf/PUOFetyaGUYiAX5/mpIt0AaEYXKwSRZk1bgnVdKZp+yHOTBZ44mee2zV2v+n0VQghx8UjYKoQQQgghhBA/QqJI0wwiYraxWgl5oaYLdb5/aI6jc2X6cnFu39rDtr40d+7o4e6Dc4wsVNBAd9rlJ69bg2mcej+lFB/YPcBtm7uZLzdIuBYnF6vMlxv0pGNcMZjl2HyFUt2nK+3SlrB5YjTPsxPLALxzRw/Xrm3HeNm69x9d4M8eHqXWDJgvN1muecQdi6G2OKVGgOeHjC/VsC0DQykWyk1cy+SRE4vcdcPwOZ/z+q4U67tSAARhRL7WGuqFAg1k4zamqdAaDKVBg20qlmseG7tTfHjPAMfmK/hhxIau1CtWmo4tVZkrNTCUIulaaA3FusdjI3n8IMKxTd59RW+rXcJ8GdcymC83ScdserNxAFzbROvW+yWEEOLykrBVCCGEEEIIIX4EaK259/A83z0wS6Hms7YzyY/tGWBbX+aC1psvNfjP3z3C2FKVhGNxaKbMM2PL/Kt3bOYTN6xlz5o2js1ViDsmVw+30ZM5e3/SbMImm2i1FxjItQLCmhfw3+49ztPjywShxjQUrmXQDKJWuAncf2SBD+7u52PXrlldyw8jvv38DFGkSbo29XyNtoRDww9JOiYHp4ssVTxCremI2au9V/PVJuVGcM7n3PBDnhjNM56vka82GV2sUah5mEpRbwY8fmKRshcSrgzp6m9PMNyZZK7YwLEMPnXTOv527yQjC60esv3ZOD/3lrVcOZg76z29MKIZRHSlXEzDQGuNsdKXdbpYZ21niquH2/lX79jM9w7OMVOsU24EmIZaDXKDKELT6kcrhBDi8pKwVQghhBBCCCF+BNx/ZIE/fXgUpRRJx+TpsTwT+Rqff+82BtsSr3q9B48tMLZUZWN3GtNQaK05Pl/huwdm2TmQ5crB3GqIWPdC9o7lCaPWUfxs3D734sB9hxd4YjTPQFuchGOxXPN49MQig7k4Vw61AbBQbvDdA3Pctrmb3mwrzK00AvJVj2zcZmK5jkLhWCYNP8SPNEnHYsKrE7NbfWRjtoEfakKtz/k61L2Q3/veUZ4ZX6bqhUwu17EMxY7+DPOVBiOLVcJIY1sGoPAjjWMZWIZBwrX4wJV93HNojgPTRYbaE5hKMVmo8ycPjvBbP3bFWV+Tbb0Z4pZJuRFgmQZhFOFYBp0pl5V2sECrX+7uodbrfe+hOf6vh0c4sVAmYVuUGj4bulJcv9JCQQghxOUjYasQQgghhBBC/JDTWnPPwTk0MNzeChTbkw5H58o8NpLno1e/+rB1armObRqrrQGUUqRjNmNL1VMed3SuzB89cILJ5TqR1nSlXX7upnWrvVPP5vnJArZpkHBaH0vDSKM1NF6SMHYkXU4sVpgq1FbD1nTMoiPpMFmoY5sKjSYIQwDitkXdC8glbGzTaA3t8iMMpelIOuweyvHg0QUsU7FzIHvKMK9HRxbZO7bMYHuC6eUa1krAfHi2jBeEeGGEqRRx22R9Z5JCzWex4tGTjfH+K/u4fn0H3zs8z0BbYvU5re1IMrpY4cB0kZs2dFLzAh45vsSx+TLZuM0N6zu4Zm0bVwxmGV+qYaxU92o02/uzrO04/X176mSeh44t4oeahh8St03ec0Uf77mij9yr6BUrhBDi9SFhqxBCCCGEEEL8kIs05GseiZf0B1VKYShFse5f0JqDbQn8cIEw0quVraWGzxUDL7Yl8MOIP3t4lLGlGms7kxhKMZ6v8ZVHRtnckzpn+Jd0TYLoxWDVtQxQEEYRNS8gZptUmj5x26TtJetYpsEHdvfzfz00QrEWEUSapapHe9JhsdLACyNu2dTF8fkKDT8EBU0/oj3p8E/7Z6g0W60E+rJx/pfbNrC9v/V8RhZaIXLcNgkijVKt17BY91t9Wr0Qy1AYChYrHh1JG6Xg/Vf289GrBxldrEKrtespNLRCZD/kS98/xt6xVj/aMNLcd2SeX3rbJj59y3q++tgYS5Xm6mv/ybesxTKNU9Z6frLAH9x3nKoXko07aO1hmwa3bu6if6U9gxBCiMtLwlYhhBBCCCGE+CFnGortvRnuPzpPZ9LFMBR1P0QB6zuTF7TmLZs7eeTEIsfny8Qdi4YX0pFyeNfOvtXHjC1VmViuM9AWx14JBte0JxhdrHBopsyNGzrOuv6NGzp5YnSZyeUauYTDctXDVLBQ8XjgyAKaVqVrTybGvYfnyMRtOlMuADdv7CTlWjx8bJFDMyVmig2mi3WmCg2ycZuRhTLb+7IYqhVCr+tM8vjIEl7YGoAVac3JxSp//shJ/uOHduJYBtm4TaQ1WmuycQel6tS9AEVrAJbWraDXDzWFmtc69m8ovvnMJNm4zdu3drO2M8nBmRJrO5IYCiaW63SlXLb3Zdg7tszT4wUG2hLEbROtNScWKvzdM1P85vu2s7Uvw+GZEpZpsKM/Q9I9/eP6vYfmqTQDNnSlUErRmXI4NlfhwaMLbO5Jv+J7qoMIb7qC9iPs7gRmWiphhRDiYpOwVQghhBBCCCF+BLx/d39rYv1CGQBDKa5a28YN688eeJ5LdzrG//7Ordx7eI5jcxV6czFu39rNxu4XQz2lFAatys0XaK1RqNX2A2dzzXAbP3HNIF9/eorZQp1mEJJNOCQsg+lik2ozwLVMcgmHb++bZTxf5/Pv3YZrmSil2LOmjT1rWr1df+97R/m7Z6ZwLU0QRkwVGlSaIV/8wA6uHMzxg+OLfP/QPBu6UhgrFb/9uTiThTrj+VZf2hvWd3Dv4XmOz1fIxW1itkm1GYBq9XNNxywiDaVGK4BNuiabutMYCv7umSlu3NDBJ9+ylv/xwAjj+SphBN1pl5+9aS1tSYfJ5RphpInb5upr15F0mVyuUfEC2pMON23sPOdrNlduELNbz/+FNRzbYL7cfMX3Myg0KN83gT9fhyjCSNokr+4lvuPCfj+EEEKcmYStQgghhBBCCPEjYF1nkn/3vu08PrpEoeazpj3BtWvbVyfWX4jebIyPXz981p+v7UiyvjvJvskig20JlIIDU0VCrfmbp8aZKtS4c3vvGas0Hz2xxD2H5qivHPWveiH92Th92RjFxhIJx8QLIkxDMdSW5MhsmX2TRa5Ze2ov2IVyg/9n7yRzpQboVpWvbRnMFhv81rcO0peLYxuKZhASao2xctA/jDSmYrUid6g9wWfevolvPjvFycUq169rY11nksdG8swU6nSlXSaX69S8gL5sjM29GTqSDl4QMZ6v8c/7Z9nam+E33reNkYUqfhixuTe92he2LeGgXrjvShBdaQb0ZFwS9vm9R9t6MxyZLa+uEYQRnh+yuSd1zuu01lQfm8GfqWJ1xMFShIUm1SdnsXsTre8JIYS4KCRsFUIIIYQQQogfEV1pl/dd2X/J7mcaik/fsp4/fnCEkYUqU4U65YbPQC7ObLHJXz42zshClV95x2aMl1S6TuRrfPkHo1S9kMG2ODUv4OhcBUMp2pMOYQSubeCFEWGkcSyDUOsz9p/9532zLJabaK2J2RZhpGl4AUEE+6dKHJ2r4IUhCkWpHrBrMEuoYaZY5+rhdobaXhxCtb0/w7a+NA0/wrUMDEPxU9d5/NO+GZ4eX6Yj5XJ8vsKWnjQJ11ppBVBlcrnGnz9yklTMYktvml9860a6M7FT9nndunbuPjjH8fky2biz2k/2nTt6V9oTtAZwGeeoCL5jRw/PTxU5MV9BqVY/2M29Gd62pfuc71NU9fFna5gZB2W3wmUz5xLM1/CmqxK2CiHERSRhqxBCCCGEEEKICzbYluAL79/BU2N5/st3j7K+M7kaNFYaAU+PFzgyV2Zb34uDtZ6bLJCv+WzqbvUedS2TzpTDfLnBlt4UrmVQafiYRquXaqHeGpQ11J445d5BGPHYaB7bbIWxWrcGWwUrc7dCHZGJu4BFoeZTbviczNdoS9hcs7adf3HzutPCTaXUKdXA7UmHu24Y5q4bhgnCiP/vPx9h71ieTNymWPM4MlvGNCBfbRKEEU+dXOavn5zgl2/fdMq6uYTDr9yxmW89N82+qSKD7XHevrWb7b1p/uiBEzwzUcC1DG7b3MV7r+zDtU6vdu3Lxvnsu7fy6MgSc6Um/dkYN23oJJuwz/0mrbQdeGm7h5UfoIzTHi2EEOI1kLBVCCGEEEIIIcRrYhqKTMzGMFqh4guSrslMMWSp4p3y+DDStOoyXzSQi1P3QqaW6ziWQdSAuKnIVxs4lskd23vY1H3qcflIQ6g1nWmHuVIDvbo2KCBmmattAhwrJBuzuGlDB3ddP8xgW3y19+n5skyDX3zbBv7xuTiPj+Y5PldBo0nHHAxlsFj1cEyDZyeWKTX81RYCL32O/5/bNqx+3fBDfutbBzk4U6It4VBrhvzPx8epNAJ+5qa1Z9xDR+rVVy+bSRt7KE3zSB5lKpRpEBabGEkLe/CVB2sJIYQ4fxK2CiGEEEIIIYR4zXozMdIxm3zVozfbqmwt1H0Sjklf7tQj9Tv6M6Rdm9lig55sDC+IqDQDPrhngLds6KTUaLULyFeb+IFmW3+Ga4bbTgtHHcvgmuE2Jpaq5BIO9WaADwSqFQC7K71Q/TBa/bo96ZxWIftq5BIOn7hxLe/a2cvPfflJKs2AhGOhVKtX7HK1SakeYJxHkPvcRIGj8xXWdaZwrFYovFBu8J39M60AO26zeyj3mvb7gtQNfWgvxJ+uQKgx0g6p6/uwsu5rXlsIIcSLJGwVQgghhBBCCPGatSUd3nNFL3/z1CTH58sopVDA7du6Wd+ZPOWxG7pS/MS1Q3zj6UlOzFewTMWO/iw/e+Pa1aD2fH34qkFmCg2emVhmqeqhgL6My2y5yXy5dbTfMBRp1yIbt7lqTdtFeb41LyRum7i2QbUZEHcswijCDzVrO5OkzjAU7OWKdZ8o0tjmi8HsctVjZLFKpTGOaxm0Jx0+dfN6btzQ8Zr2ayZtsu9aS7jUIPIjrI4YxmsYniaEOH8LCwt89KMf5YEHHrjcWxGXgIStQgghhBBCCCEuig/uHqAvG+epsWX8MGL3UI63bOw8rSJVKcV7rujjmuE2ji9USDoW2/oyq9Wdr0Z70uGz79nK4dkypbrPmo4EA7k4Dx1b5H/cf4KJ5Roxy2SwPc57r+jj6uGLE7b2ZeOs60xSaQbUmgE1LyCMNB0ph5++Yc15rTHckSTumBTqPm0Jh2rT58RiFdcy2NaXwTQUY0s1/vrJcXYP5U7pJXshlFJYnTIMS4hLxfM8vvKVr7B3714ef/zxy70dcYlI2CqEEEIIIYQQ4qIZbE/QlnQYaku8YjjYnYmtDtN6LSzTYOdAdvXrpUqTDd0pvvovrmOqUCdf9Rhoi9Odfu33eoFjGfzkdUMUH/CZLzfxgpC4Y/GRPQPsGsyd1xqbe1K8dUsX33p+hpGFCk0/Qkea7X0ZrJVes73ZGHOlBhPLNTb3SH9VIX6YOI7Dz//8zwPw53/+55d5N+JSkbBVCCGEEEIIIcRrVm74fPkHJ3l6fBkviOhKuXzsuiFu2tB5yfZQavj85WNj7D25TBBp1nQkuOv6Ney5SK0DXm7PUBv/4pZ1HJwukXQttvdl2NGfOe/BW2GkMVCgNXUvBMB1TNKxFz+qN/wQxzJInkdbAiGEEJffqz+jIYQQQgghhBBCvMzfPjXJ/UfmidsmPZkY85Umf/bwKONLtUu2h798bIzvH5zDMg3SMYtDMyX+8L7jLFe9i36v8aUa//FbB/mDe49z/5F5jsyUySXs8w5aAR4byfPdA7N0pWO8ZWMnO/sz6AienyyyVGmyUG4yX2qwZyhH/6vsZSuEeP2USqVT/tdsNi/3lsQbiPzVmBBCCCGEEEKI16TmBTw+ukRb0iGXcAAYbk9wbL7CMxPLrOlIAFBpBtx7eJ5nxwukYhY3bejg+nXtryqgPJt81WPvyWU6Ui7tydYeEo7Fifky33x2it1DOTb3pF91hWip4XPf4XmenyySiVncvKmTHf0Z/vD+4xybr9C3EoI+M7GM90DEF96/fbUFwCt5ZnyZUGs6Ui4AuaTL2s4EdT9Ea42pFO+5so8fv2boorxGQoiLY2ho6JSvv/CFL/DFL37x8mxGvOFI2CqEEEIIIYQQ4jUJI020Eg6+oBUOasJIA+CHEf/nvcd46uQyrmUQhBF7T+Yp3DjMu3b2veY9NPyQINLE7Bf7xFYaPieXavzVE+P80/5ZejMxPnXzOnYP5c57zS997xjPjBdwbYMg1Dw1tszbt3RzcqnKcHsCd+V+Q+2KkYUKxxcqbO3NnLJOueFTrPt0ptxT9mcYpweojmWyqSfNv333VkylpH2AEG9AExMTZDIv/nPuuu5l3I14o5E/tYUQQgghhBBCvCbpmM0V/TnuPzpPyrVwLIP5cpOkY7GtrxVIPD9Z4LmJAoNtcRJO66Po1HKNbz8/w22bu19xmNYr6c3EGO5IrPZPBc2zEwX8MGJtR5JM3GYiX+NPHxrhtz98BZmY/YprPjNe4PmpIms6Eqsh6Xi+xgNH5wlCjfmSsNQyDEKt8QO9+r0gjPi7Z6b4/qE5al5IR8rlw1cNcMumLgCuWtPGw8cXmS81aE86lBsBTT/kpg0d57U/IcTlkclkTglbhXgp6dkqhBBCCCGEEOK8jC1V+YtHT/Kfv3uEbzw9yVLlxT6FP3HtENv7M0wV6hyfrwDwY3sG2NqbBmC+1CQI9WrQCpBNOBTrPkvV197v0DAUP33DMAO5OKOLFfZPlaj5IWs7EiQcE8tQrGlPMFtqcmi6dF5rzpcbRC+rls3FbcJIk0vYTCzXiaJW9e5koUZPJsaG7uTqY793aI6/3TtJM9TkEg5zpQZ/9vAoh2Za979+XTs/tnsApWAsX6MRhNyxo4c7tve85tdDCPHGEYYhURShtX7lB4sfelLZKoQQQgghhBDiFR2bK/Nf7jnKXKmBY5k8dmKRx0fz/Jt3baU96dCbjfEb79vOgekS1WbA+q4UA7n46vXdGRfTVNS9cLWKtVjzycZtOpIX5wju5p40/+GDO3l2ssCB6SJ/8egYc6UGi1WPXNxhbUcC0Ofd/7Qr7WIoRdMPV9sFFOs+XekYH7lqgL98fJwTC5XV5/ezN609JUy+/8gCtmnQm2n1dU26FkfnSjw5mmdbXwbDUPzEtUO8dWsXM4UGHSmHgVxc+rMKcRF90f9ZXD91UdZq+hXgj17VNV/+8pd54IEH8H2fT3/607ztbW/jrrvuuij7EW9MErYKIYQQQgghhHhF3943w3y5weaeNEopgjDi+HyZh44t8MHdAwC4lslVa9qAVp/SmWKdrpSLZRpcMZBj92COp8ZWerZGGttUvOeKvtfcQuClsgmbWzd18uiJRfwgwo8ikobBbLHBQrnJdeva2daXPq+1rlrTxo6BDM9NFIg7Fl4Q4lom77uyj5s3dbGtL8OB6RJKwc7+LG0rg7leUPMCbPPU4FQpRc0PTvledzpGdzr22p64EOIN6ZOf/CSf/OQn+cpXvnK5tyIuEQlbhRBCCCGEEEK8opGFCpmYs1p1aZkGpmEwvlQ75XHNIORvn5rkoWMLNPyI/lyMn7hmiD1r2vjM7Zv4/qE5nh0vkIpZ3LShkxvWt1/0vU7k6xyaKbFzMMtEvkalGWAYoNG8dUsX6fPshxqzTf717Zu559As+6aKZGI2t2zq4tq1rUC5I+Vy6+aus15/1Zo2/v65adqTDq5lUm74mEqxvS97UZ6nEEKINx4JW4UQQgghhBBCvKL+XJy9Y8to7aKUItKaIIzozZ5akfmPz03zzWemyMZt0jGLEwtV/vv9J/iN921nqD3BB3cPrFbCvl5qfoAfanoyLj2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      "text/plain": [
       "<Figure size 1600x1200 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"=== 1. Basic Dataset Info ===\")\n",
    "print(f\"Total Samples: {data.data.shape[0]}\")\n",
    "print(f\"Feature Count: {data.data.shape[1]} (8x8 pixels flattened)\")\n",
    "print(f\"Target Classes: {data.target_names}\")\n",
    "print(f\"Feature Range: {np.min(data.data)} ~ {np.max(data.data)}\")\n",
    "print(\"============================\")\n",
    "\n",
    "fig = plt.figure(figsize=(16, 12))\n",
    "\n",
    "ax1 = fig.add_subplot(2, 2, 1)\n",
    "labels, counts = np.unique(data.target, return_counts=True)\n",
    "ax1.bar(labels, counts, color='skyblue', align='center')\n",
    "ax1.set_title(\"Class Distribution (Target Balance)\")\n",
    "ax1.set_xlabel(\"Digit Label\")\n",
    "ax1.set_xticks(labels)\n",
    "ax1.set_ylabel(\"Count\")\n",
    "\n",
    "ax2 = fig.add_subplot(2, 2, 2)\n",
    "ax2.axis('off')\n",
    "ax2.set_title(\"Sample Digit Images\")\n",
    "for i in range(10):\n",
    "    sub_ax = fig.add_axes([0.55 + (i % 5) * 0.08, 0.75 - (i // 5) * 0.1, 0.08, 0.08])\n",
    "    sub_ax.imshow(data.images[i], cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "    sub_ax.axis('off')\n",
    "    sub_ax.set_title(str(data.target[i]), fontsize=10)\n",
    "\n",
    "ax3 = fig.add_subplot(2, 2, 3)\n",
    "mean_images = []\n",
    "for i in range(10):\n",
    "    mean_images.append(np.mean(data.images[data.target == i], axis=0))\n",
    "\n",
    "combined_img = np.zeros((8 * 2, 8 * 5))\n",
    "for i in range(10):\n",
    "    row = i // 5\n",
    "    col = i % 5\n",
    "    combined_img[row*8:(row+1)*8, col*8:(col+1)*8] = mean_images[i]\n",
    "ax3.imshow(combined_img, cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "ax3.set_title(\"Mean Images per Class\")\n",
    "ax3.axis('off')\n",
    "\n",
    "ax4 = fig.add_subplot(2, 2, 4)\n",
    "pca = sklearn.decomposition.PCA(n_components=2)\n",
    "X_pca = pca.fit_transform(data.data)\n",
    "\n",
    "scatter = ax4.scatter(X_pca[:, 0], X_pca[:, 1], c=data.target, cmap='tab10', alpha=0.6, s= 15)\n",
    "ax4.set_title(\"2D PCA Projection of Digits\")\n",
    "#ax4.set_xlabel(\"PCA Component 1\")\n",
    "#ax4.set_ylabel(\"PCA Component 2\")\n",
    "plt.colorbar(scatter, ax=ax4, label = \"Digit Class\")\n",
    "\n",
    "#plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93f40d6e",
   "metadata": {},
   "source": [
    "데이터 개요 (Overview):\n",
    "\n",
    "    총 1,797개의 샘플이 있으며, 각 샘플은 8x8 픽셀(64개 feature)로 이루어진 흑백(grayscale) 이미지이다.\n",
    "\n",
    "    각 픽셀 값은 0(흰색)부터 16(검은색) 사이의 정수 값을 가진다. (이 때문에 학습 전 StandardScaler를 이용한 정규화가 필요함을 언급하면 좋습니다.)\n",
    "\n",
    "클래스 균형 (Class Balance):\n",
    "\n",
    "    첫 번째 그래프(Bar chart)를 보면 0부터 9까지의 데이터 개수가 거의 비슷하다.\n",
    "\n",
    "    분석: 데이터 불균형(Imbalance) 문제가 없으므로, 정확도(Accuracy)를 주요 평가지표로 사용해도 타당하다.\n",
    "\n",
    "데이터의 특징 (Characteristics):\n",
    "\n",
    "    세 번째 그림(Average Image)을 보면, 같은 숫자라도 필기체라 모양이 조금씩 다르지만 평균을 내보면 숫자의 형태가 뚜렷하게 나타난다.\n",
    "\n",
    "    이는 픽셀 데이터 간에 일관된 패턴이 존재함을 의미한다.\n",
    "\n",
    "분류 가능성 (Separability - PCA Plot):\n",
    "\n",
    "    마지막 산점도(PCA)를 보면, 64차원을 2차원으로 줄였음에도 불구하고 같은 색깔(같은 숫자)끼리 잘 뭉쳐 있고, 다른 색깔과는 분리되는 경향을 보인다.\n",
    "\n",
    "    분석: 이는 데이터가 비교적 선형적으로 분리 가능하거나(Linearly Separable) 패턴이 뚜렷하여, SVM이나 Logistic Regression 같은 알고리즘으로도 높은 성능을 낼 수 있음을 시사한다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8ad8d28",
   "metadata": {},
   "source": [
    "## reproducing Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "df8d0f6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n",
    "    data.data, data.target, test_size=0.2, random_state=8\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d341c075",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = sklearn.preprocessing.StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "X_train_tensor = torch.tensor(X_train_scaled.reshape(-1, 1, 8, 8), dtype=torch.float32)\n",
    "X_test_tensor = torch.tensor(X_test_scaled.reshape(-1, 1, 8, 8), dtype=torch.float32)\n",
    "y_train_tensor = torch.tensor(y_train, dtype=torch.long)\n",
    "y_test_tensor = torch.tensor(y_test, dtype=torch.long)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5d94d91",
   "metadata": {},
   "source": [
    "# Preparing Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "55952278",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DigitCNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(DigitCNN, self).__init__()\n",
    "\n",
    "        self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=3, padding=1)\n",
    "        self.relu = nn.ReLU()\n",
    "\n",
    "        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "        self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, padding=1)\n",
    "\n",
    "        self.fc1 = nn.Linear(32 * 4 * 4, 128)\n",
    "        self.fc2 = nn.Linear(128, 10)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.pool(x)\n",
    "        \n",
    "        x = self.conv2(x)\n",
    "        x = self.relu(x)\n",
    "\n",
    "        x = x.view(-1, 32 * 4 * 4)\n",
    "\n",
    "        x = self.fc1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.fc2(x)\n",
    "\n",
    "        return x\n",
    "    \n",
    "class AdvancedCNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(AdvancedCNN, self).__init__()\n",
    "\n",
    "        self.layer1 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(32),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2)\n",
    "        )\n",
    "\n",
    "        self.layer2 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2)\n",
    "        )\n",
    "\n",
    "        self.fc_layer = nn.Sequential(\n",
    "            nn.Flatten(),\n",
    "            nn.Linear(64 * 2 * 2, 256),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.5),\n",
    "            nn.Linear(256, 10)\n",
    "        )\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = self.layer1(x)\n",
    "        x = self.layer2(x)\n",
    "        x = self.fc_layer(x)\n",
    "        return x\n",
    "    \n",
    "\n",
    "class SelfAttention(nn.Module):\n",
    "    def __init__(self, in_dim):\n",
    "        super(SelfAttention, self).__init__()\n",
    "        self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1)\n",
    "        self.key_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1)\n",
    "        self.value_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)\n",
    "        self.gamma = nn.Parameter(torch.zeros(1))\n",
    "        self.softmax = nn.Softmax(dim=-1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        batch_size, C, width, height = x.size()\n",
    "        proj_query = self.query_conv(x).view(batch_size, -1, width * height).permute(0, 2, 1)\n",
    "        proj_key = self.key_conv(x).view(batch_size, -1, width * height)\n",
    "        energy = torch.bmm(proj_query, proj_key)\n",
    "        attention = self.softmax(energy)\n",
    "        proj_value = self.value_conv(x).view(batch_size, -1, width * height)\n",
    "\n",
    "        out = torch.bmm(proj_value, attention.permute(0, 2, 1))\n",
    "        out = out.view(batch_size, C, width, height)\n",
    "\n",
    "        out = self.gamma * out + x\n",
    "        return out\n",
    "    \n",
    "class AttentionCNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(AttentionCNN, self).__init__()\n",
    "\n",
    "        self.layer1 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(32),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2),\n",
    "            SelfAttention(32)\n",
    "        )\n",
    "\n",
    "        self.layer2 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2),\n",
    "            SelfAttention(64),\n",
    "        )\n",
    "\n",
    "        self.fc_layer = nn.Sequential(\n",
    "            nn.Flatten(),\n",
    "            nn.Linear(64 * 2 * 2, 256),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.5),\n",
    "            nn.Linear(256, 10)\n",
    "        )\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = self.layer1(x)\n",
    "        x = self.layer2(x)\n",
    "        x = self.fc_layer(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d6022185",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MicroViT(nn.Module):\n",
    "    def __init__(self, image_size=8, patch_size=2, num_classes=10, dim=64, depth=4, heads=4, mlp_dim=128):\n",
    "        super(MicroViT, self).__init__()\n",
    "    \n",
    "        num_patches = (image_size // patch_size) ** 2\n",
    "        patch_dim = 1 * patch_size * patch_size\n",
    "        \n",
    "        self.patch_to_embedding = nn.Linear(patch_dim, dim)\n",
    "        \n",
    "        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))\n",
    "\n",
    "        self.cls_token = nn.Parameter(torch.randn(1, 1, dim))\n",
    "\n",
    "        encoder_layer = nn.TransformerEncoderLayer(d_model=dim, nhead=heads, dim_feedforward=mlp_dim, dropout=0.1, batch_first=True)\n",
    "        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=depth)\n",
    "        self.to_cls_token = nn.Identity()\n",
    "        self.mlp_head = nn.Sequential(\n",
    "            nn.LayerNorm(dim),\n",
    "            nn.Linear(dim, num_classes)\n",
    "        )\n",
    "\n",
    "        self.patch_size = patch_size\n",
    "        self.dim = dim\n",
    "\n",
    "    def forward(self, img):\n",
    "        # img shape: (batch, 1, 8, 8)\n",
    "        p = self.patch_size\n",
    "        # Flatten Patches\n",
    "        # (batch, 1, 8, 8) -> (batch, 16, 4)\n",
    "        x = img.unfold(2, p, p).unfold(3, p, p).permute(0, 2, 3, 1, 4, 5).contiguous().view(img.shape[0], -1, p*p)\n",
    "        # Embedding\n",
    "        x = self.patch_to_embedding(x) # (batch, 16, 64)\n",
    "        cls_tokens = self.cls_token.expand(x.shape[0], -1, -1)\n",
    "        x = torch.cat((cls_tokens, x), dim=1) # (batch, 17, 64)\n",
    "        x += self.pos_embedding[:, :(x.shape[1])]\n",
    "        x = self.transformer(x)\n",
    "        x = x[:, 0]\n",
    "        return self.mlp_head(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a8d6d064",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model Declaration\n",
    "models = {\n",
    "    \"Logistic Regression\": sklearn.linear_model.LogisticRegression(random_state=8),\n",
    "    \"KNN (k=3)\": sklearn.neighbors.KNeighborsClassifier(n_neighbors=3),\n",
    "    \"KNN (k=5)\": sklearn.neighbors.KNeighborsClassifier(n_neighbors=5),\n",
    "    \"KNN (k=7)\": sklearn.neighbors.KNeighborsClassifier(n_neighbors=7),\n",
    "    \"SVM-rbf\": sklearn.svm.SVC(kernel = 'rbf', random_state=8),\n",
    "    \"SVM-linear\": sklearn.svm.SVC(kernel='linear', random_state=8),\n",
    "    \"SVM-sigmoid\": sklearn.svm.SVC(kernel=\"sigmoid\", random_state=8),\n",
    "    \"Decision Tree\": sklearn.tree.DecisionTreeClassifier(random_state=8),\n",
    "    \"Random Forest (50)\": sklearn.ensemble.RandomForestClassifier(n_estimators=50, random_state=8),\n",
    "    \"Random Forest (100)\": sklearn.ensemble.RandomForestClassifier(n_estimators=100, random_state=8),\n",
    "\n",
    "    \"FDA\": sklearn.discriminant_analysis.LinearDiscriminantAnalysis(),\n",
    "    \"Gaussian NB\": sklearn.naive_bayes.GaussianNB(),\n",
    "    \"MLP (100)\": sklearn.neural_network.MLPClassifier(random_state=8, hidden_layer_sizes=(100,)),\n",
    "    \"MLP (200)\": sklearn.neural_network.MLPClassifier(random_state=8, hidden_layer_sizes=(200,)),\n",
    "    \"MLP (256)\": sklearn.neural_network.MLPClassifier(random_state=8, hidden_layer_sizes=(256,)),\n",
    "\n",
    "    \"Ensenble (Hard Voting)\": sklearn.ensemble.VotingClassifier(estimators=\n",
    "        [\n",
    "            ('knn', sklearn.neighbors.KNeighborsClassifier(n_neighbors=5)),\n",
    "            ('svm', sklearn.svm.SVC(kernel='rbf', random_state=8)),\n",
    "            ('mlp', sklearn.neural_network.MLPClassifier(random_state=8, hidden_layer_sizes=(200,)))\n",
    "        ],\n",
    "        voting='hard'\n",
    "    ),\n",
    "}\n",
    "\n",
    "results = OrderedDict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "355b4d31",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scikit-learn Model\n",
    "for name, model in models.items():\n",
    "    # Model\n",
    "    model.fit(X_train_scaled, y_train)\n",
    "    \n",
    "    # Prediction\n",
    "    y_pred = model.predict(X_test_scaled)\n",
    "    \n",
    "    # Evaluation\n",
    "    acc = sklearn.metrics.accuracy_score(y_test, y_pred)\n",
    "    results[name] = acc\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cb5a927a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20, Loss: 1.4835\n",
      "Epoch 2/20, Loss: 0.3041\n",
      "Epoch 3/20, Loss: 0.1692\n",
      "Epoch 4/20, Loss: 0.1133\n",
      "Epoch 5/20, Loss: 0.0763\n",
      "Epoch 6/20, Loss: 0.0634\n",
      "Epoch 7/20, Loss: 0.0397\n",
      "Epoch 8/20, Loss: 0.0345\n",
      "Epoch 9/20, Loss: 0.0372\n",
      "Epoch 10/20, Loss: 0.0189\n",
      "Epoch 11/20, Loss: 0.0167\n",
      "Epoch 12/20, Loss: 0.0132\n",
      "Epoch 13/20, Loss: 0.0096\n",
      "Epoch 14/20, Loss: 0.0092\n",
      "Epoch 15/20, Loss: 0.0080\n",
      "Epoch 16/20, Loss: 0.0074\n",
      "Epoch 17/20, Loss: 0.0044\n",
      "Epoch 18/20, Loss: 0.0039\n",
      "Epoch 19/20, Loss: 0.0025\n",
      "Epoch 20/20, Loss: 0.0021\n"
     ]
    }
   ],
   "source": [
    "# CNN\n",
    "\n",
    "model = DigitCNN()\n",
    "crit = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr = 0.001)\n",
    "\n",
    "train_ds = torch.utils.data.TensorDataset(X_train_tensor, y_train_tensor)\n",
    "train_loader = torch.utils.data.DataLoader(train_ds, batch_size=32, shuffle=True)\n",
    "\n",
    "epochs = 20\n",
    "\n",
    "for ep in range(epochs):\n",
    "    model.train()\n",
    "    running_loss = 0.0\n",
    "    for inputs, labels in train_loader:\n",
    "        optimizer.zero_grad()\n",
    "        pred = model(inputs)\n",
    "        loss = crit(pred, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        running_loss += loss.item()\n",
    "    \n",
    "    print(f\"Epoch {ep+1}/{epochs}, Loss: {running_loss/len(train_loader):.4f}\")\n",
    "\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    outputs = model(X_test_tensor)\n",
    "\n",
    "    _, predicted = torch.max(outputs, 1)\n",
    "\n",
    "    correct = (predicted == y_test_tensor).sum().item()\n",
    "    total = len(y_test_tensor)\n",
    "\n",
    "    acc = correct / total\n",
    "    results[\"CNN(Basic)\"] = acc\n",
    "\n",
    "models[\"CNN(Basic)\"] = model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c12d6f80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30, Loss: 1.0062\n",
      "Epoch 2/30, Loss: 0.2038\n",
      "Epoch 3/30, Loss: 0.0946\n",
      "Epoch 4/30, Loss: 0.0699\n",
      "Epoch 5/30, Loss: 0.0722\n",
      "Epoch 6/30, Loss: 0.0523\n",
      "Epoch 7/30, Loss: 0.0287\n",
      "Epoch 8/30, Loss: 0.0869\n",
      "Epoch 9/30, Loss: 0.0896\n",
      "Epoch 10/30, Loss: 0.0572\n",
      "Epoch 11/30, Loss: 0.0138\n",
      "Epoch 12/30, Loss: 0.0083\n",
      "Epoch 13/30, Loss: 0.0079\n",
      "Epoch 14/30, Loss: 0.0040\n",
      "Epoch 15/30, Loss: 0.0025\n",
      "Epoch 16/30, Loss: 0.0031\n",
      "Epoch 17/30, Loss: 0.0030\n",
      "Epoch 18/30, Loss: 0.0014\n",
      "Epoch 19/30, Loss: 0.0074\n",
      "Epoch 20/30, Loss: 0.0023\n",
      "Epoch 21/30, Loss: 0.0028\n",
      "Epoch 22/30, Loss: 0.0028\n",
      "Epoch 23/30, Loss: 0.0015\n",
      "Epoch 24/30, Loss: 0.0014\n",
      "Epoch 25/30, Loss: 0.0014\n",
      "Epoch 26/30, Loss: 0.0028\n",
      "Epoch 27/30, Loss: 0.0019\n",
      "Epoch 28/30, Loss: 0.0016\n",
      "Epoch 29/30, Loss: 0.0009\n",
      "Epoch 30/30, Loss: 0.0011\n"
     ]
    }
   ],
   "source": [
    "# More Advanced CNN\n",
    "\n",
    "model = AdvancedCNN()\n",
    "crit = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr = 0.003)\n",
    "\n",
    "train_ds = torch.utils.data.TensorDataset(X_train_tensor, y_train_tensor)\n",
    "train_loader = torch.utils.data.DataLoader(train_ds, batch_size=32, shuffle=True)\n",
    "\n",
    "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.5)\n",
    "epochs = 30\n",
    "\n",
    "for ep in range(epochs):\n",
    "    model.train()\n",
    "    running_loss = 0.0\n",
    "    for inputs, labels in train_loader:\n",
    "        optimizer.zero_grad()\n",
    "        pred = model(inputs)\n",
    "        loss = crit(pred, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        running_loss += loss.item()\n",
    "    scheduler.step()\n",
    "    \n",
    "    print(f\"Epoch {ep+1}/{epochs}, Loss: {running_loss/len(train_loader):.4f}\")\n",
    "\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    outputs = model(X_test_tensor)\n",
    "\n",
    "    _, predicted = torch.max(outputs, 1)\n",
    "\n",
    "    correct = (predicted == y_test_tensor).sum().item()\n",
    "    total = len(y_test_tensor)\n",
    "\n",
    "    acc = correct / total\n",
    "    results[\"CNN(Advanced)\"] = acc\n",
    "\n",
    "models[\"CNN(Advanced)\"] = model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2302086f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30, Loss: 0.9964\n",
      "Epoch 2/30, Loss: 0.1444\n",
      "Epoch 3/30, Loss: 0.1252\n",
      "Epoch 4/30, Loss: 0.0942\n",
      "Epoch 5/30, Loss: 0.0990\n",
      "Epoch 6/30, Loss: 0.0206\n",
      "Epoch 7/30, Loss: 0.0215\n",
      "Epoch 8/30, Loss: 0.0290\n",
      "Epoch 9/30, Loss: 0.0399\n",
      "Epoch 10/30, Loss: 0.0283\n",
      "Epoch 11/30, Loss: 0.0218\n",
      "Epoch 12/30, Loss: 0.0047\n",
      "Epoch 13/30, Loss: 0.0032\n",
      "Epoch 14/30, Loss: 0.0030\n",
      "Epoch 15/30, Loss: 0.0016\n",
      "Epoch 16/30, Loss: 0.0017\n",
      "Epoch 17/30, Loss: 0.0016\n",
      "Epoch 18/30, Loss: 0.0011\n",
      "Epoch 19/30, Loss: 0.0014\n",
      "Epoch 20/30, Loss: 0.0015\n",
      "Epoch 21/30, Loss: 0.0016\n",
      "Epoch 22/30, Loss: 0.0009\n",
      "Epoch 23/30, Loss: 0.0013\n",
      "Epoch 24/30, Loss: 0.0005\n",
      "Epoch 25/30, Loss: 0.0009\n",
      "Epoch 26/30, Loss: 0.0011\n",
      "Epoch 27/30, Loss: 0.0005\n",
      "Epoch 28/30, Loss: 0.0015\n",
      "Epoch 29/30, Loss: 0.0011\n",
      "Epoch 30/30, Loss: 0.0005\n"
     ]
    }
   ],
   "source": [
    "# Attention CNN\n",
    "\n",
    "model = AttentionCNN()\n",
    "crit = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr = 0.003)\n",
    "\n",
    "train_ds = torch.utils.data.TensorDataset(X_train_tensor, y_train_tensor)\n",
    "train_loader = torch.utils.data.DataLoader(train_ds, batch_size=32, shuffle=True)\n",
    "\n",
    "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.5)\n",
    "epochs = 30\n",
    "\n",
    "for ep in range(epochs):\n",
    "    model.train()\n",
    "    running_loss = 0.0\n",
    "    for inputs, labels in train_loader:\n",
    "        optimizer.zero_grad()\n",
    "        pred = model(inputs)\n",
    "        loss = crit(pred, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        running_loss += loss.item()\n",
    "    scheduler.step()\n",
    "    \n",
    "    print(f\"Epoch {ep+1}/{epochs}, Loss: {running_loss/len(train_loader):.4f}\")\n",
    "\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    outputs = model(X_test_tensor)\n",
    "\n",
    "    _, predicted = torch.max(outputs, 1)\n",
    "\n",
    "    correct = (predicted == y_test_tensor).sum().item()\n",
    "    total = len(y_test_tensor)\n",
    "\n",
    "    acc = correct / total\n",
    "    results[\"CNN(Attention)\"] = acc\n",
    "\n",
    "models[\"CNN(Attention)\"] = model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d37f1199",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50, Loss: 1.7488\n",
      "Epoch 2/50, Loss: 0.5374\n",
      "Epoch 3/50, Loss: 0.2700\n",
      "Epoch 4/50, Loss: 0.1750\n",
      "Epoch 5/50, Loss: 0.1070\n",
      "Epoch 6/50, Loss: 0.0909\n",
      "Epoch 7/50, Loss: 0.0724\n",
      "Epoch 8/50, Loss: 0.0588\n",
      "Epoch 9/50, Loss: 0.0535\n",
      "Epoch 10/50, Loss: 0.0513\n",
      "Epoch 11/50, Loss: 0.0481\n",
      "Epoch 12/50, Loss: 0.0489\n",
      "Epoch 13/50, Loss: 0.0683\n",
      "Epoch 14/50, Loss: 0.0676\n",
      "Epoch 15/50, Loss: 0.0413\n",
      "Epoch 16/50, Loss: 0.0394\n",
      "Epoch 17/50, Loss: 0.0257\n",
      "Epoch 18/50, Loss: 0.0174\n",
      "Epoch 19/50, Loss: 0.0241\n",
      "Epoch 20/50, Loss: 0.0203\n",
      "Epoch 21/50, Loss: 0.0109\n",
      "Epoch 22/50, Loss: 0.0087\n",
      "Epoch 23/50, Loss: 0.0105\n",
      "Epoch 24/50, Loss: 0.0111\n",
      "Epoch 25/50, Loss: 0.0082\n",
      "Epoch 26/50, Loss: 0.0050\n",
      "Epoch 27/50, Loss: 0.0032\n",
      "Epoch 28/50, Loss: 0.0040\n",
      "Epoch 29/50, Loss: 0.0050\n",
      "Epoch 30/50, Loss: 0.0060\n",
      "Epoch 31/50, Loss: 0.0035\n",
      "Epoch 32/50, Loss: 0.0024\n",
      "Epoch 33/50, Loss: 0.0023\n",
      "Epoch 34/50, Loss: 0.0038\n",
      "Epoch 35/50, Loss: 0.0020\n",
      "Epoch 36/50, Loss: 0.0019\n",
      "Epoch 37/50, Loss: 0.0021\n",
      "Epoch 38/50, Loss: 0.0042\n",
      "Epoch 39/50, Loss: 0.0023\n",
      "Epoch 40/50, Loss: 0.0025\n",
      "Epoch 41/50, Loss: 0.0022\n",
      "Epoch 42/50, Loss: 0.0039\n",
      "Epoch 43/50, Loss: 0.0018\n",
      "Epoch 44/50, Loss: 0.0017\n",
      "Epoch 45/50, Loss: 0.0018\n",
      "Epoch 46/50, Loss: 0.0017\n",
      "Epoch 47/50, Loss: 0.0020\n",
      "Epoch 48/50, Loss: 0.0020\n",
      "Epoch 49/50, Loss: 0.0030\n",
      "Epoch 50/50, Loss: 0.0021\n"
     ]
    }
   ],
   "source": [
    "# Micro ViT\n",
    "train_ds = torch.utils.data.TensorDataset(X_train_tensor, y_train_tensor)\n",
    "train_loader = torch.utils.data.DataLoader(train_ds, batch_size=32, shuffle=True)\n",
    "\n",
    "model = MicroViT()\n",
    "crit = nn.CrossEntropyLoss()\n",
    "optimizer = optim.AdamW(model.parameters(), lr = 0.001, weight_decay=1e-4)\n",
    "scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50)\n",
    "epochs = 50\n",
    "\n",
    "for ep in range(epochs):\n",
    "    model.train()\n",
    "    running_loss = 0.0\n",
    "    for inputs, labels in train_loader:\n",
    "        optimizer.zero_grad()\n",
    "        pred = model(inputs)\n",
    "        loss = crit(pred, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        running_loss += loss.item()\n",
    "    scheduler.step()\n",
    "    \n",
    "    print(f\"Epoch {ep+1}/{epochs}, Loss: {running_loss/len(train_loader):.4f}\")\n",
    "\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    outputs = model(X_test_tensor)\n",
    "\n",
    "    _, predicted = torch.max(outputs, 1)\n",
    "\n",
    "    correct = (predicted == y_test_tensor).sum().item()\n",
    "    total = len(y_test_tensor)\n",
    "\n",
    "    acc = correct / total\n",
    "    results[\"MicroViT\"] = acc\n",
    "\n",
    "models[\"MicroViT\"] = model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "60671a49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algorithm                           | Accuracy  \n",
      "--------------------------------------------------\n",
      "CNN(Advanced)                       | 0.9889\n",
      "CNN(Attention)                      | 0.9861\n",
      "Ensenble (Hard Voting)              | 0.9833\n",
      "MicroViT                            | 0.9833\n",
      "CNN(Basic)                          | 0.9806\n",
      "KNN (k=5)                           | 0.9778\n",
      "SVM-rbf                             | 0.9778\n",
      "MLP (200)                           | 0.9778\n",
      "KNN (k=3)                           | 0.9750\n",
      "KNN (k=7)                           | 0.9750\n",
      "MLP (256)                           | 0.9750\n",
      "SVM-linear                          | 0.9722\n",
      "Random Forest (50)                  | 0.9722\n",
      "MLP (100)                           | 0.9722\n",
      "Logistic Regression                 | 0.9667\n",
      "Random Forest (100)                 | 0.9667\n",
      "FDA                                 | 0.9583\n",
      "SVM-sigmoid                         | 0.9528\n",
      "Decision Tree                       | 0.8556\n",
      "Gaussian NB                         | 0.7750\n"
     ]
    },
    {
     "data": {
      "image/png": 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DdP/9999ybH/n7e2tpk2bqlSpUvLy8lLVqlUlSe+9954GDBigkydPaunSpZo+fbrr+3DlyhVt3rzZ7f6ZMmXSmDFjlCtXLknXv69NmzbVxx9/LEn66quv9NRTTyW6f3R0tLy8vKw8CxMAAK+UHgAAAMA/lS9fXkFBQa7NMTJlypSoCCNJp06dUs6cOROdz5UrlyIiIvTXX3/JGCN/f3+36/98TWRkpBYvXqxt27a5zh07duymxbS7dbv3KlOmzG31d+rUKVWuXDnR+YTPIEHRokWVNWvWuxy1u0WLFmnChAmqV6+eGjVqpHr16unw4cNubRKKgzcSHh7uKpD8U1RUlCTpww8/dPu+h4aGqmLFikm+Jlu2bPL399evv/6aqJiVwBhz05+Z7du333TMtyOpQld0dLS8vb0VFxent956S+vXr9fzzz+vJ598UosXL07U/na+95kzZ9b333+vyZMnq2XLlq5dltu1a3fLXPzdrb5PN1KgQAH9+eefic7HxsZq8+bN8vHx0bx58yRJERER2rp1qy5cuKDs2bNLuvn3/3au34mkPs+ffvpJffr0UfHixdW0aVO1adPG9ey7v/76S7Gxsbe8f5cuXdSqVSuNHTtWCxYs0PTp05N8H/fdd5+rqA8AQHKiqAcAAFIdx3HUrFkzLV26VI7jqGnTpkm2y5Mnj86dO5fo/Llz55QnTx7lypVLjuPo/PnzboW9f87myps3r9q1a6cnn3wyWd9HUpL7Xrf6DJLbjh071LlzZ/3000+u4tnatWvvuB9/f/9EBaYEAQEB8vT0VJ8+fVSsWLHb6s9xHNWsWVMLFy5U3bp1E10/f/68Pv74Y+uf186dO92Of/vtN127dk3lypXTlClTtGrVKn333XfKnDmza9x349KlS/Lz89OIESM0ZMgQLV68WJ06dVJgYOC/8jNRrVo1ffbZZ4nOb9u2TQ0aNNCHH37odt+8efNq7dq1at68uaSbf/9v5/q9uHLliurWrav//ve/atasmSS5bUCS8GdFREREksXRBHXq1JGfn5+mT58uX1/fRP94IEkHDhy44eYtAADcK5bfAgCAVKlZs2Zau3atFi9erEaNGiXZpkqVKvrzzz919OhRt/PfffedatWqJV9fX1WsWDHRDKx/7mz7xBNP6LvvvkvU/z934E0OyX2vWrVquZZUJrh06ZJ2796daPlqcti4caOqV6/uNhsuPj7+jvupVauWli1b5nbOGKP3339fmTNnVrVq1e74cxowYIA+/fTTRLsbx8fHq3fv3mrYsKFq1qypnTt3ui15lf7vZ+Zeffnll1q9erWk67vsvvrqq+rfv7+yZcvmmqGXUNBLGNvdmD17tuvz8/LyUqtWrdS2bVtt3br1lrlIDo0aNdKOHTtcu88mWLFihVq2bOl2LmfOnGrQoIHbEtybff9vdD06Oto1Gy5hOevfdxY+dOjQbY197969unTpkp577jnXub9/H7Jnz67y5csnuv/Zs2f1+eefu449PDzUqVMnvfnmm3rppZeSvNeaNWtchUwAAJIbRT0AAJAqXLt2TdHR0a7jKlWqKCAgQNmyZVOWLFlcbSS52vn5+alnz54aOnSojDGSrs+MWbNmjXr06CFJGjRokN5++21duXJFknTx4kW98847kqS4uDhJ14tBH330kQ4cOOC6/9atW/XBBx+4tfv7M+GSknCPhP9Nyu3eK6liT0xMjGJiYlzvVZJeeOEFhYWFaf369a5zY8eOVaNGjVS8eHFXnzcqHsXHx7tdS/jvf55LOC5UqJB+++031zLT06dPa9myZYqLi9OpU6eS7DOpfvv166ddu3Zp/Pjxio+PV2xsrN566y35+PhIkkaMGKFRo0bpxIkTrj6WLFmiJUuWJPk+pOuzx4YNG6YGDRpo0qRJOnjwoH744Qc1bdpUhQoVUqlSpfTQQw+pfv36GjdunOt169ev1++//64XXnjhtj4D6XoBKqn3OXToUO3cuVNdu3ZVp06d1KJFCw0bNsz12f3yyy+utrt27dKuXbsUGxvrmpV2q+9Vws9HkyZN1LNnT4WGhrrGExYWpkqVKt1WLpJ6j0m9zxvJlSuXWrRoof/973+uc3Fxcfruu+8SPeNRklq1aqWVK1e6inC3+v4PGzZMc+fO1bx582SM0dWrV/XKK6+4nqmXL18+5cmTx1V4O3/+vAYMGCDp//6cuNHnWbBgQV27dk3Hjh2TdD1Xn3zyidvP8Ntvv60xY8a4luRGRkaqc+fOKl++vFtfHTp0UOHChZMslh4/flxHjhxR48aNb/l5AgBwV1Jqhw4AAIAEO3bsMNWqVTOO45jmzZu7drR85ZVXzBdffGGMMWb37t2mWrVqRpIpU6aM+fLLL40xxsTExJjg4GDzwgsvmF69eplmzZqZn376ya3/zz//3Dz99NOme/fupl69euadd94xkkzTpk1dO5Fu27bN1KpVyzz11FPm+eefN2+88YaJiYkx+/fvN82bNzeSzOOPP+7affSfVq1aZUqWLGkkmXLlyplvv/32hu/3RvfaunWradCggZFkmjdvbkJCQlyvWbp0qXn44Ydd1/6+M2toaKhp3bq16d69u2nfvr3p06ePuXTpkjHGmM8++8yUKlXKeHp6mj59+phz5865Xjd+/HiTJUsWU6pUKfPdd9+Z8PBwU79+fSPJtGnTxsTGxpqFCxea/Pnzm/z585u5c+ea6Oho0717d1O5cmXTsGFDU6NGDTNq1CiTPXt28+6775rNmzebChUqGG9vbzNixAhjjDFHjx41TZo0MZJM+/btTVRUlDHm+k60jRo1Mvnz5zdFixY1w4cPd+0SbIwxK1euNNWqVTNNmjQxzZs3N2PHjnW7fiOffvqpKVeunPH29jbFixc37733ntvrLl68aHr37m3at29vunfvblq3bm1+//13Y4wxp06dMk8//bSRZFq0aGGuXr1qvvzySxMUFGRy585t5syZY4y5vjurJPPEE0+4vhe1atUy77///g3H9ccff5h69eqZxx9/3Dz++OPm2WefNd27dzcBAQFm69atZujQoSZHjhwmf/785p133nF7XZcuXYwkU61aNbN69WpjjDEvvPCCadWqlXnxxRfN888/b4YNG+Z6nzfLRVRUlGnfvr2RZBo2bOjaUXbcuHHGx8fHVKhQwXzzzTe3/JzDw8NNtWrVTGxsrDl06JBp2LChyZo1q3nttddMdHS0q92OHTtM06ZNjSRTu3Zt167Mt/r+//DDD6ZGjRqmQIECpnjx4mbatGlu91+/fr0pWbKkqVKlinn22WfN4cOHjSRTqFAhc/78efPaa68Zb29v8+CDDybaNfm///2v+c9//mOeeuop88gjj5gJEyaYvHnzmlatWrnarF692pQvX94ULFjQlCpVyixZsiTRZ/DLL7+YkSNHJvn59O7d2yxbtuyWnyMAAHfLMeZv/9QLAAAA4K5Ur15dzZs3V58+fVJ6KP+aBQsW6NKlS+rUqVNKD+VfY4zRpEmT1LBhQ/33v/9V//79VbBgQbc2oaGhGj9+vP773/+m0CgBABkBy28BAACAezRkyBD9+OOPmjp1qr766quUHs6/plWrVrp48aJrWXBGEB8fr/nz5+uJJ55Q6dKlExX0pOubx0ycODEFRgcAyEiYqQcAAAAAAACkMczUAwAAAAAAANIYinoAAAAAAABAGkNRDwAAAAAAAEhjKOoBAAAAAAAAaQxFPQAAAAAAACCN8UrpAQCSFB8fr5MnTyp79uxyHCelhwMAAAAAAJAsjDG6cOGC7rvvPnl4JN/8Oop6SBVOnjypoKCglB4GAAAAAACAFcePH1fBggWTrT+KekgVsmfPLkkKCwtTrly5Ung0QPoTGxurkJAQlS9fXl5e/NEPJDcyBthFxgC7yBhg19mzZ1W0aFFX7SO5kFakCglLbv38/OTn55fCowHSn9jYWGXNmlV+fn78RQ2wgIwBdpExwC4yBtgVGxsrScn+uDHSilSlXtmJMnE+KT0MIN3xyuSoedfC6t1inWJjTEoPB0h3yBhgFxkD7CJjSAt2/TkspYeQ6jjGGBKLFBcVFSV/f3/9J/dAeXpkTunhAOmSVyaHv6QBFpExwC4yBthFxpDapeWiXmRkpHLkyKHIyMhkXZ2YfFtuAMmBjW8BOxwpSzYvMgbYQsYAu8gYYBcZA9IkinpIVby8+C0C2ODl5ajRCwXIGGAJGQPsImOAXWQMsCsuLs5KvxT1AAAAAAAAgDSGoh4AAAAAAACQxlDUs+jSpUspPYR/VXR0tGJiYlJ6GABuICaaBx8DNpExwC4yBthFxoC0J0MW9U6dOmX9HitXrtTOnTut3yc1iYmJ0dixY3UvGyqz2xJgR2yM0ZIPj5IxwBIyBthFxgC7yBhgl5eXl5V+M0RRb968eXr55Zc1aNAgVa9eXQULFtTRo0fVoUMHeXt768CBA27tg4ODFRAQoGnTpunChQvq16+fHMfRunXr3NpNmzZNAQEBCg4OdjsfGhqqNWvWqFatWm7nL1++rPz58+vKlSs3He+3336rkiVLasGCBffwrv8d48ePV+bMmSVJWbNmVYUKFTR9+vS77s/huayAFY4j5QvyJWOAJWQMsIuMAXaRMcCue5n8dDPpvqg3d+5cbdy4Uf/97381evRofffdd3r11Vd1+PBhDRs2TI888og6derkthPJ8OHD9fTTT6tHjx7Knj27JkyYoEceeUS9evXSxYsXXe169Oihp59+WsOHD3e7Z79+/dSnT59EY1mzZo1iYmK0du3am465Zs2aqlKlyr298X/JG2+84XbcqFEjLV26VBEREXfVnye7LQFWeHo5qt04LxkDLCFjgF1kDLCLjAF2sfvtXYiJidGQIUM0duxYOf//nxwcx9Ho0aOVOXNmOY6jJ598UsWKFdO7777r9lrnb/9E4TiOSpYsqRYtWmjQoEE3bCdJhw8fVlRUlIoVK5ZoPKtXr9bw4cP1v//9L7neYqrjOI6ef/55zZgxI6WHAgAAAAAAkG7ZWdSbSuzevVv58+dXYGCg23kfHx/VqFFDv//+uyRp0qRJqlChgho3bpxkMS7B4MGDVa5cOW3ZskXVq1dPss3q1auTvHb16lVFR0erRYsWGjx4sK5cuSJfX1/X9eXLl+urr75S0aJFdebMGYWHh0uSjh49qj59+mj37t1677331KRJE82ZM0djxozRoEGD1Lx5c/Xu3VulS5eWp6enfvjhB82cOVNZsmTR22+/rWnTpmn8+PEKCwvT+fPndfr0aX344YfKlCmTJGnWrFk6dOiQcuXKpV27dumDDz5Qnjx59O2332r69OkqU6aMDh48qHHjxqlAgQKKjo7W0KFD5eXlpSxZsri9hwQ1atRQx44dNXTo0Bt+lteuXdO1a9dcx1FRUTdsCwAAAAAAAHfpuqh3/PhxPfDAA7dslydPHo0cOVKdO3fWxo0b5eGR9ARGX19fTZs2TZ07d1ZISEiSBa0jR46oXLlyic6vW7dOtWvXVr58+VSqVCmtXr1azz33nCTp4MGDeuutt7Rt2zZ5eXnp1KlTKlasmDp06KDChQtrxowZKlWqlOrXry9Jql+/vsLDw/XSSy9p9erVWr9+vWbOnCnp+vP8Fi1apHbt2mnAgAEaMmSILly4oMGDB0uS6tSpo2+//VZ16tTRl19+qW+//VaffvqpJKlt27ZavXq1GjZsqJdeekm7du1SYGCg9u7dq3bt2mn9+vUKDg5Wnjx51K9fP0nXnyv4T0FBQTpy5MhNP/MxY8YkehahJFlaZg5keMZIkWdjyBhgCRkD7CJjgF1kDLDrn6s8k0u6Xn57J2uWO3ToIEmu4tiN1KlTR1WqVEmyICVJ0dHRrllwf7ds2TI99dRTkqQmTZpo0aJFrmuLFi1Sy5YtXbuh5M2bV/Xq1XNdz5MnjypXrqzVq1dLkhYsWKAXXnhBklS3bl1t3rxZcXFxOnTokI4dO+aa5efp6Slvb2917drV1VfBggV1+vRpSdLUqVPVrVs317VPPvlEbdu21fLly1WzZk3XDMeHH35Yv/32m6KiojRv3jy1a9fO9ZoXX3wx0Xv18vJSdHT0DT9DSRo4cKAiIyNdX8ePH5ckxcXyWwSwIS7WaPX8P8gYYAkZA+wiY4BdZAywy9PT00q/6XqmXlBQkA4ePJjktdjYWLdjx3E0ffp01ahRQ40aNbppvxMnTlSZMmXUvHnzRNcKFCjgKqoliI6OVkhIiGbNmiVJOnPmjFasWKHLly8rS5YsOnHiRKKdcvPmzet23KZNG82bN09NmzZVeHi4goKCJF1fxjphwgR5enqqRo0ayp8/f6L39c/nAyb4/fffVbBgQddxwg/ZqVOndPr0adcutsYYVahQQVeuXNGff/7ptpzZz89PWbJkcbtnRESEChQokNRH5+Lj4yMfH59E5510XWYGUo7jIRV9KJvCfrkoE5/SowHSHzIG2EXGALvIGGBXfLydYKXrEkqlSpUUHh6uPXv2uJ03xmjcuHGJ2pcoUUK9evVSt27dbrrdcGBgoMaNG6eOHTsmmpFWu3Zt7d692+3cxo0b1a1bNw0YMEADBgzQxIkT9dBDD7lm3hUoUEB//vmn22sSZtMlaNq0qTZu3Khvv/1WFStWdJ0fNWqUcufOrcmTJ+u5555TQEDAjT+Qf8ibN6/ruYIJLl68qCJFiqhw4cJ6+eWX9fLLL6t79+5asmSJ8ubNq7x58+rMmTOu9hcuXNCVK1fc+ti9e3eiIuXt8vRktyXABk9PR488EUjGAEvIGGAXGQPsImOAXRT17oK3t7cmT56sli1bKiQkRNL1ItSrr76qRx99VMaYRMW7AQMGKCwsTMeOHXOdS6pd27ZtFRgYqG3btrmdr169uo4eParIyEjXueXLl+v5559P9PqFCxdKkp5//nn973//c80ePHPmjNauXet2z2zZsql+/frq16+fGjdu7Dr/xx9/qHjx4q7jsLAw1+uSGvffz7Vr107BwcGuDSsOHz6s999/X88884y2bt2qiIgI1+uWLFmic+fOqXXr1pozZ47r/IIFCxL9cC5YsMBtWS8AAAAAAACSV7ou6klSy5YtNXbsWHXp0kVFixZV7dq1VadOHT344IMaPHiwFi1apK+++srV3sfHx+25elFRUerZs6fWr1+vuXPnus4nLNf95xJSDw8PjRw5Uh988IGk60t1v/jiC9esPEk6e/asjhw5oqVLl+r9999XqVKlNGDAAL366qsaP368Xn/9dVWsWFGff/65QkNDXa978cUX9dhjj7nds2/fvvrkk0/02muvqV27dvLy8tLcuXO1f/9+jRgxQtHR0RozZowk6ZtvvtF3332n2bNn6+jRo+rcubOeeuop1a5dW40bN9a4cePUu3dv+fn5ac6cOerVq5f69eunIUOGyMvLSzlz5tSIESN08uRJjRgxQsHBwQoJCVGmTJlcMx9/+eUX+fn56T//+U9yfPsAAAAAAACQBMfcbJ0p7trYsWPVunVrFS5cOKWH8q8xxmjAgAEaOnSosmXLdkevjYqKkr+/vyrkHyTFJ37WHoB74+nlqEbDPPpudQQPQAYsIGOAXWQMsIuMIS3Y9eewlB7CXTt37pxy5cqlyMhI+fn5JVu/FPUsiY+P188//6zSpUun9FD+NefOndOlS5fcNt+4XQlFvf/kHihPj8wWRgcAAAAAANKqtFzUS6h5JHdRL90vv00pHh4eGaqgJ0k5c+a8q4Le33nwEwlY4eEhlamcg4wBlpAxwC4yBthFxgC72CgDGYIHuy0BVnh4OirzSA4yBlhCxgC7yBhgFxkD7KKoBwAAAAAAAEASRT0AAAAAAAAgzWGjDKQKCQ+NPHfunHLkyJHSwwHSnfj4eIWFhalo0aLy4GEpQLIjY4BdZAywi4wBdp0/f145c+Zk91ukT7Z2ggEAAAAAAEhJ7H6LDMHWwyOBjC4+Pl6//fYbGQMsIWOAXWQMsIuMAXaxUQYyBH6JAHbEx8fr9OnTZAywhIwBdpExwC4yBthFUQ8AAAAAAACAJIp6AAAAAAAAQJpDUQ+pCjstAXZ4eHioYMGCZAywhIwBdpExwC4yBthlK1teVnoF7lLTB9+Tl0fmlB4GAAAAAACwaP2fb6b0EP41top6lOGRqnh6OSk9BCBd8vRyVOWZADIGWELGALvIGGAXGQPsiouLs9IvRT2kKg6/QwArHEfKXSgzGQMsIWOAXWQMsIuMAXYZY6z0S1EPAAAAAAAASGMo6gEAAAAAAABpDEU9pCpxcXampAIZXVyc0U8bz5ExwBIyBthFxgC7yBhgFxtlpHGXLl1K6SH8K+71fZr4ZBoIADcmXjr+82UyBlhCxgC7yBhgFxkD7KKoZ8mpU6es32PlypXauXOn9fukBsuXL9dPP/10169ntyXADk8vR7Va5yFjgCVkDLCLjAF2kTHALna/TSbz5s3Tyy+/rEGDBql69eoqWLCgjh49qg4dOsjb21sHDhxwax8cHKyAgABNmzZNFy5cUL9+/eQ4jtatW+fWbtq0aQoICFBwcLDb+dDQUK1Zs0a1atVyO3/58mXlz59fV65cSTTG+Ph4vfLKK27njh8/rrFjx97LW3fTs2fPRMf/vOfdaNmypcaNG6cLFy7c1evZbQmww3Gk7LkykTHAEjIG2EXGALvIGGCXrd1vvaz0mkrNnTtXmzZt0ocffijHcWSM0euvv67Dhw9r2LBh+vXXX9WpUydt3bpVnp6ekqThw4crNDRUPXr0kCRNmDBBW7ZsUa9evbRr1y5ly5ZNktSjRw9t375dw4cPd7tnv379NH78+ERjWbNmjWJiYrR27Vo1bdrU7drx48cVExPjdi40NDS5PoYk+2vXrp2cZPgT3MPDQ23bttWoUaM0bty4e+4PAAAAAAAAiWWYmXoxMTEaMmSIxo4d6ypeOY6j0aNHK3PmzHIcR08++aSKFSumd9991+21fy92OY6jkiVLqkWLFho0aNAN20nS4cOHFRUVpWLFiiUaz+rVqzV8+HD973//czt/6dIlTZ482e3c+fPnNXXq1Dt/00mIjY3VwoULFRYW5nb+kUceUeXKlZPlHvXq1dPKlSszzHMEAQAAAAAA/m0ZZqbe7t27lT9/fgUGBrqd9/HxUY0aNfT7779LkiZNmqQKFSqocePGSRbjEgwePFjlypXTli1bVL169STbrF69OslrV69eVXR0tFq0aKHBgwfrypUr8vX1lSStW7dO586dU2hoqKZPn65HH31U+/bt05UrV/TDDz9o+vTpqlu3ru6//34FBwfr7NmzypIli3x8fDRixAgdOXJEw4cPV+bMmRUUFKSsWbNqx44datasmVq1aqXdu3fryJEjOnfunKZPn65ChQqpZMmSGjp0qHLkyOEqHi5fvlxr1qxRkSJFdPjwYb3xxht66KGH9PHHH2vYsGEaPXq0wsLCZIzR/v379cknn8jPz0/S9dl6ZcuW1datW1WvXr0kP5tr167p2rVrruOoqChJUlyskSdTvoFkFxdrtH35GcXFsqMZYAMZA+wiY4BdZAywK2E1aHLLMEW948eP64EHHrhluzx58mjkyJHq3LmzNm7ceMMdSnx9fTVt2jR17txZISEhrqLc3x05ckTlypVLdH7dunWqXbu28uXLp1KlSmn16tV67rnnJElNmzbV+fPn5evrq5dfflmSVK5cOf3222/KnDmz69z06dP122+/6bPPPpMk9e3bV3PnztWLL76oOnXqaODAgdq1a5eCgoJ0/PhxVatWTa1atdIjjzyiPHny6LPPPnP1JUlPPPGEfvzxR0nSL7/8ohEjRmj79u3KlCmTjh49qoYNG2rv3r3q1KmTRowYoV9++UWjR4+WJHXq1EmLFi1Sp06dXP0FBQXpyJEjNyzqjRkzJtHzByXJGEkU9YBkZ4x0+vi1WzcEcFfIGGAXGQPsImOAXcnxuLOkZJjlt3ey00iHDh0kSTNnzrxpuzp16qhKlSpJFqckKTo6Wpk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4V69e2Z4XgBvD7eCvfMAkMgaYRcYAs8gYkP9w+e0NsmbNGk2bNk0BAQEqU6aM6tatq0aNGik0NFSSNHjwYEVFRSk8PFyjRo3S8uXLtXTpUu3Zs0eRkZE6e/asduzYoe+//15r167V1KlTtX37dk2cOFGBgYGXbI7xd+Hh4erdu7cefPBBWZalH3/8UatXr9avv/6q8PBwtW3bVs8//7yCgoI0cuRI9erVSxUrVlRMTIwmTZokSXrkkUe0efNmvf3228rIyFD9+vX1yCOPSJKOHTumyMhIRUREmHkjAQAAAAAAIMu+mqVeKBAWL16ss2fP6oknnsjxsYcPH65u3bqpfPnyV/W65ORkuVwubRr4gvx9qTUDOc1tOXTwpvqq9NdWObgBMpDjyBhgFhkDzCJjua9G+NTcngIMOn78uEqXLq2kpCTP7cdyAutrC6FWrVqpWrVqOT5uenq62rdvf9UFvQvRbQkww7LdKh/zGxkDDCFjgFlkDDCLjAFmmep+S1GvkLrllltyfEwvLy/VqlUrx8cFkDOc6Wm5PQWgQCNjgFlkDDCLjAH5D0U95Ck23ZYAI+z/u6SCjAFmkDHALDIGmEXGALMubEyak0gsAAAAAAAAkM9Q1AMAAAAAAADyGYp6AAAAAAAAQD5j2bZt5/YkgOTkZLlcLiUmJsrlcuX2dIACx7ZtZWRkyOl0yrKs3J4OUOCQMcAsMgaYRcYAs5KSklSyZEklJSXJ398/x8ZlpR4AFBJpaXQ0A0wiY4BZZAwwi4wB+Q9FPeQppjrCAIVdRkaGtm/fTsYAQ8gYYBYZA8wiY4BZdL8FAAAAAAAAIImiHgAAAAAAAJDvUNQDgELC6XTm9hSAAo2MAWaRMcAsMgbkP3S/RZ5wvvvt/0Z2UIkiPrk9HQAAAAAAPKq++VluTwH52PmaB91vUaBRYQbMsCWd8ilJxgBDyBhgFhkDzCJjgFmm1tNR1EOeYlss+QZMsC2n4krVJmOAIWQMMIuMAWaRMcAsut8CAAAAAAAAkERRDwAAAAAAAMh3KOohj+EuDoAZtnzST4uMAaaQMcAsMgaYRcYAkyzLMjKul5FRgWvksN25PQWgQHLYblU8ti23pwEUWGQMMIuMAWaRMcAsp9PM/Srz5Eq9devWqWnTpqpSpYrmzp2bo2PXq1dPX3311RX3W716tebMmeN5HBUVpQoVKmj//v1Xdbw//vhDTz31lAIDAzVmzBhNmjRJQ4YM0TPPPKM//vjjaqd/Q6WkpCg4OFi//PLLDTumLTPVa6Cws2XphF8ZMgYYQsYAs8gYYBYZA8xyu80sYMqTK/Xuvfdede3aVevXr1f79u1zdOyhQ4fqnnvuueJ+UVFR8vX19TyuWLGiIiIiVKFChas6XkhIiF5++WUlJyerb9++nu3r1q1Tt27dtGbNmqsa70YqWrSoIiIiVLdu3Rt2TNvKk3VmIN+zLYeO+t+sYmeOybLNdF4CCjMyBphFxgCzyBhglqmiXp6toFiWZeSa43/84x9XLMwdOXJE06ZNy7TN19dXXbp0kY+Pz1Uf82Lnctttt2nHjh1XPdaNZFmWOnXqJJfLldtTAQAAAAAAwAXy5Eq97Pjoo4+0a9culSlTRvv27dOQIUNUvnx5nT17Vm+99Zb8/f1VvHhxbd++XXFxcXK5XHrrrbfUt29fvfbaa+rQoYPi4uI0aNAg1apVS0eOHJEkRURE6JtvvpHb7dZPP/2kxMREtW3bVkuWLFFYWJg2btyocuXKybZtjRkzRikpKfLy8lJkZKQ++OADFS1aNFvz/+yzz1SjRg3P42+++UbfffedatasqX379mnChAlyuVxKTk7WG2+8oRo1aig1NVX79+/Xb7/9pqZNm6pu3boaMmSI3n//fS1fvlxffvmltm7dqvj4eL377ru69dZbtWvXLg0cOFB16tS56PmOHj36ktsXLlyovn37avr06WrYsKHS0tI0aNAgzzmeOXNG7777ro4ePaphw4bp4MGDatSokZxOp3bu3Kk77rhDvXv3zsE/dQAAAAAAAEj5tKi3fPlyzZs3T4sWLZJlWVq/fr06dOiglStXasaMGTp48KC++eYbSdKUKVP022+/afDgwSpTpoxuvvlmzziTJk1SixYt9NRTTyk9PV2hoaFKS0vTSy+9pPXr16t58+bq0KGDJOm5557TwIEDM7329OnTGjZsmCSpadOm2rhxo+6///6Lznn//v3617/+pY0bN2rXrl3y8/PT9OnTJUk7d+7UwIEDtXXrVhUpUkTz58/XW2+9pY8++khjx45VqVKlFBYWJknq27evQkJC1KtXL5UqVUpDhgzRsmXLNHHiRPXu3Vsul0sNGzbU4sWLFRISori4OLVo0UJbtmy56PmeOXPmktsfe+wxjR492nMOI0aMUOnSpdWvXz9J5wp/w4cP19ChQ9W+fXu1bt1aw4YNU/369XXmzBmVK1dOPXv2lJ+fX5b3IzU1VampqZ7HycnJ//cd3ZYAM2z5pSWKjAGmkDHALDIGmEXGAJNMdb/Ns5ffXs7MmTPVtWtXz5vSsGFDxcXF6cCBA0pLS1OdOnU8+9aoUUPR0dEqU6aMJMnh+P+nXKVKFU2fPl179+6Vl5eXpk+fftmOJBe+dtKkSerZs6fn8c8//3zJgt75Y/Xu3VstWrTQk08+qV9++UW33XabJGnOnDlq3769ihQpIklq1aqVFixYIEkXPZ/Y2FiVKlVK0rkOKufnUblyZa1evVoVK1ZUSEiIJKls2bIqVaqUdu3adcnzvdz7cOE5f/HFF+ratavncZcuXfTFF1945tG4cWPVr19fklSkSBG5XC4lJSVd9P2IiIiQy+XyfAUHB587Ht1vASMctlvlEn4nY4AhZAwwi4wBZpExwKxC1f32SqKjo7PcF69ChQo6cOCAOnfurP/97386deqUJGn+/Pl6/PHHLzpOt27d9OKLL6pHjx5q2LChVq5cKW9v72zN4cCBAypXrpzncXb/gDp16qRFixYpMjLSsy0uLk5RUVH64IMP9MEHH2jatGlq0KCBJOm1117TTz/9pPT0dLndbi1cuFCPPvpopjFLliyZaawzZ854xvrggw88BbNLnW9234fY2FhPcVQ6VzCMiYnxPL6wAChdvhI9YMAAJSUleb4OHDggie63gCm2LCUUq0TGAEPIGGAWGQPMImOAWYWq++3lfPfddwoODtahQ4cybY+NjVWlSpXUr18/FSlSROPHj1fx4sV1zz33qGPHjhcda9OmTWrXrp3atWunP/74Q82aNdNtt92mypUrX3EegYGBOnjwoKdgJkknT55U8eLFL/s6p9OpYcOGqVevXvrxxx9lWZaqVKkih8Ohl156ybPfK6+8Ikl66aWXVKFCBY0ePVre3t7q0aOHWrVqdcnxq1SpooCAgExjnf9+48aNFz3fuLi4bL0P5cqV05EjRzyFvbi4uKvuBnyer69vpu7C59H9FjDDthxKLB4s16lYOpoBBpAxwCwyBphFxgCzCl33W9u2ZduZr+fftm2bfvrpJ3Xq1EmfffaZ5/lff/1VZcqUUeXKldWuXTutXbtWq1at0rp16/TVV19p8uTJnn0vHHfOnDlat26dJCkkJES33nqrUlJSJJ1bZZaWlqaMjAxt3Lgxy2u7dOmiQYMGKSPj3F94a9eu1ZdffnnRc3G73ZnOpUWLFrJtWzNnzpQkPfvss5o7d65ndaEkffrppzp79qyeffZZzZ8/X7/88os2btyoadOmadasWZd8nxo2bKj4+Hj98ccfnm0rV67Un3/+ecnzvdz7cOH4zz33nD799FPPuDNmzNBzzz13yT+vi20DAAAAAADA9cuTK/XWrFmjadOmKTo6WkOGDJFlWTpw4IC+/PJLhYWF6aGHHlJUVJT69OmjsmXLau/evZo9e7YkqV+/fpoxY4YefvhhSec6tLZq1Uoul0slSpTQsmXLFBMTo7p166pdu3YaPny46tevL9u21aRJE9WuXVuS1LJlS0VERGjdunXq27evRo0apZiYGA0dOlQjR47U0KFDNXjwYDVp0kSBgYGqUaOGxowZk+Vctm/frpEjR2rjxo2aPHmyXn31VVmWpbFjx+qBBx7Q7t27NXz4cI0aNUrPP/+8qlSpIm9vbz3yyCNyu90KDw/XsmXLVLduXUlSYmKi7r33XlWqVEnLly/XwYMH9c9//lPDhw9X2bJl5XQ6NXfuXA0cOFBly5ZViRIldPPNN+v++++/5Pleavv06dO1bds2TZw4URUqVNDAgQM1aNAgDRkyRJJ06tQpDR8+XPHx8Zo8ebI2b96s+fPn64knntD06dMVGxur4cOHa9KkSVkuzQUAAAAAAMC1s+wCtpSqbNmy2rx5sypVquTZ9sorr6h+/frq3r17Ls7s6iUnJ6tKlSqKiYnJ1EH2scce05tvvukpXBYEycnJcrlc2joyVK4i2buvIYDsc8vScf9qCkiOkoOuZkCOI2OAWWQMMIuMXVnVNz/L7SkgH0tMTFSpUqWUlJQkf3//HBs3T67Uux5ffvmlwsPDVb9+fRUvXlzx8fEKCAhQly5dcntqV83f31/Tpk3TG2+8odtuu01FihRRTEyMHnroITVr1iy3p2cEHyCAGQ7ZCkzem9vTAAosMgaYRcYAs8gYYJapqxcL3Eo95E+s1APM4revgFlkDDCLjAFmkbErY6UeroeplXrc6Ax5C91vATMsh074lSFjgClkDDCLjAFmkTHAqELX/RYAAAAAAADAxVHUAwAAAAAAAPIZinrIUyzbzJJUoLCzbLdKnjxAxgBDyBhgFhkDzCJjgFmmGmUUuO63yN8sbsoKGGHJVqmUg7k9DaDAImOAWWQMMIuMAWZR1EOhEPzSFJUqVSq3pwEUOBkZGdqzZ49q1qwpp9OZ29MBChwyBphFxgCzyBhgVkZGhpFxufwWeYpts1IPMMG2bSUlJZExwBAyBphFxgCzyBhglqlsUdQDAAAAAAAA8hmKegAAAAAAAEA+Q1EPeYqpm0cChZ3D4VC1atXIGGAIGQPMImOAWWQMMItGGSgU+BABzHA4HCpTpkxuTwMosMgYYBYZA8wiY4BZFPVQKER/2lUuP7otATnNLYcOFr9TlU5ulkPu3J4OUOCQMcAsMgaYRcauLPjFr3J7CsjH6H6LQsLK7QkABZSlNGdRkTHAFDIGmEXGALPIGGAS3W8BAAAAAAAASKKoBwAAAAAAAOQ7FPWQp1jcvwEwwpJbFVK2kzHAEDIGmEXGALPIGGCW02mmdwCNMpCnWDJznTlQ2FmyVTQ9IbenARRYZAwwi4wBZpExwCzLMnO/SlbqFVIpKSlGbtSYkpJyXa93i863gAluORXl35iMAYaQMcAsMgaYRcYAs9LT042MS1GvEEpNTdXo0aONFPU++eQTHTx4MMfHBXD93Bb/SANMImOAWWQMMIuMAfnPDS3qbdy4Uc2aNVO1atU0YcIETZo0SWFhYerUqZMOHz6c48cbO3asihQpkuPjXigmJkYvvviifHx8NGjQIE2YMEFvv/22KlWqpAYNGhg99oXcbrdee+21bO3bv39/dezYUQ7HuT9+27Y1Y8YMjRo1KtN+kZGR6tSpk4YNG6bQ0FBFRkZ6nlu4cKG6deum8PBwde/eXadOnZIkdevWTe+8844yMjJy6MwAAAAAAADwdzf0nnoNGjRQp06dtH79evXp08ezffbs2Xr77bf1xRdf5Ojx3nnnHYWHh+fomH9XsWJFhYWFadWqVRo+fLhn+4ABA9SyZUujx77QgQMHdPbs2Svut3nzZiUkJCgkJESS9Ouvv2rZsmX6/PPP1blzZ89+tm3r+eef1/Tp0xUSEqIdO3aoS5cuWrt2rY4ePaqwsDBt3LhRPj4+mjx5soYPH66RI0fKz89P999/v6ZNm6ZevXoZO18AAAAAAIDC7IZffmtZVpYbBN52223asWPHjZ5KFm73tXX6udg5lS5dWuHh4TdkxVpKSoomTpyYrX0nTJigF154wfP47rvv1oABA/T0009n2m/Pnj06ffq0p/h36623Kjk5WZGRkZo3b54efvhh+fj4SJI6dOigGTNmeC7n7dixoyZPnnxNl/daYoUfYIKlDFU+sZGMAYaQMcAsMgaYRcYAswps99vzl37WqFFDknT69Gm98cYbqlu3rpxOp9avX69p06apaNGiGjFihKZMmaKxY8dq3759SkxMVHx8vD788EN5e3srLS1N4eHh8vLyUtGiReXn55fpWLt379bYsWMVEhKi/fv369FHH9Xjjz+utWvXql+/fqpRo4aCgoJ06NAhlS9fXuXLl1dSUpI2btyoF154Qe3bt8/WOf3zn//UsGHD9MQTT1z2uCtWrFBYWJhCQ0N17Ngxffrpp/rggw/UsGFDvfnmm6pcubJiYmL02GOP6R//+IcyMjLUt29fBQUFKS0tTVu2bNG3336rn376SQkJCYqKivK8/vbbb88yL7fbraVLl+qTTz654jns2LFDderUybStVq1a2rJli3bs2KH69et7tgcGBio1NVXx8fEqU6aMSpQooRIlSujPP/9UzZo1Lzp+amqqUlNTPY+Tk5Oz89YCuA5e7tQr7wTgmpExwCwyBphFxoD8J1eKetu3b9e//vUvrVy5Un/++acqVqyoadOmSZJWrFihpUuXeh5HRUXp66+/VpcuXdS/f38NGjRIJ06c0MCBAyVJzZo106pVq9SsWTMNHTpUZcqU0VtvvSVJmjJliueY6enpatu2rRYvXqybbrpJZ8+e1T333KOaNWvqvvvuU9WqVXX69GmNHj1aGRkZCgwM1JgxY9SnTx/t2LFDoaGhly3qHTt2TKNGjdLx48e1cePGbB33gQce0M0336yZM2dq5cqVeuWVV1SsWDG98MILat68ubp166aMjAzdf//9qlevnvbt26czZ86of//+kqShQ4dqw4YNevLJJ5WYmCg/Pz+99NJLl5xjQkKCfHx8PCvsLufkyZMqWrRopm0BAQE6derUZZ87Lzg4WJGRkZcs6kVERGjo0KFZttt0WwKMsOVUlKuxqiWt4TewgAFkDDCLjAFmkTHALFNXceZK99t69eqpd+/euv322/XOO+9oyZIluummmyRJzZs314oVK5SRkaHdu3crOjpasbGxks4tV/Tx8VGPHj08Y1WqVEnx8fGSpFmzZqlLly6e5zp16uT5ft26dapSpYrnON7e3nr66af15ZdfesZu06aNLMuSl5eXypYtq4cffthzjKNHj172nEqXLq3+/ftrzJgxuuOOO67quM8//7x8fX0VFBQky7K0YMECPfvss57nH374YS1atEgVKlTQsmXLtGbNGtm2rUGDBqlWrVrZft/T0tLk7e2d7f2vh5eXl9LS0i75/IABA5SUlOT5OnDgwA2ZFwAAAAAAQEGQq5ffvvXWW2rUqJFat26tgIAASecuyxw3bpycTqeaNGmi8uXLZ3rN3+9fd+H3hw8fVmBgoOexv7+/Z0VZdHS0KlSokGmsChUqaM2aNRcdy7IsT3fYv98v70pCQ0M932fnuCVLlvR8f/z4cXl5eemzzz7zbEtMTFTNmjVVp04dzZw5U+PHj9eLL76o7t27q3fv3tmeV2BgoBISEuR2uz3ndinFixfPtPLu/NyKFi162efOO3LkiCpWrHjJ8X19feXr65vtuQMAAAAAAOD/y9WiXvHixfXKK6+oX79++vDDDyVJw4cPV1BQkKdr7fbt27M9XtmyZXX06FFPYe/EiRM6ffq0pHOXgx46dCjT/ocOHVKlSpVy4lQyueuuuzzfX+1xg4KC5Ovrq+7du8vLK/Mfz+bNm1W/fn3NmjVLiYmJateuncqVK6eOHTtma17e3t6qX7++/vjjD9WuXfuy+956662KiIjItG337t2qX7++EhIStGvXLs/2o0ePysfHR0FBQZLOLSvdv3//Re/rBwAAAAAAgOt3wy+/dbvdmbqiduvWTWvXrtWqVaskSTExMZ6mGZK0b98+z/62bWfpqHrhttDQUM2YMcPz3Jw5czwdbRs1aqS//vpL0dHRks7d6+7rr79Whw4dcvycLnSl4/79nHx8fNS2bVt98cUXnm2HDx/WDz/8oOjoaE2YMEHSudV9LVq00IkTJySdW014/nLXDRs2XHKuvXr10uzZs7Nsz8jIyHSNd82aNVWkSBHt2bNHkrRz506VKFFC1atXV9u2bbV06VLP8ebMmaPOnTt7VjT+9NNPevLJJ6/pUl/u3wCYYSmDe6QABpExwCwyBphFxgCzCkT321WrVmnKlCk6fPiwZs+erdDQUHl5eSkiIkLPPPOM+vfvrz59+qhfv37asGGD51LUmTNnqnXr1vr666+VlpamiIgIDRgwQCtXrtTq1asVFxenRo0aaciQIRo4cKCGDBkiy7IUFxcnb29vjR49Wv369dPcuXM1dOhQ1apVS1FRURo8eLBq1qypDRs2aO3atUpISFDz5s21evVqHTx4UJMnT9a4ceM0efJkHTt2TDNmzFDnzp0znVN0dLT69u2ryMhIhYWFaeTIkZnfYC+vSx539uzZWrZsmaKjo1WzZk3deeedkqTx48erX79+Wr9+vQIDA+Xn56e3335bbrdbLVu21JEjR1SsWDEdOXJEY8aMkSTde++9ioiIUI8ePdSuXbtL/hm0a9dOs2bNUlJSklwul3bu3Kn//ve/mjFjhpxOp4oUKaLWrVurZs2a+uyzzzRkyBDVrFlTv//+u2bMmCHLshQUFKSRI0eqV69eqlixomJiYjRp0iRJ54qUH330kT744IOc/NEBkAPSHb7ydp+68o4ArgkZA8wiY4BZZAzIfyz7UkvMUGAdOHBAn3/+ucLCwnJ87Hnz5qlUqVJ66KGHrup1ycnJcrlc2vHvdnL55Ur/FqBAc1/Q0czBb2CBHEfGALPIGGAWGbuy4Be/yu0pIB87fvy4SpcuraSkJPn7++fYuLl6Tz3kjuDgYLVp0yZbDTOuVkhIiG655ZYcHRMAAAAAAACZUdQrpOrWrWtkXAp6AAAAAAAA5nGdIwAUEg6bSykAk8gYYBYZA8wiY0D+w0o95Cnn7t9ArRnIaQ5lqFrymtyeBlBgkTHALDIGmEXGALO8vMyU36ieIE+xZeX2FIACyZalU16lyBhgCBkDzCJjgFlkDDDLVI9aVuohTynf6SMFBATk9jSAAic9PV2bNm3SzXfdZey3REBhRsYAs8gYYBYZA8zKyDBzeTsr9QAAAAAAAIB8hqIeAAAAAAAAkM9Q1EOeYlncwwEwwbIs+fn5kTHAEDIGmEXGALPIGGCWqWxZtqm79QFXITk5WS6XS0lJSfL398/t6QAAAAAAAOQIUzUPVuohT3G73bk9BaBAcrvdOnLkCBkDDCFjgFlkDDCLjAFmmcoWRT3kKXyIAGa43W5FRUWRMcAQMgaYRcYAs8gYYJapbHH5LfKE80tRd370mFxFqTUDOc0tp6J8H1K11OVyyEw7daAwI2OAWWQMMCsvZqxS6PzcngKQY44fP67SpUtz+S0AAAAAAABQ2FHUA4BCoqj7WG5PASjQyBhgFhkDzCJjgDmmut96GRkVuEbnlnpTawZymkMZqnB2S25PAyiwyBhgFhkDzCJjgFlOp9PIuFRPkKfYMlO9Bgo7W5aOO6uRMcAQMgaYRcYAs8gYYBbdb1Eo2PxIAkbYcui4181kDDCEjAFmkTHALDIGmEVRDwAAAAAAAIAk7qlX6G3YsEH9+/dXVFSUXnrpJXl5eemPP/7Q7Nmz1ahRI6Wmpmrv3r16+eWX5e3trYSEBJUrV06vvPKKvLwy//jYtq3q1atr5cqVqlSpUi6dEQAAAAAAQMFHUa+Qu+eee9S5c2etX79eAwYM8Gzv3r273nvvPTVv3lzr16/XwIEDPc9NnTpVzzzzjObOnZupg8vWrVuVlJSkuXPnqnfv3tc4I/saXwfg8mz5Z8SIjAGmkDHALDIGmEXGAJMcDjMXynL5LWRZVpb2yg0bNtTTTz990ed69uyplJQ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cngZQYJExwCxTRT2qJ8hT3DY/koAJbtuhI6m3kzHAEDIGmOV2u7V3715u4g8YQsYAs2iUgUKCH0nADIdOnK0sMgaYQsYAk9xut+Lj4yk4AIaQMcAsinoAAAAAAAAAJFHUAwAAAAAAAPIdinrIUyyx3BswwZJbpXz+IGOAIWQMMMvhcKhSpUp05gQMIWOAWXS/RaFg0f0WMMKy3Arw+SO3pwEUWGQMMOt8wQGAGWQMMIuiXi5Yt26d+vXrp7179+rll1+Wt7e3EhISVK5cOb3yyivy8rr2t++VV16Rw+HQpEmTLrvf448/rgceeEBvv/32NR/rYho3biyHw6HHH39c8+fPV3R0tF599VVt27ZN3377rU6cOHFd53et3Lbzhh8TKAzctlNxZxqobJGNclgZuT0doMAhY4BZGRkZ2rNnj2rWrCmnk38vAjmNjAFmZWSY+fchRb3LuPfee9W1a1etX79eAwcO9GyfOnWqnnnmGc2dO1eWZV3T2F26dMnWa3v37q3g4OBrOsal2LYtb29v/fjjj/L29lZQUJDWr1+vvn37SpJ69eqlP//8U7Vr187R42bPtb2fAK7E0qmMMiJjgClkDDDJtm0lJSXJtu3cngpQIJExwCxT2eI6xyuwLCtL8a1nz55KSUnR8uXLr3ncu+++Ww0aNLjifg8//LBCQkKu+TgXc+rUKY0ZM0be3t6Ssp7j4MGDr7lYCQAAAAAAAPNYqXeN2rZtq8WLF6tZs2ZatWqVPvjgA91yyy3atWuXRo8erYoVK0qSPv74Y+3evVsBAQHavHmz/vOf/yglJUXh4eEqWbKkJk+erIyMDPXt21dBQUFKS0vTli1b9O2332rHjh0KCwtTkyZN1K9fP0nSRx99pF27dqlMmTLat2+fhgwZovLly2vEiBGaMmWKxo4dq3379ikxMVHx8fH68MMPPcW784oVK3bZgmK5cuVk27Z69eqlw4cP69FHH9W8efNUvnx5ffLJJ3r//fe1ZcsWVaxYUcePH9e4cePk4+OjgwcPqm/fvqpdu7b+/PNP9ejRQ40bNzb3hwAAAAAAAFBIUdS7RsHBwVq8eLGOHDmi5557Tps3b1ZgYKC2b9+uLl26aOnSpfr++++1atUqffrpp5Kkzp07a/HixerSpYseeugh/frrr5Kkn3/+WWfOnFH//v0lSUOHDtWGDRvUsGFD3XHHHZ5lmsuXL9e8efO0aNEiWZal9evXq0OHDlq5cqX69++vQYMG6cSJE55Lhc8XHJs1a3bV51e+fHm1b99ebdu21ZtvvqkFCxYoPj5e//3vfzVnzhz9/PPPcjgceu+99zRmzBgNHDhQ7du314gRI9SsWTOdPn1ad999t9auXSt/f/8s46empio1NdXzODk5WZJkKUMS93AAcpqlDAX5bvu/jAHIaWQMMMvhcKhatWp05gQMIWOAWaayRWKv0dmzZ+V0OjV//nw1bdpUgYGBkqR69epp7969Sk5O1uTJk9WzZ0/Paz755BN17txZUuY/0AoVKmjZsmVas2aNbNvWoEGDVKtWrSz7zZw5U127dvVcGtuwYUPFxcXpwIEDcjqd8vHxUY8ePTz7V6pUSfHx8dd8jk6nU7Vr11bTpk3l7e2tChUq6IsvvlDnzp0983riiSf07bffau/evYqJidFDDz0kSfLz89Odd96plStXXnTsiIgIuVwuz9f5+wZaFvdwAEywLFv+3tFkDDCEjAFmORwOlSlThoIDYAgZA8yi+20eExUVpRo1aiguLk7x8fH64IMPJJ27+eEdd9yh06dPa//+/Znagl+qi1CdOnU0c+ZMjR8/Xi+++KK6d++u3r17Z9kvOjpaFSpUyLStQoUKOnDggIKDg7PcGy8n7otXsmTJTI/j4uL0v//9z3O+J0+e1G233aa4uDh5e3tr6tSpnn19fHzk5+d30XEHDBigPn36eB4nJycrODiY7reAIW7bqZjTTVXRbxWdOQEDyBhgVkZGhnbu3KlbbrmFzpyAAWQMMIvut3nMokWLNHjwYO3fv1/R0dF66aWXPM/16tVLklS2bFnt378/U/fakydPqnjx4pnG2rx5s+rXr69Zs2YpMTFR7dq1U7ly5dSxY8dM+wUHB+vQoUOZtsXGxmYqHJpWpUoV3XvvvQoNDc20/eDBg/Lx8cn0PlyOr6+vfH19L/IMDToAMyyluUuIjAGmkDHAJNu2dfr0aTpzAoaQMcAsut/mEtu2s7z58+bNU6lSpdSoUSO1bt1aa9eu1ZEjRzI9n5CQoC5dumjo0KGee8ft2bNHkyZNyjJudHS0JkyYIOncyrgWLVroxIkTWfbr1KmTPvvsM8/jX3/9VWXKlFHlypUvOs+LbcvuOV5qe9euXfXxxx97qsy2bWv69OmqVKmSqlSpohUrVnj2/f3337V27dorHh8AAAAAAABXh5V6l7FmzRpNmzZNsbGxGjVqlBwOh+Lj4xUQEKAvvvhClmXJ399fM2bM0Kuvvqrg4GD5+fmpQYMGKlWqlLp3766TJ0/qgQceUFBQkIKCgjRp0iRt3bpVH3/8sWJiYrRw4UK1bNlSLVu21JEjR1SsWDEdOXJEY8aM0YoVKzRv3jz5+vqqSZMmeuihhxQVFaU+ffqobNmy2rt3r2bPni1JGjJkiNLS0hQREaEBAwZo5cqVWr16teLi4tSoUSPddNNNFz3H+fPna+rUqYqLi9O0adM89+T7/fffNWHCBG3cuFHTp09Xt27dJEmNGjVS165d1bFjR1WpUkUOh8Ozau/zzz9X3759NXfuXJUqVUqBgYF67bXXbsCfFAAAAAAAQOFi2ayvRR6QnJwsl8ulHT80kX9x7uEA5DTbtnQ6I1B+zqPcyB8w4EZkrPL9PxsZF8gPbNtWUlKSXC5Xjtw3GkBmZAwwKykpSSVLllRSUpL8/f1zbFxW6iFPodgAmGFZtop6XXs3bACXR8YAsyzLytLADUDOIWOAWaaK5dxTD3mK26bODJjgtr20L+VRMgYYQsYAs9LT07Vx40alp6fn9lSAAomMAWaZyhZFPQAoJCg2AGaRMcCs843aAJhBxoD8h6IeAAAAAAAAkM9Q1AMAAAAAAADyGYp6yFMscQ8HwARL6Qou+jMZAwwhY4BZTqdT9erVk9PpzO2pAAUSGQPMMpUtbv6CPCW48Q9yuVy5PQ2gwLFtWxkZGXI6ncY6LwGFGRkDzPPx8cntKQAFGhkD8h9W6iFP4easgBkZGRnatGkTGQMMIWOAWWQMMIuMAWaZyhZFPQAAAAAAACCfoagHAAAAAAAA5DMU9QAAAAAAAIB8xrJt287tSQDJyclyuVxKTEykUQZgADfxB8wiY4BZZAwwi4wBZiUlJalkyZJKSkqSv79/jo1L91vkKfu2Py7/EvxY5gfVbv85t6eAq5SWliY/P7/cngZQYJExwCwyBphFxoD8h8tvkafYNgU9wISMjAxt376djmaAIWQMMIuMAWaRMcAsut8CAAAAAAAAkERRDwAAAAAAAMh3KOoBQCHhdDpzewpAgUbGALPIGGAWGQPyH7rfIk843/122+rGKlGc++rlBzTKAAAAAADgys7XPHK6+y0r9ZCn2Dbt0wETbNtWYmKi+D0OYAYZA8wiY4BZZAwwy1S2KOpdo3nz5qlTp04KDw9X165dVapUKb3yyis3fB4pKSkKDg7WL7/8kuNjDx06VF5eXvroo48ybZ8zZ46Cg4PVp08fnThxQlOmTFHJkiXVtm1bjRkzRuHh4Wrbtq22bdt21ce0bZZ8AyZkZGRo9+7ddDQDDCFjgFlkDDCLjAFmmcoW1zleg5kzZ2rWrFn65ptv5OvrK+lcAaxDhw5yu91yOG5crbRo0aKKiIhQ3bp1c3zswYMHa8OGDRoxYoRatWqlihUrSpI6dOigxYsXa/z48bIsSy+//LK+/PJLvfPOO7r33nslSVu2bFGXLl30v//9L8fnBQAAAAAAUNhR1LtKGRkZGjx4sFavXu0p6ElS5cqV9f777+vEiRNyuVw3bD6WZalTp07Gxi9Tpozatm2rXr166fvvv5dlWZ7jnv/+Yo/r1aun/fv3G5sXAAAAAABAYUZR7ypt375d5cuXV/ny5bM8d9ttt3m+HzVqlE6fPq2KFStqzZo16tevn+rWrauff/5Zr7/+uj788EM1bNhQe/fu1Ztvvqn77rtP/fr1U1xcnAYNGqRatWrpyJEjkqTRo0dfcvvChQvVt29fTZ8+3TPesGHD1KhRIx09elQpKSkaOXKkYmNjNWzYMB08eFCNGjWS0+nUzp07dccdd6h3796XPecXXnhBM2bM0OzZs/Xss89m63364osv1LFjx2y+qxewbEncVw/IaZZlyc/PL1PxHUDOIWOAWWQMMIuMAWaZyhZFvasUGRmpatWqXXaflJQUDRgwQH/99ZcqV66scuXKady4cfrkk0/04IMPqlSpUp59b775Zt1+++2emyZOmjRJLVq00FNPPaX09HSFhobqzJkzl9z+2GOPafTo0Z7xpk2bJqfTqZ49e0qS7rjjDvXs2VM33XST2rdvr9atW2vYsGGqX7++zpw5o3Llyqlnz57y8/O75Pk4HA5NnTpVDz/8sJo3b66goKCL7jdnzhytW7dOkZGR+uuvv/T5559fcszU1FSlpqZ6HicnJ587lpUhfiyBnOd0OjP94gFAziJjgFlkDDCLjAFmOZ1m+gfQKOMqZWRkZLln3rZt2/TSSy/p0Ucf1YwZM1SsWDFFRUUpODhYBw8e1M6dOxUbG+vZ/++vv/BxlSpVNH36dO3du1deXl6aPn26nE7nJbf//fVhYWEaM2aMTp8+rc2bN+vEiROeYzudTjVu3Fj169eXJBUpUsTTUvlKateure7du+u111675D4dOnTQm2++qf/85z8aP368WrZsqWPHjl1034iICLlcLs9XcHCwJLrfAqa43W4dOXJEbrc7t6cCFEhkDDCLjAFmkTHALFPZoqh3lW6++eYs94q7/fbb9eyzzyoxMVG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      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"{'Algorithm':<35} | {'Accuracy':<10}\")\n",
    "print(\"-\" * 50)\n",
    "\n",
    "results = OrderedDict(\n",
    "    sorted(results.items(), key=lambda item: item[1], reverse=True)\n",
    ")\n",
    "\n",
    "# --- 5. 결과 시각화 ---\n",
    "for model, acc in results.items():\n",
    "    print(f\"{model:<35} | {acc:.4f}\")\n",
    "\n",
    "\n",
    "plt.figure(figsize=(14, 8))\n",
    "sns.barplot(x=list(results.values()), y=list(results.keys()), hue=list(results.keys()), palette=\"plasma\", legend=False)\n",
    "plt.title(\"Model Performance Comparison (Accuracy)\")\n",
    "plt.xlabel(\"Accuracy\")\n",
    "plt.xlim(0.75, 1.0) # 정확도 차이를 잘 보기 위해 범위 조정\n",
    "plt.grid(axis='x', linestyle='--', alpha=0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02290a5a",
   "metadata": {},
   "source": [
    "## Error Analysis\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "19c824f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = models[\"CNN(Attention)\"]\n",
    "model.eval()\n",
    "\n",
    "with torch.no_grad():\n",
    "    outputs = model(X_test_tensor)\n",
    "    _, predicted = torch.max(outputs, 1)\n",
    "    \n",
    "    # 정답과 예측이 다른 인덱스 추출\n",
    "    wrong_indices = (predicted != y_test_tensor).nonzero(as_tuple=True)[0].numpy()\n",
    "    \n",
    "    # 실제 라벨과 예측 라벨 가져오기\n",
    "    wrong_labels = y_test_tensor[wrong_indices].numpy()\n",
    "    wrong_preds = predicted[wrong_indices].numpy()\n",
    "    wrong_images = X_test_tensor[wrong_indices].numpy()\n",
    "\n",
    "# 시각화 (최대 10개만)\n",
    "plt.figure(figsize=(12, 6))\n",
    "num_show = min(10, len(wrong_indices))\n",
    "\n",
    "for i in range(num_show):\n",
    "    ax = plt.subplot(2, 5, i + 1)\n",
    "    # 이미지 형태 복원 (1, 8, 8) -> (8, 8)\n",
    "    img = wrong_images[i].reshape(8, 8) \n",
    "    ax.imshow(img, cmap='gray_r')\n",
    "    ax.set_title(f\"True: {wrong_labels[i]}\\nPred: {wrong_preds[i]}\", color='red')\n",
    "    ax.axis('off')\n",
    "\n",
    "plt.suptitle(\"Misclassified Samples Analysis\", fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "2025-02-ai-hw2",
   "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": "3.12.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}