3d_teeth_seg.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "66a943d9",
   "metadata": {},
   "source": [
    "# Imports and Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "96a21392",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import layers\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import numpy as np\n",
    "import trimesh\n",
    "import json\n",
    "import os\n",
    "import os\n",
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c73dbe0",
   "metadata": {},
   "source": [
    "# Data Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bab84c45",
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_point_cloud(points):\n",
    "    centroid = np.mean(points, axis=0)\n",
    "    points -= centroid\n",
    "    max_distance = np.max(np.linalg.norm(points, axis=1))\n",
    "    points /= max_distance\n",
    "    return points\n",
    "\n",
    "def process_file(obj_file, json_file, output_dir):\n",
    "    # Load the point cloud\n",
    "    mesh = trimesh.load(obj_file)\n",
    "    points = mesh.vertices\n",
    "    \n",
    "    # Normalize the point cloud\n",
    "    points = normalize_point_cloud(points)\n",
    "    \n",
    "    # Save the point cloud\n",
    "    np.save(os.path.join(output_dir, 'point_cloud.npy'), points)\n",
    "    \n",
    "    # Load JSON data\n",
    "    with open(json_file, 'r') as f:\n",
    "        data = json.load(f)\n",
    "    \n",
    "    labels = np.array(data['labels'])\n",
    "    instances = np.array(data['instances'])\n",
    "    \n",
    "    # Save labels and instances\n",
    "    np.save(os.path.join(output_dir, 'labels.npy'), labels)\n",
    "    np.save(os.path.join(output_dir, 'instances.npy'), instances)\n",
    "\n",
    "def process_directory(base_dir):\n",
    "    for jaw in ['upper', 'lower']:\n",
    "        for person_dir in os.listdir(os.path.join(base_dir, jaw)):\n",
    "            person_path = os.path.join(base_dir, jaw, person_dir)\n",
    "            obj_file = os.path.join(person_path, f\"{person_dir}_{jaw}.obj\")\n",
    "            json_file = os.path.join(person_path, f\"{person_dir}_{jaw}.json\")\n",
    "            output_dir = os.path.join('processed_data', jaw, person_dir)\n",
    "            os.makedirs(output_dir, exist_ok=True)\n",
    "            process_file(obj_file, json_file, output_dir)\n",
    "\n",
    "#if __name__ == \"__main__\":\n",
    " #   base_dir = '3Dteeths_segmentation_data_part_1'\n",
    "  #  process_directory(base_dir)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e9638744",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LBhLZbHYk2AtOFggkAABoTlzcVJ6W+N6M1+OhVI4jLV/u9YxYuFBatUr6z/+U9twzlCGitQSDBim/ooPjOEqlUqO2I5AAWgcBBNAkKLkEoF6CgYQxZuT1yA8j0um0JAIJAACaDf3k2tDAgLdSIZmc3AqITMb7SKWk1aulpUull1+WvvpV6fjjwx0zWo6/0ioa9U5LlhJI+OdAmGMAzYcSTEAToBZr5TKZjIwxisfj9R4K0JIKAwm/4ZxEIAEAQKMKnuzzV1bzd7o0xhil0+nmXY0+OCiddJL06KNeGaahoXD2G4tJ3d3ShhtKTz4p9fSEs1+0pcJAorBkU7AkLK9dQONjBQTQwAprsTbtm9w6sixL5KxA9fhv+oMrJIIlm1ghAQBAY6HkUpvr6pI+9SnpjTe8VRBh8cs6rVgh/epX0llnhbdvtJ2JVkgMDQdn/nbBkk0AGg8rIIAGFZwYUHKpcv73kBUQQH0UBhLFVkiwsgsAgNoI9pOj0XRlmnIFRH+/9N3vSg88IM2bJ33iE17/hpNPDvc4nZ3eyoejj5auvFKKcs0rqiMYSPhzfskrHVvYQwJA/RFAAA2IWqzhIYAAGstEgYT/mkcgAQBAeOgnFx4/gGiaeZrjeI2h//lP7+toVJo6VZo+XXrmmfCPN22at++zzpL++78lTgCjBvwLOP3XOQIJoLEQRwMNpLAWazweZ2IAoKUEG8lJowOJdDotY8xIWScCCQAAJscYM9IXjVWHbejss6Vnn/XKI0leADEwIL31VvjHsiwpkfDKPF15pZTNSl/+cvjHAQr4JatjsZgkjbrgKZPJSCKQAOqFFRBAg6AWa3WwAgJoLsFAwr+KScoPLggkAAAojf/3lJJL4WmqFRCvvSYdeKC0eHHtjhmJSBts4K2CmDVL+uMfpblza3d8oIjCQKLYCglWhgHVwwoIoAH4JZckNVctUQAIWTBoiMfjI4GEH0akUqlR2xFIAACQj35y0HPPSd/7nrRyZW2P67rScINgxWLSK68QQKDubNuWbdslr5DwA0ZeN4FwEEAAdUQtVgAYX7GSTcHJAoEEAAD56CcHPf+89OMfSy+/7JVBqrXBQS+ImD9fmj279scHJjBeIJHNZkcCieD8gkACqBwBBFAn1GIFgPL5JSSiUe8tTCmBhB/u8hoLAGhlwVWD9JNrQamUZIzU2Tn+dsmk9NOfeisPliypzdiCjPEaX69f7/WC2Gqr2o8BKFMwkDDGjMwx/DAinU5LIpAAKkUAAdRBsBYrJZcAoHKlBBKpVGokkAgup2bCAABoFfSTa2GplPTb30r33Se9+aZ3cn+LLaTDDpOOP16aMiW37YoV0ne+Iy1c6G3rr0SoNdv2yi+tWiX94hfSWWfVfgxAhfx5AoEEEB4CCKCGqMUKANU1USAxNFyT2N8uWLIJAIBmRD+5FpZMeoHCs89Kr74qrV4t9fVJL7wgPfGEdP/90oUXSvPmeWHDRRdJd9whvf22F1zUI3yQvLJP6bS0dq309NPeeDbZpD5jASapWCARLNlEIAFMjAACqJHCWqyUXAKA6hsvkAjWd/UbzhFIAACaBf3k6qNm3+MnnpCuvFK65x4pkfBKGvkrGgYGvA9jpOuvlz79aenb35buvNMru1TP8EHyjr1+vTeW6dO9RtgEEGgRxXrUEUgA47OMMabegwBaWWEtVj98QG34K07i8Xi9hwKgAQWXUzuOI3d4sk4gAQBoZMF+cpzYqr1UKhV+g++hIenvf5def12aO1e68UZvJcGDD0rr1nkn8R3HCx2MyZU5mj3b67Pw7LNSf780fHFF3cVi3vPYYAPprru8IAJoA4WBhOM48k+9BkvCclEq2gkrIIAqohZrYyBnBTCWwhUShZMFVkgAABoN/eRa0IsvSpde6q16cF3JsryQYdddvRP4q1Z54UNwVYPreisdli2Tli6t74qHYjIZbwXElCleOSagTUy0QiKdTssYM1LWiUAC7YAVEECVBEsuMTGoH/+q5o6OjnoPBUATKgwkiq2QoOQFAKAWKLnUOEJdAeE40qmneqsE0mlvZUNnp1duaepUb4XDG294JY0cZ/LHqyXblubMkU47Tbr4Ym9VBNDmgoGEf75Cyg8uCCTQalgBAYSscGIQi8X4o1FHfO8BTIZt2yOv5dLEKyT8kxG89gAAwkQ/uRa1dKl3Yv6vf/V6OkhSJOKVUvJ7PSxfnru92QIIvxfEK69ITz0l7bFHvUcE1F0waIjH43llux3HUSqVGrUdgQSaHQEEECJqsQJAaysnkPDDCP4WAAAqVdhPjpXVjSOUYhJXXOH1fMhkvH4PxuSXKzLGCx4sq/nCB18qJT3wAAEEMIZiJZuC8wsCCbQCAgggJNRiBYD2M1Ygkc1mlclklB4+iRCcLBBIAABKQT+5FrdypfTOO7nwQfIChyDL8lYR+I2nm5Hres/15z+XDjpI2nrreo8IaGiFPepKCST8knz8jUCjIoAAJolarAAAXzCQMMaM/I0gkAAAlKOw5FIovQYQmlD+bnd0SImEV54omy3eRNpxcg2pm/m9gjHSP/8pnX++9Pvf13s0QFMpJZBIpVIjgUSwJCxzDDQKAghgEqjFCgAYi/+mvzCQ8Ou7EkgAAArRT66NTJ3q9Xro7x91l7EsWf6KB//fYBDRbKshLMsrxfT4417QQrUAoGITBRJDQ0N52wVLNgH1QgABVIBarM3HGMPkDUBd+YFEPB6XlPtbEuwfQSABAO2LkkttZnBQikalri6pv19OZ69WfvgrGtzlYJmObik1oI7F/9SMm3+s7pcf807iN2P4IOXG7fe5ABCa8QIJfxW25K3ULuwhAdQKAQRQJiYGAIAwFGs4N14g4V/BxGo7AGg99JNrQx0dUiwmTZumxJxttOKsH0vxrtz9nd1KTZutZdvvLWvJ69r4B59QbNU79RvvZEUi0g47eP8CqJpgINHR0ZFXEtafY0gEEqgtyxjiZ6BU1GJtPv4f2ng8zgk7AE2lMJBwHGdkNZffc4hAAgCaG/3kmlM6nR75/zUpf/iD1v3ubq05/r8ka4KTf05WM376Oc34+18md8x6iESkWbOk++6Ttt223qMB2lqwJKzjOHKH+88QSKCaCCCAEjAxaF4EEABaRTCQ8Ffi+YFE4WSB1zsAaHz0k2teEwUQxhgl305qcNmgot1R9WzSo/i0+KjtBpYNaNltSySV/v898tYLmvf9MxRft6LS4VdXYZkoy5LmzJEOOEC65BJp663rNzYAoxRe8FQskOAcGCaLAAKYACWXmhsBBIBWFexH5E8YJBFIAECDK+wn54cPaB7FAojB5YNa/8/1GnhrQMoWeVBUmrXHLE3bZpokyck6evN3b3on68s+K2OkxHrNuO5bmn7fdWXEF1Xk96hw3dzn0ai38mHBAmmXXaR3v1v65CfrPVIA45gokPBXYTO/QDkIIIBxuK6rTCbDxKCJEUAAaBfB1XoEEgDQmLi4qTX4AUQkEtHa59Zq3T/WlRwiTNttmmbtOkuLb1+s1LLU5JsyZzPqvfM32uDab9Q+iPB/dmMxr8xSJCKlUt5t0ah3e2+v1N0tHXKItN120mc/W+tRApiE8QIJP4zgbxkmQhNqoAhKLgEAmk2w4Zw0OpBIDZ8QCAYS/t83/sYBQPXRT651uFlX659br/4X+8t+7Pqn1suKWF74EIZoTImjPq6hbd6jTb7+QVm1vMY0GpVsW5o5U4rHpb4+KZ32VkEMDXlhhDHevy+8IJ14Yu3GBiAUtm3Ltm3FYjFJ+YFEJpNROp2WpLwLnggkUIgAAihALVYAQCsoJZBIpVIjgURwOTV/9wAgPMHXX8uyWJnb5FzH1bLblsntdyvex7q/rwtvQMOyW+yiJf/xM230o0+Fvu+iIhFvhUNnpxc6DAx4IYRtS9lsrhfE0JC3CsK2vW0BNLVgIGGMGfkbl81mCSQwJkowAcOoxdqa/DJaTPQAIN94JZuCy6n5WwgAlaPkUmtxs67euuktucnKw4dRQj4l0337lZr7+0tC3eeI4HsCy/K+njbNCxySSe9r/+fbdb2ve3ulefOkvfeW3vMe6d//vTpjA1B3fiAR7FHnn3YmkGhvrIAAlD8xoOQSAKAdjLdCwr+CSco1nCOQAIDyBPvJxWIxXj9bwNK7l4YbPlTBwJGnK3XX1epY+Wb4O/cbTEu5fwcGvJUNs2dLa9d6qyF8kYh3XyYjOY40fXr4YwLQMPyV1PF4XFLuQl9KNoEAAm2PWqwAAOQHEh0dHXnLqf0Jg0QgAQAToZ9ca1r51EqllobUt8FXlYIUlhZ/5Q/a4j/fW4V9KzdmY7wVDrGYV2ZJ8lZCGJMrv+S6Xl+I2bOlnh7pwAOrMyYADSnYe04qLZCgFHprIoBA26IWKwAAYyu2QiK4nJpAAgBGM8Yok8nQT67FrPvnOvU/XX7D6XFVsxr2tFladcJnNPumH1Zn//7PdCbjhQyO4zWa9ssuucOrRFxXSiSkNWuko46S5sypzngANIVSAwnLskYCfH9+wd/S5kYPCLQlarG2D3pAAEB1BCcLjuPIHT7ZEAwkuOoXQDvxV43RT661pBNpvXP9O+HvuNqnYrJpLTjrXYpkQl61IXkhgx80+H/nh3tJjax+sCyvBFNXl7ThhtLuu0vf+5600UbhjwdASwgGEv45O2NMXnBBINGcCCDQdvySS5KYGLQBP4Cg7i4AVNdEgYR/BROTBQCthn5yrcsYozd++4bkhL7jkHdYXM9dv9aGV180uZ34zaYLAwZfPO59DA15gYS/+sEPIHp6vO27u6VTTpG+9a38ZtYAMAY/kAiuwpZEINGECCDQNqjF2p4IIACgPsYLJPwwgkACQLOjn1xrW/v8Wq19Ym34O67VaZjBhDb79Ltkh3G8YPAwXJ5x1PPwV0QEV0b421iW1NEhnXii9Otf5+4HgBIFz+sRSDQXAgi0BWqxti8CCABoDMFAwj9ZJylvskAgAaBZBK/KtCxLsViM168W9PpvX5eyVdhxDU/DzPz1hZq+8OrKd+D/XAd/vuNxr+G0lL8ywl/1EIl4KyIKn2c06q2EuPFG6ZBDKh8TAKi0QMK/+Ji/0fVFE2q0vGAtVk5CAwBQH7Zty7ZtxWIxGWNGJgzZbHak4ZxEIAGg8dFPrj0MrR6qTvig2l4DuubEz2naPb/V4NZ7aGD3w2WMo9iaZep98I+KDiRK35FfWknyGk5LXhDhBw9++SXL8sKJYNkmKRdgZLPSTTcRQACYNL/nUnR4VVZhIJFKpZRKpUYCiWBJWP5u1xYBBFoWtVgBAGhM/pv+wkDCr+9KIAGgUQX7yXFxU2tb8+Saeg8hHN1T9cZPnpQ6e4ZDAEsyrtac+N/qefQWbfCbC2SP16i62GoN/7Z0OrfqIdiM2hjvtmAAYYz3tWVJs2aF9vQAwDdRIDE0NDSyXTCQ4G959RFAoCUV1mKl5BIAAI3LDyTi8bikXGkTf7JAIAGg3ugn115cx9XQkqF6DyM8XVPyv7ZsKd6p5H4fVGbWfG303dNklVsWKrjywQ8colFvhYNl5cKG4H4tS5o6VfrgByf/nABgAuMFEtlsdiSQsG17VA8JhIsAAi2lWC1WXjgAAGguwbqtUmmBBBccAKiWYD85ws/2kFhURmmicjVSF07LUnq7vbTuoJM1Y+HvynusMblwwZhc+aVIxGs2PTTklWHyt4tEvJUPhx8u7bRT+M8FACYQDCQ6OjrySsL6cwyJQKIaCCDQMqjFCgBAayo3kAgup+a9AIDJoJ9ce1r39Lrq7LiGzadLZttae8I5mn7vtbJcZ+LtxxIMJIavKh4JJaZMkTbeWNp1V+m44yY/ZgAIQbEVEn4YQSARLgIItIRgySUmBgjixBMAtJ7xAolsNqt0Oi1jTN52BBIAykHJpfbiZl1lB7OKdEVkXKNsf1W6T1fOGvlPEWbyqyp6Zyq59bvV+/JjRY4dOO54AYoxXvkln23nPqJRac0a6cUXpTPOmORgAaA6/IsNYrGYJOVd8DRWIMH7g9JYxjRiBA+UhokBJmKMUTqdJpgCgDZSGEg4w00wCSQAlIJ+cu1jaPWQVj68Upk1meqXRqrk1Mu4wcO4Byv7+dhLX9dmXz5ijDuHSyn5PR0Kn0uw1JKUCy2iUe8jk5F6e6XNNpP23FP6wAekI48sb4AAUGeFgYTrupIIJErBCgg0LWqxAgCAYoJBQzwez7tgwXEcpVKpUdsRSACgn1z7MI7RykdWKvFqFXs95B2wkvBhMn+PLMkqL4RwN9xM6e6pig/0FbnTO8k25vNwXa/HQ3BbyWtE7Tjefa4rrVrllWf661+lQw6Rhq8yBoBmYNu2bNsuaYWEXxKWc5UeAgg0JWqxAgCAUhWr70ogASCIfnLtZem9SzX09lCNjlZm+FDxqociOyonhLBt9e9/ombd/stRtxdd9VDIcbzQxLK8xzhOfhgxOCjNmCEtWyZ1dkqJhPc1ADSp8QKJwh51wY92fH9BAIGmQsklAAAwWeUGEv4VTJZl8b4DaEGFJZf8/jJoPZmBjJbcvkRO3ySaLZernPwh9L8xVlkD6Dv01NEBRCnhQ3Dctj36efj7WLFC2nBDafZsafr0kscFAM0gGEgYY0bmGNlstu0DCQIINA1qsQIAgGqYKJAYGhoa2Y5AAmgdhRc3xWIxfqdblDFGqx9brb6X+6rf6yH/wKVvW62fvTIyCDNrfpEbx3mw3xMiEpE6OqRUKrfqIRbzej/YtnebvyLirbeko46q3vMFgAbgzxMKAwm/P127BRIEEGh41GJFGEwldVcBAG1pvEAim83mBRLB+q68PwGaByWX2odxjZbcsUSpZalaH7n0Tav6s1dGAmHbSk+bo/j6FaXvPhLxwoZYLFeuKZsd2Z9cNxdiRCJe+aV77pH237+cJwEATc0PJOLxuKTcuc52KdlEAIGGxsQAAADUWzCQ6OjoyFtOXdhwrrCHBIDGQz+59rLq8VV1CB9Uev5Qi/mtv1Jh4g3Vv/sRmrXw6tL2a4y3b8fxejr4IURvr7caIpXKHdeyvPBhYEB6803pnXekjTeu+CkBQDML9p6TSgskmrkaDAEEGha1WAEAQCMqtkLCDyMKA4lXXnlFkrTbbrvVa7gAhtFPrv2k1qXU/1J/7Q/cxKuvB/c8SiolgIhEcs2mneGeGq6bK8VkWd42rptrTO26Une3tHZtdZ8EADSZcgIJ27Z1++236+ijj1ZXV1c9h10yAgg0HGqxAgCAZuJfRR2LxSQpb7Jw1VVXKZVK6ac//WmdRwm0N/rJtadldyyrw1EbpfRS4bFU0tAyGy4Y4/HDY41GvYDFDxSGw3i5rnebbecCiN5e79+hodwKiM5Oae5cVj8AwDjGCyT6+vr0wQ9+UMuXLyeAACpBySUAANDsbNseuYhiYGBA06ZNq/eQgLZFP7n2lXgnIWfAqf2BSy69VNVRVMx0Ts31cgiu5PDLLfm3O07ucz+QcByv8bRlecFELCb19eXCi1jMCx/OP78+Tw4AmlQwkPBXW/f29tZ5VKXjnRcahuu6SqfTMsYoFouxJBoAADS9RCLRVJMDoJX4Fzdls1lFIhHChzaz4p4yGimHpazSSw061+3oVGb6hsWfi99g2i+75IcPrut97bredn7ppbVrvduzWa8002abSVdc4YUQAICK9Pf3j/Smaxa8+0Ld+RODTCYzcrUgEwMAANAKksmkenp66j0MoO24rqtMJiPXdUd6tnBxU/sYWD4g1XrxQznhQyP/KFq2BrfeI9fjwe/f4H8e5IcPknef/+H3fYhEvOBhyhSv5NLOO0tLltT+OQFAC/EvcGqmc6eUYEJdUYsVAAC0MgIIoLaC/eQsy1I8Hmd+0YaW3VPj3g9lN52ux89kiU0gLEvZ2fNzqxj8Xg+xWK7Ekv98/fuGH6eODm+1Q7AfRDTqfaxYIT33nLSsHn05AKB1JJNJdXd313sYZWmeqAQtxZ8Y+HXLYrEY/R5QFfxMAQDqiRJMQO34K6sdxxkpucR7wfbjZB1pqJZHLDd8qKMSfx+S274318dByi+xFAxbguGDMV44EYvlVkFks9LAgJRIeI2pV66U7r7bK80EAKhIf39/013gRACBmgvWYqXkEgAAaGXJZJIAAqgB+snBt/qJ1bU9YNmLH+r8c1nC4TNb7OqFB34IEYl44YLjFB+/3wsik/ECB78/hB9CpNPex+abe7fde2/4zwsA2oQ/v2im9zmUYEJNFZZcikQi9R4SkG/5m9ItP5dWL5Y6p0jvOlja7wP1nygAAJqOMYYAAqiyYMkl27YJHqDEPxO1O1jZpZcaQClD7ujyQoR02gsg4vHcqgbH8QIJP3TwyzAF5/bxuBc8GJMLMebMkYaGl6asXFmVpwYA7SCRSFCCCSgm2GhakuLxOOEDGosx0rXfln76n9LbL0oDfdKaxdJdV0vf/qi0vsZXUgEAWgIBBFA9xhhlMhk5jkOjaUiSUutTNayIVMGBmuXH07bl2sPzdb8JdUeH1NvrffjNpqVcWSY/bLAsL6SQpM5OL4zo7PRuTwyHQ1tvXfvnBGB8r7wive990g47SEccId12W71HhDE04/yCAAJVRy1WNIXbr5JefLj4fYMJ6fv/7v0LAECJWAEBVI/jOEqn05LoJ4ecVY+uqt3BKgo6GuBntJQhWLZSczfzgoRUylu5kEp5qxr8JtN+3wd/FYjj5O5Pp3P/xuPe/bGY1N0tbbONtO++1Xp2ACrxj39Iu+0m/eUv0ssve2XSPvxh6Vvfkvr76z06FKAHBFDAdV1lMhlqsaLuzHjLo5cukh65eaI9SD86M8whAQBanOu6GhgYIIAAQuSveqCfHIpJLU/V6EhNWHppRAnzcctSepPtvc+NyQULAwNeEGHb3kcs5pVe8uf4fiDh93/we0J0dHgfZ50lfe5z3uMANIYVK6T99/d+V13X+0inpfXrpZ//XLrkEu9zNIxmvMCJd2qoimDJJcuymBigsV37zdK2G0xIj/6lumMBALSMgYEBGWOaboIANCr/4ibXdRWNRllZjTxO2pHcGh2skvyhkX5US/i9cafNyd82WGpJyu9/ESzJ5Pd8sO1cWabeXmnXXaWnn/aCDACN4eWXpfe+V0omR9/nutKqVdKzz0rXXFP7sWFMyWSSFRBAsBZrJBJh1QPqbtyfv0XPS+uWl76zv/4iV9MUAIBxJIZrXRNAAJPjN5qmnxzGk3itRuVSK2483URzYmPkdvaMfD4SJEi5YEHyTlD6twVXQfjzpWjUW+3gut4JzvXrpWeeqd3zADC2739f2nFHadGisbfp75f++lfpnHO8EPHtt2s2PIwtkUgQQKC9FdZiJXxAw7vl5+U/5tcXhj0KAEAL6u/vVywWU0dHR72HAjQtf2V1NpulnxzG1f92LeqUVxg+NNvPrGVpaPNd8m8Lrn4IhhB+4OC6+dv6pVyiUa8PxJo13n3DQSKAOvrSl7xyaKVcXOn/jr/4onTccdUfGyaUSCQ0ZcqUeg+jLAQQCAW1WNGU1iyVVlWQ4L/5nPTEHeGPBwDQUvyrkzhZClSGfnIoR6avBie2m730UtAE48pssm3xOxzHCxGM8fo6+D0giv1uGiP19Ehr10qvvio9/ri0evXkxw6gci+84DWXLofreuXTXn7ZK8mEuqIEE9pSYS1WJgZoGrf9svLH3nK5NNAX3lgAAC2nGScHQCOgnxwqYZLVbgxd4f4btl/1BHP2Uub0sZjX38EPImzbW/EQiXirHqZM8VY+pNPS9OnSlltKN9/sBREAaq+/X9ptt8oea4zXhP5DH5LOP5/G1HXUjHMM3sWhYoW1WGOxmCKRCOEDKpdJSS89Jt1+lfTIrdL6Kl4dk81K/3xicvv42X+FMxYAQEvq7+9vuskBUG/0k0Ml0n3p6h+kYYOE6jCZCb6nrislEt4JycIm1P6qiEzGCyWmT5c23VSaOdPb5oEHqjp2AEUsWiTNmDH5Mmivvir98IfSYYflSquhphKJRNP1mIvWewBoTv5VSa7rKhKJEDygcsk+6YbvS689Nfq+266Q5m8tfezrUiwe7nFv+MHk99G3SrrvOumAf538vgAALSeZTKq3t5f3SECJHMdRNptl1QPKNrRqqLoHqLjxdPMyvdMn2CDQiNo/oRlsQO2vhrBtb6XEokXS1KleGEEfCKD23v3u0no+TCSblQYHvVJOl18unXfe5PeJsvhzjGbCOzqULVhyiVqsmJQ7fyN957Ti4YNvySvSZZ+W3II/lIMJ6YWHvZUSyxeVd9xsRnrhwXJHW9zdv5OGBsLZFwCgpSQSCXV3d9d7GEDDCzaapp8cKuGkQjiphnydPeMv+rAsL1zIZr1/IxGv34PkfW1ZUmend3si4d2+cqX37x57VHPkAArtsUe4qxWyWa+02t13h7dPlMQY05QBBCsgUDK/5JLjOCMTA4IHVOxPl0v/KLGRc98q6U8/kd5/jvf1X/9Pevwv3tU2khTrkLZ7r/SB/yytVunCaysa8ph+9VXpU98Nd58AgKbXjJMDoNZc11U2m5UxRtFoVLZtM8dA2RKvJKq381Ze/WBp7NJSE/0eGuOdhOzp8VY0dHR4t/vll4zx+kDE49LQ8AqVWEw6+GDpoINCegIAJvSLX0hPTLL8dDGO4/2+o+aacY5BAIGS+LVYjTGUXEJJnlq6Vov6hjQcEWh+d0x7bDRLEduS7ry69PDB9/RC6aiPS1deIK14Pf++TEp69j5pyavS//v+qHJNawczWjKQViwS0fzeTs188IbKn1gxS1+TVi+VZs0Ld78AgKbW39/fdJMDoFaMMSPhAyWXMFmZ/mqV9Gnh8EHS+AmEJDsyeiV6oUzGOwmZSnkll/yVEem0tHy593VXl7TFFtJll0nbbBPqMwAwjnvukT71qers2xhWM9VBs66A4B0eJuQ4jtJprwEVJZcwkVX9Sd3w8lK9HggfJGnJQEZ/emWZ+h+5TXrg+sp2/rP/Gh0+BK1eIn33416JJUnJTEZ/fmWpHlzWpzf6hvTPtUnd89ZK3X70V7R+6tzKxjCWH58T7v4AAE0vmUzShBooIlhyKRKJED5g8tyJN6lIq+cPE8hssOnoG/2AYWSjjBcwRCLeigjX9cII1819+GHEfffVbvBAu3Mc6V/+pbqruIaq3H8Ho7iuq4GBAQIItA5qsaJcr63u031L+sbd5o6pOyrZObWyA6xfMfE2qaT0jY/otWUrdfvrq5RxJcl4f3SNkWQpOW2e7j7iC1o1e4vKxlGMyUq/vSS8/QEAmh4BBDBasJ9cNBrl4iZMWnYgW70AIgyN/uM91vgsW65d5E5jvFDBsnLlltat84IIt+B/hGV5ZZe6u71Q4tZbc/0gAFTX+edL/f3VPcZPfiKtKOE8DUKTTCYliQACrcGfGDiOw8QAJVkzkNLTq5Ljb2SMZEd0+7EXaihevRMy9+z3KT29LpMLHYoE/ibaofsO/ozWhbkS4pUnpNWLw9sfAKCpJRIJAghgmH9xUyaTkWVZisfjilA7GiFILp5gDlKp0K4abtJ5tN/HYSzGeFdY++cJ/J4QhY/JZqX1670VEI88Iv35z9UbMwDPD34gXXpp9Y+TTkuf/3z1j4MRieEQt9nmGAQQyOM3ms5kvBI2sViMfg8oyT1vrxl/g+Ab0ViH/nrMhcpEYqGP4779z9Sa+duXtnEkqnsO/4LSkfjE25bqsrMlJxve/gAATSuRSGjKlCn1HgZQd3744DiOIpEIFzchXHkX3Rt1RIfUE0+qIzYgq9KlEWGFD03xYz72IJ3uabkvbNtbzWDbuTJMtp1bEeGvipBy3z//Pv/roSHpiiukF1+s0nMBoIEB6Utfqt3x/vKX2h0L6u/vVzweV0dHR72HUhYCCIygFisqddeilWU/xnR2647Dvyg3xMnnS1sfpFUb7VTWY9xYh+44+rxwy7t+66PVrbMIAGgKyWRS3d3d9R4GUFd+PzljDP3kUB22ZFtZzehZrc1mL9b8Gas0Z9pazZ++Rgs2WKI5U5eqLs0cLEtNkkCMKTMvULLWdb0VD11d3od/riASyX1I3jbFOI63GuKZZ7xSTACqY++9vZJntTLcMxa1kUgk1N3d3XTvpTi7DEnUYkXlliUGtT41wRX/Y5yMH5o+TwsP/lwo04GBjil64V0frOixqd5ZuveAs0IYxbD0oPTL88LbHwCgKSWTyaarzwqEhX5yqImhlKKvvqZNZi3T9O5BFU5hLUk9HY4WzF6seHSgtH2GcSFRi8yl3cKeDn6JW8vKBRJ+4+liPSBG79Db/p57Jt4WQPm++lUv5KulefNqe7w216zzC94BtjlqsWKyHlq8blKPXz9nCz2018cmPY67D/987iqcCqyZv4Pe2miXSY9jxNsvSEtfD29/AICmQwkmtCv6yaEmkoMyD/xd3SahYr2Sg2xLmj99jWb2rNT4qyFYxRw0tO0e3id+ySXL8q6sTqe9VRB+EFFqmOA4UmenNGWKtGhR1cYNtKVsVvrWt2p7zGhUOuGE2h6zzfX396unp6fp3lcRQLQxarFisp5f0VfCVhO/iV++4N1aOmfbisfx5ka7Kd07q+LHeyz9fa/TlYmGWEfvyvPD2xcAoOkkk8mmaxAHTAb95FAziaR03+NSpvTea5YlTetOaaMZizXmHKVd84cxfkVTW70r94UfQEi51Q+xmHeb/zEevx/EDjtIU6dKTVa/HGh4J58cTjmkcv5mz5snXXjh5I+JkjXrCohovQeA+vCvSrIsi+XQqIgxRi+vTZawYQk7syw9fNBZOvqmc9WVLnFptKSsHdGrWxygF95dWemlQibWob8ec76y8W7JjkiuUXdytXZ4+iZtsuTZ8iu4pgekoQGpk/rfANBujDEaGBhoygkCUAn/4ibXdRWJRAgeUD0Dg9KDT1b88HhUWjBrsd5cPU9ScPV/WI2nm/Hn3lKx529iw/MYY7zAwW86bVleyaWuLu/rbCAIsqyxy1h1dkorV0oHHyxttFH4TwNoV48+Kv3xj+Hsq7CZ/FhsWxoclB55RDrooHCOjQn5PSCaDWed20yw5BK1WDEZb6wrIXwohx3VHUedJ9ea+OcxE+3QPQecrZtP/IFeeM+Job7Jz3bPkKIdkh2VojENTJurJw74f7rxpB/rT/9yiVZNLbO+4Y0/CG1sAIDmkkgkCCDQFugnh5p65mXJnVxYYNvSgllLJQUaJo/apSl24/ha7ce+J1BG0O//0NEhdXd738R02lvVEDynMN5JS8fxHrv33tUbM9COzgqxp6XrltYLx7a98PF3vwvv2JgQKyDQ8FzXVTablTFG0WhUtm0zMUDFnl/RH/o+s93TdO/+Z+rg+34y6j7HiuiNTd+t53Y9QW73tNCPPSHLktM9Xfcdc57kupqx6lXtc9/P1ZEdGv9x//x7bcYHAGgoxpimnSAApfJLLjmOM9JPjvkFqmptv7S2lDKwE7NtabPZS7Vo1XzJeD+3tpXRnKlr1BnPjlzI77hS32CP+genyDUT9UtssZ//aExuvFN2enjOY4zXA8JxpN5eKZHwvpGOU9rV0lOnSjNmSPF49ccOtIuf/ER6svJVYSP8Umql9nTx+78MDk7+2CiZ3wOi2RBAtAFjzEj4QMklhGEw7ShTyoalpOYF1s7fQYs32FYbrXzZO1Z8ih7b63Stnrdt4yxntm2tnbONbv3At7Xf3T/UnFWvjb2tcaXVS6VZZa6cAAA0NcdxNDg4SACBlkXJJdTFMy/mfz3JqkmWJS2YvURvrZyj3s6kZk1N5kUIliVFI9LM3qSmdSe1fmCK1g9MUcsFDdIYVZgsZeZuro63hr/v/iqIbNYLIrq7vTChvz9Xhik7Rl8Oy/Lu6+qSdt21Sk8CaDNr10pf/GI4+7Isr69LJpP7XR9vW0mKRKQjjwzn+ChJs17gxFnoFudPDLLZLCWXEJqX14Rz1dFYHj3403KsiBZttpf++i9f0+r52zVO+BBkR/TAIZ/RYMcEL/5/+XltxgMAaBjJpFeqsBknCMBEXNdVOp2WMUaxWIySS6iNNeukgQlWH1fAtqRNN1gxKnwoFLGl6T39mj1lTehjaAxjPPtMkaa2risNDEjJpHf1s9+MerwTlq7rhRVf+pK3EgLA5J12mvd7GAbLKm01k5QLKLbYQvrIR8I5PkqSTCabcgUEZ6JbWGEt1lgsxsQAoXh9ffhv/PPYEf3pg9/Tk+89xevH0MjsiB7f+6Pjb/Pa07UZCwCgYfT3e6UKCSDQSugnh7p69p9V27Vtlbamwbak3s4h9XQkqjaWxmIUX/b62Hf7fSAGBrzVDY4z9raWJc2dK225ZfjDBNrRjTdKf/5zOPvyzxW6rve5bedKMo11HjEalTbZRHrooXDGgJIkEgkCCDSG4MRAkuLxuCKRiWpVAqUZSpdUfGnyotHGXPVQxKoNt1c2Eht/o/61tRkMAKAhJBIJdXR0KBab4O8D0CSMMcpkMnIch0bTqL3BVFVWP1TCsqSZves16fpPzcCy5PRML36fbedqwE9UrmV4X3rjDemUU6S33gp9qEDbCWvlgW17q5gk7zyMlAsTx1vZ5LrSyy9LDzwQzjhQkkQi0ZQXOBFAtBg/fHAcR5FIhFUPCN0Lq8duPr2BuUuHmX31L2YLHWO217b6pqQSGxg1M8vWW5u8e/xtnri9NmMBADSERCKh7u5u3oehJTiOo3TaK8MSi8Xo94DaW7pi1E31PP0fjUhdsYLGq634K5HJaGjL3YrfFw20FLUsrxb8WGxb6uz0TnK+9Zb0xz+GOkyg7fzgB+E0f/ZXOHR0eL/Ttp37mKismuNIL70UTgNslGxgYIAVEKgvv+QStVhRTUsSqVG3Rc1aHWl20X46VVP0hiIaUofWawddpqO0qyIaqMNIa+u1bQ4af4Pn7q/JOAAAjaFZG8QBQf6qB/rJoe5efbPeIxhles/6gltaYO6d9xSM5GRkDxaUm/JPWDqO11A6HvfCh1hs7BAiEvFObvb2eldNv/ZatZ4B0PocR/rKVyp/vB8uBH9v/aAhk8kPFCda2WSMdPPN0iOPVD4elIUVEKibYMkly7KYGKCq0gULGrrMmzpWO6lbq4pu36U12k/vq8HI6qt/+vzxN1i7sjYDAQA0hP7+fvX09HAxCJoW/eTQMPoSktN4q6o7YoVjaoGSTIVPwbLUuejZ0dtFIt5JzMFB74SlMbkTl8VeJ4yRpk/3wop4XNpuu2qMHmgPX/6y13elUpGIFwZaVm4l08CA18/FcXL9XEr9mz80JP3Hf1Q+HpQlkUhoypQp9R5G2ThL3eSCtVgjkQirHlBVbkH6bZm0Dte+sid4sz1TL2lL/bCaQ6s/O6LBjqlj3+9mpEy6duMBANTVwMBAU16dBBhj5DgO/eTQGAaHpCeeK35fnc/3W5bUEQueBGz2efjob6idScvEOgs2M95JS7+kks91vROXwTmjH0gYI61eLSUS0jbbSCecUJ2nALS6/n6v/NJkZLNeeOhfuBz8G+//vrru+E3lC734orRidKk8hI8STKi5wlqshA+otoybf5XP4dpLkRJ7POyo76rus4QqWzF7y/E3WPxKbQYCAKi7/v5+dXd313sYQFn8ldXZbJZ+cqgvx5VefE265zEp1bgX8czsWVvvIVSV2z1FmZnz8m/0m08b45249IOIYq8VfgARj0vd3dK220qbbSbNmzd6WwATO/FEb6VCJfyVS1IuZPBXPfghxFirmCaSzXohBKrKGNO0ZV4JIJoQtVhRL8mhzMjnO5lz1aNlJT82Ildb60fVGFbDeGuLPcff4PmHajMQAEDdNWt9VrQv+smhYbiu9NjT0qLF42/XAD+eHTFJMpWdsGt4lhSNKztjbv7N/rkHv/SSf5FasHGtlLuS2v+YO1fackupr0/6xz9q9zSAVvHcc9Lf/lb54/2ySn5g6AeJmUz+7/NEfR+K6e2V3my8Xj2tqFnnGJy1bjKFtViZGKCWXlvnLTGOmdXaSr8q+/Hb6ftSiSsmJmNj/V6H6CAdqd21p05Xh5ZX/ZiSlJyy4fgbLHm1JuMAANRfIpFoyuXRaD/0k0NDMUZ65ClpXf/4m438p768i4Wz9R5G9Vi23Blz8m9znFyd+KEhr3Z8KpV/4tIPH6RcKJFISMmkd9va1l45AlTFhz40ucf7v5fZbH55tEoChyDb9lZCrV8/uf1gQqyAQNUV1mKNxWKKRCKED6ipdSnv529vnVLRBUdRZfQufS7cQQXEtVzHaUvtof/SNL2ibi3XRrpDx2h3vVv/r2rH9aXiE5TaWFubIAQAUH/NOjlAe6GfHBqK40j3Py6tT9R7JGXZYOqa8h5gDX80xdkYV/bqJfk3+ScsI5Hc546TO4kZPKHpfx6Pe6VeFg+vatl++9o9BaAV3HdfZSWO/EbTsZgXFPi/k5mMFxq6bv7KpUrE47kVFagqx3E0NDTUlHOMpviT1+6K1WLlqiTUw1DGlW0SmqWnK97HJrpelsKv49qhJTpauyumoaL3b6o/6yjtpLhWh35snxPvHH+DwX7+KANAm0gmk6yAQEOjnxwaStaRFj4qJYu/l29kXfFxVkAU+5Uyw7c3+rTAGGmgX92vFZRL8k9o+vOawiupLcs7oRmJ5D7PZr3SS4sWSUceKW2ySc2fDtDUzj23ssf5AaGU+330f0/9oLDSsku+oSFp6VLpxhu933VUTSLhBfQEEAhdsOQSEwPUW0bSzjpvUvuw5WozXRXKeHL7TOlw7TPhC1qX1upgHazqvduPyIy3NsS40pLXqnRsAEAjoQQTGlXw4ib6yaEh9PVLdzwoZZrzxJVtSdZY84ti5/QaNXwYNVajjsWvyB4MrEjxQ4VsNn/VQ+FriL86wr8qemhIGhyU+vulNWWuGAHa3aOPSo89Vtljg2WXJK9Ukt9wOkyu643z0kvD3zdG9Pd75QkJIBCawlqs8XiciQHqzxgt0PWT2oUlaTv9WBElwxmTpH31fsWUmXhDSd1arQN1tKpSNNa2lI52jL/NWy+Ff1wAQMNp1gZxaG3+xU2O49BPDo1hyXLpweZvSDy1c/yeFZJypZcaoHdFSdJpRRLr8m/zr6QONpcOXmFdyC/x4rreSc9sVvrud71yMgBKc+mlla8siEZzK5KM8UqhhdH3oZAxXtD4+9+Hu1/kSSQS6uzsVDQarfdQysYZ7QZELVY0omTa0Sb6vSIa481lGTq0WlvolyGMSpqhhzS7zJJQM/WcdtBFI1/bQ4Oy0iGUhTLSupkLxt/mufsnfxwAQMMbGBhgBQQaBv3k0JBeel16+uXKHttgJ/GndpUQQDSCcn7lLckaKPK8/BOaRR9j5VY9FDak9kMKx5FuuKGsYQNta2hIuv32yh/vh3+xmPd18PeyGl58UVpdvbLX7S6RSKi7u7sp3781X2TS4hzHUTablWVZLIdGQ+lLpbSLLghlX5akrfQLvaKzVd674NH204cretw2+l8tX3+Ipt/0kqa+/orcSFRrdt1d7xxzvIz/x7lclqX1U+ZowxXjTKTWr6xs3wCApsIKCDQKf2W167qKRCIED2gMDzwh9Q9U9NDqZA/+Xiv73SipoknwEMHDNGQ5Jq9RhWUCF59ZlvdE/aupJSmV8k5w+sHDtGlewDAw4AUV2WzuhGcmk/vob5LABqi3j3zECyEq5ZdfymbzQ4ewV0AE97vddtL//q903HG54AOhSCaTTTu/4Ox2g6AWKxpd37onFVdi4g1L1KHVmqvbJrWPBfqFoiWWXipkSdrr2k9q6uuvSJJsJ6vZTz6m+XdNbkwr5m0//gbJdZPaPwCgOSSTSU2ZMqXew0CbC/aTo+QSGsb9j1ccPlSfUUURhyVZ1jhJQvDXzgT+rXX4UPJTM7KcjOzgCghjco2n/WAhFvOCBj+AGBjwTpaOVeIlm/VCi622muwzAVrfq69KN988uX0Y44WG/kokPzys5vnGVaukb39b+slPqneMNtXf36+enp6mfC/HCogG4LqustmsjDGKRqOybbspf5jQ2jZIfjnU/Xm9IL6vZTq64n28SxeW9wCj3Jv/lVJ8cZ+6tUgD2mxkk5lP/V2Lj3pf+YNxHM149h+a/tyj0kMvecdZuk7KOlJnXJo/XZo/U9pkprToeWmzHcs/BgCgaSSTSUowoW78kkuO44xc3MT8Ag3hhVelxGC9R1HAjEwRKr0m2JI0paNPfUPTxzpE/sbu8L+2ZMUsmYxprJUQjivLyajjrefzbw/WkM9kcic1Rx7neLfZdvGa9em0d/uOzIWACV1ySeW9H6RcMOh/SLlAYjL7LcVLL3mNs197Tdpyy+oeq400c4lXAog6MsaMhA+UXEJDy67TND0U+m6n6wV1aqmGNK+sx3Uvfls7PfIFWf2Stpa0p6TClX0JSY9JenL44zVJQ/LWfU2XtEDSUmkD50ElI4uVmj5D6ZmzpRkzc1f2jPH72Ll0saa8+ooiA0nNX/g3bXj/QnWs8eocjjm1f3Zx7vOrHpfuf1jadNOynjcAoDkYY5p6iTSaGyWX0LBSGenNJfUexbgm0yO6tys5dgAh5epP+BdFDX9YxpJppKYWruuNK7FeHa/8Pf8+/wpqy/ICiLHKuLhjpCmW5a2AWLQo1CEDLWftWulPf5r8fvz+K5YldXTkSjEFA4lqSCSkv/9deuQRAogQ+SsgmhEBRJ0wMUBT6b9BdgjNpwtZMtpRl+jv+nHJj+lZ9Lq2/tUvNN+9z7vhjeGPU+S9mX9G0i8lPSAvcCj23nfZ8Ie89/29WqTetxZJkoxladM/3yAnFpOJRSXHyE4Nys5m88KFSf22vrVEWrDAq5H62GPSNttMZm8AgAaUSCSadoKA5kU/OTS0x56q9wiKMGO8rw8unS5N1J7gRJ4rb8VDNLdfY4yM4zeFVf2baw+fjLT716jrn4/LLgwSgucsIpHRzaYD+xhz/5mM9MorIQ4aaEEXXOCFEJMR/F00xgv/xro/bLYtrV8vvfBC9Y7Rhpq5xxwBRB0UllyKlNSxCqijNd+p2q7n6a9lbb/hA/doQ/ev+dOBByTdIekFSUlN6o27ZYysbEZ2NiNVe3X4+vXStttKBx0kLVxY5YMBAGrFXwFBDwjUSmHJJXo9oOEMDoVXeqneJ+nH4JVZz8qYcU6zWJIdt+WmXJms90QaY/WDyfvccrKKJNcpsnZZwWYmv5mtbed6QxiTH1CMJRqV+vpCGznQcl58MZz+CcWaTheWTasG25bica9HzGRDFORp5gucuCSmhvxVD5lMRpZlKR6PEz6g8ZmMlHm1aruPaUgL9KuSt5+29ll1a6X3RULSo5Lul1duKaGGnZCM6557pFmzvKWRAICml81mlUqlmnaCgObiN5p2HIdG02hcL71e7xEUUXz1Q2B9Qll7syRN704UvcOKWlJEsiKW3Iwr45q8Mkw1Md5x/KdqWZLryhoaUPfTC72HFHs98cOG4AoI//aJGEMPCGA85503dhmzcvk9H6LRXGDo314tti319Ei9vZRfClkz95gjgKgRP3xwHEeRSISJAZpH6iUZVbdB0bv0ZfXqxQm3s5TWJguu894gvypv1cObaqyGbZVas0aaMYMQAgBaQCLhnYBiBQSqyV/1kMlkJEmxWIyyrmhcK1bXewRlqTSEiEUyRW83MrJjtuTKazgd7AMRUXXPzERUeshhjDTQp47FL6vjjWdytxUTDB/8cm/B2vJjGRqSjjmmxAEBbSaVkm69dfL7Kfw9dBzvw5jSVytVyhgpmfTKrR17bPWO04aaucccAUQNuK6rdDotY4xisRjhA5rLuquqfghL0mE6TD16eZytXB2swxQ5wEgvS/qHpOLv7+uu4msJ+vuljTcOcygAgDro7++XpKadIKDx+Rc3ZbNZRSIR+j2gsSUGJDecq23Du2Z3rN4POZXM2G2ryMVEfmsEx8iy/S/kXURlDf/rKvyVEP7+TOCjmMKAobNXnc89IDs9VLA/K/9z/yMalTo7vQa3luWFERO9Hl13XenPA2gnZ545uldDJYJhoH+Rox88+CFEtdi21N0t7buvtHRp9Y7ThijBhKKCJZds22ZigObUV3p5pMmwJB2qQ9Wlt4rev49O0rTka9JX5DWabsZSS6VYtkzab7+JtzNGuv9+6V//VTrgAOnss6Xly6s/PgDAhBKJhLq6uii1iarwSy65rkvJJTSHxcsm3qamyp1IlL59RzQryypYnu1Kcrx/jYx3Fsb/lXWUvxoizF9lf9hlrha3smnFVy3O3RCJjK4b75/A9JtKp9PeSU7Xzf07Ftf1atwDyJfNSldfHc6+/N4svlKaxIfFcXIhysqV1T9eG2nmJtScDa8SarGiJWTXSm7tlktHZHSoDlanFufdvq2+pQ1TD0kfkHR3zYZTPw8+KP3858Xve/VV6Wtfk/bcUzrsMOnGG6XHHvPeqBx0ECEEADSAZDKp7u5u3vshVMGLmyTRTw7NY+Waeo9glFJfnct9FbdtqTNavNm2MUZy5f1tCO44eD5wMn82xgoxyg42jKyh/tyXpdSiz2a9j5J2b6Rtty1nQEDTGEgNKOtUWML63nu9QG8cT0+XjjtM2u790lFHSM9MK7JRsUbT/u9xLQIIvwTTokXS1ltX/3htZGBgoGkDiGi9B9BqjDFyXVfZbFaWZbHqAc2t/46aHzKmIR2mg3Sb/q6spmqKntf2/T+SDpRU/L18azrzTOmII6TNN/e+dhyvfuLChd4VRkH+FQZ9fdLOO0vf/a700Y/WfswAAEnNfXUSGpMfPriuq0gkQq8HNJf+gXqPIKD6J986YikNZoZLZAyf/LcilmSUaz5tS5ZtyWSLjMeqcJjG269s5co6+beXuZuON18I3FDGDmy7tMDiqKPKGxTQoLLZrD5yxUd06zO3KplJypKlnniP3rfb+/SzU36mqV1TS9uRMdIXvjDuJiccLP1pc40Eii/PlG7fWPrga9J19yrXNL4WIcN4/NVR/f3SbrvVdywthhJMkJRfi5WSS2h6xkjLP1KXQ8c0oIN1hOJarX2HTpa1v9orfJC87/8++3j/PvKI16D69ttHhw+Fj1m5Uvp//0/65jdrN1YAQJ7+/n719PRwghih8FdW008OTWkohFrmofFOypX722MFHlsKxw2sTDJSpCsiO2574YMfDLje+QMrankfwwHFyAErGWRMud4SJQ+3oKyS6yq6fqXcnun5m/llmCzL+zwoeM6jlPBBkubOLXWAQENyXVcf+d+PKHZmTH/4+x+UzCQleWXWEumErnnsGm3yhU305JtPlrbD666Tnhx72/ceK/1pCxVd4XT9VtKxh1T2PKrGtr2LKXm/EqpkMqkpU6bUexgV4ex4SAprscZiMSYGaG5vHiGpwqWDIejV29p/4Eh1HbhaaqR5Sy35/SD23tu7eqBUqZT04x+XvgwaABCqgYGBpr06CY0jWHKJldVoWitCLr/UBH3geuK5FR9W1JKbduWkHG/VQ3z4HMFwXwjjN+e2hu/z+0OUG0LYkqXAvsv6PuX6OVhDSUXXLJWVDUzALMtbce1f1ewUabRdzmvTjBnSJpuUM0CgoSxbu0zTzpmm3z3+u3G36xvq017f2Et/f+PvE+/0ggvGvOv720qPTZDZ/XUz6fEZEx+mZmzba0KNUCWTyaadY/AOdpKoxYqWNPCINHRnfceQlKYetVTqq+8w6u6hh8p/jOtK69ZJA4203B0A2oe/AgKolDGGfnJoDa8tqvcI8tTit6gz7kiWKytqyTjGK7PkN6F2Tf5ZmOGyTCZrZDJGlm3lr2Io5YxNVLKjtizbW02R1+R6Isb/cCUno0j/KnU/c4/swYR3v23nPgpfg/zzHq7rBROlvkbtuqsUj5c4QKCxfPrqT2veF+YpkUqUtH3Gzeg933iPrrz/yrE3SiSk118fdbMr6doF0uf2K+FAlnTKQSUNqTamTZNOPrneo2gpxhglk8mmLfNKD4hJoBYrWtZbR9b3+EOSjpe0sr7DaGqdnVKTLs0DgGZHDwhMhuM49JNDaxgYkobGb6hajnoufvDaMhiVdGbfkqIRR1nHzoUBfmUiV7nVDSbwIe9fkzW5baX8z8c4lhXxgg5ZXhDhuE6J3yx/9cVw6JFJqefx2zR1YeCqbn/Vg196yV8JYVle8GDbuXrzpdad55wJmtA/Fv1D+397/5FSS+X6+K8/rv6hfp1z+Dmj77z44lGllp+aLr3/cGlRGVP6f06XEhGj3noXQrBt6cADpXnz6jyQ1kMPiDZELVa0rMQdquuyg+WS9pe0uH5DaAmbbMKbewCok2ZeHo368Vc90E8OLSHjSPc+Vu9RBIQVX0y8H0uScbyT+lbEyi+NNFx6adzdl/oWfrjhtMl4YzKukZN2Si/BZIb/Y1zJySq64i1Nv+WnsjOBE6F+AOG63odl5a+G8O8vp+ntqlX1b5ILlOGPj/9R777k3RWHD77P/OEz+t7fvpd/4zPPSP/zP3k3/Wh76d0nSIumquxSbLdsNKkhhqO7W9ppp3qPouX4KyDoAdEmqMWKlrf0zOrte5XGzzZWSTpUUmmrGTGedJoSTABQJ818dRLqg35yaDn3PlrvEYwy2d+och4/pcub9JiMkfFPtgd3MNb5d39FRCkH8wMNa3h1RtkNqCW5uRUOkcRaZTbaKnefHzb45zv8UkvBRtPBMKLU8yJz5nChFJrGX57+i/7tin8bXgE1ef913X/pvpfvy91w8cUjfVWMpNP2kz6zt+RWWNn959tNfoyTZszoZvWYtEwmo3Q63bSrrDlzXoZgLdZIJMKqB7SezGLJeS38/S6T9GNJl0n6nqTfS0oX2e4EjX9FEEr3z39KK1bUexQA0JaauT4rassYI8dx6CeH1vLS61Km3jVAgkxovR+s4f1NpLdj0D90LhSIqPQUo9RzncP7tS07OMDSD2BZUjYjezAh2xjZqaH8zYLhgh8wWJYUjXofJrCfYn0iCsXj0oc/XOoggbp6YtETOuHyE7yyZiE66odHad3AOu+LhQtHbj93d+nX22hSaemjG05qaOEYGpIefLDeo2g5iYR3pW6zzjEIIErkOI7SwzXZKLmElrXi/PD3aeQFDisDX78o6Y7ANq6kT0haHf7h66EhFhRns9KFF9Z7FADQllgBgVL4K6uz2awikQirHtAaUmnpjXfqPYq6s+3Rqx4sWeWFEBOxJNmS5Vq5q7Mn6hnhC05YIjHJGEVXL1Fs6av52/l9Hnp68gOGbLa8xtO+adOkj32svMcAdTCYHtTh3ztcGTe8PjYj+84M6vjLjpf+/GdptXcS5Klp0rd206RfHwajUrreZ3odR7r3Xunxx+s8kNbS398vSU07x6j3j2XDoxYr2kr/NeHvc4mktUVufz7w+aWSHgr/0PXgr5huiBDivvsm3gYAEDpWQGAi9JNDy3r06XqPoEB4qx/KMzwjCJRUMo6RJUtWxAothLCjtowxXh+IUsOHIGMk48gaSmjazT+WFezNEIl4qxxsO9cg1z8XYowXQgT7QkQiEwcS06dTfglNYecLd9a6wXVV2/99r96nvf/veF2ys/S1XaX3nqBwXhcs6YZNQtjPZA0MSEcdJf3gB/UeSctIJpPq7u5u2pWynEkfR2EtViYGaGlDr0samnCzskUnuP0Pkq6ufPdWwUe9+G/VG+oVIkEzDQCoBwIIjIV+cmhpb7wjJQert/+yr/CpTvhgjfxnbO4YTZaNa7zVCmGcPzKSm3ErKGFr8j63+1ar859PKJIK9I/zA4Wurlzvh0jEuz0W8z6PRHIlmGzbCyQmOl8yZ065gwVq7pJbLtFrK6tQmrrAI7ONzttTuvA9Unqs8yYVuGt+ePualP5+6dvfltavr/dIWkIikVB3d3fTnpfm3W4RhbVYY7GYIpFI0/5PBkqy4oLq7HdDSRsXuf3dkh6WNInDFvuNrNdvaUO+OsyZI61bV+9RAEDboQQTiqGfHFpaYsDr/QBJUtSWLKsghPCXSbvD5ZjCOBvjL78uZ1/BYbmubCer+Iq3FBnoC2wzvMIhnfb6NnR2euGCMbn+D7bt/St5JVfGCF3y7LtvGQMFam/xmsX62p+/Vv0DVbFkwsvTq7fvsmSzXvjw1FP1HklLSCQSTX2BEwFEgWK1WLkqCW1h4Jbq7ftkSTvIe3PcKekASXtLOqPyXTJdL8Hzz0sLFkj77Se9/HK9RwMAbSOZTGrKlCn1HgYaCP3k0PIef6beIyhQ3dJLE62CsCypI5IKDie3veuthLAiIYUQZdV/DWxojGSM7MQ6Tbn/uoLNjLfyQfJChmw2d7vjeB9+fwg/kPADirHEYtL73lfqQIG6OPR7h1al70MtPTWr3iMY5r+OLFhQ75G0hP7+fvX09DTt+8cQF/k0P9d1lc1mR2qxEjygbbgDkqq4LK5X0knKvfF2JX1QFdUpLeWltmF6MDSCvj7pscek979feuaZ3FVKAICq8Wu0Av7KasdxZNs2wQNa0/JV0lC63qMIaIyZQCya0VC2y/vCX/kQGW4YbSSTNZMbqv9S4veZKIXJ/yKy8m31PnaroutWFOzbkjo6cqFC8HUrm/WCB9f1Pjo6vNsnWgUxa5Z3YRTQoC758yV6eXkNLtyr8ktUf6y6+y/LnDnSZpvVexQtYWBgoKnnF5xh1+harPF4nPAB7WXdb1STN+r++9ZfSXqp8oejTJmM9Mor0h//WO+RAEDLM8awAgKScv3kHMehnxxa2ytvVP0QdY8U/BPrxpQ8KYlFsgX78FY+yJXXt2GyT8qovPAh77FGymYUSQ+q+9n78u/zX6ccxyvB5K90CPZ46O72SjNZlne7vzKiWABhWdLs2dLHP17BQIHaePjVh3XezefVexjhsKXBRuhTHI1KF15Y71G0jP7+fkowNTM/fKAWK9pa3x9qd6z1kn488WaFzaXL/a3kt7hANiv99rel1WYFAEwKPSDaG/3k0FYyGam/io2ny+a91w39ty3v99cq+mmhWKRIGRdXuVXhkxmkLa+eha0yzuoMpxWuK2XTsgf61Pniw4ov/qd3dySSX0bJDxSGhrwgwm9CHYl4t3V0eE2q/fBhrIs4p0yRjjhCOv74STxhoHoSgwkd8O0D6j2MUD1c7zJMHR1e6SXKroWm2ecXbR1A+LVY/ZJLhA9oW0MP1+5Y50gaqN3hEPDXv3pvAAYbaZIIAK2FFRDtjX5yaDuLFtfmOI1yEU3R8wXFx9YRG6OOfHDzSq6ysgo+L+lbYyTXSK4juY4iibXqev5Bdb75fG4Tf6VDLOZ9BAMJvx+EMd7tfpNq181tM9bqh1hMOuMMac89y3yyQPVlshntdOFOyprsxBuHoUYvZb/apjbHkZT/umhZud/7T3zCW/2EUCSTSVZANJvgxMC2bSYGaG/pRZJqcEL6TUnHSnps4k2JAavEcaS77pK+9KV6jwQAWlYqlVImk2nqCQIq45dccl2XkktoH+8sq/4xSg4fqrT6oYxjB437619p+SR/e1uyLEtWtJRnOxw+GCNl0oqsWarIupWKJNep64XAhWjBoMHnhwt+maVIJL/0kh9CuG7x/08dHdK0adLSpWU+UaA2fvnAL/XmmjfrPYzQ3TW3hgfz+8QEw4fOTuntt71VcggFKyCaDLVYgQLrrylpM2Ok7Oo5yq6dWf4xHpP0Pkmvl/9QhMiyvMnBzTePnlwAAEKRTCYliQCijRTrJxeJNELxZaDKjGmg5tP1DB/GUMUrne2YLcuyZNITNbL2VyYYKZtSJLle0b7V6nzpYU2/5aeK9K8e3qHtzRWiUW+e4DjeR3b4qnD/xKJ/ojEeH979BE8ym/WaT69bN7knDFTBmsQaffYPn63dAWu4kGtxrRfi+qug/KCyu1tasUJ66qkaD6R1NfsKiGi9B1Arxhi5rqtsNivLslj1APgG7ptwk+xd75dWLZD/lj4rV9p5oaI7PDPx/lOSzpBUYvDdUJOGVmOMV791/XrpnXekTTet94gAoOX09/dLUlNfoYTS+eGD67qKRCL0ekB7Wd9fg4OUfsauJr95RX6/x6qCZNmS1/QhpPMOlmRFLRnXeB9ZM0EJpuFwYngFgz2YUGT9Ck299/fqffBGWSZwQVI8nlvJUGw1g+t6VzL7zadjMWnNmlyfiLHKL3V0eD0gdtwxnO8BEKLTrzpdg5kalSeudRU5S1raKc0bquUxA0Flf7/XI2b9+hoOoLUlEgltvPHG9R5GxdriDDy1WIFxDN4/5l3ZZK8y154jrdpM+W/pbenZQ5V9cafx920kHaGSw4fQMf8vrqNDuuGGeo8CAFpSMplUT08P7zXbAP3k0PaSNWjsVmJ/g8b4zcsfrCUpaodUVz7Y78FIJmUkR16+UXQogZURxpUyQ4q//ZJm3HSZpjxwfX74YFneRUqOk+vpULgvybsvnZaSSW9Fg/+Y8cKHadOkHXaQ9ttvEk8eCJ/rurrt2dvqPYzqsaT/meB0Tej8FVKuK/X2eq8VhI+hGRgYaOoVEC0/M6IWKzCO7CpJyaJ3ZdZNl7nlDEnjlBB45jC5zji/TxdKWlH6cEL9zbSH9xdRKK90DdL2bvL8xnIvvCAlEvUeDQC0nEQioe7ubt5vtjD6yQHDlq+p9wjKNMaV+iEY6xU/tMMZSa68VQ8TVVL1Sy5puOl0Oq3IuhXqffyv6n7mnuLb+6sebNur3T7W37BsVkqlpKGh8Uu6+iuvN9xQOu88ryQL0EDO+d05yrg1ulKy2icTxvh1fWBOlY9bTCzmrXzo6ZGOO06aN68Og2hNiUSCAKIRBWuxSqIWK1BMdnnRm910TLr9NE0cCVhy/3ZS8bvelvSHyQyuQgULNUb+2Fvyis5V+KrXMqeRLEvq65OefbZqEzAAaGfN3iAO46OfHBCwrsqlNUa9Vy323nWC1Q95+7Am6AwdBpP/WbAuei3eeweP4WRlpYfUuehZTXn8r5py3x9Gf6/8i5Oi0VzpFP/zYiKRXH+IiZ5PJOKVX7n22sk8IyB0g6lB/fy+n9fmYHUKHySpr6PKxy4mk5E22kg66STp4x+vwwBaV7PPMVqyBwS1WIESDb5U9GbnL6eq5DP1ffOU7e9SdEqgdmJaXumlMoT2G2qUCx78fwtDCV879mF2Xe+KpcFB6aWXpD32qPeIAKCl9Pf3q7e3l/eeLYZ+ckARqdrVWc21Ohhd5qgovxRI8GtpcgFEyY/1G2Jb6okn1TcU9RtCjB5XNZjh1QyOI3vtCsXfekHT7vpN8e+VP55IxPs3m5UGBrxgwg9N/PEa451cLJVte6slnn9eWrzYOykJTNLy9cv1l2f/ooH0gDaesbF22mgnzeqdpend00vex6V/uVRZE1J5tAaWiNXhoLbtrXz64hfrcPDWlkwmNWVKrbuLh6flAgj/qiQmBkAJ1n5r1E2ZtzaXUtPK289fPiaddHnu68MnOa7J8oMFZ/hf/912tuDrduRPHJYvl379awIIAAhZMplUd3d3vYeBEHFxE1DEmnXV3X+Rq+vL/q0r3EcYIUQxwysHRveDNrKyKe8CoIjfxEFVDCGGyy5lM1I2o8j6Fep+7n7NuvabslxnjIeYkSbVikZzn0veiUS/nnslslmvDIvk9YwggMAk3P7s7fr4VR/Xkr4lebdbshSPxNUV79KMrhnaf+v99bnDP6ddNt1lzL/V37vje7UYcl1XP0hSrNYXXFqWt6JqxQpp5Upp001rPIDWZYxp+jlGS52ddxxHmUyGWqxAqdJvjL7t74dVsKMOZV/awfv0yyqr74NUxTzAL3/qN2izlGtp0c7Vh1xXWrXKWw69eHG9RwMALaXZ67MinzGGfnJAMW8trfcIRguWOfJ/Ty3LWxHgfz7R7+8kyyQFYgZJkjvkeH0Y3CJhyEivhjAMN512HEmWIom1iq1doSkP3zR2+ODzyzDF41J3t/dvV1cugAh+lDUkIy1Y4DWj3nLLSp8Y2lg6k9anr/60Oj7VoaN+dNSo8EGSjIxSTkrrBtfpjTVv6NeP/lq7fX032Z+0Zf27pfn/PV+X3HKJ+of6JUl3PH+HkpnifTBDVe/zDUbaaKDWxxzuI5PJSH/+c40P3vqafQVES52ht22biQFQlvw/vE4mIqUrrCn39BHKPiHpxvIeVrXfVCvwryXv1c5vSN1ZzQM3iWzW6wXxzW/WeyQA0FKavT4r8lmWpUgkQj85oND6/nqPIF8weAgGEWa4Hmvhe39/m8Kr+/3HT5I/BZn+8kJFV70tOZnifROMQggijN9wQnIdRdYuVXzZG4ov/qfi77w89sOiUS8c8MOHTMZ7/p2d3vfFH69leScVy2Xb0sYbSx/9qLdPoAzfv/376jqrS5ffe7nSbrri/Sxdv1Tn/ek8bfi5DfXtv35bZ119VoijbGyb1+NlOpuVpkyRnntuOBBFWJLJZFPPMVqqBJM/QQBQgswKSYN5N7n/3EUVn5l3jSKnlveQqmYApsi//iqIrOp/RUIjSKelhQvrPQoAaCkDAwOsgGgxzC+AAsZIg6nq7n8yivV+KFyB4G9XuG2w34EfRlRy9f+w2MYbaspdf1L/vh+QM3OujKLyAhE7/6S+GfnP8CSp2PEKGmr74wze7Tqynays1KB6H/mz7Mw4J279k4N+7wfb9v6NRLyPYB+ISppod3dLn/609O53l/c4tLXrn7heH/2/j2ogG+7l+4OZQX3xhhr1JWiAcw0xV9q2r8YHjUa9MDPYVwah8EswNfMKiJYKIACUIXnn6Nte3aXi3Vk/vqqsQKFuf4oa4M1AwzDGq83Y1ydNnVrv0QBAS+jv72/qq5MAYEIDqVBWCYRivHEE77Os/BP7Yz3OdQtWTww/NlrhqZNMRtNu/6WMZWtw+33kzJort2e63HinZMdVdFZkJFkm/75R4y0yftdRZP1KdbzypGZe921FE2vHH5u/AiSd9lZCZLPeR7DkUvD7US5jpP4GWymDhrbrBbvqmSXP1HsYzc9I01PS/BpUmspjWd5rSTot7b13ZSunUFQqlVImk2nqi5wIIIB2lX4970vXlTQ0vbJ9DaUUWbN+0kOqGv+9e4PMkxpKX5/XD4IAAgBCkUwmm3pyAAATapSTym6gt0Oxxs7Frr513dztwfJLwZ4RluWtDvB7IExGJCo7k9KMW36qnmfuUXrjbeXGO5WdPkd9B3xIZupMSYWrIcqctAxvb6WH1POPuzXz+u/KzpS4QsUYL1zxm8f69dv91Q9S5WWpbFt6+GHpoIPKfyzaSiqT0iaf30QrkyvrPZTJq/ScQ0HmOOlhWHXoAeE40sCAt+rpQx+q8cFbWyKRkKSmnmMQQADtau01eV86b26jStvCWL/4XXnbV3SUSfD/mBNEjDY0JC1ZIm2xRb1HAgAtIZFIaObMmfUeBgBUz8DgxNtUqtQT3X7g4AcKE5VJKmxOHVR4m99I1f+8lDJslorPMYZPFlnGVcebz6vjzedH7oqtWaa17ztTbs90mcjwqRk7kjtmMFgp9n0xw/VlXUfKZtSx6FnNvOmHpYcP/vOOxbwThx0dub4PjjM6oCk3hEgmpddeK+8xaEv7fXO/9g4ffK5KPyUzwUmVdERa3D3J8ZTLL8F06KHe6wpC09/fL8uy1N1d6/+p4SGAANqVeTP/6xf2rHA/RpHV6yY9nKrx/4AH+8sRROS79lppv/3qPQoAaAmsgADQ8lasru/xgyWS/DJCtp0fRpRiou2CqyUm3pmKTi5mjB1IT7339+p88SEl9j5e6TkL5MyaL6dnukzPNDk903JjtGwvbPCfo5OVHEfW8NjsgT7FFz2nmTd8T/ZQIve4YIgyVg+HaDS32iOTyT12MqWXfK4rrVgxuX2g5d3y1C164q0n6j2M+gteMBnCvoyRXq91uwC/hNtvfyt98IM0nw+R34DabuKyVgQQQNsqaEiWmFHZbh5+cvJDmayovPf7wYbT/udu0UdIEUnO8L/Zqo6u8T37bL1HAAAtgwACQMtLVHEFRKlc1zvZ5a9WcLJSNFZ8NYMfSuSdUDdeY2rLDw4KSjBJ4dQvtyPeuLKZonfHV7ylmX+6TJLkTJmlvgNP0sCuB8nKpIYbQ3vPwXR0K7bkFdmDA7JTA4r0r5YzdbaswX7Fl7yq7hceUmz5ovznHQwRgiWVgtv45ZakXJBTacmlID8AocwrJvCxqz5W7yGEYzK/MuWWXypx20wJi7fy+CHnZESj0tq10k03Sf/2b5PbF0YkEgl1d3ePBM/NiAACaEfOkKTcm+DM+imq9OXAvuP+cMY0GdMkTZe0TN7TSo+7tfdGvkveU26QErZ1Y9vSnDn1HgUAtIxEIkETagCtyxgpU6Wrdyo56T2QlLp7NHL2r7BvQWF/CP9+19HIqgXX5JpMB7cLI4CIDIcjpWzav1ozbrlcUx74owZ23E+prd8tE4kpum65Ol75u2JrliiydoUiyXXj78ge7inhX43shxFBwZ4XHR3e56lUmas+xmBZXhmWjg7phBMmty+0tNeWv6ZVyVX1HkZ9FXvZGy+QKOXX00gRI7271G+tHzxMNni0be+1dKONpKeeIoAIUSvMLwgggHaUfjn/6xf3qmw/g0OyJ/tHaiyWpE55YYIzznazJP1C0oCk70p6eZxtfY68wGJXSX+Z1CibX2en9J//We9RAEDLYAUEgJa2Zn19jlsYJvhX2PdOyb+6v9hV/sF//ZPrkWiuLFGkSNAQPEFfqmJVmPzjlCG6boWmPniD9OANpa9GCAYn/kc0Orw6xBm9GmJkfJHc99L/PpY7v/NPOvqBRywmdXVJe+4pfeAD5e0LbeVHd/+o3kMIx2ROiRS+xIy3rzJejrod6eClJW48Xq+ZckQi0oYbSrNmea8Jb70lzZtHP4gQ9Pf3q7e3lxUQAJqMPSv/66WbVLSbyGVXTX4shSxJG0raTtLnJH1MUkGZWf/PorWppMskbTN842pJQyUeY0BeeSZL3oqISa40bErRqHTJJdI++9R7JADQEowxIzVaAaAlLa/S1crjnvgKhAiO450oy2aldCpXRqi7x/s6Pnw1v19KqDC0GO/4/gn44Mn8ep/sKVzRUUw0mmtc7X+PHMf7OtjXIRjGxOP5z21oKNeAuhx+eBGPS/PnS1OmeFc/v//90sknh7OKBC3rhidvqPcQGk8YLzlG2igp9Rav/FbkmCGED7ada2j/0ENSf7/0q19JM2ZIH/6w9OlPe68PqEgymWzqBtQSAQTQnvp/PfKpMZLSFfwheOV1WQOV1X8dt8RhRNIekvaRtImkZP7jRj6f2qvsyadIS99RbJtbvBsD247LkhSXtLOkhcpVoyozhHAtW5ZxQ+sVVXOdndLs2fUeBQC0lGQyqSlMsAC0qr5EHQ5q5SYPEdsrmdS3zrvddaSsI3V0Sn3rpZmzvJNgwRJK5TSmDir35Hmx83eZiWrDTnB8f0VB1ms87R0nEEpEIrltXTd3AtCyctv7/O2DVztHIl7pJf8xhduPdVIyuApl2jRp002lAw6QPvIRaY89Kn/OaBuO62jxusX1Hsbk1bL3w1jbF4wh6kobDElOqfuebN8HyXutGBiQXngh/7VkxQrp+9/3/r300lzJN5QlkUg0/Qpr4migHa37zcin7rJNVXbMbowiv/3TpIYQ7BM9wpLULa9E0nslxSQTk4wtmcJ+cjtu4y3tXba1MjeeLg0qt5qhYJejxCS9R9K/yQs5YvKCjzK/Dak5G2rdtjuoaSOIZFI691zv6gQAQChYAQGgpfWXesVPGUq58tZ1h6/Ql3dyq6fXu/K/s8trdNwR98p+RCKShssIOVnJjHNizVLuRPpYvSImy19tUCnbltLp/NDB/4gGricNhgpdXblgwt+HZUnd3VJPT25lhOvmej4UC1uCx/Tv90s1RSLS9OnS8cdL77wjPfmk9IMfED6gZH95+i8ykzp738T8kyFW4OvC+ybDkqanvTJMNeP3mslmvdcX/yObldavl554QnrssRoOqLW0Qg8IAgigHWXfGvnUfWM7lX3m/dmXQjvlPupva1TSqVImOluZG86SmbGRFI1Jli3Z3ptf09kh98D35h6TnqHMi++R5spb2TCRTSSdK6/HxJWSPiRvNcQBkjYaHsN4K7QlOfEOLdvvID1+2RXq23Kr0p5bozFGWrtWWriw3iMBgJbgui4rIAC0Ltf1VhvU47iSNxfwSy7F4l4I0dkpdXV720Qi3r9DqdxYreFTHkUDhTHChkpLLxV7jONK06aXv6/gPgtP/vtf+yWT/JUN3d3e9yObzT022MsinZYSiVx5Jsm7Gtmvzz5WqarCvhDRqLTLLtK//Iu0007eMYEy3fvPe+s9hPqxlH++YazPK2G8E71b9U1yP2FxXW+V1cqV0urVE2+PogYGBpp+BQQlmIC2lJI0/D5yZfn9HyI33xHqaPwLmTRV0jZS9vl3SS8fJElyjjtEkev+IqvfW+5t4jG5B+0z+o3uq/vJnP2ErK/KK6mUzd010g/OktQl6euSZgzfOVPSlwL7SUm6QtKfJA1KbsSS1sZkZbOSMTKWJTfeoVc+fqZeP/XjSs+YqaE5G0qvvRLeN6SWJntVFgBgRCqVUjabbfoJAgAU1T8Q/j7HWmngX5mvwJX9Rl74YNveCa143Lstm/FCB0veyfV02ivF5K+GcCfoATEScASaWRc2ag42cS7WbHostiWVW7a2cIVDR0du1UI2mxtv8HsXj3srH9at87YLhgb+v47j7dP/PvgnBjOBQvGF/z/80k6SF1TYtlfTvbfXO+ZBB5X33IBhNz51Y72HMHnltkwxgcoO/gqIwn+lyYUQltTpeB8NZelSrz8MKtIKKyAIIIB2YzLyahxJ7kCPNFTmSZJ/viGrClc+eXOKDpnsxtLiraQFw3fMmS3n4x+S9frbUjots+Wm0tRiV5ZaysYOU+ynd0qXS3pK0lrl3hRE5K142EBeg+uxdEj69PDHWinTP0W3z3pcG//5Rs1+8jENzpuvV07/pDIzc70TUrPnTOq519WMGdJhh9V7FADQEhIJLywngADQksIqSzTe/vwT58Z4wYJ/wtx1h8utDpf/8W8fCQwsaSDplWTq7vYea0ckExl+3DinPvy+CGY4ZMg63j4k7xjxqJTJ5kpAjfVtKPZ8LEtKVlDuNBiW+M/Tf+6Okx+Q+Pf39XnhS2EZKT/M8IOE4L+pVH75qWKrQYLj8Us4vfmmdMopBBCo2NL1S+s9hMmp4OXQBMJLS14gISO5haWYxgsgJtjGcqWIkbZotCrLg4PSSy9Rpq1CrdADggACaDeZd0Y+Ncs3VbmV2Oy/VWmpZCQiZWxpKCqz4Qb593V1yuy49cT7eGcHad87pZ9ISkj6pqR7lFsNMVXSv8sLGUoxQ4rMSMmxpujNkz+qN0/+aNHNOteulrEsWWFPymph773z68cCACrW398vy7LU7Z+4AoBWsmxFePsa732z6w6XGop4J/6HBqV4x/BKgOHVAf4KXsvygoNot7fP/j5v9UPWkRJrvc87OvNP6BeebB8pPRTxVhiM9IaQt3oiEyhpVGTc4/aRTZXZhNqyvJUM/pj8HhDZbH7jZ59te4FEKuU9Jhr1tg0+3m/K7Ti5ZtPB5zFW+ODf5//rN7ju7PTG+MQT0sYbS0cfXd5zBCQNZspcGdRCrMCHLMkeDiFsPxssZQXEGC88xpJ6MtL+y8IabUhsW7rrLunUU+s9kqaUSCS0wQYbTLxhA6MHBNBuBp8Z+dQs2ay8x65YJXvV2nDHM8JI0YjMJvOkzlITgkIRZf65s/dpr7xSS9+QdJykE4e/fn95e7SVmXCb+KpVRZd0N3xrav8E2ZIl9R0HALQIvwG1VUntcABodCvX1eY4ftkf/8r9KVO9wGGkLNMw/0r+yPDV/R0d0vQZXmDQt87rEeGaXLPlYBkl/7E+f9VFNOoFFsGxOI4XaIzUjS1DOuUFKeXwSygFvwd++SM/LHAcL2jIZr0LuWw7V6rJbzrtrxzxm1j7t0900VSx52iMNDDgfQwOSvPmebffdlt+CSegBKYZL9wLmuTw/YVU7vCHZaSo652gdcsp8VZkLJak3VdJ8xox34mU+VqIEclksukvcOKyV6DdrPlR7vMxAoi0epTWaZKmDd+yVp36o2LX/7p643KNFItOvrHdM/tJ2zyb+/qA4Y8KmRJihOjAwOhJTDOIRqXp06Vly6T58+s9GgBoen59VgIIAC0pXebV/OUIBgP+iXIp/6R74UlLv3SS40iJfu+9bTLh3T6yYiLqlWOyrFxD6kgk11/CHQ4e/JN+/n2uK0Xs4bJMBWMs6zkNl3Yqlb9qwQ9E/Obac+d6/R2Sydz3KFhKKRqVhoaK94fwt+noGHubwu3HCiqyWW+1hR9ADA15pZ9mzSr9OaLtvbPmnYk3alSlvgwUW6HgV4xzvapxxSq6lZs/FDvstusa8GLInh5pv/3qPYqmlUwmKcEEoMmk/i7Jfz+cv9LASErq/0nW7IIHzdaQ+aQ6ln+leuOybcm2Za1eK72zVGbjeZXtx+mQM9SpSOdQSAMb/0+3nU4pOjTQeH/gS9HV5V2xtNlm9R4J0LacdFrLHnlEby1cqGWPPKJMf7+658zRzmedpQX0Z2k6rdAgDgDGlA7pSvcxS/0Ebg+ehPfLBvmhRPDkuH/CfspU74xeJOq9fc9kNPI+3r/AKR4P9HvweyoEGk/7QYe/X3c4mIjYuaCiHJblhQblPM5fsdDZ6T1v1/VO3CUS3nOKx72T/n5I47q51RCF/J4R/sqPaDT3fZ3o4qmxxuyXYlqxQtp0U6+f3IwZpT8/QNIzi5+ZeKNm5r88BZpOW8O/cpYlyfZWPsSd3MqHrOWtfrBs7/NKQwjbSNv3TW74obMsadddpRNPrPdImhY9IAA0ofWSJDcxVYUn15P6L8nqKvqo2C3nVvUku4lFpN4eybJkLVlReQAhS+6zeymyxz2hjMuSq6jpV9Yq1vhamvbyizJWE1azsyzvaqX995dmzqz3aIC2Y1xXj196qV793e9G3de3dq0e/Mxn9FAspv2//31tfPDBdRghKtHf36/e3l5WQABoPcVOcFei6Iltf4WD8gMB/2S530DZtvODh5GVCyZ3ot0vQWQPf51JSdG4d+I+eGzH8VYmRKL5oUNhA2f/KuZKSsYYI62voHyt32DbDxCGhrzwwQ8kgqsjxhMsWWVZ3uoJxxn/uZTy98t1vfFEo9LJJ+f3pABKkMqk6j2EypT6MhBYxmC7XrDgBxGR4azVltQxvMDKWN7X6eCqiHGbywTuCy6ZMN4+t2q0AGL6dOlzn5OmFD+ngomxAgJAc0m9NfKpu3izvLsS+vCY4YMkdT1xRbVGJVneFQFuh7ciw0Qm+SZ20XZSiAGE0di1CqN9fYqvXxfKsWrKGKm317uCCUBNZQcH9adjjlFqxfjNPE0mo/vOPlsL3vc+7fONb8higt/wWqE+KwAU1Zes4s6HG0kHT5ZL+SfKC5tGm0BoEbzNP3kfiUhOVrLjuVUR/kl74+ZqkTtOfs+EYiZTr37V6vIf44cNfv8JyQtQOju98kfBMkoT8b8nxuSaU0sTN50eSyzmNZ7+yEekE05g9QMqsqyv0Tokh2g4F7WH/3WG+9kby1sR4Q43nY65UiqSCxuMnesLUfGVn5Y0LSVtWUoAUfgaUOx1NwyW5b1OcHFOxYwxI33mmhkzWaCdJP+c+/z1HUc+TWm2ZG0x5sOiz95U3RcLy/aWTHd7Dd/MppPsR+B2TrxNObsb59lvfMuNiqRGl3tq+D+vwyWv9OyzE28L1JKRdJ+kL0s6X9LC+g4nbAOrVumP++47YfgQ9Oaf/6zHvvGNKo4KYWmF5dEAUFRyYPL7GOuEdzaTf2V/4Yd/st0/Ge+HD/4JND9AKAwSjLzwwV9VkXWGT+xHcu+F/SCiWk1xsxX2zfCfs217AcKcOd5YZ83KlVIaS2Gjbr+ptd/Q2n9sJScEe3qkM8+UPvYxwgdU7KUlL9V7CNXhhw/y/nUDv2JWoCxTxEhDESltD4cSw2WXLEkxk9u2pBUXJv/zrfqk6aW87BS+5vmvt2GLRLzg9K67wltJ14aSyaSmNPkKEgIIoJ30/yX3eSL3hjGjj439GPf/s3fecW5c5f5+ZkZli3fXvcSxEydO74100gmhhQS49F4uvZfL/dEJECAQSig3IUDoISEFSEjvvSdOsR077n29VdKqzZzfH2fOaqRVGUkjaVc+z+cjr3Y0c86RvDM6837P+34duq/+QAMHBZgGYuZ06O5CHH4QzKnXxMwgs2lhECPDAHpZUfS1Offeye7/+RdGo25YGk2perEaTat4BJgDnAJ8D7gAOB15Ir6R+hzZJgk3vOY1OKnq085X//WvDK4ofi3STB60B4RGo2lbhmP1HV9KfLCz+SJC4b6OLfcBT8kR94njuFE+h/FJgjc7Qnk+4Po5RMJSjCgsuaTEiKBX6KZTMKvQW68KslkpHqTTsHOnLJ/a3y+3l8uA8Ao0E0pKiZwAUcx4utLn8O53wwcafG+oaXseW/9Yq4dQPZXuQ1zxwXCf266ngzrMVMKCu5Pl/h525Guq9JJt5ASMkqsaS2w3BOwehy67qnfWWEIhWfI5FoOVK1s9milLO5Rg0gKERrMrkXw491zI0z/NHDCiJQ6Azms+htHgqJ+IRrHPfyXOGScCYN71IOZNd2E89gwkajSTfu74wMbXxcYJ22Y/cC9H/u9nCCUDWA3WChxHrkSY4l9imjbiC8CxQKlKBf8AZgEB+V+2gvv+53/IjI7WfPx/3vQmxFQVPHcREonElL850Gg0mqIkG1Cz3bYhkciZRIOntJJHSDBd42jTyhkvO44MaKVSMkCfcScIliUfeSWVXI8Iw5QZEY0oaVj4/ew48n1t3lxfu+q9jo7mDKq9ZZSUaFCIV2RQRt7lDKtVe52duayQQmbMgB/8oL73o9EAQ4mhVg8heFyfBwfXTBrp/6CuQsoHIuTkRAkLyLiG1Gofxy3V5GvKX6ghAiduq/+tBEo0Kh8gxQhN1TiOowUIjUYzxRBDnl/kxDLN60vv37+K6LK/N3JEMvth9wWQzsLmbRhPPQejcchkMbZsx3zoCbliqVqG5gcyPAEkmZe3Lbp9G0d883/o2uG/hMqkQ6Vgj0w2hyrNLscIcCBwkY99B4ElQXSaBR4CHnOfN56x7dtZ/69/Vd6xHLbNzW97WzAD0jQEnQGh0Wjalhqy98YpVurDcWAsIUWF2KhsXxkk23bukc7kjvGWCDEMWQ4oEpVZDWHXZFo4udfH9/eIEYbHsVUF973+E0Gycyfc8u9g2wyHoatLzuMtjyCjxAi1TZWrUr97Ub9HIvl+cI7jCkIl5kZnnSWP0WjqpK+zr9VDqI5qwhEeI+nxjAik+CCQpZa8ptRpM/dcuNuzKgWiWL9lLlWGgKP92s40w5MhFMqJn3Pnwj77NL7PNiSZTGLbthYgNBrNFCG1DpATUScdAUwcDDDmljyk53fnNnxYwgpByIJIBHOtm2kghLzZsG1Zb7YW8zZh4SQreEHEgRuAy4Gniu9iAGPke1LsedWf6dq4oWSzTfF/qHfCoFaUaQFC00oEcBTwQhXHbAJeXk+n/w/oAI4HjgHCboONTa24+Z3vDKSdgWeeYUCnL09atAeERqNpW2rNSi7ZXlyWExKODFKlU1KQUOWU4qOQSecEBS8qqK5MmkNheZxpyCyHQqNlbzaE4y4/9mYPGOX8FGp4b44D8Thc8kPYNDGTumq8wosqy2RZOYNp735KxFG/F5ZqUu87Esm97s14KLbs2jSlUFGPCKXReBgcG2z1EPxTa/Kxq0BYbmmmkCP9HdQ1xXazJZRXhCkgnJU/a+2/LyNLMPkbX4OzqpU4ms1KYfN979NG1DUSi8kSiFP9HkPnv2g0uwqJ28afOhuWApDiVErNqs01D2DFtjZ+XJ1R+UU0a7pMUx5LYuwcBNudLE/rwkhna/jeNxHDs6BjU/GXLwV+CaSQ8cfpwGuRZWAKh8h6kq4IYY0lWHzNlZh2C70TTLN83Ve/DA7qGwlNa3kVsKqG4+5FekPcTpWBgdMp7mp9LxABPg9ciMoQC4pV11xDYmMAAQiXW9/+dt786KOBtacJjlgsxrx58yrvqNFoNFMJtYK1FkoFuRwB69fA0IAMhi/e081mcAPrPX3IQupuDRPTyhcRVNuppNwvHAEjLJ97fQ4K/Q685YrysiJK4dZSKRes876mgvqrV8LwcJl2a8C28zM7RMF7LeWzAbn3aFm5rAdlTl3uvXk/I716WRMAmWyG9TvXA3Dwanj5MzB3FMhCNgwb58KtR8KmOa0dZ1242QvC4+3gIFeAC5HLhohmZWVs23Af3lsQj1jhCwELEjB/LLB3EQyRiLx+/O1v8OUvt3o0U5LR0VFM06Szs7PVQ6kLnQGh0ewy5AzQxMhMAGyOLrn3tD++ueEjwrKguxNx2IFgGIjZMzF2DOTEB4D4mFxBVANOrIhCvA658PlipPgAcuHzDuAvwOMTD5nNQ+PP9/jbH+naXELUoMHZD+Gw/AKPRIKrXfv008G0o9FUyx+Bm+o4/k6kaOibr1BcfPByETADGKptTEXIplI8dsEFgbUHYCcS7Fi2LNA2NcEQj8fp6upq9TA0Go0mWAS1lUQthePA6Ags2VtmPvRNl+2bRi7Ibppym/Ju8KKMklVJUSssvR28vhGF4kKeJ4R6Xz4KrfvZp5BsFsbGYOuW6o6rhBqHKrkUCuXKLkF5IUW9j2w2V+5KiT1++uzqgo9/vP73oNEA1vAYn/wHvPNO2HMndKWhy4HeFBy4AT7yLzju2VaPktqyH1zFwcQ9PR2I2Ix7RHRloSMrsyEMA6anZNaD17BaGVqXHVORTIn9hyA0Wezi1LU4mZTXnDVr5ENTNfF4nO7ubowpnkGiBQiNZldh8Lu558kOskwraT5trrgV026wubIBoqcbsXABYs9FcltPN3R6yiaZBmL2DClK1MKm/fJ/t4GPUzq2mAR+WmyzXM1qZLPs+7tfYxZLBW8GapVSJhNMBoQQsLUJWS4aTSFbgHcF0M4N+BQxhoHv+Gx0FClCBFPm6IH/+R+cBmQa3fbudwfepqZ+2sEgTqPRaCYSYNDDtmHnDilARCKQddzSQVkpNKiyHUKAY5cOqqvgllqcg/u7ylJW/g7ZbH4poloEhVIUa0v1deP1kA24vKMSXsJh6OmZaDStyCsvVeTzU2bUfj6Hzk5YsADOPRf22COY96Fpa5xsluTgIKLE/eqGzav55DWw+2DpK0vEgdc/BHsHl0DcPDx+DuD6PljyiSkgY0GnnRMdkoWJ1654IQo/nAqna1jAYTWGTRqGujYNDcGLL8pSTJqqUR5zWoDQaDRTg/RzuefbF5PilJK7dl/zscaPR4CYNwfnjJMgLKvBiXAYMWcmYt5sRF8PYu5s6OrMr0taDaO9+b+vAtZXOOZRoMBbOos0W5tz311Ed/aXPNT310E9XxyZTM5ULggyja17r9EU5agA23odsLbSTnvX0PB+QH3XwrGhITbecktdbZRCpFJsuu++hrStqR3tAaHRaNqSoRpLCRULcKdSMtt5+gyY1gvz5sqV/NOmyYC5ZUlTacOQJZlUIL2w3fHMBlN6RSTishxT1pYiRDabC7Ib5PtBeMcWVFaxajOdhvvuhicCLpWoxq18L8bGckJD4b2Bt1SVEiy8YoRl+SvpaprwqlfJx+teF+z70bQVO559luve+EZ+NnMmP+7q4pdz5/LLBQt4/Oc/z9tPCMEtZ72W6T4sZUzgXbdV3K1x1KFTjhtPI4WEsCMzEyy3olwyBJ0ZaTadCEHGlFkSIbfskuNtoLDREqGErgzsPtni+0rsDIWk588ULyHUKpQAMdXRAoRGs8vgKQaYnIbggKJ7hZ75B1ayBtPnGnD2X5qf8TBnFkYyibGtH2N4FGPrDowdOxGLFtTWQbyvoEOfx50JeFZbdLu/7P3HyzFKBP+rkhQabfhUDdksBFibXqOpyCeQGRBBkQH+q9wONwK1XtN+iTy730/eNdQnN557bo39+uO+z32uoe1rqieRSGgBQqPRtB9BLn4Jh6GnVwoOwoGOLoh25ptHq5m1YRSfZKvAuxBS0MhkIBZz/SLUa04uG0GQb8wsn9S3KMjbnuonk4Hlz8Nffld7u6VQ70uJBum0fKjX1E/vwi2vQOHNkrBtfxnV06bBzJmwdKkUITSaAtbeeiu/3mMPrjjkEFb94x+kBwchk8HJZhnbvp07P/lJ/nziieP7/+d97yO20r8BXGcWTnymESOvQJ2368I1n+7MyqBr1HYNqJFFGVImxMPyOcgwRdoEw/WHGDeOKKSYMOHSl4ZXNvq23jRzng7VkExKD5nNmxszrjZHLXDSGRAajWby49io6LsQYNMBRkfRXbuv+UhzxmQYkCkwco4nEJYljakBQhaiIzpxP7+IUH6sf29gvo/jMsBZwJXyV4dOpj/zJLMffbCxHg+tQAh47LFWj0LTTggggfRVKYxXPIOM6QfNo8DfSr34xgA6+C3QhSzN9HGgdCaUYvMDD5Dqr7xfPdixGDuff76hfWj8I4TQGRAajaY9KfRg8CCAbaEuVkRnsj7cQ3q8/kgJQ2TTdFfxu14ElgWRsBQMlKgwvr8jfSAKA+WqvJLa3tkFc+a5WRSGDK5nMq7fQVKW/Ui7vgfjYoqK8tVAYckj25btP/EIfONLsGN76WNrRWVvzJghVxFHInJVMUh/hqhr4G1Z8nlfn3xdGYhXswDKNGHePPjmN+F//ge++EXoKH7vqNk1cbJZbnj3u7n6Fa8gtr58iYEtDzzAta9/PY9fcgnPX3FF1WfdKx+lbkEgMEoJAwX7GAIiWZiTlFkNtgFZ5fFg5PweVBkmCyk8pK2C5v2WYRKw7ygsaLQBtSprV0sgfGQEdtst+DHtAsRisbbwmAu1egAajaYJxD25i8IoWX7JfPxPGCLAFU7lMA3Ya3HeJmN7P4TDsvQSFjID0cbYvhOxZFENnbhOT4b7TR0BfoxcLe0nG+IbwN8g8vHNnPD/3k5IrTKa2MvUxTRh//1bPQpNO7ADWQ7pYfInxxHgvUjj99fiPxOpWt4HvAEIezdeTS2ZC6UZAn7hPo4E/g0Uz9C679OfDrDf0tzzmc9w3s03N6UvTWW0B4RGo2lPike9shjc1LuElyLTSZohBHIGv1dykLNH1tAtChYRZbOyXFI6BaOjUiTo6IBoR34pJOFG+cYTIoyJvzuOW6op7Eby3AwKgcyG6O2Ti7Act13LDX04jluOCDBqKPOqAvkGucyH4WFY8Rz89AeNLW8qhHxvhiE9IIaHZf9j7lwn5BpxW1YuG0II/34Ppin/P97/fvjc57Tng6Yo2558kuve+EZGX3rJ9zGrr7+e1f/+d02VAKICjl4OjxUv4NA0LPcexva5jHssDFtN6MnAaFj6WpiudjlmupcmN+PBG08wHRBmdZqLKeA1lUpNB0m1AkQ2Czt2wMKFjRlPm9MuC5x0BoRGsysg4uNPswOzcDis6G49//xsc8ZjGDgL5sHsGfnboxGyzCPLEWQ5DJvDyHIEyegJNcYsDbIFPhDGQWD8xr3n8NPEcjjg45cSHR6qaQSTngULtAChqZ/fAXOBh5g4W04D/4dMIGjkxHgM+FPhxvc3sMMngN2Awyks8fTsb39LNh4vckzwjG3cyODatU3pS1MeIYQWIDQaTXuSmrgIJ4nBZTMP5dnOuSSsCI5hIgyTrGGxsnM2v55zBDvNaP5BSnRQGRXJMbmSP5nMrdJXk3SB6//gcXJVmQcqG0CQa0uVFBKOXP1vGlJ06OqUQXXL9ZdQWQP1LiESuJkPaRgdhp/8QIoDjUBljShhwbalqStMNJQ2TflZpNO5z9Rv0LerC044Ab7+dS0+aCZgZzI8dOGF/OXEE6sSH3IN1L7Q8ewWJ+wbbqaC7eey4a6BFEL6OoQdWBCH7owUCiK2DMTaKhPCyK3PUh4RJc/YEv1bAs7eUMUbqtb7plBwqFS+rXBf04QDDoBnWlFPa+qjPSA0Gs3UIbM293xketHyS8ZL92BMqJfSAEIWYuF8nDe/Nm+zwGJ0wedxug7G+80qzBCZvd5MwvgiaaZX399YkVS1g4CArt9TOvsBZOr7ZPKk0Ew93oHMPpgMfBRP2ae/AiNN6PRpYDbwNsDBTqd5psBwr9Hc8d73NrU/TXGUANEONwgajUaTRyzf2TQL/N+swxkOlzYUzZghLptzBMtC7mIgOwvJhBQdbBu6p8HM2RCKkF/XxCA/08EzT1WeBt6HIhSSAfqOTgiFwXTLESkhw+sxoUpBVUue74MD2QyMDEnT6XiDxAfIlZsKh6VgM57t4XkfpilXGSuxQmVu+53nGwa8/OWyTrs2itUUsPbWW/nDUUdx35e/jD3W6Do/E+lJQXezui1WPc5TPskPhpDlZrpsiDowGpXG01kDkpZsxnIzIkKeEEzI8RRw8NjVlBsbwIw0xCN+BuYxqK8G73WklpJuQkiBM0g/oV2IRCLRFvcXWoDQaHYFRq8Zf5re/AoKvzk3iRHSf31zw4chIhHs00/E/q/XQE9uhagA4rwHwktInfBxsouPwemejT13f1LHfwTRtxCIkObD2Pn1VXx0WuQy1w0skU+nvIBQL9u2wRVXtHoUmqnKF4A/t3oQHpLAl9UvzQ7K/xXo5amffC53098kUtu3E9+2ral9aiYyNjaG4zj09PS0eigajUYTLHZ+sOqPMw5iLBQtsbMHw+CG2Qdx4bxjuap3H0a6eqRfQ/92GBmG7m4Ih+S2UDgXTFdeEFlPCSfT9AgOBaKE8j5Qr6vgvG3LMkxC5JdGqqV+eWHAzRGy9NHaNbDsyerbq6X/ZFJmWXjHorI61GcQicgMZ2+2h5/3q8pJHXecDBRqNEBqZIS7/9//47rzzmPnsmUtG4cJnNiK7gV0ZFyxgAJhoAwGMishbcFgVAoNtgEZCxIhSKoqcEjBwXSrx2WsnBxruP1NiGYU9G8ImJ+AWSk/70eVkKvhGmgYtWVOmKYUT20bDj20+n41ugSTRqOZQji55QLpre/Me+kFsZ1zBn/CnEYHzExwTjseccLR0JsLzjhYxPlvMKQ7tOiaQfrwt5I8439JHfchnFl75dowQozx7ur63VnE6MgCPN99vssxFdA24sXf/97qEWimIncDF7V6EEX4IbDzn5C24eKPw4FPw7x18LJ74co3NNTETog4h3/mEuYeM9q4Tkrw0Ne+1vQ+NfnE3NIb7XCDoNFoNHl05JbWPhuZwbZIlUKrYbK6Zz6/3P+V/GzxiazabV9ZFsm2ZcZCYVBLiQcqeOUNlqlsBvXcu10dq1b3hiwIu2bN4XDxzAlfeI2xRU4MePIxuOMWeLaBZUUKx+oVU5Spdjaby2pWn1soJLf5Xa1sWXDQQfC2twX/HjRTjtHNm7n+jW/kl3Pn8uh3v9u00qLlOG55EzopOFVMwLGkmGC55tJ+rh6OgIwBsRCkTSkwmAI6svInSEFCPQykR0RYZUU4bn/IMk1AyY5DDiwdgT2rScKqtgKCuqZUe5y6HofD8MlPanGzRtpFgNAm1BrNrkDWszJW5BumnsiV3HDddQ0fgrP7QsTLDs/bJoAEbwFjtv+GjPlkRQ8hfAb4kiW+5E4ArgRv1amy9RYLh+Fzv0mPqhGr0VTDINLwebJy3L6wOg7CkzG1fRG85UR4VwweOBmOerohXZshOP3yDfznTYsZXtG8ieK2++9HOA5GLSUlNIEwOjqKZVl0dEwsc6jRaDRTGjfgLYB/T9+nrqYSHT1cvfRE5icGeeuGx4h6BQE1Gc9mpWdDofjgZfwYIycKOO7EXggpPhieUkXe0iNWFebTokB8sG1IJODf18Lf/yxNtRtJYf+Qq6le6O+ghIkdO3Lz+2L7FWPWLPjSlzweGVWQzcLatTBtGsyfX/3xmknF8Lp1/O2UUxhdt67VQ8mjOy0Nmp1GTHWLnR6uEIDH+yHkCgsTji3cZsoqber1pAW9rhm1KaRFjeM5xkYKEALodFy/CTWscoEHd9yf86OBqmtlLSjhU4nC1ZRwEgJ6emDevNr61hCLxViwYEHlHSc5+i5Vo9kVcKRBqnBCQK6m59Hid1i2zdlNmFw4Z5w0YbKf5ggw9qy6rWQ1WRCZEoGgg4AiL/n0lWovdLkQTbUcR6Hv8iRCwKr98sUHL+lpcPRjsH1Gw0ZgmHDO1esxouWCEgKrwyFnO1cnQvDUz34WTFuamlD+D6YWgTQaTbuRlqWQ/jltSS77oC4MtnbN5PIlJ2LnCQnuy+FwvnhQKWim2jBd0SEczveRMIrs64fCftVq3rE4LHuq8eJDISroZ1n5ogrkSlGBLA2lxBs/n19XF3z0ozBnjv+xCAGDg/D00/CpT8H3vgdf+xr89KcyO0QzJelfvpzfHnzwpBMfQJ7Gh61qUmfCNY9O5PqOZCFjypJJuNkQ4y8WYDrS+2G3BMwbk88jdm7/iD2xnJMQUnjImG65JrPgLqHEaTwzBTOyxV/Lf08BpIF7M8z8YlnyenTDDfX3v4sSj8fpaoPsEZ0BodHsEshJYGrT2SDkaf8HsYyXiPGpBx5ouBIpOqKwx8K8bQ5dZIxX1Nag0UdWhAjh45t2qESQcT4wCyiSTdp2AkMldAaEpho+C6xs9SDqxYLd10OyJ7ClGIVzesOA/7pyFRuOjTJnKI0lBA4wZhmEXyPo/gwYJzLe//CaEDefvwQnW6XPjYcXr7ySIz796ZqP19RHLBZrC4M4jUajmUAoRBqDF7rmBtrsSKSbH807llcNruTgzJAbUHcDXKqUkMFE0aMw+A75q3vVT9t2g/JFSjVVy3j2QxyefRrWvlRbO/XiFSEg937C4XzBxq94A/CmN0nxwC8rVsC3vw1PPgnr18tyWqYpsyceegh27oRvfau696VpOcMbNvD7Qw7J916ZZBy1Ap7cN+BGi5wiBtCXhp0dUiywDbAtKRoIVe2t2KXELZsUcUs2DUdkZkPGlAbUpoAOGxKWfG4gRQaBNKlW28aHVNhHQcmGsAOLY9BT6na+WKZYs3EcmT2xZUvz+24T4vF4W5Rg0ku0NJpdiNTq948//zR3AfDNu+5qaJ8CsN/0mgnbE5xLSQ00HceI7SjzBWmQ5NU+BxAp/dpe7IJqQwGWBTNntnoUmqnCAPDTVg+iHFVMqjPd8JY/NmYYvwemg7kv7DGYoksIosj8s5m2oOd6ME8FIwzGEjA2wvS9srzp0RcxI3a5lsuSHRmh/+nGlJbSVGZ0dFQLEBqNpj3JZrmxZ+/ag/dlcEyLf886gJ/OPpKdVocsnu54V9kWeDYUK0mknjuOfCTH5O9eY2qF3/dQeB+SycjMgueehQfulSbazUAZTBeWo1L+D7bt8bwIyYdpyvHaPuYUXV3S98Hv55JMwrvfLVczr1wJ8Tj098PIiCxNNTgI//qXFCI0U4Ydzz3HZUuWTGrxAWBhEzKwDderIR6RokHWhE71sZTzY3BLMQmk70PKksJFxIGurDSYDtuyHJPyfXBcQSFqS8Ei5LnsKDHCcjzdeV43hGz3iAHYPVHizYyXp/PpBVMthUJoMRxH+tEsXhx8/7sI7eIBoQUIjWYXwhneG4DrxYsA9CUS9PiZmNaIAOwPvR323iNve5p5YOw18QDHIfL03+m85et03vE9Ou74HubAmhKtH+hvEGam9Guvpfl5YA24cauL2bPhvPNaPQrNVOEcAqsYNCm46m1FCrlWz/h8fjswG3gfMOLz4PXAHkAnmOvhvDtX1DWWRy+4oK7jNbWjVicZk+06r9FoNHViAys7G1e6EGAsFOV3sw6h33JNqS2ruFeD8owoFB9UbXLLyhlb11PvXKGCZ/3b4dor4Te/gIfur63dalF+DOp9RTyG2spUWwUXk0kpBoyN5cynK2GasGQJnHmm/zHdcAMsXy77ymZzn1Uy6ZanGpNjvO++6t+vpiWMbNzIFYce6k+wajFdWQiVub2vmsJLhJCCQUhI8cFxsxYyJpUXLnrECYG8bmZMaUTdmZXtCWQpp6jHrsYkJ0gg5D4hN4PCdEUNs8ilzHJgwRh89cna3nrdeK/P5a61hiEF0cMOa/yY2pREIqEFCI1GM8VIS7Pn/+FuAK77858bmgCQWXwU7DbRbCjNe4ruH1p9J6F1D2G4E2Yz3k/0kd+CXWSWYVik2GPi9gmddZZ+7RVAX+UmAsVrtNdq5s6FL38ZXve6Vo9EMxW4BXik1YMoRy1BBgPe/Ndguv8NsrTbQI3Hp4B9IPIjWHj6YM3DGHz+eWLbttV8vKZ2YrFYW9Rn1Wg0mkKWdc3FMaowbq6RrBniL7MPIouRW/VfzFdHBbuMItsgly1Q05y7MMNCwNCgzHr4+59h+9Ya2qwB5engLaVkWRCNSvFBPe/qkr+rz6qaMiuhEFx/ffHPuBTPPiuDicVWyiuT2t12yxliayY1G+65h0v32KP6uv4twgCOWR5QY0XEB5WVkHLLIZki5zE9LgKUO71cb4iIDZ029KblsVkD0pY00A4hxQXHYNzg2nZPwawlSzulzZwAYhs5E2yF4Y7t2O1lsh8ahbo2qYyzSvuGQrDPPjA62pzxtRlCCJ0BodFopghCTg6zQwcCUbLCYRtjAJy8aVPjugXGXn0xTsFlJsbpYBRPOwhtmijfG+k41o7iBecznOVjJGVuPELA6TT+SuitvWh4bqhaSVcX/OlP0jQupO2ANBXIQAndcOpz/evrOlwMA3OADwUxGOACOOnBLXWlST/8la8ENBhNNbTLzYFGo9EU8uyYD38iIegcWsO0/ucJJQdr/h5LWFFumL60vHgwngWBR4wwKosOfgQJNWzHkUH2oSF47hn4z798voOAUJ4KS5fCjBm5skrJZM4fQwX/CsUKv+yxB+y9d3XjOvRQ6R9XrB/bluOdPx+OPrq6djVN54Urr+TKU06ZMuKDYnYQ1c9K+D5YQmYuqFwQS8CMlBQFlCBRrByS93dLQFiVU3JFhLQlX+9wMyFsI5fh4M18UA8lhIgSfRkCOhyY32zxAXJ+OOoaoDK0ClHiw+67Q2+vFCY1NdEuHhA66qTRtDvpDQAknv4aYPBrZJD/8PXrGxp3d3oXIuYfRELMZBq/BcAmAhxX+iDTgkwCc2g9RmoUEerA6VuEMEutuJpdeSCiwoTqIOCfKJ/uxlCsVq3fiZ66UQqyZqNhyNJLS5YE16amvbkDaFffMCcC3/8MfOni6o4TwDuAvwQ/JPNxOJ8XuH7ffbFrEAi3PfQQqdFRoj09wQ9OUxJtQq3RaNoRRwi22qW/i+avuJZD7/0yIfLLtzjAxqXn8+wpF1adifBC5xxOjm1gplPKWdWlVLv1GE17nw/0SwHiwXth6+ba2qwFw5CLhXp6pODQ0SHHMzQkg31KAFBBP6//RTWceGL1Y0skZOZFYQaEynw4/HA46SQ444zq29Y0jZXXXssNb3lLq4dRE3sHuYbSLYFkIP0ZhJuVADKLoSOb83Kw3KB/wnL1gGIG0eRKKHVloDsDOztlJkTEluWdhqLydYd8g2rHcE2uccehxAmHCQsm1b57xgL8LKpBlbyD/GuPd5FlNCofpgmnnAL7Bu0evmsghGgbAUJnQGg07U5IKs32zmMAuIynALj9979vaLfpw98qnxgLGEMGusf4SNkbguxuR2Btex4zth1jbAgzth1r52oIdRQ/wAjhUGc6+Mn1HV4ValVSNfU1q13JVAnThIUL5eqlpUuDa1fT3nyw1QOoRJ3nyLe+Xt3+m4DZIBogPiiiwPkrV46XpKsKx2HlXwMqLaXxTSKR0AKERqNpO1YPZ8kWmW9bmQRn/P5Ijrz3ixPEB5CBhsWrruHkv50GTpW15Q2Df/TtV+OIS7VZYru3xJF3WyoJW7fAzy+S5ZeaiWXJAH8kku/n0NUlVxSr4J/jyKwIx6m+fn8kAt//vv/9YzG4/Xa45Ra5kEktkFCrnKdPhwsugB/+EN7+9tZne2tKsv2ZZ/jnG97Q6mHUzPx6K/kU3DZMy8C+g7A4BvPj0J2FOUmwgGQYRiPykIwBSdPTRonbDxsYDbn+DxGYM+ZmVpgQD8OiGMxMSXEi63o/qMuT8F6nVAZEkVPJFNCXhv1qzQapVaRVmVmRSO66U3jtNE1ZFk4ImDYNPvlJ+OIXaxyoRgkQ7XCPob8VNJp2Z+xR+dOO4gjBBuKE02lmNDDV0umcSeqEj4z/bvMG4pwIRnnVVoQ7EIaBObwZc3Qr5ugWjJHNhJ4vnfKcLpdRARCtsHJqATLS10z81qRVX95B0tsLe+0FF14YbLua9uUrwIZWD6LBJHrgOZ+BDhtYSu1eD1UQAt6wfHlNpoDLr7gi+AFpyqJLMGk0mnbksR1FNgrB6VccQzRbORLYE9/ESf94beWs5AJ2RqfxYNeCqo4piyrXVCwzOW8/IYP6LzwHN98Aa1YHNwa/OI4M8oVCcg4wNgYjI7nyS8qg21vWtdqA4sc+Jv3g/LBlC7ztbfCBD8B118GmTfl+FJGIvL844wwZcNRMWtKjo1x55pnBLnBrMgbQkarxYJH/PCSgw4b5Y/DadTAnBbOSEAvLXVOWKxIIWUZp3IuhwulmCEiE5G1D1oShCESzUuAAKVBgQNiBkLo0qrEViBBF2wdmp+Blxa7PfqmlDLPKvCr196O2R6NSqFywQJrc65LPNZNIJBBC0NMGme1agNBo2p34P8mkuoAO7hLrAfjD1Vc3rDuBRfqod0Pn9NxGI4owTq14rBHvxxpcJ02nhQBHYKZGiD7+h5LHZNmnfKMRH4URJ/pkB4tapWQYxVdZlULVng1qBVFHB+y5J7z5zXDQQcG0qWlvskAVi+NaQxA3UAZ8/iJ/u86jsSXbCggDr1uxourjMkNDpGOtysveNdEChEajaUe2FvkqOe4f5xImnV+0vMyK4N7hFzn4jupXwN7dswcbQjVeV8vNtUvNxZX4sGE9bN8G69bU1ncQOI4UHeJxGB6W9wTptByfeqgFCuFwLjPCD52d8OUv+x/Lt74F990nS0ClUrJfrw9EJCLvV66/fkoHtncF7vz850ntqCdq3XoM4IgXaziw0EcBGfwXBrw4HS7fH15yY7xZA+KugBBypHm0MqF2vA0U4sgga8TJeT+MhmXmgwWs682ZT5tOznza8nhB+HkfEQfO3ATd1a9RctsQOS8ZvyivGdvOmcx74xwKx5HXB8OQ162LL5YiqqYmYu79XDvcY2gBQqNpd6KHkXzsl4DB71mG4Ti8aWVxU+cgEJ3TyS46sqZjzVQMI51Afq0L+dPJYg5vxBgpVYB+VvlGQxUyIKCxAoQSHRyntgm5OjYIMhl541DMJEqjKcZ3kCLErsBtPmoVvwbYKZ828/a6G9h3c/W1px/99reDH4ymJFqA0Gg07cZQyqFwofG09Y8zc/CF0l+EJbYvWnM9fZsfqW4AhsGVMw8gZlRYPasEBVWL3LvgZ/z3EnNxb4nUVAo2rIOXVsGKF2DtS9WNNwgsSwoKKsg/ezZ0d+dWEBe+B1WGye8CJ8OQBtGzfXjpKW6/XWZfjI7Kz0hh23Ks3d1wwAHwxBPw3HP+29U0lZENG1h26aWtHkYgzK9WQylyaliu/0PGkA8MaR69tSuX6WC6WRKqVFLJ9tTvZs4DAmSbaTfqmjHlA7et3gz0ZOQ2x8j3nyiLAZYrQNRFNaWelchg2zn/l2LelkqkUOXjxsbg2WfhZz+rc7C7LqOjo4RCIaLRZpftCB4tQGg07U7sJrIDcrX7zazhi3fd1dgTPxzF6VtU06FOR0/R9GzDThNafVfxg4wOnHI1lGZurdzx3vj7sq+FRq0CqqVuo23D+vXw2GPBj0fTfmSA77V6EE0kG4XNZW7G/xe4oWmjmcCRQ0NVi5Hrb711wrZMPE5GZ0Y0hHg8TldXV6uHodFoNIGxaij/eyc0MsaJ17+98oFFpr8GcMyN76l6DFkzxFUz9pvYpBDSW8LOyoBYKgXxmPyZTkNyTG7PZqWfg+0UiBJOTniIx6F/B1x2CVx6Cfz193DrjVWPtW683g6OI4N3O3fK4L9acezdt1Bo8UNvL/zoR9XdSwwO5jIfIF+0iUTkY9UqePJJuPLKiQbVmknBv6eo6XQxltbhCW8ImX0QcaTJdG86571gObJkkuEREMKOFCvAzVQQRcQII/fcQLYRduSySktA1JYiQ0fWLSHlQDIkSz3ZrgH1eFuVcP0f5tRahkqVg/ZbFhpyQq4SSKNRKYqqWIfyfgiF8v1pAGbNgpdegtUtKGfXBsRiMbq7uzHbwFdn6r8DjUZTntSzYGWIO2kywJfvuadhXQkzjOicgej1Ua/VKHgATt/uJXePPvybkg1ly2RBGPN8LA04iKl3NaxV2HAcuPZamOKpt5om8HuYsOxx0hGkwGfAjz5d/KWvkyfGtKK4gAGcXmX2mkilSA4OArD6uuu48thjueplL+PqE07ghte/noHlyxsw0l2XeDyuMyA0Gk1bsWIo/xvviE/9EqvDZ82PIl+WETIsebT6lbDbwj0sj8xw23UD344DY0nYtk36NDz9OGzbCtu3ysyFzZtgxzbYsgnWroEXnoXREfnIpCGdgURcBvdXrYR/Xwc3/RuefVq2U4yCYF3R0F29QSIhZBBPlVXKZPLLHXnHUVjitRyGIbMevvUtOOYY/+PZskWWbCrV/uio/D/YulVmWj/1FFx+uf/2NU1hbGCAzQ880OphBMb0MeiN+9y54E/XRBpDH7Md9ohDly0FgrAjBQGAiJBm1F2Z/GaEQb5RNEy4EAgBKROSlvwZD0kPiJGwzHYYiMjGlFG1IUq3VQxLQE8WFtRT1UhdQ6oRIoXIFy7UdUp50Shz6mxWiqeJBAwMyJ+OI68PmqppFwNqkP6GGo2mjXGMRRBfwjWs5KSXXqK3Qf0IAMPE6d0NMW12wQuU+DLNl/qd2fu62yZOcENbnsJ68TbspWdM+KLMciQRii2DEBhdI5UHfwRyJlJrDcVyeMc6WWqixuMylbqNVsFoGsCXWj2AFvCH98KPvpK/7VbgWy0ZzQTmOQ49Y2OMdnb6Pua53/2OVX//O/ZozihU2DbDL77IzW95C2f94Q/MPvTQRgx3l0OXYNJoNO3GJo+V2oJrHmP64v9U10CRwNoBT/+c2Mz92LH32f7bMQz+PX0fOgZfYElyUAoPo8Pw/LPw0mr4zz/lnLtvOnR1yYDYhnXQ2QVnnQPzFshsiaefhPm7wbRu2G13GVhfvQoefQjuvq10/6ZZnYdbtaj7BW9WQXd3roRJIZWMtIsxdy58+MPwiU9UN7ZEAnp6ivfjLTW7dav83NWYzzvPv8m1pmE8+atf8eQllzDwwgutHgoQ3CIeC9i9H56vFJct0mF3GpbGZAbBnBQ8MkeKEGEHUh2y7JIpZHmkrixs7oaoYxC1BUMRT7mkIhhIMSNrSQHDdrMebLc001hIlnNS3g8hW7ZlOG7Zp0p6gJDj3G8Y5tUqQHivZdXGJ7JZeX2ybXm+d3TI7coXIpPJZUvYtsyeeuQRiMVg331rHPCuzejoqBYgNBrN1CC9aX8gwrd4gI1/KG3mXC8OQGcv2T2Pz3+hrPBQsCnSiQhFMbLFv027rv8MyVd8jcyhbyrou3TmhNnlw4TaQjq9ZirtWAOTRXTwIoS84dJoSnEvMNjqQbSA/oIb5Z8Dn2zJSEpy9po1XH3ggb73X1FmFaLIZLjrIx/hDffdh1FLWTdNHolEQgsQGo2mbbAdZ9xs1cja7PW7ewi9Zl3tDXoWJR115ye4bfdHyUb7/I/HtLhm+v58+tGrsF54Bm69CV5cIYUFRbLgHiKVgqv+IoWIbFZmPkyfAfvsJ4NkK16QZZvKzdcNQ+7byO9JtZIYcuWXQiEZwAuFZAZEMdSK5Er3G+EwfOEL8LnPVTeu/n5ZrmnFiuKvW1YuS0P9vmOHNM3+17/g/e+vrj9NYDz7pz9xywc/iJNMtnooDcEAeitVFC2mmQlZ/qi/A17shWgW5iVgqAN2dkgxYEZSZhlMc0y2djpkDIg6goyVy4Io16VtSZFAZTckw1LQyHiOs4QUMmzLXQPp5/Ii5HELEvCexll6TkSVhvOWXlNZaF1dMH26PO+zWXkN8F6vlCixbJksA33aaU0ceHugMqzb4V5tqhUd0Wg0VZLddDAAxz73RDmnhLoxANvO4ExfzMT6SkXqLU04WmKX8Y+wRjfTcc9PMeI7C14ppQgLf/cKY7Smpkqr6OmBk05q9Sg0k5lPtXoArcKEVXvKp9+iqPjQ6ktFGDhhXR0BoALSQ0O8eOWVgbW3qyKE0BkQGo2mrRhM5QL7u//1IULmMEYQ0QMBJoLjrn9T5X0LyFghfrXfGYir/worns8XH8oxlpDiA8DQoMx4ePwRiI1WFh9qXSlcDZmMLAUlhBQLTFMG8W27dJ12v6WeTFPWaz/11OrG5DjwxjfC73+f834oRI3Pm/Vh2/K93NBC46xdmMHVq/nV7rtz0zvf2bbiA8jowb6bCsoXeSlzumYM2NQlyyINR2WGw7YO2ZZtgGmAZcD2qEPSkkH/tAVxy+PVUKJPAzdDwjWuVvsLd7tt5uo9OPjMevC86e4MHDoIr6zXgLpavOWXQMYTIpGccGqaUnwoda1IJuFjH6stQ2wXJxaLtY3HnBYgNJo2x07tx2hqmOuuuqrhfaXtMczB+gJjmf3Kp2NbO14g8vBleduWz1rD/556D+e/8TrOf8P1fPLM2/ntoct4YeY2fOQ/wExgV/JKO/poaQal0RQjAzzZ6kG0CgO+dCFcjvR9mKQsjscxA5zAP/Wz6utxayaiPSA0Gk07sdyz3mfRtY8TPvCR4BoX0DuyhjkvVVnSCYjNmMuVX/51cxYEqKCb8lqA+j0eSqFMp20bpk2Tv6sAsqqxXri/n+wHgIMOgqOOqm48994LTzwx0fzaixByzGpVdDabE1HWr6+uP03d3P6pT3H50qXENzU7Ou2PIM9ZAfQm5KMaTAGxCKQsGfw3gOGILI2UsuQ+/VHY0Ck9G5TAYYgCo2jvmxG5tk0hhYWk214iBGOmbN+B8awyu1B0qPThCGmQbQn45HPg14pnAup6Zln+s7pUmTXHyXk9zJ8P8+bl2ly4ULZZ7nqxZo0WJmugne4vdAkmjabNcbbtxrob3kfpvIJgMAAHQWRbrfUlDTAE6aPfRceDv8QQpb9Vo/f/gsShr+Or//UnLj32MpxQmUCcgG4HXjcKP90Oc4p9uXfif+XBVMc0YfHiVo9CM5m5tNUD8EuDQg+PWHBNY5oOCgM4Z+VKbth//0Dayw4P8/wf/8isAw5gxj77EOnzXxJDIxFCtNUNgkaj0axzy5v0Pr6WcDxNaOHaYDsQcMQdn+WWJa+surzR2hNewW2f+QFnXfzF2vr2Bu79+Dt4Rf9Gr+BNpWQgr6cHRkZkYN80i68s9iM+hMPwt79VPw4lPlTzftXnaJqwqNF3nxqFEIIrzzyTjXfc0eqhNA0D6BqT5Y0mUHBaGA6E3G2OmTttQkKWSUqbUlxQAgSC8TQFlaDgkNMdDHJCg3B3MEQunGC4uqU3I8IS0hfCJv9yZ7o6YkU9wZD77paAbD0aqF8vm1LHqutBPC79H/r6YMYM6fEQCpVv27bhuefgta+trf9dFO0BodFopgTCERAPcdxTZUzVguoLMG277kQCMWsvnFlLsfpL1BoFklaCfb52MkN+FvEbMl3yr9PlY480fG4A/msU5rnfnyIC2W6wMnIy0ZaYpnyEw7BkSatHo2kgWeAeZAy9A/gYUNX/+M8bMKgpw/2wsfqSEK2g13HYd8cOVs6ZE0h7T1144fjzhWecwct/+tO2qDXaLBzHIZFItM0Ngkaj0Qy7k/q9f3s3IDAiwacLh0SW3Z//MxsPekd1B5omj7/9k2xbcgBv/+RrxwN/z575Ru7+5HdITJ+Flc2wx6N38coLP0H3YH/uWMOQAX7IBcs6O3Mr+RUqkN5owUGVNVEig23naqgrH4hMZmJgz7JyZq+lCIVg//1hzz2rH9dRR+UyGiqN37uPYcjSLB/9aPV9aqomk0hw9TnnsOmee1o9lKYTScusgLIIGfTsciP8aSEFgqzr05A18nbFRIoKAvd1U/pEOCaoNY+OITMYBG6GhEd8EK7wYAppRu0Y8nnEcT0gDBiz8jMqHKi4GNJ0ZNbD7BR01nMprvea5jjy+jQwILMehodlibdMRvpBDA2VPta25TGaqojH421zf6FLMGk0bYw9ZGM9d0NTlEYTCAnBH3evvz5d8tTPU+pbWAB7voQ/8aEI6yLwyfkwfx8w9gFzX/no3AodMZizBY56CN70V/jWl+GRoyrUemw2tQYEQ6FcuuRb3xrsmDSTghHg5UiPgDOAXwA/AvYCeoGn/TQyCJTW/tqcFcDJZfeYbPrkkTt2YJSqtVoHm26/nWte+crA221nEokEQgh6enpaPRSNRqMJhEQGjHSGnlXbwUw1zIN5ydNX1HagYbDxxFfwg7u38/i57+aHd+3g39//M6ML98Tu7iHdN5MXzzyfn9+0jhUnnSOPUaVHVEmjaFSWO8p6InpqhbDX26CUD0MQeI1dVT+ZjBREVJmlwv6VYFGuLrhhyLn/Bz5Q/Zg2bIA//Ul+Pn7G713o1NsLp5wCZ51Vfb+aqtjx7LP8+YQT2kp8qGaubQGLtpdvQGUwdGZzooIw5IKttJnb3xT5WQxRG6LCIOLkTKdnp2Bu0tMwELalwCFcUcIxcgJFypKeDyC3hYAZKZiWhZ4MhIXMwrCUiFFY2kl1JWSsxRQwdwyO7p+4j2+EkNcFb1m5WtpQ9x99fbBtm8yAGBmpfOz2wv8wTSXaKcNaCxAaTRvjJBw6//mZ5vUnBH/crVgeZHVk9j8Hp2/3oq99+ofQv6DuLiRmLm3SDkOmE/rnwRMvg6vfDF+/AI59BLri8Nrr4MW9A+q3HqpNmezokDcCfX1w7LHwr3/BHns0ZmyalrEWmAPcW+L1UeBwpCBRlu8GNqQpRhw4oOwek018AHnvc9aqVQ1pO7VxI498d5f9g6iaWEzWKmmXGwSNRrNrM5KSWc2L/vIgBhDa59mG9TUtvg7DTtd8vJjWx61fuxS7p7f4DqEQ1/7kWp565X+5B4jcIxyWgf50eqLJamE2RCNNqL2ih8psyGalB4QSJrwBQyHk66o8U7FAYne3XHT08Y9XN5ZsFn7+c+jvh9mzcyazfujuhhNPhHdUmdGiqZqnLr2UPx9/PP1P+1piNCnwE+6uJiRu2ZBSf55FgveG65lgOTAa9ogDJnQ4UozAkGJDTwYiQm6LOjB/DLoygu6MzDzoS0FPFsbCUpQIu2WdHNOTDeF5E8KQ2RNpQ/pAJE0pSJhuVkUylBM2Qm57lhIhCj8TV4DYZxg+vww66/V/qJQ55QfTlB4QJ58MM2fC4GB5/wd1zEsv1dfvLkgsFtMZEBqNZvKTueUZQplY0/pLh0IsGRhgzKn9JgKAcCfxc3+OCHXkbY53ws8/XV/TVeGKE6kO+PfrYN8X4JAn4bL3wuhUiTFZFvzf/8HmzXDXXdKETtNWqNC5n7Pu88Abyu1wdRAjmgyMAOuQjtp+mNvAsTSWWbbNwnLpznWw6s9/bki77UgsFiMcDhP1s1pUo9FoJjmrR2RwauZjawEwuoca1pchBLut+GfD2pedGNz07SsYnOf6Eqha5bFYfuaBEgG8NMp0uhiFQUEVKPSWafLum8nIuX4kIj0jpk2TC4/e+1545BH47W/9951Kwc03w8c+BjfdJE2kBwZkv+Fw+dXSypw2mZSroF/xiuret8Y3dibDvV/7Grd95CNkY827z280gqIaQsVj1s0rfZBhSPGhKwvTMvLW3hIwPQnzE7kMhJCQJZo6benF0GnLQOmMFBzZD4cMwPSMzJqwEYQdCGelcOAYsoSS4Q7IcDyZDO64DGTJp4wBG6fJPkLKVNpxDald0cKgQIRwyznNTcBZW+Bl9WY/KDPpekRVdT3avBmWLYOdO+W1qJIAAbB0aW197sLE4/G2ybDWAoRG08ZEvvLuhrafxOJv7M/RvI/pfIn5mc9z5XUHsM/vLuXqp5czlBiruW1775MZO/NrYOQuUx/6NTLXslWE4NnD4EO/gZk74cBn4MGjWzgeP8Tj0nQ6pC1/2hEH2A9IVtrRwzVIT4gJi2dsYH35Yx89FA5/DLpGYNognP0v2Da7is4Do9SEWQB/Br6ETOf4EvB4hbYuBWq/Vk0GTt68ma50ncJvCR74xjca0m67oQzitG+GRqNpB5JZgZnMMG39TgBEtv4M51IIw2Cvx67AtFMN6wMA0+TPl90mg2+JRH4d9FAoV5pJBeW8ooS3dFMpCgUCv3hLLKnMBy9qmwoaqnJHqoxUR4fMVDjkEDj/fLjlFik8HFA+szMP24bPfAbe/374wx+kUeyTT0pRwo8PhGlKEWTOHClCbNpU/eegqUgqFuOvJ5/Mw9/+duO9SZqMmj055Bs+lyMZgWnFpvBuJoHpZhWYQmYvdGVlNkPU9YAYs6QJtWPAYFgKAWEBHY7B1g7Y1gHrpsGWLjdDwfWMSFk5MUMNXuB6R7jPLSHb6rTlWCzHsz8yewNkNoYyrHZwTbK97wM5/nlj8Np1VX6ohQRRSk4dHwpJ4WHZMun7EIlU/psMheDdjY1PtSOxWIyucuX2phBagNBo2hXHwdryfGOaxuC/eRVd/C9v4808wSJG6CBNhKwIk1jn8KF/3MJeF17Gvhdeyr0vVYhqliB9/H+T3u8c1KXqqsniDWtANgwvHAwnPAIztsHtL6+2jSYGqa69tnl9aZrKmUAtt3hrgSMKN27BdUHL4QCf/CF0DoBhw8uegqePhLFpEO+DW14N87fCr99XwyAawr1IC271RuLAb4HhEvvvAD5csdV6kpSbcaYbwFmrVzek7bVXXdWQdtuNdjKI02g0mm1JmHv7c1gpGQy3usYCr0WY7eklu2AR9sLFdM5Jctqtr+WU29/Avs9dgpltzMKA2PxFbN7nkJyw4Dg50+dMRj5XwXZvqSa1nyp5VIxaVxR7+ymH8oMIhXL3EeGwFCI6O6XR9BVXyJKr1fLww/CPf8gyKqlUbpW0Kk2lskRKoT7PTEZ+PsuXVz8GTVk2Pfggv9htN7Y+/HCrh9JQqglQdqaKe0D0ZKA3I4UGQ8BYSPo9mEDGkuWYRsIysG8JKShkXEHCAYZCgqwF8TCsnwaDERiKQjwkRYqsAWlLPkxX6Bg3m1DlnV2fiZQlBY6MW6op5JZTGnM1XZXtYImcKDF+8+BmRFhA0oKDBgveayVBoVg2l2XVvzDRNOV1IpXKZWilfAjIp54qyzVpqkJ7QGg0msnPFVc0JPCVweBE3sNlHIOfS0h/Ism5v7+On937WE39JV53MdkFh/DCUsh2VN6/FQzNgTPvgllb4bHDK+xcWFfWshovRujsh7bkLuDOOo5fBuTZkd+Xe7p8H1iyUk6Ef/45SE6ndCTdhI9cBj/5WB2DCYwnimzLAs8U2X43MK+xw2kiXUJwaoNECNvPTcUujsqA0Gg0mnZg7SjMv/258d+dsa7AFHUbyM7bDfpm5s2DTSCSGWWPDf/k5be/hen9TwbToRfD4M5Pfa900N/PnLzcKt9asyD89K/aVkKIN1siFJKlj/yYwBbjyiul+JBIlC4DVe59KxFnZESWgNJBxkB5+Pvf568nnIAzOtrqoTQM5aHg/eurdDaGBMwuWGNkkAv4p12vhbQFOzpkEN9ypMiQMqE3zXjmgTKQzhquOTVye9qCgShs6YRtXbLsEmp/XBEDT9kkJUgAws2uUNkRWVP2qzIvlCih3nzYzdYoxBSw3wiMRgo/NJHzgCl27TAMaSTf2ZkTK71+M7XGIJSQAfLak81OzNwqxle/Wlt/uzixWEwLEBqNZhIjBM4HPxh4szYGJ/FuHmZx1cd+49YH+N7tD1Tfafcskmd9lc//pJW1l/wxMBeOeRxec42cbAD5xnBeAzllMqcmA6FQbkVTkLVmTRPe/vbg2tNMGl4TQBt/A/6hfrlI/rjwU3DAcli7FP/BBgM+8zP40ScCGFRdlKq/792eBvYBTvPdaq0xF6PgZ6NZkEpx9IYNgbf77G9+E3ib7UYikdAlmDQaTduQzAq61uzIbbCDmYc7gJi3G4QLI2nkRR7DYoyjH/sSBz59EaZdTaHJymw89LjiL9SzKEjN3ysF6gvxzvlLmUkriokmqj/DgN12k2VQqmXDBpktXal+e6XPRvlA9PfL8q+aQLjtk5/k3v/5n1YPIxBEwc/C7er+uYrbD2bG8xsykBkPSStXbjaLDPbHQ7KP/YakJ4Qqi9SZzZVHUsbQwjMeJTAokWF6GiKO3CdjStECdx8lIihPiHx3asbLNTmuIJF1hYiMIcUSx7uvkILHwrj0rJhRbD2QN2sL8s9TlbmlyrZ5zeuh9owtJThYlhQt/WAYslScpmrUPUY7oAUIjaYd+eEPMdQXS4C8lfN5lD1qPv6Hdz/Gf5ZXv0I3u/dp3H568O+nIRhww+tht3UylXN81RDkTNy6u+VqBMvK3TSoVUzV3rhU4sQT4eCDg2tPMym4AllcKAjeDAwCPA8HPw5fvpjaIuYGfP4nsLmli96K1ULrBQ53n38X6AAakykwGdhndJQjAxYhVl15ZaDttSM6A0Kj0bQTHVuGCCVzAWmzp8aV9QU4s+YUFx8AoyAsaQALt9zCabeey6GPfoVwaiCQMYiOLkbm7lZkAEZuXl5Tw1WWYFJli9SCpFIm2F5UKaRIJHcv0d0tMw5OPln6QVTLD38ofRsqUc7/AuRCqu5uOPBAaYCtqZvHLr6Yp37+81YPI3AK/8LHF+yI6m5BDCAWyf9dQM4Dwt1uIoUFy4G0AZu7ZeB/uENmOAgDojZEbNdU2i2hlDGlaJo1ZBsdrjl11s2scEyP74MauCs4WI6bzVDsDXn3dcdmmxOFGQPZRsSBM7ZIo+yyKEHTW2opHJbXld5e+XskkvO8qQd1vLpuVUIIuPDC+vrcBRFCEIvFtAm1RqOZpDgOfO1rgTf7AAu5moN87l36S+i9f7sR264uwJ4xMqRLLWyepGzbHeZsLvgkvOJCXx90deWnUQfNnDlwySXBt6tpOZVdC/xjA6dnoG8dPDfBGKJKDNi34bH9cpPcg4D3AwuAsPv7Z91j5gFfqbnXasMRpW6umsF+o6Ms7u8PrL3Uzp2INjM7DJp2qs+q0Wh2bTK2Q9fa/jxfKJGJ1O0BYXd0QEf1Rpomgnk7H+Hld76VRauuBFHn95FpsPq4syZuVwuBal0VXNNYTBm0V4uSKqGyqCORXECxo0MuNnrLW/z36ziwZQusXCn9H4aGKh+jxI9i2dqWJe9r+vpypZg0dbH6hhu467OfbfUwmoZR5OHrOM+OSghwTBnQt43cT5CXMNuUvgzT0xDNygwDx5AZEWFH/t7pPlflnKK2fDiGzKwYDTEucqjroiHk06yRy2IQJuWvm4VvtCD7wXT9LM7cDOevLfPmVYaDWvgohLxGKJEgm5XnZTabEx+8JZOqLcdkGPL4sTH50+89wq23+u9DM0473WNoAUKjaTcuvNCfCVCVvIa3EUQILe0ILqiyFNPVB/6judG7gBieA2fciPySVw/1ZT06CvF4zuQtSAwDenrg9NNhXvvUuNdIvgsEW4wAngrBSEBZsfE++OObg2mrNl4GfAO4BPgk0oC6B2k43Rwmw+Xq+O3bmRbgd8HwmjWBtdWOjI6Ots3NgUaj2bVZOeQw44m1ed9lzmhP3V9uom9W5SBXmWCdiWD/VZdz7D3vx8rUY1JtsPXAI0u8VMeb9CtCqHsC1d/YmAwKRqPly7AahqzlbpoyY2FgQJZNGhqC7dvlww/LlsFb3wqvex2cey6sX++vfrsXIXLlY1Xwc2wMdu6U23T2dV1kEgmuf3NLJ9MNIej5sQN0pku/Pl5Oyc1oSLpG0yMRKTD0paHTlq8NRSARkkJDh+sTYbkiQEhI7wZV1kngZli4r5nCU2nJ7TOrxIda3rRrjj09BUfuhJO2ldrPveaoWILXazKZlNuVEBGPy/M8mZRiROH+1Yio3tJO1TBrVnX7axBCaAFCo9FMYr75zcCbvIr9GKL6FUuluOS+Ykaxpfn1sb8OrO9mc+fZ8NISMTGtW60YaAThMMyYIUUILUC0FRng+60ehA/e+7tWj0DxB2QWRDDnmp97iMkgPoCc4L1q9WrClWo6+2T5H/8YSDvtSjwep6sruO9JjUajaRXrYoLZD7yYt82a5TO4XQI72iHnpwHQO7aJU24/l96dz9TcxtCCPfM3eD0cGo3KJFCGsI4jhQQlTIRCxY8TIrfITNV9V6Vd16yBv/2tct8jI/C2t8Ett8CqVfDSS7BjR/n3rfzq1Ng6O2XWhaoFr8rI2rZ8fffd4eijq/9cNOPc+7Wv4cSDKrY6ufAkDdSNiRQPTD+NukF9Q0DIhuGwNHWOhaRY4JhyDm+bMByRv6tgacqS+3TYEHUzI1TZpPGMByOnNxhOQVkp7/j8vnnXI2JBHE7bUu5D8IR0vdlJQshzMh6X2Ulqm7c8tDKx9osSGwv9Lf1yzjn+99UA4DiO9oDQaDSTlOuvzynaAfIDTg60PRv46o13+95/3fR1gfbfVAz4rz85uTJL3hucalYbVJMa6TjyBiWRqGwop5lSXAdMhaR2uwM+/61Wj+JjwHsI7jZHUu4srHSGNlucsICzX3opkLY23e3/mr0r0k6rkzQaza7NlgSEh/ONRY1wuq6vU9Htr361gfDVjwUc++jnOfH2tzJz24NVl2Ua3n1JwQAD9mCrhNcfzjDkfD2ZlGKCt3SKF1VrPZ3O3UPYNsRiMhviCR8LvP7wB1i7Vt4jjIzItir5BnpfVyWfvAurVFAyGoUjjpAeEPVkkuzi2NksT/z4x60eRsOoprySn7ZSHdK7oWJg0+20Oyv3HeiQQoNtyOPDtsx6SFpym4N8GEiDaJD7qfGPez64xhOGyBlZO2a+3qC8LYwqrqFhG5aOwodWwMxyCc1eP0n1UCKBEjq95dMKz81qfG+UeKF+VlNG2rLgsMP87asZJ+4KkdoDQqPRTD7e//7AmxwlzHKCT5f7xUNPc+APLmP7qFrdUfwbWSAY6ZgKIdfSPH4k2IbITQaUwVw1k/NqVmTZdk6I0iZwbcUXWz2AKvjR/7pG7C3h88CvGtZ6MX8H37Vqi+37cuAa4CbgX3UNbQI9ts2BW8otnfJHakfzSlhNRWKxWNusTtJoNLs28bTAyObPO514b31Rw0hjzNy6Mjs56smvc+rNr2fB2n/6Pi7Z09eQ8fhC3Qsoo1g1x1ellWxbZhmoEkeqzJEKMiqUGJFMwvCwFC4qcdNNuRIsfgOHKqjprSNfuPJZ3deMjMCiRf4/C80E/v6KVzQnE6dFBClNOQAhGfj384nZhsxmSFrguKJBhy0zGTKWFDa9uoJjuKKBm2WRsnLCg+nJqIjYOfFh3BPC047wvOmK7981nd4zBkf0w6GDJfbz+rEUZm8pgUC9rjKsSsUf/Py9eftSAmk1f6fRKMyd639/DSDvL4C2ucfQAoRG0y5cdZWsuxkwLzCHBMGkTBeyNTbG/j+8nLtWuxkORb7Dbt/zDjkbmMpY8MgxyC/sbLZ2kzu/+wshb0Q2bJAPTVvwNLC2EQ036h7HhHMCDqb742dA41eOGVQnPEw4fjpwO/Lu6S7g9cArgFcTuMnHoYODdNXrByEEmbF6am63N/F4vG1WJ2k0ml2bjmc2TggSiGRnze05plm6rFBAhEly8PJLOPXm19ExWjlzOtNRZTAn6BX9avWwELI8Sjicy4RQJrHegGGpoKFty6znZLJyfXVVt72aErCFfauMCzWnUKuhVRmpZBKOP95/+5o8tj39NJvuvLPVw2g4btJA3eWYQsgsgY6sW4bJR79pM1cyyUAKDwaylJISFUIO41kNUVsKDDPSkAzljhfIUk5KeBBu1oQw89/buFeE4/nplH/jBjArBW97SWZsFMUrMBSLESihUwh5bqpyabVmeqlrQS0xDJDZXfo+ompisRiRSIRotDEifrPRAoRG0w4IIet5NoDfcASipkuF/y+m86+4nrUDQ0Vfu+qQv9fQ9yTDgGvPA2ybX4v3sScv0s0A89nIp7iIFA24KXMceOEF2G234NvWtITPtnoANXD7GZANVECsdF25Efh0kB0GjwXcDQwCp5XYJwKMAQHNNQ3g9LVr624nmwza/rx9iMVi2gNCo9G0BXNufW7CNrNnuOb2RGdXDYtuausrLJKcfP8HWbS6vB+CXW0wJ8gV6aotVdooGpW+bSrAp8SaUEj+rhYuKSGgsOa6EiIqZT2n09BXZeZHsZKxXs+Hwm19fc0tZdVm3P2FL7R6CE2j2CKeagQJAylAhIEZGZiW9QT2CxsZVwPkcco82hCQMd1SS+6fediWxtSm20nWhEMHpMAQdnJ9G0KKFwKw1b2O4enbyM+C6M7INsczIoppmo4UNBbH4OKHKng/KFT5s0hkolCpBEJ1vatVSFXZV6rkUi3nuCovp6mK0dFRurq6MNqkrJ0WIDSaduDii/NWswQ1RU4Q5mb2phmVy0+65M9kshNrkL44c1XD+24GD7wMjhH38hEuZR17k2A629iNn/FZOkjyLi4NfiH66Cgcc0zQrWpaxAOtHkAthOD2U5vV2VXAa5rVWW38L9IP++Xy17JzySgwBEwPpuse22av/v662ojpjKqSaA8IjUbTDoykHCJbi4gNkdoFaIFZe+CrRvZ/8bcc/MR3SntDmKGGJYD6QgkKtg1DQzDo1llxHOnPYBiyXElHR25/lTFRGPxTn+2qVeWzG5RxdKMIhWDdOpkFoakaO51m4733tnoYTcXVBLwxe19+amqfEPC2jTAj5QoLSHGhWBuG26H66bjigiHAcqRAIAy5XbURdrMVVvZC0pS/225Gg9rXdsdvudkQ3vEJQ/pBZA0Yjsr9MioC6z0VXYHEAhYm4IzNcFS5KbvKfOjoyJVYUt4w3vJo0ags52ZZMvgfieSbSPsliOt3KgWHH15/O7sY7XZ/oQUIjaYd+OpXG9LsenrZTG9D2i4kkXX472tunrB95ZyVTem/0Tz588t4jBMoPiUy+SMfwCTNe7iUOLWnuefR0yMnHZopz1MEXpWnORhw/bnN6Ogl4M3N6Kg2zkf+B36nyuOiwPb82rH1cMz27XTXERjY9vDDwQykDYnFYroEk0ajmfLsSAo6Ng9MfCFdR0peNc6rXuqMlS/YfjdLl/9f8RcNg80Ht3iRjgoUKhPYbFYGCk1TihAbN8qfXkPqYoFAlTmhyiAVI5WS/g+bNjXmvYTDuVKzM2Y0po82Z+XVV+PsguKNktPKZT+UKnvaDRw3Ah95AU7YCl3ZErFyQ86lVR+2Kwo4bpaCJaDTgS5b7pcMwawxWX4pY8mHY8qDLVcxMdXD7SKj/CFKvAlhSA8Kddz4m3YxkX4UCxLw+WUVArWWBdOm5WcjFfqyhMNSgFBZVepaU0sJJXWNqkeI2H13Wb5NUxWjo6N0d3frDAiNRjNJePxxOTltALezBLuuy0R1lR2vf241maeuIXr/JUSe/jvp5CYS0ca8t6aSNklc+14fO4a4gg8wjVFO5jZWsrS+ft/0pvqO10waftiohpuw/O/OUxrdwxrggEZ3UhtdwH+Af1B7OaUQMBDMf5UJnLluHWaN5REyDfquaQcSiYQuwaTRaKY82axNZ//EIJE9UkdQubu6xUxGgJOTPdZdR2e8SPaeYTA8p8VlSr2rlQ1DBhWj0fya7arMUamAofLXMAzYa6+JC49uuw1OOw2mT4fXvhbuv79x7ycUghNOaFz7bc5jP/lJq4fQVFTGAkYuE6JYSaZyKJfKd62CTz0PhwxAbxq6MiV8FjxChPJ4MNwpcSIktzlIYWIsLL0lQkK215uEtJXLYnCUHYISNlyBwTE9C4e8qR3CFT8MKYCoN2s5EBYwMwkHDcIVd8Peo8U+MEOKCqGQ7DiRyF0fOjrktaOjI3c9UKJmR4cUEMbG8sumVUut3g+KeFyLkzWQSCTaxoAatACh0Ux9PvzhhjX9IIsCaklUeKi9BP99+/NYm58i9NLdPJj8UjOqPzWet/2J6i63JvdxOvuxgh76eZRDa+v3Xe+q7ThNXQghuOV/b+Gn+/6Ui/e6mL+d/zeG1g3V1eY/gxlaS1i/RyNbTwBHACVW/LWStwNx4JXld/O1oKUXlldbt7kEXbbNCTWWUrK1eVxRhBDahFqj0bQFa+/bmFuS7MHsqO36LyB/BX9NDdSOieCAZ35S9LXRuZNAgEinc8bQti3LFynPB3cfJ5tlbPZstp5xBjuOP56sKssEuZXJ06bBhRfmt3/XXfD2t8Pdd+fa9YOfiYl3pXU0Kvs/4ICGZeXvCux49tlWD6EpWLilktxHyC09VMy2oVJJpjSysqkl4BWb4Jrb4PCd0OW4Hg+lDnQbdpBlmNIWjLmPaRnZ95gFOztktkIqBKMRaUadNyb3ieGKGcUEjwnPC8UJYF4c9h2B16+H/UZKjFmI3HVCPdSCIlWOqbc3Z2pvmrLk0tiYDP6r8zoUkuesMrtX2xu9wt4wpBCqqQqVAdEuNMD5VKPRNJUnnmhY0+vpodkKwPUj02DV3YQsk2VvtNpDgLj2jTUeaBBjJi/jKUxSvJffcSkfw/RzN2ZZMtVR0zTsrM1P9vkJsbX5KwdH1oyw4toV7HnGnrz1+rcS6a7OgGsQiNsOL//mnbzskkfpGJaFTuOzurjv/53Mo584tum1lash0bCylQlgCVBqpt5CnkDqIj4xjPKLigwDXjh4FvvcPxzIxG1RPM7+27ezfO7cqo5LbNsWQO/tSSwWa6sarRqNZtdk6Nl+ikmpRo0m1JNhdjJj+HmsTBw7nB/E2XDUyzn2yl+1aFQ5nHCY1PTpjOy7L8l58xjbYw/G5s0jNXcu6TlzEKrOuwdzdJS5N9/M0t/8Bsuy4EtfgpNPzm/4O9+R3hJ+Vy17Ta7LrZQ2TZlpEYnAbrvJrIeDD5Zix6xZVbxzjWJkwwacXWSRh/rLUoF8b5KAFz9L9yzkWh+1RGduCr73GHzhZfDMDHDCUqAodiHyljc1HNlfxJEZDGE7l+2QsqAzK/uy3ZJNUVsKF6qkklCllZClnUqecQXjCAsI2fLN7hGDD79Q4Q2rc1ldD1RpJcuSrw0NyZ+dnVKMGByU5dcsSwb/EwmZXaW8ZLxZDY30hwHYd9/Gtt+mtJsHhBYgNJqpzCOPTDQiC5BVzG5Y2/moNQ7SqO7TyZfxS+MWbjmxce+taTx+GIj6L7UOUS7nw1zOB/kQv+JXfLL8xCwSgfXrtQjRJJb/azlXvu7KsvusvX0tP178Yz740AeZtY+/G7SHLnmImz97M1/NTJw7925P8KpP3cw5n7qZ1SftzpU3v5NsV3XiRlMw4YV94IAXg2xUAAcDO4JstH4sYB2wsPpDK4kQc45KsmzFbI6o00hacXh/Py/19pL2rqSsQCYeD6TvdkMIQSKRaKsbBI1Gs4uyeaS4gauorXCCqLdueA3YwmazvYMdYoAsWbrppueFHzN40BcxrFw9xERf6blY1oI1e8BLSyCcgQXbYGY/TBuD7jqqEQrDYODII9l07rkMH3QQTnd3LohYxefk9Pay9Y1vZMfZZ7P/r37F7FCRe43ly/1nPUDOrDaVkiuoU6mJExNVBmbuXHjLW+DrX5erqTV1sfxvf2v1EJqGNxFAlWFyqF6sNJGVTguLXx67A376ILz5dNhiQsKS5ZJKDcZwIORIoSEbygkM6hDLkaKCyqiYnpLiQ8w1sLZNN55vuuKKt69i83qPUXWHDbPHYJ8RKZzMSPt88+o8jESkoDA2liu7ZFkyGyKVyl1bslnpv5DJ5HwjqqHSTYqf419ZISVcU5RYLKYzIDQazSThxz9uWNMJwgzXXLS8FnJfar/jCH4pbuLxI5vYfaP42CUBN2hxKR/nN3yAq3kT53FD8d06OyesltI0hgd/9iC3fOoWX/umBlJcsv8lvOPmd7D3mXuX3G90+yg/2eMnOEnHI88VxwCW3reRL3d/j79e8yZWnXdgVeNvBjefHYQA4Z34HgOsrbfBYIkikzEapAGZ3RmWz53Lgf39gVyZDeDVa9Zw3f77ywCRD6wqxIpdCdu2265Gq0aj2fUQQjDngZVFX8tu2x1rdvVZcMIKINxQaSKkdhOCNdkNPM+aglcGYd0FsO47EJmDMe81mPv8D/3TLVYthqcOgw17QqJLCg/SlbZEnw5E0nDqnXDsox4zWR9setWreOm//xunpycYUcYwsHt7ee5zn2Pxiy+yxPuaKrniN2homvKx556werUsDVUMIWRgMxKB//f/tPgQEBvvu6/VQ2gqSngwyGVEVIsAlpLzgfByxIA0pv7HnvDoHEiXuYY4Boy5woMSB9KWFEVC7nECKUL0pGF6GkaiBo4tcAyDhCHIlhE4xm9f3LaVUbXlSJ+K+WPw1SdlBkTuuArnrhIRsll53kajsvSSYchzPx7PCYmqLWVwXkxULLbdS73ZEbvvDu94R31t7KK0mwCho1MazVTmppsa1vRLzCBUrAhsE0gR4XYWM9aIxaTV+WLXz1OHN6RZhw7O51/swUp2Mn3iDqkUzJnTkL41ObY8vcW3+DCOA38660+svnV10Zc3P7mZH8/7MU6yuvPPBN5+/lW8/Ku3VTeeJvDY0UG29gZkjaNJRBgYpmHiA8C0xSkA7l68OLBLWIcQnLB+ve/945s3B9RzexF3M0O0B4RGo5nqWMPFy8CI4em1NWhnm5YBsSy7soj44EVAejtiw2+x79iX0RfO4M/vheeOhJGZkO1Afp+HKC14mJDugFvOgW//Lzzmw6ZNGAZPf+c7rPr853F6e4P/PEIh1u+/P88991xu2+bNcGAVC1L6+mSJFMuSpVsqjXHjxomeE5qa2XD33a0eQtMoiMnXHG3ogmJ3wOO8cxWcsEMKBmWDnkauHJOBFCRsd1vGkMJDxsz9HA2DjcB0YMwUE8dfxgfCFNKvwhJyXCdvhUsehJO2F7ZRYaavsh2Ukb3jSIFhaEj+jMelOJFKyewIlc0khDzHQ6GcT4S3zUYQjcKNN0qBRFM17VaCSQsQGs1UJZOB4drqsfohSrZF8oPkfecvbUzh2EpuVkEyMg3Sjf2yXc9SZtPPm/gzGTwmf5YFl13W0L53STIJGHwa0sM4WYfLj7u85qb+fM6fie/IL2kz8NIAlx1Z3//baRfcz2G/erjyjk0U4jbVUJKoOJ8Hrg2qsWDoBJLQ6ISx6fvIoNDOadPYFuCKw8XxOPttL7zzKUG5mtC7MLGYXLbWTjcIGo1m12N7wikZhAoteqm2RmvMxjWqnKSsy2xmPVtr6qtmQnDDeXD9a8pPqVZ+5CMMHX98Y4UYw6C/v5+HH36YbDYL8+bl6sD7oatLlm5du1ZmP1QaayYDt99e97A1kEkkyDTwnn6y4SDPFxvXn6FGOoBMmddnp+Drj8PucRnwr3RJUeMyhXzuGLKskvJ6sAR0Z+Xv0zIw5ooSJcs7eTA8j1ljsHQEjtkBv34AjtxZ7IAKpetCIXnOdnfnSiwlkzmRQRlMK5RQYVnyoXxevD4QjfCAiETgbW+THjGamtAZEBqNZnKwtbGT7J10kiqa1Ngo8r9kN5wwCY1lq+Wiz9IctcPkat5KF6P8i7PlpKKjA1aulPUeNfWz+gq4ag5c1Q3/ORyunk72z30sPeiZmpsUtuDn+/ycbFpOv0e3jPLzpT8PZLiv++hN9K4bDKStINi8oN4W1gInA40rO1cTS5Fe2E2YTUWm5SThpxcsCFQgPqK/n712FrsDysfUpRaKEovFiEajhMPN/M7UaDSaYHl+WxozUyKkF8rUtHDBMQOq+Fym74yTZTk1CiQB8PSRcPtpxV9LT5vG1vPOa3wWiNt+Mpnk/vvv5+4nn+SRD32IVe96F6OLF1c+XpVxSaXkvcO0afkBzEIymfKva3wT31Z9abN2oN5w9wgwo8I+3Ta8aqP0bag4VTdyPg6mkP4O6mEifSIGo245JtPNmqhwWhtI4SJsQ6cNXRnZx7QMfOkZWFjMT0aJtuUEAcOQ4mJHhxQSTFOKCUpUyGblc/XTazStfCBKtVvu92qIRKTw8O1v196GRmdAaDSaScLy5Q1t/kb2aVkJJgCO39S6voPihlc3tbssHbyOG3mLc4Vc+TBjRtON/9qSu18PD78HMjnzXwGEQzHe/OmreO83f4UZKrcGpzSp4RS/Oe43pBNpLt7j4gmz8Von5ybw5tf9tcajg2drzQKEDXwQ2Bu4P7DxBEIYKF4quwK1nZOmJ7Y90NXFugBXwxjAMdu20ZMo76xpp1KB9dlOjI6O0tXVhaGvtxqNZgoz8PBmjFTxTDd766LaGhWNv5dYZa8jU3Ml+QAw4METIF4k6fml971Pzsmbibuaeayzk01veANPXHEFd99yC09+//vESokRg4NSeFCByUoLmISorsSTpiRP/eIXrR5C4DRjNiSQ1U8r8ellMuMgaksxoWK7Rs6I2lS+DUKWX3IMKSTEQ7kxVBpkyIGoAxEH5qdgtzG44Al4eTndqZz4YJoyS2n7dnmeZrM5UUFlNKjn3va8r2Xd0nihUM6ouli2Wq1ZEZYFL385/PvfsDCwNPhdkng8rjMgNBrNJGDZsoY2L5pWp6gEh/ksCdIK/H4XP3NIQ4dRHIMreTsv23494uBDZGqmpnZuPgE2XT9xu/obMGDR3tv49E9+hGnVlki87cltXLTgIkQm2NTX+c/sIBwvYSLYZOLdtYgp9yKru/6G2ivENpAnqfEOq7b/585Z+cc9uvvugX4qJnDGunVl94loj4OitNvqJI1Gs2uSGErKUiVFcEZmIezqQwdmNUbINZAR2eaXXiqCCMHNZ07c3v/ylze+c6/4XeqzDocZednLeNwVI56+8EJGl7i21d5yLKpkk1o5XYrOTm1AHRAb7p9kC2zqRJlLex+NwAL8LIuZlYY/3wVH7YCOLBiq1lIx3O2OASkLbNO1jzRk1gNueaakVbn0kulIwcNBihlH9sMBg/CB5XD2xjIHVrpeOo48ZzMZmbEUCslrgPJ0KFf2znHL7Kl9TDNX7kkJFEFw3HHw+c/DgrpT4Hd5YrFYW91jaAFCo5mqrCy+9DaoKf6rWYndVBHCM/K5ozJXcbLi52NZtzvYrZuYP5o9igMueEvJDEuND244GnY+OGFzsXNs2vQkn/35D6k1UJ4ZqS2Dohwm8IrPNM6ovhocE9JVGTRfB5wKTNISYpcBBzW5z4JVpLZlsaGzM9AuOoXgiDJG05YONhRldHSU7u5unQGh0WimNJl06TmMEbIhW/1Kfse2G5qNu9XeQaauSvLBsXI/GbD0Ys+oVCQmQPwGD8Nhho49licuvZQVn/402d5eGXyMRGDmTFl+SQU1i6GMqrWpbCAMlrinn6qEkOKA+utplPwYBmb53HdJDH7xIBzTD9OyZW7lXcVEKGNqNxsi5MhSSkkLtnZC2oRMqQpkrst21IEOG/rSMCsFe43AUf3wrhcrDNaPAXUkkjOhVuJhOFz5WmuauYwHyJVtCtrjzTBg7txg29xFicVi9LTRAjAtQGg0U5UVKxra/ACdZFqVBRG2p/7V6ZcfoXlu18VZMTCPWdOzbNnS0mFMTW5/JQw/Xvy1EvPCrt4U7/nq5DL+PuI3TxJOBC9uVIswINnhd+87gPOZlFkPIeC3wAea37UjJl5PHtxjj8A/pf2GhuhOJou+lqlQomlXJZFItFV6tEaj2TXpe6pMFpzIYkRqyKoMsvxQkfnXNtE/cWOLSHXBJs+C3/iiRc0rhVrLyuVQiK2vfS3Lvv517GnTZBByxw65slp5GhUbvyrvsvfe9Y15CrFjxw4eeugh1q5dix1wsDYzOhpoe63ERBpLh2j8rfwcYF4V+x8wBEfvhH1HZBmlohSkbhjuvbyNNKKOOPJ9OeUSu9yyS91p2G8Yzl0Phw3A55bBV56CvnK3Zep8K3XdUNkL6XTOUFplPqjshnLXAlV+SZViUv4RQfu59PfDIa2oBNF+JBIJutpI7J3qIT6NZtelwYZV/+AAmnuJ8CRpnrpWLp2YyjxydKtHAMDQqMXixbB2batHMoV4/Euw7eaaDl287xYW7rs64AHVjiXgzE/f2OphgAFbfS2E2QCcQePWS9WBBVwDvLdxXZSLU5jmxM9EmCYvBbwqxgBOWb++xIt6hX8xVAaERqPRTGX6ni3jvzYtVtO6mnoywwwfc4ERxmpuvxHc7am4NHjIIU0zn64Z02Tk0EN58ic/IbN4sRQfenpkYDISyV8trRACRkbg2Wfr63uSE4vF+K//+i/C4TBz587l+OOPZ8mSJXR2dnL++eczMDBQdx/JwUFEA0uUNQsD6ASmkRMhGkkHcCLVXZLCAj60XGYjdGd8+kEgpNhgSgEiGYKsyo4ofsC4b8S0LOw/CJYDr9oA+49UGK/yZChWQskwpEgQCo17vIxnP6TT8phUSm6rdE1Q4gPkhIug/wYXLZLj0tSFEIJ4PK4zIDQaTYt55pmGR5SXMY+WreB/59Ot6TdInp08qn82K1iypOGaVXuw7QFY8YOSL1ecnhnw9i/+2c+eFQlqKnj0ZU8xoRZXs+91DFhfwvcwRwKouFPr+F/gta3rPlyi2tJjAXtBAPRlsywYGpqwvaOZpSSmENoDQqPRTHV2Jh0iO0qvxDZqNHk2so0NRY7VI0A0YC60Zbfc84TyWGg09QYPTZP4ggUs+8xncGbNkqax06dLAcIbrFSoVdY33igFizZk27Zt7Lbbblx11VVkC/6GM5kM1157LQsWLOCxxx6rq5+tjz8uV6RPYSxkUDECZJCnlUNOhAg6mmAC+wDzazh2v2H4yYOw/xB0+v3YvSWZhMx+cAwmRFINIUWO6WlYMgqH74TpWThuO7zFz9o05cMAOSFBiRFKgBBCeq8o3xYh8gWLSh4QhSghI0i6umC//XSJtoDQHhAajaa13HUXvPOdMNbYFT/NLX5i5D9fMtjU3gMnHYKdc1o9ignMnw/33NPqUUxiUkNw+4nl9/ExR+vocDjpdXcGMqQgMIHXvu+61g7CgOX7l9vhV8AkXkG+D/DNAMtI1IAoMWMThsHGBqy+P3nz5gk3JaEO33W0dilisZjOgNBoNFOaZTttDKf0JMcgjCi57Lc0wmmcp1vSTk+6fMkxz9dkshkGrEEFDy2L0YUL2XjccdJnMBSSAc5weGIZLcOQQfM2Kh3kJZPJcPDBBzNa4f2l02le9rKXcfvtt9fcV2pw6t7zWp6HQC4jyrq/qziCIFidzwCmA4vqaOOAYfjK0zB/TGYr5A2w1GANz08z97sSHSxHZjwsjsGpW+CVm+B390mx472rDKKVAiuqBJIqo6QyHdTvqtxSRwf09RUv1aREiUqCltewGoqLjLVimrDnnvCJTwTT3i6OEIJEIqEFCI1G0yKEgMsvh+efL/5yUN0gPSBaxtwpXmf8wWOZrJfXU06B//yn1aOYhGST8I9a1tIU55Q33MNk8jA47IplmMkmrlITgqUvvsghzzxDJJUCIBkutfPngY82a2RVEoWzgWci/nK1G4goE8N5cNGiwIMwFrBff35tbdv9v9TkowUIjUYz1RlIglEmCGUPz0SMVX9vYEDwK2xdRp067hca9ZVuQcz9mOxIpEGdNIhQiA1vfrP8/9qxQy52y2YnBidVyZYZM4L1+JgkfPOb36S/35+3iBCCV73qVdx///019WWGS06OJzUqBq+yH0zkKWUj/R8K4vSBEQYWBNDuKzbBb+6DRTEpIOQhCh4KT7VoANOR73V2EmalYWZSlndaMgofegGmp6RnREWUj0NnpxQXOjqkSKAeKvOhr0+eb4lEvoCgMiEq+UeMvz8hM5eUeXVQGAa8+tVysey++wbX7i6MbduMjY211T3G5IyQaTSa4qTTcNttDU/VfJwF7GzaauSCL8lpSZg2xdN5xzpptQF1OV71KnjwwVaPYhKRHYOrZwPlg6vV3KtaFpz1tsmj9FjAyd9pTvpLz8gIX7ngAj5/0UV87Be/4Ptf+hIHPvccLxxUbO+LgR81ZVzVcwqwGn67B3SohPLWUS6j2jFNEkEbyAGH7NiRv0F7QBRFl2DSaDRTHWv7CEamtNLtDM3CCFcvQtfjAVEUz2RsC9vrbiNwDNjslmFKLqpnnXYLMAyyvb3sPOww+Xs4LMuolKpJf9RRE+YFQghGR0dJT9H67yMjI3z3u9+t6ph0Os2pp57KrbfeWnV/25+eemWHTWTgXcXnTaQngxIhkp79giYLDFCd+XQpTtsC19wGR/TDtLT0zStKoSAhwHDk/nOTMDdpcMggfP9R+OPd8P3HDQ4a8h5f4YLjODlT6Ww2J/x5yyNZljwPw2Hp9aCM4FX7KvPBNGWJJj8iRJCEQtIT5vrrYc7kqwIxVYnFYgDaA0Kj0bSIwUFIJivvVycDdBCjGat2inw5HrVp6l+Zrj2v1SOoyAknwJo1rR7FJGD4RSk+OPHK+1Y5VzvmrEdrG1ODOOK3TwXTUIXP4Q3/+Ae7b9w4/nvn2Bjv++1vcUShcPoI8NlgxhQoYaTT9A3AAsi4RV9bLCpmE+UvjI/OnRt4TCUMLN65f/GoBAABAABJREFUc/z36fvsE3AP7YHOgNBoNFOdGeu2l/2as+ZvwAhXX05JGPVN6ssZUQ8yXH2DTUhm3D5b/sxOxRrohsGmc86RAcWuLrlSulQmx8qV40/T6TS33347P/jBD7j44ov53ve+xw9+8AOWLVvWpIEHw1vf+taaTKGz2Sxnn3121eWYlv/lL1X3NVkwkEIESO8Hk1w5JoEUCxpxuqUgsDoNRw7ATx6C/YehNw1R25Pw7P0p5HbLfUQc6M1ATxoO2Sm4/B548xrYdxgs2z1QZTAUQ2U2QE4MGBmBWKy4L4NlwfCwjAOZpjwvlf+DypBQQmE6XbpvJWQEzYoVcOCBeqFSwKgycO20yGmqh/k0ml2LwUHo7W14Nw+ze+tqqs5MtDrOVz93n9rqEfhi333BE1vc9dhyB9ywP9STwl+GUBj2P+bZhrRdC93bY5hlVjf6wkcc/pAiN5vTYjH65671bNkJHFvfWAInDHwdeBK4HOgCBKxbjFzb1QlMc/crRkFudsDY2fJX5S0zZtRjxVmSwz1ZEKGpGExpAvF4XAsQGo1mSrNmwTzMMol+5swtNX7FNeCOwg0IppmcGdPDffKnMRUNmg2D2N5758qzRCLFyyyFw/DSS9DfTzab5a9//Sv33XcfyWRyPIA/NjbGNddcw+WXX05mCnwWK1as4MYbb6z5eCEEZ555Jl/96ld9HxPfXmMWT4sRSNPpKIzb02eQM2SLxt7KdwPbAmzv+B3wswfhrI2wdBhmpKXYYACRLEQd6LDdn1mYl4C9RuGMTXDyNvjW47BHjInBfW9ZpEIKRQZvKaRC8cFxZHZDJiPLMCmxQZnBh0I5jwhVjqmUiBZ02SWQ5cH32ivYNjWAvL+IRqOE2qjUnRYgNJqpxJIlTREgniK4WvilKfGF/PK1Tei7weyYGqmH2SyccUarR9EiVl4Gd55Bo8vqnPXWmxvafjWEsmCm6ijf5vOjGilxjVq7l0ofTQGLax9HoKjKtRbwDqQAcWD+Llllp6cq3aqqt4XXMD9We7XfkoV8xP5XzZhRc/ul6HIcTFveXoopWlKh0egSTBqNZqoT31LecFfU6IPkqwRTlSvODQQGAjHpLKglO+eAMAzEVPOAcMn09uJ0d8sMiGnTYMGCXMDTNOXDccC2ERs2cMUVV7B27dqS7W3cuJGrrroK226cIXkQnH/++YG0c8EFF9DR0cFFF11UMZsik5g6vocq4yGKnA3bSNFBLb8xPfs1CnUrEnQfx++AXzwIP3gUPrACTtsM+w3BooQ0lt49AeethXe9CIcMyrJNCxPwjhdhScxtpNCHQZ0nJd+MzxsrVW4pEpE376FQrh8hcqW5hZD7RKO53xWNykw491x43/sa07aG0dFRuru7gy9l2EK0AKHRTCU6OuDlL294N/002sOgTNuHb21gv00gHYKh6a0ehW+eflouYNqlePLL8NiHqjqk1lvcvtmjWKHJs+qrpjJMnri64SPGfutZZ03YtuyQQxjtnucefCjQ6huuPYAe5PqtTuAVwGVF9jMh0YnMgFhCzmJPpYJUm/VQe7Bk4LmOivusmTUr8HCMARy+aRMAu515ZsCttwexWEwLEBqNZkoz6+4VZV8X/bvVtmZD1TIv23iZFbvlmmZyBrRH3HUYzlTNGoxEyBx9tAxmjo1B3C1Tqv6fDEMGQcNh/vj442z0lN0sxYsvvshNN93U4IHXzq9+9Suef/75wNpLpVJ84QtfYN9992VgYKDoPmvvuGPKLuzwFicNkct8sMllRHgFiaCiCgaytFMjlkrOTsGrNsJ3HoNTt8rshqP74cidcO46+OUD8MsH4V+3wMUPw/cfgZd7wxZKUFCZCaWuacXKI5ULMCvRL5mEgQFZYkltV4bwqs9MRj4KCdrzAWD//eG664JvVzNOOy5w0gKERjPV2HPPopuD/Fp5jrkBtlaMMqOd66MW/2TmkWNATK00ude8ptUjaBJCwMMfgxcurOHY2ro0TTj05KdqO7gB7PbghtoOdJc2CR+zhgdOPJHfvfe9rFmyhG3z5nHz2Wdz2Qc/yGAvwLnAygotNBIDuAZYC2wB/umO5wZKTokSnUDMPaaTnM2eg7+sh2AYXV/ZZDoRiTDSgDTdpa4JWrrETfSuTjwebyuDOI1Gs+vRub28n4K9YyEiXaoEYRkqeUA46rvUZ3PqMCFwJmkGRCYEqblzp249dMMg09sLnZ2wcKFcUR0O56/ujka57ZxzWOMuUPDDY489xooV5YWuVjA4OMgXvvCFhrS9atUqFixYwD333DPhted///uG9NkI1F+yjRQAHPe54z7SyCU9SpRQZ3XQwUYBzEFmYTSKkIDPL4Pz1kmB4U1r4P89BdPduH/YNZ8OF7v8eLN8ygX9lXdDsX3VeaayjpS4YJqyfSUyOI4UAlXZp3A4Z0jdCMHBSzQqDac1DaUdMyCmVpRMo9FAg1egbqKHwcCsncpRopj8kqEm9N1AHj2m1SOomhdekAsqZs5s9UgazAPvgnV/anq3x5zxCE/eOTn+LhY+vrm6eHnhaSoKtqvfC07lh487joePOy5v26cv+h7wryo6bwQfBpRJfDdQ6XoqYMsCcvZ6aWTmxDA0euXlS8B6YCGwD0R9np9PzZ/PKT5WI1aDBXSk00xbtCjQdtuFeDxO11Rd6arRaHZ5BpI22Uj5sIBhOh53Vv+IaLR8IN4ARPngiiy3pPYR7r+TU3wASHRDaHR0SgsQA11dTAPpPzgykivzEgohLItHDzuM+5csqbrp66+/ni984QuTKqD20Y9+lHi8cQvg0uk0p5xyCt/4xjf4+te/Pr59bHCwYX0Gibe8khIcLKTgkHF/Tkc6p21FzpQVaplOUP/bFlKAaDSdNpxT61S6XPC/0HzaNPOzJZShtHpNiRDhsMx8CIVyJtNKxDBNuS2VCt7foRRnninNJDUNJZFItJ3HnM6A0GimGkcc0dDmH2M3zyS/BUzNcqk57j+x1SOoiQYt/JkcCAfuOLcl4gPArIX9hKPVpVg36ra6b/1Q9Qepxf4q9uAdnM+86uPvvpvvfvV/q+87UI4FflnlMQZsXoB802rNVxx5C9QgBPAb4PvAX4GLgF9C/5P+Lo5bp01riLPJ4Vu2TNlSAY1ECKEzIDQazZTmhUEHK1G+XKQxvV+aSVWJyFutULRlOU+Diqt2vdONuDNW9ViahROCRE/lsomTmZhacJBKQSIxHhQd7Ojgog9+kP+cckpNAsvY2Bi33XZbwKOtnR07dnDVVVc1pa9vfOMbHHzwwThukDg1MtKUfuvFQooLyjHNmwOsXNF6gVHk0hy1DXJZEF4Rox7CQNOXwgQtlrn+KePZEqYphQWVwayEB9uWfTuOLIXmOLnyZ2p7JiNLM3nbawbvfW/z+tqFURkQ7YTOgNBoNHkMEKWx/g9eCtZEmE6zqpk0jntPanAHDo3Qjq+9Fi6/PPBmJwe3vwK2317z4fX+SYZCgsX7rWH1M/vV2VL9hFICM5XFifr4+i/MdnAzgCdsr8CcrVu56/TTJsGKhxiQRPo5VMELB7hP1Jt3kGJEg3gGeMT9uRV5uj8HXbv5+0t0TJOdkQhzAhYLFsbjiBIG47s6sVis7W4QNBrNrsNYFnrW7Ci7jyGMmm4PzEwKu9x8Qa32FQ6YpcV9ebjI3zBJ7xkcE0YX7RZM4NLru9DErIGxuXNhwwaZ+ZDJYJsmv/7Qh+hfsCB/bDWM6cEHH+SMM87ANFs/M/zhD3/YVHPs5557DsuyeP/7389ujzzCZJ1VWeROWVV4tLAAadrdp9uzPeRuV0KFNwOi3r9eAyl0ND1hX4kBQeA9j1X2gsqKMIxc+SSv4bt3fyVeeDMdmp1N1NsL551XeT9N3bSjx5wWIDSaqYgyHWoA22hhEGVebOrnZQ3MaGDjjaimKRkclIucog0oqrk9tp0bVt7A1c9fzfbYdjrDnSzsW8ibDnwTr93vtYStGmoK++Wu19clPgSBYcChJz0xKQQIA5i9YifbD5034bUFmzez34oVDMycybJDDkGYZtE/OQNAgGmDmYVMh/eFgn1tm+cO2J9wo2uRViSK9G+oIcVqi7K6swt+NgbjaRD/AgY8XW2B3Z+Is3xPB9vHDfsjCxfyqjVrApWSI0B63TqY0chr3NRDCEEikdAZEBqNZsoyKwoj8WTZfZyh2TKyblZ3/2HYNjh2rk550Z3c18oGtPPnEV3VLiYY72tCU8HWiHHZsbin5gB90eO8ZVmaQHr2bLm6Op1m44IF/P7d78YO6CZBCMFdd93F6aefHkh79YzjT39qTXb05e6qr07gTcAhLRlFaQQ5EUKJDQI5LfVmQii/B3VViJIrWKoyHyzksp16Z88WUnxoeqgg6JhLKCTPZceRj0hEnmtqmyrLBPJnV5fcNjpaPEus2fdYn/hE065DuzrxeLztFjhpAUKjmYo0sL7fdqbRkJl4STx97bOjed02CruBwfQGfziPPQYnBlRB6pmtz/DGK9/Ii0MvFt9hI/ztub8BMLtrNj8660e86/B3BdO5Ytm3YHMABlkBzOuWHram/kYCwAD2vHX1BAHi3Ouu45z//Gf89w2LFvHjz36WsU63rr27hGl8NZSbY224sQinxKLFf73m1cweLm9s2Rws4HWUvm3ZDgwBezJBpHC8551KLm/gZP/fwA7Iq6OUgl4zzf79/Tw3d27FJkY6O0kaBp0B35SEf/ITmEKmic0gm82STCbb7gZBo9HsOvSMDjJcwYdBpLoQmTBGKFVd42plbylqXL0bFwGWYFJf60FNswWkF9ZYKEZ9b6tyK5D/GSlxosGZEYa7GjsVDvO797wHJxJsjdz777+fE044gR07drBz505s2yaTydDV1cWee+5JbxMyLv/5z3/S39/f8H7KMQb8AdgP+EBLR5KPMpY23YdD/uxXnS4RZOmlbnefDuSMe4zcGqYglu0YwDRgVgBtVY06Jwv9GoqhAvNqsag6VyF3nqo4jmXJDKNkEjo6pAhRmG2hju/shFhsYlvNFh9MU5dfaiLtmGGtBQiNRpPHfEbxNwMPshSQO43ZeyCg9lrE44fTnHUZjRGIfvGL2gWIVDbFSHKEWDLG6//+ep7Z8YzvY/sT/bz7+nfzxVu/yDMfeYa50yoHWCsy/Dwsu6D+dgKioysto/alIvVN5NC/PMMjnzth/PcFmzfniQ8AizZs4Kxbb+Wfrzs3L9ZuunNix5KekY5ZOhT/qYt+yDk33xzw6GtlKfClItszSMOFp9zfu4B3AkfmdumLFRzTwMl+CngSipk4hBw4sL8fy3F4du5cnAqrj9b19bH/0FCw47vxxmDbawNiMfn3oTMgNBrNVOW+zVn2ciqECc0MhlVdKNEAHCuUy3AopFIwrwxZyntWVE2A02oDmLPNYX09wkChsOANPKoAptqn1kyLct2nUgjH4ZKPf7y0+FBHn47j8P3vf7/k66FQiNNPP53jjz++5j4qccMNN5DJBPx3VCMrgK8A7wWWMHkKAqhyS95sCDU2C+nJYCHXzXQihYeUu10dq8SLepbvGG6bB/ra2fB/XakmkK+yE9Q5qObh3v7CYSkqqNcdR26zLCksZDLyEQrJ30MhuY8Qcr9kMn88Qsj2RkZy/TTLaLoY4TDsvXfr+t/FiMfjbVeCabJc2zQajV+KfOkEGQ6L+LYvbcBq/GgD66o3g2vPo7FZCup/ujF93HSTv/0c4fDv5f/mwJ8fSPibYYxvGnR8p4O5P5rLXr/Yqyrxwcu2xDbm/WgeH73ho2TsOm8IHnw/BH1zWgeGAUef/mirhwHA3Ge25f2+z4sySyWaStI3MozlBiH2Xbkyd8fgusfZISk6AFgZz7Wn4CL0ihtv5KIvfnESJTQdSPHyS7cgxYc0subRMPBbpNG0y/QmCqNDwKbSZ3gIOGBggGM2bap4s/Ts/PmBSyVGf3/zV1tNckZHRwHa7gZBo9HsOvTTgZmtMP83bKhCgFCeDYadrRCoFpBJyzJNVdDL5L3mCgFirIbsz8KMBlXr3bu92HdwAzIgsuEw9x5/PLG+vuI7NLjufDab5ZZbbuFnP/sZ6YA9rRR33XVXQ9qtlRTwa+AipGtZKzGK/Ay5D+WE5gAJpOiQRo7ZRAoFyi/CJt83olYiwDFAib/GfKqZp1azr7c0ksrsKpaFFInIjIVoNJfloLIbIpH88kWqvXRa1kJWJtOWlfOIyGTka8qUWvlDtILdd29Nv7so7ZgBoQUIjWaq0WDVe5gGmAD4QsA7lrWo74D4+381qGGvhVfjGByEeLz06zvjOznlt6dgfcvitVe+lhcGXiDbADPeXz32K5ZcvIRNI5tqa2D5z2DgoUDGEmSo9fBTHwuwtdoJ22Cmcv9vO2fOZL+VK3jZI49y6NNPc+xDDzN3+3b6Z8/OP1AtvnMfYRtCReIFM3bu5LrXn0vrcz28XAnMBn5WsP1JpNvzDcDtwL+ARwHPtSjWxJXt6xnPVS91tpvAnqOjnLRhA11lbsozphn82SkEPP980K1OaeLxOB0dHVjW5PqL12g0Gl/E4wjHxkxXEACEm/roGzlpMOxs6SCfYcjgnBIpqghq20YLVwFXwoANJx1UvWBfKDCo1dbjRt0if7s3GyJgsjNncu9ppzXf4LaAwcFBfvKTn5BMlvcoqZZ7772Xl156KdA2g2IHcilMqyk0jo4gTaAjMD7H93o7OEASKaSkCCbzQdEB7OZ352r/Ziudp94sCSUKmKbMBgiFckKDEihCoVyGQ2dn7jXl96BeVyKC99z2Co7ZrNwvHIaZM6UXxPTp8ndlXO29FjRDlDjttMb3oRmnHT0gtACh0Uw1duxoaPPDdPraz0RgFM2WqGOiuudg7cdOBtYvblDD9U7+/R9/xx0TtznC4RV/eAWzL5rNPRvuqXMs/tgU38TBvzyYZduqFKXsNCz7ZmMGVSez5k+ev++D//DU+PO527fTNzRM38gw04eGmBaLsXTVizz8spflDlDig5APKwvZMNgqJ9v9E4skkyzfdx86ssVD361bOy+AncCngR95tq8FXkSu4Uq6j1XAA7ldeproYdFH3ulaToSYG4ux78BA2RuntaVWLtbDj35UeZ9dCLU6yWhxkEaj0Whq4j//oXfFxsozRSeMyPjzARhvS4Co1LIVgoi7+KmKgH1SBLgqvhGTk4HttQXvC1dVF8t88JZnaVDQUYTDMrA6CRgbG+PXv/41IsAMzCuuuALbDsKdoDFsAFrpTuFd+qb+ijPIRN0sOW8IAylAeB8Zz3OnSDu1UPUSj2rOCxW894oLheeeVzCwLJgzR4oJ0Sj09Mjns2dLL4dsNpexkMnkRELHgbExebwSGmw7V6LJKzgqgUOJDYmELM+UTOZECZWFocbV6NJMpglveENj+9DkoUswaTSa1rOmsWa22/CrshpMI0P+rL0wYbNKpnoJpuxkNKCu7rh7CvSFgbEBFv14EbeuubXG/mtnKDXE0f93NGsH1/o/aNtdkJmcXiKRqI0Vmhx/48f/6MHx52fefhvRlFxZFspkiKRTDPf1MWvA8zm6dw7CLcOUCUG2YDGk4Tg8duQRzBmcPELLRATwA8/vQ8AIMoE8iSy9NALcl9u/o4n/Zwv87xoBFo6M0FWmfvGT8+f7LqrnBwPgn/8MsMWpTywWa7ubA41Gswvxpz8RGvJnLC3Slee5qvSS+kVEo+WnomolL1QlQNiBWNuqMQTXlGLPJzZXnwHhFRYKt3nxBkcbhDDN0rpMCwT34eFhbg7QV+zuu+8OrK1GsbLB7bvVVUv++avXI8gMhCz5xYDHYDwSkCVXlsnytCs8x9QqH5lIk+uqJMdqzj3DkJkKSoRQYoAK8HvbVOLAyEjOQDoel9vV/Y/Xp0EJENlsbrvjyP7UvoYhBQUlfhQen05L4SKTyQkRymfCW6at0RkQHR2w776N7UOTRzveY2gBQqOZagScglrIBvyUGxEIIImFmTcV8VLD5LRr8tTsr5pVS3LF8ScN1f8fFHrMnnj5iWyObQ5oPNWTFmn2u2Q/bL+1gZ+7MNgBBLgqzjBg4dJ1wTVYBzNfGsBw5Js78PnnmTUwQFciQdi2CWcyzNmxg2gqlX/X4H2YBduB61/zag5evrxsv5Njjbg3o2EL+bdHINdsPYD0hDBg2kjzhvYkssCuh3I3htOyWZaWEXxsy2Iw6NJAO3cG294UR2dAaDSaKYsQiJtvZvCQvXztbkT8hAALJk6mSpUs3r/bMhgmCMd34DAh6rwfKtVNQPO+4fk1BI282Q+lxIhG1bcvQtEMgRZ+1z388MNs37697naGhoZYtWpVACNqLEHn3xoFz1UWQ7G/EuX3oGbIaSCKFBeU4OC1iYOc54NXgPC+Vg8hYIbfnZWI4Fekc5xcZkFvL3R359qwLJnl4C1/lkrlRAWVoaAEhnRaZkOodsMFoq0ynR4bk8/VsZmMFBOUT0R3t/xdCQyq3JPKxAiF5H69vTnPCCWaNIpZs2QpKE3TSCQS9PQ0sRRwE5hs0TKNRlOJpUsb2vwWXwKEgQAyQV9C/GV2T07+9Hba4ZK6dWvu+Xl/PY/lO8sHlJtB2klz+u9Or7xjagh23NXo4dTFngesbfUQAAhlYcZqmeEwZ8cOuuIJoskkkVSKaCpFz+goO2bMzjnHFeJ1kzPgW1/+Mq/24WI+OeyLp3ueq+TxQrYDHwN2wNxtRV5vEC9SdIlXWT+IoSE6y2RBLFtQRVqFX9auDb7NKUo8Hqerq6vVw9BoNJrqeeEFBucsZNo6f5mjhlk+p67od1W2zOIidYBa7VtFwDzrIwOiYtmXYt0FEV83YeMh82s7trD0i3p4xYlq2qpBhFBHRCIFN2aTQGj//e9/X3cbV199df0DaQJjAbblrVFgIgP6Jvk+DXie2+QyGhxkpkOcnPjgFOxvetotfF3dHdcaGreQ/g++6gyokkQzZkwM/hfup/ZV5caiUZlhYNs5I+ne3tx+KuivzgNVIimdlueZ8mnIZnPZDEpUUOKA48h21PHe81rtowLOQkhhIpvNlW5SbWWzsm0h5JhU240qK2YYsp9GlHbVlCQWi7XdPcbUj5ZpNLsa//53Q5u3fBXsEBhuVVdRbmVTNYTsybI8ujaePaiFnRfeXNReaXPYXW5z84s3c93K6+oZVKDcs/EeNg1XMKV+7vtMlhB3KebvuaXVQwDkX0fPphEMx2F2fz8YYDoOoWyWUDZLMhol1jsdq9TlwJBeEBjwnt9cxv/7/oVT6PT9iud5qOResgzTr2HzwgaPx8MgJf+ES32+XbbNnkNDJZvc2tMTZKEKyXXXBd3ilGV0dLTt0qM1Gs0uwh/+QKpzGrtf86iv3UVFE2ox4VdDBeeKYoBpuJkPTq78iA96fJaMLV1GiFyNmAaQilBbwN4rOniNaQv38UuNIgRAKFRujtQaxsbG2LSpwv1ABf7whz8ENJrGsobia4CqwSsAqL8CbxaDSmouLJnkfXhLL6mfjuehUIKGOq5wn1rnojOB2X53tqxcOSOVYVBYmkiVfbMsKRgsWgR77SUD7JGIFC8MIxf8VyWRlLeDyloYG8sJDI4DAwMQi+XKLnm9GUwz56mSyeRKKxUTNUZGZNve814ImaWRSuXaz2Rkf2NjuZJMjUJ5XcRijetDk4cQgng8rjMgNBpNi1nX2BIunVSud264/1pWCLPs5LqKiffcuP99JyPZBqY8+qZ24UFh27B6tcPr//b6QEYUJOf8+ZzSL9oZWHFx8wZTI7vtNXkEiFnL+5m9YwfdsRgdY2OEsllMx8GybbLhMP1zZsnggTrA+xPpB3Hcfffxf//9oSkkPhwPfNzz+5wy+24DHoFIY3138phN1XebBrBkcLBkgEEYBrGgVy3+/e/BtjeFacf6rBqNZhfhX/8i0TOdzu2jvnZ3RksHQop+yxg+ZqUCd0VvdeHJDiobJJcNu5eqIFt1Q8XpS9TgC+f9Hle14r3ZEOq7vNJ3euF8oIo5gPfIRCLh+7hm8te//rXmY23b5v777w9wNI0jA2ytuFdpCqfuSmRQfg3e/+tSQUHvX47jOc5bfsnxvObtJwiUOOI7l1cZPw8PTyxj5j0PvOeVacKSJTKDYbfd4NhjZQmkcFgG/dU+3qwJIaSAkU7LhxIolCChsheU2OE4MrvCixJn1Xmezcp9VDkm71gV3owo5f2grhWNQpWFWrJEfi6apqHKvLYTWoDQaCYDg4OwfLk0MSpHJgNPP93QoUR8RL/kygYT0zIxTQMjiCuJms1MVe5/eQs7r1948PLeX/wfSaexXiO1sGzHMl4aeKn4iyt/BcKfiaJfGrEoLuKrfnJz6Nk8ysHPPsuMoSFMx8FwHEwhMITAtixG+npwXF3NzLoZD57Lw6FPPcVtp5/qLyV6UtAB/LBg2yGUPneywP2w4OGGjiqPCtfBUi9Ny2bZvUwWxLPz5tUzqok88kiw7U1hdAkmjUYzZVm9mo1HnkRqpr8AhzOwW5lXS4nglA6AjwcIcVcL+5/LDhhDFfcp2Vq10+Yaptj29LnVH+Qtv6RWaAOGbcvgpKKSF0Sxz7uGLIgOZZRbqs0WEY/HSdboiXjnnXfiNDJYGyA24E8aLI76H1d+DpBfQVVhun15txeeIt7paZE1SRPaC4oOYDH4zHdC/p0rTwUV4PcKEaYpV/LPnw977CFFhxkz5E/DkD4HmzblshfUuRgKySyAcFiaMff1yW1enwZVAkkJD14jayFy+wqR83LIZGRWQzabExVUO4WZUF5UVkUzzkvHke/9E59ovNG1ZhwhBIlEou0WOem/II2mlQgBn/0s7LcfHH20VJbPOw/uu2/ivps2wf77w8035zcR8JAOonK9c8MwCYUMwmFTfr+W/e7z+cXYlZm6AoRjwIDv5NCACVZ8AMG9aybvyqC3/eNtxV947nvNHUitmJOnRNTMVQO877JL6RwbwxBCrjJyHKxslnQoPH5xMbLuX5n63YHO0Rj3H38sXY2qNdoQ3gycWLBtNuWnQjG46ikooXsFTqTCcEpgAgfv3IlRIsCwfubMutP485hS/++NRWdAaDSaKcmmTZBKseXgozF8Zh8YncVXxJedhZplMoSzHtNpg6pW8WZEGW+J8XH5+EJV9WJKvVYjqbnlMix94q50NhwHU33vFgZUC38v0061dE9icf0mH75jxfjwhz8c8EgaRxbw586Sj1cgUOJCqbtFg5z4oPb3lmIqsHzLM5f2PpTxtGCimFE4Lj+YSNFhHlVkP0B+loLjub4o7wTI+SYccwzMmydLGPX3S8+HrVvh+eflotBkMpfRYBhS0LAseazydyh2zqmsBFU2SZVY8p6D6nxW2RLeEkqlgvyF/jDNEtLCYRmDOvbY5vSnASCTyZBKpXQJJo1GUweOAxs3woUXwgc/KMWGn/9cfunF47Bjh6ytffLJ8gvukktyx370o/LYBjPHh+WVZUF3d3j8ezSQ7795sclevr80O2e4S7yaTYP6XH5uY9oNgEc3P4ojCv7gkv2QridJuXmEQ5VLnDXrNFi8cw3n/fOfE25KTCHIhsOYjquXhGS5JWFIESKczrBu8UK605Mnm6My3cDXimzfnfK2eFl4cQS+hMyFbzRd1DQzM4C+dLqsF8SoFXCZuDLG17sS8XhcCxAajWbq8fjjAIzO3Z2uLcO+DjE7J2Zqy/lDqcA3OE4ZjzfLzGU/jAfi/H23xKmc9Sr8zKjKreOpY5qdSWyu/WDIBTbd1dXCsmQmRGEgs5gIUVjKyaf4kLcC3jByWQaTKPtB8cwzzyCqzOoYGRlh9erVDRpRsJhI0+WdNRyr/qRD5IybvV4M3j9573Y1Syz1qXr9HQrFCcezrViWRbl2iyHc8fQiMyCqQpmne88VlVmggv2DgzKbNxaT+69fLxeEdnTIgLvjyICHEFJ4yGRyGQ9jYzlxolyGghL+lFcE5J+vKhPCW9apXDmlYhkRNfq7+EZljEQi2v+hycTcz7vd7jG0AKHRNIM1a+C002TdvEWL4Mtfht/8Bq6/PmduVIjjyFS3WbPgc5+DO++UX4AN5hnmUX6KYGCYJoZhks0KHEdgGAZm+TSIynRWDsxOWvpGaI160qA+EzMa024AODjcsfqO/I2Pf7YxnTXg4zVD0NldvtRac27zHD720C/pKpLCbgBb5s4FNy5gOO5H4d5ZPHbEIcweGWnw+IKenuwP7FFk+2LKCxBA2oZ7gd8FPKRiTK+8S7k4yWHbt9NRQhh4aUbA5/UVVwTb3hSlHeuzajSaXYALLgBgrG8WZspf0F+kI9X1YajEzxLfXKaBIcCIpd2dzVzQrwIjPorT+BIgGkSyf1XtB3vNpw0DJxxGhMOIUChXysVb/x1KBzjrEA9mz25VdndlhBAMlVl0UYyf/vSnjRlMA1CeCrX876m/enVnrUynhef1wjNDZTf4adv7KNyu2lFt1frXZyDFh1ORycH5L5ZpVQkG6m+/0PdBZUbYtlwAOn16rmzSihXyeXe3bCOVYtzLQZVI6u6WIoW3j2IoQ2uvYODNwFA/S2VRTBZU+amlS2FOAFldGt+MjsrvuHa7x9AChEbTaBwHXv1qWVaplnqVAwPw4x/DaD1VIP2zmMqBxWzGIZ22sW0HMNzv0/KiRUV2G2luCaZis6da2TyfqVs/qgiZyf1Fd8G9F+RvWH9lawZSI53Typv6NXoaGiXJm/kLS0fXlux//ZIlOAY4JtjunYsh4J6TjuXglSsaPEI1iqAwgU9TXGjYByq5WAigH/gT+FhwWT81zswMoMO2Wbqz+Hq5F2fNCvZv61e/CrK1KUs8Hm+7mwONRrML8NxzAKS6+irVUnUROAXlRstmP6h9DMoG2ARgREIy4mqasmSTj+Fkgy0sGDwz5td3vNc4V9WRVwFUd9W0kc1iZjK5LAeVIeFdTV0Hs2bPnpTZD4pHqvSj+uEPC33AJjdx8FGXYCLesy0JqOWL5ZJ9BDLR1+9fjIWcPZvkyi95XVyUGOF9VEMIKUAULQ5Xyf9EZRF4sxDUedHRAV1dOQEhkZCPZFIaV4+MyIoUXmFA+bGoskvejIZi/ZtmzpAa8v0k1Pmkzk/l7VLpXG3Veeg40iPjc5+b1NeCdiQej9PZ2YkVdAZ7i9EChEbTaO66S2ZAZKfGCv/TWEmlXGSZvWgTjVrBlSHsTTcnicBbyFL9rLffs2+l+qnVZMT9IIYXtnYYFXh4k8cUeNN/QEydUkCGAZ29zRETS3E2N7If5Vfm3XviSeN/0oZbium373g7Jz7aLPPhIM+no4ES3iEYyLpHFbCB54DbAxtUcRL4uh6Vu0LvNzhIqMiNkW2awVaRev75IFubssTj8barz6rRaNocx5FBN5Cr630YixqAEWnAvYxl4IRN6TNlhQDh63swWmnxQCUaPG3vnXlobQd6bqiMwhssb+DVMBCmiaPMbJVQ4Xl9fHuNK6w7OjpqOq5ZPPXUU9g+PanWrVs3vqJ4qiCQFmTVlGFSfgyQn4kgKB/4q2ZNnje7QQkQ3swHb6ZFqXJMlRBI4aTqYGVnZ06wU6hzo7MzZ1Ctyr5t3Qrbt8tsiA0b5MLPdDqXKeE9/9JpKVQoYaPwvAqFZB+hUC6TyyuIqN/V6+rc9IqN6mdhsL8VWRKmKbMeTjsN9tyz+f3v4qgMa6PNhB8tQGg0jWZoaMqIDwBXchj+pgoGoZBFJGJhmgaWJcswhcOlLisVLp5JszkxfKPI83r7XbOkzgbqoQETksTc4NsMkKSdZPmO5fKXp7/S2sHUQN+s+soX1WM3sjtrOIxlZf/kk1aIa89/A4ZbyNUAPvCrS3jn3/9Se8dVE+TKxjdSerrTjS8BAmAIWYapkYsuR6lYEUpR6v8wLAQH7NhR5ACDbXUYSk7oL5mcUt9tjUKXYNJoNFOOO3KlLA1HYNg+vtgEEMmtx/aT/eBvD2QwPWRiWCGMUNTPEUTqFSAaTCjSW3vQ0A16CiUiQH7ZFsgFMlW2Q5Hj8/avQLG9QqFQFYNuPslkkv7+fl/7XnTRRQ0eTfAIZOJtLcW81JytMCvB+1o943KQa3PUwytgePv0HlMNyoS6qqJvliX9G3p68rMeQJ4HKnNBZT/YtvQ1SKdzJbFtW55XQuR706gsIzXvVaWJlCl1OAx9fbBkiSzrpPpUIqDXBNtbQi3vQyo4x1X7rSIchrlz5Vi171vTicVibef/AFqA0GgazytfKb9ApgghZFmliUzcFgoZzJrVSWdniN7eKNGoRWdnuIwIUYbeGspTTRbsVl9KgxYhqqzx2wL+8PQfYKwfhp5o9VCqJjFS52Sixv/umfTzFq7ErNDAH9/xLlJdHVLoMOHIhx/gl5/6xBTN8ekCDqywzyx/TTnAnchSTI0igGpuBrDPwABdRTyDVs+YEax+8sADQbY2JUkkEjoDQqPRTC1+J02NNu9/BJ0bdoIo/81gqHmDUUMQquLqTU8qsgAz2pUL1JUhxOS+t7Kz8dpKlnjfeyaTH5QsNJ9VK6q9IoR3FbX3ZzlK7DMVVt4uX77c136XXnppg0fSGGzwURw5hyAnCBQaT3uzEcpl0pavg5ArrxQid/aqdh3P6/X4P3QAB5XdqUjrliWD++FwzoNB7asEBCFklkI0KsUEFVwPhaRwEHaFTa95u2nKtrzZFbadK8ukPCVGR2HdOvlTiYCqD9VPYdZDufdVbr9moLJGli6VZas0TUVnQGg0mtro6oKTT271KHyzJ0O+9uvqCmEYBvF4BtsWJBKZ8e/ISMRf/dY8pk1RZX2sA0SlpNaphgHZyb2y7Lrl18ETn2n1MGoik6rvprmWacgCNvNf/J1uygt9DvCRyy8b76h3YICHTjqxyeJDkEGFxcArKuxThanaAPBN4I5KO9bI/lSVYVHq/yUCRbMgtk+bxlg4HNxV6be/DaqlKUssFqNL35hpNJqpxAsvALBzj30JDyexkhXK2KgvjWz1C1QE+BMhTAMhBE4mDWblfkwfM5NWhm1SqWoK57h4RATDtgnF4xNNbFUA01t6yLvSWu1XUK6pJBX+b+bPr9PLosE88UTlhUg7duwgXWRRxlTAT6HQYv+DXkFAZSsUvlbs7rVYKSav8KCyG5Tw0OU+LM921V8tc031fhe4j5J4xbfubikemKbMZhgYyGUqGIaMxXR1waxZ0s+goyN3zihPiFBInlPZbC4bQgkHylslFMplO4DsK5PJZUMokcGbUaHOV6/3QyVRQS1cVVkSSujwnuPNIJuVGR3vfnfz+tSME4/H2/L+QgsQGk0z+NjH5AV8CiiYmaLBv4njjsczJBIZotEQHR0hTNOgo8PCth1s2yEaKZYyWOb9903RDIh/voqy78ucKgJEwTife11rhuGTTSObYN3UMp9W7Hv4i4G04/cvy8DhKB5jLtsr7rtt+nRZC9q9A9m0YF6TJwre6rFB8E0qmkyzm//mBLAVuBwYrnlQpRnGdwmmchjAXsPDLBjJXzdnWxbPz5xZU3tFuf76qttqJ4QQ2gNCo9FMPbZtkz8tk+hgrEKgPvedLEyn6PZyhwmrwqICw7Ozk0UIBwOn4j1TGn8lAKu+8wpoCpLKDFZ/kDdrwbYhGsVwHKxkUgY1VUDScXKZEqUCk34yIHzcl052ASIWi1Xc5+1vf3sTRuIPZdjsFxs4uMI+xf5kPXlFeRWHlVcDlF/vEiI/w6GwHTVbH0OWiVKiQz1ZthYy82E6cBI+zt2ODik+zHEXEvX1yRX74XB+2aBMRgbTo1GYOTNXhkmRzUpPnOHhXLB/fFBWrgyS40gRw7sNZFtKEFQ/lQdEodjgx7NEiRyRSE4kaUUpplAIvv99WYZJ03RGR0d1CSaNRlMjp58Or3udVOcnuQghJkwdio/Xth3SaZuRkRQ9PRHC4dwXY1dXBFFS3S/x/mOTv+xPUa54N2WnSKIaW69WUWR8t/2o+cOogkRmFIK11M2jkf9jdh0mDsIAYRrj6c1+WMhGDqng+6C4+ZXnjN89LN9rT7qbXuPfJH+dVr1tvcrHfr1UFaJIAncD99c0qPIMUfWfdamRh4Ajtm0jXHCzM9DVFdgnzNBQUC1NWbQHhEajmXK4dfPNbJaOLUNld837jslEJm6rdKAKxPk5wDQxQxGMcGfFXkIV1HoDsMbXZVcYY6VtNTB9eo0m1C4CsF0BQhhGzpDau/K70j2lCqQW28+zrfQ9G0Qik/v+zHEcMmXq08diMW699dYmjqg81d4V9kKF3OXiGOQLDvJ8yM9g8O7rfW4yMYvB9Py9GMipqvJ/yJATHmq561Vjs5C+DychRYiKZDKQSsnrmSqtZBgwe7YM2qsAPkhfiHhcZkdYVq7EUiIhRQGVETE+KPf9ekspxePSsDqZlGKGN6tBZU+ojAnVZqkSaqUQQgoa4XDOA0J5VzS7JFMopEsvtZB2vb/QAoRG0wx6euC734Vf/hIWLw6s2UZ8BU3Li36VntgapjSdBsHYWBbHcYjHM2QyDkKIvGxDXzw/u9Yht5aV+5Z/XRSz4poCjO4OMZ+18VtAFsGOKep/m0nWfjMnTJnK6/8vSnAETxLxGdVevc9SMOH/3vMu9l23rtZh1kFgoXHkrYyflSPTqOocdYDNwCeBl2oZVxliBHph781kOFitdHUZDYfBMKrIoKnALmxELYQgkUi05QoljUbTpixfPr76t2toJ1YyU/77wPOiM9pbdXdGxk/pG1d8CEdlADRUuQxotIJfmQCy2C2ZgZtmhGik+mzDcQwDIhGEZSFMUwoQIEWIatoo/N378Ek4HCYcntxlWTds2FDytf3337+JI6mM14ehEgYyIF+LAKH6UZ4QaptyejSR+cEqyyHkbjMLjld/KSHPCnzVhrcPqH36qvqPAj3A7r4OMnLZQKlUruyRacrfHUdmR/T1STEiHJbbVTDfKyCYJvT2wrx5uVJMKstBCX3Tpsl2lNAQj+cLguMfjmchp/KLUD/VuL3vwbtvJJLb1+v/Aq3xg4hGZckqTUtIJBJagNBoNHWwYgVcdpn8YprEptQzSLjPKiRkCwiFTCzLJJHIYBhSkDBNg2QyW+F7skjb6+qYqLeSeLmbMQeMQC1fm4gJj3y41YMoy7OpVo+gOE6F//IZ8wd8tWNiM4+t7M4GIsg3a9oCo8R5VWzzTPo5mGd99Qewap/9+OK3v80H/vhH38cES5CT6z6f++1D1SKhANYA7wRqqLJQEnUXVsNhpbYvHRpitqdEQdaySLvBjEC45JKgWppypNNp0um0LsGk0WimDnfkTIzmrHoWu9P/l45hGe7XtP/vasNr5lp+T8xIN1hh7PTY+LZaMYAwIcKECPAbzxeOU0HU8YP7mQnDwLAsnGgUUfg5FrvRqlJgKJf9AGCaZsV9Ws2zz+bPc59++mmOOOIIDMNg06ZNLRpVPoVZB34QyClhEAVwlCm1FObkQ2U4eF/zmlar4wDS7mIT71+CCZVyjHyhBI15SAHC11+bYUhBIBSSGQ9CwOCgFAbicSlIDA/L56mUfB6Nwvz5OfFBeT+Ew1KASKdzpZmU0KCyG2xbCgTd3YybWiu854dpyvYKy6lBri+VweQ9RghZHqqvT46rszN3XCsEQNOEs89uTeknDaBLMGk0mnp45BFpRH3HHbBhw6RcMSqAmxct4ovnV/A0cHEcwdy5XUQiJqmUjWWZzJ7dRVeXvJGxLCNP8K/Is3Mmf6WiYpQLNhu4HhBT8Y0Bj/93q0dQllWT1E+u0t98KFx54F3EOY07OYZHOZynOJPbmM+W8RuFUvVeFfKmJcPZ3ES4ipo+7/2/X3Hh1782FXN2inCEz/0OoabpkAM8Dlxf/aElWUxly4oqCQHHbdo0foMkDINEKBTcVem224Jqacqhak+34w2CRqNpU55/fvzp2PTZ9L2w2fehItZTfRC10BA570UBjlxnbVohsLOYoTCGYYAZKns7Mt0on41hYmJgkMVpwZxGkMkM1/89667mEtEoWJb8XJQxbpNEAcdxyE7C+1Yv27fnPM4+9alPcfjhh/PUU0+1bkBF8JYZ8vv3GEEaMXf6aNvPtkJURkThMaWyNNT9h8FE4aIepFgo56t9+JyRK2EgGpXPOzpy50U2mwucq0wCx5FZDENDuRJM6XSu9NLoqBQYurrkT9WHMoHOZCAWy8VwVJDDNPOfG8bEOI/yaXGcfIPrQgYHpVgihMzUyGRyJZ2aiWHIih2//nVz+9XkEY/H2/L+QgsQGk0z+NSnpAI/SUkBZ73tbZzz/vfLEiB+EDA4mGJ0NEU4bCCEYGQkRSyWxrYFmYyNEIacLPsaRLh8MF/lkE62ObBVZkD/n73/jpclO+t74e9aVdVpp5PPmTmTZzRBWUIJJRSQAEkEkyQwJvvy4teAETgbkS4XjC9O3Bd8DZdrm2CyEAjbkpBEUEBZGo00GkmjmTPp5HN26FxVa71/PLW6qnt3qO7dvfc+e+p3Pn26d3XVqlXV1V1rPb/n9/zsNf4T29jfplP/dWOvezAcyo3MR6B+5dDENp7KZ6nSQmHRxGgMz+JT6ERIPepblSUnbuZhbuaRqSber33/IowNJsFNy+YdIvjunOvdNvu+O8CvzrbpUJxkJgUEjD+C5Tjm9qupVOPsykouo8BcZ2VM6YODDkdAHESJdIECBQ4oMqRx0GpQPXt1jIpuYDDjdRkpwxwGS1oqZcT7KjJovwLKw0QdonYda2K8yrIoIkbg0IQSiwZLTIzF7EkaUL3+xfk0lMmmVqWSZFGP8nZYgM9gGIb553J7hEuJp8kP//AP8x//43/c495shyt3BPkD9iWkFNHpHOuOam/Q82HYp5gtn5RXs78Tv4dhCJJHDNw1zYaOXGi1RDFQLguBUKnI8+qqfF+cqbPniYLBbRuGqRpia0u+VwnZ1yuJVKmk3zfnz5BVJrgyTUGQkgyDZSAcMTG4PKuEcMRFHKd9HiQtFg2tZd833ggf/GB6rgrsCRqNxoGcX+zfOjAFChwkfO5ze92DkfjpF7+Yn37ta9MFU8TMo8hQLgeEYUynE2OtxfNcAzopywSt1jDmfiBK65vx0S4FGL2/ShpZoDXOnMmKa3CBheC+xXlQ7xxjPvZOZ3KK+zEussY6q2yhMXQpcZkjHGKDK/SXK7Mkl5lWKJOWaHodf055gUbd88M00568eCbw7TnXPc+OyI+PAO8BXjV7Ez18EbGkuMJMs7pR3JcCnnvuHFcqFa7Wanz+6FFu3txkdYxxY2586Uvba+A+SVCv16nVamh9jZPNBQoUePLgkUd6Lw+dPYNfG6PKHLih+MenND5SoOIxYUqtsL7CEiWlmiTL3wI2DsfeV9bZnLRrSIovKSDeZRqiWs1VyX48kuCmK4FkXDmrOZVFyVNaKQxDPM/b1yqITqfDJz/5SX75l395r7syFK5U0TQjXY2UI3rKhPVGjfuy3g+T4FQNE/Kn+vanMn/POoJ36UcV4DDwfHKaT0M67nTKhpUVUShkDaAdUeCC+82mkBLWyvo33STbX74s63S7okAol1N1hPu+OeVRFEmQ3iknarX0Pd8Xg+ooSkkD9xvmTKVdiSenglAqJRXDUPq8uZmSEO1ZHECmhCNCjh6Fpz8dXvtaKVVVYE9xUAmIYsZUoMBuoFLZ6x5sQwz8+rOf3U8+AEzhL9dui+l0EHgYI+bTYkwNlYqP52miyOL7OQJTT7k8OQboGRmp7Bc8fPOYTDAL5Rbzyw+ZBDvwmAP0Lgx6doDm5FX2JQ4dmyzdKNPlGJdYZosl6qyyyUnO06G0rdQSJBOHDPnwPP6Ww6zPuefb9zvfFufZ6nunWPcyOyJALPBdwJnZm+ihChN8NWeGB7zyzBnuvHQJz1q+cOjQ2DOem06o1+Hzn995B69B1Ot1lpaW9n12aIECBQoAEvQaCGgFnbxyUkupfHHqXSobgxoTctAaTARosAaszCds1MF0GyNJiIZtDV3uYJJxxTI1vHlNHnIOU7QuUS4fzb/Btv0k2+2De8u1UIIJ4B//43+8110YilLyCJG5d95PtIak0jxrwjZzGceRfxTuyAcfOa4qs0/NLSkBcV3ymAonTwqR0GrB2bOpqiGO0+dsqaWrV+Hhh8UPYnlZxq8bG6KeOHRICIpGIyUyrE1VFtkyS74vsR1HTFgr3g3Hjgkh4YgHp3qI49Qo25Vect9ttw/nMeFUFSDr7kaCi+fJObj7biEgvvmbF7/PAhNRr9cPpMdcQUAUKLAb+O7v3use9CEGPnv8OD/21V+9/c0pRhFxbOl0IrrdOCm7ZOh2I4yxtNuyfG2tTKWSQ2xlcgyT8qRm7CZKHWiOYaaNB0EEfsTud3wOZESwT12eEyw0t3+BH9flc0cnrlOiQ4UOPjEeMQFdSnRZZavXvWwGUvajPsE5vpp3LKzmcbb+6/7Eq2FAJTIea+xYgfEY8FM7awIQ7bnPjkZno0tpQNlannPhAq956CFuuTq67MbU+NjH5tXSNQVHQBQoUKDANYHO9nHdUvvCkBW3l19SNiaIp699aceRD5AMKjywMVZpTBSCibAmsabV/tDyI4eYHJixWFp0xt/rFjBY8rzq7A1nyYd9QEBcC+QD7M9SiCVkOBcmr8vJ8kmf6hJwFAnufy1TqAIGYEnVF/NEVl2xEw8IDxH9Xgd8GSPOy7jvgFM/LC2J4sCVK3NlkbKeDO6xtCQkQaMB589LGdGzZ8UbYnVVAvFRlJZmyhIIjmwwRkiDo0fhxAm46y4hQR5/XMgLRxw4hYPzqbBW+uheZ4/NKSpA1gnDdJ+Lxl13wUteAt/5nfCzPwu33774fRaYiIM6xygIiAIFdgM/93Nw88173Yseup7Hx6+7jmYwpBTMFIXZ5J4sZIMxon5I75MK39eEYUwc57h5PpFDepF1vdoPePgW6JZHv281lNsLLsM0rrJnryPMdOJiD+KiUt+80diYPJgoI0ECTYyHQWPwCVlLVA0q+c+VX3KfvgW+m9/AW+CXZBz5kEe6vXj82ZTrl5iL8/NvA7+1wzZcWtkO5xvjzrMGlqKIo/MMKjzwwPzauoZQr9dZXl4uFBAFChS4NlCvbwvmd+KV7WQDdtsNvRJfpNydPjHFlMeowC2opGQrAFEHbNxTQqR93f4be9hbm7hviyUiIspdjGYCcv7UR1GdOGrm36BvH2rfkA8A7d0oAbNDWGs5duzYXnejD26sbOgfZU4K2LthYBV4dvL6LmYTx+rk4YiIecIgtoydzH6mgUaUD2vAcxgxCvf98d+DWk3IhkpFiAOnUnAljVxpJEjJhDAUFdjVq+L7EEXyu7i5CRcvpooHV6JU6/R3SGshI7rdtKST82pYWZFljvhw+/T9lBAZbFcp2d71rduVhzOfHmVWPW8sLcErXwnf+71CwhTYF2g2m4UCokCBAjNCawnQvPSlciPaQ1gg9DzOrqwQD6shWp2hTQtB4KG1Igg8PE9z+HCFclnT7Zp8vkmlcPI4PVukcj/gN7+DiZ0JAykdtSuYloyY8MEoNd5ku8DUsBauu/lcnjXxiNAYFAYPQ5ku3qALe0I+OI7r6/gjqoyp5zwnZAkPO2T53uHrmf5HLCLNS9sBQuCfANNXqEjxYeAqczmRO2li6m0/+tEd7O3aRaPRoFYb5wNUoECBAvsI739/358GRaxXhAiwtvfYNjy0Mdc130dlMwaTY1CfXcUbrmDorViqgPZE8WBN/3suMKe335W6dvJYR8You5+1ZG1MbGYM3O8j8sFaS6PR2Otu5MKRI9MoXxcLj9QAGmR42E2e8wTfPODFpE5mtyMj22mvCkd2OCJkXlCkio5K8jztt0wjSo9nANuoI6UkoO/7onAYljAZBGmwvNNJ1V3WCjEwmGST/Q3aTPxjysnYP1uqyRFuTgHhyiM5MsARCs4zwhh48MHUt2FtLSUgPE/60WptVzy4PjmfCucv4QiObCmmRUIpUXL8yI/sm9+dAgLnM3fQUBAQBQrsFspleM974FWvEsZ+j37kFXClWuWjp08PX2HKOJznidKhXBbiwY2bW60I3/cIw5huN0fm0er4Wq497Kd745lbR7+njPhDGE9KMOmdiFRH7mTCe3kJiREobc3Uq91EuF/UMDmhFNQ3JwfIDSohICwai0pUEEe4glXJRCKZVajk+Toe5zl8eqH9H6SurFLEWs/dQnp2/N4M2yjmQkAAnAV+fgfb/yWSBjan37lZmplp15/97CxbXfPY2tpieXl5r7tRoECBAvlw773bFtkcBVp82+Rw+ACVy1MUv3QDhbA7es6jFDbuYE1SHV/7iV+Eq49uRBExJGTRIG+AX2GxiR31bkERBPs7czWPATXAxsb0Zbf2AuXynMZxc0BAf9kjD3qpQeNmZi4o/xLgFxF1AEiJohuYvpSSQuYLi7ryY4RUaSf7yBNYVIi/xXFE4dFnsu38FUCC9kGQliFyKgLfl5jKyooE/a9ckXJKhw6lygHY7qPiCIB2W4L8Ts3gzKEd2elUE41Gup5TLzgiolyW/a2swBNPCHHg+thqiRqjXJa/3X7ds9uXUzi4NgcRTvFbuxNoDX/n7+xLv9InMxz5WyggChQosDMEAfzKr8A99wxn83cJh9ptPnjDDcPfnLJbxliMMTSbIWEY0+nII4pims0pbp63r0+Ozy8ijWMnqIyRV/shVNriE2GSIeOekid5yIgBnLxvUZ2ZG85fgwKNw8c3J6/D1b4btAzsDYe50vvbuhdIqabv4jcW6vuQ/XragQG9VYMFHPYCP8dsRMKNkDuQkQO/Cnxuxm03ksccsSs/Oxd3Ivu4dnFQ67MWKFDggOJv/7bvT6NKTB58G6rReTwboY1B5cn8cMNNC2pUgM2tmJRaUp6PUokR9bY+bQ+jLuVUO2oUKiEhxvZ3jgiCI3ieBPSmGhvtswzkKIr2PcnuiBS9G2a9E+CC8O5ZI9PqGLmC3WNUKVMfOI0QD2cH3ns+Erif5grJJg35U247qd0uoh82A49hUAh5UkU8H04iJMszBlf0PAnQ12qifIjjtLxSrSYB/6NH4dQpCfq326KCsFaICq1l3WpVShuVksJV7nsVx6lBdRiK0iD7++R5qeogS0Zovb1U0tqa+EY4c+kgED8IV8JpdRVuukn668otOZVDHvJvN8ovOaXJ933fYvdTYCY0Go0DOcfY+1/qAgWebLj9dvj934dnPnPPutD2fbxRN78po4jORymOLXFsMcYSx4Z2W5QPvu/lG0+XJ9xkHfGw4UMz2B8kxPmTjDxhYUnKL5XbUGuAjvePd0UudYSFoLl7XZoR79hPynCbc0yZoxbxUa5uW6aAKg1RPJDcwK389038AZV51Tgegm1Xi7XyFVQKZcw+uLRXgX8x47YPMNfhUBt4EyRc0XRwRXXn/PuWd9I58+T0GqgRvQg0m80DOTkoUKDAAcUnPtH3Z6iXyVP/9Ej3c2giVEy+gY5sJpgo1tUorbE2xsZDEpdUMvoYaMebZG7d15XdHqXs/ajoyQalFMePH9/bPiCjUffpO/IBZJTpTKlHDfFCoIWoIE4NvHcMUQzMUCm5t89ZrspxX19HrIyhF/GQ81ADbgFeAfyd5HVf2y5IX6lI8N4RSktLEsSvViXY327Ls1MgrK7Kc7st2xw/nvpBxJl5kVKpmsGZVTsFgnuALHMqC99PE0YdAQGy7tmzQjw40+utLUnGcWqJ1VV42tOEhHDESp6AiM1MJvP+1s6KWg1+67fSc11g38ApIPY7ATwLiqutQIG9wG23wY/+qPzw7wE2y2XiUTfBGS0qlIIgUASBRmvVe3S7Ud/9f2Cr9OV9JybsABnFVAx48f4oxVRfZuywK/IhKoGOUhXEvsSIk3n1zn0/h/rMPuJIbO+/8ZhkWPtt/JdxtFDP78Ht6mX8JffMnHKfD31llxAFhPF9It9HWzua0Nw1vGMH214GpjfWHIt7gf80w3ZnkNnnAk7nONpxSm3UdsSxyM6fZChKMBUoUOCawtWB5AYTgRo/PlW2y7H2JwEIa17+mFhyU1FKj7jB9FZILN7Sv/vQq53evzyvsbRJyi/NZdqQ89ijaIM4mpKY32fqB4drxQMC4K677tqzfXuIH8IxZCrtIfkkLkhfQYiFwfJMg7gM3I0QGYN4PuINMW30YJw6YRLyFCcYBqcCKQEngOcBX4MYavcCkI5wWFmRZ/d6fV18HKwVg+hz5+Q9p2oIQ3n/iSfg/vtT9UMUSUmmUknWd3WhXYDdlUXSWp4dEeFMqx3ce9kfu04nNb3WOt2ne8+VX1JKFBrNJvz1X8Mjj6TxnryB/qyR9aJw7Bj8xm/A61+/2P0UmAmdTocwDIsSTAUKFJgjnvlMOHlyT0ypt0olzq4OG9owswm1UoogkGPRWiVKQ5tfPXg5x45lhpJkLUzfz5kwbj8nL0zYVkE3gPVD+z6QPxRhBa6O8bnYB/gP+2hepFRSungCHvvijSPfexP/hTt5eOT75znRK3+kgGNc4GX81Vxv5tkqZ1niwShFpHVKRGhNp1RKyzGxV5f564EX7WD7h2De6hEL/BIirsiLK8CD7NpJnKEo23gMBraeBDio2UkFChQ4oBgwZm37kzPGA9PuxcZNSYkf9FQkhB1uXO1uPsYISeFnSpwMW1kHfUH6CqVcXbBYYgx60kgpz80w5w3T2pgonpKU3/NEjuHodOacoDFHDPpYvOxlL9uTfmikAKgCLiIEREx/6SMPaGaWD4OHlCf6/Ij3FaKCeD1Syigv9uLKssjxHEU8LJ7GkMDj6qqULXJll8JQvBcgLX/kFLbOY0EpWWd9XQL+nY787ftw443SZqUiMZYjR6SUk9uu00lLLgWBrLe0lJZ8qtVSIkRrCdIfPpwaT7tyUG6dIEjLJDn/CaVECbGxIftaWhJSZRqCMVvyaRGoVOB3fxe+9VsX036BHaNerwMcyDlGQUAUKLBXuOsueOUrRUK4i1kvBnh0bW30CiETfhkGbWhdIoEiDGOMkfc8b8pjWs05wPXtnKNmEzBuP3d8EdQYhsVoaFfBeuxup+cEqyHON8HbKxjg4j7ygcgqZ0chKHW3LfNp8yP84ljyAeBjPB+TJBP6hHwrvzutbctEuKylHt/nlrk6u8lrL4oodeVYjNaEibx4dyc6AfBnO2zjY8ydgAAhFL4PeCzn+meTbRZXSavAnHFQ67MWKFDggGKAgChHlyZushI+hG8lCFhajzB6irGsBaKk1vooKIVXrqGsGZ3FYWMgAr8MsU2atlPXw98tKOVTKh3axT0uBlEUUa3OUvRnwRgx2C6VSpw6NVi8aLFwageLTKGbiPLBlR7y6FdDDHqqDWtrnfG63JuBm5jelHq34NQgR4AvQ1KEhloct9uiWHAG4i6g7zwcXEDfWrh8WQIOGxup6bO1qfKhVJIy13feKW3VavJ31tPBlVzqduVRKsmy5eWUVFBKyik1m0JsWCuKBmNk392u7K/VksdgX1ot2d6Vf7hyRRQcWYPsQWSVGs4rYtxv5k6gFPziL8KrX72Y9gvMBfV6HaUUtT2qlrJIFAREgQJ7hSCAH/9xeOpTd7X2XqwUl8b9mPmMGBkNGzLJ30HgYYwlDA3GWKxVybPNz60cypkppC109eJnEnnav/8eCdIPhTtwK0oIe42RDyDkw5Ev7nUvJuK+/ZKcZdMx5DgcPnW57+8yDX6Uf8sa4+tJGaDOYfxIqhl/DX/K8ZmMBvJhWJkebQwm+b1SxuAlg25tDF5eY7W54pNDejoNLPAhFmYq8yHgJ3Ku2wXOcW2qpQD+8i/3uge7joKAKFCgwDWDIQGttj7K+Huo5XTzL3t/Ka1QHTPVbVcBdAfGN5mBkq6sUD50PbWTd6JL1dEkhLEQdcGXOYDJo2ogNaGWZIrdGYt73hJqQmmrawFKKcJwiC/HXsAFkK0joIbjjjvu2LUuVUiTdmJkCu0nf1eS5ypCRHSRkaZLSRsGR1zcAhP1PXcgpZh2v47CZFSAG4EvB56CqEO2wZENYSgEhDFCGhiTGju7oLwjDlxZJqVkG1eWySkZzpyBxx6D668XIuDhh0UR4dYJAnloLfu5ckX2tbkpZMPamqx79Gjq2RBFss7qalquyf2WOjVFFMnDmVtnl7l50Uh1V4Lse079sYg51d13ww/90PzbLTBX1Ot1arUa+gD6cxy8IypQ4FrCPffAP/tnwqzvEmKtOTNOATHDr0K3G/fGCJ6nkiCsmljrvg+d0qQ5kMAoURaYBU8iJjW/sQqfvzNZccQAwSood4Q0UfMeRCx6EqVg+RzEQ4eN+wpv29zrHiTI+ZGsrtX7/v67/A5VJme6PIL4pFjgadzLc/j0XK+C7dqmdHnPZE4pYqX6SIhsSajs8+LxMuCpO2zj08AXWFjUPwJ+G/jVHOv+NXO3othVfPaze92DXUe9Xj+Q9VkLFChwAOFtD4hfrjx97CbatKQ8UrYZmMzZD95Sg6H5z4DMFzob5+hunkNrT0otDR1JZEYoSuHhEedIHrBYNAqNIpgUrp3TACaKNsmehGs1ryAMw70vwZRHWpzBF7+4O4lTJVIywQdWkmU+0Ca9lGLgEFKKyM2oRlFTPrAGfAOTL8XTSJDfER37AR5yjMeAFwC3j1pRKSEbqlUhBNbXUyPnTqdfKRDHqRG0K8NULqfkgCtl3elIO0tLooK47johFS5elDZ8PyUhXIklV37JEQePPSbLDx+WvkWRtFGvy3a1mlyLnpeWYsoqLBycgsEpOCbFQ5wnxaIrYqyuwt/+7WL3UWAuqNfrLC0tTRdLu0awX36vChR48uKlL4Xjk2uwzgMW6Hoe779xdA364ZWCxg38kkwUC76vKZd9SiUvSRJQOcaMyc46E1QNMdDW0PGhGUArWFjSci50SxA50e2oiRLQqUiZJi9kflOQXboZtQ+Dv/+NZf90HxlR50EUpxPg23mAG3h84jYW+J98PShY4xLfyB/P/SrIfvXtiOUg5ZdMMqBWCKlpVJpXuHtfy7/c4fYfAL4K2Np5V8YhBH4G+PCE9d7HtV1+6bG8taYODtwEoUCBAgX2PR59dNuikhk/gApsg1K80fu7erGL8dTkYWgmN8daJKt4BOJ2HWti4vYWNo7AjKuSnzSuFE0mmzxLdrqiQgkPj/Lci1aOgiWK9u/gdNA7YRS2thY8PpqEKTPAwzDk3LlzC+qMBM5cSaWsVxpAHVE5aFIyYi15HvRVi+n/CimESLgVKVn0ghx9WUJUEDciSoj9oLcpA9cDr0F8H0bCWim/tLQk5IAri9TppAoCkPddAFZrIR5OnUrLHhkj2xqTBvF9X9o5e1Y8HFwpp0ZDCI5WS/Ydx6l/BKRtbW1JySRHfFgrfQoCUUisrEibcZz2w5EM2b4GgbzfbMqxuRJNWbhtrE33vyhoDf/wHwoJUWDf4yDPLwoCokCBvcbKCnz5l++aGfWVSoW/ueWW8StN2RV3//R9jVIqGdgqomgKl7qnnR8/oXEpJqGGcghXy3ubUnT8EqxtMLrTCnQsKghtoNIBr8M1lQcVl8Hs1mRtdqzvJRE1iBwf7/qFQwBUqPOt/H6uZg1wiZNgLf9ffnWhE41hJETvPZupuWwtylo8Y1AJKWEyJtWLxX9kZ0OYx4DvR2oe7QLOAf+U8ezMgxPe3+9497v3uge7jmazeSAN4goUKHAA8aUvbVu0FJ4Zu0ktuoRHWoLHbxu8VjSZgMgOBHx/cmDNWowxxFEn8XuY1LjF5BhtOAVnmy4GS5fdKiektilHrjVYa3MTFXPP0p1S9eDwJ3/yJ/PtBynp4MyknfIBRLRqEeJBIaWWNEI8tBE/iAruGpQAfcz2oZ5GCIXnAndN0bc7gZcAz0/a3qsrzhEopxFvikMTN0jUC1tbEtx3vglZH4RyWcgBRy5YK+tduCB/nzghptPLy0IMHD8u6zz2GHziE7Kd76ePLLLkBUgfIC31tLkp+1pbk/ZXVuDq1fTRaKReEK5Ukns4RUQQyH61FuXEsBJM7nuTVXwsouyS7wtx89M/Pf+2CywEhQKiQIECi8Vtt+0KARErxReOHCEeIsPuQ19Xxt8IA2UJAo+lpRJraxU8T1EqeUl5xSluopcnsLwa8IyYVR/qwPV1iHSmNswe4FC/xLofVoiHagNKXai24FqrB+t1wOzH6qL9ODrn9ma9nPKOGZcO1QHLP+SXCXKmvHvAa3kH38N/pjTnNPlhZZfUwHtGKSLfl98Oa1FJxo8zp1aAl5ARi7/Kq8BO65f+KhLx30W8D/GDGJa0uQE8sbvdmTseewx+4Rf2uhe7Bmvtgc5QKlCgwAHDMA8If5wC21KJL25bqpSePODJxkysGW2+mqxswnaSzeSUxZOxrPKac0p7Pl6+lucQ71HKR+v9n8AzCWbs55YiL1GRs7GZNms2mzw6ROUzC7JKh+wyEAKhCqwm76+SzkMihIzokhpOryM62xawyXblBMmyLWRU+jVT9vUQ8Gwk+F9iN8ugyr6c4fQaQog8Z9JGTgXg/B98Pw3e+74E8DsdCe6D/G2tEBJZY+rsuvU6fO5zabmkRx4RFcPWlmT8V6tCJFSraXvO7NkpKBxhUSrJOloL2VAup4SE+z5k2xi8Xh3h4MpFuWWOXMki+/1aVKD5+HF4/vPhHe/YtWTXAjvHQZ5fFAREgQL7Aa961Vh58rxgleLhw4cnrxjlb1N5mkrFRynFykqJe+45xnOfex212pQD74cPT55zaNJ6MNqC7/Td0+1qLmhW4XN3M/ZnVFsoJUPQxvICfCAWCSvkgzfZm2Cv8ZzRpYV3FW7sOHHuZCwv5y+oTVnw/wV8hJs5O1vnxmCQbMjCkQ8AKo5R1mKVwnoeRqk94v7eO4c2/gpy+G7MFRHwC8Dr2F6O6X+x8EpQu4Kf/3l4+9v3uhe7hkajUXhAFChQ4NpAsH1c3tGHxmxgWAkf2bZUdwcLyIyAG6/HZiDwNbCtibAmQmEl2zNn4L6be7Ki0GgiYgxmVwK01oZY2x+8v5ZmACCkQrs9uczVHHe4o+zvK1eubCsZ5VQJVcabP4PM5gIkiL+U2cYhe9W76agL+HeRzzdMXm8gQ75a0k6HlJAYLGvq/l5O1pnFeU8lfV5NjmHRkNJmcn6WkLJLrwPuIUcRBado0FqC/9lrrNuVZaWSBP7X1kTlAKn3w9KS/J6cPQuXLqVljjqdlCTImkc/8YQQEe12SmQ48sFdc1kioVwW0sKZZHue9MeRCtVqalDtyj5lPRwcseLIEZD+DZZpcnD7XYTy4cYb4eMfh/e/H54+3u+nwP5CvV4/sArrgoAoUGA/4LnPhZe9bOG7UdbyP+64Y/KKvRHXpJuh1DMMQxlWXb7c4tFHN/nIR55ga6szHZmvc9x4ByOkag8JiA++EJoTsq9iD+pL0K5AGIh59o6xi7ktlQ1or+3e/mbEtFzXImAzl+JkrzHFy3n/1PtY5CevMg/3dbLJQNlCL3vHlVzSUYQeGCzvTvWgFwMv3GEbV4E9Mkw2CPfx95EZqsO7YdcqQywSm5vw67++173YNTQajQOboVSgQIEDhke2kwlBdHXk6p5psxZtL9tUuRrlD5ZZiy1XYGw5IgsmxnTb2Li7PUt4BFo2E7i0DM+iAJxDVUhIRLxrKohOZ/S5vRZgraU7RDWzgB3lu57GrBPH8VDvB+fVEJJm65eRIH2F1DS6hJAFtyfLs+trUuJgFTiVrL+MBN9DJH/EeTvECAlhSAmKMiMsFjP9PIJ4R3xm0nkYAo0oIBwBscgAnyMfloDDwFMQ8mFqNbrnpWSBIwOcH4RSQjQYA+fPp74QcZyWRApDIRaazXT7dlsUEJWKrBdF/e06nwlHHvh+PzEbhrKNe+52xdg6DKXtbldIDkeUuGvS84SgOH1a9uv8HyDddxT1bzOIRRAQv/RLcMMNize3LjB3FAqIAgUKLB6/+ZtyQ10gYqV43003TV4xp0JPIXU/jYFOJ6bVilhfbxNFhji24xXXgzjVyLfDwdduVLjbtdM3V8BOmlBpWcd6QkbsmIDY5QFE/SRU9/8Eaj8IS4xRmDjfJ3TbxgP75uY7Yr6OAYzWUnJJKZSRrEHn8eCR+kGMm1TNH7+xw+3PA6+nP/o/J7iMqEkwwL3AyxAfbIBPcG0bUGfxqU/tdQ92BcYYGo3Ggc1QKlCgwAHDY49tW9QKToxcfTl8GM9uD0Afub8BeT3erBICIhtcG3ebNCYxoZ58Lw1y5npbLHEux4h5Y/tNfR8MV3NDa527BNPUyNbMH/U+gDHoOO49Rq2vlKJer29b7nrvkSoaAtJpbgkhB2rJ87lknRDxb4jovxKzSoYmEvBXyXpOj6NJ1RQbSNmlmJTIGBx322TbCwi5MWva112IAuEIiy3FpJFz+TSkXNQryanayBo0O2Nn97d7BIEE88Mw9VpotWS9OE5LNrXb6bpZ5QHI3+02bGwIibGyIs/Z8tNZxYK10pZbp9ORvw8fFrUDpCbREvDoL+GUvY6bzdSg2j3cuq6dYSqIRUAp+JZvWfx+CiwEzWazICAKFCiwYBw+DF/1VQtr3gL1Ugmd56YXuS0mQRPHNjEqIyEe3GPKYfalWr7RUvZXy60famj5uzuyv3KEsR3WBpa2YHkTgg5YJY+Zh4R7kL1gLYTV3d/vlFhTezupS+dDOlcCy5q3i5L2MXBd7VM9kJAPifLBi+OeybRKJoJu/cFDXfyA4nVMZ883CAP8MPAxFsJYOll53hqrn0bcA8vAx6fs0rwmL0NKcuwYjzwCH/3o/NvdZ+h0OkRRVJRgKlCgwLWB++7btmij9JSRqx/tfm7o8urlLn4rzjfwUkgpIhd0nAgLJmJs40oK4JTViPvXUBWExcdDTzOW3uFtdpQHxNizsIgs6B2gVCr1kwXjHpOQd91kvKmMwY8iqo0G5U5nrBmrMWYoAWERIsCNbVeQIZePkAkdRK3QQUiHrFoiq2gwyXMTIQrayTZXk9dufUc8GITIcKP9bOmlYUfvxs9l4MvGnpzR0MgI+RsQcuA0ouaY1+xRIedtCbgDeCkwmr7MdiwT7B/0THDqBEceOGWCUzN0Oqk3Q7Uqy50i4vDh/rJGzhcCZLswFDXE1pa0eeyYqBNqNSEJnIdEpSLLqlUhIZRKyzU5wsEpKbKEnCNN3D7rdSFMQPbtyIcs8eGOv4+QVbJOEMyXmMiTcFpg36IowVSgQIHdQRTllh5PCwNsVipczMOm5uyCJTWa1lol92FNlDczKov18vhRuYt4ZoN0zoC660PkQVfvnhLi3ClGdlgZQEG3DHEApRD8ePT6+xIK/O7+kBdMwP1JEo0deOwW3Lg3ijQ2x1Bf7VGt/8FzkqXD1MAyjZAPWZWDpn8i44RHg5lcizv3v7nD7e9F/CMWVFLAmdk52XVeOK3+NJhXkGLavuaBMfD7vz//dvcZXLDjoE4QChQocMAwJLhl1Cj/OcvRzr1D34lLmtJmzjJMClTYzaw7LmMkf1knsPjKS8ckY/J7LGAs6G2jmBzYQTzQxKN9vq4FEsJaS7U6RRLSTgmKAZS6Xfw4Ji6ViD2P2Bn5DulnFEVcuHChb7lb05ELbYQwaCAKgRJCFnhIYN0NxZypMsnfZqA9V2JJk5IPlaQNNy6uJO2UkDJFJdIqm8MuKWdm/RXM5gGRRRl4PnBL0uY8SAhHrCwjpte5iIcsnHoAUpIhSxo4kgL6lQdu204HLl+WIH21mgby3fjLJQC5oL/bVxxL+47E0FrIAefRkL2mgkBICkdGNBpCTmidju/d+u5v99qRGdYKEZFdLwzT/rg+ZEmT7LHP87v/X/7L/NoqsOsoCIgCBQrsDk6dYrq6RVNAKT6Qtw7gFPpP15xSsLJSotOJMcZOfw+NJ/wcuQgoZIgIBUZBOQbPQOjhdRV6N0qZnDvJ6NlOsjz2IfSTUkx29Pr7FVEApeZe92IimqMU3Cw6IJ7AgIkVSllUHsLm8qI7NBx5rz6FlFcahSgZPA9e0e5cL+YqPww8vsM23gFcmkNfJuBaqrW6qEDHr/3aYtrdR6jX6yilqNUmeAEVKFCgwH7AM54xZOHw+5VvtvBG1AX0Owbr5Zeeqk5W9TnNqGww5SFbe1VQY0iAvO+QNEoprIKuDTGzZCnNeEtf39iuOMmN/UJCLGpOOnan4jVWazQ4dukSpU4HMyY5z1pLo9EY6gEB6RXnlAxdoI6oIapIwH4ZmWK6gL2PlGUa3KubirqSS53kuQKcRIgGL1nWRQiKVrIPj9EIk/3lcGnMhWXgRcCXI4qFgOkvY/eNcwRNhfQ4R+umhjWkJLgfBGkA3ikQsqWPKpU04N/t9pNX7jo0JjWlrtelnSNHxDjaBfKDIG3fERPtthhWx7GsU63C0aNCOKysyLNS6X4bDdmm2UyJBuhXa2TVG658lOujO+5BhGGqeHAKCleWKW8Z1zzQGr78y+fTVoE9wUEu8VoQEAUK7Bf8yq/A7/7uwpqv+z5/+LSn5Vo3eEq+EjHuXm+tKB+6XSm9NNP9sz2hFIjTrfbmH0pGk6EHdR+CGCoRxrOU2wgJscjxe3tCjoqOwYvEfLqxJASJ74a/02KPAprdFSF49jkuRJN9CBamjrBgzkOnVcl33RsWYj8wDdxEzL0e9j5sVzz08hcz5MPgeV/coKIO/PEO2/hTFvqj4LKa9kngYE+xvg5f2m5eepDgDOLGlYUoUKBAgX2D8vZxqx4xJj3UHm2FqwzoyOYemiqXmZzFuNuk0kNWSoJ2Xr9iY6mXq56utn1Hnryhdrdep+9Vxr4/sSv7YCyxMA+IUUiO2WhNq1aj1O2i41j8yAagkv41m03uv/9+Qucp4Jpi+zl2f8ek5ZFAfBNeBLwwabeDEAdZJNp2FEI0HEcC8z5Smsmla2V1L0cy/XBlnEZ9qg2mDOznwFOApwLXkRph54ErtXQdcCfwCuDlwGuT56mK8zpywQUM3MN5OMSxqA6CQJQO6+tpySNIx9VaC1lQKolKwvdhc1NIg5tuku2zaoUgSD0aSqXUGPrwYVhbE4LB98VjwrVz5UpaksmpJVw7w74Ljhxpt6VtZ1zd7aZVLRx55o7BGHkv26ZTZcwLd9whBE2Baxb1ev3AJjgVBESBAnuNz39evB9+6IfkprsgnDl8mHtPnsy17t0X86RoyzBGyH/L5maHRqNDqaQJAm96EqKUw8E3G+00SlQT2sJymAwCFEFS7ciLoNSFUovFlGW6fGzMmxb8KOlEB7xYShn50QylmPYwuGVKcOnuvdt/Tjwaw8WkkoxieGB8EHMjIwxwSEyo3Vh54pxx7+eUeGxXK2QKJAxFr0yTG9CzLTywQITAHzL7yXscMV1YIDLnpQDwr//1XvdgoSgIiAIFClxT+Mx2UiGwbZQN6RsV2XCk/4ODX59Caqx0YizdWzBqRVl36H00CSqaaGBpngGXQWsPpTy0V0ahph+vzPAzr0Z4QEyFPRxTWGtptXfZs8xaMXGOY0qdDudOnaLufJaGlRCLYzY3N/nABz6Qfxek5ZM2EaXCRWRc/BrgWGadbftDSIQg2TYaaA+EmDiW2T4mzTsa1qZCAvrXA4/mPor8uBs4CtxASkIMu5ydusM9lhHt8Y3AM5J2TozYdiycn0McCwlarUqwvd2WQL0L3rfbKUnhlBAgy1yJo6UlOHtW2rnuOqkcUS6LuqFalfWcKiFrcu08I0oleW99XfbXaKRkSLMpBMjVqylBYG3qQzEO2f1liZNB4iI7Txg0p86SLjvF0pKQKAWuWRQKiAIFCiwG990nErl3vWum0kt5BwEGOLe8zJlDhyaue6jZ5FWPPkyeQJ8kGVjiWPpurSIMDVFkeuOG3LGZVmn0LrNtuBQUC0QKIpdZoKEe4FloV4R8OHwVyiEEEfMnIVpj8j8UEAVU4pgjnToKI4SJgZq/gR4ha993MBo6+99gNQI+M6Ks/7RkhHUL8kKDqkBtuYnWcb6xY2OK9hcANfCcXT6p+wrQ1s5UbmnnYdoHgf8047YfYmHeDwWG44EH9roHC4UjIAoUKFDgmsCQEjVHOveiiVC203vU4kscisYr2PyOzT9WiiMJDOY1Kh7XsO0fzAf4A+8PazLGmAjPC/A8H2divWh0OhfHvq9Ago7OO8rVrx/EjB4K80A0z6zsSUg8xyxglaJbKhEGAXbURNJaYmP44z/+Y1qtQb3CcLiW3LRQI6WPykh5oT9KXpcy6w8rBGZJSyuVSEmJNaVYTpZb4DywlbxfYrgfg7vi15jBWyEHVhF1x9OA2xEyokpaLspPXlcR8uQm4KuAlyCKkGcO6fNUcCqAbOmhw4eFZCiXZXkYpmbPWS8IV5bILTt7VtQK9bqUXopjIROcgqGT6E+USksnOQXD1auidrhwQZ5bLVE8RJEQGFkfzm5XAviORMhzjEDPdDqvGtoRFPP8flerooBo7v8SygVG4yB7QPiTVylQoMBC8KUvwd/9u3LzWzA6nsdnTpwYW0PT4V/91V/xxq17+WVehMk55JDkAZ3cPy3G2L4SibkxLqI5GB0NjCggQg1NUUJ4yx0iRGxgPNhcERsDSEgIxJJhYkQ6D0qjzeUAMBodeYDHatjEoulQJmaZGm3qQ6uLDsO0Yd55QsOh/V9GxQCVHKdo2KB/R7Dpp6O0xRqNUmY86ZYoJnZsZzAnDBIJ2dJKg8uzCondmb4PogX8a2Q69JIpt307LIL4c5OMovTSdlx33V73YKEoFBAFChS4pnD8+LZFNzX/AoXlcvkZWHyWoie4pf521IQRUmmzm78EE0hwz3eKgFHj2kmTAPec9u16jnN20NtpSDNKKaw1GBNjZ71X58nSyKBe/0KONhUqDNFxjHHB9jEB9930mLLW7l4JJqeqVQodx5Q7HbrlMspaUd2OOO5Wo4HOMbft7QYZvyZFuaggBIQBPklKFpSRlJWY/jGvC9q3kr+dabWHKCB8oKI1m9YSWdszptZJW04xkYVr5w5EbbAI1JByTHcAjwH3IrlQFlFnVJCpSRUhKW6b586Xl0Vt4ALtYZh6H7ggf7ebLnOEnPNVKJXSwH6lkqomPv5xKaXkSi/FsWT+Z4kMY2BrKy315MoxZfswaDCdLRNVqaSKilG/G267bHt7OSe48064+27xuChwzaLZbB7YJKeCgChQYK/wF38BDz+8K7u6WKvx6Nra+JWs5Xs+9jF+8CMfoYyhQodmjiqP4pukk3uvIooWeMNVyEAzNhgL1D1A48Vgj7ewGuIkUhp7QkQoCzqCahf8UCwZtpy1wU40YOtHxrwpkemIgIA6R9ngAidQWCJ8AkJW2WKTlR12YsHQXQhX2RXj3h3AA07PcDcbVn4o9+TSItdg0pBSoHTOidouJpRNQi/bLPN3Fr3ToRSxUnh7mIknOAN8B/AbwCtzbvN+4M+Rqd+ckbvu1pMQ/+gf7XUPFopCAVGgQIFrCkOCuAq4qflubmq+e6qmypvTEfoqCrG2kunDCLJBuzrxhv4B2fB77JpeRpkxAmrlo7WPtQZrDb5fIYosxkSQFGOy1iwkoyLsjpa7KugLdCpjKF28SHjkCMZlhQ8Luu8iCaGUIooWMG6aAD+KQClMEkC2SmFHkAylIKAzQ5moGPE4UAjpAKIUgNREehW4QurbECCBs+yVqxCiIsApqC1eohJWpObTIRLwH/WtWUJUCouGQkiOG5P+hIgZ91WEQDmCkBVzhTFw4oQoFRyhFcdCJDiFTVb9474XzaYQBysrQp7GsazfbKY+CvW6vK5WU8+DWk1Ih3I5LZ/keWmJI2cE7fbZ7fb3wam12m0hNFyfJiFLXgwu2y0cOQIvehF893fv3j4LzB3W2kIBUaBAgQXg8uWZyi7NguUw5AtHxgXM4fUPPMBb/vqvqRhDC582pbHrO0jZQjkOz9O9cbHzWZoKOcbUVlkCr0S1rdG6jR/CxhEIk229WCoHOfIBEolpCGEJqk2otCHyYWMVonyHOaQjY4gDq/FVB5+YdQ4DUKHLMS5ymeNU6BAScISrrLOGmfhTvFcqCA218RLy/QBNPgXEOMhkUF6PHSrGYJWQD735kEuAH5O4lt2ezZ31dd7IJvUNdj9Gvj9YK5NRrQnieC+dSYCHgTcBbwZ+nKSHI/A7wD9GKvzOgELZMDte+MK97sFCcZAnBwUKFDiAuPfeuTVVvRxCZKGUczQQRxL46xlhj9nOK0HcyXXvrejK2BKr1hqU0oDFWoW1NonhayElsgO/PIcyhQrippu/c3S/kAC/y/JWjQZeFBE89hjtY8eI1tYWpoTIqwDZFQXEQF+0McSeRxyIWsa6rPIRxxu7Wv1TQCMZ/xYZ4xqk/JGHkA4ecBkZqmcF85bU5yGrEjZIIL+KeCZYxIh6OVnf+UOMIh+cIkMzeiy+CGTTJ8a5Gu4Y9boE9Z1ZM6SlkpzywAXuHTHg1A/OnPqRR1ISoVJJVQyupJMjEaJIyjGVy7JeNUmkdISEIz2cOsKYVD0B6XXm+tBqyXZOwTEMjrRw7WTLlu2mQnplBX7/9+HlL5dzVuCaRqPRYMX53xww7OPU2wIFDjie8Qy5Ue4SnhiTqelHEf/8b/6G05ubmYHW9MMfYyxaK2q1AK1VT5WYv4HJq1gsKFiqHiFa8tg8BEEoxEMQJj7PkSggYg+UkbJLWytCQqhYKjfd/QA87TPy90xQ4z47y5JtcYwL+IS0KVOlSUDMMnU0MRGaNhVKhOihotztbe4+FLQP78F+p8eXdvpVynN6E6mAMQqdXNiifKA3W5g4xmwhKVX7CG6CNTggcMvc19JaSxDH8zHv3jGuAL8LvGvMOh8H/m+kCu+MPS7Ih9lw88173YOFo9FoFAqIAgUK7E9cuABnzvTfw7a2Rq8/Jfy2IdjKP4BWrqRKrrro0/k1jW/REMcdrLV4nkccd1DKiurBDdySaLIaFF2MQs55zfLyuII6mUzpTMDS39igcv48utlMg6JDN599bDJN2cCZy1VNblhKK4GUWUr2o+IYmwSH/WSObL0RSSbW0rp6lagzoSRuAjem1QhhECJExCnEe+FFiBrAlWcieX85s12MZO+6tDF3pXrJeh0k3SUg9Ya4zHgHMleZNTsFtgOPaxLO68ERBFqnng+VipAApZKQDKWS/O0ebt1WCy5eTD0bnALCqRicmXWlIuWYnN9EHKfqh1ZL4i2dTmqG3emkigfnFQHp39lyUMYIwTDqGJ3nQxSlhIkrGeXa3I1z/S/+Bbz61QX5cEDQbDYPbJJToYAoUGCvcNNN6c1pgbBA2zHzI/D1n/scT7t4sReArBJRIqaTk6OU+6zcvI2xdDpRr1ziVPfdXrr1cOjkH8AVrrBUPYTZuky3LKbTYQk6FdcpeYoCeekpKLdhqQFN4MHbpQzT8Utw6ah4RkzFuTRHB54UUGeZkACDh0VxgROc4CJLNNhgDYOmTJsOFQJCuuiky+M6sdtKCAPt1cmr7TEiINxJklZe8sGAsQqLEiJsQC2cqwztE+xLL+RxVZcjz5M6t8lkeH9kLkTA54CfRaZ9FWTa1wU+C/wlUuX2AvN3oC8wEf/rf+11DxaOgoAoUKDAvkOzCT/0Q/Cud0mQ7eRJ+JVfgZe+FFbnO54rbYWEh33QOcalykX5x5VgUmBcwZt80KgJogTJK9faI45DrHVBfRfalRQMFwDvLdrhQEepAK3HBQKVqGaT4HtUq6G7XcKlJczSEn6nQ6Q1plIZPbhccDmmON6BX8YoGHEXVJnMcOfxUG42icplUV5MIqusxYtjgs9/nuNRxJkJu1WkxEIynMdDiIIXAJ9Jlt8FfAAhJ9y6TjER0e/1YOhXNdST9yxSemmL1B8C0ms0e1QKUU4cIi0BNfRwB7bZt8j6orlSRmtrqZn0xoYE9LvdVDlQq6U+DI4U0Fp+yyqVdJLlyABjxD9zaUmC7fW6vHfihPzGOZNqa1Plg+el+xyG7HK3vyCQbcOwX9WQhVNqOBP5zc3t52Gw/UXgK78SfuzHFruPArsGY8yBLvNaEBAFCuwFjJEATXfxkUiFEBCPjPGA+Hv33stSt9sbbyvgKVzmPk7l20ev7KHcYGe+z06IE3p4KBQRERpNW7UJS0IetBMXMQugxffBZpKbym1oLUnlpLUNKdO0uSbLv+p/wbteO2U5piMbjCIELBqDoo1HhQZlujRZ4gInqNIgIsBLDtYCPhEmsTgLCYa2mW1914afykD56u7sawdQwOYiY8xOpw3ggYfBWjFdN1JBGIvCxEbmieM+nvlVQJgrhl1VPSm4MSiX4bOvFAFt4IPAq5HhTNbmb3BqWGBXcffde92DhaMowVSgQIF9h5/+afjDP0wzfK9cga/7Onj0Ufj0p+e6Kx0DkYHAmzws9XywJl/QXHmyLjCJjFBKUSagPcZcSymvp3hQSmNMdmyQzFs8Usdht3gHQ+2VlafkWCvNttadDnG1io5jwtVV/K0tKR/jDG3nSTRMGMdZa4njmFarRTgq8OraGfg8vTjGKoUyRsonqYydubWUul2UtXTLZXSyrtWaUrtNOQzxAKM1ke8T+b6Ycg+DUqxtbvL6D3+YdyEEwTgnCDXwuoIE/FeQop6HST0bFFKeyJAaNQfJey2EUHA0WVYp3Erad3Npp7FxfbNst4DTmX2+ZKCfoz6lweX7jpAIgpQscOWXfD/NzHdlj4JArh33W+UIiyzCUNQRS0uiXpC6z6lKoZJkHSolv3EusdORAo7MGGciPQxRJI92e/zcx/XDlZiK437Da4dFz59+8AcL5cMBQrvdJo7jogRTgQIF5oAwhJ/9WbjrLnjLW0Yz6nNGvVTi8ggW9Rs/8xle9dBD24QHwRTBO2tF+bAzf1o7cRQVEmIw+PgEBGg0UUkRK1EzGHcQRsiHXu1OC7EPrQo0lsSIemNNttk4BPc+G6otKLfIr3d96qcZ32GLJiYmIKTMSc5hUGyxQkiJEh0sHgpDTJD4QFi8XvB0HHYpCOyFUFvfnX3tAB47yHHPW/kq0V8rZXuG09qzaG3RnsXTRkoxTWrrc7N2dLEYdiW7Zb61qCiSTL3d7FQuWET10ETyz1rIFK8gH/YMP/VTe92DXUFBQBQoUGDf4a1vTUuVuIzi9XX4hm+Ye9nX8tVQgm95BBBxKCREFtvGX8mCXsA934jDZ0jgLY16o5QYTWvtofXoTCNFQqpMqn2TYwiudSUhPcbD+j7G9zFBgEnq1eukXE1pfR3PlZEZV39+joFN5/tQr9c5f+7caA+IRLmhgaDbpRSGlMIQL/EIc4qSXiILUG63xbQ5KcNjlBISwhg6lQqNpSXCICD0fWLf7yMuBvet45gT58/zHGv5esTDYFJWbdZMmmT9y8AZpFDn48DfJO+HpIqHbrLtUaSsUkB/mSa3zBEYlcx7ziOiwvBRqUm2+T6kDFQWauAxCvuqVJOrvewC7q78UrMpv0OdxNvFxT9cGSVXuqhUkmdXSkkpIQGuXpXfNRBiLgik3atXU+VBtyvtd7tpOaRqVdqcwSukh3HkgTuWbrffYHvYeos0j/9P/2lxbRfYddQTQ/WDOscoFBAFCuwm/sW/gN/4DWg0UgOmXcDZEeTDTVev8lPvfS/VMNw2uGkOG9DnhIw9FMbkHQ4l68XjhdRAT/0AYHDmcrEYTjtFL4DqbylMEqTDANoVOHIFlreEiGgsiSm1Z6DWhG4gpZvGjvgatbHH4xGjEKlxQIcmS0nZpQ5dStRZISBEBOQxCk2Mj8LiEycD5XHDzl1QQlgNfmvyenuMGLhxlrtZHvIhO3tyi5NTL9e57Y1LlZrwiWRnPtcIshL0/Uc+FNiX+Mmf3Ose7AoajQYnT57c624UKFCgQIpGY3jA+i/+Yu67ql4N0cZi7KTBDxB2yTeKcFn1Gmy+RAJ/XO1WQCkfrTXGhBgzynNNgbL4caKhzNbNyZOWPoBW67Eca7lMKYVKgq3R8jLB5ibB5cuYahXdbBKtrNA9fnx8UzsMcBpjaDWbRFFEvdFgY32dS5cujdyXshZtDMparJYSsl4co6wltpbI94X4SdbR1lKKY6Kkj9YYPGuJk9I8FlE+SPNJWSZXemrwuIyh3G6zknia/ADwCYRMMGxPSHLll3yELPAQguFs8n41WecSYjxdRUiBrHG0Aa4iY+GtZLnLOXJXtjOjRimMtb1tl9iufMj2qwZ87/AzvW19h32rjnBKg2wwPltOK4pEIWCtxEFKJSmdVCrBuXP9hNrKivye+X5KWDQaQio4Ys5dI8aISqLTScnXble2bbV2TtJlSyllj8f5TYzza3FYpALisTy/NwWuFdTrdbTWVJ2J+gFDQUAUKLBbuHwZ3va2lCnfRWw6ieIAfvq97+WOy5eHDt2fx1ke2JaPkQ/ZckxToRFAbfK5cQRETAxKDKidmXTsZ+YLAySEjkEbMaGuL4tvxMlzcOZWwEpZpnIozxooN2FrjeEjuHaN8SSApUqLiBIGH4slICTGwwJR4g0RElBKhq82qVVrehbgdoIZ+JDo+LzRPAYrFxbX/hzgA61pL7c86w9+vKp/mZvvKRLFzaSPIUY8IPYxslfULlxdBQ4afu7n9roHu4Z6vU6tNo6ILlCgQIFdxtOfLoG8XYDXsehWjAnyqBU0tFtQrU0eLNl4yBgtWxupH8dYY92OMtiWTBJrDcZEY1QJFu2X0O0uHpJ/Y0fvciKCYJUoqlMqHZqwpgWtML6PiiLiWg2sxd/aIq7VqH3pS7Svu47usWOTd7oDEmJjfZ17751QI9QYIR2SjHCd/B17Xq9UkkoIB5UEkoVfkYB8p1wm1rpHNMSZ8lI6jjGeh7VWhtmJV8S2EkyJ6uLopUucuiBzk1uAFwIfZbjFWolUnVAGNiCZhwkBoBHiwZAaVGtSj4i15L1O8nDTALe+IyPKyf7DZP7r/B+6pCTFIMpIGajHgZvHnvx+DLaVh5DYlbF81hQvrc+cdEAJURBF8tqts7Uly9bW5HWzSc/UWal+k2drhVBwZZWiKI2pbGV+AxwBshPlgzueQXKhN/lbcFmlaVCMRQ8UnP+DWqRqZg9RlGAqUGARCEP4wAfgT/4E7rtPblCbm6nyYZdvWMP8H67b3OTFZ85QGtGXv8cnFt2t7bj/6PDlA100GDQam/wreZXeZEFHoKwQDeUmaSqMEsVDrSGvIx+uHJYSTOUOBJEQEioWX4jlLcmAUqM+qpe9b8yBKEzG7qxNiRiNR0SEn+gaDG3KaCwhQUJMGIR00BnqIc/AaUHi2ziAaP+z7xGwMU3FnWnIhwH9sxtzDo49c40RLiAznz3GNFJuxcKurgK7DW98luiOsboqKr8nCRqNxoGVRxcoUOAaxW/8xq7VAq+dbeNFFuIJIwRj0Z0u2iqSYe4UUKA9eQwbaFlYYmnswEbKCoVJYtSwFRVaB5Qra0RlTexO3zA/7JwDonb7Asa08ydjaY31ffFNMIbW6dOUn3gCWyphqlXxhMgTSJ20vxHvd0ap8l02uzH4cUyQBGO9OEYn5ZYqrRblTocgDFEJKaEBL0MiWM+jWyoR+T46jtEZ8qGnflBKCIdkfev2n3moxEdibWuLu++/v9fNW4BnI2oDn/5P2REAToVQStapIuqDTrJ+hBAOjlToIgTCKuIRISli/cGz7JjZJG267N5sP2oMD7oZ4CRSAmonmLZU08LH9G6C5BQRWVUEpCRFEMjY1Jj02a3riAmQZ+cl4YiHwe+DU365x+A+s2buswZ2s8fl9rkfsEslvQvsDg46AVEoIAoUmDeaTfjFX4SzZ9Nlz3sefM/3wLFjcGH3s8k/ePr0tmUvPXOGmzY2hg6IFPAFcmTbDNt2sFTNNHhslVQUOxqOeNCJy0JbdWRgqMD6QiIooFtJBnzJnGNzTQiFoAulEMISbK3C6gbUV6BdFV+IIIRmWUgKm01zycIfX0vXoGlToUIbjaLBckIpGHRiOV2l1VtPTptO6AaFlGYylOnSpYSdIDF3Z4be9nOACaC0RxHzKSpMGaA9r9G0G5m7kXyeU5rNkhu33nt33LuFY0DkUaggDgqUyicRnxXlstTifRKh8IAoUKDAvsOXvgTPfz68b1ySzHzgxXDkM3XOveCQmDgPGyhY0JGlfLFDqxJjAgNenvzHTNaHtWPLMbUYXypUTKctSumkPGw2aOhRLi+ztnYLrdYl8Hx0p0uUlG11x5DtUh5E0SaNxhnK5Ull+vpHW6rdxm80JAhfLhP6EqrxNzcxlQqmVOoPpA7DoPntBBhj6I4LYCqFNoZqo4EGukFAtd3GaE0YBJTCkLVLl6jW65y94QbQGmUMURAI+TDQD6M1XlKapy/ZZbDf7rNXChXH+HFMqdvl+KVLPP2++6i10s/9NOKzcBJRE3RIyye5MW0bcQtzwa8aqULBkBpNryDlnDoIoXEVMaN25Z2c6bTrtyIlLQxCcJBZ5jwlhm1bS/pz++izPzX2hTrClV4aBXc9uNJJxsDNNwvB0G6LisuNW1uttGxTGKZljyZhMAjhvvdOPTELnPphmDJiL9Fs7nUPCswRjoA4qCgIiAIF5o0/+RP4sz+DM2fk5rSyAh/9qLz3oz8KP/ADu96lhw4f7vtbGcM/+Zu/YbQVGzTG/jwMH6q48YTnSRgzn9+dTZ9vaOTZACCxdw4oUaJNG3zQoRRvtW6+koz+VKKOcA8AbYVs6JZhc1U8IVpNuHwcOkGGfHCPwdHmg7czKeJsUUmpJSFNXAMmKb8UEKGwKExCMKT55hbTU0xoLAaTlGjKgymi92OhoXkcltbn0NbikOUJJmLSitnTZgdeDzmlKrvzfRStn3QFjDpnbhs3UZrXoeyDU/LkhefJY6dS9FH47u+G//f/nX+7+xzNZvNATxAKFChwjeHMGfit3+ovRbJgHP1sg/pNVbZOl8HX28ZQuhVz5P4GlY2Yq+UWW0sV4iBPqaBkhJIjk6nG8DKzKUyGfIjJdlJrD2Mirlz5AuXyKksrJ2l2Hu1PJ88OqEaQLMOWX73yUQ4ffh5K5UggSs6H1+1iymXiSgUdRfgbSRKQ76PbbZQxxJVKev4mlrOafP6stWxubvaXcMpul2Swt5aWUNZyy4MP0q7V2FpdJeh2aVcqdMplwtOnKSfEhEKIBuvq5Q+Uh4o9rzfGtImPxOC5yL62nkeoNeVOh8OXL3P6if56pi8F3gV8gf4pWxaKVJWQVfeWk79PIEqHLyCEwRpCWMSIOsKpHxzRkG03SzJ0M+u4S8Nk/s5u1wKegRhcLwqjpjWMWD638borsQQy9sxeWy6Ar5R4Otx5p1SK8H0Jpvt+v/oFZP1xhux54fqyk2oUvi/92S8lmG6fJ4VVYK9x0BUQRQmmAgXmiTiGn/op+OQnxfNhfR0efRTuvVdqY3/sY/B1X7fr3SoPsPRv+PznuX1Cturf5b4x726/4SoFpZJGa7VN9ZgHHpbg4XzBHIslJiYiwmLx8CipEp7y8GLxeuhlpSjwIimrpIyUZlJWCIZSR9ZtV+HCSfAsLEniEdqNFLNB5ewvZqXJpGFajE+YeEB4CdmQPXNtKoQ9wa8IeIWQEK1EjNcjK1RP4JsXcxLZRvu/rqQBDue5m006HYPqB4dJumY18BjyNpb5pjgNwbxl1ftkWL0/cS0NCn0fbrhhPuRDtQr/1/8l97InnpDJ15OQfLDWFgqIAgUK7C988IPwznfKnGOXoIDT771C9XIIHZNEWi1EFr8ecd2H1rnuIxsc+mIDv2MG7p3TjDKyA6z++68/McAv6gdjbF+WvVIe1sZEUQdrI7rdOu3uJkbJPGFoUsm4zI3sOgasDbF5jLTdOdGacG2NcG0NtCZaXiZcXSWq1egePYoNAlQYojqduQY/W60Wsau3nyULMg9lLX4c48Uxj918M+dOnqRTLtOs1VBxTOT7GK3plstYoFMuSzklNyHMfO4KpL1ulxPnzrHUaknZplEZ5clx6ijCKMWxy5d7BtQONeBngG9HVAuDH4kjALKJd02gnix3Fm2fSpY7c2inWKiReklkyzBl848GlRDuuY2QE8M+rWXgxuFHvRCMmar0MLdSTZ63XW3grqlSSXwL1tbEhPrf/Bt4/evh0CGoVODwYRlvVquyzBhRP8xjHJuXvBu1TjbIsR/mArUa/Mt/ude9KDBHHHQFREFAFCgwT7z97UI4DN4go0hKL733vfCc58jNdRdxan297+/nPPEEy2E4ohKq4AYaKHJJGGQ7Bd2uHLfUW827pQTcNZbKqBHa0H5KOL9DJ9ELGOJAgYJaS8oqBR0hEqwSNUS5LQ8dg2fEsNok6oiwBJePird0FEDsnMmy853sSPPQeo4jk6PTGIKBYrLZobjGDFT8sfjJ0LVMmzJtwFKhizdyGDu+F7MNIxX49Rm22yGm7KoCTk8qeZyHfEieexPPPKP0wY6MWKRg4f4P7vrJ+9UbdWiD2WEFMtA6lZXvh4lHHtx9d2rSt1OUSvCmN8FznwvXXTefNq9RNBoNVlZW9robBQoUKCD44Afh8cd3PSs36Bhu/p+XOPzFBpXLXcrrEUtnO1z/vnWOPCClQZSxLD26IUrlLKbwSJB77sB9V0E0cdSjE+LBoJRHECyjlMZag7UxSnn4fhVQxHEXPJ2OAweH0MNUr0OgLATnz6JMNHLFvqNxpryehymXwRjKjz9OtLoq/gidDiqKMKUSXrdLaX0dNQdVo8zXDPV6Pe2HvJF+NkphPK9nNh0GAbHvE5ZKGN+nW6n0yipFnkc3UWhoY9BR1J8Jn7SrAW0MtUaDSruNH0U9H4ihiGOqrRa1ZhMzws+qCvxz4JmM9ltwioaQ1Dy6ohQriSIjIFVJbCFEhPOKcOFAp6Rw6WNO4bCMqCn8TBuuktcoRcYppMTTXmBC7lQPM80iHcEwqILIkkwnT8Itt8Dp0/DUp0qFiBe8QAiHlRUhMMIw9c6c1+/aNGP3UfvMelvsJWo1ePOb4SUv2dt+FJgrDnqCU0FAFCgwT7zvfaMHg+22ZIz+4R/C6143uYZnTuS5jX7VQw/1XntxzBvvuy+Xo8A0lqXZe7HzlpoMifJ6GK5jkxMfXptqhBMR9ZQQXSTAZko+YSAkwlId/FDIhE4JwsTXIQog8uT1qSfEmNoLpZ5t5ElpponR2ZseG7OSrChZMjErbBISYNF4xKhE6VCjTY0GZdp4iZrDqR8UUCbEJ8YkxtVivW1Q07v4JZh2GBnPUYubEzMcVgk4Pu5izat8cJg0Gs+uN80pXXxJ5lTSvsN2XCbXPD7+ayRMP35S4rK2BmXk897PIvDoo/2eRLNCKfi2b4OjiywWcG3AWkuz2TzQE4QCBQpcYzh7ds9MUf2O4fT71rntbRe49c8ucOufX2TtTL83w6H7L1O+2obI9A9S8txKrZHHkAHXsq6O37QXR/fQWhHHznA5LZAThm2M6RLHHWLPYJIBkHbx0kFV7OAy+t+3GpoXP43faKLs9mDv0E1dR5NAbfummzDlMkoptFLoMBSVgOehjEHNQQURRRGtZpNoVM3cTMmbKJnY2aTEkoWex4PRWkouJX4NzozasxadmRx6xsg2yfKLJ0+ysbpKOM44PSFnupUKpW6XYIxfRQm4jdHjV5ekY0nJgS7QTgyzfWTu65QL9eRxJWmvRKpZryDkhCjWhdzI5i85IoIRfYmBS8Cto498VzE3dYRSsLwscY9BVY1736kf7r5byAZrxSfzJ39SKkWcOAF33CHjzTy+J9MgL3mQd597kYxUqcCtt8J3fAf87M/u/v4LLBSNRqNQQBQoUCAn7rpr9HvWSn3DL31Jbrp33CHGS7uAK7W0jM7rvvAFbtrYGKt+cNBThjElPmdzlmDqj/gGWL7xne1cv0oKhenZNaukrxofn7KuEFV92hUhHPwYVjZTJUTsS0kmbYVwaKzC6hbEgUwYzGDX+nec4gt35OinJcRjnTXiZGOLxiemRBuF4STnOcw6SzTwsQSElGmxyjrLbNGmktAVEOITESTHvNP89BzDSL8DK08Mf28RGOxG3sQ4UrO5iW2OWsddd6Ouv1EZb2NG7NsWjZ8jzw2OPMi77r6DywTcbYybjAzWod3JpH83M6bKZajX5xOUuu02+Ef/aOftHAC4EkwHeYJQoECBawxHj+56MGxwBKkjS9AyQ8cWXifm0H2X8NqjVQGjdzRCcmrhkFkmsON86war9qdhYq0le0XIhyjZjcKLQMVgvMwmU/UXapstjn7oo/m3ST47bQxmaamX6a/iGK/VIk6UBsb3iZaW8Le2KF25MvOYwlpLq9nk6oBKfts1pJSQBloTlkpgDF5Sjsn110EjiW4qIRuM5+F3u5Q6HZS1xJ4nigqt6ZRKNKtVYt+X0zvh2o2TYPbNDz88dr27ES+HUXlJ7tPv6dJtWpprJfN+RDpGdoSE05LGyd+OtvERVYUzv3YKiy7jL502sDr2aPYGudURSmFLJfHw8H04ckQIhVYrJa+Sz7wvkafbhUuXxO/h2c+WbUG2//mfh3//72X5rbeKOmLexGqe38m8+9xtFYTWEj96+cslllTgwKEgIAoUKJAfb3qTkAujEMdyU/6rv5Kbx7h154g4c3P8tk99itqoOpsDWGG6sh2z3YMtVbqcok7173x2+EhNDW5hM28pAgI8PAICNJolb4UgMX+GJNkiUUP4EZS6UK2LB0SjBvVled8qkU3rcQIDt7yZxxtBJdbSigpdyrTRxMnANqBNlce4gYscp0UVRcxhrnKIdZoscZljiN+FxvZybuQhg8IFD3pWz0CpNaP+dkrsoO0ycGXYJZ1H+RBnXmeOsSe/H3YtTBiTjhqwq90s9DoGi/4od4y9ljQ7XCsllkZBa7jxxvmQOcePwytfKXL5ArRaLYwxRQmmAgUK7B/8wA9IydddQo7h+jYc//ATrH7xysDGk+75yahKeaBdcZt005CImhqX4ZH1fnCllwxaB2gdZPafjK+Vxrj0eNXXTO6kliCCGx6F6971F2hX3igPlML4PqZUkiB+q4XXbqPbbXk7yd42vk+0ukpcraLa7ZnGTU71cPnSpUzfh2SrZ2CgV25peWuLpXqdWqvF0UuXKCVEQxDH1JpN6WdSsqnri5K75+dgDFZrYs/Di+M+EmPUefGMYa1e59T582NXvRN4LqnCYRjccucNUVMKoxQt5COvIHOL64GTyd8WKcXkiihHyaMEyVwvnR646UXWjHoQAXAEeHDs0ewPDCUjkvJK5vrrsUeOCGkWBNhE5YLW/Qk77jN2JbQ2N0Up8Xf/7vYdftmXwT/4B6KQOHRovvMC3x95fV8T8DxYXRUVyRvesNe9KbAAFCWYChQokB/Ly0JC1MYEp62FrS0hIgazThaEb7n/fulep8NLH3106DrDbsF3cXFBPeofSFSI+Foe4FkPjpg4jUtMJjWk7iCy6mWWCSoreAY6ZWgsibeDtlBtwbFLUOnCcl1KLsXJfKZTBuMnGU/jSjABPOXzUx2jQSTLASEai0/EEluEBMSJssHgcZETXOAkUZK7o/vacrSDU1PMo9jOKBg4dGb74kVEr3fY3qqGlcG7WZ423czDzRTc6DrRT48clmZToka8tQ0WGHI69xK7QUTMNLR3ku29xiL6sJuTnbU1eOEL5fWImskToTXcfLMQD9///aKoKNCrl32QJwgFChS4xnD06K6pB0eJQSdBWVh74Cre1kCC04TbrfJL6KAM1pVBTPfmWY+leLwRmLUxxoTEcQdrLUr5+H4JzyuhlI9SLnwsg0DrhiEuTX5S2aXMcSgrSU03PAFrn/kMpfX16bOprcV4HpXHH0eFId0jR7C+T+nSJSpnz6KjSHJkfB9bKk2tzLRRxNb6Opubm/3ll1yWevZ1ZpnOKB9KYcjNDz/Mcz7xCd74e7/Hl3/gA5w8fx6MobG0RDchUbAWpbVkyatEt65FY+/MpydeO0nJpu64Uk0JXomoIE4gQf5RUAhxEABtawmsxVOKkNSIep3URLqNqDAUImh2ptSuINQyQno4UfW4YwoQMuMQqbfEtYIeGWEMyvPQV6+iGg3odlHr69gowvo+plwWdcSwRioVGVfeeqvETobhGc+A5z1PxrLzLMOUeK0A+2OukRdKCXly9Ci86lXwz/4Z3HPPXveqwAJQEBAFChSYDi97mTD2oxDHQkBcuCDmSruAY00xgXveE0+wPMSMdNQg6VlcWGCvBB6GNTp8C/fzwg8zvSo78y9K/hkMAQHhWoXYS42mO2VoLMOl47C1CuUOVNtQaUG7LIoIlTY8vi+11oQVZIimMfhElOgS42HxemREl3JmRzY5GoXBw6IpJ4SKTcgImQM5nwgXOZ/VDyIH1h4b/d48oteT2si5jzUNJ536Pm+/XGqSMxsfJrNXoNwsYpDrMZllyYRz7AQqYu+c5jLY97k+5fLswfJrAbs52fnu74Zf+RXJlHLyeIdSCd74RrjhhvFtvOAFck/7r/81JTMKUK/X0VpTqVQmr1ygQIECu4FSaVfunyOKIeVG5WobrxUO3A9H3RslaG3jCBO2hYCwbuwr23laczQeokYb0qTWHr5fJgiqGBNirQTTpStekv9gZPynkkBJXvIh2WcQwtErcMcXwWu3OTbOH3AYjIFOBxsE1O+6i+7x4xBF+OvrWN8nXloiWF+XIH4QSEc9L61tPwlxDFFEKQhS8+k8sJYgjql0Opw6e5ZXvPe9HL90ifrKCh99/vN5+JZbuHz0KN1MooIfRduG1xYwSUknZ2qNtUJWjOu21mhjiPxx5bYksP9PgS8DbkaC/INw+UcKKZukkFJIdWsxyPSglbznzKg10LWWGDiuFGtas5S07yHllpyo2q0/8liSfd4JvGLs0exzhCGq1ULFMdopC3wflpexQZCWuEo+a2stNookAfPMGXj/+0e3rRT8/b8P/+pfwUtfKkTFToOyrn97UeZ1HqjVZEz+f/wfcNNNe92bAgtCUYKpQIEC0+FrvkaMl0bd3JKBJfV6T764aOgkW+XvfOYz1Lrd3JOFZ3BuAb3plzrXiHghT3AbV6Wq0QzxOYPBw0Oh6NLlIhfZYAO0RhsI2kmA2IjBdHNJTKkvHodaU0yprTOcc5zQpHSuD7xkYmddLNzDJOWXIsBggS4lWlSJKCW21NIBD0OVJh4xHUpEOLm5TY5V9x6qV+1/EddQDGGOMlPTfl4LKOe07L5qedt0nE321I05hcoRDHGmNFNm/Vxn3wJX5/tJWWY7nXnquu4JlBJS1vPSmrEFZsOhQ2JMt7oK//v/LqWYVlbg1CkppfTmN8Pv/I4YVP/Jn4DLKnT3JK3hxS+G3/ot+G//rciyGoDLTtLX6iS2QIECBw8nTy68TN5OyQeA6tk6lavtHGsmY19rwMbyPKQXKjSc7qxNLI8p5ZdA6wDfL6O1j+/XEhJCSA1r00askjlDbsRJ6aUz8PyPSclXgLVPf1qC/nmQ3H+V7+NtbgLgNRp4nQ5eGAoBUavRPXxYFAWQlk0a/HsQiUGfimMxhtaaZqOR//i0JgwCwiDgypEj3H/PPawfOkSzWuVz99zDozfdRLtaBWvxwhBtrQSds2O5wdI31uLFcS9APZJAsZYgDFlqNvvbG4GTwGsRpYIL9m87HFKfhxC4nPztroCsz0MAnEZKJgVaE9RqeEoRISbVrWRdN7XIUmSD8BCS5Hbgx4CnTDyaKbAXY5IwFPITUL4vhulXr+I1m0IqaY31/b7P1hqD3djAfvjD8PGPj2//zjtlnPojPyKvDx2ava++L/OLIOj1+ZqA1qIEef7zxR/jxIm97lGBBeKge8yNp5ALFCgwPZaX4RWvELPpTmf0YCoIZEC6vCyKiAWiGsfccekSr/vCF8bKUQdRHm3tOxaZceW4tdAYlunw0qQ2zUqdqWhRhUIngXid/DMYIiIUioiIUqmM1+0QAqWEXIi0DDi7JbhagjCQUkxTzaQsEzorYWGR+IaElLiVh7nASeos0elVERWKQtQPGqFTfLr4WDSamDjJpfGIiXv7zDpAuBD0PAeeCvxWvlWzFaLGvT9DF3rbj/lsNMLr6ZzZaYRJe670r6HfrW5Q7eBeJ+uoYTPwPEqOOX08g6c7S+lNGwzIrj/v3PyJfRn2Q+FM61zt2GHbXEuS6b3CHXeIzxDA930fHD4Mf/mXYvx3++3wnd+ZTlS//uvh138d/uk/hY0NuTe94AXw1rfuPNvsgOKgTw4KFChwjeI//AepCz5n09Zhd91ZUwSUhZXPX2XzziPgjWslz71eoZSi6lWpRJp2adxxx1ir6Ha36HZBaz/xgihJZrbNloFVOaSt/V0NokRVHcKtX0rfqly4gNftEpdKkxMrEv8Fm5T6UQnpYDyPeGkJqzXGKe+cV0O2zWHLBhFFUK/TaTQIcpQ0yrbrlAuVdpuHb7uNE+fOsX74MJ1yWTwdtO4FnbEWk5RacqWYXBkmjBiVezYpKuuu12EB9MTQutZqsbqxQZCzesD3Av8LeIjhpIArweQjZIMjI9xYuoooIEpARSmOaM1j1tIG1lstfGupIASE67XbT8/gemB/PkJifBXiJTF3C2Fjdn+cbK2UlfY8CfBHkYzf3bXgeag47j8fyTVqt7bo/MEfYO+5hyAI8EepW5aW4Gd+Bt7yFtlfuTzbMYahPKY9R9MojOYNrSWB6NZb4cd+DK6/fvf7UGBX0Wg0DnQJpoKAKFBgEfjhH4Y/+iO4dGl01kuzKTe0Tmfh3fGBN33605yq16eaMLyGh3a0X60H50D9N25JQFe8kc8AsLqJjNpy/jK50ksA3cQwW6GwWEqUiInpqhBVlSymyAM/TnsSBjK/0K4Ujxt1Dga+h0V2n3bfxP7J4DbuqRgucIoOAVEfDZQ27CiLNmU0MQpLjOcq0iYqCbeFO/JptOFTQMWwcna6beY0LlMmB7+TwVUz5dFnk16yn7HbZ/bzzqYzwfZrYdT1wcA6Tss9sLtZsJ002GmLabu7OrR2A3k3EcgaH8axTGTiuP9H5MlIPvzZn8HXfu1022TvO1rDN3+zEA3dbkpMZPGd3wnf8R1w331CVty4TxzT9ym2trYKAqJAgQL7D696FXzTN8Ef/MHcmpwn+dBDoNHtCFMr7awxZTG+QnmKUuzRniiDyKTuWIMxEUr5eF5AHJtkuRubkNbpmYRktyfOw/M/AuVMtVvreVQfe4z6uPK8suO+52hlBRXHRJUKplTqDy47pSL0B1OT4P64UlzKGLytLdCarjOwHkVYZNtNnmNga3UV43k8duONhEGAspbY83oG1bHnoRIFBNZS6naJfJ/YtaM1NiEoXJ81cu6HKRyUtZQ7HZ73kY+MP4cZlIDnAOeA+4FBrYefrBMgU8BBcsKNomLgqrXE1lJWipIxbFlLCbmaVpG8pk7SxqjUPZvs6ykI+XB9su2O4cbKA9fPrsLt3xioVoWEsLYvybJv7pKQVMpaaDbpdDq022201vi+3yMjtqlMfR8+8pGdH2Pe7d33zD3cce0GXGm1tTV46lNF+fDc5+7OvgvsKQoPiAIFCkyPu+6C//SfRCLn6nNCf8avtXO5keXLD4Lv+/jHqQ0hQ8aNq09TJ4emeXufbDoOGbMWJWKexnkOIVJsz6aS5bwwGOLMcM+F5SOkIY0mzhQgDX2RVaukDI+TV9eaoLPq7uyJHXaSHrhzQs9Uj0yI8InxaFGlS6lHJGxnO0Bj0cR4GAK6yd/JpCizrjhFOCODUZ1M250afgeOfHG2bWdBppvWVZbKiaaBK3kv02z9IUcc6MzzsJSl7LMl3/UxDDOqfQd3N7hs8Ft9zYToBxUQbrA9SEY8WVGpSKm+N7xhern1HUPy6oJgOPngoDU885kF+ZADrj6rerJfowUKFNhfKJfh3/27ud0/hw2J5tGyMpbKlfbclBo6slS7eXvmct9loKm1h1IeSvloLQlCSk13pJ6BpSYcWYcbB+zTqo8+iooiybyeIvBpQdQP2axwRzAMBpsHSYgs3ITMWlQc429sUG006G5s0BjlAeH2kVVTWIsyBut5RJ6HBSLfT+v7K9UjIpyKw4si/CjCJsFmNWBy7bbRxqCNGV5eKen30+67j5VpSkYBLwJWELIhO61wn66ryDooUraIp4MmNZauG8NmHHMMqCXKjjjzvkWICNfmYK6SQlQWR5ASTN8x1ZGMgFIS/M8SUnsFY6Qv3W76uHpVYh0DShulxQtR1WpUXv1qVldXqVareJ5HGIY0Gg02NzfZ2tqi3W4TZ+MXv/Zru1M+yZ3POE6VE7sFpaSE6pd/Ofzn/ywK5oJ8eNKg2Wwe6CSngoAoUGBR+IZvgHe/G572NAn6DNQ/nAfytqaA443G1JMGjVgmzx9SbOgEdV7Ll3r9Knch2O6RPRJqzBEZDBaLh0dZlVkyVcmsVxB7GfWDFWVEp5KcTzXwGIVz+SSQFk2XCjEeEX6izxjWuJRfKtOhSgufiC4lLCopweS2EUrC9tQQw37GB6Pj0153Fkp1OJ0/02ge6JU2GnbexxxCx8ClaaqFZXXYWYIhG9XPvs76fGfJign96sMKMuuYAVm+ZNgu9Ry1MLsaTh38PcySsu738smoeAD4gR8QSbsbgL7pTdNtf+ckgrTATlCUYCpQoMC+xenT8JSdV5YflY8xDyw9ukn5cgsV2h1mTUhA2yg4Elan2M6glISdxYzaoLVGKXn0MlLyHHSy2pEr8GUfBW9gPOq3Whz61Kd6fnyjDyVVgKokaGuyZZtcFrbnpX5N2UQO9/dgIDpZrsKQYH2dyqVLnHr727Ef/jClbFA1S2oMK+2EEDMqaTMIQ7w4RlmL8Tx57QilhMDwowjPWuKEqFDZTHkrKWM22Z+O4+2nOzGnXtnaotKdYoKY4DXAi0nH0QH0nPVihGSwQBk4lrznqrNWEaIA5GrQyXsRYkYdJm1cRNQVHbYLqN10wUfUDtcjCoifRzwgxiKvobw7n9kyVpPKcC0K1kplB6dejmN5tNsyrs/2a20NvuIr4DWvQWtNpVJheXmZ1dVVlpeXKZWkNFq73WZzc5PNzU0ajQaRUuIpsRvHkvVF263SVkrBDTeIV9s73wnf+I17Ty4V2DVYa6nX66ysrOx1VxaG4mouUGCRuOce+LZvE1O4vAZkOTHNLVAB1RmzjEoz+kBMQoWQp3KJH+SjvWWRl/o05IHNcRYiIkqUWA0Os9RRBF0hHXScxJgNWA+MJ88DOxiN6/KUJ5Ihr0VRpk2VJj4x/RHtLDRtKgnBoIZQB+l2CoPzjJickj/DINTvwMpj25fbRD0yR+hk8mYndXPE56E13DTNWDSrenDtZsmPQfIhHthWDXk9CZoda60Hd5sVccD+Uz7YgUduxLEQEXtVbzWLvcpw/67v6v/7R35EJmt54PuSBVtgYTjo8ugCBQpc4/jFX9zR5oskHwCWzjZZ+/wVvHY4OrsiF2QjW/Ep2bxZ0TIqMcYSx12UCtDax/PkvimeEAA6PwFhoNqE4xeHr3Live+lduaMZFFPmo9p3R+sHyQWXDAUxisfev2T5I5gY4Olhx/mxt/7Pa5/17vwokhKIA0pszTMo0sZI2WTlEIZw6mzZ1lqNFja2sLvdqWcjlKiZrAyQ4mCAKM1RimMU0Ek/g9qIMnE+L4QGAPjPmUt1XabcIagswZeArwOOExabdciRIIrvdQGmsnDTQ/aiL9Dl1Tl0ETKMTn/B9dWTL/PRPaSdlW8jgJ3ISSGcwEcCad0GfWZjiMY3Dnc6/FzFi7BSGsZoy4twfd/P/z2b0vJpgy01gRBQK1WY3V1taeOUEoRhiHNN7wBU63ONr/Ig+x3zZEQjgjcDWLn0CEpmfrDPyxK6AJPOhx0D4iCgChQYJEIQzGjvnBhrgOBaScGO5FMB3MjINLhmIfhWZzjv/FWluhnHErTJ7hsg0r++fhUqWKxXOYy4UpFFBZWzOK0Fdm0dvOfaUYyz/hkjpWkMYOiRY0uJTqUkh/ewVR7+VsT06ZCSJBIgiP682hkG4OHRREQ4RFn1tvpdZYREcdDBj5KBuzVUM7dTqFdwtUOxnNfUYHqLHezbBQ/6xg3SDL4mdc7wZzVu4Nz9nkNiRc1tJ5qsuAyp/Yae2U496IX9S+77Tb4f/4fuO66yduvrQn5XWBhcCWYChQoUGBf4uu+Dm6+eerNht2j5z4mSIKra5+7wvKjW+h2CCbZ8zT33Ozgx1iq4TQDQZUEOquUSsusrFzHysp1lMuriRLC0JPK5hi4BCFU2qPP1fKXvsTJd70Lr9mcfIxJOUqTlDLqIxyyyBIH44LRcUz54kVu/dVf5WlveQvHPvhBAGqNBl4UjScuoKdqsFrjGYMfhpTDkLBUIvY8GkmgzJVPckoPC5jEmFob01NKoFL9ulaJjtsYOc2DfUmObXN1levOnRt/3kbgqUjQvwO9wrWOGOgmy1vAVdIZkMy/kj6SEhUhonZw2wG9gl7ZhKAsnHKiCtwEjPxWZrPcB0tsDWbAO4JhP5EMeRDHQjhcfz28+tW5kmWcOmJlZUXIiNe8huh/+9/61pk54WkYsufUEXRhKGWkRn0X54VyGd74RvjxHxciosCTDtZams1mQUAUKFBgRnzwg3LTmqPR9DwnBnm2rc5ZASESWEOZmKO0+t7zY/FimGb0MKwMk0YTEOAnkeOYmIiIjurQWhLfh24JjIZyR9QPyo0cVV/jo/G3L8nVO/d/iE87yXkp0058HfpDx1JuKc3wKdHFEAzpiEJjeg+Dhwx/BzHLkCyJuAdN2BxeCz5KksKONKHaTUy8ZxzxGZXI1fNuP2S9Hzw027577bnTl1VFTFkGKtd+FkBAqOQbsEd5+iMxqU+5rswna339O+4Yfuzf9E3w0EOSMTYK5bIo7r7maxbWvQIFAVGgQIF9DqXgvvtgijIOw+7FC7kLJ5nyXjdm+cwmXieGyKSlMa2dbrxlLV4zRF3ZyrmdwvdrVKtH8f0qUdSi290iijpEUQtjwt56E89Aon44tDE5Kef6t72Nk+95D5Xz56U+/jAlRFbtMKnsy7gxUlKWR8Uxwfo61/2P/8Gpv/xLvHbbdRulNTedOTNakTFQgkklXgwnLlzgyOXLXD56FAtU2u2e94NFiIre+E7rnsrCjyL8pGSTjmO8MMTvdvGjCG0twYgSSzbxiFhKzIynxQ3JYzBZx+Ufdej3bnC9cBpzgygfXD6SU0w402nX3mhte2p0vQx83aiOZj+Hwc99mioGg76Tg0HzvR5bb26Kv1l1mpJpAq01QaVC+Xu+BzWmLJHJXIMzIUvqORLC+ai5MmiD5OBOyyQdOQLveQ/86q/ORB4XOBhwJZgO8hyjICAKFFgkvvQlePjh8TLKKbDb5APAPZzfwV4G4YomKWI0bbbLaZ/7MaYK/roWHemgUMTEhJl/JUp4LkCvFSghHWIPtlbA6iQDf5oT2skvi0wHvZaAMLOjwRx2eeUTYlBEfeenf+iskqFuhwpiSa0y6wwKgKc9uAi6K1Ab8dlbaPlwuSZkxFII1Sgx8Z5q0ijEj9GJEmLa7ROs5iyTOrjvba9dHwbfH0dK5IWB7FdpHsN/R1ul/+8PDHJ42QeQ1oHNYCgZca1lds0D110n6odRx14uw7d/O/zVX8Gtt8qEqFSC5WW46SZ4znPgF35hvNl0gR2jKMFUoECBfY/lZfjAB3LdD4bNLxYapkzuccc/9DjLD63jh0kN+15Hct7/LajYoo0l6rQmrw+I+kHRbq8Tx0I6NBoX2Nh4hG63ASiU0miduAVMiJYEsfjXnZqQoK/jmOv+/M9ZevBBdLs9fl7o5o2zlHxx4wdj0GHI8he/yKl3vKNvFQUcvXSJsFym7MpCjRh3OFWCMgatFFePHmVzbY1yt4u2lkqng5cQCM4Two8ilLVY5/OgNRhDudMhCEOUtQSJ54ObpYTlsuwre7xxjFaKI5cvU99BTfRTwI1I6SOXb+TEz4Z+ezd37buySo6IcOu6Ql9u+yh5jEKEEBZd4HsQH4g+TFK3TAsXGPe81EfCtZPXV2LR2NiAWm3yesMQRfB7vycEG8N/q1SGsJlJGREEQpBkSYc4lvlLEMh5DAJ5uNJgO5mzLC3BZz4DL37x7G0UOBBoNptYawsPiAIFCsyAMBQFxL33Qh7J7RjsiiR6BE6Qd0A/DmnwXAERGg+DP8Tg+nmfgCGLhx6wHvhnkg3d3xZLSEidOhaLRqOUEBAqCXarbPcGT/K4j+zZHx3z5rDOKxQ2MaP2cLnrahuxACGlZLkb9mY7ZNHEWLZnd6QBaZVQEm5YP+XwSyso18EfnbKvLPjJ/Cj0wSnfPSOKhkk+ESo590FIaj6dVSCMQ+YwPODWWbzIhildRnE1w2w2pkV7h9uPwSjZ904wa3uTtlOAiiL5Hvr+0IyhmWXUe53VtVO84AXwqlfBK14x+Vhe/nL44hfh3/wb+O7vFlLiq79azOq+4it2o7dPahQERIECBa4JPP3p8L3fO3aVXZlfjMjAVhZu/tMvcOzDT4gSwtjRY/IsLL15ldcMqT1R58QT+ZQTSimMifC8AK09CZJbi7XicRDHXYzJjp9VZp+DfYBaA2oteNpnJ+97+Utf4u6f/3lWH3gAb3NTgqnbO5hmWOeFMyJ25r/tNv6VKxz9m7/hqT/1U5QvXdq2ydPvu0+C7nE8nuiwVs6A1nSDgFalwvqhQ2wtL2OU6pVa0kC508FLSjApayl1OvhJoNgEAc1qlTAIiHyfTqlEWCrRTf62yTaDJXCcOfWh9fX852MAp4HbktfZdC2QIb5Hqm5w72vEnDpOlpnMckinLJMuOaecqADvHrbCuPjArESE81xw14NrYxaD6kWMreMYPve52bZ961vhP/yHbYuHJjwNwJJjjuFIBkc+eJ6Qucn3Ct9PverCMJ3LzBrnWV2FRx+FU6dm277AgUK9Xgc40HOMXbCQL1DgSYq//mshHlqtHZMPWYy6qeYZBE1qYxiexXl+n2dMscV42GQYd4r6UALi9gdBRbDNS25IlNURDhJul9eu7JIjHAymR0S4ck1KKayLeruR5zCMO1EXZhsoRARYLB4RBj8hJdIgrO3RCLLMwyTHkJpSG3SP0kg/d4tKHmkbs+awWTh6P5RGyJ2TnWqEaGj76fXnJaoGlJRmip1nnkm3jVUqVY+SbVGy3bReEEcQafTM+d4D88veLGPUqevni/LjccanSE2JYQKNeWOa35TpG1eoIJAyBEphB38jkwm4NaZPej72WK9lxcSRI/DsZ0uw6I1vzLeN1vBDPwSf/jQ88gicPi1t7FQGXmAiGo0G11+/LY+xQIECBfYfBrLfs9i15Kbs/XngXq0jy/XvPoPuGi684Dqi1XKiVk4GhWM65TcignrI6uevcvIBeNeroJsdEA6ZO3heCWtj4jjEWkO5vEoYtogilykiue3GuBx4lw+faSs5BC+C1U14yQfye9j5rRan3vEOOidP0qrV6BlND2KGoLPXaqE7HYLNTW787d/m1LuHhrwBWKnXedEHP8h7XvlKWpXK8Oz4JFhtjREVg7Xo5POLPY9OpUKU8XzojT6SElLaWnQY0klq/SvAs5ZIKazngbVYlaZi9Z2LZD9+FHF4fZ21zc3pzkcGrwTeD3wSESO7Ib67PLLFhj3o06qDBMwSC8GZqqmuJNs9OsO2U18Hw8o1OWInnKH3ixhbh6EE9WfBP/tnMIRQG0T2rI06gqFzKWuFaCiVRHXsCIkwTM/hyorEeIyRMtuznqO774bPfvbaT6AqMDfU63U8z6Ocwx/lWkVBQBQosCjcf7+w+zswLNor1UMWr+ChEe/MmndtKWF4Hk8Mffer3wG+hXCK5m3yT6GIiLBYfHxRPCQhebce0F9uyes10h9xnbTvrRWmPQcmGZrrhIJQGOKkA9leqoRKAJMhJ1Tv/fR1NtCoExlxnCEeZvl8DHgNOPE5CNqj10qIBKWkfJJOiIesZsOqRJoeCzEBMkZrlIWYcERFn8uIzXwMObp/uw8rM3IsfaSDe87OoLKKiGy6zLT7s8AXZ+hjDkzTlVlO08Bce+ftu7q21qY1kJWSrLcMLKQZRYl03w60cWCG65WKKBm+8RunN53TGp71LHkU2DUc9PqsBQoUOEC4fHnbomH39IXeUweCyn3Lk2XHPnqW+s1r1Cs+puSBN7lHpqTR3ZjD912g48PxC/D4reO3ieMuQVBFax9rLd1uHWsjXIpPf/eSBIlBQTKSgOMbIR6uf3xiV/tw7P3v58pzn0t46BBRtTq0NGUuZMotqSgi2Npi7VOf4vhf/zVHE7Ppcbj14Yc5evUqF06cwIwiQqAvsUHFMUYpdGIqrY3BJOO4dkmyx1TiGdFNyir1yjglahM/jomS0jXKERDWCsmRVcgYgxfHPPdjH5v+3GSwAvwr4AvAx4AnSHOCLEIulBBTbGUtZfoVD9l1p4UjLWqIF8X0DcyJABjnJ7LbsFYSZqbFhz4ETwyPH/RQqwkpoJQoFQbmC2PJCKcSKZVQ1Wrq4enUSi4oXC5L262WEHfDlEyTcNttkkBUkA8FMnAKa32Ak8kKAqJAgUXhwQfl0WrJTWpKzEI+5JWCToPb2GBnRV76e6SAFVqcoj507dUtWN6Cq8dm3B2iDHDG0wqFh4eP3yMnbDbK7V4OHt6kQz5xYcIK2YbAI6JMG4NHjIdNLKShPwvHJrMcjU3UD7JMJSJgoSm8HuUy2GExpHbn3FU2nQYW1h6Hp//O2LV0kpgWq/R1JYKuB5Eny/wYaiHUS4mwQIkSokdGJGPh3vkfMT8d1U0UfHkVKrPcoweVD3bgObvOsH279/Pivql6NxELVSdMsb+pfxUyk2VpQG3P1vJ9lOf1ZM7W98GVbXJlBrL9udYICaUks0pr8Xx405vg275tJkO+AnuDRqNxoOXRBQoUOECo9HuW7Tr54AIpLqEgSSrojQeSZX475uY//hwPfsfT6RwqEy+VJElolAoiMhDFBPUuOrLYElifieN3a+MkD8ISxyFx3GHUiMra7fM3lcwdvBiOXoa1GbyRvXabU3/xFzTuvBN74gSxu5/MEoyMIvxGg9ojj3D4Yx/jxLvfTe3xfIyIH0Xc9oUv8Lk77xy978ygXCUkhW8MnjEEiacDWlNptbh65AhxQiLEkBIKyXbGWqzniVdEkliinApiSGkgBZS6XVZ3oH5wWAG+BfhApu3sp26BitZ0kjl7lFkPUu+ImOnG305RcSfw9bN0fBYMkg2LIh7cZzaNSTZIaaPDh6ff32cn1DlTSto9dAguXoT1dYnBZOIww6b7vde+D4cPY7VGNZuoTgeSEmDEsagftE4TqNx5nfb8rq7Cj/1Y6h9RoECCJ0OCU3HVFyiwCDz4oNTndje8wYGAG3yPwE6UD+MCk7naGejbcZp4RMQEU/RiNKTkkM9X8+DIPgY5Zcz926lEV+AREBAmIlmL7ZVnKlMmJCQiwpXasckJ00YC5Lni9QZKt95H3m46L4Yu5WS3GmedpXHKjPTTSWPhloCYkACNK9U0jUxjhlR93YXrPw7HHxi7mlUQW+lJKbnMW34yT7Sw2pb3tsqSIeaOsOvL+6VYSIquJ+fdugOfRrhh4JU5JpsjMUg+uPFp9jSPUkpMA+deN2T3+yQXKRfmFqDIZkIOG7Q7kqHbBc8TMzmXlRTHWJd55LL1kknAIHe0L3HPPfBrvwYPPywS8qc/HV7ykm0BogL7GwUBUaBAgWsGd94pWcMD90nYpXvl4HzH3b8hDWAmy4JmxOl3PsRjr72VTsWXRJ2hJIQFT6FbBq8dYQJNrWlY3YQncowJjYmSLkwxCsuMC70Yqi1YW4fl+mzn8dAnPsGRD36Q9qlTXH3Ws4gOH069H7LzxgmkhDYGr9Hg0Cc/yU3//b+jpyyz01hZodRu0xqnwkhKLzm1qtWaSGu8MBT/Bs8j9P2eMiJMyjkpY7DJMkcyWKWIfb93fCqTkGIHguZ+FHH48mU61SrLzeZUxzUMh4DjwCapwsEhBppxTIm0zFI2nSsrkM4LBVSBlwD/HthBbt10WLTSIWt0rVRanijPfj1PxsKzjKG+4iskgWdU2aPlZXj96+Ff/kv41KfgzW+GRkNUYN3hs/a+Kz45FtXtyvpRlP5uOiLVERKzKkqCAF79anjlK6fftsCBR0FAFChQYDZ88IPpTdmVEcmqIEaQD/PKShqMq07VxkDfPCwrhKzPiYDQWG7mKkdGmFt3SmBmyGh30yqDISbGx0/cE0zvvQ4dst4Q2YCykUZy+RAEIdz56SXuy6UwEPohTgpCOXjERPg4bwfZvXLdwRVqCpNyTHFGMbE94j4Yyp51Smmgdgnu/pNJh4S1yQ0kUTBkd1+JoBUkhA4yqF8OhXywGcVEpNNzrZFzHw/sZxzKNinVtVO4/fTVjyIlQ7JfpFlP7QTF8CzYt0H2SZg0YHfBCWtF5qxUn7xZGSO/q7UattkcKn3ekyDLOPi+1K399m+XiddLXrLXPSqwAzQajQM/QShQoMABwS23iKfSwL1yV+6L2YSDpOY/2aQCt06SYACw/PAGlSstwkMVTC0zEBscnMeWoB7itWNUJIO3Wx+Gz90zuVvWxkmcXyeeEBZjJqQVZZSzlRaUQ/Ezu2tGL10FnH7b2zj7hjcQr6yw8fSnYyqVfiWAm5M55YhLvMiYTpcuXmTpzBmuf+tbpyYfADxjKIchLWNG+0AgihGbKQviRRFWaxrVKtoYlLWY5P3YteMIi0wZJve3g9E6LX8zQLwYoBKGOzKgzsICNwLnYFsSmRs3Oq+HJE+tj4SYMs8fH7gH+EF2kXzYLTija63TEmJJwL4vQSgLrYUk+MmfnG2ft90G3/AN8Nu/3R9XUUpUFXfcAf/3/y37f/RRuPFGISs2NlKSZBSCABXHoppIlNd9hFjig6K0Fn86R6BN03/fFxLln/wTmQsUKDAAR0CoA1yaqyAgChRYBN7zHiEhWi254bnB9RSqB9j55GBeP13lYenbM8FSI+QeLo3sm8sq2glCQrykTFFA0CMlHJwiYlhweWI828Cpc/CW/7jBt+bOnEoG4ElZJSm/5Pd2avvqAZmk5JJN1pOCUrYXHXfp+WS2gfl82okUZJT5dBZKyIJyJESDb6ESy2ujRNngfL4DC82STNSsgsid5+TZydmz3chzOEsx3Dir+sEZTWehSfXVWV5pkNHLLsu7r1VgY/tb41QQsx7agYD7rex0ZELseTLZUEoG8N0ubG2NlVKPWr4r59QR0NbKpOjf/Tv43u/djT0XWDCstdTrdVZWVva6KwUKFCgwGa1WX+AYdnFskfUVcAFDV5Ynk1HcV+YHWHpsCxUbGjes0jleE1NqabC3no4MGMvaA5d7482rh8EPIZrg3ykKCINSCq3LGDPa8yw9FumciiHy4fA5eNV74PD65E1HoXz5Mrf81/9K99AhLr/gBZz7mq+he+QIWIvSmqhWI06Mqo0r3egCn1FEsLFB7bHHWLn/fsozBulvOnOGDz/veeikRNJIFUQyptFRhJesFwUBOlE5GK2xWmOy2zvyIvO6F6DOkCkK0ms0u55SnH70UbwZShkPw/MRAuITyd/ZmVWqPpfHtGRDFgoIkLJPLwS+egdt7Utk1TnWpgREVgnhPl93zXoeHD8Oz3sefNmXzb7v//Jf4NQp+KVfkrmC54mKuFSSkqbWwrd8C/z5n/d5zY0lHzIqrB6xMsQ7Qg2J4+ROeLr+enjd6+BnfkbKrxYoMASFAqJAgQLT45FH4L3vFQbdMeQTMn73XbbuAI7Q4Dw7D7Z4GCrEvIxHRq7jx3DPA/DoLWw/ERMisq4Mk3u2WGJiLJYKUuKkRaunObDKso2LGCdosFDqwPf/Ojzj6llS4+jJUECZbkIkSFmldGfZ1Hr3LB1zqgkLA+vbgeedwoIOYek8dA7l3YKuJ54OvhE/iFaQ9spL1BFtX567SfKb665N/uszBZ9C47zWgeeWmf4UZMkHtz+beSj6SYidnl4DdHbYxpMVbiLg4HmpKdyQicDgZGFok2O2mRuqVTh5Ukz23vIWeOYzF7GXAnuEogRTgQIFrgVYa7FB0FcyZNfnGINzIBeYdEHlbNA7WXflwXXaJ5dYfnSLeCkgKvvgqyQl3aIiQ3m9zeoXr7LyUJrd0SmDH00mINxgz1pFFGVL+4xLC5G3vBgqHXjGfTsjH7Iora9z3TvfyeoDD3Dm7/09rNb49To2CIgrFbqrq7Svu454aQmMQccxXruNjiJqjzzCdf/jf8y871PnznH0yhW2VlaG16TPfH7aGJTnEQE6md/Wmk3atRqx1n3Khh6GGJD3hv0JeWEHSAeQYO+hq1eptXOQQzlxHPj/AJ8GWoBLt8rmQWXJiFnhIXlHrwL+OQcw4JZV6bhyWlHUUw70eSO45CGXQHTrrXBsJ0aPCv71v5bScv/n/wlbW0JAfMVXwHd9F/zyL8Mf/3H/74577ZKZhsFaaafTmTi/gCkTng4dgh/+Yfjmby7IhwJj8WSYXxy438MCBfYc73yn3Aw9b2S9wSz2O/kAcCdXuJ+TTN+7/qMzaI7Q5Pv5+NitXvMueOdrkBHcGKgMBaCTkkUq+eeWOyIiJMRgeuWX3PsuKcJ5F2QD5NsQw7Er8NXvgOs4h06KPU2GxmJoU4VE76BG5tYonFZCYfAJiXonwk2MFpPPrVSIrWzA8tmcGySKBwtbJTmHFnoqeVeCybPJepnu6oScMFnyAfIfjoUTG8BlmIobyypeBveXfS/r5e2uiVnFJjFwYcptCgxH1ldnQkbTnqojlII/+7NCYn0AYa2l2Wwe+AylAgUKXNsQk+UY7rgDr1KR2uZ73SmgVz5o0OMgU4apeqnFsQ+fZfOuo1TPNugcrRJXJJvF68Tobkzt8S2OfKp/cHXTo/D5p5AjMUUnZZg8lEqzXoaZTvfBSPmlk+ehO5/KtH1YOnOGu37pl9h4+tMJDx2i+thjrDzwAMoYOocOce5rv5b2yZMoYzC+z+GPfIST733vjvaprGW5XufQ+joba2vizzCoYsiWRVKqZ0ZttKaxvEyp25U+urKZDoPZ8BnYbLvu/QwRoa2l0u1y7NKlHR3fIJ4DfC/wC0Ab8XvIVl/No7UYR1MpxPfheuAHkue5YFbPgUUgo8TpIxwchplgd7tw4QJ85Vem6qed4Pu+D77pm+CjH4VPfhLuvRf+6T+FP/iD0edpkpKm3c59jvMmPNnlZexP/ATem9+cq90CT27U63Vqtdped2OhKAiIAgXmDTeAznEDuxbIB4A38Rnext3stIcKy1O5ODHB/RvfCv/qZ6AzIb6T1R+YIQF9Z0AtRYziodtlS8qO9X6w4m3wjHvhuZ8AQ5syXZpMTLNi2EzI9kopjQqTKgxiXD1L+7PAMz4sPUJ0+iP5N0r4kKx4wKaHIKtYKdUUeonvA/S8IBTp9j3kPJRSCPZB4AbIbVHiTnn2lA37qmb7YDLLZjnNV2bYpsBkTDkRm1YdsaNvVBzDxYsFAXEAYYx5UmQoFShQ4NqFtZYoijDG4H/nd6L+7b/dP8FLSPsSBP39ygSga+eb1M43OaHh0vOvp3n9Et0jVUzJY+nMBkc/do7qxX5j4mfdC5+9B76wTP8Yb9sNvV9uq5SX1HsftCbuh0bGrrXG/NQPg/BaLY58ZPs4vLy+zs2/+Zt0Dx8mPHSI8rlz+K0d1qxFPCBueOwxvnTbbf0+E1moxM9OaylbpJSoIZK6+JHnpcPr7PaD6ockMN0jHwbLLg3g+PnzXP/E/E3UjgCHgYsI4TBtuaVRV4gHLCO+D1+LKCB2BKUkoXFYkH+3kU38iePUlwT6CcVx6HTgfe+DN7xhPn2KInj72+Fd70pVC7MoZrLX5wxEz7j5hbnlFuqvfS1qYwPf9wmCAN/30fMgYQocODwZ5hfFlV+gwLzx8pfD4cNjWfbBHHbYv+QDwBv5LPPooQLKbDeMHcQtZ+CWh5l6RJhVPzizaYVCJ/9KlHrreU5VkOewLJTacPdn4E2/L2WiPGJWhhX1H9Gz7I40Eaqv5k9/2FMlVmeylVhQ9683aIU27IqaFpYb7CNcd/pPpRRTXihAg9VpTwZ75ZYFRlQmLv4fZ8gIso983eVEE/z7kPSlaTFsYuo8IAaVDsOUONOc7h2oH/ZRuGB/YYcTsTyX26hv6OTGE5l5IbM+kGg2m1hrCw+IAgUK7EsYYwjDEGMMQRDg3XILnD69190aDhe4dKV/PC/1iEigreLE3z7O6Xc+xE1/8nnu+tWPc+Pbv0jtbH1bc8rCy/8K9MSphuS6WxtLmSobARalPIaGR6y0XekqggiWm3DT6GqyC0Xp6lWWHnpoLuSDw50PPECp25WxzjjzU6VQxogPRBiyUq9TazTwjBFKJ4q2bz9YYmswwDsYvLYWFcccP3+emx99VNQWc4YBnoYQESW2D/NnETy7MeUx4ATwuh32EWBbGdK9gtbisQD9ShWYjhyxFnZQLmwb3vpWeOCBlHwY52GSBznKZk9C35S2VEL/6I9SfcpT8DyPMAxpNBpsbm6ytbVFu90WlVqBAgnq9XpBQBQoUGBK3HEH/IN/ACPkU5OSrfcjFBDsyI5LoLHcnCNory08+9NiWjwtXBkmoFdqyXlBOBWEhPgzjWdHmiMECaUI3vA/4dv+u/wtfhZdpgtNSrQ+JkjohWEqCKFOPCICuvg4yXxW8Osi5VndgWLGcGnSuuUoWzyr+9BM573XRZW+BAhiMajuetD22D6qn/Hir4bwFWeAh4DHyXfIs3z5BvuZPeV58cV8uxi2fNR7037C+/03Zi+Rl//K/e0qlcT74Y475tK/AvsL9boEvQ76BKFAgQLXHhz5AFAqlSTLNo7h3Lk97tkIxLE82m0JImotJES5LPXYe3VSLcFWl8rFJsqMvwtfOgm1JjkHSnJXT9UPZnv80oIXwXLL59Cm5thlxZe/X7wmDgo2Dx3ipjNnpJRStmwSbAvIxp6HCQLiIKBbLhP7PkG3i/F9jOcN3UZHETqOe217cYwXRfL3AOEEyVhMaw5t5E30mg7PQWZRzp5t8FKxI5aPgoeUFaki3g8/ATx7Z13cHxgkGtz30yllhnglTGwvnCVrbAQ+9jF47DH5fXv8cTh7Vvo3bZ+yr3dCYLg2PE/mArfeiv6mb6JcLrO8vMzq6irLy8uUSiWstbTbbTY3N9nY2KDRaPSI4wJPXjwZTKgLAqJAgUXgx39cDJAqlb7Fw1QP10pg8ORQ4mDc0Gz7exrDq3go1/5e804xehs5KhwBlfknq6dG1CFhryxTn330uA9CycTjRR+En30LlDO2His9+7Jp4ciHUe9BTECMj8biDa0BNFDnaNvraUPUFo+Yn7z/Po60Ztg8CyWZYqUooUUUGJ08XLmlQRHHYLfH7d/A6Tq85FHEA+KJnP2d9cuWJRym/dJa4G9n3G+BPcGO1BFaC/nw3/7bzicxBfYl6vU6vu9TLucpj1egQIECu4M4jgnDEK01QRCg3D3obW/L5Um3Zxi8V8ax9LfVktdTBuSCUJJf8o1jB8fQCmtNutyCNqJ8Ljdjjl9U3P4lRbV7sO7vtUaDbrnMUqOB7zLuB8soJcFZZWUGZbSmUypRbrdBKXQcyynPEhfudVK6SSUm6BawzrR6WLknpQh9n6MXLy7keG8H7gbWkAqus055FEI8lBHi4QXAvwSeN4c+7gs4sgHkexjHQiDkLbk0CKXgqU+dT9/Onxffh4sXxXuzXpdnZyZdrUqJt7xwpEre41JK2j90SMb9d90FKytCnpZKcOIE/OEfwtpaZhfy21yr1VhdXWV1dZVqtYrWmjAMqdfrfeqIgox48qEowVSgQIHZ8aIXwetf3/vzWiq5NAy3sb7DFizHaPLMnPVoXv4+WN1gqlGhIxUcwaCGnGU77TDTCunwhrdvf+sQm9N1MDekkJR4QJSI+ux6JOQpGgiL7tWrHSQkprnCDGU6PJ+P8mXn4DVfglrIbIeWdMUoiBPPB2WFTPJMMn9x3cuKOty2KvM8AqUYnnoRnuu8su8jn2tcnr6Pwyxf2jaQwz9vispTUwswCsyOqdURt98u9W1vvnk3uldgD7C1tcXy8nIa3CtQoECBPYTze4iiCM/z8H2///fpi1+cb9bxPKGUBP5cCaYoSkmHcYHAMb+/Nz0CQZecOTnu7q2x1mTIB4sfe/iRIogU2ipOXoDbHoJnf0pNn2W9z3H0yhWOXbpEu1LBaD32/NuEbHDB2k6lQux5lNttAqdqgL5gbs+4GsRA2/NkP8MCvtbiRRGrm5t0qtWFHfPTgBcDR4Elto/x3N8e/SWa3HjQqR7WgBuB5wNPAV62sB4vEE6FMvi9iqLhBKArdTRNgFwpCdb/2I/tqKs9vOc98ltx4ULqvelKVq2uwvOfLxUplpfHm15nr9e8vgxBIGTD9dfDDTdIzOdv/gZ+53fgzW+GX/ol+Mxn4OlPH9uM1ppKpcLKykpPHREEAdZaWq0Wm5ubbG5u0mw2C3XEkwRPBgVEYUJdoMAi8La3iQri0UeBa598ADjN5g5bsNzNJQ6Rzxzqji9CpS3B67xxcOf54EotTU02jMDhK/A1/2v78qfyGd7HS+ayjxSOYIhww9x4SHVSw6igaFZCkPdKUxxinRfxIRTwE38NHzkNF6uwUaHn75CzKXkywgn4JpnKaSmtZSD1fMh2N9v9Cd1e6cKLH8ssMIgPhD952zx9H4pZo/6PML27XQ5M9+kWmBfGxTMsYIKAZqOBHwRSe9vzCqO5A4ZGo3HgJwcFChS4NtBnNu37eMMC49Xq/qgjPwzO0BamK38yhpxY2YIbH4ELp/J3I7UlEDICwCpLpas4thlw+gnNa94Rp+a7qytw9epsWeD7FM+8917ue9rT6JTLqMR0eiiUwngeutvFeh6dIMCzlgBROGhjiJOAtrK2TxlRNoZOdkzkSIgsrEUlJterCyrBBPAs4I+RIbojGVwu0zDRs5tilDLvlRDPh2MIofHtiP/DNYfBcluDPh0uuJ/97KZRCtRqcPfd8IpXwItfPJcu86EPwSc/OfT64coVicE8/PBoj4phZtO+P5p0yaJSSVUWz3qWEB3Hj4u59owG21rrnkICUkVbGIZ0u106nQ5KKXzfx/f9tMRegQOFJ4MCoiAgChSYN9bX4Ud+BM6dww6RPF+rwcC/x3389x1UtNQo7uAq5Zyp6gq44/Pw2I3T7cdg0Oh+j4e8OxwyPvFieMGH4c4hdfyfyacp06VDmfl9ss5G26NEG4OHQSWahyxE42G3dXz6fihirucsX8efAnD3Zfimz8J/fRaszzi2sRp0LAqIGCEd3LTOZlUOORQP/Q1Dx4enXM4sayIExOISpXr7BqaTK/z1Yrpyrf6OHCRs+wyCAPuc56C03jZZCBJCopgsXPt4MmQnFShQYP/DWksYhlhrx99frlzZvyUBhwUB54BjVyQRxmb5mDFjTWvjvjeVBawh9MFrdLj7055Ie60VNckCA+N7hVatxo2PPsoDtRpxpdIfjB6AshYTBGhrsUoRaU2ckF/KGBnrWytqByV69NjziBNioW+GNqQEk4pjbnj8cYIFEmc3AK9HqqQ2EcGyIyEChFwIk2c/ed8Ax4FTSAmnO4C/BywDh7iGS4sMC+Jnv5vuedoM/CCAkydFCfCyl8H3f//O+wpSaumP/mi0siuKxBPi+HFRKzlidvB6Grz28pSqq1TkWL73e4VUueee/MqJKeB5Hp7nUalUMMYQRRFhGPae2+12j7Bw5HMxx7j2URAQBQoUmB7/83/C5cvYMNx2Q9+nw/9ceGHP6XfwKPJEjy0+ES/h0an2efpszuZ7e0n/TY1hm1hY3YTv/M3hm7yWd3KM8zzOTdPvb2xXhFgIKSevswMKlawBEPeKMe0EGsv38WusZjwt/sFH4XeeyeyZ/1Y8H1Rme52QENvanLJq1FIIQXYM/DgyS5ilr9maRpOkSmliXP62h3vRD8UIDmxslwrsH6ibbiL4hV8gWFnpGYGGYdjLYlJK9SYLbsJQ4NqDU0AUJZgKFCiwV3D3GKXUZHL7wx9OM3t3C9m68aPgFA/ZUjxzIiNOnR9CQEyE27dBG40fwfKWZW0Dbjxj0j57nvTT8/avsmQGrK2vY7Wm0ukQBYGUSRpcKQlM20TdoIxBISWWsBZlLaUwJAKIY4yrwZ+8bxHviN6yIe3rOObY5cvc9MgjizrUHv4O8CHgYeCx5OE+UQ9YAZ6LDOW3gLPAU4EjiN/D13CAg2k7/S6WSnD77fBv/62UQzp6dD79Avie75HSS+P62O2KKXW5LEqFbld+j9xv0izHVy4L4fDt3w7f9E2z9X0GaK0plUqUSqK/yZIRnU6nR0Z4nlckPF3jeDKorA/sb2aBAnsJOyD3OwhhigoxJUK6PfHpOGyP5FaJeQnTDSaf/Un47W/Pv77zfZhX6SW/C69+N7z2XcPffwoPcg+f43FuZH6fsvRdJ7oHs00iIK/lf4XCDFFHTLe/Eh1u4Fzf0hs34aUPw+89XbwcpsbA6fAjmWuEXhJoHyfaGEckKIiV9K+HTWRWcHiGfg60vW3f2dnXtKc5twligQOBN79ZasEik4VyudwzKnaThTAMi8nCNY6tra0DPzkoUKDA/kUcx0RRhNZ6u9/DMFy6tPseEHkJWs+Tskae1x8c3CEObUCpC+1pS3NaMZ32YstSQ3HD5QrVKAQ/IUqMkazuMJTnA0RArNTrnDp7lidOn6babFJfWZE3XDZ8FkphrSX2PLS1+FGEjmPCUokwCCi3WrSWljBai9g5jntllSLPG3t9aGPQ1nLDY4+NXGdeUMDfB/5/iILheoRoeHXy+DSikDDAncDPAwfa4WtQkeRIwmnJQc+D174W/uAPRDEwL5w/D7/7u/DOd+ZTKxgjZvblcr8KYhYT7VJJ/CRuukkUEHsIV4YJGJnw5PyAioSnawfWWur1Oivut/eAorgaCxSYM8zXfi2qVIJOBzgY5ANAjZDDtDifi4AYhOVuLk5tZP3q94Bnh/gLj5NRz0v9YESB8bM/AbXW6E1fygd4L68izh2dnlzHR2Gp0CSilPg/DNYqsklL4/aZv17QEk3u4Avbln/Xp+Ct90BrB157TvFgnHo9zxdighdDLYRnns8si4B15ELRE7Yftq9BP4rsszPb0ExzSgUhQowUeHLgK79y5FtuslCtVvsmC46YcJOFIOMdUWB/4skgjy5QoMD+g7WWOI6J47hXniOXEmtjI/VZ2A2USqmR9LgSS+69LPk+J1XB8paM3TvVZOiWZ9yW6aYfwcqmwWt2uP58AEESNA9DObZpDXivEbzgox/l3HXXce7kSZq1GrHv95MF2c8zWW4Svwjl+3hhiPF92rUaNhO4djMXPS7o69a1llseeojSLpFmdwC/AHwGUT08jTRA9lzgm4EW4vNw4JH9bLPEw7TB+uc/H/70T+dX+s1a+Lmfk7JLly5BozHd9o7cdMfk+jVItow7zmPHJMnon/9zISH2CbIJT8aYvjlGNuEpS0YUCU/7F0+GOUZBQBQoMEcYYwiDAP+bvxn9O7+DauczXL5WcDtXOM/a1NtpLG8YEuCehOd8ClavwtXjU2+aH0OdZOHkOfj3Pwp3f3785i/mA0MamQQlOxlBIFgUHaqJumFYJ0dJBrIR8ryDvpi7+Dx3sf1Av/JhuOMKfOaElFMaizHVuSyiWpgXXviYGFr34WHgOUjh1lmQPYVZAkKTMmAe033UFpnRFFg8siUc9gp33plrtcHJgstYiqKIdrtNq9UqJgv7GIUHRIECBXYbg2bTWut85MPZs5IBvJtw96usoevgvdkF8F1QMBvwnAM8A6cfhfoydMv0j/HGnDZlxPut1rTUWnDd44a77u3IINgdlyvB1OmkqogDAi+OeeGHPsTvvfGN24O1DtnAdPK5Wa1l+hQEcnqT5doYrFLiA2EM4QT1g7KWpXqdE5cuLfIwt6GMkA3DsJQ8npSYhRCsVOBbv3V+5EMcS7mjd75TXjtyMy9KJTHBThJDMWb4MY377anV4HWvg2/5FnjhC6fr/y7CGVlnE5663W4v2anb7RYJT/sY1lqazeaBn2MUBESBAnOAtbZnEKS1Rv/Yj6G+8AV4//sP1MC0gmGWQvsBMV89AwEB8PL3wdu+nl1z9lIWKl148d/CG94+ef0uJZbZZCNXbkw2sj16JiT0hEZj8TDE+EO2GaxhNJsBwhItfoKfZUilV7SFb/wcPHgEmpOEL8N2ne3muJJLU8A3cLo+5I0zSIpSbcr2s/1zp1DRr6ZwIpRp58Ut0mKye4SDosAai+uvlwnPhQsiyXYTlN2E50mQpzqdE7qbLARJneRisrD/URAQBQoU2E3kNpsehkceEcPW3YRTCDgMC+wNZh7Pa67U8yKA287A2RuhGUG3BFHA6OFyMgb0DCxvws0PwaveC7WWI0USEiUIpAxLvT762K5xlLtdlre26AYBKgiInEolmzU+5vPty+dJrlWVmFUrO16nro1Bac3tDz6YLpx0LRWYP9xnPMv38ilPEY+EeeGP/gje+14hDWZJNHIl3hxxWCpNR6poDd/2bfCWt8CNN0637z2G1ppKUgJrUsJTqVQqjKz3GMaYJ4UCorjCChTYIZwkOoqiXr099dSnwg/+oNzk9ht2kJFwDxdyrLVdUnCMJjcy2wToW38fvGhos/OHhaADd34O/sn/CX6OGGZAxBLtKTs0bF23zAAGny4Gje1Ly8/OmlTmAbOFmg0v4X18FSNMLhC1QXXY+Z+EjBhDIcSOmrWbGQQxvOKhIW88AFyYoX13CrNe3tnT7UowZdfNixnIhycFYTBvPOMZ8IY3wCteIYGBnQTofb+/HMQ0282BGHCTheXlZVZXV1leXiYIAqy1tFottra22NzcpNlsEoYh5gAR3NcKngyTgwIFCuwPuHIawGxeQUePSgmm3YQzaA7DycHCUUHOWecqmf0961Nw46OwsiVj0L6xnYMR1YOOodyBpTocvQov+AjUmpl+OLNskOOq1eRxANGqVlnb2KDc7YJSorQZVbJmxOdks6qWzLp21PWbBJZ1HHPd449Taza3vTdUjVFgsZh2jKkU/OqvwsmT8+vD298u/XDKh1lIqEZDtg3DfN4RDkrBa14Dv/7r1xz5MAiX7FSr1VhdXWV1dZVqtYrWmjAMqdfrbG5usrW1RbvdLuYXe4BGUlrsoHtAFATE/5+9+w5vu7oaOP7V9Laz94BAEkJCIKwQwsgmIdsOlL1H2RTKHmWUllFoy9tSKBtKoRA7IQNCNntvSAgkkEH29h6Sfu8fJzeSHcuWZMkaPp/n8eOlcS1L9r333HOOUk1gUqK9Xu/eGt97U6Lz82HAgMSbLDVhAjeGX7DVc1K+MYewlY6EWa9xj4HfwcHLwBaLA83Wvh+nV8PFT8ExH4d2E0fxKfZ9VjTBNBQs2FshFTsWFnYs7Pj2/Jn2XyPU+2qMRS4lHM1nDV5q3CrIrQwy5No3FzTLwbL535rEgrwqOLi+zGwLCUI0pWSsGb8X/68jsAVHuCJc8yfYX4zE5nLJ5v/w4ZIJYco9RMJul7q1l14KHTqEd7399496wNksFrKysvYuFtLT07HZbFRXV+9dLJSWlupioRmVlZVpBoRSKuYCG4pGFHwA+Oab5j01npEh91ddLe9NiaX61FdyqW4ppiasoWzAqIWyjsgukcNMDp+8t3n2BB4saTqdXgU5pdBpM3TaBO237rmRwP+rZhO0qir8jcwk0nb7djIrK8ktLq6VxVDr9Hngx4HBCZMpUed3aCFr5qD2ZEdklZfTcUsDh900A6L5RDKnPOUUGDo0uuNo1Upec6ZkWyjPAfOcdLkkGOpySaa019t49oPDIZfNy4Pzz4d585r8IyQic+ApJyen1oEnn89HRUUFxcXFeuCpmZkARKqvMTQAoVSEzKkkn89Xf1mMtDT4v/9LvCyIJtRYHclq+rCdcNIRHPjIZ1lE9wfQb7mUYcqsgH1iH02Zh9Zz3fRK6LYeLnw69JtpzS4G8nWYgZm62Qu1eXHgwUXgrr6FDVvUgg8ij2KGs6TRy7UvD/Fu60tpj7JWlbD/riDf3Ixs+kearWE+Diy9FOnPYAErI7yuCl27dtC6tSx6PR5/JkK4mxY2m1zPbofHHoMVK6B/fwicBNrt0LMn3HqrfN3plL/vnTvDCy9E9+eqh8PhqLVYyMrKwuVy4fV6dbHQjEpLSzUDQikVM+ZwkynrWutwU7jWro3u4BpjNuXtdvkf6XLJ/1e3Wz43/2uDrUUC+grs/T9eN/ASxmORWQ7HfgAH/wBdNkF2qWTSujyS9eCsgawyyCyDnGLosgGO+mRPxkRdNpt/A7O6Wt6nYLmSjIoKev/4I5UZGbhqarCbgx0mCyRYzw6TzWICT+DfNIbaZZzqsizsPh8+p5M+PzbSfE8lpoMOgv/9L7q3aYJa4ZRVNX9j0tL8z8myMigubvx2zHVbtZKDTXff3aThJ4vAA095eXkNHniqqqrS9UWMlJSU4HK5SEtLi/dQYkp7QCgVARN8sNlsuN3u4AuDY46RCHpDpzmaWxNOj2RRw2V8znWchK/ezfN9bzsDD8fya8T36fDBtX+DJcNheZ89p+ijfUTcJ/fTdjtMLZIsiHCM5w3e51h20zqEwZnvN9TTwVwu8LKBlwhWxDYcPnqximNoPNWjU5mcEvM1dHeB3wvspWA+33PKDPY0tI7gR8isgYO31NOA2liFZECE+9DUvaytge+Fygd8EeF1VWjatoWBAyXzYcsW+PVXOZXocMhmR03Nvie4zIaI3S4nqkytalMf9rDD5HKtWslC6oEH4Pvv5XbGjYObb4bcXLj2WigqkhIM+fm1AxXNwG6343a7ce8JcJueER6Ph+rqaqqqqrDb7bV6R2hd1+jQHhBKqVgJbDbtcDhwOByRBx9A/m/9/vfN1xfJbFS7XP4STDZb7ezEYGOp22MgsMGz2fAOPAFdX1mgIHr9AuUZkFYJJTlQ44a83TBqAWSXg7Nazp6kVwa5gbr3YfpBmCBEim3IddyyhY6bNlHdvTvuykpKcnL8ZZUC1ROEsO15TCzzOwz8fn2ZLZaFbU8Aovvq1dhT7LFsEW68UebL0bZ8OWzaJK8100S6IYGBsT0lxGoFwxq7bk6O7Nucfjr84Q8pW2atMeZ/T3p6+t4ep2aNYfa/TNDCrC90jdF0JSUlZGVlNe1/fhLQAIRSYTL9HkI+lRTKP8wkMpZVPMIu1tImhEtbtKGCg6ivZk7oDvwZpsyEXy+H4jzq3+wOR92MbyC3GI7+BE5/JfzxTWI2L3A2H3M0cmw+FPUFHeoO0lbnstHYGRcOfBRQSCYVjV522C+woBdUNDa3COj7UN9w95ZgCrxcqCzIqYbjfm0gALEDyYKItPRosJhaJA+1BwmIqNhwu2HQINlc+c1v4Nln4auvpBF04Gm7jAzo1EmCBtu2yaJkwAA491y46iqorPRvmHTpIr17jP79JbPBNLAL1L69lGpKEKYEIPgD5DU1NbVKeAQuFsxlVfi0B4RSKhYCm007nc59M6sj0bOnbKI1VyNqt1v+X+bkwK5dtUswBZaANV8PzIowBwhcLvnfbH5+E7Cob/M6lKxum439f7bwOuCX/SGvGDpugQHfQkawgIORlubfyAQpzZKe7u+rkaKb5VmlpWRUVJBRWUlZZiZ2pDrpPhkP5kPLwubzSeDBbse2p+zSPsGHBnpApFdVYQPSU2zdnLKcTskALiqCI4+MzX3s2CF/C0LdkDVZNuZ5F87r0+2WrIc+feCee+S1r4IeeKqpqaGqqorKyko98BQlLWV9oStQpUJkmk17vd7wTiW1bt38DeBiqDc7OI5f+W8Ip/1twP7sxBFKHZu6NV/rLCgueRJePQVK8ppY2afOle0eScke8iE8djl0iiBZpRvruZpHuYCnqSKU0xJ1UwQCBXtMo5H14L//NmxnKB+GdOlRv8BTu2B5W7AamlPYJG3dqpvUsefjvQGICH4El1cCEFd90sCFHEAFUAbkhn8fezX06wlVVdOu31h4KiEFNmkMTMGPJrM5MXAgvPiiBBdAggu7du1b27VrVwkiDB4sl/F6/c3xOneWRcbGjdC7N1x3nQQd6rvPJGK320lLSyMtLQ2fz1crIBG4WHA6nXuDEbpYCF1ZWVnKN4hTSjUvc8oUImw2HUxJSfP+DzN9ErZurf11t1s29EwwYU9j7b2NZe12f+AiPR22b/eXVnS75X1DJZvq+54pA+XxYLMsev8EvX8KcyZtgg+mbFRurhwsczj8840mlLZNVC6Ph74//sj2tm3Z0aaNvw9E3XXvnl4QNp8Py2Q97Pm63efDF/g8bqgXCFDtdpNZXk5ucXGUfxoVdenpcM01cPXVcngnVvr2lb8RDWVw1VcOLLBHSajS0+Vg06WXavChAebAU0ZGRq31RWB2hMPhwOl04na7oxNIbyFMhnWqZ0DoilOpEDTYbLoxZ5/dpCZqicaORT7L6+l5sO8/eideTiCM+rOmWVQ9eq6FsfPAVV3PXYUyx6ivfYIFadXQ9ye45u+RBR+M4bxNT9aEOJi6qQDma/V9vb7r1Se8iVY31nMoX4d02UO2wFUfQ3ojfbugToks83Hg0Hx1Pg+hrYXNgqwaGL0SnA3taVcA65FjWpGsBYNdx2LfcTemjKY1xG6ABVTjxLOn/XnCCEzPb+ikW7DrNsbhkM2E/v3htNP8wQeQ7IeaOg+42dQ44gj5vF07f/AB4MQTYdEiWLYMXn9dPk8xJtCQkZGxt5F1ZmYmDoeDmpoaysrKKC4upqSkhMrKSrzNVaYjiZWVlZHZQtPylVLRZzLVIMrBB5Bygs3ZLLmmxl9uyWQ5mHm92cz3ePy9FAIPKmRkyGnnrVvlsjU1tS9vyi8FZlJA8PmDzye3U+cwRNgrMhPISEuTA2Xl5bU3RFMs+GAcsGoVHTZvpv3mzdIHor4yVAA2Gz6Hwx982JMF4TNBm2A9IwJux7LZcFdV0WnTptj+UKq2zEw46ih5fofK4YDp0+H++2MbfADJNna7ZX8gmMC1R+DfiXAddZRkRgeuLVSDzIGn7OzsvY2s3W43lmVRVVVFcXExu3fvpqysjOrqau0d0YiysrIWUeJVAxBKNcKkRAdtNt2Ye+6BESNiM7g4aUsFaTS2USXll6byQ/3frm/B0MjE4dwXoO02otYDwlUDPdbCoC/gsND24oPqzCYG8ylOwlno1f1BzG58Y4GIusLLjLDh4Sg+wxFG4+wRq+HwjWEMKXBogR/X12+hkaHbfZBdDYM2h3B/i5BARCTPkboZGoG/jnB+JT6kHFQMzWUcD3Iz8xnFbnLq7cnS7GHPuq/pcCaajS0WTO+G9u3hwgv3LX/088+1FyFmPE5neAurFNfQYqGysrLWYkEbWe/LsizNgFBKRUXdZtMulyv6Jx9dLtnAa26BG841NZI1UF0tZRJNb4i6l6+okMuY3kwmmyLw/1BgCSdzG5YVmyyPwKxOn8/fN8rrjeyEdRKy7HYyKivJrKiQOWV9JZjq9uIIbCReV31fsyycXi+tdu5MyabeCcmUHd24EU45RQIRoR4E+s1vYPz42I8RYOdOqSTRuXPDQQioXXYp3Nem3Q49ekCbUMpLq/qY/2GZmZl7DzxlZGTogacwmB4QqU7/yivVAJ/PR/Wek0NutzvyU0mzZ0NBQRRHFl8D2EpaI8e73XgZzHoOI8hplvomsY1sdg36CnquA7tpNFzr9hq4Yj3fs+1pOt1/GUycAx227nuZcN3GfXRkcyODaUikTRLCa6aQQRUHsyyM60CbCrkXe7j7kTZqb+BHUNrIBrSuhKlBYlm1lAJrkRJIoaovESVYoCSU2kg2CBZ3i4Yq3CxnIB7cbKD7nruM80LYbpdNjsCGg6YWa92ggGkSbU7HNcZmk5OR3bpJuvdll+3bGC4vz39fdrv/rUOH6P2MKSbYYsFut1NTU0NpaWmtxYIGI0RpaWmLqNGqlIqdwMxqU64iZmUXeveOze02xGzQ1xc8CPw8sFGxyZwMDD7U3egPVoYpVsEAm80/t3E6/YGOFMpsb0ibHTvw7Nn4tde3sWuCTMHmB409TnuCFa6aGrDb6bRxYxRGrRrVoQM89piUFBs2DLKzGw/+ZGVJtvBLLzXLEAF5XjkcUh4pM7P+MVqWP2gZKbsdDj008uurfdjtdtLT02sdeHK5XLUOPBUXF+uBpwAtpQeEBiCUCsKkREflVFJGBvz3v7Fr0tTM2lHOAexu8DLtKedx5ja+12wmr3UDEvU83g4fTCmCzPIgt1Xf+qO+r/mgzTY44R149CqYMLexQYamLz8xnjewh5UFUVe42Q/h8tGGHRzOl2Fdq105+GxN+KfRhPYVTi+MWSVjCMkOpARSOKWY6rucCTiY8kvmfWM/R5QyIILdTTVO8tgJQGc2kEVFvfGSZmNeq6ZMAvgXpIHBCIdD3ux2f3mGUDYNTNBi4EC4+OL6LzNpkixOTFDDLFiuuKLpP18LYRYLOTk5tRYLPp+PioqKvYuF8vLyFrtYsCyL8vLyFnFCSSkVG4GZ1WGXdY3E8OGJdbI8MDvCzBV8PplDVFZKhkQ4p5hNwKIp4wnG5/NnUZqsSjOPaQH6LV9OVmkp2GwSgKinjJLNsrAHK7VUX+PwIPb/5Rc6awmmpjMHcOrLCrLb4bDD4J13YPJk+drhh8OUKQ33PcjNhX//u/kyH4y2bSX7oapq39KuwZ5r4UpPlwBMnz5NH6+ql9lLy8rKIjc3l5ycHDIyMrDZbLUOPJWWlrboA0+mB0Sqaxn/PZUKQ2BKdFRPJbnd8NZbKZPedyrf4wxShsmJl6v5kM6UBr+Buim7gV9v4PEeuwD2XyvlkxoNOAT5vqsGDv4B/n0JdI3yYZtTKOQgVgS581AENhuIdhDCwk0NR/A5Q0JsQB1ov10SBIrKjxaqPf0fupSEcZ2vAQ/yH66xl27gQ+0LeAvsTWGr83EIYyaGPfSyKWcqRbRmGwewiug/T8JkTiyaUgn1nXgMrANtNhpCXSy4XDBmDPzhD1LPuj433SSNpnNzpYll69aSdTZ1asQ/VksWuFjIy8sjNzeX9PR0bDYb1dXVLXax4PV6KS8vbxEnlJRS0WeadgKRlXWNxP77N28j6vqYYEPgWzRvN1wm88LhqP8AVOA6pKLCP8+pqvLPcVpAFkROaSkTZ89mv59/xm4yUur83Jbdjq++7JbA9w1wejxklZVx/LvvRmvYLZfT6d+Ud7nkzWTvZGRIMHLBgtqb7Q4H/OlPcNFFEoQwAQzzOujSBV54Ac44Iz4/09FHS1P6ioramQ5N/TvicsmaIT0d+vWTdYZqFg6Ho9aBp6ysLFwuF16vt0UfeNIAhFItUN1m0w6HI7qnktq0kSBE9+7+SUKSOoXlHMR27HX6CDjw0p8tHMuGhm8g2KShkVNPh34DN/0ZcnaDLVj5wGCNjS2we6H1bjhlOuSFs6kdoiF8yJF8AY32yAjGvmePuwkpA0H56Mlq7uP2oMGjhgzY0oR/GjbkyhEEIFpXQucGYln72IL0gahs5P6ClV6y1bmMuVyo4/cCofSrCEF9zwA70J4tTGAWbqqxR/xciwKzwGmowSDI69rUfQ53IpmWJgGFQw4JfpmsLJgzB15+WfruzJoFzz6b1H9jE0moi4WKioqUXiyUlZUBaA8IpVTY6gYfotpsuiElJfHpAxEq8zg05//rwECICUYEzmECSy45HPJxTY2/B4TJBghovJyq/abSqquZMmsWHTdvxt7Ypm/dNVxjG8SWhbuqio6bNknTatU0Ho//9VRTI5vr3btL1sOkSTBzJrRrt+/18vLgb3+DW26Bnj3l8/btpSnzunX+bInmtmqVzOdbt45e8NJmg3PPlcdkv/1g3DgpK9VCspoSjd1ux+121zrwlJaWVu+Bp6qqqpRdX0DLKcGUmv8plYqASYm2LCu2C4Mjj5SGqQsWwE8/ycLg1Vfhxx9l4uDxxOZ+o6wXO/kv0zmXKXyP1FnPppqOlDGS1RzF+vqv2NAmZbCsiDrOfAU6b4LfvAzb2hHaSXcAC9ptg5PfgPOfDeHyEciinAe4mUWMYD3dQxxYIAsrRrFhN9UUUER/lkd0/UM3Q8dSWN2qCYMw2QQhPiw2wOmD4avDuI92wE6gfQg37qszlsCPTTaEWROFOuctg8bib03lwkd31mFha/5m04Esy7/g8Xj8pwnrWyjY7f4STeHo3RuGDGl8cyItDUaPljcVM2ax4N6zoeXxeKipqcHj8VBVVUVlZSV2ux2Hw4HL5WrejbYYMwGIlrBAUEpFh2VZ+Hy+vc2mY15yqa7MzMYbuMZLYN+o5m7q7HDULhsJ/mxNh0NOjNts/oxNm002QouLJaDj8fi/b66XJGu4cNksi45btrC1XTsq6jYsrhvIqU89mROm+XROSQmZlZU4tCltdHi9EnjweGRePHKkPF9/+1spNRSMzSaZxldfLUGHAw/ct99ac/vwQ9i9G/bMvYJqaG+hrpkzJRizcyeUl0uGhx5WShgOh4OMjAzAH7SvqanZWxrdZrPtzdJ2uVw4UyjwW1ZWRtu2beM9jJhLnd+YUk1gFgbQTKeSnE6JuI8bJ59fdx1ccgn8+issXRrb+46iQ9jKZzzFUnoyg35U4WAEvzCJH0kLdiq7sQmCqbFqThgFMWIp/OEe+ONtsKUjWI0FISzIrIST58Ftf4bsRuYyTdGJLdzPrfyWf1FGdiMDqzPIWjv00cyCsGjDDkazMOJb6L0DuhfD2lzwhRr0qT2EsK9jB45bB53CyYDYuee+nI3cX2NzVRsSfAi3/NL3IVwuDIEVoAI5g6b6NCMTfHA6/QvyuuUMzOeRnFpxOCRVvKHsBxVXpoY5BF8smHKGyb5YKCkpwe1240rUzTylVEIxmdU+nw+HwxH9zOpQjBsHt94a3iZdc2nsRHOsxhxYIqhuM2yXyx9UMA1uzfzFNMK12aC6Wi7nckl5mOqm9H9LfK127SKvuJiq9HQpuVT3MaxPsK/v+Z2mVVaSUVlJj7VrNQARLSbjuHVr6NtXSgsde6xkQoSidWt5SwQrVsD770tvmIbWEKH+jbjsMgk+QGL9nKpedrudtLQ00vb0JzEHnmpqalLywFNpaSk9e/aM9zBiLnlXgUpFidfrjd+pJCMjA+68Ex58MKkCEAB2LEawmhGsbvqNWZZsZDqdIaVaTn0d3pgAnw2Cbe0bCEJYUrKpoAieumRPH4MYO5P/8hqn8CZjqcEdZGD1CWxKEG7zgYZvtzW7ODjC7AeQAIRJCPBFMpwIrmOzoFVlmFfaBJTjz1xobDxWnY/N9+p7+EP5GX4ObZjhCBaECHbZZmMW7aa8UmAzyGhsHGRlwdChMGhQ08eqYi5wseDz+fb+f627WAgMRiTTYqGkpISsrKz4zBOUUkklMPhgyrrGRa9e0KMHfPddfO6/IcEOLNT9vxDtIERgOcjA9UZgCaHycpnbmDr6Xq9kP2RlScChvFxOmrvd8lZdLVntKarvihX8sv/+bI3Gjdls2Hw+XDU1DPriC/p/H+WTOy2ZmYfn5sLJJ8NvfhPvEUWmqgpWr5bXXU2NP0DYlGbTjz4a1SGq5mUOPGVkZNQ68GTWGebAkwlGxO1/boTKysq0B4RSqSyw2XRcgw9G795w0knxu/9E4HLJxMJsZjai6wZ47DIYsQTydoKjhtoNhC2w+yC7VJpNT53ZPMEHkE3gi3iKvqzAhRlYKNcKfAsMRjSl+7OFAy/9WE6nJjQn8NkgrwpyK8EW6XCsOu8b4fLJ7zBsm2j4Iavb26G+h9487EYofx6qgBitPxNyy7Nu7wefz9/ArqlycyX4cMUVmh6dhEyKdEZGBrm5ueTm5pKRkYHD4aCmpoaysjKKi4spKSmhsrISbxKcfmwp9VmVUk1jNkdMWde4b4Tk5SX2/1GHQ8bndvs/NnOLWGVt+Hz+gIPPJxudluXf6PR6/WUlHQ4pZ+P1StZDu3ZyeCw3t3a5phTWdscODlq+nIzychweD7a6Ga/h8PlILy/n0K+/ZuB332FLtMycZOdwyFp6woR4jyR8lgWLFsnc/513JOhXVeV/fUbq8cdTtk9LS2QOPGVnZ5Obm0t2djZutxvLsqisrKS4uJjdu3dTVlaWNL3pSktLW8QaQ1+FqkVKiJTo+vTrV7smaUtht0vwwTR3q69WaBD7rYVXzoT5o+DRq+GbgbC9LXgckFkOadXQaRNMfh0mzYnxz1HHibzDSJZQQSa/0BNfyDHfukfybdiw9uyX+/B3Qw49q6ITmziF6WGMfl82C3ruho9MFm84L5n6EjkaS+7wQV64DaiN3Ugz6GCVUhqrcGW+7mjkcnVVAitDvGwEwsmEaBZmAQ/11wSOhN0ui/sDDpCG0iol2O120tPTAfZmR5iTS5WVlVRUVOz9f+x2u3E4HAmXHVFaWkpmZmZizBeUUgnJBB9sNhsulysx/l6YgwGJur4w84jA0keBa4FYlmEKDDqYwEd2tv/UtQlE5ObCtm2S+dChA+y/P2zYALt2+TNBU1xaTQ1dNm5kfbdu1LhcVJsADYQegPH5cPp8ZJWXk1FVFbvBtkSmF0lODhx0EPTvH+8RhW/ePPjf/+DTT2HHDn9ps6b8DRgyRBpPq5Rkt9v3HnoC+R9cXV29NzOiuro6KbIjWsohJw1AqBYnsNl0XFOi67PffpIi2FizpVRhJvqBjWsDa4mGEa0esxBOfAeW94UPjoWPB8PmztB5I4ydB1NnxOhnaEAuJVzHI2ylHVtpTwlZexpMh7KpFnhE3wrYdA63N4SFmyoO5wuGszSM0e/LBpy0Cmb1hV3pYW6E1x1uCMO3Ib0fjv01nDvaYyP+h7CxIAM0HAwJp3rWJmBbiJePULAgRNy3OAIbSYYbSM3Jkbqs++0nf/86doTx47X3Q4oKtlgw6dR1FwtutzshghEtZXGglIpMQpR1rU+8m8k2xsz/TTaC+XsfywyIwACHWW/Y7RIEqaryl7Ix2REm06F1aym15HJJMMKy/Jukdntk/a6SRMfNm/llv/1ot2ULG7p2xebzYYW5jnZ4PKRVVZFbUkKnTZtiNNIWyvQw6dYNpkyJTjZycyopgf/7P/j+e8l88Hj8WUqRvrby8qTxtGox6jvwVF1djdfrpaKiYp9ysIly4KmllGDSAIRqUZq92XS4tm+P9wiaj2niZrP5sx5MaqTLJZP/MKVVw2HfytvlT0R5vBHqwTru5F5s+JjDRMrJpAYXDW8X1204ULcvRHipB93YwO95mI5sCW/w9Zi0Av42GDZl0XiPhfqHE1oMxQK3D8asgqPXR3A/NiQLIj2My9cdY7h8SAPsZpAgWxrBhRN8GD0aBg+WbIdE2axRzcosFtLT02stFjweT0ItFkwPCKWUCmRZFl6vF6/Xm1iZ1Ub79on//zUw0NBcm/h1e1AYe9aKe0vDVldLhsN++8HWrbBzJ7RpI+sWj6d2o+oU1n7rVg5ctYqt7duTUV5OWXY2Pp8Pq6FG1IZlYfP5yKyoIL26mt4//kiPtWubZ+CpLvBAX1qa9E07++x4jyo8lZVwzjnw7rv+wB40LQCZlwe33ioZS6pFqnvgyWRfm8NOVVVV2Gy2vf0l4nXgybIsysrKyMnJafb7bm4agFAthjmVlFAp0XWZUzbRaN6a6Hw+/2Q18ASSZclkPvA0VJLrw48M4mvW0YMV9GEHbUMIQkDdnXDZtw+nKbWFi2oO53OO572Ixl6XDbjkc/iwO3jD3ai39uR++ALyO4Jc3wbsvwMm/RjhQKuA7UA7Gv9Pt2/Fq8h7f1eHefmWzu2WNPGzz078zRHVLMJZLJhU6uZaLGgGhFKqroRpNt2QHj2S7zR0LDS0rjDZFllZsg4xm7k1NfJ5cbEEIkx9/d27/YGKFqTf8uVs7NSJbW3bsq1dO3a3akW1213/hfc81nbLIq2igla7dtFz3ToO/v57uv8aSXqz2ofpnZKZCW3byvtJk6SMWDL5xz/g448lEBGNzKe0NGnC/bvfRWd8KiWYAwLmwJMp02TemwNPLpdr7//z5lpjaA8IpVJE4KmkhEuJrqtTJ0ntTdWU1LoTf5tNFkQOhwQf6jY0S4HgA8ge9tU8Sl9+4FnO50OGsJkO+Br9E2yr9bFV79cb1oadHMuHUT0xf+Ja6LUDlrcP40oW2C1/9wqf+aAeNh+ke6BLKfSLtJzRGqAi+H3UvkNql2uKNPjgAzaEeZ2WzG6HP/5RGs0lenkIFTeNLRZsNtvexYJZMMSK6QGhlFJQu6xrQmZWG263v0xQS1a3r0TgusRmkzK44J+T2GzQqpWUhqmo8J8y79xZMrVNEKLu2iZF1i/BdNmwgbKsLHJLSynNzsbudOIzz/06a2ybz0d6ZSXd165lwty5ZLeUMsPhMD1awglomevYbFIazJQ/zcuDww+P3VhjYe1aKCyE0tLoHEZyOqFrV3joIcliUqoedrsdt9uNe08ANXB9UVVVtTcYEdg7Ilb/4y3Lory8vEVkWSfoLEmp6DCnkkxKdEIHH0Amvpde6p8ApxITbDABh7Q0fz1VI4Un7C48TOANCjmFO7iXPqzATg2h75Cb96E+f32kU0F/lnEkn0c05mA6lcJRG8DlpfHh79nYN6P2Al7znyfIj2LZpPzS2JXQpiLCQXqA95CMhFAfYju1H+pIno5bI7hOS5SeLnVeb7hBgw8qZGaxkJWVRV5eHjk5OaSlpQFQVVVFSUkJu3fvprS0lKqqKnxRLonRUk4nKaUaZ3rXAAnTpyaoTz+N9wgSR+Dp6rrrjupqCSyYskrl5fJxmzayZklPl4BEp06SEVFT4y8jG3j7Ke7AlStpt3072SUl2C0Lp9eL3efDZh7bPeWW3NXV5O3eTc/Vqznuvfc0+FAf83cjkvlKZqY0RzeBsYwMuPJKKRWWDDweuP12mDgRvvlGesCFU8I1GKdTGnBrk3MVBqfTSUZGBjk5OeTm5pKZmYnD4cDr9VJeXk5xcTElJSVUVFTsLekeLaY/RUtYY2gGhEpZCd1suiF/+INMbK+/PnWaUQf2enA65c00k6qo8DedawGnhgAmMoelDGczHdlJa0JvphDq0XwLGxYd2cIJvMsQPox8sPWwW9C5FDI8UBNs6OZAmQk+2MBhSZKAzydBhvp+DLsP3F7oVAKnfd/Ege4CSoA05CEOteJV3RYcobCAMiLri9ESPf88nHpqvEehkpyp2ZqRkYHP59tbqikwO8IcPnC73U2eB2gJJqUUJHCz6WC++CLeI0gc5v+AWXuAf01iyuCarAavV/rz2Wxykrq62t8Yt00b2LxZsktMFreR4msZp9fLkA8/ZFerVrTeuZNVvXqxo00bfHY7XqcTr92O0+slrbKSDlu3st+aNXTduDHew05MLpc8f2pqwrueOeXfq5dkDpx4Ivz2t5KdkywefRT+9S957VRXR6ePitMJXbrIxz16NP32VItkt9tJS0sjLS0Nn89Xa40RmB1hysE6nc4mHUIoLS0F0B4QSiWrhG823ZhLL4Ujj4Rzz4Vly5J7IutwyMTd5/NP8Kura6c8t5DAg9GN9dzPzbi4h5lMpZx0rAZ3rsNvPp1OJWfwX+7knpg0LM7wQPsyKHeBJzBzwAhsNG1JIMLtlcBDjR085ldez+DSvdC5DNqVN3GQZcCvEJMYT7D7K43gei3NQQdp8EFFXd3FgukdUTeVuimLhbKyMtq3D6f2nFIqlSR8s+lgWkBZh5DVDTyY9YdpOO1wyCZmXp5kQOze7c/WrqqSoERFhWRyZ2XJ52Vl/pJOgSWeUlyrXbsY+v77pFVXs6FLF7a3bUuNy0Xu7t2037KFHmvX0n77dtpti7SeapIy693GmMN54Z76dzjggANkn2D//WHIEOjePbKxxovHA88+Kz0famqi18Td4ZB9hhEj5HWsVBOZ3nSBB56qq6v3Hnaqrq7ee+DJlGoK98BT2Z5Dxy3hkJO+KlXKMRHKhG42HYojjpB0xNNOg+nTk3cia04TORwyuTef+3zyNbtdTg/t2hXvkTar/VnNfziHWUzgWh7hF/YnesfnLbqygat5NCbBB5DgQLsKWNeq4WpFFuDyyWU8dnD4JIMC2HeTf8/X25fC4PXSB6JJNgJbkIbUThp/eJvyYNUgGRermnAbqc5ul3Txa66J90hUiqvbyDpai4XS0lL2S5bSBkqpqEqKZtPBnH8+3HZbvEeRGMxGp1mLQO2ghMl8sCzIyZG1iinl4nbL18rLZT5TVuZvTG3Wm1Eu/ZfonF4vx3z0Ebvz8qhMTye7pIT0ykocLexxqCXUNbtlhd/I3OGAQw+Fp5+Gww4Le2gJY9Uq2LHD/9qq+5hFcjjR4ZDMpNxcmDw5OuNUqg673U76nnLpdQ88VVZWUlFRsTdgYbKvGzvwVFJSQlpaWkz72CWK1P8JVYuRVM2mQ2W3w1//Cj/9BF9/HZsgRHNkH5jJvNcrP5MpwwT+GqvBxmG+nqJZEpOYwy5acQX/oJQc6t8FD+957MRLd9aRRVNTCIIb/TO8dAhk1IDXBd4g63CbBR6HxBbslgQhfLbgAQunD3KrYfIPTYsH7LUI6A30wp+REW0W0mviTYjhQ578OnaEKVPgkkviPRLVwtS3WKiurt5bczWw0VxDiwUtwaRUy5Q0zaaDmThRyp1s3hzvkUQm2lkFgZkP9d2XydbeskWCDiAntS1LDlOlpcnaxWREmNuKcl3wZJK3ezd5u3fHexiJIVbrVZcLDj4YZsxI3vJC27fDnDnwwgsSwAsM/BmRVEew2eS1mp0Nffv6yzApFUPBDjyZck11DzwF6xdVUlJCVlZW8u9dhkADEColBJ5KSqqU6FB07QoPPghnnSUT4Whrrk19cz8mEGHqqZo6q/UxmROBJ5NS0Dn8Bx92LucfVJBFw7vkje2i+0ijkv58R04M6wH12Q4jfoHiNFjRDmnuUM963NozVJslgQesPV8L8it3eeG8r2DIr1Ea6GbgJ6ArkE7sAhArga9icNvJzmQ49e8Pr78uf8+UiqO6iwVzcqnuYsH0lwhcLGgTaqVanqQv6wrQs6dsyCVjACKwZ0O01iyBWRCBt2m3y/35fP7LeDzyZtYjpaXyvdxcuXxGhgQiKiujMzaVGsw6N7CniAluRfI8ttul3NKLLyZv8GHdOinBumyZvF7q2wOo+/oLR8eOcNxxkiGSlhadMSsVBnPgKT09vdaBJ4/HU+vAkykHaw48taQDTkk4g1Kqtrop0SmR+VDX6NFw2WXQrZv/FFCysyx/zcfGFhWZmc03rjg5jxeYwkxcVCO7+cEej4Z//w587MdqLuHJaA9xH/k/QI9iyKsM8s9kT9sKCwk62EzbjyBzSrsFbSrhtO+iPNCPgFgdSvMBlUBxjG4/GZm/US6XvHXrJqniGnxQCcjhcJCenk5OTg65ublkZWXhcrn2ZkcUFxdTXFxMeXm5BiCUamFMgNKUdU3K4IOxfn28RxCZwBPS0V4D1V17mNr9aWmy9gjMlDAboyDjqKqSDVQTfEjhg1IqAibj3+WSgzhud2SBh7Q0SE+HsWNh8WI45JDoj7W53Hwz/PBD/X0fbDb/6y2S17llye22bQtnnx29MSsVIXPYKSsri7y8PHJzc0lPT8dms1FdXU1paSnFxcWUlpaye/duMjIyUm8Psx6aAaGSWkqcSgrV1Knw7rvS+Gz79ujcZqhNsmLBTDQCT4PUdwoiI0PSoO328Jt0JZkbeIRVHMB3DKCCTKx9tvUbzoyw42V/VnElj3EI38dyqAAcvhF67IQPuzYytD0HzKyG+mhbkOaF49ZCq6ooD3QTsAP5j5dO9ELv5unqofkCEC6XTNoTWXo69Oolr+sjjoB77wWtm6+SgN1ux+12495TciPw5NK6dev49ttv+fnnn+M8SqVUrKVcWddt25K315rd7n/z+WK3FjC3b7P5m0z7fP6sCHO/TqfMw8wGqsl8iOeaSiUWp9Pf5zAz01+CuKJCSneFGojIzobjj4err4aTTkruQ4heLyxaBMXF9b9OzD6AyTQKl2kef+ih0gdCqQRjqrSY7AjTl87j8fDqq6+yYcMGLMtK7rlGCDQAoZJWyjSbDtWhh8Jdd8EFF0QvABHvibJl+U871DfZMAsNp9PfRyKFDeIrbuV+budeVrMfVaThw44PO2Dbs99tHid7wMc+0qihO+v4L2dyFF8025gzvNC6UsorlbrBF2xzv5GXpw3oUgyjfpE+EFFVDnwIjAHSiG4viCok+LA6SrfXmOxsSf9PpCCEy+VfaB15JPTrJ+nhQ4fCiSfGe3RKRczhcJCRkcHmzZuZOnUqY8aM4fLLL4/3sJRSMVQ3s9putyf/GmPzZjnMk2zqnoqO5TrABB9MIKGmxl8C1uPxH5oC/9rEjK1uWR1Twz7e6ywVHy6XZP2ef770OfjyS/j8c3nOmObloWyyT5oEV10FxxwT+zHH2htvwO7djb8mIn2Nm9fh6tWRXV+pZmQOPLlcLv7v//6PuXPn8vTTTyf/XCMEGoBQSceyrL1Rw5Q4lRSO44+XtMK7707+SW1g0KGhSZjXK2mreXmwY0fK94OYzGyO5hOu4v9YQR8204lK0qjBhQ87HhzYAB8WNixc1JBLMUP4gGv4R7MGHwAqXNB7O2zcz9/vIaj6Nv73/OpdXui9E07/NvpjBGAx0oh6IBKEiMafDAvwIsGHz6Nwew2x2eQ0XklJ8zc5rK8RnMMh42nXDlq1gj594J57JPigVArZunUro0ePpn///vz3v//d2ztCKZV6kr7ZdDBvvBHvEUTO622eeb8JGoBsIIPMt8zX09IkiFNV5Q881N0sDVyPtm0r2RElJbEfu0ocdrscFjrqKLj8clm/fv89vPIKfPKJfP/DDyUToCFdukDr1sldcgnktTtzJtx4Y+z7pHi9MGBAbO9DqSixLIsnnniC22+/nblz53L88cfHe0jNQgMQKqkEpkSnXLPpUE2cCM8/D2vWJH9GQCingxwOmfA7nVLapWpPfZ4UDkJ0ZjPPcgH/4zfMYTwrOZDd5FJBJulUUEYWGZTjwkNrdjGA77mORzicL5t9rL12wo9t2NPogUZLMdXHYUGHMrj4c8iI1d56BfAOUoLpYPb2p2iy74EnkUBErGRlyduOHc0ffABZiDudEgg0wYguXeR0V2Eh5OQ0/5iUagY7duxgzJgx7L///rz00ksafFAqhaV0WddFi+I9gvAFPv71HYSINnP7JuBh7tNkN5iSS2ZcZj5msjPMW+D/iRReq6h6OJ0ScOjdG665Rg7ogGyK33EH/OMf8OOPsHUrfBHkwJjpHTFkCFx8scz/k9mf/gTPPAO//hrb+7HboUMHyRpRKsFZlsUzzzzDDTfcwOzZs1tM8AHAZlmx/m+uVHTUTYl2OBzxHlL8TJ8Of/gDrF0r9SSTPRARjDlh5HBIympVlb8sU2ONq1NIDU5W0Jd3OI73OJ5K0smknINZxiRm05/vo1ZRKFyrWsPYM2FrFpS5wGvfk9QQ6oAsaF0Bh22G52dC91j2UsgEbgM6IVkQTeEDqoFngfebeFsNsdtlIbN+vZReam7m1F+PHv5F94EHSgBi4EC44ormH5NSzWDXrl2MGTOG9u3bU1RURFpaU/9oKKUSldfrxePxpGZZV59PNkL1JH5twYIadR8xuO4AAJ+tSURBVH/3gaViTS+Kmhp/uab6siDMdez2+BwcaamaI1DV0H3n5UnPs5NOko33+oKYK1dKT5bLLpNghMkKMOvdVq2k58Mtt0ggIllt2QJPPw3//Kf87Skri+1+Rbt28OSTMGVK7O5DqSiwLIv//Oc/XHbZZRQVFTFmzJh4D6lZJfFfNdWSpGxKdKSmTYOTT4adO+GMM+Cdd8K7fjwnaOEyG6DmNJL5mjlxBKH9LEncHM6FhwF8zwC+53KeiPdwaulQBgM3w6ddodqxpypRqLFBC9weyK2G07+LcfABoBLYAHSIwm1ZwHYiy6IIp5G0zSaBt4qKCO6ojk6d5NRVqGXMbDZ/I73DDoMNG6SkQMeOclunndb0MSmVgIqLizn55JPJy8ujsLBQgw9KpaiUazZtzJ8Pjz4K334rh5WSTXNmPAT7uun5ELjWsCx/WaaGetMFrlFU84nlcyYzU5obr19f//1kZkJGBgweLKWXgu1VHHigvD32GDzyiGQGmBLDRxwBN90kc+5ktn49PPggvP++9H2I9WHJjAw480wYNy5296FUlLz66qv89re/5dVXX21xwQfQAIRKAoHNpt1ud2osDKIhM1Pe0tPDv24yBB/M79nhkPGa7IemBBFcLlk4JMPPnySyq6HfdljRDrZkNdCEui5Lmk23rYDJK+Ci5mhd4QM2AWVIKaam/CmxgFJgW5jXy8mR1+z27aEHATZvjmCAAXr1guXL5cTV+PHwww/+LCKfT4IMZmFg3puFt8MhC66HH5bAw4oVUoqpd29dXKuUVFpaysSJE0lLS2PmzJmkR/I/VimV8AIzq1OqrOvTT8P118up4yQ9eJMw83QzTzLzIVOOyeEIvqFqLhvrptkq+uo7rGZ+nz16wIUXyny4uNi/LrXbpVfDscfCDTfA0KGhzY+HDIH775d+ED4fHHkkHHxwbH6u5uTzweOPw2efwapV/tJlsWC3y5rqlFMk40QPi6gEV1RUxAUXXMBLL73E+PHj4z2cuNAAhEpoJiU6pU4lRVvXrg1PhJNR3QyHmhr/hmlj1wmmoZNKKmI2IH8ZzOoDmdXgSd/TDiGEhtQZNdB/K9y9NDrtGELyI3AsjferCMb0ujDBh5/CvP6558J334WetWQWPuGy26Vu7E03wW23yde6dIEvv5S6s99/L4GEWbMkLXrrVti4Ud6qq+X6LpcsqsaOhW7d5Da0uZtKYRUVFUyZMgWv18ucOXPISvbay0qpegVmVqdcWdcHHpAN0kTZxE82gSWXTNABZA0RrOxSILPeaEGlYsMSzyx887utOw7ze01Lk0M7P/zg/72bTe5zz4Vrr4VffoEZM/yZyXl50mD5zDPl43AccIC8pQrLkuDDvHmSeWWCD+FULAiV6Q05ciT87W9yKFOpBDZ79mzOPvtsnnvuOaa04FJhGoBQCUmbTYehfXtJPYxHffhYMRMUc9IolAmLCVjUvaxJnw617EykUi0IFIb9d8GgjVDjADuwIx2sYC/XPb8ehwV5VXDNx9CqspkGCrAayYJogww2El6k/FIhoTefttmkPun118Nf/yplEXbtku+ZBVF9dYJ9PgkUlJWFdj9mAXXAATB5Mtx8876XOfxweQMZwxdfSHZD797+RtebN8vtnHhi/behVIqprKxk6tSplJaW8tZbb5GjzdWVSkkp3Wy6qkr+f8dqgzcWG4mJJjDjwbJkDlZT41+PNLQeDVyHpPJj1BTxeFxMSVGnU+a2Zr7doYP0bPB4ZP7cqRP07SuXnztXDuV07iz9GKZOlev8/e9w9tmSudC6tZRFzs5u/p8pEf3wA3z9tWR7ezzyeFdXy/dMkMd8rSnPA5tNAg5DhkiPCdPsW6kENW/ePE4//XSefPJJpk2bFu/hxJUGIFTC0WbTYcrLk8lxIoj2qZZQ+zaYyzVUz9XpDL3ufrhcLslEWb06Nref4FpVQrcS2LkDtmTScGaBDew+yK2CfttgfLgZBE1VAuwEagAHkQUhSoCPgC0hXt5mkwXN7bfLQufcc2HZMklPNpPwnBw5sVhd7U/pNtfNyZEmdeXlDd+P3S6XPfRQOOccKChoPHviwgulFu0338gCatgwCUSA1jBWLUZ1dTWnnHIK27ZtY8GCBeSFe4pRKZUUUrrZNMh8NJZlSLKyZD6Sqo2VTflJ8zHUbjgN/kyIYGsOE7gIdjBKhcccJGvqIbKMDAkmjBsHd90lz+XSUpk31zdX/sMf6r8dpxOOOUbeVG1r1sjzPTNT3ldU1O7f2Nh6PVRut2R1H3usrL+VSmCLFy/mlFNO4Z///Cenn3566s07wqQBCJVQtNl0BGw2ObWcCKI5yXY4/KckzAS+vts3p8cbO5UVy+wEu102fdeujXyCnMSnymzAGd/CH1tBukeyGzz1/W+1wGaBywd9tkvwIS7/gpcCByF9IMJhAVVIFsXbIV4nLU3KHB15pPRSAMk+eO01mDkTPvhAFkSnnSYN5b//3n9ds+gaOhROPVVOWQVb9DudcNBBcOWVcMkloQcOnE5JXx45ct/vtfAJkmoZampqOO2001i7di2LFy+mdevW8R6SUirKUrbZdF12u/R6eu652N1+Kmf7mpPyXq//5zQZEeGWkgksJ5uEc/u4qtv4uymvVadTAnNZWdCnj2Qim0MGenI+eixLMkbmz5dMLJerdrChbmCvKczvsgWXsVHJ4e2332bKlCk8/PDDnHPOOak57wiTBiBUwghsNq3BhxBVV8Pzz8d7FLFTVSXvg03c7Xb/YqjuoigwaBGNkzON2b279qmnUAWeDDHXCzXzI4H03yqNpEvT4Pt2sD4Hqp3UjjDYpPF060roUA4Tf4zTYFcBnwNDgRwkEyIUpvfD60AjyQh7PfoojBmz79dbtYLzzpM344knZDJdXOxf6PboAU8+KaeJzjgDXnyx/udXZqYEIC68UAMHSoXI4/Fw9tln8+OPP7J48WLatm0b7yEppaIsZZtNB3PbbfDSS9HP+nW7a9fQTzVmrRDYvyGwJGwogYT6+s1pEKJx9b0eAx+vSNZE5rnqcMhpeZcLjjjC39NMRY/HI02g33oLSkpk/W7+/liWf73u8/nX9pFyOuUgV79+qdG0W6WsDz74gEmTJvHnP/+Ziy++OLXnHWHQHV6VELxeLzU1Ndjtdg0+hGPZMkl3jCdzStukLUerZFZDJ6wcDnkzf8hNyrlpnFt3oh/rSb/PJ6fb7fba4wqFWeCYxU1zBEti5OgN0GM3DNoMmV7JhDBNmx0+ecuugoO2wvUfQq+dcRqoBfwP+AqoBHzs7U0RlA8p3fQkEsAIRVqaNHAO1dFHw8KFcMopUtf02mslQ8I0Vnv0Uen5Up+cHJmUp1IvGKViyOv1csEFF/DVV1+xcOFCOnToEO8hKaWirG5Z15TNfAj09tsyJ4i21q1lfpqqpXHNRqnJfqjbhy7StYSZ26vgAgM+kQZrHA7/WtS8paVBbq4c+jngAMkQVtFVUiJBz1dekVLEZWUSkPD5ar+vqWl69pTdLtkrw4fDNdfIukepBPTJJ58wfvx47rrrLi6//PLUn3eEQV+1Kq602XQT1dTEf7PaBAPMeKKpvg35wMwGt9u/GDKlaQInrc114sjrlRqUnTrBtm1SHzfcSZbT6f9ZzOmrJJNTDed8DS8NhIO3wFedJOjgs0m0O6sa2lXA1BVwQpzjZviA55AMiN5ANpKtYVE7a8Pac9ldwELge0LncEgGQzgOPhiefbb+7+XlSTZFUZHUVTULtcxMee/1+tPKlVJBeb1eLrnkEj788EOWLl1Kp06d4j0kpVSUpXSz6YZ88UX0S7NmZ8Mnn0iZR3P4KRX7QISbxRzqberaNnShZpsEMutF05/DHIxLS5ODaYceKj0bOneO3bhbmjVr4KGH5O9CaakEHkyzdlOZIBplyMzvMyND1toFBXDrrdH9WZSKoi+++IJx48Zx8803c+211+reZh0agFBxU/dUkt1u1xdouHr2hPR0KcUULx5P7BYhgadhAuuvmomlaXQF/u8FTj6bKzhjt8tE7L774LHHJAjx88+NPy4mJdXtlolVZaU/iOPz1R94SXCD18PAzbCyDTx6NHzeBbx2cHohpwrGrYIrP4lT74e6PMB04FTgQCADfzkm81D7kHJLC4E5Yd5+sGyFpjjrLFixQp5f5eUSuHI45Dk0bpwucpVqhM/n44orrmDx4sUsXbqUrtrAUKmUU7esa4tZX5SVSTnHaHI64f335UDF5MmwZUv8s69jJd6HupSIZL1jNr3tdllTgQQfcnKkh1p95VBVZF55Ba6+WkrGmrWuyUAJzCAymrJ+NX+73W7o3Rsuuyzy21Iqxr799ltOOukkrrnmGm688caWM/cIgwYgVFxos+koyclpei3FZBDYG8EEJEzGgJn4ZGbK9ysq5PPmPJllWXL6Iz8fJkyATz+VWvxr1zZ8PZPeXVnp/z0GvhbMZM78LEnSGyLDA4dsgcfegDl94JOuUo5p6Fo4aRXYEymOsg6YD9QgQYgsJF3DPH1+BX5Cgg/hjttul+Cg2x2lwQInnggjRkjwoaZGnjs5OdJE+vTTo3c/SqUgn8/H7373O9544w2WLFlCz5494z0kpVQUWZa1N/MhpZtNB3PUUdFvEn3HHTBwoHy8YYP/ZHmSZuuqFJOWVvsQWlqavDkcsN9+MGiQzJFTtXRYcysvh5tvliyrwCbT9fU+iQZTSqtjRzjnHOjePTq3q1SULVu2jFGjRnHppZdyxx13tKy5Rxg0AKGaXYtNiY6F6dPjm/1gmDRLszke7QWJ+QPu8/k36E12hKkvWVnpz4yI9uKrMV4vrFol2SgZGdChQ+i/l8DasibAYpqmmWZ4DocEXMzPmiRcPpj6g7wltG+BH4CjkCBED6Q00wbgF+A9wg8+gJxE/OYbOPLIaI1UnmN//CMceyzMmydfGzxYAl/p6dG7H6VSjM/n46abbmL69OksWbKEAw44IN5DUkpFUYsv61pRAT9EecLVuzdccIF8vG4d/PortG0rpVA2bJB5jgYhGhdJWSEVmqoqWSNlZNTOIm/XTt4GDYruQaCW7sMPJQsqMPgQKybDOy0NbroJTj01tvenVIR+/PFHRo0axTnnnMM999zTsuYeYdIAhGpWXq8Xj8fT8lKiY2XVqsSYzMZ6U7xuQ2mbTTbjvV5JrzWTIBN8iMckf8sWeP11mDJFSmOVlIR2vcDmdOZEmfnZApuyhfMYm9szp31SsU5vtNUAHwAfA2lANeACKppwm1VVsclQcjph0iR5U0o1yrIs7rzzTl588UUWL15Mnz594j0kpVQU1S3r6miJp51nzozu3Nflkuay3brJ52ZzNyNDDtlUVibGGiRZ6GMVO5YlJ/MzM/0Z8pYlmQ9nnRXv0aUWm83f6yGW92HW8hkZcNJJcO65sbs/pZpg1apVjBo1ilNPPZUHHnhAD1c3QgMQqlkEnkpqkSnRsdIST3Ca5lbgL0lkNuej3QQ7HNXVMGeOBCByc0NP9Q3MgCgv3/f7kTSlNhkU6ekyEbfZ5JSax1P7/tS+vEjPB/CXYYqUKZGklIoby7K49957eeKJJ1i8eDEHH3xwvIeklIoik1ndosu6+nzRb8yallb7xPF++0n2w7JlUn5FD7eoeAjMdjeb1OYQmt0u2TlZWfDoo1KSTEVX9+6x7zfncEjj+1atoFMn+P3vtcedSkirV69m1KhRjB8/nkceeaRlzj/CpI+QijlzKsmkRGvwIYrKyuI9gvgyJYrM5DOevF5pDmyE8xxvaOymBJMpcxXq7Xq9EhSpqpIFY+vWcorE4ajdyFvFTl6e9ANRSsWFZVk88MAD/P3vf+ett97ikEMOifeQlFJRZJpNA7jd7pa7+H/vPSmJFC12O7Rp42/ma742Zgxs3izNZ1XzM2uAlrSONusfkPcOhz8b3qyPQA5cde0KQ4bAeedp8CFWKiqgffvY3LbNJplXaWmydm3bFoYOhcMPj839KdUE69atY/To0YwcOZJ//OMfLXf+ESbNgFAxFdhsusWmRMfSG2/EewQq0I4d8r6qKrrNzsyEzPxjq6xsvCST6clht8P27TJZ79AB1q/ft1eH1qWNjczM+rNalFIxZ1kWf/3rX3nggQd46623OFwXsEqlFFPWVTOrgR9/jE7/M7PZ63BIRm9dFRWwa5fOG1Xs2WxS/9/tlgN3geuqwGzutDR53mZnSwBiwgQYOzY+Y24JDjgA+vaVtWW0y8ya32vbttCvH3TpIo2nlUowGzduZPTo0QwZMoQnnnhC9zjDoAEIFTPabLoZvPdevEeQmOK1MFq9Gj77DI44InrZBSaQ4HJJSaW2beWUW0VFwyWnTJaDzyf9KFwuf4Ck7uNjThLpYjJ67HZ53A87LN4jUarFsSyLxx57jLvvvps333yTo48+Ot5DUkpFSYtvNl2fMWOi048tPV02eocOhQcf3Pf7TqfMP1V8BG68OxzhNQI2wSWXS36PVVXxLV0bjAk85OTImE0DacuSzG6TwW2y4D0eKbl05JHwwAPQv398x5/qvF7o2DF2/R/T0uR33qMHnHEGDBwYm/tRKkJbtmxh9OjRHHbYYTzzzDMafAiT7girmPB6vXtTojX4ECPV1bBzZ7xHkZjitRCtqpLJ7+bNsP/+MsmPBpMKf9xx8nl2tgQiAlPj6zILEqdTFilVVZIybxqH1Ve2Sl+n0WNq0Y4ZE++RKNWiWJbFk08+yS233MKsWbM49thj4z0kpVSUBJZ1dTqdmvlgvPpq0w+RDB8OTz0FH30Eb74pG4F1HX20zEH1wEr8mOd7qP3hHA7pTZeWJusSn0+CSCZLOpFkZMg4TSNp895u969n8vLkvcsll3e55PPu3TX4EGu7dsFjj0l2d5s20V9v2+3yO73tNvjnPyUQqlQC2bZtG6NHj6ZPnz68+OKLOJ16nj9c+oipqNJm083E54Pjj4/3KBKT3R6/E/1eL7zzDsyaBcccIxv+P/4Y+ThsNplwp6fLBLtrVzkVkp0NmzbBokXBr2tZcirI6ZTJfHW1/+v1jScR+mikCrMo2m8/OZWllGoWlmXx/PPPc/311/P6669z4oknxntISqkoCSzrqoeb6njssaZdPy0NHn8c+vRp+HJ5eVJf/69/jX75lVSQmSlrtJqa6JTEqk+oc3WHw79h36qVrAM2bNg36yFRMqDtdpkzmzKzJrujpESeaybYUF4uP4PJgsjIkD53b78Nzz8P554b758kNWzeDP/9L3zzjfR8yMyUry1aJO8rKqL/3HE44MAD9XeoEtLOnTs56aST6NGjB6+88gquaB00bWE0AKGixpxK8vl8mhIda6++KhMCtS/L8p/oidXkvyFbtsCVV0qGwv77Q6dOUifTTPjDmaiZoIDDAQMGSDr8Y4/B999LuSeXSybqDV2/okICEaHed2Cj61il16a6jAw5GTRqVMtqFKhUnL388stceeWVTJ8+nREjRsR7OEqpKDHNpm02mwYf6tOUflNuN/z7340HH4x774V162QtkoglfOLBlA068EBpgnzSSZCfH7/x5OTIuuGAA2Tuf/DB8MEH0geurkQpw2q3S6DBnCiuqvIHITIzZYw1NRJICcz+sCw5md+uHSxdCuPGSc87FTmPB373O3j/fem/YVny/DaH66qr5TLRfM44nXIfJ56oayeVcHbv3s24ceNo3749r732Gm5TGk6FTQMQKiq02XQzW7xYJ/3BWFb804pramDbNigthcMPl0laZaV/EhcOj0fedu6U98cdJxNsr9cfLGjsNkN9rpjHzCxGIDEWJcmkdWvo1Uuap11zTbxHo1SL8dprr3HxxRfzyiuvMFYbUCqVMrTZdAjatZNTyeGw22WT+oknZGMxVE4nnHIK/PADfPmlHlZxOGTDv107OPRQuO46mQvGi9MpB6D+9Cc5hNStG/TsCc89JweXzKGkujIy5HvNvb4MXGfU1PhP1pu1XFqajC0jQw50paXJ+soEISor/RnjbjesXasBiKZauFD2GkyWU3W1POZffSWPd7SfI6avR7t2cMst0b1tpZqopKSE8ePHk52dzYwZM0hPT4/3kJKaBiBUk2mz6Tg48MB4jyA0drtMzKN9SqIxJggRT16vTNj69ZNTUP/6F6xcGdltlZXJqaWVK2HwYHlbt67xHhOmHFWop5vMY2Yua8o3VVUFX7AkusBmdc2R2dGrF9xxh5zgadUqdvejlNpr5syZnHfeebz44otMnDgx3sNRSkWBNpsOQyilIHJy4JBDpITrYYfJx/36RXZgZ+1afxPklsqU2+zSRbJHuneHm2+WDf8pU+I7tn79YNiw2l9zOOr/Xdts8r3jjpNT71ddBatWNcsw987LzbgCy8Waz6urZdO7pEQ2vs260jz3fD65jOmL17lz84w9lb3/vgSCzGE3Y8sWf4ZKtNb1NptkuPTqBa+8Iv1KlEoQZWVlTJo0CYfDwcyZM8loqP+mCokGIFST1E2J1oVBMygpkVNOyfBYW5b/lER9G+CxPF0f71P75mf/4ANZkJxyChx7LGzcWHviDLKIaew0SefO8PrrMGgQnHUWfPGFZEWYxtLBmAWH6QlhHpe6z5/6fjfp6XJ9m01OviQb07QuPV2CKCYwFasFs0nHnDw5NrevlNrH3LlzOfPMM3nmmWfIj2fJC6VU1ASWdXU6ndjtdl1jNKRNm+Dfc7mkefQ118DJJ0enN5XXG/mhmlSQnQ1HHilBh0MOkRr5o0ZJ8AHg66/9h1/iob4a+hMmwDPPyKGmQGaufPrpUr7o++/hoIOk1GusmQNSdrs/oyHwdW4ev5oa+Z4pv1SX1ys/1+DB0i+vpVqzBj7/3F8uqV27yG5n924J+gT2BzRrqGj3N+naVUoXn3++Zq6ohFJRUcGUKVOoqqpi3rx5ZGdnx3tIKUEDECoilmXtzXzQlOgYC9wwffddmDpVJgbJIHCz20wu6/t+qrIs+PZbOVU0ZowEDp57TjbzKyr8J3mysyWQEGxC17q1nA7ZtEkyHxYuhOXLpeZpQ5NAy5Lvm0WQOWFkfhcOh4whWDDCnEQKvI1EZcpRBWY6mJ/P1JCNZWaMwyET/nj0HVGqhZo/fz6/+c1vePzxxzn11FPjPRylVBRos+kI3H67rBHqHkhp21aa8/bvH937y8lp2aVgc3PhoougoEAOudTVpo1kLtedc2ZmNq1fRyjcbjmsVNd118Evv8Bbb/kPFblckuncuTOcdpp8LS1NSr0edFDDfeaixRyQCiz9apg5vQk+1LeWNNfJy5NN7JZq1iy49Vb/IcXu3eGRR+CEE8I7tLh2rfSZ9PlqZ7/bbPJ8iebavW1bmD1bMrKUSiBVVVVMmzaN3bt3M3/+fHI1MydqdEanwmZSoj0eDw6HQ4MPsbJ1qywoxo+XSURuLowYkTzBh0AOR/AJS0t47mzdKtkLO3fC9dfDwIGyGBw/XrIiunWDHj32PZVms0lwokMHeWvbFu65B55+Wk4mlZQ0vKFuAj+mnJI55WRu124PnnpvJvsmWyORgw/gDzCYWrGBwRYzfrs9Ns83u13u0+WS0gZKqZhbunQpBQUFPProo5x11lk6D1EqBZjMatCyrmEZPhz+/ncp/Wi3y3zkyCPlEEy0gw8gc9KWuCFj5tEdOsCZZ9YffAC4+GKZ07tc/kzk7GyZu4fa7DtSPXpAx477fr1zZ3jhBXkbNEhKR7VvL+WannnGX8IIpGfEG280bz+9wJP2wb5X31rEBFF69GgZa8r6VFTADTdI0MvjkUNtX30lGS35+fDxx/tex+eTUkuPPSZr1E8/hQULJIjxww/Bf/eBB72aoksXKbmkwQeVYKqrqzn11FPZuHEj8+bNo5WWVI4qm2Wl+hFkFU11U6K12XQMlJXBP/8pEwJTYifZtfRGxibLoUMHadiXmemfvK1eDQ8/LKeM1q2TkydmcldRIYuDXr2gUyc51fTss41nPoB/seN2++u7moDE7t3yddMU22Q61Cew0XUy/w4De0FE++fIyZEslYMPhkcfhd69o3v7Sqla3n33XcaPH8+DDz7IpZdeqsEHpVKANpuOkg0bZGO8obJMTbV5s2xib9wYu/toCqdT5mVbtzbtdjIyaveyM8Gd88+Hv/0t+PWqquT0+csvw44dctDo8cdls/Whh+DGG5s2rmBcLpgxQw44NcSyYMUKycbo16928CHQnDnQ3H2VzJrDPNYm08YcqApc/5i5fatW8nO31ENACxZINk5Vlb9clZGbC337SpDB9McoKYFLLoF33pG1oNcra1SXS95++sn/nDdv5vGHpq2jHA5Z1z75pPTLUyqB1NTUcMYZZ7BixQoWL15Mu0jLmKmgNAChQmZKLmlKdAx5PHDBBZKOuHt3cm/4hivVgxR2u6QHf/vtvvVJN22SSWBxMQwYILV67XZZ2H35pQQLjjpKsiVWrmw4YBDI6ZTN8exs/4TU45HU68DmbQ0FMwI3AEyJo2Rknl/m54nWc83lkqyWLl3gpJNif7JNqRbuww8/ZNy4cdxzzz1cddVVukmpVJILbDatwYck8cUXkgHwww/xHkn9srOl/v2aNZHN9+x22ax98km4+mrYts3/vXbtYMkSf7+Hhpha+oEZCTU1clBlzZrwx9UQh0P6fDz8cHRvd9as+PQ2M9klpodbYGAisHeE2w2XXQZ/+UvzjzFR3H8/3HWXv+xsoLQ0ycb529/g7LPlaw88AA8+KJevrPQfzjKH1eqWXwpFqOv4vn3hzTdh//3Du32lYszj8XDOOefw9ddfs3jxYjrWl0mmmkx7QKiQBDabdrvdujCIlenT5Z9ySUlqb8bXJ5SfN9qbx83J55PMhfx8STkNnHh16gT11S/v3Nl/WsWyZAEUzs/u88nJFtNwzpxsMR9XVDR+e8n4WNfHZHKY+rHRei4ddRT8+c9NH59SqlGfffYZ48eP5/bbb9fgg1IpIDCz2uFw4HA49HWdDN5/P/ob6NFi5nvp6bI5XV0d3lzPBB/uuENK2Dz3HPzf/0nG8oEHws03hxZ8ADl4lJdX+2sul5S76dEjuj0WTAAi2iZNkg3um2+O/m0HY8pWmUbIgZvbliWPYe/eEgzq1g2mTGm+scWbZUmPhl9+kfXjEUfAli2SXV9fmWZThjbw9Tp7tgQfKir8zb/NZZ3O8PrZmdebzdb49VwuOWinG7sqwXi9Xi666CI+++wzli5dqsGHGNIAhGqUpkQ3o9dek8mWNrLdlymfk8zPP8uCZcvgiiuktmq4WrWSIEY49wf+ky3V1TKxdDgiW/QkejAiWLkou11+5sBm1NHyu99F77aUUkF9/fXXjB07lt///vdcf/31OhdRKskFNpvWsq5J5rvvwj8h3ZyqqyVgYMZo1hCNcblkQ3voUDjnHPna0KGSgVxdLafJo6F9e3j+efjNb6JzezablFFq3z46t1fXTTdJWZ6nn47N7dfl9fqbdQdmPoC8z8yU4FKfPvL5QQc1z7jizbLgX/+Cr7/2f23BAnluH3OMZObUXd+ZBt8nnOD/2oYNcjitvteEec2EmtFgMiYC12D1cTjk9XX00fL7UypB+Hw+LrvsMt59912WLFlCly5d4j2klKY1dFRQ5lSSNptuRqWloZfXaSkCGyk31Mw6WVRVwXvvwVNPhXc9m00WKunpEkQIhWXJCRcT0DILsOrqyHohmOZ7od5/c7Os4D+Xzycp+aYJdzS4XPFJS1eqhfn+++8ZPXo0V1xxBbfccovORZRKcnWbTWvwIQm5XIl5KMjMfU3mg8slm9U5ObKOCKZTJ9nEnThR+jUE9kWw2aIXfDBOPVXGFQ1OJxxySPBeDtHwr3/V3sRuLoHlU51OWQdlZvpP7+fnSyZES/Ddd7WDDwC//iqPR/fu0luh7nPcsmR/4ZxzJJB21VVS+jdaGfCBmSkNBR+cTslauf760G5XqWbg8/m4+uqrmT9/PgsXLqRHjx7xHlLKS9BdJBVv2mw6Trp08Z/wUCLwNIUJRCRrHwKQyXJ1Ndx3n5RiGjYMRo2SFFpTHimYP/xBTrY8+6ycXKmqavyxMI9dNLJqAieXidizI9iYfD6Z/FZX1041bqrDDmv8d6aUapIffviBUaNGceGFF3LXXXdp8EGpJGcyq202Gy6XS1/TyWjSJCgslNPSiXhwKvBEdkaGvN9/f1i7FrZvr31Zh0OCC48/Lifp+/RpvsDKfffBDTc07TbS0qBnT3j11eiMKRiXC956Szaxv/wytvdVV+DaIz0d+veXbI/7749d1kciWrZMMlF27ZLfe/fu0my9SxfpLzh7dv3P3ZoaWLdOmsd/9lnDgbhw1bemMgfWTE+J7Gw4/HBZv+qekkoQPp+P3//+98ycOZOlS5eyv/YlaRa606n2YVKifT6fnkpqbmPHygkdVZuZ3NTXXCvZ+HwSOFizBhYtgjvvhDFj5He/aVPD13U4pGnY2rXw0kuyAd6c2QiWJRv4kW7im2yWwM9BvhaNxV5jYzLBgmjcV1qa1ARWSsXMypUrGTVqFGeccQb33XefblQqlcQCM6vtdrsGH5LZuHGSKZCdnZhZEOA/OW/Gl5YGt98uG9hmgzQrS9ZdvXpJRmvfvs3781xwQeTXtduhXz/JTPj0U3/PuFhKT4d582TTu7mZbJbsbOmJd+mlLSv44PPJunHjRsn+2LVLMiJ27ZIAVEWFPKeDrZN9PjnEZrKDGtrfaerej8MBubnSm+Ozz2Scb74pWUZKJQDLsrj11lt5+eWXWbRoEQceeGC8h9RiaABC1eLz+ajec5LF7XZj19P4zWvyZDj00HiPIrGYiVSinbZvqsCU1ZISWTxceWVo183IgKlT4S9/Sa6TJIEZFGlp/nJIgenVsWKzycQ7ktJTdZkF2ODB0RmbUmofv/zyCyNHjmTq1Kk89NBDOh9RKomZ4IPX69WyrqnAbpfTzP/5D3ToEO/RBGeyjh0OOOAAuPpqOfjTvbtsZLtcUr7nwQfjM742bWTzuL7/b06nbOK2bi3zfjNnttnk5+nfX34H558vl2suHTpIz8Lm5nDI72q//aSUT8+ezT+GeFq2TNYvOTmSebR7N+zYAcuXy9rmm2/kgFsoaxzTmDqYpmTNOxzSeL1fP8nw79evZQWKVMKzLIu77rqLZ555hoULF9K3b994D6lF0RJMai9tNp0A0tPlJEvfvsl/0l+Fp7wc3n9ffu+hbrQNHy6T8fXrYzu2SDVUpsk0OTPpuT6fLK5i1dTQ65WFZlNLUXXrBr/9rZTOUkrFxNq1axk5ciRjx47l73//uwYflEpi2mw6hY0bJ30T5s/3N79NtAND1dVw8MFw220yv77hBskg/uAD2dgfNw4GDYrf+B54QOryl5bKXNjthmnT4PjjoVUrGDEC/vpXCTbs3i3XGTwYXngBOnaMz5hHjoQbb2zewI0pv9Snj3weSgZ4cbH8nnftko3wgQMTN2OnMdu3S4AhLU1+LsuS5/b69dIkvKJC1pKhvv5i9Ti4XBKkOugged0plUAsy+JPf/oT//znP1m0aBH9+/eP95BaHJtlJdosQTU3y7Lwer17TyU5HA4NPsTbeefB88/HexSqublc8NFHUiczVCecIE2tk+lPuTnBZbf7Mzi8Xn92Qqx+lvR0fwPuSGRmyqLrzjvhyCOjOzalFAAbNmxg2LBhDB06lKeeeko3K5VKYj6fD8+egwVOp1ODianmzTfhnnvgq69ql0lNlDmpyyUZBE8+KX0rEtUnn/j7u02ZIuWt6vYY27JF6v8fdBC0bRuXYe7jyCPh88+b577cbgkiDB4sWRC33NLw5bdskQBJSYn/a8cdB2efHdNhNpnPJ4GmnBwJOLz4opTd/flnyXhwuWTtVFPjDyKYz8vLQ78fk1Fj1l/RkpcHQ4fKeunqq5u3VLBSDbAsi7/85S/8+c9/Zv78+Rypa/m40L8ILVzdZtN2u12DD4ng6adh1SrZWFYty6OPykIp1ObGI0dK+SZz8iyebDYZt2VJJkOwBagJPtjtMvF1u2XyXFkZ20VrUxsldu4Mp56qwQelYmTz5s2MGjWKo446SoMPSiU5n89HTU2NNptOVdXV8PLLUpPe/G4TJfAAMsds10426+OVKRCqo4+Wt4Z06JB45a4++USyNAI3+YOJ9Dlis8k6ISdHSvkceiiccUbj15szB779FlaulPVFmzayQT9ypDRtTkRLlsDDD0tPQNM8/euva5eQraqSx8MEDtLS/D36wuFwSA8Uk00Rjdeu3S4Bv1tukWblGnBWCcKyLB599FHuu+8+5s2bp8GHONIARAsWmBLtcrn0VFIicThg7lwpsfPVV1qOqaWoqZGg08qVcsonFFddJQGLeJdhsttl8momyA0tNAK/5vVK2nBzbDQ25XXUqpWUXjrrrKgNRynlt3XrVkaPHk3//v157rnnNPigVJKyLGtv5oOWdU1hJSXw4Yf+skCJFHwAmfOVlcnhkVDn1Co8dju8/XZ4mdvhysqS5sVjx8L998vnoVi0CL74wl/atbxcMgh++CExAxCffir9PHbvltdSRUXtwILP5y9tW1Ulj73PJx+DHAAzX2uMzSZrzp07/eu3aHC54OKLJdNEqQRhWRaPP/44d9xxB2+88QbHHHNMvIfUoumOcwtlTiUBGnxIVLm5UFQE554r6YyqZVi3zr+YC0WbNtKILt6bdWbCaybI5q2uwK+ZSbXJmIhGoM1uDz17JJzb7NoVfve76N6uUgqAHTt2MGbMGHr16sVLL72EK9qvYaVUszCZ1R6PR5tNp7rqaulbALXLL0Hi1Nn3eOS0+D33SDBCRd+gQVI2qjGRnrIfNAgeewz+7/9CDz6AlCyq21euvBy++y78McRaeTlcc41kPlRUSMaD1+t/XdVX2sx8bN6HU2LWrNEiyZwIxumU9ehNN0Xn9pSKAsuyeOaZZ7jxxhuZNWsWx2lwLO5017kF8nq9tVKiNfiQwHr2hGeegR9/hCuukBMgiTKpV9FnJoSrVoV3vTFj/Km68WQms2bCbzIhAmuUmqbTHo9MeqN9Ys40tI72bQ4dGv8gj1IpaNeuXZx00kl07dqV//3vf7jd7ngPSSkVgbplXTX4kOK2b4deveTjuvOuRMmGqKmRrOKiIrj9dti2Ld4jSk0zZkhPulCF+nfB5YJzzoHRo8MfU3b2vveTkZEYJWvrevxxCZh4vRJIqKpqPDBgWbIuCcw+D4UpgWt6QESDyyWlrZYs0bWSShiWZfHiiy9yzTXXUFRUxLBhw+I9JIUGIFqUwFNJmhKdZDp0kJMf55wDPXroP/dk5HD4G4cFywxwOqXR8f77h3fb06bJdXJy4ltvM3ACbIIP6enyc7nd8rHLJR/Hapxeb/RO8xg2mzT9U0pFVXFxMePGjaN169ZMnz6dtLS0eA9JKRUBk1ltyrpqCbUWoGdPOOQQOOCA6G5mRlNNjQRKtmyRzdHXXov3iFJXYWHoGcihbJbb7RJEmDYtvHF4vfDqq9KbxGQFpKdLKdUOHeCoo8K7vVjbuBFWrPA3hA4neBfJgSufTw6B1dREL1A4eTK88YZUb1AqQfzvf//j8ssv59VXX2V0JEFMFRMagGghTPDB6/VqSnSystngT3+SNOIePcJLQ1Xx53RKM7zMTGkYlp4uJ3EcDtmQz8yUt0MOgXBrE/bsCa+8IvVR+/SBvn0To+Ge0ylvrVtD796ykHC7E3ehGowJpCiloqa0tJSJEyeSkZHBjBkzSNfXmFJJScu6tlA5OXDKKXDEETJ3TUuTTePAzNdEmOtZlpx637oV3nor3qNJXe3awfjx0bktu13KD99xhwQOwvH00/DHP0p/g6oqKRO2Y4d8b9y4xOtPsHUrrF0rYw03IBBJQ2+jqdnidru85nNzZY2nf/dVAiksLOTCCy/kpZde4uSTT473cFQAbULdAgQ2m3Y6nXoqKZk5HHD22TKp+ugjqWOZiLUs1b5qauTEidPpz4YYOFAmbxs2yMRzyBAJMkUyiTv4YPjf//yf//GPcOed8UuDN+WWTIO0TZvkZI8p0+RyhZcyHE9OpzRbVEpFRUVFBZMnT8bn8zFnzhyyNKCuVNLRZtOKkSNl/vnRR1Kn/6uvpNeCZcmhk+Li6J60jpRlyVjC6bGmwvff/8KwYfDJJ5Hfhs0m5ZyefBIOPDC861ZUSPbDhg3+/iQg64/KSsmE+flnOaz19tsyTqdTyqwOHhz5mCM1d668ffZZ860zzNqsKa9Jh0MOzZn1rJa2UQlk1qxZnHPOOTz//PNMnjw53sNRdWgAIsWZhQHoqaSUYbNJP4gjj4QvvoAbbwy9nqXdLpOFPSfVVDNyuyVrpVcvmQgPHChN244/Xn4nXm90mye3b+/f7I8HUwopI0N+dpdLPk5Lg9WrJS063gvSUDidsogeMCDeI1EqJVRWVjJlyhTKysp46623yMnJifeQlFJhsiwLr9e7N7Pa4XBo8KGl6twZpk6VPnWPPCIbqWvWSGPdmhrJIC0tjX5/rnCYsjNdusRvDC1BRgZ88AHk58OsWZHdhmVJSaJwMx9A1sNr1sh7s9b1+fzroU8/lQNaI0ZI0MxYsUKet6NGRTbmSPz4ozxG69fL57EoHwv+krjR2gMyWeEOh6zpRo2Sx1OpBPDmm29yxhln8NRTTzEt3PJtqlloACKFeb1ePB7P3mbTujBIIea0xtCh8N57UnczlIlLz55yYuGHH6I/0VENM0GH11+v//vRDg7ut5//lEs8WJY/Fb+sTBYlJuDQpo3UPE0GbdvC4YfLa00p1SRVVVWccsopbN++nQULFpCXlxfvISmlwlS32bRmVisADj1UghE5OdLseds2OWzSvr3MB3ftiu/4PJ7EK7+TihwOWeu0bh3577yiQjIDzj03vOu1bi2HnkzwIbAv3datEpjYtg3mz5eDRT17+g9/vfWWZPQ0x36JZUnWw+rVUskgFtkPpkm1yyXP/bp9+iJhs8nr++KL5bE89liYMEH2JZSKs0WLFnHqqafyz3/+k9NOOy3ew1FB6F+LFBR4KklToluARx+VScXMmTLBCKZDB5g9G5Ytg+uvh3Xrmm2ICpkMN+cm9kknyWm0DRviE2wyf3MCa5pWV8spuDZt5PRMeXniZ0Ecdxzce6/UolVKRaympobTTjuNdevWsWjRIlq3bh3vISmlwhRY1lUzq1UtmZlw2WVw7bVy8KRdO3+/s8pKCULEMwsCpPyOah4vvQS/+U3tUkihstki39QeNgx++WXf0l8+n4ylulpuf9kyKWl8+OGyUV9cLOulWG6mWxa8+aYEQObNg19/lXGFUqYscC8n1LWTaWrtcMjPZQIRkcrIkLJYf/lL5LehVAy8/fbbTJ06lUceeYRzzjlH9z4TmM4aU4w2m26BOnaEf/wDpk2TRlB1J042m2z4fvEF9O8vG9PHHhvdcj8qNO+807z39+qrEniKx8kUn08mvqb3RXm5TPzLyqQfRFWVTIgT/e/TtGnQr1+8R6FUUvN4PJx11lmsXLmS+fPn07Zt23gPSSkVJp/PR3V1NQBut1uDD2pf++0nb0ceKWuNIUMkCOF2yxolnnM+pxO+/FI2nVXsnXyyZEJ06hTe9Ww2WdsOHx7Z/Z5xhpTaqm/t4/XK+sNkQqxaJcGADz6Qtcmvv0Z2n6F6+215TFat8vfIq6wMLaBgSimFc3DLZqu9HmtKKSa7XV7Ld94Z2fWVipH333+fSZMmcf/993PRRRfp3meC05ljCjGnkkxKtAYfWpCOHeHll2VScNxxcpqjSxdZBJx9tpRc6tpVLpubC88+K5dRzWv5cv/HZWWSnRDL02DHHAMLF0pKcSJs+AVmQni9EphI5AwIu136diilIub1ejn//PP55ptvWLBgAR06dIj3kJRSYfJ6vdTU1GC327WsqwrObHBmZsoGcOvWcPTRspk8dqz01IoXj0c2nd3u+I2hpRkxAl57Lbzfe14eXH01fPONlCgK19ChsuZ1Ous/6GTWXZYlh6N27JA1WVqaHDoaPx5uvz36lQI2boTp06W81C+/yP3abOEFFSJZM5rXpM0ma69wb8Nul8eyRw944AHQpr4qgXz88cdMmDCBu+++m8suu0znJknAZlmJvPujQhXYbNrpdOqppJZs+3Y5wdGli9RdDWb3bjnZnSy1+FPBkCHw/vtw660SMKqslPqjf/mLNKOOlYUL4b77JAOjKQGPwIZmTbkNM9lO9H8/ublSm7V793iPRKmk5PV6ufjii3n33XdZunQpXU0gXCmVFLTZtArbU09Js99Ahx4qp7+XLpUgQDxKMTmdcMghkhGumo9lQUEBzJix7/cCgwRut6xdW7eWptAgh4DOOEPWMDU18J//yHOodWsJFpx44r63uWED3HSTXG7LluBlh9xu+XrbthIg2bDBX57J5ZIN9wULmr4G8PngmWfkNfHZZ7JG93olEFFVFfsyuXZ75L0fnE7JpO/SBSZNkvWr9vxRCeLzzz9nzJgx3Hjjjdx44406N0kSGoBIAT6fj5qaGm02rcL32mtw1lky4VKx99hjMhm+/37/ZNhmk8nvihWxPRm2daukxK9dG7v7CFci1AOujzntc9RR0uRdKRU2n8/H5Zdfzvz581myZAk9e/aM95CUUmHQZtMqIpWVcsjms8/k8yOPhJUrZQ5cUSFrjnhsPzgc0tT46aeb/75buupqCf6sXu3vSZCbC6+8AkccAR9/LGWJnnlGMh/MSX0TDHj4YdnAf/NN2bQHKS/8t7/BxIm17+uzz+Duu+X99u3+Pgj1PefsdhlLYGaAWQM4HHDaaTKmpnj3XQmcgBxqWrbMX37J9H5ItO04u10e9169pMrCuefCqadKZpNSCeCbb75h5MiRXH311dx+++26/5lEtAl1EtNm06rJRo+Wkx2rVsV7JKnP4ZBTX489JhPOwADEtm3wxBPSHDxW2reXZnCPPuqfvIcjMH03WhIp+GDKLZm/oR06yIJHKRU2n8/HtddeyxtvvMHSpUs1+KBUktFm0ypi6elw/vlSAhYk0/q226TcDYRfxz5a7HZo1ar571dJtsFXX0lg4IMP5ET9734HgwfL9wcPlgyJNWskYyFwreH1SkaDw+HPUHA6pYTRH//oD0BYlpQ4mjtX7qOsTL5u+iCYj43AbOzA9YhpCu31yphDtWuXBLeKiqTKwKGHyvP+q6/kPrZskV54Jviwp3JFrbHEi8lOt9mk0XSrVhI4vOUWOOwwLVumEsqyZcsYPXo0l156qQYfkpAGIJJU4KkkTYlWEcvLgylTZFO6pibeo0ltPp+c1Nm5s/Yk07LksTfpxrHUqZOcaIkkAOHzRd64LJGlpcGBB0pwpl07KRHQowdceCH07Rvv0SmVdHw+HzfddBOFhYUsWbKEXr16xXtISqkw1M2s1uCDiohpAjx7tmQ+gP80ejx4PNrXK54yMiQDvD5r1shzo7Ky/oNOpaX+bAXLknWMyyV9GsrL5WT+Z59JydmtW/3BirrPtcBSsnb7vsEOwwQlQtlbsSzJ5HjwQclwMBkeP/wAr74qawuXS557xcX+QBzIa8RkQcRKY9nmDodk4p90knw8YYJkPhx6aGqu+1RSW7FiBaNGjeLcc8/lnnvu0f3PJKQBiCSkKdEqamw2OO88+Ne/NAARa5Ylp3WCfa+hfh3R8uuvkZXbMv/czWQ80VKFI2HSuvfbT+rLXnaZ1JS97LJ4j0yppGVZFnfccQcvvvgiS5YsoU+fPvEeklIqDF6vF4/Ho5nVKnrMBm+8n0uWJRutKvF07Ajffy8BiGB8PvkdmudRTY2sacz61fT22L5dAhbBbsPprN0XItiaxmaTjfjGLF0KN94I69f7byswqLF1q3w9I8OfWWEuF/gzxWpt1dDt2mwSvGnVSh6ziROlX4dSCWjVqlWMGjWKU089lfvvv18PRyQp/a0lGXMqyaREa/BBNdkBB0gZJv0jHj8OR/PU1fzpp8gWgOY0T2Ajs2TmcEjgYdIkKbN0660SfFBKRcyyLO655x7+/e9/s2DBAvr16xfvISmlQmQON2nwQUXd+PGy+Wrmks6A8482m39+GWt2uwYgEpXdLuWAA8sS1cdkJpjSSR07wtdfy/fS0+V93fJG9d1GYFmmYOPp0gUaO0RRVQXnnFM7+FCX6S9RVrZvVkakzaHD0VCAJStLgg92u/TUGD8+duNQqglWr17NqFGjmDhxIo888ogGH5KY/uaSiAk+AJoSraKnvFzSLNPT4386qaXyeqFz59jfz9atkTccD2zQluxM9kOnTjBsWLxHo1TSsyyLBx54gEcffZT58+dzyCGHxHtISqkQmeCD1+vF6XRq8EFFV69ecNFFss4wwQfT5Ndmk83P7OzagYlYcDjgxBNjex8qMq+95u/ZEAoTgNi8GR55BEpK4Pjj/eWGTJCiPqGUAXO7oX9/OO64hi/397/Dpk3JdTjLbpe3jAz5+fr1gxNOkHLMHTrEe3RK7WPdunWMGjWKkSNH8o9//EP3QJOclmBKApZl4fP59FSSio22bWXy8fPPsGyZpId6PMk1mUoF69bF9vaLi+WkTiS/18bqhyYTE3yw2eC3v9V6wEo1kWVZ/PWvf+WBBx7grbfeYtCgQfEeklIqRNpsWjWLu++GbdukOfDWrTKvLC72Zz5kZ0Pv3vDll7Gbb+bmQk5ObG5bNc1338n7cNYoliXPoaVL4brrpPG5yaRo6nMoO1tKsh56KDzzjGQ49OwJJ58sPR2MWbMSf31Udw1nt8troVs3CQ6OHg1jx/ozSJRKIBs3bmT06NEce+yxPPHEEzpHSQEagEhwlmXh9Xrxer3abFrFzvnnw9q1coKkpESyInbv1iBEc1q7Nra3v369LPoikeiT61DZ7RJ8aNUKzj5bTjcppSJmWRb//Oc/ufvuu3nzzTc5+uij4z0kpVSIzOEm0MxqFWM2G/zhD3D99fD55zIfa9XKf1q9d2/4y1/gnnvgf/9rvBRPJDIy5JR9q1bRv23VNLt3R3b4zeuVNev8+RLcqq72Z9c05TlkmkM/9JC/afSvv8K338Jdd/kPL5WUJP5aue4azuGAgw+WtyuvlEOISiWgLVu2MHr0aAYNGsQzzzyjpedThAYgEpg2m1bNpmdP+Pe/oagI3ntPJiv/+lfiT6pSyeLFsb39BQtgy5bY3kei8/kgLU1O/Pzud/EejVJJzbIsnnzySW699VbmzJnDscceG+8hKaVCZJpN22w2XC6XHm5SsdexI1xxhaw3HA5583plo/fss6Xe/p//LJvIRUW1G/lGg9stB3G6do3u7aqm8XikAXUkAQPLkufJzp0S1Cot9fcZaUoAoqQEbrlFDi3l5Mg6uXVrybj46CMYORJWroSNGxN3rVy3sbUpd9a2rfwcHTtC377xG59SDdi2bRujR4+mb9++vPDCCzhjXaJPNRs96pKgTEq0z+fTZtOqeTidcOqpUgPywQdl0qWaz+rVscs0sCx44onoL+bixW6XOqWjRkkzabc79P4lBxwAM2Y0T9NvpVKUZVk899xzXH/99bz++uuccMIJ8R6SUioEdZtNa/BBNaujjpJAg1nXOhxyKMTU2u/eXdYirVtHvy9du3ayUa0Sy6xZ8nsxQalwmF4QlZVyyKqkRLJcqqqaNqbKStixQwIaxcUSICktle/t2gUrVkg/kUgzy2PJbpc1vdsta3nzeWamlJayLMjLg2uuaZ7m70qFaefOnZx00kn07NmTl19+GZfuSaUUDSUlINNs2maz4Xa7dWGgml9mpkzU16+P90haluxsePNNePZZeez795e6pj16NO12163z19xN9nJKXbvKpPm662ShsmMHPPUUvPMOfPWVfO7xyIk6w0ywnU4pN9a+fVyGrlQqsCyL//73v1x11VVMnz6d4cOHx3tISqkQBGZWa1lXFRdOJ/z+93KKfO1a6NIFjj1WslONtm1lDVJTI6V5osFmk9s76KDo3J6KjrVr4cUX5ffjdvszGsJhTvnX1ETvoJXXK8GMigoJaNjt0p9k4EApWXTTTYmZVe5yyeORnS1vpaWy7nM4JOMB5H1BgbzOlEowu3fvZuzYsXTo0IFXX30Vt9sd7yGpKLNZVqLmjbVMJiVam02ruPvnP6U2pGp+Jk02PV3Sfv/3P2jTRjbWu3dv+FSYqacbmKr4669y6mzLluQOQOTmSmmwM86o//svvQQLF8LmzRKQqKiQSbfNJu9zciRI0blzsw5bqVTy6quvcv755/O///2PCRMmxHs4SqkQBDab1rKuKqGVlMDkybBmTfSyg9PS5MT6W281/bZUdHz8sRy4+uQTyTJYuzayPhDgP2BVt+xQUzid/s17s47o2xemTJGDT+vXB78vm03eAktBxXr95XLJOik31x/M2bpVPrcsOVxos8GwYXDvvRqAUAmnpKSEsWPHkpGRwezZs8nIyIj3kFQMaAZEgtBm0yrh/Pa3cMMNsomrmpc5AVRWBj/8ACecIBNLp1Oahj333L41bL1euO8+ePppSWXu3FmCSKNGQbdu8vn27ckbgHA4JBgzfnzwy5x5pqTx//yzXO6RR+Sx8PkkmHPllRp8UKoJZsyYwfnnn89//vMfDT4olSS02bRKKjk5Un//scfk859/bvptpqeD/s9KHD6f9PmwLCmpunatfC3S4EF9a5umBiM8Hn/mjNstB8EsCzZskMbUDd22KQ1lsrFjfd7Xbpc+Kh06yCGsVatg2zY47DD5WlaWXGbMGJg2LfrlzZRqotLSUiZOnIjL5WLmzJkafEhhGoBIANpsWiUkhwPGjoXXX0/eTetU4PP5a9babHIy7PTT5YR/oL/+Ff70J2neZ1lyuZNPhqVLJb39nHPg7ruldmmysdkgI0PKJ+XlNXzZnj3lbfhwOe02fbo8JhMnwpAhzTNepVLQnDlzOOuss3jmmWeYOnVqvIejlAqBNptWSWn0aDjkEDlwc889TT8M5XLJnFglhrIyWY9UVMhGuccj606zcR/phn1TNvrrC1iYz202WY+ZgEKoQdzmKjTicskY77sPvvtODpz17SuHrmw2eXy1ia9KUBUVFUydOpWamhrefPNNsrOz4z0kFUP6lyjOAlOi9VSSSjh/+AN8+ils2uRPIVXxYwIL330Hv/wC++/v/97jj/uDD0ZNDVxyiVx+1Cj4y1+SLwBhs8npnbPPDr8k2IAB8qaUapK33nqL0047jSeeeIJTTz013sNRSjUiMLNay7qqpJSTA6+8UrunVyScThg8WObNBxwQnbGppsnMlP50P/0kGQU1NdE/lW9Z4WVBNHQ5j0fWXwccIOMuK5OASbR6TgRyOKBVK3lMiosbv7zZO9q0ST4eOHDfy2jwQSWoqqoqCgoK2L17N/Pnzyc3NzfeQ1IxprvdceTz+aiurgY0JVolqEMPlZr7XbrEeyQqUFnZvgGhHTvqnzz/8IP0Rvj6a38DsmThdEqt0sJCuP9+OeGjlGpWS5YsYdq0aTz66KOceeaZuompVIIzmdWmrKsGH1RSWrpUNlWbyumUHhA5OU2/LRUdH3wg77dulY12n2/fQ1TREK3b8/lk3ZWXB998I18z/fqM9PTgG/3hVLfIzIRrr4URI+TjYNc192+3+8vUKpVEqqurOfXUU9m0aRPz5s2jVatW8R6SagYaDo0TbTatksaECfDee9JPoLQ03qNRIJPNus3Dunf3l2oK5PXCRRdB69bJE0iy2WTSPWCANAzUwINScfHuu+8yefJk/vKXv3D++efrXEWpBKdlXVXK8Pn8TYAjzcI2jYCzsuDww6M7PhW5zz6DFSsk6OD1+gMFzVWyKBIul2SRm8wKEwAw8yK3O/hzNdRSxnl5Eny47TbYuFH62f34ozxGpkxV69aSGWECNy6XBB+0NKZKIjU1NZxxxhn88ssvLF68mDZt2sR7SKqZaACimWmzaZWULrsM/u//4j0KZRx/vGzQB7rrLsjPr//yNTVSD7SyMuZDa5K0NGjfXppmjxwJt96qwQel4uTDDz9k4sSJ3HfffVxyySU6V1EqwWmzaZVSDjsMOnWShr9VVZHfTs+eUsZT55OJ48MP4ddfZX2SyEEHw+mE3FwJOOTlSd8KE4Qw9ttPmmkHk5UlPS9MWSibzZ/dkJkp/f3uvlvWQSCHxj76CF57TQI23btLxsjWrfDxx7Kuy8mBfv1g0CA44YSY/fhKRZPH4+Hcc89l+fLlLF68mHbt2sV7SKoZaQCiGdU9lWS323VBr5LDTz/JAkDFn90O550nqb6BJk6Edu1kUlyXOV0Uz99hQ3VYO3eGmTPh6KObdUhKqfp9+umnjB8/njvuuIMrr7xS5ypKJTifz0dNTY02m1apo3t3uOkmuPde2diNpBG1zSb9H44/PvrjU5H7+ms5vR/r4IMJwoaagRDsNpxOCRZ07Cib/uvW+YMndrsEJw4/HMaMgb//3X9/DoccrkpLk+998YU8j71eeeveXXr1nX22ZFDUlZYGZ50lbyD3t2wZrFkjAYjqaujRQ57f9V1fqQTj9Xq58MIL+fzzz1m6dCkdk608tGoyDUA0E202rZLa9dfHewTK6N5dTt+Y0zOG0wm//a2UyjIpwoF8PpkkZ2c3fymtM8+E5ctl4l2X2w3//rcGH5RKEF999RXjxo3j97//Pdddd51uZCqVwCzL2pv5oGVdVco580w48UR47jl48kkpS+PxhL5xbUo41T20o+Jn927pZZeWVrv8UjSZQ09NCTwYJgAxYAC0aQN9+kjGwjvvyO3n5EjwoU8fCZh98QV8/rn/uZeVBVOmSMCib19YvVoyetq3h4cekgBZOD9X//7yplSS8fl8/Pa3v+W9995jyZIldO7cOd5DUnFgs6xkyHtLboEp0SbzQamkkpGR+OV7WorMTDlFc955MHly7e95vXDddfD00zK5r0/nzrKAay5dusD69dLM/JZboKREJuUmddkETZRScffdd98xfPhwrrjiCv7whz/oRqZSCUzLuqoW5bLLpCTNqlUyxw1lc9lmg2HDYPHimA9PhaimBo44An7+WTbiI+3vEWuBfR5GjvQ3eR4zRnozPP00fPKJBFL69IHzz5f+fKWl8Le/wdtvS7+GCy+U5+ALL0hwwuuVUrOXXeYvt6RUivP5fFx11VXMnTuXJUuWsP/++8d7SCpONAMixkyzaU2JVklNg2aJo6ICPvhAgkITJvjrh4J8fMghDf++ggUmYuXgg+X9xRdLI7XZs6V5WkYGXH21ZtcolSCWL1/OqFGjuOiiizT4oFSC02bTqsU58kgpJbp2rcxzQwlA2O2yQawSh8sla4J77pESQg2VaI0FE1ho7PljWZL5kJYmmeU9e0rmwYQJ8rUrrpDDYF6vlGAysrPh9tvlLdDFF8sazOORTHalWgifz8f111/PrFmzNPigNAARK4GnkjQlWiW9ESNgzpx4j0KBTIi3bJHfx5YtktEQaMWKhoMMlZWhL9yi4ddf4b334Ljj4K9/lQyNLVvggAOgVavmGYNSqkE//fQTo0aN4qyzzuK+++7T+YpSCcxkVmtZV9WinHIKrFwpp8YrKho/OW+zyWGXM85onvGp0F15pWQ//OlP/szo5lqX2GxSkquiovHAh+nzUF0Nd92175orKyu8+w738kolOZ/Pxy233MIrr7zC0qVLOfDAA+M9JBVnOmONAXMqyaREa/BBJb3CQuk9oBJHWVn92QO7dzd8verq5pvkgywQA1Pfu3eX1GsNPiiVEH7++WdGjRpFfn4+Dz74oG5mKpXATLNpALfbra9X1XLk5sqp+eOOg969G76szSabvaec4m/gqxLD++/DRRfBkiWSUeByRe+23W7JWmhIejrst1/omTEejxzuaux2lVK1WJbFXXfdxXPPPcfChQvp27dvvIekEoDOWqPMNJs2KdEafFApwe2GZcvg7rv3Pf2h4sPng/nzJS040OjRiVUyy+dr/rJPSqmQrFmzhlGjRjF27Fj+/ve/62amUgnM6/VSU1OD3W7Xsq6qZXK5pNb+jh3BL5OeDh07wrPPwjPPSDBCJYZ33oELLoCZMyUQ8emnkmUQjd9Rero0hHa7g1/G5YIOHeDBB0MPKNhsUmZp9uymj1GpFsKyLO677z7+9a9/MX/+fPpr43S1h640oyjwVJLL5dJ6rCq1ZGfDnXfKW1OZ+puqaYqL4aefan9t6lRIpPRGux0GDoz3KJRSdaxfv57Ro0czbNgwHnvsMQ0+KJWgTGa1x+PRzGqlhg5tuJRNx45wwgn+psEqcTz4IGzaJBv6Pp+893jqz8w2DaDz8iRrOidHggcOh38daf4OpqfLc6JbN8luyMvb9zCWua2BA6Wsktfb8FjN/TscEqwI7POglArKsiz+8pe/8MgjjzBv3jwOPfTQeA9JJRDNJYsSbTatWowuXZp2/ZwceOUVSYneuTM6Y2qpvF74/ns46ij/15xOePttmYBXVMRtaHutWgXabEqphLJp0yZGjx7N0UcfzZNPPqkHJpRKUNpsWql6HH64lMWpT2mpf8NaJY45c+CTT6QXnen5YAIPgb0YTGChXTsYNUrWEJs2SQnZzEzpI7dwodyOZUngIS8PjjkGLr8cBg2S7Jdnn5WG5VVVUm6pfXsJXp1zjmT12+37HoZzOCTQUFxcO8DRsSOMH988j5NSScyyLB599FH+9Kc/MW/ePI444oh4D0klGA1ANJE2m1YtzvjxMtFrrNdAMNddJ4GHdu2k8VhjTeSawumM7e0ngs2b9/1adbVMtNeta7zBWqxZFgwfLo2oAwMlSqm42Lp1K6NHj2bAgAE899xzuqGpVIIyZV212bRSdVx/Pbz8cv3fKy6GNm1A640njpUr4ZFHoLw8eMaD4XTKYbdzzpGeH4bJWnA64dtv4R//kHVOu3aS/T11qv+y11wDgwdLD7offpCvDRwIw4bBkUdKKaaMDCn/5PPJbdtsku3/8stw000yZptNDnQ98YRcXikVlGVZ/Otf/+LOO+9k7ty5DB48ON5DUgnIZlnx3p1KXoGnkhwOBw6HQ4MPqmX45huIJJ2uUyc5sb9zJ0yaJAGIdeuiPz6Qky2mDqjHk5qBCJcL/vznfZtRb9wov5+dOxPn5+7VS7IhlFJxs337dkaOHMn+++/P//73P9wN1UpWSsWNKetqs9lwOp0afFAqkDloU1y87/cyMmS+qT3rEkN1tWQefPedfNxQ8MHhgNatYcwYOPpoCSQ0xAQOwuXzwWmnSS89E4Rwu+HKK+G+++QyW7bIOqp378TqradUArIsi6effprf/e53zJ49m2HDhsV7SCpB6V/TCGmzadWiDRwIxx4b3nW6dJHSS23awAEHwCmnyKQvVqdvfT6ZMB55pKTnhtpsLJn4fJJyXFebNpKmnEgT5p9/llRopVRc7Nq1i7Fjx9KtWzdeeeUVDT4olaDqNpvW4INSdbjdcOGF+24+m3m/Bh8Sx9//DsuX+zf6G2JZcrjK4ZDfY2Mi3Xux2+HVV+Ghh+D442HcOHjuOfjjH/2X6dBBsmj0769SDbIsixdffJFrr72WGTNmaPBBNUj/okZAm00rBdx+u9TUDNW338KJJ/o/v+suCULEskZrZiZcdZUEIFIx2SstDbZurf/rJ50kk/hEmjgH/v6VUs2muLiYcePG0bp1a6ZPn05aOH+7lVLNQptNKxWGRx6Bk0+WOa/DIe8PPDB4aSbV/HbulF4MjWU+GKYxtdMZ/kG3SFx8McybB0VFUsJJ/94qFbZXXnmFyy+/nFdffZVRo0bFezgqwSXQzlRyqBt80FNJqsUaNw7OPDO0yx5yiJzKr+uII2RTOhYTPrtdGonl5spGfKpNKm02STPPzKz/+w88ICd3XK7mHVdDVq+GK66I9yiUalFKS0uZMGECGRkZzJgxg/T09HgPSSlVhwk+eL3evc2mNfigVCNmz4YFC+Dee+Gpp+Crr6Br13iPShkLFkgwwbJCOwjmdkumhGkCrZRKaIWFhVx00UW89NJLnHzyyfEejkoCunseItNsOjAlWhcGqsU77LCGJ4h2u2yCv/pq/d8fNQq2b4/NKX27HYYMkdtPS0ucXgjRYpqxBcsqaNUKFi2SOqqJlKX12GMwYID0qVBKxVR5eTmTJ0/GsixmzZpFVlZWvIeklKojsKyryazWNYZSIbDZpITOLbfAWWdpo+BEs3w57N4d2hrMZpO1m2XVn92tlEoos2bN4pxzzuH5559n8uTJ8R6OShIagAiBCT5oSrRSdRx0UPDgQceO8J//wEcfyeXqU1IC3bvHpgxTu3bw8MMykY1Vo+t4S0uDf/0r+PdbtYLTT0+sLAiQRuRDhkBVVbxHolTKqqysZMqUKZSXlzNnzhyys7PjPSSlVB2aWa2USknffCN9FdavD+3yNpv/wNSAATEbllKq6d544w3OOOMMnnrqKaZNmxbv4agkorPcRtRNidbgg1IBRoyAnj33zYLIy5M06NNPl03wYDZtklMx0e7PYLdLTc+OHeW2Kyuje/uJIiNDfs6SkuCXadNG0pkTzZo18OST8R6FUimpqqqKadOmsWPHDt544w3y8vLiPSSlVB0ms9pms2nwQSmVOjZtkv4KW7dKtnaoMjNlXXn55bEbm1KqSRYuXMipp57KY489xmmnnRbv4agkozPdBphTSYEp0UqpAA4HFBbCUUdBdrZMHPv1k5qfnTo1fv3u3eHjj6G0NLrjcrlqb8qn4mvX4ZCmbnY77NoV/HKtWiXuz68BCKWirqamhtNOO41ff/2Vt956i9atW8d7SEqpAIHNpu12ux5uUkqljrVr4eqr4YcfoLw89Ovl5Ejj6Ucegf79Yzc+pVTEli5dytSpU/nb3/7G2WefrXMXFTZnvAeQqEzwwWaz4Xa79cWlVDCHHQbvvQcrVkijsT59Qu/psHw5bN4c3umYUGRnw3ffwXHHwfjx8vnu3dHPtIgnh0MyTw44ADp3Dn65AQOgfXvpueDzRX8cbjeMHStlts4+W7522GGh/U5/+in641GqBfN4PJx55pmsXLmSxYsX07Zt23gPSSkVwAQffD4fDodD+z0opVKHzwc33STZ2aWloa27HA7IyoKXXpI1m1IqIb3//vtMnjyZBx54gAsvvFDnLioiGoCoh+n3oKeSlAqRyxVZvc5//ENO8UeTzQbdukl/BJASRL//Pdx1V2o1os7IgEMPhQsvlMBPMFlZcOml8OCD0c80ASgu9j/WRrt2ElhqjGXJ5Tp2jP64lGphvF4v5513Ht9++y1Lliyhffv28R6SUiqAaTZtWRZOp1Mzq5VSqeXqqyUzvrHSrzabrAEcDllDXHSRBh+USmAff/wxEyZM4O677+ayyy7T/VEVMS3BFCAwJVqbTSvVDJYv37d/RFPZbHDggZL9YNx2mwQhsrKie1/xYrfDaafByy9LtkFjbr8d7r03+qWY7PZ9gw8AN9wgmRGNadNG+oUopZrE6/Vy8cUX8/HHH7Nw4UI6hVICTynVbOo2m9bgg1IqpWzbJlkMDQUfnE5Zi3XoAF26wJFHwpIl8PDDzTdOpVRYPv/8c04++WRuueUWrrnmGt0fVU2iAYg9iouLKS0t1WbTSjWnzMzYBCBOPRV696799SuukAlvKsjIgMGDITc3tMvbbHDttZKlkpYmC4BoPO69etX/9fPOgx49Gr5uZiacdRakpzd9HEq1YD6fjyuuuIKlS5eycOFCunbtGu8hKaUCbN68meo92Z7abFoplZJmzJCs6GCcTukPOGSI9A587jkp4Xv00c02RKVUeL755hvGjh3L7373O2644QbdH1VNpjPgPZ5++mm6devGeeedx4wZMygrK4v3kJRKfSNGxOZ2f/11369165Y66b0eT2T9LP7+dwnCZGXJxr/dHnq/jvr06VP/19u2lUZyDU1SRo6EP/0p8vtWSuHz+bj22mt54403WLhwIT179oz3kJRSATweD/379+eYY47h/vvvZ/ny5Vip1I9KKaUAFi8O3mvO7ZZecWeeCZddBi++CKNHN1xCVikVV8uWLWP06NH89re/5bbbbtPgg4oKDUDscc011zBv3jy6devGTTfdRMeOHZk2bRovv/wyxQ1F85VSkTvqKCnDE02mr0B9fv97OXmf7Gpq4Pjjw7/eiSfCm29Ks+iTToLu3UMrlRTMPfcE/96f/iSZGvVJT4ehQ6NfEkqpFsTn83HjjTdSWFjIokWL6BUsI0kpFTdOp5MVK1Zw9dVX8/HHHzNo0CAOOeQQ7rjjDr755ht8wTbslFIqWXg88Pbbwb/fuTP85jfw5z9Dfj60atVsQ1NKhW/FihWMGjWKc889l7vvvluDDypqNACxh91uZ8iQITz00EP89NNPvP322/Tu3Zu7776bjh07MnnyZF544QV27dqlJ5eUipbSUmmkHOomeCiXs9mCl//p2RNuvDH08SWqzEwoL4/suv37w//9n6RKX355ZJkUINkPRxwR/Ptdu0rj77pBBrsd2reHiRMju1+lFJZlcfvtt/Of//yHhQsX0rtuyTmlVMJo27YtF1xwAXPmzGHz5s3cdNNNfPvttxxzzDH069ePm2++mU8//VSDEUqp5ORwyJquvk3KtDQ4+WS4/vrol91VSkXdqlWrGDVqFL/5zW+4//77tWykiip9NtXDbrdzxBFH8Oc//5nly5fzySefcOihh/LQQw/RsWNHxo8fz9NPP822bds0GKFUU/TvD1u3wp7ayI36058aLxmUkSE9IIL57W/B5Qp9jIkmLU1qqEaj1Ep1dcPN4oL5zW/g++8bv9wNN8Avv0g/jvR0aTh94IFw001w8MHh369SCsuyuPvuu3nyySdZsGAB/fr1i/eQlFIhsNlstGrVirPPPpuZM2eyefNm7r77blatWsXw4cM58MADuf766/nggw80GKGUSh42G3TsKIGIwCCDzQZjxsAjjwTPilZKJYzVq1czcuRIJk2axMMPP6zBBxV1Nkt30ENmWRY//PADhYWFFBUV8d1333HiiSeSn5/P1KlT6dixo6YnKRWONWukF8CqVY1fNi8Pdu2S4MJrrwW/XEEBTJ8e/PvffQcDB0Z+8j+eHA5o3Rpuvx2uuabpt3fCCfDuu+Fd57jjwr8OyO945UrJmmjXLvzrK6WwLIv777+fhx9+mIULF3LYYYfFe0hKqSgoKytj3rx5FBYWMnfuXPLy8pgyZQoFBQUcd9xxOLRkoVIqkT3xhGSZV1b611jdusGPP2qvB6WSwLp16xg2bBgjRozgiSee0OCDigkNQETIsixWrVrF9OnTmTFjBl988QVDhw4lPz+f/Px8unbtqsEIpRrz+ONwyy0SWGjMp5/CkUfKxw29tv77Xzj99ODfLymB3NywhpkQ2raVjJEHHoBjjonObfbsCWvXhn75nBxpHDd5cnTuXykVMsuyeOSRR/jjH//I/PnzOeqoo+I9JKVUDFRUVLBgwQKKioqYPXs2brebKVOmMG3aNE488UScupmnlEpETz0lgYjiYjj8cMl86Nw53qNSSjVi48aNDBs2jGOOOYZnnnlGDz2omNEARBRYlsWaNWsoKiqiqKiIjz76iMGDB5Ofn09BQQE9e/bUYIRSdf38M9x/P/znP1BR0fBlu3WDdev8nzud4PXWf9mPP4ajjw5+W199BYMGhT3cmLPbIVjJBacTHnpIykelp0fvPrt0gY0bQ7uszQZ33ikBo7S06I1BKdUoy7L4xz/+wR133MGbb77JkCFD4j0kpVQzqK6uZvHixRQWFjJz5kwAJk+eTEFBASNHjsQdag8tpZRSSqk6Nm/ezIgRIxg4cCAvvviiHnJQMaUBiCizLIsNGzbsDUa89957DBo0iPz8fKZNm8YBBxygwQilAObMkQyIN95ovBxScbGcvjfS0urvG2G3S0+DhlIG77wT7r03sjHHisslP5/XKz9r4ONhs8HQoZGVPWrMccfBhx8GD3wEGjkS5s9vvAeHUiqqLMvi3//+NzfccANz5szhhBNOiPeQlFJx4PF4eOedd/ZmX1dVVTFx4kTy8/MZM2YMGVpjXSmllFIh2rZtGyNGjKB379688soruJK5T6ZKChqAiCHLsti8eTMzZ86ksLCQt99+m/79++8NRhx00EEajFAt17vvwvnnN97/YcwYeOut2l/LyJAao3X17AmrVzd8e8ceK5vuiaJjR+nFMGyYZDrcdpu/JJXdLqWXFiyQ8kvR9t//wgUXQFVV8MvY7XDiifDkk3DAAdEfg1IqKMuyePbZZ7nmmmuYNWsWw4cPj/eQlFIJwOv18v7771NYWMiMGTPYvXs348ePJz8/n3HjxpGVlRXvISqllFIqQe3YsYNRo0bRrVs3pk+frhmVqlloAKKZWJbFjh07mDlzJkVFRSxcuJA+ffowZcoUTjnlFAYMGKCNXlTLsnEjHHSQnPgPxm6H8vLaJX88HujaFbZs2ffyF14o9Ucb0lD5puZ26KEwYwZ07+5v0LZ7N/zf/8EHH0CvXnDttXDggbG5f49HAh/vvx/8MkccAa++KmNRSjUby7L473//y6WXXkphYSEnnXRSvIeklEpAPp+Pjz/+mMLCQoqKitiyZQtjx46loKCACRMmkBOYQaqUUkqpFm337t2MGTOGtm3bMmPGDNK0vLJqJhqAiAPLsti9ezezZs2iqKiIt956ix49ejB16lSmTZvG4YcfrsEIlfoWLJDshoZcdRU8+mjtr1VWwoAB8MsvtUsHpafD119Dnz7Bb8/ng0RqqnTzzfDnP8d3DP/+N1xxhQQj6kpLg6IiOPnk5h+XUi3cq6++ygUXXMArr7zChAkT4j0cpVQS8Pl8fPHFF3vLNK1bt47Ro0dTUFDApEmTyMvL0+xrpZRSqoUqKSlh7NixZGZmMmvWLC3fqJqVBiASQHFxMXPnzqWoqIg333yTDh06kJ+fT35+Psccc4wGI1RqOuggWLEi+PdtNtkUr/v8f/99uOceaSa9e7cEFZxOGD4c5s5t+D7LyiA7u8lDj5o//AHuuiu+Yygpgfbt6y/DNGiQlGk66KDmH5dSLdiMGTM466yz+M9//sPUqVPjPRylVBLy+Xx89913e4MRP/74IyNHjqSgoIApU6bQpk0bDUYopZRSLURpaeneQ01z587Vco2q2enOdgLIzc3l9NNP57XXXmPz5s089NBDbNiwgXHjxrHffvtxzTXX8Pbbb+NNlLIxSkXD2rUNf//qq+tveLxzp/R6OOII2Rg/8EDp6zB+fOP3uXBhZGONBZut4WyN5pKTA7m59X/PZpPyUEqpZjNnzhzOOussnn32WQ0+KKUiZrfbGThwIPfccw/ffPMNX331Fcceeyz/+Mc/6Ny5M2PGjOHxxx9n06ZN6Hk0pZRSKnVVVFQwdepUampqmD17tgYfVFxoBkQCq6ioYMGCBRQWFjJnzhzS0tKYMmUKBQUFnHjiiThNzXilklFaGlRXB/++x1N/uaRVq+DBB/f9+plnSjPnhhxxBHzxRXjjjJWcHNi0CTIz4z0SKWn1889S3sqyJPDgcMB550nzaaVUs3jrrbcoKCjg8ccf58wzz9TTyUqpqLMsi1WrVjF9+nSKior48ssvOe6445g6dSr5+fl07dpV//YopZRSKaKqqoqpU6eyfft25s+fT15eXryHpFooDUAkierqahYvXsz06dN5/fXXsdlsTJo0iWnTpjFixAjtWq+Sj8NRu4dDoP33lw3xYKZPlx4SxsCBcOml/kbOwbRvD9u2hT/WaHM44IEH4Prr4z0Sceut8Pjj8vswvSByc+HjjzUDQqlmsnjxYiZPnsyjjz7KeeedpxuASqmYsyyLNWvWUFhYyIwZM/joo4845phjmDp1KgUFBfTs2VP/FimllFJJqrq6mmnTpvHrr7+ycOFC2rRpE+8hqRZMAxBJyOPx8Pbbb+9dLFRXVzNhwgQKCgoYPXq0NpJRia+yEhp6ni5ZAsOGNXwbmzdLkKJTJwlYhOLAAyWDIlJOp2QINKUcmtsNF14Ijz0W+W1E265d0vD7gw/kd9OmDdxyC5xxRrxHplSL8M477zBhwgQeeughLrnkEt3wU0o1O8uyWL9+PUVFRcyYMYP33nuPQYMGkZ+fz7Rp0zjggAP0b5NSSimVJGpqajjttNP46aefWLx4Me3atYv3kFQLpwGIJOf1ennvvfcoKiqiqKiI4uJixo8fT0FBAWPHjtXabioxvfginHNO8O/H6s/StdfCM89I4+VwORxw6KFQUwO//AKlpeHfRlqa9Kp44glItAmAxwPffCOPTf/+iTc+pVLUBx98wMknn8y9997LlVdeqRt8Sqm4syyLzZs3M2PGDIqKili6dCkDBgygoKCAgoICDjroIP1bpZRSSiUoj8fD2WefzTfffMOSJUvo0KFDvIeklAYgUonP5+Ojjz7aG4zYsmUL48aNIz8/nwkTJpCTkxPvISolnn4aLrqo/u/l5sLu3bG5319/hZtvhvffh9Wrw7tuejqcfjo8/DA89RTceGN417fboVcvmDFDei4opVq8Tz75hJNOOonbb7+d6667Tjf0lFIJx7Istm/fzuuvv05hYSGLFi2iT58+TJ06lWnTpjFgwADsdnu8h6mUUkop5JDy+eefzyeffMKSJUvo3LlzvIekFKABiJTl8/n44osvmD59OjNmzGDdunWMGTOG/Px8Jk2aRF5enm50qPgpKZFAQ32GDZMSTLFSWSmNla+7TkophfonMDMTrrxSejeAZAhs3x7adR0O2G8/uOceLWuklALgyy+/ZPTo0Vx//fXcfPPN+j9ZKZXwLMti9+7dzJo1i8LCQubPn0/Pnj339ow4/PDDNRihlFJKxYnP5+OSSy5h6dKlLFmyhO7az1ElEA1AtAA+n49vv/2W6dOnM3PmTH788UdGjRpFfn4+U6ZMoU2bNrrxoZpfTk79ZYxmzIApU2J738uWwVFHSTDCskILQrRpA5995u83ceed8Mc/Nn5dtxuOOQb+8AcYPhz0taZUi/ftt98yYsQIrrzySu688079H6yUSkrFxcXMnTuXwsJC5s2bR8eOHfcGIwYPHqzBCKWUUqqZ+Hw+rrzySt58802WLFnCfvvtF+8hKVWLBiBaGMuyWL58OYWFhRQVFfH9999z4oknUlBQwNSpU+nQoYNuhKjm8e9/S+PjmhrZxLfboW9fCQ7EmmXBkUfC8uVQXS2f+3zBL5+WJgGEW27xf62mBk47DV5/vXZTaodDGmy3bi2llm67DYYOjd3PopRKKsuXL2f48OFccMEF3Hffffo/VymVEsrKynjzzTcpKipi7ty55OXl7Q1GDB06FIfDEe8hKqWUUinJ5/Nx/fXXM336dJYuXcoBBxwQ7yEptQ8NQLRglmWxcuXKvcGIL7/8kuOOO478/Hzy8/Pp0qWLboyo2Hr9dXjwQdi5E0aMkI8zM5vnvn/6Cc46C1aulAbMGRkSVNixo/blnE64/364/vr6b2fjRvj0U+jeXca+aRNUVED79tCvX/P9PEqphPfjjz8yfPhwTj/9dB588EE9HayUSkkVFRXMnz+foqIiZs+eTXp6OlOmTGHatGmccMIJOJ3OeA9RKaWUSgk+n4+bb76Z//znPyxZsoS+ffvGe0hK1UsDEAqQYMSaNWv2BiM+/vhjjjnmGPLz8ykoKKBHjx4ajFCpx7JgzRpwuSRQ8Pzz8Ne/Qnm5ZD3k5sINN8D558d7pEqpJPfzzz8zbNgwpkyZwt/+9jcNPiilWoTq6moWLlxIUVERr7/+OjabjcmTJ1NQUMCIESNwu93xHqJSSimVlCzL4s477+Tf//43ixcvpn///vEeklJBaQBC7cOyLNavX09RURFFRUW8//77HH744eTn5zNt2jR69eqlwQiVuqqqJDNj1y4YN04yG5RSqgnWrFnD8OHDGTNmDI899pgGH5RSLVJNTQ3vvPPO3r501dXVTJw4kfz8fMaMGUN6enq8h6iUUkolBcuy+OMf/8ijjz7KokWLGDhwYLyHpFSDNAChGmRZFps3b2bGjBkUFRWxdOlSDjnkkL3BiL59+2owQimllApi/fr1DBs2jOOPP54nn3xS66ArpRTg9Xp57733KCwsZMaMGZSUlDB+/Hjy8/MZO3YsWVlZ8R6iUkoplZAsy+Khhx7igQceYP78+RxxxBHxHpJSjdIAhAqZZVls376d119/ncLCQhYtWkTfvn2ZMmUKp5xyCgMGDNBghFJKKbXHpk2bGD58OEcccQTPP/+8Bh+UUqoePp+Pjz76aG8wYuvWrYwdO5aCggLGjx9PTk5OvIeolFJKJQTLsvj73//OXXfdxVtvvcXgwYPjPSSlQqIBCBURy7LYtWsXs2fPprCwkPnz59OzZ0+mTp3KtGnTGDRokJaYUEop1WJt3bqVESNGcPDBB/PSSy9p01WllAqBz+fj888/3xuMWLduHWPGjKGgoICJEyfSqlWreA9RKaWUigvLsvjXv/7FLbfcwhtvvMHQoUPjPSSlQqYBCBUVxcXFzJ07l8LCQubNm0fHjh3Jz88nPz+fwYMHazBCKaVUi7F9+3ZGjhzJ/vvvz6uvvorL5Yr3kJRSKun4fD6+/fZbpk+fzowZM/jpp58YNWoUBQUFTJ48mTZt2mj2tVJKqRbBsiyeeuoprrvuOmbPns2wYcPiPSSlwqIBCBV1ZWVlvPnmmxQWFjJ37lxat27N1KlTyc/PZ+jQoVqCQimlVMrauXMno0ePpnPnzkyfPp20tLR4D0kppZKeZVksX76cwsJCCgsLWbZsGcOGDSM/P5+pU6fSoUMHDUYopZRKSZZl8cILL3DFFVfw+uuvM3LkyHgPSamwaQBCxVRFRQXz58+nsLCQOXPmkJ6eztSpUykoKOCEE07QkhRKKaVSxu7duxk7diy5ubm8/vrrpKenx3tISimVcizLYuXKlUyfPp2ioiK++uorjj/++L0Hnrp06aLBCKWUUinj5Zdf5uKLL+a1115j3Lhx8R6OUhHRAIRqNtXV1SxcuJDCwkJmzZqFzWZj8uTJTJs2jREjRmiJCqWUUkmrpKSEk08+GbfbzezZs8nMzIz3kJRSKuVZlsXq1av39oz4+OOPGTJkyN4DTz169NBghFJKqaQ1ffp0zj33XF5++WUmTZoU7+EoFTENQKi4qKmp4e233967WPB4PEyYMIGCggJGjx6tp0aVUkoljbKyMiZOnIjH4+GNN94gOzs73kNSSqkWx7Is1q9fT1FREUVFRbz33nscccQR5OfnM23aNHr16qXBCKWUUknj9ddf54wzzuCFF16goKAg3sNRqkk0AKHizuv18t577+0NRpSUlDB+/HgKCgoYO3asniJVSimVsCorK5k0aRIlJSW89dZb5ObmxntISinV4lmWxaZNm5g5cyZFRUUsXbqUQw45hIKCAgoKCujbt68GI5RSSiWsN954g1NPPZUnn3yS008/Pd7DUarJNAChEorP5+Ojjz6isLCQoqIitm3bxrhx48jPz2f8+PHk5OTEe4hKKaUUAFVVVRQUFLB582bmz59P69at4z0kpZRSdViWxfbt23n99deZPn06ixcvpm/fvkydOpVp06YxYMAADUYopZRKGAsXLmTKlCk89thjnH322fo/SqUEDUCohOXz+fj888+ZPn06M2bM4Ndff+Wkk04iPz+fiRMn0qpVq3gPUSmlVAtVXV3NqaeeyurVq1m0aBFt27aN95CUUko1wrIsdu3axaxZsygsLGTBggXst99+TJkyhWnTpjFo0CDsdnu8h6mUUqqFWrp0KRMnTuSvf/0rF154oQYfVMrQAIRKCj6fj2+++Ybp06czc+ZMVq5cyciRIykoKGDKlCm0bt1a/zArpZRqFh6PhzPOOIPly5ezePFi2rdvH+8hKaWUikBxcTFz5syhsLCQefPm0blz570NrI8++mgNRiillGo27733HuPHj+fPf/4zl112me5xqZSiAQiVdCzLYvny5RQWFlJYWMiyZcsYNmwYBQUFTJ06lfbt2+sfaqWUUjHh8Xg499xz+fLLL1m8eDGdOnWK95CUUkpFQWlpKW+++SZFRUXMnTuX1q1b7w1GHHvssTgcjngPUSmlVIr66KOPGDt2LHfffTdXX3217mmplKMBCJXULMti5cqVTJ8+naKiIr766iuOP/548vPzyc/Pp3PnzvqHWymlVFR4vV4uuugi3n//fZYsWULXrl3jPSSllFIxUFFRwfz58yksLGTOnDlkZGTsLdN0/PHH43Q64z1EpZRSKeKzzz5jzJgx3HLLLfz+97/XPSyVkjQAoVKGZVmsXr16bwPrTz75hCFDhpCfn09BQQHdu3fXP+RKKaUi4vP5uOyyy1iwYAFLly6lR48e8R6SUkqpZlBVVcWiRYsoLCxk1qxZ2Gw2pkyZQkFBASP+v707jZKiPvQ3/nUGkFXWgKAIgoICghvIzvTsw2zdVY3mYlyuRmPMoiYxnphjEmPUq/FGr564X+MSkqhd1bMyGzM9rKKggoKIsigEEEFg2GZkZur3f5GcPv97400IaahZns974XvkTE9VPV1Vqanq2rWr3xMBAO3U2rVrlZ6erttuu00//elPuWaFDosAgQ7JGKM///nPcl1Xrutq+fLluvzyyxUKhRQOhzVq1Cg+2AEAx8XzPN12220qKSlRLBbTqFGj/J4EAPBBc3OzFi9eHH8vXUtLi/Lz82VZljIyMtS9e3e/JwIA2on169crEAjolltu0b333ss1KnRoBAh0eMYYffbZZ4pGo3JdV4sXL9bEiRNlWZbC4bDGjBnDBz0A4Ct5nqc777xTr776qmKxmM4//3y/JwEA2oCWlhYtW7ZMjuMoGo3q8OHDys3NlW3bys7OVs+ePf2eCABooz788EMFAgFde+21evDBB5WUlOT3JOCkIkCgUzHGaO/evSouLpbjOKqrq9PYsWMVCoU0b948jR8/nhgBAJD0l98Zd999t1588UXFYjFdcMEFfk8CALRBra2tWrlyZfzu67179yonJ0e2bSs3N1e9e/f2eyIAoI3YtGmTUlJSdOWVV+qRRx4hPqBTIECg0zLG6MCBAyopKZHjOKqpqdHIkSPjj2m6+OKL+UUAAJ2UMUb33nuvnnzySdXV1WnChAl+TwIAtAOe52n16tXxOyN27NihzMxM2bat/Px89e3b1++JAACfbN26VYFAQHl5eXr88ce55oROgwAB/NXBgwdVVlYmx3FUWVmpoUOHKhQKybZtTZkyhV8MANBJGGP04IMP6je/+Y0WLVqkiy++2O9JAIB2yPM8vffee4pEIopGo9q8ebMyMjJkWZYKCwvVv39/7r4GgE5i27ZtCgQCSk1N1TPPPMM1JnQqBAjgKxw+fFgVFRVyHEcLFy5U//79ZVmWLMvS9OnTlZyc7PdEAMBJYIzRf/7nf+qBBx5QVVWVJk+e7PckAEAHYIzRBx98IMdx5LquPvjgAwUCAVmWpVAopK997WvECADooHbu3KlAIKCpU6fqhRde4JoSOh0CBPAPNDY2qqqqSo7jqKysTD179lQwGFQ4HNasWbPUpUsXvycCABLAGKMnnnhCP/vZz1RRUaFp06b5PQkA0AEZY/Txxx/H74xYs2aNZs+erVAoJMuyNHToUGIEAHQQu3fvVmpqqiZNmqSXX36Za0jolAgQwD+hqalJtbW1cl1XJSUlSkpKUmFhocLhsAKBgLp27er3RADACTDG6JlnntGPf/xjlZWVafbs2X5PAgB0AsYYbd26Nf7OiLfeekvTpk2TZVmybVvDhw8nRgBAO7V3716lpqZqzJgx+uMf/8g1I3RaBAjgBDU3N6u+vl6O46ioqEgtLS3Kz8+XbdtKT09X9+7d/Z4IADgOxhj97ne/02233aaSkhIFAgG/JwEAOiFjjP785z/LdV25rqvly5fr8ssvl2VZCofDOvfcc4kRANBO7Nu3T+np6Ro+fLhef/11devWze9JgG8IEEACtLS0aNmyZfFvLh05ckS5ubmybVtZWVnq2bOn3xMBAF/BGKMFCxbolltukeM4ysrK8nsSAAAyxuizzz5TNBqV67pavHixJk6cKNu2Zdu2xowZQ4wAgDbqwIEDyszM1KBBgxSNRnX66af7PQnwFQECSLDW1latXLkyHiP27t2ruXPnyrIs5ebmqnfv3n5PBAD81auvvqobbrhBr732mnJzc/2eAwDA3zDGaO/evSoqKpLjOIrFYho7dmz8zojx48cTIwCgjTh48KBycnLUs2dPlZSUqEePHn5PAnxHgABOIs/ztHr16vgL5nbu3KmsrCxZlqX8/Hz17dvX74kA0Gm5rqtrrrlGCxYsUDAY9HsOAAD/kDFG+/fvV0lJiVzXVXV1tUaNGqVgMKhwOKyLL75YSUlJfs8EgE7p8OHDys3NVVJSksrKytSrVy+/JwFtAgECOEU8z9PatWvlOI5c19WWLVuUkZEhy7JUWFio/v37880lADhFSktL9fWvf10vvvii5s2b5/ccAABOSENDg8rKyuS6riorKzVs2LB4jJg8eTIxAgBOkcbGRuXn56upqUkVFRXq06eP35OANoMAAfjAGKMPPvhAkUhErutqw4YNCgQCsm1boVBIgwYNIkYAwElSWVmpcDisZ555RvPnz+fzFgDQIRw+fFgLFy6U67pauHChBgwYoFAoJNu2NW3aNCUnJ/s9EQA6pKamJgWDQe3fv1/V1dU87QL4XwgQgM+MMfr444/jMWLt2rWaPXu2LMtSKBTS0KFDuTgGAAlSV1enwsJCPf7447r++uv5fAUAdEhHjx5VdXW1HMdRWVmZevbsqVAopHA4rJkzZ6pLly5+TwSADuHYsWOybVs7d+5UTU2NBgwY4PckoM0hQABtiDFGW7dujT+madWqVZo+fbosy5JlWRo+fDgXywDgBC1evFj5+fl65JFHdNNNN/F5CgDoFJqamlRbWyvHcVRaWqqkpCQFg0HZtq1AIKCuXbv6PREA2qXm5mZdddVV2rx5s2prazVo0CC/JwFtEgECaKOMMdq+fbtc11U0GtXy5cs1efLk+DeXzj33XC6eAcBxWrFihXJycnT//ffrO9/5Dp+fAIBOqbm5WfX19YpEIiouLlZLS4sKCgpkWZbS09PVvXt3vycCQLvQ0tKib3zjG1q3bp3q6uo0ePBgvycBbRYBAmgHjDHatWuXotGootGoFi9erEmTJsmyLNm2rTFjxnAxDQD+D2+99ZaysrJ0zz336I477uDzEgAA/eXi2bJly+Q4jqLRqI4cOaLc3FzZtq2srCz17NnT74kA0Ca1trbq3//93/XWW28pFotp6NChfk8C2jQCBNDOGGO0d+9eFRUVyXEc1dXV6YILLpBlWZo3b57GjRvHxTUA+Kt33nlHGRkZuvPOO3XXXXfx+QgAwFdobW3VG2+8Idd15bqu9u3bp5ycHNm2rblz56p3795+TwSANsHzPN18882qr69XLBbT8OHD/Z4EtHkECKAdM8Zo//79KikpkeM4qqmp0ahRo+KPaZo0aZKSkpL8ngkAvnj//feVmpqq7373u/rZz35GfAAA4Dh4nqfVq1crEokoGo1q586dysrKkm3bysvLU9++ff2eCAC+8DxP3/nOd1RZWalYLKaRI0f6PQloFwgQQAfS0NCgsrIyOY6jqqoqDRs2TKFQSLZta/LkycQIAJ3Ghg0bFAgEdOONN+pXv/oV8QEAgBPgeZ7Wrl0bjxFbtmxRRkaGbNtWYWGh+vXrx+9YAJ2C53n6wQ9+IMdxVF9fr9GjR/s9CWg3CBBAB3X48GEtXLhQjuOooqJCAwYMkGVZsixL06ZNU3Jyst8TAeCk+OijjxQIBDR//nw99NBDxFcAABLAGKP169fHY8SGDRuUmpoqy7IUCoU0aNAgYgSADsnzPN11111asGCB6uvrNWbMGL8nAe0KAQLoBI4ePaqqqiq5rquysjL16tVLwWBQ4XBYM2fOVJcuXfyeCAAJsWXLFqWkpCgUCunRRx8lPgAAcBIYY/TRRx/JcRy5rqu1a9dqzpw5CoVCCoVCGjp0KDECQIdgjNE999yj5557TrFYTOPGjfN7EtDuECCATqapqUmLFi2S67oqKSlRcnJyPEakpKSoa9eufk8EgBPy6aefKiUlRVlZWXryySeJDwAAnALGGG3dujUeI1atWqXp06fLsizZtq2zzz6bGAGgXTLG6L777tMTTzyh2tpaTZw40e9JQLtEgAA6sebmZtXX1ysSiaioqEie5yk/P1+2bSs9PV2nn3663xMB4Ljs2LFDKSkpmjVrlp577jkeMwcAgA+MMdq+fbtc15XrulqxYoUmT54sy7IUDoc1cuRIYgSAdsEYo4cfflgPP/ywqqurddlll/k9CWi3CBAAJEktLS1atmxZ/JmuR48eVV5enmzbVlZWlnr06OH3RAD4Srt27VIgENDkyZP14osvEh8AAGgDjDHatWuXotGoXNfVkiVLNGnSpHiMOP/884kRANokY4wee+wx/fKXv1RlZaWuuOIKvycB7RoBAsDfaG1t1RtvvCHHcRSNRrVv3z7NnTtXlmVp7ty56t27t98TAUCS9Pnnnys1NVXjx4/XggULeKcNAABtkDFGe/fuVTQaleM4isViuvDCC+MxYty4ccQIAG2CMUZPPvmk7r77bi1cuFAzZszwexLQ7hEgAPxdnudp1apV8Tsjdu3apezsbFmWpfz8fJ1xxhl+TwTQSX3xxRdKTU3VqFGj9Nprr/EOGwAA2gFjjPbv36/i4mK5rquamhqNHj06/l66SZMm8R4nAL4wxuj555/XD37wA5WWliolJcXvSUCHQIAAcNw8z9OaNWvid0Zs3bpV6enpsm1bhYWF6tevH99cAnBK7N+/XxkZGRo6dKgcx1G3bt38ngQAAE5AQ0ODSktL5bquqqqqNGzYMIVCIYXDYV1++eXECACnhDFGL730kr773e+quLhYaWlpfk8COgwCBIATYozR+vXrFYlE5LquNm7cqEAgINu2FQqFNHDgQGIEgJOioaFBWVlZ6tu3r4qLi9W9e3e/JwEAgAQ4fPiwFi5cKMdxtHDhQg0cOFCWZcm2bU2bNo0YAeCk+cMf/qCbb75Zr7/+unJycvyeA3QoBAgA/zJjjD766CM5jiPXdbV27VrNmTNHlmUpFArpzDPPJEYASIhDhw5p7ty56tatm8rKytSjRw+/JwEAgJPg6NGjqqqqkuM4Ki8vV69evRQKhWTbtmbNmqXk5GS/JwLoIF5//XVdf/31+uMf/6iCggK/5wAdDgECQEIZY7Rly5b4Y5pWrVqlGTNmyLIsWZals88+mxgB4IQcOXJEeXl58jxP5eXl6t27t9+TAADAKdDU1KRFixbJcRyVlpaqS5cuCgaDsm1bKSkpvAcKwAkrKirS1VdfrZdfflm2bfs9B+iQCBAAThpjjLZv3x6PEStWrNCUKVPiz3QdOXIkMQLAcWlsbFRBQYGOHDmiyspKnXHGGX5PAgAAPmhublYsFlMkElFxcbE8z1NBQYEsy1JGRgbvhQJw3MrLy3XVVVfp+eef19e//nW/5wAdFgECwClhjNHOnTsVjUYVjUa1ZMkSTZo0SZZlKRwO6/zzzydGAPhKX375pSzL0p49e1RdXa1+/fr5PQkAALQBLS0tWrp0afwLT42NjcrNzZVt28rKyuJRjQD+T9XV1bIsS08++aSuueYarkcAJxEBAsApZ4zRnj17VFRUJMdxFIvFdOGFF8q2bdm2rXHjxvHLH4Ak6dixY5o3b562bdum2tpaDRgwwO9JAACgDWptbdWKFSvkuq5c19X+/fs1d+5c2batnJwcHt0IIK6+vl75+fl67LHHdMMNN3D9ATjJCBAAfGWM0f79+1VcXCzHcbRo0SKNHj06/pimiRMnKikpye+ZAHzQ3Nys+fPna+PGjaqtrdXXvvY1vycBAIB2wPM8rVq1SpFIRNFoVLt27VJ2drZs21ZeXh6PcgQ6saVLlyo3N1cPPfSQbrnlFuIDcAoQIAC0KQ0NDSotLZXruqqsrNTZZ5+tUCgk27Z1+eWXEyOATqKlpUXXXXed3n33XdXV1enMM8/0exIAAGiHPM/TmjVrFIlEVFRUpK1btyojI0O2baugoED9+vXjAiTQSaxcuVLZ2dm699579f3vf5+ffeAUIUAAaLMOHTqkhQsXynVdLVy4UIMGDZJlWbIsS9OmTSNGAB1Ua2urbrzxRq1YsUL19fUaNmyY35MAAEAHYIzRunXr4ndGbNy4UampqbJtW8FgUAMHDuSCJNBBrV69WpmZmfrJT36iH/3oR/ysA6cQAQJAu3D06FFVVlbKdV2VlZWpT58+CgaDsm1bs2bNUnJyst8TASSA53n69re/rUWLFikWi+mcc87xexIAAOiAjDHauHGjHMeR67p67733NGfOHFmWpVAopDPPPJMLlEAHsXbtWqWlpemOO+7Q3Xffzc82cIoRIAC0O01NTaqpqZHruiotLVWXLl0UDAYVDoc1Z84cde3a1e+JAE6A53m67bbbVFJSovr6ep177rl+TwIAAJ2AMUZbtmyJx4i3335b06dPj999ffbZZ3PBEmin1q9fr0AgoFtuuUX33nsvP8uADwgQANq15uZmxWIxRSIRFRcXy/M8FRQUyLZtpaenq1u3bn5PBHAcPM/TnXfeqVdffVWxWEznn3++35MAAEAnZIzRtm3b5LquXNfVG2+8oSlTpsiyLNm2rZEjR3IBE2gnPvzwQwUCAV133XV64IEHeIwz4BMCBIAOo6WlRUuXLo2/YK6xsVF5eXmybVuZmZnq0aOH3xMBfAVjjO6++269+OKLisViuuCCC/yeBAAAIGOMdu7cqWg0qmg0qiVLlujiiy+WZVkKh8M677zziBFAG7Vp0yalpKToyiuv1COPPEJ8AHxEgADQIbW2tmrFihXxby4dOHBAc+fOlWVZmjt3rnr16uX3RAD6y4n9L37xCz311FOqq6vThAkT/J4EAADwN4wx+vzzz1VUVCTXdRWLxTRu3DjZti3btnXhhRcSI4A2YuvWrUpJSVF+fr4ef/xx4gPgMwIEgA7P8zy99dZbchxH0WhUn332mbKysmTbtvLy8nTGGWf4PRHolIwxeuCBB/Too4+qtrZWkyZN8nsSAADAP2SM0f79+1VcXCzHcbRo0SKdd9558ffSTZw4kQuegE+2bdumQCCgtLQ0Pf300/wsAm0AAQJAp+J5nt599914jPjkk0+UkZEh27ZVUFCgfv368c0l4BQwxuiRRx7Rgw8+qOrqal1++eV+TwIAADghBw4cUGlpqVzXVVVVlc4++2yFQiGFw2FddtllXAAFTpGdO3cqJSVF06dP13//938rOTnZ70kARIAA0IkZY7Ru3TpFIhFFo1Ft3LhRqampCofDKiws1MCBA4kRwElgjNHjjz+un//856qsrNTUqVP9ngQAAJAQhw4d0sKFC+U4jioqKjRo0CBZliXLsjRt2jRiBHCS7N69W4FAQJdccoleeukldenSxe9JAP6KAAEA+ssF0Y0bN8pxHLmuq/fee08pKSmyLEuhUEhDhgwhRgAJYIzR008/rbvuukvl5eWaNWuW35MAAABOiqNHj6qyslKO46i8vFx9+vRRKBSSZVmaNWsW384GEmTPnj1KS0vTmDFj9Mc//lFdu3b1exKA/w8BAgD+F2OMtmzZEr8z4u2339aMGTPi31w666yziBHACTDG6IUXXtDtt9+ukpISBQIBvycBAACcEk1NTaqpqZHruiopKVG3bt0UDAZl27bmzJnDBVPgBO3bt09paWk655xz9Prrr6tbt25+TwLwvxAgAODvMMZo27Ztcl1XruvqjTfe0BVXXBF/puuIESOIEcBxMMbo97//vb797W/LdV1lZmb6PQkAAMAXx44dUywWk+M4KioqkjFGBQUFsm1b6enpXEAFjtOBAweUkZGhwYMHy3VdnX766X5PAvAVCBAAcJyMMdq5c6ei0ahc19XSpUt18cUXy7IshcNhnXfeecQI4P/w6quv6oYbbtBrr72m3Nxcv+cAAAC0CS0tLVq6dGn87uumpibl5eXJtm1lZmaqR48efk8E2qSDBw8qOztbvXr1UklJCT8rQBtGgACAE2CM0eeff66ioiI5jqP6+nqNGzdOtm3Ltm1deOGFxAjgr1zX1TXXXKMFCxYoGAz6PQcAAKBNam1t1fLly+N3Xzc0NGju3LmybVs5OTnq1auX3xOBNuHw4cPKzc1VUlKSysrK+NkA2jgCBAD8i4wx2rdvn4qLi+U4jmpra3XeeecpFArJtm1NnDhRSUlJfs8EfFFaWqqvf/3revHFFzVv3jy/5wAAALQLnufprbfekuM4cl1Xu3fvVnZ2tizLUl5ens444wy/JwK+aGxsVH5+vpqamlRRUaE+ffr4PQnAP0CAAIAEMsaooaFBpaWlcl1XVVVVGj58eDxGXHbZZcQIdBqVlZUKh8N69tlnNX/+fL/nAAAAtEue5+ndd9+NP6bp008/VWZmpizLUkFBgfr168fd1+gUmpqaFAwGdeDAAVVVValv375+TwJwHAgQAHASHTp0SOXl5XJdVxUVFfra174WjxFTp04lRqDDqqurU2FhoZ544gldd911nBQDAAAkgDFG69ati8eIjRs3Ki0tTbZtq7CwUAMHDuS4Cx3SsWPHZNu2du7cqUWLFql///5+TwJwnAgQAHCKHD16VJWVlXIcR+Xl5TrjjDMUDAZl27Zmzpyp5ORkvycCCbF48WLl5+frkUce0U033cRJMAAAwElgjNHGjRvlOI4cx9G6des0Z84cWZalUCikIUOGcByGDqG5uVlXXXWVNm/erLq6Og0cONDvSQD+CQQIAPBBU1OTampq5DiOSktL1a1bNwWDQYXDYc2ZM0ddunTxeyJwQpYvX665c+fq/vvv13e+8x1OegEAAE4BY4w2b94cf2fEO++8oxkzZsiyLFmWpbPOOovjMrRLLS0tuvrqq7V+/XrV1dVp8ODBfk8C8E8iQACAz44dO6ZYLKZIJKLi4mIZY1RYWCjLspSenq5u3br5PRE4Lm+99ZaysrJ0zz336I477uAkFwAAwAfGGG3bti0eI1auXKkrrrhClmXJtm2NGDGC4zS0C62trbr++uu1atUqxWIxDR061O9JAE4AAQIA2pCWlhYtWbJEkUhERUVFampqUn5+vizLUmZmpnr06OH3ROArvf3228rMzNSdd96pu+66i5NaAACANsAYo507d8p1XUWjUS1dulSXXHKJLMtSOBzW6NGjOW5Dm9Ta2qqbb75ZixcvVn19vc4++2y/JwE4QQQIAGijWltbtXz5crmuK9d11dDQoNzcXFmWpZycHPXq1cvviYAk6b333lNaWpq+973v6Z577uEkFgAAoA0yxmj37t0qKiqS67qqr6/X+PHj4zHiggsu4DgObYLnebr11ltVVVWlWCymkSNH+j0JwL+AAAEA7YDneXrzzTfjMWL37t3Kzs6WbdvKy8tTnz59/J6ITuqDDz5QIBDQN7/5Tf3qV7/ipBUAAKAdMMZo3759Ki4uluM4WrRokcaMGRN/L91FF12kpKQkv2eiE/I8T3fccYei0ahisZhGjx7t9yQA/yICBAC0M57n6d1331UkElE0GtW2bduUkZEh27ZVUFCgvn37chEYp8RHH32klJQUXX311XrooYc4SQUAAGiHjDFqaGhQaWmpHMdRdXW1hg8frlAopHA4rEsvvZTjPJwSnufprrvu0oIFC1RfX68xY8b4PQlAAhAgAKAdM8Zo3bp1ev311xWNRvXRRx8pLS1Ntm0rGAxqwIABxAicFJs3b1YgEFAoFNKjjz7KSSkAAEAHcejQIZWXl8txHFVUVGjw4MGyLEuWZWnq1Kkc9+GkMMbonnvu0XPPPadYLKZx48b5PQlAghAgAKCDMMboww8/lOM4cl1X69at05w5c2RZlkKhkIYMGUKMQEJ88sknCgQCysrK0pNPPslJKAAAQAd15MgRVVZWynEclZeXq2/fvgqFQrIsSzNnzlRycrLfE9EBGGN033336YknnlBdXZ0uuugivycBSCACBAB0QMYYbd68Of6YpnfeeUczZ86MnyycddZZxAickO3btys1NVWzZ8/Ws88+y0knAABAJ9HY2Kiamhq5rqvS0lJ169ZNoVBItm1rzpw56tKli98T0Q4ZY/TQQw/p17/+tWpqanTppZf6PQlAghEgAKCDM8bo008/jb/AeuXKlZo6dWr8ZGHEiBHECByXXbt2KRAIaMqUKfrd735HfAAAAOikjh07prq6OjmOo6KiIp122mkqKChQOBxWamqqunXr5vdEtAPGGD366KO67777VFVVpSlTpvg9CcBJQIAAgE7EGKOdO3fGY8SyZct0ySWXyLIshcNhjR49mhiBr/T5558rNTVVEyZM0O9//3u+4QYAAABJUktLi5YsWRK/+/rYsWPKy8uTbdvKyMhQjx49/J6INsgYo9/+9rf66U9/qoqKCk2fPt3vSQBOEgIEAHRSxhjt3r1bRUVFchxHixcv1vjx42Xbtmzb1gUXXECMgCRp7969SktL0+jRo/Xqq6+qa9eufk8CAABAG9Ta2qrly5fLcRxFo1E1NDQoNzdXtm0rOztbvXr18nsi2gBjjJ577jn98Ic/VFlZmebMmeP3JAAnEQECACBjjPbt26eioiK5rqtFixZpzJgxCoVCCofDmjBhAi8a7qT279+v9PR0DRs2TI7jcDs9AAAAjovneXrzzTflOI5c19Xnn3+unJwcWZalvLw89enTx++J8IExRi+99JK++93vqri4WGlpaX5PAnCSESAAAP+DMUYNDQ0qKSmR67qqqqrSiBEj4u+MuPTSS4kRnURDQ4MyMzPVv39/FRUVqXv37n5PAgAAQDvkeZ7eeeed+GOatm/frszMTFmWpYKCAvXt25e7rzsBY4z+8Ic/6Fvf+pYikYiys7P9ngTgFCBAAAD+roMHD6q8vFyu66qiokJDhgyJx4grrriCGNFBHTp0SDk5OerevbtKS0t5di8AAAASwvM8rVu3Lh4jPvroI6Wnp8uyLAWDQQ0YMIAY0UG9/vrruv766/WnP/1J+fn5fs8BcIoQIAAAx+3IkSOqrKyU4zgqLy9X37594zFixowZSk5O9nsiEuDIkSPKy8uT53kqLy9X7969/Z4EAACADsgYow8//FCO48hxHK1fv15z5syRbdsKhUIaPHgwMaKDKCoq0tVXX61XXnlFlmX5PQfAKUSAAACckMbGRtXU1MhxHJWVlen0009XMBhUOBzW7Nmz1aVLF78n4gQ0NjaqoKAgHpvOOOMMvycBAACgEzDGaPPmzYpEInJdV++++65mzpwpy7JkWZaGDRtGjGinysvLdeWVV+qFF17QVVdd5fccAKcYAQIA8C87duyY6urqFIlEVFxcrNNOO02FhYWybVupqam8uLid+PLLLxUKhbR3715VV1erX79+fk8CAABAJ2SM0aeffirHcRSNRrVy5UpNnTpVlmXJtm2dc845xIh2oqqqSrZt66mnntI3vvEN/t2ATogAAQBIqJaWFi1evDh+snDs2DHl5+fLsixlZmbyIuM26tixY5o3b562bdum2tpaDRgwwO9JAAAAgIwx2rFjh1zXVTQa1bJly3TppZcqFAopHA5r9OjRXNRuo+rr65Wfn6/HHntMN9xwA/9OQCdFgAAAnDStra1atmyZXNeV67o6dOiQcnNzZVmWsrOz1atXL78nQlJzc7Pmz5+vjRs3qq6uToMGDfJ7EgAAAPA3jDHavXu3otGoXNdVfX29LrroIlmWpXA4rLFjx3KRu41YunSpcnNz9fDDD+tb3/oW/y5AJ0aAAACcEp7naeXKlfEYsWfPHmVnZ8u2beXm5qpPnz5+T+yUWlpadO2112rNmjWKxWIaMmSI35MAAACAf8gYoy+++ELFxcVyHEe1tbUaO3asgsGg5s2bpwkTJnDR2ydvvPGGcnJy9Mtf/lLf+973+HcAOjkCBADglPM8T++8844ikYii0ai2b9+uzMxM2bat/Px89e3bl4PUU6C1tVU33nijVqxYofr6eg0bNszvSQAAAMA/zRijhoYGlZSUyHEcVVdXa8SIEfHHNF1yySVKSkrye2ansGrVKmVlZenuu+/WD3/4Q87rABAgAAD+8jxP77//viKRiIqKivTxxx8rLS1Ntm2rsLBQAwYM4KD1JPA8T7fccotqa2sVi8V0zjnn+D0JAAAASIiDBw+qvLxcjuOosrJSQ4YMkWVZsixLV1xxBTHiJFmzZo3S09N1xx136O677+Y8DoAkAgQAoA0xxmjDhg1yHEeu62r9+vVKSUmRZVkKhUIaPHgwB7EJ4Hmevv/976usrEyxWEznnnuu35MAAACAk+LIkSOqqKiQ67oqLy9Xv379FAqFZFmWZsyYoeTkZL8ndgjr1q1Tamqqvv3tb+sXv/gF520A4ggQAIA2yRijTZs2xWPEu+++q1mzZsVPFoYNG8ZB7QnwPE8/+tGP9Nprr6m+vl7nnXee35MAAACAU6KxsVE1NTVyHEelpaXq3r27QqGQbNvW7Nmz1aVLF78ntksffvihAoGArrvuOj344IOcpwH4HwgQAIA2zxijTz/9NB4j3nzzTU2bNi1+snDOOedwkHscPM/TT37yE7388suKxWK64IIL/J4EAAAA+OLYsWOqra2V4zgqLi7WaaedpsLCQoXDYaWmpqpr165+T2wXPv74YwUCAV111VX69a9/zeOtAPwNAgQAoF0xxmjHjh1yXVeu62r58uW69NJLZVmWwuGwRo0aRYz4CsYY/fznP9fTTz+tWCym8ePH+z0JAAAAaBOam5u1ZMmS+HvpmpublZeXJ9u2lZGRoe7du/s9sU3asmWLAoGACgoK9F//9V/EBwBfiQABAGi3jDHavXu3otGoXNfV4sWLNWHCBNm2Ldu2NXbsWGKE/vL/6f7779djjz2m2tpaTZo0ye9JAAAAQJvU2tqqZcuWyXEcRaNRHTp0SLm5ubJtW9nZ2erZs6ffE9uEbdu2KSUlRRkZGXrqqaeIDwD+TwQIAECHYIzRF198oeLiYjmOo9raWo0dO1ahUEjhcFgTJkzolDHCGKNHHnlEDz74oKqrq3X55Zf7PQkAAABoFzzP08qVK+MxYs+ePcrJyZFlWcrNzVWfPn38nuiLHTt2KBAIaMaMGXr++ed5kTeAv4sAAQDocIwxOnDggEpLS+U4jqqrqzVy5EgFg0GFw2FdcsklneIbOsYYPf744/r5z3+uyspKTZ061e9JAAAAQLvkeZ7efvvteIzYvn27srKyZFmW8vPz1bdv307xhafdu3crEAjokksu0UsvvcSLuwH8QwQIAECHd/DgQZWXl8txHFVWVurMM8+Mv8B6ypQpHTJGGGP09NNP66677tLChQs1c+ZMvycBAAAAHYLneXr//fcViUQUjUa1adMmpaWlybZtBYNB9e/fv0PGiD179igtLU1jx47VH/7wB17UDeC4ECAAAJ3KkSNHVFFRIcdxtHDhQvXr1y8eI6ZPn94hbh82xuiFF17Q7bffrtLSUqWkpPg9CQAAAOiQjDHasGGDHMeR4zj64IMPlJKSEo8RgwcP7hAxYt++fUpLS9OIESP02muvqVu3bn5PAtBOECAAAJ1WY2Ojqqur5TiOysrK1KNHj/hjmmbNmtUubyc2xuiVV17Rrbfeqmg0qoyMDL8nAQAAAJ2CMUabNm1SJBKR67pas2aNZs2aJcuyZFmWhg4d2i5jxIEDB5SRkaEhQ4bIcRydfvrpfk8C0I4QIAAAkHTs2DEtWrRIjuOopKREp512moLBoGzbVmpqaru5vfhPf/qTbrzxRr322mvKzc31ew4AAADQKRlj9Mknn8TfGfHmm29q2rRpsixLtm1r+PDh7SJGHDx4UFlZWerdu7dKS0vVvXt3vycBaGcIEAAA/C/Nzc1avHhx/GShpaVF+fn5sixLGRkZbfag23EcXXvttVqwYIGCwaDfcwAAAADoLzFix44dcl1Xrutq+fLluuyyyxQKhRQOhzVq1Kg2GSMOHz6suXPnKjk5WWVlZerVq5ffkwC0QwQIAAD+jtbWVi1btiweIw4dOqTc3FzZtq3s7Gz17NnT74mSpJKSEv3bv/2bXnzxRc2bN8/vOQAAAAC+gjFGn332mYqKiuS6rurr6zVx4sT4nRFjx45tEzGisbFReXl5+vLLL1VRUaE+ffr4PQlAO0WAAADgOHmep5UrV8ZjxJ49e5STkyPbtpWbm6vevXv7squiokLz5s3Ts88+q/nz5/uyAQAAAMA/xxijL774QsXFxYpEIqqrq9PYsWMVCoU0b948jR8/3pcY0dTUpMLCQjU0NKiqqkp9+/Y95RsAdBwECAAAToDneXr77bcViUQUjUa1Y8cOZWZmyrZt5efnn7KD9NraWhUWFuq3v/2trr322jbxbSkAAAAA/xxjjA4cOKCSkhI5jqOamhqNHDky/pimiy++WElJSSd9x5dffqlwOKxdu3appqZG/fv3P+l/J4COjQABAMC/yPM8vffee4pEIioqKtKmTZuUnp4u27ZVWFio/v37n5QwUF9fr/z8fP3mN7/RN7/5TeIDAAAA0EEcPHhQZWVlchxHlZWVGjp0qEKhkGzb1pQpU05KjGhubtaVV16pLVu2qK6uTgMHDkz43wGg8yFAAACQQMYYbdiwQY7jyHEcffDBBwoEArIsS8FgUIMHD05IKFi+fLnmzp2rBx54QLfeeivxAQAAAOigDh8+rIqKCrmuq/LycvXv31+WZcmyLE2fPl3Jycn/8t/R0tKi+fPna8OGDaqtrdXgwYMTsBwACBAAAJw0xhht2rRJkUhErutqzZo1mj17tkKhkCzL0tChQ08oHLz55pvKzs7Wz372M91+++3EBwAAAKCTaGxsVHV1tRzHUVlZmXr06KFgMKhwOKxZs2apS5cu//Sf2draquuvv16rV69WLBbTmWeeeRKWA+isCBAAAJwCxhh98sknchxHrutq1apVmjp1qizLkm3bGj58+HGFhLfffluZmZn68Y9/rB//+MfEBwAAAKCT+vLLL1VbWyvHcVRSUqKkpCQVFhYqHA4rEAioa9eu//DPaG1t1U033aSlS5eqvr5eZ5111ilYDqAzIUAAAHCKGWP05z//Wa7rKhqNavny5brssstkWZbC4bDOPffcrwwL7733ntLS0vS9731P99xzD/EBAAAAgKS/vL9h8eLF8ffStbS0KD8/X7ZtKz09Xd27d/+b/8bzPN16662qqqpSfX29RowY4cNyAB0dAQIAAB8ZY/TZZ58pGo0qGo2qvr5eEydOlG3bsm1bY8aM0WmnnRZ/l8RNN92k++67j/gAAAAA4Cu1trZq6dKlchxH0WhUR44cUW5urmzbVlZWlnr27CnP83T77berqKhIsVhMo0eP9ns2gA6KAAEAQBthjNHevXtVXFwsx3FUV1ensWPHas6cOXr11Vd13XXX6aGHHlJSUpLfUwEAAAC0A62trVq5cqVc15Xrutq7d69ycnLU1NSk1atXq76+XmPGjPF7JoAOjAABAEAbZIzRgQMHVFJSov/4j/9Q//79tWzZMuIDAAAAgBPieZ5Wr16tV155Rc8//7yWLFmiyZMn+z0LQAdHgAAAoI0zxsgYQ3wAAAAAkBCe53F+AeCUIEAAAAAAAAAAAICEI3UCAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICEI0AAAAAAAAAAAICE+3/ed3DrQzMKmAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_path = 'processed_data'\n",
    "upper_folder = os.path.join(data_path, 'upper')\n",
    "lower_folder = os.path.join(data_path, 'lower')\n",
    "\n",
    "# Function to get a random person folder\n",
    "def get_random_person(folder):\n",
    "    persons = os.listdir(folder)\n",
    "    selected_person = random.choice(persons)\n",
    "    return os.path.join(folder, selected_person)\n",
    "\n",
    "# Function to load a point cloud and its labels\n",
    "def load_point_cloud_and_labels(person_path):\n",
    "    point_cloud_file = os.path.join(person_path, 'point_cloud.npy')\n",
    "    labels_file = os.path.join(person_path, 'labels.npy')\n",
    "\n",
    "    if os.path.exists(point_cloud_file) and os.path.exists(labels_file):\n",
    "        points = np.load(point_cloud_file)\n",
    "        labels = np.load(labels_file)\n",
    "\n",
    "        if points.ndim == 2 and points.shape[1] == 3 and labels.ndim == 1:\n",
    "            points = points.astype(np.float32)\n",
    "            return points, labels\n",
    "        else:\n",
    "            print(f\"Invalid data shapes for {person_path}.\")\n",
    "    else:\n",
    "        print(f\"Files do not exist for {person_path}.\")\n",
    "    \n",
    "    return None, None\n",
    "\n",
    "# Function to visualize the point cloud with specific colors for labels in subplots\n",
    "def visualize_point_cloud_with_labels(ax, points, labels, label_colors, title):\n",
    "    for label, color in label_colors.items():\n",
    "        label_mask = (labels == label)\n",
    "        ax.scatter(points[label_mask, 0], points[label_mask, 1], points[label_mask, 2],\n",
    "                   color=color, label=f'Label {label}', alpha=0.6, edgecolor='none')\n",
    "\n",
    "    # Remove axes and grid\n",
    "    ax.set_xticks([])  # Remove x-axis ticks\n",
    "    ax.set_yticks([])  # Remove y-axis ticks\n",
    "    ax.set_zticks([])  # Remove z-axis ticks\n",
    "    ax.xaxis.pane.fill = False  # Remove the background pane color\n",
    "    ax.yaxis.pane.fill = False\n",
    "    ax.zaxis.pane.fill = False\n",
    "    ax.grid(False)  # Disable the grid\n",
    "\n",
    "    ax.set_title(title, pad=20)  # Adjust title padding\n",
    "\n",
    "# Define colors for each label (customized)\n",
    "label_colors = {\n",
    "    0: 'red',\n",
    "    11: 'blue',\n",
    "    12: 'green',\n",
    "    13: 'orange',\n",
    "    14: 'purple',\n",
    "    15: 'cyan',\n",
    "    16: 'magenta',\n",
    "    17: 'yellow',\n",
    "    18: 'brown',\n",
    "    21: 'lime',\n",
    "    22: 'navy',\n",
    "    23: 'teal',\n",
    "    24: 'violet',\n",
    "    25: 'salmon',\n",
    "    26: 'gold',\n",
    "    27: 'lightblue',\n",
    "    28: 'coral',\n",
    "    31: 'olive',\n",
    "    32: 'silver',\n",
    "    33: 'gray',\n",
    "    34: 'black',\n",
    "    35: 'darkred',\n",
    "    36: 'darkgreen',\n",
    "    37: 'darkblue',\n",
    "    38: 'darkviolet',\n",
    "    41: 'peru',\n",
    "    42: 'chocolate',\n",
    "    43: 'mediumvioletred',\n",
    "    44: 'lightskyblue',\n",
    "    45: 'lightpink',\n",
    "    46: 'plum',\n",
    "    47: 'khaki',\n",
    "    48: 'powderblue',\n",
    "}\n",
    "\n",
    "# Get a random person from the upper and lower folders\n",
    "selected_upper_person = get_random_person(upper_folder)\n",
    "selected_lower_person = get_random_person(lower_folder)\n",
    "\n",
    "# Load the point cloud and labels for the selected upper person\n",
    "upper_points, upper_labels = load_point_cloud_and_labels(selected_upper_person)\n",
    "\n",
    "# Load the point cloud and labels for the selected lower person\n",
    "lower_points, lower_labels = load_point_cloud_and_labels(selected_lower_person)\n",
    "\n",
    "# Create subplots\n",
    "fig = plt.figure(figsize=(16, 8))\n",
    "\n",
    "# Visualize the point cloud for the selected upper person\n",
    "ax1 = fig.add_subplot(121, projection='3d')\n",
    "if upper_points is not None and upper_labels is not None:\n",
    "    visualize_point_cloud_with_labels(ax1, upper_points, upper_labels, label_colors, 'Upper Person Point Cloud')\n",
    "\n",
    "# Visualize the point cloud for the selected lower person\n",
    "ax2 = fig.add_subplot(122, projection='3d')\n",
    "if lower_points is not None and lower_labels is not None:\n",
    "    visualize_point_cloud_with_labels(ax2, lower_points, lower_labels, label_colors, 'Lower Person Point Cloud')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0d163251",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(dataset_dir):\n",
    "    data = []\n",
    "    \n",
    "    # Loop over upper and lower directories\n",
    "    for jaw in ['upper', 'lower']:\n",
    "        jaw_dir = os.path.join(dataset_dir, jaw)\n",
    "        \n",
    "        # Loop over each person's folder in the jaw directory\n",
    "        for person in os.listdir(jaw_dir):\n",
    "            person_dir = os.path.join(jaw_dir, person)\n",
    "            \n",
    "            # Load the point cloud, labels, and instances\n",
    "            point_cloud = np.load(os.path.join(person_dir, 'point_cloud.npy'))\n",
    "            labels = np.load(os.path.join(person_dir, 'labels.npy'))\n",
    "            instances = np.load(os.path.join(person_dir, 'instances.npy'))\n",
    "            \n",
    "            # Store in the data list\n",
    "            data.append((point_cloud, labels, instances, jaw, person))\n",
    "    \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "987acb22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[-0.26072201,  0.02727386, -0.04917371],\n",
       "        [ 0.20292237, -0.32549577,  0.03434551],\n",
       "        [ 0.0822811 , -0.22177595,  0.05316779],\n",
       "        ...,\n",
       "        [-0.18279812, -0.06748716, -0.05578711],\n",
       "        [-0.38600658,  0.14346454,  0.0353543 ],\n",
       "        [-0.06522278, -0.40355844, -0.02158385]]),\n",
       " array([ 0,  0,  0, ...,  0, 25,  0]),\n",
       " array([0, 0, 0, ..., 0, 6, 0]),\n",
       " 'upper',\n",
       " '01346914')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_dir = 'processed_data'  \n",
    "data = load_data(dataset_dir)\n",
    "data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "91d21ed6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum length of point clouds: 15230\n"
     ]
    }
   ],
   "source": [
    "def find_min_length_point_clouds(data):\n",
    "\n",
    "    min_length = np.inf  # Initialize to positive infinity\n",
    "\n",
    "    for point_cloud, _, _, _, _ in data:\n",
    "        length = point_cloud.shape[0]  # Number of points in the current point cloud\n",
    "        min_length = min(min_length, length)  # Update min_length if current length is smaller\n",
    "\n",
    "    return min_length\n",
    "\n",
    "min_length = find_min_length_point_clouds(data)\n",
    "print(\"Minimum length of point clouds:\", min_length)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0da38081",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sampled Point Cloud 0: (15230, 3), Sampled Labels 0: (15230,)\n",
      "Sampled Point Cloud 1: (15230, 3), Sampled Labels 1: (15230,)\n",
      "Sampled Point Cloud 2: (15230, 3), Sampled Labels 2: (15230,)\n",
      "Sampled Point Cloud 3: (15230, 3), Sampled Labels 3: (15230,)\n",
      "Sampled Point Cloud 4: (15230, 3), Sampled Labels 4: (15230,)\n",
      "Sampled Point Cloud 5: (15230, 3), Sampled Labels 5: (15230,)\n",
      "Sampled Point Cloud 6: (15230, 3), Sampled Labels 6: (15230,)\n",
      "Sampled Point Cloud 7: (15230, 3), Sampled Labels 7: (15230,)\n",
      "Sampled Point Cloud 8: (15230, 3), Sampled Labels 8: (15230,)\n",
      "Sampled Point Cloud 9: (15230, 3), Sampled Labels 9: (15230,)\n",
      "Sampled Point Cloud 10: (15230, 3), Sampled Labels 10: (15230,)\n",
      "Sampled Point Cloud 11: (15230, 3), Sampled Labels 11: (15230,)\n",
      "Sampled Point Cloud 12: (15230, 3), Sampled Labels 12: (15230,)\n",
      "Sampled Point Cloud 13: (15230, 3), Sampled Labels 13: (15230,)\n",
      "Sampled Point Cloud 14: (15230, 3), Sampled Labels 14: (15230,)\n",
      "Sampled Point Cloud 15: (15230, 3), Sampled Labels 15: (15230,)\n",
      "Sampled Point Cloud 16: (15230, 3), Sampled Labels 16: (15230,)\n",
      "Sampled Point Cloud 17: (15230, 3), Sampled Labels 17: (15230,)\n",
      "Sampled Point Cloud 18: (15230, 3), Sampled Labels 18: (15230,)\n",
      "Sampled Point Cloud 19: (15230, 3), Sampled Labels 19: (15230,)\n",
      "Sampled Point Cloud 20: (15230, 3), Sampled Labels 20: (15230,)\n",
      "Sampled Point Cloud 21: (15230, 3), Sampled Labels 21: (15230,)\n",
      "Sampled Point Cloud 22: (15230, 3), Sampled Labels 22: (15230,)\n",
      "Sampled Point Cloud 23: (15230, 3), Sampled Labels 23: (15230,)\n",
      "Sampled Point Cloud 24: (15230, 3), Sampled Labels 24: (15230,)\n",
      "Sampled Point Cloud 25: (15230, 3), Sampled Labels 25: (15230,)\n",
      "Sampled Point Cloud 26: (15230, 3), Sampled Labels 26: (15230,)\n",
      "Sampled Point Cloud 27: (15230, 3), Sampled Labels 27: (15230,)\n",
      "Sampled Point Cloud 28: (15230, 3), Sampled Labels 28: (15230,)\n",
      "Sampled Point Cloud 29: (15230, 3), Sampled Labels 29: (15230,)\n",
      "Sampled Point Cloud 30: (15230, 3), Sampled Labels 30: (15230,)\n",
      "Sampled Point Cloud 31: (15230, 3), Sampled Labels 31: (15230,)\n",
      "Sampled Point Cloud 32: (15230, 3), Sampled Labels 32: (15230,)\n",
      "Sampled Point Cloud 33: (15230, 3), Sampled Labels 33: (15230,)\n",
      "Sampled Point Cloud 34: (15230, 3), Sampled Labels 34: (15230,)\n",
      "Sampled Point Cloud 35: (15230, 3), Sampled Labels 35: (15230,)\n",
      "Sampled Point Cloud 36: (15230, 3), Sampled Labels 36: (15230,)\n",
      "Sampled Point Cloud 37: (15230, 3), Sampled Labels 37: (15230,)\n",
      "Sampled Point Cloud 38: (15230, 3), Sampled Labels 38: (15230,)\n",
      "Sampled Point Cloud 39: (15230, 3), Sampled Labels 39: (15230,)\n",
      "Sampled Point Cloud 40: (15230, 3), Sampled Labels 40: (15230,)\n",
      "Sampled Point Cloud 41: (15230, 3), Sampled Labels 41: (15230,)\n",
      "Sampled Point Cloud 42: (15230, 3), Sampled Labels 42: (15230,)\n",
      "Sampled Point Cloud 43: (15230, 3), Sampled Labels 43: (15230,)\n",
      "Sampled Point Cloud 44: (15230, 3), Sampled Labels 44: (15230,)\n",
      "Sampled Point Cloud 45: (15230, 3), Sampled Labels 45: (15230,)\n",
      "Sampled Point Cloud 46: (15230, 3), Sampled Labels 46: (15230,)\n",
      "Sampled Point Cloud 47: (15230, 3), Sampled Labels 47: (15230,)\n",
      "Sampled Point Cloud 48: (15230, 3), Sampled Labels 48: (15230,)\n",
      "Sampled Point Cloud 49: (15230, 3), Sampled Labels 49: (15230,)\n",
      "Sampled Point Cloud 50: (15230, 3), Sampled Labels 50: (15230,)\n",
      "Sampled Point Cloud 51: (15230, 3), Sampled Labels 51: (15230,)\n",
      "Sampled Point Cloud 52: (15230, 3), Sampled Labels 52: (15230,)\n",
      "Sampled Point Cloud 53: (15230, 3), Sampled Labels 53: (15230,)\n",
      "Sampled Point Cloud 54: (15230, 3), Sampled Labels 54: (15230,)\n",
      "Sampled Point Cloud 55: (15230, 3), Sampled Labels 55: (15230,)\n",
      "Sampled Point Cloud 56: (15230, 3), Sampled Labels 56: (15230,)\n",
      "Sampled Point Cloud 57: (15230, 3), Sampled Labels 57: (15230,)\n",
      "Sampled Point Cloud 58: (15230, 3), Sampled Labels 58: (15230,)\n",
      "Sampled Point Cloud 59: (15230, 3), Sampled Labels 59: (15230,)\n",
      "Sampled Point Cloud 60: (15230, 3), Sampled Labels 60: (15230,)\n",
      "Sampled Point Cloud 61: (15230, 3), Sampled Labels 61: (15230,)\n",
      "Sampled Point Cloud 62: (15230, 3), Sampled Labels 62: (15230,)\n",
      "Sampled Point Cloud 63: (15230, 3), Sampled Labels 63: (15230,)\n",
      "Sampled Point Cloud 64: (15230, 3), Sampled Labels 64: (15230,)\n",
      "Sampled Point Cloud 65: (15230, 3), Sampled Labels 65: (15230,)\n",
      "Sampled Point Cloud 66: (15230, 3), Sampled Labels 66: (15230,)\n",
      "Sampled Point Cloud 67: (15230, 3), Sampled Labels 67: (15230,)\n",
      "Sampled Point Cloud 68: (15230, 3), Sampled Labels 68: (15230,)\n",
      "Sampled Point Cloud 69: (15230, 3), Sampled Labels 69: (15230,)\n",
      "Sampled Point Cloud 70: (15230, 3), Sampled Labels 70: (15230,)\n",
      "Sampled Point Cloud 71: (15230, 3), Sampled Labels 71: (15230,)\n",
      "Sampled Point Cloud 72: (15230, 3), Sampled Labels 72: (15230,)\n",
      "Sampled Point Cloud 73: (15230, 3), Sampled Labels 73: (15230,)\n",
      "Sampled Point Cloud 74: (15230, 3), Sampled Labels 74: (15230,)\n",
      "Sampled Point Cloud 75: (15230, 3), Sampled Labels 75: (15230,)\n",
      "Sampled Point Cloud 76: (15230, 3), Sampled Labels 76: (15230,)\n",
      "Sampled Point Cloud 77: (15230, 3), Sampled Labels 77: (15230,)\n",
      "Sampled Point Cloud 78: (15230, 3), Sampled Labels 78: (15230,)\n",
      "Sampled Point Cloud 79: (15230, 3), Sampled Labels 79: (15230,)\n",
      "Sampled Point Cloud 80: (15230, 3), Sampled Labels 80: (15230,)\n",
      "Sampled Point Cloud 81: (15230, 3), Sampled Labels 81: (15230,)\n",
      "Sampled Point Cloud 82: (15230, 3), Sampled Labels 82: (15230,)\n",
      "Sampled Point Cloud 83: (15230, 3), Sampled Labels 83: (15230,)\n",
      "Sampled Point Cloud 84: (15230, 3), Sampled Labels 84: (15230,)\n",
      "Sampled Point Cloud 85: (15230, 3), Sampled Labels 85: (15230,)\n",
      "Sampled Point Cloud 86: (15230, 3), Sampled Labels 86: (15230,)\n",
      "Sampled Point Cloud 87: (15230, 3), Sampled Labels 87: (15230,)\n",
      "Sampled Point Cloud 88: (15230, 3), Sampled Labels 88: (15230,)\n",
      "Sampled Point Cloud 89: (15230, 3), Sampled Labels 89: (15230,)\n",
      "Sampled Point Cloud 90: (15230, 3), Sampled Labels 90: (15230,)\n",
      "Sampled Point Cloud 91: (15230, 3), Sampled Labels 91: (15230,)\n",
      "Sampled Point Cloud 92: (15230, 3), Sampled Labels 92: (15230,)\n",
      "Sampled Point Cloud 93: (15230, 3), Sampled Labels 93: (15230,)\n",
      "Sampled Point Cloud 94: (15230, 3), Sampled Labels 94: (15230,)\n",
      "Sampled Point Cloud 95: (15230, 3), Sampled Labels 95: (15230,)\n",
      "Sampled Point Cloud 96: (15230, 3), Sampled Labels 96: (15230,)\n",
      "Sampled Point Cloud 97: (15230, 3), Sampled Labels 97: (15230,)\n",
      "Sampled Point Cloud 98: (15230, 3), Sampled Labels 98: (15230,)\n",
      "Sampled Point Cloud 99: (15230, 3), Sampled Labels 99: (15230,)\n",
      "Sampled Point Cloud 100: (15230, 3), Sampled Labels 100: (15230,)\n",
      "Sampled Point Cloud 101: (15230, 3), Sampled Labels 101: (15230,)\n",
      "Sampled Point Cloud 102: (15230, 3), Sampled Labels 102: (15230,)\n",
      "Sampled Point Cloud 103: (15230, 3), Sampled Labels 103: (15230,)\n",
      "Sampled Point Cloud 104: (15230, 3), Sampled Labels 104: (15230,)\n",
      "Sampled Point Cloud 105: (15230, 3), Sampled Labels 105: (15230,)\n",
      "Sampled Point Cloud 106: (15230, 3), Sampled Labels 106: (15230,)\n",
      "Sampled Point Cloud 107: (15230, 3), Sampled Labels 107: (15230,)\n",
      "Sampled Point Cloud 108: (15230, 3), Sampled Labels 108: (15230,)\n",
      "Sampled Point Cloud 109: (15230, 3), Sampled Labels 109: (15230,)\n",
      "Sampled Point Cloud 110: (15230, 3), Sampled Labels 110: (15230,)\n",
      "Sampled Point Cloud 111: (15230, 3), Sampled Labels 111: (15230,)\n",
      "Sampled Point Cloud 112: (15230, 3), Sampled Labels 112: (15230,)\n",
      "Sampled Point Cloud 113: (15230, 3), Sampled Labels 113: (15230,)\n",
      "Sampled Point Cloud 114: (15230, 3), Sampled Labels 114: (15230,)\n",
      "Sampled Point Cloud 115: (15230, 3), Sampled Labels 115: (15230,)\n",
      "Sampled Point Cloud 116: (15230, 3), Sampled Labels 116: (15230,)\n",
      "Sampled Point Cloud 117: (15230, 3), Sampled Labels 117: (15230,)\n",
      "Sampled Point Cloud 118: (15230, 3), Sampled Labels 118: (15230,)\n",
      "Sampled Point Cloud 119: (15230, 3), Sampled Labels 119: (15230,)\n",
      "Sampled Point Cloud 120: (15230, 3), Sampled Labels 120: (15230,)\n",
      "Sampled Point Cloud 121: (15230, 3), Sampled Labels 121: (15230,)\n",
      "Sampled Point Cloud 122: (15230, 3), Sampled Labels 122: (15230,)\n",
      "Sampled Point Cloud 123: (15230, 3), Sampled Labels 123: (15230,)\n",
      "Sampled Point Cloud 124: (15230, 3), Sampled Labels 124: (15230,)\n",
      "Sampled Point Cloud 125: (15230, 3), Sampled Labels 125: (15230,)\n",
      "Sampled Point Cloud 126: (15230, 3), Sampled Labels 126: (15230,)\n",
      "Sampled Point Cloud 127: (15230, 3), Sampled Labels 127: (15230,)\n",
      "Sampled Point Cloud 128: (15230, 3), Sampled Labels 128: (15230,)\n",
      "Sampled Point Cloud 129: (15230, 3), Sampled Labels 129: (15230,)\n",
      "Sampled Point Cloud 130: (15230, 3), Sampled Labels 130: (15230,)\n",
      "Sampled Point Cloud 131: (15230, 3), Sampled Labels 131: (15230,)\n",
      "Sampled Point Cloud 132: (15230, 3), Sampled Labels 132: (15230,)\n",
      "Sampled Point Cloud 133: (15230, 3), Sampled Labels 133: (15230,)\n",
      "Sampled Point Cloud 134: (15230, 3), Sampled Labels 134: (15230,)\n",
      "Sampled Point Cloud 135: (15230, 3), Sampled Labels 135: (15230,)\n",
      "Sampled Point Cloud 136: (15230, 3), Sampled Labels 136: (15230,)\n",
      "Sampled Point Cloud 137: (15230, 3), Sampled Labels 137: (15230,)\n",
      "Sampled Point Cloud 138: (15230, 3), Sampled Labels 138: (15230,)\n",
      "Sampled Point Cloud 139: (15230, 3), Sampled Labels 139: (15230,)\n",
      "Sampled Point Cloud 140: (15230, 3), Sampled Labels 140: (15230,)\n",
      "Sampled Point Cloud 141: (15230, 3), Sampled Labels 141: (15230,)\n",
      "Sampled Point Cloud 142: (15230, 3), Sampled Labels 142: (15230,)\n",
      "Sampled Point Cloud 143: (15230, 3), Sampled Labels 143: (15230,)\n",
      "Sampled Point Cloud 144: (15230, 3), Sampled Labels 144: (15230,)\n",
      "Sampled Point Cloud 145: (15230, 3), Sampled Labels 145: (15230,)\n",
      "Sampled Point Cloud 146: (15230, 3), Sampled Labels 146: (15230,)\n",
      "Sampled Point Cloud 147: (15230, 3), Sampled Labels 147: (15230,)\n",
      "Sampled Point Cloud 148: (15230, 3), Sampled Labels 148: (15230,)\n",
      "Sampled Point Cloud 149: (15230, 3), Sampled Labels 149: (15230,)\n",
      "Sampled Point Cloud 150: (15230, 3), Sampled Labels 150: (15230,)\n",
      "Sampled Point Cloud 151: (15230, 3), Sampled Labels 151: (15230,)\n",
      "Sampled Point Cloud 152: (15230, 3), Sampled Labels 152: (15230,)\n",
      "Sampled Point Cloud 153: (15230, 3), Sampled Labels 153: (15230,)\n",
      "Sampled Point Cloud 154: (15230, 3), Sampled Labels 154: (15230,)\n",
      "Sampled Point Cloud 155: (15230, 3), Sampled Labels 155: (15230,)\n",
      "Sampled Point Cloud 156: (15230, 3), Sampled Labels 156: (15230,)\n",
      "Sampled Point Cloud 157: (15230, 3), Sampled Labels 157: (15230,)\n",
      "Sampled Point Cloud 158: (15230, 3), Sampled Labels 158: (15230,)\n",
      "Sampled Point Cloud 159: (15230, 3), Sampled Labels 159: (15230,)\n",
      "Sampled Point Cloud 160: (15230, 3), Sampled Labels 160: (15230,)\n",
      "Sampled Point Cloud 161: (15230, 3), Sampled Labels 161: (15230,)\n",
      "Sampled Point Cloud 162: (15230, 3), Sampled Labels 162: (15230,)\n",
      "Sampled Point Cloud 163: (15230, 3), Sampled Labels 163: (15230,)\n",
      "Sampled Point Cloud 164: (15230, 3), Sampled Labels 164: (15230,)\n",
      "Sampled Point Cloud 165: (15230, 3), Sampled Labels 165: (15230,)\n",
      "Sampled Point Cloud 166: (15230, 3), Sampled Labels 166: (15230,)\n",
      "Sampled Point Cloud 167: (15230, 3), Sampled Labels 167: (15230,)\n",
      "Sampled Point Cloud 168: (15230, 3), Sampled Labels 168: (15230,)\n",
      "Sampled Point Cloud 169: (15230, 3), Sampled Labels 169: (15230,)\n",
      "Sampled Point Cloud 170: (15230, 3), Sampled Labels 170: (15230,)\n",
      "Sampled Point Cloud 171: (15230, 3), Sampled Labels 171: (15230,)\n",
      "Sampled Point Cloud 172: (15230, 3), Sampled Labels 172: (15230,)\n",
      "Sampled Point Cloud 173: (15230, 3), Sampled Labels 173: (15230,)\n",
      "Sampled Point Cloud 174: (15230, 3), Sampled Labels 174: (15230,)\n",
      "Sampled Point Cloud 175: (15230, 3), Sampled Labels 175: (15230,)\n",
      "Sampled Point Cloud 176: (15230, 3), Sampled Labels 176: (15230,)\n",
      "Sampled Point Cloud 177: (15230, 3), Sampled Labels 177: (15230,)\n",
      "Sampled Point Cloud 178: (15230, 3), Sampled Labels 178: (15230,)\n",
      "Sampled Point Cloud 179: (15230, 3), Sampled Labels 179: (15230,)\n",
      "Sampled Point Cloud 180: (15230, 3), Sampled Labels 180: (15230,)\n",
      "Sampled Point Cloud 181: (15230, 3), Sampled Labels 181: (15230,)\n",
      "Sampled Point Cloud 182: (15230, 3), Sampled Labels 182: (15230,)\n",
      "Sampled Point Cloud 183: (15230, 3), Sampled Labels 183: (15230,)\n",
      "Sampled Point Cloud 184: (15230, 3), Sampled Labels 184: (15230,)\n",
      "Sampled Point Cloud 185: (15230, 3), Sampled Labels 185: (15230,)\n",
      "Sampled Point Cloud 186: (15230, 3), Sampled Labels 186: (15230,)\n",
      "Sampled Point Cloud 187: (15230, 3), Sampled Labels 187: (15230,)\n",
      "Sampled Point Cloud 188: (15230, 3), Sampled Labels 188: (15230,)\n",
      "Sampled Point Cloud 189: (15230, 3), Sampled Labels 189: (15230,)\n",
      "Sampled Point Cloud 190: (15230, 3), Sampled Labels 190: (15230,)\n",
      "Sampled Point Cloud 191: (15230, 3), Sampled Labels 191: (15230,)\n",
      "Sampled Point Cloud 192: (15230, 3), Sampled Labels 192: (15230,)\n",
      "Sampled Point Cloud 193: (15230, 3), Sampled Labels 193: (15230,)\n",
      "Sampled Point Cloud 194: (15230, 3), Sampled Labels 194: (15230,)\n",
      "Sampled Point Cloud 195: (15230, 3), Sampled Labels 195: (15230,)\n",
      "Sampled Point Cloud 196: (15230, 3), Sampled Labels 196: (15230,)\n",
      "Sampled Point Cloud 197: (15230, 3), Sampled Labels 197: (15230,)\n",
      "Sampled Point Cloud 198: (15230, 3), Sampled Labels 198: (15230,)\n",
      "Sampled Point Cloud 199: (15230, 3), Sampled Labels 199: (15230,)\n",
      "Sampled Point Cloud 200: (15230, 3), Sampled Labels 200: (15230,)\n",
      "Sampled Point Cloud 201: (15230, 3), Sampled Labels 201: (15230,)\n",
      "Sampled Point Cloud 202: (15230, 3), Sampled Labels 202: (15230,)\n",
      "Sampled Point Cloud 203: (15230, 3), Sampled Labels 203: (15230,)\n",
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      "Sampled Point Cloud 205: (15230, 3), Sampled Labels 205: (15230,)\n",
      "Sampled Point Cloud 206: (15230, 3), Sampled Labels 206: (15230,)\n",
      "Sampled Point Cloud 207: (15230, 3), Sampled Labels 207: (15230,)\n",
      "Sampled Point Cloud 208: (15230, 3), Sampled Labels 208: (15230,)\n",
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      "Sampled Point Cloud 211: (15230, 3), Sampled Labels 211: (15230,)\n",
      "Sampled Point Cloud 212: (15230, 3), Sampled Labels 212: (15230,)\n",
      "Sampled Point Cloud 213: (15230, 3), Sampled Labels 213: (15230,)\n",
      "Sampled Point Cloud 214: (15230, 3), Sampled Labels 214: (15230,)\n",
      "Sampled Point Cloud 215: (15230, 3), Sampled Labels 215: (15230,)\n",
      "Sampled Point Cloud 216: (15230, 3), Sampled Labels 216: (15230,)\n",
      "Sampled Point Cloud 217: (15230, 3), Sampled Labels 217: (15230,)\n",
      "Sampled Point Cloud 218: (15230, 3), Sampled Labels 218: (15230,)\n",
      "Sampled Point Cloud 219: (15230, 3), Sampled Labels 219: (15230,)\n",
      "Sampled Point Cloud 220: (15230, 3), Sampled Labels 220: (15230,)\n",
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      "Sampled Point Cloud 222: (15230, 3), Sampled Labels 222: (15230,)\n",
      "Sampled Point Cloud 223: (15230, 3), Sampled Labels 223: (15230,)\n",
      "Sampled Point Cloud 224: (15230, 3), Sampled Labels 224: (15230,)\n",
      "Sampled Point Cloud 225: (15230, 3), Sampled Labels 225: (15230,)\n",
      "Sampled Point Cloud 226: (15230, 3), Sampled Labels 226: (15230,)\n",
      "Sampled Point Cloud 227: (15230, 3), Sampled Labels 227: (15230,)\n",
      "Sampled Point Cloud 228: (15230, 3), Sampled Labels 228: (15230,)\n",
      "Sampled Point Cloud 229: (15230, 3), Sampled Labels 229: (15230,)\n",
      "Sampled Point Cloud 230: (15230, 3), Sampled Labels 230: (15230,)\n",
      "Sampled Point Cloud 231: (15230, 3), Sampled Labels 231: (15230,)\n",
      "Sampled Point Cloud 232: (15230, 3), Sampled Labels 232: (15230,)\n",
      "Sampled Point Cloud 233: (15230, 3), Sampled Labels 233: (15230,)\n",
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      "Sampled Point Cloud 235: (15230, 3), Sampled Labels 235: (15230,)\n",
      "Sampled Point Cloud 236: (15230, 3), Sampled Labels 236: (15230,)\n",
      "Sampled Point Cloud 237: (15230, 3), Sampled Labels 237: (15230,)\n",
      "Sampled Point Cloud 238: (15230, 3), Sampled Labels 238: (15230,)\n",
      "Sampled Point Cloud 239: (15230, 3), Sampled Labels 239: (15230,)\n",
      "Sampled Point Cloud 240: (15230, 3), Sampled Labels 240: (15230,)\n",
      "Sampled Point Cloud 241: (15230, 3), Sampled Labels 241: (15230,)\n",
      "Sampled Point Cloud 242: (15230, 3), Sampled Labels 242: (15230,)\n",
      "Sampled Point Cloud 243: (15230, 3), Sampled Labels 243: (15230,)\n",
      "Sampled Point Cloud 244: (15230, 3), Sampled Labels 244: (15230,)\n",
      "Sampled Point Cloud 245: (15230, 3), Sampled Labels 245: (15230,)\n",
      "Sampled Point Cloud 246: (15230, 3), Sampled Labels 246: (15230,)\n",
      "Sampled Point Cloud 247: (15230, 3), Sampled Labels 247: (15230,)\n",
      "Sampled Point Cloud 248: (15230, 3), Sampled Labels 248: (15230,)\n",
      "Sampled Point Cloud 249: (15230, 3), Sampled Labels 249: (15230,)\n",
      "Sampled Point Cloud 250: (15230, 3), Sampled Labels 250: (15230,)\n",
      "Sampled Point Cloud 251: (15230, 3), Sampled Labels 251: (15230,)\n",
      "Sampled Point Cloud 252: (15230, 3), Sampled Labels 252: (15230,)\n",
      "Sampled Point Cloud 253: (15230, 3), Sampled Labels 253: (15230,)\n",
      "Sampled Point Cloud 254: (15230, 3), Sampled Labels 254: (15230,)\n",
      "Sampled Point Cloud 255: (15230, 3), Sampled Labels 255: (15230,)\n",
      "Sampled Point Cloud 256: (15230, 3), Sampled Labels 256: (15230,)\n",
      "Sampled Point Cloud 257: (15230, 3), Sampled Labels 257: (15230,)\n",
      "Sampled Point Cloud 258: (15230, 3), Sampled Labels 258: (15230,)\n",
      "Sampled Point Cloud 259: (15230, 3), Sampled Labels 259: (15230,)\n",
      "Sampled Point Cloud 260: (15230, 3), Sampled Labels 260: (15230,)\n",
      "Sampled Point Cloud 261: (15230, 3), Sampled Labels 261: (15230,)\n",
      "Sampled Point Cloud 262: (15230, 3), Sampled Labels 262: (15230,)\n",
      "Sampled Point Cloud 263: (15230, 3), Sampled Labels 263: (15230,)\n",
      "Sampled Point Cloud 264: (15230, 3), Sampled Labels 264: (15230,)\n",
      "Sampled Point Cloud 265: (15230, 3), Sampled Labels 265: (15230,)\n",
      "Sampled Point Cloud 266: (15230, 3), Sampled Labels 266: (15230,)\n",
      "Sampled Point Cloud 267: (15230, 3), Sampled Labels 267: (15230,)\n",
      "Sampled Point Cloud 268: (15230, 3), Sampled Labels 268: (15230,)\n",
      "Sampled Point Cloud 269: (15230, 3), Sampled Labels 269: (15230,)\n",
      "Sampled Point Cloud 270: (15230, 3), Sampled Labels 270: (15230,)\n",
      "Sampled Point Cloud 271: (15230, 3), Sampled Labels 271: (15230,)\n",
      "Sampled Point Cloud 272: (15230, 3), Sampled Labels 272: (15230,)\n",
      "Sampled Point Cloud 273: (15230, 3), Sampled Labels 273: (15230,)\n",
      "Sampled Point Cloud 274: (15230, 3), Sampled Labels 274: (15230,)\n",
      "Sampled Point Cloud 275: (15230, 3), Sampled Labels 275: (15230,)\n",
      "Sampled Point Cloud 276: (15230, 3), Sampled Labels 276: (15230,)\n",
      "Sampled Point Cloud 277: (15230, 3), Sampled Labels 277: (15230,)\n",
      "Sampled Point Cloud 278: (15230, 3), Sampled Labels 278: (15230,)\n",
      "Sampled Point Cloud 279: (15230, 3), Sampled Labels 279: (15230,)\n",
      "Sampled Point Cloud 280: (15230, 3), Sampled Labels 280: (15230,)\n",
      "Sampled Point Cloud 281: (15230, 3), Sampled Labels 281: (15230,)\n",
      "Sampled Point Cloud 282: (15230, 3), Sampled Labels 282: (15230,)\n",
      "Sampled Point Cloud 283: (15230, 3), Sampled Labels 283: (15230,)\n",
      "Sampled Point Cloud 284: (15230, 3), Sampled Labels 284: (15230,)\n",
      "Sampled Point Cloud 285: (15230, 3), Sampled Labels 285: (15230,)\n",
      "Sampled Point Cloud 286: (15230, 3), Sampled Labels 286: (15230,)\n",
      "Sampled Point Cloud 287: (15230, 3), Sampled Labels 287: (15230,)\n",
      "Sampled Point Cloud 288: (15230, 3), Sampled Labels 288: (15230,)\n",
      "Sampled Point Cloud 289: (15230, 3), Sampled Labels 289: (15230,)\n",
      "Sampled Point Cloud 290: (15230, 3), Sampled Labels 290: (15230,)\n",
      "Sampled Point Cloud 291: (15230, 3), Sampled Labels 291: (15230,)\n",
      "Sampled Point Cloud 292: (15230, 3), Sampled Labels 292: (15230,)\n",
      "Sampled Point Cloud 293: (15230, 3), Sampled Labels 293: (15230,)\n",
      "Sampled Point Cloud 294: (15230, 3), Sampled Labels 294: (15230,)\n",
      "Sampled Point Cloud 295: (15230, 3), Sampled Labels 295: (15230,)\n",
      "Sampled Point Cloud 296: (15230, 3), Sampled Labels 296: (15230,)\n",
      "Sampled Point Cloud 297: (15230, 3), Sampled Labels 297: (15230,)\n",
      "Sampled Point Cloud 298: (15230, 3), Sampled Labels 298: (15230,)\n",
      "Sampled Point Cloud 299: (15230, 3), Sampled Labels 299: (15230,)\n"
     ]
    }
   ],
   "source": [
    "def sample_points_and_labels(point_cloud, labels, min_length):\n",
    "\n",
    "    N = point_cloud.shape[0]\n",
    "\n",
    "    if N < min_length:\n",
    "        return point_cloud, labels  # Return all points and labels if fewer than min_length\n",
    "\n",
    "    indices = np.random.choice(N, min_length, replace=False)\n",
    "    sampled_points = point_cloud[indices]\n",
    "    sampled_labels = labels[indices]\n",
    "\n",
    "    return sampled_points, sampled_labels\n",
    "\n",
    "# Initialize a list to store the sampled data\n",
    "sampled_data = []\n",
    "\n",
    "# Iterate through the original dataset and sample points and labels\n",
    "for point_cloud, labels, instances,jaw, person in data:\n",
    "    sampled_cloud, sampled_label = sample_points_and_labels(point_cloud, labels, min_length)\n",
    "    # Store as a tuple similar to the original data structure\n",
    "    sampled_data.append((sampled_cloud, sampled_label, instances, jaw, person))\n",
    "\n",
    "# Output the sampled data\n",
    "for i, (cloud, label, instances, _, _) in enumerate(sampled_data):\n",
    "    print(f\"Sampled Point Cloud {i}: {cloud.shape}, Sampled Labels {i}: {label.shape}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b647ccd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300, 5)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampled_data_array = np.array(sampled_data, dtype=object)\n",
    "sampled_data_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "60e3f0cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate features and labels\n",
    "point_clouds = np.array([item[0] for item in sampled_data])  # Sampled point clouds\n",
    "labels = np.array([item[1] for item in sampled_data])         # Sampled labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b7833b8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300, 15230)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "71199ef6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300, 15230, 3)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "point_clouds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5bb3bd28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique labels: [ 0 11 12 13 14 15 16 17 18 21 22 23 24 25 26 27 28 31 32 33 34 35 36 37\n",
      " 38 41 42 43 44 45 46 47 48]\n",
      "Number of unique labels: 33\n"
     ]
    }
   ],
   "source": [
    "unique_labels = np.unique(labels)\n",
    "num_unique_labels = len(unique_labels)\n",
    "print(f\"Unique labels: {unique_labels}\")\n",
    "print(f\"Number of unique labels: {num_unique_labels}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d70d408d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Data preparation\n",
    "point_clouds_train, point_clouds_test, labels_train, labels_test = train_test_split(\n",
    "    point_clouds, labels, test_size=0.2, random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f1da8959",
   "metadata": {},
   "outputs": [],
   "source": [
    "# point_clouds shape: (300, 15230, 3)\n",
    "# labels shape: (300, 15230)\n",
    "\n",
    "# Create a mapping for your specific labels\n",
    "unique_labels = np.array([0, 11, 12, 13, 14, 15, 16, 17, 18, 21, 22, 23, 24, 25, 26, 27, 28, \n",
    "                          31, 32, 33, 34, 35, 36, 37, 38, 41, 42, 43, 44, 45, 46, 47, 48])\n",
    "label_to_index = {label: index for index, label in enumerate(unique_labels)}\n",
    "index_to_label = {index: label for label, index in label_to_index.items()}\n",
    "\n",
    "# Function to convert labels to consecutive indices\n",
    "def convert_labels(labels):\n",
    "    return np.vectorize(label_to_index.get)(labels)\n",
    "\n",
    "# Function to convert indices back to original labels\n",
    "def convert_indices(indices):\n",
    "    return np.vectorize(index_to_label.get)(indices)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40dffd12",
   "metadata": {},
   "source": [
    "# Model Architecture Definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6c7737d",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TNet(layers.Layer):\n",
    "    def __init__(self, K=3):\n",
    "        super(TNet, self).__init__()\n",
    "        self.K = K\n",
    "        self.conv1 = layers.Conv1D(64, kernel_size=1, activation='relu')\n",
    "        self.conv2 = layers.Conv1D(128, kernel_size=1, activation='relu')\n",
    "        self.conv3 = layers.Conv1D(1024, kernel_size=1, activation='relu')\n",
    "        self.fc1 = layers.Dense(512, activation='relu')\n",
    "        self.fc2 = layers.Dense(256, activation='relu')\n",
    "        self.fc3 = layers.Dense(K * K)\n",
    "        self.reshape = layers.Reshape((K, K))\n",
    "\n",
    "    def call(self, inputs):\n",
    "        x = self.conv1(inputs)\n",
    "        x = self.conv2(x)\n",
    "        x = self.conv3(x)\n",
    "        x = layers.GlobalMaxPooling1D()(x) \n",
    "        x = self.fc1(x)\n",
    "        x = self.fc2(x)\n",
    "        x = self.fc3(x)\n",
    "        x = self.reshape(x)  # Reshape to (batch_size, K, K)\n",
    "\n",
    "        # Create identity matrix dynamically based on the batch size\n",
    "        batch_size = tf.shape(x)[0]\n",
    "        identity = tf.eye(self.K, batch_shape=[batch_size])\n",
    "\n",
    "        # Add identity matrix to the transformation matrix\n",
    "        x = x + identity\n",
    "        return x\n",
    "\n",
    "\n",
    "class DentalPointNet(keras.Model):\n",
    "    def __init__(self, num_classes):\n",
    "        super(DentalPointNet, self).__init__()\n",
    "        self.tnet1 = TNet(K=3)\n",
    "        self.conv1 = layers.Conv1D(64, kernel_size=1, activation='relu')\n",
    "        self.conv2 = layers.Conv1D(64, kernel_size=1, activation='relu')\n",
    "        self.tnet2 = TNet(K=64)\n",
    "        self.conv3 = layers.Conv1D(64, kernel_size=1, activation='relu')\n",
    "        self.conv4 = layers.Conv1D(128, kernel_size=1, activation='relu')\n",
    "        self.conv5 = layers.Conv1D(1024, kernel_size=1, activation='relu')\n",
    "        self.conv6 = layers.Conv1D(512, kernel_size=1, activation='relu')\n",
    "        self.conv7 = layers.Conv1D(256, kernel_size=1, activation='relu')\n",
    "        self.conv8 = layers.Conv1D(128, kernel_size=1, activation='relu')\n",
    "        self.conv9 = layers.Conv1D(num_classes, kernel_size=1, activation='softmax')\n",
    "\n",
    "    def call(self, inputs):\n",
    "        transform1 = self.tnet1(inputs)\n",
    "        x = tf.matmul(inputs, transform1)\n",
    "        \n",
    "        x = self.conv1(x)\n",
    "        x = self.conv2(x)\n",
    "        \n",
    "        transform2 = self.tnet2(x)\n",
    "        x = tf.matmul(x, transform2)\n",
    "        \n",
    "        x = self.conv3(x)\n",
    "        x = self.conv4(x)\n",
    "        x = self.conv5(x)\n",
    "        \n",
    "        global_feature = layers.GlobalMaxPooling1D()(x)\n",
    "        global_feature = tf.expand_dims(global_feature, 1)\n",
    "        global_feature = tf.tile(global_feature, [1, tf.shape(inputs)[1], 1])\n",
    "        \n",
    "        x = tf.concat([x, global_feature], axis=-1)\n",
    "        \n",
    "        x = self.conv6(x)\n",
    "        x = self.conv7(x)\n",
    "        x = self.conv8(x)\n",
    "        x = self.conv9(x)\n",
    "        \n",
    "        return x\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "782c9e47",
   "metadata": {},
   "source": [
    "# Model training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "80eba2d4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Pro\\anaconda3\\lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Pro\\anaconda3\\lib\\site-packages\\keras\\src\\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "Epoch 1/100\n",
      "WARNING:tensorflow:From C:\\Users\\Pro\\anaconda3\\lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Pro\\anaconda3\\lib\\site-packages\\keras\\src\\engine\\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
      "\n",
      "96/96 [==============================] - ETA: 0s - loss: 2.2138 - accuracy: 0.4554INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96/96 [==============================] - 192s 2s/step - loss: 2.2138 - accuracy: 0.4554 - val_loss: 1.5662 - val_accuracy: 0.4965 - lr: 0.0010\n",
      "Epoch 2/100\n",
      "96/96 [==============================] - ETA: 0s - loss: 1.4252 - accuracy: 0.5123INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96/96 [==============================] - 181s 2s/step - loss: 1.4252 - accuracy: 0.5123 - val_loss: 1.3794 - val_accuracy: 0.5343 - lr: 0.0010\n",
      "Epoch 3/100\n",
      "96/96 [==============================] - ETA: 0s - loss: 1.2762 - accuracy: 0.5377INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96/96 [==============================] - 177s 2s/step - loss: 1.2762 - accuracy: 0.5377 - val_loss: 1.1932 - val_accuracy: 0.5715 - lr: 0.0010\n",
      "Epoch 4/100\n",
      "96/96 [==============================] - ETA: 0s - loss: 1.2002 - accuracy: 0.5575INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96/96 [==============================] - 181s 2s/step - loss: 1.2002 - accuracy: 0.5575 - val_loss: 1.1571 - val_accuracy: 0.5763 - lr: 0.0010\n",
      "Epoch 5/100\n",
      "96/96 [==============================] - ETA: 0s - loss: 1.1637 - accuracy: 0.5765INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96/96 [==============================] - 187s 2s/step - loss: 1.1637 - accuracy: 0.5765 - val_loss: 1.1077 - val_accuracy: 0.5799 - lr: 0.0010\n",
      "Epoch 6/100\n",
      "96/96 [==============================] - 183s 2s/step - loss: 1.1577 - accuracy: 0.5684 - val_loss: 1.2217 - val_accuracy: 0.5461 - lr: 0.0010\n",
      "Epoch 7/100\n",
      "96/96 [==============================] - ETA: 0s - loss: 1.1330 - accuracy: 0.5734INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96/96 [==============================] - 189s 2s/step - loss: 1.1330 - accuracy: 0.5734 - val_loss: 1.0431 - val_accuracy: 0.6047 - lr: 0.0010\n",
      "Epoch 8/100\n",
      "96/96 [==============================] - ETA: 0s - loss: 1.1307 - accuracy: 0.5812INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96/96 [==============================] - 189s 2s/step - loss: 1.1307 - accuracy: 0.5812 - val_loss: 1.0327 - val_accuracy: 0.5978 - lr: 0.0010\n",
      "Epoch 9/100\n",
      "96/96 [==============================] - 187s 2s/step - loss: 1.1146 - accuracy: 0.5929 - val_loss: 1.1295 - val_accuracy: 0.5776 - lr: 0.0010\n",
      "Epoch 10/100\n",
      "96/96 [==============================] - 185s 2s/step - loss: 1.1186 - accuracy: 0.5860 - val_loss: 1.0476 - val_accuracy: 0.6051 - lr: 0.0010\n",
      "Epoch 11/100\n",
      "96/96 [==============================] - ETA: 0s - loss: 1.0702 - accuracy: 0.5966INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96/96 [==============================] - 188s 2s/step - loss: 1.0702 - accuracy: 0.5966 - val_loss: 1.0129 - val_accuracy: 0.6182 - lr: 0.0010\n",
      "Epoch 12/100\n",
      "96/96 [==============================] - 183s 2s/step - loss: 1.0566 - accuracy: 0.6033 - val_loss: 1.0292 - val_accuracy: 0.6075 - lr: 0.0010\n",
      "Epoch 13/100\n",
      "96/96 [==============================] - 185s 2s/step - loss: 1.0656 - accuracy: 0.6024 - val_loss: 1.0537 - val_accuracy: 0.5923 - lr: 0.0010\n",
      "Epoch 14/100\n",
      "96/96 [==============================] - ETA: 0s - loss: 1.0096 - accuracy: 0.6380INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96/96 [==============================] - 188s 2s/step - loss: 1.0096 - accuracy: 0.6380 - val_loss: 0.8755 - val_accuracy: 0.6899 - lr: 0.0010\n",
      "Epoch 15/100\n",
      "96/96 [==============================] - ETA: 0s - loss: 0.9707 - accuracy: 0.6544INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96/96 [==============================] - 187s 2s/step - loss: 0.9707 - accuracy: 0.6544 - val_loss: 0.7862 - val_accuracy: 0.7199 - lr: 0.0010\n",
      "Epoch 16/100\n",
      "96/96 [==============================] - 187s 2s/step - loss: 0.8774 - accuracy: 0.6826 - val_loss: 0.9257 - val_accuracy: 0.6618 - lr: 0.0010\n",
      "Epoch 17/100\n",
      "96/96 [==============================] - ETA: 0s - loss: 0.8607 - accuracy: 0.6889INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96/96 [==============================] - 191s 2s/step - loss: 0.8607 - accuracy: 0.6889 - val_loss: 0.7641 - val_accuracy: 0.7114 - lr: 0.0010\n",
      "Epoch 18/100\n",
      "96/96 [==============================] - ETA: 0s - loss: 0.7814 - accuracy: 0.7085INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96/96 [==============================] - 187s 2s/step - loss: 0.7814 - accuracy: 0.7085 - val_loss: 0.6887 - val_accuracy: 0.7370 - lr: 0.0010\n",
      "Epoch 19/100\n",
      "96/96 [==============================] - 176s 2s/step - loss: 0.7421 - accuracy: 0.7186 - val_loss: 0.7115 - val_accuracy: 0.7241 - lr: 0.0010\n",
      "Epoch 20/100\n",
      "96/96 [==============================] - 177s 2s/step - loss: 0.8013 - accuracy: 0.6972 - val_loss: 0.6987 - val_accuracy: 0.7180 - lr: 0.0010\n",
      "Epoch 21/100\n",
      "96/96 [==============================] - 178s 2s/step - loss: 0.7635 - accuracy: 0.7053 - val_loss: 0.8592 - val_accuracy: 0.6607 - lr: 0.0010\n",
      "Epoch 22/100\n",
      "96/96 [==============================] - 175s 2s/step - loss: 0.6884 - accuracy: 0.7331 - val_loss: 0.6956 - val_accuracy: 0.7309 - lr: 0.0010\n",
      "Epoch 23/100\n",
      "96/96 [==============================] - 270s 3s/step - loss: 0.7065 - accuracy: 0.7245 - val_loss: 0.7759 - val_accuracy: 0.6948 - lr: 0.0010\n",
      "Epoch 24/100\n",
      "96/96 [==============================] - ETA: 0s - loss: 0.6445 - accuracy: 0.7440INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96/96 [==============================] - 259s 3s/step - loss: 0.6445 - accuracy: 0.7440 - val_loss: 0.6431 - val_accuracy: 0.7416 - lr: 5.0000e-04\n",
      "Epoch 25/100\n",
      "96/96 [==============================] - ETA: 0s - loss: 0.6305 - accuracy: 0.7491INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96/96 [==============================] - 239s 2s/step - loss: 0.6305 - accuracy: 0.7491 - val_loss: 0.6367 - val_accuracy: 0.7457 - lr: 5.0000e-04\n",
      "Epoch 26/100\n",
      "96/96 [==============================] - ETA: 0s - loss: 0.5985 - accuracy: 0.7606INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96/96 [==============================] - 218s 2s/step - loss: 0.5985 - accuracy: 0.7606 - val_loss: 0.6183 - val_accuracy: 0.7497 - lr: 5.0000e-04\n",
      "Epoch 27/100\n",
      "96/96 [==============================] - ETA: 0s - loss: 0.5931 - accuracy: 0.7606INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: best_dental_pointnet_model\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96/96 [==============================] - 219s 2s/step - loss: 0.5931 - accuracy: 0.7606 - val_loss: 0.5940 - val_accuracy: 0.7608 - lr: 5.0000e-04\n",
      "Epoch 28/100\n",
      "96/96 [==============================] - 216s 2s/step - loss: 0.5879 - accuracy: 0.7607 - val_loss: 0.6223 - val_accuracy: 0.7510 - lr: 5.0000e-04\n",
      "Epoch 29/100\n",
      "96/96 [==============================] - 220s 2s/step - loss: 0.5811 - accuracy: 0.7644 - val_loss: 0.6365 - val_accuracy: 0.7482 - lr: 5.0000e-04\n",
      "Epoch 30/100\n",
      "96/96 [==============================] - 216s 2s/step - loss: 0.5866 - accuracy: 0.7658 - val_loss: 0.7398 - val_accuracy: 0.7263 - lr: 5.0000e-04\n",
      "Epoch 31/100\n",
      "96/96 [==============================] - 242s 3s/step - loss: 0.5847 - accuracy: 0.7632 - val_loss: 0.6416 - val_accuracy: 0.7390 - lr: 5.0000e-04\n",
      "Epoch 32/100\n",
      "96/96 [==============================] - 243s 3s/step - loss: 0.5697 - accuracy: 0.7691 - val_loss: 0.6178 - val_accuracy: 0.7543 - lr: 5.0000e-04\n",
      "Epoch 33/100\n",
      "96/96 [==============================] - 233s 2s/step - loss: 0.5508 - accuracy: 0.7752 - val_loss: 0.6120 - val_accuracy: 0.7523 - lr: 2.5000e-04\n",
      "Epoch 34/100\n",
      "96/96 [==============================] - 236s 2s/step - loss: 0.5340 - accuracy: 0.7821 - val_loss: 0.6508 - val_accuracy: 0.7386 - lr: 2.5000e-04\n",
      "Epoch 35/100\n",
      "96/96 [==============================] - 234s 2s/step - loss: 0.5441 - accuracy: 0.7768 - val_loss: 0.6529 - val_accuracy: 0.7484 - lr: 2.5000e-04\n",
      "Epoch 36/100\n",
      "96/96 [==============================] - 222s 2s/step - loss: 0.5459 - accuracy: 0.7763 - val_loss: 0.6175 - val_accuracy: 0.7496 - lr: 2.5000e-04\n",
      "Epoch 37/100\n",
      "96/96 [==============================] - 244s 3s/step - loss: 0.5380 - accuracy: 0.7807 - val_loss: 0.6275 - val_accuracy: 0.7538 - lr: 2.5000e-04\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Convert labels to consecutive indices\n",
    "labels = convert_labels(labels)\n",
    "\n",
    "\n",
    "# Create and compile the model\n",
    "num_classes = len(unique_labels)\n",
    "model = DentalPointNet(num_classes)\n",
    "\n",
    "sample_input = tf.random.normal((1, 15230, 3))\n",
    "_ = model(sample_input)\n",
    "\n",
    "model.compile(\n",
    "    optimizer=keras.optimizers.Adam(learning_rate=0.001),\n",
    "    loss='sparse_categorical_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")\n",
    "\n",
    "# Define callbacks\n",
    "callbacks = [\n",
    "    keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=5, min_lr=1e-5),\n",
    "    keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True),\n",
    "    keras.callbacks.ModelCheckpoint('best_dental_pointnet_model', save_best_only=True, monitor='val_loss', save_format='tf')\n",
    "]\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(\n",
    "    point_clouds_train, labels_train,\n",
    "    batch_size=2,  \n",
    "    epochs=100,\n",
    "    validation_split=0.2,\n",
    "    callbacks=callbacks\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "91d0fcb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: dental_pointnet_model222\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: dental_pointnet_model222\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model training and evaluation completed. Model saved in TensorFlow SavedModel format.\n"
     ]
    }
   ],
   "source": [
    "# Save the model using TensorFlow SavedModel format\n",
    "model.save('dental_pointnet_model222', save_format='tf')\n",
    "\n",
    "print(\"Model training and evaluation completed. Model saved in TensorFlow SavedModel format.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "980e5e20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Pro\\anaconda3\\lib\\site-packages\\keras\\src\\saving\\legacy\\saved_model\\load.py:107: The name tf.gfile.Exists is deprecated. Please use tf.io.gfile.exists instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Pro\\anaconda3\\lib\\site-packages\\keras\\src\\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "Model loaded successfully.\n"
     ]
    }
   ],
   "source": [
    "# Load the model\n",
    "loaded_model = tf.keras.models.load_model(\n",
    "    'dental_pointnet_model222',\n",
    "    custom_objects={\n",
    "        'TNet': TNet,\n",
    "        'DentalPointNet': DentalPointNet\n",
    "    }\n",
    ")\n",
    "\n",
    "print(\"Model loaded successfully.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a1db3ce6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"dental_point_net\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " t_net (TNet)                multiple                  799113    \n",
      "                                                                 \n",
      " conv1d_3 (Conv1D)           multiple                  256       \n",
      "                                                                 \n",
      " conv1d_4 (Conv1D)           multiple                  4160      \n",
      "                                                                 \n",
      " t_net_1 (TNet)              multiple                  1853376   \n",
      "                                                                 \n",
      " conv1d_8 (Conv1D)           multiple                  4160      \n",
      "                                                                 \n",
      " conv1d_9 (Conv1D)           multiple                  8320      \n",
      "                                                                 \n",
      " conv1d_10 (Conv1D)          multiple                  132096    \n",
      "                                                                 \n",
      " conv1d_11 (Conv1D)          multiple                  1049088   \n",
      "                                                                 \n",
      " conv1d_12 (Conv1D)          multiple                  131328    \n",
      "                                                                 \n",
      " conv1d_13 (Conv1D)          multiple                  32896     \n",
      "                                                                 \n",
      " conv1d_14 (Conv1D)          multiple                  4257      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 4019050 (15.33 MB)\n",
      "Trainable params: 4019050 (15.33 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "loaded_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a08365d",
   "metadata": {},
   "source": [
    "# Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e60df7a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7c+eOMnbsWMXDw0OxtbVVOnbsqOzZsyfb56ooirJ8+XLF399fsbCwMLhOTp9jZmamMmfOHKVevXqKhYWFUqVKFWX48OFKRESEQbnc/l4L+vv18/NTgBxfo0aNUhRFHa345ptvKn5+foqFhYXi7e2tvPjii8qdO3f019m3b5/y+OOPK35+foqVlZXi5uamdOnSRVm3bp2+zE8//aR069ZN8fT0VCwtLRUfHx9lyJAhyokTJ/KNU4jcaBTlgfplIYR4wMcff8y7775LeHi4zDgshKhQpAlMCAHAggULALVZKD09nR07dvDll18yfPhwSX6EEBWOJEBCCEAdLv/5559z5coVUlNTqV69Om+++SbvvvuuqUMTQgijkyYwIYQQQlQ6MgxeCCGEEJWOJEBCCCGEqHQkARJCCCFEpSOdoHOg0+m4fv06Dg4OOS5tIIQQQoiyR1EU7t69i4+PT74TuEoClIPr16/j6+tr6jCEEEIIUQQRERH5Tt8hCVAOHBwcAPUX6OjoaOJohBBCCFEQ8fHx+Pr66r/H8yIJUA6ymr0cHR0lARJCCCHKmYJ0X5FO0EIIIYSodCQBEkIIIUSlY9IEaPbs2bRu3RoHBwc8PDwYNGgQ58+fz/OcP/74g549e+Lu7o6joyOBgYFs3rzZoExQUBAajSbbKyUlpSQfRwghhBDlhEn7AO3atYuJEyfSunVrMjIyeOedd+jVqxdnzpzBzs4ux3N2795Nz549+fjjj3F2dmbJkiUMGDCAAwcO0Lx5c305R0fHbMmUtbV1iT6PEEKI+zIzM0lPTzd1GKKCsbS0zHeIe0GUqbXAbt26hYeHB7t27aJz584FPq9hw4YMHTqU9957D1BrgKZMmUJsbGyR4oiPj8fJyYm4uDjpBC2EEIWkKApRUVFF/n+wEHnRarXUrFkTS0vLbMcK8/1dpkaBxcXFAeDq6lrgc3Q6HXfv3s12TkJCAn5+fmRmZtKsWTM++OADgxoiIYQQJSMr+fHw8MDW1lYmlBVGkzVRcWRkJNWrVy/W31aZSYAURWHq1Kl07NiRRo0aFfi8uXPnkpiYyJAhQ/T7/P39CQoKonHjxsTHx/PFF1/QoUMHjh8/Tt26dbNdIzU1ldTUVP37+Pj44j2MEEJUUpmZmfrkx83NzdThiArI3d2d69evk5GRgYWFRZGvU2YSoEmTJnHixAn++eefAp+zfPlyZs6cyZ9//omHh4d+f7t27WjXrp3+fYcOHWjRogVfffUVX375ZbbrzJ49m1mzZhXvAYQQQuj7/Nja2po4ElFRZTV9ZWZmFisBKhPD4CdPnsy6devYuXNnvlNXZ1m5ciVjx45l1apV9OjRI8+yWq2W1q1bc+HChRyPT5s2jbi4OP0rIiKi0M8ghBDiPmn2EiXFWH9bJq0BUhSFyZMns2bNGoKDg6lZs2aBzlu+fDnPPfccy5cv59FHHy3QfUJCQmjcuHGOx62srLCysipU7EIIIYQov0xaAzRx4kSWLl3Kr7/+ioODA1FRUURFRZGcnKwvM23aNEaOHKl/v3z5ckaOHMncuXNp166d/pysDtQAs2bNYvPmzVy+fJmQkBDGjh1LSEgIEyZMKNXnE0IIUXl17dqVKVOmFLj8lStX0Gg0hISElFhM4j6TJkALFy4kLi6Orl274u3trX+tXLlSXyYyMpLw8HD9+++++46MjAwmTpxocM4rr7yiLxMbG8v48eNp0KABvXr14tq1a+zevZs2bdqU6vMJIYQo+3KaOPfB1+jRo4t03T/++IMPPvigwOV9fX2JjIws1ECgopBES2XyJrD8BAUFGbwPDg7O95zPP/+czz//vIhRlZyMTB0xiWmkpGfi55bzRI9CCCFKV2RkpH575cqVvPfeewYT6drY2BiUT09PL1Dn28JM6QJgZmaGl5dXoc4RRVcmOkFXFgdDb9P24+2M++mwqUMRQghxj5eXl/7l5OSERqPRv09JScHZ2ZlVq1bRtWtXrK2tWbp0KTExMTzzzDNUq1YNW1tbGjduzPLlyw2u+3ATWI0aNfj444957rnncHBwoHr16ixatEh//OGameDgYDQaDdu3b6dVq1bY2trSvn37bKscfPjhh3h4eODg4MC4ceN46623aNasWZF/H6mpqbz88st4eHhgbW1Nx44dOXTokP74nTt3GDZsGO7u7tjY2FC3bl2WLFkCQFpaGpMmTcLb2xtra2tq1KjB7NmzixxLSZIEqBS52qtD9+4kpZk4EiGEKB2KopCUlmGSlzEXOnjzzTd5+eWXOXv2LL179yYlJYWWLVvy999/c+rUKcaPH8+IESM4cOBAnteZO3curVq14tixY7z00ku8+OKLnDt3Ls9z3nnnHebOncvhw4cxNzfnueee0x9btmwZH330EXPmzOHIkSNUr16dhQsXFutZ33jjDVavXs1PP/3E0aNHqVOnDr179+b27dsATJ8+nTNnzrBx40bOnj3LwoULqVKlCgBffvkl69atY9WqVZw/f56lS5dSo0aNYsVTUsrMPECVgattVgKUjk6noNXKMFEhRMWWnJ5JwHub8y9YAs683xtbS+N8zU2ZMoXBgwcb7Hv99df125MnT2bTpk389ttvtG3bNtfr9OvXj5deeglQk6rPP/+c4OBg/P39cz3no48+okuXLgC89dZbPProo6SkpGBtbc1XX33F2LFjGTNmDADvvfceW7ZsISEhoUjPmZiYyMKFCwkKCqJv374AfP/992zdupUff/yR//u//yM8PJzmzZvTqlUrAIMEJzw8nLp169KxY0c0Gg1+fn5FiqM0SA1QKXK+lwBl6hTiU2SBQCGEKC+yvuyzZGZm8tFHH9GkSRPc3Nywt7dny5YtBoN2ctKkSRP9dlZT282bNwt8jre3N4D+nPPnz2cb4FOcAT+XLl0iPT2dDh066PdZWFjQpk0bzp49C8CLL77IihUraNasGW+88QZ79+7Vlx09ejQhISHUr1+fl19+mS1bthQ5lpImNUClyNJci4O1OXdTMridmKZPiIQQoqKysTDjzPu9TXZvY7GzMxy4MnfuXD7//HPmz59P48aNsbOzY8qUKaSl5d3F4eHO0xqNBp1OV+BzsiYBfPCchycGLE7TX9a5OV0za1/fvn0JCwtj/fr1bNu2je7duzNx4kQ+++wzWrRoQWhoKBs3bmTbtm0MGTKEHj168Pvvvxc5ppIiNUClzNVOTXpuJ0o/ICFExafRaLC1NDfJqyRno96zZw8DBw5k+PDhNG3alFq1auW62kBJql+/PgcPHjTYd/hw0Qfa1KlTB0tLS4NlqdLT0zl8+DANGjTQ73N3d2f06NEsXbqU+fPnG3TmdnR0ZOjQoXz//fesXLmS1atX6/sPlSVSA1TKXGwtCYtJkgRICCHKsTp16rB69Wr27t2Li4sL8+bNIyoqyiBJKA2TJ0/m+eefp1WrVrRv356VK1dy4sQJatWqle+5D48mAwgICODFF1/k//7v/3B1daV69ep8+umnJCUlMXbsWEDtZ9SyZUsaNmxIamoqf//9t/65P//8c7y9vWnWrBlarZbffvsNLy8vnJ2djfrcxiAJUCnLqgGSkWBCCFF+TZ8+ndDQUHr37o2trS3jx49n0KBBBqsSlIZhw4Zx+fJlXn/9dVJSUhgyZAijR4/OViuUk6effjrbvtDQUD755BN0Oh0jRozg7t27tGrVis2bN+Pi4gKoi5FOmzaNK1euYGNjQ6dOnVixYgUA9vb2zJkzhwsXLmBmZkbr1q3ZsGEDWm3Za3DSKMYcJ1hBxMfH4+TkRFxcHI6Ojka99uu/Hef3I1d5o099Xupax6jXFkIIU0tJSSE0NJSaNWtibW1t6nAqpZ49e+Ll5cUvv/xi6lBKRF5/Y4X5/pYaoFKmrwGSJjAhhBDFlJSUxLfffkvv3r0xMzNj+fLlbNu2ja1bt5o6tDJPEqBS5mKb1QlahsELIYQoHo1Gw4YNG/jwww9JTU2lfv36rF69mh49epg6tDJPEqBS5qYfBZZq4kiEEEKUdzY2Nmzbts3UYZRLZa9XUgXnkpUAJUkNkBBCCGEqkgCVMlc7dUIr6QMkhBBCmI4kQKXM1c4KkIkQhRBCCFOSBKiUZS2ImpCaQWpGpomjEUIIISonSYBKmYO1OWb3VoGPlX5AQgghhElIAlTKtFqNfih8TII0gwkhhBCmIAmQCeg7QstyGEIIUWF07dqVKVOm6N/XqFGD+fPn53mORqNh7dq1xb63sa5TmUgCZAL3J0OUBEgIIUxtwIABuU4cuG/fPjQaDUePHi30dQ8dOsT48eOLG56BmTNn0qxZs2z7IyMj6du3r1Hv9bCgoKAyuahpUUkCZAJu9pIACSFEWTF27Fh27NhBWFhYtmOLFy+mWbNmtGjRotDXdXd3x9bW1hgh5svLywsrK6tSuVdFIQmQCUgNkBBClB39+/fHw8ODoKAgg/1JSUmsXLmSsWPHEhMTwzPPPEO1atWwtbWlcePGLF++PM/rPtwEduHCBTp37oy1tTUBAQE5rtf15ptvUq9ePWxtbalVqxbTp08nPV0dMBMUFMSsWbM4fvw4Go0GjUajj/nhJrCTJ0/yyCOPYGNjg5ubG+PHjychIUF/fPTo0QwaNIjPPvsMb29v3NzcmDhxov5eRREeHs7AgQOxt7fH0dGRIUOGcOPGDf3x48eP061bNxwcHHB0dKRly5YcPnwYgLCwMAYMGICLiwt2dnY0bNiQDRs2FDmWgpClMExAvyCq9AESQlR0igLpSaa5t4UtaDT5FjM3N2fkyJEEBQXx3nvvobl3zm+//UZaWhrDhg0jKSmJli1b8uabb+Lo6Mj69esZMWIEtWrVom3btvneQ6fTMXjwYKpUqcL+/fuJj4836C+UxcHBgaCgIHx8fDh58iTPP/88Dg4OvPHGGwwdOpRTp06xadMm/fIXTk5O2a6RlJREnz59aNeuHYcOHeLmzZuMGzeOSZMmGSR5O3fuxNvbm507d3Lx4kWGDh1Ks2bNeP755/N9nocpisKgQYOws7Nj165dZGRk8NJLLzF06FCCg4MBGDZsGM2bN2fhwoWYmZkREhKChYXaJ3bixImkpaWxe/du7OzsOHPmDPb29oWOozAkATKBrAQoRmqAhBAVXXoSfOxjmnu/fR0s7QpU9LnnnuN///sfwcHBdOvWDVCbvwYPHoyLiwsuLi68/vrr+vKTJ09m06ZN/PbbbwVKgLZt28bZs2e5cuUK1apVA+Djjz/O1m/n3Xff1W/XqFGD1157jZUrV/LGG29gY2ODvb095ubmeHl55XqvZcuWkZyczM8//4ydnfr8CxYsYMCAAcyZMwdPT08AXFxcWLBgAWZmZvj7+/Poo4+yffv2IiVA27Zt48SJE4SGhuLr6wvAL7/8QsOGDTl06BCtW7cmPDyc//u//8Pf3x+AunXr6s8PDw/niSeeoHHjxgDUqlWr0DEUljSBmYC+BkgSICGEKBP8/f1p3749ixcvBuDSpUvs2bOH5557DoDMzEw++ugjmjRpgpubG/b29mzZsoXw8PACXf/s2bNUr15dn/wABAYGZiv3+++/07FjR7y8vLC3t2f69OkFvseD92ratKk++QHo0KEDOp2O8+fP6/c1bNgQMzMz/Xtvb29u3rxZqHs9eE9fX1998gMQEBCAs7MzZ8+eBWDq1KmMGzeOHj168Mknn3Dp0iV92ZdffpkPP/yQDh06MGPGDE6cOFGkOApDaoBMQPoACSEqDQtbtSbGVPcuhLFjxzJp0iS+/vprlixZgp+fH927dwdg7ty5fP7558yfP5/GjRtjZ2fHlClTSEsr2P/HFUXJtk/zUPPc/v37efrpp5k1axa9e/fGycmJFStWMHfu3EI9h6Io2a6d0z2zmp8ePKbT6Qp1r/zu+eD+mTNn8uyzz7J+/Xo2btzIjBkzWLFiBY8//jjjxo2jd+/erF+/ni1btjB79mzmzp3L5MmTixRPQUgNkAlk1QBJAiSEqPA0GrUZyhSvAvT/edCQIUMwMzPj119/5aeffmLMmDH6L+89e/YwcOBAhg8fTtOmTalVqxYXLlwo8LUDAgIIDw/n+vX7yeC+ffsMyvz777/4+fnxzjvv0KpVK+rWrZttZJqlpSWZmXkvoxQQEEBISAiJiYkG19ZqtdSrV6/AMRdG1vNFRETo9505c4a4uDgaNGig31evXj1effVVtmzZwuDBg1myZIn+mK+vLxMmTOCPP/7gtdde4/vvvy+RWLNIAmQCD3aCzulfBUIIIUqfvb09Q4cO5e233+b69euMHj1af6xOnTps3bqVvXv3cvbsWV544QWioqIKfO0ePXpQv359Ro4cyfHjx9mzZw/vvPOOQZk6deoQHh7OihUruHTpEl9++SVr1qwxKFOjRg1CQ0MJCQkhOjqa1NTUbPcaNmwY1tbWjBo1ilOnTrFz504mT57MiBEj9P1/iiozM5OQkBCD15kzZ+jRowdNmjRh2LBhHD16lIMHDzJy5Ei6dOlCq1atSE5OZtKkSQQHBxMWFsa///7LoUOH9MnRlClT2Lx5M6GhoRw9epQdO3YYJE4lQRIgE8hKgNIzFRJSM0wcjRBCiCxjx47lzp079OjRg+rVq+v3T58+nRYtWtC7d2+6du2Kl5cXgwYNKvB1tVota9asITU1lTZt2jBu3Dg++ugjgzIDBw7k1VdfZdKkSTRr1oy9e/cyffp0gzJPPPEEffr0oVu3bri7u+c4FN/W1pbNmzdz+/ZtWrduzZNPPkn37t1ZsGBB4X4ZOUhISKB58+YGr379+umH4bu4uNC5c2d69OhBrVq1WLlyJQBmZmbExMQwcuRI6tWrx5AhQ+jbty+zZs0C1MRq4sSJNGjQgD59+lC/fn2++eabYsebF40iVRDZxMfH4+TkRFxcHI6OjiVyj4D3NpGUlsmu/+uKn1vBRikIIURZl5KSQmhoKDVr1sTa2trU4YgKKK+/scJ8f0sNkIlIR2ghhBDCdCQBMhGZDFEIIYQwHUmATEQ/GWKCJEBCCCFEaZMEyESkBkgIIYQwHUmATOR+H6CiLzwnhBBllYyvESXFWH9bkgCZiJt9VgKUfQ4HIYQor7JmF05KMtECqKLCy5p9+8FlPIpClsIwEakBEkJURGZmZjg7O+vXlLK1tc11WQYhCkun03Hr1i1sbW0xNy9eCiMJkIm42qn/SpI+QEKIiiZrpfKiLqwpRF60Wi3Vq1cvdmItCZCJuNpZATIPkBCi4tFoNHh7e+Ph4UF6utRyC+OytLREqy1+Dx6TJkCzZ8/mjz/+4Ny5c9jY2NC+fXvmzJlD/fr18zxv165dTJ06ldOnT+Pj48Mbb7zBhAkTDMqsXr2a6dOnc+nSJWrXrs1HH33E448/XpKPUyhZNUCSAAkhKiozM7Ni99MQoqSYtBP0rl27mDhxIvv372fr1q1kZGTQq1cvgxVsHxYaGkq/fv3o1KkTx44d4+233+bll19m9erV+jL79u1j6NChjBgxguPHjzNixAiGDBnCgQMHSuOxCiSrD1BccjoZmToTRyOEEEJULmVqLbBbt27h4eHBrl276Ny5c45l3nzzTdatW8fZs2f1+yZMmMDx48fZt28fAEOHDiU+Pp6NGzfqy/Tp0wcXF5ccF457WGmsBZapU6jzzgYUBQ690wN3B6sSuY8QQghRWZTbtcDi4uIAcHV1zbXMvn376NWrl8G+3r17c/jwYX1bc25l9u7da+SIi85Mq8HZRjpCCyGEEKZQZhIgRVGYOnUqHTt2pFGjRrmWi4qKwtPT02Cfp6cnGRkZREdH51kmKioqx2umpqYSHx9v8CoNLnayIKoQQghhCmUmAZo0aRInTpwoUBPVw0PfslrxHtyfU5nchszNnj0bJycn/cvX17ew4ReJq6wIL4QQQphEmUiAJk+ezLp169i5cyfVqlXLs6yXl1e2mpybN29ibm6Om5tbnmUerhXKMm3aNOLi4vSviIiIYjxNwblKDZAQQghhEiZNgBRFYdKkSfzxxx/s2LGDmjVr5ntOYGAgW7duNdi3ZcsWWrVqpZ+CPbcy7du3z/GaVlZWODo6GrxKg35BVEmAhBBCiFJl0gRo4sSJLF26lF9//RUHBweioqKIiooiOTlZX2batGmMHDlS/37ChAmEhYUxdepUzp49y+LFi/nxxx95/fXX9WVeeeUVtmzZwpw5czh37hxz5sxh27ZtTJkypTQfL19ZfYBiJAESQgghSpVJE6CFCxcSFxdH165d8fb21r9WrlypLxMZGUl4eLj+fc2aNdmwYQPBwcE0a9aMDz74gC+//JInnnhCX6Z9+/asWLGCJUuW0KRJE4KCgli5ciVt27Yt1efLj1tWDZCMAhNCCCFKVZmaB6isKI15gABWH7nKa78dp1PdKvwytmwlZ0IIIUR5U27nAapspBO0EEIIYRqSAJmQdIIWQgghTEMSIBPS1wBJHyAhhBCiVEkCZEJZo8BS0nUkpWWYOBohhBCi8pAEyITsLM2wNFc/AukHJIQQQpQeSYBMSKPR6JfDuJOYbuJohBBCiMpDEiATuz8ZYqqJIxFCCCEqD0mATEwmQxRCCCFKnyRAJuainwtImsCEEEKI0iIJkIm52qoLuN6WJjAhhBCi1EgCZGKudlaA1AAJIYQQpUkSIBNztVNrgGQ2aCGEEKL0SAJkYi6yHpgQQghR6iQBMjFZDkMIIYQofZIAmZgsiCqEEEKUPkmATEw/E3RSGjqdYuJohBBCiMpBEiATy+oDpFMgLllGggkhhBClQRIgE7Mw0+JgbQ5IPyAhhBCitEgCVAa4ykgwIYQQolRJAlQGSAIkhBBClC5JgMoAfUdoSYCEEEKIUiEJUBmQ1RE6RhIgIYQQolRIAlQGuMlcQEIIIUSpkgSoDHCR2aCFEEKIUiUJUBmQ1QdIOkELIYQQpUMSoDJAlsMQQgghSpckQGWANIEJIYQQpUsSoDJAPw9QgiRAQgghRGmQBKgMyEqAEtMySUnPNHE0QgghRMUnCVAZ4GhtjplWA0BskiyIKoQQQpQ0SYDKAI1Gg4tt1mSIqSaORgghhKj4JAEqI+5Phig1QEIIIURJkwSojHCxswBkJJgQQghRGiQBKiPujwSTJjAhhBCipEkCVEZk9QG6LZ2ghRBCiBInCVAZIQuiCiGEEKVHEqAyQj8btCRAQgghRImTBKiMcJUESAghhCg1kgCVEfoFUWUUmBBCCFHiTJoA7d69mwEDBuDj44NGo2Ht2rV5lh89ejQajSbbq2HDhvoyQUFBOZZJSUkp4acpnvsTIUoCJIQQQpQ0kyZAiYmJNG3alAULFhSo/BdffEFkZKT+FRERgaurK0899ZRBOUdHR4NykZGRWFtbl8QjGI3rA52gFUUxcTRCCCFExWZuypv37duXvn37Fri8k5MTTk5O+vdr167lzp07jBkzxqCcRqPBy8vLaHGWhqwEKEOncDc1A0drCxNHJIQQQlRc5boP0I8//kiPHj3w8/Mz2J+QkICfnx/VqlWjf//+HDt2zEQRFpy1hRm2lmYA3E6QZjAhhBCiJJXbBCgyMpKNGzcybtw4g/3+/v4EBQWxbt06li9fjrW1NR06dODChQu5Xis1NZX4+HiDlyncnwxREiAhhBCiJJXbBCgoKAhnZ2cGDRpksL9du3YMHz6cpk2b0qlTJ1atWkW9evX46quvcr3W7Nmz9c1rTk5O+Pr6lnD0OXOzl8kQhRBCiNJQLhMgRVFYvHgxI0aMwNLSMs+yWq2W1q1b51kDNG3aNOLi4vSviIgIY4dcIDISTAghhCgdJu0EXVS7du3i4sWLjB07Nt+yiqIQEhJC48aNcy1jZWWFlZWVMUMsEldZDkMIIYQoFSZNgBISErh48aL+fWhoKCEhIbi6ulK9enWmTZvGtWvX+Pnnnw3O+/HHH2nbti2NGjXKds1Zs2bRrl076tatS3x8PF9++SUhISF8/fXXJf48xaWfDVr6AAkhhBAlyqQJ0OHDh+nWrZv+/dSpUwEYNWoUQUFBREZGEh4ebnBOXFwcq1ev5osvvsjxmrGxsYwfP56oqCicnJxo3rw5u3fvpk2bNiX3IEaiT4BkFJgQQghRojSKzLqXTXx8PE5OTsTFxeHo6Fhq9/31QDhvrzlJjwYe/DCqdandVwghhKgICvP9XS47QVdUsiCqEEIIUTokASpDJAESQgghSockQGWIq526/IUkQEIIIUTJkgSoDHG1U4fix6dkkJ6pM3E0QgghRMUlCVAZ4mRjgUajbt+RofBCCCFEiZEEqAwx02pwtlGbwe4kpps4GiGEEKLikgSojJGO0EIIIUTJkwSojJEESAghhCh5kgCVMVkLospyGEIIIUTJkQSojHGzlwVRhRBCiJImCVAZo68BkgRICCGEKDGSAJUx0gdICCGEKHmSAJUxWQmQzAMkhBBClBxJgMoYl3sJUEyCJEBCCCFESZEEqIxxtZUaICGEEKKkSQJUxjzYB0hRFBNHI4QQQlRMkgCVMVkJUGqGjqS0TBNHI4QQQlRMkgCVMbaWZliaqx+LjAQTQgghSoYkQGWMRqPBTUaCCSGEECVKEqAyKGsyxBipARJCCCFKhCRAZZB+LiBJgIQQQogSIQlQGeQis0ELIYQQJUoSoDLITRIgIYQQokRJAlQGuchkiEIIIUSJkgSoDHK1swCkBkgIIYQoKZIAlUGudlaAJEBCCCFESZEEqAxykRogIYQQokRJAlQG6YfBJ6WbOBIhhBCiYpIEqAxyfWAm6EydLIgqhBBCGJskQKUp5hJsfgd2fJRnsaxRYIoCcclSCySEEEIYmyRApSkuAvYtgEM/QGbuiY2FmRYHa3NA+gEJIYQQJUESoNLk1xFsq0DybQjdnWdRmQxRCCGEKDmSAJUmM3MIeEzdPr0mz6KyHIYQQghRciQBKm0NH1d/nv0rz2YwV5kNWgghhCgxkgCVNr8OYOcOKbFweVeuxVylBkgIIYQoMZIAlTatGTS41wx2JvdmMEmAhBBCiJIjCZAp6JvB/oaMnBOcrD5AdyQBEkIIIYxOEiBT8GsPdh5qM1hozs1gWTVAMZIACSGEEEYnCZApaM0gYKC6nctoMOkELYQQQpQckyZAu3fvZsCAAfj4+KDRaFi7dm2e5YODg9FoNNle586dMyi3evVqAgICsLKyIiAggDVr8h5ybhJZzWDncm4Gk2HwQgghRMkxaQKUmJhI06ZNWbBgQaHOO3/+PJGRkfpX3bp19cf27dvH0KFDGTFiBMePH2fEiBEMGTKEAwcOGDv84qneDuy9ICUOLgdnOywTIQohhBAlx9yUN+/bty99+/Yt9HkeHh44OzvneGz+/Pn07NmTadOmATBt2jR27drF/PnzWb58eXHCNS6tmTop4sFFajNYvV4Gh7NqgJLSMklJz8TawswUUQohhBAVUrnsA9S8eXO8vb3p3r07O3fuNDi2b98+evUyTCZ69+7N3r17SzPEgtE3g62HjFSDQ47W5phrNYD0AxJCCCGMrVwlQN7e3ixatIjVq1fzxx9/UL9+fbp3787u3ffX1YqKisLT09PgPE9PT6KionK9bmpqKvHx8QavUuF7rxksNQ4uGSZyGo1GXwsUkyAJkBBCCGFMJm0CK6z69etTv359/fvAwEAiIiL47LPP6Ny5s36/RqMxOE9RlGz7HjR79mxmzZpl/IDzo9VCw0Fw4Fu1Gax+H4PDrraW3LqbKjVAQgghhJGVqxqgnLRr144LFy7o33t5eWWr7bl582a2WqEHTZs2jbi4OP0rIiKixOLNJqsZ7PyGbM1gLnYWgHSEFkIIIYyt3CdAx44dw9vbW/8+MDCQrVu3GpTZsmUL7du3z/UaVlZWODo6GrxKTbU24OADqfFwaYfBITc7K0ASICGEEMLYTNoElpCQwMWLF/XvQ0NDCQkJwdXVlerVqzNt2jSuXbvGzz//DKgjvGrUqEHDhg1JS0tj6dKlrF69mtWrV+uv8corr9C5c2fmzJnDwIED+fPPP9m2bRv//PNPqT9fgWi16qSIBxbeawa7PyouqwZIlsMQQgghjMukCdDhw4fp1q2b/v3UqVMBGDVqFEFBQURGRhIeHq4/npaWxuuvv861a9ewsbGhYcOGrF+/nn79+unLtG/fnhUrVvDuu+8yffp0ateuzcqVK2nbtm3pPVhhNXxcTYDObYD0FLCwBu7PBn1b+gAJIYQQRqVRFEUxdRBlTXx8PE5OTsTFxZVOc5hOB/MbQfw1ePpX8H8UgKB/Q5n51xn6Nfbim2EtSz4OIYQQohwrzPd3ue8DVCFotRAwSN1+YG0wWQ5DCCGEKBmSAJUVDQepP89vhPRk4P6K8HcS000UlBBCCFExSQJUVlRtBY7VIC0BLm4H7idAMVIDJIQQQhiVJEBlRdakiKBvBtPXACWlIV21hBBCCOORBKgs0U+KqDaDudwbBZapU4hPyTBhYEIIIUTFIglQWVK1JTj5QnoiXNiKtYUZdpbqKvDSEVoIIYQwHkmAyhKNJlszmIwEE0IIIYyvSAlQREQEV69e1b8/ePAgU6ZMYdGiRUYLrNIKuNcM9t9mSEt6YCSYJEBCCCGEsRQpAXr22WfZuXMnAFFRUfTs2ZODBw/y9ttv8/777xs1wEqnagtwqq42g13citu9BOhKTKKJAxNCCCEqjiIlQKdOnaJNmzYArFq1ikaNGrF3715+/fVXgoKCjBlf5fNQM1jneu4ArDwUISPBhBBCCCMpUgKUnp6OlZW6Uvm2bdt47LHHAPD39ycyMtJ40VVWDe83gz3R2AVbSzMu3Exg3+UY08YlhBBCVBBFSoAaNmzIt99+y549e9i6dSt9+vQB4Pr167i5uRk1wErJpzk4+0F6Eo4RO3m8eVUAftkXZuLAhBBCiIqhSAnQnDlz+O677+jatSvPPPMMTZs2BWDdunX6pjFRDA81g40MrAHAljM3uB6bbLKwhBBCiIqiSAlQ165diY6OJjo6msWLF+v3jx8/nm+//dZowVVq+mawLdR31dK2piuZOoVfD4SbNi4hhBCiAihSApScnExqaiouLi4AhIWFMX/+fM6fP4+Hh4dRA6y0vJuBSw3ISIb/NjOqfQ0AVhwKJzUj05SRCSGEEOVekRKggQMH8vPPPwMQGxtL27ZtmTt3LoMGDWLhwoVGDbDS0mju1wKdXkPPAE88Ha2ITkhj48ko08YmhBBClHNFSoCOHj1Kp06dAPj999/x9PQkLCyMn3/+mS+//NKoAVZqWQnQhS1YZCQxrK0fAD/vu2K6mIQQQogKoEgJUFJSEg4ODgBs2bKFwYMHo9VqadeuHWFhMlLJaLyagEtNyEiB/zbxdBtfLMw0HA2P5dS1OFNHJ4QQQpRbRUqA6tSpw9q1a4mIiGDz5s306tULgJs3b+Lo6GjUACu1h5rBPBys6dvIG5BaICGEEKI4ipQAvffee7z++uvUqFGDNm3aEBgYCKi1Qc2bNzdqgJWevhlsK6TeZVR7tRnsz5Drsj6YEEIIUURFSoCefPJJwsPDOXz4MJs3b9bv7969O59//rnRghOAV2NwqwOZqXB+Ey2quxDg7Uhqho5VhyNMHZ0QQghRLhUpAQLw8vKiefPmXL9+nWvXrgHQpk0b/P39jRacIFszmEaj0dcCLT0QRqZO1gcTQgghCqtICZBOp+P999/HyckJPz8/qlevjrOzMx988AE6nc7YMYqsBOjiVkiJ57GmVXGysSDidjLB52+aNjYhhBCiHCpSAvTOO++wYMECPvnkE44dO8bRo0f5+OOP+eqrr5g+fbqxYxQeAVClHmSmwfmN2FiaMaRVNQB+lvXBhBBCiEIrUgL0008/8cMPP/Diiy/SpEkTmjZtyksvvcT3339PUFCQkUMUDzeDAQxv54dGA7v+u0VodKIJgxNCCCHKnyIlQLdv386xr4+/vz+3b98udlAiB1kJ0KXtkByLn5sdXeu5A7B0v9QCCSGEEIVRpASoadOmLFiwINv+BQsW0KRJk2IHJXLg0QDc/fXNYAAj760PtupwBElpGSYMTgghhChfzIty0qeffsqjjz7Ktm3bCAwMRKPRsHfvXiIiItiwYYOxYxRZGj4OwbPVZrBmz9Clrjt+braExSSx9th1nm1b3dQRCiGEEOVCkWqAunTpwn///cfjjz9ObGwst2/fZvDgwZw+fZolS5YYO0aRJWCQ+vPSDki+g1arYUS7++uDKYoMiRdCCCEKQqMY8Vvz+PHjtGjRgszMTGNd0iTi4+NxcnIiLi6u7C3t8U0g3DwDA7+B5sOIS0qn7extpKTrWPVCIG1qupo6QiGEEMIkCvP9XeSJEIWJPDQazMnWgkHNqgKyPpgQQghRUJIAlTdZCdDlnZCkjrgbEag2g206FcXN+BRTRSaEEEKUG5IAlTdV6oJnY9BlwLm/AWjo40TrGi5k6BR+PRhu4gCFEEKIsq9Qo8AGDx6c5/HY2NjixCIKquEguHFSbQZrMRKAEYE1OHTlDr8eCGditzpYmEluK4QQQuSmUN+STk5Oeb78/PwYOXJkScUqsuibwXZBYgwAfRp64e5gxc27qWw+HWXC4IQQQoiyr1A1QDLEvYxwqw1eTSDqBJz7C1qOxtJcyzNtqvPl9gv8vDeM/k18TB2lEEIIUWZJO0l59dBoMIBhbatjrtVw8MptzkbGmygwIYQQouyTBKi8ajhI/Rm6GxKjAfB0tKZ3Iy9AhsQLIYQQeZEEqLxyrQXezUDRwdl1+t2jAmsAsPJQBLv/u2Wa2IQQQogyzqQJ0O7duxkwYAA+Pj5oNBrWrl2bZ/k//viDnj174u7ujqOjI4GBgWzevNmgTFBQEBqNJtsrJaUCzo+TQzNY6xouPNWyGjoFJv16lMu3EkwUnBBCCFF2mTQBSkxMzHVl+Zzs3r2bnj17smHDBo4cOUK3bt0YMGAAx44dMyjn6OhIZGSkwcva2rokHsG0sprBrvwDCTcB0Gg0fPh4I1r6uRCfksG4nw4Tl5xuuhiFEEKIMqhIq8EbS9++fenbt2+By8+fP9/g/ccff8yff/7JX3/9RfPmzfX7NRoNXl5exgqz7HKpAT4t4PpRtRms9TgArMzN+HZ4SwYu+IfL0YlMXn6MJaNbY6bVmDZeIYQQoowo132AdDodd+/exdXVcAHQhIQE/Pz8qFatGv37989WQ1Sh6JvB1hrsdnewYtHIVlhbaNn93y1mbzhb+rEJIYQQZVS5ToDmzp1LYmIiQ4YM0e/z9/cnKCiIdevWsXz5cqytrenQoQMXLlzI9TqpqanEx8cbvMqNB5vB7t4wONSoqhNzn2oGwA//hPLb4YjSjU0IIYQoo8ptArR8+XJmzpzJypUr8fDw0O9v164dw4cPp2nTpnTq1IlVq1ZRr149vvrqq1yvNXv2bIMZrX19fUvjEYzDuTpUaw0oBqPBsjzaxJuXu9cF4J01pzgSdruUAxRCCCHKnnKZAK1cuZKxY8eyatUqevTokWdZrVZL69at86wBmjZtGnFxcfpXREQ5qynJYTTYg6Z0r0vfRl6kZep44ZcjXItNLsXghBBCiLKn3CVAy5cvZ/To0fz66688+uij+ZZXFIWQkBC8vb1zLWNlZYWjo6PBq1wJGKj+DNsL8ZHZDmu1GuYOaUoDb0eiE9J4/qfDJKVllHKQQgghRNlh0gQoISGBkJAQQkJCAAgNDSUkJITw8HBArZl5cHHV5cuXM3LkSObOnUu7du2IiooiKiqKuLg4fZlZs2axefNmLl++TEhICGPHjiUkJIQJEyaU6rOVKqdq4NsWUODMnzkWsbU05/uRLXGzs+RMZDyv/3YcnU4p3TiFEEKIMsKkCdDhw4dp3ry5fgj71KlTad68Oe+99x4AkZGR+mQI4LvvviMjI4OJEyfi7e2tf73yyiv6MrGxsYwfP54GDRrQq1cvrl27xu7du2nTpk3pPlxpy6cZDKCaiy3fjWiJhZmGDSej+HJH7s2CRnfzHKx7GeKvl949hRBCiFxoFEWRaoCHxMfH4+TkRFxcXPlpDou/DvMaqNuvngGnqrkWXXUogjdWnwBg4bAW9G2ce/OgUegy4bvOcOMUtBgJj+XeIV0IIYQoqsJ8f5e7PkAiF44+UD1Q3c6lGSzLkNa+PNehJgBTVx3n9PW4PMsX2/EVavIDcGYdZKSW7P2EEEKIfEgCVJEUoBksy9v9/OlUtwrJ6Zk8/9Nhbt0toaQkPRl2fHj/fUosXNxeMvcSQgghCkgSoIqkwWOABq4ehNi8h/Kbm2lZ8EwLalWx43pcChOWHiElPdP4Me3/Bu5eBydfaDVW3XfyN+PfRwghhCgESYAqEkdv8GuvbufTDAbgZGvB96Na4WBtzpGwO/SYt4tNpyIxWrewxGjY87m6/ch0aD5c3T6/EVJllXohhBCmIwlQRZPVDHZwEVw9km/x2u72fD+yFd5O1ly9k8yEpUcZ9sMBzkfdLX4suz6FtLvg1QQaPwU+zcG1NmQkw/kNxb++EEIIUUSSAFU0DR8HO3eIDYMfHoE1E3KcHPFB7Wq5sf21Lkx+pA6W5lr2Xoqh35d7mLnuNHFJ6UWLI+YSHP5R3e71AWi1oNFA4yfVfdIMJoQQwoQkAapo7KrAC3ug6bPq++PL4auWsPszSE/J9TRbS3Ne61Wf7VO70LuhJ5k6haC9V+j62U6WHQgjs7CTJm6fBboMqNMTanW9v7/RvQTo0g5IjCncNYUQQggjkQSoInL0hscXwrgd6kKp6Ymw4wP4urXaNyiPPj6+rrZ8N6IVy8a1pZ6nPXeS0nlnzSkGfPUPB0MLuJBqxEH1Phot9JxleMy9Hng3VZOjM2uL/oxCCCFEMUgCVJFVawljt8Lg78HBB2LDYdVICOoPUSfzPLVDnSpseLkTMwcE4GhtzpnIeIZ8t4/Jy49xPa/FVBUFtryrbjd7FjwbZi+TVQt08vciPpgQQghRPDITdA7K5UzQ+UlLhH+/UF8ZKWrtTIuR6ugsuyp5nhqTkMrcrf+x/GA4igI2Fma82LU24zrVxNbS3LDw2b9g5XAwt4GXj6oTND4s7hp83hBQYMopcPY13nMKIYSotArz/S0JUA4qZAKUJTYctr53f7JEKyfo8ga0GQ/mlnmeeupaHLP+Os2hK3f0+5xsLHB3sMLDwQpPezPevTIGt9QIztd7geg2b+DhYIW7gxVONhZoNJr7F1vyKIT9Az1mQccpJfCgQgghKhtJgIqpQidAWcL2wsY3IUpdEwy3OvD4IrXZLA+KovDXiUjmbDzHtYeawkaYbeEDiyCiFUe6ps4jAVv9MUszLd7O1vRu6MXTrX2pFfYb/D0FvBrDhH+M/XRCCCEqIUmAiqlSJECgLlIasgy2vw+Jt8DMEvp9Bi1H5XuqoijEJ2dw824Kt+6mcvt2DN239sYm/Q6/eU5hjXlfbt5N5dbdVOKSsw+lf8TPnB9uPotWyYCXDoCHf0k8oRBCiEpEEqBiqjQJUJbkWPhzIpz7W33fYhT0+x+YWxX8Gts/gD2fqTVJL+0HMwv9oZT0TG7dTeX09XhWHY4g+PxNdAr8YPE/epgdY5fXaDwHfYC/VyX4XQshhCgxkgAVU6VLgAB0Ovhn3r2FSxXwaQFDfwGnavmfG38dvmyhzvA8dCk0GJBn8ci4ZFYdusrtA78yK30eYToPuqR9TlNfF55p7cuApj7YWZnneQ0hhBDiYZIAFVOlTICyXNwGq8dB8h2wrQJPLYGanfM+58+JcGwp+LaD5zapMz4XQGZKAvyvDmaZyQxO/4CjmbUBsLM047FmPjzdujpNqjkZdp4WQgghciEJUDFV6gQI4M4VdSh71EnQmKmTGQZOyjmxuXEaFnYAFHXOId82hbvX72Ph1O8ktRjPL04TWHEogtDoRP1hJxsLbCzMsLE0w8pci7WFGTYWZlhb3N+2euC9vZU5Tas509LPBRtLs/zvnxgNig7sPQoXtxBCiDJHEqBiqvQJEEBaEvz9KpxYob5v+Dg8tgCs7A3LLX0SLm6FgIEw5OfC3+f8Jlg+FOw84LVzKBotB0Jvs+JgOBtORZGWoStS+JZmWppXd6Z97Sq0r+NG02rOWJo/NO/n3RuwsD1kpsG47eos1UIIIcotSYCKSRKgexQFDv0Am95Sl65wbwBPLwM3tamKy8Hw80DQmsPEg/f3F0ZGGsytpza5jVgLtbvpD91NSedGfAop6TqS0zNJSc98aDv7vpiENA6G3iYq3nDdMxsLM1rXdKV9bTfa13ajoY8TZr+PUpfsAPXZnt8OlnZ5hpupU0jP1GFtUYDaJSGEEKVKEqBikgToIeH71SU0Em6AlSMMXgR1e8OiLuo8Qm1egH6fFv36f02BI0ug+XAY+HWxw1UUhSsxSey9FM3eSzHsuxTD7cQ0gzIDrY/yBZ+h05iRYemEZeptLnk/yvraM4lLySA+OZ34lHTiktOJT85Qf6akk5CagQYY2ro6MwYESCIkhBBliCRAxSQJUA7uRsGqURCxX31fp6fa9GXlCC+HgJ1b0a995R8IelSdlfr1/8DC2ighZ9HpFP67eZe9F2PYeymGs5fDWMNUPDSxLMgYyO7MJvxq+RHmGh3T0seyPLN7ga7bqKojC4e1xNfVNv/CQgghSpwkQMUkCVAuMtLUhU4Pfnd/X/cZ0Glq8a6r06lrg929XqBh9MWlWzsJbcgv3LGtwetuX3MzCZ7NWMMzcT+QobHgl4bfk+TWGEcbC5xsLHC0Nld/2ljgaG3B6etxvLoyhDtJ6TjZWDB/aDO6+UsnaiGEMDVJgIpJEqB8HF+hNls5+8ILu8HCpvjX3PIu7P2q6J2pCyqr3xLAmE3gF6huKwqseBbObwBnP3hhF9i45HqZa7HJvLTsKMcjYgF4+ZE6vNKjHmZaGbIvhBCmUpjvb22eR4XISdOn4Y1LMD7YOMkPQOOn1J//bYaUeONc82FpifDXK+p263H3kx9Qh/gP+kZNfmLDYO1LalKUi6rONqx6oR0jA/0A+HLHRUYvOZitr5EQQoiySRIgUTSWdvmOmCoUrybgVhcyUuDceuNd90E7P1bnOHKspjbdPczGRa19MrNSa4L2fpnn5azMzXh/YCPmD22GtYWWPRei6f/lHo6F3ymZ+IUQQhiNJECibNBo7tcCnfzN+Ne/egT2f6Nu9/8crHOpGvVpBn3nqNvbZsGVf/O99KDmVVk7sQM1q9hxPS6FId/t45d9V5DWZSGEKLskARJlR+Mn1Z+XgyHhlvGum5EG6yapMz43HgL1euVdvuVoaPI0KJnw+xh1wsR8+Hs5sm5SB/o09CI9U2H6n6eZuuo4SWkZxnkGIYQQRiUJkCg73Gqri7AqmXBmrfGu++98uHkGbN2gzyf5l9dooP88dXLEhBuweixk5p/IOFhbsHB4C97p1wAzrYY1x67x+Nd7uXwrofjPIIQQwqgkARJlS1YtkLGawW6eg133Jmns+2nB5yuytFP7A1nYwZU9EPxxgU7TaDQ837kWv45ri7uDFedv3OWxBf+y6nAE0QmpRXwIIYQQxibD4HMgw+BNKD4S5jUAFHjlOLjUKPq1dJmwuA9cPQj1+sAzKwq8Ur3eyd/VGiCAZ1dBvd4FPvVmfAqTfj3GwSu39fuqudjQzNeZ5tVdaObrTEMfxyLPJq0oCjfvphIanUhYTCIeDtZ0re+OprDPKIQQFYTMA1RMkgCZ2E8DIHQ3dH8POr1W9Ovs/xY2vQmWDjDxADhVLdp1NvwfHFwE1s4wYQ84Vy/wqemZOr7eeZENJyO5cDMh28h6CzMNDbwdae7rTLPqzjTzdaGGm60+iVEUhVsJqVyJTuJKdCKhMYlciU7kSkwSYTGJJKVlGlxvWNvqvD+wkcxHJISolCQBKiZJgEzs6M+wbjJ4NISX9hbtGnfC4Jt2kJ4Ej86D1mOLHk9GKizpC9eOqH2UntsE5laFvszdlHROXI0jJCKWY+F3CImIJToh+7xBzrYWBHg7EpuUTlhMIokPJTkP0mqgmostPs7WHAi9jaJAzwBPvnqmuaxTJoSodCQBKiZJgEws+Q78ry7o0uHFveDZsHDnKwosHQyXdoBfBxj1N2iL2d0tNhy+7QQpsdD6eXj0s+JdD7V25+qd5HsJUSwhEXc4dT2etAydQTmNRp14sWYVO2q42eHnZqtuV7HDl1tYnlwGx5aRlAld42dxM8OOln4u/DiqFc62lsWOUwghyovCfH+bl1JMQhScjQvU7QXn16t9cAqbAB1friY/ZlYw4MviJz+gNnsN/h5+fQoOfQ82ztDx1WJNBqnRaPB1tcXX1ZYBTX0ASMvQcS4qnnORd3G1s1STHFcbrMwfqM3JSIVzf8PGn9UpA+6xBVa2v85jBxtwJOwOTyzcy0/PtaGaiyzWKoQQD5MaoBxIDVAZcOoPdQ4eCzuo3lYdku5eHzzu/bR2yvm8hJuwoLVaU9NjppqkGNOOj2D3vVFlDt7wyLvQ9BnQlkJz082zcPQXNcFLvt+xmlrdwM4dTq4C33acf/R3Ri85SGRcCp6OVgSNaUMDb/k7FkJUfNIEVkySAJUBaUlqIhN/NefjjlXVRMi9AXj4g7u/+n7dy+ocQl5N4PmdYGbkSk5FgVOrYfsstVkMwLMR9PoAaj9i3HsBpCbA6TVqv6irB+/vd/CB5sOh+TB1pFz8dZgXgDp67gTXNR6MXnKQ/24k4GBlzqKRrQisXcApAIQQopySBKiYJAEqI9ISIeok3Dqnzudz6yzcOg93I/M+T2MG43eCd9OSiy0jVR0Ztvt/kBKn7qvTA3p+AJ4Bxbu2TgfXj6pJz6nVkHZvIkWtuTqcv8UoqNM9e61TUH91zqLuM6DTVOKS0nn+58McvHIbSzMt84Y2pX8Tn+LFJoQQZZgkQMUkCVAZl3wHbv2nJkQ3z6kJ0q1z9xOjzm/AI++UTixJt9Uk6OD3aqdtjVatmen2Djh4FewaigK3L0PoLri8S01ikmLuH3etDS1Gqk1tDp65X+fIT/DXywaj51LSM3l1ZQgbT0Wh0cD0RwN4rmPNYjywEEKUXZIAFZMkQOVU8h21D1CVeoWf8LC4Yi7Btplwdp363sIOOrwM7Sfn3FE6PlJNeEJ3q0nPw019FnbQoL9a2+PXvmDPk8vouUydwqy/TvPzvjAAXuhcizf7+KOVuYKEEBVMYb6/TboUxu7duxkwYAA+Pj5oNBrWrl2b7zm7du2iZcuWWFtbU6tWLb799ttsZVavXk1AQABWVlYEBASwZs2aEohelDk2Lmo/IFPMhOxWG4b+As9thqqtID0RgmfDly3UjsuJ0XBmHax/Te3bNM8f1rwAIcvU5EdroQ7Z7/q2eo03r8DgRVCjQ8GfJ2v0HKij5+4x02qY9VhD/q93fQC+232ZqatCsg23F0KIysSkw+ATExNp2rQpY8aM4Yknnsi3fGhoKP369eP5559n6dKl/Pvvv7z00ku4u7vrz9+3bx9Dhw7lgw8+4PHHH2fNmjUMGTKEf/75h7Zt25b0I4nKrno7GLdN7bi8bSbEhqkr0WejAZ9mULML1OwM1QPB0gjD1Rs/eX/6gO7v6ZMnjUbDxG518HS05s3VJ1gbcp2YxDQWDm+JvZXMhiGEqHzKTBOYRqNhzZo1DBo0KNcyb775JuvWrePs2bP6fRMmTOD48ePs27cPgKFDhxIfH8/GjRv1Zfr06YOLiwvLly8vUCzSBCaMIiNV7Ru0+1O1o7S7v5rs1Oyi1uzYuBj/nmlJ8FldteP0c1vUKQQeEnz+Ji8tO0pSWiZVnW34YFBDHvHPo2+REEKUE+WmCayw9u3bR69evQz29e7dm8OHD5Oenp5nmb17i7ikghBFZW4F7SfBa//B/11W1yPr9z+1b09JJD+g1iL591e3T/6WY5Gu9T1YMb4dVZ1tuBabzHNBh3lx6RGi4lJKJiYhhCiDylUCFBUVhaen4b9UPT09ycjIIDo6Os8yUVFRuV43NTWV+Ph4g5cQRmNhDXalOAdP46fUn6fXQGZ6jkWaVHNm69TOjO9cCzOtho2nougxbxdL/g0lU1cmKoWFEKJElasECNCvkp0lqwXvwf05lXl434Nmz56Nk5OT/uXr62vEiIUoZbW6gG0VSIpWR5jlwtbSnLf7NeCvSR1p5utMQmoGs/46w6Cv/+Xk1bhSDFgIIUpfuUqAvLy8stXk3Lx5E3Nzc9zc3PIs83Ct0IOmTZtGXFyc/hUREWH84IUoLWYW0PBxdTuXZrAHBfg48seL7flwUCMcrM05eS2OgV//w6y/TpOQmlHCwQohhGmUqwQoMDCQrVu3GuzbsmULrVq1wsLCIs8y7du3z/W6VlZWODo6GryEKNeymsHO/a12jM6HVqtheDs/tr/Whcea+qBTYMm/V+gxdxebTkVSRsZKCCGE0Zg0AUpISCAkJISQkBBAHeYeEhJCeLi6xtK0adMYOXKkvvyECRMICwtj6tSpnD17lsWLF/Pjjz/y+uuv68u88sorbNmyhTlz5nDu3DnmzJnDtm3bmDJlSmk+mhCm5dtGXcE+LQH+21Tg0zwcrPnymeb8/FwbqrvaEhWfwoSlRxn302Gu3sk/kRJCiPLCpAnQ4cOHad68Oc2bNwdg6tSpNG/enPfeew+AyMhIfTIEULNmTTZs2EBwcDDNmjXjgw8+4MsvvzSYQ6h9+/asWLGCJUuW0KRJE4KCgli5cqXMASQqF40GGj2pbj8wKWJBda7nzpZXOzOpWx0szDRsP3eTnvN288Oey1IbJISoEMrMPEBlicwDJCqEG2dgYaA6y/T/XSjy0PuLN+/y9ppTHAy9DcCnTzRhSGsZKCCEKHsq7DxAQohC8AxQF0bVpavLcBRRHQ8HVo5vx8uP1AHgg7/PcD022VhRCiGESUgCJERF1jirGSz/0WB50Wg0vNKjHs2rO3M3NYM3V5+QpjAhRLkmCZAQFVmje/3jrvwD8deLdSkzrYbPnmqKlbmWPReiWXFIposQQpRfkgAJUZG5+IFvO0CBU38U+3K13e31q8p/+PcZGRkmhCi3JAESoqIzUjNYljEdatLKz4XEtEze+P0EOlk6QwhRDkkCJERF1/Bx0JhBZAhEXyj25cy0Gv73VFOsLbTsvRTDsoPh+Z8khBBljCRAQlR0dlWg9iPqdhHmBMpJzSp2vNnHH4DZG84ScVuawoQQ5YskQEJUBllLY5z8DYw0emtUYA3a1HQlKS2T//v9ePamMJ0OTvwGsVJDJIQoeyQBEqIy8O8H5jZw+xJcP2aUS2q1Gv73ZBNsLMzYf/k2v+wPMyxwZAn8MQ5+G2OU+wkhhDFJAiREZWDlAPX7qttGagYD8HOzY1o/tSnsk43nuBKdqB7QZcK+Ber2tcMQdcpo9xRCCGOQBEiIyiKrGezUajVBMZLhbf0IrOVGcvoDo8LOb4Tbl+8XOvaL0e4nhBDGIAmQEJVFnR5g7QwJUerEiEai1Wr49Mkm2FmacfDKbYL2Xrlf+1OttfrzxErISDXaPYUQorgkARKisjC3hICB6raR5gTK4utqy7R+DQDYuPlvCN+nLsL6VBA4VoPkO3Dub6PeUwghikMSICEqk6xmsDPrjF4jM6xtdTrWqcIo1ERH1+gJcKoGzYepBY7+bNT7CSFEcUgCJERl4tceHHwgNQ4ubDXqpTUaDf/r6Uxf7QEA1lgNUg80GwZo4HIw3AnL7XQhhChVkgAJUZlozaDRYHXbyM1gAN5ngzDTKOzJbMS0fXDxZoK6HlmtLmqBkGVGv6cQQhSFJEBCVDZZzWD/bYKUeONdNzlW38y13+tZ0jJ0vP7bcTJ1CjQfoZY5tsyoI9CEEKKoJAESorLxbgpudSEjBc6tN951j/4EaQng7s+wZ8fgYGVOSEQsL/xymDXJzdFZu0D8Vbi803j3FEKIIpIESIjKRqO5Xwv07xeQnlL8a2amw4Hv1O3Aifi42DLjsYYAbDt7k1f/OMdPCW0AOP33ArafvUFCakbx7yuEEEWkURQjLQxUgcTHx+Pk5ERcXByOjo6mDkcI40uMhm/aQeItCJwEvT8q3vVOrII/ngc7d5hyCiysATgSdodtZ2/w78Vo0q+fZKPlW6QpZrRL/Zp4rRPNfJ3pWLcKHetUoamvMxZm8m8yIUTRFeb7WxKgHEgCJCqF85tg+VB1e+SfUKtr0a6jKPBdZ4g6Ad3egS5v5FgsNikNvu+G851TLLB8js/iexgct7M0o10tN3o39OLJltXQajVFi6csyMyAlDiwczN1JEJUKoX5/pZ/bglRWdXvAy3vLVS65kV1ssKiuPKPmvyY20CrsbkWc7a1xLn9cwBMctrHnv/ryieDG9O/iTcuthYkpmWy/dxN3lh9gq92XCxaLGXFpjfhszoQusfUkQghciEJkBCVWe+PwLU23L0Of09Va3MKK2vZi2bP5F/j0fhJNVG6dRbf5LM83aY6C55twZF3e/L35I5M6FIbgPnb/2PHuRuFj6UsSLqtjoZTdHDgW1NHI4TIhSRAQlRmlnYw+HvQmMHpPwo/N9Ct/9Th9Gig3cT8y1s7QcNB6vbRn/S7tVoNjao68VZff0a080NR4JUVIfdXly9PTqyCzDR1+79NkHDTtPEIIXIkCZAQlV21ltD1LXV7/WsQG17wc/d/rf6s3xeq1CnYOVlzAp36A1ITsh2e3j+Aln4u3E3J4IVfjpCUVo5GiynK/cROawG6DHUhWCFEmSMJkBACOk5VV25PjVf7AxVkssLEaDi+Qt0OnFTwe/m1V5vd0hLgzNpshy3NtXwzrAXuDlacv3GXN1efpNyM1bh2FG6eAXNreORddd+xpUVrWhRClChJgIQQYGYOgxeBhR2E/QN7v8r/nEM/qJMp+jRXk5qC0mig+XB1++gvORbxdLTmm2EtMNdq+Ov4dX78J7Tg1zelY/cWfA0YCK2eu9ff6RxcO2LauIQQ2UgCJIRQudaCvnPU7R0fQuTx3MumJ8PB79XtwElqUlMYzZ5V+x1F7Idb53Ms0rqGK9P7BwAwe+M59l6KLtw9SltqApz8Xd1uMRKsHdVECOBYzomeEMJ0JAESQtzXfDj49wddOvwxXk10cnJiJSRFg5MvBAwq/H0cvKBeb3U7j+RgZKAfg1tUJVOnMPnXY1yPzSWesuDMWrVZz7UW+HVQ92XVdJ1cDWlJJgtNCJGdJEBCiPs0GhjwJdh7qk0322ZmL6PTwb57nZ/bTlCbz4oiqzP08RXqUho5hqPh48cbE+DtSExiGi8uPUJKehldTPXeQrA0H3G/RsyvA7jUgLS7cHadyUITQmQnCZAQwpCdGwy8l+Ac+BYubjc8fnErRP8HVo5qU09R1e2lJlqJt+4Npc+ZtYUZ341oibOtBcevxjFz3emi37Ok3DwHEQfUZr1mz97fr9VCs3u1QMeWmiY2IUSOJAESQmRXtye0fl7dXvuSOrlflqwO0ln9XIrKzByaPqNu59IZOouvqy1fPt0crQZWHIpg+cFCDNUvDVnNePX6qM17D2r2DKCBK3vg9uVSD00IkTNJgIQQOev5PlSpBwlR8Ncr6lDuyOPqF7nGTG3+Kq6sGqSLWyH+ep5FO9dz5/Xe9QGY8edpjoUXcemOQkpJz+TrnRdZfyIy5+H4GWlwfLm6nVONmFM1qP2Iuh3ya8kFKoQoFEmAhBA5s7RVh8ZrzdX+K8eXw957y140HATOvsW/h1tttZ+MooOQZfkWf7FLbXo39CQtU8eLS49y625q8WPIQ2JqBs8FHeJ/m88z8dejPL1oP//duGtY6PwGSIoBB2+o0yPnC2V1hg75tWBzLAkhSpwkQEKI3Pk0h67T1O0Nb6jLZUDhJj7MT1Zn6GNL1Q7WedBoNHz2VFNqu9sRFZ/CpF+Pkp6Z9zlFFZeUzvAfD7D3Ugy2lmZYW2g5EHqbfl/s4eMNZ0lIvTdDdVbn52bDcu8Q7v8o2LhA/DW4vLNE4hVCFI4kQEKIvHV8FXzbqSOZdBlqjU3VFsa7fsBAtUP1nSvqJIz5cLC24LsRrbC3MudA6G1mbzhnvFjuiU5I5env93MsPBYnGwt+fb4d26Z2oVeAJxk6hUW7L9Nj7i627z+EcmmHelJWLU9OzK2g8RB1WzpDC1EmSAIkhMib1gwGfweWDur79pONe31LW2j0hLqdT2foLHU87PnsqaYALP43lKX7w4y2XMb12GSGfLePs5HxVLG3YuUL7Wjm60w1F1sWjWzFktGtqe5qS1R8Cif//gYNCknVOoJrzbwvnJUgnVtv2KlcCGESkgAJIfLnUgNGrYPBP6gjnYwtq/PwmT8huWCdm/s08mJit9oAvLv2FGOCDhFxu3iTDV6JTuSpb/dx+VYiVZ1t+G1CIP5ehiPduvl7sOXVzrzavRZDzHYB8M6VZny66VzeC7d6NwGvJupK8Sd/K1acAMRGwIHvID2l+NcSohKSBEgIUTBVW0CTpwq/7EVB+DQHz0aQmXp/OYkCmNqzPq/2qIelmZbg87fo+fkuvtt1qUj9gs5H3eWp7/ZxLTaZmlXsWDUhkJpV7HIsa21hxis1ruGjiSFB68CGjFZ8E3yJnvN2s/l0VO61Ufr+TsVcGiM5Fn4aABvfgN3/K961hKikTJ4AffPNN9SsWRNra2tatmzJnj17ci07evRoNBpNtlfDhg31ZYKCgnIsk5Ii/0oSoszSaO4nB1mdigvATKvhlR512fBKJ9rWdCUlXcfsjed4bMG/hETEFvg6J67GMnTRPm7dTcXfy4FVLwRS1dkm75OO/gSAXatn+WqEWv5abDIv/HKEMUGHCItJzH5O4yfBzBKiTua91lpedDpYMwHu3Fsg9tD3kBJftGsJUYmZNAFauXIlU6ZM4Z133uHYsWN06tSJvn37Eh6e8yRnX3zxBZGRkfpXREQErq6uPPXUUwblHB0dDcpFRkZibW1dGo8khCiqJkPuJQcn4MLWQp1ax8OeFePb8emTTXC2teBsZDyPf/MvM/48xd2UnJfZyHLgcgzPfn+A2KR0mvk6s2J8O9wdrPK+YcItdfg7oGkxkl4Nvdg2tQuTutV5oDZqN7v/u2V4nq2rutYaFL0z9D9z4b+NYGYFjtUgJQ6OLCnatYSoxEyaAM2bN4+xY8cybtw4GjRowPz58/H19WXhwoU5lndycsLLy0v/Onz4MHfu3GHMmDEG5TQajUE5Ly+vHK8nhChDbF2hxSh1+/exEH2hUKdrNBqGtPJl+9QuDG5eFUWBn/aF0WPeLjadynkSw+DzNxm15CAJqRkE1nJj6bi2ONta5n+zEyvUEXFVW4JXIwBsLM14vXd9Nk3pRGAtN9IydEz89SgXbyYYntt82L1rrCp8/52L22HHR+r2o59Bt7fV7X1fS18gIQrJZAlQWloaR44coVevXgb7e/Xqxd69ewt0jR9//JEePXrg5+dnsD8hIQE/Pz+qVatG//79OXbsmNHiFkKUoN4fqUPuU+Pg16FFGi3lZm/FvKHNWDq2LX5uttyIT2XC0qM8//MRg9XkN56M5PmfD5OSruMRfw+WjGmNvVUBFnZVlPvNdDnM/FzL3Z6g51rTuoYLd1MyGPfTIWKT0h4o0A0cq0JKLJxfX/AHiw2H1eMARb1vi5HQ+Cm1FijhBhyXWaaFKAyTJUDR0dFkZmbi6elpsN/T05OoqKh8z4+MjGTjxo2MGzfOYL+/vz9BQUGsW7eO5cuXY21tTYcOHbhwIfd/TaamphIfH2/wEkKYgLkVDF0KTr5w+xL8NjrXleLz07FuFTZP6cykbnUw12rYdvYGPebt4sd/Qll1OIKJvx4lPVOhfxNvvhvREmsLs4JdOOKAuhishd394fsPsTI349vhLanmYsOVmCReXPrAhI3aBxZMLWgzWHoKrBoJybfBuxn0vdfx2dwS2t+blPLfLyAzj1FoQggDJu8ErXloRImiKNn25SQoKAhnZ2cGDRpksL9du3YMHz6cpk2b0qlTJ1atWkW9evX46quvcr3W7NmzcXJy0r98fY0wxb8Qomjs3eGZFWqCEboLNk0r8qWsLdRmqQ2vdKKlnwtJaZl88PcZ3vj9BDoFhrby5Yunm2NhVoj/FWbV/jR6HKwcci3mZm/Fj6NaY2dpxr7LMcxYd/p+M1xWAnRppzqcPT+b3oTrx9TZpIf8DBYP9GlsMRJsXNWJJM+sLfhziLzFX4fboaaOQpQgkyVAVapUwczMLFttz82bN7PVCj1MURQWL17MiBEjsLTMu71eq9XSunXrPGuApk2bRlxcnP4VEVGA/yEJIUqOVyN44ntAo45yOvRDsS5Xz9OB314I5OPHG+NorTZzPdehJp880RgzbSGG9afEwek16nbzHBY+fUh9Lwe+fKY5Gg38eiCcn/ZeUQ+41oIanQDl/kKquTm2FI4EARp44gdwMWzyx9IO2r2obv8zX22iE8WTcAsWdoBv2sGt86aORpQQkyVAlpaWtGzZkq1bDUd7bN26lfbt2+d57q5du7h48SJjx47N9z6KohASEoK3t3euZaysrHB0dDR4CSFMzP9R6D5d3d7wBlzeVazLabUanm1bneD/68aal9ozvX+DAtU2Gzi1GtKToEp98G1ToFO6N/BkWl9/AN7/+8z9kWFZM0PntQba9RD4e6q63e3t3BdbbT0OLO3hxkm4uK2ADyNytfU9tbkxIwXWvZzvGnWifDJpE9jUqVP54YcfWLx4MWfPnuXVV18lPDycCRMmAGrNzMiR2f+V9eOPP9K2bVsaNWqU7disWbPYvHkzly9fJiQkhLFjxxISEqK/phCiHOk4VV1DS8lU+8DEXCr2JV3tLGle3aXwyQ8Ydn4uxPnPd6rFUy2roVO4PzKswWPq8iKxYTmvgZZ0G1aNUCeHrNsbOr2e+w1sXaHlaHV7z7yCP4/ILmzf/Q7l5jYQsV+mGaigTJoADR06lPnz5/P+++/TrFkzdu/ezYYNG/SjuiIjI7PNCRQXF8fq1atzrf2JjY1l/PjxNGjQgF69enHt2jV2795NmzYF+9eaEKIM0Wjgsa+gait11NSvQ9VZkE0h6qTaD0drAU2fLtSpGo2GDx9vpB8ZNvanQ9xJN4fG9zpRP9wZWqeDP8arI79caqhrsWnz+d914CR1HqXwvRC+v1DxiXsyM2D9a+p2i5HQc5a6vXWG2idIVCgaxVgrCFYg8fHxODk5ERcXJ81hQpQFd2/A990g/hrU7g7PrgKzAgxZN6YNb8DB7yBgEAz5qUiXiElIZeDX/3L1TjLtarmytI8Z5ot7grk1vP4fWDupBYM/geDZ6v6xW9V1xApi3WS1lqpeH3h2ZZFirNT2fQ2b31Y7m086AjbO8GMvuHZYncDy6WWmjlDkozDf3yYfBSaEEPly8ISnf1WbJC5thy3vFu06iTFw8HtY/zoc+lGt0clIy/+89GR18kPIce6fgnpwZNj+y7d577A1iru/2tfk1Gq10IWtagIE0H9+wZMfgA5TQKOF/zbBjdNFjrNSio+EnbPV7R4zwc5NnbLgsS9Baw7n/oYz60waojAuSYCEEOWDTzN4/Ft1+8DCeyOjCiAjVV1lfvmzMLcebHhdHVm2fios6gqzq8Kibmpn46O/qInDw/PpnP1bHQHmVF2dyLAYDEaGHYzgsHM/9cCxpepQ9qzJDls9B82eyfEaYTGJBP0byqy/TnMz/oEZoN1qQ8BAdfufz4sVZ6Wz5R1Iu6s2tz44ws+zoZpYAmz4P9M1wQqjkyawHEgTmBBlWPAcCP5Y/Vf5yD+hRsfsZRRF7QdzYoU6bD0l7v4xrybqObfOwbWjat+ih5nbqDUvPi3UleoP/6hOgNj1bej6plEeY9HuS3y84Rwemjj2W09Gq2SAS011kdOqLWHMRnViSCA1I5NDoXfYef4mO8/d5HL0/YVWa1axY/nz7fByujc3UORx+K6zWhM0+Si41jRKvBXa5WD4eaD6OxsfDN5NDY+np8C3HSDmIrQcAwPmmyBIURCF+f6WBCgHkgAJUYYpCvz+HJz+Q50A8Pkd97/kYy7BiZXq686V++c4+KiLrTZ9GjwaGF7rzhW4flRtDrseor7S7uZwYw1MOQnOxpkoVVEU3vj9BL8ducqP1p/TnUPqAVs3eGE3kbix89wtdp6/yb8Xo0lKy9Sfa67V0KqGCxG3k7kWm0wNN1uWj2+Ht9O9Fex/Gaw2FbYaC/1lVFieMtJgYXuIuQBtxkO//+Vc7so/EPSouj16A9ToUHoxigKTBKiYJAESooxLS4KgfmrS4u6vzoNzYiVcPXS/jKW9OtS86VB10kFtAZe60OnUf+nrk6JjarNYkyHQ37jNSqkZmQz/4QCO4dv40XIuikbLSv8vCYr041yUYRLm7mBFt/rudKvvQYe6VXC0tiDidhLPfL+fq3eS8XOzZfnz7fBxtrn/ZW1mpSZtDnlPLlup7ZkL298HOw+YdEjt+JybdS/D0Z/ArS5M+MdwRm5RJkgCVEySAAlRDsRfV/vuJDwwm7xGq/bRafq0OpGipZ3p4iugmIRUBi3Yw4C7q7iseLNJp07ZodFAc19nutX3oJu/BwHejmhzmLX6WmwyTy/aR8TtZKq7qjVBVZ2s1dFLVw9Cx1fVTr0iu9hwWNAGMpLh8e/yn94gORa+bqMuPtv5DXjknVIJUxScJEDFJAmQEOXEtSOw/Bmw94AmT0PjJ8HBy9RRFdr5qLs88/1+dIpCl3pqLU/neu642uW91E+W67HJPL1oP+G3k/B1tWH58+2odiMYVjwDVo7w6qn7Q+zFfSuGqaO7/DrA6PUFm9zy9Fr4bZQ6H9SEPYZNqsLkJAEqJkmAhBClLT1Th1ajKdzaZA+IjEvmmUX7uRKTRFVnG1Y83wbfFT3g1lno/h50es3IEZdz/22BX58CjZnanOUZULDzFAVWPAvnN0C11vDc5oI3r4oSJ/MACSFEOWNhpi1y8gPg7WTDivGB1KxipzaLfX+QmOYvqQf3L1TnMhKq9GTY+H/qdrsXC578gFpL1O8zdRmTq4fU+aREuSQJkBBCVBBeTtasGN+OWu5qEjQo2JMMh2qQeCv7chuV2T/z1dF/Dt7Q9a3Cn+9UFXrMULe3z4K4q8aMTpQSSYCEEKIC8XS0ZsXz7ajtbkdEfAbzk/uqB/Z+mX2Cx8ro9uX7k0T2mQ1WDkW7Tqux4NsW0hLUmcWlN0m5IwmQEEJUMB6O1iwf3446HvZ8n9CB2ziqI56yltuorBRFnc05M1UdLRgwqOjX0mphwJdqZ+j/NsKZtcaKUpQS6QSdA+kELYSoCG7dTWXYD/vpEb2UNyxWkeZaH8tJ+/NfWb6iOrMOVo0AM0t4cR9UqVP8a+6cDbs+uTeP0EF1IdWy5Mo/ELpb7feUkaK+0lPub+f0XpcJdXtBt7fVEZbliIwCKyZJgIQQFUV0QirjF23np7jncNAkE9b7R6q3ewJNQYZ8VyRpieqcP/FXodPr0H26ca6bkQrfdoLo89B8BAxcYJzrFtfdG7B5WvFq/SwdoNNUaPdSuZn0URKgYpIESAhRkcQkpLLtqxcZmrqaeMWGCG1V0m09sXD2wdnDFw+fGlg4V1XnUHLwBlvXgs2JU55snQH/zlcXtJ14ACxtjXftsH2wpI+63e8zaPoMWNkb7/qFocuEw4th+weQGqdODtpwsPrZmluriYz5Ay8LG3XNOfOsn9bq+ng7P1JnQQf1d9ZzpnqdMv53IQlQMUkCJISoaO7ciMDiu0DsdTmtc2ZIMbMEBy80Dt7gWBVqdlabRJyqlkKkRpSZATdOQfg+2PIu6DLg6eXg38/49/r7VTXxAHUZloaD1Boh37allzREnoC/p6gThIK6kG//+eDTrPDX0ung5G/qKLf4a+q+am3UjuPVWhkpYOOTBKiYJAESQlRIKfGk3PiPsCuXiLp6hfhb4WTGReKUEY2nJhYPzR2qaOJzP9+zMdTrrb6qtix7EwAm3YarhyHigPq6dhTSE+8fr9cHnl1ZMvfOSIV9X8OxX9SRZlnc6kCzZ9VaIUefkrl3agLs/BgOLARFpzZddX8PWo8t/meUlgT7Fqgj59KT1H2Nn4LuM4y2MLAxSQJUTJIACSEqC0VRiLidzNHwOxwNv8OJsJtER13FQ4nBQxNLHc01epgfp6nmAhoe+LqwdYM6PdRkqHb3vBcRLQk6nbqCe1ayE3EQov/LXs7KCXxbQ/VAaDuh5JumFEWtcTq2DE6vuZ+AabTq76n5MKjfT21uMoazf8PGN+7X0gQMgj6fgKO3ca6fJT4SdnwIIcsARW0qC5wEHafkP5VARqq6wPCt8+pnlPXTIwCe+N64YUoCVDySAAkhKrOktAxOXo3jaHgsfx2/zpnIeFyJp4/1KUZXOU/duwfQpD5QU6Qxg+rt1GSobm9wr19yzT4Rh2D/13Bpp9pX5WFuddVmJ9/W6s8q9U036i01QR0ef2wZhO+9v9/GBRoPUZMh76ZFu3ZsOGx8U12SA8DZDx6dC3V7FjvsPF0Pgc3vQNg/6nt7T3jkXWg2TO1ork9wzsOt/9Sfd66oNVMPq1JfHTlnRJIAFZMkQEIIodLpFDafjuLzbf/x340EANxtNLzbNIF+1iewuLhF/ZJ7kFsdaD4cmj4LDp5GCCJT/aLfuwAi9t/fb26j9kepdi/ZqdYa7NyKf7+SEHNJrT0JWQ53r9/f71xd7Wdl564mE/aeYJ+17aH+tHO/X2OUma4ubRI8W22S0ppD+5eh8/8Zt2N3XhQFzq2HrdPvN/dZOUJqHs2nVk7gXk9NevQ/64NrTaOGJglQMUkCJIQQhjJ1Cn+fuM4X2y5wOVpt1qlib8mLXeswvL6C1eVt8N8muLIHMtPUkzRmar+bFiOgTk8wMy/cTdOS4Pivat+arC9aM0toMgRajlFrT8wsjPiUpUCXqdZehSxVk4is31V+rJ3VZCgjBWLD1H3V20P/eaZbkT4jDQ59D7vmQEqcus/eyzDBqVJP/WnvWSqdwSUBKiZJgIQQImcZmTr+DLnOF9svEH5b7RTr6WjFpG51GNLaF6vMJLXvy9Gf1cVCs9h7QbNn1JFRbrXzvknCTTj4PRz6AZJvq/usndVOvW3Gq0O6K4LkO3DjDCTeVJ854SYk3Lj/M/GW+lP30BImNi7Q8wO12aksTGqZEge3Q8GlRun3BXuIJEDFJAmQEELkLT1Tx+ojV/lqx0WuxaorzVd1tmHSI3V4smU1LMy0cPOcOirq+HJIirl/sl9HtVaowWOGzTa3/lNHHB1foS5XAWrflsCJ6pe9qebWMSWdTu3rlJUUpcaDXwd1riaRjSRAxSQJkBBCFExqRiarDkWwYOdFbsSrSYuZVoOrnSVudpa4O1jhYauhXcYh2txZj+/tfWhRO8TqLB3QNXoS89pd1aTnv433L1y1pdq3pcGAsjfcXpRZkgAVkyRAQghROCnpmSw7EM7C4EtEJ6TmWs6bGJ4028UQs134am8ZHNOhIcQmkH2ezxLv0RI3eytc7axws7PEzd7yXlJlhY2lJEQiZ5IAFZMkQEIIUTQZmTqiE9KITkglJjGNmIRUYhLSiE5Uf8YkpBKdkMbtu8nUST7GYHbSXHOB3bom/JjZj1Al//lrbCzMcHewwsvJGi9Ha7ydrPXbXve23e2tMDfLu39Mpk7hTlLa/bgS04i+m0rMvVhTM3RYmWuxMtdiaa7Fytzs3s+sfWYPHNNiY2mGv5cj7g5GmuNHFJokQMUkCZAQQpQ8RVFISM1QE5B7ydLtRHX7dlbydG9bfZ9GWmYO88nkQKvhXpJkg7ejNW72ltxNySAmMZXou2nEJKr30pXAN2CtKna0ruFKm5rqq5qLTeVbfNZEJAEqJkmAhBCi7HkwYbqVkEpkXAo34lLUn/EpRMYlExWXws27qWQUIrNxsbXAzd6KKvaW6k879aeNhRlpmTpS0zNJzdDpX2kZOlIzMrNtxyenczk6kYe/Vb0crWlT05XWNV1pU8OVuh72aLWSEJUESYCKSRIgIYQovzJ1CjEJqUTF30+OohPScLQ2p4q9FW72lvqfrraW+TaVFUZcUjqHw25zMPQ2B6/c5uTVuGzJmLOtBa38XGlT04XAWlVo6OMoCZGRSAJUTJIACSGEMIbktEyORdzhYOhtDl25zdGwWJLTMw3KeDhY0b2BJz0aeNChThWsLaSTd1FJAlRMkgAJIYQoCemZOk5di+PQFbWWaN+lGBLT7idE1hZaOtZxp0cDDx5p4IGHg7UJoy1/JAEqJkmAhBBClIbUjEz2X77NtjM32H72BtfjUgyON/V1pmcDD7o38MTfy0E6U+dDEqBikgRICCFEaVMUhTOR8Ww/e5PtZ29w/GqcwfGqzjb0aOBBl/rutK3php1VIddWqwQkASomSYCEEEKY2o34FHacu8m2Mzf452I0qRn3pwCwMNPQvLoLnepUoUPdKjSp6mSUztw6ncL1uGQSUzP1I9zSMnSkZupITdeRlqkz3H9vJJyZVkM1F5t7L1vc7a1M0rFbEqBikgRICCFEWZKclsk/F6PZce4Gey5Ec/VOssFxB2tz2td2o2NddzrVqYKfm22ezWVpGTrCYhK5eDOBizcTuHDv56VbCQaJVlFZmmmp+kBC9GBy5Otig7uDVYk050kCVEySAAkhhCirFEUh/HYSey5E88+FaPZeiiY+xXDF+GouNnSsU4WOdavg52rH5egELtzISnbuEhaTlOtcSZZmWhyszbPNgG35wKzYlmZarCzMsDRT36dl6LgWm8TVO8lExqWQmc88TFbmWtrUdOWXsW2N9nuBwn1/SwOiEEIIUY5oNBr83Ozwc7NjeDs/MnUKJ67G8u/FaPZciOZo+B2u3klmxaEIVhyKyPU6dpZm1PF0oI67PXU87Knrof70dbXFrBjNVxmZOiLjUrh6J5mrd5Lu/by/HRmXTGqGLtuEkaVNaoByIDVAQgghyqvE1AwOht5Wa4gu3uJ2Yhq1qthT+4Ekp66nPV6O1iYZVZaeqSMqLoW0TB213e2Nem2pARJCCCEqKTsrc7r5e9DN38PUoeTIwkyLr6utqcPAePN/F9E333xDzZo1sba2pmXLluzZsyfXssHBwWg0mmyvc+fOGZRbvXo1AQEBWFlZERAQwJo1a0r6MYQQQghRjpg0AVq5ciVTpkzhnXfe4dixY3Tq1Im+ffsSHh6e53nnz58nMjJS/6pbt67+2L59+xg6dCgjRozg+PHjjBgxgiFDhnDgwIGSfhwhhBBClBMm7QPUtm1bWrRowcKFC/X7GjRowKBBg5g9e3a28sHBwXTr1o07d+7g7Oyc4zWHDh1KfHw8Gzdu1O/r06cPLi4uLF++vEBxSR8gIYQQovwpzPe3yWqA0tLSOHLkCL169TLY36tXL/bu3Zvnuc2bN8fb25vu3buzc+dOg2P79u3Lds3evXvne00hhBBCVB4m6wQdHR1NZmYmnp6eBvs9PT2JiorK8Rxvb28WLVpEy5YtSU1N5ZdffqF79+4EBwfTuXNnAKKiogp1TYDU1FRSU1P17+Pj44v6WEIIIYQoB0w+CuzhIXiKouQ6LK9+/frUr19f/z4wMJCIiAg+++wzfQJU2GsCzJ49m1mzZhUlfCGEEEKUQyZrAqtSpQpmZmbZamZu3ryZrQYnL+3atePChQv6915eXoW+5rRp04iLi9O/IiJynzhKCCGEEOWfyRIgS0tLWrZsydatWw32b926lfbt2xf4OseOHcPb21v/PjAwMNs1t2zZkuc1rayscHR0NHgJIYQQouIyaRPY1KlTGTFiBK1atSIwMJBFixYRHh7OhAkTALVm5tq1a/z8888AzJ8/nxo1atCwYUPS0tJYunQpq1evZvXq1fprvvLKK3Tu3Jk5c+YwcOBA/vzzT7Zt28Y///xjkmcUQgghRNlj0gRo6NChxMTE8P777xMZGUmjRo3YsGEDfn5+AERGRhrMCZSWlsbrr7/OtWvXsLGxoWHDhqxfv55+/frpy7Rv354VK1bw7rvvMn36dGrXrs3KlStp29a4C64JIYQQovyStcByIPMACSGEEOVPuZgHSAghhBDCVCQBEkIIIUSlIwmQEEIIISodk0+EWBZldYuSGaGFEEKI8iPre7sg3ZslAcrB3bt3AfD19TVxJEIIIYQorLt37+Lk5JRnGRkFlgOdTsf169dxcHDIcwmNooiPj8fX15eIiIhKNcKssj43yLNXxmevrM8N8uyV8dnL0nMrisLdu3fx8fFBq827l4/UAOVAq9VSrVq1Er1HZZ1xurI+N8izV8Znr6zPDfLslfHZy8pz51fzk0U6QQshhBCi0pEESAghhBCVjiRApczKyooZM2ZgZWVl6lBKVWV9bpBnr4zPXlmfG+TZK+Ozl9fnlk7QQgghhKh0pAZICCGEEJWOJEBCCCGEqHQkARJCCCFEpSMJkBBCCCEqHUmAStE333xDzZo1sba2pmXLluzZs8fUIZW4mTNnotFoDF5eXl6mDqtE7N69mwEDBuDj44NGo2Ht2rUGxxVFYebMmfj4+GBjY0PXrl05ffq0aYI1ovyee/To0dn+Btq1a2eaYI1o9uzZtG7dGgcHBzw8PBg0aBDnz583KFNRP/OCPHtF/dwXLlxIkyZN9JP+BQYGsnHjRv3xivqZ5/fc5fHzlgSolKxcuZIpU6bwzjvvcOzYMTp16kTfvn0JDw83dWglrmHDhkRGRupfJ0+eNHVIJSIxMZGmTZuyYMGCHI9/+umnzJs3jwULFnDo0CG8vLzo2bOnfu258iq/5wbo06ePwd/Ahg0bSjHCkrFr1y4mTpzI/v372bp1KxkZGfTq1YvExER9mYr6mRfk2aFifu7VqlXjk08+4fDhwxw+fJhHHnmEgQMH6pOcivqZ5/fcUA4/b0WUijZt2igTJkww2Ofv76+89dZbJoqodMyYMUNp2rSpqcModYCyZs0a/XudTqd4eXkpn3zyiX5fSkqK4uTkpHz77bcmiLBkPPzciqIoo0aNUgYOHGiSeErTzZs3FUDZtWuXoiiV5zNXlOzPriiV53NXFEVxcXFRfvjhh0r1mSvK/edWlPL5eUsNUClIS0vjyJEj9OrVy2B/r1692Lt3r4miKj0XLlzAx8eHmjVr8vTTT3P58mVTh1TqQkNDiYqKMvgbsLKyokuXLpXibyA4OBgPDw/q1avH888/z82bN00dktHFxcUB4OrqClSuz/zhZ89S0T/3zMxMVqxYQWJiIoGBgZXmM3/4ubOUt89bFkMtBdHR0WRmZuLp6Wmw39PTk6ioKBNFVTratm3Lzz//TL169bhx4wYffvgh7du35/Tp07i5uZk6vFKT9Tnn9DcQFhZmipBKTd++fXnqqafw8/MjNDSU6dOn88gjj3DkyJFyN3NsbhRFYerUqXTs2JFGjRoBleczz+nZoWJ/7idPniQwMJCUlBTs7e1Zs2YNAQEB+iSnon7muT03lM/PWxKgUqTRaAzeK4qSbV9F07dvX/1248aNCQwMpHbt2vz0009MnTrVhJGZRmX8Gxg6dKh+u1GjRrRq1Qo/Pz/Wr1/P4MGDTRiZ8UyaNIkTJ07wzz//ZDtW0T/z3J69In/u9evXJyQkhNjYWFavXs2oUaPYtWuX/nhF/cxze+6AgIBy+XlLE1gpqFKlCmZmZtlqe27evJntXwoVnZ2dHY0bN+bChQumDqVUZY18k78B8Pb2xs/Pr8L8DUyePJl169axc+dOqlWrpt9fGT7z3J49JxXpc7e0tKROnTq0atWK2bNn07RpU7744osK/5nn9tw5KQ+ftyRApcDS0pKWLVuydetWg/1bt26lffv2JorKNFJTUzl79ize3t6mDqVU1axZEy8vL4O/gbS0NHbt2lXp/gZiYmKIiIgo938DiqIwadIk/vjjD3bs2EHNmjUNjlfkzzy/Z89JRfncc6IoCqmpqRX6M89J1nPnpFx83qbqfV3ZrFixQrGwsFB+/PFH5cyZM8qUKVMUOzs75cqVK6YOrUS99tprSnBwsHL58mVl//79Sv/+/RUHB4cK+dx3795Vjh07phw7dkwBlHnz5inHjh1TwsLCFEVRlE8++URxcnJS/vjjD+XkyZPKM888o3h7eyvx8fEmjrx48nruu3fvKq+99pqyd+9eJTQ0VNm5c6cSGBioVK1atdw/94svvqg4OTkpwcHBSmRkpP6VlJSkL1NRP/P8nr0if+7Tpk1Tdu/erYSGhionTpxQ3n77bUWr1SpbtmxRFKXifuZ5PXd5/bwlASpFX3/9teLn56dYWloqLVq0MBgyWlENHTpU8fb2ViwsLBQfHx9l8ODByunTp00dVonYuXOnAmR7jRo1SlEUdVj0jBkzFC8vL8XKykrp3LmzcvLkSdMGbQR5PXdSUpLSq1cvxd3dXbGwsFCqV6+ujBo1SgkPDzd12MWW0zMDypIlS/RlKupnnt+zV+TP/bnnntP/f9zd3V3p3r27PvlRlIr7mef13OX189YoiqKUXn2TEEIIIYTpSR8gIYQQQlQ6kgAJIYQQotKRBEgIIYQQlY4kQEIIIYSodCQBEkIIIUSlIwmQEEIIISodSYCEEEIIUelIAiSEELnQaDSsXbvW1GEIIUqAJEBCiDJp9OjRaDSabK8+ffqYOjQhRAVgbuoAhBAiN3369GHJkiUG+6ysrEwUjRCiIpEaICFEmWVlZYWXl5fBy8XFBVCbpxYuXEjfvn2xsbGhZs2a/Pbbbwbnnzx5kkceeQQbGxvc3NwYP348CQkJBmUWL15Mw4YNsbKywtvbm0mTJhkcj46O5vHHH8fW1pa6deuybt06/bE7d+4wbNgw3N3dsbGxoW7dutkSNiFE2SQJkBCi3Jo+fTpPPPEEx48fZ/jw4TzzzDOcPXsWgKSkJPr06YOLiwuHDh3it99+Y9u2bQYJzsKFC5k4cSLjx4/n5MmTrFu3jjp16hjcY9asWQwZMoQTJ07Qr18/hg0bxu3bt/X3P3PmDBs3buTs2bMsXLiQKlWqlN4vQAhRdKZejVUIIXIyatQoxczMTLGzszN4vf/++4qiqCuST5gwweCctm3bKi+++KKiKIqyaNEixcXFRUlISNAfX79+vaLVapWoqChFURTFx8dHeeedd3KNAVDeffdd/fuEhARFo9EoGzduVBRFUQYMGKCMGTPGOA8shChV0gdICFFmdevWjYULFxrsc3V11W8HBgYaHAsMDCQkJASAs2fP0rRpU+zs7PTHO3TogE6n4/z582g0Gq5fv0737t3zjKFJkyb6bTs7OxwcHLh58yYAL774Ik888QRHjx6lV69eDBo0iPbt2xfpWYUQpUsSICFEmWVnZ5etSSo/Go0GAEVR9Ns5lbGxsSnQ9SwsLLKdq9PpAOjbty9hYWGsX7+ebdu20b17dyZOnMhnn31WqJiFEKVP+gAJIcqt/fv3Z3vv7+8PQEBAACEhISQmJuqP//vvv2i1WurVq4eDgwM1atRg+/btxYrB3d2d0aNHs3TpUubPn8+iRYuKdT0hROmQGiAhRJmVmppKVFSUwT5zc3N9R+PffvuNVq1a0bFjR5YtW8bBgwf58ccfARg2bBgzZsxg1KhRzJw5k1u3bjF58mRGjBiBp6cnADNnzmTChAl4eHjQt29f7t69y7///svkyZMLFN97771Hy5YtadiwIampqfz99980aNDAiL8BIURJkQRICFFmbdq0CW9vb4N99evX59y5c4A6QmvFihW89NJLeHl5sWzZMgICAgCwtbVl8+bNvPLKK7Ru3RpbW1ueeOIJ5s2bp7/WqFGjSElJ4fPPP+f111+nSpUqPPnkkwWOz9LSkmnTpnHlyhVsbGzo1KkTK1asMMKTCyFKmkZRFMXUQQghRGFpNBrWrFnDoEGDTB2KEKIckj5AQgghhKh0JAESQgghRKUjfYCEEOWStN4LIYpDaoCEEEIIUelIAiSEEEKISkcSICGEEEJUOpIACSGEEKLSkQRICCGEEJWOJEBCCCGEqHQkARJCCCFEpSMJkBBCCCEqHUmAhBBCCFHp/D9HzeMUfauK1wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plot training & validation loss\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Plot training & validation accuracy\n",
    "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31d9f0d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "# Convert labels to their corresponding indices\n",
    "labels_test_converted = convert_labels(labels_test)\n",
    "\n",
    "# Function to evaluate the model in batches\n",
    "def evaluate_model_in_batches(model, point_clouds_test, labels_test, batch_size=32):\n",
    "    num_samples = len(point_clouds_test)\n",
    "    num_batches = int(np.ceil(num_samples / batch_size))  # Number of batches\n",
    "    total_loss = 0.0\n",
    "    total_accuracy = 0.0\n",
    "\n",
    "    for batch_idx in range(num_batches):\n",
    "        # Get the batch\n",
    "        start_idx = batch_idx * batch_size\n",
    "        end_idx = min((batch_idx + 1) * batch_size, num_samples)\n",
    "        batch_point_clouds = point_clouds_test[start_idx:end_idx]\n",
    "        batch_labels = labels_test[start_idx:end_idx]\n",
    "\n",
    "        # Evaluate the batch\n",
    "        batch_loss, batch_accuracy = model.evaluate(batch_point_clouds, batch_labels, verbose=0)\n",
    "\n",
    "        # Accumulate the total loss and accuracy\n",
    "        total_loss += batch_loss * len(batch_point_clouds)  # Multiply by the batch size\n",
    "        total_accuracy += batch_accuracy * len(batch_point_clouds)\n",
    "\n",
    "    # Calculate the average loss and accuracy over all samples\n",
    "    average_loss = total_loss / num_samples\n",
    "    average_accuracy = total_accuracy / num_samples\n",
    "\n",
    "    return average_loss, average_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ff516411",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Loss: 0.6306386967500051\n",
      "Average Accuracy: 0.77159663438797\n"
     ]
    }
   ],
   "source": [
    "batch_size = 2  \n",
    "average_loss, average_accuracy = evaluate_model_in_batches(loaded_model, point_clouds_test, labels_test_converted, batch_size=batch_size)\n",
    "\n",
    "print(f\"Average Loss: {average_loss}\")\n",
    "print(f\"Average Accuracy: {average_accuracy}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "686ff390",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_colors = {\n",
    "    0: 'red',\n",
    "    11: 'blue',\n",
    "    12: 'green',\n",
    "    13: 'orange',\n",
    "    14: 'purple',\n",
    "    15: 'cyan',\n",
    "    16: 'magenta',\n",
    "    17: 'yellow',\n",
    "    18: 'brown',\n",
    "    21: 'lime',\n",
    "    22: 'navy',\n",
    "    23: 'teal',\n",
    "    24: 'violet',\n",
    "    25: 'salmon',\n",
    "    26: 'gold',\n",
    "    27: 'lightblue',\n",
    "    28: 'coral',\n",
    "    31: 'olive',\n",
    "    32: 'silver',\n",
    "    33: 'gray',\n",
    "    34: 'black',\n",
    "    35: 'darkred',\n",
    "    36: 'darkgreen',\n",
    "    37: 'darkblue',\n",
    "    38: 'darkviolet',\n",
    "    41: 'peru',\n",
    "    42: 'chocolate',\n",
    "    43: 'mediumvioletred',\n",
    "    44: 'lightskyblue',\n",
    "    45: 'lightpink',\n",
    "    46: 'plum',\n",
    "    47: 'khaki',\n",
    "    48: 'powderblue',\n",
    "}\n",
    "\n",
    "# a sample from the test set\n",
    "sample_idx = 38\n",
    "point_cloud_sample = point_clouds_test[sample_idx]\n",
    "true_labels_sample = labels_test[sample_idx]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fa3f4650",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.57055597, -0.1601968 , -0.06835842],\n",
       "       [-0.16087969, -0.41825282, -0.20182836],\n",
       "       [-0.49467562,  0.08825079,  0.14224854],\n",
       "       ...,\n",
       "       [-0.13109185, -0.42943553, -0.16669425],\n",
       "       [ 0.48112779, -0.19842731,  0.10277508],\n",
       "       [ 0.52022024,  0.30006776,  0.08077139]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "point_cloud_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "86c2e8e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of point_cloud_sample: (15230, 3)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of point_cloud_sample:\", point_cloud_sample.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1be3f273",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add batch dimension\n",
    "point_cloud_sample_ex = np.expand_dims(point_cloud_sample, axis=0)  # Shape: (1, num_points, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "bb889b0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 243ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = loaded_model.predict(point_cloud_sample_ex)\n",
    "\n",
    "predicted_labels = np.argmax(predictions, axis=-1)[0]  # Shape: (num_points,)\n",
    "predicted_labels = convert_indices(predicted_labels)\n",
    "\n",
    "# Ensure it's a 1D array\n",
    "predicted_labels = np.squeeze(predicted_labels)\n",
    "predicted_labels_sample = predicted_labels\n",
    "\n",
    "# Map labels to colors\n",
    "true_colors = np.array([label_colors[label] if label in label_colors else 'gray' for label in true_labels_sample])\n",
    "predicted_colors = np.array([label_colors[label] if label in label_colors else 'gray' for label in predicted_labels_sample])\n",
    "\n",
    "# Create a figure for true vs predicted labels\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "\n",
    "# Subplot 1: True labels\n",
    "ax1 = fig.add_subplot(121, projection='3d')\n",
    "scatter_true = ax1.scatter(point_cloud_sample[:, 0], point_cloud_sample[:, 1], point_cloud_sample[:, 2], \n",
    "                            c=true_colors, marker='o')\n",
    "ax1.set_title('True Segmentation')\n",
    "\n",
    "# Subplot 2: Predicted labels\n",
    "ax2 = fig.add_subplot(122, projection='3d')\n",
    "scatter_pred = ax2.scatter(point_cloud_sample[:, 0], point_cloud_sample[:, 1], point_cloud_sample[:, 2], \n",
    "                            c=predicted_colors, marker='o')\n",
    "ax2.set_title('Predicted Segmentation')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "762e385f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}