Visualizing_Point_Pillar_Image.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "import numpy as np\n",
    "# import tensorflow as tf\n",
    "from glob import glob\n",
    "import cv2\n",
    "\n",
    "from config import Parameters\n",
    "# from processors import SimpleDataGenerator\n",
    "from custom_processors import AnalyseCustomDataGenerator\n",
    "from det3d.pc_kitti_dataset import PCKittiAugmentedDataset\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# DATA_ROOT = \"/media/data3/tjtanaa/kitti_dataset/KITTI/object/training\"  # TODO make main arg\n",
    "DATA_ROOT = \"/media/data3/tjtanaa/kitti_dataset/\"  # TODO make main arg\n",
    "MODEL_ROOT = \"./logs_Car_Pedestrian_Custom_Dataset_single_process\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = Parameters()\n",
    "\n",
    "gt_database_dir = os.path.join(DATA_ROOT, \"gt_database\")\n",
    "\n",
    "training_gen = AnalyseCustomDataGenerator(batch_size=params.batch_size,root_dir = DATA_ROOT,\n",
    "                npoints=20000, split='train',   classes=list(params.classes_map.keys()), \n",
    "                random_select=False, gt_database_dir=None, aug_hard_ratio=0.7)\n",
    "\n",
    "validation_gen = AnalyseCustomDataGenerator(batch_size=params.batch_size,  root_dir=DATA_ROOT, \n",
    "        npoints=20000, split='val',  random_select=False, classes=list(params.classes_map.keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# # get the min and max (range) of every axis\n",
    "# x_max = np.array(-9999.0)\n",
    "# x_min = np.array(9999.0)\n",
    "# y_max = np.array(-9999.0)\n",
    "# y_min = np.array(9999.0)\n",
    "# z_max = np.array(-9999.0)\n",
    "# z_min = np.array(9999.0)\n",
    "# for i in range(len(training_gen)):\n",
    "#     [pillars, voxels], \\\n",
    "#     [occupancy, position, size, angle, heading, classification], \\\n",
    "#     [pts_input, gt_boxes3d, sample] = training_gen[i]\n",
    "#     for pts in pts_input:\n",
    "# #         print(type(pts))\n",
    "# #         print(pts.shape)\n",
    "#         x_max = np.max([x_max, np.max(pts[:,0])])\n",
    "#         x_min = np.min([x_min, np.min(pts[:,0])])\n",
    "#         y_max = np.max([y_max, np.max(pts[:,1])])\n",
    "#         y_min = np.min([y_min, np.min(pts[:,1])])\n",
    "#         z_max = np.max([z_max, np.max(pts[:,2])])\n",
    "#         z_min = np.min([z_min, np.min(pts[:,2])])\n",
    "# print(x_min, x_max)\n",
    "# print(y_min, y_max)\n",
    "# print(z_min, z_max)\n",
    "# 1.349664568901062 78.88325500488281\n",
    "# -52.52265167236328 50.9976806640625\n",
    "# -1.0496952533721924 3.1484153270721436\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: /home/tan/tjtanaa/PointPillars/visualization/custom_processor already exists, operation skipped.\n",
      "WARNING: /home/tan/tjtanaa/PointPillars/visualization/custom_processor/data already exists, operation skipped.\n",
      "WARNING: /home/tan/tjtanaa/PointPillars/visualization/custom_processor/html already exists, operation skipped.\n",
      "36\n",
      "37\n",
      "38\n",
      "39\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f5afd8ce650>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "[pillars, voxels], \\\n",
    "[occupancy, position, size, angle, heading, classification], \\\n",
    "[pts_input, gt_boxes3d, sample] = validation_gen[9]\n",
    "# print(pillars[0].shape)\n",
    "# print(voxels[0].shape)\n",
    "# print(occupancy[0].shape)\n",
    "# # print(occupancy[0,0,:])\n",
    "# print(angle[0].shape)\n",
    "\n",
    "# for i in range(len(occupancy)):\n",
    "#     print(i, \"l \")\n",
    "#     print(np.sum(occupancy[i][:,:,0] == 0))\n",
    "#     print(np.sum(occupancy[i][:,:,0] == 1))\n",
    "#     print(np.sum(occupancy[i][:,:,0] == -1))\n",
    "#     print(np.sum(occupancy[i][:,:,1] == 0))\n",
    "#     print(np.sum(occupancy[i][:,:,1] == 1))\n",
    "#     print(np.sum(occupancy[i][:,:,1] == -1))\n",
    "#     print(np.sum(occupancy[i][:,:,2] == 0))\n",
    "#     print(np.sum(occupancy[i][:,:,2] == 1))\n",
    "#     print(np.sum(occupancy[i][:,:,2] == -1))\n",
    "#     print(np.sum(occupancy[i][:,:,3] == 0))\n",
    "#     print(np.sum(occupancy[i][:,:,3] == 1))\n",
    "#     print(np.sum(occupancy[i][:,:,3] == -1))\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(10,10)) \n",
    "  \n",
    "ax = fig.add_subplot(111) \n",
    "ax.imshow(occupancy[0][:,:,3] == 1, cmap = plt.cm.gray, \n",
    "          interpolation ='nearest') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: /home/tan/tjtanaa/PointPillars/visualization/custom_processor already exists, operation skipped.\n",
      "WARNING: /home/tan/tjtanaa/PointPillars/visualization/custom_processor/data already exists, operation skipped.\n",
      "WARNING: /home/tan/tjtanaa/PointPillars/visualization/custom_processor/html already exists, operation skipped.\n",
      "32\n",
      "33\n",
      "34\n",
      "35\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import numba\n",
    "\n",
    "[pillars, voxels], \\\n",
    "[occupancy, position, size, angle, heading, classification], \\\n",
    "[pts_input, gt_boxes3d, sample] = validation_gen[8]\n",
    "\n",
    "\n",
    "@numba.jit(nopython=True)\n",
    "def _points_to_bevmap_reverse_kernel(\n",
    "        points,\n",
    "        voxel_size,\n",
    "        coors_range,\n",
    "        coor_to_voxelidx,\n",
    "        # coors_2d,\n",
    "        bev_map,\n",
    "        height_lowers,\n",
    "        # density_norm_num=16,\n",
    "        with_reflectivity=False,\n",
    "        max_voxels=40000):\n",
    "    # put all computations to one loop.\n",
    "    # we shouldn't create large array in main jit code, otherwise\n",
    "    # reduce performance\n",
    "    N = points.shape[0]\n",
    "    ndim = 3\n",
    "    ndim_minus_1 = ndim - 1\n",
    "    grid_size = (coors_range[3:] - coors_range[:3]) / voxel_size\n",
    "    # np.round(grid_size)\n",
    "    # grid_size = np.round(grid_size).astype(np.int64)(np.int32)\n",
    "    grid_size = np.round(grid_size, 0, grid_size).astype(np.int32)\n",
    "    height_slice_size = voxel_size[-1]\n",
    "    coor = np.zeros(shape=(3, ), dtype=np.int32)  # DHW\n",
    "    voxel_num = 0\n",
    "    failed = False\n",
    "    for i in range(N):\n",
    "        failed = False\n",
    "        for j in range(ndim):\n",
    "            c = np.floor((points[i, j] - coors_range[j]) / voxel_size[j])\n",
    "            if c < 0 or c >= grid_size[j]:\n",
    "                failed = True\n",
    "                break\n",
    "            coor[ndim_minus_1 - j] = c\n",
    "        if failed:\n",
    "            continue\n",
    "        voxelidx = coor_to_voxelidx[coor[0], coor[1], coor[2]]\n",
    "        if voxelidx == -1:\n",
    "            voxelidx = voxel_num\n",
    "            if voxel_num >= max_voxels:\n",
    "                break\n",
    "            voxel_num += 1\n",
    "            coor_to_voxelidx[coor[0], coor[1], coor[2]] = voxelidx\n",
    "            # coors_2d[voxelidx] = coor[1:]\n",
    "        bev_map[-1, coor[1], coor[2]] += 1\n",
    "        height_norm = bev_map[coor[0], coor[1], coor[2]]\n",
    "        incomimg_height_norm = (\n",
    "            points[i, 2] - height_lowers[coor[0]]) / height_slice_size\n",
    "        if incomimg_height_norm > height_norm:\n",
    "            bev_map[coor[0], coor[1], coor[2]] = incomimg_height_norm\n",
    "            if with_reflectivity:\n",
    "                bev_map[-2, coor[1], coor[2]] = points[i, 3]\n",
    "    # return voxel_num\n",
    "\n",
    "def points_to_bev(points,\n",
    "                  voxel_size,\n",
    "                  coors_range,\n",
    "                  with_reflectivity=False,\n",
    "                  density_norm_num=16,\n",
    "                  max_voxels=40000):\n",
    "    \"\"\"convert kitti points(N, 4) to a bev map. return [C, H, W] map.\n",
    "    this function based on algorithm in points_to_voxel.\n",
    "    takes 5ms in a reduced pointcloud with voxel_size=[0.1, 0.1, 0.8]\n",
    "\n",
    "    Args:\n",
    "        points: [N, ndim] float tensor. points[:, :3] contain xyz points and\n",
    "            points[:, 3] contain reflectivity.\n",
    "        voxel_size: [3] list/tuple or array, float. xyz, indicate voxel size\n",
    "        coors_range: [6] list/tuple or array, float. indicate voxel range.\n",
    "            format: xyzxyz, minmax\n",
    "        with_reflectivity: bool. if True, will add a intensity map to bev map.\n",
    "    Returns:\n",
    "        bev_map: [num_height_maps + 1(2), H, W] float tensor. \n",
    "            `WARNING`: bev_map[-1] is num_points map, NOT density map, \n",
    "            because calculate density map need more time in cpu rather than gpu. \n",
    "            if with_reflectivity is True, bev_map[-2] is intensity map. \n",
    "    \"\"\"\n",
    "    if not isinstance(voxel_size, np.ndarray):\n",
    "        voxel_size = np.array(voxel_size, dtype=points.dtype)\n",
    "    if not isinstance(coors_range, np.ndarray):\n",
    "        coors_range = np.array(coors_range, dtype=points.dtype)\n",
    "    voxelmap_shape = (coors_range[3:] - coors_range[:3]) / voxel_size\n",
    "    voxelmap_shape = tuple(np.round(voxelmap_shape).astype(np.int32).tolist())\n",
    "    voxelmap_shape = voxelmap_shape[::-1]  # DHW format\n",
    "    coor_to_voxelidx = -np.ones(shape=voxelmap_shape, dtype=np.int32)\n",
    "    # coors_2d = np.zeros(shape=(max_voxels, 2), dtype=np.int32)\n",
    "    bev_map_shape = list(voxelmap_shape)\n",
    "    bev_map_shape[0] += 1\n",
    "    height_lowers = np.linspace(\n",
    "        coors_range[2], coors_range[5], voxelmap_shape[0], endpoint=False)\n",
    "    if with_reflectivity:\n",
    "        bev_map_shape[0] += 1\n",
    "    bev_map = np.zeros(shape=bev_map_shape, dtype=points.dtype)\n",
    "    _points_to_bevmap_reverse_kernel(points, voxel_size, coors_range,\n",
    "                                     coor_to_voxelidx, bev_map, height_lowers,\n",
    "                                     with_reflectivity, max_voxels)\n",
    "    # print(voxel_num)\n",
    "    return bev_map\n",
    "\n",
    "def point_to_vis_bev(points,\n",
    "                     voxel_size=None,\n",
    "                     coors_range=None,\n",
    "                     max_voxels=80000):\n",
    "    if voxel_size is None:\n",
    "        voxel_size = [0.1, 0.1, 0.1]\n",
    "    if coors_range is None:\n",
    "        coors_range = [-50, -50, -3, 50, 50, 1]\n",
    "    voxel_size[2] = coors_range[5] - coors_range[2]\n",
    "    bev_map = points_to_bev(\n",
    "        points, voxel_size, coors_range, max_voxels=max_voxels)\n",
    "    height_map = (bev_map[0] * 255).astype(np.uint8)\n",
    "    return cv2.cvtColor(height_map, cv2.COLOR_GRAY2RGB)\n",
    "\n",
    "img = point_to_vis_bev(pts_input[0], voxel_size=[params.x_step/2, params.y_step/2, 0.01],\n",
    "                       coors_range=[params.x_min, params.y_min+20.32, params.z_min, \n",
    "                                    params.x_max-40.32, params.y_max-20.32, params.z_max] )\n",
    "                       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f5af84eda50>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NodzDoT0iSjY+Pq79v6IoiEQiEELA4XBgdHQUg4ODsNvtGBoaQnFxMSKRCAMpyll+v58LJWhBMY8U0UUkHo9rQ39SSjidToyPjyMejwOYWFW0GHqpiWZSW1ub7SbMid1uR2FhYbabQRnAQIroIiSlxPj4ONxuNwBgeHgYUkocP34843NLysrKWEiZUqKqKoqLiwFMBCa5xOVyoaSkJNvNoAxgIEW0BOl0sxu1j0ajGW7JVDU1NYuqBhpNb7bvoYVgNBpht9uh1+tRVVUFADh9+nSWWzU3wWCQQ45LFAMpohylqiqMRuO097lcLiiKopWKURQFqqpCCKHtI6W8YE4pIYR2fDrt3LmTS8AXOVVVMxbspvK+Ki4uxvr16xEKhXDgwAEIIbBp06aMtC9TBgYG0NnZme1mUAYwkCLKIYqiQKfTQVEUlJaWYu3atdPul5jzZLfbYTAYYLPZ4Ha7YbPZLnj+RKAlhIDNZoPD4UipnZMDNsoNOp1O64WKxWLaird0/1taLJY5v68ikQi8Xi9MJhNaWlogpcTvf//7tLYr0xwOhzaUTkvL4um7JaIZNTU1we12o729HSdOnMCZM2em3S/R4zM6OgpgIpfUbJaCO51OeL1e5OXlIRaLYWRkZM5tNJlMsNvt6O/vn/OxlD2JYV6DwQCz2ay9X/Lz8zE0NASj0TglR9lc2e12CCHm/L7q6elBT08PLBbLeXthF7tUPkuUG9gjRZRD9Ho9FEVBUVGRlkwzcfvVV1897TG1tbVwu91wu90zrnQqKCiAwWDAyMiIljbBaDRqE3xnIxgMMojKYecG3YmgfL5BFDCRimOuuZ0KCgpQVlYGYCK1QSJzP9FiwR4pohwSCASSJojn5+djw4YNiEQieP3117F69WocPnw46ZihoSGEw2EEAgEMDAxMe97CwkKthMy5Wc1DoRD6+vrS/2Qop+l0OkgpEYvFkm43GAxpzYzP+XS02LFHiiiHmEwmSCnR09MDRVGwYsUKvPrqq9i1axfy8/Nx9OhRVFdXJ81rGR8fn7EsxdDQEKSUGBkZYTLOi9TkOVKzEY1GpwRRAGYMooxG44wT2RsaGpCXlzftfYqioK6ubtbtnK2SkhJcdtllaT8vLX0MpIhyxIoVKyClhBAC9fX1WLVqFSwWC1wuF8LhMEZHR1FWVoaurq6kpJqzSbCZ2IfJOC9eQoiMLxJQFAWxWGzGNADt7e1J++Tl5WnDe/F4HB0dHdMeV1FRgYKCgmkf94Mf/OAFH3NgYAAHDx6c4RkQTcWhPVrSCgoKMDIyoq1iy2VWqxUGgwEejwcGgwE+nw/d3d2w2Wyora1Fe3s79Ho93G43otEoBgcHl8TzpoUx25xier0e0WgUiqJASjnr95iqqtr+iYBqsubmZvj9fpw4cWJKr1YgEEjqKT1fr+ng4OC0vWTxeBxvv/32BdsXi8WmPZZoJuyRoiXN7/cviV6WyspKmM1m9PT0IBQKQa/XIxQKYXBwEAMDAzh9+jR8Pp/2Q+Dz+ZbE86aFI6Wc1XsmFotpAdFc3mPxeBx6vR56vR5SShgMhqQ0CEajEUNDQ1i1ahWMRiPy8vJgMBi0x5zNZPdgMHjeIOt88wOJ5ouBFC1pgUBgSQQUbrcb/f396O3tRTweh8fjQSQSQXl5OTweD7q7u+Hz+bQl1kslgKTFJ9EDNdvAKyExMV0IAb1eDyEEzGazdn9nZydKSkpQVlaGVatWwWw2a49lNBpTzmmmKIpW466+vp45zijtGEhRTpjLF6DFYkn671Kgqiq6u7u1q/LED0ziBylRu258fBzBYHBRBlHl5eUZyZROC2e+/36JotiJ4cFAIKCds7e3F8uXL8fIyAh0Oh1CoRBsNhvsdruWiDZhcgA2G4ljc6HG4+bNm1FRUZHtZtAc8FuNcsLy5ctnDKSEELBYLNpkU4vFsqSuPh0Oh/aDUFRUBJfLhfHxcbhcrqQkhcPDw9lq4gWVlpYykMpxqqrO+xzxeFybjzQ2NpZ0zv7+fhw7dgzvvPMOxsfH4XA4UFxcDFVVtTQI5/ZkOZ3OC76v4vE4ent7AQAHDhyAw+FYtN8Lzc3N2LJlizaxnnIDv9UoJzz33HMzTmpVVRUlJSVasDE4OLgoe2bmymg0QgiBqqoqmM1mSCkRjUbhdDpx6aWXoqqqSktyaDQaodPp0vKDl2579+7NSpFkSp90pMaIRCJJ6Tgmf0ZbW1uThuM7OjoQDAaTks9KKZMuFpYvXw69Xj/tYwkhpmRCX7FixaL8fADAAw88gGeeeQZvvfVWtptCcyAWww+NECL7jaCck/gyTEyw1ul0KC8vh5QSp06dymbT0qqhoQFOpxOHDx9GIBBAXV0dKisrtcm5b7/9NqLRKIQQaGhogM/nQzAYxNDQ0JxW7amqylVLlFaKoszqPajX6xGPxxGPx1FTU4P+/v60LJgwmUyora1Fa2vrvM5DF5V9UspL53IAAynKCdN9Ia9ZswbhcBjvv/9+llq1cFauXAmHw4Hx8XHk5eXBZrNpPW9erxe7du3S9i0qKsLAwMCcf4QaGhrQ3t6e1qzUdHErLS1Ff3//rAL0hoYG9Pf3Y3R0FNdffz0OHjyY9D4+9/082yCNaI7mHEhxaI8WDafTCZfLNe19t99++5R5EIcOHboogqiEcDgMu90Ol8sFh8MBu90+7YT6/v7+lK7k29raGERR1rS1tWFkZARSSrz00ktoaGjAhz/8Ydxyyy1oaGhI2lcIgY985COLdq4TXVzYI0WLwhVXXIHh4WEcPXr0vPvcdtttePHFF9NSPDVXNTY2wuFwwGKxwGg0oq+vD/v27ZvxuES5Da/Xm+kmEs3IZDLBZrOdN7fTVVddhaNHj2JwcHCBW0bEHinKQZs3b0ZVVdWMK7qee+65pCCqpqYGLS0tWL16Ndxu95we0+12Y/PmzSm1N5sikQjMZjPy8vJgNptnneLB6/UyiKJFo7CwcEpdu+bmZq2HadeuXQyiKGcwkKKsqqqqgt1uT1pFs2LFCtx3330wGo246aabtNsnz7Ooq6tDfn4+IpEI/H7/nCvEJzIzGwwGXHfddfN/IgtMCAGPx4NAIIANGzZMuf/KK6+E0WjE+vXrp609dq4bbrgBer0+KcUC0WxZrVbU19drf0+XPFNVVe1iqaenB6+88goMBgNaWloAAIcPH8bmzZu1iecbN25EZWXlwjwBonlgIEVZUV9fj2XLlmFoaAjBYBChUAgrVqzAzTffjKKiIjz77LMIh8PYs2fPtMerqop4PI6BgQF4PJ6k5dGzMTo6ivfeew+RSGRWQ2OLQX19PRwOByKRCAYHB+HxeGAymWCxWLBq1aqkfQ8cOIBwOIy2tjYtNUKCyWTC8uXLk25LrPzzer1cuUdzJoRICsC9Xi/cbnfSHKbJ/x+LxbRyLkePHkV1dTUikQhMJpO2XyKVB02vsrJy2jQOFosFJSUlWWjRxYuBFC24xHL+kZER+P1++Hw+eL1eRKNRjI6Oor+/H0VFRZBSYnR0dNpzSCkxNDSEgYEBjI2NzTkJZSJokFJOCTQWq76+Puh0OvT19Wk192KxGAwGA3p6eiCEQGNjIwDA4/GgtLQURUVFWr2yhEgkMmVuytjYmFbCYzHMm6TcZjab4fF4ZkyWKaWE1+vVfvj379+fllxVF4Ph4eFpVy2Gw+Gc+U5bKhju04ISQsDtdiMej6OgoAA2mw0AEAqFtDwyer1+xi+C/v5+rScLOH+iQLvdDq/Xm9PLpJuamjA6Ogqr1QqTyQSDwYDBwUEt83Ntba0WcE5+3UwmEwKBwJQkmLFYDOPj4wv6HGhpC4VCWvZwYOJCJRgMQlVVLTCfrqdTp9Nh+fLlOHPmDICJz3XCiRMn4PF4Mtzy3OXz+aa9PRqNMvHtAmOPFC24xBdraWkpdDqd9qULTMytMBqNMybUHBkZScqOPB2r1boklkcLIVBbW4tly5ZBURTY7XZYrVbtdevs7ISiKCgrK0NXV5d23MjICAYGBmaV0qC6uho1NTXnzRBNdCGJbPsJqqpCVVWttl5in8m9nQaDAVVVVXC5XFogNVlXV1fGAv7S0lK+1ylt2CNFCy4SiUBRFIyNjaG7uxvxeBwulwsGgwFCCAghkJeXh3g8Dr/fP2W8PzEE5fF4IISAqqrTXp2ZTCYMDw/n/FDVgQMHcNlll2F8fBx6vR6KosBqtcJoNEJRFHR1dcFkMsHpdKK7uxtmsxmBQAAjIyOzfozCwkIIIdDT08OhFZozVVVhNpu1wMdms2lBf29vb1JvlMlkgl6vh5QS5eXlSb1OpaWl6OvrS+pBzsvLg06nO+8wfyocDgdGRkb4Xqe0YCBFC0pKqQU9bW1tKCgoQG9vrxYcOZ1OFBUVaZXhT548OSUZXywWg6IoOHbsGGKxGEwm07SB1FxX8i1mb7/9NkwmE1auXKlldDaZTDAajaioqEBvby+OHz8OYGI4MxaLzSm55t69ezPVdLoIhEIh9PX1aX+Hw2GYTCa4XC54PJ6k4XWbzQaHw4Guri709vaitrZWG7LeuHEj/vCHPyS9dwsLC2G1WtMaSF0oXx3RXDEhJ2XVPffcg9dffx2nTp1CNBpFdXU1mpqaEA6H0dnZqQUH547519XVYXR09LwJ/ZYag8GA+vp6VFZWwuPxwGAwQFEUHD9+HKdPn0ZhYWHSkGiiNEdieCWX54hR7nE6nRgdHUVxcTEKCgpw+vRpbXGHEAJ6vR4mkwnARKqOzs5OreD4q6++yp4iyiYm5KTc8uMf/xjr169HdXU1ACAej2uTJRMTUWtra6ccd+zYsSlBlKIoUFVVGx5cSjZu3Ii8vDwEg0E4nU7thyoROMVisaR5ZT09PYjFYli7du2s8kgRpVOi92h4eBitra0oKSmB3W4HMLE8P7HIZNWqVdixYwf0ej1aW1u1uZJEuYQ9UrSoVFRUoK6uDl6vF6OjoygpKUFHRwdOnz59weMsFgvq6+tRWlqKEydOQEqJY8eOLVCrF86mTZsQDodhNpvhdrvR09Nz3lxbRIuR1WpFWVkZ3G43+vv7tV5nANiyZQtee+01OBwOjI2NsfYjZQN7pGhxUxQF3/jGN7Qeo/vuuw+lpaXa/fF4HFJKKIqCkydPJq1CO1dlZSWam5vR3NyM8vJy7N+/Hzt27IDBYFiSQVRdXR0MBgPKy8tRXFwMn88HVVVx4403QlEULFu2DLfeemu2m0l0QdFoFEIImM1mHD9+HCaTSatg8OKLL8JsNmNoaIhBFOUMBlK0YEwmE772ta8l3fbkk0+ip6dH+zuxpN9gMGDt2rVob28/b2+UwWCAxWKB3+/Xrmrj8TgOHz6cuSeRRVarFXa7HUII+P1+xGIxGI1GLQdXZ2cnnnvuOQBAeXk5ampqpiTjJFoozc3NWjJOm82mpRuoqKhAQUEBXnnlFQBAMBjEiy++qB03NjbGOX0XmerqahiNxqTbrFZrzpQI4qo9WhAulwv33nsvvvWtbwH431xS535hVlVVaVenXq8X77zzzpRzrV27Fnq9HqqqQq/Xw2AwwGQyoaysTBvWW4oOHTqEWCymLS1XFAWRSATDw8PQ6/W4/PLL0d/fj6NHj2qZzlnuhbLl0KFDqKmpgdlsxsmTJ1FbWwur1YqBgQHs3bsXqqrC5XJhYGBgxsCpsLAQxcXFS/Yi6WJ36tQp3H777XjvvffQ0dEBYKKw/IYNG2ac1rEYsEeK5q2srAyVlZUoKys7b20sRVGQn5+PT37yk/D5fHj44Yen7S05fPgwfvazn+GNN97Arl27pj2XXq+HxWJBXl4epJQwGAxobGxMGiJcitxuNzo7O7Vhj0AgACEECgoK0NLSgvz8fESjUWzatAnLly9fchPuKbdEo1GcPn0ax48fRyAQgN1uRzAYRDQaxdq1a3HVVVchFAph06ZNACbSdlxxxRXaar6E0tJSuFwunDhxAsDEd8nKlSvn3b6amhpYrdZ5n4fmLx6P449//CO6u7u127q6uvDSSy8BmMhT9md/9mcAgE996lPae+T666/HihUrFr7B5+Bkc5o3k8mE8vJy9Pb2wu/3T+kRKiwsxM0336xVh+/u7sYf//hHvP/++9Neiep0OrhcLpSVlcHj8aEClM8AACAASURBVODkyZNJ99vtdjQ0NCA/Px/ARFmJ9vZ2CCHmlIQy15hMJlitVi2LtNvtRlVVFQKBAFwuF6SUCAaDOHjwIEKhEHw+35LtnaPFrbKyMqknYfny5XA4HMjLy8Po6ChOnz6tJZUdHR3Vsp4n3s8dHR3aHCmTyYSCggIYjUbtu8Bms827fIzVakV+fj7GxsZYMmmRE0KgsrISp06dQm1tLTo6OhCPx1FUVAS/3w+v15vOh+Nkc1p4wWAQ/f390wZRABAIBLRAR1EU1NTUoKamBrfddhu2bdumBUQJifQF4XAYVqsVxcXFSfePj4/j5MmTOHz4MFpbWzE+Po7KykoUFRVl9HlmWzAYhN/vR2FhISorK5Gfn6/9OAWDQS3Le35+Pnw+H4qKilgGg7Li3CBndHQUnZ2dWgLdkZER9PT0YHh4GCMjI1i+fDlsNhs6OzsRiUQgpURRURGsVqv2vldV9bznn636+nqtp9bn82FsbEyr10mLl5RSS+9y8uRJ7QK8v78/3UFUShhIUVp4PJ4pQVRJSQk2bNiAiooKHD16FEIIrTelpaVF6zU5dx5PIst5OBxGOByedp7P0NAQzpw5g9OnT6O/v1/bd6kLBAIIBAIIh8OQUsJoNMLhcMBkMsFkMiEejyM/P18rs5P48bFarbBarVBVdcrQSbo4nU64XK6MnJtyy7lZyIeGhjA0NIRQKISSkhLU19cjFothZGQEQgjk5+cjEAhgdHRUSzabmA/odruhKEpSQeNUnZvoc3x8nIEUzRsnm9OsORwOWCyWpFV2F1JRUYHVq1ejtbUVg4ODiEQiOHTokPbj/vLLL09bpVxKiVAoBK/Xq12RXojH40FbW9vcn1COSgyZuN1u6HQ6GAwGVFZWQkqJSCSCaDSKqqoqOBwOxGIxBAIBWK1W+P1+hEKhpCv7dErUSiQ6n3g8juLiYtjtdoTDYfT29iIUCqGnpwc6nU4rf+R2uzE4OIjS0lIYjUaMjY1Ne77Exdlstbe3p+upEGkYSNGsVVVVoaKiYsZAKlGuRFEUdHR0oKOjA5FIBMeOHcOvf/1rlJWVAcB5V5SpqgqDwYCBgQGoqgq3270oum8Xm4GBAQwMDMBqtcJisQCYmDui0+lQUlICKSXKysrg8/ng8XgQj8dhNBqnrUuYDunoMaClbWxsDENDQ6ioqMA111yD3/72twgGgzhw4ABaWlowOjqKY8eOoampCT6fD/F4HB6PB7FYDMXFxRgdHU363kj0XCW4XC7k5eXB5/NheHg4G0/xoiOEQEVFRU6srssUDu3RrBiNRuh0OsTj8aSVLomyD5Nddtll+MhHPgKDwYCuri5s2rQJXq8Xzz77LIQQuOGGG3DnnXeed/5OOBzG8PAw4vE4uru7WTZiBj6fD88//zx27NiBwcFBDAwMaIFTNBqFlBKqqqKsrAwrVqyAEEIrEpsYEgSg/Xvo9XooigKdTgeTycR5VjRviqJACIETJ05g7969GB4enjI38q233oJer4cQAgcPHsTIyAi6urrg9XoRCoXQ1tYGk8kEIQRsNhucTueU3EOXXHIJbrvtNmzatAl5eXnTtsXpdM6559Rut2s5sRIMBsNFuepPp9Mlve46nQ533XVXFluUfQykaEY6nQ6XXXYZbDYbTpw4gVtuuUW7/c4775yy/3PPPYejR49CVVWcOnUKzz//PGw2G+666y5IKfHMM88gGAzOukt+tkOJFzspJV599VXs3LlTy82TCKT0er0WINntdjQ3N6O+vh4tLS3YuHEjhBBYsWIFjEYjamtrUVhYiPLycqxfv15bbUmUKpfLpaU7OXXqFPbs2QOLxYLPf/7zSWlQQqGQFuQoigKn04na2lpUVFQgFovB7/fDZDLh9ttvx6c+9SlcffXVWsoVvV6PP/3pT3j88cfR19eHrVu3Tpti5ROf+MSUYCqRk+58PvrRjyYFZqqqYtWqVbj22mvn98LkoIqKCjQ1NWmvVzwex6FDh7LcquxiIEUzuu6667B69WqsWrUK1113nRYARaNR/OhHP5r2mHA4jNWrV+P+++/XypYkekYeeOABPPHEE6zwnkH79u3D+Pg43n77bXi9XtjtdphMJrjdbjQ1NeHdd9+F1WrF+Pi49oVYVFQEnU6HtrY2bf7VyZMnz5sbjGi2EhPNE44dO4af/vSnqKysxGc+8xktqPnyl7+MNWvWwGg0YvXq1bjttttQWFiIjo4OGI1GuN1uBAIBPPPMM/D5fEkrhe+66y4t6N+3bx/27t2L+++/f0pbvv3tb+Puu+9GYWGhdttVV12Fbdu2nbf9//7v/66lSBBC4PLLL0dhYSGeffbZ+b84OaajowNDQ0PYunUrgIkpGs8//3yWW5VdzCNFM9qyZQuqq6tx6NAh9PT0YMOGDfj1r3895/MYjUbcd999eOyxxzLQyotXIuXBwMAAamtrk/JuNTU14cyZM0n5tRI9Uq+//vqUc1166aU4fvw4ysvLUVhYiGg0Cq/Xi/fee29BngtdPJYtW4Z//Md/hNFoxLPPPouf/OQn2n1btmzBe++9h8rKSgSDQQwPD2PdunWIxWLYsWNHFlsNfPCDH8TAwADefvvtrLZjLtatW4f9+/czr9zszDmPlJYILZsbAMlt8W7XX3+9bGpq0v4+G/imtM3nWG7Jm9lslqWlpbKyslKWl5fP69+hpaVF6nQ6CUCuX79elpWV8d+KW8a3goIC+dRTT8nHHntMApBr166VK1eulEajUQKQa9askXV1dRKYeM8KIaSqqhc854033ihvu+22C+7z0EMPya9//etyxYoVc27zYvlcfPWrX5U2m23W+zc3N8/42s1nW7du3aJ5bea57Z1rDMMeKZpRYpI5C4kuLmazGWazWettms9nWVVVbfWTqqqIx+O8eqUZffjDH0ZxcTEef/zxlI7Pz8/HE088gXg8ju3bt0NVVWzbtg27d+/WspmfOXMG3d3dKCkpwQc+8AH8x3/8xwXPmRiKni61SoLBYMA999yDl156CatWrUIgENCKKOeKRMFyANi+fTva29uxd+/e8+7f3NyMgwcPzrv+ZnV1NfLz86fUQZ38HZLj5twjxUCKFpQQAnfffTeefvpp/lDPk9VqhcvlQigUwsDAAICJFBWJDMBEmXbXXXehpKQEjz766KyPsdvt2gXAsmXLUFFRgSNHjuCRRx7B0NAQ2tvbMTg4iL1792JoaEjL0N/Z2Qmz2Zy2VCg2mw2BQABGoxHxeByBQCAt582GvLw8RCKRCyYXNRgMCIfD2LRpE44ePTolaeps6XQ6qKq6lBOZzjmQ4ixSWlBSSrz00ktzDqI2b96MnTt3slfsHJFIJOkLcTa5nFRV1SaaE83Hyy+/PO3KuAvx+/1aGpVEb5Pf70dJSQksFgva2tpw7NgxDA8PIxQKob+/X8sXlc58cokyMxfqucoV574uFosFtbW1Savpli1bhmAwiGPHjs3rdYxGo0viNUsnrtqjBZdKOoOCgoIMtCT36fX6pFxeM+Xc0ul0MJvNzM1F85YoRN7d3T2n46LRKOLxOOx2O6xWK2pqarT7VFVFfX098vLysHbtWpSUlCAUCs26t2jTpk3Ytm0bmpqa5tSm+vp6XHrp3OYXZ9rq1auxfv36lI6NRCLo7e2FoihobGwEAAwODmJ0dBTDw8NJgZDT6cSyZctmfW63243a2tqU2rVUMZAiyiFmsxk6nQ42m02razeXGoOJWocXQ11Cyqz6+npUV1endGwkEsH4+DjC4TC8Xi+klHj66afx3HPPoaioCMFgEKOjo3A6nVi9evWsf+jHxsZgNptnVcD88ssv1y7QfD4fTCYT1q1bN6vHsdlscDgcs9o3VYWFhSgpKUnp2EgkgsHBQQghtAstRVG013qyaDQ6pwurSCSCQCAAk8mE5cuXp9S+pYZDe7RoqaqK8vJynDp1Ch0dHZBSory8HKqqor+//6LqVTGZTFBVFTabTRsWUVVVy2I+W7FYbKlMCKUs2rBhA0ZGRnD8+PEZ9y0pKcHQ0BAikYiWzXx4eFgrWKwoCkpKSjA+Po49e/ZAURSUlpbixIkTcLvdKC0thcfjgV6v1y4CJveCrVu3DocPH0Y4HMaRI0cQiUSgKApMJhMcDgf6+vqmbZfZbNbqTnZ1dSEej08bsJWUlKCvry8pAFFVdUqm83SqrKxENBpFR0fHvM6TqA7hcDhQUVEBRVEwNDSUNEXC6/XOaahvdHQUo6OjMJvNcx7WXarYI0WLkk6nQ1FREUpLSwFAy4HS2NiIysrKKaUhljqTyQSXywWLxaIVfY1Go0t5wictYnfccQe6u7vx1ltvzbiv2+2GXq+Hw+FAaWkpnE6ndp+iKMjPz4fb7cZf/MVfIBqN4uc//zmuvfZa3HjjjbDb7ejp6YGUEuvXr0dVVRWampqShvpXrlyZ9IN+/PhxvP/++zAajRecEvDKK69ocwodDgei0Sh2794NYGJRTKLH1+12Tykpkxgiy5TS0lIMDQ2htbV1XueRUuLUqVPIy8uD2WyG0+lMW9HyQCCAI0eOpOVcuY6BFC1KVqsVzc3NaG1thc1m0273er3Yv3+/Vg3eYDDAZrNpRXuXqtHRUW1YLpER3mq1oqCgAHq9ntnHadE6ePAg/H4/qqqqEAgE0NnZqd0Xj8cxODiIsbEx9PT0wOfzQVEU9PX1oa6uDuPj49i9ezfa2trQ29uLY8eOweFwoKGhAcBEIPaLX/xC61FxOBywWCwwm80QQsw6EKmursaaNWu0vxVF0YatDh48uOCLXN566y0cPXp0TscIIZLK65jNZu2+rq4udHd3o6ura9qJ4kII1tScBwZStCgl8hhdeeWVuPLKK7Xb33zzTfh8Pu3viooKXHXVVXOeXJprFEXRhvKOHTuGwcFBeDweDA0NobS0FG63G8DEkEO6rjhp8cv0PJ3p2Gw2BIPBOc+zO3jwIMbHx7WensSPvtFoxN/+7d/iH/7hH1BXVwedTofvfOc76O/vh8PhgMFgwJkzZ3Do0CFs2rQJTqcT+/btg8FgmFKYeMuWLVi7di1WrlyJG264Iem+Cw1Dvffee3j11Ve1v2Ox2LQ5mXQ6ndbuc4v3ZptOp9MuqCwWC1avXp10f29vL6qrq7Wam5N72UwmE4qLixe0vfM1eWg267Kd1ZyZzbnNZ3M6nbKmpkYWFxdnvS2Z3MrLy2Vtba00m81y+fLl8tJLL5VVVVXSarXK2tpaCUxkXF61apVctWrVUskwzO0Cm91ul7/85S8X9DF1Op189NFHkyodpLLp9Xpps9lkfn6+/NnPfqbdfsUVV8jm5ma5du1a+Xd/93fyiSeekNu2bZOKokgAMj8/X37605+W27Ztk1u2bJly3paWFllXV6dlQE8cpyjKtPvPdVu7dq10u90SmMgUvn379qy/D863nfsdcPPNN2sZ48vLy+eUFT0b7Z1p+9SnPiVramqkoijp/r6bc2bzrAdRDKS4zWdLBFKVlZWytLQ06+3J9FZWVibXrFkjm5ubZVVVVdbbwy07m9vtls8888yCP+5TTz0lKyoq0na+yYHUdJ/f++67T/7whz+U9957rywrK5N33323BDBjqZOWlhb5la98JS3B02Le8vPztdJOkze73S4vv/zyKbffeOON0mq1Zr3d022bN29O6Tv89ttvl9ddd10628JAihu3i2GrqKjQeqK4XXxbtgKpdF7519TUyO9///tSVVW5efPm8+53xx13yCeffFL+5V/+pSwpKdF+NM8XTN10003y9ttvl6WlpVogpaqqvPPOO7P+77YQm8FgkFVVVfIDH/iAvOSSS7Tbb7nlFmkwGLLevhzYGEhx47YUNiHEBYcrEwVcs91ObtnZ3G63/MUvfrHgj/vEE0/IsrKytJxLCCENBoMUQlyw6PbWrVvlj3/8Y/nlL39ZCiG04TqHw5G0n9VqlTqdTqqqKlVVlUIIWVZWJr/whS9I4H8Dr61bt8qKioppe3JyYbNYLLN6bS0Wi7zqqqu02xLPv6GhYdH2SqW6XXHFFXLr1q3pOt+cAylONidahKSUWv28891/9iKEaMEYjca05U+qrKzE3//93wPABVfFvfDCC/j5z3+OlStX4ktf+pK277kljvx+v7aCNbFYJR6Pa6vRYrEY3G43XnzxRfT09OR8mZPEysRz2e121NTUwO/3J6WnSOSPO3HiBPx+/4K180K2bds27xXHW7ZsgdFoxAsvvJCmVs0dAymiRYp1BXOXyWTCP//zP2fk3FVVVfjsZz+Lr371qxk5/0JRVVVbwXchwWAQO3fuxE9/+lOsXLkSX/ziFwFgyoWElBJr1qzBihUrtBV6w8PDeOqppwBMFPYdGRmB2WzO6c9WIhFxUVHRtEGtz+dDV1cXgOmrHkSjUUgpUV9fj6qqqsw2dgavvfbavAPat956C3v37p1zguZEMth0YCBFlEFms3nONbwURdGSFk5OXki5Q1GUjNUjM5lMKCsrQ3t7e0bOfz5f+cpX8P3vfx+Dg4PzPteKFSuwdetW/OY3v4GUcsbklj6fD/v27cPPf/7zacuS3HTTTbjtttsQDAZRVFSE5uZmABOBRCKzuaIoiEaj8Pl8sNvtGc1MnkmJINDj8UwJJq1WK+x2+5REvZPrcQJATU0NQqEQhoaGMtvY8xBC4JprrklLUtOxsbE5VXdIkFJqGfDnKzffSUQ5IhaLzfmHR0qpXUmyJl7uycvLw1133YUf/ehH2W5KWl122WU4cODAvEszrVq1Ci0tLTh06BCOHTsGALPK0O/1etHa2gqz2Yzt27cn3VdfXw+v14tTp07hxIkTWsbyyUNfiXaHQiGEw+GcHxofHx+f0rN2bh1NIQTcbjcikQjWr1+v5V3yeDwYGBhIysmXaQ6HAxs2bAAw8R3X29ur3VddXZ2VnFyJIeCEyUlM54LpkIkyKBwOz7lelpRSm8OwWOYy0OxZLBZcf/31uPvuu9N+7tLSUqxZs2bB54Pcfvvt+MMf/jDvwL6xsRGNjY3o7e3F66+/PueEoj6fD6+99hpuuukmjIyMwOfzIRgM4ujRo1rFg0TVg3PnD01ueyAQ0Gr35WpAtWHDBrz77rtJzysUCiEUCkFRFBgMBi1ALSkpgZQTtUpNJhN6enoQCASSzieEgNFozGgNU5PJhA0bNmDfvn1TMre7XC4oigKPx5NSD1Mq4vF40uuX6nuBPVJERDmgtLQUGzZsgNVqxa9+9asFe1whBO699148/fTT867teOmll8Jut+OVV15BNBqd89CSx+PBM888g0OHDuHjH/84PvShD6GiogJvvPEGRkdHp+x/oR/Gc7N755obbrgB+fn50w5RCiGg0+m0RSsulwv79++HxWJBfX098vLypjz3RCCVKWNjYzhw4ADKy8un3NfR0QFFUVBZWbng0xmEEFqJsVSDSAZSRHRRWrFiRbabMCctLS1obGzET3/602w3JSXFxcWIxWIXXI06G5FIBL/73e/Q1dUFp9OJF198cUrvCjBz70IoFMrpSecApi1CnCgnlag/aLFYcOLECdjtdrS1taG9vR1erxfxeBw2m00LXKSU076O6eTxePA///M/097X3d2NwcHBKb2eQghtyG1y3dV0UVV13qWWGEgR0UVHURR885vfzJkJx1arFUKIKUv+F0o6hr+2bt2K3t5e/O53v5vXecxmM772ta/hkUceQTQa1YaELjbj4+M4c+aMVsQcmHhf5+XlobCwUPu7rq4OGzZswIYNG6CqKtra2rQgq6mpCRs3bgQwEbCkY+L1fJw4cUJbHJCgqiqqq6sBTLQ33b2IkUhEm1OXau2+i+/dRxcVnU4HvV6f0134lDuEEDAYDGm/sv/4xz8Op9OJ733ve2k972yYTKa0zJv58Y9/jD/+8Y/zPo+UUhti7OrqwiOPPIKSkpKL7jP+b//2b1pAlFBQUACXy4Xu7m4AE6+Vx+PBK6+8og2nLl++HBaLBUIIvPnmm9ixYwcAID8/Hw888MCCP4+ZRKNRHDlyBACwZ8+etM1pS7xfDAaDNtyYyLU1Z9nOas7M5twyud17773y05/+tCwpKcl6W7gtnk1RFPlf//VfWpbsdG0NDQ3y8ccfT3t777//fnnPPfcs+OtkMpnkf/7nf6blXF/84hflrbfeOu/znJvV/6//+q/lY489xpJJc9yKi4tlRUWFzMvLy3pbZrtde+21aanoUFBQoBWfnmZjZnO6OFx11VWoqanBypUrta7p6Tz55JMYHx/HjTfeiGXLls3pMW688UasWrUKq1evxtVXXz3fJlMafOITn8Cf//mfZ7sZC46Z7CcUFxfjJz/5CZ588knttm984xsYHR3FnXfemXPz3rKpr68Pfr9fm2idTY2NjbNqx6uvvpry52DyY/h8PqiqitLS0pTOdS4GUpSTotEompqa0NDQoHXHms1mXHvttUn7bd68GT6fD3/4wx/Q2dk56/Nv3boVR44cwdGjR9Ha2oo//elPaW0/0Ww9/PDD6OnpydlJ5ukkhIDf78f999+fdPvAwAAqKyuxfft2LRnnqlWrtCzouaSlpSVtP/Dns3z5crhcLgwPD2vzg7Jh7dq1UBQFR44cyWiql6ampqTSOMFgEL29vejp6UnL+ZlH6iJy6623wuFwYPfu3Th58iSKiopQV1cHYCIw2bNnT5ZbOHt79+7VJpjG43E4HA40NjbijTfeSNpv9+7daGlpwerVq9Ha2jrrFUMGgwFCCK2UgtPpTKpbRbkvV3p4EhNgc32F2eOPP576HJSz+vr68KUvfWlKWZHvfe97UFUV8XgcV1xxBb7+9a9Dp9NhcHAQn/3sZ/H9738f3/zmN+H3++H1evHtb397QZNRztYdd9yBvXv3TplwnW7t7e3a+99msyEvLy9tQcWFmM1mrF27Frt37wYAHDp0aEHe162trYjFYigtLcX4+Dii0SiEEGnLmcVAahFzOByoqanB/v37p73/gx/8IA4ePIjTp09f8Dx33XUXFEXB66+/jnXr1mlfzENDQ1AUBdXV1Thw4EDa259J536RJvKmTF7BAkysyHjnnXewZs2alCajrlmzBg0NDfNesk0Xj1wJ0GYjFArhC1/4Ah599FF88YtfnFcg9NGPfhQdHR147bXXUj5Hfn4+HnzwQa3YccLkyf1vvvkmDh48CGDi8y+EQCQSwTe/+U3E43HE43H4/X48+OCDcLlcCAQCePPNN7Fr1y5UVlbitttuw+OPP55yG1Nx00034eWXX4bRaMTtt9+Ol19+Wcv6ngmTgxefz5fxtAcJwWAw6bdmuvdTVVUVhBBzGkGYSeJxBgYGtP9P1NpLRyDHob1FTFXVaVPWr1mzBnq9Hna7HZs3b5629tRkb7zxBqxWK/x+P958802cOXMGwP+WL2ltbV2wD1I21NTU4MyZM9Mm7Duf119/Hf39/ejs7NQKgFJ6PPjggxmrQ7fUPPDAA9i3b1/Weov1ej3uvfde/PCHP5z3D05BQQHy8vLmdQ6dTjfjsJfP50NfXx/6+vowPDysJf3s6elBX18fBgYGIKXEc889h1/96lf47W9/i6NHj2LlypW44YYb8Pzzz8NgMOCee+4BAHzmM5/RiiBnyrvvvotYLIZAIAC73b6gaQji8fi8CwfP1uSqDZNNnr80MDCQsQvXaDQKg8EAvV4PIH29vDP2SAkhKgH8BEAJgDiAH0gpHxVC5AP4DwDVADoAfFRKOXL2mL8GcB+AGIDPSSl3pKW1FxGn03neydGJvClvvfUWNm7cOCVJmdFoxJVXXolXXnkF11xzDaSU2LVrF3w+35RkZ9FoNGu5aRZCfX09xsbGMDg4OKW36kIS8wZKSkrgcrkW5TBArqqrq5syBLuQDAYD7r//fnz3u99d9L1HdXV1eOedd7JWXDYWi+GNN97AVVddhUOHDi3612suTp48mfS3qqrYt28f2tvbtWLjQggtyLnpppuQn5+PXbt2wWazQa/Xo6+vD+vWrcPvf//7ebUl8X2Tn5+PwcHBKWkNcs3atWvR3t4Oh8ORVDj6fCZ/P2fyot7lcmnz7NI5pDibHqkogC9JKVcB2ATgQSFEI4C/AvCylLIOwMtn/8bZ+z4GYDWAmwE8JoRILcvVRcxiscBqtWq9R5OdOnUK0WgUZrMZqqpO+XKTUmo//D6fDz6fD++//74WRBUUFGDZsmUoKCjI/BOZB5PJhA996EPzOkdJSQmGhoa0D2l5eTmqqqpmfXxhYSHy8/Pn1QaaKps/yFJKjIyMpP3ftaKiApdccgmee+65tJ43m4QQyM/Px/DwcLabknEDAwPasFM0GsUrr7yCq6++Gnv27EEsFoPX68XY2BgikQiCwSCCwSAikYhWF05RFFx33XUAJlb8Go1GtLS0zOl71mazoa2tbc6FzheDK6+8Ups+EQwGEY/HEYlE4HA4UF1dDZfLBWDidTq3k2ChsszHYjGEQiHt9yBducdm7JGSUvYA6Dn7/x4hxBEA5QDuAHDN2d2eAvBHAH959vZnpJQhAO1CiOMAWgC8mZYWX0Si0ei00Xli7Nhut6O/vx8jIyNJ94fDYW0o4O2330ZRURHy8/MxNDSEWCwGg8EAt9sNl8uFeDw+5fhsKSwshKIo6O/vh9VqxebNm7F9+3b893//97T7OxwOSCln7FFzu93o7e1FJBJBRUUF3G43wuFwUvXxC+HS8/8lhMDmzZsBADt37pz35OG5WLVqFcLhME6dOjWv88TjcXR3d2uru9KlqqoKzc3N+Ju/+Zu0nG/jxo14//33s9YbBUwMpd11113Yvn171tqQDZFIBL/5zW9QUFCgffZ37tw57b6Th6ESGcWLioqgKApcLheamprQ2tqK/v5+OJ1OFBUVobOzE2vWrMG+ffu0Y2tqamAwGHD69OkFK9qbTpMvTLxeL6LRqPYdW1FRAafTCZ1OkicWzQAAIABJREFUp5UKmmy2SZNdLtecfq8m72+1WrXSOKqqQqfTacHbfL/f5zRHSghRDWAdgD0Ais8GWYlgq+jsbuUAJs9+PnP2tnPP9YAQYq8QYu/cm31xMBqNKCoqOu/9b775Jl544YUZf1hKSkpQWloKnW4ibu7p6cHp06dhMBjm1DuTKTU1NWhsbMS1116LdevWAQDy8vKwceNGtLW1aRXjGxsbsXr1aq2wZmVlJWpqamC1Ws97bikliouLk+Y4lJaW4pJLLplV2zweD3p7exdNsJltqqri85//PLZs2ZJyOYVUNTU1IRwOo62tbV7nMRqNePDBB/Hoo48u6gD5vvvuw0svvYT29vZsNyUtFvNrPR0pJZ544gnU1dXNqgRNPB7HL3/5SwDA008/jUAggB07dsDpdGrzuvLz89HY2Aij0YhNmzZpx9bU1KChoQFWqxUlJSWwWCxwOp0oLy+fdx24hbJv3z643W4IIbBs2TIUFRXBYDCgt7cXnZ2d0Ol0KCkpQWNjI44cOZL02zM4ODireVoFBQVz6kWa3BuYl5enVbowGo3aKs/E+ebzfTbrVXtCiDwAvwbw/0kpxy/wZKa7Y8onSEr5AwA/OHvu3PqEZZher4fBYIDP55syjj+Z0WhEJBKZsUt0uhV5fX19iMVi2hXUQistLUUgEICUEtu3b0dlZSXMZjPeeecdlJaWIhKJ4Otf/zr0ej3+5V/+RXuzl5SU4J/+6Z+0VTnl5eUQQqCjo+O8j3XixAmtZy/xwUkElTNpa2ub9w/3UiOlxCOPPLLgP4wvvPACbr31VmzevFn7wSLKtIcffhgPPfRQynN3Jveonzx5UvtO/+53v6vd/pGPfATf+c53sHLlSlx66aUIh8PIz89HfX09Tp48iZdffhkOhwPDw8OLaqhVCAGbzYbx8XFcc8018Hg82LFjB3bt2oV169bBYDCgq6sraQL5wYMHoSgKbr31Vnzve9+b0/fI8ePH59S+yfsn5mkVFxcjGAxqk94TvwkWiyXlnsBZ9UgJIfSYCKJ+JqX8r0S7hBClZ+8vBZDI6nUGQOWkwysAdKfUuotUVVUV6urq4HQ6Lzj8sGbNmnnN8xgcHERbWxt0Oh10Ot2C9DAkeo++853v4GMf+xjuvvtu/OQnP8FDDz2EH/7whygrK8O3v/1tfPWrXwUw0cX+uc99Dg899BAeeugh9Pb24nOf+xzq6+sRi8VmHFqKRqNobm7WJuRHo1H4/f6LfvK4EOKCPXmZYrFYUi4w+8lPfhJer3deQVTiead7Mq9er4eqqmlNKpjtHpzEj8timvgspUQgEFjQbNw+n0+rTZcp3/rWtxAMBvGv//qveOyxx7BixQpEo1Hs378f/z975x3X1PX//9fNICFhhCErbBkiKOKsSq1VP9qqtRZtFXAU666CintUse6Fe1Rs3au2Fqu2dVVbxIp7TwRkirJ3QnJ/f/DL/RISQhKywDwfjz4queee885Ncs/7nvN+v1+enp6wsbHBuHHjYGFhgaioKOo8NpsNNputN9FmDoeDzz//HABw+PBhSpCaIAjQ6XT06NGD2mWojVgsxo4dO1T+jmtijhKLxXLjihuznapM1h4BYA+AJyRJbqh16BSA0QBW/f//x9d6/TBBEBsAOAHwBmCsZKgCtQtNKvqiaSJ2h8fjITAwkFrZunbtGvVl1WT8i0SV/IcffsDw4cNBkiT27dsnFXshFouRnp4OgUAAHo8nt5+pU6dK/S0QCGSyFmtz7do1dO3alboJ3rlzB3fu3FHabklRzuaGtbU1Nm3ahBEjRqh0Hp1Ob9T3YuvWrTAzM9PbNbWzs8PatWsxatQojfbbv39/eHp6Yvny5RrpT7LtoM/vnrW1NTZu3IiRI0fqzYa65ObmYtGiRdi2bRsiIiJ0MmZUVBS2bduGJUuW6Kye3IkTJ6h/S4pXbt++HWvWrJFqFx0dDQaDgSNHjuD58+fUfY4kSakaSbXnFE1SVlYmU3E/PDwc8fHxuHnzJm7e1GzkTkBAAO7fv9+o34U2PkNl9je6AxgJ4AFBEJLKkPNR40AdJwjiGwCvAXwJACRJPiII4jiAx6jJ+PuWJEndRaQ2cQICAsDn85GZmYmHDx8qbHv79u1GjSWJnbp+/Tratm1LBagfP34cNBoN48eP18iXztPTE5MnTwZBEEhMTKy33fXr11FaWoqQkBCl+m3bti1cXV3lZjbWpX379nj48KHKcggdOnSAUCjEvXv3VDqvOWJmZoa4uDgMGzasUf1ER0c3WERWHu9T0L9kldZQtnCM6J+SkhJMmjRJ6rW6jvtHH30ENzc3XLx4EV9++SViY2NBEATWrVuH0tJSrFy5Uic1A8ePH4+jR48qDLlQB1Xvw+3atZNb0FoSa6Wp7EhlsvYSID/uCQB613POcgCaeTRr4nTv3h0BAQF49eoVzp8/32D7hw8forKyEp6ennBwcABJkhAIBI3Wetu7dy/u37+PsrIyBAQEwMnJCZcvX8bff/+NAwcOoKqqClu3bsW1a9fw5Zdf4tChQ9i5cyfmzJmj8r40AEycOBF+fn6orKyEnZ0dnj9/TgWJAzWT4oQJEyAUCqUmx0ePHoHD4SA0NFSl8QoLC3Hr1i18/PHH+Pvvv6WOXbt2De3bt1f5PUjw9/eHhYWFwevtMRgMHD58GMOGDVPK4XhfnJK6vK/vW1Xy8vIwevRofZshQ1lZGb755hudjjl16lSsWLECcXFxat0PNc2OHTsQFRUlUxfw8uXL1Cr6xo0bAdR836Ojo6l/AzVFRpOSkmBubg4LCwvEx8dDkzx79kwnW8Kenp4oLCys94FDnhNlZ2cHgUCgUoHmhjBWNtcykjL0qu6v1z5P3b15giBw8OBBMBgM0Gg0nDx5Ej/++CPi4uJw+/ZtEASBx48fY86cORCJRJSWnFgsxpgxYzBixAiFwe512bFjB/h8PoYPHw53d3eQJAkrKyvQ6XQQBIHS0lLQaDRMnjyZem9Hjx7FBx98IFXpWpWJTnKdJOfVd63u3r0LW1tb9OjRg9IXVIbbt2/j559/VriSpm+cnJywf/9+7N69G6NHj27w+nl4eFA3WSOGiSE4e9bW1ti5c6e+zZCBw+Fg69atOh2zdnaXIaAoVkjy3an9Haq7miuZA/755x+pYqJcLherVq3CwoUL1Xq/3bt3h42Njc5itlJSUlTOqH779q1GnSjA6EhpjcGDB8PV1RU3btygnBZlSU1Npfa7G/PjJUkSM2bMoNJKhUIhhEIhHj9+jJSUFLRo0QJdunRBq1atwGAw0LlzZ3Tr1g3dunVDVVUVKioqVNpTNzU1BY1Gw+nTp3Hz5k1UVlaCIAgwmUy0aNGCKjIqKcwmsUni6NW2W5mJ5OnTp0hOTqb+rqysRFJSEoKDg2XazpgxA5aWlkhKSkJFRQW6deuGwMBAWFhYYNOmTfWOISkqp8t6SXXZsmWLwsDwN2/eYMmSJWCz2Uot29NoNFRWVmLGjBkq29LYCd4QHAQjykEQhFyJKn1TUVGBmJgYqWxeXbBq1SoMGDAAgYGBOhuzPubNm4eFCxfCwsJCrfMFAgHEYjFEIpFU2YHy8nKsWbNGpur/uHHjMGfOnAZX9n19fcFgMPDff//pZFvaxcVF5fIQkvdlZmaGFi1aaMQOo2ixFhgyZAj4fD7Mzc1x7do1lRWmHRwc4OzsDOD/Kq8SBIEOHTooDN7j8Xjo2LEjevXqherqasTHx1MF35YuXYoRI0bg559/RnJyMqqrq8FgMMDlcqmbJZfLpRyfLl26UP0+e/ZMqR+F5AtaWlqKixcvIikpCZ06dUJoaChVoA4A9QRhZmaGAQMGKAwWV4RAIKCKbPr7++PRo0eorKyUe/OXyOpUVlYiNzcXJSUllLbVrl27sHz5cqqQ4pw5c7Bv3z7069cPT58+ldE5Y7FYWLRoEWg0GhYuXKi1irwEQWDZsmXYvn27QgdJJBKpHMumzjmNJSYmRi+Zgk2N77//HitWrGh20k1Hjx5V+V4oD5Ik8e7dO41NgspSUFAALpcrFaKgL/Ly8vDTTz9pPPuYJEm59/ozZ86AyWRKHbO3t8dnn32GuLg46jUmk4nc3Fyq8KW2efPmDWxsbECn01UuXFtRUYGqqiqN2GF0pDTI4MGDwWQy4ePjQ9UBkaTp2tnZoVOnTrhx40aD/RQWFiIvL4+adExMTNC6dWukp6cjICCAaidxsrKyaqpLeHh4QCwW459//sHXX38NKysrtG3bFmKxGDQaDZ6enggMDIS5uTkcHR3h4OCAXr16oby8HFu2bMGrV69AEAQ++OAD8Pk1NVQl2XaPHz9GixYt4OrqilOnTtVr++jRo7F//368fv0aBQUFcHR0RElJCbhcLlW9VnIDtLW1RVlZGf7991/Y2dmhuroaqampKCkpqVf5vEOHDlKZY6WlpcjMzISDgwPEYjFevHgBgiCkCm4+ffqU+re9vT1oNBpKSkpgb2+PtLQ0mJiYwNfXF0CNeOY///wDOzs7+Pn5gU6nIzU1FZWVlbCxsUF6ejp8fHzwxx9/oE2bNggMDMT9+/e1smJFkiTOnj2Lfv36ISUlRSOTUO2+dY23tzfi4uLUCvAcNmwYXr58KVUJurni4+ODly9f6kxIVlcEBQUhOztbI8VtxWIxtm3bhkmTJiEuLk4mVkhb/Pbbb0orImiblJQUhIeH488//9R69XvJHFObkpISKqMQACIiIpCTkwMXFxfQaDR4eXkhPz9fqytTVVVVKCwsVMtpU6Z8jrIYHSkN0rp1a6qejCT1lCAIZGVlwcbGBn5+fhAKhXID4GpTWlqK169fgyAIuLi4UK9ZWFjAwcEBNBqNmggJgqCekKytrVFZWYmSkhKQJEkFRUpih8RiMTp06ABvb2/w+XxYWFhQNXVevnwJDw8P5OXlgU6nU9pI+fn5sLGxAY/Hg6urKxgMRr2O1PHjx/Hll1+Cx+NR1dbT0tJw8eJFfPbZZ1Rpfi6XC5IkKfkFPp8PMzMzqgZPZWUl3r17ByaTSTmCkh/Kp59+ChMTEyQnJ6OwsBAFBQWoqqqigvIlVYj5fD6ePn0Kb29viMVimJqawsnJidLGEggEcHZ2ppwqCY6Ojnjz5g169uwJW1tbZGdno2XLlhCJRLCwsICVlRVsbW1x7tw5WFlZobi4WC2nJCQkBBwOB5cvX1aYdXj16lVERkZKPfU1ZSRbq6rStm1bnD59Wu4NvblAo9EQGhpqULE4msTX15cSAW4sJEni+vXrsLGx0cnKhwR7e3uUlJQYTDZlYGAg/vnnH73ICJWXl0tllqenp+PFixfIzs6mwkYqKipQVFQEgUCAzMxMmUD9Vq1a4fnz52jXrh1sbW1x7949GYFjBoMBb29vVFVVycTsSuY8TT5kqoPRkdIgEueJTqdTDhWfz8ebN2+QmpoKHx8f+Pv7y3WkJGrZLBYL1dXVKCgoQGpqKphMJuzt7UGSJDw8PADUPB107twZ7969Q2ZmJvh8Pvh8PjWhs9lsEARBleCXOFEMBgN8Ph+Ojo4wMTGBSCQCnU6HmZkZPv/8c9DpdOTm5iIlJQV2dnZwc3NDfn4+EhISUFJSAgaDATs7O3zwwQdSTyIS/vjjD/Tr10/qtZycHNy+fRsDBw5EVVUV2Gw2qqurIRQKwWaz4ejoiMDAQBQVFYHP50MkEsHR0REuLi4ICAhAcHAwCgoKIBKJwGAwwOPxKNHLvLw80Gg08Hg8vHz5Eunp6fDz84OXlxcuX76MlJQUtGrVCi1btoSpqSnatWuH8vJypKeno6qqCm/fvgVBEFISO1lZWfDx8YGNjQ0YDAYKCwtRUlICHo9HFSc0NzdHq1atYGZmRknPeHh4ICcnR8pJsLGxga+vr9xAdTabjV69euHp06cNlm8wxhW9H9eARqNh0KBBOH/+vE6dg/qorKzExYsX9W2GQs6ePYsePXogKSlJJ5Mpi8VCmzZtZO4bRoALFy4AqHl4dnd3R35+Puh0OlgsFqUmYWJiAm9vb7BYLHC5XCroWyLb4u3tDZIkkZubCysrK+qBmMFgyF09otFoVGiIJovhqorRkdIgr169goeHB5V+ymQyERAQAAaDgZSUFLx58wYWFhZwdnamJk8+nw+xWIz+/fvj2rVrKC0txZs3b6jlbyaTCVdXVzg4OFAOUU5ODgICApCcnIz8/HxKdV6y8iSpVP7111+jqqoKBEGguroaTCaTCp6WxBJxOBwUFxdTDom9vT1evXqFJ0+eID8/H/7+/khOTsZ///2Hd+/e4auvvkJISIhcR0oRVVVVePDgAUxNTantSR6PB5IkweVyQafT0aFDB7Rv3x6WlpawtrYGm82GpaUlaDQaBAIBzMzMKE2mVq1a4fXr1xCLxeBwODA1NYWvry/odDq8vb1x+fJleHh4gM/n49WrVxAKhWjVqhUyMjKo7bzk5GR4e3tT9khWvxwdHZGXlwc+nw8ajYbq6mpqVYskSTg5OaFly5a4efMmevbsCZIkERQUhEuXLkk5Us7Ozhg6dKhcR+rw4cNKieYGBQXh3r17eg12B2omEGX1CY00jri4OL07jhwOB+7u7ti7d6/G+kxJSVG5jpsyhIeH48mTJzpxpP78809MnDgRAIyOlAJSU1NlakiZmJiAz+fDw8MD5ubmCA4OxpQpU6jVRaAm68/Kygq5ublwcHDARx99hMePHyMnJ4eK6bS2tkZBQQGVlMRmsyEQCIyOVHPh8OHDGD16NDw8PMBgMGBqago2mw1/f3/Q6XTcu3cPnTt3xieffIKTJ08CAPr06QM6nY6ysjL06dMHly9fRnZ2NpydneHu7o7w8HAqe48gCIhEInTr1g1isRgBAQFo06YNRCIRBAIB9cViMpmorKyEhYUFFfckEolQXFwMFxcXpKenIzs7m4qTSkxMBI1GQ0BAANzc3BAbG0vdyFevXk29v5s3b4IgCLWU4IuLi7F3717QaDTMnj0bIpEIHTt2RGZmJkiSpGpmATVB6EBNrEhxcTF8fX1RWloKFosFExMT0Gg0cLlc9O7dG5WVlRCLxejTpw9oNBpEIhHS0tIQGRmJlJQUuLi44L///qOCCrt374527drh2bNnuHTpEiZOnEg5vlOnTqXiyRgMBjgcDj7++GN07NiR2i7t378/ysvLYWZmhqNHj8LFxQU9evRAcXGxxlcRCILA/PnzMXz4cL07UhYWFhg6dKhexubz+SgoKNDYjVLfToohY25uDmtra9jZ2aFv37549OiRxvpOSEjQWHCvkRqys7NhbW2Nt2/f6n17qyHMzMzg4uKC06dPg0ajUTsstbl69Sr17/LycmRnZ4PBYMDX1xdVVVXU/Hfu3DkIBAJ4eXkhKysLBQUFoNPpoNPpOouXq43RkdIw+/btw+TJk+Ho6EhliYlEInh6eoLD4eDixYsYOnQowsLCIBaLUVVVBS8vL+zcuRMhISEICQmRipGQTKCSeKu8vDwpx4rNZqOqqopaeQJq9OSSk5PRrVs3KvgaABXo/ffff+Pvv/9Gz5490bdvX/zyyy/U1t3Ro0cb9f5JkqS2vGp/oel0OiwtLVFUVIRVq1YBqKnevHr1auTm5mLgwIHw8vICk8mEqakpTE1N4eHhgVevXlF1qNLT02FlZQUulws2m42ioiLk5eWBJEkwGAwIhUKwWCyYmZkhLy8PZmZmyMjIQGhoKKW+XlhYiNzcXHC5XAwePBiZmZkwNTWFtbU10tPTKSfU2tqaWsETi8Vgs9kgSRLJyckwNzdHdnY21q1bZxBOji7Iy8vD+vXrsW7dOrXO5/F4KCwsVMuJmTdvHjZt2lRvAoK+MTU1BUEQzUK/8cMPP0R4eDgePXqkMbkbCSNHjsSzZ89w9uxZjfZbXFwMLpeL/Px8nfwWy8vLqdIQuqgSrojY2FhMnz6dKmtjyOTn5+Off/4BUDOfrVixAhYWFnKzU5lMJt68eYNff62R9k1MTETv3r1RVlaGtm3bIiEhAUKhkFrJkoSd8Hg8qetgZmamk8KgRkdKC2zfvh0TJ06Ek5MTTE1NqRUWe3t7BAYGSjlCksKU06ZNA0EQKC8vh0AggEgkkmon+S8mJkZqMho0aBDS09Nx584dSiiyuroabDYbXbt2xd69eyEUCqn2Dg4O1L/FYjFatmyJ5cuXIzIyUu57UWfii46Oxvbt26n4CrFYDGtra2zevLneSsmnT5+W+tvd3R3z58/H999/L9N26NCh6NOnD+Lj45WqFg8Ay5YtQ6tWrXDx4kVqNRAALC0tsWPHDgA1JSJqM2/ePNy+fRt//fWXTH80Gg3Hjh1TamxFNHR9DWX1xN7eXkbnS1kIgsAPP/yAsLAwtTLRNHUNJKU9NE1YWBgqKyvxww8/aLxvXSJ5YLlx40aTKtg6a9YsrF+/Hjt27NBJ1fH9+/fjiy++wNChQ3H48GG9P0ht2bJF7zaoS0xMDBYtWiTj7LRt2xZubm6UIyUWi6l7fWJiIrp27YoHDx5Q5/n6+qJHjx4QiUR4+vQptbMQGRmJlStXUg/bIpGI0iGU9KsJjI6UlpBUBO7bty86d+4MGo0GJpMJkiRRXV1NfZA0Gg1VVVUoLi7Gr7/+iszMTJXGqZ1B16lTJ4SEhGDu3LkAaiaOI0eOYPjw4dQEVntSkmR7TJgwQarPxgr11j336dOnmD59OrUS1VhOnDhBlStQlbq2FRUV4euvv8ahQ4c0YVqzJTs7GxMnTsSuXbvUOt8QHEJfX18sXbrUYFeO9J2tN3LkSClpESP1c/LkSfTs2RORkZGIjY3Vqy1z587F77//3iT1QKdPn47169dj+fLlUpmQt27dUljq5Nq1a+jUqRNevnyJgoICPHr0CI8ePYKdnR0+++wzxMfHU6teEmbNmoXDhw8jLS0NvXv3BpPJ1NjqqLGyuZY5d+4cli1bhqVLl2LRokX47rvvsHTpUixZsgRLlixBVlYWCgsLceDAAZWdqLokJSUhLi4OO3fuRGVlJb766iuZCWzp0qVo3bp1vcKzBEHg+PHjYLPZ1GszZ87EwIEDqb89PDzqnVCnT5+O+/fvyz2m6clUE/3xeDwcPHhQA9boH1Wuh42NDfbv3690eycnJ8TGxiIsLEwd0wyGN2/eICIiQt9myGBhYYGffvpJaZ1EbbB3717s27dPK31///336Ny5s1b6BmpWwcPCwrQ6Rl0uX76Mixcvyqxk65ply5YhODgYH3/8sV7tUJfo6Gi1ykncuHFDpiZZbm5uvbqBK1euRFpaGgDg/PnzUk6UhYUF1q5di1GjRqlsB2B0pPTO7t27sWvXLo1p/9TWm1P1hlxdXY3Q0FCp8xYtWkRp8AE1TwoLFy5s0IamQmFhIcaMGdOkbG4sr169QlRUlMrvWd8rJprAUD/n4uJijBkzRq82jBgxAuHh4Vrpe+nSpRg9ejT+/PNPrfSvLxTpe+qSxsqJ6ZNVq1ZJyYbpg+LiYsyfP1/tnQmjI6Vn6modaYPt27crlOZwcHCglqfrZjwIhUKpfWSxWKyxrAiSJLFixQopwWJ9oCiTSF8TL5vNpmK3NI2rqytiYmJUPo/L5WLbtm1asKh+YmNjERsbq5J4dlPE3Nwc69ev19v4EydOREFBARWTommmT58Of39/g6iPpUmePHmiduygJvnpp5+QkJCgbzPUwsTExCCcwMZoqhodqWYIj8fD8uXLQZIkZs2aBQsLCxk17v/9738YPnw4kpOTsW7dOqknAl05D4sWLaIKbcpDk3Zs3bq1wYryqqJMoHh9bRYsWAAnJ6d6zyUIAjwejyoVoUkk13vRokUqnUej0XT+5Mjj8ShdxMbQrl07TJo0SUNW/R8SWYzGxFrY29sjOjoaa9eu1aBlqsHlciEWi7WWhbZ//3506tRJrqC4pti1axdVxFdXVFdXg8PhYNasWTobUx4DBw40CDFldVi3bh1GjBhBZVY3RYyOVDMjOzsbO3fuhJubG4Cawmh1J/PTp0/jwYMHsLW1RWVlZYOVtbXF69evpTIKVUFVJysnJ0elNNiIiAi0bNlSVbOUxsXFBYcOHVKYZUQQBIYNGybjBNfFxsam3qzL+qiurla5oGBFRYVeJ/vGwOVyUV1drXawfH3Y2dlBKBQ2Sj+OyWTCyclJbwUeR40ahYcPHyoURG8sOTk54HK5sLCw0NoYb968AYfD0eoYdXF3d8dnn32m8ZIOqnL9+vUmu2qblZWFv/76C71799b77oS6GB2pZkZ5eTnu37+v0NFIS0trULNs9OjRVNmGCxcugMvlomvXrkrbERwcjA4dOijd3tB49uyZ3PomyuLr64tPPvlEYZsnT56gqKio3uMkSeLu3bsNOo1sNhu+vr746aef1LJVWYRCoUZ00vRFaWmpRgtMahJ9xm49f/4cd+7coSpHa4tLly6By+WiS5cuWhvjypUrOv2MS0pKkJ6ejlatWulsTHmkpqbC3d0dQUFBerVDXZ49e4ZHjx416p6rT4yOlBEANU/Fksy8U6dO4eOPP6Yy9+7evQs2mw0/Pz+qPZvNRv/+/evtLyAgAD4+PvWOYegkJiY2amJxdnaGq6sr/v7770bZ8ddffyk1yQoEAqVrar2PKNpmfZ/p06cPnjx5ohXplrowGAyQJAlXV1d06tRJa2PQ6XSt9C2PvLw8PHr0CB9++KHOxqwPkUgEFxeXJrvFx2QyG1x9N1SaptVGGoROp+ODDz4AUDOJdOzYEaamptTxuhMLi8WiMnYOHjzY4JYbh8NBaGio3GOPHz9Gbm6uzMRlYmKitawgbdDYiTc7O1um0Kgux5eHlZUVAgICVOrbwsICPj4+Wt36MaJ7OnXqRAnI6gIHBwckJyfj+fPnUoWBNUn37t31vjqkL+7evYuKigqVdg4MiZ49eyqMGzVkjI5UM4XJZGLGjBkAgJcvX2LChAmwtraWamNhYQE+nw+9G+YDAAAgAElEQVSBQIDk5GSl+66qqlK4H//LL7/gwYMHSvdnCCsF6tigyaKlusLLywshISEygqKKcHZ2xqBBg6gis+qgzvv18vLSyBOqlZUVJRdk5P/49ttvcfz4cZ2sRjk7O+Py5cu4c+cO7t27h99//13rY76PFBUV4c2bN/o2Qy2ysrL0KjzcGIyOVDPCxMQEPB4PYrEY7969o17/7rvvZKo5l5eXw9vbG+PGjcO7d+9kpFgUTXxZWVlSYsbKIBKJpGxqaBx9b8XweDxUVFSoncWkK/uZTKaMg9wQGRkZOg0at7W1lfvZN0RMTAxKS0sblTJvZmZGPenGxcWp3U9zRFffUSsrK4SHh8PLy0vrYxUXF+tc/04kEjUq2UCT3L9/v8lu8R88eBBZWVlSxaCbCkZHqhnRrl07zJ07FwUFBZg+fbrCtmfPnlWpsnXdG66qN+Hc3FzMnDlT6faq2qPsOcqeN3/+fFy/fh2JiYkqj6NLfH19MX/+fKUnD3UnTnXPY7FY2Lx5M6ZMmaJWvbSZM2ciLy9PrbEBYPjw4WCxWNi7d6/afdQHi8VCdXW11uvAaQM2m43KykqtO1JsNhvz58/H8ePHdSJhcuTIEUoYV1fk5ORoTP6qsXTr1g2hoaEwMTHRtylqERoaim7duunbDJUxOlLNGEPYMmvKzJgxA71799a3GQ2SlZWFqVOnaq3/xmovqjumIUMQBJYsWYKLFy/i0qVLje5L14Uq9+zZg2nTpml1JYUgCGzZsgWrVq1CSkqK1sapzbhx49CvXz+dfn/4fL7KK/Ta4tKlS7h16xaio6MN/jckj6Y6ZxkdKSM6pan+UN5XevTogZCQkEatJqr6mdPpdBw+fFin2Veqsn37duzcubPRRV7btm2LyMhIrRQL1TcHDhzA1KlTG7WiqCrbt2+HhYWF2pppzYHbt2/j6NGjWLlypb5NUZm4uLhGP5joA6Mj1Yy4ceMG9uzZI1fGgyRJrF27Fv7+/kr3t2XLFq0WpWwIKysrrddGktDYrUtl+lT1XGXPN1Tn1MrKCtu3b8fIkSPVOn/kyJGNineZPXs2UlNTcezYMbX70CY9e/bEp59+irlz5+psTAaDgf3792P06NEKpZGaMseOHUNmZiamTZumk/EyMjIQExOjNUkndWmKK1IA8MUXX2Do0KH6NkMljI5UM4IkSYjF4nqf5BkMhsyPq1WrVli+fLlM28mTJ6OwsJBqf+zYMeTk5Ci9haSJyZ0giHrlYwwBbTkw1tbW2Lp1q8b77dOnD7p164Zly5ZpvO/6oNPpasm7kCTZaFkYGo2mlYDqbdu24fvvv0d6errafQwePBienp7YvHmzxiWAFFFdXY3JkydrNa6LTqdj586dmDRpEiorK7U2Tn2QJIkrV65QWaaWlpbYvHmzVscsLCzUaAzo+8yZM2dw6tQpfZuhEkZHqplSVVWFGTNmYOPGjaDT6Vi4cKFMWixJkqDT6ZSgMZ1Ox8aNG0EQBEpLS6UmoKqqKojFYplaVNpE35l72kTR+6LRaGAwGIiOjtbomEwmE0wmU+VVnqb4GURHR+Off/7BtWvXNNrvunXrsHr1auTk5DQqrsnExAQ0Gk2nGWZcLherVq1SSSpJVTgcDpYvX46YmBiUlJRobZyGEAqFqKyshKOjI6ZNm4YVK1ZofIzOnTtjxYoViIyMBEmSMpnR+iQjIwPr1q3Ttxlq0bNnT/Tq1UvfZqiE0ZFqhkgckKysLDg5OYEgCOTk5MgU2bx37x727dtH/U0QhFRBNHkTqC4n1ZKSEr1mwxw5ckRuEUoWi4UFCxZodWySJJGdna2Vft8H7OzsUFpaqvG6NE5OTsjOzm6UE/X5559DKBTizJkzGrRMMba2tpg0aZLU713TWFtbY/Lkydi3b59WvrvqwGQyYWtri5ycHI33/fz5cxw4cAC///47uFxug5nSusTW1haDBw/WtxlqYW5urlO9RE1gdKSaOYomzsLCQqlsGkObZE1MTNC9e/d6j2vb3rS0NOTn58u8TqPREBAQ0GRjEJSlQ4cOcHJyUmuZ3dbWFkOGDMHu3btVOo/FYmHs2LHYuXOnzjPZlEET3zlnZ2eIxWKdORt8Ph8DBw5EQkICnjx5opUxnJycMGjQIK2OoQqtW7dGv379kJeXhwMHDmhljMLCQjx58gQpKSkQCAT477//ZNqEhITAxcVFK+MroqSkBM+ePcOIESN0PnZjuXnzJpKSkvRthkoYHan3CEWTgEAgwC+//KJSfwwGA0OGDGmsWfVSt7BobVSd0Hr27KnRG5pYLMbPP/8sd7L39/eHtbU1/v33X42NJw93d3etany5ubmBx+Ph/v37Kp9rZmaGoKAgXL16VaXzGAwGgoODG1ULaMCAAUhKStJohWcajYYhQ4bg119/1WlMkybg8Xjw8/OTO9FrCktLS7Ru3VqrYyhLQEAAfH19UVxcjLKyMp1MykKhUO42ckFBAQQCAfU3n89Hjx49tG5PaWkpHj58qFWBaG2RlpYGkiSblOi90ZFqBrRs2VKp7DpFKygCgQAnTpxQyUEhCAJ2dnb1Hm/s1mBFRQVOnjypdHtFWFlZydUUYzKZajkjYrG43uvl7+8PKysrJCQkyD03ODgYt2/fVhirosx1cnd3R5s2bZTSwFP2O6JPTE1N0alTp0Y7oJp2pJhMJj766COEhITgl19+adRKWdu2bZGfn9+oQHVVsLe3h4uLC27duqXVcYqKigxGi5HL5SI3N1fj8XHq8Pfff0t9D1ksFng8nk7GrqqqwvXr13UyliZxc3ODn59fk9reM9yUKCNK8+GHH4IkSUovj8lkonXr1lpfYhcKhQaX8lsfd+/eBY/Hw9u3b6Ve53A4GD9+vMbHU+QIjRs3TmPFEDMyMnD06NEG23Xv3h2+vr64cuVKo8dsCHNzc7WcNktLS4SFhTXq8/Dz89N4pieHw8E333yDR48eNaofLy8vdO3aFQkJCY3uSxlatGiB9u3bg8fj4ciRI1obx9LSEkwmE8ePH9faGMrg7u6O/Px8g3Ye3r17p9YKrzpUVVU1uS0yAHB1dUVFRYVO7lWawrgi1UzgcrmU5pqFhYVKtWnU0bsTi8XIycmBo6Oj6sYqMXZDx1Rl4MCBSEtLk3sTI0kSmZmZWhvbUEhKSsKJEyeUait5albH2fPx8cGoUaP0Ip46Z84clJaWyiRWNBahUNjozK9JkybhypUrOnGieDwePvzwQ5iZmWnViTIzM0PHjh3xySefaG0MZWjRogUGDx4Md3d3vdohDysrK3A4HAA1zt6nn36qk3G5XC6GDx8Oe3t7nYynKe7cuYPbt2/r2wyVMDpSzYSPPvoIY8aMgUgkktoyqusQmJqaauSJvaioCN999x3Wr1/f6L50QVxcHPz8/ORu4wmFQo2XGlBEU3DShg0bBhqNpnYxy4yMDCxdulTp9jQaDWw2u9Gp+SRJIiYmRmNZWjQaDRwOp9F2cblcVFRU6Cy+atiwYVQcn7ZgsVjo06cP+Hw+du3apbVxGoLL5SIqKgrx8fE6W+1RBg6HAxqNhuHDh1PxPvfv39fZKn5RURFWr16NOXPmAABV5sbQ6devn86cTU1hdKSaASKRiIrbSE1NxaxZs+qdrGfOnKkwE06T0Gg00GiG8RWbO3cu7t27hz///FPfphiRg4+PD6ZMmYIZM2ao3Yc2JGX4fD4WLlzYaC3DDRs2YMuWLXjx4oWGLNMvNBqNqlivivi5pqHT6Vi9ejU2btyoMz0/ZaDT6fj+++/B5/OxY8cOrSee1IekniAArFmzRsqZotFoBpd5LCmi29QSOowxUs2AAwcOIC8vD35+flofS5XVlMGDB8PGxkYrVbpVhSRJTJkyBdbW1k2uam59NIWVLV2izRpJjaW5fVazZs1CYmKi3hwECbt378a3336r08KmyrBjxw7MmjULRUVF+jaF4ttvv5X6OyIiArm5ufj999/1ZJEs33zzDZKTk5uc3p5hLBcY0Qj1PV0cOHAAZmZmavW5bNkydO7cuTFmSUGn03HkyBFqpSoqKgojRozABx98INNWV1p7TCYThw4d0vo4mmTIkCEYN26cUm2nTZuG3NxcHDx4UOn+1a0q37dvX70WJvzmm280Nnm1bdsWkyZNwpQpUzTSn66YO3cuHj16hN9++01rY6xZs0avThSdTsePP/6IcePGGZwTBQATJkwwKCdKHj/++CNoNBpGjx6tb1MojKLFRvSOPOHdiIgIiEQiEASBefPmqRToGh0djdTUVCkHrX379liyZEmD5/7444/11qWq3R9JknIdwNevX2PGjBka2xpcsWKF3GyeoqIiTJgwwWC2IJWFIAhcvXpVKRkIgiBUcoxmzJjRqO3fu3fvqhQf9cEHH2D27Nlqj1cbbRTxbOxq0s6dOzF//nzk5uZqyCLFSD5vbbFkyRL4+vpqrf+G4HA42LRpEyZPnmyQW0Dbt283aI1QCSRJ4syZMzh8+LC+TaEgSRKDBg3CF198oW9TVKJpzR5GVKb205pEL682z58/x65du7BmzRrqta1bt4LFYsm0v3z5MuLi4sBmsxscVygUymROlZeXIyoqSim7SZJsUPB02LBhGDhwoFL9CQSCem+66jzR0mg0bNmyRWsOmDIToUgkkir2pylYLBaOHTuGP/74Q63zxWIxqqqqlG5Pp9ORnp4uVzxbFTTpPHTt2hU9evSQ+l2oi6mpKaqqqnSyvTdv3jycO3dOqzWd2Gy23h48bG1tMX/+fCxevFgvgsiKYDAYWLt2LRYtWqTxrFFNMnPmTHh6egKoEbE2NFsvXLiAoqIihIeH69sUpTE6Uu85QqEQxcXFsLGxoV6ztbWVu0pUWVmJ4uJitcciSbLeSuWqsn//fjx9+lRrmSjbtm1D586d693WJAgCLVq00Fuw5qVLl/D69WutLctrQ6dOEUKhUO26WgwGA4sXL8aSJUtQXV2tEXtMTU1hamqKwsJCtfsgCAKLFy/GsmXLdLb9xOPxUF5erpIjqwozZ87EsWPH9BLY7erqivDwcMTFxSEvL0/n4yuCy+Vi1qxZ2LFjh8HZRhAE5s+fTzm/R48eRVZWlp6tqp/y8nLcuHED58+f17cpSmN0pJoRXl5eCA0NBVCjUzdt2jSVzheLxYiNjVW5uvm0adO0kjGliKysrEY5dQ2Rnp4OS0tLWFpayhwTCATYtGmTXgOI8/PzUVFRAT6frzcbDAUajQYvLy8kJycbVFA3QRBo2bIlkpOTdaIbOHbsWJw+fRoZGRlaG8Pd3R05OTk6j0vy8fHBRx99hEuXLiE1NVWnYzeEtbU1wsLC8Ndff+HVq1f6NkcGkiTx119/Ub+NjIwMg1vNq0tJSQnYbHaTEV42OlLNCGtra7Rp0wZlZWU4fvy4VAD3V1991WDJfZIkVdLKqq6uxqFDh9ClSxeVV2bCw8PBZDJVOkcemlwRGjFihFIOoUgkUlg9+d69e8jLy0OvXr3kHj969Cg++eQTWFlZqW2rEe2ibrC9BCaTiWHDhuHIkSM6c+6CgoLw4sULlJSUaKX/r776CmfOnNHqA0x9SER4Hzx4oPOxG0IgEODFixcGWUSSwWBg6NChuHXrlkE9ZChDeXk5ysvLm0RNKaMj1UxITU3FnTt3ANRswdWNb+nbty/Mzc0BAG3atNGI7ppIJMKZM2cUtnF0dESnTp1kXq/tRF2/fh329vZyqxLT6XT07du30bZK8PHxQevWreUe04RjBwDPnj1DUVFRvYKhf/75J4KDg+Wudr1veHh4wMbGBjdu3NC3KRReXl4wNzenfk/qwGAw0K9fP/zxxx86mcB69eqFxMRErW3H9u7dGyYmJrh06RLKysq0MkZ9uLu7w8rKyuDkTpycnNCrVy+0bt0aly9f1rc5MrBYLPTo0aNJBL7L4927d7hx40aTsN/oSDUTHj9+jIsXLzbY7sWLF/Dz80P79u2V7tvDw0OhOHF95OTkgE6no3///jLH7t27RwV/nzlzhhLgrY1QKMSDBw8QERGh8tj10bFjR3Tr1k3qNbFYjNu3b+PevXs62YIx8n+0adMGTk5O+Ouvv9Q6n8FgIDAwUKM2tW/fHjY2Nkr9nuRhYmICf39/3L17V6N21UdgYCBGjhyJ+Ph4rawWEQSBkSNH4ujRo1qLvaoPZ2dntG3bFi4uLjodVxkkOwDe3t76NkUGDoeDtm3bwtvbWyktTkOloKBAps6Vv7+/zkNJGsLoSDVjJNteaWlplINw7NgxPHz4UKYtjUaDm5ub3H6++OILuatKdcepy7Vr13D69Gm57WfPng0Wi6XQ/tLSUsTGxipsowpv376VW9tFJBJh7dq1mDVrlsZWpXQFm82Gk5OTwja5ublgMBgGu5XI5XLV1gMzMzPD1KlTkZaWphFbbG1tIRaLkZ+fr3YfFhYWGDt2LDZu3KgRmxRBEASmT5+OjIwMrZYCSEtL08vWUPv27VFaWqq2o60tLC0tkZmZiU2bNhlcDTpTU1P4+vqia9euepXu0RYDBw6UmjusrKwaDFvRNkZH6j1gwYIFDS7Hc7lcrFy5ssG+hEIhysrKGrUtlZ+fr7GbMpvNVjpz78iRIxpfgufxeHqVWXBzc8OYMWMUtjl06BCsra0xYMAAHVmlPJWVlfDx8VG6uKg8BAIBFi5cqBF7hg0bhsrKSsTHx6t1Pp1Oh7m5eaOy/VSBx+OhsLAQS5Ys0VoAOEmSiI2N1UvNpvPnzyMxMVHn4yqCw+Ggd+/e6N27t75NkYuXlxe6d++OzZs369sUrbB69WowGAwqC7FXr15yCzrrEqMjZUQhQqFQ6gZ67949HDx4EKtXr1arP5FIhEmTJmlsC+2TTz5p0JHQFgKBAFu3bqWU3VWFyWTKdcJIkoRAIICJiUmDfTx//lztz8IQOHfunF612urSWAffw8MDU6ZMwdy5czVkUf2w2Wxs2LAB06ZN06qTw2KxsHr1ar1spwwbNsyggo0ZDAbCw8NRWlqKEydO6NscGWg0Gh4/fmwQslyapvZuwZw5c6hwk19++QXnzp3Tl1kAjI6UkXqQTPBLliwxqEDg2jQ2s6oxiMVijB49Wu0JjCRJxMTEICgoSOZYXl4epk+fjh9//LHBflq3bt3oQpZGmibarmAO1Dhr27dvx4QJEwyyiriumTlzJu7evav3ibs+evfujbFjx+rbDI1jYmIiFeaxYMEC5OTk6NEiaYyOVDOmMTfZvXv3wtbWVoPWaJ49e/bg2LFjWut/zZo1cHFxoWpzaZIpU6Y0WA+noS3DK1euKLUdqw7jx4/HkCFDtNJ3c0cXzr2NjQ02bNig1dVYa2trxMbG4ptvvtHaGIqIjo5GcnIyTp48qZfxmxrDhw+HpaVls4yLEggEBq15aXSkmjHKxu5kZWVhwoQJ9Z6rzxighsbXxaSljfe/cePGeoP7JTT03j788EPMmjVLk2YBANauXYuEhASN91sf+lxZrM3cuXMbJdDdsWNHfPnllzrZ1tMV+v7tGxrr1q3TqvyOukyYMAG5ubn49ddf9W2KxrG0tNSIVJM2MTpSRgBARm9p6tSpOpc6GDJkiFZWf/SFn58fFi1aJPcYk8nEypUrcf/+fZljkpWAhqDRaEhOTsb333/faFtrU11d/V6WgdiyZUujakfRaDTQ6XSNydTUh5ubGyIjIzFnzhytjeHi4oKoqCiNiUmryuzZs3Hp0iWVCgRrm/nz58PZ2dkgnP66HDhwAAkJCc3qd+vn54cpU6aguLhY4/c4TWN0pIzIpaioSKUbxsqVK+VmzxEEofRTrUTfrDlw/fp17Nu3jyqCKo/S0lK5k25RURHWr1+v1DjV1dVaq2T9vlFWVmZwAq7yoNPp4HK5Wq0wrosxFGFmZoaqqiqD+Tzmzp2LkydPGqRG3bhx4+Ds7KwVAXN9kpKSgmPHjoEkSYO/xxkdKSNyiY6OVqk2h6ura71ZPa6urjJbh3WJj49X+PQ5d+5cnVS4jY6O1ogQcllZGXJzc9U6VyQSaVQvbfDgwSgsLMSlS5c01mdzZPjw4Xj58qVC+Z/3gZYtW6J3796Ii4vTy/jjx4/H2bNn8fr1a72ML4/4+Hi8fPnS4JyViIgIPHjwANnZ2fo2ReM4ODigZ8+e+jZDKYyOlBG5BAYGKpV+T5Ik4uLiFGb0mJmZyZVlGTVqFMzMzADUSNzIczyqq6uxZ88etGvXTmPxGn5+fujXr5/M63v27IG/v7/ei3JyuVyMHDlSqbZOTk4ICQlR2MbDwwNBQUFwdnZWqs9+/frBy8tLqbZ1efz4MZ4/f45BgwYpfc6rV69w+/btBt+HtvHy8kJRURHevn2rVzv0jYWFBfh8Pp4+faqX8f38/PD69WuUlpbqZfy6hIWF4eXLlwazOibhq6++QmpqKh4+fGjwKzaq4u3tjcDAQLx48ULfpiiF0ZEyQiFxVE6dOqVSnMeFCxfU2pvv1atXg1t5YrEY58+fV7lvRTg7O8stO3DhwgWDSPEmSVIpdfb09HQ8e/ZMqWJ0QqFQ6YkgKCgIubm5ePbsmVLta8NgMMBgMFSSEsnJycGrV6/QtWtXlcczolnc3d3h7OyMq1ev6mX8vn37IiEhwSCcKIIg0K9fP53L4ijD//73PwiFQly7ds0grpUm8fHxgZeXF3JycnQms9RYjI6UEQpJTNThw4c19vT19u1bhYrtus4KSktLQ3Jycr3HDSFLqby8HD///HOD7UpKSpSupXL79m2VAqmTkpLkSgk1hI+PDzw9PVWS9LC1tYWLi0uTuWnqEysrK7i5uTUqKF4R3t7ecHNzU1tnsLGEhITg3LlzeovNqg1BEBgyZAhOnjxpMKtRBEGgY8eOcHV1xe+//67UA1dTg8fjoaCgoEltsRsdKSNaJTMz06B+EImJiQYTK+Tq6io3HovBYMDT0xPPnz9XeL6zs7NCDUQJ6mQZ2dvbw9raWuXz1MHHxwddunQxOM0yQ8TT0xPdu3dvlteqZcuWePXqlUGsCtNoNHh6eiI5OdmgsvRoNBo+/vhj7NmzR+vZobrGyckJHA4HSUlJBpWtqQxGR8oIhTZWY1q1atXkCztyOByFgfe2trYqy2e8e/cOY8eOha+vr8wxHo+HmTNnYseOHQr7uHPnDvbt26fUeKpMBkVFRejbty8+/vhjpc9RdywjhgOXywWNRtPbatDcuXOxYcMGlJeX62X82piYmODrr7/G6tWrDer7LBKJlFI8aGrweDx8/vnnDdbWM1SMjtR7iq62sG7cuIGNGzdqdQw6nS6lBq5JBAIBPvroI0RERMg9XlFRgVWrVqlcBX7x4sX1ZuaJxWKIxWKlakkpi4mJidJB9Dt27EBSUpJa46g66TAYDNDpdIOIQzGkCVMfDBo0CFZWVnrRPuRwOFoTXVYVyb1RU0LYmsTMzAwLFizQtxkaZ/LkyTh9+jSePHmib1PUwuhIGdELmiocR5IkOnfujHnz5mmkv7ocPHhQYbzSuHHjUFZWptEx8/PzNSqHIBaLMWzYMAwbNkxjfWqK/v37w9/fH+vWrdO3KUb0hER8OTIy0iC2q+zs7AxSCNzCwgLLli3DjBkz9G2KRiEIAitWrEB6erq+TVEboyNlRC+MGDFC5glUleKdEk6dOtUslc41yebNm3UmsqrOZ2jk/YXH42HdunWYOHGivk0BUFMCY+nSpQa3Ouno6IgFCxZg2rRp+jZF46xduxZWVlb6NqNRaL/CoRGdoqlJTNV+mtrkWZ+9ERER2Lx5M9asWYO0tDSl+2pq719bGIpunhEjqtKxY0d89NFHmDx5ssF9h7Ozs7W26q5PYmNjsWDBAoOIi2sMxhWp94S6E/2gQYOUVnWX5yhYWVlh586dGrNPQq9evRAVFSXXBk3Spk0bLF26VOZ1kUikcuC4Ivh8vtJyL82Bv//+G1evXsX8+fP1bYoRIypx584dbN26FSKRyKA063x8fDBz5kyDsklTzJ8/v8k7UYDRkWrWKHI+TExMwGazle7riy++oLLv0tPTsXjxYo1IqdQmPj4ev/32W712rVu3rl7tupYtWyodHJqYmIi4uLh6i4HKu25BQUFyHTxFPHv2DGvXrtX4daqLg4ODXKdQH3Tp0gXt27fHrl27lD6nffv2mDRpkhatapg5c+bgjz/+wL179xrVj6GtZBhRjh49eiAkJMQgkh7qkpqa2uwy9Wg0GhYtWtRs6mAZHalmDEEQiImJkVlhUWd1x8LCApaWlgBqZFvkybnMmjULLVq0kHu+jY2NjFo9QRCYNm0aJV1SUlKCoqIiuefHxMTA1tYWNJrsV/bhw4c4dOhQvWPXpby8HPn5+fUeX79+PQYPHgxvb28AwNWrV3H69GnY2NjIbb9mzRqEhYXB1dVV6nWBQIB3794pZZO6pKSkYPfu3bC3t9fqOMoikYgZPHiwUu2vXr2Kc+fO6axmVX38/PPP6NixIzw8PNTuw+hEqU5paanWs3qVoaESJ/oiICAAAwYMUHi/amqw2WxMnjwZR44caTa/GaMj1cyo7SQRBAF/f3+5jpMqztTZs2flVlKu3cf27dvh6uoqtwxBZmYmjh07hlatWkm9vmvXLtjZ2YHD4TRow+PHj+v90RUXFyM1NVVlB9HW1hZjxoyRef358+fg8/mUDmBeXh4yMzPr7f/Zs2f1FtdsDDQaDZMnT5brPEooLy/Hq1evNDpuYygoKEBhYaHSDkleXh6ysrK0bFXDdOnSBW3atFF7Mg0MDISnpyfi4+M1bNn/0aZNG7Rs2RK//fab1sbQJTY2Nhg9ejS6deumVzu6desGJpOJy5cv69WOugQFBaFly5Z4/Pixvk3RGBYWFggJCcG1a9fw8uVLfZujMYyOVDOCIAhYW1tj0KBBqK6uxtGjR6WO9+/fH/b29rh+/bqMbItAIMCJEycwfPhwqq9BgwbB2toaT548QWZmJry9vSnbi3wAACAASURBVBEcHAygZgL/7bffqPZXr16tV0ahuLgYN2/elHFEEhMT5S7t8vl8uaLCihylt2/f4sqVK/jiiy/qbVOb7Oxs/Pvvv0rp1EmwtbXFgAED5B5TZJs6K4DV1dU4ceIEgoOD9RLI3tgxrays6r1Whsjbt28hEAjUPp/P58PGxkYtWR1lcXJygq2trVbH0CVcLheBgYFyV7d1iYeHB+h0ukLpKF0TFBQER0dHJCcnq6V5aYhYW1sjODgYOTk5uHXrlr7N0ShGR6oZ8fbtWyQnJ6N///6orq7GL7/8Qh1LSEhAcHAw7OzscOvWLZmnHIFAgPj4eAwdOhQEQeDKlSvo1auX1JaLt7c39fRIkiRKSkq0UrXc0dERvXv3lnssODhY7spPXl4eEhISMGjQIKXGePPmDS5cuKC0Tbm5uUhNTcWnn34q9/h///0HDw8PmS22srIyJCUloUePHjLn3Lp1C05OTuDz+TLHqqurcfLkSaXtYzKZcseojbOzM/z9/ZXq79mzZyBJUm7l9YbIy8tDcnIyPvnkE5XP1RcXLlxoVtsnTYXKykr8+eefehu/devWKC0tVTpDVxf4+/vDzc0Nr1+/bjZOM1BTfJcgCIOR6NIkRkeqGZGXl4dr167JrQ57/fp1lbIj4uLiUFJSIvXau3fvkJqaCqDmaXL8+PGNstff3x8mJiYyrxcXF+PFixdyz+ncubPWg7flkZycrNCxOXLkCDp37kzFVUkoKCjAkSNH0K1bN7Rp00bq2C+//AJTU1N06NABjo6Ocvt98OAB/P39FWYSVlVVISUlBWPHjq23TXZ2NmxtbdG3b99629TmypUrIEkSPXv2VKp9bdLS0nDo0CEkJycjICAAAQEBDZ7D5XJlrp0RI9qmT58+yM/P15oItDr4+vri8ePHzcqJsrCwAIvFwpkzZ/RtilYwOlLNiFatWqF///7YtGkTCIKAs7MzMjIyQJIkZs6cCZFIpHSWhJOTExgM6TJjSUlJOH78OJhMJhwdHSEWi5GRkQE+nw8+ny83lofD4YDP58PBwYF6TdJ+zpw5qKyshKWlJfWalZUVXrx4gd27dwOo2WKqvWKzYcMGaiuAx+NR55iYmMDW1rbBeBsLCwvweDywWKx6A7QdHR3lOniAegHF5eXlWLduHRYsWCCzZfbzzz/D2tpa7gocSZJYuXIlZs2aVW+GIVDj4G7cuFHmWtXmwoUL+O2331BeXq50UD5Q8/mpKn8D1Kz4/fjjjxg9ejTmzp2rsG1FRQXYbHajHXMj6mNlZaXzBxShUIg3b97odExDx9bWFomJiXj9+rW+TdEYXC4Xbdu2bXDFvCljLMjZDGCxWDAxMYGpqSnodDrMzc0p2YUxY8aAw+GAIAhs3rwZOTk5MDc3p4LC6XQ6FewtcZwsLCywcOFC0Ol0sNlsmJubU4UWzc3N4eLigujoaOTl5WHx4sX44YcfUFpaCpIkpWqdmJiYoEuXLggPDwdBECgpKQGdTseGDRtQUlICoVCIpUuXIjIyEi4uLpQ8xP379wHUOFFWVlZYvHgxxo8fj+LiYsqRMTU1RWhoKD744AP8+++/uHLlCsLDwxXWL2Kz2ejfvz8YDAbu3r2LCRMmUAKt5ubmKCkpgbm5OWbNmgV7e3twOBwwmUwQBAE6nQ6RSCQlByM5B6i5WdR1PIGagHEOh9OoNF9l4pVIkqQctrCwMLkOX0JCAiorKxEZGYlFixY12GdVVRV8fHzw9ddfY8eOHSpL4RQXF2PhwoXYs2ePwnZ37txBeXm5whW1hqj9WRhRnbCwMLRu3Rrnz5/X2Zhv3rzRSi06VTC0rLGIiAjY2Njg6NGjuHv3rr7NaTQsFgsffvghzMzMcOjQIX2bozWMjlQzIDQ0lIpHodPp2LNnD0QiEQiCwM6dO0EQBJhMJr7//nuIRCIANZPzv//+C39/fyxcuBAikYhyBHbs2IExY8ZAKBRiyZIlaNmyJQ4ePAixWIzdu3cjOTkZY8aMAYvFwr59+1BdXY2JEydK6WTRaDR89tlncHR0pCZIBoMBBoMBoVCI8ePHQywWg06ngyAI7Nq1C//995/U+7KxscH69esxevRoAJCqNRQVFYWAgADEx8dTWniKnCg6nY4JEyYgJSUFp06dAgDKLlNTU+zatQthYWHYtGkTZsyYgTlz5mDSpElgs9kwNTWFh4cHtmzZQq2umJiY4IcffsCIESMgEomwdOlSODg4QCwWUytzJEnCxcUFs2fPRlRUFHV9GAwG9e/aq3g0Go1yRCWOm7KUlpbi22+/xYEDBxS2kwgiK9P/2bNncfbsWbRu3RoxMTGYOXOm0vaoisQJV/V9AzXXatu2bRg1apSWrNM/ku+JqtdGFX799VdcvHhRa/3XxdnZGVOmTMHs2bN1NmZdDKn4Jp1Ox9q1a/VthkYZPHgwCgsLceLECX2bolWMW3vNAJIkcfbsWaxfvx5isRjv3r1DREQEhEIhwsPDERYWhoKCApAkiRUrViAsLAyhoaF49OgRFixYgMzMTIwbNw5isRgikQjh4eGorKzEpk2bsHfvXoSFheHs2bMAgJs3b0qpj4vFYowYMUJGbHTs2LH48ssvqSc+BwcHHDhwAHv27MGIESOom9eGDRvg5eUl857c3NywZcsWhe/7p59+UigoXJtFixbJzdCztLSUWjEZM2YMCgsLAdQUAL148SJOnz4tZYupqSn27t0r9TQbHR2N8PBwJCYmYujQoRgzZgwCAgLw7bff4ttvv0V1dTVGjBgBkiSxe/duSltqxowZyM7ORmFhIVXw09bWVqWClqpw+/Zt7Nu3zyBq99Tm+fPniI2NxbZt21Q6r7CwEBMmTNCSVYbD8OHDYWlpqbXvxfvK9u3bcfXqVX2bAQBYuXKlStvuTYFjx47hr7/+0rcZWse4ItUM2LdvHwCgU6dOSE5OxoIFC8BisWSWrWfOnImMjAyp1x4/foylS5eCy+VKTfZ1GTlyJAYMGICbN28CqImp2LBhA8LDw2Xazps3D23btsXJkydx7Ngx6vXS0lKpOJi4uDhwOBwsXrwYz58/p14PCgrCzJkzUVBQgG+//Vam/5UrV8LV1RVJSUnKXB6KLVu2IDExUeZ1oVCIr7/+mvp769atDYpoikQijBo1Su61kmyDPnjwQKYI6f79+6UCx9etWyd1vGXLlpgyZQrGjRtHvTZy5EiZcUJCQmBtbY1r165h1KhRmDNnDqqrq2W29WJiYnDq1Ckq3bhDhw6YPn06CgoKFL6/9401a9YgLi5O6ntopPkzZcoU3L17FwkJCfo2pdkxadIk3Lp1S+X7dFPEuCLVTOjbty8iIyMB1Dg5GzduREREBABg9+7dMDc3l1rC/vzzzzF27FiQJAl7e3usWrUKY8aMoSbhHTt2YMWKFVLFHs+fP49NmzbBzc0NGzZsACA/xoAgCPz0009U+QUfHx+sXLlSpj1BEJg9e7bcDL309HRMnz693v5jY2Nx5coVpa7N6tWr4e3tLdOXs7MzYmNjZYR2CYLAkiVLqFit2tja2mLdunXUtavLuHHjIBAIKOe2bhuCIDBp0iS5jkyHDh0QGhqKOXPmSJ1Xt48xY8Zg0KBBIAgCT548kYp3qt129erVOHjwoFRG0p07d/DNN9/I3abbsGEDlRQQERGBzz//HN26dcNnn32mE+28N2/eICoqitqeVraWlUAgwPjx4/HDDz+AyWSqNfbChQvxxRdfoGPHjmqdb0Q99B2jZBQc1w7R0dG4fPky9eDd3DGuSDUTGAwGHj9+jNOnT2PZsmVgsViUbhSLxcKcOXOkMmQYDAZu3bqFhIQELF68GAwGg2q/ZcsW8Hg8CAQCkCSJsWPHIicnB5cuXYJQKARBEKisrJTa4pOwYMEC+Pj44Nq1a1JxQEVFRYiJiaHabdq0CWw2G1VVVVI30+DgYAQFBWHlypVyda+WLVsGJycnCAQCpeNFWCwWNm7cKKOjRhAEBAKBlJOwfv16WFpaQiAQyI2dIAhC6trWJjIyEu3atUN8fLzMVieDwcD69esxbdo0KsC9Nh9//DF8fX2xefNmhYUhp0yZgqCgIJw7dw7x8fEQi8X1tmexWBAKhVLvQywWS9lOEATlFK9fv57KiDx+/Di11fvgwQO1ilVaWlriu+++oxz8hiBJElVVVaDT6WCxWCAIQumJtrKyUiXtyLoIBAIwGAyNClY3FaKiovDff//pPLg5MzMTK1as0OmYtYmMjERCQoLBlBlYvnx5s0iYmD17Nk6cOIHXr18bTPyZtjGuSDUDBgwYAB6Phx9//BFlZWUgCAJLly4FQRBYtmwZGAwG8vPzKccjJCQEBEHg0KFDKC8vh1AoxPLly0Gn07Fs2TLY2tpi6dKlVKyQZDWrvLwcrVq1wuDBg7Fy5Uq5BQwtLS1x6NAh6kkkMDAQ33zzDUQikdQqjLW1NZYtWyazMsNms8FiserV3LOyssKOHTvqrTNVlwULFsDKygpFRUVSzoC3tzeGDh0q8z6sra2xdu1aZGdny/Tl4uKCiIgILFu2TO5YFhYWOHXqlMxKGZfLxeLFi2FtbS33mvXv3x+ff/45TExM5DpZEqZOnYq2bdvi9OnTOHv2rMIsuvnz5yMuLk4p+RVra2ts27YNWVlZ1I2vrKwMFRUVEAgEDd7cx40bhyVLlqBr165Sr9NoNFhZWems0OXixYsxb948vdQZa8ocP34cDx480Llgb4sWLfSaIGBhYYHKyspGVbTXJKNHjzZIvT9ViIqKwq+//orXr1/LPEw2Z4wrUs0AW1tbkCQJMzMzhIaGQiQSISMjA9OmTYOnpyc2b95Mpd8PGTIEPXv2xD///IMWLVrgyy+/hFAoRGpqKuh0Ojw9PbFx40Y8e/YMYrEYoaGhePr0KW7fvg2gxilo0aKF3DonEyZMgI2NDbKzs6nJ18zMDCRJYu/evQBqJtfIyEjQaDSkpqZK/diCg4PRq1evBifejIwMVFRUKHVt3N3dceDAARmHgsPhwM7OTqqicVRUFBgMBtLS0uTeXNlsNhwdHamipLUZPXo0EhMTcffuXcoBBWqclDFjxsDl/7F33uFRlGsfvmd7yqZXUkkCSWiBABJ6kYgIogY9ImCBI00sR0EBEUXBeuRYj4iVo4CAiCBFBEEIKJ2QIL2EhABpkJCebJnvj7jzsewmJCSk4NzX5YWZnfLOlpnfPO/z/J6gID744AO7ERZPT0/S0tKua1YXFBTE+vXr2b59u9UxruWpp57i119/5fjx41W27YFKN/SJEycClR3ma/v0OGHCBLRaLSkpKSQlJXHu3Dmr14uKivj444/tbnvvvfcSHBzM3r172blzZ62OWxWpqamEhobecFRpxYoVxMbGYjQab7kWFtXRq1cv9u3bR2pqaoMdMzQ0lN69e/Pbb7812DGbMmPHjuX333+XTJM7duwoXZ+aU4uYzZs3k5qaelOrS5sickSqmdO3b18KCwvZu3cv7u7u+Pr68sMPP6BQKOjSpQvffvstu3fvlgRLZGQkJ06c4ODBg3h5eeHm5mbTaHXPnj3STbVNmzZcunSJrKwsIiMj6du3b5Vj6dSpE7/++qsUzWnXrh09e/aksLBQ6u0nCAJdunRh8eLFNjf5oKAgysvLq7y4jhgxgvXr11crIuyRkpJCUVGR9HdERAT9+/e3Wa9r164sW7bMrkgLDg4mNja2yoax7du35/z58zYi0MHBgaioKJYsWWI3X8Dy+a1fv77aXl/Dhw9n27ZtJCYmcunSJbvrKBQKHnroIU6ePMn+/furFVGOjo48+OCDdO7cmSVLltT6wjdixAjS0tI4ceIESUlJ7Nu3z8Zc0WAwSAL8au666y769OmD0WgkNzfX5nWz2czixYurFHatWrW6KeZ+x48f58iRIwQHB/PQQw/dkKt7cyQqKgo3N7cGPWZxcTHZ2dmEhoY26HGbKjExMRw9epTIyEicnZ0JDQ3F2dm5WU313XPPPRw5cuRvJ6JAFlLNmu7du3PXXXdRUlJCSUkJnTp1Ij8/XxIioijy888/S1GQXr164enpSUpKCmazmfbt23Pp0iUSExNRq9U2jYL79u2Lq6ur9HdISAhBQUF2K9/i4+PRaDRs375dujmGh4fj7e1tt2pjw4YNdkO/aWlpVUYDBg0axI4dO2p8cbnjjjvYtm2bTWuc4OBgQkND7ZY9//LLL3anOPz9/YmKirKb4N6/f3/2799fZSVceXk5GzdutPtabGwsxcXF1T51Dhw4EK1WS2JiYrXROoVCwaBBg/jll1+qvZi5uLgQHx9P//792bhxI7/88kuNc5EEQSA+Pp4777yTrVu3smHDhiqnYe3Rt29f9Ho9+/fv55dffrE7RWv53tqjVatWREdH23XRh8qCiLpM1SQnJ5OamkpISAidO3eu8XZnz54lKyuLrl273vCxG4vGSPi2FHhU9Tn+XREEgejoaPz9/REEodqHoaaCQqGgT58+f+vP8u975rcAQ4cOpaKigqysLKKjo+nYsSNHjx5FrVYTExNjI0juvfdeioqKyM3NpUOHDkRGRko3cK1Wy6hRo6y2GT58OLm5uVYC4dy5c3anoEaPHs3x48dtojlnzpyRfESUSiWdOnXiwIEDNhdvew1/68rDDz/MypUrraJRFs6fP8/atWtrtB9fX1/CwsKqfP0f//gHW7Zsselir9fradOmTZU3qqioKHJzc8nJyan2+CNHjuSnn36qVkCqVCpiYmJISkq67o3R09NTsrJYsmRJtetejVKpJDY2llGjRpGUlFTrJ8+YmBhGjBjBjh07pF58tSE0NJQ777wTV1dXtm7daned7777rsYu8p06dbK7/ODBg+zevbtWYzty5AgpKSm0bduWNm3a1GrbvyMBAQF07dpVMsdtDE6cONHkIj4pKSlERkZiNpspLS1Fr9c39pCui0KhICIigh9//PFvk1x+LbKQasacP3+e7777jrS0NFQqFX/88QdLlixBr9czYcIE3nvvPWnd4OBg1Go133//PVlZWbi7u3PkyBErQ0uTycS8efOsbsSLFy/m5MmTeHh4oFarbcTC1XzyySdVTjtBZY7RU089xX/+8x+bm/3QoUMJDw+vcvvQ0NBalynbExTu7u54enpaLRMEgdDQUFJTU+1u06VLF/r161ejxO2rCQkJYcSIETa5QxZGjhzJn3/+WW21VEhISI3O28HBgUmTJvHBBx/UaGyFhYXMnz+/RutCpVBr3bo1Tz75JGfPnuXDDz+s1dNySEgIw4YNIysr64YiRn5+fvTt2xdPT886J69bPm+LhYQ9CgsLa90H7uTJk+zYsUOyHZFp2iQmJl73Iaah8fLyorCwkN27d7N161bOnDmDUqnEzc3Nql9pU8JoNPLVV1819jAaFTnZvBnzySefAP9v2Lho0SKUSiUuLi42N5uZM2diMpkwGAwMHz6cvn37kpiYCFQ+Ubi6ulpt4+7uTkFBgTT9NnjwYJRKJd98802Nxubk5FRto117bNiwocqE6zlz5pCfn1/jJx5Lhdy1wuiOO+4gPj7eKl9JpVIxd+5cRo8eXeX+Dh06JDVSvhpLReC10RmtVoteryc7O5u3337bZjtXV1eKi4urFRWW6su8vLxqz1uhUODi4lIjk021Wo2jo2OtpuOg8gL//PPPk5uby+zZs2u1rYeHB7Nnz2bcuHE3VMnj6urKyJEj2bJli+TPdaMoFAp8fX2ZPHkyzz//fJXr7du3r1oPHFdXV4qKipp9PoirqyslJSVNpnKtobB8p9asWVPjCuCbieXadv/997NlyxaCg4MRRZHk5GRcXFzo3r07sbGxVVYMyzQuckSqmaNSqRBFEaPRKIVYn3vuObs3iblz53LmzBkUCgVGo1HyhAoKCuLll1/mX//6l7TuvHnzeP/996VoV3WVUPZ8lR566CHuvvtu6W9BENBoNDd8noIg8Nxzz9UoFK9QKPjwww957rnn7CaO//777zbNUquaDrP0B6yK9957jzlz5tgkTXfv3p0JEybYvUFpNBpee+01li5dyuHDh697PlOmTLHJ87IgCALBwcFMnz69WmFgoX379gwfPpzXXnvtuuteS3FxcY2OcTV6vZ533323TqX1M2fOZPXq1XX2ORIEAX9/f2bOnFnr84DKz83yHZ4xYwb+/v4261Tn61UXjEYjoihW+128EWbOnMkPP/xQo+/hrcTMmTNZtmxZkxBRGo2GOXPmUFZWhiiKDBkyhHPnzkm+d3l5eezduxeDwXDDhrMyNxdZSDVznnrqKXJycli6dCmdOnXipZdesuumbVk2depUevbsyerVq/nyyy+Jjo7mjTfesHH2vjoCMnnyZO644w67xxcEga+++oonnnjCRuRc7Rju6+vLRx99VKVgudZdvKrx3wyq2//o0aO5//77q3RYN5vNVW57+PBhXnnlFZttPv74Y1xdXa97Ttd+DvaIjo7mlVdeuekJwzfi/uzm5sYHH3yA2Wy2aWrdGLRq1YrXXnvtht4rjUbDJ598wieffIJarWb69Ok27ZagMifQnlFtXVm+fDlXrlyxarEk0/xRKBTMmzeP0aNHo9VqOXv2rN2Hv5ycHD7++GNefvnlRhilzPWQhVQzZs6cOTaVRampqVLzWwsLFy7k+eefl3J8PvvsM6l9C8DFixclPyGo7Af35JNPStM4Xbt2ZfHixVVO61V1Y1q9erVV5Ke4uNiqp52F2bNns3fvXqkx8tVoNBq++eabemnjMHnyZIYOHWq1zNXVlU8//bRaY8B169bZNNNVKBQsXLiQcePG2USLhg8fXmWezFdffYVGo+HZZ5+168VlQafT8cUXXzBmzJjrTh9duHChVu7htSUqKorXX3+91ttBpWP4448/fkPbAvz3v//Fy8vrhre30K1bN6ZNm0ZOTg5PPfVUrbZ1cXHhgw8+4PHHH+fxxx9vtEqqjRs3snv37psi1GQal2+++YaEhARiYmJwcHDgwQcftLHfKC4utmoHJdN0kIVUM0YURT788EM2bdpEfHy8zQ1CEAQWLlwoTf+98cYbVhVFPXv2tOrrplQqWbhwoZVosURQ7HXw1mg0fPXVV4wZM8Yq2jB16lQrvylLrz17N/F33nmn2oo4C2PGjKnRDczBwYHPP/+cxx57zG40Z8WKFTXOs/nXv/5F//79qxUfVb2WmJgotV65lsmTJ1ebz+Th4cFHH310XfHYs2fPKvsR2uP222+nR48eNySKsrKyePbZZ2u9XV0RBIEXX3zRqufjjXLq1Klai5AWLVrw9ttvN4hFgEqlkvIe7REfH0/Xrl1566236u2YL774IiNHjiQmJqbe9ilzY/j6+nLu3DmOHTtGQUHBdX//t912W53SJWTqDznZvJkyd+5cqaIuISGBQYMG8eeff0qVWFqtlvfeew+VSsVTTz1FcXExKpWKjz76iKSkJAYPHkxCQgKnT5+2uuGrVCqefPJJysrKmDNnDkFBQZhMJhtR4urqyptvvolKpbISOC+99BIREREsXrxY8rMSBIG8vDybaS7L8ebNm2c3R8PV1VXqxVXTKIAgCKhUqiqnka4+lxYtWvD000/bRPAsKJVKli9fzq+//mq1XKfT8fbbb/PUU0/ZvC+PPPIIvXr1YufOnVVGkqo7l5CQEKZOnYrJZOK5556rcr3Bgwdz9913c/r0aT766KMq17OQkJBAfHx8rQ3zunbtyiOPPEJhYeENTc3VhwCx5AfVFVEUa50cLggCpaWldr+79YmzszOzZs1i2rRpVa6zbds2duzYUa8J7kajEaVS+bfzAKoulaAhMZvNzJgxA7PZjCAIeHt7Ex4ezi+//GI3J9BiaLx3714OHDjQ6NPlMpXIQqoZMnv2bBYtWsTp06cxGo3odDp0Oh1Go9Gq/5per2f69OlcvnyZWbNm4ePjQ3FxMQaDAa1Wy5EjR1i0aBFFRUU4Ojoyc+ZMpk+fTn5+Pq+88gpBQUEsWLDAblNPhUKBTqdjxowZVsudnZ353//+x549eygvL6djx4488sgjmEwmmxwqS/+54uJiuxcEQRDsHqMqPDw8qrwRTZgwgcOHD0s+Wa1bt2bcuHHodDq7CexPPfUUkZGRHDx40CpR2t3dneeffx69Xm+3L56DgwOJiYk2HlVKpZJXX32VWbNmWQkpPz8/SktLpSo6pVKJ0WjkzTffrDax3vL5LV26tNqee1CZ+N+9e3f279/PypUrq133WtRqNVlZWXYrFqsiKiqKkSNHUlxczJw5c2p1vKaIKIo33W9IEAT0en21x7lZlXULFiygsLCQwYMHI4oiGzZsuCnHkbFGq9Xy7LPPShHG3Nxc3NzcWLVqFadOnbL7wCWKovTQKYuopoMspJoZzz//PKGhoWRnZ2MwGLjvvvvo3Lkz+/bt48cffwQqBdSTTz6JKIpkZGQwZcoUwsPDWbhwIWlpaQwePJjevXtz/PhxcnJy8PDwYOLEiQQEBEgJtAEBAXz++eccPHjQJvnR19eXESNGMG/ePLsJtzk5OdLN3cnJifLycr7++mub9Vq0aFFlY10fHx9GjBjBf/7zH7vHsIdKpcLT05N///vfVsvHjx9Pp06dOHjwoHSj0ul0KBQKm9ynq89x3bp1JCUlWS1XKpV4eXkxb948m21GjBjBmTNnbPrtQeWNMiAggNDQUAICAlCpVFRUVEjGe4GBgQQHBxMREcGWLVvIzMwkMjKSoqIizp8/T2BgIJmZmRiNRu68807MZjMrV66022LlWjw9Pdm7dy+bNm2qlQdTXFwcwcHBLFq0qFaeSjqdDrVazY8//lhr762/Ix4eHowYMaJGkcWbgcVLyc3NrUlEaW42kyZNYsmSJdV63jUEBoPBquWUXq/Hzc2NwsLCak1lq6rglWk8/l7x3FuAdu3asXjxYoqLixk2bBi9e/fm1KlTrFu3jvT0dLy80mQVrQAAIABJREFUvHj44Ydp3bo1X3/9NWPHjiUmJoalS5eyZ88eqTHwhQsXJB8prVZLWFiYJHbGjBmDWq3m2LFjditIHBwcCA8Pt5mOGzlyJO7u7jbrFxcXc+LECatlY8aMQaPRcPz4cbsXBp1OR0RERI3Lsn19fbnzzjtZuHChTQQtMjKS3377TWrKGhkZyR133EFZWRnHjh2rcp9nz561KzxMJpPNuBISEoiLi6OwsNDG5E+n0zF69Gj27t2Lk5MTfn5+eHl5ERISgk6nk8wu+/bti5eXFwcOHKBz586o1WrJlb2wsFCaRgwMDASokUgZOnQop06dksRZTenZsye+vr4kJSXZbdJcFVFRUURERPD999/bfOa15ZFHHqm3cu+mMpVzLf7+/jzwwAOEhYVx9OjRm3YcV1dXHnjgAbuvDRkyhKCgIPbs2WO3ndOtxsGDBzly5EidLDnqA6VSKeWmDRo0CJPJxG+//dboAk+m9shCqpmxZs0atmzZQu/evRkwYACpqan88ssvnDp1Cqh8qunUqRM//fQTW7dupX///qxbt44tW7ZQUlJCTEwMmZmZbNiwgWPHjuHp6cmAAQMwGAxSH7l+/fqxceNGuyLKz8+PmJgYu73jevXqxZ49eyQh0apVK7y9ve32tLMcw56I8vX1rbY5sj1cXV3p0qWLdOwOHTpYvX7gwAHJlb1Fixb4+fnZ7ZsHlUm9SUlJNlEYd3d3unfvbnfqo1u3bhw5coTz589bLddoNLRu3ZrevXuTnZ2No6MjarUanU6Hg4MDZWVl+Pr60q5dO/R6PRcvXqSiooLy8nJKSkqkKb8rV65gNpuJi4sjNze3RiJlwIABqNVqUlJSbMZVHbfddhu33347QK06z0dHR9OqVSsyMjLsNiuuLX369OHXX3+9pZ/A3d3dad26NZs3b75px/D09KRnz55VTgGXlJRgNBo5ffp0vST1N3WcnZ3rpQq4riiVSnr06AFUNmwXRREHB4dqPftkmiby1F4zY8WKFUDlU2RGRgZr1qyRIgYeHh506NCBkpISVq9eTffu3QH44YcfMBqNdOzYETc3N9avX8/hw4fx9vamb9++9O3bl507dyIIAnFxcQD8+OOPNuFlPz8/BgwYQHR0tE0ZblxcHElJSaxcuVKqSIuOjsbPz88qfH01q1atsjtt2L9/f3r06MHevXtp06YNer0es9mM2WxGqVRiMplQKBRWRqSWpPiuXbsiCALdunXj0KFDdOnShcOHD0tTesHBwYSFhXHx4kW7YrBbt24kJCTw5ptvWkVwPD096dixI3q93qY/XZcuXdDpdGzdulVqB+Pg4ICrqytubm706dOHrKwsqQG0wWCQpj8zMzNp3749Op2OHTt2SIL40KFDeHh4WB0nJiaGsLAw9u7de10jwa5duxIcHMzatWtrNP13Nb1798ZoNNZ4ShUqRfMdd9xBdnY2a9asqdXxrsXy+e3evZuVK1fW2W6gRYsWtGrVqk77uJkUFhZW2aS5LgQEBBAQEICzszMODg5WlidXYykKaUjy8vJITU2lY8eOdTZarS1Dhw5l165dTcrNvaKiguzsbFxdXWnTpg1Hjx61SQ+oT1xdXSkrK2v0qNytgiykmiERERGoVCpWrFghiSh3d3d69uzJ0KFDOXToEGq1mokTJ3L8+HFEUSQ8PJxHH32U0tJSCgoK8PT0pF+/fgwcOJADBw7w9ddfo1QqmThxIidPnrRrHdC+fXt69+5tt3XG+PHjeeaZZyTB4uvri7e3d63PrV27dvTp04f9+/ezdOlSHnvsMWJjY8nNzcXJyUkSTjqdDrPZTElJCUqlEmdnZ7KysmjRogUJCQmYTCZat27NmDFjWLhwIU5OTrRq1YoBAwbQo0cPTp48SVhYGGazWXIM1mg0jB8/nvT0dJsLTFhYGHFxcXatA8aMGUN2drYkCnU6HS1atKB169YEBwcTGhrKoUOHEEWRoqIifHx8OHjwINnZ2fj6+uLo6EhycjLLly+32u/VIf6wsDB69erFmjVrbPynVCoV/v7+aDQaFAoFZrOZsWPHsmDBArsJ8VURFhaGUqnE0dGRLVu22DS9ro6BAwfi5+dXZ4dsizt/v3796s12IDg4mOjo6CbXoPZm4uvrS6dOnYiKiiItLc2qp2ZTID09nc2bN3Pfffc1uJA6c+ZMk2vt4+fnR2JiIufOneOuu+7Cy8uL0tJSBEGocRPumuDs7ExJSQkeHh7k5eXJQqqekIVUE0Wn09l0/raEo6dNm2ZVju7s7Mztt9/OoEGDOH78OEuXLsXb25usrCzmzp2Lt7c3U6dOpaysjK+//pqTJ09y7733Eh8fz4EDB/j0008RBAEfHx+ys7N5/fXXbW5gzs7O6PV6jh8/zpdffmn1mo+PDzk5OZL4cnNz44EHHqBdu3ZSHta162dnZ9uINcsxTp48yZdffomzszNHjx4lNjaWpKQkevfuzdmzZ/Hy8kIURbRaLVqtFpPJhNFoxM3NDa1Wi5OTEyUlJTz++ONcvHiRhIQEysrK0Gg0qNVqMjMz0ev1PP300xgMBi5fvoyHh4dk/Lho0SLMZrOVEHR1dUWtVuPl5SVFxkRRRBAEjEYj3333HUVFRbi5uREcHExAQAA+Pj60b9+epKQkNBoN58+fJzk5mb59+5Kfn4/JZOL+++8nPDz8unlIzzzzDG+88YbdpG83NzfGjh2Lj4+PdJy8vDxGjRrFBx98QH5+PgaDgfLycjQaDXq9XsoZskT4VCoVo0aNwtHREaDGF28PDw+USiVlZWWsXbvW7jRuTVEqlQQEBDBlyhQmTJhww/u5lpSUFNRqtZWHWlPAMr1b1ybM9hg8eDA5OTm8++679b7vmuDl5WUTCXVxccFgMEgPHJcuXbJ5eGgIFi9ebDdtoaERRZHc3Fz0ej2CINCvXz9++OEH1qxZg7OzM4GBgVLBUH1Fz8LDwzl27JiULypTP8hCqonSsWNHRo4cKUVfLPPmgiAgCAJz586luLgYpVLJPffcw+23387+/fvZsmUL77zzDleuXOGZZ57BwcGBt99+m4qKCt5++20yMzMlQbFv3z4+++wzBEHAy8uL119/nbFjx9odz7Bhwxg4cCB79+61Wu7k5MS7777L2LFjJWE3YcIE2rVrx+rVq6WpSAuW3muPPvqolVjTaDTcdddd9O/fn0OHDuHo6EhMTIwkJl1dXTly5AhQ2d8uNDSUiIgISUgpFAqUSqWVMPjqq6+qjGhERkYybtw4HBwc8PLykm5oJpOJZ555RhJKFn8Xy98vv/wyxcXFeHh4UFZWhqOjI2fOnOGRRx6RbhJarRaFQiF9Vpak8WPHjnHp0iUSExOlz1WhUPDzzz+zdetWdDqdJAo1Go30tHi95s8KhULKvRJFkbfeeouysjLGjh1LRUUF8fHxXLhwgaSkJNq0acODDz5ISUkJKpUKNzc3MjMzCQkJYcqUKde1UrBgGdO0adNwcnLi888/l3qD3Sienp68/PLLUoJ9faBWq+nfvz9eXl4sWLCg3vZbH3Tt2pXY2Fg++OCDet2vVquVIq2NgbOzMzNmzGDq1KlWv7+EhARSU1Ol3EQ/Pz/GjRt30z26rmXq1KnMmzeP3NzcRi1AKC8vZ968edxzzz0ALF26VHqAufPOO8nNzSU1NZUOHTpU20C7NtT1NypjH1lINSCWGyv8v1FhVUmPlgoaf39/XnnlFRQKBSaTSeoLJ4oiL7zwgtRWZdOmTRw/fpxp06Zx6dIlpkyZgiAIzJ8/H1EUmTp1KkVFRQiCwKOPPkrPnj3Zvn07UJlD8sYbb1QZ5rWM+7fffuPbb7+Vljs4ODB//nyryJJlaslkMtlcpJycnPj4449tIlEKhYJx48Zx5swZJk2aBICjoyMqlYp27doBlRfn8vJyioqKMJvNuLi4cODAAU6dOkVISAitWrWyMc6sjuPHjzN16tQqX09ISGDv3r1kZGRI52GpsFIqlXh7e3P69GkEQaB169bMnj2bxx57jJYtW1JeXo5SqcTBwYHy8nL8/f0pLCzkhRdeICcnB19fXyoqKigoKECv1xMVFUV8fDwFBQWcPn2a9evX88wzzzB79mwEQZCMVRUKhV3jRKVSKTkcl5WVMX78eObPn89XX30FVOa79erVi0mTJknJ52vWrGH8+PHk5+fz3nvv8a9//atW0x3vvvuuJO7feuutWlX22cNyboWFhfXqoD5ixAiKi4tr7GZ/K/DEE0/wxx9/sHv37gY/tpOTE2+++abdNjwLFy60+jsjI6PBRRTA9OnTefvtt/nggw8a1Z7D0dGRuXPnMmXKFCZPnmx1vUxOTqZFixb06dMHpVJZb0JK5uYgC6kGIjQ0lKFDh9KiRQtMJhMZGRm4ubmhUqlwdXXFaDTi5OSESqWSRIharUapVGI2m7l8+TIODg6oVCqefvppysvLmTNnDs888wxHjx5l48aNxMbGkpqayquvviodVxAEHn/8cSla9MILL9C2bVt+/PFHfvzxR6Kiopg+fTpFRUVMnjzZ7tiffPJJunbtapWc7e7uzvvvv4/ZbLbqn2dxXL/Wy8mCyWSyiXq9+OKLREREWFUMGQwGMjIyOH36NGFhYeTn5+Pm5iZVsW3dulVaNy0tjbS0tJp9EDXEYlzZt29fDAYDf/zxB1DpRFxcXMz27dtRq9Wo1Wqee+45ysvLOXXqFKtWrcLNzY3IyEhKS0vx8PCgsLAQZ2dnJk6cyMMPP8z58+f59ttv7UZ/WrZsyZtvvkl5eTnTpk0jPDwcg8FAdnY248ePx8PDA1EUpURRhUKBj48PgiBgMpmq7LmnUqk4ePCgVaXi1Y7277//fq3en6q+KzdCXFwc//znP8nOzm6UNjQyMg2Np6cn06ZNY8qUKTz55JP4+flZPSSdPHlSKihxc3OjU6dONp52Mk0HWUg1EGfPnmXNmjVMnjwZg8HA559/zuOPP85XX33FzJkzmT9/PiNHjqR169ao1Wqb6JWTk5MUlbK0VPn++++pqKjAaDQSHx9PVFSU5CSt1WqldjEWXn/9dQIDA/niiy+kaBRAZmYmL774YrXj/+6772zK/isqKqxyWd5//327PlJQmfz61ltvVRlKnz9/vo2HjVKpJCIiAkEQ6NChA+fOncPT01Nanp+fX+uKtNqSmJhoM2aDwWAVmbvzzjtJTExk1apV9O7dm/z8fH799Vfat2+Pq6urFJkaNWoUTk5OGAwGq3327NkTs9nMzp07SU1NlaJylhysq7n//vsJDg7mjz/+YNeuXQQHBzNgwACbp/1rqcrqoaa4ubnxj3/8g8LCQnQ6HVqtFo1GQ3FxMatXr77hz2Hw4MH4+/vXa06Uhaeffprk5GS7eXo1oW3btgwfPpznn3++nkd283j55ZdZtmxZrWwr6gsfHx+ee+65KlsuNSUsbVkaG1EU+fjjjxEEgenTp/Ppp5/a5Mzl5+fLIqqJc10hJQiCDkgEtH+tv0IUxVcEQfAAlgGhwFngH6Io5v21zQzgn4AJeFoURduOt39D0tPTefnll4HKH5CLiwuPPfYY77//PvHx8fj5+QFIN9qrzQgtlWWWJMFly5aRl5fHl19+KV0Qtm3bhtlsxt3dnTfeeANBEJg0aZIUjVIqlXzwwQckJycjiiJxcXGMGTOGvLy8Ki8q06ZNY9u2bezbt0+6+QcFBfHSSy8BSNt9+OGHuLq68tprr9kkMkZEREjJ7tf2j3vttdf47rvvOHHihF2RZRl7RkYGRqORK1eu0Lt3b0pKSupcIVYTrh6T0WgkMzOT1atXAzB8+HApDyUhIYGKigrJdLNXr14YjUbOnDlDmzZtyM/P5/vvv5eE0dXJo7t27bI65tWfxbXviSiKbNy4kcOHD2M2m0lLS5OS42t6HrUhIiKCVq1asWHDBhYuXGhX3Fneg3bt2qFQKEhJScHDw4Nhw4bh5OREbm4uy5Ytk9aPi4ujZcuWtGrVipSUlBqN/0awWGTcyLn36NGDkSNHkpub2yRuuDVhzpw5eHp6NkreT3h4OA888ACzZ89uFu+XZYzjx4/n0KFD7Ny5s9HGYvmOzps3r0lZMsjUnJpEpMqBAaIoFgmCoAZ2CILwM5AAbBZF8S1BEKYD04FpgiC0AUYAbYEWwK+CILQWRbFp1Zs2ApYpGQtffPEFjz76KOXl5YSHh0tGcZak4dOnT/O///0PrVbLkCFDEASBZcuWIYoiBQUFiKJo94dnSY6eNm2aZGY4e/ZsvL29KSsrw2QyMXDgQIYNG0ZGRkaVbVJmzJhBREQEmzdvlgRNmzZtGDdunDS1aMHR0ZFXX32VtLQ0qwtpbGwsjzzyCEVFRbz11ltW1TKvvvoqAQEBVFRU2OTnGAwGzp49y9q1a3nuuedQKBSoVCopqfrcuXOEh4ej0WgarALl0qVL/Pzzz5Jw2LBhA7fffjtbt27FbDZLn69CoSA/P5/AwEB0Oh3p6ekYDIYqk39btmyJ2WyukRni+vXrrd6vqr4D9UVaWhoXLlyo0XGuNgnNz89n5cqVqNVqHnzwQQBGjx6NTqdj3bp13HbbbezatYvCwkISEhJIS0sjOTmZbt26sXXrVh566CG+++47SRT06tULb29vDh8+XCMz0qeffprff/+dQ4cO3dB5q1Qqzp49a1OhWl8MHDgQLy+v60YSa8orr7zCZ599ZtW3saHo2LEjXbt2ZcGCBfVaqn+jxMfHo9FoWLdunc1rs2fPZu7cudL1TKvVolI1/MRMXl4e77//PrNmzZKuo7IVQfPlut8gsfJKZimjUf/1nwjcA/T7a/n/gK3AtL+WLxVFsRxIFQThFHAb0HiSv4kyaNAgfvjhByoqKliyZAkajYZBgwbRtm1bFAoFBoOB3NxctFotFRUViKJ4XZM2f39/HnzwQd555x3JyfuFF15gxYoVVFRUkJGRwZAhQ+jfvz9nz55l+fLldsuvp06dSnh4ON9++63URqVLly4kJCRQXl7OZ599JrmIz5gxg3fffZe0tDQrQdSjRw+GDRtGYWEhX375pc30j7e3N59++mmVrtvl5eVSEvOZM2cIDg5GEAQpMbm0tLRBy5gt+VEWioqK2L59uyRqrx07VOaElZaW2k3G7ty5Mw4ODly4cAGVSkWbNm04cuQIOp2OuLg4qzwwCzWtqqsvqhOA13K10DKbzRQUFCAIgtQDcvPmzSgUCnJyckhOTiY9PZ2cnByuXLmCk5MTZWVlUrR0y5YtVu+pWq3mxIkTeHp6cs8991BaWoparZZuhHl5eej1elatWsWkSZP4/fff8fT0xN/fv9ZCu0+fPvj6+rJkyRLJXLa+seRD1pfo8fLy4vLlyw3+/YiLiyMoKIh169bdtPeqthw4cKDKIp5vv/3W6hq1YsWKBn/PoPL3kZmZyeLFixv0uGPHjuWHH35ocLF9q1MjKS4IghLYD0QA/xVFcbcgCL6iKF4EEEXxoiAIPn+tHgBcPVeR8dcymWtITk6W3KMtjtiFhYXSReDaEHlN2hoUFBSwbds2zp49y4QJExAEgaioKD755BOKiooYOnQoffr04ezZs/z888923asnTJjAH3/8wY4dOzh06BDFxcV0796dO+64g/LyclauXElmZqaU17JlyxbJ+NNC3759GThwIAUFBaxatcpGSIwfP55FixaRkpJS7ZOY5Sbu5OQEQGlpKTqdjoyMDDQaDS4uLpSWlhIcHCxVkTWkwV9VHkAWJ3ZLFaNFLHbo0EEy53R2diYnJ0dyaBdFkW7dugFw+vTpBjuHm4koily8eBFA+hcqv/tlZWWUlZVx5swZHBwcMJvNFBYWct9990niCypd01NTU4mIiCAqKgp3d3cuXLjAb7/9hrOzM/feey/ffPMNDzzwAP/85z9JSkriyJEjeHh42LVSUCqVxMXF8fvvv9OvXz927NghRShiYmK47bbbMBqN17WdsIder6dVq1bVtsjp168fRqORHTt21Hr/1yIIAmPGjOGbb75p0GjQoEGDOH36NO7u7uh0uibVnLply5aoVCq7eXvX/q6u/k42NGazmdTUVB599FG++eabBpmS3bNnT5OIGt5q1KjXniiKJlEUOwKBwG2CILSrZnV7d3ubb4ggCOMFQdgnCMLftq7TXsPciooKysrKMBqNCIKAVqulbdu20lNUbGys3RJ4C8XFxSQlJUlGbhkZGaxcuZLy8nLi4+Pp06cPaWlpbNy40eaiIggCw4cP5/z58+zevZtdu3ZRXFxMXFwcAwcOxGQysW7dOs6dO8d9991Hly5dyMjIYPfu3VYXgV69enH77bdTVFTE+vXrJf+nq+nWrRsHDhyoVkTpdDrJY8XNzQ1BEEhNTcVoNBIYGEhQUBCtWrWiQ4cO+Pr6UlhY2GTcq4uLi0lPT5fM9CwXr6KiIlq2bIlSqeTMmTOkpaWh0Wjw8fFBqVRy5coVLl++LAnrW5X8/HzpPSkrK5OiGaIoSpFUCyUlJVy4cIELFy5w6NAhtm/fzv79+zlz5gypqakoFAo6duzI77//TmxsLH/++SfdunUjOjpaEuBQ+X0KDg62iuxenR/Yu3dvBg4ciFar5cqVK4SHh0vC9nr4+vrSsmVLjEbjdd3kLY76dbWMsNClSxcOHDjQYG7d/fv3RxAESkpKOH78eJNKhO7cuTM+Pj7NJuIiimKDilBfX1/pgTM6OprQ0NAGO/atTK0mh0VRzBcEYStwJ5AlCIL/X9Eof8By9csAgq7aLBCw+aaIovgZ8BmAIAhNry17A9OmTRtcXV3Jy8vjzJkztGrVCk9PT7p3705gYKC0XnR0NAUFBaSmplZ74bSIHgvdu3eXckx+//13qaebBZVKRc+ePdHr9axcuVISRp06dZI6k2/atIl9+/YREhJC79692bp1q908hLi4OEpKSti0aZNNdEgQBPr06UNiYuJ1L/w6nY7BgwdbJTgXFxeTm5uLp6cnRqMRlUqFXq+XIlVNJdHVMu2o0WikaAcgfbaCIFhFIbVaLYIgkJeXZ9Xj7++G0Wi0cUe3RPPsCfKKigr27NmDh4cHq1atIjQ0lIqKChwcHNBoNFb5L4Ig4OzsTGxsrOTLo9FopO9W9+7dMRgM7N69m8zMTLRaLc7OztWO18/PDxcXF5ycnCguLqa0tNTmt3U17du3Jy8vr1kL5V69erF8+fImFYWCyvc2MDCQEydOcPTo0cYeTo3ZtGmT9P+xsbGcOnWqVq2daoOjo6P0IK7VajEajbi7u+Pm5ia7ndeBmlTteQOGv0SUAzAQeBv4CXgUeOuvf1f/tclPwBJBEP5DZbJ5K2CPzY5lrAgPDycgIIDffvsNs9lMWFgYfn5+3H333SQnJ5OZmYmXlxcZGRm0aNGCc+fO1eoJtGPHjixbtszuVJRGo6FDhw6MHj2acePGSctbt27NAw88gMFgYO3ataSmptK2bVtcXFxISkqyO78fERFBTk4O+/fv588//7R6TaFQ0KZNG7p06cJ//vOfakPZOp2OyMhIqaLFYDCgVCpp0aIFly9fpqioSMqP8vDwwNPTk6CgICoqKrh8+TJZWVkEBwdz7ty5RnUvrqiosIk8XNtw2BJt8fDwIDg4WDoHmetTXl4umdICUkNpe02AS0tLycjIICEhQRJS7dq1Izk5mZYtW5KVlUVycjJHjx61iZQKgkBkZKRNFNnb2xt/f3/S09NrlAR/xx13sH37dpvfxo2gUCiIiori6NGjDfYdb9WqFRkZGY2SV1Qd4eHhxMTEsGvXrmqFbEMTGhoqNSiHyqlfDw8Pu753rVq1IiYmhszMzJsmpK5uKG55yA0JCSEgIEAWUnWgJlN7/sBvgiCkAHuBTaIorqVSQMULgnASiP/rb0RRPAwsB44AG4DJcsXe9VmzZg2ffvopx48fx2g0YjAYpCdlo9HI7t27OXfuHAcPHuTgwYO1rtSaP39+lSIqOjqaiRMnWiV9t2jRgokTJ6JUKlm2bBn79++nU6dOPPHEE8TExNhtt+Hv78/dd9/Njh07bG4UKpWKsLAwnn76aebNm3fdC7+3tzfjx4+3qeiz9CB0c3OjrKyMwsJCKioqyM3NJTs7m6ysLDp27Iher6dPnz6NUpFzo1y+fJkzZ87QunXrxh7KLUtBQYFVpdzXX39NRUUFd911F1u2bCE/Px9XV1eb7ZRKJffdd5/VMk9PT06cOMHGjRvtTtPfTJRKJcHBwUyePJmPPvqoQSKxlt/3Tz/9ZDe3sjF5+OGH2bFjR5MSUVA5Derr6yv9HRAQQJ8+feyue/fdd7Nq1aoGj/SlpaXVKl/P0dERNze3mzii5kdNqvZSgE52ll8Cbq9im9eB1+s8ur8pWq0WnU4HVD4JazQaBg4cyNq1axk6dCilpaVs2bKlzkmDarWadu3a8cQTT5Cdnc3s2bOByqeml156CYPBwPvvv096ero0pmPHjvHpp5/a7Euv10utF669EFgu+jNmzLhu1SFUii4nJyeMRiNpaWm0bNnSqqVOWloarVu3pqysjCNHjtidPhw0aBCLFy9u1GjUjVBQUGDjKyVz8/nwww+rfd1oNPLmm29aLbv//vvZsmWLTYTRHs7OzvUaxXF1deX555+/aZGLa3F2duaJJ57gzTfflCxVmhLFxcXodDpUKpXVVHpjs2zZMqvo5rFjx6oU3Vd3GmjKdOjQgdDQUJYuXdrYQ2ky1CjZXKZh+eWXX/juu++sys4VCgV33303giDwyy+/1EvlRZcuXZgwYQLnz5+XnM1VKhXvvfceSqWSV199lfT0dJRKJQkJCdx77712pxNVKhVvvfUWOp3ORrhYetG9+OKLXLlyhSlTplQ7JkEQaN++PVOnTsVoNEqmpJYInE6n4/z586xcuRKFQkFkZKTUrNgy9y+KIhs2bGh2Ikqm6WKvYnbBggU1ElEAs2bNwtPTU+qXWV3BSE1QKpUUFRUxY8bqL9ixAAAgAElEQVSMOu2nJgiCwJtvvsns2bObpIiCShFy5513Sr05mwJKpZJJkybdchHmXbt2sWzZMilpXQaEpnCzkZPN7RMUFMR9991HQUEBxcXFkjD48ccfb0qFjlKp5LPPPkMURSZPniw9SU2aNInOnTuzefNmvvvuO6ttBEHg888/Z+LEiXafBOPi4nj88cfJyspi5syZ1x1D//79GTVqFOfOnWPevHk8++yzBAYGMmHCBJ599lnKysrQaDScPHmSiooKfH198fLywmg0cvr0aZs2NjIydUWlUtGhQ4dqLQ1qw8MPP0xmZqZVknFtCA0N5Z///CezZs2ql/FcD0EQ+PDDD2vd2LohmT17NgsXLqy3Ssj64LXXXmPBggVV+uQ1Z9q1a8fAgQNr3aOzmbBfFMUutdlAjkg1Yc6dO8c333xjs3zYsGE35HFTE0RRZPz48ZKIevHFF+ncuTPLly+3EVEajYbPPvuMcePG2RVRQ4YM4fHHH+fkyZM1ElH3338/o0aNIiUlhVdffRWlUklgYKBVNMDJyYnff/8dBwcHAgICUKvVFBYWUlJSQlhYGIMHD67jOyAjY43RaCQpKYno6Oh62d+3335baxF1//332+RoNQRarZb33nuPp59+ukmKqPfffx+dTsfs2bOblIh65513mDdv3i0pomRsaT6ZuH9T8vPzWbRoEYIgMHToUKCyTcjNaCdgMpl44oknrBJXLRGnvXv3Wq3r6urKq6++yuTJk6udQtuzZw9ff/11jY4vCAKJiYlS5ZVl2bXrdOvWDZVKhUKhkBzfTSYT6enpREZGolAo7NoyyMjcKKIoNnhC+dVcbVCalpbGa6+9hlarZdasWVLfy/rG1dWVadOm8cILLzS5aXKFQsE777zDtGnTmmRrFUEQmtx7Vp8cPny4UX8PTQ05ItUMsFTx/fzzz/z888839cJxdTXgiy++yKpVqzhw4ICVuAoICGDWrFmo1eoqqwfvv/9+1Go1ixYtqlGLkdGjR1NYWMiKFSvsRre8vb2l/9doNAAkJiayc+dOSkpKMJvNuLq6cuLECTZv3lzj85WRqSn1cWOMiorCw8Oj1tuZTCar/ooGg4Hy8nL+/e9/13lMVWHp+9lUG+lqNJomO7a5c+c2GXPgmuLl5WXTVL4qLN0YLAiCwCuvvPK3zZuShVQzwtJSo6H43//+x8mTJ62EUOvWrbnnnnv48MMPeeutt+xuN2rUKHr27AlQo+TUMWPG0LVrV0RRtLu+2WzGwcEBQRBYvnw569atY8WKFTg7O6PRaDh48CAmk4n8/HyUSiU9e/bkoYceqlFLHRmZhiI6Oprc3Nx6rbS7GQ7eERERPP/884waNapJ5sDodDqee+453nnnnSYb9RkzZgyOjo6NPYxakZ+fb5O+8eSTT17XlBYqhdWXX37ZJKd/r4eXlxdjx46t0z7kqT2ZKrl2fr99+/a0bduWX3/9lfT0dLvbPPTQQ5w/f57Dhw/XyKH74YcfpmPHjmzfvt2q1YSXlxejR48GKn+klihUTk4ORqMRo9FIdHQ0YWFhlJSUoFKp8PX1RRRFCgoKpLY1gwcPZtu2bU222kjm78OFCxcoLi5uUuX59sjOzmbDhg0YDIYml+Pj4uLCyJEjCQwMbHJeVlezadMmhg8fztq1a5uNua7RaOTixYsoFAoeeeQRFi5cyObNm2v88N6UP4/qUKvV+Pv712kfspCSqTHe3t6SCaE97r33Xi5dukRKSkqNLh4JCQl07dqVHTt2kJiYKPVY8/f3Z8iQIbRp04by8nLUajWhoaGSNYJKpUKlUtGiRQs0Gg2iKKJSqXBwcMBoNJKVlcWZM2eAyh93U79xyfw9aC793woKCjh06FBjD8MuBoOBo0eP2m0X1JQIDw8nPT29SeZvXQ9RFCVj0+bUaudGKSgoYOPGjXXahyykZGpMVReGgQMHApVl4rt27arx1EW/fv3Yt28fmzdv5tKlS9JyLy8voqOjSUxMlJrGWqr32rVrh4ODAwaDAZVKJbWKsTSnNZvNVu7GTfWGICMjUztcXV2Jiopi27ZtjT2Uaunbty+CILBz585mJ6TUajXdunWrldP5rUBd00DkHCmZGnPq1CmbhrIADzzwAK1bt2bt2rU1FlGdOnVCqVSyfv16GxHl5eXFrl27WLNmDWq1GlEUcXZ2RhRFnJycUCgUnD17lpycHMn53YLBYKhRzzMZGZnmhZeXF/Hx8Y09jCoRBIEOHToQFhbG2rVrm52I0mq1tG3bltDQ0MYeSoOi0WgICgqq0z5kISVTZ86ePcuCBQtqPJceEhLCxIkTuXjxolUiu7u7O506dcLLy4uVK1dK/ZxMJpM0bWdJuM/IyCA1NZXc3FwKCwulPns5OTk35RxlZGQaFi8vr2aTsK1QKAgJCeH222/n66+/bpZJ166urtx7770sWrSosYfSoOTl5fHDDz8gCAKBgYE3tA95ak+mzlzbg6w6lEolM2fOJC8vj3//+9/SU5uTkxN33XUXoiiyZMkS3N3dpSlDCxUVFZSXl1NSUkJsbCz79u1j3759TbZyR0ZG5sYZNmwYKSkp/PnnnygUCoxGY5V5Zs7OzpSWljaKgFEoFPj7+zNy5EjeeOONBj9+fWE2m5tNHt/NQKlUMnr06Cqr0atDjkjJNCharZaysjKmT58uiSi1Ws3o0aPp0aMHBoMBhUKBu7s7Xbt2lbYTRREHBwdcXFw4dOgQV65cwc3NDVdX18Y6FRkZmZuIQqFAEAR69OjBsGHDSEtL46OPPrJZT61WM2rUKHx9fRt8jIIgEBAQwGOPPdasRRTApUuX+O9//9vYw2g0jEbjDYkokHvtyTQgTk5OvPPOO0yePNlq+QsvvEBYWBg//PADmzZtokOHDkyaNAlRFKmoqEClUnH8+HFUKhWCIJCens758+elfnsajYbc3NxGOisZGZnGZMqUKXzxxReNEk2Jjo5myJAhvPvuuw1+7PrGx8eHJ554gtmzZzf2UOqNG3SYl3vtyTRNfHx8mDt3ro2IsvD555+zadMm+vTpw6RJk8jIyGD69OlV7s/d3R0HBwcKCgpkESUjc4vh4uKCSlWzzJN58+Y1iojq3r07vXr1uiVE1K2IQqHg9ddfbxBjZjlHSuamExERwejRo5k6darNa6+++irffvstp0+fBiqfIOz11ystLUWv15Ofn8+FCxcQRZGAgABCQkLIy8vj8OHDDXIuMjIyNx9L9a+joyMuLi4YDAby8vJQq9VNqhruVuue0BRmqOoDnU7HjBkzmDVrVoOckyykZG46qampvPPOOzaJoHPmzMHT0xOTyYQoigwZMoT4+HiOHDnC559/jlqtlta1VPdptVpOnjxJXl4eAEFBQQQHBxMXF0deXh7Hjx9vuBOTkZGpVwIDA7lw4YLU27OkpARRFFEqlZjNZmkqv7F77MXHx+Pi4sI333zTqOOoL1q2bMmQIUNuau/GhiAoKIihQ4cyf/583n777QYrPpBzpGQahVmzZuHv789HH33EqVOnGDZsGN27d+fo0aOsXLmSvLw8XFxcmDNnDiqVir179+Lu7s7ly5dJS0vj4MGD5Ofno1arJcFlMpma1NOqjIxM7VCr1VaWKA4ODkDlg5QgCNK/jX3fcnZ2RqFQ1GvfxMZEo9Gg1+utPP2aI2q1Gr1eX9e2PLXOkZIjUjKNwvLlyxEEgdOnT3PffffRuXNnDhw4wObNm6Vokz2cnJwICQlBr9eTlJRERkaG1YVXRkam+aLVajEajXh4eFBYWChFq41GozSN1tgiCqCoqKjBjuXn50dsbCzr16+/KfuPjIwkIiKCdevW3ZT9NxTh4eFERkbetPepOuRkc5lG4fjx4xw7dowhQ4aQk5PD6tWr2bp1K1lZWUDlE1LLli1RKBQYDAaUSiVQmZPg4OBAixYtaN26NT4+Po15GjIyMnVAEATJdNPJyQmDwYAoipInlKVBOTQNAdXQBAUF0aZNGzIzM+ndu7e0PCEhQbom1hUXFxcCAgLqZV+NhUUMWnJtGxo5IiXTqBQWFnLw4EGbKJRer6dXr15A5ZSdTqdDEAQKCgqoqKhAFEWKi4ttGhIrFArat29PcnJyg52DjIzMjSGKopQPZTabpan58vJyzGYzOp0OjUaD0WiktLRUWvdWJywsDH9/f9RqNdnZ2Zw+fZqOHTvSv39/Ll68SEFBQZ2FZZs2bSgtLZWKd5ozFRUVZGRkNFqOrCykZBqVzZs32yxzc3OjTZs20sUUKkP+lgvpyZMn0Wg05OXlUV5ejqOjI3q9nqysLARBkE06ZWSaEZbWUlcnBguCgEKhQKVSSf02b7UKOXsEBQXh4OBAp06dcHZ25uDBg/z5558AHDhwgGHDhlFYWMivv/5a52O5uLggCAKpqamcP3++1ttrNBq8vLy4cOFCtetdXYmt0Whq3EqsNpSUlDRqWx5ZSMk0OQIDA+nVqxf//e9/ad++PVD5Y7x8+TLe3t6UlJSg1Wrx8/OjtLSU8vJyAgICKCgooLS0lMTExEY+AxkZmdqi1WqpqKhAqVRiNBpRq9WUlJRQWFjY2ENrMDp27IhWqwVg165dVrYu5eXlfP/99/V2rF27dt3Qdg4ODpK4DQoKuq6QUiqVkpBydHSsdyHl5eVFmzZtEEWR9PT0et13TZGFlEyTo6Kigvz8fDQajdVyS3Sqb9++KBQKjh8/TmlpqdS42M/Pj9TU1MYYsoyMTB2xCCZHR0eKi4sRRbHZTznVlrKyMv74448mVz3n4OAgXY/9/PxwdHQkJSXlulNpgiBYpV9cXU2nUqlsUjNqi4uLC3FxcVy+fJk//vijTvuqC7KQkmlynDhxgvT0dGJiYoD/TzItLS21CvFnZWVJrualpaWkpqaiVCqbZed1GRmZSiyC6u+SD3U1lmR7C4IgoFQqEUURk8lUL+LjRujatSsREREAHDp0iAMHDuDt7c19993H/Pnzq9zOErmyV1nt6+t7Q1OKVzN8+HBSUlLYv39/nfZTV2QhJdNkMZvNlJaWkp2dDVSWt1ouMqIo4uzsjKOjIyUlJUDl/HtkZCSHDh1qtDHLyMjUPwqFAqVSectbnWzdutXqbx8fHyIiIjAYDCQlJdGjRw+2bdvW4ONKTEyUUiaCgoJo27YtKSkp1YoooFrj1LqKKKi8RzSFqKVsyCnTZNHr9UyZMoWCggKys7Px8PCQnsby8/PZuXOnPJUnIyNzSxIUFERISAhFRUUcPHiwsYdDTEwMly9f5ty5czXexnLNLikpQaVSXTc/KiAggNLS0roaatYVuWmxzK2FIAgEBgaSmpqKKIqcPHmS7du389NPP0kiytfXl86dOzfySGVkZGTqh/DwcMxmMzt27Gh0EdWrVy/0ej3Jycm1ElFQWUBgsa+oSZL5+fPnUalU+Pr6Xnfdp556ivDw8FqNx0JMTAyxsbE3tK095Kk9mSaNyWRi0aJF6HQ6fHx8MJvNeHp60r59eyoqKigvL+fSpUucPHmysYcqIyMjUy9Y2uD4+/vj5eXVqOkKO3fupFOnTpw9e1bKSa0pmZmZ6PV63N3dq+1YcTU5OTlApUGrl5cXaWlpdtdbsGABRqMRNzc3TCYTJSUlBAQE0KVLF1auXIkgCAwYMMDGYqdbt25cuXKFEydO1OpcqkMWUjJNmqsN+9RqNQqFAkdHR6miRxAE/Pz8MJlM0g9QRkZGpjlz9uxZKfensSv4TCYThw4duqH8NBcXF9zd3WtleWA575KSkmrzqCz5VxZzUotJs2VaUBRFu5V8Bw8exGw212sxgzy1J9OkUSgUdO7cGVdXV86fP48gCOh0OqDyqS09PZ1t27Zx5MiRRh6pjIyMTP1gNBrx9fUlMDCw2oTthuJqc+Sa4uvriyAIXLx48YbEoKXH4vW4OuE8Pz/fqoKvtLSU9u3bo1D8v9QpLy+3EoU6nY6oqKhaj+9qZCEl06RRKBS4urri4uJCeXk5giBw6dIl8vPzKSwspLy8nPLy8r+VaZ+MjMytSUhIiPSg2Jx9tNRqNcXFxej1epydnRus2tJkMlFWViZZNQBcvHix2vfRYDBIleE3ijy1J9OkEUWRK1euWJlzlpaWcubMGYqKiigtLW0UXxUZGRmZ+ubqfoL5+flWkZTmhL+/PwaDAaPR2OARNcsUn4Xr5XWZTKY6Vwk2z09J5m/F5cuXEUURlUqFIAi4uLiQm5tLXl4eJSUlN6V3k4yMjExDk52dLQmP0tJSK0HQXFAqlej1ejQajTRz0JAYjUYyMzNrVPlXX8hCSqbJY/EfUavVGI1GXF1d0Wg0ODs74+Tk1NjDk5GRkZH5C6VSyeXLl7ly5QpQef1uCCz3BKjMn/Xw8GiQ44IspGSaOJYIVH5+PqIoUlJSwpUrVzAYDOTn51NQUNDYQ5SRkZGR+YuKigouXryITqfDw8NDasJ8s3FycsLPzw+oTEA/evRogxwXZCEl08Qxm838/PPP+Pj4oFAoMBgM7N27l6KiIrp37y7145ORkZGRaTpkZmZy8eJFqYXXzSYvL49Tp041yLGuRU42l2nSiKLIhQsX8Pb2xmQykZubS/fu3TGZTGg0Gg4fPoxCofhbNjiVkZGRaaoIgoCvry8Gg6HWRp7NDTkiJdOkubps9ZNPPsHb25ujR49iNpvZtWsXBoOBgQMHNuIIZWRkZGSuJSgoiJKSklteRIEckZJpwpSUlLBs2TKeeeYZBEEAKl1sO3XqhEqlIiAggPLycioqKvDw8GjsRpcyMjIyMn+Rnp7e2ENoMOSIlEyTxWQykZqaiiAI7N+/n5EjR+Lh4SEZ1YWEhODv749KpSIiIoIBAwY09pBlZGRkZP5mCE3BOVUQhMYfhEyTw9HRkVGjRuHk5ER2djbOzs4IgiD5SRUWFlJRUYFSqUQURRQKBWVlZXz//feNPXQZGRkZmebJflEUu9RmAzkiJdNkEQQBR0dHsrKy0Gq1FBQUSK0GkpOTSU1NpaioiKKiIoqLi6W+e/fee28jj1xGRkZG5u+CnCMl0+QpLCxEEAScnZ1Rq9UYDAY8PT0xGAxcuHCB0tJSfH19SU5O/r/27ijEsvq+A/j3N3dnZt3tzLqJs6tRo2EjbDdNTE0wEgOxIo0lIQmBgIU0PgTyEiGFQoh9KX3LU8lL8xDSUCFtRWhDRLDVmJZAKEZtUsy6Jitq1mXXuOLq7DqrszPz78NcZTRbnTnZ7Ll35/OB5dzzv+defvDj3vnu/5x7/nnppZfesGglAPw+CVKMvPn5+ddDVFWlqvLOd74zy8vLOXLkSH7961/n+PHjefHFF5MkzzzzTM8VA7BZOLXHSGutvb721GsXmbfWMhgMsmXLlszNzWV2djZHjx7tuVIANiMzUoyslZWVHD9+PNu3b399FfRXX301VZVnn302J06cyNLSkvX2AOiNIMXIWlpaysGDB/Oud70rVZWJiYksLCzk1KlT2b9/f44cOfL6bBUA9MGpPUZWVWV+fj6Li4t5+eWXMzU1lW3btuXHP/5xtm/ffk5X9waAMzEjxUgaDAaZmZnJCy+8kJ07d6aqcumll+aCCy7IxMRE9u/f33eJAGBGitH12iLFF198cXbv3p2qypNPPul0HgAjQ5BiJFVVdu7cmdtuuy2nTp3K6dOnMz8/nz179uTmm2/Orl27+i4RAAQpRtPa2x5MTk7mve99b5aWlrK0tJSLLroon/vc57Jv376eqwRgsxOkGEnLy8t56aWXkiQLCwt5+umn89q6kFu2bMnLL7+cV155pc8SAUCQYnS11lJVGQwGue+++3LPPfdkeXk5VZXWWj7wgQ9k7969fZcJwCYmSDHSVlZW8tBDD+XkyZO58cYb88Mf/jDPP/98tm3bliNHjuTw4cN9lwjAJub2B4ysqsrk5GSuueaaJMnBgwdz6NChLC8v55prrsmuXbty7NixnDx5sudKAdiszEgxshYXF/Pggw9m7969mZ+fz4EDB7K8vJxDhw7lxIkT2b17d+bm5vouE4BNzIwUI2txcTE/+clPMj09nQcffPANzz377LOZm5t7fQ0+AOhDvfZLqF6LqOq/CMbO1Vdfnenp6Rw8eDDHjx/vuxwAxt8jrbUPb+QF/jvPWNq2bVsee+yxnD59Oh/5yEeybdu2vksCYBMSpBg7k5OTue6667Jnz55MT0/niiuuyMc+9rG+ywJgE3KNFGPnpptuytTUVK699trMzs7m9OnTrz83MTGRlZWVHqsDYDMxI8XYuffee7O4uJjt27dneXk5rbVs3bo1s7Oz+eIXv9h3eQBsIoIUY+nee+/N448/noWFhRw7diwvvvhiTp8+nUOHDuWGG27ouzwANgm/2mNsDQaDTExMpLWW1lqWl5czPT2dj3/84zl69GgeffTRvksEYLxs+Fd7ghTnna1bt2Z5efkN104BwDpsOEi52JzzziuvvNJ3CQBsEq6RAgDoSJACAOhIkIIxMBgM+i4BgDMQpGAMTE1Npar6LgOANxGkYAxMTPioAowi384wIiYmJjI5OXnG57Zt2yZMAYwg38wwIubm5vKhD33ojM+Nwv3eAPhtghQAQEfubA4AsGrDdzY3IwUA0JEgBQDQkSAFANCRIAUA0JEgBQDQkSAFANCRIAUA0JEgBSNgeno627dv77sMADZIkIIRMDc3lyuuuKLvMgDYIHc2h54MBoOsrKykqpIkKysrPVcEsOlt+M7mW35flQBv7aMf/Wh+9atfZWZmJisrK3nyySf7LgmADTIjBQCwylp7AADniiAFANCRIAUA0JEgBSNkeno6l19+ed9lALBOghT0ZO/evdmxY8cbxpaWlnLixInMzc31VBUAGyFIQU8WFhaytLT0hrHl5eXMz8/n9OnTPVUFwEa4/QEAwCq3PwAAOFcEKRghExMT2bp1a99lALBOghSMiKrKzMxM9u3b13cpAKyTIAUjYmZmJldeeWWmpqb6LgWAdVp3kKqqQVX9rKruGe6/o6rur6qDw+3ONcfeXlVPVNUvq+oTv4/C4Xw0MTGR6enpvssAYJ02MiP11SQH1ux/PckDrbWrkjww3E9V7UtyS5L3Jbk5ybeqanB2yoXz2+TkZC655JK8//3v77sUANZhXUGqqi5L8skk31kz/Jkkdwwf35Hks2vG72ytvdpaeyrJE0muPTvlwvnrggsuyNzcXCYmJjI7O9t3OQCsw3pnpL6Z5GtJVtaM7W6tHU2S4XbXcPzSJM+sOe7wcAx4C8eOHcvDDz+ckydPZjAYuOgcYAxsebsDqupTSZ5rrT1SVTes4z3rDGO/dcPNqvpyki+v4/3gvLdjx47s3LkzKysrOXDgQBYWFnL8+PG+ywLgbaxnRur6JJ+uqqeT3Jnkxqr6XpLfVNUlSTLcPjc8/nCStauuXpbkyJvftLX27dbahzd6B1E4H506dSoLCwuZmprKyZMnMzc3l127dr39CwHo1dsGqdba7a21y1prV2b1IvIftda+kOTuJLcOD7s1yQ+Gj+9OcktVTVfVe5JcleSnZ71yOI9s2bI6OXz8+PG01rJjx45ceOGFPVcFwNt521N7b+EbSe6qqi8lOZTk80nSWttfVXcleSzJUpKvtNaWf+dK4Ty3uLiY+fn5zM7OZnnZRwZgHFi0GEbMli1bsmfPniwuLuapp57quxyAzWTDixYLUgAAqzYcpCwRA2NoMBhkMBikqjI5Odl3OQCbliAFY+jiiy/Ou9/97szMzGTv3r19lwOwaTm1BwCwyqk9AIBzRZACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6EiQAgDoSJACAOhIkAIA6GhL3wUMPZ/k5eGW8XFR9Gyc6Nf40bPxo2fj5c39umKjb1CttbNXzu+gqh5urX247zpYPz0bL/o1fvRs/OjZeDkb/XJqDwCgI0EKAKCjUQpS3+67ADZMz8aLfo0fPRs/ejZefud+jcw1UgAA42aUZqQAAMZK70Gqqm6uql9W1RNV9fW+62FVVX23qp6rql+sGXtHVd1fVQeH251rnrt92MNfVtUn+ql686qqy6vqP6vqQFXtr6qvDsf1bERV1daq+mlV/e+wZ387HNezEVdVg6r6WVXdM9zXsxFWVU9X1aNV9fOqeng4dtZ61muQqqpBkr9P8mdJ9iX586ra12dNvO4fk9z8prGvJ3mgtXZVkgeG+xn27JYk7xu+5lvD3nLuLCX5q9baHya5LslXhn3Rs9H1apIbW2tXJ/lgkpur6rro2Tj4apIDa/b1bPT9SWvtg2tudXDWetb3jNS1SZ5orT3ZWltMcmeSz/RcE0laaz9O8sKbhj+T5I7h4zuSfHbN+J2ttVdba08leSKrveUcaa0dba39z3TED3QAAAJYSURBVPDxiax+yV8aPRtZbdXJ4e7k8F+Lno20qrosySeTfGfNsJ6Nn7PWs76D1KVJnlmzf3g4xmja3Vo7mqz+4U6yaziujyOkqq5M8sdJHoyejbThKaKfJ3kuyf2tNT0bfd9M8rUkK2vG9Gy0tST3VdUjVfXl4dhZ61nfS8TUGcb8jHD86OOIqKo/SPKvSf6ytTZfdabWrB56hjE9O8daa8tJPlhVFyb5flX90Vscrmc9q6pPJXmutfZIVd2wnpecYUzPzr3rW2tHqmpXkvur6vG3OHbDPet7RupwksvX7F+W5EhPtfD2flNVlyTJcPvccFwfR0BVTWY1RP1Ta+3fhsN6NgZaay8m+a+sXpOhZ6Pr+iSfrqqns3opyo1V9b3o2UhrrR0Zbp9L8v2snqo7az3rO0g9lOSqqnpPVU1l9QKvu3uuif/f3UluHT6+NckP1ozfUlXTVfWeJFcl+WkP9W1atTr19A9JDrTW/m7NU3o2oqpqbjgTlaq6IMlNSR6Pno2s1trtrbXLWmtXZvXv1Y9aa1+Ino2sqtpeVTOvPU7yp0l+kbPYs15P7bXWlqrqtiT/kWSQ5Luttf191sSqqvqXJDckuaiqDif5myTfSHJXVX0pyaEkn0+S1tr+qroryWNZ/fXYV4anLDh3rk/yF0keHV5zkyR/HT0bZZckuWP4i6CJJHe11u6pqv+Ono0bn7PRtTurp82T1czzz621f6+qh3KWeubO5gAAHfV9ag8AYGwJUgAAHQlSAAAdCVIAAB0JUgAAHQlSAAAdCVIAAB0JUgAAHf0fXONDtWmp82QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = plt.figure(figsize=(10,10))\n",
    "ax = fig1.add_subplot(111)\n",
    "plt.imshow(img)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}