eval_ssl.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bb4a298f-71ef-4ef5-94b5-cfc6e35ff2d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "from datasets.data import *\n",
    "\n",
    "parser = argparse.ArgumentParser(description='Point Cloud Recognition')\n",
    "parser.add_argument('--num_points', type=int, default=1024,\n",
    "                    help='num of points to use')\n",
    "parser.add_argument('--emb_dims', type=int, default=1024, metavar='N',\n",
    "                    help='Dimension of embeddings')\n",
    "parser.add_argument('--rank', type=int, default=-1, help='the rank for current GPU or process, '\n",
    "                    'ususally one process per GPU')\n",
    "parser.add_argument('--k', type=int, default=15, metavar='N',\n",
    "                        help='Num of nearest neighbors to use')\n",
    "parser.add_argument('--dropout', type=float, default=0.5,\n",
    "                        help='dropout rate')\n",
    "args = parser.parse_args(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "07e93c30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "args.rank = 0\n",
    "\n",
    "pretrained_path = 'checkpoints/crosspoint_dgcnn_cls3/models/best_model.pth'\n",
    "device = torch.device(\"cuda:%d\" % args.rank)\n",
    "pretrained_model = torch.load(pretrained_path)\n",
    "# pretrained_model are saved in DDP mode, but we test on a single GPU, \n",
    "# therefore, it is neccessary to do the following step to make key matched\n",
    "# https://discuss.pytorch.org/t/missing-keys-unexpected-keys-in-state-dict-when-loading-self-trained-model/22379\n",
    "pretrained_model = {key.replace(\"module.\", \"\"): value for key, value in pretrained_model.items()}\n",
    "\n",
    "model_self = DGCNN(args).to(device)\n",
    "model_self.load_state_dict(\n",
    "    pretrained_model\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "348c3368",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " DGCNN(\n",
      "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (bn3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (bn4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (bn5): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (conv1): Sequential(\n",
      "    (0): Conv2d(6, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): LeakyReLU(negative_slope=0.2)\n",
      "  )\n",
      "  (conv2): Sequential(\n",
      "    (0): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): LeakyReLU(negative_slope=0.2)\n",
      "  )\n",
      "  (conv3): Sequential(\n",
      "    (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): LeakyReLU(negative_slope=0.2)\n",
      "  )\n",
      "  (conv4): Sequential(\n",
      "    (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): LeakyReLU(negative_slope=0.2)\n",
      "  )\n",
      "  (conv5): Sequential(\n",
      "    (0): Conv1d(512, 1024, kernel_size=(1,), stride=(1,), bias=False)\n",
      "    (1): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): LeakyReLU(negative_slope=0.2)\n",
      "  )\n",
      "  (inv_head): Sequential(\n",
      "    (0): Linear(in_features=2048, out_features=1024, bias=True)\n",
      "    (1): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): ReLU(inplace=True)\n",
      "    (3): Linear(in_features=1024, out_features=256, bias=True)\n",
      "  )\n",
      ")\n",
      "bn1 BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "bn2 BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "bn3 BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "bn4 BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "bn5 BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "conv1 Sequential(\n",
      "  (0): Conv2d(6, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "  (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (2): LeakyReLU(negative_slope=0.2)\n",
      ")\n",
      "conv1.0 Conv2d(6, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "conv1.2 LeakyReLU(negative_slope=0.2)\n",
      "conv2 Sequential(\n",
      "  (0): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "  (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (2): LeakyReLU(negative_slope=0.2)\n",
      ")\n",
      "conv2.0 Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "conv2.2 LeakyReLU(negative_slope=0.2)\n",
      "conv3 Sequential(\n",
      "  (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "  (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (2): LeakyReLU(negative_slope=0.2)\n",
      ")\n",
      "conv3.0 Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "conv3.2 LeakyReLU(negative_slope=0.2)\n",
      "conv4 Sequential(\n",
      "  (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "  (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (2): LeakyReLU(negative_slope=0.2)\n",
      ")\n",
      "conv4.0 Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "conv4.2 LeakyReLU(negative_slope=0.2)\n",
      "conv5 Sequential(\n",
      "  (0): Conv1d(512, 1024, kernel_size=(1,), stride=(1,), bias=False)\n",
      "  (1): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (2): LeakyReLU(negative_slope=0.2)\n",
      ")\n",
      "conv5.0 Conv1d(512, 1024, kernel_size=(1,), stride=(1,), bias=False)\n",
      "conv5.2 LeakyReLU(negative_slope=0.2)\n",
      "inv_head Sequential(\n",
      "  (0): Linear(in_features=2048, out_features=1024, bias=True)\n",
      "  (1): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (2): ReLU(inplace=True)\n",
      "  (3): Linear(in_features=1024, out_features=256, bias=True)\n",
      ")\n",
      "inv_head.0 Linear(in_features=2048, out_features=1024, bias=True)\n",
      "inv_head.1 BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "inv_head.2 ReLU(inplace=True)\n",
      "inv_head.3 Linear(in_features=1024, out_features=256, bias=True)\n"
     ]
    }
   ],
   "source": [
    "for name, module in model_self.named_modules():\n",
    "    print(name, module)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "73b4c6bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# seg_num_all = 50\n",
    "# model = DGCNN_partseg(args, seg_num_all, pretrain=False).to(device)\n",
    "# model.load_state_dict(\n",
    "#     torch.load('checkpoints/crosspoint_dgcnn_seg2/models/best_model.pth'),\n",
    "#     strict=False\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64a570cb-d50d-4231-a40e-aa565b0c6eac",
   "metadata": {},
   "source": [
    "# ModelNet40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9f999b8d-d3b1-43ab-8910-d4f64ae4cb19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done !!\n"
     ]
    }
   ],
   "source": [
    "train_loader_1 = DataLoader(ModelNet40SVM(partition='train', num_points=args.num_points),\n",
    "                              batch_size=128, shuffle=True)\n",
    "test_loader_1 = DataLoader(ModelNet40SVM(partition='test', num_points=args.num_points),\n",
    "                              batch_size=128, shuffle=True)\n",
    "print('Done !!')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04c7743b-f0f9-4bcb-81e4-f8da2cf585ba",
   "metadata": {},
   "source": [
    "# ScanObjectNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9d0dc924-2763-450d-b9b9-bcb171802f42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done !!\n"
     ]
    }
   ],
   "source": [
    "train_loader_2 = DataLoader(ScanObjectNNSVM(partition='train', num_points=args.num_points),\n",
    "                              batch_size=64, shuffle=True)\n",
    "test_loader_2 = DataLoader(ScanObjectNNSVM(partition='test', num_points=args.num_points),\n",
    "                              batch_size=64, shuffle=True)\n",
    "print('Done !!')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "706ee9d8-6a97-452e-9987-8042eae5b90f",
   "metadata": {},
   "source": [
    "# Load ModelNet Train Feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "89241966-45c3-45ec-a6d0-94e40cb8e0dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9840, 2048)\n",
      "(9840,)\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "feats_train_1 = []\n",
    "labels_train_1 = []\n",
    "model = model_self.to(device)\n",
    "model = model.eval()\n",
    "\n",
    "dataset = \"ModelNet40\" # Choose Dataset [\"ModelNet40, ScanObjectNN\"]\n",
    "\n",
    "for i, (data, label) in enumerate(train_loader_1):\n",
    "    if dataset == \"ModelNet40\":\n",
    "        labels = list(map(lambda x: x[0],label.numpy().tolist()))\n",
    "    elif dataset == \"ScanObjectNN\":\n",
    "        labels = label.numpy().tolist()\n",
    "    data = data.permute(0, 2, 1).to(device)\n",
    "    with torch.no_grad():\n",
    "        feats = model(data)[1]\n",
    "    feats = feats.detach().cpu().numpy()\n",
    "    for feat in feats:\n",
    "        feats_train_1.append(feat)\n",
    "    labels_train_1 += labels\n",
    "\n",
    "feats_train_1 = np.array(feats_train_1)\n",
    "labels_train_1 = np.array(labels_train_1)\n",
    "print(feats_train_1.shape)\n",
    "print(labels_train_1.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "048233c6",
   "metadata": {},
   "source": [
    "# Load ScanOjbectNN Train Feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8f71ec5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2309, 2048)\n",
      "(2309,)\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "feats_train_2 = []\n",
    "labels_train_2 = []\n",
    "model = model_self.to(device)\n",
    "model = model.eval()\n",
    "\n",
    "dataset = \"ScanObjectNN\" # Choose Dataset [\"ModelNet40, ScanObjectNN\"]\n",
    "\n",
    "for i, (data, label) in enumerate(train_loader_2):\n",
    "    if dataset == \"ModelNet40\":\n",
    "        labels = list(map(lambda x: x[0],label.numpy().tolist()))\n",
    "    elif dataset == \"ScanObjectNN\":\n",
    "        labels = label.numpy().tolist()\n",
    "    data = data.permute(0, 2, 1).to(device)\n",
    "    with torch.no_grad():\n",
    "        feats = model(data)[1]\n",
    "    feats = feats.detach().cpu().numpy()\n",
    "    for feat in feats:\n",
    "        feats_train_2.append(feat)\n",
    "    labels_train_2 += labels\n",
    "\n",
    "feats_train_2 = np.array(feats_train_2)\n",
    "labels_train_2 = np.array(labels_train_2)\n",
    "print(feats_train_2.shape)\n",
    "print(labels_train_2.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84930173-3beb-4ed2-9efa-5a343f128df3",
   "metadata": {},
   "source": [
    "# Load ModelNet Test Feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8d1045cc-2a11-41b9-8a6c-b2851fa2c685",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2468, 2048)\n",
      "(2468, 1)\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "feats_test_1 = []\n",
    "labels_test_1 = []\n",
    "model = model_self.to(device)\n",
    "model = model.eval()\n",
    "\n",
    "for i, (data, label) in enumerate(test_loader_1):\n",
    "    if dataset == \"ModelNet40\":\n",
    "        labels = list(map(lambda x: x[0],label.numpy().tolist()))\n",
    "    elif dataset == \"ScanObjectNN\":\n",
    "        labels = label.numpy().tolist()\n",
    "    data = data.permute(0, 2, 1).to(device)\n",
    "    with torch.no_grad():\n",
    "        feats = model(data)[1]\n",
    "    feats = feats.detach().cpu().numpy()\n",
    "    for feat in feats:\n",
    "        feats_test_1.append(feat)\n",
    "    labels_test_1 += labels\n",
    "\n",
    "feats_test_1 = np.array(feats_test_1)\n",
    "labels_test_1 = np.array(labels_test_1)\n",
    "print(feats_test_1.shape)\n",
    "print(labels_test_1.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "999608d4",
   "metadata": {},
   "source": [
    "# Load ScanObjectNN Test Feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "de6d78d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(581, 2048)\n",
      "(581,)\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "feats_test_2 = []\n",
    "labels_test_2 = []\n",
    "model = model_self.to(device)\n",
    "model = model.eval()\n",
    "\n",
    "for i, (data, label) in enumerate(test_loader_2):\n",
    "    if dataset == \"ModelNet40\":\n",
    "        labels = list(map(lambda x: x[0],label.numpy().tolist()))\n",
    "    elif dataset == \"ScanObjectNN\":\n",
    "        labels = label.numpy().tolist()\n",
    "    data = data.permute(0, 2, 1).to(device)\n",
    "    with torch.no_grad():\n",
    "        feats = model(data)[1]\n",
    "    feats = feats.detach().cpu().numpy()\n",
    "    for feat in feats:\n",
    "        feats_test_2.append(feat)\n",
    "    labels_test_2 += labels\n",
    "\n",
    "feats_test_2 = np.array(feats_test_2)\n",
    "labels_test_2 = np.array(labels_test_2)\n",
    "print(feats_test_2.shape)\n",
    "print(labels_test_2.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33a1abab-94a9-42b2-ad9b-427acfcf12c3",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Train Linear Model on ModelNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b830ca46-9842-44bb-b703-90300f766463",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C = 10 : 0.8865478119935171\n"
     ]
    }
   ],
   "source": [
    "# Linear SVM regularization parameter C, can be tuned\n",
    "# The strength of the regularization is inversely proportional to C\n",
    "c = 10\n",
    "model_tl_1 = SVC(C = c, kernel ='linear')\n",
    "model_tl_1.fit(feats_train_1, labels_train_1)\n",
    "print(f\"C = {c} : {model_tl_1.score(feats_test_1, labels_test_1)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18e246c0",
   "metadata": {},
   "source": [
    "# Train Linear Model on ScanObjectNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "18e246c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C = 100 : 0.7418244406196214\n"
     ]
    }
   ],
   "source": [
    "# Linear SVM regularization parameter C, can be tuned\n",
    "# The strength of the regularization is inversely proportional to C\n",
    "c = 100\n",
    "model_tl_2 = SVC(C = c, kernel ='linear')\n",
    "model_tl_2.fit(feats_train_2, labels_train_2)\n",
    "print(f\"C = {c} : {model_tl_2.score(feats_test_2, labels_test_2)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "389250e0-9c3b-4524-a438-0c8754c3ae2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Results:\n",
    "# ModelNet40   -> C = 10000 : 0.8731766612641815\n",
    "    # C = 0.01 : 0.8861426256077796\n",
    "    # C = 0.1 : 0.8942463533225283\n",
    "    # C = 1 : 0.8869529983792545\n",
    "    # C = 10 : 0.8865478119935171\n",
    "    # C = 100 : 0.8865478119935171\n",
    "# ScanOjbectNN -> C = 10000 : 0.648881239242685\n",
    "    # C = 100 : 0.7418244406196214\n",
    "    # C = 10 : 0.7418244406196214\n",
    "    # C = 1 : 0.7418244406196214\n",
    "    # C = 0.1 : 0.7573149741824441\n",
    "    # C = 0.01 : 0.7314974182444062"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a62f9f3e",
   "metadata": {},
   "source": [
    "### t-SNE visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f3ec6d69",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch as t\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "X = t.randn(1024, 256).numpy()\n",
    "X_embedded = TSNE(n_components=2, learning_rate='auto', init='random').fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2a11e293",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1024, 2)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_embedded.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f73c66b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Random data T-SNE projection')]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LtOlt10va+7tK28q8MzimCsC27VXA/GN5DWXenti2jZ3LlbbnS9sOlWjSHLEOuMDfl5nceuIn0cV64rnthF2zCLlmIEtvXqpv7xQnlXmZRHRFyTY92QGHqABUTeLkc/3c/5viYItpc0tiM8q8QzjWM4AyZQ4JIQSBcy8gvWZlUbvvtEVH7BwRv4IQxZOMkFdGkyup8ZU6et8sHBGV4LwIudebyPVvKtomu0KH1feMeR7SCZPnHhhA1STOuy5M09TyGoV3KmUFUOZti2vKNGr/9YsMLP0nkqwQvPASnJOPXDx9fYXGLZdX8euHe8jpNn63zKevrCbsO/aFfTSvh4pZ76P9xR9gGxkA3DXzcQYnHla/vpDCWVeGmLvYjySDL1gWEe9kyt9umbctktOJ58S5uGeeAIBQjuzPWZEFp8/0MbHOSTxtEvEpVASOvfDfgysyhcazvkc+2YGkuND8DSgO/2H3K4QgECl+lnreonVbjvYdOTx+icbJLiLVb51nUebQKCuAMoeEbRoI+ej8fPJ5i2i3jiQLwtUqkrTvGPkjLfiL+haC2rBGbfioneKw0Hx1aL66o36ejStS3HXr8AK3ynqVG79UR6iqrATezpQVwBHgpptu4qGHHqKqqoq1a9ce68s5quipHpLtr5BsewlXZAa+psU4/I37P/AA6e/SWXpXL2tfTnHCxV4qz3bSJuUZ73Yw3eOmQisLnDebZMzg4T8Wr5bvadNp35krK4C3OWUFcAS48cYb+dSnPsUNN9xwrC/lqGIZWXrX3UmqrZAhMy9Xsd1I0Z3tozrsYFKdk5Dv0H9Stm2z/Jk4a19O0TjTSc8C+EdscNHVAMzxuLilsQbfYS52KnNwGLpNKl6abiKXtY7B1ZQ5khx3CiDesoz+dXdjZPpQXBHCM6/D33h46wIWL17Mzp07j8wFvoXRU91Dwl/2j+fV3DX8/bksMADAgqluPnpJNV7XoQnoTMpizYuFlApNZ7r5o95ftH1VKkNbJss035sTlmjbNrkdfeR29CFUGcfEChz1wSN6jqSZoDW3k7gZo0qtoV5rQj3EkNJE1GDrmjRvvJykfoKDWQu9VDccfg1jf1hhwfkBXvhndKhNkqG6sbw47O3OcaUA4i3L6Fl5G7Y5mA4600vPysF00IepBI4LhAAEYKNXX8sDDxYnMHtlU5qLTs4yrenQBLTmlGiY4KC/S8eW7JIYfABdzwPF/esxg3RLHjNt4arTcNarR6QOc3ZzN23fWQpGYaQr+RzU//uFOMYdGYdAykxyX++feDnx7FDbjVWf4hT/6L9FvS+FlcqjhFwl6aNN0+bFR6I8e38UgI0r0ix/Os7N32g4bDONJAlOuzCAqgmWPxUnVKlw/vURapsKyiW1PUv/aymMuEl4oRfvFCeyo1zn4O3AcaUA+tfdPST892CbefrX3V1WAAeA6qnBP/5c4jsexxKVmFZporBMUiedz5Du30q2dx2yI4C7YiaB0Lj99q8ogkWXB9nyRproOp1JZzvYOmJhV5UsUbuXwzc/YLDzN91EX08DIFTB1K/U4p9xeKUlbMNk4ME3hoQ/gJXIkVrdcsQUQHu+pUj4A/yl93eMV5sI4UUZjOm3LYvUilZ6bnsBM55FawxR/clFSF4H2U1d5Ntj6DPHs2zECB0g1mfS2ZI/Inb6UKXKee8Oc+oFATSHwDE4y0vtyLLhW21Yg+kjep6MM/kLNYTmew/7nGWOPseVAjAyY6SDHqO9TDGSrBKeejXO8BRyaZ2JVSrbuofTBPjcEnVeQbJ7DfHXfjrUnnUE4LSvEwju31lcP97JJ7/bQNeuDHNVlVc1iVV5g2nC4nyfiwq/r2j/9I7ckPAHsHWbXX/oZfq/16N4D91XYJsWRm+qpD3fF8O08sjS4Zs/UmaipC1jpRnofJXE+qVEZrwbb/1pGJ05On/6FJgFIZtvGaDzF8/hXdjMwN9WASBbDrBHMfeMNo06RIQQJesCEhsyQ8J/D2339uOf6UI+RFNgmTeP42qeprjGSAc9RnuZUhRXCP+4RYRq67hxksWiSQ58Lok54zT+9WQNV8givfGeomPMXIzcwNYxeiwlUqMxY0GAyRGFa3Jx/p0sH3DKNFdUlOyrx0qdk9nWPGbm8ByUkkPFd94oi6pmyaT10nz/h0KVWouy1xhsnNqM1teClU/Qs+q3ZHrXo3clhoT/HvTWaFGbWLGVBWcUm4X8IZnqcUfXTm/ppRrG1otXTx8JckaUnDFwZDstc3wpgPDM6xBy8QshZI3wzMNLB3399ddz6qmnsmnTJhoaGvjtb397WP29HVACLsZNCvIu0ctX6+J8yD3AxHFuUGxsPV2yv20cfDF1xR/AO3U64SlTcTeOGzXe31lbat4IneJFDR7+6FOc4MT73onIIRdqtQ/fJ6eyO/QYR2pYXaPV8/HafyOiFOpcTNamcI10Bkbrq0P7JNtfRakoNadIXgdWbnj2ZezuZ6bdylUfjjBhhovFlwe58St1hI9ymKZ/phux16N2XGywXF9G2kwedv+6maI19hQvt3yFl1q+zO7oUvKjzJzKHBrHlQloj53/SEcB3XXXXUfi8t52OBpDVNb6CSdzSB4NSVXQbJvkhItJr//z8I5CRgtNOirX4B7vYPzHKtl9Rx9mxsI/y0X9tWEk9fDHNs5gmE0n3UFg1gRM8my1/0mFew5utfoIXDlIQmKG50T+zfFdMlYKWlaQWPNnRioYzVuLWu0jcPEMYg8PZksXgsoPLaT/3lVF/TkSCeYu8nLyeYeXD+hg8ExwMO3f62l/pB89ZiItyfB0/QOs7nmVj0qf5STfwsPqfyCzgfU9tw193th7O6rsp9Z32uFeehmOMwUABSVQdvgeOYQiowSHHa5CCHwNpyPJKrmdjyEcYXxTriQYObwcNWMhOyQqzw7gm+XGylkQBsW5b+Gfa42id8SQPBqOxtCYBdmdapiZ1TfTnnieWGYbEz3XUuU9GVk6sNBKK5cjs3UTyZdeQPZ68Z5yGs4Jpc/BrwTwEyAbzpBUHEOzJUnz4amdj+zWCF9zEt4FzZjRDGq1D60hhFobYOD+NeS29eBd2Iz/3KnIzjd3YZaQBL5pLjaEn6Ulu5Ot+gZ0uzAzWRZ/6rAVQGfyhZK21tgT1HgXIsRxZcA4Khx3CqDM0cfnCeObdCHZxjOQJRVVPfxY9H3RG9PpJslm5w6e69lAtRrgqtA8prhqS/ZNr++k44ePYecLvgPPwmYqb1xQpMRG4tUamBK5Hss2kfa2deyH9Lo1dP6/Hw99jj35GPVf/RbO8RNG3d8ZmkjDmd8hF92FkCQcgeahNA+yR8M1tXjm4ZwQofpTi7AyBrLXgdhPyozDwcxmsBIJJLcH2VMa5msKkw35NUVtFerBFboZDdcosy2PVlsW/keIsgIoc0Qws4Ui5bluAzUo425y4PQd3VBAw9LZ3a1z+6N9NJ69myfzqwBo16OszbTy3YZraXIMO47NZI7e218eEv4AqZd34j9zMspJ+w4b3Zfw1y2LzrxO3rKp1lS8ioyZyzHw4D+K9rN1nfT6tSUKwMrl0Ls6sA0DtaoGx7gDT60hqQqSemCvsZExyezKk+szcEQUXE0aygFE6mR37aTv7j+S2bAOrXkCle+/EdekKXT05dnVnceybGZXXMJy+UViZsFRqwkHp/nPPuD7GItqz0JaYo9hWAW/kiwc1PuXHHa/ZQqUFUCZw8a2bHqfi7Prd8O1cqsvDNDwnvBRCwXcntnMqvgKNj9/KlMmyzyuF+dg0m2T3bm+IgVgZfLkW6IlfZmxzCFfR9wweah3gAd7o9jABJfGLfU11GBhm0bpAVZx1JIRHaD/738j/uyTADgnTaHqwx9Hqz2yCd4s3aL70Ritdw+vrm64LkzNJSEkdeyZgxGL0vXLW9G7OgHI79xOx0//E/krt/L9v/USSxXux+2Q+OJ7f0CfZyU2Ns3OiTQ4mg77uv3OZk5p+Dbx7A5sLPyO8fgc+19TUubAKM+j3kbYtkU8u4P2+DK6U6+T3StVwoFg2RZd+Rgd+SiGXRpCeShkO3V275UsrGtpjEzr0Skl2Jrbxc/av0MyZ7KjzSKXs3GOkj5BlYqVjxxw4ZlXOrpWa0tTKJvmgUX6bE1neWBQ+ANsz+R5qG8A2+EkdMkVxTvLMu4Zs4uaMps3Dgl/KNQ5jj/3NLZ18GGstm1jjvGdZjt0Wv9S/HtpvaefbEfpYr6R6L09Q8J/D0LVeG1Lekj4A6RzFi+uyXN64GzOCCw5IsJ/D16tgTr/Iur9Z5aF/xGmPAN4G9GXfoOVHf+JTUE4BJxTObH6X3CqB7aOIWakeSy2lgeiK7BsmwuDs7kkMIewenimGjNjYY8SD24kj4yC2Zv23G50W6fVXsf0iafx8kqdCyaezN/M54b2qVB8jHdUFh0naQrh6+ZhJrJkN3UjnAoVHzgFR9Pw8+tuzbPiuTg7NmSZvdDDzFO8hCrHdqzuHixL6ZMlspaNbtusSKR5j2HiO+Ekaj79eWJPPobs8xE49wIce5l/slu3lPSZWvU6ocuuRHYfeEqNXdltPBd7gm69ndP95zDTfSI+ZbgYfT6aKo1etfb/HUlOF8gymMP7yT4fbQOlx+3uymNaNvIYvgjbso+qn2I08p1x8rv6sXQTx7jQEVvF/U6hrACOAC0tLdxwww10dXUhhODmm2/mM5/5zBE9R95MsLH3D0PCHyCW3UQ8t/2AFcC6TBv3Drw29Pmf0dXUqkHODey/hqyRHQCbofQEI3FUKDhrVbIdwyN+ySlw1BydiBRlcLTfqm/nktltdHTXs3aZj+sWnM+Aq5s6l59Zngaq1UDJsY6GILVfOg+jN4nkUFGqvEN5g2J9Bnf8Vzv93QXTTcuWLK1bc1z98Sq0MXLbNDkV3lsr6DG6cEtuhB2iM6PikSVk1YV33sl4TpoHQoyan8jRPL6kzTV9ZkHwHiBtuRZubfsOObsQPbQtu4mrI+/n3NClAOjpPvK55Si+yRiJ4d+P4pdx7GedgFZdQ+Sa99B3z50jrm8WC6Z5eWlD8XqPs+b4i4S/bunYWEgZhfjaNN1PxFFDCtXnBvBMdhyRfE37It8epf17j2H0FVZ0C02m7usX4ppy+M7pdwplBXAEUBSFH//4x8ydO5dEIsG8efM477zzmDFjxhE7h2nlyBo9Je26VZquYCxeTW0vaXsusYmz/TOQx4iqMPMpkm0v0b/hr9i2RXjq1fgaz0B2DKdkUAMKEz9Tza7f95LclMVZp9L8kUpctUdnFWqjNp6QEmHA6ONh4xfMPe8cTuAiqp211IYnoir7tmzKbg15cCSYiBr0duSRZYlMyhwS/ntY81KSs64KUTNu9EimFF3cOfDY0OcK2cdnai5FlSSMWAbbslFDYzuYXVOn4z5xLunVhQLvSlU1gSXnI6QDt8625XYOCf89LB34O/O8pxFSw+jJDpKddzPuI5+n/a8esq0mzgaZppuCOCr2rQCEouA/+1wcEydjdHejhMM4mppxSm4+cG4Ff3u+H8uyueLUEHMmFu7TsA22ZNbz2MADWLbJu7d9grZfxYf67H8pyYxv1eOZcHRrDaff6BgS/gB2vpDfyfmZsxDllOLAcagA2uPL2Np/N1mjD6cSYVL4OurGyL54oNTW1lJbWwg59Pl8TJ8+nba2tiOqABxKkFrvGbQlnh7RKvCoB+4sbNYqeJnilAwTHFVjCn+ATO96elb9Zuhz7xt/QHb48TWeXrSfp9nJlC/VYsRMZK+Eehh1AfZHpVbNp+u+yqb0OvqMHqa7ZzPeEcIpH1y4aXdbnrtu7aCrpTBzufTG0lQTQgwmQR2FmJHmrr4Xi9p6zQS9eh/Vr/bT95fXsXWT0OWz8S2ahBIsHdWrkQqqb/4k+fY2bF1HratHDR7cQq7RQiJloSANtkuKA8vIEOv8TyqvOhtZHodp7sZZfw6w/3PJLjfuqdNh6vShNh9wyYIgC6d7sG2I+JWhEX1LdgebuntpSFxGxOElvq3Y9mTrNolN2aOuAPTu0hXDRkccSzeRywoAOM4UQHt8Get7bsOyC46vrNE7tMrwcJXAHnbu3MnKlStZsGDBEelvD5JQGB+6Ass26EguwyEHmVZ5I35nqQlhLE72jueJ+Dp6jcKL4ZddnO2fvs9jEq2lC3HiO5/E23BqieBR3DKK+815sWq0emq0+kM+3rJsXnsyNiT8Abpa8tQ0anS2DDtGT17iJzKGKcuwLVJmrqQ9l8vR/athU1vfn5cjeTQCSwoF61P5dvrS68gYXYRdswg6p+A6jGL2jY7xeCQfKWtY4F0WfjcBJVi410wl7orTSPe+SKq7MFsJTroU1Xv4kUYRf+mz6er08Pe/NpHTbcDm1IkmZ5/uJfvCiNQQRzpZ0Ci4T6gn9s91RW2+JVOQXeU6Bns45gpACCEDy4E227YvPZrn2tp/95Dw34Nl59naf/cRUQDJZJJrrrmGW2+9Fb//8Itz741bq2FG1c1MDF+LLDlwDL7gB0q9FuYb9VeyO9+HZds0ahFqtFI7+UjU6llYFXXIhoG88xWMVBeqt+5tvxDHyFtsfaM4/HP5U3Gu+UQVmaTJrs05pp3kZuJsN8oYaSVCioeLgifw94HXh9pkJGpjpQIm/sQmfGdMJCv6WdH+QzJGNwC7og8xvfIjNAbOPeR7qdHq+Gz911mVfI1uvZO53oVMdhUUu5E22fnrJM6GiwnMmodFF8KuxeWfjCQfeR9NMmNy3xP6oPAv8NK2NPNPDqMOjiUkh8Az/uiLHueUSio/djp9dy7HzhkEL5mJd2EzAJZto1sWmiQddV/EW5ljrgCAzwAbgCMvMfcia4ye9nms9oNB13WuueYa3ve+93H11Vcfdn9jIUsqbu3Qc9FUqn4q1QN71N35Dh537MCj1gAaDZXvJrzlJfzN+1+I06f3MGD04ZV9VKm1Q+aIA6FP72FXbhsJI0adYxzjHBNw7CP9Qm9eR7dtIqqCth/b+e7sdl5PvkzU6OeUzy5gzR/C7FpTEFa2XSh+cvrFIU6/eP/XKQnBuf5ZOCWNJ2PrqFR9XBs+marnY+z9i1Jr/AhZIpHZOST897Cl7y4qPSfhVA49K229o4n6UUIvjbhJcmOW5EbofbIayVGDlbVp/piKq87e5xqAQyGTs2jpLg3/zQYEVfOcKF7wNVk4a47+AEJ2aQTOnoJ7TgOYFkrYg5AErdk8Tw/EeCOZYb7fw6Kgj1rH8TkrOKYKQAjRAFwCfA/43NE+n1OJkDV6R20/HGzb5sMf/jDTp0/nc5876rdxyFh5AzOaQbhUlDHy3+xBt/KsSLyGJU/grwObMbGoVv18bObVOD2jpzLYw+b0en7d+VNSVgJFqFxXcRMn+89AFfsfcQ7o/fy281Z25rYNtX2w6hYW+BcNfc7mLdp68+Qsk3anwV3dfaQti4V+L++pDlMzxsvcmtvFT9u+PeQwfY1lXP3eT9C5pYFcxubEM7yMn3FwdumI6uWK0FyW+KajSQoOSSU3zU28xodrRhihyaTW9BC8aAZClrDsUuFoWjmsg1yTYdk2nXmdjGlRoSoExlgNLHsknPUq2TYdbLCyBWVnRE3a7u2n5tIg6mHUTdgbv1tmziQ3K7cWRwiFc0kcPa9jd9m4Tz8FNfTmlPUEipzwA7rBT3Z30J4vfA+7e/JsTGX4/LhaPMehX+BYz+NvBb4IjLnqRQhxsxBiuRBieU9PaRTMwTApfB2SKBYOktCYFD68dNAvvPACf/zjH3nqqaeYM2cOc+bM4eGHHz6sPo80+bYoXT9/jl2f+Rtt//Ew6Tfasa2x7bAD+VYsKcA/YxsxB7+eLj3OXwdWkTHHXjwU1Qf4fdf/DNmjDVvnzz230ZlvO6DrbM3vKBL+APf23sHA4KK3VNbk7y8M8LXft7I5lec3nT2kLAsbeCmeZGlfDGsM+/KO7JaSaJll4h98+AdhbvlBA1d+uJJA+NDMIj7FhWMwPFWtdhG6por0xntJvHoHvkUgvDaZjV34e5sJytOKjh0XvBCXUuqAHousafFYf4wvbW3ha9tb+eaONnZmStNtZ3MmLQmDmhsqkJzDI/3I6V6SW7J0/GOA1LZSH8bBoFt5ovoA+mB1OIcm8d6zI0wYjABzqIKbLqhg0gQ/FR84hdrPL8E1+8iucj4Y2nP5IeG/h/XpLJ35o7No8a3OMZsBCCEuBbpt235dCHHWWPvZtn0bcBvA/PnzD8tztMfOf6SjgM444wzsN8GpdaiYGZ2eP7xCZk07AHpHjPYfPk7jDy4bc2GMZcbR7dKfx4ZsBwkri0sefZQdN6ND+WD2YGMzoPfR6Gge/fosm1jKwKVJZK1SQZaykhiDvpvd3Xnuf3EAVRYkVRP2em+XxRJcURkkpJYKcssuHWdYtkWwQsGrjD3yz+YtcrqF3y0fkL04u3UL3b/95dDngfv/hq3LxB/LYKXyTLj6UqKnzaLLeJE6/9nUek8b06di2zbppInDKQ35InZlc9zeMTyT7cjr3N7RyxebanHLhVFsIm1y7/P9LF0ew+eS+MKnKvHs1BFCEF+fIbGu4P/ItuXhxEMrn9ma28XD/feyKbOOqa4ZXBy+lgZHE41VDr56fT29cR2nKlEdGqzR3LBvf9ObgTLi+wsoEktCBoqIYaFg2g3I4q1gFX/zOJZ3ezpwuRDiYsAJ+IUQf7Jt+/1H86R1/jOOWMTP2wWjPzUk/IcwLfT2+JgKQEFQrZT6CiZoFXj2YY/3KX58coCEGRtqEwgCyujhhp39eR55LcqytUlqwiofuLJQJctgOB5/gXcRoUEz3UCy0G6YNk6rVGjWqSpKFhhlID/eOQlFqBgjzDAXBK/Eq/hKdx5kU0uGe57tp70/z+LZfs45yU9VcN+zhPTaNaVtq1/ANf18Uq+1Eb33DWqnX0DT9ItQ5bFNIX1dOsufirP6xQR1zQ7OujJEw0Qn3XppjqGN6SxxwxxSADs6syxdXvgOEhmL+9YnuLwVUmuKHd+O6kMTATEjym0dP6HXKFRHW5V6jdbcLj7f8C0CSgivS8b7FiwJWe/QOMHjYns2x9VVvSwd+AU5O8fTcZnrKm9igX/xAZkq3ykcMxOQbdtfsW27wbbtZuA64KmjLfyPVySHguQpHbFLnrF/6D5HMwF9F+f6hsMTPZKDD1WeiWcf8fYhJcIHqz6JQxT2kZC4tuKD1GkNJfvmDYt7nu3n0eVxUlmLbe05fvlnwc1VX6bZMRG35OGswIVcFL5maPVvVaDwvw3E20zGj0g1rQrBFVkfLT/uJNdTOqWXO2q5JvVF5iqLmKzN5ArzFiKds0v220NLd47v3tnOul0ZBhIm9784wD9eGMAw9j3bU8KlPiU5EMFMDJtbjN7UPoV/Pm/x2N29PHv/ANEeg/Wvpfjd99rp7cgTGsVWXa8VVh/voS9R7FNY05Ihc7oHNTB8bPh07yHH4vfoHUPCfw+9Rjc9eucYR7w18CoyH62v4tMNDp6I/pqcXfhOLEzu6vkNXQdoqnyncHzNd45T1AovFR9aSPfPh3PluOc3ou0jL4pDCTAhsIhwvpNTPQ3kkKjXqqjWgvs933T3CXyl8Yf0G7345ABVWu2oo6q+mMFL64vLBnbHTJKtTXx66tfIWTn8SqAogqixSuMjF1Xyh8d7efLFGP/6rgoM4SWds4j0Caw7B0jGTBKbshg+iW3tOXZ356gMqmgJm0d/7KJm3GW4PYIXtubZVJdm/DdNHKOMVlt68uh7CftnVse54rQg1aGxo0bcM2cjh8KYAwW/hVBVPCcspvcPm4f2USv27QSNduusfbl4lXc2bdHdmqfpJBcXhgMs7S+M8J2S4CP1VfhGlMysDBS/2jbwdGeaW75dh95tIjkFrnrtkNdtaNLoikMTh764K6v3kcjvxrJ1vFoDHu3o+AoqNZW0lS1aNwGDpkqjn4YxTJXvRN4SCsC27WeAZ47xZbyj8S5oQq2+BL09hux34RgfQQnsO9+MQwlQrQQ42KBTIQRVWi1VWmlBlpGoisDtlEhli23zDk3CJbtxyaW2aYcqseQkPzObXLh6d5Nf+Rztz5yA1Gcw0p2Z7czzyiqDPz45HJB5YrOLGad7WT9iQdKe1DVWLke+vRUzGkWpqECrrccxSvy/yyGh7CehmVZXT/2X/p3czh3Yho4SrqHn9+sKUlhA8NJZOJoLyte0bFq6c7T16XidEk3VDoJeBVkRKKpAzxcrIFWT8Cky76oOcnrQR8o0qdbUkjDG8TUOrl0U4r5lA1g2VAcV3n1WBHe1g319oflkJ3qqE0nxoPnqkbXR/QPVah1nBi7g2dijQ22L/edTvZ/vfCzSeherOn5CMr8LAEVyM6/uawSch15JztSzWHoK2eFDkjUs26JDj5EwM3gl16imytBhRgS+3XhLKIAyB45l29gWyPLBxW9LqoJrchWuyW+dRFgVAZUPnFvB/z40HBc/pd5Bc/W+UzpIQlApp2m97VaQVQInzKLn6eJ9tAaVu54pjrdfvTPDifPCMGJx81lXhVFlk9gTj9F3z2AdY0mi+uZbGD/zFMZVqeweEdf+viURIoH924i1mlq0mmFhWPelevSuBJJbQ6sLIDkKr96a7Wn++54O9gRkzZno5mOXVhGqUjnnXWGW/nlYgTVMdOBrsFkaXcMLyc1McdZypm8atY7CbMLMp8knWrHyCRRPDZefWssp07xkchbVIZWgd9+ve6ZvMx0v/RBLL4Rw+prPITLjPSiOUl+QQ3JwcehqZrpPpFvvpEqtockxEccYM4M9mJZOIreDnBlFxouwqwh5IwykNwwJfwDDSrNz4AFmVX8KeZRU3/sjO7CdvnV3ku3fjKtyFqEZ7+FV0vym91l026RK8XFT5cf4Y/fPyVhpFKFyfeWHD2t1+duRsgJ4i2NkBsj0bSAbb6fHeRaPrsyTSJucO8/HiRN8eJxvPUcbFPLp97bnifYZ+EMKlXUayiiLjhZO91IVVGjpyRP0KEyscxA6gDxCel83Rl8hEsY/bzP5E6cRW20gaYLayzxkqrIYo4TWRxpUTr3QTzJqMvdMP83TneTbd9P31+Fsl1gW3bf/msZvT+IL19axuTVDb8JgUp2TplroyLWiSQ4iamXpCcZArfCiVhSn3Y6nshjx3Xzh/BhJM8B9yx2s2pZmV2eOOZM8zD/bT3Wjxu4tWSpqVBqnOLjPfJGnBgrF4bdku3gpsYVvNVxN2Jbo33APse2FVA9CUqk99YuMqxrbxzESM5+iZ83tQ8IfILHzSbx1C1CqTxj1GJ8SYJYy94CfgW1bdCVfZlPvHehWApdSSXPwGlp3TMUzinJK5Hdh2TnkUTz6eTOBhIoilyocPdVDx0v/iZkrjO7TnSvQEx20zrwIfXC9RbeR4E99G/nXum+TsRP4ZP9BL1h8J1BWAEeAbDbL4sWLyeVyGIbBtddey7e+9a3D7tfUM/Stu4tEy3Pkpn6D7/8lijloLVm/O8cnLzdYPPvA48ffLGzbZu0rSf76iy4ss5BM7YoPVzL3LD+KUqwEnJrEjCY3M5oOLhRRdnlAUcAwiD9yO57pcwhduwDnlKkMOF9jhbOGSY1VbB2R68fjlGiud7BgVnHUTyoeK8lNY2ezWMk4VROqqQoVBFBXvp0/9PyB9ZnVOCUX74p8kLm+U/e5SnksbMvA7FxGaNdvARu/kPnUopv56TPjiGcGq2x5ZcZXR2mQehCaRloL83TfhqJ++s0Uu5M9eDMy8V3Pjuhfp3vlr2k48zsozv2HX1p6mnx0R0m7mT34okMlfdgmIEjmdrKu+3+xKdxfxuhhR/Sv1Hs/QU//NFAEI4sW1HrPQJWLlWbOGKAj8SItsUdxyCEmRt5F2DWjKIxWT3UNCf/htg4mWMW/vVZ9AB1tKG3G8UhZARwBHA4HTz31FF6vF13XOeOMM7joootYuHDhYfWrJztItDyHpHrY1BfAtIpXVz74Uoo5E5343Ue39u7B0tepc9//deMLK8y53k86aNNlmrTFcjRFjkwGSLWmlorr3k/vn24HILthFWqkCsvQ2On1cU/6Vd61+Gwib/hYv1WnuUbl6sWhIuetbVvkYrvBZQ8pkz3IwRByKEK+vY30hnUYtsHDM7ayPrMal+Th/NxNRF+uYrmUYPxkm7rmA78vw8wQTW8k4e7Fecq7EN1tZHa+gNj5O66Y91XqIoVrzGzZRPuPvo89WHTGNeckLr3sBB60i9N667sH6PrdTiLv+QQDA7/DzBdSLxvpbiw9BQegACSHj9DE90FcBsUgGXsKPd2B4jn0tCNZM8/aTBuPRFejSQoX+CfjUGvI6sORNlmjD0VOsWm3xZIFH2Zz7x8x7Ty13kXU+RaX9NkWf5at/XcXno/RzYr273NKw7cJOCcN34s6mm9LoEvF4i4gu/CP4mc6njjuFMCr8WU80H83/UYfYSXC5eHrOOUw1wUIIfB6C0JY13V0XT8iCaZsa49AspHl0tBDVRbkVr9B765tuOfMxTlxMtIoC6DebDJJk6tuyFEzrpMEJl3Oen6fdPJ8X5Iv+Gqo0g4/74qQJPxnnIWjeQJGTzfC6SO7MYMt6bTaWWzgHuNpmk+o5JQTG8hIafLBKcCwskx3rabj5f9GcYSI3PAeBv5yP1YqiRKOUP3xT2MbBu0//j5GXx/Suy9lZaaQ4fPizMdZ9qMwuYwNRFHUGB/5jzrGTS4VPMZAmvS6DtKr23BOqMA1r4FO7QU29f5haJ/KihOpNE4m2/oasxsMKqsdmJk0vff8eUj4A2RWrWTx6Qt5cMSSilopQOVWHaMrQd//bSH4ycsYaC/4MpzhKcjOYOk1xePoXR0IVUWtrkV2uTDa06T+aJBv6UGoMoErr0Y9UeAINB/yd7Q208aPOodXxK9K7+IzkSvI9g0vklMkF3ndT21Io8F/DhH3bGzbxKlUltj+c8YAu2OPFLXZWMRzO4oUgOqtw998HvGdjw+1BadeRY+nBimzAQsbh1D5ZNU5RA6zGt7bneNKAbwaX8adPbeRH1xV2m/0cudgOujDVQKmaTJv3jy2bt3KLbfcckTSQaveGlzjriWVrWZmxM2EGpPtncNpGC6dnCP+u19i6zrRpQ9R+7kv4zlhzmGf93DxeVrJp/+L5NosAqhVnHzypH/jv6MOtmVyR0QBAEhOJ65JU2DSFAA8s030dA9NqeEyizv1HnbSwxneyeSsYXOQkY3Rs/p3YFsY2T4GrAfw3bAYd2A2jppmlGCI5GsvY/QVnLBSew81M2pJk2bg5Qi5zHDkkqHbvPpEvEQBWLpB//1riC8tmG2Sz2/DH0iwpfquov16cquprnk3Utd6KioqUWSBkcmQb9ldcs/eRI5PTT2X5ckdNKW9zNjhxL53LQB2zoBUQaCp3noq5tyErBaPcPPtbXT+7/8jv7vgcPWdvpjQle+m988ryLcUVnDbukn0r1upm3Eh8qij6f1j2Cb/jK4qarOBDXmdSUoVGaMbgcTE0A3c96SfqbLBiz0xTlpcgWeM3ERCqCiSh7xZbN6R9wo9lVUX4RnvwlM7DyPTi+KuwhmawMmqix+6qoibaSKKj5pRKsYdLkY8jq3nUYIhhPzW9M+N5LhSAA/03z0k/PeQt/M80H/3YSsAWZZZtWoV0WiUq666irVr1zJr1v5LLe6L9pTGFs4h1qqz7nf9nH2Rn8UzFbZ15zl1nELwgd9g6cNCrf/Bv+OaOh3JcfA26SNFTyxPunUZtjGc0sE2soR7XqHGcw6x0TyzI0ibKZJmAg9uHDmQ3Z4DfpGEIqP5a5gmmSwhw1ODiqBJi9CwV+pry8xipIfTKVj5BLH2h3HUjkcZLMgy8tkaL7/O5We8j4c8z5PulwCL8fNU6s82yDtyhO08ummijrhWvStB/NGNRddomNmSlOQAlmRTc/JnUT2FKC3ZH8Az72SSLzxftJ+jrp4z/FNZkK2j9ScPY/YXmwXzzvHEjK/jC1fgDNQUbbMti+iTjw0Jf4DEC8/hnnUimXV7rRQHjK4kjGIeN2yb7ryObUO1po4ZFqtJpeLFKbuYXfNZ0vkuMCJs3VDByVUysW6dVS8mqBon45sSJ22mCCuVBNXh6Y4me5kSeS+rOn80/Dzk8KihoorDj1Izp6R9nCMCHPlQT9swSK9dTc+dd2AO9ONbdDahiy5FrXzrRN2NxnGlAPrHSPs8VvuhEAwGOfvss1m6dOkhKwDLtlmVSPOrri4SDovwiTLXzIyw7Mf9XPfZCKedr6MvfYmB7cXCxc5miG5/FCPbjW/cIpzhyW963v5o0sSR7y5plzM9eP0STc6xlVNreiednWtJmkm2OttZlJpN+LVW/EvOw1FfupJ4LCq89bzLEWSBfyrt+SgAk101jHMMO8wVRxBP3XxS7a+NOFKgeYdDNx2N4xCahp3Pg2Hg/tX9vOdfbiB6qod8Po1+9U7usAqjbwmBK3MhJ3tHZEq17CLnshTxkKmYiFedRFIfrswmCxeB0Fzc7uHiPkJRCF96JWZ/P5kN6xCag8i7rsfRVOhfqfASvnoOPb8ZrkimLp7OI48Jtm50suQalSnzip+LlU6TeWNVyfPK7d6Ba2YtmdXFSkAOl9rHo7rBQ71RHumLYgHnhPxcVRUispfpUREylwbnsCa9e8itqyBxsnciQWc1Lpr48887aThd4pF1UbpjOhcsdrO7+mXub7kDw9YJyCFurv0c452Th/qNuE9kfv03iGY2osp+Qq5pR23B2L6wDQMrl0NyuxFCkN21k46f/Wjo+44/9RhIEpXXf+AtPRM4rhRAWInQP0o66PBhLv7o6elBVVWCwSCZTIbHH3+cL33pS4fcX0dO56ctneiDP6Z+w+QeeYCLLvPR124z9cQmMjOzDNx/X5GA8S4+hYGt92EbWeK7nqZ+0TdwRaYc1r0dLJIQpH2no/SsLGrP1Z7OpeEgE12jK4B4Xyvmo0sJPvk8QSFoPncRq0/ZxeygSvpX/4/6L34NxX/gU/aQ6iGkepjhakAWEtJePhlLaHgnvwfbNEl3rUB2BKk88UM4gs1D+zgam6j74tcZePhBjM4O/GctwRueRm1dALMxy4+Sa4f7w+a27qcZ76ikQi1EGanVfrynjif5UiG6Jn35fH54f5aPXPoBAv5/ktBX4lGbmFb5AXyu0spuWm09Nf/yeYy+XoTmQK2sGvItCUngWzQRrSlEcleMnrjCKxsltm7UEQJmnOQmuTVLrtdAC8m4xmnILheuWSegP/V40XkczRPwzJlIx+YerExh1uNbMnl4sVoyR75lADOeY82EAA/1RYeOfWIgTpNT47xIsOT6p7nq+Pe6K3kluQ2HpHCydyITHYURsaJKTF3i5RdP9QytgQg29HFv7LdDx8fMAe7o+hWfq/8mvsG8VLKkEnZNJ3wMI3dyu3cy8OjD5LZuwXvyAnyLzkJvby2JJEs893RhFhB560Xq7eG4UgCXh68r8gEAaELj8sNMB93R0cENN3wQwzAxTZNrr34XF154ABVFxqBX14eE/x5ipolcJREcXEDkHD+Rui9+nejDD2CmkngXLSQj1mInB00vtkmqY3mJAsi3tZJr2YWQZbRxzWjVxWaCA0Xv78fOZ1FCYSTHsA22PpCh16hBmvYhcjvvB8A/+WqU6tlMdo+ddC2/dh36Y88MfTaWPs3MmqtJ1cm4Wnejd3UelALYgyqVjr66BvLc/2KU597Icsb067liwfVEgh40d2lqDNekKTg/+RksPY/sKoyIFcAy81CcxYKElSVt5ShUzC3kYIq8dx6OiRUkX97JTo8Xw4zzv/fLTG+6ihMmXMG2qMK0RYXFRzszWTams5i2zUTNiSsNfq9GqGHcqPcmOQqL+6iL0LEmTWJllKknuTnryiDyjhzrfzs82Kl/d5iaS4MEzrmAzMb16O2FSBzPgtNwTplGUvaT//T5ZNvjGJrCQLUfj6LhSubovWs5iSc342iO8OqHTyq5jmWxJOeEAyVKVhUyM9z1zHCXLq6SZYGuwciM5Kajf2QUaOG70ttJmLEhBXCs0Xu6af/xDzBjBT/EwEP/INfagu/0RSX7KuEwQjt25tgD4bhSAHvs/Ec6CmjWzNk8ufQVMqlhx2C8zyBYqSLtJ23AaAQUheKIaHAIQcgl0zCYvEsoCu7pM3FOmoJtGnS+/jMy3auL+rH3KjKS3bGNtv/8Dna2oCTkUJj6L3wV7SDMK5auk1rxGr1/uh0zEcd94klUXPcBtNo6Mr0b6Xr9lxjpHkITvoDs+AqZVp2u5RoN16rY02zM7AAgobiCRf3mXnmt5Fzaiq2o5y5EByT1yDiODdPm/hejPLWqECr5zNocz66D795YycQxIgJtWyLXK2NbOZzVCrJTploNICGwRnxLjVqYkFIcVaJW+nBcNIWuJWHYNvy6bdhlsmEX1IRAOQu2pbN8e2cbuUGJqAi4Xq3g4b928ekrq5nWOLYz1uWROeFUH1OmOMl16UiWYOMdxWbNtr/2EzzJg2d8A/Vf+nfyne0IWUKra0R2e3hteZTfPRrf85SAfj5/rcYsM0HiyUIOI2MgxQRLsHyv8091OUuE/4EQ8BeLHzkfKMniGlYq8MpjDxzebPId7UPCfw/pVa8TvvIaHE3jye0aXEshBBXX34Die+tc+2gcVwoACkrgcAX+3ui6XST8AdJJC2/QRnMc/ItR71B5f00Ff+wsjOAEcGNlBXNdHtx7rZKVVBVUleDEC8h0rxqxReCtO2Xok21ZRJ94dEj4A5gD/aTWrDpgBZDWu9C3t9Dzq/833LZ6JX0OJ5EPvJeOV36MlU/gCi+g6/4IyfV7Qhiz9Na2o/MGsa0PICSV8Mz34K1biKwV0hg4Jkwks35t0fmUpgaMJ17Gt3gJau2h5ZjZm4GkwbNr4kVttg2tvTmqI31IkopbHXbc5QcM2u/rp/uJONgQWuCh8X0RGirDfLb6An7d8wwJK0ujGuaT1efi22tlasbMc1//azwUW8WVvkWEfF4GEsO/leuXRPC5FR7oiA4JfwDDhg1yBp9L4tb7Ovn+hxoIj1KAfeg8rXm23tpBplWn7qoQtr7XUNoGYzBDqEmCZGoZ6Z61eFIn4ZlwGc/slSYaYMWWFNMrhsNQzViWmW1JauocdA4688OKzKLQoQm55moHM8Y5Wb+78JtctdLHBedey2Pxe7GxcQgn7/N/BLF8gEwkj2NcCMlxcGHOuazJjg1p1r2axh9WmHmy56DWbOyNUEYRmbKMpDmo+ZfPkdu5AyudRqtvwDGu+ZDP82Zx3CmAo8IY2YEPtUaMKkmcG/Yz3e1kwDCpUBXqHdo+k5C5KmZQe9qXiW1/HElxEBh/Hs7QcGy0bRroba0lx+XbS6M/RiOV72BF+w+Y3H566bbXXiZ42UVY+UJ2RVWbQ3L9iEVVLgnv7C0MbPzLYEuGnpW/RnGE8NQWUgn4Fp5B4oXnhzJoyuEw3smzsOqn4p48ucjMtC+SZo7WfB8bMu3UqEGmOKuJqMMCyqEKIj6F7lhxTn1J7ufFlq8gCxdTKt5HrfcMFNlJfG2a7seHFcbAKym8k5yk5zlZvdLLpfUX0VArGOf3U+kozfDZku/jodgqAP5pvshVly/G7g5hZVVmNrmZVOfEsmz6cqU5/lOYuBwSO7vy9CWMMRWAbdl0PxEj01qw35tpCzUkow8MzwAll8BRpWBkBuh45UcYqUIq59j2R8ln4zRVvoftHcXVweorNNQapbCUe/DHLP/2ZT5z00Ki8xuwBTQ6HVRqh7b2JORTuOWKSnZ26iQyFvURlbpgPdNdJxHNxQjmg2i3baNrVcGfVHnzafjPnnLAa2ziRpQNy3Pc//PhrJ8vPhzjY9+qp2bcoZlmtIZGnJOnkt2yaagteOGlqFXVCEVBjRx4epC3AmUFcARQVIHqEOi5YYmvOcWouW8OFIckMcF94CMVSXHgqZ6Dp3pOybZ42qCt1yC88CxyO4tXkapNJ2Lb9j5fqr68zra0wPB8FNlfGjGlVlUjOV0gZLBNbJFCcgmsTOF5eKfJ5DLPlRyX6lg+pAAcDY00fPUb5FpasCzoM6u47bcWsV6DRVfpjD9/M3104pG9NDrGExylwMyOzFZejD9L3IrR7DyBpbHtPJtw8cmqc/ErBROK363wwQsq+dE9HUN6e0KtgtPzGhkDTDvDhp7f4NHqCLtmEF1dCLNUK2S0S3UMT57UQIIHns/w6tY0rCr0ceMFLi6cX/jbtm2yVhpNchI3h0fWl9vjmb1+La7VG9CmTCUw4TQ0dRx9XXmmphy8uJdTYbZwc19bPw5V4NtHcRUzaxFbMxwO2vNsnIb3ROh+LEa2Q0erUJjwiSqcNRrpni1Dwn8PmfaXOGf+DbyyKU06V5idRPwKJ03yoAUVaj53Nj2/fhEznkVrilA9sYrxgcNbQGWZOtne9eS2PkyNbTFt0sW4Kmbwxm6DH/8V8oYfsLj6hOnMz5lYGzro/cOruGbUotUcmD9gW/9ult1b7DfKZSxatmQPWQEo/gDVH/sUmY3rybe14Jw0FeeUqaPPDN4GvD2v+i2GrAjCVSrphEk2beF0y7h90kFn7DwaRJMGv3u0h1c3prho2gQWn3cl1rMPIxQV71lXk2itI2gVZPdodOTy/PeuPUW0XVzjG8eps08g+8Zg1StZpuIDN6FFaqk48Sb6199NJvoIdVdPp3UwuaYeBVmrBYpr/Sre4jQDamU1amU1a19JcudPC4VFQpUK3kWbuLX7V9iDInu66wRuqP5EUZWx3dkd3Nr+LfTBal9vpF7lkvCHuGdgJ616PzOUYUfkiRPcfOfGBnb3ZlHUDB7Pq/Tp9xZdSzLXQtg1A99kJ8nONAMf2sIK5VWm6OdihyTO9FSxszdPd7Qwcr//xQEWTveSU3t4Mf40a1LLmeyazim+JWgoTFRCzH9iM+ayF8gC2U0bSL3wPA1f/SaG7mfbvSne/+4wz0hJLGxOU31sXJ7FtODjl1ZSHRp7lC07JYJzPXS2R4FC4feWO/uY9PkK1LCM5neiBQuvujRGKc9GX7zwTLpzyLKgqcoxdE7vyU04J1RgpvMoYTey5/Adm9m+jbS/+IOhz5meN/Cd+mP+98E8+RE1GO5bk2Xqgil4NnRg5wys9Nj1qEeSMGJsTW/AMkoXZJr7XoqyX9SKStQzzjy8Tt4ilBXAEULVJPxhgS9UmDEfiVQQR4IdnTle3VgoLPLIRlgZXMCV71vANEOh7W8Gje8NIO1DUa1MpIqKaN+bt6i59jomnzkLNe/A2zAVrWEceqoDYVsExp+HI9CM7HHjm+0js8NECyhoDReQ6X0N2yyYGWRHAE/16Jkkt4wYzV79+Ry/Tv95SPgDbMisoTW3C2cyRqprFZaRZXONc0j4D1178klOdC8ibxWbVxRZMKneSdS/gpWJF5hpRkuuwTGoXAInuumub+FleRkNuz/In58bNIVIUT5wboR/vBglmjRRZYElZbiz+9dsyRaydXbp7WxIr+Erdf+K1pbFfOHvRecw+/vIt7USmnoifo/Maz8YYM4pbkK1Ks6AYMkMH+9bHKG+QiNpZunS46iSTK0aLFpkJSRB5RI/iQ1pUtsKAtJ/skS790Fkl8kk/3XsedVVXx2eugWk2l8ZOt7XtATVW0O9olFfMbqCUCIelMi+i9gcDPHdz5a0Rft7GUiWniNmy3gAtdaPsp9COnuQhcxOeR2zL5nHc7cPtyuqoHHSWzsy582krACOIEII3iJyf4hYqni40xk1+b8X4D9mB6i9QiKwn4LgO7O5krZHdJVxk4ME3TNxqBHyyQ7aln0PM9uPFplENuSkM/kEbt9kKhaehM/RhCxNpuHM75CL7UZIEo5AM5pv9AU8NY0FIVQ/XkGSW0jk4iX7pPQYPRseQa6djO1SqRZ+BKJIUdjYuIRGnTZ6PeJtmY2sTq/klIoPkM63DK3QDTlnEnAUVpc6azTSziizei8aEv4ApgX3PDvAkpP8PPRylPecGSEl+tmaLc7W2Wf0IKXddLYZVEtS6fBTEmhOiYveFyFcpbLmxQRm0uaca8I0TCqYAFvz/fyi8wl25HsQwAWBE7gqNI+AMvzdueo0xn1eo3d3O7IqyLizmEYVidRrpP0d+AerXMmqh4oTPoi3fiG56E6coQlIvhl0t4Oi5glXH1rk2sEijVJW1CNFqQ0F6RgYVuQCCKPjmBjCd1UlJr0oNO63f7fs5bzQpbw0+RHO/vgFbHvaiTtsc9oFQerGlxXAHsoK4Ahhmibz58+nvr6ehx566FhfzhC14VLTwZknePCdlaJX9JKT/dRYDWOmND7J5+G5aLFt+rSAm3H+MxBCIhkzMAa2FdIGCwl58insNFeQ95zL35JtkFrOpcE8cz2TcAXG4QiMHtM+ksknuqmsVwlVgtazkdmVs1mTGy60LiHTIIXoa3TRkf0bAN5kI+8Nv5s/9/9laL+FvvOZ7JpDlTq6zbjJOYln44/x54G/c0nwMiJSAFWdilcOo44I5wxqIVozpTWG0zmLhgqVn76vCnd7D7kH+viX+k+yvHoNLzCcwqG3V+XhrSofOf08zOeWDrWrtfVo9YXnEanRuPB9ERZfHsThklC1wgpuwzZZGl3DjnwPtYqf2XYFO1OdbHF1Md9bvHhMd/bT6v8bzs6P8eTPFRJRk6knT2Hcu7z4R8hM1RVGbTgVX8Op9LTneeD/9bBtbQZFFZz77jAnL/Hj8sikzRydegyRyxPqiKKk0miNTWhVh54hdA++cYuJ73oGhkKVBZGKaj55RRU/vbeT/oSJQxXccJaTGnUr+oW76W29h+qaT+Pw718BAMx0z8FV62FLcDWnnNREg6ORqgPIjHo8UVYAB4llW+TtHDkrh4yEQ3KiSho/+9nPmD59OvF46Wj1WNJc4+Bjl1RyxxN9ZHIWcye7WLConf/quhVj0GRyafjdnBO8eNRqTjM8Lq6qDPFAb6G04FkhHwsDfgwdNr6e4JE7+3jPDQUTkyM0iU5zHZbnHP6vb3hNwv90P82/1bqZ52k+oGuuqNW46at19LTl0ZRxnKdXITsUVuVWElGqeHfoejB66ci+PHRMMt9Cnd7FhcGr2J3bzumBc5jknIFPGdtZOcU1g9nuubyRXsEOPcdmcxrPRPMIurgwkOO8gJ9qn0aDo5m+YBuyxFA9BoCwT2ZyfjfS4y9h1UwmtyqKfF+U08+fStfZXWw1NzPddQIiprCty+TZqaez8JpxOHeswahpJrRgPraSpn/Ts+Siu/DWnYKrcibKiJxFaTPPyvQuPmzPYOILW5FWPozd3IR64RSYVAjvtdJpJJcLt1pDMP1+7v4fMbQGZOOrBpqic+0nLJS9Slyahs2yf0bZtrbgqDZ0m6V/7mP8LIFdE+eO/hWszRQWjJ3paOLcVZ0of/w9dV/4Ko6GAxPCY+EMT6Z+0TdJtb+Cbdt460/BGZrEZEnmex9qoHsggxhYjtrxd6KZnqHjRps5jIUqaUx1z2Sqe+YB7W/aBv16H7KQCatv3dW7R5LjTgEsi2/m7v6X6DOSRBQv14VP5Qz/gadLyFoZevTOoc+yUNC7TP75z3/yta99jZ/85CdH47IPGYcqcfacADOb3eR0C4cvwY87bhsS/gAP9d/DdPfsopwrewgoCtdWhVkc9GHZUKUpqJLEjg0Z7vpZIZokkanHKWRsy8Cj1vF0ZqCknydj6w5YAQAEIiqBiIqeXojY9jDnRdOcW3s1Xuc4wq5ZbI/9o+SY/sw6Lmz4PqrsPSAfTFit4IbqW+jOd7A5HeD3vcMLfB6KRgnnJRaZfnxBL3NqGvjklTl+93CUVNYi7JP5+Nws2d/+eHBtxVNErv0o+ZYExuMtXLHoKvqr04yTZqE7XIS8Mks3mTypNFEbnsTcSjdXVQh6VtyJwiKs6GxSqRxmegeBKScM5XByyxoXO6cx6d4nMN94Awugtwd782ay//plEi8+T3r1ClzTZxI470Ly/eOw7eIiLm+8lOL860zCVRKGZdOay9Oj6/hsif5YsVP15Asg5ryf1YnaIeEP8Ky5i2knzWTck8+SXP7KISsAy9TJDWwj278FSfPhb15SYgoM+VRCPpWYJejZPiz8HYHxaMEmevUudmW3k7KS1GvjGOecgCoOLw16v97Hk9GHeC72GKrQuCzybhb4FuOWj5zfAyCdNdnZnaM/bhLxKzRXa7gcxy5X0HGlAJbFN3Nbz9Pk7YJTsNdIcttgMdkDUQKmbRI1+vdqM/jXf/08//Vf/0UiUYg3zlsWhm0jIVAlgfwWcAxUBQsvSGuus6gQ9h7iRnTMY2UhSoqOt2wdXlD2yF/dXP3BL0DiASLyRHx26SwoIB9aWmHVHSEy870EMr0gJFR3JfmuOF651JQUds1AkV0H5YD3yB6anRP5Y0dbybY1VpaZXS58QQW36uH06R4mVnvZsjFDg9yO9fv/LFpYl3jlMdxzlpB6rZ2I2kzrTpkfvjCAJKJcd3aEroE8yzenOetEPwumedCTG7D7LmbX3Xt8Cw78c1w4PxLDWVHwWyhC5rRcmL433ii6NvfM2XTf8RvyOwphvXp3F+kN6/C869sl9xGsUNAcAtu2eTme4Bet3UOeksuuDlCXsOnbnefSSzTGBaLY62Ywo8HFM1oLCWv4/jZqSSaEK4qyiR4s6e7VdL48nM1TdkXwn/p5wv7xRd9bzEizK9yI+5RP4Up04nJX4Y1MIy6b/LL9v+nUC2taBIKP1XyeE7zzD/maAFYkX+TpwVoDpp3hr71/oFKtZpbnwEte7o+8YfHPV6Pc+/zwAOn6syNcfEoQVTk2MuK4KoB5d/9LQ8J/D3nb4O7+lw6wBxuL4hW/Tz78FBWVFcybV0i9aNk2bTmdjrxOWz5Pv25gWjZgk9F7yOqHX2LvcPDLQSqVYhvuKZ6TqRIKvanVZPTSTJ6j4Q0Mj1oGeix+/xMf61o+gvHGBE7XpqKOiCtVkDjbf+jJu4Qko3qqUd2VpN9op/XLD2AtzVIpDb/0LqWapuAlSGLfY5rstl4GHt9I9IlNZDZ1YRsmQggmjJKltNZUScaLnbYeSeKVvwwgtm4sEv4Atp5HKBKOqVVsTEr86sFuOvp12vp0fvVgN5PqXHz7g/VcfEqQiF/FTkXoLA4MIr7KItteLAw8mgf2yiipVlYPCf89GN1dVAZSTJo9rGyFgMs+VIk3oNCV1/lNe0/RusUH0zEmX+zhqis0Qo88R+y3y4n/Zi3+76/glvTJRf1PMNwY0X68C04lZSZZn1rNM9FHWJN8nZhROuvbGzOfpG/tncVtmT76+jawNTu8NsG2bZ6Kr+f73Y/y9cxqvq728xVjK32qxu78jiHhDwVH/729fyRpHLjpVe/vI71hHdkd2zGzWTJmhpcSpVFJG9NvjHL0odPeq3Pf88XP6S/P9NHRf2ChrUeD42oG0GckD6p9b2Sh4JP9RbOA119awdKHHqG5uZlsNkssHuezN93IT373ewDipolfBt1MsWzX91EkF5Mj76XGuxDlEEfFh4NfCXBj9af4TdetDBh9nOc7l3FmH290/CcAmuxnbu1X8DtLs1OOpGmKk4oahd7OgkIVAhoqPLT+phfZJ/HVf7+Mdfl2bMtiptbAZOehJZ0bid6fousXz2FldDIPtVLZM4PqU+ejTQnh9TZibc3T8djTYJgEzpuOc1pVUeqA7I4+khtTDGz0ku+zCc428JsDDNSu4CT3JJbFZKKDUToRWaG6UybQWPyKuDwSDROdpP2T0GS5KKrHt/BcjLiXwIVu/vBaqUB6cX2CuZOHTQoCL1auNAWDnS8+p1pVTejSKxm4f3itguwf3bHtlpJc+8kJdOzMkUlZVNSp1DYVlFvStMiOSDfhlATnSwFqHCreCg2pMYK1sWPwGkxCz/Yy7tIIu/U+piuVTNjQR+iSK9BmzODx6CM8PDB8PSd7T+c9lTft02RimzpWvvRdk4w8LyS2MNlV+I30Ggn+MfD60PaMrZOxdXble7Gt0ucVN6MlIcBjkd25g45b/wszWhDEgSXn4b/mWmq1RjryxSvlK9XR049Yhk2+T0fSBNo+1mfsTSprliQNsGxIZ61R938zOGYKQAjRCNwBVFNIpnCbbds/O5rnjCheekcR9pF9OAr3xiP7EAgSZhxZyPzohz/m5//1S4QQPPHUU3z/v380JPwBFASWncSwMtgY6FaC9T3/h1ONUOE+oajvHj1BS74P07Zo1MLUaMFDvtd9Md41mS82fJcBow9V72Zd13Bun7wZZ9vAfZxQ/WlkaewEbJEajRu/Wk/b9iyZfgNvzib3zyi2WQiH9TzjYMLThTTbvnOciOtHn+IauTh6sgPsQoy64hg7r4wZy2BGhwVA9pUueAX8XxiP5cnT/p1HhtJypJa3UPuV8/CcOJznKNtpsPOvBla2ICxS28GyZVLBTjoyf+Szke+yKxoglwG1zaa5SiNQ10dHYrB6lqMJr1bPWVeGeOC3eZZc+29oax+HdIzAORegNTfSs+EXZFsFQfcnS67f6ZZ4biDOFLeTGoeGs8aLb0aCxPrhmYTkEDjrip+7kGWC512Ic/xEstu2oNbWYgV0vKctJPnisCPcfdI8tNo6XB4Ff2j41TazWfI9A4S8PiKqQp9uIAEfMCO88rMB1saiACxcNIvZbifmikJCM6knww2B6Viyj0bLh/dkUMIROvQ2lnb9o7APEpVqDevTa+jItzLRNXXM7092BglMvJD+DfeMuDmJqCdM0ioNNx6NWq2hJNx3UeC8okWBY2HlsvT97a4h4Q8Qe+px3HPmcd6US1mfWkXWLvy+KpUaprlnl/SR7crTfm8/vc8nUfwyTTdUEDzZg6zt35hSFVLxuSQSI6rJhbwylcFjNw4/ljMAA/i8bdsrhBA+4HUhxOO2ba8/Wie8LnxqkQ8AQBMK14VPPeA+FKHgV4JDikAaUXBFEoK911S5pEIeHtkO4Gn9Op5wmpT3QQYy64oUQHt+gP9sf4iuwamsT3LytforaHaMHo2Qy5hkUhZun4zmOHhLXkAJEVBC7EivK9kWy25md2+U2mAFzn38sMNVKuEqFT1mEF+XoX+SA+ciH5ImaL+vH9sABATmOMnu3F4Q8jU1Q2mV9VQXXa//imxfobCNMzyFqnmfRPOOPluQAy7kkAtzYMQoUIBa4yf26IaSnEyxRzbgnl2HkAr3kIsKrGzxTl2Ppph80hm0qo+yO/4lgtocGtwfwjcuAq5WXmv/DoZViHJSJA/z6/6dmnHNXPfZOrrylQycMgFFspBdbtwtT5BPtgCweFKcVzZ70M3C+RyqoKpZ5Zdt3Ux0Ovi3plocapTI+zNID6okXjdxNak0vq8CV52GbVvE860k9V5MSUVSA9SeeAKeOXPJJ9rZ/cTnCM6/lur5X8PO55GqFSSXiuQqjuTKtbfSe+cdZNauQa2p49Nf+Bq/7EvQJBxs+lOCZGx4BvPy81kmvGc8zkEF4DxjPKsfcjH5hEr0aQ4GXFAhBHk7j4XJdNcCxmkXsCPjYrzLJGftO0WDEAJf01noQGbnk+AMkp10Hn/Ud/GR0NlD+1UoPq4MzeMv/SMWq0lOmrQKqlQft9R+mb/3/ZkBo4/T/eewOHBe0Xs4FmYqRXbzppJ2o6+XJucc/q3hu7Rld6PIKo2OJiJqcV4f27TpfCRK73OFQaQRM9n2P11M/2Y9vmn7n81XBlQ+/65afvtIDy09ecbXaNx0YRVhj4SZySC73nyLwDFTALZtdwAdg38nhBAbgHrgqCmAPY7ew4kC2oM8Su6EJWefzelnnkl3Xidv2wjAK8vocT+pWAf//F8n4OSCD96I+7TOomNXpnYNCX8o5JZ/KraeD1UuKnFqtmzN8vAfe2ndlmXSbDcXXB855NwmXkepM9Utn8RtD6Z515kZ5k7afxSEGlCInOYjcpoPM2MSW5PBM8mJ4pKovtCL3beCVHcHQpKwV4HvjMVolVUkO14fEv4A2f7NpNpfI1lzIW19OrIkaKhQ8XsGV7GGPVTfspjOnz6NlcqDLFF500LUah/IowgASdDdlkdzyISqVGRP6YxGKAJ9204is2fTq68iZqxiYlWMhBwkGls2JPwBDCtFR3IZfmczcZfFT7s66R/MiumUBF+pmYvMHYXn2vpLvnzRTWxPNxNHwl0p8YBeGHluy+boyuvI2WfYbv2NwOVTCV06l7S0G6N6CZY9hfb482zs/T2WraNKPiLhq+jJd3KS7xQkzUdo3AfJPycRfWU5QlPwXzUFMS9FessD+GtPQ/PXYGYy9P7pdvJtrfjOfTdCdSB+9TNu/MzNWDGJv28pHXUnTRVP0IW0eCZPr/KSD2qsiGR5eEvBRn9ZRZAzg9VMd51AhXw5d3XZQOEZvBxL8Y3xeaodY88cVVeYwORLydTN5eX0LjYb/XywcjFTRpgIhRAs8c+gUvHzfGITTY4KTvdNpnZwRjzDcyLNzknkrTx+JXBAwh9A9vpwzZhFetXrRe1KZSXRPp2tL7lZ/lQdlfUa4ct9RPYKitNjJn3PlVoQ0i35A1IAOdNinSNN4zka84WHbnR8uTZ67niS7JbNeBYsxH/qItQjsM7iQHlL+ACEEM3AScAro2y7GbgZYNy4/S8i2h9n+KccksA/UBySRJ1DQ7dsJAHoMBAtdjw/eSdMnjUTRqxJ2ZUrrVS2LdeFYVtFDtX+Lp3bf9hOJlmYRm5amaa/O88HPpXC45NxBCcgKQeeOz/gnMSE0NXsGPgHNhYeZRKJgSXs6DR4+JUoJ453I2zI9+oIReCo2LfNU3bJhBd4Cc51gxCk166k5293Dk27XTNmoTU0oFVWke4eXtyluCLIjgC9VjP/88e2oRw70xudfOLyKqqChXtyz6qj8QeXo/emkP0OtNoAQpbwnTqe+GMbiyqM5GdP4tdfbMXplrj641WMn+xEDUro0eEpeM15NtnVz6LOKhSDCbtOZmWmkuej3VzqbCm5v2SuYCdelUgNCX+ArGXzRMrm8srZ5HrewMpFcWz/KbPO/hnfaIuRH5x51DtUajQJt0izPVGIQIuZm4ixCUzwZxtRZDfre37NnimNbiUYGHiIuHcBjc5mKhxV2FsrSL08XAw+dvd6vDWT0Jsl2PoM3oqFSKqGZWs4Tvs3Wh8TWHmbqrNm0p7rYr3ZRcOMmbSsK7ad+xu9bJp3Biue1clldOZ/w8ufB4Z9Xvf1DFCjVXFF+ON8c0eUkdOufsNkZzZPhCz5RCu6kSAu9xG326n3LSbgnIwQAqes0exvps5bD4hRawcHFPc+31W37DnoEE1J04hc827y7a0Y3V0gBMGLLkNrnsRz/4jx/ENRAHradbasSfOJ7zZQ0zg8sJIcAke1QnpHsdNW9R9YGGdrLs9fu4fNT+9XbTK//DHmoAku//e/kd+9i+qbbzng7LeHyzFXAEIIL3Av8FnbLo0ftG37NuA2gPnz5x9iguU3F1mIoURwWdMsSQtt6DZGpnjEPtfTzHPJ4unpIt+0kopWfV35IeG/h542g962GNGe/0f1KZ/FV7/wgK9Vk31MCF9NNjWPrmia1bu8PL+mINgUWZDv0+l6IErP03EkTaLh+jCRM3wo7n3/6CVVwjYM4s8+VWRzzaxfi3vWiQB4qk8i27ue/Inv5zXVJiUUxJoKuqPDo+4NLVlWbkoTSWdwumUaJjoIVvlQq4p9Bc7JldR/4yISz23D0g3S45t48Ekb24ZMyuLun3XyqR80MukzfgZeiZGPynib0xhbluI6eT692jYm+a/Hdp7FT7b3FL7DwKmQKS6yU+9fDEBHvtTp2JoziJz0MfTetdhGBkdoEnjDnOQzeT2R4tpqjRZjGy35Lnbqs3Cp1eT2qkftUEJk9T72tmflzH78kpO0mcLM5Uhlk2T+rZm0miUU9aHc1Yq1NU97/XKanVOJPfcYwbMuwjHzcnb+yR7qr+txg/EXVHGn8So3XTuNeLdMrMdEkuCsq0M0THHh8Cp463LUNGn8TYqW3OeyaILZDdXodmnkT40ZpfPV35PtLZgWFW8tvpln8Frbt5le8zl0xU+DNg5V0tCkw4vdPxQcjU00fO1b6N1dSE4nak0dsSi8uLSjaD89Z9PVki9SAIpHZtz7K9j0/fahBczuiQ48Ew5s9h3fKw1Iw0DfkPDfQ+r119C7u3E0Hv5g90A4pgpACKFSEP5/tm37vmN1HbZtY1sGYCMk5YgWUpcVgbRXd/6wTLCy+NFPd9Vxbehk7o+uwLJtLvTP5uRRFk45R0kLLMmgKjlsoHfNHUjBKWw2HQzkc0x2RVGtBJrix6tWoymluX8koaBJdfzun22Y1vCP9IpTQ/QvSxaKoQBmxmLX73pxVGsE95NDCMBMp8nvqZA0sj1RWIfgrjmJPoeLW/WtxNIZ5jom0N+ql1RD27Q7i/5ijr4OnYZJDt73rzUEIsXCQ0gSrqnVuKZW07krx2++VDx6Nw2I9hpUzQogWR3En3kSc3MS95xT0cadwKzgxShumdWJFBaFcN6XUg2c5r+OaLJQ2nJC6BrCrlkAzPW5eXKgeLyyJOTH7QmA56yi9g9UV3B+xOKNzBbWZnYSM9P8suc5vlF1AfHsFqzBCBaPWkfIOQ19hNlpD5ocQEcQUsIkrRTPnPUGr+iFgvCKW+HDn/0onnaFrNGH5AiSb9+BGYuS6a4AivsTdwq+8slL+PvA60z7XBMT0g1UeTzU1rtQVQl/SGXizML329yVZVWqOPJmvMtJQJG5rCLIfT3DSiCkSHijGxnoHfYrGckO3D09OAMRejKruTPxAldH3s9C/5mHlTDRMvOH/K4qgSCS209qe47Y82nsahXNIZExigdWyiix+b7pLmZ8r5FsWx7JKeFp1tAiB6bIKlUFVYihcq/WaMXiZflNLSJ/LKOABPBbYINt28ds+axtmZj5OEZ2AGyQVA+yHEDICpJ2+I9H1SQiNSotg7Hd1Y0qV3+sGv9e4WMBxc3V4fmc7ZiIsrsNa2ULSvAN9ImTUSuGnVGV9Srzl/hY/tRwkYszLxXIiaUYgJmPszOd5Fe9MT5Z1U5ftoNNRg+bc9uY6pzG6f7zqZDqcWjFP7JJ9U5u/bCXroEsGzrczGj2MiGksvHZ0nUBiQ0Zgie6MU2blq1ZNr6eQlYE0+Z6aJjoGHqxZY8H9wlziD/zZNHxjvGFQjWap4ouPUqs9w1CsptFXh/BSzeRzih099Vx5xMKumHTGFBZ2VsQYq1bc7TvzBcpACOeJb+rDzOZR6v1ozp8OFyCXGZYjQgB3qCMUFWczVOxF/pJr2hF5ANoPg0zbdHelSebtnivEmGTkmVZIsWm7BzeVTGfE7xu3IpGzujDsLNM90T4UG0F93T1o9s2l1YEme8vNUkkzQRrMy/w8MB9CAQX+C9gay7EinQbP+tbzzcqv4yZbUdWPQS8k3GplZhWnqkVH2Rz7x+xsZCFg+rQ1XidU/ApATYkXx8S/gDjxFSiqQqS2Qr8HV/FDmbJbLiTyLXvxVnnYm8FoKoy05z1eNVKWqJ5DEVGC6ioaqkwPS3g5emBOLFBc1dAljk94CVn9HKGzySkhHl6IEWjU+OSiiDGusdK+jD7duKtKIRUCgT39v6JSa7pVGoHb+vW072k2l8l0bIMR2gigeZzsdwu+jPrSeZbCLmmEXBOxbGfGsLRlWm2/qTgh1ODMksuCvDPv41QZpUKtc2lI3shCTzNDjyjbNsfdQ6Nz4+r4Vdt3cQMk5ZwJSdNmER++9ahfYIXXHLc+ABOBz4AvCGEWDXY9lXbth9+My/CMrIYmWEbp6UnESjY/TJKlQ9JPXxt7HDJeIMyn/1RI56AjMc3+mOXhISyYg09v/u/4WPHT6Tm059HDRds1E63zHnXhJk0zkHGMvD62nAbj2NEB0MVx53N76IW5wdNMNbzSG4rLfnCto58K5szm5jS/ik028dpM7xUBTXMfIrE7meJb/grTjPPWRMuIFh1CbLqxFmvkusqNnc4qgvXv2tTht9+p33IxPXcAwPc/I0GGicP1i2WZYLnX0xu925y27eAEATOvxjX9BlDfaVTBWf5J8NT6ez5JXnZh8e9iOqGNm666GQ2bA9gtJqceLoXf0ihdVuO3IgwOiOWoef3L5N6eeeeh0jdV87j6pur+MvPu7DMgvC/8H0RquoLfoTki9vp+W1h8Z96Qj3Z+jq68mlufa6XbL5wM/Omu7n0pCChtMzAGpP0eJNdyu10ZZehSC6mVdzEuaGFnOzzYmETVpVR6+JuTL/BPb23D31+YuAeLot8GIlmklaOgVXbUX/3Z4TTRfCLX4cJlciSRmPgXMKuGaSMPmTZh0uuxjuY1C4+onBMSIkwa+N7efR2GygMCiZMkbnk/Z9Gra3H75DRKhXyPQWfiuQQ1F4WYmtnlu/+uX0o935FQOEr19dRHyn2H41zOvj2+Hp25/LYwDiHhmJs5KWWn2FYKRxyhM/WfYaIsw5ZkolXzCCx+5miPpSqySTza5noPJubbQ+SaaDGu7HDFQjpwN8v2zQY2PwA8R0FJZOLbsdUBLsD24nlCkJ0V+yfjA9dycTwtWMuCtRjBrv/MOxz06Mm/g1Z3v8v1ezcniNSrTBhpptw1ZE1UUlCMMfn4fsTG0gaFiFVxvXxT5PesI58yy5c02bgnDLtTS0ucyyjgJZRyPZ6TLGMbGmblUZSA9g5A46AAgCQJEFVw75HDXp/L31/+VNRW27HNvItu4YUAICctsj+oRfvRJXqqy3S/X3Ymg9vzRk4Gs9ke6fB2YEseTlIS6J42X673sLMQB93/11nd1eej19aRb5/E71v3DG0T2zbwyjuCKFJl1B/VZjEugzWYLUzV4OKf7oLy7J56ZFYkX/DNOCNl5NDCgBAq6un7vNfQu/uQqgaanVNoY4xYFk2apuXJY1TyCWfxe85lxaxiLsGJFwSXNvgZpqp4fVJPPuPAQZ6DMZPdxKuGv7Z5nb1Dwt/AMum+39fYMp3L+VTP2gk2mvgC8lU1WmomoTRn6LvLysQDoXcDYt4sFfF266zrSM5JPwBXt+Q5nMz/Di7DXg8Q3t7Ht/JlzD5vFls4X9Z2/0LPFotYedw2c3ReCn+TNHnEz2nk7M99Br9BGQX+foaHKEw1kA/A0sfoubmWxCKgiRUfI4mfI4m8slOUq3L6OjdgLv6RCqrhovbzNcv4pW/FJ9z+2aL5FWzqXW5cNXDtK/Xk96VwzZsXI0aaq3Kg3/vLCq80hszWLcjXaIAAKod2lBkT1rv5uWunw1FR+XMPtZ0fIeFjT/Eq9XjqpyJp3ERqZZCNlRHxTSoHs90eT6JV/4Psv1YQK94CO20r+CuKo21Hws93U185xNFbVbAPyT897Bz4EHqfIvwaPWMhpW30fcKzMisyzDhTB8z3n/0k8BFVJWhCWxVNYE3ccS/N8fcCXysEXKplhdCxdatgyrq29zcjM/nQ5ZlFEVh+fLlB30ttm5gpdMl7VauWEkZAtT3hNneodO/sZH66i8gx2KoVQ5kfyUN/R206hIV6uiRBMIuKLWX1ie5dnEItWt1yT6JXc/ib1qCd7KLmd9rJN2aQ1Il3E0ajgqVfMogmyldwZgbZVWj7PEijy9dbCdJArXfzUUTJ9GeWUaHWMTfegthhVHgf9oTfKammgd/1DW04HbHhixL7+zjhi/W4nTLWPFSBW70pRBZg5px7pLwWFsSCE3GPH82t66xiaczXH5qkJae0uX4oSzEft+HNagYYs+b+BLNRK6bR5/xOmm9i8B+FEC1WseGTCHayScH8KuzuLt/5dD2tbTxlfddhfPnvyXfsgsrn0ceMQI0cnG6lv+C3MAWAFIdr+GZdDE3jPsEf+37A1reXWTq2kNOHx5bOatVnNXDv/N0zqS9v9SJ3bNXneTRyBnRotBYAMvW6cq2oktBLMXPk7VX0lx9FpJt0iGHmegI4ut6CTs7Ig2KbdG3/h4coUnI6gHGvwsJIWRse/g3Zo1SkNvGxLItcmYGCxuXXOyvUkMKFWf76XlihA9HAmfDgUfPvVM47hWApLiQZAfWYKUqIWQk4cXIZVEO0gfw9NNPU1Fx6CMIJRzBe+oiki8O188VqopW11C037p1GR68e/hlqqhRue79IezWFtJrdvO1OfW86PLgkKZyknseK9PDcc/TtXlsfcOHxwmL59tktS60qpmw43EY8WJp/nFIg8rR1aDhGvFyGCmTjnsHOGm2eyiV8B5OPM1L2jRpz+notk2tphJUx36O0+d66Optp7L6Qu7uVoBiwbRNz+LyykULlnZuzBLrM3C6ZdTaAHt7jV0n1qOEioVKW76fFxJbWJ9p45SvTSTUVkl8S8GUsrU9x8wmF2t2FN+LFjWHhP8eEqsMKq6cT5/yOpq8/9zyC/1n8nLyWbJWhpnuBSxL7i7abmKxOyQzVVXxnb4Y2V0srPRE25Dw30Nq68PMafwvJjX+ED1n0TfXYuOK4YGDqgmq6sYWZm6HzDknBbjj8eLQ41njh8+d7ciTbskXQn+bouQGVpPr34KzahZNrnPYlRn26whkdhhplg2sYLo2h4cGMsAexZtlWi7Kh40ke686MLMD2Fah1OiBoHqqCE65nIGNwykotLyMJgfIj0hwWB+4mG16B490/RILk/NDVzDLPQfXYNiopBTMYEKB3mcTOCpUxn0gckh2/bc7x50CWBaNc3dXP326QURVuK46zGn+Gmwjh21aoAuspFWIL9fe3DStkqoSufIaZJ+P5IvPo9bWEbn2uqLUu9FencdGCH+A3k6d7pYk3r8MCvrHN3LGR0/DXjyRK5zvZbZnPjuy2wjoE+jY2sjmdnjXtV08Y/2BV7qiNDkmcs0pH4JXbwfbRFJcBCddjBglPhsg05qn6+EYzqlOrrkuwmuvp1BkWHRZkMB4hV+3dfNSvDBKbNBUPjuuhoZRkq0BhKo0HK4gScMkpEi07zUQ98sy+VzxrMLlldCcBYeloylEzWfPpud3L2HGsrhm1VLxgVOQnMMj3n49yX+3P0ynURASG+ngw8HhPC/rd2V475II8bTJzq48miJ495nhUWvHyi4JU05R7zsHn6Np1HsayTjneD5f+XVa9RY8cpjV2TeQEEUjV01xkPv4N9nijFDZnqWhUkNTJcxkEjIS7sgc0n2rKdJytkGFWsVA1uD0q3Xcfpl1r6SoalQ577oIVWOMZvsTOru68vhcEv9yZTV/eaaPdN7i+rMjTKkvfEepHVk2fa8dqU7F91EFefXPMWKFaK5k20sExi0mVDmdgfwGBBIVoXfzp0QbXXqCqtCsknPuyuaxgqUzpcCEC1AcB1bgHUAIicCE89G89SR3v4yiVCBts5hecRmdoU0kpFaqvaeSd0zkts7/Hjru913/w801n2OO95ShNme1StMHKqm9LIzsECjeY5eS+VhyXCmAZdE4t7X1kB807fTqBre19QCVnBEs/BBtzUL2CsRBlsUTQnD++ecjhOBjH/sYN9988yFdo1pVTcV73kfooksRTieys3h0ZJo2er7UzGLsNaPv/8sKGuY2EgzVU+WoZ2HgbDr786z1Z5h+WRd35n+ONbiCc1duG3/TbG5e/FXUdBzNPw6Hf3T7KRSWwANkN2UR27IsnuHGNm0aK1RW5XNDwh+gNa/zeH+MD9ZWljhJbdsi3bWGnlW/xsj0c9lp32Njes+6UggoMjO9LtJz86x5cdjxefENFWQlm7xuoaky3gXNOCZXYmcM5LAb2VVs1mvJ9w8J/z1sdGxh1vhJrN1RMCHd/XQfN19UQX1ARXbK/HZpNz0BB3OnOcltHDYz1b3Ph6txGh51Caq871BY2zRJrV6Jp1en6u+d+M8J8MXwHBJ6hr56mdvdq9ExCGgT+fbSJFAYkX/4wgpOC/bT9/tfkW9twTl1GhWX3sxAWse2FdzOblRvDTs7s/z4b530xAzG1zi4+JYInXGDnz3VzYcdlZwwwV30zHtjOv/v751sbhusyyzBJ66pYpuSY5czxxzJjdOS6Ho8jpGyMN/jxTK34o8Vh/Imdz/PpPH/zjp7Fznh5M+JNtr0GAKoHWXQNMfr5h95lYtO+he0rfdi55MEJl6Ir/H0fT6/0VAcAbwNp2JuiNJ//71YqcJvLTR+PBNu+Aje8Az+2P2/Jcc9E32U2Z55RSv4hSxwRI6OCOzVE/QaCXyyi1r1wFcrv9kcVwrg7q7+IeG/h7xtc3dX/5ACEKOlFDgAli1bRn19Pd3d3Zx33nlMmzaNxYsXH1JfQpJQgqMntwpGVE45x8/Ljw3bLzWnICylixJVW3mjaFUsQE1YoyassTK5EauzeHi7O7+djMdHOLz/6kmOarWQSNwC24DkmjSuRg0trLAtWZoFc00yQ8aysEmzM7uVltxOqrVaxlFF9OX/HioL6F/1U7426+PslqpxmAqTvBrhXf1ceEKa80/30G16yMgy967qZ9vzeRZO93D92WFyrnbalBa0gEajMp5Kip1q9ih24hf19Xz77ImcPsGgM6bSFM7SpKzE+ttzdJx7M9s68mzryKPN8TPrZDdKxqZ6shOPr5/M42+QVDfimj4TZ9PYWVPzba1Elz6CxDwC504j9tgGzFhBmUQUif/4ymLijV5++H+JouNuf6yXxhPbUVoLaxl0R4gVq8fzzKMmpmEz+9RpnFMtc+fTvUN2+x2dOX7R2cMHzomQ021+9NcOfvDhRhorh2de2ztzQ8IfCtXNHnguSu3ZKq/0p5jsdnKqy0t6ew53k8YTjiyLR/WD2cjCwV3x7qJMuqd7JzPJ5eTmukr+1NlH2rKY53NT61C5ryfJ66Kai6f+CxeFvAS8kTGf2/4QQuA+cS7pdW+QXr0SZBn3jBNwRRoK+YZGMc35lQDSm5T9fkOmnZ92LiVuZlCFzI0Vi1jsm4o6xoz6WPLWu6KjSJ8+upNrrPaDob6+MGKuqqriqquu4tVXXx1VARi6Tls2R58NQVWhwaGh7b1SbB/IimDx5SH8YYUVzyaoatBYdKEP6xf/LNoveNlslPDoI1SvXDrt9kg+XNL+F3dBwScw+XM17Ph1D0bMxN2sMf5j1Sg+mYmmEygebZ/oc6MJi0cHlhalEJ7pnM3Fjadg7i6EZFrpHrRXv8MJ7m+SXO1Gce2k44VtQ/tXffBkftLhY2tnYbrz8sYUC8/o5U99P8QcnDdElEo+XfdVqrRhE4+KTJ0apF2PDrWd6Z2Euvm3NPZvoVn1YLYmyACheVdj9XYDBXvxg+viPAioCvxgXoDod76Ee+5CXNPmk35jG0gyzjFWberdnaiRanJbclipPGYsizougvu0Weg5FbVVxuf1ksnHOf80qBjXgyVlEakqct2ewsspBPEJ5/LE3cO/0TUvpgnXOOgaKHXk5pUci67pYzzN9GPg0+UhH0w8VWrT6hnQmSkKs8z1qSxnBP1ULPbS/UQcly3YLkeY66nBSg3nrvLULcDrq+er7stYltzCmvRuTvNO5hTvBLyKxpKwxmyvm7xlE1RldmfzuCWJgCIzxe0ksI9cQXuwTYvs1h5Sr+1CyBLu+U04J1YMzcy16hqqP/EvGN3doChoVdVD4ZNzvQt4NvYoebug7HySn8uki8i3tSIFgnRmNHpiBgGPTEOlhmOU9Q97Y5k6RrYfSdZQnKWDM8uwwbaJiwy/6HqCuFnwJ+m2yW96nmG8o5IJzqr9nufN5rhSABFVoXcUYR/Zh5PyQEilUliWhc/nI5VK8dhjj/Ef//EfJfvl2ttYbtj8Mp7FpOC7fF9NhPPDgYNSAsEKlbOuDLPgvACqJlBUieznlhB9eB16exz/OVPwzB835krLem0cZ/jOYVmi4MgTCK6v/HBJHVTdypO2UnhkH4pQ6NJjRI00ftlFzbwAM8c7MNMWWkhB8cqYeoaJIsUZfhfL4oUXoMmpcV4oQJ/RzdKBfxT1vy77BmdUXotnhF9U0nyYMYXARIvEX7cV7T9w90o+85nT+bc+QTZvM3uSwvPZ+4aEP0Cf0cPWzMYhBWDbNg9FV7LAO5GMpdOlRxnv8DLTdmEMJqIz8yNmLbJNZddmGirm09o73O9lC4KoLzyE4zPfIPtEK32/2QSywIj6UC6rJC9ptO3IEu0xCFQoNExwIvsCZLdvwH3iDMxkDjnsRjtlPjvuTsOgS7RiicxHr1B42XMby83tYILslLl5+i2gKMheH61dLtirENEbLyZZdLmXv70ULWqXvVkqvJX8tTdJVzxKRb/CJ+qrmOl101BZKnjnTPOwzih8V5NdhdlC6BQv2U6dk1Mqt+aTVM34OA19y1EHNuKsW4C//hR26LvYmH6DkNC4uXIeTc4JRf1WasNmuOkeF9M9Yzt6LcvGMu2imsXZTd20fWfpUCTewEPrqP/GRbimDAtR2elCHlfqh2lyTuRz9d9kS2Y9qq1wwq4AAz/7EbFYDLW+keiFN/LTF5zops11Z4W56JTgPpVAPtlB/4a/kWx9EdkZpPKED+GpOQkhq5g5i8T6DB0PFRaRRS72EQp56GV4VmdTqD44gbICOKZcVx0u8gEAaEJwXXV4H0ftn67/z95Zh0lWnmn/d6xOubVUe/fM9Lg7MjO4u1sSSCAkJASy2RixzRLfuCcE4gkESAhugzPYMO4+067levT7o3qqu7p7BALJfrt7XxcX06fOOfXWked530fuu7eXiy66CADDMLj66qs588wzy/axLYv2Xbu4s6qxZK5s4E89g8z0uJjgeuvkTy7PcDzT2VpF5KMrsA0LUT38bXVLHi6ovIpFvuNJmwkqlRrq1GKieUDT6dcNRPKszzzMmvRLLPAeQ6v7BH7W9xxZS0MVFG6KnMyS8CTUiqKTMfUcsR1/J7XrQc6JLOSE2mVIgYk0+CvwyzLteb2UcxgJSx1ZHirgr3kvbb+UqD917OzWLhhIHfs5a0YLK3cKBH0W3dZYEr24OdzRKQgCPtnFA7E1zHM30Sz382r8fvyuk5mqBjALw6sV2VWB1ZdG3ryFW248hk3tNu1RkdlNEtObRArSBPI70uirhzyWaZN8ZCuOWY28stnBy0NkYgDLzglw/AVN2Nd+EGkwi5LzgC9I+6PllUYDz6bwX9RLZ2JY3cvE5BHtIa5euhB79wHCLWO7eesmOFg608szm1LE0sXrunyhiqOywAO9EomhDPaAbvCdth6+OamBiTUqHzm/ij+ujJLKmSyZ7mbpQon7Myaz3C5mDhlptVKh6X1VRAY0vuBU2VDIkao7h7mTL6bC5WJ3bis/7PxqKbTmiKncWvklWrwTEUbzoR8BnXvzvL4ySU9bgYUn+pm+0IMvKJF4fieOxbWQ0tC2D4BpkX5jP8rkMCLiEWPqTc4JNDknUOhsp/0nny0J9+id7fgfuZPTjv0Yj22xuef5KLMnuJlUN/77Z5sGsR0Pku5YVbw3+Rg9b3yf+hNuxxlqJbMnT3pXHrVCYfCVFKktea785FK+GnyoLPQYlt9ZbeF3Cv+rHMDBOP/oKqCD298uJk6cyIYNY2vpy2CaZGSZ3Ki4vA3EjXHKTd4GBEk8Yg6jkDPJZS28fjdT3DPKPtuRzfHdAz0kTRMJOLtyGRXyXiQxUDL+AAVb56e9K2lsCFOvFp2nlmwjvqvImWP2rsHVuwbJ4cd10jdArqBSqWaycwa78sNs317RR0NgFp7jP4+eSaD1hOi6x4WRNBEDHgRVLjbjAUpLENc59ZgTQ3gFP8dMt2jGQbP3FB6N31P2O1qd5aIkp/pnsiq1i3YtyiypmjNyNyJEXajzZqHt/CtGbB9qcBI+93EM/u0ewh+8isF1n2a67GSWO4h+oA9dX4455VKEB18ac01jCYFVj8bLtq16LIE6X+FXOGiu8fBxnw93h431yCgCNRHS42g0D5h9KGdfRVop0BtfS3XLdPr2F1cBTo/IivNC1EecfPW6Bg5Ec+wzu9kkr6HSnk5i1OOUsyz6dIOCkuKJwEo+eM0SBvM9bLKf4U/ZnZzmvxR17RIGYzrVC4urBFEWcNWoTAGmMBwaNGyDlfFHyoybZhfYmF6Da281lVPcyJ6jq6jp79S466td5LPF39Wxu5/EgMHSiyV2nZvmZeFFgmaQFenleFbn2XVCnFc6bycsV3Ji4EwmDLGLHg5GXx+jy7nM3m5aXBkOlp/G04d+/4x8jFT76HtuY6STDGxIcuC3A1gFG2edQuPVlbT9cQD5RZGpl9ewPd+NgMDV/mNodPxjk8x3C/+rHAAUncA/avDfFgQBb18P4Yp6vJJEwbaLnPACVP6DIaijRduuHE/8aZCufQWmLfJw8sUhqodK/5KGwS86+kqMhSbw8IDFNTXnoAsDZK3esnPptsmgmaae4oNt5scaMVNLYukZoAKX5Obq6ht4Lv4EGzKraXG2clboIqqcDeBsIGUk6Q3vJdnaS73QgssI4I6cyMDvX8exopLogp3s1O5H0bzU+67kjVwzK3MW/xE5hjPI83ziCdySh4sqrqF5VHNWq7OG2xsuZn8ihvVKmOfvTWLb8Lpb4LLbbqB6roBXU9D2txH58C2YwTh02FhGDitdnLGne16FmrMRJ1VAR7zs/Jokj+kZtG3I5ExwwYGCzheNON+e1ICrOUPuwHCtqyAJ1AhjE6KLXIvJ+0ReT7/Ba+Iqlt1wAdMHp2HrEoE6k/qJxZBLRUAh4BfZONDH9kQnM4RWZEHiYJOvUxQIyBJeSeSB6Ju4JYWX9T+zSxvWu30i9RcurGnigV9o1H29YQzRXvnvssmYYznxo1Kau4MpzsiJzPAc3Wy3p71A6zQRp1Ngy0abXNpi/csppNO38Vet2JneDmxzbub9F3yUP0aLgoF72cn6zGo+WX87TUeQLhV9Y9910eUiZRWfe0nksIpcguxEcVejZ8rZQq1UI/t+2V/6O9+lM/hyitBCDwhw9YHj6FeTePIOmqlEfYdpJd4p/K9zAP8ySBJel8z11X2szr+OQ/TSoCyhUmqm/iiSYv8oBro1fvP17hKPzsZVaeL9Otd+pg6XRyJhmONSHBcsN4KYRxUUCiN0VyVEQiP42BVPNaM7shRvPdKIhFnEUcdlVddydvgSXKILZUhyMm/meHjwPl5NPct1xr+ReUBkYEs3nskqjR86kW7fY3RmnwVAMxP0xX/JWcFPsSMX4O5eg882XcqKwGlIgoxfHlsBIgoCk5wR9F1efv+X4Rc5n7V56AcSV322hopGJ2qk2HCX7hwjS4HsCqN27yd+Yivq+i7MRA7n0hrkE4LILUlmLHWw9fVhwx6okIn7rVJfW9q06FQMJn0kwv7f9JPensdRJVJ/hUZm90reM+lyHsw9ScZMscizlJmuuXjlALqtYWPzEn+HIT8xyzmPY+y5pdmvLEicH5xPvSPE1mwnl1TP497eFNeKTibui+HpTuGdbROokon4Q7wcH6u5pHmjpBMBchkLf8gm16VR6DWQfSKuBkeJ/lsRFU4OnsVdveXNaTXKPJ4cyLI+l+Pr3kbqneM/03pvEr0vhehRaJQ68Gf/BoMZFpx+Gms6pyDWWTyTeLjsGAODdqsNGRmD4orQsHUOFPYc0QE46hsInnUe8ceHzikICBdexyPbFTxOmxvPrqa+8tDvn6z6qJx7Ld2v/lepUdJZMQ0tOvaYzN4CgXnFFVDbjwdQEJFbRLyf/edw+78d/J8DOAxM28K0LURBQB5HAeytQBAEeo6r466+YeLT9cJz/Hv9lxGFQ+vgvlMY6NbLSNQA2nYWiPXruDwSflki4pDp1cqT5KqYZVXqKd5bcSO/G3wd3TaREPlg9YklhSYAh7+ByOJb6F//Kyw9i+KpIbLoI2M0fiVBGmOkD2Q6eDn1NCeL5yL9soJsd3EM6W0FBjbqdC96dczvUaw2XOIc0qaJJQgExDCdgxp7kxkCHpmGirHsloX02P6JxKCBNCoCoAYnovib0JMHs9MCrqmnMmBvxe9qpfCZk/E6EnRyP4P5DRAVmH/VKbTMPI2n/mDQMt1J8/kefjuC618APJKIu1llymdqyfdESXc/RXLwaWwjT018Px9fdD0Zr4+klSKgVFGpNrDYeyxvZt4oG98y/0ljQh9hxctpgVksV2qIta9iqWcG2s9WY7THyQG5lTs49eIZPH1Chnq1mfbC3rLjQ4qfCdOdeN02iU1Zdn27m4PKqdVnBmi4LIzskdCzA0xJyXxZuYEeh85L5iZa3SfwTDQIWBQsmy5NG9cB5Hb10f2tlVjpAhXvmcTgX38+TLfy2O9ZdM61ZGYsYr3oYHS6SBKkMbQPIkd+JyWXi9D5F+GZvwgzGUeuipAP1fKp2TZet0R18Mgzc3f1HBpO/Bp6shNRceMItpDdO/b3qdUygflu8r06wUVufNNchBZ6UP6Fmr9HwtsemSAI02zb3n7kPf//RN7S6deTaLaJLIhUyX5covK2Ocxt2+bJxINl2wxbZ3t2E2FBZTC7noIZp9I9j4BzMnZ/gdz2XvTeFM6p1TgnVyO5j36lkNejpLU2TFvH62jA6QmO2UdWBJQhPeGALHNTfYRvt3WTMS0E4LJqHxVKH9dUf5BmxySmu5oZNNOEJA+1jmCZUxREGV/DsThDkzD1DKhBekUDI99HrRLALR26zT6aL4YU6lMT0brL3/xCh4hraTUFc3TsPIBm2ZxTEcQjSbyxPc0PH+jBtIrG9r2nVnLSfC+uEXQeFaOW4aFqiTnznQSD5Y5C8VRRu/ST5KLbyWs9mC4HO/WV5IxeIr3HYNyzl+QteQaNg3kfm870SmYtnc4nFizB6RV5KZNG77aJOGSODfiocyj4h3jeZZeEpymI4JqBltuAqaUITjoLn38qta7iiilv6WzOdbBLg/dW38rq1LOYtsGJgbNodRa7beNpg7Z+jXzBor5SoS4sk9z1MJm9TxIOfphse7z8Wj68gynz59BQdTaD+m/IDnH6HONZToOdZtoVmzF2NbP/jy5GyGbT90SC8BIPruYUXa9+Bz1V7E/wuyq5cMEtfLZbRB9BI+Icp6LNzBQY+O3rWOkCok/FGGwfy7W15ikmnLKM85UruKv3h6XNHtFLi7O1rIjAJbqZcBgeJtPSKZgxJKHYae6aMpwXcgJvJQosCCLO4AScweHVhrvZpPr0AH1PFUOfgiIw4cZqfJNd+Ca7qFr2Lwgzvw38I67pKeCfI1vzT4Zhm/TpidJDbdgWvXqCekcIxyEoZo8GFmNnoKats6H7O2SNYp31gfgjLPF8hcT3t6C3DRu9yuuWEjxzxpjjx0NW72V99/dID9FAy6KH+Q2fY+YSD1veGK4mOeXyEBUjSMKmeVx8Y2Ij/bqOV5aocygo4nBTlR9KMf9DQfFUk9TT3Dv4Oi+mt2MDs10N3FB1IhHH+Nw5QraSSqkan6sG6+IAimGTX5VGHzRIrjZovuJSEtq3sIeW/265ATctXF9XwSKfh96Yzs8f6cMcurw28IeVAwiVkPCYLA/5qFcdRBodnH99JY/9fpAzz1ZpSHUiaFkGD4QxtAiB6mFdWrNrkLjdww7pbzBUuBNQJ5N7thul0UO3ODZMlNQ6SLgX8Ho0hYjAbc219Gs6v+sZRLdtIorMRxoiTHapiJKCp2Y+zvAUbMtAdpZfm/WZA/yg98niNUXiw1XvpTfv4q7ONNM9KU4JCNz30CBbDhQby1RF4HNXRVA6Dq6Wxk4WbMNkrrORHtXgo7VfIm50ETOibMlu4OfavVwWPJcWqRPPMXMxn0thZoef11ivRt6xHT3VjiC78FffSG53Nfn7Fb640Mf9gQwbjTxzPC6axpn9WxmNwt5ixZatmQjqOMJEXh8Ot8Isx3xuqfs8mzJrCUghZnrmUSFXcXPtbazLvE5QCjPXs6hUuXYQuS6N1LYc1MboCTxCT24VqhxmeuX7qfTMOyQ99NuB4pVouDJMxTIvRtrCGZFxHoZ/6VCwbJsBo1gyWil7/+kdw4e9IoIg/OhQHwHBd3w0/01g2FbZjAaKrIOGbeJ4mz5TEAROD13AnT0/KG2TkJiktnBgRHMUQOFAf5nxBxj8y1o8CxtRqo4cLopmt5SMPxTFzNtSj3DWdTcyYaGbWL+B5Bd4M5qjvsdN64gSuAqHRNKOslcbpNtwMFGtplo5+tlMclCnb4/FcdkFnBaazwbPHu7LvcGq9C4uDi8a95iAUMGl8S/z0M8TpBMxHKrAWReEUF9K4W1WyTziYdGFnycZ34eoS8ibVfQ3NrH84yejKDL9uTy5UXxBoghBQyOTTHF3NssNjbWotkCbbHDWR0M0PLeGwklenqh5ha3GJmoy9VyRvI4pvllgWcSffAzx5GZGRhksW0N0y2j7E/iNSaQYSbUtkHecyLf2dZYCFU9GE1xVU1FSgAraJvK2zXS/9Cyiw0HglDNwtU5BcBRzKWY2i1XIo3ld3BsddjAn+OZyb69Fr1acbb6USLE3l2dKtbPkAAq6zV9eiHPDnDNwaQpiGkSfipUa7vz1Lp+EOxKg1SGTMdP8ue8ndOrDymm/Sd/Drc6r2EOW6gtDOJ5LonUXkxg92FQmiiEtf/X7absjgpmxAA1eHOTaj1YRmxeizukgMA6fvehz4ppZS25zN3bBQHREED1erMxQQlkQqLjwUiSnEwmY5p7NNHc5VfQMz1xmeOaO+wzl9neRXrsVwWnR7d9BT65YtlkwBlnf812WNnzlkMyttmlS2LeX3K4diE4nrinTcNQ3jLvvSMhuCd+Uo2QyHQcJI8vKxBb+Hi9yeJ0TnMeZgdkE/4klo0eyZu8H/h3GEPkBXPXOD+fdgWlb2NhHHccXEcYQdgGH9c7xeJwbbriBzZs3IwgCv/71rzn22GPL9pnhmsOHaz7Fi4mn8Ep+VgROxVHoGrGHQJ20AlWoQLl0HvndA2TXF0XI7byBbYxdQYyHrN49ZluqsJ8EOX70chRBGF59J/IDfPaqWtxq8dpsynbwre7hMr96JcRn6s6hWhk7e9fNLFm9G9s2cSu1ZGJO7v1xDwd2Fh8Xf0jk8lsbmFUVIy7qaJYxrgB4xC1zx7cSJcZPrWDz8P1Rbvx0LR6HhaC0k71rH/qGojrZwYcxt6MXpdpHyCsR9Eqlcr6QV+SWeQk8d99FpK+XhUuOQzvtLAao4G+vxPnIIgdWwOZvNY+w3yg2m/WYnfys/7/4rPoNImIEM5FA2hHHN6+ZlFU09CntAJ4Tm8l+6xUqu2cyWLuJvFWsBKn1nsDTcbPsiTGBfbkCtQ6FPk3n6uQA9s++f3BBQebNN6i/7T9wTp5Kbvs2Bu/9E3pfD97jlnHeMa38knjxOooVY3IznZrO0tpyiu32Pg25cjnxb38ZK5MhfOH7ye/U0LuzeI+bgO+4SSWVu5gxWGb8ASxMorJBtN/mpccGufryMMZDMdwXhfj9zhRXL23GLYjoAzVDxn8YA/fFaK7qxllTA8GxiVnJpeC7ej76D9MYvSlij+yn6sO3YiW7sfJZXFOn45wwacxxRwN9oJ++X34XvbsT7zUX0JsfnTOySWtdh3QAuR3b6Pr210ovhejxUn/bf5SRML4b2Jzr4L7YcH7n77E11CgBTvRPf1e/dySO5ABWA5tt235l9AeCIHz5XRnROwjbtslZGlEjg4lFQHLhlZxHdASKIFEpe+kzhrv5QrIbx2GOu/XWWznzzDO5//770TSN7Di8/k7JzRzvQmZ7FpRyCQlBQZVCFMwYU6yrEH5vMbCzeLndc+vxnzqV5Mod+E5oRakay6k/HkLO6eynvJKi1ns8Pb3K0HUZ3r6rK08mb+FWJdJmnj8Nriqr8e7UY+zO941xADl9gB0Df6Av8/rQd86AHR8rGX+AZMzizefz1J/5LHmzh5wzgiC2kE6YON1iqZEtl7ZIRstj/5YFGROqp7qJDdagd24e8zv13uL9CfsVbrkowg//1ksiY3LtnALKH76LZgwZzedXYhTyCGdfB4CBQH6Ws2T8S+ezdfr0bmq9DQROPYPeX/6EFvl0jKlL0OUCPudEkn/fQ8WVC9F3pJnpvhGjsYDiduFxNPFYRzmnD4Bh20iCwFSnA9djKykjO7Vt0qtfQ/T56Pru12FovMmnn2RyOs3UMyLsMPoRheL9qFBkZqs23YbAjryBaJXno5ZO9+BOx0nGimyxg/f9AkdDM0pdBPfihSjVxefHtC3AxfG+M1idfr5EmQDgkerZtsbAtiEfkOi9PMCjuxJEUyYPbgxy1bzrsQ9IjBatt3SbfHQP0d2/Inj8p8i5PVQrtShDwu95S+cO5xpqb6lgQqqZgmpzh3MLH55zMhP+QYqEwoH96N2dxXEMJHC2VpAz+8v2UaTxZ9VWoUD0ob+WvRRWJk1u25Z33QG8mto9ZtuLqe2c4Jv2D+klvxUcyQFcCoxV3ABs2z58/dV/AxRsnR49UXpUB40Mr2/J8vCLGQaTBhV+mStPCrNsVnmIQxAEPJKTBlFGH0oCOwT5kCuARCLBiy++yG9/+1sAHA4HDkd5PNCybdYlM+zPF6hVHUx2q/hjUYSd7czoXY44uR5jq0xq587SMdkNnYQunkvo8gX4lk1EkI9uBRN0TWFyxTXsid6HZevUepdT51tBPlU8vq5C4vjZFumsQH/cic9V/F2aZRA1xgqSZ6yxC8BobkvJ+APkjH4SbWP369wpMvHMRjL2AQa6TF59oJftazJEmhyce20VE6a7cHslXF6R3KgqHV9QwilJ1FSFGFw2ifjfN5Z97po8rJU8o8nN1z/QwEDSILz/TRJG+Yw5+9oqImdfTMAj8XSbxQ1LQ6iCk4Jd/ni7hwyFe9Zcqq7/MPFHH8R+UyZ0+nmkH+umsDdO4c0epIALaYeLus+fjuwuhgHOqBBZly53/It8Hl5P9jHd6cAep0lPF2z6pQKCIJSZ1Pxrr/Cek7/Ebxyb0Ywk/15ZRbj/deS217B8zdgtZ7I3JuJ0FGkxlkyTOedYAzsPcnUEo6/Yt6F1HEDrbCN82WUA9OlJHotv4LnkVoKyhzPCN/JG8m/06Z0sc5/CtkcrMIeSKd6wTFdXgWiq6Jy3tNt8JzaRb5xZgaD0Y+vDI6463SIXfwlLzzIY3cwPBv/G5ZUfoEqpplvrwCeHCMsSD9tb4eA8xoROPf4POwBdG76HuRdeY+KiS9ki/ImDTirknI7fMb65sk0DMzmWwNBMj3Xm7zSa1UrezJYzrU5Qq/9pxh+O4ABs246O3iYIwgLbtte+e0N651CwjLKXav02jb8/lecgHdBA0uCOR4szhdFOQBQEVEFB5chlYvv27aOqqor3v//9bNiwgYULF/LDH/4Qz1BDTHElYvGdtuHQzE0ehQm//QXageID4J69ALN/2phz6wNpIjctf0sPhSJ5qfWfgabOZcBII8sBkIK01EjcdrWKIT1HXH+GKsnHKaH3oMiVgERQ9nCqfyYPxtciIDBBrcSybZodYxuVYrnyWvKCEaVusgZPlu83ebFBxt5OhbScp37toH1X0cF079f47Te7+OjXG6iuV7n0pgh3/6AHQ7cRBDjrmgoiQ5z2giAQOHkKxmCG9Mt7EVSJiisWorZW01vQaMtriIJAk9NByJkg3hUbozUqer2IVj+fvqSBp9br7I1HuNX9n7Tpe3nT9SK7jW0s9BxLUAyzKbOWvJVjUlOEymtvwPS7EPIqWuf+0vnMRI7wFfOxzTyFjgHkYJDpbg+faa7lsYE4kiBwdkWQiU6Fz7fUsSObQznlTPRNIzrGJYm+OZO4I/0CH7/yYsQ//KVsvAdeU2ntORZ3WKB2xgPke14uthWkuxEHNjHr2E9zy+UHcIg+fL4UexKPoZkp6m8+Ht8am+yDTwMQOO1MlOoaTNvisfgGnkgUHWmvnuAPg+v5VM2NSDmdA8/4ObADQGPqfDd1LSrnNzuQRHhhY4rasMJ1Z1Tiq3My7YsKvU/EKfQZhI/PYXmexRxMlO5XWKokavTxl4G7Sr9pgnMqJ/iW8kJq2Oj5xLdfI2/YJm+m90HIpkpRsHUdO59Hv2sl8z/4EbQKG4fixedowamMX7zQJ8VRTzkB/Q9/LtvunjFW1+DtYlDvI22m8MtBQvLwu7TUO4lnk1uImcVJg19yscI39VCneVfwdjKadwIL3umBvBsYzT//9EsFRnPBaYbNPc9FxziAo4Ft2ViaTT5dYO3atfz4xz9m6dKl3HrrrXzzm9/kK1/5CgC6bZMzrbKEYrCvu2T8AbTudtxTFlHYP1j2Ha5pNW95RmDZFi+mdvDrgWFlsUtCizgvuACP/3X2xh4DwDQKbO3/AR7HfxByTUcUBE4NzMRruxEHw2xZL+CUJfT5PgYrdpHSd+CUKwg4Wwk4JtHFC8PXApNgSzvHnzWFV59MYFkwaa5E48Id9Jr9BAqX0r6rvNFML9gMdutU16tMne/mY99sJNav4w3KVNUX6/h1S2d/fhdbpQ3UX9VI6wWn43H4UKp9tOULfHVvV6l7uUFVOKtyG+mKXmZPaMHat3/4el94LhmxE0N6k3NnT0F7upW+n+s4aeScE25APE9nj7qV55JPMKj3cUrfLGI//QW2Xhyz97gV1H3uIlIv7UPvS+E/aQpSoEDHf3wWM5nA0dBI9Q03Mb9lIrO9bgRAGrpvsxWFSkWmT5lI879/lsRrL2IpCqmFM7nLvY+olaV3wlRqR1wb6eSreOUxyKTyXPA+g3zPqvJ7rGewkxsZMP9Ka8WV7Bj4CwdnvAe0p5hw7PkE6j5HRqrCrvSBJBM30jyX3FZ2HhvoNfLMd+oYyx+habmbiPNUvHITvoCMD7j29CouXQpWfCv5ngdJ61Px1M9l0s01pDvX0Lfux9iDxdWfqHjodloscBzDC4mnyr5rX34HZ7iHNQAWeSbQoo6voGfZFrvzvbya3o1l2xzra6XVGSkL33ZoUX7U+xQ+1cUtt9yI57HnoK8P79LjCQSmoQSrxj33QfRqnfyg63bmtEzjuKvOR1j5GoLbTeXFl6NOPLzU52jYto2WbENLdiAqLhyBFiRnkC3Zdfyu92dkrQw+KcAHIrcw1V2kXG9SK/hy/cW0aYNYFCdaNSN6a/4ZeDsO4F8u5H60UAUFWRAxhip64qnxNX4Hk8NewTYtbK2YzBMVCUEeP+xjmTZ6zMBImFTLEepr6lk0ZzEAl156Kd/85jeHz8noiClIerkxNAb6kY7L42gJoe0vVgC55tXjnl3LW0W3nuD3Ay+Xbftb7E0WuBvpTD4zZv9Efg8hVzHxVKX4aUq18o2/Dq9WXt2a4eOX5UkIRcH6gDqZqf4rCKsziRa2AOCVG/EaA5x66RymHuMhl9VxR/bSXvg7Dvz43fU4VAGtUH4lnEMdpqIoUFXvoKq+PHTWkdvLr/p+QNosLtNdoodPVP0H9fh4LpYkaZo0Ox0cG/BiWDaWPY2op59t10xhcu8i5LSGURvCrgqwOXcXtmYyafdyBp8ZDnUlns/hagxSWDKZV5J/4QPeD+K47wGMEfco/cqL+JYeS/UNxwFQ6Gij/T++UuKZ0Tra6f3lT6j/3JeRx6EfqJEVKkyDTm+EX51ZQ87S6dY3lUg+hVCImo9+HCOZxFHfSEe2FkGMFauZqlSEbgXb1BAVD55wcf7ldDYywXEx2OBSavD6jidnm7gQ6E+vpa37OJ65J4OsZDn7Gi9TliuEZTfdejlth1uAjb3fKf3dn3uNxfVfBqYUx2YVyO65l3R78ZlKd6wiXTGVmqX/jrt6KtULbiLd8TKGO0h/ZRX35x7gZPXsstzCQdTKPm6JnI5XUml2VBKQx6cg78p3EM1tZ5poEMfFd7of5xM1ZzLDPSxU1K+nsLBJmFm+6trEvCtmU207Oa1uCZXqkfUG2gr7SJkJVvE6Gyb7WDBlHqIkc1bdJET5rVE35Aa30bXq62AVbYkaasW38Hru7PsB+lAHfcpMcGfPD/hs49epUIrOKeIIHLI8+iBMq4BmplBEL7L0znYVvx0H8J/v6AjeRThEmVolSMHWsWybCn+GweRY4qcKf/EyWLqJ0ZfCyhdvoqDKKNXeUuXESFiaVVLGilTVUF/bwKbXtzB3+SyeeeYZZswYrtmXBQFllMJYtDJCvdOFnR9mh9QH91P7mfdh9KYRJBGlLoDkOXQDVcEq0K/3YNomlckatAMWiCbJSTGMUT0HNpC2NJxyxZimqoPatumEQV+HRrRX48zZfp7anMQq0pyzYZeXGTNryOo9JAq7yFtRanpc1FVfio2NEBsg3/8Cct0pPLE9ycub0/jdQVbM+TSiCH24OO1KgUd/N8wjM+sYD9WN49dOa50dJF99CXnbFj4873h2TxP4u/AYOSvD7tx2apQG9uQKBCSJpX4v9/QORytneI7H8jzPo5H7ESMS53nOxEEGw8oQUFvJrXVCeToWaU2BgXl+WtTJuHQJo3tsJZWZHDac+jgkY3p3F0Y0iuzzM6inGcgM4hpI4BQUzJ4wtiTz5BM6U97XyBPWcBRVFRQCDgUWzCQoFct8pwA3f9OLodu4BIuMehFadBtqvpXUAy8DAq4LZjJx4bn05teheY/lrugDWJgogsKV4atIxSTAwNBtHvptiusjNtdMPo7v9jxRSvQ3KCHCVh+aYw6anSClH8DGoj+7nqCr6AD0TB+Eq3HUXFxk4ozFyO5+Bj3ViatyOr6GY/A1HENHvo2/dH+Ngp1nf2E3011z2JbbOOJ3OmlyNlPjqBv3nh9ETh+gfeBOUoViPkwWXHy44nqeSmxiuquutCIOjFBks7FZZ3ThE52cJ47/TCUGDZJxA49PIlytYIzodktbKV7kJby2j9Ptyw87vtEw9SyDm/5UMv4Ahdhu3Im2kvE/iIyVIm5ESw7gSEgVDrBr8B6iuc0EnFOZWnE1/lHU2/8IjsoBCIJwPLDetu0M4BUE4XvAD23bPnCEQ//lcIhyqXb/qpMquOPRfjRjeBbqkAWuPKkYH7QyWsn4Q5F+2Mpo4zoAe1Qo6btf+h4f+Nh1GOhMnDSR3/zmN6XPJEHAJ0mcHvCzOpWh1eWkuTpE5NNfIPbIAxTaDuA7bjn+5SeihLwooSNX+ySMGI9E7+eV5LNcZL2P2C8ERNum9uo9ODr7CLs9RM3hWa5LUIgoQRwVV7Gm6+vYQ12VbrmGgHMy/fsL3HdHLx17i4axolbhirPD3P3GkGEVypW1bFFA69uB1fFmaVvNko8jObx4nMWYZjJr8shrEAmLnH/RTt7IruLkz5yFNeAjEvYzcVIYj29sYluPxej+8XfRe4aM8J7dTJk/h9MuPg1VkvCaTl5KbGeax8ckl5fHBuNlx2/NWMzzLUTgaRZ5j2UWE9HsYsljzhigqlUnNYq8VZyiYJMnZSbI+1WC8+ZTWLumbB+lZng1Jg3N8gWHimvmDOxCgcKBNiSPh7iRZVvnZuoffhFtzVo0wHnMcaTnXEPbdo3ZL9VzyXEu1jp3UmUGONU7nd/33s4ZoYs4NXRO6Tv8IRk9Y7Ln+31I/jlULwrT/9uflz7vu/MXRFweshNCPBD7aanRULd17ovex6m1rTBCBSvWq9E6Ocy/1ZxJn57AKSrUWl6q1vRSeGE2YkRFPNHNdvfvytSzMlKCLfajmPkhgRV/M83TzsRGIDe4g1Tbi1imTrhpBbfV3U6P2Y9TdKEKKi8knmRN+jXq1SbOD19BjaOOAb2PmDGI23RSZQVx+MsFVhL5XSXjD2DaOfTMc9SpJ5Xt1+gIc2FoIX+PFe+ThMiHqk+iUhnulcn36mhRnb6Czb0/7ytVoF1yUzVNsyYiCwrGCCN9Wuh8gsr4anyHgmXk0UcI5pRQyJTxFwE4BHVcQabxUDDirO/+HjmjmNCP5Taztvu/WNrwVVzK+KGzt4qjXQH8HJgrCMJc4BPAXcDvgRP+kS8XBOFM4IcUo+N32rb9zSMc8g/hYJz/nuei41YBWfmxZGhWTscO2mPi8KJc/vecGXNZ9eSrOGuUcfWEJUHg2roqLjHCuCSxKAAzcRKRm27FLuQRPd63FOvfldvGquQzyMjUtk0k02XS+H6dROdvEUSZjyy6gd/pbbTrcSKynw9Vn0yNI4it+Fna8BVSWjuSqOJXJ+LQKnjz9XjJ+AMMdus0tRtU+mWiKYO5rSkSevFBFAUHPtcEwsu/RLZ3I2Y+hjsyF2e4OGNcNtPHM+uSHGS5vvBMk7+m78DCZJdvI/iKCmSf9XwdGDsT0rs6ho3/EIx1GznhhBuItf8Jxb8XdfJp7JOSTHBO5XFzbH+E22rgC43fplKphnySRBZEQUEz4zCnDccrzWjdxePUiIS0UKbBNUhIX0RfyoV93jn4czmMbVsRXS4qrr4WtbmldH5HQyMV112H7c2QSa5BlJxUX/lh5HCI/lychgEDY9360v6F9WuRF18BwKbHNZwv+pky41gyUYv8e9JklDSPRe9ngXdpSZhHH+wnt3Mf4ekZHPMmkn6gPKwHkFr1Egecy7DU8muQt3MI/gwwbAw9Pp1tZpwf9T+LDVTIXj6zfgbJe4YS+rtAWC3T+tnL8XuKVTOmVWBv4iHMEeGclH4AI3QM2CadL3+9JOmZbn+RuuVfYlrlcAL1sqrrOCt8MU7BhSo52ZXbxh3d3yVjpZGQuFA+j/k7GglMn4PkLY41p/eN+Z0FrZMloYayd8QlObgwuICFnhYSRo5qxU+DY9h4J7dm2fXdHjwrfPx1VZrM0Oo/n7X4y496ufkbDdxa90WeiT3MgNHH8sBpzPWM36x4OMjOIL7GZST2lldAuAItXCK/j3sHfoONjYjI1VUfpFqpOcSZypHTe0vG/yA0M05W7/mnOwDDtm1bEIQLgJ/atn2XIAjX/yNfLAiCBPwUOA3oAFYLgvCQbdtjqQrfQSyb5T9kwld0K1gZbdQ2x7iGWVQFHJUy2qABNggOAUeFfFgxeUkQCIyifhYVBVMWyJgpnJIb+Sjb1Xflisk8VXQiRGXARlBTkDWxTRPn6l9yU9PxaP5GKkNzqRyKnQqCiN85sWwZme0u0HFgbLx2YJ/G6Wf5aa2XUTzPkEsFcDvqaAmcS07rR3I1EJp8zpjjWuud/Of7GtiwN4tt26ihfVgD5eGSjJUiacTGXwqPp44mCNiFGLaRQ4vuxLWmnaaF17Jb28xszwQ2ZYZLASUB/IZK7VAcWHf6MaQW5jo/RXfqeQaMF5jw8UuxO5xYWhqlMkOy1sukWD1vPlbFqtc1KuolFl/2QdTLOpjibcRVXf7SSi4X0gQPfWt/W9qWi27DpXyOHyV9hAONXPDJLxL89c9QJtRjndFKUnmeyfOXsmudQT5jsWt1gUWnuXjeeS+YYGKUVll6fx9dP/w2+pAusHfuByA8tgtcDFUgpQLIavlM0y16sOPDK8nlZyt46wx+EF1bWsedZE3EeHhn2fnsgoGvrwH/tGJTlmHlyWgdY++H6iLTvrZk/EEgNPUiMt1riO18CG/tYtyReSjuCgJy0SgnjTi/6/0pGasYBjQx+avxd2q5HGXLZnxLi42TvnEYPis9S6hVx4aOnJKDydJYg6rHDfb+og8zY6E5xZLxPwhDt4kPGkxpmEKz81ZMy0QdxVdlFnRESTxi+bUgiAQmnY2pZ0m3v4zo8FI5+304g5M4TpzMRNcUEkaMkFxBjaPuqCd6kuhiNMMugCy+/e7j0ThaB5ASBOE24D3ACkEQRDiK+sjDYwmw27btvQCCINwDXAC8qw7gcBBdDkSPXnIColtB9IwfTxREAdkvIblEbKtIBiW+RTUkgO5CB8/GH2VrbiNTXbM4NXjuGI6T8dCsTuQlIGOl0SdmADdWNoAgObDN4vitAy/jkFUCJy097LnMgkVLk8qWteU17DMXullx3JDgS89SqnvDaHqCXNU+djsexylVML/2k7gc5XXcgiAwqc5ZUll6NjqAiFjGheQS3fjGoW4GcNQ1oLZMorB/uFHLs3QxueyW0t+2kaNF06nt1VCCKVweL6szBWoUhbNdQZpcQw1IZo7nEo/zSPQ+bGze67+OmZkI6e4nyQ68ieQMEWz8ND65gj/f083ONUVH0rnLou87Bks/76cl7OfgK2fbNlnLwmHpxPc8NmrkNoX+TeiOY9lRMPguCp+76lok/352cD/oMPdimLp0DonuEGpTiv2Rx6lwhZjluAyP6CNtJgnKIfJ7dpWMP0DBjOM6biH5197E1or3V1BVHAuO5Y1fypz7sRt5XLyLgl3AJXp4r+fDBPIWDRcpuIMK4WlRslX1JLqGezcMrHGNmySrJSPlkHzU+I7nQPyRsn18zgkY9nDYwz/hVNKdr6Kniyu3XO963PUnoPuvxq432Gf1kTazHBu4mNeTDzKgD89sky6d5KMr8S5eiiCKBNRWplV9gAE9TUZwEZQCtLgnoUpHz7WjJ020vqJDdJg2DqeAlh82pIIAvkDR/MmCjCwNm0IjniOzpo3Ek9uQq7yEzp+Nc8rh6/Md3gjV828kPO0SBMmB4iq+NyLQqLbQqLYc9dgPwq3UMiF0IftiD5S2NQROx6McPofyVnC0DuAK4Grgetu2ewRBaAK+/Q9+dz1FvYeD6ADGWCpBEG4EbgRoanp3uedERUKp9mLrZnFWr0iHVdgSBAHB8faLolJGgrt6fkjXUEv+a6kX2JPbzicavlyaNR0KU92zmOqcxY78Zp4PP8TZN1zD4FMSddd8jN5QkgOKiIjIFE8Diidy2HM5axSqJZiz2MPG1cW8wdQ5LmYsKvYxFDra6fqvr5aSoIKqMv3Wy9nk+APxwu4yB5AtmMRTJm6nSNArU8iZDORDnBq8jpXx35WSlBeGP4Q3HiKbL6BWKUjO4essBwJEbvoY2Y3ryO/aiWvWLArKPjI95bTIoYKD6M+LMfGLp8/kwmOWoU6dg8frIegtPtpdWhsPR+9FQOA95rXU/lVkYM02HM0zqLv0UhzNPmR3mL7OAjvXlMs16pqNfzCIf4jvpVfTeTGW5NVEmlMCTubKYysybMmJMdRVagKFSCUp62+lnPOA8ABCw0O0zrwY1bUYwahnS2YDj0TvK15bBN5XfRMTfOXGzplysrG5Bs+tn6Gyow0Egb76JnoCESbOK7Dm+42cdsHnEQIZJlSGcP3XWvTOBG6g5hMn4W2ei9vSWeBuYW12PwDPsJvFF8+F3wyLw4g+FSFiU0h2YiYqyXdqVKnn4qxsYkfh50iCk8kVV+BzNKHX20NhDxtJ9ZeMf+lZ6HwROXwS3xh4hcSQSLqAwHWVl/PwwM9K7J7+vIJSHUEYWvkpkpu0MoOfDj5BxiogIXJdpY8T5QqUcahExoMSkFAjMoVeg9zLKc65MMSD90WxzKLxP+faSirrx5/Dpl7ew+AfVwOgtcXIbuik4Svn4Jxw+LCLKCk4vEcX3jkaSKJCS/Acws7pZI1+XHIFfnUisvRPXgHYtt0DfG/E320UcwDvOmzbvgO4A2DRokXj13G+gxBEEUH95zDy9es9JeNf2mb00qd1H9EBVChVXF9zCz16F6ZtUN0YwrXAw37FyTf6HkTXii+XK7uDLzn8THAeuupA8ck0LPPh2Zpj/nQnclCmeoKKr7ZohDLr15RVwNiFAtbLO/Cd0VyMqQ+hra/Ar5/oZ3t7ngqfzA1nV+HPxplZLfOH+GRO9n8BW0gh2gEi8Ro2fb4N24TQYg+N76nAGSl+n6lZaMkApncZntNPRI3IWL0FGJFnc1bNQ3ttmB7C2LYFtm0h8LFP4F24pLQ9bhQrnhbIi6m728DYUjxJfksPPXsHafj6eeAGRRFRXQKFXPkj5tMdDOzIYdbJ/Ck2yOpU0UH+uV9n1oRzYHA7B5fogqQyEJhKOj680pFcnqIe8AjYmGSSNg9+1eLkz9ewNfe7EZ/Z/H3wbi5vuJZQuAIjWuwLMVfv45XwArbaNkrDFGxsDBuO1XPUNMvMbQjglUQm1zjxpaPE/U5Ep0LovFm4ZhdnjE5R4T2Vx6EMiryR2YtTUEhMrCL8nhUo+zvQIwL9swrknduo3trKvp/mS92+vukTOe6mnyKGDNxK0eFr+SaCjZ+hkHse8RAEZoZLI5Ebdqw2Ni+lOpnmnsv27HrOF8/G9cwG/Je9r7RPzMjw075nSh3oJha/HniRSc4IE4+ya1gJyEy4qYrd3+1Fj5o4nk/ygVvDRM0+qiMRahv9Y/QioDj7jz88inbEsCjsix7RAbwbUCQvFZ45HLmo9e3hSGygL9u2vUwQhBTlgSgBsG3b/kdIrzuBkbGOhqFt/2ugHKJc7VDbR8Mr+2mVh2+BHbJZ2bcF3R6Od+Zsndczew7pAGzTxsiaOKsV6modWIZNJmWyf0ee9pUJWqa7CPeMrXCwugdRhQB+tRivzeRNfvFIL3u7i1PdwZTBd+7r5ovnpXC9+RPeN+P9dBUiCEoTzYaHxO39pfBxbHUGV6OD+suKy+boKyn2/WKYy6XqFB/1V56A6m8in+hEUEKozhq6fvu5sb9nlL7ywYTqjMz0kvEv/YacjtaVxFEbIFStcOZVFTz462GR+QlTnChbNXqjFjsKAqul4aoqE/hZNsznj/kkua43ECUVs2Yp34i5OFjcXyGJVFoCzvCFDHZt5uArJAlOjIHpRHsNugcTY4KpCTNGl9FJ3a0fIvvdn2Jm0qg1dTSrCluzeXTbRhbgRiFM43oLrUPHmKiyXzKobXECdbinVNNjdrHd6EAxB2nUWwgrldQ5QrzPcRLTt84m2Wnz0DMaumYTaWxh5umd/FX8GR8Vb6HnYbW4Eh5Calse7UCQcPVwR218bZ6OP3txT7gE8UQT2VWLkRteBSiVx7PdGjtnS1saJ/nP5AxrBRU9Fp6rV6A2Dq/uE2aWQaNcdtIGBo00Ezl62gj/NA+TvxIg3t2F4Ryk3/0CLRXnUOmZfGhiR1FAUMeJ+QvFIhHR+d9T2vHt4khUEMuG/v9uSFatBiYLgjCBouG/kmKY6X8kbNvGMjXEEXHMaqWW4/2nsGpEc9YS7zIibzPGZ2MzoI/Vax3Qx+c1yXUU6Hk8QWJjlsA8N5EzAihVCs/+LcrrTxcbr1Y9luDDH5gPLz9fdqznmGPwSy141GIyeTBplIz/QZgW9GdkavUMjg0/oQUBUfHgj3yBaLa8amXwlTQ15wQxUiYHfjtQ9ln/MykqV/jxTZ2Fu7pYYWJbFqGzz0cLWBhVIrLkho2dZYYEoM7RyBWVH0DvKyAoUplRAxCdw6/A7IUe3JpNNGniVkU8UYP0M0nqrgswK2BQlwhTGQWHRyBep7FWTNPrm0rjBD/p7nVI6U4+XrOQXVkDPzDFqVBfEcayQyyu/zL9qU3kkjJ6/xQe/5UTsFATlQiVQlmJ7WTndLq0dpz1JxO+/Zug68ihMKdoJlVZmZhtUmlLBH+QIBYb+j2rMsy/Ztg4H7D28cOur5aasS6UL2VpchGyJYPbyyu/LWCM4PLpadNpjalQAT49QH/XWMOtxcprn41U8R5m9xXIHoDGaz+Mq34jen47umMhO/dMBr8EoxYHJ7in8LvBH5Ox0lw/7VYWeMvvWUB0EZbKy5ihWLX0VuGrqcBZ5aRghKgVZ+NUDj+Xlv1OKq5cSO+PhrvcJb8Toz9N709fpPI9S1Ai776C3z8LR9sHcKpt2ytHbbvWtu3fHeqYI8G2bUMQhJspssdIwK9t295yhMP+abAtCyufx0ynEEQRyetDUNVxE0E7duzgiiuuKP29d+9ebr/9dj7+8Y9jWyaWrmPpWbpe/RahyefjqpyBKCmoopNzw5cx0z2XjsIB6tUmJjin4BrR4DISuYxJMmbgdInjCneLgsipgZlszZcvpI7zTR6zrxY32PW9HvJdxdLX/qeTpHfkqLgxwhsrh8mxbBteWF3JWVd/gMRD92EbOv5lJ6Dt20/uicdxffJzUN+ASxXxOEUy+XLD7lFGOgUbS8+ihkRkn1gyIADeVhVRFTH7daz8WONjpMsNtyCKiKfNZlv/f2FYxRBD1YmLcIf0MikUh6iyLHAKXc4B+r8exezPEN6Rx35oF+659TgaQ/QWNHo1HY9LItIok/+vQWwT0oDkEXFqO1B3N5G4K0NsaGjeWQpzr+0jTRQltIDKcPEaVwGzRkXvREEi5JqKnJ/InT/ror9L5+AqYcO9bt7/H7fw1+jvSZgxJrtmMMM9F5fgRulykew2cAScyIaF9UyKqicT1HpF6i4PcyBWfk36/xYncpwPMQRPRB8oGf+LrEto/ZVE/57nAJBCLq68chl//MOIyikJBH+a2Y7ZkNpEcPFSBl8sOz1SZfkzF5znpufhePEPC9p/IzHp1jORp5/BGw8kWfdintYehU9eew59YhcmFo5sEH/cSVooTkrGE5gPKV4+GjmV7/Q8Ts7SEBF4b/i4shLPtwJF8hySDXQ8eBY0Uvf5M0ivPoAgiUg+ldiDm7ALBo7GEOHL5pfZAa07gdYZR1Bk1KYQcmj89/e/I442CfwlQRAuAT5JkcvvToq07G/bAQDYtv0YMLqU4r8FrHwOvXc4ZGCmUzhq6hCcYxN/U6dOZf369cX9TJP6+nouuugiLM3AjGcxMzp2RsArnUbfmp8RWXIr7spip3BADjLPu4R53iVjzjsS3QcKPHBHHx17Cnj8Ehd+sIpp8z1Io/oRZrsbubHqRP4WW4OEwGXhJUx3jl1RaLECznrQYyLmkFZwrk1Hy5hjlPq2brQ56cplhC8WMHq7Sa9ZjTFQDNFkNq3HUd9AVUDhA2dU8ZMHe0tz2RPneAjmnizV/gjVs4lOPp3njV2EPu9nckcE7Q4dURWoOSeIKBdLaV1NDnJtw45DUATUEcplUT1NXI/RF7+7ZPwB+jNvEnRORhZVPI7hpq0ePcn3e1fSpcfBD03Hhrn5hNOocobYI9n8154OMlZxlOdUBTjj9jqST6ZwhMDl2YcZ76L7yYayIGh6s05DTwM/NL5KhfzZMnFyPWGQ79ZBAFedA3mo2c0XUjj7vZX88TvdpSbi6Qt8zJSbidTW0q/30Kt1U6PUUdM2gc1f6QAbHBUy4eM89DxSzMPoMRN9YFQnImAZNlig2QV69OIkwCGoTNwbwdwzXFFlxnKE2vbTOKmJ9j0asiJw9vUeglUq0zsrMTqeIbB8CVrSTWp9Fskt4jszwKBhlXVteKc4mfypGjrvj2LlbWrOC6JOLfBG/FPUnt7AnJOvo6sDdqVW8Yr5GBYW8+RlLHCdUOIYrnOMX/E2acDkP+1jGDCTOAeT+B5fiXGyhDJj1rvOlik6Fdyz68jt7iPxyJay0vD0q/sInjMTyaNiainye/vp/c5rWOmis1UnhKn5+Ekokf9ZkpAnUBSGWT/095ds2777XRnRu4x1Lyd5+p4o8UGDYIXMaVeGmT9Kv9O2LMxEvPxA28bKZRHHcQAj8cwzzzBp0iSaGhvRe1NY2eIM2zYsBn+1k9DNF5LperPkAI4E3bJoz2rsTOZoOcVNMmGSHDD48/d6+Og3GqlrKa9d9klOTg7MZJFnIgLgk8srBmzbIj+4g2T3wzjnRwkuP4Xk6glEXyp+HgxINE91cmDH8OwwXC0TCELshWcpjCCwA8oatpZM8/LVsEI0pRMIy1R5ZJzZUxjQezELCfZOPZ074sOVPP56F5/92mnUiEHc9cXrqvhkJn00woE/9pPakketlGi4RgTnAWxrIvH+bvb278FVEyCt7R9zvQwrR6qwv8wBvJLaWTT+Q2gzomwI9BP21XLnvs6S8Qd4NJpgfrOLpoV7MAYHiN77Z4KXfgQtOtbgGqkCc5vm0K/3lBxAem+e5OYcolPAylqkd+cJLfXgrCyuSybPdXPzNxsZ7NXx+iUijQ5Ul0QjLTQ6W4Bi9+qWb7eXHI5/povoq0PhEBG88904GhxIrmHnDRA5QyXfnye3xuT68KdZFX6SrdJ65A6d0S2O+p5+LvhYC8lECL8zj9X7O4SdAuEp50PLxdz9Ew3Bspl2cYh03uKp55JcelN5/F1UREILvfhnuLBNkL0SPalXi/fA2oUu/Rhh6kW8lBnWplhnvESVp4I6s4mzKy6mSR1La2BmMiSeexqptwf/1mJCtgB0bVhP4398fUyY792CHHKP6QtSW6sQ1aLZzPZsI/V4tGT8AQr7omS39hD4H+YAQhTr9vdQTNY2C4Ig2PboueJ/b6x7Ocnf7+hH14rDjg8Y/P2O4kx2tBNAkhEUB7aujT7NYXHPPfdw1VVXYRtWyfgfhK2b2AkXov/oyrh0y+K5WJLfdA9gC6BEBK6+Ocy678dJJ0wGe7QxDuAg/PL431GI76Pz5a+WGni0xF34F7+X5IYZBBe4USpkjn9fiOYeHVmz8YsiLa1OvGEV88ST6f/dXWXn88xbWPq3Igv4KyXelDI8PzhAdUrmikgTU467jYSZ5b6ecpGapJmjzbmXKkNCN+eT1jrQzRy6r5r9pzpxnyFT5eknvuu7JAedVNW8n9hdvyKYSeNcMA/t0rn0Z1eXnVMSnNhD5H9auhstPcAWY2xtwfZcN8d6ZtFeGHt/B3MFnD//EVXXfojQ+65DmyIQ3KUSf31Es5wAYiRLxsriGeLvia/PsPuHPVSfGqCwSyf2ZgbJJYINVadIyC4JURSINKpEGg/N8WRmzTLDbvtF5EoZI2nivbKC519J0ferfq58TwXitiyFLoPgUgPFp7Lj9uH6+vnHn4p+roE6vRp9ZVvZd7gW1aD0t6EWfDi8lbgW3YyoyIhycVynvD/PmhcSPLsySSZpsvAk3yGfNck1nDQVRrB1SqLITtaN2X9bfgMfq7uNwDiUC4UBnUK3jjplOZl1Pyj/0DDQo4NIHg+CqiJ53npO4K3ANaMWtbWKwu6ijRB9KsFzZiLIEkYhRa5nG3rb2DHonfEx2yxDIx/bRbZ3A5LDhzsyFzXwr5dUP1oH8BrwTdu2fy0Iggv4FrAKOO5dG9m7gKfviZaM/0Homs3T90RLDsC2baxCYajG34Hg82GmktiGgeg6fGxP0zQeeughvvGNb4AgIIgC9qgqCEEVcNcsPMQZytFZ0IvG/+BYbZv7rBgXnufntT/G8fqPTiBmJPLRXSO6N4vIRh+n9VNLUCqDPLMlxR+fGaakPmtJgL4DFlMNiwnzF2HE48QffxhBUQhffAXOycP85aZt8+hAjCeixRxCzDD5+v4uvjqxEb/sRBsiUJor1bEi04hoQtDlQqOL/QO/pTtVXIbIooeqio/zvXtd1Ic9fGjRBfitAr2//Bl2oWiE82vXU3/i+8mHBkgV9iEKCo2B04nltlFduRAt1U3P6h/iVy5lYW0N2ymvUV/oacEtiUx1O9mRLReFqbQMKi67kczqAvbFTrbkf8r8i76GjUziDQNHWKbyPRrp2kGMPLgEF/k+nT0/7sURLBrq2FA/hZm1aP/jIM5mB/0TIWOnyFoZIg4vjY4wqjg2l+MIy6g1CoWe4gQiWinhqPfibVH5+yOxknrab3/Vz8QZTi680ULJ2ez4SjnLZ3KVxhmXXcArtU8w7+xmzCfbwLSRF0cwFodJfH5I6E+A6puW41s+ibxpsjaV5S/pKMYCm7NPDDLTchIJOkoKbgDpnEnnoAYOm6hi0qZpNDtVpjqbUOUKCsYgBTNBzThJ12bHZPo0FQuD0IjO+OSWLLt/2IuRNBGdMg0Xf5L8Sz/G6C+GYoNnnUt2w1p6f/4jlMpKKq98L85pM0jmkvTaCQTBpkYO4neNz/3/VuGo9lH7yZPR2uPYuomjIYhSXXT2giCgFfbjWrgC/ZFyQRnn9LG9ANn+jfS8Nsy4Gtv5d+pXfBnV/+6qjh0JR+sATqXYDbwEcAL3A/2HP+S/H+KDY5fxUFwJZHImggCqrQ2FNYbMbiaDUlWFICsI6qFnbQCPP/44CxYsIBIpNl5JFR6M/uEkl2t2BPfUVpyh+kOdonxchsHoJVbWshD8AotP9hFpUjEtnbzRjyBIuOQjqwkJ43RTCrKKp9lFV8Li7ufK9QieeCPBJSvC/Ndfu/nspbVMvOAS/CtOQhBF5FD5ixbVDZ6Olb8Mhg3thQLLXX7ODy0gPhBlxRMi5gvF5iPHpAqMf6+m+2AMiqKIvaHez7nHf5CHXs7Ta00hlNtcMv4HkfzB72j85m2IgTSaGaVgxGmtuByv2kiy9yXc7sVE79jBtHMncMz8Fl4z9yMAKzxTmetuxC1JXFdbxffbuunTDSQBropU0JzJM7gyCifUEvduhjRsMP+Tae/7AMGLazCVOIpkYeUDyILM1txGwvE6zIxFeImX+LqxqmrR3Tm21vWyN91Fo9ZItxCjN5xmSbB5THOTEpBpvSXC3p/3kWvXSIs2922M8cFFFSSfKDfye7fm6d0fpVLWwRrb8GcIOk9bj7Nj+QSWLTkeyZJY797A7JxFyUzZ0H/XqzgnV7PDK/GjjuFVxB8GB7m+1o8oZai1G1AEhYGEzl1P9JM3LQJLZV7LDT/jywNe3lN7G7HsejJ6F173fN7MbKBbL9JJ+KUAfulYvrivi4hD5pNNtTQ6VQqDBnt+VDT+AFbepv0v0HzpxSQf/BlqcwtGPE761SIfktbRTtf3vonza7dzl7GebYXimOc6arguuIhaf3NpTPGhe+sbR6z+SJCDbuTg2Imf5PASaDmVgtyNe0kt2dXdCLJE8MKZuKaU3wdTzxLdel/ZNkvPkB/Y/v+NAzgduJVi+Gc9cAzwKvCVd2dY7w6CFTLxcZJn3pBET1xHVUSqzATlLQ82Vj6PUnnk0q+7776bq666qvS35FVLpYdSyknkphORw0dfIVChyEgCmCOGE5AkZkx2UbtARVCjbO//G52p55EEBxPDl9LgPwlFOvTS2BWegqh4sPRhI1Ux/Qokh5dMPsdoXjUbsCybVM5iX3eBSU0ulIrxG2IUQcArSiRG0STbdvElPME3jfyWA6RfeK30mbZ3EDs39jFMa3uZO0vgydcFNFMCl1wsVRlxbkGSSMRzuDujCPNnkXQaZE2JCUYGwcgjGD6sbBLh3h1csKuG0447BsGyqa2tI1RbvJ8TXCq3T2ygX9dxiSK1qoNCfw9mWqNtziCBoVCaZWtsjf0CUXAQERYR+HUflkvFukTBVm2UQJEWRBs0UCMKenxUuWmFSD5lEH95Cm8cKM7sF7Q6aVxSoK5GQHaVr+Y8E51M/1I92VieA2ae6GqTtrSOIJRrOgsCOFQdy9qBq6mOXNvw98o+EbcQZIZzAVvza/mzXMzfCIbAicnjGTmHswsGdsHgTWusAuzz8Tw78vez1H8ci33Hs/VAjnW7s1x6bpj7c+WigS8l0pxR0UBr6NzStpvrbqNbaydh6OzPBflLrwjY9GoGTwwm+EBdFXrMQE+Mem5MkCqnUvW+61Hq6uj69tfLPpe8Pt6UBtiWGXZYG7Qe1mX2UeUIkpE8vJJM81B/DIcocEV1BQt8HpxD3f16NIMZzSL6VBxvI2bvrlmAqOxA8O/Hd8YCFG8lamNtqaN55A+xzFHXVZQpuH10FzrwSF78cvAtf/87gaNteb0VWAwcsG37JGA+EH+3BvVu4bQrwyijqBtkh8DCs4eofTmE2s1RpDoymQxPP/00F198cWmbIApILgXZ70RU5bdk/AHqVAc3N0RwDhHM+SSRWxojTKp14/ZKdKVeojP1HGBj2gV2Df6JaG77Yc/p8DdQdfwXcU+/EqXlTBxLPkMiWExIVwUUQt5yQ+R1iehD9NmmefjrEFRk3ltbvuSvdSjsyxf4QVsPpqUg7RvVk2CDIzHWYXmdc3kha7F4loeGKgeeyQuoeu8HihYPQBRxfepmcq4YO+dYbLZ282hiLd/qfpQ7+15Ar5yCHQ4g+YvJZXNdD+pPN+K4YzMpCWIjGo2Cisxkt4sGp4okCEgeFWl2Bavkl0mKbhzS8EpHQKAq0Yq2ezfGpi3MyU/iGGkW2fijNL4fUjtzhI/xIo54zlwTHeRabHJtYXYeGM4Lrd2dY9P2HN0PxzGyY3UqZJ+Ev8nDtIYQHzg7wJquLAvOKDdUx50hImefJtO/kprLeggtVZDcIoG5LqZ8to5HN+Voil7AFGUuAH4pyAeCN+J8oLzXQgq7kYJOwspYZxySBNJmknv672JA72Nvd3ElZgnjPw+aVT6LCCkVzPDMY19uAg8OCOgj3qfNmSw50yryarlHmSMBnI1BAiefhqO2HjkQLL8+dfWs07rGfP9mM4Fl5FiXzvK77gFihkmvZvCjjl52DoX7cjt66fj8I3R84RHaP/MgqVf2YhtjWWUPB0lx4amZR8XMC/HNnIuzuX6s8Qckh49Q67BDFCQVY/E1/LJwP19p/yTf7vgiO7L/mgr4o10B5G3bzguCgCAIqm3b2wVB+OeKV74DOBjnP1gFFKiQWXSWn5YFRcMsyTa22wPZkUt4AdF75Nm/x+NhcHDwiPu9FUiCwDF+Ly1OlZRpEpZlKh3FmLFuZuhJjaUHjuY2EfGOpbQdTOrs79HIaib4wjwkzifvtumPGfiSfdw+0UGt38G/X1rLHY/10danURtWOH1hgPtfiuJSRZqqDx8CA1js8/LFFpmN6RyyUHSoPckcadtkezbPnAkVJEYdo7R7aV54JW3x+7ExcCvNaOq5bBs0uWVxJa2VEQRBQD2+AefESRixKIVqL38y7mVbYXOxRAQ4NXgZg4aXN7P7ODM4m2D9BAo3RnD88hWsVAFBlbHfv4SNPoOafB+LvOOvlJRaP96p9VRb1fw1+nfODZ7NVLsSYknUAYHsn4vkXILDwXTfPLJb/0a2Zx2i+hRNH74YZJmWz9WQGdAxHQKORoVNcop9e8dOL7bFNJo3GlgTHJgNChNqyntNjEKSqJXk9fAPmXviaVSY9Zw/x0cyWqChMogr/kfMWNHpJzp+SOW5N1J7cSuuSC2xgskT9yUwTYlZrVdycuvF6AUH1Q4Xocvz9P/yNcxEDjniI3LzCuSgm3lxnUckkczQUlAW4AzB5DHLIGdlyVs5pjSEeeLNBIPdBg0tDjpGFEpEHDK16vid7BNdYyvo5nk9uCURsVpiwk3V7PlhT1FrQ4DmaytxDfH1yIEglVdfS89PSow0CMA8Ry078uX5nZmSn4yo8OTg6CcN3kylmWEJ9P7oBcxYkfzQzhv0/uRFHN8Ioja/M/mD0fDUL0WQFOJ7HkeYsIy7Cg8TN4urp0Gjn190f5tPBT6L/HgxL+NbejzOSa0I0lvP870VHK0D6BAEIQj8HXhaEIQY8N9eDGY8zF/mL6v4iaUMoukh1kAH9EsaFdVViOkMIGD5POgOiXf3NhwagiBQqzrK9GIBREHFrdSR0ctnQA5x7FK2P67z/b/2sLenaCklEa65sJI/WcVZYMq0aM9r1KoOWuudfPGaevpjOoMJnSfXJVg0wc2ymT4mNhxZjk6VRBqdDu7q6udcS2bCmk6kdZ0YU6sRTnLinF6DZ3ETmdXFqhSlzo9vxgSynmlY1jRkQWdr3svzPSbX1gaZXDFczSQoCmpTC2pTC+3ZTWzrKudseSnxEMcF3seD8e1kLI0Jnjp+E9aY++8rqMzopFwKD4k6p4oaA0Y582nZtVVkgse2crJ+Flvjm8jG08j9YdQeG1GWMWrryO/aQfiiy3F5nQz0FCtdrEKMhLufv7gnsTnfDcGiAf20t5ZW3KQnZNg5qiBpashBoT+P1qXxvWd6+dJ76pnS4MLU0qS73iC2/W+kZp5Ct95ON78uHuQv/vexyKdw73SSig/1GTStoBDfguXuxdt8FQ7LIuiVGEiYbNptsmm3BJhMutRL44Ia1G9UYaXySEE3cqB4nav27eKTmSwdVbXoCDQlBnHf9TuOvWkFumoQksN4mlycONfHi2tSXFYTpsOvsUPLM9vj4uzKYFlit+y3up2cEvLzzFCeaILTwekVgZJ2d2ihh5nfbEQfNJADMq56BXEEX4977nzqv3A7Wkc7kseL6PMxp62HddVV7NSK4ayZjmpmuxp4M7aHSrGWfaPGUKHIGPEsxuCoPI1lo/enSw6gM6+xK5sjZ9lMcqtMcjlLGs9vB7Lqx998Ip66YzigHyDe9aeyzwt2np72DfifKeooJ599mvrPfgnX1Olv+zuPalxHs5Nt2xcN/fPLgiA8BwSAJ961Uf0T4XWJ6KZEeigJXLBNuiUTNahiC6BZOWqFI898/9mQRJka33HE8lsxrKIxcyt1uJSxicA93YWS8YciRcMLq5IsPMnDG7niizDy4fa5JXxuiYn1TqY3uhBFcHsO/6iktU6i2S3oZpKQex4fV4MIv32DwrbeIudjRxxpUzd8+SyqP7QM/bwElmHiqAsgB9002jZZq5nHBuMM6gYfrq9gvq+8e7NgxMlondhY5MyxidaCXUARRBRBok4J4pElrq6p5Aft3fSrJoptcXZIZXV2LVdXHr7xTvI7mcRMviD8J9m7fk9uy7McbDmruPxqKi69EkdjE6YZB0EqVVb1h+ZyIA3VikyfbmDY8MeeQb4UTGEFulkXmcze3mIYaFqtk5YkCFUSUj249og88UaMSbUOsv2b6V93BwCuQgFFcKDbwzNtCYmAUoXsqiI0+TwAMj1r0FJdKAu+zauvxWjrK3DJsjBrd2dZvaN4vRoqFSZEis+zEvZAuPwaC4KIcufPmCBJxXCbYWA7HDhlD9dWfxSP5MPjg+tOr+KMRQFME04N+0EBlyQhH8ZIhhSZ99VUclo4gG5b1DiUssSsIAq4G1RoGP99ExUFV+sUXK1F4SHbMKg74OAWq46+sIAgQJXs5fuxV3jvfjeRkJt1opODAoBeSWS+14OEjuhTsVLlRQVyqOgEO/MFbt/fRWKIV0oAPtdSx2zvP97hKylO3LZvjBIZgEsb8Y7ZNsmXnv/v4QBGwrbtF4681/8/UGSRyoBA0COBCJqlkjYL5O2i0ItLVHAcpUjLPxtupZbmwNlFTV5BQEDGr04kb+aJm4OogpOQUkFynPjyQMJgmlB84COKTJNz/GW713fk357WOnmz83Y0s7jknmG04I9ZDGwrVzMy+9OkBwqkEJEUP1VNCrKzOHsVBIFpHhetbieWbRcV00YgVuhiX249STOFT3DgkUKogkphhFJVq2smMcPis7Xn0uAozuR8msi1/gA9SoK0nWJ19g2We6YxUT08RfZBuLpTxLaUS1REH3qApq98C8ntIaEpOI79Itb2P2Mm22nKCXxm+1rEZJLszDnc7w0zaFlke9Yh7XuUG+acTdp9BlqPjGuPRnWgD6PlefSH2vjM3OPZH5pFoT1OsvPZ0vdJu1/minkXcnf6r5iYJWWpiFqP0bCUnte/j54urgTlGf/Odx/W6IsXJwUvbkpz8bIQCye7USSR1nonVcFDE5o5GpqQIzUYI7rgA+ddSG3jybhHMH6mhH6kYJYquQKPpGJEM9iCMMahjIYqibS43pkJlSDLGA0O0tntyGaCSvdcsqJMAQPnlp04djzCbVdfR3ugAhmb5nSMBrkRscpL5MPL6P7+c2BYIED4yoU4Gop9Cduz+ZLxh2IhxH19g0x2qTjfgZBMtVLDZZXXcnf/naVtZ6pn4Xx2HSPf1Lfag/R28N/Tsv2TIQoCDqU4c6kQvLgFBzlbxykouEQHtgnZvIlp2CgOAYcqvi3xl3cC2bRJ284cuzfliDSGmbhwEZrQQa63lU0vQV+HQeuyHvY2PcV2cTVXVF1HQ9X8Mec5bpYXpxPe669gntdDlePILIdmPo+VzyJ7/QgjZm6J/O6S8a/zX0Da2oWlhMaIGYmXHssf70rRfaAY+1xwgo/Tr6jAHy6eK69ZbG/P8eLGFH63yLJZfibVqWi2xgvJZ3gq8WjpXJeEruCm6o/zUOLvdGltzPcsZUXgLMJKBJ/kxDLyGIUk8bjAj/6SZM4UB00T/SzSjqe3z4GwVCC2P42esnDVKrgnqEhDNOBauhst2YEgObCyY0NFdj6HUSjwWizJ73sGyJgyyyd8mCsKUeLf/y5kM1iA87mnuOaDN/Ny8yQEvXgxrM5H8YhPUV11Bu7j5zPws+8Of0fbfmaefBbpjmm4Fs0k11cUVDdzA9RufIF/n38TWbePgByixlGHJEhI/kbql38JPd2DIMls7ovQFy93vA+/Fuc7NzYRCQ3f47RhEs8bSDaE3EqpMkaprKTu3z5DdvMGtM5O3LPm4Jo6DWnI+OuWzrr06yS1DoKCREEIUjnYQHSHyr4+lbSeYuZSL83T3DicR1tj8vaQ1jp4s/N29CGFsf3xh5lZ/REWqtUYTRK8/jrqz75PqySBbRM49QyEBQsAcM9vpPGb52P0Z5CCLuI+Lxs7Crgc+phCEYCEYaLbxRr4fxSiILLUt5xGtYVBvZ+gHKayx6J/x+Nl+/lXnPwOfNvh8X8OYBRkUcInuvANaUCZpk20X6cwgr0yUCHjDUjvOifJaNi2zboXUzz6++EKjspahctvXsQfv9lFPlMc476tcOzlp7JvwSZ+3ftjPl3zbW65MMLvnh4glTU5Ya6Pc5eEygzCkZDbvYvo/XdTaG/Du+QYgmecg2NIJN0cUTroc05hR9+3CbmnU3fiseSeKwa95boAG/u8dB8Y5u5Z+0KKqfPdzD6mmGTftC/Ld+8fnnk+sy7Ff15bjxTqKjP+AA/G/8Yn3Z/gIxX/RkEx8UmBkpxmLtNBunct6R1P4JBd3HbOe/jBygBvbi1en4vmujhwRx/JjcNjmfiRaipX+MnH9tK16mulMtnqyf+G4FCxteGVhnvufPp9fn7aPqxduyqV5dyebqxseWjK8ejfOevmDyLJrlL5rW3ppPseQxUaxjiYzAtPUXHpDNyRhcR2/bWk7mYbOfzqFJxykKAsIQkSlqah9xfHoFZPQlQUrL6x5GqWZWOaBkYhj6z66MkVSGzPoz+RhpRF9gwvlfO8BALF58FRU1u6t6PRpbWhG/3IqReImjGiQLdciTP8MZ79Y/H6vvFcmqv/rYZZS9/dTt14bmfJ+B/E/tiDLAueR9esZqpW1WF1dYFpIoXCxf6VoXdWEAXUhhBqQ4jdXXm+8bvOEpHhMTM8HD/Ly6rC8LnPCAfxHUEa8q3AIaq0OFtpcbYCYDXoKJ/8HPGnHgcBgqedhXPKtHfs+w6F/3MAR4BesMqMP0AyauB0i+POFN5NxAcMnr63vNJooFun+0ChZPwPYu3DNgvmnspK7qbX2sdxM1cwvcmFbtiEfTKyfPRj17q76PrO17DzRUOffG4lxuAAkY98HMnpxK9OQkDExirlI2LmNjyn1BGeOg1rdwHH3Mns+tPYHoz23QVmH+Mjr5k8sCpW9plu2mzal2WyfyydtWHr5Aop7D0HCM0qljhatkEss42uxE4MRwzv3GPwDOQw93yPq475PL94pvi4Lwo6GNxYXiFy4HcDeKerxPY+ONwjIcrk+sMELvkE2Vfvx+jtwDlzCb4V5/CyNqrOHwFdG7tkt7UC7vwg/bseJjT5PCyzgC6qdHtbMTtSY4oLBFnG0RhGDTfQsOJ28rE9IDloC8zhRz1xerQksz0u3lPhx3nfn0kN0XT7TzqN8HkX0VTlwecSSY2gkjhlvhuHtpbOlx7A37QcybGExH9lSquz7M8LqDeJBE4YX6JzJJJGAqfeR8Ycvld5cwB/ZAO+0GJSQwylK+8bZNIsV1n3MBQnMT0HNHraC8gOkfoWlXDk6CciI2HZY6+3aedJ5HcwKXIGxsdvRe0eQEFArW9EqRyriZErmPzpmYEyFtvXtma4eXIl7Y4Cacvi7MogxwXeXWcmKkpxtTWjSHc+Xjnpu4H/cwBHwHgtALZN6eWxbBvN0vn+D37AH379W0RBZPbs2fzmN7/BeQTiuLcKy7LLONwPB0EQsId4OL1SsTIodBTx/PGgdXeVjP9BZDeuJ7N2NaLThXfyFBbUfZ69sb+iSh5EQcGydTp4hs6G5wm0TmbSgJOJE+sZHKUtU9mg8MqWFC01KuNRS9lApVKNKjgp2MNjCMkVuHf3UdDieIYcQH+inzd2uHhmzSLcTpuzjilQV70NqctPpWMAQajh+JlevILASDcqOgSqTwuQ3l5AyJ+Lv3Yeqf7fo3giJNY4SO2UqFrxIdSpGtENAvEHRHwfKp95arZNtmlCMUY8olkteNb5OKsnIO30Et12L+K0q/meMIeBhME1YRczK6uwBoYbskJnX4QRrmHf1izeYB0Vzc10aTr/tacd3bYRgY6Cxp29MT5gWaUHNPnsUzgnTqJm2Ql8/pp6Vq6Nsa9HZ/G0PHW1L7Ipv5LpU85ncM2f8bfkUKuPodA77JCjjyWoWeId05A2GrViNfuM3jHbdWUfgfCxJQegazbWOGIwbTvz3PXVrtJzHKqSef/n6qisPXq934MIOCcjIGGPiJzXeI+nK/U8TcEzqaxuhurmQ58AyBbsUl/DSKQ6M9y09wDypErqmuqRDlHZ9E7jn2X4D+L/HMARIA+JvVsjmqCcbhFpKGeQNnNsOrCLn/34Jzyx7iWqvCE+es313HPPPVx33XXv6FiCFQpLT/Xz6pPDdAuqS6S2WcXjE8mM4NdfeJHNKvFpFjuX0dDXStemGFbeIjDHjafVifgWVgDiOBQYgkNF62gn/thDhC+9kvDZ5xOs/QympSNWf4KdA79EM+O4lAjN4lLEbIK5LQL79tUy0F0c54yFTjbHCzy1KkmFX+KqEyv5yUPDxkUSYXaLm2pHmA96buTu3D0Mmn3USw1ckjoZacMGpBHyjxv2CPxpJTDEffmzByT+7fLJNNT301gV4rsfaqIqIKN36QgyDFETUXdRiJ7HE0M0BAKSu5mmD32U9OCdSKcLdFwQ5zFnG1O1WqZX1eLaD7VijiZVpa1QPIksQH+tn/mfvI3sk09gxgYJnHYm7rnzkb1+6ld8CS3RTsIZIdFVdGT36ALXX38zTXt34OnrxTd7LnHXRO68rQetYCMrApfeVE1hZrF56hTZy5I+B9IBHateRll4Guaq4ZqMzLo38S87gZaIyqnHrKUvvYm0vpO4UVzRRKVO3J4I6c6nCB23mJ5hrXEkVThsSDOZ34eVjZLf9iSVEyYTozwxruYW0ds+PCNfcV4Qz6gJh65ZPPdArGwSE+s32Ls197YcgF+dwPzaz7I//iCamaTKs4Cs1o3X0YJbPjrlML9bZH6rm9e2lYfuqgQD84U9mC/sQZ8SQWp5t0QZ/7X4X+cABl5O0nFPFG3QwFEh03BlmMrRTKAjoDhEKmsUkjEDXbNxeUQ8/iKro2bpDA69XIZhks/liSlp0pkM4UgVGbOAQ5DKuF5MI09hcCfZ/k3IrkpcVTNR/Q1HNXZJFlh+XohApcLaF1LUNDlYdk6QhklOPvCFeja9lqa/U2PGsSq+yUlmej9BqKuGXbf3lkRWuh6IMfVzdQRmH31Jm6OxEeeM2eS3DouHB045jfTqIqVD7KG/4V1yLI7qCJKoUuebj1/9GpqZwurZQmL1n3F4mgkFl3D5ihQpRz1yVSVrozaPrCk6s8GkiWaYfOryGp5ek8DvkTllvp8JtUXnM1Gr4/o1c9ErPSj7+zFeuQf3pVehTpwEFMXon3qzPCRgA7s6PEya0Iw31ERgqEFJbnQw9bY6Dvx2ADNvoifNEgcNFAncUhsqcSxZwn0Vm3k9tw8KsIVOmidWcOuUxcTSu5jqr2SJGMGyJWQxy73xlXgaV7D45o9jm2aZ41TcVSjuKly2zdmFQR4ciGMCd5gS4Umz+dKpZ1KIwZ2fbUcrFO9VRa3C6ucSLJoWYrrDyXEvQmblMPVCeq6Kf+EKsmuKyi3OofJIM58nlttMvFDOxJm1B/CrAQzbwsqLMHLmfGEI6RBJ20R+N5t7f86E+GTyfRtxeUPUV66gK7cKgCbfGXjTM5g8yyKZtFgwz0NLsHwlYZo2ve0ag72jiakhMQ49y9FAEEQqPbNR5RD9mTfpTq+i0jWH1sCpyJKLqD7A7tx2OrU2JjhbmeScik8uD3Mpssily8P0xXT29mjIElw420VozbbiIl8UEOR/7qz8n4n/VQ5g4OUk++/oxxpiBNUGDPYP0UEfzgk4nCLhiIJtgShRmimZtoWFTU19LTd8/CZWTFmA6nJx2imncNrxJ5LXNfqFPLVKsHSuTPeb9L35k9LfkjNM/bIv4vCNn3QbjWClworzQiw91Y+siCVBmNpmlYpGAcMskNfbaE+sxJtpphD3IKkiVn7oZbeh68EYvmnOsiabw0H2B4nc8GEKe3ej9XSDbZPbsgljKAFp63pZ2APA66gAKugZfATb0imkdqOJB3BVziZSqXHP7ipe3Fg+69rdpXHjOUEWtHpK1zimG6xLZXjDUrh87grCO7die6twfuhjKDW1qPVF5ymLAr7RVAJAwO0iULMUSfVh5nUwLSSPin+mm+n/UY+lW2MkKAEK3TLq5HN4vfv+su0HrEH63EkCbe2YqhdTT5MTcrxibcYtqtRTgS1KiIcgHhMFgTMqis1Sz8WSNKoOzqwIUqM62B/PoeVtvAGJJTeF2OTJ0SGazLVtLrZ8ZJ4p519MbigQumIRYqWf5IzZWE3NyIkEhXVvEJm9gv7s2rL9q8RpFBIPE1n4EYwpNQjeLGbGIrTQQ2DKocOVHcnn8MkN6H07AcjtfQHvYDOzm85DcUegs4U9/5Vh3mQnokci+3CMTodAqNWJY6jQoG1nnr/8qIdZx3h5pbs8/zJhxtHRox8KPrUBtxKhwX8yiuRFEETSRpI/9d3BttzG0n6nBM7hgoorkUcxsDZUqXzuqnp6B/NYu/uRH3kds7M4xuDZM98WT9D/L/hf5QA67omWjP9BWJpNxz3RwzoAAFEUxjAnyYKEQ5Dojw6y8pEneG7bagLBAJ+86sPc86vfc/VVV2FUOIibWWxsjHyCwc1/LjuHmY9SSOwf1wGYmQJWTkcKOBFHxSDVUbHaPbkdPBr9Kyd7FhCNP0xz8Bx606/TVfEckU+fhPjaLKJDdPxGxmKwoFOlHH09thKuQAlXoPV00/GVL2Blho23b9kJyOMk2AACzSeTbl8F2NiWTnZgHaHp5zG94OLFjeVx9MVTiqWGB42/Zds8MRjnwYE4AOsRqG6dw+db6vGOohtwKCIXHR9ge3u+lLfxOEUmNxiIoofMhk6i96/Dymj4z56OMd+H4vcQ9IaoOM5H7PVRIYCT/GSk4djwdLGRhelp2FkRjyOE5VMZeNHHs3sL+N0ebjjvEnbsz/O9J+NMjyQ4ZYpAQ6UDpXpsv0FYkTmzIshJQT+yKJSa8LxBCYdTYNH7g/xOGUQfWglsy/XwXbmGznHSP9ml0/h5ppr2gsH0WJqlfi9vNE3HnRI5pfJ7ZOI/oGD20uI/G78ewXncZ1GDrYiyg+CEozO8eX2AghGlItxc6jfQEwfQNx0gNPUitIE6QCK7azhHY5t2KcQG8MbKBMmYiVawWXSynw0vp1BdImdeXUHj5H+8L0ASFaQRhr1b7ygz/gDPJh7jWP+J1KljGTi9bgmv24PutMhJc9DaojinRHBOrUZQ/lU8AO8+/lc5AO0QdNCH2n4kSIJIUHby2POraGhpoqa6mgrbzYUnn8Vra1Zz1YWX4TZcDMpDRsk2sUezAgK2NbaaIbeth/4/rEbviOFZ0kz44rk46oLjjqOz0MaPur5GlRIhn9tKg/9Udg78qZQc2889NB17MY7XFqP1mwinefhTNMZHXNU4pLe2vHXU1FLzqc+RePJxtAP78B27HN+xxyMq41dyOCumUL/8SyQPvIAgCPiaVuAMT2aBC95zisWDr8SQZYErTqhgWpMTIxFH6y4aGS1Sy+Oj+Fz6dJMDeY3IOHwzddXtfPyyAge6/agOqI900V34A3Xx2+j95nOlxP3gna8hvn8yf5j2AFdWfYCJM6bR+N5Kuu6PYls2tRcE8U5V8CpOlnpaKGRFWl6axSuPFx1C39wc8dYQG/dmEAW4Yrmbh19OsKtTw+MUEethdZeE1LGNumNcyP7xJxfqqGtfEVG4/OYIW3x59FHJ/heULHOnqGR2Djsl7ywnL9h52gsGiiAw1+fmtz3Dq5m1afhc9W34pP3Uq5ORq95eJUudbwWb+n6MXbcMaWA3Zra4EnFWzcJyRJDq/AhKFnvEmCNnBnFUjOzyLf7/zWeTVNYqHHN6gFC1zIIT/vHZdcFIABaqPCwwo1tjQ002NqZ9+HddqfKhVP3PEX0/Ev5XOQBHhYw2Trxx5IP6VmDZJjG9l6bGGra8sR5fn4moZ3nu5RdYMHteaT+vpJIRBCRniMCkc4htH+YGF0QF1V9eqVDoiNH1jaewh0oN0y/vxUzmqfnEyUjOsYa2W+tAtzVsbEDAtLWyygiALp5k8nlLSEhhnq7L80YyyzU1FVS9BQeQN/N0a4PcLW8he0Y1Sx0LWFAxnbB6aLFuQZRxVU7HVVne0h7wwLnHhDh+phdBFAh6ZLSeLrp+9kO0tiLNlGPCJN57xbX82hBZ4lBptAX6ZAl7AP60pp9FlS6CWRuHLOBqUjHdGRLCd6ls8GDbBlGzAAjoyUxZQxqA9FQfLVNaeGDgbi6v+jTiyU6mLK5HHMwSf2A1Pc/ECZw1g0sX1NCTruWBx4fr9WumuFi5vVgGedOpJkFHF7s6/SxpFTh/2h7k3kcQLHA1nYce7TmkAxhzrQSB6Qs9dPQb0Ff+2ZtmjvM+VEN0ZZL4mgy+GS5CFwfZEC+WVc3yuHgjWb6KsYANcZ15goIw8fCz/X25PFvSOQzbZpbXzUSXWuLo8TgamBi6hP3pF6ifu5yQVYVh50lajXz9AQ+WnefmGyuQV2UpdOtUneSjYrkPBMjv3UN2y0ZOrLWZec1kHn7Mw0C3zkuPxPnA58dqVb8VGGaO3swb7I7ei20bTAhdQI13Garsp8ZRT1AKlwjXAKa4ZlI5DlXKSNimRaEtit6VQPSoqM3h/69E3t8q/lc5gIYrw2U5ACiWADZc+fYYAEVBRBFVZiyawjkXncUxJy1HFiXmzpjN9Ve8D0EWsWURn6jQS/EFD7ScjOTwkNj3NIonQmjKBTgC5Q5A70qUjP9B5DZ2YQykkRrGGltFKM6Gu7UOJN+JiPbYVYYiutk5R+EP0Rh5w6ZJdeA6ypIz0zbZldvKw4P3kjQTzPEuo9/08KfUOl7UD/DF+gsPKUF5JIR8I7pTV79eMv4A2r49zGrfy5ciM3HdvQnrQAx1YRN7pk6gETeZn/eTGCo7VCokWj8zBUlwIQoKXt8laAQIKwrS7rFjs70KXmeEBuEcvrS3D8OGRlXh+pSJvKkTbBj842oCzEL3pBkZ/0sNGESCCrJs02A9T9aciiz5OXtaN+y9i4NTjMS2O1HnfwzVnlwypqOhmWkyWgeWbeBR6nAqYRYXLOriNgVV5g2fxAuGxiXVYXxBFe97K6m/JIzoFDELWeZmBLq1YjpXHofMXLYF3IYX6TAUBntzef5zbyeFodiZ1BflCxPqme4pXje3o4ZCMkFAnUiKXvblH0cWKmjfdxPRVPH6f+3FAb79gQZafQryEKV4bvdOOr95OxjFK6JKEle99zaefTXM8WcFaJ72j5VJx3Lb2NL389LfOwZ+jyx6qfevIKxU8tG6z/BM/DH25Lcz17OEZf6TcUmHN+bZjZ10f/sZGCphdc+po/qm5f9jncD/KgdwMM7/VqqADgdRkAjLFfTpPdz8xY/wpS9+ETUjYWd0BKeCGHCiOCREYdh4yK4QwUln4mtagSAqiNLYGb3oGke1y6UgOsa/XY1qC7VKA916Bw8mV3J9xTWoUpjCiNlPVeAKvtWdJ2/ZOASB6+oq8R5lZ2NbYS8/7vr60AoDnov/jRMDFxKS3LTrUfqNFH7ZRdIw6NV0HIJIjaqgvoWaZtu2yW7ZNGa7S/SR/f4LGLnikj730m6a+lOIS5fQExt2kvqgSeINg8Xn/ifrM/DTXoOcZRGQJD42sRJvlXdYnU0A47wqHIqT+3qGS2fbCzoPBBxcOa8Bc11RwSrz9AFC1y9kZMXMtlfSXHhTJTt7o4ipLbjzvVx87PtxJ19kdDAv1fMqbRVhPJKbRmctsjh8b3P6ANv672Qgu774W+VqZvtuIfqFV3EWDJzA2YubOOuahVT7igZIEIWSgS2097LML7HNkNiSyXJVpIKduWHnrwgC0woyUq2DHi1OjSM47rV/I5kpGX+GfukTg3Gmup2IgoAkOKgLXEAqv4H+9Js0+i5gd9t0Hnlt+BiPS8Sn9JOLt0PMQg21knrphZLxL57YxLn/Vd77qRsO2YiYLnSQyO/CsPME1MkEnBMRhPGfo57k64zmG+lIPE2t7zhEQaZebeaa6g+St/K4RHfZezgejESO/l+/VjL+ANmNXRT2Df6fA/ifgspl/rdt8MeDKjqpUeoxbB1REJEdDoQgxfKxw9RVS8qhHyhHUwj3vAay6zuGx33N4pIe6UEYWZN8t45ieLi5+gvskDYQNQbJii7m191GIr8TzYwTck5DUSbyqWbImia1DoV656ETb0beJLtPo9Cv4wjJpCOpkvE/iHXpF5jvvoAXU/twigod+QI/be9loe7B0w078wKTWp00TXJh9SfROhOIqoyjKVSiHh4Jy7KxzruR9PQ4TjuFc8dzGNvWIogB7Fx5PNfY3kvgmLHNO5k9BSDCHT3tJQbIhGnyo94BvvLlM1FXt5PPphmYlOcB/31Mtz6MRxI5VfDjTgsYLnjBTmHNroEhByAFXDQ2hDnnCpMn/1asYfcGJOrzMGlRBLl3LpkDT7Bw4mpkvWKMA9Ccfn41+CZRM8ONlcs43j+jZIhiua0l4w+QM/roSD+NryaCfqCY+9BXt1F78hTcNcExv1fza+yPf4NLfSehS9NwSRk+UR9kbVLFpQnMx4lXzvLFxMNUZH3cVnceVcrY+HbKMMdsSxomtg0Dus4zsSQroykWu2dxnHwcehbcioEo9JXU6r58qc3g67djacWubWfFdKT0WGI4K5U6jPFvZ3Xn7ehW8RwCEgvrPkfYPXPMvno6R13ieKo7ZmPLUKhLsEv+C06xCtsSONhiLQkynsMo5I2EXTAwBsZSaZiZsc/a/xT8SxyAIAjfBs4DNGAP8H7btuP/irG8E1BEBYW3184+HuSAi+oPHU9+7wBmPIdSF8A5obwRpTCo0/bHAWKvFuO+7mYHc285Flf98AzTP6raYcZRDNE2bfqfSdH+h+FkYviMGmacMI+t1vrSNrfkIWsZXFlxDNWSnzv7Bligu9nz8xSJ/uKs73kB3vPxKpy/fhwzUeTdcU6PEPnoCpTK4ZeyR4vTsdngoe9nMHQZCLF02aUsPn86cmissxYcElZQAXJl28PHeukaomAeiaRpknDJTDlzBrqlo2ntnG9chW5Uck1WYNXPo+TSFrIicMG1IeRof7GVTBCouGw+oubC+2I3V50TxBBBiZvE7xpg0scieKedhh7birb3bpTjPs1A7RQKQLWWx7nzcaLV0+jNFlc2v+x/iRZnLY1q8V4mC/vH/LY4Owm1TCo5AAAznhuzH4DmyGLZBWLpJ4AnOGi6TnF/hX4J7jNeYtdQ126XHuNAoX9cB3BMwFvi6D+IMyoCCAI8HU3w4ECcJaoHbSt8bW0XNlAbVvj81XXEUia1YRlX4hE02yRYdw5CwYGgSshLK8msebPsvP4TThr3twAM5jaXjD+Ajcne2AMEnJORxPJVsd4WJ/6N9dh60XlJfifT/u29KGo1G1ZlmHOc7y3RnQBIQReepS1kXts/vFEAR+2RKTKOFjlTY3O6necz2wkITpar05gkV+Pwv3P2463gX7UCeBq4zbZtQxCEbwG3AZ/5F43lvyXkkBvvwqZDfp7ali8Zf4DsAY2+ZxM0XVOJIB75wc/rRRoBp6M8DJTv0em4u7wuPvpklmMXncZW13qgKIt4evAS/HITzWolGctmYzrL+T3+kvGHIkvBU/fFuXBGPearu4vn39ZLfmdfyQFkTY21PZ1sustT1iH6+ssWs794MtWNEu75DWTXDa+GvBfPZ7Mu0XC6n+QzSRCg5pwggdluMrI5moQUlyjgH4qBK6JCk3MiNXoL6/anef3OQXLpYhjI0G2evSvK5E9OpLLCjbO1CnViJYVBE33QoPD3YuL3oDkWJAGHv566ZV9goBDl54k32TEkTq4IErce+1F+NTCs2mZgETMzNFJ0AEHXFNoSj5Vd60phDvqO8sonueYQVURKiNEhEElQ6YsLvODdXjL+B6HZwzP9nFlU9/JJfqa4nXyyqYYH+mLots35VSFme93EdZOno0XH0JJXuXftcEixO6qzcm2Sm86rRpFFerr6CFdfRvT3f8NKp0AUqXjPe4jc/G/EH30IbJvQeRcelt/+IKPsSBSMGJZtIDHsAGzDIvH/2nvPADmqK3/7uRU65+4JPVE5oQRIQmRhssFgggGTjDHGOC3+2+u02O9613HXu7axvV5HvDbrxcYGk7HJOUogCeWcJsee6dxVdd8PPZqZVs9Io8QgqZ4voOqq6lvd0+fce+45v/P3tYPGH8Dsy+JfE6E7UMF9d7UTb3QSb9y/9FLFoRG96gQwLVJLd6CGPFTcvBjnhEPXJeyt1BZ+0vH04L9fTG/kK7GLIC3wqk5q9DAO5d0zy+PiAKSUTwz752vAleMxjiOZ1Mbyjd7E8jTmFRaaZ/TYfq5gsWpbmgde6sEwJR84Jczxkz24ncVrzIzFSJlyDcYkrqu4laTZzxT3dBpck9FFcdaiC4vjvC7MTHmierLPAm/pZl+urY/ViRd4Lfk8J/kvJpvWSIyQipvss9ACXio/firZzR0YXWkcdUGck2OcpqooJwnMC0MgwFmhI1RBraXwkXiM37V0IgFVwEfiMfx7bIL2Z0xadxUGtWt2IyWkcNFw4VDYwRkT1FwWZtefhgygo0rD0zBQWewMsL3QPmj8AQrS5P7ECqa44yxLFftSaShEhoUjQq7p1AXOZVffU4Ak5JpBje999AVXYbQmEU6N6HULcE4cWYbA66hlQuhqtvX+ceCIIKrfAKKaGlcLKwfiUZMd1RzvmUlAVJE0TNoKm7i/826a8juZ713IBZHLWBCo5TivGwl4Bj4rS5oENRXTkPR3lYeJ3tmaJpm1CPsUAvEldPz410XjD2BZdP3+99R+/ZvUfPnrAKgjaGMVurswurtQvT6iwbls7Xmg5PWG0AXoe2zcStPCaCsP1RhdOV5YbSEl9HYZ++0AABw1Qao+eyZGdxrFpaGFDl3sP2Pmeah3ecmxgjRZlt/Oc11ryVh5Lg8v5KLQfDzq/ktjHAjvhT2Am4E/jfaiEOJW4FaAhobRZ8THGt6pLvh76YwpON8zajl/R77AO8k0ohN+8eehitIf/7WNL1xZzcLpRcPkrNBwVuvkWofi7qpHwR/3cmpwZH1yXVH4QCzCxro0QpQK6J10hhvr9W0l5+caFH7f8TMAJriOp9npoXrSZFq3lMb6I5UD/WAjHnyR0kyp3eZc30NDRlcUZrhdfKgyQkFKVCG4r70HECwZCCcVrAJJfReNU3VW7aGhJAQEwqXOQiiCirODuGoc9L6dwlPvIDjfi7NiaNneO0KLybZCH9NdxVRHXajcVnEWNcM2Yl1amGmx66kPnoMlDTx6NbrqxfulaozOFMKloVf6R91LUhUHjaEL8Koz6Up2YhoRPEodDbVu6pT5BDQ33YUcljGR+1rTSNq5OQ6P935nsJHOG8mXSJi9fKL6C7jV0r0Zv6ZyfXWM/9zRgj9YPqmY2eDGO/D3plheCm0tZecYnZ24J08dcfyZXdvp+MXPyO/cjnA4iN32KeZN+wKbuu/FtNI0hi6m0lveuU1xagTOnU7Hr14pOZ6sqmbrs1mEgGD4wIu3FIeGY5RV1266C50UZJ6wFsWhjM3RWJhle2nA4DEJ3NfzJrPddcz0HFyK7Fg5bA5ACPEUUD3CS3dIKR8cOOcOwAD+MMJ5AEgpfwn8EmDBggVjk8I8BvDPdBE+2VuyB1D5vuCI4Z8+w+DnTW0kDIvateWxxieWJjhxqhdFEehBjSmfq2L7/3SSXJfF0+ig8eYKXNUjz0isgoVVkDR6nPinCaq+pPPcn7pJ9pqcdG6A+QtcZJtDpDuSCF0leOVcHgg9MRi1WJt+lajnDCZdb2L8RqOzycDpFlz80RhVDQc2C1qbznJve3fJsb929LDA70VXCryQeJIHu+7hOnkjl15/An++q59CTqIocOENUSrry99XD6hETvIRGUXjvt5RPks/1TeV89zTONFRQ1j3E3fHytJBNcWJ37mHc/M6Ub1jMypOzUVteDqVVi2ZNW1k168kP7WS4HFxLo8t4I1EPz/YObQySVltJV3UANZnVtFtdFKjllfIzvV5+OeJtfSkDM6c5+f5FcUZfkVA44rTIzgGdHK0YAS9Ok6htdQJaNHyzyWVb6G17xU6WU74o9Pxb51P6u4H6fjJndT9y3dZVPsNLGni1EY3wt4TG7DSeXoeegfFqSHOmsvTb6sIYXHxTTEq6g7PDDpn5Xg7+Rp/6fw9aSvFXM8CLo9dR6Vj31IuXtXNxYEZ/KxzyHHpQiWseUkPKwbtMsvlzw8Xh80BSCnP2dvrQoibgIuBs+VIOsBHGHfeeSe/+tWvkFLy8Y9/nM997nOH9f2cUZ2JH68kfnEBaUhccR09MPLX2ZwrsDqVpULXcI2wQvB5VIbbJe8EF9O/HKfQZ6J5VTSfSq6pl9zmTqRh4ZwQwTkxSnJjlpaHe8g2F6h8X4DwyT7mHO9jyjQ3RkHiDxXHY/7DEowBB9AXzrBi55BI2c7cZqJ6Dc66Ok78gpNQMkjM76E2PnprQWkN9GpVRp7ljdSXVhPFaHlLfhd/7foDZyhnUX1XBtn5DB+5cC4p4cIbUKg9xY0+Ro2k4UxyVfKJirP4365XSFs5FvumcE6+msw3voIrncaaOJnCzZ/AWT+2VayUkszOPJldeRSngneCA0d05I1CM5On655lJF/aAkDfUxvwHF9H1afPYHu2NC9JGaG/tVM4B2tJhr+/0dmOVTCYGo2hVPqZd56H804IkitYxCN6SQ2HFghQ+bHbaLnzP4b2AK78cNnz5o1+VrX/N4lsUVcowUaCkyZRt+QUMs+9QqEvScpRg6I6cMTkqKsfLeQm/IE5+E+bDIogkVE5Y5rB+4MqsRrHfm8Aj5Vdua38fljtwcr0Unw9fq6p/NhgQ6K9McddxWeiJ/Bqpp2A6mKSq5a/dL9Zck5Me/cqkccrC+gC4EvAmVLK8rXzEcaqVav41a9+xRtvvIHD4eCCCy7g4osvZsqUKYf1fTWPim/yvpe65oB/7SgY1E9yoC8XFAZSZVQFzj8xiBACM1PA6EqhOFX0Cj/qgN5Qbkc3Td/821ATbU0h/uXzWf/91GBR3Y67uyj0m9RdFS1rAqK6dHqrdbbk2klk01xd+UVeSvyFHbnixvD2zBquiF5FNDCyntDgcxhZMh2r6d30GIrqIDTl/bijMxB71FJM87hwKYLssHzuKysjeDWVjdluTnWdzPuM2ajnZrBawyQefhO3YWEB1uT3Q3D/C5Rcis5ZwVnM8dRTkCb+9j5av/alwddzWzfT+X+/o/r2f0R17btoLrkuy7rvNOM+z0dygYNcT55GzU1jwFlmFAut/YPGH0BdUkPHIpXW3DJmeOOEehR6jWKYqydfwemBC3ml7wnMgdqGD0avJaYPySebmTT9L79A15/vQeZyeE9cRPSqa3FXVTO5Zui7lVJS6G8i39+MontwNDRQ/43vUOjsQPX5cFTFEXtIhKQKzYPGfzcJcwv1s45HFR/i6VdjLH1uB5ouOPeqKCec6S/7exrO7vz8aBCio6xSDyUt+eayY0uTr3BR5ErC+r4lo8POGhaobma7K5GmpFPxDJYYKgiuipxEwwirycPFeO0B/BRwAk8O/DG/JqW87d14476XNtP9x2UYXSm0qJfINScSOG3yQd1z7dq1nHTSSXg8HqSUnH7Gqfzpz3fz+X/8LLrqRRWHrjFMLm2ya0uO5m05QjGN+ikuQrG9p5DVOB3UOHSa8wUeNnq58vIIfS0GbqEwb5KXyXEn+aYEHb97jczKZhSPg+iNi/CfPBHFqZFe0TRk/AEMi8Tjq/BMmEZyw9AMs/XRBJXnBHHuMZ4eI8VP255kbXbox/Ox2JVU6S9Rrdcw13sGEMSQcsTZ+24yHatpfe37g/9Oty2n5vSv44nNKjmv0e3knyfW8noiRY9hcHLQxwxP8TuotyIEt/bQ1/kTALRAlOgt19H18/XAUEOO/rTJrs48uYJFbdSx10bqw4kNpFn271xVPv61qzF7e1Gr9+4AzIzJzj92ErzZz70NWVZl+sEEfZfgqxPizPJ6yLTkyWzPI6XE5R/aQFfOq+WJJctZYb0FPaAJnevjX+B3zQEurcjSa75Gn9nBtZUfxyXceFU/jc5JJU4lt3kTnf/7P4P/Ti17AzUcxXPxh9m6Ls/WNWlqJzmZNn0X7W98Dzmgu+OpPpHK+beUfR/DGa2oS9FdbHOdyBuPFzd386bk0d93Eq3WmXHC3hvNv5v41fKwVJVeg1MZ+2/cpYVxDegWhYFv1X+IjkIfHsVJ3BFCF++e+Nx4ZQEd3qnxKPS9tJmOX748KLNgdKbo+GVR0/xgnMDs2bO544476OrqQncKHn30YeafMJu8mSBv9uHV9x0f7C4UWJfKsjGTZarbxQyvi8gesycpJW+92M/Dvx1K02yc4eLa26vxh4e+SiORIbe1C6M7hV7pJzAxxucb4jzX08c7qTSFgMV5DcFBQTWrYNB939tkVhYNtJXO0/Hzl3BUB3DPqMLoLl+kGV0ptFjpj1l1CUb6292e6ywx/gB/6l7K9+o/QktO5Uc7O2nPJzgt5OcDsaI08p5Iy6R3U2nKJEIh1bYKXTZgJrNoIc/gjHCi28VEd/mPUu9txuxcB4AzMBmP9xQUXcN/zlSM7gyi0k93f4G7/tbB0g3F5w54VL5yTZxJcRe5jgLZ1gKqR8Fdow+ukvZEDYRKh+p0on/ydja6vCQTSRpcDgKaxa58DykrR7UepMZRNApmVlLoteibabGqM48A5js1pkqTlzu6iKcVtv5/zRgDm9cVS9w4p1aQ29hBz0KlaPwHMGSBZ3rv5p8m3M7PWn5AaqCH7orUUq6K3cTx/pPKxp7bvq3sWOrNV2irOZd7fln8TE48QyVq/H7Q+AOkW5eRS5yN5h5dG8qr1xB1z6crs3zwWNQ1F49vEcvvLs9s27I6vU8H0F1I0mOm8KkuqvRDl7M/Eo3OSUxzHceG7GoANDSuiN2ARz1wJxXVfES1w9tycjTeC1lA7xrdf1xWprEj8ybdf1x2UA5g5syZfPnLX+a8887D7XEwZ97MYdorciC/efRtjrRp8r+tnbySKG7oPk6CU4M+bqmpwD0sfbGnw+Dv95T2BN6+LkvrzvygA2jryJLY1I3jtc0Yb2wFKYlcdTy1l8zluuooeStSpkJpJrIkX9/OnhRaErhnVOE9vo7E46UdoALnTKf376WfZf2Ho4P678PJjKDMmLJy9Bh5vretZ1Bk4emePnKWxSdqK9GHyUhIKekpdA2GeoTqxF/1Uaz+evROwc4fPoSVzKFFvYQ+PZ+W2EtEPXMIuWfgUEvjqbnEDgB8sTMR66fR88xOsNpxH19H6qzZPPZSHyfN8A4af4C+tMl9L3Zz6+IIm7/VMtg8pvLcALUfioy49+JsaMR/2pn0v/Q8ajBE8KO30v/aywQefxjttCW8NXki25WNvJAccEZC5ys1FzPTXYMeVKm9yslbMo9DCD4n8kQe+jPmxg2os2ajXnQp5jCf3PF8hsmfXog5fTtpbweUtoemrdBMTiYGjf9uHu3+C8d7TyKolxpsLRYrex6tpoG3Xhta7VXGLYzkrrLzCukEG1emSfWZxOI61Y3Okni8rnqZWXkzXakVdGVWE/XMJuqZi0NUUN3YSkdz6d9KtFon32uQXJ+lf0MG7xQXgRlDfQbWZ1q4s/XvdJsp3IqDWyvOYpFvEuo+ZB/GQrajQHZXHkUXuOocOEIaIT3CR6s/Q1NuO1krS5WjhhpH+eb5kcIx5QCMrtR+Hd8fPvaxj/Gxj32MTKGDr93x/1FTO5QANVLq13BacoVB47+blxNJ3h8NMXlYTr9ZkOSz5ffKZy1yBYtX1/Tzuye7yOQsplVP5LqbalF/+yLd9y3Hu6gRZ10Yp1oeYlFcOo7aIPkdpU3Z1QHJBte0Sqr+4Uy6/rgMmTMJXTIb36JGps5S6VudJtduEJjjwT9t5MyVWkcIDQVjmGVa5J1M0tDZM7v85USSD1VGSuSeV6dXcFfrj7it4UMobSvwV93Crl/XEj/fQecDT8NAfNvoStHzk7fRvuhmed9/MjV6HRNCF5eEN9yRqSSEiiM7h+6nNg4ez7y9i1BtiDNrKzDTKtPjOmfVKWiWxap+leVNOdpWpHBNs/Ce3Yfp6UGkw6Q7NIKB8kIh1e8nds0NBE5fglUo0Hrn94uNcwB1/Voqvvs17h4w/gA5WeC3HS/w/9V+EJ/qwjfdTZwCl2mS8H//GKO3+N0Yb71JT/Muqt7/D7Q+PNTkp+1ZgydurOMEvwtKE6CY4z0BYwQnbCGRovzvyTV5Ks4p08ltGgiLuVw4zrqcDT8cCjWtW6nSsGQBhe7XS65N9Ef57XeLqz0h4Jrbq5izuNQJe/RKPKFzqQ+dW3L89IvDbFieITfQ0L6iRmfSLDfND3TT/vc+4peGSW/N0v1yP6EFXpyzNX6cfIJus/jbyVh5ftL2BN9zXEWDs9yJ7Q/p7TnWf6eZQqL4Gftnupj0ySqclTpBLUxQG32VcyRxTDkALerF6Cw39lr04GOM7e3tVFZW0rKrh4cffIInnx/qJOVQg0DvqNcWRkmC2vN4MKYxXiOGCgAAQupJREFUa6GXNW8OPYPDKaio1dnWmuPnjwzl929ozfOAw8HVc2ox3mlCZkfXQVd9TmI3nUTLd58crK70nFA/WIAknBq+kyfinl0DpoUW9pAz+sg6tuM8XSHsqMGxF5XFekeUf6q5hLs7X6a50Mup/ql8IHQ8Ldny8ElAU3EMS2XtKnSwvm0r5yY+SbNTo2HR/yO/NI7Rn0MxM4PGfzdmTxpf7hQ051Ns6f4L1b7FuPWhzWVXdHqxickL5eGG7PJd+Fv7ME3Jp+bW0v/b10FKaqdUcMYHF8LSDOLyN1lvPVC8wA8TPJfjsy4raUYy9Ln6cE+fSe9Tfx80/rvpz/SUnb8j30XayuFTXbhiQeo724hk+sj0lp5baG0hcFUXPBwaeu5TXLyV7KW94OOC0Md4ru//yFoZprhmcmnkwyDArXjIWENLhwvDlxPSIhRyFq078/R2FgiENaobIvhv+Czpt7cgjBy9spI3Xw8wZa7BxhXFvaCtaw36zrqIUKyfXOcaFM1NcMYN/OVuH7uXIFLCQ3d10jDVRXCUDKbh1E128alv1dG2K4eqKcQbHTjSFlv/3kflBQF63uwn21T8O+5dmiZ2no/oWT66GFrZWEg6jP6DcgCWIWl9tGfQ+EOx8r5vXYaKyvGRbDhcHFMOIHLNiSV7AFDUlYlcc+J+38uS1kClaXGpecUVVxT3AHSdn/7kx8Qi1YDEoQbR9rFBFHfoNDh1duSGjESj00HcWfrH5nAqXHBdlGBUY+UrSaobHJx7VYTKWidrV/bteVuW78hzxcxa9O4UWuXeY4zumdXUfecDAzroDhz1YQg4WJ3exROJVRSkwfnBOczw1ZDLN7Oy9af054uZJxH3bI6ruBW3Y+RG3EIIZnlq+VrtpWRlgaDqRhMqTlFgmtvJhszQBvNH4jHCw/Y++nZpbL5zPr1txe/s9Es9zLAUIIfURmhW79ZZpSWIBN5Pd+/9SFnqIDRXiPDUS+hr2kjy2Z2ln299mPzOHvK7evFMjiIcKv5TJqKGPIQTveTeJ1hhPVhyzbbUX4kXTirL5R+O4nEjr7mSvM+FZ91WzFdeJZoVsMdWxxxXPbk2jV5XgVBMxx+sR+tYS5kSkBD0+9K4GirItxn4L3HTNEWQ75dsyUp6O6dwVuQOTvTrxB0VgxLIt9d8jdf6XqClsIuT/UuY6ZmLZUneeqGPB38ztK907tURTr4wTLd72uB+k6Kk+Ni3NConCDYu06mbZkJFJ/3BaibOuQFN97JpvYftG1pLhprqM+nbnqP/qT580134prrQ9pLVU1HroGKYnlVySxYkOCeqZB8vncR0Ppnkg6efwL+L0r2h0EHE4wGsnEX/hnIBuMyO8sZNRzrHlAPYHec/0CwgyypgyjyWtDAQ9Fh5fIoTn+LixRdfLDl3d2nD3hRBdxPUNW6vj/NUT4IV/Wnm+T2cEw4SHKGvbKzawUU3xjjz0jAut8DhKv6YQiP8qGoiGi5Hnorbz0QL7D3zRAiBsz6Ms35oabs208y3mh8cDGC9ld7O12suxZV5bdD4A3RnVtGZXkG941z2hld14mXIaEd0ndvrq9mSydFvmtQ6HUxyD71eyFu88kB20PgDvPhgmuNvL46xa5kkdsV8+u9bXnxREcibZnO/tpwPizgNoQtw6eUzQUVz4JnbgHPaVnIbiqsmNewp1ja8WpRtQFWIXnMivY+vxmhPolX6iN66AM3jwbCGryIlBXP0EGLOMlg6I8TdPevIyAJT6iq5ft7NVJlePhI7nXu6XiUvDer1KKe0Hc/P/6MFj1/lw5+rImJK0s2VOE4/hfyLQ8VD6nlnsjbYjvbpMGkjz9PKy8xzTeM6z1Re7Usz1e1kkT9Io7vUQTa4JtHgmlRyrL0pxyO/K9V+eurebqYf72XBkgB1k130dhQIRjVC8U5Cp/yM006uJ8dOWowdzPF/Frd/IgChSA5FAWuYz5043UXfU32k3iquPBpvilF1QWjUz2tPXFU6/lkuDK08hAUQ1/2ohoI5sOq4JrKYOsfBhWdUj0JksY+WB0pXXr7phy6b773CMeUAoOgEDmTD17QKZAqtmLI4CxAIwloFbUYKS5OEhbfE2I/F8A+n1uXghuoYV1ZauBVl1AYiUOxPHAiXfnUTqp2cPMvLq2uKxkhXBR89L0ZNvWvUPgL74tX+jWW7F++ktzNxWAbHbrqzq6lnyAEUulNF7fp9aKlEHTpRx8jL6nTKZMuqciXMpr40Ez6p0/oQ9E2qpPuOeTj7TJJhwf2uVfSaKWLOOhqdC1FGSalzVPqJf+Fs8tu7ye3oxuzO0P3nYoGacOvolX467noVK1X8vo32JB0/epVJX7mUDQz1dXaoQTx6Je35VlryO1GESq2jgciA49me6+CX3UOTg01GJw/E3Xw2upjzXV7mexroTeVY8dc8Tz+dK2oR9Zn86cdtXHGaj97HssS+cSbG3HocXRlyMQ8vhjdQ45jF/emlJGQaacJT/cv4aryGC6J1aGLvUuS7SRgGyX4Tc4/o4O4x1ExwUj/FRf2UouEzU2GOC3yULmM1OcvFdN+1hFzTB6+ranBwwxfjPPDrDhJdBlPnuDlljofEsMSFnX/qInRiqYzG3tC8KhM+Xklnph2tQsHoGPIu3lNV9Er4N+1q2gt9BFUPdY4wzhHCcfuDEIKKJX7S23Mk3k4jVKi+OITfdgDHLqbMDhp/KG7sSqsft+ImYWQIqG40Di5/VxEC7146N+2NoFfj5vMrOfeEHKmsRXVEpy7m2G9HVDqe8kyKbiPDIu+JJAaKuHYTcc8BwOhN0//8Jnoeegfh0IhecwLeRY2oIzS52Rcer8r8JR4c9T1YjhzprQGWP26hORL09/+Q6Xf8AKm5WW+2879dbxQvsuDy8IlM9kzHuQ9VRS3oRptbixb10v3ASlAFrslVRK46gfyO7kHjvxsrmcOXWoQvMoVkbhMB5xRmVtxEh5Xkx03fJjUgZVyp1/DJ+BepcsRpLZQrXC7P7iSpmniFIO4IkVidYuUTpRIK/b0mOU0BC/p+78e8rp4XKl9EV3UWu8/Dlejgi/2QCU7lKZHizXwrEc1bkj01Gj0Fg5d6+3m8q5cL9AD+sFoiiud0CyKVpZ9dvrmJtt/8N7nNm1DDYeIXXkLglCmow/SDFEUw/Xgvn/q2k1zGQnQU2PzdlpIEOCsnkebekyL2xB13ECh46f5sB643grDBAYsyiHlpIu5GFKFQ5zh0ip0ArmoHU/6himy7QcGwSBjQ0WVS4VTQnQefYfRe4ahwAFKOXjJ+6N6jfBNVygK64qWwj/d+N5QupClRuwxqExI9rOOOHJzxB1jim4pDZlmb62FjrhgqOT0wnSpNpyv9Dj3ZYmpopXchMc9cAFJLd9J1z7LiDVJ52v/7JeJBN975dfv9/oaaQVzwCg8n7sPCpCIe5/LFnyDQ9ArO6HT0gAtFc3COeRxTXdV0GP1EVC8TnBWDxj+TMlFVgWMUkTwAR22IyltPwbrmRITHgerWUVwaKKKkOxSKYJ3iZKX8GHPCklmBCnyal0c6fj1o/AHaC82sSS+nyhEnOMLmeK0exjNM394fUstE9NxeBd2SZID8Jgv+NcSpk66k7joHvWt/RCFZDFW5gEtnfJAl9RcO1hHsi5d7+/lDW3FWfr/Vy423RVn+Pwk6WwqEKjSuuK2ypKpWGgY9f3uE3Oai0zd7euj6v9/hiMfxDut9PfQ8Gv4QZCxQnAJrWOZaxZIAjn0ULo5EUA9TN9FgR3wrGTNFWI/S4Jo04iRFWhaMcRW0N1S3Sns6x+//vbXo0AS87/Iwp10cwjlK/ceRxhHvAFwuF11dXUSj0cPqBEaq5lUUL2mrQETzoY0SapBS0tXVhWsEKdxDhbQkXa/0s/Xn7UgThAoTP1VF9GTfmHoDjERfdhs9vX8hnlnPdPdxOINnIrQKprnj6EJlfvzzpPItCKHg0ePoqgcrb9D31PrBeyhuHcXnJLVs5wE5gB25bTyWuHfw3x1mCy867+VyXyXRydegDGwCu1QH091xpjNUcJfqM1izNMXLj/bi8amcdUWESbPcqKNoxCi6hhId+jk468PErltA591DOi3i6uN5RDfZ0WfyUh98w6UxSS2wI7el7H67csW6ignOGKf6pvFysih/oAuVmyvOwD/QQ7nQ1YVj/Zucf8k0/v5QUcpY0wVXfrKSSgH9ukAWJEKB4FwP+eTmQeO/G2vjY8yqP2tMue99hsHj3UOrkqwl+a3WxWc+HSK2vBOPH6qmDRl/w8rRn9lM9rQI7hOvwnppA7k3lwOQ27FjRAewG3etgxlfq6X5gW4yO/NET/dTcWYA5QB1eqJ6BVG9gnzOoqu1QMKSaNXmoDE2M3kyq1tI/H0tqs9F8PyZuKZVDFZ37y/JhMH9P28fTEuVEp6+r4cpczw0zjiwHtjvNY54B1BXV8euXbvo6OjY98kHgZQSU+YwrBRSSlTFCSKJQCEp1L06H5fLRV3d/hvAsZJtzrP1F0XjDyBN2PrzdrwTnbhr9j/0ki108nbLv5Ezi5tgXanX8BVaWFBzx2CZuq76CLlLZX6FqqDHA+R29mDcchyb6vK0q2nmusL4zHyJxrklLQR7n6V1Ge1lxzbk1uGadQtO197lcle/meKBX+3+myjwP99t5tZv1NI4fWw/XKGrBM6ZgWt6Fan2fjZ7VB5zwQ5jmEy2EDgUJwt9p7Jzj+5e01xTkNIiqHm4qeJ0zg7OImXmiOshaofN1PtefJbEA3+hvm4iN111MRnDRcXsBqqmexHAtH+pJdmUx+FQaL2/i6q68kwUaRYYsYnDCOhCEFQVuobtqRakJGFkiD7wGilTYsyrxFEdQEqLlr4XWNv5G1AAL9RdeDq+7Czy76zBUTWS2G8pvikuptxejZWTg72MD4bergJP/qmLt18opn7OXODl/JvddLt20plswefQCAQsrJe3knxjG3XfeD+uqSNnp+2LdNKiq638c010j+2zPhI44h2ArutMnDjxXXu/rNGNJQ1cWgRlDOp/Y7pnS55Mcx5FV3DXO3CE9+++hYRZ9vuXBUmh1xiTA7CkJGtZuAY2n1OF1kHjv5tkfjvpQhuOvUj0ClUh9P5Z9M/w8tPG1XRaSbDgifR6buk3OCc0m4yZYX1mFc8nnsCn+jgzeD6TXNNGXMqH1PK47gTXFLx6aK/Pk0mZvPxob8kxKWHLmsyYHQAUdeddUyqQEyO82tzOumHFeif4PNQMpOnOcE5ise8U3ki+hipUlgTOQkmvos81k6CzFr/qYpa7tuz+Rl+CvueK3aHMXVsRu36CB3D5rkOZ+QEAgpNcBCe5kFLib3BgSZVkjwerMJTP76s9Gc09trx3t6pydVWU721vGQzNR1SFxl39YFgofifKQJe4dKGV9V2/L7l+l/Uic864Ct3hxbWHzn/OKtBc6CVj5qnSg0T1YuqxoitYSoZ0oR9d8aIfRJrmppXpQeMP0Nef4YXsszzT/VDxgAeWXHQ2C3urMFd3kX6n+YAdgDegUFWv07azNAMpVHHEm81Bjp4neZdwaYdms8mSJonsJoztbnZ838BMFZeZvukuJn+masxZEgB6WCvGWnNDsVbFJXBE9v31NufyPNPdx1vJFHN9xfRT34gNLkRx1bMPXFMr6Yr20NldKjvwx+7XmJavocO5lt90/GTw+NvJN/hC3TeY6CpvGtLgmsgZwfN4IVFsIOdV/FwZu3Ewr300VBVc3nKH4nIfWCjArRYboxzv97I+lWWqx8Vsr3tww1630kwptDI3fClSmiRTb9JTaGWFayFBw2S2p27E8IxwONCiMcw9Cr3UQLmejRACV9wB1FHj/ho9Gx4kl9iGv/YUAhOWDIbDxsJxPg9fD7rYbAncBUH9tgTa795EArEbFqJFigbasDJYsjz9UplYTWzmuai+oQrffjPLgz3LeGSg41VY9fDF+EVMclXSl93G+s7f0ZNdi98xkZkVHyXknjbm8Q5nw4pSXaqp52Z4MP1wybHnrKeZ875Poq/uggMM/wB4/RqXf6KK//3PFvp7TFQNLrwuRnXD/ncae69iO4BxIpHdyKa2+/A/fN2g8QeKmifrszgrdFKmyc5snqRpEh9Q9BwpZOKK60z+bDVb/qsNM2OhehQmfboKZ9XenUi/YfDTXa1syRTDCs25BO/0p/h8Q5Rq3+m0JofSFxtDF+PZh6hdv5Ggz0xQ0Kyy13LSYN07GczJpT9gC5N16XdGdAA+NcAHIx9msf8MMlaGgBrFkA66C0ki+uiFbQ6XytlXRvjd91oGN1bdXoWJsw48bhvVdc4I6ZwRKl8B+Rx1ZAttJHv+Ongs5F3MY6kmmnrW8eWai6knhsOplHx/qstN9IpraP7P74BZjN9pVdW4pozcQQuKociW1jjvrLoaXc/jzQWYU+knvB+Tak0IJgsL94//Hd8Jp6O6arEunIh7Rh3u44Z0bVxaDLdeTaYwVNylCifCW8MuJUeV5cI1kHK5Ldc+aPwBesw0v+98iX+sPJMVrT8kM9CfuD+/lbda/o3F9d/Bo1eNfdADNE5zseq1oZWYqeVGlFrJ6wUcuopn7sF11qqf4uJT366jt8PA7VWIxh2oI8ipHKnYDmCcaOp7Fo9VQ3Zb+WvZ1jz9hsk9bZ0801PMLtGF4MuNcWb7yme/QgjCC7wc9716Cr0GekjDtQ/jD0UNot3Gf3BceYM3+7cwx38a83yLSBfa8DlqCbomjyh3sJut2Y38ru2/aC+08oHILTiFTm7Y7PFMZTarHitwwrU1sMcEV9lL+qxLdTNBncLadDPfaXmSXiPNxcpCjktNJuxyE6tVUYx2jHQHqjOIw1+DojmZNMvDx/+5li2rM7g8CpOOc+915iYtSaYpT67dQPMreOodoyp97onXEWdBzdfY2vsoydxWXJ7jyRdmcuUuBfeODJ7qbt5qM+nNOTjxrACVwypd3TNmUvf1b5LftRPF6cTZOBG9cnTD2LU9RX5dO3O9GfIOFys3Jnmxw+CiGytG3eAeCWddA7X/+FWyW7cgC3k8EybhbGhECIGR60caGXRXiHnVt7Om/Tf05Tbh0eNUhq/hjpan6beynOydwrWxk6nQA3Qa5T16N2RbSRttg8Z/N4aVIlNoPyAHMOMEL8tfTtK0uVipq/SGiVTH6DaGitmCapgKRzWRf56Ha9LBaQIBBCM6HlWQacqT6svgrnWgB48O03l0PMURiGnl6BcbqF60hO7HS1/zTXGxLZsbNP5Q3Kj7VXM735xUR2CECmEoVk2OxfDvRhslQ0gIgx+3/piv1n+Pib6F+7xPwujh160/oscophY+0/snPhP+Es/2baJD6eXE/DTEqxVsacoTVUqFwXShM8MzZ6/37yj08WjPUi72NxDvncaTdxZ4p60Lt1fhli90kd744wFZYkFk1jWEJp+PpruYMMPNhBGyNfJNu8g17UJxOnDUT0CPREisTLPxP1oG91KqLwpSc3lkr7IFwwm4JjIp9lHu63qFnZkk172Ww7p/PRJIAfFp1bRXzOaeH6W4+Y7awW5pQlFwTZiEa8Kkvd4fQBom5uubUP/6NgbFfdmFJ03hxdZGEt3GYA/lkUhlTTa35NjZniMW1JlS4yQar8URH9qbkNIi3b6ajhW/oZBsxRs/kcisazix5qvkzT625fv5Vtuz5Ac+pFdTm5jmjnNhaO6IXaxmumtwKl4EKnIP2T9NObBm69FqBx/5Upy2XXmQRemIWe5/5K9d/8emzBomuadzefR6ag7hvmC2Jc+mn7aRHnA6nklOpny2aiAkd2RjO4BxojbwPt5KfQdx8gYC7dPpW2YgdEHtFRF8U1wkRmoynjdImRajdH7cb+IOndOCPl5KDM3e5npVmvJvUJB5EkYP1Y59L6ETnSant92CUCXt4VW8aj3G84l7WLD6NnZuybH5nQKp/jzTZysE1r/GJ+Zdx9vB7ahqgNMCp9Do2rvxKxidnC42YPR3s/7vE+kZyMw49TyL7NZfDdOkl3SvuQdPxXG4IiO3nMhsXE/z97+NzBdXPs4Jk4h9/Mts/UV30fgrUH0euLybSK1QcE9uwFG1734OAH7Ny2zvZOp6O7AeWlfymrGhlRknzOD1l006W/KDDgAG2i92d4EQ6JHRu0HlW/vof2h56Wfz+iaOv6YRbS9+35KSZ5b38Yenhypy507y8OlLKgl6h8aR79tF86vfA6v4+aZalmLm08RP/iJeR5zHOt8aNP67eT25iQuCc5jorOCD4RN5sOctJJKo5uP66Kn4nWGmRK9mY9dQ9XRD8EK8evmm+FjxBTV8w2bgASbwierPk7L68Sg+XOqhTdHsfjM1aPwB0ltydL+RpObSQ1t8Nh7YDmCcCLtncELNV9ne+xj+6xNM/dCZuJxBXFVOhCKoSpd/NbM8LkLaoStAcasq11bHmOtz8k6qlypHjoxcxqv9r6ALnZAW2uc9Wrbn+L9/66ev24+iwIS5Z3LuFWGeNP/AVYstqirDVIZTxNw9xPqWU/jbA/i3z6biqhvJer1McO39RySlRU//8yTSS4kp57F9WFuCYCiLmS4XwTOyvSPey8pl6f7rnweNP0Bu2xYKHUkKA5WwdZea5Jf9gr72oqSx4vVR86U7cDWObUY5z9tIQnfRa6wpey3qgcWn+1GH9WMw+hL0Pf8MPY88gFAUIh+8Ev+pZ5RssA5+FlkDRqiirYhCYIQ+DLtp6ylw7/OlGtErt6TZ1ZEvcQCFZMug8d9NtmsNRqYLVa9jhruGZeltJa+f5J7CpuYsO9ryNLiO45uVszC8Saq0AOGBfZr6wDkEnVPJGu04tSgB5wQ0de91MaaVJ2t0oQoHrjG0WnSqLpz7uOeB0reqfDKWeCdDzaWH5e3eVWwHME6oioOYZx4R93EAZSmljS4nt9VU8D+tnWQtyQSng4/ESxvEHAoiusZpoSARxzZ+2/ZTMlYKXejcWPlJKvax6Wuaktf+nqCv22T2hQ58C5N0ONvweybzEe/nqfKFaYg5mbXQS9/OLO07aknN/jwrglHeyFt8udq3z+K9nJmgPVkUQksr65k45yzefrb4Wnurh0hVBWZmeA2IQPeMHPe1sjnyzU3l75HZibsxTr7DRLXWYbQPdS+zUkkSTz+B86aPj6mgSBcq4XgFubk1gx3WABSfk1ynznFhlYq6IWOdXrmc7vv+BBQVEzrvuRs1HMW/aHHZvbUKH3pNkELzUCGX4ncSmBLa65gKhhzsAT2cbKF0s17Ry3eSFd2Lohb3ThZ4J/JS/3q254sriYmOGLGuCdy/ogePR+GdDRn8HoUvX11D2D2sYbzqIeKZCczc6zh3k863srH7T7QlX0NTPEyP3UiV76R9quoeLsIneulbWapJFVn43mlTeTDYDmCcGa2WwKEoLIkEmenzkDFNYrqO7xDO/kvHoDDLM48v1n2ThNFDUAtRqcdHzM0fjpG32L4hy4TjddrOWM9D5gZ2B71PE9OY6y0adyEEwYZGZLyWbC7PPCm5yOUgpu97v0ITLryOWnqz60gb25mxpInmzbW07TB57RnJvK/eRm7rTzGzPQjVScX8W3AERi66U/1+/KecTu9jD5Uc73C1YX3EhfeBGGb3CA5i2xZkoYBwji39T3XrVHx0MT2PrCH91g70+gjO+TPYfm8O1V2g4aIQUJQs6HvxubLrk2+8OrIDCLqp/twSuv74FumVTbimVBC7fiGu+Oi1GQCxoMbsCW5WbRsyYh6nQk20NIbtCDbgjS8i1fLG0LVzb0L3FvspxB0hvlrzAXbli2mrMSvCyq4C/fNNmmWBM2b66VlvsKUlR+UYeyjviSVNtvc+RjKzgxmFG9HSbhxGkKy/E29FDUI7tDo8GdPElOz1txU6wUvf6gw9bxSzj0ILvYSOPzocgHg3dGoOFQsWLJBLly4d72HYDOPpP3eRq+3j95WPlb327boPMdl1YEU4w+nJrGNZ87cHctIFdY4bcKdPxel0EYvrCNlbDFM4/Ojeqr2uKgrtbXQ98BeSr76E4nYjLjuPRyavY6PczA3uTzN1h6Tz53eWXBO95nrCF1y83+Puer2fvrcSZNss+tcVw07uBgczvlYz2Eay/e676Hv6iZLrwpdcQfTyD416XytnYPZnUbxOVPfYDG1zV55HX+/ljXVJJlY7uWpJlCk15TNqI9tLLrENM5dA98ZxhiaiqCO/x5r+DN/c3lSShHmxN0Rtv85Zcw+sN2+20M3Kph/TsOYckn/YBKZEcetEPnQ8hd40ofNmoscOvn9uwbJYmczwl/Yu0pbkA7EQi/xeAvrIEzIzY5JtLYAspl2PNUPsvYIQYpmUcsGex+0VgM1BMf90Pyu7+0d8bc8NwwMl7J7B4rrvkMw3oSou/M5GXNpwAxNBd49tQ06vrCJ20y20nzONFquF59UXMDHBEkifwfPxDcy79HzMx55Bmib+M8/Ct7B8Nj4WPHVOdvyuQKF7IANGQMVZAbbd1U784uJmf/CMs0i++jJWuji7VP0BfAvLG7UPR3FqKM6xG0EzkyeaTnHjiS4+dEYEl0PB5Rh5Jq25Qmiu+WO677ZcriwD/7V8ko9VVWBJuVdJ89FQFRcN6fNI3r1+UEXUyhToeegdfCdNIPnqVsIf2HvW2FjYmMny/R1DCqy/bu5AqxUsCY+8mlLdKt6JR5bRHwu2A7A5KKLVDuaGKqhrC7OrMFTRWqOHiO9DsmF/8Dnr8TkPTfNtzeFEr65iR2IHFyi3U9+WwpPo4RXfFp4sPMHbx8c5Y/aFaCgEKyZSFYqR3pEjtTWHUClqLNWWhoN6CgYb0lm2Z3NMcDmZ6nERHhBD61+XIddWQPWqdDzdR2ZXnsSKDMd9qw5340Tqvv6v5HbsQAiBo6ERR3X53kvS6MNCEtD2b2adb+ql/Tevkl3TinDrxG5YhOPUSRSTSA8O9whpxF5VYbPMMMty4TyA/Spd9eBORknu4VnM3gyKW6fvuY0Ezp6O6jm4FMyV/eV9Jh7r7OWkgPeQ77O9l7EdgM1BE3V5+Vz1BTyeWMHK9E5mu+t4f2g+Ie3Acr3fDRodM1gmq6hZ/Q7m739N7vj5rJvQC0C70cJfRHGfYHH2DCZvmse6bzYNSm1oPoXpX6/F21h0AhnT5P9au3gxMbQSOivs5yPVsaIWk4Cdf+gqqfi2MpLMrjzuWieOPfLxh5MxM6xMLeWR7nsxpcn54Q9yon8xPnXvcX8AK2/Qde/bZNcUK3llpkDHL1/GURvEPX3/i7D2ZJrHRVBVSQxUMQvg9JAfFQ7I+O/GGYsWbzbMCahhN1Y6j7MhMqhVdDD41XIHGNRU1MMsK/9eY1wdgBDiC8B/ABVSys59nW/z7mNKk+3ZzaxMLUUVGnO8J9DonFwWZ69zRvhoxRlkzDxu1TGqPPZ7hbacha+/H+W+e7AAa/tOZhWOZyfbSs6b511E+/29JTpLRtKid2lq0AE05wolxh/g2Z5+zosEmehWEZoYsQmKoo88C883J8jv6kU4VLZMbON3Hf81+NqfOu/CLRwsCi7Z5zOaiQyppdvLjhdaEofEAdS6nHx1QpyVyQxdBYOYrpEzTc4MH1j8fzeO+hAVHzuFjt++BqaF4nUQ/sAceh5ZRfwL70McgmSIOT4P3s4eUmbRKSvAByvCOA5CO+hIZNwcgBCiHjgP2DFeY7DZN1uyG/hR078O6q082fMQ/6/2n5noLter0YQ6qHP/XidfsPBZJla6mONtdHdx3FYfmyZNYbNZbHxyovdkJjqmsqu1fI8j1z4kc5EfJZEiP9BMxhnTqLs6yo7fdeKKazirFKy8wN1YHsbIbu6g+Vt/x8oUcNSFeOv/lWclvdj3DPM9C3GMkLY5HMXtwFEbIr9jD7G54KH7jia4XTS6nHQUDCwpien6qBXmY0XRNQJnTcU1oxKjK4VQFcxsgZo7zsdZGzok425wO/nGxFrWpbJkLYsZXndJP+pjhfFcAfwQ+BLw4DiO4YinJddEe6EZt+KhxtmATy0vIDpQpJQ81/u3ErEtA4NlyVdHdABHElUOnRedXqacsBBj2UDa490PcOVZp2JccjW620ulHseluqk8R5DckC25PnzS0CZstUMn7tBpyQ85hTqnTvVAr2MhBLEz/Pgae0m9/hz5bevxLliMsE4GhrKkrIJB919XYmWK9zFTecJqqGzsERHEyvbCPhyA6nMSu2kxLd99Alkohmm8CxtwTjx4fZzhCCGoHKWv8wHfU1Vw1oVx1h1cg/e8laffTOBS3HjV0o3zepeTetexZ/SHMy4OQAhxKdAkpVyxr0IgIcStwK0ADQ0N78Lo3ttY0mJHvouWXDc+Jctv2n5AXhbL1I/3LuKqipsJjqGCdyxIJFmrfLMsZaZY35rCapPEYg4qag9eE0WaFvmmBEZXEjXoxlEXOuBm9mMh5NE5JxAlecFl+Hw+rDdeRauqIbbwbNyhmSUhruA8D/U3xmh5oAehQd1VUfwzhlIow7rG5xuqeayzl5WpDPN9Hi6MBgkOSymU2R46f/MfGF3FSGdu+1ZyO7ZSdcttKM7ivaysQX77UMWu2ZNmtpzL88ozZKziSkUXOovFDBSGxmdmsxSadmEkelFDPkxXDtXpwRGowzOrmvrvXUK+KYHi1XHUR2h1CDr6UwRUlTqnA+cI8fCjgZbcLh7s/iPvpJYR1+u4quKjTHXPPOztY48kDlsdgBDiKWCklkF3AP8EnCelTAghtgELxrIHYNcBwPLUDv695REWeRtIFl6kOV8a4/1U/MvM9h5/yN7vneQy/rv1+yXHLgl/kd+1BDnXEUB/yuDs90epmbDvmZS0TIQycvy2/9WttP30+aLUgYDY9YsInDcdZZS87ENFuiNPJmfgMpI4wl704Oiz6nyPgRCgh0YekyEladPEo6poexiZ1DsraPnP75ZdU//Nf8dZX5zYSCnp/tNb9DywcvB1/3mNJM83aRI9WMIinndSkdMIT3k/QlExs1l6H3uYnofuK16gqkRvuIre9GMEJryP0NRLUPWhkM9bfSl+uLOVwsDv/qrKCO+PhnAdZU4gY6b5Wcu/szk7pMmkC52v1H2XuPPwded7r/Ku1wFIKc8ZZSBzgInA7tl/HfCWEGKRlLJ1pGtsivQZGX7b8TwWkirdw+bUrvJzzN5D+p7T3Mfxieov8FTvIwg0jnOfz9M9YSQWT+T7uPHkCMtf7N+rAyhkukm1LKV/xws4w5MINr4PZ2jC4Ov5tj7af/nykM6NhM7/fQP3rGqcE/etA3MweCocWLkOurNrKZhJwpnpBJxTRpS+3lenNk2IUZVaxUgGVoiShiVCCAJnTaXQkST5yhaEQ8NRVUm9r5LK/s0Ukq04QxNxxacMOtJC064h4w9gmvT8+WECN55Bz/q/4qk6AXe0GK7ryhf4eVP7oPEHuLe9mzk+D1M9QysaM2eR3JCl+/UkekAlvMCLd9L4yDAcKN1GZ4nxByjIAu2FlmPSAYzGux4CklK+w7DA5/6sAI51sjJPu1EUP1uf7WaaZx5r02+VnFO5D/2e/cWpupjnW0idYza/aG7nzT6D00MJDNmLJiI4kgF27CzvGrUbaRkkNj1K76ZHAcj1bCK561VqT/kGmqsS1a1j9WWRmT3uIcFIZDjcEdpUvomlzd8kP8xxHh//IhXeEw/p+zhq6nFOmERu21AD+eA555dp/+tVASo/cSqRy+chNBWtsqiX5AiWV1T35bZR6N5WdtxKJRFm8ZMzc0O6Qf2mRZ9plp3fWygt2Ot7J83G/xiai7U+2susf6nDM4ZV3sGQzJhs2pVha0uWqpDOtHoXsfCBvadTceEULnKydO/GfYAy1Ecrdh3AEURQ9bLQM4E30ttYl23juujJpM0+tuc24RROLotdT71zwmF5b4/qIKapePXXeLx3qPPVRYHrWXzBaaNeV0h30rv5bwAI1Umo+mLohfSylQgrgh6vwxEPogbdmIlh+w2aghYbm95KIWFg5iSOsDpqauVuejsLtDflUVVBZZ2DPmVTifEH2ND1R0KuGQfVu3Y3hpUllW8ip/US+cTNFNZsJrt1M97j5uGeOQtlBD0kxaHh2Ee2S19uO5t6X6M2PKXYA3OYYdcqqjBFLwgF3TvkOEKaSpWu0TbM4AugYtgGrpExabqvVDnUykn61mQOqwMwLckTb/Rw70u9g8fmNrr4+CkR/D4d1156HYxETK/kytiN/KHjl4PHjvcupsZp7yMOZ9wdgJRywniP4UjBqWhcHV5IwSzwdq6JR3s3cVvwSoI+C5+3hpi+dx2cg8GtqpwRzvKTlgdKjv+t7x6+OG0uEBrxOiEUhFCQ0iQcv4ze/3kEK1XsPyAcTqIfvBVFm0zV55bQ9uPnMHsyKF4HlbedhqNm7/nkliFJrEyz/a4O8l0GkZN91H0oMmqjjtYdOX7/by30dhUNYMN0Fxf8Q/kmt2EmsQ6BjIVh5djR+zc2df8RAIHC3EW3U332pw7qvpaUrMs4ubt3EaqEz9/yKYz/vQsrlUKrqCJ09QX0dDxE9cLP4fAPFZiFdI1P11dx5842ugoGLkVwS00Fdc5hn5cFVr58X3CkOoZDSWt3nvtf7S05tnJ7ls2xLPqfW5l4WyXhE72I/UgxXeA/lSpHLR2FFgJqiHrnhEOaJXc0MO4OwGb/qHVX8pnYmXTnu9EKGYJSxxloRNXehRityJb1XzUxKajlRnQ3midGePplJLY8gbGla9D4A8h8jsympUgtQvi8mdR96wOYvWlUvwu9ct8/1OS6JBu/3zZYMdr9ShIpJZM+WYW6h9aNZUnefLpv0PgD7FifRck1IlCQDFXpNobej3M/JRdGIpXfNWj8ASQWq9p/jt814YDaIe5mRzbPnbuSgz22vh2q4cbP/xNTjF2EYtOQap66mQsGVTyHM83j5luT6ugqGPhUhao9+kxrXpWaS8Ns+Vn74DGhgn9mae2Amc6T396N0ZNGq/AVK3SdB25OCjkTozw6RUFKlJTF5jtbOe579Xjqxr4KcSpOprinM8U9/YDHdbRjO4AjEK87jNd9cPnRB0JUr8CteMlYQ025g2qYiFZuaHYjhEJg4jk4Q5Poe+j5stfNRBeqvzgD1aNe9OhQ2CWVbyVjdOBQfXj1WlRlaKaa3dJJcm2WPdXIel5LUbjGQK0qXQWYBcm2deWOas3zERZe+lU2d/2FnNlLQ+h8qn0n7/2DGCN5s7xZjSkzFMwkHIQDaMnnSxosJkyTn2TgWxMmUeXb9x5QWNcI7yW7KnSil8m3V9H2twR6SKX6whDeSUOG18oZ9D70Tkm2UuzmxQTPmT6mngkjURnROa7OwepdQ816fG6FUEqSAaQBmdbCfjkAm31jOwCbMVOhV/HJ+Bf5Q/svaCu0UOto4NrKWwnre1fi1JwBtOr5cJJF6pVXS15zH3fSiMU+3ek1LG/9PoaVAQSTI1fRGLwAbaDdX3pFE4oSKrtOD6soznIjpDsV5p7so783QbrfxBqY8Nc0eoh65hB0TsWigOMgQgSmlEg51GvZpcUQaEiGVh1ONYJTO7hWgt5hOjvTHC7m4kEY4MwfmgpfzasSPdlPeKEPoVAWdsk39ZYYf4Cuu9/Ec1x8n3sXo+Fx63z0rAiPLU/x1tY0E6MOLqjxkvv9QBtLAX1Zi6Ah0TQ7j/9QYTsAm/1iinsGX6j9F5JWPz41sF8xVde0GVR87Da6778XDIPAORfhnX8izvpSg5gzelnV/t8Dxh9AsjPxONJxBtvzeRSgfk41vhd24ptWQXLDQAaRgMabKnCMkKff02+gTdapucRLWNdIb83jd6pMHAhtFFsUHlgYzZSS7f1pejqTdAtJOOJjls+Nz1HLvOrPsbr9FxSsflxajDlVn8WlHdzqrdHlYIHfi5mX+Leo/GVpcdP2Ca/Kl66OMzl+aMKByiiG1uzPlR2TBRMznR/h7LFT1+BjSZekql9hbr2Tzl+0I02KdSEfDPP4I718aKLrkBQe2hSxHYDNfuPTAvgoqlG25JroKLTgUb3EHfVl5fbDUd0egqcvwTvveLAkWig04nkFM0nW6Cg55gt+in/d3juou+NWBP84O0Ik30p0fhzTUPBO8eKfW565k8mZPPBKDyLdT19e4amtBqfM9PGBc8P4PAf3E7CkRWdTD8qDqwi+vJVI0I1x7QlsmF3F/HCASt8C/M5G8mY/Li2Cc4xV2u35VnqMTnxqgEpHHF0MZcEENY1baipYvz3LD5cOpWsmUiZ3P9XJl66K43EePjE+vdKHcOslqbtazItecfCNWqoaXLz4aIL730hxxmUR3JYk3Ojk4Qe6adqexzqCGlgdCdgOwOaA2ZBew3+1fI+CLM78FvvP4LLo9fi1vUsVa4G9b7A61AAePU66UGzY4XPU83oyVNJgJmNJVtT6OfvNXeTfWknoouPwTPeWzVpTfQY9O7u4IPMGuZefQHh8nL/kMn69XqE9YRyUA7CkZGOqmeDrnZhv7ABLYvakET97Cf0rZ5MLeHGqKm69Arc++j7JnqxLv8MvW35AVmZQULgidiOnBd6HPmwPJKRrpFNW2bUbdmZJZa3D6gAc8SDxL55N+y9exmjrxzEhTOXHT0MLHXyOfTCqc9VnqmnakqN5W44+BA/9poNUn8n803xEKg6t5tCxju0AbAYxsj0UUu0omgvdVzNqK0CApNHPPR2/HjT+AK/1v8BC36nM1OYd1DgcWoDZlZ9keesPyJu96EqI7oIGlKZmdpkWlZ84FWnJEXWDkgmDJ+/t4gTva2SfuGfgaAfq3T/k2hu+Qn6ERun7Q1d2G1ZmKc0LNxOZPQv9DTeZR3eBBF97CsH+x6p7Ct38ru1nZGUx/GVh8efO/2GyaxoNrkkl51YEy595ZoMbn+vwyzp4ZsWp+9eLsJJ51KAL1XfoNmf9IY0ZJ2iEYhrLnusjFNM489IQxy3yoY+wv2Nz4NgOwAaAXO82Wl7/IUa6DRCEp32Q0NSLUB0jL+uzVpq2QnPZ8YSZGOHs/Sfknsbi+m+TKXSiKz4KmQCrU6VVnaeE/AhNHdXMtm7Po+ZTyPVPlb4gJaHurUQixx3w+LKFLta13UnGKIZgOtXl1J72PvybqhE58ES9OIbJP3QlCqTzFhG/htc1+uw8afaRMHvKjvcYXTRQ6gAmVju5/LQwf325BykhGtC4/pwompYlkS2unjx6/KAL2kxT0tVawMhbhCt13N7i+LWgGw6htPSeVDc4ef8NMUxDou2jwM/mwLAdgA2WkaVz9T0Dxh9A0rPhr7grZuGpHLn/ql8NMss9lzWZ0myQSn0k/b8Dw6VFcWlFLaC5qsnHayq4v6MHFbiyMsIs7943O1NJk1RaQXj8QOmeQiDiJ+g98D//ZH7noPHfTbP5HFM/83n+nn2OGleWE3JQpdWzbGOKXz/eTn/aYkqNk1svqqShcuQZs18NElIj9JpD1bgCQVgrl3D2ulQuOzXMSTN8ZHIWVWENp6ubla2/pivzDgBR9xxmVt5ywHUH6aTJa08kePa+bkwTGqY5ufwTVVS+SxuxQgg03c76OVzYbtUGM99PpnN12fFCumOEs4s4VReXx26gwVGclTqFi2srPk6ts/GwjNGnqZwdCfLdyXV8e3IdZ4QD++zdGqvWWbvSwFx8WVF4bQA1GMQ3Y8ZBjWfPgrjdx7Yqm1mRW8rjifu5s/lbbO9v4kf3tdKfLsbrNzXn+M3jHaRzI1Q9ASE9zE1Vn8GrFFdeGhofrriFuGNkATNdU2iscjKjwU3Yr9ORXDZo/H2uuZjOs1iXytNTGF2vaW80bc7y1L3dg0oTOzbkeOHhHoyDDJ/ZvDewVwA2KLoPV2Qahf4mrOh19CRi6JpBSNl7vnqNs55/qP0nuo0unIqLmFZ52LXWR1PbHInqBidX/0M1Tz2scfrlX8Gf2YYn5sc3c/qoPXjHis9Rh0uLkTWGNAwjvsU8m35n8N9Js48WYweSUiG39buy9PSbo27UTvPM4iv136Xb6MSn+Kl0xFHH2GKzM70cgIDnfawwz+HZVgvIUak38YWGOI372fWqbVd5aufapSnOu8ogEBl5jyibN9FUBU21Z+7vdWwHYIOqu4nNvp6OnRadm/IIS/L2Oj8vPW1x7ecKhPeSeeFRfXj2kvo5nqia4LiFPuonO8llaghEZuN0H5rsGLdewQnxL9OSfJnezHoqfYtZnm9ia6600E1RymfKYZ+K27l34xjVK4juR+bQbmKe4xES0s6zebZlKEuovWBwf0c3n66r2q++tyN99/WTnbg85Z9jT7/B6+uSPLO8j5qozkUnhZlae2TJSB9r2A7Apkjej3zuHvxvvQLAWfMWs6nmInZuyu7VAbwbtOR30ZzbiSY0ap2NxPRyaeS9MdpM9WDxOeuZ6ryG3U2V1vT8teR1j+Kl3tXIkrk6z60s9hVWBFxyrpPHs29yhmM69c5D1+/ALKQJp4KoWwq8NVkDSsM+q1IZkqZJZD8cQP1UF7MWeFmztCj/4fYpnHNVFMcemUaWlDz5VoL7XypuYO9oz/P2pjTfvKlu1P0Om/HHdgA2AGRWLSc/YPwBCiteY9IFU2nt2/9Z6KFkW3YTdzZ9a1DXvUKr4lM1X6bKUTOu4xrO7rDXqYGzCWsxXu17jlpnAycHzqTeWcf1Z5ucOsfHlr4eTF+Sx8UztCcSvJ7azDdqLyOiH5oVVKZjNe1v/hiAion97FnZPNvrxrePfZM9CYQ1LvtEJadelCefs4jFHUSryh1qd5/Bo6/3lhzLFSQ72nO2A3gPYzsAGwDSy8tbbWpb36Jh0dnjMJoihjR4sufhkqYeHUYb69Kr3nUHkDFNmnIFMpZFtUMv0dDfTUALsjhwBov8p6GIoRmyz6PirU5xj/Egw0RHaTf6aCr0HhIHIC1zsO8CQEXby5wTO5enksU3rNI1rqiI7Ff4Zzde/5BkxmioCjh0Qa5QGvLSjrJWk0cbtgOwAcA94zgy76woOaZPnUW4ZvzCPwUrT2uhqex4R+Hd7RyaKBjc297F0z3FME5AVflyY5zJnpHj28ON/25UUWzjvueOgD7CuQeKMlwSfOeznJ3p4PTpH0Z6a6hy6HtVAD1Ywn6da5ZE+dVjQ5ljEb/KhGpbt+e9jO2ebQDwnbgAvW6oW5JeW0fk1JNx7qVo6XDjVj2c7D+z7PgMz8i1CYeLrdncoPEH6DNN/tDaSWaE9oqjUa0HeV+gtPBstruOGv3QyHoLRSU05f0wrCzO6l5PoyaZ4XUfVuO/m1Nm+fjKNXEuWBjkI+fGuOPaGqrDtgN4L2OvAGwAcFTXUPuPXyXfXJxxO2pq0ULvfs+BPTnRdwq9Zi/P9/4Nh+LkksjVTHZN2+d1UkryfTvI9zehaG6cwQloB9hDobtQ3h1sYyZHyrT2WYuwG6eic2VkIbPdtazNNDPZVcUsdw0B7dBV0rqj06k94xukWpchFB1v1Qk4w5P2feGhen+nyvzJXuZPPvhWmjbvDrYDsBlEC4XfE0Z/OGE9ymXRa1kSPB8FhYheXhE7EpnONTS/8l2wisbbFZ1B1cLPorv3P+tmpHj/XJ+bwH5uqIY1Lyf7p3Kyf+p+j2EsCEXDHZ2OO2p3wLIZG3YIyOY9jypUYnrlmI2/mU/R+c7dg8YfINu1jlzPlgN6/0luJ1dWhAd/LDUOnasroyVaP+8FLDn2kJSNDdgrAJujEGlmKSRbyo6bufIWjWPBq6p8sCLCSUEfWcui0qET3I+K5MNNKt9MS//LdGVWUulZQJV/8UH1HLY5dnjv/BXb2BwiVGcQf92p9G1/puS4w3/g8g+aIqh3Hdp8dlNa9BopnIqOTz2witmc0cuK1h+RzO8AIJHdSHdmNXOrP4euHrw+v83Rje0AbI46hKIRmvYBTCNDquk1FIeP2JwbcYYmjvfQBmnLJ3i0dznP96+nQvNzY+xUZnvqRkwh3RupfPOg8d9NV2Yl6UILQXXyoRyyzVHIuDkAIcRngU8DJvColPJL4zUWm6MPhy9O1Qm3Ycy6CkVxoHmimJZBKp/Arfv329AeSgxp8kDPMp7tXwvArkI332t5hG/VXckk1/7JXIhRnmO04zY2wxkXByCEOAu4FJgnpcwJIfbvr97GZgwomhOHLw5AU2oDL/Y9w/r8BmY5j+O0wFnEPe9eiuRwuo0Uz/evKzlmIWnK9+y3A/DqNYRcM+nNrh08Vu07BY8WPyRjtTm6Ga8VwCeB70kpcwBSyvZxGofNMUBPuolfd/w3bUZxY7it0Mzm3EY+XfNl/IeoEGt/cAiNkOqh20yVHHcr+1805dACzK66jc7UCnqya4i65xL1zEU7wD0Fm2OL8VonTgNOF0K8LoR4XgixcLQThRC3CiGWCiGWdnSM3qDExmY02vJNg8Z/NzsK22jL7hyX8YQ0DzdVnF7SynKys5KJzgMT3vPoVTSEzmNe9eeoC74P9xjTZW1sDtsKQAjxFDBSf8A7Bt43AiwGFgL3CiEmyd26usOQUv4S+CXAggUL7DZENvuNJkb+Mx/t+LvB8Z5G/qX2cnble/CpTiY5K4keIlVQG5uxcth+AVLKc0Z7TQjxSeD+AYP/hhDCAmLs2bjVxuYQUO2oY65zHitzQ2J3izyLqXYfnvaVY0FXNKa540xz27F6m/FjvKZADwBnAc8KIaYBDqBzr1fY2BwgPnclV0avZX7mBHbktzPBOZkp7lm4VFuzxubYZrwcwF3AXUKIVUAe+MhI4R8bm0NFzNNIzNPI4vEeiI3Ne4hxcQBSyjxw/Xi8t42NjY1NEbtaxMbGxuYYxXYANjY2NscotgOwsbGxOUaxHYCNjY3NMYrtAGxsbGyOUcSRlH0phOgAth+m28c4emoRjpZnOVqeA46eZzlangOOnmcZy3M0SinLtEaOKAdwOBFCLJVSLhjvcRwKjpZnOVqeA46eZzlangOOnmc5mOewQ0A2NjY2xyi2A7CxsbE5RrEdwBC/HO8BHEKOlmc5Wp4Djp5nOVqeA46eZzng57D3AGxsbGyOUewVgI2Njc0xiu0AbGxsbI5RbAewB0KIzwoh1gkhVgsh/n28x3OwCCG+IISQQogjsk+gEOL7A9/HSiHEX4UQofEe0/4ghLhACLFeCLFJCPGV8R7PgSKEqBdCPCuEWDPw27h9vMd0MAghVCHE20KIR8Z7LAeDECIkhPjLwG9krRDi5P253nYAwxBCnAVcCsyTUh4H/Mc4D+mgEELUA+cBO8Z7LAfBk8BsKeVcYAPw1XEez5gRQqjAfwEXArOADwshZo3vqA4YA/iClHIWxVaunz6CnwXgdmDteA/iEHAn8Dcp5QxgHvv5TLYDKOWTwPeklDkAKWX7OI/nYPkh8CXgiN3pl1I+IaU0Bv75GlA3nuPZTxYBm6SUWwZ6YPyR4gTjiENK2SKlfGvg//spGpra8R3VgSGEqAMuAn493mM5GIQQQeAM4DdQ7LMipezdn3vYDqCUacDpQojXhRDPCyEWjveADhQhxKVAk5RyxT5PPnK4GXh8vAexH9QCO4f9exdHqNEcjhBiAnA88Po4D+VA+RHFiZE1zuM4WCZS7KP+24Fw1q+FEPvV53S8WkKOG0KIp4DqEV66g+LnEaG4xF0I3CuEmPRebVe5j2f5J4rhn/c8e3sOKeWDA+fcQTEM8Yd3c2w2pQghfMB9wOeklH3jPZ79RQhxMdAupVwmhFgyzsM5WDTgBOCzUsrXhRB3Al8Bvr4/NzimkFKeM9prQohPAvcPGPw3hBAWRaGljndrfPvDaM8ihJhDcXawQggBxbDJW0KIRVLK1ndxiGNib98JgBDiJuBi4Oz3qjMehSagfti/6waOHZEIIXSKxv8PUsr7x3s8B8ipwCVCiPcDLiAghPhfKeWR2KJ2F7BLSrl7JfYXig5gzNghoFIeAM4CEEJMAxwcgWqBUsp3pJSVUsoJUsoJFP9QTngvGv99IYS4gOJy/RIpZXq8x7OfvAlMFUJMFEI4gGuAh8Z5TAeEKM4kfgOslVL+YLzHc6BIKb8qpawb+F1cAzxzhBp/Bn7PO4UQ0wcOnQ2s2Z97HHMrgH1wF3CXEGIVkAc+coTNOI9Gfgo4gScHVjOvSSlvG98hjQ0ppSGE+Azwd0AF7pJSrh7nYR0opwI3AO8IIZYPHPsnKeVj4zckG+CzwB8GJhhbgI/uz8W2FISNjY3NMYodArKxsbE5RrEdgI2Njc0xiu0AbGxsbI5RbAdgY2Njc4xiOwAbGxubYxTbAdjYHARCiFf28/yvDiiDrhdCnH+4xmVjMxbsNFAbm3eJAfXMeyiKxNUATwHTpJTmuA7M5pjFXgHY2OyBEOIBIcSyAd37W4UQjUKIjUKImBBCEUK8KIQ4b+Dc5MB/40KIF4QQy4UQq4QQp49w60uBP0opc1LKrcAmis7AxmZcsCuBbWzKuVlK2S2EcFOUc7gP+Dfgv4E3gDVSyif2uOZa4O9Sym8P9AHwjHDfWoqS1rs5KtRBbY5cbAdgY1POPwghLhv4/3pgqpTy10KIDwG3AfNHuOZNijIiOvCAlHL5uzJSG5uDwA4B2dgMY0Ai+BzgZCnlPOBtwCWE8DDUjMa353VSyhcoNudoAv5HCHGjEOKygZDQciHEAo4ydVCbIx97BWBjU0oQ6JFSpoUQMyj2hoBiCOgPwHbgVxTlqQcRQjRSlOb9lRDCSVF99XPAX4edkwH+TwjxA4qbwFMphpRsbMYF2wHY2JTyN+A2IcRaYD3FmP2ZFBsEnSqlNIUQVwghPiql/O2w65YAXxRCFIAkcOOeN5ZSrhZC3EtRstcAPm1nANmMJ3YaqI2Njc0xir0HYGNjY3OMYjsAGxsbm2MU2wHY2NjYHKPYDsDGxsbmGMV2ADY2NjbHKLYDsLGxsTlGsR2AjY2NzTHK/w9hlF7GaR7iqQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "df = pd.DataFrame()\n",
    "# \"y\" represents the number of samples\n",
    "df[\"y\"] = t.randint(0, 10, (1024,)).numpy()\n",
    "df[\"axis-0\"] = X_embedded[:,0]\n",
    "df[\"axis-1\"] = X_embedded[:,1]\n",
    "\n",
    "sns.scatterplot(x=\"axis-0\", y=\"axis-1\", hue=df.y.tolist(),\n",
    "                palette=sns.color_palette(\"hls\", 10),\n",
    "                data=df).set(title=\"Random data T-SNE projection\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d46f8eb0",
   "metadata": {},
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