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processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current 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PM.jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/478580000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-30 at 2.35.28 PM (1).jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/491000000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/436830000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/491660000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-21 at 7.40.27 PM.jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-30 at 2.35.48 PM.jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-21 at 7.15.57 PM (1).jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-30 at 2.35.42 PM (3).jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-30 at 2.35.49 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(1).jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-30 at 2.35.49 PM (2).jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-21 at 7.18.51 PM.jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-30 at 2.35.29 PM (1).jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-30 at 2.35.43 PM (1).jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-21 at 7.16.31 PM.jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-30 at 2.35.39 PM.jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-21 at 7.26.27 PM.jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/491380000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/479160000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/490820000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-21 at 7.19.33 PM.jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/479230000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-21 at 7.15.57 PM.jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-21 at 7.15.57 PM (2).jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/491760000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/437270000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-30 at 2.35.39 PM (1).jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-21 at 7.59.37 PM.jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/491630000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/436210000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-30 at 2.35.46 PM.jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/49139.00000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/492050000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-21 at 7.42.24 PM.jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-30 at 2.35.39 PM (2).jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-21 at 7.33.58 PM.jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/437330000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-30 at 2.35.42 PM (2).jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/490720000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/436250000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/478590000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/437180000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-21 at 7.46.20 PM.jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/491650000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/490710000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/479330000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-30 at 2.35.43 PM.jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-30 at 2.35.41 PM (2).jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/491700000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/436060000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/48067.00000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-30 at 2.35.44 PM.jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-30 at 2.35.46 PM (1).jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/491480001.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/492400000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-30 at 2.35.47 PM.jpeg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/436780000.jpg\n/kaggle/input/nohanpneumonia/NohanDataset/train/NORMAL/WhatsApp Image 2025-07-21 at 7.15.56 PM (1).jpeg\n","output_type":"stream"}],"execution_count":1},{"cell_type":"code","source":"# STEP 0: Install & Import\n# ============================\n!pip install torch torchvision torchaudio --quiet\n!pip install kaggle --quiet\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torchvision\nfrom torchvision import datasets, models, transforms\nfrom torch.utils.data import DataLoader\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport seaborn as sns\nfrom sklearn.metrics import confusion_matrix, classification_report\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T17:51:34.942645Z","iopub.execute_input":"2025-08-13T17:51:34.943146Z","iopub.status.idle":"2025-08-13T17:53:11.19533Z","shell.execute_reply.started":"2025-08-13T17:51:34.943119Z","shell.execute_reply":"2025-08-13T17:53:11.194727Z"}},"outputs":[{"name":"stdout","text":"\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 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MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m74.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n\u001b[?25h","output_type":"stream"}],"execution_count":2},{"cell_type":"code","source":"import os\n\nbase_path = \"/kaggle/input/nohanpneumonia/NohanDataset\"\n\n# Check what folders/files are inside chest_xray\nprint(\"Inside chest_xray:\", os.listdir(base_path))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T17:53:11.197266Z","iopub.execute_input":"2025-08-13T17:53:11.197625Z","iopub.status.idle":"2025-08-13T17:53:11.203162Z","shell.execute_reply.started":"2025-08-13T17:53:11.197604Z","shell.execute_reply":"2025-08-13T17:53:11.202382Z"}},"outputs":[{"name":"stdout","text":"Inside chest_xray: ['val', 'test', 'train']\n","output_type":"stream"}],"execution_count":3},{"cell_type":"code","source":"train_path = os.path.join(base_path, \"train\")\ncategories = os.listdir(train_path)\nprint(\"Categories:\", categories)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T17:53:11.204026Z","iopub.execute_input":"2025-08-13T17:53:11.204307Z","iopub.status.idle":"2025-08-13T17:53:11.228755Z","shell.execute_reply.started":"2025-08-13T17:53:11.204285Z","shell.execute_reply":"2025-08-13T17:53:11.228112Z"}},"outputs":[{"name":"stdout","text":"Categories: ['PNEUMONIA', 'NORMAL']\n","output_type":"stream"}],"execution_count":4},{"cell_type":"code","source":"from PIL import Image\nimport matplotlib.pyplot as plt\n\n# Get path to first image in NORMAL category\nnormal_image_path = os.path.join(train_path, \"NORMAL\", os.listdir(os.path.join(train_path, \"NORMAL\"))[0])\n\n# Load and display image\nimg = Image.open(normal_image_path)\nplt.imshow(img)\nplt.axis(\"off\")\nplt.title(\"Sample NORMAL image\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T17:53:11.229568Z","iopub.execute_input":"2025-08-13T17:53:11.230022Z","iopub.status.idle":"2025-08-13T17:53:11.927984Z","shell.execute_reply.started":"2025-08-13T17:53:11.23Z","shell.execute_reply":"2025-08-13T17:53:11.927327Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAUMAAAGbCAYAAABeXvJFAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOy9ebidVX0v/tnzeMbkJCEBEhMQZGjVoCAOYEVoBbStqNV6K5b62Do8tk+v1VrvD1rvc3G43qtW26r3OrQ3t3hxKhY7QAstIoISiYBMIZKExAxn3PP8/v44/az9eb977ZOzIYEM+/s85zl7v/t917vGz/p8h7VWJAiCAEMZylCGcoJL9NnOwFCGMpShHA0yBMOhDGUoQ8EQDIcylKEMBcAQDIcylKEMBcAQDIcylKEMBcAQDIcylKEMBcAQDIcylKEMBcAQDIcylKEMBcAQDIcylKEMBcAQDI8LiUQiuO66657tbJzQct111yESiTzb2RjK05AhGP6H3H///bjqqquwfv16pNNprFu3Dq9+9avx53/+58921p5x2bBhAyKRCN773vf2/Hb77bcjEong61//es9vDz74IN761rdi3bp1SKVSWLt2LX7zN38TDz74YM+9X/nKVxCJRNxfPB7HunXrcPXVV2PPnj0991988cWIRCI4/fTTvXm+5ZZbXFq+vAHAX/zFXyASieD888/vW/ZIJIL3vOc9fX8fyvErQzAE8P3vfx/nnXcetm3bhne84x347Gc/i9/5nd9BNBrFpz/96Wc7e8+afPGLX8TevXuXde83v/lNvPCFL8S//Mu/4O1vfzv+4i/+Atdccw1uu+02vPCFL8S3vvUt73N/9md/hr/5m7/BX/3VX+FXfuVX8H/+z//BRRddhFqt1nNvOp3G9u3bcc899/T8tmXLFqTT6SXzuGXLFmzYsAH33HMPtm/fvqxyLVc+/OEPo1qtHtY0h/IMSzCU4DWveU0wNTUVzM3N9fy2f//+Zz5DAwqA4Nprrz1s6a1fvz44++yzg3g8Hrz3ve8N/XbbbbcFAIIbb7zRXdu+fXuQzWaDM888Mzhw4EDo/oMHDwZnnnlmkMvlgscff9xd//KXvxwACH74wx+G7v/ABz4QAAi+9rWvha5fdNFFwdlnnx2cccYZwe///u+HfqtWq8Ho6Gjw+te/vidvlB07dgQAgm9+85vB1NRUcN1113nLDiB497vfvUTtDOV4lSEzBPD444/j7LPPxvj4eM9vq1atCn3/8pe/jF/6pV/CqlWrkEqlcNZZZ+Ev//Ive57bsGEDrrjiCtx+++0477zzkMlkcO655+L2228HsMikzj33XKTTaWzevBk//vGPQ89fffXVyOfz2LFjBy677DLkcjmsXbsWf/Znf4ZgGRsN7dmzB7/927+N1atXI5VK4eyzz8aXvvSlZdfJhg0b8Fu/9VvLYoef+MQnUKlU8IUvfAFTU1Oh31auXInPf/7zKJfL+PjHP37I97785S8HsNgmPnnzm9+Mr33ta+h0Ou7ad77zHVQqFbzxjW/sm+6WLVswMTGByy+/HFdddRW2bNlyyLwMIj6bIVXuG2+8EWeddRYymQxe8pKX4P777wcAfP7zn8dpp52GdDqNiy++GE888UTo+TvuuANveMMbcOqppyKVSuGUU07BH/zBH3gZKN+RTqdxzjnn4Fvf+hauvvpqbNiwIXRfp9PBpz71KZx99tlIp9NYvXo13vnOd2Jubu6w1sexKEMwBLB+/Xrce++9eOCBBw5571/+5V9i/fr1+NCHPoRPfvKTOOWUU/Cud70Ln/vc53ru3b59O97ylrfgyiuvxPXXX4+5uTlceeWV2LJlC/7gD/4Ab33rW/Gnf/qnePzxx/HGN74xNMABoN1u45d/+ZexevVqfPzjH8fmzZtx7bXX4tprr10yj/v378cFF1yAW2+9Fe95z3vw6U9/GqeddhquueYafOpTn1p2vfzJn/wJWq0WPvrRjy5533e+8x1s2LDBAZmVV7ziFdiwYQNuvvnmQ76TgDAxMeH9/S1veQt+/vOfu0kFAP7v//2/eNWrXtUzcals2bIFv/7rv45kMok3v/nNeOyxx/DDH/7wkPl5unLHHXfgD//wD/G2t70N1113HR566CFcccUV+NznPofPfOYzeNe73oX3v//9uOuuu/Dbv/3boWdvvPFGVCoV/N7v/R7+/M//HJdddhn+/M//HL/1W78Vuu/mm2/Gm970JiQSCVx//fX49V//dVxzzTW49957e/Lzzne+E+9///vx0pe+FJ/+9Kfx9re/HVu2bMFll12GZrN5ROviqJdnm5oeDfLP//zPQSwWC2KxWPCSl7wk+KM/+qPgn/7pn4JGo9Fzb6VS6bl22WWXBRs3bgxdW79+fQAg+P73v++u/dM//VMAIMhkMsHOnTvd9c9//vMBgOC2225z1972trcFAEJqaqfTCS6//PIgmUwGBw8edNdh1ORrrrkmOOmkk4Lp6elQnn7jN34jGBsb85bB5v3yyy8PgiAI3v72twfpdDrYu3dvEAS9avL8/HwAIHjd6163ZJqvfe1rAwBBoVAIgqCrJt96663BwYMHg927dwdf//rXg6mpqSCVSgW7d+8OPU81OQiC4LzzzguuueaaIAiCYG5uLkgmk8FXv/pVrwofBEHwox/9KAAQ3HLLLa4eTz755OB973tfTz7xFNXka6+9NrDDCUCQSqWCn/3sZ+4a23rNmjWuLoIgCP74j/84ABC619dO119/fRCJREL959xzzw1OPvnkoFgsumu33357ACBYv369u3bHHXcEAIItW7aE0vzHf/xH7/UTTYbMEMCrX/1q3HXXXXjta1+Lbdu24eMf/zguu+wyrFu3DjfddFPo3kwm4z4vLCxgenoaF110EXbs2IGFhYXQvWeddRZe8pKXuO/0Yv7SL/0STj311J7rO3bs6MmbejapdjUaDdx6663esgRBgG984xu48sorEQQBpqen3d9ll12GhYUFbN26dblVgw9/+MNLssNisQgAGBkZWTId/l4oFELXL7nkEkxNTeGUU07BVVddhVwuh5tuugknn3xy37Te8pa34Jvf/CYajQa+/vWvIxaL4dd+7df63r9lyxasXr0ar3zlKwEs1uOb3vQm3HDDDWi320vm++nKq171qpCqyrZ+/etfH6ozXx/QvlYulzE9PY0LL7wQQRA4s8revXtx//3347d+67eQz+fd/RdddBHOPffcUF5uvPFGjI2N4dWvfnWoX2zevBn5fB633Xbb4Sv4MShDMPwPedGLXoRvfvObmJubwz333IM//uM/RrFYxFVXXYWf/vSn7r4777wTl1xyCXK5HMbHxzE1NYUPfehDANADhgp4ADA2NgYAOOWUU7zXrd0mGo1i48aNoWvPfe5zAaDHvkQ5ePAg5ufnnf1O/97+9rcDAA4cOHDI+qBs3LgR/+k//Sd84QtfwM9//vOe3zmgCYr9pB9ofu5zn8Mtt9yCr3/963jNa16D6elppFKpJdP6jd/4DSwsLOAf/uEfsGXLFlxxxRV9wbjdbuOGG27AK1/5SvzsZz/D9u3bsX37dpx//vnYv38//uVf/mXJdz1deTp9YNeuXbj66qsxOTmJfD6PqakpXHTRRQC6fW3nzp0AgNNOO63n3fbaY489hoWFBaxataqnb5RKpYH6xfEo8Wc7A0ebJJNJvOhFL8KLXvQiPPe5z8Xb3/523Hjjjbj22mvx+OOP41WvehXOPPNM/I//8T9wyimnIJlM4rvf/S7+5//8nz02v1gs5n1Hv+vBYTiBgXl461vfire97W3ee37hF35hoDT/5E/+BH/zN3+Dj33sY/jVX/3V0G9jY2M46aST8JOf/GTJNH7yk59g3bp1GB0dDV1/8YtfjPPOOw8A8Ku/+qt42ctehre85S145JFHQkxH5aSTTsLFF1+MT37yk7jzzjvxjW98o+97//Vf/xU///nPccMNN+CGG27o+X3Lli249NJLl8z705Gn2gfa7TZe/epXY3Z2Fh/4wAdw5plnIpfLYc+ePbj66qt7+tpypNPpYNWqVX2dR9b5daLJEAyXEA5SMqLvfOc7qNfruOmmm0Iz/pFSLzqdDnbs2OHYIAA8+uijANDjJaRMTU1hZGQE7XYbl1xyyWHJx6ZNm/DWt74Vn//8570By1dccQW++MUv4nvf+x5e9rKX9fx+xx134IknnsA73/nOJd8Ti8Vw/fXX45WvfCU++9nP4oMf/GDfe9/ylrfgd37ndzA+Po7XvOY1fe/bsmULVq1a5XVwffOb38S3vvUt/NVf/VVIJT0a5P7778ejjz6Kr371qyGHyS233BK6b/369QDgjZu01zZt2oRbb70VL33pS4+68h4NMlSTsQhmPlb23e9+FwBwxhlnAOjO5nrvwsICvvzlLx+xvH32s591n4MgwGc/+1kkEgm86lWv8t4fi8Xw+te/Ht/4xje83vGDBw8+pXx8+MMfRrPZ9IbHvP/970cmk8E73/lOzMzMhH6bnZ3F7/7u7yKbzeL973//Id9z8cUX48UvfjE+9alPeQOvKVdddRWuvfZa/MVf/AWSyaT3nmq1im9+85u44oorcNVVV/X8vec970GxWOyxCx8N4utrQRD0LAJYu3YtzjnnHPz1X/81SqWSu/5v//ZvLoSH8sY3vhHtdhsf+chHet7XarUwPz9/GEtw7MmQGQJ473vfi0qlgl/7tV/DmWeeiUajge9///v42te+hg0bNjhb26WXXopkMokrr7wS73znO1EqlfDFL34Rq1at8trTnq6k02n84z/+I972trfh/PPPxz/8wz/g5ptvxoc+9KElVZqPfvSjuO2223D++efjHe94B8466yzMzs5i69atuPXWWzE7OztwXsgOv/rVr/b8dvrpp+OrX/0qfvM3fxPnnnsurrnmGjznOc/BE088gf/9v/83pqen8bd/+7fYtGnTst71/ve/H294wxvwla98Bb/7u7/rvWdsbOyQ67FvuukmFItFvPa1r/X+fsEFF2BqagpbtmzBm970Jnf9Rz/6Ef7rf/2vPfdffPHFXuZ7JOTMM8/Epk2b8J//83/Gnj17MDo6im984xveeMD/9t/+G173utfhpS99Kd7+9rdjbm4On/3sZ3HOOeeEAPKiiy7CO9/5Tlx//fW47777cOmllyKRSOCxxx7DjTfeiE9/+tO46qqrnpHyHZXybLmxjyb5h3/4h+C3f/u3gzPPPDPI5/NBMpkMTjvttOC9731vzwqUm266KfiFX/iFIJ1OBxs2bAg+9rGPBV/60pd6wiI0PEUFntCNn/3sZwGA4BOf+IS79ra3vc2t2rj00kuDbDYbrF69Orj22muDdrvdk6ZdgbJ///7g3e9+d3DKKacEiUQiWLNmTfCqV70q+MIXvnDI+uiX98ceeyyIxWJ9V3n85Cc/Cd785jcHJ510knvnm9/85uD+++/vubffCpQgCIJ2ux1s2rQp2LRpU9BqtYIgCIfW9BMbWnPllVcG6XQ6KJfLfZ+5+uqrg0Qi4cKQAPT9+8hHPtI3nX6hNctpa1/egyAIfvrTnwaXXHJJkM/ng5UrVwbveMc7gm3btgUAgi9/+cuh52+44YbgzDPPDFKpVHDOOecEN910U/D6178+OPPMM3vy+oUvfCHYvHlzkMlkgpGRkeDcc88N/uiP/siFT52oEgmC4bnJR6NcffXV+PrXvx6a2YcylEHk+c9/PqampnrsjEPxy9BmOJShHOPSbDbRarVC126//XZs27YNF1988bOTqWNQhjbDoQzlGJc9e/bgkksuwVvf+lasXbsWDz/8MP7qr/4Ka9as6WtzHUqvDMFwKEM5xmViYgKbN2/G//pf/wsHDx5ELpfD5Zdfjo9+9KNYsWLFs529Y0aGNsOhDGUoQ8HQZjiUoQxlKACGYDiUoQxlKAAGsBnalQWDShAEiEQiKJfL+OlPf4pOp4OJiQlkMhnEYjG3UD2VSjnPWLvdRiQSQRAE6HQ6SCaTaLVaSCQSCIIAjUbDpd3pdNwSo3q97p7lHm2RSASVSgXRaBStVgupVAqlUgmtVgvNZhOJRMLtYNJsNlGpVJBMJpHNZtFut5FMJpHL5VCv19HpdBAEAWq1GpLJJKrVKiqVCh555BGk02k0m02ccsopOHjwIA4ePOjKs3btWtTrdYyPj6NWq2HFihV49NFHsWbNGpx++umIxWIYGRnBunXrvHX4yCOPYOvWrdi5cydKpRJWrFiBnTt3IpvNYtWqVWi322i324hGo67smUwGhUIB1WoV7XYbmUwGyWQS9XodO3bsQKVSwS//8i9j8+bNuO6665DJZHDBBRfgvvvuwx133OHardlsIplM4vnPfz7q9Truu+8+NJtNdDodt1qi3W4jFoshn8/jZS97GXbt2oUXvOAFmJycRL1eRzQaxbZt2/C85z0PCwsLaDabiMViiMViqFariEajiMfjSKfTiMViGB8fR6FQwMTEBA4cOIBEIoFIJIK1a9fiySefxMzMDCYmJtBoNLB3717XjzKZDJ588knEYjGk02nMzs4iFothdnYWuVwO5557LjKZDCKRCEqlkusvMzMz2LhxI1auXIlTTjkFf/d3f4f9+/ej0Whgbm4OxWIRa9euxerVq7Fp0yaMjIxgz5492Lx5M8rlsttUg/304YcfRiaTwdjYmEt/165deM5znoMdO3ZgdHQU5XIZyWQS+XweO3fuxK5duzAyMoKJiQnMzc1hcnISd911F4IgwPj4OJLJJNavX49SqYRisYi9e/ciEom4/rtq1SosLCygUqkgCAKsXr0aQRAgm82i0WjgpJNOwszMDM455xwcOHAAY2NjyOfzSKfTuPPOO/G85z0PsVgMa9euxbp167CwsIDHH38cTzzxBNatW4c77rgDGzduRL1ed5vZrlu3Dg8++CBSqRRyuRxOP/10bN26FZ1OB7VazdV9Pp9HqVRCoVBALpdDPp9HEASo1+sIgsCNy7POOgv79u3D3NwcVq9ejTVr1iAIAlSrVdTrdfzkJz9x7w+CANddd53DksnJSZx99tkol8uYmppyfWblypWHxKhl2wwPBxgCi7u+3H///ZicnEQqlcLk5CSSyaQbwKOjoyiVSqhWq+h0Oshms4jH45ifn8fo6CgSiQRGRkZQLpdx8OBBB4wLCwtYuXIlKpUKDh48iEqlglgs5iosm83isccew4oVK1AoFBCPx9FoNFCtVlEulzE3N4dYLIZ2u41qtYp4PI5Op4MNGzZgz549GBsbQyqVwszMDAqFAiKRCGKxGKamplAsFl14Aw83Gh0dRTQaRa1WQzweR61WQyqVQrvdRr1eRz6fR7Vaxe7du1GtVrFx40asXr0ar3/967F582bX0bR5vve97+FP//RP0el0EIlEkE6nEYlEUKvVHCi1222Uy2U0m00EQYBEIuHqstPpIB6PIxaLuY43Pz+PiYkJnHrqqXjwwQcRBAGSySQOHjyIIAhC77c7OffrOr5T4qLRaE96y+03T+XUuUGfm5ycxPr16/GTn/wE+Xwe5513Hnbt2uV23F4q79pWPJSK3zU/nDTYdtVqFVNTUzh48OCSW4n1K4ttG97nu87xxb6Ty+Vw2mmn4b777nN5++xnP4sXvvCF2L9/P+6++27ceuuteOCBB1CtVt2zmibfY8u/lLB+SEr6lWWp53WTimg0ipUrV+L3fu/3EI1GsXr1ajzxxBN4wQtegIWFBWzatAlnnnkm1q5du2S+gMPgTfZ1EC2MdpBarYZ9+/ZhxYoVaDQaiMfjSCQSmJubw/79+11n4aydTqfRaDSQSCTc2kmymmg06jYkWFhYQDKZxM6dO90s32w2USgUkEgkcPLJJ6PRaGB8fByVSgXxeBzZbNYBxsTEBJLJZIjxkeHkcjmsXr0aiUQC6XQauVwO7XbbMb5EIoELL7wQ27ZtQ71ex8jICEqlErLZrJutR0ZGEIvFMD8/j0ql4thwtVrFqlWrMDMzg1QqhQMHDmDv3r3YvHmz62ztdhutVgutVgv79+/HzMwM8vk8Vq9ejUgkgkajgVWrVqFWq+HAgQPIZDLIZDKIRqPodDoYHR1FJpNBsVhEJBJBKpVyszGwuOTv9NNPx6ZNm3DvvfeiUqkM1NbLvY/leSr+OjtQotEoxsbG3CT0dPMLAPPz8ygWi2i325ifn8fWrVsRj8eRyWSQSqUwNjaGn//8547FxGIx1190cPoAiaKAVy6XkUgkUKvV0G63D5nX5ZQlnU4jGo2iUqn0AHIymUQkEkG1WnXtf8YZZ2Dbtm0AFjcF+eu//mt86lOfwq5du9BqtUJlYzqHAi0CL0GXY5osnZpTIpFwZCQajWJyctJ9LpfLTsvgWCfJOOmkkxCLxTAzM4NGo4FoNIoXv/jFKBQK+MEPfoA//MM/xAMPPIB0Oo2HH34Y3//+9/GOd7zjmQFDVgD/7NZERPJKpYL7778fTz75JE466SQAiw1UqVSQzWad6ggApVIJQRA4sOp0Oo4Btlot1Go11yjz8/NoNBrIZDJObdZGmpubQ6lUQiaTcZ0gGo2iXq9j//79SCaTmJycRK1WQ71ed2wymUxifn4e+/btQ6VSQSKRQDabxezsbAhQCoUC5ubmUC6XnSofiURQr9fRaDTQbDbdzF+r1dBsNjE2NubWM5PhttttpNNp3HHHHbj00kvRaDSwc+dOx+46nY5bSM+JpFaroVgsotVqYe3atWg2m8jn89i/f79TOZvNphvAiUQCmUwmZBog6DPfrL/DHWTwVLac8kk8HkcqlcLKlStRrVbdoH26QhMDRddvl0ollEql0ADP5XJIJpOOiSvTGRsbQ6FQcBMOxTLFer3ec89TlVgs5iZ4KyQiKs1mEwsLC6F833XXXaH0otEootGoA37WTz8Q1P7DSSuVSiGRSKBcLqNSqaDVamFychIrV65EIpHAvn37XB1EIhFcf/312Lp1K8bGxnDBBRfgXe96Fw4cOIB4PI5TTz3VmRXWrVuHZrOJaDSKCy64ALFYDJdddhm++93v4p577sHCwgKuuOIKbN26FQ888AAuvPDCQ9bh0wZDFtxSZkqn08FXv/pV3H333YjFYli9ejVKpRJisZjbyDOdTiORSCCZTLpGiMfjjuFx4BM8qcKSMTUaDczOzqJYLKJWqznwAIAVK1Ygk8mgVqth9erViEajKJVKiEQi2L17t7OtNBoNtFotBxa1Wg3z8/NotVpYvXo1KpUK5ufnkclksGLFCkQiERw4cMCppitXrnSTAWfgUqnkOl0kEsHU1BQmJiYwOzuL7du3uxlQVZiZmRm0Wi0kk0k3m9H+yfOEs9msU3XJjJlGOp1Gq9VyrJrsJZFIIJVKuXJGo1E0m02k02nE43EsLCy4zn60RFuxH3CSA+DslI899pgDJrv64kiIghbZo08ikcghGeuRkFarFQJwC7zMm17fs2cP8vm8tyw6ObTbbaRSKdeXFPgs21dzCG2GZKocF3v27HGHjCmRikQiuPvuu/G6170OIyMjeOCBB5DL5fDSl74Us7OzjrBUKhXXXzudDj7xiU+4Pj06OopKpYInnngCK1euxMtf/vJD7sJOOWI2w+A/tpznVkLRaNRt5V4sFjEyMoJ0Ou3Uyna7jWw2i2q16kCLWctmswDgZpZEIuHYWqvVwsTEBDqdDvbs2YOHH34YY2NjSCQS2LBhg7ORxWIxnH322Zibm8Ptt9+OU089FQ8//DAKhQLWrl2Lhx56CMDibiE//vGPEYvFHNubmprCqlWrsHv3btcxuHMzbXJUDaanpx3IEJjYqVKpFJ7znOfg3nvvdTM1OxgANwOfddZZePnLX46NGzfi9NNPx8qVK1Gr1fC6170OMzMzWL16NbLZLGZmZpytknbX1atXY3Z2FolEwjkpADjHBO2GQRC4+84++2zE43HccsstqNVqrqOTCamdJpPJeJd/HSk5FEs9Eiz2RBHuKL4cdso+w8kbQAjEtA8TCKPRqFOHSRT0fp/qHYvFXF/l+Mvlcjj11FMRi8WQSqVC25vF44t8Lp/PIxqN4uDBg8hms8jlcpiensZ1112HF7zgBU4bXUqWDYbT09NL/k7HA720N998M+677z5UKhWUSiWcfPLJmJycdKDRbDYxOjqKSCTiGGAul3NqG2f9drvtKpRe0Xg8jmQyiSAIHJASnJrNJsbHx7GwsIBYLIZKpYKpqSnnrc5kMti5cydWrFiBhx56CJFIBNls1nkfzzrrLNx7772u4TizJRIJTE5OYmJiAtVq1THWer2OhYUF17HI9rjHHhlYJBLB6tWrUSgUMDs76xqSdg9S/lgshlqt5jxnY2NjOP/88/Hc5z4XN9xwA4IgwMqVKx0zZd3H43FEIhGMjIw4ACajSiQSyOVyaDabrq4bjYbzmD/vec9Dp9PBHXfc4YBdje3aocnYhwA0lOUIx/DhFvbRfmlzEr/00kvxkY98xG3UvJQsW02msX7//v2YnZ3FgQMHsHbtWkxMTGBmZgb79u1zHqpPfvKTuOuuuzA1NYXNmze7jOdyOUeVqa7R4EoATCQSDjw4IEnj6SUtl8vOqE1GRDsYAOfcqFarTmV+9NFHsbCwEAJiquuq6tNuSDW83W5jYmIC+XweiUQChULBgUShUHDhOqlUytk4CZLNZhMjIyOh8JNSqYRkMhlS9Wu1Glqtlgs3SKfToRCYO++8E//+7/+OsbEx5HI5rFmzBoVCwdlJ1ZBfq9XQaDTcDM5zk4HFI0Tj8bizJabTade+NKwDYfueOlsAPGOMcCjHhxypA7eW43AKggC33norXvnKVx5eMAyCAPfccw++9rWvIRqN4rTTTsOGDRuQTCaxceNGrF+/Hrt378ZHP/pR/Nu//RvGx8fRbrfxyCOPYGpqCmvWrHEqZTKZdPbBSCTiQlJSqZRzPpA10fhPehyNRp0NrdFooFarIRqNIpPJIB6Puy2vGA7AsJtyuezYZhAEmJubcwZwMqZ2u425uTlXkfF43HmCyaSARUpOACObI9siy5ubm3N2Oh7WvW/fPpcHlps2sGq16swEBEuyz2QyGXLU6ORCsE+lUli7di3i8Tj27dsHAM6J0263MT4+jkgkgomJCdTrdZTLZeetY4iSqsVP1fM7lKEsV/qZOHyhSVaW2z/b7bY7SfCQ+VmumnzgwAHnMKB6y/CVHTt24Hvf+x7+/u//HrOzs1i5ciUymQyy2aw7q2NychKrV692Bm+NLaxWqxgbG3PGZ3qHk8kkyuUyMpmMY3GxWAzFYhHJZBJTU1PYs2ePC4UhOAZBgGKxGDLOUg1dWFhwDhWywGQy6YBscnISpVIJs7OzDugY3pNMJpFIJFw6BFEyrEajgVQqhbm5ORQKBWSzWWSzWaTTaUxOTmJ6etqxSLJieh0rlQpyuZwLj+AEkEgkMD09jUql4uIuaTfVOEOWo9VqoVQqOaYKwHk/yZTr9bpj4GSozWYTTzzxRAgMVR1mB32mHBZDOf6FJjMrJFKHOnGxn7BvU0gsDiXLZobVahVf/OIX8bznPQ/NZhMPPvggpqamsHfvXszMzODAgQPI5/NYtWoV4vG4U1MXFhYc42EgNActPZ60r/GZeDyOcrnsvMXlchn1ej0EdOl02q0GIcMjy8tkMk6l5Wd6XQm00WgU2WzWvYdxfzMzM6jVapiZmXF2ONri0uk08vk8yuWyMwgTEMmqGIJAlttqtdDpdNx27ZwIyCpXrVqFYrGIRCLh/th4BCUalGnELhQKGB8fd20zMTGBSqXiYhVpemAYSrPZRLPZdKEXK1asQCKRwMaNG/HSl74U8/PzKBQK+O53v4sdO3a4/K9YsQLr1q1z9kt67fft23fEzxseyvEt0WgUyWTSe84NiYxPlmKT7Pccl5Tl9tVlgyFj1D7zmc8gnU7jec97Hh544AEUCgWsXLkS69atw86dO92yqdHRUcTjcQcuDAbWIOx6ve5iiMhYqCbSTqUhL0EQIJ1OIwgCVCoVB5z0MpG1celZOp1Gu91GpVJx8XitVit0kDnVVMZC1ev1UMAp2ShVazpt+E4COmMT6/W6C6om0FM1JiinUimUy2UHMgQrqvbZbNbFaTFUKJ1Oo1aroVQquRmVKjkDubPZrOsorVYLU1NTTr0nm+x0OqjX69i1axceeOABpyJfeOGFuOSSS7Bq1SqXz9HRUdx5552Yn59Hs9nEnXfe+ZTOTxnK0SOTk5PI5XLOsUkTlbUTJxIJnHbaaXjooYeOiLmE9m2fHGo1zlLXfekuN//LBsNGo4EHH3wQY2NjeNOb3oRbb70VpVIJe/bswYoVK0LhI/SoTkxMOKYzPz+P3bt3O7tWp9NBo9FwrvRKpeJiCckCATiP0ezsLBqNBkZHR5FMJkNHHVLFpSOFqjVZI1VaggHVZtogqXZrxD3zRRslg7vJ9lKplGOO8XgcxWLRsT+q0fl8Hq1Wy4EVy02HBoPR+QxBmUDKCH2q83RysO7GxsYcM+V76BjRcjO/tFXW63WXB656+cd//EcXqsQAd10dMQxhOT5kcnISL3nJS7By5UqcfPLJmJmZwcMPP4wdO3agXC4DWAyjO//88/GmN70J/+W//Bfs27fPMS4SAK6n3rt3r/eQqqNB+qnh/WTZYEhG87KXvQz33HMP7rvvPszNzSEej2P37t0uSl8BhOt90+k05ufnMT09jd27d2N8fBxjY2MuTIOOFXouOVvpwKQazGVlXKecy+WcWs7gY13GQycFPdV0VpDtcXak/bJer7sYQTpwqGoTmBm8zNUzBAwyOuaVzBRYZKB09nC9sm5EQRVWVWN1aHDpVjQaRaPRQLlcxtjYWMjrTQBdsWIF4vG4M00AcN+p3hMMgUUbzfT0tDN70AzACYae6aFqfOzL9u3bMT09jY0bN+Lkk0/GK17xCrzvfe9zk20+n3f9d3x8HDfccAMeeugh5HI57Ny5E7lcDiMjIzhw4AC+8Y1vLHmc67Mtg656WjYYRqNRrF27Fvfccw/uvvtutFotZ8sqFAqYnJx0DIXOCgZLF4tFZ++q1WqYnZ11u06Mjo66ZxgbSHZEsOLABOCAcXp6GrFYzKnPHKxkQLRDcg0o0PWu0o5H5wrBj7Y6vp9gorvh0JFBIy3j8qhOM7SFtj2GyXClDO2aBCWqu3SOUGWm+k+Q1gYmG+V3MtRWq4V8Pu/KrQBGdkc7K0GWatMTTzzhHDHa5qyjodPk+BGuvd66dSv+/u//3i3H5DK3X//1X8fU1JRzmt5yyy2YmprC/v37sXv3btdH+61sOVZloNCa22+/HQ8//LADHdqWyHBGRkZQKBQc66DnsdFoOJZElsEwGD6n64rL5TJGR0cdYJF5EXT4mQBA9Y9gQ+9oNpvF6OhoaJAzKn5sbMztWMO1xgQYqqkEPWVI9H7TI0vgjUQWtwhj3CHZMB06qVQKqVQqZJOkAZlsko4gBWrmiQBIFgt0l+kxLIkhSQQuXRVAFs16Hh8fx/Of/3zkcjkUi0Vs3rwZ3/rWt0JLs1j3Qzl+pdPphGzoe/fuxQ9/+MPQBKxAd7QD33LCcvrJQGoyl5opKJHxPfnkkzj33HORSqWwsLAQWmpDxwXtbAQWflaniobCAHCeIaqDZFRcvkemx/0GqRpTxWUaCsIMdOZKEstkbeXStklgIADSrgjAqaoEZm7ZxQBsOiRoU6WRmmVnHbGcZJrc6kk3q6BjhyAdiUTcMjm1QRI4CfbFYtGZCE477TRs3boVc3NzTm3avHkzvvOd72BsbAyzs7Pu+cO1ycJQjh05lKPiaBY6GwfN67J3utYgR/VkcnA1Gg3s2LHDbXs0OjqKXC7nVp3MzMwgCALnaQXgjLEc6Iw9pF2MLAmAAxl+p9eI71PQJZDShkaP8sLCgltBwvLo7hrWLa+Ao06EVqsVsg8SCFkOdTrQgUQAJvhSvc1ms0gmk6HVIMwDQVEnDzpMmA/WC/NOQGWIjwZ4c4sqhuXs3bvXOZ8efvhhTE5O4uMf/zi+/e1v44wzzgDQXZM6lKEcCxIEQWg1ld04ZikZSE3WQcGgYAIUPcJUH5khqmyk46lUCtls1jkDyHT4X9PUzRrI3rjjMgGBKjEXalMNpV2MINdsNkM7b5DlkVnRm0xQ4W42apsj09MQHwIN1W8CPZ9jvpVhsnysA+aDEwNZLFe1pFIpt11VKpVyeaPqrDt7My3Wz/j4OFqtVmhJXTKZxN69e1EsFt0OOJFIBD/+8Y9x+eWX44knnsCuXbuW3YmGMpSnI0dS9VZ/w6HkKZ+BwhUcOgjpNGAGGN9GYGJsE9kIbYhkPMViMbQ5K4GO6TEoOQgClwZVZYaRkGnxHq664IoZgg3j+wC4PenIMBUEydi4+iSdTmNiYsKppmRutAkWCgX33vn5eQdCBER6jOn8YZm4SQUdJ6wz5kXjHQm6rItodHGjW4IsGbLaVnk/AOzbt885Wsh8U6kUHnnkETz66KN49NFHnT13uR3pcIk6h4ZyYsiR1jyWyw4H6uk6OAhy6migB5brXtUbyg0IGDiss4GGvXDbHqqJBAXdBYZxhrQL8L3FYhHz8/Nu3S53r+a7qGKTIdKuxnzSMaIsmP+5EafaI8mIAbjyjo6OYnx8PKTuc2NLhiFMTEw4+ybfQccGjzngumbdCombLABwa5a5gez8/HxI9de0ALgVOMViEfv27XN5UHtgpVJBu9122yVZT/YzIQTuoQyFQn/CU5XlOgEHshkCcKCkTg5lKPQGFwqF0Fpe3qebnqoqSECkOsjfCE4MAKZKByC0cSTQjStSZwdtehz4jLEj4yLAj4+PO8CkY4fhNrrSQ1kWHSUAQk6iRCKBlStX4rTTTsOaNWvceS9Mn8sOCXR0Uqi9j/ZIps/3ERR5aBKFeWJd0xzBLcMYNE5V/5FHHsEFF1wQUt25I46q1E+3Iw4qXCEzlKFQrInuSMmywZCDQleGZDIZd8KVbvip4AF0N5EkY9T4vE6ng3w+j06n4+xwDDfhIGRMI1khKyaTyThVm9vy890aS8h10Lpekeo1VWbu5MKYQJaD+eHyv4WFBSwsLACAA58gCNz2Yuq04TVd3sc0fL9TjVVvdTKZdI4mMkaWlcBMOyaX/3Hn8IWFBbedmcZvplIpPPjggzjppJPcKpZWq4VNmzbh4MGDuO+++xwgP9Nq8lCG8nRFTUKDyLJ7Ou1lFDondAss9apSNS2Xy86+qAyOm7ISrLhihIOcS/oU7GhnJAMjeFJltCyL99KpQ491PB5321/RmUJPL/PBMByWnXnnZg579+51QMP3E6h5NglXy+TzeeTzeefM0eB0rrZhvRDwKcrEqYrzwCyaD1jXmUzGLYHkChldVkjgJhO888478bKXvcyZB3hEo+7ZOPQkD+VYE44vjqvlajbLtlRTtaOoDY5OFGURulKCrIXf6fUtl8su6JceVA5uMjYuySPYAHAeXz5DlZaMk/nkfQQNbg3ElSRMn3mkA4LpMoSG2/fryhGGE3GbITpDaGsDEAqPIYjR/sdQG2XPquKy3FyWR0cT64Lsk9uLabxhsVh0gbQaGqPglkwmsX37dmzevBkjIyOYmprCaaedhoWFBTz22GOufZ9pm+FQhnI4hOOZzsjlyEA7XZNl/OIv/iKefPJJt1ROV08wAFoHni7/4uDiOmOyKXVycCE4bXQaO8S06cjgdlhU1bk5AwDH8prNJmZmZhyIkJXqztS0R/IPWJwAZmZmHFjQO067IO17PIiqVqthbGwMIyMjzjvOuEKge7g47XT04jIdjVEk82U+ga6TRjdz4BnNnGwWFhZCzh07YfFdNCPcc889WLt2LS688EJ3jCuPQxiqyEM5loXYtNx+PFCcIQBs3LjRLaXTOEIyFwCOonLwcRDzOwcaV4/wOdoKyb7odCC6M8iaamC5XHabsNr4RN2sgWCbzWadfZIrWCgah0jmxkok2+TmqwBcmAwApwJz6Z3GSGrYEXflYZylzlxaNxqjyAmG7JpMTz3ZauNst9uh3W3IslVFDoIAF1xwAX7xF38RO3fuxIMPPohkMomxsTHcdtttbldwGq6HqvJQjmVZbv9dNhjmcjls2rQJjzzySOgsYTop+tmXFBCBrl2M/4vFIiYnJ0OB1AQZ61QgQ+Q+hqpyql1RAZhqJACnetIry/xp8LTaK3WlShAEbi9BBkcDXbe9rlYhwI2NjbldcThLUfWmDVVVfp1AyFYJyHyG9cI/qvBk7XbFjqq5WtYf/OAHeOSRR3D66acjnU7j3HPPxYEDB/CDH/wg5LEfAuFQjgVZyr6tNvilZNl6EHeVoVrGawAc07KxefyN9+paV14nAFFl1V1VCGoMgdH1yepc4aamuk8g1wbz2FAALkyHy9bUpslQH12dYsvC8CGG0egqGD5Tr9dRLBaxsLCAVquF8fFxF3u4Zs0aBEHgDo0fGxvD2rVrMT4+Htp9h4yPKj4bmsCp67sJbgRzBWfNs7JmTgbcMZtbn91xxx2hFTPaTkMZytEs/YjYIE7AZTPDhYUF7N69u2enYwVAAoNu5GBthVQVAYRi6sjwVPXmwNSdr8laCArcJ5DqbaFQCKnNupchmZOq7LrfIdkpAQ9AKOaRrNIu99NdYtSZxAD0fD6PXC7nbKtUdxuNBnbt2uUAi5u4ttttF7LE/BK8dQ9ErUO+n/XMncPZLj5AbDabOPvss/Ga17wGu3fvxqOPPtrjMBkywxNHjtfogcPuQFEvqRX1wurA1IpVANMBRwfE6OioU3vJ1KjmctkbBzXTpzpKJwNXUABwDBNAj0NHaTPBT5fpEZQBuI1cFeCtnY/7FzI9xvlR5SbA69JCAM4Tze90Jmn5qS5TdWZazIOCna7IUTVc2aK2S7PZxF133YV4PI7//t//uztigOVg+wzlxBA10xwvQgKxHBkIDE899VRs3LgRe/fuxd69e0NhKADcvoVALxjyXl1upbF72WwWuVwOABy4EcDUnkZVVgOedYmgbw2zBnHTZke2RTsfPb1cB8z808Or+wSSpal90wI8zQJkgEEQuCVzBDJla6wzps+zYJi2buig72F9ESTJdvW6zRuZL9Pi77rUbxhac+LJ8QaCHDeHnRmOjY3hgx/8IO6++27s2bMHjUYDN998c2jplKpsZC128HKgEcgIWGR2GpSsu7d0Oh23nrdarTqboG5bValUkM/nkc1m3ZGj9MjyQCqgC2YEI560B4Q3RGUQNvcKVM8swVGBg+BBJskJgOu3GXZE4Yyltg0tM9BdvaMTizJGO5PrJgcEMgK+vo/v2blzJ2688Ua3FFK9yPa9QxnKsSJqtjvsQdedTgff+9738M///M949NFHcd555+HSSy/FzTffDAAhtmNDS3RgaWiNXmfYjPWCBkHgttonY9NYwlwuh5mZGQBwgdc8LhOAix+keq5L8hRI+TyBjOCo9k2Cspax0+m4cBo9v4XP8D6mr44mbSg6gwjQZMdcMcPfWaeaFwKzOnT4zomJCczPzzvVn+3RarVw6qmn4o1vfCMmJycRBAFuueUW3HLLLT12yaEM5WgXjfu1/Xa5/XjZ3uRGo4Ft27bhnHPOQS6Xw7333otkMuk8pHwpQQPogoEu7dIQEwUiqpV2OZ+GzXDnZ6quDNBmnJ+eP8L3A8Do6Cjy+Xxo0wX1Giuz0+29gmAxIJtqNg94pz2QeeRSPq4L1o0p+KfMDEAIuHS5nzqcmB8N8+GzLJ8yST0igMDHHau1UxCwX/va1+Luu+/GP/3TP+ErX/kKTj/9dJx33nkh4F2uvWUoQ3k2JZPJYNOmTU8rjYE2auCKkBe/+MVot9vYunUrzjnnnBDjs4PH2rWs+qXAqOnQAzwxMeF2zaZdT5+LRCJuL0AGWfN5HgVAFZBL35hP5k3XDPN3psMNWJX50kurwMmtybgBhQaPa+gNAHeuNN/DSUHXVuvEYdm0sjaNCdRjAbT+mR/WO9MaHR1FOp3Gi170IlQqFWzduhVr1651dTCUoRwLEo1GUSqVsHv3bm+/PewrUHga3MzMDC677DL8+Mc/xv79+/GSl7wk9FLdLcYOQKu/6+DmMjUCj65QIeuhGsvfGVsYjUaRz+cdAOrW+LT9qaNDwc6qnJlMBpVKxameCkwaDK2go2wuCAJ3FILaP9Vho3tAMq+MeeRvfKf+Md8ETFXHFczZ+Cy7qglMp91u4zOf+QxOPvlklMtlTE5OYv/+/di2bZu7z7LZoQzlaBSOw37nnhx2NblQKGDfvn146KGH0Gg08Cu/8isurARAaGAqECog8ncFE6tik+lwMHJHGCB8WDxBRZ0kANw6YQKp3WvRAonGDEYi3XOTaWNUINcwFmWRtHWyHFRbrX2Q6icAt90X8wAgVDYb5qC2VAVHrT89YIvXdOWPtgmPeN2+fTuq1Soee+wxPP74447Z6juHMpRjQfptCrzcPjzQFl7JZBLT09O4++678bKXvQxTU1PurAyyJiC8o40duBzQDC4myNBbS4aoTgQGX/M9TJMOE+6xyNUm+g6NNeRv6hTRpXBkneqk0bhIskhljEDXBMDAbiB85rCGsCiYEbxUbdd6Ypyl2lY1zlIdOXokAGMUWS6KTlLsOL/yK7+CD33oQy4QnFuhqYo+lKEcjWJBbrnqcD9Z9tOxWMxtbHDXXXfhzjvvxCWXXIL3vOc9ocORdPUG0GWEHOTq6dQ4O3ViqA1NvalMl+Chjg5+55pk7mOoYEKwYQjJ+Ph4CLS5CQTPYVFAUDCytj0+r/GQyur6mQZ8DEztqrqjNn+zbFUdPzohablsp1G1odPp4Kc//WkPcA7l6JSnO+CPJ7FM0Jp0aPZa7jESA4HhGWecgXw+j2q1ih/84Ac4ePAgRkdHQyoi2Z3Gx/FPB73PoaKOCTIjOkXoOmcaBF1VQ7mPYSKRcAceqcrMuECGuPC0Pu6Ck06nXRiPmgBUbaX31gIVxTqICFqW2ep3rRM+A8Cp38oeLStVUNZ8ar2rA0XzmUgk8O1vfxsf+tCHQkHuakIYytEj8Xgcq1atOq7PiNFzfg4ltn9a2yDD3Q67mlyr1bBu3Tr88i//MkZGRvDkk0/i4YcfRj6fx4UXXoj169c7B4F1oujgBBBSv5Qdcd2ueqY1dojPkxGqKso/3Uafq2G4RphhN7rRATeWJcirnc2yLIKxqr16ELzmURtAVVMfSOpaYV/DKVO0E4gFQqbNP+ZbOwrzykOn6HXXw6OGLPHok3a7jf379x/Xpgs1Yw0qWi/a9zW2eClZNhjW63XccsstuOiii/DGN74RL3/5y/HBD34QIyMjeMUrXoFVq1a5QRmLxUJnnPgAQgGGouEy3FvQqpNaUQoEdHrwRDwyODJEPs/QGJ5wR08y7XM+R5Cqqer8sWWz9lHmn/dxowjr+bXv4HVVgzVW0LeiRN9jnUR6jzqnFFTp0NF3DuXoEp30j6Rw/D0bomcnPR2xtvLlyEBrk+fn5/Gd73wHn/jEJ/DDH/4QMzMz+NjHPobbbrsNc3NzALrgxmVsZIuqYvYDDhaCqjaAELgpWPJeDly7ckW91iMjIz071qj9st1uo1QqhTY/UGcQ7/eF1FjHh1Y+VX1V55m3ftvxMy2G9nD1jJoaCOjqKWaeGIak6q56y322RAuMQzmxJZvNYs2aNXj88cef7aw8ZfGxxEPJssGQIHb33Xfj6quvxsMPP4xXvvKV+MAHPoBOp4Nvf/vbAODW3+ryMIKgMhYFNKDLCql20omhgdS0IdrnMpkM0um02yFGK4ErV6i6kzJHo1GnIpZKpdBpWsqutGKVUSmgq/1P64uMi4Crjg/er9uJ0QNMMONSQt7L1Sw8ZpR1ovWhDNPHbrVurL3S52wZyokn5XIZO3bseLazcdjkiIBhOp3Grl27MD09jVgshnvuuQcf/vCH3aoFAM55QiBgELTa1lKplAM13TkmHo+7rbe43ld3ZFHQULBiOI7aArl3oD4HIBSqE40ubvpg7X660QJZqvXoAuG1x2rPU3ugBR3d55EgStBjedVEoIBGdVmX4+kZMNpWfL/1aDMtTUPDdYYyFODE7AvLBkPu3kwJgsBtksA4PwCh3WZ03z0e12k3HLAqIuP6uHqCA15ZmLWV8RmqrNbJocHPZIhcL8zldqoGa+gP/9sYQOsQso4NYHETCTp0CFjqcVdQVeBS1draVAmYS9Wd2gX5G9NVZ4sCrponTsSBMJTjUwbRdJbtQOGApnQ6HezcuROrVq3CunXr3HVlVhQNBeDqDFU5+ccga6qz6ijgPWrgVyeGnhyn3lkyTQKOnqlCILTxgrpXoto8NQ8AQsBO4bV2u+3OjNbgcutQUdaqjiIFV6rPqqJb9RYIq8rW0G6BU+td8zSUE1OOR/OI9UccSgbaqEErLJVK4dxzz8UVV1yBK6+80u0VCMABGu2ACmKaSQo/64FOvK42MS2Y77MuzbMDnXmnys5dsa0zhqDM8vpiulTlVBBV0LRltI4dApuqqWTSDAqnvVNX4Ng8M01+pqnAiq0vDVDVoPQhIJ6Yciy0+6CAbUnCoWTZYKgDJRqN4kUvehHWrl2Ln/70pzhw4ADe9773OUCkWkxmxf82Y6rmxuNxlMtl58UlG+K5JNb2xXSVTdmKIAvlYCeQ6GegeyQn7ZYEFHpyFWA0RMUGObNuLPDZctuJQJ+3y+jsJhP8TZ+zW3Sp917TtR3eMsRjYUAM5cQVHkQ3qBx2MCRLAYBTTz0VrVYL27ZtQ61Ww6c+9Sns2rUrlFndmYX7jNntqNQGRnWU7yBzU5BT9qJAQCDTvf80jk+Zkh7gBHQZE9NgPmk/Y/rJZNKtb2YF22WFTI+/K4gqU7X3WhslP1vPO1fEWNqvrJn58jHxfuA3jCkcyrEg6rOwshTgHXY1mWuEmaloNIozzzwT+XwepVIJd999d49Nkcvg9u7dG4qrU3ufMisNfdGNXvlZVV7LdCw7s6fz0amijFLzqBtH6Dpo5ldBjkDJ76qmKtBoOJFNg+9R+6PmneVW4CfAaloapsM8+GI5FRiVuQ4BcSjHgxwOrWbZYKgMq1AooN1u49FHH8X/+3//D89//vNRLBZ7jhEFFredZ/iKAhYBiiClQdNcSeLb2FQ9uNZmSBbJ9ch0yNgYP3XYxONxjIyMYGJiIvSb2vIAhMBPK57sz67/5XdVr/nZ7mytey+qQ4f55Z+yYNaXmiHU1ql51XT0XgXkob1wKMeq6Nh6OjLQRg2q8m3duhXPf/7z8f73vx9bt27FwYMHewZTtVrFwYMH3WC0aigHoPUMq7eZA996bdUbrbvM2MGvYSbcnt+yw3K5jEKh4C2z9SIz72ovJKDQcUSg1fKpA8Pn+KAXm9et91zTsTvp2LhLirJOTUPNCUMZyrEuJFVPV5YNhrqBKlnM2NgYRkdHe1Z+UDqdDgqFQsjhAPjX8Srj4/I4DcJW9ZP3AF2WRQBk/J5loWR2qlaqV5U73vA3pkNgVnCyKrFPbVYvs6ar3mr+Tm9xvV53u/XyPgVurs22qrcekapL6/RoU6ZJVjgExKEcL8Ld8J+uLDvoWoVs7Gtf+xq+9a1v9ewjptJqtVCpVDA2Nhay2Sk4+tghQYdOA4oe4E7ACIIA5XI5xJY6nY5b18z3qlNC362nywFdR0YulwttOAEs2k6ViZI92j0JtSxqu1SGp6ySW/7TEcQddhhwzolBTQdAF3wJ2jwwi7ZFO1FoG/D5oQxlKAOCoVVT6/V66Nxkn0QiERQKBWSzWTdIfc4J3WGajIz2OD3gXVduWJueAhwHvYKQ2tQUKGxQNyk32aKq3OVyuQdAFPCA7tkjvvcT4Oi00fNc6KAiKGaz2R71nPnhKhVNWxmjOpp8cZ8K5EtNZkMZyrEuhz20ZpBNF30ZmZubc6Ey6t1UENFBSvWRa519GxJwIHMlh8YcqlqpoGHVVgUJZXIAUCqVXHgNfydTUwZq7XFaX8yjxjIqgNmjBeLxOLLZbOjAe75H1XEth3UmKWjqGTXKSilDp8lQjndZbh9fNro9VXWKgcyNRgOFQgErVqxAKpVyrEttazbkIwgCF6/Ie9RxQlHWR3AiM1Q1lGnzu6qK6nnVdHnkgJ6novWhB0oBXTVed5tRkFYmZsN5yOw05lHrROuL6VqTg93QgnkbeoyHMpSl5Sktx1sOMNKxwQGYSCRQLBZRqVTcDjjq6bXsUK+pc0JZoAKCrvRQ76nuzkKQ0TAhvUc3MLBqp/XuWhbpcwJpWI+yW+Zb2SXrV7cYIxhrTKWuh/aBt3XcsKx0RKlpYeg8GcqxInpK5pGSgcBQVbxDiZ5Vks1mkU6nEY1GMTs7684VphB8rPrJwU1vtdoUVaXWOD8LEEDvDtUKmMq4LMiR9dmNY3XjV6ALXHw+l8u5/GSzWecUsbGAdicbzb+GISmwWzZLcNQ8kHHyPlWjfd7woQzlaJdnoq8OtByPp+AdSjhQddBx49ZIJIKZmRmkUil3ALxlVxy8umtNo9FArVZz19WuSIDxxSKqDVLZmK7xBborP3RnG1seyzYJXPad5XLZAZRugqCgbcN/mCfmU9mfz5SgNkCNX9Q8MR2e7cI0VIZgOJRjQahpHUkZyIEyMjKyrHsJflRpacTnFlvtdhszMzMhD6pvsNqBTjVS1Ur7Tg1bUa+zMiploLrtFxDeFYbP6fZlGppj09X8qOPEB9SqvqsdNBKJuJMAlQFbu6lOGlpvfCdVCu6KbdvH93koQ1Gt5ESTgVagDCLc8QUIrxaJxWJIpVKoVCqYn59HMpnsSdt6aikEhVqt5liiVX+BMEOiOqosTFVl7gqjG7Ay8Fntg6p2WkbmKvM/OtGaNWt6wFfZpY/J8bdoNBoCfCCsKut7bL2wbEEQuB3GLQgPZShLifalE00GOip0qV0jrHCQazAyBz/V5UqlgpmZGRd/aJeUWbuZqrjcuJVL7HzOA1UZfeot82QdC+wQliVaJkpPubLCTqeDn//856H38n26RhpAyEOtHmbL/CxbtkxRPdLMF50mQxnKUJYnA+10rdv7H0r0pDmfnYq2uUqlgkKh0LP2mX92gwbSeLJJAiM3ZvC9i/GG1mnD9NQeaO16vMfHyvicAp9lbbrqpZ96b3/TIGgFT60D+11XwqitcihDOdql34bEz3g+lnvjIKqWtZ0pmKhxn/9p4J+YmAjdbx0IGlLD77T5UcW1DJPMiSCnNkXNq6rQyuaU4So7tbY66wxhLKPP+27TViC1IM209LPWrd1NR9X/411sv/I5s4Zy9MtS8a++DUgGERtZspQcsaBrFoAeTVWX7SzAA9zn5uYwPj7uXXanYKQAYsGTwEUVlw4bC66W6VnGRlGQ1LIpY7VrrJkfWwYFU2WMWse2jBo2Y9uDS+14up/dGPd4lhUrVuADH/iAW6kzNzeHm266yWkvmzZtwgUXXIBcLheaSD73uc/h0UcffdbyPZReWQrsnq52M8iE+JQ2ajiUaJwbB6lPPQTgNhQAFnfGKRQKGB8fRzqddpsVKIMiiKRSKQDhrfCZBu2SBCcfe+NzFlytMG6PYsNWtCxaNgu6/K/bkRFUbRiOisZP2t+sTZPl54a1x7Pkcjlcc801bh/K6667Dtu2bXOM+LHHHkO1WsWXv/xlrFmzxrX33/3d3w3BcCheGcibvNwIcAUMfud/C4jqpeXSt/n5eXcYvYKBMiTaCJkO2SOBy4bZWI+rApLd4JR//M5gaQVmLZdV5fm/32oXAjmvAeENXynqzVZQD4Kgx+PMCadWq/XYHY93iUajOHDgAFqtFt785jfj//v//j+ceeaZuOWWW/DFL34x1GZDGUo/WTYz7HQ6A7ENDY4GetVsdYRYmyCweE4zV65EIhF3wBTFLsOzwKdOGrs6xX6m7U0B1wK49eDqbwpUtBVqUDdjLhV8GUKj79fgbBtSpPVj88k65i5CqVQK6XTahR+dSPKGN7wBV155JdasWYN3v/vdePLJJ4dAeJSKz57+bMpT3sJrOWKdE3rN52Rh5VB15jI8LmlTj6uChg/E+BuBSPNhwdDaAxX0fEZ52iGZh35OGAqB0NoSNfbS7qWoarSaB7S8tt7q9br7nk6nMTY2hrm5uYHa7FiWIAjw+c9/HjfffDP+9V//FYlEAq9+9auPqgE3FLj+aQnOsy1HxJ+toKBn/irzs0CkIKaDvFKpoFwuu1hEpqtMTZ9RRwtZGG1oPq+VppFKpVw6qnbzu/Xw2rwoKDIfyWQyVE6tGzI62jc1XQt0h2KKrOtIJOKCrUdGRkKOqxNBfvjDH+JLX/oSqtUqPvOZz+DKK688KsI2htIVTtxHm9Yy0NrklStXLmtgWTvgyMiIAyprS+RKFZ9Xl/a2Wq2GYrHYE+Ts8xCTdWm6GoICdBmfxhQq87TrpZkvvaZ2RRs/yPe1222Uy+XQbt28J5vNhpifpmPvZd1YVqj5J9jrphC1Ws2B/FPZi/JYk0gkgt///d/HySefjEqlgkQi0bPOfChHhxxtQAgMAIb5fB6ve93rlrUsj2CTSCSQyWRCXk8APQNfHRAKLApazWYTpVLJpavAo2zPMjYFTe6NqIBnHTrqJNE86OoRu8kD36fHnCrzIzPkewmSWgc+9mIB2XcPwb/RaIRYqzqI+NvxyhC1Hc4++2x86lOfQjQaxZ/8yZ/gtttuO6pUsaEcvbJsMCyXy/jOd76zbC8lZ2VlgzqorZcVCG9k6rOLtVotFItF56TQZwGEAEvVZKbN8BW74zZByW7Dz/SUVfrsj/zfD2QViPT8EZZVr6kdVMXaXZlPloHHHqhKraaI43mNchAEGBsbQzweRyaTweWXX44PfvCDmJ+fx1e+8pXjuuxDOXyybN2pUChgz549y+pU6XQao6OjIccA0B3E1sZmGU8/xwsHdqlUQj6fd7u7WNZGdkcGyfTJytSLq3miU0SB0rJXZZz8U2eKPWLAsjEFRfWmKwj6gNY6hxT4aIjWGEzd7l899seLVCoVfOlLX3LnxKRSKbz85S/HAw88gB07diAej+OVr3wl1q9fjy984QuuPZ988slnO+tDOUolEixzlDz00EO4+OKLceDAgf6JRSIYHR1FLpcLndXBP+7aDHQdCD6V1J7spuCgrIeASBsZ7yVI0olh1xNbYGF++J+gqQHZfJaAx7JZe54Cml0WqM4Qlk1XyDDPdkMHpkGvNOux3W6jVqu5A6SYbwarx2IxFAqFo85rd7jEmirsd9u2QzlxZTn9/ykdIt9PxsfHMTo6iiDoBgW7F8naYGVVQK8nmdds59a0gEXVvV6v96StDgmCr29XHJ+XV9VczRfTtt5jX2yg5l1VfXV4KCjrpGDzx/9kkar2NhoNNBoNd53OAr6Hav/xCITAoTeq1TCpoRwfcjgOi+8nh83FyJ2wC4UCgG6mCQh2SZ5VUX3MyefgsCoiN3lgCIumpeCkNkCf+mqv2fXAfC9Zl6rPZLJqAtC1y0xH7Xq6flqZoN6v79Hy0MOuG1BwR2s9GMvaO4cylGNdjuTk9rTAUAGB23E1Gg1ks9kQ27F7GtpnLQj183z61GY6VjSmz4bJ8D1M3zqBFFzVAWFF1V8LunoMgb7P2j9VfddtztQm6mPMCpAEQk1T88Nyc+Pa5Ug/Fn60SSaTwate9SpMT09j9erVjh0XCgU0m02cdNJJSCQSmJubQyqVQi6Xw7//+7+fUMHnx7McyT76tMAwlUqFziCp1Wo9e5P1Az3L+Ox3y4p83lYFEF7L5XLOtmZDeoDerbeAbngKEN6O34KvAqtVdZmOZaPK+LTcBEO7htoyUgVf3blay65skmm3Wi0HhIeyrfW7djRKq9XCY489hoWFBTcxaL1NT0+7qIOxsTGUy2VUKpVnO9tDOQZkIDDMZrOhQaObBfAzT8EDujvSWHD0XbPqsxULEhYoySg12NancvqAjADWbDZDzhsgHOZj863v1fs0NMdXTr6Xxx8A4R2ubV3RA06V3J7VzLwwv4xj5LpsLafPCcTnjgVpNpt45JFHAAD79u17lnMzlONJlu1ASaVSOOecc0IDUFU1Xfmh4OBzhCgg2LW9/Wxm9p36rN7D4GdVJdVho0vs1KlBIAO6IK7b6TN4mTGBqlKTiVkA1e/pdNrtvccycu215l3rhuey6AauFrQsINJ2qM4TzQ890Vp+3445QxnK8SCD9OtlM8NGo4F7773X+xsHmrJCC1S+AGtVAfUeflaHgYKkdTQQ0BKJhIshpFoZjUbd1mPqAabjQu2H8XjcLfezZxxbe57mkWlZr7CyV+ZFwZn1oaAKdM980d999+tOOARNDbPRvKtwUlD2yro7FlTloQxluTJIfx5o2/9MJoN0Ot1jg4lEIkgmk1ixYgVKpRIA9KiTlqH5MqvgpuzQAiH/6zvU/ka7JeMPdbMFa5+zTEtXrTB9hhXp+l4CoAVXWxafGuoDNx/YatyhFeaNtkFVo/uxXuvt5l8ymXQ73tg2GcpQThQZaNt/9ZZSuBEAwYj36p8FH2sjtPY8oPesD1WnVdR+ye921Yvmi6CmHmCCiJbJgpiyUYYNaZn5bqvKWmbXz9an8VMELL6T2x1pmciAeaypsj3bProkUfOpEwVVaKB3yeAQHIdyIsiywVCZFg9gArqsKRqNolgsOvuTTzVjOkuJtS/aQGzLuKwtUlVqu7KD9/MZsijdTsg6RWgLtKtC7Lt912x4j48d2t99ajHDlbh5K1VilonPqzqv9RGJhDe3UJbNsnFDWgBIJpN9V8UMZSjHqyzbgZLJZLB69WpEo1E8//nPDzkjAD8bBPzgYAeVHfxWFfZ5kVXNUyeOOj0AuDxy0wj+1mq1UKlUQsyO1zWfPnVdl/4RUDW/kUjE7Y1ogdnWCfPPHXWUOWr5CoUCKpUK6vV6yJbJ96tN1ufBtkHgFhAtA+YznOx859gMZSjHkyybGdZqNUxPT+Pkk092sV10OCgzAcKsR0VBg/99nmPrIFBRsFAWx8/KcJgG0HvkoIa/WECnTU3zqaomAcJ6jRVkuB7Yqv9aFxofCSDkgKFJQs960f0ZrfQzP/jAUZmoAqVdKqnAae29WlaKnbSGTHIox5IMdAZKp9NBPp/HI488gkQi0bMETlkSn9HvFgyBXiaj91ogIQDbNcB8l/5XwEqlUg7g1E7I321Ii2VwfIe+u16vu+/9HD9kovqbquPWLkpwJkPl8j1OCjRVaJ1pXeh/tSP62CHvVQ+3DXOy363t0YKyj3kOZSjHigxkM5yamsL27dtDS984iDTomGJVZssWLHskW1IQoXQ6HUxMTKBUKoU2V1XPr32OYKUrMVQFVVXasqJkMolqtRq6n2W0a5y1fBacyFQV6GzYkQIg7+VphKzfpZxTtu4V3G24kgUqH8v0LWdU1mzLq79pyM4QEIdyLMlAK1Ci0SgqlQqSyaRjgariKbvy2aI0HWDpGDoAIdCLRCIoFApulxber0IQ0UGsAKErSBQUdPMFPl8ulx2I0oHASYDpaH41hEVXd3BfQY39Y34sqKZSKXcIug1NsnnW61p/ynx9oKWMWPNi2bkyRtvGGhNp8+Vrb19bDWUoR5ssGwyz2SxOO+00PPTQQ4hEFgOsuSrDskGgv/qrYKRqmv2dAdwcaMlkEo1GI7ShK59XdZcSBIHzett4QP5ubZssj82nbp6qwKRqczwed+BpPdO0/6mNUVV1pkVnjwUUHwPl/6VsqkA3/Mf3jNoorfhYvdaLxiz64iN9Zgetl6EM5WiTgdTker2Ocrns4t6sTQqAd1DY60Cvk8WqblwRYYFW7ZSNRsMFG9NGqNtkJZPJECiTmSnTi0QiTnXlWSFB0D3rmGnyOXV6KDDqQfSqCrMetB5ZFqZJJ4ll1ur00c8Kprb+fB576xDiNQ2+9k0Mtu1871WVn79pzKSq26xH36QwlKE827JsMGy1Wti7d2+PYR3oXQ3iGzRAmG3RxqdhIspYVKLRqLPfqRE/mUw654iyN7Vb2YEciUScHY/v4SqOeDzutsyv1+uo1WpuwwcLhJpfgifLx5g+PbFOTQd8hu9W25/WIUXVcBUfY7RsUtOy16w3mff4ntV8aXtbZqh50O/ab2xcpVXXh3swDuXZkIHWJvO4zmQyiZGREffdN9A1ULefjc/a3uxAVTXMZ1uk/TAajbqjNzXG0J5Sx+cqlUrI8RCJRJDJZBxQAl21VwOv1baoA5sqOcGaQKjltGxSy8jvduUHAZ/vpVhWql5mPqe2QZ2EVPgcJwKdhHxqLv9rOJPdTZvvU68+y6pOKP6maSo7tWFHQxnKkZZlg2EikcBznvMcPPzww2g0Gu7YTp35CTzKHnydOQgW9x0sl8sOBHRHmFQq5Q5A96leyjwJflyeFovFHFtUMAUQ2pTAsluqyGQmCiKpVCpkm9Q8KUArmOn2XdYzbsFFy8St++moUTVf1Xg+Y5/X/3yHBWAtO8tEeyfLqXm1wNvPFsj6s04q7Qd8t7Wr+nYjYv3qahhbb7b8QxnKU5VlgyG38Prud78bAgY7qPTAJ4plJ2Q88/PzTtU966yz0Gg0sG7dOtTrdXzve9/D2NhYj+3Mqm36bv6RySWTSWSz2R4HAgeO2vk6ncVttggKiUQCtVottOxQB7dlm3pCHvPajxHqPb4VLEAXTGn75PI7jTP0sWqto36mCgU7DWViiI+CPJ+xtkyfik7RteHWpqj9xWoDar5Q1q6eessafRPLUI5v6Ucynq4MFHTdbrfdMjMLCKom92OFkcjixgK5XM55odPpNKrVKrLZLD7ykY9gbGwMd999N2699VasWLHCqYiWrejgszYzDhLu5qIeUHvqnQKcgiQDna36r84YBSabJusMQGiVDvOpeSHrIQtSp4kyba4ZtodtWbC1edL3quppGZtl0NbWp3WgQOtjuNouqjL78qtt52OmWhadTLS+td6GcnyL7deHSwaKM5ydnUWz2UQ6nfYOAB08drUC0O3Yb3jDG3DnnXeGzvZ98sknUSwWcfDgQXz72992ITSqgqvRn+9bKqxD86R2PQsYVg2PRqPuQHIAbj0wB6lu/qpgonGDBDKdxRSMCLYEax3oVJVZZ/SYszx8v8/j2w+ktH4IZtZTrSBk79WJwMcGVXws3KrMamLRNPV31qnPHKNeeU5eOpkMgfH4liOhCSz73OS9e/fijW98I370ox8hn88jmUz2dFbAv1TLqtKcyVOplBv41WoVyWQS09PTiEajmJiYcKExukefnhmiAwhAiPVYda7T6SCRSIRWhChwMA09x6VeryObzSKbzeLgwYOh8sZiMQdkfA9tegpq9HLbyQMIO3jsUkYFAA27AbpL+fQ4AC0Dy6vmAd5jbZn2nZzELOBZhkix7Fmvc5JR4LUTlaahz9lJUMvuqwsVpkW1v18XH9oZTxxZTjsPFGeYzWZ7VCa7u7Ne43O8RrZHlkbWE4vFXJD1unXrXAfnfz5H4EilUgCAkZERtNttzMzMuPfpemG7c40ORLXvadp0rtBGV61W3Wa2ymx0Cy2mS2DnO3iMqQ46AhGBSp9hnhSwFMA0DS7Xy2QyqFarbhsygrANT9FJygdsOnko0NjPOjkpENnDt3wA6ZsgrWnB/uYDQv1NAR/ojZ/kmTHapyhDIByKykCbu65evRrpdLpnz8J+LI2f9RkdcFQVlQ0QlHRWV29iJBJxAd8HDhxwgJLJZFAoFEKqqeZDVSdlRHY9MweY2qD6gZSyMQZPazlYHzqIyeii0ajzemve+rEmrScFIKrz6XTaHZvpO0FP82zNGvxvYz5tu/LdBCDLNG3ZWV6dRGw/4WefzVXv03Jr2ciq2Wa6Qomi9l/2K+0DhwLFIYM8MWQgZkggisfjTk32qT/0COZyOedwUWBTWxSdAWo4V4bZ6SwukdNT69T+xI5fqVQQiURCITLcYgzo2t6A8MYMyh454KwtTkHDshAdZGSxZCKWGbGueHqfDlpN36q79v0KitzgAVgMx8nlcu78alsGaxfsB6794g19z9o6IUBZBqY2ZAVe1r2W3YKpmll8ANZsNkOb17IM9r1Mwzqv9HcfSx0C4YkhAzHD008/HUDXK2hnfXbcTCaDfD6PaDTqzvoFwh1NN1G1zEIHXyaTCQUdW7WNgMABpcHSZAr6Dh2EfD+XF3IXaTtIGdrC54HuoFL1i7GB/WxhAELHd2r5rUqoz9jPFA01USabTqcBILQfoh5roAyr0+lgZGQEY2NjmJ2dDZ0q6FOl1dygrFDbxD7nC7pXxq55V9DS7yo+sGLguLV5ajiSAjdNJ6oF2MloKSfMECSPPxkIDK23UQGJoJLNZh2A6ZZUCnD8r/Fj/N0e6J5MJpFOpxGJRByw6mxv1XUAIdskEGZwCoTMN21uuimsng9CJ4iyD4Ij2SfTisfjbvPbUEX/x33KxAgIBBkyVgse1hOtpgPWH+9Xpqh5Yxl8AzwSWdylJ5fLhU4X1La1tjsgvILEd49PVee9ys7tTjvK6Hg/RUFSVW62GVXmfmDsY/l0htl+ouXrlx9rahjKsSsDh9ZYdTUejyOdTofsMpVKJTRD92MTdjcZ7YTKGghEBCNlZBYUOp2OW2PMQcFBYlV5qyonk0k3cBQY+Vk9ydZ5wsGt8XnKNJgPq7bxd3qG1Tus96nQBGDBScNL+G7Gc3K5IgPJlb3V63Xkcjl0Oh1ks1m022231NHGZer7dBJSBw9/s2vPFRStLdUCnr6bAG/v9U1w1BIUzC1bVUAlYyYgan9Sc42dyLQuVL3XMg4B8tiSgY4KrdfrjnVx5QhVyFqt5tbRsjOrV5cdVB0cyjQTiUQo2FlVatoN7coIqpzW9qZ2SXZUy3ToxbWDmOo70FWlNPRFmakODpZFGQY95nZAs34IkAQo5tGyJmXRZLSWHavarupiIpFw56vEYjFks1k0m02nDpP50jNNk4QCmcYhWtXfMkIFBR+b4v36m2Xwtnw0l+g7WF7bJ6yGYVm01p+yV03bZ9bRutA8a33oOmzmfQiGx44MtFHDnj17Qo4RHqrU6XTc/obKgjjz6kBVRqUqpzIoVZN8xnR7v12+RWEaugJEWY06DtS5oMBkmaSKdnbr8eakwd+odtO4T0Cy6qO+k2u2AbgYSb1PgZzvZ/1qeak6slzRaBT5fN6ZB1injUYDqVTKmQxU5bT7LNqBrmxY21FtqP3KqZED7CuMQVWtguVRwOQf38t3q/1W7aW27xH8+Md6sKt8OCmxju3u6cp8mQ8F/KEc/TLQcrxGo4Hx8XEXh0eVjhsZKMBks1n3HA35yg6ULaktjHFyVHW5mwzv13ObyTg11MOyLwDODmYHKAcvBxxZjarzZFS+2V7VKObd2lU5yKmCt1otB0JAl10SJH0hSAr0yj500iCzZv3oYV3xeBz1et3FSwLd867j8TjK5bIDX16PxWIuckCPHFAHlNZXP4C0zFvtpZwcCHosu04+Ckq2bpWF8jndzEJX8rBO1FnFawQ6fuZ7K5UKyuWyswGrI4ZtZyc+VcuHW5EdW7JsMIzH48jn827bfzZ6Op123wlOwOJKDjXGA13vJ21jujGrnf0rlQoqlQri8bgL9qYzhQHG/KMwEJkAawcuWZJlk6paEYx1ticgqdqt3lD+aUiK2gcJhBxcOvBVDVZ7lIKTAgTPrU4mk67MCjYEL8YcptNppFIpt2GFzXcikcDY2JibsAA4kGQ7plIp9261nVmbHRB29rAsPvCgiquqt1VlrYOFom3CttU+wHvZT3K5nLN1853atrqJrzqfxsfHMTU1hUqlgoWFBVSrVddvmQ/Lltm/7PWlPNNDOTpkIAcKvZLsUNlsNrSGOBKJOPsUEF7CpQNaQcKqVMr0gEVmpYdAERCZBw3RsQOUAKdL3cgYlZFqmAXF2i2tqqV2RYbw6EBmWvSE89zjRCLhQl+sGm0ZLMtn2awNl+Fg1phJMhnaBtPpNEZGRlCpVNx7yIDoBKvX66FNbjnZVatVB8DcZFftmiyLTx1WU4ZOJiw32RbbRoFNtQ3Ws7aN2l35PtYR+wnzpkyf9+l3XYbJemF+RkZGEIksLjpotVooFosoFAqo1Wohs0u/qIih3fDYkIGCrglCrVYLY2NjoQFIJsgOpLYboNshqRLa+DS18+jA4qAm26lWq2i3u+uaue2WsgS11fA7B45uCMt8qfDddhATyMn+9PhOVZt1mR3LSQCiA0MHtdrx1J6qzIL/Nb6ToUu6qSzQdSoBXWdOu91GuVx2ABGNRp2jhHmIRCKOAWooEeu5UqkgFothdHTUtbUyec0j86zB1Mr8CExaTtseCqzqCdb/dgLkvdls1k0grHcbAUANxaq3dACqZtFqtZBMJpHP55FOp93Z4QcOHMDMzAzK5XKo3+jEObQdHjsykM1wYWEB2WzWGbgbjQbGxsbw85//PLR6QVUnMh4AoeBpIGzkB+CM+QQTHTD8Tq91s9lEvV5HKpVyah3VHI0r0zAcDcy2KjbLqAOZ+dIVJT7w4uC2Kn8sFkOxWESz2XTeUKrPHCS6QsaGGmnd8D1q3/QxUqDLUrjcj4OeQeUEZQ2wVqbLMisbHx0ddWpiOp12MYnqfWd+rcqrwKZ5Zx9Q26eaC5Qh+4A3CLormAhWAFx/4ymOrBs+q3ZnayPm71p/nDz27dvnJolcLofR0VGMjIyg0+lg3759KBQKIeZuyzqUo1sGPiq0Xq9j5cqVaDabKJVKbqkc7TKlUqnH9qX2LgUZqj4Kivos0F1NQVWI8XC8jwyJYT68pqoQ7YjWKaFCgOVgVnXeDlw7WNUWpTZIhsyQjdGGRceBOiCsg0QBle9WsfWkqjHZtzJuXudgpS2t0+mgWq2GwELtdJ1Oxx2byjqu1+toNBouvApAqGxqHmF6+p/lseFBFDVv0DGkqrDmUR0wNCvQcaJHtrJu1JyjbUi1XxkeP3NC12Nqi8Wiq/d0Oo2VK1di/fr1AIBdu3ahXC67yUfLRRmqzkefDKQm53I5p5bW63VkMhmnEtNbaWdwDnTaqdh5dQCqPUhtiJFIxHld1ZhOTykHYBB0j/Pk4CSwKavT8A3mjyDBWDueysdjDZRR8l3Wfqh55h8H08qVK1Eul0MhKlTRdHCqN5iDkgxD36M2WK4Rp0NGbbK6PZhVwelBnp+fd+yGk4o6ihivyLKUSiUAcOo2yxqPxzE6OhrKM/90ArH2QJ50qEIg14B5tZsC3TAjBUEGiisbtZMV06YmwfZsNpvI5XLuzGpdX06NhHtbkg0TGBliVi6XUSwWsWnTJkxMTGDFihXuHfv27cP8/HyPCWAIiM+MqGNzKRmIGaZSKQdqVEHotWSn1HOHVY2kvZG/EbQIUpYp8TcWRlU97aA0+pOJUWXSwGMOSj3nGAirXBzEBHba4mg74n3KGnhNWYUCXLvddqoly6IOA3V88DQ+AjfzS2DVd5FtcFJSB4kyYYJiu912ExfLyMiAUqmEVCqFfD7vTgUkcClrV3slQYZtyZhJAhQBUAFZ7YP6m7aHr9PqBEZmajdiUFOMdWTo+5lvPfWQ0QMKmto/otEoCoWCO5cnGo2iVCphYWHBtTfP847H49i/fz+q1Sri8bizs65YsQKZTMaZk5jPIRgeXTIQM3zFK16BW265xX1nR+WgZGei0V0HCgewMjV2XCC8NEp3dWGHIyNUNZfGbno+GSxLIFGvnqo/CjgsAwcn86c2M7U9qkpPcOKAIKPipAEgpI6p+kmhc4WTh9pIfXYyC4yxWAy1Ws0NcNahemaDIHADVJclkuW3220Ui0W3FZgCItvFOhv4p2yVLJt9gMyedaNl0TLwM9NmHTD9TCYTCsDm5KUTp3qq2U4auM9y8LruHs425Pu4AxLVbYLd3NyccyqtW7cOtVrN2YTL5TImJiZcXCJjNdnnM5kMVqxY4TYJVtvqUI6sLNdmO9BGDWeccYZzWgAIGeQZHE31iDYTsjV2Lh3QqmapWksmRVDUzqrxgcwXOx0Ht93GiyBnQzP4vIIz0LtET1Ux5pFimSbrhoG6zIcOANYdHUZaL6paZrNZFAoFl0+Kqptk1awfVQ2ZTwJTp9NxbIWTlYa8VKtVF5NIswPbg4yU+VMbnbYjr0WjUbdxR7VadVEALIu1gWod8h2ZTKYnoF9trOr0sQDKe7ScfDf7hDXNjIyMhPo3A89ZdvZRhizl83mcdNJJKJfLmJmZwdTUFB5//HFXFxwbtM+Ojo6iVCq5/q12UO2TQ3l2ZKC1yY899hhWrlyJ888/H7fffjsymYyztRQKBedMUVBQhqLfdeAqA1IViMDGODgCAo8DUE+oOgvYccmEyKB0EKl6q8/q7xzUOrg0/wBC9j8+T2ZFmxifVaeMb60164D/S6VSiJW6Rot3jy5QIYPRem61Wi4/9LyzfgikrHeWudPpuHXK1WrVvZ91a22LFAVklpXOtWw2i3K57Fa7aJiSmhA6ncWlnQzmt7GI1mRh+47mSc00GoPJz8qS+V8jE6ht0D7K9qSHfm5uDmNjY8jlcli5ciUuvPBC7Nq1y5k92O/JvPP5PCYmJrB///5QcLZ1hg3l2ZGBQmvuvPNOPPe5z3UxhpVKJeTEIGjZ3T+UdSk4sLOMjY0hmUyiWCyGgqEJOrTnsTOmUimn9umCfF0PrSyQnc23qwtXZjCfPkZnB6A6BwA4DybtjVRBCS4c5MwPGSsQPuZU1ToCNb/zWYIjvzMNlq1arfZsasD0qbLS8cLfyH5YPg52miqoIqutWFVpNUEwv1pPjUbD7XFJWyVBURmzbv7B5+2GC+pEYT3QhGLtiNapwue0T7LeyVzZT9XkwAmD4M530zzEOn/ggQcwNTUFACiXywAWz/ZhHyFA6p9qLTqxDIHxmZeB1OTx8XE89NBDeOCBB9yZx0D33BFVmwCEQEcbWu1CkUgEc3NzoetAVxXU/+zUHEgKLMoQeT9tS0B3W35lVVa1snlnempzUCbBPKsaTXsfQdy3rldVYWuH5PvVg6ohSGSiWjfKWGmv4m92swXmSZf1tVot5wDhHz2mmUwG8Xjcef8Z20jmqG2sIGMZF1khQTGXy6FUKqFSqSAaXQyUViat8Yt8r5aTgK4TVK1WC9kSNR9qk2WdEcjU+8zfdELX9dlMixMO+06z2cT27dtdyBEA53Rpt9soFAruPq0j5otsXc0uQ3lmZSA1ed++fXjyySexsLDgBol1iChwWCOxz1toDed2dtQZnKJ2HLIBZX1MVwcFAUWN67xfVS12UKu6EETJ2Nix1aYJIGRPtezUggTQ9ZZadZD5YOiMNQKrs8eq0dx9xpojNLyFAMUYTabJeuE7eaY1PfcAQqs6WAZ+J1AryLOtaLMkYGvwNkVVWgVBtgGZKUOR6AGnPY9gyfzrRM327nQ6jgnqaijtezpZ892ap1Kp5GzdfJ6OPLY9T3wkKBYKBccUbV9gPmw41VCeORkotCafz6NWq4XUESC8wzQN9GQfqh4rI9RBT9HVG0CY+VjnBsFKDfi0iamhXe2VHCAECpt/Vcf4LmWHNgRDgVpZKo3kOpi0XFTj1HaWSCSQzWZDdiymSzsa36V1wEGuHnuCNN+r5VD2QzZMNqvqu7ZdpVJBJpNxJ/Ex3xzENAfoM0CvBsB+Q7UwCAKMjo66d2o8pmoErB+1+6pTRoPYte/4mDfteTS32InQthnjThUwbd6AxQlIVX/WC2NvO53FFVxc2qf9UrUGltOu1hrKkZeBQmte+MIX4m//9m9Ds6QagNeuXYvdu3c7tmJnXe2s/K6gwJlef9eOCXTZpbJIjTVLpVLI5XLuu9rMKFbt5UDVGD4Kr3PQlcvl0Iyu3tNEIoFyuYxSqeSeUxuphm9wd3Bd6cFBoWEfZN/W5qi7q5Ap0RNMlZrvs6oX1Wm1zQVB4Iz89CZzAgAWbV90hBAQWZcaQqL1yvxaUNTQKzIlqspUdQkaqtpqWbgdmdrzlG3pShQ+02g0XAiSZeiqTo+Pj2N+fr5nYub9qlXw/WS4OiZYZi5jVRuzbh5i38HQJN30ZChHXgZyoDz00EOhAaKdOxaLYd++fSHQU2ZDtYaDXDuien0VNKzqyOeUmQFhFlAul9Fut5HP5x1oE+zUdqaAqF4/BS/rEAiC7qFPCtTK8OiAseEg7XbbhZpoGWmbYz5VtVQAVIeNlkVVf92qn2BL4OTKCZ1UOFEwr1T7SqWSW2XCZyKR7jkpBEStL/YBPS6VwntisVgI4BQ4WEZuOaa2Vi6VVEZptzDTemb/UBss+5JuHMJyK+gBcKuPbP+wwomLbaQ2TpaP5aBXOpvNIpvNYmZmJlQ/vF8nXpZbnSvWZDSUQ4u25VIyEBg+8MADLmHOdGRlAEIGYBtmwc6ns586C9SmRpagHZR2HlUfNE0FIAZfEyQ6nY4DXDsAeQ8HowZM6/utUZ+qIcutsZAa28jBzLXbylgJCnrou9r1LHBZsKbzQwcI1dVMJuOAQ1mv2uQ0v2ShrAvuW8kdzPnuarXqwl70kCnd+kt3xFGmozZjax9lP2GcXzqddu/UXXJYb5ZtcoLWJYTMG9/FVVBsO58pg23NyUHtl/yNfYnpsS9xNRbLDMDtBBQEgQN0qunavxSUVQ1PJpOhrcJOZNXZ1tlyZbnPDGQzZGfnigDueqJsUdkNvwPh9btAeEdgdmZ1flgHBOPVuK6ZnUfVHQtUan+jWqi2s1QqhVqtFmKGfJ/a7XSw9rMxKViRfXK/R7u6hQOaA4T1o2EkLIeqhmQOQPcEQDJqAoA1XQDhDQsYyM5NSrUOlY0qCOXzedc+LAedGRz8Crw8d4V1rDvz6CTEcltPL9tkZGTE1Y86wBRUlb0r4+e9qVQKCwsLLg1bD7S1KvBpveuGEbRH6ySq4Vy0+9ZqtZApQbUl3UCD4nOQ6fhhe53IQAgshuAtLCz0rYenCpaUgWyGU1NTzklBVUI7BQeSMhFteB0E1svJwawsjaDC71RB0+m0C+0gSFGYFm1OCmy+lSPaKQm4hULB2QF5j7XZaT55H4OeCQjKqCwT8rFTtTPyHVpPbAc1IShz4TMEIA4gnQA4eBlHp6yD6ROACIC0JVJto1pvAZFl4hZXWmcEIN/yRLUl6yQzPT2NfD6PNWvWYGFhweXVOr9Uw9A+E4ksbqrLutE2V8eV2viYJp1W3HGI/cdOvKqSs0xc1thoNFAoFFx76gTFcqttV00rOoERQNXrfiIKFyH4JJPJYO3atdixY8dTBsSBmCHDBQgABDzL+jRkQQe4Mimrgqp9URkjEN7jTgc/O4ymD4RXhdjnmQf1sDJPjUYDIyMjPSqJMkMFQnZQPSZAl6kxDbIGG3NI4NC8U83VpV/MB9NRhqgeYJZNbVlafp2YyAqp6lFltPYxssVSqeQ8ynye79RYRqqQZHAaRK9toHWlIKH563QWA8IPHjzorlMDsauH9I91RSeL9iW2CfPDOmVANPtCPp9HJpNx2o+q5kxTNQGmqzZcrtDSYy64mqXZbDr2aNNQbYN1o2FsJ6pY56aK7jf5VGXgA6FUbdTAYNrGCIBqv6PKqw1LYGGjEzQJogQz3wCi2qBOGQDOo+vz9ulgUDVVOxgBhqyKwnvVk63ATRaqarllkPqMlokqUqVSCdUv7+Hgp50J6K57JthTfSNTYwwoB6eq39Y+puuIOUh99cX3AggFXLO9uO8k0J1E1PCvdaHtrhOWgjknCus51wmQAKUgxWtqN9Q8qRmHn+PxuKv7eDyOTCbjnEEqOqlo2kxLxwYnZE52bBe2CU1OxWLRfWc+te5V+7LM+kQGRivtdtut+nmqMhAzVPWJqpYOKnvEovVgasYtQOi6Uqp51hGhXlSKqjwMwga6wKYbclo2oSoWwWNhYSHUAXW2V7saBzJtdjoQmC9lWWpCUDAulUruFDoChnXokCkS6O2BVapG6TGfdFyoPVbrTVUy64hQZq02Oq7Y4KH0zC8QPrNa36E2Ta177QfsF9a0ovmlQ4GhN7ymqq6aWAgezKcyQraD5pOOO+u9tWYK1ps6zXQC04nHahc6UQDAxMQEFhYWeo7MsCYGJRk+7epEEmXRh1OWDYaxWAwbNmxw1D8Wi7mdrgl6zCgZHkVDGIAuMGgHBboqAk/Fo6pBFZMshioeQVQ7n2VmBAJlIXyW77OOB40BIxComq6sz6pPOmiAbkyZVYdYd/SyKjgpe9JyWFVd65n5VsAmQwqCwHlomScFLbV76Zb5VFO5jJEsinXJoGULalaUEfru7XQ6IbVb65L3KsCxD9jgf9UwgqB7bCjbgte13VjfXJqok6V67/lutVVyErYOQ+1rGppk+yZtviMjI5idnQ397qtLTnxaVyciIB6pMg/EDCuVClqtFmZmZkJqnHYyoDeux9pZ9Dobnr8py9DAX4Z4KPCoB1XT1BAahkCorZGAbQGDlawdWPNM0OA7+B7LCK0qrGVvt9uhjWSVNVmAULXaelD1nbYuCYosj55VwjAfhsAQFHm//tEjznQVaAA4RmsZvi2/5knrhPXBdcq2DlgPypQZwsNyaZrqfLKTigKbBsOzvRiorxODZfbaHpwYdAJj2+hkyb6lfVfv54Rox4qCpi/dExEEDyVnnHEGDhw44PY6GFSWDYatVguPP/44FhYWMDIy0hPFzwaydhxlPKq60NbG+8n6tMEBhACEKh8BRG2GyjrVJsUOzjT1GapdCqbMk3rDLdCrbU1tiQrU9l6g67TgO6yqbmd6TYtODn5WNUy/8znNiwbJdzodFItFx7q5y5CtJ2WfzKvdjYgTCXfR1jRUbfSplGxX1oeW1banghbZKoPKrYqqNmQ6RrT92FZaDt11Xc0jlqnrpKB1YFm/9iUKTRf97Jvsl3ZTCCC8v6aaHbTfDmXx7Jmn43FfNhgmEglceOGF+P73v49cLuc6szaYzngEI2VuCgK8h8+q3QroDgJlHhqnp7YTq75QFGx00KiablUTqmBkIvyzrIXApuyB5eC92jB8luE7TFcB0p4IyPKoCsz3cRBpjBqBSEXVSF133Gw2Q8d/apC7HWRqn9P64CBVLykHqTJZbRe7CobqrObbgo/Wg9qj1QasYKiqK1fR2PrRNqcZxgKdqrPaJ5UEaP/XetaJgGVVj7vVVFKpVOjIUaB33072I3WOWVPTiSxc2vlUZSA1+fTTTw8ZqYHwmk3bCdRTZwe5qjORyOIuw4wjUobCRtbBoGEG6pBQ47s1jjOv2uH5O9O23mKrpkSjUWc/U3BTYOSztFdR6HigOkZWyhUJZEkK2tZeqYzZp4Z3Ot3jAtSepkBEVZmrS9Q2SIanji8LjOrFVfZO9dVOLBoLSVENwLJpq55qu3EC0FAXPsMycrLVyVE1FGV/rC/VLmz96kSo4Kjv1H5qJ3sFV9WS1FbtGxvUlnz55zu13YehN09fBtrPsFgshjoMF/RrwDUHLGdjZXEc/ExP1YRqtdrD+BQcdBa2agljxNRmZI/+1E5JUcaotiUgvJkD06DjgSFEOuB0tuZ3nTg6nQ7Gx8dDJgXWoXpDVRXVsivg0GOvg4x1CoRZExA+WJ5eZpaRsXStVssxRZ4SaAOkla3rpEUvMJ0QPnXaevktu9L/tq21T/B9tVrNHTWhAEbWpaCqE5+2LSdr26cU6K1WwkmL+WBdaJA+AZsAxc/sQ6plqPquxMLH+JQlk2Sw3EMQfPoy0H6GQHfPtkgk4gKwuZ6XQKcnpLHhGLyqTEMblwNebWn6Xt7PgaGzaBAEDqDonbW2IU1TP6s9yzp3tPPqdR/7TKVS7iAh6+2jXSqbzbpT1bTMFObfqu78zAFjVSzeQzCyZdA6ZrkJYMViMcRw6LXnjtPWScG80w6p5SQQxWIxt1pEg7CtfU/zqPVq+4fWF/OppgVlwpwstP3Vhsx0rZPJqrxsd8sCLeNWkAbCW8ppH2O9WJao2gbzafuYBXMCvW9CXiow+UQVa7LpJwOB4Z133olWq+VYnKoEqtZaozPVEWUrtB+qIb3T6YQOgLcDQ5mG7pySTqddIDZ3BxkZGXHpa8dSANLOxTJo51KvIv8TdJkOB1ar1cLs7KyrE/7vdDqo1WqYmJgIBZJr59XBRMDTgUsTAL8rqPgYmw5sFaahefANOAJ6JBJx4GrXTbP9FNQLhYKbmHSZHutLA8etWLBiG9iy6PPWUQfArVtXFscJT7UXgpMVVT8t+Fq7NBm95tXaJDVgn/mx6Wi5yUptme3EaMeID9SHMpgMtAIlElm07XGTTw4qqqm6BE87nn7mFk72N3YU3Zbeeuq04Wnj0jg7/l4ul93W9LrMTGd6O6j0mnY8ZSHa2dUWp3YstSfS7sO64UDlRq3KWHQSsSowVWS1XelEpNufESA0HYpVOTWfbA9ri2PQujXaA4sGft3slfVMxsYJimX0MWy+l9dUVBPoxx5ZJtY/TQlqi9P3sH7UvqosjvlXxmeZcSTSjXfku30MTvPM/qLMUj3M/E0nPasVsK3U9m1tukPpFWuz7icD2Qw3btyI73//+5iamnKbXyrAqPdVOwE7C21iquJoR7AL/IEuoPCMDt5nZ2CdUcm4lGUx+Bno3Z/Op/pqJ7QDS5eEcTDq+xSMotHFA9sZJ8mQJB2U1omgedC8+dgg0PUS21g6HfzaBlo+nWj0fk2Tz9AEwXtKpVKIlWrZqWrzczTaDWvyOU3YDlpGvabgoNf1eQUNW2fK8Akk7F9q96Ot1IKP2hGZjlVRaXrQQHw+q+1BlqjB9pqu5tVOitpnNSyN+VDtZiiDyUDeZDpQyAxtiICqHQpS2rk5E9JIr4OaHYZ7uJFRjYyMuDTpebWeMw33INhy5tUdg3WQaXC15pu/K7hYdhCLddeushyq5ihbsU4P/U07vQ5umw8V1qt6TnXgsz6Yd04i1tGl4M82VHMHy8Z3co9D1p2ehWIHfBB0N7Hg8zQpqJnCB27aDhYAFPh04Ksqyz5nmTfv03Ky3Jou82HVWguu2t460amd0QbnWzMR30tQ5vpvzYdlxFZrGcYdHh4Z+AyUWCyGAwcOOEDSGDEfy9BzQ4Dw+mC7WoHGZAsS2kE4e7NDsJOp6s3rakjXPGhn0uvWVkOxKjVFPc8EXussIFBSBdVBZNVYy4oUFPldRdmsskEOYt7DfBKYyRRV7fWphMr82+3FwHe7e40yTfWaqoZA+yF/V+ak9avltyqnLbN9hu2n2oHer3Wp/y37tn1TJwVtM5smED4oSwPZNcaQ+eamr9RyOp2Oc57Z8mmeraaggAog1L56/1AOLQNv7koVVFmGhtHojMXO72M2ui9eEAROhWq3226FAb2yNt6KQpVZ1U0NfWDn1PXJFvjU3qPqSD87lrJY/ufg0cGRSqVQr9dDm7syLbULqZqtDE8N/MooNQ92oOjEwusK+kCXQfsOaGe9Md1Op+M2eOXvLE+tVgvtdKODTjUE7uijZdT1tdYswfco2PiAz9aBtYPaslv1WT9b04HWAdV/VVk52ekEx/zQJBCNdnfyUfBUGygnL8t47f3Kfi2wWdDUuhjKYDLQ5q4rVqxwth/7m3YUqgDKilQ1ZEegd1VtbgS0SqUS2gNQgVZVDp+3WHev0c82r8qgrBqv4GPZh3XCWLZAhsv7FWCVxWocJOuU9+iGCLoyxarZzJdlmsrsAITqXvOrmy0wHXVeEUgUnIIgcMdzjo6Ohgz/tu7UXmmdBQrEKgpIWj6yPWVPtu21LXO5nMsn61bFMkK1hzJN1rXWh77DAjb7nDVR6NZy2m+sx1vrwAd0+l1VZJ1UtayWDQ+lvwzEDMvlsjujwi5fArpqChteAY+dTNUFnRmBcIf3AY6qcnbGBLoGdHYQoLtrDK/ZAcEOpAPDMhtriyHg87Mu2FdQ0sHFe/mfZdUyERw1to8DWbf4Ilhq/bFulbnogFVzhToSWAd8L5c06XGW1laloFosFpHL5VxAvXUCKAPX7evVRGFVTgVBfreDW3+jqN2YrJZgo4DO/qjl4ISldc2J1TJJ/WwnaE5s2kc44TBNDclRm2M/8LPv0zHB/s665L36N5TlyUBgODk56VRlXe2h64fb7XbobBRVI3wdXjcf4OCOx+OhgF/LFGxojoKxL8RFB59lVFb9pArHeEdfvtUupyEu/N2Ch7IYsgHmj6s/LEvRHZ352QKz1jHLr95q9dwybfX66lJAPpvP5917dPKw5aCK2Gq13Kl5VhVVgFCbqh4boI4wq/brNV+dan1rPvleC846UemkqO3L/tfPeWZZqQKZMjPeY+3C1i7La0oKbN58TFj/K9O2+bTvH0p/GSjoOggCt/mlBlFzQHE2VFYA9Ko1OjCBsCOCjcczOpiutYP4QAfoMhplebYza+fggNeBrEAHoAes+Md1vcqCgbCTxarjBHwCLjsy0D1zRAexDZtRVVtBnp+5SwuBh6xEwYCHtROQfUvFNAxGRetP318ul5HNZt1kYuucQME4U6ara7u1ftW+q6qw7U86yaioeUDTYhsoWNjPbAMtry9fbHd1kKhzSPso7dcWDFmH6hTU8WZZon5nv6H5QQO7bV5PZPExbZ8MxAyDIHDxcjwBjJ2BqwsILOxUZAQ6GIGuM0YHsLJL3fGFA0hj/rSzKGj41EZVe+zMqg4UDiDde4/vsx1b2R2Bn7ZCpqP/lZHwebWpkiVb1qO2KuaJXke1FylbU8eW3dJIPct8B8UORt03UPuAig467m9oWYpdbaTmA2Vimh9tJ3UkqQlDVV0+a9mSNRfo5KggqmCrZdM6YZ+y7atMWMug9mUNO9J6VPONfb4fY9Y28DFHvvt4BUId/8sR9uND3jdIJg4ePIhareZWiQDdDqO7BOvMSLZIO4p2Gt6vaol66fg7O2Cn03FeUA2U5XOxWAzpdBqlUikEjmoc13Stim9Bkt5PllMHpg56nQTU8aFedKarKiKvq5rLZ/S4TQVcftdnmI5OJOwo9PyT7SWTSXdmit6nQKPga1ki8+IbdJHI4pI7ngejzFJZEydCBQofu6PwGU3LN9tbu5sCos9codeso431QGH+de2vb821T/uw4Gw1Jwao+44D1bSW+qx9wdowdcwdL8Jzu5cjy93jcNlg2G63sW3bNuzduzcUD8VG1qBedl4eXKSNpCqb2vgIPtqwlgGq6sPG1qBmYDEwXI3lQO/aTr2m6RME+Q6r0qjDxgK/ZYtcongodUUBVBke1VdlRQqCvKZOF6qbukJHnSz00gNwy+V0QOpEpDMv36tgbScPbV/uKKPmCk3LOk2sM4OigKsqpVVd9T7Wi/WyMj2rFmtAtI/dWQC2LMwCpgrbUJ06+l72M+bRMl/tf1rn9n28riYH1WiWy6COFel0Oj2HdR0OGVhNpmhYjKrLOnAYh0a7ms6Q+p0OA+vpZEfkNa7i0A5q1e5yuRzqsBTLahR49LMCgg3JiMfjrkzclMAyBgV1LTsHAPNMRqnnK7N+OYCYbwbwaoyg1hUBkW1g9+hTIzrbqNlsolarIZVKYXR01Hl6rcNFQYjgrPGhbAetpyAI3IYZysIUmHSy0wlD36ttZ1mbqrQKTDYtBRAfoPK7snjNh03f9z7NhwV51oc6SNSzr6xfx4ROIlbdt2NR60v/+wD7eJEjUaaB1iZz/0JlHWwkywKVtcViMRdxTzBjh1MAJeO0e75phyWAAF2A05PFrBrNz7azUaxNidf4pzOrT21W9U87Lz/7VsEouCh40slA54o6j8jWmBa/6/I4HXwKgD7VjWnoQec8IlOZmh3YANyKElsPrDsKN9Pgey0L5KSodaL5BnpVPtsnl2JmVo3l79aeSWcg88g/LgXVbc54TKqybaZl65xpWFBUe6m2j27GoWVQcLZMWycPnZj7AedQ+stA3uRKpeLOnqjX66EYJw21IQjRXmVZCRkSg6qVMeouJ+wwTJMDVWP82DGq1arbfp7MlML0fIOnn/qqZeF3emb5XePauGmBMh/Np/UyKuAxDTIzbvdu4+Aike6WWrxOx5XaL1mPWj/KQlTlZp47nUWnWCwWc0vtrDeZn2mb1R267YBjH+BKFbUJMz9kuBqgzrIpw1Enid5j/2uojgUDLX8/Nmft4O324rZkvGbPU9b3KPvTwGfeHwRBKL5Q36uTg1WL+7FlzT/fxYmGE7Dv/qH0l6e0a43tQAoc7HQ6o/EkNmDxNDBuZlmpVBwY0PCv9hXaE/nd7jpjAY8AkEgkkEqlQmxSWQjF18n0GaaraotlYdFo2BveTzWxKqXurMPfaS7QnVN4Xdf08pqqmRok7YvvtN9VZdRyttttFItFZDIZB8rKQoAuKOoRruqBVxWfk5fPNKI2T7Yb60XrSttYAZwsiM/b40Y1P9rOqpkEQeDMBSyj1pf2A4JaPxXZPmvbz9Y9n2P+rHNN28j2J7VD+2zrljEyP/0mr6EMuJ/h9PQ0gK66SO8kd4VhA1j7GL2iNNjbwGegy560YbVTsSNaJqedRWdfdT602+2QWqdiZ3Y7c6vRWwexGt6tWkLpB8AEfN0TTz3HnBSoRhMkWBa1MSkokl3qQNB7tcx2UKZSKbdTEAdmp9MJnZ6njIltSNOG2kHtxKBrxJVVqS2QQKwTnPYLoHcTVLa5ahG2LdV8wXxrHpku8+Lrk9qvVVPRyUSf4X8LTlo+y34ty7T9k9etiqz1x3f5+p2mY73iQ1mUgRwoMzMzoTWoQDcExLIsDafhH5/V3WrYoOr1BBbPaFY1g79ph49EIm6XawChgarLvrQjW1F7jqq16jm2g8s+owPTginFsjCmzTLxbOh0Ou3UZ+ZPAY/BuToo+LuCBAC3B6Qtv6rwysI1j2R05XLZHQGgKijLShsy2b8drCyrHjjF6+12u+f0OpZP61DbwLIqW7cKJOPj45ieng7ZJTVNTYMTPPuh9kWf5sM6pfgmGE3fmjy0fux6eJtGPyaqk5P+rnGbllXafA+lKwOpyel02i1+p6Gf537YgQ8gBC78zplM1SMd8Jbaa4wcgBAYcKBrOAE7lq6wsKBowYSOHqqmqiZblVTVeB1gtOUpA+Bv6glV1c3HclutltsRWxmd5sOq4jqR0FnEyYiDnHm1pgYd2MqCdPBz70quSWf5NB+sA7vRKIUagoIq0D1P2K71tsv0FGh8wKDvZD3Ozs5icnIS09PTPeYAX3tof2OdMv5PgVKft31BRdvPqqcK3mRqVp3n75ZB6nM2L5qO1pWv3oYSloGYYb1eR7VaDS3x0sGjoSgcuPTusvEZbsH7lNLzO39Tqq+Gf8s0dQmSxi0C4a2d9A/oemTVEaEztwIC0J1VlQ1zm3kFYxuMrc9qh9R3sfwEG3qJdSbnem0gvJ7WquvKAGn3i0QWYx/tEQ3K0ph/Pk9Q0slF60zbRMN+1I6n71DPrbYP1VP+6e/aFlpPrAPeoyqjgtPs7CxyuRwqlUqPjY1pWPsb+5S1QZP5KhOzeeBnBSpry7WAqv1a+7cts06eatv2qdwKxDpZaN0OJSwDMUNGfEcii541eiA5iDiQlCWRMeiGDJa1aWewZxArM9J7FWQsaFEl0bAapgH0bvCgnckGWzM9Pq9pK9MiaCmrBMIxa1ou6/VkPnV9sw4ofUZVOH2P1qfPsxqPx0ObPhDo1P6pITW6QgSAOwgsk8mE7G/q2GH9atyk5rFWq4WOdmA5re1LB7e2vwKs2u7YDrR1qumCO3RzAmO6Cohad2q+YDA7PcnsSxpLqvmwE5j2K9vfdVK2zh+9V/su61fL4ANSxpraZy0zHkpXeo1oS0gulwsxjEajgVQq5c74oAeXnZG7oqhRn+o1O7BV+YBwuAS9pNop1G6nKpuq1gpSmq4+q0DK7z41gsDAe3R9Kn+35dAOqvlT+xzvI7AoQ9Z7WU7LxjSvep3ii2+MRBbNHaOjoy5MSlVSoAvgOvFo+RiCY1Vt+4xv4FNdtntiEoQVuLX9fCDJawTjSqXiwJDXWS9k/vqcpqPp+4CnWq2GbN7anmrm0YnWThD8b/s7DwjTfNm+4+uf9jf73/bBoSwtA6nJAJwxnSxRnSD6neKztfE7zxrWsyMsc6ITRkFLY6g0sJa/kQFFIuG945TZ8TvzqN+BXrVHO7E+Z/dnBOBWc+hg5J+yXd2lRhmBbuZqg4E18BvonQz62Ytoguh0FnetYd0yBlAjArTcbFML0pVKxTlErDddTR6q1jOPzWYzNMnp/ZwUFDyYFwtg2qdoZ9V+aG1w6onXdtY2sZOn9j0yY4Y/qcqukQsKPpxI+9lkY7GYW7Bg323F9lte8zlytL0tgB6PzFDtzk85jeXeGIksBh2nUinkcjm0Wq2Q19N64nSWU7WYqpl2bKrO6qggwGlsmm5oqkG86kBQpkB7pQ5EO1v2Y1nsxLYDKvDyXqp5tgw+0FVVWkNUGKaipwfqBMGBozZAsjTWsdY360G9mMoAOVnQ46wrhJhX5t0yZ+av0Wggm826ctkwGR76ZcGeTI2rOaxJQlmWBXUr7Xbb2UH1fgt4QNjLqm1uWaGPXbNe9F3WHm3VbU1D+4/9ozqu9WM1Fs2n9immbx0wbA/18Pu0h+MFGPvZQfv1G58MxAxrtRpqtZobmLqFlbrymQk6GzTMBeh2ymKx6DqCBSCrdjFNflZgARBaSsbKofda1QkdXGq3ssyDIMW09FneA4TP++DspDGDvId1oO9SZwuBjRtN2LypjZLPkqVrDCIZnhrtmQ89LwbontfBerexgNbuZSewaDQaYojW3krnEgerqsI6EWh+FVxUq1CWqvcoq+rX6bUc9j5tT59aq8+ybqh5EBR9E7zVjixY+gCJ5dUAbRXbDnzWd58yZ4oeo+HL57Emmv9+5eBqquXIQMvxstksgO6gIyCycW1YAg3myhhHRkZCDRIEQQgsFcC0oAQNNdar2szKsecSK0tTpmVtN6xYX4yWVdGV2SnAWceGArDeQ4cFPZY08ANhh4JdoULQI8ujcV+Bkd56BTrWgwIjy2hZCyewSCTiVE+rIrM9WH9ccsc8K1j5PKj83Gg03Npl1j8/a161HPpdt7zyAY22O7/zeWoOFvgto7SgFo1296zUw770fi2Lem9t3qwWZUHSxwI54ep9OkFYc46appQAHA+yHCDnJjLLkYF3rYlEFjdsYOyVNjzvabfbyOfzoZUUHNgcqHaW1iMTyRr4m6pgtjMRfK3XTWdR3dhBbT1WZbLgqJ1IVQyN3VO1xFdfTIt5bLfbKJVKjt0BCNnPlC3qRENhfbI86kXlGSZkeFwhRDavZfI5R7Q+YrFYaPWJHWBMi88SENUzrYNcj3FQ1gp0AdkyK4KiioZiqUah9a1tatkt6xkIx8H6WJempc9xMtLtuZg2f+9nY9by+4BP32MnauZZ+6oP/C1RIChrf7X96ngV4s1yZCCbIQeIqlo6iDj4WPHczIF2LbvVFAB3HwcRVSmqIso2lXXqGbXKdnSjWOZP86uqonYQ7aTaWZmWhrVw3zgyC9vROBjUcaLxeqpecRKg7cwyE6vuWnbG++y9pVLJrTEmMAJw+xmq2qksRU0DtFFSPbTgZVkNt+1SRqjrjG24D59h3Go/dmTVXK1HZX5sb/1vB7yqs1qnvv6u4KqTBweYahmqYmvf8rFevU83PfbZOfvlxwKgr8yapjW7UBR8jwfxlUfrdSkZCAzZcNyYgINHVz0Ui8XQxotqy2PjqMFXPcls8GazGdqNxdrVlPaqSq6qLmduZXC2Q+o7FRDtagHm295vB4AOTj1USp1AqkIS8NVmxnq0zMsGFNsyq2oNwAVtc4ch1qe+U51L6rTgdfXs0vbrE3W2cGNXa6ZQ1muZEj8ra9V+p8L+YcFB79X2tPZItkM/R5fvOX23tnuz2UQqlUIikQjtuqx9hBOppmHLpWx7KdVPJyDLCn11Zc02QNhBdTyBIMVXpuWWcyA1ORaLuc1T1THBNazKviz4UBXM5XJYWFhwg4wGcA3MpseUqhAHJkHRqkdUC/mM7qHHhldVjIDp855ae6SqLwqWBCK1nTEvBEFur29XhvB+ghftfmpfs9c0b8yP1jEBQgOl4/G42yaL7wHCB53bTmOdIPoOagUaQK/mBrUTU8W2dauDUM0iem60vtd2ZDJLBX69n58ty9Q20nv4m4plepq2pklnCvuXtWn68mB/903SPlWZYtmlLb8SBxsUb+tI1eehPIW1yVS7isWiW32iQKDqIoCQmhSJREIshR1ft4HS8BSCAQecsicCIGdqjV2zZ0lY9sd3Mg/WxqX51fyrN9QCiXZCoAss7HA6qwPhgUEWp+m0Wi0X7qIMjvcoKDN/6nzi79Vq1cXH8ZoGjXNHHGXbylTsKgZOJGR+PnWu1Wq5bbH0Xfyv7UwWzTog+FtQYLtqu6naaicvfX4pVdI+o/3GNynYPkVnCutP+4uKpu17j9phNX0dB8pqbf6A7mSpEQHWDGCBeLkq5IkgA23htW/fPrTbbRdiYw3BbDx+JoAQ7HivDniq0c1mE/l83gEsB7ZuDGr/ADhGoeyIoR46WHXg+GZuO1Pb2Zmgxg1YVdVvNBqoVCoOKFhf2um1XvjdxwwIcJoO88+y6sCwzFTVOx2oasNUpslQHC6nJEO3ziVlnVS3gbAdWEGHzJZrldUrzud9EwMHu/UAkukwT2xbbWMd2JZt9VMpFUwtaNj+ofcS0AnkzK86irTfWabnK2s/lVdXcGnb0aSi7wG6zF8D8n2az6HU8hNNlj0tcBZkkC7j3TTeUMXXsKpeU51UTyfPFcnn8yE7pDIuNrLuWKNnd6iXke9R0LEgZfPKvPBcEF7T/yqcHFg2der4AM+yOabJyYHAQeeRMjeClw0kt2poJLIYFkPnDle66ODg4Ga6zWYT5XLZqX4M9bHlZx1a84JOhMwXnUIKfLzXBwA+tse61KBkCuuF11VL0Xz7JlFtP5/KaZ9XseCmZgPdjYltou1k86J2Pa0/7SP6mwKj1cIU5HTC0onKskE7CZzIMhBHpnG8Wq0CCDeaeoiBsBdX1TACAhuBAKDLtprNZmgZHgOLFch0y3ztDFx1YgeGqq0EStsp9P/8/LxjVrorjToa1ObJ8jN/PtWHg1vtNArgWj5dLUKHErfZZx2xLpXJMU+qzgJwXmHLBhS4gS64046nG10wvwru0WjUeYN1qSDLzvoJgkWbI9uH6bPtLOvTyc+q5EzPbkThU/ksAHIyZnv4bKdMS3/zaQ8EHJ1orHPPgrNV6/tpD/Y9vvL4Jmdl8TphWRs77z0RZLmmgIEcKByUBC672L6f8VsPVtcOAaAHLHmOCdOwIKpbT/Fd/E9w1Tg9BWQ19mtemA8OPGVPNhaNg6nRaIS86v1mar6fn9kxafvSDXAJ+tzmiveps0qBgeXh80EQuA0FmC7LZsvAPJGR6m42ZHXJZNKZMewqC02PoKCb8KqdiyE3rC9tUwrbS9uSZdQwGmWz2p7WFuZTAbWvaP41L1aD0GsWoNj2nAQ4ebBebT1pOzHPVJOVEWsd2Hza8jAP1lxgJ1xrX7RpH8/q8mH3JkejUZx77rnYt28fgmAxvMbnoleqruDiCw1hI2hwtj0kiQfWqxqsDahBvlpwy+J8ncunRmkZAIQ6lQ4IZZ/WU62OHKsKkWWmUikHfGp3UuagTiNVUQlolnXqAfFkd3bJnQVJZao6yTEv3NjVt9qC7+XA59pqoHswOn+3sZ9qv1WgYNpaVm0jZc8MEbLpahrablaYnjVnaF/Q9uY4sIHgbGtOuPqMApTNj7Vda8ysbzxpnvWaz5GlbWTrg+3d77kTVQY+KrRUKiGbzYYOxwHCG7rqgLHMTjuaz6gbi8Xclv/KBtgJ1UNrwwKUPSl74Ps1T5pH7VQ+oCToKrDwd1WhOYAJggQ8rSMFKKruNixGwV93j1YQBLr2Lh3ICkwaCmMPF2cZdB217pajDhwyOuZFBznvYb50E1ROckD42FBl0bQ5a7uyvHxW1U3tT1YNtSzPgqPtm1Z8QMjy8XfLMjS43Mce1XRif9f6Y+RAv7yo2DxaMqK/ad/rV74hIC7KQN7k7du3hzqxBQ7LYpQRAd2wEcC/ooLPKqPTPQ8ZfwjAMSl2RrWPMG9WHbPsj2LBypaD6o+yQQV0Za0UBRyClIKoVZ0olpGowVxFB7aqvJom882T3/R4Uu6+wjpS9kh2qZMZGXo2m3XAreq2BTetC+aJa5F1aSbbiRODZYqqDut3rUP90zqgZ1cniqVA0NcWCtzab339SXc91/bXurETLn9Xcwj7tw3WtiBq+5zmybJG33VNeygDMsN8Pu88mmxcNphlg2RxQLjTamcnA+FgVUbCjkLVGQhvU6UhKOr1VQZhHSx8B//bAasM1QI8nUaavu7I41OdONCtN1x/Y1C0BpLrZKJB0BYUrarEcihY0DHDa9ls1r1bT6ojI9UjHZhfDqggWLRJMnSJNkLmT51pBCa1BZIF07vtAzArzLsGituBzecsMGid+BiTfd7Wrba/9hOf5mDtl9q/fAzMMmvNE8FR7/WBoUo/sLT36ES1VHonogzkQGEjaaNzna6ulLC2E+247NA6Q6oNg4Of77INxs+0f7Xbbbdzso9pMt+WTajaAIQj8i0LsIHjqsbp4ND36OauQRCE2BbT5sSiTho1BagZQctPG5X+piBIxqfszdalnlFcrVbRbrcd62N6Vj1k3dTrdfe8Zcr2XvYVMmuCIfPO+iNT1PbXOrb9kKzZx+a1jq1qqBONil5XUOo3+Whf5H81b+g7fe/VCUfbhZO/ptMP6Hwag5ZX01eNSft6P7Z8IspAYNhqtTA1NYUVK1Zg27ZtzialKi3QuzuxznRqiLczs3YQsj69T3ez5cAhAPtCG5SFUjRPKgRjzY+dya0aBXQ31qR6qPlnh7PqsXZiuyFsNNo9Q0YBTuuKdkde1zYgGNl3sj7UsUSmMDIy4lV3dcCRvZPFasC8nWyYRzprlAUGQXdzBgVotTfrNdYh07aD3DJvtSPa/7a9tU59fZH/tX7Z13z54WSuMbJaJ9ZhYvu62qQVrPV+C46aRj9g5H2sK52stC7HxsYQj8cxOzvbMz5OBBkIDNPpNE499VS3dCoa7W60oHYdyyrYuGQKOnhUVPVW5sJ0NMRCGZG1w/CaLufzOUyUQfiAk7/pvSoczDYO0YId86774DF/rFcFTXWkWCYLLA54VWdrtZqzjVF0kPB9iUTCsUCtR4Jvu912S/d0oPFeqtwEN65B9tUhQcRqDuwD/K7tZvuQTnjW6cW6tecsq1bgU2dZN/pO1Vp8E6cCpa480vtUm7GrZ3QisJMh24gsVycXm2fff32/vovlYX2w33GllI0JBoByuRyKNjjRZKDNXaenp/GjH/0IQRAgk8kglUqFZmbb4Dp4VWUCem2M8XjcBeiyAavVqmMv2uE1wFk3FdVZ3M6gVqXVDkOxzyhb0c+8V/OlrMqyDB9IKcAQ+Pg8tzRjkDWdRVqv7MyZTAbZbNaBEb38ymaYZw44tgV/VyBNpVKhtiJA6KYLbCOCJ0+7U1akExbtojq4m82my7c6oJS5q5nAN1GxHnxahE5OVmX19Q3VDLSfaP5YL1YNtX1F+5Uv33rd5sMX5G4ZqJZdHXMW/JUxWyJigZlt3G9nohNBBnKgnHHGGbj//vvdgFV1zIKL2gGXWrzOQUT7mjIGfle7hj0CkZ8JDvpevWbj5Ngh1CnDZ0ZGRjAzMxPKr3qodWDodmVMV8GT1/gcgbxer6NcLrvNWHkv2SLQPTVNQUEZb7u9eHRCsVh06i497nq2CPOi9cgdsdV5wfyxjjkwfOFM+ky9Xncgqmq2BW6rAlt7laqH9jonPAtCOsH62Je2rarhzKN6qe0kpuA2MjLiToTkPRbkIpHwvpf6vIKSBXcCkS4v9Nm5VUtg/VmWadmtlkNJwdCJ0isDn47HHTHYUHbXajaWMjzbCKqaKENRIOGg14ay9iC+i+kRdDQYV50gkUgkdHIdAUUHYDQaxezsbCg/OpNa9VdFB6CGBFl7HNWiIAjcMjnWhXWI6G4uvEeBi3knqwYWO78e20qnF+14utZZ1SXaIllPQRC4LdvI7pg3qxoCi55qBRVllWw7bX/mx5pM2GY+RmjZjwIo7XTajyyoWiav6Vq2piyzXC73AC8QdgIxTZYxmUyGzA76Hk1Dhe2q3mQFcb5Tn9c8W3Zs+20qlXJhVbYsRxIUOcEeTlukNec8XRl4p2vuVmM7jLI1zm4an6eAR3DSApF1qAoGdIOadRmeZZ3WgcD/XEmhR3IqKFuGYGdMHdQ+dYn38brmgU4GtdkReDTGjxsjKEulc4HXqP7y3RqkHYvFkM1mQ7YmbrLL0B3d1MI6Wizz1Hd0Oot7Q7IsllkpEAVBeGNX7aSsK7u1FO1rFqA0Hs/3ThXL7G0bqlhA0vTt7wogPtDV91hg1ThKTcunslPY52nbU8LhK4+Plfb7TfOh44NlOZyA0k9UZT9cQozRjXWfjgzEDBnEmk6ne2ZzNhjBR1Uq7m+n9ixr/wC6y8CAsIqojEJVI3ZKgok2Khvf7n/H3xQINS0FZN5rO7FPXeS9GhqjQMvfuZdjNpt1eziyDni/AoKCkIIE69wG8uoAikajTg2nfZcrHXS3INrbOBCB8L58mUwmxBpVbNmpMlvw5L0WJFqtllPtlcUQ1C3A6Tu1bi0DUi3EgpLmQ9meb7DqNVVPl2KoGhpl2dlSIKZgaN+rfdv331dOO4EoK9e6YV0eTtZm5UikHY1G3WR9ONIfiBmOjIyEBoaGPBAEVW1Te5oOWGUDasNjesp+VL2yZ6KwU3LDWWWMBEhlo/qnti0KB4e+k9cV/O3sSkCxu6gQIPi7BUWyPeaXM506V+yWUKpi8hn1JhNY1AZJJwnzojZa3yDTkCVV+5Sx6CDXCYUAp7t0KwulCqgslmVWsLVMx9fZLXOz3/V5fUYnSb3XgoiaUNTB4mNT9t36u05a/fKnfVd3F9K+ynvHx8dRqVRCq5O0DEt9Vm2A/4/VWMNms4lCoXDYgHYgbzIHNb2c7NAM8+COKTpYWMk8UU/ZkzIZbRwgzN5UnSG4AQipl0GwuA/ixMQEZmdnXZ4tI9TyqCpmGavPLqPBwtZR0m633WFLdrYl8OgMpsZydVBovegh7EyTLE4ZjY231BUf2naxWAzVatXliWunyUCpui7FpLn2WL2qHLCsQ9oCNQbT2tLUwaOgo/Vn+5BVmTVmzj6n7UZheqOjoygWi6Fr9jmdpOhI8rFiy8iAbuyo9gHL2mxZrSOL9awTOWVhYSE0OVrWbMthJ4ql/p/IMtDmrjQi201Hq9UqKpWK6+xA7x5q/J0AQABRFkOw6HQ6ztZlBwY/E2CYh2g0inq9jkKh0OPJVCAnA9X86WBj57KsknXA8lvjO1mePqMAx0mC+VVGqmoggYmbszJfvJ/2Oz08nr9ztxzm1TIY5o35qdVqTm0neClb1gGsA5rOGZ04LKDoZhuWRWl7aiCwZVXWFKNpLGV/03stEHQ6HZTL5dB7lDFZjUAZoW0vH+BoX9HJXetS2aZloxS7vtvmwzdpK5Gw/30OE+3bev1YksPqkBnkZs6mXLbFjqWUnYNSwYTC2RzoDmA7q3NgaLyVHUwMYNYgYN5XKpV6DPPKMLmZrOYvCLqxjMyTVZ/ZGZkX7bzKAtRmqgyRaagqqempusnv9XodtVrN3asMjnVF1Zggxw7OMjJN1lkQdGMz+a5ms+mOW9D7NXZT7bKRSCSUBsvI+uO91Wo1tEmvsmk1qajazPrUCUX7j/YV+9+KvZ/t5IsV9IECJz8foAG9K600DbvdmU54mpZOAKwbtqlOQpZZWiCzDid9D8vB71q/VmU+HkX76VKy7NJHIhFMTEw4T6jaDVXdA7oVrjY0VaE0/k8bJZlMhs5dVoO+ZR78zp1YaKeyhxWxI/QbOMyTbgSgHbCnwmRg2FALdijmXVmpApd9Rgcf09W0KNYuyLQVWMk+CXKWMWjb8BnWDzfvTSQSzkOt5VZmzGBwLQ/rk/kgALMNdeAraPM5+7ve53NW8V0WqClWq7BMTUUnJT7jE8soec2XN32PjzkqqFpWyg1M1I7uy4f2JauO2/rQ/mYndWW6x5sslz0O5E2uVCooFotuhUQ6nXaAx4plTJltdNsIQHfxvqoO7Ny1Wi2087PO5gBC7wC6O6IQhNRArgPKdlxVqRUE+U7Nm9r2eF2BlnnS2VfNBgQTHSga3BwEQeiAJptvoMuuLXNTpkV2zg5uy0G1mIAFwNV1s9lEqVRCOp1GLpdzy7cYvqD1xXKxbrSN2Tb1ej0U3mTrkmlY54T2I9/E5FNrKTYPKpqOBRP7m71m79N3+MBQnRWaT510tTwKslp/GrNq88336XVVx30Ttt6n9tzjFQyXW66BHCgHDhxwOx93Ot31lGrkV/an9iCg2+DakXXg6u4zZC+66N2njrBjKXBYO592PF5XW1Yk0g3P0dUd2oEtc2FavKaxfPbdLCfzp4yY9cN7uCGClg1YBA89nJ1nidDjrJ5bsjaaEjSEh+XirtQEQ+6qw/qoVquo1+uhbdvUxqqgqGczK8vh/Y1GAyMjIyFvOvOkfcICnLJ01qf+V/bMvqYA4LOxKdD0Az+r0uo9PvC1eVJ13LJW9i2ta9uX2Bd4VIIFMfYNrRuKTp4+FuwDVDXpHC+ipGi5ZRsotIbBw9ohdMcWVct0Eb0CjwUhPmtDdfifA0S9cgpgQPi8WD5D8NHB5bMd2A6pndU6WezgsozGBpZaQ7lVYZTJKQBTDdZBqyoe00un06Gty1hOu2xNN8Ll5MJJyw40thvfw8X7jDFlPVq2attCgarT6TjzippPOOh5Dxmx2mB1lZCClQKmvWb7rQ9MfYyObWZZqr5L09FJWX+3fcVO5rYfaL51krHRCz71W8tp09Qxpu1iTQ7Ho7C8+XzeRXkcSgbyJi8sLLjK1Y6tnlJ2EP5G4eDgH9U0hqRoCIYOEtqngHBH1U6laogFX+ZFK0gNxVa11TT1PrVtKjvV9ylD9RmoWXcc5GRxTEfrKgjCu9Zo2ZQtUlSF17hL2u2ofuvKGAVbH1NhffPwK524VBNg3dJm6zMVEAy1/nRyBMKB3lpmrRvLcliH2t5sV73ftpkCIz8r8Op1OzHZP5ufpb6zrjQ/+p2TFABXn/q8Tor98qV9UtvXPqNyPDpRgiBAsVgMjZOlZCBmePLJJ7uX8D8rj2f08ruqZXpdB7QCHUN1eA9ZC9kmO4Z6ZhXcNPBXZ1AFKooFAt7no9W2w+qszg6sW42pmkbw08PcNe9aL8wnQ17IxK1ab/Oqg5fMnfWfSCScOszYUNri1MvLd2igus+GB8ABogVnnYzYftajz80ILDPxlYV1pOnbfqfsVNvOTiyWLdq0OIloXu192lc07aWuaXvZe/lODSui6MTE5WZWe9H+oO9VdqjX7XdbR9aGeSLKwPsZMowjmUyiVqsBCM/QvjhCVVEtqwTCQcPxeNzZw9hg6qW2qp06B8h4OGD7eeL4fqu28F7rJOB1n5qhTggFXfubD6DVJspDl9TGxE7PPxv8q2xUr5PhaJ3T9qen5mkIkaqiTNsyZeZJvfbKOig2RIhp0iask4I1j6ipQdVDTcuCltaFj9FZQLBtyvJzUlOHlqavZbLXLAjafuJLy2oFWgaWXcHQ1ydtPSu4q7mkX19XFn+sguLhyvNADpT5+Xm3u7VVd3ydTUGHQcnKDKgqR6PR0FnJBCRr57CdjoBLwNFzazXfVvWzg4npKwhq2ZiOlov3WYDTsBgFFIoOYLXh+UBVHVUUq85qGBJZA+uXn7WjZzIZxONxF9StA5YOMbXF8Y/pM4+qDivrYH2ojVEBzW4Iy+eUHXJnIU1P86BtpI4WHRA60doJSttTGaYeHmX7sfYn5tl3r4q1Pau65gNvXleHhqrJlH6MVMePXX1kwVyZparHx6KafLjAeyBmSLuP2n9suIkOLs0kGSGN8bYDEkSsk0PBRgep7fzaqW0n0wFggUk7is+uokCpQMRyKxtTgFD1mGVTjy+fYZm59M6q5cqy1QarYKyf7QDUjg4g5ATJ5/OhMB0ClWWDzA/zSzuiL8ZQ1WR65rUNudEHPaUsi5o3LOOzDMbHAO1yNm1jqwXYvrUUa1SGz/daTUPTUOaqk4m97utn+gz7CgmDgj7Va82rzYstg80D20PH0FLAfiSF/fNwMlLV3Jab7sChNRMTE9i7d29Px9AGZsOxMXkPZ3xdUcH72Mg8ItECkw9cI5GIO2CILEp3gVGQVpWZ33VwkLXqbtDMnw2F0M7F3wmEOiiZto3lYggN1U12bIbI2Dwpe9Q61jRpD/QNKmXbCsSscw442hbtCXlA2PbK8tlAdb7DTkzKACORxY0ldIcibY9ms4lMJhNqexXLaFRsvfsYvm0/e13T5TXL6LQs+i7LINU2bNmfj5Wx72jYEduHqjL7ix13Wgf9mCPrWZ1e9h4eSHWsi7bLclegDORAsXvH6W8KgmoXYqNms1m3tEzVY9q3qB5ZLyuvB0Hgtp9SY77tsDr7WdXBMgYN0iZLVVuneimVYehMFolEQqtkCBZ6H9+r2/e3Wi3nRQfCB2VxELDjcnMFggydIzachexNVSRV361zif8JYvF4HJVKBbVarWf9cT92zXRVnaMzjWlnMhm33I/1zmBi5o2grzuxWEeXgqMCsAU3bWfrrFGxLJTvsf1Ef1PQs041HQva9ywQqkrKMWXHk5pXdM8+ve5jqJoHFcsmmRf7jM/Zc6TlSNsoDzszBBa3Pq9UKqGNVhUEdddmZoKVagc0n7cMjetZtVPxN9qUCDTqjQW6S/N0uRoHud1CywYp2xUt7OjMK9BdPK+gyLS00pXxWrWeXl0a6pUVM7+q3lrnUbPZdIHvBEwui4vH46EdmTUNDk6d0DghqSrFc0loCrGqMNNVptxsNl2dq42O+WYeaTdmW3EvOj5HUNb6tXWs6ep/K9p3WKdqk/WBoI8t6m+Wcfb7rte0njQ9nYh8jFT/q/nFpuPrs4eqI9UUdML31cuxLNrPlyMDqckHDx7EwYMHkclkQmqSLgXTDNjP/TqbgqMFJu2E7FgEOP5GVqKMiXmjqNdS1TgONLXH6LPsuJp3/qa7Nqs6qgBDAFdQA8LrsnkvN0W1DciOq2yT7261Wg64RkdHQ4Hxas9V8KY6ZNmCmi5yuRzq9bpLW2PeVK3XeNBcLucFiVqtFmKxanpQu6JVuXWi1XqyjIbp6nftO0EQOObNeywYKciwTzNagtfVZsvr+owCiuZf82Pz7QMq/te9O7WtNP9aX4dKz7JY7Yusw35q9rEoLMNhjzMEFk9iU7WUKyAINGqH0s4WjS56i6vVKpLJJIrFogMVsg8FT10loYOHaiTzwcEIIGS/IhPRfPAeDgy1u6j0o9QEXf3dxheq/U5teup8UZBV9qMAQHBWZmknEWULHLTlcjk0EbCOKBxYaqPidasCcpsw1rs97J5lVgZUrVaRyWRCbFQHn040QbC4TI8OG+aDdWajAqwaynz0Y2X63TJCHytj2/N3a7u1aVrWxbqzjg2fOcmXZ01XAd7H4jTtQw10BWwFTR/rZV7tuDmahOPFTkyHemY5MhAYsgEymQxGR0fdALCsiJlVNZmbwlKlImCxU1DF5Xu0sZRFcScPDhgyDl2Kp6osQUzj6bTDMc86K+r7VVX22Z/0Xn5Xlc83i/MzAc8CjVWnfIM/Fos5GydPpotEIk4NZ52xfjR2Tju9pqlAzd9o+iiXy27yYd7JoLjTTbu9eFogNQctC50COvh0KzWWU0HQp7b5GFA/9Y4e87m5uZBJgOn0Yz+dTsc5o/oBrbWt+VghxUYaaFr2v9Wg+C7dZJb5t/2uX3l8ZdaJT9NjGxytR4bqeFiuLPf+gcEwk8m4DQNoA1JvjapoOrM1Gg3kcjkAi0BQr9exsLDQo5Ky4ZW9ceAwDwRbXqPaTIapbIvp6pJB/a4DSe2YrEAfSNrZ2IKdMiod8MyLZQeWKVibG8uodcs60B2zVS0ge9QVPJZta9n4XgUmoHvWMpk9mW4kEgnVIctNtZ11wPqxZghOEnSoab508CpbZD1qHWh9KvOLxRY3OuAenOyrPgDSNtT3WuDyOVz0vn7qKidu7idpQdOq9pqutU+zbLzHTtCatjJgy/S0/bVu+2lMR5M862AYiUQwOTmJWCwWYgIKHpbFEHyoCmmgL9NUAFDAsc4HCkFWvY7chr7dbjvmqOlqaAvzawFEy6nv88UF+tQNpq1OFF7jdeaFvys71LrUAc+6pGOKZbUAqwNFGRjfpQHeTFsdVVq/+qyyRzI+rXsN57EmDQ2e5gSktko+b1mTnYy0b1j7qwUT2iBbrcUTAq2W4WP22l/1N5bF9mkFY6ah/cayd73fp1347J2+tKhd2fz7yqXpWPMO0+Xztpya7vEgywX3gZghz1zN5/Mhr6TGBeqgJ4MslUpuVuRvCgrs3HZVA9ViLiFjp+KJb+oYIJBwcClYs7MSnMlIFNAU1BW8gN6ZhSAGhM865m9qItCZV9+jAKIzv2+AK5NVUCAw2fwQbGjrY10zPEM3ybDvsXWirJeTih5lqvWvdWDDlpgeQVmXiqkjhe9W+5tlcT4wYT0TcHSlE+tFn9NJzbJj5smaOTQPrO+lHCe8zrawrHcph4VvstN7rVajE5h9ph971HyrRmBNAMe6HBFmmEwmkc/nXaAzK5MAwBmM7Ixno2jHsQCjs5LadeiUUTWTHYudjRsRsDHJRGjvULVP361ODe3MdvBZ7512IFVh7DPKcCw71HRYPr5DmYaG/dDGynrWsCBNW9+pQeAKFrRR8h7fgON33wTBtqBqa1fFsN4jkUX7ZS6Xc5ODLR/zTGeN1o+dzXWwWxZmf2cb076dSqUwNzfX059te+pvFuTsfZaNqfqqdcDfNG0tB+/VfqL3ap3YKAtfPnwgpiYFLfuh6uBYF9bZcp0tA23hFQRByGFhqXcqlXJHiS4sLLgD51UdZCZDmYhGQ4OMjU6DvNofFTCbzSYqlUpITY5EIiGgBrrM1aqu6lm1DNMOyH5shHVjRTeLZaNoZ2aZGCNIsGH8oc8myrLrDuBc3sh643v5HjqqOODs9lsKrra9fezDOjvYH3SwqabAFS18XkGZkx7ZovYPH/BYUwrv5YoV1qveMzY25vW4LmWI18lRwU63qFMA0fxYrUfTtCxQ06Zks9lQWfReZWz9wMoCnuaV+fKxUDvxHQ+i2say7l/ujUGwuDcYVTCNiKeBPRaLoVgsOq+j2n60M1h2qMyO6VPl5e/KxBRceQSpbgcWBN0NUtVpYBmeDhzbqX3ltx2Z6et3BREFYJaD6dMGqF51zYcCAAAHknrwFutMVUQyeAptd1RNrSrJfFo1Ug321sGldaCbSWg7sY0YKG47Jicttl2lUgmp39YJpu+3NudqtRoCaS3Hvn37Qs/rc5YVWbCyfUHNChZs+N2uWtK+4VO3dYKwqit/V2+0DbHR+vSxSn2ftU3aZ3RSVDPFsSYsVzqdRrlc7pno+8lAB0Jls1lUq1UAcIv8aYdrt9sol8shW5xlRppRMjllJtoZqMoR9JS5Ab27SCsDVdCko8DaeMhEeF3T0jLrnzIly77srKodT2d02lK5cwuZGoFOAdTa9JSJEYDUZqkDSfPKNkqlUs6soIOD7cC827awbcpyMZ8EZd8EEYlEnIZgwYftxbTYLj4w8k1Slj3qhGlBfal02D9VS1hKfKyL9WjBjGKBSO9hep1Ox8WM2jLZdrX1sFTd2LzYeuM92neXIgZHu7Ac+Xx+IFAfyGY4MjLiwEb3puOAtqqE2jqYhp0ldfCoPdCCim1c7RgKmOposV5lftb9EpVlWTCxYGRjFLVuFICBblQ/G0I92mrXVMAH0LNcjteoUqudTwGPkwvzqMxTVTe2H4FU94BkPdu9DpXtUbTdmK4GodtBX6/Xew4Q03pRJ4O1rWo61lZo2Y013fgAQe+3DNECp03PvseyrH79g7bffuWwedTvHA+xWHfTBgum/cS2g2Xb2s+17tVef6wKtYLllmEgm+G+ffucDYg2QwZSs4LVLgd0t4yy8XB2QNA5oINFO4PG7ZGR6oYPBEheIwjrJqQKcnyHgqiWFQiDLu1zBHv9s55tpqEgovcROLQ+1FmjYTbcMEHBVN+lwGiXIyqA0QuvXl61Qdp2YV3rRKOMWAczQZiDVQFMTR8ajqKTlDrHFIAt0Fk2o/XMtPS6ZTv2WW1j+w7bhrzXl4a+r19efQ4Sm39fvvld+6gFXmW0+pu+35IZFVtvQG+UxLEovvpeSgYKrQHgvH4EORtUPDExgUKh0GMrY8aUXXGw2gGmg0fBhANHtwWzHdQGgFOtVNWQ6Vk2qyxWbYwEGuYvHo+7rfktw1Q2SaAh07Ibluo7WT/aMQGE1Cbmy97DDk7QYnl49omWXwN/gyBwzg1VuVm/BC87YfBdCjD8ncyVtk0NwWk0GshmsyH2r7ZFn82P9eMzYfC/jQLQ+rHAQlkKgBTU7ORpNQXbX/Qered+QOj7rKIqtNaHje/UMvgIRxAEKBQKAHoPVdd397O9HqsySBkGAkM6KyKRiGMTOnt0Oh3Mz8/3eI0JmtaWpKxOgQ+AYxlAOCCborOhemktU+P71HbE/2QxTNvmUe+t1+s9Hb7fLK7PW7BlnWlHVdVet9S3szzT1YFpAUDtrwqKrFMF7kikG/SsoTi66oTt67NVqfrGQaRl4ISpExF3FdIdbDgZKPNVVVljLH3vt3Wu/5muBTvLcjlpsl76hWP46p3xt76BR3WTdWM9wpbN2fb0qePWds37VEvQ5XQ+p5PWmY9NHusgSOGO68uRgRwoNG6n02m3Px3QbTgOHja+ApU2pgICEPZ0pVIpjI6OIpfLOVBQW6LmhaqrNpwOEPubvl+ZqnqVfeXmfztr6jM6sBSQdeNZfc7HQnzXlWFrXS+lDgHhLeczmUzoUPl+jIT1ylAcZYL6TmXJmn8LLrpmmiCgO21r3jnAfcClA1YnEp1UdYBb5uxre817Op0OrVxhHpWt66oeTYeRFcp0bX+0nxUQeb3fpOebAHzrnO3Ebyd1bR/b57RumD/f5HMsijLoQ8lAoTU8LjIWi6FSqbhDhgC42dTOuPxN2YPdlzCRSIRWteimBarSqu1NG5EdmWoply0xDRtcrN4yO8szj9yNxw4cZTF8hvlUIPQxBN9szt8UOJguVVeCiA40vkdFAVTVTzL5VCrl7Ia6eatOTGrX1EB6BUZro6JYNsN2sg4abuZAwOb7ODFxsGs5OLGpg8bWo7aRDgI7YWmfBtCzVZdOetZuZvuMmicsONn8WSDX/PjS1WvaVmpnJYvWcvV7jx2TrAPt/0zfLvs7ViUIAkxMTCzr3oFOf2k0GiiVSpibm8Pll1+OlStXOuDQDsRGYydn3BvP32XsWzabRSaTQTabDQXqKlDpgFavL9/FtGioT6VSIbuYzsL8runwHUB4xYh1EPBdfIZpjI2NAeiGvagtzap7fBdZE7dAA7rhLe122+02nUgkMDIygmw26+I4ea8FIQvuZNR8Jx0i6XQao6OjLqCdYoOglQ0xFpB1qGo368jHzNg3tF45sbHddPJUm6sO1lgs5vpNqPN6WBjzy2d9bJhiWSufsSYVH5NSkPSplTrB8BnNv9UsFPT0GdVg7GTZT7XVMvjAX9O3ZbLgeTyI7sK+5H3LTVA71vr16/Ga17wG//7v/+6Ynqq0QK/h2s7yAHoYiHYMZVm0g1nPsVWRgiBApVLpiRnTmU+B1ObRqpz6jFaqlrFQKIRWmyiQsgy8rvZRAG7NteYnFos5IOx0uhtSUJXTU+dYfjvgdTszW/86eQHdrb18rJTeeLI1BXXLULS8KlTrFEx1gtGJinZGdUrZAe5T+3x9lXm0QGuf96VFkPYd0clyax+1fciq6bav2UlL82JBj+9QT7ztlxaILaAvh+Fp3nQCP9YlCBY3pV6ODGQzpCr35JNP4qMf/agDHp86rCoc1bRcLtdz1KgOCssGNR5PjfxWXWbjc2mbDgSmT6ajeVKA5TvUzqh2M1asDi5lmBq4zPeNjo46FZchSAQ3pq/hMMxrNpt19cdBXK1W3RJHBVvm23b4RCLhjl/VUBzWOScgvotpaayigitj5LTObUxhqGOJWSIIAqcdEFh9wfZMy06OWv/8b9VKBTOrHrJ99Def8LdqtRoy+RwqINv2X21fC3wKgEyv36TM3+01nXh9EwLrUNNfKu96n9XAjgdZbjkGUpO5w3Q8Hsfc3JzbkMGqIVr5ChpAF3A0nEQblLOSOk4ikUhI9WXaBEg7U+vBS9ZWSFVWO5O1UVnvL9NX4NS8Mj96byQSQalUcsxPQYnPapq8TrBT9sm1x7SF6sDWDsz7O52OW6/N6xoMz+9k5WwPjRdlWkGwqBZz5ZFlORos7VPJtG3shKQbqPIZtU8yD1blY3ra17QN+F6mqf1Lwcf2W4rVQHSC0GdteTVPljEvxWh9da5/fEbr2Jp/LBj7wFnbRceRfV+/xQXHqiwXDAdagVIqlUJeM4YNqPrJAaiDj6EUjLfzzc68165mUVuZBgezw5JxaGfUJXiqMnGQqZGeQMs0yNz4nNpmVHVQsAL88Vl04lhbpHW+8J5UKoVKpeLYqYYxUU1iZ6WjSNMjsKuHnODjYxFaPqZXq9XcfcqOeL+2s96jariqknYCSyQS7jxp7S/qTOG9Nq/Wqw3A2yYq2jcVdFW0zWj26aeGWlDVeletQPukpqPmBeZf82DT1fLaSUJjHNW2bvOsedJ32bKzfZSZL3cp29Es1mzRTwayGarXjR1XZxMgzPx8sz7QZVRKx3VHZr6D6qGyplwu1/NubUi+i+nR/qPGegUuywDYyRTYlMVp5eryMabBw9E5yNXbSNVFQVqZI8tPACQb5j3WRsVwGQUw1p2WS8uq9j+1Q/FzOp124SKsCx24zKOq1gBcvKJlzhpWZNX1SCS8uasCqfYrrXsFFguW2lf1mgKN/c2Xpn7vx5BsPjnRW7s036WAre2v71exfU/fyd/j8XhoIrf5tOX2TTD6mROBrZenI/3Y9zMpy33/QGryypUr3WBpt9s466yz3Muostp99AC4VQ7s/HaWpk0qm826malSqbjtuXxn6eoszvzQ48hG5+DngFJGZgcl86eDWVkfmbCvc6n6q7ZDPkPPrY8xqROCdaGsU80MtImqc0UN7swTmTkBKxqNYnx8HCtWrHDl4aSlqis7TTweD8V5KqDqQNcJww4mBVkFTYIx38dnNAyH/9Xc4RPLwvhfGZhlzqxXCw79QNKCqC2/73d+tm0L9HqZOUks5dyzbcAys27sIgdbFssKbfkoOq702tORZxsIgSPkTf7Zz36GcrmM0dFRRKNRPPDAAy5mDQhvEc+BRJbEWV4HGAdiMplEvV5HqVRy5wLzN2tzVKO7hnYAXTapoOZTPchuFFhVdVHGqQzIhv8oU1SnBH/TfRT5DKXfLM66U7sNB4ouiyMj5G/MP4FUy8NJiPWqjFjfoeoh2TTbzcY08h1WDWT+LLhpfeuzrNdEIuHOUdZQJrtahf3MeuFZTpZNtRVb1748WKZmgcjHvOwE62OS9prVZgg+to/0Y6UKkj67oe9e2/c1b/qfz2iax4Mc9hUoAEI2JDKTdDqNbDbbY5tSu9LmzZuxdu3aUCdMpVJu3W6hUHCn7AFhVVsHNBtT10eTfWnjaT59KpAyEgoHn25woOcAW9sJy0JbEdOiGqSDWsHKsimCOn9neZiuHfTMu3q+FSz77RqkZ6dwzTKv6c7gCgK0vaZSqdCKFh0kCsA6Qag303rx+ZxllMybpq+OG/63pgVtQ+2DWse2LzCPPkao/UUZsZ1grS3K2vT0PptXrT8LRr7vyhqZP59KruVX8NNJzseQ7fenywifaaGJ7OnIQGuTR0ZGQut+GTjNU9N08HHANptN3HPPPQAWGymdTiOdTjumQnVIDdC+lRXaqak2ExAJAgo2yhCV3ShjAbpxXQQxliOdTjtvsA5adkJ+1sHLsms5lG35Zm/Wi5YVCJ85QlMA7Y9qW1OmpyzMTg66xEzttaqCaX3Z72wze0yBggbribasSCTi9m20DIVl5HPNZtOduWyFbWPts8thL/YeC2y2DfS/rmRimVmHuqmumkk0Pf7e6XRCJwBa0Na60XFkNQkLetZJaOuiHxHQa5Y12uu+dI9GIbF6OnkdCAzJFhgATEBjZ7VLg1Q9Zpxhu91GqVRyncwG5LJzAQj9V7VPB4cyFgVVDbBVGws7mzphKAoqdNwoEOogVOBUYFBgVPXHrhqxHVR/00lFBy7tiVY9UjbHOtEJwLJaa2PSsmmd2AFIe64GRfM+rUdOTpyg+pVRAbndbju13zJqqw5bNuf7banP9l6bNx+o6ATBa9Z7a1kWyxkE3QO9+t3rY3EWyNnP1LxA84ZvkrWThn2PtUH2Y5nHgpTL5aedxsBqMsNb9LPGDXJAcsDxM20/CwsL7kApDmreZ2cmDk4bwM3/VlWo1+s9AbPKCHWQxWIx5HK5UPqqzpAt2oHXr9P57Dd8NpfL4XnPe15IJdL36J/+pt5Zls86I4D+A53shnmxnmGyPE3Tllc/k+GQ+elelUy30WigXC67uqe30w5wZbR8h41XtANV253XNCDcDmK9V51fqhno/frHstLkofnQydHWmxWWQe/VyUkZKvNkGZ/mT9fts037gZdO/PourUsbvK39+ESTgVag0HHCGZGbK+isSdWQnUBtepz5Va2wA4SNoXYwvQ70xufR0WLtVQQ6tUkpMJZKJdcJUqmUYyh8RzweD9lDddArK1D11IJmNLq46P2RRx5xE4WvbnWAWiaq97DOWF7WlTJG7cx6TrUODj7HcipL0/zrRMHfo9GoOwvbqnVMj+8Lgm6YE/NuJz3bbj42qWBnHUPWVOETu6rG1r1vQmHYj651tmBsGboPVPX9tiw+EFfvus0X+6fVnjQt7SsqOu60jGTpesibBfATQQY+HY+GykQigYWFhZ4ZTIFK1SQG2mo4gF3Lqx3DdgY70EdGRtzOJxZQFUysA4aODc0L388Ooe+yu3hrZ9PZlGIHvd6ns7jtbFoHWo/K7FSN0fvUTko2Zm2dunO1thkdRjqhLaUmKdPgiiS7ZZtVBwG4Ac5wGYp64DkZ2VAfX1+0ovXOtrRAYrUP/c0OfPYVBsMD4UgGnwfY5k3Zp+ZP+6wPcDRt7dtAuN/YPqa/628+jUX7Lp+xzqsTTQZSkxnHR5DToF01DhNUfOEPGg6jbBBAaDDZcBrb8SqVCsbHx50hWzuVTx3QtBUI1cHCcviAxzdT8rqWR9mOnfV9YNEvzzqoFTxYF7phqP5ZRqkmC8sImb5lO8yrsj6WVcEgCAK3FRgnO213HYhkVwxBsvXH9uB96ojSycZnT/MBuJ1ctI20rRR49B4VO2HYiZlpaB9SUmCZsy9Myda/TgrantYBo/nQvuzrl3q/2gutY8reS4nFYs4hdjzKQGCojE87mx0stLMore9nJ9EOzf/2s6qDyvz27dvX45igWHXAB1S8boFPn7WDScuqaqW+l+XTWVrBlKyYAdSsN/WK815lsQoyVPuts8HWmTI2a8dV0clMB72tI1unZJsaIG3rmO/kCia187JfTE5Ohpi6dfowD/oO24fsvYA/4NaCGJ+xrFtVRtanb2LmZwURn+eY1+yhWHbStmWyda/p+iZeBTffO/Rdmh9bR5ZF08l1pOXZUtEHshlynXCn03E7ojDTutaXjaOGZ9sB7aYAyjqsvZDPW4ag4KLqn4KDdmh9vza+tb/Zd7DT8JqChG/3EO1EtKFZNkGGpDF6OuCTyaTb7zCVSoV2emE+APQsx7IDSweIvkPVaAVHn5qkbaJ1ru/gQe7K+nQrMaarnk8F7NnZ2R57rIIA82HbTsW2g9aT7x7LfPQ9ei8nomg07LlV8LSgw98tYGqZgTBYW+Zqd9zW+1T6mRV89cQ+q3Wp/Z51vtRkc6RlqTY+kjJQaA03JI1EIu5AeUu3gd5VH7ymhVR1Q5/Tw57sfRb89B6g96wHXWtM4bO6RtTOfkAvuDB9XucA0fAV5pHMmXYyVTdV/eM1sg12Ul1+SGBk3VWr1dDgUGDRurAqlNaDTggEaguWWtc6OCxjVNUYgPM007ZsnUbUGHyxefoObTMtm2X6vrZi+5K5ahvadx5q0LFdSqUSMpmMY622f1t2rnZUy/b0v90MQxm1nYBUCMwskwVLHzvsV25bP3ZiBsIb0x6vMpCaPDEx4UBNF+BrZbZaLWdb1M5gVQteV1amg4R/yWQS69ev72ETmpZVVXnNDjrtqBawbeem+qpqrK8Tq0rMzqNl4vvZqazDRzu7tTMRMOkQ0T0hlelatcvWiwKiZa+q9tn9IllWWzca26kTEK8lEgm3d6OmxfvUO6t5AcKODx/TteWzjgY+0289qr2v3z16ry4lJEOn+MCVgGnfY80QWr9aPzYfNu+alv5mGa0FYZuG1qlOWjoxqo33eJeBtvDi/0ik63kiULBjstPoQCRbUMOudiJVsVU4w8/MzPQs6dKOquEyuq6XoQIaiG3tEQrsnU7HsRxlbr7OZDu1qum6dZc+Z22l+g5lW6wLvV8Bk+VkzB/T4Z8vLIOiAev83arM1o7G9tT6sMufyOo5qHj8gn0Xy8FYUx20ykrUCcHPOmB9A92CpK1z33Pal/S7thPTbDQaPZqFTjS+yUffqRMwlzpa9q7v9IGh7QtaRgtyPrZpJ0Nbbz6W6MvLMynWJnrE3jPIzaeccgpGR0ddpwDCnUk7DtBlUNb4S8ZH26GqcTYAOhqNuu2kgO4OzplMxu2sAnTZCgGSDJDvteyTAM5Bxk5l1WRtCAUupqGhOCwP36PeQOsY8TEuO3j7sVa1GzJt/vliOK2ZAUAoP5wo7MaxvM688jeGmmidsL51gHFndKZjJyHbf5gm10LroF7uYLBszFfn/KzltHVmr7Hf+ICH5dG2U9upL137Dh0zer+WS3+zLNISCb3u+63fe2mfXurZZ1qeCSAEnoI3uV6vh7bG94XPsAOm02kXzGw7ggJUECyqlyMjI45J6I7UAByAWsO9dkr+1m63XaNSjeTvastimWj7s+oz1R0FfrWvKVvqt1pFO5OWlWXj6h3mhaL1quqLXW6oQEd2lkqlXDk1dsyCCv+rWk8njR2IZNBqd1Rh/Wqd8lq/gasrTvhnV5Nov9G+YweIBTOKbzLWctlnbVvp+1UtV/ZtTUBcl23bXsUSB5v3pZ73AYNlphbk9LO+wzJnmrieTSZo5ZnKy0BgCHS9UaxEOwA4QKPRqFsdYBsym806tqeerVKp1AM2BFU6bthhuPkAB696Wnn6m4IHZ24NVeFaZt2HUQeez5utoG/fy525FeitTZTv0Pzr1vtA2GHAeuV3vofvZ53yuwK2PZmO/3VLI+1oBGkd4Pysv1HFU6eZhv2wD3D1C5dtsh4pdpLk2mfLqihWhbeTYr/Jx8cGl2JLyiyVYdr86iSlZdKDpPoxK1vHPrCzTNACvubXOiP7lU3r1Oe1ZR32Y8DHswwEhhxgZBu0eajKqY4EMg2gaxiPRqMuWFqX0uk7lCWQKfj2EiSo6XNBEKBUKgHoqiq6zZYNZbEdimnZDkmxDhDNt4KuDhzbQS2Tpj2Kk0MqlXLBzGqMt6zBpx6T5TJ9sk++h2kybMOqxaxTtUfRFqjBx+oEUaDVelSgSqVSPYwkCIKQXZF50nwq+7LlV9BiXlQ7USCjaJ37AMi2lU4G9hltT17nklOd0Ow7+n3Xaz5mbMuov2tefXbxfmLHlKa7FLgejzJQaA0j0Lmjiz2oWweQnVE583c6i8HSyjBVxeV7FCR11rPqNQGAjUoVXhuT60sJ0uoU8KlsPm+hOmg4EbCsyuDsILWDjAAUjYY3fyXAEHy0rD6HhuZZWTrzode1rvleZZtWzWOefWyZdcv6sLZXvlsdV/ydXmY1OxC8ffvR+ViL/az/NVBb29gChk3f/rfrkVm3CsjWfsyycwMTHyPlZwVkXuN1X/60TKqd+P7bdIHwGFQbr3VMWNOLrXsfeB9PMhAYVioVZ0+g17XfLKXshPF2ulaZAKgqN4FLhY2mKqCl7nxGw1c0EJuqGt+hMYbKJFgGpm1Zkw4IGwTNThaLhfcwVDDTZXN8B9kbO3qr1UKtVgupm8qEdPCpqsU8qGffAqruKK3v0XL7mAWFAEiWzfsJZKxby+yA7qYHVIU1IJthQyyv7yAq7Q+23MwHIwmYl35MzoJfv4lV02aaFuT0Xp5Foxt2+MROojYPCliaT372TbY2bV/daRn0eebF2q7thHO8y0AcmJuxxmIxrF69GkCX6QDhhslmsyEmQaETg+qddiheJ4gpEKjqpV5nZZB20PBPzwrhM+xw7LjqHbaxjpZlRSIRx44VKFkfHBDMB0FC9wJUhqv51bIDiwDKmD27w4jex7xxsmLZbMwjf6MJQtVf5k1XNCizYjmtXVfZno9ZqApPMFfQJJvV+rXAYNVF2ydYV1a91O++/qH91gdMvvZR0Xto6ujHCvvly/fZ990HcvqefmXVNOwk088kZOv7aJFcLodcLresewfN+0BgGIlEUC6X0Wq1sGfPnpCKZzsS0B0EOmNbNmfVSA4426F0xtOdT+zg538drMpArDpl1UVV69hZCNp6nx1M/TpcECx64LVzad4sE1Cmqn8EDK4PpXOC5dV4S+ZBvYK2faia5nK5EDMnSHFiYpkVHCKRCCYnJ3smHHsOstaNMjqN6yS4KhvsV68+NU0nUzt41fap5bBp2brplwf9XeskCILQgV/ap2192H5j+1K/AewDeu2nts6XAgIth4916l8/BvpsSbVaRaVSWda96rhbjgzsQKFNRAe0ndU7nQ7K5XLffeC04TmQAbjNC6wHVkFE4xP5XnogVcWxnVg7DEHaOjv4Ll7jH0FUbWvaGfk+q0Yxb9r5bOeNxWJuz8RUKuU2nNU60hAjtgOBi4OQkwi95OqU0pAhO5i5ZyPXnas9luYG3zK+2dlZAF0bLddIWw+lZXIc1DRbaL6UNfvYmY/xWHaog9eqvP0Ym6bZb2WV7cOaXwChQHvWr9ZXvzyyDD4njx0ztj/3Y5Vad/3aQtPx1Ynmy9bxsylLOYOskIQsVwZmholEwq2PVVuRriJhxyc4WBBhRXPQKji5jAlLIehwQ1Hd8dmyU7In2gmpllmWqLO4xowxDXud+beDm+WwS9l8aoZ1/PA3htjU63U361nGrGozARLoLn9kILxlQWojUjsm36GgzkO6CLLMp4bV6J9tT4pv+ZYOLk5inNi0vbW+fEDiY1r6md/VIWPbQdm3nSDoaNNntV/qs6w3mjJ4Pz3nPueN2jH5u4/BWxar6dgJx1fX9ndfP2ZedBKyiym0/o4GMASOHEsdCAy5LpazOlUt7Xi6bhboeoanpqZC7I4AAiweNAV0VWQKOyHfWavVUKlUeoCQn7Wxo9EoqtVqKIhUmRu9m0DYk2wdFP06oLJDC1x6X78T2LQj8ll2Rgu4FlCVCZKtk8EGQXcDW+ZfzQ5aRr5fdwFnuJRORPpuWwbWnV35YgFRgZgD0afGqo3QDj4Lxr5Bz/us+mnrH+iaSHwTlrZjPxCgZqO2YAIqd/Gx+dZr/f77yuTLgzK2QYCq38QChHcb13uOFAAdLjkcQD0QGI6NjbmgYmWCaq+y2w6xY87MzABASC3mgC2VSk5NrNVq7nkuvWs2m+5MZR08tA3S6ZFOp90AVVYDdFciWBWC4EBRoLH32A7B+3xqlY1Ls78xfcsWXcNEw7FqynSth5nApRs2MD2Cjg2B0byx/dhW9PpGIl1boLI6VWu1TFbFVY+z7Q8K1FpuXUKpz2r72PayamUkEgnVhW0z+70f+7ROMe03tKsqs9IT8Or1euhkQE3fB3x2otXfD8UCtey8ZttC79c6W6oO7G/PtiwFeP3yqO14KBl4p+t6ve7CR+xgVE+jMgBgUSXKZDI9sWtkNrFY92xfXRFSrVbRaDTcNbIQVwBhHDyICAiHuvDQIB5FqYPfgqG1AdrfLDtihes7Fax8qraPYdr3KGj4zA0+r66aATSPGuakaXEAWzVOBz5BlGogB7yGJ1kmZj3N/Kz5VZVeVTOfamzryjcotH5su/gYmW3H/7+9d4mxLMvO+te98X7nq6qLLnd1q91tsEXT3aYwpiUekiVk2WIMA0aWGSIxYgoDDxjgGYIpQ4YeICHBhAEIYZBoGyHcbozpemVVZuUj3hEZEff+B8Fv39/5Yt/MuNVVptv/3FIoIu49Z5+9117rW99a+3F6xj9vzHydx8c7ewBE9nFnqqjHyvxZr68JeI50sq89B92TTf6d8uo9+6epTKfzTzDKshAYbm9vV9XsxJGq4bIGh1WAFiDmI7HMTLjObA8DZd0Wy3TSyK0E1Gsm6Bm4i4uLxjp7YRCglZMImTyeB3LJxEgH0Md0Dq7T/xuQetf0QMfbBrnG9Xj3Du2D1WAwlhdMkjq9H9oTSnYCrt8yZSzQEYORr7cMcvF6j+GY+cx7gfi88UuQSCdgGfrZyajJFeb6Vtd5dnZ2I4Xg770H3e1xH/nM/Umg7jHLBFzrSMqx9/9P0sRJ1WfLFS7S9oXA8OLiolF/swKDmnc/wB59wAICNgCanRDGEZp5PeHy8nJtb2/fAEXqczu8li2ZGffm6dypsD1gTOVzu/29w7lUsGTGvsftyRCSugAmfvM5wGLm6wkBh+dmif7b7TUz8ZZGt6sH8naIngjju55RWn7e2cQ9PdbET76O1PX3WF7PGbodfobTJtTtzQNELZ5gcv0pm9SvN954o6qGrz3I4vZRZzJhj1f2OX/37nE7e/nx3t8/LcWM/WVlITB89uxZG3CmrT3xkLOwhKc2rvPz8wFgAX5W0tPT03boQX7POxigv2YgfJ7et2oGfFVD5gSYAOJVdcOTJ2BRh9s1LyyhbS8DW//tZySQ4FToS26fyrwm1xoAnZMzi0u26x/kw5j59bDcT5nHurneM/vZZv/t8egxObebvxN8fJ91MEEwWa4LbbKuosN2JkwqWr/ymQkwk8mkPvnkk6qqevr06WA2PcHOpRdd5AQVsst29sbKvwnrkyD8NJfbMsqFd2Hzdryk6RkeMcucoNK7B6MFLLmfZTKbm5u1s7MzmAQwKzKoJfNz3ipfzp3syEpMvf4fVsB9yXDMZFH+TN46p+rQ021CPj7GzCGxmarzq4TmZhjuCzO/lpPBifWaXtDNvX4mx7LljLFByPcZ1BKAUzbc65PE02jNgBIQ5xWPI9cmi7MTtU5YdvQlwZqzGzP0zTrdnmSS/s5ttDxdR4IiDtLf+ZqeQ2FM+d9bNHNy6vMsPfv5IsptgbBqQTD0Ug4mQ3igvT2D6G13uavEISghLZMnnhnllJyjo6OBQXoxMNdlCGhwyTCQMhqN2mLvvMeKQLt6C4O5xspqRmsG67WZ/O7Ndpkd0g6DGf3ONs8LzfydFcTb4vjxwm07NQMojL/Hsv08GxTOzPJJnUnASxn3ACSZbDoWrumVHkjmPZmny2dOJpPmaJ2f7UUK+T9yMyjZ+bpkntrXJCOe97wEtdSVlGvK5PMCMOeZf1LKQgc1HB8f19raWpv5RZAMomdtyO15kTDXYMA+vYXcy2QyafUQJlYNDclK6NnOzCU6j5ghikHDS0B4phP8ZjiemSU8Mgv0QQw+hMKTDxTatLq62mbSXTd1GRRoj9kkMuCzDG8yF+mVAHYoDgXTUXmSwqCZ6ygd5rqfPreyZ/CpS4wpWzkz1zgP5HpMz07UjCiZVwJBry7LNJ0LS8tYTO7tjNnXeW2zviS495wAdWRuu6fv2W/usx2lDO203JefpnJbdnhrmB+Nrt+Ihxe0ImFInMFHiGvFNvU2wEwmkwaEBiDYBx63NXjOpAThJIzFyzV6YQhGzv3U0VNSFC3fcVw12z0yGs1eNOV1l8zSevLCTBDQQzGzf8jPB1hYaZNN0D/65e+43kyV/pj9GCiTbdupra2tta18uQOJ0mNJOCC3K6MG5Jny8DW9EDOBJuXi8HVeKPsq48n+0UYchuux/vi+zCHbkfWezzV+fg9U854E015JRpkyzRz/n9ayUM/u3LlTOzs7DbyqZl4f1gTAGfCm01luDCMDUAE+MxFKT5kNGnzHLoB5A2+jyX3UfjalBxI52VI1yyGiwPzg1WF7loP7lc8FSHts1hMpOcvt+s2+HH45sd57bi8EMyNO5mHAXF5ero2NjUGqIhP6yY7ytO0egOVxXC7JdPxZgozLPLDMv3u5yl7qwuOSobLzh26TIx/Gyd8nI7Xsc3x6OcFeGJ0yMJFI+WUbzAp7DuhPS1kIDO/fv1/37t1rnh0h3blz57oygQP7MxmM7e3txtrygIGqPvD5c+rL0zsIrbnW+a95Ck6baGuGnt7exqRC5rv8XhUXh3hNyHOSxWY7KJkBwICdBuln+fMMJ8nb+hSabLeB32CWkzMpUxsqTH51dbUd+uAJK8aO51XdXPKQzKnXn9SNnkzzmnlgabn32GEyxR7zsmPb2NhoOuhxpR9cz3ZJiICfO08edjDZJo9PzyFkm30vbd3c3Lxxby+yuA1z/mktC0+g/M//+T+boZBcZ60UwgUsptPZqwGOj4/bNRawmYOFDxhlftHn+I3Hs6Pi05P3wgIMFMqPguYhCw5zDfAOc1nkzRq3JlCBSIbqbmMytATFHkPK8x97OULLiTSDmSTt9xId2uPPGQu/3CgZpsfNdeAMeSmXDdVbJWlvr8++PsH3VaEhpcf8/NPLQfYA1jproON6f4Ze9Rihw3M/PxlX9smkIfvltjlMN3C+qn+j0WgwQek2/P8FCKsWmECZTqf17NmzZmgYztnZWf3gBz+YVfh/mRqnqJD38QxxsiwG16A2Hs9OqamaMZ/M9TFATrRbOa04L168GCzwzhxhgo8ZHZMZNl4rjoECJpme3GBnAEv2g8xYEsT9zt+lE6AtvTyr5eb+JOtMoPGyG7eTerzQ2H012/HY8lkvDPP489tjmizOded40P7UHerpLT8xSLhu/s/veiErcmbDALneBCDL3DLwRFivLclW7YSow9FSjwH3SoJyypDfXtr20ziR8qqy0EvkOVT0xYsXtbm5OQALe0jCH/5nds2GYkVzCGCDzG1fVdWAJkHMyuH6AWSMIJmZFWBeaGRDtpGx7Me5n+yPwWCeYifLgk05BZD9snJPJrPXGnjSg5Ks23+/jJEYtC2TBEkbnR0dnwEIpBxYq2qdsaEZcDNviP7kmPgay5s2pJH772RBFD63nlxcXAzeG+0ZYxixT4SnLXZc6cQSYDM0TWflvnh9rSMy21H202Bmh5b9puRzf5rY4TwH0CsLLa05Pz+vw8PDWl5eboszLVgUOQ8T9esw83o3GCXIjdW+vpePyb8zfEwFNCh63y2Fdvhg1h4bNSPk2dSfSu4lQu6vmQ/1ZN2wOrbWGYRIVaRc3RY7GurPzyxT158GZcZhkLBxWwE9Zg6/6QsO0mtM3SYAMUHH/aNP/p06xbUYvyOM7GfP2PnsxYsXbdWEC7JxyoX2ZqRiYKGdXlFAm5O5Wi5m35Yv0cTa2lqdnJwMADjrzc9T5uirc+q2+Z8WULxtOxcKk5eWltrLhNbW1tpEg0MmD6hnlz3oVbMBtWL6kFSvLTNjSADMUA2FzF0ANkrnDvk+gcSHC8DS0rCqhicbY7T02cZH6sD1WLbU61DYYEafq653CbC+EZkZMBgP12/ZOazy/wZHXtzk73s64bbSxmSafp4dklkc4+Z7bJi58Nss0aXHBOyYEiR7bN3y8DX5DhtfQzvJuaIfuU3PcnLbcoycL58ne2Rgx4nOczhv1uVxoR3pKA2aOYbzdOEntSzS1oWYYe4o4fQaL8B2GY2uZ6m8XswDaDZG4bUCOVng3RtW+Mz32LNiLIAtg7u9vT3weORBbaBeN2cj4JlVQ4U0C07w4u882Znw1sdcYVj85job/+7ubo1Go8H+cNrj+3ogOM+QzRboc89o7EB67DxBnteDplHzOZ85V+Z2W6/cjx7bssx77Nb9NqNKVpnMjbryxVkJrG47AJ75P4NSgvt0OnvrotuTwJmkgIKNZPqmNzZuk4vlmRsd+O62Bx/8tJWFZpMnk8ngVY8oMBMdGJEB6vT09Ma+YRtqLhuxVwYADAhOGhvsvAzEwFRVg2eZSdEmG8g8xkYbcjuewyIvxnbxThIKBuA22sNb2WGmKDoycYhsELSh5v9WdIf4NlI+924h2uhx9LUe16ZckTLxmFG/xz8XYnsMesCVINZjPqm/brsnN5LlJogy1gaIbA//52SeZWi2xfcwuclktiSr1z8/w6yPZ+7s7NTdu3cHKxl8bZbUi7Q9Pzfz0D8NZdH2LsQMeSPayspK7e7u1sHBQZugQGDMcvol60nFq2beHQbId14a4he/j0azkDHDLoeePLuqBnkpGyWglqCTgDEajQbhRs9T0hcchJeOwE59/mM+1x7YoVbmznJiwgwbQzWguvQAnufyGf3osR0MJeu0nHqAyf/IxUylapavZZw8w+22ILd0NOheljRsg5ZB1WtW5xX3Y3l5ub1GApnntbB93mXTk1HK18+h7ek8XZfHjnpfvHhRl5eX9ezZs8HLtnphbdZr/bYu+Lv87ZzrT2qxbdymLASGo9HsiJ/Dw8NBfsuKZwVIoVK8uNdAxXMMAD2QQukAQ+cvyZ+trq42Y4G1ra2tNcOywVQN90ID5i9TTr63N/cMovvRY51W6qw3++tEONfkQblmJr3QMO+3czBgeLwzn5jgwv3j8bg2Nzfr6OhocC3j26vT7aVNGf467+z8WDKVeQaefbV+5TUel+w7xQvX0/mk0/cSq4x6ek4n86Apd/fTbbIuwwo9+ZdysV71ZFc1BMMkAz9NDBGnd5uyMBiinF4+A6V2PiwNNRf+AmbO2dkQUhFRCp7hxde5xW40GrVJHtptoISRwtqSrfDeD+714DtfZXZq5uBZ0V54zA+zkhiND3nI56Yx0xb+T6djZulikOoxlZcZSs8QfB2zlx73rIM2WEHNUDHiTJGkQ+R656Kz3b5vnh5lmWfo6eB6DMq6a7CcJ2Pf7zxj9rOqBrl5yxEdnE6nTd9T53qA6rZZJ7k+IxO3O6OUn4TSA37Kbdu6UM7w6uqqLakxIOSLf6qGb4VLw+MzOrG2tla7u7sDOusBASyrqh0Q4EMT0ugYfAOfZ48N3hic7888Wi8P53tsiMjDs59eG0dfeCXn6elp257Ftc4D5lIcnk2d5Er5nPv89kHnVns5QN9Ln3oMBFnktZ5UcRtTdr2/XYeXCJklzisGQvcjx8M6lYbtz5Ox+TlZjx1NAgvjY8dk1tjTnarqhu09UHVhm6qv90nhyUCTmaZskF+OKf3oOdifhDJPPouUhZgh70BBqJ4JTWUwA0kGk4p1cXFRz58/H+ycyFDLOUa+9+DZgGEm/ACGsMBc8Fs1MyTeWeF2O6zGiD1RRF3OgVZdL6s4Pz+/AWaTyaRNLCG7PKbKKQja12sv/aIsLc2OV8OLG1RtFA47ARSe4Zltnpcz6tSTOdsegHnMPQGRAMx+cPKsjFWGr1mn/7e80/DTwLkeUOqxzgR6yyrBwfrKPWa4uX7PzsFLmTIyypwrOsOp23t7e4P7enLnXq+kyAmhjEAM4rkW909bWegIr5WVlbp//35THsLJHv33IlKKjdNKgCcFTPwdxraxsVGbm5s3ZiCTfY5GowFTot7pdNrygDZqt5Hn94zAExQoUhOigLNqlldip4VPNnnx4kWdn5+3+7K/9BmWYPaabMp9MQN1H7jXM+mp8IyfZ49zJ0s6NvfbY5rbz3pG0wvLKLBmPs8cInJNdtary0CY0UkC9srKSr311luDU74zjER/vVd8e3t7wJwsK/Ql9ykbYLAlSqYYXLI97svFxUWdn5/X1tbWjf67LwbzfLaB1s6h54CyDX8ayq3BcDqd1v7+fj179uwGG8JwPPAMNuDlgXeoZTZoplI1A+DpdHYclo2ba0gYO1dDGwBD2BN5Oo5p39jYuBFSZdKeZ9DmXGfGd7ndz8tICJ8NGMnAkCNLhbLwXS9UtdLy0ztjkOfYYaRzSkNz25KZ5mfJKhwu98LnnAghXGYMqRcdNOgRlbj/2dceAJrtUtfZ2Vnt7e3VX/yLf7G7G4m/HcJPJpN2wIFlhd6l/bgkE+1d53p7usD1/jGQz5NL9gtZ9Jxt6qnt5Ce99PT5ZWWhBMDy8nK9/fbbdffu3W5Y4WUlLwMAG9Y8xeHHJ58QEubuDyfd+Rwm5LV/Xu/HQmoWLiO8VCQ+z3DF3jJZMErpgx041qoHGAaO/DwV3f0zaOLxM2xzaGXgc14rmSL3ZXsyvOUz529dXwIgsrE8ewxvPB63lQC5HtQsNFlOssMEitQv37e0tFT/43/8j3rnnXcGbM9ttlxcb44jejsaDbdmMj4JWP7xxJ2fb/mgW2lnVTVI02Q/3d78nt8mGWav2dbUs5/E0nM2LysL9Wg0up4xPDw8vL55PDzcNHNaKdjeaTC9AeFvZmUdhvTyRbAJQizaRSFnaENEYTFmG1syN/oyr92WDwZgVsn1m5ubVTUETzuKZM89OXI/P54Zh50miOXi6UwjeJlTMlqzP4O5gZW8KPJwSsEG1QNI/zg/yJhYVhnSZV41w8ue4VoGros2/c7v/M7gWDauzzWZ3GNHboLQm9hLvfX1dra+Jxlez1aoy21O0JvHFhPUsDvGwE44Hf6fFCCORvPfjf2qsgggLnRqzWQyqePj43YQpA89ZUmJByHXhc2bAElG5p0VTFRYGcnDGTw4yIBDIazg1ANosi7R2wu9NcyGR5sN5LkLxctoPAvn71AqlkhQVzIzZO3v+dsyQhEdSgJYdh728NybSXOPgT8zII/H4xthafaB/z32Zib8zhCNwpZIwIQJqHlLZHLbGoWxyMJ1rEPNthnYNjY2BgcQ25mYGbpuM7V0TtZfHG6uBbQjRea5LCTl77GmbX5WOorss8cRvXaayGOWhCfJyhdVptPpn8gWwIVmk9fX12t1dbWePXvW3o5HqFk1y99ZqSxcvzrUCjXvLEJ7WA+6geXq6urG8fq9ha4AAOcs2uBz0B3aY5x8Z9ZDmU6vZ9ZPT08HCpjrH93XDF+SFfYYsAHYMrTi5v3IP7245UtJ1msQdXrA11A/dXpZi8ctn50yt+Ojfowtj80y0Hr83G6Dbq//rDG1w0mHlKDqdidTMWD07ve45Ssqsn5HFT2Wx2/XaTnPO/rN9VjunuBLFm5SYcC/uLi4cbDxF1V6TuE2ZZ7+zX3OIpUfHBzUxcVFbWxstLfkcdR7VX9tlnN3NlCvK/PEAzlC50Ac9gKA6+vrDdxsRDkL1hMO9wGkMB6zml6YmmyTOnkuxSDi+9kVQ/9hpd5L2mNMZm/8b0BMuZoBJgPvhadOA/SemX3254BWXoscMlRz/clEPQZma72cLDLvyYs2JpPh3nQ+2ZbR6Prsztw5Y9Zt4OUzAOxlqxdSR3pGng64F44aKB3K2om49ByG65nnZC1vt8lj9EUW69NnKYu0cSFmyKwtA82asM3NzdrY2Kj9/f0BawPwbOj2jpm38Tt67Y2qZm9Uo57JZNIU1sqYXpV7bQQIN0NttyUZEMVrA+krBpvAvb6+Ptipk8c/cZ/b33tmFoNtMjx/Z4NLw/czDBrJlGk7n/eMqicjtwEg6QGT2+yTXsxELy8vWxrG7aIkQ0rZGegouTSKNmffewDAJF5vk4Bltra21l53kfn03pgiJ05ucmrEssqxof/Ob1MH16asE9T9d8/ZW8bZ5rSfz7M48lj0vrSrV5WFIJdF1xg2Dzk6OqrV1dXa2toavBKAvz37S57OjIm6/GJyBozvUBTCJUIcOp5hscNpX8NvzgNMoHYYlkzF4TXv+PC7PmgvoAFQTybXi6zPzs7aDh7AATmYtY5GsxfbZxhrZueJl1RoG8k8ZmdlTidkxTegencMf7sd8xhohnUUZFF1zZzv3bvXPvfz7bjQJUr2t/eTxXrVC6/dPutTstMEXjtSy9lOJIHbDr9qNolCvtp6mP21rOzQfK37km21LLe3t284y14/PZbj8fgzT27cpli3Fym+57ZAvTAYLi0t1d7e3mASY3l5ufb39wcnzhgUt7a2BrO0gCGDR8hN8YA4j0ieBzD24NN5L/rtDSog5fWQCaYeaMJY+oQMqIucKXWxe4K/mfVdXV2t3d3dqpqF4WZ3LMXw7hj3z22yfCh+TacXpjt0M3A5LE4mnYDoZ+eSjqohWzYAUJeNx5/z2/U9fvx48GxPuvVmX6uGYXP+Td/ToKjXIGh5ZLl//367rwfyCXKpSznDznU4X8vBwOnr0j5ckhm6X5YTDtu6xufHx8c30gYeoxw75PFF5Q57TuyLrGMhMNzY2Kitra1aXV2te/fuNTaB8SXrw8i8V5KBIgxYX19va/Cm02kDHzri8I9Za7Mugx8s1MDUCwUJH7x+0UxsaWmp1tfXW507Ozt1fn5ep6enbRkJoVK+tIm+5xrHqtl5fQZDM2Be5lR185xAMzfav7a2doNNGxD53KCYiu7/E+QGiiLwcqiW/+cz/J2NM9lwj8lmnYTOOI4EVLcpwd1jlG2jOO/cAzgzPK4za0zQz/FN+XFNL79nJpxjlE7Iz4fxEnnww7PW1tZqY2Nj0F/rrmXmdvZ0J1cMfN6FMf9xQHGR9i2UM5xOp21pyMXFRQs1EeT6+noT6Pb29oAxMEDj8fWs4e7ubh0eHrbcY9J7WIxnAs/Pz1totra21o4RQ9EyJ1c1OxYpPTpgnKGST7VmixP7P70XNGdtWbpDcT3cy0w2915dXdXa2lrbS2ym3Qv5UT5yQr0F4HmuYbILG7iXd1QNAcv3++8EB3Sit+A+HSBjZaBbX19vZ0b6h2swWGSL/lk+CULJvnISxuCUzGkeUB4fH7d6cIRen+q63IfxeNwm63xKU4/Zui3k1HusOp0N/xsc0a+cnMvDly2TlKWv6YX5X1SekPJF15/lM70DJZkPxotRrK+vD0Cu6uaJKVdXV7W7uztIjpL09YGqAKJzPEzcwCDX1tZa+NzzBACK9+zyPIAo2cV0er1r5OTkpB0akCGfiwGEdYtmd9yPYQCogKtDOq5ncoVrvSwHRwTYm2HYcJz/tIOgmIlSV4ZT1Om+IleegTzdpl64x7Pol3cFmZXkhA6yJOfqdrlv2UbrgH/b4N2WHqOtmuWocSDWF2wjZZWgZZBnHLL91HVxcdGikywGJDtMjykvhPK4+Nm+107QzNyvPPU9OWljsPxpLgsxw7t379a9e/dqf3//xisJUQaOprJR+TuAZXNzs1ZWVurg4KCqZmETx3NVzRLJKB47RgBLh1kenAxDuBYA9bpIz2DnW/0Mnp5RNZPJXKfzclYwQnscCSBsr0199CFzWlyHMdrr50Jw2jwPJKzIyNDGYmaTLMpAYVbRlEqMxtemPGi3ZZxMzcbI+Blo3R7+TpDy8/13MshkxnbkTjfQ7gTYDDu5nt/5/AQnOwvG03ly35Py9W/rqnWqx0gTELMdtuNMZ/Tk/3mXnqP7ospCO1Def//95rGm02k77gpj8ar+3uJiGAPfETZW1UDJCIMNIAwmO0eqajBRYZaXYGClh1mSe3PIZhZCvRS3/eLiorEx5+bMdGzYXl5TVYOQ0oZpb+/QErlgHLSl6uZOHvpCPb7X1/J9Xutwy2Pp/BP3+bNkaz0FTodlhujf6INDZL5PwM/npUwN5q4/n9nrN/rmOhMoe1ECJa+zkzML85i5LbBzrrFMekza19sW/fzM8dphZt/nhc9cy3N7r174PMr29nZ97Wtfq4ODg/rggw++cPa5EDPc29urra2tev78ee3s7LTcSYYIptK8UrQ9MLa/EQ6akhNOVtWNwWEZC2EjxghIWokcgplheFGqB9iJZwDNW/FgJRgnz0KhHHInW/UMOu2yvJwuQJGt/J5R7c2Y87/zrKn8gCz1+BorGjlJpyXcVodldi694v7ZSZkpZT7XSX87APShx5aTEbruXPTs72BeCWi9OpO99/pKMdjyLD5nfDN35zKZTJrTtX0Z4Bgnf0aqyZN3ydzdvx6zd98Z28y5OqK4TYFIEJXdpkwmk/rRj37UXiXh9n31q1+tR48etV1f80qy4ZeVhbfjMfFxcnIyyOXY69GIquHGc7Oo3qSGDccGbIPFOBKQUDJAzKBIWOvZ7qphYt+ej7bCROgDi39RzFyrRj/H43HLnybY0D/qtpIkqBioka1lnW3vMSfayPPTIXAP40ZfOUE5F7wmAJnZ0B/GwMDJuFs3sp92LPTZ91mpbZRul9vWA0Bf24tavPsDHTHwIzvvZU/WSN0ZjTBWfp3sPJBCl51y8NilQ3U/GYeq4SsAemw2+9Zri0Gw1955kYDLZ2GQ5DyzTKfTev/99wcOdV5ZJMReCAzH43EdHR21l6FvbW3V+fl58655FL9BLU9KgXk4lEmFNuDRMSusT9UwI/KpwPkeWe5h7eDS0lKbgHFYRJvM5ACOlZWVOjs7azLJ6X+H9O4/4TIy2NjYqLOzs7q6uhqkHgxymcdjogr2RN09L+3lFTmRkmE515E6wGjtbDwWCUbJJPjM9Sdzc1sSvNABG6TDOuTLdQmUOWvtNswLDf1ZFrfBY+vx6fURnZkn+x6IUpiMrJptQzXT7cnM8jHhyGelI7Eu+HvGtMdMew5oXuk51h+n2Bm8rHxhYLi6ulrHx8cN2Ahlnbzf2NhoYQen7rKVyjPPDj8ZPAaQ+23A+QY9K7fD46rh+i+u9TKI6XTadss4L+klOtTXMxCDn/OBzsEBuM7leH3h2dnZjVwibNJKbIZl4wPESTHggAxEgKCV2qySergO+fM8vz6AOucVM/rl5eXmfGyc84w4c5sU9zknTJIF5/f5+bw++NoEJrfBzzXI5v3+vGrGwD2WXPOyEDNTJ04lZbt6DsO2xPPRQ9uR+5nyMSnwZ35Oz3H8JJTUmVvdc9sLUXK2wwFsLFYmzDo9PW27MI6Ojurk5GQAEGZeVcPwKgfSeSN30AOClzZzc5tsNNTL954IsTfkGQmItMFswAzJIf1oNFvLyPUsFua0bfedvjosA8SRt7fCGaxYeE2f0+j822kC2mZHxf2kQRKAnTelLRlCoQvJDG2wHlOudX7HoR7/096XMS33tceWPW69eqyTvfrclnQsPWeRTJHSe1YCEc7L1yWjy/47YsBWfX/vWZZHj50bTJFpssp5OeP/V8XjflvAXrgHXm/n8MyswqEyCu0lEQy0QaBqtoPEBudBzBwBHQYorCyZn6JNCAdv5+OUUCRvwDfAVdUgbKwarjFzOIpDMGPFS7O2kGU2hO7USwhPH51780k3eTqJmZnvQxlyVtYyHo/HzXjIizKeea8ZMH8zruPxuIEzhTZ4hrMHHD2w9DjMYzQ9NpfOL0Ekr+XzV7FD6jXrz3b0gI/xSwfr67NdXM9ze+DXk0dOulVVF8BeJkPusdPoTcg4j/95l7t3734uQPuyiMZloadcXl62QxkIMW0wOeuas69OHFfNZm8BsgzL7MUwVhidjdoMxgyCZ3mvrnNf1O+lMGYzyaCSRbKjoMciuM8b7Uej2eLrZMbem4xyWjZcn4loszBP5hgMKGaOnpEGwK6urhqT7/XJ7TaTs7Oqqtrf3x8ARU8uvWfY4fUAoifrlKOfwfNzoiEBNwHFIGDHnszKIMdn1qEeY+3JMJlp1WyTgidqXI9BKPtBXc4Z955B6TFo96U3SZiM8PPMB/IMJlC+CKDtlYXA8MWLF3Xv3r0GdD6ROtkGoaiNIl8FamV1LgM2BthSAFO/kxgF6L2EnvYYlKuGzDBziXzO9QbAquF6PE8CcQ99Qybe4YFD4FquYRkF13myyXW5Pfl97ufuvWiL75FV1TWYbmxs1OXlZZ2enrZr0jFYtlXV9kc7oU9hQiiNJtkExfU6LPQ9FAN9zoT6nkx9OFqYF7Japv482V46M9qd9fZYm/O21pVeOI+e2nkkiLse58pxrOlc3K9sZzJO62FvpxCymOdUfpwynU4HmyOyvbDGz7MsBIbT6fUWNUJAK4/DYjzreHy9X3lzc3MQWjs8c04saX3VzWPkk8Ex2ZITLFXDGWm/H4Xv+G3GZmMzoNqo3X6DdQKv1yqi9A4hDaK5eJvv3Q5C48ytVfXZL/Kjzc5B4cgAwtyW6HqRD2PrENHts4z8WQ8kkmX4e8A6ZzF9Pl+Gj667Z9z+rmdc1nGnRuYtPYG15QRNpmPMeM3uLYcegPKZX13K9XaizhHa/nKlgdudJWWELnlpWNXwYIbeGP5Jlel0Ws+fP791+HvbshAY2jswG5xGMBrNEvkIjuUjGDuMyu83AdCqZqwMQ2RwyK05hPFSkPTkno1GQX32oMEvWVDVzR0sviflwX3OCRlweb7znGmcyZjMTnNiBTZoEOdZNj4zRzPqtbW12tzcrJOTk7YqwA6J7ZI2NPK8fkOhQ0Vf691ACXYJUgkInnk3s+Nag22OoXOXWdIBJvD4PoeZyAbZU3I/fMqi5wB8DX2ZJwfrovW+xyIz6qGuHEOXbDuO1qspLKN01H8SYJgy8ee3LbcNsxcCw/Pz89rc3BwcXZ8MKpdPMCA0yEf9Y6SZ/wBQk4WhoPPqybCqB0bUgdFfXV21/mRIlaGpDWJ1dbV2dnZqdXW1vQ+G56yvr7cZYyulZ92rZmfBEU779QgwZiskn3s3StWMvaW8cCRmFCsrK+3INA6hQEZm+YAZjgOAynSGxz5l3gOzeddatnye7NuTQtzn+u2QJ5PJgEkmU7VsE5DsiG38/Lbjo81pnHk94JKTVgavZIy019s3GYPsV0ZMXO/0UY+FWj9yPa2LJz+p3/d+UYCYjnReSefn/2+bz1z4cFfPuDr0RUEyN5eKSyOtCGYCnhDgh+PCbBzplbiXa6zMgESGvQ5d3D4vyUlQ5CAJFI3F0jY01hDyGYyWflEANgOMvS/PtbPw5JHbzv8YnlMVsOqqakDoEMgyzbwZdTPBY1lhQO4/9ZmFpgE66V81PIjVcqcf8yYoqC8LAG65U1xH9pvnXF1d3TjqivHgx4d1uC09Buz+UV9vZhmdSN324nI7GcsvC59BKtKR8Dt3haTjcN97ji7B2Hr5eZTpdHqjjfOu83OzvbcpC4Ph5uZm67xniieTSWONZhgcJjkej9u99kT2wnTKwGRmwPfJEM2EcpB9LXWaGaKogA7gzIx5zgbjpem3zy10n624XHNxcTFYbO4+8VxPGpktWRYGOfcZEKd/OCqOdCI/SM6XunM9pNeT0geuN2P1GNEO5Mn1dizI2PndZJh5iEWySusK7ec7fgMy814latlmuO8+9xiP2RmOJ++3nHxvhshmnrTn/Px8QATQn14/LQNskuKUisN921lem3X7GbYP/jabtXy+CIb4qpJjnHp0m7IQGC4tLbV3JueCUDM9Qix72tFo1N5ol0pOmIphYsAoxGg0aifcmI0uLy8PztBz/o6ZbIPtaDRcCJ0G5L5k6JI5PoOyGZ2VGI/MG/yurq7adjvaZs/NZ37bH8Usgv7jjABty4uQGKZrtuP22ehXV1fbnmQ7JRu+101yPywQR0Axu6OPlp3zaqwvdZscsmax/hiItre3b7BMO9MeGCSo5xgnU7OzdprBxuclLX6OHaltowdY1s2cqEnjz3utP548c196fUqwzrG2zqfzsAw+a3E66lUlnzNvRv7Wz771lXWdMD49Pb3R4NFo1IzAzARjxNtZqQ1Oo9FsttLCNBPiSC/qz32+hEWZeEdIefAoYGr2RZ6vF6rA7KbTaXs9KkpgJmaFWl9fb0sAMPTNzc3a29tr14zH1zPuyMb5JCu4d44YvHthvoF7ZWVl8AJu5OIcpwHdoOv8FM+oGrI9Ow50xN/xjAQcyzb7ZKXuKbzbZMBxGOjrHML5mZlq8DPddo8JITL3+/kJKA7xPbbWTa7PF8qn/vKZf1N6qQ0DmidRLPeeY/B3yIb+8JnPE+2Foz8OGOb4uiRI9hh/lu3t7fYiu1eVhcAQwfIy+aqbe3Kds/OsMCzOi4szN8cA+vgoswnnIlFUAHB1dbXu3r07yDnmTKtDemYCqZ/Q0OvjbAgGWe7Dq8PuvLZveXm5jo+P68mTJ+375eXlOjs7q62trQaI1E9O1OzCDoJ+OyfnPKjr4jOYeB6WS31+qxkgagBhDFkaZdbjv7nf4N0z5ASB/NwMxSsJ7Pj8Y4NlbE5PT1tahrZQPHtqPUmW4/Z5jPkOMEAPPKuc9uL8qOvsRUhuI587ndOrz+1wPxIMYd4JuG5nhvoJkpmrtO7589syuz+JcnR0VIeHh7e6diEwhGV4BtcGWVUDRbPBMBAoF7kHvmc2lfVvVTMDtTe1wTuUJcww8JjJpNfCoJjNdu6Q6wyUfJfhFC+ocp3uC0t5ANalpaV68uRJTSaTdtbj1dVVy6daqRy6+36H1VUzh+Tnc1IPsiZExqi98d+fOT0Bm+Z4MRyFc7A2xjSWquGMsJ2KWfXu7u5AT8xs0/i5xsBGMdgw7k4JWCeT+RsUzXrok9eoOsoYjUaDnF6mGKxzBqhcEUBJpp+64L7a7nxPTw5uG/f2WGgWy8cpEYfTHuf8+0+qzHtez0nNKwudWoOXMRBhoAYOBM0RVQwarJDGsaAU9ug3zVmB+c3fPB8wY6V6L9dRddOI6Athr++rmq1zgykB8A5DqcOr/GkfC9MBdc880g5ODIf1nJyctPt4jr15hmP85rluB9cjd/KthEsAIG1CllXD8ydzlpv3t/As+uL1ljbu8Xh8g43mDOpkMhkc+OnclkHDhuvF2MjJDspOkeJx7wFpMl2zM9roqKJqFmmw2sEAnozX40Y/cWqZwzYYe0KKfK5ZWo+hW7YG9Ux5uP/J6jPk9nXop9lqbxLmtiB025Jtc/k8nrVwmEyCOmeAq4aglfm4i4uLwXFChF9eXuJdIgyCBzkViwEGuPINbWaIyayqhics81kmms1SuKcJbzxbD+jQ7OTkpO3qOD8/b0ttuBeWhoExu4tzcIiYxpIeMOVs1o2c+C7bboYAyHiyA3l5jJOVObnP8yxPh1L+3v1wGJwOjbpzfWYyThfaZefaAyS3ufdZRiTuswtONcGuV2/ms3ssy59zbToJszyDYrI+numtsaPRqHZ2dgYOwbac9bkf2U7Lw4778wAnIhE/64ssC4MhJ5pkSAljwgg2NjYGS1P8OknCNq/l6oUWXrjMZwCx6brfHUxbbHQeaOpjmYvZrMNZ+udQndnW8Xg8WHpCf3JGlLb4PbUo9cXFRXef8nQ6HWzBsjHS1mRBrH204+B39s8vwMrn0h/Gy0aaYV0yAo8PffSyE57zMgaR45WAyGcGRoeLacAJgKkT1lcDiVmaWVU6aINR73vkZf0zILndjA/O0fUQobhN7pcL9ydgrq+vD5aA7e/v3wjtE2zSaZlk9MASJ/WysHuRkodU2Ml+EWXhmmEuGFTVzOh8sjMn3FTNjinnWkI2mKDDQueUmGThuRhp1TD0tUKlIaXSA0IY8mQyW/fm5xtQrABmRVZ+n5RdNWOYhJaA7Orqanu/NPc5zHPe1DnNnFAy80V2NlL6ZbBIRaUfVrqctUYGGY6bWdIe6rO8nB5wDtSOzWPlPiTIoQcJnmaHbofX+qWz7dU173cCNsV1+eBVO7IEP5ys63FUkkBO6S1l87OyvgQ38v2Z984+9Yqf13uu5dNr46LF8nBZFGRv2z/Kwi+RPz09bUbrEKbnkUnimw0CcOTNYE4koQFLK0YuneGtfJ6UWVpaalvbqm6eFOzi0A/GmmzOZxGi1M73Zfvor/dhe32Xw20zJvfP+Tczba9F5LkGab9S1YbnvFTVcIE5z/crS3sLiGk3hmTml4zCIXQCTz6D9mYuz0CNrMyC09mhU72Fyb2ws/cdf2dI7/YhW/QgDY00SC6q9zO9DpYognExsDgv1+uD+5k6ZQCy7uLk/M7nHriYUPRKzzlYH1O3P0vJSSV/3ltz+qryhYBh1WyFPOzOywS8nszgsby8XOvr692johxespaQ+gERZjJhMQYLHxoAc8zzDs3k0mPDEqtmnhkApx9mH2noGIHDUK6BmflkHr7zfmjPvvPbnhaARAa93Q1mjs49Vg0PHUAmZ2dnXaM2QCOjq6urdt6hWXXVzOkw3l7XmIzPTCydVbI/tylB3+2kPoME/bTBmnGmrG18GSr6WdRtJwD4OcLogXrK2ezNn2V6hO98mrtl6msTPHMJD/aaumP5I7uUhXUiHQr30T7aYmD/f1GSrb6qLMRjx+Nx7e7u3jjtpLe4dWNjoyVAnciuqsFaQ+rlPodgAAqTI8kszcC45+zsbJCnBPisWDwHMOQzSqYArq6u6vz8vIHb6upqbW1tDSZnHHo43+MZXDsGDnPwguuqWU7MbYAF83e218tRuIacbY8RsQuGZ+UkURaMIJdqUF+G6wk+PZZgwEsQtD5Qh+XTcxq+nt95Wni2IZ2ki52oP8v2uOQeYjMmyyPD4WR3frbb4Fea9uTQAx7LlHfm2PYsr5SD/zbL7LFq654dxudZeH6vfFYW6rLwBMrW1lZTvK9+9at19+7dNrA+6BXGBvC9ePGizZjaODyDjMGZATBhgcFV9Rd1EkL7lA5PaFg5AVk+Oz8/b1vmmAF2snk8np0WgzLzhkCH61Wzfb0AHWcGepua8z8Oi72Q3eEMfWbnSwIYrIEfQBDj8oQJwJD12HCtxL31cDBAG6vDYxvM2tpa3b9/v6qqCwA9gzET528+t+647RTve06GSB2ZM3PdPeBOtkUxyBGlzGP31ONnJKObB0RcM2+vdU+eyWQnk9kpPtTh9Inr7TE/+guR4P9sb47R51E8VvNSX/OcufXnVWXhdYZe9PzixYt68uTJIPyzwGxAX/va11qITBgHgGHMgOnJycmNgfA6Lur3i+ZRfPKHzMgBEFVDEPX9VcOQwiDhxcl+BsBInmxtba0xRD7j2TkraEZJ23NyI5fVkH/1BJKZddXw1ZL0jXwaQM+zuM999su6DNaWGZ95VxEGkMyIkN0rECaT6wM9WKzeY1t8bmPlf8YEXfGMfBok6QHXbeB5leHiIJwHc/3IkjqdY+bvefXyMy+Vg/7YEaSDNoglYPG5bdCTYx4nt6vHtntMsDdOeR91zQOw2xb6+zKmOY8xLvL8hZmhw70nT5405uPixD3e8tGjR+2dy2Z/zoX4RBgvY7HSGgRyO58NywPCDK2F45CPtjrkQwlzrR0MkZ/V1dV2Wg/h9NXV9YEMADKsMNlVb90jYTiAT8hLW+lr7qrxJI3B3zP8yNVhLwYD8Dp8TYO20ZFzrBoyBud8bXjUub6+Xm+++eYNA+J7MxqPazpi5J3Gy99O6OcaxZcxQn/nfvn7XHtpMPRBHD2GmQwnQdHXsp2y178ekLmPnnhyO8kbOnXlkHaeg8hrfJ2ZOs8xSH4eJcnRF1FuzQzptD06B5syIYIRMENLCAlbQbGYkUaxYBgoFIbko+hhkawPrKr2IvecpfTiYUDWITZ1np2dDbyfJ1NggyyDISw0kADkyMfhqpXBk060gWf4UIjT09OB7FA4y3c0Gg1CbgAMGcPUAHTa5zZgfO6vFZowPXNbXutGPQm4nm13vWbZH3zwwUCnDGoZQhr8/X1OxtipNuXWgmsCnAAAOWJJREFUxFRO2CRbrKquU+A6syxPErkNjL1zcwlmLnbePcAzq7XDti74J/vF/2aYvPvc45Jj1Nv1k05iHqP2JIojuR8XGF/GCj+vcmswdFhQdT1Q6+vrLaRFWVEA1tNhcCcnJ7WxsTEILT2jtry8PAh7MdiqGfNh4SihtCduHBrkABt4Hca4Xy5+fSfMEeUHBBkcZGKmZdDMBLrDqqrZZFJVDUDQ+UlADSA0KPA9z/dhEVzDkiOPHfU5x+oZX8seRowTS8fja8h1uh7qdXjpa5JpOHyvmk1C0R7Gw2CPMzKoUjyxl8CUyf5kXNYRO2Z/jqxw1gbVnn7RRvqR6yGRw9HR0Q0boB1OQ9mBvIzhWZ/9OeDnE4d6dRgc7QR7wOzQ9vMAQ/fhiyqfaVUkISzbzsw2GLyLi4s6Pz8f7Bv2oaIYeoY1ZhDeOTGdThu1904RA5cVhbq438rPIPleWODW1lZtbW3VdHq9xOb09LQpocN7h4Fmg1U1WMKA0ji8ov8YD/+zkwSngVzol/vpHCa5OQ57qJrth4ZdU5f7YAbApFCe8Mz1nuF2CO86uN/gkGyPwjjytwHYzyffaUafoZ1DacuX67kmn8/vnFDphdKMX46jn4XuepVDAqnbnb/TOVhmbpv/N+gwxr6fKIn0Dnprh35+fj6Y/Ezmlww3nWVOwhn4Ps/w9rZ1oKuZpnlVWejqyWTS3pvsvESGHIQKvg/gcujjfBwvVp9Op20WFsDgx+//cDjtCQiHAwbEqll46plaKxUgRL7PuUMbIOCD4jNTDmiagQFYBkoOceB61z+ZTAYvcaeNsGnupa/JnjwWsDUrhWWRwGJnVjVU9LOzs9rY2GggbxaMsdmQbCz8dn1m2GY1yMz3uT1mva7XMuTzXk6R68xuPQY9ZpThotuRjLpqeDCHHX6CN/ckwPnZPdBJeSZD5Dp/Ti67qtp6VddDGsW6Y/n1nu/xTwyg3akHP065LStEtxZlowuD4draWsvVOUTwMVDT6ez4J4AM74PwcnreQgXonJB3WGXgZFAYaK5z/g7gRjj8vry8bHVZqd0+BtyeFYaaoZMnXxIsYGMAh9tnYH7x4kWb9XWbvc4SFkn9rHlkq6HDl0z+A6wpNzNbM0mnATiP0uNG+3pr5JwmyOvzHSn+Pj16OiIMM2fFHcLSpmTjlgOfUdKxG6wyRLWxuy5HAgkuXJvg2GOMCZgGV/pm1u0lUPPAn2szzHf/uc5tzX6YdRuU+dyTN5QMzT/v0tMZ+nTb5y4EhoAcNJtwKQUACzRrYHcJLyTyxImZjg9X9ZoxFAPD4rneb1k1XAqC0XOPl88gIIfcDJgZLG2gAEgGVu43a3Bejr5SN57L1yNDZEdbLauNjY2B4qEEpB+s9N7yWDWbOGIiij553AxUKcvxeDwIpwyYbg+GiQydRrFxG3y5Dx2zvjnXaVDIbWV870kUr1f1mPt3grWBKO81Q7KM8lqzQbM+k4Me8CVQu8/WP499XmM5pnzcZzvzZHX+bF5eMsGQex2l2V4N2IuW24BZgnovWnhVWZgZumMkgKtmb/XygHvQYUNeqGwWyOA4/PTR+qbpfMazUgFtsPbo9uxV1XaBVM2W+HB/1fCQBofhBhNA1qcdHx8f3whPkRnGw6JnFGgymQxk0mN1BhJyqmaqllGGLwYWhxAGdvqUrN4Gcnp62k46twx8YAb1O41h3cjZShfG1jq3sbHR6svnOBViIzb4ZA4sDTuZX48tJth4mZOfbcZG+3kmP5memGes80DAbbQjTTbnSUK3i0gn++w+0C9PjKbzduH5Zun007nQz1IWvc/R5CLlM5+H4yS68zwM/vHxcVNGwI0fK2FVtSUu5CKdd7IwT09PByyoashsGDgLhMHzmjorjPfROvznu6WlpcFiZQbG4XSyLdplBYEFA/Jmj+lpPSNrA3YKArC1Maa3nk6nbckNn/nsQIqB1OBHPz3mVTVYOJ9hHKzUsk7jQS7oh50PzzSge1KG9vp5yb6si3kMlMHPLMffJ7PA6TjHa2dnR8U9Odudz08mmHqbzBH58Cx0wfbkyRIf3ks91o08aBkboRBVOM1gYK+a5cR9f0Zo1stk3Lcti+b+XNIuX1YW3pucawKrZozREwbktJhcmE5n5w6en583o8MoqoaHfGZolHkulNahLyG2gTS9Hu3NcMHPdeLY3rXnHQ2y1L+1tVVVNZhUsCJ5cD3RQz99iAPgRxmNRi1ctiElMLgvyY58nY0kj/xKGcPMfE5lj41kezAQ5/kYA+pOg3fbnALw8o8eA+a73ntQ/DsB0H8nwFrH0Ie9vb22vc05Wp7vvLLtx9chh2SGlmte17vWDN3v47HeIj/rWQKgi8fNsuZZOTEEQFfNUk/ov6/7rOww+zrve//9sgikVxYCQy9xYG+uFdJskY6z44GcE14JzwQwev2TcyH+3Gv/3GmDmxWOOgxaVTVQWOfUdnd326Am2MFOvDwhd4G4nygk7cp1mDY2ZDeZTNquGy+5Qa6bm5u1tbU1mFQx+DM2BlM7HYe2znnSrtyHnLlBMxL328zWUQKfARZ+VhqGjcbhvMcXIHUf/D1lOp3WycnJwGFwnw08QZXP06goyHc8Hg8WLrs4bdMb52yrZUIf7QzMYj02fO/PYKRnZ2c3+u9xQC+2trYGjBfA9Fg4Z24gt4OzrtjO+JzvWJJntvmq8rLx6F3rcSA6u21ZmBnu7Ow0BoZx4I28H5jtWsvLyy0MtpIcHR3V+fn5YGudjcE5PBgB99rLMSGDglrZnOszkFXNQIYBWl9fb8fzA/ocsprGb+ZKGErdvOTJMuuFPc5/mTkS5lA/uR2AjGU/Du9RuKur4RvQaDMy8LPpQ75mgTbbIDJHWjVzjDb2ZIr+7fHPcbETdLhnB2MQRq8MZrTB7c2IgDE1c53HGqxn+RnjC/t0Hd5V5BA9ZcFn3lnjMfK1ToeYVSa4uz/ud0YEvs87oJC3F4HnKgGK01OMQ+a8cwLUx/XdtqTtAOTzrqUPxo/bloXAkINLMQQGickDdp3Q4aurqzaTTGOrZiBlT8JLdVi3VzWk4mngGBHgZWVw2Ec9NlpCUZ8oDZA758GA2riow+yQwWV7og9tsNcmrMrcj5cFofBOcAM67Hu1UmcOFPl5DzLPYdKFHzsny81MJIHGOcqq4d5u35tMLZ1B1Wx5jdMtVmjG0AZluZmVJLCl0+SzTL9Ydhkeu25/xv+52yd1zWv36I8NOU+hSYD2OGco6n6aVTp/7bHL/DPpBgiBgSbHj+fbqSZjQ97OO2fEwLMNxosWj1HvO+uT23+bcusWjUajOj09HQwYs8NmXZ40wOuYenMfA+JE7fLycgsTWwPHs3WDNgROYXEIYJByKJyeND0x7XO4YAblttgweC7fZW7MoG8lykGy4bFjZ2dnZ7DEJyeK3F6zutFodsAt2/Acol1dXR8mke/65RraDlA5FdDLXzkUpH9mFu6jUyZVs4kUs0TqMYjlrHqCRg+8aW/eQ/iYAGSw9WdZtwHFbfezDYZm/JaRjdpLUVyPwd1tnpdnxKZw7Na3ZLU81zqFvRDJuW7q70U6Bsl5jjAJxSKhcp62k6SEv00+XFJW88pC8PzGG2/U3t7eQNAwqBcvXtTR0dGAUVVdzzwiQMfxDhGZRT46OuqeUm0QNOCMx+PBC70RCmEUwrKhWUBZp8HSOVAf5NoEp/Y5l8Q99NUHHph5AqQGTANL7lIx4KViVQ2Bhef5JHIDg3OYnIxiNuZZf48B7DYneDIFkQpJvYCDGY+X4fQAwUaYKQ6ikQRK/017EmA8zsn6Ewgyd+r29UI+A5/1jT4a6D1Bh95mRNRjaW5LsiW2dSa4u02eLMt+WPbpAHpAzPgR+aSuJyDyvNsAYrJpF2SJLOYxxtuWhRddb2xs1Je+9KVBjoyCQA08hDMIEDZHyJd5QIcbZntp/AZMjJFikPM+XA9S5lq4xkYCc+XUmAQl30PInexqaWmpzf7aqGBmzLaTUtje3m7PMLB6sTsOwxMeyXi99IYxyXAW4yNv6DCHfngiZzQatVc3ODRLIIdpAJ7OU3EfxsqqA8ap96bDZFeepTTQ2WiT7VES+HpOMv/v/VCP9S6ZXNYDCLK2swcqW1tbTZ97YOb7fH4khfHwO8HRTWSXTJbfjuSoK/s1L9/n6Iz/LWPGibGzQ3hZmU6nA4b9sut+3LLwqTUc988AOdzyyS6Alc+dM1AwYTEej9teSS8GRrhXV1ftNQNWXJiTl/PYIwO6TuqazdnYuN4GNBBSzBrz22Du2U+AC0CfB+r+DDnAms0YmYxKsHMonyyLcbAT6DGEk5OTATOsGp5qs76+PlBGH7xLPT3Q6S2/os++zu8chpXSNy8mdx+5DhlbrrBhM+QcMwOMfyyXnnH1wj+nLHzdy4wX/bDjpw9Pnz4d9M/tdjhKQQYuMGbWgp6dnbUURbI26jcAOm1Enw3C6JxTV05l9JyCn2GnyPN+3NKTt3XnNmXhw12dlPcgmaF5cgNAcEg1Go0G+T4GzefgOcmPAZoxkGtEyLlN0EJIgK0avtfDYSQAi5KlsQFSKXwrWbJW2uEz92CMrNvDc/dm4JC72VCe5egTfehXGrllYhA1EDGjjqz9DuecmXfoauNJQwI0zG4cpnmyCX2wU+A+69NkMmksmv6jQ+k4PP5uk/U6Q0OuyVyVQYGxTWN7GRAa+G07/N3LjTFetiMDk//PnDDhq+93G21nRDh+vpmg9WUe+KRcvV3WOmx7uw1DdBtcf6/0CMut6r51K+o6xF1fX687d+60JR6m9DQAas7LjuzVYYYMoJfNuANVw8M5qR+j8p5XihkowGPFs+LyHZ+ZJVpJbXxWhJwkMTsALFlPuby83E7iAbCqqq279DmOyPPi4qIODw/bi6hsCKPRcC2hQ1/vGpjHgnrsFznbIKnTTgV5eUWBHV0Cj89rNMObN7HkkBidIDT3dbD5nKzxT6ZDnI/uAaP/T/brv+2oGO/bFOdUDajuu50h33t8kmH78I4M4QE3b5RwztaTf1XV9NxhvBdqY3cUy9GO0O3tMUP+twxuW26TF0w2e9sQeiEw5LAFhEWHWEKAl+E71v1Np9M2S4yAneTPI9wRLqyN75gpo8MZbqAAGRo5X8bffgmVw2vOYDTTs2E6l8dnTCqQQEY5k5nmglZAF6dB/46PjxtQOnfkZ6OYnoSx57bR+kxDs0DaZ9kYXJGd62b8HCEYTMwgq2ZHs3l83Xc/z8X98M4k/5ycnLQcmw3SoNEDNU9a+LteeEf/krF5HG/DbJKhUrdDTedL3SbLOJn+1dVVWzxN/pfX8m5sbNTZ2Vk7kzPbmY6I/fLYsPXW+uPnM970yyDu8Jn+uv/UvcjMcra/VxZhmi4LgeHV1VWdnp7W6upq3b9/v3XKTMfn+gGeMBYGloFj4A2g5Iu808OhaXpGK2VVDRihPQNMBBAj/MtQhLbxjPR+NgSHinhVAwT9AOjcHhQPoCUv5zwXYFc1PE7KoGUmxTX2+ACqwxOHZO4LdTtUyvptqB4LrsFpGXQpCR6uK1mDWS9OkR8zfnKMCRIJ0BR0lutswJaz25vyT+a2yCJi2uR2ONec4MFzzc4sw+l0Ws+ePWvjxjXepODn5Z5krjMBoU/pIKwvHj/ag21bdtZnb4+z3LH7z6vchj32ykJguLKyUjs7O81wmP3E4I3wLBGwMXn2GYFwOnLVjDl5MS7e03tSvVTFobpDtqrhu1E8gIBisqmqm1P5ADl5LAOmAYXCwC8tXR/wkCGrr0VG9CmN3AzPbTOzZsY3GRHF4aXDJxshsvf9AKMZeFOa8XDCwDK3w4OBc0967ASe5eXl2tnZuZFb4n6PFf8/f/58kJ5IBmzwpxiMe0uIsk9uL8V6ddswL5+DI02dtnytDzmOBkbbAvp69+7dQWSB7jrHjz5Z5lXV8ozOPbuNfjayTcfjPnilCJ9l+mVlZaW2trZqZ2fnVvLMYsD9LOxw4WXgT548qf39/cHEQ64fm06nA/Comm0rg0E5weska4+BWEnNLBnYPHnbg+oEbbbRYbPB1znN0Wi2l9rvC/GuF4Mkbfbugr29vfZiKYCbNvBmPX4D6hyT5Y33PiOSgceZ8OPQE6Xwj2WKvP2/Dcezf/SXv2kTMmwKpfyrwy/6bcdJHzwje3h4OGBDufSq6uaLkpB17iLyGGaYmGGfZWTQ4Rm+L6OURcI8658XEyfAcW3qNPd6Eo/P2WtPnfv7+zcAlMlIbNEkgr9NMJLZ03+3N5meX6XhqMyy4nufXr+6utpeF/KyMh6Pa3d394bsrSMJvLcpC/F7hImhsO/Y78Glsab+VjT+97IYDzpr+xzuGtAQPoPv2WPnYHpT/anwzpUh2GQTzodUzQ53dc7Te6/53k5hNBq13FkersCSIVgy7WZyAGdhpbPisWja/SBHVzULUwxsBksblhmAQx4XFBhnlvkkh7YG7tzwn4ZMvU4NpGPrhWW9krOHyfrsKBKA3C5fw2fWIcbB4PCykm2wPKgTB+fj7Ei3ePzdN55NO+iDD2vIRdYUdBebc1SyurralnU5gnB6zNGfIzLrkR1SgpSJCfVgMz150h+v/c1iJ+92vaoszAwvLy/r6OioptNpHR0d1fHxcZ2eng72YTIwXi/Gd6bL9gD2ljlziNBS6AABQOL8kQ0ot/OY0fWEZqoNy+O3w188sUGAPnjmkDaQq8TgM9xHmb3HmVBze3u7gdzl5WWbZUb2DuUpLNuxN3aIzWcGQgoOyCBcNdyS6DSB2ViGYQ6J7HiSuTnCyPb4uTmWWQBytz//dnnZ/+lM8xqHnbcpPWDOVI3lyDXWfwNTrqXkXkcPEBf+dk7ZDNwpIFg2NuYUDu1IB9d7Njrr/nCdnSLXZV4yCzqcr2pNR8thMjluLysLMUMM7ezsrO0gYfLAkwR8VlXtHbJV1+f8HR8f32ByFgwgU1WNJSLMzAlWDWeo8AhWOOr0bDDenP8xQO7nWDGUhOcDgnxOfsYKzaRR1WxW2exrMpkMZs89e+eBg30ZuDc2Nm4cqpr5O1govy2jDPscVmIwyMphvkHIz0aWdlIJOgYNM0bakw7OoXAvn5fs2/3iHjPvZHXJTPJ/Gy/j3Osb9+VSmJcV39fTUeSZeuxIoNd+6sH5pO66HwYbHDf9yK2NZ2dnLV1zfHzcZDwPqDLXWTVLjzH2mV/MMc56blvSccIcbwuEVZ/h2H/yP1/+8pebFwEUABmS5m4ILzpi8JMKIxzW3MGORqNRW5zM0gHPCANSuY7KTMWLTeexBRsNs+ac6MyMG2Dh3JfrrLo+YGE0Gg1OmGanDfcDjhw/ZsWxoqIUvLL09PS0ga9L9sV5u8yhGZAc4jlUpv7MiSEby9dAkAoN2M/TJYeadgyZG3V/DZqp6L7ebU0HyvPMWBNs3JeUGyUndF5VrDN+h4r7hLNzcV8cCjO2jhhOTk5atIAzdISDfjmNYqYHoBJhcdgHDtxhsmVOO1MvepNaVcNcn23CE5uLFOtCpkFuWxZeWkM4d3h42Lx0MgOvnTo7O2sHwXK/G+nN3QyMmQnA5ze/2Ws6oUwbUY5kTFxvhUgghN3lYBFy8iyzQq/i58XfsEucA2cm7u7utleSmq3SRgzLwMuasclkMghf+e1DZPm/qtosfIZSycySxZlReclNyjJZnpkVY0FxOsKRgRkYBu7dSclE+NtGyPP9vXcS2cEkwNmAaZv7478NemaeOds+rzgSYhukx8HRjRm4GSRt6uXp3Gbuw24yBLU9OFfnhek46+l02iYRuS4juqrZ2Z69tjMmAKpxwE7Rs9uvKh6TdIw41y+MGcJcEPzGxkZV3cwd0GEPsBWGMBjFv7y8bC9AR7h+FzEDaaPhb4eiGWLwjNbZ8XDxtQXqCRfamkaCInmCxWDhtmxubtbdu3drd3e3lpaW6uDgoM7Ozurp06dtESxKSuHdtuQivZyIermegc4lTSg/6Qn6Rts8TnYMBkKeubW1NXgu8s1wzG0k5Ku6dnRmJzw3mRrPzTYxfg4XKTZk18144jQNwLm9z+3mfk+aGQR7faAOf/eyQlttxGbI7r/TP+g6405dBiODEm30EjU78JS9+zeZTAZ2Zwdl1pYMejy+nuHNCTWnpUaj0eDsxJQBemYdfFXJOQKPDyB+23LrnCEDuLm52RrNpMloNNvlMBqN2oEDeCYfFmADdd0+K9GsgMFxcd1WSgOVDYPPzSLzOxsEisf3hOkMFDkrJlOm02lLH9Du09PTevLkyY2wdDQaLkq2MdmwqZdkNm2rurktzgCOfMw67UDS+zqnxG/uTfbi8TKA0jY7FcbHTMoydj0JymaMvoexS1aUzLpnHMgl+9C7hryv9TsdCe3k/9TReYU2egxtvClnpy8sz14xGBKxWM9yP7j779yxQZNJPNJX2E4uY7q8vKzHjx8P8vOun35dXV2fjM/9zm96jIiCeG6vvErurFe8bf7x1swQT7O1tTUwGIDAeSWUx4zRoZEHuKoG6G3lz5AYJuUcAwrl/E22w0Dg/IpBz0DkmUjaS59ZF+WdMrCwy8vLdq4jp4LDSBgU8jE7Ozs3jKDHNviOZ5tFOE8K++sdx+XnOBzhebRpPJ4dRZZrwzJ8S0ByuxLgua5nHB7vZDZ8l/vbcRLUY6divXqVPpvBuD2MnfUgQd0ONGfxX1ZyxYOZXcrGzJf2+pzCHjCaRHCv16NmuJyOhGfxHOcwcarog+uzPlmW8/KqTvcAqmnPydIpjoZSly3D/f392t/ffymgDmR3q6tqFoO/8847bdkHi4S9DhCwsaCdQ8x9u1WzY5ySJfSOMJ9Op4PQyxQcg2WAbLQ+7IBrPXtqYzJL8iz56urqYC8s12A8VdeGQb7Q7NVhwNnZWcu5uj0Z0iCH7e3tWl9fH8xcAwj2/lUzheX0Gacs/LnH1TPmnh3F0dHuVG68dwKTtx4mmPi56IvbDyBzH0CNs5kHIPl/skbqzhC4d31GBx4n9I12X1xctPV8ty0+TMNjTzusK3yGTppF9QAR/WVFh08dMlD6eSlP/rfMLTvGhWPCfNBKRgCZV+b5maJAnxz+8xyPT1W1MzApKysr9eabb7ZTjNyHZNsvKwvPJj9+/Li9GYxQzmsM7W3H49mBCICpO+aFnhxWgPKPRsP3GGdYkQDJAJt6+zp7Owuf2TIXX7O0dP3WPPZ0Ehpb0AywD04wINN3swrAEiW3Mqa39swgdXiWlrrsYT1LDGCapZohJBM2GwbMvXKA98fQJxTeRuo0iMfBrMfytgN1SLa9vV2/9Eu/VN/5zndqc3NzAFbJpnvAYqdnmXKPc3NVwx1LbnsyHOpjR8dt99bSV1he1WyJE8VtMkAwjgkg85giQOaJST6/urpequW0g0Nfs1DqsE6jB5ubmwPAM5hlCsBRkB2+I0ie562q2S+fhEQa7fnz54MdNdT1srRCloUXXds7pSdx52GOCI0OOLywEftsQthIsrXz8/M6OTkZbD3z+2Fdpw0hDQRDN4D6O7cRVgLbIY/G850jyZC+qtqSBFbyI0PvlEnlNzBVzd5E59kxOwfA1uyb5yQz8hjY0LgeI7dRWKlgG57dd8rBoIj8MSQrPNfyN23mHiKOw8PD+k//6T/VD3/4w1pfX68/82f+zOCdyO6nWYkdp9mxgbHHyHrgwni4bkJP3vDoxe23Kb2ZeAOvl5fQN78fyAQh5c7fsDczdZ7LzpPNzc1GOpzbRl9zIso6sry83LaR+sAHnGTVcF0iThdmR+TlkNu25JSOi/ejgxl+9wtt6C3xe1lZCAwZJITAEhRAyahsg7FxGyST4aSHAlQcOjn36AmU6XQ6CAmoz+vyzMgQmPMSBkG8/fr6+gC4qmrQfgA02VzVbCkSC1oz/9HLwfG5n0eOKUOZBHQ8N0zds384GOcZKTZ215ugakCzw7HsUGKzEK5x7pF7rBseC2QHKz48PKz9/f0mf5ZxUE/mjc1CaGemcHqJft/XC/2QB47ZMp23ptLF44eTQ060HWBIxuT1l+iJgcbytF5a561H+/v7tba2Vnt7e7W0tDQ4Zi/1wjpnGV9cXLT1wIxbys2yA2D53JEcfTT4ecLVdtMbMwPqyspK3blzp+nHbcpCEyhV1fKFGJ5DqapqBwrk4aNulCk19VgwGKK9M94LlglAbm9vV1W19XgIhbyYJy8cjqB0Dg2sRNlGgNUzyk2I49lC1XlGBPNlz6mV1z84C08S0Q7azQ9gbvBMb2qvaIVCTjA9CvWicMkKkAXFTIr/DZIYIfcaULkeppWgbCaHPPzeaKICyyaNJP/2vmfLM2Vl1pjfjUbXZ3XSBvrmV4fOK34WThAdNRs0i59MJg38/YrPXp7bwM/nCWK04cWLF7W/vz9YAmU7Qa7eNuuo0PJnHay3mRqoPT5ghFMTsMk8Wo/P5zkajzFyW1paqrOzs3r+/PkNx/+ystAECoq9vb1dm5ubbTFwj1l40mRlZWXgSZ3DwJgtQDqPsAASBO/nnJ2dDba0ZZgxmcwOzXT4k7OFDlFM309PT9szqmYzh2YWyAcWxHMcOjmU84JqANA7LwBdTrpZWloanHpD/axLzHyZ0wM2TgMRz79z584NpuoJlzQqFNDytGc3CzRL9z1mKTYawic7AYMH42E2nGze/cvwiOsSaNKg3GbkQvtYRcC6O+/5RadeVvw8M1X64Ry728WKC4OeyQay9Vihq1zraI3+MZljgpOTFj5NnMLpMnt7e4PTi3AItI00kp/pVSFLS0sDgEcWPMufEfamjK2H4AK/sZvblIXCZIR3eHjYwsfMXdnjZQhg1gJIOvQ2AyDfgUBQRFjT8vJye6nU8vJy2yvt2VUrCgpr5sLAVM2AIP/GUz148KDefvvtVgcKkAZjUHWYatDwBIfDoul02k4p3tnZGRgfzNN5LJ/WbXCywiUzzOU5BwcHAzbtUJfnOA2Ss/uMLyBmoOF+xpDf7j/tWl9fr62trSZD65x/cDiZC817Mi/Is8gBZ84xw8EM4biOLW+AYOp9AvC8Aovi73RuFNbpwsj8HEdc5NT9vmzshXYSdfiVHPSF9Aq2jN5CMmivxxpAZBLF+T6PC/Vhh8jbE2X8zlPvsWkYdIbxqSMuttXblIWP8GJwQPO1tbV2MECGVcmgSDRfXl4242Jg3UEAgGd6kgLAYP8lSgRwGsgY2N6CZLzm8fHxAHAwfk/iXF1d1Y9+9KOmSJubm42hUO/29nari1lyA8B4fL29yWEA4OJwoReGI4Otra2WtLezoa30H7lm8jrfXuhwHZl5Ya+NLsE+2SHf8bl3GVEy58l9gLC3Ozq0tm6Nx8NdFAaqnpH0UjN+Ntf3+pBGNplM6uDgoIWsnpl3frW3y8OFZwEMAIxzwwYWdnohG9icAZE62PFUNTsKixQPz2Yt6eXlZTuC75NPPhm0L3PA7BLjtCQOEnnx4sWNWWmPFwWmRmqBvqQjsxN1tIUOshA7mbPHzv8D8Lcpi51XXrOZY14ZyqwUyWDnQczSUMKcUXLux7NUhNQopoXtXS0GOxQoZ0QdurMurKrq7bffrp2dnRY6HBwc1Hg8roODg7p3795g8scM79mzZ4MchoXOs7x0wO8ahu1RUDzPik+n09rZ2anJZFKnp6eDPtqQDPZmJZkOyJDQzsXGSxu8N9gs2iCSIakNZ2VlpU5PT2+ExcmwkNHV1fXrYLe3t9uundQ5/zZIGwDTGHoM0mEg91G3jSbvW1paakfX5YSbZcoznELJ4kkiZAOgOhcP+3cOl/vtSDY2Nmpvb6/dh8OwHcAS7aS8VIZ0En31JCaO29/hrNltdvfu3fr4449vjLdJjMfDKZmeY/OEUlW1VNLp6emNnSm2UZOI1J9XlYXAEBq+srJSu7u79fTp08FrPB0K0yF7YoCCQXTY5iPyq4YJ7DwJG68Gs0kP4/CYv1GqBw8e1FtvvVXf+MY36o033qjNzc2qqsYSnz9/Xn/4h39Y5+fn9Ud/9Ef1/Pnz1hef2Aw7vbq6GuS6PIC0AWP3WYa028lv7jFbtHI5p2Njc3oh868O6c2SUBxAEEdhZs7zcGxmtYAwbTGo8NtgSOkBKEwdHdja2mrtxbEcHh4Oxth/Wyf438zZbx80e7lz5059+umnA0dm3eU62nd0dNS2o9pwYW6OLF4GhtgB4GSZAYqMC7lCj51tbW9vr21vYxUBDjf1jHXBsMuLi4taW1trh8kiA5+G7Rlsbz1Fv8CE+/fv18cff9xYaG8pHbqVM9+9VAY44/Dc7BZ7ZhvvaHS9VtkL4J2PvU1Z6CXyzCQ/efKkdZZV6FxD5wmlDVS8LN55K2ZXGXByH84dYniEn3Qeb+U1Tm4Duza8hW48Htf+/n59//vfr6985Sv17W9/uynM7u5u7e7u1ng8ru9///ttZfvx8fHgmQYoDl1lIbr3YVfNUgNM4qCIXlfHbxgAoO+Xx4/H49re3q6Tk5Mbs38GSp6FEvk7FN77nmFYa2trjTFTpxPpmR7x5Bftow9m6HZgyCjBhL69+eabdXJy0maM06kZCHN21KetOPxEBk5dUOezZ88GjNgM22z6/Py8nbzk3Db9R895lm2iV5z7MxDQL8JQZMhYerLMgJCLpW0jnsH3Nch2Y2OjnbbunVZOiyCTTDnYyVkPaRMg7ZB+Op0O9BJ9MXN0P9BDzvJEfgmyo9Go6e/m5maL8EzAXlUWYoaj0ajefvvtevjwYe3u7tZkMqmPP/64Dg8Pb6y12tzcbMs2QPh79+7VycnJgNkZCA2CTrp6YCw0Gx1MwHWS4zNIwf42Nzfr8ePH9emnn9bHH39cq6ur9dZbb1XVdfj81a9+tdbX1+uHP/xhbW1ttVwJxobXpr2eUGGCByUxsHON10+5r8iianZ8Fm2HSV9dXbX8IUCD0jvXRDswsAyfSA9Y5gY3ZG+WCJgT8vvEYfoD0zBD9U6fZIrj8fUs8cHBQR0cHLT2ZFhsUHROyyDJteRnrTdVs6U1Hguu8Qw2RgswM05m2fz20piU5cuKQ0Z0tOra2Hd2dgY5d/qP/Dc2NlqIyj2WEc4Jx0f9ZsssmGY7aW/fM2N4cXHR8vJeiZHOON9lDcAzBrDJra2tAdtL+3a+2/LKusjVW9bk7m8bHlMWzhnu7e3VN7/5zXr48GE9ePCgNSSBj2QpNB/mkbNUDBSeF6BE8Uy1rbhmVM6LLS8v1/n5eW1vbzdA8FIRcg8ozYcffliHh4d1cXFRH374YS0tLdUf/MEftJB6b2+vhQ1eEgNI+VBb55K8a4T2e9cM7U1mAPDZK/I8FIcwCO9JHW4Dfa6aHSpq5UywpF7AlWeRPwTkYPcObzEK2DvfGWxstJmDJPRiu51Bz4wilbuXE8TRHB8fN0M3QNh40aWq4e4hPid8dsKfcSHqQBaedKuqlkaZZ5AZImZekDHLk9RxmBsbG83JeTypg3F1+sVLgEh1HR8ftyUoflOl7Yu6fTJVyun8/Lx2dnbqyZMn7T7s/+rqegkcentyctImXr0iAt1k3BhLfpOSIjo5Ozur3d3dxtwpsPlFy0JHeNH5N998sw3OwcFBffzxx226noFll4DDYe/EsEcAOHlG1SynhLHbc+eSHr5jgLxomZxOTrLwv5WKsKHqmjVBs6nfuTwULttmVgpbdp7PC1XdfrMuv07VyyrMRDxJkwCSjgMZY7D0FdbgcQB0HRoBVlU1ONfOQDQajdoSCzNcs2WzZH9WdW3cBwcHrT4Dpicn/EyDJLIyoOTuJ/TKAOvcJd8DCo5KPAOPE5pMJi3nXFWNYTmdM6+YBaF/DmUxfFZMYCMvXryotbW1QR7NztRMnTFn3AENbNOn7TilgExIWWG/lvtoNGo5ygcPHrTJL9oOqB0dHTWdgAxxH3ImH21dQuYAMHUgA+zi+fPnDYus7y6fe87QDV1bW6v79+/Xixcv6lvf+latr6/X//k//6eFuVZYDAkFWV5ebuHl1dVVOxnauUV7WbMlfnhJjcGR+swovA4MVmgFAay3t7eb0i0tLdXx8XGrD2DCy5MrhX2ikBgXrCi9tAHCAIaiYWBus7djwVAwNq4jlPOEiHNm9Ikx4RUEZg8GBgzLOUfnmWyw9BsW9MYbb9RHH3000BePh+tAVl5Y7vWrNuzUP9fHZ7Qp/89cF6GodcTs+vT0tI0Duuu+OmTf2tqqjY2N+vTTT9uCZtoHq3tVqGbQ9L0wLpZwGbQBFJ5Hn6wvTt+QhyW0hnlxv1MlOQnl55HLzHbyYjivk6QubJIohP56Jt1LZS4uLhomkNLhc+MA+swEkCMfdqthN7/6q7/60jGgLAyGDBogce/evfrzf/7P18bGRj179qzNvsJAqqoePHjQ6LOnzGk0JypXzc57w/jN6GBNKDchnZPLZgNV1Xa/EOKyqBlQOjg4GISUo9H1LCPK7RdYraystIXBgI/ziHgu/seoGMT19fU2m+ZXgQKepAk86cNzUWwnn2kzAAJYmsnZoSBHPLFDMgzCCjwej2tra6vlzQxoJNodzlxeXp+w7fDQ+UnGzCEfzAMZ0WaDjkPaDJ2rZiwSh5bJdT+X8XEeGkDA4JE7aQOvinDe7Pnz53VwcNBmZs3iUw+z5OeAGnp8dnZW29vb9fWvf73+8A//cJDOACQyBYT+k6oyaDNG3oFiOTrnSP/G43EDGy9twbZhgMie8aGd6L5fWcoMek4+eYxt5/QJnaBfOzs7dXx83CY30TXLdTwe16/8yq/UP/kn/6Q7Blk+ExjyIOj69vZ2/bk/9+fq+Pi4eclHjx61kArlQFmragBgTkJ7wsMDBVs0aC0tLdXe3l49evRosOiaUMDbd3KJAYL3khmM0wPDAlvWzrHEg/wW19pw+b21tTXYI720tNRkZuZGLoXnAVim/icnJ223gMGHur2Vb319vSknwAcYOllPO1HQs7Ozlrg/PDysqhqEYQ8ePGgsA+DY2Nho7OXhw4d17969QeiysbHRWAx6w9gD9BsbG/XkyZO2ppKSbJTi/JYZsdMNXO+JBC/p4PTyra2tevToUZ2enraDi91GT0AAetZfCkunAFtmaW9TAHCABf28uLioX/iFX6gf/vCHA2cCCcgIgv4D3uTqcMLch/74RBunbAwojjSch0T2VbM1gLBEO6Orq6sma/SScQBUd3d328vOtre367vf/W6dn5/XBx980MAeu9nf36/z8/Pa399veknfuXZ9fb329vbqr/21v1b/+B//43rzzTdvNw7TV/H4/1tgdlnsre0ZLi8v69mzZ3V0dFRV1RZoV9WAyi4vD7fSAXwow+rqap2entba2lptb2/X2tpaHR0d1eXlZe3t7dXV1VU9efKkLSk5ODhooAYQk6i1RwScADcGmGc6XPbCcACEUB0jwehRLEJXQuvLy8u2fZBFoycnJy1Uf/DgQasHUCPEYRM83vL4+Lg2NzdraWmpnj9/PkhDAOa0gzbt7OzUo0ePamdnZxAi4XBWVlbaxnbGkt0uXk4DUFdVG5eDg4Pa2dmp09PTdgIKR62RuD86OmrjCkCzVAv28t577zW9ARDW1tbaK2Ynk0kzbudWWYg7Ho8bAG1sbNTR0VFzEgDtzs5O3b9/v+kFDo4JF96IWFVN/maPsMuUk3WfCAQ9zeKUhye0bNyE2e+++249fPiwHj58OGCad+7cGTBDdMyggx7ev3+/gQ0TEPSFXCLMljY5bQQAMimJcyYiITe4srJS3/ve9+p73/teffTRR/Vv/s2/qY8++qhN1LAd8OjoqHZ2dlo+EaD2WyRhswAljmk8Htd3v/vdRpZIMREBrqys1N/6W3+rfvEXf7Hu3bvXUnG/8Au/8EqM+7HB0IWqJpNJPXv2rB4/flyHh4f15MmTJjjAgaUXJFWrZoC5tbXVkrxMwzOQ3M8i3Tt37tTp6WkzZE6FxttjeGaHXjxsBkFI5JxP1YwFMOO5vb3dlBN2RL6NsNrsBEWH9a2trTWj/uVf/uX61//6XzcPysZ3FLFqeOrH0tJSe+Mgz8QwuY89o1x7fHzcDBYjhSkeHR0NZp3ZOeLZa8AbNsD1L168qPv379cnn3zS9hYDrA8fPqyzs7NaXb1+5evz588b8F9eXrb7AEvG7MGDB/Xee++1tsJcHjx4UF/+8pfrv//3/97GCQNBnhgSrOjq6qqBNOM7mVwv8Xr69GlLeezv7zdHQoi/sbFRX/nKV+qDDz4YrJKA8TFzyiqKpaWldi/XccjHvOI1qQ71nB/c2dmpb3/72/Uf/+N/HEQeKysr7R0fOBaHl6Qsjo+P2/ITcnee+EEmx8fHzZZ6+T4cdVW1MSEEZgXHV77yldrb26tPP/20RWYffPBBk//29nb92T/7Z+v73/9+6/83vvGNev/991vKibF9880327jQThznyspK20bINbDc7e3t+kf/6B/VX/2rf7X29/frX/2rf1W7u7v1T//pP30lfn3mMLlXTNXv3r1b0+m0Hj16dOP7Fy9eNCVlCQu5KZaeYETO/VFH1XDLHcqIZ/zkk08aq8M4zZZQOIDDoAhI473xnAzo0dFRHR4eNu8Pi8BLMXPtZTV+LrnSk5OT+pmf+Zl68OBB/fqv/3odHBzU+++/X0+ePBmsj4TZsC7LSwwIR/b39wesy7O6gLpPLDHbJYT69NNPW8gO+6NOWMVodD0juLu7W9Pp9XFaH3/8cW1tbdXBwUFLjWxsbLR9rMfHx/Xw4cOWZiC8hkmRhzs8PKyTk5PGdDFI+vjRRx/Vs2fP2jiyQHtpaan29/drZ2enAf6bb75ZH330Ub3xxhvtM9g/rHN9fb3eeeedeu+999qOF8b78vKytra2WsThEPv4+LjOzs5axAOTgwmjny8DQUqPNVZdL187OjpqkwlMOlI/zoNDSsgzOw/vVMTJyUldXFzUz/7sz9ZoNKof/OAH7T4mSDzh4zw2tkYoDxB6p8fa2lrdvXu3vvOd79Tv//7vt4motbW1+o3f+I36nd/5nXr48GEDqPv379fJyUltb2/XwcFBY4537typp0+fNuZ5dXVVX/va12pnZ6c+/fTT2t/fr6urq6aPS0tL9eDBg/r0009rOp3Wz/zMz9Sv//qv10cffVT/8B/+w1pdXa1vfetb9bM/+7OvBq/6nJlhlun0epHz7/7u79bdu3dbKIMxAVYoNYO6tbXVWCC5DC/TAOAYlKurq+aBJpNJCxF8KobzIlWzdVdWYOde+PHugkz00gfCDU+08GxPPnDd/fv36+7du/X2228PJo8ePnxYv/u7v1tvvvlm84KwnlySwtFbADVboZxLIiTH67548aKFHPa0l5eXzXngXLgexfZMHbL1chT27eLZkTNOBMfAtrbd3d3WLsDo6OioRQOkQ2AujA+GCRNDtm+++WY9ffq0Njc3a3l5uR4/ftz6znXOR56entZbb71VH3zwQS0vL9f9+/erqurg4KDp0N27d+vu3buNuWDgXg5itnh6etrGiTzb4eHhYDKnN3HizwhHvU7unXfeqfX19frBD37QdI++LC8v1927d1uYX3UNpjBBQsvV1dX6K3/lr9Rf+At/oX7rt36rnYyEPkFMqmaTmCYURDew8aurq/p7f+/v1S//8i/X17/+9VpfX6/Hjx/Xhx9+2CKl999/v8bjcX3yySf127/92/U3/sbfqN/7vd+rL33pS/XkyZN6+vRpXV5e1q/92q/Vb/3Wb9XKykr9y3/5L+u3f/u3B5N9PP/dd9+t3/u937sxwTYej+tv/+2/XX//7//9evz4cf3BH/xBvfXWW/VzP/dzbULv3XfffSVefa7MMAu5oK997Wv1+PHjljdzvo38BJSbGTwzwJwRzNlUQiXAgRykl+fwLAoDzjIfDBXlJPxlrSGMz1sMq2aH2QK05KLw4G+99VZtbGzUW2+9NTh+zDN3VbP1m++8806boCEk98DTFpgjcnnrrbfa4tfT09O6d+9eyyu+9957tby8XM+fP29pguPj48bavZYLgCJEury8rDt37rQ0BPk3pxlwVLAG53eYmKGPtJfDUWHe5DFJ8PM5AL2xsdGuWV1dbVvIAO333nuvTk5OGvOsms1mMt45sz0ej+vnf/7n68MPP6znz5+3iIBUQlXV0dFR6y/GCcD95b/8l+v3f//36/Hjx03fab9n1O3Iua5nK3yPA6X86Ec/qm9/+9ttTOgPkQ1jaPbK/z7lZXNzs773ve/V3/k7f6f+9//+3/Wf//N/Hqz75Vr0DcLg5zLm3/rWt+of/IN/UHfv3m3tPDk5qUePHtXXv/71Oj8/r29+85t1dHRUW1tb9fbbb9d/+A//oa6urmp/f39gA9/4xjfq7bffrul0Wn/9r//1+mf/7J/V6enpYKyqro/7J+XiAsZ86UtfqjfeeKOWlpbqyZMntby83LZ53qZ8ocywanYc/8cff9wAA69DaEnyFWXD2AlnPdvsZRkMzOnpaZ2enjbFPT4+HqzD899mT8xOk5/EsB8+fNhyT0zwEFbAZJeXl9vJMtTpfN7V1VXt7u7Wz/3cz7W2I4+eMTAMzNayjMeg6dnlXvG6MN/jI86Q28HBQf3oRz8azJ5XzdYhEtLChqqqyZY9xkyskFPjWdvb220ZBw6C9j19+rSWl5drd3e3sWfuY0yYxDg4OKijo6MajUb17rvv1h//8R+3KIKEPzpFimIymdTW1lbTBa8bRH9YcvGd73yn7t27V//23/7b+uM//uP64Q9/WAcHB/Xd73635bV2dnZqe3u7vvzlL7cVAsvLy/Xhhx/W48ePazKZ1KNHj+pv/s2/Wd/61rfqjTfeqMlkUh988EH9+3//7+u//bf/Vru7u/Vf/st/qel02pirJwwnk0n93b/7d+vf/bt/1+xsMpnU3t5eLS8v18HBQX3lK1+p7e3tOj09re3t7Rb6PXnypL2xEoZOJEOEdXJyUj//8z9fv/RLv9S20b733nv1z//5P6/Ly8t66623mt09fPiw/tf/+l/18ccfNzDd2Niod999t+Vxz87O6i/9pb9Uv/IrvzKYOZ5MJvXpp5/W6elp/dEf/VE9evSo3n///frVX/3VWllZqf/6X/9r7e7u1ieffFK7u7v14MGD+vjjj+sXf/EX2wTH06dP61/8i3/Rxo+88/7+fn3yySctX7+2tlbf/OY36+7du3X//v36zd/8zbpz584gomN52nR6vTD8VeULB8Oq4d5QG7S9jYsZ3KCxr/CorsfhrMHTIUbWhzd0nZ6gSDBynZ4JvE27X1YMmC9jE7epI+vKsOxl9TuXWjXbG1o1HCMA2BM9WR/P9kLwlxUmNEgR7OzsNGbudAfOEieZbc81pL6W/7mWAz+c56VOM3H6fHl52Q7T2NnZaW2hvmfPnrU8J/1n0gbWi9N944032g4RnASnyJM2QMZEND3n576njK0P0+lw4bN1wOtnSQ3Zmfp31u82ICfkeEuoGRTuQR88u725uTloz8uIxucKhq/L6/K6vC5/msvCrwp9XV6X1+V1+dNYXoPh6/K6vC6vS70Gw9fldXldXpeqeg2Gr8vr8rq8LlX1Ggxfl9fldXldquo1GL4ur8vr8rpU1WswfF1el9fldamq12D4urwur8vrUlWvwfB1eV1el9elqqr+P2QCZlEJbXAjAAAAAElFTkSuQmCC\n"},"metadata":{}}],"execution_count":5},{"cell_type":"code","source":"import os\nfrom PIL import Image\nimport matplotlib.pyplot as plt\n\n# Base dataset path\nbase_path = \"/kaggle/input/nohanpneumonia/NohanDataset/train\"\n\n# Categories (e.g., NORMAL, PNEUMONIA)\ncategories = os.listdir(base_path)\n\n# Loop through first image from each category\nfor category in categories:\n    category_path = os.path.join(base_path, category)\n    image_files = os.listdir(category_path)\n    \n    if image_files:  # make sure it's not empty\n        img_path = os.path.join(category_path, image_files[0])\n        img = Image.open(img_path)\n        plt.imshow(img)\n        plt.title(category)\n        plt.axis(\"off\")\n        plt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T17:53:11.928782Z","iopub.execute_input":"2025-08-13T17:53:11.929378Z","iopub.status.idle":"2025-08-13T17:53:12.894251Z","shell.execute_reply.started":"2025-08-13T17:53:11.929352Z","shell.execute_reply":"2025-08-13T17:53:12.893492Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAUMAAAGbCAYAAABeXvJFAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOy9aYytW3rX9+y9a9pDjeece865PbgHaHenbYhAMQ6T7dgxjowdiRBsd0IgivIN0k2wlChAut1BQRFByYcEJIQUAr62Y0dYNgECJMEBA8ZDbBPj9GR3u+9w7pnq1LB3jXvvNx+K39q/96l316k699y+t7trSaWqeof1ruFZ//V/hrVWq6qqKq7TdbpO1+mrPLXf6gJcp+t0na7T2yFdg+F1uk7X6TrFNRhep+t0na5TRFyD4XW6TtfpOkXENRhep+t0na5TRFyD4XW6TtfpOkXENRhep+t0na5TRFyD4XW6TtfpOkXENRhep+t0na5TRFyD4XW6TtfpOkXENRh+RaS/+lf/arRarfKzsrISH/jAB+KP/bE/Fvfv34+IiJ/6qZ8q93/hF37hXB5/9I/+0RgMBrVr3/zN31zL1z8f/OAHy3Of+MQnotVqxaNHjxrL93Vf93Xxzd/8zeX/L3zhCyWfP/tn/2zjO//ev/fvRavVOlemiIiqquKv//W/Hr/39/7e2NjYiF6vF1//9V8fn/zkJ2M0Gp17nnp813d917l7lOW//W//23KNtvpf/9f/tbFsf/Ev/sVotVrxO37H72i8f52+PNPCW12A6/T80ic/+cl473vfG0dHR/HTP/3T8Zf+0l+Kv/23/3b8yq/8Su25T3ziE/E3/+bfvFSe73znO+PP/bk/d+76+vr6Gy7vyspK/PAP/3D86T/9p2vXR6NR/MRP/ESsrKyce2cymcRHPvKR+NEf/dH4Pb/n98QnPvGJ6PV68Y/+0T+KH/iBH4gf+7Efi//j//g/4vbt2+fe/d/+t/8tfuEXfiF++2//7W+o3C+99FK85z3viZ/92Z+Nz33uc/GbftNvekP5Xae3Saqu05d9+p/+p/+piojq537u52rX/9P/9D+tIqL6oR/6oeof/IN/UEVE9a/+q/9qFRHVL/zCL9Se/SN/5I9U/X6/du2bvumbqg9/+MNP/f7HP/7xKiKqhw8fNt7/8Ic/XH3TN31T+f/zn/98FRHVH/gDf6CKiOqXfumXas+/9NJL1eLiYvVd3/Vd58r0X//X/3UVEdX3f//3n/vOT/7kT1btdrv6ju/4jnP1ePe7311tbm5W3/Vd31W7R1n+/J//8+UabfVjP/Zj577x67/+61VEVH/jb/yN6tatW9UnPvGJ5ka5Tl926VpN/gpO/8a/8W9ERMTnP//5cu2P//E/Hpubm/GJT3ziLSrVLP3r//q/Hu9973vjh37oh2rXX3rppfiO7/iO2Nraql0/PDyMP//n/3x84AMfaGSr3/Vd3xV/5I/8kfjf//f/PX7mZ36mdm91dTX+xJ/4E/E3/+bfjP/n//l/nrnML730UmxubsZ3fud3xh/8g38wXnrppWfO6zq9vdI1GH4Fp1/7tV+LiIgbN26Ua2tra1cChclkEo8ePTr302Sbe5b0fd/3ffEjP/IjUf3LneQePXoUf+/v/b34yEc+cu7Zn/7pn44nT57ERz7ykVhYaLbw/Af/wX8QEWcqcU4f/ehH3/BE8NJLL8Uf+AN/IJaWluL7vu/74rOf/Wz83M/93DPnd53ePukaDL+C0u7ubjx69CheeeWV+F/+l/8lPvnJT0a3243f//t/f+25/+Q/+U9ic3MzfuAHfuCpeX7qU5+KW7dunfv5k3/yTz6XMn/kIx+JL37xi/GP//E/joiIH/3RH42VlZX47u/+7nPP/uqv/mpERPzW3/pb5+bHvf/v//v/zt1bW1uLj33sY8/MDn/hF34hPvWpT8X3fu/3RkTE7/7dvzve+c53XrPDr5B0DYZfQenbvu3b4tatW/Gud70rvvd7vzcGg0H8+I//eLzjHe+oPbe+vh4f+9jH4id/8ifjF3/xFy/M8z3veU/8/b//98/9fOxjH3suZf7whz8cv+W3/Jb44R/+4YiI+KEf+qH4t//tfzt6vd65Z/f39yPiTOWdl7i3t7fXeB92eJmJIKeXXnopbt++Hd/yLd8SERGtViu+53u+J37kR34kJpPJlfO7Tm+vdO1N/gpK/+P/+D/GBz7wgVhYWIjbt2/H137t10a73TzfffSjH43/7r/77+ITn/hE/MRP/MTcPPv9fnzbt33bGy5bq9Wae+8jH/lI/IW/8BfiT/yJPxH/5J/8k/gv/ov/ovE5gA5QbEpPA0wmgo9//OPxi7/4i7G5uXmp8k8mk/iRH/mR+JZv+ZaaDfZ3/I7fEX/hL/yF+D//z/8zvv3bv/1SeV2nt2e6ZoZfQekbvuEb4tu+7dvim7/5m+NDH/rQXCCMuBo7fFoiBObw8LDx/sHBQWOYDOn7vu/74tGjR/Ef/8f/cdy4cWMuqHzoQx+KiIh//s//+dy8uPev/Cv/ytxnPvrRj8bGxsaV2OH/9X/9X3Hv3r34kR/5kfjNv/k3l58/9If+UETEtar8FZCuwfCrOH3sYx+7Mig0pa/5mq+JiIhPf/rT5+4dHBzEyy+/XJ5pSu9+97vjd/2u3xU/9VM/Ff/uv/vvznWO/O7f/btjY2MjfuiHfmiuWvrX/tpfi4g4Zyd1YiL4iZ/4iUtPBC+99FK88MIL8WM/9mPnfr7v+74vfvzHf3zuZHCdvjzSNRh+FSeDwi/90i89cz7f+q3fGktLS/GX/tJfiul0Wrv3l//yX47xeBz/1r/1b12Yx5/9s382Pv7xj8cf/+N/fO4zvV4vvv/7vz8+/elPx5/6U3/q3P2/9bf+VvzVv/pX4/f9vt8X3/iN33jh95gIPvnJT174XMQZ4/0bf+NvxO///b8//uAf/IPnfv7YH/tjsb+/Hz/5kz/51Lyu09s3XdsMv8oTtsNf/uVfjn6/f+7+7u5u/OAP/mDju//+v//vR0TECy+8EP/lf/lfxp/+0386fu/v/b3x3d/93dHr9eKf/JN/Ej/8wz8c3/7t3964FM7pm77pm+Kbvumbnlre//w//8/jF3/xF+O/+W/+m/in//Sfxr/z7/w70e1246d/+qfjB3/wB+NDH/pQ/M//8//81HzW19fjox/96KVY8U/+5E/G/v5+o4c7IuIbv/Eb49atW/HSSy/F93zP9zw1v+v09kzXYPhVnjY2NuJjH/vYXFB45ZVX4g//4T/ceA8wjIj4U3/qT8V73vOe+B/+h/8hPvnJT8Z4PI73vve98QM/8APxn/1n/9mF9surpE6nEz/6oz8af+2v/bX4K3/lr8Sf+TN/Jk5OTuL9739/fPzjH48/+Sf/ZCOoN6WPfexj8d//9/997O7uXvjcSy+9FCsrK/Fv/pv/ZuP9drsd3/md3xkvvfRSPH78uBbXeZ2+fFKrqq7PTb5O1+k6Xadrm+F1uk7X6TrFNRhep+t0na5TRFyD4XW6TtfpOkXENRhep+t0na5TRFyD4XW6TtfpOkXENRhep+t0na5TRFwhzvDx48fl76qqotVqhaNyqqqKyWQSCwsL8corr8T3fu/3xquvvhpLS0vRarVicXExDg8PY2FhIU5PT6PT6URVVVFVVXQ6nWi32+VcjIiIwWAQo9EoFhYWYmFhIcbjcbRardjf3y//TyaTmE6nMZlMoqqqaLfbpUytVqushiBPYt3a7XYsLy+XeiwvL8fp6WkcHR2Vd/i9srISJycnMZ1OY3l5OU5OTkp+nU4nFhYWYnFxMTqdTgyHw/gP/8P/MD74wQ/GyclJfOM3fmPcvXs3/vpf/+vxz/7ZP4t+vx93796N+/fvx2uvvVbK+8u//MtRVVX8vt/3++K3/JbfEr/rd/2uGI1G0W63o9/vx9/+23877t+/X8q/u7sb4/E4vuVbviXW1tbih3/4h2M8Hsc3fMM3nHXqwkL84A/+YKytrcX29nYcHR3F7du34+DgIJ48eRLj8TiWlpai0+mU71RVVercbrej0+nEeDyOiLNNCuifTqcTk8mktNvh4WEsLi6WdqZfx+NxWVbH+/R1t9uNqqpiaWkpptNpjMfjOD09jePj49Kvt27dip2dnTg9PS2yRv1brVYsLCzEyspKLCwsxOrqaoxGo3jy5EksLS3F8fFx6VvkYDAYxObmZvzhP/yH46d+6qfiF3/xF2Nrayva7XZ8/dd/fXzxi1+MyWQSW1tbMRgMotPpxPLycvT7/bh582Y8fvw4Xnzxxej1erG7uxvr6+tx586deN/73hfvete7ot1ux+HhYa1etCnt2G63yz3GhOUT2XU9kcX8DGPH7cM4oq2RS54/PT0t7znPk5OTWFxcjOl0WvrP47PdbsfJyUn0er2SH4kxSHk91pCDVqsVk8mkvOvx3pRHXsW0t7cX9+/fj8lkEkdHR/Haa6/F7/k9vydu3LhR+tmp6dplYj8vHWdoMJxMJnFyclIW308mkzg9PS2D6K/8lb8S/9V/9V/F0tJSjMfj2NjYKAOz1+vF3t5eAcXpdBqLi4uxtLRU1pu22+0YDAaxvb0dCwsLMZ1OYzqdFoHrdDpxfHxcrp2entYaIlcJYaGzyJNnLUyAOp3p/PIEYPAln7W1tXjPe94Ty8vL8eEPfzh+5+/8ndHv9+Pv/t2/G5/61KdifX09dnd34969e7G2thaPHz+O119/PSLOhGRzczO+5mu+Ju7du1cEtqqq+LZv+7b42Z/92VhYWIherxdHR0dxenoay8vL8ZnPfCYGg0F86EMfivF4HC+//HK8/PLLpcxum0YhaKjXvLa8KLXb7XOC7LwzqLn9udfU1rl8ue3z9xh8rj/9GzEDl9XV1TIp0PadTqdM1gsLC9HtdmM8Hke73Y6NjY0Yj8exvLwcGxsbsbi4GHfu3Inv/M7vjFu3bhV5RLYnk0kMh8Py/vLyciwtLZVJgvIsLCzU2qbVapW2dJs0tVVuI/Ikj+Xl5Tg4OChA5/rzDhMXkxxt5t9VVcXy8nJt3XgGer/LmAWsDYidTieWlpZK3kxeTX05nU7jN37jN+LVV1+N6XQax8fH8fnPfz6+4Ru+If61f+1fq32T5DYhvSlgmDuACvP/yy+/HN///d8fn/3sZ4twcXJau92Ozc3NePXVV2NxcTE2NzcL8BweHsbp6WmZOZeWluLg4KAGZFyrqioODg4KS2MAPm3w5s6dB5rPIyGMq6urUVVVvPjii3H37t34lm/5lvjUpz4VX/jCF6LX68U//If/MI6PjxvfjZixKhjq4uJitFqtOD09LYK2tLQUJycnsbCwUAbx0tJSLC0txfb2dmHPpH6/H6PR6NJ1pTwGmKa2i6gDrieU/HdE1ICz6dpFeXvwLi4uxsnJSbk3r1/RAKbTaWGUy8vLJb/FxcWiMZjxA3BMpLdv346jo6NotVrx3ve+N777u787lpaWYnl5OZaXl2tsbHd3t4AUJxcCTLRrt9stz3OtCQwnk0kBlHa7HePx+BwLgp0DTktLSzEajQoIAV58B5bI35Y9jxf6hXzyM2ablhXyRXvjeUA1Y4knSuq8vb0d29vbhdmenJwUzeqDH/xgfPCDH2yc9C1rlwHDKy/Ha7VaZeCByouLi3F0dBQ/+7M/G3/mz/yZ+MIXvhBbW1sxHA7j5OQkRqNREdwHDx7E6upq/Kbf9JviyZMn0el0ot/vx2AwiMFgEK1Wq7AeWGCn0ykNNRgM4uTkpAABlR0Oh7VZommgu5HmJdhh06C8SkIAdnZ2IuJMtX348GF867d+a7zvfe8rO1J7ENO+ZggMiIODg2i323F0dFQrP20HE+H96XRatubPaoyXxmXW7G9yH8Y/nU6LNnB8fFwrKwzd+eV6RcwYBG2UGSBqeETUGDz9AggY3K3em5lQZmsENrEcHR3F4uJiKb/rYBOIy9/pdGJ3d7eA2erqamxvb0e73Y61tbXC/gAxtBsY6MnJSZmoKDOTPiBq0AZErJ5a9TTI0EYLCwsFTJlouUf/zlMt3YZOud/QBo+Pj+P4+DgODg5iPB7H7du3Y3V1tZGkeHxCoGj3JhDmnV6vF8fHx/HkyZPY2dmJw8PDmE6n8Y/+0T+KH//xH4+PfvSj8dt+228r7xwcHJTNgWHll0lXBkNmWBf2V37lV+LP/bk/Fz/7sz8b4/G4dBw2km63G6urq7GzsxOTySS63W688sorsbe3FycnJ0XILfgwIjMKZvSIqA2GXq9XVHfKRfLg8DXPiv7f+dIR73//++Mzn/lMYaLPuqvx0dFRLC8vx1/8i38xXn311fINgwrlQeBtLyVZRRwOhwX0DCzLy8s1wDLD2N/frwGR3zN4OE+Alj4yYNAfgBfMwIPLNiTqYBbgAcoEe/PmzWJCsA0u9zECn1moJ0yAAUY5nU6LjTizNdtKFxcXi32SPnRfTCaTePLkSWxubhZZpv1PT0+j2+0WNRD1EELBfdTUrLp6TLjNUEHdVlV1ZiN0n9s26bbLMtE0SWbw4j2/P51O4+WXX4719fXS7siA6+LJkXueqHJ/+v+qqqLb7Uan04nV1dWCI51OJ973vvfF48ePYzgc1jTPbrdbvp3766J0aTX54cOH8XM/93NxdHRUDNU///M/X2aw8Xgcw+GwzLioYRjBGSAI3f7+fplZuGa116oKea6srESn0ykzA51vO48peVaFrQr4vpmP6T0/OAoiopTTApM70wOz1tj/UjBpD96zWmEWBJNhEKMeOxl0XL93v/vd8fnPf76ozhFxbmChdhhcDXYG52wP9MCw+sU1q9RNtlXeye2U2b1ZIykDOPc9gCmr2R1OEeqN7GDKoI2Rh3a7XVOVYXfLy8sFJD/84Q/Hiy++GHfu3ClEYWVlJbrdbnG8AVSorWhAx8fHtcnC32LiRFasphosx+NxIQm0KXUGnFdWVkqdzDTNxFx/JgW3u2XdWgRaH2zXNsumCdZ2SWsVHjN+9+TkJI6OjuLx48fx4MGD+NznPle+uby8HOPxOH7jN36j9OkHPvCB+J7v+Z7Y3NyMo6Oj2Nvbi6qq4uu+7uviaenSYPgv/sW/iO/4ju+I7e3t0jjj8bgwQby/bmhYAs4Rd5ztIQgLM57VRLMwhK1JJaQDPMiumjz4YRCUDwHh2lXzvGo5smDkQe40b3Zteu4qzzTZ8VwO2ikD9NsxUW5PDMhQt9uNXq9X6svgRN2k7zudTnH24Vz54Ac/GDdv3ize6pWVlQKUbkdkstvtRrfbjaWlpVhZWSkske/1+/04OjqK8XgcKysrhell5szYQuWnnGZmJycnBQBht9TZ7UKbUG4z+gyK+W+rz00TlJ2fEedZPCDeJHdoewDhZDKJBw8exMOHD+Phw4fF/PDFL34xjo+P4/T0NH7lV34lPvCBD8THP/7xuHXrVhweHsb6+nrcvXv3qTJyaTX5H/7DfxhPnjyJ9fX1WFhYKAfuABB4Nl2ZbEPyLGj1i3wyu7F6mNUDpyZQbGIbTXnYk2cQZoAD7OTfBCYXGf+fxfbod8yomurme09LV50gmsrexICfJTEAv1QHKdGvvV4vFhcXiwNuPB7H0dFR9Hq9MjCXl5eLtrK4uFjsT9PpNHq9XpEDyACOPVRgT06AVMQZEBigaAe+NZlMig3OWkqeBAE9ZDSbMdBckHcmduzOAC91MtObNwlnYDTr83imPKQm0wYTg8N3XBb+5t2HDx/GvXv3ihaK2oxT0WN3MBjEv/gX/yL+o//oP4pv//Zvjz/6R//opY+1vTQY/v2///cLwzs5OakJMWqejf50GJVmdnJMUW4I/0aIbCymsfCoZTuSv01yB0fUwc9CaEO0O5PvWk3I6Y06Wy6bnhV4nsc3sF36mczcr5oua8t5I4lvUM79/f1z4DKdTkv4CRMhgIGaBgve2NiIvb29aLfbcXx8HL/2a78Ww+Ewtra2aqqnVev9/f2aHRNGZEA4OTmJbrcbw+EwImZxgZZnkwzGIqAcMQMZVFazNjshnVc2C/ga5cissMnWl81PXOPHmiDtmxloBkXKBVs+PDyMra2t4n1/+eWX4/XXX4+Dg4NYWFiIw8PDmEwmMRgMYmlpKf7BP/gH8a3f+q3xnve851Kycmkw/KVf+qXC1hzUSsNmZuf7eaawfS43uhu0KQ+DZtM73MvGZ657xvWM2sS8mjr3yy1dVoV+Wmp633bCZ0kG2Dcr5XLPm7iIXCA2NsfdYZ96+eWXSwhOu92O4XAYR0dHMRwOY21tLW7evBmnp6cxHo+L2pm9v7BJvoH9lqgM1EbLus0tVnVRk3mn1WqV72JOMkPNbNXA5vFoR1cOuqdd89j1eKXO4AJjDabqvJpMXraH9nq9Ep/86NGjWF9frzmdxuNxHB4exsHBQfnGwcFBbGxsxC//8i/HyspKfPCDH3yqrFxpBUqv14vT09PSwUZwz6hUDDugG5TkhsszVnb925ZgRpjtV015NzG9DM78b9tFBlLbI581XcZm97zT8/re81ZnPZjeDqmqqhKPZ5CCwRHjyT3YGZ7oJ0+exKc+9alYXFyM9fX1Eq/IoLasMviPjo5qoIsjEEY5nZ7F2+IctPZCGSyvts9lkxFl8H3XdTKZFEdLJgDkke8xFpqcJvwfMSMuABX1yo6VTGhOT0+Lp3h3dzdWV1ej3+/H9vZ2HBwcFCes7flMNkdHR7G1tRU/8zM/E7/5N//mS8nApcGQEADsGhlQIs6W0O3v75eK3rhxI3Z3d8+FZZgyR9RXPLjxAFE7ZDyLNKmu8wy9/E0c2NbWVty9ezeqqopXX301jo+PazGRjY31LwXHZb1Kuuo7sJQvlRp+mfQsDqGmRATC88rveUw00+lZbOZgMCggZraILS7b5gDM7e3t+MIXvhAf+MAHaja2XE4AAYY4Ho9jfX099vb2Slwt5QFwiLRot88C8G0vjJhNVjAlVHGPHz9n77Tt9yYm2Q5InvzPhJE93R7ngDCA7PAlCFOr1SptydLMqqri/v37MRqNSl2XlpbiC1/4Quzs7BRTHREBW1tbsb29XfOW7+/vx8nJSe2c64vSpcGQMAF3Kg0EQOFNxtZx7969muMCNXreDGJwzDa8eYwxDwD/b+cHwdrveMc74rf/9t9e1sF+4AMfiNdeey0+/elPX3geb1WdxSy5vG6HN5qa8pnnsHkr0/MCZliXlz46XbXel3n+Mn3FGvV+v18Gc46fxMvMoIZlTadncXe9Xi/e+973FnAhAgLmFTHzZgMWqNaUE9shAdy0GevjWRZoDckB48QWwmoNWAD8dDqNw8PDmtrs5XYeex632blDH7ovTWYIj7Nn3rZQ+wLG43F88YtfjN/4jd8obLWqqjg8PCyhMoeHhzEcDkt7TqdnMaO7u7tlomm1WnF4eFiWrl4mXRoMjfpUGFpvFcANwUziAmYbHY3i+D3PTk0A2GTAdedxHUFpt9vxwgsvxOnpady6dSuWl5fj1VdfjVarFX/n7/ydePDgQYkjnBc6k/N2eZ5HMuCT3mx72luZqqoqXtMvVXJUwEXp6OioOCGsfnrgkwfAaPX1c5/7XLTb7bh9+3YBDUDT9nVABSeLAYuYQzsObc+DTVVVVdR1PNVmZ37G9jvMXVZlHbOa7fk8E3GevHisoary3HQ6LfGUsMM8tokjPjk5iVdffTXu3btX8sUmizf59PQ0Tk5O4smTJ9Hr9eLx48eFhDFJRJxNIi+++GIcHBzEr/zKr1xKPi4NhvOMm26c7BkyiNkmmJkewOoG8oxnY3K2M2bPdAZOliN9+tOfjna7Ha+99lr86q/+am05kWfeeQPlsqD3NPbh+/l7OdTgKz09r6WPl02XtXtiP/SyOq6TD6ABqABcLAT44he/WLzPhJ1Zi4qoBy87RIQJmWBxvg0Dsp2eccNyVIO3GezJyUksLy/XQA4i4jo6Tte2PLzTtj26TaxmmwyhpptB2zEF+97d3Y3Pfe5z8eDBg6I67+3tFaDsdM52hTo+Pi6LLkajUVGrMyE7OjqKR48exZMnT+L+/fuX6vdLg2HuQKgzDUGj+DlosStPymqvl83kxnZc1jwgaVK1rWZHRBEmAPJpoOMtuxCMN5r8TU8uGIzdTl/JoOjB83ZM4/E49vf3y+oUT/IR9ckXtoMKvbCwEPv7+/Hrv/7r8b73va94dllBZZDhHuyJvBxaFjFz4BlQAEzetQMmIopzx0yU8ZRXa1nmYIlmsrY1ZlOUvc0k8vECC9syHUO8u7sbn/70p+OVV16pRZoQfE7sJYCaVxBRLkwNrPsejUbRarUu5UmOuOLa5OxN4lqm1HaZm+ZboOwRNpCaKdru4BmVe6QmG2T24rmT/NvJ4NztduP27dvxyiuvXGmFxbMAWBMgfCUDIYm+fVp6qyYHvL0wFcAuMzzYzeLiYm2vyL29vXjllVdiZWUlBoNBGbx4cSPO1Gw237DNDY2FEBzYnQGy3W6X57DBApatVqvE39lOZ60OtkrKgdge68QXOwIAZkxYkJmqx7TLR5/zjfF4HJ/97GfL9n6UbzKZlPX1JO8lavND056JR0dHpf9YV/60dGkwNPKbktIAdnpQeZ5zA3ngZ1DNjW8Qyw3clIfLlgfZZQYSIMwsw4afX+r01QCEEXHpSeZL1R5NJg7U5eXl5RooG1yQa3vHASBC0t71rneVQQlL8jpjvu84x4ioxRBmdtbtdgtQEq7i1R3eoAGV286OvHMM/9skZRbr+kI4AGNCjwg/svpt5yleYDzBn/vc52J7e7tswMDyQxwgTAgAsZ1Ru7u7ZXNi2x9PT0/LjliEJl0mXRoMc1hMFpwm4yqzBQW16mtjrt8xkDrxPSo3z7aWZzTydbpocFG/t2q97VcLEL4dU1PbV1UV+/v7ERFlsPtZ/7b32ePjwYMHUVVVvOMd7yjrkhnweI0zuABuNqFkDQr1mPwMwgYIkxc8uzBRwIt6ZUcP4MLzfN/hbjC+iKgRIq/Phtmi+g6Hw3jllVfi3r17he0eHBzE4eFh8X6PRqNiPiPkbTQaxcnJSRwfHxf2B3ECG9hQF1b9a7/2a5fq/yvZDG0vtJHUggOI0Zj2MGegymqSG852Qtscs9rt75L3RUD4tHQNRm+v1MTW3qz35z2LB9P2Mcu+ZQ57l8fI0dFR3L9/P1qtVty+fTsGg0GNaWZnohmgAREV215mmCVqKJu5mp0RshIx80TbiQLrcjmwu2GH83ZoMNxWq1XbWcpgzLuUEZvfyclJPHz4MF555ZUYDoelzDiiYH7Ly8sxGo3K/0tLS+WICavGxgz7BCjbeDyOd73rXZfq/yt5k93pzF628TWBY7bluSNIfq/JptikFmdDLveyofs6ffkls5I32o/PSw6Oj49r23BlB11Wma0aUg92HWe7L6/6sIc6YmaPGw6Htf35YGloLjhJAByAzaDn53NMoIO8YXKsySYR0xgRNe3OY9AbynrHet6fTCZxeHgY9+7diwcPHpTYP2yOtBlxj8R7gi2AIQ6Vfr8fe3t7sbW1FQ8fPox2ux2PHz8uO+HTBpTtMunKm7tm4OK3O9NARsNZTeadDKwks86cRw5N8D3+fh7pzQLUy+Tb5Kj6akqwpoODgy/pdy9qb9b5YveyIR9Wlif0iKhpRhERT548iel0Gi+++GKsra2dcz6yvMxqJzvL+3uMNYDWIApgU8YMjHwLVum43ojZ8jrAksDnVqtVgMskyBogjMwAjEr72muvxf3792vf4xsc+wErZuJhRyHYIOzz9PQ0tra24uWXXy4APBwOzzHsq5i8ruxNzkGnVlkzUGZA43nPoM7D3zDDa7LPOGU1/Hmk5w1ETbbMecnt8dWYCIB/uyW8pqilDnWJOL+VG+ojXlPGzsHBQbz22msxnU5jc3Oz2MIBF8YGQHNyclIYE9/HwQBrwrSEDTAiStgOewREzLQz2K3XRlMH6mHTGOMra3VZEwMYJ5NJOc5gd3c3Xn/99bK0zt8DkLGVwgjZe/H09LQsU2RiWFlZidFoFAcHB8VmyCbIxhvKcVl5eqag6yb118/MG/gGs9zgpBzMmdPT8nbHZTZ6lfS8meFV8/tqZYVv58QgZpDafgYTyeuKcVJEzGxs2LI4/vLmzZu1IOqmwGTGy/HxcW2ZWradR8y267IaC9tsmmR5njwYw5Tb30ct57rl1F5nNlI4OjqKhw8f1uySHBnMpAKw2fvNJgy0M+07nU5jb2+v2Aa9GmcymUSv1yv23U6nE2tra2Xv1aelK4XWNDlD3AkGuAyM2e6XZwcA0pSb1PStfD0LxBtVM6/67tPA7u0aXHydLm+WAGDoa1Q328uaAqMNksQY4kHd3t6Oqqpic3OzqIMGLjtibEPlmstOmfiOgZJyeqwBGHiZvbaZ+uAtpp0izu8bih2TieLw8DDu379fjgTGgxwxixWEBbpeo9GojBPazHug4lDysRsANfZFs0KcQ2zO+7R0pdAaGiTHANo+kkEhC5jfs5EzO1+sijfl0yTA84DyS5HeDLvlVdK10+jZ02Unqjwh46UFwPKSUgCHa17m5nAzlp1tbm7GYDAoYOngbAMYoTtNu1VHzExUtkfa3uc1ybb1W00HaFgjjQPJJgEHRbOc8Pj4OHZ3d+P+/fsxHo+j3++X7wBkABh50i42D2EacFB5Xu5HvWwfxDFDfQzET0tXZoZPU5X9/0V2Brv7yS8DbGaMObkM+f5VgSGD77OkecD/pQCpayB88xODLqIOOIRy4PV13B0TPqEq2LFgYIwTNiY9PT2NtbW1AnpmgAbAiPo+gpkh4lTIMY+wPoM0rNBhPBEz7zJMjCVylIeJgK3vWPGCSgwzc4gcwAdIcc40JgXa2CE9JPAC22FmxNmpwzk1+VzyeenK3mR3MCmzPRqZe5e1A2Z7n/POAdtW15vKcVVweFYgzPV1ugaor6xk9mK5ttpntgZAEUvnUBvvdgNbq6oq9vb2YjqdlrOHI6LYGSkD38dBkhkhDhtIh1V4gIN8AC9i/KzSA2KU2eUm9pLjVgkAPzg4iOXl5djY2IjXX3+9eJJhprQTIE/QucGQCcP/2/FiULVa7brS7tgVL5MuDYbepYaOprGaDLlUNquPVjXMGqmMG5/rdLDViyZ13OX4UgHRtS3wqyfhhc2AB8h412XvBr+4uBiHh4fF+YCKme3bjBO2rVpbW4ter1cAwE6HiCheZH+bsYNKmp2bNmV5nHEIlkN4qKPHlQ+O932+vb+/X8YsXl/e8S7h2Codb2jbIOyPNswmBjuIjCWeQBYXF8tyvcukKzPDJhCiADaGzvP68hz3sl1j3neyI+Yie6LzumZn1+mNJMJZYFSwksXFxXJKG3LspXoAGI4UngVEYJGcb0xcHe+dnJzE7u5uycshNQADB01FRPm2CYjBDhZpwOR5AqSbVnyZAKEmG1TZOR72ximDhAJ5xVhElBUlVoHZXsyOGN6xahxRxwWr1bQ7daI8jtl8Wnrm5XjZaEuD+VmzQ2aPJvWa5+xFzrZA3zMwGhT93ecJgtfOia/OxGA3M6mqszX3yLW3zCJ5JxfkEyCNiAIWgKE3YgAUYWh7e3vFZuednQFaf9vjYTwen9sYAnZpexuqZN781WQFhnZyclL2TYTZLS0tleNTYXFWX21bjIiiirNHIW2YN9K1s4QQmf39/RrDdb3z+HQZvC/kRelKO11HRK3AThkAfZ2UKbpnjAyIZpfOJwOwUwbS55XeCmfMdXrzE4PassggZzssQjb8TkSUEJJ2u108wPmgNAahz87pdru1GMTJ5OxoS3bXBnSx41XV2Zb3Ozs7ZWswl3swGBR2uby8HCsrK2U1h9dSU5/sADKLpdwwNpwcsLgnT55Eq9UqoIxqyxpjM86I2a5EtMl0Oo3hcFiz+R0eHsa73vWu2N7eLjbIiPoG0uPxuGzo2rQDlvHIWirPPfega1idXfY5VtALyOd5fymoHSIu+Dzwy/cy6L6dmNs1EF4+eUOOL2UCrACFlZWVODg4KLYtVDk7GswOeQ8QxGtroMK5ARBlZyL3GDP2QPMM5IBnUA3xTtvDu7m5Wc4Ksf3PXm4Okqf+OHW84evJyUlht6j2BwcHhW3yjOMUbe4CF3CsUH7y89K609PTePjwYezv75eAcrNR2sDbnJHcPnn8057ziFNTutKBUE36OX/nOEF7sp6m3vp+nq38XkQ9HvHtBIDX6dnSVYHwsgHST0vdbjeWl5drKitBz+yj54m+iVHB1Lxm1iADAABMjqljGR1t0Gq1yioM4gwZ6KioeKQph3fTAVh2d3drIM7uL5Q1s0vK1ev1yhI6ArQBwsPDw7J1Ft8jfpDys16YUCOYXQ6roexmo5yg6bYmJCdrosYCfme2CAjy93M/EMrJBbIdMINhBj+HGxgQzfxcCVKTPYDf2bHypUrXdsQ3L13Utnkifdb8fVCSt6gy87Gc5UiKiPp5PrAqsysb+iPqtiue9cYH5E/8nceHgWgwGETEbCduxhqMkNi94+PjGA6HhaV2Ome7b8OEq6qKtbW1AqR2chweHpbv7u3tlbbyNloRUdt/saqqUgaeBdRoD1R/AxygCRvPmqWdOU1M0DIB0+X6VcbolVagZL0c4ycCYOcKP75H4fIKlizcTepCpscWnjfDafK0dA2EzenNtJdeVbh5HgbEgMSIP51Oy4C1g9CbMSDj1I1rOEcAQ8t01oaQYzMu1FtsbXwP26E3YXXw89LSUgyHw3LdQIvTBvUVlgkgnZ6exv7+fsmb3WJgrl4TzYYJ3ANQCQmyHRIvMOqygYtttwBbmC7jlbLRdnjuveyONjUour9sUouoH3SFB/4y6coOFCrBDJnV5ozYPJ+dKmZ0Gej43fR+Dt9xma7TW5+eBxBe1JfPAoYA2srKStnlhAEZMdtGanl5uQAFYGetZzqdnttWzFt32bsLgHgQ+yB4ZBevqhnePGYJAKGNAdLebn9lZaUAIvVBFT08PIxut1tbgXJwcFCYHECEA6aqqsIgh8NhrK+vx9HRUXnH6rhNAiTsl6jk2AoJhAasCFUy2fLhaBnwrEU6ua2NLW/afoYZeBASz4p5vSOVyBWhUxwtT8PlCmeV2DNE0wB5XralNzu91Z5nt+/bPV21jNkcY7tfXr3BCgw0F06dyyFdZoQrKysF9GyDw1bupW+E4LC9Peo5oMQYwP7HJqWAFGPC3m2r7nbeYBvkKE2YbMRZoDb2UgORHT88Z28u9eTdiNkOOrBDe66Pjo7K9vskO2C9xI82oM+8Q7b7nGezmk3K5jWzw8ukK+90bfDJs5wLR8fzLmzOsx0zoO2I5D8PIJpAkrJY8JvsCl/KAX/Z773VIPRWf//NTLkPYFLYBr22F48raiHv2tsL88qbi8AA8wQMG/Qa5Ig68K2trdXW+/q8ERMGQAIwdsB2RJQ9AAnJQf0FHEwcOKUPB9Ly8nIZx7DmTqdTNlVgTB0dHcXy8nL0er04OjqKbrdbziGx3ZAQGU8m1BfbJ4HmEfXVaG6bPBnR1tyPiHOeeLNFMMZge1G6Ehj69zw9PLM//53V4eyB9r154OhyZGBs8lDP+/+iRMe8kZCPy37vKxmM3ox0lUnNWgep3W6X0BHb60ajUayvrxcgsgoI6ORve6lonsAdbuIVHaizDOKdnZ0CSjgQqKfB1c4GO0kAcNv/OFMEG1+2jXa73XNhQ9THh63B8kajUeNBWI8eParVGQZJm8McscEuLy/HwcFBDfDpD9oLkKQ+Tf1NnbPXmJTNcrY9XpSuDIb+f55Tw8Jh7w7P8YONghnJFc3C0FTpJlvj09JlBhMd9aVMX2rm+uWarjqp+R1kChBkgB8fH8fKykqxZRFaE1FfJw9okacnfG/h5e+h9tqeFVHfbxBnikHIA91rnllF4jAenBB2zHhpG44Q5BoPMMcrwCi9kSrA5rNTXF82WMU0wPpjApz7/X4p//HxcSmPwR4vsusWEQW8MwGzQzVixrANfhmHaOvLpCvZDD1gm2x47szMBvP/NJQBrkm1dazis7rMnd6ugPN2LdeXc6qqKvr9fhmMETNzCoMYlra4uFjU1aWlpTg+Pi7rbg8PD2MyOdv8wNtcsWmD8yF/2wwtv5AEBqhDTgBAa0yAB8cC8C7xgnwHhseY8rpdB1GPx+OyCgZABLDNoqfTaQHVVmt2Wh6ThXei4Zse07Qlz5nkeKNZq9FuKy/gwDRghxbP2T5Ln2fQvKxN/kprk426eabzc/zOasRFTg/yMnj6mWyPdKCny3edrhMJ21cOxQDErL0AkD6fGAaGegdYsEkDjMi2KQ/miChsK0/oVlMBCLMsnsdZkTUlwAYg5dsOIRqPx7UT5vr9ftlOHzA/OTmJx48fF7U2IopNEKAiKPv09LQwNrNJgBnw2t7eLuefMFbZbdoraHASWV23x50JxiTLXmw7kNz2JmrT6bSs0X5auhIztP3CHU6HGMnd0VaXM7U1+FFZGsPfzcZpN8Bb6Y29Tm/fBGi0Wq0aA2KlBxsMnJ6ell1lsMnBahjUgGWn04ler1cM/KiwEXFuMrfGFDGzI5KsDps1+nlv0IqKzNjj2aOjoxKDCDDCpjwGUUsBc8YNGzUYyD1OHfNI6nQ6JYwHD3NVVfHgwYNzYT959ZpZbsRs1xrGMzZPnh2NRrUIE9rXY58+I1/6a55foildedca/+2PmDVaoEgU2vq7KTL55rxcaYNp0/cvW49rBvn2Tdnkku/N67uL7qE+5rX1JycnxcjPYMUJgePBcru8vBzdbjcODg5qAcp5UvayORwKBhfHvZnFWJPK8bvebABGBfEYj8dx+/btGI1GtWVvrPN1jC52QntjbePEiUI5ut1uVFVVnBx4jwEtGOje3l5Z281zlIX6sixudXW1th8i36Ws7BxeVVUtWNvtlbHCQO76vGnMkNQkeA6YzKEERmeryw60JBkIyc8V8z3PotfpKydlQc+G9KZ00T2HeJnl4WW0Cmubn+PTlpaW4saNG7UwGLQT5J1k7YYwmCZ7um1trqPHgBkdzJIQG4Cs3W7Ho0ePzq2VNongPep7enoa/X6/5GEPNd8n3IglfZPJJB4/fly28WJbfTzFAKxDkHDuAI44qjAvwHCXl5fj6OiotoWZy8sSwSwn+QfwpN6spLlMutyuh/8y0VGetW1DdDS+7+cO9kzq+5kR8qy/f9nBMbfC7StV+VIp202v07Mnm2L4/yqpyY6d1auIKIwB1gKj4V0G7+LiYvT7/XjhhRdqQcqoolYvI84fbJbHh+XbDhY/y33YkkNeAB2fDcK4IKB7OBzG9vZ2qSPg4h1hfHQn7BJygR2Qv9nZmg0bUN9RgR0IbUeNA9mrqiq7dh8fH5fve7WP8cCmMsc8erKymc12WRJ7NLKW+2npDTNDGyp9v+lv/29gdF5NDJBkMMwM8rLpzQiZ8WYV1+mNp3lOtsukp72DfHnFBP2HWoeTZTAYFAcGKzJgX2aFztv2w4j65IuKnI38fs4MkjxQ3w0QsCocL6itvV6v7FTDMaRra2uxtrZWbKdWKdvtdu2I0ogo23yxCocysXMN9WPcw1jNqMnPnuNWq1XOMKaO3GO5JODJzjdmwQ6wxvtPwqPvdeY4WvCEPy09c2hN0+zrhLBkp4efx1aQHSZG//zu25GFvZHB+yzpK/VbTs/yzauUFVsXoIPjYWNjIwaDQSwtLRVwtP3PQGIgnBddkbUdq68us6MuyC+r46iddhJ4/bFDe7CTPn78OO7fvx9bW1sFcNiclU1p2+122fyBe95vcHt7O/b398skwfdZ9eJljBFn5GAwGNR8CBFRvNisXAHUYH0RMyeTgdTABsP1GnEmOEcImHQ9dzCE0tKpTaqqhSHPHJ5NM3hQGefl/7P68EbS0wbMswz+eUz2zUpf6UD4ZpQhq6NMtgyglZWVcn4HoMN7DnL2+7l8TaqbzT0eE4wD50G+jC9Ucjs+DNy2u/ENNlYYj8/OLK6qs00WTk9PY2dnJyJmThPyfMc73hGj0Sgiomy/j/2N8m5ubpaYzeFweM5OChOkXgApanWeKAApxjkmCwMedcVe6EkIBxZ5oepzHUDv9Xpx//79S8nIlbbwAhBtAHblbOfhp0mIKGzT7Ejj8K7fI++I2QYHTYPlogH0tIH1rAPvrQCNNxusvtR1cvjV8yoDQGLGYxXRDhBscwAlu8FwP9sGsz2ba02TNwzTz1g19m8zPP5mzbG9w4BJRNSCyDnXhHfW1tZq9lHAg8PreZ/NYtvtsw0ZVlZWSqA5XmXKA/BwNAAgVFWzbbnIj8R3vfzP6jJhSoAf16kj71ql9ze91BEQ9xrop6UrLcebN6O5sp4ZEbgmG2K2/zXZTDzLeNaMqJ+j3FTWr4b0lVbPbHu+asosjRUkBjyzNhviM4NjIwRrQbzHcwzqbMJxfk1qszUs5zNP4+IdxoUdOIShYFfEBsh5xQBMt9uNjY2N4ulttVpx69atOD4+jsFgUMaaA6gBNHuz+/1+rK6uxnA4LMsXuWd7bLvdrm0W0TTuvf8i38Huyp6T7N/odjQ25M1il5eXa23S1Afz0pWDrm3zyI4Mzxq5c7mXDce5kSxQfAMQzg1yHWz95ZEu21cZeK6aeJd4QLOv7PDI4Mh1Bm6OHbRqze9sysl2Pq6T/AzjgdCZTqcT6+vrZa9AMzbGHCAAY20ai3h3vYKEOEBWoxDCYpMVCQADiDJRabVasba2FktLS7Gzs1O87q1WqwAwkwHlpI0AfR8ib1aISYB2wnlF23nPx9wnOQ9rlva8X5SuDIZeepQF1w4To7sLTT789i4XEfXNW6mw2aDZ5HX68khfqokLeRoMBrVVCAxiBzvb4WGQM0jOe8Yqc/YW58HKcxfJLYD15MmTiIjaFmLkQ/ltP0RlZs9DnBM2Nzjwmd1n+B6hLrA5g7DNYrYH4uFFBWXvQpge78IObW+tqqqwNreNlw8S7E698vELNqlZS3W/2SmTQfWidCWb4crKSty6dSs+97nP1bxffNQdvbS0FP1+v5yf0MQGs6qbjcj5+9luyLPXwddvr5TB7yphR2/EDrq4uBirq6vnWJrBKYfC+FrWPAyETQwS8M2TdQ6fsZknaz5ZbQeQPBYch9ftdgtj45uowqj2ZoQOn9nd3a2V06FCrMRxWxGTGFFfCXZwcFBsiw61cZ3430HQrg+q9Xg8jlu3bkWnM9uyi9jN09PTePToUakTy/TsPPHfOIUGg0Hs7OwUUvbcbYYREaPRKL72a7+2/H+RXeTw8LAcHmNbI+Bp0KOx3FHOy7OqBYq8st3yGhjf2nRZG83zTqurqzUQM1vIzM62rAyQVtUizq9QccpsJSeDo+U8a0lNDDViRhgAOgKQyddLDUejUa2csC4Ynr23HlNeLsh91z3vtWiWiEkiIoqaTPIxobZ1OoA6IsqO2FU1O6ArIoo3HaAm9hDAXF5eju3t7cL+6aeNjY2yqcRl9zKMuOLa5Ha7Hb/8y79cs99l43Ke7RzSkPMzuF0kZP7t+/n7Tf9fpy99umpge1P/+jp/R5xfhcTfCwsLZSA5hCartlnltCxbpc2OFedJOeaVPatw8zQfe2B5xuUhXCQzLYOhGeHS0lLs7+9HRJSNJ8gbZwPqqz3orVYr+v1+WXYHW1xYWIj9/f0CXhAbh/kAXARzE39oELQdNZOhDPp4f31cAc8RRgTw4TBC5ihPVVWxtbUVOzs7sbu7e2lMuBIzxG1tFz2JSrrT59lH8nsZ8LLR2l6hrHp4KdB1evskT3DZ9GEbkkHH8XO8677nHmoi18bjcQn+jagDV1aDUdMiZkHUucw2vmetg9+57L5vZtUUY5fbyYAL07PTweE0PGd266V02ODZ7SYiSqgLNjnGHGrnZDKJtbW1AmCUGzWaMlFWArSxGwKABkqffwLDIw+fzkf/wVr5m77HIWLbrzEhT2AGzbW1tTg9PS222KelK4GhG8Q022qFvWS8Q6fnQZFBzECZ1WLPnswyfO/tnr7cPd9ZPbQK1TTIzbT8v4HBAyNi5vwwGBhEzGbsYLBaCYg5Ns0qow36eZ2wy0uZyc9y7vrYu+myZKaZv9F0zRqU75MvDNFt1DSWALTxeByDwaA4VLzaptVqRa/XK7GXrOHtdrs126HZK98cDofF/AVo0f5mcaxpdh+47vRDRJRzYGC8nLXiUBvKz5Go1NlY4PpRh/X19bh169aF8k26Ehh6UDcxOgsKHUtFmux6pvDYIJwv38lC62s2oL5d05cDEHqy8uDMIShNfR3RXEdPjpkp+psY02E1DPQ8ePCEZhB26BVAa3BkQBlMs0mHumU7YQa67FjJThPe8xkrHqx8JwMi30Edze0McOCNbTI9YY+zukpsoZ0pVVXFYDAooS8EZHu7LINrxBlLG41GxcnB9wC+qqpqzhazQTN9l9vMkc0jWq1WiQ+FvbqNsTsiW97c2dqiZfDFF18811ZN6cqhNU4WWP+N5yt3WGZ4eZD5XtN3/X5+761Knp3fLulpZUJg6DOEzPedzNIy07/om2ZSmVlZNrwaBBUnA16r1SrsgIFC2AWbjGYWCRjmcBnbrTJrz5O765LLTZ5mLs6zSW7zuPAEQNm8Q7fVSX4on0OF+B6sl114tre3Y2VlJe7cuROj0ai8B2PkpDzWYpttubwEcKO22mvNc7QB7ck2Xt5+LIfkTafTwkZZ8cK3sD3SznmSsJy47wklcjkuk94QGLqxLGAZ2DwYnEdmeh5o+TtZzfCs/Vamt/r7TWnepAXoZVuZ3zEbyZMWfWigyn3clF9+P1/385lNZXXXhv+IKOqZ2WxmuE1MqknFb9JuMoNrahvKlJlmk0Zj8hBRZ7U3b94sy+hMKgw4k8kklpeXi63OJgP61vF57F7TarXi8PAwlpaWYm1trWbroz0fPXpUVqsYdGFfg8GgqL5msrQztsDsRR4MBlFVVe169i/QDoeHh7GxsVEmg/X19bJumomQNjIYGsQpH2z3svsZXmk53jzwyZQe4XM8FB2f7SQXqZDzBLBWgX9Z4bcjKL2VCXZk8Mtsbp6ZYx5wNbVxE6B4Nuc7Llf+PikbyRF2BrwdBlxvt2dBtQ7gt9pqtcllNuj5mmUShpFZSNOPPZ3ZsWdANkg77MxbUMECB4NBDIfD2oYMqOGZvVn17fV6xUTR7XZr5xXDAFmGd3BwEPfu3YudnZ1iRwS0+v1+rK+vF3UYNortDsbGQVGsYMm2Vve1D9+aTs82gQCwcMB4V3IcPX7fcum2trzAlJ87M+TD+SCbPHAsSN7LLL+T85430OapxVmg3+7pS8FiEQSCVhmAuY2yqmJQyNea+sDJk1uOobMqmt9pYpcIP6qe4/N4JhvLDTzk4Yk2q8i2Ybqu/r/dbke/3y9L4/w9D27KxpigLLZjm10ycGF8JMDp8ePH0e/3i8qfl+Qxubn9AH4ACbXULJAdX7zV1mg0Kmt+q6oqO3lTVjuZvIPM+vp6PH78uHa0KSALa1xeXq7teOMQIsspbcD+iYD9yclJOUDKXvKI2Zko9kjzHfrDfb+yshKbm5vn5LYpXdmb7GRBzTTVFaEB8js8Y2HMg28e2DmPtzsrZCa77BrJqyQGGCDosJU8YXgmJTnINtv4miYtMzQGFvf8vp/PqrTBy2pmDpLmWwZZywWDyuEnBh/vY2c1O5tsDMqUAebCvVzmi+rMtwEmyz6/sdtRR7yprDBxkDOkAhZMOX3ynlkx4MxmrIClxxZtAohSx5WVlRoL9moYYgk7nU6x69k+Z5tnxCwsis0TbEbgHYCWzSMo/2g0im63W9uJxs9zZrPjOcl/Y2OjnPhndv60dGWbYWY4FpBsM0FQmSUyANIh+RvO055idyaD5I14ap83W0Ngso3CNpLnmTqdTjnYPDPBrJ4YQDxpZZYY0RwkTP14J2sE9AvOBJ43sGZ1qQk0HXvGzikGyfxNbzYKW2DAZm3C7/q7/J0ncurqJXduR7ePB53tYZkELCwsxOrqagEU6te0mzMg1+v1Yn9/P46Pj0tf20yAAwRPs4OeI2aBzG4Xlsu6zYjr8xj0mAVgqavlyWPcJgq8yRGzse4VKYA17HV1dTUODw/j9PS0trdBVVVlv8mVlZWShyc4+nI0GhUmyS7fl0nPtO2/E0JEp3oWn06nc5fDZDWtyaOXwTf/3ZRP07Pz0vNmlFVVNRprnzcQYo8lzMBgk0HP7ZGZDu2e1a4MhvztlAGXa/k7lDf3jYEIpspgcXiN49EMEv523sEFkMiqs+vj/91+TezT9j0DBWUymLk+3oCE4GWfmQI7Qj56vV4NjEww1tbWyuoSVFO+M5mcnUsMaOI8cf0AHZgcjph+v1/+9/6GZuV2auF4sSPLHm+YJDZNbLxZI7BMOoIAdkycIke1mnHCiGGAlJHvYWccj8/OjbbX/aL0XBwogGBWlS30eWY2vb4sQ8uMNKvlTYz1Ky0RF2YAyUyLawg5KfeF47KaQM1te5Hdr0n9yiCaQSiXyd+02u6QEedjA73LZ+A14Pp7+X8Dx9MmEJfFsmsbYWawAKHBg28Cfma2gBZlIuzk9u3b5ZhQmzWq6mxVCKfd8T51gax4DC8uLsbBwUHZGRstDnXWbcvKFYDccX6Li4sxGAwKuE6n07Ilv+tk9mn2v7i4WJgc5WTdMaz/4OCghP4wofA3ziG0JFgofQUoXyZdyYHSJGCZLiMYWQ3mf2YVOtHCnGddz4x5wPH+V0NigMEGPUBz+/K3+yG3bRNI2ACd84m4OPSG32apTX3rfHkPMM7Aa2bm5Hp7aShqY943k9TESjNoI8NeD5zrmkHeoOzvwZwc/0g7w2AAlYjZAU+sHrG3OGK26UGv14uTk5Ny34zsyZMnpdze5JTvoJra6cC3bty4cW6ijJh56E9PT8th8WgmOGxgcKjHto0bXN1X7kNHDWC3ZONYawje6AF7sPEEtRrVnPzelJ2um/5uAqqm2bfJ9teUv4Utz7zZwJ/B8a1MmaU+j/wQdKuBWcXMv0l5AJJfU7vPA56sRjZ9Z961zLCyiur8cz5N9Wu6bobi9nedPKFyL4fwkJe917QH37AKFjHfI8+3vGM0zwHW/G/GRnt5qRkAQnvhrWUdNmpzRBQWldcxR8yAttVqlaM8I852+QGoADOr7ZQNxndyclIOkSKekLzQVuxlZlJZXl4u3uHhcFjqTT1hg3kjhn6/H7u7uwXUrZ6j+tN3EVFW33hZoL3WT0tXYobZQcJ1qxgGPTrdTNEqdH6uCdjyrNvEEN8O6XkAoQd4tqNlT2tE8yagzisDitW7eYzPqrNVVqt9vOeJMKem/HNZDY5NwJLtd9ZCMrvN7eXUpM5bZgEAq7v2qGZg5zcTNoyHwcoRo/aA5jp60BuscEbwbWx9ABKMaWVlpcQgAnKZLNAmdibxG5WS7xwfHxeVGfWW38fHxyXQm11grH7adkkZ6Cu84wDeYDCI0WhUHC6WacpBm7H0j0nKdm6bANymBJwje51OJ1ZXV8/JRFO68lGhF12jcBTCXkXu01BNqoWfaRpgb4ZH9q1OZoBZfTQ45uf5m5TBhWtOVkm8XtxCZdCwI8J9yXss1rejIINKBl4LfmZhTZOeWaTBycmhQvPe42/aBwDM5TFzzCYal9XlzPYzvKMGOfcv5UF9oywcWM+h7W43q+6TyaTY+1ZXV8uhTJkomOHxPd6nPMQj4kzZ29urhdpQXtYBe42zTQQwR4Nrv98vq1m8ZyKOosXFxeh2uzEcDgujJF+844TcnJ6eRq/XKwyWehlLvJsWdlaA8zLpSsyQBvagbWJ6POfZsKmTuD6PNWQQbZrZv1wTFL7JexlRX6WQByT3zW5yWxg43EcInI3s2fmQwbndbpcBR0KQuWa7JgZz73YSMVMLKQu/bQ5pYp88Z1AxUGWQdF7znDlN7ep+oI8ykJqhm2lzAh/xf1VVP7w8v2+Vlu8dHx/XogSw6VnmYUmwxKqahaPs7OyUuLwcDpTlZTqdRq/Xqx2YdXh4GBH1U/coK+Vw0LqdFRFRsydypABbhFHH8Xgc6+vr5UCp6XQaa2trheWyFRg2v6Ojo+j1elFVZ5EatNHKykocHR3F2tpa7O7u1vDGE3S73X5z1yZnIbWthms0kg3zmeE0scA8qLMg8+6XI0uE8tsOFTEDPntMsxqYB3AekDzT9Lzb3cx9Y2Oj9rwHPn2G6tftdmN/f78MXjsrvBknwMBgxCFgVmOZaGJ6pKa6NclH0wSRZc5yilxaVbUpILcdk4cnY1gdKitHaWY2Sb9aXs2ebVYy+FEfYupgj6iAABh2s5WVldja2ireZjN5x/tOp2dHB/T7/bLfH9/b3d0tYGRAps75uFPvckM7sy0/bJB2RV44lsH23v39/VhaWop3vvOdMR6Pi+qPzfLg4CAODw9rHm/6yeTMO3lbhi577MQzqcl50PnjmQlmoMt2GRtSm/LI3553/zJlv8rzzzMhPJ6hmcnMXjxwMyPKqlkTIyJ5snI/9Hq9WF9fLwJJYDO7hkTUBypCjfrBpp7YlsgfdYRrNmT7UHPAk4HNIM8szUDm/w1yeeLIAJsnbrNPJgP3T2533gU05xniV1dXo9vtFmDzhEdyX1ubssOC8gMwfj87dwBMgIx4ul6vF7du3Yrd3d3iqIDtMaFFnAUlP3r0qJw94j7CEeNwq6qqCgCZ8DB+c718uFR2NAGAljFCbB48eFBso7BDABmtwyBP/WlvVHgiLvzeZdKVg64zaDWBlP/GPmUB5Z08EEyn8zeyML+Rcn8pEsJCR3mwGhib2B7JRnzy9MDN6rHBgLZiMN+4cSNWV1eLLQb1xaYLt63bHFDE6J6B1nFzrht2Jg4h5x7qjsNIMhvLwJhZYmaErr+ZSp5YmvL2wHF+eQL1/+12u2x0QAgI14kdzOtx+Q2IeGWSmRVse94k6RAcM0bAhTXJxAcS/gKgLSycnSDI2Dw+Po7RaFTsnq3WmS2RvHL/OA4SkMbmFzHbp5BJ1L87nfrhU7l+EVGW4bXb7cJ8DZ60HZtFODogawSTySS++MUvXjBSZ+nKcYYkd7K9PZk+28aSZ1aDYY58t8B6wFqw3yqmd1EyM3C9s13OtrqsVmWQ9DN5ps0mi8ze2+12bG1txeLiYuzs7BSQyu3IoM5tCmOAWfqAbtvMrHKa6bRarbKIH3bS6/Vic3OzCPbx8XE8efIk9vf3i3d2ns3U9TQo06ZW390OpKxK877lOYNiDpFZXl4uHkoAhnq7P9hNp6qqokY7DGgyOTt9DkBCTnBMsQECdkTXBTkjL1gmAcoOoWF8AlKo+dj3vNEEebFHJDGNy8vLZWxTJoMp9SJPs2PGAqBIe2fzDap8RNScSYx7L9RA3ffej4w1PNfU87mH1ljgbOcwbc02EKvETYPMDNHXyS8/l99/OyU623FhGdx4zoPcbM5MsGkQ5wE+b8D7/5WVlej3+3FyclKi+iPqKlpEfT0tjNKqDsLOFvEu1zyPsctNXQkNGQ6HsbKyUkJE7ty5E61WKx49ehTb29txcHBQ82pm1ks7GsQsK5ndGfS9GiMzSdrH18h7MpkUp0Peeh4A4bc97DBAZMOxhdSPJWjj8bio3cgTu+cASPQf/cYSuYi6iuqYP5ML6oOTw/21vLx8bs9L5IaxbG9uVlGZiFBXLTc4fQB9q+RMmsgLGgX7GfKOQ4i8t6LHFYk+a8KPpnRlB0oTCDV5jimk352XLLD+vymPtyMjZCY3+83gxrWIODeIMwtqYil5kGdwcMosE5UHIUONwyngUAbAKmLG+KxGkgfX2u12ialjIJJnToAaAsq3dnd34/Hjx9Hr9WJjYyNu3boVx8fHsb+/X358HobrDCvIjK5p8qVtmkJmaFfq7HxwBBA2w/cAEwZ+jg4AlBi0bJjAdUAUFddgQzuhDuYdcCiz25nnaWcDJj8A9enpaQGZVqtVTBm0CbvCAI57e3sREUXtpjx5z1L+B+wAcjZi8MRCfSaTSTly1MDJ5APgv+td74p79+7VIhgoP+3iEwWRy+e+7b8FI4Oi1UEzuqyy5Pea1BXf89+ZebwdAJElQqb7jnVqYnkesE3Ok3nvPk1dbGKS9AcqkwXdk49V78ykUE/MYiJm6vN4PI79/f3CbvBwRswGocNhKJsHMeBsr2G/349+vx+3bt2KyWRSdmze39+v2ZvcDnxzHlv1YATUmBDcB25jwJYVFK1Wq6wAcWC1+wagIBzJ9ljawfY1QBSnh9VL1GXbGmFTxOqRmLhQZSk7wBIxA+RWqxWrq6sFGIldRKY9vuzQwE4HKwR8vcaa+gG2VTVbxeLoAsxqpKqq4mu+5mticXExtre3y8YRBG4/fPiwtnMPdaYMfBtwX11djd/5O39n3L59Oy6TrqQmm/U0AZmFsKrqS4t4j/+bGF+m8rYd+rtvJhBeBmgR0qZQmHkAxv8Rdbthvsf1rPY5Nc2I88qfn7MAYrOyzY/8Sczi9AmCzCRA6MfR0VHs7+/H8vJycSz4G9hvLEc5Zg/ZGY/Hsbe3F8PhsCzG73a78e53vzvG43Fsb2/H9vZ2bcdraycwkmyfNRi3WjObWxPrhrG02+0CzIAo7UR5zXawAdImvsffHjs4OQhEPjg4KLbCiChhOzC1HPw+mZztRg178iargGi73S4rWehDH7dq4PeRAAZu6snkaLmmHjn8yO3uLdncJjkm8vXXXy9noXAONH1848aNaLfbcXBwUGsLgyP9Sjk+97nPPf/Qmia7nu1MNJydBWaMCJs9jxnkyCvf9zctTM87PQ0IAQHbzcwqmpiKy5nV4SbQNBBloONak1rM79wnEVG2ckewEcCIKMCBV84C7MEbUfc4myGiIna73Tg4OIjHjx+X/wFH4tYYELk/c/vzbQYmA7vb7catW7fi9u3bsbu7G/fv3695YGEeztNMK1+jPQFp2oDnNjY2ijPBgceUz+1g7zg2PhLlsQeZ/vJkMRgMYnd3t9xHvQQwqCN9a8YP6FA2xpuDu1utVtllBsYGe51MJjEcDmssi3Zz3ZhQLffuN+7xDW8CaxnnHQd5s4sNph3Uelj81tZWYbmenCmn++P09DQ+97nPxYc//OFz46gpXXltcr5m4MqDNAOaQxA8ezgvOpBnPGtbwP3cm50YaKgXBq4M/H7Hv/Mgo05+N4NhE+g1qbZN7wB4HiAMKAYxAsZ5uQhWVkNzWflt1gkjwzs5HA5jb2+vxqIZzDBEG9m95x3l9GoWBifATfhIv9+PBw8eFJUK5uKBR508Adgpk7UQQGJtba2UzzLuPjAJoJysmIiIAnTeZSZi5nygvCYSeJ45rAmWiLPC3mI8vDgmslPMsXeUcTQaFQAiDwAT4LJazkawh4eH5YwS0nQ6LVrC8fFxKbPZOn1qmbQmiM2QvqF+PhiLNlhdXY2tra3Y3d2tmQzyOCFfyniZ9Ey71uRrrlhWd+1hsqqS88kD3yzSdJyUVejnkTIrYUZmmZHrmlXk/DcpG/yzCmwAnAeEPnwnz6oMdrNvhNOqSv4mgGFQ9eTE//nHYQwAhdVbhH9jYyPG43EcHByU8BNA0EwuywfthTB3u90CgpTNXsnl5eW4e/dudDqdAr60WZ5s8xZfmdVERPEGw0BQOR0WwsTmpXHkj/oGk6V93A9W7XPC1oczg+9b1cT+BtCZsXoig2keHBzUnC+0D4BI3CDfoS4wc1T1fr8fEVEDKtqWtuL72WPvHXtYHudxTbusra3F3t5e8eofHh6WCWI4HNaORnj48GGt/B5PZsP03dPSlRwoGZyoSLY9+F6T3c/sw04X3nGnkHIeTX8/z9Ruz3aTzkBiUMpl9A8De9797CRxfnnmtIqd65sHYy6rZ2LnO51OS4ArHk0H5lJH22Zy5ED+28DWbp95YHu9Xpyensbh4WEZXIuLi2UzA5wTXtIHSGKH9Lm+MEMAYWlpKe7cuVOOkDRI2YGDSYD/aSuYoGPvVldXY3t7OyLiHHDSzmZ3gP7JyUmx77kfAXqYnQOX6WPanjqwSQG7NTN54JjJNlF/czwel/aGZdNPKysrpc2pO3WgHx0+Ywa6v79fzCIRUdvun3NcaENkmTApZMoHlgHYsE6Hp7VarbJGeTqdFtvo8fFxdLvdeOGFF+LBgwdlUqDs1tgWFhbi7t27lxrzVw6taWJkZoJZlbCtKrMaD3zu54HOYDawZPX7eSY6i5kph274h/L5b/LwIMiMmXxzOzYBbBNT83P0B0uq8jfzgHEZYXMAQa/Xi8ePH5dwCAA2MykzZ2sEtgWaoTCzs4KFQN6I+o4tABzAxQCzI8JqmlnA2tpabG5uxv3792s2T1ic2Yf7otVqFYePbVHD4bDGdMljYWGhDPTx+OzgogcPHpQjMgHo3A/IMOEqnCUM6LEqCFWWOnrrrul0Gqurq8WTbSbm8QPQ93q9slSPQGmcUZado6OjsskEKz5IfNta3tbWVjm/hUnq4OCgqN8+2mB9fT0Gg0E8ePCgyC11QXWn7VZXV2Ntba3UjYkD1duaCZvN3rhxIx4/flzAFjMMnvFbt27F+9///kuN/Ss5UCxECFZensWzNjJ7IPu5pm8g5KQsuG8mI2QtbQa8rOqaGbqMBh/XI4OQ322yGzrfiPPedq4bRHjesyozr/PgPdqaWZWyv/DCC3F0dFSM2LAKJjTeX15eLtu1A5hmb7AeBp2Dcfv9frExMaCxVaFGYZ5YW1uL0WgUo9GoOIJoM5cbT+jDhw/PTVyuu9uY3U+Y5KkHA9TgHzFbUXJ6ehoHBwcFANh3L/cp37PsYh+EPbKu2/bT3H6ciHd6ehp7e3vRbrdLMH0+c8eAYRskZYF9ud8tJ8gQ8Y30GfW2phIRhQ3jbc/yD6uz4yjibMnd6upq9Hq9WFlZidFoFDs7O/GZz3wm3vGOd0RVVdHv92sagNk5dsSVlZXY2NiI7e3tMkEhn9xz31yULg2GBIdG1A+8sYpkQ3RmL03gcRHD88Dz/08D1GdJCJdjtjzoLgIqnrXQcD8zvaZrzt+paXB55gek8LahunDP5+hGzAz2gJptf7AGwkEWFxdjbW0tVlZWivoHWESctysR+8WgRhAJM7HN0gMJhkMe3GObpl6vV+xnEXVA5z3alOVbuc8YoF6s32q1ot/v12LfHCtnlRZVGvsnbYraz/JEM7M88Tjle3jLaQuHv+SYQpgbcX7sQQizsqPH440+abXOvOMLCwuln4k7NJBlUwdABngeHR0VuWASBFxtWyUP7KCrq6tFTtksBNC9detW3Lx5MyaTSQE3H3NqTQY1nj7e3NwsTiRrnzdv3oyIiM985jPxzne+M56WLg2GWQXK6l0WgotSE6AZVBmweVb39zJIPmsCUDJzyGyuCawy22tiH/OYYpOpgPo0AW/+PgNnOp0WJoXAMsg86Jg1czkiZoG/gJvP4+33+9HpdMpux56kEHx7bx27uLi4WFRj7Hy2K7psABPXKX+/349erxftdrt2UhoqGnVut9tlV57d3d0CfgaFyWRSnAHZs+5IgYiZlsA+gYAFKjCs0O3ptb+WzzwZ8h0YD7/H43FZ7tdqtWpaF5MtgdJeM8xKmNFoVO5hLzRzjojY39+PtbW10n7r6+vR7XbL7tOYClCJ2TcQNkk/TKfT2NraKitEJpNJ7Ozs1NqAw5p6vV5sbW3V+t+qsm3H2Ok5/CpvdkvfexXU0tJS3Lp1K+7du1ezadNm2EKflq4UWoMg+po72/colGfGbGfydQNqBgoPEoTojYIgbLApZtCDwmzOZbJgm7k1MUa30zygayqD28fftn2NVR+0tVUKt2c2WeTv8C4qku1sLJPCjkP7w2iy8FFWjOms2ADMrE5b1YVFwLwePXoUnU4ntra2ChP1lu58z4vx7969W/bkc7/lqAADMrapfr9fHDatViseP35cQji4j4rL5ID8GKABMUKG3C95sjNoOpSEiQTw5bpZqx01DtxGU3AQNt/mTGK85XjgcVQQagVo2SbJ/2zfb0cVgIZ8YhdFfhYWFmI0GpVJxH1OHjDQ4XBY6o73mF14Dg4OYn9/vwAcjqK1tbW4c+dO3Lt3r2h4yMG73/3uSyDCGzxE3n8DWHRu3i1inq0v/8+1/I5VkTcaX8guzQ7GzQDRxPJcNqtQZpP5nXnmgYuYptvF76D+ciSkjwy1sFp4KSsAllVdvuHBgBri9bDedQVgsq2NPCOixtgAtl6vFy+88EKZ1Q2o2KKsfkecscPXX389ptNpbG5uxo0bN4qNzmBmm+by8nLcuXMnHj58WLyg3W431tfXC/uy7SlitoKBpYBPnjwpKjHv40FmNQjaRNMkTv4GYBgK6pxlwyCH7dRnGqMuRkQBWFjq8fFxAT47/gATykTdPSEiv1/4whfKNVYUUTeHdWHf5HscSMXEho3PajdydHh4WPote4uZaDFnuB2Pj4/j8PAw1tfXy25BmAaGw2Hs7u4WJxHhXAA8bTUYDC6Cg5KupCaTMnhlBkhHWegMlE4e6Jkh+nvO41lZIYM6B09TjotYoMsKEzPgeWAYMPP3myaDJmD0PeqPrYW8ERwYGNcRAgCLCcSAY+bEoHBYDQMQhsO3cZqQFz8OleLb3W43tra2Gndbga1ge8treHn+5OQkHj58GOPxOG7evBmbm5tFzTO7xOPcarXKJqd7e3u11Tb2cFN+PKwOizEI0u7kzaYRMGjYDL8t57Q94wPGRFA2z3lvx1arVVZh4JgAXMgboEWtBZx41/YzJlA8yJhXYO6dTqeAhduU8jLp2xbHmmPGOE4QZG9xcbHWvzYxZJygbY0ZjkmMOHPcPHz4MG7cuFG0DNpgdXW1BNwPh8O4ceNGMSGw8a59ARelZ1KTMzu0naDpmYjzzJF3nTd/267kvOaxy0tVdGGhGOSbGFxmhU3MzGqgQ2MsKE0skZSZbm7bpnah7TqdTtkDcDweF6cCg4DJKrMT7iE8dnA5lMUME1UUtQ0wwazA/+SNEb7b7Rb1CYaAqmPmiprPb8InrCbSxgywhw8fRqt1Zrx3/GfETB2i/RcWFgpo7u7u1kJ5POkuLCzEN3zDN8T29nb86q/+agEPvukJg8mCAOZs16Y9zHhpf0CAZxnMMFdvjBoRRc1ltxhW29AmfAuQJPSE67Q1jq2Dg4OiqU2nZ/F63n4f2cEm7KgK1Gz6J4MgKjVsHzNCxIwxOo6VbyKDtktjduA7KysrNbspZgEYN9/b2tqq2Qrf9a53xauvvhrT6TT29vYKE39aujIYNqm+7gQGIYXzIM92q8z++N3kHCGfJqB9WvL2QHRCBpwmdTYDpSPZM6BmMM/ld/5ur8wofT1idvwiDOvg4CAGg0G0Wq3iRSQPO0K8ZtN9lgPBzV7MzGEGnp0Z1MRwAWSESTCwGISwHXsaUb39fZbH9Xq92N7erk2EVuNheg7ghXUhc0x2sBr2w2OwYsPsdM52/3799dfjU5/6VA3I8uTAPRwHXDfg0c6UB/B1wDp90qTSZkDs9XqFwaFiUi9vrEp9ASDUSiY12t3jE1NFq9Uqm2Eg3wAN5WQys3rrjWadF23AqXaYXtz2TMIeN5gIiCDAKYO8Yksmb8qL2s4YYaJmonj48GEcHh6W4PmnpSufjmdQarqfAS4P7vw8z2U1wNR5HgA/LaEW58afB15NZTN42lHCvfysVzXMU7kz68xtYUaHA2FlZaUYljudTtnuyROQPaQRM1tYXq4UUd+D0s4ThMv2UPLi75OTkwJMN27ciM3NzZrBPWJmVoHt2b4II6ItzWpv3boV29vbZSBxHVmgHsgJ7APGSR14nxUOk8nZmlxUptPT09jZ2YmdnZ2agw4w84RmxoPNEPsbg9CTTN48geSAYdgfTNQMDccDk4J3kqG+sCjOMwY8sTcSnuQQnclkUju4irI5vpI+BAjZMg15yCot/Ut5rZGsr69HVZ1FJ/T7/XOECvmziYRdamCyXrVDHTCJ0Ld7e3uFgSNXm5ubUVVV/Pqv/3rs7Ow0wcO59Mxrk5vALAuSZ/eIGUOx8Dm/nKe9b37/MoAI/Ub43UlNIMQzZkcRM0ExGM5jjgh7zsv/u+0yKPo+XrsbN27E4uJimcH7/X48efKkDCbKhcDa+wjoAJR5srF9zj94RFFVGDi0IYOTd1GrM0vjXccbwgQjota/tj+ur6/H3t5ebVkabGd3dzc2NjZqgxNV3oHDgFer1SqMg7jJJ0+eRKvVKud+uNxebeI+91kbMHMDCvUELAgpok+x5SG/3IPpLSwsFCCwUwkAoO3JZzwex9ra2jnZpP/d77A3JgpAlPqPRqNot8+WTgKek8nZIe4cI0AdMRGwGzf2QQM0E/Dy8nI5l9nvGi+QIZ+vg4rOsz74iTrRTpYf8iKQ/tatW8VOe5l0JTDMgzWDiYHLTDE7RehUqxM863f5O7/rZxsr9S/tgzCLrB5nMOt2u+UUscwY83tOBhU6h+tNgJlTfoY64Sm+efNmtFqt4rXr9/vlKEhsJplBZYZN+8EaUZtscDegUhfHENKmeaLDRmgvrY3iVVWVfrAHlfIBLlaJGPyDwaAcVMQAoDx4fa1yeQWLHVyAu72weCPtgLEd1GXj26iheIRtBzUrd5QBYEdgvCc++pn/AdjhcFhAgfbOYUi8b3YaEbU6MQkAWH6X66xgcShVRNScZ7YtG7xwjlj7wkkD60Zl9wHxgJb9DKjJEbNdaziA3uBHvxLUTzvBgJEjMAWtig0mnpaufFSoQSkzOa7zf2YgBjafd+D3PNvO+/ZFiaDaeWzO+fHjcxW4lx0tTTZFs77M8ua1Wf42beX8B4NBmfWZtdm9BS9hHvhWdSNm4GVmwfMItgeu1cIcKuOBafXWcXsAHoyw1TpzdtiW6P4224iIGqDCBlgv7VAPGNjBwUFRhQBh8sHeyfMMSsrO6hO8x/Zc2i5M+XZ2dspmstSd5z1R8jdB5kxIOJgIz7FDBYcEZ9QY2LOqTl+hDnMNUKBvsxzQPtj9kMnV1dUCloxLAvh5Zm1trQB9Xv3hXYeOj4/LTtTYstECaDfaxeM4jwMm5+FwWGSMuuGwqaoq7t69W94joB+5QkuKOJtwnjsznMd8aEQPeD+X7Sb5emYFGUydF9fnJU5ec5npyMzWDExmrzS8DeiZUbo8lJtr2UTggdLEfEmAzdraWu3ch3a7XWj/aDSqqRjZq0l7WaXNZcqOLUDH7YXtikHlfK3WMJio2+LiYlmqhTrGDiOrq6tFnXKoCgPYkwYDmbIZTBksHsC0A+/yDHF45G1V1/mwu3Sr1SqeU8DGKrQBj7K67JZhnhuPx2VCw5HHcQHIF2BCHwDkTHjZUQNbA2QMsLSFmRzPmGHbgzwajWrREZhokIdOp1P6DrscJxlylgqmEoetYVO0PFn2rVXB4HGO4VVmkmAVCksgCeoeDAYlNpK6o41gn75surI32YLQxAZ9z//7mkHUwJjBNTdcE2Pk+9iEXB4DXRYmP0eyuuSOyozOAzi/n9vEIJvL7DZk1vMuJQhvVVVl9YYF1mXgNyzKKm+eBAyeAL8HjycFM0KutVqtYjOy+tVqtWrbMzEosW+xwQFtxWDx/7Ad18m7sxCMPJ3OdjthwHq5IIOQ++Tta1wnDYfDmmMKNmeVLsux+9N2zyaCwHpeJgfYKSq8w4UAddtmTQ6QdQfaWxYc3wcYeaLDTMWSOa67HoyF4XBYvLhMHhzURVkIj6F92u12bG9v10wrjAebASJm9lZPQF5KiCmo3W7X1O7bt28XUwzxm5bh8Xhc8xs8LV3ZZpgHcaa8+R1f8/9NrM+DO7/rcAwnbEAMBFSCDGS5XJ7hEbZsmLUaTDkt4Jlxcr0JdJsAkY7v9/tlsTlswIPVgOFVBFmN4loTmwbIqLdtgFa3CfMAlNhJJeeFwZsZHcFl8PED6yA+cH19vQQFe32pHRiouR7MeCRheJT76OioDAbkxGuwHSAeUfecehdq1EZWbjDgDHDU3zJPec1SM3vkOfocULx161ZhzHzD3nhkwGON65mJevIzo7Rdzeu/iR9st9slfo96REStb1jlUlVny/n29/eLQ49QGFg1INrtdsvmq+TXap2tNV9fX69NRFblPRHST1U128gDR9N0Oi0OoOPj42IXZGUZeGCn5tPSlQ+EygwjszvHk2Um6bxM3+epvplV5tTpdEqQb0R9pxnT/ibAIpkdmcWZUWU1ODsSmvLMoMnfbis22czOJDMwQMVhMzaquxyoBfMAmf7zelpUE54zi7Bty3WhvRx36F1kCLamTqihyIY9v1b13f6o2RFRVmJ4MsDJZJUSNsEPzBWAIhFAbGcDdcLeSR1sT7RN00zNk0qWd2s8njCm02k8fvw4qurMO20gZOxgC3PMZtMkHhE1JuiywZq570kMeWGCcFuRBxML7WXV1atjbAu05gJDY/dwTEBuF7NbhyXZ/MJECANkB5zJ5Gx99e7ubty8eTNeeOGF4oxin07vWHRReqbQGnd6BhnPoPm37/O8gbEJNPM7JDxO9gLyO/9cBFzYpPhek1qcVV0zxiZb4DxVOWIWRM1SIe4hHP6+d21GoA0c2bQQMWMqrreBAJBw+IIHgp9vmlAiZisVbEOMmDkv7BUmJowBQVkNsp69zQ4BKMrs5w2qk8mk2CnZ85B8bH/EA2qHjPsHbyyTE/ngkPMWXi5rDhmx7CAPeVLh7+3t7ZqjyUDgPqbtHMDcJFu0FTY2dmWaTM52Dd/a2qo5c2g3QqRsb6VuLtPCwkIMBoPaChCPG1jk4eFh8aSvrKzE+vp6tFqtmvfakwkmAXZFr6qZk5VJkAnPwf6ALRt7nJ6exu3bt2tL8Sz/F6VLg6HtF7nTI85vzOoZLIOZ1bwmlW4eOJJwlDSBVh74TYyQawgEKavVLlNmxBZC59n0HdeHGRXbGjY1gqkN/hloDTzcj2gGE4ObwYX/89rliKipZDnw2moMA87fdBt55s87Uztkw7YuywT3ATMmgtPT07J6hPsY9vkGbAp7HB5jWDff9URiVmZ1kTpTJlRx8oV5GQxgk4Cv5bhpgmq1WsUpsLa2Fqurq6WM9j5TNkwXNuuYCVN2nDTEC0ZEcQyxV6WdSYAKIIfsWVPAoUVolpmqJytMILdv3y5thmmAfrB9z4H0rLQxbrh9PZnSJlVVlWgLh3Gxjv+5g6FnCKvHTbYq2zPmqbqulI2efq6JSfb7/WI8tqByP/9u+puBZIHMDZZB0YwlA34TgLpDq6oqAdPexYROsz0pO2ZyvF9mn37Og4Ly8m3y9Uw8j1nCABiIBisGom2XHpSoxjYn2MNJHQy8Lg/5OcRlOp2WDQZI7Xa7bK6QgdqOGrySsJF2u11jfhEz4KftLOs28lMv1Gmrg/YGZ6AiTMSJfqHtJ5NJ7O7uxnQ6LUBhpk7b0vawOK/vZv0yKjVhJvzPdygT25MRIG0NKU/8NiEQ1YB8YZ6g3sQddjqd4nyxvETM1hlnYsR4oS+8HRdySf/l4wA83lZXV2NhYaFsKnGZdKU4QxoK4cjqo1livueB55RV2Hmp3W7XgJBrTWCUy+O/adB5KuS8fFwfC4rByXnRMayggLJz3V5C8sZOZ3Cy2paZqP9GEDNbA1h8Qh19YVsa91DN7WE0iJCvQ2rMLL0yBubgMjEZWH2BpdJubm8DlcsKSBMXR7sRTuNQENrBE6dj5/hOjq90+dwePMu3PQi99rrT6RQtAKdRZpHUjfYhTGV9fb0m357MeBZGizqcTRd5DPIeAJoBFtD0OAV8IAJ5eSxmB9qDZzE7sEkGdcU+6p3NI6LmJLQWQ+iO5Y7JIjuQPBGijnsd9dPSlc5AoRFWV1dLJL4F1yif7QIRzd5lfjepxgwkGrFpgTcdbLAjZRXMjhKXo8mm2ASSTSpddqYgkAwEdspBbfUGCm4fBokX3yPsvOtZmsGBzcyCwQBj5YDDFJgMzHa5R7l51ymvYgDoaVfKaucJ4A8gowJaXjJA8w0GS0TUwA71FyeA84M9w5T4NqtH/F3azKtlYFHWejJ75h7fov6APH0Ja+V5bHdsrppNHpYf1n6z5tsyxnuUi7axjY97mYHTfp6MzKqRTdoANggIUlfHONI/7Xa7FmyPWcNyh92SsUydDK4wSq9F9thBzlmJlb3PtNfp6WnZdzITsHnpSkHXDHS2F7fAUCDr8qxNNHtyxxhYGBhZlV5cXCzGZXdeZqT+4ZrLnoEwgykpzyJu4MxCyTuDJGfMelbCxmQQoj3tzXUdI2YOFw+crMYY7BEe/rdNx2zTYEf9GCCAkduUvAA7v+uByvtW0dvtds3gbobsZWo5HzMx7iPk9A3MATW1SZ0HoFAr8c7ykx1CgK1XJtmuRr1Q0c0kqT8sG6CiX9iEgTFkQLDjazI5258PQLQ66LZCPrCn2hsLmCAL1la8YgjQhN3R3jgmqKdjLr3Sg8kVwCJygHNwADhML9iAXa9WaxbuExHFzGHZZmOG1dXVWh/wbcsaeb+pQdcUMIOCB3pVVTWDdWZ9Biyrf7YXRERxqVsA8mBzYzYBGe+Z1XgA5zLmfKyi5DqYCTIAvOcfHjoE3YzPi/Zhd+Tr9spOjiaGYFuPEywBIzYCnW1RZnsApeMQ7fSIiHKgexbuPPjcN64fxniuRdRtowh1ZsMGFTzIOB+yV5MBSf9Mp9PY398vg9QHgHlJHu1q0KTcrid9yHPeaJWyA8he/ocZod/vF6DBo5uJAKcU5h3NaV8Ahn62Cku7Yx7xOLBH2iYj2ov7bu+8J6DHU2bb2B/zJgkAPGYMB1p74rNcWpVutVqxt7dXFihsbm4WZypOq6x5MPlcJl0JDJvALDOwzBINGAYvM5qm5GDOecwvg2AGsQygefCTMqhyrYmp+jtmNKyAsQrOAAHwELSmdjLI875nTjtBDCJW2b0PnVVmqykRda8xyYDe9C5lBdwZnHYCUB4PUMeAemBRx6zm+PtmQ7QlNkHOU6Z9GdQwFwZYVisBUUIzYEQALIyIOtmWid2PtsGeRrtnQz0THqYKWDH1IA6u1WoVoAa4ARHsgTAfxhLlR0axo/E9jwOvk8ahFBElNIW6oP7SBj5BzzJJO2c1uN2eberAe+x8w56MsEzLmPPNJhwzOyYx+urx48cF4AnfWVlZKc6TiNnGGpdJz7QCpQkoMgM06BhkssDkQclM2uQxzuw0q7kZmDOTNFBlMG1ir7nsTQBKBzGgPAizrSfHbJGaGK3NDbS9AZQfbCsIvdkaZbEaRz5Z3QJsqT+g4kGGOpYnGZfL7ZyZrdVdrjGwzU7NML064fT0NPb398uqluyAcJ60HRODvfEMdlZRrK6u1gYZjIW/bdznUCza3LYr1My8k461AIfb8N5gMCjhL9jSvITMdbIs2BTB9yiXyQdl48dbsEXM2BjOmIioHbtJGew0M8Dw/YgoZrTRaFSOSKDslkOzdgMi8gqzjIjaROJIA/JBvX748GH0er340Ic+FHfv3o12e7Z66zLpSjbDp7G6zBD4TcfkdwyuEWfCtra2ds6eZdC7iAkyGMwE54Ep+8JlsPPfeYY1y2VHFe+ua0ZBnWyfamKgrqd/IyyAANc88J1/npxyuamPvbMRdc8//9uDSB6Uw+9TbzNbrsOA7VFGuGEfLH2zJ9RhOFaxObIT0DBrM+szaHggWt31Ur/T09N4/fXXCwCZCXY6Z+egdLvdUner45YTgJPdcTgHmYkJ2yF1z06htbW1stHsjRs3SjgKTNKaBRNO1oyQH8pu0CL+kjr0er1il7OMIJ+np6c12xvM1JOcQ1/G47OT6/ihbNyHve7s7JQ2axrLONPwOEfMdjjim66/owh6vV70+/2yw/WtW7cK+71Meua1yVzjd2ZYWZX0s86L55ih6XTbJAwgmQ26TPPYoDuc570hqX+74XKdGFiDwaCo8HmAexAjRPbEZVB0p1rYeB4BoE1cNhiUVRfAzOohM6hVSupMnSij1yLDMg1I1JEZ2nY9+gH2aNChbc1oAELbp5oG92g0isePH9dURdrbgzVvMkv70EZNjC2zFpidd4mm/QaDQTlCwHFzAJ7lCDWeA4xoS5fd6mu/3y9gShvZjsYYcTt7hZIByzLmcCjA3/Zgj0dA/OTkpGg8TPKeUClLVVXFeUVAN22Qx3273S4TmaMf3B7Z4WfVnrLwfNYyWCJIOjg4iOFwWDtW9GnpSmDoxmhieSSjfZP6iXAxCJaWlmoeYxopM7rMBJ0/DW67Q37Wf+f88mzfBOJW4XMdIuq7DJPoNPIAbLKqjHBQD8oHs7LNj/wAS75jEETgYC1+x2zOaonVensJI6LsNE0f2QPr/qWsAJEN/1m1ZqDamWKP6nh8tu387u5ubd12VgX5Ftds+I+YHSWK/ci2bNqZMh8eHsZoNIrBYFDair7hLA3eMXunjtYeqqoqZywDiisrKwU8sKshs5ABtycsKSKKXc/OOH87q7UmChyihZrPu/QD7Uafw6g4QN4234ODg3j06FEMh8Oag8PmEsaH+7yqqnKQmSdZ5M5qsfsHLYUfnFT8T/jM6upqrK2txcbGRgwGg+h2uzEezw5Pe1q6kpqM+tMEcBaEzPwycwDJq6oqLCt3ZGZ0JOdBZ5oRNqnETe+7bBepr/yPukQn5I6ELXHdA9YD3/YsBkxmsNTB4RsWdgTHbczARzg9e5qtZvth/qZVXgMEA8XCCEOy0dtgCsCZwfJcv98/V2ez1pOTkxJ+gjpmlu1QoYj6QGTSabfb5US7zCSzDBvYW61WbQ/CXq9XlpJRDgAVVdjxoxF1G2xVVTEcDkuYCeyTVRX2hLI9Gc4a9wUTFBOkx5H7H3nwBElbe4JjvPDt7GDzph/YdpFnHIbUj2T277Y2DnjC43lYO9+3KcVrqcEhs2RAkcPtb926VTtqII/peemZDpHPbCAbd/3xbPtxPhiuDZR5IHqQ0YikphnIA65J5eVvl4NrmQ3i1SNm0J1re6PBykBo6m+V1G2VJwnUOdrCwu/8uG8mzTc9Wxt0bVukDtQzr15BwAAk6k/ZHDjMNwEyt6H7j9+unx0KDHYOis/yRT0c8sLg5Bnsj4CJHScMvCwbWQ74PZ1Oi/rHYMxy7+NUrYqSAMqIM4C/f/9+rK+v13bpJr/xeFwbD+SZ+83mlNzPtLfDZ9xP9LVNKvSLnW94sQEUvoGphMkMEwDlpH0In+F/b7vGShwfpJWdPExoOJRYa+3Jy+FTJgGozI7xvEy60gqUPAvQuHSEr1n4PCBhSWtrazV206QSG8CyGhtR9zTbxkiyCuE85rE/fiNErCDJ6jNezIjZOtDMFPmmGVLOnw7LjIqy0mb2tjouLKdOp1MGr8uKsJjB5XZiratNFXgDGSB8G7W2SS2zykRdbPvJap3rNZ1Oy0FQNqIzu9O+Drr2QIUJAqRZbeW72VTgZzNYol4vLCyUgZ9VQzMozldhIFJOm044WZAYWlTUbBNGPmh3ygRIZe3DckRdzWLJx6y3STsAdMx2T09Py64+tn/6PGTKs7y8XFOhV1ZWYmNjo9TB8pknfAgFDigDO2aCVqtVQI++xwa8v79fnJudTuec1nlRutKuNd6Jg8Y2QLohKTxCSoWIA6Ljs6MkD1QLh0GJ9x1/18T8+H8eAHoAR8zYYN4n0aBG45sROC9/K9vCKLOFgvz5Fr9ZMZFVC4J1mfVsczEjNftkwLrfSAghNht21famrQ7fsG2RZLZiZm6Wae8zbQIDYLABorArh4BY7niftsMOR3sb5Ohvy2t2nACSPG/woV1ttHc/UY9er1d2yjZbsomAfJ88eRInJydFzvDe5s0gGHMAJswRIkHZLV/EYmai4knV7ej4RcthdgriVEJ2bSLCnsj2XbA/FiK43wE/x6KCE3aiuX/pNxgr45C24EgF+urhw4e1yeYy6ZnVZAOPGQ8N6XATBJs9xhgcsMR56qz/b2KKBqtsC8p55Lyyisv//X6/qAAGXQ8gB956hvb/WQUySDQxYf5HNfDMizrmHw/6iFkMnOvc6XRKYDFCO4+BR5x5QJn92WDAoQy2wXhyov3thLFMMEj432opRv2Dg4PyHW/ZbzWa78MiFhYWyioNM2mez6pw7guSvebZo5/fs3fZatzW1laMRqOySzj2RU/UfJN2oq0ZFwCnQcJhPRFRY6EwJ7N1MygmTOrw5MmT0m68i8wxVgEa5BfWyRplryxDvi3zHA+wtrZWU32ZRO3UI1/OOEFGbCPMbcj3vHgB9uxyERGQV85clK60hZcFqUnQQPaIWagGs8v6+notfpB8PNtGPN3zm8GjyWZGykwx50k5AGUOl+E52wBzrKBVGgYS72WjbWZMEbPZ2L8d58ZgYQcO72TMoOXbmRn7m6y2sD3PdWeG9hph1h8zs8MCzBLNpBByTxj8j2EbGaK/7EmGiUbMVu0wOC1X9nyj7mWHXtYAuM5vgMzt7Ge4lpki+TIAmZTG43Gsr6/HcDgs0QZmalktNHOnvuzSDLDirEEuYIz0Fe0A4DJ5ut+pV1P7eHkfsms13czbfUV5rJbbkcFEApBRD7cnP7YX29HHyhpkksnV67ht5qCsts3CRPmbvn5autJyPAsODZLtciQ6jH3W8mDM7KIJsCLO7yhjFpjTPLU4q8JZfer3+0V1d6dZAJvAMQM0AkI+vm8HjAWXdkEFweZ3enpa7C4IInUnn2wrchkjZoDkTTUBI57lfQTIS6/29vZibW2tZoDO6jz1cRvZAG5AyeYTmKjDg5AbWLFj7WwXxDnSNDFHxLmB4j7x5Al7MNNxHBx1NEvy/1VVxePHj2vnBtuuOxgMIiKKnY7J0rbZ6XRaVk+g2sGwbE/MTIzyWJ21Q2Jtba3UAbWZg947nU7ZPANQd9v4TCHak92rM7C7nXnfbWzvd0QdoLELo4bTD1nLxAwC00SOqL+ZNH1IHs/dgQIIWPBymAEDhcJxyHhmfVavfN1/+4fvZk+qAacJCHOHZfUHOu+ZBKZlw70N7U1la2KgLhMCyjcN7DzD1k6AYX7X+Zl50O42E8xjyLxvVZm/UTVsgx2Px7G3txcrKyuxublZW1zvlRqeEDNryODEsxZu1Cba3IOFOjHQfCxlk4zAImi33C6UhXbz5rCe5Ax4Lr9ZCZMA3xiNRmVcADQrKyvFWehJKTPOqqpib28vTk9Py7b62NZInrw8AdvmZ5ICkLh/COuxqYpn2u12YWX0vzfGdf2tSUTMAsJdDtuXKbvVVrcBNj+TFnYsd8yo62jnEm1B39NONrU8LV0aDFF1yNh0mYohpNPptGy1444kZfDjd2Z8FmIzogycGaT8ftPf2BQ4Dc2C7mVTptfUk7J48FF/1zMDTkQ0DlC2ijLT8cRC3nm25HdWAflOBmk/O+99b6vE/clktvIEh47ra2Dwtx1nh2B6RxcLMgZ/Mwpki3wxGVBPq4I2kLdaszNLzExs53Yd3ccAeNaAcntYNnN+MCjqNZlMipPEjiKA17IJ6x2Px3Hjxo3odrsxHA5rfQh4+Xu8m2XE5IX7vEP5rVr6f9sauZ41Gsu2QcemkIgZkQKkmLytNkdEAcqFhYWybnw4HBa7KcsDeYb/TZSQPbNUy8dF6dJgyB5vBiDTTz7a6XRiY2OjZqClAS8DggatrKZclIc7wckzL402GAxK7KCZgJdZef+9iCjny/oawtcE0k3l8WADBLEHIhwM9szm7DDJIOvneNYsgvt5svD1DIIGVzMB7Jp+Bu0A4Wfgu50AQ9gRKzGyiubykjcBy3nyMQMwOwagM5BRF9qK+EmHdkTMTAFuR0+GzsNMye3LNmfcAxzxrhJUbqCAaR4dHcXOzk5tpRMDnLb1t2g3m24ccJ1NDRH1yd2OK77HM2bm5O2/kQur6PSbZc22Y5YdsjTQpAT1nZ2+bTekzgSvLy4ulvFDEDjfxCk3mUzi9u3bcZl05dPxELQ8M2MfXF9fr7GaeWytCQSbOpjOyc/lcvkdX/dv4p3waDOLIICeYWw7i4hy3oIHrVXAixiqBypgQCwa9yPqwupZvmlA57plW2Bmwm47fye3Kf3rcnGNtsqMyYOg3W7XGCACDitiPS1Cbtue1dSIKDGD3inHcpfNDryf284y636LiOKEIAHqVn9zeEZmhDYBWKZgS14PzLOLi4uxsbFRNqelXvQNYOm4RrMgANGmBqvtETMtbjKZbX8/mUzKMQTkafNWE+P0OORek8mIvuebEBDanJMASbA7ZAhtIZtNxuNxWQPtlUkwVsrKUtnNzc1YW1uL27dvx/r6elw2XXltchMQTKdny3M4kIbGYZDg0cGmkgenBYlv8dzT1Oumcjoxq2xubpbDqxEKb2LpOma1invZjuWyWyjsiKEOqN54T12fJvBpqqMBz7+zjSSXsYkRUiczrQy6+Xvkm9VCD6Zer9eookdE2XGFZwEMe0dpP3Y/sb3WYOLYNPLI7UZ7uPx5Amu1WsWJ4yVhlNf2KRLgAkN2aI7jChnITKwwRNu7CA7e2dkpdUB1Pzw8jPX19ZoNjnyJEMAmD6vLzDpiFjrkvqVNqDNaD2umASWDMO+7Pa0dtduzJZqo+2hAlht++DahaoeHh/HkyZPSDj6rGRMJ4VRMzlbXaddHjx7FvXv3Yjgcxosvvhhf+7Vfe072m9KVvMkWIjo94ix+kNg8g4MHgVVszyTOG8ChYfOzHpAknmfRu69HRNkRl4OlPeswE3o28iyfWYvL4XJ50DGYbWiHsvtAINc3ImreZguc69qk9mXGnq8bTCzg5J3tkFlVd12b8q+qqrC9Xq9X8rITBM+86wyId7vdsnUVjJH98PwN23XtkDJIWUZd7jxBUkYGE+8iD0xc0+m0dt6K2x71nnfpd+yqKysrBRTyPoO8w6YJvV4v9vf3iyyiKnoc0Aa20cEwvTTSY8LtzzdhiN5og/xhoTxHe9jRlLWvqprZvfkB6AE0x25aVYb5T6dnXu6Dg4OyEqrf75eJhAmX8rJemSgAq+rIHufN/PN//s/jne98Z3zd133dORnI6cqhNXwUAVlfX6/tfWdbkYXRQbckb5Pk580I54GmZ/+qqkrDUL7FxcVYX1+PjY2N8gx2J2YiZnaHdJC/U2aETfcZpAgzeXpHZoMpApvZoAHxIpufZ2T+d7ma1MJssLdK5XY24zLY8w72H5gK37ea5kkqOyQAFgYOXsS8jb/b2+aSJoN4Bm6DhuXQzCtidp4vah2hJ5kpe1KyCu3/s7OJ7f6pq4/jpJ357tbWVlmK6OV2djjYdANbxLzj/na9+RuQRhZQT2lbyAr5UQdPXPztiYIJnnbIbM0mkaqqCvh5tRCaY3Z8IX8eTx7vji7xNZ6HOT/3OEOrKUSqb2xs1JhEFj7/ZFbHzEhqEjy/75T/J9F4a2trcfPmzZIXM6dZn+0q9pxZQJu8fR7MrNCIiHObvKLm5YHjAeuBkdumic3wfh6c1N35uN0Nmu4jMwMzUgOOGTHfZLb32nLvJZfbGYDgm25XAJGVJNkpFzFjzdnG6GfIz3LowWSWyS42ftcMzHZHq7+WRZMDTyg8j3wzKBnoTSyTjVdXV1eL1xZwcllozzyBeVKjTy1HsDfYn8tpJhhxZndz/5uReddw2i6H1ADaDkq3/Q+TDvVjey3bLyeTs7XJu7u7NSLl+hqo0SpQn1kHbUJymXQlMMSG4iVrNJgFMQ8go31OTcDX7/dLRZpUY/9ttWtxcTFu3LhRAoVti7IagRBEzAJ9ySsLvRlaHjzk6e25jo+PY39/v2agtnBl4Ml1ahrAfs+zZtMk43ZxPaibZ1PKl5egZUZm1ZpyszOI2ykbzdko1MBhlYdveROA7IXktLbMfHI/+LseqNSblS3u49yuJMudJ4wmec1ta5mkbRk7sBQCql0HzAg492DU9jYbADJAux8M6P5Guz07J8d2T2+rbxBBHslvOp3tCZm1mul0Wg578ioiyAH55XcB/9FoVBY+dLvd0kZedWOTiJ1G2CapEzZVTz6XSVcKrWm327GxsVFz7WfQIxkc+d+DOau6ni1ZFWDvlpPz4Gd1dTVu3rxZ7Bv5Wc+y2X7VZI/0NwCEiPoMxk+73S4g2BRUymBqYmfZNpdtcrntXDbfy9ddP+djJsMztvEg2AYZl8vqUrYVul5srGnbIe1nVjqZTAqDdpkxN5ihAAx5cqF/XF4Gktfn8v08iN3e5EWb8F3uuY1z//IM1/AiM2B3d3djY2MjVldXi5y4X73rt5mt5cMmKdeB7zet022KxkB2AUFUXsYGy1LpC0dcoO4vLS3VNmU4ODgoB74DVF5hha0P0F1YmB0PgZbFyXc40dimCzbPWMOU4g0+YJCslHEbXSZdGgwXFhZia2urNlgy+GWBcOCtOy0LDx1D2t/fL/cyw3GiAW7evFmYKsLNb3st/V5mYhFRG0z+2+VEOFg6RMwUKh4C6IFC+7itDL6ZBSIo2DabGEDOJ/8meZBbncmOEmZVP2NVNzNAywGA0e/3S6hHp9MpApvLk20/9BFt7gmGbxq4XJ/ct6iYdoLkCY+2M7umH+izzOQ9UXhyoi60p2XAQB1xRigePXoUL774YvT7/RgOh2WAOyLAbWsW7/6yLLucrq8nCFglTAy2Cntn8vDuRpTJtkFWxrBdlrfw4lyVdvtsJcvq6mrZvduRGzYBuC/oT+Sed+jjdnu2kxPaH7uIe/FCxJkteDAYFG3tMunSYMhh1l7qlBmNmWKTbSeDgBlhkxri3wZF8l5fX48bN26c8wabVtsGZdWWxqZTssMhAw9C6T3tOAHMKzcyM/NA8qDMDMl2IOpgAJqnDnuwGGT8fac8qDPouzyeAKyakQ+DLOJs0hkOh7G6uhqt1iw8yWobbYzqYvMFbdjtdssARD4YFG6fiPNBwXh+UftynbMc+jeJ+tljnQGINuJvl4X+QG3mPn+3Wq148OBBbG1txWAwKPYsmyOYrGivhYWFElBMuVCjs22Xb2eHHUBCWWCrblfu+zAqJhVUbPdbq9Uqy+W4jw0Rb/DR0VEMh8NzsgwwUzYmQPoKlup2Q46wP9LWxDc7T56/fft2TRO6KF0aDGm8JtDj7zxwsjqRZ1nn40GMcOVKWIWCDfIslNnvO0+EK69TbAIGM1KEqNWaHcvJYTN4sLNH2HV25zd1itsoM0pfmzd43S5N/zcxapfHfcB3HZPWBAaAIp5D7DW0MbISEefam76CRQK6rJdlEqNc5OstmzxRMpjzcsY8+cxrC/LLzC5PEk2snzxz+7idSdnTvr29XUJoaCPsebSL14lPp9Pa5rj0D5Ogmb6ZPRMF6jN5ZFbszQ6oD8sKe71eYXSepGCb/lav1ytxgTA891e73Y6jo6MSYkN919fXy+5FqOBenUQekDG3Aw5LH9K2trYWq6urpQ6XSVc+AyWrCR5IJAOLBdHrPrmW8/L3eIYBtL6+HoPBoLYlVcQs1g1h8KxCXpn9WfWycBgc8JhiBCf6nRg4r35oYoSuZ1P75PZtAlCzEFKTau93cxkye2maqAyQttX5XgYYh41QL1Qih6o4Hoy+JLYQdtnv90u70l/M9Pn8GPoL4MzraN3mTdpJnvj8v/PPgGhZ8iQbMQvEdn85ksDyhzw9fvw4BoNBbTEA8YZ8i77IIAggWBty/VwHAAxwIlAbgMvy4vxZs4wJBVCnHyEY1JFnWH+NKQtHDdqYx+vS0lI8ePCg7PzjzSUcsJ7DcbifHYBoH+PxuGwmfZl05RUoVsuykDSpxx7kbvAMonk253kadGFhoThvzGiYrSLOBjtGXmZ552MG4GBVCxCdCRBGnAn1cDgsu4o0xbu5PfIAavqfOvN/9v450S6wriYWmwHSjDe3cROrp/wIuwHTZbUxnjaMiDIrE97gdl9dXY39/f0yUeEp9HdRhbNMWcgpK4PYamJuq4vYcbvdrgG4VVLLZTY7+JlsfrF2wPcAKdRN7hs4Ocry1q1bxRFFGSyzfMfjC5BBTgEGZMFeXWQEFsVBbMPhsMg034Gxuh25Rv3MWHHAGJzNPGlrh9UguysrK+XALTZkRduAzWKTx65JHdjLkW95hQ9mhCa79bx0pZ2ue71eKaQHnkMK/JsOzCDoZAaYGQs/g8Egtra2avud4d3yzJFVAwuxB1oGaFKr1SrnxTLTHh0dxe7ubvGSud62rXkAmObnus1jwbl9mtqB62Z4BjH3RbZpuZ0jZkeBYhinXF4vnfuRdmQAor56014GHO8yo8NQDMD5wCHbzagf9UG4GeAMunkM3BNgVm09iVBmt52BK8sp7/teNslQ9hynimnB7c3kQ8C54+6Qa96xFkO+Nl3xnDfM8Higz5l82u2zZaqj0ajWt3hzWUGDp5e80Mook9klajB1sM0PlZtxgX14Op2WwHvqBIPlu243QJ/YVhZvMFGSD1rJcw+69sBpspFY2DzDeNYlZdbRxB75zsbGRmxubtZCPwiZsOpBxyCYzC4RM2H3yW4IKvliT7FXbX9/v+bxy+oTeduWQ13YN451yK4//89jghmEzEJyHJ7XVSM0DlkAPDNz9TGUOX7N7WkmwDVCIGzoZyBFzMKwHCGA6YK29PbytCOy4bq7noApQk7K5XQ+GQid5k04fs+AC4PPWgZlyEBO+ZscHWwQ4ckVxwAbvDZNmiQHvXty8DcoY9YIAGCf/Oft9fOqMI8pEhOnQcqxgA6FYmxZW2u1WrG7u3tOo8ERRl8bkDmgjSMFWq1WDewcjmV5fe7MEBe8G8NCQsoNz7V5YGemZHa1tLQUN2/eLN7JiDNhODw8rKliPG+1MeL8QdSoaGZG2AWtatD4T548qZ114sHi+jIzZuP2zs5OrQ2akgcSYOt2nGdG4N3MRCJmuwhn0PR7WS22+p3DoXiGQWRbDyuRmME9M/M+pgtme1SjpjLlulMe3kewMbRndTUDmtXDJnUXmWpqU8qPvZJnmRBpEwOB2VPWhjwW7LGm3Q8PDwuAjEajcuC8242JGhOD1w5nTcHJ4y2X05EVOCdsp8yTKMBjgkCdsk0XxnZ8fFzOh4Ed21Ntpw4METWfvvOu6Dhv2NUaosNJeLZRm0k/LV35DBR73SxodJaFLNs9mmZOAxbvrq2txebmZqyurkZVnRnhWa7FYM8zcp6JKbNVIn8XEESlpfGfPHkSw+GwkbllQedvhxshfK5nnuF5Jv+df2c10J3qumQW5/bPzMaTVWYNZpe2qVVVdc6LaDDKjg/AhoFgNkCICP1nBkEesEzswT5/N/d1bmPawxNCU7+ZJfnITLN/b+LQap0FkrNSxofHWzas6jkAGQDlWQar25IyT6dnR6bmxQ32pHoMcM0mB/evbaAc7IQsOeaQZxkLdmT4m7zr86+rqirb0i0uLsbe3l7t2IqsLe7u7paJlUmv1WrVSExVVSWukODriNmJicvLy/Ho0aPCOldWVmJ9fT22trZibW0t+v1+7Ozs1DSUi9KVvMlubIcKeIZw45v++v9sv4qYDfxbt24V+yCd5mU+UOds6M+szazJMVcMZqg79/f390sHmQlmBkb5c7yd1SSDvO1glCsDo9sgA3dmy7ABMy8nM6M8EVF2P+MA6vycn6Gu7fbsmEov/EeYDUC7u7u1MAizLFQfy4Zl5+TkpGgA5Ov2dF/kSWIeO8POlAEjM8LMICkTtjXK57w9AXY6nXLqW/Z4Uw5kxmBBPhj++WEjAwAKR0YTk7Ujx2qq5S+zYdiaCQt9hV04j2vL/GRytkEFQdie4JwXbZrP9vYEGzFjnV6S6PcpI9uNMSmdnJzE48eP48mTJ7G8vBx3796NtbW1Yod9WrqSA4UCYgfzzGMbkRsB9caARWfxLHagO3fuxMbGRqnw4eFhbbG3O922G8qWv5PBF1sVp4PBeHZ2dsqqlwysWfXKg4x3bK8xmNFOBsTMFpsA0sLEM1bbDbbcdxntcKB8nrAok9mUBc6OmMw62YHZ98ifchOCBChkZwnMkZPfGBTYpmEVtF9E1ADFA3KeXHibeNodRtbUzpk18g71zuDidqEsgAvvsjIKrybxkMgJa7xzGSeTSQG/yWRStrZqt8+cEIAsk02OwzTbIpmdMR6yppCdPvQL77VarQLSPtPak+BkMilb5h0eHhYTQKfTKe+SH++fnp4WWyn21BxYnrUcJ5ty0PwePXoU29vb8eDBg7hMulJoDeEtDo2g4TxL8nwGBQumBXl1dTXe8Y53lNCC09PT4im28ZYBRDJweWD6W9zHDc8AQY158uRJba9Fl518sgPIs6sBH0BwOZo68CIgzEwUoHEbZFbgd53v2tpa7O/v1wJXzSgz6LsPzRj5ttlbkxbAgEK1JZ8c6Gu1Ec8j+R8dHdWCdd2/uZ1sK6YMbiN/i3rlvrbMkgc/Bl9sbRk03X420/DuZDIpdrBs12LLegdju2zj8bi2rx+azGg0Kh5fwNIxfrQNeQJE/ob71WzSGojNAFafMRVwHWzodDrlQClvqcdEbtUcguMYQephG/zh4WFE1DfPMKMHwGGITBiAsZ2lT0tXVpN3dnYKoHhQZhshQsFz2X5DBe7cuRM3btyoNZi3/7G9wx6izKQMUBaEdvtsmZBjuE5OTmJ7e7sctpNDg5zsTOAZDwZmZOrUxP4s7E1Cb1Aiua3cnvk5sxob2yeTSezs7Jxrlxwj2TRAuJ7ZLeCPALp/+Q4mBwSVECOHeSDMLn9EFCDkXU+YOA9cH79vsDaIMeDdd3lyo72ph9fnNj1ncM3Ay64pHjMHBweFieOo63a7sbS0FE+ePKmZfmhrJhW89pTFMYV2aHkcmu1xzX1Fe9IP9A2AxrvWRNrts3X4Zuz+TkQUYkD7sYkH9csmDY9VbMmAMSBP2bJ5h/J7rE8mkwKIlOH4+Dg2NjbiMulKp+NR8TwrZC+SwbGJgUynZ6tJ3v3ud5cwCRqMwWDbndmg1U0zgyYmChOk7FV1FuS6vb1d20rIqps7imSGkhlwRP28h8wE5zG/pnwAsqZJxd83w+Y+93A+ZPbi3xbIzHZcX/9ve1UGEwb5dDot27bDhOjXplUCDLRWq1XsTVVVRbfbLSsxDJq5PRjItAd96pVOTYAVcd52ahA0cHug8Zs2zvLhwW3WY5lFizg5OYlutxt37twpbIiBTHkIf4mYAWTE7EQ/55vlJjN3j0mXG9OF94ykDRw/yNZdeTK1bFEPlwkHFapxNs3Y7uiJCvMBGEBbmlzZ7GIQx+yQwfdp6UqhNZ6ZaYQ82PLgtzDSEHfv3o07d+5ExOygJZa6QcXNvNxxUHiDcBNTYuaFUUwmk9jd3Y29vb1otWYL3Q2GTpmNuS5cd93zwOV3BpWcT06uV1Mb+jlmbBvhXb6IumkC4TSbjTh/8l5ElMFgAUTQPCG6HOzU3Ol0aowgIoqa57Ih5KjyCK8HlJmhbaBWve3Iy8zNwE2feUBb1cpMx6qZgce2ctiU7YiU0XLg/MlvOByWEBpUacsLW17ZBON60e55jNmmbFC0/OH8ouz0kc+Rpq28kiVrDC6Pv4mZweW2NuHJI3vl0SjyGnXao6pmu1qz4zptwf9VVZXzqnd2dhrHWk6XBsMbN27EwcFB7O7ulgbIgzyivt2SkZl1gu973/uKex8bAZ5i2zwQGHcy+WWbHN+NiOIlZtBOp2feK8cNejDnjQQysGa21PQMyRNCtmdl8GuyLTYBgAek24KymxFEnN/KPzMqWJrzzUvjIqIYsLHZeHYm3pSJh6Mvh8NhEUgmLATdQeARs1UDe3t75xwltEuOFvAWY+67JrbtQZlNHVlVsx2Rd9k4Ij9vIGdwwxSn07M4uZWVlaIus8s337f2QB1PTk6i3++fA0/3o89E8QofwIQfT+wGS75lk4H7A4eM+5ox6hU5Hp/IcUTUFkHwu9VqlaV2gFi73a71IxPb+vp6sTWvrKxEv98vsY+EbjneFACk/mbz9AmTwnNfgfJbf+tvjcFgED/2Yz9Wa0Q3bGYlFGwymcTm5mbcvn277FYSEcVJYgaCrZABSyd49uFvCzlA2uv1Cos9OTmJvb29AuBWGQxc/J4HhCQPUOfDc+zl53jLzOicl1WGnKya0aZZ7cksyEw0s1YYZFa5ec99WlVVCXClXQG9/D6DgxgzT0IRdVsvTArhJCoBFupJxPWh/FkdpryZ6VG23N5+xgORUBhO4xuPx8UDOp1Oa2Ycvuc+NNsh3GN9fT263W4x5HuTAdrYdmQA1X3hPux2u7W1wbYPU2cOlkJeKBf2SLbYyjLiuqBJMUZta3T4S2a75ImK6uVyo9EoBoNBAUybHGB46+vr5ZvY/DgbmUPd6I/RaFRkhx1+aFs0SUxwjx8/fv5xhj/zMz8T73//+4v9DZCz4GY1AMp98+bNuHv3bm2Asxkj61YJrMQIaqHIcUYWeNZSRkSZPQnW3d7eLqo3HegyPg0ILTTcRyDybtlNrGMeCEacPzEuA6JZTUTz8jDe9cDINhmezU4Bh1dQbjyB9BGDCPMEbIc6A5ZsqpntSS5vZvD7+/vn4j9pBzzQXroHqHrzjAwYmYVbVqgHdXe7HBwcxJMnT85NuJY/95PrwoRgrQX7Gt7jmzdvlpAh7KKeIPgbNkN5KSOhYGZ1dhhRx9FoVNihQdLv0baomLSdV7bYREL9skpOGcnHTLqqqmL7h90xYRKfabIzmUziwYMHpZyMMZtBrB12u93a6hMIFWFLg8Egbt26FZ///OdLuS+TruRN/n//3/+3FB4Ay3ZE32+323Hnzp2yMSwzJNv72FuEkNo2afDMoEsnM1iIVzs9PY3hcFhCZjLjaWJq7uCc8j3HTTY96+/wLasv9qrzbP5t8PaAJnkyyMyY9rKqDaj0er2itpGsTmDv439vWGHDfm5/h8dE1IHHZUWY9/b2apNGjhTgHmzBNkEGs0M1rE7a5uz2ZwB6IuZ9myhcx1z+7Jhp6gN+M9gfP34cJycncePGjVhdXS2qM8yG9udblMW7seAEzDJmG2ie/OgD2h0bq1ddIc+0j22nPkvEebmNeC+vR6YPCVLnftaYSGgd7jsY4e7ubplY3Kc4fZggV1ZWio2wqqr4zGc+EwsLZ7vze5/Ti9KlwZCttXF4eEB78EbMdud45zvfWU60Z7A56NQUPDM//87qCI3qGYlZN6vFWRUwUPg65W/qrMwk87s2Hud7/p7vG+Q8izsfA0POPxvOed5qb07Y/8iviSHbfuetl2w/YyCwbtj90mrNToVz/1Ge7e3tMjjdLqhzBnbqCWDY0bOyslLbrs31oT3t6MBuyWB1ELjVKLPCiJlziXu2mflZy3Nmj8fHx3H//v3CEhm0HIw+mUwK+7MtHdCirQE9QIPy0faOyzXzzGckkxdMjZhFE5yImd2YNjBztbmDicXOFk/69Cu/sUPa3g1jBqTpb9rE9mrjA0yz1WoV7aXf78fx8XH0+/24d+/euXEwL10p6NrA5cZFSFBzl5eX4x3veEesra0VYUHPb1raY6HLgAQb4G+zFtRilm5tb2+XMxiaZvYMcFmdys9zrwnwMsOcx95IFqTcpmYmtj9l8Iyosy6X02V1/Two/Z3MIprYH+WDAbpMlIOVEQim25OBF3E2eAi7YcNQt6VlicOkbPD3EkzLkPMxU6JuAHNm5NYaDGZuS18zEFm2PKHZTJHbuKrOwrrY1XljY6PsIA2IMXYAODY6tZmBvrVnnn6AJVlePRl5ImsKj8tqsb28tGmexExUsBl7b0PyzkTIbeZ9Ak5PZ+ey2ClkwEMuHHWAXDApsIHIrVu3CiF7WrryRg0WKArH7FdVZ9t+3717N3q9XvEacVYIyM9M7d8ZrNxgDGJmSM9kbLXl2EELTWZ6FioGexNoIkC+38QMs/pwEZtznSwouR1yGfP3fK+J3VF2hzK4nAhUBnwvzjerRuWz4wBV1WW2OmvAJrgYp0QTEFo2nB/MwqDE+5TLWgP18eav7ucMZDxjNuW2zu9Qf/rRwdKWmdy+PHN6eho7OzsxGo2i1+sVh59BhYgIv5vLR/29yYTZcC6fgYeyOMY0YnY+tVd7kSdt70SbZN8B92Dj9A2AZU0CUGP9NvsGYMsl9Ii2yEsyPQHADJEdTskbDAZxmXTltckgNOCIvWgyOduH7c6dOwWsIqLo/DQwsw2NFVGPB8sC6IZjW3AGLrvMEEfkffncWXmmNuA12eQ8GHmP6xncmtgh5WbgNqnJDGLbY8x+XT6+nZl5zpN7/qHfIupnxXgQZDZpQet06mddeAdk1E3qQsK2Nh6Po9vtxt7eXmHsETNW5jwQaC9tzPWyd53r9gRjR3RoR8QMQM0EPYDdvnny8OSaQRfgcZynwcaynE1JEVFIwvLycqytrRW7me22lkf+zoHZ1M2AzzdXV1fPmTbw1tJPgKXV3ixzjF/aBeZu1oYsu614dzqd1rbf5/veMardPltGR/3oJ/LLdvemcWGHDnk899AakBc7TQaBjY2NeOGFF8oB8Hh0aTTPDAgQA8lOkoi6odUCTwU5nvPJkydlmyQajPI0qbRN1/MzrpN/k79Bq6mNDMBNDKaqZpsouMyZFeby5rybAIz8PPj9DTMX15My5nw98Eioct7b0sGxqH2wFjZdsNAi+Niz7DTzQMaYnvvQbVVVVVnjaxbIO2aSZk7OI0+WJNty/Vw2Zfge3zYAGpQN1LYR7uzslEOMeNZt4bzMFm2fzEyesceqIex/BjuvPOl0OmWJqpfmWV4gM24zABz5cBvB8lCR6Sfvn8g3AE3MKJ1OJ1ZXV2uygKwSZ2gnDmXGDjoYDEpA/2XSpcHQNgUSwrO1tRW3bt2qudYRUmYGdwDJwh5Rd3hQWWiyI8uPjo7i9ddfb1Sxm9SafN31yYOhKeV8mv6mblZ3rN7Q0RiArcJmhtYEfk11aHovl81qDs/Oy9914DoDKH/Dqp+BjDrzHg4TAxQAsLa2FhH16ANPElyzMBug0A7M5PLk4PYh36a2MmDxXLYbZnbENZ6jXwE5gwTlyN+3zEA0NjY2auDnsub1w16hYsCjL9DAmswE7i+Cm/mG2WdW0/OESb1ZTjscDstmDqw3Z4svt7nrUVVVWTE2HA7LWAEMzZQHg0EJp/OOR8vLy/Hiiy/G7u5uTCZn5xYRaXKZdKVt/w0gNPidO3difX29NN7h4WGxJyHMjh+MmM0anmGZHfAOUQk2WMCmsLu7Wxa3G3z9m2/QYfyfQSQPGndUBtMMQlbjfD+zOWbBvFlBttdkYDWTyQMwl9/qiMsDA8/J383/85vQBUJzqBPlQTgdouK+jYhyAl522qB6M4uTX1aPc11IBAUbrLypB22Zv0ubmtW5r9yWLocB2OBIXmg9trf5HVh3RH0zVd/jeermGE+Dke1/VvWJ9nB4Fd+zmcqMGGYFCGczDOWypmE2C7vDvsfkROiUveLdbreQI4/Fdnu2P2av1ysb6BpvGPswRALbcaIuLCyUVT+vvvpqdDqdckRAlvWL0qXBkLNOEYalpaW4e/dubVt+vDg2xKNOeW2pvVQefHQeMUY+4+Ho6CgePHhQU4szS+W3O9TpaWDYxPR8rylPnmvKy4AKMGUAawJwqyVNYAbAmD04L9scs3BnxmRmjU2Jb/C3g6PNROibPBktLS2Vdcr+PmVgHa4N68hVZkxmNe12u+xCzvMGZ/J7//vfH5/97GfPtUEGRNoKO6WBzsmeaE86mfXRBrRdXm9rdlNVddMEIEeew+GwnPtLm9pBxfftmCGP3MdseGBCYoB1WzPGM5v25Mo2XoAUQc8LCwvlFLuImfkEwKV8Jkle1eIyYRJD3trtmZO21WqV77O+e319PdbW1sqhdbTN4eHh848ztK2g2+3G3bt3S2wUaI2A4+b3u54JPWitUmJQ9dbw0N0HDx7UvJfupKx6GISawCsLfLY1RlwcW5if9TUP6sw8MrDxbLY/ZmZBecyiKXcTG8rMiAGUDeP+nkM4EEqHYETMTtRjgiNf8mMgEfhuEKKvBoNBmRTRIvyc1WIzSdQ4217RIFyeyWQS9+7dO9d/ZoOU2XY12iWzJurNQKbNYW7Y51qtmX3Vhn7qmpk437UHHbYcMVvrSz6EnETUt9tvYrn0Z5Y/9xfxhC73dDot38hjBHkhRIYx7xAdmKFZqE0f1I3rgH9m1ZjVcCghm3bMIW8QpaOjozg4OIgbN27EnTt3ot/v1zbAuEy6kjd5NBrF+vp63Lp1qxg5aWw+6OU33vg0qxk5RAJB4PxdBGB7ezt2dnbOeUBJTUBIMnDk6/PuZxWp6Z2c/zwQblKtcp75fTs98r3c1vPe97dcXp+AZkbeFDKRvXDkAxugjK4rs3deCw5YsNzP8sBzAFkuV1VV5UAhaw+olG4rgIEIh4i6+cLvZmbLwPTxlZlF2sZrk48nD/JfXFysnduTvdfUkW9xnXATx81Rz36/X8rl8QNQkEe7fRajyIRkO66ZJO1OP+OFZY29tRlACHXYrI9+Y7J1QLjbMcuMv8u4Y4UUsYqE29CnZtEOul9dXY1erxdHR0fx8ssvx+rqaonlzPI9L10pzvDFF1+sGXdRi2F+jlynY+lsG7k9a1AxM8LJ5GwB9+uvv14OH8/2tQxOmcHxTAavzIgyoOQ8PLM2gU/E+TXIufNtG/VgymohQueB7fwNqn7GDArmltmhHRJN4ONvkbcZPSzMTN4rDzDeP378uBbvSb6sJaV+2ctL+Zg8YYMwkcyW501uZtsZfLzME+BiIAIY2dvabrfPHU3qdsKuSlt7sieOMG9abAClb5qCpWFABBnzm+sOZclOGzS1bKKIOFvDf3h4WAN3volpwyDtFSRuZ9qRMjg/g6D71wmww+6HGYa8WEnifiH6gPIdHx/H8vJyYYB4x1GjJ5NJ/Lbf9tviMunSYPj1X//1RQVyR7Gkh231J5NJLaTGsUhU0rYDn0nCteFwGK+99to5tRjByELpDuLvnAxkmR3lgTWPUXpg5QFoocs/uXwe8H7PdTX45m9ayM0KLLAZvPNkkuvDwCcfL+43U+QbdtrAYLwQ32YBBNXL7cgHAPcad86bZkswG+2RIergkIvV1dUSxsH3s03UkwEJGbWc0CaU02qcvbVmR5ZNmI9NQHjPWTEFY8ztzDhgDb9B0uXim1ltzn3I/5SXcUWijACO1wFTNuqcJ2MmhHweitsd0DJD4/tmhXiIvQYc77ltuzjPUIV5HnslS/IoszWFi9KVdroeDAaxs7NTZjfYIBX3eQWgdB6Qnt1XVlYKENLoDx8+jNdff710ENfJdx4jcGp6hr8zuHAts8MMcvOApImBNgGpmbFn3aZ2cV0RGjM2O1rMEOcNGrNTTwL8ZpB4gBg4GNgwN9eZwXh8fBwHBwe1ECj6GHMJs78nyuxZbLfbtS2azCoyO8yMfH9/v3YSWvaC+v2mHw/wJhmyrZLyQhAM2qiM2cZK/mtra7GxsVHiC1ky2Gq1yhI8MyoDgetvoMiTvWXSoG3Z8iRaVVVxcjSxONc9y1H2XnvM2Fk6TzuEHfId4gRR2dfX12s7bePoYTku79hxA9HKcbIXpSvZDPf29kq0fw6+tr1gMpmUA7D7/X7s7e2dm3ExbkacCdvJyUncu3cvtre3z83cPOO/zajmPdeULNjuUMrFbw+cpwFl0z0HlPoZwCl7FvOkkb9rpoNQG+hcB3/TINA02P23WYadAYQuRERt/bGB+ODg4FwwLgLtQexzcamTPa2sJMmTluWBAZDBaTqdFjvZ4uJiWecLQLhslCnbaL12NoOn29eTh22ZJG9kQJtSVyYGtqLCFDCdToupiPw8GaJO06cuz7ywKsuS295mDOeb2Zvb2PXmWXtqrQ0YkM0mnYc3hfBYcdkWFhZiZ2cnDg4OynvsEek+Q05p74WFhVhfX49+v//8V6CMx+MSOJmNwTSCAz4R1uFwWAtMxWPsM4tHo1G88sorcXh4WGMj5JMZ4GVA0ADT9EwGviYQneeI8Pu+nwdb07OuUwauJgBEOOzx9GA2MM6rB2Vy7BbvmOUYoGE3DDSHuJgBtVqz80sMLJ1Op0yIthlHzLYVQ1Yo197eXk3V475tVbk/zIQ8SLvdbk2FzpNgnoy453rBXB3y4U0FHNZj1kOZAXXahb6iLYmX8/58mKD4pvd1BLAMgAYcL5vke3ZYRESxfzqUh286qNxlRgMgZXnIsm/WP52e7e9oU1nEzJnHNyaT2a7g3gxiYWGhZi7LkSa0p22/gCE7teexOi9dyYHC/nYHBweFYvtwd37TWTnUAOMn109PT+Px48fx2muvFUGjQbMQk8wC5jE2P9sEMDxLPjnN+6bzb2KGdFCetTOzyP83fSvXEVbRZDN1eQ2OZoPzyu8yOs92exYA37QulWcIkXG79fv92tb/ngDsHaV85IG9ysCJSSYzJPebt5GnrVj+ZfYaETUAMTs1aDjUg8HVbreLEwXghxkZ0CzzqL9eXuYy5GB2gBZgtE2V9mKc2HyRZSizOzNwy72dLjybWbH71WXnN6FN2HRtksH2uLe3V7MJwwjxFNNHECVP0vQRG7/gSLKs5kmC9p9OpzEYDIpW87R0pQOhOBDa8Vm2h9jwaapPIXFzV9WZfeKVV16JnZ2dguikJgY3jzX5nfxMEzBmtuiU7zWxrXllyCrBvHeavum/s/ruMniHFN5rUqmzCpTrZyDMZfD7CFqus8sGYKL+TSaTssW7v20wZBBiE9rf36+xJpfR67qdDwzC4OgyOlDc7cjzHpy8RxkAv4goca72Alu9hiEdHByUHVI4DN2qMmWg/lVV1VRM8sWzaieVzz+hzNYKnAdlbmKFeRKwU82TBhMeGonbknwBQyaw5eXl2NjYKADHxI1pjT7kG7af44AD7NyfhGN5DJgVOj42a5Hj8Tju379f28fzonRpMDw+Pi4hDiwZwktIxT2bRMyEyrMEzPLzn/982dKpaVDOA8Sm+/mZzAgzcDTlkQWm6Ru+ltVbOiNPBhYAM6Rc3qaycz17iOeBfWawTQwhgxl9536D3TAAsuOH/Oy5JbEdVa6vwQ4Vk814I6LsRsSPv2vbFeog7Ahbm+uVy+wE2OIFN9NigDmOzn2MLDe11XQ6LWf6AIwAGau0cmgKaq2jKfJPp9Mp+4J6rJjNWZXPgOc28NI/1zkzTupVVbNNVx0RQB3a7bNNdo+PjwtDhOW3Wq2yTrndbke/3y/vHh8fx+rqagnaZifvTudsYwbbWCeT2TncrVar5O1t/z0meAesscb5tHQlNXk6nRbq71nKu5YgGNbd2fbp4OAg7t+/H6+//nrN4GkB8f/573kgkAV+HsA0qcQ5ZfbYBKRNM21mJQx6ZtWmPHne387GZtq9SXXIxn+SV/nkesFKsNkYtMw6vQtIBmAGnlcgAFI4LRj8vNvr9YqHmEG4u7tbvK3+vmd42/HsGUQ9BeSbWKjz8D1YG+9j38tmHtvusm02JwO5J0ecI4PBIDY2NmJtbS2ePHlS2jhithMQAEgZuXZ4eBibm5u15WtZnvimQdCamidkQHFeG1EObHW20+HwYfza5gizBSeoB8yPNmQipF2JyaQOnpyXl5fj9u3bEXG2JJjTFM0O3ScOf6JtsmlpXrqymkylHEDKRxFKGnAwGJTYpcPDw3j55Zdje3u7NEJmSeQz7+8mtdR/53wsnBepqn7HLCSivowrz7oWQINhxGw5lZmOBTILNO9yDRDKbIJnAIEmNukBnYWE5P3sqKfLarDlvlkdNjlrBVkdctuxxT2xqKxSMZjaeeJ2NQBYA7H9kXfs3DMDtoqLo4+gbgN89qDabpsdTPkZt5u9pKenp7G7uxuj0ShOTk5ic3MzIs5CgZjwTk9PS1gNXn3LB5MTG5cA6GZ6TTLAs5YJt50Zpx1BtDGMHRn0ZML1fr9fO5Wv0+mUyZY9DZhcLEMAMyEwnoDdvrDPTqcTW1tb0Wq1ytG0qMAcUUAbeQOHy6ZLgyHAxw9qhmk7wu6IcAThC1/4QhwcHNQq6vec5gHiPKdJRLMjIrPNeYA4b4ZsAtImdup8KAcGdOKn8uB0/gYdC7Nn16Zk43dTOZryz99xmbmGsNuRwfcAaU+ErVartnEn37TqCZNot9tl+/8sOzzr92Al2CP9TfeZ18Nmpswg5x0YLcDgPjWTb2ob8nF8rScv6gpT9mQRcbbZ8f7+fmxubsba2tq5FUPY+yLqtks0rclkUgtJc5u7/rAr6mew9ySVJ2mbNezVdXu67fgWB7LRf7aB2k5IJIlX5pAXGqQdqdZgDNAbGxtx8+bNOD4+jr29vRKgTv7IW/ZiX5QuDYYOeLTwepATEW7hu3fvXnzxi18slaTBDWxNAJOF3rOTfzcBnK950Od3mt7N+XtgZSZKB+ey87xDUwxy+VmuuePnnWmR64eAOATCM3xmd9Q7G8/9M69NyBe7jG1NhEBY+PLKksXFxXjy5EktDIf2wRBPAHPeUdvlpi4Oncms0AzTdiTfR72z2pjbyqzK7ZGZufuQZwBJZMQOn4cPH8b+/n7Z4RqbGkCACm/A9Ql5HmOWb2QBbz4JNkY/AMAOhG+16ssLAX3axdEB9IPHVLfbjePj41hYWCi7C7Fjjid12hsAhelRbuQY5wogy/sAH2XZ3NyMGzduxHQ624mdOOjNzc3nv58hlaaTXMCqOrMJsSMJO4y8/PLL8ejRo5qK5krkma1p1kcImzxjTXa8/LfzIJntZKDNQGlBy+CXmafLwuDwOlK/53Jl9ayqqtqO4Fnwc9kpY7a70TcGPquLBkDuNU0UDGDaG2bF88QTevDbTsOMvb+/X2JJnew5NTOwCm8GwaA0SGYg5L7L0qQtNLFyA4ufIc/cXtn8YXsV78MQHaM4nU5jf3+/BJr3er1aTG6r1SqTBPZHe3Mzs+WaZc2ASj0cbwp7zeceO8bS/gHu0d62+ztkyu8zKbILjT3UNj1AtvIEbxmz/RG5AHPw5DsOMqJ5G7ymdKXleBQMo7CDG/EiTiZnZ8X+xm/8Ril4BhQanEHm5M7N75neNwk2+fpvCyd55mczW8v58L+BsAlQDST+m8GeGUhmFQiW1RALCuU3MLmsHrT+Ds+77jyLF9ADn1g3wqg8mJipEULUIKuCTgw0QmhcL/rPYMWJcU3sKwO0+zUzQVZCZcD3xAcoofW4PfOEYdttk1cXcPGETh9hu/J+fO6TTqcTBwcHZRmaT5Wkztjh8mTq71jeKT9lziwTFjtv/0K/k2XKskcbui+wD7fb7bLBKiq/z7qJmE2EyCAeaY6C8EQfMTub2zI3nU6LbZOyszs+msdl0qXBcGNjI5aXl8vhPtBrbIOEzLz22mvx2muv1YQtz5SeYd0RHiCZOfp/C2DT/SYguwg8PUCa8mDgm434uzyTwdtR/VVVlQ63gPl7dL69aS6fGUhTm+S65HrZYUI9YKBmCuvr6zUg9CBiF2arY956n8Qsj3d5d3f3HGtzH1q99EoK1xEbEAzJ9jTYAY4RMzsDuvuMe02TRFYDDchmSplpO9GueFhtCyOvHFrGAAc0YEHUnTHl9mGMcR1mhOzxDQMsk5gdKXjoPS4tF1a77T/Ism85wGvstcM5f3atxoPOhhuow9SFoHfqCZA7NtHtx73Lrk++NBhy4t3S0lL0+/1SKAz94/E4PvOZz8Te3l5N6DJFzTNbTk33M0DlmSyDXBP7mweUEef35cv5+of8m/LJ3/BaXewfWWU2c4F5Wf3y4HOoh8vXNFE4WQW3/dLe2MXFxdjc3Iyjo6PiobPxnzKz2woBxgCQ24V+Z6Du7Oyc6zMPNGLGMMS77fxjddqggPqUPclWs82IMsg2TcowN/eFGRjPZxAkfwYoDAc1j/JYVeU5Bx1PJpNYXV2ttSN94rpThiyH2elm5kvdIDF+xhMm9fREm3c696qcw8PDODo6Kp7ziDMShZMIB2rWMmmD0WgUnU4nNjc3i7bJ+nbMckyEADokg0kJuT49PY319fUYDAZvzhkoGDyh9QRi7+7u1oKorctbADOgZBWZ66Q8gHw9g1ZmhvP+z0BiASdl+6RZYS6Hfztfq2xWd6yCZFYJYFolbmIouX4AnME34jwIMDvDGFg9xFkzT548qTlxVlZWavtJcpYLoAVbmAfuvV4vtre3y76XjjukfDgFEGQ7OjKLtM0L8OTH8tPE3s1czSizjBhQc39nEM1yk7/nPKgP9TQgcX06nZZQkMPDw+j3+wU4se3B6Ghzf4fr1sYsD95FypOi5RLgNXPP5AZ1P/e51X7HcHY6Z2vF0Si8eQdmEUL10D4AVS/fxYTAmmPiIO0cIhaTTXrRFC6TruRAYZ8wjt9jg4WHDx+WxrZA8Z7zmMcKff1pAncRK8vfmvcdkgXeHX9RuZsAyc8hXNhCDECoBZ7VKYOXNrrcZidOAKBtMghZxCxMw+zS9ePv8Xhcdp1hKRnAh7AhaCSA0IPBg4oNAQi09owdMTuBjfaDATVNHi4vP6jrWe23zS7Xl+u5DS+adKmrgcJl5rfblz06I+phJtSbfiEWzkB0fHxcDncajUa19c4GT7PjPAHkchowAdG86MFj1zGE5EWMoPNAlm2WcJwg8Zx4mAFIq/GW9ay6w/w4cGpnZycWFs72hez3+6VfAFzKAxjjyW7q26Z0aTB8//vfH4eHhyVw9PHjx/H5z38+9vb2zs3qFjCu07BNjI17FkCneQLtQXcRK8z5Nl3LqkEGNj/XxByawBtDvoUHFcqAZRXQrDC3QdOgRdDNCg1+FhYYg/vBbenYQoeU8B796BAb1zmrlcPhsORTVVUZVATI0n7Oz2zcbUs5aVO3kdU2t0meYDNDyB5H943BhvtmITmIHZsl95CPPIlRBmxk7jcmzbw80LLJgI+ob1Dh9jQwmzGSHKBtbzHglc0jMDezP5OCiKixPcpmJxFtYkeKIxLMuglAR1Ny202n09je3o69vb3CFMEf2ofNc3u9XpGNy6QreZN3dnZib28vPvvZz8bnP//52kDPDI4OyGs8I+oDO99zmsfO8rXMAptA6qJ8L7qXjcPz8sq/KQu03aDgwF4GaW5LBmYGrDwArW5nQEbAESiEOwsf9wwifqbVatXW62LzYfD7ezADtIc8MPv9fq0Ncr3yxMnfVVUVT2PWPvIkm9uiyRllFsv7ABSb1VpNN4vPDjCYOWzFDiD61uYRy0LemYbkaxyOxHX60TboDGzZEWRZMWs0UFkm8th0uJNX/fC+nVoA+mQyKcyRVTUAOd511nOTTk5OSl2ZgO0YIX+2Bdvf34/BYBDr6+vlPfoKUH3uzLDVasX29nb8/M//fDx48KA0ggtp4XWcUbbpePBEzFdxfa+JOaJWzMvrIqBrqp/tmjnPDERNf2fgNNDZW5q9bVZnKUum9xZQt0fTPfqDcmHrNfPM7QL42lMZUd+DD9UGFsB36GsGJoeGm2FUVVVbjZBVu2zno6yAIN/ODBJQoE08IfK3HS9ue2/4QJuz+YTlwg6LiBlLNKC7Xaljp9Mpdr92u11s7E3mGECLTQuQHTMv6sX3rHFk2bB8UD9v4JonEn6oF0QmYhY8T9tkdd11pr2YuLzwgG9ncKM8gBmgCY7wG5Xbk0BVVSUsaWNjI9bX18+RiOcOhp/61Kfi53/+52N7e/uc2mewsJBkgafzXVBfz/+7w7ItqOm9qzA+J4M3913upnddJsqa2Rb3Tk5OShwmMyuzaQaoPKCb2KGf9Xes4lKm4+PjWFtbq4W/GCgzADt/BB7hnU6nZaC6rwEbGONoNKrlbycBE6PL0MRSGTQ+vwJAturpd20ayGzRgxYm5LYlb5eZ9simhab2B0CyCYSJcGFhoTAY2mF7e7tm82QpGepvNjn0+/1z/QT7d39ZfpqISpb/vMaccW1Z8dJQj+2mtqAcEfX9D/2ubcc2x6yurpY6jUajmk2ddcfYsy33nKDHt1jnfVnnScQVwPD//r//7zg8PCy017p4Vhuc8uDKNoL8bBbgefldhuXlNA8QM/BlNbvpW5dlta3WmbHfZ71MJrNdffMs7jzm1Z8yZhCNqHuQqQe2OzMynrGAWjCZIAhrABCw9bjt+H6r1SpnVVB2AN0hGy4zgxB58nswNLySlN8DzaEWfj8Duz2OlMN2MXs+SQ60Jn/q4wFqlbTVmu1OnZeu2nHSbrfj7t27JSyNlSa2RdJuNp+QyDfHEmbGlx1nGdAyK+SZ3Narq6sFEG3uoR39m3Ai1ghTX3CDv7FFGijtaMQfQB6elM1wKQsOk52dnTg6Oiqe6Ox4nJeuZDNs2tW4iQlxzSojjexkRjkPGP0tX29iZh5ICP1l2KI7Kufp5+YBsOvB/2bM4/HZkQlsVInH0Ifg2GbkNmUA+V7Tt10+swALuL23ua0QxJwn704mk5rR33WLiFK3fBKZgTBrC/PUGEwHBhIGfC5TU1+4boCTAcUTQ2aYGRgAdgMjwGY1mwFNuey0gAmRJ6DS7XbLEjwA0UDf1I+2CxoA3YZMMk4ZwDJImHFi92RccOphtpd60kMmACkWZuDQsMrt8CFkzxMK2ga2WAgFE2e2bfMMTh42sjg9PS0H2F0mXWk/w3ymhAdNVtn4PQ/QmphbE7DOA7/8zEV/NyXfz97HJvBrAhyrOPkZhA8BYhLJTgXnnwWa32aPbs+mcpohPU09yt/N9rxsWHefMdmYjcEKAQIM/03RBLm+AAvn3WYm4+/mNnbd3S52QliOslwaIL3SgkFl2529pdkGapbC89jNaMvDw8PauSaEkHS73WJKoWxWu21L9PUmYDTAGzwsEwZr2tHhL9g3Ca9iEwbnv7KyUjsgzMDns5KYEPmuIwKsUpMPjNK2Rctv9lB723/yoB45DvWidGkwZMdaCxApD7h5bMysYH19Pfb392uA4vxzyoAzb3D7+XmDP//mHNbMMJvezYzJdfaP64WQTCaT2gDL9r2mejWxbA/qiDp7zODJgGuyPWYmhGpsEIUBePBQP+rPLii2SzrukQHsctkuRVloI9rHg536Ala2f+YVMJTVdkkzxfys29XhLN5hmTpkI78jBfibPjHwVFVVwAQgoPwwGnawgYlSVgY0bZ1ZsSfz7NTg22aWBm2eY4UZThJkk1VG2btOX9APHMDFhNjtdstB9m4nyslYsK2esmETBFi9jNURDKxh9sTtyWo6nZZ13pdJlwbDd77znfGFL3zhXEgEyeEi3LPgZyBh5sjs6DIgRJ753jwQnnffP/NS0zNNDNRC4vuoP3Qerv7JZFIW5ru9LLjz2jO3rWdFD3AvajcwcM3ldPCvAYWBZ7ucVWWrRbbfebcVUlb7GPCESgAKPGMDvkHRTpRs38Vu5MHudvH/ACb9YUcCwNPv92u2Kramot4cYs43qTP2Vu5hG/MkwjPUGabJeySrrZn1Z7khX8AbgKOd3RadTqcshXTYEu2SY0tpe9fDURKUlXqwYQPB5bl9AODhcHhuDNg3YXJh+bUGwv/YGtkzIR9LcVG6NBju7u4WYbXaYDWK/7mXnSSZzbgB8sD3gM9pHoA1Mbqcl8vWBGp+72nfdl6+R2cTm+d32aqJwZpZYC7nZTsyl91gZyDJzJFBbGEiAYYRUVQ7DzLsZtQLNkT4jfvDcmKB73Q6JZB/nhputc/sxE4S75bcVNcm2aKfDCoeZOPxOHZ2dmqBvwawdrtdnEsGCGJGycvPGAQJHwF4aXOrwBEzJw3ltD2RvqBfqKMny7xBgtVmykx53VeWP/oDB1omAfQBcu0jGQBWQJc8YX0OwAY02a0aVop85QUWeYNY6sHyYTzMl0mXBkN2kMizOqDigWSBm8ds3DkZ+LLqmdmj02UYoMuUO3Benk9jgvk9swcGRJOtwmwQNoJKOW8C4JtNMYIZcAx0TZODn/P/Zh1mHt6hxuqaBwt2vohZgDDfzQ4KAJP8AcKI8+f8GtCRM+8KDTjSfjBYm0gACU88GVzNeG0bJwG6qP9eJ2xW6EkBNc9rr7GL2mOKY4pkG6DDUgzSuY8y6Dc5iJy/Pes5VMmAmJ1OXgOfJ/HJZFI2YrCzBns5qnREfd/FfPiTmSnyQftFRNnRhh/iWfkm756enhYgfO7M0IXOyYMr01mDR9M9PzNv8GZwMqg+DQz9f5Mam59vAsF5gMo1s1zsXvNmo+l0GoeHh2UjXA+YbDdFSA3emYk7X7+TJyIbnXMdLXx8xypXkxPCe+ux/pgBz5I7AKrJxoUa5ZnbjI3nmkCJ5/ASO9yCgQDLyMDIb5gp9WE3nmzrs6qI/csgZJZsptlqzc6DbrVa584S2djYKJudtlpnm1qwOsepCZw8mWS2aDsubWBzhFmU8+IbeWI0CyV5qd10OjskzvLFt3wAvHdtZ2s3y4i3LYuYOa7oA8YJfYd5hUBse8hzn18mXelAKDsFMvDY4DsPXLIBluQBkPNtyusiwM3PmWXmb/hdg4fVsaYfv+f644h5WiK0hhU8DiOZN5NlkPB1EsCFgHsQ82zTZOD8bYPkuaxmkdrtdu2EvOy0IGXVe2lpKYbDYRwcHNQGKYPCA9KsOKvXZpqeODKrzfbRzLIZTNw3C/YEkNud7xswza6tspEnYSoRETdu3ChbXOGEAuTNxluts+2q2LiA7zexRdvaso0xt1OWIQOJwTZrfTxL38Ewqau3X8NWuri4GPv7+7GyslKbBAaDQfFaM7kRP8p3sAN7LTllYccl8jC2YKt97t5kqydNTAvBcGoCnaZkAMiMJt+fB7T5Gr/N2uYxRg980jy7SB5U0+m07KAyr35NZTw4OIj19fWoqqrEUHkXjwzoXMu/eSZi5jnOalRuv6xK89uDhHcjZjutcD3ibHJEFcyM1O0JwPEM2y+NRqNz9cqD0W0cEefiBSmvWZnrQLmznAL8npgzYJC/fwxysBQb+Q3clAO2Y7ZKP7MC5caNG7VT3uhznvfmpGZXtkt6wqLfbFMlAZwGRptoAGBv2mGtIQMpfUNQNmMBltbr9Wp9Sz85KBunCpqF9x9k9RbRLGbei4uLxUHS6/UKwcDWaLPGZdKlwZBBZpBwqqqqbCHP/+jyTawvA5HzaWJITe80scf8fBNTawJU59fkEMmMChXXk8RVEuEDPt/CG2e6fBZ+X7ewNoGnWV5E3fjveLo8AeW8bIOybYYga4QOhofg5/0LEX5v/28wclncBjzruDaXtUlOsB86UJe2NSga+HifAWfm5bbw1lsO63HgPmocdaFdvJEDYSjT6TQ2NzfLMaDkB2i6TthI86oexzLa2+962kkSEbX/uQ/QZy3C8pYnUK9QQv2dTCaxtrYWEbOTNWG+ZtV48C0/tBVtTd/Tp4wd78Zuj/v6+vo5BnmZdCUHivX7LLQIQ0R9s9ImlsfzTWBl1tMEkvm93GFNoJbzz+/5egYiDyCuORbrWVNVVaVNUZFWVlZq2yhl+2H+O6vTTWYA19Xs3Qwwos4iDI70uXcfYqb1rtysBSW2jHbiuwj19vZ2rQ4LCwuNu3sz+KtqFi7DILOjJfcV7zo8JNtDASkGsNVht3Fm6bRN0yTPN/2/9+wjwd4pR6fTiaOjozg4OCiHGQGytuW6DTwBMsHMs2NaS6CfvaWb29t2R8uBJ1OnzCJhwL1erwA+qrGXOhq8VlZWyviyDZdy8E0vOxyPx2UFFyCb24ux1DRO5qVLg+FgMChIPI/puSHt4WliHRF19W4eM/PviPMdkkEw2yPzN5vyze83XUO43ygIOuF1tPpoZ4VnYa55wGZW6PLaMO53M6OMOB9KYZXGnlQP9ul0WsDO26/zntsRAX3y5Ekjk3U5eY+BDEtxv0+n05o5wCDuwe82pN0sa2aLBhP/8A75A/JmfZYVtkwDbAECjxHb29AMptNpOWTNzkoDFGpkLrvVYvenwces3XZlM3TLkM0ktG/WTMjbJgfCX7wShdAY3ge0cBoBXICYJyk0D/cH7UM5rOXAnLvdbqyurp5Tuy9KlwZDBLCJueXZMyJqe+jNY4Uk0+2mlL+ZKXxEnGM8+dv5nu/zs7m5GY8ePTp3z9vdP88EI2FWI0Cb2bJJNW5qD4Mk9z3R+B3ybGKR2WlBv5g5MThtA0M1crlIvDMcDktQNtfNXgAX3kVtwmhulkNZc1hIZrMRUatvbi8zR54183PeeRJqkiurqfkd/0+ZUHVtWkLl7PV6NTttq3Xm8R4MBgXYABXKylZtBjO3WY4pzZOB88zJbJnvQYxYv5ydUN1uN/r9fo3NUn9Wq7DXIR7pqqpibW2tAB6yZTMDarfjNZlYWNaYnTCXSZcGw9FoVDNGWxWmo+cNsBxqYrsNapIFZ54a0gSCWcjnAae/7Q42m3r8+HEtb5jgVbYBumpi9mdgs8NNTggxf2fw828P5DxoPSAizm/7lPOmjBlA8qJ4DzqbFhigu7u7tR1eeC6z2ojZLtAEL5Ofv9vE/igHtruImcPAcpIdPS67wdasKbMj2sSTFeXhm7YBA2CEgJCnt7Lif8qO5xXm7T5z3QBgJpsm8uH+d39RR08gTm7jzAItV3bi0D4LCwuxsbFRgC7nD4iR52QyKVt3PXnypCzDM+BFRHGaAHh8u6qq4rzpdM72kvQKn6elZ/ImW5A8eDIL9Cxppuh3PSuSmjyzOW865CK7oJ/NApIBNLNMwi3e7MSyNeoDAOdt1HNigBnsfa1JxaZuZmJ8M2J2hjLAw9pR9xmDhgmMM5EjzofuMFj39vbKQPJyLoNnRBRDudlXVoftNLDKC1MxW+R/e0s9uCmfASRPFh7AyGWTF9TgCJCa+VJPmKifA4xsIqEcqImE1vDMdFrf/t/lt1OHa7mt+E42BeS/nY8nhawdzgu/6vf70ev1Sj1yBMJgMChthSrNhEaM5nA4LCtSkAHqbDbscCv6azwel9M8n5audCAUQpgByIbrDDrelqipsfys88hGXL6T7/t9/51BL5eviVHmzvpSJADCKsvx8XHZwSSzWwaqf3O9SVW2qhdRZ3D5N9H8Nq6bxfND9D+hDHzPExRq297e3rm1owYoT5pN9k/XMzOdDHp+Fy0mTw5ZxrIN0W1kUHG9zMjYvh6QA9QoowEZ8OJZgNubWjC57O7uxvr6enE0+DAl2wKdH2BLGzTJgq+TD32cwdvvZ3kD/G2PdH5mnWtra3F8fBzb29sluJ12PTo6itXV1Wi1WiU0B1btpX/tdjtGo1FxxKytrcVgMCjfh1DkLb9wPF0mXRoM6cisuhpcSBksM8PyOxexynmAlfPJ/1+G/WVwfx4e4mdNBKYi0ACyV3k4zQN6ruXJyQ4CkpmR2YH7BgboAdJut4s6Zk9mRJ3RM6hZotXkJKG82Aezp9jM2I6ciKdPnGZp2bRD2WG0GRTNsrINFRWM9qSdDPIwG4DQNjYAookk0L52oJA/bUMdHTtnVRlAdr+bqWYbaAZ3A0eTem2TV5480BayiaHX60Wv1yu7VKGJrKysxHA4POcpdrmm02ltdxt+dnd3o9VqxcbGRvR6vaIus9UZ9baz5Wnp0mBoO1YGQDqn0+mUnSqokIGN384jD/aLAKtJ7cvPO695NikPorcSBF1OMwu8aLbB5FmdwWUVkGd83W2YbVoRzQ4uszUSfUybORbNByihPkZECax24hkGKkG57mfft2EfeyF1skMvOz0sb96Y1ZNCE0AYeLKDZ2FhoRxqHjHb0NbA6LAjgyh5+2wPe0BhMORVVVXZ65CyzDMVWNWmHhH1DR6yRnCRzdh9brbnMWK5c7tTxmzfxLtLcDnvj0ajWF1djW63W+JPq2rmsUc1ZoLhd7t9tvppf3+/nJDnteI+JjSTiXnp0mDIFuwWWg8kVKcmFjNvwM1jPDbAR8wPzHZ55rGl/P2ImeriHXjfyoTgmxVhSM9OAtfL4RI8Y/tXjj+zkDa1oQXcgOm/WUvMUY4GO1S05eXl2NnZKUDCd612okYDXGZULktT+fLE1dS/+Z6BIufj7zvwGGZh08Ha2lphsbZ9AnC8hy3M44J2wykHeDpez+ql5d+sOdenyeyR2X5+xhOD75OyrHHN7zUxcptweAZVdnV1NcbjcWxvb5d2mU6nsbe3V5MRh1N5xU2/3y+b42Ka4H3WfjvMZnV1tXjtL5OutAIFD5+FLIOcG8mDwQ3ud5tAq+l5v0e6iPk13YMl0JBvp8TyJMrqTQMizpsAmma8LLgGGoOmATKrzwx0sxMzBFghNi87HKrq7ISzo6OjYn+MqB9MxG8fB5kHE+8AEsTXASIGadqK/xlETm4LGB67MCFngJLteG5PnB7ejYj2yeYcVGRYswfk8vJyGdSApb2lVlkzg8Oc4Ou5D80K+Z2jNTIbdP/n57KMWbUHnGkbj+esujOZsBaZPQxpP9rB4UTe65PrsMtsrsNZcnJyEuvr6zWgdpkvSleKM2xiWk1UOQNjfs8VaWJ2/iZpHuj5uXksEdtX3jPv7ZQAaoSqqs48ifYU5hm8qqqaXbHJAWGWYDXO7NHBsB78Bi8DG7FfJISe4NadnZ2aQT1PiDwHEzBYWcXP6qsHNWVxPCEOCG8bz6AHaNrtdgFVnBqOceRZmyEWFxfP7c/o8sLqbFezjZM6sov10tJSrK6uxsnJSbGpOo6S78879Y5r7kdPcFxvYo/52Qxe+V3eb/rbtmmPfeSB+jOZLSycnX/Cmny251pZWSkTgu27gK0nAPqZtuZZolLQ+mzbzIA+L10aDCOiFgZj9mVhz4ws36MRm55xyiwwg2DT/02OGwT/sg3yLAlBeqNA692hW63ZNvgGPLcVM6Jte3Y6GPB8eJEZpwdANrhbBSTsxs6HrMIvLy/H48ePz4WTmCX4yFSXLwO6VULvRjJvQiZhu/TqDgDLnl2DowcW9ruI2aFJBkODmz3W9qpHRAkhyh5jQJ2laDdu3KiFL2U2lVmwJ8R5dr9MSppAMdsYDYwZCJvIhxl87gO/Rz3oj4gogdj0hZfW5fXtZnY+QM318AQKwGLPdczp09KVmKFnYVJuqHmgxU+Th9T3aUTPOvn5eepxxGxm+FKqwrkMz5psvOd/q898K6Ju07HwWEjNeKzaeeBY8P23bS82e2SVm0QALOqx86JfUXsi6mtkXXdAlV3CARzn5UGe5Yjy8r4BtqqqWhiGVTwYB20KuJk5Akg5rMTsyGEmNiFQDyY53llcXIzNzc0SzoWNjN8GhexIoQ3ozwxwtoMaRN0/eVK0XLm9M5C6/bnH2DZWmCnaXNDv92t2U+S13W7XVnvRXo4MwDnivUMnk0nxTnOUQRNBuig9k5qc/3bj+Z4pdVM+Tr7WpPbOY4MR5x0ibyYLfLMTIRfU7+TkpHhcnZrYNMyFQWHQsi0qt6Wv8T9gZceHVReriQx47GAkA063263ttcc3rBa5HgYzXydZDWNwom6Sv3dRsnpuRxX1Ql6zecDtg12K/wl/oq28bRUOAjNvg1G2wWMPhEHyt4Gkydbr//0dTwJ+znW0XS23r/PLE47bB/lw6J3HKH3uzTjou/X19RLJAWukXPv7+4WVE8+aN7hgPT+Jvj88PIyNjY1Stss6Sa8Ehuvr67G7u1tTa0xZmwAwz+pZwPOAzkGj81SiDIDP2xbYBDZfikQ4Bd/2zi8RMfdv/veEYsbCNdvxnLKqby9gxOysDavAZkKHh4flfBd2vo6YGcK9PZO/Q158ZzKZlIX9Ltu8VUlONr6z3RcsL6/WMfAbDMnHgMPz9AXhNayT9bIys3uA0+oufdzpdEo+2Bzz1lm0b9acMhia7RvUzXKbtCn3o9sjy4XfyROFAZH/3b6AmDdMmEzOllXu7e2VzYHdv0xggF6ON6TNmYhMgqbTaQm5wX783B0oo9Go7Eyc2WFutIjztqvMQvJ10jz2xzd9/sFFDPSyKYOAy/FWJNukmCURJq7b0eGyZluLhT3HJEZEjW0g4FbtvMeiVeasgrdarbKUqt1u1w6PWl5ePufkceye80bQKfPCwkINFM16PCFTFtJgMIjRaBRVVT+ZzQ6V3C52tLj9vLyPwTedngX95r6iPmZJ5On8rV5HzCIJIqIAosNs/I6dKgY9ADXXj3bzRJgDqw2i88ZSVs3zxMl38rgxG7Ttz3ZCPPSnp6cxHA5LqI2XbrbbsyNvDW7WVtxWh4eH8ejRo9jY2Cj7Kj4tXRoMTcUzEGb25oLmNA/s+D/nRyNZbXoaQ7hsfZrKcNl330ywRLCpP2DItyOiUXhdFwcOGwjskctMMuJsVuYsDse+eTKzd6/VasX+/n5RCXnHbCe3ndXjiCjqosO2Op1O7WyNiPr5wNj9HGoznZ6dk8s+kfmo0mz0z2q/gRZGQ/udnJzEaDQq6p6dHJ70eQ/GQ1stLS1Fr9crYOe15wxg+os2sYrM/3w7mwyaZCI/M+8e9zOxyMTFrNATk+WvKXnydRkcSsMuPbQPMYncZ+zD9t23BlvboE9OTmJ3dzfu3LnTWK6crnw6XlWdxZIx8zY1WkTzUjs3lgeDhRkB5xxXG1LduG8UjN7I+282a7SqTHv42xbaeeycQWNGBghGzLaP5z1+bKg2Y8yqkMuAZ9lMlp1nXK4mbaGqqppanBkHZTID87sG1sFgUILCYaOOPcyD3Qwpoq5m23Hj3YscRkN9zcg8ULGHcXrg7u5ubG5uxmAwKIPfK0yoi89bZlsqb3wKe80s0P1v5ug+cD/kyTTbSJtkK5sO8rjMk7P7MYNxtoNyHTshNl9stQAcLJH3WM4aUd8wwmdoXyZdGgwJ3qyqqgAjFc5sz9dpjNxA3GeANoGfZ6EmlTir5l8pKduw5hmAsxBZ6NxmTaCSB5HZI99HkByiggCjsngLf8pMPJlj/fybZ5jpYZWwMoNExIwp0jYRM0eTQYiwGoDDLDliFirjgHDaBTZrgPYKCMetodrxrhmZbbyAi80EOzs7MRwOYzwex9bWVu3cYLcvdc7hNXwzog5MBr/MHn09m1HyD+1NanLMZLnxWOVeUx6Wb+fhkK/FxcXo9XolOqHb7ZY2gqmPx+M4ODioheO4fbwXYtOquHnp0mBIYZvAL/9NozaFttjul4MjbbOisXLD5ZnqeabnwTifRwKYSPbY+pncFk0zdm5TrpF/XgLGNYAJ4cqHd2PLYab2dwmbADSzDZSysk2amaC/YXMBtrjsfbb90cuucvuQn+tP3p48vEs112ygRybNaOwJjZipdLStnQOAHfb39fX12mlx/u2+YRLxMZ0ut1XQJsLg8eNJJN/ze/Pkib7yxJnJi5+/yHOfZZiJwTZY223dNvbYO2yHkJv3vOc9sbe3d+kQu0uDYQZCG039O3sJqQwFt/pBB2aPWQakpo4i5RnrjaS3AxCSMpueN/u6nfN93vVzTeqK/2ZgOTiZEAbbjDqdTmE4Zki23eAYQF30ILaN0MzL5XGsnUGQOmEXNNuFwdrb6QHoA+vNlgx4BsONjY2iegP8blPbHFnl5Mmn1+tFVc1WE0VE8UTznf39/eh2uzVHgAOVAUG+DZNy+TPYOXl85HsGJMtNE6BmefT7xoEmZujJIOcH6KO5eFKbTqfltDuXB3mifYkoMeNfXFy8tPMk4hmCrl0Qrhv9ofs5aJbOspGYRjI7dIWb/s/CyEB6o+ntwgpJGfD9twV/3sSR72VQyP1GwlZFn1gtjagfNQk4wH4iohY+wW8GLs8DGvRddqZQD0+SbgezG8uOTSx2HNl+Sn1grbBBGEa/34/9/f1iBuBcGnu1ed922GzqoY3Yqt8g7ABkynNwcBCrq6u1OEOzP+pkFp+vuV0oY54g8/tNcjVPFjOLa5LFDIZZTsECygmI8b81GU/0yAiOM2zS2GaRWzPye/fuxcLCQu3UwYvSlbzJmb0BZKg7OV7IwaJ0jgXYWys1dUIGgfw3TCQ7WZ4F1N5OQBhxfi14xPkYw4hmx1JmexH12Rlh5O8mFskgwnhvVR1PL46TiCgxXYBYRNRU4IgocXmo3P7Jg5jkMlEGO39c7+w0QsY8ASAzntQdNrSzs1NjuLC6PGHDSpFnr6DhN98GUFmBY+cIKvLp6WmMRqMSYO8NX7PpyO1jcPP9PLkYFJuY4zw5cj/Mu+5Jx9fM5P28nXN55x/HWtJH2At5F0Ds9XqlbWGCgCtyS3tfJl2JGSI46OSoPxZmz+ym7zBLz84edDzDdXujLWDusKySv9WpiWm90cTsH1EH7HmqS5O6xKRlNpBnda7ZO5uftYqD2sx9bHY2p3iQZhsijNDxdn6HsiDcOBfyhgmWn4g6I7VqZTm1yu26wn7JD3bm67aJZZl2PewJNlCyiw3rnXEULS0txenpaRwdHcXa2lrJn3Z1W9t+aFloAjVSZoJNcnZVQtAEgjlPrnsysknEkw59b+ca+XQ6nej1ejEcDsskW1VVWetOv7MbEn3zpux0vbu7G8PhsAwqU9s8qPLMPm9zzcx45jG7ecyRe96mqInKvxmpKf83AwgjzoeyZHXP4ONZ1d7JPGA8gbkP/Qx/G1i94sSqJyf6RdRDVmxecdygZSSrcwYPBhvB03m1kSfSJgDPahqgTPsALNYuMqt2+9IGtmNhNzUDGQwGxaNuuyr77rXb7ZpNFJsgk8Xp6WkZ6N4Rx2aKLO+5/7MckZpkpQnImvLiff/O7WWi4j7iHbNYTyjU0wSK7fs3NzdjOBzWWDLxqUwYgOfS0lL0+/0aaF4mXWnXGu/i4RUgFAzBQoDdQU48S8oDb17n8bevZZbpd97M9KVSqxlETUzOQMb/ZtUMmgx2mbW5PrzPe954IOKs7zjiEUZjsLbthkkTIETdzPY2OyEAJZsJmg5Qt2xZ1XW7+RtODEADrt/xgDSLoTyeYKy628FhxjuZnB39SR1YnmgWHDEbX7lvSPSH6+5+dT9kgHK/N41Jnp3H7uYB50VMdB7Acp+2cb/Qv8gWfZXP5CYftAdPlITo8PxFjNjpSqE1dJJV1jx7unKuuAdoUyB1Hjzcv2jmagKkedeb0lWefSuSQc+G56ZZOzMg/25i8WYWVv/yM/Ysc51DexBCb39v1mqGapuPgcAAF1E/Y8MeVrMz8nA75YkiYnY6GgDldvKmtW4Ht2v+G+C2DZX+caIdDJTZM768vFxjifZuA+ws/2u1Zt7krO5mcOHa00Av388sz+3K303Mm3eyGcvjtklW+e3+tdZJf3hNe7vdLmyZ546Pj2sH0POcY06fu5rsClL5TEHzbN3UoFlozVLyrEae82Yrl6vp78vUZ9433g7JzG6ecNkrnIUuYma/mjcoDACZOXiRO7F0rBbhXbOZVmsWdIytDYElb/LJ6pJtjRFRhJ77Zm8GDssgIMLzTU4Ne8pz21q2HeTtiTxvttvkeafcXF9ZWaltzeU4TtZu+6Cp/7+9c/mRLLnK+Ml65KMe/ZyeHpuRZXnMIMECedixQCCEgAXsWLHCSOzZ8ocgsUGyBAu2yAuWIMEGWbwkQBZCFmjcnhl7PP2qqqzM6koWre/W73514r4yq7p6qCOlMvPeuPGOL75z4kRcgijVYg5qjqnMHtgEhpQMBPU/U4ez/hVx2cUmG8M+uWesjfWvvq3J89Wri3cDyeneHdBHo4s977PZrPOJNRER3Y5ziDpoqaNJfMWYHb1KaKu+W4CFZ4Vk9JphmR9+ryMl2v+mRfUdUe60PgiziUEg4AMrq3efvTVo5QrCI9flg0jfMKWpY5f0Bjq6VLmLiNiW8sG3w3ln12ICBxLthbIjERizumKcBFXWH0GQdcM+qgGr8jrLE6hp4UQTgrNF5TVLg+1KkKPNl+3sbM/NCg5U2SeTJi2N/cb/My+Mh5jBMlMLZb+VFqL+kgEhF0w4WXeRXq41Ejq5ulc/QcptUtnsyVnDbYkZSJElUJyB9inTUGZ5leKdS9KmdjhD1IeqcsTF4a/OjGjP42BVGtxDrGPsFZ9EB0vI20Bxq5PSnsaVXbJG70sR+d5ZXecKdXZ0llRc2rY1eDixq9+SGaqOODAdlDRYeWCF1HNNGhrErE8nEmKPo9Go2uPt6VAcSAkyrKuMgWW/XZr6m0sT8DGODBecBLFt9ZG5Rlv01Ed9Aca1nK5jutcOFM9kNlNw8HEQ+szmGWTnY1xMU7+bQK+t4Ey7L/AJJErxloB6iHCwOGPxcGwHdq6IZsfYEiNQ55MKqIFOtVOzrlQ9MUjFLzcR9hMyPeWNLjaZvVmuNBmjkcj+p7j0jO75pEA3I9qnJFT5aO7hKwuUV9oclT7tYHo24sIGqI8YI8uofd0q42KxqN4imLUd85uNRaXfFob5bAO9rF+V4mUenDH6NcZNvFF9UhuQTyHfH03tIKI+4XeRXsxQg6Pki8ZvryR9C+G9kmRjyhqA4Mo0mvKahekzS/R9tgmwhqZHe0mWjncufjM8V4OzeLJZlK4eyot8C8VeqBZyO5RcIrjow47q7FVpccO9AE47WrJBK6DSdjmqS1yU4TmD2iJIxkh2xW9XjfkM+63KpbaSIV/q8Gw2i729vZp5giYLhfUFgpLK622f9QUHNl73umxjiU3PeB/ydNr6quNFlme1kfZlUxult8NodPEie9llS/byTHqtJms7ldtrvGEc8dnhWIESdX4HnNIMVgKmNsbXBFZdgPI6VWgOvgy0vP4ztucAzc5G9xCCCFU22gbVRtwOpbjE+rS6LKd83yomwFK8cqRVPPpmOKqxAlUu3gnkBNyuXgmgOCHoOpmWvwJX4blfWe5EAi0BHF/hyYUQ7SbZ29ur7TRRm7nPHO2ndKdy21qJYVGyMM6Qmp5hnykBVZY+69S/PQ3XaHideZXbDJ+Xyqy640q/4pSdduMHNbCTcIC5gZ8V1ARs6uyZz6GzH4+rK0PsIxxEJZvkdQoByvNR6lzKN0EuAzoyE7IQ3Yu4OCSB6Ui91AqoDwDFcXR0VPOHc3YgHzK6i1D9Vr+QvxhfLToajapFHLECnaytNJzRKX7WhVii0nCXLroDqayHh4e1HQ9ksgT45XJZU4NXq1Vl/Fce5Ds3Go0qNVg7UgSGtDU2ibdTxvQyBuZxlOJ2oMrCllziPD2uGWTgx4nQJzAKNQy+GtbTupIFFNpesgopqcRq+Ew0+6mjKp6IHARZSZ62nhELGCpdKfVVioMYOxklA0U3npc+bownmAmw6LZDW5tcFvQMX2r07Nmz2vY13RdoKQ0xSy7Y+ABaLBa1BQRtAR2NLnZ7kFlxYY+i8AIlDSLuXXWXH9URD0zgSTMRF4dQsO64mKPdEaPRhW/m/v5+zGazyjfz5cuXtUUCn7xUh96u3l+y/uCSscM26QKETK8JCDN1uylNJwQiYxEX3gk8wou4s7OzE/v7+6mJqCS9/QyV2Pb2dnUENweWD84ScCkM6S7BTxXiRlMNhiamWJr9KBkD7PLcdUlm6/C8lTo8WW7ExXuTPRwHXKYy01aj7Xeygemkc7aJDmEQQAh4BESyM9KtxNuYg4kvFo+4WBEWuHHl2E0kZIN6Vio8NRL1ZYGc7wVWWygOffuOE6UtIOQJ1sqv6mg6ncbZ2VkFjHp/DBdT9CwBMZsYabbKJgH+bgOy0r0SuDU9U4o3A+wS4GZqPQGP3xJNiKrjroc0RAxwrdEMzUzTJug0NasQPlcCsgxcNbtm0hfESqrwTQBDB6aSlGxFVIc1mBQ288/iN+NhGDFDAYW2ZE6n0/jiiy9iMplUJ75ogPpAFqvkiqrnnavKdO/hvlOC0v379yvGmNULJwQCf0RUair911RXbifUb8XFeqdfIfPJtFRe2jAXi0UcHBxUar6ecZ/QrG+wjRz0miQjEW2SxV+amN22SABtAkcf9+x3TIP9Wc/TiV1mnOVyWdNEukhnMJQx2Q9izQrmv7N77n6QCSuxBKwM579dbhLza5KSvdAlY46035H9cmDpXSHePrqf+RfqPRNiatpK9sUXX1QGbm6BWq1WtROgt7e3a4Djg13lFSsaj8e1I8LUyaUqEbQiorK7cbEl4mIlWYNF7JTvPGYeIqLGNnnIiI6ZV/7odiNA1vtrIqJSh8mItWCkulW8NBdowvF9yDRjlAhDE/trUnPXkTaGVxp3BEF9SuxWdcMdRe62R/uw/vcpb+/DXR0Im1S1UuU7uiueNqBixSmdPs93jf9Niw9OV1F8Fna24AxPq7Ky3fEMuGymZ4fy1WGqrdvb29XeUL0LRewz4jUYaPvUdDqtrRzTFud9RKAlIJpOp5X7DNVSlY1qLM0LVF85SUiNcm2E4La19Xp/q4zzZCAOSqoTqbkHBwfVO5Enk0ns7+9X6i99LbkCrfh0j+o1y+RtXWJiGdtinyhJifll6fi9NmnKi+5lYzorJ8FR/+n07mdedpFeq8k+yDJ2SPsFC+q2QJ5VVhKvnIya9wXEJrkJQBhR9iWLuLxYVOq87tzMk6rJFn1Hhe/SGI/HcXR0VHNmVjixoMViUYGeVn/FcHTclV7yQzVbeXOb8dbWxW4DMjI6SAv0udtEYTLAEHByvzXT032lIdDjqrLe1ibmp9XiiKjqgKvWEjkH7+7uxv7+fkRcLNoo3N7eXiyXy5pjtvpCk4bl/cLDOTj2Acau4bJxl4XPQLwE5t7P9Zs2Uj3PyZkYRTNLF+nFDJl5B0KfZdUAXOXLGF0X4exQimddFaCPP9JVC/3PHOz57Z2cHYegoLLRFYR1pedor6JzvYDJ2ZSOpnr69GltAUCdUh1R7wvWCurBwUGl5rC8VH8ExM78FI4vFKcdjivK2eQt1xVNxlwNVr8VExTL4Mk3BPGI125E8/m8OsBC71KJiJoaLvDzxRmxTZ8gBKpqh9JiQgYiJcDLxkcJXNcZSx635yn779edDWbMUO+i1kufVO/u2N5Ver8dr5RJNagX1E8jicgXThxAuUuF0kaj33ahqtRmLM9m40yd4j1nCpJMtSajou0uImp9gSAr5jUajSr3Gb4HmMDH5zgR8SVPWplVmgRBgTfrja4zEVEtyEh1ovqpeGgLV73Sj5Gb/3XSu14orwlBcWkFXPHodBXt0FF8bBPVNZ3baUbw9sn6gcCf46gvQRgKgA5UjM/BtgngsvHsY5uTnPqDwvgibp/Fk4iearL/dkqbMbdsELdVetNiyTpSAk3vaE1hr1rU2A5aJduJxFeQM9WIixeZKq4OJcYmh+HValVbVNHg3t7ejpOTkzg8PIzj4+PKRqb8aMGEgEl3FIJgBmIs52g0qlgd3XXE6lS+xWJRqa7K76tXrypgjng9iOQATSBRflTHWhgSMC2Xyzg5OanyMZ/Pa47ZVOsnk0kcHh5Wb2/T6rHYtSYInsKjOlbdkEWS6bONVac+ubE/Zf14U+OrjYyU0sn6cWly9wmZ9+kl4NjjXi9tMujUGmd1pcRYEKpvetbve6Uy/iZ/xaZrXUSz8SbiWlfo/lFqyGzm16BnnWXskO4enEl5j+BAFsd86fvs7Cxms1m10jsaXfgH0j5HBudnILLjChQjLsBI6jLVfjrZRly4znBVmQxMcSl/TJ9mAO2pFqPk3mzWm+Lhy6J0f39/vwKz7e3t6ngv9TO9TpQsUvmMqB/o4PXEduMzJeLR5X+b+Hgf+kwTbnhfzyZ711Z4jydb6/nM1NQkvbfjkYo7cHGmzxqn9Bx/tzGyzN6wLnBlab4pZuggmM34vOcAxWecTTiz5AD3FVIBAt+frDaWyifWpIGtHQFaRKEaq3Cc+Og4TdcS5UfsU245dKdgGSIumBMBj4tDXJSgTdYZBf0BeaiofAG5cnx+fh5HR0fV5gMt9Ny5c+fS9j9uxYuIylFdKrj6MSed0oTmQg2tjwbWJF2f9XFSAjj2O487m9wdMDN/w2xrHz0I2lz3XHqrybJ7ZJVOh1lXvzwsgTGb0VjJJQdpD9e1DBRWalvY6xBXBZomCJ9IBFoRcYlVeGcTWNLRWDYv/XYDvquQEa/dXvSyJi0SEDz1TQAkCFPtc/YnESg7i1JZeIwY4+VKogOOTwqr1cW7eKm663tnZ6d6oVVEVC+EunPnTm0nFgFNeVadMv+aBI6Pj+POnTtVvrWFj36YbMMS2HXV1jYhJaLD+32lacIn4WF68mflrjRqSn3roPc7UNixI+qvYOQRXETlEs3OZgiXtootgUUpTQc/qUk3RUorh00zKweNAM4ZM58T2Ljax46ngS3nZF2ja44YlFaRZfeiXU/17aCoD8F2NBpVNr/ZbBYvXryowEUsjfnPHKwF2gpPNqr0Ce66LoCeTCaVs7dYm+piNptVjEOv9tQ3zQlaNOGKue/SkqlA5z7K5YaLRO725G3IvlECk67SJ7yzvy7P+xhtY4pt+VNfL8Uj1TmLvySDzjNUhjmA6A2ezRpdgE7xOb0tgR3ZSqmy2+SmuNNIyGwoWQfkBEX7HmdHmjUYl+pP/8nIaJQWCBwdHdVWuSPi0gkrERfqsGsJyhtVQIGW2nB3d7ey2/HkG/kq0t3EAV2go6Oy2C+42ELV3W1v2l5IIJIfpcLu7e1VYOVlj7g4dFQALId11rHqiXVDkwAXR5z9dQGOvtIVLLI+2DW+DLD9eil+Mn61M22IdN9TeGESTSJtMmg1mdd03dVWZ2YZS/TfAsISsLnaPEQlyFTimyQ+QGlO0DV9U70tsUVnmmQYYji0B+q6QCobmGojvtuDH9ohGZY7RLIZXYzqzp078fLly6o+lE/tP87KxMFCZkjmx7TFOJWuwJXnJ2onDIFXu2nIZLkgJHZMZ3DGo3hV33Ryp2rMNm3SrFieLuPAAXUdE5Pfa0qfuMDx7Aw3i6eEGbzH8eDmkq4yaDVZslq9NhpzT6o2v/sgygaAV4yn55XneeDqUl/J2OabFnZUn2gyQIu4fF5fKS795sKC7jENZ/kcrLwmwJQdTGBB5qXryo/UWKWZsSDtlOFKMNVbXZMwTyofGQHrR+V0m+F0Oq3ttCFocnFIedCxYtxNw3oUy9P17e3X75oWWLLd2GYCUrf3bkIy9qX/pTHWd3w0jcUSoHveSozYtZsSyWL4rL80Se8jvPh7NHp94KXUCNpgaCOhuuQFyRglOw3veV6ywT6kLDdFOEnwo3uSEkMgM2FYqoViKQ6wYj20efF4Na2UivXQD853gpB1ZTM41XA9IwYo0OEBqQI25cftlmKfBGM3D9Bnkiu1KhtPcff64So0XWZGo1FtS51WnblN79mzZ5WJgYxRH4Ks+xaW2jnrN9nvpufa/m+SMWbpNLG8kjgOkHS5FsB+1lV6MUN2Lg2sn/70pylwOaCVKs1nA58BKCX7Qlu8b4s0rYBxWxoHUsRldse49O2HG/iqbsTFwQt8XuqyjuxSG/jeWtoMFRdVReZPzIz+eW7H40SqRRaCnC8AebkjolLNHWjJRlerVfVeZ5VHh1qo3sniuDqsSUMDzxeuFIccrTXZcHsf20n14KSjz4BWG3eVrmMkm4wdxEraXeleKR0nN1k6tBnyHp/P7OVN0uvteFSjmHgTnc9mIKq3GTN09M/AUpWxCckas4nWX5VkzFnfZDI+C0bU1S5nHIzLnd5pd6R6qzAOMuoHfCEUZ2OyQme4Ov05ImoMiMDrphQK2aaeIeDpGdaTQJQGdvo6Ms8COAK+n4PI8yClkrsJQQAugOWqty8E6LrKx3yxTlkfpf9Zf/I+xLbtq01l8WfSNI6zMeZ5LWlBmTYoHCBD5L0rYYb0MXP1KnOILIGdxN9b4A6UWcE9naHiec4A702wSnbw0iTDwe4dXf/pkuGdXoPcbVJuJ1PYiLrLiuzDcpgWQEjU4R3YJApLgCF70z3u/KAaTKbGfPqkwLgktJX6xK44uEUuImrslyp4xOuVZW7JIyCq7FLBOXb4zhbVn09MzoJ8AsxApYmUvCnJQDGivKDapFp7f2bf4H0P07VOOoPh48eP40c/+lE6w2igsBOyER142LHZudRBstXirMCUtv9d7w2VUkP2jYPfERfH1UvIFjTwNajd8J6pXOwsGZPkARl012Gn4q4S+v9xEuSKtIMJbX5krwJQ7tPlaTseBxkfV2SpitO/UUJQ5CRAQJpOp7UTcHSPNkwtIMlWKKDmmXqcLJgGJxMHdv/ouk9+JTaV/c9kaH/18d+Uh0ylLrE93m8qD++T7VOTKZGJJukMhp9//nlKzany6rw3NhaRm9cVTh9f/eM9FVZxMK6mGaUkVwGGilPn0kkd7CNO6R30s3zT5kQwXK0uFhzofOosgoyFIKoJTqDmMy3zpvZztVfXBbBamPAVW/YLflz9Zb4lBGw/BccnXR9o0k7IOPUs3Y28rulvm638CiA1NlR/HLCcAFSHBEYvZwn4Sr/9uetkjE1plbSwjAg4AJbiJgh6X+oz1nsf7spDNz3TBACnqz6b8Zudn86ypMEOhv6b6bxJOTk5GZwHrVJGtC84ccKgyikQlGuKnslMF7ynuqbaSJAjoyFT8snL1VJXmcmAyFz1280lipuAQvBnOfTt2wwdTKWqbm29PpxBL7tS+fnWPA4yV7O5sCNxNZYMlPXrqpyryF2AkP1kHdDL+kTfPtw1XS9H6bmm+JoYZzaxdpXerjXqTJp5M1ufZs7SLEA2ogHgDems0GcPDuxNqKebAtF14tFkkA1y/82BozR5OEAGkqrrrJO4mubqhurbHazpQsXnPB+ZCqMwcufRcx4Xy+rquoOKh/e2dVvlarWq2LwmEtn0CMTcNuimCa20k5GKCXOS1rP6r/3JdOXh4HbAKDG/odLEKvuMia7AVQrr/bqUdmky1z3957UrY4ZKhDMvXw3JAcfGpTtBRP0dG25PpAriBcxUZN1fB4Q2zSaH5idTC/yesy8yaA7UiPpb2zJwILjwOm198n3TFjT6jBLw3MdQbeygTCAjKNJfMKLuSqRvloMqq+4pX3RxIWBnaUslpguRAyYBzCdyhmVeCYT0ieSzVOUZRwkwhgJf6blSH1tXmlTaLK2M0WZjqMSKGb6NcTZJbz9DblJ315aM2hMcJero3BfcB+iaQKNvea5C1om7SR3yexpAvm1O9ziQuRDTtKDlbidkUBGX/Qm9zR1Y6dvnqq0ASjY2/VcefEWaIMb/uk8A1n1OHAQsgrDKI3CUUzlBimCq9AhydFCnqwfTdbZHNZ7x+jPeNzxsJlnYUlpDyEXTfWdrnte2MpXU/RIjbEqvDyj2OrVGlF4DhBvglXA2iDOXGLpTsBBdZ4lNANlVAeJQ/0en+E35ywzGEtrLFK+Hp8pMhufpEqAYjoBErcABitcUB984NxqNKvDxeuDzrFum4Wyg5CtJUPTrmgR4fJnq0cPzuax+nbUT3FlvWbsSRJvC8pqPlxKQvEnpyhBZR1n9ZLgQcVmDHMoOe6vJmgH12zuVMkEmQkDktw9CV0GYtn+vC2RXxQqHCgeU/nt5S8ybnYBsjhMPFysczHz1kpNdSR30NnGmRFMJ80URM3PVmyDkrjJuo/YFJwdR/XaGyP+cmP3QCoKgszmq4O4uxHbKyu/gqDI4uWBfL7ErlxIglp7vAx59wDYjR1n6Q9Vbn7zbytwmvRZQJP6yJnVSDjgNDv/vrCEztJcK0VToLNzbJhwIpWuqR9+aRgaVDVyGIyOXnYx7kn2g8KVeZINkexFRY4BUt+k3qDQVt8fnMztZrO9sUZq676qpL8Y4KCkO13C8Hgmmep4MzndAcGLIFqwYJkvTGXiTtuTjqalved01Sca6fALs8rz/bgvLa97vs0milN4QdtwLDNk5Iy4GamZg5jMltVEDw1efOdDYKNnvUrxvuzhLKAGlgIesI1PvfLFD9whAtKW5Qzzj4GIHQYJ9IVNLlTbBicBNwIqor44LlJvUUZWdblij0YXt0fsMy097X2lgeRwERIXlM02AwrwyrNdbmxAw28ZFX4DItLNS+uuk01W8PnWtlLe+bLNzzVMNdqF6kKm0+s46h56nl34TeG5CRWbaN0myycbv6zoB0CcjZ+AR9RVNDX5f3CB7FGs5OzurFsvI4iSK1+1oSosMLQMwuumwTbSKTbB1EM/iI3tVHAQ8Bx6CMFewaSLwOAk+GRjqt7MxxUHxSaDEarKJMEszkz4sLUuv7fmM1Sts1/Sy8pfqoy2cX+8qncGQ9N0zxVk+om4XzBhORH21bLVaVX5mTdQ3+z1UbiJ7ZF1lEwLtcD6IGF7bwzgBcTLjvlh9LxaL6mVOOmhAExQBM2OKrkLSB9W1hxKbzMBGdSLgJtNjh2fe/OP9jXn3RRv3tVR+MrBiGZqAwBmiq7a6zmf8d1cpgUcJSPvE1TQWS+UuxZd9SumXns/ywv9DMKLXAorvAuEM6ZINUl3nKR5caXPWV4q3K02+arnKtL3RnYH4wPIBe3x8XDnzEnQ4odGFhG/Bo3CS4iJDRH76C8GrtGWNACahDdT7gdLNFmAUXmcFuu2O4Thx086p8jW1AxeSeE9tw3Q8vqZNA6U0myRLoxTHVfZP/38VaZXibbo+lCz1MlBkahkTZgf31cTSLK14susehg28CXa4rlwlCLOsbownCLhrS0T9pA4Oel9U8Ps8b4/3fCYW2Dm70n1NggRhxecTGbezOWgwfrJHgpnskNnEoHL5801MSXXKMmT14PWdtRslY62++lwa3F2kjf2tAxJNUmKPvLdOun2eXbd8vbfjORPMGqFE1b0DOWvM7IoeJ1lD1uDXxRI9L5sWsmPZsrjiqIUTuoMwLxrMZGTOuAUOmb1W4TPfNy1keH4i6q/kjKizOu5k8TISIDIGqHAZa6TTs9uvHJBVNg5g9lXaLt2O6eycfaBUv562j4+SyudtSckGvbNOptVFsvHaNbzX0RBZh+VuCuR7LaCUkL6Jtmug8DkHRhZSA9wbVPFkgNxXONt3DX8d4gNU5eV3RP0EH2cyBAyPg4OWR2stl8vanmgBkt7h6+wuA0//9g68Wq1qLlmc0NivvF0c2GgLHI0uXkRfsmVmbNDtl26D5T0CnjNEn7DJbL08maqcAV5Tv87GXunapsUngVJaQ9hgU1jXJJomsmyS6ZOPzojAVcOMkUVEzR/NM6/rmUrhuxaymd2faQLlYmFthu4q2UDKgHxdUVw+IehaRLMtkd+0ywoU9bwveOk/j8DS4QN8haUDqYAmazMxNtoLuW1Q6i1BKFMtVZ7MxqgXNbEMtEOzrvh8G+PySTvrs/6/BIIE0iZG17UfZeH69sESaekLxDdFWL/ZZNhVei2geMdQRrLByBlVoiPaM3ZAGxPjUjjmgyqVg0aT+ALQEBm61a6LkPWwHhwUfbBKBEx0S1F9qXNkqpP+U32l6uknMWeTENsuc1rWde7YcCZYYhxifwR1sVMt8NAnUiBcKmuJUTSpsJw4MuDgc0zXVeZS3TVJ6X4bC1oHvJy1lsZj9r/L+NoksDpDdxLVVQvs/apQrxSqaltb9VcyOovkgZfe+bLOxG8OKLl+6J5YzNsurkoREF0FZjjZFJ15ZQNSH7mnCKAEHjxtnOmW7HFKV0ClcAQwssOIuslF+aMK6oOJEwAZammBgzZUZ2hZf2u6JnHAzOrV20dlLYGsA2EfYGwDnDbA6hJPaXJqk+tmkJtKr/dL5B2FJQ6CvqOEooFDu1VpxtY3jeuK2/3m3mYhQ/ZJhKDk9kJd07HzBBbfHqnvzNRBQPEdLM4ulcetrdcuOQJTgh2fITjwqC8JVRoBgoCObjwCbqaf9S0HHy8f6yBjDYy3CZwzUOO3hysBEsNm9zPhsyVNrO3ZEhA2scBSXNctbJeS2aOv9F5AyRL1o5IUfrW6eB3k4eFhdbCDBttHH30Uv/iLvxjvvPNORFxWQTXQBZwRUR3CyXyso/Zm5dzb27vkMnId0tSZCYa+3Syivl9c9eHb15xREbxcrVa7+WKJgwRZY5ttzdkTO7SOziKjE0h4p/e+5s7SrJvSx8HaJ+MSo8zK79ezNmwaqCVQbpPSeOwjrvL3HUubHHublBIRa5JeO1C80lmRGd1XZ3v8+HF8+9vfjr29vVitVtXq5c/93M/FX/7lX8Zf//Vfx0cffXSpMyveksri6W5CVqtVnJyc1M5avE4hgBH49N/BhLZTfw+vviMuDtT1Vd+ICxuhhAciMH5fxKA46Eq8X7hKrFcdKG7fW8x42CYqPw+fILg5E2Sd8DfzzGsZu8zUXQdUL2fXCTtLr2vfbmNGbQzzTbG765CM2Zek12pyNnuWZnxW8I9+9KP40z/90+oF5RERX/nKV+JnfuZn4r/+679isVjE48ePLw0USqYOZdfXFZ8pr1N8N07EZXUgY3YKQ7MDVW4f6PqfLUIxbXc5cUblIBRx2U+P6XOXicK4IznFmaYDccbGMoDyidMBktcycMsm4hKAkAhQ2uLa1GRfYrqlsG1x3BS5DkDvdbir+6zRbqUwftyTVLDVqn78//Hxcfz5n/95/Nmf/Vm899578cknn0REN3qbAVYbeL4NIkaU+fxF1NVf7T1mOcUMCYJug5MQZAlSuqdvmjuoMut+5tjtZwySUdIvUMzObYAOlK7SMv8+KWaTKfOW1UGTjZDPldJym2Fb3y2FcRbq10px3STQukrJ2iai/2JSSTqDobsqZLaGDKD0jAa4nn/58mUFrEdHR1UaPmizAcrrWbi3EQgjoqYGZvWrVfTs/dIOePoWiGkyktsNRQPKd39k51PKt4/234xlcWWbCyzMI5ksXWcE6BmjIxt1EPK+kZ0Q43E22cJZt1l98X9J2B9Lz6zbZ7sA51XJVafpbdqnrpomnkw6gyHfBZtlqq1DcZbnwPGDQjmLM25fXPEwmZr1NooPPoKiFpK4oBRRZz60v0k1VRgudBEA/aRppetuIVS9Iy6rsW4rdlbnkylB1Nub4TPTgML5ty/mOJv0396Psgk9A0Mvt99vYzAlhuvxd4l3k/I2sMyszjcxIXQGw+Pj42KjZ52J17hLwO/zmwUR+4ioG/tLSJ+pkxK6gdxkybYb0uSgMAKw8XhcqzMCoLMmP4dP18mQyMpWq1XlJO+uNsvlMmXqCsdFIImAigs9fN6ZoJfL88ny+jMZ0DCf2UKJfjO//G6TLD7ey8C+9D+LtyR9QKHrtZL4JL1paWJx64Bc12d7ree3vRGttNpIW1hbRgkCmR2LYFFioVn6b2p12KWpYbLdND7ZKIxcjBw0Ja7+kXm7i4yzw4jLx25lrNJBxhfZRqNR9QIxhclYFRdRCMYEbwczX9jJwNDtmWS+WXtkrLHECrPnmgCiKc5SfiSZCaotvVvpL53BcG9vL+7cuZOu6PmgpGSzYBul1eDM3DT6ztjM502QpnwIGBjO61d1k6nJfGnS+fl5zW9PbGw8Hl86MZrHcTnwUpVUvDr52lmX7zSJqNsFBUYCYJZbNlHeI4vLnJKVR68HlSnLj8KV7I2ZtIFYph0NZXpNUhpjXWSddEvxbTpOj7+LZFpOafJok85q8suXL+Po6OgSw+oLTj64GY+vUktF4wvoFVb/qTZtUpoo+1VKtmrrv2U3lMopUZ7JhrRoMhqNKhDl2YUEu4ioAaPSIhtTXgRsHPjZjiLGldkYKfJG4Ep6xuq4gEJ7JNOl3VDXma6X259tky62uyyuNqDkgC6xzyZQdpa6CcC6StDzNK4jrZJ0ZoZNJwI7wDWhdGbD4XWJBu/+/n7R98tneMY3RDyeq579Mmli2WRQpYUP1gldXASyBAhOPpp4OLmUGBG9AtwGR/bH/HjZ3A6ovPI8RD8Rx+uFi0UEdqUryVTlEhD6s0yPcXi9NNm6MlDzMFl6bdfa5E0Cy9soncGQDKEWgTn1Zp0wonn2zDqX/j979uwSMFHNG0qJM2mz112HcBElImcX7mLDI7YkBLYMvBS30tR2Ple/Iy7AivfV5s7QlIaDi69uO5vnnuxs0hO4l+zSPqmWJs+mSTXTVjyNIf3B2zOL26XLeOmSbpe01k1nXWkq69Bnh0hnMJxMJhGR7x8uqQPOVvSbM3TGhEqzuO7zWPrMJtQmN3nG9L3HEh+I2tJ4fn5eMT7faufg4ofAsn6Zrk9oJXceHsbhx2ZlzD9jnEyL+W5iYZ5H1lGTeF9rWthp6iNdQNHjy4hBU/m8TM5CM1bbpY663L8uAOwCtpsA5K4TWGcwPDg4iIjLK2dUezxhurJwNiejK6kRJZtJU6d4k7KpDiTQ0WAtsd/lclmtKE8mk0vtwaP5XTUVcAkcpWL6jg9vW7IonlK0Wq2q02s4QTWt3DKvnPh8R4ruuwbi18k+/XqWvk/gDjptNsE24CnZ9rqq06XxwbAloOgDIFnYTWtcntYmxkrXOPqUoTMY/vjHP64i56DIWCHFwU3/mxxt/bnsW7/ZaVoLu3VxUOmmZZOqOv39Mt9DhZvP53F+fh6TyeQSC8/CK25vQwdgxUV/QLJMHq7AA2Vp4/NV3hIwevv5JODA5mqwpM3fkOl2AZC+zLMEjm0MtxRXW/pvktV9WaUzGPJdtRFRDQCxkhIoZYcAsFMrrmwm8jB+z6UNkOgWctNFAJXVkcp5enoay+WyMmH4DiGCKt9lIuAgcxcjEzi6Wuy2TN2jak5wjbhwxOY1iUAua3PlJ7MNOqhkANEFNLI4h0pf9ZRplsJ2UflvomTgvyk22JRmW166SC+na++EMuL7dc7ema2k1JCZXyHj9DxkqsUQuWmzqeyBZMrZwsr5+XmcnJzUGFrE5Xqkz6Y+PGSVAKg2k62Q4mq02KTyQsDTPYbxvcLcFcOy+uTq5c6+S31Okl0vgWrGqhk+m5hLKm0mJdbX1n+bGO0QwGF9XzeArSt9xnrXsL1eFaqIydjoKJt1LleB1CmprmVx+wDg4NO1rgXlQCvd7xrXdYirx6wjr+fj4+M4PT2tvV+G4QUAerFTxMWrEyIuOzbrGWeRfK3CaHRx8IYYJN9pMhqNKnMEgc0BS9eYntLnwplPgJ5vllPCtvSFklLfZPzZpOzSFSzawmUaVdMzTf35JgPYTZfOzJAd06/xt388jojchSWbjRwEFJad2cNlchUqx1V3OjqaZ/VKRvfFF19UdjxfUdZ7irmQsr29HYvF4hJgKD62q2/TU34IQvzWfT3jLJ7t+OrVq9T8kgGzfnu6njdec3cjv+/lYV5LfbcpzXWla5wl1qr/XVjm2yRDy9K3HnqryePx+NI1Jew2Hh/MDphZHPwu5cH/r9vwQztPZtPa1DNkbllHZ56fP38eJycnMR6PL/nhCVjIqsk8nX3xPEqqt8yHq9MS2jkFdiqzT6YEPf1WH6LPIcP6wM9cq3wVOZu8feJ1TYRpdlEXszDrqppd0x0qpTp6k7Ip9XwoHvQezbT7eCfUNjKv5Kwjt0lWoCzeLC/XJUPAsGtDybHabYWZ2rlcLuOLL76otcFqdfGmO38roRgZAYeLJgIkgp9Yo+8pjrj8alDVjTMzAah++3H/BEQODF8p98mB9zLVt9QOGfvranbRd8Zq19VUGKbPeOk7BjbNIjcBYpnW2OU5z8eQvPRSk13N8U4pu1Jmk/GOlnXYptk4U73Y+deZgYfev+pVafkRZumwPs7Pz+P09DRevnxZrSwLDKUO81WgJWaotKSic8WY7Zj5lHo+vW14n4yQE4q78VCYT2eEZLBZv2lSgUu/M1ln8h0KPH3Cf5lUY8m6ANvn+V7Uxg3dWYIZaDFMkztFNugzVcZ3MjCOvrKuPaJPZfcdTAKuTD1muQVgz58/j1evXsVkMqntNhEoim1SHeWrPmlzzFiSt52DHZ/JGCTjcRVZaYol+s6jjGlm+WpTV7MJ2cOXZBMqXFPcQ+71eWZTamgp3quSrnG3Eak26QyG4/G49vJ2z6g6MgeHMsKBoo7NgeT7VrNCsrD6fR0zYVsafWduB40mEYPLGJeEBxssl8t4/vx5jEYX7xvWxMVV/8ViUXvlqvwvXe30SU/tpDAOQltbWzVHbT6nkMpcbwAAMU5JREFUMErP4ydYyReU4Kh77EdM19kqB4YmjGwSZluU1F/e82e7CPt6l7j9Xlamvnl4G2QoqLqmOFR6nVqjwZQlqA7HAcPtX55R79Tewdkx9e0rl9fZEd5Up+OqL1mifuu+rh0fH8fx8XGt3tVuAkR9FAfVYT3jrxXl7hLed/VYoOqA79vm3Bbqx3WRbUZErRwezuOSsI/5ffY7hXVpa3Pm7yrYUVczkNdHqSylusjS7AosbwMYd81jZzBcLpfVm80iLsDPO3xE1FQt3uMAcaM4wzc1lL4dPEvPbKqxShPAkPj7zF6vXr2qsTjGoTYg8AkQV6tVtetE4QlwdKjWb2d1BDln7dlkJhbH9pEq7gtCisPj98FMFxt9l5hVSSV2QHEgLElTn+rT7m1hM02rS5x98nAdWlQX6Qu2m0ivq3QGQzrtRlx2YVDH58ztq5BsPJ/1OOt7IbLKywzomTTN/JuQ62jQ5XJ56YBbbqmjTXC1er1IIkDU6nJEfdJwfz+BLl1rCIyLxaJ6xt+aJ7bnwFgCLTJONwH4nuZMI/H+4HlxMPZnyDq9T7p4mOx3iYl16XOlME0sr8vzXSRLo41h9snb2ya9VpPPzs6q9/WWwkTEJdCUuJqc2UPcgO+d1gdUaf+qy02ZGSP6z+pkV76gomuj0ajG/E5PT2M+n9dOmWYd+rOs52y/MZ8T+GULaQIwmkw4aXkfkMqs8A6uBEw/7DVTUTOzC68zbf89tN9k5Wp6tim9puvrShege1ukbRIbIp3BUIZ3vZEtWy3kAKXKlHVWH2TyifNOTibjHY5gqWtM4yaJBnjEMPWa2+E4+MTkaP+TfXC5XMbp6Wm1sEHn60zNVbtmBzQwjJ8nmTlf87fSIjByZZjMT6BOZtoEpvxkbJDPZeaVJmlihV1kHeC5TlWyrwzN15CJZUhcQ/PXGQxXq1W1jWs6ndbulWblbAsdB1xJzXW2GJHbihh+aKfp2lEZrs3ZOlsVdxWwr9B2yPLykFcyKbHJ+Xwei8WiyneJbS8Wi6ptFFfmSkNmyHbLFjayFzyJAZYmN/3Wd4kBeJt7fA6IBNamNm9jHH36WQa+fYCxS/iuff+mMcGbqL31OqiBs7fP1mQ+EfV35zKsD6JMnHnqWX4rXBPj7CJDZ7imdLq8lnQIMC4Wi0s7R1x1JjPU7+Pj44iIuHPnTg2oInL7H11ZWF6FySYvLuCMRqOKiSoO3S/5BFI9JpCVfFvdD5F54W+fcLO+l/0vsces35bYo4fNVOmS3DTwusmyKY2wt9O11GWuVLJTZqBEAPX7vvJMJigpdeJMNjW7lPZZD01jyNY9l1evXlUsj+YDZ3X6cEFEKjPV5Ih6O9DmSJvd7u5u5Qi9Wq0qQN7Z2am9yN7b3BdVfAJhf6EnAvuKL7xJsj7nqrD3uT7Sh0H2jdOZ7zrqNKWkWm8aWIfmdVPldPEyD02j1wKKd1Rd1yztBnOqU8o0GaQDXxvgEBSdKW66gp25rdOIZDvrymKxuGRfJUAKQFhPYowCQ39FKMulgzh0fWdn59IJOkqbR4FJWE/Mh6vemYpc2oGUsS3PT5MNm2n0BbguQNqkqpauZ2C+jhD8/z+xyk2q1r1shuzMZ2dn1SDnCSsUBy8fJD5gXdVm2t7Qbf5mmxYv21Wr4k1xzefzGmvS8f/cbic2KNebiNdAOp/P4/T09NKE4mo3Fzj0f2dnpzo3MeLCNsojwjgBahHNnaJd/VXeCZSZust25SozncazdmL/8z7i6r5Ln77U5/kmkBwqm+xn1yFdTBDrxtlHejHDiMvszcHL/dcorhaRVXo6+k2wzGZ/rlpft3St+E3n7dWrVxUgRkTM5/NLtkLVF4/2Zz0KfLgXeLVa1Q6AZX2THYoRCmAchMlOuesl4vKimE5Kp1qeAaGXwfuEL5hkYNcGfEOljZGtO3Fuij3eBNkkGy7FP1R6n2dIEHKVlRmSGpWpxlS5Pe4SC3T1m6zFDyBtq5BN2YLeZAedz+eVu41sibT7+QouP2KNEZdtdRKyNo8n4uIdJV7nmTag3wI/MkO+j9k1DE6WHEBNDtglwMtYSBtgrqNyrmtTK00gmXQBl5sKppk2ua6qPxRse4EhO56Dl8R/NzlFZ89mth3v6F1UjKEzddu9q6D2Q2U+n1fMbz6fR8RFG5Gp0WanPAtAl8tlbG9vx+7ubgVU3DFEwMu2UPo2OWeG7kTvA9xdebK+5BpBKQ3Gy+czwO7D6tcFkiz9UpgvgwytM2f966Q/RDq71vgs6x01Q3j3PWM4+ZspbGYXYhxNIEyWkOWX8a3bsTMWvKm4+8r5+XnlVD2fz2M2m9VUU6m67tC8XC6r1eDMRihGqN1GAioe86W4nLGRBapeaMZgOzvgKi6q7e6XyMW2TFvIAFe/Myndbwvf5br3CeaxKe2SZJPxkLy+SVmX8XUZZ0PT6P1CKGbKxVcPHTT9vDzFpf8+cKmqdakE3s9WcK8KrHZ3d2O1WtXsc9clAj+pyaonMT6qzqzP8/PzCgzlzE3/QgERgcvVUYGuq9MSgqaeUVjZCiUK5+9EYbzZpBhx2Q2rtE3QJ9yuIOL/h4JZV7W3Sdq0plI+byIwRmwesDNS1lUGvRCKA4XMgWzA7XzMmLvokB2QnfCev24gi78qVE+fvnU7i1Zt36TM5/OaqkyfwYi6TZCvA5hMJjWgWywWtbbgfmDFrftaXfZJr6SmSsj6FCf7gPJIx3FOloqTfSDbFsg+1qZJuPSZOEthm4Cqq/j4uWrweFPSVIechEtgtw4ISnof+5/NyqvVqvb+k9FolLIlDiQOAIoPJtqwWMg2u0LfRvbwpSOlukjpkNqrlPPz12/J4yrzcrmsGJecsCPqpgq9iF4M0utb4oDjTtpytRKA6vUD3JmiXSm+wKP4xWg5uRA0/UxFZ6oKrzCcWDP1tK8MYXbO4obYxJrSum7G1yW9N8lE10m7MxhqP7I3pACOnVcvl/fVRjfilwpRUmsUDwdDFlfJXlhSf7LwfZ2kCYBvarbVSdfHx8eX2BQBiP6HAkKBmxiZ6jdTZ7N2iYjaMwJisjV/4x/Z5GKxqI4hY91r9wvNJ3yRVJPaSzZRMu24NIXpC2Rt6fk4aoqbAF/qx01jahNyU1Vtb+c+7U3pDIZ6UXhErob6YokyqI4bUW8cNyzTJ1GD2DsL09I1pnWpcLbTpSto8vmund9PellH1u108/k8Xrx4UTslW3ZBTVpnZ2dxenpaLVConNqlEhHVfW8PB1CyfK7w6rcmxoj6aTWLxSJOT08rdkrw5EJNRFwCP2/TbM9zE1BSsn6WqfptYNSk6jU97/F3EY+vlKcu8dwEGZKPjJj5/T7SGQwXi8UlA7vE1dqIOpD4gCupMVWmALal1WpXfZiXrHPSzsk8+7Ndrl21DGWWzpZPTk7i2bNnl/Yzi/3pUFgtvmirn7Oz0WhUhctEbav7Dp6r1ara1rdcLuPk5KTaCaNFk/F4XO15FmC6L6N+8x3QSs+lT7v1BcqbJDcFzK5KXCvpEm6o9Fpp8NOulQkNwowdZXYSPZPZpXjfpUR9Pfy66pDu+8LNVcsmGlRAMZlMYmtrKxaLRTx9+jRevHhRgY8OcNAqtJ+izWuyQXJv89bWVs25m2ct0kdRrFDp6DgxTaw7OzsxnU4rN5/R6OJFVq7q+EQr4FQ7kZ2WWFMbe6JW42lL2Ie7Mrm3HbA2rW6vK1leNjFR9XKtefXqVezu7lYuFew0ctOgbG1tVerPanVxzFPJVsI4JbqmAdhFHWBc/s0wLtyNwTI3qeKblE2kQTCcz+cVUGjL3qtXryr7r/YUa3vfeDyuJoC9vb04OTmJiAt78enpaQ3IVE9cHeaCidim2v7s7Cxms1m1v3l3d7e2qBJx4X9KW2NmZpFkbVOyJXe9Vhr4XSbQPrIuwDhY9+0/TfU0JFyfNCl9JhRnitQ++sSXSW+na4GbixiDOrVOP6FqI/HO7TM7F2QknLUjomZU133/dl9HlqNJsriuAgzb4u2SrodRO4hp+R7w4+PjOD09jfF4XNt5osmM71OJuNjuyDbRkWGsVwGhbJGc2HTM12w2i9lsVgM8TXRSjfU/oq7208ySgaPKyHphn+mqblGGDtw3LVeVx4xUXHV9ONh1mahoiusjncHw8PCw8mXL0LiKEINKOxYY1hc/WBh19NIsoG9ed+dqDhjaFbssajBMNiA3LVnn8vtNIgDwxSb3/WN4pXd6ehqff/55HB0dxd27d+Pu3bsxGr12iRIDlLuUwFLAKFVb7lNUh6UyTyaTSgUW49TKcETEbDarwnp76pvtV7I5Z4OAcXapw9L1m2gj7CIlc9KXWby9h9RBZzB88eJF7ZglZYBbqiKitsXLXy2aMUEyA19RblKpmwrsDEPX+FssJlOd3Xn3KmWdQed2WsWn+nTXpogLhsX7epPeyclJHBwcVEC2v79fCxsRld1vd3c3xuNxjTUK+PSZTqc1pkqWqh0yEXX/QeWxVEclR/7sOYbJ6nmoOrwpabNLXrW8CcDsk2YfranPGkNJOoMhZ2LfV8yM8fw8z5ADmX4LPJ3luZpKp16BGdUg5kO/tYrpeSmtRnvaVy193XCyMrr4pEWV1FXH0ehiJ4lAabFYxLNnz2I8HsfJyUmcnJzEvXv3qjj8tOzt7e2YTqcxmUwuqeY8KkzlJftzIMzAIaLusO8qMcM4cPJen/rto2Zl4bqm12TXvqmyztgYOt66Mv2h4SMG7kBhIizE9vZ2LJfL+NrXvhb7+/vFjfueYYkjealzu9tGFmemUuk6DfuZ+i02dFVy1UDrb5eLuOx246xMZea5iFJteSahXvcgG+H+/n5MJpNafNIIdnZ2qnv++lgy0zbWl7WxTyIsq5taSiaWkgxtH9dcmuLP1PzrlK55zZ67CZJpjZK+7S3pDIY+sDwh/T4/P48nT55UqpezGGconoYbyZlmSUq+iM5ieb2tktZ1nHYZylKarmXx+B5uDyMgoxmCzGQymcR4PK4mDNn7FJ8AcDabVYswKp+YuNRin1B4j2DQtJOk1NcyZtjE0K4SBPtIUxp9GWkW95sE2OsQL1vWN4ba+XutJnsndWSma0VE7qricfmgzAaFG889fb/uDDazzWQgwfvL5bLyeduENIFr9i6RLI9+zcFO6q7XJ5/VTiKF4Q4TmTi0Ur9YLKo9xufn57G/v1/ZAX0lX+4yYphyl+Fb8ThhkpES3JyhN83yTYN+6L0h4YaG7xJXxnjetPSZYPqI9+cuZoPs/tAJoRczbOq4EVFjCbT/8bcK6Cug7kCrsBFx6VmF9zyUZtY+My7j4utOr1JKK+iZZKqBnpnNZtU97rbhM86itTrsJojV6vWhCZPJJA4ODmI2m1Wrw/5R3Uod1m/ZdLPTaSIutmqyzSXOHF3WUYO9TobKpljY28rorkNlLtVJG8Mekrfex/7rQ6BjBtweQl+yiMun0vj7S3zgstAcVKWFlpLIhtWmejKu0vuPmxjKkA5dKn8X0QThwOQdgtd8cuH109PTODs7q0Bxb28vptNp1eacFAmk7AcRUXOj4TY7fqRWKy/8uImF5cjqjJrAkHos1eutNMt11VGTjTCzKQ/JW2cwlOtE9r6RTB1z1scN+rpGsMyYjBcwU6t4X9+ZIZ7bxFyyStZ3ZnNsUrGHMMk+ZyGW0vB9vc689WxEXWVXOG3Ri3i94+Thw4ext7cXZ2dnlW2Q9ar0eBjDaDSqFkwIjlw9Vt44qWb16YyxVBYfCH3qsYsMZRm3shnpa19fp606j1z6iTFRZxk8zVpOu1I59ZtMRKepMM425uUfPevA5eCr+w6cWXj+54BuEpazr5QaMVP5M1FdE3Ao/p/Hd0W8Vpdns1k8evSocqNxFVqgLaCi7VEASKYo52/dE1BzIsxURFd9+e1s19lkqX8MlVIcpXi7MtOu+bsqFfo6VXOO+bZwTWrxVee1l82Qp5+U1DGJBqS/rY0AJFYg9ZWqtgaYwhPoFM5VM6Yt8c7pdkDmiwPOVa+mBZC+jdTHDpJ1AmeyAh7G4ZMPWVzExYEKOjXmwYMHcXBwULP7jkavfQ41EWplmd9yo5FLDQcZF2eUx2zlmB83p3QBRa+npkHXhxHeSjfheFmXSQ99dhPt1VtNbtLbqUZzEDr7UkenShdxeVEks0tKBMzuAJ6BloOoVlRpF/KGHMIGukpp8sjYZ0mNpJB18WVKLI9WiVV++Q7eu3cv7t27VzlMCxwFWrIf6hmeYl2yUzrQZe2cLYA5u2O9MC7F0WdSKUkTQ1pH/X6TDOdtk6H1zPG7iTrtDIZuF8vUVK4a8zlnWN4h/JSYjPkxDg0KLr74AORzmbGd4EMA6VIHVyFD4lY5uf9bHUQgSoDlzpG9vb24d+9e7O3t1VaQOaGpTsQOeeCqWB5twXSuVv1yZwvZn+eN7eh9i/lzVWqTQNYlzlsw6yebHDOOAxG5RtBGHkrSawdKxMXWOZ8BOdNzUCozDqKuNvN5FtwPT+Dz/DAPUoWzQTMajSrVjva1TTtZD5FsoHcJr1Nh3KbLOhQj3N3djTt37sTdu3crn0SvG00O8h08OTmpsTvaBZWmTzpeDt3j6nGbWiXAdBBqqpe+rPC6pasauQmVU7IO81qnjq6KPGR9yzWSIdL7JfIZo9N1rjQTED1zDpL872q1Mz8Obp8dssr3HRkCjtXq9QqqjhpzoZG/SSXrK01gl9VVl/jcPYkdmeB1cHAQh4eH1QELbsvNTjLf2tqqTqTRkV/6Vr3QXikVmyfn+Eq00uBCTPZKiUxV3pRadNPZnddV6XOVsk4afZ7rYuP1MN5+2YTZFxw7g6H79WUFcHT2hZLMdki12Q9UIMgJbBn/zs5Oba9s1lG4T1dn95HZ6oXqEXVb1mq1qtkV29SxrhXuKnvpHuMvuRFFRG3RwheGZOebzWZxeHgYe3t7l0wDXi7fRaJry+WyemG9Dr9QHDygNWtnfbMusxdoOdg5IJQYYhfmuC7LuW7w3BQjHMKW/LUKfdO8Cim1H4lUaWx1LUuvU2ucyWUM0MEr4rK7B1mL/rstkQXQINM9d/XQtSYw0fYyH2B8Xqqzn8HI33QpoRvNpmZp1qHKnNlhJVyJZ93pDMLDw8OYTqdVnr1+nWHLT1B1IbWWK8M6kEPPq465iFN6EZhrArqu+NmOJebsLlTrskWfxDfFPjPpGmc2DjIzRJ+8lkiMyxBf2U1L3/GUYVNf6bX5lg3gDrElnV1hCHxZISIuACkDV+8IvjeWgOEA3WQT1L5gf/cGGYzv9xUTbasXlt/rsdTY2ZmOJZGK6vbW8/PzmE6ncXBwcOkVr8q7ysYTsXn6teKJqPuLyrlagCngI4tUOLFxZ3Uql5sw2GZu/ywxnE0AVhNrbIrf26cp7BATSJ+8dJGuAJOx8k2n4ekNCVeaHJwcdI2/92pyRJ2FZaqWI7Q7Ovs9/c52exDIqIKRjXCbnTOPEn3mN5mQiy/0eJz6Vj6Yp5K9squ0dSzf6SGg29/frxZIJMq7bH+sR55jyPcmZ4Nd12gv1ESi8is91aur8rT/ZQ7Y7krlnToDr02yuCZwXEc2pT1cl/jYucp0Mmlrh6H3StL7cNesghzgnKV5OKJ4BnCr1ao2sDTIs3cr8xQWz2upQggMDpQlHz0OWKr9YlRUw3Wf+ZVq6YzX3ZGa6tlFixSaMLa2tuLu3btxcHBwqU0E+notJxcusrJxsmEeR6OL1XiBIE0HDlx0q+Fk423GtmkCOmcsXdXD7HfTtabrfcN0SWco6KyT975xNoVvwoR189A0Hpz8SIaoy73UZO98rjpqYBIQvSB0aXFQlHqla37qieLLwLfJZshwygPVMT0nYN3Z2amAy1VtDuzJZFIDVIKs0hI78tO/eZBqqa7bhK5E0+k0Dg8PayfXaBIRS1XZGL9POmKKBEmVSa45PG1GC1icRFjnBNmsTbK2Yv49LCfP7Jk22YS6OiTdtrKWwCNj50OlC0ANjb80yfQFxKbwjjUOkG2TXpv0thmK8Ti7ccYTcXFSTDbgOXBc/RHoCJwILLR5RUTNSVji9ibF7epXZpdSvsbjcbXSTJchgYDen0LWmC2okKW6c3lTPbeJgEhAeO/everZ7HRqPXNwcFCd1ajzD1WmyWRSc5JmGQiiUnv1Fj0xQ/5WezG/nEyydveyZ2aLrMM3AVwbCN3KzZKsfTKW1wSapcm2TTqDIQeIq5Lc75sxBDIYAiaZg287y0CwNGj0n0Cqa15pzL/bCfWc71/mwaWuqvsM5YtA+jjAMD2n9xlQ+rNiZ5PJJO7evVtzC9IJ1b5Dh6xd8YkF0m9QItDTPbFD3VN+z87OqjpiedxWnIFgkxmlxPbd1JJJiam0hbkOWZdVvk2yCTaaxeGT9RC12KW3mky1kYlLvSJz0zUugtAG5c8KLGmcJ4DSduUVUZo1soHHOF39cjuhq8dkg0z37t278fTp00vmAYk7pGdhmjqOXxfLm0wmtUUJbyueKUjwI5iSNavMqmt3xOaL6FWPVKn1kT2T+SJgexs6aDapQKVrXdlh37Bd7vWVTam+Ll2AxdN308NV5GvdeLNxUxo7Q00hncGQR22VMqtBJBbBY6WUSX5rwAlAfcZ39VqDj06/bRVA8NbgpBqvSvVtf7Spyacuor4PmGz2+Pj4UtpkyZlkAOdgn8loNIrDw8M4ODiIiKhYIH0DfYFE9kK2kYCQdc/VXy5+cCKZz+e1iY72SN/ZwjYYjUaVy41rCFmbcZCW7IddOn3fwbEJlnHdMiSvXHhb15aaMbfsOq81TVJd7ac+vtYpxyBmmGWElUpfM93j8xlLk/DMPJ6UQ7ClGsnnm1hWKUy2mFNaBPK4OJj1tj1JtqijlefT09Mi7S+xRsr+/n4cHh5GxOuXtRM0mDeBq1aPVVbuPpFzNkHR1XrWjdjhcrms6k2r+uPxODVn8DnFk8Wd1a3H59I0CTK+dQf7TZcSGJXCqY/zPT99Jpe2dNYV9o8mYtA05vu2eWcwfPjwYfz0pz8tMheyJKqabl9kGJ544oXyAUAG5wyqqdLIMJxpuOrng1NgIRVez3PLmuIgI2adeD7okuJpsmwEGj4/Ho/j4OCgYnY7Ozu1cya3t7djNptVB7fK6VlgJTD0ttKzEfVdLconbYR8IRSBlOyUh72ORqPau5zZZk12QbaD32sCyJIMfe7LKNki43VLU7vzvwOcf5dA0DGnTTo7XT99+rTGBiSuuhDIqGbxHsN74XQ9e70AF2LczSUi3+TvtjQBEFmQwjlLVHyyESofGaNs2uWiBYjValVzbFZ8PknwumS1WsVkMon9/f2aU7fi4aLIfD6vgTl9IbVCLmdphdve3o7xeFwdXEHVmnuQuRd8NBpVfYJHdym/tAEzzsyXMOsTGXCxf3UJx3hZl00D5DoAM5vw+zy3KVkXCF2b2VS8HrcTqrY68PHURTqD4e/93u9FxEWBs72npNxNHbtkS/MO7s/XMg6GVmJRfJ7PMT0HPlfrXF3MZqSmgR0RNfDTsw7E2bPM3/b2duzt7UXE67fgzWaz6tAJtQXtg2wT1RX3GW9tbcVsNqv5DNJvkJNP5ucpuy1Zte9FZx2VNAq2X6mt+VzGGLvM/qVJuYt8WdjkVTPArG03LaW+wjHVxe6eSWcw/Md//MfaC8UdtUsZpCqUIbszB7/vcZOBOBPLaH9p0LBSyXSURqa6EiCUvlP0Uh6yfLLMZLlUM5Ufqca7u7uxv79f5TkDQbE5xcd6EttbrV4fYSaGyMWQDBi5AMVFEtYpt+ZF1Bei2N7sF2yXEusrdeoSQDUB15cB1JqkD3B3UVPflDTlowl0fSz2ae/ONsMf/OAHVQIRlw9e8NmZACIQ0bczLN97zMHhYOa7RyIu295YEQQs5j0bZFwA8utKX9+0I0qYfwpBROGczTIO1m/EayYoN6M7d+7UzBWz2axmNqB/pmyKUrFZZ8qXyrK7u1uloTKIGUuF1m8xSvppqiyyUUq4F1ppOcCWOi3riXlm3q8D+LqoZbfyZiSbbHm9j3RmhmIaDoJZGN2nHc4ZGUGDg6StMK56OdtTGDI9v88BTFXQwZZp8nkeCNtl9qEKSWnbmbNarSo73mq1qlihntva2qrcfgRovv94NBrVVnkZvxZhyEb1HN1zJNy65yo8Jybd8z3dBF/2g1Jde7266s36uirZtIq8ibiGlLfPM67F+ecmitdrk2ZWkt7H/mdCWxSZjbM4V4kjohrsZIcZg/HB53GVgNrz7aqbVMNMbVfj+9a2bAFHICRV0RdYmH5TnXKQ6zxCpcH3lXiZlSeqzNoyyLYRYO7t7dVAqeSITmY3Ho8rZ2p/13PmRkSgU55Y91kblxZXlB8HwlI98v7/Z7nJANZV2sxMDOPkKNP0StJZTc5UBQ0i36KW/SYjiagf2CDXEE9PcZTcaZiO/BJdbc+Ypq7t7u5WJ804m/RyazDLZcXVeX44ORCgubqaNbAPcqmjcmDXcf1eDq720ubn9SgRs2V41TPBj/Go/GwTXaMvqMqp/NAkwcmSdZvls8QKvexZXH3lpqjA66h4fP7/k2QM0E1sXeul13mGzt4yxsfwfkKKG/NXq1Wl5rkKq2sl+xvVroj6K0P9HEHmuY25Mm8O7DxlhiyMLjjz+bz6+PmAfBkS68XLF/Ha3qfV4+3t7ZrNj+c3ipHKJinVdzQaVSyO9aY6VtoCRmeUql++CJ6mEl0j+KnelReVmXWZdcyme369xBIy8GwbBG1hNgkufQZlUxyMq6RZbJINrgPMTeXdNFt1QsJrV6Ima0Dx5GN+uzOv7zXmbhENrkyyBiWr4IIJ85LZEKkKOng5qDrzIKPJ/AidsZI9eXnOzs5qR+VH1N9f4rPabDar7GsENeVZiyosm2x+s9msypPY32QyqeyBu7u7MZ1OY3d3Nx48eFDrtFoEcYboNkde5xZFb3PmQZKpwj5p8Zq3iT/De02A+mVkTiUAaALKq5a2dLqweC+TP+/hHDP4TAlnXDqryWJCfJm4Mua2NWbAV1EVV+b07Lp+VhmKj6xE12nHY0VJ6HAt25/bFJoakfnScxlbavKp42qt2/8UfjKZVC9qJ/PjZ7FY1NiiJiLZYLWIojyJoQmY9D2bzeKdd96JxWJRU4s5cWjScZvfaHTxUi63D+qbbaVP9ipXPuedfYgK3MZG/79IkxaySeEk2ZbmJtujrVx9yt1rb7JsSuqg6tR8VaTcPvRfnfL8/PV7Ofb392O5XMbJyUkFCjwIgZXqdj8yFAIRPxysdO+h8LoDXGnWoTBPtH0q7jZq7i48DKuFkoioAM1nOy7YKBwnh+Pj4xqY7ezsVOAqoCS4HR0dxf7+ftWezvoIns5ENel5e7AN/MQe2h6bTAXeDtclm0praDxeL+vmJ2NLffKyifj7qKuMs+m644OH61veXsf+R0RlxBcLofpItqeBwMwvFosK+GiX0rPMvDOQbDeIhIxFqp0OMNXzGqzaa0wAZvlcZW1qFAfE8/PzagW3qeGznTISHbBKdxcdpkA27vZRqrTj8Tj29/eriYduN7qn14ZqYSYiagx0MplUO1wI3nxhPV1wlC8eJMtvCRm817t3Xk6I3k6UjAGuAwBDZNMA1qQaluLPylxSHZukVA72eY7zm8C8S2y0T956MUM/pl4qszoCjfhkZK5auq2OqjUXQki7GdYbRELwpZ3AwwkQdc9PzpE0qW1+Xek3ScY+/f50Oq3CyCzhK7YEJ51aQ4DVCvmLFy/i8PCwOnxVLjVykRFTX61WtfMHeTq5WGBEVGA/Ho8rFZ1l12IR8+p12VZPBHo3e3QRn0RLchMGcB/ZRH6b2FJT/JlmNIR5bUI4Jpm+m7qG5K0zGL569aoaVGJatJv5KnBmtHR1kr5tKpDvGCEjdLtipk45m9F1V0udkVL9lmSgRWD3+lE90AevaQYXu5IaOZ1OK6ZFVkuQ4j2qqpw0uLo8Gr02XRwdHVW20vPz1wc2LBaLODg4iJOTk+pkGu1a0W4X+haKtYohjsfjS689cDVb5WRdCOi90yq8T4gZ82uqX06eWZghgLBJ5ldiMetKiU225b3tv0/+N4kRRuSudpSu+ewMhh9++GF8//vfr6lnW1tbcXp6WjFBqXViNK4SMfMSDQrakbKOTFui7yOWLZNqntifdwSyDtnC6N7j9jyCt6t3XcFTz3s+nDlNp9Oa+pkBHVVogRfdXqTibm9vx/7+fuzs7MTdu3erMk2n0+qUan8f8vb2drWQoonl7OwsptNp9U4YLZgIZJUXqtAsN/97HVAyM4Vf6wJwJTZaujZEdSxJl7jeFIBwQippPC4+mfUF73UnjDa7YQb21FL7AnZnMHzw4EE8ePAgnj9/HovFomIxeilQRFSsg0DiDE2FoC8a1WrampwFCmz5nK6LETFdMUsxIgEqK1k2NbJSr/isYZS+L/T4uX0RF25Eci5neVRXUj0Vlmrr7u5u7WVNW1uvj/wfj8fVS+LFFGUzHI/Hsbe3V00Sx8fHtcWd/f39ePToUWUzfPfddyMi4tNPP40XL17EYrGI3d3duHPnTo0J0OZK9Z11UGIO2Wo/25x17PapJrB0KQHqTZBMs9k0Q6Rk7dCF2Xlb+HObYsldJMtLJtSk+OkqncHw3//93+Px48exWq3i008/jfl8XtvYrw36DlKZf56/Uc0XXvTb7Y56NmOeBD2BpVin8saFHsbJ/22uARxcWkBy9kf7WRNAcNLY29urWN/u7m7NzzCivrihSUdg6GrranWxuLW7uxtf/epX4yc/+Um8ePEi7ty5E9/61rfi8ePHlb1QE8n29nZ88MEH8fLly1gsFjGfz+P58+c1lZyn3NDuqnJ4J/RrNKe4mtxmcy21SRcpAehQ+9Kbspn1kWwyaZOubOwqpI31e9rZuPJrVwKGERGffPJJzGazys50fHxcuWlEXHZCdhamzAqkNKgzOj6ZTGKxWNRWJ1U4vr1OAKR3GAukyB6VltRhZ24CUJVBYWhPVL60uhsRl47vp4+h++QpDzQzEPQFflRR6TjNSUJ1zlNzuGihOtZJM+PxON5///14991342tf+1rcv38/jo6O4tNPP40f//jHFcubTCZxeHgY9+/fr9x6Hj9+XDuP0d+vwnbXR3XFNiUQeuctSQZcJXEm2UfamOZVS5NK2PZcJk3mmk2x0SF15eDcloeMgXIcOGNlOGpBXfPa+73JT58+ja2t1weDHh8fx+npabXCKGFGIupuMyoEje2j0YXrS3ZWngBDIHB6elqpkbonkJQqykUdrnrTOM/3rfDofN2j+u4NIXE1j53OV1Xpp6nfq9WqWkEmqBA8uWihU2YEzGSF2st8cnIS0+m0mhjOzs7i4OAgtre348mTJ/Hxxx/H0dFRbG1txeHhYW0FW6xQ8ame+XY9+ijSod4XyNhv9J1NkCXJBkJX6QoUDHddoLiJ+PtMIpQuANQl3FBZJ94SyDPPHN9Xxgy3traqFciI14cIPHr0KLa2tuLJkydxcnJSbffKdoWImSlzestaxAVw8dRkHYjAV3a6Sq3BqEEmFVEgcHR0VANAgYgWD/x9JbRTesUSlNkQelaVr/SXy2VMJpM4PT2tHQah8tIRWXY/gRsHpRZGmM/RaFSxuEePHlWn20RElabaZmtrK9599904OTmpvfxpNpvV8k4WrDqSvVHpCxAVXs+QtTepLK5Oqw6z+7znZWeYkrxptrcpKTGbIWp6FyB6E2pwlzw4AfEwHLc+MXeVzmC4s7MTT58+jePj41itXr9n4zd+4zfi6OgolstlfPLJJ9XJyRJnbPKL80II5Lgy6bSXYXWNg20ymRRXe3n6sgBTK7fMb8b6sh0sXYRs1l+zSkAVq6MtdD6f1xZUVD4xMoV7+fJlHB8fx2w2i8PDw2p73XQ6jWfPnlXqrJj7wcFBBYQEe4E3HbvpQqX2V1hOJLqW7SpxZkf1yFWY7J5PTkOArS8objrcm5Am9rupOIfE26Yad7mvvDiDVV8V8RoinZ/69re/HZ9//nm8ePEiPv744/jhD38YH3/8cfzO7/xOfPTRR/Hd7343/vu//ztOT08rw/29e/dquzLEBMX6yJRcVXW1W4sLetXmgwcP4t69e/HkyZNYLpfVYsD+/n588cUX1W8xWrmYPHnypKa2Ejx9ZZgV3aQqS3zgyhbI3S50Q9KAf/nyZQWcd+/ejQ8//DB+7dd+LZ48eRJ/93d/V7E4rjDPZrOKAWv73uHhYbXyfHBwUJkUBFyLxaKyDWoSINhyNZp7jpVvlc07nU8gPkiotnhdsRP7PT6TXWuStjDZ/SHPdJGu+S0BwdCJoK9kk1Qpf+vkJ2N1rk24UJvIPFSonbg21VU6g+Ef/dEfVb9PT08rVvLkyZNYrVbx3nvvxdHRUXzjG9+I73znO/Ev//IvlW1P6tWjR4+qQXd6ehrHx8fVQJYLx97eXjx79qwaoHwf8cuXL6sdFI8ePapA8Mc//nGl1t2/f796yfnDhw/jl3/5l+Pjjz+Of/qnf6qAgUyLCxH6TKfT+NVf/dX45JNP4t/+7d+qBuMna6yskZfLZUyn02oiiLhwttZzX//61+N3f/d346OPPooPPvgg3n333RiPx/Gv//qv8fd///fVYoZeGi/VVT6DAhOdRDMej+P4+DgePnwYi8UiFotF7O/v106/ZpmUd01QYqm6T7U+4mLxhxNWxuAI+M5EPRzrJlOz9Z/fmerkK9J9QG8oK9wUWJXyukl22wS6pXruGk/G1iRNZciAtytL9ImWoNlXRquOOuDnn39edbjvfe978Rd/8RdxeHgYH374Yfznf/5nPHr0KP7wD/8wDg8PY7lcxve///34m7/5m/jOd74Tz58/j4ioLZCsVquaPYoLFhEXp2dnLEPsRa/ElHouEOSKLN1Ujo6OKhvkZDKpDotwV5udnZ2YzWaVna1JlVO5MnVaDcXTZY6OjqrwOmrrT/7kT+IXfuEX4n/+53/i/fffj69//evx4MGDWK1W8cd//Mfxk5/8JN5///3Y29urQEhHch0eHlYA9uDBg3j48GE8ePAgPv/88yqO4+Pjqh6kSnsnEpBokYrnHBLEuGqse7rOOmK8BFammanMni/abb0/ZIy0bQAzXb/W9lybtA3gvtIXBEvPU9bNYxcwLaXvbcc2cyDNTFb+7X2XY9/l4cOHrWXrddK1ZvdvfvOb8fM///Pxt3/7t/HVr341fv/3fz9ms1k8ffo0vve978X5+Xl88MEH8du//dvxz//8z/EP//APtd0VAqEHDx7EZ599Vg1CUVza+ZyRjEajyv4oW6Qqgw7MGqTL5bLyiVT+9ZpLb5SIqED0xYsXxXoQGPOEbgfIiIuBfHp6Gnt7ezWWKxVX8Z2ensY3vvGNmu1QKq3qTmDGMwt5JNdsNoudnZ3Y29ur7Lv7+/u1xRXl012iVH654/j+ZK8DgT93yCgs64Jqtvtdetr6T2lTnzxcl3tdQaYPCHUBVZdNAmcXaVN9GaZr3oZOIgTArkzd88sFUU6kQ6XXqTViV/v7+/EHf/AH8Vu/9Vvx2WefVcb6hw8fxnvvvRf/+7//G9/97nfjnXfeiXfffbdaFGAFvHr1Kp48eVIxDBYsIqqdLTwWSquhEVFtFYu43HB8q9t8Pq8GIhcBMjuexyX/Q1Uyd8nwlBjfljYa1RcetK97NKofRiun6V/5lV+Jn/3Zn73EbM7Pz+PRo0exWl0cpEAw1G+BnLbDTSaTePz4ceUHyhVj5dHPISQzzE7HUR/wj8I5q6N6zDDKR6bW0M2K6VIFLg3ejO21qWZdgGsT6m/XuJuYVNO1rkwtu94ViLrea5LShOYTYEmyvqSJfRPt1GvZRX5pysT7778fX/va1yIi4unTp3F0dBTvvfdefPLJJ/Gbv/mb8R//8R/x8OHDePjwYXz22We1At25cyfm83nF8NxfjfY5qVnn5+dxenpahdvZ2anYpVTm0WgUp6entQErMBST85cZqTwRUUtLZWYDjkYXZwZGRC3fdIJWw9N5muxUK8Pn5+fVKm9G8e/fvx+r1evFp+Pj47hz505VjoODg7h//3710igx4IiobLMCUR4koXJq77ImB7kAsZ30WyBF04D7O6rcXmdq0+VyWU1KBFqyRGeZlCaV2a9lzKMruLwpWQdkJG3s2ftyk1w3c83SzsrGw6U32X69wJA6vsBIGbx//3418L/1rW/F9vZ2vPfee/HOO+/EBx98EH/1V39VMZHnz59XLPKXfumX4oc//GH84Ac/iNVqVR0KID82AYVOT9GZiC9fvoyTk5P4yle+Ep988klEvB6g0+k0zs7O4uTkJJ4+fRqj0SieP39eY57cccEdLnJQPj4+juVyGXt7e3FwcFCB13w+j1evXsWzZ89iPp/H0dFRzGazODo6qjG0xWJRO7xWgKnFmuVyGd/85jfj4OAgvvKVr1Sr3i6j0Sh+/dd/PSJeLx4xHm2n40q7JgHuxuErA8hstbDFWdlfSyCgU5tzoclBK1OVL3U2ex9KxvT0fHaNzzKfXZ7vcu9W3oz0naR4MMlG87HqCP+ff/554303aLrBUyqbBtXW1lblbMxwEd07bGanYx7oxM0VzYioVG4OUIKGMymJGKaYlFaKdT5gRFRHZrFcq9XrLYNyK3r48OGlPb5tapOLAwTrw0Ejq6cmcOH9Ll2ki62pLa2ucbXl6RbwvlwyBBtcuiygdAbDW7mVW7mVL7Ost/xyK7dyK7fyJZFbMLyVW7mVW4lbMLyVW7mVW4mIWzC8lVu5lVuJiFswvJVbuZVbiYhbMLyVW7mVW4mIWzC8lVu5lVuJiFswvJVbuZVbiYhbMLyVW7mVW4mIiP8DsneyXeykA2YAAAAASUVORK5CYII=\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{}}],"execution_count":6},{"cell_type":"code","source":"import torch\nfrom torchvision import datasets, transforms\nfrom torch.utils.data import DataLoader\n\n# ============================\n# STEP 3: Data Preprocessing\n# ============================\n\n# Augmentation for training\ntrain_transform = transforms.Compose([\n    transforms.Resize((224,224)),              # resize all images to 224x224\n    transforms.RandomHorizontalFlip(),         # random flip\n    transforms.RandomRotation(10),             # random rotation\n    transforms.ToTensor(),                     # convert to tensor\n    transforms.Normalize(mean=[0.485,0.456,0.406], \n                         std=[0.229,0.224,0.225]) # normalize with ImageNet stats\n])\n\n# Only resize + normalize for validation and test\nval_test_transform = transforms.Compose([\n    transforms.Resize((224,224)),\n    transforms.ToTensor(),\n    transforms.Normalize(mean=[0.485,0.456,0.406], \n                         std=[0.229,0.224,0.225])\n])\n\n# Dataset paths\ntrain_dir = \"/kaggle/input/nohanpneumonia/NohanDataset/train\"\nval_dir   = \"/kaggle/input/nohanpneumonia/NohanDataset/val\"\ntest_dir  = \"/kaggle/input/nohanpneumonia/NohanDataset/test\"\n\n# Load datasets\ntrain_data = datasets.ImageFolder(train_dir, transform=train_transform)\nval_data   = datasets.ImageFolder(val_dir, transform=val_test_transform)\ntest_data  = datasets.ImageFolder(test_dir, transform=val_test_transform)\n\n# Create DataLoaders\ntrain_loader = DataLoader(train_data, batch_size=32, shuffle=True)\nval_loader   = DataLoader(val_data, batch_size=32, shuffle=False)\ntest_loader  = DataLoader(test_data, batch_size=32, shuffle=False)\n\n# Check class names\nprint(\"Classes:\", train_data.classes)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T17:53:12.895249Z","iopub.execute_input":"2025-08-13T17:53:12.89575Z","iopub.status.idle":"2025-08-13T17:53:14.848852Z","shell.execute_reply.started":"2025-08-13T17:53:12.895721Z","shell.execute_reply":"2025-08-13T17:53:14.848278Z"}},"outputs":[{"name":"stdout","text":"Classes: ['NORMAL', 'PNEUMONIA']\n","output_type":"stream"}],"execution_count":7},{"cell_type":"code","source":"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torchvision import models\n\n# ============================\n# STEP 4: Model Setup (ResNet50)\n# ============================\n\n# Check GPU availability\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nprint(\"Using device:\", device)\n\n# Load Pretrained ResNet50\nmodel = models.resnet50(pretrained=True)\n\n# Replace final layer for 2 classes (Normal / Pneumonia)\nnum_features = model.fc.in_features\nmodel.fc = nn.Linear(num_features, 2)\n\n# Move to GPU/CPU\nmodel = model.to(device)\n\n# Loss function\ncriterion = nn.CrossEntropyLoss()\n\n# Optimizer\noptimizer = optim.Adam(model.parameters(), lr=1e-4)\n\n# Number of epochs\nepochs = 10\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T17:53:14.849539Z","iopub.execute_input":"2025-08-13T17:53:14.849752Z","iopub.status.idle":"2025-08-13T17:53:16.499528Z","shell.execute_reply.started":"2025-08-13T17:53:14.849726Z","shell.execute_reply":"2025-08-13T17:53:16.498773Z"}},"outputs":[{"name":"stdout","text":"Using device: cuda\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n  warnings.warn(\n/usr/local/lib/python3.11/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n  warnings.warn(msg)\nDownloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n100%|██████████| 97.8M/97.8M [00:00<00:00, 177MB/s] \n","output_type":"stream"}],"execution_count":8},{"cell_type":"code","source":"import copy\nimport time\n\n# ============================\n# STEP 5: Training Loop with Early Stopping\n# ============================\n\ndef train_model(model, criterion, optimizer, train_loader, val_loader, device, num_epochs=10, patience=3):\n    since = time.time()\n    \n    best_model_wts = copy.deepcopy(model.state_dict())\n    best_acc = 0.0\n    best_loss = float(\"inf\")\n    epochs_no_improve = 0\n\n    for epoch in range(num_epochs):\n        print(f\"Epoch {epoch+1}/{num_epochs}\")\n        print(\"-\" * 30)\n        \n        # Each epoch has a training and validation phase\n        for phase in ['train', 'val']:\n            if phase == 'train':\n                model.train()\n                dataloader = train_loader\n            else:\n                model.eval()\n                dataloader = val_loader\n            \n            running_loss = 0.0\n            running_corrects = 0\n            \n            # Iterate over data\n            for inputs, labels in dataloader:\n                inputs = inputs.to(device)\n                labels = labels.to(device)\n                \n                optimizer.zero_grad()\n                \n                with torch.set_grad_enabled(phase == 'train'):\n                    outputs = model(inputs)\n                    _, preds = torch.max(outputs, 1)\n                    loss = criterion(outputs, labels)\n                    \n                    if phase == 'train':\n                        loss.backward()\n                        optimizer.step()\n                \n                running_loss += loss.item() * inputs.size(0)\n                running_corrects += torch.sum(preds == labels.data)\n            \n            epoch_loss = running_loss / len(dataloader.dataset)\n            epoch_acc = running_corrects.double() / len(dataloader.dataset)\n            \n            print(f\"{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}\")\n            \n            # deep copy the model if it’s the best so far\n            if phase == 'val':\n                if epoch_loss < best_loss:\n                    best_loss = epoch_loss\n                    best_acc = epoch_acc\n                    best_model_wts = copy.deepcopy(model.state_dict())\n                    epochs_no_improve = 0\n                else:\n                    epochs_no_improve += 1\n                \n        # Early stopping check\n        if epochs_no_improve >= patience:\n            print(\"Early stopping triggered!\")\n            break\n    \n    time_elapsed = time.time() - since\n    print(f\"Training complete in {time_elapsed//60:.0f}m {time_elapsed%60:.0f}s\")\n    print(f\"Best val Acc: {best_acc:.4f}\")\n    \n    # Load best model weights\n    model.load_state_dict(best_model_wts)\n    return model\n\n# ============================\n# Run Training\n# ============================\nmodel = train_model(model, criterion, optimizer, train_loader, val_loader, device, num_epochs=10, patience=3)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T18:08:44.537518Z","iopub.execute_input":"2025-08-13T18:08:44.538379Z","iopub.status.idle":"2025-08-13T18:11:48.892233Z","shell.execute_reply.started":"2025-08-13T18:08:44.538351Z","shell.execute_reply":"2025-08-13T18:11:48.891554Z"}},"outputs":[{"name":"stdout","text":"Epoch 1/10\n------------------------------\ntrain Loss: 0.6093 Acc: 0.6713\nval Loss: 0.7215 Acc: 0.5602\nEpoch 2/10\n------------------------------\ntrain Loss: 0.4185 Acc: 0.8426\nval Loss: 0.4653 Acc: 0.7778\nEpoch 3/10\n------------------------------\ntrain Loss: 0.3436 Acc: 0.8380\nval Loss: 0.4593 Acc: 0.8009\nEpoch 4/10\n------------------------------\ntrain Loss: 0.2276 Acc: 0.9167\nval Loss: 0.1998 Acc: 0.9213\nEpoch 5/10\n------------------------------\ntrain Loss: 0.2380 Acc: 0.9028\nval Loss: 0.1225 Acc: 0.9722\nEpoch 6/10\n------------------------------\ntrain Loss: 0.1599 Acc: 0.9444\nval Loss: 0.1089 Acc: 0.9583\nEpoch 7/10\n------------------------------\ntrain Loss: 0.1483 Acc: 0.9306\nval Loss: 0.0462 Acc: 0.9861\nEpoch 8/10\n------------------------------\ntrain Loss: 0.1198 Acc: 0.9398\nval Loss: 0.0382 Acc: 0.9907\nEpoch 9/10\n------------------------------\ntrain Loss: 0.0887 Acc: 0.9537\nval Loss: 0.0434 Acc: 0.9954\nEpoch 10/10\n------------------------------\ntrain Loss: 0.0797 Acc: 0.9722\nval Loss: 0.0996 Acc: 0.9676\nTraining complete in 3m 4s\nBest val Acc: 0.9907\n","output_type":"stream"}],"execution_count":9},{"cell_type":"code","source":"from sklearn.metrics import classification_report, confusion_matrix\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# ============================\n# STEP 6: Evaluation\n# ============================\n\ndef evaluate_model(model, test_loader, device, class_names):\n    model.eval()\n    y_true = []\n    y_pred = []\n\n    with torch.no_grad():\n        for inputs, labels in test_loader:\n            inputs, labels = inputs.to(device), labels.to(device)\n            outputs = model(inputs)\n            _, preds = torch.max(outputs, 1)\n\n            y_true.extend(labels.cpu().numpy())\n            y_pred.extend(preds.cpu().numpy())\n\n    # Classification Report\n    print(\"Classification Report:\\n\")\n    print(classification_report(y_true, y_pred, target_names=class_names))\n\n    # Confusion Matrix\n    cm = confusion_matrix(y_true, y_pred)\n    plt.figure(figsize=(6,5))\n    sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n                xticklabels=class_names, yticklabels=class_names)\n    plt.xlabel(\"Predicted\")\n    plt.ylabel(\"True\")\n    plt.title(\"Confusion Matrix\")\n    plt.show()\n\n# ============================\n# Run Evaluation\n# ============================\nclass_names = train_data.classes  # ['NORMAL', 'PNEUMONIA'] expected\nevaluate_model(model, test_loader, device, class_names)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T18:12:01.080559Z","iopub.execute_input":"2025-08-13T18:12:01.080841Z","iopub.status.idle":"2025-08-13T18:12:10.253709Z","shell.execute_reply.started":"2025-08-13T18:12:01.080818Z","shell.execute_reply":"2025-08-13T18:12:10.252998Z"}},"outputs":[{"name":"stdout","text":"Classification Report:\n\n              precision    recall  f1-score   support\n\n      NORMAL       0.98      1.00      0.99       113\n   PNEUMONIA       1.00      0.98      0.99       103\n\n    accuracy                           0.99       216\n   macro avg       0.99      0.99      0.99       216\nweighted avg       0.99      0.99      0.99       216\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 600x500 with 2 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":10},{"cell_type":"code","source":"# =========================================================\n# RSNA Pneumonia Detection (Faster R-CNN, PyTorch)\n# Ready-to-Run for Kaggle\n# =========================================================\n\n# If needed, install extras\n!pip -q install pydicom opencv-python tqdm\n\nimport os, random, time, math, shutil, gc\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import Dataset, DataLoader\n\nimport torchvision\nfrom torchvision.transforms import functional as TF\nfrom torchvision.ops import box_iou\nfrom torchvision.models.detection import fasterrcnn_resnet50_fpn_v2, FasterRCNN_ResNet50_FPN_V2_Weights\n\nimport pydicom\nimport cv2\nfrom PIL import Image, ImageDraw, ImageFont\nimport matplotlib.pyplot as plt\n\nSEED = 42\nrandom.seed(SEED); np.random.seed(SEED); torch.manual_seed(SEED)\n\n# =========================================================\n# PATHS (Kaggle RSNA dataset)\n# =========================================================\nDATA_DIR = \"/kaggle/input/rsna-pneumonia-detection-challenge\"\nIMG_DIR  = os.path.join(DATA_DIR, \"stage_2_train_images\")\nCSV_LABELS = os.path.join(DATA_DIR, \"stage_2_train_labels.csv\")          # patientId,x,y,width,height,Target\nCSV_CLASS  = os.path.join(DATA_DIR, \"stage_2_detailed_class_info.csv\")   # optional (not required here)\n\nassert os.path.exists(IMG_DIR), \"Add RSNA dataset to Kaggle notebook (rsna-pneumonia-detection-challenge).\"\n\n# =========================================================\n# READ LABELS & BUILD ANNOTATION MAP\n# Some images have multiple boxes; Target==0 means no pneumonia (no boxes)\n# =========================================================\ndf = pd.read_csv(CSV_LABELS)\n# Normalize: NaN coords -> no box\ndf_box = df.copy()\ndf_box[['x','y','width','height']] = df_box[['x','y','width','height']].fillna(0)\n\n# Aggregate per image\ngt_dict = {}\nhas_box_ids = set()\n\nfor pid, rows in df_box.groupby('patientId'):\n    boxes = []\n    for _, r in rows.iterrows():\n        if int(r['Target']) == 1:\n            x1 = float(r['x']); y1 = float(r['y'])\n            w  = float(r['width']); h = float(r['height'])\n            if w > 0 and h > 0:\n                boxes.append([x1, y1, x1+w, y1+h])\n    if len(boxes) > 0:\n        gt_dict[pid] = {'boxes': np.array(boxes, dtype=np.float32), 'labels': np.ones((len(boxes),), dtype=np.int64)}\n        has_box_ids.add(pid)\n    else:\n        gt_dict[pid] = {'boxes': np.zeros((0,4), dtype=np.float32), 'labels': np.zeros((0,), dtype=np.int64)}\n\nall_ids = list(gt_dict.keys())\nprint(\"Total images:\", len(all_ids))\nprint(\"Images with pneumonia boxes:\", len(has_box_ids))\n\n# =========================================================\n# TRAIN/VAL SPLIT (patient-level)\n# Stratify by presence of boxes\n# =========================================================\npos_ids = list(has_box_ids)\nneg_ids = [i for i in all_ids if i not in has_box_ids]\n\nrandom.shuffle(pos_ids); random.shuffle(neg_ids)\nval_frac = 0.1\n\npos_val_n = max(1, int(len(pos_ids)*val_frac))\nneg_val_n = max(1, int(len(neg_ids)*val_frac))\n\nval_ids = set(pos_ids[:pos_val_n] + neg_ids[:neg_val_n])\ntrain_ids = [i for i in all_ids if i not in val_ids]\n\nprint(\"Train size:\", len(train_ids), \"Val size:\", len(val_ids))\n\n# =========================================================\n# DATASET\n#   - Reads DICOM\n#   - Converts to RGB PIL\n#   - Returns image tensor + detection target dict\n# =========================================================\nclass RSNADataset(Dataset):\n    def __init__(self, ids, img_dir, ann_dict, img_size=512, augment=False):\n        self.ids = ids\n        self.img_dir = img_dir\n        self.ann = ann_dict\n        self.img_size = img_size\n        self.augment = augment\n\n    def __len__(self):\n        return len(self.ids)\n\n    def _read_dicom(self, path):\n        dcm = pydicom.dcmread(path)\n        img = dcm.pixel_array.astype(np.float32)\n        # Normalize windowing (simple min-max)\n        img = img - img.min()\n        if img.max() > 0: img = img / img.max()\n        img = (img*255.0).clip(0,255).astype(np.uint8)\n        return img\n\n    def __getitem__(self, idx):\n        pid = self.ids[idx]\n        dcm_path = os.path.join(self.img_dir, f\"{pid}.dcm\")\n        img = self._read_dicom(dcm_path)  # HxW (grayscale)\n\n        # Convert to 3-channel\n        img = np.stack([img, img, img], axis=-1)\n\n        H, W = img.shape[:2]\n        boxes = self.ann[pid]['boxes'].copy()\n        labels = self.ann[pid]['labels'].copy()\n\n        # Resize (keep aspect ratio by simple warp to square for simplicity)\n        out_size = self.img_size\n        img_resized = cv2.resize(img, (out_size, out_size), interpolation=cv2.INTER_LINEAR)\n        scale_x = out_size / W\n        scale_y = out_size / H\n        if boxes.shape[0] > 0:\n            boxes[:, [0,2]] = boxes[:, [0,2]] * scale_x\n            boxes[:, [1,3]] = boxes[:, [1,3]] * scale_y\n\n        # Augment (mild)\n        if self.augment:\n            if random.random() < 0.5:\n                # horizontal flip\n                img_resized = np.ascontiguousarray(img_resized[:, ::-1, :])\n                if boxes.shape[0] > 0:\n                    x1 = boxes[:,0].copy()\n                    x2 = boxes[:,2].copy()\n                    boxes[:,0] = out_size - x2\n                    boxes[:,2] = out_size - x1\n\n        # To tensor\n        img_t = TF.to_tensor(Image.fromarray(img_resized))\n        # Build target\n        target = {}\n        if boxes.shape[0] > 0:\n            target['boxes'] = torch.tensor(boxes, dtype=torch.float32)\n            target['labels'] = torch.tensor(labels, dtype=torch.int64)\n        else:\n            target['boxes'] = torch.zeros((0,4), dtype=torch.float32)\n            target['labels'] = torch.zeros((0,), dtype=torch.int64)\n\n        target['image_id'] = torch.tensor([idx])\n\n        # Area (for COCO-style; not strictly needed)\n        if target['boxes'].shape[0] > 0:\n            wh = target['boxes'][:,2:] - target['boxes'][:,:2]\n            target['area'] = (wh[:,0]*wh[:,1])\n        else:\n            target['area'] = torch.zeros((0,), dtype=torch.float32)\n\n        # iscrowd\n        target['iscrowd'] = torch.zeros((target['boxes'].shape[0],), dtype=torch.int64)\n\n        return img_t, target\n\ndef collate_fn(batch):\n    return tuple(zip(*batch))\n\n# Datasets & Loaders\nIMG_SIZE = 512\ntrain_ds = RSNADataset(train_ids, IMG_DIR, gt_dict, img_size=IMG_SIZE, augment=True)\nval_ds   = RSNADataset(list(val_ids), IMG_DIR, gt_dict, img_size=IMG_SIZE, augment=False)\n\ntrain_loader = DataLoader(train_ds, batch_size=4, shuffle=True, num_workers=2, collate_fn=collate_fn)\nval_loader   = DataLoader(val_ds, batch_size=4, shuffle=False, num_workers=2, collate_fn=collate_fn)\n\n# Quick sanity check\nxb, yb = next(iter(train_loader))\nprint(\"Sample batch images:\", len(xb), \"Sample target[0] keys:\", yb[0].keys())\n\n# =========================================================\n# MODEL: Faster R-CNN ResNet50 FPN v2 (ImageNet pretrained)\n# =========================================================\nweights = FasterRCNN_ResNet50_FPN_V2_Weights.DEFAULT\nmodel = fasterrcnn_resnet50_fpn_v2(weights=weights, box_score_thresh=0.05)\n# num_classes = 2 (background + pneumonia)\nin_features = model.roi_heads.box_predictor.cls_score.in_features\nmodel.roi_heads.box_predictor = torchvision.models.detection.faster_rcnn.FastRCNNPredictor(in_features, num_classes=2)\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nmodel = model.to(device)\n\n# Optimizer & LR\nparams = [p for p in model.parameters() if p.requires_grad]\noptimizer = optim.SGD(params, lr=0.005, momentum=0.9, weight_decay=0.0005)\nlr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=4, gamma=0.1)\n\nprint(\"Device:\", device)\nprint(\"Model ready.\")\n\n# =========================================================\n# TRAINING LOOP (with validation loss & simple early stopping)\n# =========================================================\ndef train_one_epoch(model, loader, optimizer, device):\n    model.train()\n    loss_sum = 0.0\n    for imgs, targets in tqdm(loader, desc=\"Train\", leave=False):\n        imgs = [img.to(device) for img in imgs]\n        tgts = [{k: v.to(device) for k, v in t.items()} for t in targets]\n\n        loss_dict = model(imgs, tgts)\n        losses = sum(loss for loss in loss_dict.values())\n\n        optimizer.zero_grad()\n        losses.backward()\n        optimizer.step()\n\n        loss_sum += losses.item()\n    return loss_sum / max(1, len(loader))\n\n@torch.no_grad()\ndef validate_loss(model, loader, device):\n    model.train(False)\n    loss_sum = 0.0\n    for imgs, targets in tqdm(loader, desc=\"Val  \", leave=False):\n        imgs = [img.to(device) for img in imgs]\n        tgts = [{k: v.to(device) for k, v in t.items()} for t in targets]\n        loss_dict = model(imgs, tgts)\n        losses = sum(loss for loss in loss_dict.values())\n        loss_sum += losses.item()\n    return loss_sum / max(1, len(loader))\n\nEPOCHS = 10\npatience = 3\nbest_val = float('inf')\nno_improve = 0\nbest_weights = None\n\nfor epoch in range(1, EPOCHS+1):\n    print(f\"\\nEpoch {epoch}/{EPOCHS}\")\n    tr_loss = train_one_epoch(model, train_loader, optimizer, device)\n    val_loss = validate_loss(model, val_loader, device)\n    print(f\"Train Loss: {tr_loss:.4f} | Val Loss: {val_loss:.4f}\")\n\n    lr_scheduler.step()\n\n    if val_loss < best_val:\n        best_val = val_loss\n        no_improve = 0\n        best_weights = {k: v.detach().cpu().clone() for k, v in model.state_dict().items()}\n        torch.save(model.state_dict(), \"best_frcnn_rsna.pth\")\n        print(\"✅ Saved best weights.\")\n    else:\n        no_improve += 1\n        if no_improve >= patience:\n            print(\"⏹️ Early stopping.\")\n            break\n\nif best_weights is not None:\n    model.load_state_dict(best_weights)\n\nprint(\"Training done. Best val loss:\", best_val)\n\n# =========================================================\n# SIMPLE EVALUATION (IoU@0.5 Precision/Recall on VAL)\n# Note: This is a lightweight metric; not full COCO mAP.\n# =========================================================\n@torch.no_grad()\ndef evaluate_iou50(model, loader, device, score_thresh=0.3, iou_thresh=0.5):\n    model.eval()\n    TP, FP, FN = 0, 0, 0\n\n    for imgs, targets in tqdm(loader, desc=\"Eval \", leave=False):\n        imgs = [img.to(device) for img in imgs]\n        outs = model(imgs)\n\n        for out, tgt in zip(outs, targets):\n            # predicted boxes after score threshold\n            keep = out['scores'] >= score_thresh\n            pred_boxes = out['boxes'][keep].cpu()\n            # ground truth boxes\n            gt_boxes = tgt['boxes']\n            if isinstance(gt_boxes, torch.Tensor):\n                gt_boxes = gt_boxes\n            gt_boxes = gt_boxes.cpu()\n\n            if len(pred_boxes)==0 and len(gt_boxes)==0:\n                continue\n            if len(pred_boxes)==0 and len(gt_boxes)>0:\n                FN += len(gt_boxes)\n                continue\n            if len(pred_boxes)>0 and len(gt_boxes)==0:\n                FP += len(pred_boxes)\n                continue\n\n            ious = box_iou(pred_boxes, gt_boxes)  # [Np x Ng]\n            # greedy matching\n            matched_gt = set()\n            matched_pred = set()\n            for i in range(ious.shape[0]):\n                j = torch.argmax(ious[i]).item()\n                if ious[i, j] >= iou_thresh and j not in matched_gt:\n                    TP += 1\n                    matched_gt.add(j)\n                    matched_pred.add(i)\n            FP += (len(pred_boxes) - len(matched_pred))\n            FN += (len(gt_boxes) - len(matched_gt))\n\n    precision = TP / (TP + FP + 1e-9)\n    recall    = TP / (TP + FN + 1e-9)\n    return precision, recall, TP, FP, FN\n\nprec, rec, TP, FP, FN = evaluate_iou50(model, val_loader, device)\nprint(f\"IoU@0.5 -> Precision: {prec:.3f}, Recall: {rec:.3f} (TP={TP}, FP={FP}, FN={FN})\")\n\n# =========================================================\n# VISUALIZE PREDICTIONS (save a few images with boxes)\n# =========================================================\nos.makedirs(\"pred_samples\", exist_ok=True)\n\n@torch.no_grad()\ndef draw_and_save_samples(model, dataset, device, n=6, score_thresh=0.35):\n    model.eval()\n    ids = np.random.choice(len(dataset), size=min(n, len(dataset)), replace=False)\n    for k, idx in enumerate(ids):\n        img_t, target = dataset[idx]\n        img = (img_t.permute(1,2,0).numpy()*255).clip(0,255).astype(np.uint8)\n        pil = Image.fromarray(img)\n\n        out = model([img_t.to(device)])[0]\n        boxes = out['boxes'].cpu().numpy()\n        scores = out['scores'].cpu().numpy()\n\n        draw = ImageDraw.Draw(pil)\n        # GT (green)\n        for b in target['boxes'].numpy():\n            x1,y1,x2,y2 = b\n            draw.rectangle([x1,y1,x2,y2], outline=(0,255,0), width=3)\n        # Pred (red)\n        for b, s in zip(boxes, scores):\n            if s < score_thresh: continue\n            x1,y1,x2,y2 = b\n            draw.rectangle([x1,y1,x2,y2], outline=(255,0,0), width=3)\n            draw.text((x1, max(0,y1-12)), f\"{s:.2f}\", fill=(255,0,0))\n\n        save_path = f\"pred_samples/val_{k}.png\"\n        pil.save(save_path)\n\ndraw_and_save_samples(model, val_ds, device, n=6, score_thresh=0.35)\nprint(\"Saved samples to pred_samples/\")\n\n# Show one sample inline (optional)\nfrom IPython.display import Image as IPyImage, display\nsample_files = sorted([os.path.join(\"pred_samples\", f) for f in os.listdir(\"pred_samples\")])\nif sample_files:\n    display(IPyImage(filename=sample_files[0]))\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-13T18:25:35.862245Z","iopub.execute_input":"2025-08-13T18:25:35.862982Z","execution_failed":"2025-08-13T18:46:35.732Z"}},"outputs":[{"name":"stdout","text":"Total images: 26684\nImages with pneumonia boxes: 6012\nTrain size: 24016 Val size: 2668\nSample batch images: 4 Sample target[0] keys: dict_keys(['boxes', 'labels', 'image_id', 'area', 'iscrowd'])\n","output_type":"stream"},{"name":"stderr","text":"Downloading: \"https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_v2_coco-dd69338a.pth\" to /root/.cache/torch/hub/checkpoints/fasterrcnn_resnet50_fpn_v2_coco-dd69338a.pth\n100%|██████████| 167M/167M [00:00<00:00, 212MB/s] \n","output_type":"stream"},{"name":"stdout","text":"Device: cuda\nModel ready.\n\nEpoch 1/10\n","output_type":"stream"},{"name":"stderr","text":"Train:  15%|█▌        | 913/6004 [20:50<1:56:36,  1.37s/it]","output_type":"stream"}],"execution_count":null}]}