running script

{"cells":[{"cell_type":"markdown","metadata":{"id":"Wf5KrEb6vrkR"},"source":["<div class=\"markdown-google-sans\">\n","  <h1>Welcome to Colab!</h1>\n","</div>\n","\n","<div class=\"markdown-google-sans\">\n","  <h2>Explore the Gemini API</h2>\n","  <p>The Gemini API gives you access to Gemini models created by Google DeepMind. Gemini models are built from the ground up to be multimodal, so you can reason seamlessly across text, images, code, and audio.\n","  </p>\n","  <strong>How to get started</strong>\n","    <ol>\n","      <li>Go to <a href=\"https://aistudio.google.com/\">Google AI Studio</a> and log in with your Google account.</li>\n","      <li><a href=\"https://aistudio.google.com/app/apikey\">Create an API key</a>.</li>\n","      <li>Use a quickstart for <a href=\"https://colab.research.google.com/github/google-gemini/cookbook/blob/main/quickstarts/Prompting.ipynb\">Python</a>, or call the REST API using <a href=\"https://colab.research.google.com/github/google-gemini/cookbook/blob/main/quickstarts/rest/Prompting_REST.ipynb\">curl</a>.</li>\n","      </ol>\n","  <strong>Explore use cases</strong>\n","    <ul>\n","      <li><a href=\"https://colab.research.google.com/github/google-gemini/cookbook/blob/main/examples/Market_a_Jet_Backpack.ipynb\">Create a marketing campaign</a></li>\n","      <li><a href=\"https://colab.research.google.com/github/google-gemini/cookbook/blob/main/quickstarts/Audio.ipynb\">Analyze audio recordings</a></li>\n","      <li><a href=\"https://colab.research.google.com/github/google-gemini/cookbook/blob/main/quickstarts/System_instructions.ipynb\">Use System instructions in chat</a></li>\n","    </ul>\n","  <p>To learn more, check out the <a href=\"https://github.com/google-gemini/cookbook\">Gemini cookbook</a> or visit the <a href=\"https://ai.google.dev/docs/\">Gemini API documentation</a>.\n","  </p>\n","</div>\n"]},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":304},"executionInfo":{"elapsed":121759,"status":"error","timestamp":1731946014526,"user":{"displayName":"Sai Akshitha Reddy Kota","userId":"05755385555264415224"},"user_tz":300},"id":"bGUc28EVD5-H","outputId":"abd9ae2c-b69c-43fe-b5af-a5da3c334fc1"},"outputs":[{"output_type":"error","ename":"ValueError","evalue":"mount failed","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-1-d5df0069828e>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdrive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36mmount\u001b[0;34m(mountpoint, force_remount, timeout_ms, readonly)\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m120000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreadonly\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m   \u001b[0;34m\"\"\"Mount your Google Drive at the specified mountpoint path.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m   return _mount(\n\u001b[0m\u001b[1;32m    101\u001b[0m       \u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m       \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforce_remount\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36m_mount\u001b[0;34m(mountpoint, force_remount, timeout_ms, ephemeral, readonly)\u001b[0m\n\u001b[1;32m    275\u001b[0m             \u001b[0;34m'https://research.google.com/colaboratory/faq.html#drive-timeout'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    276\u001b[0m         )\n\u001b[0;32m--> 277\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'mount failed'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mextra_reason\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    278\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mcase\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m       \u001b[0;31m# Terminate the DriveFS binary before killing bash.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: mount failed"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":282,"status":"ok","timestamp":1729619883585,"user":{"displayName":"Sai Akshitha Reddy Kota","userId":"05755385555264415224"},"user_tz":240},"id":"scYUMlHx12sc","outputId":"5b9a974f-da47-4046-a4eb-80c867449f46"},"outputs":[{"name":"stdout","output_type":"stream","text":["/content/drive/MyDrive/yolov8_tracking/tracking/nicolai-tracking\n","/content/drive/MyDrive/yolov8_tracking/tracking/nicolai-tracking\n"]}],"source":["%cd /content/drive/MyDrive/yolov8_tracking/tracking/nicolai-tracking\n","!pwd"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2871,"status":"ok","timestamp":1729619888424,"user":{"displayName":"Sai Akshitha Reddy Kota","userId":"05755385555264415224"},"user_tz":240},"id":"dQ-W-dezXxIR","outputId":"a8c1f889-d5e6-4477-f0ea-7074183cf411"},"outputs":[{"name":"stdout","output_type":"stream","text":["Requirement already satisfied: absl-py==2.1.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 1)) (2.1.0)\n","Requirement already satisfied: antlr4-python3-runtime==4.9.3 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 2)) (4.9.3)\n","Requirement already satisfied: astunparse==1.6.3 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 3)) (1.6.3)\n","Requirement already satisfied: beautifulsoup4==4.12.3 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 4)) (4.12.3)\n","Requirement already satisfied: certifi==2024.8.30 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 5)) (2024.8.30)\n","Requirement already satisfied: charset-normalizer==3.3.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 6)) (3.3.2)\n","Requirement already satisfied: contourpy==1.3.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 7)) (1.3.0)\n","Requirement already satisfied: cycler==0.12.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 8)) (0.12.1)\n","Requirement already satisfied: deep-sort-realtime==1.3.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 9)) (1.3.2)\n","Requirement already satisfied: defusedxml==0.7.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 10)) (0.7.1)\n","Requirement already satisfied: easydict==1.13 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 11)) (1.13)\n","Requirement already satisfied: filelock==3.16.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 12)) (3.16.1)\n","Requirement already satisfied: flatbuffers==24.3.25 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 13)) (24.3.25)\n","Requirement already satisfied: fonttools==4.54.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 14)) (4.54.1)\n","Requirement already satisfied: fsspec==2024.9.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 15)) (2024.9.0)\n","Requirement already satisfied: gast==0.6.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 16)) (0.6.0)\n","Requirement already satisfied: gdown==5.2.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 17)) (5.2.0)\n","Requirement already satisfied: google-pasta==0.2.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 18)) (0.2.0)\n","Requirement already satisfied: grpcio==1.66.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 19)) (1.66.2)\n","Requirement already satisfied: h5py==3.12.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 20)) (3.12.1)\n","Requirement already satisfied: huggingface-hub==0.25.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 21)) (0.25.1)\n","Requirement already satisfied: idna==3.10 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 22)) (3.10)\n","Requirement already satisfied: Jinja2==3.1.4 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 23)) (3.1.4)\n","Requirement already satisfied: joblib==1.4.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 24)) (1.4.2)\n","Requirement already satisfied: keras==3.5.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 25)) (3.5.0)\n","Requirement already satisfied: kiwisolver==1.4.7 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 26)) (1.4.7)\n","Requirement already satisfied: lapx==0.5.10.post1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 27)) (0.5.10.post1)\n","Requirement already satisfied: libclang==18.1.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 28)) (18.1.1)\n","Requirement already satisfied: Markdown==3.7 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 29)) (3.7)\n","Requirement already satisfied: markdown-it-py==3.0.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 30)) (3.0.0)\n","Requirement already satisfied: MarkupSafe==2.1.5 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 31)) (2.1.5)\n","Requirement already satisfied: matplotlib==3.9.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 32)) (3.9.2)\n","Requirement already satisfied: mdurl==0.1.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 33)) (0.1.2)\n","Requirement already satisfied: ml-dtypes==0.4.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 34)) (0.4.1)\n","Requirement already satisfied: mpmath==1.3.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 35)) (1.3.0)\n","Requirement already satisfied: namex==0.0.8 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 36)) (0.0.8)\n","Requirement already satisfied: networkx==3.3 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 37)) (3.3)\n","Requirement already satisfied: numpy==1.26.4 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 38)) (1.26.4)\n","Requirement already satisfied: omegaconf==2.3.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 39)) (2.3.0)\n","Requirement already satisfied: opencv-python==4.10.0.84 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 40)) (4.10.0.84)\n","Requirement already satisfied: opencv-python-headless==4.10.0.84 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 41)) (4.10.0.84)\n","Requirement already satisfied: opt_einsum==3.4.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 42)) (3.4.0)\n","Requirement already satisfied: optree==0.12.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 43)) (0.12.1)\n","Requirement already satisfied: packaging==24.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 44)) (24.1)\n","Requirement already satisfied: pandas==2.2.3 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 45)) (2.2.3)\n","Requirement already satisfied: pathlib==1.0.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 46)) (1.0.1)\n","Requirement already satisfied: pillow==10.4.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 47)) (10.4.0)\n","Requirement already satisfied: protobuf==4.25.5 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 48)) (4.25.5)\n","Requirement already satisfied: psutil==6.0.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 49)) (6.0.0)\n","Requirement already satisfied: py-cpuinfo==9.0.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 50)) (9.0.0)\n","Requirement already satisfied: Pygments==2.18.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 51)) (2.18.0)\n","Requirement already satisfied: pyparsing==3.1.4 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 52)) (3.1.4)\n","Requirement already satisfied: PySocks==1.7.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 53)) (1.7.1)\n","Requirement already satisfied: python-dateutil==2.9.0.post0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 54)) (2.9.0.post0)\n","Requirement already satisfied: pytz==2024.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 55)) (2024.2)\n","Requirement already satisfied: PyYAML==6.0.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 56)) (6.0.2)\n","Requirement already satisfied: regex==2024.9.11 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 57)) (2024.9.11)\n","Requirement already satisfied: requests==2.32.3 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 58)) (2.32.3)\n","Requirement already satisfied: rich==13.8.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 59)) (13.8.1)\n","Requirement already satisfied: safetensors==0.4.5 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 60)) (0.4.5)\n","Requirement already satisfied: scikit-learn==1.5.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 61)) (1.5.2)\n","Requirement already satisfied: scipy==1.14.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 62)) (1.14.1)\n","Requirement already satisfied: seaborn==0.13.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 63)) (0.13.2)\n","Requirement already satisfied: six==1.16.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 64)) (1.16.0)\n","Requirement already satisfied: soupsieve==2.6 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 65)) (2.6)\n","Requirement already satisfied: supervision==0.23.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 66)) (0.23.0)\n","Requirement already satisfied: sympy==1.13.3 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 67)) (1.13.3)\n","Requirement already satisfied: tensorboard==2.17.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 68)) (2.17.1)\n","Requirement already satisfied: tensorboard-data-server==0.7.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 69)) (0.7.2)\n","Requirement already satisfied: tensorflow==2.17.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 70)) (2.17.0)\n","Requirement already satisfied: tensorflow-io-gcs-filesystem==0.37.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 71)) (0.37.1)\n","Requirement already satisfied: termcolor==2.4.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 72)) (2.4.0)\n","Requirement already satisfied: threadpoolctl==3.5.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 73)) (3.5.0)\n","Requirement already satisfied: tokenizers==0.20.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 74)) (0.20.0)\n","Requirement already satisfied: torch==2.4.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 75)) (2.4.1+cu121)\n","Requirement already satisfied: torchvision==0.19.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 76)) (0.19.1+cu121)\n","Requirement already satisfied: tqdm==4.66.5 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 77)) (4.66.5)\n","Requirement already satisfied: typing_extensions==4.12.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 78)) (4.12.2)\n","Requirement already satisfied: tzdata==2024.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 79)) (2024.2)\n","Requirement already satisfied: ultralytics in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 80)) (8.3.20)\n","Requirement already satisfied: ultralytics-thop in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 81)) (2.0.9)\n","Requirement already satisfied: urllib3==2.2.3 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 82)) (2.2.3)\n","Requirement already satisfied: Werkzeug==3.0.4 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 83)) (3.0.4)\n","Requirement already satisfied: wrapt==1.16.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 84)) (1.16.0)\n","Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse==1.6.3->-r requirements.txt (line 3)) (0.44.0)\n","Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard==2.17.1->-r requirements.txt (line 68)) (75.1.0)\n"]}],"source":["!pip install -r requirements.txt"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/"},"id":"htfv3RZU9VPR","outputId":"e2d616b7-940e-4f9d-c1e9-fb39cd279b6d"},"outputs":[{"name":"stdout","output_type":"stream","text":["Creating new Ultralytics Settings v0.0.6 file ✅ \n","View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\n","Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.\n","Ultralytics 8.3.20 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (NVIDIA A100-SXM4-40GB, 40514MiB)\n","YOLO11m summary (fused): 303 layers, 20,030,803 parameters, 0 gradients, 67.6 GFLOPs\n","Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n","100% 755k/755k [00:00<00:00, 107MB/s]\n","\u001b[34m\u001b[1mval: \u001b[0mScanning /content/drive/MyDrive/chicken-dataset/chicken-dataset/valid/labels.cache... 450 images, 0 backgrounds, 0 corrupt: 100% 450/450 [00:00<?, ?it/s]\n","\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ /content/drive/MyDrive/chicken-dataset/chicken-dataset/valid/images/CH01-20240605-024419-035638-101000000000_middle_frame.jpg: 1 duplicate labels removed\n","\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ /content/drive/MyDrive/chicken-dataset/chicken-dataset/valid/images/CH01-20240605-035638-050817-101000000000_middle_frame.jpg: 4 duplicate labels removed\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 29/29 [00:42<00:00,  1.47s/it]\n","                   all        450      22999      0.967      0.955      0.985      0.799\n","Speed: 0.3ms preprocess, 8.4ms inference, 0.0ms loss, 14.4ms postprocess per image\n","Results saved to \u001b[1mruns/detect/val60\u001b[0m\n","Initial mAP50-95: 0.7988015497141385\n","Pruning model...\n","Pruned model saved at: /content/drive/MyDrive/yolov8_tracking/tracking/nicolai-tracking/yolo11m_pruned1.pt\n","Ultralytics 8.3.20 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (NVIDIA A100-SXM4-40GB, 40514MiB)\n","\u001b[34m\u001b[1mval: \u001b[0mScanning /content/drive/MyDrive/chicken-dataset/chicken-dataset/valid/labels.cache... 450 images, 0 backgrounds, 0 corrupt: 100% 450/450 [00:00<?, ?it/s]\n","\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ /content/drive/MyDrive/chicken-dataset/chicken-dataset/valid/images/CH01-20240605-024419-035638-101000000000_middle_frame.jpg: 1 duplicate labels removed\n","\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ /content/drive/MyDrive/chicken-dataset/chicken-dataset/valid/images/CH01-20240605-035638-050817-101000000000_middle_frame.jpg: 4 duplicate labels removed\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 29/29 [00:14<00:00,  1.97it/s]\n","                   all        450      22999      0.967      0.957      0.986      0.799\n","Speed: 0.3ms preprocess, 9.6ms inference, 0.0ms loss, 8.5ms postprocess per image\n","Results saved to \u001b[1mruns/detect/val61\u001b[0m\n","Pruned mAP50-95: 0.7992246941513608\n","Fine-tuning pruned model...\n","Ultralytics 8.3.20 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (NVIDIA A100-SXM4-40GB, 40514MiB)\n","\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=/content/drive/MyDrive/yolov8_tracking/tracking/nicolai-tracking/yolo11m_pruned1.pt, data=/content/drive/MyDrive/chicken-dataset/data.yaml, epochs=50, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train8, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train8\n","\n","                   from  n    params  module                                       arguments                     \n","  0                  -1  1      1856  ultralytics.nn.modules.conv.Conv             [3, 64, 3, 2]                 \n","  1                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n","  2                  -1  1    111872  ultralytics.nn.modules.block.C3k2            [128, 256, 1, True, 0.25]     \n","  3                  -1  1    590336  ultralytics.nn.modules.conv.Conv             [256, 256, 3, 2]              \n","  4                  -1  1    444928  ultralytics.nn.modules.block.C3k2            [256, 512, 1, True, 0.25]     \n","  5                  -1  1   2360320  ultralytics.nn.modules.conv.Conv             [512, 512, 3, 2]              \n","  6                  -1  1   1380352  ultralytics.nn.modules.block.C3k2            [512, 512, 1, True]           \n","  7                  -1  1   2360320  ultralytics.nn.modules.conv.Conv             [512, 512, 3, 2]              \n","  8                  -1  1   1380352  ultralytics.nn.modules.block.C3k2            [512, 512, 1, True]           \n","  9                  -1  1    656896  ultralytics.nn.modules.block.SPPF            [512, 512, 5]                 \n"," 10                  -1  1    990976  ultralytics.nn.modules.block.C2PSA           [512, 512, 1]                 \n"," 11                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n"," 12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n"," 13                  -1  1   1642496  ultralytics.nn.modules.block.C3k2            [1024, 512, 1, True]          \n"," 14                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n"," 15             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n"," 16                  -1  1    542720  ultralytics.nn.modules.block.C3k2            [1024, 256, 1, True]          \n"," 17                  -1  1    590336  ultralytics.nn.modules.conv.Conv             [256, 256, 3, 2]              \n"," 18            [-1, 13]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n"," 19                  -1  1   1511424  ultralytics.nn.modules.block.C3k2            [768, 512, 1, True]           \n"," 20                  -1  1   2360320  ultralytics.nn.modules.conv.Conv             [512, 512, 3, 2]              \n"," 21            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n"," 22                  -1  1   1642496  ultralytics.nn.modules.block.C3k2            [1024, 512, 1, True]          \n"," 23        [16, 19, 22]  1   1411795  ultralytics.nn.modules.head.Detect           [1, [256, 512, 512]]          \n","YOLO11m summary: 409 layers, 20,053,779 parameters, 20,053,763 gradients, 68.2 GFLOPs\n","\n","Transferred 119/649 items from pretrained weights\n","\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train8', view at http://localhost:6006/\n","\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend. Please refer to https://wandb.me/wandb-core for more information.\n","\u001b[34m\u001b[1mwandb\u001b[0m: (1) Create a W&B account\n","\u001b[34m\u001b[1mwandb\u001b[0m: (2) Use an existing W&B account\n","\u001b[34m\u001b[1mwandb\u001b[0m: (3) Don't visualize my results\n","\u001b[34m\u001b[1mwandb\u001b[0m: Enter your choice: 1\n","\u001b[34m\u001b[1mwandb\u001b[0m: You chose 'Create a W&B account'\n","\u001b[34m\u001b[1mwandb\u001b[0m: Create an account here: https://wandb.ai/authorize?signup=true\n","\u001b[34m\u001b[1mwandb\u001b[0m: Paste an API key from your profile and hit enter, or press ctrl+c to quit: \n","\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n","\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.18.5\n","\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/content/drive/MyDrive/yolov8_tracking/tracking/nicolai-tracking/wandb/run-20241023_014117-hryfr871\u001b[0m\n","\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n","\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mtrain8\u001b[0m\n","\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/saiakshitha33-university-of-georgia/Ultralytics\u001b[0m\n","\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/saiakshitha33-university-of-georgia/Ultralytics/runs/hryfr871\u001b[0m\n","Freezing layer 'model.23.dfl.conv.weight'\n","\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n","\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n","\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/drive/MyDrive/chicken-dataset/chicken-dataset/train/labels.cache... 2287 images, 10 backgrounds, 0 corrupt: 100% 2297/2297 [00:00<?, ?it/s]\n","/usr/local/lib/python3.10/dist-packages/albumentations/__init__.py:13: UserWarning: A new version of Albumentations is available: 1.4.18 (you have 1.4.15). Upgrade using: pip install -U albumentations. To disable automatic update checks, set the environment variable NO_ALBUMENTATIONS_UPDATE to 1.\n","  check_for_updates()\n","\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n","\u001b[34m\u001b[1mval: \u001b[0mScanning /content/drive/MyDrive/chicken-dataset/chicken-dataset/valid/labels.cache... 450 images, 0 backgrounds, 0 corrupt: 100% 450/450 [00:00<?, ?it/s]\n","\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ /content/drive/MyDrive/chicken-dataset/chicken-dataset/valid/images/CH01-20240605-024419-035638-101000000000_middle_frame.jpg: 1 duplicate labels removed\n","\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ /content/drive/MyDrive/chicken-dataset/chicken-dataset/valid/images/CH01-20240605-035638-050817-101000000000_middle_frame.jpg: 4 duplicate labels removed\n","Plotting labels to runs/detect/train8/labels.jpg... \n","\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n","\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.002, momentum=0.9) with parameter groups 106 weight(decay=0.0), 113 weight(decay=0.0005), 112 bias(decay=0.0)\n","\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n","Image sizes 640 train, 640 val\n","Using 8 dataloader workers\n","Logging results to \u001b[1mruns/detect/train8\u001b[0m\n","Starting training for 50 epochs...\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       1/50      10.9G      1.255     0.7807      1.073       1017        640: 100% 144/144 [07:18<00:00,  3.05s/it]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  5.41it/s]\n","                   all        450      22999      0.946      0.914       0.97      0.717\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       2/50      11.9G     0.9242      0.425     0.9286       1195        640: 100% 144/144 [00:24<00:00,  5.85it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  5.93it/s]\n","                   all        450      22999      0.966      0.938      0.979      0.759\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       3/50      10.9G     0.9002     0.4061     0.9183        703        640: 100% 144/144 [00:24<00:00,  5.94it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  5.85it/s]\n","                   all        450      22999      0.963      0.946       0.98      0.763\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       4/50      10.9G     0.8832      0.397     0.9117       1119        640: 100% 144/144 [00:23<00:00,  6.09it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  5.75it/s]\n","                   all        450      22999      0.966      0.949      0.982      0.764\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       5/50      11.3G     0.8747     0.3919     0.9055        886        640: 100% 144/144 [00:23<00:00,  6.12it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  6.16it/s]\n","                   all        450      22999      0.963      0.946      0.982      0.771\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       6/50      10.3G     0.8659     0.3858      0.906        916        640: 100% 144/144 [00:23<00:00,  6.11it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  5.75it/s]\n","                   all        450      22999      0.969      0.942      0.981      0.765\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       7/50      12.3G     0.8629     0.3836     0.9035        895        640: 100% 144/144 [00:23<00:00,  6.15it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  6.00it/s]\n","                   all        450      22999      0.969      0.946      0.983      0.775\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       8/50      12.9G     0.8541     0.3795      0.899       1003        640: 100% 144/144 [00:23<00:00,  6.14it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  6.33it/s]\n","                   all        450      22999      0.971      0.947      0.983       0.78\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       9/50      12.2G      0.852     0.3764     0.8982        796        640: 100% 144/144 [00:23<00:00,  6.04it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  5.72it/s]\n","                   all        450      22999      0.969       0.94      0.981      0.775\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      10/50      12.8G     0.8462     0.3756      0.899        915        640: 100% 144/144 [00:23<00:00,  6.14it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  6.08it/s]\n","                   all        450      22999      0.971      0.944      0.983      0.776\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      11/50      13.3G     0.8431     0.3718     0.8982       1129        640: 100% 144/144 [00:23<00:00,  6.19it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  5.82it/s]\n","                   all        450      22999      0.967      0.948      0.983      0.786\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      12/50      11.8G     0.8359     0.3692      0.898       1027        640: 100% 144/144 [00:23<00:00,  6.02it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  6.13it/s]\n","                   all        450      22999      0.967      0.949      0.984      0.783\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      13/50      11.3G     0.8435     0.3708     0.8965       1083        640: 100% 144/144 [00:23<00:00,  6.14it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  6.13it/s]\n","                   all        450      22999      0.965      0.943      0.982      0.778\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      14/50      11.8G       0.83     0.3666     0.8926        970        640: 100% 144/144 [00:23<00:00,  6.13it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  5.95it/s]\n","                   all        450      22999      0.961       0.95      0.982       0.78\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      15/50      10.7G     0.8274      0.365     0.8932       1059        640: 100% 144/144 [00:23<00:00,  6.18it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  6.33it/s]\n","                   all        450      22999      0.965      0.953      0.983      0.779\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      16/50      11.4G     0.8295     0.3651     0.8903        829        640: 100% 144/144 [00:23<00:00,  6.20it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  5.69it/s]\n","                   all        450      22999      0.969      0.945      0.983      0.784\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      17/50      9.96G     0.8187     0.3592     0.8887       1056        640: 100% 144/144 [00:23<00:00,  6.17it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  6.22it/s]\n","                   all        450      22999      0.968      0.954      0.983       0.78\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      18/50      11.3G     0.8204     0.3613     0.8909       1010        640: 100% 144/144 [00:23<00:00,  6.19it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  6.23it/s]\n","                   all        450      22999      0.968      0.952      0.985      0.789\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      19/50      10.3G     0.8168      0.359     0.8899       1014        640: 100% 144/144 [00:23<00:00,  6.10it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  5.68it/s]\n","                   all        450      22999      0.966      0.951      0.983      0.783\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      20/50      10.4G     0.8119     0.3573     0.8888        771        640: 100% 144/144 [00:23<00:00,  6.16it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  5.97it/s]\n","                   all        450      22999      0.967      0.949      0.983      0.779\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      21/50      13.5G     0.8141     0.3545     0.8902       1123        640: 100% 144/144 [00:23<00:00,  6.06it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  5.82it/s]\n","                   all        450      22999      0.964      0.948      0.982      0.778\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      22/50      11.8G     0.8066      0.355     0.8868       1038        640: 100% 144/144 [00:23<00:00,  6.07it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  6.30it/s]\n","                   all        450      22999      0.965      0.952      0.984      0.781\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      23/50      10.6G     0.8085     0.3547     0.8862        881        640: 100% 144/144 [00:23<00:00,  6.15it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  6.22it/s]\n","                   all        450      22999      0.968       0.95      0.984      0.784\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      24/50      12.9G     0.8033     0.3503     0.8853        827        640: 100% 144/144 [00:23<00:00,  6.16it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  5.81it/s]\n","                   all        450      22999      0.968      0.952      0.984       0.78\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      25/50      10.3G     0.7996     0.3489     0.8838        958        640: 100% 144/144 [00:23<00:00,  6.21it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  6.36it/s]\n","                   all        450      22999      0.965      0.951      0.984      0.785\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      26/50      12.4G     0.7965     0.3456     0.8853        951        640: 100% 144/144 [00:23<00:00,  6.16it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  5.94it/s]\n","                   all        450      22999      0.967      0.952      0.984      0.788\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      27/50      11.9G     0.8009     0.3507     0.8855       1076        640: 100% 144/144 [00:23<00:00,  6.17it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  6.34it/s]\n","                   all        450      22999      0.967      0.947      0.983      0.779\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      28/50      11.2G      0.798     0.3492     0.8829        870        640: 100% 144/144 [00:23<00:00,  6.21it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  6.21it/s]\n","                   all        450      22999      0.966      0.956      0.985      0.793\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      29/50      11.3G     0.7923     0.3446     0.8835        678        640: 100% 144/144 [00:23<00:00,  6.15it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  5.99it/s]\n","                   all        450      22999      0.966      0.953      0.984      0.785\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      30/50      11.3G     0.7915     0.3446     0.8808       1222        640: 100% 144/144 [00:23<00:00,  6.10it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  6.43it/s]\n","                   all        450      22999      0.964      0.953      0.983      0.784\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      31/50      12.4G     0.7865     0.3422     0.8833        643        640: 100% 144/144 [00:23<00:00,  6.15it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  6.07it/s]\n","                   all        450      22999       0.97      0.949      0.985      0.787\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      32/50      12.5G      0.787     0.3417      0.881        838        640: 100% 144/144 [00:23<00:00,  6.16it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  6.31it/s]\n","                   all        450      22999      0.969      0.952      0.984      0.791\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      33/50        12G     0.7824     0.3404     0.8798       1038        640: 100% 144/144 [00:23<00:00,  6.16it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  6.30it/s]\n","                   all        450      22999      0.968      0.948      0.984      0.787\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      34/50      11.9G     0.7813     0.3391     0.8793        759        640: 100% 144/144 [00:23<00:00,  6.09it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  5.70it/s]\n","                   all        450      22999      0.968       0.95      0.985      0.789\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      35/50        12G     0.7831     0.3402      0.879        857        640: 100% 144/144 [00:23<00:00,  6.11it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  6.06it/s]\n","                   all        450      22999      0.969      0.952      0.984      0.785\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      36/50      13.6G     0.7791     0.3374     0.8788       1010        640: 100% 144/144 [00:23<00:00,  6.18it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  6.03it/s]\n","                   all        450      22999      0.967      0.957      0.986      0.794\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      37/50      11.6G     0.7734     0.3342     0.8749       1095        640: 100% 144/144 [00:23<00:00,  6.09it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  6.39it/s]\n","                   all        450      22999      0.967      0.955      0.985      0.793\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      38/50      11.5G     0.7741     0.3346     0.8781       1246        640: 100% 144/144 [00:23<00:00,  6.14it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  5.85it/s]\n","                   all        450      22999      0.967      0.955      0.985       0.79\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      39/50      12.6G     0.7683     0.3318     0.8748        833        640: 100% 144/144 [00:23<00:00,  6.06it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  5.94it/s]\n","                   all        450      22999       0.97      0.952      0.985      0.794\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      40/50      12.4G     0.7717      0.334     0.8757        947        640: 100% 144/144 [00:23<00:00,  6.18it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  6.43it/s]\n","                   all        450      22999      0.965      0.957      0.985      0.791\n","Closing dataloader mosaic\n","\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      41/50      8.92G     0.7703      0.329     0.8847        553        640: 100% 144/144 [00:26<00:00,  5.44it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  5.96it/s]\n","                   all        450      22999      0.967      0.953      0.983      0.784\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      42/50      8.98G      0.761     0.3278     0.8811        570        640: 100% 144/144 [00:22<00:00,  6.42it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 15/15 [00:02<00:00,  6.13it/s]\n","                   all        450      22999      0.966       0.95      0.983      0.782\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      43/50      8.87G     0.7548     0.3241     0.8816        921        640:  89% 128/144 [00:20<00:02,  6.41it/s]"]}],"source":["!python pruning.py"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":265109,"status":"ok","timestamp":1729092850992,"user":{"displayName":"Sai Akshitha Reddy Kota","userId":"05755385555264415224"},"user_tz":240},"id":"HcHrXZWyX3I0","outputId":"cd7e4182-d6d8-4fca-83a1-4039bb06137b"},"outputs":[{"name":"stdout","output_type":"stream","text":["Creating new Ultralytics Settings v0.0.6 file ✅ \n","View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\n","Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.\n","Using Device:  cuda\n","YOLO11m summary (fused): 303 layers, 20,030,803 parameters, 0 gradients, 67.6 GFLOPs\n","Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n","\u001b[1m58889256/58889256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n","\u001b[1m87910968/87910968\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/efficientnet_v2/efficientnetv2-b3_notop.h5\n","\u001b[1m52606240/52606240\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n","Model weights saved successfully.\n","SupervisionWarnings: BoundingBoxAnnotator is deprecated: `BoundingBoxAnnotator` is deprecated and has been renamed to `BoxAnnotator`. `BoundingBoxAnnotator` will be removed in supervision-0.26.0.\n","weights type  <class 'pathlib.PosixPath'>\n","Downloading: \"https://download.pytorch.org/models/resnet18-5c106cde.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-5c106cde.pth\n","100% 44.7M/44.7M [00:00<00:00, 184MB/s]\n","\n","0: 640x544 44 chickens, 58.5ms\n","Speed: 17.3ms preprocess, 58.5ms inference, 819.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 1\n","MOTP frame: 1.0000\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 42 chickens, 16.6ms\n","Speed: 2.8ms preprocess, 16.6ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 2\n","WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n","I0000 00:00:1729701363.512851   13807 service.cc:146] XLA service 0x7af604001440 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n","I0000 00:00:1729701363.512884   13807 service.cc:154]   StreamExecutor device (0): NVIDIA A100-SXM4-40GB, Compute Capability 8.0\n","I0000 00:00:1729701369.853075   13807 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n","MOTP frame: 1.0000\n","Mota frame:  0\n","42 detections\n","frame saved\n","\n","0: 640x544 43 chickens, 15.8ms\n","Speed: 3.7ms preprocess, 15.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 3\n","MOTP frame: 1.0000\n","Mota frame:  0\n","42 detections\n","frame saved\n","\n","0: 640x544 45 chickens, 15.9ms\n","Speed: 3.8ms preprocess, 15.9ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 4\n","MOTP frame: 1.0000\n","Mota frame:  0\n","43 detections\n","frame saved\n","\n","0: 640x544 44 chickens, 15.6ms\n","Speed: 4.4ms preprocess, 15.6ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 5\n","MOTP frame: 1.0000\n","Mota frame:  0\n","42 detections\n","frame saved\n","\n","0: 640x544 44 chickens, 15.9ms\n","Speed: 3.1ms preprocess, 15.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 6\n","MOTP frame: 1.0000\n","Mota frame:  0\n","44 detections\n","frame saved\n","\n","0: 640x544 49 chickens, 15.4ms\n","Speed: 3.4ms preprocess, 15.4ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 7\n","MOTP frame: 1.0000\n","Mota frame:  0\n","44 detections\n","frame saved\n","\n","0: 640x544 46 chickens, 15.6ms\n","Speed: 4.3ms preprocess, 15.6ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 8\n","MOTP frame: 1.0000\n","Mota frame:  0\n","45 detections\n","frame saved\n","\n","0: 640x544 48 chickens, 15.8ms\n","Speed: 3.0ms preprocess, 15.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 9\n","MOTP frame: 1.0000\n","Mota frame:  0\n","47 detections\n","frame saved\n","\n","0: 640x544 45 chickens, 15.8ms\n","Speed: 2.9ms preprocess, 15.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 10\n","MOTP frame: 1.0000\n","Mota frame:  0\n","49 detections\n","frame saved\n","\n","0: 640x544 44 chickens, 14.1ms\n","Speed: 3.4ms preprocess, 14.1ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 11\n","MOTP frame: 1.0000\n","Mota frame:  0\n","46 detections\n","frame saved\n","\n","0: 640x544 43 chickens, 16.0ms\n","Speed: 3.2ms preprocess, 16.0ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 12\n","MOTP frame: 1.0000\n","Mota frame:  0\n","45 detections\n","frame saved\n","\n","0: 640x544 43 chickens, 15.1ms\n","Speed: 2.9ms preprocess, 15.1ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 13\n","MOTP frame: 0.9837\n","Mota frame:  0\n","43 detections\n","frame saved\n","\n","0: 640x544 42 chickens, 15.5ms\n","Speed: 3.1ms preprocess, 15.5ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 14\n","MOTP frame: 1.0000\n","Mota frame:  0\n","42 detections\n","frame saved\n","\n","0: 640x544 40 chickens, 16.0ms\n","Speed: 3.7ms preprocess, 16.0ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 15\n","MOTP frame: 1.0000\n","Mota frame:  0\n","42 detections\n","frame saved\n","\n","0: 640x544 43 chickens, 15.2ms\n","Speed: 3.7ms preprocess, 15.2ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 16\n","MOTP frame: 1.0000\n","Mota frame:  0\n","41 detections\n","frame saved\n","\n","0: 640x544 44 chickens, 15.4ms\n","Speed: 3.6ms preprocess, 15.4ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 17\n","MOTP frame: 1.0000\n","Mota frame:  0\n","44 detections\n","frame saved\n","\n","0: 640x544 41 chickens, 14.8ms\n","Speed: 3.0ms preprocess, 14.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 18\n","MOTP frame: 1.0000\n","Mota frame:  0\n","42 detections\n","frame saved\n","\n","0: 640x544 44 chickens, 15.7ms\n","Speed: 3.0ms preprocess, 15.7ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 19\n","MOTP frame: 1.0000\n","Mota frame:  0\n","44 detections\n","frame saved\n","\n","0: 640x544 42 chickens, 16.2ms\n","Speed: 3.1ms preprocess, 16.2ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 20\n","MOTP frame: 1.0000\n","Mota frame:  0\n","46 detections\n","frame saved\n","\n","0: 640x544 41 chickens, 15.4ms\n","Speed: 3.6ms preprocess, 15.4ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 21\n","MOTP frame: 1.0000\n","Mota frame:  0\n","43 detections\n","frame saved\n","\n","0: 640x544 41 chickens, 14.7ms\n","Speed: 3.4ms preprocess, 14.7ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 22\n","MOTP frame: 1.0000\n","Mota frame:  0\n","44 detections\n","frame saved\n","\n","0: 640x544 41 chickens, 15.8ms\n","Speed: 3.3ms preprocess, 15.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 23\n","MOTP frame: 1.0000\n","Mota frame:  0\n","43 detections\n","frame saved\n","\n","0: 640x544 42 chickens, 15.7ms\n","Speed: 3.6ms preprocess, 15.7ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 24\n","MOTP frame: 1.0000\n","Mota frame:  0\n","44 detections\n","frame saved\n","\n","0: 640x544 42 chickens, 15.7ms\n","Speed: 3.7ms preprocess, 15.7ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 25\n","MOTP frame: 1.0000\n","Mota frame:  0\n","46 detections\n","frame saved\n","\n","0: 640x544 47 chickens, 15.0ms\n","Speed: 3.1ms preprocess, 15.0ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 26\n","MOTP frame: 1.0000\n","Mota frame:  0\n","46 detections\n","frame saved\n","\n","0: 640x544 46 chickens, 16.4ms\n","Speed: 3.0ms preprocess, 16.4ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 27\n","MOTP frame: 1.0000\n","Mota frame:  0\n","47 detections\n","frame saved\n","\n","0: 640x544 43 chickens, 15.5ms\n","Speed: 4.8ms preprocess, 15.5ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 28\n","MOTP frame: 1.0000\n","Mota frame:  0\n","47 detections\n","frame saved\n","\n","0: 640x544 46 chickens, 15.6ms\n","Speed: 3.0ms preprocess, 15.6ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 29\n","MOTP frame: 1.0000\n","Mota frame:  0\n","45 detections\n","frame saved\n","\n","0: 640x544 45 chickens, 15.8ms\n","Speed: 3.4ms preprocess, 15.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 30\n","MOTP frame: 1.0000\n","Mota frame:  0\n","47 detections\n","frame saved\n","\n","0: 640x544 46 chickens, 15.6ms\n","Speed: 3.6ms preprocess, 15.6ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 31\n","MOTP frame: 1.0000\n","Mota frame:  0\n","47 detections\n","frame saved\n","\n","0: 640x544 47 chickens, 15.6ms\n","Speed: 3.2ms preprocess, 15.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 32\n","MOTP frame: 1.0000\n","Mota frame:  0\n","48 detections\n","frame saved\n","\n","0: 640x544 44 chickens, 15.9ms\n","Speed: 3.1ms preprocess, 15.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 33\n","MOTP frame: 1.0000\n","Mota frame:  0\n","48 detections\n","frame saved\n","\n","0: 640x544 48 chickens, 15.3ms\n","Speed: 3.1ms preprocess, 15.3ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 34\n","MOTP frame: 1.0000\n","Mota frame:  0\n","48 detections\n","frame saved\n","\n","0: 640x544 43 chickens, 15.5ms\n","Speed: 3.4ms preprocess, 15.5ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 35\n","MOTP frame: 1.0000\n","Mota frame:  0\n","48 detections\n","frame saved\n","\n","0: 640x544 48 chickens, 15.7ms\n","Speed: 3.2ms preprocess, 15.7ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 36\n","MOTP frame: 1.0000\n","Mota frame:  0\n","48 detections\n","frame saved\n","\n","0: 640x544 44 chickens, 15.5ms\n","Speed: 3.2ms preprocess, 15.5ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 37\n","MOTP frame: 1.0000\n","Mota frame:  0\n","49 detections\n","frame saved\n","\n","0: 640x544 50 chickens, 15.3ms\n","Speed: 3.1ms preprocess, 15.3ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 38\n","MOTP frame: 1.0000\n","Mota frame:  0\n","49 detections\n","frame saved\n","\n","0: 640x544 48 chickens, 15.4ms\n","Speed: 3.2ms preprocess, 15.4ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 39\n","MOTP frame: 1.0000\n","Mota frame:  0\n","50 detections\n","frame saved\n","\n","0: 640x544 46 chickens, 15.7ms\n","Speed: 3.0ms preprocess, 15.7ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 40\n","MOTP frame: 1.0000\n","Mota frame:  0\n","49 detections\n","frame saved\n","\n","0: 640x544 41 chickens, 15.2ms\n","Speed: 2.9ms preprocess, 15.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 41\n","MOTP frame: 1.0000\n","Mota frame:  0\n","46 detections\n","frame saved\n","\n","0: 640x544 43 chickens, 15.5ms\n","Speed: 3.0ms preprocess, 15.5ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 42\n","MOTP frame: 1.0000\n","Mota frame:  0\n","43 detections\n","frame saved\n","\n","0: 640x544 41 chickens, 15.2ms\n","Speed: 3.0ms preprocess, 15.2ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 43\n","MOTP frame: 1.0000\n","Mota frame:  0\n","44 detections\n","frame saved\n","\n","0: 640x544 44 chickens, 15.2ms\n","Speed: 3.0ms preprocess, 15.2ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 44\n","MOTP frame: 1.0000\n","Mota frame:  0\n","44 detections\n","frame saved\n","\n","0: 640x544 44 chickens, 15.3ms\n","Speed: 3.4ms preprocess, 15.3ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 45\n","MOTP frame: 1.0000\n","Mota frame:  0\n","46 detections\n","frame saved\n","\n","0: 640x544 46 chickens, 15.5ms\n","Speed: 3.6ms preprocess, 15.5ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 46\n","MOTP frame: 0.9687\n","Mota frame:  0\n","47 detections\n","frame saved\n","\n","0: 640x544 45 chickens, 15.3ms\n","Speed: 2.9ms preprocess, 15.3ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 47\n","MOTP frame: 1.0000\n","Mota frame:  0\n","49 detections\n","frame saved\n","\n","0: 640x544 49 chickens, 15.6ms\n","Speed: 3.5ms preprocess, 15.6ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 48\n","MOTP frame: 1.0000\n","Mota frame:  0\n","50 detections\n","frame saved\n","\n","0: 640x544 49 chickens, 15.5ms\n","Speed: 3.1ms preprocess, 15.5ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 49\n","MOTP frame: 1.0000\n","Mota frame:  0\n","51 detections\n","frame saved\n","\n","0: 640x544 47 chickens, 14.9ms\n","Speed: 3.1ms preprocess, 14.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 50\n","MOTP frame: 1.0000\n","Mota frame:  0\n","49 detections\n","frame saved\n","\n","0: 640x544 47 chickens, 17.8ms\n","Speed: 3.4ms preprocess, 17.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 51\n","MOTP frame: 1.0000\n","Mota frame:  0\n","51 detections\n","frame saved\n","\n","0: 640x544 45 chickens, 15.1ms\n","Speed: 3.1ms preprocess, 15.1ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 52\n","MOTP frame: 1.0000\n","Mota frame:  0\n","47 detections\n","frame saved\n","\n","0: 640x544 46 chickens, 15.6ms\n","Speed: 3.3ms preprocess, 15.6ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 53\n","MOTP frame: 1.0000\n","Mota frame:  0\n","47 detections\n","frame saved\n","\n","0: 640x544 45 chickens, 15.6ms\n","Speed: 3.2ms preprocess, 15.6ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 54\n","MOTP frame: 1.0000\n","Mota frame:  0\n","48 detections\n","frame saved\n","\n","0: 640x544 44 chickens, 15.2ms\n","Speed: 3.3ms preprocess, 15.2ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 55\n","MOTP frame: 1.0000\n","Mota frame:  0\n","46 detections\n","frame saved\n","\n","0: 640x544 45 chickens, 15.1ms\n","Speed: 3.6ms preprocess, 15.1ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 56\n","MOTP frame: 1.0000\n","Mota frame:  0\n","49 detections\n","frame saved\n","\n","0: 640x544 46 chickens, 15.6ms\n","Speed: 3.5ms preprocess, 15.6ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 57\n"]}],"source":["!python run.py"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":211},"executionInfo":{"elapsed":360,"status":"error","timestamp":1728130681846,"user":{"displayName":"Sai Akshitha Reddy Kota","userId":"05755385555264415224"},"user_tz":240},"id":"UziQGaHwX51y","outputId":"2a28a56a-34a7-4efd-e182-5dfd181ee6a1"},"outputs":[{"ename":"FileNotFoundError","evalue":"[Errno 2] No such file or directory: 'json_info/trajectory_data.json'","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-1-a0257e0da0ff>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Load the JSON data from the file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'json_info/trajectory_data.json'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mtrack_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'json_info/trajectory_data.json'"]}],"source":["import json\n","\n","# Load the JSON data from the file\n","with open('json_info/trajectory_data.json', 'r') as f:\n","    track_data = json.load(f)\n","\n","# Convert it back to the original dictionary of lists format\n","original_trajectories = {\n","    int(id): tracks_info[\"tracks\"]\n","    for id, tracks_info in track_data.items()\n","}"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vqZu0ehHYDrQ"},"outputs":[],"source":["from util import plot_given_trajectories\n","plot_given_trajectories(original_trajectories, ids=[1,2,3,15,5], save_path=None)"]},{"cell_type":"markdown","metadata":{"id":"a3GKZZMp78yz"},"source":["# BELOW HERE IS NOT NEEDED WITH THE NEW RUN.PY - EVRYTHING IS IN THAT FILE"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"SWlfTX9g78ei"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13756,"status":"ok","timestamp":1728060008821,"user":{"displayName":"Sai Akshitha Reddy Kota","userId":"05755385555264415224"},"user_tz":240},"id":"tZ9t9Ujo2XGu","outputId":"30ef9b37-c094-44be-f3a3-74b443db79b4"},"outputs":[{"name":"stdout","output_type":"stream","text":["Requirement already satisfied: ultralytics in /usr/local/lib/python3.10/dist-packages (8.3.4)\n","Requirement already satisfied: numpy>=1.23.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (1.26.4)\n","Requirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (3.7.1)\n","Requirement already satisfied: opencv-python>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (4.10.0.84)\n","Requirement already satisfied: pillow>=7.1.2 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (10.4.0)\n","Requirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (6.0.2)\n","Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.32.3)\n","Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (1.13.1)\n","Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.4.1+cu121)\n","Requirement already satisfied: torchvision>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (0.19.1+cu121)\n","Requirement already satisfied: tqdm>=4.64.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (4.66.5)\n","Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from ultralytics) (5.9.5)\n","Requirement already satisfied: py-cpuinfo in 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(1.4.7)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (24.1)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (3.1.4)\n","Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.4->ultralytics) (2024.2)\n","Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.4->ultralytics) (2024.2)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (3.10)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (2.2.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (2024.8.30)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.16.1)\n","Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (4.12.2)\n","Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (1.13.3)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.3)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.1.4)\n","Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (2024.6.1)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.16.0)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.8.0->ultralytics) (2.1.5)\n","Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.8.0->ultralytics) (1.3.0)\n","Requirement already satisfied: supervision in /usr/local/lib/python3.10/dist-packages (0.23.0)\n","Requirement already satisfied: defusedxml<0.8.0,>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from supervision) (0.7.1)\n","Requirement already satisfied: matplotlib>=3.6.0 in /usr/local/lib/python3.10/dist-packages (from supervision) (3.7.1)\n","Requirement already satisfied: numpy>=1.23.3 in /usr/local/lib/python3.10/dist-packages (from supervision) (1.26.4)\n","Requirement already satisfied: opencv-python-headless>=4.5.5.64 in /usr/local/lib/python3.10/dist-packages (from supervision) (4.10.0.84)\n","Requirement already satisfied: pillow>=9.4 in /usr/local/lib/python3.10/dist-packages (from supervision) (10.4.0)\n","Requirement already satisfied: pyyaml>=5.3 in /usr/local/lib/python3.10/dist-packages (from supervision) (6.0.2)\n","Requirement already satisfied: scipy<2.0.0,>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from supervision) (1.13.1)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6.0->supervision) (1.3.0)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6.0->supervision) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6.0->supervision) (4.54.1)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6.0->supervision) (1.4.7)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6.0->supervision) (24.1)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6.0->supervision) (3.1.4)\n","Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6.0->supervision) (2.8.2)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.6.0->supervision) (1.16.0)\n","Requirement already satisfied: lap in /usr/local/lib/python3.10/dist-packages (0.4.0)\n","Requirement already satisfied: deep-sort-realtime in /usr/local/lib/python3.10/dist-packages (1.3.2)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from deep-sort-realtime) (1.26.4)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from deep-sort-realtime) (1.13.1)\n","Requirement already satisfied: opencv-python in /usr/local/lib/python3.10/dist-packages (from deep-sort-realtime) (4.10.0.84)\n"]}],"source":["!pip install ultralytics\n","!pip install supervision\n","!pip install lap\n","!pip install deep-sort-realtime"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":48992,"status":"ok","timestamp":1728060058956,"user":{"displayName":"Sai Akshitha Reddy Kota","userId":"05755385555264415224"},"user_tz":240},"id":"anqf4KtLFUNg","outputId":"6ca27748-33c2-459e-81b0-35e8f93361d7"},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting absl-py==2.1.0 (from -r requirements.txt (line 1))\n","  Downloading absl_py-2.1.0-py3-none-any.whl.metadata (2.3 kB)\n","Requirement already satisfied: antlr4-python3-runtime==4.9.3 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 2)) (4.9.3)\n","Requirement already satisfied: astunparse==1.6.3 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 3)) (1.6.3)\n","Requirement already satisfied: beautifulsoup4==4.12.3 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 4)) (4.12.3)\n","Requirement already satisfied: certifi==2024.8.30 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 5)) (2024.8.30)\n","Requirement already satisfied: charset-normalizer==3.3.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 6)) (3.3.2)\n","Requirement already satisfied: contourpy==1.3.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 7)) (1.3.0)\n","Requirement already satisfied: cycler==0.12.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 8)) (0.12.1)\n","Requirement already satisfied: deep-sort-realtime==1.3.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 9)) (1.3.2)\n","Requirement already satisfied: defusedxml==0.7.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 10)) (0.7.1)\n","Requirement already satisfied: easydict==1.13 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 11)) (1.13)\n","Requirement already satisfied: filelock==3.16.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 12)) (3.16.1)\n","Requirement already satisfied: flatbuffers==24.3.25 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 13)) (24.3.25)\n","Requirement already satisfied: fonttools==4.54.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 14)) (4.54.1)\n","Collecting fsspec==2024.9.0 (from -r requirements.txt (line 15))\n","  Downloading fsspec-2024.9.0-py3-none-any.whl.metadata (11 kB)\n","Requirement already satisfied: gast==0.6.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 16)) (0.6.0)\n","Requirement already satisfied: gdown==5.2.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 17)) (5.2.0)\n","Requirement already satisfied: google-pasta==0.2.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 18)) (0.2.0)\n","Collecting grpcio==1.66.2 (from -r requirements.txt (line 19))\n","  Downloading grpcio-1.66.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.9 kB)\n","Collecting h5py==3.12.1 (from -r requirements.txt (line 20))\n","  Downloading h5py-3.12.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.5 kB)\n","Collecting huggingface-hub==0.25.1 (from -r requirements.txt (line 21))\n","  Downloading huggingface_hub-0.25.1-py3-none-any.whl.metadata (13 kB)\n","Requirement already satisfied: idna==3.10 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 22)) (3.10)\n","Requirement already satisfied: Jinja2==3.1.4 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 23)) (3.1.4)\n","Requirement already satisfied: joblib==1.4.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 24)) (1.4.2)\n","Collecting keras==3.5.0 (from -r requirements.txt (line 25))\n","  Downloading keras-3.5.0-py3-none-any.whl.metadata (5.8 kB)\n","Requirement already satisfied: kiwisolver==1.4.7 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 26)) (1.4.7)\n","Collecting lapx==0.5.10.post1 (from -r requirements.txt (line 27))\n","  Downloading lapx-0.5.10.post1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.1 kB)\n","Requirement already satisfied: libclang==18.1.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 28)) (18.1.1)\n","Requirement already satisfied: Markdown==3.7 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 29)) (3.7)\n","Requirement already satisfied: markdown-it-py==3.0.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 30)) (3.0.0)\n","Requirement already satisfied: MarkupSafe==2.1.5 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 31)) (2.1.5)\n","Collecting matplotlib==3.9.2 (from -r requirements.txt (line 32))\n","  Downloading matplotlib-3.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)\n","Requirement already satisfied: mdurl==0.1.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 33)) (0.1.2)\n","Requirement already satisfied: ml-dtypes==0.4.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 34)) (0.4.1)\n","Requirement already satisfied: mpmath==1.3.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 35)) (1.3.0)\n","Requirement already satisfied: namex==0.0.8 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 36)) (0.0.8)\n","Requirement already satisfied: networkx==3.3 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 37)) (3.3)\n","Requirement already satisfied: numpy==1.26.4 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 38)) (1.26.4)\n","Requirement already satisfied: omegaconf==2.3.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 39)) (2.3.0)\n","Requirement already satisfied: opencv-python==4.10.0.84 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 40)) (4.10.0.84)\n","Requirement already satisfied: opencv-python-headless==4.10.0.84 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 41)) (4.10.0.84)\n","Requirement already satisfied: opt_einsum==3.4.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 42)) (3.4.0)\n","Requirement already satisfied: optree==0.12.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 43)) (0.12.1)\n","Requirement already satisfied: packaging==24.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 44)) (24.1)\n","Collecting pandas==2.2.3 (from -r requirements.txt (line 45))\n","  Downloading pandas-2.2.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (89 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m89.9/89.9 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: pathlib==1.0.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 46)) (1.0.1)\n","Requirement already satisfied: pillow==10.4.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 47)) (10.4.0)\n","Collecting protobuf==4.25.5 (from -r requirements.txt (line 48))\n","  Downloading protobuf-4.25.5-cp37-abi3-manylinux2014_x86_64.whl.metadata (541 bytes)\n","Collecting psutil==6.0.0 (from -r requirements.txt (line 49))\n","  Downloading psutil-6.0.0-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (21 kB)\n","Requirement already satisfied: py-cpuinfo==9.0.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 50)) (9.0.0)\n","Requirement already satisfied: Pygments==2.18.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 51)) (2.18.0)\n","Requirement already satisfied: pyparsing==3.1.4 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 52)) (3.1.4)\n","Requirement already satisfied: PySocks==1.7.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 53)) (1.7.1)\n","Collecting python-dateutil==2.9.0.post0 (from -r requirements.txt (line 54))\n","  Downloading python_dateutil-2.9.0.post0-py2.py3-none-any.whl.metadata (8.4 kB)\n","Requirement already satisfied: pytz==2024.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 55)) (2024.2)\n","Requirement already satisfied: PyYAML==6.0.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 56)) (6.0.2)\n","Requirement already satisfied: regex==2024.9.11 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 57)) (2024.9.11)\n","Requirement already satisfied: requests==2.32.3 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 58)) (2.32.3)\n","Requirement already satisfied: rich==13.8.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 59)) (13.8.1)\n","Requirement already satisfied: safetensors==0.4.5 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 60)) (0.4.5)\n","Requirement already satisfied: scikit-learn==1.5.2 in 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requirements.txt (line 73)) (3.5.0)\n","Collecting tokenizers==0.20.0 (from -r requirements.txt (line 74))\n","  Downloading tokenizers-0.20.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)\n","Requirement already satisfied: torch==2.4.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 75)) (2.4.1+cu121)\n","Requirement already satisfied: torchvision==0.19.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 76)) (0.19.1+cu121)\n","Requirement already satisfied: tqdm==4.66.5 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 77)) (4.66.5)\n","Requirement already satisfied: typing_extensions==4.12.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 78)) (4.12.2)\n","Requirement already satisfied: tzdata==2024.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 79)) (2024.2)\n","Collecting ultralytics==8.2.101 (from -r requirements.txt (line 80))\n","  Downloading ultralytics-8.2.101-py3-none-any.whl.metadata (39 kB)\n","Requirement already satisfied: ultralytics-thop==2.0.8 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 81)) (2.0.8)\n","Requirement already satisfied: urllib3==2.2.3 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 82)) (2.2.3)\n","Requirement already satisfied: Werkzeug==3.0.4 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 83)) (3.0.4)\n","Requirement already satisfied: wrapt==1.16.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 84)) (1.16.0)\n","Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse==1.6.3->-r requirements.txt (line 3)) (0.44.0)\n","Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard==2.17.1->-r requirements.txt (line 68)) (71.0.4)\n","Downloading absl_py-2.1.0-py3-none-any.whl (133 kB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m133.7/133.7 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading fsspec-2024.9.0-py3-none-any.whl (179 kB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.3/179.3 kB\u001b[0m \u001b[31m18.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading grpcio-1.66.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.8 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.8/5.8 MB\u001b[0m \u001b[31m87.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading h5py-3.12.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.3 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.3/5.3 MB\u001b[0m \u001b[31m108.1 MB/s\u001b[0m eta 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Found existing installation: python-dateutil 2.8.2\n","    Uninstalling python-dateutil-2.8.2:\n","      Successfully uninstalled python-dateutil-2.8.2\n","  Attempting uninstall: psutil\n","    Found existing installation: psutil 5.9.5\n","    Uninstalling psutil-5.9.5:\n","      Successfully uninstalled psutil-5.9.5\n","  Attempting uninstall: protobuf\n","    Found existing installation: protobuf 3.20.3\n","    Uninstalling protobuf-3.20.3:\n","      Successfully uninstalled protobuf-3.20.3\n","  Attempting uninstall: h5py\n","    Found existing installation: h5py 3.11.0\n","    Uninstalling h5py-3.11.0:\n","      Successfully uninstalled h5py-3.11.0\n","  Attempting uninstall: grpcio\n","    Found existing installation: grpcio 1.64.1\n","    Uninstalling grpcio-1.64.1:\n","      Successfully uninstalled grpcio-1.64.1\n","  Attempting uninstall: fsspec\n","    Found existing installation: fsspec 2024.6.1\n","    Uninstalling fsspec-2024.6.1:\n","      Successfully uninstalled fsspec-2024.6.1\n","  Attempting uninstall: absl-py\n","    Found existing installation: absl-py 1.4.0\n","    Uninstalling absl-py-1.4.0:\n","      Successfully uninstalled absl-py-1.4.0\n","  Attempting uninstall: tensorboard\n","    Found existing installation: tensorboard 2.17.0\n","    Uninstalling tensorboard-2.17.0:\n","      Successfully uninstalled tensorboard-2.17.0\n","  Attempting uninstall: pandas\n","    Found existing installation: pandas 2.2.2\n","    Uninstalling pandas-2.2.2:\n","      Successfully uninstalled pandas-2.2.2\n","  Attempting uninstall: matplotlib\n","    Found existing installation: matplotlib 3.7.1\n","    Uninstalling matplotlib-3.7.1:\n","      Successfully uninstalled matplotlib-3.7.1\n","  Attempting uninstall: huggingface-hub\n","    Found existing installation: huggingface-hub 0.24.7\n","    Uninstalling huggingface-hub-0.24.7:\n","      Successfully uninstalled huggingface-hub-0.24.7\n","  Attempting uninstall: tokenizers\n","    Found existing installation: tokenizers 0.19.1\n","    Uninstalling tokenizers-0.19.1:\n","      Successfully uninstalled tokenizers-0.19.1\n","  Attempting uninstall: seaborn\n","    Found existing installation: seaborn 0.13.1\n","    Uninstalling seaborn-0.13.1:\n","      Successfully uninstalled seaborn-0.13.1\n","  Attempting uninstall: keras\n","    Found existing installation: keras 3.4.1\n","    Uninstalling keras-3.4.1:\n","      Successfully uninstalled keras-3.4.1\n","  Attempting uninstall: ultralytics\n","    Found existing installation: ultralytics 8.3.4\n","    Uninstalling ultralytics-8.3.4:\n","      Successfully uninstalled ultralytics-8.3.4\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","cudf-cu12 24.6.1 requires pandas<2.2.3dev0,>=2.0, but you have pandas 2.2.3 which is incompatible.\n","gcsfs 2024.6.1 requires fsspec==2024.6.1, but you have fsspec 2024.9.0 which is incompatible.\n","gensim 4.3.3 requires scipy<1.14.0,>=1.7.0, but you have scipy 1.14.1 which is incompatible.\n","google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 2.2.3 which is incompatible.\n","tensorflow-metadata 1.15.0 requires protobuf<4.21,>=3.20.3; python_version < \"3.11\", but you have protobuf 4.25.5 which is incompatible.\n","transformers 4.44.2 requires tokenizers<0.20,>=0.19, but you have tokenizers 0.20.0 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed absl-py-2.1.0 fsspec-2024.9.0 grpcio-1.66.2 h5py-3.12.1 huggingface-hub-0.25.1 keras-3.5.0 lapx-0.5.10.post1 matplotlib-3.9.2 pandas-2.2.3 protobuf-4.25.5 psutil-6.0.0 python-dateutil-2.9.0.post0 scipy-1.14.1 seaborn-0.13.2 tensorboard-2.17.1 tokenizers-0.20.0 ultralytics-8.2.101\n"]},{"data":{"application/vnd.colab-display-data+json":{"id":"76173ea42aea4d85bf1dffabd541756f","pip_warning":{"packages":["dateutil","psutil"]}}},"metadata":{},"output_type":"display_data"}],"source":["!pip install -r requirements.txt"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10727,"status":"ok","timestamp":1727091297146,"user":{"displayName":"Sai Akshitha Reddy Kota","userId":"05755385555264415224"},"user_tz":240},"id":"-iWJQWJI4X0e","outputId":"cc81b329-bba5-4c72-d6a5-b500345510bc"},"outputs":[{"name":"stdout","output_type":"stream","text":["Requirement already satisfied: ultralytics in /usr/local/lib/python3.10/dist-packages (8.2.99)\n","Requirement already satisfied: numpy<2.0.0,>=1.23.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (1.26.4)\n","Requirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (3.7.1)\n","Requirement already satisfied: opencv-python>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (4.10.0.84)\n","Requirement already satisfied: pillow>=7.1.2 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (10.4.0)\n","Requirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (6.0.2)\n","Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.32.3)\n","Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (1.13.1)\n","Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.4.1+cu121)\n","Requirement already satisfied: torchvision>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (0.19.1+cu121)\n","Requirement already satisfied: tqdm>=4.64.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (4.66.5)\n","Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from ultralytics) (5.9.5)\n","Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.10/dist-packages (from ultralytics) (9.0.0)\n","Requirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.1.4)\n","Requirement already satisfied: seaborn>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (0.13.1)\n","Requirement already satisfied: ultralytics-thop>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.0.8)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.3.0)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (4.53.1)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.4.7)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (24.1)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (3.1.4)\n","Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.4->ultralytics) (2024.2)\n","Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.4->ultralytics) (2024.1)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (3.10)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (2024.8.30)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.16.1)\n","Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (4.12.2)\n","Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (1.13.2)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.3)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.1.4)\n","Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (2024.6.1)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.16.0)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.8.0->ultralytics) (2.1.5)\n","Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.8.0->ultralytics) (1.3.0)\n"]}],"source":["!pip install ultralytics --upgrade"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":80004,"status":"ok","timestamp":1727091381435,"user":{"displayName":"Sai Akshitha Reddy Kota","userId":"05755385555264415224"},"user_tz":240},"id":"i6z6t9IIoa0D","outputId":"f8026366-ccec-491b-97ff-4effd1a82fd1"},"outputs":[{"name":"stdout","output_type":"stream","text":["Creating new Ultralytics Settings v0.0.6 file ✅ \n","View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\n","Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.\n","WARNING ⚠️ best-6.pt appears to require 'omegaconf', which is not in Ultralytics requirements.\n","AutoInstall will run now for 'omegaconf' but this feature will be removed in the future.\n","Recommend fixes are to train a new model using the latest 'ultralytics' package or to run a command with an official Ultralytics model, i.e. 'yolo predict model=yolov8n.pt'\n","\u001b[31m\u001b[1mrequirements:\u001b[0m Ultralytics requirement ['omegaconf'] not found, attempting AutoUpdate...\n","Collecting omegaconf\n","  Downloading omegaconf-2.3.0-py3-none-any.whl.metadata (3.9 kB)\n","Collecting antlr4-python3-runtime==4.9.* (from omegaconf)\n","  Downloading antlr4-python3-runtime-4.9.3.tar.gz (117 kB)\n","     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 117.0/117.0 kB 7.1 MB/s eta 0:00:00\n","  Preparing metadata (setup.py): started\n","  Preparing metadata (setup.py): finished with status 'done'\n","Requirement already satisfied: PyYAML>=5.1.0 in /usr/local/lib/python3.10/dist-packages (from omegaconf) (6.0.2)\n","Downloading omegaconf-2.3.0-py3-none-any.whl (79 kB)\n","   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 79.5/79.5 kB 13.6 MB/s eta 0:00:00\n","Building wheels for collected packages: antlr4-python3-runtime\n","  Building wheel for antlr4-python3-runtime (setup.py): started\n","  Building wheel for antlr4-python3-runtime (setup.py): finished with status 'done'\n","  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144554 sha256=eaa3f6267f59b413395963254b2bc5c97b1bfd5b4fc148f39e847442e30dae76\n","  Stored in directory: /tmp/pip-ephem-wheel-cache-rs1crqc0/wheels/12/93/dd/1f6a127edc45659556564c5730f6d4e300888f4bca2d4c5a88\n","Successfully built antlr4-python3-runtime\n","Installing collected packages: antlr4-python3-runtime, omegaconf\n","Successfully installed antlr4-python3-runtime-4.9.3 omegaconf-2.3.0\n","\n","\u001b[31m\u001b[1mrequirements:\u001b[0m AutoUpdate success ✅ 8.7s, installed 1 package: ['omegaconf']\n","\u001b[31m\u001b[1mrequirements:\u001b[0m ⚠️ \u001b[1mRestart runtime or rerun command for updates to take effect\u001b[0m\n","\n","Ultralytics YOLOv8.2.99 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n","Model summary (fused): 218 layers, 25,840,339 parameters, 0 gradients, 78.7 GFLOPs\n","Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n","100% 755k/755k [00:00<00:00, 23.9MB/s]\n","\u001b[34m\u001b[1mval: \u001b[0mScanning /content/drive/MyDrive/yolov8_tracking/yolov8_detection/Chicken test,train validate data/demo/validate/labels.cache... 17 images, 0 backgrounds, 0 corrupt: 100% 17/17 [00:00<?, ?it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:17<00:00,  8.90s/it]\n","                   all         17        324      0.981      0.941      0.974      0.585\n","Speed: 10.2ms preprocess, 32.5ms inference, 0.0ms loss, 42.2ms postprocess per image\n","Results saved to \u001b[1mruns/detect/val35\u001b[0m\n","mAP50-95: 0.584873834016588\n","Pruning model...\n","Model pruned.\n","Saving pruned model...\n","Pruned model saved.\n","Ultralytics YOLOv8.2.99 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n","\u001b[34m\u001b[1mval: \u001b[0mScanning /content/drive/MyDrive/yolov8_tracking/yolov8_detection/Chicken test,train validate data/demo/validate/labels.cache... 17 images, 0 backgrounds, 0 corrupt: 100% 17/17 [00:00<?, ?it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 2/2 [00:01<00:00,  1.47it/s]\n","                   all         17        324      0.984      0.932      0.972      0.575\n","Speed: 8.6ms preprocess, 23.9ms inference, 0.0ms loss, 3.1ms postprocess per image\n","Results saved to \u001b[1mruns/detect/val36\u001b[0m\n","mAP50-95: 0.574805523081249\n"]}],"source":["!python pruning.py"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":250811,"status":"ok","timestamp":1725440341768,"user":{"displayName":"Sai Akshitha Reddy Kota","userId":"05755385555264415224"},"user_tz":240},"id":"9yS1tbiq4KfL","outputId":"63933023-5076-405a-e808-dd80106eceec"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[33mWARNING: Ignoring invalid distribution -ltralytics (/usr/local/lib/python3.10/dist-packages)\u001b[0m\u001b[33m\n","\u001b[0mCollecting git+https://github.com/mikel-brostrom/yolov8_tracking.git\n","  Cloning https://github.com/mikel-brostrom/yolov8_tracking.git to /tmp/pip-req-build-d5z3m9ji\n","  Running command git clone --filter=blob:none --quiet https://github.com/mikel-brostrom/yolov8_tracking.git /tmp/pip-req-build-d5z3m9ji\n","  Resolved https://github.com/mikel-brostrom/yolov8_tracking.git to commit 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(/usr/local/lib/python3.10/dist-packages)\u001b[0m\u001b[33m\n","\u001b[0mInstalling collected packages: yacs, triton, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, loguru, ftfy, nvidia-cusparse-cu12, nvidia-cudnn-cu12, lapx, nvidia-cusolver-cu12, torch, torchvision, boxmot\n","  Attempting uninstall: nvidia-nccl-cu12\n","    Found existing installation: nvidia-nccl-cu12 2.22.3\n","    Uninstalling nvidia-nccl-cu12-2.22.3:\n","      Successfully uninstalled nvidia-nccl-cu12-2.22.3\n","  Attempting uninstall: numpy\n","    Found existing installation: numpy 1.23.1\n","    Uninstalling numpy-1.23.1:\n","      Successfully uninstalled numpy-1.23.1\n","  Attempting uninstall: torch\n","    Found existing installation: torch 2.4.0+cu121\n","    Uninstalling torch-2.4.0+cu121:\n","      Successfully uninstalled torch-2.4.0+cu121\n","  Attempting uninstall: torchvision\n","    Found existing installation: torchvision 0.19.0+cu121\n","    Uninstalling torchvision-0.19.0+cu121:\n","      Successfully uninstalled torchvision-0.19.0+cu121\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","torchaudio 2.4.0+cu121 requires torch==2.4.0, but you have torch 2.2.2 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed boxmot-10.0.79 ftfy-6.2.3 lapx-0.5.10.post1 loguru-0.7.2 numpy-1.26.4 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.6.68 nvidia-nvtx-cu12-12.1.105 torch-2.2.2 torchvision-0.17.2 triton-2.2.0 yacs-0.1.8\n"]}],"source":["!pip install git+https://github.com/mikel-brostrom/yolov8_tracking.git"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qvp3tW1HY1RB","outputId":"ed0b7ddd-2d5d-4c05-aab5-1bb1a82721ed"},"outputs":[{"name":"stdout","output_type":"stream","text":["Using Device:  cuda\n","Model summary (fused): 218 layers, 25,840,339 parameters, 0 gradients, 78.7 GFLOPs\n","WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n","I0000 00:00:1728060080.276775   13796 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n","I0000 00:00:1728060080.294152   13796 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n","I0000 00:00:1728060080.294422   13796 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n","I0000 00:00:1728060080.295073   13796 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n","I0000 00:00:1728060080.295316   13796 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n","I0000 00:00:1728060080.295505   13796 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n","I0000 00:00:1728060080.433704   13796 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n","I0000 00:00:1728060080.433952   13796 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n","I0000 00:00:1728060080.434165   13796 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n","SupervisionWarnings: BoundingBoxAnnotator is deprecated: `BoundingBoxAnnotator` is deprecated and has been renamed to `BoxAnnotator`. `BoundingBoxAnnotator` will be removed in supervision-0.26.0.\n","weights type  <class 'pathlib.PosixPath'>\n","[ WARN:0@13.065] global cap.cpp:643 open VIDEOIO(CV_IMAGES): raised OpenCV exception:\n","\n","OpenCV(4.10.0) /io/opencv/modules/videoio/src/cap_images.cpp:430: error: (-215:Assertion failed) !filename_pattern.empty() in function 'open'\n","\n","\n","\n","0: 640x544 20 chickens, 46.1ms\n","Speed: 5.7ms preprocess, 46.1ms inference, 563.8ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 1\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.7ms\n","Speed: 3.8ms preprocess, 35.7ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 2\n","WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n","I0000 00:00:1728060092.079494   13846 service.cc:146] XLA service 0x7d2d94005fd0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n","I0000 00:00:1728060092.079529   13846 service.cc:154]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5\n","I0000 00:00:1728060098.437953   13846 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n","Mota frame:  1\n","19 detections\n","frame saved\n","\n","0: 640x544 19 chickens, 35.9ms\n","Speed: 4.9ms preprocess, 35.9ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 3\n","Mota frame:  1\n","19 detections\n","frame saved\n","\n","0: 640x544 19 chickens, 35.9ms\n","Speed: 5.2ms preprocess, 35.9ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 4\n","Mota frame:  0.8421052631578947\n","20 detections\n","frame saved\n","\n","0: 640x544 21 chickens, 35.9ms\n","Speed: 5.1ms preprocess, 35.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Frame Count: 5\n"]}],"source":["!python run.py"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":505105,"status":"ok","timestamp":1725921879490,"user":{"displayName":"Sai Akshitha Reddy Kota","userId":"05755385555264415224"},"user_tz":240},"id":"n-MsIAefEaBo","outputId":"c2cbddf9-85d9-493c-9169-776dfc1049a4"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","Speed: 3.4ms preprocess, 38.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.21052631578947367\n","frame saved\n","\n","0: 640x544 19 chickens, 42.8ms\n","Speed: 3.5ms preprocess, 42.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.21052631578947367\n","frame saved\n","\n","0: 640x544 19 chickens, 37.8ms\n","Speed: 3.6ms preprocess, 37.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 38.7ms\n","Speed: 7.1ms preprocess, 38.7ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.1578947368421053\n","frame saved\n","\n","0: 640x544 20 chickens, 39.5ms\n","Speed: 3.8ms preprocess, 39.5ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.1578947368421053\n","frame saved\n","\n","0: 640x544 18 chickens, 37.1ms\n","Speed: 4.1ms preprocess, 37.1ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 36.0ms\n","Speed: 3.5ms preprocess, 36.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.1578947368421053\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 9.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 39.2ms\n","Speed: 3.7ms preprocess, 39.2ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.10526315789473684\n","frame saved\n","\n","0: 640x544 18 chickens, 38.2ms\n","Speed: 3.5ms preprocess, 38.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 36.5ms\n","Speed: 5.7ms preprocess, 36.5ms inference, 2.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.21052631578947367\n","frame saved\n","\n","0: 640x544 19 chickens, 35.9ms\n","Speed: 8.3ms preprocess, 35.9ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  1\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 34.3ms\n","Speed: 4.0ms preprocess, 34.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 18 chickens, 34.3ms\n","Speed: 4.0ms preprocess, 34.3ms inference, 2.3ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 19 chickens, 34.2ms\n","Speed: 3.5ms preprocess, 34.2ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 34.3ms\n","Speed: 3.6ms preprocess, 34.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  1\n","frame saved\n","\n","0: 640x544 18 chickens, 34.3ms\n","Speed: 3.9ms preprocess, 34.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 18 chickens, 34.2ms\n","Speed: 3.4ms preprocess, 34.2ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 18 chickens, 34.3ms\n","Speed: 3.4ms preprocess, 34.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 18 chickens, 34.3ms\n","Speed: 3.6ms preprocess, 34.3ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 19 chickens, 34.3ms\n","Speed: 3.9ms preprocess, 34.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 34.2ms\n","Speed: 3.8ms preprocess, 34.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  1\n","frame saved\n","\n","0: 640x544 19 chickens, 34.3ms\n","Speed: 3.6ms preprocess, 34.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 32.2ms\n","Speed: 3.3ms preprocess, 32.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  1\n","frame saved\n","\n","0: 640x544 19 chickens, 32.3ms\n","Speed: 3.5ms preprocess, 32.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 32.2ms\n","Speed: 3.6ms preprocess, 32.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 19 chickens, 32.5ms\n","Speed: 3.7ms preprocess, 32.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 32.2ms\n","Speed: 3.5ms preprocess, 32.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 32.2ms\n","Speed: 3.9ms preprocess, 32.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 32.2ms\n","Speed: 3.4ms preprocess, 32.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 18 chickens, 32.2ms\n","Speed: 3.8ms preprocess, 32.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 18 chickens, 32.2ms\n","Speed: 3.5ms preprocess, 32.2ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 18 chickens, 32.2ms\n","Speed: 3.5ms preprocess, 32.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 18 chickens, 32.2ms\n","Speed: 3.4ms preprocess, 32.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  1\n","frame saved\n","\n","0: 640x544 18 chickens, 32.9ms\n","Speed: 3.5ms preprocess, 32.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 18 chickens, 33.5ms\n","Speed: 3.3ms preprocess, 33.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 18 chickens, 34.0ms\n","Speed: 3.5ms preprocess, 34.0ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 34.3ms\n","Speed: 3.6ms preprocess, 34.3ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  1\n","frame saved\n","\n","0: 640x544 18 chickens, 34.2ms\n","Speed: 3.3ms preprocess, 34.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 18 chickens, 35.0ms\n","Speed: 3.5ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 18 chickens, 35.0ms\n","Speed: 3.6ms preprocess, 35.0ms inference, 2.1ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.9ms\n","Speed: 10.3ms preprocess, 35.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 18 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 18 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 18 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.9ms\n","Speed: 9.0ms preprocess, 35.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 18 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 18 chickens, 35.8ms\n","Speed: 4.1ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 18 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.9ms\n","Speed: 3.6ms preprocess, 35.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  1\n","frame saved\n","\n","0: 640x544 18 chickens, 35.8ms\n","Speed: 3.9ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 18 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.7ms\n","Speed: 3.4ms preprocess, 35.7ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 32.8ms\n","Speed: 3.8ms preprocess, 32.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 18 chickens, 32.9ms\n","Speed: 3.6ms preprocess, 32.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 32.8ms\n","Speed: 3.6ms preprocess, 32.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  1\n","frame saved\n","\n","0: 640x544 19 chickens, 32.8ms\n","Speed: 3.6ms preprocess, 32.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 32.8ms\n","Speed: 3.8ms preprocess, 32.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 32.8ms\n","Speed: 3.3ms preprocess, 32.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 32.9ms\n","Speed: 3.5ms preprocess, 32.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 19 chickens, 32.8ms\n","Speed: 3.8ms preprocess, 32.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 32.9ms\n","Speed: 3.6ms preprocess, 32.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 32.9ms\n","Speed: 5.1ms preprocess, 32.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 32.9ms\n","Speed: 3.4ms preprocess, 32.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 31.6ms\n","Speed: 5.1ms preprocess, 31.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 31.6ms\n","Speed: 4.3ms preprocess, 31.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 31.6ms\n","Speed: 3.5ms preprocess, 31.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 31.6ms\n","Speed: 3.7ms preprocess, 31.6ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 35.9ms\n","Speed: 5.1ms preprocess, 35.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 33.3ms\n","Speed: 3.9ms preprocess, 33.3ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 33.5ms\n","Speed: 7.5ms preprocess, 33.5ms inference, 3.4ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 33.4ms\n","Speed: 9.2ms preprocess, 33.4ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 19 chickens, 34.7ms\n","Speed: 3.6ms preprocess, 34.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 38.6ms\n","Speed: 3.7ms preprocess, 38.6ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 8.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 2.3ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 4.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 5.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 8.2ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 2.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  1\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 8.4ms preprocess, 35.8ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  1\n","frame saved\n","\n","0: 640x544 19 chickens, 38.6ms\n","Speed: 5.3ms preprocess, 38.6ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 40.7ms\n","Speed: 3.6ms preprocess, 40.7ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 19 chickens, 35.9ms\n","Speed: 8.4ms preprocess, 35.9ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 39.5ms\n","Speed: 3.7ms preprocess, 39.5ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 19 chickens, 36.1ms\n","Speed: 7.6ms preprocess, 36.1ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 6.6ms preprocess, 35.8ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  1\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 36.0ms\n","Speed: 3.6ms preprocess, 36.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  1\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  1\n","frame saved\n","\n","0: 640x544 20 chickens, 35.9ms\n","Speed: 7.5ms preprocess, 35.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 35.7ms\n","Speed: 3.3ms preprocess, 35.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 4.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 2.1ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.9ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 39.7ms\n","Speed: 3.9ms preprocess, 39.7ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.6ms preprocess, 35.0ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.7ms preprocess, 35.0ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 20 chickens, 35.0ms\n","Speed: 3.5ms preprocess, 35.0ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.0ms\n","Speed: 3.5ms preprocess, 35.0ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 19 chickens, 35.0ms\n","Speed: 3.6ms preprocess, 35.0ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.0ms\n","Speed: 3.4ms preprocess, 35.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 18 chickens, 35.0ms\n","Speed: 3.5ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.0ms\n","Speed: 3.4ms preprocess, 35.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 35.0ms\n","Speed: 3.6ms preprocess, 35.0ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 36.7ms\n","Speed: 11.0ms preprocess, 36.7ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 32.2ms\n","Speed: 3.6ms preprocess, 32.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 32.2ms\n","Speed: 3.6ms preprocess, 32.2ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 32.2ms\n","Speed: 3.7ms preprocess, 32.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 32.2ms\n","Speed: 10.4ms preprocess, 32.2ms inference, 4.4ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 32.2ms\n","Speed: 3.8ms preprocess, 32.2ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 32.2ms\n","Speed: 6.1ms preprocess, 32.2ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 21 chickens, 32.2ms\n","Speed: 3.8ms preprocess, 32.2ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 32.2ms\n","Speed: 3.9ms preprocess, 32.2ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 19 chickens, 32.2ms\n","Speed: 3.6ms preprocess, 32.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 32.2ms\n","Speed: 3.8ms preprocess, 32.2ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 20 chickens, 32.2ms\n","Speed: 3.6ms preprocess, 32.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 20 chickens, 38.3ms\n","Speed: 3.4ms preprocess, 38.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 34.3ms\n","Speed: 3.5ms preprocess, 34.3ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 34.2ms\n","Speed: 3.5ms preprocess, 34.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 19 chickens, 34.2ms\n","Speed: 3.4ms preprocess, 34.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 34.3ms\n","Speed: 4.1ms preprocess, 34.3ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 34.3ms\n","Speed: 4.0ms preprocess, 34.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 34.3ms\n","Speed: 3.8ms preprocess, 34.3ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  1\n","frame saved\n","\n","0: 640x544 19 chickens, 34.3ms\n","Speed: 7.8ms preprocess, 34.3ms inference, 6.0ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.3ms\n","Speed: 3.6ms preprocess, 35.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 19 chickens, 34.3ms\n","Speed: 6.5ms preprocess, 34.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.9ms\n","Speed: 4.0ms preprocess, 35.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 21 chickens, 43.0ms\n","Speed: 7.4ms preprocess, 43.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.9ms\n","Speed: 4.5ms preprocess, 35.9ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 7.3ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 20 chickens, 38.2ms\n","Speed: 3.6ms preprocess, 38.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 9.3ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 21 chickens, 36.9ms\n","Speed: 6.8ms preprocess, 36.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 38.8ms\n","Speed: 3.6ms preprocess, 38.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 21 chickens, 39.3ms\n","Speed: 5.0ms preprocess, 39.3ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.9ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 4.7ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 7.4ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 39.7ms\n","Speed: 3.5ms preprocess, 39.7ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 18 chickens, 39.1ms\n","Speed: 3.9ms preprocess, 39.1ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  1\n","frame saved\n","\n","0: 640x544 18 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 18 chickens, 34.2ms\n","Speed: 3.4ms preprocess, 34.2ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  1\n","frame saved\n","\n","0: 640x544 18 chickens, 34.2ms\n","Speed: 5.5ms preprocess, 34.2ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 17 chickens, 34.2ms\n","Speed: 3.3ms preprocess, 34.2ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 18 chickens, 34.2ms\n","Speed: 4.0ms preprocess, 34.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 18 chickens, 34.2ms\n","Speed: 3.7ms preprocess, 34.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  1\n","frame saved\n","\n","0: 640x544 18 chickens, 32.2ms\n","Speed: 4.8ms preprocess, 32.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 18 chickens, 32.8ms\n","Speed: 3.5ms preprocess, 32.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 19 chickens, 32.3ms\n","Speed: 3.6ms preprocess, 32.3ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 32.2ms\n","Speed: 3.6ms preprocess, 32.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 20 chickens, 32.2ms\n","Speed: 3.5ms preprocess, 32.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 32.2ms\n","Speed: 3.4ms preprocess, 32.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 32.2ms\n","Speed: 3.7ms preprocess, 32.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 32.4ms\n","Speed: 3.5ms preprocess, 32.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 32.2ms\n","Speed: 3.7ms preprocess, 32.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.2ms\n","Speed: 3.4ms preprocess, 32.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 21 chickens, 32.2ms\n","Speed: 3.3ms preprocess, 32.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.7ms preprocess, 35.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 20 chickens, 37.1ms\n","Speed: 3.6ms preprocess, 37.1ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 4.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.0ms\n","Speed: 3.9ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 19 chickens, 35.0ms\n","Speed: 3.5ms preprocess, 35.0ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.0ms\n","Speed: 3.5ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 35.0ms\n","Speed: 3.5ms preprocess, 35.0ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.6ms preprocess, 35.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 20 chickens, 35.0ms\n","Speed: 3.4ms preprocess, 35.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 32.3ms\n","Speed: 3.5ms preprocess, 32.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 21 chickens, 32.2ms\n","Speed: 3.6ms preprocess, 32.2ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.2ms\n","Speed: 3.8ms preprocess, 32.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 20 chickens, 32.3ms\n","Speed: 4.0ms preprocess, 32.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.2ms\n","Speed: 3.3ms preprocess, 32.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 21 chickens, 32.2ms\n","Speed: 3.8ms preprocess, 32.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.2ms\n","Speed: 3.7ms preprocess, 32.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.7894736842105263\n","frame saved\n","\n","0: 640x544 20 chickens, 32.2ms\n","Speed: 3.6ms preprocess, 32.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 32.2ms\n","Speed: 3.5ms preprocess, 32.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 20 chickens, 32.2ms\n","Speed: 3.8ms preprocess, 32.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 40.4ms\n","Speed: 5.0ms preprocess, 40.4ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 7.7ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 38.2ms\n","Speed: 3.4ms preprocess, 38.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 22 chickens, 39.3ms\n","Speed: 3.5ms preprocess, 39.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 6.5ms preprocess, 35.8ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 20 chickens, 37.4ms\n","Speed: 3.6ms preprocess, 37.4ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 45.0ms\n","Speed: 3.4ms preprocess, 45.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 22 chickens, 40.4ms\n","Speed: 6.6ms preprocess, 40.4ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 39.6ms\n","Speed: 7.8ms preprocess, 39.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.7894736842105263\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 6.5ms preprocess, 35.8ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 38.0ms\n","Speed: 3.5ms preprocess, 38.0ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 21 chickens, 38.8ms\n","Speed: 3.6ms preprocess, 38.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 39.2ms\n","Speed: 3.9ms preprocess, 39.2ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 5.0ms preprocess, 35.8ms inference, 4.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 39.9ms\n","Speed: 3.7ms preprocess, 39.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 37.7ms\n","Speed: 5.8ms preprocess, 37.7ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.7894736842105263\n","frame saved\n","\n","0: 640x544 22 chickens, 40.6ms\n","Speed: 3.4ms preprocess, 40.6ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 3.2ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 6.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 21 chickens, 38.2ms\n","Speed: 3.4ms preprocess, 38.2ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.9ms\n","Speed: 6.7ms preprocess, 35.9ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.7ms\n","Speed: 3.3ms preprocess, 35.7ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.7894736842105263\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 37.5ms\n","Speed: 4.0ms preprocess, 37.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 23 chickens, 34.2ms\n","Speed: 3.6ms preprocess, 34.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 34.2ms\n","Speed: 3.8ms preprocess, 34.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.736842105263158\n","frame saved\n","\n","0: 640x544 20 chickens, 34.2ms\n","Speed: 3.5ms preprocess, 34.2ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 34.2ms\n","Speed: 3.5ms preprocess, 34.2ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 34.2ms\n","Speed: 3.4ms preprocess, 34.2ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 34.4ms\n","Speed: 3.6ms preprocess, 34.4ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 22 chickens, 34.2ms\n","Speed: 3.4ms preprocess, 34.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 33.6ms\n","Speed: 3.7ms preprocess, 33.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 20 chickens, 33.5ms\n","Speed: 3.5ms preprocess, 33.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 33.5ms\n","Speed: 3.7ms preprocess, 33.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 20 chickens, 33.5ms\n","Speed: 3.4ms preprocess, 33.5ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 33.6ms\n","Speed: 3.8ms preprocess, 33.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.7894736842105263\n","frame saved\n","\n","0: 640x544 20 chickens, 33.6ms\n","Speed: 3.5ms preprocess, 33.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 33.9ms\n","Speed: 3.5ms preprocess, 33.9ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 21 chickens, 33.5ms\n","Speed: 3.7ms preprocess, 33.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 33.5ms\n","Speed: 3.4ms preprocess, 33.5ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 22 chickens, 33.6ms\n","Speed: 3.7ms preprocess, 33.6ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 34.2ms\n","Speed: 3.6ms preprocess, 34.2ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.7894736842105263\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 4.3ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 5.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.9ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.7ms\n","Speed: 3.4ms preprocess, 35.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 33.5ms\n","Speed: 3.2ms preprocess, 33.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 33.6ms\n","Speed: 3.5ms preprocess, 33.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 33.5ms\n","Speed: 3.8ms preprocess, 33.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 33.6ms\n","Speed: 3.6ms preprocess, 33.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 33.5ms\n","Speed: 3.4ms preprocess, 33.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 33.5ms\n","Speed: 3.5ms preprocess, 33.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 33.5ms\n","Speed: 3.3ms preprocess, 33.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 34.4ms\n","Speed: 3.6ms preprocess, 34.4ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 33.6ms\n","Speed: 3.4ms preprocess, 33.6ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 33.5ms\n","Speed: 3.5ms preprocess, 33.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 4.0ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 4.1ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.7ms\n","Speed: 3.4ms preprocess, 35.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 36.0ms\n","Speed: 3.6ms preprocess, 36.0ms inference, 3.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.5263157894736843\n","frame saved\n","\n","0: 640x544 21 chickens, 39.6ms\n","Speed: 7.5ms preprocess, 39.6ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 3.3ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.4736842105263158\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 37.1ms\n","Speed: 8.7ms preprocess, 37.1ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.9ms\n","Speed: 4.5ms preprocess, 35.9ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.9ms\n","Speed: 9.5ms preprocess, 35.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.9ms\n","Speed: 3.9ms preprocess, 35.9ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 41.7ms\n","Speed: 3.8ms preprocess, 41.7ms inference, 6.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 39.2ms\n","Speed: 3.7ms preprocess, 39.2ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 6.1ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 38.3ms\n","Speed: 8.7ms preprocess, 38.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 7.9ms preprocess, 35.8ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 37.7ms\n","Speed: 3.7ms preprocess, 37.7ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 40.2ms\n","Speed: 7.9ms preprocess, 40.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.9ms\n","Speed: 7.9ms preprocess, 35.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 36.0ms\n","Speed: 8.0ms preprocess, 36.0ms inference, 3.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 36.4ms\n","Speed: 3.6ms preprocess, 36.4ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.5263157894736843\n","frame saved\n","\n","0: 640x544 21 chickens, 35.9ms\n","Speed: 4.0ms preprocess, 35.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.5263157894736843\n","frame saved\n","\n","0: 640x544 20 chickens, 33.6ms\n","Speed: 3.5ms preprocess, 33.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 33.5ms\n","Speed: 3.7ms preprocess, 33.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.5263157894736843\n","frame saved\n","\n","0: 640x544 24 chickens, 33.5ms\n","Speed: 3.5ms preprocess, 33.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 33.5ms\n","Speed: 3.6ms preprocess, 33.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.4736842105263158\n","frame saved\n","\n","0: 640x544 23 chickens, 33.6ms\n","Speed: 3.6ms preprocess, 33.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 33.6ms\n","Speed: 3.3ms preprocess, 33.6ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.4736842105263158\n","frame saved\n","\n","0: 640x544 22 chickens, 33.5ms\n","Speed: 3.5ms preprocess, 33.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 32.9ms\n","Speed: 3.9ms preprocess, 32.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.4736842105263158\n","frame saved\n","\n","0: 640x544 23 chickens, 32.9ms\n","Speed: 4.3ms preprocess, 32.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.9ms\n","Speed: 3.7ms preprocess, 32.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.42105263157894735\n","frame saved\n","\n","0: 640x544 23 chickens, 32.8ms\n","Speed: 3.5ms preprocess, 32.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 32.8ms\n","Speed: 3.7ms preprocess, 32.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.5263157894736843\n","frame saved\n","\n","0: 640x544 22 chickens, 32.9ms\n","Speed: 10.2ms preprocess, 32.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 32.9ms\n","Speed: 3.5ms preprocess, 32.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.4736842105263158\n","frame saved\n","\n","0: 640x544 21 chickens, 32.9ms\n","Speed: 4.2ms preprocess, 32.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.9ms\n","Speed: 3.4ms preprocess, 32.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.4736842105263158\n","frame saved\n","\n","0: 640x544 21 chickens, 32.8ms\n","Speed: 3.5ms preprocess, 32.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.4736842105263158\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.368421052631579\n","frame saved\n","\n","0: 640x544 22 chickens, 36.1ms\n","Speed: 6.4ms preprocess, 36.1ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.42105263157894735\n","frame saved\n","\n","0: 640x544 24 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 38.5ms\n","Speed: 8.8ms preprocess, 38.5ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.5263157894736843\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.368421052631579\n","frame saved\n","\n","0: 640x544 24 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.4736842105263158\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.368421052631579\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.42105263157894735\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.4736842105263158\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.0ms\n","Speed: 3.8ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.4736842105263158\n","frame saved\n","\n","0: 640x544 21 chickens, 37.5ms\n","Speed: 4.5ms preprocess, 37.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.5ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.4736842105263158\n","frame saved\n","\n","0: 640x544 22 chickens, 35.0ms\n","Speed: 3.9ms preprocess, 35.0ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.1ms\n","Speed: 4.2ms preprocess, 35.1ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.42105263157894735\n","frame saved\n","\n","0: 640x544 22 chickens, 41.2ms\n","Speed: 3.7ms preprocess, 41.2ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.0ms\n","Speed: 3.6ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.4736842105263158\n","frame saved\n","\n","0: 640x544 22 chickens, 38.5ms\n","Speed: 3.5ms preprocess, 38.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.0ms\n","Speed: 3.4ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.4736842105263158\n","frame saved\n","\n","0: 640x544 22 chickens, 35.0ms\n","Speed: 3.3ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 36.1ms\n","Speed: 3.9ms preprocess, 36.1ms inference, 2.1ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 8.7ms preprocess, 35.8ms inference, 3.2ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.9ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.7894736842105263\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 9.1ms preprocess, 35.8ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 40.2ms\n","Speed: 3.5ms preprocess, 40.2ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 6.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 42.0ms\n","Speed: 3.5ms preprocess, 42.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.7894736842105263\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 4.7ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 22 chickens, 36.2ms\n","Speed: 3.9ms preprocess, 36.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 35.8ms\n","Speed: 9.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 35.8ms\n","Speed: 5.9ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 40.9ms\n","Speed: 6.3ms preprocess, 40.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 39.3ms\n","Speed: 3.7ms preprocess, 39.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 40.1ms\n","Speed: 3.6ms preprocess, 40.1ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 39.6ms\n","Speed: 3.5ms preprocess, 39.6ms inference, 4.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 5.8ms preprocess, 35.8ms inference, 2.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.9ms\n","Speed: 10.3ms preprocess, 35.9ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 7.6ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 40.6ms\n","Speed: 3.9ms preprocess, 40.6ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.9ms\n","Speed: 11.9ms preprocess, 35.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 4.2ms preprocess, 35.8ms inference, 3.2ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 21 chickens, 35.9ms\n","Speed: 3.6ms preprocess, 35.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 36.3ms\n","Speed: 3.9ms preprocess, 36.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 4.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 32.9ms\n","Speed: 3.6ms preprocess, 32.9ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 32.9ms\n","Speed: 3.6ms preprocess, 32.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.9ms\n","Speed: 5.5ms preprocess, 32.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 32.9ms\n","Speed: 3.6ms preprocess, 32.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.7894736842105263\n","frame saved\n","\n","0: 640x544 23 chickens, 32.9ms\n","Speed: 3.5ms preprocess, 32.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.9ms\n","Speed: 3.5ms preprocess, 32.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.736842105263158\n","frame saved\n","\n","0: 640x544 22 chickens, 32.9ms\n","Speed: 3.5ms preprocess, 32.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 32.8ms\n","Speed: 3.5ms preprocess, 32.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 21 chickens, 32.9ms\n","Speed: 3.6ms preprocess, 32.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 32.9ms\n","Speed: 3.8ms preprocess, 32.9ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.6ms preprocess, 35.0ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.7ms\n","Speed: 3.5ms preprocess, 35.7ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 21 chickens, 35.1ms\n","Speed: 3.6ms preprocess, 35.1ms inference, 5.3ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.0ms\n","Speed: 4.2ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.7894736842105263\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.4ms preprocess, 35.0ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.0ms\n","Speed: 3.5ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.5ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.7ms preprocess, 35.0ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 36.0ms\n","Speed: 3.5ms preprocess, 36.0ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.1ms\n","Speed: 3.7ms preprocess, 35.1ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.5ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.8ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 40.1ms\n","Speed: 3.6ms preprocess, 40.1ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 36.1ms\n","Speed: 6.0ms preprocess, 36.1ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 32.9ms\n","Speed: 3.4ms preprocess, 32.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 32.8ms\n","Speed: 3.7ms preprocess, 32.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 32.9ms\n","Speed: 3.4ms preprocess, 32.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 32.8ms\n","Speed: 3.5ms preprocess, 32.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 20 chickens, 32.9ms\n","Speed: 3.3ms preprocess, 32.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 32.9ms\n","Speed: 3.7ms preprocess, 32.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 32.9ms\n","Speed: 3.4ms preprocess, 32.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.9ms\n","Speed: 7.3ms preprocess, 32.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 32.9ms\n","Speed: 3.5ms preprocess, 32.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 32.9ms\n","Speed: 3.6ms preprocess, 32.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 37.5ms\n","Speed: 3.5ms preprocess, 37.5ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 6.2ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 40.1ms\n","Speed: 3.7ms preprocess, 40.1ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.7894736842105263\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 7.7ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 36.0ms\n","Speed: 4.1ms preprocess, 36.0ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 38.8ms\n","Speed: 4.7ms preprocess, 38.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 37.7ms\n","Speed: 8.6ms preprocess, 37.7ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 9.0ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 9.0ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 7.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 7.8ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 21 chickens, 42.0ms\n","Speed: 3.7ms preprocess, 42.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 38.7ms\n","Speed: 8.0ms preprocess, 38.7ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 19 chickens, 36.8ms\n","Speed: 3.6ms preprocess, 36.8ms inference, 6.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 18 chickens, 37.6ms\n","Speed: 6.9ms preprocess, 37.6ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 8.0ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 8.6ms preprocess, 35.8ms inference, 2.3ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.9ms\n","Speed: 10.7ms preprocess, 35.9ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 38.3ms\n","Speed: 3.6ms preprocess, 38.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.9ms\n","Speed: 3.7ms preprocess, 35.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 5.8ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 4.1ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 36.4ms\n","Speed: 3.7ms preprocess, 36.4ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 44.0ms\n","Speed: 6.8ms preprocess, 44.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 12.6ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 36.4ms\n","Speed: 3.7ms preprocess, 36.4ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 4.1ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.0ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.7ms\n","Speed: 3.4ms preprocess, 35.7ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 20 chickens, 35.0ms\n","Speed: 3.5ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.1ms\n","Speed: 3.6ms preprocess, 35.1ms inference, 2.1ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.4ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.5ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 36.6ms\n","Speed: 3.7ms preprocess, 36.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 36.9ms\n","Speed: 3.6ms preprocess, 36.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.0ms\n","Speed: 3.5ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.0ms\n","Speed: 9.8ms preprocess, 35.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 35.0ms\n","Speed: 3.4ms preprocess, 35.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.0ms\n","Speed: 3.4ms preprocess, 35.0ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 8.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 37.4ms\n","Speed: 3.3ms preprocess, 37.4ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 21 chickens, 35.7ms\n","Speed: 3.6ms preprocess, 35.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.5ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.0ms\n","Speed: 3.3ms preprocess, 35.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.5ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.5ms preprocess, 35.0ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.7ms\n","Speed: 3.4ms preprocess, 35.7ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.4ms preprocess, 35.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.0ms\n","Speed: 3.7ms preprocess, 35.0ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.7ms preprocess, 35.0ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.0ms\n","Speed: 7.9ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 4.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 6.8ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 39.8ms\n","Speed: 3.7ms preprocess, 39.8ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 21 chickens, 35.9ms\n","Speed: 8.5ms preprocess, 35.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 4.3ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 38.8ms\n","Speed: 3.8ms preprocess, 38.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 37.7ms\n","Speed: 3.7ms preprocess, 37.7ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 37.9ms\n","Speed: 5.3ms preprocess, 37.9ms inference, 9.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 40.9ms\n","Speed: 3.7ms preprocess, 40.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 40.0ms\n","Speed: 3.8ms preprocess, 40.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 40.3ms\n","Speed: 3.6ms preprocess, 40.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.9ms\n","Speed: 9.6ms preprocess, 35.9ms inference, 6.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 4.0ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 39.0ms\n","Speed: 5.6ms preprocess, 39.0ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 41.4ms\n","Speed: 3.6ms preprocess, 41.4ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 39.0ms\n","Speed: 3.7ms preprocess, 39.0ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 21 chickens, 39.2ms\n","Speed: 3.7ms preprocess, 39.2ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 39.5ms\n","Speed: 3.5ms preprocess, 39.5ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 6.7ms preprocess, 35.8ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 4.4ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.1ms\n","Speed: 3.5ms preprocess, 35.1ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.0ms\n","Speed: 3.7ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.4ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.0ms\n","Speed: 3.5ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 20 chickens, 35.0ms\n","Speed: 3.6ms preprocess, 35.0ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.4ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 20 chickens, 35.1ms\n","Speed: 3.6ms preprocess, 35.1ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 33.6ms\n","Speed: 3.4ms preprocess, 33.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.9473684210526316\n","frame saved\n","\n","0: 640x544 21 chickens, 33.6ms\n","Speed: 3.5ms preprocess, 33.6ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.4ms\n","Speed: 3.8ms preprocess, 35.4ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 22 chickens, 33.6ms\n","Speed: 3.7ms preprocess, 33.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 33.5ms\n","Speed: 3.5ms preprocess, 33.5ms inference, 2.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 23 chickens, 33.6ms\n","Speed: 3.8ms preprocess, 33.6ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.4ms\n","Speed: 3.7ms preprocess, 35.4ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 22 chickens, 33.6ms\n","Speed: 3.7ms preprocess, 33.6ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 33.9ms\n","Speed: 3.4ms preprocess, 33.9ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.736842105263158\n","frame saved\n","\n","0: 640x544 22 chickens, 33.6ms\n","Speed: 3.7ms preprocess, 33.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.736842105263158\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 9.2ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.736842105263158\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 42.1ms\n","Speed: 9.2ms preprocess, 42.1ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 37.6ms\n","Speed: 3.8ms preprocess, 37.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 7.9ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 5.1ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8421052631578947\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0.8947368421052632\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 5.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 37.1ms\n","Speed: 6.8ms preprocess, 37.1ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 41.3ms\n","Speed: 3.5ms preprocess, 41.3ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 3.2ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 4.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 5.9ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.9ms\n","Speed: 3.6ms preprocess, 35.9ms inference, 3.1ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 42.5ms\n","Speed: 3.6ms preprocess, 42.5ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 44.9ms\n","Speed: 3.5ms preprocess, 44.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 8.2ms preprocess, 35.8ms inference, 4.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.9ms\n","Speed: 3.9ms preprocess, 35.9ms inference, 7.1ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 5.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 48.2ms\n","Speed: 3.6ms preprocess, 48.2ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 41.3ms\n","Speed: 3.6ms preprocess, 41.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 7.7ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 37.1ms\n","Speed: 8.3ms preprocess, 37.1ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 36.9ms\n","Speed: 3.8ms preprocess, 36.9ms inference, 3.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 13.3ms preprocess, 35.8ms inference, 7.2ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 40.5ms\n","Speed: 3.6ms preprocess, 40.5ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 42.8ms\n","Speed: 6.7ms preprocess, 42.8ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.9ms\n","Speed: 8.0ms preprocess, 35.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 38.1ms\n","Speed: 11.1ms preprocess, 38.1ms inference, 2.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 8.6ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.9ms\n","Speed: 3.7ms preprocess, 35.9ms inference, 2.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 36.8ms\n","Speed: 6.9ms preprocess, 36.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 39.3ms\n","Speed: 6.9ms preprocess, 39.3ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 6.4ms preprocess, 35.8ms inference, 3.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 37.7ms\n","Speed: 3.6ms preprocess, 37.7ms inference, 5.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 38.8ms\n","Speed: 3.6ms preprocess, 38.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 6.2ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 9.6ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 42.8ms\n","Speed: 3.5ms preprocess, 42.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 8.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 7.2ms preprocess, 35.8ms inference, 2.1ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 18 chickens, 40.1ms\n","Speed: 4.0ms preprocess, 40.1ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  1\n","frame saved\n","\n","0: 640x544 18 chickens, 40.0ms\n","Speed: 3.7ms preprocess, 40.0ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 12.2ms preprocess, 35.8ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 36.4ms\n","Speed: 10.1ms preprocess, 36.4ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 4.1ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.5ms\n","Speed: 3.9ms preprocess, 35.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 34.3ms\n","Speed: 3.5ms preprocess, 34.3ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 34.3ms\n","Speed: 3.4ms preprocess, 34.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.9ms\n","Speed: 3.6ms preprocess, 35.9ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 34.3ms\n","Speed: 3.4ms preprocess, 34.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 34.3ms\n","Speed: 4.3ms preprocess, 34.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 34.2ms\n","Speed: 3.4ms preprocess, 34.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 34.2ms\n","Speed: 3.4ms preprocess, 34.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 34.3ms\n","Speed: 3.7ms preprocess, 34.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 34.3ms\n","Speed: 3.3ms preprocess, 34.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 34.3ms\n","Speed: 3.6ms preprocess, 34.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 37.3ms\n","Speed: 3.7ms preprocess, 37.3ms inference, 2.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.9ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 4.3ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.9ms\n","Speed: 3.6ms preprocess, 32.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 32.9ms\n","Speed: 3.4ms preprocess, 32.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 32.9ms\n","Speed: 3.8ms preprocess, 32.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 32.9ms\n","Speed: 3.5ms preprocess, 32.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 32.9ms\n","Speed: 3.4ms preprocess, 32.9ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 32.9ms\n","Speed: 3.6ms preprocess, 32.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 32.8ms\n","Speed: 3.6ms preprocess, 32.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 32.9ms\n","Speed: 3.5ms preprocess, 32.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 32.9ms\n","Speed: 3.3ms preprocess, 32.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 32.9ms\n","Speed: 3.8ms preprocess, 32.9ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 36.8ms\n","Speed: 5.6ms preprocess, 36.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 43.7ms\n","Speed: 4.0ms preprocess, 43.7ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 7.5ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 37.7ms\n","Speed: 5.8ms preprocess, 37.7ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 36.8ms\n","Speed: 7.7ms preprocess, 36.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 36.7ms\n","Speed: 5.8ms preprocess, 36.7ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 11.7ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 36.3ms\n","Speed: 6.6ms preprocess, 36.3ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 8.5ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 7.5ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 41.1ms\n","Speed: 8.9ms preprocess, 41.1ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 36.6ms\n","Speed: 14.5ms preprocess, 36.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 8.0ms preprocess, 35.8ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 37.3ms\n","Speed: 7.6ms preprocess, 37.3ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 36.8ms\n","Speed: 5.4ms preprocess, 36.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 11.7ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 4.9ms preprocess, 35.8ms inference, 2.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 8.4ms preprocess, 35.8ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.9ms\n","Speed: 3.7ms preprocess, 35.9ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.9ms\n","Speed: 3.4ms preprocess, 35.9ms inference, 2.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 5.5ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.7ms\n","Speed: 3.5ms preprocess, 35.7ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.6ms\n","Speed: 3.6ms preprocess, 35.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.0ms\n","Speed: 3.4ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.6ms preprocess, 35.0ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.5ms preprocess, 35.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.7ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.0ms\n","Speed: 3.4ms preprocess, 35.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.0ms\n","Speed: 3.5ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.0ms\n","Speed: 3.4ms preprocess, 35.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.0ms\n","Speed: 3.3ms preprocess, 35.0ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.1ms\n","Speed: 3.5ms preprocess, 35.1ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.1ms\n","Speed: 3.8ms preprocess, 35.1ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.9ms\n","Speed: 3.6ms preprocess, 35.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 4.0ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 4.7ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.7ms\n","Speed: 4.5ms preprocess, 35.7ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.7ms\n","Speed: 3.6ms preprocess, 35.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.5ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.5ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.4ms preprocess, 35.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.0ms\n","Speed: 3.7ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.6ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.0ms\n","Speed: 3.3ms preprocess, 35.0ms inference, 3.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.1ms\n","Speed: 3.4ms preprocess, 35.1ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 35.0ms\n","Speed: 3.3ms preprocess, 35.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 33.5ms\n","Speed: 3.4ms preprocess, 33.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 33.5ms\n","Speed: 3.8ms preprocess, 33.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 33.6ms\n","Speed: 3.6ms preprocess, 33.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 33.6ms\n","Speed: 3.5ms preprocess, 33.6ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 33.6ms\n","Speed: 3.5ms preprocess, 33.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 33.5ms\n","Speed: 3.3ms preprocess, 33.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 33.5ms\n","Speed: 3.5ms preprocess, 33.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 33.5ms\n","Speed: 3.3ms preprocess, 33.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.2ms\n","Speed: 3.4ms preprocess, 32.2ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.2ms\n","Speed: 3.4ms preprocess, 32.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.2ms\n","Speed: 4.4ms preprocess, 32.2ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.2ms\n","Speed: 3.6ms preprocess, 32.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.7ms\n","Speed: 5.9ms preprocess, 32.7ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 32.2ms\n","Speed: 5.1ms preprocess, 32.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 38.9ms\n","Speed: 3.6ms preprocess, 38.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.2ms\n","Speed: 5.2ms preprocess, 32.2ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.7ms\n","Speed: 3.5ms preprocess, 35.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 7.9ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 40.7ms\n","Speed: 3.7ms preprocess, 40.7ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 38.8ms\n","Speed: 3.8ms preprocess, 38.8ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 7.7ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 38.9ms\n","Speed: 3.8ms preprocess, 38.9ms inference, 2.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 41.8ms\n","Speed: 3.6ms preprocess, 41.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 8.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 36.9ms\n","Speed: 3.6ms preprocess, 36.9ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 37.5ms\n","Speed: 11.7ms preprocess, 37.5ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 6.0ms preprocess, 35.8ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 18 chickens, 41.9ms\n","Speed: 3.5ms preprocess, 41.9ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 37.5ms\n","Speed: 5.4ms preprocess, 37.5ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 8.2ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.9ms\n","Speed: 8.2ms preprocess, 35.9ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 38.5ms\n","Speed: 3.5ms preprocess, 38.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 2.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 33.5ms\n","Speed: 3.6ms preprocess, 33.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 33.5ms\n","Speed: 3.4ms preprocess, 33.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 33.5ms\n","Speed: 3.6ms preprocess, 33.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 33.5ms\n","Speed: 3.6ms preprocess, 33.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 33.5ms\n","Speed: 3.4ms preprocess, 33.5ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 33.5ms\n","Speed: 3.3ms preprocess, 33.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 33.6ms\n","Speed: 3.5ms preprocess, 33.6ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 33.5ms\n","Speed: 3.3ms preprocess, 33.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 33.5ms\n","Speed: 3.4ms preprocess, 33.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 33.5ms\n","Speed: 3.4ms preprocess, 33.5ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 3.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 33.5ms\n","Speed: 3.5ms preprocess, 33.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 33.5ms\n","Speed: 3.4ms preprocess, 33.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 33.6ms\n","Speed: 3.9ms preprocess, 33.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 33.5ms\n","Speed: 3.6ms preprocess, 33.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 33.6ms\n","Speed: 3.3ms preprocess, 33.6ms inference, 2.1ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 33.5ms\n","Speed: 3.4ms preprocess, 33.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 33.5ms\n","Speed: 3.5ms preprocess, 33.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 33.5ms\n","Speed: 3.5ms preprocess, 33.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 33.6ms\n","Speed: 3.9ms preprocess, 33.6ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 18 chickens, 36.1ms\n","Speed: 4.3ms preprocess, 36.1ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 4.1ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 34.2ms\n","Speed: 3.3ms preprocess, 34.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 34.3ms\n","Speed: 3.6ms preprocess, 34.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 34.3ms\n","Speed: 3.6ms preprocess, 34.3ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 34.3ms\n","Speed: 3.5ms preprocess, 34.3ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 34.3ms\n","Speed: 3.4ms preprocess, 34.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 34.2ms\n","Speed: 3.4ms preprocess, 34.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 34.2ms\n","Speed: 3.3ms preprocess, 34.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 32.2ms\n","Speed: 3.3ms preprocess, 32.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.2ms\n","Speed: 3.6ms preprocess, 32.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 32.2ms\n","Speed: 3.5ms preprocess, 32.2ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.2ms\n","Speed: 3.5ms preprocess, 32.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.2ms\n","Speed: 3.3ms preprocess, 32.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.2ms\n","Speed: 3.6ms preprocess, 32.2ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.2ms\n","Speed: 3.5ms preprocess, 32.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 32.2ms\n","Speed: 3.7ms preprocess, 32.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 32.2ms\n","Speed: 4.2ms preprocess, 32.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 32.2ms\n","Speed: 3.6ms preprocess, 32.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.0ms\n","Speed: 3.7ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.9ms\n","Speed: 3.6ms preprocess, 35.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.9ms\n","Speed: 3.5ms preprocess, 35.9ms inference, 2.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.9ms\n","Speed: 3.6ms preprocess, 35.9ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 19 chickens, 35.8ms\n","Speed: 4.0ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 38.8ms\n","Speed: 3.6ms preprocess, 38.8ms inference, 3.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 38.2ms\n","Speed: 3.5ms preprocess, 38.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 37.6ms\n","Speed: 3.5ms preprocess, 37.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 40.6ms\n","Speed: 3.5ms preprocess, 40.6ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 40.2ms\n","Speed: 3.9ms preprocess, 40.2ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 7.7ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 7.8ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 36.5ms\n","Speed: 4.4ms preprocess, 36.5ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 41.0ms\n","Speed: 3.6ms preprocess, 41.0ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.9ms\n","Speed: 3.5ms preprocess, 35.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 8.3ms preprocess, 35.8ms inference, 3.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 37.0ms\n","Speed: 8.8ms preprocess, 37.0ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 37.5ms\n","Speed: 3.5ms preprocess, 37.5ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.3ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 37.1ms\n","Speed: 3.7ms preprocess, 37.1ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 4.0ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.7ms\n","Speed: 3.5ms preprocess, 35.7ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.9ms\n","Speed: 5.2ms preprocess, 35.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.9ms\n","Speed: 3.5ms preprocess, 35.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 25 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 35.7ms\n","Speed: 3.4ms preprocess, 35.7ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.0ms\n","Speed: 3.8ms preprocess, 35.0ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 36.5ms\n","Speed: 9.0ms preprocess, 36.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.0ms\n","Speed: 3.6ms preprocess, 35.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 35.0ms\n","Speed: 3.7ms preprocess, 35.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.0ms\n","Speed: 3.8ms preprocess, 35.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.0ms\n","Speed: 3.7ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.0ms\n","Speed: 3.6ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.0ms\n","Speed: 3.7ms preprocess, 35.0ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 35.0ms\n","Speed: 3.6ms preprocess, 35.0ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.1ms\n","Speed: 3.7ms preprocess, 35.1ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 4.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 4.1ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 25 chickens, 36.9ms\n","Speed: 3.8ms preprocess, 36.9ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 25 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 35.9ms\n","Speed: 3.9ms preprocess, 35.9ms inference, 2.3ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 25 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 10.2ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 25 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 25 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.9ms\n","Speed: 3.7ms preprocess, 35.9ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 35.9ms\n","Speed: 3.8ms preprocess, 35.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 25 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 36.4ms\n","Speed: 3.6ms preprocess, 36.4ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 37.5ms\n","Speed: 3.6ms preprocess, 37.5ms inference, 2.2ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 38.4ms\n","Speed: 3.6ms preprocess, 38.4ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 37.4ms\n","Speed: 14.9ms preprocess, 37.4ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 25 chickens, 38.8ms\n","Speed: 4.6ms preprocess, 38.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 35.8ms\n","Speed: 6.2ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 36.5ms\n","Speed: 3.5ms preprocess, 36.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 40.0ms\n","Speed: 5.0ms preprocess, 40.0ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 40.4ms\n","Speed: 3.9ms preprocess, 40.4ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 46.8ms\n","Speed: 6.7ms preprocess, 46.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 8.2ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 7.1ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 25 chickens, 35.9ms\n","Speed: 7.5ms preprocess, 35.9ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 25 chickens, 39.0ms\n","Speed: 3.6ms preprocess, 39.0ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 40.0ms\n","Speed: 3.7ms preprocess, 40.0ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 40.5ms\n","Speed: 3.5ms preprocess, 40.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 7.0ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 25 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 36.1ms\n","Speed: 3.5ms preprocess, 36.1ms inference, 2.4ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 10.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 25 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 25 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 35.5ms\n","Speed: 3.7ms preprocess, 35.5ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 25 chickens, 34.3ms\n","Speed: 3.6ms preprocess, 34.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 34.9ms\n","Speed: 3.9ms preprocess, 34.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 34.3ms\n","Speed: 3.6ms preprocess, 34.3ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 34.3ms\n","Speed: 3.7ms preprocess, 34.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 34.3ms\n","Speed: 3.6ms preprocess, 34.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 34.2ms\n","Speed: 3.8ms preprocess, 34.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 34.3ms\n","Speed: 3.7ms preprocess, 34.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 34.3ms\n","Speed: 3.4ms preprocess, 34.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 34.3ms\n","Speed: 3.7ms preprocess, 34.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 34.3ms\n","Speed: 3.4ms preprocess, 34.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 35.8ms\n","Speed: 3.8ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 4.8ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 35.8ms\n","Speed: 3.7ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.9ms\n","Speed: 3.8ms preprocess, 35.9ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 35.8ms\n","Speed: 3.5ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.3ms\n","Speed: 4.2ms preprocess, 32.3ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 32.2ms\n","Speed: 3.4ms preprocess, 32.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 32.2ms\n","Speed: 3.3ms preprocess, 32.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 33.2ms\n","Speed: 3.6ms preprocess, 33.2ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.2ms\n","Speed: 4.1ms preprocess, 32.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.2ms\n","Speed: 3.5ms preprocess, 32.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.5ms\n","Speed: 3.4ms preprocess, 32.5ms inference, 2.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 32.3ms\n","Speed: 4.0ms preprocess, 32.3ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.2ms\n","Speed: 3.5ms preprocess, 32.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.6ms\n","Speed: 4.5ms preprocess, 32.6ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 4.7ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 20 chickens, 32.2ms\n","Speed: 3.6ms preprocess, 32.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 32.2ms\n","Speed: 3.3ms preprocess, 32.2ms inference, 2.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 23 chickens, 32.2ms\n","Speed: 3.4ms preprocess, 32.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 32.2ms\n","Speed: 3.5ms preprocess, 32.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 32.2ms\n","Speed: 3.3ms preprocess, 32.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 32.2ms\n","Speed: 3.8ms preprocess, 32.2ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 25 chickens, 32.2ms\n","Speed: 3.7ms preprocess, 32.2ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 25 chickens, 32.2ms\n","Speed: 3.6ms preprocess, 32.2ms inference, 3.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 25 chickens, 32.2ms\n","Speed: 3.5ms preprocess, 32.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 25 chickens, 32.2ms\n","Speed: 3.7ms preprocess, 32.2ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.6ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 21 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 22 chickens, 35.8ms\n","Speed: 3.4ms preprocess, 35.8ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 35.8ms\n","Speed: 18.8ms preprocess, 35.8ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 37.7ms\n","Speed: 3.7ms preprocess, 37.7ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 25 chickens, 39.0ms\n","Speed: 3.8ms preprocess, 39.0ms inference, 1.5ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 39.1ms\n","Speed: 3.5ms preprocess, 39.1ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","\n","0: 640x544 24 chickens, 39.1ms\n","Speed: 3.4ms preprocess, 39.1ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 544)\n","Mota frame:  0\n","frame saved\n","Tracking data saved to tracking_data_full_frame.json and tracking_data_top_half.json\n","Mota overall:  0.0\n"]}],"source":["# @title Default title text\n","\n","!python yolov10-tracking-newest.py"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":50998,"status":"ok","timestamp":1727091595713,"user":{"displayName":"Sai Akshitha Reddy Kota","userId":"05755385555264415224"},"user_tz":240},"id":"b77fxSK1X9jM","outputId":"a6baf185-b1fe-4cce-a70e-eda299b1135d"},"outputs":[{"name":"stdout","output_type":"stream","text":["Using Device:  cuda\n","Model summary (fused): 218 layers, 25,840,339 parameters, 0 gradients, 78.7 GFLOPs\n","SupervisionWarnings: BoundingBoxAnnotator is deprecated: `BoundingBoxAnnotator` is deprecated and has been renamed to `BoxAnnotator`. `BoundingBoxAnnotator` will be removed in supervision-0.26.0.\n","weights type  <class 'pathlib.PosixPath'>\n","Successfully loaded pretrained weights from \"weights/poultrymodel_feature_extractor_weights.pt\"\n","** The following layers are discarded due to unmatched keys or layer size: ['fc.weight', 'fc.bias']\n","Traceback (most recent call last):\n","  File \"/content/drive/MyDrive/yolov8_tracking/tracking/experiment.py\", line 251, in <module>\n","    detector()\n","  File \"/content/drive/MyDrive/yolov8_tracking/tracking/experiment.py\", line 191, in __call__\n","    results = self.predict(frame)\n","AttributeError: 'ObjectDetection' object has no attribute 'predict'\n"]}],"source":["\n","!python experiment.py"]},{"cell_type":"markdown","metadata":{"id":"Nma_JWh-W-IF"},"source":["<msg desc=\"Text shown above a link to https://www.youtube.com/watch?v=V7RXyqFUR98 which highlights some helpful features in Colab\">Colab now has AI features powered by <a href=\"https://gemini.google.com\">Gemini</a>. The video below provides information on how to use these features, whether you're new to Python, or a seasoned veteran.</msg>\n","\n","<center>\n","  <a href=\"https://www.youtube.com/watch?v=V7RXyqFUR98\" target=\"_blank\">\n","  <img alt='Thumbnail for a video showing 3 AI-powered Google Colab features' src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA4QAAAIACAYAAADaGKJBAAAKqWlDQ1BJQ0MgUHJvZmlsZQAASImVlgdUk9kSgO//pzdaAgJSQm/SWwApoYeg9GojJAFCiTEkiIiKyOIKrCgqIqCu4KqIgmsBZC2IKLZFQMHugiwK6rpYEBWU9wOHsLvvvPfOm3Mm853J3Lkz99z7nwGAQuaIRKmwAgBpQok41M+THh0TS8cNASwgAgKwBsocbrqIGRwcCBCZtX+Xj70AmrJ3zKdy/fv//1UUefx0LgBQMMLxvHRuGsKnEX3OFYklAKAqEb/eaoloilsQpomRAhG+O8WJMzw0xfEzPDEdEx7qBQAa6QpP5nDEiQCQNRA/PYObiOQhL0TYSsgTCBGeqtctLW0lD+FjCBsjMSKEp/Iz4v+SJ/FvOeNlOTmcRBnP9DIteG9BuiiVs+b/PI7/LWmp0tk9DBElJ4n9QxErh5zZ/ZSVbBkL4xcHzbKANx0/zUlS/4hZ5qZ7xc4yj+PNlq1NXRw4ywkCX5Ysj4QVPsv8dJ+wWRavDJXtlSD2Ys4yRzy3rzQlQuZP4rNk+bOSwqNmOUMQuXiW01PC2HMxXjK/WBoqq58v9POc29dX1nta+l/6FbBkayVJ4f6y3jlz9fOFzLmc6dGy2nh8b5+5mAhZvEjiKdtLlBosi+en+sn86RlhsrUS5ELOrQ2WnWEyJyB4lkEYsAGOwBvYAydgBYCEnymZasJrpWiNWJCYJKEzkdfFp7OEXIsFdBsrGzsApt7qzFUYE0y/QSjTZ87HGwbAdgEAsN6cT4Dc32bkUtLK53zGTwBQWgLAORFXKs6Y8aGnfjDIN0Ae0IAa0AJ6wBiYIxU6ABfgAXxAAAgC4SAGLAdckATSgBisBtlgI8gHhWAb2AXKwX5QDY6A4+AkaATnwCVwFdwEnaAHPAJ9YBC8AiPgIxiHIAgHUSAqpAZpQwaQGWQDMSA3yAcKhEKhGCgOSoSEkBTKhjZBhVAJVA4dgGqgn6Gz0CXoOtQFPYD6oWHoHfQFRsFkmAZrwoawJcyAmTAbDoeXwYnwKjgLzoO3wmVwFXwMboAvwTfhHrgPfgWPogCKhFJB6aDMUQyUFyoIFYtKQIlR61EFqFJUFaoO1YxqR91B9aFeoz6jsWgqmo42R7ug/dERaC56FXo9ughdjj6CbkC3oe+g+9Ej6G8YCkYDY4ZxxrAw0ZhEzGpMPqYUcwhzBnMF04MZxHzEYrEqWCOsI9YfG4NNxq7FFmH3YuuxLdgu7AB2FIfDqeHMcK64IBwHJ8Hl4/bgjuEu4rpxg7hPeBJeG2+D98XH4oX4XHwp/ij+Ar4b/wI/TlAgGBCcCUEEHmENoZhwkNBMuE0YJIwTFYlGRFdiODGZuJFYRqwjXiE+Jr4nkUi6JCdSCElAyiGVkU6QrpH6SZ/JSmRTshd5KVlK3ko+TG4hPyC/p1AohhQPSixFQtlKqaFcpjylfJKjylnIseR4chvkKuQa5Lrl3sgT5A3kmfLL5bPkS+VPyd+Wf61AUDBU8FLgKKxXqFA4q3BPYVSRqmitGKSYplikeFTxuuKQEk7JUMlHiaeUp1StdFlpgIqi6lG9qFzqJupB6hXqIA1LM6KxaMm0QtpxWgdtRFlJ2U45UjlTuUL5vHKfCkrFUIWlkqpSrHJSpVflyzzNecx5/Hlb5tXN6543pjpf1UOVr1qgWq/ao/pFja7mo5aitl2tUe2JOlrdVD1EfbX6PvUr6q/n0+a7zOfOL5h/cv5DDVjDVCNUY61GtcYtjVFNLU0/TZHmHs3Lmq+1VLQ8tJK1dmpd0BrWpmq7aQu0d2pf1H5JV6Yz6an0MnobfURHQ8dfR6pzQKdDZ1zXSDdCN1e3XveJHlGPoZegt1OvVW9EX1t/kX62fq3+QwOCAcMgyWC3QbvBmKGRYZThZsNGwyEjVSOWUZZRrdFjY4qxu/Eq4yrjuyZYE4ZJislek05T2NTeNMm0wvS2GWzmYCYw22vWtQCzwGmBcEHVgnvmZHOmeYZ5rXm/hYpFoEWuRaPFG0t9y1jL7Zbtlt+s7K1SrQ5aPbJWsg6wzrVutn5nY2rDtamwuWtLsfW13WDbZPvWzsyOb7fP7r491X6R/Wb7VvuvDo4OYoc6h2FHfcc4x0rHewwaI5hRxLjmhHHydNrgdM7ps7ODs8T5pPOfLuYuKS5HXYYWGi3kLzy4cMBV15XjesC1z43uFuf2o1ufu447x73K/ZmHngfP45DHC6YJM5l5jPnG08pT7HnGc8zL2WudV4s3ytvPu8C7w0fJJ8Kn3Oepr65vom+t74ifvd9avxZ/jD/bf7v/PZYmi8uqYY0EOAasC2hjk9lh7HL2s0DTQHFg8yJ4UcCiHYseLzZYLFzcGASCWEE7gp4EGwWvCv4lBBsSHFIR8jzUOjQ7tD2MGrYi7GjYx3DP8OLwRxHGEdKI1kj5yKWRNZFjUd5RJVF90ZbR66JvxqjHCGKaYnGxkbGHYkeX+CzZtWRwqf3S/KW9y4yWZS67vlx9eery8yvkV3BWnIrDxEXFHY2b4ARxqjij8az4yvgRrhd3N/cVz4O3kzfMd+WX8F8kuCaUJAwluibuSBxOck8qTXot8BKUC94m+yfvTx5LCUo5nDKZGpVan4ZPi0s7K1QSpgjbVmqtzFzZJTIT5Yv6Vjmv2rVqRMwWH0qH0pelN0loyFB0S2os/U7an+GWUZHxaXXk6lOZipnCzFtrTNdsWfMiyzfrp7Xotdy1rdk62Ruz+9cx1x1YD62PX9+6QW9D3obBHL+cIxuJG1M2/pprlVuS+2FT1KbmPM28nLyB7/y+q82Xyxfn39vssnn/9+jvBd93bLHdsmfLtwJewY1Cq8LSwokibtGNH6x/KPthcmvC1o5ih+J927DbhNt6t7tvP1KiWJJVMrBj0Y6GnfSdBTs/7Fqx63qpXen+3cTd0t19ZYFlTXv092zbM1GeVN5T4VlRX6lRuaVybC9vb/c+j311+zX3F+7/8qPgx/sH/A40VBlWlVZjqzOqnx+MPNj+E+OnmkPqhwoPfT0sPNx3JPRIW41jTc1RjaPFtXCttHb42NJjnce9jzfVmdcdqFepLzwBTkhPvPw57ufek+yTracYp+pOG5yuPEM9U9AANaxpGGlMauxrimnqOhtwtrXZpfnMLxa/HD6nc67ivPL54gvEC3kXJi9mXRxtEbW8vpR4aaB1Reujy9GX77aFtHVcYV+5dtX36uV2ZvvFa67Xzl13vn72BuNG402Hmw237G+d+dX+1zMdDh0Ntx1vN3U6dTZ3Ley60O3efemO952rd1l3b/Ys7unqjei9f2/pvb77vPtDD1IfvH2Y8XD8Uc5jzOOCJwpPSp9qPK36zeS3+j6HvvP93v23noU9ezTAHXj1e/rvE4N5zynPS19ov6gZshk6N+w73PlyycvBV6JX46/z/1D8o/KN8ZvTf3r8eWskemTwrfjt5Lui92rvD3+w+9A6Gjz69GPax/Gxgk9qn458Znxu/xL15cX46gncRNlXk6/N39jfHk+mTU6KOGLO9CiAQhROSADg3WEAKDEAUDsBIC6ZmaWnBZqZ/6cJ/CeembenxQGA6hYAwnMACETsHsQaIirvAcDUeBaQA2BbW5nOzr3TM/qUBJoDQBq2CmSzu3b55IB/yMz8/pe6/2mBLOvf7L8AZxEDzrF6e+IAAABWZVhJZk1NACoAAAAIAAGHaQAEAAAAAQAAABoAAAAAAAOShgAHAAAAEgAAAESgAgAEAAAAAQAAA4SgAwAEAAAAAQAAAgAAAAAAQVNDSUkAAABTY3JlZW5zaG90XoFeLgAAAdZpVFh0WE1MOmNvbS5hZG9iZS54bXAAAAAAADx4OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6bWV0YS8iIHg6eG1wdGs9IlhNUCBDb3JlIDYuMC4wIj4KICAgPHJkZjpSREYgeG1sbnM6cmRmPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj4KICAgICAgPHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9IiIKICAgICAgICAgICAgeG1sbnM6ZXhpZj0iaHR0cDovL25zLmFkb2JlLmNvbS9leGlmLzEuMC8iPgogICAgICAgICA8ZXhpZjpQaXhlbFlEaW1lbnNpb24+NTEyPC9leGlmOlBpeGVsWURpbWVuc2lvbj4KICAgICAgICAgPGV4aWY6UGl4ZWxYRGltZW5zaW9uPjkwMDwvZXhpZjpQaXhlbFhEaW1lbnNpb24+CiAgICAgICAgIDxleGlmOlVzZXJDb21tZW50PlNjcmVlbnNob3Q8L2V4aWY6VXNlckNvbW1lbnQ+CiAgICAgIDwvcmRmOkRlc2NyaXB0aW9uPgogICA8L3JkZjpSREY+CjwveDp4bXBtZXRhPgqS9wCyAABAAElEQVR4Aey915ckSZafZxkZkTpLtJienu6Z2cHq2cVicYgHkOALDg/JP5mHD+ALyAOewwVWAZhVM9sz06pUVmWlDpX8vmvukZGRkapEd4prVZHubuLatZ+7m/vPr9m1hedff31cMiQCiUAikAgkAolAIpAIJAKJQCKQCNw7BDr3rsXZ4EQgEUgEEoFEIBFIBBKBRCARSAQSgUAgCWFeCIlAIpAIJAKJQCKQCCQCiUAikAjcUwSSEN7TE5/NTgQSgUQgEUgEEoFEIBFIBBKBRCAJYV4DiUAikAgkAolAIpAIJAKJQCKQCNxTBJIQ3tMTn81OBBKBRCARSAQSgUQgEUgEEoFEIAlhXgOJQCKQCCQCiUAikAgkAolAIpAI3FMEkhDe0xOfzU4EEoFEIBFIBBKBRCARSAQSgUQgCWFeA4lAIpAIJAKJQCKQCCQCiUAikAjcUwSSEN7TE5/NTgQSgUQgEUgEEoFEIBFIBBKBRCAJYV4DiUAikAgkAolAIpAIJAKJQCKQCNxTBJIQ3tMTn81OBBKBRCARSAQSgUQgEUgEEoFEIAlhXgOJQCKQCCQCiUAikAgkAolAIpAI3FMEkhDe0xOfzU4EEoFEIBFIBBKBRCARSAQSgUQgCWFeA4lAIpAIJAKJQCKQCCQCiUAikAjcUwSSEN7TE5/NTgQSgUQgEUgEEoFEIBFIBBKBRCAJYV4DiUAikAgkAolAIpAIJAKJQCKQCNxTBJIQ3tMTn81OBBKBRCARSAQSgUQgEUgEEoFEIAlhXgOJQCKQCCQCiUAikAgkAolAIpAI3FMEkhDe0xOfzU4EEoFEIBFIBBKBRCARSAQSgUQgCWFeA4lAIpAIJAKJQCKQCCQCiUAikAjcUwSSEN7TE5/NTgQSgUQgEUgEEoFEIBFIBBKBRCAJYV4DiUAikAgkAolAIpAIJAKJQCKQCNxTBJIQ3tMTn81OBBKBRCARSAQSgUQgEUgEEoFEIAlhXgOJQCKQCCQCiUAikAgkAolAIpAI3FMEkhDe0xOfzU4EEoFEIBFIBBKBRCARSAQSgUQgCWFeA4lAIpAIJAKJQCKQCCQCiUAikAjcUwSSEN7TE5/NTgQSgUQgEUgEEoFEIBFIBBKBRKCbELwfBI6Pj8toNCqDwSB+/X6/9I/4sR0OhmU45DcalnJcyvh4HNv3o0lKTQQSgUTgDiGwUMrCwkLpLHTK4uJi6Xa7pdfrlaWlpbK0zI9tb6kXcZ1OJ/LeodZnUxKBRCARSAQSgXeOQBLCdwSpBNCfhG9/b78c7B+Uw6PDcnhwGHHj8TjSzTMdZo+n03I/EUgEEoFEYD4CksLp4HEQRUigxHBlZaWsrK6U1dXVsr6+HgSxzTNdLvcTgUQgEUgEEoH7jkASwre4AiRzEj2tgDvbO+XV9qtyeHhYBv1BWAcVnYTvLQDOoolAIpAInIPAbN/aHrcjM/wwZ1jsLpal3lJZXVstDx8+LBubG0EOtS5mSAQSgUQgEUgEEgFG3jz/+uvTJqtE5VIEfPGQBO7t7pVXL1+V7dfbQQLbF5JLBWSGRCARSAQSge8cAYeQOrz00eNH5eEjyOH6Run28rvod34issJEIBFIBBKBG4VAEsIrng7Jnj/nAW5vb5dXr16V3Z3diSXwimIyWyKQCCQCicANQEAL4ebmZnn8+HF58OhBzD2cHYZ6A9RMFRKBRCARSAQSgfeOQBLCSyCWBDosVCLokNCXL16Wg4ODJIKX4JbJiUAikAjcBgQkhs4z/PDjD2NIqU5ptCRmSAQSgUQgEUgE7gsCOVbmgjMtGTw6Oio7r3fKixcvYoio81MyJAKJQCKQCNwNBOzTd3d340Pfq81X5cMPPyybDzbTYng3Tm+2IhFIBBKBROAKCCQhnAOSRNCXBOcIPn/+vLzefh1zBudkzahEIBFIBBKBO4CAfb59/f7+fnn08FH58KMPy9r6WixtkUNJ78AJziYkAolAIpAInItAEsIZaCSDegrVWcyL5y/iq7FxGRKBRCARSATuNgL29XqJdkSIUwM++PCDcECTw0jv9nnP1iUCiUAicN8RSEI4dQWEVXBvrzx7+qxsv9qOxeOnknM3EUgEEoFE4B4g4Lxxh5E6ZUBiqLXQtQxzqYp7cPKziYlAIpAI3EMEkhA2J91lJPQe+vTJ0xgqmlbBe3g3ZJMTgUQgEZhCwOeCI0X6/X75+KOPy+bDzdLtdksOIZ0CKXcTgUQgEUgEbj0C954QSvx82G9tbZVnT57FF+Ekg7f+us4GJAKJQCLwThDQWujcwuFgWAajQXn86HHpLfWSFL4TdFNIIpAIJAKJwE1A4F4TQomfQ4KePXtWnj99no5jbsIVmTokAolAInDDEPBZscd0gtE3ozIejssHH32QXkhv2DlKdRKBRCARSATeHIF7u9hSkMHDo/Lk2ydJBt/8+smSiUAikAjcGwR0OPbkKc+MZ89jZMm9aXg2NBFIBBKBROBOI3AvCWFrGXzy5EnZerGVlsE7fYln4xKBRCAReHcI9I/64XjMUSVON8iQCCQCiUAikAjcdgTuHSFsyaDzBZMM3vbLN/VPBBKBROC7R0Ai+PTp05huoOOZDIlAIpAIJAKJwG1G4N4RQh0DuMbUi60XuazEbb5yU/dEIBFIBL5HBCSC7YdFlyzKkAgkAolAIpAI3FYE7hUh9KH98uXLsvWcYaIsPqy1MEMikAgkAolAIvAmCGgpdB76q5ev8nnyJgBmmUQgEUgEEoEbgcC9IYSSv53XO+X58+dFxwBJBm/E9ZdKJAKJQCJwqxHQU7Xz0fd29/K5cqvPZCqfCCQCicD9ReBeEELJ38HBQQwVPdg/yIf2/b3es+WJQCKQCLxTBHy+7O/th/fRdDLzTqFNYYlAIpAIJALfEQL3ghAOh8MYKvr69euScz2+oysrq0kEEoFE4J4g0C5ev7W1lc+Ye3LOs5mJQCKQCNwlBO48IfRBvbuzG3M8dCiTIRFIBBKBRCAReNcI+Hx5+eJl2d3dzVEo7xrclJcIJAKJQCLwXhG404TQoTwO4Xn16lXJoaLv9TpK4YlAIpAI3GsE2qkJL7de5tq29/pKyMYnAolAInD7ELjThFDr4M7OTnm9/bq4nyERSAQSgUQgEXhfCDglweeNPwlihkQgEUgEEoFE4DYgcGcJoQ9jvb9tv9oOK+FtOBmpYyKQCCQCicDtRqA/6Jft7e1ydHiUpPB2n8rUPhFIBBKBe4PAnSWEWgT1/JbzOe7NtXyvG6otIi0S9/oSyMbfEATGo3EsQeGzJ0MikAgkAolAInAbELizhHAwGIR10AXoMyQCdx6BGJ22cOebmQ1MBG4DAj5/XPfWUSoZEoFEIBFIBBKBm47AnSSE7eT+vb1cKPimX4Cpn5a9d4HCOxHyLhRJGYnAvUfAuYR7+3vpzOzeXwkJQCKQCCQCtwOB7u1Q83pauu6gaw7m19nr4Za53x8ClfSdT9pMX4h/EMQ3MvTVQsfjpo4ZGR6eqp18/psOCwtNoXZL4kTMVNx0mdxPBBKB+Qj0j/pld2+3bGxulF6vNz9TxiYCiUAikAgkAjcAgTtHCLUOOlznYO8g51TdgAssVagITMgWFOv4eBxWQbfuVFq2UBYhXccdKBiM8Kr8K4imMvhplRgNR6WDjMXFxVqP1ZPmnFrTxqMh22EZNnmLBJJxAoudTlnsdinLASp0OouxH9vuYllAnnkW/JlhwhTzDCcCicA8BLwfDw8O43mUhHAeQhmXCCQCiUAicFMQuJOEUGcyh4eHNwXj1OO+ISDDmyZMlbVFJNQs0JDwdRYgba2VDmIGTSwStEoeJYXTQmZAlAQSpQOL8RiiR7kBFol+/6h0sUY4FvzwiHsAIjgY9sshw6ePeDkd8rFEIrqw0AmSGHNs41gS2RLCVgeaATFcXl0tKytrZRVLx8rqWuktLUEeIYmSQ3S8UM8ZtfMwEbgvCMTUhf2Dsr+/X1ZXVrlfLrif7wso2c5EIBFIBBKBG4nA3SOEvBi7CL1WwgyJwHeFgC9/EWJbX/yMG2MlcFs5YtjWYkhozbEAKSQuLG8QMlikljzLhKWO+DZMk64qF4sf1j7zDwfDIrE74MVTwmfZ4WgACeR4eIT7e6zlyMT+iGw5IqQUBcznT955rJxRH1JInMcaCiGNI2Qr53XnReks9sryykpZW18vqxsbZYn9Jchhb2U5yGRLEFudc5sI3HcEfA4dHRyVETdet3PnHrf3/fRm+xOBRCARuDMI3Lkn1GDIi/FBDhe9M1foDW9IEEHYXmyD9km4RkHMfBnUYjcaYXGjHWFNC5JXyWMHwtVlmKZDNbW4dRmWKQkL6x2WvyBx5HeoZiWUMfoTkjeM+bFD1jvr9/th+Ts4aKzijYVRHY7JF6TRoamEkUSQ9LEElZ/3ygJb9XCYqYNBjyGMZPJPJardXlg2FmwjJPNg56jsbb8qHXTuQgZXJIgPHpS1zc2J9dB2IDDaG4LyTyJwTxHw44uWevsCh3FPf9i5p5BksxOBRCARSARuIAJ3ihD6kusLcg4XvYFX2h1TqSWCY8iW1rU6f2/YELSDxkrdnyJylQTyRlhZHeRLItiFCPqi6HBNrW3LyysxJLOz6FxDh5XKxPxJ047DGniIxW9nZ4e1znbjWh9wzUsSzaNNUaujB5JBtzFnMayJkMSxaTVorwwLpXQVoug/rXyaD22fcw3Hw3FYEbsMHa16LhZmKpLWL4MxcxF52d3deV06Pcghw0k3IIfrG+tYEleboaXtMNSTetv6c5sI3AcEXKBeBzN+PMmQCCQCiUAikAjcRATuFCEUYB+8OVz0Jl5qd0OnIII0xWGdYQFknTG92e7jTbDPi1+QM6wBEkXJltY4nblIGINwSb4Ik+GaWtK0zmGl62GNW2L45QpkSlLlXL0h6cZLxqzPYaF60H31civIoDMPNfyFhY/6Fjiwzhh6qmXQn+NEsVJG/ebxhw7VJkj9Ek7JoOSTWOM7nWOGsEL+lCUZ1SENcnSOEZZMeOMIK2YIIs+Qtm8zVPv1y5eQ2uWyyrxDrYZrzDtcW1svi7TFNtjuIMXUlCERuOsIxEdKLfn89AC8wIeeDIlAIpAIJAKJwE1D4E4RQh++WgdjjtRNQzr1udUIyJm0tA0ZZukXf4clH0AC91lrLOYIYTHTsUsHD6GSNK/FsMwRN6LcOAhZhUDyF0RqCpGwy0GWJEzdpV5Zw9rWxeq2urZW1iCHht2d3bJHnVoGnStYyZgp1NgSvqbesA5CDmOOIBklgzqxiRfSaEy1DqpLDGNDRy2dlTZKJKt49VqMSYUaEbVQQnaZZ9jFonnMyFDnL9ru6oW0DokbYrF8DUnWchhzDplvuEI7Yh+S2KNdDjnN4XOeuwx3HQEdP9ln+FGow72TIRFIBBKBRCARuGkI3DlCmGsP3rRL7BbqI/lDbQxpEeIrP5bnvd2dsoN1TkImIXRIpSRKutV+94cbxQeJmMNHms4kJEwwppAnCYIe1gokZgRtcjGSk0PzO/yzj0dQh2/qsGXp5VK8TGr9luhFXSqnXEVLApETZE7S5vBQfhj5Yihq0EFJHSU1GJpzga16+C+slUYToi1kVU9fXW17+4ElyKX684I7wAFNx3mP/AYDdMapzcIijjOcD6kl0HmJSBtBDHeYR7nPvEOti+uPHpf1zQdBELtYEs0bxND2ZEgE7iAC3kM+l7wve/zLkAgkAolAIpAI3DQE7hQhFFwfvD6AMyQCb4QAl45XT/ATdiRge1jmXjx/Xl69eok1cB/SVjM5+su1Aw3VUYuWwWBakKiTIaJdrQKx2ryWvPDaIiWDiDXDNydciNi4dp3/px4DLHD9ckA+LYBWpfMZSZTXeCxCjypB+CxHuvVKBg3h9ZPoKKjlUisg+aplrlYqaTsJZiY7G4e7hsdRjusQVC2dal2Hp0oSJcQdLIUry0tlvOT83QFeSRkaN4LkOedQiycgLUIOx+QdUn6XIaVHDC1dWV+DFK5XRzSQXpfKqPMX1SBDInB3EPBecW67FsIMiUAikAgkAonATUTgzhFCh7BlSATeCIHKqYJYwZximNfzp0/K1vMXZRfLoEs5LEJ0dLAiM5IcVWIWZreGcLEvoQorGYRPyxwE0nmEmudicXeZHfEOH5OQOVCTP7HVWui/wp05doF6uGR4HdXcR9BKB92CX0IuyabnUF844yMIx+rovnpyGEM6LaNSWvaMjKGjkarMqI1ts2chytfhruzTjmjvIpZH60KGtNAF64MEczw+lhiyXiGkbjhcgPipj8QU66NNi0bgKZX94dihc1hAcUZzwNqIOqLRkc6qjmhYzmJRYtiQbHXKkAjcdgS8H53/21rab3t7Uv9EIBFIBBKBu4fAnSKEPngdlpMhEbg2ApAVuZB/vI4OdvfK11/+tjx78m0lVZAUyZtWwboIhPwuBmlGuVpWKxwEC6KndS3IU0OKukEAcQ4jmYwArSJO8iNRcj5e3WGLMOODZiqY/djwN8gSBSRlQSTdtzxkTQuEL51BAInT6Yvxei912Kr5JG4GJURAsPFUUQP7kULkEBJrWrdbrYU9iCU2y2i3eoTKoSRlyGt7liR06tIMlR30R+WI/UXWMDxGjw4CtZJ2F6uFccCc30PKOB9zNZzQbJZlHNLkHMPmfOTmTiDQ3pt3ojHZiEQgEUgEEoE7h8CdIoSenRhGd+dOUzbofSIQhMhrR3IEmdnZfh1kcIthoiO+7He7Wti0iuFMhV+QKwhWXcOPFIiQceaSpOlUZYBQSdaSc+q00AXjkgzVDxaSStf8OyZeS58jTXXe4j9lOTzTtLAwGkueWCKisfyFbbFjbdLUai10XuDxgHK0wRSJLTH8aggyya5Oboy2PXpDRTRbia6hlqn7pEAqYXvo7ZqJjbWQxLB2kLUltVoLtVYeQ/66WAQd/hnWSxSXHO4zTLSDt9QNLII95Mg0QaaxOrqMBVZDyOEBy2msYCncfPw4iKHOajIkArcdgfg45E2WIRFIBBKBRCARuIEI3D1CGC/BNxDpVOlmIiCpQTNJ1YDhxlsvtsqTb74qu9vbEdcLb5iSMSiYZIzMUJxK0CA9cLiGeEmqKhkzTgtXJXh692yaDvmK+YfU5WyisDBSJspxPER4WPeoS10kXc4ZtN6Y2Ece9dDDp85bgqBishxTTvLXGUsOGbIJubKs1R5TuaRPtrcQjLe+lPo3CBxbcoUurZrObVT3WDaD1FiNAnmIDXIbcwPVCbm+42o1dSmKIfK1SipXUrgIke5g+oylKnrMhTzA8yiWwBWcyUiUx+NuLK2hxbCP/l3KxT7E0CU8Hn70YSxdIZYZEoHbjgB32W1vQuqfCCQCiUAicEcRyDetO3pis1lXQGDyfsZyJXjwfPLNN+X5s6c4PdmnsBa+hmhBeGLphYY4aT1rHcJo6ZJMwn8YKgmRwgoWZf1LWrwDMv9OohZOXpBlfCVjzSsicab7N4gc9Yb3TWK0Fkq8GvMdx3j0hCwusTRFr7ccbM2SkkP1QFHsbl2GTuvUhmRkhWz1ZL86hqn6WBfUMeo1c7TDo2MthzVPWDCRpTVUgrewQJeBTjrTCW+i7IfOlO9JVJk0qM6Dpn6J4YB6VlfWyvrqRswfrEtV4KmUdkggO8rl35jyrEhRFsl/xHIeL58MY93FRx9+VHo4rqFytMuQCNw+BKq1/vbpnRonAolAIpAI3A8EkhDej/OcrTwHASnZPvMFv/36m7L9agsrWL8Og3SkJMRG8hdOXCApkpa6AHzEkF4dxUgKI5C/9eSpdc0fRYIgHQdBIh+WN3OHdZD8khw9clbyaYKptexiD2+iHDn803TjHQyqUxbJlM5mdExjG/Tk2cNKp2b9Y5aE6HTJ5xIWWhnrmn8aCNkLQuoLKk2iPlVCLmnGWZ8E1JGtGBzD8qenUK2LkuZDLHdreAXVyqeqksIeJPhocFTGzBfUouo8SQnqUTjSACMqOtw/ouaFsgyRXaW8DmoGyJO4dqnfOZeu8WjbXNje49FgoexsvYihpB9+8kk4nak4qGSGROCWIRA31y3TOdVNBBKBRCARuBcIJCG8F6c5GzkPAa1tu9s75em335btl1ulD6mREjlEMn4Uai2DkjGHRQZ5gsw4J8hJgNI0rX3+DPK2GMrpkEpkaWmTYMme4n0w/lRSKNGLuYekhydR8yAnrIMIgjuy70y7KjcsfMwbXISgSQOrRc8hqVgFyez8RufcGR/DRNk/wg2M7dQ6qV7qrwpa5sbIcphpKG3dyFFLdY6hm8yFVK5DPleWGObJr++C83gHHZC2xmLzOpFZ7LH0xOJqELe6FIVLY3RiOQoylgWnTeJQpl1jUYtiD2LYXV0JgqnFMKyN6CspPGQNxrDAoq8WyrAW4u318cc/KCssch/6IjJDIpAIJAKJQCKQCCQCicDbI5CE8O0xTAm3EIEh1quXzBfcevas7DOvrd8/hASxfIKkqvkXpKTZdy3BIFqup+cSEhAnyZ8Ezp+ETw+eWsskd62lUGiUh+DYaqUbY7WTHEnpDOFABnJpfYYBpMh5grC4IH7um6I1cHm5W2Wja9SNlCCOId1qFrDArYSTFmt2rt4hJE5roVTR9qm41kQJZBBDyaGNIUhrlTtGn2oxZMgn7R2PD4IQ6gF0NOiUfR3AIHcDBzDrWDpdcmIdhzHOXzyCBMaSE+i9LKHF280ydR9BPo+OBkIXGC5DJDcgeIcsQaEekt8lxoy6lujersN2IbkQR/EejXbgloPy0aeflrUHD6IN6pshEUgEEoFEIBFIBBKBRODtEEhC+Hb4ZelbhoAk5wgL1PMnT8vW1nPWGjyIxd8lcDpLITnCgp5SmqB3TefQHUPCtLYFo/FvEMFq3QvHKZAvh5hqAbMeiY9ErI8FzOAi7hIuZYTlEBJWCaiykE9+h3c6hDLqkZhBhoakaXGDH9a5e060O9YpC6RQfaMu6+NHGYegaoHz0DUHl8l/4vYeGmo7kTlC4LjLVkLIb6BekLDWSU0sFg8MoS9yXXi+MEXStQI3sPjpTfUA/LSmdiCDnSWGhDLXrwvROwBjcbB9y1r+wHcZXYYdLIAQPklzH3Lo8FCHnw46tAUd1K2HbMnfNt5eN1iKome6pJa4J/w++elPIYWbSQq9qDIkAolAIpAIJAKJQCLwlggkIXxLALP47UFAwrHDAvPf4jxm59UrLE91sWhJHMwFQiXHcFiltimIE4zKJSegT3UIJtnC2qf1TOJF3h5kSxKnJ9BK/vqxVZaWLefRsYulCwcwhPAAinyHeUp+2FM6x1FpkCh1cQhlHa6KWtSlPTGIJm1YhjBJuvQ0SqmGFEI1yRd5IHaLzCEMT6TWzjqCZQESCQnsM2/PQp1etJo5fG0Z0iC8R1j4tAi6lqJDWiWOC/wksC563+8foZ7ErxI5rZGHe/tl69XLsrmxSYnVSFtfX4MUHoRuekrVreoxBHaDYaIS1AOInfMHzaNFUHmSaoO21OUVhrtCOPd2dwuDRGlLD6sn9ZP/2Zdflo8//ywthYFW/kkEEoFEIBFIBBKBRODtEEhC+Hb4ZelbgIDWOonYzqvt8vzps7K3txPzBWNNQMiODDAsdUEG2YecSbTqVmsfhI1fWPXMz/+YV7e8whbnLcQdMoTyiKGPQy2IpCOFP52yuroO+VqEqLHYPXkkQTFHj2MdsARp01JohdYJEbO8TGoBi5rRDJgMoqc+Wu8kUJJZLWs6aalWRgiqqoVFEqcykEMdnva6kEcIpmore1VvnaQ5vFUCKyF03qD/JYIHkDAdvqivBFGC6bzA8IpKHnG03BHOZVwaQougw0Wd6+fcQgmjhPXB5kY4j9ERzQJytbIqQ7k9ySZEW8I7xiupQ2S12tpmvYkGOabdq7TPtu6yNuHGxnptB/j2D/fLN7/+onzy+Y/LBusVAhS5MyQCiUAikAgkAolAIpAIvAkCSQjfBLUsc2sQiCGiELFn3z6BWOzG8FCHH2KukrLBkuI/xAtPlxALrV9aqCRZjKOESGkNdLioSy8wXBJL4PIyjlTYuii7njKDCGEJ1KpmnOQIOhdWLThQ2ceqNWDeoEMvtQpKAiUx2h5lYuGpUyIIr4ETRT1UjmVOUsccPNKc86huS65L2BIg2mBNS5a1HH96WCLXV5chcVoJqaGpbwmSFo5wIIkW1+pou5yjJ0Z1Ht+orOsBFJIoeetLEBkSekj6wDwQN72ARghdIblgeQQGmziYWV1xfiELzGP9ew2J22S45xpzDvsQZZ3cSBS1+rnvnMzB4BCd9TzK8FPqkkxyWJYggrbVIbp1EXv0YBkK5yuaPmKO4oC8v/3lP5XPf/f3YhH7JIX1tOTfRCARSAQSgUQgEUgErotAEsLrIpb5bw0CDhHVuuQQUUmgpOoQchbEDKIGhwrCJAGs/yqp0mImYepAghZ0AAMRcsiiVsEgKJCSAbKGR42DGY5X11YrWTE/ch1OWh2vSICMkAjqfZPhkzAyPYdK2NRFEoWtsKxA5Bxe6bzB43DqEiLDUtaBWGnLk5SGZRE5EsBKYCF2ECjl+dPzp6TzeMRQT0iVcwQXaIODU22X5EsSKNHsjCCmyFpiKKdqOt9xwPIRfUjXInm6tGsJUirJW6XsEds+Q0dDM3joYDAK6+grrXiSQgip8sfkKxDwBziNEbdDh5riUUeLqqRQ8i0RlFBqfZX41iG3lfiG5RUSLjEVszH6S07X1jfATDIPoUTON7/5IobsrkE+kxRyWjIkAolAIpAIJAKJQCJwTQSSEF4TsMx+OxBw2YSXW1vlxROGiGJdGkJwRpA4CZhkSPaj9QwKFcM1jdL65HBO5w56MIL4SM4cuuhcQef9OVTTRdcV0JJELV4DhlJKlgwu36DQmg+i1g6XVLZ1kkYOc1YdqLda7ZALUe3FQu11DUITSA4SqFaWW4KcxsL0ELplhqPGXMPG6ubw0EVYIzWVY1knIiVVWhVt9wjC2mP+oG1XLzVxSKj1x7xF4pY4wJkppBBCRqV9rIgHPef8Qdy6WPEOHDrKcZBlLYa1vn0sgQpa1gJKnZLA/stBefzgIfrrLVSLH2mNpdBlPFzO4kinO+gvLg4p1Vo5HjsUFp2RE8qhr45m+r0jhuGuMWSVoaj823+9w/DRX5fPfvazXKeQM5EhEUgEEoFEIBFIBBKB6yKQhPC6iGX+m40ABEaCs/XiRdl6/izmoLl2noQwLEsQn66EjW2QIkhHEDPidO4iAQnLnUMjISebDFPsLeHRE/LTLkuxBDmpC8lLeuri6sqWUPWp2/mEesp0iOPuzh5yIVsQIS1iWsIkZ1oazW+tOjRVDTVRJwtI/LTcmRCkCMLkyNAu8wpdxmERC6JDSrUCSpBWIG1jiNVwiNMYLYKU7+JYRo+gHsR8SeO0Qob+VEM96j0eWaekjo2WU3QbskREb5GhmgwH1VNpFw+izimEsgUBHQ17MaS036W9g2rdi7mFkECtf1ooux1IIDo9e/kSS+F6zDV0nqLDVGNIKgTdZTG0nkoKjXPYrf+0Mkoe9ZjqMFXxUN+D3b1YkkMvqpLwznihbHOu9Yj6Kd5Hl3ByEwSS/BkSgUQgEUgEEoFEIBFIBC5HIAnh5RhljluCgBYwCYhk8CU/iVQfwhYLzks2oBXO/ZOwQC/CAlXn42k9gwlBAE2XGDrfboNhmhIbnadIRmIRdo4lnAPqcVhlkEwIltY2HcZoNZOBOYdvFU+Zmxtrsfi6hHANMqZV0Tq1jjkUM+YEUiJ4IDpKDk3TmqZFzqGnjCZtOE6dL7iCMxtJnev49fAguoJFsYfMMYWXdSIjkQwWqFzi0WWIXOcR2nbXQJT9KfoYAuZv4TgoV7RdJzkStCGks8vPlOCmlqHOZeobjvACKga0K4aUQu7Ew+GpngctfSwkURYlfwej8gJS+CEOYCRunhPJXjirIW+P5Tgk2EfoKaFmFz0lhXUBe9Mkm0EOsUzu7x+UjQcQazAPrYdHZevJt+Gp9OPPfgQJxnGOQjIkAonAnUKALsie7YJgDkKzqQe39e/lrX2/LZsF8WLk368uSp/V521qvKwt8+p6kzJvo2Nb1nrn6dOmv6vtdPu+i/ou0rvVRT3a/endqbhGzEzOKeHnp0xlyl0QSEKYl8GdQEASoiVQIrj17HmQM52ZHGGtc9kEh3vWX+1aJUouhG634txCyWO1zmEpg3xsuK4eaYd4v1yEaDkPTicsypOcxE+SiVwXlpc09iErC1jFVlaWIq8yHz96FNYu1wyM4ZxY7axHAij1lLpJqJwzR6HQQ4cra1i6nKeIaOp3riH6Sd6sE8K6xjDWsLotIQXSRoMhX5JMh5MiC14qKQ1yRjmXnZDk6qjFdQ51fiMhNJ9rBKof9DHIZAcLZIEgdlaXIKysF9iQU+f6OUxzvACZQ6bFF8YM3XSfOK2dgwWIm/E00LmCBq2AzGgs2yz58Qg8mEpYjpEZS0lggSyLFfuubJj/Wv4chqtuYtDDiU8cg4WkUM+ji4cQUYabqoOeVvXw+uzbr8sS8xgfffRRta5G7fknEUgE3hcC9i/fZXAofO3B3dpd0F/b//nzQ5drs9qX0U+GbvYjoeN19Tybv9YY1VLr/DA/z3Ts/HKT2ElWa2hqaePi0B54Nsxoc62PYWelnUgHu1NhOu/0/qlM5xy0OrbbNttJe2ZT2hzxUAgszssxG38V3c7PY0qdBHGiQeydX2SSsY6zmRw2xeYXnNX6dKl6NL/kvJwncT5fLwreMxXT2VzTBS/XbjbHdOlZyfOPZyWYq5ESG/+Yx6c8P19YvLZ5h1rgd8w7By8fk/RodyOyLUkiYV49NeVd/fWj9V0ISQjvwlm8523wwX+Es5jnT5+WV8wbjCGikAqdwSzwQiD503FM8z/2Wy+hksERLxDamuwoJY+ry6tB2IaQlViHEMKjNSuWnaAuu5d4VNIJuMyEa+k53NG5g8pYgQC5LmGscwgpe8jQ0X7/MF5QrDfIKHmdB+gwUucAajWU+Gn5c7uKFdCOTGuaRNb67BZjiCfDQrXSdXwJYsioDmWWVpAFqVQD5dtWLXRjHcsg23Lqs8D8wq5EGD2UX62FvjRJ9mg/jXNm4QJtcFjrKjpKCA8gvAP06kGGtQyO0akTFkfyM7evi3Ma6fURC8zrUdXu0TUK91kiwiGfttVztP16myUkNtGLIaFY8hxCq2XV4bNufXeLIbvoG/+QJXl2DqLk0LbFPMMD10MMTdU2rINxDXzzdVnGsquTmbvSSQNlhkTgHiNgj2uP4l/2g/gxX5uRAeOjvTI62sGJ1XZZONwpx/xGR05yduh8QxQlhWeCMttQ+8v2qG5Jr1XGof1ie2jJ6dJtuTa9zXlsoUvDSamQOn0YtZyKiPfhEwql8LMU5HSJixQg5zmZK6mo9PtcCVd8CY4q/BPEfEraTN0VrZnIyN7o0SRNo1qjpmMsMCNj5nBKg7m7SptX5NLmRqFZXZB1QcGzuU+rNE+PucpNFYsPrm0bpgU0lYU606zRPNP5lBV5T2un9/TpEPfidIT7keUk32kJZqgxNRv7sxlO1WEuMhgn8eN5f7yIh/cu70ZL6+W4xzO+y6/3sG6NX+iRnWky8bY0K9z6M1yEQBLCi9DJtBuPgGTwkCGEz58+KdsMSxweYVXjRWAMiZEsSSDsVupPT5wOGa1kSCclOpqRkEnCfH47fDGGmkJ6tI5JPhwKqYdQCZKWqhFeOLUQul6fRND5gmsQHtcN9NHlsgld6hn0mXfHy0lnfbWsQ/AsI/GJ+XUQruoYBoJIh+cwzyCG6KKlTfKn1loP68tI7Rxd07CH5Q51IW4u34CFjDxa4YyrX8VpCLhU5zhuIWgcK8sO1jmBynZoqE52JJGSMzgee9beEE+/tqPbiuXEBv1duP742OGs9Mlg1ZPAUlCSqAXUtg2wRA7BPjymIldSWy19OJCBWLpMxypeTRep23rFOOSDQaxJGJZFygdxBUPOEYNAOZcOI62WTM/bkHyuswgVp+2QVUjj9tbLsrr5FIKsI6DleuIpmyERSARuMQLx8Ysh+hDA8d6LMt55Wo53nrB9UYb7r4nfLR1JICMWOhJG+gR7ft97g5fZfTbh1DtnGzm7ncofSXaKTTBJGdPv1PZfhijWlJ0UaY5rjrN/2+TIHweTkjWzhxOZtU2tlLZsexwd6ZUaeFJi7t604Hk6zRaazj+b1ip/Jj60PRuLrBMSNYMFuS+syvQ5GWbjZj8RtEXinLYHZzW7MCbqmFt2bmTIOkOqZrNOHU92pxsT8Jz+KGC+ebq0HxImcqZb0368iET+nIG9fli2SFu+3YaYUwdV8Kn7o0bxt7mYqc8is8Um1UaCf2ibjSEh9GfXD9zHkL5xZxkiuFIWliGFqx+WzspHfIX+uHSWP2bs4wNekCSI9V1nUn3uXIhAEsIL4cnEm4xAkEGsTk+fPCk726/4cGw37/w/hw1BgiqriibYtYQVjmEGdp+xwDoWM3uaShLpbiBNsSRFEC2GSmqRovPRimeo6/ONIDwSIi1hWLQkIpI5yMsSBETSYqcmuVtZX4MUYlmTFEJ8WIMdZzCQJpTxt9CQStU8Jv/Ijo6XmREvNuooqdR5is5hHEbaEkjnDWoV7NCpGh9k0gYGsapdqu2XFJJNZSKvcwW1zDmn0CeGD91KJEmPjIgIkuhwq1reB5bObaqlUOujxIzhrFoB6WyXyWZZi3eYqNgh3nmKQ4adgg7ObpbCUquFUjIczmDEFYekJMfQD+dXOifTYaYOeR3QfoeNGhxWKhn33IaH1mgD55i2aQmWwIcFFflBdCGrW1iK1zcflEcf8pCgzgyJQCJwSxGA4B1D9kaQwNHLr+pv+wnepV6Vhf4+IzoY3UHfY3/qh63o1/iQV3vB6GKi4aScBLN5NB3ZFpiOm0ghK2+3dGunwuwLryLM0sa/Uc8zqWNGoUn8SZtUxmj1ckuXeOVgmenslje0cW5buRWnNiWyxZ/4+EimtmzV4iS9Smtws76zIiZYTZdy/yJCOJ1XHS8L87I0j7uTourHUfurZepHhakGnuSf2Yv8MxXNi5spRn3zCp3kmidjts3H7QXXFpsROYk+J76mm0jrp4WfOl+kxwfjs2e5lT/bFI9PiYiMzfUS9dD6Gd0jf/sFJ/Q9ocxxqJamHzsCAN8Ow9eMCuDj0Osv8KEAAVx+UMYrkML1H5aF9R9BFD+hc3iI7qSdUXCiee40CCQhzEvhViLQksHnLCuxwzBECYNDIl20XCueJK+u5UenY+fD/5gjCNnTqhdDJemuJG6SKp9WkkG3zucbIMc5g6ux7l21EtpZOS9OS5/cS6cmDr1UfpAs5sKtMEwyyBUZlpjTp8MXLVlD1+Ejn/X6EjOmnPoskm7/hrPMID70ckEK1d8F59eYP6cVLCyLvPlohauEsJI8yVDtW9XOtsYG5SBnNNpYu+Bj2GgliegacVj4IJvxYAQvH9YxJBZlJH0S105PYSGVfJQnzl+HuXtLXZfagJiRdx1nOZLvDtbZxQ6WUy2J1Ow6kB0I2zpDOPewCoaXVgiaBDbmCRaXqcA5DkN0JXsYFeO86TRHXSXgfTp+80uOqRrdsJhSLxmQoROaARZSLIE0wGeLQ1AP9/bL06++pH1LZf3Bg5AdDck/iUAicDsQGLPkzwH9+vaXZfjsn8vgxW9Kef2sdAb7MR+57dPpMGqfd81W2VdMUxkOpoK9pmHqZfRUOn2lybVzdW8Sask2cytnknxm54T4KI78bdGTnaaMCZHjjAz7brO3tcUz4EyuGmFe09u8bbbZY+NPx00UiyJBBqm0xk6lxW5z3AiYyJnK1spv02aSoo74c25CzdKWPylwstcWneEcJxlm9kIWhaJcI9hNK2cm++TwXLwvKziRcP7OZfXHuT+/+DVTTis8i5vvKRFOZ7uwjnOzhqzTqa34eJCbdE7jo1Tz5wR77sjxPh+K9tl8W45f/T3E8IOysPl56Wz+DuTwp7hR+JB3CN4VLj2jFzbpTicmIbzTp/duNs6HUZ/5fVvPX2AZ3PbDVcxzMy46CEiGhMJ8PnDjXxA3SIdkEMIC/SMe8udQS/LEfD/SJB6tkxkdu0hijvpYAyFyWhWdTyexC7nUo0VrmZ/kSyLlbL8epGXEECeXqujxwqJTFIMdqsM3HVjahaCZX0cqYeGD9AQvhRkuYMVcocwi4+VjzUHXGox0h2dWq6DHzgeMfrHpSeN1gf142QkdqXTSq5OzwWK8WAvYDgmhXxiVZbqLxx+zgKFE0w5Zy55ooXaQNQlfZcOk8wm8DynTU+rCAkM3+BeEkDLhyfWQNRvZdx1HcXQe4lidaXiHc6O18shlKmwblkBOTpwzh7YOR8w5hNiJITMfg6RDC6kLSytAudyEQ3Yd9qsFtZJfz7eksYNzoS2Wt1gqn/dYnxBHM9MvXnEy8k8ikAjcEATsOWrQN/H44HUZvvh1GT79h3L8/Nel7L4oHawBfpRyaD8d1URvezKPao9WoyVrkSP6uxo3/dc+4rIwLW82b3SpF2ZoEi+o57z+aPoF/7SeVeZ5IqfLTe/P6n5a5mzqCbL1sdEQ0HPaWmXNaGRenyVtaJOnok4lNelttjZtsp1T7vTZprpzGhUyG8FzxTSVnJ/mc3Giyfe2057PVpf2eFahSXqT4HGbt02bLfPOjluc2u0VBc9i77HXXquv4s6InC3U1mU59utHbnw2HHzDUPKnZbT9y1I2f1o6D3+3dDZ+hhXxI/Lh+K8tl9sJAkkIJ1Dkzq1AgB7OpSVev3pVtl8xZ5DF4yUoOpCJ4ZowF9NjXqCMi1vfB0YMKzRN655z4yATzr/rteQRMlKdXPIQJJ+WOPO4uLwLtB8hXwcyRMUQ0SBT9CiuBehN5PxC61mA+KBQca3CHkRuZZm5eehs/iBJ5NeC6Jw3Q5RRvyCIrk9YHc24rmD7oGvnAPbwYKo1U+thtLV+p669oLKgRUGCm56udqwSvKjKyiBSlVDGQ5V2tvWbRZKmxc5e1VcBLaRortT45xCoZciqpFhcbLNBg90ybT0eO7QWgmYdyqGk6xhqedVKqNxDCJxy2+B502voCvljqQl1InEFEukw0r191nEkf1hvtf6uHFcCbq8PN9XS2ANzh45GIK84a3F9xlIUK9T7wx//JHCtLzltzblNBBKBm4GA/QtheFCGW1+Uo6//rgy//cfS2Wd9USyF3M50S/Rj7JjP/qEN7X67PelZ2hzvdqv8qMMK24O28resKmgxZGrSX7+lvDcpbpOu3pzQ+Pxqri7orIzrKXK2/GUxrW5xMqcyc+z1dg0QpgrfrN33ch21uE03NQAzYhbM6UxX3Ef+RNwVi5zK1uinDHsLPhszupQlyF4w2mDnizLe/FnpPP5jrIY/Y/4hHs/9qp1hgkASwgkUuXMbENBKtw0ZfPH8ecwh8+kZzmEcikkDdDaip9BqQasPLPftqsLyB+GQ1Ul+lhYhMGyNN1jeAZVBBomXCEoCJR3OGSQqSIuEQ6tgLBeBbOfXaSmUpBnnMgjOK9T6p+XKgksQJjsoiY3eMSWsBufsuac3U8leLaeFDlJF3KJDJMlgnnB8E72lTy3LIRHZ6uV+EEjm7tlW/9Qa2G3KG29qWNisn4jIGxlDWsiyYBxZB21AGwhW+Pxkr1pFzaEGnR46Qqa1vEpiVdahnDFkVz2QpYV1BO461pFwuo6gJM+XPL2gOjxXgrwMCZSUa1W0rZt4Z9UZjU5p1HSAnHDks7IaOMVLInldXkPPqbYl8GBPy6/De59+/XV58Ohx2WQNRJoTeVqirXoZEoFE4PtGgA9tu89K/6tflMFXf1vKq29KDw+iYQzku5L9TPz1Bnf3msE+wRBFo/+8TEDtC+flqpKalKpW7VSUL2lt6ppX9qpxV1LxqsK+y3xvcG7OFGkbHzieSX1nrWklnzqfjfT2FLaqXLfSNy13Xj1vK+/K5VtQzlOE+CtkuaD0e0xCsbadvplMh3jVwrfAQn+rjF/ggGrvqzL+4I/K4uN/WRZWPuO7tfMLM4hAEsK8Dm4wAm33U29wh3q+Zojo02+/xbPofhAwrUgO9zRISuwLdEJi7+DD2TllWgclFlr+JAXsBKHQqjSWDEJoTDDJdQXDuQx54CxBQIZYFNUkCCAWPMnfOsNJ9ZTZo7fxJtI5i95GtWytsA6g8+fCOQx1LkIG4XaQH4eTQrDwgCABqkNF0Y99CaIdl5bEGtDdeN6KYk4i8aZJINHMJqCTVjhCPYg2O0k7WtKIiWUqyBsWRfDxWDm+vEQ58kdW4uTKEuI2hPVQacgPJzPskw29ILfoJvbqE3MwQ4hLbkgfq5yx6yFSj5gesk6gnkdXmX8oDocMH43XLnCX0A06da5gBxzbdkry1hnuGedO/TrMKcTCiBp4E1uFPDZEn+uiS7taq6tDUZUhwTxg7uK3X33FXND1OD+2zfYkKRSJDInA94wAQ0H7z39Vjn79V+X4239gCZvX9Kfc4LULj1vdHu28cDbF/uZ07unj6f3Tuc45amSdrafJT7ppZpvud01t492fhBndZstM8r3jnbbdVc+zws9t39msk75zpim1weafTpjenyPrTNTkWWbKOVpNZE52zoiZjpgnpY1rR42EJP5Y/WWhxXJevovSzhV9tWbMq+4kTt1Pjq6/N6PDPFmTtrnjOwG1RLF4qbp+lVFipt5rS2nKT3QLrar2bRtiSz6zMiGIIel4J36Co6r9p6XzwZ8xlPQPsRY+pGTzPnVtJe5OgSSEd+dc3sGWtLe0lrRx2d/ZY3mJ52WPBc4leSOIxTAIQlAkCA2Eq5kTqCXOJRDMp3UvnLnQJehwRkLgQvNSFy2AlnMOnU5iTFOunYNdniTUeXXKWmbBeefDLTOPbZWhoC73oEVrCYLkPLolHcBI/iRVDAHtLUnoeL1B5thhrOSVrEoEJWQOudSBjZ4wJTASQIlWw8yi3pa8RDxn2KGlBkmrJLa+LNVv6Khb05rOj9y0T8tmU4ZUrZtBBpVhPomhO4pje8x6ghSIOEkk7C2silr8Wt10PoPW5HdZjur8ZYExms6ftB3VKipJr+1CIkESiNMf9hw+qoWwrrEoyXV+JpbAAXMEqdPygRmyV8krCdzDm6xzBUeo5LzFDnFaJCt5RAesuAuQdevy55xFybRYv3j2NKyEn3yG1zHkK7vFlawZEoFE4DtHgD6mv1sOv/pv5fCf/6IsbH1ZejiQCqugutROo91U7Zq4WVVr79ZkOSdPpF6U1naes8KnjqfrMXr62P154mfj/QjWhu+KDLb1tVs1mNU90pqEy9ZPPGlBK3F6e9Li6Tqmc7h/sYzZ3KcltUetjOnj5lHWCCBHS1baTKRM7Z5U1AprS84cn2Q8f8+6rx3epMxsJRfIuI5Oc3Fp65qtw2MKSKhnk9oiV9lGnW8h4KKiprVtms636PSc0S4Oq/6ROYbb5Xjwsix+8Oc4nfkB+e/3ENIkhFe5ajPP94qA5OFg76A8e4pHUSyEQWDQyKGGEiI7pbB8QSUkMVqItBZVMlidwTScKKxREgUteuaTSFA4iFxYrKQ/Te/hUFTXNHTZCT1yOhR0LQhhtfhpn1qFWG5gKTRNy1lY8yCd1QCnDnRKVK43Uj2Kqp/izRfrEUIYJWWSMclNVG0vLtlr4loLYZAkejjjJWMGOzyz11C7Pw/NYwhSF/UT5z9IaKRYfLo3J7LKZAc8FKoIB4ya36GjbtVBzFzD0Di9l3aw3MXi8uislXaRYzXBnkcVlNJJDZO4bfmB60SSKJkbKQesJGlaGiWFknItg1GnJkuo5/rGetk/YJK45wnSF95FIdjirZXXa0Cr45h6g5D6sQCnPscOJYVwO2/x26+/KpsPH9QF620Hv1CkwcnDDIlAIvBdIMD9uv88rIJ9LIOLu0+5y+mHp+9FbtC4R+kI7Atuc7AdN6UNgelbgDl9is6KeVvpZyVeK2YG5CDcVxTgM6kNF7exzXXzt1duxwxul7asASvkX7fsjPCL7o3ri1ba1Ink6DwZvh9pLSyH35bRE6aw8HGq+/G/YZmKz5FQnQDOqHovDpMQ3ovTfHsbKQHR6YhzBre3qxOZcD4CeTAteA3ExuGDEqchHk7aoZYjrEQSRUNYkiAIdhCujWeHcKSTEi2JkkeJGonRgbATVkIIiUsXOAx0meGPWgS7kB5vmiXqcmjoBksurLNQfCWYEBK7E4dKErRK6mW0i7dQZUuWrC+Gi0rMJHUkVILXdmUSJDLTr/mlti5roTQiJI5qON0h03bzRxT5xcRgO9wVqyC4ESEWRFKnZNlQc7ute4EDWLQhlnpA7xBLFsstMOTVxZ9dFkKJ4u4yFWHZBIGQTT7bezw+IgPf3cDEdtIimTxl8fMFKXRBezGxfs+X8zkljrVNxELIuziyWV9dL692XofGYeVDtkNWzSsuWi39cKAa0Xp0dahpryOZX+KDwi5Djb8pP2FtSK22NihazFa9MiQCicD7RiA6oTJivuDBL/9TGf32b8rS4WvuYPtl+oZ5t6E36bz466qKjOg755aLnmBuSu0k3kyHVu0LpM+v8/uMbZV+FzrMkTUn6to1zTuP53bh1wT/XDlTWvosnM03ezyV/eJd9TsHlKvKvEDExXWbek7dbcGr6tDmf5PtXDyvLUgU/F0WphrM7qIfnQevyuj53/DueFi6n/zbWKICrw+XCbqT6UkI7+RpvSON4v7WY+gu6wzu7+6UIcQQzoOFiOGIvPzbmWmBgg+wC/GBJHi7u5i59inJYF2T0GGCJwRthXlsLgDvUFIJkAuj2/FpwXOZAzNbj0RCJy/LEIhYwJ08yxyvr7AOH85UVklfXWbYKB3RWOsWZSRHUCIIjV5G66Ly6qUFrM4bJE3V1VvF0dihpGHJYxuEibT6z2NJVy1TU8kU6UIAIVbxOEao0jhUpu2VHNZkBUuApKu0T1LXkL7ITgmxskyQ0ag9xFR5sCwtcJJkBBCHzpIqXuQkfZXmWQft8A+hzudzsjY1DiCFRLuUhjpUi2DF2dz9Afv+p3OWFI4g0dhTQx/j5K4bG8wlxOPgkVZbFHXoqYR7MKTexvIqUsEIidKRkNfL0eFh6THfU2HPmHv6wUcfl4c6mAnN+JMhEUgEvhMEnJ883HlS9n/5/0IG/7r0jiSD9lbNK350QO9BFSqxnrcKdlRvLeStNHhvhW3WXDJ+3RrfBT5vIKM+866r7M3Of502vQFkN7rxV25PZPRPfee4UqNa4fY1zUXv+9vCeK8MX+HUCucz3R8yJWX9XxB7/0hhEsIrXUWZ6ftAQHLgENGd1ztlH+cg3vhakyQvMZCRXjMsVirnDU6QCLgfQwbJ38FyKKtxTpkhSBqE42jIcFM6hHYJBx29VGJjGYYvUtbVBbuUXYZwrGAd1Eq4ubaCt0wJoRZDF49XE4lXtYwFx4vORmoH+TGNejuwPuuuliypS33DQJVKwsgU5C5KhMr1Dyu/W/7EikW55mlRjWFtD2frapD6WWvN5rZqIQeU8EnIbO10UD/DSWzNUQmk5YLmBhmjEZ6Kih3tDiJHizpgKPH1/FjjyoJeRxF64HITfdCoy3NY3DzKCKgo2++jM+fWOZsjSXmcDwv7wyrLeXywsVFeu8B9QwqNj7mhQ84Z+o0ZmmqcxHyR89PrLZW9Q9YyVArzD/uDvfLk62/KxuZmDNclW4RKnE9a3kTnJhFIBN4Sgfauskca7WkZhAz+5q/KEkO0fBGLpSSavjtGBRA3Hdry03Hn7UcfiSwlRLmmn2yOzis2P35OxdOamTwny1xZV803t/AbRE6afe2y0y08W1i5s8+NNlctGRnaqKttFdqc/ysDOit5csLt/d8sXIbZKTXf5oReoexlulzUwmuVvYIuBIiZRAAAQABJREFUc+tqK/HL9nSYOg/T0RfthwpvqEdVw0rb30U1kdbqbTb32+uOQ9/bemMc3zGv0I/NC5/yjrT6M/qndvjoGzTOem5ZSEJ4y07YfVFXwnDA8gRaeF6+3MIhyX5Y6xwW6BBP+4BKenhE+RLAT6uU3jqr5agSQJduiP6G9LD2YdWzH9CzpWVgEuHopQchGo+Y8yahMR4aoRVsFWugyx+s4lBmjX2HiS5jHZQg6l1T2T4k7V8kdN3KCOM0LZAuYW2XmNA0GKrzx77UrP5CDxsE6VGeQbKq0GqxM4Z0dIuXHiuL0G45YFet/eplbGRp89GeIIKhZ+Qib93WlyiLU8r6pkRan9bNEE667XARe+f9WVyZMTwUhzIhj8K2D17HOaCtDi0Nxss+2yMsgdC9skQ+iaZEMqjxMfM5F7UOVgc/Cm8xUZ8FiJ7zPfXuura6hmxIHnrV8lWfIJLkcV6jaxDqFXUJxz9ak7UqLiwiE2Evt7bK7s5OeaCVUOGEWlfs5p9EIBF4hwi0vc344FU5wHmMlsEunkT9OBQh+troTuyB4tekvNXGeuvd/VZi7nxhMarn6CpNJfc5BSZYTwnzEXZxuDTDxcW/49TmcfFea712HVMQXrvs+2jJd3njXaeueeCcivPdinfLMWuhQgqHrvn86TLDmn4MSvZWU0C/D9xuiMwkhDfkRKQaJwj4gr7PshJ6EN1nYfID9lcZ9jdkzp9ErQ4LhHxxQ1f6Q8/gvmyEGEmMBCU8YxITRAOZPR2/QExcDF3SaB6JSngmpegIwmKcRMFhpetr60EG1/B0uQYBXIUM6qEKnmMtSDY3/9jVbUpUL8MjSBVDN2RJqWI4q544SQ9jHPVxxK9KsUwIsh38bM/pUHNGnBU26WqhhFobWrXFQkRzYP7IRTEySKTEOOYLnqqr5ovMakYHGZjSgCCmCmc9Qgd0ui4h1Ub7oxFaSI2w/VYb8ws5ps3O2TSy05GE632UrctPKEJw+DkcVVLogvehm9FkUL2Ynoh8CaqW2TXmbY7xnrp/wPVABtsxoj0jlrXATliPkWeR5WW8lEL+xziw6WLZHTJ8dfvly7ASOlR4giPlW4JI1RkSgUTgHSBgV3Dc3ytHv/nrMvzir0r3aId7tAZ7m9qNmYt8RNTjOLzyn8l9qwDC5LiRUKU3BxdsamnKz8nTiD6T2Nblc+CmhImuKHQenvPaeJn+UcZmujNpLjtNJZNnz2WCvsf0Fo9W/fa4Vak9nsbQtPa4TZ/N3x6/7fa8es6TO30qLDur33nl3jj+wgraq6pF9/xaFBO5miJtyfNLTKWQ+UI1prJOdi8Dp7l4lbuIpXDw8u/KsLdSuj+AFPY+QYyVTqTd2Z22b76zDcyG3S4EJANHB6xNxe8VTmS06EjIRswl1ImIQUIThCt6BQnZCSHSc2UQCXNwA4djGUiG1sMePy1z5nGdQIngSm85vFq6oL1DFZ1/2ItlJZbLAwjhGoupW07nMLFWIITHl4BY9gGCYq+mzNpXYPdCbrAy2Ejka3sRyyFHvlSjGjnqhLfMtodD1GTf3UmInorbNdpMrBlDZpVz6oWEOuJFxbyyIuVbjzpRRgIVZJAtGeJ/q1PUZ+cpqY4t+9YVvwZn5HSQGXpT0KQo3+hm3R2G01qH4MRwWci0S3KsMOdyCcvrMk51tL6u8AsvrpwLz4dlPWcKFdd4EoOzsLmIvRpL6l2OYoWPBFoCg8ajq1bHKOIeZcJhEHmDkJIQw4Zp18421ordvcb6ST2N3ojOkAgkAu8QgeNxv/Sf/mM5+uI/l87BFh/DvENroOdix9+7CfXePyvrpMazacZMlwtt3p1K8yu8wbFX7grnYBTPQcBst/OaOfdcWOmVK54jdY4uc3LdmqirQjE331yA5zT9KphdJc8c0e87am6737rSBjg2ftjujhiF9Py/lvGrv+bFgbnONxSLt272jIC0EM4AkoffHwKVDDKOG4tOn4XMv2GpgC7kQ+cvehp1SGYMwYylE7hDJSxxA9e7VStQEAGIgS/5Wp8kaN7MkrpYQJ0yEg7X45MouA6gecJjpcsY4JVSK97aClZByEqPIaeLLGHQwdNlF9Me/IJXGKmHHQfDShvvpkEqqEdNrM9t6KdFLYgbBApdTTOe3Roio/EIVuFok43yeJKrERiVn+SRkCLbbEFQ2T8J7IcibRzbMMm10XZ7/Iv6jGvyhTDL8r9RpzKzGifZRQs+o1V8nWvpoPtJ+zn0Y5vDM+McmNl6aZ9cWSve8hIyOLB+128cSgYhi4MxrmRGi+SD5GEBjJrQR9K3wFxKSb37ri2oSK3Gkr4+HwpGCzq40drrPELODNhodXTe5jKkXo+yfZ0Roccuc1KfffNNOBNaZUkLcQr927bbvgy3EoEVloDpeV/zW2IO6SLXwxLWaMMy93MblrAWTwevIx1YGbwnBsx59cOSnmr1fOu2f8Q1RD/k+pkZzkGAe98uwbBAvznY/rocMVS0gzOZHh2gyYa69d6O269GXuNvU0UtoRBC9Dexdyo1Yi760+o0KeVOG9kWnDquRLYmTMq0+aa3U2Xa6MvW+Gvzvc227covkhF6n9JvtiWnEu0iT0KbNeKmE06yzO61udqiNf3kSPnNaZwpOpPnzImZyT7nsLVcqkMr7VR75pSZjTpfv9mcZ4/bOs+mnI25il4tlpZuZUe59uCs2NMx0wJOp0yOZvVoRTus8vxA2iWyPRdmac/J+bKumnKRPlMyZhs0lRS7pscFWBsQH64Gr1mS4q94cHxQFh/8GYr7zKjpvgNesebZmm70cRLCG3167o9yQQZ52XKumGTg66++jGF+Cy5NgLVQMhfDLrkf4+U9LEAyFr2KugA9S07IEoiQNErZ4gVh8hLCywh5jl2fjnxao3ouB0H58DaK3EWsg1oBY7goFijJivTCeYNLECDsa/KgkCMB88XAYakyJNOUH6SVMjqmCQc3xksI1SzKypDoTNSVPC1xqm9GpNnLtJ2THZT7hth3h2OJn/kiKJ8dx1W28iWTyjfPhGQqR3zMZHwVUMUb4Y9gfJMWx239k23E8of8VBMDRckfxDJi7SoRwb/wghr7xok5biR0GEPZSGeV+RHnYbiMUxnO+aKW3KFkD0IImXO9yFYXrXtdybqlafCYOYE6kdFKOD7max75HRpcPa/SUtvNC/5YB0ANQejv+9LPrz9kGZOnZYUlKJYhEB10yHBzEehC6iX24RCKrXND19bWyiq/FX+cwxXvV87zdxGir2Ju8yG/I9bHdEi7Tq9imDvXXEsgJZVek/cleE/Hh6noDbmHmSt49Ju/LMMnvywM+PfGDSjc1C7GPtmoGn9dnGoPdlJKKbX3OYlT9GXSWznT+dq4KUknu9MZT2Ln7tWevyaJz00JJ5qc7Klb2/V7Xq7RzGjWVV7yrybztE4nmF2t9En+03vTUs+T1Lb/dMmKS3P5ziZ998fnKX9e/DvQMNp+mfzL0tHDc+DvCllPa90UiM1EQCvtdNY3PmpPsGIJksLxwdMyfvpfSmflY4jhT4yNtDdsRVP25m7yTejmnpt7o5mdcHydh+Q5rPLFk+d8oR/GsMARL1XOG3N9QTlOD4cu3owuhK4DGEmY97Fbw4IuJutOfRnjMNYlJF2roFajdsikxNMn4LEWQ0iBFijTlrEsuOi5QSLalRjCBH3ZsHZJnyRPS5XVmmLtdctfIn1pRL3I64uAVi/JU5A3Msf8NStQgKXdtFubIKExzn3l8YtaBCGKoIllJX9msjLTYp+toLYyg0ByHKQxIiOtvrxV+VGuLaNcnu7io0XOtQxPybMWcUOkQQIuAQvJ1kWooiDIUVQ6SJs50GKjd8EuuhxzXo8pa9tc07CPhddF58OTLPIk1fGCR+H4UOC5wFvoAmlafLHbhvfRZYb9Ho6wLCNDwteJa8S5m8jUsoyVdxkr0dGRc0chjtQzGnXLDs5lHjNP1aVE2utHxcX5Ki830dD8804R8DzoBfbxhx/E8iAPHz8KIugQZef7eg3VucLvtNprCVNHCai/8vh00fiYwTXm9Wpfc8RIh+2tl2X71asisb3LIe7VpoH2qX2I4NFv/mtZZtiozp7a0PYbTQfVRr/V9kT6lJi5kVPp73DXrvA7rO4dan6eqBP6Gs+d87K9o/iTa6J5qLwjuXdVzCxK8by9JRegrxfXDjTYcnGf8V5wrXDdCtWvuSC7Hd4ptr8ox1t/UzqfPOYV6uEdu89PI5mE8DQeefQ9IODLk7e6FqFnT5+W1y+3y/rmOouJ7/ECzws6P7+6dyEQHV7enU8o+fG+1QonidPKV2kHkU0HMOTFv8eLpC+RMVw0SmjFowyFpQxBKjnQ+mAf4DCzGErKC0yPPA4ZjXl3EBfXKbROyZp1Rt3UFeSBspb3Meqx9kSDL7FhrSJGOeoW5IPyTQHyVxISBZo/x+SdkJQoQ37L+7M/ZBPldenZdF4muBsP8AaDyOd+W0b5bZr7k0Ae46vaIdM2RN9Le6NCiaH1e0QjJZSKDasc7bSusBSGDpEtyCRwBwa+LDt8VotuPQEx1tSzAAlnaCc/CeEAIjpmG6SYOqzd4DxPGzhGjw7XghZHvYh6frX4eg20x15LYuHx0miZ4aVrWHSOyuHRAUNMJYmLQRb3IYXLWJw4mdH+ILroExjWavPve0DAa3sN773rLCWyhqV248GD8uDhwzj2Pr+tof3YxEUeTVhdWy2P8GjbBu+B+CDBtVw/TLjMCtf7HbEk+snMfmG8/7wc/vqvyuLBNh9xaD33YvSNDRBzu6AWpGtsozc6JcwYe6WpDVGRr8ae+Xsq7dTBmawXRjS1XljXhQLeQeIpKC6RZ177uTgvrfJNGftOw3Xk1RKX/G3xnanvklKT5Lb4+wa5bXeLw0SBqZ02z1TUW+2+a3mnlJkAdyr2zMEZHSYRb3jCZmpQnKq00q6o1oyU6xy+aQ1VUUsvjo7K8Pl/K92Nn5TO5p8Sc3ufT5chd3dbdlnLM/1GIKBzFy1CWpn8mq4nUeeT9WPODouKYxEakq4DE4lEvETxku+Ll8Ex7eE4hpeqeswLCXexRM/5fTGcNFKM42UMWQ4bXWBOoERC0ug2hn7SSznszDIuH+EyFf78uC2h1LFM7dDM3zws7TF8rFbm1NQElYJf+LPrs/PTShmWjUbvYCtNZ9uIaMqyIcJ186KSloxFpFmQFk+pKMVuJWtBYNA5SOS0QPNqPTQ0ZK72xk1cJDQyQ58qt7apfvPXihdkdtKVN41XT/6Jn3jHP7YS4Fa6IsV2kXhm+wXfdC6mczJrJuqAXI4YNip500p4NOD8kzsshegXpL1pv0TxWCuw1wPnJhzLHB5wDTjU1DJ80WMrIYzmkNWX7cXlDsMMV5HNsGRkaDnc390t2zguWn/4oCx5fijgL0ht4JJ/3iUC3rMffvxx+ewnPy6f/OjTyT38Luu46bKCMDZDmKd19RrtxxBU3J774eNWBu4h9cZaf/TN35fR81+X1Rix0TDC99Am+5kY6RA3e62g3b3oZf6NVWmFzwhoe82IbvrFmSw36xAdpx4jN0u3OdqcA/ucnO8n6rr1x3V5DVWuI1/Zs6Et33y6rffhbKYLjufJvCD7O0k6dc9cIjHea3hhaNvZvDxcUuotkwMUPnH5unPwogxf/G3prX5eFrofqclbCr+ZxZMQ3szzci+08sXbF3gtbX2sN7s7uxC+OszPxejlL8PxIF7QJR0SCAmcc8hqByb5cPgoTh70QAkJcDin96rpkrg63FFrYn3Rt7zE0I4zLFvIbWW7jSGivMRIAnusL6GFUMc2EkRlSzTUS3W0dklm6lBQa6jBeIPWzTFDDnwJrMPc2hwkhp4cRw+ntuyDwyRMsvoyZUDjusBePMipOcpa+0RW5JsU5KjmmSgW6fwxS1NlbCOeSOMMVhl50Nvhl7S/1k+G9i1LAhyHbaFWLAVtv0bfyCDOFQOtu4DCC1zNorfRjmm2DbmSdeeBaiUcNlbCqkpURBa24LWgyZFyWimXmfc5Xhpj+cMJiOli7keGbtgqQoc4Jv/KKl5jGSK6s/Oa4ryAs3902C8Dfq5x2OJv0yvIsedRhmsi4ANc69/jjz4MC9kmFsCNzY2TodLXlHfXs3tvrGAx9Wf/ZD+lRTzmI+rg5pZYEB11McSBTP+3vDwN9utHsegzfJlr7yfu06YTmkSdc4Kj6FQatzwl409ImPRHTd9Qs9Z6TmRb30mYPoqcNftJBvemM3lslScC52Qw0+WhbU+IauqwTYYOO/brPptq49ypHxRr/nmKqpf5rhtq5a0+bem2jfa1Ve6U8KndNv9F2zPZjWhxPZM4X9K0Dm2RFq/5Jebj0VZ7XpnZ+BaX87A9L35azlXrvEjWpM0Kbg+mK2mj48aoNKW+iVh7W+CsJmdj5uM2U9VbHjY6sVEzf5edSy/C2oqm0HU1aCF403IBlO+DvJe+/FUZP/qnsvjIYaOsZx0y69/rir+p+ZMQ3tQzcx/04uXdB5BWvz2sNa9YgH739WssNzvM64NRYKnzdouHFCRDklA9djJ/jOGB8cTiRV9SGS8GbKrl0McqJfnvi5RzADtY3HRMEuvRNcO5hDi4Gzr4ANShzITw0QHIXySCcMDQQanhNEaC1/Tilg/CpxXNCu2A+Kl3WB3JV0kqkZYxIUKTN/abtEZm5PGJRNsmLzySskh3uYdGRFtfe+gWUZMyKmd9xtU/NU3ZbV2RZplmx02bZuGQYaT1ow+7vrAuOAfQYsiKl4dWvsXJ2yGvBC3+RVvEo4OjHqy8EDb+h0iJtZ3QMs6DWjJ4xPUwxIrbL0dRlzAgLfYl+DFUNSJ9/XSYL45GsLoc+eJsfGjGxtqp25dryd/yymLZgKQcYFHUEmmajj8OD/bL8hprDrXn1farO7/2PCstw/kIiJOW9WWc/Hz6+WflRz/+cQwFPb9EppyHgB+PWk+pbR6J4RHOa7Rse/95bd68wD3D8Kr+038q45ffYHVvdIz+xD6kDdP7bdzVt0qdSEB21OK9ekUR5rs2elcVfkUdprOFaBQKSyf9YXTvbX30y/Zo9kdV6zZhWsI19y8RYbK1ta/i15T+/rJfovd1Ko5LkgJvchu1ZS+qr8XwojxvmjaBYWqn1tfW2l7dXlRXrOWq+a4obn4237NIoS6r87q+tFoLmGmSsW0bcRcGCkzKXJjx4sTmZPu208Hr6HjrF2Vx8/fK8eJHF5e7palJCG/pibvtavtC48BChzzusSbc6+1ttjuQw0G8hMeaeTRSEiGx8N4mN3/pVIIA8uCErBgfxirz+HKAPGPjNYFE89ThoLz881JlvZX08aAlr3xH8uKQVYeHOnRQZyaSSI+jjqZTmEsOJCikm0XnKKEQdYRSIZs/hHAKE+TKg4gwmnw1PfZbK0AwPjJFc5UZrwhmrnKjXJTgD2mKCJmWiUJtImmtfMsSHYrSFYthyIvImhZ6m49fux+SWrm1fKw/GPWQdapNFfWWRKFXm4di9bxohNOBC5jxMcChnUEySa9kcRFiCKkYdCFsWOwor7OhcG7DkhIFa2tcN34MwDWNjkb04qiX0E2I3gCSp6fHsBRaJz+b6HkOT7Kcf+enreOZss/1NhxKBvfK3t5u2WT+ml8AjltSaNkMlyLg+fzhZz8qn3z6KfMAN8PzZ/0oc2nRzHANBHRI0+UaXeOeiGHzEkSGmA75oHEjyGH0Gdxr+y/K0Zd/V7o4kuFWIti/vHmYdF+IiPs5RPncqDKVHjVMZ6xJF/6daMWO+4prt6cKTjKeir34AGHxrArZpwW0XWsIaCqMHP7xWYcW9syOPPGV2fz+hDI+hCk5Gm96DR5etfmWaqBri5/ahmyExUfMUylTBU+qns1x6ritJ7JHxcawc8Xyp4Rx0J7ziL9ARptvgklTZRtv+en9kDfnz6T8nLQ3ilJnf1Pn6yp6nK6LNxvbg3LD5mNBg2qcs64vQ1bAMy9OdJt4Wsh3fmSzUdD/BN4UouEqd3GIcxBlpvNdXm469zvZRwcdzAxefVGOP/yilEePwFcnYd+DLu+kQfOFJCGcj0vGvkcE6ku9j70SX7z3cOxxeLjf9JUQRTszbkCJg2TI4ZsRx71Xt+xE5xJvHGHJ80Eq0bOg8xLtQxyGpYVPa6KETA+ThpbAISxuZ/OabxliISF0GOLaMm7uGdKos5J4sbFK8kg0HW4oKaqLs4eiJ31vkDepLl0Fnd6YDjoIkD1bpJliaLf1SL3JWH+RNn3c5pkqRnI8Gabzzj5drCJ6VHYiv1vlimsjs02Pwyay1SPKkABOJ4F9inuKatWNZTQOSPO/ekS7G3kWATtp/TFDe51vKaYgGEN0JYsx55Pzp6VvVYseHwZimBx19zmfHSyGYx6ASnGuoLIkiG78iNDFi+gGw+36/aMy0Crsz0pUhU0sSeF1wDWxjHfILg6LJJuHe/tljyGkg/5HYZWpDypb2zb+pOW5VxHQu6bk7/Of/OTezgX8vq4F+64ghxDEZT5sODqif4DFG3IYzmmaPu4718/7jKUl+s++KOPtb8tKax18z4pQLfU2/czcusxxQQDP6EuQEU+DK76oXiBxbtIZLariKC8BXCh9+rbdw+OytTcur4+Oy2Esz8O0BUaprC8vlscrC+Xxaqes8MbmB0w7ttnufm7FNyryovN0oxSdUuYdPgeai8DH63WD973XyoCRObtcHztcJy/3j8tenw/dyHWJhHXW93201ikPN0rZ4MKJdT/jWrlube8hP8//fXTdYXq01rYNdF3hd72LWAAbEN+DipeJ5DWCOUt7ZfDy78vSg9/n9YuPyHcsJCG8Yyf0pjdHsuCLS0sKD3yZwZGMQ0QHbF3XSwugZMvnfFgbfPLx304xyAYJXZySBD0gTcLFe34ct+2XdDi8MBaqp74lSF6VSyni7ZPr1rogJlgEHc6op8tV1q6rcwatn87WPwRJi8H8LkPhEgpayOrHuJoWOikfvXzRsA6FLKigWWyLQQX8026NttNHXnwiNtl9w6QMx+ZpO0XaWElhZGrS3Def2zY0B1GWOLfqc4roGY884xzPaZHQnR2b72dJ9eA8SYgjn+1SFr/AYKKnxSXovOwgb8yi8QrR8qpcvYRqIVzoQNrUQ/HMG5TPL4PpGl4/B4Pl6mmUfKOO3kPJI6LWRT3q4BBgvYVa7RAiKFwuQl7Xg8PxEB5j1SM+BHANaAWuzok41yxVsY910GtvHwv1IcNIVyGUaN2g69nLMI3Ax5/8oPzO7/1e2YQMurB7nPvpDLn/nSNgX+S8Q8mh/duAIaWuixgeeb9Dbbgry3F/p/S//WXpDQ+i6+C2nLqHPHr7cOaejAj7lTbl6vXoLGuH6ecvD+zyjssqb0M/WKcHkMw2YlqpVfOLZc/eD/ZX/psE+6444K/6Mhf6gLVYv9kZlX96dlh+ze/bV4N4ye+jj6FLvlXcXX+4vlh+/NFy+dnHK+Wnj7vl0bLTF3SQZr7TWkbBc/5ENz6t01S+CYRTcWd2r17V6aK1OafjLjqaU49RIWZO2hlR5Llula2MwIHyFQ8/QDYIG9dmmrOdW9+cAu1jUhFnMFeIZfhFPo4dQj7m93yvlF89HZZ//PqgfPn0qGxDCg+GjKIho8++FfwdPFrvlM8/Xiq//6PV8rs/7JWPvJ59oVBYq+AcnWajQo2IbAup7RsG5bDm8z98vV/+4y/2Cv7dyv/0883yJz/DuzfP5asF9XgHulytsnNzdSHYw+3flPHht6Wzukk+3xe+f73OVfiaCUkIrwlYZn8LBLhvfGlpO0SHcA6Z2zXgZd45hJLDIG08qCVhDX+Ih7V9ii/32t4kYb4IVctStcbZscZQwaYXjblmxkFgtE5FB0vZWHJCWTwZrYNapCqQx+qR1LlsQVGClPYivZK7ys+sN6yGLkGBXCTGy0886lHCzlu9nCfncDrnJdahpOggmQKDeAjQxsnTwIiIVDH2JWAR3KEzb8hxjRPEukfmutNs2tjYWtR8/pQZDwQOzNuW90kX6eZpf+Ylnv/1BFTCxlE9Vq58kBeWluCFbKJrFZ4PMJnIa6qIiObcUdkYi184UVAe2CtvRGerUx8JuWsK6mBIi6IW3viIIK+kkmgCOkoI4wB8wgkHNeuh1jmFnneHiS4yd7TjyxX/taZ4zXmOXOpAr7YuYeJ2H0vh5qNR0dFNhoqAL7gumfDhDz5mTuDn4SQmsbmZCHiu/OgkMfTnUFKHlEoQvXfeb4g7soxePy2jrS8bdwttx+J2KpC13r9TcVfctY0z0qJP8rlw3WCfPOCj4t/ykvoXv/HD0HH5o4+Xy//+rx6WxWPWTbxEZKvHhdlmEqNM9F/0f9T9/GCh/PevD8tfUv8/bx2Wgz7PDJ4XSzjycqkO2yUx3D0Yl68xrfwCEvDx5mH5E172/4efrpYfP+ID5gJO1i5pfNQ7ydRqfrpQXD+no84eNTJabOzvDZLM6TBzOJ10zv6JTq2suecUwco+yX1WXKTNKNAetnqfLVXlGu9jz5EoWm19bPi6r0XLHM1rBI+gsxqcjYki/Dk/zNWnUdZ3H6v1A+bReLH86smw/N9/v1t+8duDsrXPg5C0JT4U9Pjo6uuBur4+GkEaIYxP++Wvvzgof/qT1fI//uF6+Rc/oF+Ij7IKnYNfC1Ck1j+h25z4qSxX2o0akWPf9M3LUv6/fzyEuC6U3/38QflTns3HXL8B7zxpb1L/XFDnCX+TON95aAtzCUc7X0AIf4rqrEV7h0K+/dyhk3nTmyJR8qXcm8oXfh28OMRv1yGjWAZbYiGRMpP/7HztF2IeINuWMHrfB59pOmcfaA4LNEjgnFdmZ2RyWPiafD7ptfSNke+HYC141qB8LYRBEklzuGitUyk+DPhJIP01Q1MlIbJBF5mXaIaiKmucMmmH+kabrct/jn9tdSFrBBsjKG2QLFpt5DMt3g6MqE8ss4Za7LjfHhvpvn9CpvuEVpb6um9drQ7mb39RjgzmiTjz+Yg0uF/3bF+o6LFPIhIkdJ7fGqqAsA4SHy9y1KdKbRW++HAlBDY+/LTQjsijR9AewoMUoq/WPfGLYb9sTx7G6kNBfm1cuzyFuLdLUHBVoF091+rDhYcHUogfpHGFZSi89rRKO2x5xEs0jBIlZamIpop4MKv3PQvOC/z5n/3LtATe0vPedfkcft4ffgTZx1nX+1nOwpvD+5C+/MVvSzl4Tb8paOfcNGQ9L8lSs+Ek+xwyaGar9j6dLXjJsZ6Kt7AM/u1XB+W/Q7R4Ry1//jsPYmQITblUnvWp23WCLYjnFE+HL7ePy3/61W75y1/voceQkSkLWP5WymdY/z7cpG/i2Dr6WIBeQQC+ftUv30IKv9nulxd7g/IC0+a//+MH5Q8+Yokkhuq+bYg+lA76qjja9V4r2Plb6JIKZpOtpvbG16rt2pmtJ94bICnP947LL77plwM+Eki6/+CzZd4V6lIw1273OZrEs3AmLSAVAGHionZ6w/6wW/7yl4fl//qvr8ovnx/F9fPRZrf86PFS+RTdNhkiGtNiUGyHDwdPsTB/+WJQnu9AIH+xU55w/L/++cPyr37MdBjIl9Ysn4fTOE/vz6j0Tg6jXUiqU27YocI6heRi8aFX/FFCK+XiMu81VV3AuYPn+9H2b8vxBzu8uKy81yq/a+FJCL9rxO9pfT5wtNrUl3dmm2AZPMC74/arl+FUJkgTJKELEatDOSsBsx+w81gkXouQ3Zlv6cppCYiWOsvHVlIYfQd/iPNhIomTaERRjqsOZoo7PP5q9Ys1B8nreoNamoIMUayzxGw3SRC9uLLcagezztYraa00ei/yspX4KZn92JPUxd7JBkUinZgmtDpxaCGDsgzmDcIV0pr0SSaUrNnib0TzB/1quaae0KHJp9xWdkRZdxOC7TX7Ed2kGU+IYmDeksH23IqL1rcTsbWc8RE4dE/aKNEyejJsF9kScs2Pzhn0XKwsr0AEIYSc98GI154BVj9Iot9sxbt+YODxBrP3ZSusyspuqmM32u/wUgml5879IXMOl0L+MsPrqsfRfeYUailcwboSa0oixH/RgkbXkHdH/3iOHjBR/tPPq4MYF43PcAcQ4LzqsfThRx8FIWzXOoz5ue+ged4h3iPHDBPtP/9tvCzxTj25b+ZVEffUvIR5cWQ+RfjsBw3TN7nPghp7ktT2AZafSnPXpBF9wW9e9MuXvDAPGYXw0w+Wyx/9aD1e/OP5EPe/maug0zUopcqpe1f/y5gFrH2l/Md/2Cn/Gcvg7mBcfvCgV/7lZ6vl55+ulc8fdWIuWHUOUrvYg0GnPA3Lz1H56y/3y2+2jsqT14Py+oCPW/Hp8Wwbr67R9XO2p2C25BTkgfGpdAu1GU4lzB6czjR9mmdzvqvjuD54IFnzAs+gp5Cp//C3u+XZ7rD8+z98WP7gxwwNhATEs/RdVXqOHHWJp2xYBnvlL355UP6P/7IV16lzSf/485Xyr3+2Vn72yVJ5vKbfg/ZpyMcDHqwvIbP/jDXxP//TXvkFQ0v//pvD8oONRa7tx3wERXh7MzU3xRl8A37+xJfQc5R8w+jpMzu9P0/cRC/1vCzztIC4zq5TYLrwZfu8EYCLS1Ac7z8rZbDFB+QPwZR3xTsSkhDekRN5o5vBTS15CtLGi78v9br73+MlfC/WHqzzu6p1jiUlGCrYw9ukJMxHnS/ybrUA+rC2swjHIhCkIBsSJeIdHuhcRMmd/ZnWJUtK0IZN3vqS33T+8aqAR1HITQ8LleRORyRufQGQcNRhn84VhOhQh7JqH6WbfYaXQiyqlZGKSFO3SQfGfi0X6kX5+tRpOqzYtAWaOIrXPGhuu7RqsdwGILQJUc8kj/kNURydsYBZrjmsaf61o5TEGZTZhklcG8F2+mFguabFVajpqsAfiJje8CrBruWjBjGKcuarvygUTarnLxLQI3glf5z77vmXh2s99pys8CIrp+1j4egx12/Yw2LoRwEjPbPUIVnsOnzXGCrzmlGOaUb6/Kv5wGURZ0CcL6+/LrJ6nL9lh6dyHUoItRaubz7gY4BPTgoiRii8FiYPUlLuUtDavbG5Wf7oT/4khobepbZlW04j0DqjWeN8HzLP8IDr3T75bYO32Gj/ZRm/eooVggO7DEK76/aNw2zhVug5AqNb4v6H4zkYgH7Evj7u4CgR4kjf6y+UXz47KC8ZZudSq7//6UZ5hOOWIRa5dmRI9GG2Zbo/nLTqrAIntdS06AKpMOoMOZ2yTb1/gVXwr75k3jJk8Hc/XC7/9vcelj/9Ubd8sEYXFX3+yTmxPStLOApZ6ZQfPVwrnz7slr+BSH6w0Ss/+wgrsC+nDd7TGrVR1u0vjqczhlLTJRo9m6hQdzr5VP5WepvhVGJtb5s0tZ3oMRXnbn0muzcrdypKICKc5JmWdxLbZJvekBilGxHTedt4n5jBlbAc+8lx/2jMb1SOhnxM9plSHwQTDRpRE5VVb1ru2YzTCp2/Xy81xrXwLvN3v+mX//A3r8pX24OyycS7f/fHD8v//PNVrMg8J31gMu2i/ShrA103eZ20Tx4ul08f98r6X5ewFP7RT9b4AOqzuqm33U4aMaWPaVNtbVNaHtkeX3V7gsu8yqqUqHJK4HTO+uwlB5HGX6T6lIj3s9tUHh+MB/Sd+0/K4urPUCwJ4fsBPKXeSQT0DBoWOjrWEU9p19Jyftc+7v4PmTdoT7XIS73Ez3jvu3AqYz9AnKRLhzN6ntRa6EuMz83KNijOvgQlOgzJBKwihiTgkcu+LciLyJKhuadrURIlERJInc7EQvaSE14QLKi1siPZUHb7Iz3mBCIoiKqZI81trcOqJiGE0a1hmfIF42Ro6Uzmtrc22uCxxM0tGMREAbeRj0zUWfP5YGDPFzt/4BfBvCpkPkFog7sWbbfuqCPtCiDbrG2eyEx+ZVegTwmwcwxVop4qtupItqi/CjQ5aDhb1akkX7ERS94qRzUk5cZq+ROzo/4S536Ja0PHQ/xoY9RJqRgayjn3OvG66HDuV1gLr88HB62Lzg+1Qkmk5bUMqp/zVXWM4hIUltNS+PL5i/Lw0WPWGXoQcAvNSROaA6LuQvB6/vynPymf4Sn0IQvt6pE3w/1BQEu43nZjHU4+hjjX8I1C9BN8mNl5Xo6PdqKrqnd8lVZ7ZfeJNS8hijT7NeaSv1yrJzIpaJ/TCIr4RrTPiSEvZy8OjstvXxxCrkr5gx8tl16pw/3UJermZfvb7SGWNuZZ8vHxh+vd8oefPSgvXh2WJy8Py49/uFY+2fClW8dVtX+ND0uN7k0L5ivdtqtVmL6mEo3qIfJXz/rlbyCD2xCO32HY37//I8jgZytltXPU1HWC2EkF9utj8nTKzz/pls82N3hmdcqDFXCI58FJzrqnDBRQB+GiC+S7D+HkHvdjp31rzLsH31ZtczXF6qMjJNEG4u1z41mKQPcNExkn1UXGyD+lm9n92b/HRdKUV/ACuvioq16ka/3kmoS2XjQmsWkbkT7jFWpdTheIXyPXTTx6wSz2laYgzwf9vWXaoETnCpruKCNRass4Fsga2o/SZmubJSptvrZsRMQfM/KfutrAYQTVmA1tmvELXJ/Pdkv5f37xuvz2ZT/m3P27P3xQ/rd/vVk+3uDZx3tQCJ8WogAV4z2jh/ftf/Fxt6z8+UbZg9T+5NNlvKf7/lELhM7TOgSORDRxoZ94WqApE9vpMoiKfG0W0/why3+T0FTaipnEn7MTJdUndALfiOCPAvgtTOF5Rqd5wJ5Tz7Wjox0qEwpxP7Lm8d7XZfED+s2F5WuLu6kF0kJ4U8/MHdHLTiUse969dDKHODrgMcS8rYOyw7yWgUsG8CCvRK8O7XMJAXuCEZaxLp2jnYNrzClLougLPI+DuDftJ3yoKSM6euqo/QmkwieCDz7y1AcX2+Z+9oEZXRfHzifTu6gORSxiv+KDQTJYvZ3WLk4dK/FQos+1KiweDLKc2Q6Jh1EoqTw7EoeRGhf5atmqXPPQcHa4EYo3DyTVYjWP8U0aUXXfY8o2otyqX9OApix5Wr3sTDls/jQyOLR8K2OyExmbeBOtS1nsGtqtu+AAda5qRuLUH4vFYf2rKpyiWp79wJlDRXseTdday0mOl6Ae6wauQNxcWH7UwylMl/XXIIUxXDjOL1nD4rdEHGcCsqjVb2V1LRwV8aqBcL/wkk8LM3IXkal30SGVra6tl01esA6fPSsvt7bKo62XvCjzRTVwVHGV5GpRN5VUwVscxEYnMX/4Jz8Ho7s1If4Wn5bvRXVf7v0o4i+8k0IM32xdQ0Z8vGYIFet62i23t/d0H2EDp++c6f3rN97e2/67kcm9OnJO4NFC+dWLI4bKHZYvXx6V3/8QT4ufrZdezP9qaqTPOMSz5z+/2C9Pd3EwhYzf+WgNq8ty+Ycvdsv/+d9fl0+fDsrPf7iCM47l8vEazwa8HEtabFfbm8Xu7J/pRrHvI6Ft9bFEFS+R/40hnw731OL3b35nE8sgy+V0+HiFtYcn16zE08fowNTC8oPN2h9FH3g6R6NjrdWu6phnyt54IeaXafWy79UiusH6FRtMMV126R6fIVNBPXzCjhgKpwxtZjFMjmfxLsNXX+2zXA99sB9T1xjG+IC2LDv6IuRYadtqhdqn8/TjNwaDg8FCecVQ1wOso8KzTMf8APeu6+jiOm9VRoiIc+NZHqDBOIblOVyPeinHmCK8bB6X14c61TkGz0WsaJA5QPe5bK596tqmriPGUrr8kw5YHtDuTX6Sphh5U1Xkr4um+A7hjEyvrqrDCOz6xyinJzXOkZKZvBI5aGl9H+Ca6vOMeb07Ljvoo2WaV4mysdxlWCeOfxZ9d7E+8ltoTmijfa9h7Er5e66Tf3qC0zNO2J99vl7+lz//gGuROYQ86y4LEuPOwqB8/pFI+XP95TmluG/8ZHqAFXSbeap7fT6AUNYpMw/Re5PHwxLn1WexBkkafyrE49AY8HboslZ3hzEfQELN67XxeJ37Zwm8wL+9g04L8c2oKhcfG9CpjxOd13hQ3d7H4SAfbBwttL66iAfVRTwBV32qvwBOCUXPw/RUPe/koKmMSm3x6IAho6N92q+30Rlw3kl9372QJITfPeb3qka/sPrzC9sRVpvoaul0fPk+wLOj3kAdumf3K+lzyJ8Wu1g3zpd3PvMO6WErGazWOopPOoFwFPL/s/dmMXodWYJe5L5nMjO576S4iqLWqlKt3VVdqBr0tBuGMTMGjMFgnmz4zTAGGPhxAAOGMY8GDC+wYQ88Xrpnyu7u6ZnunqquRSqpSmupJFGiuO87c99Xf9+Je//8k0xSJJWZpFgZ5J/33lhOnNhOxIlz4gRpha+pdSUdtTJ21jIDN+/QZYITFW9aXsqfu8qNWhaF6DiDB3HhXf/YWyS+wz/OukVg3pmMy9lNA2HQBcMgVP2kUuHMzG/SU6YIo3xlUIQxScbEJAMU6WUKcU4gxjULdwQttLDBq+ICtvAyDhn5IjxwI77pKUEwWAHPd376GRavpC85ZaPraRkMizx8Vr8bsOCCkJMt1UcSCH+BTsQoUMhZ5ok2gAm/CDN9GOoRDSddiqRRHtPUg1czd0JOTiIlxCJoA1dSNGgZFAZR1FxAKBStrXdqgz47YQLADQJV5OxHom4/cEJSXVQmVGe/9Ns+J6Ok5HqgH6awpwe1UUmjWGQXZay0a+H5JXo4Pnbveybt2L0rLo+P8nyJ8F9DdWVroIH+rxEaN03GUCX1rOGDunl2yyeG+xgtLNYKJzkpx07p9yDPcog5tpd0Jd0g3D4ts3CTxeMZJIKfXp+E0ZvgLj8X4AmDIBh8AFCmSxma2h23RmbTmVsYkoIpaed8+IEtbTzZbILgXGNhfBUJnnC2X2lKhze1pAMbG9ImDb2odldoeiyJ2308pU6q/mkYRLr2zMaWdBhjJW110naZGqgNf+5V7GrQec7LPnfGz9/MWJ5Bo27O3ppKn1ybxCjNNPccsuFK5o2oFnbD6O5ErfDg5qa0dR2boVp7JMy5ToNs57ne4MMro9DRlI5irXJDZ1M6xZm0Y1eFhcEVmAfpbQdwdvc0piOUZRtGTuph2PJcmPGzvp2Ph6dqg8E5cR3DOKRXHZNqiKsSNKKzh6sSDm5uhNmF7mrVx0DcPGk/vTydzqDe247a5NFdbTEFfoza7Gcw/ra1DMXR7S3pm1jU9B4+7+c7CZ4nKcP1wcnYQLTOLfe61sa0C1XbQ1saMMyCVpB9lkrz7N0nl4E3OY30GOaoYFjP3R5PP/5NPwwR+cAVbe+pS8/vzFJn1wRT8/VIo2fS8SuT6QJ9agB8Qu2Yeuukbrb3NtLO1BF9qAEpXZ6Ho1tGH82lzH+j7ZibZKqOXxyNZwdM1TcO96TNXbQN817EyY1cnXThnbAyODYx+CjH1EIk/GTuYeDOXJtKn1G/9o/RcTZdmYM1cNTNbsGuDQ3p0PbGtK2HegoJI4xhCRxgsWSgDkZs2ytT6filom3pZ7p2mLgdqEQf3tWYnqHdGLDhX/4pQdnU1qVj+drt2bCkehZDT/0YUVKCX0emna11aZt1iXGcvdsaGYv0H5AtYSxCrMxgOZ8W3Ios+iXocs3OCF11JNU0bLQEy5nbY4O1xhA+tqp/+jN2wT3Nwr0ONT7fPf/lQvs20pjbt1AxYjHf1NQSftPTkwxwVPqaWyEATGYsMpQUuXB1oe5IrGeiUkUwXIxIOQ9GaEGlPDOolM+dyzx4CwbEcVxSRZ75lTjEE5+QUPIp4YlzjAxueYaAg5/xPZMSjAvfsZiWuBHHPwHbSNI7wnMGcjcFATRuEEPCAm/iysQpEQx/wvWKtDxlBoXH5MoffriA7YsRccIxSO4rClTE82GUgOefIqGrIj3Nw3xLri3SElTtiqhl1oFr5YNAssx5Foki6zLj7FeCpbYiS0NlyCT8unl2XjOSouQyxKpwxxL4bABY/6rw2oJN3CfY0uRuIYwgkj0N/kwzOZpeldCwRKoaKZOcbaG6qPVqfwk1ogJlr5+QAbR4Oa+CIYR5VArpYnhkaDg2KlrbMTARdZU3IUgS6cTzy+SampvSxs2b0579+9aujfgyNdxjwFW6psXkDq4aqVYlrdDOJXFyvE6kOayLate5HB+OMV35nb8e/G+ZvpJCggIwh6QcgdKc26Oz6fSN8fQxkpRzqH/2IbmaglC3saDdts4FbSOxMuOR4WQp0EUYkstY65wm7n6MyezqZbE+x5UOXRjtQDJ4DmMzN1nY97Ew14DLJ1dhIDY1p0MwTxqAaaxTYqJELJdXcnhfB81zjXwFyaAwNQ6yZ2Mr0hPpXd7AkjwHiV5UYQv1meHnuch3o/mr1JMVk6uI+uGMJFKmt0+PpLf4XR5SoreweI60NUpS69KJ643p2wc6KXdjSEJta7ViLg7MpJ8cH8RoCVf0cO/o2f7Z9NqxgXQNWNPM5TYHD2juVDp5bZx6ak7fO9KV9vZq9bSoGwo0x3x8e6Iu/eqz4fTuudF0HUMt05zTLJ1vdSz+jwUuTem7hzvSzvW0x6xqkZS3FmnZtbH0C3DZyNm4pvbOdKNvNL11cjDdFhY03RmlHebrhfnmNIQq8JufDqQPL47RP/Ixgaw0WtBvyv2JeV1rSt8+RLk5u8kRTa4AmU1vnRmHgZ6EyePcuhZayP88pmivjTCfUGAlVV/Z25ae3YnUGeZwHENnxy5NptePD6WzqAKrnmm96HywcoGZhYm+UM/5v870lf2tqY36ivndSDZZEd/P+Gbeus7GweV+5jvKtp+7J/du5T7dWa7kKuJE3KX+2LBVLuLzx+eiINrk1kgtKqlYuT2LxJo6m6R/6IznWx14HrtQy4/+8Wx7enFPM/WkRD23vXFdg9wer02//nQ8vX1iNO7QnCzaNmZ5gH1G/Xx6qT59d6IZy+6t9K0SkwIxsaPME0hmj1+aSm98PJJOwMwP0x6BCNmIv+6TC5Ppo3N16euHW9O3n2MTp8E4rpFW2THWLEXNLNpu0zCEzeBQrGtWGZNlz26NIVz2Kl0DGDXAKNaaYy3bni6sJ5HeeZG7zN3N6zfC4l0DC/16GDIH/Aw7Qe5q1jCZedbL+S0khTAuqo5qgdJFvExlSOtIYzoXMX6HGinv5iET4WQd59Mgqu4kKSFyEOe7CiF5MmsAUDokfjKfjeASKidElKCZnyqY7qoKHxbFBDx1fvNOeqYt3v354GkUts9CrUF75sDInkXakoj5KVzTGCdmB+HizMd4EhqlgsEk4m8cD5MXefMR//NqwoR8x8MnaVWNsTLdzqMcMYsHjnzzP4dFCv4Iq0gfaQz327hGxkWw3wXeFb8iHnFrQh2HNiBNLl6xGLFOIk8S+YwkGa51GAtS+sA8ZwfzLrjxyScYtkb6kOqgPJUWsrM+TT+phQkketwnWIsuVCbVwrdf5F+gSLmiZkljWBTJ5aJ9jbq2nf15pnVwcDB19XYz6Re42TZVsIT3ZXDbdu4I1dAmzlSuubUaeJga0DKpPyXuIwMDQa+XTM+4qpkaT7WT/KAVMf7ysIkhXtKlwmtJEKWn5CDoQ+nBMw9BaHiMPyQyjNkbXOR+EonRcRgFz1jJZGkptA0Jkgvoo1jr3ANTsakDVXHURUvK5EbYENKM0zcn46qHFuamfTB6PS3gzVy1Fwuff++lHsKn00fAPoeUUPXIQbi5C6igfny1Ie3f1JoObmlJW2EM26BztdBot62CtlThHa8lnaUmxuEybiKt8hqJjZ31GIdpSC1IHGPPz8iLKsgPGJAoc0C6608Z3Ro3saQ9cOBznHK+DgP202NDcWddJwzybiSSm1ChRTszjSHdu0TZLsHsfoTUb3wSpqpmPeWizVUvpESTzI/DqJi6LveexP7BMdT4ZlCjbUay1hS09dbQVDp3cwLGayZ9AAPWxPzZ9XJ32tyIaqdzFBu7QxMN6WdcmfDGqWHqcS6tQ2p0aFt7Wt/ZQi5z3LE4lS7cHOU6jen07llVSefTH36lJ+3BeE49awKIcjBG4lKH9Pa9M8Ppwo2hkDBu5gxmK6qinivvammEyZlL758YTO+fHQnmbD157d7Rmjb2sLlHLY0A48xVGGQ2D34Lvm4eNDSsw6AQR1SQONHdg1lvAvVBGCRQYd2h1VfmX2rFDeMW1ie+T7AW+OjiVPrxB/k6iEb60l42ILZuaGOOohbB/SrSxbPXx2C6uUR+fEA2OX3nYCt1DCNB38htZ+9ccHNzWJRls2KIulI/xY2Djibqgfmf7ElzR1dZSLrorRpq7iEE4znPXNY3Xp/+/W8G06+xdGs+Sl4PYuV263qYPubQIa41ucAmi5Lcjy6MpwEY77mZbhixZvBmc97Oxj2aQ5O16fVjo+nnHw2lfuK0wlnvB862Dc1cmyIc2vYamzXci/jv3rrBhkt7tKdMpcg4apxzp6jLY5dn09+8O4SkcSLKuZM7QfcyNttQ71UaraT3NFJYJZqDbAS5NPr+y9S1k7/jbDWcDaCT5vFPyf78DHrgeeRF0Jf9zxpD+GVvwScUf+++cpxqsVGJjdIXz+rdQFV0eGgw/L1KYhai7CXlLgRUHY0FAcQvXwEhseeMIURDwpGZMmhKMTBj55p3GbpQFyVOSIVgAB26oUvPYj9ID5yJ0r/gb8ArYDFxhkSQ9KqISmplGGUStSwmEa5DkhX/ZBoEqr8u3n3yXQLV30JHmKl4l5GL6Yh3n1IyuSTjeNhA4qpzotFPp5dx9BB+cFVlgOFFmjJfn+VKqggydsALpIs0wpLRDJwKP2GJg+ki/+Kl+t005YpDeAFTvIzLj2duC2Hk+oqiWgSIZa4S/XM2kZdBZVaAKlHiNdok0gHXHUXmZHaqOUuIau/kNAwhZ0zHG1xcQZApu2lkDmtQa9GKqPn4Cwkf4dESUWcZdyXT7m5nlzcR3BX3Nzk5hTrz7bR+00YuZV8X5SrQLBM80U83MTZt2ZJ2PbMndaP6mtvqiUZ5WZCbZKNJQ0FjqKF7fYiM/cQEfQR/pcLSEX9uKOnccAhJctEZm7jipBwqblSFKjmbD1oSbkbK2kifa27B+AfnLlswyOIzW6NdFvSfWCBKDLt6e6Mex4szhtXIBm3lLtn5Gc43GRB/Ko/yszrJw73H4PPklqqhqPVdHU3HkEipfulF3LZjFxK33VtbUQNsinv5NqI22BjjXtqzMHpnWcReRcJ1kUvgZcx2djWm3ZtbkPgRh3gt0JqdnM/b2A4szvad5TziJ6F6yGXgMD2D9KfzSG6OXR1HotaSXnARzbmmBs4YLuUkydGn+MPRsjQMQ2N9dbIA7+K8W94KLWjiIgDinIIpm4cxWyjBokhFz80kXebKyq6BKTsBfq8dG+Q+vdm0njNcf4DhmiNbmlHvZOMT6eYkc+71kWYYpzEkRKPpHKqO757oh0nbkDZxdkxANqNkX8bms0sD4Fubvn+kO728uwX8wZmw4an2uNrgNaR3F2GyTl4ZQ70UBmybV0dBY+sa04cnx9KbMB7DSNz2oHr4e4fXpT2bO/J5P0og3b7Q35R+dWIofcKl659ydm59e33a+Epn6lDFUqYhsPGevel04vIAaVP6wdFu1FQZjxjWsS2bYD5R1mRzEAad+XI36qA/eH4d6sCoF8I0Okd7dvTq7tb0FhK9t88Op9MwKx+dH0+bkSZvX1eT/oNX2rgEvgV12ZT++r2rME6cJd3Wkb77Ui/rgInYW+1u0cjdfLqABPV1yq0KsGcSv7a/I73yTHPqhdlX48jK6x/r4A7B4fTah33B8P7io8FQoTywkZb3GAjlt5795RZnY5OvQfqaqpI0ZdpQqOE6x9qXcm2UsUn4AM7Y/pgaYPcb09ufjaY32TCQQVal9dtHOpB6NnFu0M1x59IWVEjb09vHuRrlxEg6j3/ToJMAAEAASURBVMT837/XD2O9gfFC2WizGSR6xy5MpF9+PJj6UMHezAaHcI4+08JZP+rIvGabuBexLb350Wh6l2swbnDFmIbeYsIWb8vDmuR6/3x680OYQVR2W9lAfwEDOq8eaeNcL2ecQWiG9hyZbk2fnplNv3j7drqKKunrXAuiJPm5vXDxSpMtoBW5Si5WlTKE06rWVxYTq5T7ymWzxhCuXN3+zkJW6qLKkYsmGSwvRPaMlgu2Piw5Gl4Hcxjjl3Anbc8Seo4sFvGENLJd58Ldn4tcJXwSkyCFUEYZxHkGZMiE+NaSKewdRNP9p8zUmUFOSx4SIvKQqOZ8efIiQ+il6FmVkbxYAGVmJk+wXkFhHHfWoMkx2SjFDOpaaeHAqvJVcLV8k4HMHAvSnGtBOORggiqSjonKndRAxtKZCf/DObHIxFFfLliCWkcBBFAWxCc/nekiC170k7r7C8fTMHF3Nrc+qLPIr4gR6c3PiO7OlvkbHvniV4Izr8gXj5AIGqCnvxwtiCZxIiTiRxDxCRcHFzswyLaZ7WTSYMjjlTDwCGafsrspoJTYX1Mzk/x0Mwv/afhr69YExKaelPZZVTrzLVESCxdjEUZ4SI1ZGGn11ogaWHEiVzl5aHAgDdy+nTpQlZIhiERRVgE+oQ78WmFUnn/l5dTdCyP4FDk3b+JH+/q8hbr59avX0pUrV9L1a9fSzRs30+RDnHlbjqqxz7ahVrwJddyNmzalzTy3bt9Gn8FCLX01NproT+Xm1XLk+dhgUNZGpMz+JrmuYhRjYJnRyhjNot4/z6IsxtsyIblQbwxWaNIUC9BPro2mn50cSjdlrBjIvVgikTE7wp1s21D9XAdz0CIT4QajYzxwKegPbTI+W8Pdg0gHWVDCU3BurTmkiPWcWasjgaSYlXoYWtnCwr+Hs0r7OG92aagVtcWJYASvIRU7hVTsOlKcGYyWdCLx6YZZClp6R9lzzhmPafKeYN1qHSk9UaIknc045oS5/qBJ1PcEC/dfYuDGexLvV7FQ5DASo+GRIxjQmQWnD2DCbsAMdrK4/s7BjvStA0iZUAHMuc0h2eSMWHcdTF47ZZhP72NM5+T18bT/xkTq2oN6PohkXMCdepESf+2ZjvR7B9tTZwNaP9SvMTo419Wxuy0kNn0j0EwYmaswSNOoYWoF9eZEbfrVqaE0AOO+Ccb5+891wFA2MUtjAZZxzOwW9LoDJqO5uRt6ntJxmNlj58bS8xjcObSJeg0RaqbbqqpqjOc7MJXf3t+WOhtV/6dUVGINOMlM/eBoe9pr2TiLeID2a4MBpkbDqEwLOHUidWqqW4fkdxp1TqTASO9uwtwd3FwLE8ncz2bQFBKpsCkAfuvoBwc3Mz9R0TLnCqRGkPZ+en4inWJjQpXarz3TjrpsGxZAKRPGXJg8Y9x3IaHe+EJnamRO///euZUuI3H7zakRjBitq5zZtKIrfYB2V+NyDOmk8yDLEtZNTMOxRgAskZ3FlnRlgy0ZWHgyl125lVCphUFH6r2Fs6M/fHld+sq+Zhgx2pX1VDimvK4tdamnrRPJ+Vz61cnRdAaG/zUkgZs3rgd3ztpjS+Wdz0bSjaFZrkCpT79/tBOGkDZBfbI2pPLgiWR6XcDpCg2wt1ArzXSjQJZxTddIJ2EET6JaS3RwaU3fhzHf1EXywhjUTAPSf66E6T3alNiOSP/69b50i3w/ODme9m5vCiYyj78HqYT7VdADhtFONoNtUTOHCjD962lxawzh09KST0g5HPAT41zwLSVj4EwjcQnpG2Ooj4XcMAYLZL4k4pkJgNjLBBGemUH5EBb+MIReBRCOydzFfkj1HItEN0G58MIrwlQZjDAmm2DajGVamA6nheBBTBoO4s35hDA+gtqh5wOUEBHZRIFPRMNfGBqriUWKmelkpsTDp34SCV3gFn8KGPiXDKEUHlgRPyISZl4iLZxw+pmep/UisREv0/E/JIwWJPIjXHyL+gtY4BtpfZhHgK16wgTl/Hiah/F9NWLkV4QLt+ICWJEnnmVZA0++wScWP5ajBIe37WMU+0S0ne9GB7ZPs57jj8lse6V9AYAiS2xzf8CP/54n9L7BYAiZtO1fkw3jWDZjAi7K7IaBu7e6OGsqngCPew1lbnmPCUl8I4hv4rp5oXqc1hYnQtI0kW7duJ7WY42zg8vadVEO0jyJTonVvkMH06atW54KqZUSvRvXrqerV2H4YPz6+/uxGMs9kSPD8XRD6XE7+9EId6iODJ9Kp0+eyujQPxrpmzKK3u3Y3t6eepCubdq8KW3ZujX1rl8fY+Jx4/5F8m+ir2mAJu4xRGIYqvlsyrixE87B7OAqx/IXyaxI67BzgaxEUPVNz1Y1MOa3cnHfS3u7sSYKk6NRFI2PBa2XasZWYTAzgmF5jZRvFmkHUmSkVV1IdVSj7MCYTMkMaonSvLJpe863Q79kZHraW1JXW0ucc+of7w/jLEPgoQXLGZiEkuaZdpGr8oj5R2KDn6RcWmdVVbtMjfhL2aZRHfwMFbljMLBFsuqo8V4m1wZmJ5tkB7m0/MptzmChEitjsQ2DOK8gbelqZrGPxwx5a+nTjTjW2NRfQzq6l/OBSICuo3p76vpwOsgl7Gg8Rp5m4pyneuxXiNcFM6gVZ49/ZwrOeUguntwNY911nqsSoJ0DMOsInrAsWcs5PRiJASTHlOcg5+6e39uZWrG4GV0FP6vH/tMMbd+/qTNd3ZU4izgNwz+D9G4y7dvcXkh6xUTGFxVfVFa/ugcpVCO4AKisM6G5xbwFJmzDYQzPMFfwP8qR537mE8pcRz9RKvbsjrZ0Fga4H/XIfqzNzs16pyP4iJxGbYoa0DBLPTjaJ+L6LKydDo5gjAWV0wl4gb1bm9Pz+zvTJqYJ9JkCz5yW9QS1hL2c9M2j69J7qLGqqnwCJvLmcGfa1ilyi+lYrhH7uvVLP6E8qg3r/Gs58ld4PdQfwSBzTJ+eG05XaOsm1GBf2NMRDFhbfbbfQLZ5jUTm9JK0BWnnd17oShcxTHTK61LOj6VvYUBnD+dsL3CX5imY93o2Ng4gKX+F85FdzZkOiHvAot6U22qQ6Q9e7kHyigopjKVhuUQY0BmbSyeRDA+Pz6VdbOy8xNnArWwE1LnRyyaKS52yDhrpfy+/0JHeOT2efsOmwRn6yHXOtu5x4+DOwRR5rOwf1yhpjjYv14Arm92qQF9jCFelmn83MnGRNAUDqOpnLczPLGe+XJg3oOo3zLms27cwTS6RYHGuZU+ZsVkX9BBb749zV11m0Pv/ZCCUDio5lEAG00g1Ou6dpPI/SaYMH3GhQqoXhlTReJJUIkt8siEaU8gw4hcDOKuXCj+kgDF7gL/STIilqlJO3EG4zC9++TMyE7BUNii0T34FQxVMhxhmACKTmZbKt7jhJ+XUwZBmQHriKE98u4tsGgutX0j1CBIvGcnSicOibz1IE+D446cf1E+4YAp5K6IF/DJukTXcF+FF/Ehb5FfGi7IbTgL9Iq71zwfe1nbGiUmsgBNnOkWA71xHTNKUzyQBljS1bsVGAWHq9Q/w9hnVnWAItQhrH6PveJ5wfAzT3AAIU+MwCrOueoAZUHia95yqHfFuX3LCBydnGotEHho8st+qEhgbEagc9iMh7GcDo4WFvWrIGUPSPEFOvDbAbDz7/NFgZp8g1B4IFftAqHoidRoaHEpnzpxO506fSRcvXow2eSAgT1Ik+pJqqFMsevq5vqTauSjeyb2Pe/buDeZw/YYNIZVWcyL3r+rYT/a7m2Nebi9zODpwG6m6A9XFLU/HfTGaH7YU9x5dGAtD8rcBlbYezoVNcF5pBlp76hobBGw+agjmhR3taTu6hE01rNKZczKsvIBmOy+uBVBCc4nFsHKlrT3NWIzEkEphXZO1J06FPIgC72qKTMJEXudc2vErw+ljpEmeIxyHmTSqaqobUI1rRoIlU2PJy9ILSSeN0WCW04LTiyqARtLwhufSg8BFTFOWNQdthHip57IBKddmBRAF/YxI/iG6UsQRpHvDnMGbkTEiL2W0XrvRx7mwwJFzeiMwOheGmF9hMM1lLmCRh3CQ4MHuhKVPTw7eQg13YhoDcHHkWAoKYwDeuznPtcGrLqSjkVLozs/QXZ6qkLbDTV3HdxyJktOLDO25G2N8I7WFOWwDl1sYlxlQ88Q6DgR4FRSqvPLVdY1of3j2bAKVVhj38ek2xoY2AYyUUgthnivrod6VSAuiDAOVqM4az2Xy64PRuMD1IVdgdoepp1naQW3Ybd3NaT2WL2u5EsI5ZQraP0G4WFm2uHQ8IJuj9ZXrTAba4+zWbP/wPOcQqW1wbm3mGAPXJJzv8zoK2ajsxM203pYxjSXSLhj2eiSjqoN6RnALBoy8YEL8jZubWAYKCTJ/fLoJErbTrC/iGbOoPb4f3EUVg+zYTB3WV6eRts+nLWx0HNnVwplJxm0YlDGWmOScnHsdRzs5F3lkVysbKTDO1imbFLuwynuRc5zDtFOXd3hi4XW9DG5IywIEuOoyTKW3uzDI5FlNGcLSzdHot+mbF7W8S9TW1iZqpBmrra7nmLstLK6sm4AG7enstIOOoRI9R/tOp11cD4Olh4i7Yn/sYJZHJKqca4s6B+xT4iRRa26tBr5wDUhAtPaoayi2GL0uwsWOhglcYLur7OCWEfT8jep+kQZi5X2DdfyC7LlA56B4SHzY0YwFPXBd3Hu+z0kiRqZUhBQyhBJLD32Lh/Fi/BIa1w1AAGX6Qp0Q2E6espKqIcpIiFN2MhakIbEwlDIGASY84vDMjGkmDl6cXqZD1FV5zWcX+ZQBi4mfpzhbPyaR0eN/LKICBLthMoX6mZFIGNH3KIjveDkxBCJ8Cze8gBv5CBs/3w0yvPrdsEgTgDI8CydOPv1jnDL/yIe4RXRjVJx+mVPzJf8CT+uS7/ivfybSUdvg4iKnlrp3URJVl6PmdjE6MARjP6hlNrBd2VIAtdzGIhMSQvrUBH1IaYx9bYat2loWFaI8Q5/iwokoR6gRI111V9g+WOtGBf0nNhCizsySvoPftNZLYTZlCBvrveKCXXOkVD0bN6aOLvRXqiqirCY8H5tb19MNI/h86uRSefvql8l5/+jJEyfSieOfhbXhUSRNagPkTYIvU0keDlc3Ha5cvhw/20xjP8381iGF3rFrVzCKXybm0I29Ds4VNda9lEZ/+1ccKht6uApZKvZdfZm+zYBrgh68jIGQbtREf3NxJH16lQUhUoXTnH87j3GU31wY49L25vTczg6shmZVwRqlDEF/MY2POqJXF9weZ8kJLd69oT11o34YpvkDD2g/dEAaMcUq/vzAXPro0hDGLkaxDjoTjI2ETUZQQzQv7WxPh5CMtSs1g0ZJvqRw1c6i6O+k1ATDpNqb3y6kRzFuIzMgTcy0JUOI6IS4NP7hyx3pe6jKBpsQoAXo3ADzx/m83yJZ+cVHV9MoC/VGmCkZsZsY3NFApjPkp+cG07lrI8FORMYCL5y5QYqxi5HP5pl2gOsaxqe8ggltGEx96JyqeriCgGLHFBG4Bqq5tDK9YYiNYL2V2XmOW6ZjYJj6x9NrGN441ofBl4HAxXil89168K/WRweR2MGfwXwoaaxL7dDyqCeiNMJYrkPaVO8cg28JJwwO8RXXW8D0vMMZwffPjGIpdZazi5mJMj9Jfkv9KDCGaGfagLVAG7h6H22JQ1FIo4dzf5Gjh2FgLM9t3OmI7q/XUjh1nr08nK5jiTSmWuevMiHPWJ5E2WDeMbBiHU9SJUMoPmUr2+Rr0XGmEwsNM3Vh+aeJtYd3GvZrKZWQzM4b0wRFLkVafe/riOdaZoQzuLcZCNIeGblNqsgi4Qq6W8DKjwxf/yYMBO3c3Er/HYozh7c5fztN3+1DdVqGVYu5G1HXrkc1N7oyiJRo5d6ON/G0zLt9E/FQVy2dq7ZRDNOMMo7dLD7DWLvOpkttZV1VQsop8hdpUPcVQw0e3UJ11TtIvbRiVZ3IgET0G+jG0+LWGMKnpSUfYzkkHPyPQe01DhIcF+BOiDJi3mulOf85GETDjKM0cIaZSyMPYbChmfOGMgBsicU5PssDtQwrkwKXKALLycqFiCqkcf4MvxibEBRh+x6Mn99QaVNGavLzKaL+Ux1RxlQGIFRFCfQiev7yX+bRd+BZCOI75btjVcLybTZ2O4FlPKODiwhEEl+MLO7BfPCkTsiEX8EcGlHw/qSm+gvINLHrFADwF7hwhF+EB3DTmtj4RVwflD38ABl5Z4Syf4TlYP4Wjvj6G888/AUcAIRfIJjjlq/lrlj5TZJI4wxHGr3tF8GsI/XTJ/4x4Tm98Ycs80QgYOvZicNQ/+cKFQ9qnnMZUcuktYmatTRKP/HqCQ2AjCIl9KxJqCDTx2ZJI4MoiqJvOxufBovNBMGXasri5zUWummMYzRiXKSJPqFa4q2bnFXjnFozkhANImXEImqULfeN/L1af5Vi7tizJ+3Z90yMg9XK91HzsQ8MwQDeunkzXTh3PhjBa1euRr94VJhPQzrrJQzfcK56AJXYc2fPRv/1TGKpXtrJRoQSxCfZOQaa121Ke//hf5MG3/iXaeL46yG9WR6cJUKZrEjJOxpm0vNb6tO+Daif9XemD86PpA9hDK8jdbnIAvUyZwPf4f62w0gjXtzRlQ4g3WjjbFQYrmDReY7FrAZVtsFUbEddEq3RLIWAIKhKOVnTGHfK/fZ8f/rw0ni64TUW0EVJ2zoWvodQV3yRs20HWNh2omrqmStpmFhm6ia2C07yG6SMCG3QmF4kG3W1WC5lJeuVGTNc0RCWjCVUOOH4kx1jhoPOmd6FLrRLF4SLJ/R5EonpibmRoGmSrw6kK1Kx6ZD2AId2UTXW82jODcK9nwsSCB4h8ajnRvJUHNcgU6eDcEsBkcDi8l/QIY7WwqWr03J2BJhshDKPwoyHWwoOAeFNfI16zkPHc7HNPCewjt3ArWQWwPClfBDDNDBdn37+2+H0M5gOzy2qAtrT3pA2I2lt5IAaVZ5u0UduYLHS85y2bIf6trhFKJFuwRnCz3nNeqS83oGr2rKtJmM4HgZiQOsO1IqUBXDUnEEobKCzbohquyO+edbDDHVrgRajQwhJsfaJkazZdtSXWVOR/wJjaOwHd+IyNeX1Ekg2yVdrs1linfsGXne57McdvTDi0SbEsE9pzG2c8/tWu/0GISnufn3MllMCaP9faE/r1CXRFOJ5w2eoy4HhUqVUmPd24tNIXdnPcicDmajUe6dZ1hD7AgC1cv40uaerNE9Ty3xJyuLCJugl+DqoY1GOnwtqFwveQziIkQ7PDhpDddC4MFzmLxhEiI0GZkgsc5gZRKWHLvBZBkB4zSLOowkfpst34RLEuwt/zkjg50QacWGa5jgHEAt26QWeMoAybvEJwsZX2hTMKZNJRp447jZJtEhj3nUyuAaCrxLLfJ5QCigMHkGQJA7BykRdZPIGcpQhM3DEc9IAdjCNBU7BrBEtZy4s4odUjfyCieQ7/IpvD9gH52kanJUW6f0TLxlUgT8FIH3hr58wrSB/vC6CpUexMIlAw2Vwq11Z3ognDCL5i7x96niaJXFr2CJ1x9PP/NdwprTIn1qiLKr3BHNmSK7QjLKJACXKtqPXidgSs3HQPKsWu1iWoY8NAspZ43Y2ThXQQCt/Rpvp4eQxx+TjhkP0zwJX81fqbDo3LTyvKKgp1JKuXrjEWcKNqQGLnYLL5u/BCfxEcTWdVkNf+OpXsHLpgu3JdhqSOv7p8fTrN98MZjBbGrbjr7l71YD98yobEP4cC2pRbKDvHXr22TBcc690T4J/XVNb6vnuf5pmXvrjdPvf/LdpZlAFQlwxBvPHw/6NEReJpB8MT2jKFFKjmnQUC4P7MJ70Cozhe+eGkOaNpOsyWvzewHrmJ6i2fX9fV/r+811Inma4Jw7GEaZRYy7bNzai4qbcjdUog9hcPKrwG86F/et3b6cbMI+ObWlAL/p7h7lm4hUYwf3k2VHPRiT3j8ELhgSoJKWB5F1lLSgE9LIZidRWrJp2srgeQs3zDPgc5Nzces7gLbgcX+MlMYdIj8DEf0HPioimGEHafG14AjXA2bQRznZjR10Y1HH7wHBIXPrOsxg7wkql+KoJISzDlKfEbBjIU6ls2CmUaYFh0TDKrCqoVfOMvJAEL57AyDlkXJ0SDNZlH76AK74lzj1a4Ty8ASuWTkeZKczpMj5521YIzs+0M3FUD+5umGKfr4Rewjfegou0RJmeb0jvnRpLr2GttI/zneuQ/v4+dxq+igEatIMDspKk/uJ84htY2jyDIZ06pME0jbPSAtCqN+m8vwWXcRZ/JZbP7uQuw+3tKDqykb2AKomMUdA7Ki7akDZo4Azlrg2sYWJeX4Dqm81RS5wttFkvmxZXuR/wDGcOr/TPYAwH7RbU0SuVHQn4U52nfnc5kc/rItvDIzL2BVcan+eKlPQHzp869kigopNwQrOKb3H2F9WXE1SBzcjFX+iZUsCY+ysxSECgtaN9gIO729ORvVr9XVBNjjVE5Cxws3Fk+BdJPzC3qy5apapaAb1KLyFMqBorq5TtimWzxhCuWNU+/YBjUSzNg/aFSh4Dw+GqeXeJhgRgkjMe3utmuAttGTAH0QQTmup9jajoONTdvZLhUmVKoxKlut+MjB0xhCURcuJ2Ae/5RGFJdD0jKKMZpAViV7pMOiTFqowSHjQlkxSNyIhPTLwmkGpB2UJtSMbEQS5NJ8CFWmZ8gcR74W0gccDOLMFJZk5cI2E5O8AUml5GUn4w8jGOH04KwTTqDwJKCM1bCuvqJyit/pFj4Ye/uOoXPz7M0p9/nLXN2zihXsRT3IQXrgjLiDBDCAc/z5i4XYd/1InpyzJYwJCG4hc4GU04BV7GLXH1PfAwjr+yFfSPkIirNNBFb5lXoE+4Ut3AVw9+tnswhMG0I1UOPFjO8O3Ggip3GpgZx8qkxmN0tlWu86I1qNdadtTdZAgVZepdmMbzaX8zX/uefUm0o6+hsqQ645WLF1IrBkK0slg6GcPVchq70WjMzj27A9/Vyvdh8rEu+273pbOcAzx7+kz8xlARX3OPVgP2X89XXrp4MX5tbW0hOfRKES2aPpF3SzKe67u3pg1//79OI7/5yzT68U8wy15Kmh6tHhiJjEcIAf0rBiZglJTMslkjQ3egtxbrkJ3pa1i7/ACm0PvwrqNuOAqduM1ZulkMhw3AgJ27PsrVD3Ncw1DPfXGo6TKUlfA5jGWG5sBdy4t9qHNqYbIXhuIwVzW8hOGN/Vga7ZBrIE/bRVXAkjkSN48giBv/l3SOjXqYrh1chbAd9boPuZ7hsyujXHQPHlrkRPXEOEIIeMCUkpXq99k//3Vjc662MV3jLKT3u3nlwgYubO9pQy2vlisQmsiLtJZpd29D+ipWG+uYW/PsKIwMJ+iu9WpORqaeNcQzLf2LBXampQRkhijw8yu7hfQZZukfX0V5s9QRJoJMn93RRHmh29ShZYw2JVGF2SoqLzCq9UwYYeA9BQ1e7Izhr3TAgnMfQd319NWJaMNWpIHf4z7JH7yAdVW0S2oUW0aa6dTZheVP7sPz7OR1VI11lVOKBQ65jiIo4xlldxVhVWHN2WtomDdVqt3KXYjfOojtyzruz3OepC5z37AmKEX0DSFaDiEwx8LEu44ggv/DVUrEebte7mD0vN0p7gK8xXnZN4/1cx/hRqSEMEqkK+Hn1NYlDgA+A07xYt+eZ56M+ga3FqzhtsRxnhnucZxLo4yP9V41QarcvAWQgAT20TioFMNEg3KMlbY2z9mjKs69j6kGwzrU7QhSw7DCTn5mrcv9o+jP4kEGtwfHgaP6qLHMi3c2WNB2TuzBpN6e+vTqc62pt4matX6qoAkzClfJgE15dXmd811Hlf4RcRX+ROVScxwvcew8LU46sebWauCRagAyEAtpJ0kHq9K7chcopD4QoZHRkTQ4MBhhMhBZmkNKBnGof8LUSWqDAWT7SRIyheqeqoDuZMkfaHVNilRb5xkwGE6kbXkBj1Ea/GUsm1H1C9II4ZE2SBhlOkKlR9ojI6mUDpjukMogmocGcOKeQwe46XiYh6queeJnApZpo4wyDIbFDhuESAmXZTav2DGLMPISX8HJiElpxSm+qSdVF3ExIfAURjjwCkDxNL0JcPE0B5wMYETyXQ/SBDjDTaOf+eFvhAJExNUriKxxChfhpuNn3butbNoSb9M4KZiOcsbTfI1v2J1OeDEBGpBxlhGOsgZatjQv+GVLfhmUMc0mT3RMREX9ZaMM9in6C/Vmf7MNVOmcpN3rmHy9P64ZdWM3GFR3kqGPOYI09i+zs4obSDdJO5qXkuksjRRPi4UaDBLnPIlRVMpq/5iN8xUcXL90JfWs38T9WlsWpcupzULkLYsVsLzOKyS+9u1vLZnv8ub08NCc5K27Sxcupp/95G/T+XPn6OrW8Jpb7hpwY+LUyZPxE/aeZ55JL7z0UmyIVPfl5c73UeDVtnalzm/9w9S879XU95f/PM2NL3220J5S9pY8dqp9ipxjUeq4WhhbmU7kuHOcNdcK4R7UL3f2dqWvImV4j/OEn8J0aZyDoZ4uI2G5yPUNptgM87Qbc/tt6v8XNCwbWsEyJnRiMwZL9nNm6tW97WknC9TmkJ5BW2C8cteGSgSdi1EfxMXvshwF1pVH+BOu7GsD5vmf477Eixh+uYHq4lunh7hzrzvwqYN2ZToiFTJ2hh80qVJ0NCTYSOwbnw+V1msYgWmG29qNCqtXbjjv9MJ0Yi8lrkY4d2UkvbizOdVDEJ2PazBuEqDQNAlKyIez4RQTXbay7WanklNp2eISmS6jkf/6lWPwd1HUAOrtDWkLF53XXppIfRycO39jgCs8OlIjc3XML9YQeThbx3RDMqh3qA/Ks7A1F7TEud/pQCduZe6++C4zLuOl8ZpBGBO1VLdjFfWFXe2pFW2SOTf74l9ubmZtjNVw8fwQxlWoFzeFc01bP+bi/F6WjTyZPPzKmDIv8LWOux07MG7kfXg3+zCINYwKcQcdjbxmmZNm+Imb9ZhdLSqqrCngkBs9PkFHsi+ZXfSlIlb0awJUhXyRqyw+wpLmSZj+t09yVQV98tuHufu0Bmmv86FYkUlMx0V6H1EnPpljvfD9DOdM27Gms3VjQ+rkCo0t9I/Pro1hGGeGS+On2KTg/lXqWmxc72Tnxjg5UPFj8GanOds3QSM10tc2bwAHpNobubtRq6/DqAGfQ9J6COu07KFEP8tYRM8FHE8KOTCJwaPrnKOs5GD9oB7LeNN4Uj9ht7BmOoiRmJ711EzM1dEguZ4CUlEul3HgWo92UcWgS1Rm/ClyWNlHtB/aaTX17WT09LBRT09JVrb916DfWQOMPYe8QzBL76AGDFKJlQRASYxGY25zdkgVFFUVXMy70J6ZgTAw4GUOJYiq6pnWdFNc/lsPk+i5Lxk/4TuZuehx53aaXXMvD5dYORHMsWgXiOEuRiXgwgrn09kGZ+w8/wsrS4OUMCllFJZpJbLBHPKt00iNTrwksPnbWEwSxjHcevCVcKm8+QS1LylfxAYI9QH3GPACZryRRhfURez8EaeMF/565Xwy9c/5RFS8Y7aM+Hy4Uxa4+17AK8pAzIxovJR/iEc9R57ibWHE28VSlI2nOASaFrQorMnLfHwPBtQn4eGMx0vM9Hr4wV/SyExHHUX68A3wmnw3vhDyxGwY9RkoYoTIAMo3QzrVR2XwptltVNqsap1MnKuB+XL3kT6lOrGTXBg+oH3sG+WZHxmZWEiLGj/7rX6qqOjsm7VM8FoztM9du3I5revt5m7N1oJxjWjxx/KI3nI6DdzsP3wobdu5M+O5nMC/ICzr6eKFC+nj336Yzp49G4ZhYgPoC8JdS/7gNXD2tBZZT7N4Wh8Swz379mF9r/PBAaxCzMZN+9LG/+Sfp+F3/wxp4Y9j/C57to5dBp/aDS7tdyJd2cKdaF9FIoVgEEnINCbzx1M/0pBWVPx2r0eaplQkGLCMTWxiILny/rp//PVe1NBauIsPjQLmKelhZWxX09JImmmFrwtvGeZdf6F7LRjWOLy1hbOMk+kd7v47dmUchq6W+/m6wZuNSbVhKEyeRaAqvGe4+EH3NHCCoVTu9htBTXYkpDPPcg/jYcz+N9WxyUUeOyhfNwzAIBtcn2Ad9eUbLengBjf6YGqh6+o/BP0FwTk2Qy/2zYUBFhmc57jTTdXTKLF1elchlvIg1hKFF5fdWzpS5yec5+Ns2Mdnx9Nz3JO4o5NNXOu+mEvqWNmb3EvsT1zFONDpMVQqG9NztF8HTO4Ck1KVd+TnnxJDZmTglLNrPtfF2oK1gdCdvmxj2V10ktK1gfl0EmnyBEykgi6hVGt8CDmyIGCWOSbme/z0rKEOO7G4uhO14ytYC72IUaOzqHVuQHImQ2RedTHpGJlv5p1BrKv+mIvd56lv7/zbjpRSiXEFfWHjIk9fWPfsQJL8jUOdGAnqw2LqbPqzN28Ca2N6dT/SSe6U9HoM+CHwIZUDwAc/Z33XD5OzDbTrZPqrtwdTJwzs3/+93rR3S0oHdzSnt9mIGALmB2fG0jNcVbKZNo+6siIKJ1il659emEqfXpykzebTDpjAvdzXWDc9iqGZurSedNe5D/AkdzIe5RqPZ1BrrYl7GHPRatBQEqfZ+qb00UnuGmSTRpezybXazXjdhZGmM9wHeYVrQE6R1+Yuxh9jReukZQLL6RVV06j4/vSdYep0nitVmrA661oRiAGUzFbK3TX2yYj6qalHDzpafaUyXl24df/0n/yTf7a6Wa5cbi5UrnFvVRD4lctmDXIV4ZDxsL7jsDDPILz4SQgH+gbS9evXgrlzxDY1NgUBUwKYjc5APh3LTB4EAIoPnk1sLwZDCRxdVuODCSNM/5KZNA+N0MhQNrOAdi9PgErdxMNFvmqoMhousD2fOA8jatwu7grrVA0wGEKIOEnlKXzKMDpBWEzhmK+Swez4Jt9MiGVucjgeUZaIE3njH4Uj/wDMpFyJYznJLEBGLjlM5gzYwRDqbXrjxDvPqI/iQ1gRl/hGsg6tP7fEyT9+hFRc5MWX8XSmF4cStn6GUfaAa3g4IxTvpZf+pi1ddVzzNn6gRQ3wndsVL+vBUII1PBBgKzBpW9oqNheMUODl8kiQTuzWtkUMlTHqwoP99gUlft4f6GF/SxetQqJyc0AYMaGQVvi2SwE+cDBMPI0fZwIIVXIsajPADyaSdO0d3KPV1roAlzTRDyxUuDu/S/+He7Z3cj/U17+eNm7ZDD5V9fxwYJY1tvWjCugZmJA//9H/m177+c+RnF5OY6NM8pXKXNYsnypg9hPvJVxuIzHjtMnNGzew2Ho8jNOoWuqdmm5oPAmupoGF8q4XUSXdlqauHEfSjwGoJVw5juhm4SQB+SWP/eLrroejufifwxinSg26YPq62pvSxf7p9MbJwThXtgXp4Kuolm5nQQ+1iXQxyM2LjDu5aL23HaNlagYErb0j7wpSxDfbEse7sLqXB0wpYssWdOQGUAW8hSreFaR8t5BWeVa9BescWuiM83vC5uecNl/TkEbnYN4GZ9OvuAz8jVMwWai3bkPS+T3OyB2A4cPWcgxDYQxxVcY5pJDDqAWOY4F5fVczFk7Z+ISZUfLn3DbDYvY69+m9dnw4vQ6DeYbFeHsLKq2ombpxe+6mVlaHoXU1MLGtaS8MUE3BRC8Um3kR+uQl5ccwwmN5tiBxemEHUiTUG5swdHMLDvYKP9Vxa2DANq3jjBjzr7jk+TpLss71z6Wf/HYgvXUO66BcQ7AJSdZGpL7OtccvTaI+OZ5aEX0+h6XZbTBUEP4K2bE1p2E+znIv3WWYtEkY0M0cHNwCg69F0qxxgpSOerw8VJ9++XFfXCMyBbfSxibBga0wquRVb/0wL93mWonfcs/eEPXXjjTs6F6MO8F0OK/MOu+hPjxJPzt/gys+aEfvI3Q90YE1Vi2uKkUUby7cSkPTDeQ3kn78wXA6QXyqMO1jU0CDRtWEc4GE5tpVQrehm00NzsReRbo9yIbGeepgml2BdiR9rmdKTRo7ioaTppFUjXH048ZwTXrzk7H0N+8PRP8aoF16GQ/7tjWlNqzJXEQal2HKcHFulLrSMEyoiJK96p9euXLm+iwMJQavkNqp5vn9F7rTy3sxwjfLWVwMu93Gou1F7iccBkfn1V7a3v6t5k70W9qEGw/TqRvz6d++cTPdGMjaUQ3AOso43LOZOqeWZmCgz8EQ3gDeKLA6YGC7kBoaL5YZrElm2QQegcl99/hk+rNfD3IVDGMUw0EytC0tIm19rp6bA+f5xp5Uv/HFVNPQTcYLo2L1sFj+nNYkhMtfp085RDp+9H0X6nkUhuSHEam3i+d4MqF6uXfscuIRdwxKJPGPRT+D3HTGNaW8iHfEaWBGhQjPdYV6Ju/uTml9y0VDqGmSrh7CHUtlJ+7Aw7AAxrdsgbiBI2lF2LQynv5cLNXDdCqFNA+Jl8Q18Da60iU/4k+GxFd8KyWMUgcs3szUBxNUvEvB8CoXOJHOOOIic2pcF2vhFZB4z2kibvnH8IBd5lE8g0LybtJi0RLlL+ogcA68c545Iu9l+KK8AGL9MBEGE1qUN5Ar8w5EC1jhl8sQ+etdugjDo4ganBl5RRsX7WENK8nLqNgeuS6pGd7zTwZf02Ua98n9y42CLL2znmQqQ9WYRUs9iw7f47oIztnBsUT/CmYT2Pa1kArCMGp2O9oY/KLaCjyjfwTaVflQplp2KO0jtTKZ4DaG6vNlpGIdMGvewWZZcp8q+2WuYnFe1PZl/Tzgc8++Z9IzBw/Eov4Bk6x4NK2Evvazn6czp07FJfFr0sAVr/JHykCp7eVLlzgn1ZV279mT9u3f/8TcT9my7+upYePeNPiL/y1NnP8No2eZHWRMOu7YC2vFGCOZYiye5zLwmyzam1lc7uS8l5eso6MS5EsSqitxkQkMOhSejnAD+auYIwiaHl/AkWE9HMF+GLiZZztTzXEkVeB3DEupV7jAfd+mVs7ZtWPwBnU86A+yNDak6riXj/vf+idYBA/FpeLoxAQz+N1DXenodlREtcJMWZ33Guvn0quHutMFJFfHgP0hxnUmZ/rSSxjFkdlrR9VimsXsVZjQD2C+PkFqo7qlxjl2oA4If1RxZf1kj8Vf+inkUvuidMYof64DWuqm0jee7YIp8WqQ6fRLVB8HOev3PAzWlnXc4cjqU2uf529PcNk4DBNn5nQ7UDXdyjnPWiVElutznOuGNjiaXUhLPwHGTS6a//kHN9jNg1mGkW1DB9Wzbpcw0vL2CTZPKHMdmyYNUzJEuJgTaW+zog90tnEtAwwU11zG/XuvfTIU0lPvjKjT8A5S2APcxffcrqn0zsnh9BkXtE9M3Uqv7OvgknTP1zm3wVjCKL9/eiC9x4bEKPPsZgzFHNjeBq7MgSElJT/yrNSgL+U34R1YGv3hV7rZAFUqPJr6MJP675A0Hj/XmA7v7aQ969O6FjffUY6BEXST4ArM9Geco/V86TiMcSvn/V7kvsEX97Yh2ZtCelyXvouhJTchrsBovvbxYNyN+PKeVtRAnZ+5GgMG9ywM2q+4vkM1T7eWXgDGN5/DnNMc5y5p2/bGWQwFtaVLNyfSiWuoP58YSSP00xf3c7E853pbWEtMcuXH2RujnIEcjHsb13c0w0B7bpO5nsqWxW5AEr93Cyq+BzrSwEfmN5n+4s2+dKWvPR3ewdld7KfN0L6DlO0TpMxvf5Slgxrd2cPmQCcqp5V1EJBX3tlJbF8ejdRHqIxWWnDls1/hHNYYwhWu4KcWPAOipNV56nRBL61gh4zFdz8GJoa5lyrOFcIstWCQQ1W/aXTOldTFECJB8CNMujJqobLHjOSuY5ytI1LJ0LGiz35MksHCQXAkKZ4flOl04S/TKQzHqkwnICOO8GQG8q5vVjmU8Qy1QPLIh8rFKEsWTevh8DqNsgA3JI3ArWEX190z8/W/5Q5pJCnNM8rkHz9K54JCV/jJmIBo9jNFGa6PUZWcGdefDFoZHpVthMLP13B+8wJ+4cAvJjgXME7WVkLAwV+Y1emESTkzTFMb3+/8midKP/Ar4cuGB4winvHNQzxLXC2edagjb2qfKHwXeNhegQt/ytfMSBEzmGXrOEsCjeh7Rss3wAC/EdXjSVWQMSuvirFGNlQdncKIkfhFPZPIhYl9QjhK/+wLIfEjzEkpzikWlRKMJ301dnc1jAQ8d8vd1VXtWfXnEazlNre0hKXSzPjlySEXPyomcHzYPxqOOfTcEVREdzxs0hWJ74bMtatX0/vvvpc+eP/9rNa9IjmtAV3OGrAPe43FB/yOf/JJOnDwYNxx2OVdmuX4XM4MHwJWfefG1POH/2UaevtfpbGPf4IW43gx8haALBpB8bHIZyHinW9FXKmF5EfSN+m1C2OTaSNn9zq1Fhpn7QzMNKUahJQlg8j5Vf+VdmS+J9Of6nQP++4818i9qEeQjjQ1dKUuVCQ/g4lRivPOueH0yaVRFsFIR2A8oFQs9DG4gaRKwx0yBqq97utpTF/njNnR7d63CHGq0GYwRfV1c3tt+uGLPWn2t/3pDOqpnyI5uwBD1sMiuoXNtmk2Jvu570+pk1PRfpjB7x/p4owfG6Veb18UM9dHLnvZdXK9QIMtOB/OwlLXLPtZOE2lfwNWMfdy190fvNCZfvrhUJzl9FqQz1Dh7UYK5CX0EzBlfZzFG4F5aOb7MIZ2/s4LPQmhXWzMzpTzrfnZDuLGz4e4ZFS5ygrG4sjO1mCY3z4znC7B7PzFO7fSJqTCrcwN42zsyYyMk8+2jdxBCXNy7NwAcwHSrYACJAsOne9F0ndwW2u6jERrGCnUX713M717ig1kMtfQ0B/3tqQt7fPp9w51pFna5aPL4+nsrXGsglIu+prMqT3MS+hvcVZPPLch7fvec53plb2c6UR9905XliUkuLlQSFSnkezNpz/6WlfA/dXJ0XQDRu44zN7pW7dCUujdlm5S2ze8EmKYOyk9SuF5Pw3evASj992jHdQDGLFWqqWvHNnZmAYxuvOzD/tDUvgWUuLjF7yfUfXLGrRtuHKJuzc9P9iChPTQtub0d7/WjQEaNh406gayGnTavbE2fffFrjT1/lC6dHuKOqJtUYPuAY6q0FPgcwuJLfsN6QhnDNdv6Eg/ez9r8HkUw3rP0vyUvnm0LSS778FYXqKv3kAy+c7xesrItghjxmtLbvWrrUO5UDP9FkztV5/jqhU2hfHMneHOSl3276JheHhNTW1rLwPhybf4/TDVsKYy+jC19TsfV7KFK6lyQUgdJkHQGLgyXloZPX/2LHeeslhn4e7kkO/7Q62BXSoNeCjZYTmO6mhO4+LaX76CIjN2QnVhKtmvR4UvM3WclSCei38ZS1UFXbS3YlhExiPwgLC7gJexc4Ekg2r8aSYoCW4nalXrUP9r4063uBsIv5AeyWlQtszgkjv5hNohGBguwxlMAAUuSEP+JjxqgOQkYfFQQOCZ6wZP0ofkCqY4RyJJADFB8W7+AlDa6M+JPgMQgRxmCXmtuGiLyhcvBhKH/5kxNMxv8/UnnCLM9wjTz3edcfk5y/s0rkhE/qQvw/WWwTNMHH2vxI/A+M7oGQHHI+qgzBdYTmQWUzVQYXsxtLh4dsO5J4wG0FdMF0w+QIya03gnEhI8FjhKkSdRRfYnpIy3WfIlmvyC6awERCxjhot2LSL6HqEm9b2o4ynOFNXSfkpg7E+W13D+fyHXxcXkX//97yQNyDwJ7sL58+lHf/qv0huvv54uX7xUKf+TgNuXFQf7yUqojN6vPqSd169fT+fOnImrPzZu2pT77f0SrXCY47h5+9HUuHlfGj/zDgMZOlc9gIqxGuOK8fh5Q2tRuHDy/yJdNlpxaGtzOoKa3i5UCFtQ/csszEJBqymB8AKmcEq8wqMIqERYSP8wb4KUntShldGLxdOd61tSL4tejbqEKidhYyzIZURGYQRVf7QWurgHcReM4Mu72tLvH+xIh5F8tXBuMDbjFiFAaTiLuI4rLjZ2c38qpZ2BWE5yvnqIRfUATIrqm85yqsce5fzhHyCtPITRkSYYVZ2baddhqK5yX18nuB3iXsed3NvouTWABz65SiSQSDDB80If98fx+YzSs62NML2ccSO2FlS1mrkBs64Yeg66PkGZvP7hNpLbEcqpYZfNnKH8yp729L2jXWlPD/M1BmFsqXnOgV/C2MgAl5H3wmwd2so5x07mKsqUceCvlcpcoVrsehhANx9lOMZg/vrI5+YQ6rNYkmmCdntFxHee3xQMVt/ACNLDGiSzTWk70jFVRpUuO0V3dzSxtoARAb8J6s46sw63oH57BMlUI8qQShE30SbeYxvzD5yPddxPHcsM6tfVVoeRoqZg0L+6j+spuIfSTYHcv63tKmdR+Cy6cdSncVuR6u5G+rmVc4VeDUKlEIn5DnxGMM+pZE5crf9WLCNt4qzfcztp1+c70zcPcR1MK5Xjrqaw+TWwCby5txlJNIwrQR69sK8NjMCc89MCaSMM3Rba/FUkfj94pSvtWk9KYZA12QSOquNqxKiHvuYm7xT4yOAPIslUJXqSd9WwX97dmn749fWpHWnepWujrNVqMULTnHZuoE/RVy1LO3zVZg0iYSHWuX+auhzmnKNqqQOoQM/w3cHZ2L2cN/w7L7Wlbz6PpLWetEE+wM2CrbgzE35UwEwtjP0m1EVbdzFuXeM+Ha7m1pUrtu9T4ZwAP3j/A2gFvXzNLXMN5C7vUtkJTeIjNZGABnUovh0wtzjTcvKzz4IAl2o8LqTrIe6jY6MwhNi/QhLk2S/byjMFhqleqNTO6wNCkgMBMq7vTY3Z5P+si3K+ZQInJlDV4GLn9haIA1KbGgibhFYJ0Piku8/eV4jFUpgMJZSjSCydiLdu2JB2bNrApABhdyeWosnwlcyrs4sTtIyJTKD5+XMSsrxOmOIqYVBCGDVDsGWPRUR4FNVvvRhXSuqDtPHitpkAIswAfobZd6XSOr8XzQ56mikP/QOEcXkXP/3KfHz1PZzh/PI2d+FXpCvhGN+8qcOKg1BTYdlfTxk5g41X4mH7xzdeZhcwMuxgpCDuce4zFn5K/lTNJB7OcBlC23tGA0LmTTkcx1PorcTCif7gZoLnT83aayc8azhOvxgjzjDWF8cwRDTO7/bAAFbf+pgo2QmViaRsopfrkD5LvrkKnHRcFEWgqFC06Kn5nXqvcwfQKuWPeE0TX9WVes7/HDp6NG3dviP6sJGizSPlw/2xT23dsSMdPHL4sav2WR+nT55K773zDmP3BG1UNNLDFWkt9j1qQHrhdRGqHD8uJ23d+8wzae++famnt/eR++1y4T8zcCUN/Px/SZOXPy1AOh6zu/eYWohTxi19Ig0DvqR62vZwMzDTATcHc4o87stUmaSVsCSD4XipZkcjTSWwjPTwzyC3C1mHBg2KiKgXziORmsJIB5KlkQloWN74bIRGdMHoaI1xC4v4DSziW0MqotVlHDS6ClwgFBIv6VZNIxfTz4f66CXUMoeQVs3CTKi1085CfQsSpN29jUihILsszCW/sZkJ7bw5jOGo/knIWw1MkPccUo9lBZqt1NOMiTuCBc1zA0hjYSQ2oW65Y50q/xmvuIqBepvHaEz/KBeto8p6DallH5pCCJBYI3DfIGfiNIaze1MbqogwS0g5Yyphrp4F/hUkQ0rGZFK2wjR0t9Iy0GKnJ531YFuFVhGWyAenajjbhpoqqpwDMBUyhy1IH7dxVu4ZjJ/0wDQOcIfjtdsghPOuxvXtrD2EZB0Ii/WFwR8j0fRMoycZWpGW7YMxe343qrWhdsy8RT2PztSny7e1pDoBAzMdjJD11omkdxMS0j0wnNvWYT+BcsGqRp539gM9nZvsYtXdLNoYvziqwub66CRM1W3qAxXim0jfZOTEuYHKaGHToBPp3AaY1G2oB2ObBQkc85wNG5VVQLbewHuONdetoTnuOpwIVVMZS5lYJbc9nfVpB5sOu0Plljxcq+VkZhfghBbGjjiPehM4Z7Eoe42zq6Mwqc70GiraioEgz/l1I52+NTqFKuo0MuiEcSf6H2d5nZSLngJKXPNEX7rCmcWL4HRrIG+KmF+XFlLp/zs2taDabB1Rj66hrDQ7S9kZeF1RZ5YQlqmmDanxwN+DIdyHhxvtK5rrqgFfYwhXraq/zBlV93ZHBEOYySFUKymWix0nB2PJwJ07czbdvHYNFRAPGKvW516hvAK7T6MwakR0kvaeLQm5hhZcHMt8SRwkSjJnU0gT4/5C0ssQmk4G0PxkKMdRD1RK2N3RFfcCeam4zIM7omNjI2GSXbVVr5dohWGUIWwk7XaYwR2bNqZ1rUoIQQyiJMxG4lmGWSWb5OnEKV7iat5hDZVwpUOGkZCfKawDvo0UH/wxSKdftb/x/XaXzzRRN0YWHk6JqK8yX4IzrsxUORnHLKx/ERbMI3EDH57CrDgiRV54QGwDpvGFZXr/hD9P4ZZhIh/pyFdwxnU2CDTxMH00OAFONkw6GR5xZNgogzub3jmkNHgCptzLoW1X75uc0hAQyWQUrVv9nbRsA+vbfjJFG8REBhwn+lYuY29vx2y5bQSzpgGZceCO0B9GRjCLDUyZw+uodQ5r6AS4TjBKGMNZRF7cLCgXiOZfQbxk7I1MWiXYTuriZP4al1HqPEtZN2zekg5yWXgHksKs4ioUMiiyEsSDuMNHn0u79z3zIFFXNM7VK1fTj/7kT5Ai3VrRfH6XgduPHjdDWF3/O3buTN/49rdjY63a/3G8D/zif407C/MIvQODGFbVA8tRXO3ycrIYgREgyTJWGTOeBRzJmiFlmF+lK/2ME+O5DOAZKarRqAp79NciRzK0f0hsp7gaYsrFt5ui0B3LwjQaVxForEOyGyrv+IelzrsyX4xkbGgBZBK6rAqfElqtdWskxPvfgskDaP4nm2d6/vKQ/vkdYdLnqrzY1oswIAaes2oCgV+Dlj3JK998aIkKGgvAOD4CkEnmBS18Ogdo1buR85KWMXK2SsAnDJyU+TmniZCYiCtxSkzxihwiqnmID7DMmSN//NwIQKOI+VH1x1oPPjqfAi5ACjVglnBzy4uG9xvOME97Fo8mwaiktgvQdoL5UL+p+E+5M36ezZxiTlGa5RzTCGPlOclgNMkWEGRrfSzhLB6ueOQP/lrr4hnHLPSNvKhVsrdszrE607mWaSDPWvuJEWJ+i+ClAIffPPUyRxmnLSOoWbcuJRotK9L0yFdYOI7lVupM/quCqwjSV9xs8F5MVVZdS6h9JZxoDmDEdRww9bLEdeDmdSi6zAgDw2wCFlJL1w5UmEZ0dM20XRPwGBKAJh2/fH6V8EXIRPSV+WPlUOoZ7sacW88m7u7/kGXc+sjL9n4anMvhNbdWAw9YAw4IBqTjPX4QDAkUTjVOGcQxFufDgwMQE4mkY9wFuJfRQ6BhAJ3MnOZk5Fxgu8DXheStAJwX7FIn0wo75xFWKPWD8GoWQBgNUH/zMkzC7jSlv6AyQ1DiTDrycQJqgEl1gogFP54h5ePpJFwPwVJiKZ5ibxotk4blN97zwC8qoJoIGLHyTXi8Cxz/pZz1w25mziDjSHYgDfkMqRwfEuIoOkBggFXPiYKF+oeRhW89G4lf0RaLsoswkSjiWjHxLrwqf4lqUGb9jMLT/CW+kbftgb/VYvoAU8RlpmAKCv9ZJpaxsQmYQBlBziEgxZ2EGTTZBFI8GcMpZmoZrJAAgrPSY51SKSHG5gB51rFL6wSvqu8QzF7T4DCW3DpSB0ZdcpmzRFkjPZ6N8JL6FlSHx9gocFNA+OJa8oTWnc3qXqcSZuZNJjHztDz2HOuSJMST8XMn0L4a1UqQ0LSDfvvWTSxsXkq72cjLCnwPAABAAElEQVRowbqczvLnpUR83vdPM6rKz3GH3AY2JR6nc+Pm12++GXfbhTGfx4nMWt6rWgMaoLn6p3+adu/Zkw4fOYLk8vFdWdH1rX+UGnp2pKFf/98cM1h8kX2mMDHy7lE/edRVx/A9fiTO6T8fyp3AMz2r8s0gqjyW41WgYCpdCh5lFrVN6BiLaAxUgnuez2Iqgma5fq6UM/Apkar4BrwKHQrw0DdgI2DBGqPUVLhBxIOWk3WGWQEFLrz7KT3OM2BZj3gWrpTDApb1OOqhzBVq6GQ6aiTyAIiYRV0yv8RCngQarmligR/zPPHcPBQPk5uv+ZvOd121tkJlis1BRYKcRsYgNJZkSCi09+RxTS2ODKOgEn8h46zzyMyPnFOGTXgRMEfcOhjIjsLSjnu4sQkeIPK8EinB37nEeyybnLttv3DkR6FiHiq9ipDqx11lqgq0D0RllH60CUCZ/mpgNqlDLJ4W6OS6c2Uk51kkW+gLEStD8RV8fNgmbsM3e69TNY4GAsfHUi6qiACXDcaSAc1wwAv7btZhLCmE4H8+DK9h/pfpoKoqLmdLJF9sJ/KNIUAfwUhppM/A6CcWX4CRiGe8ZwiGrLQzx9larMl27qIQzv2rl/dKl034awzhatTylz4Ph0F2jtfS+SrBctksWfLeQdVFpycnIArq8rOohnjJePmTAdTJbM34DiVUjSkzgPoLi3+MsZgEeLqraBwnjXwhvecBMVaMREgipP6+DIQqb2U6JVRSnMxgAI9w8TYf70EMZgC4xteBWoSRgnjEJz8SE0LpyNdoJeMrRYpJB4AywcI0Xna5RvK7QH0r/HImwOVbr3C++zNuEV9G0YTOMIG0n0U4uIdz1ww8shOY8flVO9M4g0VYQX6NEkwoaeVy/FnO6nwqlN7IRbqIxzuLlcDdMghXFPRi4lT91/b1OcrdX4PDqAaz5VhLW2EADSYNtU78DVdCKHSlsDKbMn5CnIHx0198tMg6jYn6UNmFSa+l7OOoiE5M94Vk0PNYMsjRxkX92E9akAQ3joykWTYfKhNxlClPnNHXwN+JXeneLCpVZZEtjLuOEdMnzLTwDS+bRwRnUW+9xIK6pRUT9rt3RX8Uf/PL/cFCLO1ceH/1W98IIzhLx1hZX3EcHBhMP//bv0W9/jcrm9ka9Ce6BlTN9rL7s2fOxCX3qpNq3Gi1XU09xlGO/jDVdfSmgb/9H9LcZFbly3g4sj7HOT6rokiWSpdTL8AIUlcduYzIs+JtdD8qHlWR7vta5vMwCYu4xcPxGdKdO/IpIetd0itpkoSzOszw8Pel2hkp4mY6aNkiXpGvhQ04lW8T54+KV+WlAGwC/Jy1g2bz5mxZ4pfDirg8Aj5/glHgT9DnhWCjhzNc5yNeKwHhXYlXRFsoLx45KjgYyIfTW4UBNrkRqoGW3zmhMcC/qCPfiesaINK5ZIkY+U+888dnZBdxffOXXeWNl6XaZSm/Mm31M+pUYCUCUcAFPM2yEqdImKOacTWkAkaBTyWOsP3dwwl7KVwrbR3pAODaoBiA1WElCj5dVcR36VmdZ7VfrDOKwGr/skCL/KqBrMy7/Vxpc2rthlbtIZMKt7oyGT4GqGsM4WOo9C9rlo4/SVCWnpUEYmFUjo1xFxEqexqG8SqAeUxmKwly4T2NxMgFSFYJhKliwe+7kh7HfZYwZr9QBQCsanvBfMlMEt8JRIZOciI9dPEdzF4wb+ERjKH4ZSaR6Qn4qpfq6niXaVCF1bTsuYYfKYWY8eFd6ZBO/8iH/EOlpyBQqlHEJCHDVRY/AyFFrqUCwUwgI04ZkaeU1bKH4138wC1cLhivUFXfg6kr3vULxtL4pDNcx2t86xd14TdxlDZKnIVtHEmx9Yd3xQlP5tx0MqHGE6x68RVceJdR5aB/hk+bwRQpVYofbTMBs+a5PsFMEjY64RkR2gHQGn6ZhjnUmMA0aWrZRa2HMbXtgokkrc4NBPuB2YfKEPlPoDY8Ny7zBlNIOTxbODExAcyZ1NbWzsZCZsxDVYiyeFZUA0OqlIahGstQFt88oogSdvuIak6Z6SNEFMI5keVFmX3Cvkbc+CdupAXHyXHOlpw7G/cSbti8KeBEFQsmXgpgVY+NnCF77qUXHhszqDr3T3/8k3Tsw4/S8Ag21dfcWg1QA26mvf/uu+nUiROhCv3Mvn0VWriaFdS8+5W0/j/6Z6n/x/9dmr598Z5ZlyP1HsMsD+V7Bd7Lvzo349wRr0IKq+Pd9X5HorvC7+1RSVl5uXfcSggVUdZFxe9+L3fAtkylu3PxbpDRK0kqL2WK6sAFvwWQJYSFMN8CzBKwlvDKCasDlgYZ8e6MVsmLlxInp8eKu9d7JQIv1XGq/aveq6OU+VQFV2BUxzN8ES6LEizxUSYunvfKp4y2CMKSniKwKNbnfhi9Ot8HTR7xishlmvL5+ZneHfNun8+FsqwRlG8qHaxpUruHhcVT5tYYwqesQVeqOLBEUAQYIZmioAwFefBBkFI5pYMybU3oaYQqAgvuWuK7+PYcmS4YNeIKS6KYF+YsygVagiRMps2w4F3wVw00n9vLCxjhy+AJL0vxMtMmwyGzWQdTqhRIRi5wL/COMwsYmQlJTpFfEEcZI15kJlVJ9TN8TF+oahq9IgHiI+/PGW7JcBHBl9KjeNXfn39k0mQGgwE0E96ppwUnB1c6w/kFc5aZ1OCehV9JUwAXDvUW8YUfxIqnjJyFUZXFdNZDvIKHT9PofFCnlcK4QhCmhydKlU7OB9QSP6R3MoAwZiNI/ka5mVez3p7jU4rnAjPOi5DX9PRYSHaVAmo8RoZehjAcsJQYikMw7eAwhzqs7w3Ec2PB9nVTQKmuGwpj42OooU6kwcFB8sNyXFML+XmeVRVP42HFUOuxxJsDp2gjimVxhMP/Im++C6lyrge8CYuFkXGMb11FxfidNxnq6Quz9A8P04+gGn3uzOk4A9tVWAg1v+hvJCmdfWbH7t3pyIvPl16r+rQ9PuPi8h//1V+n/r7+Vc17LbMvTw143+Q7v/51unDuXHrplVfSuu7uGH+rWYL6nu2p54/+q2AKp66dYPiVAzZjsfgLv4KU3IVjMYYX+RM3j827oFSiOVZz6N1xRKVC6yspVv6luojVWFW/fy4W1UA+N/Ljj1Bdzw9VTlG/R1kr7XeP8BUptcgX+VmmsjtXcFmOTIG7ZJGW9Hy4DMt2qG6DhwL7UJGrcCszrvJ6rK9UgFdepMauVNd9ABrhdROPWrjHWpL7Zl69Er1vxLXA3+0acKFbqrMoNYm1sovfYkz03bqd+m5e54wXetUMnmkvceVQss6FvJJCpXUyeaHSSZCTs4xdLKKB6eLfxAtMl5IhpYvcZQMM1Zlk/mT4XNxrQU7mQenNPAt2VVddxBsm42Je5mIOGpzRNcBYxPlB8ZZRCvz9Ey9GCfyDMQh8MsNnSbJUkBfQrMbReql8Byj/kMJEwghJIn7mF654GiaDGDMFfnrHO3h71qFMrwRRvcsIw7OEU4JTwqcr8A2Jo8AsvhJB4wWzR9qywXwGbmbCuwWG6Y48tYYmY0xdcckf4RzwRvqrsQCVgCdgtjTkMopa5hBGggaGuRCetp2hHrwGRClfWPkkpW09DdwGwlT/bLA98fPsnv7GVb1Yf4sVVgHNEXwpcZxLaUbt1E2G2SY3G5pSMxZk+weH0mD/YGpuBTaGhGTucnWhNoqEsAm/bMgGWOTNn0rRjedHlnRXLRHJ37JaHybxihL7kDHmeLfKNJLk/gBdkDDMdHMf1OVLF8GjJe4nDAiRVwCjHrhb6/mjafuunQatursNfj/6kz+N6wdmizGw6kisZfilqoHrGAT7yd/8Tdq1e3f66quvBg1ezQLUtffAFP7TNPDT/zFNnHn7kbN2BMZYF0IejtU++i5yJQ1flG5RjMfzkSkSeVfKUI1HpYThKY1a0lWALBl6l+ddYB4o/V2p7oL7eR6LS/N5sZcOfyQsivItR/6LsAKZEh/nlId2ZeKHTrg8CSoof2E8KpA+H7F7duLPT7rsMYpGY/ZXnyzVrtuNZdEd0BUWAQvUhfcvXEHLjvqjAFxjCB+l1n5n0tjJ80B2UZ0ZrKLwLnr9x+BVFa0PC4Wq7dXDGIyMjrBwRsLHIr2OM34a+PC+QQVDnjOMRTYL5YBO2LwrbL/CQ/bQxXbIDCMz7zXM0h0lQf6y0ZgwOmI+JFCdUCaCPwHGxbqSkXCBez5kLkMYFkTJS2bEf7p4yp8FJ4ifhID/ShjFx1ixYKg6X2C6CrMhc+UvgytSGCFiCUgAGa6eEbcot4kMB/dwFCiYTJk5GUYZQuOLU2TA07L6XuYb3B9xdDKgkcb8+JUMY4mLcTRMUyOzR3hwuD7Ih7hRjzBtwZHBEE6hXjjOubwmTovLCA5g3GWMNh/FSMwIBmSUDoa5csoQ7UA+UzCG+ewnpJNy+7N8M6iNhsotdSCD6J2QHah+NiDZkylUEugFuvYp23ASiaObAfNzYxQrS4QbUDdt46oR211LtPl8p2dUPVMKg4mE0Mvjx5FczoYBHspGfqUqsrVU6V1RJ4byjyoVzfwHv7K+eLF/zNGHa+qVFOZas2+I43WsdGoBdStMn/jrbD+t0R7ksvkdnDMsF5sRuAp/xsfG4lJ57xIc0+rqmlurgYeoAcfc6VOn0rWrV9MLL7+cdu7aRb8v6MtDwHnUqLWNran7h/9FGnrj/0ijx35coWEOz4pb9FHxLYdvjONS3rcQeo+3GPj3CMP7c4LvnfA+IQ8Kc3ExC4K1BFzhlfPZEsESuUy4lgz8HM8qJMwn08a701SX6d6Y3p1On0p84RdRAl7xUQ17aQj3971neuBbvDLP+0NZCL0nvIUoAbfqs/L6IGnLxA+LV2RS1V6VTB/iJfIsYIir3w8Fksh3x3+Qktyd6iHQXt6oVZ3c5dZc07rU2HMIhS0NcGU8nyBsl6XsawzhslTj0wzELs9AdiwzQEKFr2RYZMLwG4NZGJ8YS00tXObKNQD6qfKpdGge5sTFhQtnF8Wq9/kMKaOjzHfiOJHF+CspD3nkBTfMJPHqkRJlB2NaMH0yHMEoFuHTrugBIvyQPMIMBL6F2a2QUIFXSBVlwMyTv+IS49sPXKTnKfOppCsQMwpwxTeb6jaCeUUK/OUS+Chg5G/CAicj5bTBoPlO2qwPW5VG/0CIsvrk7qaQ0Fkx4ujPd8VTgb55ZtCRj/VZ5pMjFGmIpEEYpWi0BZkTi7QyjpVFnoDwgvk2nmcEVfvUKtjk8EgwbLadKqEjWPEcpp2VDk6TJ70iF513mTIZ7XoYyUAXWDJJ1GK0Re4/WXW0m2sb1nEvWzPSv5amRqBkC7FlkYTltSFap9V5LlCmdBo88j2BtZgv96ygUmhNcGOshkzrsVyrcRmvOAlrttGxrFLaCwY5eH7S+K3L5tvtC/zDK+7QKtJEX8DPmrU89mPb1qauof4s1wR3a165eDG1Ih3v4Y5Ly2j/f+Ubr6ae9dkstfmslruNtP5f/u//IvX391nINbdWA49cA6NY932TTYUzMIe/973vVTY8HhngQyT0ioSu7/zjVNvUlobf/dHilJlcLfa748sojPj4e0fQXZ8PAO6uNF/EI88bDwFhtRG8EzXpCDiUePssSGQlZhlW8XiUlxUo5yPhdR887gevmtzeB8Tn18wXSWzaL5Je7EgfTV48Px/hqhikWZy9kKprpiruk/xaNLTr2Tk2z+u69yMd3A3G2Mfgb149Li7pk1ycB8GtXGU/SNy1OL+TNeAiuZAOMkBcNJfMnYNcFc1JznTJnGkJcnp6JO4C9PqGqUkYEMaLFkEdNjJYMofhWNTL2HlpfMlLhT8RQxoj04eapOfCXGB7psx3F/QI6ZiYXJQLNU/5kTZGKRjKGJHexXrE4ZvYwQg2wliKWzCBpiaN5TNpnDnkabjp/GdY4Ic/nuRtXPIFF0pPWGYODA5gQjJBgcuiwlkZoiwzll/8wIUn3mAcZeIRXAvxlJ7KieiU+in19C4lpYcymzBEOYy4nhcscAuGM3AhmtkGE6kxHZlC0gVMkdSJMz8Y5znURWuAaUubxjsZkYPxry4NDg1z6ex4XAg/ztlB68gL44EWRVeSa/taf9a9nk3Ut3XmWcSQ7GGZ06tGVOlc19WZ2tta46wgmBEf5hIcjC5s1Va9L0tp4TRXWAzPw5hS7pZmjBbBZMqw1sEsqro6OZclifYp+6KqpY1NDTCRnkUtJMXRKCKGi3Yko8KvNuo59+18djDXedn+xtPqaNxdCG5xDlIYIkv/GuzrS1cuXAzVVo3MPP/Ky6vODI7TNm++/sv09q/fijEZ5Vz7s1YDy1ADSgr/8s//PD139Gjad+DAMkB8UBA1qeNr/yDVNrenwV/+i4VEDt3S5aFafj11T4saRfRPdbmrSgpJIs79K8LNMl3QrKq0d75GLAHqpG+l4zVwuX82ZexFzxJc6ZnpexXsMuCOZ5n9nemro5VhZdzqsDvfHzTfO9M9yHelNGXVkaisqofB8UHyMk4Jc1F8PZ3LV8BF2z8M3LLwD5pmyQI9aOJljmdnEh8e8x7Xaduc6nuPsDjsioxy0VwlZfewRV1mbJcN3BpDuGxV+bQBWujijg0XyZ7vkqmTyklYHS2a4B9jF9nJqDyn5+XwptGCpPfSuSCXVZlW8hQMnvyBUir4kpBQ5bxMI6NWB0MxyxlEGZk5GAyNxygJLOFETRMWUkATmXc88fMTpiBoIuk8SyhDotRKRkFDJV56nxlKykKY9w2RsQlZ6MuU8SlUgMzPy8Bma5SV0W8aPniEi7j5lb+WxR9OTkk9WQsSePEd9ca3TGMZz0AZEvOWqaPOguGT6fPn2S/xC9VachYueIWqZBiXwUNG0WwDf+IW7WTWs0jWVOGtUX1SBjZgEVcEizJEYQItgWRk56aVzHEPIAzk7ds3Qk3US99HUROtpR6buF/Sc3rGl+mfRn3Tum2EWasBSSWxXuDrezMSwO5165AIdgUzp4qoFkHzhgA4yWBHwQAnPMtDBXves460pm9BFVQp4Gx5vjJienntXJqs9SJ7ziUCw/OqMv7G9+7DGcJzv7VsJiIv/1nthZd1J75KGiMu4blCiQduLqbiSTzD7T8ZVAYiI+x5wo1bNqcffPWPkBSunmTQBV4/DOn/83/+X+nGteuB19qftRpY7hqQzr+N0ZlLSMNf/eY3Qwq/3HncC17b838IU9iRBn7+P0P6slXie8V93P4lTZG+LIeThuviIfAvCPjBQRQFKPLPWCzX32WqnM9Bp2yLz4m2LMFWU3Wplqq2B8JnqYQPguEDAX8QQMsZp7pGlhPuSsOiERhnXkM2W9+e6jYc5SLPnWQaW9eL2nmlMVlN+GsM4WrW9pcqrzyQXWyGERiJTRAc/T07FVxJGsfYyAgqhTKDSmfqYRQaGlAd5UJy47nIj8U3X6Yp+K5YXNdzNUUJM+AJWikZTqbFeU9/z/xloyEs1oVHQIOMDfG8qqC0uOm3jKDqkJmRE5JSRnBlAe+CX8M09eThcj7i8yczpcaFrS3zBWuHfjC+IiJu8ZAZlFbokb2jDEV4ePpHZlJGQgYvEhch4qJX4C3zyS9ACdwfYSEpZGh6Hs3VgNdBRMXlsBoupC0SFYWQ7SIvYZMnBSVcRhLpHjC1uGq9BD6WOpgZPAIHournuyq2MLBa0PS+HetN5mpgaDAsinpWcBQ1TOtIJnp8ciQYbZnxKcLcgK7HkNAcaqWqibY0whxS/mbeN/T2pp7untxuRT8iB6SQnBWECbPcng0NRpBiZIkzFYW/9WJ915NBY10TOGUpoLiCJnExFEQZiYIEk0vpkVRqWEZjNUoip7j/UMme+eX+5vaF/2xJfE1o3VEvMqj5/KLfObooWEZ6UuHhmVibxDOTRBIKqHaiAvvH//E/WFVm0Lx/+YvXkmcFJyj7mlurgZWugSuXL6ef/PVfx92FOzhbmDcHVzpX1mMHvh2Z9P/0vw+aEB8MSYdvtQvyhoc08V7OkDxyF2JE/II2LYYJnQp/U9wbZglpcdrSNz8LcrHYc9HX4jzK3HyWc04ZXZzuLEOElZ5lYj3xuzN9yWgWwZG08qcsRAmrEnA3nDKIWipf43m/spbgywTGNXWUs/TkeT8YVdHi9UHi3lkHOSH0vQS2uAil78M9LcQdcMryPgiOUQl3pL8fAmUz5/ntfjHvHRb1XpWnr3fiWhV8b0BlSES+szXLwDued2Z0R/CqfBYN5JjI2lnmCjOIhfcaVUXXIR0sLqIv63tV8FrlTNYYwlWu8C9Hdo7motsrueFX3wDTEYtqF9AsiGEUPJM3yvlBpSONMCEaDPHMXZ6IgQGIWIjz6oI6JED4ydTVqFYIs5CZRBkj2DgW/Pp5FUGWxqial6WDMiYRX7SMq/SJ+B4PlBEJHosBXC5O5IvCSiT5yjpKc5QaNbLgDybV8ADFX/+zijBOMKaUU+ZRxsc4liOYSr4iezOUXeThJGjZamCoUErNkbV4StqILKHx3SSRgG+ZD8oVfob7cxXjM6SB1gffUQjSyRiaxnATyTAKU64kpH6Uz+/IJ5DKeFAGECcF6QjLywcrTNikjbOVImZ04FPHvOQseGjEZQppoMyZkr8prHtqNEZmb5iNANvd7xklg/g1oQpqPdpXurg4vhWpXgfWN1th1to408dFIEnDs8FEAds2nkPd07aNslk++5gozVtmGK5ALW8qiNgsdWv71fBUnbQNhi9ikai8qzL3H8LBR7XR+olapNMAsu4qf6veyFYXfdqygq/qr7a5TVAkA3uYa76jzV1C8L+8QmNdT0/6R//5f5ZUF10tp1Tw3/7Fv+H+uJOrleVaPms1EDUwPDwcmxCH6YPPHjkSG22rUTXBFEKLBn72P3HemfPqj5Dpo6R5hGweMclS2FX7BXEM2AXlfsR8nv5kMY88QDGjdh+wMh8I5gPCuidqXzT9PQE/RMAXwCHXkbVa3W8fIu/HGtWCizfrCTfF27eluvUvcGxQjR9XI0+3W2MIn+72faTSxTk5RzULb5kdGTGHicySDJeXzPt0gSxD6MpYiZIqolmyAyOCi904xpbPadR8skEZwSLhg4GUiZEHKNXveIkFuPnIbJYMpHBlFv0pwi8ZNdM6cGUOA13wzcyo/qXLg1t8NUxTpwRM5gfGpTL0gUl24S9MmRzhShdix4gvcfa9god+kYXGTP5/9t4EyLbrKtPcmXlzznyTnp4Gy7YkJE9lY1uWbNnGNMaAoYoCGwjsAppuGpq2oSC6u7qa6LGIqqiqqIjuiK7qoGmKIYqxjI3BgM1gGw/CWMZDQWM8CGHJeNT4ppzn/r5/35PvZr58Uj7p5tN7mWdn3nvO2Wfvtdde+5xz13/W2msDYAEcIwDCQYBuynI21kERhea52kCto5ul2bp7Ns+YEFNuArMccI6tjcqvWdLww6GCSv/5AkYDMLvlJKgwwj+VkGUKuqi8FQSCEnPfj8FmpClR5hemfMCt2BTLH2DP/sycJXIscpmePsSSD6cYy7pkhO6gEwR6GTOQC8Fhpom2OQkI1Fo5OjzIubEyAv1RrqFB5zESvDnz8Ngz8IquoqwyDy+8TLALbGW/GkW5psKmrrzIFTlqmfR6sJD8jNGGEU6lvDFNNl05MzMXOr4AmICXeazVywA8m0+3oR8RQbtJ9VplLGnfoDRex02QokZU8seVjriUMfvySmCZq645UX7sp37qkoLBe+/5m/L773hHce24NrUSeCok4D3z6U9+sjzwla+UV33DN5RJXgJdijR+80tzI59677+rN3TTKPfkbtJmsc2dC9fKc8KbPanngXHhKo95Jo/chtwOJbec4uD837NzleRmS3lP9Wb07p+rtud7VWYX0cxTxOdFcLjroo/VlV3J5bEI9HBxHi3r7bJuD5kdd3tpXxRJCvfW3ZH45ZoZxr2j+LAffXb0eBm65vYyMHkTeQT4OwCpBYQHYJAvtotikNwYfqPYq4ybVIS1Cum+OQIIOHPqNJEMT8WaJmio2jb3DgRU+v0xU4GWVixsKNDa6zKfjVxd+9xPPenzRKtzDKs1awWzjvMGvU0FkEnwIPYxeU7nz1gR0fZXDLCSNmwTPrpPSC1SAjmtfhVUyg8f8iv4hU94lqxzxbrkYzESSAYgNvkcy08SO3ElxO1zAGBSJ+1TW3GJHuxbABkZ+jayLz2X2QgIVjtQPqIY+I7FT8IerzJ/D2tmgJ9F7L8L1CdBi+AvdLi21atlKAPrmySvFZHi9QBCjqXoVXTUBVbh1SIW9N/qfK0SLGgFC94MoN+xdGmFs8wjckkJZWlgl3HcMicBgpMsED89PRlQmL7Q1zFcgkdxGxXc2d8NrpvlpUXEwFXRBcX2b5WXBb5kMOke7MuAyB1LgEuLbMCzbqIr8qtckaEyrr1kmQnKb+T6MPrXRK7RBQLReF1MYJnUdXRxgTaUTXriGNTaeTmQluuXl3EshbQr8AzY55SYPle0cqtiyvVzlCiiP/hjb75kYFDZ//ndHyl3vf8DsWL2sN7uthK45BLwEfPoo4+WP3rXu8rtrFn4zBtv3HseuHfHb3l5nmOnPvDzWAqd63zxiduYp8GlT72P67633tshO3i5p208PlVj8nhi8icnP9ePV/BSnN8ms/y+b897gnzYzyeazlX1Iuy9EJ8oxUtcD5btg75eq51pwOCLWXfw+Sgal+ZF1yXu7Y7NtYBwR7G0mUrAsP/NbR23SO6WWO9Qlr1xTp98FMWe6JEAAufyOY+r06lvUlSqawAQACRPUt34TOrz1VXUPALGjAOOUOhNgkcfuoJE53FFWadCJ09jCnBSgxaoDjp+sBJJEG5sQ6AwSKCTtTUiYJqr0q8Vh/0OdRKZMtUBfZRNkBpPAi3WqO8cNQFKmAS0GATHOonlApVYJqnXWJNsN9CVdgJskZY0lZvWwU6Qq6DQICm1v8ooFj0jqC4rEx4/WBWlBQrkQ/uCG/mwT5aXr5wmmIIBZ+BJsJnphwE55AG8kgShlqdqPWY81gFC9EUkbUAG3VsD2iykxTBDYx2PocVG2ccC5kLs/A1D3wikLko/zssA+zEF2JoAbE0TOdR9ZeU6k4go0TZdvqGZ17i4NBfQlzGXR9rIywWBIfS1HhugZn2BduHBwEQDWn7pS4dIpi4+HxkArLXieWXmugnblW85HQVEW3cRsGx3Rqg7CX9zc6yXSICZAPLaQehRwGTBJHvqnNkKTp1PuBpZ1bP1JQLFuZa1IE4TJfXNP/VPy7VPe1q3/t5udGN9y6/9Rrn/vvtgueF5b9tsqbcS2I0EFnl59Gd33VUeefjhctvtvFX3IbDHafzWV/Ks65STf/x/5dmw6xYpuL3suV+6PWYa8k9WNE+ovo+Lnk737ekRmj2Ee5p5sm3spp8XKnOh/Cc6uj5uL0Tzsc417V2obnN+11tEvVXa5x/vmlafC267xPpMvY/kNn871bscWD4yT1JbXBsYL4PHX1CGrnoJOtMRcrdLPEX35VcLCPflsPanU4FpPMmCEbrAKlY9lGGDxsywDEHm2nG/VPBQrXUrvLEVIG5oTSFpLXRPCyNqfI5VaAWGQwEcnEw7nHVLWyrcAhsDjVRg5772RbL9BIQJCuvtKogQ5AziJrixYit+JEtp2wJkCSq0OFULlNkpEb6kvYKin0AhAifq2IbbNfNhZ83lGqgSF1rPeeB5wBabCgrJsr/LBFYZA7wMspbg4jLz5LB2irtGAIYCWfnTWmn/OwRKsf9rzqejrDw6j01QGMVK4l1AXRkBsNhx+6dkcx6Vhv5sQH9AEGm/fdAJSgXc5DuQAx4jp/oQrHlxFZWY4Ie+aK1bBVCFZ+poKevA5wJK33SWiZC/TpnCHXSSj5bAWGHpoXIK8KaZxVkC0MgmNFzXcAAEOzExAZiscw1tY5n+1u7hgkq7rjE4T3AU+7KCVU+X1eHhhTJhu7RlO8o9/YC0lsOMFUQE5yNYFUfpv+XkW0A5Pj7BQvVz5czsTMbG5VFgqosHVQX9c0xMfPO/AvgdHnR5jFGWruDlhH+Iq34Vlsw4XH7kv/vJSwYG/+7zny9/8PvvLA9+9YFw2X61ErjcJOB9+NlPfzrTCO64885LEIWUe/tr7ixHvv6Hypk//Q951nifbk9NTr2/Petezd3cy4OqqcmzoHlZ1GT1abulmR1oNrzucCpZ1m+eVBcqUwvWs5u/Hxw2tPOzt1Nliech50kbamrsVJii23h5vL7tRAUSTzhdqL0L5T/RhnZLryn32FJ7DC52IYxdFHmMBvbw1BbGdimBRmB7yNaFSW9hmGtdnY+XzYNMdzn2/NI5cSd6VI0Unt/+3d11F27uCjnTAsIrZKAuBZvN8z8/GM0Pg8AjwATlWxdOFHCtQ7NnZsoKAGEYMLCBsp8AHDCpuq6bneUEdj4a3BdcNT9O3nha1AxEosKuIuHPVXUX1Z3SH2SWEkARd57Y0DDKfZ4xuqDWHzZvZ8+p2NuOlrn8UIqPyPVZI9khdgRewwAal0Pw2HMpRA3rxjBn5z0neKKi1qDMFWPfMo27rBEtYR4CfLqEPNLCZYRLCAg1ADqr5eFTZ5ket1oWWUJDa6mA1EAnukWOEuwkS2BghRpgYXNlpBUzcAcwad98EI2Nd61jsRp2m5RH+24/tPABgEGRPMBGk11Yky/mQ0E1VqVYCOU5gI/yqUcdBdQMOqAttOi3XfMaGMINtkM9LWUdAsSM+GF/1C2BYyrwA0pJ1wryRHWDzGhFM8WSK8Al6qdAdR2Z6Ya6AhBcIvqn14plHN8F6mhJXWR/HCA9PIzlGVq+hHjk1Bm6OBcXUBenX6fPFbwTxIjxqeAPGdAnwWCNTOvVomhY4gILptZCo+Ju4MMadj3pGHpASmnarnlcx7RrVFojlS7Cs9eR1+gI1/x/+RM/Xp79fCKP7XHy3vi7+z9f/uOv/hryemKucXvMYku+lcAWCXzxC18oDz/0UPmm1762HGapmb1Ok89/bVmfP1Nm/+J3uZfrc+ex2vQZe7mm7qPoguzltzK/OzymLlTqgicuVGGH/H7Q2IHsbrK63dtN0cuizGPx+1jntjD/ROS9SbwP1zPtb5LbwtguDrbU7QMvu2iyP0WqfuDjQJ1tlSiiA6w12Ln2lWVg9Dqym0Fptv1p9XKm0gLCy3l0LjFv3h4+FIIT2G/m2MmGlhgDiWgZstAs1hbnaGkhXOdHWEA2iqKsAq0laWEZcARwCTBAYRcroIlX2ijWjTLf3Goq2hTIv4r3/PwyCvAy0SknUPaFRpCjrcbqKDALYMs5bma2HcCKdMR4A5qqrMNWLOScR+kGOGhL6vaT4qQaxKZauTgCHFWAW/NdpkLGpSUPgr9VLEjWjTssOwuAQUHNKO6zWrlOnz5TThGJb0lLG/Ssq1uqYNdgJ86JFLRoLRuG5hhgxQAsLuoOkwGk66w1OMqcO+fh2TdlP8xHC2IEovAcLFPmKAL27KzCdo6hQI2+OXcvcrYo+1Vp4qARvpbPAEILVHpiToHaMMAv8/AIzmKeUq1zMS1a5SQQly+361pCoef+yKiA3yika4A9rH+AQLrGMiUzACxeIlBGWdJSQLegS4DouE9NrqVd+7JO3wcQ7pm5WRahX2F5h+mMo3MDwWqwjnsof1p+h7BC6kacKLTIWpkL6MeQresSzuPyusaxfas9pflGjt0MlS7HSZqurZgxQuYurSEY/uH/9ifL817IukSXIN31gQ+Uu973AeT0+IruJWCnbaKVwK4koAvp+9773vLi224rz7zpptxPu6r4BAtNv/R7eaytlNm//P1zz7UureYx52FeSp67859gaxdfzd+bfqXm+bQbegGQPNcu1Px5+f1kdDcMbiuz+Uzeln/ZHyLIPef9vMG6DKQCT1svmT2XwpPvtHJs9CZ20QbKyuB0GcBNtHMNc5MDBlF2tqRU2pKzHw9aQLgfR7UPffK21vKi6yHwJwAlCjeK/xI/9mtYT1xmQFCQdQq5wQQ1WroWtGRw/wRUATSq22a15GkDyzw7niKDnNNl0lR/5AR9KOIArBWAVCxTKOKLgCKtiQIRFXz5qNa0Wq/WBQwAsFxzLo3n/gWw+JgGjAXIsivgWHH+Hq9i4QhuKAiAqAwHPpkjYbvG/D7BBQ8HgQYPjmWAzTxLMRi10tqCvVmO5xeYnwZfgtd55LMCeAZC1vaY9zc+gSsrfV7E6rkEDQGJwXlMWqAEiVoFJydYrB2ZCL6NiBoLJ1a8sbGRcmh6ms9kgG1kmtp0Kvyxda4gwNCX5PZ7sIMrKiA94NhOCRLTOw7sHFWSAgYFZiROOe9PS5tAynmQU5Os3Wg0T44dvwGikUZuXQJxwVW29GsZa2iALn3S7XKRhewRouIsy1wbSBMZrJZZ5DWC1U4surRs+Hgsh1gIdbWdnZ/niuOlA9eRMhKQTQFIMbGWWWV7+mx+hJSv9ZSTYF7agv4KWJVD7aDXky7IAtuzWCkF6Rljv7Pjl59cLeG12fdFCJzl+vPa+67//PvL7a+4U0ntafIe++M/+MPynz7+iT1tpyXeSmCvJOBC9h/58Id5Jq6Xm26+mXtzu5LV35anX/bGsjb7SFm478/r8+2iyPusyMPgomo9FYXjQXORDdcn4blH/kVWP794P0S1Aw0tpP7GmOrT+PymzdkKQmqZnfJ2rr0tdwc+tpV47MML1N81Pxeov73RXRbbXu0JHe+qLQpt7WNzlT2hJi9tJXhXJ0HDKavDx8rg1V9bOlffgRfT1Vx3e/ucurQdvbjWWkB4cfLa16W9DfKz6M2CMu0NE6zED3rAgBYYwJWRDk26EC5gcRE4CN60ws2hzAvm4saHMu/WH1p0cohCJ08Q7ExsBUWCOK1sztOrAWKcywfY4jPeDWNuVRV+GfLHIq6fgi9Ak7QFLwJE/xLN1J8SKsXK40Ory3eXBUAa1VxQzo2dDujl4QCTBpZx/ogBTgYDVgV5gAvaXwIEnjxzhrlos7FiCRBWQDSuqz4A6FlhkfUlApc0FsGBQeew0QDddVF33WOVhhY029XKaR/mAiABbvA9gxVMaDOCVXCK/ruwu/MxtYDGquoIMR6jWLs2F73vyoECCAqrHOBZcJSEVTa7gs8IwFyEYoJOHRj2My5u4Qsr5ZpzN/l11kJouzrr+ueYSagOR+Up/QiYq1Y/g/HMnDnd5XmI8WJktOiCVLUcrHBtzQPcl5HrEkFuFg32wnWlMiBgXkDOg4w/gsp4jiDnJfYN8DJI3wSKAuLhMfo0tFrGBnBbpm51U62dlONcb+HW64fIhM4lBFguMoaOXUC1A0KngZXpeyzMZnkBkbyGYp2j/uv/0RvKq7/ttcnfyy9dQ9/y679RPn///XvZTEu7lcCeS8B756N3311OE416r4PN+Aw58g0/ytqmPJ+/9Mkd+9a8ePSkz4ytqd7zW/Oe3FHzWN09lcfnYQvbWw4u0Eq3TG9/z6uWjMdru57P7ypN9dLo7efjUenlMo/fnozQ5Pe3+/jtOXNut7etc7kXt9cri8eqaVtNezvx5Lne/jb79sPyTd3z2kiBrblN3a2559NoyqkvbBmE7RUv4ngHdnZXW2asvEUKu6t6yUqdE1i3SSSH7rFGjID1iesBgreXoWN/Dwe2Q/QinTmPtcj6vNz9l9ECwv03pn3pkYqx+EKwV936OAac6HK54GL0KO3OLxPwDHBzsZN9gR0VKtDwiZibkcc+dAR+ArtYGqkbyxtPZq2KAsdY7djX4pgIk5RdFZjQI+uYso+1xx/2zLvr7mc9O877VtEynhcE+ECOC2G3vid1GdSKFrALf0PMKQsQoG2Dp7jWnUtejGCV0ya2AHg5M3O6PMo8Ni1Uy/Qx8yQh5vIFARbOS0NmkCUPcCHA4c+5hMowLq6cswcqSYsLC3H9dB4hzFKOZRJknKSM1peQw8bZcmjqUNwxl3BdnJ2lT3CkfCYFzFgTm+A76TTtKYHs02YGUGBl3wHe6bQN0M9aLpJK+2Ynr0tCnpSdeQODjB1txhLIsf30T9DpCwLhFMUDspz3uUibBtJxDqDW4CHqTxDpU8uq6+YZ7fPs7DwWQgLIUE4QGPkAGhdpZx1e58jTddf5qquC6YFVXE6XCS4zmQiium7OYDnW2jqJ6+gwltyM87qyx5jIddEBkMvrIMzZLeuM4dLb6cxmbmfk7bVLGmD87Id9TsRa83Kmft3xdS8v3/r67ww47snu+67LuLz9N99avvSFL/addkuwlcBTIQHv43sINuNz74UvfjH3IZ4Le5QGhsfLkW/88XLyD/9NWXn4/j1q5akjKxjbknywbcvacr57sFsAtFPdnfJs0qafcLoAzz6DnyTlJ8zS9orbRb39fF+OLyCHTdqPd36z4JPYuRRtbGfvkgi3abR7pXITVM2FF/HD02Xj0M0Ej3GdwZvR2fbumdRwcSVsW0B4JYzSpeCRe6Z5wAumYhHEapL5dJypc/GGUOrnmAPmYvSUBgxp+dkQLODyqHudwEZ0IDCUjinWpe6+Ln2xdFHXFOsL53wmCawEWs7VEuiYK/CMOyAPkGpFrMAzgMx65As6MGflh7Gq/h6q3AtZsHJhVRK4yE3mj1Fci58/kllInhMCBwsIlbQ0Do8QBAVwOMtcxkdYb/HBhx4pZ7F+6oUo6BMIC8+00ikbedWtdVDgFcCELAyugjLkjxyLKvBd19gbA+S4lIYyRsLQsg+CZuUMPfnu5p2dORuL6ATz8RYAxzhmIjPAEZY1l3iYmp6CC9oM5zBnktYmAOYWh16SQJXSEZSdsCfKzWT/I6E69jDOcR0jgbe82YRjWcvabyWFGy1jtIir5zxA7/TsHCCeMshlecPAMasEcllk0Wrm/XFNzOJC6tjNw//Gxghl1stiZCToXEDec3GdtT1lPKVMadXoq4L2uTNn81LiOMDwFC5pI1wnwPsyvo5LMTIbpa8drKtaVlc5Z//WhrDi0vYwebqenp2lXa5ZX3QEzDMyysz+5Jqhq3bXbEVy4623lB9404/mPDl7lh595JHyS//+51kiY27P2mgJtxJ4KiTg7XTvPfcU3Ui/4TWv2VMWhiaOlKOv+cny8O/+M5axwb18W2vNrb0t+yk99BEtXxdK9RFe4dJ5ZfrRoYug4bOx+Uk5j5fHyeit19vf3vzHIXHe6Yut29vuecR2yMhvwQ75Zm3+TnTPN9faBXnq/qakeFO4W3fXG+pdkP6uidSCDZ1GJhfNUio0tZvt4zDRNPo4xfp5Wv3MzzpLSmxMnCB4DC6iRwgKN2IkUfWiNimBFhC210Ek0NzKub8FB+wImAQt/gwloAsljdLoXDGDnCQPRV3rjsFUnP+V+YSUc425KPXc/AFb5FWFWyABxQAWLXNaBuu8QIHbApYzQeA4c9AERvLh00+wKb1YxMgKgOKcAM85Kv6aZl07wWGqpCJgQrdHrHAcavkRVBACB/cAeag/sNKynDwNxrpXEnxkAeCrJesUy2s4L9DzHUCryxoIBnUhFVQImIbMgxdpOe9PaxYnsyi6cwOz9AbHiVxJ2QlcPrVoaXkT4LnwvD8u1s9afoCXocEKHD1eW4M/7lYtb8rDslrsdC0dZW6cvbW9JEGeMhHPMfcvA+k55Wkmp7KvTBtAaEX4P6eV2AZ0+OiymhOe7yb76jnn+C2xBMns3AKur4JCXT65DijnfEoawLo6W2YA+boVz2PV0/q7QP0zWMMWkeEqdHT3EkAbkGiAc0Yzddw2lDmycCmKDnMIZwhmNAW4PEOby1wrWgc5WRYEe1wzzhUdgl6cZuku2ZlT6Dh5vRqkSCth5nka/CaCq52yvwwJefWatbfX3XBdefP/+E+QM/Nl9zB99StfwTL4NkL2t2BwD8Xckn6KJfDlL32p/Mm7313ufMUreEm0dws+d45cV4695ifKqff827iQPsXd7kvzPY+qvtB7MkR8su9FygvdvSK+FwzvMCi9vyl70mTa9OtyEtQuedlr4fQKHJbkSl1tYxBvpdFjpWgVPPbcMjBxIz/0gMPe8u1+Cwjba6CRgO9P6tNNi0sAG3dLrEPeUijmWuKMuiioAgMGEKhoaxkTFC6vElmUG74CMxRw9gNwvOvY14VTwKVFrXmO5oZFERfceF5A6Y+C4BC1PHe0LpcuFWAZP2ZbX36z1RIET6txM2SemayBepwD2BFYct524v7qnDrKhjqVV+sEQEBKBRNroOAzAMCzvMl2K3AYJljOYdads0XlIE2tYAZBMUCJ8yhdzNy5k/Kn1XAM/l0TcQhgE1BF/wc4jsWQMsN+AIECwfU1wHUAKnXZLgF+dKfVemW+8/gM7jJC2ZRDXoI3SAAwXc5Ct8wqF2AYKMjzdC4gkH2Dy5jfAdRYTqmJfNwGtFmew6QKhiCfLKO6eg004FwgGDfMSBkrqO6hWAZntAxCwzl+C+zPMw/OIDL2f42+P8ycQpfcmAHEOR6en8NyiB0ZFir4NBCP4FhAbdiaBa4FA8zYZ+msAgK19G1AY35lMVFZh+FnhXmX44BHgeMoAFOrLySgw3XstUp95ScYlocp1kKc88UGQXjsvgAwCblqOW5cgF1v8s3/9J+Uo8evquf36PtLX/xi+YWf/bk9ot6SbSVweUngga9+tXzw/e8v3/yt31pf1u0Re6M3vKAcevn3l9N3/XseZr3PuJ7HnW1vPvt8FmxlxlPbsjYLbM+3bA+p82htVtzljrzI9pNKO9DYzvdF099GoDlstg29nVjfnmed7XlN/Wb7eHLYqf52Xhpa/dqmTRq56HZ2YnbXTDWtQeSxLoym2OPQ3X6tP07xrac323hSHdpK84JHO7WxyUC3ljKpu26cfuR0niIQHLuqDE7dyBrzX4N76DPQuQ5zXj2oTdsl0FoIt0vkAB73PlsaN0/F4L6WrWrZA1KgoBtQRjdPIzm6tp51Pe+jUaDgfK8VlfLu0ybzsbh3U4a8WhYcogWLJLiUiDewYFNQ1QFcSl/3U90Ms15h6lYIKF8+DqQliBRAOgfMGJo1SRPjmCBBQAgA9VjwusxctGEAQv0jm76sQ6e+RRosJ4lgeRLL1Qr8zQMaprFGTRLdM8CWvDGXr5A3ZUHdAQHj4ERAk+BMnuR9nOiezmOzrI0bpEdgE7dS5UUH7EO1tBnNk7IAPi1RS1jZ7JMAR3AumJGumEax1nl51RXW/jougs8k5Dhg6E4L2s6KFkXb43zXelr5gXeK2M4mKJQjB9QPqVor4UBQSGGD7TiHVIDYGTKQi2v1eU0s177QrC8DdB+dwWIoqCsd6tLGKeYOahl0jqFuxloF15GHPAo+c01h7cPZkwPbCwO5lryGOh2oWZb807NnE3XVdR0NMDMoiIS2gXvGXPqEj8t3jHF+Tas1DcTqx1Y5T01MldlJ5zrOhH/FVM/7GqHua438oX/8pnLDM5+hKPYsfeHzf1fe/ta37Rn9lnArgctRAqdOnoyl8Otf/WrmF0/sGYvjt35dWT35xTL3yT/kxvZ5R+r1isgxX/WR59Huk8+onuRh99GZZ6unmiLbyTf5qb55cK6Uz2Z/o/J8TqGer83y5J2rkmeYpTZ/w5tyzZZzm7t5+PdWtua55OktqVvUjaearWW2FzVvp9TTWk43v8GOR17KUWDH/lJ6O7s70e9H3nn97rYt7WZsm3a293unulul05XA9ooNwW3bSq+ncDIUUk/etjr1cLukdygErZBJUb4ej2QPic1+nleHDPO2C6qpu1mxydj9dnuPepvevN5pPEAQz6r1YbwPxq8GAF5fhqZvxCL4dFQgg8ZUT6zdt3ywSraA8GCN9469rT87ftfkzeYD2rleAi6tdZ7TnW8ei41P5wbQDeOyqDVLy9ciFhyjSGqJ895X0ffZ0AA3YE19XlA2yn3O8wUoFHAICgQUoyNVQXBuWixkWtusSdHwBjcCVZX7piF/SPzzvO3HaommLx+mrD9ou8w1oxh8gQIIRGIdsmkbixVg8GEUlbiVknfsyLFy3XXXxip1+vQp5qjRHNFoBECrADzdJQf4aJ0anx4HALq+IIAQfgVvghT5Dp80orunQNmgCmI0U/iVr3BSgd8hFSR+JK0nnFKIljOaqiAs9djXKuhRBauco99DICm7lh/YtMGXy03IUDw/MyB1H7kPhHylqdxCkEPr2z8/An35W6dtwaAPYBx1sbC57j1WNvJGXXJkyWA5M7gVL2E1JdoqgE+r7QL9Xga0KWije67pZgzgyvxL+piwNKtDrC94OJZY6+gO6rqFut7qZut4C8KNNJoXBPR9jutN+Y6yT+ifuDuM60KrZdaF7QHYw2s1+qug2oiwXqsTuI1OTy2XOXj0GoMl+uu1opRJbL/uNa8uL33VK+vxHn27gPdv/MqvxMK6R020ZFsJXLYScM7se//4j8s3fcu3JFjUXjA6wIurQ6/4wbJ6+qtl8Qt/UZvgfve5n4fqeY12nwHmUySbzaxuRs3Oc+N8IpahQm/dZr9bb3PT0O0l283zmRQeK7VU8TindwIEls+DjKJu3eS7aYQDds2rNHrLkFcPU8OvLYcc9FCp58wwvzmxpcImmR463QKbFc61krbheZNEQ/McmaZLW/jYPC0fHDT1d6i+WbTZsWzKbRautavoNjOb4udtLdG0t3lyk+hmDm2cV2qHiufKN6U36fcMjL//pp2G/xyFrWPVm9+770vaLaxx6NA07feWbfYbqXS56LlmurXk1d3uZrNeKj4O8abwedue6yLnKheoHrkm8puN19kGEUM3OoDAkSMAweNlcOI6QOD1ZWDkavQ89cmqw55Hvs3YIoEWEG4RxwE86N7L3sXeaj5zcsuxoyXKdQET9IRjI2PqHhjXRRToAB6iOnpTOq/QBcUFkabG8ucDsbob6vZHWa1zPNH881Zv3u5oBRIM+lAKwIQPgcYwFjkjcQo4xTda3RoAZDuCyHrs9lwgG62DPvTykIO2wCUoDPoGhtkAuDlHz+4LqwzSohVLi97IKC6c9O9awKCA9+zZM+F2HCCnVdSbxoiVg85zww3T+W6uwTg+jvWQBm0T3EEjwLmuPDxcXsJiZR8FMBoOTQIR+WRX0KML5TDz/rSSSqemCsI8r7xsQzdIK6UObVQX3wq0bSvCErGlIQoiD0rzoceOqROpsb56nJFQEPAbTMj4ZFxoy2xTM071xxtKussis+Vl3FWRmSY9HT3TbQQcd1pquxCFKzYOAgC1Do4RDObGr7mZ+uvl3s/di+w6ALTJjKPzUE9cfSLrBS7isnsWEH765CnKMs8QuZ/Ghdd2rZMlQeiebp2RAXLNdaZ84FfRCZhrJFx7yyhHMK772CmTXFeu+Ti/tABfBPwZhn9cjBXAi176kvLGH/4vkL9U9ib9zWc/W972G2+JFbzOZXUU2tRK4GBJYIZn7l0f+ED5xm/+5j2dp3vo636oLP7258r64uyF77Ptt/vmsTtb787NUzsNFye3nO8eVAoe1Of9ZqlG6e8tR2HL+5jf2vJODZK3+ayidJeOJa27ecqMx0rUa1jhMbpJZ0v9Htq203u4nbTA5bF4t66/Gr7kTHvd43NMnOOhtmWNnSn2nnH/wp2uv2nylnKW7Uk1z5M7tLNDhd5S9qWm3twe4uxukeXWU9t6Jg9bCzSH8v7Yqbm+LlzqfBrnuK8N24et/ajgy8Z7z20tkxZ34K+X+oW52umM9L1Gup/8jgP+BpkGM0TE8OFJQN90gOAArqEDo8c5Pgr+AxyyHFVVhLbTbfqwPb89bgHhAb8G6u1cb5Dc5t1fAgFYQBjKtzfkOhEudQfVaqU1qFGWdfP0ZheMBfxQP0Fk3EJQZdcHiBvBnLR82GYuGvkBOOaRBHbS1QIUMNpV6D2nLm99o4VqMfLBqkVLEBma0tLSmLawtKUtJd3jowAAQABJREFUAQNtUlkL4cYK56ljYJghlH/Xy3N/hXNa/YZ1NxT0AToEeKu4W548+Qjsb5SjRw4FqNjHEc6NAF4EuNLRfVIZDPojotzgQnkJfAWNDSgcHRoF+PCmSrnYZT5wHb7rwxaMZn1OCiir7CyLPJQnfbUfERd1hZIgIwhxTlDIXyyKbANMyadxygjYBH8kQLbZZEQuEPaABA2LK0PKCsaTQaZW2jqmlnMuKW6ijEEsbt05oS4PkmA4lB8fGytHkeUsQO4M8yvHvV4A4Mv07dCRI+X6G24oZ06fCWicHO+UI0d5gJto37UtTxxncdijV5XZ6cNlHNnOAARncBM9hGVvnr5lrCgrGPTlwSjXyxTjNk2gnsOTEwSOYXwAfZEjLsJKSAOpAWroVSy9EywpMjU5Xmbhb5FrWFCpmI4eO1a+5we+L9egLO1FuufTnyn/x7/61wnEdPjokQTCae6nvWivpdlK4HKWgJbCP8FS+KpXvzprr+4Fr51DJ8rx1/+L8ujH31HWea7zONhMeQKakZ3N7LpD/rmyvQXO5W6r0Vthy6kL1tg8UelvHm6p/RgH/F7sVEdqwTWe3KlAD0me8D1HAorzKzhlQEJNyfNL9JDIblMy1baf3JJZaVX6OxSsv5eeSMFzdHt56N1/rA7bg8csmx9n2zjXTnjaUqkebMkK1W11UrHna2uFnhPbd7cW3HKUcdhevud4S2Hzz+fpvCKbEumeSRvb63GukY3KQopuL9PDR8/udon3nHrM3YyV1zd62gZ60AYvysvQeFkfAgjyGQT4DeAeugE4LIJEp8bkF/+xyO6O58eisF/PtYBwv47sRfTL+7q5RYAFHGHrARR4z2sp8xmwDkgSKIwyL0ugopNegB2KdLVO4QqIFadakiq4yLND2oIwXPgEFtzV4UzXv9oWjwoVdUFIlHJA1iaYMx/LIXQH2eoKKE3bUIHO/DWUeYGczIYePHtOWKabYGN9saJtrK0CtnAVFQe5RuCiQIq6cU3FiqVlDnYpR0CTmRnAxXisgYewbNl3zwsaBabyG9DMw/PcD6hgEJrUFyCJ7JyDpwyUpcAkD9Iqhuw7X07RCGIFw7pV+lddQnkkZkjgkfOxhoZn5O84QDOVlbNwEN4DesjP2EmYM2hBbCnrAowZA4lSMqfdJ5uGpK8VLgCUbPvlw1iX3soV5xGen7gCywPjh12TLQGHoD8FMLv6+PEyw9j83QMPgMBwpQWkOcfvFEDwYx/9OPMMWaA+VlsY4N+gROknB9LTSXjIKISAw2FkPQmAE5SePnu6PPTow4y9y5tg96PuBNa+w7Q5hWV3YmyYcaLvdhVaWhNdjsIxNS+Wa/qaFxtYTSdxz51fZm3J7rqTP/XP/1k5ce01kcdefN3/ufvK//zf/w/p/9GrrirDAOBp5qhuEAgoQ5HvvWi5pdlK4PKVwElc9T/yZ39Wvum1r90zJkcOX1MO3/ZdZe7UQxdxl3lXmuozsu5f4LspeoHT52fXO/78/H2as1v57CTq3dbdp6K7qG7tpay2XLI7DVSX0/N4OC9jF11q6Ne6dVoHP+L6+wj8jAiPnlB/OXdBri3yuBJoAeHjimi/F6hgprn18rvH/RfFma3KuUlLnFYjrTImLTMCH0GKRZYWazCZ5odTcKKPXnWhrJY9aauIB2xCowIA7mvBHfQFJLqHDqHEW2/JgCHR7OERXhoQZnkfEUaltD3pyJtgpgKYCgC03glcrWsv7ZMvvgQWBnlxXtsSLrAGHxlnEXXLaf1cI1DKCuDlmqddz1wz3j7Rz3FdV7F8Sa+uO0jTtif4s2PhASuq1iYsY/IhqBQwyq9trtF3XT0DmmlLd1xTeBdYDSFPQGmWW0B+zrFThs6dM9nu4KDz6KgjwslkQQ8EbTBAErQNgPkSBIbnZQArADIIWPQEXxBJSXnmwC8L8ukCQuXDvnIN4LSHVFM+a4QSNSqoVtImDQ+Plg3ktUTfDYxz/YnryvGnPb08gKvtqYU5oowyD1ULHsDnwYceLmfOnAmfYyNjsSYqG51lbavTHe9YVQGmIxwfZQw6Rw5jGVxgjiRWXXhdYN/rSPflKV4UTAEYBY2jRGR1vcGAVIEsPx7OgXVMFadAV9k5JuZnXJH56vpS+fbXv25PweCDgON//dP/nKUlZrEKVsuxy6wYSGgMQCtPjmDGqBFuu20lcEAk4P3xvve8p7zy678+86z3otsTR47zpCEiNnPd+5nyTO8nwZZWK4FWAq0ELrEEWkB4iQV+OTXX/IhpTdtMAgEOBFda6rSwuS9gUwFPNEgUaXVXXTdN0lkmAIjnmxQgRF3nZY2NjQfgnZv3ppWKf4CAijtQCKChks7bHvIFRM5XlG6na4kTKMRSBS9JMkDZBjDKl1FEkwlFy2YeHjxIJy6qtgmYoVmsjvaJPuLSadmqmLO0AcDh7OnT5cSJE+Wqo6xbI1CCPyNaup4gBDgmDyIDBmuhzwl6YvAT5tTpViswsk/yJshJja48tfoJpIVAAt/IBJoC4I11zlHYm3IQa5egUiDWBPWxD7YXguELKmRJSygRCyxHtjcA4B0clPdaZZAorPaDztb6OWN/uvQ4FjQHjIQXgZOH9oRz9tu69GcIN07X+9Ma2GF8pXt25hRWttVyFZbBpz3j6WUUd89lwdoDUyw1MZ9r6RjuosrqLGsJ2s6hqUlAnOsxyiPRX3HDPYTL5xgZywB1CpdJwNoY570eVphX2KHNo8z9O4TFcUPe4Xq8axmcIE9roe6iAYT2BwA9vsrcTfvCNaKMgenIG5sm17drSo7Sxs3Pflb59u96HYX2Jp0+dbq86Qf/Kxadn8010cz9NGqvc1Tj+gpoblMrgYMsAdfj/ItPfKK87OUv53Hj86jPCZqThw4lWFXjyt/nFlpyrQRaCbQSuCIl0ALCK3LY+sO0P7fqyU2K0s9BgIcgBcXcPF1BnbclDlKR94d0CYCY+Xucd5t9EITzyzKXTOLVHBcc4b5ugZaTfn7qbYMfaJcFaPiwCS15i6zFZ5TIuF9y7MLvFrKcyr4gNfsAlAawNnQ9bzAR5/hpwdwAqJ3rm3wxb1CAy8cF4tM/AK3gYJXt5MR4OTQxWUagkXXv4LZ2hzbhRYvhOqBvHXCj5WkesOLagYJgk+6UmVfIvLlhwIsgVM7lWCAQxAeIEqSs50t3UoAfnyHOu1i9sodIWtalVtqSUYZyswoYVc4hnVahDy0gZJU3e5jmAuhhICVqMfYFztCMALt1LUENPrWvsaTRD/FeAxTTD+ppxZXhQVw2R7AOnjlzNtbcIwC+q6+5loXfiThKm0dQvG647vpyz31/m/mPE4C/G552HWWPBbyLUYcS3Id5gbhsTmopY7zWGYP1ZdYJ9JqznwDDZYDU2uI86zsKtHHZxbqqq7DXj0tMjON6aTCgjmAQoK08Bfpj8DtOf50nqGXVUbDfntdAK0C/9rrryo/84x+PJPbiS9fYf/m//3TmTTqu8XRhrObm5mMp9Lo9Qz+OMH8xsoXBOh6OSJtaCRwsCdx37715gXTb7bdn2+/e+5swxbNqBjfV5neh32209FoJtBJoJXClSaAFhFfaiPWZX9X/Rv30xzF/0UZR+FGWVeydU7aCUq6yPYSFZQWrhiBDVzcB3hLuewaXUdPW0qeCm7l45BiN03yBjBYsQVFgh2BRfZeP1j1Bj0sMjGNNVFE2T7C2AeiprpLyQm2AEVAm2BQdOingUMAET9JrgKDWNyEV0wZlN3PzBKbOX9RlSKvjkgEGqKvbqBjsEIFGxghyEvbADi4lsQLIXdMaqjLv/Do+GwZWQQ5zLMJu0BOBnoFmjJQ5juviGJEzwVMJZLLkXEXmzDkXUp6NIpoImALWCEEJCaztA66NAcjIcF3ZUQE+IVXlFQTIqAF27JNfkSu8Bjxb3A/lnJuooBzHJI+tIxPk11TH3FO9x6FNuTo/lLMudi/QxTKoxVeZu56gdLQhHsOaOjl1OIF3nJNpHzpEcb0Rt9v5+dnyJdzBBLqCuCFk7fIkAvHVRRnsAMAnyjFcSgfIO8s8wZMPPlxWANqrXFvOHZ3Ccnj40CTdFTxXEKiFdYRzzrs0cqjA0L7m41jRIUGfbqWC/WXkG6tD7VxA9+joeHn167+jHGM+314k+/nT/9P/Wj725x8NP3FbZq0Pr2mvI6+hSean6qI8A7A+QpCZWObDTIXi9XsvuGtpthK4/CTgI+ree+5JsKlbbr213rN9ZtN54OO4os8zT7xNrQRaCbQSaCXQtVW0gjiAEuBXt8EGFW0ADFCUBYi608X90ALkOZfPteES4RMlts4bROHmvArtKoBnFQuPyq4WGOfuCRgELSq+oKe4gTpXLgFWOKelzVTBHO3RrjlDKPiCS3+wN6i/zjy5Ea1igIDNt7ko+Sr7umMKWP1jN2vqybcAwY9tWy5z7QBaaYtDF7wXIrn0gNYnI4pO0N4E4PUoa+EJcHQhXdJaCEDcAEgIRldYMH4NYKeFbZ28GaxWswBC+RokaE4Ha9koQFAL1UIsh4vlDOBWhR9mmPOohRBQ6JRA+u+ahYKbQepn+QXaV6bryHIwsgIo8zZb4KVra7WriuSUVAVr1SJIL+EvLrNdwLQ+hMwz+ZqeMgSCpAFlTnuOqeWzD6UIiTxPVTetOhbmB4xwQhjut4I2zzmBILty5qFHWDNwqUzg/pn5n7bBNbKyivxmCPEO/9cfu6ossD/HGpYdTneoewSwr0VT0D4+NpEeaRk8/fBCmTtzOmBwjD53AJCuLahbqOBPy6BJkKdMO5Zh3qCWxlyznNZKrRlOd2PzhqmnBdGlMAzYs75Se+M1cMerXlle8JLbQnMvvn7tl365fOwjfx4RS19AC+bNk1d5awnXgjiG9XRxfqHMA5anDgGM+fO6blMrgYMoAZ+pn/joR/Pi7OZbbtkTEYzzXF7iOa+nSJtaCbQSaCVw0CXQWggP6BXQq2pWVR9FGcuZ1gmPqwUKlRQFVndRwZxBUFRStfZpMXPOoCCrRhdV0TVwBxElUdAXAQnjginOS9NyAjG3/POpACQ6Lz/+nlPVF7iME2wk0Tahp1KvtdHygqeAOrbyB5Xup+IbeRvMMgj0wDKOLV8q3uKUzIEEAHpgEBKDvwg6Thw/ES/OAYCEvAqWYAPL3ym2FYbZj7XMEQQAI4/5uQVA4yK4gzXtcI0UgOj2uIxS73IMq9KA1jxtrGIlMgjLMu6PmTcJ2wIVxNeluUIkTdwmmyUTNFXSlxVA5YjMc2hP7b+ATBnYu8xpjADrOc+vCjqdW8e/MnP+IVgp1qgh69s/NhEKm8gH2QRY24jn6lfFjQJFhAF74SEul5QQ/God1qLnsg9rWLjmVk6XBeRBx+krVlR4HaLfHdq9GVfSeV4eCMhGcaNdQia+aHBMlgk8Y0RXLcSC8nGseUcnjrCMxATXgVcjIyuQZqs7qtfkgB/aGcLamuA84bt2Sx5pvibGxWvB60rLcYegOKtDWLt50XD1tdeVF73yFTnfLd23jWP0B7/3zvLrv/wrosAtdAMK4c/5ubokK4txotn60sGAM6OA5RHmNXav4C1124NWAgdFAnqJOJ/QpWD89Dv5XDsE3dMse+HzvU2tBFoJtBI4yBJoAeGBHf1zSqpeiJkbh8KtEhqLlwAABTUumgBBoyK61l2sfwIOzgvUljeWAlwCKFC8/ZF1cXrnSsV1D1DQwerhouSuXQfB1BW8OQFOXXkRYOCi9tOHpmJlMlNwIDQdHa3zwSogrcDQhWyrK6MgFoCpxY6tfAfQwkOWGgAYGBFTNMPZWIgCOrAMaj3U4nnkyNEyrQUPcLOM+6JufFSPq+jq2nKAyjhLGWi5W4HHFYKdzGP1WwD4CYomcHMcxoXSJRVOE/RkifqBa4Ik2nSxe+Whwq8sdV0UXLmEBc1E7gIdG53B+ui6eKNgtsNY3Ax20mGJDNvG4NdNgiM+kWM9p0zFegGMlNJSGPk6HgIiJusN4qYoAs0cNk2GTUJmjkGsu2wzOHwnz203J/RhuK79RyZAeZU5fcuLWEgBWYIzLaPWN3CMVs1hrHqDWOcG6L8W0HXGdAXer548VGa5Lr586hSsIyXHmjrM9iwTgLbrjl/F0hWjAbN0C17qvFTlp0XRuZPsps8JyqNA/chk/gX17MC8stWoqHuwEW47HYD6KlZOjl/1uu9k/FjaYg/SZz/9mfKz//b/lnmow8CW5OhoLfYc7sS8PCksxTjFdahlesG5hYe0cHuFtKmVwMGVgC7XRh791n/wD3BJ7/+9qtu5lsLWdfTgXmNtz1sJtBKoEmgBYXsloLMKCgANKK5aU1axarlVATfqpq6ZRv5cUSHHIrNOsBLLLgJeFuYBQbj6+Ta3seAJR7TsCQzGCc5ico5UXPk8UA/OZiBLRywsEnqfuXcT/OAbPGZu5myA2RggSkVegBPlWGSkwg9vAkDn7cmjlk3Bq4BAoNcAI4GiVhgXKLfJHLMFqqZP0heoiC50QRzCamgwGttMcA/m8C0B0hYAes6XtP7s/CJz4uBX91D6Rq/L6VNnygx5S1gIFwS+yEIDla6Rc/TbADbL6fNGmYa2lro1wOW5CKRYAtcGy5GRaQAXAUZmzyRi59VGOR1gWQz6JKgTMAm0XGIiEfLos+6mkQkWr2bhYIFGlQfjhX+qFl2BseJLYTfZd8e5iI4/MpKWxPgoX+WpWGPNVGZY67RcaXHcwK3X9iYBVPKQIDpEH12lnsuBkFPnarpUBFZCx+qY4Mu+AySv0sWUYC5G3bQRQa7Xh9D9MGBwnE+it5pHO7rXcqqCW6y6cpprlr5tOMb5s1sVfNXu8Q1v9BwgqMupS4340mCt3HL7HeUY7e9F+vx992fe4DzXTU1yACuVtcopfNUxHOAemi9nz8yw5MWJYpxRZeL9No7FuNbskrnMNnlG0I82tRLYSwkICj/6kY+U/+wbv3FPXpIICJ1T7vOmTa0EWgm0EjioEmgB4UEd+Z5+d/VUwIaWFRVz1HkUcBXuGjEUhdpzzOcSgOnOqRvp8hLRH9kKBs1XeVX9rouWAyQACIKLuIqKLEgq/nwHGIAzE6XTNqUfBVMwBYjSeid4MAnqbHMDYGVeBYS0xGmBhq6VUrWMboHyIUhcA0gMEgRHIGMdU5aiyB78mUX9NcDNhgFz4Mdim2stQksP0zlBIZbBNeaezc7Oy27ZAGCsAMIEg7NaeODfmSh+5im7Kp8CVTopzIl1cYUF0LEgdqDrXErdOSVGswFAQmfnk2llnBzvlEWAdpayoL2RYZZPAEQNA8hcLiEBWXBhhTQ80z+I6C6b/usVC/ARcKwD4B2bghwMnqMlNGsUyp8WqNSvoM9+hR/OGfRmTRpaFjlWtuk4RchO37Qajxyp1lMtq3QJcMjLg4wlfQQc6wq6zksDQbn9lQXdRFeWWIoC3iaxAAuQ65jCL1ZZXkkgEyyMNGkQo7q8BbTNIDm+smr/aC5jFgFAB40x169W2dhJue6AzQHfHYQkCB9Dhs996ctCq99futH+n//q35SHH3hQRnuSHG/JQJzea6y3yfXn2ozTzB2cPnwo10aWQcF9ViB7uSYD4Lz2739beftb35YXPpcrny1fV74EvvLlL2fh+le86lV970xcRwkqderBB3NP9r2BlmArgVYCrQSuAAlUDesKYLRlcW8kUK1CKtVVWVXJrup2VbgFIBsbWLOwqjAbLW9RM79QICW40BqkYo5ymz8BH0r4oGCBuirBsTapwlfEkY7E5RFwoHVxBMVXi4hAcgarygqAwWMVeOkGxMGec/Nks9tS9Gtpaw2Tfy1ZWhLdF4CkAOdWcWm0ebNy3nKWsQ/yvyHQFdDoimh5rVKy6Tw+lisAAc0QFGUJACxNXUVPzWIRXMO6BXBdtM+AT+fEzQOCVgFEy9Cbn5kLiPH0PFYgI412RrQu4kpJpi6xgiSXuRif5i01ebqjTnBey6rzNMcA4QiZf4Aic+0ENHVZhTpKzsljINIv3WUFboJrLbtaNA2II2hqXEI3AvLohm6k9oaOeo5uVwGl39Xay6oSkccK4+vafkYPFWjFUokMM98ycqSu0UMVA/SrKyR9G8ClFh4cHwO7qHgFpHKsq6fBYPTnTB597LDuxqBzBB0Xor8qf8dLgO5LAftuqvNH2fpHfl5CaD70XLcfthV3VNpK3yxL30bh8yWv++4ygnW438lovD/3736m3POZz8ATXDUmQaGpckU+MtHsum+WY7LES4RZ5g9O4YI8gcViFiuheUOTWoQzKP1mty/0nvO855X/+s1vKne9/wPl03/9qVwvfSHcEmklsE0C9993X7kGq/7X3HLLtjNP/tDnzJgu29yDbWol0EqglcBBlEALCA/gqAeXbet3QBSac1wNUcC1vAmOXGpCF1KtgFqBGmXcJRSWlmuExAA+QEUDCLX4oMKjzGst1IJnMBVBW9Rf9gEgtL/O3DBzsjg8bW6gzc8BeoZR/EcILJM5adSrAVhYAB4+tK5FpaYTARJdMCrA6wx4OaM820HqqYRrHaJBDivICxCRhkq2AAea4DRwSbW82YdV5kgOAGS0PAb0wq9WuyXA1ejoBACwlJNEFz2L5WuaJSrmAS9zp+YzT3AeAKlVcB6g+yiLkcuD81SUa3oPX6sGU4FWFkYHCC2uAn6QjW1tINPj112TOW5GJx2NqyQATeCDzBcBCkZgNVDLOkqM4xBAB31BkP1KdFW2nltbAxzSl0H6DLQIDxWS+F0Bt8AlMkYejhvIDNkiO/lBgs67xPhI4BuhoLLlBYBypZ8BOtTRoqjsXSze62FjnTGn6BhuuPY760Iyxs01QXeqJdaxoR2vk4A7aCsrR1mZSGQg9Soo3MCi5tBVovCtHCzKl/NYUw1ag8pbWUAj1wF51rn21meXI3vkKvruP/jD8sfvfBfXCI6ftJXrPozaFZmEb1PDv32zj8jRsq7neJj10aZ5ORBQj1yaQE/nKlcSl9P3iWuuKd/9hu8tN970sfInzPfSutnt9uXEZsvLPpCAkUe93qZ5cdLv5HzwZaOO8pvSplYCrQRaCRw0CbSA8KCNePqLEtpNjY6qLm1yq3If8OX8LPJiadLSo3UIcKPVThdIXUEDHCnk4vIqrSrrFZQIcOr6hYIx6whBBAvSsaBz21zyQTpaRZx3KHAcJujLMKDEH2YDgpgESyAi6tdjQcOqFj2hJe0LVOUtuKQaksjUwiRI4QMdXWIFTYIIyWbfvtKO4G8Y4CGxLPAufdpcxCq4tCj4JUoqlsYNgpIsACIXBXKUPvXQQ+mLAXOWoaW8Zlh3Lwva07YgZwAwtjS3BC/wiNU00VkpJ+DVkmjo8xUA5AzLLRzDLfTU6dNllP5MHzX4DVFXsSAmaI8ylF9cRdkgGwEZOwPKE4IkQZBlnFfnRMY6PsqIfvFxfULLikOq3DigbKxvyEFw5viPEhDG8rU++YJjI3rSXoaA+oOOJTKVTpYTCQe67uLSyuKPjpHyd6wFijaZtQs9BmQ7vuyKDDMmuU4oFYvgZocAdvDhx/64rZeA1JKVPlvcaysvBnQ3FrAi2xUb4EWDbOjiesOL92aJiS994YvlV3/xF+t9w7hWIMycRcB/OgkfdrUmYXd9QRChxKxZMh/3NItlu3an6z1GRtxDytxhhlC3/uW38Z6//WUvLTfefFP5k3e/p3zmU5++/JhsObriJeCLk7ve977ymm/5FtZ67a+V32t4ihd8Zx99tD5vrnhptR1oJdBKoJXA7iXQAsLdy2pflqxqde2aOneUe7f8OKqYq7QLDlVKtTIZpVJ3xMzbExDwifunJCAQ1z4AhaBEQOBHxbZGxawugwF6gADdJZehJZjTLdTAM7oljgISVfsFh1pK3KYdm7A9tgEqgNQABXLky4icBoTxh73Wr1Yj5yNKX/6ECqkTvqQlvKj9XGSdQVAEPIs8cGcFDM4vrJQFQeEKyj1Fz2IZXMAKuGYb8D+HK6nLKSivRfgx2ua8FhI1eHg3wIpgcfQwb7SpL++CPEGKC7S77uEka9B14G0ZIOm8wwXmJM4MzxKVc7FMc26QuVqjBBixWvpOEJ6lRWgSiEdQ2PAL6Zxv3GV1J9Xds6PbqFPR7CpIsHFlFMQ5XsEZlBX4C44z19DxFyiGJpZercMuwYGcAx6h49iKRTfwSVWmfoQ6ugoPA2oSYIa+Cyx1PY7lmOa0CFt2ALTvWJl8SaA10z7G2ii97nXDnuLNscxyyhqbbXokp0af9dqynusXdlgCZcMXF4Bc+X7GHa8sY9MsEdLndJb5f//yf/nfCAxzNpTlwWt7ghcbXq/L3D9ekbJdLYV1n8zaF04ohTXuhbNn61zCQyxl4osS5+h6bQ9h/e6Kqs/c95fc8auvLm/4/u9LEJAPffCuTZn0t5WW2kGWwGlemH3qk58st91xx+bzo1/ycKqCa6waZKZNrQRaCbQSOEgSaAHhQRrtXfZV5TWKOhqoIM0FvcfHsRg5Fw/VVWudc+8aa18FaSi5nFYJjyXOcoAbXd4EXYI+1WDLqPwKKoweqjuiETxVdg1A4nnf/LqIukq1ICILiqNYW6gBHgEQ8BXQAN1YoqgriG0sgAIRrXz4Soa+3ReMOc+uLr/gPEgAC0rAKqCJDloEniuwWFxeDRicnWe5BGjPYcWbAwxu0AZFiFhJeUCfi8cLpOaY85VEG7azqCsp/qjXP+2Gcucr7iyfv//z5f57P8dC7GNlFdrDw2Pl+utvKMd5Kz0qMDNIDOVHAVqgAaxyS1gLzyTgyCECjhw/cbyMszTHMLEoYwnDnZRmqVeBr0DO5RQ4CXir8hbkOQYRLHTdta5AL+CJclpWm0S3Mq6xSlEuQWqUR8SDpVf6jiAFybI0RxxY0Y/dQC6Oo9ZAwb2NNW04r09bWQXi1qcK/AoCHRMaDw3pVCsavEpM4lrSuG68NsxyfAMsKan4/FJ0a7oiM92zAwg1iqzuuaPHTpQjN95kc31P7/jNt5WHYik+R1qODQgzMQFPzAsN0Pa0fbAr7KZb5pG8N8zQ3fJR1kXLPFN4N5Kvchkbo9/KJzVT5bL+uuNlLyvPee5zyzt/9/fK33z2nsua15a5K08C93z2s+UQ7tW3PutZ/WWeZ94UdPX88HetTa0EWgm0EjgoEjinCR6UHrf93CKBqpxHHU2+eyqqAVgon4K6EawtHrt8wgbBSFxIWyAn2BJUNIq5IGKIyKJafrRObWAdqgFatNp1AYhWJQ604NS5bxtRfg1OswIAGmcx8rhCotG7hp71BZ4ypRJtUtlehS8tY1oeBV+xKqUAPVLhhl9BkaUFRALSqoQDHtkXuMqU9Ac5r4ul0TdrId0ynTe4UWaxGi7IG6jmFMq5cwSHAbAGmpnHUqcFzr4aREerpSDXuYGC3LlVwB2LjAtA//Zz95WvfvUrWAU75arjx3G5NWDMWLnq2FXlBua0jWgtg/YAoHMYnlyoHiRTHn3oq2WRZRqcB/gwSspReHWO2SAWOPuNTQ/LGEVpmxUmcE9VQgI5xqHrguuxY2QZKgUM2vcK0sVgjnkFKlpTey2+ylX4VvMElC5DAhnACZv6UcxkVhK24xIjBp8xeIzlpMkYBDzDoGjaD3TrGLGbY3kRwNZrJCNNMVrni2/KuJ6ivHgN5aWCY0vbsmCxtKdASKvIU7Ku/XjihbcFoOZEH78+8qEPlT8C9DSuqr4zyUsLGl4FmPqCwqBBswRLMmqrcvIv8pfh2ju2yI8+an2fA0CeZf3KIyyarSwWAYlaLgSEtWd97MAekXJ8DmH9fiPWwk987OPlTz/4wdZauEeyPohkvVf++q/+qlx3/fVlqs/rE/pcdT7hHJb/NrUSaCXQSuCgSKAFhAdlpC/UTzVM9FKxkOqpirfvRZ3PF7dMFFKV0SjqngPkLRoxEyU1YA3lnOykxqqE1hvlVnAU2ijBAY31MIq8CmNcUVXmAQprrOEnK2MAnWrRwkiGcqxJK4vNU17waT3pxrrVtCvI4ZOlKqJYAxjgQUAZ5gAIARCU2Ywmah6mQ4Gu7Qkc5GODNRbldQXNfn5hFQC4ljUEnTd4FgvgAqB1CMvkUhcQq8gb5OUoVj5dQ12j0IAHYxMEfmEe3qxrzOEG+IUvfgle1svTn3Y9AIH15cYHygTWs6MsM3Acl9BhecM6uA7ozAf69kG30Impichz0bmJuKcugH4E1KPD3Tk01E0/6YsiidWsO6AB3eTqugrBMjACTKN/uubGIkd5o4IG8HneQeBrHauaMnHcheLKT2BmOYMLBQMKYime5DjKBsFddN203Aagfo31FTewfA4xJw5pQ9sxsRbE4cG38DmivFlcdrk+7IcXorTcpky+uoDQ8aW+NGIFTXsVYFpFXuXZvembby0T11wnxb4mLXm/+DM/G+uf7rWZT0kL9SVGDQSkddK5shNYvee4FuQLBNvlA/7gMf1IPsectk/OlXKeroB5mWvOY+dmKqDaq752Zc+IeU/dcefLygte9MLytv/4lvK5e/92z9pqCR8sCbjO54fvuqt887d9W/de71//9VJZcMkcnk9taiXQSqCVwEGQQAsID8Io77KP6s/+ACZKpQAFABb3QgEbVisB1xILjbuQtsEyquKrBQ5wgUIcixRtqe42VhL1WwFIBRYeVZ3X+VFVya3Wx0Xn4ZHiPtotI1CTlgq+4LTqyrqBYoESbHoO5VowqJac+XDkZQ7dptJNhkWjRUtBcKOlCWsSIMI5h0PMPUw7fAtP7M88rrJz8LgMeHEG2FmsdPMAVNcRNAKp/bGsiv7RI0fLiRPXYClcK6d5qzzBfD9dLZ1Lp/ukrqRaiSaxFh5mbpjtGyX0MAu1HwIcbkgXa9Yast1wgWTnCKLsGERHi9EoZj+XaDjCQvVDgLAIRF7hM5ZPxqeOE5ZNA/jAWYAbcrfHLtcxOKgljb5qUQP0Wt5jg8zYkwSjgXQzblSjTrPUh+CaDIEcNFexAOr6OKiVlQasA4nwIgAIMDdPWVMnLxZYXxA2aLdKOgRTxlKS4c/+eERjuliKSL2udBn1r3eupDyTVcEU45jri3LyKQ1lI5jsjI2XQ7c+lxb6m2zvV3/hl8rJRx6t81NpU5lqafYFRqyxaRKLKderLqCrvOxYYHwrUK39TBGZVhZspRurO/yvUM/gSl6dayw7srbGPEwGJTLqb3f2nNoYbtJv/IHvL//fX/xl+dMPfLCcYR5Ym1oJPFkJPPzwwwQw+lR53vOf/2RJbanvs7EJMLPlRHvQSqCVQCuBfSqBFhDu04F9ot3S+qZS36QADhVdFFZdRgWBfrJ+H2XVY1XBBZG1HuoqSr9Kre6mKrl1zpdlUzikG0CXRdpVfgFEAiijjgrWVlHyTdIUHKgokx26RvDMMQXzZxtCupCvliFxh7kCI2cx1kAq8gkQJN8180yCzQBgeBOOCEq0TM5jnZnDrXOZ86tUWqL9FT5G9bRvdnxibKIcO3y0HDl0mO0RrHZEEAX1rKC8u95dBxB4BAvg5ATWHQCB1j77qJV1EEX/MGBwGJrzp06WNSyDAsE1LYNdt0z5G0Eew1gaBYLOiesY6ZN+C4hiJQ0qQjIKTQFVIWQbl1L6YwTYBJdZ1zoHzqJNP/bbJERrQLWuqdW6J0kAJLJ13J0DiHRSXiLO4VylTXmpc22QHvsUr8DcMcm4QAdwnDUBGTflppwb610AaEMVXjPWlAl/5ntdOWi2Tb4fxzWdZeOZlE0WspHLMBGMWqaecXMZ3oNAMu/9wz8qH3r/B+iHHJjsuJxxbcFvZCgAxs1T/hJYiPG3C8u8bEh/5LNJ2eckWXwzttUdO3NikSskcj8NMoaJINvUu4K2ehrc/tI7yrOe8+zyll/9tfLAAw9svti5grrRsnqZSUBA+DW33srzlfnKfUx6fozwIqMNMNNHobakWgm0ErhsJdBoM5ctgy1j/ZdAVcbPp6uyqvLaez6BYQAJq7hOrqLICuS00Gndi9WOSiqwAYQowoIGldm6Pp4AAICBoi/t7Uk3O61JAg7pjhGV0WigsZBAQ7Cg1aW6kNZ2Ym3qgsXQtXUVcRXqbhsCiKpq1wzpaF2KZRAmPBcgEt4r+Agt+m7/jY4pMJKPRZeD0EXWvnO30BvAGXNMtDyxRMQhlssQFE6OTZYJXUcBH0aLNDqkVtURAO01zBM8jnVvHJBosE+XlDgMmBzn3PLM2bJw8lSZJ3jMKm64Q7Q9CvgyoM5oLIzr5STnHj15BlDnupBEHSVf65y90xJmh4InyBGQpP9dmQSkWUAZ2D+LW1gDW4CtLo262wKm4EtXYedwhh5yNs+x8SP4cTwcM2kECHbHKO3aOsex7Cnk8CMogiZgoIOFzDmUrm05hOuq+0Oj7uNMSoCczAXkmnDNQgF1QDX59sfry4izAlOtr0PJr+Pu23z5gb0km5bP0UNHypEXvLhm9vF7jsWrf+ctb03/aSbyh+2kyofyBLg5QvwrX2W1xj0zgYJpv0y+KOlW9yh5br3efelijvNPzUt9xstz9U7oFr8CN0ZQ/eE3/TflO17/uqzdeQV2oWX5MpLAIlb397373bln+s3WOM/3NrUSaCXQSuAgSKC1EB6EUb5AHxsVtDmtVcOEDpsP2meUUoHEBq6Ia8wFm50FwBhMRRBHKd1BOyjnKunWqqCDLcqrSrltqMAGgEmeDOAF9KqyaxmjiKr0D3dYxBwlWUC10c0TYMpHrHLUrREsGw6l7kdAChoIIKggwbxGcbYN28+8uYAZXUOhCZlVlXK1el0nOd4EnEaqBERaTje/ZYChTGsnc5mIo1PTuIBOlOmJyTJtIBzbAzQfv8pAIHX5gDWsnviYsjwFTqcQHwE1HMOaOD0+WQ5PTpUxwO+K88ooZ3+nJ8cjy4A3AFgifCJbBAmowAUVC5Nz8NYZB+UM4/5HAue2sZWSp4w4R/W40lJA8OjyESOMVWPZU64GoFF8SlIR1mGqAXccp7iAKic6GTlb0sKaV914yFfcQcmqvMk2QIi/LGMB/6kjX1YAJNloHV/LSB9eNngk2bVQpqxNCDL9uPSCjJrpsTLgWknb0kuCIrQsM/2c55UBgGg/k/L7Dz/7c+XhBx8MD/YxVzl9kufMz4THCmLtHf2ijPeIL1GWltaJMjsO+1iieckwwJIdysO6cdulfO4hX6rw4mVwcLK+ILGv6bZ9u/KT1/OLbrut3HzLLeX3fvt3yv2f+xz9pI9taiXwBCRwkvU77+Maetazn/0Eal+4iktQtFbCC8unPdNKoJXA/pFACwj3z1j2pSdVga0Kucq3FgqDW+gKqZUoEUZjHaSkOjlfujXq+ul8t4ABT6i9Rs2NqpvjqLJ8RWFXCUZR1honKIllhTzdQf04Ty+ud5zPEhNsVaLlwYYTuITygr3o/5pooLeZpC8fbGtCMffPOpQdFMxIm5PCXvtq8fCDIi5RMUaHICnDrm+4QhkKa706BCA8wbzBY4Qn161oFGtXhT4ulTFSrrvqeJkEiMSyhkVHq5a8DyOjI1OHyhQAcgRguo57qABzAECmO53zJ4WqAROj8AhgtG+TvKWWvxECs5gRd0S5tmt2m489t28bmRMYEaWf6wIO+gsZPp5HjsqQaLHWjeVJaxSAxfHweJXxlaLjJIjMOASNKHuBndTgj09lQSb494BErdR1yqFJl1XtZUmbhbrHct7lJcsqOAh22m1TRFQqz9YN8OueoG/223GUE689XYNzDTMuY0+/MU328+tjd99d/uyuPw3YXeWFgUzKqu17XRmdNvwwXgLpBOyhlGPaIQKv1gzHcpJ5psxUDUhM8CM6UqWphb26WwsgnWtrt1dxQy4bvBCw6366smXvik5aC1238LOf/kz5/d95R/p7RXeoZf4pk8An//Ivy4033ZTnaD+ZcBmK00R4zm9bPwm3tFoJtBJoJXAZSSD61GXET8vKJZJAwEO3rUa3FLSobMbSw7nqbqgyupY5fpk3SBmVXLFXFiZHCUb9zxw13UulIQCIxrqp/HMYRRblWZBBmVjtyBR8CEI8Hz44J5DzLwo2NBvXVH+QtSJY3xasEOuXtLtJECLwUUu3j12qdZs2oMuRbpEGlKk2qgp8EojEtlH0/VhOA52WvQmCurhI/GFd/jgexAo4KCDAurdC9M/1pXmWfSACKxFF17D6HWV+4NWsHTgB0JsCnIwDZNZYcP7soyfL3ClcRM+cLiu6iMKjcxplNUF0BBJYTwRnQhulKyATiFZxKm0BkP2rcnJbz9U8BFrLwKd07Icp8qBgrKABUdX6B3nP+pUyWUsSQXbCA5nKMhvac6z4FxNGutS1bcfTJNAOOcck1wHjhazWkU2uLwtZGZqgJ2jpVmwmSRqVmIQcpO4HEAqYrmCwtlOLC5qRVayPwqkKBq17+PaXYx0cCdl+fQnQfvdtvxXem/my3gtej/5lzOA/9wWNZr1LuxQGGGcupg6gfpEXLL70mOAayRzayKnbdcY5rymg40sCreW6UTuKzrOtMmwE1q+ePbV07OcLXvi15U0/8ePl2c/rfwCgp7Z3beuXSgK+bPkwy8DEm6SPjfoyZ4TgYW1qJdBKoJXAfpZAayHcz6O7y7416qWASNCgAps8vpxL53puWgaXADSLRMwUFMSaBCASkAnUkmctFf0oyFU9h2SUWJVmUwUzasnWqwCSBQADeOJ+F8BRAYgUnEtlVflKO4KdfDgLYAkdqVbyactWmkS18CNEsm0pmyeAiZUwCLKW13IjONOi1+mwFiC8aAOaAIwMsu9cMOexYTBk+YfFcubRRwNatey5fqK8zszOJfLoOhFFjRLpwvJHsCY6X3IVGY44BxBagkxZG6Av9Cz8DAM642ZJXvoKNyojtmtawVU0fcNUOaBVLb0RTFf+BQvKxl5KPGMp+OKA5rqJ1sjyI8xQhE1ZC8iVkS21wC7Dfwd6gvAAec5nLOSH+pWytdyr7YVWjrp5ZpAEoa4hWIGsY9BlSGSfvjQMdrfNeSub1TAKP/H17Z6vrVjI9ut25Pi1ZeyGG+tBH79/+ed+vtx7z99gER5lPOsLiwBkeHO+qPeCQWQ2sKY7DloIsxwFQqsBfHCvBhCucN27huUYrqPjgEKXo9AKPsB15hzOjUHGEJl7rdk/FVyDErm1XNkg8ijjU++SPnbwKSZ17Kqryhu+7x+Ve//mb8rbf/OtmYf7FLPUNn+FSeCrX/5yeQh3btcn7GcaZ63DRSM/t6mVQCuBVgL7VAItINynA/tEuiWgqJaPbm0UbEHbCnOdtOS5HtrCvAtsV9AhljL4iSBK9TTaq4ghqKEBHucASxR7KsWqRxNxHbUsSeVXcKRibURKgYmujesq2pQR5GiBrJFLqaD2z7/t+qdVJtYTyAmmBFIpwlfAInkQ5lNBZHjhS6Bk2UEmyKm0D1lYEMWJkbGNBJPpLCyW4W45FXZ588bZwI1P+54AcY05hrMzs1HaRwg2I+izziQBUyZwKZ1mzuAyUUcPMffQoCLDgAj7P8L5AZdwgM8EIpFH+pOF3+WLD4f0oc5pFBeF56ZfnoRfmnIvXUxWuLR/Who5a58sxEnn61kmlqxutpErA9JsPoKjHOW1osZyarbjkPMCu4Ym28jPauamEchb34/9oq8hSy4EBN5Uqu2EkRD3i48baWeHrzBat+Z50Xk+NNj1+pEnv1KHtSxvusWMvqa/BaS86x3vCDsbCerDdcC1kOtTX2KugyTl6dgqKz721TmUjqMyqSARV1KA3TIvFVzvzHUp54ky67Vvv4Z1Feb8CtbBkVECDznXEJpaKLWmDfMCwrb3Y1JOz37Oc8qPEHTmg+97f/nMpz8dcLwf+9r2qf8S8Nn/l5/4RLn6xIlY6PvVgl4bgsIFAkq1qZVAK4FWAvtRAi0g3I+jehF9CojaLA8QUdnuatYBAgFIFgCQ4eamkhoARr7Kre5sAYBRylX71YgriQrQODZ7S+pq7m66xaWVoBJYHbUmqRR77L7JH/rGohjgAp+6lNqW5AUa8pWDLs+S92Omf2KiKOqUddkHrTeq6cITgZ5z9qSjAWx0bLhG9ZwX4rDEBNEwAxpRzCUvgB1DMVdBV+lXWR9jeYipycPlMEtQjAH0tBKdBi1OT7FW4dHDZRFgadTRYVwHI2fpIL7MIaMNAdoQYMNF59GCA+akYXTKAdbJkN9z7qF2q/bXPrIXPu2fx4GB7MSKajnzkFn+lBt5LjBv4fCCmFXGUx4ZiCw5TBO1nQpubCjWXmkqCAuZbJdj8XaTbDVyt115ybFf0PdAyVvIgxy7S0b2NzPIbPK6W/n3Y/tJ3S2bIQL2jD39pm5+fzbK9HewWOkeaj8NbOQcUDn02jjHvKcrL27qiwmv0yoUZe++90yuZeq6nmcDCmfnZkPblyxehwYyMvKugFDLIpde5tiNjXFtYE20/f2aTlxzTfmeN76h/KePf6L80bvelfurXiv7tcdtv/olAQPMfPqv/7p87Yte1C+SoWPE0SWt+T4f29RKoJVAK4F9JoEWEO6zAb2Y7gisGgXWeiqsVctU8cXqpbUOzVbAoEVD19FNCw/KsXOiLBcwRjkVVIOauBO9mC+tYBSp59JIBQep6DFJPqrSrBVQ4IcViSvTpSJs3/oBhOwL+2wnKnZX0U4JM0kpD+AIzOvyIjhKrVSSGkeCK0Eg+5kn2AV2G2tYaHDdXIenAYDY6opuf+MVBKXl2tAY8wKNMCq1pWUsiASTEfhNTUyxnQyoXELZnx4byfzDIRZzH+RjcJDhwTGWWiCiKpUFfALCIeoPDROcx8Ax9EvLI8JLcJ11wqEusWj9GnPxxl2aQ/4Yj7hyQiTjBid2VxblyVQ55cg8TtbuQzNArALQzBdlrp7W2UjVsl3gM8QgAH+sHHoZZ4nlumFTSdd2KOPoBBHKfxqvY+eudc0MmK/UzKxtNvU2OZdwuOU8td1tOpe61jOfciHBvucR2dRtL087Kdanr49++O7yIRZT13XX69B7YaMjYMYNOJzQc/KUF1zQC/hSZh7wFYsoh/VIq7ovP2pAJYGhY+v6lOPMT3W5klrdAE4LZRXroe2Jf4dHx2Ot10re39iplbfL7dtr5SV33F6e8cxnlve/970o+Z+63Fhs+blMJaBF/5ZnPSvzdPvFot4zw9yjgsI2tRJoJdBKYL9JoNG69lu/2v48EQmggMVaRF2tQAFm5GUNQiyDmT/IOXXbACqUdhX9Zn6gSnCj8AswKphTPa5/1lSxhWSlEuAh/tE1soLT2qbnVbwrbUGTEM/k91pX2a5tCSYFVLpehnDK2HZ1I6UC9GMNDFCxTAVRumUa1GMEi5yumyMArRGsfLrkqfxrqRHc+ZlkSYgx1tEbpcwYnwnqjFN2BL/QURDlFBbF6QkiW44A5QYADMz3G8TNc5L8YYDLAPujtJW5Z7CgG6UgwPlozkEcZj5Zh3mHikT3U5eWWF12btkSsgYcABoMzuLcwQaAB2TRGwGhMqy9J8M8DjyuAX4UQbVldUWXMsFj7FmzkZ3AKm6OjO0Aax3WuW/QEs8pfAF+FzBKpKFXx1DLJ7L245gIuAG7sTp2Qah1KmdwJ718+BIwSswMt4Ce+hGQ2rgdskem5rHFVhOrVmqUtc7R46Vz/EQt0qdv3YB//mf+H+ZvCv0cN+b1wZ/XZMA0eXnpIc98qhV2s4c5Vr5e9xRAprgbex3ArzFz7JIu0loKvSaMPpq5g8jDOak1UJP7gELOe82uEHlUSR2UdPWJq8v3Mrfw27/zO8rU9NRB6XbbzychgQUCdn3qr/4q9+STIHNe1QncRjefleedbTNaCbQSaCVw5UoATapNB1ICaJTqqJuKpQotR7qJVoCHcivIQIFdxFKxhBJqCPyqlAtAmvqVQmqHIHtouZKTlhajCkXMBmxoyVG/pzU/JiNt1gqW12KmJcZdYGCXnyjd8Gd16WgBtA3Bi8p1x7X0pJMeOW/QaKcUoFwDHwQ0dW4epiS08YBBtsNd8OcbYNsNKAWPyNcG8/+WcNkbXOPcIKCAAoLL0WFBLEtJ2CYAUXCgTJhYyD/KPb1LMBiWGhgEtGhN1VV0YHCUD0Cr4Vk3VarZb7R+eAUIAyjXcUssWBRdisExmABsClAzxzP9qnKIEJSLglEe9hFyrrFoUiImgV2VqxZYgY3jK3jtSod+RNGxP36sySmvAd1pXXXSkbS4S3M4hh7QXeRCudSvtFLb9ul7pWVbBkhJpZDudrp7nrIm6SZJwdTtRMjyZb/T9+a8ZdhPg7j53nAT8urvI+2jd3+4PPSVr0auXo8dQLxjIyh0XGw7AFn2wm/tg3JKPpdaxMn4WLymCuC9br3WalqPW6RrFDrOztfNSw2sgwLAFSzYdt0xrK7V1Nqk1yWxzzcveekdWH1uxYX0D7JMxT7vbtu9JymBv7333nLjzTdnPuGTJLVZ3RePo1jt2wAzmyJpd1oJtBLYJxLor/a0T4RyULohhlCHVfE0BXxFQa3HggGTc5kWCXqxiiVj8+2oGi8pinHdi0IcwEbU0C7JnFFztSm/o8MKFJPBMdsAOfTiDcxQCXRC2XVQhsUEdSrNWmTkSttegAjwJPu2AJ/BnuQI1tS8Vc7lQf5c8DuJfXcFarapQu7afoI6rTICRIHUinPrSNXVDwspoFEgObgxWkELdAbJW9cNlLrNnMBYfuBlhI+0BwF+ugSq8js/UTAhcJUJ3VUH9IsVVPEXoOoeAAAHXfjQtRR+accyo1iO4tZpf7pjBJnsK6MqG1tyX1CvVRUIZxkGRSDu/MMIxdKcCIiD1wpsKMh+HSDOp135pG2soUPO54Q36QuUgb3u5mNETO1nvgBQ/EO043Ui2MzC8GlX0l4zFAhT8KoV0OS1JCA2SZMiW5KnErilye0pQN+tMIg75egzbm4K9GU7MzNTfuFn/9+4Mee6p6m8vID/6uqrfNm3v7SYa7Q7PsrNY2WTFwNy6Vg7dpQXZOfFg5wqHygov0UshS5X0cF9mAHJvF3X95R+tU4ia2juLCiy93FS1keOHo218OMf/Wj50AfvKmfPnN3HPW679mQk4P31mU99qhy/+urcp0+GVm/dMQChbqPN72bvuXa/lUArgVYCV6oEWkB4pY5cP/hGed1UrbuKuMBExRQoEAU2bmtYRFyLUCsYWK+ri6rAophSLyHwVczd97RKMR+tdGCabqbgQqZri4EwqdvAhGq5cr4cOKoCGhVqeBGIriXYTG06C6sPYa1B6Rb4OJ/KtpwdV+lXJslKsi8q15mTCL0mVxAS9gR7gEGV+ESEVMln30ihqt4JAkL79n+AhdlV6jkNEGROCUBPVz+TvFRgWQPQBIhxflSXUD51AXJoWB5XynwABkOa2QA8GwBMQea60UtxMR1wPiHFoFwxA8wGZMhXxqgL/jgOKIFvBR7Z2mX21kXolF0HdNgXs+11gDvoLUCnkZsnU48v+MpBWNB1lPqA3CVcWZ3nqQyUXQXGlSrEKvi2e7SyxhIlI+QNGCRH3vKhkqkZhmbrxZEBazIoI8OmKt66n0bZtWxDk9bGn/UC5p16JfQvve3Xf6OcfPiRADGtqcoq17xNeB2ln3XXa6xhP4oi8qnF2FqU/gVMen9xnDUGHfduqnWVa+3amEtbeF1Sb9jrgOQxVxguxcxhgkYdq5w6UF++qHrpnXeW5z3/+eV33/7bWQrkQAmg7eyuJfDlL32pzAPeJgkI06/kEjBaCo283aZWAq0EWgnsFwn0qlr7pU9tP3Yhgej9TTkVXTRMFV6tEyq9gRVop7qraeWoET6p0CiiaLB1+QKVWDLN30zUVsPlP2pxdutxFN9gA1VoFWUUabYBGIDOCsGwDMKDZM0REK6wtpuuiiuU8SNAFRQBHwFYAo6udh4eKqAVMIS3rnJuW2k0DWSmlsMAAEAASURBVFK36uypa5b9tq9q+irimZuIEq5LKa2EshbFifFRoo4y9499rWcq7/no7uc6g0OAP4DgsPMMUURGCAQjCIzVjr5oBdxAmXCeoW6hZR0ZL9bF7T3uYJ1zLcCAMoXQfMKBfDSfCjTi+gn9WPq6fCpdS8UixV66bJ4y6FLI/EBNeiHn1o+PhO4+/U5eaiBpZcH10ViORUABptYXYeoejNVTEKrIYzlk7UoGq9IFUG7Stw2P04b1bVZCJIcgxzmq/UdmXBQ1X3l4PuMKX6NjZeTqa7uF+7M5e+YMgUzeg6UOwC+P8Gu/7aYpy5/IhykXUle29GFTxu7bz8gDPpF1ItkiN2kFJLL1JYIvFXQzVgQGc/K+s1VfRozSP08YydYXAloKt6c6wttz9/fxFPO53vD931f+/nf8QyyHR/Z3Z9vePSEJ+Ey/6/3v73o3PCESO1aaPHRox/w2s5VAK4FWAleqBNQ52nQAJdAotl2VNsq1iqyfWOCQiT+mKqEG1MhahGzNy9pvKKaxOXV180qnqqWChDoPUcGe2w/IrBBRNVkdOx9Bi5bIuKSSxWEFoQGDWJq6bUhXoOhnBavlMlFPTY0CHrCZnOjPsarV6Jlyx4f24p0XIKPTI4q5febsuoAT4CJo46iWBZilX1jvtAaOAfASgIY3xAKFuIRSW0lAqOvqxzmsgSMEiBkGOEIevCB45UMfN2hjjTmJ6/KOJXB9ifXn+Limoc6jwUe0RYNUhG461RWUbDFwAoJhAQTgLcFpKB9X2QgO9mlUwBF3U3vTbb+x6tmnWEMFZIIL22v2Zdjk1o+AkX4PEEDHOkMBKATD4bgDs1kvz/7QdsYHOa7h4ug8twEAonkby4A5yjBoMgPxSLWCQWgGJAaY2ibHzUB6zo/FmzyKbMpFQManc/SqMshyE/1M72Ge2le++EWC+RDAhWZyncCbzTeA2OunufZs29cQSWysI0DfPE+G15eldL1VbpYOPeQccVPelwzKdc3rG5l57QRIMu6CQWUhMN+eNtvefmKfH/sC66V3vqz8yJvfVK69/rrIfJ93ue3eRUrg5KOPlq9+5SsXWeuxiw/zQlCvjza1Emgl0Epgv0jgfM1iv/Ss7cfjSgDdEiXTb5KKd1IFcFrWnLukoq8ynFD7aKMCgi2LYqvMxn4mnFHft76EQr0qsGi9KrU5l/MCO9TplFMlrsljdV4tJOIDEGIFUZywqO6qqxRYJtqmIDVWSxRmzvCxTC1X1XbaJDcV047EG/6q9VE+m4/ROOs6c5UfFfkhg7+wlS8VT61jzv3STXSYOV5GCbV07KnsCAAqftF51fZQ0+VdIOhyAij5AwI/3U+dH2jU0BwjVwPOqOiHQBiFcohKOIyGDrRCl7ZgLSDStgL2qGaSJwVoHxy/Vdqx8/W042SN+qmCpgbt0+Havjz48Zg+09mAwgTPMTgO50bIizyQhUkA6Ljlw5i4Vp9WXI16rl25yhqMa0Ts3JhbcJJm+tC9UCrDDvhmuwJU6ApWwwf7utjaMy8Qe2InRfdsJ275exz0Lz3y4EPlnb/1WzSNiyxz+uyHsjbKq9eC+bEW0mTAITzWFyxVwrKntL2HqtU2R3S7AudEEeW8AYIMOFTnkwK2AeajKpq+cOCcoFDZeXl7jTkv1UvBO65NWyWgtfBHf+zN5R++7nVlfHx868n26MBLwGUo+p1G2+us3yJt6bUSaCXwFEqganNPIQNt05eHBKJfd/XMCt60aglsAGFYCRP2XgUWxT3LQaiXkxrltFGABX1R2P1mV0VaLdZ98w2sUfeSkTypeJS5iAF3ABk07IA+zYPQiLWlW9omKoDDUthVsuVLgBEXScBEtVAa2MRsLYFVSQ9/1BfomaO1UVfUikfkrQKpetp5ioBCFpePhShMRj/nC9AqOBQUSqr52EcpAyIE24LqauXR8iOogQigJtFWKRgrno13g7gE5FAqYkjZEEu9Ci48ibyg0Vj8lIdyHcCSqRyGkJ1gwiQwixtsR+arRbRjWcePfodxgZfwuTs2EYanBIR+HDyPBWc0ZrEhtwAYrYYGX1WOtmDfwjZgTYvvIHW9jgSPyqzD+WYuXegpvJhtqdsI0nb8mGzb/DqAZLjvhi/6qavo0FR/3bfu/sD7I9sRXIETwIX2/cu1xRIgusXabzsaUMdYp/OwZopVMIPiteBYeV3aD/8HWGx+rMzNzYG1kTu0vbccz1SnXKyH9o+MFSzJ7o4NsQZhXEbJZkwi6+63NNvkJTNYbrv9JeU5z3tueftvvrXcf9999dpvhXPgJfCVL3+5PPrII+Wq48f7JgvXJCy4lreplUArgVYC+0ECajVtaiXQVS3FCCr8KJwqpijw83Oz5dSpUwECWsYqOKiub9FYkV1VeAEEKu/d1LMbdTW6fldxTbnoxyiybAVslldnDkahnBZJrYFkJX8DrXgDF0TbrFY7XRR1u1Ph7paR527DqcdxeJOK/wAIVXTbiZbdravSH8XffJJ1pCN9wUo1TrHERNdyk3zq6rqpq6ZAIUp8OgARjtdcKmDJ4DAa35iHVgUAPTjOXQdwcqtljiUsEn2nAUYykX1RGDzIhx1oaIjOyJNdraF1vmOd8+g+WZy3in2VN8tTjllqzk3U2hkAbRvSFNykHgDFiD5h7ByNav6iTKx2tkgJylcral3LMXPgoNOAVkvJn+Ooa+8KllCjvQp7dJuNlVDrlxZPXUnjUsp+xo8WvBDkWyI2aNsCanmVv3QeQHjdMznub/qLu/88BNMn2s3LAMbZ/npvVLdiWIC95IVBryyYgn+vn4DCmlP5lyKnY2m3j3aRrbS1qnq/aR2sY+P8Qo2zWqIJIgMQXCDK7zJBepKkX/eypcX8dbMO/GaCKJBvZG7h67/nu8v4RGstPPAXBALwnvzo3XfzWOGm7VPSYu8SFG1qJdBKoJXAfpCA2l+bDqAE/IE0VcX1nM7aiELQIzhcxtVxDYXeyGq+gY/y2tRNYemoBDc1oxPnQKxhak41YCEZnut+rHuOHxV+lOQAHkAZf4JBi1awV0GHFhV/2yvIqGBUiNSh0bQnTUgJKa0tVvOEm1W+NDxqhcySDQJPjnUN7KiJd5P8R4Fgq4ukYEC+VNTj9ke9rAkH/bgQwpB0lKlWQfsxaMAbk4BCMGP3bEPiWgUDcsJMZc58U7YWppMa8DKRknKkqP+SYF/LYJ0/aD91e3V+GnzIC+d1e2UUsy8wlW8/BkrJ2NuOBdWT0qaEBV58tA4KFC2w5nxNzgle2Q4gK6OwBhWRg+D9jpyB8hnPYFqy1wCAiwtrkeHYKFziLjuUNhkx+pU5qZENtM0XCMqLeQrUwdOkaROisBSyCG6rR67iuH/p7j95X3n4wQfShCA+Q8WXbHngVtlmLD2SnZrLaSLQIjavmVj8kF21/FUw6b4W2+GN6n7svFmDDwnOfVnhuAgA19c7ZUkajGUC2iCDxflF5iwx11Q2lAsJqhyHsxy3X+ck4PyuF7zwheVpT396+aN3vqvci8tgrLTnirR7B0wCZ7DmPfLww+XENdf0recGl1leWOD+be/Dvgm1JdRKoJXAUyKBqlk8JU23jV5+ElAxxY1SiwW/b2u4Yy4vLUblVDGtIExll5N8/NNa0qyDZ3/O/2HUKpTidcu+ej2YoG7/f/be/Mmu5Lrzy9o37ECvbJJNtrhTJCWFxpIV2mfThGfC8iKJ4/FPE2H/XXZ4IhwOhydkjTzaSGodSRRFNckWm80m2WBvABpo7EDtVf58vnnz1auHWoGHxvay6r57by4nT57MvPd878kFGg4rVLXNIV1oSpf/OEOYToiiXOMQNc7FNVSaHZ6p9VKAI22xRPBEFHNBZdXbTZYVMLnIEEfykmZT4JNnylSHyxJsMVHK3TNunQ3qffHXYZkGuEWGWMVhg9rfKr9yUJ2Kvsq7W1qMu0iM1sBpzjMsRsCG92G2FUbgFeZJS7peIeLfddOEU8ZG02GY0iV/+Vx3YRysbpnPx72kTZJLmFO+Rgy/pInrZJxrQEkZh7eZI0zMO1rKHMcsX8AZOlncF08gIj/kGx64z956JA4ZwpSVS7JXC6RJahyHRWZopPLmOjKnfgSvqQvqcLNZDLmmMNV66Dn35CBQTIngAV5nnv1wGWdl12G5RYZx/hHbGMT6Cd/uGem1srXl2VYcPm07qaBM6SJXG5vhuaPdcm4yt73YTxJFf8qslVD5RXbSUm7cay3MBwji1e1fjOs+hnXuoA1slT1B7WP+mYsto+bu78gNSuDUqVPZt/C3vvzlMsNqvyP35ErAfvejH/xgqALwOeAiYiM3ksBIAiMJPOoSQJscuZEEUCpRRjN8DSUzlguE4oIaznUS7Ki8aulRGe45FdOq+XZKcy+ku+iUVONBP4qr8eO6i5wACYQaHlWXLARaXqM/Y+kzHJBABLN3rmEU6i6+L/oo6YTHithIc68iLo85E5/bKPZR7rXArFM6h1MK1pJfOIiSjkesNjKj5c0hoi4qIxNKIcNFoV1XjFRKwgaUeWSl5cdFQgKiBHha2zyjQHQFrUzJmImi2ktVPnCa17y1LMYxnUKBduKbpgsTEEcGACp5SXk1j3phEqJ6HQutNx7SyxBcA7n2mBQMslon8yLjlTxgQjQe3vFADqFrMmhmuKzAW97EuPAa6yn0bUdKU7mxVm3ATtoQZMICcQU3XlMCVl5dDfjJQiuWWcDrOc6ycCh/5Qv9uZc+04UN5/T6d77DPL1lsrAuxdlaUskPZ3sh9/AwzrVcyZp+KoUJMxhReR22+cmf8boIUlEuGbrrBw3+jGs+WuFNrQy9ty0HKKbNEYSbZQuK8NQIVu9KJ1x1HqNTTwJa9D/zuc9m/tjX/uRPyve/91r6Sy/C6OKJkcDZN94on//CF8qRo3zsGpKbZqj3Ku/KkRtJYCSBkQQeZQmo94zcSAIolFWRRZWN9qqlYj3bBDh3rn43aHv/qajWoxNclN1KIdaLgMZOA+5F4d5/0woguCan3LeYnj0SzlXAXfxQm0ngn0BJ/woMa/NVwfZYZyxoLH5YVcQnGryiuufc0Q5dyqaCT5zES56bTGtzj0NXOK3WGqkHJAO0PGdhFDJ2W4669UYFBpWynFdwkLmFAkA1/UqE6+5eQMXczDjBhvGaIl8ZlgpxZNpCcOtheuML5AQISdtnGZR36wyrpcMwTaMsHPao9VRgMQXoFazFyVtoCLqg6SEgZB/FOo8Q/w38BKXQLivw7LWCR9bOL11nXuAGK6bW+jSwlkRAA/WMdAW6Qdo7rF/hq869RMLxM66XxhDMuv+ec+YCMFN2y+KFjli5ZLGfU2ewDg53/s7Lf/VXMNHyw/oG3xO0fT+G1BLAAfxa3jAiO8gj1kJ4TBzkLFhVnsZNmw3v9UfJWDYBfBzXphO0TGNpcBVfLZOCvljlE4l65N569cOHiyDZH3T+1qvcjn72kMDTzzydfQv/9W/+t7VNKviRe6IkYL/79re+NdQyj1YbHao4R8RGEhhJ4AFJoGr6DyjzUbYPjwQq4KoKrwqu1sHlpaUo8e63l7lpgBl1YfXdOHVnLlR6q37aVNOoxgmJEk94VtU0UafIqjQbW9U64MowHeEBfCi+6uaChV4eBqs+d4qcWMLLCjIFWqTFB3UZXwM4jKTjlLwogAq9h8PxNjOZjTTm5+RCFO8NcRdJXMmUMbNR7EPFwodmPWVYJPECsQCqFQgyvA8wIFCwLBr6krkEwjCUY20jlX6Nx04ufQWu6QQOAW4dkMM3IDIgi2s2tbduBLPuWxewArCMnODXvQplUEBoXrWu4E3hpvfDhHH6KzV8GVmZ1nLkrASVmQCcOYUZ7ml9CfR0hOm8y1BJwqybWMnwEyAJcgSlqRaE08Ci6VyxVZlJwD0bbSFjlj1lgMdWl8Sdfuo5kwzNvfvGG+Xs919L26ltg7ZO/hkyyvxF+XRfRfnbRC6Wqw7xFDhiDd2s8yaVY0Bfx5kKqOECOuspAJhyeK2zDcUeiZ91E3CJf/ojcrAqspJpV99rblgPcE8enaxsT/6N3MEk8KWf/qnyoRdeKF/7ylfKd7/zysESjWI9NhK4cP58Wbx9mwWHhvNByf4pKFxmLuHIjSQwksBIAo+qBNBwRm4kgU4CKrkCA3RL50plSwdBAYqqQ/2i3Hd6p7poBW4qyCqwqM+cjatTd2/3NYnqriAP0AAQiH6bdF2SECGcWP45LzGoDDCgX/LirF8DFubVrDB1SX6HTrYFTSQuJxWkeFWdFhpHQWppcZ88ygU4clEPt5EQzImBqhWyWnIC6rTOIAOvA2AFVLio/xSwzQOr5baMZm9kI8F0Dm4EN23Rlgx/1K/zb0q9pFu8nLs4xne+ikMLsSQRicNfyoS8XLAkIJfrCkiJhrLiRufWa62vWi8O9cQj6VMZYRy/1aV6rHFec+9EDi2asJ0KJUmsVFiP1zg2MrnTMPyVqfMYOWKphI8MzUVuzhMMv7BcwQ61Gj98LUaTT+q3ZpeKEBBbYYLN8OwZEZwZ3sIQ8vGnv/sf4bmCNHPXgizQTR3Cnq61NYc/2zwHXVZzlVeF4S/xTGPf0a+BNsurv86zYN27xOvoaiXUMq+yqfUw3jajWBA7QB0Ko5+7kcBTTz9VfuvLv1P+zX/3m2XhCMOkR+6JkcBtpkG4uMww3Rz7YI7cSAIjCYwk8ChLYGQhfJRrb8i8R+FFoa2WDyxOy4sorCr1WKBYaVRFvlo0qjWjp/h2CnC7b5azxl5Pd1ahhz4qcFJEJe4FRs+PfwOabti9qfWD4ZMq7W0eonO83MOtWl+cszaVrQ0EFTNRniEqkDJN6FdlPAo59AIwARhR+sUXzFEU75gmcUjn4jMmjl2OQMvuPLcJ5KFFJ5YtiRNNebkICMKCT8Kdjyi4NFwQZ5gIOPE5B+ThZ2kFBvLY5Zc4AXsyhHNVT+N4SMewlA2QwZBNVx81zzZUVlqbyEd+TSMQNCdrTFAmaNtgKCgwJQv7sDEg0aDNfV1JFIGEP/myvrp8tGhx73DZAL6APvzCG2XUKmk5kiwSJkhg6g4TdYP1Gea/9VvAZMycQ6MKgfjkGS94DF8WSMK4DsBOsZfYxMLwFLCrKIfn3nwz2aQ4yMwc87HAvLs2K6+2TeWpa7w30GipG481nNZE/btIkzJoLum6G6/TxqFm3WhlzqI/MCJd+1KGi5Jv2jv+xum5HjE57t30gkcXe0vgp37mp8tHX3yxfOWP/3hkLdxbVI9V6Ldffrm88JGPdM+Yey+afdTFtOzrIzeSwEgCIwk8ihIYAcJHsdbuA89NlRQkCQi8d6K8+rnDEbWWCHx6Tv0zsbio/1HcqxJf4wkI4lBm86cyTVCNY3TBVZJ38aqf4MaUZpcFWgLCjGL8OpfPbSAcPhfrHn5RuGNN6RbjMHGIQ4t8Qky/jlf3OKz5JCf8K4jSeOcOB9lrkCABTA+Mypdk3S7CZLru0jyMFzAMHwFV5kv65N14UJmPH/4BPxIgngfpCaxHLz7hVRodTW9lkPjOmWR+o9bcNQC71sHk39ETtE7Aq8MutW4aR2rKcJNCjo8xLHOD+WqQqj8d7QY4zCfCQMmBdoaKMoR2PYvXuDhKle3KBvnHClYXlNEi2aHQ5OdQVuefZoEUeBmjnGZpe6rz7RSR8zZJB/jPKqTkzR7wKXPakQnC/BiLyXy6u+E0BPfW66+V5dssngT9SL+rXwGYANX2aphMKzsvZKfWMVtH8CdIi4WxayMGm8gy2y5qgvjGv9Y5csjCPhKugNAY7ncpeJdmPopAUzqpQ1b+nWZlVUH55HQFzfLiIWcjdzgJWLenz5wu/8Nv/1b5xosvlr/88z8v169dPxyRUexHTgJXr14tDh199rnhDD33OeaiUCNA+Mg1hRHDIwmMJNBJYAQIn8CmEIC0U7mj9XZgiHAB0wqgy0OFNVYxtM6An20abiVWFeeqMPeTj/oMSNHuor0qcDNaLCFdnsY3nsMRPY8BKpJfLGuAwGkAYDccdJKhdC7AIWDNPDYUd4eYmr8YYhXwMiHKkefQJEzFHjQnzWRN2cxpjfMUyjenaNWhYyrjq8xDl0sAEwdO4JKzPKqCm44frTu5d2N3y6QY/IFGrse6rua5V+aW3viCyBDraBpW8+rFl5ZRHNooAKOcG6tY7FjwZZUj1knitHoQHJqVVASyqwCzrPIJr5MdAJucEvAIziq7/BKZTCw08WJGBIQo2A32pFxdcs5iHRJ669ZSuYRideXqNaKyzyB1Mjs7W44szJf5hblYcZXfCiBSkBPLlvWUzDq5mJ8yhYlYVJGP7cMFZmKNUybyo4VSnpn344Iyw3Tf/NOvVXJko0s9Ig/LJCBTNBFSrWx+q7VuMu3DIIAfFuuJdYfl1qiWW7m2ubKKM2Cd8tivKFREG2BuWzcPnFbJKSzetn8bnXJYsb6TK2ngZ5ptS9aoE/vBIF/hXUIjdygJ+OHnn/z8z5VPfebT5fd/7z+V77/6vUOlH0V+9CTwox/+sDzz7LPpv8PgfjSPcBhSHNEYSWAkgQclgU5LfVDZj/J9mCSQoY9osYIG5+RFGdXyhDbrpuY9rbWPaXV1XVNo672ePZ/o8wKKWIEENYa1hCbGVTDIr/4BNSj/KL9uXCCiEVCsLda5bH6JVQHHaBnryxjASCvSkkryGqs0sjjJlOAPP4FPIGDy3+ILKIVSjXUNcLLOEfAkzhIEmQYW5dTFPjfgo8KCWqYKBQWNci278GoagQ5x4xLAfZJ4lhrOOPpt8yey/g4HVfkXMEjHNCKJyIRweTMsYBDrFWeHcFag51Alicq7g1q5UtYBH1yLCHHLgEItvvJrfc8A4gReM5B2C8LGJo0A2hWImK/F8V5W1lY3y6XLV8uP33qnXLh0MZbJY0eOlvGjbC8xvlIW4d0hvRvr3PMxQZBz/OhxgE5dFRXGwiPMQRW+KmuyFwbG2ORe/pJ/itTJDi4mj52Ax07GNcU9/b712vfKFTaiT/mgJKy3LQSPcW9Ohulsj+NYTBE5ZaMFtacn8ZFmwrUGxupJKgG6FWERnNvq4kwWeXMcoMmfcvfsYjW2N+vaeH78EFxTWVncybwFkdbrDIDbcOswHy3IQBq1JRpTbvsFqt/IHVQCx0+cKL/9b79cvvH1vyt//Zd/Wa5euXrQpKN4j5gELl64QH9bzxzdYbDu9hMO9/a9OXIjCYwkMJLAoyaBptI8anyP+L0HCahYBnh1NBqw0V/nS9IV05x87zA199sTW6hr1nQq7FWhrfdNZTZ1n0KKwlv/qopaASeERBVmlfyI0dFVQdYopneokG9Wm/QlGwuRAGYmh7QCwIiYBWhMAcC7tbTMC366zKE4Zzgp3sYL4Ou4k8tEp5xraywgMoFFkT+iBaIIf6QvD1rDpuAr7Krd5zCeFp6q0KcwJBYIGC8gTGLNWSAtXaKtEOoKmRyIZJiZ+dPO3pou+QkSRSHcegY0VFQCF8R3qOgaRx3ealzSyQ/RrZ8OE5I9sBZPrZnOhxRrrjD0cIXFYeZn17Nxd7alMA15CJjDeipfa6lpN8tNVuh759x5QOFl9sWbKyeOnyhPnT5VFhZcnMP69NgoS7cZXgpIn8d/FoCT1UWJIV9pUB1ITZnjGY4TY4z5OFrJNlPvLREfBp5miFfkIpF7c/L57b/887Sf2o7DPfwBCGlzXW2mzNZ1ZKpcmYPZ4qfPIJPMIYUd/W050ojFtru2/Tp3sy4gU8spyQoarSPqL/BTP4fzCjChQlPxus4jrNtT+GFlEtBsejlWov7V6y0/r0bu8BJQ1v8V1sLPf+Eny3/43/738u7b7xyeyCjFQy+BmzdvlvcAhc9/6EPD4ZXnklbCReiO3EgCIwmMJPCoSWAECB+1GrvP/ArKnJPml/EbN25G3XQvO/8EQVF4gxJgBEXYv+jyHV8VbHqjompodVW5raBCGrHxSIcI5um50fZePOVKiw6XUyF2xVP3AZyfmy/TWAAdhpg5VpDPEEYUbvPYYJEUrZuNJ8EiXjhKYH44s7Ms5iP4zTBKwtmjPgvCGOYYS3GakaUvxwLLgFC8AxYkJEnOnip9iQPKLICuo5Fz/ASDHAkzUEdc0Zm3Dh2VbigahgsNL4gjbUGh8XGxTgLYBBbh27mBmrBIHw46NixL23sR6QZESmIFWa0yJ9BjliG4s1hfBW7KJUN0SS/YGYMvsCMWq5Vy9fqN5PvC8x8qx44dKyeOHitHWGXPVTGtl9t8TLjFnDw/Jhw/drycPHmCOmMYJPIPtpSn1oZSVu4t4zZnxuaLZ8L44X762SEpb5BdBthefOdtxInkIG+1OGVSV+VaPwq0cCMFwClpv1yQRvAQR5jtKW3b+kD6WqFtu6FbI9W4+a0Fs2i9dmkqPJIHtGJRp660OBjHvAIMSaOFXD+ice4jy2Vobvca3d2FBPzA8e//1/+lvPzNb5Y/+cM/YquC0bYCdyHGhzqJdfvc88+nLw2DUa2Ei3xITcccBsERjZEERhIYSeADksAIEH5Agn6Ys2k6t4qk1okVgMG1K1cABFoHVXi7GCq83Z0Xme8XxXVAI02sqtgaP9a8TmvVcuTLMnMHvTRPflSkvTYv850BDKrpRqEmYGZqJla/KSxNbtC9AjDJsET43cDCpRIt+Kv8Cn6YiwUt6QrozL5ayCqwJTqZcg0PWtcmQQIq7htY1wR9sUjCq0MBs5UDCVzd1HluPQXexCaC8/glEzPiCPhTLmbkWeeZI3lzSfLcSwe6NZ30uvj6KS9BoGcTBhAKCmsswZqbmQcQyi/8YIuCQgWE/pqdZV1lvuGNGzfKNYD+EpZUyQt8JlkV9SjK73GW31+bm8UCq+SQIYAOySJfsxM8F9KtxHJ48oQWwSPUk9bYGYaDssIeoHJ5cZk4Syn+aayGJ48fK7N8NZdCFocR8UQ2lgdnRjKof4rNj4zp6SmeNa5bTYyzoMqw3OXz58qVS+9VIFczo7ZrexXw2w6q2OWFtgUg82NJY0sIPuEwUFz8iKO0lelGtmvRPyEp81a7IR6001wM5qh5cUF3M731Zr1qUWzDtx02nbxsz/ASizTiaiLsvzJfaYzcvUnAOv+Zn/3Z8pNf/GL5P/+P/1DO/uiNfEC4N6qj1A+LBK4wyuEq77qTp04NhSVXGvV9l4+cQ6E4IjKSwEgCIwl8MBIYAcIPRs4PdS6xzMGhZ+d8Xb92rdy+fRPFVKUUFRk9VcDhoRLrvVaTO196FYBZ2BpGxE5ddZ8/05u4KsaqzqjU0vXPsJpV0jpfyzCtgYbPAjqmAYUBZ4A3LVoTU9U6Y7zkA08O9JskrXvkGZ5VHtHq65BKQ3UCjMqrV1rOnDuoIh4OkYMAQLryKoiVB3m3HBapWpKq1abOr6z+ERDWm1j6lJUWP+hWy58kpEOuOYekwqr3WVgGv+bgCTObyAwf43jfHZKJnVXwoOUVfrUMBsBTNwLVoJnkhmVvtVy8+H659D4K0NXrZQnQrwVqli/aAsJrDLFdAcBtnD7JMFLmwgQMQRO6q/AQKxlgWVk5j22OoaKzbCMxxr1z/VbJa4ltSpYA6nOA9iPHjmDNdU4N/Am2bUcxf4XxWsImA/0VaifbyDBCNprlruEzH/54TTek329+5Y8iTsknC8SXeu7YCaDq8UimtIUAOeJZtcqELwlJ3KLJmnXRzq6+Wts9adN5zAPXta1c2vaoC+sjUQiznbtozBoK5ubyUviTjtu/TFJn0qCKKi2J9JycdflzHoHCnmDu6cI5nb/z7/6n8r3vfrf84f/3n8utm1iBRu6xkMC5d98dGiB0DuEEH242eL6O3EgCIwmMJPAoSWAECB+l2hoWr/3aKzSjXHpGMb2JBem9c+8y9G+VPfewj6GhCgpURo2nUitQ0jrYFPVox/UnHCa+efCvVaiuJGr6UEhMrwJi9JOeyrWOPCe1NgGC3LduhhUVBS/OHdT6p4rrUMZVjnyNBQxpzZM/VWGdIMRtKNz+INsuZBgm1i7yqPP8qiZtjiTLIdCpLFheLYPVf1z+ua4H14AW8YtbTwggVeTNOHkbqceEF1LpnNq7gcrNTEORexFAMjO8k4HBXstQDjy0OHVWJ2lsMh9NGtZZHKdsB4FC4kTM+FN+s1oFsF25ch3L4A2ijmHZY6VOQKAAzFVBq1w36lBQAMjCEcGxUEL61cJquoAWLGLGtzi1SLYFeOPfNMeOLrDK6EKZnmNRFNPXZYFS9ICTgOO+8gcc1yIonq02RZygI/mk3hzKilVyWO7m1SvlR//4nZDrATju6scFoLbtEF5dzTN8+YMsGsCyBKlGzqZPNRI/wNnAzt+ht95ufQSxOFSW8jURRz6eAAAnqBO7Tdo2K9ZMTNLmqQ/zTFTobDgcusneTHARW73sfu/02RY8urkrCQgKv/ClL5UPs3/df/rd/7e88aMf1Y8wd0VtlOhhkcD5c+fKZz//+aGx4zxCV8AeuZEERhIYSeBRksAIED5KtTUkXjt99Q5qDtX0a+kVrEgq/xmKiIaaoaHEVpdXu232MhXaquhKqlJt4FGf6qLO9q6bb8AgSnCAJZ5cBfug/2YO2yIv1KOsKOk8wnGU8sQXGKDruthJwKoKuGnlEVCorUyFPKs8qsxTHod8ulk93EcRN74g17jh2bRd3uubWCXxdVioQ8UEIt6bqXwGUHYrYFruWBQtNvG74hub+AoK+mYhIGBLiLFZ/LTeGd/gBBIhafEUZbpsqmDB9FoGDRN1cJ3FVeAhBPCSTpISn8GDqS+Bs3KaZsgniZhz6WqsAAqSzWFVcs6f8tQKfOP69QxFjHwpSwUudZXLReYAZvET2LOMysIVMtucNuUj6J6eFqwo+wogteC6AmZdOAWEaPkpSyRMXVfwTHsQSUJDXuUzMoGHVC5nL+MMNi55jWGRHOcDwbDcWcCg2QcAmk+XveW1PJZ5Cuv0MgBMJ7S1/o3ndcoMo5bJIbr5ICHvpE+BuLZ8ytWhxtJNualP+4/VrbOoAunIqYsf+omljGqbN/0kdZm2jiwLexBGfuYTSls/0pSydEZu+BJweOGX/+d/V1579dXye//xdzNEevi5jCh+UBJwtVHfKfavYTgB4U1G2eQBMwyCIxojCYwkMJLAByCB4TwBPwBGR1ncXwmgYpYbvMTeYwl+FdZp5/CpVGbOGoqlSiuuAj5jE4oy2u8SFiV/y7cpp/0+qqmxPAmyjN+lcXFMbTBiIBXrKeaz6aYZOudiJlUBds4fS4XjJ24SbjZ6mHTgXb6AgaQPyIFvw9HgCVAZr66x7n34JpbKt0P8Jt2YPP8o8SrcpBMMeEwAGnVaPaXbK18jmECouuiITjrmDd06vLDjIEwZ3sUzonJw5RYRnIghsuGa9A7NxCM8BhdKGyf4mmE47XHm6gnkbrHC3Rr3WkcdQjoD/RmAzfwc8/0AhVoEXUH0OoDw9q3bmY/ptiIu2mN261j7AtIFHbCUfe9IHzAnC+aJ4rRA3czOuFCM4MhFTyhehugaCRDMSdlFLIIgPGLUlCyiaMVNjRBX8GKdZw5oh6n0hQPi0hZOPcWCP3UOHZ737N76/muhbpuyvecGqrBAjlp+2fsyC7cgR2QiCM78V+LIo/Mr0x7DCYWHQKyq4mDSKq+0nRCu4dWaW/OyZPommBubgTKI4MhfwOlRrYlyZALaHLLUeh7XI1Jvd/qNXGtOOwWP/O5SAoKHz/3kT2Yfuz/+gz8sP/j+63ne3CW5UbKhSiA9C4p2kP2d75PXX3utfOZzn9s/8gFi2O+d6z6yEh5AWKMoIwkcUAK1V6t3HaxfH5DsKFqfBEaAsE8YT+qlHU2r0XsXzpXbN2+UOVfyZIimq6XFgkKEdEb1VcHNbk4UwH+NS7flxRjlOPHtxlFro/Q2q6O+W91b8DXGHDYWhOGF6lYFLuKhtryyyhYGKMLTABoXkxkHcLWN6kFM+ZuZcJiiRjn30GOLArV7uMkCMVoQc19zU5nXIIfanjw3xgEGKNsBmwIP8ozhBgoWK0NKOQtA/dXPwlo+FXX96qE3gVr41PJ10fZhKHp8EurJ4XV3L+DzEiBXgwSCpGnozzPgy1T+CAwCVLh29dXjrPQpAHz3HGAPYK+f8y/dtN6VQLPtg+SpP62rR1icZQI+TeOedisUNnswbvJIEIySiRYt52M6hFcQZDFVhKcYxisYdDiuVTrmQjwEalS1ENWqRVjqm7OX8ovipTPumPMFzQNeCKntjDK68E+tNeiQprIyVqafeyFph/Fj3V/48Vn4tJy1PVvX8lMHeMqj9V9BXdoIUR0+ahtynpD9YB2rQq0f6VSZSSX1r1wgWq2l9SWW7CIM4+qqfyySxI1czJOQAE7rig8fE8uCUmVEvwDUmy4WcWRlXnXqJekJaZS55L7S93rk7o8Ezjz1VOYWfudb3yr/z//1f9+fTEZUDyUBW72uvy/sR8C5oZ/+7GfTB/eLe5DwESA8iJRGcUYSOLgE7M+tbx881SjmYSQwAoSHkdZjElfFU2XYI9eU6xbg7/y756KYnz5zJpayG2wvsBkFtXvBqtF2Wmf05z55xJrR3sDG48jQzESseal0CypcJTThpBckrqP1R6k1kOtxLH3jgJCbLNzg8EctcysupoGfWyKYjaDP1RdV0FW6N6Koa1kB3BB3hrhZVdQ8Gt/y4nVclYEWMXkwfy1F4EJ+5RdwgBKuem45ZNyk4ra43ODvoiLNVVQBAcGbyjpn+IpLvhV85D7JWlqJcjQ/z3oZXcaSp3RqvMiQa623GiLlz8V/jgP8jHIR0HbjxnUshkvMBWVzc1b9PDp/pCzML5QVhnUK6mRrSisiaQXYAkjlZg5uEaFcrdNY7Yjn8OGkAaA4LLQOjXSDdS2GEIOOC8eMBbjAhog8ZYYORK0XAUrAlP4pVgV8lsc6tJ7dd7IuQFNLOzauDMnzzLPkNBx38e03y80rl8mAGoeXSJb8A1K5Zwmdjr8Ui3K7mA6yhj/boKt9bgLG9EuNpnz2pVoX0tzA0ptFfsKy5YU+R0rlNTTxtRbTxlpfRITEUziEcjjsV7lZjwGptH9BomB0bAo6/hFdJ71BZ/jI3V8JWF/OLXz+Qy+Ur/7Jn5TvfueV+5vhiPqeEqAnEl77YxYB2zN2DXQEih/Sjp84cYDY+0eZ5BmRh8j+UUcxHmoJ9D9VH8dn6cNdviZx35O61rcf6ibzCDM3AoSPcOUNi3UVXZffvsl2BPOABq1Jl66+H4tOrGoquGTmb8DVgJJpV83BT1NOjRLFlguBhc6hl1rhtHbklc2P6RLPhByquJNYtgRz1wA1ghwtWKaZhC+BSLXOaG1Ce4aA4HFCQqR1lctpwM380RksXoLDDaxZWg47QEeCWNfIy/0V1bsdtipoka4AyWGjWThFXEAWsRqqqaPEWxZhovP2dJWWSj1lEp3pbXE9CxDlK1G5iLmLe0Genp4oV43vPem1opE/GRFGGs/615GqmqnwFqgpK61YlZTDbadmJsuZ0yfKsSPz7AO4mCGklmWZvQGXuHc7kVksvzPIcW5eSyug2fltyG9mFoviGhZF4uuvZTAgCGCnchMgDj8Op82IWliyFgVLyrGWEV5w1mEkAp+yrwwCXIzEdZOzrCuKFgbJnhUxljU8iF6mTp5huOjwHlUvf+0r0JVypV/P3Ke+4Abxy5tOEGbxdLFqW1/dvX3B+s89fllhED+HKvtlwXTrDgGOzGoih87a2GrfqDzoNx46clXjKXvpZOsJ2x6uAk0XS2LlUe7X/EgieKdxVEqJNvp5QBI489SZ8ltf/p3yrU+/XP7g939/tG/hB1kPXSetvceMvbI30RcP4By9cJWPRMMDhM6vrvOPD5D9PlG2SrVzxFHv31kuI9/HQgI0f7Wd3ouXu16Lz0Xv7rEo7oMsxPC0rAdZilHedyWBWCVI6T5M51lMRu3dveXc42+RbSeqUi4IokeqyO7iVPCjGKebqkCjVAPctFwFQJHWYYrmp6IbWnmBp6fTvT3nNxTcdPsGFssVhugdZcPzZl2a1jqShwG8QEtLjcvwV0CplQ8rE4BJAKciv76+gjog7xWkcNXxaXbkF4BWoYG/m+G5Ktdu2C7QGZtgaKRh5GcaAYygdhMEKiCihMF5GrLQ6v2BuPxx7aQ5r+GFxFV60AlD3sW854PO+J5I08IFCPqZDL5CIzRlwFsto5WmvI+HP+LhPz02jdVuopw8cbxnkXO+oEOABXzmGAufYI5jnLl/LqAyOzYTS5TyU+YZmoul0/mI4Z8yuEBKcC/lchXOKnsIpgzyL3H5kn95rbLLhwXlQBkciqk11qLWEviA94Y7TtmIHQq2GdNPPTU86+AK8yx/9J1vJR+yiCNLnHUpxJYNBRxPLHRsYbJGLYc92li8u8DcETeR8VM+yCSWVXy9tpXwlQFAC6I3GYQCABuJeFYZhTRCieUX/zW2VnHoWeoqeWG9pH1rHdd/BaAv0exJmMR3/kSuNdadgSOf+yKBL3zpi1gLP1T+9KtfjbXQZ8fIHVwCrWvsLjX7qs6+peUe4LW2yLyH22VjZZFh8kt8iWFOtF+jeN44amGwDpK+ZdSx9s6pqfLRj328u7u3k3OvV3/w1bK+6MrOu7kBBro+3hWuS9TF4Vk6GHvLw4fTbnns5E/k/vh913kltiT4D8qtBeW5Y/iWR49kI+eTsTo/D7a4Wynax69ewhb9Hs/mn1dQR6fl2M57kW+8b8XpfEL0TgotviHteivtXV7tRqh7PzZJblEnQf2PV33mN14bscphk0t7L/TTaGH61dSNxlas/a+6dtqyvSPBrgG9mNticKP6iaZSPwqP8z6cWijj0wt8KWZE1OQRFAUOdJfoXsRsZZOT/nJIt95vXfUyHV1EAiNA+AQ2hPaQF7AJfG7duklP0ZI2U+bZnPz6latROu02UWY5+UBXKdYNPiaavtOsKVkYhHgBg7yUAwZMlxd0fQ0IJKIwk8kWXePSjYm3vMSCJ6TJsv8AFPPXYpKhdmToojK6xSUtZjwXsJpIc4W5e66AOeXQUu4dtie4ybBGlYMUypQ8NnjJ+tISzK5nDiH5uWfeBsq791h3fLFrMfORFPZJX8sPR/w3WWRze61CidpFEvySvgcQBUjQiYOX+Asa9ZdQDpUYLgIqOSe9KbrXK8BirFvYpmZOHPivj74aZwI+BHlaWUF8yfEID84FLIexziETgXO1NAqkIW8aAJgL+FgHgl5lNmk9BcBVWSh/rYoZ1inlVL6M67iXlmWTdS6RZobeKjznHKZoxDHMOYtyLDAPsOTahBmKCQ04oBxYJ0+eTsgwft4/904Ap/lWaZm/bMu4Tl4E27Xepzb46ICsVgBnLYYSdYjyJkCxlUfr4QRI2bY3zhssbZpAafUc9/a9uuooZYOg98k7Z6urgk7lYZhtM5Zc7l3Mx9VPAxRZcVXrof1jahMLuIUYuYdCAtbdU08/Vf773/ofy0c/+tHyF3/2Zwx/5xk7cgeUgM+T2p77niw1rQBwnX05l2+Wjdu8p25f4bjOvn/Id3WpjPMRkBcFHYvevV+XGAh/c/FS+fn/5t9u77MH5PiOaLSBOT4mXn/tL2oQeVmWpqzqafaD3bY9kwxvrhevRY5Q+Ondd1IaKE9Lv+uZ9Hck6UjVV5LPr8pn590jdUc6Qnw3xA1GxtNpIdVtT9l8u8DeqVe0/lTbk/rw7MXfdgEfeacNBMdPGvr3h+nX0e73bjRbtknfecaPn158LgxvcVvadu6Vp5egizuYoJ9mS9zyHIzbH56wGsE2ZjbeNW9F5XWvDDVqKPRd5n53z63g3lWIcsfZy9z607m+4ias+e97bgLr0QplysW7lffg2jiL403NM7/lWBmfOwMmfIbz04BERxMBFAGO/Xk3edR8+0P25eSJijAChE9UdQ8Uln6xdPt2ee/8+XLxvYtRRq9cvYbfrSihFdDZBXnA2Ic4MieDvtkUVoGDznv7cOaacSF4q5YSOnBNCJDxSyEWFOK1eW95UNnXyQWcUsO5yBA6AEQWiCHUfLISJtd1zht7DQoQuRdYBrSYJ0fABpaw8IZioLI+jqVrk4x9VPoaDNtENK78+SL2LA9jjBmcGmdz4U2tMQzdg/46LxnnG9phLEXmF0IvANayhxMC4zoQQLoIxUK2N71CEiTIuNf1oibzpdnz4oIHn1bLMSxCeZJLJ28dI3ntUZPkwhsdvBqD17CCy5XgQvgVsINcHSYagE7cDGM0RYAZVljYc2pk5Ip/HZ4KwJRfAEkimEGyg77CVAGTHx0ngy2fkgiAgg/BkgFaIFN2rkOTBKkr6Xdlsp7cnH1zChpzfA0ckrv4zlvhIflDMxxTH1rhknXHe912YirDjm2rBvJby0oqxIYY9OffgO6szJS4QE6xNIDHZeRtORWhDULpdEm5M3490rcIWMPKYdvVemu9KY9xPgjY5szQIaouuuQCNBPIqdW61JvTT1m2dt/8R+f7LwHbwj/5+Z8rP/GpT5Y/+s9/UK4xEmPk9pdA19OIWFtvVghbWyobi9fK+o2LZe36e2UTMDi2cpuPd8wjp3WnSyUFPQO55xnbZWVv2dENBCxde69ceeeH5dQLn9gx+mE9Fz7zS2Xx27/XS+Z7b9DFq8/faRXbXNPguzgtan8se7hup/6vf0JbQj10eMZr0L+G5hnmZR1FU+N3Qb2Tj8UtIuaiRzv3ouWi8djv6wfbmr7fd+frsOnLpN+lYHj44NYlEqcwtuXVi9bi4NEXJRFb3XQkauIu/h1+W6Hbr4i4W9zB/LYn3LqzuuV3Jzo9v95Fl64VUH8O3yqNxmDUXk67BtS8G8le/MGLLq87vPvo3kGDsL7gwaTb77uIymOr2qsna8GjE93kgyzH4oWyee0H6GeAxMmjZWz+6TJ+9MMAxA+z19Yz1YrIiCnftfW5cgdX2/N9wu9GgPAJbQB2rVW+tl66dJFNyy+XaVeOZH7ZbYYVOhRTZfc2887q46lfSPVRE+tFvLc6mAqQDz6BYMAG4d4btyrU3PBvB9fy5tFAmx0/1ifybQDFAaIqwVr4BBHuRygYnOTrjwr06qbDgujmpHX44mq3kIlzAldWiKtioBXK/PNE5kGZ68pzVHL8eyXgeoMXMnAlQzBl1rRtjqG8CA5d5sM0eXg3vlHY5UkgC3S0gF2eRGwuGVWAFinIi0d75Anecq+QKu9rLKzjsEVMoJVKB0LqTbjgkrPl8+hoppw1Et61XpS7INfoAQiyCL0MzZQH80wFuSAC1wDH0JMv8094jVL5hFD2koCmxSKf+MuHdODFl3OAIMJKXevPf63jGk0dqIHTDI/k7ZyvzfDm3MEJ5rUOy7335lmKgTywiMIE/FUeRXi2pbRr2ps1bB/QUuhHjrExhh8jh/piIS3OjxA6FbHQbGfKnMWKKH+NmWj8mFcXX7l4g0uLMC70N2nnltmQVaySymnGFV2R/zrWb/OST2XrQkLKyf42epBHlA/lzyn2Lfxt5hb+4yuvlB++/noW8HooGX0omKp9wv7nUFAtges33ytrV94tG9culLJyk2ezH/jsifSlACjT0DM4pS/1lcOQ1gfbdWL7k15meItRytlvfG1ogHDyFErp7NGysdQNG7XbJt+Wc8fbVvZw0kXoL0OfV1/UXoxemi4ej4Y73YCfshrwujNNf5w+HlpE0+tdg1qEdu4FJHqPx9wd4Kd7NjYuw+sAwylDY8LsujQtmpw0eesXubTAPhb6vfqvjRIafXF3u2zs3pG+E8eg/250Wrx23havT7S7+puQeC1qP53G47a0gzckbGnaeTBK6JvBDhG2eTUm+ghsC+/z778c5LM/jSTz1uWixvONKCuMeFq9Ujavclx7vawxrLQADMdPfKKMH3mxlBm2rcKq6Mf8g9cqUZ8wN9IjnrAKt7ixQPB0dHPySxcvZhGWaYa3tG0BZgCGDieM4tnJJ4pynqhbvVwFtir5YAesFVGQ41dBmF1Vhda0vuA1jbhYiS9ggZ2KNnYQQB+hHW03SneelE4+nSOoq1sZiEvcbmIV62BdZbSmM9yD/RPJYxPgtobyPM/2GSvLKNUq0D4IAAFixji9OPwy6KUPCXnNAincrZNHlG/eKL5UBHtaBRmwlCOPFRLWfEmNh9eCV9R0/jqnVazdxVNiRGzz1MzcI2GoM1rPDGclz02GrK6vrbBYDEMjUPzr261LkDJ5rYv6ZOb11rMgsN4FaLSHbPxUqAKKvOsOwV6ODvjJt6ynUORDvVl/YZYyxhlmOQJHurwtjJkp2MSj7r13ZVmc9aicY33mXi7Tbsi7vrwFV3woiHzYz+vMM5kjl8RD+Lnw4zfCnzzJVkCZ5eamtoVq2eu4jXQEY/Jk3ejfPnYIdAOoTY9zeDKvpVwrX8uIV6pOb8tdLcqSQg60R9tbrNsWviYlTgSvsKrFmzjuC3qb+Y8uJCNBaY8DHKfoq9UaX2kn811+7Hd9LXOXWCPv+yEBP3R8/gtfKC994hPlr/78z9niB3AzcjtIwI7CM3v5Rlm7dr6sX36zbN64VMawEE7w7Ei/9dlENHpMXCxNXd/pJ9jC9eu/3rq5M9E73/3b8lP/5t/Tr33W3bubPPVCWTn3PbpyH8ON7FaXbz5P1nlbpQwUPVWzg8yI5nP7btzdpEu17ZPf3dC9G/4frjQKZav/eDUUOUB2i2rNInS3Z0dmvMvUzfq4UAerb070k7VrZePKDT4iYT1ceK6MnfxMmTz+yQ4YzpHOmCM3KIERIByUyBNyr0J5/drVsoSS6XyydRR2+9z8DPvTobzcZssCX2KZA8WLuAIv77tOuK3XqlwLqFBy1YDpbonn05Tr1mmzKExABRACZdbXdCyFRHAhjViHSOOWBc16ouIsXz4UqkWmKsNaT+S7GbGMI9+gB+JjPXQj+/n5vNhdedSVU7X4hO38mHs4BewJBlC+CfUhE+MRd+bXRZXB4LQAGnh18UiIJ34U/R5h8+CG+JAgCrnUjDh7wZGnlnG4Tjoieq1LUjgiA+fqzR49SnwTJEPCkyBRQ8sv5OGjAvgqbepDOpK37kgqGzr9Kx/dWdrtkDHjRY5ecHiKbpQA7iWGRzKAmCgqTsIhzrlLpw+ZC0IyL1FGDCVYy5tDiE2SNoafNSCgVsELVcJmn/tw0gzj58qF82Xx+rW0g6z/J5sUWa7j4Md809b66kbpVyd46+InrhbEOm/QtuOcU/tR7TehROQUsIrcjPTuXCA7fvablJ64WqPHBfTQs/2t287pA1lcxr4KKJ1BdvnwwFxSrYQOq/bettZJrmWRc/WrLWNbwOjmA5fA3Nxc+bV/9s9iKXTvwiW2hBm5TgL0g80VFr66dq6sXTpbNhkaOr6+nOHr9lMePL2+mkdpknUdqq9fddS24jYPzjtE6wtlCsXNq2XxxpUyf/zMNv+7vZk88RyA8NXa//uJ7MdIf9wDXvuo2c1tyWu3GNW/RyL8tTvPdzLsO/6wrr3mWro7KDSaLesWse9cdZE+j5amz8vLwbwGgve93YVsL91geLtv9dDuewl2uEgx7xBCfQcZvdHaIemWF+n7xbUDua24e10dMGGL1p9nr3m0wL3y2SNsUB67kqNyt+fvWw4fEiQNP35AGt9cYpj52bJ+61xZvfLdMn76c2Xi+KcZSsp8w3EXohm5fgmMAGG/NJ6ga5XIrFKIIqmSqVUi1hp+3d+v7ntGJ+NFvL6mksvL2v7W9VAfyu0+w3YACLH6EC8KPrKM+k8aLWsCN7cu0NpiOnOLg2AFC+RlXPgRVDbgKZqJKosS7Lw3w1x9NMAzdCpDmbmFcuw2E2bgw8El/81rmT3cBLhH5ufCv3zKY8pAPJVprZSTFlZyJsJfUGreASmMUddbw5wcZb89ymAZBTYukFLjp1SEGFGJwA17AABAAElEQVTAKq2um7kKaSe/xKJsWx5dgFgr2o9x4SfgLJlWwpLzsCI8x0knnDbWt8pG3Qq087iET3n0L4nN36Rx5tWu9fSGeIK/OK/N03iEt/wpYy/TLmY9VXriR9nMvDcuKsvIL7K1vrWiVbBdQ2u7UtbjJJ57/oVtVO/l5o3vvCwjaTsVhEGNIllFvXu9lFFPFnJV/7zK3pbQcGhn0hLRso0zdHbD/SpwytiPBEmJjMecv+pf6rvJk+TWLQ3KvGEDOtDl3g8dtoEpFgai8dGMAIGzbBPC3N5V2nL26bSFESY/sUY6j9CPLdbLyD3UErC+PvGpT5WPvfRS+bOvfrVcYA73k+zylF1bRmm7VNYu/gir4DsMDb1dpuj/eRb2Nem+y57IdvLrBe5wMRi//3755rWyeO3y0ADhlMNGd3I+Hsh4O6jS88E6ZbGdi+13g9z1y24w7O7uu/zugrAp7+bxd2eZ75JzGGj5t/PdUbr7VP1iazXna3tfd5A4EtkxXtUoBlvOvnnuEEHyje9twTt6bsUIW4O8ca/XJC/4icIc5GtnecYw//j4j8r4mZ9iruFPEHicGFvv5C2KT+ZVn9rzZArgSSy1QGiVOXbLgKSlpcUAOZVYrTYqmW5QruWhreTZ30Xtl9F1u26bFxppK2Cs6MLwpmCrCJveeCpCPjrMSyWaU1wFotyQMEvue04+/kqb9MbkvAxYXVvFipQ4ABzouHqlSsUU9LVCqkSrbAtEBWpScTXVFRRtUkiJ9IJF0QqAhEOQ2Hg1PKBAlrgWsKxygBlR+lX28TNNjg7UhI6x6yF/upwJi79+XvfOXgMi9AOUchHAEhAmmxY6Beesa2mDSokrfyEJ7/rhGiiXlkBCYGJZWngj6L0WLYHNJlao5J/8+jJsFeS5eefsPXJGxjkSr4sgG8gqB4msOYfsyoe81vonnUCKurJNeK1LWZLGmgBgM3dwnCGRw3DWw5uvvqJYsmKoPPV4MW/lh3wtSgXN5lp51kJnGzVOr14NtsiKgtC0bRJ3UkjaZGY8nOkUTX6Il3vpdV5mnLSywVHp1HhaCbWkTyOLleWl9FtaX48XV9iVcPiruZjTNpe8t/mMbh60BBwl8cu/9mvl537hF4qWwyfS0ec2lq6VlfdeL8s//LuyfuGHZXKVedN2gO65cBi5JJlJ+45eejzt33u5dd59LiwzLDd58nAftPIM7J4BO/Nwf3tyxJOfmk99D/Js2k9w/cyafh8590cf5rXyO4xLse6S153yyrP7Lun1893aQfPbS/w78dHSed4vvD/ufte9ovUR3Yu3/ei1cGkM0unl1SLted674qWlaji9wUJUl18rG2/+Udk4/+csU/9jPggvPajmumeJHkRg1cQeRM6jPB+IBFq3WWao0u1F9m1CeXfO3hKbljtEzX3qVE3nWdlRJbT2+9o102H7HgT9BQiggpaKvEM+BUuefZEINKfczw/l3y++HlG6JUu4dKsSXhXgChhVwfmAk/lbpqkrjsqreRAzyrXzEKezCmPFJjNuRyGPHFobBbh1qKmLdLBIgaAqlE3eKdEO81M5l/+UL4x112AbyAUAEt+zC4ZY3li2OiFsAwpyZ7nyaY7EEvAwbw8ARgBT+IRA8pQQcXQBWjy9FIy8tvSynusuvXwD6LSWBgjCkyTGklcF0dalf83VrLgnj0qOBPLZOwRo5B3g3Pmb3CPEvc5N52kc4lNPSZNxOvhZXPjyMDGcwrog2jISDo+b8G88azqWgMQj2HCOqeMnvRmKW2JrlZtXr0bU9WOFfFRnbjETdvdmHz+iKFc/JnQsdfxvRcyHhcjdNlmd53rU3wqGBea1TlMfBCmTO5ztA38/aNgPFJfzBq3nWAAJvcJCUKv0V10bai24ThuMfHegm9ijn4dNAoLCj7/0Uvnnv/Eb5elnWDrdvv+kOEYXrN+8UJbffqWsvfWdMn77MmMp/ChD57Dr4PZryYYPHkl4Dz/nvveNe0i9PemuFsLt0R6yu+1Sr1Ux4LdVRcPlfXs2w6X9iFLrvW4Pyb/11nWjQ6bcOfqOfPQyeLgrTu6aajLJCKfJ5ffLxoW/LavvfrWsX3+V96sLPz3cZdi5Vobr241lGy7REbWHVwL2X4eIXrtxrdxiCJpKqUqt2zIsY31wEZcTxzCj0/tvsSVFAzo+DCqY8Ly943iv4hywwyPIeztfgF8UHDeRnwQz+LonLeF5uJDEGX8Oe4uFJmP3AAikEUQK6GZY8XQ5+ZmHQLPKVvwiwJxj4RjLtLR4K2f3H8x8KvmFJ4dzZq4a+W4wTHadPQZdVTNk5FNYBDNaXFTcA1qgFGUcumYdDJYzgBDZrYmZ4M8yB0g66bBzreyCHHKuvhLh0uLJk5lH6YFGF6E7c4K3Gpmz4CAMcE66vny8x1l3WrsiP8rQ5Vj5khJls4zOn4yck8hskEPHtxubM5YXzw7UdcClVlJjnLwVRPRVeeO68aenfE/CUwCgvBEueKchtA8AxsG3K1KVvffxFBaSVQCWntxMHTtl6FDcrWvXsIovJw9XbV3BOlrbbLIKKJUj21UFrbZD23L9YCLgshy1OqrsvYn1M+NiU7JaODi2HjKnT3oCX84TDDuWZmsLicOPIvU6xK0v5GytxZGHFkr5UD5TAIib9NtbN26USSyG9hNFXwE1afwnXuhVCvkNCG00+/xHlw+HBBaOHCm/+k//aXnrxz8uf/f1r2do8MPB2X3igkVi1q69W1bPv86qgBfKJCtGp/93DbfXfnsXQ+DDLnUAeude++YQMqskxueOswT+aYDv+0OieYACDCmnRqZ7EqV+mt+BBLkV+eBXhy3eYeP3cWJ7u8Pht5P3HfHw2DH9ThHvt1/H8EH5PjQ7uxHGvwZ1LWS3eIfO8BAJGg+9vFtr3YVGF89pTpNYBteufL/uYfrUDVYk/Ryb3p9SC+gSN1rt3MtkF+KPvncr+aNfklEJDiwBrWyLt25nawbn2alMOjxUALiwsFCOHT+WeUyuOloV09oh/FVpzm/rI9zFIiVAUU9WmYUedsGAzCwaw30WfFFxNb4arForNFSQteB5qLRm6F3OY8XVTl11VJARhdY8yUdg5f3szAzzAufpvg4dJY9umOsk1kidm3ar9PdbhFSuBaWSEry5iEe75jIhSSMBnvgBeAIfnHjIrTrchD5YCO8egLH4eIYaDEozliH4DMrgvtLvLjjd6YyLbJxwSVlyLSUT6rqzoHKrXNU/1k0Z5J/UvbxSRsFPOKNupE+ECjCQoqAUoMJeB6QSEJq6c2ZrvVLeejCsleG68Ys/EbI0JzRN6xYU7Z4FT2wDzjl0VGzxPnnwUQC/DeJuOh8R/w3bArETDj+SIMcydZQNZofkbrGAkhZii5c2iRwsXv1J7lY3VWXLqq8EQWCdq0pMZR8NAFnSLwRt1fLX6oI6Rya15uE/0Wu50v7MrN5CqmsfxufaIadxhAsirRvnEdawem99y7dEpugTS/Tf1aVl5Bjhdumk2/Hquc+Z9cg93BLQAvzixz9e/uW/+lfl+Q99iPru2sXDzfahudtcWyyr758tK1gGy9V3e2DQ/tFrp170bvbOwpbef+wVu3uE7hWFOYTvlxsXmcc4DMczY+pZFrHonB/o2tH87ubsoyiPo30S98drIt1NrPrnUdxHM88qn1cI2Cfj1l9fpP7L3Yj3x+G68TLg3bvt57vn2V00+Xk+cCMZILKX7Czj3bi9eD4svUZrLz4bzUEp9D/5G50Wd8fzHsVNehLtFCV+LfOdIuyY2cE8D0puu3xaydt5K6/GZs+nSzhZ2Mf01ttl49x/Kevvf4NFrS5SVqbQbHt/3kmvR+cxu3g83ziPWSUNqzjt4a7lzYdeQBrE3YNwEeXSTa9PnjiJMrqeTeCzz5mZ7/EWDc10nq7LCeywMOVAwVGJzT5unTJbXyghSlgHANM567U8BWQBQuZcSAPLlVtISF02omhzLXxcYI6ZC9W4Obd7DmbRGvKZYt82aQsI61y/amXxsRbrD/mZpd28AjoVc2lXBV213n+Bq0M/9VdmGYJKRJVzh4uaPhhMP+6krcUoDvqCmlgE9ejiGBocJKpNPp6JDL9hilPOhpFHDtI2FzAIPymHniGTn64M8lFjS6q58GL5KEfvpS9dj+Tj2YQexI4/YKOBQcMkorXLc8CjAKUDgiw8VFYIWCaN18YJsgPQcKZJMWeRa0lmyqJzC7ECcy1OWyXNKrTXOK8T31l7EwussDok9/6775AfTMFadVx4HXk3v61zvlcQv22BkipAfqaRToV0nZg6ohEnckobTxuroNM2pqP0/HpUFxEpV5/CrUEahJeW6HyQkT8Iu5iMgEElyH1CXRRK0JjhwvlwIwkihwmJbHdbuW73H909fBI4euxY+aVf/dXMLXz4uLs3jjaZH7jKwjGr73yvjN28xNODXtA9qHLyp7s/bE67Jm0B7XwAwpfOvnqAWAeLMvv8Zw4WsS+WXXkvl25+yE5t9HbsRXuYYS2/nLsy7VO0ZL9X+Qy7n64923fLw7rZr352S3v//X37HMIdKvIA3XtJ20fqrmXZ8m/nA7fumsBfj3EWgxtfvsgQ0m+wqNXX2fv0Av4oKj2XF3Tv7nG+GAHCx7l2+8omcGvOxUaWlhcZIsoQOoEIGqmK79NnnmY1wxnCWAqd+IKx/RqIdHvgBEqCqAoCHZKIAgt9z80C2HhQea2WGi1T8qBlsbtCSVbh1QIoKFWPNzB5EebZuTcOn3PbDHl1GKBg0n0Kpzmy2XoSVp6cGymf/ANQOqW9Y0Z6AjxloCJuJJV+1f76FZIFajpAKA0XqmmH6dZV6kO4yji4obvXJ1ZJL3JU/lPS5hc+WuE7z6Sn4NI2IfnoBIT9ZRGsNiBrvApwEzWyd68896tT1rEG1qDUUy5lVto5atkrQjEzY8iXraA7Oisf40Pxa2CQ9MscrPC6sYp8FgEqgEMXIN1YBdwBBD3WVggjfJ3zKsfK0gZtbYN2uMZCKcxjXVwti5yXl9ZZPIVx/kPckP7Cm2fht7muDmx2Hvx4ylzTlLX7YBBZ183g20sr1WIKPGrbML33iItA24w3WhfjmTrRL7G6vlLbftJUIZN3IoSGl7Yz25ZOS9FaFpbBam6f4N7Y63zwsH5ts1mZtGbS5WXKVKAXcTWHdjc6P8wSsM5f/NjHyr/+zd8sH/7IRx5mVg/MWyyDbCexeu61MrZ4madH6xeVhK3VNno37XS/NI3ufvFaYd5/6/vt8p7PU8984kCl8tmi6x4F9WbX3+19uz+a6dvR77/vdftIuW/EXSIMCLcb+LGtUgeibC/sHYG75NOeqQPBd1XmjkZLe2AWBvJ+GG7rO2mLE8s0bLfV6oZDfCceD0p5e9otzvYscyp6S7Uxrvm5ovnEyvtl89I/lPVL1VLIW3hPUo9joFrdyD1hEnBLiStX2G9piaW9GS64vna7nGQBj5OnTqG4O8+uLmAh6FpFMUHj7Elox24XT7tVBVBuLeF1LIAACXXsCjwrmXR40qjMNqXZ+CrUcSrDABXnHeqlcjQOnxkeJ4ChC8+wr2CGvrJKqsNdKyAEfEI8VrTeFx6WHIaftRX3DqTTk14FWoC05QAsZLQG+BpPmJYdFH5ApPhHZd1hjQI/wZ1MaY2slkNsWdDNMFjnkhls2S03f65KOpZwQMCkoApnMS1YLiINfassvPUQDCh36qLnSONDX7nGSolFLSAEnnrJpOty7cpMUuYvKGxErAw/AjQzoWzoWj4O0XUOYfzhIa4DN5bPnFxuVTkgn80VUB98+pFBoO2xxhDVceYnhgT8KAOv10GIsbxxpxXa+rCYDsNVljJpvE2H3HI/yfDlYbmLb7/VI9U+DMQD3ixT2igsON9UEVYzLmKBj5QEv9Rx95KIXCVgZI+ujNJSvtGtSMkVt9WyZ5DycaNLs3V+pbTj1+sLUKDuxpBvtURKjo8i6yu04VWs36w0ylxIgaFzIrUQ2k/zMYOhLhPUXz6GWKyRe+QlcJR9SH/xV36lvHn2bPmbv/7rjIZ4FAu16ZxBNplfu/CDMr54vQcGD9tM6T10nD4JQOCwNPpS51JygzSunf8x/bI+RwfjH/Z+Yv4EqyXP8XGMOfnbmJdSf2EOS3m48X1abXFTr3zO3VeXh+1hcqi1JVuNsy2ed6YzGG66Qb+dUw7Xt+XZ8m/8HzqXjkDocd3oqAsMq7oar9t46zIyrL+lbIuzz03SNoaJO5iP+kFf8N7UKiMdlUFKeyW1pXfOC5LW9zT+q1cAhN9CZ+Ej+pmfZfjcaSL0tKeW6rE9jwDhY1u1uxcs+wxiBXQFyOXV5XJk4Uh5+tlnUTanyw1WH11lntgsi7WMjy+Xmzdv9gilE/HQqc9wHgntuutUdiuBmhYprReowQEv4o+khZJnXzIq5bxxO//aQSsQsGMDuGJVZPcY+FPh1eIiUNU5dE6QV1dJXStTs/PZciLbT5C807lD22F3rkIKEZRngAeK9wYKt8Cu8sWjoHuK+rIO8IM39xlcZ2znRC1sjSNtiFuGNWhpqfHL0iRxLE/wksq+ReiUiQmuvY3zohNEwK/AraEgY5mXyKQl0EtQKqO4PPBJMQk4XttgOGwAFYCFs0mMlTlv4DdlH8wXvgRbeOooa1a7NIWCai6XPvg4HOqp84wcwpAyynBR6wDabv2BDN2yYp1rwYuASU4k60IqAkDlbL06SDkbqCtX4gpgVpGh0mzzGrnsQNBmmT2FEkWdDcPdvn6dFUYvpw0pYuVsCevhL/nmt/5EFHp3cl+F1/FJfPFrCp0vkLR/5ci1ZUg6f/03Iz1Ikw8ays/7znlru8vHC+JaUoND0zDaqXkpq8iHsBVXAoanfMwgrX+WbYE5vw7Ldoi028hMAxY9anaRcHKtpc7l6OcRk8BHXnyxHD9xonzn29/OwjNpX49KGXhWrTFXcPXc66wkeoUnjE8DXNc/DlWMvj50qHR7RA4vA+GL1y/zDlth4abZgZC7uOV5PT53NICwFvxOGnku4e1z4WDuwBEPRC7viu6JMZjA58yOTu/Ux8Er8uAx+3PcXun9Muq/7k+x7XqA/e3UasyBKNuS52afCAfi406qeT/s5L2vX8fPTmzty8tOichwJ7n0+DANxy5Je9H2u7CdpQ3cA6E7y7cn5x1L/cwPZN7dqrehmfKivVrWL7Jn8eRMGT/1M5zdq3AgzX4FfUTDh6NxPaKFf1LYHlQenJNXVWCUdxTOp55+ppw4eTIKp2BRhdPVO7VIxHphD+SNlZcWnaa+vFRX7YjpRRGlQM10AQGFlynKqzEq4FL5bcqpnavSg4Ok9U045tMi/wDLqRnoOJdxDUBX5wRaDhXizBskTEvnNMBwGquIexCan2gEDALoAzRCWV7MTRqrgEtpZp4inpuxnBmaUgTUxQpIelfltJwuOqObyFOolkeMJDAU0GTFUsCCvG0IoHiqTFCOyAbZbkJHnqP8N1FxNjxyMXv57ujnrBw6sZi3oFDAmWGFgi7oNjBouEBMsFXBxUaZRha6YDnyEaSNw5clMY6LiwaI8lvbhrwIRMmXYZ+WIWUUCBm3Il0LTT60j1gQkTNA0D3yMoRVusqMPLI1R1c/S1ixljmqNRCAaASyGseqq6yVoRawyJug6sbKiU/+RLu55/P751ggAvm1sipjQVqb71nbp4xVGcUirIRoQ4YpW9PgkXsZ8jarh+bGID8OVCuy+bR5g1atsFenn/QE+Ka3zIJu68S89VMmrIodet736EB/DT6Wl1b4WDNLvxCEIz9A4m1WHF04fjzWw03u7bcCxAxbtUzQ0XmSE3L3duQeMQkICH/hF3+xvM72FN95+eUM+X/YizDGR7w15gquYhkcY6VNPuulndsQa694OEuwdOMKox6Y3DwEQDjGImfjsyyQdf3Cw1nYPA6214bPngO5xDto5PoMOhDdPSIdPLc9iNxl0NDyltB2kfc42k/2LdnQeOnlPHDRnwGZmm+8+v0Hknzwt00a++R8EJ6Nw8vSOYWbDB8NKJw6UsaPf54X8vBGK+3D6QMNHgHCByr+DzZz27vAQoXR1UUFS0eOHimnTp8uU1j23KjexSuch6dy7357PS2yj9X0m743evoaSv0UljiHga5JQ4tUIqLg+odCW/ekU9lNik7Zrepp8kM59s9rh4iudEqvFkKdX3Cq4u0QUJR0yqIT4Lk9RRbnhI8VyGudco7hOrwIHOeZG9lAQIDAZM1XCrmXuAyTd/ygXefmEQ+gK1QQ0Krc6y8mGmelzDUUHsFZyiQNHiimj4OG4Ai4E9LSkGfDjeVfHv7+UAZ+usN4AjP+8beezMPv6gF2hAkKhRGRK9fS9J4EHCTEBQDlAVeHmDYAFH6op+RJmSJHUYPGPxGbMrB9APakqUWSDPkHhPgnb0SzDWnpc5++VcCKSznLl3tauoWJwHyMStFavAIt2RqjTo6cOF6OnznFyNOxcu78hfL+e++RpcCLPKEnaPrYUw7VGI67+PabcE07hOfIgLMSMh+LnY8eZmW7VB6EGq5sm+XPdAHGBBhWXQXSFdDhk+RKDBpdHn6AUL7j41ORn+A3wN5sOpdhqpS9WYJNXMGrMl6nX9m+bBuSqnNI/fjikFGBn/NobUtHTpxiDvAs/Zj5wVgKZ2bn0h/NP2Xt8hudHl0JWI+f/NSnyrPPPVde/vu/L2+/9dbDWxifU8vXWUTmh4Ch93gK+iESV7tYvT7kL8VP8gMls6P29bMDpekiLd3ASiAgHIIb8+PM7JEdKdE14yzXg3C7y7M+A+87T4cud5fg0Om2lyTlvsu20U9pmPV2EFpDYPlg/Q/59os4+frT79kviENe2+77y3tgsi1izjIUzg6Z+w7RG0PShaQjvzYWLwAKv8lwbz7mLPiBmuk0j7kbAcLHvIIHi6eiqQKvomqHdJsJ+8AGyr3L2KscZ79A7mPR6++1IWYHTG/kXDukQxC12iUdiq15uJCMSmqNC5AhjYtPmtJXTeZFBVhUUNQUXoJx42V2dhoLDC9kAMb45DQppFmBpRQEIs7Bypk4gr8pgaypyUTQYhmXGfazpmVwejLpVewDrjirVJN7+M1ehaRLyfhxdVHBlWUR5Dbr23hnBQwgg4aWL+lNM+a8KfSt3J4deuoKmsqjzq2EQWgqmfxw3XMyH5mIpLj2YN7hOENX17JIC3AUXsJa/I1vPAE0oJMEKozZLqHeBUiYRXihnuSDCHikkIkV8CEdImaeH9n74SBAB4A4BngWGWpZtt4CSgCAWm+1KC+hOAmMrS8/KhjuMN35o/PlaPa0LOUGQ48FlNNzc+Xkc8+UaYaECgiXqbfbpJ+hjm/dvFUuvcdG1YCZY88/B0PDcZfPn7NocbG4WTbu9NMyuqkFVHnoywmvlNcw22q1BArsCbBxda4jSblqX2n+7WxetZ8xt5W2Jv36McQ2l6zMTtFG3g7zFZBuOuzYGiOOvGmBtX94OK/WxaBmsMTPzDCcjSTW/fIi4HDqZjmipZDtWrKZPXwRGHas49o+t/hPwOjnkZTAsW4l0u9/73vlu6+8Um6zZdBD59aXy+qVt8ra+2+zRenKNvZa39nmecCb/hZ8L3T2ys7hom49MTeMvVB5jkzMnbgjO/v3g3JmrRwPxcKhE9zn0rVC3GU2vkcPUwf97e4us0yyQ8l8l4wGebEsw3SD5HLvz2DAMDPdjxZ5D7ucW1lCvNee1KNQvdgbdeX6m2Vt9tusU3GyjE0/7Rt8K8ljeDUChI9hpe5aJFq5Sr5WBxV+nRYGLXAqrytsSj/HcLRNQIDD04wrwKjPADsJafKvsqoaawdFSWVIzHQWdqnNqVpVKn3TZjgiVLyOQqxSbf5G6egZ5tYRDpkTOE0CKBZXWO2UCAJBO2i2weAioIX04ZkVKlXIzbO3Kio03KttYmIF3rA0UpY1hjeqTDvRz6yJgiIuTx0TOWORAbQIwBz+uYmiXcEjabl2Y3vl5vDRDJuUOwBwrIQNCMN7HWZJeknjVgFScMtQUsAH9/rLu+KUb8sehoQGYc6znpxU6pFxQKkcWx/mTwR5Wdeixz1CCmAwnWC2ShsCghoOLgLmAvSo/waAjBsLmekwEVJEDq2vkCRMYK185ctympd+y7SVNc7jUxNldv4Ic22mobNWbt+8kbznsU7NAwanUFydDzd17Uq5zubwApaZM6fL2MJ8aB09c6Y8B/kjcwvl1q2bgM+Jcvn998spLCDDcM4bvQE9PwSIgz037Cf9yF4ZURZF732VHTc4gZTldesMyx5QGP8az/ZjzDoMFDl7QxqiJr7y9jJAWgZwylseHG5sXVZP4pHW/tElTd5Epo79+BG4GDr2A4fuzs8dCVicwDpOLTHkG2s41sFprIRa/OVQZx5ymXrmPAKGEctj8fPJT3+6vMAqpH/7V39Vzp0798DLZItLi+dZsO5Q0ffOlgkWU3Fkg67+1nNaJz+1lSZ4eD+N7j0Qv3T2u+Xpl35yKDxNHH/mDjqt698RMASP/mI3mTey7T7PwV6NtFDP/an7/FvCPq+DXt5BMYXfn2CV0R2p04CSupHYIUqPN+N04YMyH7zvpem/IG0/edP4rthyPLPjt81zK7i7kkaVefXov9ZHmvvx089HpdLROkDabYXoTzx4vVMm+u3kP5h2n/tWvkZqb4kNEGuRW+KB4J1viXyQ+P1xumvfmnx+LeuXXyub88+UsVNHeXcu7Nhjds770fMdAcJHr84OxXGsP30pWp9S0VTPt3VPqlBy79PIxShULLUwaH3zKRXg0tGwrzQlWC+x3SSgQBoCywbOhFsSj2UiXUhFlMy6J0L9zgK4S5xKp4YJ5gCopHepfS0rwTMkVak2jpamCeKsY3lbAjQuuvUEQCNWRsCO0QSRsRoCDFc7sBbl2/RSt/yUTeNXhOAv9/If0NTFMY1ymJgG6MkL/nFdOZIG0KH1zKGV49D1K1IUcJ/wOCGfR6sLwaDDkQSkLpzSPX8ia+Ud150EpFHkieQLZIPKcMihcxMFg+YWljjJa5R/IrrFhHQzJy1iE0Rg5fNjAIBEjoJcjSUJ/JrFbw15rWP5CxhFjlZytcSmVLSR2TLNUOP5Y0fKLMBuinv3sHSu4/SVK2UJS5+rYU49/VQZP8ZegtTXDG1kwnpj1cSx+TkqqJZ7GkvX0VNnGNI7V6axVi9DY4JFjur8tyqKe/m1HCsslJQ6tW4EYZYdyfU7xa28arV6p6vtN22YejFMuOa9f4L0zFG1ExjiyRABH3/WpW0y9+RtWv2iCNjdaHxa96yPmtL8pV/P0rP+iZBw513qN4msrVfrR3Boe/JjyBLlvIUlVmr248qLGZEIHgWChnGXM6eRewwkMD8/X37513+9/PD118srLDqT5+EDKldtXzyXWLl65dKPS7l1haGi+m651gb1sS0+rO79t14fGmsTR58aGq3hEdpeL4em21+Rh05sgkfjSbRTG/WR+ri6h7ZodzB2gPZ7R5pD1Bpps6jg6nX2J/xOmVr4UBmb+zitVtiUN/whiD0aUUeA8NGop3vmUkUzTm0T15TOWYbwCboWGXKkgjvF0D0VXoftxUroy5x/U5tSMiq2qtR2CUFaXXxDEERYLF4qsKbw0KrFidAoxN21PvHr+InizLV8SjNWTACEFpMAkxCng8KrfsYfA4SukObarVvlOUCMQMktDlyow3gzDJ1bZHhdzVIC6sWAGywu68z/ixCkg+XOZMIr1fYo6Bnhx53z56SL0k22tSyQEgho0VTJdp6g1jMGpUoEfj3VstTMyTg0DJQLgYLDN0mfWzzzBT13+lAG0JtxsPQ4J9PVQ5WNkpWXOn+PODhTCTpSL6TRJ2DBa9BHtoEQJXOdTd+hZX5SM57DPpeZV+pCJSmkVs4jDEckvXNBNwhfBOjJr2Dw6FNPlSMvPM9ka6x/lD8wQwECnIXyguJJQN0Y4I8EchfsOT5/k9X2AIPyBT/y7EBMV4DdVBaA95n5o+WY19IbgnPlTefaKXebmmStm1RDH/1ebsbp/AP80j77PJOyxrCNGayVOFZU0qVMfPioaSkqfw4BbZZCy2VqZ2NWftwqRQmaptaxHMqn82jXGWqXrSXom+aRjzSkt/07HFVroKBRK+wEgNE5wCsM/c7quoT5sSa8sVWI/Vtr8WDZu+KOTo+wBGwPn2Bu4cdeeqn82Ve/Wi6cP/8ASlP7xaaril47X9avvMOsG+YEy0l/o6vRwl+6Vx+ntvu7dS2Lll+7l4F+sj3/LqP+sP68r7z9g/7be7qeOHLmntIfNHGT32AZD5p+K157Rtbn1ZZ/31UnOPMcrEdjNbnKi8ewnunS7q/Tw5S18dnkFFqH/Gk0wkYK6bN7fyL7RdmNp93ku3+OQ4jRY2o/7g+elyI7NDUSRdT5OXhedx2zx6A6Lh/8b71T1q68wsKjz/LyZU7httZ917k8dAlHgPChq5LhMtQewlm4ouvcWvI8Aq5QalexBqo0zmOhcR2ZW2vXsyhIVVC7h7kgzGFzOp9+XkJPYBPLnYAE3V7FxAGSgrKeRYJ4KshJQtgmYMf+Jn09HTrn3LZYKQ2BjovGBGqQTgAn62sMk5PGHBYm5zyq4B7FwnR76Va5cesG13NYSlhUhmGkDoVETQ5/KsFaVLY57p2DaGZuiyD9MOWJPOVNi9o4Sz5uOtTT+JaDeJaTUGRWh2kKIOvQUh8e5ipVLHQhqpgElORveTklHCLKLnkGoEmYQ9SZYaAQiZdyJH+YCdiGjpZBSzPGPELntwnq5LmCRvMWWMAB6aUpf7EqGtey4b20upgtChweuuocPmR3jJVmZwF5UwC5iePMecFvHLCNGbYcu3ylLGN9mgfozT31dBk7cbIU5gimTLQfMgmw0wI4joV5nKGixWGLkQFlnXEIKVtJMCQZlAsHJKWxuYqrEhPaCg5dgGHeYaZJl2j39KOFcFkLYSd4xZt2QZ7x66grExjalpe3DejJTw2FABf5SIGf9Kwo2TVulW58Qt/+oBVPlzbkJelNZhujapLGtFALPWlYZ3TNgHCBX9mcSp6Cd/uuczTbKqL2YwHhDDLWOuQ+hQJCw7WkBwRaGP/zISBMy9LIPWYSsG388q/9Wjn7xhvllW996wOcW1jblL1kY+UmC8mcLeMrt/xEFkfTq2eiGdOj+dWQe/+tHNw7nUbh+ntv0ed87rZStJDDnyeOsEhWf4GHzewhWDqI7H1c+Mx7gGzuWiKfmfIV3h5GBnfl/P4E+O7Z1x0kDkQORGvfzA4e4YBsUdfG7O9A/dcHz+9uYvpMm9xkxNz732ck2idZeOMzkPGZ8MHxcDd8302aqqncTcpRmkdKAqjrAQMqqDPTbNOA4iDIuHH1aoaIzs8vZJEKt2Zw1UKHFgawRUmu3TEvCIBGfVGgvHYKrB0jQ/JQap3/l+t0FkEisTj6XUgKIHB2c8HKOOhFS4nKswvUmLfpBH26pOFcFWf4Icz5jyePn0TxHi+XKId7A9ZyqvRXkDZOHOcT1rdHSEUJl8c6NwuwFK0emiI9XMAV4doLBanmLT/66Cy3fLsKq0AwFjveUlHk8Y+VKAzXx52PjYSSvObLFyfk25v/ZzTlkYNrXRKRI8NmtZaaTwWj8CibOfiBZ6HEmBupMpdT+qsAvzWOZUDzMsB6eRlQBJBYArjdxHr0PvP5rt24zhzNFayrNzkvlwVW/jzJQi4L7Ec5A+DTwpetCxy+eepUOeIiL0dYevkoixC5Ybz14ipBblLvw9FlV2FqAlDiUu0sP9MxWf1hrkxisRoTJFIfgnGLmw8HlMHPBYrMjwiC02G9mQS8zquzjQUYhS/yzZtPIVdRR97e1CrzqueVa/wNsh5bnFhXoa+zrUqfXHJvObrYhNWPJubWwLn9LxvIJxdDoJw2VnnKiq60qfQdvFwp1xDvBYhLtxexBro4FEIkq1ickesRwLR92fi3bqCYE8f+2uhHyNyN3OMrAUHhT3ziE+Wf/8ZvlDPM0bXt33/X2jDPNlYUXb/2XkYL3Gu+Us1jojvXXGzN9dhG32LudGyLdPAbh/Uv3bx28AR7xJxYOM1qhVN57IR3f4bgLK6PGqs45wPS9FVX0/hcHExUP5Bu996d4bzqOhKtXvpjS6fS6g9tMfpy4dI73xzNtVjt3rP89qXqD8r1Hbl0kau/1NvHPa7yUfhOasnDfO4MqvkPhA3mudP9FqOG3umSlXQHghJbz3b0he/EX1/wrpc78tefcS+vFnNXUnsGmLrXfzv6Kc+eqXYKbKkaP+1+p7hD8OuXBeT8uD6+xFoEl1+hzfCRPE+gIeTzkJEYWQgfsgq53+yoHLo9hIutoE12iiWLrzAHz20GMC1kKKSPTYGICq6LyURZJ60P0NZXpKV1AhRC92DhFkDBNBHzlVhtn7eO4M6HlspzgKTXneJrl45fQB8BxM/LgARZGIU3XN3zznjgCHjWGingskOq7E/NTIM/5soVLFlXb9+MxdAhiBsAW/OUPxfYGFuHD5K1PFW4VawFeRkWCk2yqC4XiSmz8ffOhWbyIuU6w0XlhVCVfMFoNqsnP0EZCA6myb8jajkjRMqeeWfQkLfIBSV/K3NZoPy+3YOYFG8FnWNaaPM+U8IOIZQkJeBs3iqC49TtKiBwhcN0sd7Czgar5i0yDHgFJecooOHMyVOM5mSFT/xctXLh9Kkynvl9WP1cfEeAI2FXsgRUsOwrW/HMlw2UGlbYsTIqKLScMmU54UVQOnfkWK0jh9saoDwEgByT1l3aGek6mVMzDCmlHBySIXJ3zs09/SyyyI1zUYOmpUz2NiZbbHSBUMfTjClGwDxh8ajMyE6dbplIpjM1MVJHFAMZmIcfGJKETAz30LlQUQWIhtrikBNxVNQn6DO2wlqtti+BZeXP+rOdOVTXbT20DsYayH3m/VLnWkAdKppymSOFmmVOmcOY33/vYj4knGYup+BeS/A4dRqgD43GX5gc/Tx2EnDEx6//i39R3jx7tvz93/1dPhLcz0Kmda/xoYK5gxPrS3lmb/WxmnP6R35ql9uLn/4+tFe8FtaRbbe7nneMt4On+btB/XBWGh0rr944Wj46d6vMTfI84t3adddd+TxMgOz7ijlQp/ZF2HvC7vYUgFiTSYvS7ndhbLCut0XbhzmDQ77LI6t+WxyI9tNtLKQE3DTWtue17a5343uGwT6+lvtkz7sGIv159BIM+SIi6Gj2lzWFwEO/nr88GTceXaLu1OjUZ/72sB3vdqCxY7xBz7tN19FRptvqp49e3+Vgrnfcp7xJ0Ki18x1Rt3s0QW33PfjdtvRaCdGrLv+gjD/1JqOnPkW74cN24O5hSnPw7B9EzBEgfBBSf8B5TqHUT7LohM9FLW5a0FRC17AU2bRVR5eWWMZcQIHiKOJoQw1rBBVYlHjD6JuCK53KrA8pLW7rbHDuQiPGiZKN0uwCKnnCOayS/JIXoCoKMOBABThvNeiZTqBVHynEJX4sLfh7nXz9Ib1bZ7zLkNGzzJt56UMvZJN6tzTQijVL2VyxVKVc66dl0aaWYXjdhvIhAzeWO4o7hchWA7AbiySk2ld2ycbJWq7lgWtObrI+aTnhUb8AqlwqQ0ECdMm/ATWvM/8rpIjDfxz+EvRhk6Go0K1WS/wz91FOJWkcgBZ5rXK9LnBDHmvIfhkLkYBe2Tu/UTAxzpzLp1i98ynm/83wAUBZzCwzdJStIiaQEaZjUWrlwbcmQ1JTMOsN4DLDqqGbAEuEUcOiWBDPxYJkQgcInJ5dAJATL29ZwgSNkBKICFTHJgGX5OMQ5DH4c7sTF6ex7pewXJ44xop8PUFXsnf7e+Py5cgwID2ttskOnnSyDYvJrmkF3CDGFDP1aByOKm3SGx8Z2FybiwUXf/5TVyFoRB1lzBBQLIVp48az7swDOmnrXdQQJ4n8ZG6i6ZU3svJDikNBBf6TbKMiCBWMGm8ckC5tW7F5LRxh8R4KcfXK5XIVGRzHyuvHGecgrrMdwDRzO+2vI/d4S8APCB976aXyNJvZf/1v/qZcYCXS2heGXW77Fc+rG3yEuH6pzNhnPkh3n/Jzg/phua+dHSsnFq+U//oTx8qHjzFCgBEmPlPu1eUxQ192VWzfy9lGZw+iisrDvOvzSB7unY89stwlqMuzO/n0cqRQHr4+y3wP+pwMt5WEUdur4aAc2zLVHZzh7kfJiQk+ijk3Rvp7tJvktQvnB/ZuRNBFXNAsL0J+w/tueee9Sgye4/V9oGQefffBlYGc7kNmWWV/mf1JGTo6+aGPUyGP376EI0D46Pezw5WAp6kgwf6iIqqlQeV8DcCgIqmy6cb1LmRSh7RV8lX3FQjykCKxQzuloXIhHRVcH9QquDzWK9j0YZ7nmoCRh5rhJsIvRPTIQ7Lexj7IwzCvSV4IeVkCRBw2Wi1dWnq6hzhJnUeoQjyPFUur56Xr18pp5radYMicyvAGX3TMfwagEyCGhcTPgZPy6DL+5C/w22CeYNsCQPZSME4Bg8Rd5cXh4ipRny2+keqPsZNHABvy0wI3DQjVqiMhZdpbcl2eYWhDRYDzmoBojQPlXmn6VXSMMIf5Ka8AS2WVdDmFNb2yLUZouVAMIFBrkfUmCCSCwGuFoaJahdwo/iQWwBc+9tFy4imGkCGv9dmZWBM3rkPXyhX0UUYyrRkmXwvKBR8PBG7TqwtldRlrFFbAWFUJFQDCDBccJvXSH5Mqb9sGBzVF2uXyw9deDRBxqO80be8cIP7dC+fLcYaszgAYl6/fLM+/9KJEhuJuXeMBrlxpD0g4TU/Ze5Hmx49/tc3x2ykhFiCKs2nhxGYn8M25M/v6kUFh24Rd+XNzg75DmdMnqiBiIbZ/1Q8a9j17hzniyDvUnaPaa++EJ0/iEKny0ImWOvDevioBLeZpR4A8H+QpI+0VfceEmRMsjSsAwqXlpXLq1OlsXC8NrZkT49Rj5URuRu4xlsDCkSPll371V8tbb75Z/oYtKny2DdPZkjdZAGn58jtlfK1aBwfp55Ey6Dmk+/SnIdGSDF0kbvkWD8ghuSM88/7slVfLO9dWy69/9mT5zDMsojXWvdPuIQ+nC7x7ba3847nbZTlD+PcnJhCc46HxiefmygvHGYXASyDPRZL6rIo8Oxn0qCkUHyh345pAd0qOn3neXpsor755u5y/ulYWpsfKlz6+UE4yeyDP5LvJs0uziQ5x8fZ4+ZvXrpcLV1fLp1+YK1/62GxZUJ+nHwwW02Q7FfWwRZeuHw4Zx1T+4Qc3y9tXWUEbXqrbKdcaooh40penj06UL760UBbah+uWcvekXYzutJOst8fYurMOtu6GfjUou8hmr1waP9uYOmjB9yJ8L2Hqv4wEu/pGmXzqIlNjPgyxbQzeC/GHIq16xMg9ARLwgetTzoe+Cuoc87RmUPRdcOPa1St1XiFDzxax0DiHcEolFrkIaHKhYm9/THrm5aHMO2TTxWACIgEkFQQR3YRGjvasMo5CC70JHo660OGRp9VN0OPXO8FfkvjljnShQd4qufLpg3tVpVmyXdxqkQQ4ARhPsI/dtRtrgIsLZeXECbY5EJTRvMcFSlhBydv8LIIgpb30YnWBdhb+4OthFt1I/u3BTdZa6FS+Yd/Xh/sU1pJIzPLIUXXKS2tR7Vi1TImcAvmYd3ipZYdv400QB90sm5F7QWRfIgK+KjgoC2YAHbUOzc+8oER6t95wEZ0lwJ9gxBUms2Io1iO/GE86X5R6PXL6dFlgmOimlkCHewKmZVJwuEEa8/XFKVzxuneQ8SZxx6AxSVxZdDGdCfmrFVYtgJWpXOfLLrwtizQZknrt0sVyAUX0vXffLu+x8bwLn7R2c505jJeZp+P8wpde+Ej57IsvlWOAw2G565ffh03BdQVlqavwKhTjj2ubeETfVWLKqD8fGwRPxjSd8Cnt1XTWp1XTXXtuH0mUC8Wv4bQ1glKVlilgsGuHtkf/bHNGzgcIYmgEtgZ0OZve/gdRjzHAu9bf7LXJx4e0OerejzvGd7EhWEi5ZxlOfYq6d9XJd956uzyDhXiBuaAOWx63f3V9MpmNfh5rCWgtfPFjHyunmVf4D9/4Rnn7rbeGVF5bHf1k8Xo3d9CnXHVpvztcd14P9wnmhwkIf+WLHylv/uM3yg/fXy6L37pcVj5/snzx+XsEhfC4yfPgzWsr5Q9fvVmuLNZpHtZHk32ti3bXRL7JO7PO637uOO9m3id7OpN3lZpHX6vgPRMdLDCcQXR5Y6L87dnl8vKbi+Xk/ET5yPPHyskFMvLDG6Ux37tx6yxi9vU3bpbfexndhmfej6+slWPzU+ULH9ViSFsli0HSd5tXP3+KyPfE2thU+evXl8o3f7yYMgzm1Z+m//rTz86Xn3jxBMC1fnjsD/N6GDwO0rwf98Phc4gN7h4KOclH4fUlRh3d/HGZOM2Ko5voU4+RGwHCx6gy9ywKTz27lIcLv3igipblxdssPsE8K6wHk/Rchw9qyXGBl/w5HLB7Yqrk2rm1THhkJUMBHaBBG5A2NOFEHV7pg9ZHn0onAEJqnFV6Va71r+HE8R8vldPVVS44q1wblyRRwlXaA1a4jzJLHIfAJS2R3Ivr2s3rmUcoABpDYT5ytG6LMG15A1zhsEuflUvlKaz4g6JNnhFQ+LYg3OOUnFamKPCJShlUpJMYGgqlo2MZvY/iDj3LKu9+GVTx31gFoHJvko75vIod9ue9oFkQl30KTch/LIXKnj9uI/csNMPQ0NUsHMP8QECWlsBl95AknUMCj1Onx04wnw8+5GuFl1/mPgIi8Mg8lgzDpS5hFJ6hHjaQq+c4csV/c5Iy22YE2QBkVx6lUCmC8jFB6hZgss4WJuff+BHWiDfK2s3b5eK5d8s1NqWXezdRtzzLrHJ6E4vmMkNQj1BfH336+fK5j75UnnVuI9bLYblF9+WjbJE38ucSPiiu5e/qJmAwGWoV5IDPlIj2Fwud8fEN/8rS6paQjlOsdnwcsT/4AcPUqV6DlWkokpr8bRtZ8TdxROTwI7i2rZGfzmYlDdtcrH1cE0p6PxT48cKVQ8eY2rlWZrTsEn+VDzsq/A6PlkDdhxJesH7PTsyX5194obx3/kK5fOkSvGMdwIpuG7WvRR49jsPC6OcxlsBR5gr/4q/8Snnn7bfLf/mLv8gw4nsrbm3ja7cu8Vy4wWPBvmI/GHA0tNYvBkL2vd013T3Q3DdT3lo32FN1WO6TL5wsv/nTp8tXXr1Wvnf+dvnjf7xapidOls89wwfWWAqV2B1S2zP7XmzkkNT8HJlmGsUMo0baM4qQ9kozUpPl8dkJng16UF+RYwsZyDLeiZCAHtmBaAe9NS9do1OftvJvQF1x2g9nPpv0bR9yjd/Smn4nR5Rtzvg+gq/dWi98F06ei+gYNxb9cCt93PZT9RvCr89s3xmSV/8R1jo66fgs2zfx7mwfGxOhCSNpKp8n5tWbfCcMlgqvYToY7ESwJWBfQsNykjqkjBO9x5QEDsFPL92wCrBFR9LjG0sMi3+jTJz4HDentgIfgyu0iZF7EiSQ7sRDR6U1i1OguMbKh1K+gRKvQum+fg4X1TIyrUWIoYFuUl8tGHRJHhIqnVr7Mm+wezpHsYR2um0ebFzZc/5/9t70T6/juu+s3vcFCwEQ3EmBiyiJlk0pthXLiuMkn4znRf6B+efmfTIvMp9xJhlvcWxLFmlJFCnuOwmSWLqB3vee7/dX9z79dKMbaAANiQC7gKfvvbWcOnVqO6fOqap29Dasfe8QOyxumFO41gx7jkGu2HnBe8uIC0gNiAx4QJPeODGPIw/9EpeB1n107oXzZFHv1FthT6SC78T4SbSMlA1t1QYaNVHbxmS0zaMbtSp8MQnBoCvoGreORZYJ5pmfecroV7q0DHWdeBJGKqJUfPGoA78+lDk4uzewZZicGBBBqixAvsEujL9Zm59CYq6+EASQhadQrHnoCoLZIkL8IkLYMpq+Nep3EG3pFKeFnuS+wBH25nnhvCdOqjkcBoanmyr7iozazGX3EFLWIQK30S7lmHXztBTOphII4dOL57e5+mMLGm71clUJ4Rsbq2WdEy9XyX9tabGsXp8v1y9dKp99+GG5cpmTBknuiZpTXBUyrIYRWIiSqW9NXKXzI+ceLk899niZxqzNhQj3Oh6Vm8Nc0mJIR8ubSjEP3jXdjWuEsUy9tQIT33pR8DZZXAuniWN4TJmlIyaY2csH7KYqQ78IdYRLy3YhI6RvQNrmBSfDoJAeLINrDWhxsg3Y5hUIpZsnsZrTGosAHprk3kA1+yP0zWjiiZe2HJjb1O1IeRih8Prs1XKFw2amT25gpsvVIuRcXWobmG1hG+/jxwNJAfvDo489lpNIvcxeU9LOIsedlNhDj65dZgGj3h17JyC+Vmmgzyrz0sXLR3PKqGXrHxorzz/EnvZe+h3d++2vlso/vHsdbdip8vikPS+9nZi31wdrz62pHFteemSi/PCZE2xfYO7GQka47SJXxhdzIH+1HecwF2W2DT5435W7PaxvzKqDW4LqPN3Gcoy8E2c6y/fi+ZHyESa1V9GgXjgzUB457fI1p5ILl4zvFvdb4Zay8WcKO92ffPdkeeIENbXFIqI8QZcLV5BJgQO9R3owF13ft19miuhKd8ev97jsd4Tn3VTGHWV4SOoJmwalkL6xcKlsr19jOw19mTn2QXHHAuGDUpM3KUc70ctMaiJq8z15+qGywP4qj+SXmfRCazjWGg7zOT42EeZzRXPChlHtY1WrvQbCPWutk7loTc8UlBRYIlDBaNu38+0g1468TafVR8FSRtegTGx2OJhaBZUIfmGEq+BS+x2xyMPp03yFocCjG/RKAzzUcroHUoHXCUFcRrn/bp0JcGVJ4UuBty8aFvNX2NKpObGs2QeZzo+nQS6vOleTvlNOAePEw//B3vTArmaoCgAkkTYAUajtVduKC2jS65cL7dEemqcgo1kUTj4oay10aGM9qkjU5FTN4CJ1c409d3OcsLqK4KIw3ONhIdzNODgxVXo8bRJhLFpLaJrTNtEqeRKoZeF4EYSDS+WjN39TTnPVxNAol8lHQwb92KcmQ+GMKW4UC9otlGuffpoBUU3jwtwcguZcWeK3Or9IG1rNqZdrpFWDfBINrcKg9WDd9KOZlAJr1Jf1O0B+Z86cKQ+fORuTZfdPSv8e6uao3OIcg7Z1EKGvWQwAeNsn0vZSLTbAhrUifoRuyu5pnVZvjVLbSTxqJYY2aS/EgUSpW2t8B75ftAA01EKxWmXO6qo3H1m652kzEmZyMr748N+2gAOVjhC/xuFI3i/opfQb4ifNiLwFXbegvXAsrns34wIbwRzNs/1eLeJ1rmkR/tTJ6dSL8cz92H2zKDDN3aM/+vGPy3vvvlt++eqrWSy8bQrQcLbWF8vWwiwLOjSqrpZke9eladfXO/rbgOmkvZ222qa9Ic0NHg14EmwzXl6+zoLLV/STI3K9Q6MMu2vl6ZOD5cfPTZaF1c3y7qWV8gv2zZ18frRMYIWR7n/I/DIEhdb2fn/VnZkaKd9/fKJscx/kVuYc5tLUS42R2HznHxNKO1YdMtvDR7uh0mv+aRMH0f4G6HXM1PsGcDfE3e2R+GTZw1kCL5zvx/RyqlxBU3ge+p+bdk6DbxAP4lTMdudxu/ntzp0vYGdM7yrrGNrb5x8dL98+T44sxMtg1FnJ1L7RBoKTtUO9aS7bImcU3KHw6sqzptr990AYVo6B5nkgjDZgD2JNFh3YDZjdOd+nX+1ABvqZmlfmyvbSldIz9Bi0Yp5tSXKfFq9F+1ggbCnxgD67B/ucMAjTOIimRrOzMUyHrl29ChO/BrO/iOaGNTPCNb/04utlThpVAGr0FpWppe0bJ9wvvSD9gBEgZqN8eHpnNWvDfI1JNU5pl9BaHQAAQABJREFUotnDlRQOFHC4MrL2Mwc+TTr9rYNLYGluSeIIVOTp6WM9CooINApNEeTYcyB+Ci8KIFMIGEuYwCrcesrmCMyyjL0atCn2TamhkmkWR0tlL15FiImGjtzq3XhoKD0UBni5RsDOTh7ZF6hWz0++uwVDB8DQAYSFBXKUiKEdhrxqkCxJ/RlPoct6qeWADpDJsgM2cKQLis06GZiA+L0IgUCOxmppaaXMYYo5ex0TWTR/i5oLDo+Ws2e4WB5BfgVhcU1hgDIo7LanS37B/soN4o9x+fwGjMC1a9fDCF78+KNy/vz5Mn/ZawrYNI3WaQlTyyXahHzEgPUCzZah7TIH96j1U6PlBeiWwwN33KPpVSYe7jM2PpL9bLYhf/q5D9QrQjaZhDfQOitQTk6cwKx3vAxKJ+iTogLvKDWEmkPrhK0gVnOJV/On9YO+lIvsm5qyLls/6E/bay+ozlUowmxmvrQk6kzTq6RvhDjbuu1XV5/48B1tIhE15bVvma8JjWp6nfHELJVeQaTP2GzV0HrPoIK25tEK+ikX8VZp6ywJZGtDD+01gjDwhGv7crHFE0cdA2bY27l5dbNMT/vNYoLxzPzYfaMoYFu78Oyz5Swnkf7qF7+ItvCwBKh9gIUT9g9ury4gEJqy9qnbbUu3G/+wON52POixutlb3r+0UJ6cOzqT0d6BIfohcx3moc+eGSxfPTlW/sdvNsqvPpkvz3G4y/OnWbzbVkg4vOvus747R2i7sYVZW88mJ4U7HXV6dqWwa1BZh0o2+Ont4HMIl1gOJk1058y88idjl8/MreZa3wLbSMmUtmGW5hnXeWk9dj0NtUzJo312cMU3AcTYBbNGdEw0ag80H8Ki5dmHB8qFnkHyBgcXgoNhBdGMtiSsTrApWeDr54u43xxfYx7khNAHLpocum/Ee6ClUDfE+qXPXowOgmrUBskAaqBVEDuJ2u8QJOBrJeAvfXVCCagsHrQfPFNhTQQee11ANyh01h0anCzFDn7GvL9dbcLUmmajixfLwIkXadYPzj7CY4Hw/m6fN8W+e/BSCFKQU1umgGD/dS+Jwp97Btcxp/TETfvxENochive2c/moRUMCNFwEBYNYAarzDRhOJ3kjK+LRsP4pM2AyrvjSRhi0/vhDxdmQkaamNnLplDEr3WBARwFzMp4kJRA89qEi/ZQFgeg7L9js5WnVi6zNyD4UkbLo4A4jnZQc70BTFEV0GRa1HRqTrqBCaSCWSaJlCtDGPlVYS6YigP51oETH8umUAgcXQRYi2H6cPe+USb/EkkhSwOVMOTAdW9HzVMasgJIaJzwAhPaix/voRdP81DMVKu4iED4xVeXy1UEui1xcU+gwgGXyE95iijCYswxGb00/ZU+12dmy/tv/Ka8S35e02H+s7OzMSVV4LvEXWWXEQyltZOltNW5xcS8QxUKMEw+HtYjmsNoIqWjgpKHEKkN1NxXE0ZNGt3HqMDiqasxa1QYROk4zAr2BIKrWkNLn0mQcuXuSepW0+WjcJZR82grKvWYP7VubDjkVBsU8TST9sNyJTb41FNf+abcLhDoFKiMk/ZCPda6VwuLL33MjMzXelcYo2sYO23SN+lkNL09W5YGT96mM6ExfCV+RYe8zK26YIy/zxwgtLaCMO0xfDWtB/8obLo/dnC7nkCasgSufbTiblPVlPhkeahc5pCfsj1TJqc4iMnTZC3HsftGUmCSq2j+5Cc/Ke+8/XZ5AzNS9yXf2tn2WARcvs6ildpp22fT35rE+t2pu4ukd5SlXUXN+sz8Zrk4u1qebM3K7wja7kQ9w81hWcBk+17MGN/9cqW8/eVyeQtzxsdPTpSxzqmjltxfOu9uQIbsIUz3t1sZtjqmotSFYx1g2iSB6B8nT+sqiR1p+EckZ7vMOwnlT7dLBOci65jxD8DWd/12JnPhjPmZeP5bZavBCmOrcZzPhplzBwh3zM8Y2gU7Q2XXt69iqL+4Z7GButlgoWuVRbF1hSoCXFZzgXEQmvbWI5aTLsUzgvTwmYPQhFnTATJ4u8AXmiQnx3TigL8nti6zuEo2maHFnat7yc05ssGWuLd2FXotS50LFKT9FpduJ6Wqa5+VBt31JO1M7dy/Di1WoDFockq39IVnMwwsQxzfJASFkm6WZ43pZgBijqKRZvoOWZwTnLO0SFojzgr7LIWp3yCRAlc47jd1rm6wlAqb8bfmqXsmvHBL1IfL7sts3FyFgNJ0lKuRBtnTfiuSJTx/2lzaZ5PpQQ9wvXdO2NK91getkEUwzEa3GCP7xvC9l3nfu1LthXwsEO6lyH3+3Q7k3cXQL1o9PDU10ykYjsGUDw+xF0mGmElKobGfPWSeVuiAp3DQz2DgICFzm/1UdeSoAwJ9wD7YmrW1vaW7X+7Gx07DoNP2byJ2TEsJUrumUGF6BZ9OXEadFk6GQiL4D6SMAi7EdWIivcKg/9RK6edx+wKMP99a0Ci0RCgmL7WLK2hbzE9NnXGdDIzvwTZBlW+dONR/+UxctYXZV2k6uX9RIXhHM0MK0sXxqJBqWVt4CWNQ9V9iQOsQwVLi5SmfEcQZWFc5UXRm9nr5in1gq/iPw8SNYpq5RVmsH/cCjiKAXeEAkT7qcxLGf27mWpm5+EXZQJC8jpnwtd4r0FnhoKdMqLlDSJFW+TmpgoV7+erBQrYV3hXW9Qc36yw1gH/qj/SGa5I4yCr4MIfCDNGuBofZz2lZaEdqBntgDEbJbwxt5qgX3avBBKb0VojJQT/Gd/Y4ArfGIoftWPwbyu6BWineaogNtKprNRJGWgqaMrcYuWCR1WUASoPQQyERtBWmKv8oEH71f/K0L4UWHQwqTkbSP0JhsEwy8kgGYtTwbRUDMZb6W9BrxX2/NNqYjlLXpulhorevb9EWgisFCl4SwPAuWnjS8BnMdecw/Z3DhHQMba2HzezGs4Pw8cs3hALPPvdceYT9pq/87Gc5eOaWxWYBaWNpnnap+f8tY980gs20290luIC6AcYNHt050k0oycXZlTIP5zy45QnMR+NkrnX24R5odnpsqDx7dqR8eGWlvI9AOPMUVwyM3QI50nfTeL/YhvuL/JN5pSbaS9sMN8RbXOspX8yxl5y1s5GBnvLYyb4yqmDlQJhRv46D0mV+tad8eZ2tJRgJjTHFnmUvnIz/Z1gauPZ2bry3nJ4eKNfZ53xxFqGaE1WvLq65BR2hoperFAbLk6f6yzn2TI7BitQDtmop6l+y1DUfmaV432KAZcc5gnopH89slK8Q1udXWIggoge1TDLXnD/RXx451VtOchgLs2GmM0uwxTyzvN5bvrjMVUzgPT6yXR4G7wFMXTY3+8qXc9tlZpGDtliHfATc+mASvgD3Dy+tE7Yai5theIHT44PliYcGQ58xBK/tzmKByLbjtcgf7JDFwFlHmiTZXStOG61rX12cnl2uNLa8D5/oY799f7k8t1He/3ItCxcK3ueg++89NVrGOZPtS+pjgbqaHi/l4VNDZWl1q3zw5Xr56PJKWVjmlFVWJL73xGh58pz8IPMFeczDKn05u1UuXlktl+bWuQYEHggkPJH13Inh8vipUh5iz6lCnXONmIuvAt8l2sT8/FaZGO4pZ0+50N5TPobeH4Hf7LwWXNvlxce47uMZLFGYu2Q3LN+u0uOx07YDvSXFrZ87CW8d905iiGwwrn/lhbYwye7ZwIKgD8I0Yca6n92xQHg/194e3DuCxx7/mHHCMIYxTk+UCe3hoInhMnViuswzaXiwjH3K/UV9MParHBQiPE3lwrjD0Hqyppo5mVud8eskt9OtY/NeQ+uAl47a9H67v/mbFlhqAL2/LaajGWAq02yUlKW+kMo8wYU0Lbjw23wbzzy9CsEwO6qM+hDl2GTA1nS0Pc1UvGXghymfwl4vgtMmpnJrmKkKw7KKnbi5Ule1nGJbnYLDFiaYjZhYw8lUGpiGZdnk2daDeNd/BIG/yIl3JqmUl1DwD0XyR4K2uflO+WDuNfX1ABkPxFmiXuYxgxxgH+AIB7UMUIdDlGMF/L+8eDHmmD3Mep9+9GH5hJxGKN8qGkNL1otQ5uEuCoCaeQ5KB4mG04w1F6+Cjz4WR2FU+cy4muRKu/punRGGn/WnAGGYiw22HwXDCJDk4x48cbfuxtBGj3C4Se58TH7m1AjreeYLWiqZ371raSt+TTHrk2wjcFG+kJ28ay00f6GBwrWHEokhCFFPFZ8srMSzftt2dbY5nRDMN/ShwvW33QExdKrvxiduw3DZzlztJRLpeAZUg5nfXa7mA77AVKtp2+hDM2PjUkjURNh2Gq2u5W7a5Q4zKjBhVuFwGKHQBaAlrv9YoF05VnhnXasRNfax++ZRQCsCtYXvvfNO+c3rrzPuYH64r5M55GTj1cXf/jXNu7vGvtjp2XTd2q0OjFUDmJWiGfri+mr6qydCHplzMYkFnOyVZowYROh64vRQOcmFeJcXED7Ys/jomGNGnV93ML85BnW4aEtJXGC3c9BOyq5wPUnk+O64oMbotY+WyptfrWHNUcqfvThdXn5iBI0vEoLjBP/8u7jRX15lv+NP352nznvKS4/BPyBxeFDLX/9mrlzmOoc/fP5keW5guLz2wbXy2seL3A3M6Z7REFZM3EP3OHv4fu/JkfIS9wGeHHE+RXg7oC5dwGXZrSxtDZT3EC5++eFieesrrjJaZE6ETG2p1GI9hJD03Pnh8kME66cQ3PoxnlVk3O4dKrPcn/vfXptBoFwvLzwyUv7jy5PFql2giL/8YKG88uFSefjkcPnxS6fKzNxy+fk7c+XDy2ydQJBy6gYJBMZehMGB8sML4+Xlp0fLJALkjlBYy3ervxbzgKLWpE1eLZzEZ179DCH4P//jTObGH3/vVHnoZG/5h9c5rfYzFnmXLGUpT5waLI+eGYfeveWvf3WtfHx1vXzvW5PlDwYnyi/evFL+5f2FconyS7dJ1IlDbBt44tEhtuGwIIAA+dr7i+XNT1cQMNdZJGARt0ECOb5MchjOU+cGyssXRjkNfIiTwQECYWwbq6u95V/eWiT9cnmaA42+/+IUQuVC+fmbc+VzcFiBb7EcG2u95aUL3BENr5I22hbyPnxmut6AR15HKBxqW+F9WJA9KB8LhHsIcj9/OrjLuHbzkDEVZenOCUKBJ53YeApAMIvjmI26f3A9zKWMZQVgfBn83OVHfO8ADFOJfwTCRDO/2hkM01/zSH0y6PGnTiYNVYlrfP9lHxbhCg66DZlmwuRf60ADYxshqs2jhRrIgVFHavz5bzkteBVEEPw8NIYTONfBZxVByAFTs9EINf1oroir8LI9tBVmx8vnIQoDrkw0I2CyEVMdOOTZ/DHLMPMVd0lg9AiSjpPCwpkmv6bc+pmv+bQaS/2qg1IExbyUZ7SV4K7mSGF9k+sqXPL1apDT0yfK5PgUpqdM5tDIfZBXuUvyKiagX338CZcdQzvMgBVcues3Jpu9mBP2Q5MBNHSa1iowD1K/EbKtJXBMGUBGPHx3qvYgIeMpEA4w6Xr3nZqnxCEfyxzBGY8cOGTeFDpaN+gtOzWC6ehI8q0nwEpfy2992FqceKRn0mtOBK5H4VZgYqW/Aqj/UigB66mzEFZe48Q7n/i7Z9J9euJpbbpSqhDmgTkK0tGiEVafpiOxAG5wUtK07aKGa7zVmcRy+0s/ISB05attecY0jxqvhjfJk6cmoutMtNH8kzjaS/JiF238ansGhgsgAo+reLb5m3Z8kitaoNMSBxQp9Ppd7zVs07S5Hj+/KRSw3T37/PPliaeeyvUUX7DgtK/TDnx9/8vo2/idptd63OXzduAdtgXbKyyz2w7m0KJ4NcBwj3PiETnHS36aEQQnnqcmBhBkBhAG18rleQWY4QjW+40kB2OxO7Z9vpc+zaDNOKJ4Wcf2Nn0b2/HMcWtytL88dg4h7svV8smV9dL31hzaJoQFNE7sPWDQQWTr6S8fIpT8AxesfzzDwTinh8ujDw2XCew0LyHIzqJh+4J7gN+Z2SqfcK/smx/OoSXaKqcnhyJMOL9cR6N3hTK+8cVy+Yq4c9D5RwgYp9HoKfi1eLV4+ozpJieCv/7JWvmrN2bLp2ivHI9PQreTCIBaNK1QRTPA/RwafsVzFs3Zf/z+6fLkae9lZR50jEVI+hIz4K/QfJ1FIFWglTJePTGzvFU+x3+d+wp/+t5S+fAie/MX1shjsDzOaaQidnVxnXKulTe/QDOJkKjl1B8+M1wGkaodmw/taLiehcDKavJPobsa6M4rcKGfztpb2ugtn1+joAPb5Z3LWxxENFfe/GgxeJyAFtbjsDstgL9Gmb5a2CifUabp673l+mtz5RdvXUdQ3C4T1HWutiLqFkwG51WXTy+vlb8lzhvAs2yjaA8fOTOMphFNH3PJLLCuQtd/fne9fEb7mF2YKH/84niZ4H5Et5S4FWF2CQ3m9Y3SP858/vZKeffT+dSJsKbQeq8iYNpnax3vU9OEdZe9jWn5vxbOyVJnIRpct+kbagn71BSHYatR7ue/R8N53c8UeIBwb9tsmFPLhYfH0nuVxPDocEoagU0hg8nAyWkD7ZiDtRoyDxLxcAkZWxlIzTeFGWHL/sCH9+PJhNqzIyBWb/+GYTZ+02eSn3k4aIdhxSdCHk/jtDAErdNLISFCBpOaERTgWlcZY/zAzbQyrn3CRkDx0BeFSSfDfgQKYXoSKkUpCwi8vhOVVUMOP1ErCaIKONsw9wo8mhfGbE9zWdK6UlwdgzIe5i1+CeQRQYE8c6+fEwKTlqjmiohmICcVJCOVAP3hQjMKX+tIhAOVtE5QIX1oZBHj8IzwKvJoIKc4sGUcs74VbHbmFpY5UGYtq5ys55ZJtIYe+DIEzCHMgYdY0exzT4HMQUqEcIe57BAC2jD1rLZPfCyZaPis1xsYuwp6Tnze42iYNFCYDFPDd4u7q6S2K+uuMhnyIqxADg6nLqRL6ERbCDmgfQRCcGXuMrPACv0UBv0+AieNo1mGDpbPX60K/9ZMbAedytEvuNW2o1BomewLxhae1dK2BSfqvBNof1IINoO2jsOIEdbpJzAgaphtqGr3pKrZBxszaJzCoPRxBT8zLg9hGNdCVMEOGHioIUy5xNN2LbMBd7WJ4LqOuXQ0/JBUTa0MIi0iba1tv9ZXtOUI/OOTE/J+mI9ywTjxT7Dw0McCwLH7ZlPAvaU//slPygfvvx9t4SKLBh1nm/S0RH+N06vjutp1x+8QLwcmOzBgB2h3/p3o7Ut34E6SvKW/0TcXV70fdQvT9gGsSdqEeyLf4afQRMGfY88o8E8gENE7Ma1D07qNQNhGwm+vs6/vdXXm2PGdQ8D5ZJY80NxkfN0J6rz1U9gJ7iocQ8AY7Nks30FrptmfdyS+h1nh3yFA/KeXT5SpfuZQxrmriz3lFYTBz2YRlBAqfv+p8fLMOfaKg08fgzojT8bIj76cZ98gliiYDv7g6ely4SxCI/GZGHIX4LsInb/8ZBFTzLXyT+/Nc19iX/kjBKuxwUoVx762iuJDfawxZn5+eRkzzlWsYXrKtx8dLd/F3PHMNMIN32tquGbWy6sfLZS3Ly6XX32+hCXMXDnDCbpjg1g+UQJF41imQIHuhbHk5aCHuzq/WubeXYsp7L9+fqq88Ogwwmwdoy8jTL4Kvr9A63kRwexVLrp/moOBHpsmrfNeIOz3xxCI1NSb2thLmL2OXcUDs8pu18Lwab3YLnIliNGanwv37342l+umHke7/N0nRzHjhN+R/2EefgiBTFPaNr/PryyWD5CYxxDM/vUz4+VRTGIHkOI2EPzO8n7p2kb5n2/MlVc+4JRgmKVnzg6V7zyDhvUcWkBUqGoTryJgv/Xpcnn13UXqf738f7+ah08cKD96AYufHq8lg0ZM3rbhy7PLZe76MvP9ZvmjZ8fLU+ddDEagRYt8ku3uPZ7Z0BY0ha+8R+0R8bjNPxBmNxlvM/0ho2eS7Y7L/LzluKflhEvaD4aTUzx2DwgF0ma7etsak7R3kw1jVqjQ5OmbCj32HxnZVcLUosisr8I8ajoZDRuDsMKCPTeCC4O5mgcPsljn5+CjUKDglnGKP2GIeSqQJdwwv/mjUCdLncFexpT3yuDSqQhzYqy4GwemWfgyrwosSVfhGEfmvA/hS2c+zngytJvY0Dsst4yuV1EI15XHOUzhFhEKBzGXlB+3TICSLw9uCi+iZVoHpmhjzFfcg7n58wNXXcpBeAQAPQxsnHEUIjYYSQfd5Q5IhUIHW4VTJ6MqIIBbgLZpeQJezWkHHC+92e9oXEoHHE00+/ow89hgUzjml/OYg06sYHbEe64OkXYNLq6MBmUKKq5b4NQP/Jz8iQBQD4updJDO/qsCdhV0spIIAA+mIfu0mYAGLyklcGm2DgOlYIJHhY0QoRDaj0AYmmnSSDzJm3ZCPrYCYfgNIggy/JjcexDeICi/o3LBtNIUvIUsHm0OLR41Vs1TnGw/mroqGNkndHUluLbLLB5YJv0BpolpBGKBA73+zWvNDI/aftqchZfUDW6131Tc9Cecn/RNO7ZRJKlPymHFgqcLObYFGmqcJbRducCwzmmu1snQGPuEKcMmpjtwGLR1TzqtFgL2sbbB+T42zn5UiOLprAv9Czl4KpYFHYrVfI7/frMo4ILeBfYWenfh3/7VX3FC8bW0X9toFghdJMQ1zT/v/kmT7Xzd/cvtwhO/XWn40G+vqyM7voQrDNoHBlmE69c04Ehdd+6c0IxgJuNtf/Uaig0nqGS5g7ldXxfa1tcb/zbjg6neRGC4hnDjnG1pu3M0oeAmMd38IYz/C+wh04RvHMHvh0+PI+ysYRa6WF75aJ49Y0PlR8+NRtOnEPf6Z2w9AM8XEci+9/hwDiUp22rhKhtpbtcXV8vDE73lz793ovzgqZEy6QplwzBvTvdhIjtepif6y9+8cQ0BdL38Aq3Uo5g6PnvWcairbhqkxb4P3L7zNAeh8c9DS15G0DjPPrpBhA7HUM3knziB+ekk20Q2ruaQnt98tlD+cHaiXHgYuI6hwG7ICD7mU7+EWbNCaGEeO4Hw+sfPT5d//dwYAgyH8GWm6sEcs7+c5lTsRQSpX0MHzSo/xRzykRNDjLd1T1wA7/unyZmMNO/8m19dLa9A/1RoN1LBiz8wChceHix/9r0xtHBiynxCWqPKV8zOL5Vnz42Uf//9KeL1Z7+nc9MWB4l5o9Q8CwI6eY6r15bYt9lf/reXz5TvPz1APbMTE0DGWEbr+E9vz2AuPM98t12+jQnwn31vsjz7CCeFD8I7EEfaPM3pt986Pxn6/refz2bv4j+9Oc/+w+HyDPdqiqHxnKuuL62Wh8b6yssvTELHkfLQpDwWvM8WQit8kfsHd7m2/O0zkIR2SNdJd8j4dxPthrygombV0RDeDeCvT9pjgfDrUxe3jUkEm2a2cMCo3beCCVMLQ+jhKjnNEQbQ+K1Gwz2C0QiiLZJPzIExdGyZwgyydEzv8lOIsaNnDxuaxhpPWaAKesmVOOZfBR3RqMNthEY+TasM5+Er9vcIg45I4G4fC9NrsmYw8OneNq8x6AgMRjStOGZ0r0KWA6EDnF4EJ4wYpIPhRWj18l3vXpzhRM4RzR0ROKL1IpE4+8fvaPYcVOUExI10BstI1+Gu5oEXE20QqUKk77En5IlkEMGXfD29tJ9rMdoyO5D3c6pXD0yGeFUBUUGvsiIKbBGb1UZZ1nY4TtmqVjMrccDx1LB+Nnv09Y3m1E8vlve6AUDEpLRfmJQre8qERXzplnoAX7FM4Zp6kp45QCZ0tRjE4V3MFEY8oAQv0lGXtKNadWALk+iiwAAHyeQqEwVBZqQImgoswPBo7QhP5oqfbasK7gIHK8kmHolv3kI/Ote2dyFaPLKr9evTj3jwaN7NXW2zpqHrrHQnfaLt4GUdu5dWGlWBTbpglkw6aRmQArR8oVqF6WArPCvKxQb+d1wVNvls/HwIIpv3Q/wmAM/4E5624zf52jYQDWudCZhvYa4sc3UIbS4Hz7C3dINf2g51lwWXth8E61pHXgVi/8liEX3QE0kjOHawPX75plJghH3A/+Ev/qJ8+P775Z9/+lMaKN2Jtm9bs4Xa+tP+24Z8m4RqWvltptod/W5hyN/psrDS3Umr95H+de7KAWc867aJOgRUGu7O6maoOE43hEcDtRbTzN2pd3+dZE/YM+fHGIsdzag/6lDB4Y+fnUTYWSufYB75d29eK2dOEweB66fvs0DEousFzEQVHM94sKI8BWaWjnHi67g+SIF+/8m6x26iD+1coz0znPW+cgpN1R88OVyuLU6UvwW+GscPMFl8nHzqgLi79pwb2OlfnkbwOMsp2gq50xMeYgZEFracf4Q9QpznHx4ql55AqOUgm9mVjXJxZqVcYE8hE1hwJNpNnfPdc+dHyw++hTA4zNhP+TLY4u+i9qPsMXzhsbHyFqfCLq8hbC1wkA5RvOanQ/yb5CCeHv7yAenN62ZOq6pVLIImKJeuu+7dz/fH354sLz4Ob8e9lgzWiQPrQKM1lx3nlb4vP8eeUAT7iR40+5ZJxzx9fWW7vIWgP8fdjA+fGCh/9MI4B7+gFe9FaIMpdKHT8oNKhLyffG+iXGbB4H++zt5ATHc1WX30FPcdM2d72qzOuBfQ3r78AgeWcahuP3dAWkG286wb7EYPzxs8BHM4102Uw6U48ljeZan2+UFxxwLhfVqTDpS7Xf2uwhYMLcKcnXmIEx9b5yqvzKNawwXvk8P8T1lmiT1oakE89MN+u47gp0mbZoAOujK9ahRkenXmoVYueRHm04HZqcy4TgwxEeRb9sAJowo+FUf3JYbRVthSSKBTOZaRFc4VwaoZVBsoLI85VrgUdnVEVBDD6eWb8LP/ToYbq1cVVrmSAdzUXs1yefo0p3H2scHc/dBeTG86wWgWp0bLfYQKavEH51qeWt4aF6bHsvIvl9lDPP3j8G9eMvhJZwWhCAAE+B7YJLDMETqg8wjaW5EwvuZ+HVqKmBMeaWVM1JgqnG5vtvcmUkD8hzBrkqEI/bBH8TmAFtDSbVMmpwrvcFTjqzDfklAZQ1fhK6SSB79oUBNABCJLe5AL/taLQqDmIZahRy0jCw5Vg0RcYWpLT13E0X5cfQ7ewKpZGuabixMuKgiPb2YSFjiJK0Y1ZmDc5Z8IPcFHuJWeEDbvMrFpP2RnjvpaXLWDK5ha25I1m66htS3z0eDoizQzjabUzUQb9ClrA9hwvRSwrP+siZBHEy3tozXJDv2lU37mlCzybNu+ceIvBAiu1tis6oKCgip4YIokDK8u0X+ZuydNV80/LSVtIn2voUcFGX//uIjh3uIF8F3BlNpxwwOo2rw7EY9fvpEU8DoK9xPWOajpOTZCXdvg69dt/U2fuK0U9yIyWDTDV+2HTbnuRVbATN9l7PepSaPd+nZydFT1X+uk4SOcCPnYKezz2NsZph5P/TuO6B7ucmbcRT9n2Op62RPl/YgKhTNosT6B+f9/OYjFIbk1Ff0hZodqjHo5SCgptVxxHAKEeZxlX993Hp8uY55S2pSrKj3r+Ol8MI2Z6PMIam99NsDBJwifCBcLK2PMyeLDuNXg48OpAYtWxiv2bmNCucapoF9d3y5XOIRHocx8FYQ83fI0msaT7H0comyeEHuVOJuY4HYOTmvgtrOAn915jWNCe+G8B904l9W50rjJhQrqQyN5emqQhWVNKVloRmiCTeq0lwb8gQ/zGiWPp8+OYYrKF2PzboefkcjLQ3E8zbN1divr0rI8cmKwPI82b1BzTbVupmmdDamd3PE7hcbzpae5zqSfuNAkUUmgiPwF9fsp9Nfs9hnuwfwWWskhhMFt9hoKVNq6cJk6YX6bQkj+V89PlF9zsM81tJAfXOQ+5G9PZz6E80u30Tz4AqeJnptk4V1hVSAPpGsKltWjB6eQxwLhfd5Y7f/dvc5JzLvXvE5hZ7+XAypDPwO02rIlzMEUsAbcNExHHuSkwUns7WX+vRi+HU8ycDIbeJG45qLtsO+8FYHOQYP8andgqHG0AYbCoNo9BzDvAvRgEoUJmeI4oxqnOTwkY5iJE84ABLMqMHGMxqUWMvgrROhajUoYZbwUUNXKybSaZxWIpQzwgHN9YbFcvo4Q3HeCX9USiuAmsCOwAFMNGufNwNwzuGnSISz+mWMmBoQd4flzmIwfYTErFFeFJP5V7R/wIhAqICFaQPvs9wJ/BVWH+lVouqYWiu9eBSJoF4Eo8wC54o+0FVNKn4CW8KknrwkhuoSIn/t8tvrQEaEBykmw+Lu3z9Mi+1gNdAU5QhxAnFRy0IqwrExLpCDnq5lIY38Jw4t68bRTKiy0Sp7ADT5KOP50IhSJh7SUPfVp2RD4cwkv9LVI7YmmHTNR8UBzq0DIoujROjMEUYvim0xM/oU+kk86EcDPetugrKv0nVWE9YYgmbcVrrZd7WycsKzvLETYNwSCpw/BxQm7ebVlVAcuIKOw2WoUXQiRdLadmppnoutZCSIk85KAEcwC23g1h+QfIHX/oPeQ9WKmY52urbGng7qbwKzK9Na9CxBBzvYFGKGYZYXGAgLxR8fHyvx19qsgAAy4eBThuJbi+O83kwLvv/deeY3L63ffUWiradthmtihiJMUNVltfLdKlT5xq0h3Ed50APde2ye8j03G/2jdTiHs75vMQQvsr3LsH2U+1oS0IeUdZ/vtR8bLv/8e6pmVxczBOT26DigdmC4Ej2TIJ+/44oGAMoJA8MNnRtmDuMoBKwvlN5ifiqcH7Hz38RF+WKUgoUVoIKWlCXxeHKkeOzVWzk1hRrlN3sI1gkVqy8U3nABXbvSx920oB9TMIrjNr2yVk25NkPCN860HaVK/1d6BmLO+8fF8ef/SBqeysminaRPOqWqMvZjnT7lnmhPSnXrwX2OMk+PIeCcORt7jagmq59hwPyaOLLB6rULGczHYGcON5f19Huy27vjbto0arQLp+qu3rn36MgXRf/LdKYRCxmDmjd0RmsggiiIVk1yw78whFXumITRv/ZwSSlzKV8W2Tg4VHn/bsp4a7+PgojoH7yDC9Mw9gzMcBjQH3UcQUh9GAzs+Kt1IKTjrqwErHxdveICHTw2Xh6eHuKZjmYOENtiywhUWXDXSVtsE2stTaJq1zFJWuhGzDor7vLRY7xO016vNcK//b+27u2S3gfdvDb87y+hYILwzuv3OUzV9tHZEemw7bCl0rCHAudcsh4nIbDOoqIXwAvqVXCeBEMihIvoNjXjvGAdKYPugoLjC8fNDMH9qCBXswiCzv1DhxsFTRlZtokytfTLMLfDzTrhjZBhl0rb7rCSWJhfRmjHpOHHoKjNeO5MDeAZnYSafmsbTLasgqaBlLFIDI2mTHyUnf0+/zCBODNOLo5DVmPRzQtoadLl45VKEIW7nSZjHfqvyn0NYvHhlFisJ7vLJQShq8GTAmVAAEj6crBXk1IaRnZmk7LxBRwQeJiw1cVUjVctmxJbpN75miCk8M5jCmRvEV8Er++14z2XC5Nsj451MhU5CJQYJLC6eC+6qID+/pY3Xa1BZERh6wSOCDYL9BuYpJmuFIMvnt/XtIL9NGvEgQcXLvJhQQajJP5EJY3ZCKOjhKHokBSRZ7OYRQEMIBFpWHmqaRitNYwFv4JrOJV5wjOCZidYiUUZooGbQq0x6tSUCF/fB2b6O0gE5ZSVT3ySGf4N66pH3CPDQXW2amluFQffTtvFiNgzh2vZlO3aBwwUN6anLMzNnvuLnH/MwXejsN/lonuwigH3MBY3Ab/EjfhX4iNvAThBppZlhtqmab/0mZk1t/SbPWlYpGYaKp4cmra4Mc7gUsz5OnCxfTuAjo6qZTVD+4IOpOYcPEd8FJseKHFrTIrMT9fjtG0CBee6qfO2XvyyffPRR2k13ketcQJuzsafN5k93lP3fbYC45nHzlgXIQ0KtQP17yARtNPHwTt5xDsxw/+Aq45iXfh+lS1nbstCfvfvvGqdt6k5yz91AKzgF+WDUyV48a8yO174vw2jaHhpjPNP83rzaAu6JnfGsC2ByY6w+iYDwo+88xOEy6+WrayuZM5/EVPT3n55EEGEsd2EvsBwlMqR28BpFQzSUfW8RLQhtMmgfPBnBuMKhnwNl0DQCYA3hZC0miswBDa4+8sqf9Z7B8talrfI/XrtePuTKiWXqxL2d7r0cyT599q5h9nhpjoNRSLUCL2NTdLHX+dv5psmej4pRHTHb93gj7LHf2n3szSQht6MTj+708cyfYLjzufeNYMfr7lgKdNNoIM9SP9uNQLsrQgcuOVqIJuM2f+tySE2qc6jB3ZWbSGK9U94hBOUBtbWWqYOIcTyF3XMhvJeRw4XQsHqYEehlXt6FdYsD+HiX5IlxDyhcIm3dWqFJlvH9N0QBh+WDxP0Qrhv9Gv0Q6W5MdIicjjpK24KkWIewR53Jbx3esUD4Wyf5EWRIZ2u7TcukCtVTN91L5imbruiHsdRfYRBmPcfoIwC5r1AGdRBhaxDGXq1gH2lkchVOwsDSxs1lmUNnqukjmkJGV//JzFanVgwf+f/GKaDErJCJVea6NXVrw33ahdppJIwpTLKDXx08ZbwJ5ScDqnDZmval45G3jDtoZKIzzMM8xF1Y5i9z68mhTgiJyN8B3lcRiD+79BUM/xqrWgjCAPHi+hmYneucsCrNJqbQrHIVBex+8NiZFMRDNIUJfpStvvmuMAH+5Fs1mJp3KrTWsIzc4K1w3csBIJrxxkR1owra+m9wLHavWh1gCDcMurY2hKWgZursiXDLjMwQRCwy6PxrcTAtg3IvmzbV6nlSpjgrkC9TVs1wFVqlIWIckYXQCPkKg1waT+b1p/DnYEd7ibSGsLxNm9lWe0YVeI9jTESFw+SwzQ9iAJKn77hoFhVeYU6iXxM0tFAhuWV5eGZzid/iYtojcv2D4G0eqaj8AbJ0E01LbVuqdZe2ip91Fg07TI8p7Ce76gQ/616zSiDnl32ieattAKA7wlyTX2IK0DQkTN/kGVi01U6bNgLxrKO0e/KquUA4/MVHzCVTj8J28k0Qf6zL6qUW1m+ZBvuQAYtoyM13AEFPOI4T5i8MSknECo1UQkkc77rcYq+MFgf2j9Azocd/vgkUcOxwv+Crr7xSD63aU2jbSqwL6NC26/qnbUd7In+NP9NvwM89aiOMG1NoOi6hAfHEzqN15uSPEZxxdoY8rnBxO8rBcmaSOVgTTAehpg+2cVscJHGLa/UL0bviE+44QL9O33asJYqxbu5qr1eTl3U86LDdEfwYp0jsmOTePa+Ib13qnI+2VPo7PumRfY1txO6nYSDksCRijlrmrvl7xq3qnRTOEV8sbpe/+uWV8sbnixl+njqDpvKZifLItJfbk4bDUZa4hP3jmdUckPIJl8qLT10eC5jg39akYTXfWq5812g1INSSYrtDjFLrxrc9bh8C7+NFIusGuI67Pi3/DdlAjV2Jpc+Oq0HdPjthN74Ze09cvOQRWgcFqVMaoHNG/ptmr6sw1Aan3gjWp/Ht5GBKZq4kztjQCYnXzp+a1c73ffhmKV1c5+99iP3+KB8LhPvT5evt62ixZxRxAtDk0UvBNfdqTS1l5BQUvXpC7aDMoeF2/OERbQRIR5xRtD+ePrmGAOhhEppgeqjGpmaYDMoxQ4SxD3NJBxCFFo2w1sA1voOLwo4MrjjJTLuCzFd+0S42THod9Oqw4tjom+nzJI77y8gmRZWBjwOmA2r8DeNnXk5+4qlWLBOM2peE1mQpN3gsoSn5nPuUZhbmmpMkPQwEKAyGa9Do+vJqGR0dR4B0wq65pKwZBWHGHceZFLP6SJ7ZD0kEBQvlHqNZZgUw72HaKVVITlkq/v1qNLdWInxsypgr0FoWaSY+7RijZyUUI62eUAjJShPT/qwAkwdoKjuSOid+9iD0u2dQIVlAMv+AJTSpg3sVZ0gk/GQGEJFXyLNtaRKqIOi7AoRl4T2HBCgwYpoqLTKxuLeUNqYgqCmRZXSl3YWEDfaxeNxJFQIVrsAFvDfFnSwsB1xQg79pwZP22Eued+ukse1JdOMsKxm0DFdaGn4teW0vK5hUaw4XpooAqBcSiVe04YJIfUNr2pr1bJ1VQdb2Up1vtkdpZP2YL39CB2HbXtz7qUteTZuOwGdlWVvEqbhWHNV2p22RzkWe1gU0sM0BwGn3qX8LZp0AylfTL1y/VkbGuZKEPYHbtg8DcMnHLMG5lrj6qxHX7FgheZD0/YTXAiXZ8Z8HmALXOUn0jV//unzy8cdp4/sV1dbQS/toze5drnAsTVs0QW1G+yVtm17Cau84OPpNwOwLuwO0Db0JgBbXGsUL47cRzgbLF1yNsMyRIUfm6D9ZPa0kYs2st3x8ZblcRig8NdZfziIQ5tL6AzJs8bwxuKXejSH6OD403Xz/CF2+1uPsah+Xnl/hbrl6F6Oj0UUOmXnl3WtoHifLKbdTMB9kTG8qWNqJxRIL0psb8BUcoHaQM54nua44h/Ahv+Be7QrD0B23hcXLB18slHe/qCaoz54dKf/7H5ziAnYWtNlD14f1jIufFvCFxyc57Ka//JefXy5XOVSmuoMrfndOTfQ6mO4g0Hk7GE4nSvPS0rqF3z73xvtdfTtTeBDMIFd9OOW70L++CjZ1s+eB7cUFDNeC55Y8vVp2RF6v0r6lTnp+Ctz6JOoh/pjoVpQC5u2CPUTOdxZFROAO+tyGUefxO4Pz9UrVskpfL6yOsbmBAmEaHazSb2rHCbPbjD4yiN6vN8DqZoRBmErvIjOdAt8iGjAZVBlBgbR7gpYwEdV0bURzQOJqKieD6yEZEbIYBMzNd9MJox3wnBBahjX8K3GqGR2CGQKC6dz3ZPysiuHht0ytHTvCZeDySUusZazrepls9BRKM0hHmOI9AiBPcbGsbXmFHdwpjwy3Gctgm0/N11Vs46AtxKxkBQGWYqZD9yB8qtS6OjefC1y9S0mTThH1ABllNHHWRzMUck/59HegdFAQ88QzKnkqLChQ6JLc9ABQozmghonv3BMprYXDT/h5afBPynD0+JtAkpgdv5Qr5UMARVjsx3ymj/0v1B7/EAoxyenD/CfaX/w9IVAhKXkQv0Orpg5cHd+m/pN/nhAHYS1YqWqyThGoe8FfNKIJXON0U38bHnOOcI3Wc4s9a1scOrDJzxXFPo86g8ly3NzmGPIebFP6WRIf0GSINutewoZMqcvkD/y7dZVhkWyWgbx5WA9tm5XETo6tllB6LGNubd27ANLGS2JjkiAaaOCZtnXur+04/JNbwu2hravtNjka1gFQkao4NkD14lUBVddZDOEzwjZQjV+bRe2P3bg6FqgF93Ac+7Hf/sRlg6PVl9CIe+WMCzatk0TpX/g1WCTINI4VoZM0aRMcPx9YCtiWPDTmv//lX5aPPvww48SBhaWBaCngAVP3qnE03fdAFI4mwJZenSP5w9McHsKYOeedbkfl6LBqzZw71Kp6yuN7mkBy2MeTZ0fLKfZiVSLu4HJUWR8KDvPpGuaZ//zOfHntU8wv6ezfeXKKayYmGU+24vca9/CxI5kFvcwATgId0L7NKERz2IuTVPeYVCNZLn6k9WqEy8R1j6Z3EQ5Da83XW3BCMLYj1JccOuMBLqMIoi8+Pla+dZYtLoUT1JmHpGf20/M9rGWPcwxp/N2VO8IquGtcbrMgDZVDv+73CqbOSXIJExwqpHnxCpsuZ7gCZM3ri5Jqb4a1BC4OXlvc4rqQ1cw/o+y5nBjTXJRURPFp2yZwL4BbfAv/EFS6XbC3yPWug+G9YGIAY194MNyxhvA+qceWqRVdmcU87UT+h4mLYGQDtTPK1DlQ8vR+QZm/4RH2D6ERlOnf4joEtYReMr+GRsT9hF5Qf332akZgBRn7tWdoKBNVhhTBC38Fneq6eqc4OJibhnfvKtRFeCS+fi3zmfRMCIhOiZxTEYmrkFiFUBkMNiUjKIi/5pQKgApR/nrQgAIueYmnJ17mh1/iKdQQIaegJhdXwSrOfQh23mHoljz3JooC0EkJKjwVGue5r3AReo0MTrCYCx2dYSxY+0vcyhgrCDppCldpYys/ohIHapGvdWDhHYRJI2Lko3liNC9oBjXxVQDpZ7VUk5xhyq32sU96ihp06TbdCXDCBRWhlyh1NGYoxk+hcBs40lsaSCyFASngUdaW3bTSxDJXnBTgiYN/zzYXrTaaNYAQkfL7E5YCHrCqaaiMkoewqGHlHbigKlL+CSwR6meiz1UbrkJCbSeMQeqwx1NeoamU1ExJGFk00K85zRZAd+WGWeTI4kOIJijz8lFxtG78ma9tbZ0VgWjF0+ZYNKF+xc9yRTtIf5BewRMwMbFWe9bj5ceWpYanqcgQCRcatv0nccybCEGD8EooyUf7Fya/VhNpvuq5WyFUuJvQ0ZVZ/ym62QbqIocLMO6tqfckKhBuIdxuaS1AXWsSVLU4aLIRFL1yRsZsjNNEq/mQLQQnfsGrfgazVCxvFvDYPdAUcOHwF6++Wj5FK3i46qbd93Og1QAaZxu1jfSQTvg3zQNQh4fWlemeRHvz2BPcJNyJpUXDQxyMcZ4TJfswkz8ql0UzSuw8s9EzUH7zxRL79Fa5IL6fy9bH6smcTriN657zW7+9T0ed0LEOOg29nAsYg42cwhLDft261FGlgn/bkF7q8YOvNsrfvzNX5tjX9ySnlf6H75/NmQQzs0vlCw5/+fl7S+X89AgXzjt/Ms8LgF9w4PULLjF/+8utcuJbIywcLtT1RMLriGfc3rLCYVcfeNE8F7x7WM25k4OcEkq5HXcabASr02cJf/V9g8wl0xyo0sfhaT3NlgTLlTGQcXh2tbe8dXGpLCBg341rinQ3IG6a1jmwVkdL+Z3otdxQiyD7kz/rOERuolmv0tOa3x3SREhHbN7zqFB9da41LTZJ5ew0J81ODZT3vlwpb9MWX5wZ4vRX5gnn3y7ASS2/wP3Hv3xvFoFQzXEpT53sz37IBe5mNHqS7GSVnA/6s3uYMOV94FJpqRCoh6MPF67+YhK/D5A/HIoPTkkOV94HLlYEv3YSaXqZfmqeZq9cKYtovMYwERufnISvhzHkN+CBIMRdWYb5ryNTmbl8JWalQ8NMgghkeqstVJjQOYjIXGcwIm00FERKcuJGKHXQCAPMZMS7aTJMEElBMBNc42WYp3PK4Ip2ygFTrrBkGk1Lc+ddNBsmYr6I8KHg6YBK3kwUvte8xKEOLIbpqjaz5mscTd8SnzQ5ZdM45GX+dmoxbq+oULjLnknLAzwFBsG2PzdUexqpzr/BmkDDhRonYP/nxx9giqKCrRGHFIyg6QpmlWsI0QpXCsCCiAAtLUgcRl7YJs4MoSCKsKxwJv5tL3awV3ZD2HJ/WGQ55m2FRPfpbbG5XA3ikJpDl4JyrHVNX4ULhTImA7R8Cn/bnFK3PHOprM/PcKgMdxjxvbnCZnI0gBvry5TfeAr/npSKySjw+t3EzsphH6ep9bPp38vm1Q66Y72X3+AIZeaqjAiYBIU4KbCFtnxg0rQ5fO7aDboIIsxQFXDQ07rKAkMEv7rgYB2vYjezSJ9YQZgSD52o2XaUCm0Dth/rzPC0b15l8FIO64L4xqlzhO1BCJU5iz9xTB6YDfwspvBumzSOSQKCbxd62nQiU+HRP1g40NRK58JI+24TqXsa3Qupltb9qWi7sfXJtTGUIRpDhMKVxcUsFiVD4NR8AjJ/xFzcTCu+jh0p806U47cHhAK2CU1D/5//+l/ztO4P52jMMIp97D22R7Ru5631Ofh5O3EPhnL7Iea7X96OsyOMWRceHuXgkqMzGdVywhxjBsm2hX/+cJ49ihtc0zCO8MVCjmPvHbjK/+/UWPb6Ye6vtY7jglYhLgB3fvg5fvSxQJg5Uqz4xnq1/PXrs+Xi/AZXK/RwsMyJ8tjJHu4I7C0vc1m7J2x+yD69n7+/UOYQvpxEHG8ySAZvNEgMnX/7+tXy3gwnXHN6uQettTR2DNmEiX7ny7Xys3evl3k0U2cQur/FFRRjsiQpSDuZVbAunE6jhXIKUUs5w72F3ovo+OoCmIuYQ5Rjc3ugvMqpqL9GIFzRhOeQznF2B8OuohwexK6chHcz5wyRK6SyKOrCKPN054cwxtztD3Mv5nLebwZsT9hhUXavfg/z9ZmpnvLtJ9gqRF1/hBb2H3+zwB2W4Je8pQs//9A2mLjLq++ulL/lYJ81JqgTXC/yItdZOJVnzvbR/Hjc3O0q1CGxvhVhb57jEYaCL+V3vysMDgtGExAJ+jwgrnIUD0hhvgnFaIUdy2q/8jt9hT8RXDAPXUHLtcTJmQo706dO5VJqJ3wvEE46mLy6rxDNFBPFIvuK1leXy8Q0piHsM4yZ2Qr33THah0EFUnhe8wsAcia/qm1UYIJ5dn5gwLejyDbKJOf0TccSzFjVTq2x5ypMJ2l9ej2EaQe4GzBxTdfMB9GSwLyKQy6OB+omB680GMQ/DDrctROg5RGmOPhuef0OnNAIhjY4Np2XfP1HlmHQLVh7mKd3Fi6hXeljtNOkpd6pSDhxKGHiS3eFVx4ZIMRTfAzcERDI31xgyiWgDLXMRhXmONyHCdUJW62UV1D0rtcJWg1QHxOG9dnr5AftvEPIayTMX6STb4ca5oO/1WJ0aWvjwCnWSLV+Bq1e6QcqCtuhI81BpsAYCre1rIQ52PGd8ijwAWRzxVNLpTE4Aj+0dKKgTOabJc08fW8yV/IRUUaZ1A33Sni9hHGFnXnbcKIp9PoibdxDeFROU+h1T0VNPmQUwkit1EzqVI2sh+8so61VO+zeRxcw9Jfh2HHWve2IxQzoKINj3bVm1imjdUwC40UDCoy0M9sF8W1DFB+XGgOOfYKJv5IFNA30V8Nt2y09rWXzsN1RmxVehFraKO1IoTeLDYS5J2cZ3Nz75yFSmrVmQQj/LfpHPSiqLweFOAYMYFrcav8VAuFa+bac9UAZrzXRvFfcKnZNHeNz7O5vCnhwmJfMayaaPn+bxbHd9I9MYrTH08Ei7bsB0tVM4l0bDy25ur3P28z6cNFbfNrMulK1QV1eDW6b5RxawqmzXN9wRG57dSH3tn620Fv+5q3r5b1Lq2hZhsoPnxwrE2i9ZKxvx6U4GWstRS2JED5Dg/NPn/DGQSvt2LEXrktkQyzgnZ/o5QoINJbslfzpO9fLG184/m3n8vnff5q7R9fmyiiD0x9wGf2nl5bKv2Ay+qvPFssj3Pn3ry5MIEw4vlVz9GwPYHD7ZHal/Jd/+LL8u++fLi88zJ17nEyqu7rSW17/ZLn8zesz5UPuvxtl7nnpsfHyzCnmwW3HfLWxzke4pq40I32MvCaHesp1BMhX3p4rJzgV83vfGi1s88Rapb9cXtoqr3IdxV+/ca1cZT+m42RLj8C62R+iOt35+224JYr55hdbZZG68TA+56HWZXzHRz//Tg1tlycf4tRWJwf71V27ljK1rSDTle8/PVbeu7hcXv+UMeCdBeaDzfIn358qTz08hImu83Qps8u95RXC/vLnM1z3sRnz3pefnS4XuIKkZ5P2Qp1tNHO+pWl/+/UtA1v24K6L89sEkHlZ5MmUgvm5Peip6/zaNvvbxOce5XUsEN4jwt5LsGEaZTT5RSAkM4WOdYQY7wyT2fN0wJGxcZhATPOMi5+TvQ1ZJs8X5vGcIurl0yfPnE0cL7F2FFBA23A0oAdUs7v0A7QYVeiyX7jPKpMYHzK7auCqEEEgQfZ8UpOVmjcFDPNsBnyCxd+ROEJbelrDNDMhRTNCWhng6kxneBUcerRnNV/CNYNIcuE5ePI/NCI/rzYQhmD8aVKXbIEmbbx70X+ad6owU0uzBA0vX7taJh5+ODhHa8lKZ8w4AeL1E67dKdzlrkG+YvYIc++paDktDTjmW/F1YBUPaMjVUS8AAEAASURBVGEmCj3mRf6u4mquqbC1hjDhya/rfLuq6/2NCt3SUORTh9abdCFPBWUFKPGQ3ClYO3FQ1urns9JHYcz9elW3JW78pJdl4eFCgBOPK5NgbAVHIBpg0vfFvYgQzYz44chDu9aqgZPFILgRbipG+hBJodF657Me1FMheHS2/6STdVIFct6bhQsS37UbQzM+PzsbOkk3y5fy5qnQR7+hnN49OAdj7NUgaRfSxaryD/i5V6WXtiq9ol20D+EUtCrdKEDoYd3g+GOZfPGfZU+bhub2kar5IwvrD7j97GGt/ZOIJjRPk+NsN8YPEILsm+uYdCkExpsQ69LvrWhsNa8a5L2erFv7HPuDsBogGmnEwYMEMFd2UcJ9x6T1qHCFwIo3GdkEKGcYfleNKwYdvPg8dvcxBWxvXiPhCaLeT3snjlaS9tk3iol9xgb7V3oN7WSnDR8EO+kPCGzb/wHB99ab/jHEqc+Pn5s+sny2sAT5ZK6U//76tfKrTxdykMxPXphCO+hWBiQFClz73uGzrONsTWda6fkvH86U1z++Bji/ql9euv4Y9wSX3f3F908i2I1zSfxS+cf358sii7aPTQ+Un7x4okwh4rN6xEzTV85i9fHHz54sF2fWypfz6+Vn782X0xyC863HsMDICpfj41a58Oh0+JAPMQn9P//mYnny9FB5CNNT57yvuNvwE9IvsMfQ6x2+i2b0D7nK4vSgbQZn+RucIX91m6vlyXN95aWnxsv/enehfMJet//rp1cp4zL3GA4zDtKGry6Vz4A9wHUh33nqVPn0q/lynYNPWhACbnJogHY/aKPtINrtfcN7C+3OW6U0987F//sfL2a83q9mWujS4TvnRsr/8e8f4mowkDF7f4nQxroByY5H2xZarDsBgsDThc5t7pM8j9nnv/m9Exy2t1U+vLxS/hGT4De/WCmPnx0spyc4ZBCt7GdoDz9Fo+3VIO45/CPay7/7/XGWENR4M385J9Y3oTe/Og50UMZXdyhS16hf67+eK9EzdALexsp5cNyxQHg/1aW9HFenWYYMPu1+MpUypQ5s3h2mcDiIqUtlbGX+1C7BhCoAEUcGbwOzOJmARTSJkydPleHhkXL1qy9zNYXmi0lDZ6/MZJuvjKl7lBTyxAQc7OGNUCc2sJT6Gkg+ai3UDGpOWAexKoAQxiCC/iHCgvEVJxqQgbvpRITwojpeQUjYMjCWPVkKLwlg0hU4iOIuKr3ELzTi6YE5ZIUfDG/8WzNLtCicYKawp+ZUvAWZ+30Qcr66epUJZ6oMTcroQGMFM0bSTcseyYU8oGkP2hbTKZyFHhDGyaweH17z7eGqCDe+JwDkFQBCP8ujAEV6DwDpXUdwhjnHO/vZSA3OwLI0MFtb1LE//cRTmuh88ycNZfQr8FCAV2rAcvHHOL0IhWqXInxYLuEo6IB/BMbAwk+kSOMJgkitvBOQ/IRivqbzP3kmjD+moY351PynMgo8RdgkhvsiiDji6YVfktomKPy2JptH5NxHKOxMgNBVuvvLqbk+aR9qRr17UA2hqLmwEWdFiBMANjmEyDarEK+QHm0vsTUxTtlSLoGr+aQMJE0/kT7CTDmFCj2Ei4d4pRrja9+qWmfbrPimThLGH5AWtyx08NTZJhUCsyhiILDNszraN/Wmyegm/S8HwwBjjb2qHjzVmpWuIRT2LVZNtftlI/Q3EFyksL1FO9gsxAjd8aaWuYl4/LjvKOBJuv/r7/6uXGVbgWPBHbu0O9ouGsIyMFo2V6/HQqH2/R2oabt+2uZ5JNlOcOdN/3SV/Ol4H/jSwjlM9L1x2rT7AU9c2vn42NExfJ9fmSv/mRMw3/5qlb1XfeXffnu6fPeRYU60Vng5fB3sxdvFKpfpBhivNes0vC7m7leyHT9z3GRuunhtq/wDmje1a5NYCvzpiyfL42jtejgsTGDOe4MsNF04M1j++MJU+as3ZhAMV8uv3p+LsOfc5Hjv2HAetd1LCGX/+IuL5ddfrZTfUNZt9qiJkzTV+uXkaF/5PlrRP33hBPHxZK6vU4RzqfugjYc/NLFs4yxI/tn3p8NDvPLBQplBoLz8yQIwOSivgXmC+w//Dfsdz3Bj+3+dXcDSA3qAN9nh5EIa2Aii7fzpuOlI3088aVfn/qb9kWY3nf0SnjwRsPnypE6pTdH3bdCAzfieNOYRfsZSUduU6yAXlAnUnkvMTQHXUS1/MpHID5meOSTPJnujko/ir9G8q9Gl3YjDDR1qKuLpaN8DvWvle08Mcu3JqfI3v5otb19cyb2YMx9YZ3WRSBpKqzOTfeVHL0yUP/+DqTI1TJttFgtdVLf9mR+cBW+VFzWLXa4tWMdTbA6mQyfa1+JF5P1VfL1+a2D8HF7snW5CvhZo3iUSxwLhXRLwXiV3MKmuYQTbT54yZFVIqk1RBrKu/tdTRBX8duIQv5nwIxDRsT11dHlhnkG3r5w+dy6M7tLc9bKOmegSWhIPm0n2tP8IkD4d8InfMqA5DKbFkXBfxTReGXEVZmSmGxiMKhFCiGG+DjKINzCmHnrhwOaAzADGbKC85b2Jaic0/1TI0T+cPPmIU82xMtXiFUaYzGsYwTiFNPdNOUzFP4MwkYIkcYlDiJx3DZcBdkKC8V/GhHaG/ZcnOHTD6yOYR4KfA3ly9w+A/Hbf36aH1RCigOlPwbfFs2p4mAao1DDv4K+AvqVZHu8K6LWsCM9ocQajHWR0RxIdwKwmzLsEE3VwUeajwLXMvEq7ylDUOBEKeXUy1XloiIJn6AucPDNVJLSmJ6oTVWdyI73KTOOCABnz4Q+K4Rn8W1xkCLJXhDLvcuQvBhWLiqfhTStpogJ/l6OdHKFAOHHiVJpNaGIJpQn/pZl9SLNV63CZPNX2WlcEJY6llVFozXwtYwRrKkCTTGGmHxC/7a+WVTJZpxGmgSllXdgAOD8+kfbEI6/8yYJO1/6htg0b7i/ppKVphIFvqiR1UiNF69+ESd/sDaL95CoZVvo3iBtzavrD5jJCHgtA/ZglK5xurK2y0r6GdprFJIVR2otPy9JnW4ypKB+puWAU+uhz7O4vCthOPv7ww/L6a6+VBQ8WukvXtAYO+xovfWMn2HfMXZcOEbXl7gvddqWrLbm+t38Djz8t3Nb/bp7pFncCAESH0Xwelfu7X35S3kJAOj85UH783FR5+cnRMsLVCc4FLY7t86Z5NsRJPyeBmrlHpnrLn78wXrif3d6f5DeDJRPPVYvlCZQcPRvL5dlz/eXhkxOYY/aX33uSPaHsD2d0yzhS+/0Gglkpf/TMMMLANNYUCI8q/tB6ZkWQLFOvLDhdODFQnv7RufKzj+fYL7jIFU9aYZhfTzlL2PMcoPMi+wanh1mIpT1qxeNmiKG+9fLyU8Pl/Km+MsrC5ZTbN9FKsURaHhvfKv/p5anyNEKp2sxLaApXgTnEmHz+5ABC6ER54dH+sgj/8m9eGC4Lq4PlmTOUg3FV+k4NrSGAj7JvcYhDcQbKcC/iFuOpOL306AAC+kQZ4xTT06MS0LGWvG9ohOy7m9gu/+57Y8wb2+WpMy4yt8vQRO+K375aR30I/D+8MFIePsN4y7yw1xnX+WiXA+ezE33sZQVPaHB+arv8xcsTziTlGbR3sZACdtuXTBsIjOfTnHPyJy+Olm8vWFbumma/YOa9ZmozbuZ5nur2hntWy3ef6KP+T5Q3Pl4p736mhRQ8YrPWOYlJ8SMPDUVL+xw07rfNNucnMAnSLjbKD56i/bB47t7CE2NgBX06JeIl7x2PYCAWvtzadRP21rHvUYwGedsGpdnqHyk9Y2cpGJ1i37Zyj9C4x2CPBcJ7TOA7BV/ZsSY1na6uKNkoYf0RQBgvmsFaLUddkRliE7cCmw52tKyxqreGOaiDTa6jgLldQ+hbZo+hAtQQq58OyPPXZsvs5cvcUXY9V1W470gtYWtO6OQeBtfBDBQcDBWSmFMYBPFr+rUP4+aJf7QpxNV1D3igQZo6YNR30yEsyUQ3eWygUTNlTDIb/9CAtDLFlj+MtQ/yUnOpc7it+FZG3ncZYdOYPmUyksDFwyIhgSqkas7pWpzeCnkzCMlnT57kkuLseI+AafoIcyCwhRDoqmIEBuJvMHDLqavZtBh94GTdROOERwZl4rf4R9NCXp54Ws0QNcvoy5UBub6DME1ipL1MeeqWLIzbCvkdASaCi0SxYFVQtQ1EgHD0V2IRd2nX0NBy6kek1E8lS51kEqZvE06kzsTvaw4AIM8+61HapyJNZf4+8G9em08SiV+C8tzvD6jQJo9uD+HoxCT1FFGX7CyPD8sIEwKOorjCVQwLCyyE0KDbhQVxM2rtA8aqYdLTRYZBDkdo+6F1HbyTouZhmxOWMKSFGQklNCGfmDLrx08zZZ/C86UukNT4Jlewt+7VRtqG7VfC9dCjth1IWXFL2woM6944rr7TDhlDLK8pbduaiY9hBqrmMNfSXJ8D7sloib2LMG0VjJOXgDrOPlT7UgrU8T9++bpTwPajVvDi558fOao9fewTnzpd1mY+pY3KSdY2091yjjzTewjQvqobPEKBcIZ7HV96ZBQt22R5DmFiCMaalRdyOTyVWryCHOnST5krH+HEyMdOYr6pXwPO8fxWTmsHYT51ir1Q1Jnjk9cFOaeJlmEtdh5689Bob/nJc+71a/JmrlM4bEcrFya9cmgScH/+nXHMTIfYL+cebQ9/KZwm2stJ2qQGlotOmRGSCVdfoQn8AQJh6eUnzwNujnc2JU++nEJg+5ML7Ll8eqjMcx2IMD2ldII9hUOk3d5cYqtMKf/2Re+GIxFlyEF0wJ9iP9yfvQhc/C2b87J7JYfdx/h4f3npCXR+xMsYbNn3uIoiJpaTPeXRlygcxBWOvEBcnTw7qQJB+mXMRWD61lD5QS/8VkvMTsydF6i/8yEypN3a5BAdyPso9fvoD+riRKxomnoLPKK2KV3gOznaU3787UoD/WNZBLz92kPSkcZFhbNce3Lmu8Plj56nzrgShWmRuQOhmf2b49Sb9x5vU7cSSvKKon9GBzfLH1wYKL//HPcVA8u7iKV9x/m6K3M9usI7Eb/GLx1mxkV/+J7Rh5grT1NF8p2Wpa2Br3EZDoHasUB4CCL9TqMwKNSu0/RAvzMQgRVjkcyjl9Fr1qXgEKGJOBESiTI4NBwNgEJgmixhHvIiq+rg7QE0cwiE80xWG6zueUjGCloq97ApLJimZTQrg1s1K3YCsqNDVDgOAGomFQzEISaSCDMOl14h0TKqfNZ3Bo44Cmee0bYwSKtVcZANYw58mflBmI1oyUCmmtGlJMQjMf8dSBXoIm/wHa1WygcjDU6BVXMLjpYqJ3SCgzRTgxahUmGOjm+JPEjGKyjmOIlxYpTN9eBRBQbzs9xSz3Ly4SSDb/ZUsuKmttAL3BWmNM31fvd2f6VMeQZ+BSgHT5JbJq+IUGPlfkK1NaurrD5i9hthRhpwNUjMXqGP+8Skb4QyIZC/eWnCIjy/NTmsr+BGnraZZvolw516lYDi02pvo1kSBP9C3NSgoznfLdAKGNpR38I2LPlRZsN0fnc+6jtRd/vxudc50WyjsToqN3X6dECJY/oNiFXNnqainKAJzdUGL3vwjM5GrQuyvIOPwpQTrdoy27Hmo70jtCEY7NoKpGelWNIq4PFiWUyfFz7SZqjLlgw+Qz3jkF8V7qrQZlsI1cGnXTiwneQKEuJb307iAqswKnzzovbjZ3+07dcFI+JRBhpb+odCoNfR5GJhsl9FQFyirXsacY+TnEIhbbipXcDutIgUChwMO3ZffwrY7t5/991cMr9IHR+lqy3AtsGC1eRDZY2DFrY5iMTh7XZah3HtBrfrdqXZ9XF4SN14tiDse/qPTJ46PKBbxPzuybXy2JkTaKGI6KKX/bEptcNNxotbwEhwQyzj+2r9Zvygv9bVPn3DHuTpn7Zc1cMvei+PzmiBxiepEhFfnhnXa4LEMyhjlMMIP8eZOi4kURMTKFzD46JAD/P/OEPJBJquuIxxbntoIJOs0tnM+DH35uA7BQrxC9YVdnygF6MhezupF+9sbAjgydjbwDS+5q2OkbUsNW3A4ONhWpY3VG+CGBSJ36DHIyDbsOrd+Rt8AFxxNFJySSLT7ecsny6aUMZ06bXbCaP6NdA6XymPkZMVcyv07Dj84t0magICyfYADWpWlrjNoZN690sSgSP0lfdia2kZ5rChSgwe5oFArnN7iBCltn08jnDnCibKliJNwF0+bqDVXcK72+ShNbw1i8F9k4/D2E51aHG3oL8u6Y8Fwq9LTXThkQG+/aZTZNBtGqMDf+3paqFqj2zjp0OTzkMn3FMYIYr4MoROPoLw3sFN9kv1c7SxTKGaweszV/OuIOIhGSNcXm+nrwJOzUMGVHhVO8jAKl4O5wQ7YBjLwS+aRAYVBS4FQxnSfvbqGV88jSdcBSlCw5TLoGc1EBzJJpDqyYgKin5WHBT0qiiDH17R3vDM4MRKodqOdVcVA4Fw8YV5l29Wa6e/kCqTrHjkoCYd1aUZVr/dJK2AuYY5zCymtWdPn+AbelCmHKpDmkCyEPhbztAeONZPFTysh6qV2Yamwwhyao1crTOt5ZAabd2pxXHF1ANlVqifHmz7oxUEvnu5xMkVVstsPTgBSjdkBFylq2Wp0PWrdAk+vosyBFAzFeEwo3yFZwWCNlCskQCshBIILqanpu84Iuvwy5tljkf9a8to22i8yTgk48MY5uDTPBOVRwMpbzTC+jyCvxMnsIkikxSfhmBd2f4UBJ38FOoWOVk35qLUUe1T0pY0XNNR6VfLF4EMaH7ZH9I2xZFyaA5sLv6zTqxX4VvutFPyrAJZjZOyk862F2Lwp23boVXT5iOoA2cNPDVTZSts2kk9Ml7NIkBIEHzMjx8oBDfbv1fICGOT9qL5dR9XkBh3m0WFFcYCryjRssC2s8LVIrEy8PAgSxJEpJwo1vLlI+96WoHH7utMAbcAvPKzn5XPPv30HqJJ67CdD0/CKJ0uW1fmact1TOpuIbak7u97iNC+oA+VP5GaoTEwhseP7pTR3zu1XPo4xdFxXkI4EtwRPSxI41KmtmD214TlTxslz04+TZB5VyQcp3jj08XdihdBTbwWSJu+ScVeeqMy/4RYAuhKkrTMrjwzRqriArddMAhLtGQlJPOvMdqRtPGtMSwb4YkB+eriWbINIMsjvNrqqv8O/DavBoc2IJDrnxa3dpRrciLwRtcejBYwFLIDzpcWEK/Vv/Uk707Ebpik38/fyI2/INsoHfDdnt3geE8QCUILE3Y36D1xd30a18T8Ws1xnaubMhJewRGhqY8W9yZZB8994Zq4zWBXhPvlgzqEL9geYpybeAIaDKXvVKrcL2W4OZ7HAuHN6fM7CW2FpzZzu2N7QIcdUQa9jZN3mcrWMQJrXlbNEKu2yfQDHDaT6xhgCle4S25zbq1cu3K5zM3OoJHipETSjY1NlJPj49GWeGKprLuMoi5CkwIWworjtoxzHb9lZJsuEWYUYYc07sNzUBVPeVuZ8JhugmtrBipoGXRHEQ/iSETe+R9hyAvuzce8nSzakxjFJ7GIGGbbnMB/iFPG1ll5dQXPy8+dNLzbb7DX86mZoMChTSqzbnjKJRyDggvUIr8elsIswzWuoPAqguFxVr4Jr0w9THVIrpCp8KD2sJbbQVhBzYNo3LcVrSmgc0hO6Okap5mRh0IEca0fBdBNTv1S86fGtK+fe+PW2V9JmaSRgotayxCb1JWpF5LpnSDBA0bDdwL9n1zqn0pnxUXLJW76i0UOH+Cl3YcpjVI2/NLG+Fb16rsp6qRtSvOov5opXnHGC3Gabx/El7bSla/6E1Z9rxMW7SlRKRP3AdZAY96dG3SvHP3Bu/Sko8WhpQW8bXLZfbPUr4WubbouCkhqyxHqEi+4gY51nfZhGPCsh1p+Ajt0d5EAWm+2fUTw1nGli6usQSQ57JQxJMZPmJ4GK33qH98VZsWctqSAbdsiXtUgi1+te3EzmX3MRZW6sAIuCLcbHNvei7/0CCQWT1a9XgXNp/HMbw1t6SDWBnXf4A5uArW12beFf+y+/hR47513sldQofBeu/Cc3MvVf+JcWZ+9iMYAjXtX80n+dpEDENG/bf8HRDnYey/QtoF2+Xe9HggnOBjKSwtiePLkgfFvJ2B7ncWX5Zna75qE5nFnZa7YZXxokWgK2IxUFGFPif00WVOwKpw4rjSwjN+BUV93Q2ghtxn6rGnNyzm+jV9HohqvzacNi2/z0aBSI7awGjiGtWVIvA6eTXTjtXGJ0IHVvJiFv0QibSdcPz720t24xtk1ugWACRpYxAhOHWB8tXE6fjW+f2vZ9wnYibLvW1K0cIkhbXUtJJ96td++d/DY885nW7C83vQPANtsLWf73oJo03bKRYQWB8O647dx9dzBzdjdKTqxbnzZSXRj2O/MBz7MTRcTj5SekUcoihN5W559S/87w/ROMz4WCO+Ucvc4XRjwtLXa4CpDTvNjcKsLPjKjtbfZFCNY8VTAqYyvJgPNqaP9CCxq//jlMBoYXA8vqSdWygj3lunpE2WcC+yXMJNUU2g+8q9tvmqlaj4M903b9+k47XArwxuNlX4gGG2K3DdOGGGtAWjSfOMnQ256BzxNKvr6YUoV0mI2qmbT/U9oxBrG18TtBNbCFZbCrAKTprMjMLMKkn6roWuZZi7K4V1GvQ7qwlXjJzyFMY1aVoNnIAdHmetV6HT12vUyAdwhTfYYDU2Tk0ZjCVOnEDVzflpkzT4jyLZ764Cr5k/ZoSVeHeRJC2dufRpfJtzN2godCjCrA+v4KThWE1dhtLJWyiLASuLUq+n8JjqkktLgyUf7btkVfKWAP+M2f8jTElgX+KsdAynztW6TLhkLnEjJwMI07z6AnRkQmtWCJjTxkXMJst2YGGdcMWi/4ymM+CLxHJ2GUNPngcHhCD5mW4V3NbcIP9Bzbn6eMOlc7/FTwOplo3htKxVn00i3aE8J1wx5EzujaADBPRSm3JYudWDpyCympsRvy6lA1U9bq6uv1AUpJFd1ZCC1feBSZy2tbST41LZvSBVGedGbvgMtbVAdetquzJ/DY1gI8v7APgRUFyeyyMJTJ/65sH5wnUNmsAowHAF5dXC5XlkDzCYL2gA42Lw0/zZtIBz/+TpSYH5urrz2y1+Wj7lS4rflbA+2y/6Js2V99ETZnP+S8fDr0Upsr7dybZxaDmLjMTDM0fpHtIdwc4mrb26FxCHC7eL7UTXjBmG1x1b8d0XsTtQWNvnt+jgEBhW2qUIr5sNsXeBp+dz9zUzHX3Fx9mgwwsvxvfrXZzdK1aeByUeL1X5xMsy1EVqA+0Vs4OwN6gyTbabNsxvknqB87g0/CM5+afXbmz5++3keBKDLvy1Tku+FYeDtItcFe99X8qiC4L6h+3uCg23g9t3eAt0+hHuRQt5ui3sH+088V7b7OB73Top2LxA7QpjHAuEREvPIQe3b222H6fG7BxhmBJnRMJ+8h3llBI62Cn81e+toQlYXuWuN/VIbCDoO0AMIYZOTU9j5T5RFwjylS640AiACR2VmmeojDMmw1l5QUVMgUavlsE+OTXzjRMOhgOKggH+v2i8+ZYpbBll6ZcAQJL9oUIirdjMnWCrQCRmuOTxxE8+C1/FO2ECAIV6jfJqMuj+yh8PzVti7JxwZbvHR5FImX4FT5ta4ccCyrNKr19Oz+LaI5uuhMBsw/lfYY3kCzekptIQR/BKHwhAvB6pYCt6jpQMohq7RxAxoBkqAPL915ruIkzx4Kzh5qqlwDI/wm/vo1BJyyhf7uqS7B8xUuqOdhZe3HNkbaXmEiVPDJ/JqpqqWjzDzMcx4lkcGP7hbBzWwthc99agpOin53FVXO9KLldVAr/mTccpGZvgb1GbQfqYiIJLx/PHtT/yt3JSj4qWJbb4Do6a/07/10nUWGuagP/9chFB7prC2jMnkHG0+7bdBW1pZD2lzHbmU+qS80kJTbPfdxQxb1IFZ20DtaxSq1i8wutG3TiIsKhCSzjytj5BDForIIQEQFdRCG95th1vQiSSNs52070QjLFWfxHVxRNyFp7Bnu7BfuXtmAOF4jX4R4Z/y2h8pCNdtLMP8DpXxkanQxutopJtCou3SvM0nWkr6pPtdG3LtIHL89jungGPBB++9V371i1/EHPi3jVBa69BE6T/1SFlbnGGfGXOM3bxBZKcN74eZ7b8Zl/YLvh2/7gzb95uk747S/X7i/NM3SXV7QZsLV/ZNYLftHif2jdR4itvNaFjp15TgoIjdBbxZZocMy+jA3M6QmAVTl9V2CX7k18my83IT4ODtVNSJ2rxneNsnWaeYnQQ1UvvZzo9t0sPSuo2/+1mh3g2Mu0m7G5dbfN2yYVmWDvUOBNbi2x2zpe3BiWpI5SPaWN0QWr+Dnsa9ZS4HJb5n/ptqBMfPl77xpxmrHkzR6cEs1T1rEr9FwPv1CTp5TBZBo2XU7TYymmr+HJxz2iYMYA4ogflbh7lbR/hbUxjkXU3cJv5r3LlmulHMRL3E3rsCV/HPpCLTSoeUCQ7rBy4O8jGDI78wzc1IsRGcCMTEzcNZ4vBTOOSIlGj3wuxWuSf+xglzbDzyifZBxlZGln/ulQrjiqCneVv218E5q7EDQC275pEUXq2a5qbCUCj08BXTUJgwsMTIsGfSTr689HOstRpFtYhqV2IyiWAoXi1DHa0dNxXNs8fsKw7dGYdBVmOHJSBoGFeIVlRzpD/lUVOGThNBkme0bjDSqJfce0jR6uTPjMdraCGNEVO0yoyLXEZc68G78fr7KQchJsfLSoiWyY+qhRQS+EM/y+G/KqQgACZBajD5WQ9pN82MbUpd9c9b8mn9TZQw1WMiAPwAEm4nEoT13XBfEqchtt/G9dNwy5i8eW/j41XffaluC4HQohLrrp2CTT9twr4RrRntRDNZ9+TNcQenbb6HhQMzFCXLm+sleJp//UsRaBuhnX40pgiEQbCWNfuCjF0TdfCOIEU/sDz+ibCXegkgvqsgmTs3O6mMmsgVqS5/Mao/8bDexVDNH8sQwBJ0hEz8xdGfMWq/drHFdku8BoxCslrDZdr4+OR0GeaYvlUvrFdIxIw0WmvoZZnTv0lretteLewu5I4/fkcUuM4J0b/59a/LRx9+mLr+HaFhRyn90+fLxsznZWvuy9Jvm7St3Utn57oHeZx89FtHhvVBAuHtZGAx78odKY3qyLjNgunoyGZ5/vHh8ggX0H+L+wuHueZAJw9Rn7V6Do1/2kyFHwAH/DkI3pEW84C87w/vm3WMg6i3u2Q3g7A7ZtfXvokOl1+F8jWrQdsjbXmrf5zFrufRohyNGXkXxb42r8cC4demKvYgsk+f2OlSBMr58cv1DwgeDp8RLnhXi+GJWjJ1y9w1tYm2yW+1gquYhK7gp9laLpyGQfRU0XX2bWlmGWZXzUK0azQPmUGyyw+GvgpWMIcu/cq2prPQXYgTJpHZX75fflEUZaQVVjjTYrczXcNoGqRGMSZ1AJLRlWmP5gYGQ0Y8XbKR6ipjXpESD94SR/NOtX1q1IwaZjvaFmiDRzR4wBXvra16AqNxdB6y472F64S3WqJ6UijhJLmCQHgKLerI9FS0TLnENhgLDZbcdGr3hE0SzSTVFHrzrUZ27hszf7VC0qZ17lNUYMLAlXDKzrcMlPRUOFVQ7wGuWhmkfMxaiUfdGKbmR22VZa4LBWACcHIIPZKHwIgr/XQ1a//W71QSQpo0lcoi578gYJ7+pCH1E6IGMQtoHMJ0qRdh4nz4TZ7V4eFrGgNP0/jur0myi2Mk/21MlmM26t7Pu3T9aIwHOGlXerlkomaw7h1cKdc5MMhDklIHTT4pO/gqxFcnrhRfOuz4QJLa5g1TyGsFrhTKcgHXdpF2R1uoRW1rQQ28CyBV4xiKh5YVjjQKCMHwI1rHSXZd2i2Bwsi1JgQo3LZh9p0IiBC/9mWEWPBUqMvpoWkTxKastKgsFi0vL5WTY+PB2WspcjUF3+JQ9y3XtlnzaMvk17H7XVLg4meflX/4+7+PVcHvEo+aN+16eKoMnH68rC9dY97h2qOu9rsvfgm3ld3cpR3uE8Xk+6Zu+8o+afbz2ovnqccu7Bftjvw25y7tm25vnnsjdZfLuO0Isjee3zeHdRD19oN0E7+GpmKSV8aZk9xT96fPjWZ/+hDHjw54BCnZBfe2AJ10B8Nuo96sIJ0ydiLvA++AvDpp9yS5Gag9UfNJkeMOgrdfmtbvbtIK4yBcu/1349Ud0mJxuKcpJWUgHEDTAyGZaGfKPDDavgG3m9e+QO7SM4VPyVP+dU+On3q89E64SPTgik0Pbsnusj3ck+RpZHcGuTKAdRBWG+bBI2r7dBGQGAXW2QuVU0QR7KINkbFWCIHBdF/gsvulMAl1b94w2i41AKv4+5OB7UeQ8hAWGckhtGwKG+ucihYmGdyjyeNZ8yOJ4z5xZXqjQSFmhBmeBGVcV1hxVGknMvt6hB4mDU09c5gMfmroIiDSItW2KJyaTgHHQdSfznL5mr+8qAmtewUpPwfKmE4G2T1hCsaJnD/mXIEAouMUFERUodWyhM4y6/4wEYg2hDCZ7Uuz12I6Oobw6H6+7T5oFqj1tFRKilYQJp/SmxuvpId6wPfwmC0nSp6hVztqA1sSpUweJNJgBmnwlA6Y4wyajm/SKNrBZSllAq/SIvWR+MKuNKsihWkESNl8NK7mQkCjrfM7dayX8Sl7EiqJaAtkZnk2ABLMH8MFbKK2PE2UTAbSNo5w31OJDSZ+R5A0AuE6G5SVQ9jGtZky8NC56n8Xf6XN9ENnygdvvx1B0PamEKip6CJacusihaAeKhbQmDjUvmQDFcsgdaChuAEvR2yLU1OUtJm2CFYU7SZ1QpSkb+K1xfSpOTMIVRh8VzPRClNQ0bA3p/N101YSumChs93r3O9Z2zztwUUdccTfcqghtIz2Q/cUJoV9SjU3sZoWFA3jAnvPJqamsRoYpd2tlyUWjjQzHWD/bGhi525cp6+0HsfP3zoFvELi/2fvTZtsS67zvF1z1R26b3ffntGNBloYGgMFGgQEWhRJBSlHSLbDpCJsf3L4B/irQx/tn2HZCjuCH2hJnEkRJCiQIEGBpiQCINANYupGY0aPdx5qrvLzvGvnqVN1q+rWcO7U92TVOXvvzJUrV67Mvc9698rhb7/0pe6HzBVsXey2C7FbgfTteAkvvtFtsC/htMPYt7rOthx1nxxd+mG2jctw3LbC9rnob6ltFI+8+4Vt18e5WLv8xrbsw+U1eZv82wi96AnqXr0hNRHqsfHZlaIvsJWbRzGEO9tlVx67RloKEvPPNnXdwoxvvWhno/asyK6SbY8cKks27bLJvZ34YFfHydtKkMdwvY7D80B5W8URYF91DtE1WXPcK34b0d4X9ouUO8Rn6HTvjC3lUMRDmY6Ur+Uf4bGXQx1saAfOPoRjkOfBzCPoRZvhnRnGgPA2tqsPdCdg1w2+923ejMndRNPAXAWcZCgYhp0LovikcpEYwaBpeeCkJ5d3z+Xlr166QvpiDDvBkkPpfNrW4ivrDPsoL8rVK6xmifE/m6XnCyjEA4XUlq0hiO2cMptXynQ9FdYvhrReCIxnjegyuM2nB01vnwYt2WXiBXLCudLIIVh0jpegUKPYeXwhgtQQo5yogCr4xeMDvTJsUC8N2YBdDNms1mkedUH5ZZwXINMyDsBFhgBUSfg4hA7/YjdLfuwYQB3SAfxw4XUXWHH0TRaYeephHg7SA9bSVsi2ioE9KYjV0Ie3HkSNb71RVlUjPuVbD3UIneVZquULCqWfJK3iycs49dSPhWZcGERZ1J3eU2FnDQmt+jQvV8CJDNStdbPyKZ8vTluSJefCNADDwJxQviQgjXnZcyfCDcAbGWQYOk6RKedeC5gGgQgv5WE8bTQI6QBcCbYa30ZnHMBl7fzbIwGElnn2qadTlODezdxX2G/z0rWr0eOs7UXZVR37Av0YOqcLGFIj+7byERS9aHOZr+r3+oFt4zJ+7Y+VWPl6wuQVZEmrd3/ADTKbzBjvlQzRVBbvG3rkVq8wS8lpf8qQZwyxGcqbYwh0gBrxdd810OgMQrPZbtyT9iMqoj6skBJ6LzikfBGgfIK5sgsnTmYItvMLXYXU0JoqF3z54mgcbr8GbPcf/eAH3f/3hS/QhvaYuyXYy7yX6GfMJZx59N3dCl7C9aWLuW/sLTfcO60P9Qk3pB+jaiXN0RjMzHMPjGqFUV/ELF3eU5Dj3kU+N3bqba/Cmrp3S2/6OiivAQ+Y+ijZEqI4KNftDiMrc4fsw3rbrwzThmlvaf0pSzGH+0/ZjsMxR5dgv3ruy1W5FCzhELIM8rS8d/ro7y/21gRThR7+QHkHtYcGGr/rBD62wsaA8NgqPDiDBvS0ocsErZvFTtcMObk1kFH9rmg8F+w4/FMgNgn4idFHZsGhPTfDB6cX8pBYY3PvxWsOGa0houvknWH4nAamINLVJx0S5tyjOeJ9tCxdx2OiwRgwiMeNsmruEGAEr2IMVejKm2aR5T10JUeNAI2TSVfFhFs8fBwFQjXHrx5evQ0aXiSXQYoXrYxqb0CMdoGVMZxruMYA1shQFbkUaGqg6vkq/Qg2Z2YmAMaAZQCUdRIIy8M6SJa9DmNgUx9zAs7lUcZzXw7y+jDT47I2iXcO79gGIMKVU52j+GO26pgHTD96+mRk1Nvk6EKNY72G0R+A2HiHkWqu2TwTIgwZKwoHhEldAsWId06i8wwdWmqN/FClgIbafoMEvZbEqwPLm4CHmnM4sGA4/YaM9q8KvXagt+jobjiNOPlFPxKERgHUTV0KIKLs2J1m4Nq4hAGzXmiIzNiCp7nmJPk52p52AtMM7VVkrk2jZdDj6rm3uoWiOPb3Y+96Frb0Z8Ytr9I3LuElv8qcOetrn6alFCTl+K1XOnJzwVnFUw/163VReuJZcoQww3Vp59zLoU/WfKnjtBm6s2/54sY5ubZAioOP95D9QbZytV8GYNpbiajnhymqEUlsBw7ed7mv6QOz9E292aYLSn02mJZ+SZwtZ0nJKCOCVwblc8EpRxrMLczhLXyAzeqvZw6yXsKUAc94zb1vlGEcbqsGruG1/dLf/E33kx8zR89nyF0U7JneIXnqOMz9gceZc/NMt/YGw0ZZYGbfUN16qzPuS3zrE+dPneH5Wy9Cjl2aWyEtsdLZXRx2v5ONbQ1zEOF353KQnKOgudOPo+HyPT+M5o5S/53art8HOO1MOAJzWRxV/huLPyqnIwg+wiz+5rqQzMQDT3fTD3+UYT1n4F61q+fcCAu7S1iNAeGdaAj6VLpVvIVlnjUxapiXhn5PRK90/0ANSIGg3j1XyIwRTyZvtYAaQRxpy4C863g/ljB6/QgOZhke6obT/lQvM0zODendhkJj0XmEWpvXGE4q8JllGKlGvx46DT8dHRqCGh/a8Vm5E0NUz5XeTg1NAab1SZ3Im2X14RkDGWtXuzfeiVanEGrIahBTgE9PbBsHtNWwPJmRT2+LFAixgsxE8CMN8EmyXhTL5Ya1zJSH3ORwTuT8/CxePj2GBdIQNMqSTsM3F/KhDk3+iBdeFq9BTT010vH+KSmx3RUM5B8DVubxxpxij8BJlGKpCiqNALutPKrxPW3d0KVttwk/PZ46HDXl8mKA9AYKp5FR+fUtbgiUuHbYavaPpC4uPCKIlWXyQxkwigx6IhG9qil/mMtXmf23X9kG5lVW2zw/IEZ4rbACDGSUqBa5IU2mjn2NzjhasIW2fFxWmsf+XFpIE+G5HcfrnLcjjMLbTCHmaLAMXgqcP1eXI/h+7KmnIpp91jmZ5y5dTH8SmFm6VTJERL6yeEpENrWq5dEqC9nSRtRF+q3gFe2F/uI5J12KouopYWc7xCuPLBXqvnKdW0MbIp2XK/ZF4uLJkxc8VZlBVeZFhkJB5X3jvajnf5WXN/Zx7xv7v7ymaDOfGw7t9sVSSTT0swYfeazwXPAFxOwm26ywh6MrlbpP42meL1P0xZIlElT/8XQcbrkGfFb/6Ic/zCbzzpW+G4O9fSvQt2ZOdNNnn+vWl69wPzN0lCekFP3dUMd2sZVxz7OQ7qC/gd8Q/z0ZHSBB72DzjB+AfF+STadhLBcgzO26L/WOxEF9h3W7g2ag0Z3xXA/y70gjfphj0+OAvH+u7Mi147KoJR3mJZEpPqMM+9U5eRsdtDv5hMFNvvbiv1f8ruwGFd81NZEH5rebPg7Af1DyYWjJFPIDCzcoZc8TWWl+tDB02qL2Pko8yGBrHrBFRyj/3sLtnZJfayvdZEdsbbCNubM8w36KMdHvIrOOE+uz/Um3N9d7L2UMCO9om/U3i/2QG0KA4xw0l5mvpyjQQPcSwZUzBYO1SEsZhwFB5NUjJmgQDF6+cCGAb45l5B98+JECj/D2zf/l8+ezlQFWXfhNs9jGGgayi0loFVuGRrPpGpGai94fAVFeY406zzBD6jBsvYkMGrEBH97U0CmLIHKaLS0M0teWEtBRt+wzCI1Gjl4Gc+dGG6ij6mfhpheIg5byXfVQJBWvXYzeyiuN/CxbY3geD4dbUGxssEFyeyhZGa+QTVrE6uuGp1M2lhfgaJm9V0XpkMFoTXh5XwA8v3niYjf78FkAVAmdb/NDs86XdVonn8M6Uw4ypI05xrPSy6Js8kzxFDINYExdyJS6w0Nvo/K6B5yeXecXOrRUIKtMLoRjIfmD76aogXT7UFY3hUb55BsQyLkh7QvNtiBDK9t4OKxR5GowzhJX+cg/oT8O2LRrjvKSTgUYouScwMtrzw2m9xfKYz0BJZt4uSfYR++4Qe/Wmcce6y5/7/sMFb3WXb1yFb0I7apUq+tZSaHOBGK0C/EBdCREbxJAbLtkVd/EmxVNFhMZcS4olJjQ6ym9XBoaj+WdKt5yG+/I0OdJuf15SDxHIvSZubbyhCb9SVp/uBLHCr16tqdrxWAykVZ88kKHfuJ96POl+oFpKVhSeNR9n3Nexth35hhKvrrK8FqeE4LNRt1AajKOv26pBtxY/q8ZHvrWm2/muXxLCxslc55lk+xJOPPoe1m59nq3dvUtnm/eWdXrhu2v4xRbPfw4HG7Mu/DgI6MDhHoIF7cPGfU+upncPgpLWzfK12KkafdkizvI8WZl31y6rVLyiBkSwtN2efNyemIJW6Yt1nfw7AgCkSX1PU49DpjXdr8VYSffwxXjr5whWhg6JnLvr52F7k15S1O817KnJqVoda9Nn+4mz360m3zgg/ymL/S1OpxGbqnAt4D5GBDeAqUenKU30PZHfr31hwPGmW/39eY1D2Dscp6+GnnGu52EQ0E9dw6hXkS9AqcefJCNpU9y7sIqAL7eY+im83owZliG36GngsT1VfcQAmhOAxqJiwHJDRpjU4s5opSMGpPeuwEnWqfeQFy7Oqfep3irABAu5uIjwSGsPuU1RgUTgkEzxEsSLwYGau6veoBYpsb2Gi5Fh6gql/xX12oLiSnAaHkDVY96gH37FdGYBTQZsrUGMmTRHN+mF2Hd0DJU6+TVIHFBnUX0cIJx4rZGvGi9cR+gC70rg665CAeZpFlFZ28CvE/iQXFvQlcjVVWmCiLEAhrM+GFTn6woSbpxwujoA2+LfGMUwbc0ECbJI8AW/KXtOepNtT+wZzqhes2kmT1HvxPoK16mEoTCSjObKFi9q7cCMVWS125EXsBAnoS0KQwkgZ9tliNlVEC/puXTTriWLjozjiCffMxHXJRtWskrybbQkuTBeTys1HmdFxzTIwCElvX4s8903375le4Cq8Wu8QJFL2Bqpe6DsEsi29fhu7aUHbpejFg9ZaOdJxlGhpzVhioZraedCsh7/wikNnsay7BfGASaBvuuMereljePcfadBI4WV8qoQ11V/0s+dToU1uwftNkcbeqLHYcvV9v1vJDH+7u8hABWhs8Wh8bHmlM12tJPbaOxlnvoxMmFeoFiP4ts9hHztbxDgoxPR6YBPck/ZK7gi+wr6AIy92Tgfpl+4Ilu47FFtnphmHSbT2g/OkCFbkoz3AVvSnyAAiGR5UNPPd9uwoNl2odqnQVlNtdrBeB9yJI0oircrJhDpXvPb3/cDCu9seol90Byo6jnWKO58djodnaGm+W7kdNWzHHyDnHZOj3EWXS1C736G41cN/JRh3e03wwKt5Kt8oOWbRF39TG/fhrYNFResAIAJx/+IENF8Q7OPER/rt/uu7oSIxBuDAhHoMQDs7C/+ajE+Kvbxc4nUBAAARU417bWwF9eYpEYgIpBg1MAl3uNr/V49a4lj0M8Gy9BYAxoGF1n7uAaYCdDRzEm3HPvzJmHwuc6y+07185yBG2CoOV+3z6NSW15AYMFlidAb5XeObqL8nrT9KUWYNFrxz9yulCGC11oeDq30OAiMQ6HlEbj1/SaJ1gAsHkbApyQZ4M5TKioABP5Y2Q7PBT+ZQw7zE7d1acMWMGgw+RYlAYP1vLKUrcAYFMOy1L+6Ik84cFV7n+ODpFbW29DUQtAKXf2o3NSnwvGqAuf6DCx7lcYRvfGhfPdAno9OQugpnjLEBRnoQ8eIIIGdek2GEytpPp6ZwDz1H8q6N5IPklRujBXTdADWORl22PcB0zCbx3UQClkE0wgE99CG72Irn5pqrEeIE9dA1iQwZQMZ+RYgJAY8oUQgBp0GlRPRjPbBwSGCsSlQ19pFf4F+gTjDfJQmBTAkf7MuEXioFdnKi+hLy91J85G7vtFypFePjI2+TrDrNgbbxTh8Xe9q7vAUNFrDqNu8igbBdme9qUsemQaYmbIbNq7r1uEkJ6gjNaZdGP0KqtjozPU135AQvPaTaLTooQgod3rKkxW5ZnzZU6YA/SrHfuyU44M9bw3UGhOyrTQPsRDric5kXXPuu+g958qt+0DZMnn88ZSImPywxehzer9ZJDGW3gGUL7JfFxf0kwyqX7rpUTIxl+3QAO25Rc+//nMFbwF7G8by9wx06xo/fC7+SFY6dZe/xbPBjz0t02CoxV09tkPHC3jLrmWX/vGLrGjioqGj81sPy7Dz5jhguq3uB5A5s9ZXfJk4Xo/po2R9NAN8rf4YxyV10f3bQ/7lHkgefbJf7O69Gq8GdktSz+G6LdMpkMx7juNv528Bu26B5/vph/9eNfNu9L5/QOT7p+aHqp33Dpib5w2BFDryw7oj79ePo01P6sMlVsFoPmI1TjTUNQLl8VbNDxnWDSFOYE+9TTSBAyCNR/QSwz/dLXRay4GQZoLj+D8Y27gCbwCa8yBu0o6+/VhtGZInOUDiASlgicfXJkH5/BVy1Je7Xr+4uHwYUuc1xrSzrPQ0xSPHdcan3or5ibnAjrKm0g98PZplVrfGprWG6DwqiANZgLMq0xApScJlIYsyhYdCRwI7QfJI4VtxXHmELepk1NZcdS36wUCFF7pS+eeuwqngFHjV94Bc/1dIV+HqE6sNf56PksoRTh/+Wp3knaYPnMGOudVYkzzpW4kyxxE+Gc/Q64nQRlZZZZyLFteAeXqBmPc8gQS6t28q+QJyOPchVby0oD+UNhNmWj/8IDQS84bQFQZxKZd19H7JONY5S/4r1AyhggZqTjRxlkop5RJZs6VlWv6Q4aKChZMN05eSeuvjTfRo3RkTf6B64trZBgEKyuxANKP+QIWOQIW1/Fojyo8zkqjzv+0+CzeY1EKz3/rk9MshJG+JEAjXQ+taUoZ2iaMHkX+B1XhxDYbgCuTzWR9/ISDRz2OJlbe4mmbVVoO/bn8+swpW9C5nkVx8PKRFk+2JFLBU1Xal1wISk+h28xYdF7s0LbxStIm9vH0afnVjZ06NhnT57kPzZeXBzCx7PLO16q+jqfJ8yalj79GqQH726uvvNK99NWvdg4VvdeDfdC+NTF7IkNHuw365xuvsCJz3YuVXrXsu3NdHON7mOeB2HjvDBF6D5x9jiXmRxRWXvvmDZyGy2uJ2+KQ6SD62EbjxTYmjfOtPG6TIAUdRgRpb7fYh5HvMJprdTlMnpvR7iZri9uu+RZ7M477px+Ji5m2C0PEkTjtL9zIUptsw0L7W4/dOIGD5YH3dNOPfxIw+G5K1OFSdNpX7/QwBoS3uoXpQwERHDXGBH52PO09wZLD+PwBEgwJ/ARLHk9kyGd5xcwgTeu+nnsHbjA3QTBTxh5x0E0y7nnh1KmOKRDwxFvGMNJVDIslN6RnQRmN3kkWmRFEOpxUThohegb1JqxA735lzmfUda5R6CcGInQBJUM3Rg3rdPEV5+5pkLrtBfUBSDnENCtkcj6LJ80FK9SGq2K2OXqw1PaP0akw5S3kuqpIufCyrnjq4kmhjNSXdNWgsWp8Ow+uoRR1q9HsojmLDD00eEPLtrxtDOd0qCwyKtfSSs2Pat4j5SqeDu0UHDv8k7JNkAv/Gt9vnLvQzQOKp9m0HhMbPOPwUZdPwMxHNlcXFWpZh8mmR4Ug2Ab431IOJjby2MaCLI4AowmHqRLiAYVfgHT4qwv06Dl1tH8Nzo03E2XJz+09FNl+Fs9P5A/XvCxI4cQFeKILiExMX6JzpZ5Y/1S2ATbSbByVY1AdKiOBi6gH8KgLIN5G8iHL9nSvQsjRNM7lFzZ8RWDqe3X7vBtzHTWcBrQ7p9S9B6PiVjwM06ZcC8LsTxFNnQzk7mlIUERBd2gi75bYkS1J5DU/7dMCV5wKGokjLfdwaIjnBpjwxUOqnq/KZpLtmCap4dLxFk9txSkzd3L6ND0KVgBp7l+fC754Sr/r66QMptvTZ7jfV9eWOKv2jg7glTm6lK631HpGauKV1+eSdfIeNz3KaBUcH4+tAbf4+I9/9Vfdaz/5ybF53X0M6EOzp7qZJ8rzVqCQLU3sYf19dCyZD8mD7jwI9vFhGR588rlu7uSDg/RjnXC/rb7+jdzbB+ETWRSnydci+szG+yuxb9gleT9v3TbyoYuBDBRGNfy+odhhGrM2Co9DrG7IZ0RoIWp5jBvm5/Vu4WY0N0sPz12F21vqA/HcRdij5BvWxzaWyKzYg/SeuXbNTZW9jdGNF+HZdNKON5LtHiP9tjwDCXenb7FHUU7Le9yjN8SQzPbv8gwCBp/8h93kyb/Hi/davyD6PW5590j+MSC8VQ1FByujsIxIF2vRaxXwooXHzeBbdodyZpVLOqe3kR3T+6T11cZDwzDgDMNeUClAE0S6iESMOXJrtM2S02vpl/CMXT53Lsd5PFmnTj/QrWB0uMLoygTzDfXwEGKkcnSOmlsrBIRFCISxHNLkLaiovdPMhc1P+Q45Va7mYbPcACjyWZ8M1xQY6XFioVCDYK2CQA8DWUSYuVjlwQgP6iV/y/WjyjYFV+QV0JVeymCWXnGHg+nreEpmZxk22g9dFUiqZf/WOJ/GsHa/QQGhQHieoXGWGc8e5dgW8Y5Qx0m8hA4LrUVd5GOYAGAsdq+fBxTOzneT7tArBuAwK/CGTEo9MWpR4722ikBnkio3GdbjvZOCyPpPPitlPar+AjtMf3kBTKVd59ptPgSFycDRtounCsN9Q6VRhuVmIRGIlIcioEdQ6hO9cZ79CvUYuRm6FPTNJNInEhfdkYROE6hfHS0bOY23fpbJf3jk4IU84B1g0nRHtDLk0konk5H1ob+ss+/jqMJDjzzM/nqnuvOX4BkFFOfog2uPSIOBaqJ6Vxd170ppW9kXbAvbQR2nxawzstsGDgeWU76hl0cAuxF9oe1lhjEQFL9c+CWvnkPzlBJrf6dbUj4eOr3M/E1F/1VWWFOGnnlfSCiPL2McZu72NFOM8fS+cSVadZ4VgqGJGuzklJV7g4gsHENeXybZpN7Xer9TKdItd4URDFkhOPUaCD8+OaIGvMdfefnl7utf+1rnthLv2GCfmz3dzT7xQfocc1pff5nfhOv25xoqAABAAElEQVR5d2S/v23hJmU9/cInRibK2uXXeTYebP7goNCbyDegG8HJrSrqtrbnCPRQD7jdGR2qLir0Fj0Xb1VbpdYy31HAjsvdlXNDrJW/RQq4oayjRlgzZeTIs9df+XgGH3xvN/PkP+omTj6PSXP8xeyOKt2dzDcGhLdI+zHV6G8akAZBUt6+0w9j5Bupgckht48Gc6L6GPJJp+GlseZ5AGEMUww0DW9DsYdJf2J5/K8urXRXWfhEUPTQ2bMUNdEtXr4coxbzHBvf7+IbKTRAyacnL3P/oNcYRIoMUYznLgZln4fyNToDNpCpre45268sqmgGjdLMPTSvslHNeO9IK93AL5SkcSZZFj/hvIxvy0MOPRIxsrnudYV9Eb0ku5IWI+qIZxLw7efECVZKZDXVJeYUagjH0EcO5zRqrOsZdYidmQOEY/QX+FL3ypCPyiHUN8dUp64uMCdz/vxsN3P20e4U+hNsUhg88ZpSj1mMa0V25GljkCF/vf4Uu+1JpzLMU2UCOpCH6md4b3k29RT6CLMuppFIHewNAnsdedZNCgG+8XqfSn2CBYSwThGl5C+lw2cgHLmsA/lSOC8JYMo59CHjSwXIlDokONeScgX28k+wYsZJp7fRa5MANbnwvDWavD2XxgDf9UWGjBrX+FXKkb4XTpzonv/A+7sf6X2x+D4ogmVGj6lzLweR6t79C6WxTzUxci9EULVc9Y/YfV2ssm2dODNVYvqffXAGvcpzwNBzQtoG4rQuMlp+vMPIpdevGHmPeD+Qzle9zJHOvgCdbU6anns9/tN4RQVwg5cw5Akv6AXv9VLCPs5e4rwQOXXqdO4J5VESwe8mOjD/9CZeSrzOMzNzedGTakk4DkfWwFVWLP7Kl7/c/eD73z8yj3srI50TUDjz5IfopPTNnzCncOVyZul4T9z5PjXRPf6+nx6ZSt1u47CPsDwyUIT336HCIclvxrt/nO1JFjumPRSHqHaJGko9/mmT61aXcxRJm2yHypsfg31y7JG+l/734TT6pD1kO1BBh70xDsT0cETaUGsTrCDKAjIzeAa7hWe562rti8NxemdQjwHhLWrHMu6Kubazz+r2gNd40wIzXuNfo8yFYvRMBfhAKxhzURi9igm5ecorl/k7GoDECcRknjh4muc6npVreEIWTp5g6OhDDBtlaKnDRTEQF51fyIIzZhKkKkFkxcgMQJrDo0C8YEq54tGQmrIivwCkl2USb4H1yvA0jFFN44BEji6KIl3mIzrkkPq4Z5+rH8YIJV0PZ3kmMIHzYOFL9GN+PRvk0/jWKzZDWQFC1ps/eaRwaAVXyldD2uQFb+gEw8bPsqqqK9xlVU+Bi2Y8dZSf+g+ohVYg6sIxGQ4HVYCr13zUb4Aj5cqhGeLqzjq8wZYeM9TnqYcf7uYxb6zF5DSyr9A+5M2HuMjsXYf7cAaPZzxq8kCRzr+cZBsLZVYhpk1DI1jBDg9PwYLy+yBzSOAEfIB7eAMBL8wFtY4O1xXcuo+j9UOTYDBkIV9b3EdReqVDod77y1KPRRSIQw4mrJIOILGx4Rt3VQhsY/qQ/XbevkSaHkbz+KE+EVrhBZfIkPalfepIlgTS5c0hYLE/33D4MnNepwApowif+rmf6/7iz/+SotQERVJOREXPFilQmtn0x0Dd8Y2Ytn2IjfWcTGkfritwTfv4oiEvG4j0/hH4xbuWe8zqFpBTIRNZlUW+PgPUvtL0/DmfzD1APOneTxFGYUmzbIds2tedHOw9bV/MPSUf8gS8oe/so8a1Xm3nBwvKM8TcMiDN/YKc8vVl1TxDyQXOqSfpvhjyXku/7+WzfO+tKTbtDogmfhwOrwH1+B28gi+9+GK3+A6YK3g4DdDjp092s4+/wKiKU93KD17sVq+dyzsnuuqNocV5v3obEPL+iOMwvWR9siRHCjPzJ7rTZ58+Ut7dMq1d/NEg+mbyJZ2vojtiTcx8lHCkfDdmGm6Pw4hx1HyWcZy8WzLeWJettJufKcORWox8ybtXZtNvXvyxKSxjLxH2Y75d93I4IJftGfcr4hak+avGLzHmiVtLTD320930Yz/L6IXHiC1b4BYUek+w1DQdh1usAX+8BveJN0J/h2usB/hhqGmcOUxLkCI4XHZVP4y1Od7wZ4l88kmjsabhKlDSIAtoEHCQb+n6IsNDWUEU8CcYdKjkEkOQ1lmgZh3jWp5uaqzxF28TYgmeNpgf5y0ib0Ghsjo/zptG4y8eES8oI08vfpX1XMinr0o8i3rUMkQUIOd8LQGLgNCdA1ZJWwUgZMVDytDQ1RAOsKJc2Q8MW41dwprLlFNPy5phhRnIkidywLsZpDFgoRfITWMkT04uJ08BVRbVid6sVpO2jPOs9kke67zCsB5ljZGMgR3QjlDSuPeiYD3eGsotLvXgUO4M06Rur597myGoU91Ztv3AnCYPQ3jhLbi2guKhaQCcMFQQhaatUEBHAKwEpLvtQcAvHafALTngQ81i7K9yPjNjW1XbrCNvFquBIkBZAGga1xqebVXTxEE71RZ5oawIoGz0JfXg3MUAEBZ/SJrDbaN42kRy5E4QSHgOMKCi0HAunQAIYJo2SjxxemBN18toPoGiFUe2lJV8RBlPNIg2RZi28sZrzIkdDSD8xH/5s+gNsK48VEbdqN+ALmO4HgB9RFUfm+ijemdde79JpyrMq04V34i8YKB+ppUuS/+pEzTe69V7qu0gk0n6j/eCfKW1Dzb57BIG+x9cUR99lZcMviTxtjeYN88D8lsub14ig97CCV4wOCx6iXvfe0/+6tVmsAfapy1C7+BD7lvKyxNrZ39qQ0VXllldlPKtb4Ff5PHeT8WVYBwOo4HLly51f/fSS933Xn01uj9M3nuftu6AfE/Osqz7c93U/Klu6cdf75bP/aCb3XQOevVp+6XddQACOe9vh8HxKPqQ515h/hRbNrEp/ajC6vkfHZKVvw5Nwv7ZcpjaNqUdoNRWSkgPke8ArPckscy04bbC9yQ/UIKPTcOteBzdCp4l7f7frdzUTV0Z0SraZz3O87e1Q2v2XoUDoQ7fPHLYyWXAbvtJq9z22BFeIUcTZbgs9ce1hzVGu6yfeByv4M92Uw/9fX4MtTGaVkYoyj3Gqjcp7jGp70Fx6warDoc9lhCvApaZ3jANrhiEWID+zbMhtF6peNDs3f0DQcMvxiNxGnOCEVckdWVRvYMzDMOZOe2G7OvdIsOR1oh3ddFl5w5iEEYO7giN04AAy2XVRLlOAUSyTYOGK4QxiPMQ8u7ygxiUN4mHIzJTvgw1qN13bWUJQ5tz66LB6HxF5fZaj8kaHx9i2ZgeXs5p0ogW2KR6xGnKbhnmgkL2I8Sgtd71gQJR4sUjk/mlL+O8dKKcyqsO3J9xGq+H8yXVpeVnHiCyaFSrA72ZgtcAyN6TEv7USS/bBF4wQaULcViW+xxWEdUuakb6FcZrvvb2OYz36e6h06eYd0j1McinKVNQNpGhkkgWPEIu/mfJK/RCtHgBrZx7BypnwJ3toM79EI86kNs9GgENkRu9Io8eVNsiOoXZZg/21It68yXCNIDMciw48IILiklZ8SomjYjqJeEfIVm8iAYdRJOg+4gP0ntsIWBEzoSmI9Mx/AIKjRfrZCkddciNgAc0Qli2wtEeRRNhAIQ/6Raef7+Zjh3OPPRQ9+73vLt75VsvU0y9BLF+6sDKqW8BT9qea1RenghEiZ6MIOSe5NT7w3bJCwHarQX7iKDOeyChv9+jVshy33OvhZvZqPcmfcOhmemfabvGjWMIKYtjQBlR8jA6wz97Urgk/zRb0Xiv+4JjRt0T7/UsbSG9QNF+VvWW5wSLWJ3IysU+CxwW2oLieZ/ohTbUvVYimW8cDqeBHzI09D/99V9nzvLhcr5zqOtOqW9v9skTj3UL7zndrZ4+26289q1uYvESz0xfUFnnrT7mWXuXNayNLYrh2BvP9+uu7fY99fATLCjzwI2Zjxizdukng/us1bixyvVA+KHUJkxPyFOhZdn9uC3ZiyFeu+fY4qhSdpS3R5Yd0T6rdkRxmefXzfgN5duNx41cb4zZmW/n9Y05Dh/Dz+tNVbNfufulbTXA7mXcoMIbIqjPkB4PX7vKYU/R1muyHoqlxDI4TKZWUBV/a76tULsHmt6IUtQNFupbmzrZTT7yQjf3xCe7iYXniK2XoIM8xNyvYciau19VcHvqve0R3W4g38LHsO8j6Lx6KGr7BwEiRp8GJ3+57/qbKUCOp5XG6xqAR3A3y2qac4DIKywis8iQUAHbInPb3PJgnSGj2XcQfnr+Kn95xjQU441CDf4Am28db5lGcQAYm3jH6KXsrHzJ0YVMNEwNud/IqPGpbT/HnnzKK08XXEmgjpYjcXseWB/5+guvvRzAxrHN84v3zycywTwa8Mpj8NuhkdEL8f4IyWsdY9ajMhXYY+N59HPiBJuMqmdyqt9pjOTJCeTlT3A1wz5Zen5WqMMEW3IszLu4jHOwWEBjnflX6DmAEO+SfDWqtdlj53NUPnnBpLtG/jdog2mM6kmMbMZyZj6Z24YgMIGjVo11UdB8GDoLQxeBEdC5dYXyyjcYizyCj4AMssTY14OL11Qw7Uqu4Rx96UFGXodyVoE5xhMNEo0c0MFm6IegXizIY5DQzpGFgm0s2FkBzr3WMzjwHhIdQYmn7NDqKfRcGfQMSo/uU4B8PA3IUAee85U28jQRiVq9fIWV6umPgJxRhA9/5KMBhPJqfSxeNYtM/SIx0vA3/MNCcrxp1MkhlOUxox7IWhvZ9zInqr20qHs0faPPX3W1cOihrS/7rOUVD3IPvJbe+fQGM9BlvAft9ZU9AJY8eUnj/eANSGhAzfRrrC7sKINTvBRZ5F7ISxF42RTWJ8Nbubb/G5zLyusPKShHudJQ3PP1U6GM5VWM8Mkz/rq5Bi4wpPzLX/xi9+brLDJyc/L7iKK/O6YXWGzmBVZrfoTFZr7TrV74cTfFgjMZfD/U1dL3j6idYb0PsdzG7Zmf+kfbro9zscnw+fVLrx2HRfIq917yDjNv933/GBlO2v/8IMx3cOgfldtjj8BHWXfltZ3ztqud9DuvtxEf8eK4PI+bfyD2HjptbT2gO+JJ5NyjjH1Zkuco2fblOdLEXkA6mL9kLki4ykIxE6efYnjox/AKfgR74wzxFtrusFyMVIp7jdkYEN6uFhvcPc2gs5sSNOS4K2MMcsyCLvRSAZDxMdQw7ELLda306Vt+5rzxCaiCjfmXGTLqpvOWsMZCEs5Pck9DN6nXArTzC4jkqdHpD1Z52DQO8WhgOLpNgd6sDcBVZBiSTxk0BvOH8Z45fgF65WlziKrbWjjGR2N0ael6+Fq2wxq9NY0XsChDbj/qZPDcRVDWAaBFBy1D40wVmNVcLuK4zjw58pumUd8ejurA88zFFKDICcaCV2XVM7nIUvvO9ZtliJz11Lum3e9qqct4WgXMDncFvoRPgDF0m+hHw1+6WhWUvNFnCsivmvLoLXURj0sXL7HSKO15Yh4xTCENEApMVIlcCfww/Elad3sJ5RUkkicVQy6jYuQzBNA46zGpbnl7LijV05gVVJFvAt7OFZTvKuRTxGUep32G2NbeMkVDpU+KE5yWF9q+RiGGyJACueAomIUvS+Jy6TkfAZ8AzqP1SV5o08mI8yhqNq9DZkND36AfRCAdhM5LpP0qgkMqCb19hfgCPBhVzLGafHA0gPADH2J/sd+2LIqpQ4qNvFzn5YiyB4RJgzx9tTzJOwmu1YP9LXNOOdqN1WNkVi/SpN17vUJTfVV+3n+qh95Axug9POucb+L0IJZnN88G1QnfTV4w+GLAcixUngLadg/ANi8M2vNjWjmRawkwuMxQ8niCU349X7JtDDyscoZx9/StL1jODC95fCESzya8smKwBVnpcdhXA+rvG3/39e53f/M3u0cfe6w7/cBohj/vW+g9m8hz6dST3dxzj3TTj/ykW3v7e93a5be6yRX2keVZke7mrXmTbieJ4QayPRP6rkwBz/0Xv5i8o/ha+fFLPKOX92e1j0xmzL3vCZXxubBX8P4f0O5FtFu8N75hb9aVvu27iPM9lG9QlTTUkOx93kbvsdGapAh9lp7yzh+aWo4iya2uS571O5U41A4HkrnRt+OBMm0RJdsg73BrbtHc6TNNDSy7bn1qvttYeLybevj93fRDH8Uh+Djx/p5ZAe3wkv/urIV1uH1hDAhvn64HJZWBt/UU9FrDroJwSPtZw7F+Ahzi6FNzDU/QKt6+Rqs3zw6toeaQ0EUWk5kBzAh2ltly4hqG9FUWl5F+MHyNHDFaW+/vj/FAAeb0gOkB0XOmUejDp+i5cZBJciWNoUm58ZYRH6AIvUvfazxmvhTEyligs+olMBSUlrlLHFVrz5XKY3oZ1tZf+KLRrHwZ7iaAy9Na45zyuY43LXEIJj3y9CqL/NbJoCEdXVOLWYaGLgPcltGp4HoaA5wNN1KGQ0cn8BIGJANoBKoFjq0nRgv85Rio1TdbHi39kFCB4yr6u8qCKEz16xaYl2W6K4C6LQBKiXyO3FNOgV62o4AvlYKWSE4dQuiRKELfUGRQK7kmwbbRY6vOHPZqcM6N8V6rX6knqHtaD10EhHAlTX5c+jpAlX8P+cislZtTCOETD+cgCe7Utz5EUm86isLzURA+vXeJC3NVSAP154rdPkaZDdkE7O6PuMpLjpkHz/TExzu87wMfCIMC12qGYNmpMPpCXlqbNrbv29bqOATpO+rZIP6t+5Z0LtyOQa+h6XVfsnginnZpjFcPqiSqUr+wUQWGMuaabuqeqyS/izhq9BReRQ+/XpbiVQah94RBenuK7e4iNNmSIkIjO/3MPq5u4+kkft37wHs3/Yb700VjbK/IaRvDtDprnVtIK9/zcbhBA+cZKfDZz/xJ9/K3vo1uW/veQHZfR1Tvtpv1Z95eU8wtPPNsN3XyUbaeeaNbv/Cjbu3K29xQvBjiLdikL5JaMBvBbIcJu9E/8q73MX+QDXxHFBZf/sINnHYr9waiXSK8l0cVRsfpcBJZ976VD91eg5J2CL/jckC28+Soet/JZ9v1UOHyP0wZQ1m3sRy+2JtmWJPDOe7Eeat1O+4jQ/vB24fkxqTGV220eu9C1ciSpKXD79fMyW5z4Ww38eDz2A8fYHjoE9hDLGgxoB2cbEXdyPq+ihkDwjvV3EM3h0aotlUZoVuPgdwC9lkSYyD3K39qcNYKoRivLBbjqqICwiz0AP0KG7FfvXypu8I2Ey6gIa35413kKLDTW2bABsxDeorVRQWA2fhaazKhjMwAUOQt0Ede50ZxLT9558ccb6P5BU9z7Mm3zryzGMM9IAw72GoXadBKG4M2VrKwz3h4uwKjwznz9sZcZIJe/WR4K2V6bYYGHDPkT29UuJgMf0nCT4wCPyKU1ZKy2AaLz0gkX+c2LgCkrV/qj87Ul3rWY+twWIdymsU2ih7IuCm9xgk8mriFg8qQ1tt4DWBupR2GmpChnJ4BHsjvGqEF8NAndvdU6kGyIBFk5+IerhCKpKlD010ABmQamviMZBjdZyVVz40ADCBy9LSGvALB8nAKok3RzKh465Cg4jxXMZUZQIe/NCtjEk95yp4govXS/qIXE91UOtfS9eCkaCSUMTS2YSswdMSbZPAUoBIQxrkAZwnD+sTTz1T6Mb/f/Z7n2FrhVO4NxbW6AznlnQbkSGNM0OATgvLUgzawz+rBVjeS8mf7KKs4OS8LROP8B+TThsJL7281bf7oyuay3PqKDD3LCNQAX2tre23dlwJ4PYT9QkWWTwXyggWkWfJRfNO7JVgmwXtV+ciePALBGsZtW9Z94H2eBXcgaiv0Oo+5BeuQfmH1i21LGh+HNCDYfvlb3+r+3e/9fnf92vWktHYYIhufooG6k9IDt+kjnnkMuikXnXngSbaguditX36j27jyVre+xF6iqywc5sJXmZBdWesZ3Dgat9VJc5/3JYRiK6mP7bqnP/ypwflxTzb5/Vv6/he3sRmWLAmRYXusz+gW2nNmUI2tpEYyOBYtBPz7XNotbMvuRcgabe7u3bJtxYWBvxiVdTh7E3sg81aubWct77bIm1w03jx5eRJuyTmowk3yt9/m/chaGfvRDNphiKhpbxCFUDfEDRLrZFjuqHSXdKOSJrMdRMWf7x3xO9jse+nP2lHD0bKS62aK2UWgVD8F0quTPzE9Jec0rrZdnhfOl59iRfRZFvU78UTXPcCz4+SzjGxikagJX3DyWzf4od0qzF/ncSgNbP3ajzVyWzTQHpjN6LNQz2vopx3bTl6d3geZxp2eMwGKwGdyxqGa1bHdoF7g536DziF0yNcqw8IuvPUW8wd7MBjDj+XmAZOW7Z50dv8y+DihkHn26XP1RcMqAFODxuCPU4zLyKe3pPbti+Hf31gamct6Lbn2D9KsynmN1U6JtHIYxHpc8EqAPlMPDWtBBCGgjbRBkF68QEgRyK9nL6BJ5n2CPwtlwKIbFyAAFKnDMoY10PHYwFfAtIKe3I/NuZnuAadxvApzh9RaRwHfHPPxBIDxQpJHHWgMN/BtfIYH9vLEe+UwpiZPZItUCE4kvAWsgsJFQQKoaH6Oh5KAL2m0o/Xm0rpJIRg0zh8+h38mHWinMakGXPEy9JbVDH1k1Wts+8EWnfDRU4nXMO3lEF44ChrUvQQT6CEaJ0P9vBYkVYbBz63IIWUQq5w57/UfaSH26BsFEd8q5wO5pCOuPwQEDUAg8Wn7Pm8T2jjPkTFtyDlXVqa7zt6BD//Ux7w6drBN/5tf/ZXuX//ar0Wvimg5aaJUz6v+ozztnLNmrHm/JpgcRSq2L23qHvB+VX0G71k9+/bh9E/HGffxdc9TwtCvc+M9fD9FrxQZcbYETR/N4kKwtH0ti9cdvd6UyfmAgsACf96rzie0T1bjSEN7kO695DNiOvuIVpz3i8Cx7inLN76vu6U0PaRG4y81cIkVRP8QIPjqd74TED/WynE1wI3E/MKp03zwGG6evd5tCA6vn++66xe7zWVW0V5d4gfNkTM8K+2jPuPon62neuyfJhEmd2DdhgPhfFY++tyHB9fHPVlj7qBe97pNSpJ6Onjn9ZLtkMEykX4obNFp9PaP/6H07afhC8+NPCOG0no2g3IValB2Je7kPUju2RSVz5cKpmufGDzk2ZSrfb4oZKtVis68N80PQT0PefmVZ079bpnPcLOyqy47atQyF4t6/vfnux+2tDdI38Ej8U0pdUHldpRL/CCGk8G59I2fkdazXZvWB6VIX96WsaVyHOTZi6Bot16491n6Z/n+uap3tt/BUnxfoPkj95AsIznVpmt66u9qfud4Xc9vkU4MpjmxBoR7m04sPMbnSRapeoItJPD0Cw6hqzop5/61G4m49ziTMSC8zQ3YDL5txQ7upXpc5r4yDiNNgy0/JBh3xhdIcG7gCqDvaj4+LDOvCKP78vlz3RU+Zawxn815cfwwBZRplLrISXurykRbPWAaycqlF2KF4WXSlheRG495bhqBecMNjXQxfvOj601mEFhBi0Hs8E23i7i26PxBjElkctiaAE1gJq3wY+unpR5I1k0D1kdOG17lM6Z5WuSvh08OGr+m5bffp0UTgxN5GBUQCZFgb2JiOXsRzrJp/Bzz4BaX2aReHsjnyqHqxx9vdVv1KJClx9VN7ecA20t4YAXgsxMMZ6I+rOtKOdYFiVP+1g+GPLSb/S3Q1rc+y7SXHr9N9G17Wodpxw9Co7zyQhS40d5r6JL0GUGtfFJAConxbqYAapXAuYDDdnE+WIAr/CpLDd+cIk09qrAVwAorDZX+0acgU88nyiDOvtbTJo54dGIVBz9s8AhvK6ZXkFwo0wpw7iXXVsoQ8OB5n56Kkgd9FH1Lk4RzARVHDilSWZzjucR2HqsMf55hYZRRhF/9H/777jd//dfzUsTKeI9FR6mYJVj5EiktjEA0OYF45BNYleT0Ie4hfyDjmTXSFLP39fHceydgCroG6hOPzgdgy6wEtdfuMX/+ZCY7+5TBK9jk44sNCoZK/lVuhoYzJzVzjNVlvN3IT7+zL/uCw76YexyeDit2b0p17n0ww/1jmONecdsJ+43NZbCIGAOUb3kps5Lu+29B9re+8Y3u3//xZ7rLDNMfh1ugAQ3AuQe6KfYu1GvIimrd5uoic82vsbUSH4aUGje5wbw97w37sv20f5rsJ9Gk264wZHRUYfUqgPXMu2FH+d7AOdt+rCu+h0CD9/LO0GK4/Sq0iHY9OLYEbth2OkhTEiP7EvoyvacHYVseL7YiGpnH4jLIBcdtpFsJlTJ0Tek+vLYF22cr7Jo6FBlgjARNjq2ce5/V42uoFPj5HB2KGa5qz2hbauLqCTxczpBgRnO5PVeezMMZ+nIiQE+8PYfE5grnHeyLEXIP9Zdh5tvJ4bs9YojUhK3ESLB1OZw0lMfTkrX/ORhcDxNl26rhiJxXfdLSN1b3BurhiOTMDx7a117CxtkA6G1Msl/uDCu5z55h2xo8gOwn2LG36QTzBTNcdKh+JfchCx4W4j46HwPCO9jYGn7e25rhBju/N00ztnLEGsvQLtIc7mfcCitZulDMMisIapy6mbTG/uULFwGE5wf0GeronC54xsi0LK8x8gRrCd5s8BTMOacuxjFGo+nxaGgpIqRGbbwFZBKsKIfPEI8JsuNcULWCwak803gYio56ca3nxFoaAvQ4hpdloAXPY+lCE01YtHIi76Cc5CVd3fFRxtQlYjBAL2VguPb10/A1b6MTFDpvMEAR3eltXV8DOOIdEfgJaDcCjNmvcXUycfMM9xQMXnOTdNIFXoLTieiSqiJTKSMaqXNOVW1qq/yQxDODLHlgwSPWP81hmh/BIHCzzpUbvbgATvYYzI+AoA1aeFQbeJ4IdFseTK/j9VnXmBc4Oz8ST6dqLGmq7mSbnZFGn1IfyCv/ElzJ+0rYLhHQVBnZjkBzQSF1S4gcnJlUTMgjDxMIyq9w+chPY80EiassL/WqCqA9rgE0l2mLJTyg57/33e7xD43mDf4jj57tnnvve7vvvvIK5VBoZOLYb7dg34tMVs/koWsu07fy4iF9Exr7Iv2wVhlKhqjF6pu/wDg8ZUtIkT3XipGKeISx3uxUknzGVUr18+rvvBhB5xuCUmid5yhH79/2csK+HnlQsbr0nnRbiVlehti3fRnjvMeAPfqN90oWJ7LtSHO1Yvt5BI0APpMqTRnIWHJVpAn3dXA0wb/99f+3e/WV79S9d19r43ZUnucpfXaCZ/nE7CnswLPcMz4vefm46XOJh2qe+wUIq/P2N98e4j3+9DPdyTNn90g9fPQ6wHXmI/9t3Sd99hslaDHtuF859STYxnAHeX9XEsuN3JOHZDf2LT1pfHlf3+Kwnxh7Fz2c62gy+mgeDjsuh5MOdb4bnwb+ZVTpw/I39sM5d0/fLbblrgfz1tXhzyh/WIQDM9gpVWNS8e3qwOwORChv7RTshPzeapu6uJ3TWFibQQ8ho2BahbYk3Do7UDFjomhgDAjvcEcYtpn5SeNeL+OuAFAPBomeZqio4Eawdf3K1e468wN9A7pwinkW3CiXz73VnX/zjQIdPNz1IApy4MYNhGeQu7V5I+pcg5DKp8jag8+hh14L/DIUzXR6iPMCveE0BhsY1HiNzxIvUcBirN8CiVnoBRASTwr85ClQ09ht5flA0hgXvAT05QllgXyk4+APXAE78kK37g+9sRoDPCB8PmRoqA8Ls5LNrxitnGkYV0IlWX/pBX4rGMKR0yF11EvPmjp3r7ZlyhIwTgMCLXdueo6FYeZYpIfhSeRzeGYAJuyHMG5EsMQWShvWwjOFoxzyp22nAakA5rSB1SLdFSRNmxEYILv68uNqoakizCepg3WPMY/M5U2FDpAbrydpZMk8vDLey2O6AXiTj3tNqgfnTU4iS+ZsRrZCcuI2JQ6YK5GrOjaWw10bsJPGLIJDG8KQgj3yURFWzo/XzUjxHLmprASJj3aQx/5dw6MFhbz4oN8t8pJihX785ne/2z32wodgZ57jBXm88JEPd9/7zneQQLCDUOh7wDnyWh3vnq1gvvwwMYxXkOhf6jcgMSOf1K36lXUy2I/lV20SKq5bGlLQPlEVNOGbXMrACTpXujZUx/m/MxBngamep/0k+XqBm0fRPJad+4UK6q0PWITO+9z+5L3suauHzrIAkl5zhVG+tAm85cN/+CiLYRRtEUb36JfPhq98+W+7z332s91VnsnjcPs0MLhXKdK7wznOE75kc9GIPtRTyRvo5uFDH/+5mxMdgmJ9Ei/mmecOkWNM+k7QwHC/PE59RsXnODLcjXlzrytYf1tv/Vbmh/JuFPmekmkMCO+S5opRiizOa8vDgC/HeWvMxZjEaMtiKNDonXAO4AL7683ivbrCHMLzb7/VLbMJvUaahqLDM7U3Nf4KaFrResxodIuVBJn5LeW8DTFTDtNiRFo2Bn8tSKGBWHehBmFWLYTjVC+vxiK5Ur4AsozFMmIFcKuUmfzy8D/GcAHM2qrCuOKvF3MygLIkjjcmgIHbHxJKsTBO4I8Ray71tjMYFbmsH+BHA07Ph56Sqall6rCCbqwj9YFYmdzUewa6pZX1gKboZYpNvRlSNDczB+gSHMu36uacBh9K4iQLi7GteIm3qv7Vkcg6o/1WnXOX4LxCTphbln0IkWGd8tS/oFydbGqsu8iJwMByodez6fCZAgIyIo066hVUQ+sMC7a+GvubgE/fqFt+wDQgWMC1ygIpFBygGADhizbKKQBS0qpjh6ikfuSpE45N/L5OllnBtD7dqFTOo52R+iNTPiHudQN92oc+IsAVZNtfFhnuvLSMNxd9reH5XmHY7tyIho3+zKc+1f3R7/9Biey3iJsQffXAt/pdYpNmndOvPRJjP0yLIr+6TrCOqSZEBFURXQXE2351bc2TxmWy2D6RQQIj6xAuxSrgVCntr75/kYcvaKbd45G2N5N92D6hvuuFiP2GfmLfoA/PzE1loaPc0/1IANk7X3B+vvcMVtHkc7gobyskiOB9QiLa+f15vM5iUb/xr/9N971Xv3t/KuCuq7WdlNAfdpwmaa+vhYWF7ul3PbNX8qHjfZY5H3ccxhoYa2DUGhi6wW9gvV/aDcTjiF00MAaEuyjldkYFBGlo80um4eaZYEBDXIsxb+gxNmOccr3EXCr3Gzx58mS8g9cvXeze/smPuyvseydtfowAhJqnbn8AfEh1tBGbTReDEfZeO0pOI7IWmcFopFyHpup1zHBK0hQqG8dzVDZ+7iKPQ0JjuGKdWrZpegUFdPpWrI0ATNDmME3rWkaqZWrOgg16uTSoESUGrmnKljl31j0eSqshhfHO2dJYFdj29SM+cxuJq/wax1W+BRWYdh4ieUE32QNOk14h4Bcjm3N5TlGvSRcp4M9rN6yfI24Bg/kaK7iqv3hcAK1TAK9sGaG8CgdP/1I/dOdwhpxHG6Yrl0CO1WAF7fy5BQYlJ98GgMw2dEjqxrS6VwY9kqW7AnS0F6uVqiMkiYypMzpRZ8psWEc3BbwZVpERgHrmnDNYOhNoFWC0XdU97TwJQORET6o6tgzzpLVIrzrKnQuDEWIRQVSL4yyhMEpOSw4oIpvyRfjIJwBU5jUAqp5LweCSW4Lg5Vb39p3lt8/hGb8yMkD48U9+ojsBuHQYcHRnPaLQvl5cqhvrLuwzqRaJIE5aD1bB9kHeovHlgv2PdNoz+TkrHVJJ8zUdFaKL3o0tXVc5mYvr/d8HeSeffMMbWZAtl8pIkCI5+LJP+7Jghpcbxq0KBjlxfmH2MuRc4K2HWBBqml5v99802L/TQ0zg4yuXPJP6wq1zMpl+nwX7qRvM/9VffqG7cIF5YuNwz2vgiSeZjzjCsMxv9DiMNTDWwFgD95oGxoDwDraYhtWW0VjGVzwQyBRwiPGRVS992wjtMgu1nH/zTfYanOlOnn6YBWMWu7dYgfEyholz3DRMM6xSG1FjHqNS299LPwK0GHKeEy8ImAoiFHACbEhfYP6Q84kCKgA0LjSR4WLy0BDk4yImMRr1YBFvyJHysqgJQCPyUyUXptD2laK3LysNPg2Yyjdz/6QTHZQqoItZWnSJVPwCg4LMRBXG8RTRrEdJ5Lm0ZchKqrENyCLekDpRbsxqyhFwWZ5yZDEZjOPNAFE8dQyjW/Wc+slPuQOYVWDvuRP6BSRSZqpLGV4rg/lSF45Z3YuokpJcAmX/AIXejNQgYJVCFDO8CiAgo+AaObOAiaqRbziR37qoO0Vi7h2XcfbVfD8BPNfytM0CLpENFgFodC9XOF1n+G/mpzGJbRq5BRUZnQ9htIbuBAepi8JZpgWZmiGPEFpfk/JdX5HNGGiVge8kSGNzGOeqrXosV3iZsExfFhAKBh02ep05s0vo6Qcs5f/Q424qe/xwghcq/+Sf/dPu937jN2CmTKXryG3XSjWUGZXSznlhgBKNdwGavnm4tk1LD4J4en7qUy8+zM9HXnzSF1KS/c2+tFUP0wT7hiaNujHK7HApXvDzvja/92juIfUqMV3Ulzhhw02XvkpOPcdy0Stoe0tjyHxCVhTKSroZbmc/TVJkSLl9/e1rfbNZEbj1hEV+X3y7guiffPqPstF89fv7otrv+Eo+PkJAaL9wC6hxGGtgrIGxBu41DYwB4R1usRhgMbAQhKM/KGWI1bw+gZ6GnfHXWVXUcPL0AwFqbwIGLzB30PmCggCNQ8zAgIIYbDCSV8xvDUYASvhTjp67LBxhejxF7B8I8Dkxv1CrgWLxCu5cMEHPQQAitAJO5ybGi4aHrBapKaNZo3VlmZXfPLFgPnqaluHRyh0Yk9DEa8SxeBtR52b1IrrJmQmVrkewfZzzNIlHS48aqQkaytq/goyA3T4+YAhNNEDY6i4gF7RuYEBriGuYu7jOrPME8agogwB5iTq44mWQNDwDzlgNdBN5BHEUSKRgTdBWYFzD2w3vtaWrLp70AnGo02rnxDKEz3l+tiMLRcbAFzFtTgHEHEKLrO4f6PzCmhdKK3NefOw7aA0adaG+1atzIeVXafQNz217iNRV9AadxymRu1hBYQWX1CtR1ENwWEyQwQJTIWSnL6yxMu2ksqMn665A5k5PDm9lilSRz9LyB5GrXOrtcpjxysoaemb1XPQvIMyiMvR/+89VFlD627/8fPf3f/7n4Tya4PYTf8QWAcv0WauUrxJz6BwApSfYevUE6i266GMCyqhReKRmfd3hlRcTKpXEAnHyQdfwsx/bHwWbfQeBvr+PKcNiBjzhFZWTV3oXNip+NRQ6XZDVY+1zAXzoNPcF/ASwtk8bSm7fd4io97ZzCOXl3MEWWjntnm0vAFSN8pRMjfqdf/Q++vIXv9T9h7/4i+4SIzHG4Z2lgSefempkFfKlS/utHBnTMaOxBsYaGGvgNmhgDAhvg5J3LQJDDWtwy7rCCtMA09D0oGduiXkqARcYc1fZfP4qb6g13FbwFF5kzuDbb7zOvEGW2MZqF8gITchaRneGRWJoy48456MJHjTV9QhkHh1GqcafXjHjTjCXwpAhcMRrrBovb0GjBmg2ecdAEujIWGNRA9QfQT8CEuksVzBomXol8oF3+aWKd/OOxmtkpfsQb4mCEeL1Czip63gGsaktq+ojTdWxGbDFpgdIpG3J3xvjyGW9rLx8prNiVQ2tm2eYnSBKVZ1giKg89YhqhAv2WLql6sgKnVmRVG+oDhiCQ/Wsu8a+W0poiBcoFMDZPlvNXbXt60i83hoHHm7gsbFsgdIEABMB035T6hU958/25looaP8QuKrX7D8FyJJ3dMJ8R8FK2gZDZY06b2zygWKa+Gm3TFCX5CUDwuM9pBK21YT1SiJ8oc/w3RI65658apC3QzzZ8IJ4gKOR1HtLBq+rT5vDtram5lvzXLk4unDMEi82BILX2RZkGZBJNNfs40jcJZby32Cu7Fs//lH36NPvspRjh8eeeKJ75t3Pdq++/J0BL+VWZ8pKNfKl3gsUkYpQqsrA7VFTJKELLbq0J5pefVEiAR5biACW5cEdJFH0YyHRI5nNb560FcdJ9GN7VLz8uSYEfJJu22R4NnTyhzXsio8XiBxd57kArSDwOvu1LTHk+QRzj104xpdI9h+fKQGntMXmBnJaD2S07Dx/7JAKi9SWxTq7inJfhHMMVf785z7XvfjVr/aNdl9U+76p5Lufey5Dx0dV4fb7OCp+Yz5jDYw1MNbA7dLA/fPLfrs0epByNAgxLGNox4wjkxaYgaPbE1xlFVGNv3mMtetXr3Rvv/Z6vG9zLCLjENFzb72RRTaSS1uNoKdF4KYhHeMzx1owooxHViWdP5GN6AWB2nl6dQR37kXo6pt6/2L/FcvEO/8uhiqyCpa0FGt4m6CvPGLlgQOg4G2b3MCQxbg0TaNZC1ND0mOG3nFudc0zjSdOcLbq/oepTBXcDOEM4QQYeW01NVC1ppVHY1rwWmWrOuk0ijXge5QWdtBpOJuH/PkolmnkQdp4wlaYNwhSjtxLtIHeUgteAZAI/vSKTgtg9NoSL8jewEsoYBiUDa+AtL6uARLQBlBaILILGrIITgQoOYxzoR6mZ8JXPWn8CyAEtgAnwF3AsXHUIUNcLUtQF0hnnYt/lcB3MAS6hpnAq807M3odF+E6fOI9IuMGOl7Twwm/8oYhKPnKM5hGTB1SiHqEh+UJRmf5s+nsr4LJoiGR/5JZIFGyuRJqZIF2jfPVDBFdzzBRF0UKGETfq/Cx7a8Rt9jvAelQ3pe/8pWRAUKHjX7s4x8fAoTWygYqUFd9qW8fZI1+pSC9XkYUbZ8lWVt7mbevsi1YOpV9f9enL/JiYtM5h8Sab4M21gttX1GGkOfL9HrpIq+sSIqn2DLkY7Cf2Wfko1FqH4mnkDT77jIL9NhvjW+rBttvvffmF9i2hry2SwICWWz13YoafKsb/iwngg8S3nknLwECP8MQ0Wu8mHun1/Wd13o3r5H3z0997GM3JzwExXi46CGUNSYda2CsgbtKA2NAeAeaIyYV9pSmfCwvZNAo1Ah0sQeHhvrDcuLUKYy/ze7a5SuQbXaPO7QF0HDhzde7NTyD2o0aj+YRRJZBpylXbDXWHQaqp87tKhwmdorhphsu3rEuyAHgEKe3yT+9kgVsaqsJDUkLWabMeKywPTXsAwItQ8MRQ1QZzaf89VGuil/H4C0wBvhzB3TFi3fJzALSGvpm/vAoTfQ1KH5NJjPEkCa1tsNguKIyS015FbzyGo0pT4JePpAWlIK11BleAkEsZL41t5Mcg3wWkKyHSs/X/PxUAIkrMLpIxyTHRfTtnlca1Frhk6zYsukYTFnAMoCQcmpRH9KRJWVosfcyCdbU+XAQUFk/56F1DBPNPoeAddZTj3wx/dG1ADBevNRRr1xfB3VAnMFiBGMes3k6NAyuZSNnvXJ6rNi6QEAQOUlz/iDnLHBaQFdZkANV0Oae6e3U65lem2LUedpf4Nj6ALwpse8btoHzWvViWTf6mIAaGnkKgJfQ82I8gywgw9BN5w2uAYx88XC9B4PynvZFA/K+/r1XuQXUvbMbjx/+63/+q90f/NbvVH1gF+2hw+oPKo+YgU6JR0eG0FGntCAXiGiDJiWrwXpGvnwSb1qRmEtdDNqfdPuNPNRT8S5qLqt8j3URnvQyui5zFrmP83IHj7U51b48HArqfV/PBuZlouN6uUJflQ+F+Hw4cfp0N7swx8sOvYTmRbfWnvN49y1WEeSuYJEjEf0Fh3dYOHfuXPcXf/pn3ddefKl09Q6r37g6pYGHHnqoO8lv7KiCz+QVnlnjMNbAWANjDdyLGhgDwjvUahqKBg3tgWHIuR4vjc7TDz4Y4NLeOJ59/IlunqFer//wB8xjuYDhzLwt/rTPBIM1zFFjVSPdOUp4/Jj7NwXo0zMgGDyN8aepu0Ze5w3NAW4c2ohpHoDjfDBXGHXFUY1HV9bUkHTuUQxb8sg3exQqeQzEMm6th74OPRx6gQRaAW8YrfEaWVcyxAw2XUmLAfLrgSxLcwvEaf8aZ/BY+goYQD8BTpQlSahEp1JykG3LOtAzcZbhvMgZNjR1jqDeURd1WaO+zWuo/G7Y7vBQQfQkOsvKl+hmHg+qXsJlyne+m2Bb4ItLlJLRIwaBemjDRvWWZu8/QaNNg2DKFs+m+zeWyErNp+T2KBTTO8gXV9CZh3YyxRVNLY0o4tA5Fc3ehJbMtX2ngePoBl0XyJQVi+bgvdUbKCBz9dJ1PE0Z2ko9ymNI/6Dt7U8u6DLLucrUa+kLB+urPDlSZiQfVEQZgNnIbpv7EiEeQvKpQ3XrMXMGmYvqXE0BYVYTRb/xIiOm4N3VXPVqCQYnlFEwigznXudlCP15dkSA8Nnnnut+5lP/oPvif/xP0V+8r1TQ+aku0tP6oHodBsbeI7aDzZqgsgku+IPa0tdsJHVlsJ3UY0C01+rVQHrAPf0nl5btvUP76An0nvI6Q5whqHzFtOar0gb0ad+Q5N5Cv8qaD7x8qSEIN9gW3v8KJc9TeEh9QeQzQl6VHzkjd9XMkqpmnJCHDhid1L0s13dQoH2+9tJL3e/85m9ttc87qHrjqmzXgIvJ+FwZVXCEQ3tejIrnmM9YA2MNjDVwuzQwBoS3S9O7lINZRuzA3Mq5P1ALGGoakit4ARcZruSiLnPM73MY6UWGiy4yx0qsoKEcAKYRiAHbgvMD3WBdL4pbGwjgzjx4JkaixrSGtfOGpibxDsJjBhC46FLZsQVjDQIW2XMPvs45EhzEaIQ2gulK4F/+yqEhmXmDzEmKl5HIwXA1aAuUVR7L4L8ClfQHVEM1XHKuATwcL+jwz6KrfPPEYO45eV26hAuEkgn+9NRE0D6vMgl2BDZ6T2JkQ6uhK4/Mi7QszvWWKZd1l04J1Ec8akmrWlhvJ5Kp/w2MZcuI0U98PG4xztEV134sTiHBZCVsyjO26h3hQ0MM8sdjZF0EkP5BSjGFRHArTSCjbaBu/CtdlgyRreflUGK0kr9N3IAFBvEUCraUFR1ILzDQM+TfFCBtmZcBviTQo2h7G29Pq7qU3pQ9xaRypCJfvRiwir4kqIV/9Aj6okE96glcWqFvCayg4R+ZytOdzehJD0BEFoc7O6Q5W58AIF998avdBz/5D1LmKL7+6a/8d91XvvSlyBplwtQ6usekwE+wZfuu8+LCUEYklVXo1D0Vj94E3Zt42Wwkgb/ask3ixYNpvMXwta4FEm27egER4C7LYmdCylCGAvq+AEC5lNvawHh5NzHsC5ahaA4NrQWjHB5eTH0hMjeDt5tnit6RYU9r2rens54GxRkO8RoqAxLUfT2ceu+ev/3W292f/+mfdt/8xjfGYPDebcYDS27fff597zsw/U0JueHcJ3UcxhoYa2CsgXtVA2NAeEdbDnNLAyyGZQmSuUlE65XL3CkM41MYbs5ju/D2290V9mIT7GgI6sFZERxiXGqjaaY9cOo03kDAHKBnBQ+Lc/QeeviRAJlltqnQxHOu4Byg0XMxkx5GmCR+RW8gxp4exUXK0kvm4hOzs/O9wVsmoh4iDeNNymn2a7x8XMOKegGeSNeYr6GsRFVWEim5LFcJc92+Au56o7TUAliANiAFUuuaHEM6sx7SqJPognIEMZ77MZjmkE9+tmNAz82zoir70F0FcEcsypxxiF3Py0g9iMpueTGo+zLlJX+BovxtBwGSui4QKLDSOyiQ8FzPkUx4Gx0e1KH3+CmbdbYOFfQKEShU0CgASH2JkucwGExCS+zzu61F1Ee+zDWjnfg3N9f6mwBnYJxN9k/c1EO4Tj3STtST8mxT66e++aYOvAxYZ3gpeS1fwYTo0Ym0nAXYEKfctsP6Gh/LN81rCgwYhIfgMCuTCg6hERxaf+kc/uuiMr5U4JLcgkHnalI+unTVWM//7j/8ZfeBn/kEMqZiVu5Y4YMfeoF75OHu4vnzAYEIvmuwbqg3fUFdVLMVseDdoLwJXkoLnXopQA7oJi5JNhJ1FwxKp/aq7xePaB8ahx1bjp5BSauv2C8E0XpsBX/qj3PbA178Ewc/lUiwqPAmUa+n/fbkAzwneOmjcD5bArh3yJ68fPVVqaM6556gdJPv+aA3+juvvNL93m/9Ni/Fxgb9Pd+gB6zAu555pnuQUTijCt6PNS1hVBzHfMYaGGtgrIHbq4ExILy9+o5hNihSS204aNxhJK47fE5DmT9Bht6J65evdlcuX2Ll0esxDmNMA0Q0FgUBXp85c6Y7feoBViS9GiAnqDv7yGPdCQDg1WsASQy5aRZNMT5v9/MjxpBJyls4cZJ0vDYAzHn2InTFx0XmdPlDFzCEPP7gBQwBbMpbhITK2+YdKQzmKKY++fBEUB9t0toYW0tVAx7jFTqBDpAycqSmyVvKkKd8NJZjKHNVhrbzJQVfGtEtXcM45CUnp17XB0gSY7hAb8iM4OPQRQHFMgvJZKgsxnSVWW2gsV5Gv/zxGCKL3kXBk0DFAgRDgkj8aClbkD2NUa14AYwa6JzrgYv8ZDMrtfGrD03Y2OYluCnWjyTJ4xVSJQRHkrqQjDKoncjCd+prWvhXvvIKyV/9cQiigB+N4seWso8JHpq3x83KMwyW8pVSud2yYIoXBQGk0Fd9bDvLEfRZRP8HX1iEs+2s96o8ZABQ6O0PawwXVacFCMtraN9y3qZHSODKh36vfp3zKYjRO6v+V+jL5378o+7sM89Cdfzw0COPdP/wF36h+/Tv/m7PTJ3RvoIqdUNsW9VVuflPvZOQroouiaQV0g+kV/f20wEQs+IGATuKTd+qGJWX9giA68kSCUd5uN2I14jCoe4d5VEKX1qoM9uv2lspSOPfvLaBwDFHaGZ8GcRoAxeSmWXIeF7WrHqfUlf4OBTXc6G+wbrcEGx4i7/Hw6WLF7s/+nd/2L3y7ZdLD/d4fcbiH0wD3nsf/NCHDkZ8QKpVfyt98I3DWANjDYw1cI9qYAwI70DDxcjSqCJoMPYnWF9laAm29NT4A+MCJwI1t5zIsM6ijgHZW+Qx4PT6zbOC6MqSc/5WWE10vnNZ/dOnTgZIrjvnkB9Ch0XGmYHRJ+jU4+RcOD0Fy+TVA6NEV65eCzDV4FfGVfLPzS3EIA8f5SO/xqgAU0/gUha6wZB0uJyGLJavcx1dTdIhh1qX2rEBChSICTuwOC0zGtGAVQ/1T901tDVuy7vW1KVcgp+STy9eGd+qR+NYmZUvXkzKj+FLBr2EVnAZ4DtJXSeoxyaAOPQawlzH+4n8BWT6H3mKrybTCJ+mLVj8h2G2Dq2d6L2Rbi0w7bBaAI/z8jT8/Sh7AUJrGQSh4Eram91l7OtLEqhRcNKH62ouEYFtx6FkUcbowESCFwYPQ/FGCzbUiwuRuJ3FcL8DmqXI5CWjQyVb8HQCj53gXH0HoBDpMeUgT0RWqfwrQ+YOclFVgbsAkAvbNXEiSNKdS+iQUYc16x0UILYuYbtatkNwZ2YYskq7aMhN4TETGL7x6ndGBgjVy//4P/9P3ec+85nueu8xTh9KBRGibw9fzPjygJpElwOlkT9VTyNZM19xCJirH6pL65PgQWKCp/YL2yIecM/h4f3Rhlx7bvBbwJfnAkS0RvqpixltsFCQ/SaLNkG3zqJR9jO9fgb5Ww4SAQS5h7lfpXdRGe/TeFrh7T0ibdqKPqLMqZc8+PQ16C+G+pCF3EPBERHf/uY3uz/+w0/n5dk9JPpY1BFo4NQDD3RnWFBmlGGx3yN4lDzHvMYaGGtgrIHbqYExILyd2qasZiQ274/2XjZ3xzDT4NPYNAQ0YaAZ49zBc2+91S05z890LDOHiAl4tK2d33eSeYca7NdWlmLgPfb4Y52rqF1l2Oc1fqxME5Tp3RHo6C2ypMwfYzjedeY/uMjHAsMor127CuCpPRC14GNsAqTKcZUbqQAAQABJREFUwNQYJiMfjWMNT0THqAf4IS/kpLMVAcNWNV71MlIgecsDp8VruTE8OTYrU5bDoYzR3uikQEFn2/4inhDiNHKVRV4GQYsfhxbW0vpbHONttC7IKEBzCB5wlqGIDGmVSf7hmXNAH/MrZduGjG7MlCyC9fkFwC/6WmR+pfUSZDvM0TmLAdR4ZGQTABNAqOFv/TXULUMlKVu+oh8WKk15fBPPh3/1apAqsQi00SNA49Skw0oFWl6Hjjpm/0LirWHpZEtP8qkq2g+4CGePFZI+0CeplJc5cXJDbkaakt86uD/lVh7z6VkSwA/aNnH2nwLVJbOVEgy6kiibztOHomN4Wwuaj6C85Q20D6lHQUv2dnSfRs7ffPWV7sO/8I8lHknQQPy5f/yL3Wc//emBXqhJKVXNctrqVWAYcGubR9a6pxwOm/5DM5t308YhSBNe6tUbsaKTFp5G82ddBWPy8M/7TjCsotcpS0AqN3WnruPJ5VqBfTFj/pRvWxHnXoa2Ef+RfX5uvhaRmfWx731MCiw3VwGP3F95Lgy1vQI2UeVh0Ded1mwJFX3PfC/zPPqtf/NvM0x0sLDPPSP9WNBRaOC9zz+fefmj4CUP5+X7GYexBsYaGGvgXtbAGBDewdbTSNSw02SLkYkx5nBR412CfwlQtsFQvSvMbXI5awGRwzMFYjUUD6MPoHHy5KlugSFgS/0y/e//wAe6J/AOCgYvX75YxiAeQxeSaBaexp/mZUAbG91fZyiqq476w3aZbS6k01jN8DHKcNuFeBQxIpXVOY4a9Fqc1wBHeiUjH0ao+95N69XBazkBaAqAEBCFXxmoFYeZqWHJx2vLayHR/bVGrkMFmwejeU2UQ+DgPDRM6cgFaR/K4G185a2RbTB/eKDPWQxhgXABP2VIlWJg19w6PKbECWqs4ypeRfeGdCjvxLILjTDMDgM70B2QOQVYXHcbCoz58mgplzqjYGR1aGGMdq/7kFPi112dk8VjbBuyx3urxF4LNjXGUwPS+LciWTHU0/BMJGnow14FB+KtT3kSOUms9TF4pX56bkYNgn2wgZVEmqcyhNx+GH7G82FBzMgpbxdTsQ8YMvyX80hOonNO9Qrad1Yd8mxm8yJHyUJdUJb9ctptUQBBXnuMPgHgHq+8/WZ34bUfdw89+XTKGcXXz//SL3V/9kd/jCzULfWyDXlEqr8IaYVaP2MFT+4BEXmGlqJw7w8bOvOAq/oRy2v7gxXNuwDqWveWy9UQRweQhprxioLh4vQjswP70EPVX6+r/B0imnL6tlRnTW8W5r1ge8fbnMaodF9kLDAX2WG4086lpT8rkYVnGLZ7h0YWZCM68iKXfTPXkvbnxgzfq0Tf9UGdvcgelp/9zJ/wgox9BcfhvtSA88bf/8EPjrTubSXwkTIdMxtrYKyBsQZuswZii97mMu/r4gqYqIKYYzF2M3SLGEGJFrx/AsDlxSVA3WWGJl5j4Rc8cAEl7jdYIFITTRDnFhUa1JeZO/jMe97bPfv838u+Ym6oLHg84XwhwOB0DEVKthwMVL1ueiFctXSGuYVuxXCRLS30CLnvW0xDjErT9NBoVAluPJZHrIbFuRKlC6tgyhZAwXKUPsZ+akMEfAQHAQgaqnw0jj3mI10s4zI7Q2saXDSKdYw5bHWNOX8CJONjVPc8MwRO3aGHGMy9fpvhGo+q/EgPwES+zFcjzgVLAq7Ui+XBx3lu2vDqWgAiOw31gABknZ+dy+qsykOlAhDViU6heYelEpTDIZq2k/qQj4vMWE3PMyeOsjKcj+vsCwjg1JPrXC5BkLQBlNFjOMFPzWrQCx4pUH0MfdRP6cijdMBV4vIhDxpCBwJTYY7XHgN5FLsPtsPW44FS4OWn9N7KFXpSShLVvfVy5VqqlU+GqApPUOYKfVFPsu1oe1jCVtlb7eJiPDOAwegAAJR5hOhxij7ZtloQJH7jzz/Xyzqaw0d++mPdw2fPInf1a1+2pE/SjpZn36n+pDZsF/pllJIk6mKNCNA1kGZMreBKftVEmrXOC4mc1nWy9X1BnmijeHgOSfGrEvy2D3uH+oKBQ/pa8kmcuGo78zkn2JdGvhBy/uAMLzQcrus95r1lHaWrecFyt1UI0HinpYAc68s02/NeCQ7t/n/+r3/V/f5v/84YDN4rjXaL5BQMOuR/lMH5g+Mw1sBYA2MN3Osa2LL47vWa3CPya/61oEnVDEff7Buc37PO8C/BnPsaaTi7CqBz8eKVwVjTEBVgnGRF0SefehegcKG7cOFi9zQrp73/Ix/upvHMXcMIcnGZBQzAWReSwcAWom3AG6aAjfJiOeyxyp3uLrFojcZ6VugE/ASgKhMgReMwQ9IwkstD53UZyAU+wiZDNWedzwgI1ZAUFJkumNJg1VZN4FjApUX08RyKRlp1gnkqukD2LKMvD21eYzRqJe5ZDIxmjWJqazCueVkEVgbJ1Z9Bw1ZDOQa/rOBnfTWSBTcB4ni0lF9PSrYhII9GtiDb+jn/LaA1224IphmaC6hQtBSJDDG4uXCxjgKDgifbkmPK8rqBRBZQoX0cjpphvpxrrKfdrVvqF/G3vtRLdJFKlH689kyAGNO+wOAAhAkIlc2/nm1pSK3Xx2GJbU6l5Str8iB3dGs68fm0NDI3L6z9OvMD6Xf25cwTVCz0MKg7+RgJygcdUE/nCwYECo75RBcAlwFQs0w+lxlGfYWVd0cVLPt//d//t354s31ELUTYyJ5yoi/6DtE0cdq4AJU+OfOUfqTNkM4o1trWvWCvC1/ibS9f8rQyqh2goNjsvQiv6Iz+Z7CcSV9eeO5fX5YgOUNw8e7nnkK49Le+z8wyVNT+E5nJY5im70KU89wXlslVvSSpIc8mGuenchljtmr7urp7v31+fek//033r/6Pf9n96Ic/TFvdvdKOJbvVGvAF6DPPPjvSYtYYNTIeLjpSlY6ZjTUw1sAd0sBoX5XdoUrcS8VqyMXE7A0wrZQY8oAMjWrTysOFEca1C7XoJXTuC7Z1jEzj5zHoTjAErIZ4Xu7OsGz++z784Sz8cv3qFTavvxjQMgc4mYFeT9ASi8ZkDl0WqhFoLmU1UvcsXAV0LjJ0dBbPl+VvbDA/CoN4LnMBa6EZDcHeqtQqDEjKXD2ELuM2viL2OGSoKCak8xJdMKTlk0bgZPBcA9ejoKy3Tfu00oFpmqMeSid6ttCg5ZHHFTcjTw69HntjuPFTVwF9GAN6q+Qj8GpeG8F3tosAeBToE7hSqoAHWTfdQgHD0jKldTiki1LMzp7sTrCIj3MJ9SLOTFMH5Kl96+YDqgNoNeJt67S3PMu4Vj8Vh/CWZU2sV/UOzpFRIE2kehLUVeeAKCcKKV9zVkwM+8YjyX1fk55/6eHGnxdeV7nGV6jSmwxRr2UboLUXmTYpIveMuMjMVzhajOd89EApu+3ksOPWzkUoO8Ep+SODdeQKPeg11VOdIaO0l9e24RYoFUQXqN7AO/v297/bncarN6rwoZ/6aPcCn2989cViSVUFsc6PLeBHv8frqrzK5BBPwbbaKe3avmTyHzldtCm09nv6Ut8MoS4eVW/PTfRYunIbCR7PvU5LGO4fXhJMrjnMkzmD/EmfFzXr9Ete5ngdXgggULQPCioFhB7t/xPslQhRhjvn5QXPg3ppYB/35UbxtUxlgTRyK71tFv7eEHdxuMq86U///h903/z616nDXSzoWLTbpoGnnn66e2CEW00ouIu9jcNYA2MNjDXwTtDAGBDe5lYso4pCMaoLPpXRpdUVr5VgDCMuQAZLRk/f8iJDUjAsNQY1MH3TeZqV0jQY9fC5kf3zL7wQj6H7El46fyHA5KRbSeg1wBBcZPhp9gykHI105ydpsAuwNALdg8syBT8ujqENpXfGpekFUpmzKJhSBi1EDMPy1Gn8l6HpELRZF59hwRH3M1zm7anBxWhanoYvKCblma7RGtDDuawFNgUABckaoAJCNIdQGuHlGTGjuckDjRfKbh2ULUZw4k0DQNTITUAfVxjnxY88gDzbZAYQIphT9/GSKqj60ajnz2A91zC6Beeu6jrL0Lt5VhltoNAtJ9S1cswyh3ITfuauYXnWo/RdBrWAwrjG3RIoh3/rKcjYxHB3fprjUDfc8DxxXAeQSa9OSgnqyHYNi14vPW4rvamWnrnJfpwqh8Q5NzV5t66y2uik/MNcumJc8sGDk/x5JCneKc8RsQFCE+znoZVIXpHDI9oJsEAH6NkhsgEoHvMB8nC0D7Sj7ZHhkuoadj9kk/r3fPwTSj+y8Iu//Mvd17/61bSdZdgv9NRmuC+lNJ1nJVnq4kI6U6z2KW36LVWzXb0f7E8TbhPCtX/2O98u8K0WBvFmbnxDx0sGbiPSbXept/QrYIvX1aHASJl7hDPjNgV3xKUvUJa632A4t3xcRGmDYbtZHAq53EZlDtbm33RVIwSaYm9K7ydlSVMRWaUrAJ8SmmM6FBF3V1D/X/7il7ovfP7z3UVGTYzDWANqwNEcP/PJT45UGb6MHXsHR6rSMbOxBsYauIMaGAPCO6B87OWBmaXRGBsLI8zgtZvBO1x00WGjeKCyeiGGsYaaQ/BmnL/GRyPaOYRPvOuZzr3UNDavsCLp5UsXOz2DGohugeB8RH+4NPAEVAWaMBQxdAUvy4BK53cZlE1DUYNyhqFmDmEVgAjgYphiKOohCWjF8NTjGM+DBiTyTMWrUR4VF2FJ/Ryu6hDKGMLm19jUSG5GZdVd+ZrhHIOaEoECSCQ449BbpgFp8DNX4wPLpAsWtLnVsDwMMXjh4XxKjWOs3tQlIIUzF8PRYHDbCOe4eS0wqdx9vXvZ3FNxBc+UoNEtLPw4bMh6so5/ZAgIwJq3ZdWnHlpqnbTUzzYIvzLmAwyhs7Z9FTHIqbNgSXRFrO0RPauzvrLKb1mm+y+4ajITGbKogMjwTT7hRlKJ2yqvorgeYuD8NOuwLa7nWxpMroAHWRcgrHLNmeIoJXqOiMpqm9s27UiBnOu1dc5ezRFs4BC4Y59Kv1Jner0qrq9Ed+38290bL3+7e/x97y9hRvD98//kl7vf+LVf684xHFXZfRGQuZDRIy9rHPirnpBb4Jq6WydO0t+VMemQRPFbQqlxej4JEqAr2tWmlsxPzTGFSl5cpw9bgOf5pkVgnpc5IVDPlS4YqvtN/iFWqtwP3kWeOBJgZsEVgF1VlPmlZDW3PCxLT6KAMyH86zTf8IxUfXlDKXfF6YULF7o/+/ef7b7WvLt3hVRjIe4GDTz33vcyYmZhpKL4uzoOYw2MNTDWwDtFA1pl43AHNFAmXNltBRLKgnNo2jrgzbl+be6gxrNAzhUtNcn00mlaG//QWbaXOPtoDLllhny+9drrmIDl2XJZfL1XAky5Sy/o8cKhqManjGUX+YBjb6Q6DNR5dfPMTdSg1MgUBJpR+fT+aUXWxuIFSGJQatQLXJWSPHpWBAnx6pCmHak3ReBaoE5PXeWPUAOwgGGMrJZd22KUxSz2sRxTAhwwXDXU5Ws5yuSn6kpuyjQi6ZTp/Kh4URotqQZBlUZ/PFHmQQ/+uUhMjG0YhH9fgMBsFV5UMsNNZ1hl1LytcPNEH0bBJ3W1btSpYiyCWvSf0k+BIbewyGIz6NG5c8Pz6QSpNZ/O+YR8SM8RIBp9hT+F2o79QUTipYZ8/RWUK32pM68LVERuAYptwqdAKMe8IPCafuDHPCq1p5WXaVXvrfSCw5YaWJn6Kpe6KvDXQKAvEqi3i8bQt61j6pb6lf4aGAw4tJ+pVz9U7ntf/M95uRHljuBL7+//8i/+RWSkVlSzdOi8UC77ug+gLSWWzlLTXi+oxqrCo/qT+rEdWrD+CejOtitqm06deK/YBjDpQ/oXvH0GqKvaggJ6+RPyUoEClDWhbyPLlEK95SUHEZvc3+W19kWFrwV84WN/3iovQFR5+yhP20f+ng/Te3knw1e+/OXu//6X/2f3tRf7ob53Uphx2XedBp5/3/tGLlNtAzVytmOGYw2MNTDWwB3RwNhDeLvVjkEWO6sZXwEFvd3VjHFk0pDTg7eW/f3w7jFHSPvw5OlTGRrqcFG9g2fOPpI9xKS9zBtyVzw78+ADDC1leKOrf/IWM740hkRmpVGMSYeZ6gnTu6fXrGy+MkpdREXD8fTp0xkmJzjUWJQuqxMCDgRVWVCFc8FBvBUY+XoHNTyxaANoY0AKXEizjACHGLpcGEek8/YM8U4QtxF3SRngIYCJuugt0ICUGLEUE0BFQoEbjNt4yCCGcbwl8DevnjPr4SdGu8Y5cikrswJhTiy6KGO8DPUaHuq+chbEJ0oSyNY8ROcRrqMnDXGH8K6ob0GintvIZEmyLvDrqQBJ744f+QYYkd+/AAfPAuCsg1k11i2b+jBsdJO9HdYZMxxApsx0COuQ4a+RsQdqpFmrfA2592ATfcg8sjWaHC3Qkwp1OvTtqcqUSXEvLl6mDHhynmRTiIsMnOeFR66qrpVA/SFo7S5AcTGfWmm1QLFDMtWLQ3EtU137qdDz5fr6hXN4Cs93px99rE87/uGFj36kex8rEn7za1+LjNFdCUzdhLi0q15q4xx6uyVW1OFl01SAMtfWxT5mSNtzZ6aNyawebE8ZSbcBna1k37Yv574izT6W1UJ5sbNMH0y/gUYgPbvhcFEBrGDSMgSheKqVl48lW56jDKb647S0BJ8Fm3r3PUcOPZXDIc3sF/wjK/n5v+NBL+5f/Nnnuq+99FLp745LNBbgbtPAe59/vnuYOfajDI7e8Z4Zh7EGxhoYa+CdooExILzTLYnd1dtZmn+YvWUcCuwyZNTFZDTWkHNqWq9drd7pUM8HGSa6wDxBOWTPQQDhAnPa3F5AMLjMfMIEDMlarXIye+5lgRrya1zGi0f+gDbyabDOMaRsjuGiGuFalisr5T3QK+dqp1qCGq/KkGGMMSA1dsmBMavnUA+aMlsfv+NZ0uAltHrKWwPV9BjIGNeTgJ4YyclNijx7I3rL+xE2BQ5kX2wTafnxZMkXUKKnSX4ukqMMiqPBOxXwmEsiegPfPeUosIxmzwuMFeC0XQQx8Ie1cw8FmJFP+YlfWWVoLmDZuNQhtPDRFK//qicEDdxoXHMR2QcA0UvFj26qcojL0EIACHMJUw91ZzkcBYvG1Sby1NF84dHnLc30leWir3voJMy/rVLlbhHKfyhEnrou2sor1VASBNZPOnTJn548ddd4VXsSa3uTFjCoZ7D3Dmb+IO0oCBLwuACQSo1njKNlZTEao/nTo/7DF7/SfeiX/qsSbgTfvmz5Z//8V7pvf+Pr3dQ6BaXQuj+tb9oP+W2//NmOgxpuFyB9pgf6evZaH/MFStQkOfwFavaB9IlWT+rqSxO7rpvUW5b9MXnpaybI33tzA36+yLA/O+80Larcdh77uC8ToHeF0fTPFEsaIQsj5UxRWh+itGrIlpJ2aDVNlfuU236gXl/9ylezcIwvt/ZQ/W0Xa1zg3aUBh4l+4lOfGqlQ/r4ssmjROIw1MNbAWAPvJA1g6YzD3aGBMsxixmGQaaCtOJQzw0QxnBkW6BYSrojpvoRu+n4aT6AbwAse33r9tYA7X+wvsxDNdd5gOqdJ400PlnsJClac1yeIc16igLDABfaiAIM/F0pxM26NbG3JVcoXNGoYBgACAjUEpZdH8nstMUFjX8PWMnJNvmxjQdwghJbSOPqnNRfDWsNfg9c4eGo4G2IgExUPCtfm0DD2u9EkQ7xSGtTADNFSQgE4TwUUptWQx+JRXEwtecrA97KATFIo0Lob9/+z9y7gliVXfd/u7nv7/ZzRjITeo5nRC0kjCT0AgREf2E4gJF/ifMlnnJAvNgKSAMFWDIjoS2L4iME4iWNsXiGYGGIQkngKBFgyinjYIIQky3pLMyNppHl29/T7dfve/H7/VXXOuadv93T33DvTM73r3n127apVq1atqn3O+u9VuyoiUci2602NYR3gUp5Fp9PGY4cBbmtiXMNbcCNYVW4BYgFgzg0gFBisNsfAJ12glCmjGPt9KqmGf4ATfdT35bNddci38cy5phbqDQ2wMk0ZaERvR1OxTVsVVl/S4E5omzolycZnr22j8vd6Kl5y2NaprIwLrtMWPK19qqhpaaN5rd2lD3hE7sabenPN+HIM3vPvPjyc4t3Z9Qxf+frXDy975SvTd+lHxxX1pQ851/htOshYNo42opSmFWVW5/wZ/JyO57on5GNwDHRlqm7blTwurN+4+hOvlRyOZcak5VJPnVvNVaeMDGZ5lt4/0u2jTENnrJonn8ggWRfEMrOhyTqb9FjHH3zggeHtv/LW4Td/7dcy7f1ioj7Wco31XXsauOWWW/iOWd/n3vUbOvN7du01e5Ro1MCogVEDV6yB9f2mvOLqxwIaYmXAldEnSMs0TYzBTOdMJt4RftR27d4VkOB00QM33oC3cHu8h4cPPpQyLgBznJX1srIogE2Iso0pjDvYM09DUFDT3/tz43X3NcQhR8DYFMA0sGFdeta0JZehCwVlz5w/FQPUhVQ0GDVSBX/aiN3ujGXZLVIKLuL1idcOIg3QDiClrxUmC2TFSE5NfLQ21yWElA3I0hImFK0MisLPbojH+2S7kEuhJEmcs8Y0Aoe/MglCNrGE/6aWprctxjblNIyts1eRaXjIZVs1zL12Om/2EAygwfPCuUCy4LE8lbCMdCVXiVxtL6M8hnmvxSYpswdoUhghqLRSFrFEdD7w9mQ6JudlQK/eQVcCdYqh7dysF4g/nUIUjLy6Em2b3ChBegfJEY40K5beQCTAmmgqt3plSrIs65ieiFkqXJIdXq3AhDfXsjM5ujBuH+So6bvxFDIGPQsK+zTJgGX6I3qpGmino5Zg2+1rjns+9IHh9r/0taauS3C8fPPf+pusOMp7aeg+YBa5zi/7IACPHB5i66+xp3ylGkZRpmlzmdAfjnhR00KJdFrGERA33l6Bb/oJRlmcSXr++lh0PHIR4CYvouT2QMxyyKc+HKPmlc5SXeQzUeCnl0O+LqC0om6j0FYGvpbrIXwar843eTM0nXYjz35/3fWZO4e3veUtvBs9Luixkbp+MvD2+/hLX/rSdW+KM3e878cwamDUwKiBJ5MGRkD4OPWmhnKZX2Ws5eelGbdOR9T75KIvZcW5KIReFqaYcezbfyD7Dmo0njh2bDjLwjHbAYOHHzrMqqTlGXQFwR179g27du3O+3F6EjSofPfNxVKM12+aJmjJIGATCG7H+ygo1OjSuNTui9ePAhrzMQpZHKbSyNdbRTkN1gBJ26bhSTkNS6e8erYNmrDlPZoqvuTo16WV7nE0z7iH9WpI11TCWYPVvDKerVM6vZpuyB1DV02bb/EIZTs1vkloIdzapXkBGYIo6Ksm2hQwBlGAmd7T2mNxJ7pysZ4lgLIrtp7DS2h/yKPkjtipKbz4KJ7Kg4GfOgRsXtfRPZ/WLnBWdsRiyqhSK4P0ACcAhe1dFiBi1Cua7xpySoO9rotwt4bGD3rorM9Q9BXPhQnkpXiE4jJEVJ1CBZYnOm15KdGYpqxMuJ78iQplTZptdCzUwkGOj/IaBiTi6q5xYknAjzyNtHpSngu1YFw9P3jXZ4ZbXvuVwwLjd73Cc2+7bbjjy75s+BBbGVi9C96cX3J8U7eNT6r6Vh/lJTat2t5EphMiPh/Ve17LjeZAmHdfJ+2qcRlvXQhClg/Hk98N0QUFU0dkoB745R7kgYBgT/nqTB46DvsqkEq9/xe3sEruNu5bt83Ival6IeIBQCRvMq4SI22YyvRYxR5mX9Xf++3fGT7+sY/RrrTmsap6rOcJqAHvh1e/9rV5z349xfedfGfkjGHUwKiBUQNPNg2MgPBx6tEyB6vyMm/KwCuvyAAYPAPgKiAVm48fOFf43L1j13DDjU8ZdvLuoFtJnOBdBp+EHjp4EDB4kveD8FKxAf3uPXuH3bv3xIDUrtOYFGDWVNHmQWvGrPkukKGH0cViBIO+fyhwNHTzS5k1Rj3LL0Yx560YlU5p1WOl1/IcU12ntBjrGHABtAEsTvkzN3ZpzhXvQNE8jVL//RC4OAWVK+L+0Ae4YuSWQd6NbmEt+Rq/0Ll1xg4QVAFCcsKjAJ71u4CJRrH85ZdA1frQ9NSUwd5hTGWnUdShd9Cc85RVp3phtwEGt3LOFh8Y7ROPC7S+T6nXSz0rI9rjsKaSPQY+10oRmqZj80s0arMY+vVaGOQ7dNkjkFIx/llAxL0KXZRnJfHSV1oWA986KyA2FeFBpgKjESSRyu96rasal51IsFmSh0lIioJr/9PGKmmZwFYbZYYyJ5/2cA7go0EuGjOZHqueAF3qxHtBuvQCNPaL3k/7TL9avFycHVtOhz7O/ptf/MiHh2e/8lVdgEd9drr1G77nvx++99u/o/pdgQgZi56Vr5Im56jSxK6iFpXAceWf5TllbMdjalsbX9uXPiXFsepfD+rae0H2pnZsJI19TU5Iw9+Y9TR+5oYv13kvmbPbT/h+rcVqzHIuFpNzY5iMlpWmhXcyN+7D77hPfOITwzt+/TeyV+rG1TRyfjJpYP/+/cMzn/3sdW2S9068g+vKdWQ2amDUwKiBa0MDIyB8nPshxiMy9LPg5wyeuRPHeGkd6yswAUPY6YJnAFq+JL8LsKcRefLEsRQ8Dq3AcCdg0bRd+/aw0uiB7GFo83yf7gRgcQnePl2v99qmDdeYzBYHAMEFgJ31nwaQarA73U3D26CMgsVY9c411XKEJnsMQrItC9q4pcWpMh4bsIoRHHKNekACB7+tkzA1QDWAnaoKULPxEEkWmZWda4FRwBrZwQZSUo/SxSiWmV4z6L2WzPYWgHVKZYENgYnv523j3cpatEOoIXgFLEOv91UPqOUTqDdguBiWXMqCPi2/eTtbRPj+JcCwClVb9Qlp1C4D1LNapmU03eVTKI9IGfjWM9GV9UlGeyJDzqW3dAdlu9EPKSyQHSDoZvZp6wQUyr3CtC3yKRnM632hrAIMoFbSevqUA8Q4wQT+CtX5df6tGruF0PqDWEFdIrahtSMPBYjn/UC2m3Bqse8Mmh49C5CSLy/SGiC0Tttno9K/8gtoIoF+u/NP/2S4+fkvHLbv3m3BdQk3Pe2pwzf+p39teMevvD1tQMvw5UGC8iFFPKYoLvogbaI3EnofmWi+bTLIwTHYLiuhspIXWgko5V1g+wXq8nY8e58UEGbxJvpcmvC0DMfKkuNZej7tLwoyuzgPMdzSJtOM2wMRx288htQ1kUchCfJcMyjaBocTTM37jbf/6vCZT386U1s3uLqR/ZNIAy97xSvycHM9m5SFxJy1M4ZRA6MGRg08CTUwAsJrrFN9pyfeQTweMaRFBc2Q28pUOLed0JPnlEgN49MsMHOM9wZdFfT02dPDTqaIHrjhxuE8Hj6nkGkIHmWzegGengW9jD0UMCwwqJdRYHUGuiyMouVJUB7rERrplcISjVHpojdL8OvvOkm7bXFbwE9NdS2Ph0Z7pnhhpMbYrA/ixV8DOQarxqgtprkaumbjH03Z1B8QUGVskzpRfu1Sp6nmXccJwFJM+QIcBK6iHApIK3aMzUx8O95QF97Ru2RbzmIoC5oFV/FoRraqS+M/UxthUIBEXciQeuROXepCD6sgx/RoIPxdpZV2ma7M5KnrAAqFIniSXeqRzgIkQsqpaL1Ql5btfQdhgtM/HS8rWXHUtgsQBXhKp3zqtwXiLTXVVDxUeVex1DuhsHCjl1HJOREuLKccvOyAMDRKb3v5CxXx7oGNHuiMLC5DH/apwKGXLmpUV8bVJhz851BDTpEtpkzlFCAxhvVOf/7DHxpu/4rXRbL1+vjaf++vDu/9l+8eDh88lPqrLcpifyhZUpALvRO3L+LRdAy2QCvSd5DQjwI1F9VZCA/LR+/qWlbwrMC1LGhw+jLvxzKWGGsOZtMyHiGvIlUvbwvXgw7GgF8DTbySSQ+2xKlL5vVQwwVmNuE2rgcsVWQqRUnjZ+TrCpgmr1vM9nyY9zZ//53v5EEX72uNYdTAFWjgOc997vDMZz3rCkpcHulJfkcnD3gur8hINWpg1MCogSeMBkZA+Dh31cQwQ47+YxODOcCrDF+NRQFKnwYq2Dl35vxwnB+oQw8+hFG4PJw8foq9A3cPN3/J04YljOItgEaN5ofZiuK0W1dgZMXbFyNQjlWfYErj0pSzAEqxUwE0DFDAo+X0TsbIxYvjWVCZ6axYtk7RtA3ycGsLl4AHb6SsUxKJZcEbwenmzYtpYxnRGrDd3Cwjs+zg4hlBtFUhiV4qswhbuSShC4EEYk5Cph8iqxuqYwGHR6YfQqEBvlXdNINYuZTj7OmT2TZi29btoTl9+oxVxzgO2KOOrXr/qGiFhXbEbNETRFlxFCPbxXYWmTZKxQGpemQsm4WCMLa3s0JseboKXAbkUHcAUMBNA7hWzHUZ3iV/hFEe6hX3Cjn48D9hi5XRT/7ZHjkJdjfzfpihejzR0meiVkQICfFUWJed3qSW3AhTA4mtbDuFTz56yZ4hwOi0nr0WtFQaao3nlF5IWoFB9a4Kqm9zMcvboshVgKuAvrpUUD1dd3/og+sOCG+86abhDX/7u4cfffP/HHGUz3Gp6mzfRB3E++q+8bCjf2kN/f62L3t5ZfYe08uZ+z6Z8FXvgn/TmUZr3srgyr/QW68E8NGzHxnyUbLUuOryqR/k8z7muYQLKcncfTcjU/MOpg2M200sltPErXw+c227ksKHdRsmCXW5Hp/OdPilX/jF4Ytf/GIA7XrwHHlcPxpwivdLXvaydW/wWX5DPcYwamDUwKiBJ6sGRkD4OPVsGZDzFlVd68nDotciDADT+FtgAYhdu3bxvt62gK1Dhw4O9997L0DmFHsOnh327N07PP3ZPBXFwFtYZPsI3uU7cvhQtqjo3iRMSwxLeGoQaoRiZGpQBkAIdEjnxDXeQuzrbEshraJwuKgNnwAgpkCSkEVuEFk+8ephvOo59FqPndsiWE7w2L2VHZRq4K62KKUkpRnGwKgydBuVMpZRLF3Tm7JxaFybIiYKiKBNi4Jc8xA0YFAwppwAwnhXZGNdAAlB7Ck8quZvw6DIBvYYzGRDy7ub8NmG528buj/N1Fv1t9BECDhDafZZ0dNm4upVx4z96CIzZ1iMYCdTatWJbVRg5S4ABPDR4GY6q8Z2iSbQNt4qSrP94GjljJpiiLlOun1NL9jT6cuVBfsVAtJTYednITNafSKa0E04Fmf7eZKEPC5ek7E5y0deLYSlbfE6fdxkk0nrc8eH/SkNWCdtFygnjXSxXQeDng0lhnH5mFLpxgx54OH9AgA7y/YTH/n/3jN86de8Pnnr9fFSpqG99nWvG977rndFVhU2kU85FZIwSVNUaGyXbedRCrmOWWnq3vDhirIzYXbYxKByzJqfMuFmvCI9TRr5yCOg05s2oXSqLrvulcnxqhyCQMGl7ATO5aUkn7gPSsrTDihk7K4OlnAswnZVbDXVo7nyu+bffvCDwx++573D4UOHHg2rsex1rIEXvvjFLLq2f901cNqVRccwamDUwKiBJ7EGRkD4eHWuRh91d8+CYpQhWStkCpy6IaghtpMtJ/Yd2B8D0E3o77vnC8PRI4fjlXr6s58z3PTUm4fzTPd038AdAMez7EV4DA+i4MbQgVjMOhhrSOZ9PTyPpmVap3QY1VsXBWuW0TuoT1D7UiADyDBd47JZh/IJ1qCM5qhGpgnWB/yKgSsvg7w7gEvjSdPI7XXbXnmUxClSSlJRHKkrlWmedjmaIFwnZtswlJ1WG88L6a4AusK7kWk71xqfArbNTNk7CxA8izdwGzSLgEan655Fh5rrymbV20gPmON88qRt8F1Ebx3r4pP6BJ2RSfBLml5KQSOXyF17Prry66YBL2NKlpypQx78WU5tl8FffLtOqow1FG2LYOhTyDRPHE5DtPds6/lc54L0ogsRSQnm80cml/RDT1SKIMGitqTAFDhYwIIKSjMpMPmo8uSptwhkViqhbANGLclsx3uAsB6wDgjRo3HT7W9D10e7qLrJV6a0APkDCB176Ystw90feP/w3JfdMew6cCDF1uvjP/rr/9nwJ+95T8ZQ3a9NO9SboC5tnMGxSsg7piLfyMsJktLVVO54eEnM/UY7Fnxw0NjIwxIFpL23uKIebt3cj8W20VPIhx6LENmHjtUATscnZQSCm8mfMKcOAeFmPNvyd4xOwmxbWmLkJn3avxPqq46c5mHJr731bcMnP/bxpperZjUWvI418BS8+C97+cvXXQO+O+hiYWMYNTBqYNTAk1kDzYp5Mjfx2mxbzMhuN05ELDMxUzWNdosQOheT8f2002wx8cV77hkefPCBTN38kmc+Y3j2826JB+4878LtwQBeANw8jAcxm9PDJmBQK1JzS54a3xrhegK41LtldfEkkKBBLq/+nqJAUA9fppbCpwAhJfgXN/june8X+v7dOealLbmghQapPJ22CU/jBo3OVJqrpEwvISqVdGr5lAelPJpFUNyIY5gGuFCojFnqjLHqdQEKZQ+woE3yUK54PjGCLV/G8pZhK0byKaYEediwTK1rkrqP49ZtNQU3oE4VkqcfRVm2Usci+lzCaNALE68Q4uk1zVYKLFyjl1SwHm2ggxj3yDQBP/AyBGQge/Ab5w6uAp6oZ8G+oJyAKYdxhOEyuot2SbPdtcm7/awOnLLrYbzq7Txr2rB93Hhbh/Ec0HoGoLgyq0DazeS9zpFx4diwreRxdrGeei/QMrwjCDIuWVp7ETJtUM7UqT7I86AVvms5DwY7AHHs2VLHiboPWDTNBOS23dLa15/9MPsHrnN49i23DH/zu76T6qxDWQpkqX/1lUCy4Nlc5cq9wIVtUkxlriPUEs+kOYYdJRSFPiGMKloaah6+6CLFIW5EpFmLD0HUg/ffik8lDNEL9OhmVo7EyfZbIP3Q64W+NG0zpGrBxq5DUC/v/7P3DT/14/9k+MQIBtdBo9c3i40Ag96nR0eP9fU9sMbWjxq4TjTQLI7rpLXXUDM1vrohqFgz9lye4mMVYkCWh03v0w6mgAoiHrzv3uGez312OMM0x2fgGfTQkD7Fuzfb2YpC76Cr87lvl3abxmUMQi40GWP0ad45PZE0wWAZ2RqZGIoY6YIwp1E61VHTTyNUY5+svC93HsBnsJweBze7x5TM9gpSl2GpAWxpc6yqOJlfqSaWkUyMOpXVGKF7s1QRGQFwnUCiRmg9KQK9OvAiYIB8f8gDMFq7NT5rwRy28wDA9sV1Mn2Uoq7gepZDI1/gE8a0a+dOVnUFjNsHZwCLvpe4EFBbdQkCFvGOigXOnGF1Vcp4Uy0DjAucCIqqH/XWxgNEfdGH8sZ895NAeunE9hR4SDJtKCBX/VBA06m+BX5Kt1JGG2Fj6bR/AgILZHU+9W5a50uf0FcBaQC4KYC0PjiRJ0YoINn4UHdkJM/6PQIgIex1rHWWX/hYhrjXAc0CSsEhh3KrCkNTSV3w2bo+17Y2142XxAFa6MH0O//iL4aTR45Myq5X5PV/9S8PL3rZSybsAvi48n41BAwigPeeLVF/hs26gAkFJGmbOqT95sd7Tsm6Lm9zb2u8fPYt/9578vTe88FR+IWw6QxdwDh8PKujKY36qfvJ+3bZ2QOUzYMhPIM+AKo7quRMuQmH4tOY5XtgJuWKo0fol7f98luG32I7iYdZFGsMowYejQZuf8ELhi95+tMfDYs1y57hAay/F2MYNTBqYNTAk10DsUOf7I28FtunoVbGV5NOG0zD1oNoN6a9drEWjbaDD9w33PXpT2Vj3Ftued5wy623stXDDhaXORIjeufefZkC+cB99/EjVu8LaawmYPhpDBpi5FNJDFnyrTHeK2g00gWCWV1Ueq4L2LHwCh6uc2frPSiN30hKQwQoAlp/OAU8mZZmGrILkNKmxKvVGsIBfQI5+Gv2l1FNfdJZLEZuxE28SrZrTyYYQle5flK06pUAXqZFLoxdV1p1BdQOGtSGhniMY9vOtV43eeohdRXSvQDs3bt25v3EU0zDrVA02vlOM93KlhIWO9cMdC9cqdUQ3bjVBe0U2LqZvcBbsT1HQOWkfKRXN8BD+ztjIMpIU0pXZAegCZwa0LBctAgf8+xD8wI44Bs+DXAV2JsCBgZO6NJXNiI8Wv2RgXcue1+SJ68AUkCzXkGvFdl3LT024z3U61cyFJ/EO9CM3MrXPJjwyAqh4QsP6ygxaBN9Evmr3g4UkThqC7BSkU1mBfEdR2GS/XqO/n7/O397ApzU8HqERcbFt/w3304bEC7jhrOBuPUq3GTM2xblJT23YmS1XAqkWPRLTLroE5rettwr5KHRqs+qyFcX7oOZe1odWG9lQVlBGvlFL6m/jXXHHSHed8vRDsdi5JOVaTIJ35Cu+lAmVyTtwHZV5mVcvP997xt+9id/evjoRz5yGdQjyaiBS2vgxhtvHL7s1a++NNFV5o7vDl6l4sZiowZGDTzhNDC+Q/g4dFn3Blq1RqD+tcQx4LK4Q97xIQVvVU0LZOP5Bx7E83cs2zo85/bnD8+7/fZ4A08ePxaQs3fffgy05eHzd901HD92lA3qF31ZLga1q4TCmil+TCOTLelZuIa49TudUSC3jX30FEXQojdLY7I8RIAZ8t1gXaAobzeil6nexxjqIhKbAZ2G4qbF8mK4QfxmpheaJwDWUJZMOQrQAA42IQ9GqHwCMji3SW6powpTtlXBCVtVhvAi0ThV1iIuZkJpdjyfRDRwLWticfGCfECi7THPRWg8ayQvLZ0ZdrGNxw3797G3I5vOAwBO8N7mCu3vRra6EVxtRxduTH8OAInYJQs8Y2zT9p3o1LwzZDq1djv6ifG94MI01lg60eSXgUnqKLqgjgCMGam757ADLoG00wID61VstTSncJdnUj0b88I+aH+WIVl9GUIVISJZ0gpAV/nioX4tL73gtmrIOLC+VOOAIISo6qqEqst2lLcw4tBezuqUcyuZHUOKp3DI/pSDMY/G0wLBNwV+zrOoTD1cKJoHP/f54cF77hme+pznWHjdwq14JP7Gt71h+KWf/bncOyqw6hVfqaXeCqus8QkB45u2kB/paRA5XJtm/4c0uu2AMrRpN98FbCJf9ZAAD+vJyLasQV0Qcm94iT5xaXNNH/lC6cwXT/VjyAPwlV0uHYAiWMlTLItw5jMPfrhXriQcfOih4Q/e9e7h37GlhPKPYdTAo9WA98dLeW/Q3431Dif5zncP2TGMGhg1MGrgetBAsySuh6ZeO20UAGpveRiPacQPW4w0zvVU3/yyys4xffOYU98w2p79vOcNtwAGd7Lx9jmmMJ4+cTIb1euZ+cLddw4P4UXczfYTO/AcaidnXz05Yxxaj4afALFbewJDPWga506VFFQK/CIXniG9C5s4Oy000yyh27LA/n2cI5/yWg6WGqKmV4PwgME7VZFgjba0DGUl8VCmKi9AmIDUoAPyJamCsWW9VM7s5xduTHeTBNkKOEjRitGOJlRSYmAnVlNJfc9PfdoKganvuG3NkvzLef/qxgM3AAZ3xHvlcuMBwgG2GPW2GXntp62AQT24Tt9b5FoJBNh6+E7xTqHvIrpyqeBPkKicgmjfNfQdvMhNGZvsjMJoRCYoDtK8M+h7g8aVN/og6mWBKt7Zw0uZRXJC1BRGvoEa8pd9HwEEkmj0b8ZrWZ6+ev/PBxHhgR7KuygdDPiIRxIZAoJzJtlu5qg0ec3kA83Vt22Lbsmzz7ZQp3Vbr+8VUlUOaXJQX/RqncapPmYe8YAcmgZl6nTsKZ4KqXGYzLquwZju1yv757/7u6Fc74+//B9+0/CcW29JW/vU6akeCjRnSrYVK15rR+4TLiK5Nyl50VVvNzpSHz4MyAGJDxMYOJP7y3T5qH/5qhn5GDItWR1YA2ezfWSQByTwddGZylX/1f9NmRafBuXJXzG2Pzykzf1UFU/pLxJThg+8//3D/41XUDBY3vCLEI/JowauQAMvveOO4enPeMYVlLg8Un8TT40ri16eskaqUQOjBp4UGsjv+5OiJU+SRmgY5mknXh+nLQoivHa7iZuf/ozhObfdNuwCDJrnFgi+M7id99wO8/T94UOHhxtufEpASr3fhxHoQ/xm/QkGBYguAKORb7JARqPP97g0as86pdHVMDX2tDEhCrADoGSvQTxiZsm/G6TKZ1y6bqbbHYJKeTSLdcLTvFTuOe1lGEJn/QJSL/xLojQEZVWmGLCCD/MlUcCWF7nqQuKkVy7RFlFu2xzZQSTuW6XxrSGvRnazgMzNNxwY9uzeEaCnYeAqiNYTDxa0MYahdfGU7XgANbDPaLA7bZL3CVcAXgJM3zk8dfJUjHZBmyBQgBJd4UXEpm+hWlvAyGY1sKDQNKNPH5U4+mltsYUFJArMK4fe2EwbtZxHr8Ey6oSjl5mCQlalBNTbv+YlpJ2lZ+Xpf6XHxgNCwXEtdAOtQIZrWTCZNH+WU4oCoYJBdKSeONveAMQAIAGjfCHnUFx5LROP5w39hz4EkqgjDmjUd4CXgMninUc7P/SFzw8feu9704dkr1vYzlj5zjd9/3DghhvyUMWxUu2v/lNGReBmqfFDO0t/Jb/jaBYc5YEIpI7FtCcPBdSTnuI+JZlWWw59+M5fQCLX1mua3vmabqr+GIumWWnyyzvt+FCQyOtDEAN0PcjfoEPRllwQSOrvel6QN5fw4AMPZAXRd/Cu4Cm+ryLLHM14OWrgajTwlKc8ZXCbiT5er4bHxcoIBuu36GIUY/qogVEDowaeXBro9uKTq1XXeGsu9gOmcahRr7cmUxAxBPeyauiOnTsDRvbs20d8d4xFVxvdyrRGPYUnjh0fHmRPQvfJE+C4592ZU2wyDwDR8BOYlVlXJqr2oD92BTgrR3vQaaICwk1uXTAxJrUVy9AWmGjAtxIxdwWDGofSwBTZfMdQg9JK7Ih+lk+VNNXkSCMpQaOXSjMNU49bN241ZNXXxDCF3vYIWjMVTp4BkVWd4EGWyiWAyIXGMH9yCZBMjRreyN4MYin0CN6EkbEXnW5HtwIUkuHFCqS8j6b0vj/mlgCutLkdfTtd1PqmU4vYWIIN6p1Wq2F/lGlH9qXbYNhGjXCZZppu4oho+8iLfJwLAMp1GgqMFthTJwrTvUTd6Le+rUwrdIXJePvkhQ5sR+qgjHWkfDuZ4uIivh8qWNYbnLrQlHUoRcqQV/2nFjxM9+vDsULMvuNS0RwPWVkUPRXwM00vrEetQOr4ki9J0HB4ZeFwFTCSD41UoUxbiEUO+tjy1FkAvx4m9BVtm8QOJyQtWT/8J3+cFXqVez3DM57z7OG/+m+/I+NIz6/yJOSkPhEicnpvtCcA5GU8qjfbYbNpp+NZ717vV1MFhupJ0N0N1NISeTw0qm1lSIFJ2MjJOgm5Ny3XHrQoUt339DVp1mv9FsxDkqaryX1qumUa58nzC9LyDjG8Lxbk8elPfmr42Z/6afYX/FCmSYdPNfZixcb0UQOXpQG/S90T1N+79Q7neM98fHdwvbU68hs1MGrgWtfAxX/Rr3XJn/DyldE22wEaxE79FDzo+Xsaq6ZtY3XR46wg6g/gjl2CwfPDaRY30SDeDlA8D4B7kEVkXPBEcKNHSrBYU0Wb4YmBKKgJ6KAOjcy8D6ih2OCihqygJl4KjUiMQG1bgVIC9ZVnraZnxpCFb4x4CJXr3NLZeC2U1SNA0jY1g1I+3UCPsQpgSEg96MMz9TgNU1AUoxkCbcgAGnl5QVC2LpoGt6nlSQIsUHcHFOakFug3g7hdwETAZH5ADPmWFeDtYartATY19vA9S/UhgHHKboCyfNGjgHU7fSSA9B3CbM+Bcb7Nd7zQw1a8hltJd7sK38c8CUDXSNd4STuo0bYJFJegdzpnPEvQQJi4+g3QSnPLJA9AQv4J0KVNAYMqQ320IBv1372pArTy6MDT9lqNh/SMBUGFY+Ycq6yaZj2GaKbpu1HPeLSKQUQmGuBJnR3wCULLG1h9kf6AuNi1sl5TJryt2BDBUnOi0QtpZtvEfkhqSJ/3Rx6qIbqIb7rGCO0zHH3o4PBvfud3El/vj1d/1VcNX/11XxfwlLEAsHPs516iMtuuWEv0t43yPvVeCigjP/cQ596XttU0JY/XnMK+r2rQ+xdgBpH5tlQdFKCjnP3e7h3p3aomKwp7YQHKOSY1evvelOkD+jwPK5A7dHxEl5Yj9Pu2g0P7WznXCq5w7Aqi/+//8895OOU2LhX6/dyvx/OogavRgN9tr/nyLx/2b8AG9D4wObEBKxNfTTvHMqMGRg2MGngsNTAuKvNYanumrti9mS/WTC0tRhI1gHfv25upnycAgkcOPRyDby8/fgLFk8dPBCzt3rs3RuPDhw+zQf2R4QCeLb1Vp/EMLgJIWMIEA18PoYY5IAKDsqaYOQXU9wHLkydAwNwczgFM6h1Br+vYDFCJWDEk/SigYawbiBqjLlYvsMniJhSIMRuj2BJQOvesB6KCHQGK3igaF14xNJsxKjjTS3r2TJmcAhSN1rz3hyEasISMrB/Cp/yQFz5ayp6z3o1GuHHONmIriZvZlkO9p251rSwAvN07dgLytgw78ApuRcfKpHG8FeB4mmmfR4+wSA9gL+9Kojf3eRS06kX0vcslVhTVQN7GXoUCq0XiAqIV2uBUUfvEze0t172sERbZCxjgEUVMTewOGDsYzDW8ognUFViA3IEFJBLNRzfepbPPECHjiSU/kyA484W8gA6V0ILqUR9bndJKQSGjDJfR+coWK7L/oJ+Z25g6oZKlQDeyKnsS5E3coqFBD/JQKHPgIztKRWepr7Uv6eSlPxuto0PAXEOjxpZl4xkEHKXf5UY7A5SsFdlTFx/2v+PH2vUS3so7R8+6/fbkr9eHDwC++80/MHz+7s8On7vrzmovjalabWuF6AA9mKMSvF82Owi9TrAdRDy6vnphkuqedXq2xUuv6irktjC81S/88KBnYSjp5C0wllYyxrd+9PM+wGleamcmJA6z3DOWqSLGErooBULRfc9oZ6dDf/LjHx9+7W1vz0rFPbvk61c2r3Oapo2xUQOXqwHfGbzl1lsvl/yK6JxdM53tcUVFR+JRA6MGRg08oTXQLZEndCOeyMKXEUsLtK406DDmFgEbLiJx8KEHs7edAG8rnkJ/qNw2wamiAibfczjIOzo72BbhhptuBggtZBsKyzslsWxAjS+NR8w4DUHS9Q7GmMNAXOYwuDpjlrFvxlq8Dhjv0mVhC43XTPfDcMS70T0X5mURGrxMMTg1QGlHylOfDdOA7UFDNh6uJLQ86ikApAntdEA8ebSlG/LKL+8+HU+DtTyAMLFdejqpS2M3reHswJ49BGbu5eiKoU53C+iATo+W20rs3sP2EryLqWfwFKAuYJU6jwHAz7LVRgApzNXrIttM+O6g7wnazpOAxmydQO19U3brdrP6bdu3ZQqv23pMvHSCFP7iQUIGwbT67O2bAivpBDS2H445wzjqKKATTy3pXb60X7XAd6J4yzm2SCo9Vx/YDwLh7s1UP32BGr2g1RY8qoBp9ZQN462LI0AEhjnDWZn9y4ChrsCFoLgWL7FDP+udtUj1Ux8DJPivzOZ5JmJzwj/XVsOfA9xUUZRHyti20g2Zk9CKDX/2e78/SVvPiF65b3vj3wZ/1+q7tql3gX1RrbAdPDzhoUJ0iMxpBwSTMW1Bgvn2qb3mgxrHd7WXTPXLdTUZzlbgv+kVydl7PrqAUB6ubuuWIKbZ98vsaegMg6a20mN6IyLkwzb0MIlTtw8yZoMPpd76S788vP1X3roKDBbNpKTSjWHUwFVr4Ck33TR8BR55x/B6Bx+IjN7B9dbqyG/UwKiBJ4oG1v9b9YnS8mtAzu50CYDSVIplpmAu+IABx7GgZwljUwPMqV56mVzMwlK/YZEAAEAASURBVKf/Rw4dAmycYvP0XSxmgtcK8LGd1UXlJ61gRe+KbAPQ4FxTRQsQCua0VDXtlgGEsd9JKqPNcmRqtGpocugNk0/AS4zxiFqGapPdsjFLiWh0CupilJpPWr23V2DP6xTjI4BCA1gGZEBdecbq3wzi8MOojTUMrUauHs/KqzLKaOECSSVDFSWOLtWh7wIKfvTu7GQq7u5de5jKtzCcBAwKCOV7jBVcjzk9lzJ9ewpB0/YAy+0xnV04Rl07NU+ju6ZMukBLTQ30vU4Jl/DGRJ8IEr00g0a9itz1FkXP8d3YZDWQ5iYefSTFpnXwBGN1g567rtNu0lRjdOkHRzslvdjMcgy1qi2Q4TnxAlfqW49n1cE5/QOIUcf5K45yaTXXOVVUqnTSFxisMaHwVDPhJ6A36EWsSJ3SyB61Lb0VxEsGuRg6g9YO6jN0fVnbvXfdNfz5v/qD6DqZ6/jxwpd86fCd3/d3MxaUxCP3GP1DU+nZGqf2hR0ST16ioZQ6hXxQIE0BwGqvFN5HKUu7vDbYROMZO96bfC9YZx9rlu5jyXcc9eIpjH/T69KR2nIWgWUiMJ+Guq64n+EtMcHvgg+zcuhP/9OfGD7xsY+n/sqZftbd3GttBafZY2zUwGVpwIeEr3zVq3J/XVaBKyQ6cfRo7qMrLDaSjxoYNTBq4EmhgXHK6DXSjTGTsMM0xZz+1t8Z0uD1yaXv/Bh3RVHDGUELBqDeQA00wcw2pj6e5R1CjcPyNml0FqDzWsDiYdCwJIuA90BPQUBUcspYNJqgt89phb67tjkeyiqMmcg8x24c5okt/GJQYtDGoYUBbn6Mds6bAEkBfsb5M6xA6HTJMjptPWaxAImUADoEDaCkbuWVl4cekiUWsAmdGQQNaGN6RpTRuuXhghwCNfdObKSh104XcAsMldf3LwXMXhzkPaiHjx6JgVvv/lVbdvHepiBvh/1APaeY1quCtvI+pNNLd7lVBfEAYYXBG7mLfqmVWnMZ9QmMBFkG64ZZ2uSCPsZ7sC87ELOvMm2WzGpnUpruqoS06cpOkGTKxX0q36qzSkZNrTrzWr2cpldecKQYOo1eqy770OSMgQln66o6AlKU3zw+bK/9kf6zj6xF3qSFlx1SFYVH3sODjm4tgThVsG+J+ZGDuMuRhl9RdD7y1zPrQ4DlZR6SQPPe3/yt4Zm33To87dnP7sTrdv5LX/91wyc/+rHh3b/9O9Rk+0oSdSLoF0Bp2Ir9vOfUUe2tgQgZCJVmayZtSFw1oUT42JVZCViKKKeorcsKvTfyUMQr8tW33mzH4PlzAMYFvgPUNeDPrWv87ogc0J/HG76yPYxkVvJDaop9NBt8P/Cf/9w/G+75/Odzn9jeeZpZ+jE+auBqNeD989Wvf/1w0803Xy2LS5Y7y1RRjzGMGhg1MGrgetWAttoYHgcNdGPZqv2xixGNcZipYRhWrlTpFM2AI9I18Jw26vYTvudwloVOnP6ph8v3CX3vTuCokanHSh+C4CvL8secE7d0b1oZdnpMNBhrZVCMvokd2CLIJb9l3kfSGI13UUNeAxGSPg016pMlad3TFSLKG6RNG1vBbnzGYIYmhm4owyJ1algGxDXdRD+klZ5si5WVAWqaTiVBL6nTkGtS0IM0vkenlzNTNx35lkGHLtJz5OEjTA9lRVB0dxzP4OEjD0ef8lQHGs1bObbjCdwOCBdE2o5TeGgFg9l6gTp20D/KXd6uAj0uLqO89pktrPZTeanHVkVm+28FT22/NrkAYPWlbfDQwE/cds38mS7TDiADqAVbab8N5ghNVV3U07hC0KSEAD9k3WR3k+YR2Ja6C/DKSzkMxWt6bYKyKZP9q0y1UI7pghT4meb0XQ+9vvLyn0MdpY+tOzVEc6kvMpre6c2Xvgkf3fRC0HRdycjx7rj943e+M/daY71uJ3X+N97wrcNLXvEKBU6wLerCP+vOdGfarifPEWszDF1k22KZ6EiQ2PhkBViI805u552CnSBs6r5PnTJFL6JPo/xNVtWliLpY0sPNoeewxg/plJ2EFrdsD2kPcrmNxN133RUw2PPG86iBjdDAi770Szdkv0Fl9Xv3GO/ij2HUwKiBUQPXswa0EsfwOGigzMPZijWTNQT5gcKLoLEoINyMZ8M0PXsam1mERGAHcDnHVgg7drMPIe/AlTFchqdc9WoV0MKQw4gUDGr0adaVwVwGnisfmpe6u83X7EENP+vV+bJC3eeId1Nx1mhMMYxLf1gj7KRZRe2n9P7FCG6GsGTdWC/QUKAgrdBKzsGJP72mfDaDHgqVkpTAHGLUoXS9nMwxhF04w2m1J04eZwroUcDeURZ5OYk38AQGLQfnww8f5n3Nh4bD7ON4+PDDAMPjNbU1gLz1BRX4Xl1fOVPuAnMBvNNIDXnvDmAu8FEP8c6Qnv39KHuG/nKqr2WUtxvgls2qqwAj9aP5rj6kqQ/YUSYtJDH6bBkBeyCHfg4Ao/54IOVBvPe3efKsQ36zh+mpkXRDo21J1mko9ZJHJPKTnzRVj2QFJKu0aki/Ko9ydDlTtqaNludWJnKHCXqLDhxr6EIMm3EMYHT863G1wi6PpQxpozI3VkofXYiq4Gndpbni9ymmOf7J7/1eyq73x07ux+980/cONz/taT6lyP1l/bYjD3hIaxpS4sge8IuIgmMb0elsj8F7dDbIwfuknzudZzSVcRrdyw8ivx/UawJx0xxTWVlYMKhulcXTJcKs5q3bkPuOs20Zw6iB9dbAU7mPXv7KV6432wm/k2wNNPt7NskYI6MGRg2MGriONPAIP//XkSYe66bGdioTqptRXmnYakS6CIlGod6siZ2FISdY1FDWk7WAF3GH7w9i9AkAstedi4QAUJwi1w00jcEAO6asyUxDLt4BDMEsDhFDscy7Am3GSyptV1fYlMQFVTQip8Zp0eRTgzOyUbKSm0bLMFeWDlbk1UFoIypZqavoBDFtaqGSwM/2BYAgUHl5INawTb560wz2D4OYAgUQpu0UDOvNO3rsCKDwWLwbbtVxEkB4knQNgqwwSvlM60O3ZVrDl2qsPwvJAOwMZ/GqnKC8i9S4+Mppps/pOfRdwkxBpdACcjhVT8n0+NoHZwHg1qUB3vf8s4FOI3T6qnIvt/e44mnkWnr1FZ0Rr79m3CNLtbu1Hvq0vZ3FQaX3Su+L0FjKALscfOa6f1Cc0Gh6oinydUjKuLJzCt6qkRWGfrHY8vICTmWSVXjzEeBcF02ONjYRRdaCo6yQ69hGxzVtGV694naWNt5QmROU0XYpTqetutR5Ce07on/8zt8d7mO640aE/ewf+t0/8P3D3v37Ikvao+IQzGnV6sUx5Y3Ve7SPCx8s2Gd6r/VO28zqJ+JpWC2GJI1lTGvJxL1PnXZq+2dAYGi9f5kumvQaU/Xwie8Z5DGeLtwIhYw8Rw1chQZuvPHGTBX1nt6IcIbvcB/sjWHUwKiBUQPXuwa028bwOGlAQ9CQT3/v8qPXzH0NwQC1kAAA9XSx2TzpgkX3sHO6qAucOJ1RQGRYzPttO4snRp5eAb1SAqJeX6sWXtP6NSAVoVL0ZtQPcKZC8u6dRqwGankaOlUZ8NZrinkaqPIyhCdsNMLzg277NEz9I1NjPcAo6b28C2Kwf1rzcAr+DN0gaKxT1vR4QfQeyZrr5DdwaP22w+m0Gtk7Mt2TFVvbe4OCDEGY7/3tZFqncUGbQSAeGeULH6eC7uBdQLebUBbB4Bne63QvQg1pW+XUUGn0GJbHD88YfPjPtFJBSV+0I1trUH8BlhJelbvqZ8CAclBxwIHAgbyu115GPRomus1VfUiTP3XhAY+AaioJKJShFJ4mofh52esKWfLNK+LiLODjgHf4cS7+HQRW2+Xv9iXpH+LT6jIKrKkOM1Q0hzoLQFYXtp3guE9oDCLplJktoWxrK5UV16YheHRdyCP3Ag08c/L08LusjLnE/bQR4fYXvygrj3qf2vKu62oL/ZAxRpbN9qTSOQTB3jOCVvO8zsMA7oU0GZp6oFAK6PeGbbDdxR/Ax1W/xx1vzjiQQTyt5OWhCwmurHuOdweddVAVyKmFqqJfWQqaum9NrLEwRzShHiOjBq5eA76r/bqv+Zq8s331XC5e0hktx91z0PtuDKMGRg2MGrjONVDW1nWuhMer+WXIzRpT9cOkwbYNI1KPnxaeRp372xUgxMgDLMQQj+E4NZg1IM1zARqBhysO6skSTOrZ83dPfKVR1w3FXFe1MQZLGo1FwSTGPEakhmUWW0FR8qjfT43tMljVn8asgHECoppSCyzIFXoYBuBBW3TNsCQ7xnDK4DnhPT+BlSunGuQxGwICNfIpp5zRRbdkqSPU1ZDwcn9GPXkCv/179w27du0aFrh2mw7rsuEn0K+H70AKbDJFEx6Cxp0sILObMgUkF1lY59xwhGlG9pGyqGe3rvAdw9qqgbMeWvKyQic8vN7GZvUr8M+Kpckrw1/5C/vbdmhtN3VnxUjaEw9wwA75nMtzVkBMvUSXRtBFNZtOCrhKkjgph5mlL+gAHH38qccE+7t3cM6VLFAVWMtHsCp9Heq+19HkQn4fUASwtLwJcyIkTcJsn0eurhN4uLIrTMIcsQg1Znp9Dk55lTzWre7USa9BHdT4sgylQyOvbBDPtaR3f/wTw7t/9e3V31azzuEVr33N8C3f8e2RbcI6um0Al8SMNQXzQCZ14dGneEaXybYVkPWPipAyDeq0g+eMq/BEDzD2/VkrKE9+TVu25HkAsavgVgBM+13RAXi03HUqRX03ZDy0EuNp1MB6a8CZFn/pa7922LNnz3qznvAbVxWdqGKMjBoYNTBqYKj5b6MiHj8NaABitGm3aaFqCPpjuB1ApCFYGTWNUUB49szppG/Bm6XxJ1BkfUyedB4djrEq5hlWynQazGloTzMVUuPPEBvSlwHhL/gQEPI/CSSHKDYmAuVMkl4EeWTqGsYh5mRMRGGXPDaz6oirjWbq2cSI1JBPVYADjXXboTFrQ623QFvSrZZyGrExMhFEUJStIQBRWziU2VCGMmcsfA3ctAleZicPGkEpnyzyguFPe6XU6yYHp28KMq3/bPZdxOOJLvQQnUBfHXgK5JTHMtLfsH8/nkAAqiCF8PCxo8nbiSfQLSqcIrpHwAgQctVHp+VFJuTUMxMgjE4FlKfw7PpkWsk00N37sVYxhTGF4s0jqmfWkGl8nNEi+bQ4bRUr6cFUnX5AoLDG7Tv7l3h55oqPRNlgHjJDtnaQHwVXss1DART7KPImB3kgSfd5nbqtyGBZwwxAiPymdRpj1U+J9WSKpmYZT8pYLBWkPFw5Kw3J9jcyVpvsUbmpE7nLw3/GDZu8ZyVPO5VxmzERnRS4ko/KmYAdy3H8xXv/cHgxy9k/69Zb4bq+wTHz9d/0jcPBBx8cfv1f/NIEsHbQJsi2jT44sE9LX/QdD3scj8u+cyrANt+Gqjs7eJPTSh3v3DtcWy7BBtnmfi1v0lIHBaRyTFnXMvduxrkPcrgfNutFlBaejqtwbGzDm4/iTKL1zNTR88fzqIFHqwG/D1/1mtcMNz/1qY+W1UXL+97guKroRdUzZowaGDVwHWpgBITXQqdPjDjtrM0NELpJ+GLAil4qjbhj7JO0mykughTffxP8aJadZbGSY2yTIDjcwuIrAhJXABTIuUqkRtwkcDEPBjXsBAVlNnKOsScQ0Frl/UO2pYhnrxmHlc9FM7a9jpFKJWmKZ/80cGmPQC8eSeiVRQPWLSBilGrQNmSqAVxp1slG7tDoVSugKG/qzH+de8NMLk9Ram0GMFCB+k3P4jyRp+Rwtc8sM84UOb1vZzG6Ny/wPiZTlE6fOh0Qav1y27N797Brz+6APkHqQyxAc+bM2eEAi/l4XsETu5c9DLdT1n6idaRRj6CUa6d86ulbOc8+hMgjQBRc+z6mPaOH0umRHbibpo6ie84GjfbehkqpTz2ZNH0S7ANVKYf0h3EQne2oHjFP3ZnvwbWVJQiauBDQmkea+la3lmXHEBOMBljWoj7mdbpOm4RKh9jtLoqfUoRZeAhm5NdSEg8YSYWVWkB1ImB4+hEx/CDLsZF2mEFf5l1Zx1f6AtDDPTAJpE/aZqL1w8aHLP/iH//48N/90A9mxd4J/TpG/uP/4puHwyxc9B4Wsoma6dPcH2hAAOZ9gkDp69J7eTwdhwvLLmZkX3NAxu0Yub0/+vRX2+s9Gs1FuWsIT8Xq2PGXewmSPEBxvEZPMoYPf33MVU+sweuiGWvQjkmjBi5TA47LV3zZlw23bMDDmS6CCyllu6CeMJ5HDYwaGDUwamD0EF4rY0DTWEPMH0SNu1279zBdZu9whOWwdewtYwWe4Knm/V/8Ygw6p4Jq9Gvg6nFaypYGvm8kAGFLCkCIoZvTBQs0nkmh3DSHqFaxRiBZ7hUnSNKyVKYCL75jpWHPyRg0MRrDC1BDWgE/jdoqp7FbU9akrNSc/CAhIM82WynBujp4jMcQo3ZhEUC15LTZad2hI69qMsvyZSirOw1n2yNf7eIYwBi7rpAqCHOvQL168tQLs8lZqcRrQZ5a1KWA7KZhN6DvhgP7s9WEoO0gHli9gzfgLTzPtFF1vn/vbt4Z3Ea95Q2M4W69eE23ky6wO7eZ6Xi2E70vsriM/XaWab3btwH69cpEV8qqIkofEHNRepmAQ/PRk3k2ewW03zVRehE0WHmRlaenSjBSSgZZQKBnNwCQq16jvaoQeuOKjXQQNJaTi6TVGDBTvfPfQiSwSFWcPNOkgXcOvJtkCzrURc+z/+XjWE23SlUJjTenJqzJBj2dtnoZ76APPybpphVJ6pRh6rOMdfARAMrNpZfW6WO/+8tvGf4aW0Yo53oHvf7f9j/8nTzI+VM8ksqSWvjwgUj6qlXr/adsPizQ42l+9vAk37HgtGGB4GanPRMPwENg77R+nyt/3eu0m5rkF314pm6B4BYegljK7wq/Qzxvclop+lAe89AWf8WXi4SkboCOOv/xfH1qQG+620vc9vznb5gCfPDhFhP1u7Fh1YyMRw2MGhg18ITTwOghvFa6DGOvm14afTvYAH0777wdPngwxpuAwu0TfD8wxiNGmqDKoIGnESeH5aUzoQ9g0rBvRl3opOEIOAi9udbq2Tzi2Twez4qGOhkxomOwQiGhiVZFEHRhWgZoea1BH88UcdvgdULoLUyZVq/xvMdGG7oECiIYswKnUzoVM9trNLCb9lK/AEAaZdNz6rtjgpzUT0xBbaPyLbGHolNtl87X9houBqMx7TuFWwW+0KhGTsMSIC/ghtK7eG9wj1t6MC1UXbg1xWEA4U7KrbhSKE+ZD+zfO+zfty9TQW33FsDcIiDW/lFKwTkXAVgryknbBKGCeY/lnRj1eB1Ns71lsiM53WkLA5A4CxAm9rc6om3mc7LZCepVPUbvpIQeEGvD1IXvBk76knKhtr/IDR/LoIjSrSwFgxC2ilMfH1ZpWu+11WAwmVWkl1NeDx4bFMhRYBdMKe+2bTS9EckgYI0U6oqgk7RUDq0y5JAIXv3ToaNX1n6PnJwrFwo7GHBl7fGic7a9yxRaYcqww/Ij73v/cBsG6ctf95WyXPdg33zX//im4YF77xvu+tSnkKnuMSsS1BVoQ2Z04oMZH0IYNGJ9KFTeUPKR22nOAYXwdHxmvEvsOMh9pw5KD47HzYwv6y/FMNYBfzUbu0ad4zdTcqGdKq20F13Ku4UC8T4GGsOogfXTwPNf+MLhDvfv3MDgIjI+/BjDqIFRA08MDfj7M/kdf2KI/ISVUvtoDNeIBvoT/WyOjjfw2PETMZxdiESjzif4Sy4Yww/aCnENXA1d31uLRQuNwCNOJkCRU+6AAtPWCQq0fBt5MrjTNCfjpSDBd/bgqrUcWuu07qAU64rBmZLN3DQLw1MZuG0zzY9ovEEaoO1W7uC1SvopeOGgHvFAwGZSuTCBYJ7TY31ybFLeq7IO/gQ3OTBkbVL36iBh4opzng+nEJ5GZ+fUnfqSL3LFaLZerqMv6PT4KZOLxRxguwABodNx73/wgeHQoYPDNgurf+j27d07HAAM7tmzi0VntgMKARbLTEFFe1sBqE511VMrCBTYGrbhHVzEMI+HB0Dp1CWx7FbBL+BxgWMrXpv+zqNaiLcImQIQIF4g7vuFyqlOohc+ajXTps/Wvui20SqHenTKqucswMKLlp593zL7K4ZW3h6l0/Qj/MtrKnjz8LrSjBd9lev7NNZiOvK2jpryaF3y6w8FJmPEyuA3aZDKsvHpHXt0JuZFaHvbOx1y8JcHCQIpUfUKgBsejlmn9m4irdqp7pQbPaDzmppKGqz+5dt+dbjzYx+T6YYEPdFv+tG/P7zo5XcgGaOPgW/fOIgDyJAi4Mx7jqBUjlHTij4jPHnec7knmObpTet94Ie0BsvaX0lzijL59pVboOR+pA7BccYG48Ig+Exx+6lqTPrkw0paPZO0MTJq4FFowO8QPYN3vPzlj4LLIxd1mqgP4sYwamDUwBNHA/m5eeKI+4SWFGthDI+XBjSMPQQ2rnx44sTx4d57vzDc9elPDXff+Znh5PFjZUAroIaYKIejAzAvk558o2UIdnLPhpBZLvlezQXZQqXhLlCSr4ajlBqpdVQZDeny4tV1DMsYj1VPUrGs5SOP0FuGw6CR2YPi6N0wxfQO8tSJ3g/f9euGewcd2LhpfwAsdJ1rsdUAJoV6q27aAPOqu4CIcgUMUqcAgqv8qVPpdu3aMexlZTv3HHRRnnsfuJ9+OZFpsSvI5A0TsLgPGqaECt4EdYugKt8X1LjW4BYEqu+tAMOIxMdC8wRmBVITES5p8FCPHgIWF6fR0Le8+pFn2qOkLR592BbbAa/kI8hmPLzV9tJM1V3lCwi632G927jVswcyKqcy9+0e9LQFyIVnlU+dM/Ura5dH4CqgtUzaBF0B16ZhBMnYQs/2syEgEzr13v9UVkkeCj4c79Daj0wL7alUXbiwn8nImAPgqDvbbd/nHgMMqcuMJflTpzqtNtI209qBcMPvv+Vtw2k8yRsV3JvwTT/yvw634RFJY6nb8R8ZaZggrlpq6w3t0xuToPx19kEAbaVMHvSQqKdR/aqfalOdvcv6/ZUy1JFViVUuoU9TVWl54GQfEYdNO7pEPcGcMYwaePQaeM4tt+S9QVcn3qggEHRauGN67TAd6X3EXxvntaW9MPValX+Ua2PH0fxIePLp2xapw4vdufMaGK+vXgMb9w189TI9qUtOACAei7MsSnKGDc3dMP0kYPDI4YezcfpZrs+T53uD8Q42T2AZht4e3hqc+XHTZAtPDTuT537wcjPFkCzDr5Tbbq1+h3HWDN8CMNGwFDBolPY9+bLgBEa2gEAjuocAj0AkymtQW3cM6zLyO10M1BixytxTOXNpuYSWXmAXo3aTHg3apnFLWeXRgK13GtEL6b3dZQyX8RqPCIw18pexiieGfqvWMgnWa75eK9IWWbVxB54+vYJnAKLH8dDq4eNj2Bagt2nYDtA7sHdPAKHTeQVTep1Wlln4A70UECmAgSIDYs6wYInT+2yXenClUw14+ynvbCFMPIIm+V4Xtr75AjsfZrvf5CamqWrEVzvpR2QX0ESBAh3/VCM6Uqe5mOjZBALXoWnxYTPleM9xU86V3/UoSQ+WllU/15W5pPAfMEWkeNdZeWpk1gMFr22TuvdIn1uA/4hJ3L/GNWfp5H0+Xu4mvGXSbMeXNajjopNneKsC5lc6ZtRlB0HmVSjdeUvIP/ILNL3Xcg8tD0cPPjT8X3//R4Zv/f7vG3aweuxGBN8pfOMP/r3h//zBHxo++ZGPUnfpCkEA5yxQhPxKrI0cuXKl/kpnmTHQ7oECwlLbDM7qgrhtMzh2DPH+WX5L3Su+R3j+PA+A8Aou6K3Ge2lZrwWFi8iYcvlMdyXWH6i05PE0auCqNODovP0FLxheziIyk9+Bq+J06UJ+B5wEDG5kHZeW4NHk1j38aDisXba+L9bOM/Vy6n0kHhfnfvk5lyPH5XO7esrHoq1XL9205BNFzqnEl4rZmm4bXDAmJ02dRC7Fasy7DA2MgPAylLQeJBqkbgVxlB+mo7zHcPTYcTY3P5HVQAUNyxhkggiNP6cSYsnWlFBAEPYboW4NDbbcIO17Ur4x5IP3Zm6MFtVotqx2YgJnjUpD5VT5mnKoNV0Gs+94WbZWw4SY+rL0PVa23jlDvIOhslgJ5Nmj6g0ZH161SltSp0u5liWLgKUOHuLZKSkFh9YQQAWvJUCYBq3L5QccWoZ8zPo0MADAhpZYrVZPylcGsVk5KOtUQ3kfxyt7yM2K0fvu7Wz/gVHshva7WADG9wr3surovrbq6Jkzp9i+gtVQ9Q5uwXh2ei39l5UbacwyeadP1tTQrU59ZTquegDOUDF6R/5l3nGMZzDohXTKa8gvwDMtRtd9b0RBYJoEWXKjpzL2o0+6T+5pZZQZVeTa1UZTL1fGqQVeAipRkySTkqHvH7JJUK09QnnHSf5sSos3Sk4ArXyaQr8k3yhjy/EjI+J0aRPQPjGJNAFggJlEyATIXWQ6Lt2chwEla4qTLaBrvGgDmo9+Us5kdHjunIDd1gIu4Wc9gkXTSmfRMtc8gFgmnXEPwfD5z9w5vOvXf3P4xr/+n4ceduseDtx4w/ADP/ajwz/4gTcPH/ngB8NfmZQ/Z9ocYJt7jgaiC/vfMeRY8h51cSLvmdw3lNzkiqEQZSEa9CF4c9x4bXranJ6jufDq6b6rKxCs2hnKXNsPtWTU6qZH5aFcnT5ejRq4XA04Dm+97bbhla9+9eSBxeWWvRK6ZR6oHfrk+4alU8dWF3MQrwrcDIz2jQkXVDapZj5n/neSOxraeaoqXqm9BPf2hOtVRGYLq4bZ67XYrcpfdTFHXTq9GEWk9vdkLtS3cn3XzWXl8mL81qLtabO1XE35zueKz1da2Vr0s8LPCzChn0TmKabXkoTXhQzz0zOlXBW7kHpV9mVfXIaEjL1OZa1lP/jbt2kzNpazf7C1Ni1sG1Y2b08aj/PzO1evfijKekl72c160hCOgPAx6EqfUB4BZHzmM59mSuh9gED2E8SQ00DToO0gQUeFxn+9+8StwLUd5P1RRrU3CjdIG/ABPcqvlZh04yaY1CK5qBusQFKypx+NLnvhCUownV1wQmPTzeiduul0soAHcjUwVwAxdc9ifFKhxrbese6BCPCgeuWsL3YNd4xSloHscmkQxDvTb/4mbhnCAlFKRzaNeXjRBFdfBbaVPgTMQRYU9AyRC6fU+5TWM1FFxQUZ0Ll9AjVHW2rSFpguWDh5nHcNeWdwkcJ6SQVxuwCA+5lCupNtJdzYftcO3hfEbSOQKz0sZ7VQddR/xLpXSkCowe3iLHoeqSleV/WirtSd7zV2nQgMV/RuoasAHXjyMiLtnXpla7qgyirpOdnZOZlWPZ1sPuyvnCpHVVFn0hGmxdDttHzyJty4CgMoozdzjXtY10x9ZrVyiWWcwBc622sNghZaFipTDKYHGLZ4iWte9aHjLTzob0Fha7VFw8E8Ne8qo668Ysn6rHFJB8TbJdjX2y4PeTv2ArZWyhtumVqcxemTSMm7nO9+x28New7sG17/Dd9goQ0Jegq/680/MPzI971puJOFZtzTM97kLJkKCEZmx5NpjnGktLszPfksYNf3jZ1qZ1uSkXZ4/9hF6Fw+FPTBjgDP7V4SyHMMbzGNBN+fXcIlvQlerjRqP/gdsFmvIaENI5nOXCRr/Bg1cEUacCy9+CUvGV7GO4MZt1dU+sqID/3rtwzHP/aHFPI+qKGbrwDjk0GdLD56Tr+eP1cBy3kPrh2mGd5/q8LcZc/ryZb0u3h16Llrydspe5n57+Oev8Z5yrYyu6y9YavyO/8ZPp2+J0Hfa+/f7V2dktZ3ssRTxmnvTPkeDVUjk6aX6OdON6mnJ7SzckS8GR5zJKsvZyrpdfQW57rz6ZmrS9dV47EmCcKUbiTqnNdi0nLXYkKayRcrXUWq3XEQyH6GTy/r717xuJDTDPmqsrKS+sIS5hAoOCk7iVTWqs9ZulUZdbG6jtJYeFMu9oO/VP7ObWE1h627h5XFvcOmbQeGzTtvGjZvfworxu/FYHZGDwv/TSRSoOIsx4u2YQ15rsekERBuUK9rTGnI+aNwnJfZP/GJTwx33XV3DDwHqKAr3iTGK2QJi8Q1zvqXR951MifjuRFB41L7fhkKJDqYCNFktE8i4WZeWLQrWfZg3W5I7R57yqrMyu79pJeAU0L/nfBCXvVjB18M1U2+AyeY7Iu3UUg+br0gA2mLvtKNxxDgbPCkhFUHxmzz+oEFUr95sYctF6ChgYzcxAVi4UFhAWOmUiJgZLS08qlUZFPfAkJzBYJO9VwhU32rUafWLQDAtqKLHQA/9yXcxZTBvXgEd7Da6FbbKT38ttAHGvN6GwWOVuhehGeZHup+kIZM82T/wcVt8MPb6HS+zeedZsqKsRjjGulOy3Ua7JYsIwm4iW6QBxqlSr8YQ3B1Wp4fNAIAymqZ0AVQCbYoa7s9VKot02uWS+LxElIPLU+qNUQXnKwrgcKJUT6pdRE68ydlpItiK9XPuk7tXnJd8oQ38TqbzuEYlpS4/ZtquExbQgC15wSJoKL/HO+C5fOpu3QkwzwMoC8CwOVGs+Vl6/2BtK94wsF2IS4uI9PSlz8wjpnUpUAceot9p/LU2dPD773914ZnPveW4bYXvyiSbMTHvv37hx/6pz8+/KO/90PDX/zrfxN95P5hPKoC+3XJtvPnf+mCDNogiN2kd5OQhwjQCnz9IrGsNLmfjfX+It329ncRJXT6sntz6nnctOB31/lhO7pa3C4H+zEVJy7XMYwauBoN+J34whe/eHjpHXdsLBhk/B77wDuGMx98B68DlKTttFrsSaLj+3KC30uXQ+c9tpoud9Bc2uxlJLgAEHYeUNZ/7sSkdpFbGe9TQ92ria750elWZXZBGs8pMO2VrKKumlqZ8OvlmwxT6rXLVz6FJuVMmaFdpbxKn/4ecF3/rZqZcqT4G72a75Sz1XXqSRWdF3pc3ZYpm5RZJeuUjwyT5cccjQLOJk3qNKPXa7wHiPMb2K8n0nY+EMwyjGB+IHlLr1982jJLF34zPX9B3qTCmUiYT65XX1GjFczzmb+elCZyqbwZumpNo29lksaHuokcJ3EwhCH7Om9htszWncOwA1C465nD5t3PBSQ+jd+xXdD6QFMmZYtUNZMa6nL8nGhgBIQTVaxPRIPVzc3vuvuu4cEHHgywOIM38L777gsAWGZEa7RmgAqYGLLeWBpxTNLiR3ITK06WkWquxl/QTyjbdUsvQOhFC9Im9LN3jz8P/E3yGgknqQQMWxfZR48FRYQQSwAY3187h/Gst7IvfR/vDoDIQt5aM18t8IYLU9U4YVAKmCJ5aGa/lPoXepfbs3ziBeIsH+v1rtcg1asjcKzbGeZ80efdMfSnwaxMQEDSbBPxiaeEK3kjq0CB1+TyTlasZL9RyNDIrkVe6r1I3+PznSlBn1NEF13tEy+hwHArRox8nGaooe0qob5vuQCNOiw52RSd6UnLgEwXl1nCO6iXEXiR9imhK4xu5t3I5XMCPzAq3tfz0Bm6btJYruWrHtWk4yXAl3rjdaRf0JColFzoctgu6HPResf284foLaBj8bMdFQ5kmIdOVEsPPapM6rtCpfpp7a3nUndUako6opVulZqXHmzJE15chx5ZrMJalD1kfsCrWFiX/Z2KQ+i1VU35ZhS1NphX03D1MvsunGNIQCi/M44vgmPNhAL4NQbNd+rkwPRgDVe5nmSl35/73/6P4e/88A8OT3na01J2Iz68z77nf3rz8JP/4B8O7/ujP7Jx0UX3oGxqXnvfVe19FTVJpf5oj76+dEETsANAv27yoIR0x89mp95QLlO+abT9IE91JS+IeIcZb6E68B5EtnoYYScgGmk+qMi9Saf5tx4h978PyhDYKdM+4Fkv3ush38jj0WvA9wVf+KKNe7jSJTz9+Q8PR//srZN7pac7vOt7Kl8xSTbtgjCfyBDvo5w7psh7Qi8c5v3CW2meifdpK5qyfretDvPX5k7v6Tn68LDEnDz9C2I168mV8pcOLDtX46RNRPI/V2fj0ktNZZuwn4kUj5mEC6OT+syavWjxnPzIt37OTbDopfQ5Sztt0eS7o2Xb1C63PzrTeEv3e1AxCBapWo1UrOeZvyp0/v08m9l49qyZWquCWVriF3ZdL0nmTHRSLGmVMZFvJq3T2YRJfk9c61ysWs6qi9XUa2WZtlb66pKXvFLG6GANPqabHOvS3/hc8Zu1zBZep05wHBxWHv7MsLTwZwDCpw6b9j1v2LL/+XgR+d1meqkKKB2swfySUl0/mSMgXOe+PsXqhB/4wAeGj3784xhhTuVi8Aki8GQ5x7k2mi7DKt6ejE2MOSxeDSK9avVFVqBBmz9GvoOfb78O7PwirMG9RgN6Roz5frGazmr9ctJQrFUtfd8KgIJBpnHqfodljCIzLARQ0uZ9vSZHGYn5eWHKpXBWQFtGnEZdvIcN1OWp3SoRzC9fVfcuqo+scEh9xVvQVoAtxmeEthYAQv0SoJWynP3Cy4+ckZjGygLoCuBgQ3gNV54kUWn6xNUw9QL19u8U+LXrbYDjTAGFwTa8fplAhyevAKP7I4KqmIp3bglB4+krIJvN5uGzxJfVItM8T552X0P6Hh0469OVSJdPd2+O4I045ZVYwoWF6nn1ZmL3uPkF6GbxAmB6gWmP5SkTdKoX+8bgOOkgoBL8JHFusABnKYeuSY/fKdUJDKCWic3yj3yuVgX5V33FVzqpI4LlpbZ8l4nLGkdmJEvOdWE5YvZbH6rFrWX7lS8fErdwI9CjoBbKoAvpHC/FqVXsbWWUMvarYDuAkOvch+QJqPTiOmV32AYd1wrvuFUa22w/6lWTuz9C3gu/8pM/M/zX3/vGDVtkxha7JcV3/N03DjfefNPwrt98R2TRA+i9o5wnedC0Zcf0+0HAxFMF9IMW/HcKKGkmO4a8h+rhTDKtgrY6miRvZXJB3Lbzl3eG1QF8j91373Dv/felL9W1h8V8eLSTfVKdTr2T92prgaW2PUzGKEq7gpB6+d45wvvVBw8eYjGlM5mGvW/f3mEvizgJ5mfH0BWwHkmvEQ34PfqKV71qeN6tt264RILBw7//4z7hyHdB/82cVuz31ewYdfzPhdnsuazcBKbNF1ujTK/GWzRhFQ2Jq67zVdQI1zp1JmvlXUnalfJ5JPq5RnRR8qXeLzqPi9B2sn6ekPVyZrT4JK8Tm16/nT2lzqTP0s7GJWjy5d2z+bxVjC5W7yqiNS+mEHPN7DUSW12rBF+D7BJJazXF8dfH4iWKXnbWWnVcduFLEcJ4Xs5pz5Zu6me+JOh5mi2bsck2rbAQ4Lkzw/nD7Df68J3Dyv1/Pmy+AVB4w8sAic+B+bZotmv5UqJcj3kjIFynXvdH59TJU8MH/uKDvCt4F9MNd+QJtwOvgA8x/9svQ86M/AzMpNfXnUZccAZ2W+wvvrSyl5uGFvTeBgFalMwCHNwEARAz7ZBnbhf5Sm+dRozXKecl6tqKN2QBD6ErYZ5GfsGZ2z3oXREcFGAQOABmBHCFZcMlBnlrT0BCu5NjbEIRedMOCrVyAYaUUb6Ub0amZmqWzIdHpv9pkC9Rv+Ar8zwtwQHhCitC6sVIa0jqber60WReiG794S9A5rXbJCiIxqWAMKa1bcIYdcrcMqBgi6As8lHWxuq55VrPSgJTSs+es0byZMdhHZJuiy5dOIbvJOizpcPC9uhf76W6XgLQLJ+EQmCFDDG04/FTA4Bp+huznDgBGnsg2y2YLWfyszw7Faqv9H28sgrTQos6Wuweiy7742efKiyHPRDeEUWoJVX1XEh8Hy8RCcyCPnyVqUIHj60Li4ws8ztNUcKjpVR6jfvEBbQIOZmiFN0jS4BLlYtUVB4w6qqo/NF0rmm/fZNK4GYxhBEMuuG88tbKrYyp6LKAoWNVoFXFlMKjyhkVDOr1zbuiVCSfez73ueHnfuTHhje8+U30Y3akrPLr/OlDh29+w7cON+ONfOs/+/msRIzw8dIJah0vNY7pM9qkhgRxervru6W2P8k0UXVJO+2x6tnqf9vjQwqVqBa8r3vwVjOf1OQLjs9FV1WXpCunWImVd6IdQ84IcNwLEHcDEPcA4nYzzTrvJOd+U7u9/l6LOJb9QZmiqt7t1yNHHh7uve/+vF9dYPb88MV7vwgg3DvcfNPNwwHe5dzGNi/eu/IbwxNHAz5g/Mqv/urh6c94xoYLff74oeHhf/UzOA3YNsb7I98njhfvFMe23yOJXtHHlY8567HOqte7wFDfsnVPJK9nJ7fu0kRnPqx7VuZVRSb8KdAy1mreavklXM1lpjqiUk9lWV12Stnr6d8vF3AkodP0mA/YLivQb4YLyE24oKLSz5pySnsBEzmT0YQxe57tWkXSnRadDasI+8WsgD3NQrPps0xm47P0s+mPFJ8tN1uP6f36YjSPxPsy83s18+Sz1c7nXdb1xRhX4VW51sXNsgXbYAvgcOX0A8PSvYeG84c+MWy58QUcrxg27XgWRM6SGcO8BkZAOK+RK7yOQYqVdOzoseFDH/y3w2c/+7mJ0VIABSOOQToBhX6z58uOc4tO7bH6uTB72dUvMKg0ugwaf9pXUvihjZZFMDTQMJJnQWG+5yheJS0ATZ3qhwWCvHuEcbUDQ47Sw5mzTJXjWhDgQhXS5wtQJqT7176jJ+3zJ8M2aqTp3UwZrk3LDzFnQww8blBDvIHKDAha3sQ7fCmlkZ9mpY2Z/ibgs0IMx7PwdkN139XDHMaArRVG5YdaSk44WJ0/Cp5jPGPY6+1z2wgQX+pQNgFDyaxR7XRSPGbw2i5ow0uY+vGQ+E5gwKJT6PC+HGdK6Lll3gmE3iltFsrUUTyPW7ZSFhmdZqcX+DRyq9sISDsWNtd7hsqswLZ3IR5L3vFiAASAU6aANXnIGRAHnYCLK+qjbayEmcJ8OHU1epfAZJWhPtWbSihCzgavPamf6h9/EG2/Z8n5pChl5ZHrRs+pCADnYWOv2QaJ4EGZlGt8OKVP5Jkgy8lgMtGEFlKfbB3rpJvF0cuGr2Why/gXyKS3HETohr+sJ5OC8lFN9nH1abxkyqhM5EVHRqBXDwG1XJfHmXFoVfR39mCER8qQZq0P3nvv8I5f+MXhP/nWvyWDDQ1f+w3//rD/hgPDL/7ETw/Hjh1N/d4D6kFZHWOOcQ+Guclpj3m21Smd8YIqO+1wARlD9X3Tg9e2lz+BpsEZCurP75t4563TrSnIO3dOHhYoWpVzzvvz7PGsnmyqe1keOLB/uOGGG4c9LMbUgaHfAS5SJVC1T44fPzkcfvhQvmu8FzM7gTzHge/LCjT9XnRV5iNHjgZw3oA+bmRlVoGnupB2DNe2Bvbu2ze8DjB4w403brig548+MBx8x48NS8ceWj0yHmGY1DhqY/pSUj4Cn9WV5k6Zcsv330zamrxIbHSTgopF8mVINykyH0lV83znieavZ+SbfNdfhMbv+wJXl5YydDN859mV/lYTxI6YJWz6mE0q/dCLq4tOlUb6pSVbxW16Mc9vmlOxtfJNi4wVyW/abLm1yszkR05oJm0hIexke9Gyq78JV9OFY2qYxmYqnIlO6pxJ69GUbfVb23y7LCvNI9VRfdy5XubZemXc20+8tzH18RHZ+Si5TJjS8wLPsOXsg8PSFw4OZw99ali8GW+hwHDxqdDz+xbicCJ+fYcRED6K/te4cXrT/TzZ/gh7iT300MEYQGFZI7RGqgMVI6rbUZMqGYMXpDE4HdQOT40wg9hQb16mHmoAYoRNnuoz8P2ud2qYdfSplFWSz/kKZAx36Xbu2BVj+pSLoMBHz1O919amMYZUw7maYRnvswowkhcJAaYYqAYNTg28Hvec1mAQSl551N9k6N988tXzkal+tDWgzCqakeiegmkzaRrDBst4ZAVWhTRPrVlRfcRwdCP4ZbyeWpga0NEjBm/AAm3yehuemd0AON8ltF+lc3qh8vilofROx/M9QD12ARPQxkinNumVaqntLbl9O2CQIKjczHzRLB7jFEaOgD74dj1lP0JAL5Klzrw7mIbRGvvPPOkFgMEENlCJ+OObsXtaXWhGNZRWoCBuGcvnSGYIqMt+IhmijB/y9IwKQrO3YrjUWLSE9P5XqJTEk0au5RpBcvmobihZe8mQkFQ0KUzc8ThlnVqVC53KxOmyKz5QUEbSXIm1gzm3zQhoVPkJ1Tf2Rx+L6jv18anOzfMcHqSZVzSdLzQCIYJA3em16tL6P/yn70u5b/qW/zJTPEO0AR+OtVe97nXD3n37h5/5h//7cJjvFiVVTS48FACnHh0TWSPXMVhTS+3zrCoqNbS2d8V3fJucZWTRIMtmyjJ5jhFCAU3GOryWV/Ci8yCFxNStjuxlvn1a58oePgA4aqFfkIR786GHDg2H2FPVBzi+Z+t7iFAGoOZ7UIm9hx1v6XjiyFL8IUVQpYnKAbMC9OMnTgzHWJzLVZpvuumm4elf8lSmrPL9ZT+GkgJjuGY0YN895eabh69+/et5D3vHhsu1cu708PC7f2o4d+jz07r87jR4UiBP7VxX/bPfGf16rfOFNPMpdX9WalUzTzHlm+8siGbHrt/nFwRo1ky/gPDiCXJds9kXLdJrnMrTU1YXKenXzpNyJqcpfsqxOF0o1xxF2r+61urQaVpvn98ZhgnPFpnjWERzn/M0necqsgnjaeqqJH4DUy6J8xynZS4Vs2hKzhT393naqLVKzxCb3el78kTInrCaxyR7dfKaV0W7mk+69nKYXIRmNnk155lmzxLNxpFyWv986WkTpFnUcXLq/uHs5/5g2HTsC8Pi017DIjS38tPj4jP+Ro1hy/e+8Y3/y5NFDRr492Ew5Mt2Axslf6dS3X//A8PHP/bx4ROf/CRTu9iuAFCh4R4jifpryh0jMYaPIIcvDMasXxsamPPDt8Z5fa3lqwWC8gI6JaymQWrYx9iLIVW8Y9iSH45WQPCq+E1r6THlEPxtYwPsc4Cf3FEQnwM0aRCa39tQzLhZvKNiFROFUfKrgpAor8ZZX4QmHga8BfmRhF/eLURGiyhHDPDcyVxh8Okx1NjXM6PZaV9mP7+UtRwloU950pTRMAEN5OX9KQxIg2ay7LMADMa1tzvicd5EuxdjrEZl0qlL6hOQWqfvnVm/q5DKswxbzoAEF4zR67GVqaN6B3uLNHDPAAZP8g7pAuPA6aJ6aHbu2BlZXWhIeaRfwdOo8ayOBKB7djHdjkMdkZ06ax9CDWPGA+JltdEYv1FDeMUUtlHtFyNApyopotSWSmk9NZOXbD8MXqMR+8LDuvtiPEVhfv1VJqmqPfWFgxdTCsu3w1x5VJ2rM+yzPr56P3b6FGKcyad6uHRglQG39mRk4MTZsYKLvNXGybHRSup1ckGnbawQ6/t28YJTwvQl+nlh0T5cGE4xdVHAjibSAIf5Zh4inGYLkryDCOBZYCx3wPLFz94TMZ/3ohcq9oaGGzGqX/VVrxs++5nPDAcffDAeOXVXgLa+a3yA4UONeEUZU3r2/N7ILQLtrK6jV1SWe6vpWHVKnyDBTBmnPmdrFGlVNUQehsRJ76HqqQRpvSfOMdZ9ZzPvZc54CMMrjPywHZzy0blNz45ReTtubKsewyNHH44OvJftQ3KnBa7BmPLr2fRduush3Hb77cNX8EBjO/feRoeVs6eGg7/xw8PZez85HQVtLPVR4WXiLX1eJvMe6ejfMtK1n8J5NsUk4/qiw3lVmX5vThIj5ORq3SIXafaa/GfbZ7n+2zBPnHtyPtFrGeTgI4r3ooWZqClzl0W0ZmIrv8ZpXocXtPUK+fUqLofPLOvW7au+xsy/kkPiWZ7KkvItPfGeNnMO3UzBeTptnfm0VdePlG8FhHSn55mjC5y0y+AzW7bHZ+WTn+mz91unW+vc6ZPHxzwNrCqQYT1OJ11hAZrzx+8hnX18t+7ioaUP8K3x6sL8GLw6Lo9/qREQXmEfaMBq3N95153DRz760eHQoUPNQAJMxAgTrMwyrYtMGdW21/DqPxiNLB6upJUx64D2MsYyzDT+siiKaQAVgzQ9VFFM41Zx8hpBN47LYIr5nMLxYHF3OxXSZfbz3iDGlvShna0Auk2AlT7o/YHocWXocQ12QZJgx7QODmWVKXgYrSSnceWRQHL/W5sENHrlDAJVjdb+wnfP09i1TLyV8CqQoAbK82d5qxAQ6knR07FNT4XT0EAVArod23cA8pxDXmDQxTx2kuaT7OJXAMmqBIaGLFCCTOopU2qpUmDuu1DnmG6rwbvEWfChV1AdWhcMArIdG75rpffNhwfKG3AJzS48k25rYT/Xvo8lt90pUDbdqbbq1D//ex/0NNtceTGfG21Upfgpk36ij0JpAUt4Ru/1V30fntCFRBmI1FTLpITXlLNpqbn4KKNM27mMCa6pNulWG87Fa5qWjGBNezM0yJDx6z2AlzWA2bIRuk4Z8rIqdpN7Rr5nmfrse2r2tyBeb768TdfzZ787Xs5Ao9ddJj7QcWzFWw4YElDWQ4EFeNMMlOG4/OzHPxnj/hm33JL+ofCGBacev+LLXzs8fOjwcM/ddzFGWKzIMUEblc17SfkDtJGiHkrhJWwATHXFk6geKaduajppfTukD2hbzuRFnYwJdSVQzr6k6CvUZLZY6HN/9pa3surRw3or2iN1Kd/6riLmRX3UKZklg2WNlVxFlu8Y2mwfPghAFuRvT/8K2B3b12awDdcDIPQ7/45XvGJ4BauJeg9tdFg5f254+D0/O5z+7Acz1KyvxrMDibGT77aSwvT69qtx2Eel54uFi9FcEhA2Zhn/F2Pc09sY75eTRkwS1idyWbLMVtWUUuUu1JC6vDC1MbhoBvlzeXOXxWDNxFnh5uJzjZu7vKDOudIXvXwkPvNiep20mYyZ6EXrMSNfg0bWKHCBHNKtFWbKzkRD+Yg85gusxZ+0C/jMlpuNX6T8xZLlGx3M8JiJXqxYpUM4oZ1EpkVmk6zHA3Nw2Lx0clg6gQNp6QT77QIKF3eTd3XfWfnOmVb5hI1dXeufsM19dIJrxDi98u477x4+9elPsYpkGfaZ/iTO043BaFs1AEnSkKwhm0iimttlWGGEQ1A/MBKDISSvQs24E2xinGH4ZSCb32txgPMXj1YVz3WMt8nXjEQc1sPhKoG4EVKFe+5pXOkxkdxqV9lV7Q7KqdWqUalHInVGlvqQxpAfCy5i9HGuqaBNhBA5RbAqc9P2VMp1/VwDtGinRrd6oBbiBcygjNGH+TvxsHVACMfo0UaENWeNgS0ozNU9bZvltgJ+t/K+n5wFA8sA2L7vnICwpoi2PsFDKNATwJ1fcTVHinnQzgXGwZasNqr2rUfvaE35VOAYrjHaoQV4WFBgeh6vk++HapjEoFeXHBro2W4E3sssmrMc/dFw9Qfv9J9Vc61xr6oFAp67sU+MK9PlbYa12G7rSwKfbdwx4NIVoZA/haD3fTzbKG97xOmITutz2mSBu2SnlPVVjVy2SL4Yezy84GJbOqGVOpitxDT7PWdTKuJnySN/RgX6sb+S7IminhwXbpGCqHh50QVtyHgKafFyHDkOBIR68Je4Z83pbVe24s89RPoi14JFg8ZtTYNEB/SP4zMaR2bH/rve9qvDPt5pe8lrXxP6jfxwRc9v/Z7vHp73/NuHn/1H/xgdNA8ginX8+iBBGR1L6lRj/Dzje4nxq0d0sW3Gpty21yP9ijLr4QpaoVzuWc/owHHnCp9n+H7wwYQd5p80aii6o1ipixQjUe7amrBM+pjyBleLdZT57/BLbsrbhlCEn0mJpNOtG9l4QOV4vP9+QCGe+Wc98xnDAd4aPSXmAABAAElEQVQx9B1GZRzDY68Bvz+/7q/8FaY573tMKl/hgY2riZ68830X1tducIdaRgMfDqkMK87zI6SnzzOafG/NZVxAD8MJz0lkrpD1NrnMyV0Ebd1NRdtujVxMSWW4usaeN0tfHC7+OSvWLLdJPNVwFeaVWvwrnu//i7NvOdJWTRfTnYSzsrSCSetlVrev6u90ky/vScKlI/XdMqUJt7UEaCTtK3RaoMfmxJhvRJe5k8+f54tP8i8hy4TmUpG58v3ykeSZsGwFZuXrPCZpJPS0VeUuSJzkXn4EHqmnny+/ZISatrNL289TRheISYKPm7cunxyWH/owv5Mnhi1Pe+2wafdtZDjFPUZEY9D5XcBlWsGTJDYCwsvsSI0gPYOfvfuzw6eZvnWcFTl9Oj4xjrhdarj4OR04MV8oqzmVKaQONMaXQ6wfWkP54u3jbk4mDTd9d7K1lm6Yh8EMbYy5+W+/lm+eXioXbNA4doEHjULBrF4vi/llHMmVw0gu6tZoUZIAKAAsPX5YoTGMQ85d6bS8MjS10DuLMriTjgyREcGLvZX2tsvFoLGtrJZHvzYSuU0z0TZ0MJkSCO4Xgsa6Zf2Td+qRwHZrBNMDAbBZgEceAh3k5GBd1+jgnGAN8FBT8NATRmc2nYene7fJWd75oNLZ6cEa4gJNvX4CCftIUKZXS+P1nF5BDHYNa8GW9H0FyHilmsyC1Nqsnnx4WLbaZ73COgEijFVLO5Sn9KpwaCDN0qC3CMa/xBHafCkct4nk2o8GLXONxirfKyqXt+RRMWXnh5jyyT+nMPbaQJrlZwo0Ungagyv5rSRn66l+DFCQg8w92r2mYCnZ0kMN/95C6dWSwyWNgFgaRbBftmxhcSF04tRoQZQrhjo25eAYW0FpqY64757qgXOc6CXTy1zyyNHhvzT81s//AiD/3PCyr/rKykvOxnx4f339N/0Hw9Of9azh53/8nwz3feELNX1cNdom20yIhxM9KbvyZpyrWcaS8axKSh6PRidlpJuMIRVAsCvrwQa0tNUBnSr48Cy95XrZnpfCxSI8vE7Jxjf59lFxCzPj6VczGcD2SYZN41NlJOWPem1jZg3wXea7hXfeddfwTMDvzWzZ4XTuPqZ6ufG8cRqw/7/k6U8fXvnqVw/7HjMweG44+ke/MJy6888diJdsXP9dc+z47zC8dIm12V2yjJlzY3VtLo9/6iXbkTZckuLSDVh1j69NejE1zdb6CF26NuN1Sr2cui+jmY9Kmo3mf6XCXazPrpTPPL0LLhrm23tZ9UGUcqvuvdlRVLzX/Ewn168E6+oPy0c/nQUDF556bti87wX8nl6f7xWOgHDN0bI6UQPEaWWf+xxg8NOfjgHieIphHVKNIoxELBgBiz9Q/kgKCgo0kkbI8JuM9Pw8Jb2MY02iMG2/KxJq/OQTQ64Ahq9L+QMXkxT+uSGsCwNKo1WgM7kloLO8H55c3GE7xlICBrGLmTg1swPM0MYKK5LJZ6/HBOsOSCHCvwKUmdjBi4k9pMW5UIcehnw2MsFRUibFbAteD+pwIZ1u5BYo1FMDobyQ06mm1i3wiixyhl8uZyshPYYw7d2kpwhPXra4YPVEdrYoPtDr4TNv0yankLqRaRmebj+Q9wVpa4F6iIn3fHkL8LJ/IYa2hnvexWuCBJRpTNt+/jWybaNxvaelcvuojROy9Gwpjx6wjC0VR52WjQEeCGfcjNkAF8sLeOHoBFx6hg+uUn31SXhY1kQoCjTKR72SRl6Ni1YHdJJaXzU9hUMn7bSn5UGQrW0zmjpsn21pVbZ05Uj5EPpRIZdEzY9+OMshrwvKuxWKxqTx8Bb0j3zbYFw4HA8qZe2jvFPa5KrFfBw/0MBRD9uy/Qed5RcB+L67efbs6dIJfaFO7bv+MGiJhynvfuvbh52sqHnbHS+NHEq3keHFL79j+OGf+onh/2fvTaMtO677vnrz3CN6QHej0RiIiRhIAgRJiJRFiaJESQxJmRJlWZEzrZWVL1kryZc4jlem5bXsyEpsLkWWJymSIpGULFuJRZGSIHOESJAU5qHRmLqBntDT69dvnvP//XfVvefed997t+fGUO/dc+pU7dq1a9euOntX1an6/P/2D9ITjz5q/jJwUL59paKQa5bZMsDA4A/Gropl5/qVn3L6G0mVn2ERy7YrmbhiSHJ+Jks0WV4e9aFCuhIDD31dDmfzHVWI61R8qjnh9LL4aliJpK78o3ZxwgddalPQxxJdh3IDkFu+WkwVTv1Na+b39cNHNJiyIKNwu77d1fJsBOIdd1k5QF3fdscd6X6dMUi9XQnHt91sIDN94JGG7EIu6ANaOXoDOV8KZCu4prAM6lsrxEQQ3iquCdV6j1m83Z7ahV0PLjdVg51HqddDu268820BtYJNBJwnYaWnyDVay4U8L9a1g6MdmIul40LSXxBdbfCsNV4lDMXlQkhtSNMafwPI2g+1MpyHIDlTwev9gmahj6bSwsQhr8LTYWCpa8OdUs6GlS/IaxmsTcdbIPadbwjXqUSU2VkpG4d0DtmBF19M58bHpZcgQnIIlZWpLDJ6ZFdM0lhBVTwGIrNwoRTnzCxjpNWzYP0i8AWc8VLNUTkBym0orp55ArbIvuDDwED1V7CXfkYk1xpu0cLyL74FDNfhGQ6MqjBcFWq6cnQNCgMNpTrSMTPIDCPlYZaFcpLedxmc3Ev5/Q0JxeRnfMQxQwE/Iq07dQhV54IS6llGeKp4lG3uQRYzY8IvGByzeGif4HY6UOgHDOq9aQKffr1eRqZJDi19YyOdAX3Lx7JA6orvsUSMvkPq9ZlnfMs3pKVPAzrzDGOtXzwb0XI9DGl2IsVIc92SnzJ0vajKUJo9m6eyEA4xVmjlwQixAapy800beUKXjUjNULHMjZlF4MNArqQvBoqDgs/QWxxKmOlRpnEvzyLKfI3wSopAXhCIPtIhd6ZbuNHBAxdlBTAKRVjUtYsXBoRiCYv0TkhipQg5wICC3DA2ItOiOCpn5yNW+F6nUXDCUfIzfiJt6QlfoAmY7Hd0CSdv+VlKyJLfPp0J6uWfkleWT7LJCd+EIqd8v8n3dzMzeiEoDPniOBDqokvG4NS0vjOQQUURO1lqLcTQYyrkjzJ0phefeNKH1l+/78YaFZfTw+zf+/Rd4Zbrrksv7t/v71opU6EHymgL8IF2qkeXCZood7R75I3wqDcMYuIon2UxF4B6mBNuztCkTVN2z9IbmS5KLiz22MtjBFLxpiEuioWgeqwfybMWgQ/6CDNZGaMegWIAgDvltIsHtzEGZia1coO+gc1baFPQfi046HirfUM4qPMmOV/wdhmEpU1fbl4vL8yls9/4zTSFMWi5WZnjijpHjoq8rARvP6SVKBGWw1tFNyNvRdtKmGpIO1ir8OEvYt98XwlZI7/iWQkFnrZ4WCW36s8oVwQ5gEYsZ/8KiIirXgXSipZS1iroqv6mbFy+prDV0q6Wz2rhq+FxeIs8zxtPBcfFpG1Jp3BX0FdAVo2owLThBXlTBk2PqyOBhBpwlqHVoSOGBCWRkwQC5ElamjavmEzLc+c0cTCoM+w3C5bPfdZ3K9r1+kmuSYh3ZgjXqRYUyiNHDmtm8GXtbFcxBpUuRCmMNZQkzvCyYEjQiPPSRyknKCrIHmEonP7TQ7zPUNowkBQZMVbIPfquEIJRpn2XH4WIn81GAoV7yWf8CY/8LZ3S850RIo9yiAG5pI/xoQtasu7lzKCxlKjg4plwlEQMImhzWgKLi4TlyXeBy4XyjAIJb+AEaSlQGB4GiWd54SPttaBzERXGvCdGXJcCjJEI4DMNPOK1WgoCnvO9S8YsS2UJ5RpcIiel0j8btvB9GQokuxZyKDl5YOSxQQyGdDHUuqhj4fGGHUYU2DCXlYV4pLwouPwYlJYL+CYFfnxqXIq7NjbBkBXh5MMPwxTjVpVi/oSBL0pFP3XDLo+lLBhFHpgTfufD0l2XQ4CUVzwiVxhBWRUCUXEXslC0Far8a9+u4o8ERsGTMMjpao9CVBbocv7EyM/5gyYwV0LcSOBcXU6wKLHxRjB4VAc27uBdHd7hwOegWpn1DEaXzHUaAYQZBrrk2CApoikNZz9GXtCIQbjYg+zPKI542lEMhvgbV9FjLLQJ148weMABbnDennacVT1giCGLxYgJ40WYNC37l/qmsG9wIN15//s8sAFNl9P1a+CCJaQ33357+vV/+I/S0dcPuw+AB0EXgywqLXLouoffKrvCWLrMYIui7OBJeHPb5ttL2o1iKXNPZ7cOqJ81X+v8i7SIrmvHyDLCiGq4Bs8inywyUdeGqqdD+kBpQ1Z3D7Rk6iwJyF3FuWxKDuYFGfsnTpxUfSzqaIpdMsLiaIoK+Dvei+QANbVby5YxBmlXV8ox2Dn2zf87TT37tXqWrvf642X1FbErolrubWZa689awBeRdv/VIv5igtrBWWBKEav51eOILf1EFaJ9f2CowDvD82PkxVGgEii7VuWsUHVlvOdX7NY0NeFAjkp9tU5QCW1KW4mpeVuDKLR1RC1dWx5wXCiehnTU5sXXKHKl9TRpafJwWjrxA733tCP0yLtE4+XfKbktfl0BoCvXm1+BwlzqLJhROnH8hI3Bs2NjUqAkhcidFVzUlmIYRReFgk1rLB0OLzB/+0YSfopA6S0ttrwEHEs6CtAg14SQi67yepYFvwIM5miUUwXalLBmxkM4hbNsCgWwM7+4Mebml7TLoowQkudcDc9MAUp3ZJhxBFUKcibZmAs/EDWfPAXG5VSe4YI+DAAWpkGrZxfk6ZASzTJWJh857iwUxpKKO6pnlIl8wF/LL/tCtc9pM+lAMk/omQ/lw0wcxSjfODqpEMEXf2elclMtXjLqWaAlG4MYcT0yEKkzlsthDKHzYijyPdnCvGYY9YxijTHOMtaevvjuDGPCNKN0Kx+e4bkNfUVYydZMK7iFAiTGU2ZIHZ7jzC/8AjS06VWZXC5SE2EC464BAte7tW7xGTg5+NupfOwkF46GvaBQMLyk7oLP1BtpiVeY0ZM7f4GfPK2MOxIAnsGjP3AqXytB8hNu2VcwDrS1WUNglcbO+eAzogjTtUh24BN+55mjkVuBe8a65K0oeOy6l58Ne1gOitFeZIpE1Jvp1ZW6JcwbyJD9EkeN6PgJ/WFgeMBFBn+pb3KHXJzpUtp//1u/qzMDz6QP/9RPRMQVuLLRzP/6+X+S/vSP/m3649/7gugLOdXNgx3woMges4a4JRhRmCpAyoGcdGhswgM2apsMVtACiWOWnc2nSORBraWIcUozQRcLlEBqzgytPeGxXOBxGjyZd/aY/QaCSujwhl2iAaNQEfpJJi2PWXAppFw8EUdZ4yxEonbtut4z/LTBd9zFc4CZ1wc/9CF/M3hFjUEdLTH6559P04eeWlmIVkK1Eur8QhC1qqvIK8Fu79X4qkA3hAPMfzPCZqBL/1xjyyqoq0VajTpwQHvuIRpLQaKCZDUETXmvCrZqRBOCyuN65auANnirZDdEXMRD27S0KCdpczdW7x/XoKU5r1IFayRZGdWCjpVATSHNGTdFn8+jy3w+CZpg66S0Wfp6gjqmFmHoFZ0yChfHD6aF7j7phPr0YGCfXm3tzRTWkb85fe8YhKvUGwrUaR0G/aKWiXK0hHQTOSkVakhWSCRMKPfFiJEq4hkKxNMKuDxW/v0crc8KMUJInHsAwhFA3VFYZTQwO6GEzse4FMfOieQZWJQER3qHlc7aSchcURheJti0oBA6b6UJJUtan3tyYdS/aQGX0gY2cs7OmaqcGCz6Q9nqkGKdi+E7SKCvKF3lZZlJFKKcXjBBV/BNV+dfLZnT5uxR0FHXTSu8rpLkcCdXKPUS5QUG2wjdNPDqwWUI+mK2BB6jNLMsMJbCklcYBEs2DFkiCs/CiIH/UBEY2dQFeejUBjX6ulCzR4vefbandykxawMs9eBsdeXbTo4vmPW5dnHoPcYKBqeNPUqgzLs1C2MDmbTkpzpklob6c94K9JJf4w/sUNXgKoovZbXzTWnzPZJEnLJSWYJHEULdwnXCwC2AmjxF+X0EiSEUT1rVUhiGShJIdEfmRHWmgTzMe9dj1CWgIdeRiNCSPoyLyC/aCvlADbzA+C5pVEcKty2S84bx5AV/kckllgUrsAwIGEz1j3yC2zOEEgNY53IwkEPkopbxCoddho14HiIYJLQp46E+NbLxlzLM+IbzoY//eN5hNsNextugljV/9u/8crrj7rvTF//1v05HXn5VNNEmxDHR5bNL9bzkjzAhBCYFDzKXTZ3ZStlUT6w8oB4pKxss9QjPgspb4Clzo+MZvLo1RzUCNj7lJFQ+O8WCwFfl7YEW8XdJlcN8JXVNqSJraOGB/5KOWU+MwuV06jT99lLas1tG4cgGywLg77jz5wDtaPuOHen+Bx+8YhvHFCqXZifT2Dd+K80cfDyCXOkl9tq5I4HF1UhEpktgq7tlt1XEFQwTgdF+qnnSFqMpE7pOKaoJz8+/JnNWR+V+avXoVWNKOrKt1teqCZoiSvpqcKuwavw152+H561g6HQvc2FbZdvMv5UkXEhNNmEFBZkLOUahDqNKC2Mvp4Xe4dS9Y0gjojsVydvnre3eMQhb1C9KxOjoaHpeh86/obOumBnkz+qHBIbOExi0kugoZSCgNHtJWShQxCkksGdhc3tSyEpFKhOhfGqinQUT4bfSLCUnDC3hdb4hv6QM2sKXNaUIVVrMEo/8CxG7V5puQHGKL7S4LeRAU0HG+TlyyI/cFBW0CD90SllAYSgGoUFKuO5wotBVeOA0VjYda+SkBy80GT/GROyQIoVfXBfP0RcLjwwj+AjJaSAvh2GEQF/MmMjP8szMO/IKXgT3OrX00vAKZ2dRDDbTrPTkAE7uLnymr1vHTixLwaYE7FbZr28TSVfoJw08n5yalME4Y6ORmY4enXWDscL3mNAU/AzjtFYmFVTkm3bOXsQAMgVBhBV2M0MzNYWsTGCNRiOuB0a4CxNMNCp5bfRSMeTgeGQYtVsP+mdWkSWigDgaWgAkiYxiVy8LKXO88WUYwDAvgQM8nJ6pTz3IF3jy3fIIHkCIl4cisuA5jGfVs+KNkzTCwh/wrmuHmVPiLbuKauMgzWz5SAYBUe8xSwYepdSPdOQDDhz4kA2OsvAyWSjRv8Mkkz7zT3DkQrv3bDu4VaelLTz8h/82TY6Np0/84ueM80pd7tZy1b935x3pqzJK/+QLX4pZU/cdDDipzPRbUJ4NdYqM34a24oiNMqu86s9gtvmjcGSWpdK0aM/Q1ipQkXKuO3BXw2EqglF1ZFpxfqyF1TzCRF0EXlZbLHuWUCTlugJtgQEd9UYIZcCMh/5TGtTDMN57Q5eMQp01RZ/zjjsvDtCnffChh9INN97Y0MefF5ILBF6aPpdO//t/mOZPvGIMdekIhE2SlQMRjGbICyQgJ7MY4yfDELMcE7eWdIiE9ago6dYmt0A1ZHlpHlYhEHpWibo0+V4wFnrdi3RmtvowoWnums4X89r11j62i8FjfrTLlDbhWoO1Dm2/lKtDtoN5JY9oF5ewbYDKhEjGdO/SkRTzZ/anRX1L2LVVZxR2bbiUua3OjKsY845B2MR8lJopbUzwwgsH0uFjR6UsiUUoERIQVIlQ0lEOQxhD+SVCP8EUhaOov6EkESU1CiVY+O1HtPSP/Hl2wxchqfZQkkoEEwUIJQ2Fx0uoMD4V1gMJEJXxGBuk2kiQQgvdUoBxKK1lIw0HGLF84MCRT/iCphxO3t5Ep8QXeow3AlGyyw8UnulUOs9MAgc95EcJlrRRjNXPGulRZuBRqAUm1c/wNmZJVXiCYWKFllwomokBa8ZBCsqROSw+oaAXA9rfIpEEBVi8gU4qlHrhmz4Ueg6sZ8bIiIyLfEQ/NJiOyJN4z3qJYL4xZPkUM3wkJTFp2KRkfPycNjfRhhzscKo84Sfl5NtENqmhvK5b4w/awR1SIlQ53GUFtWko4SQWXZEpsY3OtChIIM6IWMIwsquORxHCXzgl0HPhaxgPihOvAsYJBJplGTlTrUbq4C1thrIVHJGxwBRGeMBGbvVrpiFHwhdggV62hs8MF+mVB8Hyg9/LeXV3OLxQ+ZgIQ/58xIqWmy3om9keloAonh1s3W6pf/gpfJ7N1yPyQqnMdsV3Sx5YSrwoo3JeM40y+8nZznIJQUqBsciPdNQ9MvfIV//M5wB+/Bd+LvWxa+0Vchxk/+lf+tvpjnvvTf/f738xHXj2WfGC40xiIx3zFLLxUHx53fag27PYsSup+w+Vg8EAGI4Mut24HHCJWq86Qlzj5mvpC6sQ7v+UL5A11/BQCw3Cchx8ZWMm6pSgwEAhXIS6EQqgaGAzYsuInk6fHnX59+69IQ0OabOAxtxB8Y5rwQF4vXffvnTPffelYe2ie6XdwuiRdPZr/6pmDEZtt0FFCEiu5ZKq3NtIvwoIaHlNcw+ps2fVS7OcuWVE4lqapsdaeN1z/nTTrKt4mzE0Pjc+1fNt01fNiCTNzxnNiuAVAdX86jS5r1oFKU094qtpV/qNTfmRZdRf9F2GXJOOOq6ST7nXY6AhkNT0lBxZ8q3CruZvtyykr3MnsLVZhFXrphVNK/JwJgptO7NWWBWW01MP5+vctStdsBsKm6k8X4wV+Co9+IVab8PUPXc2LZ56JnUNbEtp+E7lzfFjb133jkHYVLfsKHjw4EEfMWEFXhJspcdSmEUhKx2RtK6aoEwirXT8VjCrklNJn1UmC5ZBiAtPlvaCk0BaAKKJMShTSfShssp6kZKj0Xv9kbx0SmikHCKOrgs9hNNRoQzaiEXYhZNbcwdGTHEG4yGTUA1HmfZsSKFbwKG8R16kKemtACoxyqRtuUqccYIfJ1yRRlfw5l/JgnKBq9Ac0KQIbvJsHqC8KoxZClB75k/peI6NdQxpAkO1xFhGkWcmhLQYG3GHIPLn4j+CxdcwMPXAER/CSwqMSAwBjA1CWB46MTlh3D7ce1abmRgP32NpBpL6A7l+SuocsXZsnBKOowByGBqmSIDBzxyfbwaq+XOi2rM8CDDPBW9BLNBlMneU7sRb2HWhXPCbSOSIu57gQyALcG8uYytNXYmFDjhBNeVlA8wYiAUvVzhn8IBnwARjDqaSPzjiP3AKuBMhUrzpUBwtYFlLG11EPZOPV0XqjYNCi6EOGtqO7G/Hl3CW8XrGi5lawfIjwyUlQBQoDt+Lch7h7PyM6zJmkWMW2ABOEe1M2UB0yIFmJtnR9MnvfDe9cfRo+qX/5r/2cmJAroSDd3fed69/3/rzv0hf+Of/Mk1MjDtr+Eu9uk6QKfkZHLH8il/MpsLjWHJK/8dsKXUFj8Rx1TftKerbNWi88NkVJoxUYXEEVx4dXH02LqctKer3krbIIv2gpZG6YtYwI+JWQ5EDqUdgiTup5aNsHLVn924Z5++cU1jncGsf7eajH/tY2qYjPK6GWzx3Mp3+43+QFifPNGRflRsianXeAMVDg0SsiF0vYLV8Vs9vPYxXJj7aYJt5ueG1CdsKrInFF6Lkr0TbzPmVENUQmnq7ZS7kljt4Sn22k2urfOrvuSpV7fnBB/2lD2sv1UqoanlWxrYXUi2/6WqVrDCrVVybYUVGLgiVErWqg3WzvqBEYNX7Q21kaep4Wjj9VOru25Y68tJR6K/ybF0a3iQA9bf5m4Tgy0kmivcbx49rqehzVvoxelzpWXrdcHPrrQu0ICJC4oPyoT/hwWjA0WHEjBQp6qmIA4IfsPzFjBdVgpIb8IZRPIqojy4QHMovCpwVZ/lpJc5Hd5Q8FF62h+dOuDfEEA4woiCVSofeSB6+Qo8DV7k4TyWzAo3WLD95sPSRpXiU1X/KGxqdt0vZjJ28I3/f1VNgV+DHWPL3X97lEGpDqTOX4FWGg28+wgIFlT9ogZMAyB/f4OFhthSaA49nBoWV+iWi8N71prpjICCMQ6JtfruOyA+FNByZUWaWfsLvoJm8OVoC5VsQacPwiAwK7XApJXtex2hwxAQGoglS3vAOPJ5Nkde1qyxEMgXwDwV9idkdaCOsRgOUAKNbgSeoOHpfg+tCPI5nbsJlPvkZGoIOFRxG6ae7AQHAT90SnuNydKlr+FV+LlOmHRSWD9Aqj2J0u8aEC5xFlkIygydlMMP4czmQhCgPd9FlXgR1RLgI5CF6Mcup7zIbyDJPtw3qSnnyoz7ZPRSZZRYQOoqMgQz5ofT+zpTsxEMbS+aHIuSILw0Kf5Q9Dz4Ih/uU119Pv/Or/ySd0e6XV8N9RN8y/v1/+n+kj3/609r0qM99CXXvehFBlBk6+c4QHtB3lHqCu8DyR5XHcAd1WfheLxHVYVjdzQdFWUx8CTjCGSyLATMSZB7q3sqVYOrKkNFJNIDW0Bfghtighbo7dvxkOn7ihI8RaQJ55zFzgL7pPe97X/r0Zz971YzBmVe+l0790f+0whhst5KQMf5CalunytKE+IWrCVEJWP1eZLsKQfLqjzjyvzh3HkQ1ZbSifE3xl+SxSl7Vn5EXftTyKgEtYGsw63iqHFU1002flyvdSLuJqnlE/9aYMvo71bQRN8at99RukvMt43r5El+VjypPm9O6LV1iAqiykj/+tqpQQHUySupmals9t4W9VUKH0Yt0L2tV2+jLaWn8gAifNb1r8WxVZG+CCOtYbwI6LzuJNM6JiYn09DPP6luvWRkk2nK2oum54UsZsWIiWAQC5nnkWolrCgv+ovAAJXlE+UHhUq9RLwdePceNK6Kna03TCkEmidNn5ZRWARyr/vhFSnApreKsYGMMylDBT76RN8gDJzTh8480PGOU6A98aznSFAc8eaJEolBziLdnvpQ3yjh/5OJiC7HDBF/Nwy9X5w9kwBs/BoaUdxRT51mGlsDoAHmEyIqlEwRW6sFc1WOAhSUU+ZB3zLSSF4p+GNBhRIMveBG0AGP+gxP+K97GC4avkGNQdmu2D7eo2SCMBRvtAhzoH9TytCHTNzk5admCN4MDgz4jjfMOUb6oJ5BRV+RKKfhxLS8Z0664xgHdLE9OkFMVP4TizCgRmqO1FleECi/yKW+GArLuMm8dD00qaxSWAsvvHzRnP/eKs2FrGRYGCFYy81QwHhhRnI0O8YKD0w1vIAGQKdWlunYaZMi8MRrzH6B4CctnInWRBynhx6xrMTz59rJH33kimwuaDaR+MM7ZFAhZxHlmjDz4gRsiQOw2qxko3TEeyZOiYjR5QEEgbjNKVxxGv9saZVa9MlDAMm3cMa06+Pz/8PfToRdfKuBX9H79nj3pl/6r/zL93V/5R2nfbbfa6HNZoZWSq3xeji6q4FGneJQ5ZJ5QLp7hL0ZhOdKDsOpPj3JiDr8SkTERFjId9+IHjAoPcD9lHETUHW0bEx3sSB38zzkZCD/OYbkOHaYLskddHDl8WJuEjUY9Bfg7V3GAGcHdkpFPfuYz6S5tTORBqyvMGQ6cn37pu9pN9Ncu2BhsINmCEPJQDbdMVAPeZv7Swi5VsStd4KooLyXPLyWuVQl+q0W0W+ntwr1Z+HMh5bGAFSlj4FT6xfy4Zgmf1xmFJ/Mb6M3CgPOj850lo5lfHBb+rL61OXnyhDYIGVCoJAmlQg7FhZ90QzsUwiJnBNkYVAAdo2EtMoyoS9Gy8i0o/YdiHzhAgL6MShMmS1xzljUg8vE8BdafHPmhy7KIkIMjoAkFmBiucQ4eAGxoMq/8Ay9xdgYFSzgeUaVRevF55kh3GkGhxUkUSyrCKBfwnkWQgWFFXN/PoVQAtZAZxYHsRelThJ35A03kV4iiDFKey46fKKYo1zA0ZooEq+WZzlwEmPpMVEEBLLTBb5dYm5i4PlBktYbQyzOhABjuSkg5vLmL0Lv0uthAk7FCnXr2ETgjirvlQDgjHx00r2/LZjUjaGOvb9DlXexCPjq0mcxUmpmdTWNntQ5d5RkZ2SRjsD/19ejX2+d6m12cM34b7yaO8tEJ5ZKJWHjjx1LY8kBBKCzh5SevHQZ0cxgRGR5pod4AMUfNFHMOKswkx4oXkTmBOFLIUX/UoxEEHsNBm38BZmi+cwQQi4obFSWP48iNx2wEFkOtFg+Q4FW72b4EgcKy4/tW/iiLa1Fy63M5TYMMQtHfoyWh1MOiDrXu6pYsiJ/kw8/GnZJipBoTfDO+OP+RJaW0L2a6vfxY9cgz9VNm0ZATdoctxiDpvdxSqDAgee5S+2A5+h/8+m+kh37y4+mhH/+YZTsX44rdbtGZhX/vH/9Kev6pp9K/++3fSUcOHRJ1UQXcox1E+eC5q0oXiw1tijai+upQu++iAxMfXH26WI4yLurFifMtcqCYwkUyvL7Kp394WfrOOixRpqIeRD6ixwNFJNUPGrmv5kwXoiqg2bl5fQ5w2N/vbty40WGrpXu7hO/SMtp333NP2nrddW4HV6vc5x75Qpp86iuq0OiHWtGxRjUHeBWg6m+FLIdJfNy3rAFiGbeMZflDZklX3FpZlXZRYNu/V3NoTtUYRzupumpsY0wV6tL43UeAqimjQkNTcJuZrp2quc3zhoieKNCTmi6ILgq6Ci3E5iC8a7s1SXAOa6dvI7Y5i4K1TqNKVWNwIDSMLpTN5WpG0ka+LUHA2TLCubSMuZDA88YGXQ2EnTeG8yPTeelS4ztnGi+nuYkjaencC6lr2w7h62uQqfPL4NqFfscgVN0wo3fo4KH04oEDqVuzNqiXiBzyEL+sGBGaZRGZQcEocmof2lJNiCpe0jiB7tk5HRfFFSU4jEdAsxIEfmeZM+WmH7YhDcQj5gLwCycrxkGTRsM12loOds7ZmCDioaXWycjvGRUUfHA7C1Lwo5sN57vKxh1lzMpwjgQnOLr1W5biu9SJ8ZbTCYbyFKV6SUv3bKTWDEIB1vAoGQYdCgHGRsW5XAKF7ihjxq9bJM9IchqyJwR8MMszr0JZluLaABEN9dkfZnNCeYfeeRkPGOK9MiDgbyj7wSPKxlJDvsvECO6VcccGMRjjLBddXFjyjqLT8ncqPfSyAyln2jH7wjeEpCvLNr3bqGjhWy3qVLlEGVUAzzZTJgqkC7jsXLgIi7gcHrG6OoFuyC4GKlgJ0088KTJHvFPqDp+s8Ze0xBDGCAT+UifUHXx1ypwusOQ8SEN+ck4KLH6FkQ/eiMg4AcJRT8w/4cCrH7LgNJQ9gkr9g4kolwDc7GQqcM8cKcJZK7i3V0t2NaCAUdir40RYtlsMQHYGJpFxqpycXuLslBi+LWqgyJsMga1kaNqgNZadLogvHEHCoEC4mIUubcz8xwjVzjhTY+fSn37hC+msdjH+2Kc+lfoGrtxmM5k4f8v43g98IN11733p61/5SvqmNr85rpkz80BMpq0gn4s6Z7OmdYgptCXPkIvXbsMK4ztm90PwhvCSie+unIYQHixOukd+hChP89sMbsJBfDjq0w5aJBtK5T/a53rOMOojMcwnNGP/yqsH0x133J4GxH+wvN0c9bh582bPBrJ76NV0ixNn0tjX/1WaOfjYmmRQS65phKWla6zJVlDrS0pLxIionC68bxuQlAflrejGONIonnC8ckYT3kt0pcxgj/YA0uiu1RYhhraScyp3P7ZDSBWmJK6GZbxt3VxnBcn6KVat4py0IV4PkMXP/M80+qaLw5uyXI8S+qZ6rdUTGyePjm4NU4deyxfvJ/QiDNfi7IU4/ey3fhexNZoKvO7FW9JfzH1NXGtGtplrBUfFu27iOux6tVZB1SAglfDz8VZwMIjdvTClWcL9qWvTnWm5Z7cwoeOcB03nk/dVgn3HIFRFn9TREo89rjOOtIStUwqD2yMdKT8rvpXakXR2ugVHgy5KH0qUBVeXIsAWFRp8Ts69xOHzzJ/CIh5jSvmxZSI4pFwxsSJ1q6aTOTHAhIs2SLNRoTx4CWCQcJi0N0mRklp/FWQCBEf+oCiOkJpy7JgCUYEiSHBW4Chnbijo6xgW8ajc5Ilv6phhixwwdkgeKGQcQWhOX8oBKMYQyrzjXLZ6/uTnGQdFUx9wNKpAfqOjFIUuxQkPqHDL3RhDxOkFKW3fxmzEKFTZ2bgh95TmZOSx0Qj5wZNeloRquSGGIjNLpKU+FhaCsyjzGHcca4Cbmp5MMzpeAgUZCka0ZHQOw1x10acD7vv6+7SUdMBGCctr55UOumIZo3gm44TyhNJNwZSnhIAZKD2YLr/xKKAZQAmKI089Z9l0qOHgh/Bq1lKY9Q9M0G8GkIogEigcqbSjDILlVssFwOLgG6moaIIdJz/Oz7pDC0JCnMriCOLIwqBcCACGG5fscrDh5KfNsUmIjTP4oTC3TwFoiCGjiPQhH+CWwaKlj73iPSVj6S717eXNusMKMd1lKDPeLFtzmVX3sA/Dnx1CbSApX39np11zMf7I3yjEA+rYs+zww7g1KEO5BUP9iQzXK0fYLM8tpj/94hfS0ZdfTn/nv/tvU+8V3IG0sJc78vsTP/uZ9GHNVj7+3UfTH/7mb6ZpLZtn8KCnuzfNd4sXKr/lwIYz5ZQcinHI6LLlFw6Y/Soq9UI94+B/udqrS8C6HwmvI8CFixT21i/A5QjfxEvOJETunEwXbzSUU4RkA2kpcFrXkvNjsAXau9KYjgQ5evRYuummfSFTOf3b4Tags1I5XH7Hzp2W66tZ5oUzWsL7p7+aFs4ea5OMiuCsm6IiPBl2ZYjEmzhFVDHTRVUd7+KaIAqQR94FdYeU8Ud4yB87V5enOvJqLvXUF+RT/xJ06e4ORu8QLeieVdtYUB8FfQRr1byO7UGP0LtLP/q3K+EurKTBs0JfFUdwtcSUOxCVPknljEG+KKRrqBFlSbjmnTqs5u39BtRveOWU3hkeICbn1kQF7iqCSm7Rb7HyRjuRq540fuyvOQDvkTB2awkG67/04rC+E+9PXptBE3CsGnLlqq6tM9bqtOZZk4YKOat6i563KsCViKCwq/Bx9ezPO8HqqEqMULreJo6nxXMvpq7rtqsd8VnZW8u9rQ1CGvPk5HT667/+ax810acNF+jS6USYpaLjdOfuNlYaGpIhABQ7AfCjwcargAiaLfH61yM6IXGEIaaOk8fpgMtKI15DqpMwbE6nrl6zG4SYkhoSj8orL5aOQihKGksuMZiKEev8nDayMY2FiFo4ChKGB9/YmDpTEvRBRClbZI1CVZwVQ58HYBKMAyiU55hRU1pQBMPk57meHrr9Z1roIAOeLMg1Ot2giVTmgmHlc0+vcpNGcdBi3EYfHam6bfGCGTrwBY2Rv4FKMYLfoFQHzPEQqU9HesiIEAGmwWkF7VlDwfByYMavW0Yis07j4+M6/FyKs40Cxcnow9BDhpZm47gJwvh+EN7YSBbPw9BTv64w6oCdHBEuRg1rdCqvziUMTl4GUdcxRQw3Sjnk96Mu4kMwCgahwOedGG29K1734IcTGBaWIk/+HjbzMXgZvK3l4+x0cc+IrJOXys1yXvI1fZGvaVBI7Q44zkqS7oCLP7Ui1DwiBtKoYLygqyHBT0LyhR8rY4Coh1LWWCLK8ROgZWk430d5llYv1Tm9jaknf3NLSuFVE9JgAGdFahZYBiL4qLPlZZ1nqDoubReeUVcuem53NAfocpvyOx142qmcYGI2WN+SSh6ee/yJ9Nv/+FfTT3zuc2nvu24F4qq4IR0pgFF49/vemx75y/+QHv3GN9OJI0c9k+rNjDKvVXRVH4ZYLLVmQANHWT2TDYP5wYCWLkfmeD/JzyPpxajV01bSeHZS8qSadS7gqTsA6yHUVbSlOgQyiFF45MgxH1i/fftW0ZAzqIC9lbzwgB1Db7zppnTzLbdY3q9q+dQmpg88ksa+9TtpaSZ2v70gepAZHJ3a+ThETXLbqb43GnBj4kYpqop15BNv9HreDs2JAkL4/Kz3RTYgkEVSnC+pjZTVn+jSJcg6kqcnjc4up8Ojs+no6el0ekq8nVNjVf593R1py1BX2r1J34hu6U0bB/Sdf4rv3euYGn3IisuQi9cYe4FPtXpaO33wpsbBSkteJZ0SLKhEb5xdSIdOzKSNKustOzTwqlVKxWAn64Kxbd67oiIdq1ZOTab0/GuT6re70m17+tNwr/B7RdEqdK0WrPf5nCYeTp9bSK+dmE3HRxfTueklvYdYbaGTDfo6045NXemG7b1p12a9pzSgvayVSNBNfUPWgvSEU2MLaWx6WQPNnWn7cFca0j51vDOJx5XyxlMb16YEK/rMNlCsBQL9hTbg8Ddl2Zg8RzbWVxVDI/jlfyJvvfuWtALsjAzCzfeo/7iuoUyXn4bLn8Pb2iBcUEe9/8D+dFK7//X0snQrlFwUjlD8QnCjG8+VIUG1rOpCR4OYOBWGHB2+XnR2io/lAECH8mrgeDRsXZpyoDDbsAkMkRGKplpFDDYCV2Ajf2wIcmTGCYJsREEYzr2IcOqRLj4aOd1kdBxgKss/iQs/CnIUjNEwXnylc4jRdaXiH8OBl5FgwYMDjuWTKIgcxE72BhaOMFTRKFGiczA4XDCNXuoPfsErjoeAN8zMxUtUaXIS3RxnSNMpH7QbAzgcHXyIgjsGfJ2C9yyOHthgAnqpW2hzmclDYcxC8QdV7AxaHCOsGN0sO5vXt3/jE9Oe5Zub1bdpKvOAjMR+lZ+6mNGxE+Canpn2srTh4WFvJlMMFOoJGmwgCidlMD2UCWL9r4uMdEL8LDo9o2luFKocGw+SZwMLV7jgizEQp0igo9zIFMYKdgpyE3WtMVDlIf6DI4CdDsUjnkFAkCJdkQpX2pBCJ4i4AgOt9gvECHQz7gxLHM7lDm8NP2FOGwiQAUuu34ykB5XKCG2KAf0SMivw+E5RxrZoZHMZhtL7dKbgvL6rlZRKUdKyXcnZ3ALHScT5fKYIfErPZjSLyz0+l4+RWnB4xg/cZCxZ8PmcACuOdogKAhHIGQYH39DyPS2HwXPkwbTyQn56tIR1YUGGfq+MklcPpd///K+lT/ztv5Xu++AHwHzV3KatW9NP//zPpZ/67N9Mf/UfvpZ+79f/meR7QUtH53LJVBb3b5pllWKDkc2gBv0P7Q+HP7jgx9qFqiScbbwzB5viCG/taIu5FdQAinLtmrc8gT9w1OGDpuij5I9H4RCVqjeWdr/66itpeEiDNW/h8wk36FvJBz/4QRuEtJer7iRDY9/4rTT5zMMiZfV6vxx01nKT9n1urjO9dnwunZIBRe9Y5AOYGtwKIpCuqgvIeljdB1S/Zntu2IJBFoaYV/QIw0VXgxB0qP8am+lMTxzWoPahyXT07GyalCE4L5LcJSt/2ofGLdOgDIedG3rTfTcMpffsHUjbh5Se1SvVfheCr3EHd8uYoklVH3tERtVXnjiXnjsynYZkUH3yfVvSB27ul5kY7+7GGmmvgKV+eN+OL/SlP/7uifSD16bMx4++e2P68ftGUr/6spq+txZa1YfIlOHemw6NLqXvvTCenhWu0xML2oOAFS6KQ4xEKENc+qIhbR7sTnfuHkgP3TmSbpJxKI3DM4HoD5Nz3enrz0yl770yka7f3J8++YCWfu9SH6m+GBcSKXQXUnAQtJOwlkkGV14O4qJf6KsuUr6A+PzchZJ/frm0C02b5b2u9jVxTEdRHEudI1tUUGqsMKNdXNcu3NvWIERJOKrzwQ7ou0EbNmpMHVICLc+Ko4FauXHPipKZxZM4tdxQPpB84mJWz2NSeka5Ji2KoV2+NYgBSXNAYJbA5efAHfHxzZsiyJ+fZ5FQ5qFByxylzPZ15xkpKa4BJ5rUkXFemJ8z3rhFxmUWyjMZwkX5OrS+BNPLs6OxFoVXl18q4DFOKboYMaGE88KJmTyW0lFcgfkCf8OAFG/EW8pkw8PZG8h5AqcE2q0zcEYHS1jggrehbKpM4Be8YQIk4HwFS/yWUd71suNlCP3kbaVQSiDlZNYM7jIr5JnRgPAyDGZATJPwL6AMIxdKx0HySMSsjL/x8Qn5k5Z/9nvDEpYz9ssPDIbhPEccKA2zh3NSpvlWZ2hwQDpofLMIDQt8oyjcvToTjd02zTyopVzwBFr1Bww8JQjDFVrsgGty5otkAEXBhUd5z3CuA+ECGWcHFrmi4y4vkuAfVxKVHwD6QYToiVahm51gxMMIlh+ht8xx54Ea0J0waBFvgp58Bx3Z4AyjB2Bw0GnZwFvyl1/loz6rDjQYsjQNeIxRaFpAp2eMFzIaHh5MZ8fG0pyMdZ57CFe+86rTTo2yI9PwlyVHtAHSzSs95SMOeuIegxVuA6o7qLGhSl3Jj/HZK+NzmUEa4YYW2g6OTWowRFnWTdiC7sjYF7XZzAtatv7JX/6P04CWGl9NB10/9LEfS/f/0EPpsb/6TvraV76antIqCmSxcL7MElIOvgsWAy0itDbkFldgix/5hEPulyzfxKjekA2gLWM5Vb6BqYanIYp+VnWkvgiDO3CAD/iSotwjnLyIQ6zYFIf6ZeOnI1o6esvN+zx7WyDf7Hdk88Z9+zwjyNJQb5Z1DRRq/tTBdO7bv5tmjzwnaprrp5FAYguEJcry0QhDF1PkLWJKijpcSGN0CQ5V+zt8bin9xbNn09PHpzVQU5eYeqrz85U8yp3UUMLz7pHu9CO3jaT7bxrUzBUrKlbSCHwrF5Q1lpB2stjZk144uZC+9vzZ9Oyx6TQ5rzYktL16hw9o3WE3a0XlFiTnM4o7M7WYzkxPp1dPz6RnD/enH717Y7pzh76n7tAsv+hxF1slgOSFzGqhqjBN/jbBopmDvuCvZQTCFlgUVIdtzJRFTUfOzKVXTs2ls+pqx2QQ7z86l+67UbN4soTpcxiEolday7USLcOrHR0fW0xPHdVKoLnlNCVePnd4Nn3g9g2pf1A46brkakWJRz/zGkJfYcOx2c6+9J390+mrj4+mY5rdY9UXb4RezeAOqb4YuOT9woqVaRmJk6PzMvDn09MyHH/ifZvTD989kvq69DmK3le8v6cWOtOZySXpIErD8mArDsG9VcuSaePWzI0G+nkw8ZUELbxs1gcoa2jQfdEp9BpIfJ3iVWrCUfIpNJXnFuhWBtWAG6hbCXclQrIAokkszk+mxbEXUufwTSrfyJXI/Yrl8fY0CCVf4+fG09NPP+0RcEbzcWVnvJr41TzE0WnWJLQm6G5aCi+qEkmcTPA0B7dTJ9OzIty119E439aXAKLJWXlX2mhevAHJIYwF967qTFAqrZo6c1Nl5bZTWjK6dnFWsnP+lIdfptQGUZca+QJKcG4AxKNUo6izbp2ZDmYGrGAIhlk8Gy3Kx7MFRbF3hkqrdDgr08LDDo98h8RzddMbDD+YShnBExu8KMi0wjVojXIJKDsoX+nMHjolKebER+qAKyOi0GXFXMEsfCObOtyyZvamPIPAjA5n2E0vTckYjDNoCg+wmael8NPh+/gJvXxmZ+fDL2KnJibNOzacESecBxuacPwBxqZpQKEVbCmHvw2AZ/wUWOOfMvPLQHl0YEC2cDEwoTLL4KUsGL7gjtLBECEUn52bguNdBmQuvzz2w0DVgRmeFQtViKAiPTj8NvMwYE7sOPxgID2wwkHbIl8ys+Kvu5IbzuGK486PdIZBPtSiREOQ7wSG4bkGrhTFYchRH4u1MoSMUSAMHL13vTyMjX4mJlS3QsKh83BjXt+N9mkpFbxyXSjfBRnyzJSSX5w9qJzkJ8CyC3uUoV/44M6EuS0IML47ZdmpFmhpaWW3DEEGCygTS46RAwYOMBAJ4/l7X/t62v/kk+m/+Lv/fbp+717TUsp3Ne79+tbsoR/7Uf9e3v9C+r1/8S/Sfh3LA78oQ222XYMbEvqoazNpJbWwzqsE8LBNaUs46j/S5psfSit3GJUPjpzeikftqSQPmXZiX5zAPnBQVTjqkSW8x4+fSBs3bErbd7y5l44iuyyH3n3DDem999/vFQlR0qt/pU+akxE4+pX/U7PuU1ecoJo8qU2PznSkbx8YT8/IGNy1qS/dsq0v9dPFWPgCku6q2bUKAwZpo0d2D18ykowtS85Pzyyn/Yen0p9rVqhXyw0fuEEraPgEwILYnMPqzzVw1bFOs01PH55Lf/bMWRl42j1ZyTZqZuz2HYPp9l39adfG3jSgGUHomp5bSEfHZtMLMmhe0pLKMzOLLveEPmVYuG9june3NjrrAEOTK+VoCr7qj/WmbFJ4FW2Uwb1xUMs6xxdsYG3bjH4iPaMQW2NeCWj3HkzYpGWoO7Qs88w0g+8pbd+gJZo9iqsqVi1QOlsGnVJ/+uoPRtPDT47ZqMRYv2FjT7rrhsH0rp29aesGNpvTO0PvvDHNVh88PitDcDodkpF7XEtL//CRU2lsfDF94v4R5au+VjzIb0S8/rXIfs2gKFmA0B82sXXNtLVIETE135WeeGkqPXNwMm0a7k7vv2s47d0mTZR3v9wF465lUjxVikvYlb7DJQ0kygReOHdEgnBO07nDCml+31xpui5dfm9Lg3B2fjY9v//5dEY7/aEsosR1asTNCgu8zTMimHl+XCGLOUDS7l0kBYShgWKHkmEFRnFWxktLUxLCrXSCVOHoNvYaJoStFgZIDg88JCCQdHQHepCyzVI2Nr6gQ7ExIRpwJR+SMJoPXpRs45If3BgbGHsYEoRziDfKWpcUVS2oDDxcUbb1A4byEeNNNZQXuzeCnLLH90bKXwCRmvIqmkvN4Q88pAEQjNRB7cesgzrb4AX5CowfwPka6CKuhtoe4Va9efMPlZMXA2kx2HlF8k8ZVCj92E1UjVvflfHtIPc5Kbvjyp/ydknJnWfXUC8pXNQM07C/HWQZnTeSET/hIbwgvwWN7vUPaMmoXgLjOph+enZGy0X13SCzT/rr13EmwM9qpHZOebHBDDMc0ISDcuLDOBZ90FmLow70DOmFn8GgeAZOcouRgvK1xFJT8dB8U1mKISzGCgZY6p4cA0Yhzotn/i0nTizEpiHn7TZBvYFEDnpwwCKXphEERDhhhtEzw7klnRPqmTIgB843aA5cEUWtddkABrbUt/y1jEsu0En5VT7oEN66rEccSzg5G3JiciobecKjqJA78UxpLBuFL5RA9RGDE3oR0D6Cec4fGdWiK7dBs1ToFmQ4d0oZ4/s6hgHYkGZK9U0e1CvfJGJggmtRvMJoR5YYlOLswtHTp9P/9T//L+lHfvqn049++lP+vlSZXXV3i3bk/B9/5X9PR18/nB7/3vfSw3/y5fS6dmb2wJBGqxk06dISWGqcX9VRW0gadQaPO1U/XtKLzFTqEW9AVVPX/cZNP0SbzjkVuSZdgwN3wU+k/QFRheX7INr9odcOqn2/OZeOwpftmgW89bbbvCx0cHCwgRVX+2FpdiqNaVZwav83Ve8tjI82CHSd0Vdk57ZdHta511MhXx2e8Tl4ZiHdwHK7+7elmzepr+db6CyLBd53mJtjyvhXhFSuAqTbQyZzr+jIRcnqhNTHHTI6v/b0mfSSFP07d/anjazhZMAr461gWuEle/fJll+tLOnoSc8dn09fzcZgj4yLd2/rTx++UzN+1w+kkR6tQBBu8IfrTHftHEofuGVDekH5f0MzihiGfT3qm9Sdme5cxhWZn2eA+aU0NXQ1TyOiSjU2RuiJOLEt10S+Z8SVJlxL16X+8xYZID9133B64fUuGYZaFnuz3rkdDLTFO2DVeqthiXxb4WdTresGO9Pf/KEt6dH9Y2lERv37bx9Jgz2aE/P7rIKk4nXR1d/Pdg6kh7Wc9cuPaWWK6Nmo9B+4dTh95N0b0g1bNcDu6TR0KN6pQqD6fe+Ng+mhOzamrz89lh7ZP57GZLy/+Ppk+ujdg14SW8kmklQD2vWvUjdOzrt7rfhKHrNaMvny6ZS++8pc2rlpKd18w3LavU3jg9SZcLSJpoIxp2tImAWgEar1U6tKbA15YaHC742Fps9qYOu0qmun8DAc9NZwbzuDEOXu9UOvSwF4zZ1PdE4huXS82J20zwAAQABJREFURSl0J0Ll53p2uOIxoFBHgC1x4ECRjBkyGrcSCQ4coZSClzZPhNJJoTEIYOC3EAc2MMd3dcQhfBHPznpeGgW8HDAouFas6EsUj0EI3uqMBvlDH0KsrlZ+FFOewa0fClZ+FjIi3COzwYnjXFqIUBQXyqAfxhzpUQht2CkKpXBB53wFIHRgJGYjWfmKC2Bwh29WyF+jQ7ig02mygQAvWHZr0l0yuAcO0cZfpht/1VEX0LSssoWBVSCUOtOPYj6XtARD8hD5LnqH0a5pKe0aZR8ZGk6zmvmYnp7St4F6HtE3gAqfm5uRss8MnGjLvINmwtjNkvzY1p4dLbs06zMkA4QXXJ9mWzjSZEGDETN8kyU6gHUhIB7+UEUuC+H66V9CpQsVDKh4LXotWE6rcBLBCAPk5PJjxHL2Ivz1EmAzPLhHNk5hHHqQc9a6eGZOsR7oIL5EQg+JqBvxBd52MCvkcIgjHnoiiQonWD04X4URzg8cOOj2D79DFAe9EecgoTMJmXbqlZqkTFkoSkKzJPAQz4ygBgVEo0VJd5blkm5Ay3qHVSejWjpKezU9JkXGPWiFEQOBGTCMSxztGKO/i5F0+SEbYxpZWpKC26GZRtMkAugnMP7oJ/j+lKXEyEV8L6pBJ6WdJ10U1NWLEcm5lD1aZsrAxIRWL3z5i19KR2Rw/cwv/WK6Tsr+teDgyZ59N/r3SX1r+AMtJ/3yH/279NJLL6Ujx99Q6WFg9Emlf6Qe4WmsvsCvGMt6li3H1ksHfIMTnzz7qkBYBp/hoWVBHUPZfVghQEQ9mLcFC4mKf+WddtilAZspLR0tu456kGYl6DUVQt9B37J9x4502513pk2bNmUZvIbIVD3PHjuQRh/+DS2xemPNeliP6iJH68G1iq/KFC16gu/sNHh3i2bU9J96FrSEXLTqlWdRQUZxfu9k2SGkiof45mdAecvhUBFZHToo2bp5c196UlOQU1LstYBEGSqHNWSS9FVnUKFdVvs7Mrbk2c1DmhkckGH5/n0j6cf0TdsNG6SEs+GFylY3bQMLXfOI5OU9e7rT1oFN6XkZF7u29qXbd+oTB75NE+6GJlPNvMkfZNO6cedRiCY8JXXgaREJdsqcs1iVPrXfQW0g8569/elefR+JBqSeXxWRz4t1Brq0QepKPkSiDr0Pbt/RlW7W5lPUa5feLWz0EoxrTTvZ8s3gY69Mp794/IyNwS36LvAT792SPnL3cBrp1jt0cVY7NQuOsuoad707RP/1moX81Ac3pw0DHelFzTD/5Ae3pc3DokfvBwO2zjZCA9FaEMqPsgGIi9xd2ihyBJdrHSyHCIjKoQvXe5HBTS8k0gt0QZ54S7dBRMG/4l6lrWS+AugqBYgeyFuYTsv6lrBz6DaV9x2D8CpVxkVmqxbP+V8vv/xSmptRg7RyIfFF61RcWWK4ughGa6Hp1ke4gybCUFJCWqwaWW78UgFELxwaIC8Mp1VsNJ1oW26cgV4QfrIiZExC4vXaGs7j+xuZYpGN0QpahiAuYIVX5bHhRyA9KWXDL1cUqvDrWUkJiw43oFA2WO7icLV64rwkT/l4FpLnTAJ3G6JSgjG2mXHDUIoS6GqNnLKi2DHbo1KTSC6uOX+nUNkynyK9S5RjSFE4k9NkPMTgiC0GH9hdHY4RjSoT/MZA6dCsHIYgO8wxw0q99XpDmF596zdk/7ziOVMQI5CzypjBYZknbygUfOoTA5iZQcL4VpCZZzaTmdaM0KxmfUZGNni2kGMFemSIsDxwjqWiWqambGuKrmnPRTNPXK7MQ/zE4XI4jyGzDm28uKMmjV4rKhf1GwMRAUa0DRY9RpZFNuAO/GamOGdFEjNRkABDtFD7LEkFeD7Iskd8yLdiA1Z8bnAuDmEFlljw6RkiSQdy4EwHs69QwyOtpuALWQrJIIw0JS6SI358R2p7D5rl4hsNitChJUYy9jVwMa4dhjEU9QmGWBszpuQHf6jPPilgQYvioUX892CLgJCHJQ0qqEWaRZ7VxYCUOGDU9Ujp494tObKh51lCRQqHByrEvSXtDEibYVaaXU9ZNjo7qwEJpQP3048+ml4/8GL6yb/1ufT+j/6IaLm23AMPfSi9TxvhjJ45k154bn/6gy98KT35xJOqEbV1tQmp2FF/IjtqKNdTueluka7F6zkXERD87oNUhzmJREGyUGRSEPwVuS0wGcWat4K/UMZgyynNzvK979brthjvmgiuUiQDcHu0nPi2229PbBbDElF4dK05jm8Z+/bvpcnnvi6lVxuMXQCBzWmany8ApeWoyIkHSSVPbv9qx6UHQ9riJ+mgO1PG5E1Pwowaz+AwfPZDC88h8fRO+qlPKf0t6cES/Vakd9C6l0hJQ5mY70hPvT6VDryhfkv9EQbQx/Qt4O4htQkZLWVVT8g2JcCnq9oLG+jRIe7d2JH2bBxyebQ+JtqScOdcnKKQVMIQL8pC2QK3SiGc+F0SeeyPSPkDX8il4PQuoE+2Ex7A4CGuhscIIszhkSkZAxT05Uxo/gXcy/YVy9EzZXUMg8hOAaDy4xcD4vCCTPlXWYSbOsNRt/EaKSEOrtMqA7AXWkjMu82IAqbVle/4T2jz3G88PZrOzi5pRrEj/eg9m/Qb0pHmWjGiAQmweUBLHuiFasjr5H2ngWB2Sv3oPYPpA7f1pc0jKqM2sgtKG3MkjR14KJN+uZixOh9azYsCyKPCIECOG/VLnTAoYt7p7jYAO8QYo1AMfMoQujM5gjHIpxdBmVd/6MkSYFxAh9Pj+k5AUVfrg66EaCuHlcnaDikloaxqb2wsszStCZ4rf5Zw2ySfJ+DbaoYQBf9VHUh8dnTMDcCCTYcRTaIuubSfLMZFxOgLauKAJz/Q6RlWLYZGE5eMstwU7hkFANSZsKLNHRL5ZmU3Os+SGw1PHQCKkOLLS6b0okDVlAD1tDRgDsfu0pstGqzi1bpj9Fx5koAb2bkn1kN2gauOD7zkG+UQlXSmemZpFbMkNqwEQ3jdGYsfY3kddIsqMsT5pgvld9bKA645qE5LxEWS2hWYnL6KDn6ipNfTGEhZhPFAHH5v3mFYKfeMGmLU6pkON5T6gMG46+zXkRAq4+zMrL4zG5eC3pV6teQTapkF7JUB2acdIpkFYuaQMJR9jNAZzerMaokoR5dg1PbohcBvQLsXskkIxuSCNzPRSK7CmYVgOat5QsH4jyKo6PCkwpcso/CUP2o8XElQnnOoglm2Sl14qaKCqQvkiKuxc5HMlJTF4HH9lvpXOUxULZt47sQg5qVIahSN0Hr0qHjqmJdIzZEYWN0pIPE4Vz74xAOHE5/jDEC6SGm8foRfJZDYgOGOjxcJZeSje3BRzwxCFHIs2wrjSBE2AOL8R+oCA9evMJWbMwqZeUduIrPIw/KmEL7zsDzILwEyCEXivEPGdilL3WjRnjL67pSZHOp7QQMCODAye0zdMDso4ZABqiWmgmEzGgxE6m5BSzHPjZ1N/+af/8v0yvPPp49qCen2XbuM41q5IC9br7suPfTDH/bvyOuH07e/9e302Pf/Oh144UA6I2MR/lA30WcgwXKEFS1RDAku16/UaC0e+Iqs8hh9S04oXMZJxHm4WhoRSH5sGHXs+PE0pM2HWM59rTiWqm/RDrC7du9Ou/bs8azztUJbKzrYMObcd76U5jQ7iItabQXZZpja1MXgKGlr9V3LNmTR71flgaMVx10ipyA264gWb4mMbksA9Jy13pOk+gnUzn2J/GBCOV9UhFq5Uugvwwbk+lfAcVqQnY5qI5znjk55Y5N9W/vTh7QM9Pph5audk8k6t6zMK5fKdCHd0e8GlAdlcz9MvxaOey6A73pWPzgvZWVcg1RnJhfSxAybrC1rJ+3OtGmoR0dYaGdtGS2dWjlhA0MYAosGSJf0Hl3kXSt4TZXKHvLmJ6PTKZ06N59mZBANaIn5lpGetEkbo/QKB+812jXHSEzMd6bT+m7u7FQYrUNaarl1pNewfPNoQ1I44TEp5xY1mKadm1kZ0qf89PYzMdA/g8UvmD7R2sMukZRJO8zyzeHEDGcBa2sQvfu36lvEDTpOwt9UihXmhvooODmvvpjlkbCrXzh6GfzjfVdxwBd2LnV0p2cPTaVDp/UtusLv3j2Yfvjd2himY0bGu9IpMLhdMnKQ5aNg5T0yoLWXAzDPxqDSgKxkQt565nWHnE2LB2cmlvXT+0QbJbFhzWbxbIt2lB3qhkuS2Hp2eiYx/NYZveLd2Ql9wziputGmNlKDzJMtG7rTSF/UD1wMmRc/1DhmFrTL7XyP9ldgaEFxIm5WNIzPscMrg/+qC51lS53g3GdnuXPAmpfgTuHSmqAlEt5cLlfhOV7rrzOyI7S3hE6qV66XM/PLVaiVeN82BiGzM0d0ttaRo0d0OCtdpyoQmVNroiqLsu0GU2Qxg9Bo4q/CQBI5nhcDjUE41UGAByUU4Y9uKBqxTTXB85Kx051406Gr81e+sS27IngR1ZSg3NUrjRsxKQVbDAMMEAzCbv3c6ERQdNCRWZHlCCuFy2RIofPmEAYS9vxWC+WNZ36iHuUXGJWv9iwUS+psO1hCQVkUZwPEYIRAoe7wRlxCmfY3h4TZmMu4ZLwYPyQpijLyTCp/cqL0RHBz+aDDBh/PhoqEXMlLP2jkWzqWsC6zaaPSUEcln1IGRlr5hosfszLsQskMzfDwUNogxRD4np4+da4xChbG4LRh2DkUg2KGjWUEZ/ZgdMozqFlBDqYf0tJTDAEMAx9/kGlgYx5zRy9YK75mCeWCz6IXRvgH7cF717cNlRxPhsAKBy8P4+FZHTPLktm7gyWq8AwjB47yQrejzgTLk2csFe9scr0ETIblAbkgPzLkzgsRyVd5/FP+YqDCeBa9UYh8Ix24CJczDTzL8VLN5StJIpw0OMEpP/6cL8mctMQbqHaBfECtDikdbTFo5CZeE630wGE0s7EMm7vwLR/tgE1GMNQZ1SZP+Aow4R0dsdS3A8aKCPiK9LmdKshthRjhDhmUbMnYYxaZ77omp7Q7mWSB0fo+8Ck/0vKblRFI/9Gn5ckLi/2WmZh95hvT5fT4tx5Jz/314+kz//l/mt6jmblr1e2+YU/63C/+Qvq5X/h5bT4znx7T7qRf+v0/SE889rjLBzvdRsUjRN2VVQqDXFUcdcBfs7MS2wQbNSFIwJvilGEdRSWuhBJNv0v9nTt3Lp0+fSbtuv76Wl9RT3zlfJRx77596a67704jGzaYFui7lh3L6Ma+9btp8tmH1QAxga49et2PFCYWAbCUaWVE/h5+QYI5OUffoV0gNfbFce80agyJaRkGdCn9OqM03j1q44pDiWYVQh+r6NVH557G8oifrGrZlfzbuFv+Ve8zUhxe0zLR42Nz3kX0zl2D6cYt6quW1G+0gaeAmI5qe1BE1FJgCb/olUVwWkdaPHNEu5IemdVOnjNpak4rYgTWp8GvLSN96Vad93ePzuS7Ucdq9HWFkYbWs6yNe556dSo9rt0+h2TM/A0taeW8vCe08chT2jXzmI7HYMlunwZcr9f3le/T7qv37OlNm7U8clq7gr34xlz6vpZavqpz+sZZyaU8B7UT5+4tA4Id0Mxovww3zBjeYVqKO9+dHn91xumGlc9H7hpMuzbwPtb3oie0E+v+SX0e0pkeUD437RrwktnHXp1Or53SBmPaZIduaFgG5w3X9aUH9P3hXdpoZ0jn/zljZc7g3RMHZ0XTuI3Yh24fSrft1Lm0bKZVceadaGVpPYbvc4cnvWPosAzfh96tlQeDoldLetdtFlEJBovVPfRNlYzszTWlCK1DSa/p6I3HX5zScRbT6eTYvAw2BqXDILxTdfQh0bxH9dTJ4AF9Hdg1s8es88vH5tOTqptX3piVUShjUvJL/zOkj0x3bOlPd+/tTffu6087tKEm5uOS3pn7X1lI3zswKmNwLB0+M6s2kNKoBg2++dSZ9PQr+nRG77KNWib9Yc1w7t6C7qGyi5crilEtFiRVn89HslcyqAHTpX5wdvNTWv0woe8IVafqJd4K7m1hECKIo2fPpoOHDun7Lq39ldgx70ZH44pVpxLGHJ22AhFL/Vtf0VMsLaW6icMJKgsvIRhkzBKRj12OA4m8vgLv5ZYFICDdaWWv0IIjnrhjRPlZfiunYBPB4HQsSLNyjaESM3mhzKJ41V9DoRiDD1pt9BlPpk/lxzAia/IDDyq1A8gMWO6ZGPxWyiLKceQPLUVpgR9sgqLs7Iri6wchUAypna9nHqWEeyOUXA5nDk/V3QHpcoosyLCST+kycs/IQGOuMS+V1DNwoVQLGy9p4YYOHMYTtPrbMOXNLqGUm90lhzSrt10j8ijnNrSUaalj8GHg9ffLSNRsDrtWouRjMC4LBw4jcJOWc3EYPYYgaVkOCA3w0BtxUBBKI3yFRyI3+JyjouAB5oLDY6qGOz/RbxhkQOUSx6PugMmOMrrcJRORQD52SgZeRlupkRjII1A/ZAD8OFjmSAM7KMIEB95CT04aCZSeZ1BggTkBGROQCSCcqLjUbvHIC51IAwAUTvmF7OiRqEwikUgAQcauQhYWQSN1YOOOBALCQOaHURgbD/GscI0id2mmblEzuiHLkhlpQXwnwaYvOFGm4tSosIx5eWiOJ1/kzOmVB7PDg1qKzC7l8zI4mBVclsLRo28wJxentFEER1Ho1CzSSIT6tHx5VrPJC8wUKoxBCehnM6Iv/dpvpMe//Uj6+M99Nu2+aR/kXJMOmvvURj6koyv4nT51Oj3/3PPpuWefS08/9Yz9cxqsqLpa/5kDxS447TotcMFTQrOD2XJxzWHVW46vBrXykx75Yck7tG7WN3lX8mzCAX1jzEzrddu2+Y7/zfAto3kpHo+//lya1BLRpdOvRFCuuFJTpStpxftqWKmu9eCjbVZTXrjfg7RqX3QY8zJmDpyckzKvDcFkzNx740DavVHnlUqRf1kGygEZKwMyHu7d3atz/brSOc2E7X9jJh3UMs6Roe70vltG0jbNiqjhhvCKAYUHF0JhyDVGT4eMsjhncOfGvrRX3wAOooiqf2h2pS2UfMu9GY5nx+kC3wNOupH6umOTHenhZ8+lx16d0IwPxps2RZMBp0knz+69emo6HdTvpeN96Ye1oc29MuiGZSQzsLIoHh48PZ++IwNqk3Zv3rFzS3rjzLn0gxfHZKjoE0rhUkerzVIW0qkjk+nYKOXanN6jGc8XD0+krz3L0QzzMiJjN2aOzjg9vZhOvD7hsxanFzamh27VBlBdMbunPZzTKycXtbHJuM/vu0+btlyvLFgBdGZqOT36ShiEDModnpxL335qVAabBuUoj97bHPVwXLOFR5Xn66dm09R7N6cHb9anIpppRPfjSKJDOq7jr14e16xoT3rXnuF0G+/H6DRWsJU0JzW7eVTnYKCL7N3Sm265XsdWLciAWCUNSEq94Y+6wBfO9VMLzNqTntFZDp3Rd6XPndOM5Dnh0AoY8ZfBTXaSPS3ZPHRSAwmjc+lnf2irlgtrIFID3+gKo9ov4bsHptMjz46p7Hzqo6O0xPM+jWqQ31nx6Ljq/8CRlF45PpR+5sHNMu60skX1y5EY33lxXIObRXY0y6ilsS9oSXMhfrtmJ999y7DSAFQLLkVqvFOWWgicqHKjFrHSs15HsTLFhYcUkqBVcr7EN6BzMggHVD4Z128F97YwCFHKXz98WN+IjLrBuFHKmKJZUceMBLKUsigkyBjhquYmIY0qp9EhvMTTcoqxs6w3i/ogOWLjV2SIUEmRQ+2vXgRU8q4GQwRY6GCs6vo5lN6IwawFIFL50HQJpmc8FOi2ojiMNxQ0z7Z5Ns8qrToTjCaMIuL9RZhfMIua/WH639zJuOOmqxRcZteM3IF6gYgIeOAyMDBMShRm4bahqkKU8sE7G3IKK8odtOEvC2vsZyhSsAr2j4u5R4B85Qde7CGop3MsdVEMXBvqYFYnKA4oXdSq87YgCJPKLyxKK8VchhvfmPGiIBdmhub1DQzL+DhwHtrJhw6XJcgzUxoKNN54AaHYbdmyxfycltzB115mfWQUoGz2qrMNAyQXTKPOLD3Figm+Bm89MqiCQYMdtOpn41f5yW4RHRI2BgXgDRu8BGDczCd5dYdmDAvwR9XlNAU5uKlE0sCeXB4jMh7C4R3pgMsgBKFI2fGQXTXcyXTBuKOhuaAFMCMCBgdM8UeAnhUAGK7ElXuE+hogSAhypyDlxXe3lBdw81h3eIHMwAvqGDnh56KprNRttJUwIMFIw4HvLOlckOFm2SEL8iJH8Ya6xagjvTIzvwXifOY1c8J3pRzjgBG4qKVX0IPCj+x5ObH8KFzMFNKOGWzAIOTMRAYQ+NaRZc29GrV97gePpWe1JPMTv/C59JGf+YTCOCPz2nZbr9uaPqxlpfxw1MH3HtVupX/+cHpcM59swoQRTN9Vd7lW41YPbuXLMuHmgEiSRr/oV1olqIQJDjGz3Ch4XLSc0izhHhm0tP9L7ZAv6pTl5TfceGPad/PN1+amMG0UnHYxcfyVdPzh30z95w6rXLAdhpKYC5VQEOVKKo9N92ps3V9L3AK6xNWhm4DaeuS7QNyyjJRJLXn71vOn0+PHZ3QIOfMhmmW59zopxzIIZFg8qTP/Bm2obNL3piNaxjmX/vL5cRlAszLQNNM00J223aRvinJ/CYXuQp2Dc6n52vHAS/qYaU1GndbsjXpxbS7Sna7TMQhSzddEEd/7R+EaORR8K9wTyugT1fT49vrMbHf6MxlNGHQ4jM93be9P2zfrgHTFj+v4BWbXDp6cSS/rPvesjvDp2ZLu29WTBrx4UInES/DPiMRHXxrTju4TaYeOw3iXdkLdMtyj5Z1L6TWlP6CjP05pZumvZFycmOpILx3R0Qw6M/EuLbF81/WDaWRAs1jqL1/SUtkDqpMT4/PCNyEjoyfdoY1e1NsqH/p5/ei/9awHXdR/63mxs1cdNZ8AJOc1Na13soAevHUk7VG5mEU7qwPiXz42JQN31kbhN2Qg7dqyXbuX6lt/6QQN+MmgUqGNfCWOJaxa7qqZ3Cl9Iw4le7epHFoWu4w1bAIV2MIRBb41QGqpkAogx7Sk9nv7T6vPmtVGQeKvZo636UiLBU3ZHVZ5OPLkDRl7jx+cStt0vMWnP6SBLu1suqCVXS8fm0nf1E6mb+hoi43a8OYWzZ7erI2GNkq+9DpLx07NpxdkoB+WbD96QEdoiY8//9GtMvyXtbxWu9vu1YD3or69liE9qqNNmAXerg2UhrQra4fes5t1FMiwyh2lqpG+0lMtcNW/EnJlSLsMW5nyokJMJrPzC5PCwzur6F8XhfaqJ770b7urXqRGAtgE4uTJk+nw4SNWtlAEUUb8E2iRP8uVHqwYONyqZSAjsskBVxLb+CkvAOHHyKITL7hJ2gJFpfG3ihV68Cgj8AREM8b8rMigR3mqtwslSHEKj1kW3k/qIHmBKBi9GweFRQG2UqxwKysa9aQn8w6cAWrYUJ6jfO58hQvFhh0U6SMxvqx4ixj+yKvwOmZQlb/jhF4JwBH3oMVGEPkJxoYkOLLDR0eOA3f2+pm8oa3wCwhmB1lXj8mMHwPOhqhTZCzKB0W+GJEYTb1aHtophdxlgla92HDwBbwo7CwDZG6DZaYYii6bys53aUMahYRWvgFD4Wd30TkZhsyG8L0hBqJfWNzlmHXy7KVY0yljVAAKhHEyQlRnZVaKtca8rHnJwR87bmaM0oQmRqV5dBpeWiRdcwomL9KR3g4exks06k2vsVxW5MK7mYofNnBgvLVlpSe/Ogo9gFkOvNVw/MDinK/uhiEiAwp/HUhhBV6h5hG4CRMf4lkPAqMNE2EZA1YukmYEwMgLKxnY8B9R+gVfclrzArkgXN/gaiQYWaGuaS/+5k+GmPmReU6dUregoxTA4gd2WRt8IGcobcivYYRnUXXHclEcs8/IFjiQWfBxRAXfrfbK+EA+2PESR5yXMwt3vw7A4riT5aWeWpo//4N/k575/g/Sj/xHP53uu4aXkbowTRfa6ge0IQ0/2tDJEyfT8WPHPXDHTOIL+v7w0KuHxDtetk0u14VDqcPMa56pE/o56iyeVEcOI3wNFwmVpNPfc54+c1oDO5u9dDxwrZF2nSgMvx07d6Q77rorXb/r+jQ8ovPEtJx8QH0FfHgzOvr1GRnOM5Pjaf70ESlGM2Z3fKOuEqlYLll52bRZyGKU13jexJ5GdlGjK2u1OSToyPQEaUFNBRlk0tfRI41r+SffRGk1ob6fY0MWvolL6ZyMGB0rSKeSprS0UUM66icZxBA8/BDcjGZJYkMl9aFkLHjkDwduXFV8I2Sta5SGb+6mdTC6SJRxoSWNMkr5citiI32VVdonOZ3UN2Gzog06KkVVGlKp94c2EWOcMrzYiXRhqVs7Y06mxw6Ouw99146B9HFtXHOrlkj2a+YJPMwKnRnvT99/eTI9IuPsiGb4vieDbufI1rRP35zxXin1Mq1ZwNeOn013yBD8qfs2a6dOfbetQUX65dM6PP4bz3elb+uMxqPCcXL8lL7xU7+gWaUfu2eD8OlzDRE5v6Qli3sGfKj7d0TbG/oG8SXNfN28bVjGjd5jvB8yJ4IHoR+EP7i+oAG410+cSztlLP3ouzelB27BSINvwq8l+q8J/8NPnk2Pa5nn6/ru74Wj82nv9kGdIEj/Q3kCG9fwlRxXBixqIPKsvsWb06A2ffzGTVraC7OrlaVkxVXrpoRV783xBQ1tcHRce6WLx+++YSB99L4N2jlX+xZoGheYc3OD6fsHetOXvz+aTsnofUz19X4dZ3HbdpVH/BhW+fdtlyz1ayb8puH0wJ0bZDTKuFf9dMhgnFH6F7Q89ys/OJVeODabfvDyRLpLdfOB23uUX0fat2OzviHsS9988lx65JnZtFHnNX70PRvT7XukQ0k2+V5zpI/2UCS/WqrsFzPNz8JUB5cStoBfEQRsQ+IVEJcuoJpXlrtF9Xu1ln3pcrpamN7SBiENZkpnjh09fFQH0U9ISLOipg59pcgprNJTG4Ln5tZITSF/INAvFD8JR0UmnYzIaiDp5GpgigZFODqcxkazIqkSOm09USaDxq8ZMDVwn4PX36tRMYwNPsaGBMXjKemMmA7T5oB4Iq4InmBA6LdiR0ZeaEEto4Iso7OxKMMDpTUMJOG31QG2QBCGZ5QKhZYOEYMDBxkNTvihjx/p+BXnrJvgnd5h0eGXaAyEYszwLRgZGa/zjhkhaOGbQujhDYk/SqwyqHyUie8Bmc1jeaCPKcjhlJVvNCcnJ7S8JL4bmJZC71k/K+n6+FuGH2fOeVRScf1a9scxE571YMdKPXOgvY1eF07Z+666F37qqJQvaKdpKg4jzQUNPnX62z1oxnhUWWAZ1QTvMJQK7sxLB/EVjEY6kVXqlwSAGVQXYAD3zqvgFD3kCY9ksgReE6dwMiyMB1Vx1TDowZU7rIZOMkfzAhcyYZzAZUAF2UFQ1ZVn4ATDbnLUIX/gYKijuPAp3LxBTvQTPC848ibesiZfDJEgd8F7VbtIkSyQn/KCPNrCEj8xCJLDAJSf8kCBsmIWCYOQGTwGSFx/giU3DE1Z8vKFUcjxAIOSFc5CJD0zhrP6NhEDcUaG4YYhZqV0mLGU7V4NEHjAhW8ce52hlihPayS2z3lA09GDB9MXfu2faRnpX6WP//xn0/U37nUc1L1ZnA9T37M77dbv/vffnz79mU+ZdAxFNqZ5VstMX3rxpXRIx3BwpiMG87Rm5pl1ZTCnwVFplpO462pX7g2wTQ/AqDotW+Af0/fEg/pOmGXlazlm/Hs5NkR9B4NCHDWyQ0eFXL97l8q0x8tAqce3gqOfWpScTog3LJVf1sAVA2TefTe4ZyaajxdT4ItGEJmvQNMUgLFkx13taURLuR+4SbtwauZoQIe937F7QJuIzGu2I6W79siAV1/ArNVN12kWX8cWXa+ZkvdqpmRQ30uNaGZkn76rY4fV6F3cQwVXhL8dGczUVG7uwWS0YJhKuVZMr2jTmCW9SwUu8Ls4kseJua70Z1ryeXxCna8CHZ6hSyq/YdR8WBr74Ls2pnt296c3tNTx8YMTaUqG5F4t+fuJe7bJGNNqimV9+qBy4ZgL2a1v9LpuH/YOmt99aTy9qiWzh3S+4S5ZWTS/ulsW73q8rPR2GZX+5lEWJRvb7BzuTXdrp9QDmqk6p9lCDOvbdw+lv6ElqHtGMEr0/bvK3Sue79o8kO67dbM21dF3f1oKeersgvpLDbCxhtWulIoH/PGr0SK5ZQXGg1rS+8HbBtOwlvUuezWCNm3R++6W6/tlQG9Ir47OpxOa3Ttygp3CB5KqWqiquMG/lqO+tJkYfbbSiXS990GhNwARhdy1ULQRF6ULwF2aLf2IvtO8c5eM7WXpJ+Iv2WzSaqQHVUevHJ1Nj8jo9hLQM/Oa7WXp77KXsQ5rFm9MxuIuzQBv3Sis6DfSJaF1UBvo3XNrv2RpY3pj9FQ6pSWk+189p2XRGijTopThfi0l1kiJXlnOD5nconZyPXXHCx8i/P5ro0AXClKr4AtFcKHpKJxaIJv9oDxdonq9UGouVTr6hLes4xyww0eO+Hwpt2kJDwpaGHGqwyxMMTNFE8sO4BJHQ7YuGQIAhCH16NkG4TPuikQUvAUd95K6pdwokFeHDbdqItKRDzTkcPIGP2FBcSilzDqgHGHceFZHcEBAWxQFODorCa9GMnmxeDak3NVzYbhx9hoLMcBlg1AzJMxsdakzRVnmWziMqrL0jsPozRETKOzCAe7iYrZUT2o0Lou8xAPuXw4MpTxK5CuXYKzTQTsvYx6CesWrNISzVM/Gvo1BEMcukeaH3vimSQYU9LNMkHqb0yhwUfBtGgsPI/kodBiPhDF7aEVfeGe1PHRaszh+0SvhrBR48FA+jEFG/DEIMZzNCpHBrqPMKGIcsqzP/K8yx+XTkl2lEZGu0ziDUkAUlfMfcMBRdvtVUoxEjDaAwsKTXzDCYWeBzSDaGQ0YDEnP3opeE8hdzo+6UGs2pgmOKL0Y8JM3uLmTQo4OUI+NLicE3ukLEqAURhkKfeCyXzCUw7REGGCQH2HcSa9LgeERmIJeHka5MY4U3ECW5QVYGWQY/AxeYBwWWbNMkUZpdYyk+Bo4PDggQy1kTsQwJC7MljzqoVNLh7Vhip3we/ZZMsimScwOQK7bMgTJkV+v6hKjb2J8Mm3YMKJ3rmYUJB8YCixTXpA88t0i8tanWeqpjinH93lWWXXDS1q84ric4jBOOyXX3J/Xpi0vyXB69/sfSJ/6T345DWpTpDe7w1C8+567/aMs8GZGS/cwCmf4qU1yduBRNgtTP3/q5Cn7+V4cY67MiOdqWJ8d1JugqHdmY8+cGU0bN27SLCE7DWuTBfF0owz6DdrkhTvLYDdrefiIZvzoIzmOhnZ+rR4FsT4D1obAAJzUN7Dz6tPcnuEVwyp6B3iATfy7WHcJUND81nEBQYu246aKR176dED9A/sG0j5tMIINv1k7afbIMOEYmQe1scbtO3u0gYp22cRSkPI93NeRPnwrm6JogzHNoG3V5rTLWkoWnVgryZMkKb9WMasRbViTGvQWPWFVPAJjF8hXteTvNS3no5ZIWfKs+d1vyqhS2fbu7ki3aoXEyycn0xHNwJHmlj0b0027h9VXTWoGVP0MGWbHu29QO43u2zmSntemM6fPzabD+k5tclHGsQyg4ngv37J9IL1rp45uWtQAjvoxcCM/HXqvwsdN+vay61RH6pdxd/deff83gqJdH9CGcA6g36aNbDh4/pwGxXh/L2jjHz7MrpBVsl1xp03v1JLJ+/YNpREdKk9fCxmUCJ2wW98/7taSy81aOolBOK3p4XmmhWXgyCrVBbrbc9CDjkSe6GDltbdaarHCrp1yAAjN/HADWqZ5m5aJ3qyZwR6WMObMjFIDNSOaSb5554BncNV8vWmMvl63Qa790NMNW7WZzrYey8uolpZqfzyFWiPSDqHU8WK67YYRzayOpVNayjs2qqOztOvsspZTs3kS5yLG4LqIUUFYWbSsjZfQk7IaYjovy6Vdhl2WzAMpA2LnIxuXkZRLgvotaxBiDJzRN4Mvv/KKvwlhFL8ogqh9NEI6K/TX2HVJd7G0dLZwF78g/KcLgATLubnVvLU0irYu6/gCG2Bcya84Oor6U8YI2hwIfWW5Z0lDB4Mjv0KBlV61S4weyKNsS1J6KZONNiF0KRQXsCjguXOj8Iqno49ZEZDrJxCeZUJ45q1bymxt4w0UePJQB03nbiNCfj34R57qEwQhJXhZM2Je+kiAqIAGx+R8dMO5LI7P5TMC0rhgSqNwjYqat4Ekp1OYlWI6IP1pNNG4oF1pXS8yBsiVc8YggJk1j16RL0wmnWCRF2YFCWODkOUlbb88M2U+8P0hS/oYCed4AF6GKPL9UlhHpCQO6WgKZv841xB6kLNZDUaAHWORuCgZJOCLfOVxfhiqHFsQB6Urvhh/AQAQRRB/SYdHZSkOfC6HAogqqPPdLyXF2zAnXo4kJQu/QPLLC9qjQcQtiBYi56uE5W4sujjfjJSM3ZjIoADke6GRwQMttQplKRNB9VQd9aZ6rC2VNb8ywlJOw1cywSvhpx5xlLnEhl+ZiGXeoEU4MOBggo8G8QCBzh2Ugeflu0agUXgp9mz+QnoPgKjs8HBJ8gRuigE5yItnVhWXWa47tPBEPIa47h4o0NIzLR+en48daOF9fJvKsmHJnNKUGUCOxpjTdyEMKGBguGSSEwZp5iSfpkuBnTJiaavM8k9pVuvRr30jPf/Ek+mjn/pkekDf641oc6O3imOgZljf9/Ir7rbbbyvehjvGIzuGTkxopz/NKM6I71OadWV5Lm5ifMIGOn5qisEgzhvFMYDD4A6GIMs7N+g80QHNFL5dHYbgLDzUr8h1Iy8s8Y1BbTxFCwlA2hS/siqljeQXBVLyrvcUKoMCKd+Qdswc2qw+gmdmxRQGbRulBG/U8jrOAiYc3VvNMW1W+FbNENI9ohwzuMaqgHjPktaaRry/LoBq3m69MkJ7ZDDRD7B8lF06NXUm3LzxlZ1+5h93ETKgfuHevUNp16RiRCRxxbkf07vuhL77eu3UpDGoi9Iy2M50VJungJ9yTcmo/MEhfUeob+itIxhLYPJxHOLTMc2osXGKuKRvHGVIqe+mpZQc2T+GbxA1GShDQXxz8jq1lKmP96bCN2hZPN8Gdot2F1RhQi1c4qGY3S+9ZkAISa1s9Ata9LiuYwx1h75v27aRvlZ9vVNwjbpl2Wm/gPplHEMLfHZGvpEjvyZH8kBUiyjto1+za9SOB/5YZyyGhnbSlKCkXCW4RHOvUwC+yJrjOPgWcqhXYeIRaApb4B1a3AiDGnr/8QkjckO4L3oPzsp4Oyoj7+CJ+XT01Fw6N6kZf8V3iwkbNOO9TTzbsHFAO4siz85CZVL6TK/1TD3ijDbfc7TDz+8CljYcAnpVXaHTknJVKbmUmb9lDUIUeDaSGR09K4UORV9s04/qsx8uWqboEFYKlxu2W1TEluqvJQMPf8DkyBoePa8nryh0xZVOxB0GuBQFrhqdBTCinJ3jHR6ZUwp8GHEkDCNHLyd6wvxC4oWFI6Q4qACX5hrkUTopl0wIQR7g3OkgDYOnuJwHUBhAMUPDy0k/Z6TUKOj6xagV2KJTsR1a8HAnSg6eeGaSlypvW/24wxfyhwr88Iv85FUaSqNw5Ula/JQDv1OI5qAvcACC8o0xq65P8LHhimcfpDRi4DLbylJBFEhwLOgZxZwRZEhFecQIZOnfQN5t1LN8irNRpztKOko9xqBpMrHFC1ZhEr6aU368QGzU6EVs8aBnBybDwQuXl3qAx6UiAXYCwjPvTKl4l7OwAQF+cGAQKRwecjQAm5hwJp8HDCCoxlv5hdLOiDIy/M4vxzkv+aEXeMCqlVzCMLSJYm2JBmgacZA25IP8qaMwCkncwoEfZHbykNyZwzt4WXfIpDoBL/2jfqgrysvRH3w3yGDBkr4F4DtP5H9OCjA8iZl2jpyQEqIyI0ElU/KqyaJphX+KVj7srrukOnQbVFloTyGPEYaBwkw1O9WypJhNr8iPJafMIs5pMMEzh6KRTWX4oClmmJWHylbyZUYSo9Wznwp3+1HYOc2Q/b+//f+kP/m9L6af/c9+Of3Qx3+8zoy3iQ/jkc2d+L3jLowDyPz0+PgahmDgdbtzf0CDdENsL8Na+41k6yVyFusBVeNrXUe93RKdqXQL5rmA8caILlf9hdoVLmBpc3Ieo5TPgS61/THQTN9WMEV+tVyFlJUDERtQ0YpBWtLgb+1IybELI9pQivccm7qMa7eWnZq9qqY2jQqg3gZlrP3IbdrixflWoeiitLNmR196lI1cdD4Cg7YYQ8wgjWsDF14t4Hj2tdH04lF1an7vBG31UuhZaNm8ZFLfsGGgTSstY37UU8mRrn5Ex0lgmDS4DODBrFyxfTIOKWeHzyQEOvd1TqgMtMqFvhWH0cLC3Hhy0JoXsuBoCfLAMA0CI7WvDsKUj7jagDL6goAL9Yb1uy3gqplSJOSHVSgbNXuqyTktt1xOp87MyRAb1DmI8LWaIvxryfXKuEYEfNc5onJx5l/QVsdvevTo1yp0EaULcAxojs926BiQOe3+Oq4NfnT2MjsAKRJjEBUD+O5ebaI0OK7zCcXtxqzBZt4QXKKqfuLB0dLliMbyNadumVKBq2JdLcFlCC86xrVAy6Ur3lvOIKQjQ7l/44030muvvW6BDUUJBVgCV2Su1KPCCCKiRDsqh3tmJaKBMCTx5EPnTJoSSD4X4kLJU0rSuzcWfjqdjK4Bq2DQO/MnUVEeAdhQEDHMFEIUdx96DYHqBN3AQWR6C0Z178IXwZHu/2fvzZo1Oc47vzp77xu6sZEARJAQCRIkBZJaqFFII40taUbeYsITtm/8DRzh27nyhcNfwGFHOMI3jhjfOGJshdfxSDGhjVqtkSiKEvcVJIgdjd67zznd/v3+T2a99b5n6dONbhBoIs+pt7Iyny2fXOp5KrOyOk+Z6yD0Ijmo9Nk4y6iBG+dHfi1EPxOFCKMM5ZCBD/PoSKL8q0OD3+1Rvm3eDdAozrISDFtvRA7EkRDYDLb5KVyxIz+6iiMlMUNlCOR/Zveurl2Nkb21xeDOTN8mxn/NesK3lUVD3PTrN65Gx27znzIhwSo3TDeBcLbGGQRnDNwERMciSwZh6wYYm5TX2eh1dyjkxluyVDkjWyu79dzlVxf8q+DoNRO4Om1CdFTPFkYHP5VCXN0zs1gjfssXRmIaAuGPoZIk61KHW4dRFHhx9752/eqwno1KcIAKEeQKSphld+PtELLoSplGx1ReVlLn22kAMwsAtWvBI2DPT1lIavhL6C5L0DTIaHQlU5BGGsFPkhIWocBJy0uOuj12JqSR53umK2yYdDOGBW2Odlfvf+EUXmVHQdqejqyzdn6b0fdJNZ5ucve0P43fcJQs9Gy/6jTtGL2oE8snTb/PWR+3txnrmOuIY9CR77XLHaWh45KNaKxvRuP0GeTw+4juNOqMluOZjrtL0ixn7zfKetP+ggGlk2v/TD70NbRuwONf/Lf//fAH//e/Gn7pN359+Nl/+Mtpw4jwfnhfA3tqwIcSzgg6/tW7gXuCJiMPbrxB2Lgdnjh5THpf4N7+z91SXZSkX9fo4q3W8SKLF7rsHcRy0E/lbIgEuaiU3M9ID3gI6T4UoGm+ApBxl3gz2YnNRi0ubhu8bzrD9siJ9eErL13Jh8dfZinoT/ENPyYni/dIpQRnlOAdspKly1gg5DMDd56ZoW3ef/KedZxx5TQfZnfXUjdfMfiOn+9K6tTwVLnKBGqVOiC5Hw+8P3aazUS8Fz3Ch93dSMQbTN03gANBJ85vFVcgoRHptMYzEW9vzecLuLd7g6Xq+utxrQPrxuuDhCK1E9oU86Y5FZ/+7uQwxZnS9hWGc6fWssnKG7x39wPeRzx/8SSb5HAvcfZ1J6ndUxqDnPjZDc9xPstTJ9L3cjRTKbR7mmA+9NTC+dIL14Z//Vds6MMmOn5W5Gk2/vng2dXhBDOK0r3KBkmvXNgevsUGPpcph0E61kNo7yZQoApun+xAUXV3F+4W7+647Ylli8aC4PddItCekh484wF0CPlAJrOCX2Uzgst8czAGna249QiNqW7IWY1J1hjtcc71xK+MvWRMfwDNcglwOtxu7UGKI/2GX1xIl19vQy3Ry9w4vKmQn3fJAjNizaSgJzFuilBOaXui5vfSYoRKgmzly6yWzkcQGgnoeovrPGtmiPKAZRqTG5FPZziGf4eXL3Hp6kRpvPKT2ZzM5FmuRldOmYXTsHf0IC8lkSYjfkCBceDJJx68iXAdY1ePRZm5BjzphWwCByG0ONcyR+l7wUk8iMc0xgC3DC4X8328Q76PJSD0nQW0O/f3IWtZqN8UdMYMQx1GOhB5Lwg8N5dwRnCF98c0+GvHUPiiEIunLjxSHtZnaKA3UeHTgoX2iLNnmvitYjhZI95PLXuQ0U3dHVOw8JJflBI8gAPT8tUb5XNDhK5zr/sdNsYJlwlhbTlwSHxZRjoG86N06EAuDqDC8G+mVR6SCNpaoRmECeFeuUkPYtF32YmElD08zGtHZPcSOuTFuZKOcKHd6XNpyKV5hmq3wiYZWq21Qd2U+rUtWHfbLI2y3l2ma9vzcIZu84ZPx2vWdJv3hmznLvnVwV+2frP8GW6kSyvVZP0bAS9PleFnnvSdJZT3GEq40NXRy8MbMt3MqDb04Sk98DdxAH3ybtv2oYPvsmapo3UkDdnVD5MWGnG0ZcsDvO/TCi9b5VCd3/v6N4fvf/Nbw2//T/9i+M//y/9ieO5zn83s9SjX+5H3NWCzol27c+jVS5cSP5hSHCd5oEI7TfO2ZTogGiZNvxIWfm+XPwGfd2omGftFS6A5ORwJsH3tLvnQuh+b16H1fmGfqr7ciTI+AOjDM0l1cubStXYG0moGC0jiDpU3caau8i6an1lY5d7BrWUSpDiXMMmbRtm0jPe5Pnhmg1nCVb4fx2cY2ITlo3wi4GE+c8GAsSuZPr5YstJflcCtwl5jd8ofvsk77sj1FB8fP4Ozsr7EQ6cmjjNPv/LJh4YPsNPnLe6JFjijt2OyMgfOchZNk48wth5noxEtCbP7Mae84E3Lhs4naaE2XksfWBPhk7wpamWUKHPp+11IZUapk/cOktRWsXPcRnnm6c6odIp1H3J567kTG8NT59i9lE1cXkLXf/+9K8PZ547gaLMKJTpstPagbW5TLY2qqWHKcF4UrvbOlEVn49ndyl+7uMQOpOyoyqcpTvDe5M999MTwuY/yyYoTLJ0FSDgnUi/z/cuv/PDQ8Dt/cZ7vYLZdbRGs06tWPuVtfHrN5W1Dh+/n2yK8SwAcSNhiqdtO7xKp3o4YD5ZDSHvyyfs3v/nN4ZVXX6Wy6ks9Gna20WbLZnDUWFoMptgk7bDeGGJYN6AObX5v7w4aGWiTSDpAGvWSrk6fhOpdoQNGh+3npNePPHq+8QoVm12T2oHgVYOpPGmc0tRiVwAOL7NsLSU3bkcuxgHxrmiEVJGNCpGBCDDL5uHwru68IQgUpyt3O9jFCXXWSUxuq9DQMAZTYqRLvXgqt5y81Al1WWYMZ5zGzFwJnEN4YRue8cgi+5YXQhFHignqwLcZrG8dM2vcJ4gu91xl+eah9uH4Xi55VFl8gZxvvjFQOuN3DCfAnRwPMUMUIxun6SgfFneJoaqf3WibhMiiQ1E7nepo8ERV+br4JR7XJHCUk4Xs3EALUIBCyDLFeF0mmcbBP3sdcBbeckEn4P3aJAXjbJCv+UH0lwvKJr34ZACofx2OQ0vsCofjnIcC0kjoZy5IK31a1pFw8UhbUx6ZcQSNn7HwpCXeYIyPh2kt6FCJz79ECoULk9OAzFB+EkYZGyxIyhVQf0Oj0ISwriUUHRAfP0ZP2X1YpDNoG3Rn2Zu8W+I3A31YcJ0ZOWk6y3uId8tWGUtu4SFvIU9oRhToIo9/FkGn0fbkzKPIpvEfPSuBfTXnwFEecTl0UuXjbLpNAoTWH6gjnVFgrAPbqLJWO5d4FVY8241GrXUqjMF4ZoKZBXDJ2hV2yf3v/uv/Zvjgh54afv5XfmX45X/8m8MxNrh5P/xka8CHEVd9v5J7p7ODdxTsUz5oW/UlsQVMr6spLmTsvFxEnUKk704T7jQ+lYMxxBktZ7/8sPpf/4CNYh5mQyDKwaMi3sVic5P0X5DoP1eZNrx8lX5PQW7m3rPIPJ2aRODJd3bLIjtW3GIp+kVWvXzxe5eGq/hUj+AkHIa+Y0SFgynH8WWNWb0n2fTkyYcODX/Dt+G+wff7vsZmLSee3GApouNObrQhu5NqT0GTjA1brJJ4gR1B/b6cg+2jZw7nEwyH+ITDSXYpX166Cswt3ulbGp59jHGRJaEWyjGkRm0u+Pd5V+4j0MitJz8seXepw0JjKNsjaJGxNND0kBPEGpZkik+S2s98KxDalMKawu0dR8wET70GRmgSpaaue5jFesrCmTFZ/Y0hUXREXRxbu8V3+o4NX/re5eENNmP5k69eyHf+nuIdyVvu5Cnx8Byxx0jLKhDqa5M2KBtuVfO3vxHjTiN8a/L8zeGHr/IaDISf4tMin//E8eGps/Cg//uObNfQKZzF9ZUjw1/waYkfjqkzfhZZ2TwbcqYfqMlZarJu8yP87cKE0e1A73d+FXRYWvO9852t9X6zv1/0HyiH0FmrF1gm6vbky0vNGWxWcpobxlwNTDYsOxnN1k69GEjzL4bfbjDi5CjEwBHNgJI2Wx147PUL9DNupsM4yIqY1tVkgTMd8lYz6syp3L2oFXFnGQwO2W4Ck5lROzZt1ZPl6QZkAQKpwRjeCtFDICOS5ellMzdOJ/CRJ3pTVmZGcArbZSeSs7h2lSpvlUNnyqJpAPd3xDRINHirLpRzIk/iJccoalOI9JNm2UgzWXDfrdKRM1O6zvodhoezdjoETGkCVzM6vkzuw4F1nD2dPt8JPJNPA7BjKIL6fpeOoMv31JdLarKzlDdHrnUCMxPTZOo3vlKApbc8nFq+ZfVyRedUGXtZTTQIl4KgNad1JWEFgrWE/LkeYQE0Htot3q+TRp7KDrp1TSTp0OZsdAWjpWItL7AkGVRug6tzq/uWaFnditx2YXtLsDzBU3BDO8ss6UZ6mMaBoy4qBJhovyYqrrLVT+Qay2462aEGnOryAYMoWCwiJS9Lv9CHDvvsqCWbXlsdvgwjOdvQVZbTrrBEeDUPAmo8sN3SAmayhCmX8uTHvqtKbZvSsb3HzNBgCwPfu/HtF94LhK4PT1aYRbBtOsMsHeGMO9sf/FYGy+T3u5abYdp1rhGfvgaeZdZISz9DVh+E+E6kY6NtVQfxe9/89vBdjn/1v/328B/8Z//p8Llf+gfDqYfOhAbc3w8/ARpI36XduFGMs4JvK6yzjcgaRxrfnVGyufdQo8s0pfrR2OcDOJ/fcefOrU/OpXGRrkU/eJilje4K+oe8O/VvvvLm8Nff4x1q+hSf0OP7d6eHJ0/RgXGyXru2PPzJNy/kg+XiZsJ/kajXZHaW/ZxXPkhlMo9v0m0NT5xZ57MAOG+821cOYYfcjWClZfwq8viaN4dHcGSV+4U3+EA7nwr4U2Q7fvjU8HF2mNxYcbMzRya0uKAibQlHNe9X2wxO33l9kzJfGt7EUTmHLtwB9OjaVnZC9uPzG6ycuMwunt/84WU+RXGaTWIYuxw/2liu5LmVMJ6wuWi+uXcMB/skDmRW1mTEWyhfL0wr7mxUbgkLJ0f+BQoL1+RCc3JHWqBQ2Kpiqo6kipeBdp5JRv4GHD02jllZs0A9l5MyVRTqjM9yXOLh4k9T38/xqZI/+vql4dssy/zdv35j+NusT6kAAEAASURBVK2fP83SURqaqzimgoWXXKvckvF+7+dD/pjvI2rTff65E8zC+mR4gtjKYUpxnuV6nUBldYyIB+SVKzz0ZEmor8I8zNLjcyepuZsuEUcGgL2ftEoeXn5tm+9O1j0rTOpnNCeFL/rieH8q1M5+t3P0JdIo5G5QC2l3AruAek8uq5JT2JSXh8TDqtvQNtvnnjD58RKhRA9O8Mn+33/l7/Me19ISBjeDqANG7w7pO9ZkjUYYX5Uz187IF068GPpNPWkAicccDC4gO0JI03AcnNOpgKgO1juNtLtEZjYiTRYJysujiWnSfBhxSE6hikw5VQzc7qQJWa9nL7N3EsVvNpC2a06YixjCyJbCml6lFlajX0OzBorKk354Wp4e786LsLIMiZbfSu4a9jxZJDsw/KijBM7ct7z/JKSoLS9y+ZMRSIesySOkMF0v4tKyvfHqfmmAaxRLOO/VcWdfZpmtPFw2iDvIctAjLA88wmYxh1geepTvSh3iUwDXgWPZHgNz3oVkmZ/l9zC9z8TIvolEDEGUMXImh0zP5vBXTJHFROEqx9/AmWw5MNayZpPBxnf9MnOoYmwVaVBELad8fKjv2ew8tpUoNCKU0V14jcwKNPCRGVjPkuhZFa1iJL3yE0XHeRqubsOnAXtKRQolTa+VCSGNt2RiDY5C2HYUVTopiz8NsNP20pBzo8tdTt0KYjs0qC9TezCWFgmQdZdloJG5Zud8oJLlljhhtnXbtZ8eWGdWbvvwbNYks7/QrXdtin/KDby8fSfnFg6expc3W6qOtC22XJdn3fB1zjZ5PyufSwGmnDfbk3x9QIHs4MVBnJbBsvmfaQjL6/Ct8+e34HzgcTNtVXzcSeiwkyrvu7qDaj5xgbi2f8tgez7/+hvD//w//I/D7/z2/zH89CeeHX7rP/lnw+N8x/D98GBrwHdLdQS3aNu9v9x9iemjaxhFh49D6+6pBNPx6j6EOar0L43qz/LtwHWWRX75B1fYTn9r+CFO2xr95Re2mJGnf7zBZ0J//2sXhj/nm3xr3COOuiPIpC8qJt2pQmNQxnZPrPw1+vfzTxwZPsO3DZ86zQjEWHCnegofkA4tbw+feGyDpZ5Hhz/+1sUsG1368nnubSeGT31gnW/yYZs44ITBKB1Sw5fx7To3xa/zPtjv/d1bw9eYIbRcn3ry+PARvku3wc5Vfp7npx5bH84dWx0u8gmJL33/Ek7N4eF5aK/h4Ei3P8B2dct13j3/8vcv84H6i3wOYnn4B3y0/COP+B3f4t313s8zzZQmp+PzNG/3uCP8LBifcdHO69fSljLjYlKnWA1/ppok5L4Q6tgzpHSMolN0C6bhT8h02EqCMP+RlCU9p3CQf/lTJ/nQ/fXh2+zg+f99h116aUb/6GeOD0/ggPm+ZeyBIM8o1Wel1nD6l4Y//PKF4Qt/zwMb6WKH/OonjmRcn5ekcEcKlo8LT6M8lWQK4RYOJln4Mbf8fAf3o7zugmzKHjriA/T6xdXhT/7uteF1ZskN/uZ+581W4txrcuPlylfwnX0GkbZU8JGBvFE24iWYEYM5c7lJfbf/pG2tbLAyghU2KvIBCQ+UQ2i7qvcGffLemto4Slejsx3boGeDwM6aTFtvyXM3TGnmoNO0+E7sWYq46RCNoPGKOlgVjRm0MdOUsw9pO29CHV758xRScIJoOsSbm7VjpGkahAqgCnSO5OnL4gpht+86iJwxjknBuAQ4HbryJdGZyCgkGQegEztZpxDKDgCeVbwhxn/xrARRKy+4RJWLf4K/lr1oeI04MZS7sR0ICmkZXELntfL5F5VJxtAuNPBvsHNkkvjJe1i8I7bOd4xieOPc+W2pdbaH7puIiKoj4dmZQb8jWDOJOgebMRScKezLVtWbNZQyoFOfOipPeKKHJRyAVuHcqB10ncnRcQKLJ3E9z7J3uUMt9UOMZLxBfqAljtdJlEcNuElTbwZ17qHTmcKYGKSQneMROYGVVA+2jYQuT6tPyyV7yjRrKzpcsLOiUnYu5JnAudNSnmiJk3dDsUySYDcAWx+1dhU3nwgRSPACrngrZmjDS27Ko/OmPjr71IL001qKghASF94/N3Oxf9SDG5wxDBzbWmZ8nWGHvbC2DT81ovOmuMKs841CHSqfMVSwTNKmjknPbBz889CArLrh+gDC96ysS/sqG3dAO586aIKry3xyA4A4nvRnncUe7DdeSsN/+7tLSg3O1G5t1juEcShb+1BelzNjDSKbT/l5is/yPkoW59U2+eorLw8Xz785/M2f/8Xw3POfzq6kTz3zzHDi9KnO+v3ze1wDfjrCJcduFrNFe76XYWmFJZfHzgxbtPmsy7F50sYN7ZQ4yWm3Jo7pdoYxVLv2MslzeSPQnUfCWKINlfHxJM+Lf+6DG8PHHt4YXr2xOvzul17LzBtLAoYL2yvD733lpeHPv3OZ2UQ/qn6amTT6UMZiyXRCC6JkvJql+fmHVe4BJ1gm6vcKXZJag9Qe+DPUXWKOL9t853B1+PxHjufj7F984XIcuwtuXPL6EZy7I8NjJ9nwjOWluWfD5hYPCDXSX7vErqE/vDT85XfZURJnT5V8ms9S/PyHjgyn1rkveQ9hfHj4xKHh+Q8dG1688CbvmW0Nv/fFNyj3qTiix3E4DQ5XF9mc7W9/eG34Az54/h2cHb/X+NHHbw1Pw9MXN25Sd13tQWo/07S70UKnVdQzmvaknecpszFXpbRqIK03scgyJ1CXf8/aHikaUSedltfBvrU5fPjcxvDv/eyZ4V/+8avDixe3cZ4vs8HM1vBp9P4s+j973NcV6p7gPZ5XOocLV28N337p+vAXvOP39z+8yncX2QWWlzt5rsctdE5IWc2FXYs8B6Gct4YzbFB0mp1Qz/Otyu+yUdE3f3B4+BTf39xY50Zh/dFmXjm/NPwRs5N/y7JXlxD3EJus1S+3TZZB85CR80Xa4Quvbw8fe4qlzGw2VDaPbTHu09geRj3tX5TOrp3vCHgB995f5j7MqoilNT+BtNDx7z27d4zig+UQ0mZWGdB9sm/zTRO2HREZr7m0QXvEuJ2ouoxFYIPIOEgkRiCdsJGBjngiNSDpJbcIpemLh3EnjscszK7M1bnpwoV363TdyDbX24jDXmdpzJmpOHnNIZM+5mbK4ztOm6Gjkeu7YTRWBplNDEaf3GWpJmXXGK4ZrpLSwUgZZORTQJfX2cyjq0hhuvpoTpACEY8TJxJ8g09cw1W2JpteHUY+5DVDWcUU58pVFxlAwEn3kg9HguU1Gpmq7tS5S05jeJAXSH40vre5cXqWUpwwhLFNnL/wFt8X4wlmY75xyE1iwNHZg4fJMcQ5b3Hz14DWSXFTGh2FDd4plJOzQOGPUZ/NY5DL9LQX4qPzlkri/ROMMR2P0jkyc23V9frJElTwgxfFSQ+KGPzWggZ+hZTSHBsnJ47wNtc88CyH53YZxyu6aOnk2Ya8yo9whtAyQo6Zrf68eaRtBq7qO5vN+CDQP+Xt8gkDHeFLB9Jqh7qQbudnvDNJG2k4SQ8hAWblaLRDTx6VG/q1/Ef6LbXJEKjUB3kYRuU4tvIrNk/Iq/3aVuRP/VMe3yukIVDzm/mepE7hzWy+AxJ6V1ydrLVbWJWS1rnH2c2DkTj+vheYmuMBDEOsupRDbzfM4Pv9QNuVO5na11KdAjV5/ablCvXfH0hUHTBqQMva84nuFn16nQRl3kYGH3psQMgP3RvX6bQ9x4HlQYafuBDO92Q3WArrQ460ZdqMTq994O/4wL3fMTx+8sTw6V/4heE3/uN/imN4WsneD+9BDfSPyd+b2cA9FEAbXztxdrjGmS9r0jrteXX/2ANjTKb7zAV7cNLsjPcpSNkxw3fvHmZm6zDfEDzJw8Ef0Fcu3twYfvfvXx/+6FuXeOdvbfgnnz4zfIwPd6/yEfoadcQeR595Ccma5oRPIMRk0EimqXcbvCduD0+dWh1+47lTmeH7a2bofsiH1N/46tbwxReuDE/w3biHT/BuOEa6940b1zf5ZACv0vC9wBcvbbIEkVUEjE3P837bv/OJk8MTpxirfIVCkfg5xNKEzz9zjM9RXB/+FIf4G2w68sZfvD783SMbw9M4z0e4f165diOzXl/FaXkJ52aDe+OnPnR8eJolkqvoVOurWsDe5Uz13mEdT3UrZR+W2c7uJBTLRUr7UbgT2AU6tLEVPhb//E/hHS2fHf6fP38zS0e//OL14Xss2/3Lb14Znjy7Ppw8VnsXeBu5wEz1D1he+kOc9rdY0nuDnV1OH1kZfvXTJ4df/PhR6PkU8vZm+36qXeYG9ijfY3zuqaN8g/D88AM+OfF//elrOIZHhyeoZ3eufZn6/zIb4Xzn5evDqePYTGwW9Mp5nUUC5cp9EO2v88DD5abH6UOvXro5/NlXmLHmXvIIy65v8gD+2ScPDY/yTU/XrBi6qZCLO/m5s2q+E8p3BeurG8PGKWZtj4KvJfRghNu3rPdYOd0aPk2vGZmKH0elGYkxytNbNACrGpMvoNcxnr0g2PAD4g+HJ5Iy3HFeDPt1wpAKrQWsRqcPoUWjAZpH1EGvDEJljgixfcuYLXoalbFLmxCuHMTqpAOW8ejMhZapMx1uLFKOHgYmcIJKP0vUWvmriev86USW0Vmc2q/wOkzCOzBz3WcHpRf80FRmYJpc0V6H9clYYEm13toT2EgEjKGjNVVQHmXG2KAcGuXmd0M/CGjLcmgIxZkHINv90x4uXbo4HD9ay0JlnPcMuRkqg8ZwHZQ1s0c42e6uhgYiCbrM7o2k9E9NWEYdnzhF0JeOO+zFLxGJMimHOuqOY/mIOs+lkwIDN8icTeAGK83rLO3aYMlfEnEOXIpBhiLJKf9xAIMMPXETUjhiHYZo6hUYZQ5M+ymhuRA5hIk2QuahZ3eFTRrpRhOEQWftYnYm2TYXJpGLLAudIE67lk5PTkVxTZkT0g46gGdCGrR0Obp8JNsW8o4Mui5yBZ/2SJ51nHiI+KO56sPwqvc1HDLfEdSZSztC9myyw8xbaAPtn+8UOnsn7y2+LeVDGzeDsZ40p3TqaXlQth8Ai6I0yMKfZb+hpf6QSRx3d73KDHR9moI+mocPFBN8yyF9Pz3hcmFlK2XJx1xLUGa3vFwOZh+z3V9jmfPRtsvtDRxO298qtI6yUZIOYr6pSbqfTfF7mleu8d4IeHLwwYUPWeT35htvDl/4178z/MXv/8Hw4Wc/Njz3s58bPvbpTw0PPfJIygL4++FdqgEfcG1Sx9a/juD9DOmKLJlaPfHQsMS7hLfglzbuOGg7rU55ZyLcAY49wXAHKIGvd/zsz+LysI42f4PpmT/68os4O5eGs8ye/JNPnxs+fs4HP7XjcHhlgA6JGqsrGgHUxaIc4lRasMerjnbHZ+TUyXgKI/y3Pn1ieISpzr/8ziWcws3hhfM3mNljl2yGUUamJpP3Qx9wkobsLgf9zE8dH37xI0eHx47xDlmNXn1IyZLWs2wz+VvPP8w49cbwb793YXj54ubwBo7KX36f76cyfjlbtMlY6Pk0jsDnnzk+/NLHjg9nXVqfsQpe5FviXMJ7US9juVVaGlHdohyrd4ZQQt+OrS2fU2y5BpyHceQ7jsnLI7cnBOg0y07o94kqckMfT6HOT8FKSD067pJoOkmdzoi0R0QZcu9fvj585sn14dTRh4d/86Xz2XH0LWbT3uK7f994hYfN1Jf3B+n7yo6mmufDzAo+xzujv/LJk8PPfGhtOLxUm9Eois6wqlCeXr49xEgdlPjgcJ+xQRxd3R5+9mOHWZZ6Y/jit3D82GDmBzw08GGBodfx42c3hl/9+ceGv/rbV4ZX3mKHVOrVmW/7tnfrNZYxf+jRVRy/w8P5r14efgSN3/3r87TBJcq7Opx96MjwiJ+CRdYEz8WCiBc9I7nv3h8VqOCR3bLzwOXwOfrIofdKCQ6k2wfKIUxHyQCgcUdTc/Cuf859IBhbYxTUO1M32Ex0KEsHnajQ5iCNfuS65Rt3cMrgIYzx/UI6f8kjrjLYweWZz02YxtHJdGpJSzopbeAtSGmUQ0I2g0QNFhqe6kBj1sHUgUw4nluFV9o3pDLAMQopB/8jY/Xgn8apR9eJIFKRlnpWluARnQbL5JMhtzm2DMGnUNJU7joXhrN6hmz6Ig4IPmPuM1Rh0BQiHY3tDPhCMTLJP2UrgUSWRUJME+Iaw1euXB2OHeVJW8P3AULIZpx0dtGyWDaduZu8g8USUWd7IncjKl3wY/in4AprGSNE+Gpk2/yiO4z/mjEjIcxwVIDN+4zevKUHnWQ5zFKgres8kYZG6hVnNGXBYM/i/yy9tGgiEkQ0Ddlz9zdNuZIeAOL9THpDM6WC8rcjCY0WZSo9SrdBam00EmGRH/JCnzPZVTEtQV7WrbSqwgq2x8lOCDg/0hsTuO5kSC/9tIQGZT0VjnUGuCTyBwAXYc9vFc8Z45qptYG5NHWN3WTV6Q3avzdhlzpZVzpjQQof2zEPXICRuzz9/p99yw2HrGPfD/ReG960KfkYSiaXabppDPgQ8MGDbUPhpKUDLZ44XpulEzf2O9t0K3aiLe7JhxT2c2HzXcmr18BdyTux9fkKlpVSFh+g+A3NLQwR+5qzhT5suMFDD2lYvjiE0HQVgRXuSgP72pf/7V8NX/yzP0/8uc9+dvjHvGv41DMfCc0+JoDwfvhxaoB2owN4hQ/J+xDgHQ00z+WjZ4fl4+eG7Vffou9Uu1+UIc22td3FvH5t/+htvae97XPnydl7QTreSNSZwmF46BjLwFme99UX38r7Xf/0s2dZTsq4vY1TnU4MAqj2zU4uz2ZGOntHxJlh7Q0XqEa8syzcCc7IfGt4hNnNf/fZI8MnHj80/O0Prg1fe+ny8BKO4WVmAZXUIcjPSBzlXcmzOIIf5n3AZz94aHjyzOpwEkM+y0Q7vZEFHBkTzvG5if/o585i7B8dvvSt88wwXhuucD+8RrbG/mmU9hTLIZ9/+ujwHDODJ9dcEl/3cN8pf/jk8vDxR3FiePp8iv2GMhBykt3IkkKu8z7nB1iZ/ixOxUNsmnOYZam14mMUCATGRI4NnJinz/pAe3340Bl2f+ZTHI6Hq5Tl8VNLw8egcXJjhc2BLBsZ/B8/cjPpWzxQfIyZ09yLSI8MTZAac6HPsPchdDNsrQ8ffIhZb1ZxpI7pT4+dQMbH2IGcj8CfPiTtGqcnUu6IAhVGysKajeEjZ5eHc790cvg2zviXv3tt+A5LNV+/zA6/zAL6moFBh8z3MT9w9tDwyZ86MjzHDNuZo248hjNoYRnnV5e3hsd5H/WTvNt5lpk+P/Uho/QdeNnG5e3hJzBOH741fPwDa8M1+Dx8kkJjJ/gARBr//udPDR/gcyZf/Mbl4bULvB5BHUvnJG3mmacPD7/4mdPUM5sj/Qgb6Pra8KGH2a3d20O4IA50rOtf/+xxVqIMw998+yqOrg8eq1+tcV9UzR5OWpRUnEYJjd8mBO82MPc7u8tgvaPYm7w/uHL8UQrmXiUwt2gPQFh67cUXLc4DEa5cvjL883/+X8Xw18mx//SQTk+Cxlhmhqjgbnz1uhQ8RhlwDnrdgdHgSgAgT2aa4Waaw4IdcIQ1EYI2GrE67SQ3nhXPLzLypA1D0/N6M7YdHEITGYpCyepTsPpuGpucMGhroDrqWw4Hd6GP+uSfnQWv8q6I+WghBoJOh0/3NjAONzBO17lrH8IYXuMbUhkBFJjgSTouR3MzjNVVNlnBgXIWxUxlc+C/jsPiUrrIgSzqRTkMpePavCI7ITaDVR1puKrPOGSUT0NVhm50kFk9LnwfSmcohrMahmw3sKXhLOcK73E5Q1M7LRaMztPFixcC62cjbAAOjTqVwYfQ8ePHh8fOnYuuD6MDjehNtoHWSTzKh+ePcj5pedGRM3SRAQO5Pj2A0exCfmTwO4QZdqGpHBaCmojjqPPjk0zb2bgzqA4RZYqugbXlJG40oV3TalxSep2ZGw14y9uggaKO4YvSqqKi7pEA+SQkjajJ8kydwBfdUNiSATrFGyDoF464/VoiHKFBVLltawkmEqQLrpAtJcn5iT6sV/VS7UIXGIW0NDB6GZRFWsqXc4uHd+Fbj7b9zMZGj9DMH+TAqQcTar/6d/o+5JQvD2pKCuTUIQQKPWxSnCu037cuXRleeOml4bvsTnxtsz5I79PRa+Rd4HtsthtnlQ+xvDjOl+LBM59boZ1usPnQmu/kkeZ7p87Er9I2V+lXtvUsX6Z/pO0je95R5CytS+zueNhly+jlBrxrQxrbDRvc0P42N6/x3pcOG04c3mZqAD5qxeL5tFf5pHkY586+mRlBcE+fOm3xw6M7lurlMp+e2ERGnVDLJM0ryKG6a/WAS2Zpt8go7VXam/Q3nc3EWHBc1OF86NFHhqc++szwCb5pqJN4lH71fnhnNWBbc9z03UBnAjPGvbMiVJ+VJwbrpS//v8ONr/whm7bYV28viGPHPGBSSPN/LwINppHvV3PQcxeI1kXZlS659LdX2UTmC2wiozH788+cHJ5l6dwKs+3eP+1rCdJdoN1y7tlpUW902XkVNU7ljLb7Df2TxeHD5eu38t7feZaGYo9nTHLH0BPsAHqanS15PRAboMaMKoh2y0w/XU+eHRd819gN1y7gtLz85uXh4g3HTewUnTwc6IePb+AgQYm6d3zW2ljF8ZLmVR5Q3rjJeKcjB0/fkzOMKmx6ZOsr4LivUqAVYN3tdQ14gxiW088guEDkJuPOJjQZhoDVeWKsBF4d+TDuhjQ4NkhHqNTdNsJc57MN3gd8g1s5uMNGkCYCV967PXjAxuZfPh7TkXZH2OXm5G5C9zplsmy8DjqsuyFM/pS0gnm3C94H3BzmBu/ovclOsa/ghPnunbdXbhd8ZxKnkfdWT1Ffh9aRtd2z5ZU+AQ9l3YzOlKf0tcrZEBEW5OBlgOgYEozx1B/5Qtu2vSfz4sHwJu83vobTf5ltcdXtad4xfITZwaPWL6/b+KUM75n5RjOOX+10TVtqvPzO89Uby8OLvJP42ls+HLg1PISz+gSO9RE+wTHqpsnZJYjQt/sZkW8HeL/zrQX0TxvcPvL4sPbMP6MhPEGK9bCg9Pstyn2iT9U+OCGNnB8HiOoaiWRgtIFaZak3IwBVbjVNrzWYND4Lho7bKzlpXIewWCEQuLpxcS2sNDmbG9TOgGtDd5iMQ5LuCE8cEh03dz2MwUwf1ZnIQAicJrf0HOAc1GLcyQpjzYOYubOyKAPX5ui41VIKIao84ZtyuJytZg4UJh2bdO1tmcfoFRhemWnU2MzyNehXskRbsMwmRlLS6hwa3ChqBsxBhJlJMZQxg35d19MxpTYw3EU+oiZFoDIwTC6aqMrsdk00rH0/ynydOM+JN7nqPTzeb2BmxCfph48dhTRUKFdm7yKZxi43MZzmXhJpqxIPBVIXGusu+bO47u6ogV2GvljNCWHZXqlDIUk3i0EzETNK+BLca/MN6hw+cSZJz6c5oO+MlG1ghRtRgIMf0PoJDeh3WtLRyfK6B3WpCJGnp8tbWIEm5+AIwxEc+RohdFjyQqWBddkD1/nCM1Hv2LmT6yiCoAwjnUQgTFpowSdtGxxFsqyy1Sk0kh+vjStD/cURNFGeNLrCK5rUKiyJ29ZB8926OPXLGNTIdo1ldtblKm1UPAd43xm9do0ZAvLpKYgCH26g7trpzdunoJs8GNHRrDbBLAN4ipXZROK2p1u0M/uqbUdY8+3HLnm+wZ3Wt1otW/p9coUwKDO/yJobDnJkgxrLYfBMW7fcOpjKANE4hld5oHD82HHS4EHZltzl1IdBOIE3cApd+nMNmEM8BFnjAZJORYw6lcMEk+VDWJxS5dMpXKNM9W1GHY9XXvzR8KMXXhj++Hd+Nw+Lnv+lXxx++Td/c3jsiSf4tuEJaPq+7fvhXmrAdqnudf76LqH3kv7d0Oot1R1v1849NVz91hHGqosYla2NHoQo5ZJOUO4A7SCkd4fpUrdcHuw8wnta/+FnTsYRWrOf8zDG/u5hj7Vb7B72E3iBz+4E7jg1VP2x73Nayvi0PZziPcjTzELdOseHIsjLaM14l4dmwMQeKE03ZbcxFBqOM7PAfcaLbd+b3Mr7YScfZ5QShiP8HUd9r5JZp3yfkTrMqpcG4PuZh3ww0FTgqUWlPAbf1D6qByJd28EISB2ArJmgC2dNrDNGxhFDuNDip+pHRwfXlQJbUy5pdLWHVxs4cYcyidNXQFmIxSAV5AfFWcEy7kmBH99lT7vk64HQlwa4HHu3h0XadS2aIfckxvwNGvujxzhw/tzIqPPMPRv5o3t2AA3zYM7kdm8FvxmpFP56dD17J6zeFKT8uJspr5RWADhlAM57omP9ytL14TE2t3nMnZYGjwq3WJo83IAvhXU+YA0HFSukyh+mxTf9lnv7EWZ3n3mcz5iwWVMC9qf7QjSGJWcyutQF9p75RWz1e9MHA8c/yMP5M5Qpvew9U4TbCfpAOYQW1qZmtfVYBhl7L73ATuffNJ+LhDiDxNqtCRAgA98AGuVcQaLoFLcO4TnU/QkE+TtBzKzAzb02pOASnHRQRkAdmpJHZJnVgKXjoqHm0kedqN7xww7sOjuQFZoLhzQy42DpDARilq+BamqGFQchg71bQiHC8MrNcgtD0F07lSkDGgDOMOiglh6CueNHUtJxYI0elJ80OYVbANqFtDDibzKwOKRJ21J3mcPHy4atHBpHRYKySze8vE+WLuJzwMgrUaWn0X+ZmeSTx44xs4Mhji4dTIXJU0TfxwJWHVv/pq3hyDkzoop0CHT+5Kf+9OOjP64z22nBgHXpqTMpJRhpkbsNHsIga5IiGD+5rsEzTqcOp2gyckZUFOucI4Fkh/SRfmiS1y158YQNkUbHtFEpXRZ5hpgc+JeQSMZbuklpKQ3fy9yBrFtFUA4Ob1LBl7Y0Wki+WXWzVj+FSL48DJ6UL0GNIlfaJHUXuWVkpsLYNip41tGzbquNVJ0lLRVW+CEPBA/3wtMsTZhtEq7i4N1Qt6RZBPUqLWefrX9n4py9s1+6CYzOXh7ikG97GoZrqIPvVuqQYTXpWHljr82CdCW5DC0j9HmFhrvpZbggHXqIU0h6NiFCmFwrp/XIOe0VnCztjE4lJD+WC9JeaaSRUdVdvnI5cXGuUz5n+w1rrOtZoT1fZ7MJe78PR+zbmyxDKlkckyAFrA6mvLcwYDILCpx0rJ3Sj0/rmaXC4fzz3/sD3jf8w+HkmTPDQw8/PPz0Jz85PP/5zw9Pf+yn43yH+fs/d64B65Y66isobHfjGHDn1O4Thr2Lz/OcfGxYPfnosPXapbRtmaXLHphrevA+0Ok4c/lz9Ocu5sBue6Gz5Pt0Oj08K4ng9MDxYebeBdkp0yKzPqw5vuwXOtwUZre05COnxZVk7nucYzPkXeaY7WW8k65Whc39IhgiJYGfnWH23b0aZx2LfaC+mQKAqFD81/L6Vme5HzAWQS6fIRIMw9mgYxcEfo1FGM8Gxj18yjGt5IQ2kD5IdiS2NEzyBde0Gj+lO+PNmgfGJgnWnap4KDm1yNPu3Gb46fQ995DUCCY8V8SFN91bVTiT1uknyfzgFJU966kxESd4Xjucc2H7WuLzV/xXCBFTK+QBJtHgjsh1bVWMcMAYz221pXqtfnzQrwOZqpNGkCyZdYt2cnNCDmRYsuEXYuMJgrjYFOo8dQuAKGL7SbMAiiZty0U/ssYk42Ewy1A6muZU+p6/t1Pqnoj3L8M73/YKq4JOPoVimT5NwXtJ7x/fd4ryA+cQapw6LM26laqkpdp7adh2st5ARyWnt9iY7Thl7JjXB5xUd4MJcq//aYNN38jPSHYxMqK1DKUsx6q2wK8uJA0l7ND9rIHqAMUAqoFo0CiMXB2GksaA8yO1lkXjszpw2i0onitul+ZITxefq/AlrZeVZG8EGiHyBViwhDo1WOBbckQXVqemQ4ZeGLck+E4HUyE1Tm86cEtJYrBzp0YjbqSRzVq4yjlyGmdIQ77lzJpRt1ynzkQjJB7BpFs6E/4Cm8ucPHF8OMbsiNcBQd44AOhY2V22CUFoOPvDM+Om6yw3hnbXqzxy00I3Oul+e85Qxn2bJQyDIKVcKXyKVuUDWozw63e7JZYxZYRvZU1dQzpyCB25KROyVr1JoxWcmOkJ7d1MgLjsPMDzMjDAWa+GuqOUHNZXJxfx/CGEjvokIIPB3/Qq6aGzHOFlZsGIlxlZiUrD5J6XO0mDI9nMMiaaXKRY7qDkNyACtmCOwsIjJ84k5R1iUq1hKJJGoiQ5hR7lvsKM8VtZRunDBQZ7YPpycR0inTzr1WXd2UwIp+gQy0Qvs8wys8LWO5ZCPiFBXmbw5e1TRFlBzwcFGvHpB63smSEkPZs9AZiHCThb1mtm9S2NBBSWw/ZmVeq8VTGkZx3zAElOgG2xtEcZV2k7PsS5ehVnj3eHNni8WzuMshgKy+oo39nc3vZdM3ex4/Mr6NaHKJ1v2q7WD+1BXThDyiKl1J9P7NP2Ec5+Yjl0UEuE5eGtN97I8e2vf2P4nf/9/2SH0jPDM5/65PCJ539mePyJDw5nz50dTrCDqXjvh50asD3kgQNOdt8YZifUuy2l9a9DJ4e1Rz80bL3x/bSXarszWW3JO0OlpqmnrVdsBrc71ix/Z2wHxkhyjOxEIiWjDchTKGnV/bJKM83blcg7lojOpwWdCOZ40GVWpZMs4m3svq2cNYY6kAbfH452lbFynhKZuakXxGzsaoh78QuOMIUXub1swVtMtS4SSM8tp8An46MwbRwUrCsGIeoeoPPSKe5+tiyCFGtkCfwUjzi5M5gZnSrr7HqMTcoxpklAGyMJO2ujaaFyWzlH3BaJaAuJ8zI0uuhhPr0jFQV1nfKMQCSUYJUheK7r3piH0g1nBByvO+0Jaqc1y3rPx1zktHT84WHlyJOUpbtPD05Be4ne8xU1FiBt3QqaVVI62aQXxTDrCH3w4FoQL6t/gE88A1Rw81NADbd36549NwgX+ihFww6PuvH0QcfxAUoxkJTZTtyQG5/IFLudp3Q+ofcpcRNUjBDlJA0dQrIr3yxJwVz+OSZxkkpNEkEo8/vY0OFNzAwJj5A0BL32FKMFXhqRMVZ1KsyzLFnO0IkXjjC+4Jx3/hht4+RpJAPf8TOzUlJEJ8LGSOfJlTwNcXKjQAepSgvNpulKI0M5lClBZHXMQcyP1Lu5zPbJcvpqgxryREOH1of6FSsGvgY98YhgHvFyRIXhSsOZv+iCO5bGvY6h9MrKaMiOqL1FJLOlj3IWj7SFbjBLQ1hhLE7saK9rFtMlH6u+a9jhU+RICjChRyc8xvSeF/pc5HrhHP5ghH6/UI8I0pzfFCnlIT9nhOgV1pkFtTsB8mgwTS7rVRxFsCzl5DZ48rpzJzlJzfqlKZVm+lgG44QUrencB7Ghr3Ovg4l3c5V3sC5cvMQsF8u2caToQalz8XxS7gMA38tzubTvdKpe36tbWfUTDjpKyqhz5lN0cNUJyKUGy1n9WUdTZy9LgEnWsfM9WJcbm247s9zZBY58z4oJMWhJk36NHL6/oG6y3Bu4bZdsWSrktf05g7fCuz++w+isxzXKp9z2EZeVOpvnRjrrwFy7eV3qKVtxiriSSvVl9hMH0/7gQ6h12zUz11evstsg8ut8LiOP7f2mMxPA3KQMBrfHv4Xz+dYbrw9/9gd/OHzh978wrLFcdYOXUk49dGb4zOc+M/zMZ54fPsoM4k9ycJxxCWh/F9A2lH5A+nsr2AbZROTRjwwXvvulYfvyK7SbXob0zLTRlCmdw1jLb9l3Xd5F/AW20rWdHygAJqzjTcg0tF4dnkfxD0Sw+tJeoHdKK3R2K0ors301oeuAiyn4OG5OEwtj9kteK/0srcWC1ngUiQmjMdqId1nANdp1uEi07qiVWqPHTOY+7odio5/4JCF1EvRRgFxZjzbBaepEpIkYQEAvJFtqyj9NaLljUiO6O70J6Uk0KCMBM0rL0U3gppImO6lzgpk8R6N0O5c09jvyOskO0M/w7rouJrNfQRZ5jFoks5PopGeYO2OB7Qg7s3dPuROl7k7hvqRy92buk3vr6Q+z1OYs7XkvDd4X9u8I0QfPIYx5RFPdpbXaLne2zXarsAdoxAAzQ6UJkJ7O4X16Lq/qZwZbtEcYRqnFwagwGl7Lj5NCB8juohHOH5uew2RRr0Gea3DcFTMDYO80UwGCKVbBKFHkrxRyLaP8OYeXkInUgB04CtqyNUQ1PnV+NH6Fzr9yYCB7KEycp+inKQkO8RWKdPgps0ccw4ggnrSh2w4BlNeZUI1jjWRpy7+H0NFoop6VrexPGZV+WsG4bjiRwXiVVRzLpWHsoSFfaabr2MmzDPKiYXrFEKvoyN404Gwb5WgWUAy6LPtlsBCg6ayseweQEJkRsCweJjdaOph1wckgHQoa58MsLn2JW/+yOxC9uMUPIJ8KCJsfBVUWQz/X1c5fZPRxasD6WagQK3AV4tFpV+oEpMF2nukInVYIQ19dtDh1OQZwsiTO8nbFA5cZUciKJW914acWioX8oBW5AhDKaqw4+OvBNaAO5DYpn/Zduno139XatBpsU6htnXdxBM8mR773iiy1G618ZSNvHhLQdlpC0uzwOmEKlWXLtmP+bBPOxhnHd4ILs5BE1nAIfR/R4DvEy/YnPPwUG/4ps2cxSVR2H8SkmPYTnD43e/I9v65q++kW3xLzO4OuxfJbh4Vf+lQWN0sKT8olTcsKs5TFy4TwRyPItJzlyz64QTcsOV26zlJZ5NA5FCn6EJ76SFcNEeSTrsTRC5yY8bqajXfOnz8/fOdb3x7+5f/yv2ZjmyeeemJ48qknhyee/ODw8CMPD+cePjecO3t2OMp7vg9CcAxzAy7btWN5HjD4kKEd5j8IwVpePs6yUZzCG998nW/a7VIuG+97JLx3JN2p0H1l3zdzJ61xQJrL2qVu5/J3XrxjVX+n5ZsT9eDlesfKMyff/b9Y1EAv5zT97lQshSmV+1+We8aBMdqHscPhh4aVkx/lnsZLle/RouynkwfOIcSsiWGX2qISNao0grLcshuZwNQ92GZdjkLeXubSZVkaMGVKkmCcipeuDWC8d9tL6t+cFvpTl2aIAqPTI1wCRpfcYughk7NjGgiee7q8nNmarvsuEtLCIIaWXGJ4EZM0yUUe5JKgM6xz0SyjUkB5aiyPiIWdcnYHWEyN78IlYtmF40d96oj4jpTxOITJ7eXQMMS4lotI8OvOpYUInVEpEiWQCJQ/cbAyM0dhIZ/MzBoR07COU6mOmNHRUI4DyblCUS8+IQrpMsqz0QEbaxjc1fEKOye6nM5PDKTalVMFo1yd3do4RtnBmVV8yquc1ma5Nam96NUZoOwWCQ0mZQSrYDmc6bElpVDEdRisSIFG+SXMUYThC7xF6rjKEVkwvjDIadINn3OCxIChftJYLI8MdMDkK63pIU5oGiEEvDEv5Vd6/1XOLusoC5ld3h1w8JwLMkCANAwzSjZbToK0p3RJTCsnTcxipAjC24pbe4pMvWDhENJBAcb2mCd66MEloNt0qusssbxw+Wq22lZK6WfTltQdS0X99Ad/K862kRfq/FgVtokllipX+7b1oF5ktN34HsXqMu2KtrXMsueMAeQpQ74lSdw/d/rdZPMh+5JtOJ97SJ5tTnpWmXjlQOpM8pJtCUrU781lWSvOpKsGhLMv6qytcEjENupy0bzfqJCUsjaXIKZDmDpuurVspeRAWt4sT8dzdnOajKPIc5hdV51hd/dT2EUvKkV4y4X0kV2FOp64hMuZVx+3BBo9lL6Xs7HN17769eFrX/lq8PrDFc9neB/xuU99YvjYs88Oz3z0I8MHWXKqoy3dd3NQT2+9+ebw5utv5Hjj9ddTP09/+OnhDLOjD3JYWmFH3g98bLj4o68PW5dfZZOOWWl7Dx5TWtuZtrkx715EbJC2wQM2mDyAOgBf2/x9k3kf/jZ7bYEfS0iBVaihn+tqr9+IeofyqlvDqF/wJdHTk9l+dksza8ryYJJOqd4+rmzy9hjlvD3aCHFgnGlBRuwDRHqh98XvmR34AHQPCgLpUO8sgief+8DroDK9TTjHrm0+d7L60DPD0qFHqftu8MwV8m1y+fGjP3AOYdoc7U5jyngtRcMY8aPeBnqxxsj8sJGEuZ803XHEqUpP2hyUA8KkQTR+JiU9+CbOI5WTqsOgsUiehqKnCCxuHR1PGE27HqStkTUjWzKMNnYDnEg2lxJ8mUBDw7pE4My1TlNkN59gbhywplANTHWqFsXLdeCENjSplI+o+HYdHUJLYRBiUTbTPHRW8wd8Zk0wujfZ6muUGRjlcSvqfKcdOGnnI+FddmDCQP7KQbpyuLEHwLmp+lmBixi1a6cPsauoO2s5e1MzHrX9P0awBn08LukENfQkL3wce4uVK39KzlWM2XKUMXwxDpVhGdrWT/TWiLEAzwI37N00QprVHkfC/BSIM0FDXtzgkx55SCNdQ39Mr6ygarQXQcHB9bBehZkG5ZsL4Mle+L1C52P+KK/w4Ka8DVEac7AzXol13pwDlvoFt8kbCUY5UquNsCJWPY8JvWDS9D9tWFLO7vFtJRyli7wL6MYouiu2I9uW3x1z1zorLDuPok//nCXcRL+1Ey3tzieGlNUHHzoqsnEGL59i8b1WSKzwArqzgZll5CFED/Yh25ezhNf51plFcrmpZVbYLIPGUdSJ1MEzPe8wIsumM9uBc8MX3+1DdmVEAOEtw3WdRdsccvnpC3cDjIzBs9ptWD5MUe7q42ZZhrlgmW/xWRng7R/2myOHD+XzLFfpPy4VVRhpWDeiOyZkklli8tEpVJ3m2gQNpPncQxyTIo0XhIw/nM/jVH3hD/54+MIf/nHSLY/vHx4/eXw4zk6mfkLGz8QcP35sOHX6FPEjwwnSD/Nu8BEPrtfZQdX43QT77lVmkK+z06wzrZdYWuy7o37S5w3elTy0sTGce+ihOHvOAN7IDsacgV0MfRxZTH/grmlvKyc/MKw//tFh+9tvDat+P63V625lnWZN47vB7pUmnu3ubYU7YD4OP2+L4dtB3r2091eu3Xm+nVIs4nYO96Q+F4lzfS/1cy9p7SLqHSXt2nR3TZyS7dqepu2Mv/PlvK3gO4W8bylNR5y2cQBvHeHdwVPODh5hSFu0Ne6bEO8o4QfPIZy0p9FcpFV3I0PtCrJ3dyhDVDhhtGnGqm9IYTHhI2yDDo7w4dF6UwcNOj9xpVrijBt8BGg88iSwI5KYWYbw2fkzdtpYcyDlXHA2XIk2sknUmcyL1xh7eVcCg9NZg5QVo9R5lyxtEpNEjVdnKiyVv518zXROKMfjkV8XvPLmUsKk5Ss4R/FosEnSOWPLft5RisELTP019USACd8mp4VLeRuN7oyVuRlWaQdiXsWAu8BOjCdOnBzLVd81BFkjFp617I+BQOdL2jH6y+ESVjoaysKNfp2pkMi7ZETFDD4yZxttDPA4bBKUwHhSuxG8EvuvOhIuMmk6NyTzqRM5WCfGM0ukw4Le8v1DG5G6ytFoI6tsckijB9M6bE/rZwsXORrvdhpFMS8w0FZO85VNmgbpCtPkTFrPjG7JE2Yk6KX6Ij1ono1UmDkf4vRgXJgg1Im4s+1q1qB+rQ8dKNleZHZQh9A06VtPzvXp9K3wtEFHTofQZi173wPMe7CUzU9WmHcLp7D6atWrzuY223W7O2/aBQ8hdBz8hIWzh9Jxti6lhZ7tJM6iPIHVcbCv+e5id+4Uz9lAS+FMnXpRVvuoIUuqKzpL05lkCed6+hDxzBgD3HAEV+70c9fOIpAy7QjCI2fel2Q9rbN87lB69OgxPlnBNwwvor/MWs7GBulE68pIfGzX6J0tmiJD+JMTPYxMhTRHHEOTSEH5d+ffN3HE3sBRNMd6yVjaQMVpGClmjyu/fUJn0bBGXehE9iCes8bn3zyfJB3qC2/V90yTAKGsMFEX/tOHNnHET7Kk9dd+7VfYoKrodno/uee6RyytHxs2PviJ4cpr3x+2zr+Qb8uNdTFVTqlThbbUXaGmGDviYvbbzojdyY1U29g6XldkCtbbXQPZcRpF3JHz9hOmcoxlWCBb/HfPdTyY0lhAnV02oJwgldvDAuLc5V0WuttakbaJfCekMp42qbs8d4I/K3DF3g5upyUNhoWEfj4I3SmeyAfBKS4H+92VnkrrijsYmV2hdqW9K+QuiXfL/27xdhHh7Sa16s49ZYsdRVcfenYYDj0B2bL93i79dyP+A+cQejOvDuu5jq74tDVaecwQatvbV4V+rqveJj2POYFv4B2gXY5YpJeJY8oMoWgUkjKZNxq7hVy/wW8JhVQXkvKuNwn9qp+l3gfSSit+DgyyNM0OrpGa0SJp6geTCoAyKkNlwkWiHsCBrL4KXgfRw9kBAaQNrjCkK6v1EMez8xNInrLQUMTY1vhVGncxlJ5yhRx0ajkoHS8I1BlpOYAyaNTvFVoNFy0IOodTUgYz6S6Fu8aSQB1OPjXLsZzZGmdhLNc6szfFwzJXfclRYzByc1YXylxP/6OdKn/gmryWsZUzOiLP8qsrcaOUsscVgRTSkicgoZi2NC5cqtCqEGZB9+y7XhYSVpxJDw8uVKhtp9Ps9ELcH/KEyaFcpgXISKVLK+sV23VoCgNeh7WerRPlM1k3q8MJqoNo6CjhV0km+pdM5TTPwNn2VHorxOSYHvYNrqCTJmyHTC7XpkSviKYqFOUGztybb701XPU7gjhxtlcdMOXY3OJTI/k8wxofp2YJKNhu/rJCO72VSWYeVjBb5YfcXR6aooEvD529peswovzONF/nW39ebeAUujupMjmDXDPhVV/ytI3pNOq4ONtkyIMZYMkSIjOD1U+SnR9bt0GYVHFgk0QTcHkpy4qR0zithPJDFd1FKwpDaKcdcVVsiP5hkBlWHtQ4VjhLqVO8vrHFjFm1vSIMNfKl2ccW3cDkQcMVCPZt6y9DmoLLI7/GO9cqU/JMCph02wV0qg0EMeQrVrBFtmib7k6ozupVWBpeeenl6KyXvrebBjB/ikitXUFSKVxG/NaFi8MrL786nGCmstroPNpP3lWvG/rGCT7cjFO4efmNYXXrUqvW+fqNfqLbrqm5i574E3++J1q5GyKtb853rv2qQyazPrcf5J55dyPnnsTuX0bG2rsgr0rvFvcu2N1TlLurmt4e+nkfkd51iqHEiM2df1g68SSzgx/jpnyktfADlGefor5bs7RVHriQqur1ZQ9s8Z5kgev2bkwTY5qTTDMImk3mHrwraBjM2rV4/YATssTxMjVAO+n25KIjLWA4/DOkOLEBS2Z/+yGcsmqgVqkaDjDJCS2plCzKEyq0gnrHsvI6RY3NHBrKOgUe4mjw5TrYss2hbIqpAyRn6RviWAlkIEljWMfLw+Ayu0rXcKwZE41YZ04sfyNTfDSm4/VomOosyLuacWAh5J96zgyLDITxDG8lykMDYs7YaNiqsBjoOjRA+qfRqvNsGZw9GB1BZRIu1iz64KyONP7jJMtDJo2X8kk7gpI8hg5kgtmzjMSkGiJmBF/dTACnaUqMXOosDkn0owDdYQ+R4tDIFsdpuvVLpkd4F/god6v7sXDCiN50NuLG0jejB+Akaehl9pyDdhUnDN4AleaJTstJehzvhms7DkGuu8NBQsliXui20k3EcHbWVlLP9thsheWbr59/c7iGo5DvLIHrUlE0xhJS6LCecY0PBi/jLEoz7x2iA2fubBfuIrvKxj5KY9DZcvlknH9m8Xq6edd5V9XlhO72GSeSXPubzp+S9lowXyfLtGqjlZv8VhbbWPqVOiJYXEO9U2mrMYGDbJe43sChjUy2icAWhDqO9BCo9lnZ5oY36X18CMXQo53bV9GlDqbv8+rEbmzwPab0iS5vExZaodFkTv9IX7cu4A9Y6txzK89MioVYSFL7noEVfnqogxTPiLEufzuPijIbGiRHtrl083YJYU36jH5IZIz53vdfyOZUu6D9hCaVtpbWjgwbj310WH74ab5t3d7B7Ypc0IxtrtraVMMFZMr0WEDd/1J+HNXDZqB7iDED+DHEehnfCdb78TLv7YUfn3YdQsZh5DaF6Dro5x3gC8VwvOjhoDyEn+J1/AOdF/jvhnMncuyGf7u0rpvp+XY4u+dPlLc7wLswtUqN5Tfc2nhoWD37yWFp/VHk1Ip4L5bnYCq2dA9WSA/shogVNxslUo3Jr7T0OXpVOXxVyabFSBFvPA6mIn0EP9ZZN6AyXcUMH/LK0LKJ8QffzmukDkyXws6eo+Eb3yuIU3Z4AfWZjqQXwaLViGp+6+rJTaOsO0PFo3TXnaa6WU8Md/TXl4pKoXDky9F0q8HcDbyir/lHvv8cGqgavp5rcxpqQKeuiOHY1Jb80oibwFkDOgQkQlAG86UhmvFuLBsftd/QlLXKXOdy6jDWMdQ3nV0DTpqhAaziTEPYmx72rX2lvLXcTDyz1GXeMQPB6wZSiCZIQOIa6Il7AladBSNYAIKbX36Ek5Azf51gP6sheOUdRRzCGW3gw6/hGTcET8oehBRU+sY5eywGk8K/47TrkYYAHPHrOozlE47Dnyav7alCi4VuSwqk+dYtJztU5+s16cFO3Gs0pp4L2GzAC1+frrGsdOs2CSCzNPMS74G9zgyhM8XdyehFcGlo7SpKnQAbPNJu4gA5w9VnBX1oIJPwinR+kgKnnPbb+fflwfUdQD4G71JjMj2sT9uNfC2S5XAjIz8c3524FC25FoM/0aqgKqAhVtTkHsSzPbmTZT4fUYSiH3Wkaqu9VZuteCMArjzGcaKEQ0/MDNJXnMF0xs13Ey3f+qGNOLcZRxQAXmOdgNP/pKpj7cMa+6ddzCO8w0MIjvwY4UhomXbCFMzrCuEDQkCjnIYkaAfy3Mqf6PjT05Gm44a5ABVCA9zGoTXx4uHDl5dfeXW4eIEZsPfDrLrQxS0epCwfe3hYf/LTw80Tj7B3Lu0etXU9dnVVvXE1V1k992DnVuNzwJKzXaVtzeUU9CK73kb7eQ7lABeT5nUA6GqOu+Eo3WJ5Kq00t5h3MGYHgtpZBQq4m5B7kFsETx0ckMRdlWsPOe4muXRcur9bWcbyLzauiUAHUuc++BNSO6Ojwndm3XEKtLwvTo+dDWR3qjPxu1Z3h3tXp1JwV6dtMyO4fOZZdk/m80jLG00FsxK+q8twF8J5P36gQu7n/JTzVV07xlXriWNnX7jxZxQm02FXI2PacXNTaWnz6Quq6x2I5J3kkcklZ/CIEdsM2fDKbUueNrRpYyOuw6Pz0EJy+VGm/CVemdKOM4jBZfnjYkAzhtcofyiUwSraWKDSjIaqswAxGsOhpbdZwpqVsRwaddLiV9rdMCcuRs93c4x6T6pY6fg4E+KSTJXhzJqzLh5qX5rZDINrrzRK1YF1yGVg6rfXU/ETN7MXZo5linSmVHtIrMh0g9sPil9hG351V2Wocy2tVQLf5wIR3prImXXUkFcf/BkUq2ZYm+MaWdH+RA7hCrohpCyTxH7dzurBv1BPGtzSftCIToQCI0POQKXMDTxozdSmUAzqZJinAOIYEd9guvL2Iw6iBZ4E8TXGDZ6m151OMvmJo9NpAyx8Lv2p+m1EgqE0Hoauz4IsNDcemeUXTM+vvlO4rdWFRvX9crYSLwGKIAI5A/jGhbfYYfRycqzJ1BXlEr6cFvBNb+/4WWRnlA22Wb/3lzwdPIvJUkphVmnvzgIWS+mSp+6oM78JqAPoe4CWVUctDiEFysMIru0X62w04/uLOpZ5UJC+RZ0I519XCPHoQCYE9WKIfvyp5Lx7l1lW81pmn11tIPVQSITkS8WoF9NQWnb5rJvL6BTaRtf4pmE94KkZz8Ko1jsKgc4dE+Rb77rWtSzVjtAgAABAAElEQVSb+EHr8vSLXAvTxFjsU2M7bqIWj8YLnBF+0k6FsWhjniUNvj/modfGL6fkzRICRTkcs9xw5sUf/Wg29szAfuJiczqz9Ct87/Ls03EKtw+dytAQ9XbNdAQaQP/rWXWeV3wHn8KkLlqC+f2oCuy50/MU+97FJ83rjonOl7LQpxLnPX+SF+Eytkw7z26cd1OacKQv0mvJEyVOpdiN+Hzaog6CvRuTCVp3OGYVN8k8aJSydDXsxm4xbfH6oGyE63x2w5HuSHsvve+GeJu0TrefBZ+TQ14jv1GC21Cdzxar34vHOpkHOdDVnFwHwhBoUoEHxrnHgGm8peHcQyC/vYQDePLDw8qZT3JjP03dapMuhp0pixDvpesFy++9JPrusqZK209u/IBprMdwIl5ZmvbG+42/DAiN/ISxcxVMJU763B4JoU1ep5p+Sg+xrWn4ZZkl+ePNr/GJbBqN2uWczJ/n1g2WYpzclAkoDHhteDuixmyOGOWSaOXGcCnjR8wWiNrxDU1dkbOMpV6CksJuYEMR3HJoMIsj38zcNToZDJAhxqZagK8bZaR8ygmAMyCHDx/ON8zG2UE9LsoD2cAsazBDM8Y8jEr2LoHnksWy93cMu47NEb7XvXVsXh1l0IoXhw84jdtL7jTJOTs7Kif0sylMOCmA+DoYLsFsbaSd5bWC7NlJkvjo7KvzUBJXASCm3sBLO+h0AtMVGIb104srbqMzggbcdA6nehLa2TzkaA0pccub0PPqavZrOvU0NiRzQh7dWSm2p16WlIFraebowJzT7oTn6OVrYgWql90L6UUeTp6TUpF2SUq1JWnZ4tRjxKocU+b+Cl6I0nMcDzZMqbZgu7BdLfPe4ObwCp8E0Km5ZYdDkN5lVLft2/ZgYr3zVsuX0/ZJFyZLv5ErS3RxBnv96+TpMFbZoE3h6mHGchzA61evlbOHvqXX26Ky6nD6MGSNw/Ypnvk+0omOJEeJ+/JQBN8RoiEFVA0enrh2xrP059k0MypPGPupjqx81Ii/OQOYfpi09gOQM4VX3IFzsz5p4UMe+3seANkuDEVoFKXztU2l749KL9DeXxpa0KvvF7kipNDtmlMrRotBB9mmR8qRQk2pAu4lR9EXp0hELyO1zgqdw6g24/Jc6crr+Pfd736H3UevgvV+mNcASt04Pmw8/tyw+vgnhq3VY2l345AFsDpU9RV6bfZzT5+cBd7tmIKQ30EmyftHO0I/7w9917lzTXGBiqWeC5FlR+oEpHQn2J2G3Pv3QtyP5RyjAkzfmUuvi4i/Fw9ADsxmF9q3TQpzoPbhv4PGbXD2KueUzl5l2q/ep/gHkXdXOW4j+xyPvS52obFL0l7Ys3SREvbSRs9v5xF+If2dvpyIy514uHmMzxyde54x7HEkqdebdhdpgrg7wHsmtd253zPyHkjQWfVUc9agwuIdcXt+cgukGQQzR0jDILtCBksnoGONZFrE9H5M8ui1MS5i9GCEMbtjglI0ltmavcfL4AgI1DDCNJr64cxQeBRyDJ6RlbK1ALHcYOE9Gjox0poDpEDdUG8oMRKTrqHYaHU6I0xFYlinULvoYi5JAiXDzLETwPey+Jg2Bq+zee7U14NG5zb8nc3SsIz86jDlLnqjB6sGyTMojmXutqVOivWdmgREui4LbOoHXtyeDx5/7jTpBjPuidi1KX+/s9j10x8WZGaxCR1jXXlxNDKL2w3hlo90EXN0yJBMepll1EBvfwG3OFWkOquu1AcRHQyzMT4z2xQ4AQwdqa7qt+WFBj8qKTQ4o3vLX/oTwBJ7noaieYs62sJpctlhwqhzJEenhddpKCNp8uphjANPWbap780td+DUYZKHNTcLHbOWU3rV8gPkNUfTSXRI/aSP2G7gFXxgO9vCSCqUqPOUe4X6vjK8wgYjbhdje7ONVDe1vVlHzCKmzzlqIAP5eVhA3A/Jy1PZpOwySXpXnDcTdJTSj5BJupbTZad5MAL8Nu+sZsdQl9RRF30WUOk3ed9Pp8x3QQ19FjsX8mg6U746IC8LAeRTgPXbLmzuyqlMypM4v0k3r8HZ3uW90tpwJddv6UdcsC2TBwrz/Vsfplzlg/OHjxweNvgchTO6CB55ajRVf8pZV+JadzXLWvVWWi/5e79PUSfl6ZKLb1l6v4xzAf2dAUHUifL4Mx5ClkzBSXpiBWPU8s3RlIihn4laDoB8IPAmu5NeYMZZmd4P8xqwd6wceWg49NTzw8qjH8UpPKzyqI7WHubBd1yl+kidaH4HTOrqHqs+TeAe09wp+HzKWMZe6Pns+3o18r4DLvZVu4+hd6+6uje/nfbtqO0q+53W3a5Ebsd5Pn8vlgctx/6NfJ7X9CrDzl7Mp4AHiB9Y1iktdccR3FGP90igKZ/7GW8FV5dbbCJz8/AjvDf4GZa9f5j796G7rZr7KfF9oT3zku4L+R8HURuiR2ulTQTre2yrU7HGm3hrwAIlWtd74gk04k4JzuJSiOGicYkRY8ggmlj9RCbp5OgZGk3t0Ck0PtraVYoykJqooMUYAa6MEgdqDx0rDg00Sh+DEJh8DB2WfjS6h04v1+aRFQM0opURpowaQfuFGHTOukiA0HlqRJunAaXR7DtcbnihCL6z5Zby6shleCMuvOQWmpEf2O0qo2mgjSFGItCy1ckrGpSZaN/IR2ANf0NO0iDfTT/80LbGfpbuqXNngprMgYfu1EC3dN1Ar4al3EIaWiQOHMv+4uCaXPXiWdmVS34ppQKVUFbm7LAs42E6lwYLOh5ee0zwLHgHtpDdIya10iW0cAgnb+vPesBhUfZ8a056kT8uUuDiuBXjUC3Lv6IRKOQncghLWm+ruejgoWOm/3W2SFUwTiT1UG0cKGS1PeYsSj+aTGibP+XVGUTnHOwpOrzMZwvOs0Okb31KVvwpDfn4cEKnpfrAzHi9wWY0PsyIUwt+yVftqLd124l0bYee0+6dfSPutcsMb9y4xmz52rCxvpEZQxSdByQ6/KvMoguXWTv6y/hAIc5aaYXsBOU2JJUfS5t2MSYmO/SETDm9ssyVxdmxxnqyjauzCmleXKuDmX6argQh/TpO/oVLF4YrlOcYu21uMPtvqS1zKIVp8a3SGweCdl/6ss3NGDasWZpZjYYy5zCp1dlYCoXtwXj9lxzK0uXpMO086q/hy39W2w1I0hyjGMQqTgtCP5s8MHmdGed6ULDA4Cf8Uj3xoddhlW8THvrwz/LE/elhiw8800juTQiDt0nK+n23hHeTLHehk3slfuuOdyxB+PtzR4LcEfCuMu3VDO+2HLsy2SdxYsrtA3X/suY1WKPj/eN2LyjvrDHHfb83eHPj7LByDmfwJJ+ZWD6a+8FO6Hshw7uPxn7zoO8+aQ8iUYyGAtR+ygYn3LQT6J1+vLnHPXeDoNK43nGjKlzNBDudNnc5FaZLq4yf4E9+Ol3PmfkSNKODRARMQs4md7G7ExZzEkYaMgZPMbCUwwR+pjyEGo9CCZB4qCFCmxx9oJicQ5GSNXhlsJyGzqPiMtOhRDkWnv9CKejoBuTgkzHLk0oZfqa5WYYzCBrEV/l4s+VxRmJr63reR3LXRXce1aBOWUUnZFaMc59ZkF8cXfPCTOj6UzsxzIILpEKZB2AZ1lzzL7S/5uqY6aBe4RMBJ44exuD3G28c+kTFYM7QDsvQBFkKvWFIU/gASBkCXo8yqCPdE7GMRxAF4Er5yAguevaiOWGmZUaugbcaBbiFEdHGCy1ZS8Ls/Hg2QkYaOJnCmNnz22Wle0EWTuEKL1JndlF8FSKAzqVhzkEJwZaeTH4qzXKqf2fDlldSUvKkUbVgSoWSJzt7joKoJyAF8SDe272JhRltktWBpEYccZVA7fp/k3Hg4pVrw49efZVdRpmdZuauvxdALxUpVWl7cMmo7WDN7/5JCyEcRkzL4QYz6+QpA+ndGdCBdjYxm9GAV/VsZai7KgLgfKrhKjNyayyfPjTcoM3raPpOnktOV3mHkG9XcPh+3tpwzXgL8upBvVi+zJI1OeSX5wLCmbkYWprvJXXtBQRi/mXJ53I9hErZKLSO200exBhs6jOy1b98kPImTvbymeV8k8+Z9e0bbDqDruqzLlIWWXzi6S/ELQChF6mfTRvhE5u0EMpZWEI1uI44UY5lq8t+LuiwnCtDyOTH8jbRmkwANtryDD0J5EL5qxVbt6/Qppzhdenw+2FeA1HXMitDzjw1DB/hUywobvPVbw0rN/m25rK5geiqnkc+wJXYIreqmmsf+6J3hF2AJk1pl9w7S5JW2h1o/bwb/ZQjP5SlhosRfsrRdmrYU/w9M6ZU9oiPgtHnJnS63HtgjclVk3VpX7qbMOW7G/6++XfFE6mngi8w3ZffAuxdXx5Q7jlZclEN5oDody3egRDvRoi5Ah2Iyz0AWqhsGrcpOoPbh86xM/LnhpXTn2KV6ElSW0e8B1zfCyQePIdwonWNgv7+YEbPfrcHJjf/Psr184g7adk9SouRXpGoTih4H5xH1EmkZpt0iASsZWW2f9mF7OwHY1PjSrjKi1GH8Y4ZDaqcRZwQJ1pmkgN3coVIECwzBSJoi3LtYVo2cMFocfMLHa+M+oDNVFBM6jfkyNOIq6OEgF/ryOlK4vsHTBw18ubNX9jAawm+Gk0av3nvDkPYrevLgdMYdSt/HULLWkIpt7LEKI3jZFmgRUb0gl6Nz+RFDhSea37UjHKlHUDfuCFlTx4X0LiMQ3jh0uXhDB+q16Rb5r2wudkS0ziUy7qp8itdpYVh9Mm1cgojgpGwDJIcxSBNHbZ4JTS5qXMKZF5mTKug4LUgSpayNocqS0BbbhiCC08dnNRHMMnvBFyuqUyGpNkG6rKDxmlMAaClgasMhuARbwqvZGj1Ysyd+0VDFUjU1gdLBGHQSK8vRLGNRg6zBKr/nJUis0rAW/d5eBLmVccNfBS3CHXx1PzKcP7imziEr/OJCeWCYc7GO18dIC5Rr/ozqpF/0w1UKHBmt+iXyRFFoQg1C80H0G0D0LJ9OcOYWWBgqq/Jz/cO+Xg9DtPlS5eYIVzn+4Qbw1UeRrhE12W1K+zAW0s36a84hFmueqst2y3FhSe1w18vQDsjUETiUudrzFaqyG+70rlVTsuYU2B9WGb/dWbSNixyb6LqOn1Q4g0pqFFALR996/xbw6mTp4Zjh48NV25dAXATp9AltkCCF336ncqwbrJ5Ii8yt1/hZiEMgWnlIqNS6rfDJbcncU7/lIxpkyCcOqNk+TUrsA2mg3dSHXWUqJU3dJUdAJfAnz9/nlUGV/IZjim9jv+Tfk5V8HBp9aEPR99X6R83Xv7GsHbzWsbZ0uRMS13fvT5mOXvHDowj0dRjp9Ux+/W9P8turlnvx6IV2tOiZNJp2ftROHDevaY1ynuXhOeq5cCl2B/wLkUJ0YPK83Z4vL0KrfFsfw0cIJcCHLSs+1OzBYytYH/Qe8Nwfx675lLYdEbuAak47m03XSZ6blh95HM82Pw0g/oZMOseuSuJBzTxgSux9RyjsY2+3UHZdxjtDTPtuBrz/E29unuZhxpNnVqlV9soU0OD1j8hygBqhhWaLgemcnuXKUjSIKof5ANTaWiw8UZPjN5yZoqXtlWKFgKdSnVBORU3KGiYcrGs8QzYUpxLZccxZabOnQ51eoIpGWHCQkOprutUBqSzc5Y/eWYYQAgFErtTlzR4xGC3QA1Ow1aZsjQUwZwFscx9Z1AdD6/jOCqINFunDRf5WxboxWEklrJQzBiQKV+YxaA3TTJjGcnqTlaJZNOvssrTzwK46+QVZjtwpUTkH2O8O0QQk2bNBEm7yt5K2JUXuaUfp6q040WSmvDELWc7IKCc0jMSeurNuHWILi1QVFG5UHPGhlmcZrQX8fYrHfDUjUGdhb0jn2IUIeiaKx/PhibjGBePBuTR88QVJXK1VNFI8+Sh3JHZc4i3MhDP+3qkTvunKAZKBKtaZBnHgzRbnDlVS033tgPTw8zsqpckKR/H9E9AKdzknb1tYF/nfa+3LrXlosBaumpnRIDrYsvFpczmWV6rRJZxbohYump3ddZRNM86kaMOIKg4eOpQ2tVui9dSHAdhLtPmfHfv6JEjcSzsD7bvvEuIMHmvb2yDkIJxcVAIjt5WoqUmZONna0o7TZ0oRmWkWkukVgbSzUJgy6TzlzGHNtTLWKjCFZWAd3rAe33NPnTxAnJRPpaO6vj29yFHpx+4UU+NXyNTvJQr8lIWzwnt7KmnEXec9M+QLIUgkr7UEiO/cYLZ5ufsrxGOGh8Lb6YvCYlQYYwic0JOtj2uGCsuM+P72muvzcpQUO//TjQQlfFtz5WHnh4O//QvD8uPf3zYXOGdQrqID+FGHU9wdkQh0h9SmBeaHUgCByLSEd6Zc28yvVkfhKs4vb/uCX+fyxoZlGNOyXtKM6uL+yzX3hLc+5yDlv3ec96F4h3rVYQ7RhoZ3z3mSGL/yI9ZuWnW/GzdWhm2jj42rDz2eZzBz+ziDL49Pe6vhHdXblmN7y6Z7ok0Gcyg1O2HGdHJ6AbQXKPvF/0s0gg+TZTwPO2YJjGUheMAL05UN9qlE6Ec5htf0kzWCPE8mt6gu9NmfdNMY7SZPZLeM5Txqv2fwLmMVww8E7jWydTQC73QLJPKPPXUjas5kYva7LfTD81mHMbCnGbMwGUsvxWWH2oUqhNnXNzFU+PXWRQN7nIy1INPa6osyqUqdwToWc4y3iwdF8ogNmXUKI/hbZmE5U9JY6xbB41mqw4Ug6MK9qtsNHLx6hXiyOGmH7z7suQyxzhXVUfWYdVeFFaiqbwxlNBxyMKAjMjmuQGN8BEwiSUWNYBuqh7M05mCr4UN2eJZjjDySSdHo9v5eUl66AAyCxDpOKMSZrljTJixLXehx1wiEm367IUyKQc/4vegHJRDnUVv07wGM6J6rUPVjrEewUz9UT5VkdDi1nraORA6WHFkGrxw4asXxf913lO1jq/j9HfDX3/KYHssJ6j6h7zzWQVk7/1TOB2/qoxcmEQYhUobM8W60Rlxx9IxRBfFcJX3B4+wPNkP1l9ipvDQoUPD+sZGeNor19edGdQxQ67Ixpl0sVPvIdr5ziSwoLPUkXNFyOgPanY4SkKIiMye0npgZtkNqnAvutIyz7JeZufRi5cv5oGTuwlvrB+iv7sQZUagS1j0/OWoC+K7BOUguUEWqPVPLEtf1bXkBWpBiUoq0pWPo4dOR6KidDo933Pge1U31An5gMYxIS9jDLA/+MGL1OeNKZn347tpgHcIV07/1HDk2V8b1j7088ON9RO8hznWSumW31HfRtoxpnW6PS8g1T961o/7bNdp3WdOlN3SBEjZxgLaZufQDnYx4h8MfITaS9gR4GCRu2Uv9b30sshZvczpRqb9WAR+h64PKvvdipPqWUSeU8Ji5p1dL9bb4vV+1FL2IMz34f1wkne/lbanAMiJvEq7NfA+84kPD6sf/IfD8unnh6XVU6TOu0X9PrInuQcoY77kD0rBrGlCGcwWcaF505Fyw08vq7ygpIOVIdeNiDIAQy4/nZLnuv30FBIgIgnJphE1OZKOsaQD5O6FvlujwakporMQR4XrGMIA68yIOnOUWjVBNwYsZ7GnoZyUiBADx501db58Qu/mFd2wjHHbym88Bh+WTfomP3GkwOuiR/Z+0c+WDxoFqy65lkDoqZc6km4co9a4my8IlnekuHa5qLDB49TfvwndSADt6ClESAFJAq0+NZh7CE74u0Nk331So1GdtDpttNIuEGisW8gssVT1pddeHV585ZXhhp8q4Jm19RszPM5RldlyhL2sjY8VIRGuTVQu4+1Rdvy5Vr9UNiDQh1DVu2UClqlhV9MVTktLWa0bnRIyZc5/jtAn3ugl0TxDhOSM3MHrsNM8afdDAYSRlocheZylOcLJ31qwbQlUrBJNljo1YvkspznJhTx/IOkM6GBXu5Mv+ba/UKsfOYg/Phgg2TrrR2D5yYwidR2egYFU6qr6vFSFDS9q8hKzOC+9/hpPBOVJnpkRzwg4XcdcWr5NlnUWaMsTnIR8diVlm5RO3JafCD8pc7uwRckv/TBlYZYeR2mV5aE6hM5Qu1zaPkLJSec9QiioizyQQJ6wbDL3Mi14QV2gLsLsnHJKERl0MC3/LDex6D0cKyP5wim4/BfgvTTNw1bjGOD70u486iwsjLL7qI6h7w/PhRCbUITFojxT+NLlDEJqOsgyb90s4IEwDYE8zO/to9OzPOJ6lB4D1rPrQryUbJZsrMPnLB0O486GvsbDhtdef30Hv3kKP9lX1k/qiIdtK8ceY6bwV4aNj/+jYfPk48O1Lcdpe8qC5qmLXaoiSZ1eaN6RajtRz/cn2F/bMBEGdy7j/ZFrV6oR9O510jF3pb1X4kQhdKF7GiTXjwMR3oP/geTaA/dAfG8DtG/rvC3fg9fKvnxuI+NuffN2KPc3f1KaaQfsHZLsbTaz2HTDmLM/M6w++evD8onnMECPI1YpdUKhpdxfid8t1B/AdwityqpOb9R26FQxPxoGY/sYI4KXQVozGTNjXePS92kKFMOsUZaeh1wyYIyPpk0pY7kcPvJjlJdhF4dPYsDjsoAfkyb8QWyCFt3wUv5k1I9X87fKKmcHCazkw4P3mTD8fEeJOTMt0Sw/k4m2uufAW4DgkDJlFqKVUfDFuQyi0qt8anamqNUMDZLzr2S3mP1zZlDHrXYRvRXnVD30a41w2Yrrt8wykwjdLDGzHGZyUp/u/BnCXbace0kAbLTiRKbeSubMtEhIfXogT2bdxA99361czQfLX+Aj048/+vhw8tix0NPa1OjTCbK8ak0Ug2U05LonRonwVXT1DC/5JcFE6j54JgXbX2DEC66JpGnlmlXZLV7GUmRo+L4ruOSyRB0SdBg+ISF+O0KEvB7AHUNguJI3OosnFL7iNqhs/CC5niA8R0Cq7+iQl34sNBIG1jOA3WJXDyEqnWlc+b0mgKeGROsOf9dNLfktMH9FkZL1naYhLrJKSRqeaydRZ4nZ+AOD/Y23LjD7zjeGyK96tW3RriyjdcMp+LRbP2C/yUOLNZwZd8f1Y/QC+GAn7RG+wtp2633QcmKVSeGS1xxFk6QcmTR6wRVujZlAJtWydNQZQmXy/UI3pQEIPs6ut+XUIqf9UDbpcyhvGdGeq70L1sNYZxEq6DbJyFe4QpopITJsJslvyZ64DnoDIWnvQHl96HXh4sU8+Dpx5OhwiI1z3Fxma/ta8KwZ2wsqKFl8GMI1xanydOpcl8ZIiEz+lBD5vEVJlbrscCWnekisU9pxtkw9dNx+veM8gTUv7TGRGaT1dY3Nsr773e8PZx86m/dCZ7nvx7oGprXiZ2CW+GD9+lPsPnry4eHaN/50uPbS14bVbTdcKgzbSld/2qvJPaERHeuD6yn9lj079QY2S0lMuj0Icq9DpzlhExbj9RzPMTUw4s40sCBZB+34/XoBbLfLjlLKPCjiFM4RdmeY9quduY0bzDtu181usHumRSd75u6bEb6zwu8Le1vZJnR6eaYE98Wf4E5xjC/SWgQ1P7T3ZbBIdee1d+qxL8Gk8+nnnRgLKROcknpR8gX46eXblH1Kas9479j9LCAibrJEdPvwo8Pao+wkeuqTw9LaQyTXgNNLsFMHPWdPbg9ExsRKfCDKMw6etre8p9VqtgzUWaPvpZ1VM8Nub6RODxDXmTrC0+2e3HohJ9RGogvh3IhBA8tdKdOTQb3pDBM0NO405rJEEoOhdh8EC3jbaJ8B8Z01zUONIj8g3b/Z1WVW1i6DxWlF6kXIObyUSVlIcdOWQxiYqxizGmM6Wn4UPsO44lcRZjQa0dD3Rk1Ot+O7eRX9mDHtYI1C6bqeuosdh0sdSImz31/TApSGMyzOkppVusMwZkZkdd3PHIhLPjMl8llh6aa6ueH32zDca5fCpmuVwn/VIbwxzI2rX+NexGE1EdgYiaa3wmuQ5ntzXsN3FeP8tfNv4jScx80CV6Nc+iqCc4T1TCiStBkLkDA925ZIhKwRjcUll59KD/jkQaCW8LYlokkUicM4OnOTkVjNoY+w5kb2kqe3V8uVD313eAvGcZMlkv17b84WVxkUiuD1WI+WRprwpb0iaBVQuFGuApnhAJO8GZmQ4yftRpJhgXTEldEkkFKfldlkAMAeUNnqq2awIm9UQpp/ykXo5TZuuy+HDB7oLPoqhuFnC7QinPV98eVXsptsROPH/lgPBuCPvm7h8HUpXeaozvzwunVmP+rtXweyL3dWhloSacwgLVsPerCuo0qY8S++vP1RRN8vdPOYQxvsNHqNz1mw4dI6M4bSnjl5RV/HsRxkFFJqkFLThf1OzXpYgtlfEgWUaTscZ+wX8uidPKozP7qrsxgV5MkhA4IzgXN5aQetf5GTPgztK1cuM1t4CZI+CPJhT910l3t/Ug7g/n/23rxJj+PI00wUUKgDB+9DIiVR0kgtTWt6uttszHp2v8D+uftxd832M6zZrG3PTEvdkniTIEiCIAhUoQDs8/w8PN98CwXwkNQqQRlVmRnh4Vd4RObrnhGZmT5YyG09owvjYoiNbqKl3uNGCYGbFFVrzGyA3zE3hC/H3JM4qel77384ffjRR2nzk/BW+CkLXNznHQ4/m678w/8+Hfzd/zY9fO7N6d4DnDbebNsjbe7qeTCc4vENin8A6Tfg/jiKp0VOjcerNpB/J6XicyCrj7MCyu9tBn6TzNwjM/LXtnXGLLt8G/wF6eZ6sAT+kfPfqO+eIvO7tu0pLOeqWkk0F/+wzHfq+1Mi5fFt0p/SOOrhIM+PA8eWxXA9eYgfcJEb/a/+43T5J//HtPPy/4rj+QqY+jzfthHfpsF/ObjP4Ayhxtc9qoAsP+Q6Ht0ndLyB1lwOJtjA4iAxcOqE0wnEPIyT1MFPZ0Q3I2MNunJQ68JonY5OZgZxcpTbzqqyolECFgXgt+oIqYc+mWORTX7teBTXSKPSJBLQIBcPYRUCFX/rrK6AkmDUAqi++XCf4DYckOvrahKoFSDc82ZBcrqyEpVuNeOh8xh/MLLDsmwSp7J0Uu84wIoErh0uO7MBrwc8V6PFL10+CB9nXaInuw7Yqt2lr7QGfzr62k7H+YHfKeQv2qUjOhCQh7a0jBORZahYG94uX7Oum2mbkhRD3tfjOytmfQJxAwQs8x6vkP/hmz/gRR8HXFvUgC34trU+nZHPl9iI6OQxTIOXmTY7HNlJwaO+bxoIHG1A6cLTI5dGPo2PXGdSxgRdtZ9yRoVOteNMejZJbJN2IruBy5KUZYeJTrSB25Bjx0Zwwyl23ciG4bCTgZNJsZolJVVBdvodRfKuImSgvSrO40K6vlZLp87R1YztT6G0E3cOMezDCBw4qtoJGs8zwqbIFxxdwrNkOJt0mzdAfvzJzdgTI+UlM44xA+ai4Zh21DngyFOMs4IX6DevDcqZz8/R3h7z0R0KNVG+zOqFRjUGH/EphyR4dECVZc6i0jbzjt2DQz7uDiw3kghCHZe79Jtl+71nSZVT1wu5whN6h0T1BxkRasehy2licG1deLAfmqWs6qKPympLeAkseoBzvnnMdbEa5LYTm925eyc673Fzam93f7rHNz99WU+dC1B7DiLM61Hs5jWw5QOLaOVCo92Kb0vrWsuPp75ePF5zNqT6bdTBeiP9bPwl1OuM16sTgvp33n13ev21V/Nc6BJnzZ9lgdHZ9O3O3nPT5bf+Kz7aT6a7b/8/08k7/+908tVnE3Pzud50f9ToO4tXwbr+9OjY6t8FOaK/VRJ/nALfmE4RrdcWUQOfoINyHMfK7PZv0S8KXotMT2rnAnWT7YY8Qf4GcTs3Xwe3wSnNdc174ETEkHOqKu07g9WfDXRav2+jyLcdT0/lfUa/xE3IlbEHz1M5nF0JX1mf5nCGuMfo5/bNyHI5zekxMgTOBGdU/hFBaVh25aPg0D/gptN0/YfTJT4psXPt5/zmXkVjb1D6O8gZ9g3U/yNqeG5ZPXMBYfqVseDF84njjwod+jhkDol5NEhYjpJe2RHfyqtUsBozOiZ1Mi17tRwVnWASCMqu67O0DDnKnjPm/ddB79mJArk8Vbxy7sU9K6VN4Q+C/wgMTGGhYVdIea4FL5PAlhfUENwes6QpM3M+nmQSn3bmBwcltIM28c+Atd6yqADwBs8UFKVs/qqtBbBtOqk6gpJI4wycH6J3OeglNmcHlSG74BuMaHMJ0OUh+fC16LOWKalop8rPPzqA1UVt5GgQONdRoayyK7U4bM6ypH3sq1zOeMaAQQ8zlR/c+GS68cUX06svvQQ9z7pcwGm1iQQG+dFFWWWmnVHc6tIretiWqGNm5AU0ygzEtoFl1JDXFgoCAT3jJlfjQiGkWLD330E1978uNWRUnDgbi+3s8wtZezX0kIsMFsVNeQCtV2baJUwdOc56LBnUeZT+U48wqyBKevWvTx/Yv5QYG8qmV2K7UmTwMwoooyLTJLwwPNo/6aMCCzqVYKy6SciTHXlZOgZuMvN784tbeVlTnlwF1+f1OsC1jeEfGmxIcKYthTvxqmUTpDFzWGNIGGmWWXIKVO2d+wscx5yoynAG8OKOmxArCR4Zm/VsIpLIay8/DZMxlnFbY8P2yDf2TR8p0QRUMAi2OzvZV0kRSbW0dYBLdFWETgLoYzzz3aPj5pJyrc+eXY4bW3dd1ahinY9f3fPTAl6DWC3xgJlXVizIW3z7Jqt1U3JnA0uuGHNKofWZoZvMaGA1s3hvKs/OdX93bV83urw8Nm7jdHmJ02PuUz5S//mtW9NrBMGNv8Rb82WB6t/uK/uW3uOFMxeuczPul69OD77/q+no3f82nXzw36fpy5uME7/z6HWkKKuvi5e0OS/OqXGXuv6pVDxrTP6pZBXfuub8aWWs3FcLDAuMa3xK4xpwpm3Ewx/JR+Z3ual/7YfTxZf/Ybp4/WfTI74t+Iglo/VbVb/JZ/L4KwU+cwGhHe1o0Nn3x7h+pMfl2EGU7CiPTs+PNoi528+0skGNzprOivg9DutnCCIBg5UwL8RyzAyJy73i7MzYLUWMkS9nSae/l5zKENdIX8g9+cKd90OJ4t0D2bv7zmyAT32117zBAMENvHRsDw/KMckPBrDgsYRUNZ0hAT1OYGbhIkdHzhPKitJHZqNqtIEKlSs2I1PBoHg1o1NOty+2ucKzRL7MwrfwOTNXHx+XxdA7wUL9pGcvbwWMGSlLc7JhpOgz8JIPt+IXfW0g/eobQ4NGWb1cSpk7+vC57wwRgajzL85A8JrP6YgZyXc/+HB687XXphcO+TC748mgCFr7LIaFYQf0Qx3gKsVOYUsg/FMZ/mQtySsZ8c3LkIMDtlN4hGlBsFGWEadfbGcZP0GDHQa+y1J3L9MPtDG2bV59nPWKsIbGBqVAg1qPoadLeJNG+xQ3IBl75B23dop9YVDh83yE4JQNAMt0ZbuibHorbbb6cggPWdVoKCE5x1JJeSasum7SkjY18gVX03zM5ybuEJgQEapcEaZfSo7nfuuZZnhzAqxjjsecQ3u7jBBvGOxoV2elK4gz6PUsjJVm/dAcwTtGOkaTjGHHoN1lW30W9uFFAkL+dgkwH7GU++IR45Kx6DXnkDeOqqEffDc5e+8Nlbu8AVe+oJDEcKsbLLEhApydzYw5Na0O2TkvhXCHscaJxW0oqfGt6tTO90MN6YbwxMcDIf3SyOPY9CXHZzEfsFT3DktjD/KynFwhXTJucmx4Dcp5ZQiudW1ZcwlWlRVvXY6LeoBj5ES/GJrq8FigFacn7zMGT+GHB23uIfNkaseZ2vlSHWajP7oxvfLyy7mJ8DSav+Y6rVWpjT6O9ucOb6d9gc9TPPfG9PBH/zgdf/zr6fjDX08nn78/XTr+inHuuQ2+s/cZFYsxMMqOAGWEa4tokWcen44030yR59NRt7g36qa9QyewTo+rtEl4neRDzjgbzmI0JDWdxaYdVRthAJpFMkuFrJsrZ8pTmW2E2B0eS9kSDNVP0QIfkD4GF5ZyzW8vhNsSiqD10hXon83UnIVcJE/cR9ap2rD5BrxajyZftqNhZzZgrjw78435zApIcSbV2QLOgPa4a3t4/AYmOIPTnxi0NZhaQ9qe5rNLAxrubyM3Vi/wm3rA20KvvcU15BfTzpUfsEzOt4f62y9hiOf2nj3q/sTtOqfsn8GAcNvSXrTqAsmg6XEjCidXxpJZtlyQuOKY97xzkOggOIDqPBTYPz3C/a+B5RjLjBqOjXf5dRL9ALyci6N4cl4kQPKNFuNYcnUpBnzwN2ApSVSMjBfhQD0Kc1ME5TyXhAP5kOVuLmPdJSDzmTzfvpllb40qDbwjVyb86+jmh8+2kjcZvPiCGgV4IdHBNo+fmqP5yIaBS9wsuCi1l2seXuEbazxD5HIxZ6/6235xXJGTQBwZcT5tAwlosZyPAuFd7INRhSiTcpafgi9E1MpUobmFBXX2i+Piog3SACT7jp8cdN2Zfvf229Mbr78+XfvZT/E50Ia+vYTD7Z/fdAz7DBA4hxyIwCTzXej8KHuQoDfxpW90y03bRxHmemnpi8gWucgrx14a6i9c4k67jjZ97niMjChKvw6+6WdhKUMXWnjQ3gSZysn0jQpuVKhSQLVTZHJDSQpRD3sm1kWGNwEMdi7U20AK2zJbrKlsk4zYnP2MugGoW2pbUBUGrEKLRhh44SezSyxbvDd99PGNPEfoR9/T5PT1gg34yow2g5Vj0ODFgIYn4HIegAUULGAmTeSqx3GqBOZ54yyfb8aQtpZIM3L8riVw2cvH2cmd3Qu8tIbnZ5nNvUvA6nbgNwkpez6I85DZxMwaGrQive2iru2Mef1xafgFAk9TLUk3pw22bRMTB5cfTwNW6wWa+iis/iPPYWBGG7ULHrsP9jNZmGx2IQPRG093H93luey9BEm+BVme8nA8ZqZWm8FIGtNgPa4LGwldX1jb+9TBr6+Pfd5vY41SCzizcsh3HMkPnPxGPAG3tbXPtOn7PEv4xhvfn1588YXo8kSyteIJFvAMIREY7rDUa//a96f9H/zD9ODW+9P9T34/3f/s/WnnzifThePb0w43vxhA4PevbY2Q5e/JPKgiDd5B2YypgLloNaTq6VUBA9jXzeCeuWvqTaUQf2JMzTMFYFFhC5iasaO2dRy/M9ttWOCKusUnhDPCpsT5i9yoM4BlZc4S6LdYzNSLTF14AAziMPKasJGwwB7ZRZ34bYzOdvXg0cXmE5EArd7ovekn8U7TPAnWPMfIgl84lk3Qa5vPdqloC7/5DPK5mAxkZ1FaF/hg0T/fBdtQCK92sn9M3NB8g74t25I0p+jO0iirixbUi25ZQB/PyjpdNWeepkzRR50Z/3Ge3wRSUtiraJSVIb+lDJBHBIAP8XemPYK+g++xGvRN3mD85nTh8iugHGL3+k3UDtFlIfDrtV8g/xVkn6mA0M7ti1McJcdPnYbWsDEcHBEZUAwOBlM5pMBMOtI+50UqPkVdJ2ldMAaX4LirIMQjc3VsuXDh1yjvod5i5EoF+766VSl7PWeH6ZKvaFEx3lLc5dJHfcUeo1qaOrHhAcygzVm+XRyry+T9yLrP8e1QeUQg9pCZDl+c4g9lNOMuve2MSVR85HVudSijO06o30ScPzANNMtOhY9AIzOpcoF+5oe3p912cQB3d/eyPG8z46Gd3aotOrLlzNK64VmnjTHCaGzaWv2R5/dKO0UmWVMzPADgJ7hmeO1jeKCv3/gzHvGTV1r1Es5zHHTKmUXEs49TgTN+g+cI//Xtd6a33npruu7SS0IDA0ObHLUUQBsS5Gg3FUFGDTBVIp+2iOjGPkOLPI1TlxlXsk6MoaQYYABDzk4ZS3jTLOmFKUg85UjGX1DCJwgCSYPQQ8pBtiIpQSEz5hks4aWNFzhDlybvWc84F3Bw+a+6ZCaT7EPsmyW3Q67975/tki+56LE5T4pzRLpLAiZ4pBpvC9jIiu0bOl0dcoPZwY9YxmdDhsSyDZDi7SzbKIR5yVKM54E3Vlxmol45b+wixrd9b1CDlQFUyk0QAxuSz/A+cANPkoigzH/kJmijJsHegJ/cP+ZtlXeni1euYKvik7ebQnORwNE3yvYy11y/GHO+MErb2gZnE+WvTv1Mc18johTwjFmQd5lOVK+UbUP6K8SFisaCqmNqeDuTd6HHqDQmDrFjZev8MC+tybZx0DZ+vN52eMMsN6zAUb4MdnKCeF0okZLH3tSFwxAnyyelGjsbxIwPGZIydp9ECNxrQmlaSNWmbkTRN4+Sc5pZX8d3ps8+/3x65513p6vXrublZKcx1/I3tYD2Z8Ppm/ZenC699uJ08ZW/mfaPvpge3uGZ4C9vTA9u35yOedbw5O4X08X7LE8+uccYZRWI156MqXH9K05wGzzPUKH7P8umqc85NuNtxsIMmjObMZfBLm3XjfGXoixGxePcgMwnTROL31o1x9OUXS9N1W1hQNblVuVxTo1Rcru+Sr0HWv8zv5ysXf3YcZtnVS84k/W3dAEBZZtmblkjie+JuaBb3GOEvhAb/TGVIqFkbOFQePoNn7r+LfltazpqtpgusUde1Runj0u0s2DUC5ZOmfE/ljSLvHjWn6nbAu+U0aso8dclcDZoXysl3IK/Ifo6CY/X03DPYPvHG56PLlyeTi7yssddbpzy3PGFfa4LBy9P0z7b7ot01CE8fNxCobHYzPObaTyj/9VlnqmA0M73WuGQ1Ql97GJj3XJgZoCBOxycGj6FkB9+z0BSDaLhsAYydghraA80nbhy5IpbUxdF8VuyWCoUHuwitnlTnnlDmDr1tiH+2MmSK6J/tt2ZwINr19ImHcf9Q96qRHLpqPZw2Wa/7U8nMkRDrbKdIBjhMPpjqIMpvk6rKTOIyLJMSysIDm4pqh7Bpagd9niDItl8Z803he66lHXwEq6L3W9flX8S8lWp6usoPH3K3ffEKOhlYFt41Q/i6xT7jFe+tSaivGw3Mm2PJrN9NtGAJxm4LB08ufrs3Tsfvj99SGB4+L1XmSGqmZFYQUEjLemaV6pQpPg3JqJsUfRBIRVQsSSO5umvOPvk/ZuT8sTPQbr8F82AV33hhDQ3IyAQt3GGRWPHcHOnnEFnMbhIj25DkG2ZcU7hSyPIwzjOP6x2hnA2c26JOXC90xdRris8FlapW1Rb9gUlqdh2CTIA/HeTlRgUxsgRNv2Abw/e9tsOlFVJxz8IJajycrMBgY2GjEPe4Aq8g9mEd0GFE4Gi4y3tAccZwX1uIJhk5Wz9w9xkgsr+lY+KQu+NGkDQl8Z+y846b5zs7t6PPM+9BGxEns4knly8zzJnz3vH15ABTr0cp8a6AaLT+LWE0UaAmF5QK5PXiupR5WWmuCqesJceKuyDhglyY3Ng23My25JmqcOO8hCWGw3KxW65oZbzMGfWlpbBni/iMzfZPD0t5InYuj6JyOtYxkQjIGoe74oljUMVGnBKTsD2L5n7vFzGN46+xrLz119/dUO35r61BbwWtv0d83iCcf52cAAvvvSzafch58vJETP1LKk+5iVGR1/yg3ecZ1WzHNkbfSFcim6OS9jj+b6mPV7zBAidvxkWm9xZ2GdqcEZA+HQu35jzUjGIvj1XJS2poj+AvkY/buOzdDsFWzJM1bZVtn4HF6RLrLpybCpPlzc15JBX1+/BGUYJHNTjMV22KCkspRZ6ne3imaN+waMkLACF5n4k8bd5hkFITtENCmVszobms308m3IbZ6nnqZqnFrfPB3U/rf9Tyb9TZfwVfrsvcN5fYJXNo0tXuKnoDVMCPwLDaYfN5aBbjWrbao0/vY7fqWHnkOgZCwjLwl6gHLjzN9CG82NtnaQOFgp4hzoCPVwy2HVATOPA1WLkxwVkVDVNcCnUxVCcWQLyt7CC+rU7aHrp46zYko+OlDqwq43Lgy87oezsmnfed3FI7/HMziEvNbjC8rMjnGGdWl/q4ck1z/bZNOlUM6qWvrGfQRWbH8jOa/UVOhB15AwHdexqlkM6HbxhKkrCDy7zOnHelHjvmKVwPP+UNybCz4CwnGPOZ5zZnPDwlouzrPdd5kg5PzTLq7s6kDwkqFR5krZXNWnlW0Ev68hpszBxDToNCnWSi6zaqi3yTNcIDuUoDz+mfYuXkPzzv/yP6bUXnpsu7bOED34GFOI4cuolKfQ3DOVW/U2OMVQ6lYwhUCKyGqn0hmSTkF+fKyj+iVzFNXkwduVQP2bVj1Yl2fjKgGAeAg+KcQmhbUP3JGXDd1Zhpq3qcNJGFkOvcMpUxJGXces102qDbfrSA9TI84jMoVP9BvZ5ou38Uy9lDF6DtwfHdWaRFEEfDlElkP3siHirWB5BAI/z4O6do+nDTz6ZjlwaeZGXGilLPdhJd5pXGhIEcNKoCqxcJl0z0yokA0zqZyMuwoO25SYE54XTz459x3WWjhKctW2k8yZGtRZc8dPvYZdztz9If5+Zwh2ed3TpaMYSN3p8a6lj+4KPcw7dGKrpDseiNio9GNMOVPo87WOXY4lJvs5xnzkkGFKHahK6kUbe7Ky7VMI5eI2xbdbVuMjwFP3MNMiok6DO0wqinbGtgV3ysQ31yvFQO4sAZGJSaPIDECVSEXjdhLFuMyZnRgPNwzI1a2HzWKrCAi0azeUtnYA6mgNrDPT6ghdTvf/ee9Pzz12fDq9e6Zr1+C0tsOmf7gMtXedu9hf5fifbhen69Oiw4DnXaqAgbcPhW4pe0b+BBeps+waI5wzltN5fN0p69H2bZnxbnk/C/y6yv42e5xe3esn2e2bPv+1RuKB1ep+20Kg7vw07l5o9WwHhOJvi7PCDXD/ay59pEIbnsxwu5svP2HYiCu5ehDpIPsSQCQYOlTNtouCs6eSRiyO8wSzix8oDvHUoAbOz2p4aChZPm1A/h9GFXZaAUv+At/ftj4DLF8JcPbzGMtKL05c4lzpefnw9s304j+34JBij1jbJP0f2mVFzNhGH1FlCMQyUNJQtdCZr/ri87bX97MJPnQzCkO2bHI94Luq+s4MuH2XJanHQt9eR1mEum+nM+fyNcjogTNCpXJLtfIh9UT++dOgkJcnCoLeT5aqyRcoSIBJl+q3ab9Bby2wRCpaNEJs8cAPT3/zud9Pf/Pgn05UfvhFHWBYi2dfyC98QKSeVIozcKNtRQLr/egxGTjEMTfQTtZPkQ+eNo2zjS7bqJlkZz9zjgIVWPMrDLBsNBVpiG7pJlayZ8MiugFQULTD1sUpSUYOWXXBTxS5oQy8v5IVmhQIG2xyLd4DiZSCJ1n2EFMkUZorQU1mEyXZGIVOh0IXpM5zyT3jjo09SivOw+17k0a7ittirfAdJ5GMXyoRC6IdN4aYwl3KePHC5s7PVzrx7fjF+GTfODDpea0gq2eT54ayYvIQZxNm20UGprz7LTDxZb2R4rsn/ki/Ekcr+D03xtSkZWzKznvPOl99otLZbatgVbvGQ2tncnAPqVOR9CK8ueGWwH/3P52xcbmpQiHHUXqtI7hY1ZM6m7cx2f0QfAFleDzwns/0wZHM1ADY2q82Hg8jkwBuoBejCTCK+SUDlap8RtU07qjMC5dP4rcySfMiJOsAXYke++jYkg899rsfvEhC++PJL01sHP1xye2by8zXt37VFdS6fJXKu6Y46C2mF/VEt0KfNH5XpvwOz03qfLv8xVPi2PL8t/h9Dx78UHmfbBujZFTSrK/r4l9LSP5+e/o4/O8mxMW81CPqHuxtpOduo8CCmPyQ6cP0D17CZYWPlh0b3iK1EJB8O0gevpRWXktCwJxzDV0XKAdVJcfayXcW5o+I8GZxVAyTzjajOXvhmSb836AsvXF62x1v97rOUxmeSbJfBqi+YifMGvrTRF1bxgXA6g6cziyN6iQd1L/m8D1scU9SLTjijLo0zL36CoihooFn2vYRzbPKtogaDJoNTg8tg6ExCb75kljETCALdyKlZP2cxVDh9BKXxHT5peNiOSrid6KHVNM8JNB6VEjuasRbdExDCRPgJSJldhJsMtJNLb3d49vE2bwz8Lc8S3uGTHT68rNbVx+CGn2rNCsDNvHzAGlnLuVGQQG70ZBsqRxiJryPvcj8bkW0wsGhZzvBIXuZWJw1FVEh+gXu0DEKCB13tmYA68q2DPGJfjuoR3tQn8NBZx0psPd5FTweIN/N0xNZf6oE7/+N/+hldqsehkQzZzmipXgEEblJiyaFLjY9Rt0BzLJgaVP3g+WMbLk7H9x8yO3gzL5V5RFsI45BnjzvmJNyyCIBlEiHGK1zGiD3gDJ0C85IcxshDNm0jPwMln4XNDZsEWLZYNmUHD3XDiHYzBlNltZkgigqvYWjb5zi3XQZtbnZp4JYdq9ZB3OOjb6iIm0AVBqBEh7J39ZHnk88b583CwyZhroDe0Kz+ho6U6hFr7YZ89zCv/qnjoilmw9mWej73X87t5owse6R6xbZmsAOpNod32qdMoTWKyI4U63Vh1sVrUtusjhv9ZuTODBb2m8F63SzqSo4asIQPoAXTaHsfY+gBF4aFbt36Yvrggw8WnzAK4bpbLbBaYLXAaoHVAufKAuVlniuV/hBl6oc6rsO2n/AYU6sbRUepnVMdkErlvsx1enOn0pLH7DSAs+FxiuDrisqo/xkz2sQhsVWtW/knMxJEztb5CvsDXll/lwDwMvk9gsP7R/fzsWRx1SsvnTA/t0cXq9pWzpdOZs12GAy6ZE2xBkztbFZAxayFLg8OcjmuaAeevOKMQudzTUe8REKnM7INEkGK0zVkZmYlzu6mbdGVnSqWg2ZAUoA42xbg4+yG4AqShgM56JSZPgWhHTz1yfJQ2lKzNBIr1/4vWGxA+++73I4AzcDsbZ4l/OyL29MD9PQlIflWJK2PI6sCJuV0xpx8oY1uBhHQFqzgySPTZa0oo5KhlibdHHOIK7jo5RU+HOYEXWwir/xJwCY7o2GTM0uRL6H1wJU3qkue/ecGjlun2AeawGx7k4Hr2JCvqfU3Hz4e+Q+8dAyleiJXbSvNDLdYpE56tsojJ20ANjv7rXMdByKH6qMv7x3nZTL2pYzoYeparlibZL7LydssMjETR9/Q65Jnx2vMN58PhDvUS+NeG1o/jy8YKDF9Q9uDwz71g66DF6uV1+cUaJHn+WSyn8N/wDfG0TS0WZ0MUp2hZOxmuTe0TRMmvVOvjJnqjwY/6Zg2sKvWIA/EBFq0Ny/WUW+2ssOGi+XA2PmijvrOaPFpXglA1QX9bf/jXAKcmW6XZjBkCFlsyh3duEB6UhauJTz9FazmtSBRtnrLO3YFJ0G+uA3jKJ7nq6fgp59+yvJzXoKS/hdrTasFVgusFlgtsFrgfFmgPI3zpdMfpk1+xOtHW0b1M/10lv54mxI4tNMFn4ZXbdXH+bZm4TzMjk0jfpfj4Cdp65wjO4/lfBTj0cSCA3K2UAdzn2BQhxDPdXr++vUEavd8fhBHxK0cTxyYIcNjt1H+UQFgz0DUpzNwhnkhjbN8NaPA7Bry5KIjJI/azSapoBN+946O6iPpCJG/DlIFL1gMHsrW+c3LNJqX/EjhyzF4KFZLRwnoVBKnvGZEwILvDoGmL9Lol3LIeXbIgyK3C3HqT7CNttA3U/viO/jjSMvPmgcuAzah800cuo95Du2+dk4gSHABnjNRgDiCZ/vCT17qKC2b05jwSFnetLPKPb7UVnp3MjNoIBl4BGY+kKId2QgNoQAll016BqrQWg8YKLu3Zthl+STSUxBbyuzUhbak76zaSkEqngNe7AyCQbbNMQAHUItcGtMmsEmlkMghA6K4ZU0yi7SE1/kwkBc4ZlMHh8+++JI3jN7KW2WDEvFwjmk5jvbLd5lEK4syXsUBkNlmMvaOW7XEvlZetbnbL6+MASoz3qXBlr6d196Wpmf45eMYl2ECNCrzXK02JGnLXZaBG3yp7xcQ1gAAQABJREFUr+ekR/nW+VzPxnrjRtzctGEcew7LxyXfCWI1lVu4bnaOG4HRI9pt6k7nguPOc9cNHeRXs4RkBn9hPcmY/CgbMLo9nqo9nrPagF1QPJjLNmBn0S5h2ib9OoDFqQrmU78gUD9Tt8Vy2WvUdAOClSYOHo6Qs1N4jCp5ffnlnVxDTnjRzJpWC6wWWC2wWmC1wHm0gF7bM5P6t7uO5Ri0cxDHDKfirONMF49p26HQONab2jkxkzy7ODE6dKS4CEGmHKbsBs/HjyEZu3Aj38eNzOZZEhQSxjOCJZ1LHcbLOI4uD73Om0Wfu3Z9usOLZb4aL5QRbw/n0E9TuLTNZabhHZbDiScfvwsnRgczL1zBSTshyHSLQwuCNo2DWo2c+Whbk/XHPD9z3w9rAxI3S0+dKQMQPHCKTzm06rJpfbgE15z4maEcdk7wh7NbgWu9BVVeCUT0RNUROloV507HWVjNQOhwUgnIGUMD3QTM8PaoHlk+Kj559T7Gkfu33/8+AYazbWIlKECmSTe/OsTjSAFZzzYO81EzNMx+iF0GnTwrYonOYZ0qGKJTgjSFmyyTokb4NW052KENiju2xjEjkXYR1iloIQg6A4uaBV3j9tH+6MA5SozLSZPJfgQw2rXJFJq+D0RdSx2LwYNXtckxwSahALf0i4ceR9ZFyxzTFOx3xE2Mjwnkv+BlRqojC5ub4IWRYd6kHiZb7dbJ3rdsfZrj+GALHrAZHz3kMNhFL3nUmPUVNink3OlvF9qOhIaMNwOgfkOoqDJyHPunRso3qLvMx+sTADJWAA2+vQy7Zul9mZTJ1QIiuMw7QaHnijzlTX2CNSUA8Hqwy5h2JrcC02gcPk/aVdvZ23Z45DzkaD6bMs7Yip/Kt4wllnFmnefduFwnGlXikZdqToMFkmeZXSd6b0FTP2ClY+MDyT9l25Lx6pVjAU+p8OVddXJaKmcNNq/DpgbAMc9w3759m+X7tXR+oKyH1QKrBVYLrBZYLXBuLFAexLlR5w9XxB91Uw7J8iPPT3gtF9vmbwCQX26R2dqZWWIVt4Lox5RLSDm+gE5UXLuiFXkmaGdhBmyYJEd9qtg1qmxnZ6nQrSxXRMkbXnGIQdFnV4c93ijq0W/lvfTCCzKabt2+xfcH+e4XTs7e3uVpn+0S7TQgZM8LWuAoA/4f+HZE+MX5hJezErqzD45ZKseMg86iMyHtdJZzvNHIwFiHzmeolO1bEqM7DqeBZb6VpqxAq390vnQmTdKGJzhlA+Eaho26OKuU5FNOm/1Fzw7P3iAOtNKT1oFFHXSw8S79xrksYyvWdpnKUa/gQxMf+623RCHwUwavOv79++9P73344fQqbxyNzjRF0fyzydOc7Rp8Ulruqj6QKKo9JcBeHkutItCrxN40RgSZLhDMj+Lgk3L4jDqrw69Gzszb6vAKRckMnrtkqLf/xBOHnfilKAeBA8+6zoq6TH1eAZPC/cyS3EwmP5fxOrYQG7sCs75w2EckMI7hRX3+pOU/JhgU9lnRXpi+vHvEctGbjH+Co9DorI+2yR/CkqF+pDIYGaVs6sSxK+7Tpj1w1DF24JjRXwKrq8CtescUtdSFm+eGy1ZJjmPHo9ce85JLbM7x7HN/vpzm4QPPHxMYyE1gh0zPZc8BJKR2h7f4ag+fafQmzsVL3LjBpm4+L5wzAZ47frz3Ed9T9A87OXbVw/NdEd7wkYe2elKyqmrVCSwACSStoGxdbVZW2uSqrs5t6qCxP5xxty3iaQ/t9pA3t9oqk/gZF6M8y0l1BetBpBxZ4Nc5GGhXNTXjSFuBCX7sJoaEtkWe7jyGmZlKqYuuVlS/1fnQGHUcZ2wKZetH0+HhwXSdTwHlbc3b6GtptcBqgdUCqwVWC5wLC/hr/Gyl/KiPJuVHnTwwnR8b67b4mQ/i7KRYF8dA50B34VQK74KWS2A9QhZyTlF8g2ITPwG1PBEqF9oIc8OxcYZB5+kyL4B5wGzXVT5ofQUH5M6dO9ls72WeJTw48BMQzhRAijeYpZoU8pwLOLbnIs7lLriXCRwN4nxu6oiXwvhMkjQ6nL2Vgwo9dtW/0oZqaNkZCgM8eVfQh1DKBl75qD146Y/RNnxA6nCaqZdPbCuzmIadnjVJdGc93CgF5i4zmOiYmT7tga4JHO1zNgk7cNThDCPohckZ0WmDvGxFdKACVfycGwHhpekr7PC7d9+ZPudO/yzbOolMZtKeGjtVAbD89gWijAcwuoV6saN+VkgB4rOVMQafTTk52kGDa6sOju1Ln8F62HpuqApKrILhP/AE2hb5yNd87ThKIKC3goQN0DmFpqEpzBw2vMDWDpFd9oiDjZ7VXOgTlENv/w3mm4BsAIaYliZ/u/gz+ukmbxc9kRl2lsMYRpEpODdzsHXT5khFjcHmz7gF5jgwmHCcqoOmARLctCPl1nLQikEbpZlrUC5jMXLqBoftUx/PFZc954aH9k+q88YW+FImX9Z0efdy8Dy/DBAzA65O5L1hoyzf7ms7TJ6TZQGtQy3/oyq4XgvkvTVeQvk1u8Gk5QQ7IufWzgwa4jF9aI3jn1TPAtd5n3MYe3me9k2giKmd2KFZ7uTZW7fBpakJyB9HBwUrsInb+SqrD3Bk1TUA4qYPvtV1zbDefs1xjOOisV5tIE2/P5pe51uEb3z/jVyHU7HuVgusFlgtsFpgtcA5s0B7HedMre+ujved3fLDXr/L40ddD2HjBCihq5Onru5EWyIN3Cps9ksafRT9hfYZMqvUTmy4L7E3POZcE4rW21xZmeKw3AOPc1T1Opy7uxXoXcJLvXblamYjbt3+Ii+T0Xn05TJx+FpIsUPvjVOjM+nySHmpjG8HvXfXz0X4yYoxQ4Ez2laLIwVNOU86RuXQhueYDfEFNnFuhy3Lbyp3KQ4YcmKCtIccx3KnUNBimipH8wXQAa62NIw2EAzGuR9yE6Q27bDVRWY8H6HuSZw4DeBMC5+bT7Bgd8dVLud6y04GK9Tt7E5vv/cBQeH7PEsIPTMuwqOxfa56HNV1E3mQt9HIjIGsM1qRf0UUQraTqs3bwEuZncekskXGaMpBGHTksdEFbJ/AI7an4aJgi7zdMvrYLjfSIM8x+hW4HHbPpWVKCwsg3ZwoaOtsAkflgrcWrr+5Nva2T4Mt7VCpgkHYxLbWqkcFe2qQTfzKLUzhkt9p+pyXePgx+uqfxhLfsQqC2yLpzMc+UGx4i1AaK73UoxZ1ggPMYFFazSxlDXULwl0OWvwMHgqDGS/PG0vUm69zhBYSmNU4rODO/qvzBL7iEbR5I8ZjbnoAs1wJGvK7nO8Gjbm5ogw2dWqZ6l46NpVHdEL23A9V9dR9+A6M6pmYJcMaEbHamQysTG3ZIEtZo1TbhODbmzt0YlmuuCjvSSksFwiLbA0fCJew03xic8/HUtyu28KvPtimaliNG/AH0WCRsn3w3HPPTT94883p2rWr6bNtLmtptcBqgdUCqwVWC5wPC9Sv8PnQ5Y+ixez4wK2dz/55j+OglDhw5Wz1D3vq8qM+XAfzX5PiBIATB8tj8JfehDxOb0E6tYNSJqTGNl8QMzqI5SQKnxPILv/c49t+Org+Q2j7v+DZwdtf3qKO5woJBv0ERQIf6gwUlJEXxfCcVc1eFG9x/cSEz9V99dVX4NyHjjr4JLjTYYW2TWP7/d5heAxlDc5EMmjTWc1RuZkNKCc5Zh4uV+wPfh1lUr1mObMEhUzz0JrqnkFZOou5U099ZiWlUyf1kBvlBJDQOuNpEk/P1eVyYV++ejSSl9+XM+bTTtb7ZkTfOHoLm/zm7benz+8QaPBZA5eTZkMxcZOQF0U9jGSwSmPSRwEFhZ2480ZNnFJOSW3lmWnZFHxhAhcwS21bwa1EdKBchIMPCGmQeGTMR7Z4JGXOdAtdW150kcfMJDw251ixmXkrYKhqjWMlfQZs9PA8pucxkkaLXYSlJv1alFE5XOhfg6kaA+pTzZHM/r53dDzd/PyL6R43NfocjV5SGKDB2P7dStD1GLRKHUuPMEVXP/bOuEJcLJAdKPByubIp/QwfcVMNslwiLzojlyDBZZYure5x67nRN0+iJ0QdKEUnmNXNKrSCLgEknGvpaFSIfAM6Zwhd7i2fPgesrHGiNtUqFYxuAlBfHeZrp8o/IaVdo67R5DOPTHWnLKz6uZCbDrUqSHOsBcljjz3OI/WOne3j9PY4VjtbZnE9tW8hgJOV/0hNp25npQVqGqDdvdqp5lYK4+ImTrYFgjo7O+sLuFy6/4tf/M30+uuvZWWF9ljTaoHVAqsFVgusFjiPFnjmniG8iPPu33K2L05VW1+PZPzK+7Pedf5U18+8mXLkJJlhFh5L1ooL9ZbnANyqGbbg8phPUE6FalWaMynKIsEgGQOdMHYtlFmOmf3DEcSLEjEvMPjiq9vM8PFNwj2WieIc7vo9QV4c4csjtMv8SQYl4Ixpr5p54FMR3Jn3xTS+AKGDSB1NAzGQEavgSnHchjNsW3XStaczFM5WlG3lroVoyGiaeJKBUjjQlM05xjEtGeJtEg4aBHnmKYETNdCJYVD6AAMm0JOAfN4OSb3tNZhz6auOms/x2MY4vzjuzib5sfJ4qRxs94MHtD3K1lI/1c6kILze//jG9Du+K/bc4U+mfRsgnv3RR3gkjbZSmbo0BbwExvBJyqHqbUtwx4EHPENXiFVV9hs2Ed+tAzUrjVbm/pBGR5vjEh6AwFEXvSl2UqzVwjMobYh8BbLJczNYKQtjm2HSSf+0JK9CyriAfgfbJABoWnkucCyZutpMxG7QSgX0M2D/+LPP8jIgvz8oX/s3Yxf88MCMHptvXwc27ejKEezT1/nsBFRZhhxq+RrAEOBtONWYazmgeN5GJjtnv7zR4jN9Ar1JkeXTPNOb84IBacDo+fjQGxOUDRY9ByWoWcO6waLO26eIfQp+ZvKPWSFwnPPfEeNZ6HkYoezDKzYWRi+Qz3Jsxk/OoygcxHk3d/EMMSNiepE9tqGUcwVwSwzWQrQUbnV7iQrPC2fXHQP0UlZayISxnW8yBoU6dPSv+BbDndBFgrtKMjdRJ9ZWCq/SrdFSH1sUZqhmQqTR8Hl8zPSovaBxMNoXPrt5uL83fe/1V6e3fvSD6TWWi/r8doL4Jf6WUmthtcBqgdUCqwVWC/x5LfDMBYQ6aDqX/uSXO1C/7DoR/vfvfP3oV5AVx+ob9kOcywWufKQPvwU8WdVogafrRrl9BNF0uHI0z2YwE2eRY981H2TBMFB0lkCHNB+mxyO5d49nB3l+yqBs7/Je3k7os4POHMRRxMmMHB1l6OSbWQdwdE6dVfGZvMwYwFtHJm8c5WggpazoqOPoM3+W00Z2/CtXfIHCjU+WtgkqNP7FSdKDHEm8BGocDVor6QC2PeolGQJ6psTnFeutoDqyur6REPlx5NDbt3ieHDnzh02J4cRy9lS+/WxYWgWggmWddGQOdqUBLHHSv2QZ4m9+97vpjVdemd546QUcVpFwYi8w8zPaHG1lHgke1QmAsIzNoaNVSaO8DO4KuUgHeVCjGDmNa3BmiiyOgNTfmSqXyIqylWb9qJBP6NHfAWESv2kkpp+ShInjhj1btRiz22ZH56Uk8hiMcpRHn4vhVjJw1otRwez7PrcGdQKAVm1QgmWtKWd0ZUdZ+99H3KeM/1s8Q5uYeqAbktmc9BFM7bUFq+LTbaxSENReWo8nMGDujcHjrpLBU84JbOnYNeBThnnh+ZM4MuEEzPPMt3tymyU2dilzZvDEz4Cs8+joId8QZRb/4uWx9HooLH9Tbi5oEWQ7rORtozzPPQ8TbHIuyhukRXeLVxasnCWvNbWV1sKeniJtsyvkMFS/DedSG67wF9q2NwAUT2ybFAr7QCjH2lI0tkuwWbUiFE3VDloL0Gkf+yWnE9zlP6cSUrJmYMlru85gdRoCcy1RSRP2NNd8c/NJfWnZ66++NP2Hn/5kepXrw5Urh1wL68ZYbrjZD2taLbBaYLXAaoHVAufQAs/UL5S/15d4Pftjv/Zt+PyCsxMx+a54MskGY5Nrx3VmEl6zq7JBPCVjU7HJ6XBkAxQHyKpmFQdEhxNHsj2TUa/zIvs8O2dgNojuHd2bHjozYBCILVwualCpg5hZBujaOekZOGfeZO93A30xjbguH61nCuv5Q53NeiunklwmV8/5qI5JPXSCncGrWQyBqepDFQa4quBkBtq0EUxp5TNIZwa21QC4mOq4MnQhfsgbQfPMUSioF8VDNp1gnbeBD6xmEXEWnYExwDXwBK7DqYPqTGN0EpjEsbPId9HpB8wSvv8R3yWMlzp0TYAzSFr5MCqHd8MEnBIwjgM5fT0ExbEHbhvd4grLW1z1Ec+jsOyqimpneRIALOHSDZINTwHAZWllDKaswS9g8jqxwbGKcuebxj4xys426GUh3GPrKr9OCx7JBhfa9NXQALoEPIOPeG5ZShmYzEnhbwZ6Zt383MQnt77gJUDe1BhLN60eSaruEUGRD7D5ezRgCZxjnWcVrOZco6brzBj05aYKPLKEc6iVGznoOYqznp7H0uRcso22xUTecW/qwM5xIp7DxXM1W8aG6IWbITLbIOTh47OESneMe8zsH8i92sAW5poiv9EgsPibNS5mX7Mv0m9OM0RtuKq75Is2VHvRz3sMFPraF3iiyQ25ucSVC5B4c1rQz7BkNny34adK8Cod6mitlndLq9k5k2s3/pgZwX/8+7+bfvzWD3l28Fquvxk/tK1vqkm/ptUCqwVWC6wWWC1w3ixQXsV50+o76qND9YM3vpcZLlnwk58/83GK4nzkZ1xQkqXeBujJh1PORehiwc3sxky8LSbgx+5Az8hkhhezTYbzCLyCmGrDkiSOHPVN49LJo3v3Ut5zqaizELTZ18971LEpB4UgZsiLg03ebwa6XFIb+gySm8GddHFgmTVsHXWAM1sInXwsu9Wy014qavBVrnXsBJ/Isqk2grrMoOgcs0lvb4EWHYMCYtssQWZoqw02UgoD0zRFXImSKldO+WaIZ1kask6g0YPT2bYdmbmIUkVtdlMkR7CnlfO8IDb1ZSXvffzRdOcub2DFNSzNi0JdMupoTxSzQQY6Bgdx4im3otZZqEZHp0iWVkc98KG/00jiKia8G6fkBlcMZGV2ED1tY2SFj5WWgVcUUWXwC486UxoAz5Y/K0ud/Ew5gIP9iDiKxjpnht3kP9TaklXUm306bhRVSzr0yTJPdJWF5617VxBWH2hHCtYPHOshpP7idPvOvekTl4s6XqmHY+knhvjI7HFmVYSEnqYoY/Bs9UUBlLEcGPRKM1mWl8/qZTZOW861YpAk9pA/z7vCqHHryDE5ioBLD768nH03L7WB4PzWUAB+Q9SbO9aJYbtMHvscywwkOI51z8N5KCDJmyEZ02OMOsvtCgNxckNBfsUyfJ+2K/2fhnG6rhl7LKtkP3Rve9lmbaBttfH4h6Ykpslh9bgGjpzq98J+DCPEG73qmth6beDbObSw8+p/aMEpwA2IKweH09/96j9Of/+ffjW98srLuXb2dUse5nd5ztsX96xptcBqgdUCqwVWC5xHC+C9PTvJH97nnr+OE3R/uviQt0rmZ5sfem4hlzO4cBD4cS/ncNP+pUugE2G5f9iDm1mgUSN9SGsGqnyMghRHqHUgUlGOXd3JLimNGWcIPF1Da/LU2rjljU+k3wYLsJugmLOPexyXyhkFndnjIz4gz3cDnd3bZ6uAyOVnrnGTATTgKc0AzGQrfInMfb7XZgB40RlWPMPWy3a7LNO3c+psmqTNDOHgIcwX2uyzRFWcvFkxDp3ycF6ZrdQZUs9YzfbaJBsIJP9dh+TMlKS9pW/6AD4JyMSDzrkMLZ9+iY1qmZh6VXsrCMvzSMwUJdl2aP3Wm29f1VbaN1pxjEMtYtRCAdqildOFkYUMqn0G7N2PPpo+vHljun7lzehhm4MNnzTIg/79sFlecWqdbZaJ8ARmIpoop80DvrBN6vQlExRCXEoD1nbis5kMKJzxlJd5ZWhoq8WLDBkFIJDNA+V8YN6yPNmkb74GeeqsvOVYFM8ljpo3wRzHXk9pnSk8lDn0VrSJ+iy1U17riF51vmWURJWgw8Oj50HYpmA1GXSP+tjmPnxuMjv4KW8Y9eaIgWWaqA7KG1szkU1pWftIsX2mqMVO5vy7DD31yotc8djCGt72v3I4J/rcstIucFzlhoe4bKHBbvfpq12uU0lDVXXMNzUBJuSDNucD/H2WcI+uyIw455NLuw0O/XvId0QzpMA3hDI5Y/kgy789B+tS700b9fRGSJI2kcPQE4ZcO1GG8yi6FtbX76Gv5/3gBk/Dc1P2thtmF+gj5Xgel9bKtf+sq/6RRgtL53cRVfOCF7eLbMLY0kUWo2Dh+pkJKf03pch+FKvLgaePkCWiuqQswROSODILP8aU48pgXFsL3KH8Fm8R/fnP/sO0x7ODZ/ET5nditf2aVgusFlgtsFpgtcB5tEB5CedRs++oU+5w82Ovm9DLA+MlxPHZMM0P/KaY3GlYnAnohusEzmmMuBVxLCJji1+8jTgkSyo1CxucBOGRMeiWeIK6Xv8lDlMggwZHJAEMfOKA6m/jYLo06XD/Ck4kjh/4fopCv9jgJF4MjqjPJZXTifPJkssjZgd1VvZxaOR5Au9aCodTaPAX57G00fHNDIYza+CZlOX3C3MHHHpFGSvq3KYeJ1On1mCtHDJo4dObOOLaDgPjHRzH0EIeDhgpwW1sVnjSLJ0v8aNPiYROPPcEiKmTR8l0Bs2X8Zwcf6XkbI6VPNNlYCOEsk5rOcUeN3AeJpxufP5Z3jj6fZ4Vun7AzQec1+geam0i9aJ3Rxa2EZniXN2VEpOnnQmwYl4I4n0in+cUo1CpAi4ZFQx+kRo0OquibXyjbOpkm0HAcTjRgkbTk4289Cf8xNV5rcbDQx3sUOrAiW3oUyoo2yATRx13k7ZqnVItrXB2qQuQYvELTRCoplC1xaL5Oy7LnvSLokEyCAiLUO3w/OvJ9NFnn0937h2Fh80JL3Vky5gqYaf22gpQxkvxFkFaR5B0VOVciskRqp69QRjM5XgEkJR+0Bbq4CF/UtSNmIfcRPGmQWS5Mw19I4C8cqT0HJLegNDgkHlJdKpz6z7P/nrSXcgza2W7BC5QOyNoQHLM6oHiYfe6TByBkclOHcHN7CNZ4/yAIvvrd1KHlVwoLNnmpoytGDKU4+YZV/k+CB/5tgG2Vk+3kMNY3o8MLj0vegwNZe2Z1LOPlAEPMHTwGmIU9fVJ5KEpWWexvTFhfOrbnX1h196lepPxWf0vf21usJhnq79e4IqxWmC1wGqB1QKrBf7dLeAv8jOTdHJ+9NZbBCf75TXwo12zEFUsR2GuqnbPjoeextiGRXTz3ITHKRnwKnfBI4I6DQdEXyV+fMP7qBKkHOSLEyfvfEh51MjNjdrURZ5lidyNYIqSAP4N4AgCyO/j+LlcTOcwASNKGJwIU9bR8REzE+ACM2DyuUHZ7ufD9dwfEF+u6kbQl++Bwb+fWcoLXAgoM+OhHhA7A+jbTHV8dFSdoWvnzZYkqES2QHURL8/2UVZOAkHt4F+MrjMH42FWHa0Em9BGWSpiH3UsgIZQ6Ti/ycLPIE9al8z5nI+ocvbZSHW4z9tEgxtuclROBVHRK/KjSQLy5OAhzjGB9O/ff59ZwpsE0CDiKOo21owkFkwZluBvUsmYy0YZbib0nBO6oSAwhaWS3ZLR4CNNb0Gj7DcpuSkQdpKYkUfjSZoknArHSjbabQBZyOADbz20z8g/Ake7GszPPAfHma+0ylM+uOElD/5rG/mWO+CZCYcoRXcmUdkSEJIJW+Vr39iYSuzv+Lxz94jloremI2a7qy9KfNQobrWHiWq5sc/fsjpQZYgjxrxTF2kdpCqo3Drm5kYxLFjgIUxZPM8daYQ6/lq2HOTp5l9mxlwqiY09R02yluUJs/nODBZ98fK6Z9mWiC+e8oQl2FQucF82k4AGftYZMJaGUFIf+dID9zz9tqm0eRqVPLe3at2giciSaxuCmjbVNTLKDnrbWuVqaxoNSa5bXSHbGMNMpcjbEto128fwweg5tbUl+hzyttAXr12ZXrl+dXr1uavTy9cPpheuHEz37nyRpfrSnJVcfu8LvqrPzsJYYasFVgusFlgtsFrgz2sBvZJnJukEHebNbgQ2/IAbNpR7MfsOc1uXcH/Gl5tIzsg1MFk9Ax0EfbqvSblzLU68micgy8tNIfzLVh1aL6mW+eAJFB26sNaBNLgLpUs6XcpECbhHg8BdXybD5izfXWYI7hPMJRiE7g7fKzxm+eO1q1eYHSSIHkn+BlHlpCIQZ1t+0RO6zC4qO3pcyFLRPZaa5ntyOvkkg0DI4kjFEYaN+qhLO6NxkGJWZKUNERX6MjNQAzuc1DADxz5WjkEZUHSxriyFOpkxwX0b3JAPngVx5akuzjaeEDQ40zIMSb0YFqNQ5W2fMP8CLzku63X27cbnt6bfvPPu9AWByINH6kM9fSBPdTHFhuqYJJ1bn3bAh+4DQYI52zTVALS3TvyoQT7lga+xU0d7WQ5LVFjkyjKfenF6ozr41o+NvklwGP2qrWHSfJkWcQZqh610Urb8tDH4Q5WZJhl2skqAYWbond4YCLNO8Acl6LGDY6yCgRqLNQtrPyYojTwJ6E9mjD6/fXe6dftOZrcG59Q5dnNu0C7HRkkoDPss451xkfORvuqxoK7Cys7MnjGWaobINkMvK5I4GUtVTDnjBb1UUVTb4ZgLL2EAM/7JO8PumFRecNHFpZ6RLe3QTRz5Wc45hD4Ghy7pjrxQt0T58uyafcU4v8+LpqyOPSOzbsrY1vDn6LmmTHoh7QTtG6d0V+Q/mUTNRBlmOwOx5WoFNgiq7ZtxMMDA6xolkjqre6dMRMtBI5PKqrYUhqVo4E/cqWhQ5WuGU4qxc5VVBddYs3t9f5fj5ekK17wrlHfog3tffXkmO3Xw2uoy/jWtFlgtsFpgtcBqgfNqgSf/Np9Xjb9Wr3rdu07NVtIRWGztPgD1934be3Ya4kIMNlKIWYEQ7kgVR20OwwGZ8/CRwtTHKtU+GlLRjkoFdo0txTZVglQdHx1CDjo+eHjsqq09w5IXTuC8u0TJ2TCXk93lRSi+AMG3juKbTre/ussnFL6Ko+JyMmcD4xDC74S8S0mLtXfHSzOds7ykYjhfOjt5AQ13vyvQMzBwuRm8zOjkgqMj6/NMOr5x6GA8B0bYqJx720HiEEc4fWXzUBagbTLoECvPOuLoxhGkHGdaOcjgAI40msnQjSQvZIpfjjSzgzjI1oUtx7IglIu2xTGWPH+NZJvIX9rhjZYn02/fe3f6kGWKfKgCoBuVKokizlJtpXiq1JewTVXKErF1ncUUkrGR4VlEIg1Eq22E9R4TWAAzEDbAsz4mJGPbhj2BjiSCGykzktDZT80zYlpWtecCfC8wjhIIzrhDBzugk/llWXKr3SKS3UL8XFdiigt9Zn/OfRAoDMZYsGttszO2N2/dygt/0vsRraAhgrYnqJRTiFJFJWXHhht/SeOQfLHAHDV+t3DAyxh0vOcZzCJvqZ4fmVmHNmagXS6bNpWIqnfcyr/woaYyL5VJXxnP2W+OXb9HWLOBBqeeeyIfs+Rbvex6g8ClyXMtAMsA0/5HCkOlRrbnjTI3iTFrm2bAdt0M/tpM0WW/YZZ7BiEd8quqzunkIcinKKITBY/CsEMCw6FZaQ8cs3jt6FT9SrnINm2zjxvp1LH6r0LGuWrQu2pjh/4i7J6u7V9KMHiF8/4Q2x/scrwMjKWgV/Z2p+O7d9AnI29mY0b7en31pTLbtt5CWwurBVYLrBZYLbBa4M9qgTGN8GfV4Y8mXKfqLV75/ctf/s30z//8P/Qk4mNGAD/uI0xJMd/to5bf/ko4U/rD7TjkbYNxNuChUxKsgT8cjPgrS+d1sBI3zkkzoyzuzJysZLoPblZFD/DjHqUAkFSuDICZl22CivKj8cyWsuSns3jR51lwWgwGncVyVtCP1GuLyzgmOiVfERzeufMVDuXl6WDvAIfToMxlofWh7BOcax1kndl2VB/g8PpimXyqQcdHmc74KQcHydBLp603J4yipI40eKbli2hqtqVeblGyZElrbSe7zAqmjQLkgRxyuLPRS+9SRzlLbZ25Sl0ty7MtNSsqvwry4jjSQOWaz4s5rJN9dHXHDAwwkwFrZkXJqwumHF0wCLCNL+D5/PaX0we8YOaNl19iWZg6Mltjb4SnViFfLAesCxzNxqtVIJu4yQwcYWYT+XO0Lg/OmW1c4c3LPCm8squybbJz3ew7q8ybRnvDo3FGVeFQUMeoFGWg7faFkUzCqhiHKaABkzb46kuSBNCsj3c20gmjUjRoM7TpY8drnQOOe/MVxIQ9ImSl3e4c3U1A6Hc0M47SFhD8lx+b42yTzMNPfRos7ii0824A5T8nAj3LfQC2DYHNcElyjTHf1ukzeLXMGhLOjQQJzNBlGSZjz+dvL3WZflXNGnPVVoV5DvkCFgPABELwr3Ooxr6flNlhzGu6aIxc9YiuQrQ57ahln4xpgproAZ8s1eZ8LFOAg5w6V+WldP/QnT7zxk4aLyBQj5Xm8yR1DQULPVJnw0yDeR00tFo602k9uHb0PJyoQS63o9ILYpScCrgfjg9LYuIhh+sNL9Nxybq4SUPu5voNLu2OLLiqhyizntYMmgy9YLIb4/AKQfcLV/an64d70z5yuAzU9RW4/XCJZwiP6c8Txp3XlKxIAKeTn0Hy+UGvzWtaLbBaYLXAaoHVAufVAv7sPTPJH3a///Tmm9/Lj/5Ww6grV6egOvk6CuUsVGDQfuEWHYVtuK5Hux9ds4QNgq4azPRJNiBzo7QBDswnHXRVZ3c1Ob+hp/NnJxq8uDwsswaoY0BoAJbZA3AuEawYDDlTWM8NPsxLEXz2D7cqfO4TOIpvawwEk0kBAHrmNfDw0klTbYM05eh4ViBYcIMpZybk0QGlzrgBXAI9aOOEyYdAUyrtY6fV7AV5E2WV2Mx6BDNVBn0V1KGPMygkWfR31+zryIdHnFFYRSYOpy/VcSundBDKIOGFXJQaAi0TndLmeNHUU6VTavv9vMHv3nuPb9/dIohJT1SlSJ3SDup6ECyO5aiLKC3JGaQO2k7zkD8OfBQIcukafHXLhi2kd4tRRRy6SBpvOsTsoMGOtVmstnVtO/NzufkEIM9FGwMb+pi3zW4ZRwiOwThKUsbzDoYDtWYzhZEcJ/MYAeZYkk9MJrvYUkzGnAeOzs5+9uWd6QZ9cIT9cnNCc2gGVXIA9CAIRdFVsAGO1YJMZCLLneoir8b3WBaaPt6MQ/Vxy+wjNI4b+To+e8ao6tS9bjg4A98Cba94hRtlofV8eMTMEi9qov0POM8d12hapiO4oDrjX/2yITf2bsZgAwl9nYPVlpybXAfka8p+7GxV2NBnvbSbC2Twgpu20z4Ky21GaHr5bcg21UPHOu9KvjZuXjPiqCorV987JnK+Y6vYdtgoNBpjK1UflDqLOvi2vbrv07/QDpE5Cw3dvGF4wI21l64dTK8+f2166erh9DzPCz7PM4Qusb9yeJDnrvf2WTp6cICZWD3B89nNV3Xk7XLRw8PDGscC17RaYLXAaoHVAqsFzqEFnqkZQu1rgPKjH/1wevGlF6ePb3wSk/ePfX71F/6BP97+aCcB1wGxHJRTTkYcvfIwYAPNgk8x+Jr94Ld0GJpJdDgl7zFuOrR6t42Hf4gvlbfd6VcbgO2ydCnOKzg+W6SOOlHOCvjcncHg7du3s1xyb5c73swO2nw/7u7LZgzqYECgU46r7Yx19F91xNxG8GVw6acbXIIaLBgFF5lxYnU4H1Sg+MgPN8M3CPDI5wDQPQ7ooqGhZ6fDF/uSr+4ppzD9RdvE61kZHcX8Cacivn9sxBtWXdYY7XTamfGQKXW+7n8ryTBpzqRUMy3ABMtzVGv3ekMmTQT+wUcfTm+//970vRdf4OU62A/9428nADA8kbA3+MyNG3xtb9frdEfOEGajhNn3Q38ywCxbx8GsKXkKGiFTpxztU+tVWgQVC75HtiQAZpUjnvXBYZdjkGoX25qFoMkX1SFIp1nPZtNM8rZgQDuCkcDdZfp1MMMWj3iu1T6VQh4GXP0cavrXCE7e6sbH6I95Nu+jm58yW1vPDyqy1IaL+looAJlKSnMT1+Myydpz0r+QsvNmSJ4/5QbKhQfOcPnGV6hoS+iDU2MwulGZ8QpKLRuFF+eZqZZFJitW4YUJvJDbb8OtQI5xa0BoP1Inz4vDlp7bai9exqr6RJuyncW0Ah2d25LHBc5tz1v5uJS8NGoqdPN8Uhe2CnTVaZgv/De4SosNRtAorwwfCajI3l3SyEQpd6VdquwjZ0ulBi1L3/uNusEDG70ePqprGicZgqTRVnJoIcBUPryEWY6E7OqzGGS1gzqy2Tf2AQYJS/kZDO7zO/I8weDzzA5eIei7ynODPiftTaDYTPbygNTyRW4wHbMEf//w6qyO/XJAsHiZwD7yosW6Wy2wWmC1wGqB1QLnzwLxuc6fWt9dow4I3/j+9+LQNKf2ff0dN/lbbvLHXJ/BICYv+xiOgjCdJrdOca4sQFsOY9c0TqgauDnKg63cyw3YXPRIPQXJW0ErKStzTmSDikPj6+b3cOwuO0MHwmWeUTEoNHBTNz9kfazDB4E2OebthLf4PtsRMJei7XHn2oAJ9ASDOtXS+RIalVKvtFGHCSQDTANHHTPh0ubZGB3kxke+TqSOnUdV91lEHenMksSpq9bYjjQmRRBBLnsXuGdJVMRZDTHEVxYNSL77Roeug8jUiwueDtnMRx5hIqn41bfRo7grgP+hi6UMDhVNDbXWaQ/K0tMeF8B9xbLcf3377elmvn9n3aKnFRq58oY8eXvMTLeLrIG2nZFkncglOyCFGsj6rFpmEYF2g8IqjAUWa4/VOFUGxm7GFxfeHrRl4BZMHBuP9hVIHJksUqMv9ZSu+VmfbSCmOeSbd9OpY+spCXK82aA8Rk0EaiX70pSARTj/5cxfmG6x/PmDGzemu0f3CsdqdA/bQgWZKsWHCUCS+x5zAbhTv9bR7gh9jxUAlNPEDTPwh6xSSqYDr3jl/AC/Zz5bluZt+fU2ZAAjwTE3c3Lewa/Ga48Pam1f2lgwbZbnBdG9x3edl5yLKJzzER0MJi/zJto9zl9vkmgP6fKtUAdKGSj6J5BVH9SKWajOzCI4Bk6zqdKOoThAZZQ8YANJtkpLUdTQ27PCxjHouXrQ/eoiVSXtoQ0eMv4f8HbgBLfDzmIsLVflDaQ4jH2DpWWEZZ7ZvOce59hFjvsEzS9cPcgbRa8d1DOCh1wvD3gT8wHLPw9Yeu93V/ew+S642o/JxOk+AaGrNjq5WuPKlSu5/jZsPa4WWC2wWmC1wGqB82gBfsaeveQb3b7/xve4W3sQ/759AN2GOCYB4DglSNg4m9Ytt6VlQjfqNZqbjow3qr3HrGNhGLCkP51f8pvz+iJuAKLWXDEyAtl0mWyMxV3uVO9z1zmfl8CJyVJRnWiY6E6Je8JLU5wJ8zk3nSeDQYNCZ/+0j0tFfUHMPWYGXfZo0JVnnhQDvj6S7XM2QzyXuSlbJ034RTwgv8OVN37GAdTxhJj/zGYYWMlDvuCnjXHgyoGVVflhuHpmTMErx48sxXIs5at/KHtfTuMWJ5J6k45xZJI3+AQteqmAb2AszcNQ9JpcUySbYyDMLVLW6ZRBWZr+jIwAQiBZ9ITYIFTeforhg49vTL99993pHoF1jYIgpv1RKD1M29P8khFB3TBEqE82SUMkcJEdxS2Y+s4bNWUkyDNCo6sjNIxL+VNywm2zq45a8ERh+ZvkmW2hV9UUz9a52xE6CqJnY5eOHUSDbbNoRF9ak2dGkfUQfIdS+htbaT5ZKdBxfp/nXz/69PPpY17s4zOu9pvnYlRgFxFDTlSQVJ5Lm4WbFa0BR2iKl9ZDUg21jSlAMKAqQR4cg6VTcSqhCWRL4eDEJNqY5E2SHkMGI0KVafLtwScs34Yp9wFYBkvZMZ5zLHjFVHN6fnGmJPDwWbUOCOXjcOhvFaql+lQAWG8FRiBt8/yilSLDx1bYHktu2dEJ2gSwipcdStUCBVyrCMgOXoVbfOCJnjV8OKr4GYkaiN0PqjY8uDOFN0Q4r9VRhbRh3wDofrW7ZvzkhvLqPq4XsbGz0X6ShjFz6cJDXhRzcXqZT0q8dO1wusobRJ0dPGTzWue3Sy9zPa2AWvuxDJ837Gpf30L6kGvpyf1aNqo9XSrqW68zBpCwptUCqwVWC6wWWC1wXi0w3Jzzqt530+uQQPDv//PfTT/64Q9qtqHZjCBFb2a4G3FQuvqs42nHpRwVMGEQn2Z4HcVPcPMuhyRwHZwlMuQ6MzqH5dQUnXmdynkDoWYGxKvkSyt0Cg10vFMuf+/ux93BMZKTswoGRr5cxorbd75kuei96OtMgsGgzp88jsdzL/IwuSS0gyvL7TzNGmg72mIg6AsT8rILlBDmX9qEzOHzBhbBYdatiLbglkOc5W7U62ArO7Oc6BebyXvoobN3kZmN1rXp+ihigll54ZBpB7eyPUDrxx+lSiqKTFP00dlEju2IUXOgLM5Qv/jZVgNd9MQWd5gl/C2zhJ9+ySwBTmJ9jFtihUIorbLC2BaZBsPUkxf8xDOSCj/voINM22LTUntmE/rsZNQyOktZca1LxiPl1mEuA2qbaAvb3aw8Kru3hsvmdKL/6uTyyGZ5TuaX5VHsiAM97T/HaoKuERQMa6Fx0Xq868fob37GGMfusOmQ0PPQtlWgwJii3ULEKT5VTom6Cz1gqTfNl4qoutFXqqIXVjJ6fGnaJNqb4QLAoMGt+r1M4Rh386UteZbVgAbCalXhFL5BJ8tlPZ8JCG2DZWeenJ3PrCA2Ei6/hIXYrQOQBErwveRKAgIak13hNUWdc/7SnbbIa0oCwwwxx7bn5ngZjjzbBBy1u1tp7LHS3H00xmvBMmUGVOGDLrmBsoEOPgC8ERO7kq/ZU+Wy0c42s6HrMpUdaA3tY7eoWuTVQYHQGujvagdKe7T7+uH+9NLz16cXfUaQG26HBIDOBvryrTwrjU6ZDcTmHutGFVaHnzfl1OeE77rKW9teu3Yt/aS0Na0WWC2wWmC1wGqB82wB3IFnL+lIvMQzhP/5P/3tdJUlO/GCbGYcAY+VWe7PskI7eqfrdHaWf6frzyzHESmJcU8WPor45UC1gmdyAIgbpvOHg6jHKbaOisurMtMQB7O+S9ZO4R2+j3WPYFBafSRfFqGzomOlMyowb8CjTqfTIDEOlRTUOUvhM0w6jeWMlSOl3JqNKDzQwXBjr2LQWu6k82USVrly2QUETyBbyy5nsN16Z/5YKgbKjkvdaINO4kmcVhxE6PJJC+plFv4cfYlNXvMfAezqnwN/6NeuZcusJWnWFaMKxsUKIDTmGyI0uOii/A8/+XR67+NPmLXaOMzNq2WMxiuAVI1OXRA9HaM9x3AXaQjhmHELjoHhiBwxwSIN2oy1BsNH3olwSl5qZuXNjGR25te4HIMrjrC+ZACsxhU8ullPYjwGzyN26fMtdeHljhS9yIs+gr5NyCYOFQ33CD/HusGIr1JigV+Wi35882a+pynLKAvp0KRABR1V9iB/aYo7NkU1wQBJGBwzDfMYnQVWUpe6TtDndIasxNccynE2u1/05Dlq3jZ4w6bsF4rgC88MoCDkVEAnHiN/8PRYs+S+0dbAEzzPZTblGzAmWOGoqp7P3rQxgDTg8xz3vHDMJaD0+lFNiV7qIL22d/xXKoze59pHIV0OgscKGOkbaDxDbH3qi0isIUdAEcs9vQnijGZt+hxYjtgktOokV/5iZwLWQGoXOHUm9/M5lULA4VK5aDAdEPC9wO/Di7ws5uVrfmj+yvQczwwe8rzgIUtD3fZ51voybxL1Wlf9rDY+k+0yUWyF7r1xgeS5zFq27LODV3gBjTZe02qB1QKrBVYLrBY47xaoX9nzruV30M9lka+8+sr0wnPX4yTNDkd8ER14mh6PpZyDuX4hS1hvehntW3j8rmkOMmU8GEU2u1InpW32OmY4fAkzqM7dfI8QuOH9hFmCPBzAdhKPjo6no3suYSrXSdkuN9X9cuZMfIMr+YmTWQZwWsf7LDE9ZgmUcN/kmWcBUTqvvMd+luvFFuWAPWTplTzFV4dl+3RGm69WjQM5WjnD0wx2Nmk2DwV0audPB+sCAVGCN/Xiz/b4pxMYJxJHsoI5uZRu1oWGctzV2FTnEpyNMHDgiUhl+hc+tiUBSSpoo1JLtu9IUQMD1TvMDvzLv/02b7s0WDF0NlkfEZIvU8rWFA5olYZuMQR6oCRwK9kaB47L2a9UFCuy4o1TO4EgpPLIFI55NneRM8rYEoajMBh5jizOE4xRNCE3T0ZZdmZkWtEJWHeaoLSBo3gmy4F5FDZ4cJRt7QqcupHF0hQNI/gOJMHNjU8/nT794hY3BxRXvFtEuGag2ZOnE5AI6vNpgTHgpZNqFmB5rhVxnX+OuQpSGgql7CBzpBjA9Q0LA0IDLs+Rwh6tAzf8bb9jkD+XND5wrMVWG2N67fIlN30O5/z0nIOH59LFHc7p6FS05nPjCIkZz4O38vb4hmjrVteVCrr9fMJObjzUeaUFMgdpw+ofyCYNE1EF/tC5zszSqc7xCu1QJ0n5g1303nBrGuwGsue79FKbci6jTZbRtuAl8cjXNQBbihOdyGec0yeMuSv8RhgAvuDbQ1lVcoVZ1AOerzxgqegBy0Rdlr8PzCWiu2NW8BLjKas00Eb90yL7Dpm+jMabda6eeO655/JCmTPUWkGrBVYLrBZYLbBa4NxZYPw0nzu9/mCFdMJeIyD82c9+mmU/uhLlThRrnQRfFd6fnqjjKbE6EnpCbkm6aZs/QZY2qaUsYZvazsV/1nUCXQfK46xcgFYUMOLVg8QnBnlWBYeEfHhQjvPFc1Q6PAnC9ApJzv7lpTJxPGVXS810sHy7oM6kNvJbWsqO0wRdHC+YG9zNwSBtrCWqzDYM59JjOWQ824Qd843BzDzIq+xSmqArDmxmPmKrmploJzTKZqf+w2GLMWw/+qCLDrd2yuxmAtgKRnX4dFyd1ZDWF9goUwfSRulMG2zYR/m2YhRyyFegJ30IsgPLMnbS9JGoCiNYsUqoae7zqGhF0Tlr+dt33pn+5Xe/n+7RJ3xxDrcV3aiuJXMlO0zkJTz9hUzaUIYTCIZKmEqwmbENeOiEmYAJHlUwBZ1NHtnEATf1HqWbka0cCdhsE0CiRA8z0gy+ZsMC+Fw/YBxSJ27jCzOFX2VLR5ksk/yqrwtqSEHqdgS9+sng5EuW6b534+Ppzld3QSql+oU+pzkXP1WoP/t67sfIUM7AGsc0TTAZtaqZ6ZZT+L1k2rHkNwbr5osMKrB7kGfUHjLLVMsM7/u8Gl1NOJPxmQ/QD7Hq4ziuQGa0AL6WPddyA4fzwYDD54FzDXPckMCAVvlIzvlWcFubJeXA5SjUwMWzwuQ1wDeP+rZcIbbV8y2fnfDc4s8kX21gu/K2TmEDbp35IA2+HgY2FXLBfvZt92+xnelO9UbpIVVoQB5jQD7yc/h73ZmvGbMS6jH0IVupuIvi0OdRwTzzt0+g5zX1MjbdZ3n9AbOl+9ww2+cTPQaC+wSMl8m7HDTfdsVOXsf4D6OMjxiUIscE1FwDfd7w+vXr2HCdHeweWI+rBVYLrBZYLXC+LVBew/nW8Ttpp2PzHLODv/rb/zj99Kdv5YUA5ZboLPCHd4BvsHBiUppl+ZtvKvfDXOGLtdm2acRaUlT51D5eBO5YOzh4F3HC4vwgLYJ1JuNOx4Gxk1gkGeel5510SmyPWs3BkM6kfziPJycV9CXIgV7nREcyzw3iTOpo+WxTxCkMPnILR8q+pdSXWej9GLQY8Imlc7mLo5QgLLLLluqwSeXIyzvPJdEAHaayXMFsc6fWoe/4p2/kF710InXGcVKZhTNvvW88VSFnWwqmgwhHcdFRgZkNsIXA42jPKhas5S+POr3aJmno2DbEtNFJG3VSduQLIBC9Q7D9P//td8wS3uZlMwaD9lDxRLPoN4xR+sEqwaB6ytdATvHxOmV6KgUtiqRC2tALd6vG5lD22+gaA8WmA89DESVX8tVBpUad5KEJwMIoexSHnYYXJ2VhJPOBjbqGlSe/wIXvsHMJLTmx8cyvxlie2aTpspXN57e/zPODx8yKd5Kknt9sfWU/M2q0x4+nUSCvfrf/ZFqBWN/I2PR59ak4WY6JYo557ZeAhQAB4owRm3nCudfJ8Xnfc4xkH8bM7IQn0CEfOZS9geP5aDBmnfA812aYJmMUUL561Hio2TXP+8w8UlHLHkWo86nlVKCmFg47g8F6PrjaKMeQ5Fh55GWQOq5nMJnup2EzjSCC8uZxJgF0KRdvOaTtZpapUIuF+OjW52ZmUSXSXpG7JNzO245s8oDE+WWXhR54LQN0mWAwgSCzggeZFRwzgwR2uwSJBoQ1y1unpeMp7bZd6kjfqZtLRy1f4WUyfox+TasFVgusFlgtsFrgL8UCup7PbNJh+vGPfzT9L//1n6bvfe+1ciz48Y6DlF90m64TUw5JAhEdDAEkf+uHuzGXk/lDd8iW99bWhUBLbinm4kNnB3E2dGbYLPsKg+inZ5w2uDSqZtju86ZLHciafSin0jv+EuS5QekJsEwJlAxCSDqycrvPrIbLRTvl+T2cUGf6fCHNRezqxFk+NK/MYS9tpS6bZDtBHLa2Zjb7BikkqqAujdAOsWiy16WcZxopK8uyNSI4QxKYgSNL5rL0E3iWyCI0vMFICv7ISl7Z2mODdrAFKNtNJK2jQ+6fknX+qhfKbmrg0tEPbn4y/fr3b/PGUR13Q3gQO2kAizZYvjHIgIkTVMvJCNnOq3v0r36NgxzcWcmica/i2rSDsD4Kz7ZBDTq4sXsUG3XSG3B5HCKKXyiKd9enO9h1G5d8Qjv06bZHRDP1SLLZbQPp1ZNjBwCNfeS3B3lm8zYfpC/7lr1CngBw8JMnaWnOgpzaF/kpIEXYWOWbds35ghGXUho4tU4dcEhccjLqsQ2mw24ZMVSokbP49pk8q0ywRyA3J/DkJ16eM0yFM9x1PnsuOjuY5wQz/pVpoGRXeDtIhaGXv0CSvC4R3Phcm8Gi0v1+aOvj9dBgsXma97MKXiMca3IpfR0Ddd7NARY11f6SJ8+MT/D8m1Om5to+M/SpGflm1g2ds1Jg1gQZtKmuEbZUvmhYzZ15borm7AV0Z97eF8Y8xzLRXQJBP9tTs4HYx2Bwb5+211uYDQa9iZbrjrqMftN+zp3WcOeayIXAa4H2882iL7z4YoLqWZE1s1pgtcBqgdUCqwXOuQX8TXtmk06CL1P4yU/emn7xy19MV69e5WeclB93HYQUctRVKI+inIdNPkhj186PtEW9rP1GeR3c3ppAZyb8BleKyeHgeTfbb13xvfM4HRtMHbVy1mRjW+O7+6yfzxEqI45i3fH3ub8KigguWRal01eBRcnq9sThJCDM8tPwrO+hiessnY6isyTx6d0N2fp+0kTugNms3sJPHjp3AHUVVdFU+eE+ArSNVgrXWU2ZfWYDoRcQvnphYIV+4GYWBJR6eyOOMHf34yRbr2wpBm8L3ethIg7A4hpEd9l0/pOsxK5RoGoGqXqAi43u4bz/f7/+TT6F8OgiHwG3F42gl0m2gDKbJNyyqQ1mWeWiYIRSQIKd3MiCt+qtAihtG5csPSOAf47Ce0uduwEL4Whn6mQkjRtBS+hSUTQzzoCJvpQhOKq7I0nfgWXr1PDQZlfqZw9JCG2TNqzyI+x/i7e5vv/Rx3x78Jgme5aYalwle8ZO00Yf61I4A+kJoMymYQPHUzauRIwAADRRSURBVM1SGZDYpJIsmfm0VHWbD/YwUHPMW9dBZAcXNqrfIiqJ48Fl0HIQHpVpnzdfDAqV0eexoj3fEiyN88q6eQghN7OW0BncOHPl+A49jDO8QM4ySCSqU211rvmtURWoWy1qZ6o2+hydulm2rXN7Axs7gGWfqi38JcLT82kHUirgretGywsl15viL46qbiSYi82tMB+9H/IimUvTi9cP8zkJl4we7LE01JlBvje4z7cGa5no5dirlqNXf8tL9qWLAaBb36Qqu/kB+td+8OZ0lecH17RaYLXAaoHVAqsF/pIsoE/wzKcrvEb8F7/4+fTGG9+vO7fxT8pJcV/fOsO5iO+gw1N1G8NsyvX8jIhB3qAkF27kdKrNb+hSvdiVs6KDIbBwva+v7Pj4OV7IHexdgjkdxx0dS7Z4ojhDiil3zGodRd8gWG8E1Tkqh8XlTr72HnR5ELBcMqAbuiQgMbBCEYO2OJ04n+qn8yq/zHJQrhkEndVue8ko7dGkHTQB4Fcby5HXCRWskyUf25NtwS1l6KQtEWEERiEZiBrw6cDaIPXT0mFMRr62zXLJQz/kGRwq3bbK0eeldNIjZMgTniRrYJY1cTKjMs58JEq/2QpJ6kJU5vs82/Yvv/89Lz5BMjNK4ndfyTiOLA5lpSFgbjvlzHKpTOMM9qK6iasds1Ge0+A1K54GzbUh1vapdwChX+sD2BmkjImuV4+MD1lIp93cBg8P6mCHGSHR3hyVMVCoTPV8VKUKR6oiuNDNuhhEwWbQR91B7Nl5DOqHNz+dPv70Zt40q2hnw0TXLG1pyymRiYjwG7UbpsF64s5mycUjNJFBczN+LA8+VIuVvyBFuFDf7Flj/+JFlzz7vCt4i76XKuM1/GxD0QlTZpaTosUxs6LysjGZ6aNfXGatXFNR2R117qladBefv8yqge/zwdIpxo/Ip87zIedEtIkOzoYqp9oVsWWD4FW3R9VIVwE12E7qYDDfgVRqo2hfH4BQrgBLWNF77GukkKwGSOVAgEir+meKmLlc7R1AKuu66UtjXuGzEs9fJSAkELx2uDddPbhMcOgH5wkGCej23Ljp5cxgXU+87rWunm/olXPENlnUel57LkzXX3hxev0HP8oKiii17lYLrBZYLbBaYLXAX4gF/ATTM598JubNN78//dM//ZfpiDdBvv32OxX40PJ2JMz4w15u5eZYxiknxL348dWr4in7YD6lflllcNFlMnHg6ttWuwQ4Dx/6gWqfH9K9Y9MhaXQ0yswc9QaEOpF6KjpTzmbk5RA6jgqAKDOD0FY7dfaqTe5dFpdlamlouVqhA0eHzKWiBpny1hHW7bJ+B4Po+AlJcBGuyoARuKWfDucGVnhwiK5UWUc+GemT91jM5BUHjYBW3DjZ4Uc9R52+3NGP7rLh5TUsCRN2n+812i6/OdaBXGhsPrC+EYCZIq6topw0IfzdkThUMFBF6aULYlCQi62OiVr++69/Pf3sRz+Y3nz5xRDqwLrUTCZp69A1IBlL72bbTYNfKUhBuMqNbHCWuyi8qJyDyha05LlhHxaSJTNktzLyVK5bZh8pd7JOWOslPDrMCJWRRLbYpQb64NloyoJOtLAkZz+FqPmlkxzXO9Odu0fTe8wO3uZlMlGpbTqOYRTexbfEFH/zjr0kdeo0xHWxjwNzNJ0AzfNr6JcAUb1bR+DOJKrjJnmuWPJ8ZJmm+OFhW0unOk9AoRz+1Bsg9XmTsUL1CTP3/Qyiz/+KAwWcPSeKVp08Rx2DfT55E8jx/9VXd1gKfpzrQuugySIfHdOVcsQ+jnGXWzuzeMy3HpPSDusYv/blnKodVhuY+4KupObtMfjDFgu7d9uLQ+liq8x1Mjc4xrZ5myft9nxSF3vEpfRJIbPQPLAHeQPAV3im/EWCwT1mBt0OE/yZr+807o4XyLhU1gCPy0ed2+Gp7oOnZTYtoN5eja+88NL02s9/OR3wMpnur+iz7lYLrBZYLbBaYLXAX4AF/ioCQvvBZzt+xXcJdYz+r//z/54++uij/JiXC1I9VY4IP/L9wy84zkDVb++HBzIOqVvizvA5syDHjZjBuhT+lSh9+OR1skm+pfACd/SdHXzAkjXfVlhBokGFuuo4upSsZhNCHH7ltOmIZVYBmMGgSadTp2VHLwr6zAQC86P0pjiY4Jfz2xCXnrLUFAex5AJPI5BTKMZkgGzbsj3VOmHS1exT4YWsiXMsJ6v4xlUUpVJode1qOWh/L7Gd7zjC0Yf20Bbl+SyQzrGznlESHo94+2cHBOqSWUMDhPqPaDXWPnkZiUqT4gQnp72SCc9oGU9aBQtXF/EyS5Xf//gGQeG/Tq+9+MK0lzplO1sBfUWRZJpZBZoXnE0Z7dgIGvJoxBBRdOKpe8RyDJ1lXVWBpU/gKY6y1OhRsm1P9aH7DQbVSUCWQPOISGo9U2ikUdk4S3zzNsC6GU7BcngNMG2yugWFVRrOWMe2N2/d4puPfu+RNtDW1IcN9k2haDMON4DiuCxLQ3l24KUtwcFd7qKugRJ2c1atkiOlghLpZO3YupDHdUMx+quU6ufhHI8ZXxB4PXJs2YjNETVoLz2zGS+WwHXG3hc7+Zyvy7ulsw3q5Yug8o3K/bp5ogbOhnszR9p79+7mXJC357w6G8wYxHnTLHDPF28sMYO4RxB1uH+Qpavyd5iratI87ixVWzuXenfiMtitlbd4ju1mAYB0ugwouKkceRs5eHDezC8NSrtpuy/m8Zesg1TbJxtB7H1m8JXnr9YH5/NtQZ+R5NMSwF02ar7eJOoNNJeBenrWjZ56IZFntAwdfyZksoEaXfZ4NvO1X/ztdP3V13MDKijrbrXAaoHVAqsFVgv8BVmgPZu/IJW/u6qHfGvql7/8Oc8T/jwBYjldug0j6UXg6OjC1A8/dcLiWpi1sNzmKpG2qsoZrcCkHdM+6ktteBVdghSdSery8hjl6IT5mvoEe7y6nqVMe7zhMy95ydJQqqwDr2YTdFQqRlBnW5Y3d7o8jD+DIx3DOINqoEM16OsNieVgdntVQQdQPgaWmZUYgUz0T2VqESQeyNk8iGF50HPUMc3SL+WmbaMeFnIpQRZSEpIklmLj71GoT2DULEqc3e6t4ajrZCv6Mt9Yiw4Py+GVTzngZQODyQ6SFWRzWi81cHzoGCZ5BD9jxmNcTY4yHSk0wwoXWB54gm1//XveOPrZ59DyIh5wKxCToLATvEXGkAON/R3kZhw7jnrzc1bhjbtQxAGWxNF+0B6xScOtbPmMjLSNssc5UC0OMKiMhzTA4oBF7wEPbAEP+5YxSFI9YMm7K5raUzSJkmTGSxTjVgSW3h7xdtn3b9yYPrt9O70esLigFlmNu5xPVP7/7Z1Zjx7HdYZrVg6XEYfUUAslSrIWb5LXxBdGHMP2TS6ciyCAYwQOkn+YAPkDCZALx4BhR7bhwIxl0aK4SKIoihLJWbjkfd5Tp7/+ZsixJSWAxT417O5aTp3lreqP53T1kuccJE5dUKyCRYEeQmjg10lMnqowV8wZHKmU7bHqZSExr1WdQdrc3DHDCMxg6jnGuFBQ8nnYxycvmNgeyejWDLb5BTSSb7vUx3cFKDCGJ3PbYy3eqBh5Zfo4xe2m3SIJZ+4TXFpX5Zf1AfZcefTt5woM1xTs8K3CcUK2uLuKLFrGKCEq5lPqTXtQBodxnhqziqZ9e36z/AdPtZo3OZfjDORc4TfSq/Zw6OZxwAYueh1fP9I29OH5wwoGeWbwiG4P9W2iumjDraKH9SKZNeV5ucwhLrj5dlHOfYsyS+6CyDIWOS+r+RTJ8aeeVjD4eAWD4F+pECgECoFC4FOJwGRWCHN01tfX27e+9U0Xf/rTn7UP9XKKOa+kOxRJH76PHKyhHg+oF/Z6N3IU9js480Tu6SrlejSBo+Mn81wVQSG3h0ZgGD46gSAB4e7OtlcCCO4I4lhVCKc3HC/429GTo+LAU06iZUoGThOicaWc6WUcRxzkaEQJergXFrnawZwcJQKYpIfCQWaApFKkcBizpKP4xcelCaTQAy20WQzupDLjyKp3pdlOLSx0u12+8h+73V1OLI4ZzjDOub8TRx/ZhWPtW+Vwlimn3tiMeBICYkfGdoqVV0JAzbjqCByLWh6xPvOd3c994SkCs1QWG1flaF659n577fyb7bETX2xrCgpv392Rw4nFvYM5RMdwcVXvFUc1IMtjARH0SsPYUO51bqCNqq6B7Jw197z5RnuwIa+tdxllkiMMg8Z2d3q3jvOqyCDUvNSHI10ZIJJV9S7Kg3Jq19hjewYanA8wiLlHH/J69k5HAsE3L73VeMvo7Dk8WIqafiKPcQtZwYv2PanPN8+93mSx5EOkpIUexjxYR+DIJOmtwMK5E8+ZERBKU+Zj52ktVIBX8vdFDHXk3OGMQF/m8F2XkT/q7wkZ84UgN75rqLKeK2Z+c0b7G6r01TmAXM4Z6y7hfAoBUwkk0VNk7Z5X1JCtJFO4vRQNZ4nnHOPD96v63YmP2sd4QAOmtkt6Zy9kML0koo+jCSnoH+ecNoS7g3sHj5nQP5izXPUHN84vl3WUkJDpvAi6fqyigghB3mHZsabfT14aQ57AkFti/eF5tfObafWsH/qGjhT9fCrymIGqps4zQAHkxuln28Yzn6lg8A+OXhEUAoVAIVAI/CkjMLmAkFujnnzyifa9733bbyD98Y9/0q7zzbjBPcE1nSWcOHwDtplzSSU03s2ID8jBHyfNLhQexSi5KEF2y+Rx+OUxaueqNM5efAtrxc8/8gwQ9DiCdlrwwtAvdg6MctVruAVUTjlOIt+vx1nC8cFzo4iTaYdUBfhiL5sbfbTGvqUMR4z6COjkSMqRghjecAtnsDPRIZPx6wWr0PPwG5xLPKy9Cd4K6OANHbfcIdt2Szc7yEYigmCCwsAFtaSraG4rcKDPssoR9Npq68143NVtuIGj+IoGOCVOddq6gw0jqueSKlynHbEXt5bBmUrXq7CgoHxn+1Y79+b59vJLL7QVvcSCUcbUJQQEpQY6OuJsWgEPAM3mRKOSuUeWPU17qtyYXcyrY6V8kAoddBWOxodKC81O5jDbuZOVg7DXU9nr0gbz6M0m67QcIDddtqsyy9k+NKEvktCPrswpzplI25rAv79wWS+TuSYM6dzZmxK2fQyyQ3Tbt4/5k0TBJ4lCfpTMTWR+nk9Vvigi8jv+BmZMWObg3UUu3jCXI6jlvONcIIUtgdeCvojOPCYAI0BjPsecFr368MV0LWanUe7Pzvq6evb5CT3h5kAtL5JwO3euLqI3n5XgGWLA82ofd5My7sKTFXZs8XUVlX0ei9DntWzAXn4r4lZzBVRaRbt560a7s6MLMYZrhlnkQulu8qC3M+LlcXFAqPzQf0w94zffeVZiPmCLMebL8hQpyyZ+oeJXKvj4LgAFvTv6reRCDh0J/I5oO6zbyNn4ePyqbhdd0+onL+0iPL7HPO5z2au4lhImI4Ex7Wq0BfU7/vTzbf2pZ9uKVhejpXeoQyFQCBQChUAh8ClDYHIBIeODM7S5udm+852/1K2jh9u//9t/tKtX3xscr1ngJ9dKToBT+BqRd2U2jGjUaifSbmCQ5h4na+CVlXNH8dM/+TfhbuO5U4Ejoqv1t7b1wheu7HexcdvnTCkcImRgWzhgIqQsEj9n6EhA7FTn53hEBzOqWYRQtZyrcFzRM8TgBqne3XB4oSWomN2uOQRCooEZsocgT/kIFuGgBAN4qx7rzN2ec7TjPAd+poZQTtiMJ4rc5a2n4clal/S8WcXA0eZV/cgx3jjr0tUrHAKCPFJRI5+Z8vOF6Km60CJkD3araCxGjeiPbuFrhtbmm/pLgDFQn0XpdfFtfZfw3O/b0c++1I7IGdWXHo1BBmMLYDCKse4bo6F0aGLZoSVVaKeNZmisG3ltPkQQTRPmo/uSFAfn2TwPWve1nF4mn8Ee/BA1pLnCUBs0e9rcV3Ue62yjUqk/xxrMY2yGOXBXoFgF1WsF54MPbrZzFy+0G9tb6sdnU+hlS8QoFIzzYqa2x8iCDtp1XeCSWTISAF7eaONkin82kwAKcp9DBCkudEuU9wUIUcKK4IrAK+Ydz8HyNmB4ykZDo2AQWvIaKNtlvFCDMknBnFa9uEuA7+VFgMqFDvGWbgvclsy5obSk1S8uDHELK8EmWEHf7vGSKq00QifF8sIAc4HfBa8WaiWNgNXPOqrLKh9Z54KK7aWPReioDIzZeoo2nX8qc7bRAh1zL57J5fdJ9Z1H73bgQRKtqw9iZF0tCPkIQAV+K1SQbRz96R3ld/SbubWjwFC288wgK4OrPRhckp0xJmG7RxO9FNxjAOzYxR9yRKf6hdWj7ZEzz7ejjz2l7zvqfA6jRVCpECgECoFCoBD4dCLA/9uTTDgVx/XWuW/9xTfb9//6r9qjj/ImyPmEQ4BPQLJTEtkD9lDPKNNx4Ug9joNvP/JxDxucP771JgeOZ2L8XIycMgIiPpvASpc9T3VLByX0Cw0JgHBWcIziswrwE6UOOIz7NgIr8cVhTCcaFeeSgzHVqJ6VDFI6xy64g+T2fjhibObnYy9L42RNm4M86eTVyeAa7Dr/5GOHEj6uZ0dww215OLMqy16CZZI/raFjBou+nQ67hSHOoF1U9fWYqEyQyAolb22kj8TYNoThfnvMug0WcJ+dsTBG2Nw30ZEHE0vVdwh5K+arvz6rZwm1Eq3yXZxyOZ3IFHXYJ8OybG96QGwsOFF0t2jIgI0SDMwz2qWaqkAxmuLYHWfX5o5OvaN5wKdv0WnGILugH0amSqF88BnrNIQF8GesiAx06MGVCy5HHVRsjAFBA7eKelOww+2377A6KLmxBa1VcEftBj3E5JMk2w9/GKOuL1/IZC6MWKJl0W4ayw96mvlsAQGkn+uzWrJCAZqDMgEAD+YmKdmhO3OH84OgjT/jAI02VvwQs8vnJ/QbwfzlvAxKUagxniOM881zXT1tis4F3z6q/qFlyHIJed702wGddF9RsMQvGbLgywuautXmQN5z3qU9OwwajIq2sCTs2EP9EYpYHynYhw3Wo5/r/HaQGCfe6soKM2+j5XeDlTxugV3RyqDfvgx+qAqG2oiXsTn+cVHJWdvN7wdty0ePtxPPf6Ede+JMW1aAudfO0K72hUAhUAgUAoXApwuBSa4Q5hDhMB3W20e/8Y0/00fr19u//PO/tkuXLtvRsTMlhwBnaI9vk92H46wdp0FJu7wVM9vslLnHzKkhZ3cuL5fLqYmg0J68WhHuvXvmLvq5KRotNFs5hgycIPjjyESfyJG3Ptp5hUD9w964PoDTOtz2Bo2ecMSZhTacL9Xpnex2nsTL9DhkCs5idaM7zTI+/lLXHoyIJ/K9HCLbjRF2up4G2pI7BcmzHgSAcpHtSAdnO4Dqgg7hfBNwyHEW3a6cXAePsOiYpJNsndVOog6HXzlRiZl1C32CwFTd3q6fG+iB/tlXeqjZRcaSVSwglYN9WW8c/Z9z59rmyUfaETn2CgcGfpYJP+mTq7RWwoyo1+YgC5AgVELkUDBBryQvjjaNMdlLR1kJ3sk/+cBT3RkH20SZSeR2C+xyVeUEca/PqrkifUnJI0rBLtuyPdqG7momGHKrxvbW7p12Xs8O3tCH6AVoEPd9zJtQ1fNK/eCTEmg/ONGusddhgGTU33NGcx+G5Oe4uWvOG3BXBXQa80XNU78ERrzigoraJYQ5TBDic4y8aH2hRHnmJYGeRq7rhABmCAFLXMigxEWiVX8mQat5BG2cEzY6dEEGifmft4imcZahuUmHkKuseCOHFcgVndt8ZB05u7pggs0rKi/ejNVINIo0PkezbnyUQujEphTjQG/1mzGRlD822cAgNl/tmOi+7ZsKAmTwU17PVyJvVzjeuLWlVcJdnYa6jVaB7rICQr5XusytonQTjzj/Q9WcB3EEI2koupX1E+3YMy+0tY2Tvh38j9W66AqBQqAQKAQKgT91BCIC+FPX8v9RP3wHrhq/8soX24/+4Yft2efO2G/CCcAP+MMJj6JvmaWGvFKudpEnCMDNjWBATgiuEA41gQCOIc8R2QE3sehwnLg2HRJQB8eQDS72xSXIzjttIkh5HH1V3nbIte5H+6vKR3vQB3cxUIpvcOVKhvSBP84jBmkLPhKEg6x0Tw89hU7iJf3ZCMzsCEsO3XDSHOhkf/Wj3kk0D0p2xHHwuHovD9LhNo6uugQmPUAVj0U5d9IAxGi2E8wtb3f84GSXIB7p3BEwcnspeoWDrKMccwchYjVoJUUZtSgPtWaILP71wtw4eRwkw7fXiu/Ovdvt7LnfeZXLzyoOHdUbttrM3bBKog3sDV36QARm3rqikM2UtDrQsuoTDeCNogQVsVlt8bhHEO9bCuEJW5gppQxVRpDTBbheeTviXTDePZ61mXIk40LwgR91uWFbF+OM2WiHiN7X87sHm+B18e232+tvvKFPTcSqOdMg9FU3umKL7e31OhycRjqacFAodFPRFlAt3pjnoaEsHQec0ixRSwP/8XkYxp3+Dv5EzwUVzzdVAiFzjsQcRXdsYcN8zkH6IoM5xvkXK3hxayjj4bcHa+y80qX5xXggE3kEi0kf3xZVwCjmBD3wX9QFE8sbhMZ5yhyB7x0CTMkgaOIiEOez9RUI8EU3aJlLnldUZMKOcep2UTXKjik+Uj5kMzZgpA1blO7q/LqjFzaBcZ7jYMdbU29ubbV3rl0DAQW7WiFc5MPzvDVZv3OyD07YEbZ0AwBK9SQwWN3YbI985nNt7cRmBYNGpXaFQCFQCBQCDxMC4ZU8TBZ9TFtwwl588fn2ox/9sH3ta19tJ/XtuHDv5MjsdXI+hozkNfadYBPhlJw0HDqHI6KUQPuIHPkbyR9l1XJwin6dh3iH40k5Ujq11kk7fCAcKLAwjXbU2WnCYVK3cLxn/eGBExrPS0U9dE70wfGlgnxvsEMrhzPtskMvHqHZ0NvAU+K5JjukYuA3SVLZHWoUZbUlVi/pAl/x8tFaeDeTGQ4t9kGTKxXBn8reRzycUNK8Qt/QsRNJD2ubx+igfVDZSRcp9umhLn0/73r7nYKam1vbVGijN7qbix3sznGmhyhcQI9MQT6QBjHtMxqzVBFckn90z85BjW7gQFf9c5pR9ArsUdC4q2ex7srhNmgA5000Q4eeMaMRw2STEyDLKTDL6u5vzFFPd19sUDAtka+9cb699d5V3Q6tQJ653OezxxniQYeOffK83xE56rBXPKQdiqEtggTYi340Bu6fxIMMaDTeAjVX6NyEbrKdQM1vs1Se+UaAxrOFbMjxPHaQw8+ylTSNf6TTPo1npOibt3fmxSECutt6mYoUMIa+SCO5MQnBFKxAr98qDl/DF/OQs3BHq4I+z22z+KiPQid9A5XbRsFNfzKUvPXWEftIYDrGyZX7dlB99IQEyzMGzGuwQBftpSMv+9m9vS39t+PbhLLSzxWL6t3r19uW5u7KCi+R0Qt5hD+8Mqi1LdghIPkjWZae1Tz02Om2/tyLbfX4ifg9c2vtCoFCoBAoBAqBhweB9C4eHos+gSU4AM88c6b94z/9ffu7H/5tO/P004PD8EC29lLUGj7EiKw7FXYvZvkkcDd5MrxtctHfGpTTRhCi8uDk2r2KHqbfJyZrk+v+I/6fYxsdrUWPgHCgcKTSEeLIKgYfnocwVo2UldPF69l5Hodn7XjuEMfVt1jhxIrWwVZfuQkhIcvyug3QOTgRASsOvpKPcrQHoa0zPylGlVc5RCtpdqSxjltHJT7kq4xjTRALb+zJVSI7izAj4Sx6JUxOsIps7OGPoLxdzLecqj4cQtYNkhYWhOu4yyS10MhGUqWLOhpXYwu96gl0JQcZt3S749nfva5nCa+rgYAwT7/gChfmoEGF7yBAWYzDybcGKRAaJXB0G8+kwYv2eHskz1KCUUIRDrCwkhjNAIsgyIYFIqjwgT0VUYggjJUvrPKEgjR1xfr7JTp3HuaD0E6bXSgPfJDeafRCGVYG72pOXtWnJs5fflu3AKMxHdnIx98gmWra9M/2uTy0OhMI0sDGhY8kyrosR78BryhK1QgMoYrzlKMKqoA2N5OLFuwjQGK+SZrqeBZwPM6es76oISZdPJaR6Ot+ytPkl9LA03NRvx9a5WL+M+/AzhdzPBegZQ6LEzyos6KUle1zlKMTYyoa92C+qp9fVKNmbgXnnOebhDx7m4lzjItF8wnm8zWUfCt1n2RWYz/JA2tG08MgGCJXqgvHPOi3DVv94h0FhhmUs9J5/caN9r423uIawTirg3GmwzTO9mAHS+xaPna8HdOq4CMvfL6tKO8xs7TaFQKFQCFQCBQCDxcCe/83f7is+xjW8J/+YTk+r7zycvvBD/6mfeUrX2onTm7ouRMCJZzB8L/tVOH4PGALJ3CsQDrauB+zxG1eOGLhZHc3TwT2qfoqTDjvMz+L/jgwHjycIJVNP2M7yqm98wuHRs6/5NELPulYUqCdOpil/jhNBBVU5vNQfqMnt1s50Ah+PMcEJjhSrArCCxl+OQby7IAqpJKzic3wj+BNMtUPycgO3ZRRwf6jsm7guUD12dEzTnzrzUqKZnExgkHePrqjt0/SwgswcALDWRWRdIlVGzUKamzym1eVJ3VuUej70L8XICCpn7VEUZLqsZmd7aGeim6fg0i3gbeq5VRffvfd9sbFS/7AugMehp9GKOhv3tplHU37Eu0iYFPyGI4wdme30yraznMIbtAphELgIBtejEcEvsIHbNjUtx8clCsEoEvnSQa9Q4/ZEZkWGsZ1PaHO7s5DY7pOq4kKjsiLObuoW0QX2puX32lX339f81DPtWlcGQPjbSaxcz/ZFEijHjyDL/Mg5kLW5DGoshRdZv2CMzTgEeeLVYazxtKpk/dWzccINMDF55M60Ddxo99qf6GRAxL194UIjR/nFxdiSEDmZ3I5XxgbVTM2NKCDX57kMQ965DFSvDjG46i2eLENoV30M1NpEuciPMRb3T3XkaGNFTUuumx7FVufr9DvHu2ct/DL4BNpMVIjLFRnm42NUJOeHR61BD26+c3EMOip/8x5+ngqjdogkTra4BdjETEolbRGfdDFHAYH8Ll9h98KXiq11G7c3GoX33lH80ljoTLKwc8aWt/g51vIV9faysnH2lE9L3jo0cfiFlHTIqVSIVAIFAKFQCHw8CEwu9z78Nn2iSzCEfrs515sTz19ur3++rn2i1d/1c6e/W27cuWqHTWcJycOc85Cr98jPVwP9nZB7MtEQIRDJyev85uxwt2S0yJ2cvHcL3oS0MyK0ZbtvX7uEMRQhEOpTO+PLFwsEnsHSpJFUOZVPDlvKw641FcOFp4Zqw58F5GVCfjdhlarhvD39w97cDA4W6q3aQgQkZ1bZUlBE/nc2xJ1gM5X+HXkg/OLWqGUF25e0JjOnXB34/Y5bsnjpRG8cv+ObnvD2SVA4IPfYMn6mQPcvspxb1ftarMe6OahkKLdKU6dOII5JjihXy91s7IFozBS/3DkNToqe3VEzzjyvNe2bls7q09QnHniifaZJx7T1+QIsPDKZyzMwziqkno2Gz2zPOefAxM8ZH0Lz/0g7WSeS+4bDFwfs8rCzCMqu4CYDVADBUfLJS8ZgsVHtyAjJieVkXddDxgHvjB5QIKGzYLY9bIP2LTcbukNrecvX9bLZLblmHPLYswN9OGccVzr/vMyYoTQSz1Guoh1yNE+uzH+mBJtccw8c5BbDFkdlkKaB8wliNGDA/xji55cfEBErAr7CTcRSlt9C1MltfEM35Ju29QTrMMfOnKhhDcEL+oZNyDlvPJUBXglLmos8M1Dzj1uNV1SsKNn52jmvPSqPqK1RSCkucfcoEL8wISjNx0ccApjaLBFvcRbfaTjjj5tsSCePp8k178dUoo3dW5rLGCFPgaOrj0NmKrs369eYfbeqWEoJHX2PugYeAdF4CbY+9mjTPLuLKSxgkKenYwLRAzJeb0w7JpWm08e3pRyoK9OVkG8NQEWdDvp0iMn2urJTa8IAv79fqPMtHaFQCFQCBQChcBDhEAFhAcMJitg6+vH2pe+9LKeL3yhvfpfv2j/+ZOftgsXLuoK+pYcNrmJ9v5ggmeBV4JzSJk0ZMLvUE243DTZa1RGbhPOo2ihzr2yKuDMQdcTjmvmOSJormLcOMunFjiykMcmXvDD8ySRx7OUl8WLLtjwtvW2f9PbCRZZ3oKIaD7ZkC+w4Paymf6wm+lKfbQqQ8cZQKpQQgVX9zaVc2WRgCduZeWqPnylo51cwrtIdOfZMnm2brstZzlXU2yxnD10GMSqv1/KoQo77NLVwXknQHfoh4SAnqgPy5KiNyZN7xtBiGhUzmR85WTzgpRfnj3bTqwfbSePrWn8ZRPzgAPk9IG9hfU89RbpymCbvN2Hzp1G5RjX3lcdHXDIPuPncRafwd7OWt3H84E+sEYpREXwSQ2b+jsgoCFo2M8FCKFqb3dr39FACr0gGOTCDH4hsF157/126a23h2CePuC06FtKhVkfW7N7wA52ng9ux25lrHMXRX0vx7iZ0Dv6ZTBo7FRL9w6dS5ShG4YDEfTr42nW2g1jIvrAkszIduWTly+uSCkgZtWPWx3hw4UaVsUpkF+WHFZ++Z4eASE6MD/JE+gRiM6UJUAM3VIXXioEH1W7nw7KIFerhASFnpQaJxRR5zV9pH5Hv33bep5UnCCOg3PoH3OGliHBnBaUywbXDRTzGdEd1AwP/y4IYH4duTBAcMgLuMYdGQNuTb9NAC7Mrlx7T9+xvNjObJ5sqzoH7i4qAFYXX3BaO9JWTp12MBi/MQdqMK9vlQqBQqAQKAQKgU85AhUQ/hEDiPPEB+z//Btf16rhS+3nP3+1nX/jQntHnxJ4992r7YaeTeHV7pG6k6QC/o89FKrknKRThANDELggZ4yAT+6X2kaBn0pzno1K9CYd5KZYxAFEtGeog3NmntThFPVb3bg1bFdOVKxeWqR3yMUJ4xhBMFfgdYsbNriRNjHba0bajXes/viVVBFAdoAGIb6dlEbTsOrILWzLXpEkOA+nPhiYrdrt2MoAO7UEOmKL/g5cZQsrgl5ZUQNqeoVQxx3RYAcrnchzsLbAeIQ6YJLJskZliIIs9m6S3QM+qgj9xEE6eBVSR3eSg72ll368dv58e/ap0+3YC8+1VYWlXqGCnZzasEm26Jk9M6IeAgTh9Drp2LPp3FN9jz40uE19ZKMh7UYEChD6n+hyRtA7UtgezFk5GcYJfEmqcn8GU5kIbpAZfSAZiMg6mCBDexfcaX1g1zcOrKhqZNpNPXN57tLF9p7OL565RFriRCjQxcPYbMM2qGbJAV4OBk3CQwqnJqqQwIgmZp2GXAQ3Dq6wE/VDC2FKIAYernQP59h1rIO2F00RuvHW0AVHePRnfDSfMKazsjxdOFjQah0rfStaGV0UDXnOuSWtDC7pSo2fw/WKOAHPkj6xsBsBoLh6dUwrm+jglUDmgXUHAuY9aHFu8FxwvG0XFcfo+TulksV561tUdeGCOyf4sPuWgsJ7nJPqkXc30D+TZcEM0PKYjQceuQAUQBiT+9GKJ+cxeBEE+pfijvDSxKHs4BzR2sjv3t7R6t+qbzd/7c3z7csvvtge1+df2iHdAst3CdePtxXeHsq3FmOQ7ye16gqBQqAQKAQKgYcWgQoIP8LQ8rFmPmD/3e9+W47UvXb58lvt7G9+23796/9uV6++127eutk+0Nvs4haycK3wbdI/5OhnBskQSLHJpbLDZKcUZXBjHpSCJ25quvHmPZBH+yxooIG6Xu8cXlR3nemsW9e4Is6zQ9x2tr3Nqp/upVSyYyYSfNVl1MIWHXjWCcfJ9pgH1Eqwg8h5F8J2lcd+lgMIyeuUpsFJ9apg70uABzduT12RA2rnVUEVK5I4xuHoB2PyflOjwIY3OjoQxGHvQggQl+7pVle/0EOOLA6y+plWbcsKPO30Ih9pXWHaSbZV1Il71Ko+jegVFDOGghpQfPuoAmLejgk/HOn3Pviw/ebcG+3U5mZ7cuOY8eHTGhaE6XaKpT+B5FxKjVRpiCRjDlyVTRL9kO3AyTywdxihOa5RQN9QwZonX/TC8hRtMKLOHawvZbYZ5vSJpPrx/DZztZh/0qhsliqzKnVvSauDV9u58xfkyPNSE322QbbESq6auzJ0YSwHUclGx0ypt/tIpiVaVheJYIjSXqNEb3Rh/GIzP8BUOVVP7Yd5AV8lkGZZD1N8ocO1YBiBJPOLvpSZ98z3Jd2r6VU05bktlOf1tu9olU50BJ9c6IAt89SrWMwN+HH+it7nldoZLjBZFD/0nF1Icfcuk776J2LNEH4GXO95L578Fvh2cG5J1XmDDUykO5rHS/odRD9+y/J8wzyx25ewMs6D3iR9xN7Q7iN2hZVyzhiPmLqbcTO64qzfEMBRLvUO3KWrjRM2bjO7tqHvBy7p+fA7h9fa6pOn26GN421RbxEd+gdZ7QuBQqAQKAQKgckhUAHhxxhynC/9a2fOPNVOn36iffkrLysQ/KC9+eaF9otf/kqv57/dLl665Cvv+CVcaecDz/Zt7NTqiryDmnDKQgV7Nrhm+gt/yaszLqmn/oXTyT4covupHlwsyc1Rjj0VhCRDKJaemRxKAiaCKl7ZPr4ynzrPyVI/HE5WFnAU6RtOM+FAl6XDwD47q4JWnFs2O7C0dX4cafdzd3jSJDw+KeGVDGRqJTZW/GhUg/o46BGeONCDc6eACscZ/XF5YYw8VkKpQ/8o63Y71aGPA9IePcUKRDjw0KOXO3IkURHeqIu5666oCTKopM1jpkaPr2zbkW6vX7jQHt98tB37wmfbxpFD4scFgtDZ8uho+7GTms4dhUgm6pTCYUiqylIe6Rsk0MfmnhA4o53y6JnBigbTjfQLPmqLjPvYPvq6rhM5soh+DmrRe9ANQguLg+qZNwOObmK0Ftr27t124a132hW9TIZZ4a2PDXkS3KA2trBxLZWq8dh0/q6CClE6v5QhP9vTk7F25dyOszHPON9eLB2o0YzxaJgYeXDwGAVz2yX8CFpINlNzjFs0M6h1A9r7NyF48MocJ3Xw6rUKzFUHfNIdu5innr8iRS4bc583Ae/qA+zMEtoX0dU4YFtQhjWxiu4XyEie9VEfZcRLFOoXt6kqKNRt8cvLGhFkqZ3PjnBh7MiRI+3mhzfogpFOHCiSsJpFULf1duqH1GVRtopDgzLSfX9lF9MFYAf/uG5CcHxvUS8c8lzgfI6LW1xMOXRotW2eerRtHN/wi8Ke0qr8k0883g4fPap+91NsrEjlC4FCoBAoBAqBaSBQAeEnHGcctVOnNr09+9wz7etf/2r7ULe4/exnr+o5w1sOsq6//4FvL+VFDTiEvhVQTk8GXjicbOzHjhDPFeJi4bbQzj7y4cjY17b+Kqs56CGLdpqc606Uy6rIZ2Ro4wUXh3Tb1KpeFsEzgWtbR7yigPPHxkrBId66txovWIgXx+Ao8tF3VirEQ15Z0Ee+S3UbDiOOFzTIC1VUVl3ooToRserHrXKRoApqnNyjR47KKdUKnjBbOyr9JNcep8hwfNl43okXYCR/InaecyKQZEWTl3XgSK/IVmxavyGHUI6jVyB1qxjijulNhGSWVeblGgTxXqnseKJRJsvBLlU4yNAxb2NjXGMsR9TunKPIc2HapNO1W7faB7t32nG90OIeb/PQLZ92cKGXQ4s+dlwBElffR9r0DxptIIvEIZmGRrVlsJH9CEoItiF3F3bjsenVnXfw6HUUlILzqM68+tj1fsExKaOfAFdn6izY4572RE1vkXO/tbPVbknnDTnzWj/V+IG0qFRnW4FCf4y9Vw5HekFGoGBNTU83JIBlSKIIfvN6Bo4xSu5tWx10CMcjmouHtVp9VyvorLrxbUaCkLQy54HPBbXAOz7ToKMvVrC6rotBwgC9d3TrMN8M5CKK34yr1WtW+4CICxRocEwvcOE3ZlVzm1uNmcNgYFlS7PDaYd3CGfN3XfOdb1w6wBQ+zHPkIJMXGjGfsJ7zjDp/eF48fHu1fgdE0HXT+SgZWAa9dZFOyOY8ZoWQc/BD3Q3B+TFc2BGtO+RBfQhgmW9c6oib6sMuG2ngwl66ZnJ1FvYdQbVT6ABW6MjGjttZN0+dascfOeYB3NjYaJ///Ev6ruxJBYeH+njsY1oVhUAhUAgUAoXApBFYuHLxov8vnTQK/8fG4xByayMeCsHBhzc+bFfevtK29ExUOPkdctEdlNIxiiO0WXNQr/m2fT1ckS6VjirH6oNuyfPqW3fsR2xod0AHcdfBt6HdR39z7kLtrHUTHdSMeJKd1Qkn0YXTbtTsJNtiMSEotNMJrgSDECNj6BP13MbGKg2BAobhDJOPAM3k3dGOZ6zQFR18+5t4OtCk7MAlHOBwoUPxblYUvLe1CYlq4KeDeKH7vmQGwQW3mLmwKtsePbHRHtGKC7eM8rdPjpgG2/1js0/G3gow6n/oR/ARzIIQPcfyxuUImAI3sXAaaDPT6xk7VxmAoN27B+tO3ptUyv5jYuF/S7cuX7l2TUd9T65rCGms7kUn78F6hHeqBbuOWooYS3De/Jyb9UrOWTM+svK0THTogD/k7mE0kyGdmAy+SIAmHnDZT0AnjWgmcGOOkrxaxzhboHbkVZ/tXsEWvY2hr9qgZe7GRZqg9ao4zHs7RwK5PNfcovYI7kzltmynxvOlTzUCutAJfYOPn7cV1S6fmun6029f6rYwM5Cbm+kw0RaaaF/XB1bsIYdHWBs9WBE8pYBwXS9sImFX/L7t6RjktS8ECoFCoBAoBAoBIVABYU2DQqAQKAQKgUKgECgECoFCoBAoBCaKgK9bT9T2MrsQKAQKgUKgECgECoFCoBAoBAqBSSNQAeGkh7+MLwQKgUKgECgECoFCoBAoBAqBKSNQAeGUR79sLwQKgUKgECgECoFCoBAoBAqBSSNQAeGkh7+MLwQKgUKgECgECoFCoBAoBAqBKSNQAeGUR79sLwQKgUKgECgECoFCoBAoBAqBSSNQAeGkh7+MLwQKgUKgECgECoFCoBAoBAqBKSNQAeGUR79sLwQKgUKgECgECoFCoBAoBAqBSSNQAeGkh7+MLwQKgUKgECgECoFCoBAoBAqBKSNQAeGUR79sLwQKgUKgECgECoFCoBAoBAqBSSNQAeGkh7+MLwQKgUKgECgECoFCoBAoBAqBKSNQAeGUR79sLwQKgUKgECgECoFCoBAoBAqBSSNQAeGkh7+MLwQKgUKgECgECoFCoBAoBAqBKSNQAeGUR79sLwQKgUKgECgECoFCoBAoBAqBSSNQAeGkh7+MLwQKgUKgECgECoFCoBAoBAqBKSNQAeGUR79sLwQKgUKgECgECoFCoBAoBAqBSSNQAeGkh7+MLwQKgUKgECgECoFCoBAoBAqBKSNQAeGUR79sLwQKgUKgECgECoFCoBAoBAqBSSNQAeGkh7+MLwQKgUKgECgECoFCoBAoBAqBKSNQAeGUR79sLwQKgUKgECgECoFCoBAoBAqBSSNQAeGkh7+MLwQKgUKgECgECoFCoBAoBAqBKSNQAeGUR79sLwQKgUKgECgECoFCoBAoBAqBSSNQAeGkh7+MLwQKgUKgECgECoFCoBAoBAqBKSNQAeGUR79sLwQKgUKgECgECoFCoBAoBAqBSSNQAeGkh7+MLwQKgUKgECgECoFCoBAoBAqBKSNQAeGUR79sLwQKgUKgECgECoFCoBAoBAqBSSNQAeGkh7+MLwQKgUKgECgECoFCoBAoBAqBKSNQAeGUR79sLwQKgUKgECgECoFCoBAoBAqBSSNQAeGkh7+MLwQKgUKgECgECoFCoBAoBAqBKSNQAeGUR79sLwQKgUKgECgECoFCoBAoBAqBSSPwv00Rmsli1bNRAAAAAElFTkSuQmCC\" height=\"188\" width=\"336\">\n","  </a>\n","</center>"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":26439,"status":"ok","timestamp":1725482114441,"user":{"displayName":"Sai Akshitha Reddy Kota","userId":"05755385555264415224"},"user_tz":240},"id":"zwFnJsE6vjf8","outputId":"27ee89b1-aa46-4d89-a26d-70affa167412"},"outputs":[{"name":"stdout","output_type":"stream","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}],"source":["# prompt: show avi file in colab\n","\n","from google.colab import drive\n","drive.mount('/content/drive')\n","from IPython.display import HTML\n","from base64 import b64encode\n","def show_video(video_path, video_width = 600):\n","  video_file = open(video_path, \"r+b\").read()\n","  video_url = f\"data:video/avi;base64,{b64encode(video_file).decode()}\"\n","  return HTML(f\"\"\"<video width={video_width} controls><source src=\"{video_url}\"></video>\"\"\")\n","# Assuming the avi file is in your Google Drive\n","video_path = '/content/drive/MyDrive/yolov8_tracking/tracking/result_tracking.avi'\n","show_video(video_path)\n"]},{"cell_type":"markdown","metadata":{"id":"5fCEDCU_qrC0"},"source":["<div class=\"markdown-google-sans\">\n","  <h2>What is Colab?</h2>\n","</div>\n","\n","Colab, or \"Colaboratory\", allows you to write and execute Python in your browser, with\n","- Zero configuration required\n","- Access to GPUs free of charge\n","- Easy sharing\n","\n","Whether you're a **student**, a **data scientist** or an **AI researcher**, Colab can make your work easier. Watch [Introduction to Colab](https://www.youtube.com/watch?v=inN8seMm7UI) or [Colab Features You May Have Missed](https://www.youtube.com/watch?v=rNgswRZ2C1Y) to learn more, or just get started below!"]},{"cell_type":"markdown","metadata":{"id":"GJBs_flRovLc"},"source":["<div class=\"markdown-google-sans\">\n","\n","## **Getting started**\n","</div>\n","\n","The document you are reading is not a static web page, but an interactive environment called a **Colab notebook** that lets you write and execute code.\n","\n","For example, here is a **code cell** with a short Python script that computes a value, stores it in a variable, and prints the result:"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gJr_9dXGpJ05","outputId":"beb4aaca-6eac-4b30-f2ff-ea4308968122"},"outputs":[{"data":{"text/plain":["86400"]},"execution_count":0,"metadata":{},"output_type":"execute_result"}],"source":["seconds_in_a_day = 24 * 60 * 60\n","seconds_in_a_day"]},{"cell_type":"markdown","metadata":{"id":"2fhs6GZ4qFMx"},"source":["To execute the code in the above cell, select it with a click and then either press the play button to the left of the code, or use the keyboard shortcut \"Command/Ctrl+Enter\". To edit the code, just click the cell and start editing.\n","\n","Variables that you define in one cell can later be used in other cells:"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-gE-Ez1qtyIA","outputId":"e8f0c2b1-3c1e-4476-f8c2-d8f31cdb5e75"},"outputs":[{"data":{"text/plain":["604800"]},"execution_count":0,"metadata":{},"output_type":"execute_result"}],"source":["seconds_in_a_week = 7 * seconds_in_a_day\n","seconds_in_a_week"]},{"cell_type":"markdown","metadata":{"id":"lSrWNr3MuFUS"},"source":["Colab notebooks allow you to combine **executable code** and **rich text** in a single document, along with **images**, **HTML**, **LaTeX** and more. When you create your own Colab notebooks, they are stored in your Google Drive account. You can easily share your Colab notebooks with co-workers or friends, allowing them to comment on your notebooks or even edit them. To learn more, see [Overview of Colab](/notebooks/basic_features_overview.ipynb). To create a new Colab notebook you can use the File menu above, or use the following link: [create a new Colab notebook](http://colab.research.google.com#create=true).\n","\n","Colab notebooks are Jupyter notebooks that are hosted by Colab. To learn more about the Jupyter project, see [jupyter.org](https://www.jupyter.org)."]},{"cell_type":"markdown","metadata":{"id":"UdRyKR44dcNI"},"source":["<div class=\"markdown-google-sans\">\n","\n","## Data science\n","</div>\n","\n","With Colab you can harness the full power of popular Python libraries to analyze and visualize data. The code cell below uses **numpy** to generate some random data, and uses **matplotlib** to visualize it. To edit the code, just click the cell and start editing."]},{"cell_type":"markdown","metadata":{"id":"4_kCnsPUqS6o"},"source":["You can import your own data into Colab notebooks from your Google Drive account, including from spreadsheets, as well as from Github and many other sources. To learn more about importing data, and how Colab can be used for data science, see the links below under [Working with Data](#working-with-data)."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"height":249},"id":"C4HZx7Gndbrh","outputId":"65838ba2-0ea9-41b7-e3f9-a725be449d3b"},"outputs":[{"data":{"text/markdown":["![Sample Visualization](data:image/png;base64,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)"],"text/plain":["<IPython.core.display.Markdown object>"]},"metadata":{},"output_type":"display_data"}],"source":["import numpy as np\n","import IPython.display as display\n","from matplotlib import pyplot as plt\n","import io\n","import base64\n","\n","ys = 200 + np.random.randn(100)\n","x = [x for x in range(len(ys))]\n","\n","fig = plt.figure(figsize=(4, 3), facecolor='w')\n","plt.plot(x, ys, '-')\n","plt.fill_between(x, ys, 195, where=(ys > 195), facecolor='g', alpha=0.6)\n","plt.title(\"Sample Visualization\", fontsize=10)\n","\n","data = io.BytesIO()\n","plt.savefig(data)\n","image = F\"data:image/png;base64,{base64.b64encode(data.getvalue()).decode()}\"\n","alt = \"Sample Visualization\"\n","display.display(display.Markdown(F\"\"\"![{alt}]({image})\"\"\"))\n","plt.close(fig)"]},{"cell_type":"markdown","metadata":{"id":"VYV91hbKwP2J"},"source":["Colab notebooks execute code on Google's cloud servers, meaning you can leverage the power of Google hardware, including [GPUs and TPUs](#using-accelerated-hardware), regardless of the power of your machine. All you need is a browser.\n","\n","For example, if you find yourself waiting for **pandas** code to finish running and want to go faster, you can switch to a GPU Runtime and use libraries like [RAPIDS cuDF](https://rapids.ai/cudf-pandas) that provide zero-code-change acceleration."]},{"cell_type":"markdown","metadata":{"id":"vwnNlNIEwoZ8"},"source":["To learn more about accelerating pandas on Colab, see the [10 minute guide](https://colab.research.google.com/github/rapidsai-community/showcase/blob/main/getting_started_tutorials/cudf_pandas_colab_demo.ipynb) or\n"," [US stock market data analysis demo](https://colab.research.google.com/github/rapidsai-community/showcase/blob/main/getting_started_tutorials/cudf_pandas_stocks_demo.ipynb)."]},{"cell_type":"markdown","metadata":{"id":"OwuxHmxllTwN"},"source":["<div class=\"markdown-google-sans\">\n","\n","## Machine learning\n","</div>\n","\n","With Colab you can import an image dataset, train an image classifier on it, and evaluate the model, all in just [a few lines of code](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/quickstart/beginner.ipynb)."]},{"cell_type":"markdown","metadata":{"id":"ufxBm1yRnruN"},"source":["Colab is used extensively in the machine learning community with applications including:\n","- Getting started with TensorFlow\n","- Developing and training neural networks\n","- Experimenting with TPUs\n","- Disseminating AI research\n","- Creating tutorials\n","\n","To see sample Colab notebooks that demonstrate machine learning applications, see the [machine learning examples](#machine-learning-examples) below."]},{"cell_type":"markdown","metadata":{"id":"-Rh3-Vt9Nev9"},"source":["<div class=\"markdown-google-sans\">\n","\n","## More Resources\n","\n","### Working with Notebooks in Colab\n","\n","</div>\n","\n","- [Overview of Colab](/notebooks/basic_features_overview.ipynb)\n","- [Guide to Markdown](/notebooks/markdown_guide.ipynb)\n","- [Importing libraries and installing dependencies](/notebooks/snippets/importing_libraries.ipynb)\n","- [Saving and loading notebooks in GitHub](https://colab.research.google.com/github/googlecolab/colabtools/blob/main/notebooks/colab-github-demo.ipynb)\n","- [Interactive forms](/notebooks/forms.ipynb)\n","- [Interactive widgets](/notebooks/widgets.ipynb)\n","\n","<div class=\"markdown-google-sans\">\n","\n","<a name=\"working-with-data\"></a>\n","### Working with Data\n","</div>\n","\n","- [Loading data: Drive, Sheets, and Google Cloud Storage](/notebooks/io.ipynb)\n","- [Charts: visualizing data](/notebooks/charts.ipynb)\n","- [Getting started with BigQuery](/notebooks/bigquery.ipynb)\n","\n","<div class=\"markdown-google-sans\">\n","\n","### Machine Learning Crash Course\n","\n","<div>\n","\n","These are a few of the notebooks from Google's online Machine Learning course. See the [full course website](https://developers.google.com/machine-learning/crash-course/) for more.\n","- [Intro to Pandas DataFrame](https://colab.research.google.com/github/google/eng-edu/blob/main/ml/cc/exercises/pandas_dataframe_ultraquick_tutorial.ipynb)\n","- [Intro to RAPIDS cuDF to accelerate pandas](https://nvda.ws/rapids-cudf)\n","- [Linear regression with tf.keras using synthetic data](https://colab.research.google.com/github/google/eng-edu/blob/main/ml/cc/exercises/linear_regression_with_synthetic_data.ipynb)\n","\n","<div class=\"markdown-google-sans\">\n","\n","<a name=\"using-accelerated-hardware\"></a>\n","### Using Accelerated Hardware\n","</div>\n","\n","- [TensorFlow with GPUs](/notebooks/gpu.ipynb)\n","- [TensorFlow with TPUs](/notebooks/tpu.ipynb)"]},{"cell_type":"markdown","metadata":{"id":"P-H6Lw1vyNNd"},"source":["<div class=\"markdown-google-sans\">\n","\n","<a name=\"machine-learning-examples\"></a>\n","\n","### Featured examples\n","\n","</div>\n","\n","- [Retraining an Image Classifier](https://tensorflow.org/hub/tutorials/tf2_image_retraining): Build a Keras model on top of a pre-trained image classifier to distinguish flowers.\n","- [Text Classification](https://tensorflow.org/hub/tutorials/tf2_text_classification): Classify IMDB movie reviews as either *positive* or *negative*.\n","- [Style Transfer](https://tensorflow.org/hub/tutorials/tf2_arbitrary_image_stylization): Use deep learning to transfer style between images.\n","- [Multilingual Universal Sentence Encoder Q&A](https://tensorflow.org/hub/tutorials/retrieval_with_tf_hub_universal_encoder_qa): Use a machine learning model to answer questions from the SQuAD dataset.\n","- [Video Interpolation](https://tensorflow.org/hub/tutorials/tweening_conv3d): Predict what happened in a video between the first and the last frame.\n"]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"A100","machine_shape":"hm","provenance":[{"file_id":"/v2/external/notebooks/intro.ipynb","timestamp":1721333597346}]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"nbformat":4,"nbformat_minor":0}