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        "<a href=\"https://colab.research.google.com/github/sufyanAshraf/Image-Segmentation-with-Transformers-from-huggingface/blob/main/segmentation_BiRefNet_huggingface_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
        "id": "KoLfH2RuxoLi"
      },
      "outputs": [],
      "source": [
        "pip install huggingface_hub"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import login\n",
        "login('enter your access token')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "97eLfu3l7uK9",
        "outputId": "e0ee4352-e593-4d7a-a662-2f354fe43fe2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n",
            "Token is valid (permission: fineGrained).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install -qr https://raw.githubusercontent.com/ZhengPeng7/BiRefNet/main/requirements.txt\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "collapsed": true,
        "id": "FMeEnrmS2vRn",
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      },
      "execution_count": null,
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        {
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          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 GB\u001b[0m \u001b[31m708.4 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.1/6.1 MB\u001b[0m \u001b[31m56.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.8/53.8 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m63.3/63.3 MB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m39.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m833.3/833.3 kB\u001b[0m \u001b[31m38.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m62.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m96.4/96.4 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\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 2.3.1+cu121 requires torch==2.3.1, but you have torch 2.0.1+cu118 which is incompatible.\n",
            "torchtext 0.18.0 requires torch>=2.3.0, but you have torch 2.0.1+cu118 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
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        "# Load BiRefNet with weights\n",
        "from transformers import AutoModelForImageSegmentation\n",
        "birefnet = AutoModelForImageSegmentation.from_pretrained('ZhengPeng7/BiRefNet', trust_remote_code=True)\n"
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            "09a89b8b4292478abe3f7c005df78e32",
            "744d9b024ca649509f3f54f06bf86a9a",
            "3a9c86fe0d43445abe9d9eaac4369659",
            "8836dededbb34ff3931d4e699f4e3e77",
            "49ff82108f5545fd9060fbade761beb3",
            "e736ccca10574303ad19657efd5943f0",
            "767cb9f419944ed3843f2f994a4863c4",
            "9e23f179f586438bb02e0bbec8adc44a",
            "054551e0f42c4c57889a7352dca6354b"
          ]
        },
        "collapsed": true,
        "id": "2wsq9m7G28D4",
        "outputId": "de08e822-e4d1-47c2-9dca-14ae3acc09e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/413 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "77425b0b74b14ea3b2c644fdea045bd4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "BiRefNet_config.py:   0%|          | 0.00/298 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cb6c8f6d8b9444f8a194d5841f69bfb7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/ZhengPeng7/BiRefNet:\n",
            "- BiRefNet_config.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "birefnet.py:   0%|          | 0.00/91.3k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6f241621b42445658440a3496c2384f1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/ZhengPeng7/BiRefNet:\n",
            "- birefnet.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/885M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5a4dda138bda443e869313adece83793"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Download codes\n",
        "!git clone https://github.com/ZhengPeng7/BiRefNet.git\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "5bGb2GgJ6Ogz",
        "outputId": "fe8b31b4-0ba4-485f-802f-dec6ecaa89fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'BiRefNet'...\n",
            "remote: Enumerating objects: 1149, done.\u001b[K\n",
            "remote: Counting objects: 100% (515/515), done.\u001b[K\n",
            "remote: Compressing objects: 100% (122/122), done.\u001b[K\n",
            "remote: Total 1149 (delta 446), reused 433 (delta 393), pack-reused 634 (from 1)\u001b[K\n",
            "Receiving objects: 100% (1149/1149), 5.36 MiB | 14.38 MiB/s, done.\n",
            "Resolving deltas: 100% (791/791), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd BiRefNet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aPKbHijT6k9V",
        "outputId": "b4a70e61-46d5-4d39-c819-4d76b7d60415"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/BiRefNet\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use codes locally\n",
        "from models.birefnet import BiRefNet\n",
        "\n",
        "# Load weights from Hugging Face Models\n",
        "birefnet = BiRefNet.from_pretrained('ZhengPeng7/BiRefNet')\n"
      ],
      "metadata": {
        "id": "lFgPGcD86gPE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Use codes and weights locally\n",
        "import torch\n",
        "from utils import check_state_dict"
      ],
      "metadata": {
        "id": "Ytq2DKdS5pw-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://github.com/ZhengPeng7/BiRefNet/releases/download/v1/BiRefNet-general-epoch_244.pth"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "2x8uaODm9c7K",
        "outputId": "0e060061-2546-4fcf-bb37-f25dc11664a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-08-23 11:18:46--  https://github.com/ZhengPeng7/BiRefNet/releases/download/v1/BiRefNet-general-epoch_244.pth\n",
            "Resolving github.com (github.com)... 140.82.116.4\n",
            "Connecting to github.com (github.com)|140.82.116.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/525717745/ba10f0ac-ac7c-413d-a416-85f4a6be3235?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=releaseassetproduction%2F20240823%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240823T111846Z&X-Amz-Expires=300&X-Amz-Signature=280a7bc02f1e3c131769dbf930827df80faeedf9d913a51a073aa3bb48868d34&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=525717745&response-content-disposition=attachment%3B%20filename%3DBiRefNet-general-epoch_244.pth&response-content-type=application%2Foctet-stream [following]\n",
            "--2024-08-23 11:18:46--  https://objects.githubusercontent.com/github-production-release-asset-2e65be/525717745/ba10f0ac-ac7c-413d-a416-85f4a6be3235?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=releaseassetproduction%2F20240823%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240823T111846Z&X-Amz-Expires=300&X-Amz-Signature=280a7bc02f1e3c131769dbf930827df80faeedf9d913a51a073aa3bb48868d34&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=525717745&response-content-disposition=attachment%3B%20filename%3DBiRefNet-general-epoch_244.pth&response-content-type=application%2Foctet-stream\n",
            "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 885082437 (844M) [application/octet-stream]\n",
            "Saving to: ‘BiRefNet-general-epoch_244.pth’\n",
            "\n",
            "BiRefNet-general-ep 100%[===================>] 844.08M  48.7MB/s    in 13s     \n",
            "\n",
            "2024-08-23 11:19:00 (64.1 MB/s) - ‘BiRefNet-general-epoch_244.pth’ saved [885082437/885082437]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "PATH_TO_WEIGHT =  \"BiRefNet-general-epoch_244.pth\""
      ],
      "metadata": {
        "id": "8d4GfXVH654F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "birefnet = BiRefNet(bb_pretrained=False)\n",
        "state_dict = torch.load(PATH_TO_WEIGHT, map_location='cpu')\n",
        "state_dict = check_state_dict(state_dict)\n",
        "birefnet.load_state_dict(state_dict)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jtncWKOM5Bt-",
        "outputId": "eb91a008-9262-42df-c2f2-8fee67d104b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q -U gdown\n",
        "# Download a sample image in very high resolution\n",
        "!gdown 1DGBe63XrwwSutS84FrrrjDga3BiuQsRc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zDwndN9z93pT",
        "outputId": "494cc820-ee3f-4415-b6a1-b7e8d17042a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1DGBe63XrwwSutS84FrrrjDga3BiuQsRc\n",
            "To: /content/BiRefNet/Helicopter-HR.jpg\n",
            "\r  0% 0.00/639k [00:00<?, ?B/s]\r100% 639k/639k [00:00<00:00, 89.5MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Imports\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "from torchvision import transforms\n",
        "from models.birefnet import BiRefNet\n"
      ],
      "metadata": {
        "id": "nslZGVcj-Tqb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# birefnet = ... # -- BiRefNet should be loaded with codes above, either way.\n",
        "torch.set_float32_matmul_precision(['high', 'highest'][0])\n",
        "# Let's try running on the CPU instead of GPU\n",
        "birefnet.to('cpu') # Changed from 'cuda' to 'cpu'\n",
        "birefnet.eval()"
      ],
      "metadata": {
        "collapsed": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aqNKQ-jV-6v0",
        "outputId": "1dc365d1-85d6-4ab4-f682-3810125570cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BiRefNet(\n",
              "  (bb): SwinTransformer(\n",
              "    (patch_embed): PatchEmbed(\n",
              "      (proj): Conv2d(3, 192, kernel_size=(4, 4), stride=(4, 4))\n",
              "      (norm): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
              "    )\n",
              "    (pos_drop): Dropout(p=0.0, inplace=False)\n",
              "    (layers): ModuleList(\n",
              "      (0): BasicLayer(\n",
              "        (blocks): ModuleList(\n",
              "          (0): SwinTransformerBlock(\n",
              "            (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
              "            (attn): WindowAttention(\n",
              "              (qkv): Linear(in_features=192, out_features=576, bias=True)\n",
              "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "              (proj): Linear(in_features=192, out_features=192, bias=True)\n",
              "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "              (softmax): Softmax(dim=-1)\n",
              "            )\n",
              "            (drop_path): Identity()\n",
              "            (norm2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
              "            (mlp): Mlp(\n",
              "              (fc1): Linear(in_features=192, out_features=768, bias=True)\n",
              "              (act): GELU(approximate='none')\n",
              "              (fc2): Linear(in_features=768, out_features=192, bias=True)\n",
              "              (drop): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (1): SwinTransformerBlock(\n",
              "            (norm1): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
              "            (attn): WindowAttention(\n",
              "              (qkv): Linear(in_features=192, out_features=576, bias=True)\n",
              "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "              (proj): Linear(in_features=192, out_features=192, bias=True)\n",
              "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "              (softmax): Softmax(dim=-1)\n",
              "            )\n",
              "            (drop_path): DropPath(drop_prob=0.009)\n",
              "            (norm2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
              "            (mlp): Mlp(\n",
              "              (fc1): Linear(in_features=192, out_features=768, bias=True)\n",
              "              (act): GELU(approximate='none')\n",
              "              (fc2): Linear(in_features=768, out_features=192, bias=True)\n",
              "              (drop): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "        )\n",
              "        (downsample): PatchMerging(\n",
              "          (reduction): Linear(in_features=768, out_features=384, bias=False)\n",
              "          (norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "      )\n",
              "      (1): BasicLayer(\n",
              "        (blocks): ModuleList(\n",
              "          (0): SwinTransformerBlock(\n",
              "            (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
              "            (attn): WindowAttention(\n",
              "              (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
              "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "              (proj): Linear(in_features=384, out_features=384, bias=True)\n",
              "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "              (softmax): Softmax(dim=-1)\n",
              "            )\n",
              "            (drop_path): DropPath(drop_prob=0.017)\n",
              "            (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
              "            (mlp): Mlp(\n",
              "              (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
              "              (act): GELU(approximate='none')\n",
              "              (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
              "              (drop): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (1): SwinTransformerBlock(\n",
              "            (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
              "            (attn): WindowAttention(\n",
              "              (qkv): Linear(in_features=384, out_features=1152, bias=True)\n",
              "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "              (proj): Linear(in_features=384, out_features=384, bias=True)\n",
              "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "              (softmax): Softmax(dim=-1)\n",
              "            )\n",
              "            (drop_path): DropPath(drop_prob=0.026)\n",
              "            (norm2): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
              "            (mlp): Mlp(\n",
              "              (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
              "              (act): GELU(approximate='none')\n",
              "              (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
              "              (drop): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "        )\n",
              "        (downsample): PatchMerging(\n",
              "          (reduction): Linear(in_features=1536, out_features=768, bias=False)\n",
              "          (norm): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "      )\n",
              "      (2): BasicLayer(\n",
              "        (blocks): ModuleList(\n",
              "          (0): SwinTransformerBlock(\n",
              "            (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (attn): WindowAttention(\n",
              "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
              "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "              (proj): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "              (softmax): Softmax(dim=-1)\n",
              "            )\n",
              "            (drop_path): DropPath(drop_prob=0.035)\n",
              "            (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (mlp): Mlp(\n",
              "              (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "              (act): GELU(approximate='none')\n",
              "              (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "              (drop): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (1): SwinTransformerBlock(\n",
              "            (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (attn): WindowAttention(\n",
              "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
              "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "              (proj): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "              (softmax): Softmax(dim=-1)\n",
              "            )\n",
              "            (drop_path): DropPath(drop_prob=0.043)\n",
              "            (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (mlp): Mlp(\n",
              "              (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "              (act): GELU(approximate='none')\n",
              "              (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "              (drop): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (2): SwinTransformerBlock(\n",
              "            (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (attn): WindowAttention(\n",
              "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
              "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "              (proj): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "              (softmax): Softmax(dim=-1)\n",
              "            )\n",
              "            (drop_path): DropPath(drop_prob=0.052)\n",
              "            (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (mlp): Mlp(\n",
              "              (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "              (act): GELU(approximate='none')\n",
              "              (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "              (drop): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (3): SwinTransformerBlock(\n",
              "            (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (attn): WindowAttention(\n",
              "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
              "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "              (proj): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "              (softmax): Softmax(dim=-1)\n",
              "            )\n",
              "            (drop_path): DropPath(drop_prob=0.061)\n",
              "            (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (mlp): Mlp(\n",
              "              (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "              (act): GELU(approximate='none')\n",
              "              (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "              (drop): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (4): SwinTransformerBlock(\n",
              "            (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (attn): WindowAttention(\n",
              "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
              "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "              (proj): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "              (softmax): Softmax(dim=-1)\n",
              "            )\n",
              "            (drop_path): DropPath(drop_prob=0.070)\n",
              "            (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (mlp): Mlp(\n",
              "              (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "              (act): GELU(approximate='none')\n",
              "              (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "              (drop): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (5): SwinTransformerBlock(\n",
              "            (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (attn): WindowAttention(\n",
              "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
              "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "              (proj): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "              (softmax): Softmax(dim=-1)\n",
              "            )\n",
              "            (drop_path): DropPath(drop_prob=0.078)\n",
              "            (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (mlp): Mlp(\n",
              "              (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "              (act): GELU(approximate='none')\n",
              "              (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "              (drop): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (6): SwinTransformerBlock(\n",
              "            (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (attn): WindowAttention(\n",
              "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
              "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "              (proj): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "              (softmax): Softmax(dim=-1)\n",
              "            )\n",
              "            (drop_path): DropPath(drop_prob=0.087)\n",
              "            (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (mlp): Mlp(\n",
              "              (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "              (act): GELU(approximate='none')\n",
              "              (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "              (drop): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (7): SwinTransformerBlock(\n",
              "            (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (attn): WindowAttention(\n",
              "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
              "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "              (proj): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "              (softmax): Softmax(dim=-1)\n",
              "            )\n",
              "            (drop_path): DropPath(drop_prob=0.096)\n",
              "            (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (mlp): Mlp(\n",
              "              (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "              (act): GELU(approximate='none')\n",
              "              (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "              (drop): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (8): SwinTransformerBlock(\n",
              "            (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (attn): WindowAttention(\n",
              "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
              "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "              (proj): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "              (softmax): Softmax(dim=-1)\n",
              "            )\n",
              "            (drop_path): DropPath(drop_prob=0.104)\n",
              "            (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (mlp): Mlp(\n",
              "              (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "              (act): GELU(approximate='none')\n",
              "              (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "              (drop): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (9): SwinTransformerBlock(\n",
              "            (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (attn): WindowAttention(\n",
              "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
              "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "              (proj): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "              (softmax): Softmax(dim=-1)\n",
              "            )\n",
              "            (drop_path): DropPath(drop_prob=0.113)\n",
              "            (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (mlp): Mlp(\n",
              "              (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "              (act): GELU(approximate='none')\n",
              "              (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "              (drop): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (10): SwinTransformerBlock(\n",
              "            (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (attn): WindowAttention(\n",
              "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
              "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "              (proj): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "              (softmax): Softmax(dim=-1)\n",
              "            )\n",
              "            (drop_path): DropPath(drop_prob=0.122)\n",
              "            (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (mlp): Mlp(\n",
              "              (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "              (act): GELU(approximate='none')\n",
              "              (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "              (drop): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (11): SwinTransformerBlock(\n",
              "            (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (attn): WindowAttention(\n",
              "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
              "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "              (proj): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "              (softmax): Softmax(dim=-1)\n",
              "            )\n",
              "            (drop_path): DropPath(drop_prob=0.130)\n",
              "            (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (mlp): Mlp(\n",
              "              (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "              (act): GELU(approximate='none')\n",
              "              (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "              (drop): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (12): SwinTransformerBlock(\n",
              "            (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (attn): WindowAttention(\n",
              "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
              "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "              (proj): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "              (softmax): Softmax(dim=-1)\n",
              "            )\n",
              "            (drop_path): DropPath(drop_prob=0.139)\n",
              "            (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (mlp): Mlp(\n",
              "              (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "              (act): GELU(approximate='none')\n",
              "              (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "              (drop): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (13): SwinTransformerBlock(\n",
              "            (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (attn): WindowAttention(\n",
              "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
              "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "              (proj): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "              (softmax): Softmax(dim=-1)\n",
              "            )\n",
              "            (drop_path): DropPath(drop_prob=0.148)\n",
              "            (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (mlp): Mlp(\n",
              "              (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "              (act): GELU(approximate='none')\n",
              "              (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "              (drop): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (14): SwinTransformerBlock(\n",
              "            (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (attn): WindowAttention(\n",
              "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
              "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "              (proj): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "              (softmax): Softmax(dim=-1)\n",
              "            )\n",
              "            (drop_path): DropPath(drop_prob=0.157)\n",
              "            (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (mlp): Mlp(\n",
              "              (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "              (act): GELU(approximate='none')\n",
              "              (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "              (drop): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (15): SwinTransformerBlock(\n",
              "            (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (attn): WindowAttention(\n",
              "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
              "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "              (proj): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "              (softmax): Softmax(dim=-1)\n",
              "            )\n",
              "            (drop_path): DropPath(drop_prob=0.165)\n",
              "            (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (mlp): Mlp(\n",
              "              (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "              (act): GELU(approximate='none')\n",
              "              (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "              (drop): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (16): SwinTransformerBlock(\n",
              "            (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (attn): WindowAttention(\n",
              "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
              "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "              (proj): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "              (softmax): Softmax(dim=-1)\n",
              "            )\n",
              "            (drop_path): DropPath(drop_prob=0.174)\n",
              "            (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (mlp): Mlp(\n",
              "              (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "              (act): GELU(approximate='none')\n",
              "              (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "              (drop): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (17): SwinTransformerBlock(\n",
              "            (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (attn): WindowAttention(\n",
              "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
              "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "              (proj): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "              (softmax): Softmax(dim=-1)\n",
              "            )\n",
              "            (drop_path): DropPath(drop_prob=0.183)\n",
              "            (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (mlp): Mlp(\n",
              "              (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
              "              (act): GELU(approximate='none')\n",
              "              (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
              "              (drop): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "        )\n",
              "        (downsample): PatchMerging(\n",
              "          (reduction): Linear(in_features=3072, out_features=1536, bias=False)\n",
              "          (norm): LayerNorm((3072,), eps=1e-05, elementwise_affine=True)\n",
              "        )\n",
              "      )\n",
              "      (3): BasicLayer(\n",
              "        (blocks): ModuleList(\n",
              "          (0): SwinTransformerBlock(\n",
              "            (norm1): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
              "            (attn): WindowAttention(\n",
              "              (qkv): Linear(in_features=1536, out_features=4608, bias=True)\n",
              "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "              (proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
              "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "              (softmax): Softmax(dim=-1)\n",
              "            )\n",
              "            (drop_path): DropPath(drop_prob=0.191)\n",
              "            (norm2): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
              "            (mlp): Mlp(\n",
              "              (fc1): Linear(in_features=1536, out_features=6144, bias=True)\n",
              "              (act): GELU(approximate='none')\n",
              "              (fc2): Linear(in_features=6144, out_features=1536, bias=True)\n",
              "              (drop): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (1): SwinTransformerBlock(\n",
              "            (norm1): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
              "            (attn): WindowAttention(\n",
              "              (qkv): Linear(in_features=1536, out_features=4608, bias=True)\n",
              "              (attn_drop): Dropout(p=0.0, inplace=False)\n",
              "              (proj): Linear(in_features=1536, out_features=1536, bias=True)\n",
              "              (proj_drop): Dropout(p=0.0, inplace=False)\n",
              "              (softmax): Softmax(dim=-1)\n",
              "            )\n",
              "            (drop_path): DropPath(drop_prob=0.200)\n",
              "            (norm2): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
              "            (mlp): Mlp(\n",
              "              (fc1): Linear(in_features=1536, out_features=6144, bias=True)\n",
              "              (act): GELU(approximate='none')\n",
              "              (fc2): Linear(in_features=6144, out_features=1536, bias=True)\n",
              "              (drop): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (norm0): LayerNorm((192,), eps=1e-05, elementwise_affine=True)\n",
              "    (norm1): LayerNorm((384,), eps=1e-05, elementwise_affine=True)\n",
              "    (norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "    (norm3): LayerNorm((1536,), eps=1e-05, elementwise_affine=True)\n",
              "  )\n",
              "  (squeeze_module): Sequential(\n",
              "    (0): BasicDecBlk(\n",
              "      (conv_in): Conv2d(5760, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (relu_in): ReLU(inplace=True)\n",
              "      (dec_att): ASPPDeformable(\n",
              "        (aspp1): _ASPPModuleDeformable(\n",
              "          (atrous_conv): DeformableConv2d(\n",
              "            (offset_conv): Conv2d(64, 2, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (modulator_conv): Conv2d(64, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (regular_conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          )\n",
              "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (aspp_deforms): ModuleList(\n",
              "          (0): _ASPPModuleDeformable(\n",
              "            (atrous_conv): DeformableConv2d(\n",
              "              (offset_conv): Conv2d(64, 2, kernel_size=(1, 1), stride=(1, 1))\n",
              "              (modulator_conv): Conv2d(64, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "              (regular_conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            )\n",
              "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): _ASPPModuleDeformable(\n",
              "            (atrous_conv): DeformableConv2d(\n",
              "              (offset_conv): Conv2d(64, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "              (modulator_conv): Conv2d(64, 9, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "              (regular_conv): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            )\n",
              "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): _ASPPModuleDeformable(\n",
              "            (atrous_conv): DeformableConv2d(\n",
              "              (offset_conv): Conv2d(64, 98, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
              "              (modulator_conv): Conv2d(64, 49, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
              "              (regular_conv): Conv2d(64, 256, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), bias=False)\n",
              "            )\n",
              "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (global_avg_pool): Sequential(\n",
              "          (0): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "          (1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (3): ReLU(inplace=True)\n",
              "        )\n",
              "        (conv1): Conv2d(1280, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (dropout): Dropout(p=0.5, inplace=False)\n",
              "      )\n",
              "      (conv_out): Conv2d(64, 3072, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (bn_in): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (bn_out): BatchNorm2d(3072, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (decoder): Decoder(\n",
              "    (ipt_blk5): SimpleConvs(\n",
              "      (conv1): Conv2d(3072, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (conv_out): Conv2d(64, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    )\n",
              "    (ipt_blk4): SimpleConvs(\n",
              "      (conv1): Conv2d(768, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (conv_out): Conv2d(64, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    )\n",
              "    (ipt_blk3): SimpleConvs(\n",
              "      (conv1): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (conv_out): Conv2d(64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    )\n",
              "    (ipt_blk2): SimpleConvs(\n",
              "      (conv1): Conv2d(48, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (conv_out): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    )\n",
              "    (ipt_blk1): SimpleConvs(\n",
              "      (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (conv_out): Conv2d(64, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "    )\n",
              "    (decoder_block4): BasicDecBlk(\n",
              "      (conv_in): Conv2d(3456, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (relu_in): ReLU(inplace=True)\n",
              "      (dec_att): ASPPDeformable(\n",
              "        (aspp1): _ASPPModuleDeformable(\n",
              "          (atrous_conv): DeformableConv2d(\n",
              "            (offset_conv): Conv2d(64, 2, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (modulator_conv): Conv2d(64, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (regular_conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          )\n",
              "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (aspp_deforms): ModuleList(\n",
              "          (0): _ASPPModuleDeformable(\n",
              "            (atrous_conv): DeformableConv2d(\n",
              "              (offset_conv): Conv2d(64, 2, kernel_size=(1, 1), stride=(1, 1))\n",
              "              (modulator_conv): Conv2d(64, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "              (regular_conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            )\n",
              "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): _ASPPModuleDeformable(\n",
              "            (atrous_conv): DeformableConv2d(\n",
              "              (offset_conv): Conv2d(64, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "              (modulator_conv): Conv2d(64, 9, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "              (regular_conv): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            )\n",
              "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): _ASPPModuleDeformable(\n",
              "            (atrous_conv): DeformableConv2d(\n",
              "              (offset_conv): Conv2d(64, 98, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
              "              (modulator_conv): Conv2d(64, 49, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
              "              (regular_conv): Conv2d(64, 256, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), bias=False)\n",
              "            )\n",
              "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (global_avg_pool): Sequential(\n",
              "          (0): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "          (1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (3): ReLU(inplace=True)\n",
              "        )\n",
              "        (conv1): Conv2d(1280, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (dropout): Dropout(p=0.5, inplace=False)\n",
              "      )\n",
              "      (conv_out): Conv2d(64, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (bn_in): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (bn_out): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (decoder_block3): BasicDecBlk(\n",
              "      (conv_in): Conv2d(1920, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (relu_in): ReLU(inplace=True)\n",
              "      (dec_att): ASPPDeformable(\n",
              "        (aspp1): _ASPPModuleDeformable(\n",
              "          (atrous_conv): DeformableConv2d(\n",
              "            (offset_conv): Conv2d(64, 2, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (modulator_conv): Conv2d(64, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (regular_conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          )\n",
              "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (aspp_deforms): ModuleList(\n",
              "          (0): _ASPPModuleDeformable(\n",
              "            (atrous_conv): DeformableConv2d(\n",
              "              (offset_conv): Conv2d(64, 2, kernel_size=(1, 1), stride=(1, 1))\n",
              "              (modulator_conv): Conv2d(64, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "              (regular_conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            )\n",
              "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): _ASPPModuleDeformable(\n",
              "            (atrous_conv): DeformableConv2d(\n",
              "              (offset_conv): Conv2d(64, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "              (modulator_conv): Conv2d(64, 9, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "              (regular_conv): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            )\n",
              "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): _ASPPModuleDeformable(\n",
              "            (atrous_conv): DeformableConv2d(\n",
              "              (offset_conv): Conv2d(64, 98, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
              "              (modulator_conv): Conv2d(64, 49, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
              "              (regular_conv): Conv2d(64, 256, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), bias=False)\n",
              "            )\n",
              "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (global_avg_pool): Sequential(\n",
              "          (0): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "          (1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (3): ReLU(inplace=True)\n",
              "        )\n",
              "        (conv1): Conv2d(1280, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (dropout): Dropout(p=0.5, inplace=False)\n",
              "      )\n",
              "      (conv_out): Conv2d(64, 768, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (bn_in): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (bn_out): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (decoder_block2): BasicDecBlk(\n",
              "      (conv_in): Conv2d(960, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (relu_in): ReLU(inplace=True)\n",
              "      (dec_att): ASPPDeformable(\n",
              "        (aspp1): _ASPPModuleDeformable(\n",
              "          (atrous_conv): DeformableConv2d(\n",
              "            (offset_conv): Conv2d(64, 2, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (modulator_conv): Conv2d(64, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (regular_conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          )\n",
              "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (aspp_deforms): ModuleList(\n",
              "          (0): _ASPPModuleDeformable(\n",
              "            (atrous_conv): DeformableConv2d(\n",
              "              (offset_conv): Conv2d(64, 2, kernel_size=(1, 1), stride=(1, 1))\n",
              "              (modulator_conv): Conv2d(64, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "              (regular_conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            )\n",
              "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): _ASPPModuleDeformable(\n",
              "            (atrous_conv): DeformableConv2d(\n",
              "              (offset_conv): Conv2d(64, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "              (modulator_conv): Conv2d(64, 9, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "              (regular_conv): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            )\n",
              "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): _ASPPModuleDeformable(\n",
              "            (atrous_conv): DeformableConv2d(\n",
              "              (offset_conv): Conv2d(64, 98, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
              "              (modulator_conv): Conv2d(64, 49, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
              "              (regular_conv): Conv2d(64, 256, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), bias=False)\n",
              "            )\n",
              "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (global_avg_pool): Sequential(\n",
              "          (0): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "          (1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (3): ReLU(inplace=True)\n",
              "        )\n",
              "        (conv1): Conv2d(1280, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (dropout): Dropout(p=0.5, inplace=False)\n",
              "      )\n",
              "      (conv_out): Conv2d(64, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (bn_in): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (bn_out): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (decoder_block1): BasicDecBlk(\n",
              "      (conv_in): Conv2d(480, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (relu_in): ReLU(inplace=True)\n",
              "      (dec_att): ASPPDeformable(\n",
              "        (aspp1): _ASPPModuleDeformable(\n",
              "          (atrous_conv): DeformableConv2d(\n",
              "            (offset_conv): Conv2d(64, 2, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (modulator_conv): Conv2d(64, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "            (regular_conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          )\n",
              "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (relu): ReLU(inplace=True)\n",
              "        )\n",
              "        (aspp_deforms): ModuleList(\n",
              "          (0): _ASPPModuleDeformable(\n",
              "            (atrous_conv): DeformableConv2d(\n",
              "              (offset_conv): Conv2d(64, 2, kernel_size=(1, 1), stride=(1, 1))\n",
              "              (modulator_conv): Conv2d(64, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "              (regular_conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            )\n",
              "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (1): _ASPPModuleDeformable(\n",
              "            (atrous_conv): DeformableConv2d(\n",
              "              (offset_conv): Conv2d(64, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "              (modulator_conv): Conv2d(64, 9, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "              (regular_conv): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            )\n",
              "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "          (2): _ASPPModuleDeformable(\n",
              "            (atrous_conv): DeformableConv2d(\n",
              "              (offset_conv): Conv2d(64, 98, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
              "              (modulator_conv): Conv2d(64, 49, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
              "              (regular_conv): Conv2d(64, 256, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), bias=False)\n",
              "            )\n",
              "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "            (relu): ReLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (global_avg_pool): Sequential(\n",
              "          (0): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "          (1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "          (3): ReLU(inplace=True)\n",
              "        )\n",
              "        (conv1): Conv2d(1280, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "        (relu): ReLU(inplace=True)\n",
              "        (dropout): Dropout(p=0.5, inplace=False)\n",
              "      )\n",
              "      (conv_out): Conv2d(64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (bn_in): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (bn_out): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (conv_out1): Sequential(\n",
              "      (0): Conv2d(240, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "    )\n",
              "    (lateral_block4): BasicLatBlk(\n",
              "      (conv): Conv2d(1536, 1536, kernel_size=(1, 1), stride=(1, 1))\n",
              "    )\n",
              "    (lateral_block3): BasicLatBlk(\n",
              "      (conv): Conv2d(768, 768, kernel_size=(1, 1), stride=(1, 1))\n",
              "    )\n",
              "    (lateral_block2): BasicLatBlk(\n",
              "      (conv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1))\n",
              "    )\n",
              "    (conv_ms_spvn_4): Conv2d(1536, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "    (conv_ms_spvn_3): Conv2d(768, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "    (conv_ms_spvn_2): Conv2d(384, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "    (gdt_convs_4): Sequential(\n",
              "      (0): Conv2d(1536, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (2): ReLU(inplace=True)\n",
              "    )\n",
              "    (gdt_convs_3): Sequential(\n",
              "      (0): Conv2d(768, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (2): ReLU(inplace=True)\n",
              "    )\n",
              "    (gdt_convs_2): Sequential(\n",
              "      (0): Conv2d(384, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (2): ReLU(inplace=True)\n",
              "    )\n",
              "    (gdt_convs_pred_4): Sequential(\n",
              "      (0): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "    )\n",
              "    (gdt_convs_pred_3): Sequential(\n",
              "      (0): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "    )\n",
              "    (gdt_convs_pred_2): Sequential(\n",
              "      (0): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "    )\n",
              "    (gdt_convs_attn_4): Sequential(\n",
              "      (0): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "    )\n",
              "    (gdt_convs_attn_3): Sequential(\n",
              "      (0): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "    )\n",
              "    (gdt_convs_attn_2): Sequential(\n",
              "      (0): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1))\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "def extract_object(birefnet, imagepath):\n",
        "    # Data settings\n",
        "    image_size = (1024, 1024)\n",
        "    transform_image = transforms.Compose([\n",
        "        transforms.Resize(image_size),\n",
        "        transforms.ToTensor(),\n",
        "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "    ])\n",
        "\n",
        "    image = Image.open(imagepath)\n",
        "    input_images = transform_image(image).unsqueeze(0).to('cpu')\n",
        "\n",
        "    # Prediction\n",
        "    with torch.no_grad():\n",
        "        preds = birefnet(input_images)[-1].sigmoid().cpu()\n",
        "    pred = preds[0].squeeze()\n",
        "    pred_pil = transforms.ToPILImage()(pred)\n",
        "    mask = pred_pil.resize(image.size)\n",
        "    image.putalpha(mask)\n",
        "    return image, mask\n",
        "\n",
        "# Visualization\n",
        "plt.axis(\"off\")\n",
        "plt.imshow(extract_object(birefnet, imagepath='/content/BiRefNet/Helicopter-HR.jpg')[0])\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "J-ZV57v26CVr",
        "outputId": "2d7ed16a-f61e-476c-f558-1b66ac86f3e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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+IFfww1MEA88QxhiWl1c4ePAgcRJTLlcYHBigWq3ieZ6txToOKj+dCFEsLM81hBBs2rSJf/22n2N2bp79+/YzOryBq666grGxkXV9+Y7jsHPndl796lfx+c9/liSJ8TyfgcFBllYWMVqjlK2P9zQDQL7Rk/sZQJZm9jIGeid7Y3R/wyZX+mujUVJSq1V529vewpVXXo4wkrtvv5W4OU8riFjspEzNLDA1dZTF+SXCMCROY6QSeUZBkqYZaZqgdQZAlA9lCoIAx+29Px/75KyUwvO8E563dRtzfvLrmTclSUocR4RhRBiGdLsdwiAgCAPCIKYRtojjyAYn2gZNyrWlBseVeJ6H73u4eSeF53q4rmPvqyNxlERJhZSuLVMolZdg8mAMCSbPxOiULElpTM+gFxuIrtVPpBK0qzBCgl/Gr1YQQpJkGY7v4/geXqmMdByksBoOoRTKdRFS9gdaqccRRz4fP+dZlrF3737++q//muuu+yoLC/N5kLpGd2IjVqRSCOUivTL7SzVu80ep1keIS2XK45MMjm/mnNO28qJdm7hg+0a2jg8yUC3nZannvvGW/QzEVCrlZ/W9UAQDzxBhGPHNb36Dhx56CM/z1pyEXEqlEr5folQqU6/XGBgYpF6vU6/XqVYr+KWSPR05yk7HOy5YeD4uJs8WSknq9Rr1eo3TTt0BPP7zn2UZd975XbrdDiaDN73pp7j6Jddw330PsHfvXg7sO0Cr3SIIOiRplAcANg0usKflJEkwuSWxEOSdBvYU3kuJ2/JAhhCKgXqVsfFR4iQhCWKmd9/H4kM3Ix049yWv4y3/8tfQ2uHw9AwPP/wI99//EAcO7GdpaZEsNdRqkKZxHmQIhBTs3b+XP/rj91Cr1xgeGmZ0ZJSt27azedNm6gMDlHKToscT8h3fEWDXZwW4lMsAtf7vV7MNvYyD7mc+0iQhThLCMKTT6dLtdml32nQ7bTqtDnFiJz4KtC1zSInjKZSUtmPCdZHK2GBhTVnCzdPObq8NcqiMP1xDKfs5RRgwGhJNkmQkaUrSDfA9h0qtQhQnLOw/QNLqQBhRTlIkGbE0GKHQueDRuC7KdfEqFYTv4fglnJKP4/soz8X1PJTn22yHUnlJQ52ge/jnThhGfPADH+Y97/kDllcWAPqaEcdRq6UenWec0hRjQui0cJwlDNNEUuE6isT1CaqDTN80wjfGJqnUxxnbuIntW7dw3umTXHrGJs7YPMzoQA3PdZ9zwUGWZXz+n77EQmOOt7315x8zmH6mKIKBZ4hms8WRI0cJgoA4jvujb+3vmggh0dp609tarlWi91KinleiUq1QrZapVKpUylWqtSrlcplypUy5VMbvp1OdPGh4fp8+nm6+33MaRTG33nobWZYhhcO/euu/5OqrrwL+BVobWq02CwuLzM7M8bGPfYzP/tNnCIJu34pY5yJFYzQIhTa6X/3upeV7GQX7Wtv6ehjYlHxrZZnuygIHZ6w50paVBdKoRaVWYdeucc4+e5I3vumVrDQ6PPzgHm666WbuuON2ZmZm8o3SLp6dVofrvvJ1PM+1izYwsWEjGzduYHR0mJGREUZGxti+fTsXXHAB9VrtMTewH+Q9uHpZsM+CXbztGlled1ljTJ4wWc026Dx7kGWaNLUnriiypZFut0un0yYIArqtiCRukhkN5NoGZTdfJSUoiec6uEohleyXCKSUOEIiyy44LqEwUPEYOW0rQlthh9IZJk0wSYqJQ7JuQpokJFGCDENoNEnShG6WoGzsRywVmRIYaW8HRyFdB+WXcEplSqUybrWM8D3cUgWv5ON4eUDjOqi880IK2at2PCc/82EY8Qfv+WP+9E/+iCgK6Fl898phvSC3d9/X/k4KQZIk1pnTGGIhMXQRzRUUhxBH7qNbKrPs1TlUG+Ebg1twalvYvGmCM3Zs5Zydmzn31E28YOcEm0YG7KTQZ5n5+SU+8LcfZrk9y+WXXsb5553/rL1uz/6z8TxBKWnTukCSJCcMlBFC56lKQZrG/UW02zXrFlZtMpuqhTzl6fYDCN8v4Zd8Sr5PrVanXh+gUqlQKpeo1WqUy3nA4Lr93vX1acvefXnuLSL/HAnDkKmpabSGjVs2cO65Z/dPJlLC8PAgw8ODnH76Ti66+AIOHjrIt799fX4ysu8Hk+XiOQmOK/OTsl1Abc1d53X2CmmasLS8wl/+5V9z5VWXUi87NMOErvRJk65V5ZMSRgFCGyBASoehwTrXvvQKLrzoTBYX38AjD++m2WpRqVYZGhy0Bkeui6u8vlAvTlJ0pkmSmE63S9jpcu/d91CvlpmcnMyDFmPT8LnfgMpP4b333/Gn3h8W28qITSX3sw2Pz1qNw2rwkJGmGUmckKSxDR7CiDiJCcKQMAqJ4pg0iEiTFGPS/MZN/tkBIQVu391RI3Nxm+84Vpw4UEKIMp7j4EpwpfVeyLRBYyddisSWTdIoIY4isjRBRwmy2yVdXKGTZmRZkj8Smx0S0kF6LsbzkKU8UKhWUL6H8n28SgW/VMkDBpuV6D/nctU9shcM9Z7Tp4ssy/jHj32SP//zPyWKAhvk9h5RvunbYG6N5iN/rTCGNO+MSbVGSIFS+d8Z+zOpFUQRSraIlmZQajeZcjhUqnG0Osy3hybpjl7E4KaNvP7CLbz9ledx1vZN/YD6mUZrzVe+dB333383QiX8+V/8Ce/+3f/L6Mjos3J/imDgGWJwcICzzjqTw4cP9Rdx62Vv00K9NGvP3tYY0zegAfLL28VGIzBZhlQKIyJWneBa+UJnTzu9dqueqY3jKDzfszVMz8X3S1SrNcqlEpVKiVqtTqVStmWLUolSqYTnevnCnS8gRbbhB8ZxHGq1GkIIrn7x1QwNDT7uZaWURJE1ITJG99vvMjKMsducyAzkfffiOKGUlKvvoUd3H+DAgcNUSh6bNm6gNjTBSNmjGbvs2bOPUslHuJJqdYByuYLrdvvvPeVknHbmJFmaIR1FuVzGdWy923NKdm6BcvHcXOOSp9a1NggBSrj2fmur9k+zzHZJZLk2IFrdAAQqV+/LfgfB2iDV+gg4J1geP1nWvm/Xlioej96m1PtMZWlGmmWkqRVJ2s4JK7hM05Q4ioiikCRNrF11N7QHAK3JtAZSMCmeEKANRtjXTQqJkHmnhfSQJYFyS5S9Oko6IKT9qq1glFQjDaRJRhYnZFFIFoXododgeRkdp+TGFFaDIiWZlGSuQnm2DVQpF+X5GNdB+C6u5+OUSnilMo7v4fo+rp9nHta8Jk/lddj9yB5+7//8HmGnS57TQa/Vzqx5jXrrnzEGstWMWM+YCwxmXeeIdcASCHSWkvVKanEEQZe4tUxpaQY1ez/NmbP5yPQlfPXGA/yvd17Ga198Pp77+O+Dp4tjxxb58If/AYPtAvr2DTfynVtu5Cd/4rXPSoBSBAPPEK7rcvXV19Dtxtx4043s3XuQ8bFxJibG0SYjjkOktGrx3JEW13XXbO69Oqog09rW07LEBgRmffag9992obICM2KbQhbt9R9kx3HXtVK5rkumNVJYZXvJ9/E8n3K5RLlcsRmGSplKpUKlUqNSKedT9GzWYd0kvuc5fsnnX/z0v2B27hi/9mu/8oQfcG00zWaLLLXeBPZnBiQIY8hMhnQ8Ttl2CkYbZudmCcOu7QrQGUiBlPa9oNGEUUYYxSw1uhgtUEpw2wO7Gf7GdxgYrDA4UKdcqTA0NGznKTgKjMkDRpehoUFqNatZKXt+Xm/3UMqmzj1H4LirNW2JoVKuIL0SMvcU6AnmlPJwXOs4iFFYPaRBaABNliUkSUAck7sU5pbMBhACR9m2QiXVavuhu95b4OkIUNe+j3tZjSfixO4KvRooxDFpak/9aZqQxBFJHJHGEVE3IIsCwkYXkzSIw4AsThA6QxqNxNjMg/TIXBfjKNySh+iJJisV1NCg1T9IiQQcZb8ao8kSQ5qk6CRFpgkijomDgDToksYxsUmRqYFMWMeL/CUQykH4JZxSCbdUQpV83EoJVfZxXR+3VLLvC8e+1r1gzhpoiXXZzziO+Zu/ej8Li3MYUrTJ8hdYkSQpaI2jFEZKhFD4rkOSZei8i8bGT2k+vTM35eqVxozBybVUJnfBFEr2swxCSLI0JjQZIukiWreQtpc5MnEN7/rjb9HudvjZH7sCz3vmAoIs01z3tW+y/8BuMpOiE00QhHzpi1/g2mtexkB94Bm7Lz2KYOAZpF6v8frX/yQXX3wRt9x8G/c/8CALCw2GBofwvFJ+WjFkOgaseUxPdd1bmFzX7QcHvdamVRtY+qWI3s96J6xeJmItxhjifHpebwHrfYB7abuOLUTbyDwPGkw/lZfhei5KOZRLFQYGBxkZHWXDxAbGx8fZvHkz5XLpeRkYpGnKddd9nTu+eys//uOvZmJi9AmfB51p4jjCaE2qDcpxsOlbkWsF7DCk973/fezadSYPPfQI7/7d3+PmW25CpwnQyyrZUxOQB4TG/tOapBHRaLYRQqHyfnq/5FOuVEiSKPcKyHBcSbVapV6vMzoyyvj4CGNjYwwPDzE8PMRAvU6tWsVxXDzHPibrV9BEONZJ0XFAOSLvMLCqetdR/ZY8x3FwpYujHEQ/65Sr+9Fg8sdu/RQRRmCwi3oWQ6edYcvtdkPpdXAo5eDm2az+xvQMZbMeq7vi+2Ueel/XZyBs5iGJY6IgJOp0SYOQJOzS7bRJ2l2iVpe0s0S7G6DDLmkW283VMUjPBS/vYCiVbVdFycPxfaTnoSo+ql7JvRwkVc+3XRCZxGQGnWWQZehY2/bLJMEELZKVRWKdkhlNqrElLGOQCIwQpPkGbaTALfm4A3W8WhXXK7G4uIJeaXLO5kmanRVCmdBMO/YVjBU6ztApSNdh5xln8KY3vZmVlSaHD01x+NBhZmaOsrSyRBSFoPOsgsn6Lbn5M2qzVDYdYv0p8uyCMfmamKZIKZDHHsJJMha6V/Cf//TbVHyXN7300mfsRL64uMzHP/oxksRmknoGYfv37ycIgmclGChMh54l7DCZhDCMaDZbtFodlpaXWVlp0Gm3aDSaNFeW+ieK3kIhBMRJAkjSLAWy/ma/1t++Vwu0iDw4sK1qjmMV0r3L9soRa0fpWhe73A1PqnxQT34SFAJtbO1YCBsc5JZ3tl0rXwy3bt3OlS++iuGhIaqVil2c1qQbezyXgwVjDEma0mq1+y56nut9X9OTI0eO8qpXv4rFhQXOP+8FfOrTn6RSKT/u5VdWGlx15dXs378HAKW8XBBoTYqEkAwPDXPTd25i584dfROkffsOsH/fQfbu28dDD97Pl7/8ZcIwpFItMVgfot3u0O50bH+9yTNK2iCEzi2EyYWrIhcsWutiN68vn3XW2ew66ywQ0G62mTl6lHanietKBgaGGBkeZmJ8gpHRUU7deSqbt02u9vInEWmSgI5t2UBYoaOUJhfqKZTqZRhscOI5tvzgKruZCUeCUEjRaxMTdiiSAS16C3/vWczr34j+ydFmGYRtL8wzCau6hTU19H8Gpa+1wQOGvneCzjLSJCXqBoSdLnHHlguC5RXibpewsULcbRF32pgkIctiMlIQBuVqfL+C8B3cimf1BpU6qlRFuL4VKvoeQjmoXnsmmsxIjJEII3AxmDizZZQoIU7SXDyZkEZWgxF2AxvErCzSiFp0awnT2+e5d9MBGnEXtdxGtzJMp0otK3P6pjMZ0ZPU5SAlVSaLfVqtlJWlBseOHOXo1DTTU4dZXFkkjkN7YNEpUjqAXYtsloG+F0cPKRVCuCjlY0bPJBm8kNO31fnUH72DU7dNPu2vo9aaj3/sc/zGf3oXUdyyluSA53ns2LmdT33yM0yMjz/t9+N4iszAs4SUklLJp1TyGRqyUeDauKwncLIpxoQojGl32iwtLdJqtWk0W7RbHRrNBo3GCkkckyRxP5uQJGl/w+pnBIzEdT2UI0jiBI0VocFqStQK0txVvYIQIAWmVzNN037Q0cskSKVIk9RG7CbLR+6m7Nu7h8OHDxJnKbMzM4RByOjIMFsmtzA6MkJ9cIixkVFGRsYYGOgJHsv9CX3PhX7sVqvN//rf/5tvfet6KpUyw8MjnH/eubziFa/g3HPPYXBwsF+r72GM4dFHdzM3O0sQBAwND61TLvde57V/k2WZ3TDX/DzrCwmh12Ig1aqXQblc4txzd3Huubts0JKkfP4LX+K3/stv8Vv/7Td59ateTRhGHD58mM9++nP8w9//A92wa/UGQvdtlFdT2fa9EEURUkqq1Rq/8zv/m0sueSFg35OdTpf5+UWOHp0hTVLqAzV27NiO6zqUSj6u2xPSrXY8pGmatwXGhGFIFAQEYUgQdAmjLu1OSJKE+dRG0/dacBwHTxmUFKAkRkiEAt93KTlu7jcgkdLp6wtsZiQvSQhBqm2aOGU1K2bjZJV/LnoDkxykcPJBT27+HlyfWXi2A4bjb18i172v6kOrp8n1LZp5Jk8bdJZnHuKYJIqJ2m26Sw26y0sEywu0l5eJFttErVk67RaOsPoP4YHyFI5rUKUSTrUOJRdZLuN4ZVzPw/E82x5ZdpDKxXFq+FJRFRKZKlb2TLF4+61sXZnGDFTYOiWpXpjxjTcucWwiQWYZoGlkbfYkR/C6HqWWQndi0gWH0rJPabBCqT7MxuFtTO7cgg5TjhycYurgIVrtBdLMkCLRqUGL1GadxOoAsNUnSJMlXczKPqQ/zOHpjXzo09/gv//qv3za9QNLSw0+9MEPkaQJKtdj9D4rExMbKJX8p/X2H48iGHgOsXaRUUrk3u+OdX4bhAlG2blz+wmq6DhJ7MkgjGh37Emw0WiyuLBIp9Oh2WqyuLhI0OkQhhEmzE9Pkn4NtmfxijHEUYzNJmiEtLdl66WrquPeIg+5cA2BMRlSSTslT0qMsClHhWB0eISZcIabb76Z2dk5tBZ5AG/FU67rUimXGRoaZGR0hPHxcUZHRxkfH2PT5s1s2LCB0ZFRBocGqdVq+L6fm8y468RNxz+PTwVjDNffcAM3XH8DtWqFffv2sk/v4Z677uTjH/s4Gzdt4KKLL+RFV72Ys88+m7GxcVzPBWP43p130unYCYDj42M0mw3SLOPokSPs3beXgYEBLr3kMgYHB5BSEscxQRjk9x8QGldJktRmg4TIZwE8TjuUEALPc7nmmhczvmGU226/mZ941U+wectGtkxu4vwXnM+e/fu46cYbQeh1Aclqh8NqlshxHEZHR9myZUs/daoUDA0NMDQ0wOmnn/IDPYeu6wAnLm5rrWetgVJq38NxTBgEdIOATrtD2G0TRyFh0CVNIwwabVpIBK4LSgpk3hLoqt4URA+pHIRSoOQ662GBFewZITD52GiEsBkrVD+boBw7iAlkrsUgDzhyw6O+4NHt/7w3pVGIZz/DsL5F8/jUtw9U8+839H+6tlyR5UGD/WczD3EQELbaRN0uUbtDa3GRbmOJaKHNcmuWMAjIhAbH4EiJdCXKEyjfQXkVpHGZX5zF3H0vCo1yJRPfMuxsKx78qYTQE2iVIoVGOgbf6eJsAC0M4U6oSkhSQTeQlI/cz8BtLuLGOmNmC0P1OluygImqzxIei0lGI41pRiFhHCHViTomA5iwidfai1A1vvD1e/iFN/8Yp2zd/DS9KjYr8J3v3MruRx9CmxSF6B+wPN/jhZe8kEq58rTd/hNRBAP/DDleFe04yrZgD9QYx7alrA0YMp33XScJQRAQBhGdbkCr2aLVbhIEIa1Wk1azRRTFtNsd0jjvjc5SUpNijCKJIwyQZWFunKQAO2hGOQ5JEpMkUd8vYVURriiVSmzbto2NGzcyNzfP8OA4GzduRjmKmdmjHD06w+LCMZaWl9nz6AEeuH83cRz2gw+Djeodx8XzfKrVMoNDtjVvbHyULVsm2bxxktHRUYaGbfq6PjhIrVqhVCrnxjiKH6aGbIzhnnvuYnSsjuO41Ot1FhcX88ArpN1usm/fPv7ps5+jXq+zceNG8gwuszMzKOWgdcY//MPf88UvfqG/qWaZbQ8947RTednLXsZZ55zD7NwsnU6D3qhf0TuR5eUcWwayUw2zLDuh1NK7v3fdeRf33/cAD9x/Ly+55mX81E+9GYByyeeqq67gtltvJ46D/oTEXhagp+i3r5nD2Ng4b37zTz1t6crea9B///LYzoWrbYC2BTCKYltLj22WIQi6xHFAtxvQ7TZJ09imwY3V2SDAcSRCWoGar1TuwyGQSuJIB9dRaMfaMwupQLjo1CHLNTd21zB9QW/vvmfC6XdE2O4dhZQOTh4k2FJLbyyz6nde9B7/c43jBZN8H0Fdb43RWtuMg7algjiKiIKQOAhpLC1x63XXceNXPokJVhAH53CwYj+ZCUSQ0brHUBoU6KomLUOmDJkUhALSqsaUDF5Jsiw12gHpZLRrCe2XR5TOCYneG7MxO5UFAxPbx3j7m15DpnzaQcRDj+7l69/4NkdnDiOVIo6TXHSYWe2AFBA2UEmL2TnDTXfcy47JTU/b67O83OCD7/8wYRhacy9yi2vHYWBwgGuvufZZa3UsgoEfUdYHDBJ7QPOo1arrLneiiAnSLMv95RM63S4rK03azRadToeg2yUIQ6IwYqWxQqvVIgwiojDAGIE2Do6SeH6uEkYQhbHtiMpSlJRs2byF0dEJLrn0Ei655EJKJR87bS+zJZEoJghCmo0mS0tL1phn9hjHjh1jeXGZmZkZjh2bpbG8wvTUDHFsa9I90aMAm+J1VO65UGVgoM7Y2BgbN21g46ZNbNkyycYNGxgeGmR4eJihoWF830c5+WYhJUEQcPjwFBPjm2yveZpaH3udp+wxtoULaDWaNFZWcKRHp9sFYTcP33dJkoTFxSXSVFsbYsfB933uu+9B7r33AdtHLSDs2pkBGMjSPKASJi8XQLvd5g/f+4cMDg6wc+dOrrrqxWzburWvchdCUK5U8oU54Y477uCNb3xDf5P/5V/+RU4/9Qx+///+PtPTB4miqN+yCuQbmMeGiY184APv59xzz8Fxn70lYm3AAArP4zF1F8dnGZLEDlLqdDqEYWCDhm6XMAqJk4hWK8zHN8coIVECXFfi5KdHIcFRDka5ICW+Iym5DmneHeHkIsnMSITr0TNH0sYu4qmwHRO6JwTVdviUkDZD0Z+tkJckeoGdztuFVzMZ6zek51oAsTZwUErh5mLJ6po1xphTaEUZ/+uP/pwwaYNOcB0bEDlKYRwHfa8mPSwoDwqoGjLPkCoNPsiqQA0IZNVgXIWsGJQLmSswHnipwB2IqcU+rjPG7Y/s4ZwjB9mxcydOVXHW2acSRQmf/adjhHGEUrJfwsofBUnURoULpNLluu/czU//5Esp+Sc/Va+15pab7+Dee+9G65RSqdR3ytRac97557HrrF3P2utcBAPPcx7rhOw4ipJvT2qjI0Nsm7Rps+M95nutU1mmCYKAKLRGLe12m5WVFdqtNq1Wi1a7RacbkGYpSZwQBgHLKyt89atfY+boUa6+5kVs2rSBUsk5rl72+LebpjZg6XQCOp0OrVabxYVFjs7MMHdsgSPTs8zPzbGwNE+r2WBxvsGhg0dJ08R67+fdFsoV+aAev2/MNDo2xsDAABjDgw8+wOjoKEEQoDVUq7U8qk9J08xuDBjGB6pokzG32CTNIpJ8ep+fG0D11MxZpu1wnDSjo9O8RCMIg9AKRbVGClCug9PrFMmDhTiO+fCH/oFSuYxJEyrVCr/wjnfw6le/mq1bJymVSqDB81yCbsIDD9xPEATU63XAbqSv/ImXMjM3zSMPP8LiwgKHDh1mZcWWMCqVKvWBOv/mF/8/zj3v3PzE/tzn+CyD73vUajA6OgIcVz/H2tvaLIOdh2Dtjdt0ux0rdgs7BJE1Icp0BlmM64CUOp++6OSttwrp+ggp8R2bEXBdldepnVzRrhBCI6WDMQ5oRRgawpA845DfvyBkafejSMfHH6jjVgeQlTLK9zHaCi9LtVo+JMnFyz0Aes6Dz9V2XmMMBw8eohuHxFkGCAKjbZdpZlue6YA85qBcDcKQaciyvJNEGRxXIH1DogzCFUjH4Hi2iyFsZiTthNB9mKAbMFAf5J57H2HPgSmU45ImKfv2Hlp1NqQnIFwrsAayNibrcv8jh2h3u09LMNBud/jIhz9KpiMcV/SzfADDI8O86U1vpFarfZ9refoogoGCH5jjOwCsMtu+harVE61i7dfja5EZYWQH03SDME+3t8my8RNaH7/f7ZbLPgMDNWA1ld1PXfYEU7niupdtCIPIdm0sr7C8tMzs7DGmpg5zdGaG+fl5lhbn2b/ngC2nhBFhFHD0yEw+D8D0yxY2dW8fT2Q0C8srKGk3fG1MX0+RJJpmo21PTp6D73sI7MyBJM3birCnQSN8TJYhjEFIq/jvd3nkwkG/7PATP/Fy2s02t912O+9+97v50z/9M7Zu3cr5F5xDpVzvm7E8/NDDfOlLX+JFL3oRGzdu7LfbnXvuuWgjaLc7nHbmWSRJwplnnM6VV17OxMQ4lUrlWUtVPh2sr58LlOc9ZpbhhBa/TBNF1s6422kThl2iMKIbdInjyPbBJ3ZBb+gAyGw/gzQ4jgDp5BoGB8cRuPmoZMfxkcpBSYEr88FGvmT4lC1k3RATRiQLR4iyFCMlKoWg0WbwtB3EYUQ0PYcqeTiVMn65jFOp4VSruNUqbqmMm5uGOc6q18OzJX7U2nD3nffatlcpkVYqZDN5ebnLGKstMplA510g1vTaIEJIRWZbFg0oYVvw4tyF0CDQRHSwLdJhnPD1b87lug3b3aTN+sdt8s+UUBK0QYqMLGlD1qHVWCYIIhg+uc+DMYa7vncft992K92g1beOdnJDr+07t3P5ZVc87hr4TFAEAwVPC8cvwD16qnPWmPH16rAn83aVELl3jQLXpVwu9bs2TmHrutvupZjTzA7DSZKEMAhZWlxmYXGJhYUFDh+e4p577+buu+5keWXF+jNge53TNGMpauNKq3jXaYbAtr1pnaJ1isGBJEPrtF8jdL1K3lduHep0HOaZAYHQ1lsAY2znfW4/3Gp2+MLnv8zwyAgTGzcxODyM57kYk3H3XfeQpraFzwhYWFziXe/6T2zZspn/+B/fxete9zpc1+Wyyy7hzDPPoNFoEkUhxthT9MTE+I9UEPDDcqLJEHmmqg6MAWvc7ljV4+hM98sTQRiQxDFhFNDtdomikCxJCToRnczaDBusGBQ0UmHHNCs7hdGpSJx6FUcO4ArZ64ugbmy7qRf5+ElK1glIV1aIZo/R7cYIbYglZAKcko/0XUrVCtTqyGoNVa3glys4ro9XLuP6Vvnvel6uaXhq7oKPh9aao7NHyTAYnUE+drv3HPcDsHw4Vy5rta2j2thWZZO3jIrccA3rqmi9T0Aa2XOoApOhhUBqsJ4t1rq5Z+GuM93XMpksw0grIk3TBJFlxGGHIApO6nMA0OkEfPjv/55u1LZZJeXkbdsOrufwyle+kvHRZ76dcC1FMFDwrPNspjdPELLl5RGGYdPmjf3L9Vr3lpeXefDBh/j8577EN791HcvLi+gspVYu4QjJ7OJy3iZo+rap9nQvevICu7HHMZ7vWxviOELkp4QsTWDN6dRoCcb6sI+MjDA+NgFIMp3SbDYBQ7vdotVq4TolSuUy5VyNnMQJKysNwiDg3e9+N1NTU7zu9a9ny+bNjI6OMD4+9ow+1z8KHL9hKqXW+ApVGGIIOFGL05vKZ4WQKXEU0w3s5MVOu0W326LdDEjTLllqtTX29GjsNEUlEUrheWXkaAVnom7dBoXEaINrIEsSTBhhwgTZjtCdkHBpijQKCXQu9DUZjpT4pTJJuQLVEl61hlev4dSqNqtQtpbkru/nwcKJ5k0/KFEUMTM7Y0tdUqwr+fXo2w5jyyHG2CDAHhKwEy6kxJhc8JfPNBDSBgc9d4leT6oxhiz/mdWQ5q2YQoJcnYPQf536IuUTX9+TgTGGhx98hFtuvgXf94hjmxUBSNOEU7bu4DWvfs2zPjipCAYKCn4Aeq17GzZMsGHDBFdf/SKOHft1brrxO3zoQ3/H/r2PEsZxPm1Qo4TIx7FmKMfFdexYWpsoMYyNjHLaaTu478GHwWiSOEEpSalUolwu0w0CojABZedQZFnG8nKDpaVlssxePxpKZZ8M60ehZEQ3aIMgFybZvvIkCnn0kUf47d/+bf78z/+Uiy6+mJe+7KVc/eJr2Dq5lWqtlmsXCk4Wj+1GuIY6jK7p/Ol5iqRJQpLbF4dhSByFdIMuYRiQpjGdICbLotxEzKbSHQGOVLYkoARO3UcOl/GkwFEuPoI6CmVAhCkEMVkQ0+52idtdmsuLmDhBJjGOhkBJlOejSj6yXMKrDeDVByjVqpTqdfxyGa9UspmF3uwC9diZhVazzcL8MRwl+q6ApmckkT8/awMnO1vAlgUEAon1mOg5qvbbN7EGaiKf1SH71yP6cw/WTm+0pYjUXq/KswpqddaKUi5a2M4Q1zm5PgNRFPOJf/wMrVaTJO32H6/jOlQqFV7+ipexbXLbSb3NJ0MRDBQUPAkcx2Hz5k389L94M9e+9CX81V/+DR/9h4+inA6e59HzSydPhUJvII3NFuzcuY04aKPI3RtZ3RBGRofZWpskCBKaKw2SJLaOarnmIoqss1+1UmXDxg3s3beHLMvyeREVlJQMVGvMzs4RxhEm07bN0RhmZo7xlS9/ja9++Trq9TqnnXkqr3jFy3nZy17BmWecweDAYDFf4hnGbkZWke8/hnBtvZbB9IVntuMnn3mQ61u6QYcgCInaAWkU2BkAPd+DXPTouy5u2cOZqFB3HJQwthafZagkIwpTkigj7XTIgi567ijBwf00o9gaQzmKzPXRvoeoVnCrNkioDA5SGRzEr1TxfZ8oifnODTfQXZrHFQYtRK7jMbaFE4izdF3gpJSDFIo0zfpjuTOd9X8Ppn8dfev0NXbta7MJawMNwZoMpFg1IbLPq7W/znKDqsfz8ngyGGN45KFH+fKXv0yShiCs02AcxwAMjwzwmle/5vvOvXgmKIKBgoKngBCCiYlx/uN/+nUyrfnzP/szHMd6Gggh8myAtW9OUuvS6HkOru8TRiFSeSACEBmZ1ri+x8jICJs2bSSOE2aOztJptzFYTQO5CjpJEqRQvPVf/yv+4i/+kiPT05x5xhls2byFKIqolH0mt05yZPoocWyn6YVxTBDG1Gt1tm/dwVlnnkUQB/zjRz7Fhz70ESY3b+TCCy9kcnKSgcFBtmzZzPj4OEODQ3a6ZbmEt+Y0KMTaMbg/GFprlpaWWVpaolypMFC3kzJ7jpMFJ7JeywBPtGyvFer2BiQlsdXBRFFkJ0eGAWFo3R+b7Q5xaDcpIW0t3hUOXslD1QdRctCWKoRCaoNJE0ySoiM7NyHsBERzDYKphGNJQpLPLSBKOPbgXu7+3vd4gYYl5dDSmtBx6WYpSIeOTqwOADvBcWh4mNe/7vUII9i//xCHD0+zsLhAu9siiYPVQVyyZ8cMwmg7rK03zK33f4L+fIv8SbRamlysuHZWi0AildVtbJ4YsV05J4kwjPjHf/wkSyvHkBKEVP0OAtd1ueKKKznz9DOfE4F3EQwUFJwEKpUy/+k//ToDA3U++IG/o9VscNaus/iZn3kLjuNy+x138O0bb2Bubg5jNNNHj7J58waSLEFIiXI8yFK2bdvGxS+8hCSNiKKYDRs32XYoY4ijiDD3el9uNDh0eJq52RkmJ7dw5MgRZmdmqVaq+CWHIIoZHCoxPr7LDu5Rbp5WFmzctAnfUXS7HZYbCdu3beDOu+/loZUlHnzwwVVHPeywJMdVlEoe5bLH6OgII6MjTIxvYNPmTWzeMsnmTZvYuGETQ8NDVCvVPGDw+iZPa8Vp1ka5TKPR4o/+6M9YWJxndHSYs844ndNOP51t27YzNjZGvW5dJkUu/Fpba157Xc+FRfS5xKoGxs5gKJcf25eh97WXYVgbLMRx0hdBxlFIN4qIog6k6eo8EiFwBgaQwwN4+VRFq98zJFFC1gkZ3rKBLZecQ2d5mfb8It3FFbqNDu3lFZa6TWZay3TjiDTLCOKEOAj43pc+h18bwClV2DY+wORojbmFeZYaK4RBRKvTwb6L13cH0HtMYlUfYIdeWU2AFSnSLzH0giYppe1IEA7CwCnbt1A+SW2FxhgefngPX/7KV0AYa/0u6Bt8DQ0N8cY3volK5dlxHDyeYlBRQcFJJMsyWq02QTdgeHgIz/fs6SfL2LNnHz/zMz/L3NxRpIQ00aSpzo8zAkPK6OgwXsmn1W7l7oC286BWq9pJgtU6lXIFx3EIgpBTdm7j61//JgsLC5y9axeXvPASa96UhBidYnRmPQ/SFDCI3Imy027ZxVA5xMZw/0OP0O12QUgc5fLrv/4fueSSS2msrLC4uMjU1DTT09MsLMyzsrJMu9GkGwTEWdoXdrmuS6lkja2GhoYZGxtlcnKSLZNb2LRpExsmJhgaGsLzfJI04eD+Q9x1993c+O0bWVlpEARdDJpKtcL2bVs5a9cuduw4hfGxCavqF/RthculMpVyhYHBOrVatT9Yyf6Tfce/x3KbLAKIH4y1DoO9TEMUhYSBbQfudjskSUy326Xb7RBGXbROkWRkWULF83GVDUo02FJXZjCxtTaOuwFh0CZtdIibK3SabRbnl1habrDSbNJptem0OyyFXSINUaZZigKiXACY5UJBow12iDbovmIgD2jlqntp77HY9s9VN0gjfNzBHRh/mF9+++v437/xSyelqyYKI/7n//g9PvChvwKt0Sa10z2lwPU9rrrySv7yL/6SoaGhp3xbJ4MiM1BQcBJRSjE0NMjQ0OC6nzuOw+TkFibGxzh8+CBDQ0PEYZM0ifOTsyTLUo5MH7F91j3FdB5IHOud+ETur68kGzdtotFcIYoiSqUSR2dmuenmWyiXPByl8hO9T7Vao1Ku4pf8vkDRZJq0E9JcWWG+sYwUubW0NkjX5bWvfS3nnXd2v+666vCXD86KIrrdLgsLiywsLDM3O8fhqWnmZuc4cuQos7NHOXrkQW6/7U6iKOqP4XZcB89z8DyXarVKtVIhzVIyHZGktqtiZXmFleUV7rzzbrTW1Go1JiZG2bR5I5s3b2HDhi3U6wPIfHSxdGS/c8N1FL5fwvc9vFwJ75d8PNfD8z18z+9nKxzXoVyypQ/f9/Pyx2q54vkeNBzvMOh5nj3FrunBX1uW6GUUWu02i4sLTB86yJ3fu4PUpFQqFcoVD9ezZlpCZ9TrdVRtBHfDGCWxlWEj2JJZoV8WpyRBRNTu5vbpHdrLLZaWlllcWabRbLLcaNJqd+mGIWE+1yLO71NmrIg2Vxn0N3dpBQWInsZQ5N4Prk+sJJObRk9auWrfnoNc96UvITAoT5Fl5BM9DbVyide/8fXW3Ow5QhEMFBQ8Q5RKJa596Uu559578H2fjlIkeTAwMFAnijRZJtFGr4qhoD82mlw13XNgnJubY2FhwfqaDwxwxulnW5OV1LaxxXGE5w0yNDjGyMgoW7dtZd/e/Tz80H6QmvGxUUYmJ5lpNImiFMfx0dpwxplnsHXr5LqT9HqHP596vQaMsm2b9WxYl3pOs1wRn9Dt2Pa5ZqvNkSMzHD06w8yRo0wdnmJxeYGlpUWWlprWKji2mQyTiyltn3hGvBSzuLDEww/tYSQvU2yYmGB0dJSRkVFq9QErwBJ2foOSKjd0cfoTJXspYcf1rPpc5RbMefbA9z0cp0StWqVSrVIq+dRqFapVK8os5UFDoWtYz/FlCaoVhoaH2Lp1kjNOP5MPfuij3HzLDbi+Q7nkUy7ZmSJ2AugQtQGb8arVqview2BPP+K4eMrH1CpUhwco6xFGDZzqOqRJRhInRGFE0LFzVdrNFo1mk4WlFvMLi0zPzLK00kAIkHLVy0QplZc6RN8HxHEVJn8PnLZz+0l5XuI44fOf+wrHFuYRQBiG1ljMGHzP4/TTTuXFV734OfV+KoKBgoJnCNd1eMc7foHbbruVIAiYPzZPZlKU9Oh2u8Rx3N+0yNus+m1T+ck6NdCTSgkh+j3gb3nLz/Jf/+t/xXXddT3Uq/X6VdfEIAg5sP8g1133TT7xiU9w8NABhLAntlKpwrt+/T8yOFj/oR7bulkYnsT1XCqVct/oCeCii85fr4zPNEma9m2sW60288fmmV9YYOrwNLt3P8odd32Po1NTRGmAMZrl5SXCsMv8sWOUylWGhocY3TDB6PAIJdfD8xTlcglHOfkmlfvmu70OCdW7w3mGQCCw5RIp3bztzZYZPEfh5CO6S2Xflj22bGbjxo1Uq5Xn1EL+XKQbBExPHbWzOrqCJd1AInIPAesgKBV5icnDddxcm+Ljex61Ws1OKa3YgKFaLVGplPFLFYQUlFwP11cMOINUhgcZM4btcUan3WFufombb76DY/NzmN7QKsg7GXqBgAYUwi2RCYeK57Jz2+RJyQgdPnSET33600RZF23S/mfVZIZSyeMVL7uW8bFn12ToeIpgoKDgGaRWq7Fz5yncccd30Sbrpy+DIOifXnobujGrJ+Te5bQxlDyf0dERlJI0Gg183+c1r/lJKpXy913IlBLUahXOO/9szjn3LN7xzp/nyPRR5ubmyTLNpk0bOPOs0562je4El788aBhmENjAmWeeCqzOoFheafLZT3+ef/rcFzg0fZhOqw0modsJieKUlZUVjh6doTY0CFmG50g8z57+vXxD8TwPz3PxPDsYqFyu4OStXFIKXEfheT5C2nquMNqmxqXT74dHCA4fPsydd95JvV5j11m7OO+8c/sjqAtOpJ2XC4w2SCVRUpImae6uCWjQsc2CBSLJDZR03yyoZwgk8qFNrmtLOEraKai1WoVyyZaD/LJHqVSm4lfsHBAU27Zvt8FjlJwgOO0FiloLMjwEitHBGgMnYTZAkqR86pOfZ27uiH0f5Z/lNM2QjsPmrZO8+lWvfU60E66lCAYKCp5BZO7Ctry8iDG2/99OypM4SqKzfIgLkPUGuhirmFZSUiuXecObf4pdu87mW9d/i1tvvZUsMwwODf3QJxopJYODdQYHz+Tsc858eh7wk0Tkpk3jY8O8451v5eff9rM0mi0OHT7CA/c/yP1338fRo9Ps27ePxeVlTGzV2guNRm7PbF0enXzgU5ppHOWgHGmnUyqrMbB18LLNDLgKRznUazVKJR83t4020B9LbLD3a+++vXznlpv4yVe/mnPOPud5beP8eHTaXYJuFyHc3E1ztZ0vS9P+vA1j7NAhozNrIihFPk9AkmmNJJ+mmiZ2uJiSNJodZufm81vKfTxyUaCUilKlxtj4BKnJ1gUCx+vlhfLA8TGyxCnbJqmUn3pb4dThI3z+8/9EZiIQ1jukF+jXq3Ve/vKXs2PHKU/5dk42RTBQUPAMEsUxzWaTKIryNL499ShH2UCAfHFUEiVyUxWtSbIEYzTnnH8+/+W3/guzM3N8/gufI45jfN/H/Aj3BElp3R/Hx0YYHxvh4gvPxfzcT5OlGWEU0+l2CYKQIAhp5wr0ubljTE9Ns7i4SLPVZuboLEtLS2id0mg26XbsSGMlJULYWROI3BBHSJSyI42VstqD/nMsFH7JQzm2/HLHHbfxO//n3Zy969kbPftcxBjDysoKURjmI7ptS2Iv2LWlsDWXz+v4QuSKfylXL4fsZxeyLO2P3TZmdQDTepFrQtps0213EcJ+pnpdBAb6AUlvxoFUPqmEDRuG8b2ndlpPkoTPfOoL7N+3lzgN+9qUXrfL6MjIc8Zk6HiKYKCg4BmksdJgZmaWgYEhGo0WnuehpGIlWcGI1C6AQuEIQaptfdNxHLQ2DA0N8R9+/V1s3rSJRx55lIX5ZTACx3GflpGrz1X6orVcm1CvV0+4jN0cVq1+e+1xSZL0A4dOp0un02VlpcHi4hLdbpelpSWOzs2y0miSBLaNrtFYpttt0e0GRFEKRhF0G8RxxOL8Eg8//Ai7zjqrCAbWYAwcnZ7JswF2eqgBpKOsjXDPERGJQSPQGA3GaKSyOg5s3IAW9nPQ08n0AuSeiyCsbvA9MaMx+WCkvIJjHQilNRzCloGEEdYJUTlIrTll68RTzvBMT83w8Y9/gjQfSKYcewfSNEU5iksvu4wzznhumAwdTxEMFBQ8Q8RJwpe/8lWmpg5y3nnn84IXXMCb3/xmDuw7yJ/+6Z9xbH7OLpo6s7NY1ojthJDs2LGDyy67FAOsLC+TpglKOWzatKkYOnQcvVMmiLwk0KPM4OCJ7VzrBwvZFjCjzWoQESekWW8UdkC71SYMQ5Sj2LXrzEI3cAKGffv22/ez0et+0ysPCCHYtm07559/AffcdS9HjhxGOXZUsckypBQkaYohQ2uzzqWyN/EQWPfcr3UWtEaEaxwIte4LaSEPPJAIHEq+x3m7Tn1Km3SWZXzjuuuZX5hBSN1vCzbG4DgOw8PDvP71r3/OmAwdTxEMFBQ8Qywvr/Dnf/6XzBxdZPOmBj/zllfywhdeguv6jIyMsLyySBRH/ZON6C1q+VyXkZFRHMchCiM8z+fNb34zn/vc5/iVX/nl5+wC88+FxzQkWntIrJzo5Ffw+GitOXRoiszovg4AA+QzOwwwPDzMG9/4OrZO7qRWGeALn/ssV197FUE35ebv3ESSRhhhkHl3h8mzBzqvv1t9wOrUw/XTCAVC2Jkf/TZdaUcfIXKjIimQrg/CRyqXHVu3PKVgoNsNuO5rXycIOnZsudFIYbNXjuNwztnncOEFL3hOZgWgn0QpKCh4utGZJooihNBUa2UkgkqlzJVXXc6P/fiP4biOHdGKsTMNxOoCKoQVsdmpb5pOp822bdu55iUv4eUvf2khYCt4TpGlGSsryyBN3uvfmwNgU/ylUolXvOIVDA4Os3v3Q2zfMcnExAQ7TzuFbdu2gbEGAdt3bOdl176UatW2E/qej+eUkDjozG760Js8qPrGXEquZg4gn1wIaGNLRnZ4kwLlk6IoVcpUq08toM4yTbPVIdUaIWS/LRhj8EslfuLVr3rOuA0+FkVmoKDgGUIIkAIQmqGhAYaGhyiXyziO4p3vfDs33PAt4jii1WrRabVptVqkaYqQkrHRMX78J36ckl+i3ekwNzfP4OAgZ515BrWT0A5VUHAyiZOE5eVFBJkVZua1fYxBOQ4XX3wxF110Id+9424eePA+3vymn2Lnzp2USxWONudtNgHBjlN2cOlll3Hw8CHOOON0JsY3MjuzwOGDU8zOzdLqNNE6JTO9yYbQUwqu1Q/0hgOtDZq1Nijho4XDpo2j1KpPPfvjuV7ucZERpXYyoZIuW7Zt5eoXX/2cDtqLYKCg4BlC6wywNcRKpcLY+Hj/NON5HvX6ACMjQwRhSMn3ufnGm+wENSF57x//MS9/+UuRUjIzO0ur3aI+UGN4ZPg5qUwueH7TbLSYPnwQnR/OpZR2QxZ2Q96wYZzGygo7T92J1ppWu8mpZ55KuVQliq0WA6OpD9ZIsozzzj+fiy++mGPHFilXK7zgovPodAIeuP8BXM/h/vseYnZuBljtNiDPGhij12lCeuUEIVykW8YI2L51jOpTbCs0WiO0IUsS7NQEged71GoVXvXKVzK5ZctTuv6nmyIYKCh4BtBa88ADD9LtdnFdF8/zqVWrfU8bqzy2/u9hFFIqlRBSUiqXcRyH7du39e1M0yRFZxmuo9iwcQLnJM5fLyg4GSwuLrG8vAL0nC91PkZYECYxn//856lWq2zcuJlyucxKY54zzjiDVqvBWbvOJAgiDh0+wOjIKEmSMDI6SrPZ5tOf+iydboNSqczo6DAXXnwRF5x3IVs2n8rHP/4RzjnvNDzX58477ybJfQl696FHz3BISg+pfJQwbN00jPMUT+1pmpGmdsy4oxwybYOPjRsneM1rnpvthGspVpGCgmeATqfLX/zFXxB0AwYHBzAYbrv9VjZsnEBnmt2PPkqYuxBacZMky+ubPXviHo6jSNKEvfv34roOSZJ834BgtQ9b981ZCgqeDowxPPLwXrpRjMn1/MLODYbcIjuOU5KkycpKE7DB8Pe+dxfVapkLLjiPXeecxtbtmxkcGGRufoEwDRgeHmPjhk2cd8HLWVxc5NChg9SrZdI0ZseOzZyybSsXXXQ+I4ObePje3azEy4j8bd4rF9jvldUaKEGmHJQSnLbtqYkHwc4jaHebCClIswSEoVqt8opX/Dg7dz61ToXHo+etsFaX8WQpgoGCgqcZYwx33XU399x9HyW/zPDwCHEc8yd/8ie8733vo9Fcyafn+YyMDBNFESsrK6Rp2m9t62GH6vh873vf45HdD1GtVDnrrLO49NJLH/f24zjmpptu4obrb6DdanHZ5Zfzute/nvJJcFsrKDgerTWPPPIImdawxlq458K39ivY93SaWmvpZrPB7OwxSqWbcV2XHTt2EHQiYh1yyuRWLrr4XDpBhwsvPJ/t2yep1spMHzlKuVLigosvYnhoI912QBxHdqYHa1tGe7dnEEi0dMiEg+c6vODc05/yZhpHEY1GgzRNkcqKgzds2MDrX/8GfM97Ste9ljTNaLfbHDp8mAfuv599+/fxyh//MS594aVPKeAogoGCgqcBrW3nQKfTpdls8eEP/z0bN23gvPPOo9FYpt1qAXZWwcDAAIcPH2ZhYYmVFTtrYGxsjDAMaTQaDA4Ocvc99zA2Pka1WrWlhCAkDGK67Q4PPfQwL3zhCx9zMTPG8M1vfotf/ZVfptNqo43hM5/5DI7r8oY3vP45LWgq+OdJlmVMH5nO7bYl5DV7waphELAuIOjV8e0pVxOGIe12m0ajkV8W/vGjH+X0009jcnIb3W6bZnOZoeE6szMztNpNrrrySkp+makDD5LoBBB9/cDa2wXrPCjcCpHxGKpUGBsePOFx/LC0O13CoItSEiE1pbLHq179Kk4/7bSnnBXQWtNqdbjnnnv5yle+ws233MThQ4eJ4xDXU5x73i544eMfCH4QimCgoOAkYoxhdm6OT33qE3ztq19h/8HDuI5HqVQijmK+852bOO+8c4nyU0Sn06FSqdDpdGg2m7TbbYQQTE1NkcQxcRjhOA7vfvfv8oEP/C3bt+/gtNNOZ3Zmliy1vcxf+MLnefnLX86WLZtPWHTSNOXLX/4KQRBAXiZod9r8zd/8DT/x4z9OtXaie19BwVMhCEKmp6ahZy2cGzgJKdYJ+fpOgWbVWXBtcLD2K8Ds7DxzcwtIeSvVWoVSyWPXrrOZOXqMVrvJRRedz0B9gGPHjpFmmS0RHGfT3bs9KSXSK6OUZHLjMPXqU/scaG1YWW7kJkkG13E488wzectb3mJFwE/6ejVzcwt89avX8dGP/CO7H32YIGijtRUiu67DRRddwkuueelTDjiKYKCg4CQyPT3Nxz/yYf7mbz/E3LFjaK0ZGhpiYGCAZrNFGMZUKlVmZ2cplUokScLMzCxJktppa1mvRcratSZpSqvdYmlpienDU3z39jtXDVjyYca33no7v/iLv8gHP/hBJibWj0XtdgMeeugRut0YQW68IgRTU4cJwqAIBgpOOp1OwNTUIUw+cVNgjYZ0/78FWW4G1GPt9+vNg+xurpTKPTcgTQ3NRpdmo8P8sVuwpQjDZz/zT5x+2pnsPbAnjwEEUim0ToGebgCyLAWpwQikzjhj52bqT/JzYIeOrXD/fQ/y0Y9+gla3jdaGen2An/7pf8H2bduf1CZtjGFxaYVPfeozfOBv/5apqcMkaYJSiomJCaRQHDs2T6ni8ku/8isMDw0/qfu/liIYKCg4SRhjmJ46zCMPPcjAQJ2jMzPU63UmJyeRUtLpBMRRG8dxWV5eYW5uDtd1qVQqhGFEGAZ5+6Gll/bvdLs42FNTZjVYdpCRI3EcF8/zuPvue/jSl77IW9/61nWp/ziOWV5aBgSZMegk6dukrtUiFBScLLr5vAfVE+2ZDKM1Osvse1qcWCKQUq7LDhxvNWyndqr+wCFYFcQqpdDasGfPPh59dC8Y1dcprK2cZVmGkgolZD4K2cVIwynbJnDUk9sK2+0Ov/fu9/LVr3yZVrtBmkWADV6q1Rppmv7Q3T5RFPOdm27lD9/zh9x7392kaYxf8rn62mv4yde8lvPPOYevfvmb/PEf/yEXXPwCLr/0ipMiTiyCgYKCk4QQgnPOPY/Td53L9TffgRRw5mmnct4F53LLrbczPDxEmmR4nkuz2cAYCMOQKIoplUpUKhXiOLLuZUIQhWHfOlX3hvNIgTYm750WJEnCysoKw8PDfOSjH2HT5s2cf975DA4OUi6XCLpBX4gIOl+YDK7rFS2JBScdYwxzcwvEUdLPXklptQDGSt77m32WZesHC5nVbEHvq9YamWsJIFsNIvL0/3oTIWt9LKXJhYMGrVczDD3dgO1uUAjp4LpwwTmn586fPzwzM3N8+UtfZWFxnjQL0SZFKZelxSV+53f+D+VyiVf+2I//QNocYwzHji3w13/xN/zd332YVnuZ+kCV17/hp/j5n/95zjnnbHy/xNGjM9z8nW9Trkre/vNvo16rP6n7fjzFalBQcBKp1+ucdc75BEGEIxUVF2TWYfvWTUwfXWBoeNAOYEliHEetWazsQub7PiBwXa8/sMV1XdI4sp0Fucd6lmUILfK0p2Z2dpbl5UXe9vM/T6VS4QUveAG/8Ru/gc4vI4y21rDY6XGlUrkIBgqeFvbvO0gUhRgyTD6T0AhACjKzKh7sfT3eNvh4sZ/RNh2W5S23a7MHvZJCL8tlA4ts3XX3phv2RIxSSKRbxvNKVEo+pzzJmQTGGBbml+h22wRBC4TpD1DSRjN1eJp777uHV7z8x75vMKC15uGHd/NffvO3+N53b8eYlIsvuYj//J9/kyuuuCJfF6wG6Auf+xL33H0nL33lNVx91dUnrU24WA0KCk4yaZLm89YNbtnFKTt0ww7tdgvPK9k+6yi2RkO5A2HvpNRbGLNM47ke0lP4vo+qGxqNFbI4xnM9VFmRxHHu6mY3/CiK0VrTbXf45te/gVIuP/mTryEMAsAuqEIqdJZRKpUKr4GCk44xhn379gJ6/Sa8pqWwrwkQq46Aa7MDq8GByC287d/qdWrA3HOYVV2BEBKdBwy93/Xuw9rgQwuB51UBh8HBMiNDJ06x/EEf68GDhwmjgEwneJ6HlIIoTBAoBoYGuPSSy75v0J2mKTdc/x1+8zf+C1PT+xgYqvP2t7+dd77zFxkZGVmnn9i79wDvf/8HKQ34vOOd72ToJGgFehTBQEHBSSYKQ7ROibOMR/YeJpMe8/MNPM9nYGCAKIqIwxCdb+RSKuxByG7qUkiMFrljmyGM7fWlRqOUQ7lU4YorL+eB++9n5uiRfNyYJMtSskzno14dxsfGSdOEIGjhKEGa6f7Jq1TykaIIBgpOLsYYlpeW1p3OewFBPhsalZfBMrF+M+8hRM9G2AbFWuh15QSQGGOHAfWmEkqpwNgphlZUYPq6AVjtSrAlOIdUKoRUnLZjEyNDT66tcGlphU984tOAYWBgiCQKiKMIJSXDQ8P84i+9kysuv/IJsw5RFPGZT32B3/6f/5vFlVlO2bmd3/u9d3P1i685wbEwiiL+7kP/wNyxo7zpLa/hohdcfFKNjIpgoKDgJGKMod3uoLU98UwdniUMNNVajTRJ2bJlkCCOiNKETBucXAQIIJCkaWazBcqWDhD21KSUi9YZcZjQbDYxBrZt38bM0SP5yUORxHZzt2YtO/nVX/tVHnrowb5x0drT18BAvT8iuaDgZCKEQEiJNtk6HUDPKe/40kB/ZPe60sHa32XrxIW9k74xeXdC/jsQaGM1BlKuNzXq36/8e0dVibVk2+QmXOeHtwk2xvDd2+/i3nvvya2WrSBXa83GLRO8973v5RUv/7F+ev+xCMKID7z/w7zn//0/OkGTa665mne/+3c57bRTT8jaGWPYu3s/X/7cFxker/O2n38H5dLJHatdBAMFBScRrTVLK0sAOI5DmqbML8zRbKxw4cUv5LIrLicKQn73d34fx/U4sO8At91+K3v37iGOExxpBxoJB9Am91BXVKtVskwTiQRHOezfv5+g27GNWvkJyXUdtLYWxhdffAGnnnoKu3c/ArmtsdMb8Sol4+PjRZmg4KQjhOSss85EKZcsjdeVB6ygr7fLC3pnWqnWjxu2ASv5+xp65kE9emO+jzcsAoPRYIShd/Fet8FqIKyt5kY4aCPZddbOJyUeXF5a4RMf/zRxEiOVJAwTUp3hl0v83Ft/jpe97OVPGAh0Ol3+7E//ir/8i78gSTq84Y2v5bd/+3+xYcOGxzztd7sBf/nnH+TYygy/9d9/k7PP2HXS7Y2LYKCg4CSSZZr5+fl15iZaa5IkZmJ8jJGRYSbGxnn961+L67oYY+h0OnzlK9fx7t/9XWaOHiGNU4RRSNXri85otVoopRgaGrLKbKMJggDf98ny+ek/9sof46tf/SpZluG6DsbA4cOH+y1dMp/pboyh2WySpinw+AtWQcEPi5SCK666DNfzSLJu/jPZz0z1Rgtna9oJ13lrnIDpB7DrMwL0xXq9v80yjVQOkCLEmtuEfqlCOQ7SLZEaieO5nHnazh96U42imA9+4CPcdPNNIDJ0mmGMfTxXXnklb/+Fdz7hqb3VbPMH7/kT/vZv/wZDyC/+yr/h3/7av2docOgxL6+15pabv8fXv/EVTjllkte99g14J9HeuEcRDBQUnESMMX2r4b54ytg+6UqlShzFjI6N4DhOP21Zr9d5wxtey9zsDF/8wueZOzZHN7AtgaOjo5TLFZI4w/N8Lrn0Es4680z+8A//kDAMMSZDZxopDDffdCNpHFmjlQziOOLhhx8hylKEo9BS5EYwcPvtt3PgwAHOO+/cZ/PpKvgR5PQzTuWqK1/EN7/1VbRJAYkQJv88aCRidZPOMiQCIQUY0KxtMVzfebCW1cDArNES2PZDECcEAn2MQThlpOtQLTuMDQ08YTBgjKHV6tDptHFcl8GBOlOHj/DpT36aOArAZGRZitYZY2PD/Lt//+/ZtHHj415fq93hD/7fn/C+9/01jqf51X/77/nVX/63VCqVx/2bRqPFX/3F3xBEK/zC2/8dmzZuetzLPhWKYKCg4CSilGRgYIDeicYubKCkolKtECcxY6OjJyxAWmuWlpYYHh6mVq9x5OhRoijmv/7X/8bLX/4y2xstJZ7rgrCTC//oj99LmkYcPjiFNppGo5EbsGiM1nQ6XXbv3r2uHiulJEtTlpeWeeSR3Zx77jlPyzS1gucv5XKJX3/Xv+PO2+5gsTVvN3oESimyzDoA5Bo/O+o3yzA6T/v3lPPaIKWTu2yutyyGE7sEoCcW7GUc5Anve/t7e1+EFIyNlBisP7HzYBwn/N6738M3r/8aGzZs4Mdf+eNIJDt3bmP7zkn27z/A/v17kVJy7vnnceGFL0AI6//R65LodQw1Gk3e+54/5QMffB9eSfCud/1n3v6Ot1MpP34WIcsyrvvK9Xzve7dy6eUv5LWvee3T1hJcBAMFBScRx3G4+pqX8IlPfIJWq2lTnEIiEJR8jySOGRwaOmEDjuOEmaNHUUoxPj5Oq9WhoRtMTm6hVquuu3yapCDA8z0mNowzPTVjfQrStL9gJknC4sIic3Nz61qTRF4qEECcxM/gM1PwfEEIwSWXXsi/+Te/xP99z++T6Mie+s3q5t3bKJP8PUtulW3fmwKRdwdYZUHav+61G3wv8F07lnttwLA6+Ch3MTQAEscrkyLYtnmUgXrtCR+LzjSdVhdXlXjowUe583t3MVCvsn37NrZt287Q0ACO6yAduOqaq/A8n/vue4CPfOQfmZk9ygUXnM9pp57G6OgYn/n0Z/jkxz5OQsh//o+/ydt+4W2UnkBXALC4sMz73/8BpKf5pV/+FcbGxp/w8k+FIhgoKDiJCCF4yUuu5pprX8ZXvvg5gm43VxuL3qGEgfpj9TUblKP6Kc04inFdl8c6tB89OsMf/MEfcOjgQRzXzUfF2n7sngxbG82hQ4fodDr22telU+39KIKBgqcLz/N4xy//PNff+G1uvf1GyLd5s84rgP6sjL6wkN6Gb9A6zQV/66/7eLvitQHB6t+LNdkE2bf5NljDIak8Tt2+7fuOFlaO4uqXXEmp4nD33fdw/wP3MnfsGI1mk/sfeDDvbsiI45ibb76Fuj/Eh//uoxyeOghk3HTjjfieD0hWVlaIo4DB0TpnnbmLG6//Du1ui9NPP5XJyUnq9QE8z+0/jizL+Pp1N7L70Ye4+mVXcdlllz+tot8iGCgoOMnUqhV++7f/B3t3P8r9991t66ZCsmfPHq699qWUyydOMVNKUavVmJ+fRymHJElxXPWYnumdbpelpRWSJENrG0QA1qZYa6Tj4HklVlaaJHEC5G1VvcU4X0jTIhgoeBoZHRvht/77b/Kzb7mfRnMJKUGbxy9JrXUe7AUBtnxgf9/b+HuXtZczJ2a+1kxG7GUKev8c5ZAJhXR9XnTZRd93c12YX+RjH/0Ee/Y/gjEZ1WoZ0Fx04YXs23uAxaUl/JJPtVLlvu8+wCP37iWKYuuaiCQMYtIkIwwCkiQBNEYboiTlG9ddzx23fYcw7DI8PMypp+3k4hdewqWXXsqOHduZnprhb9/3PgaGSrzjF95JrfrEWYynShEMFBQ8DUxMjHHNy17KgUP7aawsIR1Fu91mfv4YURRTPq5O2EuNplmKcmwdVSmBVM4JJQXXc3FdF0dJpFpVZIvc+x2gPjDI0vJyXpwVKOXgOg5pEpMZ+nXVgoKnCyEEl1x2ET/xE6/iE5/8R7IsREhpTYq1nZOx9pTfawPsv5d5bPHg8Zv+8VmB9d+L3KfAoJSDlhKBoupKztwx+YTBgNaa795+F3d97w4a3Qbbt08yPDzI2Ngo27dNouOYSsln6ugMqcpQSoKIUEr22wqjKCIIAtIk7N/voB3wvr9+Hw8/uJswaiGEZGVlmYMHDvD1r32D7ads5ZprruW279zB7j2P8M5ffhuXXnLJ067tKVaDgoKTiDGGQ1PT/Lf//j/49Cc/Trnk4/slSqUSjzzyCH//4b/ngx/8AGEYrv87bLtgmmakaUqSxCglcR7D07xSLlOplO3pKe/WtsNX7Pee53H22Wdz8OBBsjTFaM2GDRvYvGUzWZ5SXaewLviRx6azTT6tUp/g+vd0USr5/NRPvxFHOf1WwF6rIGvuw1otwdqNfu3Pe4/jseYZnGhlfFzrIZJMC4RfQagyZ5+2hcnNT1x/N8Zw8NBh2kFAmlpTIc/zqNfrfPe732Nm7ihCZYCh2+3Qaq3QaCyxvLxEu90mTVPiOCZNe2JCwfj4BJ7r8d1bbmZ5eY4kTRgdG+KKKy5FSkmr1WRq6iDf/MbX2LP7fjZOjvGv/tXPPWG3wcmiCAYKCk4iy8sr/Mqv/DIf+fDfgdEIKRkaHsYYaLfbLC4t8n9/7/f57f/52ywuLq1JaVqTlSRJMUAYBXiOwnEfO3nnKJlPg0vtP5ORGY10FBde/ELOPf8CHn10N0ZbF7gtW7ZYhTUG6Sh7QkuLEcY/qmitCcOIRqPF3Nw8e/Ye4Lt33sOtt97BfffdS7PZekYCQiEEp51+KsMj4wjp2gxYZkCbPJW+ylpNS68t9/hRxj2RIKxqB9ZmEda6bPZtkLEW30Iq/PIQfsnhLa99OfXvk3aXUvLSl13DxMQmslSzd+9+pqamWV5eZm7+GBrBSrON57uUK2Uq1Rqu6yMVZDolDAOSNLIdERKGxgZ47Rtew85tp4FSGKDTDYjjkNnZacKwS5JENFZaHD50mCAJeN0bX8f2ye0n7fV4IooyQUHBScIYw3e/dyd33HEHO3fu5KUvfSk3fedm5ucXCFQA2AUrCAL++q/+imPH5vnD976HgYEB4jgmCMO8rijQme7brR5/Gwf2H2B5adnWUM3qJDetNdVqjZ/+mZ9l9yOPcPDA/twi1bBt2zaOHDnSH4ZkBIRB9Mw/SQVPO2ma8sCDe7jzrnvI2itsGfQZ3ryNSrVGTSR0dz/CkaDFyClnMDI6iuueWIo6mYyMDjOxYYK5Y0cecyPXa7IAjzXNcG1A0CsD9N7Hj6cRWBtI2NY+hSzVoDLMtS+6iDe++iU2rf8ECCHYuXM7u84+hwMH96KNZnl5mWazSaVSIdWaMAyJ47RvRey6Lq7rkSYZURSB6AVcBiUl3XaDMA1IszS/DZg5Ok+70SUMQ3zfRwhBGEZIuX6uwtNNEQwUFJwkjIEjR48iEWzfvo04DomjkG6nA0YghMJRkmqlzNj4OHfdfTcf+9gn2L5tKwcPHOTI1BRhGJOldlxrmKTs3f0o4+MTDAzUUUqRpimf/PSnaHc7IAVpnOLkg18cx+OKK67k4x/7CLsfeZhut0ucxBgMmzdvyqcaGpSyeoFu0Fl3sir40SBNM6amjrK0sERtaZ6xO/ajqhWSQYUuZaRGEidNVuoDdLoxtXqVSqVEyfdxHHXybW6Vwvc8pBBkvXQ+Btkrga1x6jzRN8AiEBgNklUXw+M5PgiwkxENoHBLdcT4KVxw4QX8j//w9u/bUti7/eWVBosLiwwPD1GrVymVSigl+5mKWrWOUvZ2FxaWWFxcRErfBgjaTi+1GTzBynKbr331W7Tbrf7f+9JDa02r1baDy1i1Z3Y8h69+6Qu8+pU/wUUXPv2agSIYKCg4SQgBZ515FlK6ZFnGLbfcwtGjR9E6n6oGSCXZuXMnY2PjnHveeXzmM59BCbjwwguZmpqiG0R0u13CMGJxcYkbbvw2X/ziF7n4kkt4/RvewNzcHDfd9B1OP+N09jy6O7cZtul/x3FYWVlhdHSYiy++mOuvvwGtNZVyiYcfvJ/G8hJaW+tUpVw6nc4zVjsueObwPI/zz9vF7MwM3TBk1pTYOD3LABp58Uvwt70QpzaCVmWSJKGx1GBpfgmRxZSqHkOjE1Sr1ZMWGARhRLu9+l6zboOr6fzHagmE1UxBP1tgeiOR7XyBnnCwF0Qc70oI4KoSXn2coa2nc+3Lr+a3fulfs3XTxAmPq1dWiKKYbrdLs9Vibu4Y3/vuXYyMDnJR/SLS1J72hRBIJUFolJRondLtBgwODhLHMa1Wp6/L0WuEklEYsRQvkaYpKs/GpGlqN/7cLnzt4za5c6l6jI6ip4MiGCgoOEkIIbjowgu49tqXcMMN38T3PdI0xfM8O5LY2JapiQ3jtizQDXjhC1/IkSNTOJ7i8isuY3GlTSahXKvilzwM9nTyrW9dz+HD0xw4uI+BeoX9+w+QZXnvtoEsM0Qm4e677mHjxg3s3HkKu846m4cefIgoTLjpxu8QdAO7uDgORme0Ws0iGPgRRErBlskNvPjFV3LTjTdzWAuC2TLblo5Q+vJdiB2zRKedhj+2CfwqWkPUWETvuQsz6tC8+HV45SHK5bLNGJRKlMulPKP0wwcHM0dnmZubsZ4CZnUTf6zNv8dan4B+O6GV2YJY75ux1oRo1UdDUiqVqIxs57TzLuYd/+qNvOrayxkatB4fdl6I3cQXFpY4OjPL1OHDHD06xczsHM1Gg3a7TZJExHGMEOD5Dp5f7t9uu91hcWWZlaUl0jgm0Rndrh1jjLFWy0KutvVqnaGERAmBTrPcmdGSaSsG7s1eUMrBcR3e/OafYddZz4xLaBEMFBQ8BYwxxHFCGAZIqSiVfP71W9/K17/+NaIoolar5U5p4Pk+UdRhcnILWye3sm/fIR7dt5f5Y/N897t34LgufqmClA5pHNOMunz3u3cwObmDU3acQjfo0mq0eOTh3bTbHRC9ka5pvrBokjRj7tgMl152ca4cTxECojglSTPAiqoc1zmho6HgRwdHKU45ZRKlrub+ex5gZsAnPjbM2NE5hh4+iL97H0m5hPY8zNAAyeYtOJNnIXZsQKkqcZQQdALmkxipFNVahbENYwwODP5QQYHNkN1Oq72MNukJhkBruwQee5TxGj2AXO0s6LkPrtUI2CDDwfc8HL9KefxULnvxi/jVt76e7ZsnWFxc4sDBQ7SbTY7OzDI7M8v0kSM0mw3a7Q5xHJFp2wEgc/tkYzSlkt8XJ9ogwo4Rn5mZY2FhHoGhWrJW41EUopTK/T3s1FFjcm+PPECQSmFSO7PBsMYnAZs57GU1Nm/ZxOte94YnnH54MimCgYKCp8DCwiL/9bd+i/vuuxflSH7yVa/hzDPOZnLLVuaOzeB5HpdffjlDg8N8/OOfIEs1d955F55XQuuMWrWKGdW4rmJhaQERBmAEJtOUS2Vcx8NzHYSEer3G1PQROu0g78rSCAFSCRzHIUkSjNHEccrXvvZVMLbboFwu84Y3vJGPf+wTZNrOOOgJGYvMwI8unueyY8cWBofq7Nu3mf179rFveJDa7DTbZucZXm7gCYPRCeqUDaRjA0Syio46mNQQHdpDtDSPLvs0JiaYnx9mcHicsfEJBgcHrA5AiicMDJaXVvjIRz5KmiXr3mtrgwCVC2F1pvMWW3J74fVZg1XbYQX90d2rKX6pFAiFKg3hjW5n82mncdYpE9z13dv5xvIKS4sLtJpNkjjsB8ppkoCg7xJqhMBxFJLc+EhAmib55TOMgSAIWFxcYmFhnjAMKZdKJFlCFIY4joPjuCRJgsz/J2wcYIOLvMOh729gbNmk9xz0go7B4UF+/T/8B3ZsP+XpeGs8JkUwUFDwJNFa89nPfYGjs7OcsuMUbrnlZt7znvfgOL51GYvtCNcHHniAN7zhjWyZ3MK+fXtptTvs3bePhx9+mAsuuIBrX3oNzWaTRx99FCkllUqVmaNHqfglhkdHUEqxtLTI7t272bdvH8ZgRxSj8zHE2NNMfySsZmWlidF2oe1ZpvYMiDB2kS00Az/aCCFwXYfRkSFc16E+OMihg+NMDY7w4MAcY7NTTCwfpTrXQX3tblL/HrKBKqZaRntlmDuGF3YRGypwxSXIwSG6nQ4H2geRyqNSKTE0NMjQ4ADl0oniwyzL+Pznv8yDDz6AEApj7Ht1Ne1vBxVprRG9GUW5GLa3wfdO/msV9atDiFa/Smnnggi/BsM7MZUyngq5+3u3Io0mjEKE0ShjMCZDCjszREqJyv9l2pDpDI0hjuP+sKFe50KvtBDHCc1mgyCwVuMGQycI0MZQrVQIoxiEQAqFEgpjstUMB9YpVPW8FrQd0qRZzXxUaxX+23//Ld7yM//yaRtK9FgUwUBBwZNEa8OhAwfYNDHGVVddydGjM9x33z22jUlKlJK0Wi2SJOHmm2/utx8dOnSIpaWl/nCV0dFRfN/jwIEDeJ6HUoKtWzexvLDIfffdy9zcMVzfY2FhgSzLeNGLrkI6kn379uaOhrZFsL+AYk8hmUkR+c+PHp1ZXdSMRpoiM/B8QSnJ0GAdx3GoVEqMjo4yO3GUo0MDrCyMM3psgdFOi2rYRi50YCVCOitkjsGMD9N1XDoPH6CSeqiNpxNnECUJaZbhSIeSX6JaKTExMc7o6AjVahnHcXh09z7e+94/Io5DEFl+yrajhUU+OTDLrD2vHaBlVuvm2JM5eTDQKwtY1rccrr6HFbq8iVDU2eBraM7TFQY7Hdn0HT2Rqj81MU0zm1HTmjCOSLMMASRxhNZ5CS7PQGRpijY28A7D0AoB8w6fNE2RQhAGIVGc5I+PfhlAKYU2Gtdx+xkOx3HwZAmEJMv9QDKdUq/XeMk11z5j5YEeRTBQUPAkybKUNI0IOk3m5qaZ3LaJ+++/lyyLAZWn7Q3dbpe7776LKIrIMjt8pdPp4LpuvsitzkRvNpvUa1XKJQd0RJqEtDtt6EriKEMqyeVXXsqtt97G4uISUZTgOE6/91pKCZlVOacmDxAyzZ49e9A6Q2Pnx2eZtUotgoHnB0II6rUKJd/D91xq9QojY6PMHplmYWaGY8dmGFucZawd4xtBKhK8oTLOeWfjbNiMk0qmjjUIHrbeFZ1ug6GhEaRUxHGA57scOmT1MUNDowwODvOB93+YY3PHkFKQaZML45StnWsbkFqFnTXlEQiENqDT/n1GG4xcfQw9vQCYNd8LtHHAKaGcGsJklByN7wg8aS8LhjRNCPJuAK11HgiYfjlA5xuyjaB7XggCbeznyfdLpJkmSTKUcqhWa3iemwsMrUA4TRJEPqEx05qS71udgLAlA6/sc9GFF/GSl7yEc885l8nJSYwR7N9/gP/37j9gz/6HkZJnrINgLUUwUFDwJOl0Aqanp6lWq9x997088NBuAJIk6bcg2UXC0Gg0bH1UKYaGhvqBwvT0NPv27c2NRkI63YAw6FIuWYFflmWkmU1NJrGmXPH5xje+we7dj9rbAMrlct7GlJEmqU25ki+BxvrAz8zMrhsh27ufBc8vXNdh88YxRocHWByoMTQ8yPzEJqYPH2R2bojFxWUGl9oMdJtUjzTQi7chxsYQExNMDAyi/TLNRpuocYzp6X0sNzqMb9tGKhUIKPkl6vV5Sn6VCy48H6EEDz/0IFNTB2k2l/u+F70audHGmg5l+Xsz0/2sQe/zQt5G2Nv8jzchyivyOI6HchyUyPCkQZqUONLEuZYmy1bNgYQQCGkQOKRpnF+303cCdRyF63lIx0MqhSMlRmuiMMIYxdbJum1vzDKmpg+RZRnonhjQoFP6Kf7+OiDgvAsu4P3vfz+bN29eNxfh3PPO4b67HuDg+/ajlMJz3Wf8vVEEAwUFT5KVlQYPP7ybSqXEkSNHCIJg3UhVpRQmX+SUVChHsWHDBjZu3MgDDzyA4zgMDQ3hOB6dTofxiY2cu2EjYdBl/95HaXZDWt0OUiokKYKMIOhy3333WStXY9vIut0uV1/9Yi6++CK+ed3X2bv7UVrdkDTLbDuTctHa+rNjMkymAStSLBIDzz+EEJRKPps3jzMyMsDAQJVavczCxg3MHD3Ksdk55pvHGF50GAwS3LkW2WyDNBcLimYTd+oRnKEyI5OnM1qrEDslOlFCECYsL89gDIwMD+O5HsMjI2zYtIHlpSWWFhdpNKyHfxCGCCOQ2EyBNhkShUFY4Z20p380/RP7YzkO2gZbjRIeOgUlY8JOjEoy0iTKvQnADuyS2PHIGSYTOI7GdT1c18PzPBzHwXU9lHIQUpJqO17ZcyS1ao0d27ex89RT2L59O1EUc+MNN/K5z32emeAImsR+5kVmswBevqFLwcYNEyRJzOVXXMLExMQJA5KkFJR8H7IUz3VRz6BWoEcRDBQUPEmWl5dprDRoNJZJkqRfP1w3XMUYPMdjeHCQaq2K67rMzs6u815XShFFEYuLC2zbthMp7AnHcVySNCWOrSbA933COCJJknxjt2nVweEBzjnnHIaHh+l2Opxx+mnc99BuMm2Q0mBnwuh+C2Lv/vUMTwqenwghKJdLbJvcwNBAjXq9Sr1epzkxwfzcDIu1WRYbKwy1GtSbXRwdY3RKpVxi29nnsOOcnahTd0F1GKF80tQQx5qlxWVmZo5y4MA+Fhbm8X2FUilKZoyMDjE8PIgx25BS0m41WFlZptPp0O12SaIUndnUvZ1dcKId99r7329TxJYc7Mhja+aTiAyMzqcgylypr3BdH6UUpVIlL7E5KKnyXn97G0liBYRBHHPppZfw1n/5L9m+fRuViv2b3mfo2muv4ayzzuY3fuM3CEMrOCz5vr2uvBxR8ktccsklHDkyzaUvvPRxRIF2smjvMJHldsXPJEUwUFDwJNDasGfPoyDMupGrruvm2oCsv9FrDElqywJhGNJsNgGbpt+3b18+VCZEGxgYGmJhYYG5uWMcm58nDBKyTKOEHYvql0p0uh10BsJY0VWpVCLoBnz31u8wNjLIyOgE+w5OodOMTOfLqbSXB0BaB7WsmFxYgE23Dw3VqdXKDA1Uma2UqdYHGRrdyOzMNEvzs3RqTaqLy1TDDiZLkNpHP3KY7PA8YmgUMzIM9SrK8RgKU2TSYsBLybaP0+w2mT5wgKTVYa7Rxi3V8MsVsiTh1NNOY6vezvDIMJnOiLox88cWmJ2bpdFcotvtEnSDXBhryHIBomG15VBJlXfIpBiToPFBOChHopSHrwS+6+UnbjdX+BukyEcoA365TK1aZ8+je5hfmCPLYtrtJnEa83M/9zOce+45jznu2Pc9rn3Z1WzeNMmhw7tBwCmn7GRgcIB77r4LIQye7xCEHaRS1AcGn+CVEAjHdjWkSREMFBT8s0BrzYMPPYiUgjjO1p2y1xqq9P51u91+JiAIAtI0xXEcFhYW6HQCfL/E0PAQ+w/s4/DhKWZm52k0Gn2Rn9UNxNB3LdN2GpqB+fl57r7nbnadcTrlSo3pI0cJ4ogMgxYmr4OKvue5lNL2VJMrtgsKsDXuTZsmGBkZ4tjCEstLVSYmhpk/tpnDhw4x61dRrRbloIUTJZSjmFI3RM0dJmk+gGg3ceM23ZUlopUmLprxkQFGydixY5ALrrgCPbiZbibZc+gI+6eOkiS2b78bBgwNDTEyNkp9eJAzzzmLaqVGFIYsLiwyPTXF4uI8C/MLdMI2aWb1LkJYcaJEWkW/I8mAysAQg3UFaISyGTQ3zw6USyU2bNjAtq3b2bZ9G5OTW5iYGOfIkRl+7Vf/PXOzsyA0WieUyh7lUukxn69e945SitHRIQ5P2c/m7OwsjWYjPyTYx3f77d9FKY/bb7+dq6968WMGFr0gRwj6g4yeSYpgoKDgSdDpdLjtttuIosim/Qy0O3YyYZYZBFblbPI55lmarWvl66UKkzhBZx2iIMZzfTrNDt12h263Q5IkpElqvQKE7YG20mq7sWdZage4GMN9993PwX0H0FlGJ+iSpBlpluWOZuT3y/ZXk4sKrXr6WXjyCp6z9PQEW7dsZOPEKM1WG6kgNDHjmzeSZQlxFBG0ujTaLRbDLnG3QzQzT3rwAF6jgRODli7lNMVfbOPrGH++RWnPV3FGh/G3T3DWlknS088lrQ6QKp8shWRlARbmyXSK59bwfQc1VCEZ90knKrQedVmpCzrbT2fKbGT6yAxL8ws0myt0ut08uHUAH42LFJKBWpXx8VG2bZvkjFNP45RTtrNhwwT1eh3XdfveHO12m/e+5485Mn0YrVOUY30AXNdjw8SGdcLbOI7Zt28/995zL612hwP7D3Fs/pgtE2Yx3W6bbreLzjSOm5cOpWLb5i2ceeauxwwEIPdJQPY/n880RTBQUPAkmJ9f4PChQ3l9McEY8L2SzQBogbCF+v5Xg+mXDtYuBnESQxQjpaLTaTM1NWXrhSIvMwi7+RthVddZujq6VSCRuTVslmasNFp5DzN5zdHOLei1L/amvelMo1ynf38KCowx+SCekJWVBlNTU+zZs4/77r+fe+65l2azSbVaZXh8kMnJSTaMbWRsbIzBoa04rkeWCYJOh9byEkvzC7QX5plfWWZ6aQXZaFJbblJrN6kdWsbZO4PLnZQckL5DxfNIETitLiqIgdxQyFFkFYVzxmZif5hyN2CoqqhfcBrnjF5CmEiiKKLZWGZ+scnssSaLAbSCmDNPGebtP/tadu06k8HBATzPWzf0aC1aa2695Q6++PkvoJQgzTKSRCOVRDmKxcUFpqaniKOYBx54kG9/+9vccMMNeJ7LNddcw+SWbZx73vksLs6TZUk+h8AKE0EjFbzuda/lv/23/8mmjRsfNxjIUqttsNnEZ/5zWQQDBQU/JNbE56htIQwjpLSmIlaMZO2BjdEoR2KyzDY+5ScQbfS6MoJSCoQg01neqpRPZctvS/fS+H0VtbFTCg0IoXLhU0KtVqNUqjA3N5eLqKxC2dq7ro51zXSW92ZbAeGzIVQqeHbpueqFYcTKSoP/P3vvHadZVtf5v8+56YmVq6urc+6e6ZnuiUwgKUkdQFzABIZFUFZFFjAHDMjmVXfd9bcKQxZWXURREFEEhjR5prtnOufc1RWffNM55/fHOfepaoIOyDQM1JdXvYburqrn3vvc537TJ8zMznLu3AVOnzrFmdNnmJ65zKWpS0xfnqbV7pCmKaVSwNzcNMePp9yX34eUHuVymXq9ytjYGCsmVjI2voKVKydZtXEdtd3XEQUBKlO0Ox1a7SbJfIvZuQVaC3PEc9OwsEB5YYbhhTalbgc/D4jQhCqnZAwiS/FzqLVbmE3r0Wt24Q2MY4ZG8UODrkqkrtFtXGINHczGESqrxgnHV+JFZcbGhsEYej1L0Q2CoK/JsTTarTbv/9M/o9VqobHTslK5hNaGuJvwe7//+/y3//p7dLs9gjBi/foN3HLz07h+1y6+7/tezODgILt338i+vfuYupTAEjMlIWFkZISf+qnXsnrVqn9WujnXdu3ne17fwfBqxnIxsBzL8VWGMYZDBw8R93p02m183+PmW27i9OkztDsaKS1wsF6vE3cTsizHGAUYBFaOtAAX2kRdyLPK/v8v9vxZnhMEweI0wGEAwD7QtLHEqk6nS7vVIwwisjyx8CpjX89o65NeyBEX+gNpmpKmy1oD38phVe00WZrRbne4cPESJ06c4PSZs5w7e45Ll6aYb7XodDo05qbpdbskSYx2Wvy50oBBemVraxz4pEnev2+brSadbpszZ06jlSHPc7Qx7Nixg5tvvpWxsTFGRodZsWIUMTGOQFCKSuTK0E0yOq0G7dk55hfmiS9fwszOIBcaRAstwsYC5U6T4ZMd6ke+QEl8jsgX+OWI2kAFWS6hjU/93EXoxMiyR/XH15Ks+xXSaJAL5y5y5vQ5PE9Qq9bxghA/iBioVxgYrFMpl8EYHn/8IPff+xBBFAB+X3ZYCoknQ3bsuI7vev4L2L5jO2vXrKFerxOGVjCsKCx27drJ9m07mJ66SK4TKznseQhPcteLXsS11/zLzoNCYN1NsaqRVzuWi4HlWI6vMoyBU6dO9WWAAXZeey2ddoe5+YW+HPDQ0BA777iWcrlEu93m4MGDTF2eIkkzpJRUq1XSNCVJkj7QEBa51HbkX5iyFE5vEikkQtju3jiWtdGm/2edWxEWC0ZyU4EvsoqV0o5Yu53uVbxyy/FkRl8/P81ptTvMTM9w5uwZzpw+y5lzZ7l0aYqZ+Wma7RZxr0fS6dHpdEhTu+ZSKiXLU7QyeNLeP9IzBEFAEAROKtsjCtx9Z4xF7Pv2Hs2SzMr0akWlUukrbapckaYpJ0+e4Nz5cwwPDzO6YgWjExOsXDHOuq0b2V66FhmWUKkm7rVpNRe4fHmadrPN7Mw06fwC3sI8pYU5xhZmGGi0KU83qCQJXp4QaUPUTQnPz1NVJ4nrqxGlQfAifD+wEzSlyHJNs9libr5hi2QNly7M8Iw7n8H+Aw8yOz9HliuE5yEQDI8O87Ov+1luu+WWfzaZ1wdq3HzTDXzu8/eAFkghEMDw2Ajf/wM/9ISkhQvK4pcSKq9OLBcDy7EcX0VorWk2mhw4eMDK+2pNpVJnpDbI+Mgoh9VRO4bNc+Jej2o5YmxkiOmpCzRbC7brMoJ6vc4dd9yB53k8+OCDtNttSy90+gMYq+YWlcps27aNsbEVXJ6eYW52ljAMWb16DVu2bmF4aIi//OAHOX78OEIYQCMFVoVQSrRRjoO9aPhSFAOdToep6cts2Lj+a7oWxhiUcs6J8mvzul+Ory2KxJ+mGc1Wh8szM5w7d54zZ85x9uxZpi5P0Ww06LnEn6Yp3V6PJOmR9YV4HI5FW8MrKSVREEKAG6kHSCn6xYDnefjSg0DYLragzvbNfDRB6CONz/DYKJVKlXK5bMFwRlMqlRgYGCTLcs6fu8DFS1McCnxGB4dZu2Y111yzgzWr1yDECk6fPQtBwNDOIerlspX1Tno05+eJW23mZuZpTs/Rm5nBn1+A2WkqrQaDD3epH/oA1cm/p7R+G6XVkwwND2AyRa+doYwhqpcpjQ1iqkMoWWIi6/JDT99A+ux1XMpKXJzrcu7MBY4fP0Wr3eLyxcs0my2iMERI6xBarNqK8DyP2+98Gm//kxLNuIcQ4Ac+3/vCF7Jr5/VPaCrghQHGE2iW2QTLsRzflFGAq+bm5zh08DAPPfgwhw8fQilrODI4OEi9XqNWq/VNWAyQ5RlaK2ZnZzhx4iTtVhdjBKVSCSEEBw4cYPfu3WzYsIFjx445+1OfLE1BGwYGBrn2uut445veyG233dZ/eFs3ugDf94jjhD2PPsKJEyfwfOmEjpSbF1i5VulJWxgssY0tpgQnTx7n1ltveUI7yiL5x0lKu9VhfqHB5ctT9Ho9dmzfytq1q7/kIbkc//pYTPw5nU6Xufk5zp49x7Hjxzl99hxzM7N02m3b8ccxnW6XNE3JswyTJ65oNc7/wib+RTtgiefZ/b/vew7f4hFFUT/ZF8C7pbv2PM8X9TQArTTgIYRPtVZiZHgEIa34j8pTSuWIVasmEVLiBQHXXHsNG9auZeXkJBMrxhkcsKP3wnXzwoVz/PLP/zKViseKiVEmJydZu2Ydq9etY3xiJZs23kCtXMVISdyJaTWbNOZnmJtbYGq+iVlo4nfb6L3n8c1pBqOIeskyFIJLlwgvSEqTE2ThAAqB9DXR4AiD/jDRgGHl5Gqu230DnVaLixen+OQnP02lXKZWr7N2zWrWrF19xfUQQrBp00YmJiZon20gpWBiYowfeeWPUi5XntD77PkenufhCfsMudqxXAwsx3J8hdBa0+vFnDl3lr1793HuzBl0nnNx6iLdbgetbEdUrVbp5jlKLNEXyA1JknJx+hJpkqKFx9iKFdawxO0Doyii0+kwNDREqVRCSsmLXvhiLpy/yPT0JTzPY9OmjczOziDlEnnTJRHHMceOH2PFxBjdbptGo2E9CABjJEJ6FmOgjXtg27APXcVn7vkML3vpy5BfRgu9KBoSt2+enZ1nvtEgiVNUpt3DUOL7JR7ff5g0zdm8ecM/u+9cqsWgtVmeKnxR9BN/ltHtdJmdnefMmbOcOnWaM2fOMjMzy3xjgTjt0Eti8iQjjXso55ynlCJLNcooK+frEn8xEQL64/4iwXuehx9Y8CsCgiDC93w39REgBXmWoYwmyzKyLENpgVaGsGyxBJWojBcENBoNVq1ZxdDgIGiDMjmVqEIpjJifPY8wMT/2oz/Gruuu+4rvu+97rFm9Bqkl01PTTF+e4vF9+wkC25EHvjUJGh8fZ83qNaxdu4bVa9YyuXo1q9dtoFSp4UuJynskiSLuJaRJgsoypCfJVEqiFQ00xvomIxDIVOIpQxiGDNQH8DwfP3CsIAHlcomRkSGGhoe+LCNgbMUY27Zfw6mzJ5HSMDQ8zNj4+BO+t01ucRrKuTVe7VguBpZjOZZEMQWYmZnh4KGDHD58mFaj6ahCAeVyiSSJ6Xa71u5UCEZGR/EDjyiy4iSeb6VNPc/j+LEzCDTS9yhFpf5esHgQxnHM5s2b2bBhAx/+8If7u31L80o4cvQIjzz6CAcPHuLFL34Rm7dsoVIu9x8wCwsLtFpNnv3sZ7Nv3x7m5+eBwjPd/lcKged2mNolm6IzfOCBh+h0ugwNDfaTf5pmdLs95hoNFhpNWp0OKs1x4wak8Cz1S9iRppEeUSnixMmTjI4OMzIydMUD0J5PTqPR4uy58zSbDfbvP8DJEyepVKo897nfyU033UClUv6K9K9vxVhM/DndTofL0zOcP3+eM2fOcPbsOS5fnrYo/KRHrxeTJhlxkhCnCUqlaG3w8azdrimEr0ArgUYjpCFwSX8pij6KIoIg6FvrAn0jHW00vm8LQ+n5xFlud/5JihSSQAYM1GtE1arV8peexbVIgcQwPDLM9mu2W28BYSiXS1SiCmmcksUJA+UKxrlr/nPv84aNa7n9ttv5p0/9Hdo4OW2lHSYiJe7FzM7McOjgQYSUCGknbgMjK1m1cRdr1q7hB//N89m2ZRNCSIvNiXtoZ1SUZSlpZjE/eZ452m7u9DesAmDc61EqR2zYsJ4VK1YwMFAnDCOk/PLHXSpF3HzrzXzyU/8AQjM9Pc3s7CyrJlc/ofvBXzpV+wZQfpeLgeX4to/iodzp9jhz+gx79+7hzJlTaK0Jw5CSG5n6gUUaz8zM9cfvRiuGhoZQyuBJv5/4DOB5AUFQRuVWUrjbTZAyxRjVd1yTBmZmZun1YoZHRhgZm+DgoZPMzjUwJmduYZ48z/l/f/4X/O2H/4atW7fz0pe/lGc+8xkMDQ1x7Nhxq2BYtnroxhhsgycIPJ88V06rwHbgOtcIgzU/kpKLF6b4+49/guc977n0egmXLl2m0+mQ5wolQGHwPQ9f+Jb6aKzegZBWS8GyFTRCBghPsP/wUbZt3ki9XsNozeXL0xw9eozPfOazHD9xgoMHDtDpdOl1u6RZjB/4/M3ffIjnPe/53HHnHezYvoNNmzYSReG3VFFg3HXLsoxOu8PM7Bxnzpzl9OkznDt/npnpaRrNJlme0Ytjep02WZY48RlDluVojUX56xxtFIEforKUUiliYHCAmZkZkiRxlFIPIVnc90tpXfmEAOHhSYHvkpqQwmlQSLI8J8u1lcTNFdLzKIUlhgaGqFQqhMJDeh7VaoWBgToD9QEGBuuMjI2BESRJwvzCPJVymSjw8TyJFD5eaBibmGDDhrVs3LjpX3xv6/Uqv/W7v8r515zk0OED1sDIKJckLWUWYRDCYhaMFsRxQjKzQMNcQJYrXHfNVtavWdWf1qWpdRPtdrucPn2OR+57FKUyQnevqSwjzzKazSYLjSaZUrzmNa/mmmt2PKH1l5SS2267hXp1gFZngVazzYmTJ9i58/ontIYLogDf8/HEotzy1YzlYmA5vi2j2H93Ol0uXrrAkcMHOXX6FO1mm7iXID3J0PAwvu9jlO3yhZRkacrs7GwfMCUdayDwA/Lcgn6ks3PVyn6gtYE0zclUjhAQ+M5PPctozM1z7vwFwlKJ8bExer028wszzC/Mked2J+t5HqYGvV7Mww8/xJ49jzI+OsIdT7+Dhx/d4yRZVV/yuFgGWLaDtVS1bm0SKX3CsMTEytVs276dXbt2k2nNg4/sRWB3lZ6QSGk9FTxpx6iYAuHsigA3wZBSIj0P8BDSo93t8clPf4aHH3iQ6cuXOXjwENPTl/t76izLFr0chCaJrQ30+973Pv7yQx9iaGiEF7zgBTzvuc9h+/ZtTE5O9Efa30xRrDm0Vq6wWpxoFPdWltkJy8zMDBcuXOL0mWLUP0Oj0aDngH1xktBNY7s2UTYh5XnmkrRxIjZ27VQqRaxds4ZSqcTw8DCtVguAer1OuVzm4sWLdvrkyf4koC9QJT2E55Fr49TutEukhixNbcHr+5TCEr4fEEURYWjd/IaGhti8eRM7tm9n/fp1jI+PUSqV7I7bvdalS5f5yw/9FeWoxOTKlWhlVxdSSFatWsWu3dcxPj5G8EUrqaIYj3sxU5cvc+TwUfY99hh79+4jTWJ2bN/OmjVrMQj27tnL3Ows2uRIWeh22NVHpVJl9dbdbNl9O2969UtZt2bRJtjiIjzK5RKDgwPs33+YP/7jP6HZnCMMI/s515pKuYRWiiCKeOkPvJzrdu58wjgYIQRr169hxcQKWicWULlh3+P7+J7vfuETsiQ2xomMK3XFSu9qxXIxsBzfNlE8wFvtDmfPnuXYsWOcP3WKXq+Ndp1ROSpTCkvWQlRYe2ABdgXgHNAWFhbsbk8pi/gPQ5RSHD9+3NH5BEprur0O0rPe6H4gyXJtx7ymEBuyYkTtdhfVbBP3Yj7+sY9x9uwZ2u2W1RwwVh622+30KVzGGJrNeU6cPEacZJTKFZQ2dpQrPaCgE9pSwPN9BuqDTK5azdbt17B7927WbdzI4OCgM1LSGGMpiwBG2C+JsMoIrsCwmkc2YSAsfQr3pbSy5+P5lMpVzp27wL2f+yxpmpC7fbVSeV8EKfADwEMrULlFmy/MN2g22rz/T9/P33z4w+zevYtX/ugruf222xgeHsL7CsptVyuU0szNzXHkyFGOHj3KuXPnmJ6ZZvWqVTz7O76TFSvG6Xa6XLh4kdOnzjA1dYnp6RlanRZJnNDtdohjK4CTJIml9DlUvnbeFlpZ98soiqhUKlTKdvW0YsUKjh49TpZlrFq1GjA0m00GBgZQSnHhwgW01pTLFetBgZ1EhEHoEqK9d5TRSKHJlCLLcySCSlCiVK9RqdjEX6tWWbNmLVu2bGbDhvVMTk4yOjpCuVz6ioVZq9XmH//pn3jXO9/FTTffyHd+57OYXLkSY4xL1BWiKAQWNS6azRbnzp3n8OGj7H/8cR7f9xjnzl+gUimz64bruetF38Mv/uKbWLdubR+Ed/HiJT7ykY/xt3/ztxw8dACtc3bs2MErX/lKbr/9dlavWU2lXCYI/K9YQCZJyic+cQ/z8/MYkxOGIVmWIoAkjimVSoyNj/Hyl76McvnL+xJ8pRgZHWbrtu0cPnwQ4cN9991Pp9MmHBr+F3/WGI3Jc5TW9vN0lWO5GFiOb+lYBMHltDox58+d594vfI5uxxmJKCsbKp1XgJTSUq2U4tFH9rB37148z2Pbtm3s3GmFQ+bn5/tWxVEYUo5sR3bu3Dk3Rrd0uzxP6XZbRFHUT9K+51mr1ywHofGc86Dne1SqFSqVCoODgzaJZhkYi9TuJQkipW9+AjhamCEol2k2mmRZThCE7vUlA/UBtm3fzs233sbW7dewYsUKSlEZWNQaEELgO/e24vzlkq4fYf0VkIJM5VZaOctsd+dAiEIIdH9u4FEu17jrRS8mS2MOHTxAu9Ol3W6j9KK9c5alSOkjhQfOX96TVoSp2+nQ7ba45545Tp0+xTOf9Sxuu/12vvv5z+vjCq52pGnG+9//f/m93/t9pmdmUFlGVPKp1+t4nse73vVuVkxMsHHjRpRS9OKYbtwjVzlZmlhw2BIUfzERkF6AUgqtM9auXUu71WZ0ZITJyUnyPKfZbKBUTrVaYdu2rUxNTbkpj3DrKYU2mrAUkeeack06u17fsUcUWZbaQswYjADhe1QqFtRXr9VZM7mKdevWsnnzBjZsWM/Y6KgVGPIXRXW+UhhjOH36LP/5v/wX/vYjHyFLU5rNjawYH2N8fAywRVSSxExNTXHixEkOHTrE/fd+gQMH9qOB9es3sHPn9fzIj/8oO3ZsZ2LFCuoD9S+b0Ddt2sDrXvdaXv3qH2d6eoYsS1mxYgX1eu1fPNYiZqZn+cLnPm/BvH5gpbqhTxn0Ao9nPuuZbN2y5au+14Ig4Oabb+Yf//7j5KbHkYMHeOihB3jOdz7vX54wGEPo+2itviGOosvFwHJ8y0Wxn02TjE4vodVLyZTBaIMykjAskcQdm/h8t0t3tD3P99DGcP7iBT75yU/2HQbPnj3LyZMn2b59O61mC6XsA0R6kjiJefTRR2m127YbdzvKomCw4VDLQlj+tlakme2acTIjSRJz6dIlWq02eWbpYNJNGfIkw+5Kc9tBFLQvJGEY2Y48KjMxMcm1u3axY8c1bNq0haGhYQcIc/vhpeh9c+VmsjBHuQL8p7VVTNSFRLKdBGitnfiRY08Ybc/PFRVDIyPc+cxncvbMGTrdnjvmxSLEOC2GTBt83woj5FlmBZMEBKG1gj5x/CQnjh/nrz78Vxw98hN8/8texuTkJOVy6UktCopRv+Xxt7jv3vv41D33sPXaHYzPz3Py6HGmZ6ZotVpUKlUMMDc/x6VLF1kxvgIhxeJ9mCXo3JpEFQZVlkUyzNjYmNWIkIKxsTFqlQqjY2OcO3fOyuMazepVq+h2e3h+wMTKCfLcakckmR3tl8plqnXD7NwCqQatMoRK7ZTKF3i+T60SUSmVGRoZYuOmjVyzbTubN25gYmIFtVrNmfZ8deDNNM24774H+fXfeDNHjxwiCH12Xn8d/+b7vo8kSThw4CDHjp7g3vvv5eGHH+bixYtEYcTWrVt52tNu5d+++ifYsN4eQxiGTziZW8GuCtXquq/6fdVa88lPfpZjx4+Qq5TAYWeklBZkGfhUalW+98UvpvQV3Ar/pWPbdcN1lKtlOp2YuBPzlrf8Np12lxtvvJmRkWEqlcqXBVBqrS3uh28ImWC5GFiOb52wyGxFp5vQ7WV9IJ9yc29hDIODw9zytDs5f+YE586dptdto5zUr/QtLsDkOfv37+/vdAsa3unTp7l06RJJHCOMVWHrxTFHjh/j+MkTZHl2RfVfjEOL7lkIge/5COEjPUkYBH3xF601CwvzNJstVG539VoLtLCuh8aIPsVJiEI/IKRarbJt2zXcdOMtPPc7v4fVa9dQKtvuv3i4Fq/R3yFjBYmkWLRZFkKAO/aie11yIgD4DtgkpKQP43KrA2H1j/taBlJ6bNqwlcmJNRw7coxUpxZ34GGdGN0o2/PtZEQAQeBjpO4XZLZA6iGEYObSZf7X//xDPvbRj/FjP/Zj/NiPvfJLds9fayzd8bdabaanZ7hw4QJnz55j6vIU840mzVYLIyVCQrlSYs3a1Rg005cv02q1iSKbzOZmZwl8y67IshwEZFnm9CfgxhtupFav8cjDD1OplBkYqPfH7770GBwewmCLq5UrV1rlv6hEN07AxAwODZMlOSrPCcIIhKBSqmGMRxJPI4wmDCSlSoXcaIzJiaKQ2295Gt/9Xc9n/fp1VKuVfxUOwxjD9PQMf/S//w/v/8D76XQ7rJgY5dnPejYrJ1bzvvd9gP/yn/87WZoyPj7G9bt38yM/8iPsuv461q1bR71e67MZrnYsLDT5wAf+L9pYv4UrunVXEDz/+S9g587rvqbjE0Kwdt0axsZW0OosYDScOHaaN7zhjQwODjI5OclNN93ErbfewnXXXc/KlSsplysYY+j0uhgJuVk2KlqO5fiqok+FyxTdni0AkvRKZ0AhBJ4RDgMnQUB9YJCtO3ayet06zp8+xdmzZ+nFbZSysqkLCwscPXp0UQ2wH8J2a8XfC0Gapjz00ENkWdY3Hiq630KZrZgieJ6HVhrpKcgXR/LFQ8dz6H+Bh9EGT0rbcRts4jYGzwsZHx/n2uuu44Ybb2LTli2MT6y0QkbSQ2rnlPhFUSTpIvELCjrZ4gOvYDgUx10UMH3nRfeltXKIZ2ew9GWmDJ70ML7P7XfcwRfu/RykCdKz9Ybf3x3j9O9ztKObhZ6PICAIZB90WAD18jznscce43/8zz9k9+5d3HzzjV/2ga21ZXTEcUIv7uFJj8HBAXzf6983aZrRarW4dOkyFy5c5OKlS8zMXGZhocFCs0m7Y62kkzgmThNrJ61y2+VrjVCGwcFBet0erVaTJMkI/BAhNJcvX2Z4eBgjhAW5OQOaarVCvV6nVApZOTmOlD5giKKIUqlEqVQmSe3vGR4epV6rYowEIVHKMQP8iECG/ZF/sXJoNZvUKhWyNKXdbjDXnKIT97j51qfx06/9SW7cvYsoiv5VCdgYQxzHPHD/Q/yHt/4nHt//ONLz2LFjB9/xHc9i1co1HDt+kttvu4ObbrqR63ftZGxsjEql/E0BAjXGsOfRfRw8cNCaeQl7r/u+b6cAQlAbHODFL/5eyq6g/lpidHSErdu2cvrMMTLltA2ylJnpaaanp3n88cd5//vfT6VSZf2GtezedQPr1m7gwx/+a7TK0EpaN9OrHMvFwHI85UJrTZzkdHsJcZKR5cZykREgJJ4nECiUWlTaM8auCYQbhXqeR7U6wJbtO1mxai1nTh/j0KGDnL9wjsMHDtJsNMlzBdjRbhiGoIWl5gGeEChjLYa1UmSZNfwpHnh9tT9LkkZ4sm8Z7Bn790JIgjAEYTBG9GWETbFe8CQCSbVcZdWqNVx77bVsv3YnW7dtY2RsDD8ILUPA8xbBdRLraohBGbfJ16aP/i/kY4ukXxQ1S/0QDKAFyKIAECBN4cImwa0pMp1fcX2FEEgDuCIBYN2GDUyuXsWxY4dQzsgGon7hkaYJRjv6orRFUNxNMF5RWnh4nvVeMFqQphlnTp/kTW96E3/xF3/OxMSK/jXPs5xDh47wkY98lD179tBo2jG7Nppbb72F73nBd3Hu/HkefeghHtu7l0vnz6OzhGqlRnl0mHKtisohzTLSNEGbHN/zyJy7YyEyZW2rDVmeEUUhnc6VxZdRmjzLKVeqSE+i8ow0TZmfm+e+++5j48b1zM7MsWnTZoIgZGJiJWFUIlcaYwS+HzI6unhexhgq5QpSWOMqgU0469avZ2hoEIzgnnvu4bOf+ywXL15AqYwbb7qR333963nmM59Jufy14SyMsZbZnU6HS1OXefyxx/nY3/09n/7Up0iSHisnV3LTzbfw7Gc/k5tuvIGJiQmqlQpRKfqm1IvodLr86fv/L632vF0LBL7TDJAoZYGxd9x2KzuvvfZfdexRKeLpd97JPZ/8FJlSSCF5+jPu4OlPfyYXL15m/4GDHDt2lG63y4H9hziw/6D9HGkQwjJ/8jzrf6auViwXA8vxlAmtNZ1eSrPZI8mUA0pd+WER4Cz/Fg1/+onKl/2pgcB26kpKBgeHuOa6G1m3cRtHjh/l8lyL6OhJjLEmLCpP6XV7aCUQYlHbv9jdF1OBYn++NKQUmAIWILiiIwGQuXJGApArixMQ0qNUrbBp6xZue9rtXHfd9YxOTFCpVPD8wBmvLKGNfdGDt29brG0ix9GViusBzrfAJbbCYc0er+zrFRjc8XrCrgO0QWC7XGXcBKZYHbhzF26KURRcpVKJjRs3MnX5Ar1epy9j238tpfCl1wfXISVaAaqgRBYThByDcisSn8cee4w/+IP/wS/90i8yPDxEtxvzh//z/+Nd73oHrZaV5RVCUK3XrCDSsWP8+dvfQZR1GA9z6lHA6sAj0yndyxc5f1IQ+yHVyZXUBgf691Cn0+6P+ZVS6LyYhIBRyjFNIoSwynlhGCIRZGlGpztDlmWWH+/eq4WFBbRWBIFPkqQMDY1QrVYR0j6KgyAiyy1Qs9grB77P6NgYG9ZtYPPmjaxZu8rqOBjDZz/zed75zndz3/0Wtb59+1Ze//qf43u+53uo12tPOJn0gbZJwvxCg1MnT7Fnz1727d3HY489xszMDHGcoPKEUqnED/7QD/Dan/5p1q5dSxgGT3jf/42Mw4ePcc89n3V0SCucBfT/XK/XePnLvp9qtfqveh3P87j2+msJSxGpSZCe4Jan3cpPvfa1+L5PkiRMT89w5MgxHn30UY4fO8aJE8c5cfwYaZY6Guiya+FyLMcVYYwhd0Cudiehl1qQTUGD09qi66VwQiROUAdkP3Ev2v8ujmw1i6Ny3/cRxmNwIODmG29m546dHDlyhD2PPsI9n/wER44cJFMxRkMY2cSlXJcsPIl2nXzx+4vxPwiM0GhhnHVrhW3bt3H2zCm6vR4qs4k58Hyr/FYKWLlyFbc87Q5uvvU21m/YQKlSwQ8CJBJPWuS9J63XgCk6e7lY9Ohc9VcUwk0alOMsa2P6YiaiqFk8+3syZY1q0MV0wJA7TICX2xUFWiPx+79XIvqgQ+mEayxT2homCey1qNcGCMMSRkNXt9CZnShkxr6mxli8hnsvUp2RK4Xv+2jtzssxNLSbtKQm473vfS+VSoXv/q7vYv+Bg1yeucz3vPAukjTl0Uce4cihw7SaDVQaEnQ73FiGsUkPOTpCGtZp5hIhDZUsYaSRcv5ii6mp85bj73uLAjduwlIuRQjfI45TZ+6TgTAEgYdSoJSh0+m4Qs9iPiw+w4FKjb02p06fxpOSNMsZXzGBED6+V6j+hYwOjbBu7Ro2blzPujWrGRsbpVSK+pa5SilOnTrDn773T/mLP/8LpmcuMz4xzi/8/Bv44Vf8ECMjI0+IBaCUoteLuXjxIgcPHmTv3sfYs2cvZ8+eYWBggKGhIc6ePcPU1GXStAcItm/bwq/9+m/wrGc962sC2H2jIssyPv73n6AxP4/BPhcC6Yo3KQDNjTfu4vrrd31duvHVa1cxuWoNR48dRBvF4489Tpqmfcro+vXrWL9+Hc997neglKLdavN7//1/8r73vBeBnThe7cnKcjGwHN9UUUjxdro9ZmdmmZq6zIWLl8iynMmVK1mxcpWt3KXvcG2iv/YWCIfi1ovTAJcYl1LnTOERKtzPuB26FhoPSVivs+uGG7j+hhv5oVf8MEeOHmXPI4/w4L33sn//XhoLCxhpO1etF3XEr0ThL64oCgbBD/7gD/HDP/xDTM9M89nPfJaPfvSjdNodJlev5tprruW6XTvZvuNaBgdH8MMIz7eqfwiBR7FzNYvUQjfSN9r0sQXCONESs/j6xXXFWHVCrTXFsELiYXKFJyXSCHI33ldZTpJae+MoKFu0uec7LvyVAEOjNao/gbFJ0ioT2uvb7fVot9p9HQIf4Tpm956496nQ1l/EfBhX1Eg8L8AY3S+ytDG0Wi3+6I/+iA/86fu48xnPAiFpNpt04x5BEDA2NkZnfpZyr8tNY4I1E4LPXsqY7ymGhnwr26sN6ABpYGhiiLzZodntIQLf7fyl01KAa665hvn5WQ4fOkaj0QAW10EYD+kt3nvF/YhYvE80tqCxqyOYmprizJmzvPCuF7F161ZWr1nF2Ng4tVqVwF80CCpCa82lS1N86IN/xdvvvptz589SH6jxE695FT/90z/NmjVrviJ9rVDgazQanDt7jgMHD3Hfvffx2OOPsbAwz8DAAJs2buZ5z30Ou2/YzerVq3j4oUf4r//tv5LlKZV6jR/8gR/g5372daxcufKbbgXwL8WFCxf50F9+CINyfgyyv6oyBiqVCi95yUuo1+tfl9cbHR1h2/YdHD9xnFz3OHDgAOfPn2PHjmuu+L6CyTA0PMRzn/dc/uwDf+aopovPsKsVy8XAcnxDoxhTx3HCzMwcJ0+d5sTJE1y6dJFeu0ue5e4DIzh+9DC1aoUVK1awecs2qgODVAaGbOvlkh0sJuWlhcDSETnYB7WUHtrxeHwBwrMTBSkFNV8QeB6+P8Cm9ZPc9fzvoNHosnfvHu6//0GOHz/G4cMHOXv2NK1mE5UnGCOc1K/ESAuOw9hOfue1O3nZy15GEEasWrWGl77s5SAkY2NjXH/jLQwODeF71jRGCqf3LoSVPHa0PaNtFaO1uaLAKXAJlu63OCUA+uDF4nrQnyZIPCFAGwJZuBqavjnT+QtnaDXmSbOMgVrdocAHMAjX6eo+vsAeU4E7AM8L7b9JQ6ZTZmanLS5CK8BYr3infAegtMGgFu8HbRDCrkys5pGd49hzsjx6pXJA0Ov26HW6HDp8iCAI7boBQ54qkrjDQN5j96SgurLCXx3qMN8NELKDUiGeb++bPFcYo+h4mpUDgzQuL9Dq9qwYE5Z/nqucfXv3sWnzpn6xUvw3yzJ8b3HtUhRtxhRrDXdPKGW3D46dkqUZjcYCL3v5v2F0ZOQrPviNMczPL/Cxj36ct73tbRw9fBgZeLzwxS/k9a97Hddff/2XMCsKRb+5+XmOHT3GAw8+xAMPPMCxY8fJspyJiRXs2nU9P/f6n+Paa7azatXqvrZAu93hgx/8S37/93+PhcYc26+5hl/9tV/j2c94BlEYfo2f9G9caK351Kc+w5mzJwkCz9p6SxyV0rJYrrvuWm6//Y6v23g+ikJuuHEXH/vYx8AI5ubmuHjxAtu37/iy77MQwpmVeUihSdJlAOFyfBtEvwDoxVy8eIn9Bw9x/Ngxmo15kiTu7+BBEPghCEkURkxMjNJtt6iUfPY++DmOnjzFrXd8B1u3X0OtXrdgOldpL03+Sz/gi/Q6g5R2LOt7HmEAoXNFs49y3HoiJ+1osjwlTXI2btrGqjXrSdOUXtxjevoyJ0+c4NTxY5w/d56F+VmmpqZotRv9vXUYhLzgu76bUrmKtk4/4Pm87Ad+2PLOvQA/CDDa7qALN0C768/dNdNXJPkCo5A75br+dEN/+e8DUMU6w10D06cEuvcFi3+4cOEsc7PT9LptsjSl01yg025wzc7rqdbq/ZG3xUAU+wa5OKlxQE5jNHML85w9fw43tnBTDDdF8KyLgp0G2BWBUqpPpXTYRvvwLixdBf2VgZ0QaDxhbaSr1SpnTp+m22myMLdAvdvjppWC+uoaf7e3wULPB2nQytBsNvB9r3+vWSMquGZyiPnLKTNJTpZnTmrZWvae7Pa4dGnqClxIfz3k/CbQOGVGiwGx76ffXx8tSjHb8z548AD/74Mf5Cdf/eq+BsHSz0mvF/P5z32BP/zD/81DDz0MQvPM5zybX/j5N7F71/UW2OqOr9vpMjs3x9GjR/nsZz/PF+69j5npKcIwZMOG9dx4425e9aofZ/v2HaxcOUGpXLIrpwKAmefcd98D/Of/9F/Ys/dRwsjjVa9+Na9/3esZGx19yk0DipienuV9730/SZLg+R6lkk8Y+pbKagxBEPDDr3gFIyMjX7fX9DyPm27ezcDgAAuNGKU0j+x5hGc841lflhJrjCHupdZYzBhy97m/mrFcDCzHVYuC0nXx4iUee3w/R48dYWF2jjhOSdMUrWO7h8UaskgvwEQ5fhCQpoYjRw9z7MhhQHBxagaMJM4Ehw4dYGJiki3bdrB27TpKpXJ/PCscvUsI4RzWBEEg8X1JECzSnXItSHPlzEwUeZov2sJq15lqbbtUIQmCAD8MqdQHWbtuI8981rNRuSKJe3S6XXrdNkmS4Ac+URhSqw8ig8jy+z1JWPKQxo2Bnb6/0sJxzF1S1xpRsBKwO3/hEqlSasmYc3Hy0WcxLP37oiBw9MSi2LliiuBYA91uh/n5WbqdJknc48KFC5TLVbQ2XJ6aYmO1bqcDxTEupmlgyarEGPI85TP3fJq52RnHQFjEcHieZyV4WSzYihWBXd1YrXiHCC3uIOcFIN0gyJ5vGHj0Om3WrVnDoYMHmZ6eYSCOuXUUJneU+H8PNplpazyZI0XQB3/2umkfLGknFpJaTVDJE4STZzba9OmTWmu63R7lcqn/2p7nOWyDZRp4zpcCBL7n2esjwOtPeK7UcTBa89cf/hAvfuFdrF69pn/9kiRl797H+JO33c0//sM/0O21ufPOO3njG/89N998M1EY0mg0OXz4CPv2PsbDjzzCgf37aXdaDAwOsGPHTr7/+1/OHXfcxprVq6jVrKLfl+t8jTHMzs5z99vewTve+S5a7SY7rt3Bb/3Wm3n6HXf0C46nYhhjuP/+hzh46CCe7xGGgXsvrAaI7/vs2r2LO26/8+sK2hNCsHnLRrZu38qDD0yjteahBx+k3W4zPDz8ZX8mCKxJ0ZdSmq9OLBcDy/GkRiHostBocuToUR7ft4+LFy/Q7fZQKrMP29xyzfM8c4BA2xUaldNu22o5DK3inBE+cRyzcmKSwcEhyqWQJO5y4fwpZqcv09q5i127b7QmKoGH73sEviAIPDzPd12cIcuhGyu6aUqW5eS56lukGoy1WS1G08aCFkUBUuxL70okWPK8J5C+jxdGRNU66DHbITqnv6VJ1xMWDFhMMLTWGGWVBRECo2z3atfj9t+VS762l16y7nDJtVgHFEnqCsBk8V44fENxPBrwnPeAchKonW6XPE1BG2Zn5rjv3vup1gZ43vOeR7PZsoyCvh7BIjMBFkfn9mGmOH70MJ//3Gesw4ETUVp6nAhb9EGxUnF5VAqLwHeAQYHEOj0uLTsKtD0oMs4eOsTa0SGGB4eIz53hliHB2l0V/vZAi4sLCoOl7QVAuRSyefNGpi5Oc3l6liRN7GulmpLXY2UoOdUTdLRYpGc6kEmxey+Vov6RXJFE7OalX4BqtxrwPAsyLJL9UjxAnsa8593v5I1v+kV8P+DwkaO85z3v48N//WHm5ucIgoDdu3fz3Oc+h+PHTvCPH/8Ehw4d5uTJU6Rpytq1a7jpppt48YtfyI4d25mcnKRarfTtsv+5yPOcBx94hLe85a08/thjBOWIV7/2Nbz+Z3+WFeNjT9lpQBG9XsxHPvJ35HmK5wn35Tl/DahWq7zsZS//ign6XxP1eo07bn8aDz10H1prjhw5yvkL5xgaGvqy19UTEl9KtOd/yZToasRyMbAcT0pobYiThNNnzrFv32OcOnWSdnOBJI7JXMetnfOfMMqxA8yXVOdSClSmaLfbSCkZHR2lXC5Tq9WQQuL5Pp7nMzBQp1ar02jMceb0cW7YvZuoaicEWkBuDL3MkCY5SZqTpinWIz23o2YjiwMH7B5eORqd3f9q+qN713Fbmd7CFKjg69sds9A2MSpjEHhIX6CxkrSBLSGuTIz2ZVDa0QuFtIWR0Q6k6OyCl6wLCvnSAitRdBR5nlsXNreOwTEBvrhwABwrwaAKSWFjqY2e7/f3255jHNhz11ju3xL/AriysMFw7txZ3vfe99FYWLDfq1W/MFmK6SicHgtmh2NZIoTuF0LFvxdhHMpfOQaEZyQ71sIIJxErV7OtU2Xz1oy/eSzhxAWF0NoZUZl+V75t6zayRJFmOZenp93aI2S+m+KHHlVPk0mPPLNqkEsgKU5/gismM1CsBzwEwqos+os6AarvQlfgCQpsgSBJUsqVMo8+8igPPPQI733vnzJ9eQqEcRRCOHXqNH/0v/+IWq3Gtm3bufPpd/Kzr/sZtm7dytjYaF8B8auhErZabd7xjvfwR//7f9HudNm+fRu//Tu/xdOffscTctl7KsTxY6f4zKc+RZZkhCX7rCg+G7nRbLvmGp71zGc+KVS+IAi44YZdlKISvbhNs9Hgscf3cM2Oa78E6CmEwPM9dz/p/ufiasZyMbAcX7coHnrz8w0O7D/I4/sfY3Z2zun7JyRJ3FeVwz1EPSktEl0s7SxVnxoH9JNMnmXkqf2SCIaHhxkfG2dkZIRypUqOpN1LOXD8LOXBFWzavMnubo0h1xqVO00AlYK2I2mjjAOnFSNcazNsjMa4vbc0lnq0yA4wGG2TqBR2QqDBJf7FZAWuQyw6ZyFtwi+SAYs7fe20/+0radtNm8XkabTqgwQLfIDS2rrQGa5ISr4j5xsWC4TidxfFgBC2AMhwidwdbxRG+H5AnvuMjI5wy623Uq1WCUOf+sAAymiEdNftCrS7RrnJytTURd5x959w7sxpux7QThTKLBZAtmt2mg0s4ju01gSeZwtFb/EBbTnhwh63KwKK6YSJPNJqxOAGSZkaA+tv48zFac42j6NVDwc/cd+f0+32OH7iFMpout025XJErT7A8NgYqiQJB9pEC7aAGh6usmP9MAcPnmWhZxCePdcksXz7ILAeCgXA0TIsC8toy1aQcnEd5fue+7JCVtLzmbo8x2c/fy9//eG/4+KFi3S7HYyBMIoYHx9j165dPPMZz7Buk+vWWrZB8LVz+/M8Z8/ex/nt33wLDz/yIKVyxKtf8ype/3OvY8WK8af8NKAIpRT33/8gjdY8fij6EtiZypGeR7lU4eUvfdmTMhUAe49v2bqZ0ZEVnDvfJcsUDz70IC/9vh/4sqwP3xkzCc2yhfFyPDXDGEOcpJw9e55HHnmUU6dO02m1iJMOWZaRpanzHTeLACqjEa4NlEL2k5xVPTP9rtQ+SDVRZPf0tUqVSrVKuVSiPjBAbWAIJX0a3ZRuJugmGb24y3ynQydO+4nUyt4aS53TKT52GqHdA3sxiRcjbIeOFwIthE1GyrjzWJwYFF2eBjvid/twlEZIiXIJXQqBxAkLOeCdcZoHFl1uO2/3Gx24z/SLpqXJHpxmwJIdtDtwRzUsfvciy8B2/oXUcE43btNutcjTlIF6neHhMStoFIaUqlWSrEe1WmXTpk1gDAMDA4yNji7BIiyKHmmtQGi0zpmZnuZd73gbJ44f7U8xlDJuTK/6eAWlFGiNLzwc7tCet7T0RunOQwhr1CSl6BcAuJ/3HfVSKcPBYyljkyvZef12ypVhtqwS7G7ew2c+9Y+LAk9LOvk9e/ZY8Jjv87znPY/nPv8F5GnG2/7oD9hSD1lXbpF0BHGmOH36Mlp7IDKMsferUoqbb76ZgWqNI4cPc/bcucX7GzeKDgJEZI+7SN62kHOYk8AnyzM6nQ733fsg0jNs2rCZpz/96dz6tFvZuXOndeSr1Z7QyP+JfE4bjRZv+5N38Pa3302n02THjq385m/+Jk9/+p1PaWzAlwulNI88stdN2xaBtAWOZv3adTz9zq8fg+DLxdjYKNt2bOf8xTMYozh0+BCNZoMV4yu+5HuFA55aYPCyhfFyPIVCKU2z1eLQ4SPs2bOPSxcukKUpcRyTZj37YCwm4C6B+q5L6ucvDYYcgSAQAvAJSgFBFDI8NOQ0wj3beec2mWQKeklOc2oBfyFmaGQUZQxZlqPSjF57nrTTJE96tjMuDkJIfCeI4wtHUfNssVGA6ZQRV3SvBTvBIK3Zjnayxm5SoI2lyslineAJEJapIGyb75KloI+7N3avXKDhCzAcxqCFFQbS2tobF4//fkHg9v7F9/epghhUltiiJ1fkedLXQMiznG4cM9do0Wy2mJufp9Oet5Q4Y4jCgM2bNrNp23YIIkbGxzEC4nYDIQTlqMT42DhBUMIXAZICJOeAdwKMUszPzfHud7+bI4eOgLYdPEKA0Bil+vgA62sgkR4MVCJ6iSbJU4QzYyqKMYmVfLYPa4FWbszvyT4DwBMeUkJ9sM7g8CSBHxGGEUZ73HbrbRx47HGmpi4gpLnCL8Lz7Eg2V5okSZkYW8Fb3/IfOHbyAt2BgOsHIyYymMoFzRh6GqSxVs3Wbtnn8tQUO565hcuXp7hw+TKhH+B7Hl7BahFeX5hKOpZK4HbVRV5XOiMqhWxYv5Ef//Ef44V33cXQ0OBXNfJ/Yp9VxUMPPsrvvOWtPLrnYUqVEj/yoz/MG97wBlauXPmUUBD8asMYa/6ltR3Ze57VF1C5IowiXvKS72XlxMon9RhKpRK33HQT93zq0xg0p0+c4dTp04yPfekEpnjexMpKXV/tWC4GluOrCuOS7qXL0+x5dA8HDx6i3VwgTVOyJCHPc7I8x4hir27VAn0nWFPss6W0o9JqpUy9GhIEPqFfohRVkUGAwZDmmmazSzdOaMcpaZJaTniekaQpvSwFT7B1sx17a5UilEGlGSgr6Vp0osXgXkqBwAcpUQUAztHtzBJlwyKKpFPY9AqcCY9LUgar4y+w3bI0dgxrREGZ+yJ/hC/u8LUFthVrEWO0xSpog0Fe8fpAX3Ww6EKL5Ka1Ap0RxzFJnNJqLzDfWKDRatNpt0nilG6SkeWW6++jkQJ6OidOYh47uB8ZRWzZsoNaqUIwvpK0PoSQgnIUEYWhU8lb3INb2l+GUormwgLvfOc72bvvUYxSeHJRSEgbbb0K3DXLjaEUSnZtHGV+IeNM3OlrDhSrh2KyIfqgwQKXsTg1KkJKyYYNG6iUaySJIooUnmc1BMIgtMwFo664lsUExxjD5z73efbs2cv01DQCRaoHOJ3UMSKhLjSZ6dJRiXufF1/79Jkz/NOnM/IcSuU6QRBauqrzh2CJxbOU0tH4JAZNr9cjTmLGRkf44Ve8gh955SuflKRcTAPuvvtdvP1tb6fTabN58yZ+9dd+led+53c8pVQEv9qQUjI2NkrgB2hjAbpC2OfOls2bedELX/ikA/V832P37uupVKu0OylJonjssT3cdONNdp23JMIwREgrMPbFsuZXI5aLgeV4QmGModuLOXHiFA8/9DCnz5yh3W5YD3qlrLGG0n0NfFU8bLVBoazefrlMuRQyUKsyODBErVp1wCdNL45ptGMa3YzOfJd2HNNNMjqdlF63Q5J06HU7pN0WJs9J0wSlFVGpxKrRIavvbizH3fd86tUabqaL0MWY3q0MdJFUXFeL7WJdmutT+HCgOeP2+T7SdXSLWvxgc5TR2jIHtOk7DBbXbSnCP1fKcfBNX1cAp3mgsbt1tEEoK8QjDGgnf6y17qsLCqNRyo6Y250OrVaLdnOBhYUGnU6HXq/bX0loJ9qTK4Mu6Iy4YkLa6UOSZJw8eZJ1azdQKlWRpRJBYB8PQkhEEF6x+y7WA9ooms153vPed7F33yNoldvzx5ArZdcyxjhmhe3wK5HgOTfVUfM9TszHpGnepzpiDLpQWTQWj9GHDwrn/KgXJzfF9a1VB4hKVZTSxElCGMB8Y46VqyfZsXM7Bw8cZGZqikxklCsVut1ufyKTZSlzszNonRF5HmUvQEofL5QolTAc1GjmKTGZlY32A8KwTBRV6PZyAj+gWq0sgj49SaYUwolECbOoMYDUpFmKAV75ylfyk695DRs2rP+KyoH/msjznD17HuN3fvstPPLowwRhyMt/4GW88Q3/nrVr13xLTgOWhu/7fPf3vICPfezvaLUaSBmA8TBS8913fTerJlc96ccghGDz1o1sWLeeg4daYAz33Xcf3/+yH2RgYOCK79VKoZH2M7tcDCzHN1sopWm12+zff5BHH32US5emyLOELEvJsvRKCVm3Ky0AbkEQUKnUqNXq1AcGCVyXlitFnGV05jso3SLJMrqdDo1WTKfTptdqEnfadOMezU6bJOmh0tiZ1Ti9fBng+77bpdpE4gcRRiukF1KpVu0D2P5EnxcvhS0WtAMVmsKlQDsPcWHcBFz3d97F7lr2SX1XdveqQMS7Mbf9gxUEWlo09Gl3RYe55Dov7v01QlnQXe4SSq5yEsfAiOOYRqPBwsIczeY8C40GrU4HgcFk1qdBOSqkdOesBVafQGur/ucKHFusqf76YnZ2ltm5WdasroGwEspCCKQQjg4przhepRTNVpP3ve+9PPjggzZJuxG/xSm4P/epdQY/gFt2DrLW9PiHMzkLuQVUWkVIx7LA/oz1mjB40rOsCXftYZHOJ6WkVCrxrGc9mzvuuJPZ2VmGhoZZu3YNA4MDhIHVnp+bW+Az93yGD37wg9x33310Ol2UWpzGeI6mKIVEZzmyZPD8kFwqfAxlLyRRGdL3CaOIWrVOuVzG94P+2kKpvP8eKwzSFL4X9sh9P6DXa7Nt+zZ+93ffwu5du56UztQYQ2OhwTvufjd/8ra30ek22bx5M7/4S7/M85/3nG/pacDSEELwzGfewV133cXfffQjxEkMWrN202pe/MIXXTX63ujoMLfcejNHjhwEMo4cOsTU5UtfUgwIIZB+gFDOuvwqx3IxsBxfEsUIenp6lkf37OHQgYPMzs2TpLYIMFr1CwGt7A43DEOiIGSgVqdarRCWSpQrNTyvZL3g84w4zkhURpLmtDptOp0OneYs7eY83W4XFccIrUizmDxLybUmN9oa2wjwAt91X5rACwnDkMHBQWq1KpVqDaOt9WetVqFardoEYpGBNuEYA0iEWx/YZGnQxq0Uis7U/VyR8ByZAPBcArHgR2Hn5P1uuRAKYkn3XPw3X6I3bhzvPs1yJ2akyHPXURejbKVIk4Rup0Wz2eTyzBxzCws02016va4TJ7ITi9zJHkd+iMpTEqPsODSMKAUB7W4HpbP+nl9Iu3+XxTG6Y9JG02o3nZqehxQSIa0ssz0PO3pXKkflGe12k//7gfdx//33ofLMrU3sZKU4X4xlN/hegCBj2/qIG1fDA5/NOJeqRWqhKGYkuILKTTCEFWIqJjnYf1ksFoB6vc6LXvQiNm7c2L+Hv3gfOzExzvOe/zzuueezLCw0rZOkY7B4nsf4+DgLs3OoLKdSrpB2egT1CC8KyeIeUoKnJWEpolarU4pK7n3NEXpxdVKEL23RWQgkedKz+hirV/EH/+MP2LZly5OC2s+ynIceepi3/PZbePyxfQSlkFe88od54xve+C2LDfjnol6v8fp//7McOniIx/c/RhB5fO+LXsS6deuv2jGEYcgNN+ziz/88QKOZnpnl1KmTbN605Yr3w/N9ywxSeplNsBzf2DDGkOWK8xcucf/993Hs6FHiXo8kSfrGMkq5Eb1ShEHA4PAIgwNVxsdGGR8dQytFL05oxjmdOEMp6+WeOS/4PE1otVqcOnGC6anzxHEbL/AplUqEfkDg+/gywi+VQEC318OQIIS0WAMvsNMHY7vCwcFBoihyUwCB8DwGhkaQYeS6eoewd/vqQnHOFgTC/bvj0OtFeltxPYwb3ReMA+3AepgcT1j52QJIuJQtsRQn0FcHdAlXO4Ehk1uhnVwpp1Wf0mq1WFhYYH5ujkajQau9QJKk5LktaDRWR9+C4SwjwhcSpRVZnuMLgS8E5UqVVavWYgwcP3EMr2+bbJHKwiEp7PE4h0Nf4vm22ArDyJoXLRH6KYoUWwg0+MD/fS+f+cw9V9w/VtdA9ffkUkp857Y4XJO84EbN2QfnOdmTbm1hlQ+1o0gWzI2CdqoxS6YCONCgYxU4YZ8oipyN8FdOrnmuePvb3sV73vNeuxKSV8pVVyoV0jhhfm6OdhpTxkOWfAIJmYoRSlKOqlTrVUpR2TIkjEY4CuHSL8AWAu59z3NNq9lkcHiQ3/vv/52tmzd/3QsBYwzTl2d529vfwXvf826arQY7dmzl1379N3j2s55FFEX/8i/5Fo2NG9fxa7/xK/z717+RkdFBvvfF3/tlJYGfrJBScs21OxgaGmZ69gJSGR7e8zDf8eznXFEMCGcW5sugv2a8mrFcDCwHxhjSLOf06XPce++9nDlzmm63Y3UBnN0sFGA61e/IR4aGWTE+YTtnYLrRJc1s8u/2UtJck2U5aZaQ9bp0u03mZ2e4dOESzWYDZTR+GBIEIQKJFwQYaTn7nudRKoWUKxWyTJNlyuIT3Ii+6ApHx0bdB9uuD4Ig5JrrrgexKG8rpXRjctXnttvE5cbpkj7truD798+5SHA4ESL3OlopsDmuX0QsLQC+eDWgHIjOGEWSxNbpL8lptZpcnplndnaGxkKDbq9HnuekKu0bExVUPoUVwMFYjQNrLJTjSx8h7Pi/VqsxMjpKlim2bNzMzNwcQgiy3FE6tVuLeIJUKzwsv7lWq7Fpw3omxifwA7uCkWJR40FrjTCQq5xet81f/9Vf8ulPfZo0SxxS23fkBrvvLxKj7wd4AEJx+40VkjM99k35LOT2evTxFcIxLAR4vofKVZ8lsbSgkgXA0BTrHEOpFP2LtLhGs8Vf/uWHLLbF2PWQ5xVeAXDq1Kk+NbMRd/DLdTpxF6M0frWMZxIqUUAUlu0mRAp8305M/CDog0qN0Xiej+dJd++nNBsNtDH82I+9kht37/q6d+dZmnH//Q/zO7/9VvYfeIwwCnjlK1/Jm970BlatWvUtoxvwtYaUkjvuuJWf/unXEoQeG5ZMkK5WrF4zybZtW5n5wkW0Mnzh8/fyY6+YZtWqRdyClMI9S80Sz4+rF8vFwLdxFPoAJ06c4r57v8C5c+dIU+sTUGACjLFcfwyEQcTExASjY8MWOS2sxGovtpODOElJspQs65H0UpIkoddpkMRd4iSm2WxxeXra7sJ9D8/YUXSeGdI0IVOGMAzxnJ6/0gaVW7MgpVR/j1Yk2KhUolqtorUmyzMMkq1btzO+YtxqFbiCIVMKQaHzb3viYt2gUOS5djz4xSTel+FxQEIhLcJ9qW64ci5/GLOEQvmlXgF5npMlCWnSodluMDU9y9zcHAvzbbrdLt0kxrcVicMy5FbcRiiEMUgp0EJj8hxtAnfs1kpYYDvsKAxZMT7K2NgoK1asII5jAt+jXqszNDREmsYsLCwgtAV2pioDBJVqhfXr1rF542ZGhoYJwogwKvfPoTgnz/PI0oRut8lf/+Vf8PGPfRSVWfqTVXQrRJYCQDlqpXBaC4qNKzx21TP2PCSYSjWZsJMZjaN1utcROBYGVva4GJdaKIJlfCitnKFUoYK4SAf9StHtdGg1mnhSkuscjO4XcVmW4Xl+/73vZSnztBG+R240SaIIy2X8MMBI0e8qgyDoayQIY70IlFIYrYmzFD8I2LR5M9dddw0veP7zeeYznvF15fIbY90M3/Z/7ubud76bbq/Dho1r+Y03/wbPfe5znpIOg09WhGHIK3/kh5BSfEOuS6VS5o7b7+ALn/88wsCJIyd517vexatf/RpWrBjv+2UYaciSlGzZtXA5rkYYYy2DDx89xgP3P8DUxUv04i5ZlpIkaR84J6VH4EdEUYXR0VGGBoeIShEGQ5IkpFmPJFEkaUYc98jSHmncpdNt0Wos0G63UHmMUpCmlnLo+yEIp0CI66KkU96SzrlOW3aBcNoEWi+68xXHb7SmXq/bB7Lj/tcH6mzdthWtC/lXtx83BulAacWm2ajMehQYJy0rFnfQhaCPRNj1CHbvLYwB5Xb1SxzotNZ97MBSURyLk0hoNBqcOnWSy5cv0mo3SJLCiVC65JeDlqC01WHQhXmu3esXSnsFWr3YpxcJzAsCaoODrFq1iq1btwLw+P7H8aMyg4NDXLt1B4ePHKRlFo2KKlGJiYmV7NiyhRUrVhBEZbwgxPcWxW0WiwFNHCf0Oi3+7m8/zMf//u/IXCFQJGWLsXCuhFohPQsITJMEXyruvGaAucNd9iU+HZEXuEKcOKD9kotdf4H0XzT0cRMRY/owTvu+ySsS+VeKwcEBVq+e5Nz509ZZcIkxUkExFHiWOSE0zbRHdyEjKpep1weIymVbRBamV04EKNMWX4HW7ra2x3bnHXfwM6/7abZu2UK5XPq66wZkWcYD9z/Ef3jrf2Lf3r14oc8PvuIH+Pk3voFVk99+2IAnErVa9Rv22p7nsfuG66nValaMLc/4wAc+wP33P8AP/uAPcPvtt/PIo3tQKsV3bopLC/KrEcvFwLdRGGPoxQmHDh3hoQcf5vLli6RpTBxb22D7cFQIERCEEdValfHRFVQrVYIgQilDL8765j5x3CWLY+fU1ybutkniDp1Oh7mF+f7DFtyu1/PcGFgShKFl/kl5BbffUvAMOld4RXftfo81erEfkCDwWbd2tfORNwReiV27bqQ2MGA7/SUWu1Z9zwIGhbD6/8oUBYkda+fa9Pd0xZpAu2mEEYJU5UjhoZ1EsVmyBiiS2VI98STtkiQxZ86c5/ChI8w3FuzrOsleIUCSOeaFpeh50nM4AFsgeFLiCci0IFdWnMjz7MLCXjuPUlRi04YNDA4O0Ww26HRiRkZH8GSIynJGhodReY7KwcfHC2F0fJy1mzawbtVqylFk99t+iPTDPo/eShTbpJO69/Tv/+6j/O3f/BVJGgM2ufnSc227w01KJ56kFxP5+skyqyseD89KZoWmL44o6Hf3BejSDn+KosBpFgkn0SxAOttjjcCjALtqur34n733a7Uqb/qFN/AzP/0zXLp0EWOu9BeQEsLI4lHi2LoWhuUy9YFBSqUSwpN9XnhfLwLwERg3vfACn1qpzAvv+h5+6rWvYXh46EnBBszNLfD2t7+Ld73r7TQa82zevIU3/+ab+c7v+I5va2zAN3MIIdi4eT2jYxOcPnPceY6kHNh/iLe+9T8yMDBAEmf0ki5B+C9Pup6MWC4Gvg3CGEOSpBw7foIvfOELXLxwiSzNyPOULE/69EDf9ymXa9SqQ1QHhwiiCAzEmaabdNyo3tDt9UjimG6nRa89R9Jro7Umde5/ucr7Km8FkKwAr9XqdueaKUVuACktEI9FHX3rHqhQDqtXgOOkDDBG4/s+Y2Oj7mFrf/fGzRtYtW6d9SBQS/bNTs1PFl2xWCqYs6joJ4z1ES86UlySt5LF9thyra64pu7/2B7eTQbyPCPPFe1Oi6NHj3LixGk67a4b/6v+6sDyym1SCaQkz3K0BwqF0eA5ZH5mjL1OWPU0hJW7rVUqjA6PUq3U2b7jGqIo4tChQ7Q7HYbHx5hct4bTx08QxwkXL10ijmNGRkbYvHk9k2vWEFbLRF5A5Pt4vg/Cc6qIi/v+LE3pdjp0Wy0++5l7+LuPfpQkSd2UwrjphOeu8eJ5Fe+3lfuVbN9UZe50h+NdQfGME32goOq/nv25K+5c6K8CXBHQFwwS/defWDHByMjwP/sZkFLy/Bc8lz/4g//Bq1/9GtqdBYQQVuilwCAIHKbAUK1WqVTr1v3SnVMxRQD68sLWOGgrO3bs4Ibdu9i6dSsTE+NPCm0ty3IefngPb33LW9m3bx9+KPiRH/1R3vSmNzG5cuW3PTbgmz1GRoa56aYbOXX6GFL4jkFkC+65uTm0wgF2TR+wfTVjuRj4Fg6LZFacOHmKe++9j3NnzxLHlh2QJqnjwit8KalWygyNjDA6NobvhXR7OZ1e7JTgbKWaxDFZlpD1OjQacyRxG5Xb3xMEoZVaRbikYhMMRuF7IV7JX3Jc2ASnlQWRsYR/70Bz1v3OOhZarIBwPG7tFOc2EQQl0iRhaGSA7ddei+f7pEnaH99nWjmuOhad5sR3DBrtDHz7SPqloJ0l2AHop6T++qCfPAxWDphFcGWWxszOzbJ3314uXbpEpnIwdq//Re8OxnW36CVCO1Kgc9v5G60dZkACmtD3EZ7H4OAQ69auZXhggDhOaXXazC3MUyqVqFQqPL53H/V6jfmFBidPn8YTkm1btrB+/Xpq9TpBGICUBL6PJz3XfcsrpI/zPLdro16bL3zh83zor/+SOOk6BL8dq/tBYI9XLWr/o01feEoYiDzBRCnh+P6EmcQndx15UTzZa9kXakaIwljJrVB0vqRQcCI+ThtASB+lFNValTD4l/fAvu9z14u+ix/+oR/iPe95t9VB0JLcsTM8GVKplAhKJTzfJwqCPt0TWFI0CKrVKi95yffy/d//MiZWjDtVzSdnNG+MYWGhwTve/h7e+Y530GjNs27jBn7zt36T53zHs5exAU+RiKKQO59+Gx/5yN+SqwRZsGI0bmypnamZR5rGy8XAcnx9IleKi5emuO/e+zhy5BjtVgulc+Jer9+dhmHE4ECNkeERRkZGkIHPQrtLlnZJc4XRGUbbpBD3evRaLdrtFjpPUDpFegI/8J0Mq+XgKyEw0tpxgiAqla2Er+NcZ1lmlevyHE9Yxb7EODGhXOMZ+6BXRqOFIHfmNqhF4N74+DjDwyNg7AN606ZN1Op1sjTrS/QC/SSjXUEgTTErcBMDpfoJRmKT+VLQXN/Yxv0+bYoRvwPLYRO2NpZSmCQx586eYt++fcw35t1r4yYVdkdd8NrtqFkh3XzdJuDM7uCLPTmLwkSRLxkbGaI2OID0QzZu2MjZ06fIc8XY6Ao6us10s0GpVKLX6XDm9Gma7RaTq1exaeNmRoeH8X0LzPQ8D+m63SKBLU14eZ6TJAlJr8cjDz/MX//1X5MkMeVKuZ8Q0zTtf2+R1JVy5+OeYVprotDHJD6n25oe7ppq68pYgELt9fTRyj0QsVbQmELWdylFU/SnAsX9sHbtWqLoiSXEMAz57d/9TSZWr+QLn/8CJ0+eIM8zgiAg7qVWyKpc6ttUFz8jhCB1oK7v/q4X8JM/9Rq2bN70pAvX5Lni0Uf28Lu//bs8sm8Pnif5N9//Mn7ll3+J1ZOTy9OAp1AIIdi9+zpGx8a4dOkcUDxnvP6kTWuFNIsGZ1czlouBb7HQWjM7N89999/PgQOH6LSaxIlN5nluxWl836M+MMjo6DgrxsfxPJ9Gq02r2SHPLZhOZSlJ3CKLO2TdLt1Oh8TRsqLIR4gyRkgrOZsZjLaThCRLULmmXLaJQ+WqD+jTxgprKKXwpb31EqPd3l6QGWuDKwo6Ho62pzUe9oMSRRGbN22ydECtmVy1ljAqgzHkaepG9hrQfWtcCXac71YOeEW3Z/HsfSEgUTjkWZehYk1RJCJhnKyx64DRCqUSsiyn1e1y6PAhzhw/QafbdjRCg/DsuN9zsr92IiH7tEGFAWWLIs8TSJw1rwO0eR5OaTFi4+btrF49yZmzp8nTjG3bruHAgf2AolQKMUpx9PBhWs0GIyPD7LjlFsbGVxCEAWEYEQRWx0HAFcJIxSoHbBGZ5VYrYs9DD/OhD30Ig6JarWJH9rZoKlZAnnS2zIVqotF4S353FHkkHZjLDKnRjpwp7DVgCUhRK6TnUdD2CoGkgmFgmQTuCurCxllQKpW4/vpdFsT3BGNsbJRf/7VfptVq87a3382B/Y/R7cYcO37Caju0mggpGRmoE/i+lTiOE2655Wbe9MY3sHPntYThk8tTN8bQbLZ41zvfy913v425uRlWr1vLb775zTz/ec+nVFrGBjwVY+XkBDuvvZaLF85aISsPpFc0DG7qqKG/s7uKsVwMfIuEMYZON2bPo3t56KEHmJ+fI8+VowlmTioWyuUKo2NjjE5M4AURjW5Mu9UkzXNHo8sweULcbdNsLZB0GgiTI6RPVKpYn3apSdOUTqdLo9Gk3e7SacdorVm9djVRWCLLHDVGCKz7ryGLYzwh+jz0NEutvahL/pnOMZnCs60gqcrwBOhco4x98K9ds5ahwUGUUowMj3Htzuup1GpX6CHYjKJxGMF+wusDv0yBebPaAwBLRT7sbr/4WSflW/yFMSijkNqg8oQ8T5mZnmHv/se5eHkKmVkDIM9z9D9tR/C+tOuTTCuMsQZOnsMtSCeopE2hg+DjSUEUhUyMjaO1ptPuUKvWkELQbbV54NQD3HTTLWRpxqHDB0iSlNnLM1SqFa6//lomJyeJShX8wAnyuCnAUg38vqKfS8j9iUDcY8/DD/DBD/453V4bpTKH5RDO8McCJdM0RWKpdp6Ui0BPrfvqe5WSpNnu0lMC4dn3XgvnJNkHHgqHO8iXjOLNkqmBnfIU71BRDPi+jx/4TE5OXiGO9ETCClYN8OqfeBV/+qfvZ//+x7j15huYbzQ4c/YcjYUGC/NzCAE7d+3m537u53jWM55OuVR60rvxPFfs27ef333LW3ngofvwfMmLv+8l/Oov/zLr1q1bZgo8haNcLnHrrbfwyU/+I1Lq/n0vpUBryxBCWMDw1Y7lYuApHoVq4PETJ/jsPZ/l4sWLpGli9fydsp3WEIQRgwNDTK5eTalcodVLmZltkiVW3CbPEshjVJ7QbS/QbC7geYJyuWz3pkK6HW1Oq9VhbnaWVrtLu92m041tAvM8Go0Gw8PDVkBDW7BfmqZ23681SZ73kfLaYQbshNpOAKxbn8MMKIVyjIByqcKGDevZunWblcr1JFu2bWV4ZJgkzciyFOUUBTGWkVCAAYvfXwgNaW0R4IWHQqEV4I6CQlLXWJShA8gvsiLyPEcaQ5r0OHv2DI/v38/M/AIag4elvtlpg+34jdEQWGvbHIXSqZtC2BcswITSIeilFNQHhxgfG2X92g0cPXoUhLBypdKjXKuzfWKSRqPB2bNn6XabVKs1Nm/dxLp166hUatatLwitdr6U1pXRLDImPLlEOc+5C6ZZRhLHHNy/j7/8f39Gs7mA1lkf7Cc9icnV4rpHWQMqC/9w5kEU11qj7Gkz34TMrRH6JizGXKECCFxBv+tPY/qTC+E8IsCCKb1+cVOpfu2UseHhIX7iJ17F3//9x3jg3i9QCgNmZ2Y5cewY6zds4HWv+1m+7yUvoV6vP+lFgDGGZqPJe977fv74j/+EhcYcq9ZN8su/9Cu86Lvvolz+9vAU+FYOKSU33rSbWq1Gu91007ClktxXAlmvZiwXA0/h0FozPTPHPZ/5DMeOHKXX61pwoNtt2ge9R7VaY2zlamqDQ6R5zvxskzjJyNMUnXUxeUoaxyRxhyTuoXWK73t2pF+A2oyh12nTbDZptaxefuIshTGCLE9RSjI3N0cURRhtO/VcaSt/23+QWnCa8eyI3gO0GzGjlesuCy0BC/XDQBRFDA0PU66U0Fqxdv1GxiZX4UcRcZKSqxwtpE2ojotfTAP6+2VjOfCIAjRnO3PlpHALnX4KJoLRCGH3d8VUQSmFzlPa3S6HDx/i+PFjJEni1giGROeEBTvCCBAGT1jlPq2UNbVxu3MpBLVaDU/YIgoEw0NDjIyMYhDcevOtpK5TL7mOtNON6cUpcwvnac5Z6+h1a9eycdMmBgaHCMOQMKrgByG+S5hLvROE5yYEhc+AsboJaZ4Sxz1OHT/Ou9/5TlqteYIwwJjQrQDcysXz7HtubzCQtqtfSrEsiiCEplbyaM9bMKg2qhgGsPQx98UCR31tAXe9C5yBdGscIRcpfqVSiYkVK77mRJ2mKQ8/vJc///MP8cgj95PnOUNDw/ziL/0Sr3zlK5hYseKqdOJ5rtj/+H5+57d/i4ceeQSk4EUveRG//qu/ytrV3/oOg99OsX7DWsYnVtLpdtznxAEJWRQqS5PlYmA5nkBY5cCEhx55lPvvvZ9Ws0mWWeXAQpzH8+yeeGh4hMrQKEb6NOab9Npt0iwhT3p2/K81ca9NknQRgSCIfISI0NqC/ow29LoxC40F2p0O7XabXs/qEgjpkaYZSZoRRRGVSoVarWYFadzIW+MAgcr0ubNFkpYCAj8gx9Lu8izr0/1UbsFl0rNMhc1btrFqcjVaQ70+yKat2ylVauRuNK0BZRTGJeXiQ7X0IVp84AraoMqd0h2LOgh9YSOtkUJa6o8xfSlklSsa8zPsf/xxzp4/Z9UUwanoWc+DAn9QaCYU6PzCuQ/hUa8PMrFijHXr1nHq1BnS3DA0UGfz5o2Mjoxx8sQp2o0GtcEBSuUSRilOHjtKkme0mi06zTYj42PsvvFGVq4Ytfr8papVx/ML8ZzF7t/zPMvc8K1mvjCFBLMhSRPSXo+Zi+f50/e+i7PnTlGpRARhiOdJoqhEr5eiVIZyIMsCPd+XKza2oBOLFxsfQyQFF+MMLaW1mBbiCjOnpaWBcJiEpeuLPj21fz2LB6Z9L8Mw/Jp05rMs59ixE9x99zv58N98mIX5eQaHavzwK17Ba1/771i3ds2TYiv8xWGxAW3e8673cffb/oSZ+SlWrVnNr/zqr/PCu+6i/G3iMPjtFIODdW695TZOHDtuRazwnRGXYxDJxWfI1YzlYuApFkppzpy/wGc+fQ+nT58h7rWc2p3tuuzuWRCVq4yPj+H7Pp2FGZrNJu1OjzzpQZ7hSYMvDSZXaGyHpY3GSCupKjD0ul3a7Q7tVodur2v1BZIUoyUYD0/6RCVJdWCAcrlMEAT40kPlOUZbCpoUui+0Y5Hnbv+rbeecpYs6B4VGfZani0lFSyZXTrJiYhykRHgRW6/ZydDIMFJIkrjXH+2bYge/pBAoUPLaJSBtNCgrN1yo3/Rlc5cIfUjXOWtlLJNBaZIs5dy5cxzcv49ms2FH5cZ1rK7ACMxih4uwkwhh7O8X0iMMQlZOTLJ27VrWrl2L53mcvzDFNddeS56m1GpD9HoJadIlSXpUTIU161ZxYP9+LkydJ/QDoihi27XbWblyFeVymWqlYmWcfX9xfF4wIaDPGjDGrjz6HbzW5FlGnqQ0Zuf5f3/2Z5w+dQwpBaVStW9hHAZljBZonWNyQZ4voV0WhZcrgiSLSP9yKSBNFY1MoHJtkalYDAVaW4pi/37QVnFSYEV8hLC4AtzERiyyLopnZFHsxEnyVX1+Lly4yPve937+7wc+wNTliwSBz4u/9y7e9MY3seOaHY4i++RHnufs2fMY/+mt/4VHHnkQLXLuetGL+Y3f+A3WrV27PA34Fo0gCLj22u22ELAUJ1cS50jPTi197+oZKRWxXAw8RcICBHvcf/8DPPjgQ7RabStMobM+2hoMURRRKlfxgog0zWi3O7RaTUsF0xmeUWAUJs8wbhVgE6JN7goLDmw3m8S9mG4vJk4SOp2Oc/zDJX6fSq1mCxEnCqRyhRfIJbQsY8WNVE6eF8WAwwNoK0FXjIKVUn06jdHFbt8Qhh6Tk5PU6zW0EazdtJHJNWvwg4A8z9w1sN4B0snhFoyAYjpQJCw8ifEERll8wlJ5Y1gEprm/7KsXKqXodDocPXKEY8eO0Y3bThYYJ8Ur0MJglKVGekJY5EBfOhiMUoRhwLXXbGfzlh0MDw8zMzvLgf2Ps37DRkZHR5i6NEWv16NSrjA332JyleHxPXvIVU671cKTkjVrVrNu3XrqA0NEUZlSFOF5Xp+SWBQCVgzIRpFMi2tSnGue56RxTLfV4iN/8zfs378fKT0GBwcZGhrC83wrhqKhWqmSK+tC+cV7/aLYKCyLjTEYYRgZKDHXyEmXUAULxDRLrrP7PxSLBiFccWdwLAGHc3BTgqWTA0szHXpCn5/ZuQU+/OG/5Y//+I85ffoUQmie9rRb+IVf+EVuv/12SlepCzfGMDu7wHve/X7uvvtPmG/MsHJigjf9wpt4+UtfTqVSuSrHsRzfmBBCcOvTbmJychUXL55zbqrKTdqgUqmwbdvWqzKZWhrLxcBTIJTSnD5zln/6xCe5cP6CxQU4mp/nHrKeDKjVa5SrdYT0yHJFHCd0uj16SYLqdTBpDLkiCD2CKEILQU+50XmWkaQJc7PTxHFMIfySOUU9hCSMyoyOjDA8POhG3oq5uTlr56psIkhyyypQRuN7HvV6lU6nQ5r2HArdOKygpRsat8dXWgFOL15bJzkpfYZWjLF6zVqk8Bkfn2Tr5q1US1V0pq1wUkEg0BaApwvwH6D0ojWuvY4KLazIx6LEjltbOBMiLaz9LFpZ3IPKWZif57EDBzlz4iTGaSJorRZlizUoDNKTZI56547KoesNvvTwPZ+RkRGUMMRZykJjnjRPqZVKXDp3BjB4vs/Q8AADw4OcPneGTrfL/Pw8k+MTbNmymcGREYIopFSqWDEnz3NWw7ZLlsUDRIj+3yMg11YGuBi3585dMul1eeC+z7F//6MMjwwg5AClctnRGUOiKKLValEfqDovit4VHP9CX8AsKQCNcy6slQSz8ymZwfKnjNXxt1ManBUx1puBwkDKrltwx2pJB87QyJNXAAuFEH3PjK8Uxhi63R6f+uRn+cM//CMef3wvuUrYtHkjb3jDG3jhXXdRq9Wu2kg2y3IeengP//Gt/4nH9j6KIecFz3suv/7mN7Nl85arngCW4xsTGzeu4+de9zP84R/+IY1mE20UwyOD7Ni+g5e85MU85znPueqToeVi4Js4jDF0ezH33vcAjzz8CO1Wk7iX9Ds+i6r3CcMSUWWQcrWKMRDHCc1mi163Q9ZpkHSbZGmXclShXKvih9b5TuU5eRqTJAlTU1PMzMzQabfJ85yB+gDlcoUgDChXygyPjFKtDSAElEplSqWILMtYWFiwSVZZDYMC5GUwDI+MIIFmo21Zi9pOAGwxoPsAvkK0B+xKQUo72q7WquzefQPVShnPD9i6dSvDQ8N9ARiV258prlUxCQAnBqRxiHXtBGwKQRxzRYcJoIVAS7tGyPMMlCLPYi5cuMDj+/czNTOHUMopA5j+Hrt4beu7YHnw/YmDsOsG4YFBYjyfi5cuc+78JbZt3QaXppi670Haazewfv16ZmamOX36NPMLDRqNJs1Gg4FanRuu38XExDilUomoHOEHPlL4fSGnpcUASxJlHzhYnKdwUxc3Feh2O+zfv5+9e/cysWKcqBQhpSSKIqTnkaQZJ0+eJMsy6vU6aZrQbrf65/yVHlbCAQt7ucdCnKGUKxS+KN8qbQWGCvMke6y6OFT7Z12IP4n+e7xUCXDnzp0EwZd/jGVZzp49j/Pf/tvvc+8XPk+73WTlynH+7atex7/9tz/O+PjYVXvgWhXBJu94+3t497veyezCDGMrRvj5N72Jl7/s5VSr1au+I16Ob1yEYchLX/4Sbr7lRmZmZhBSsHr1JKOjFv/zjbgXlouBb9LQWnPhwiX+8RP/xJkzpxeTn3Aua8LD931qtUFKlTq58Gj2OrRbCySdLr3WAml7AZP1CMLQOvyVKoRRCd/pqscLDebm5jh9+jSzs7MuuQqyPKMXZ9RqKSPDwwxX6gghabea5HlOr9ejWiuT9GIrQCPtSNxoDZ4gzzJA0u106bTblt1gFFrl1mHQJf9iZGyTQjEx1hglEJ7HtdfsZHJ8BYiAzduvYXRiBZ4nSbOENEucQp/pFxdLu9VCbMiC2sSiit2SggHoMyUslRCE1uSpRdYfP3Gcg4cO0e11EUaQ5ylGAp6HEQZ7tcBIK4iEc63LzSJwsUjExuQkScyFixcoB2Xu+4sPMnTgcVYP1REm49DhoxitmJ+fp9U6gwG2bNrAhvWbqFQqhOUSMvDxpMT3PaTw+vgAuJKWJ920wPBFO30HzEzTlF4v5vSpU3zmnk+SZgnlUsTA0DDVagXpWyqiFIKh4UEeeWgPUVSmXhtk2p8mSZIr1jCFIJMN29n7nqSdQK5xayPVNyVaShksCJ3Fn4tiRhZuk0XiL5QNjRMsctd48+bNfcXGIpRSnDlzgf/1v/8PH/6rDzEzO8vIyBD/9lX/lp/52deyaePGq9qB57li397H+d3f/Y88/NCDCKl5znO/g1/9tV/jmu3bl6cB36bh+x6bt2xk85aN3+hDAZaLgW+6sFKvGY/u2cu9X/gCc3NzS+xiAzxfEoYRXlCiXC7h+yFxpujEbeYbs7Sb82TtJqrbxvclpWqNKIrwg4BytUbgpFV7vR4nTp7g8uXLzM3NkSQJpVKJWm2ANE0JoxJDQ8NEYUSW5ZAZcpU5xCuoPEVi9+Ol0MfkHpmCJE1ROkMIj16vTZYnaMejtaNnn3bb7twX7WkXPevt/t9n/br1bNu2lcAP2bBxE2vWrScql8ky6/WtCxEabamARTJcuhO3znuWPfDFk4Pie4qeVDh9gjzLaDQWOHjwACdPnyZJUzKlCYVECWMnCFl2ReWuC8EA3F6bwga5SHwSYSAMA9asmmRhfoHRHVsZf/adnJuaYrrRodvrcPnSRbIsYdWqSTZs2Mjw0ChRVCYIQoTvoaXAE4uTgOKcPc+zCfSLJgIFTr/P69e2CIvjmNnpKT75iY+zMD9LtVbFD4I+I8QPPIyUeI76eNPNN6NyRa8bI5xAUpqm/Wv6xdRAsMVJL8kBK0stpYcxqs/aKK6VtYi2+AAhF82Ji/8W04PiL/vnJiQCj3KpsmSKoLk8Pcu73/N+3vvu9zF1+SJh6PPCF9/Fz7/xDVx//U7Cq6jjX0wD3v2uP+Xtb7e6AWNjY7zhDf+eH/iB71+eBizHN1UsFwPfRFE8PD7xiU9x5MhhJxxk96lBEOL5VkimVCrjeRFSQhz3aLdjGq0W3c4CveYsKulRDkPqA3Wkb6lXURQRhiFKKZrNJjMz07RbLetT70a5pXKVUrnC8PBoX25VYDELhSiOkOAJjY+1rtV5jjSaShCQez4YyNKUXKWkqV0NFEC/iRVjlEtlkjjp75mXJmeBQKuc8bFxdu26zqrLrZpkw8aNDNSqoLXVLVC2EFBKXel4WHT/bv9sXDYs1hBL1wJuuw3GMgVMnpMrxdTlS+zbt49LU5cceNCWC0YIjPCciJBVGMyylMCz8r6WCbHosKcdBqHAVgig1+tx+PAhSkHIlltvpVSt0jl1ivaF8zSaC4yMDrNxw05WrFhBpVQhisrgxH4QgkAu+gks9RUANxmQwk1X7LQkd4ZBRcGklSJLErrtNl/43GeYnZlCesLiNZxqol0TlOx0wbPIisnJCaampq3BkbH+AEsLr4K10Pc3AASSPLOWzZZO6IoEx9KwUs0ehT6BActAKdY6LjxPginMrwpqoUVbl6Mq6zdsAGB2boG//tDfcPfb387x48eQUnLzjbv5hV/+RZ71jKcTReFVTbx5nrN3737e8pb/yMMP3Y8Wmmd+5zN486+9mWt27FieBizHN10sFwPfJKG05uTJ0/zDP/wjUxcvWdMfnRMEHn5QIgpLGOFbxX3po7Si1W6R9nq0Wm1mZ2eIuy3CQFKt1qzFqh8SlSIiZ8OapimzM7MszM0Sxz2k6xYFgigqU6sNMDAwYCmCniSOe+g8tUnI82x3p01/5G6V/qxcsHE69fV6FWUUrVabVsvul/M8J4oiBuo1VJqDWbQZXroDlgiqlQq7d9/A6OgogyOjbN6yjaGhYQLft1K5hRbBkj24FNKK+hR/JbBiOmoxWVkUg+kLCxWMBUMOeU4SJxw5fpzDRw7TbLUcQND9T2i0ycntgMMmeimsDgG2iMmNsF4KS8B0FlznLHelROUZOrUTlalLF+j1EuYW5qlWKlyzYzuTa9ZQrVSoOHCglNY+WSydBLgCoJgIFMWH9CTKUTONXlQE7BddSpEkCd1Oi2NHDtHttFmzdg25yujFMb7vEYaRRfALj9APrUYC4AUKP/Tp9bq2GNKLLIL+++gAlIWyY1EMqTzH9317nxROhY5+WDAJCvoj2olGufewEIoyRoBx4EhhLM3RDBKWN9PrdfiHf/gn/vB//X888vBDqDxh06ZNvOGNb+BFL34RtavcfReeAnff/S7ecfc7WGg0GB4Z5PWvfz2veMUPXfXjWY7leKKxXAx8g8MYQ5rlPPTwI3zhc1+g2Wygco3nSWdJWwbhxGrwKEVllFZ0Ox3m5+dozc/Tas2Tpj0qpRKlUoTn+5QrZSqVCqWohMbQ7XaZm5ml1WySdnsk3R4i11TLFaQfUhsYIIoiKxXsSSxNAcLAJ3TUNa00eKCES27Kyg1rIQl93+IF0AyKOt1uhySJkdLHGIPv++RK0+n2SLOc3MnaFtx+T0iMgJ3X7WTjxo1UqkNs3XoNtdogQRCRpjFxHJM7y2Hj9sks7UqXPmT7I2w7IRBS9MFzdtphMQtZntFrtzlwcD9HThwnzXJnpOQ6N+nQ93mGkL7jtttrYYwmN8axFYpRuRPKcTt6KYQVQpLOjMiJ7kxdmkJpzaZNG9m4YRP1wQGCKML3fDy5CA70pR2xCyn6BkpLVwT93btD8GttDZqWJuo8zy1gsNfh/PkzTF2+yPDwMH4gCaPATh28kMAPQRgH4LTnYs2IPFrNFrMzM2R5Zq9lvliQFfbCX6yYVjADlhZHxT3f//clKwYo5JLNlSsBZ5Nc/HorQ9yl2znNr/zSr3D+wmV6cYdVa9fy2tf+JK/84R9idHS0D/C8WpHnir17HuMtv/MWHn30UZTJue3OO/id3/4tdl6zPA1Yjm/uWC4GvoFhjKHZavOP//RPHDzwOHE3wRgIo4hyqUwYhaRpTi/NqVRqeF5Enltv87nZC/RaC3Q6LbIspVKpUKlWrA97KbJaAGEIUpB0ekxPXabVapF0uqTdmCxN8cOAleNj+GFIt5s6SqFC5Rmxsh2dFDj3QNUXq/GLcXUx5vUcmE5ItAJP+gwPD9FsdlDO/yhJUs6dv0iSZMRJilZYcSIsu8D3BRs3b+bandcRlkps2raNwZExqrUq2uT9HbXEGvtoY6zCoVnsNguOeyEEBIAUKAy5yvu7Z22c4mGec/nyNPv27ePy5Qvkyu64jRLkS5KbMQYkCGnFkkAipMETHkZpKzgkPas+KEzfyldg9/i5ViikBX9KDykkgwODrNu4kdGxUcqVCr4f4HkB0rk5Cs8me9/z3TTAoepZTJLFOqKfNK3OkZ14uK4at4rp9npMTc9w6OgJ4iQj8pwLoBGUohKe9BH9IsD+7kLN0mhot5p0ex20zsmyzHpNmMUJQXGd9BKAqChEhjzZBwP2732WMD+MNSZCWCEiITzQlp2iBdh3UBWSRmid4Xl2YnLxUkKpUuW1P/tafuonX8PkxNWRD14axhgaCy3ufsd7ecfb3kazNc/AcJ3X/dzP8SOv/BEG6lePurgcy/G1xnIx8A0KrQ3nzl/k4x//GOfPnbGqe8Lu9mu1AYLAZ67RBAKi8gBGSrpxm8bCPJcuXiDtzqGyBCEFlWpEKQopl8uUymWCMCAIrHqcynPm5+dZmJ+n2WyCE8YZHByiNlhHlkLLzdf2AZokPXd82nbiedb/PVIIwiCkXq1SiiL7c8p210opep0OcZoQxzHtThejpX3Iu8kEDihWqCXaEbcVmZlctZqnPe1OBurDbN68lbHRMSqVch/sWIAo+6hzuKLT7AsLuX/3PK8/hpYYPCPIlaKgFcZxzJkzZ3jssX00m020sc5hAgGe7aqFENaUSBYAOON24CCEQaHdRMMi3K2waG7H3jgMBG4ELi2IcHR4lK0bNrJychWl+gBRVMIL7b7fEz6e8F0RYLtIJcEOarwrhIQQ9F39lu7vi/euAA3meUbc6dCcneXTn/hHLk9PEfg+KydWUC6VkBJKpRLGGLIkww88fN9zhQD969loNNz9YLUivhis2XclWAJiVLlyBZEVVSmKg6XTgC+XJAsMRrH+0FoT+MGi0JP7Gc/3+Z677uJNP//z7Ni+7RvSeee5Ys+jj/E7v/NWHnnkYUBz252381u//Ztcd+21y9OA5XjKxHIx8A2IPFfsP3CQez79aWZnZ9Ba43sR1WqdqFIny3NavYRSpYbRkjxP6bZj2p0Ws5cv0msvIEVOWIoQwlCplCmXapRKJWsnu0Rjv9FocunCBRqNBjrLqZQrDA4OMlAbJNWaOEvQ2goUJUlKlimrB+B+XuUZOJEdO8nokWSKAaNI04xeNyFNY/LceiMkaYoxkKXK6g3oImGA0pm7Aou6/UoZJiZWcuttdzAwOMjqtWtZuXoNtVqN0PdJkwSV51Y4x2ikvlIpcCkAsZhU9B/AbiRfHIBSOVprOq0mh48c5Njx4/SSHtphGKwfg9XZR9iRtZAGhASkM+vRVj/Hdd9K5wgkQksMGk9CIN3e3lgRIykltUqNtWvWsHHteoaHRoiiKjLwXQEikUgC38dz6wlROA0K8FgsDvpJ2HH5tTawREa5eN/t+eSkvS7ddpOPfvTDfPKf/hHtFATXrt/ATTfdREnYqYChwBlagJ7VaPCQUqBUQrPZJE1jwAk05VYoynMeBQUQsMCTLJVA9oW9NsZIhLCyxFLKxWnPElokaLRZWuhZFg3uXpKeLZyEkFx//fX83u/9HkNDg1+Pj+VXFcYYWq0O73rn+/mjP/pDWu05BgcH+Hc//TP8xKtedVVcDpdjOb6esVwMXMUoaIP33vcA9993H+12C2Mg8EuUy2XCoIxSgtxIvKhMnuRondJuNZiZmaXTaZOnXaTQliEQRkRRicHBQYQo9siLD9JOp8O58+doNBoopagO1BkYGEAjme10SbOcLItROiOJsysQ/top/mvXTRu3AzdI2u027U7TFghaonWO0nn/563PgCsq8sVuvtgHF7kMYGBgkKc97WlMrFzJypWrWb9uE6WoTBiGVi43TReZAGIx+S/VFbAUQksPVFo5c6RFjnohsqOznKmZafY9to+Zyxf7ToJgOb9Lx9ZLR+BKFVLKVktcCEmeqyuKDm2cQE7x2m6VgieZWLmSDRs3MjoyymC1ThhGCOHjBX4fB7AUWFesAoQQCE/gs4gPKBgLbrPef2+KawJ2/aKdjHKSxDz00IN88pOfJEkTAk+itObsmTPU63VuvOEmC3JEE4aOQSAEQRDY97rVotPt0Gw2+2DIpddt6TUr1hhF2AmNb70ppbB6FO6Yl04GCv2BopDrAwfBMj2MZScIEWALScsyuO7666lWr750bzEN+K3f+m0eefRBhIBbb72F3/mdt3D99dfjXyVvg+VYjq9nLN+1VymMMXS6MZ/85Kc58Ph+2p0GnufhexFCenR6GUHJyvz6wiPp9ej2OrQac7Sb86RJjEoTPGOQvk8pKlEuV6jVa/iB7ZwCz7fANaXpdLtW9KfVIUlzBgYG8MMS7V7ap98VD/csVeTayuwap/gmWGosZPoIfaMhyTMQylEOBUrl/a47cxx8pRRZlrmEKcFZEduCwFoDDwwMcfvtd7J23XpGRsbYsGkzUblCuVLpj/KVG3cbB9QzBWCun3yszbF00rYORQfGehRYcF1OlsScPHOWfY/vZ25hDoECZasSgbXy9RAIbTfTyhUrBeCw2M970lL1fNfNKrfqKIocIwTC8/F8ycDAABvWb2Ry5STV2gBhVCbwA8vM8Ly+xXCR/JdqJfRXBUW7jUv+ZlHoR7o1RFEgFOdcOAMm3R7Hjx/nLz74ZyRJx9FUrSCR50vSNFnUKpC+oxZ6eL5debTbXZI4YeriJTrtJlYnwPSBmFJKK+tscJ4QuS2ChOgXRUhQJrMdvQMGsuQcCyYCOOdHJyttCmkBq5xksSXCoI1AOO2CTRs2XVV8gDGGRqPF3W9/J3/8x39Mq9ViaKTOv/t3/45XvepVDA4MLE8DluMpG8vFwFUIYwyzs3N8/OP/yKlTJ8iyzFG5SuSZQSvB0MgY0vdJspQk6dJqLNBqNOi0FkiTju3cfB8pJGEYWsBgpYLvsAGeZ1H7eZbR7naYnp0jjhOMlAyPjfaTsjTOzEcplxwsWrvoCHXfcrfYz5r+/lwpRZ7ZxG/IKSbwWZaiVI4UHsbI/u+X0h6rlJJut0uujNu/S+fctZPNmzYzPDzCps1bKFcqlMqWBhnHsT3GIrkt2Ykv7YSNcFK1GKt955KMdXLMXRHW5PCRwxw7fpJ2p2cNlTw31jaFQp+yHHh70vb4hYUc+thzClzXqoV1XAQcNsGWO2EQEQQBlchj5eqVrF+/nlp9yPoI+AG+F/ST11LhoCI5FuJBnqMNFgVIoSBoD23xWnyxJbTWGu3OO01TFhoNPvShv2R+bhZjLPOjXq+zfv16Nm/ezOTkJFFYsYWHH+D7Pp7vkaYxvV6Pdtv6Iux5dA9pkrhpkVUHxE1FjDG2qCmmMMZYCqC7Ofr0R0F/AvDF7yMsURh0jIL+ZMZd34Icaq+ZBZwODl695KuUYt++A/z6r/0Gj+55GGMUT7vtVt78m7/JTTfesDwNWI6nfCzfwU9yaG04e/YcH//433Pp0sV+tyyFR5IqhAwZGBxBeD5JntPptGjMXabdbJHEbbI0QUowRhIGIRVnFRxFEWEQIITX767yzCL1O13rN5CkqbW1lRZ4JoXEKNUHmKXuWKw5kHGuhMZZDhuyPHd+Ajla2bGzyhd55baYWHy450qh8iU6BFhwWhRF9HoxnmenA57ns2P7Dq7ZsYP64CAbt2ynPjhEFFlaZJqm/QmDJy0SHWkTNI6mVzAHVK5A2u5eujVEnqfkeU6mYHZ2lr3793H+wnmrO+AOt9DDL9gInpBgsKC3wNqIojVCeiit8J2KoDGgcWsUY5O6JyWellTKZcbGRti4YQPDI6NElTJ+VEJKj8D3bSdv7N5bBraw85YUA9IVAeAmEUqhhUXeF5bESmun1Gf6x1CsZoqvNEvpdpp8/O8/wrFjRxkZHWXlxArWrlvLqtWrKZfLREHkisgA300rMLawi+PYgUBb7HtsL1OXpzBYep89LtfLSzDaAkhxK44wtDbWvW4PhGU3LLI8rDsmmL5HhcQJJQlHJ3SURrv3cRMRYZkq9AsiKJUiVqxY8aQXA1Y3oM3dd7+LP/nj/0NjYZ7BkUF+6rWv5TWvfvXyNGA5vmViuRh4EkNrzbGjJ/jEP/0D09PTLsFZzYBWt0N9cJSoWsdIBxJsLjAzfZFeu0GWpijn8hcEIZ70qFYqRGFIGIaW328WR7ZaZWRpSpykzt9d4Pu+6x7t2FhhR/fadfhgyPKMLM0sSA/IHGXMOgnmbhKg+ip6V1gCYxza3lnjmgL0pSlEcTzPeigEge+Q6IINGzZy661PY2hkjHUbNzM0NIrnBwRhaAuBNL3idbQxFIK/Rth1gc0VAt9zSHVhCxitFFpl5FnOqQsX2bt3H41GAxD9PXcf7e7Q6cYYPPdfI612QSB9tCkKH65I2KG0dL8wtAyOSrnC+Ogow8PDDAwMOtGnEsL3Eb7XXwF4wk5mhO/shsWibgBwhaqglWWWfQR9MUpH0C/a7PdZFciiwMuyjDhJOHPmNN1Om7u+57sZGRmmVIosVdT3+loGhTOhcj+XpimpY4MsLCzw6KOPcuLE8f51EtZxyV0/69ToLQE2ep5HGEUkSYKRIPQiFsDRKuy9aK5kIxT4gAIvIZcUe86Zm8Kiu4jx8XE2b9n8pCZipRR79+znN379zTz8yAMYFLfeeRu/+5a3smvndfj+MlNgOb51YrkYeJJCKc3+/Qf41Kf+ifn5eefG55HEObk2VOpD1IdH8D2PXqdDszFLc2GWTrtJniTkeY5XgATdWiBwiVVrjdJ2ZKtyB9zLM/I8p91u02p3yFVuFfKkZ3fZrntMksQVA1bJL3OJxGjrGGi7PJxvvQU8Xhn2dY02fWW8Pu/dlyC0U4jDJVxnJ+zWBpOTq7j55psZGh5lw6atjE1MEoQhYSlyAEsHGHQ76SJZFoqAOMVEIQq1P4EwBZ/eMh/iOObA4UMcOHqcTqeLMNqBH11B4WyNhRDo3L4vSDspkJ6dAKB0vxsXUhL6gRWBqlYYHBhkcNB+1Wo1m2SlXdX4QQk/8BHCw/N8mwAFbpTu8AFekfQXaYGFIt+ieY8reIqumuLfCpElYxkNWrvCjr7iX6th6aebNm2iVIoIgiUiRoFvcQHC61MnY8fYyJU1U5qevswjjzzCuXPnkFIQRVbGWjiQpOf7gMG7Mj9jgE6v23exDP2gb3hUnJClPNKf0mi9KDHseY5BsQS8qfUSUCF2bSWlx0tf+lLWr1v3dfu8XnGHu2nAO9/2Hv74bf+H2bkZhkcHec1P/iSv/amfWp4GLMe3ZCwXA09C5HnOww/v4fOf/zzz83OuI7YPmThOKNfqDI+MIgXE7QbzM1PMzl2m1+v19fZ9RxEMg4BatdoHmxUPz9xR4UxuO0JhtHuwpyROZz8Qix1YwdW3iV85zwBFmuX97i0vCgNjMMqAsIC5pRr7YJHd0o3tpef1QYYWWwBI+P/Z++8wSa7zvhf/nHMqdJjunjybI3YXOedAgiAIkiIpBkskFShLsizZsu8jywrXoizpitL1fXyfa8u/RxIdZJmyAkUxAgwgCTAARM4ZWCwWu9jFxpmd0NO5qs45vz/OqepeivdaIkERJOZ9OM+Cs7Pd1VU19aZvkNY9yNNEI8ccRmBqcpLLLruUublZNm3ayty6DQRhQBhHCClJ+oNhx4vrgIupA65bxrgpQQE+87RAgUFi0EZz+OXDPLfvefq91AHZhAXp2RDFVCNHrbt9f76+EEoQBaHzdhirUa1WqVar1Ot1KuUyURQTl+Jiv58ntzCMkCoAjwsQUjqMAW6tUVAGhSgSZO6rcJrJki8KRrtmihG7IEs9qwEP2vPYjkxnpFlCv9vh6OGXiMKQqFIm9Mkf/146NaQmGWFKaJJBHwS0Wqu88MI+nnn6KdrtNmCL7lcIsNoSRAETU5OcfeaZTE5Ocvvtt7G6ujq8TsYBWIHTJKBz0KpSrvvPUqcA6W+owgJ6WByI4n1H9QWkdMDMa6+9jjAMX8lfW3ceM80TTzzN7/727/PIww+QWc2lV17Bhz70f3DxhRcUFM+1WIsftFgrBl7hSNOM++57kIcefIhmc4UsdWY3mU4olyvUahH18UlM1me1tcriqVM0m4uk6QCBLDrI3OGvXC4X4/YhOM4lPWtdp+y6+5ReMiDJ0oLulfkOW2tnW5uPkQvtAP8w1jpPMB4Zb4UX0nFfoyPd/O9dxz90AvxWfwohsNoQCMnc1AyXXnYpGzZsZOPGzWzavIkojohKZYIg8N1pisenF5F3yrmqnhLC8ff92kIbpwsQKNwoGiiVykyMT2BZ9WsON+KWUhTa92EUEQYh1VLJKTbGJUrlCtVKhVqlQhQFyCB0eAClENYg+wlpv4eK46LLd8C7EClD95kDVSQ2KZyMMD55CyHdsRsNQhIGPgFaC9YMAYw+RimO1hgvmjT8/uiX0Zqk3+X4sZdJk4RyqZTXFcX4PcuyYirkijjnl7C0tMT+F/fz9DOuCFAjIM8gDNGZJpSCt7zlrfzLX/pXbN22hbFqFWMsH/7wH/ObH/ygH+sPHQbzxC3F6QwJ9zsycB2+kt7rYnh/uQkJYIXXOsjZGu7vKpUqF1x4Edu3b3tFu3NrLa3VNn/6P/6CP/7jP2S12WR8vMHP/tw/4Z/9s5+nUV/TDViLH+xYKwZewUiSlHvvuZ9HH32MlZVl0jTzEr8GFSh0BvV6jSzp0+40WVpaZHl5qdiR5g/PfM8ex/FpqHIhhAfyOb6/TVyy7/Q7dHo9+v0+qdZOMtc49bm8CMh3+27fq1zXL0DmKHYEmabonrPMO94hvYCNGcoA29RTCd10YpTzb0e7WgBhGJ+cYufOHaxfv5516zexZet2oigmDEJCqUjSlCxXGLTWc8sNua6dsXZINfNzaYETEbLGSRQLDZ4EyNy6TdTHp+j222QeD5GPqgMlnVCOClBSEviRvXQvirASi0ZYi2xniOUTiJPHsS++gO0mhBefhzz/YiQKGUTDVYY347HCONliK8isgQywTobYyTh75UAkSQIEjiCoChqhDzsstHKMg/RUxkImeAQ4mCQJzVOLLJyc9z871CbIMu3lm7PCMjjNDN1ul73PPMUTTzzubaX9cShFFEX8/C/8Au9857v48B/+MU/dcxe/+M9+gQsvPL9QJNy7dy+PPPywp6M60SIjHOIfawlVQKAUmdGnjfptThwwnFYkFAJEcjgZwE+hrLWUy2W27zqDN73lrWzcuOkV+73VWvPMM8/zwd/4tzz00ANYLBdedB6///v/JxdfvMYUWIvXRqzd5a9AuF13xjfuvIvHHnucbq9Lvz+g3W47hHUceUpgQLvdZKW5SKvVJE2d1K0xGqUCJyEMxF5JsJCa9VS9LHU/ZyxkOkVoh+jvdrt0/RpgkGZYodBZQpr0TxOFyff2IhQYrytgjHHKfdqMoLVzxz1dFBLWClKjvZ/AELB2WkfnYzSBVcfG2LNnD9PTM0xNzbJ9x05HiQwCoijCaI0eJAht0Nag895SDK1sC0Mi60R9jDHoNEWnCdn8AuFYDV2ruhWFEN51UVEpRwjjdA3wUwxtvcK99R25cfttBj3Maov01CKVl18mOngQ8dJxSovzWGXJrriMwVveQn/DRsKohPRMDuUlc4VSbnRvDdlgANY6/QdABgGhcrKEjsYZuqLGaoJsCSsk2LqTQbYOq5CLNOV8/tHzm1+b/JqmaUq71eLAwYNYY04DtvX7feJS7HbxWMIwoN3pcPDASzz26KPML5zw999w8iSE4MqrruJXf+VXMMai05SoWmH//hdYbLX52m1f4XOf+xwrKyv0Bz2CIHAJXjiKqjO10sOVyAiwYPTeMBYCv17IWRbD+0+ivCx0ECjicpmZuVne/7738aPveTelUvyK/N62Vjt85E//nA9/+I9ZWlmkMV7j5//5L/BP/8nPMd5orE0D1uI1E2vFwHcY1loGg4Q777yTJ598km6vTbPZZtB34j6RH0fHcUySJpw6dZJOp+1G8sIipON4S+EmAHEcU/ITgTwBZsaCkQTWOrc4CQhBJg06M0RBSFf0fGI2pMnAUb7S1O/F871y7vzmdsEWPwFAO0dCchlbzWCQOHS+Lwbyz+pwAb7Dy9yo3goBShSJSymFRDA9Pc2VV17OzPQM441Zztixh0q5RhCETljIAwYznTkKnXZ2NEIIt28WXhrYGEIk1mi3+sgyBr0u4YMPUPubTxNffxOrb3wdWbXkDH6kT0bWiSXZTGO08wwQSYLo9xDNFdSpJfTxl6kcPk544hji1Dxhu02YpCgMNgron3UOydvfTmfPmdhylTCOMdJdLyVBSdcVu4mKm1YoYQkjiQwMggxp2ghRB0oIYX3RIBDZEutP/QdssJGTEz8Dyon95JMdfHI2+YpgpEDT/loaA1ky4MALe+n2uu5+UyEycNz/alAtruvqyirPPPcU+/btY3lp+bR1zmhIKdmzew8PPfgwn/3s5/nq179Gv9/l137jg2zYuIlLL72M/+cP/iMbNqznlltu5t//+3/vmB3h6cwChCLLLFINx/xJkvi1gZuCGaMRViAFnl3hjiGfjtXGGmzevIUb3/RG3v/+97Jz505Kpfg7TtKZ1jz+6FP87r/9PR5+7CGQhosvvZAPfehDXHLJJWvYgLV4zcVaMfAdRpKk3HnnN3jyyadYbbZprq4y6A/IJV3HxsYQAvqDDs1mk8GgTxiGaG8AFEWxR89DWPLCL37MWgD+gEAGnm6mSJMEIfFAwBRtEoJAoA0MUuf2lgsF5ePlJEmKEXWaZqRp4kFfjluvc4aB7/gznRZjahhFfTvQnmMquHF05jv4nMoopWRudo6rr76ajZs3Ua3U2H7GTqqNOiIICMtlMmOc74DXOLByBJuQTxtw4D637/ZyyWmGbc5T+/QtRLffjui04W+OU3rkG4jtu8nWzWLLznjHppAmCbazQtBsI5eXESunEM0VgtUWQTpA6gwFTtkRnEuehGTdJnpvvYnuVddi6+OEURkZSKwUGKlQIiBUzoooP0dSCQ8sBMWAcv8g9e7djJtnOd74MdrxdVijkJ5TbyVEuklt8Aynam8klWdgROAKvRzTYU7HYOQyw24y4HQBFhdPoXXG+Ph4UYylaUpmjCvyBBw8eIA7vn4Hq63lobSyn76MjvBzauN//+9/wp/81/9W0Ai3bNnCr/zqr/GWt76FarVKlmUcOHiQu+66i1ww6W+b8jiWQH5P5D8HeByHO19Wa4LQFTBhqKhWq2zevJXLLruM66+/nnPPPZdNmzYQRdF3XATkTIE/+7O/4o/+8I9oNpuM1cv8zM/8FL/4i7/I5MTE2jRgLV6TsVYMfAeRJAl33vkNHn/8cZrNJkuLK2SZs/5VSjE50UAqwdLSEu1OB2u1U8kzziNeSgfikgLickQUhoWMa9795c8lYzInWpNplHWUQmM9eC5QBFohopgsM/T7bYe8t7nPQK5ZnzrgocHT0QQ6M4WXANaSZX6tYJxePX5H7R7ins5mTSEBrP3oO2cjCCFYv34911xzDevXr6ccV9mxYzf1iQmEUsSlEhhDf9B3FsR5ohv6DTmMANZ19hgyD3q0WsOLBwj/4s8pPfkYNsswgBmsop5+Ep5+mkgK93mFAAMxQ6GhQgEHJ3YjLYRYbA6wExI7NkbnqsvpveWt6HUbUeUaYVRyPHcPBgwCgfTnUftCIA4CrDQo02ds8CIzS5+nevwRKrUm3YlJRwX14jk5iB5TYaAbxAeeoFp7lAW51QkJyXwKk3mZ5dPxGI7B4GiFy0unePHFF8Aa+s0m5XKJXbt2sWXLZnbvg8dNUgAAnF5JREFU3sX01BSlUszzz+/j63d8HTygUzNM/nkRlusczM7OcsEFF3Lo4EuOXihg/759/PIv/RJ/+eeXoMKA/QcOsLAwTzIYOB+DAuyYawlopLTIQKK1o10KIdG6N6Kd4N4/isuUyxWmZ2a47nXX8a53vYtzzzmbqanJYl32SoTWmqefep7f/u0P8dCD92KFZs/Zu/m9D/0u115z9Ro2YC1e07F293+bkaQpd911D48/7sBXzWaTJE0cUCwIqNfraGNYWVmh2WxihUNzCzx/PmeOGbdKCMKw4LUXY3nHoRsizp0DD1YbMpMx0NppDyhJpaxIs5R2t1eMko1XCzTWCQw5UZ58xOySTV5U5GIwFkf5ct9zY3YpnGCPHXkmO4T6UPgmR4/PzMxwxRVXsH79RqIoZtuOnYxPTxMEAZVKBSmEW1+Y4dTBvchQcneIc9BYnWKzjKzfpnTnXUSf+DTxyaPeVs8PESyexJabKbnEK4tVtfD6Au6InZWAKwKMLwRkEJPu2UPrXT/CYPcZiFKJMC6BCjGeUaGkoxMi8mmAVy+UEgJDNDjCxMonmH3qXrLHl+GskNUNl3B4/GfpVnYhReB1H9z1Qa8imqfo3K2JNz+CLb8Vq6ouWXraoMYlzpzamdM7LZCmKc8+9yxjYyVuuP56dp9xBhMT40RRWAAHW602J0+c5NYv3Eq33S7EjKwx4O+3vMAIAidp3Wq1+Madd9JqtZ0io3T0Pt3tcM+99zjXQK+ToFTk1hkyd5LMmQH52sjdK+vWreN/+5e/hNaaxx9/jBMnTwKGWq3O9PQ0l19+OVdffRXr1q17RdYAo+E+U4c/+8hf8OEP/xeWlheoVsu87/0f4Jd/+ZeYmZ4pGAtrsRav1VgrBr6NyDLN/fc/yCOPPMzKygorKyv0ej3CICaKYkrlElmW0mqtMhh0XdIyxim/ieFrDMerAVhJmrpOvDCvkcoVBNZirSg6Kk3mOlSpMNZZ90opCaIA1VzFmIws1YWKW5ImWD9BGOoFGPclrLcZ9hKx2OJ9wInMCCkxOnPUuBHQIAzR4Eop1q/bwKWXXc7GDZsJVJmtW7czNbsOFYZEpdjZ1g76ZMkAyL0EfLds81269aj5jCxJEFkGp04S33wL5dtvQ/U7DmzmtewFQ3VC4V/THdcQnG9xP+838g6Ahy2Ef+y6GbpveSur116LqU4go5AgihAiLHQCXBL3U5Es80VYQOANeML+U2w/9MekX9hP8xHNxFtDVi+5jhPT/xwdz6Bs4EbmJkNaAyKj1nmIyrP7OfW4pf66I8jpRUyp5D6FxR1bQf8cykgb/99bNm/ghut/lVrN4QJaq22effo5nn/uefa9sI9Dhw6xutri5aOHefHFF8jSBJHLT/t1jPKfS44k31ar5f0zQsIoBGFRKj4NYOgUHe1p33PX0hWQjh2TaxxY3ve+9/EzP/uPCcOgoLgOBkmxpqhWq98V0yGtNU8/uZff/d0P8cBDD2Jsypln7+K3fut3eP111xJF0Sv+nmuxFt+PsVYM/D1Da81jjz3Ggw88wNLSIktLS8VqIIpDyuWYJHFMgjRNUEr6ESxgDVnmkl4QOEnbIHDj0zR1awD3QJSODudHuE5tzlu1WIOQzuNeqRACj/y3zkNASNd55yqBefJ2fgO6SOT593Ohodz+2FrXhY4KEAVBAGZopjOKJcj56Js2beK8c89n08bNhGGJTZu3sm7dRoIoplQqIXLzoXQAeWeMVwG0Q7EZAJ0lWJ0gB33Yu5/yX/4ppef3upVGnuzx5jYIhPBytiNFSk5LzL8koIRH4lscGG+8Tv+Ka0nffBODDesRcZmScjr9xgoM4rRdeJqkQEYYBG6lI92KRyaHWHfwvzL4qxdIHjFUrw8ZvP5cjs/+AjaeBRt4rQGnB6DJYHCEqcOfpHlbF7to4cQK8txlTDYHXiRolKGZswZy3MCg1wGd8tyzz/HSgQM88cSTPP3Ms1SrFc4//3wuuPQi3vWj72FubpbHH3ucn/rAT/gVkb92wvkqGL/Pl4EaOhh+0/7/m0GG7lpJLG5Kkk9khkWBdWqCOE2KcqXMO9/5TsIwOO31S6XSK/NL+S0i1w3484/8FR/5k49w/NQxokqJH33f+/mVX/5XzP0D+BqsxVp8P8VaMfD3CGMMzz67l3vuuZeTJ08yP38Sow1hFKGUJAwV7fYqvW7PUQWDYGSU7sfXOHW63G3QGkuSDtz3hfSYAkenMj4ZCJzQjvV7ZxWEXnsAhBJFp2yE8K6ATqnOoc1dt59lzimxUBvUmaONaVMkGBjhevsHpZSywAKMAtly9TopJdu2beP8889nzksLb9q0hU2bN6GCgFKpROjdGJ10clZ05yZv6TEIrPt7o7Fpguy2CL/6dYJPfpzS0oID1Hk6mjsn+fDfI/ML/YF8SuB/TghnTexLBysNSoX0zz2H7jvfg961GxlXUKFzSxTSCys5ncWC3matJY5ClIqQAqd+53UaKsnjVB7dR/d+iOcE5TeP89LsT2OCDYBC+y5cCoMVGjXoMHPsrzFf3E//BYGyFrGkETrxidoUcsM5CyKfnGA0vV6bj//1XxMoGKtWmZqZ4oorruCf/4t/xoYN671ktSv00jTj4EsvOb0Ja4drBuv8GPDqkYX8s49c2yI/h2bk7/+WlsRo5MdphwqSZ599Njt27PgHS75ORfAZfvd3fo/HH3uUzA7YtWcXv/Xbv8V1r7uOOHrllQvXYi2+32OtGPg7hjGGffte4Ktf/Srz8/PMzy+QJsZTAUtIBe12myzLiKLAS+S6UbY1bsyfhwoCr6JnkCgw1nnIS5x9rRCk1vkPSAsy1+WTEpV3cEpgyArJV5MDwESuJOgwCplOfSc3AkCzbvSfJgMyTykM1LAgyBNG/rlHFeS0dj23EI4SuWvXGZx99jlMT88SxzFz69azcdMWwjguCoEsyzBJBtoMfQUMWOPOibGJOx+JU8izxw8RfuJmKnfdiRh0yWF/WriBP05yySd9Px1wJRMSvwLIj99jAyQWE0r0hs2Yt72J9IproTaFEgEyitx1QLj/KUnoWRjunAjCOEJKQxi6X5kcHW9tSpycRB81iExjtSIJx0mCzTjzJOedYDFOj98MqK7cSnzPHZz6eorIJCYAUQWNQFvjwIkeEJEnYmMdoFJnKUpYfu/3f4edO3YQ+cSmtabT7vL0U89x7733cs899/LykZfp9XucOH6sEJ/KufxFQhfuM4dBcJr0c14AjQoC5QwV9/lzZgkIAhBOijo3kcrBhFJJ3v3ud1GplF/B38ZvHdZamqtt/sef/gV/8t/+Kysri5TKET/24x/gl3/pXzM7Pb2GDViLtfh/ibVi4O8Q1lpeeukQX/3q11haWuLEiRNkWeathB3daTDok6YpcRRhMQSBIE0yjB4K8jhEtU9fxiKtcNr/OJBeLhtr8QnZ2+pa4TziA/8gz2WEgyB0jn1KoLUb4UdRNLIWcONcY3MnQlOMiK2xBCrAeOEikyfmEVDfN2MDhqsBJxi0Z88uzjvvPCYnp4iimLm59Wzbto24VKJUKjk74sHAySVnusA+kH9W4TQDtTbOWjntI594jPiv/or4xf1IbQo8QHFUwk0C8nWBgiLxS39u8RK8eeERSUu3Pk7/umvI3vRmsnUbkVEZGQZgFTZXfpQKgy5EjhxzIAAVIIQt1CBPn6T4hYRwqoN23iDvfpmJDV9lfvIfgQwKQKbNDKXWA0w/9nGWPtNFty0CjZwQJJsn6DHpPqcv2Eb9CYzRSAODLOGmN91AKYpotdrs3/8i37jjTu65+x4OvniA1dUWiUkcPTFNsB7/Eccx/X6/uJajDIXRjv+br/2oFHUhFuQnRFI6JEZeoCkpSAfJ8DWFoFarceONN35X8ACjobXm8See5UO/++94/NGHyWyfM/bs4Hd++3d53XXXFkXTWqzFWnzrWCsG/hdhreXYsRPcdvttLCy41YADVzm54EDBoNfBGE21UimEftJMFyA2a50ffYggS51HfRA40KC0ikCCxMm2Jq56wBhDFDtlN+k17G2epJzq0HDXLwVSRk6ed+Qhn6TaGQoZU9AZ3UjYYwmMcd204LQH/JAmJoopwZCC5jAFZ511FmeffRZTU1PEpTJTU7Ns2rydUqXmwII51z1JHNfdFz0CB0pEGLRJIXMiQna1Cbd/mcbNn0GuLGIsJD6hU66QZgMHJrROitkhK/Dqdi4dFQK2woMJpcCGMa0LzqH9w+9A7jgLUakShgFW+EmAdCqCSngHRgsCjQrcqyslQGikVK6r9qsdKSUGizABBONQF1gyyCTLX+ozse0TrF6zhU71Cj8Z0tjBCda99DFWPnMKPe9pozFMvCFmYfO1pGLc4x5cYTGqAqmsZWVlhVtu+TS33foFnnv2WY4ePUqapQjhpjc606RZirXeMyEMCVQJISWtVgvydYp1LEuVU0N9kv/mxD96L+ThdAwC8HgKN93xkw87dFcUUhIowaWXXcyGDRu+q7+fKyur/Omf/gV/8t//lNWVReJSzE/++E/zr37pf2Pd7MwaNmAt1uLvEGvFwP9HWGtZWlrmi1++lRPHj7G4uESr1ULKgCAMCAJFmjpkfLVaRduhYl++h3UKucLbBafkUqtZpl2i8clVG+0aTOnw8VHs7G+d3K0qkvdoKKVQoSpobtbKwk1v+BD3nSzitB1wnmhGkz0MVwSje+LR4mBsrMpZZ53Nnj1nMj09SRAETExMsmnTJsZqNcrlsmMNJImjSPolv/mmpKKzjCwZILVhcPQQ0V//DbUH7iNM+h5BYBFITBhQ+vF/TPfAC4hv3IEtlaHbITQQ4I2HrCsIhmBBgVCK7ubNmLe+g/4VV2LHGhC7BGkFBVYit2HG4v0bhB+ZewYBBmETItMHWUfIEKtksdO3NsXaFBEotHCFoF4UtP5mnnXrPsbB3VtJw0lMOmDq5C3o2/eRPe8QCaU61N4U0nzDZZys30SSZYTS6RVYhla+QgiydMCtX/wst99+m/NxEJYgkJRKceE7EUURY6UxpFRo7e7DXKY69ZbV1mSuwBI5c0MWoNBvhQUYxZAUjpbWeM0ADdavFjyLwJihfoFSire//e3E8XcuHfytIss0jz7yJB/63d/n6aeeIEm77Nh5Bv/2d36LN77heqLvgqvhWqzFD2qsFQP/L+GUylp88Ytf4sjhIywtNZmfP4XWhrFqTBREpGkfgaDWqNPrddDaFp1cXgxEXpAlV46LVIA0CiOE6/AVZGif/CBQAXiAIDIoJIGBAkgWBgGBzPXdnTuesQ7kF3qUu1KKMIy9mY9fN+AEc5zB0LAwcBMMhVSMPPBPN8kRQlAqlTjnnHPYtWsPk5OThEGJ6elp1m3cQq0xSalccViELHPKf3ZIW3QJxGEFtE4xWYod9LAPP0Tlrz9O6fCLSGNI8eI/WOKduzFlRXffy2TveBtBpUFj0zTdv/xzom7f3bwWUpGjBxwN0NTqdK9/Pcmb3sJg/QaCoORonUrhsXwFoj0vPJxJjyQInLKik3ewlJKjNJo3MzF4lIXJD9Acu8FVHNoi9IBK9wGmDn+FzlMp2kq00FgMvYOCyt17CTc8Qa92LaJ3mPKBu1i+NyUwgtI2S+3dVY6d8zpOTLwHIxq4eUaAszFy63zrqYj79+3jhb3PMzkxQbfbpT/oYq2m3W67IsBT8/q9PmmSnAYYtFBcB3DWwhLhdSOGk4D8eiulivsj/+/TVgTGOyAajRCuCDHWKVpqT6FFSqZmprnhhhte8T29tZZms8V/+c//nY985H+wutqkXCrxY+//AL/6K/+audnZNWzAWqzF3zPWioFvEdZaer0+X/3q7Rw48CLN5iqLi4sMBgNKpZgoDjA2K8x2ev0+2mjSNCPLjAMBSpfw09TJ6CohiVVAoAKkDEhzFL9XklNBgFAKFUXO9EYI0jQ7raN28sWRS1Re+CWUjrctfHdsjMUaxzTIwW/5l+vmnADR6HTAWicPK3Xe7Y540PsR6+TkpJeF3cTk5BTlcplGfZy5uXXUG42iEEiS5DQeu8fBOcCfdqZAJksRpxYRX/oi9c9/FtVeJQWSnB2gArJNW8muuZHV+Zeo3n0P45ddROvyi+n++UcoZQNE4HASOco+kBIThvQuvID0bW9nsGc3Nq4SRGVnLwxkPqkFQYD0Y3Ay7c6lgiAQKOVWGAhB1HuejUf+kOCF5wjOKKMnnDWfYYBKTjC18gXGn/48vY8vM3gaNKZgO2gtGcynqHQBnfWZbN7F4J5T2FWoXyII3rmel7a8neXq6yGsYjWEYYxT6aEobLAwvzDPLbfcQqfToVKpEIYhqiNIkr6Tkc4ylldWnNkUoL0DpDamwGc4aQDne2CtQfgkPzoVyIvIUfqp+yxDBkJ+X2TaMRWUlBibImVeRFhU7MCIN9xwAzMzM6/o72aWaR555HF+69/+Ls8++xSZHrBtxw5+53d+hxuufz1xvKYbsBZr8e3EWjHwLSJJU+64406efW4vnU6b5eUlBoM+pVJMvT7mDGoCBzgbDPpkaUaWDTDaJU7hEdpW+4ToKX42CP0MwDpWQG7MEjpuuwpdQYD1e35/PPkIP47jAoCoAuXwBFKBBklAt9thZaWJNvmc4fQHuDHaJTqHSsQYZxjjJGTB7aptMT6WOO+BzVs2c9ZZZzM9PcPk5CRj1RoTExNMz6ynUW8Ql8rIIEBnaUFZlHkxgDcz0gZjNabfRbx4gNJf/iXhE4+CydDWafFn/jhKM+sp/eTPsbJxHerUJugn9D/9cUpJm9LSSTeKN/6jIJFSkW1ZT+eH3kZyxXXYWg0VhigVI1RAZodFgJCCUHn5XDQigFBIwFDODlDSy3RL5yPTJdYd/TDJbU8TnhtxZPanWK5cSpB1qHTuZ/rlv8be8SLN2wdwClyWzUGMAqYgvLREX86hOnup7f8a7UczqmdI0vduY9/Gn6Nf3oMUEQKBlRajJMpfX2OcL0E6GHDzZz7No48+hJSSSmWMSnWMSqVCuRzT6XQAnGRzQT/196GSXvzHr3o8jkLaIZgvVx3MiwHtC7Z8rZAXoLnuQD45crgZWUhv534IURggBURRyI/+6HsL9sV3GtZalpeb/P/+0x/x0Y/+Fa3WKuVKxPve8+P863/9K6xfv25tGrAWa/EdxFox8E2RZZqHHnqUxx57jObKKktLi3Q6beI4cippAuLYIfb7/Z4b/2dpQR00xpAaZ0Pr5Icdbcuh5wEp3UMaZ3srlRN8UX6872Rks9NQ/SoXJwKM1m4VIBSFnKEQGG1otVpOPS5LC22A/GGfGxYJ6TpDNzOQSBW4BGJd8VGsBXDFx/adOzjzzDOZnJymXh+nNlZncnqG6elpqpUxqtUxhJKYNCVLUow1jl9uc3lj5+RndIZtt+Huu6h88uNUjh93UxEg9SqCet0GwhuvpfPkPtT/+BNK/+IX6S0soabmEI88QGnQcnsG49YCSkpMY5zkuhtpvfUGsuk5glKEFM710Ujnp5CfwyEI0iCEF4KSgkAKwv4Rti39HiU54ODU71Np301859P0ngfz5j20qm/Easv48qeYevhjtD7eIn3RolJLJnB4ESkIawFj54O6boyTe26iGWxl47G/ov/lJUzXMPb6Gns3vJ9+eTdCBk5l0jrAnfGOhiqf2mSap596micef5xSKWaQDGiuLtPutKhUyoRhUOBTcrtjV4i6qUcuKATDNYCxQwXJ0b8rgKfGkgwSLzc8xBEUDpiMTqiGngaj96oAtm/bznnnnfeKgPeyLOO++x7i//id3+WZZ59BYNh5xnY++Jsf5MY33rg2DfgWMYr9WANQrsXfJdaKgZEwxvD0089x7z33sbi4xOrqCktLiwQqoFqpEgYhURRijCYdpGCcKp3WmUdqS98B+2QrctS/9R7zXhJHOLEgGTpJW6kUMle5S9PC5jVX95M46WEpJTbAU9w8Ol8KwiCiXK6zsLBAP0kgxxNoZzYkhdMROE2Tz7ouEY1PxcYLxrikOTZWZdeuM9i58wxmZmcpl8rU6w2mZ9bRmJhynWmphJKqGBlr61QPcq0DazRZ5oCE2fxJ+NSnGfv6V5G9LomwKAQasEKiwojGe36E+UxRfu9F2P/5X6l12kSf/QzRiZfROvHrBlfEiFKF/kWXYd72Jjo7dqGjEmFcQXqkey64LJUkEE5/weJG56Fymg4yVF4DIKXavYd6+xBLk1eRaU39+Ndp35WiDBgdQSao977ExD0fo/k/VrCLDpE/EJAJELGkcZkifn2Zxc17WGjcSDs8m1rrIeL7H2LhCU1tnaBz1jlklbMIVYyR+fVQ3v3RaSlobUBrlhZP8Y1v3EmlUiGIFEkyoNPp0Ol0WV1teTqq4/PnssV5SCmJwtglZ+Huq16vV1Ah/1YS9+sBJ11tCZRCWOExFu498p/RWUaaJcX6IAzDArgqPOD1Xe9+N/VG/Tv6fcynAX/0h/+FP/vIR+j124SR4od/+B/xG//m37Bhw/q1aQBeAyLT9PoD+v0+/f6AdqtNp9Nlbv0s69bNEq6ZMK3F/yLW7hAfxlhePPASd9zxdRbmT9Jpd1hcXCRUAbVaDSkEURiitSYZJJgsc3iATHsEtcVq143nhjYOje+SfA5ai8IAGQVeRdA5F+YgwVF0/yi1SwQKb/juXj9QSBkQhyGNRp2x2hgqkBw+fpSgXML2HJ/cT4lHvAjyEAX6271H7o7oCprZ2VkuvuRi5ubmKJcqbFi/iSBQTM1MU69NUC6P+ZWFJdMJaaadXa5/LisgzfroJCNNUuInH4e/+Ri1fc9hjVPUw4IWAqIqpYsvoHfsBGp8FvvQfQSzk+jxdSQf+yjBicME2rESMiuxKmZwxk6yt72d3kUXYCpjBFFEEEQIFQ27VyDIrwVeJwFvCgVYobG+ixVpm8bqoyTHBe2Zs4jTedRLJ0mOOmXg6uFDyNn91E99g8Vb24hTroDKfGEl65K5d4asXHkOL0y/iW5wPlYGRN2DNPbewuJtPYLMUL6kwtLsVRCMYfCgTgtCKoRUBNbkQw/SNOHzn/0ML+5/3qPxXfIulUqkqWHQT8Bqf39ZxscnaEyMMxgMaDZbHhehsEZjR+WfPZMg30GNKk3mcscyUN6NMb8HGa4JtClwLNJPrHKVyiiKENLJD7/jHe9wBk7fZqRpxr33Pshv/dvfZv/+fQhh2LR1Ex/8zQ/y1pveQqn03WEofD9EriPS7/dprrZYWFhg8dQirdUmCEMUlRgfn2RmeorxRh0lv9laei3W4m/HWjGA1xI4fozbb/8SR44cot8fsLS0iFKSWq1GGATEUeR48zorjFYKX3jcKDOnCTpgln8Ye60BpQKiKCSMImSoQKiiMzNGgzWkaVagufNOyxhTgN2MtYSB8r/s04yPN4hLoRv9a82lF12C1pZ77r4HkzgTGFdgGNfheTljt/eFzEvdKl81RFHI1q1b2bFjB5s3b2ZsrMrYWI1KxdnLxtUxSnGZOCqBtaRpf5g0/MhYWw1GMxgMkCsrlL76JWqf+RxitUlupmwAJQUiLJO9/ydY2LKB4M6vET3zLNWbfojs5HHGDr+MXDqONBZtXVdvZmbo3XADyRvfSDY5iRUhUamEDAJyxQEYaiIEwlsTCa9CKBVSCNf1SkWKRdqUUnaM0tGDpC+DPUcQ2COkJwbogSXTlu49pzAbvs7+45qVYB1qusvMUpPYZIhIse4dAfOvezNHpt5DqibACILBCdYd/Cv0Z48RLBpKk2AuWs9K9QKMcMcrhMNuWEBYJ0IlcQyAJx97mG/c8VX6gz5hFI4IA3lGSRiSDDKM1dTGalxyyaUM0oQjR47Qbvfd63qcSH4fWIaj4/w1T9cWkBijCYKAMAwxxtDr9b3aYC5brYs1AeClj00BPgyCgCuvuJItW7Z827+Lp04t8wf/4Q/46F9+lG7So1yOede738Ov/uqvsnHDhu+6gNGrKdw1tKRZRrfbY6W5ysrSEssrK7RaTQRQqVSYmJhky5bNjE+MM1atEo1c37VYi79LvOaLAUdTanL7bbdx+NBhsixjZWUZbVLGx8eJVEAQOHnhNMvAWrRnCOTJOkuzwgI21xHI8XNSuFF/uVImigOvIwAIiQN/a0CjM1PgDvLXzcevURwThiFhGFJrNKhWxyhX664rAxAaKy1SKHbu3MmTTzxO1u+RaveAV1IVO18pJNgUIXwRYFx/OzU5ze7de5iZcSDBer3G+Pg4k5NT1BuTVCs14nIF4c9FljkcgD9ipIA0SyE16GyAPHSQ+K8/xtjD96OSNFf6BYRbjcxOkKQSveNsUjTRD7+f1Y//OZU/20+ydAxWTjiMhDWIUpnB5ZfRe/u7yTZvIgsiwigiDEKMcHQ2Y53nQO7giH8vgEBJjLRuHSH8isVoIiWxRhB2Hse8uIxa1khWIcvQ84YOlpcnGnTMbuLuBbDzR+FXAnqDPo9/5jPs+vKXWTel6V68kyOT7yFTdbSWiGSBxomPor/0PNkBAwqq5wesbL0eI8dxSghOntgod42sMQjrCrX5kye47dYvMDM7zcz0DPPz85yYP+lxAIAVJOkAg0apAG0tTzz1DGPVMVZXOw6wikEYJ9KUK0/mhdEoViDLsuGIHwFWUS5XKJVKNJvLOFCpLCStgQLTMqpnMco++cmf/MC31bmnacY9d9/Pb/3W73DgwD6wlm07tvNbv/Wb3HTjawMb4CaEhl6/T7PZornSZGlpgXZ7lSRJkFJRHWswPT3Fnt27qTdqlOLYT2jWEv9afPvxmi4GHAhwwFe+8jVeeOEFBgO3k02ShHqtRhiGREHokNUMH545dS4XGHKYLadMJ5SX2/UKbVIoKtUKKnAiRU7wRvvdrTfBMZosM4RRjKMADqcDURTRaDSYmJigVC4Tl0oIqUAEDhSHMyFKkoRDhw4x1qg5LIDfkVv7LdQFcaj3MAiYrDZYt349GzZuYGpqmrGxBrWxGtPT0w4kWKsRxxUq5SpWQKoztC9+MDl10D3ATDJA9/uI+75B+WOfonr0CNK6jjSw0q8lIN28nf4v/gL9j/4NlTvvxFZi7Jl7UIuniF4+TFVaBkaTqQB75m4Gb/thBhdeiIlKGBUQhSWn4CjAZE5/MFDOAKpIbEJAppFKEQYKIy3ar0EcDUGT6YSw9xhTL9/G6n2axk6BNhKVtul3JAfe/k7Cd7ydfqa4+8EHefTRL3Nq4RTr12/m/e/9EV564hHWcYJM1UmI3aQlWWTz/Gco33YfnUdTbCaRk5BevZnF2hVYGxbiPLk6Agw7/n6/z913f4OxWpmZ9ZOcOL7AwsKCXyk4ZcsszVdJ0gM9I84+62yiKOLJp56kP+i4gsdqV3bo09cEUozKDztcwGAwQGuHDeh2u7Tbbaz1HhVWImWAVJAk/dMcK3OBqVzfYtOmTVx19ZV/r47UWsvCwhL/9//9H/n43/w1SZISxgHvfs+7+PVf+3U2rJ/7gZwGDMGZGZ1Ol+ZKk/mFkywvL9Pv98myjHK5zNTUNGeccSaNiQbVSoU4iggCtdb1r8UrGq/pYiDLNHfffRePP/4Y7Xbbmb10OtTGagRKEUjFIEv8qNWp1mVpis1VBrUD3VkBgQdYGSFAuod7FMfEcYkgCp1RjrEYT99D5FKzAA5AaC1e+U56ueOIqalppqaniUsllAwQKsTkO18hMCZFG40UsG3rFk7MH0da0NoSBJGfXPSH3H/AWCcW02g0OPuss4oHznhjkrGxOlNTU8zMricslQhLMVEYFQ99x5zI/LLbgxS1Jk366IWTRDffTPWrXyHutt1JFs68xig3qjcI1JYzWInqRO96K/ZzX2Tjjstpfv0rVI4eQ0o3K2F2E4O3vonk2teRVGsQBkRRiSCMXecrHJhNKjeJyQ14/Fu6r9BNdaRySwRprZuiWIu2q8Qr9xO8+BEev73FSm8jG0oC3ZxhU7RC11i62/fwVx/9JE89/gjNTpdQQKZTji8cY9eu7Zw/t5HlF46z6cQhylP7MTZmYv5WKl+8j+49CSJ1jIX6xSHLu29AB9MOdYj0vP/AXUdjCwXEg/v3srx4jG6vwxNPPUG330eIgFAphIU4ihBkZDpBa/daWmuefuZxEg9mzR0HtTEIqRikadHJFz4ZQmIy7ddZEiuUUz60Bq3dGkKIoLj3He0wPQ0nkOtU5CsCKSVvuunNTExM/J1+/6y1JEnKvfc8wAc/+Du89NJ+LIbNO7bzb3/zN3jzm95I6bukXvi9iHzkn6QpvW6PlWaTk/MnWVpcJEt6WGOojNXddG5qivFGw+tKBKddu7VYi+9GvGaLAa0NTzzxBPfeey+t1ioAvV6POI6JorDQENDG2/dqW0i86ixzjoPFaNR1SG7/rhBKolRIGMVOD8C/pzUODu86K7fLz9cNeZeVTwNKpZiZmVkajQaR1xcw1iKMRQSySHipHo5spXR0tHwKkIMS871u/j6RVGzcsJ6NmzYyMTnJxMQE5VKNqalZZmamaDTGiUsVVBwhwxC0LTASWCdMJKVEZxk21WS9Djz9BBMf+zjlfc9itXa2wUJgsGTlELFjJ8nLBwm7GrFjK0ob4tmt6P4K8qMfYcI4x8KsViO74mp6b34TetM2tIoIopAwFIUegrHGYy5UMUXJZ9ijn1NJNy1wjoH+vIiMQe8U7QO3curYAZrynayeU+HExibjYyXqJyaR8j7KWnByaYnHH3mAanWMc7dtQR46xsr6aZ567nmOHz/GuUgGLUFyxwK7g//MYDWjedcyy89liMSgpKC01ZK+/ixOVa8hswHSDYTcft5SUE61NSwtn+K5xx/lpWefY+HUMbZumGC1V2GlkyGE95Tw7IdRH4q80FNKEceVQgpaKeVdJoeU0fw+y7/n7reYjZs2sbS8xMLCPEJAqeRGzysrK4Rh7KdKwn/lio1DgSJwye68c88lX898q8g74cXFFQ4ePMRTTz3Dxz721xx++QWikuKH3/WP+PVf+zU2rv/+Zwo4PQyHBWq1On7kv0SzuUK32yWMHAB4y5YtTE1NURsbI/bnXa4l/rX4B47XZDFgjGX/iwf5yu23s7y0hPEMgdDT/IJAOUndLENIJ7+qM0OWadJBgtEWKQWBUF5AyIsBSbcPD+IIFSiEgCAMXfEg3epAa4M2DpDlkrajYwXSIeJLccT4RIPJyUlK5apHaCu3cxfOKEepwI1+tcYa9/0sSxkkKVnm6IFany45LKVEBZJGo8662VlmZ2aYmVvH+OQk1UqVanWMudk5KtUapbhEXI6Ryk0D0kRj0VhhyJ2YszQlGXRhZQV129eYvuUWyquLCAtagFECLQPS9XMkG7Yj3nQ99o/+E2HQxyzNU/7SF4mOHEQcPe4emoGCc86m98530N1zLsRlNx0JY5RUFPA3LVDKqTT6/YznxLuIwsDLLzgdAmtTQCCkRQ6WSZpPceDZZ1mVczx+YAt33/MlFuZPkuoUJeD1r3s9s+fU2dCErnZC0Zu27+A6adiycopHr7qM5/cfcCDE1golBKsPZ3T2HocMbALKKmwkCWYEvH2Wg3PvIDUNp72ALGio0lM8M5PR7rS588tf5OE7v06312LdZJVOq8OplYywVCHJMrIsI02NB/G5z+iKgIAtWzYzPT3D8vIyBw686LtIV4xZ6yYB1tk5ep2LITZlMOhzamGeZnOVLM1QgaTdbhfrpTh2q6BcXTIvIvIxdz4VsNby7/7d/8XM9CxvuOH1bpUzEkmS8vy+F3nyyafo9XqueCkFXH/9dSRpm3/1y/+Kd7z9Hd+30wBjLFmWMRgkdDpdVlaaLC4u0en1MFoTxTH1Wo3de/ZQq41RrZT9emWt61+L73285ooBay0L8wt86Yuf5+SJ444qpYfdZBAEJElCkjptd2kcFiBNs9OMgqRQSBEglUAGEuUR2EJKRCA9wt3TtpRDsRtjyL6JPiiE298HQUilXGFycpKJyQbVagWpIrcXViNIeb8TRwxNYdI0pd/v0ev1GAz6RFF82sNaa83ExAQ7dm5jbm6OOI4Zq9Wo1xvMzs7RaIxT9oVHGMSUSiWEMKTJwDkOCgPWSR4L68acZtBF7t9P9PGPU330MSI9cKwKiePPj1XJahWiD/wczQ07SJrzlBrjhL15uO3zVJQTJtJKkW7aSPct7yC97DJMY9wBDAPX+Vsh0biOWCrl3AaDAJt3jbmgkhLgnQYC5WSejdCQJgizRKX7BMHSF9h7QHFS38D//PgXOPzSi2BdUYWwZMbwyKNPcO3Wt6IPQXqB68bjKEJ0WgyiMpQbWCzTjSq15imUNGQGdNuiFCgbEEQCs93CGxvcEV3AykHBlq1dpqadlHTOIMAzByDjiQfu556vfAWRDRBRyJGFDu1BhkWQZG0y64tGFWKVA0A6vIqzEW63upw48YzzJDDD615IWpNTSYdJJ6eVpmnmcAl+mlMYbfmuPzdCAgpsQK5WCEPaobWWo0eP8vO/8E/50Id+j/e9771EkVN7bHe6fOMb93D48MtUKxXq9VpxjI1Gnf/w//xHLrvskuK1Xu0xuu/v9vqsrKywvLxMs9lkMHDU3lK5zOTEBFu2bqZWG6NSLhW7/rXkvxavtnhNFQPWWlqtDrfeeiuHD71UKPrpLCuQ+6nXD8gpglma+YdqCsaihESFbnoQBp7ClssE+4LAitwh0D0slJLue7nKm38I5t4GQRgyVi1Tr9WZmBwnimOiuAzC0QmRTqbWeHyC0RqdZiRJQqfXpt1u0W63abVWaTVbTE3OsLy0TKfToT5WY8OG9czOzjAxOUUUhozV64xPTDA3N0e1OkYYRkRR2cnOSkd11DpzyoHWJRFrLDbL0CbFtNoEd95O5VO3MHbyBI4DL8iURUcVsosvQ1x7Ncmtt5D92ccJfumX0JMz8FP/hOS//SHBcpNAxNCYYvC6K9Gvu5HeujlEEBCEMUEYoY0FpRwmwViCIBzSA5VyZjtSogQI//4OSggajTBdov4Rxlr3MHbqHsQTh3nwwBQvXPYB/stHPsrCyWPElTrnnL2H9mqL555/1j3cBwNaA0F5IOgNBiRpShxENLs91FlnMj8Y0BhvsFEbprodTAWiWUm0WSAbglQJTkYTHJ/bwFG1jYHdjUhSDJlfKTlqpXUjHaw1HDl8mM9/+tOk/T5aJyQDSxxJdm8bpzuAUyspocz39NAf+EIAgRCKNDXMz59CBcqtt7IBWZb6iVbmplC4CUKO+tcjIkWOaQK5Z0VBQRRDNUqBKwTA0QnTJDlN6lhr7QtQzfLyEr/+a7/K0uIiP/0zP82Royd47LEnSJIB4+MN55To/12WZlxwwQVccsnFr+pCoBj5Jw7st7S8xOpqi+bKCv1+jyAIqNdrTE1NF2yccin2suNriX8tXv3xmioGkiTltttv45lnnybNNNJrAOTc8zRN6fX7CCFIk9R3WU7cQ/qELKVwEsJSojzdT6rQgalsAbBH6yHVME0zwtBT8ryJTGGYIxVRGFGr1WmM11EebyCE6yAyq5H4TsQYrLb0ej3a7Tarq6ssNVdod7qseinipNMj6XUZH59g08aNzMzMUB0rU6lUqJTHaDQazK5bx8TEBEEYIVVIHEXEkQPmZXpQFAPWUREcI0Fn9Ho95JGXUTd/mom77iQc9N2DTkmQgkxIzPU/xMqbr6d34CVmNq1DnnqeKGvT+5OPUFpYJljtYKoNOhdciHnLmzE7zyALY8IwRkknxDSU1rUegxGglEQLCiVGJV1xJKWX77UGbROkblLpPsb4/F2E+59CP7RA7wnL/JLixAdu5FO3fJmTJ44yOT7J+3/ix6hlL5FGF/LSoQO0221q4w1ka9kBPhGex19lcP4FsH07T370Y5y36wy2PvMc67dkjL++Qnvzel7KplhonqKX1ni+dSYimUZqxYb1k/R6XWpjVcBZOeeJ2BhDv9vl5k99imMnjiAtlGPB7HjIOWdUiETEAy+0EMoVEZl2WIE0TfxrAVYQRSHr16+nVCqxsrLC6qEm2mjvfQh5ks9Da+1phLpI4s4w63SJ4twdwxjrdCEQjjFhXIEmPYMjx6fkEwQhBN1+h//55x8h1YZarU4cl4iikMFgQJakBEHAYNBj9+49XH31la+Yh8ErFc6MSdPr9Z3iY3OVpeUlmisrZFmKDCT1ep31G9YzOTlJbaxKHMcotcbtX4vvz3h1/QZ+F8MxB+7lwQcfoO8TfqYN2ousZFlG3yd/l8BTBxr0/gJKKqQXFco5/yqKCEI3ytdeeMVYpy+fPxQFbrWd8/yLPb7/e6UUY7UapbKzG850RujNjNI0RVvLQGvSJCVNBiRJyvLKMs1Wi9Zqm9V2l06nTbvXcdx5IWg0qtTqc1RKZffapcCtBGbWMT09TalcQQUhIIhLVZR0icGJKiW+08uc2IzW6CTB9PqE995D9ZN/Q3TkMKHfySMF/akN2Nddy6ASYI8sEVYn6MVHSSpVgrk6wZduYeLFp4lkSH/3OfTf/BaSC88jC8vElSoqCFAydEB7KDrOfG2D30sLT4eUUpJZ7aWE8dLHPRqth1h3/GOoB5+lfVef1YOWrANGWI7NzXIkrvPcvr1gDde+/nWMP3I/25ae5Llr3ofRrnO+9IILEXv3UlaKbpYiraQ+McnU+nXc/IXPUYoll3VWOTd4ifjdm9m3/h28sFTj4OF5qpUIY1Ias7N0uz3K5TJLS6vs3r2TKHJ7cJs7SQqBTlPuuetOnn36cRAGFQg2rquzYTxk/kiX544s0s4kMgwwaH+PiaKDdlRAd3+dOnWKTqdDr9cl05nvvPFqgcInKemZAQprTDH1cfLREiut80jIdQl80Wut+9kscxMEK72jIsNx+ejo2xjD3LpZ3vJDb2N8fIIgCDFa00v6DAYDAhXS7/fYvWcXV119xfdcPyD/DFnmkn+702VpaZnl5SW63Q7GWOIopl6vccau3Yw36lSqFeI1YZ+1+AGK10QxYIzlmWef5c4776DXdfs8ay1JlhKGIZk2DPr94qHmONcGkxg3HlUBgR/TlkqObSCkwEqFUKEfp3rrXygEiOzIwzbTunAitNYS5uqD1tLr90jTlLgUE0UVpoIq2iY0V1fJjEZIRau5SnNlhXa/x9LyCoM0I+sPECZBCM1EPaZUiilHIaUwoFyuEoQxjUaDqalJpqenqVXrrpiJygWuwAkIJWida83nD3fPIBik6GMnqH7mU5Tv+jrVXptMCrRwFMBg+zmYf/wzLK+2MTM11P1/SnBqmWhqjqzVwtz/GOHBQ6jZbbSvfwPJlReRTM0io4iSCFEqcixFXwg42qUrvnJgmpQOOBnmBZYAYQ0CXxDoFhOLn2b6sU/Q/dQSeq/EpE54x2LRVtLftI3njrzkujoVMDM+xYkHHkDsupJnDp8kGaRcevFlbKuU2bzvBXqVMiu9HlJJvvSlWzHGMD3e4JrQcN7BFxlcU+FBcx7iZAvRb2MGXdI0IZ7ZRqoNM9PTVKslhxKv1XH4AEf1E14Fcv++5/nSrbeA0CBgkBqOHl9lMK+oIthRqfFSp09Ta4yQBeAvB5+CwaLJdEp/0Mdo6xOvEzRyCdvRLkfdCZ2xkStKAy94NDQqMgVwMMc0KKUQxk8QtFt/Ge/QmONoRl0NjTHs3rWHicYEWZLy4v4XSNOUyckGtVqNmdmNXHDBeWzbtsV7dvzDRl6UDwYpnU6HpcVlmqurtFpNL+wjiUslxsZqbN68mcnJccolt+//QdQ7WIu1gNdAMWCt5eixY9x66xdYba4WKOs0TQlU4B8KAwA/xncYAZNmkIGKFUK5kX4cxQSRA7eB77By7v4IuLBQYyOnkduiCHD/ThadlPajyDCM0FagonFEUKXd69MaGDqdDu1uh5WlFZJuB6MTsjShHEooSUpxlTgOCJSTTI6iEKkEpVKFiYkp1q1bT63ecAJKYUAYBKgg8hKnKTrLsQFeCMmCNhqbpahuD/XIowQf/xvGXnoRaVJMEGIrFURJkaw0SawgrU9h4woiLtPfvoXx22+nunwSeWw/qlYhvfrNDF7/JpJ1GxClmCiICYIR+2QvjCSVdNbJ0lk7qyAgULmewBCxbi0IYwmFQJIxu3wrjXv+iuX/2cGetAiboHN1PEAKSzQ9h9Zw/oUXsGnjJnadfTbHxqrct28vd3z1DqwVjI9PYoQkE4ZOo0HXZIxVK9SqMXvGx5ltDZjtJBycWcdz47ugM86Wl+9jZXobF6ybZzk6m3hyI9NTs5Si0mmjb6c66ZM5sHTqFJ/4+N+wtLREHEAUxiRmgDEQKEskLM2kz8A69D7FVCm3os47c4PONPVajW63T6fbGSZ94zUAgtPth/NGVkrvjzB6jP4eFVK6VYMYUgmNdm6clqGXhvMvsMWUK6fHlkolkiThxRf3c/z4Ubq9LnF8Jjfc8EbOPe88qpXyP1hHnQN3+70BrXanUPZbba6QZQMCJalUx5idXcfk5AT1eo24FBGoteS/Fq+d+IEuBqy1rLbafOHzX+DkiXmsFWAFxoCUgRuB6wyT8+e1xmbOFEcKiVEGAkUQhQRBQBCFbtwKzjTI763zrjbv3HLwof9u0XG7YwJwSnJIiUKgrNvHBjKmMbOTaGYjzaMvoWRMv7fIoNvBJB1KoUEEAlmKkcI98EuVMnEcE0chURhTKpcJo4iZ6VkmJ2f8HtNpF+Q0Jq3TEdc5jzTPR6Vpik4yOHkc8YUvUPvabZTaq1gB2fgM6TVX0r/sUnj4HoK77yfNukQnVugtHaDRXEVag37kdmSlRv/8y5E3XEuy6yxkqUKkQmSosMKp3hlHrnPvryTWO97lD2A1oihoGe6ztdauaCAh7O+n8dJnWP7EKuakwzdof6Jd7wyagM5qixuuuJJmENE7dpTFl4/yF3/5URaX59HagfFu/9qXiH/oh9h07XrKExOUspjLrriUDfWEs/v7eWFpPQcmdlORkpKpUFq0LD7Vo/q2ARvPvJyJyrWEQcXpH9ihwp8BEMrdf8KS9jp87uZPcvDFvWRZSkDIzPQspxbmiaRGhAEnegMWE03PeuVEf85y1cIhv1+QJBkrK6v0B13c0sQVDY62ahBCobPc3nioHgjOehkcSwVrXQGgZEEZHGWkWDtSOeCAhJXKGN1up5ggxKUKW7ZsYc+ZZ2MRtNqrJIM+c7Oz/MRPfoDdu3cRfBeBgqMj/263x3KzyamFUzRXlun1ep6REVAdG2PTlk1MTIxTrVSoVEoFxmct1uK1GD/QxUCaZdx++1fZt2+fS9BYksSQaZf8syzFal2MPrM09c5tEpQiKpWRypmyBEEMflTrqGsO5S+0KDr9Ub324UjXemCV8B0wzoJXSdLEICKB0QKLQqhxNm7dydQZs4xVUxYODmitZCS9lFJowGhKpRJxHKG8AI8KnWhMbazGxPg44xMTVKpjKOX28OVy+TQeeJKkaJOSW94WY2OtydKEtN2Cp5+m+qmbqe57ikinbloQhPC299C99Erap16i9tAz1N7xXtpHjhDe8yBhdQnuv4tqVKF75vnYG96APf9SGBtDRRG5vW0+LrEjSaWYlIwIL+XCK3khMErrtJ7iCIZS73H0Y0cxh730MqALFwQ/tREZm+6/g+SBu4h1xrFyxBO7z6DbadFoNOh2nQS1UpLeoEfnsktZbGWcUT+Pk8dPkNkOL8R1VgYtL7+siR97mHXrJJdf12P13LPpjF9FRM0fH8UHNDb3nBh213feeSf33nsPWebApFrApm1bGWQZnaV5kkzQKNUI4owjvR4mNUihyLwAVu7A6O4x6zQg/HpHCgqkPsXPZPjbBYlwyoTg9RlcIcY3FbR55Kkxv7e11p5VIKjVxrj00ot5ft8+jh45hrWW//1//3X+yc/+LOVymRMn5jl0+DCVSpmdO3cw3qh/V5KtMYZBktJud1hZadJsrtBurdLt9jDGUirF1Ot1Z+QzPkG1WnG/Q2pN0nct1iKPH9hiwFrLM888y8MP3Y+1zkVwMEjoD7ooIRwqO0sQ2iAyQ28wwAiLChVShB7lH6CigCiOneFQELsOSVg/ph3VeHeRy7PmD1Qn/qOxVpClFiGUG4cjsEKhrSQ1lkrY4OxzL+TKC9cRVxQLYor7Dz1DNbKosRJp7NTkcjcya91/12rjTrp0fJw4jgmDmCDIWQ4BBJIs05CmzgNBJ8XOWXhRGp0OIMng5AnCL3ye6t13ES6exFrLwHhApNZ0b7kFjrYpv/UyhEzp3nIzot0iUxGNMCbdtJXu668jufJa5OQUURRjc1olzphJ+ZG38IqNeQFQgAUZctnBF0/F89pidIoUuOSWGcr9QwwOO5ZFhqft+Z/Odf8xUCfDiIxMQBqVmd60kZ/+yZ9Aacveo0e4+647kUoxMT0LY3tIVk8QWIsMAhJToRxGjNUHjCfLTHYPc8EF88jzt9La8lP0y1cgcSC4LD9fCL+Ll1iPwtc6Zd/eZ7j505/0DBWFUpJqbYzGxAQz62ZYXpjnYNJD2C4IQQZ+AmUKtcL8k7lEltMGI4RwuIAscUA/JSUYW5hj5cWDkMpXTvl1sEMBJ6RTuTQWbS1K4jQmhCtYtQWjJVLh5bt7zpPDFxEXX3QRDZ/0t27dxJYtG921eIWSbuHil6a0Wx2WV5osL6+w0lyk3+uBgHq9wcTkNNt3NBhv1CmXh13/WvJfi7X41vEDWQw4J8JVbvvyF4uOzxjNYNAv9s9CBPT7XWyaIW0+hjUF5S8IIuI4QEUhQRggvG+ANcK/nnGuO9aN3UcnAvk04PQpgZMtLpwOCciMBCsZq05yzp4redPrz6I2JjFIbL/PWKVCFIaU4jLtVtslBKmoVso0GjXX5YzVXfIMA4Iw9MVA5HbMXlkxzVKUcZ2yy8XWsykybJZAt4t55EmqH/trwgPPI6wpaGU5R9pqQ9ReQD7/GOr1lzAolVDzRxEiIKnWyF53HebGN2A3biCKy8hchrngrTvgnxFuvSKEREJhyJRPL4QQHt3uqHN5USBlDmz0XWrOibeZU3WE4isvB3K75AxIgQTJ4VKFY5dcSrL3WdbdeTenpmYJd25HKkW9XmfnZMRk52Ha8QaSXotAZExVE+rBMrXGEhc2FhHladpTb2dQOQuYQhKAVd4PwCP5yXfV3j7aGuZPHOK2L95Mu73C+HiDPXvOpFqtUmvUaTQa9PodTo6VSPtdEg19p/V0WtfuatDh1CmfAKRpMjQQ8iP/ApdiXBFqrEWEEUHYwIRVRFxFq9gVjdYgrMFajcpSdJIgdB9hBph+G5MNsEYX2BchJL1ejwcffLCwMB7F0OTxnSZfY9yKot8fsNJcpdlssrK4SLPZdOBaIaiMlZmZnmZ8corx8YZT9lsb+a/FWvy94geyGNDacNfdd7GwsFCMowf9PnqQEIYOPJePbEG4BCIEkXJ79Th2lEEpncObNeAsh91qwFqBkKp4yLv31P6hqNA6wysYYLQpWARau6e7NYJuBiIOmNk4y9ZtZ7PrnDPotPssnVqk2Wmz0lrh8JGjaGPROqU6ViUuVRifnKJRrxcYAFcAhKBAKIGxgsRoTJogdOZQ48btraXVfpdu0WlCNhhgFxYIb76F8a/cRtxuY60hk64I0NaZC2HxHakhnD+E+fAfEawukFXLJOdehH3zD5Pt2YkdqyPCCJfmbbG3t160SCrldR2c6MyowU2uADnavbm3NsVawX1JrAfQGQStYJr6rCIToK3GE/cAVwRooC/gZBxx5NzzWbroQuT0OtY/9GkaqoW54HpeeGYv5VLMxRdeSPWpJ6iJBeJpx6WfXFng0g3H2bbzJL2Zq1lsfIBMjSNVxU+ArJOXNq5jlSIAA6k3uHK6E4JThw/z6N03E5Ow+8wzmJmZY8OGzSgVUKlUKZUitmxaR39xDmV6IBSrnQGHT3ZYbvawwvsSGG9B7Cc71lisL7SKc2c0AgUECGsIKxNk1S2o8gyiOkmiKqAqTqLJSKyQTmoajRB2yIyxEgJDKAaIpE/SmseuHIXWMWzWdoWY1R4DYpEy446v38EFF1zA5OTE3zsZD7v+jG6vy8pyk/n5eZaWTtFut0FAFEY06uNs3rqF6ZkZarUx4jhaS/5rsRbfYfzAFQPGWI6fOMnDDz/sNPxNSr/fJ0lTMmsY9LqEKsTYzD0IR1DXURS5r9D5AeTdDsIlf2PsadKrucd7vo93UwLrk5bfxRtIU0NnMGAw0Gjj/OInpxts2rCBmbk55jY0OPjSszRPSsoVh6I/eXLeKQOGEaVSifHxcedVEFeIvX95vlt3k4gBNjNeH913eb5LlQK/GnC0rzRNMN0u0UMPE37y45Re3EeY6eIcCpuLzuTCNkPtBImG1SX6u3Zi3vFDDC64AlmpIuMYKQOwfjntPRPyXbZQ+R5/qBWAf838nObFgM3300Y7aWMLgVCnSTALLwqUhGdS2l2m2WiTLZ9+L+QTgUONcQ7/o/fw6Mll7v/cl7n26qsonb2LU7vO5+4XFllcmmfH1u1cWA5Y/+B+2uu3U2s+y1nVBbbshmDHRlbnfpxW5QqyYAIokWGxXsY6V+hz2AtPy/OrmGTQ5/mnnuTxr93K+HjC0nJCXJ+l0ZgkjGICFVAqlSmVQnR9kvk2HD54stAPSFODlaKQGC5AqtIxL4x1+gj5VAtwlFcCrIjJZs4jnN6OIEBlCaq3jEwOIdIWJm1jdQ9rdU578SqFChCosIyVghSFidYRVmZQGzYT02TH/MMsLh3meObEpnJ9gj/4g//Al7/0ZX7v9/9Prr3uGuLY4Qu+WX8///3JMk1/kLC6usrJ+QWaKysOkJg5em+gBI16g61bz2FiYpJGo74m7rMWa/FdiB+oYsBaS38w4KmnnqTb7ZK73EVhxOzsLMsry7y4/xCT4xPoTJPqDKUCgjAiCkPiUogUygEFVeA4275QEB7RrbywEFCgrZMkt4hVXufejcJ7vT7dXkqSZARxxMxcg4lGg9pYmTAKiMKUpH2Szopk3cbNyFDQGnQJE5icmKRarlIulSiXnVSwUAqpQrcLhkL1LcsyhNUY49ThhBUePOe87LEGqx2YLU162JePEH32c1S//jVUp+VuAj/ON9a6fTcMRZTc4thJ3a7fQPJDP0x25eXo6UmiMIIgdN4LFrRPVLnLIl5nQUknlRv4AiY//oI54M9lAcIEZ0aEBelWHjnFDiHRNsUY6EZ76G0/k9q1j7D0JQmZV/ezEo1BI+ls3M4DRxf4xr13ESvJM888ST/ZxcuH97JwaoGtmzdz0xU72PPMl9h8Zot00z5KOyZJ5y5iuX4tg2A7NhjDigBHZnSf1Vqn8WeMBpNhMsdy0Mag05RTpxb46u1f4rmH72fPXERrUOalo4tMZzG1+jJhVKJScS6DURQibEgUVkkzWyR/pMAK43b5vuqQskBDeJAlDOchOZZFEq0/Gz15JnL1JVg+iO2vkJkBQhhSMpSQTDYm2Lp1O+eddx73P/gALx08TNLXGJ2RCMvs7DQT45M8//x97h6RiigK2FbS/PhUhaNJyqfbGceMQ29qYdj7/LP89E//DNdcfQ2lUpkgVLzr3e/iqquucE6Iy8usrKzQbC7TbDVptzvF9Gd8fIKp6Smmp2aYnJzw4j7RmpnPWqzFdzl+oIqBLDPMzy9y8OBBLC4BlUoV1s3O0Bivk2R95hcWaNTqjgoVSJQSxHGJQCosAumdC90Y2roOXLp9sJBiyOH21KssSzE28yNtjQpAIhmkGXE5ojpWpVqpU6mWkcr4DtslkUBJavUxwiCkVhrnnF0bObZ0mBPHl6lUakxMzjilQyF9sjVkvgDAJ4vEWxhL3HgXzHDPbxxILDMZgyRBtnpw7wM0PvNJwsMHwGTu5wBdLKbdZMAaC4EAnDOjnR6n+4Y3w/WvZ7B+AyKuEIURUkpSrdEWckAl+L0yLqGHShVj3NGVgPVo7lyoJg+3JncTFpkD5oTj1xvPbRdGkmmNUQ2OTL2fDW8+Sd0cp/kNjWjDQFhSK9EIVBxhrXC8fylYbjZ5+JGHKZfKXHLZZVx00blsGd/P3Jl7MOEkg3gHS+EudDCDJPRHg5t6CHftrHFTAA3O5hqN1Zp+ktDv9XjskUf52te+xuLJI6wbk9Qm6zy8b4Fev8+RI4fpdNpMz8w4rwUh0ZlheXmFQ4cOuSJgqMLkrr8KcbbHGqmkK44YAgCN0RhjCby5VVCqkY7vQDaPYI4/gLUaKQVRKWLr1q3c8KYbefNNb+Kcs86mWq1ijOYP/uA/8eE//rDz0ggbyMkN9KZmMf1VrFKQDRDK0B/0+Vxf8kRJ8aNV+DfrIm5tZTygI3QQUC5XqI3VOHL4ACdOLtDqdHj8iYf5wAd+glKphJKCMAiZmJimPjnBzp27mZqcpFqtUiqXintlLfmvxVr8w8UPTDGgjeHUUpOVlSYrK8so6RKO0RnN1SbTM9M8+MBDfj9bIghFAfJTgWMKgEAoZztsvPSwwCG4pRrKsjpgnF8FAN1ui36/T7lcpdFoUC5VqAuJkBAEjjooROZMa4UDy0VhxPj4OFs2b2F6eoaZuQbdXhdFhbm5ssMqSK/o5vn/DqBm/CjaC8KYfDfvU6jfKwvhzglZRjJIkAdfIPrMLVTuvpvyoIcROcDO7ZytBzlKXALWOBc/xqoMLr8a+fa30N95BkFQJgjdmH9IZ8vpak6fwbkISu/gqAq9APArFdwJzDtamUvnGr+qkMIXAm5FQC564ycJWZZhsnzaYclK53J0w79g5m1/zuSug7QfGJAdBJ0aojGor19i5+aLeeL5KjrLqFaq7Ny5k7PPPoe59RuZnZ2iWj2PRevcKItzYhXaCo8ZyVkQeKEmjfXn2BiNSQf0u106vVUeuPMu7vjGN+j3+4xFio2zMQurbY4v9wsGQKu1wqGXDtLtdDBa02q1OHbsGP1+zxWjDDEATkZYDtcEIy6bjrKYT1RkQRfM4gn02AbC9gIbNm/k7LP3cMXll/PGG29k965dVKvVEZMhQ5JmbDvzItLKRrJqA8Y206/M0W+sh1IfuWM74th9qOQEqlSi3+9x3Ahua2xlZ3+Rn500vC4s843SBCth7FYXCGrVMqvtFsvLy5RKFa666momJiap1+tU1ix812ItXjUh7DfDf78Pw1pLc7XD/PwpVpsrfOHWm0kTpxewurqKUoojR45w5OgJNmzYzOR4gyDMXQSFs8wVfiQt3DTAAbDwwCiJkMY747mHswMD+oeY0CRJghCSSqXihY2sLwZy4RyvzBZXGB8fZ3J6mqmpKaampgjDGFBoa4qkmCf+LHMwOGu0k5i1rihBaJ/4c650BsKZGWEhSQbYVKObS6i77mHslk8SHz3qaGMCwKK9QI+jweecfkkgQcQxrfPOJ3vH20j3nIeojSHCoRZ7gVwfWVlYKx0yHYFQwmsdDIWDjB16DIAosBX5eQZz2v8vduB2VPEuX4+koFMETign0wNsb5566ytMth+h1lkhTQfY0jh7w8t4cGk33b4hTRJq1Spj1SoTExNMTEwQhm4dlK8htNZYhE+yFmN9YeO2HkVhYgXei8IgMPS6Hb74+U/wyIMPsLDcJJIBs40xNs5GPPPSMs2uLpJ4EIYoFQxVCQtdgOE5EUJgMoOUwVB90PpzgkZ5HEbhdyEcziWMQuzEBfQ3v4kL1lv+4vd/gm2bN/hzT5H850+tcsf9+7jrkQMcOL7Mi0cWOH5qBVDYsA7lCrZcBwkiUMTHn0Ds/QRRZL1pkuGmG17PoLnI5PI8P5J2aMaCL9e20K2OE5UrvHTkKAcPH2bd3Byf+MQn2LNn11riX4u1eBXG9/1kwOEEUk6dWkIIQakUMz01yeHDR7AWut0uzz//PINBwtT0LLWxMcfbNgYVhG7njWMNKCXBO+XpbKjAFgQBQuZ6dq4YMNo4oxgpSVNLuTTmkdj5GByCICQulSiXnWPb9PQ0szNzNMbHKVfKlCsVqpUKWhtWmh2y1O3/nWTtSMeHN47x3fTQHMYZBblc7vABWmv0IMUMusgX9lL95GcIH36UKO15Y59Cxm4IfMP/hxDoWNLZfQa89Z0kF10CYw1UFCPD0BU5voDCmwUb4+2DhXAdrZTD4/KWukoOjZxOF2fKWQNeh/CbytL8PGAhy7LTVjQai7EatGdIWLDRNK2p99JpvA1r21ibYkSVjDo7xzssr64QyICxsTHGqlU3fcnpeVY55oaxGDM8z64IcNfbcyS8I6C7Z4TEAUezjLvvvocHHniUpWaLXPKok2mePLRMb+D4/vm23zkA6qL7z+mvoyDB/MKYfNKjDZA7BSqUEsX9OTExyfnnX8AbbngDZ+w+i9/48N280ClzZLnPs4eWiEtlOp0BDz95kHsffZHHnj3KgRNdWolFIzFWYoMYKptBhQgjsBJkFGJFgAoVSWUWEVeJdAdr3HEdPnyYuck6h4n567ES70lX+ZHeMT4TRDSDmIE2bN2xnTN37+H48eNs3bqFcrn03XsgrMVarMW3Fd/XxYC1ljTVnDixgPVyqOVKiV17dnP0+FHSxCn2bdq0iTCKiKKK7/qtk4xFYQ2uCPCvp6QqtATypI4w/oGc77ezkZEshIGTKXbyr5IoihmfGGdiYpKJqWk/Eq0RRZED0QUBpVJAFIastjq02r1ht2kNVhq/HvAiMzY/Pu0mAhh/LE5rz6CxaUaaZegkQy/OU77tqwRf/BylxXmEMW7s7Qf0mcglcvNkbBEyYrB1I/otN5JceS1iYg4ZBsgowgqvHIhEYNA5u0AEbmUhldcOEIWQTs7GyCcC+QTB5ntum681htcy/7OQv8WvMYxTHMwlcY2TcUSYACwYmyKQSC/oZFWE1Q3XRQuBNIZGPaReq1FUQjbHf/hJgxHF9412qVxbB4gDg/SJ2a2VAiROD8FtMQQHDr7E008/QRDHCBGgTUZfGwatXuEPoISzysazNTDGr3dcYZRPPgpWxQgOw2JQoSQHCo6N1di+fTvXXHMt119/PRdddAHT09NIJbnjvr0c79bRImKh1eEnfv0vKDNgMOiTZhZtlVOUVBUISiACLAHEVWSljlABttfFWo0xAnSGsALCBiKeJlltki+ZsizBGpjYsJnFdof/tNDk58oDXpe8zNcqda687FLG6nXiKOb+ex9g8dQSb37rTYWl81qsxVq8OuL7uhjQ2nLi5BJJMiAOneStFZJNG7dy5p4zeebpZzHGMDk56SmCjoKlPBAwUDnt6XREe67yIqUoOtN8vxqGDtyUphrhR+BKKGq1GpNTk8xMT9MYn6BWbzjlQhUOu3gfmda0WxlZuupslL1BkPEJUhrnd5BjyIZGR45LP9wlC2ezbDJ0lmFaq/DEY5Q+/Wlqz+wF3UfaPPG7Hbwx1nvduQiUQM+tI3vDjfSvv5FsdhpiZyaElE4jwLqU5Y7Tw74BIRVB4KYAagQgqJQTHCp8BYpJhvCAOIH2WIxcRGj0Z/Kxt7FuVeIVfIqCKcdRKLyCoZVenUd47QiJMZ4W6RMt1ulCCK95AA4zgufUG+1wD7lyIP64jf9HDsTpdRECZ2edM0eWTi1y5513UiqV2LZtGwCLi4sjzpXDdYcnWDi2hpJDzX9gMBgghHD6EUFQ0AuFFKggZnp6mosvvoQ333QTV159NZs2bvRy08N7q9cb8J8+8QDLooaQCq1qZNbQywaIwCCiEFSIDX0xIEJ3o+kEIQJsJjBSIqIYoQdgNFhBljr/hlxbQwUKIS1jtRpZ4AGO5TEae87ls70u7+41+ZFsmb1TFzAIIoe10JpDLx3i+b37uPjiC9ZMgNZiLV5F8X1bDBhjWVpq0m31iOISQrrEoISkXKpy5p5zOHb0JEpFhTiMyXfUFs9ll54l4AxzwjBwiOzAgePcw8rrtwuJsQbl0d/lUsjE1CRTM7NMTs/SqDeoxKXC6cy9jUuizryI4d5bO9CZ1kPbY3B9d85Xt14/3lgLxiUjU3SLrhDKMo3JNHrQxx4/Tvmzn0d+7VbK7RZBvpf37IB8lC6Eex+kJJuYoH/tNfRvvJF0wxZkqUQQlfyOGqT1HapfWRS7XuEc6nKuu/VaC/lXfj7zRDi6GsgldYUdFgHDnbkD5GlfBJh8LWAs+HOXAxYVnvVg3ZTA4pK51hrjQXEYv9awlm63TavdolwuMzZWQ4rAdeOZLSYQBSAzvx5SEionkKTylYI/BwaLEZAkCbd+6VaeefpJLrzoQipjYwgR0un0AOuwG74YzO8J4Vc0DqeR7/Gz05gWSikq1SpnnLGb1113Hdff8EbOOfssJifGi4L0W0UUhVx2zga+/OLLYEOsCbFRCVGuY4Oy+3xCgPQTCRkiCvCpf01tXRFLBlYjlZOBDjr7sZ15wiACaahWa0zOTDFWqzE9NUepVKbT6TF/4hTfSMd599Hn2fbYPXxWTDPfamKEK8zPPu/s4nqvxVqsxasjvi+LAWstrVaHpaVlwrCEkr6LYvgwHwxSrrzyGsIwAk8NxCeXJE2cN0GaMvBUsMEgIQycepx/1AOQpS6JjdXqVCpjbtdfLjM2NuamEWHgmlJHL8BayDJdMA3yzjpN0yLxSZMn5mGS9B+smA5AbnJj0San2OUAM4PJUrIkIVldIbzvfsqf/hTVwwexemhAJPwxgX8vYZ0YTW0MrriE7pvfhd6+lSwueTOmwA3RhVMJdKP00b0+xWpESucyiFJI4UbmRSEghq56Q/ElU7yOGEkEowVBfj600WQjdENrDFY7ymR+XoX1xZDNyLKELE0Z9Pq02m2OnzzO/EknYLO0uEQy6FCp1Ljo4ovZc/ZZBEGAzmyBQ3Bn1r1u6BkkQgiEkui8eCIvJIfFS6oznn7mKe6/516SgVv1VCpVmIJqqUKrtequQQEKzJUCGcFMDCdRlUqFubk5LrjwYm666c1cc/VVbN68iUql/HfuoqUUXH7mRko3H6A38HiIUtVJOwd5MWkLLQwhLXjjLjc+MliT+M+aYnRCaFeYlov0lx/GhgOqtQZbNm/g2muvolIq+fd1gFulQo4eOc49z+7nxe4qv9RsMtt7mnsHAYmXK37vj/0oQ12EtViLtXg1xPddMeBEfjQLC4su+SjrvjylylqD0ZapyWlKpTII5eRWrfUKa2ooseuH5UVSZpioctpc7jsghFNms/7vCspbZgvFPLeZF0USzwFoeZfvWlDr5Imt9gIyRb724SyWCy8DQErj97cagyVLEkyvBy88T+mWW5i4734YdN1KINcJAAr4OxIjBbpaQZ9/IclbfojB2Wejy2WUCqkEEXiFv9xUuHALZDgRyHf/wme0YjUghvoBuWVDfq3yf5v7DeQj+Fy+OS8ETpsSAGmSePaBp78Zp61ApknTlKTfp9vr0O2ssrK8yGqzyTPPPM3Tz+yl1WpjsaggYN36dbzu+tdzwYUXU6uNI4WkP0jIBwA5Q0SJIZWUIlm7osOOJPD8tGItzeUlvvC5z5L2nTVuc6XJpk1bkGNjVKsV2u1Vp1Fhh3bW7l7Kz6MiDGK27tjKtddewxtveCPnn3cu0zPTxN7p8e8bQgjOPWM9jcDS7RlkEGEDiUlSd986jCfSaKSwaN31lEmJESFW91xBLAUizNgxF/Ezb7iIN1ywmdWVd5MMBmzbto252WnuvvdeXnj+ebTWBMo9SgaDlGPHjjK/dJJlJfjPHcMvjQccmE+5P3WW2ffddz/vefe7UCr+e3++tViLtfjuxPddMWAsLJxaRmfaO/MJpHIJzGjtqXhQrpQdZ9xIp94mBQa3GqAYX4dATh/0mgJ5Nyry7/vkboaUMuC0B/VpFDjyYoKCNubAYu74iv04xuPNHShsaDoDMOyucwyD1RlYQzIYoE+eIPjqHdQ//xnCxVPuOD3YzZMC/LF4UF+pRO+cC0jf8TaSc85GV+qoMHT7didXVCDrGVlF5MncrexN0fXnDoNFYUBOMfSAy1zvwIP+5Mi0IAfJjdIFR7EAefctrRudp0nGIOnT7/Xot9u0VldZWlqk23La+GnSxmQJmdaUy4Ik7VCtVznvvPO45NLL2L59B1GpDODtqd2USCo1XGsIb5wkhiBLM3I9XCEjhtfWGNI05au3fZnDh/a7+0pDs7mCVFAKIqpjFZgfPY+OZRGGIVMz05x/3vm87vrrufLyy9mxfRv1Rt2BC1+BGG9U2L1ljPnDAabvxJDcFMAAKaE1VGWGyQa00wFGlSCqApJQDFAkNMYC/unbzuXn334J66YmTsMl5J/p/HPO5cSxedrtFlK48zU2NsZZZ5/JwZdeRFvDowPNLSvwT6YGHD6lOIbi3nvvptXuEMdrxcBarMWrJb6vigFrLSsrLVZXV4lDp80v5JAelulchS30VEC8j/vpjoLfPHItOt9CBW8E5c7wz/zfju64R3fiRYPvE1quzS9G3qc4BuFFjAqa3RAlb60uPOeNtdg0pddrIwcJPP00tU98kvjZJwnSgUejeckh6z5wTkDQcRW5ew/pD72Z1kUXQn0CAoWSEUqFDrRocm0DM/yc/nidbe4QOKeUKpJoDrYs1ARH1xFmOBGxXishH/GPjuaL6YA2GGswGAb9Pml/QHN1hXa7SXNlieXlZXrdLmm/S6AgCALiMEZgnEBUGDJWKjO3cTtnnnsZs3MbHGhUBO49MkfrUzJAeSlkckqfl1w2gBGuCHF3hygAfsWAxRcoGMve557hwfvvBasdbsFCv99FKUG5XKJWryGcKQRSBYSqxMzcHP/yX/4z3vH2tzEzM0Mcf3vd//8qKqWYmy7ZxOPNPq1B03koSIOwPaR1bJiB7oPNCJUhLhsa47BrU41Lt2/hit1zXLB7I5vmpgrp6G8OIQTbtm3hrW+5iaPHTnD48EusX7eOzZs3ceLESQ4ePMQzzzwFVnFrT3NpLeIflTT/pWc5dvQoz+97gemrrnjFP/tarMVafHvxfVMMWGvp91MWFhaIQjmCtnYPU+2nAkrGbjWAcZQ533mfjugeFgajHfjw/zOikT8EwY2uOUdH4KPTgtHkUUABcN3kN+/HHepdI8SwIHAZ3SCEQRvI0hS6XdIjL1P5/C1U7rwDtdpC5g59dngOJCCkJYkikl1nwE1vJ7v0MtJ6HRHGCOW6Uymdxn5uLexG5T6EQDNccbjdsuugc3BkPiEYsi/ynbh7PW2cZLC1TtFQa3esmVfuGz0PWZqSJgParRbHj59g//P76LbbRLEgjAXGup9P05RqtUKgJNVqlSgoEUUlolKJUrlCrV5HBRGCgDQ19LoppZJCyuA0YCNemVL7a5b/6ZYFAusFh6RSriDz6x+TZW79og2d9iqf+9zNdLtt5yLoV0NZlhGogEApynEVJWOCuERYGmMyrnH1JRfwY+97LxOTE6/Ur8W3DCkl11y4jeC2x93nxSBNRhRAHMGmiTpnb9nI+bumOH/XerbNTjLdGKNWqRBFQaEW+b+KMAzZsWMr27Zt5orLLyYI3P2xY8c2fvInPsAHf/OD2CCllcBHljS/MV3hjEGHfYOUvc8+y1VXXLbGKFiLtXiVxPdNMaCNZX5+0XVaUp2WiAUu4QRB4IBvACPyrXmMduGjSPfREf03/wm5ylwuuJOvAIavl8vkFpS/YhrgR85eDMgWkwfcON4j5rF2OKK2FPvxbDCA5QXk3fcxefNnCF8+5GyIc+Ga4rP5QiUIyXZsw7z5raRXXkXSmMBKRRDFBDLE+DWCO5/Gn8vhZMWNz2UB0svthZUXDwq9U2IxLcnPIw5wmOMsYNj1YxymIj8HWZZijQNULi8vc+TIUZ54/BmeeuopFpeXSQcDJuvj7DlzC+vWO+R8FAVMTk1SGxsnLlUol8vE5TGvcx+QpingNCcETuypVIpRQTgElvrdf24n7Xcpbn2UaSfDLGVx5XIgY16gaeO0BrTJ+NrXvsLLLx/EGpf8tdEEUlACQiyBFUxKxQ4VuM/dWmFydZXg4QGdleZ3vRgQQnDujlneeY7kuZMZ2+YaXLF7JxfunGPD7DjT42NUSk4y+LRC8NsM5/g5TOpKKa573TVUK1W63VWkMuxNJA8PUt5eifmDVsI9997DT/zkj1NaWxWsxVq8KuL7ohiw1rKy1KbX7RFGUaF/n3eeqR9D51RB6VHL+c4/36GPFgB5fLNJzjcXCQVPXbjX0daMyPe6f1NI8mYuGSGMR5lJhKHwDxhF2TuuvCnUEDNrndVwlmHSjKTTghf2Uv7EJ6g+8jDZoJ/L5XisAZ4dIMjCgGT7FrjxLSRXX41pTCIi10VLGXrsgsWMYhEKZsKwYHKTAIkSthijCyURSqECL5/r37/Yp3saoQNGOqGgLMvQRpOkaaEPQKbp9VqcWljg1MIpTh4/waHDL/Ps8y9ybGF5SBu0msXlRU7Ol5mcrjO3bo5qtUqjMUG9XiculZ1CpDbOmnrQdRTAMKRcKqMCt8pwVsdepGdkygO+UPFzfymcyZMzQVKFVbWQXofAus4/V2/c/8Jz3HXn1xFZRlkpxoxlnVLstAGbgiolC6kV7DKaK1NDYgb0pGAQRVSiqHCc/G7HeH2MP/r1tzkb4GBoFPUPFbOzM6xbv5GXDnaRNiYTKV9qGn57tsKOTsrTTz9Dvz9YKwbWYi1eJfGqLwastfR6KQsLSwRB5GmEo/tpD5pT6jTg2+i/H/3/o+uC0b8vEuJIwvb/otjtF3/P6N8PX+P0HXo+Dhff8ngEArRjP7gxc4Y2KaafYE8cRX7pVspf/Arl5ZMIawl8CeCc5h3VMFOSbMsWem/+IczVr6M/1UCGEaGKClCce//iw5NpfVpCGj0/zhnR0RuDMPRqgiEq8EnMemMcm572mXOAYd79D5KEVDtL36Tb49TCPAdffIETx19mtdkkSVIGgwGZtWiT+JWKo7opC5VSRLlcYfPm7Zyxcye1ulfFA2xmSRNNliVOcbLsioMgCJ0ionX2vFZCoEeuM0PQorFOzjhNE7AZwtE3CIMSKKcfIM3I9MW6oqfVWuWLt3wO2elwHiF7jGQrljljKGcSE5dZDCLaGmqpZg6BCSIGYUB/fIz1P/VeJtbN/l1v/e8ohBDEUfQP8l7fKuqNGldffTUHDjzvmIwIXko1Tyc9bigZPnHyJPPzC4w36t+zY1yLtViLYbzqiwGnMrjo1f4ChwgUDmzmRs6GIChhrERaP/0FL6SiESiHIbC22IV+q0lA/t/5nwWYDwpDGvcNM+IuezrmIH9tbXHOcpaiwy5+RmjAYI3/vtEe5JZhVpeQDz6A+tRnqO3fi9UZFkEonCCMQWClxcqQZNsGujfeSHbNtejpDRDEBMpjKcCzAiwWjcB1hRrrHAE9DiKnEVrrlPaUHxkHKigYA0EcoYIYbIbV5rQiANwKJM1SdKa9sVJKt9vj5UNH2b93P63F47Tay/T7XWf3nJdSHn2eJgmBMFRrZebWzXLeWXs495yz2Lx5E2EQk5tCZQOn9qekJAwj4pKTgFYyxPopjTG2AEA68J7ApBnaphiTYYyzudYGtLBkWYrwhk+BARsOVz/aGxcFSiFwxjx3fvmL9A+/yNuk5AwRUtMJZaGI4wA9MU5700ZsaghCy+y11zB+3bWYOCCqVlh/8UVsu/RSSqXXhi5/oAIuvvhCPvZXisTTaDMhuatj+PlKiZs7zqVx966d3+tDXYu1WAte5cWAsZZTS0263R6luFTo34N7aA/6A0qlfMyYb+nzDj2XEP7bOIDRP0dHp988RcjlBLQHvEkpGB0KnNYdnwYozAf6Izr71nrUudtFSz/VSJM+pteH/c8S3nwr8X3fIOr3/CfJD0OClGgVkG7ZiHnjG+ledw1mah1EJYRShCo4DeBYgBStdYkXkMY69LwxhcgOvhCwUmC8ZoBjDQSMjVWYmB7HCktzaZUMZ5+cZd5V0QMCsyyj1+vTbrfY+/x+7r/vUZ5/fh9pv8vuHRsIQ8i8yJPDHvhEHsKmzevYsWMXZ513Lhs2bKASBygRYrRTPcytmgOPVxgqHXp/gnxxkrMZLAh/vlOtyTJNmvW9n4SHCVqJ0BaRGWSAFxny2A2Vqy7md5Ug0ynPPXgvvfvu4vWhYGcYMT4xS3XXGZTOP5eFzjKtpIcOKyBjqo0G1/7jn2LDpo3uNb5pNfVaCCkFF198IZVyhUxnWONGNc/0FUlJcKHtc/zEsb/1O7gWa7EW35t41RYDbj2QsLi44pNHbnzj8lsySIjj2Ou/5ztdEMIgBRirvTLgqLSwC8koHmBEE9/vhbEMwX7C8xG8nGwuKjRKByxMePLE78WHtNFonMOdyYynsfmCQBuy/oD05AnUbV+ifuuthAvzfm0gHVNAgEWgg5juxu3om15Het3l2JnNaBW40bhSp7McEBjrRYBwExTQvogxYL2EsM3ZDq4AMDnuEopVRzKApfk+GzbUycolFrtN0jRFJxnWpiRpRq/fZX5hib3Pvcj9Dz7O8/sPMBgkYBMqsUIEEhQkA8vScovxRp2NG+fYuGkTW7ZtYWJyklJpjAyX/LNMY0TOcpAEI1oA7jy7yQpIR430XafFYI2T/021V3vUFAJPxuQaAZYsS0jSASsrKzQaNarVqqNMClnIILsqElKhOXb4AE/cfhtn12uce9X1nP2j72XHpVdQGatx4uRJPvupz5AO+shAEApFhmBlpcnGzZte04lucnKC6liNTreNNhZrYNVqHkgNV4YZxw8c8FLdr4y+wlqsxVp8+/GqLQaMscyfXEKhRihsDkmfJAO3HlCRS+Zm6D4XKIbJGucCKOSQE39a5++a99O6e21NoaiXh3Yy7p7S7/59Lm70zZOFIV/fgdJww3knXkRGolPMIMF0mpgHHyP61KdpvPAsIkvBmygZi5PBDUPEpq0MbroJfeU1pHNTZKHTCAhH1g9qBDBpT+uU88+lC6CftboAW+bHmTs0Fp9H5GP3jMGgx0sv9cl0l8Fg4KYKaUKSpJycX+D++x/ioUef4fDLx0m1A0KCIRCCOCpRqdSpVELWbyhz5ewsmzdtYmJignIp9tcVjPaTCykRwYgYkAU1IljkiqMcA+ASvDFOmTFLUz+1yIbiQNaBBQdpSqYzTs0vgoGlpUWqY2VvYjVeXLNMZ47OqT0rQUouueRCXnfd5VxxyYXs2rOHuU0biLw6oLWWIIgIopL73EKQ+Xvv1KnF13yiq9WqbN+5k/mFE05zwhfsd2nJ68shdz31FFmmX9PnaC3W4tUSr8piwFrL8nKLXrdDFEUO8e1zrtYarTOiOCJf6rsdeO6WpwtpYt/mFt17wfEnT+LeoIXcAEdRWMb64iFXzMu/Z/1DPzfjAdAedFdo3LofLihtwv99OkjIkgS7bz/hpz9O7YH7kN2ue13hLG4zDFkYwOZtJDe9kfSqa0mnZxFBhIqcOZCVoIRLpM5I0GkWuOKHociB9cJGfhUg/PkyEvzYpNASkL4IGFULzMyATGu0tmidkCYZ7dYqL+57kUcff46nnt3PwqmTLiFbZ6BQimLGqlU2b5jh7F3bOGPHFianxilXxpAy8MndHZe2oKQ3PAKvkjeqEOl0E9yUJr9+LvmPFnBZ5nAX2hdo2hjSvGA5eYLl1VWsgPFaHZ1ohHArG6UUyWDgir8gcOsEbVldbbF//z4uufhCzj5nF1EYsn371r/V5QshWLduhu1nnMHe5/aidYbFMjk+wRm7d73mk1y5XGLPmXu4/957UCog0+76HEkkJyqSdUcPk2UZcfy9AzquxVqshYtXZTEwSDIWFhZwecoWX/j1QBCESBn6kftQxMetA3zic5mkQNKPGgLl6HchTSFOlBcFguEUIY/ThInIJw8UpkiSYqrswk8rjPcTINPodIA9cZzgK7dTuvULxAsLp6kWCmEgCDHbtjC48Y0kV1+HmZ5GhiUPnBR+JZAfS/5WAmtAKAFCkU8DHDxQeR65P3Y/4rDany8h0CYjEMGI2+KQfpeNYAIW5ue5776HePjhJzl6/ARJYlBRQH18jInxMls2b2DTxo1sXL+ByelpqpUKkaJgTljv6yCEcwF0lAg//s+nM1J4Hwd3jAhLViR+4ZK9Z2m4z366pHGWabrdHkmWcPjwYaSULC4u0ul1UYECrZkcH2fz9KYiUefmVuVymcMvH+GBB+5npbnM1s1beMMNr3fmVfC3CoE8oiji8ssvJk0STp1aolwtc/lll7B+/dy3/wvwAxJSSjZu3EhOhtXGoGRIxxjuS+BHV5doN5tUq5Xv9aGuxVq85uNVVwxoY1g4uQjGoMJwxADHqdA5lHtITrKzNgXy8fFQR99ikbnxjNP4xfrE4RT1TtfKz2V0pfjb9ELrM73AFQJ4fr70ugYY63n2mTPzwSvnaU3W62NXmohH7mPsU58mOPAiZK6DVDimAmGJbMd2zJtuoH/1tQwmp1AqRgaKUCl0sRLIDYxELjs0nHj4AqEYo9scWe/+TgrhzGcQBSDSYDESUi+xK7XxKoUuQRqt6bZbLC2e4oX9+2l3Vjj3vB1ccdU5jJVi6vUJxifqVKoVj5KXBCrwxZS7PiJnePjVhGAo4GTz8zzy+QA/UjakWVJMAcAW6o74a5z5QuXUwgIWwYnj81gMQSjZv38/W7duJY5jZuZmEVIgkSRJRpIkxHGZdXOznHXWHjZsWE+9UcNoQ/tnPkC1WqVSLnmVy//vnb8QgpmZad7+jrc62WUpijXCaz2EEJyxa6eTptYUgmBCCh4ZWN7fmqf/8iHYsP57fKRrsRZr8aoqBqy1NJsdmq0mURgV3dsoPzyK/HrAh8hBhUZ4sSFTJG5hcnU97yVgLMZb4WptKTILnm5oxYjD3OmKhdi8yNAIadHaYAmGKnse0yAwpEajuwm220bufYbols8SPvIgQbeHE7u1BDJAxwHqjO2svOWH4ZJL0BOTEIZEQew8EKzjwwsROACWdeuQnD83WrRID2rQ1pIZgxLS/bdwSV0EAXkJkasnamORNk/+mjTLyPznlx5EaXVGGArOPPMM9uzeQZZlCCUJhXL78iBAet8C54swvGbFNSp2/qJgdwgpC/qe9oXa8DyCNgndbptc9hgcTiMHEi4tr3Do5cMEYUBzeYVNmzbR6jRRSjFVneSss86kVIoZDBK6SUar06bf6nHGju3ceOONbNu2hYnxBlEUnpa46/Xa3/u+FUIQhgFh+Kr6dfqehxCCrVu3EMYlEp1gM3dvKQ0nreWBJGH3M4+iL73sNb9SWYu1+F7Hq+rplWaGk/OnUCrvyIYguTRNCXIRlUJR0HfpJh+D69NoXIVQELj2VDqr2rwbzRfsxQoh74hHBIYKhUIhHMrdWERuvgMFgFEIgc00qU4xgz7ZocMEX/48ldu/SrC64vb2AiQBolwlO2sP/ZveSHLxxejGJMab77hO34MXhS4SeDHCt6drITgJXTucXkiJUoJhmz3yb8nXC243HwnhPg/DImhUFEkIQRjFjIfTRZJ2JY8z9FFIpLfozVceo+6H+eLEeH2C06+NLmiNWFekDa8fQ8CZHTJB5ufnMcYwPT3NsWNH3T2TpMzOThOGIdu37yBLEzLtuv+TJ+dZWlzk5SNHWVhYoNVqUR97F9u3bWV2dmqte/8HiFptjDiO6XTwJl8apCCxiq/3JW+/9VPM/shPUq2viQ+txVp8L+NVUwxYa1laWiZLM6Kw5C2FARxdDEAJgcC74WE8ZiAfPw5H5rmzYK4eVwjk5CwDbTy6308OzJBSB0Ne+Gm6BH7Unq8TlBBYmxWshTTTJL026tQy6o57iW/9ApWjzyOyzKXEQPD/b+/Ng+y67vy+zznn3rfvvaCBxr4TBIiVi2hRlMSRqKGWKU2qbI9TcaI4YyuOy+VKTXlSFcdJKpXKf+Ox7CmnyjVOKuOpGW/jkUcjiQu4U9z3DQQIEiDWRqP312+795yTP869770mZXskgmwAfT5VLDS7+7137u1+fX7n9/v+vj+dr6IPHKR3/9ex+2+jWy5hAneqzqoQIQKX5hfOYEfaAGeflGYFLEK4FjrLoBtAJ54K2LTPfuC/L0k8+Rm6BuiLCdPrE+nmnW7Gyddcul+5Wn76WDvIUrhpgziPgiGvB8NQqUKAMMMDmhwmuTcyad80ydecEE8ThkG/LGKtpdPpUCwWmZudo1AsIqUgm3XZCa0tS4vzXJ25ytXpaaamppi6fIH5uTk63S7Gxigl+df/6o85f+4C//gHv8OuXdv8oJzPmFwuR71RZ252epBh0xYVwFmtOPXWCSZPvEPh9jt9cObxrCLXRTCQegrMzs4Thmna1p1WbRxjjCGbza5IM1sxZBAkGaTAScfyDk6yyMR7Z8icxhjTb0EbzgB8fHjRcEui29zsiql7Wmu6nQ5mcRbx2huIP/l3FE+cINPtYIXBioBurUZ07Ci9r/0K3HIL5EsQBGQCNzegf6JOPPERyQnKrgxKRHJBLhuQZg8gNoZYx6hkgFO/xDC09vQ60/ttUx2BHbqPAkLhZjFoTH+iX3q/YDCHQUiZ2AmbVJXR91dwWg53j+L0tJ+UAv5j9zZ9bp18v7Wp34HoixjDMEOpVEZrQ6VaIZNxgcCJEyd54YUX+OCD91lYWKTdbhNFPTdl0Jj+lEVwwsjnn3uW//43v88/+N/+IXfffceQcZXnWlMuFdmyZTNnPni//zln7w1LseXRtuH2H/+Q+NBRwky4iiv1eNY210kwANNX3URCJSxC6n7rWxxHhGEmqdknrXE4N8DhiWuWONnw1dDzDlLlqUdA32bYJqds7IpNd3jDXBFUCJeJSJX8RseYqEPUahGeeJ/sf/gJ4oWnUEsLGCBSElGvEd9xJ52v3E9v705MvowK3ShgpMKKAIlCDLciWNdD32+NTDZtY1zrHnZobHDyEJGI8YB+3V1IJ95zojZJkGQwrHFCPKXUUBeG6MswhHE6Bdc1kdgWD2VM+vdG60HXRvq9Np1T4PwanC4hCSL6GQfTL6t83Aba4EopGJMYF5n+bZBSMj4+RiFfZsOGCbZu3USjUUMIwcVLU3x07iNOnHgTISOkMDg7akBapIRYW7ASqSTGxrz25qv8xl/9K/z93/5t/u7f/R98vf8zIpvNMrlhvROPSgHCIpOMXIzh0sgYV157mfrMVerrvZDQ41ktVv0voLWWVrtLc6nZb28DV3J2o2mTk3ui0hdCEiiZmM5A0ijIwE1waDAPQ94AH/u4v/lLnBjQin6G4JOnV8AYMG5Ajo4iTKeLuXiO8CcPU3n4ITKz02irMSqDHR+le/e99O67h97mLcSZPCoICMNsMkVRuMyGcafpdPwvSfAhgnSjdG2D/XS+sYPPJxtxevpPyyFpWj3VOcikb3/4JB6k7XJJQBCbgdZCWINO9AXpfUqzDcP3ZPhUP+zFAO70PZxx0Fq717LpuONPtm2C6yRxA48iZ+ok3DjcUqnE5IYNbNo4ychInWw2syK9v3PHVv7JD36HP/yjP+b3/tnvcfXSFFJKZ0CU/DxDGSYOh7GzHJaw3FrmD/6/P+B73/vrND7jscJrFSkF6yfWI4TzlIh1nMyNcNmwZqHCYrVE+8VnqH7r133ZxuNZJa6DYABmZuaAgdAtNftxhiS5pH8eFyEkFrpau0yBSOYPqKQWYFNbQVgRBKSbWiq6c2lvgdXuMdomg3os/b51ACENsTFuoFC7R6e7BLNXsI8/Q/VHPyJ34RLGauIgpLN5J/FXvoi56y5667dgMiEio8hKNyxJCAlCJZs6gw2cgRGSMRZhVSIKTKx2k0Bl5UlaJ6N2XTdF2j6JEKTxw+Dk7wYKpY9VyUYpEwW3krLfbqiEQFqLTmv1xkDyfRK3YevUHyEJBLRNDY2cWsD2A7fBmpWUyCGB5nDANQgm0oBIUKs3mJxcz6aNkzQaVbKZ7IpM0DBCCKqVMn/zv/tvufuuu/hf//f/gxdfeoFWs4mOY4yO+62kqTNk+pJTU1O8+sprfPW+L/ua9WeCYMvWzSiliOMo0eokmSChaXY7LO2+jdaf/Vt69z1AznsOeDyrwqoHA70oprnU/EQqutvtks26DcCmXQWkm5tEIJOaN24PSU+rcvAcP++Un35+0PqW9ron328/9jjtDIOiTg89N0vw/HNkf/RnZE6dRERdTLZA75bbiL76VaKjh4gaI4RBDhkEqCDou/vZdItO1PxpCUAMqR0GwYsLChAuaDDW9elLd6G4fELy9cR10MUug4AnvZfptbq+iUH3g0o8EvqZhfR+JfX1fpYEV2pI71/a3pdma5zHgQsUJII4dvdOBrL/ulI6PYTtd4EMOjZM4ikghKRULLBhwwQbJieo1ypks79Yv36gFAdv28/v//P/m9/53R/wL//wD2jOLxCTtpemP3dXsghUQK/X4/d+759x7PajVP043WuOEDCxfiKZ+kk/GHflGs3yYpNmocTUfJfqiXfIHjnqgzKPZxVY1WDAWkuz2cJag1RB/3NRFA3S1kKSZLxdzTmpe7tyQNLql5x+RX8QUGq4wwph4PDrpv/2N8qhk7pNxG467qHjHr2lRcwbr1P69z8m8/qLqN4yUaWOOHQH7V/5KvHeA8TlGiYMUSqDkoEz2VEq2fxdKcMFGCZJk7ochlubWtHJ0K9y2KQkgO2PUFaJuE7KQT+/xSSlBvf/w06C1g5yJX23PiFdPT4RGsokE2OMcZmAJBDon9hToaAUK4Or5JSX6gokwtkLKwUMWhXTa9JG99tAjTEEgaJWrzKxbowNG9ZRrZY/0ff/iyKEYHSkwf/8P/19KpUS/+QHP6C5uJDcM5lMkBxcm7WWp556imeffYH777/Pb0TXGCEEtVqVQAVoGSHtwBbcIui1OizOLrG4ey8zP/w31A7cRpjx9sQez+fNKgcDsNxs9lv2hBDEcUwcx+RyuUE6PzlIO0fitBTgEHJYy76yU8DCinT1cIZguHOA5LRssRgdE8cRkY7QiwuokyeRP/4zas88h+q2saN1oru/Su9Lv4LesROdLyECRSgl1gauJVIm5YhEmDfcw+9O+8nXh8R5LlxIuwbS9abTBZ0aP7kLSRCUXI+ULnPwsRN+X6GfXveQbW/aiSHSkkzqpYAb0gOpkj+9/8lY4+EyhQBSTwFjE+9H27eQNolQUfdbEt1r5jIZGvUaGyYnGBttUCwWCAJ1zTfhYrHA3/7+3+LcuXP863/1R0Td3oqOCJG0Zlg0vV6Xf/H7/4J77/0i+Xzumq7DA4VCgSAM6HZXfj5pfGVubp7e/j1cfejPGb9wjsa2HauzUI9nDbOqwYBOVOOp+5hJzHxSG9g0ky/lQG2fGuyIxDjHnYYNVg/q2Olp31kHJyduBqf+wcaTmvXYRGwW0e22iVpt1JmPyP/0QfJPP4pYXiDatI3uV76O/sJRzPpJbJhByAAZquSULhAmGatr3OyCfrkhbRkc0jKkdX4hBUa7NLlJhhwMXW4/m5CWG0ys+wKsYV3AMGkdPhVjueFOOrmXA4HWcJCklMIkjxnWWAzWK1YI/9J7GQjZH86Qtl06N8fUMEhQKORZNzbCxLoxRkbq5HM5lPrshWKVcpn/8e/9PV58/nnee++9fuvl8H2w1jVpPP/8C7z//gccOLDvM1/XWiMThsgkoIz1YAKlEgKrBIGCbrfH3LZdzD34I6q/+Xe8I6HH8zmzusGAdqdVOXSiBVBh4NoHE7tgYV1dXdtE/NdX1w8cBnW6T5nBhuva8VLv/iSz0NcKuDqysZpYG+JuTNSap3X+PNlHn6L62OOIzhJm3266936JaN9+9Og6VFhAZoKhrgfX828tffEcaWsgEkSQGPkMPBLcmmX/el32YKDo19pghHAbbbpaa/tqfIFAC+cjoLHIZLhP2qPv1jVkmpSuJt3ohz4GkimPoIIArWNgECSkwsZ0DR+v+Q/fa7d+yOdyNBo1GvUqjUaNSrlEJhOuilJ8y+bN/I2/9X3+4T/4X9DdDtrESanADs29iFlYnOeRh46zb98evxFdY8IwJJvN0lxeBBI9DAKpLGGgyFZKLM7Pkdu5k+kn/oyRX/vL1HybocfzubKqwcCw0C39/+EhL/1/k69J5ZwG+yr8JP1tSYbemNSRUPQ3MZso3webY5LCNgZhYnrdDqbdQV++gnrycRqPPkJBG+K/9AWa9/wlxJYdmEIWlQkJZYBSg1n2qdBgUFtP1iwkJMI+l3kY1iJItwlhkk6IQfuiSEWFQvS9EGxS05dpFoOkBW8oy5CWOEjWNZwtSL0AlFJJQDV4nExEgMYYTFrOSAKL4dce1h+Y5B6npQ+lJNlMhkqlxPjYSH/zD8NgVTb/jxMEil/75gP88R/+Ea+/8vKKLBQMyiEQ8+/+5E/4b/7Gf0295oWE15JsNku5XGFmZnpFZs4KiKwmU62iF5eIpORqfZKFR/6c8l/7ng/KPJ7PkVULBlwq2ym8+wdkIfpq9hXq/0SAZnUqqEsseknr4k4I1/fAV86YRybKfW10MjUv7WO3xL0O3U4bM3WJzLMvIR97lFyQRX/zG8wdPoxdP0GQKUGQcaK4IBl5KyTGDNT5xliiqI2xlkAqlApx+f705D007jhtAURgbFK3FsnwI3dTXKbBkozwTbIPyeukp1mtNVa5f5VbFLFNZwMMav19QeHQyT51HTTaoPtNDc5IKc06DHcS9LsRjCGQkjAIyOZz1KoVGo0K1WqFYiFPGIafS+r/l2FsdJRf++53eevNN8DGn9BXpN4NH354mlOnTnPH7YdXd8E3Gdlshlqt6vwd4tR8SqNjg5QhhWoNJQPaVxfI3HKEqZ/+mPqvfpfK6MhqL93jWTOsroAwVa8r1zGg1EBFPFy7BlyqPUnHk7QASiHR6P4f9BXGOEOnYylk4ncfE/V6xN0een4G/fKLZJ/+GUGlQvTXvsvSrj1QqqEyBcJsgJIhCNVX8vfb6ViZLpdKoUgmCvYZZDeEIGmrSzoi+ul2IA1phBMQikQg2Z81MLyRD6X5Rb/c4U7/isH45vR+mSQdnnYH9DMtSfYkvRYBqCS7YKGfRgdLGIYUC27zH2nUqFYrFAr5z0T091mhlOTrv/JV/ukPfpf52atJz3u8Ui9hDK12izfeeItjRw9eF1mNm4UwDMnnC0O/g4PfZSklkxs3o7Tm3DPPY0fqzGRyLLzwNKVvfNv/HDyez4lVDQacANB1EAgpQdqkfp3WpAeiOyFEcvIfiAHTtrZhYeDHXQRd6jvGxD06nTbRwiJL751EnT5FtZAl/i//Mr2JTahCERWEhEEWIQJnQAT9/uiV3QgmWZlGCCdmdKl/hTWiXz5wwYFzGMBKrHXud27McpohgL4SQDjDIG0NwZC4Dxgo+9N7Z12nhGZlWUAm96c/iri/7tThz/a7GKy1/THCSroxQ7lsjkIxR7VaplGrUioXyWXdOOkbZfP/eayfWMe+W27lZ888Och2DAVU4IKg5559lr/+X/2VZFS251oQBIpSsYTR7r3jOmLc73cQhGzavIVCPsfMmXMsXLpEfXIb0w89xPh93yCb/U/PjbDW0u1FzM4uMDs7x5bNk5RKhRv6d9XjWQ1WLRgQQhAGKknba8JsFtf/Pdzyl3YLpMI4NcgWiLSFf8h+d4jB6GE336CzvMjc+fPE01ep5EKCr91Hr1QmCLPkwhwqDBP7H9nfPK11HvupI2LfJ0CkqfNUfS8TQeBAKd3XASRpeUgzF6mzoQQrkEL1ffylHLqOpA7xcQ1Aeu/S7xvOUlg7MBOK9cp0uABkqAiVIpsMScpmQwqFHIV8jnw+RyGfJZvNEgTqpjuRlUsljh09ynPPPoPWFiGCpKMjGb+cZGFOnz5NFMU+GLiGCCHJZLJDnRyu5BbHMfl8nrGxUarVCjtvP8ybx2dZnFvETk+zY7n1c4MBay29KGbm6hwnTrzPK6+8zAcffMji4gJHjhziN3/ze5TLpc/1Gj2eG51VzQwEgUIpSRz3MFr3Ve7glPLOYW/Q/mWsG0ZkcCd+K9xJ2A6l7kVSUzc69elPNmckpcYYamIClckShlmEDBFK4jr3EhOfZJPViWBRmsTIKOk+EE7lR9rul5zb3RAlhlvWVs5EcDGBGEr9D4sQE4+BVJHfvx4+8Vzp2oRMrH37a7NYYYmMRklFJsyQC0NyuRylUpFyqUCxmCeXy/ZnQKT2vmvhFKWU5Nixoyjl7KAj67IkAhJhqru/ly9fZGZ2lqK3xb1mCCHIF/JOD2NIslGWIJCoICAIAjKZDAcOHaJYKvPMj37EbFfwyjvnWL+hx8RomXzOTTRcmF/k5MkPePnlV3nv3RPML8wSx51kxojliScf48CBfdx331duuoDW4/ksWdVgQCpJoZin3V5G69ip3RPrWyElCOe/L63r4zdJrdGYGCGT6Xsi3XyTk7JJN7ZUJa4RUlIsV6BYcX/yUwdAESSH9iEdwFDpQQ8Z8DjRoCY1Q06+4FoWRdouNejd/7jRkSsDqKFNPvVLoH8yTTMBInlurQ1pRDDITCRuhNZV+wOlyGRDioU8xWTDz+dy5LIZwiBAqYG98FpGCMHWrVvI5/MsmybEJgkUVXKL3T2am5tj6vIVNm/auNpLvmkQAqRKp21KV5KSyf1PBMNxHHNleo7XT03z0uIEJ9pj/NP/9y1s+D7bRwt8bWeecP59PvjwFFNXLhD1IoQIMNaV/9rtFrE2ZLM5Tpw4wb333uOzOx7PL8DqagaAxkiDmZkZtI6JoniFG6FNe93TqXpSEhuNsTFoV6vXSRe9SIxvTCIsdPV4m2z6LjNAIspjSGlPsrmK1JFOWPp+eh9rrXMCRg1S9Dfs9MSfDsIhfV4GSnwnPDSJA7ArFVjj1u+mF9pEWSCT2r67XpkECCqQ5PIZ8vk8mTAgCAPyhTz5XJZcLkMmDJOa/to45f+ylMtlavUGy8vLpAJPm2QIUkFbt9Nhenp6dRd6ExIolXTKDMpoWMnszCI/fvQlzs1IHnv7KidnllnSAVFQhCCHkllmLksuzMwyPvcm1fgKJtZ0Om06nQ5R1CUMFYVyiUq9Tq1Wo1KvfUzM6/F4/nOsbjAgBKVikbHxdczOzIJIxUWQptEFAoRO9AECk+gFBAJjkrZC407sqc5AG7Ai7buXKHfkHtIbGJeKt26Dd4Z+Gms1VrvXEjJD2kA4MNRRK3rs0+fsiwGHBX5ikIK3gE5S+rLfDin6gQgCAiRKCIJMiAoVxWKBUiFHqVggn88ShIE7UQ09r+cXo1qrsmXzVs6fP0f687LWJOUS228Znb4y3R+o47k2pMGqVJI47iKlQGvLwvwC/+c//ue0ynuIRnaiiw3iWGJFiLUBUmSJgcs2h8jfSnz2DK3mDEpBrVFjfX2CfLGICFzb66bJjXzx7rsJAu9R4PH8Iqz61EIlBRs3TFAplZi+Okun26XbM4mUL0Gk43Sdva227jSdBgxpK2GquJdiuC1xUHuHpK88baJLTuTOsMfV4nvdHkEQolwuoS9C7D820QNabOJomGYCnJGRu6aBu59bPwgpCISzWQ7DgDBQBGFAJgzJ57Lk81nyuSyZTIgK1IqN33NtyOdybJhc32+dTLUbDpflkVLx4ZkzPhi4xmRzWYzRfU2OayuEOG4z8+4zqPybiN33E+/4MuTyCK3QMbRip4/pxDGl7CgjuRJbxopUqiVM+h6Rgp1bt3P3nXdyYP9+arWqf+94PL8gqx4MgBN31WplSuUinW7E0tIyrXaXXrubKOOTE7hM++glVkhnkINI1MnKBQRi0IuPdj4GZqjlEJKSAST1+LQP3yJFhjAbDG8PSfp+kB1woYTqtzcCiahQEGZCpJJ9pX4QBmSzGcJk089mQwLlDIykFCtKIp7PHikFk5OTKCkxUiZeA+nvgkg6QjQffPABWhuC6+LdcWNjrZtCGkddjNWpzSbg5nIoJYjiDvFyRPHMcQoTW2g2DhBrgZESo00SXAu6skBl4x7y5gqx1Yw1Ghw7coTbjxxm44YNK9xLPR7PL8Z18+cubTUMA0WpkHW1c22Iophut0sURXS7PaI4phdpVwowBpv0LlsLxmj62QLo19D7PgRS9lP7UrqJaW44j0jq92CkGxMcJB4BgXSTCKV0gkQVSIIwJAwVmcD9G4ah64wIJEq5Dgkp0sl4PqV/vSClZOfOHYRhSJwIVQcMPp6bnftEq6rnF8Nay5WpK/zsZ8/xxBNP8ac//CG9Xo8gCFxHR3Lv059DHEe0FqYoXXiJsLKL2GYQQpEPJNgIg0HZmGyoOLB9L1+592727N5FqVT07y+P5xpw3QQDw6SufVIqJw4qZIfEeTAs1DPGuHq8SQMCJwgz1jiLYteLiEmNgkzS/y9lojOQQ68n+8NrlJKfOL0P/lu5Vs+NgRBJZkAFyUhmN/RKyrTs5MpWC4sLRFHkxxl/Ci5fnuL7f/Nv88ILz9HutgGSzpbBey6OY6IoAtx7r9tpw8VT5LYvEGfqGOPstjPCIm3EeLbH977zVb5010EK+fwqXp3Hc/NxXQYDP4+BcA4GdV4ALxTy/MUplYpubLRxPhEiafNUQVKuMdBudej2equ91BsOay3dbpepqWn+9E//A4vL89RHqoTNgHa77fw/jFkRzKcBtpSSIFDkRQ9ll7CmiLKGwGoCqxnNa37rV3fy1S8eJhOGq3ylHs/Nxw0TDHg814JMmHE6E6EAjUX3tSPpRMZOZ5lOu7PaS70hsNbS6/WYmrrKK6+8xlNPPsmpU6dodVuU61XqIw2uXrnK+++fIlpe7pdf3OYfIqVCSOlKcUohcyUi00PGS1R0jxqGWyfzfP/Xj/KFgzsJvJDD4/lM8O8sz5oim8tSKBRpLi27WRfJqGkwKCXRRtPpduh02qu91OsWFwBETE1d4bVXXuOJJ5/m7XfeReuISqVAsZQlX8zQXG4yuzDL0mKz/7hUM+AyfTL5WCJVQC5fIKiPkzeLrCsajuwY51v3HuTgLdsp5LO+JOfxfIb4YMCzpijk84yMjHB1+ioyHQOd2BFb63QqvV6Xni8TrMB1BcRMT1/ljdff5slHH+f1118jshHlSoVao4rRMUtLi0xNTdPptgmUolAssXnzFtqdZXrdbqLHcQFBEIQoFVAsFVk/uYnDR49w4NAxbtm7m60b11MrF1HKlwE9ns8DHwx41hTFYpFarTbkTSGcaZVww6akkhhjiWO92ktddZzKXzM7O8+bb7zD8eOP8Oqrr9KNehTLBcqjFZSAVqvF9KUZpwswhnK5zMTEOMVSGZDMzsyyvNRCqYAwzJDJZCgWi2zauo3b7zjGl+/5Evv23UKjXr+hRmN7PDcTPhjwrCmy2QwjIyOfMJJK5j8SRxqjWdOthXGsWVhY5O23T/LQQ8d55eUX6HRalEoFGiMVpBIsNptcmZqm22pirCVTyNKYGKNcraJUANrSbreZnZnj7AdniKOIkdFxtmzZwhe/eDdf/vK97N27h2q16nUAHs91gH8XetYUUkrKpSJSggoUpmeciBAIAoitm6i30oPg5kdrzcLCEu+eOMUjDx/nuWefZ3Zpjkq9zEijTl3WWW42mZ6eodlcAmHI5wo0xsapVKsQhsQ6RmuIehGFYo6tGzZw75e+RKFYpFQps3P7NjZv3kytWvXpf4/nOsMHA541hRCCQrHYH0IllXI2ucmUyiBQ6KH2t5sZrQ3Ly8ucOvkBDz38KE899TgzM9PUR0eojdeprSvTaXaZuTLN0uIiUdyjUMhTHxmh1hghE4ZIXDeB0ZrReoMD+/dz6y172bZtK416nVw22/fu8Hg81y8+GPCsOTKZ0J3+0QihE1FbelJ1bYc361wCrQ2tVosPT5/l4Yce5vEnnmB2bpZKtUx1pM7I+hFMFDE/O8vC3AKtTodsIeeCg0qNTD6HFQodueFbGyfXs3vPbvbv38+2bVuplMv+1O/x3ID4YMCz5iiVin1NQGp4k37sZhKom2pDM8awvNzm9OkPOX78SZ588mkuX75IuZpnbN0IY5MjtJc7LDWbjBbGGGs0sLEGodhYq6Kyrq2v1+tirGX96AhHDh3m6NHDbNo4STY5/Xs8nhsXHwx41hRCCIrlct/5zlo3pEopkg3Ntb7d6KI2YwztdoezZ89z/PgTPPLwcS5fPk+5WqE62uCWQ7sRFpQQZDIZlhaWKOVzbJxYBxY2bJiksLhIO44gNoyOjnL0yCHuOHaMTRsnyefzPvXv8dxE3Nh/8TyeX4JSqUQQBGjthIMuKEhmXWhnjBPegJa3gwDgHE889iSPHn+Uc+fOUSoXGR0f4baD++jFMa3lZYphBmst4+NjCCkZbYwgECwtNYmiiObSEvlSkSPHjnL3HXeyY8d2SqWSDwA8npsUHwx41hyZTAbADbTSzh+fpINACEkYhmST77neMca18J05c4HHHn2Chx99hMuXLlIqFVi3bozbjhxIUvwR2Vye8++fQMeaPbt3u0EfVmI1gKTb0xip2L5zC7cfPcLevXuo12s3VcnE4/H8fHww4FlzDOrbFqmSk661gJtIGQQBmez1GwwYY+h2e5z76CKPP/YMDx9/lA/PnKJYLdIYq3HrgX2EMkBICMOAQqHA0tISY2OjFPIHk1HCGYy1dLoRSkrWTazj1v372LNnN+vGxm7IzIjH4/nl8cGAZ80hxPDEvKSNcCj7XSwVrrtgwBhLp9Pl/LmLPPX0szz40IOc/PA0xXyeyfVjHDlyC8ZKtIVLFy8zsW4dExPr6PW6tFotyuUyRhtKpSrdTpd2u0e9XuOOO/Zx8OABJtat8wGAx7OG8cGAZ80RJVbDqZdA2lEgpbMiLhYL18WYXGuTAOD8JZ55+ll+/JMHOf3hh2QLIRMbxjl85AChUizNLXD6/TNEUZc77riDXrfD2MQozVYTYQezAKauXGFy/SSHjxzm4MEDbNq0kWzWDwDyeDw+GPCsQbQ2pJZC1tp+2cC1Gwry+fyq1cmttXS7PS5euMzTzzzLTx98kPffP02xkKUyNsJtxw6QVSHNhXkunjnLpo2b6PZ6REYzvm6CcqlMqVyh2W4TBgHZTI5MJmDHjh0cPnyQbVu3USwWfADg8XhW4IMBz5qj0+sSaQNIhHB+A9Y64yFjoFyqfK598+lI4EuXrvCzZ57jwYce4tSp9wgzitHxdRw+ehCBZXZmhqmPPmLPnj0sWU2706Zar5ErV6iOjVGvVBEixNqYrArYsmMntx87lnQCFL0XgMfj+Y/igwHPmkPHMUoqDHHSVmiR0v0bBiEbJic/843TTQSMuXp1jpdffp2f/OSnvPHma6hAMr5ulAOH9yEJaC42OXv6Q5YW54njmJHxMRqNBtVqjZHGGJVyjatXr2KjGAUUykXuvP0Ye/fupl6v3/B+CR6P5/PB/6XwrCmstTQXl8BahJRgTKIZcGUCYzWTkxs+k2CgPxJ4bp5XX36dB3/yCG+88SpWQGPdGHtuuxUhJQvzc5w9e56luQXanS65XI7R8VHGxkbZsGGS5eVlCvkiCEV7uc34SIP7v3YfBw8fYt34OJnM6usdPB7PjYUPBjxrjuZSE2MM1qQlApuMMhYIuKbBQBoAzM8v8Nprb/HQg4/w6msv0+l1GZsYY+ete5AqZHlxialzl7hyZYqo1yWTCag16mwfGadYLBIEilKpxPJyi+bSErnJHPfccxcHb7uNjd4R0OPxfEp8MOBZc/S6XaRQGGEQwo3vtVYghJuuV6vVPtXGaq0l1pr5uUVee+1Njh8/zgsvvUi712XdunG27d5MmC3QWmpx8fwlZmanabfbhGHAyOgIoyOj5AsFLCAtxFGMtYIwE7J3zy4OHjrIju07KJe9I6DH47k2+GDAs6aw1tLudDDaYIzF2jj9itMMqIBSsfRLPbfWmoX5Rd56+11++tBxnn3uBTrdJhPrR9ixezP5XJHmUovpi3PMzZ2m214kDBSNxij17VvJFAoEKnCugp0O2hhq1QqHDx/i6JHD7Nq1g3Kp5B0BPR7PNccHA541hcVlBiwGpSRR5JoMlXJZgUwuT7VW/Qs/X5wGAG+9x/Hjx3n+uedYbi1Qb1TZsXMj+VyBXrfLzNWrvDv1Hq3lFvlsjnK1zMSmPZTLZTIqoNfr0W13aesW42NjfOGu2zl25AibN22iXCr6AMDj8Xym+GDAs6awxtDtdpFSYIweGmHs7IjrjTojoyP/yefQWrO42OSdd97j+COP8fTTT7PcbtKo19m6fTO5XEin02V6eobZuVMsN5ugJMVKmb1btlAuVtDCEmvDcqdLx7SYGF/H0SOHOXToIBs3baRYKPhWQI/H87nhgwHPmiLWmqWlJcCVDFLxoLWuo6BarVEuFT/xOK0NzeYyJ0+e5tFHnuSJJx5nvjlDfbTGpu3rqZZrdNoRczMzvD89xeLSItZq8tUSm3Zto1yuEGYymNiw3O5ipWC0UePggdu44+hhtm7ZTLFY9BoAj8ezKvhgwLOmiHoRi80m1lrnRGgFUiqMsQgU2TCk3e6Qz+edBmBxidPvf8hjxx/nqaeeYmZmlsZ4g/VbJthZ3kW322V+ZpaPTr/B4tIiAkOlUmHXzm0UyzWMUiCcq2C3s8xoo8EX7ryTO44eZcfWLT4A8Hg81wXCpgbtHs8a4MqVqzzwre9w+tR7aK2J4xilFAKFlJJiPs89X7qX7/4Xv87b75zg2WefY3HxKvWxUSbWb6RaqdCLY2amr7IwO8Ps7AyxianVqzTqDUZqDcJMBqtjOr2YTq9Ho17n1lv3cezoEfbs2kmpVPIlAI/Hc13hgwHPNWd4AND1xvnzF/na177B5csXiOMYY2KCIECI0LkSGk0QBoSZLNt372bLtq2MNepEccz01VnmZ2aZmbmCEFAol6k2atRqdQqFIkZrbKTRsaZcKrB7127uuP0Yu3fvolIp+wDA4/Fct/gygeeakE7Ym52bo9PtYbGMj4ysuie+tRZjLTqOmZ9f5OGHH2Nm9moyvtjNI7DWopRFKksQuta+bqdNVimIY957+wTz8/P04h75cpHN27bQqNfIZYsYA1EcEbXaFIsl9h+6lduPHmH71q2Uy74N0OPx3Bj4zIDnmjA3v8BH5y4gAG0si4vzfHT2LFu3bOPo0UPk87nPPFOQigGXWx3OX57jrbNXOTffptnucealx3nzmUc5e+Y03V4LpdwsAjebwCCl6yqI4xitNVobyuUqGyY3UKtVaYyOUm+MosIQEqMi3evRqNY4eNsBDh85xNYtTgPgMwAej+dGw2cGPJ8arTWXL0+DhUw2izGaXDaLMYbf+Uf/iD17buGbDzzAoUP7KZWu/fjcXi/i4pU53jl5kRfPXOWVs4t8OG+Y64UsyQyGiHVLOZbm54lMTD6XQ8hkQ0/cB7XWGGP6wQAItI7Zs3s3o2NjxNYSxYZ2q02hkOfgrbdy1x23s2f3Lq8B8Hg8Nzw+GPB8arrdHu1Wq59yF0LS6/Xo9Xpk8orX3nyJt956k1079/Cd73ybI0cOUKl8eitdYwynz17m9//8JZ45OcNCnGVa5enYHFkdo6wmb1pIBXFlG2rzUVh4kNhobBxhktkEbvMHIUBKRRAEBEFILptHa8vycotMIc/hwwf5wh23s2PbNl8C8Hg8NxU+GPB8aowxdHtdwjCDsZZer8fS0hJTV6bYsHEj5doSCzMzvP3uS5w69S47tu/h2995gGPHDlOp/HKnamst7314kd/+f57irWaW5XCSbpghNgJpDUb0oNciMJZMp0shjKlu2oW88CrtmYvEejC+OAgUxjgXQjfy130ul8+yfv16vvmtBzhy5BCNRgPlMwAej+cmxAcDnk9NNpshl83RaXfQStHtdrl8aYrlVocgU6BSy1Iu1qg1Fpwpzwcn+N3fPc3mzdv48pe/xF13HWV83SiZMETKv1i2IIpi/s2Pn+ad9y8wO7qXTnEEGWShF2N7PSKZJVZZjI6ohy2yrUtERtDNl4l0hLUWKWVfRCilIAxDAiVRgaJULvGr3/hVfuu3fot169Zdl50RHo/Hc63wAkLPp8Zay+XL05x87xRzc3OcP3+eC5cuoQVo0jZDl46Pej067TZLCwu0lhaJIk0hX2LTpu3sP3CAfft2s2HDOsrlEtlsiEBgjCWKIrrdHs2lZaamrvD8Cy/yb3/4IFNtS2/HV1jY/kVMtoiJLHG7i7I4MaOOkbZFsXmJMvPw4VMsvfUoMrEfFsImwYAiDDNUKxUOHTnMX/2N3+Dee+6hWCys5q31eDyezwWfGfB8aoQQjI+PkMkEnD9/geXWMpeuTrOwuIAKFGEQIpUkUAFkBEEmS7lSJeo2WViYY2l+kROnXuX1t1+i19Vkggy5bJZisYTRmiiK6XZ7LDeb9OI2cRyTzYUUy2X2rR9hXlzhreVZRLaCFgZkgLACYg1GYSkgc2MokUWVxiDIoKxFCEAYisUCGyc38/X7v8437r+fvXv3UijkfTbA4/GsGXxmwHNNsdYSx5r5hXneeusdnn3xeS5PXUEKRRCGGGsQUmKNAeM25G63RbvTprm06DZwK4jimCiKaC436XZ7YF19P5MNUEoRSEVsILKSViy5tOXrzGz8EpgMutMjRBDEGqwlUAKplymoLrkzD7H0+p9QyefYtm0rd911J1++914O7N9PvV73okCPx7Mm8cGA5zPDWEur1eLEyZM89fTPOHP2LFYIVBA4IZ5xnQdCgDaauNel3Vym2WrSarfodbt0Ox2iKMZo69r/bAxCoJAgFblimSCT5WxmJ5e3fgsdVuh0NNJYRByTsYJQGpBtlF1m64VH+fbt6/jOtx5gx7Zt3hfA4/F48MGA53PAJh0G5y9c4LnnX+Sl19+g1+2ipELJkCAMscYCMcZojLDEOkLHmrjXI9a6P2EQQAqJUqGbLWAszVaLMzMwtfObLJcmafck1ghspAmsANslEC1uLS3wf31nN/fcdZgw9BUyj8fjSfHBgOdzRWvN7Nw87544wUuvvMK5c5fodjVKSazUCCmw1m34QoCOXCZAkMw8sIp2p02302FhcZG52Vk67RYtnad523dYqu2grfNgJLbXI9tbZjyreWB/ib/z7f3s3Lwe6bUAHo/HswIfDHhWBactiFlqLnP+/AVOf/Ahp8+e5cr0FZrLLXQUoQ1YbTEYjI7R2mCsZHFxgTjuUa6UyOZytDs9eiLLxdJeZtQGmq2QojLcs63Arx2b4Au3bWZitErg9QAej8fzc/HBgOe6wFqLNoZet0u706HVatNqtej1IrSOiWONNppMJkuhkKdSKVPIO2vj5VaLN949ydtnplG5CpPjoxzZu5GNY1Uyvhzg8Xg8/1l8MOC5abieRyd7PB7P9Yw/NnluGnwQ4PF4PL8cvqfK4/F4PJ41jg8GPB6Px+NZ4/hgwOPxeDyeNY4PBjwej8fjWeP4YMDj8Xg8njWODwY8Ho/H41nj+GDA4/F4PJ41jg8GPB6Px+NZ4/hgwOPxeDyeNY4PBjwej8fjWeP4YMDj8Xg8njWODwY8Ho/H41nj+GDA4/F4PJ41jg8GPB6Px+NZ4/hgwOPxeDyeNY4PBjwej8fjWeP4YMDj8Xg8njWODwY8Ho/H41nj+GDA4/F4PJ41jg8GPB6Px+NZ4/z/OmDoUAFZGn4AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Use huggingFace liberary"
      ],
      "metadata": {
        "id": "aW8ZLXiVGV2G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install timm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "tKqai4jXBKGD",
        "outputId": "2f722309-79df-4d48-eeff-07c7cff69292"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: timm in /usr/local/lib/python3.10/dist-packages (1.0.8)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from timm) (2.0.1+cu118)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (from timm) (0.15.2+cu118)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from timm) (6.0.2)\n",
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (from timm) (0.23.5)\n",
            "Requirement already satisfied: safetensors in /usr/local/lib/python3.10/dist-packages (from timm) (0.4.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub->timm) (3.15.4)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub->timm) (2024.6.1)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub->timm) (24.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub->timm) (2.32.3)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub->timm) (4.66.5)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub->timm) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->timm) (1.13.2)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch->timm) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->timm) (3.1.4)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch->timm) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch->timm) (3.30.2)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch->timm) (18.1.8)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision->timm) (1.26.4)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision->timm) (9.4.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->timm) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub->timm) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub->timm) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub->timm) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub->timm) (2024.7.4)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->timm) (1.3.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers\n",
        "from transformers import pipeline"
      ],
      "metadata": {
        "collapsed": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4lL8NYzOB9WR",
        "outputId": "4bfb1845-d1ae-471b-86b1-fcd758f61553"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.42.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.15.4)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.23.5)\n",
            "Requirement already satisfied: numpy<2.0,>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.5.15)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.4)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.5)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (2024.6.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.7.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model = pipeline(\"TASK TYPE (like classification, segmentation)\", \"Name of the model\")\n",
        "\n",
        "model = pipeline(\"image-segmentation\", \"openmmlab/upernet-convnext-small\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "6c917ff0f5b24b53825c254af300d4e8",
            "0426d1c27ae5401e86f72909a29a2e54",
            "da94d166af274587b85a047f8e3b820c",
            "286db7ad53dc49b485a261b260448182",
            "4dc0df943c1f4be8b29b2501324c028e",
            "ca1b9009e0164e299138683e7ea968a4",
            "efeeaf78ed364bd29c0e247de4c95dda",
            "ec87646e8267475eaae6e7bc9b3a1624",
            "b42f867c2fa445db82efc6e8cafd08a5",
            "a1af32d2db7749518c4d1db0f3bbf650",
            "db5ce05be7e346a1b4cf7de0aaa46025",
            "7e88f66284a240c88c321b6abadc6bfb",
            "e36f3bfafa374f88bc90fa6958775b16",
            "beb6e199d41f498f95888958636118d7",
            "d40023dc44544dc5bdef3ebdf41032b1",
            "b10aa25dabd04e5297beab37827dbddc",
            "629cb05c0c59423da500c9066a8fdeff",
            "32aca6fe341944beaca0cc6863f6847c",
            "371bf3f8468849a7815d4742ad4c7360",
            "985a12ca5a2146738e99d8f92966a3a3",
            "905b2c23024a49d8a03f32afe4fa2108",
            "a0cbc740a57547d5ba26af4bc3ad4b03",
            "fd6bccbf21e14509bb38a0892b02df01",
            "b4f4b877539d42a4886bb354a20f505b",
            "d0b64769a3bf4b8296ab1c1bcac13658",
            "a290d684a260408f96f67d0c9aa5f773",
            "97377a04b9464d2f850490793c36d6d4",
            "f9c7acea74f24dcba3cec5be2186d281",
            "b14423fdfc0c4e0384b345e454bb477f",
            "bacf33e82c4948d5bc7db9a44040df57",
            "1c40d6b2c69747a2a10c704555263f27",
            "ff16f8e2f51e4083ad394ae5ea1db6ef",
            "0f41a3a4f237449284677cc5d0a80049"
          ]
        },
        "id": "syLsRbByCK8v",
        "outputId": "79624cb1-d9aa-4ce0-a2f2-08360f33acef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/8.76k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6c917ff0f5b24b53825c254af300d4e8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/328M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7e88f66284a240c88c321b6abadc6bfb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/372 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fd6bccbf21e14509bb38a0892b02df01"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "res = model(\"/content/BiRefNet/Helicopter-HR.jpg\")"
      ],
      "metadata": {
        "id": "LbHyl4SPBmCB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "res"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ZoRKOWiBxwd",
        "outputId": "01c8f7be-ec37-452b-ad9b-11cf4ff8e94a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'score': None,\n",
              "  'label': 'wall',\n",
              "  'mask': <PIL.Image.Image image mode=L size=3577x2163>},\n",
              " {'score': None,\n",
              "  'label': 'floor',\n",
              "  'mask': <PIL.Image.Image image mode=L size=3577x2163>},\n",
              " {'score': None,\n",
              "  'label': 'person',\n",
              "  'mask': <PIL.Image.Image image mode=L size=3577x2163>},\n",
              " {'score': None,\n",
              "  'label': 'airplane',\n",
              "  'mask': <PIL.Image.Image image mode=L size=3577x2163>},\n",
              " {'score': None,\n",
              "  'label': 'bag',\n",
              "  'mask': <PIL.Image.Image image mode=L size=3577x2163>},\n",
              " {'score': None,\n",
              "  'label': 'minibike',\n",
              "  'mask': <PIL.Image.Image image mode=L size=3577x2163>},\n",
              " {'score': None,\n",
              "  'label': 'fan',\n",
              "  'mask': <PIL.Image.Image image mode=L size=3577x2163>}]"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "res[3]['mask']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 690
        },
        "id": "zUANqQ3YD2gl",
        "outputId": "751df59c-a6ef-4716-db35-ac77b8db289d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=L size=3577x2163>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.axis(\"off\")\n",
        "plt.imshow(res[5]['mask'])\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "NpZD_w3_Dh8p",
        "outputId": "744118ad-c306-4405-a8b2-1df0d2cf91e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MC-uRCalEbEl"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}