Working_pytorch_instance_imseg_ccrop.ipynb

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      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ncsu-landscape-dynamics/LAMP_assay_automation/blob/main/Working_pytorch_instance_imseg_ccrop.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "thyNLvhjnN4e",
        "outputId": "8f63b8cf-9a3b-4a9c-d85b-48f6dff681bb"
      },
      "source": [
        "# https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html\n",
        "\n",
        "import os\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.utils.data\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)\n",
        "\n",
        "class four_chs(torch.utils.data.Dataset):\n",
        "    def __init__(self, root, transforms=None, target_transform=None):\n",
        "        self.root = root\n",
        "        self.transforms = transforms\n",
        "        self.target_transform = target_transform\n",
        "        # load all image files, sorting them to\n",
        "        # ensure that they are aligned\n",
        "        self.imgs = list(sorted(os.listdir(os.path.join(root, \"imgs_centercropped\"))))\n",
        "        self.masks = list(sorted(os.listdir(os.path.join(root, \"masks_centercropped\"))))\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        # load images ad masks\n",
        "        img_path = os.path.join(self.root, \"imgs_centercropped\", self.imgs[idx])\n",
        "        mask_path = os.path.join(self.root, \"masks_centercropped\", self.masks[idx])\n",
        "        img = Image.open(img_path).convert(\"RGB\")\n",
        "        # note that we haven't converted the mask to RGB,\n",
        "        # because each color corresponds to a different instance\n",
        "        # with 0 being background\n",
        "        mask = Image.open(mask_path)\n",
        "\n",
        "        # Convert from image object to array\n",
        "        mask = np.array(mask)\n",
        "        \n",
        "        obj_ids = np.unique(mask)\n",
        "        # first is background, other values are noise, removed them\n",
        "        obj_ids = obj_ids[-4:]\n",
        "\n",
        "        # split the color-encoded mask into a set\n",
        "        # of binary masks\n",
        "        masks = mask == obj_ids[:, None, None]\n",
        "\n",
        "        # get bounding box coordinates for each mask\n",
        "        num_objs = len(obj_ids)\n",
        "        boxes = []\n",
        "        for i in range(num_objs):\n",
        "            pos = np.where(masks[i])\n",
        "            xmin = np.min(pos[1])\n",
        "            xmax = np.max(pos[1])\n",
        "            ymin = np.min(pos[0])\n",
        "            ymax = np.max(pos[0])\n",
        "            boxes.append([xmin, ymin, xmax, ymax])\n",
        "\n",
        "        boxes = torch.as_tensor(boxes, dtype=torch.float32)\n",
        "        # there is only one class\n",
        "        labels = torch.ones((num_objs,), dtype=torch.int64)\n",
        "        masks = torch.as_tensor(masks, dtype=torch.uint8)\n",
        "\n",
        "        image_id = torch.tensor([idx])\n",
        "        area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])\n",
        "        # suppose all instances are not crowd\n",
        "        iscrowd = torch.zeros((num_objs,), dtype=torch.int64)\n",
        "\n",
        "        target = {}\n",
        "        target[\"boxes\"] = boxes\n",
        "        target[\"labels\"] = labels\n",
        "        target[\"masks\"] = masks\n",
        "        target[\"image_id\"] = image_id\n",
        "        target[\"area\"] = area\n",
        "        target[\"iscrowd\"] = iscrowd\n",
        "\n",
        "        if self.transforms is not None:\n",
        "            img, target = self.transforms(img, target)\n",
        "\n",
        "        return img, target\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.imgs)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ASZUhOClbBgs",
        "colab": {
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        },
        "outputId": "42530e29-7cda-42b4-988c-4238516877eb"
      },
      "source": [
        "#os.chdir('drive/MyDrive/Colab Notebooks')\n",
        "\n",
        "%%shell\n",
        "\n",
        "# Download TorchVision repo to use some files from\n",
        "# references/detection\n",
        "git clone https://github.com/pytorch/vision.git\n",
        "cd vision\n",
        "git checkout v0.3.0\n",
        "\n",
        "cp references/detection/utils.py ../\n",
        "cp references/detection/transforms.py ../\n",
        "cp references/detection/coco_eval.py ../\n",
        "cp references/detection/engine.py ../\n",
        "cp references/detection/coco_utils.py ../"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'vision'...\n",
            "remote: Enumerating objects: 64548, done.\u001b[K\n",
            "remote: Counting objects: 100% (4328/4328), done.\u001b[K\n",
            "remote: Compressing objects: 100% (883/883), done.\u001b[K\n",
            "remote: Total 64548 (delta 3823), reused 3764 (delta 3423), pack-reused 60220\u001b[K\n",
            "Receiving objects: 100% (64548/64548), 125.92 MiB | 23.19 MiB/s, done.\n",
            "Resolving deltas: 100% (52584/52584), done.\n",
            "Note: checking out 'v0.3.0'.\n",
            "\n",
            "You are in 'detached HEAD' state. You can look around, make experimental\n",
            "changes and commit them, and you can discard any commits you make in this\n",
            "state without impacting any branches by performing another checkout.\n",
            "\n",
            "If you want to create a new branch to retain commits you create, you may\n",
            "do so (now or later) by using -b with the checkout command again. Example:\n",
            "\n",
            "  git checkout -b <new-branch-name>\n",
            "\n",
            "HEAD is now at be376084 version check against PyTorch's CUDA version\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1tX1QjxcShEu"
      },
      "source": [
        "# testing\n",
        "#dataset0 = four_chs(root=\"/content/drive/MyDrive/APHIS Farm Bill (2020Milestones)/Protocols/For John/images/New set for John/collection/four_chambers\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uC00k1lQyZ7v"
      },
      "source": [
        "# testing\n",
        "#dataset0[0]"
      ],
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "import torchvision\n",
        "from torchvision.models.detection.faster_rcnn import FastRCNNPredictor\n",
        " \n",
        "# load a model pre-trained pre-trained on COCO\n",
        "model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)\n",
        " \n",
        "# replace the classifier with a new one, that has\n",
        "# num_classes which is user-defined\n",
        "num_classes = 2  # 1 class (person) + background\n",
        "# get number of input features for the classifier\n",
        "in_features = model.roi_heads.box_predictor.cls_score.in_features\n",
        "# replace the pre-trained head with a new one\n",
        "model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes) "
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth\" to /root/.cache/torch/hub/checkpoints/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth\n"
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      "metadata": {
        "id": "iBUwqXo4MpAw"
      },
      "source": [
        "import torchvision\n",
        "from torchvision.models.detection.faster_rcnn import FastRCNNPredictor\n",
        "from torchvision.models.detection.mask_rcnn import MaskRCNNPredictor\n",
        "\n",
        "\n",
        "def get_model_instance_segmentation(num_classes):\n",
        "    # load an instance segmentation model pre-trained pre-trained on COCO\n",
        "    model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True)\n",
        "\n",
        "    # get number of input features for the classifier\n",
        "    in_features = model.roi_heads.box_predictor.cls_score.in_features\n",
        "    # replace the pre-trained head with a new one\n",
        "    model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)\n",
        "\n",
        "    # now get the number of input features for the mask classifier\n",
        "    in_features_mask = model.roi_heads.mask_predictor.conv5_mask.in_channels\n",
        "    hidden_layer = 256\n",
        "    # and replace the mask predictor with a new one\n",
        "    model.roi_heads.mask_predictor = MaskRCNNPredictor(in_features_mask,\n",
        "                                                       hidden_layer,\n",
        "                                                       num_classes)\n",
        "\n",
        "    return model"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TP1ebz8PUi7J"
      },
      "source": [
        "from engine import train_one_epoch, evaluate\n",
        "import utils\n",
        "import transforms as T\n",
        "\n",
        "\n",
        "def get_transform(train):\n",
        "    transforms = []\n",
        "    # converts the image, a PIL image, into a PyTorch Tensor\n",
        "    transforms.append(T.ToTensor())\n",
        "    if train:\n",
        "        # during training, randomly flip the training images\n",
        "        # and ground-truth for data augmentation\n",
        "        transforms.append(T.RandomHorizontalFlip(0.5))\n",
        "    return T.Compose(transforms)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T4BoIh1gz6xk"
      },
      "source": [
        "# use our dataset and defined transformations\n",
        "dataset = four_chs(root = '/content/drive/MyDrive/APHIS Farm Bill (2020Milestones)/Protocols/For John/images/New set for John/collection/four_chambers', transforms = get_transform(train=True))\n",
        "dataset_test = four_chs(root = '/content/drive/MyDrive/APHIS Farm Bill (2020Milestones)/Protocols/For John/images/New set for John/collection/four_chambers', transforms = get_transform(train=False))\n",
        "\n",
        "# split the dataset in train and test set\n",
        "torch.manual_seed(1)\n",
        "indices = torch.randperm(len(dataset)).tolist()\n",
        "dataset = torch.utils.data.Subset(dataset, indices[:-4])\n",
        "dataset_test = torch.utils.data.Subset(dataset_test, indices[-4:])\n",
        "\n",
        "# define training and validation data loaders\n",
        "data_loader = torch.utils.data.DataLoader(\n",
        "    dataset, batch_size=1, shuffle=True, num_workers=2,\n",
        "    collate_fn=utils.collate_fn)\n",
        "\n",
        "data_loader_test = torch.utils.data.DataLoader(\n",
        "    dataset_test, batch_size=1, shuffle=False, num_workers=2,\n",
        "    collate_fn=utils.collate_fn)\n",
        "\n",
        "## Next few lines are brief examples of this cell and next. They appear in the\n",
        "## tutorial. They are optional given that they are covered elsewhere. Test says:\n",
        "## \"Testing forward() method\n",
        "## Before iterating over the dataset, it’s good to see what the model expects \n",
        "## during training and inference time on sample data.\"\"\n",
        "## For Training\n",
        "#images,targets = next(iter(data_loader))\n",
        "#images = list(image for image in images)\n",
        "#targets = [{k: v for k, v in t.items()} for t in targets]\n",
        "#output = model(images,targets)   # Returns losses and detections\n",
        "## For inference\n",
        "#model.eval()\n",
        "#x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]\n",
        "#predictions = model(x)  "
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c04spXqVeu-8"
      },
      "outputs": [],
      "source": [
        "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")#, message=\"UserWarning: Named tensors and all their associated\")\n",
        "\n",
        "# Load the model from the saved file:\n",
        "model = torch.load('/content/drive/MyDrive/instance_model/MaskInstanceModel_newlr.pth', map_location=device)\n",
        "model.load_state_dict(torch.load('/content/drive/MyDrive/instance_model/MaskModelParams_newlr.pth', map_location=device))\n",
        "#model.to(device)\n",
        "\n",
        "#The output from this can be cleared from the screen. Don't know how to do that.\n",
        "model.eval()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6kwig7t_rjcT"
      },
      "source": [
        "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
        "\n",
        "# our dataset has five classes\n",
        "num_classes = 2\n",
        "\n",
        "# get the model using our helper function\n",
        "model = get_model_instance_segmentation(num_classes)\n",
        "# move model to the right device\n",
        "model.to(device)\n",
        "\n",
        "# construct an optimizer\n",
        "params = [p for p in model.parameters() if p.requires_grad]\n",
        "optimizer = torch.optim.SGD(params, lr=0.001,\n",
        "                            momentum=0.9, weight_decay=0.0005)\n",
        "\n",
        "# and a learning rate scheduler which decreases the learning rate by\n",
        "# 10x every 3 epochs\n",
        "lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,\n",
        "                                               step_size=3,\n",
        "                                               gamma=0.1)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nJRofw-Lrsc2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f88cf22-5480-4f00-f03a-fae6ee535918"
      },
      "source": [
        "# let's train it for 10 epochs\n",
        "num_epochs = 10\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    # train for one epoch, printing every 10 iterations\n",
        "    train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10)\n",
        "    # update the learning rate\n",
        "    lr_scheduler.step()\n",
        "    # evaluate on the test dataset\n",
        "    evaluate(model, data_loader_test, device=device)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2157.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: [0]  [ 0/20]  eta: 0:00:53  lr: 0.000054  loss: 8.6439 (8.6439)  loss_classifier: 0.4199 (0.4199)  loss_box_reg: 0.4778 (0.4778)  loss_mask: 7.6947 (7.6947)  loss_objectness: 0.0409 (0.0409)  loss_rpn_box_reg: 0.0107 (0.0107)  time: 2.6740  data: 1.6038  max mem: 1160\n",
            "Epoch: [0]  [10/20]  eta: 0:00:09  lr: 0.000579  loss: 1.8665 (3.9125)  loss_classifier: 0.4158 (0.4111)  loss_box_reg: 0.4967 (0.4890)  loss_mask: 1.0328 (2.9807)  loss_objectness: 0.0206 (0.0238)  loss_rpn_box_reg: 0.0075 (0.0080)  time: 0.9452  data: 0.2062  max mem: 1482\n",
            "Epoch: [0]  [19/20]  eta: 0:00:00  lr: 0.001000  loss: 1.6590 (2.8156)  loss_classifier: 0.3734 (0.3545)  loss_box_reg: 0.5345 (0.5191)  loss_mask: 0.7432 (1.9067)  loss_objectness: 0.0206 (0.0268)  loss_rpn_box_reg: 0.0087 (0.0085)  time: 0.8699  data: 0.1435  max mem: 1485\n",
            "Epoch: [0] Total time: 0:00:17 (0.8731 s / it)\n",
            "creating index...\n",
            "index created!\n",
            "Test:  [0/4]  eta: 0:00:10  model_time: 1.0512 (1.0512)  evaluator_time: 0.8500 (0.8500)  time: 2.5562  data: 0.6308  max mem: 2253\n",
            "Test:  [3/4]  eta: 0:00:01  model_time: 0.5329 (0.7079)  evaluator_time: 0.7167 (0.7471)  time: 1.6457  data: 0.1662  max mem: 2253\n",
            "Test: Total time: 0:00:06 (1.6585 s / it)\n",
            "Averaged stats: model_time: 0.5329 (0.7079)  evaluator_time: 0.7167 (0.7471)\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.01s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.01s).\n",
            "IoU metric: bbox\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.405\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.950\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.147\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.405\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.144\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.469\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.469\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.469\n",
            "IoU metric: segm\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.521\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.950\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.475\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.521\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.169\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.562\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.569\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.569\n",
            "Epoch: [1]  [ 0/20]  eta: 0:00:27  lr: 0.001000  loss: 0.9698 (0.9698)  loss_classifier: 0.1524 (0.1524)  loss_box_reg: 0.3786 (0.3786)  loss_mask: 0.4085 (0.4085)  loss_objectness: 0.0146 (0.0146)  loss_rpn_box_reg: 0.0156 (0.0156)  time: 1.3909  data: 0.6961  max mem: 2253\n",
            "Epoch: [1]  [10/20]  eta: 0:00:07  lr: 0.001000  loss: 1.0132 (1.0710)  loss_classifier: 0.1907 (0.1886)  loss_box_reg: 0.4705 (0.4675)  loss_mask: 0.3274 (0.3908)  loss_objectness: 0.0040 (0.0148)  loss_rpn_box_reg: 0.0106 (0.0094)  time: 0.7952  data: 0.0779  max mem: 2253\n",
            "Epoch: [1]  [19/20]  eta: 0:00:00  lr: 0.001000  loss: 0.9528 (1.0002)  loss_classifier: 0.1731 (0.1689)  loss_box_reg: 0.4463 (0.4615)  loss_mask: 0.2600 (0.3510)  loss_objectness: 0.0040 (0.0115)  loss_rpn_box_reg: 0.0049 (0.0073)  time: 0.7698  data: 0.0491  max mem: 2253\n",
            "Epoch: [1] Total time: 0:00:15 (0.7737 s / it)\n",
            "creating index...\n",
            "index created!\n",
            "Test:  [0/4]  eta: 0:00:05  model_time: 0.6446 (0.6446)  evaluator_time: 0.1900 (0.1900)  time: 1.4784  data: 0.6268  max mem: 2253\n",
            "Test:  [3/4]  eta: 0:00:00  model_time: 0.3717 (0.4541)  evaluator_time: 0.1760 (0.1735)  time: 0.8072  data: 0.1647  max mem: 2253\n",
            "Test: Total time: 0:00:03 (0.8280 s / it)\n",
            "Averaged stats: model_time: 0.3717 (0.4541)  evaluator_time: 0.1760 (0.1735)\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.01s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.01s).\n",
            "IoU metric: bbox\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.641\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.734\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.641\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.181\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.650\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.650\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.650\n",
            "IoU metric: segm\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.726\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.950\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.726\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.200\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.738\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.744\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.744\n",
            "Epoch: [2]  [ 0/20]  eta: 0:00:27  lr: 0.001000  loss: 0.7313 (0.7313)  loss_classifier: 0.1072 (0.1072)  loss_box_reg: 0.4166 (0.4166)  loss_mask: 0.2028 (0.2028)  loss_objectness: 0.0007 (0.0007)  loss_rpn_box_reg: 0.0041 (0.0041)  time: 1.3989  data: 0.6725  max mem: 2253\n",
            "Epoch: [2]  [10/20]  eta: 0:00:08  lr: 0.001000  loss: 0.7530 (0.9544)  loss_classifier: 0.1373 (0.1621)  loss_box_reg: 0.3747 (0.3699)  loss_mask: 0.2697 (0.3933)  loss_objectness: 0.0050 (0.0220)  loss_rpn_box_reg: 0.0049 (0.0072)  time: 0.8044  data: 0.0740  max mem: 2253\n",
            "Epoch: [2]  [19/20]  eta: 0:00:00  lr: 0.001000  loss: 0.7011 (0.7744)  loss_classifier: 0.1102 (0.1312)  loss_box_reg: 0.2806 (0.3109)  loss_mask: 0.2289 (0.3123)  loss_objectness: 0.0035 (0.0148)  loss_rpn_box_reg: 0.0029 (0.0052)  time: 0.7721  data: 0.0465  max mem: 2253\n",
            "Epoch: [2] Total time: 0:00:15 (0.7759 s / it)\n",
            "creating index...\n",
            "index created!\n",
            "Test:  [0/4]  eta: 0:00:05  model_time: 0.6543 (0.6543)  evaluator_time: 0.1712 (0.1712)  time: 1.4792  data: 0.6379  max mem: 2253\n",
            "Test:  [3/4]  eta: 0:00:00  model_time: 0.3380 (0.4346)  evaluator_time: 0.1270 (0.1352)  time: 0.7503  data: 0.1664  max mem: 2253\n",
            "Test: Total time: 0:00:03 (0.7724 s / it)\n",
            "Averaged stats: model_time: 0.3380 (0.4346)  evaluator_time: 0.1270 (0.1352)\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.01s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.01s).\n",
            "IoU metric: bbox\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.694\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.812\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.694\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.206\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.706\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.706\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.706\n",
            "IoU metric: segm\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.721\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.956\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.721\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.206\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.775\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.775\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.775\n",
            "Epoch: [3]  [ 0/20]  eta: 0:00:27  lr: 0.000100  loss: 1.8592 (1.8592)  loss_classifier: 0.2396 (0.2396)  loss_box_reg: 0.3152 (0.3152)  loss_mask: 1.2052 (1.2052)  loss_objectness: 0.0658 (0.0658)  loss_rpn_box_reg: 0.0332 (0.0332)  time: 1.3712  data: 0.6541  max mem: 2253\n",
            "Epoch: [3]  [10/20]  eta: 0:00:08  lr: 0.000100  loss: 0.5584 (0.7358)  loss_classifier: 0.0968 (0.1156)  loss_box_reg: 0.2754 (0.2673)  loss_mask: 0.2026 (0.3364)  loss_objectness: 0.0012 (0.0111)  loss_rpn_box_reg: 0.0022 (0.0054)  time: 0.8053  data: 0.0729  max mem: 2253\n",
            "Epoch: [3]  [19/20]  eta: 0:00:00  lr: 0.000100  loss: 0.4203 (0.5869)  loss_classifier: 0.0842 (0.0999)  loss_box_reg: 0.2015 (0.2248)  loss_mask: 0.1462 (0.2492)  loss_objectness: 0.0012 (0.0095)  loss_rpn_box_reg: 0.0018 (0.0036)  time: 0.7779  data: 0.0466  max mem: 2253\n",
            "Epoch: [3] Total time: 0:00:15 (0.7818 s / it)\n",
            "creating index...\n",
            "index created!\n",
            "Test:  [0/4]  eta: 0:00:05  model_time: 0.6337 (0.6337)  evaluator_time: 0.1737 (0.1737)  time: 1.4453  data: 0.6249  max mem: 2253\n",
            "Test:  [3/4]  eta: 0:00:00  model_time: 0.4022 (0.4443)  evaluator_time: 0.1511 (0.1546)  time: 0.7781  data: 0.1659  max mem: 2253\n",
            "Test: Total time: 0:00:03 (0.7995 s / it)\n",
            "Averaged stats: model_time: 0.4022 (0.4443)  evaluator_time: 0.1511 (0.1546)\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.01s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.01s).\n",
            "IoU metric: bbox\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.757\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.860\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.757\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.200\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.775\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.775\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.775\n",
            "IoU metric: segm\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.765\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.765\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.212\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.775\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.775\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.775\n",
            "Epoch: [4]  [ 0/20]  eta: 0:00:27  lr: 0.000100  loss: 0.4686 (0.4686)  loss_classifier: 0.0904 (0.0904)  loss_box_reg: 0.1963 (0.1963)  loss_mask: 0.1784 (0.1784)  loss_objectness: 0.0013 (0.0013)  loss_rpn_box_reg: 0.0021 (0.0021)  time: 1.3753  data: 0.6478  max mem: 2253\n",
            "Epoch: [4]  [10/20]  eta: 0:00:07  lr: 0.000100  loss: 0.3349 (0.5132)  loss_classifier: 0.0801 (0.0954)  loss_box_reg: 0.1330 (0.1773)  loss_mask: 0.1381 (0.2359)  loss_objectness: 0.0009 (0.0016)  loss_rpn_box_reg: 0.0014 (0.0030)  time: 0.7997  data: 0.0747  max mem: 2253\n",
            "Epoch: [4]  [19/20]  eta: 0:00:00  lr: 0.000100  loss: 0.3738 (0.5167)  loss_classifier: 0.0810 (0.0969)  loss_box_reg: 0.1521 (0.1843)  loss_mask: 0.1381 (0.2303)  loss_objectness: 0.0012 (0.0028)  loss_rpn_box_reg: 0.0013 (0.0024)  time: 0.7731  data: 0.0469  max mem: 2253\n",
            "Epoch: [4] Total time: 0:00:15 (0.7772 s / it)\n",
            "creating index...\n",
            "index created!\n",
            "Test:  [0/4]  eta: 0:00:05  model_time: 0.6304 (0.6304)  evaluator_time: 0.1467 (0.1467)  time: 1.4307  data: 0.6259  max mem: 2253\n",
            "Test:  [3/4]  eta: 0:00:00  model_time: 0.3883 (0.4448)  evaluator_time: 0.1384 (0.1366)  time: 0.7644  data: 0.1655  max mem: 2253\n",
            "Test: Total time: 0:00:03 (0.7851 s / it)\n",
            "Averaged stats: model_time: 0.3883 (0.4448)  evaluator_time: 0.1384 (0.1366)\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.01s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.01s).\n",
            "IoU metric: bbox\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.778\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.920\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.778\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.219\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.787\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.787\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.787\n",
            "IoU metric: segm\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.772\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.772\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.212\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.800\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.806\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.806\n",
            "Epoch: [5]  [ 0/20]  eta: 0:00:28  lr: 0.000100  loss: 1.5597 (1.5597)  loss_classifier: 0.1605 (0.1605)  loss_box_reg: 0.5464 (0.5464)  loss_mask: 0.8393 (0.8393)  loss_objectness: 0.0072 (0.0072)  loss_rpn_box_reg: 0.0062 (0.0062)  time: 1.4129  data: 0.6620  max mem: 2253\n",
            "Epoch: [5]  [10/20]  eta: 0:00:08  lr: 0.000100  loss: 0.4104 (0.6138)  loss_classifier: 0.0804 (0.1052)  loss_box_reg: 0.1631 (0.2058)  loss_mask: 0.1659 (0.2913)  loss_objectness: 0.0012 (0.0076)  loss_rpn_box_reg: 0.0014 (0.0040)  time: 0.8027  data: 0.0755  max mem: 2253\n",
            "Epoch: [5]  [19/20]  eta: 0:00:00  lr: 0.000100  loss: 0.3374 (0.5012)  loss_classifier: 0.0777 (0.0919)  loss_box_reg: 0.1376 (0.1693)  loss_mask: 0.1447 (0.2324)  loss_objectness: 0.0011 (0.0049)  loss_rpn_box_reg: 0.0013 (0.0027)  time: 0.7717  data: 0.0478  max mem: 2253\n",
            "Epoch: [5] Total time: 0:00:15 (0.7756 s / it)\n",
            "creating index...\n",
            "index created!\n",
            "Test:  [0/4]  eta: 0:00:05  model_time: 0.5384 (0.5384)  evaluator_time: 0.1500 (0.1500)  time: 1.3389  data: 0.6242  max mem: 2253\n",
            "Test:  [3/4]  eta: 0:00:00  model_time: 0.3709 (0.4120)  evaluator_time: 0.1186 (0.1323)  time: 0.7251  data: 0.1642  max mem: 2253\n",
            "Test: Total time: 0:00:02 (0.7456 s / it)\n",
            "Averaged stats: model_time: 0.3709 (0.4120)  evaluator_time: 0.1186 (0.1323)\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.01s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.01s).\n",
            "IoU metric: bbox\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.812\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.923\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.812\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.225\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.825\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.825\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.825\n",
            "IoU metric: segm\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.770\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.770\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.212\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.806\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.806\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.806\n",
            "Epoch: [6]  [ 0/20]  eta: 0:00:28  lr: 0.000010  loss: 0.4728 (0.4728)  loss_classifier: 0.0892 (0.0892)  loss_box_reg: 0.1873 (0.1873)  loss_mask: 0.1947 (0.1947)  loss_objectness: 0.0002 (0.0002)  loss_rpn_box_reg: 0.0014 (0.0014)  time: 1.4043  data: 0.6706  max mem: 2253\n",
            "Epoch: [6]  [10/20]  eta: 0:00:07  lr: 0.000010  loss: 0.4357 (0.5857)  loss_classifier: 0.0851 (0.0972)  loss_box_reg: 0.1480 (0.1949)  loss_mask: 0.1915 (0.2810)  loss_objectness: 0.0024 (0.0097)  loss_rpn_box_reg: 0.0012 (0.0028)  time: 0.7995  data: 0.0758  max mem: 2253\n",
            "Epoch: [6]  [19/20]  eta: 0:00:00  lr: 0.000010  loss: 0.3226 (0.4751)  loss_classifier: 0.0727 (0.0844)  loss_box_reg: 0.1212 (0.1596)  loss_mask: 0.1399 (0.2214)  loss_objectness: 0.0016 (0.0075)  loss_rpn_box_reg: 0.0012 (0.0020)  time: 0.7715  data: 0.0471  max mem: 2253\n",
            "Epoch: [6] Total time: 0:00:15 (0.7754 s / it)\n",
            "creating index...\n",
            "index created!\n",
            "Test:  [0/4]  eta: 0:00:05  model_time: 0.6160 (0.6160)  evaluator_time: 0.1483 (0.1483)  time: 1.4173  data: 0.6311  max mem: 2253\n",
            "Test:  [3/4]  eta: 0:00:00  model_time: 0.3568 (0.4301)  evaluator_time: 0.1074 (0.1332)  time: 0.7446  data: 0.1659  max mem: 2253\n",
            "Test: Total time: 0:00:03 (0.7642 s / it)\n",
            "Averaged stats: model_time: 0.3568 (0.4301)  evaluator_time: 0.1074 (0.1332)\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.01s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.01s).\n",
            "IoU metric: bbox\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.816\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.816\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.225\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.825\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.825\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.825\n",
            "IoU metric: segm\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.769\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.769\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.212\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.806\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.806\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.806\n",
            "Epoch: [7]  [ 0/20]  eta: 0:00:28  lr: 0.000010  loss: 0.4311 (0.4311)  loss_classifier: 0.0958 (0.0958)  loss_box_reg: 0.1465 (0.1465)  loss_mask: 0.1820 (0.1820)  loss_objectness: 0.0059 (0.0059)  loss_rpn_box_reg: 0.0010 (0.0010)  time: 1.4431  data: 0.6806  max mem: 2253\n",
            "Epoch: [7]  [10/20]  eta: 0:00:08  lr: 0.000010  loss: 0.4311 (0.4757)  loss_classifier: 0.0786 (0.0886)  loss_box_reg: 0.1450 (0.1685)  loss_mask: 0.1753 (0.2140)  loss_objectness: 0.0012 (0.0030)  loss_rpn_box_reg: 0.0013 (0.0016)  time: 0.8073  data: 0.0745  max mem: 2253\n",
            "Epoch: [7]  [19/20]  eta: 0:00:00  lr: 0.000010  loss: 0.3147 (0.4730)  loss_classifier: 0.0678 (0.0872)  loss_box_reg: 0.1170 (0.1571)  loss_mask: 0.1404 (0.2203)  loss_objectness: 0.0013 (0.0063)  loss_rpn_box_reg: 0.0011 (0.0021)  time: 0.7735  data: 0.0468  max mem: 2253\n",
            "Epoch: [7] Total time: 0:00:15 (0.7776 s / it)\n",
            "creating index...\n",
            "index created!\n",
            "Test:  [0/4]  eta: 0:00:05  model_time: 0.5872 (0.5872)  evaluator_time: 0.1466 (0.1466)  time: 1.3702  data: 0.6260  max mem: 2253\n",
            "Test:  [3/4]  eta: 0:00:00  model_time: 0.3881 (0.4253)  evaluator_time: 0.1107 (0.1337)  time: 0.7377  data: 0.1648  max mem: 2253\n",
            "Test: Total time: 0:00:03 (0.7613 s / it)\n",
            "Averaged stats: model_time: 0.3881 (0.4253)  evaluator_time: 0.1107 (0.1337)\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.01s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.01s).\n",
            "IoU metric: bbox\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.818\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.818\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.231\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.831\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.831\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.831\n",
            "IoU metric: segm\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.772\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.772\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.219\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.806\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.806\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.806\n",
            "Epoch: [8]  [ 0/20]  eta: 0:00:28  lr: 0.000010  loss: 0.4294 (0.4294)  loss_classifier: 0.0681 (0.0681)  loss_box_reg: 0.1657 (0.1657)  loss_mask: 0.1772 (0.1772)  loss_objectness: 0.0168 (0.0168)  loss_rpn_box_reg: 0.0016 (0.0016)  time: 1.4486  data: 0.6497  max mem: 2253\n",
            "Epoch: [8]  [10/20]  eta: 0:00:08  lr: 0.000010  loss: 0.2879 (0.5438)  loss_classifier: 0.0694 (0.0931)  loss_box_reg: 0.1026 (0.1765)  loss_mask: 0.1399 (0.2657)  loss_objectness: 0.0025 (0.0058)  loss_rpn_box_reg: 0.0014 (0.0028)  time: 0.8020  data: 0.0724  max mem: 2253\n",
            "Epoch: [8]  [19/20]  eta: 0:00:00  lr: 0.000010  loss: 0.3242 (0.4723)  loss_classifier: 0.0702 (0.0854)  loss_box_reg: 0.1234 (0.1595)  loss_mask: 0.1399 (0.2199)  loss_objectness: 0.0010 (0.0054)  loss_rpn_box_reg: 0.0012 (0.0021)  time: 0.7741  data: 0.0458  max mem: 2253\n",
            "Epoch: [8] Total time: 0:00:15 (0.7783 s / it)\n",
            "creating index...\n",
            "index created!\n",
            "Test:  [0/4]  eta: 0:00:05  model_time: 0.6457 (0.6457)  evaluator_time: 0.1469 (0.1469)  time: 1.4611  data: 0.6515  max mem: 2253\n",
            "Test:  [3/4]  eta: 0:00:00  model_time: 0.3515 (0.4320)  evaluator_time: 0.1120 (0.1253)  time: 0.7420  data: 0.1703  max mem: 2253\n",
            "Test: Total time: 0:00:03 (0.7635 s / it)\n",
            "Averaged stats: model_time: 0.3515 (0.4320)  evaluator_time: 0.1120 (0.1253)\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.01s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.01s).\n",
            "IoU metric: bbox\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.818\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.818\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.231\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.831\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.831\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.831\n",
            "IoU metric: segm\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.778\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.778\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.219\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.812\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.812\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.812\n",
            "Epoch: [9]  [ 0/20]  eta: 0:00:28  lr: 0.000001  loss: 0.2868 (0.2868)  loss_classifier: 0.0577 (0.0577)  loss_box_reg: 0.1062 (0.1062)  loss_mask: 0.1214 (0.1214)  loss_objectness: 0.0000 (0.0000)  loss_rpn_box_reg: 0.0015 (0.0015)  time: 1.4470  data: 0.6853  max mem: 2253\n",
            "Epoch: [9]  [10/20]  eta: 0:00:07  lr: 0.000001  loss: 0.3036 (0.5185)  loss_classifier: 0.0651 (0.0897)  loss_box_reg: 0.1061 (0.1667)  loss_mask: 0.1325 (0.2572)  loss_objectness: 0.0006 (0.0021)  loss_rpn_box_reg: 0.0011 (0.0027)  time: 0.7999  data: 0.0754  max mem: 2253\n",
            "Epoch: [9]  [19/20]  eta: 0:00:00  lr: 0.000001  loss: 0.3055 (0.4608)  loss_classifier: 0.0739 (0.0865)  loss_box_reg: 0.1062 (0.1537)  loss_mask: 0.1425 (0.2170)  loss_objectness: 0.0008 (0.0016)  loss_rpn_box_reg: 0.0011 (0.0020)  time: 0.7729  data: 0.0470  max mem: 2253\n",
            "Epoch: [9] Total time: 0:00:15 (0.7780 s / it)\n",
            "creating index...\n",
            "index created!\n",
            "Test:  [0/4]  eta: 0:00:05  model_time: 0.5950 (0.5950)  evaluator_time: 0.1525 (0.1525)  time: 1.3797  data: 0.6212  max mem: 2253\n",
            "Test:  [3/4]  eta: 0:00:00  model_time: 0.3744 (0.4181)  evaluator_time: 0.1151 (0.1306)  time: 0.7251  data: 0.1636  max mem: 2253\n",
            "Test: Total time: 0:00:02 (0.7449 s / it)\n",
            "Averaged stats: model_time: 0.3744 (0.4181)  evaluator_time: 0.1151 (0.1306)\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.01s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.01s).\n",
            "IoU metric: bbox\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.818\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.818\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.231\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.831\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.831\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.831\n",
            "IoU metric: segm\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.781\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.781\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.219\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.819\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.819\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.819\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0c3fJk2ArkzC"
      },
      "source": [
        "# pick one image from the test set\n",
        "img, _ = dataset_test[0]\n",
        "# put the model in evaluation mode\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    prediction = model([img.to(device)])"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zPxh5hHj0Akp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "0f37de4a-1a37-4929-ac64-87ac7a066612"
      },
      "source": [
        "img1 = Image.fromarray(img.mul(255).permute(1, 2, 0).byte().numpy())\n",
        "\n",
        "plt.imshow(img1)\n",
        "ax = plt.gca()\n",
        "ax.grid(True, color='r', linestyle='--')\n",
        "plt.draw()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lxhRAKCZdF2WdiHsdT5BmEXC6TEXQ5TYo4nIqAD23QETEKlMmo4igC6kLWeawwq5C109SgSyZ16uiXcfiWy7MstVHDDIX5btZrnqIkOR+TBYwlcWwTpyQR61+QFrfIb86aBKxNO7GGKu3AbvjuphOwssF+VLUUYlT1PNDQbdc6jfUvlA1riIXCj8uFqsuq84OHIs+pqw/j1EViCyhoSvTKfnZi8BYihByJk1CXhvjc4wiFOXOyOXTISFtofKhtSUBLQ2vvpE1o+vuKklp9im5DrJmjsb58WjvEUpmq0K9NwCbz5xJec5bEHu+SDPuL4afpQyEGF3Mfik1QUfnlK3oiTgeBk00JXKKsgOA3PPXVF9dGkWmroh3JfAuqtaagnSUmlTl4NFsIf4kwnBfNxJiskCsIWAJe56lgnEyUUcphPjR+Kyjmfsholxp73JBlqiCVKa0ALWl8nswFa5MMMye5+r4DC0xIL82GIJnSf0ky+MocFWMzQXRV2JACWkJ6hiDbGV3FuzeaP+eZ6mCXYysj0J7Ms1HQjgIrA6AkcuoeIe7kBKaCzSjWh1gaVDpaSGz5+lcAF/hNPPUjFSoQtXxBAlhF4riRj0n5l+2c7oumtHzPdjzcFw/ER8B9nInXbGsoAjr087AqMc5H8XoqmPEXNKKnrvvJpqk/SoqTuHmmQsEB530X6azZW52oWopZaUpsUUmHhSrJVP8/oXiUQDVQrAEqdzWwj8CPEtF+hX2E3F2ox4iYDFV+KEjy2MsPTlfgTbN/xARCXgLwHOYlh2aLK2gZBPqvM8VmaQSZS/qIZ/WU35t7oBneQ4F+DchTUC+KMGxXMdSoVzlTkAVxCG0BitTAW0FFK+C7vM2fq7tR8t1Dsc1MatoYA01/2EunllZ7k+E5XYhNbFNIUIHdCVU+9D1CNmpOL+yjvWwTXNGCxJGsrDcQog+fF30Qi6J5z6YgOy6BSi8QzQkxTga7b+ImktyluOcFxDDKIwwCJe8IiKZWpdSJ9WsiN8z1GiHh5MEZImAJWVd28d7+m5h9KgELU2j2PpxSFPAa5BnaE4O0+B7xGPW/v8tdR6CiAR7Z4q+iIHvDkqUTvkMS08tVoKepaL40sIXUAgyfG02x/fhUnaWK3KKCtiqPi9TtXsv6iFBLFBXE8ckWGSNdez3EcjHgSO5ZvqtpBL0SirmoMzIYY5KrgIuokN9uRx6P7WvhhPIPKzdjTouUifFyUUNYZQ1JscY4ktA1eKy7MRsC5HwJZdBOISEAtFPnvpsEa9hndvAulsX2L3O0KKNqN+QPhyrcFC1TwVlhbO8QSbluY1BrEuZj1E7wU0vMfYqirQfYai5h5J6kZJY4ziEa30BdFDdBuz+BaoA0jPtzL0BNWSnOo61yhOjOGEZ6De8X8JCLoTaqBCspzTL8lAilJD0w1Ri8JBfpESn5dDuSkc/YW1TPYT5qC90SBgKi9GMQPWTcBLPJpSFIOR+CrieMpPzJSpeIPBXSWMrom4CFxXJyNEfRFv1TllMq6jac4pq8UmIK3TrQkBgpVKRoWasipkFdobQTCsouRYBMnev6tDfP6ih7NUUuvyejSPWTuWHaEKZxlTJVBp3zxfRGEB1S7C+1ZhLOUqYCcjvUbGgd1Dcqw3Ahh9WDCLBjj1UN0FaTOaiOlZaT34W1EHaSO3UKLOhsWQCKs4t0x2GPu+O56i5AEL+BbCt4fX+oeX4J2kNj3mH1kuudRx8EoYgUzTAyh0vWruEV0igyJpS29qaJNyGIfDpyUPQBEwlICVgujTM9B0HqcDnkr2nTxXOo/a8a+//FvgVaiizQ83u0z2KbghYU9LTYvT74fLZsZ8qWBUNgqoINH9EQmKsnkseEvdEuoupOM0KiuUVdUsquxskEglwO56FweFBVRaqD9Q8A42HMjSVS7ORajloLATCjtp/9ZWUiPhAJnYXdnzDxmLZvpPde5yqROepAvEMx7ntYmTYeoQaVlJDxQRqtLRZmMuNXIc1UdZhak47VDxDWrxv11Q25Z6tJ6nMKsHgSSd6p1ybQS03h3ZOi5SJUSsZWgHDGaFTUbdVFJRZYJfXpw9bZ2iaxtJEIVDSKwFuCnGXPyyGd+BM1oMSeBSKlIk+Y+/XTMJgnK0+u1VtFeApsz/TZLRggnQ55K934MVBFaY53ueRBQlkZWdqwpjGH9gaIPJwIpNoRBbYYaqL5WM8BvmDwIoRmF4qEwHVloNUK0nuVbif2UO3sjpSqUd+zfpV/bOPaoGKueXiuOUjaxAqXqB+FI1Iccg6FA1qjDqw9RBV0Cvse3H0A/bsQrxXVsxZjnNbQOhQ54jwoIKEksqHqeaTmF6abA2FyI9SkV2ojC2zXZ2uZ2UtKJbvlgLx/7jdr4QUme1iVCHo7hfKFNS8ADEB5XfqUdK3BaCFcEsr4vwJ6CzCQJGZTnSNh2pX2X9ZDrIIFC5UfSQkFqIPV1PnaqjvpZH0jGmvYd9BFc69EqnJH4w2fgM6X+2SR7sMlherwBc+I0aRmyDBJi24ujUOYiJoJgSZFTNc70KunYTWAeDwUhHIiZrToiiRLCi1cUvcJ5c1+q8jXElYiNwE5U7I+nLLQu/xPtRMYAfj3b5XmbqmsZMgleBU3SUMx6mheeVlCIPRzNmzHOe8gLj9/VSTSkQQWYNDjUH8f5XiU03SRPkVoZC5qOnbAj09UQkqoYdGu/1dNIk//NI0gG4flpWoo8VbRiMioEMCAKoroXMdqlUirTIaAkUm8Fip8+0XQNaqVROUCWGJksU5AgOfqSliU/RFRNQrz7JAFZabKARzjBpBkT8rv15arwu3X0rTbfIcEwGE0d68DHyzjlX/G/3SOZppKBBNgNlkvOc1Kkg4R3Ux5D6Nwu1bGFo6SdaaTOfIGck52neMugrXCCXV+CBVycgN8YxR4QkTVj9ZSXF+cCXcrmc8i1PjqTFwkNEVnVwruRmyjqGZKKX0aZsM1ghlJ7j9Y9afpyiC4bnaX1wezx2iJnY5nZ7mOLcxCODGQ1Tp6Gi0+2NQtdhRiqA4Su1U7N5JSohHWjxZGYp0aKBDi9woJN0HebkQ/7LKgSF4liV4lOeg+QwSdCdq2UMcQNrTU6lfpgzm4dK2G93MlmkeZu5AURhl7cm1EPM56DhDYRoJKWUvqh2XUudNbKQuCBMa9UbhPEuQBIBZgtNQSOj/MSrmoYxLaUxFWxwYVuixLVSlLSPyceNxhkIpywXsUXJBJmJQE8VKWM/r3ZYuFSNQ9GGxWz4+f6NPESi+Homm9b8CN85T6EqYiYQIVIEmvEmWpHCv8VbbBDA6AKm2iWZcoSmqMwc3PmdjIYusRwHvL4hyrqe6sLIaz3Kc2wIiwR37qYwLdcAciINq5qvRWlnnFQpRyvxaSSGEjdTkGDGGgzrSkH2443krW5pBvu0IpJMU/13CQgOk8jzPXpp8POogQSIzX+Cj8jtOMVwr4o7jVKkvq6UHnRAASQwvxvKUYZmZCh1q2ryAXbcEXqYmTGmylRi2B3dootwgcBXHdbR+owh5LMHbKetTLkU/CfeQqS7AUn7/CWpkSEu8ZYrws6ShOw5QoxoTwPuiLiuBqVzr5MlsEpDHqcpG478ITPZhpF+ZWWW4teRh71m44zmqi6mcCY2jrIgxqiXSTmdftnLl/mmmaqbSliIuUjTZ3gPc8W1qvo1CsomqkBJlkZ+11NDr9znrcW4LCB1OhP5fjKVZnPJnBeL4yjpajksMPkXRNMqybPtu0CQEDYzfH3XI41R8Y4QirReBhdBi0pASXmK2l6m+tSP+S1GWwpgbqAJQWkQW0zoYTJZy8iw1j0PEpnpLqAngVNhLwkj96lEHCTnlZnhoTdrf53b4QipQ/foTVKGp6IdHdW6kCEtZU5pnIgFp2ZXDBCMBlBuo+RdPxLfGYsrKPErFIeSmLlEsNAnGU9Q8FwlPj4CpLPUx9i06EQ5B/BaGJUvJLV+3kGR1atr9hdEuuVYDitUnWp+MjxSQXKVF6qI6aqNm7p6irt2R7f9ZjnMeg7j/bTTN43aCkxhPWANUfEGI+JKdW6ZOdnJ0XsJFBDDGMI/9/mupvqDqIVNuqWju4UrIiixkSmqbiCni38M8BL1PxE887yCcBM26cv7+C6mp1EKkNR9EprXi/fKrHR/A+kkgpQSthJeY0DWQQrnx7P+2lipMlA0pxtKEKpnFnVyYUIQvbT5l4/FX1CiGxkKp8morNMHDDPcrTdjrvIoabhTA263PNASUNLkDrAIsnYk1rvot5RDK5P5LrA6+6O+IvUurjKsuDkIOrHwpgReogldg8UGqkFe9l+sz97+LOt4jVNqQNaLyj9l73iBR6u9tQaSULk0p/XlK6cmU0hMppX8d59ellB5JKT0d32vjfEop/bvYcu/bKaX3/CDv2buG6lKoczzRCKoklnSWySbfWdpPhCPAao4iLLTQht4jZon793o6sdDojQwJaZhrJutlCVInkcSAk5SklIsiAqGEJw20ZkwKoJJJKsT5GJBgr/I+Zmgm1eg5ZVGmAqDKckgj1OQiC4sN13KQgJAPrBwGMbNwj2CaJ2Wq67rWwAwGSVhfQdF6l1N8YS2mIutOESgh/h6zF+bifrVFMPaujfvWUAFeWWvqk6ujTRo3AaNax1IukYSd2i7mFGiqw/GFaN/eNVQFImXk0YhVVExG1pzCme42dKiZjupDV14+c9nB2PjsXWvXPRLSoygtrcAuun+DCAa8ORdjGfgfcs7XU+aE/auU0vXAbwB/mnO+FvjT+A+wjbL4+rWUjXF+6w3fkGHHX9p/+WcCVjRYgeQP3QOZW+r0SepaBEo3foXCmLM0iTXeO4ySdGDH4zSJazme92m8+o51KfNIrky0QMkc1H4JF1I1hkJR4zSXIfeEq2CgHc9RNbQSaWT2KmU5QNZsOQlZAs7BQFkHwhkEzsoi8ExPCd0wWXccpBlea+WeZGlrN7t7lAlaMvuXqIypvtUiPQrFQs1pWLQyQ0ns2Bf/lbsgxtxIif8nCn4jt0XCTy6E2qssTS2rt5oKOmrMJTw98SqiHjv2UgVJh7rMviyT1RQhpun5niUrgSiLRPSoKfb6QFMIyUUU5rERduymKkvV0duQKLQ0S53M1Q7dt46/t4uRcz5IGRpyzidTSnspOuLjwE1x2+cok3t/Pc5/Ppfc7j0ppTUppU1RzukPuQyS2IoDi/El6XWfrklzi0kO0/SflX15hOp/i/gcMMKeE1NpfoeWDZOm6diz4btmmekDez5Deg1YhLyKOuByA0S08r3FyKoLcf0I1ZQctXdIOy9RiFaEKm0yoBDrSeoyap745YCtzstVW6Si9Hqf4vkSyhKs7sd7SE/hzRFqoo5nScodk0sid8MXfvGsSYG5Erg96iQo1Xst1VqS1pQwUTkOIqvPZZEMqJjQFKy4qsvyphH635kvqdSZumgy1Il2GqsedQcYzWuRIM52r9a8kPvn4Kja7NawlmEcULGUtgska2IVRSE6iK15L2c5/kEwiJTSFcCPAd8ALjSmf5miL+H02+5dQgiZ0x4ZHt1AZTyfyDKwcyJqn4iyijpgktoym0XcImRHxz1LLST3o2uo4TFNdlLGpIdJ1fFiABcOPQrhjFMy8lS+mFcmp3CISWoSVQjDR9fR1P4ioOsSvJjLSs5aKUkmtZJ0pAFVNwlEB2WV4izBOmHvkADpw6Na+k3aZ7gILMVSk5AUw45QRlku0NspFtirVHdK7ZaV6HNT1M8a7xC8jwrdh8rsUhgaI4GSmpSV7Lz+twUFVpZAW+L/BYn00REG/UXSLOTvw6M+bfo4RQArrCuLN7JWU4+y2ZFcD6U6y2XUbwk4LcgrvEzWgwSBQPc+PKpFjcUDnpV6AYUbpeSOUnGbsxxvWkCklCaB3wf+Tc75REppeC3nnFNK+YwPn768O4i9OSdH4cMZpv8Y7r+6+Fg7vgkMiuC480dhz67y3EwPbvpZeOAvYEt06q0fhW3Pw21PAxnu3QIHp+C+rwMJHtkEn/4I7P4S0IfDo7Dtg7BzGq4JoPGW6+GVLkx/rbznngthvg+feaVcf3gKdqyBXS+UMg904ZaL4aHnYHMM5LZLYfspuDlSwe9aX8bs7iOlLQ+uhZ0b4cg3YxQAACAASURBVKGnSpn7enDrZtj1PGwIAXPT5bC4ANP7Sj0+dQFsWoI7jwH7M5+/GHYtws59QA8e78Htm2H3MzARDHbDNXDvEfjwE6XM7RfBdcvwyUjF/eyF8Fgv1r5MMD0O298Je74N3YVCazf8SJE5098sfXb7JXDjLNwRC9XcfzHs7cCOKOPRSbjzOtjzWGnrTBdumoQHvgVbjpZ7bn0bbHsFbotxu3czHExw3/Pl/yOr4dOXwO7Hy/2HR2Hbj8HFyzD9F+XcLVvh1gPwSwfL/3u2wPwofOZvy/+HL4Md74BdX4lxmoRbfhYe+hJsDgBx28dh+3fg5v3l/10/DmPLcPc3Sn89uAV2Xpt56F8WH3dfD279AFx/AKa/AIx2uOkTid/8Yp+ffCHG6R2w6Rjc+VT5//nLYdda2PnlGKcpuP1DsPshmAgFc8PPwL1fgw8HjW1/P1x3ED75N+X/Z98Gj22AB75e7p/eANs/CB86DNN/CP0EN9wMO74KWyOcffun4MbHIxTagfuvh71TsOMvYOvZ+PHNzOZMKY0AXwK+knO+L849BdyUcz6YUtoE7M45vz2l9H/E753t+85Y/rqUp4GtN/H6xCglmGjdRKWtSmLKFJUpqVDRiP1uo9NQsQyZ6qdg+juw9d1UwHOMIXDY0BRj1FWepQnl268FDlJXcpJJLtfC3SJPzDKUenofbL2GauHIvVB25GvxzjXxjFbDkoWlJByh7CdpovauaeZpuk89hjMcp/8Gtl5PtRQUNpRVIXRcoUpZUFrvoW21CHyTu6FzGh/dLxcgfOrp3bD1w9Z/jimM2DtkzmuMu1amrBJhPx17zkFjaeatVJD7RWASpj8HW+8AVnfglQFMQWd8FF7pMvj2XMU7FAIVyKiU8osoQPlT1GiHQFnRovZU9ZR8D0V3YfpLsPUWasYp0d8bou1HaaxaNnQp//d/hNmcqZgKvw3slXCI44vAL8fvXwb+Pzt/W0QzbgCOnxV/gCZIJr9LTHcJZUUcmUm+PZt8MZmZDggpi073+jNann6OuhOV/Dll+M1S9xTw1F/VTwh1LNfWmJTTC3dcjOVmvnxdT6ryCTzqAxFEij7QVO6wRoZ5B/JX5c6IYY5R3SyBV16nY1T3SOnjMpNFVDL75ZJ59Ef11bZ8F1KFpIBNAY/qb+EWI1ZmN/oPmnkpbYxA5vhaKrhoSWRDIdC1c2q7BB+8Xkir/cnuvxj4FsU1OhnXj1PB76MDuKIH74JBZ4m0JcZIUbFT1Gnpy9Qoz+WQrqO5nJ/ASY2LFj8SSCpaUSRJ7rfqKgD6ZDx7NOoqcNJdsrMcb8bF+BDwz4HvpJS+FefuAv5X4MGU0q9S5sn9Ulz7MvAzlDlus8Dtb/iGXEzSIWIs33mZsoqyawZPdNKce3WsE7IIW1pTKwRle17fwbwzAF3KBq9aYUjhNc0kdLCnT92bUprH90LQGg7yFT3xCWqmoTRzCIcZaTf1geYSSEvb+gTDHakyZUn6I8BrEf6cpaQmS8vL91WbY1HXYeahGDwssRn1O9ZXI63fml6dKVaJhCpUASc/W++WhpVFJwUhzECMHoJhZgUF8xBWoOiJrAHdK0tExrKEhA6V7ValaEIMpPwWWVIClsdhRhjHAFib4BBMvG+UmcfmueifwCt/DHm/tV1MLGvhScgbqAlpWmld1qVbwMKg2hbycoyLoj0aGwkiKTlF/IRtvAFIeW4vGLMmZW6gak8NuJuHrlWc2WRyJ5oLuIhhJXHF8CJIEaoWk1EIcJK6lfpFVCLWvhUSUAKHetDtw6ALeUOYniJcAV+yUrSgqYcGh51gdRagqd8jFIT/7RQT9RWK+E1R31ejfpcytDBSn5K3MUpd0UluhsKuIkzF2rUvhdwxJ15d07Rl5XdAMW2PxHjIdZMQg+bUeGl0jakEg8ZW2lWWwIRdp/WcM3amaeEp5KqyBMJiv5Pd7+6IBJDKXbLy5eL0KUH/ExT1mBN8JxdIXm6UR9PGqet7KBNWGb9uKTl9ydWFCsAqKiTLWf0ggauIh/OR+OT//mFdMGYADyi/XAOlXAERl6QoVO2lDhLDe0xZlsQiNZFKg635G+rQYPwHnqPm7ivRxM07pcS6idiH/ljkIBwa1MFxl6cTz8oXdYYTE8ksXgjwsEcRUJdTUm9HKTkWeyjbIcufl6syTmHSKC+rvZosJAtKwkCHnhdBBdOn5dIfvfbaAvKN5SYolVyWzAh1HoZwAo9QiLl1iOHkpilpaYrhXIIH/oRmGDrZc675XWiM2XW5GG5lSHg4U0JTSHt5PXjgS1SraxUFS3iNEtlYzHUHeu2PoqNPzUPxHeHU/6JBJe3JTXNLWef68MBjNIUjVFdE9fa0f9XhLMc/SJjzH/PYIp9NZreEgoA/hR1l9ivnQfH9HjVtV/tuygx3k1ff8suVZwBsEeHL51X4TgLIzV+l9ir27Ak+AlVlomuPCNVToTgJMezaRtjy/Shzhtdl0UF8KyPTNaaIQ+8ao1ouIpbFwvzZgTzN6HyFYZg4d2DLAiyPRZ/Ij/YkNeVfyLJaSbEmTsS9ItQO1TIZoYb8fKZtn+oGed5Lgi2aRyCwTmX37bf63evnlqT6Td96vktTaEl7Q1MDZ9hyKP6L+U5Fn8k1kpBX/1tC2xCfkKBWX4qGFJ4WzakOyvWA4RgoetcIy8rSlbsLTVfqDWZzntsCQgNsRDwcQEUvtIKzch5kzuq3tL0GVWahBkwSVybzUpQn/1vEJotlhLrmgrL+oMaq5edB1TiOjEvQyVx15lUERBaSBMWynVce/SJNE105FD1gJhrmmlFEIVdCFsvK0t5E5FD4LNQ+dQUrTQySYJKpLPdPi8xKO6tuWvnqsL3PhbOEgKITUPEMCc2Vdk5Erv50jenC3900F5gDe86pX+MhxnXTXn3ROc29jsXMUwSDBN4CRdGcoJkrIgvFrQVPmpIQd4Z22td9wr5EH6qvLDbR5xGaALfa57lFZzjObQxiMuVrroZ9U9ROdSk/oBKcADlpH3cnoGqYUQpo9gqV+aASmTTbRoZ7Y1wzB/vEIBIEsh7E3Hq3sAv50/rIlxboKsExzNxM0LGxcAQ7iPGaDPvavrAGukvR9isoU3gleHynJwkFhbp0j0BdaSkJuWXqJB+5HKNwzRLsu5pCeEq+kbAbtf/Snjo841OMrHGEat4rbXy1/cb6NfrymiOwTzNh1RfubmSawln96oyu/+pTAaeiNUV8HOOQQqFcu+YQ7NOcIbf8JAhnqbiCxl7tV9+KlrA2a7Ng0b6S0CRQBMwOgAvgmoNGHwoZS7goSdBnlI5RaOa3f1gxCGCb1h6Amp0ncEoDsETNRGuHBLsUK+NK6MoquIwSIlWc3s1ACRiZZhOwTdOAHWdwf1brEeicVpLWrFGfByAmFQEO5wfk1+MYwiTCb9/m2W8eatSSatpMSFpX0609F0JRl5gz0lhrw81guRDYs2GFbZsB9qcmyLmCEtJTn2gtAzGDzG+fTORlyyqRGzFJZQD1vcYpmHnbc1ZnFyLQ1L7uXklIuyslhtF7VlInco3bMwJLPQwMbHuG10dA9G5hBGqLtLdcYo/CzVM2X+5QcCa5ro73SAhJUEqYn4JtB6ghfAHtDvA7ljdPXS/lLMc5LyBuO0IFDcUQEhKTlFCjg0nqdLcOAE5R9lGAEss+bNfEdCISDWYw2m0vU4SMdpvWmo3rKDjI2ri2mro79Cr7r/i8Zg765B0RkIhPDOZJQ3HtNkUEJEQ81u/JWV3qxCM332U1aJKUXBO5LyJGTRWWxhPRxkI8t70ILORmLkeH5qYyR6jWizSzj48sKhG7GGuMuh6E2jZi5ch6THDbEzTDle1D/aP3en85vuAJURLyUh4S4BJOI9TJddGvtz0R5bTdGqia2i1ItUcf9ckUDJT6/1yUoUlkUBPfHKtQ3y/Dbc9bexfj/uMU9/sQdZ0MTYXv06zzGbrw3D3cx1UyjVYXEtCmufIypdxl6Nk1TYCR9JTEd99NhOPLfoloNtp9rsHdB3YfF6oJ6VaND64nSPmMRZnnAvO69ryyNTOV4OTLqu0d6pTwgZXhLlkX0ilKPsSElT2g4hwqdx2FaV+kal9hL45HeE5JogKyDiDKclKOh1spwki0EpP6ysN2etZ9cg9Puh/vYVPvc7ce5OZ0rDzNlPRQqSsPV6saX3ezRBseRvXow5LdawJvOKZagGhAnR4vAbdo79J4C4hWeTonwSOLQtaZgGFZ32c5zm0BAdx7AU0QRkQi8FEEpf9OTPK15c+JoUQM+i+ClD/aStO99z12rh0zF44Q9zawATFmx+53jSthJB/XNxR2YCwI495LqQJp1J6T2ah2z9tvEYT6UBpxgboDua+VIMtBm91IMKmvM9yrpfK04pPesYYqsHVOwKVbDQ6yqs/FfDLh2yCu6mfn732f9aX6vGvnfKwcWHThjo2PhIUSi/R+DwWq700p3fvj1kbd74ljirpJmA2oC/EKk5LyEkiuemudDKg40ICyWrjn96yAe6+kWhiO87hgdsH/BiFOONcFRIaD0k4iOiWDiBDWUn1s5fl7HoQYUedkusrsk/TH/jshJjioefyS9F6urArsGjQFQhtxF0N7TBzqegFiHOUzRFTjoExNJ3YNsiIfDky5ry+ho7CWQscxhZlR6r4izrhaR+M4w41nDoo5lJfwEpWpJ2juqg4V/FR/uB8M1cVyDe2AYNtiC+1+UPuNqm8dnDwdYOlRBI2JxlSfZSpOIyHvYVFZlzDcs/SgL3noUSO115WC2qixcOWn+gi7cEBZdBHjndV/ot0MB91dwfpcSkSgrAS0YzFnOM5tDCLDfS/Gby3l5eZgoprSK6BzBcUVcH9RLdQqQsIHJqgAlGv0NoDVhfu+RgMcU92AOugSYPIrNXAiHAfYXLjonQ6ejVCIaDUVyR+B+/bRBPVcW05YudDMvBOROuYg18RDu2qT3A5ZalosN0LL92kGzVFKxMQngMmlkcDyHaJk5kqLShhhfdROoNK3m/shlO/bTROEdEvC2w5VKHRb90kwiZEcTNW8h7ap7hsoj8J9MTOzAT67gvJ3S+DIDZLVoUMAryczRZ5P6pdPwxLR71m477s01wRVncUvHlruUNfxOMtxblsQOgQaabaiJLQSjyLluHNDYrA/l41w3dLwlaJFDNAkLBGijj6vJ0yZ5CIOmWteBlRJ7enfjmx72MyJ9XT1EENLk2jE5lv/ZUm0Q5hraS5II2xgkWo5KKFLMXPtvQHNxCtpdFku7sqpbb4GRIe62MscTW3qLpJHKdrYjgtTB5Pb4KQzpVsdYkSdVz17VoZbfqqjxtXdLPWHCyW31JZbZTp9uWvo635IGHlylOrgO5iFsEpdSnauBI3WvlRd4fVWkVwTudX+7A87SPmIkH5N2JKmgWoOBtK+/Exupqb2qGHG7uvLHnaOE5Zr6PBJH7kszvVbz7VN1lyfaeQ+DOw+mbEduyfb89IIziChfR65lJqk5ZpEgsJdINVf09HFjBIWOa6tjPK00K3mBEBTGEjbL8aYyPyWllLERf9PUZjJl8CHpmBUu2UxedKZDh8jd/+68MgVdr3N4C5YvG/dytJzPp7uQmLP6D7337vRH1dQcwzc4nHsRO2ejPZqnkymbsco11DmfwsryKOUxWaEabhlkOGRKUqfOyC6YP2q9iqU7zjMGY5zO1FqZcoTm2OWGpQGj9tvWQMCe7T6lJZZG6f40L7G4emwAf/fs/uiEycWYsaeAMm2VjgdczoyjT3r/qn++31iXgkQHV2YmI8kSU3bFVE5KCbBKJdFy7hJa+k5hUtXRHkO4GoZOc/bl7uwWPakmdF9AmKlrSXM9Q7lYsiVEWNBFY7jlMlmqygWh1a4doHrll88PzEI2mi7GCpb93oSUluruzUpYdAKH57WxbFxm5iJWcfq/zYOgZV5Uemf9GqHdEGXwaNLVZCLfhRalhXh622ov4XbmEKayDFBVDNLlymrjAnXEUYHVfhk4LEf1kSpDux+gSYBun8VtU9aU0ALycoF8Jx+qETnGsalqJv+ZrLu/gN7b9e+9bznWzgQ6WYfp6mHm9B6vyYbOXoexLz7S3Gv3KWo43DNLmEZMpE9x0AJNFouLlEySiVgpcnH7b98dF/ANcPuJ+Ka7vXY/oBCoGutju6LO5AqYeaRHu3UrnYnXt9PgS3s3mnluhuiMl1gQ1PgZprj7OPkwKeX4+a/3Kll2P3/0lQEtH7rvQLZL4O8YUCe67xegEG1xjSdfT11cyGNkSJ2wn0S7N4b9yzV8nIK/lA/eeKVXI6zHOe2iyEhoB2wDsV5aafQKlnpzRpEWQLjrfMOJBnY1Yg8OLikQ4yispxgXaDoHeaeDDWVyoFmXsPAfreiJw0h4wQ4SmNh19zh9YJQhwOrCuFJCJ2kouiqR58a3ZDltUARSsrpV/8I7XdrR1EMWSiupSUwhM+4VneT3InZBUMbGPbnHeRtuwne722l4JaeP9e2Bvzdamsbu3C/32lBzwmongEuhPy1hSZWovfIgrI1OIZKT1ajBJSOxQ7kQc2TkACRBTRj5SuXRnRzluPcFhDA4R5lwRMh0sepRHaKot3cXFSjpUVc47tmgteDbt5hRhyHlUDUsWtOEF0ry7WNay8nNg9NCtB0LecM3q/PHvY5AuNUbeuT07C+ckL3HaaVdKb7hBWIGF2oetmT5d7DAocd+CPqtZYS2ZAQgEqwYgLX+i4EXON7n6gdrun7cFjJbO7ijVh53v8SKJ4o5UzvzC4gvB1+9AiMteHweOuetrBROcIXVkHnQsqOaFOUdSOcTrS6tfACJZRJKYjJ1Z/LQGfAYdHncYpwibkcGWpejZSBK4uzHOc2BjGW8nDDFU14EROrca49Rqjm90oqSt8GjqBp/jsIBU2N2gawoJqFDkDqHn/ONZW0lPIR2gSf7R4Pz4modd2BwCPUpJo2IOaZowK1BtRsR1kOHiFxXMTBSk/HlsDwBUsk8E5SM1xlwS1RcaNkzzoOsZJiJUqTtvEE9UvbUpLQEuN5eNkFuOM67iq4EJEQ8uSitnZ14eUWjNxMt3w01u5m+FJ/MzSXn9OY9u1c1+6FmmE5ZfWQVSasSSB+l0obGnuNzSKkccraqB1g+ocVg0iw8yAlL/0wNTbvc+MlDR08kj8tEy3KalgZOieGgtfjBkE0O79i97vV4GX4t7sI7TI9PCjidYDT3ZJ+8x07/wtNQaF3aL6GGEBt1+5RIigl6ayKcgf2vNwWEZhPgJMlEZPWdj5LE3WXueuCCSqTYHX0ULOEjAS3z1FQu3W9HWHqws4vWn86jtEWvrSexfrY73H30TEgs+KGQsru3flFmi6CCzgfd/0WHuSWlAsKCTyogq9PzVVZtI8yYWdh5ws0lZ2mByzbR2MgYSUlcIbj3BYQGa5RrHYAzFAAOTGI+4mugZ34nEhpPQNN4GrZzpkwueY4TcGCPe/fIiQ3RaWRxKByf1RXB13buAR2PcM1J+y8LJhR+z+wb88qFRF2KMLCNZyHVRcghUmbHL13IHMerhEG5H3rLoGEl5hAzKl5IArVea6B2urM7a6HBKBZZNe8RnNeTVuwZitT5wZWpmNJqr/GTYK9jTHpvwm3a7TCud7vYywFMGLnVZeYoMWFvF5Qy5pZqu8ZKj7971LD/J0yDZ9EESSzFMxOdRdQHTSZoa60dpbjTQuIlFI3pfTNlNKX4v+VKaVvxB6cv5tSGo3zK+L/vrh+xQ/8EhHxCeqmI93UTKxR551Oyrs/DlVTucXhHemE3wb/2s+ICN2N0XO5da8TpAOeigb4+/04nbvhURqddwEnk7ZPM+w4Ffeov1zALsWrRiBPURhaBCr8QhSjXc59NXER+Fqq1aK6yb3R2HiEQ4yE9bVHrtpCww+3AGR9tdOvXdjm1scxhTYm0XYxna48ZRm71rV7vHzPGfHxXduB93XqlH2No28IpOn7joHJlVZdpTDVZwOKwO9EUS5gNZXc++YMx5vGIFJKnwLeB6zKOX8spfQg8Ac55y+klP498Lc5599KKW0H3pVz/pcppU8At+Sc/7uzlj2W8uZNZTOaxsxE+bQicu9AATjrqQzgWqMdAhNxOJE4gfRg83E4IIJvm6o6HLhyTe6uh3x8uUbSFMJKtAK3dvpSuZQ2bV6AA8onEPFpSq+7Sa6lpFXWxH8t5HucKrB0OHCoCIRZF5wsdd7chQO+9J40oQj8IuqSfe7yJYp709bW62kuVa96eWTD8Z7o083H4MAamsK2LUAcgHahoL46XcTKrSpNsnIromNldII+Jq3MWWoujo+3ytEYzlMTyV6lZq9qWXqvK9Tl/bVtoly62GNz8xwckCAIQDnNQ28lLI9BlqIYo4z/XNz39D8SBpFS2gz8LPB/xf8EfBT4vbjlc8A/i98fj//E9Z9Ivg3X6Y4B3HqSShzSUNK2GnDNmW9P+FEHO4NGucNn3Xrwj2nWW5+impVu+qos//YMx2FH0RQaasu6uHaMOmtPAk+LzGgOSQdu/S5N7dWhrk2g3AYxhJjFBcQodYUj336uQzX/1W9K1lHbVkE33nXrq1SNSNwv0G1AEQ6JamVIMIohlR3rZn/bf9dYtaNKZs3dutf+u0XUtgq79ZmGJeDuj4+97vV8ED9adHLrE1RL57XoB43BqtJnTKY6+1W7uQtX0EZJ7rYpyqRz2pflMHUD5L69Z9R4BYbLJuYMSx3ISq1Xe33W7FmON7uz1u8B/0u8+n8EfgXYk3O+Jq5fCuzKOW9JKT0O/HTO+UBcewb4QM75cKvMO9DWe4n3vgzsHYX718DeVbGlW4ZHJ+DOS2HP90ojZ0bgpg+UVbC3xAaxt/4EbHsBbvteKfved5eZmfc9Vv4/shk+vRV2PwSkEkbc9vECSl4TmvaWj8Fjvwsvh4a45wMw34PPfK3U4+ErYce7YdcflusHJuGWm+GhL8LmWIdw28/Zlm4duOufwtgpuHsaGMCD18LO98NDv1vK3LcWbv0Z2PX7sCFSoG/6GDz9O/BSzCT81A2w6RTc+TjQh89fBLtWw84nSxmPT8Dtb4fd37Kt9z4I934HPhzb5A233jtSnvnsBnhsEh54vvyfnoTtl8OeJ4tH1x/ADVfDK3vhhSDE26+AG2fgjhjF+zfB3nWwI4Tqo1Nw51Ww569jnHpw0y/CA39cF5299SOw7UhskZjKNO6DUzEZC3jkcvj0B2H3F8r/w+Ow7VY4ch88G9GPW36+CPJfiu357rkpxulPYpzeBjveA7t2lusHpuCWX4SH/hNsjm0Wt90K2/8Kbo563HUTjA3g7q+W6w9eBzvfAQ/9Qbm+by3c+rNw7N/B02vLWN70s/Cb34KfjGX/PvXjsOko3PlEaf/nN8OuC2Hn12OcVsHt74bdj8FECMwbPgL3TsOHY/f47ZfDdTMxTh347LvhsUV44MnyjukJ2H4NnNwD3x2H/gjc8HbYsR+2xkLBt18GN87BHYFL3L++8NWO78PWhTNbEH9vAZFS+hjwMznn7Smlm/gHEhCNd4ynPN2J7eakHecpWWVCz6eoyCwU7bSGpkZ1iS+tJZ/N3Qt9PA4NTP8ObP0Vmiajg1DuI58tFyFRNb4m0Ggdy669c4JmzkJo+un/AFtvplokMPQ1G0lP0kzu3kCdhDVDTd1VHyq8JstgnKrlOsA7KPH6ZZh+PLbeU0xeM0kHVAtu1MoTJqBvuYvSzquibh7B8CSrlkkvK2D6t2Hr7fYMrTFwS1HXdN5TvqGJN/hvWT8DO698ghT98TnY+vM0tXrbtRGgCmWnb4GIqvsY1bXQ9HpNolPqu+q0jmJ1+pgRWzO+kwqKrqPsx6HNm1dSrRWFywGePLOAeDOJUh8Cfi6l9DPU4Nm/BdaklHo552XqpufE96XAgZRSj8LmR15frB0Z7tlIc9ERIB2PMVempJi9DUg5UJia5Tb8YJmzGliFnILg77mBZuKPgChHqIXY+yIxbaBLDO8TpvQ9a+9wZl/DcH7JPVupvvyilelAoOfrC+BS1GDB7hHAqWXrFV6Ty6Ypx/Jz99d33XMxNRVYabseblyw8rsUwlQKvKPpLlgkQNsh0pa7N+zLPtzz45zeRHZgsY0ttQV5283Mrd8+Q1O0JdwlxuKe99F0ZZPdC1UgaYXwF2guRZBp5qcIn1Iew1KCQa6rpr8cfSjsbbn07z0XUOhImbAvUF1jCZguZXsDyju6g2Ygr338vTGInPP/lHPenHO+AvgE8Gc55/8e+HPgF+K2X6a5N+cvx+9fiPvPbr4kmJcGkSZaCVm+uieXyLd0v6odLnQt4NfamIFCg8Fk846kOyEIEPSwqYhBxOX16FClviS/wlRKkV1P2WRFQGwktrAc9VC93IrQOc3aU7SgR13TwEFBMbTW0hC2Ioa8gCLuxfAjNMKJ8z2gnyCluiuUt29Ac+1DjYcsPc3cdOtLfeRhN9fc8DohMe/IvAOUbiV6eBErx4W2+sWFAXaP/zbFIWti3svXeg4XUMKXayibHG2gbHSkZKWF+vxQEK+hWHO+uVAHGM3VIhW4qUxIW2VqXhZpogLZGj+1tQdZbexBX1GtMxxvOsx5muPXgU+llPZRyP234/xvA+vj/KeA33jDkhJ85mViMFLV2lofwl0GEYpcAGnVNsDUsWuOijsw1nJLPvM1KmjkrooLBhG6Zwmm1rfeD9WcdvekQ11tWKblSYYE8Zk91AVLdIg4BdAtUxJk3h7XluwdCzRnDWpRFN+wl3in5+63mPQzL1LQr/lc15aQwHSAVJoxtqAfrn8AzZmtEi403zP87+OTahmf+XPrU9fubr2pTm6FtMt30FLnXND7/S5oLqKOi6y1CYog0KrcSopKlNXUL6HpqvqhyILoU0JClpjG0i0iLW8wBp95hWZ/LlOn+y8Dpzrlt9xceGs2zsk57wZ2x+9ngfef5p554Bf/7oUTLRS5gwAAIABJREFUJmmMlFsM0NQGTihtU9HPi4DkC/v97i+7NpGp6jFuqMLICXJgv6FZBxG74xJ6r7S62unE6/3hloOYU1aWJkolKsFqUyGPtUO1HpYoQkLv0gIvIuxV1PCo3BD1kTSghKfcLdXPpxXrOaV2K0vTx1DM4ytNZapJ7thOaj3rdODn/ZwLC0UJkl13weBCxDMf1U8n7L5VFKr/Hlx+wwQvfHWGPEPZtfs1yqZExyBt6JDzoLqQLiT1LgkrpaprzNXfEhaxUNLQddGYiwZ0Te+aGlQ3c8TOn+X4x7Ag/uGODA/7hjUW0jltRqIBWECTgDTYXStDROuTcqA5aF14+Fp7p8fInVmhmrOeCOR18/JV/85prvk9MAQOH76c6jpQ6zdkGC2bf4oCgC1QiLjt23uWoMKZrqWUZOUrRZnwffhCqhZSHX0lajGZh0Ghpm6vpIKQ7mTqfxsrcPPeGPfht9EUDl6WC4S2ReHuqAtst1REA23h7slJkcH48NXUfVESvPTEDPkksA7Salj7Eei9u9Q/Pz4oSqBndfCkMLmJmSr8FfZ090KCXYDzAB6+iLoNhEL+nieiHAhtx+B9e4bj3J6stSLlDRfA4bXUzoLqXkDVgpK42sQGKpM5E8HpNY/uUZli/BHYMBcz9pzZ5YuKGTTYOvx829TVIVNS7oti04oOtJhnw4mY0QlVu3vWpCfnqA5O4NLmF1IISwk9zgztJf3cKgtgbkOGw8sUrTlGsS4cFB63sjzzU8lmLhC1L4i2/etYOZ6V6MI/xm7DDBz2/U3dDdAMXPX5Ms3+UJ+3XctMSUaaoy5bqDZICOsYlH7cMAuHr6LQ3TGq0JTLcEmHVf/NBCf+w0n4HiUpag0V6PW9R+R2KqdkEH0zSwWy9d8T2tbAhmU4LGHfYzh3htXACUgLgd/JgiPq8EM7WasDuw5RmQEqM7iUdIDNEXEHEUVoUIWDpHdbi7vb0Yddv0sTTJImU7nSRA4HS+OIuOUWueWj687sPsffy8yw6ytUAvfQn8pyP11p1jJLZZKupBLxgp0TUyqByt0zCeFIRtv1RDwn7eU+vEJz6hff68NxB2lit8i8LXp3uz+srbu+YP2sdrtQgabw8+Qov7ddD0VZ9F9p7cJ5FLmK5LJdfwCdKyBdSQGYP9FpbDY9zoD+k6eKYNDGy4tWrxF7h2jKXTdFgMbqO4ftMyG366+p4y4AUzNl5VporQlfUOgsx7ktIEQ0sgyCGJJ8dQ3k6cxJEYGDdDqcOR0991x5B9P8uhOtm7UuoPTf909w4pY20+BKiOmcl+MWQJuJ1FYHPHWI6GXOyrpRTPw4zd23lZ2n+shKgiqk5MLo9wqKlhKxyWydo0Y9NlOXq3PXqL1gqtrn2YF+vo0teVu9zx0gdWXRHg8fEwl+nRNIS/z3FZ6kkTcC750aCrrBImTNkj2U4Yp4dkMZpKWno098OzzRZztXpB39SZR+X0t1C62daYkifAb2jKyERUqIepYyj0k7vGmuzdlinFatc/MYKXkeHKOxOlSWf+ZaSITtroMzlPvrZ/JbdV3EEtrmgPaZPBPYqbLchXEt316jwOvs2Ag0rZcWxnFgsnW/zGRp/jkqcbnQhGbylaIK4xRiU8q1tL27Zfq22bMHfAYpVLRe/rH21UjUJfN9mTkxqiyctgshIez5LR5loJR/YBXNfncG0X/HFHzs3bJTOzRuHl1QeaKtjRRGHQP6c4VGJ6F7AvoHKMlJ38yljxeA4wPmJBik3QXeiuEl3DvUSI/qpkQ6JbjJFe0yzHnIg/L7gPprtF4bArGelzJHdZWEM53hOLcxiLGUy0K0XVhnoi5RfS9l4CkPQDtcqzMcX2gzs5v+UKW2rrfAucY51/LuNw/seRcUHtN3bSkClFDq2X+oBOtWhmtB+ccD6jRfD895jv8lwEHqQiXe7g7VulAWpDNXO/IgrasytBmwgLAO1bTVPBMBwyP2DmnmtVRNrTFwAS8G9SiRBK/3de8Mv9VOt+CkTDwyk1rf6kPlb1xExUtmKBrs+9C5LpE3dcj7+0U4vEy1Fl6jgMWaAzNLXaC3b58RhqDycFtEtVcuiVt9A5oYRo/mqtayRkRvcotkVUqIf+uHGIN46AgwGdwiZnEfSt9x//Db73WGxH47juAAlDN6goe+FOfF0G4GD1rluXujujjg52awNMfp0rKhCoMQDg99xe7RtxO/W1COxEtbvZsylU7CxsHLmL8yrJMLGXfTZuEhbZrrbfF+kUA7QZ2WrAllap8I3sfFQVHHU9pt1bg8GP9d40pwtaNNnuch4eoukzJcp+JboK8vziLtrJmxYer/4X8GXkrk7/ZhHyUl/SjFejrOEGDsrIS0gSKolSHrSXXzDIVR1jqsUgJaoV3Wh3AJuRtL8NDT1JmiHeoGxO5iCsuQJad8iDMc57aLkWCzBnppeKp0XphVDS3qwE/b13Rf3zWJzHAJiq49Q/m9WftPni4+rmf03+re+C8ihkrMbcxAzLNsz5t5vVmS39ulVFuvm+onU1NuxV4qA/hMQQkGd3N875ER4EoKwx8s04qBisUIHxpQBYGWvz9M1djuCgjYlUCVWe8ApfpFFgTWxi5sPkFz/kl7HNylkdCRMNKSfbpPGtjr0hZgkxRBq7ocB2bgkmMwODQoK59ZlmjnysTgSILvDeAS6G0pmYv9BUo0QxvjtF06qG7aLE2BJfBXfaNwdILN7e30YhLa8OPum6zCN5AA57aAcI0ciHjuUqSzr4nosem2Ka/U0xGavmZ7haKeXXPN5ZaGWwWOMbQESoMo24Q+oDlYLiS8Lrrmpq7u8ffrne7m6JD5LkJ8Jr41EUshMd0rN0JMp7K6wI/E9S/HufbKyB0rT/NBvO4d6hRv97tdoCk8LcRdZrLa1bayHBtx97AtjMQ4KkfhQsdqdN9+6urTPZqrRL9KwSBGqFO6ZV3JdO8APwHp+9HPcwne1YUfzSxNJfLX+uWejVTBpLR7tUn9JKtWzOwWVLZv7x8pPPWp+lmYTluIepToNMc5j0FsuBQOa2csmVVd6spIWgdCjZcJ6BOrJBBcs/jaDrIqROiu7fuwYT5WUNa9mSYx6ts73308BxYdLReKrfe3hYZHMjqwYREOex11yM2apy4e4zF+5SwofTvamk5QNoFVLoL8WzGc2iBzfByYjXpoxquY1IWUhKODpA4AQxk/zWLtUnIzfjXassv6xfMgHJQm8lM2UiMmbQGh/tQ7lFo+b/dJMEhQeGRDlpUslB4ljfpd0YYDwCuw4TU4vIqaW6LtBnsJ+h14rV+sj4iIdH6qCy/1GfwnqlsxoLmdouoqGpWlIEUnmhbI2IcNAzisUKnaJTyoY8/KglQ25bd/WDEIYLt8OZmD0kRtHEGELEIQk7m2UWc7Mu3mGjQZPqTr9m9TGcfNcH+HpLTOeYjQ3aSBnetTtUe23xpAPRNA3/ZpaqhM2qNvvwU8yiQXgWvWpYRGoNvZLSiZ67pPhzSthM8ybH+ZyqgSYmJeEabCoN5nbsWdsLLVl5uA91Bm8DiDO5Wq3zJs/5tWX6k/266gBJUYWD6/cAf1VaK4EMnK0mrqV1DmUiwBT8Tzq8u57XLdNsZHCmyQ4VS/lHMsyrtxhMELAwbfTHBFp0zqcjDbx1OYjMBbuS+u3Jbrvdu1b4noWtv8QRMrEj4hYX6W45wXEDcrscM1g0tOZzoNrKStuxRt3ECSdMHukeb1Z3qx0IvOuVWg94pJnOH1LWLVx4FStWHZzotZpAVgqAFvfpFKtEKn3YoRkctdcmbxqMUMzX0XPOID1S3zMs1/vfmolatnewx3/+YC6lRu+fQC/Dyi5AJ2Dvga8C2au2u5S9aK2tz8tPUhdp+Dv+52yfeGajFOUOZQ/DhFMB2jCmbVeZniz18edXuWEg1aBayFm5+gCIsV9j2Sym8BgbFT+2gHOJpJNybSj06R1veqWzFOneWqftf4OOjs7u9YvfdmLVXoUTONYT/GR5tMTdp9ZznObQzCDxGtgBuhr470i1ndrHJGFkGLUNyU9zi7m8zO2B4WlMmpwwcCmpbG6Qa2DVL6OZ3XNGvVS/WX9kvWDx4KVV1y6zkXgMs0268l790EDZMYqGFQx1rUP7NR/pq4RwJvtlWOT2GXIFPbFoG/oAgICR0Xpm08o320o0WuEAbUNPREVQqrKJmPl1A2++lQQ7VKeoql29hP7Xutj6HtBTWmynXowOglmXw9LP0JxRXpQhrA+nUjHNyyRF4a0OvP0R9drut0ak1Sdy1Fq+uom0ZpPUqFN+We6hmNq8bbQWN3TdyVPsNxbguIBHfJ3BTi6vtIqvMc8XfgS/+7rXL9GSckMZkIOITMXR+iErruFQO6EBFxujBxP1z3ObDpeAE0FgAZmsOhue96m/XDgJo4ox23oIa53HJQ+z1cqbi7viUwRqkgoQNYmgOwCHddQWXIdsh2grJHqspNFAaQ9pLAVt94/bUZkrt9TsDCRKIv7/qI9afPzIQ6uW6Z6lrEcwCdi0YYdJbK+d00Q+WKVpyijMOrcf670NkCg4spVsQBYA3c9XOUaI1W4BqFpaPAn1LckS5s/vgoB7+7yMEXZktE6Hchj+QSEj1CnYPTj47pRkU1jlJIA+osUg/JAndtijYI1xC4rz4aroKe4FQufeU7f5/mOLddjFzWBCyYQphs11HDapmmOyELwkEql8hu2kog+L3tZJxgmrE22OiEm+2cDo+IiIE0yHrn6SYTYe8m2vlOSnJOgrF5SgTnmLUlNk1pWB0SENIsoxTzWVECaW/PY3CfX4y+Ie5X/4ZFMKZzJkRJFMJ8jQr6baKAj+7TS6iP0zSHHZORsHLBLoFlVtaYp7x71qWPpwA/lb8AaQrWf3R1KX+hvqK7scvoFZ0iHDwhShbbAgyOAdel0glHgRMwpkWHZXkB+RXIz1AEzFE48LVFuKwL3wR+D9Io5GNLJf1ZFoGE2iDXeqnNmo7vLuUp6updIzCmfpAFvFjqN1xRzF090cYbRDHObQEB3C1wcjw67SDVh9YkIhG5Os92PB4KhPbsRwe/3DzDygqmu/sbVrY+ztgSNgJHPX6emmUNNZ0SiKQZNBlojjqJapQiFG/vwLvh7meoC8poroCEpJjLoylQhcAJCuFfYP3l808cB9HsyjU0Izahke5+Lu7zmHyiLh4zEu/aZH19qdVLVo7PIZHwmLc+dAzFQeno07u/TnP3NM/E1MrgUJktys1zcOirh5sg4OZE2jBK2tRj6oPx/CqKkFRfJQrDP5PrhKsZuHsXNTK2krovCNRsyb0weKpTxuEA5EMwEC206UXhaSWYOQaUKO5GhK87c9AJwX33S6WsJCErLGOS6gItA/3cBMTPcpzzLgZQOlwa4QilE1YWo2Jo6i/T3IBWcd82g3qo0v1fj8sv228HOkW4XSvXASEvsx1ZGFhZSxTto30JhJ+Iybr27BPA/kHRxBKIMxRNLTS7T43LyxJxolYmYOxr0Yg4HLf3L1MnVR2lrCkhl+d0WEzbhZHAUFnfp5jeWpBVAmGKmsTTBn198pLOy9LweD/2jNZG0Hs18czxEo2bBGIH+Cngz6Az2WFwcsDya3Msd2BJYVNZBQ4WbkikyUTuDhpAc+dqGCi/5GIYfxHWr4BXnoGl2EM1Ty8VWl6O/rWQ7dCqk7D2qJn6pRf9Rr2e+ok8moswj/7J2mdkfyqRl9lcFabnuqhdZznObQsiwYPrKR11CpJQ8hWUCVsCaVy7iHmH/hyVuJypdTh24IRqUv3Ba2hqL31r8NqIvMCl3CrL36noi1s7kvzt+08B++HBS6mWwgkK8zkAqXcpxq35DbII5in4gJhsnhptcADUE3WupqxobX38oNZgcEtLjKR+maEIH4U7hZcI7FTfubUjiybTXFHKQ8PSsD148PrWeQ9VS2OqXW5lqK9PAj8C3fd2Clg5Uc4PjkG6irp4MNSEpalcVoRaxzCc+OCNkH8sVTBxVZf5eTg2kli5ZaSEPmWReB+59StakHu0iro6lOhihuIqSllk6I9lBmE5PXghVbCdCBxjNjenr2uM3fo9y/GmBERKaU1K6fdSSt9NKe1NKd2YUlqXUnokpfR0fK+Ne1NK6d/F1nvfTim95w1fkGGnZlKOQV5NHXwxtXwqdYwDbB4qcxfEyh9ed7dBqHsw0s63WRnuqy9beRJCjie48HArQmi33BL3kaGCR7o/Yt87r6Jox8Vo9wwVnXcXRppT76B1n/rHzVDvz8PWzoMUkE7P9WGnxsGtM00gWm99K2tBAmdDfJ+k+s7adlB+s1ZJEuDnAK9HWfqwcwtNEx2abZO2nKAJ2s1Hm/5zqcvSXy1XDGAEJjeNlyiFTP3jlMlXRynA5D7KitExDjvXQ36mQ+pSrK7ZPnkRZvowOzsoz5+MfhV99M0VkHBV3Q2YHlp1Y9F/SshSUpvyIo7CTqjJcKdyzQlKkDpUpenKVGnzZzjerAXxb4E/yjm/gzIVaC9lMdo/zTlfS8FxtTjtNuDa+NwB/NYP8oKHXqD67L7TkRhLmsaRenc7fFkxaFoTboaKoKFq6Tj/0JdpgpQyB9tLzLVDm6qDWzieN6H/MwyZZlSpvFp9SUDZCDy0O/5PWN1P0RRAwjOgqb31TlkbHhpONPMWJDCgJqlZ9uFDz1t/tcOnC1SGFLNrgZrjUQ+5ODKxp6gTkRS+04xTd93kdgRjPfQF608HgAXceoakaEFL8p2I91xO3Wfl2hKlWO4OCiNqmXlfx+EIZQMHuY45NtL5oz75YQoI+ZeQxnrk1GXpiX4RKh2KlbIa2AL8KuR30XQhVNcORYBpHsvJuP4KJV1eoWTRT6wc9VBs1jNcGiGsbXqQtdq2J1yNUIHrMxx/bwwipbQa+CfArwDknBeBxZTSx4Gb4rbPUYJIv07Zeu/zsdT9nrA+NuWcD57xJa7tNyV4KddwTaaZBSafbZzXN9on87RzBdqCwgWGGMYPdxskFByYbANKLoLFTImiDfZTmTokuqJcHKZaEldTRC8Uwr2Ios0WqJYG9i4xryIPElgrqMuoSQAKyReTOa6h/vFFZ8SkLlySlTlFJV4xmIS2TGdFHFyjyd8fj75xvMEtlU7rne3rogsJ53UUhpKQk6CT6b6fYh3Ml3cPTsH8ywulr/ZRJlXJYp2iObdHSyHKRVimWBAvQp5YLvXYRwUvt0IaQNqSGDySCsPPDiomIPqco2AKp6hgtG98PEdzSv4EzSxeAb0+a9fBceVceMLhGY43A1JeSemOB1JK7wb+GvjXwIXG9C9TPCYo6Sgv2vMHqCsUDI/G1ntduIqyY9D9RzN7x2LrPeDRcbjz8th6L8eWbu+EB74JWyK56Nafhm1/a1vvvRcOjsN9jwIJHrkMPv1+2P175f/hMdh2c2y9F9ue3fJT0OvDdEwtvue9ZT+Gz+wp/x++Ena8E3Z9sdTjwFRr6z1g28dg++ORkZnhrnfD2HNw9zfLex+8DHZeAX+4B3If9k3ArdvKEnMb+sBauOnfwMr/CNO/DyzBp9bCpnm48/nS1s9fDrvWwM4o8/EJuP162P03saVbjq33XoAPxyrX26+OLd2+X65/dmNsvbe/lDk9CdvfBXu+Sdl6rwM3/Bis7sP0E6WM26+GG0/BHS+XMu6/JLZIfCrGaTXceTHseaKUOTMKN30IHvhb2BL1uPU9sfVeaNp7b4CDk3Dff4lx2gyffk9skUhsvffzsHEGph8oz9zyCbj1W/BLTwFduOeny54qn/lcqdfDq2HH22HXHwMDODAGt3wUHrozZoV2YNu/h+274eZHgAW465oSSlXU5sHLYee18NBXgR7suwBuvRkumYXpPyzvuenH4TefgJ+M/eI+tRk2deDOJ4Fp+H/eC390KPM7/zGTO/D4CNx+Hez+y9giEbjhnXDv4zFOo7D97XDdy/DJ10o9P7seHhuDB4KbplfC9kvgyiWYfq4EKW64EnYchK0xgez2y+HGE3DHazFOF8He2cJPWznz8f9T9+5Bdh33nd+n750HBoM3QYIgQYqUSEmUaMl6wCatcBe1WtmGEzvLWls2UrVUGHu5KXq1qSjFOJZqFYdZeZ2FI9eGFVSKWZsrbrmglRUzEWWjbNoybLpMyNDDEh8QxTcJEsSDBIjXDGbm3s4f3Z/bv3NJACrT2oJO1a2Ze+45ffp0//r3+P4e/Wa23vsgsAf4UM75qymlf0vhSx/POa8J1x3NOa9NKX0Z+I2c81/W838K/ErO+WtnfcaKlLmRwqlfopVeN45gNQ1JlvNeDP0NMHgPRRVUNYSulqDEUaoL3qi2ed0wXBfBwB7d3ALCPdGU8JkRA9HGVhrXoKL+xTAQpV5JcQ2eBt5V33UJ+H8oUk+2uoLiTjT+Qawhai5RO1DFtt9iHNDqFYpNRCC2R5E+6+t1SlFL6iuZrP7lmPn8XN/B9zU4ajG0tYay1XMf+FZ4h2O0QjgptKupEr0ol9Jcr/OU/UHWA1+u4wYj0LtTCLZPcQEvq9cZwWqRFTcc1hx7B1237hxFIzgTPkpwPS2O0zT0phLDnMt1r9KV+ifLNRyvY6bmU+MWUqJkNVscRlDesYw1LnrhnJquY+A13/3+JGvtB/bnnL9av3+RkmpzMKW0EaD+PVR/f5FC8h6baNvynfXY9TeUPHszOaMNK9rtBM6UVtN7e7A5kd6TSKvp1isYR24luuihiK7KZbBrFw0ci1hERM/juRhnEZ8VPSQRva7HcBFWv3+q1Qt8iaKD/QXwGOz6VzS/POG50XSJuEBkdtCA2wiIuVeGxOR4Wmgk0XIJKnC26+u0YCezBwVMI0quSisY6ULUZSjGUjfg5RDwu8DvUxbgFopJcjndFOw6Zrt+j4ZZiBMcp+FTZ+oYvkLxxJiGPgjtuJnQGcoCf5aGhcjktNdz/XsFDSM6BbvupTFNBcNkuN/oXDGZ12B4ojKHI7QygYRxE8SFhhlVnCVH2s/t910HaHUzoe3ypTloOzJzaMz0LMffmkHknF8GXkgpvaOe+jDwGN0t9j5Gd+u9W6o34wbgtXPiDwCLpbT5KLTU4KFlNLfTCoobaQ2FiA/C0qEh3J1hfyYvp1tYJi5+F4DfI/7gZ6mkFXe8BMNwDzTGEzWG2E600aMUtBpQXaB5DubyIqyD3g2ppQ3XoLD1hynS9GC4V0KNgKi4hnap0XUTtLqRhjh7XYy2M1BL4DIGhR2D9QJqL9CI2wXRoy3m5RQMYIpWvalHk3qZwgA+QCvEcoiySPfQMBoZi2NamexoXqI2dJqCs5ygSOIXa5ubKLq0i8V+rKJt9hzHblX4yPQmwnVPU0Tk8UqjukTHsa0ISkcP2AlaMZgYSRppSBrRo2F6fIwTGTKigfUyJuM4ZAIyiejqdSzPY0C82UCpjwO/m1KaogzZrbUbX0gp/SIl0vyj9do/BH6KAtucrtee+4iL19LsLmyjDPUVm0E5R3FBrYP8NuA7dE0MF2yfpgrHjNBIDJH4YqCOEx6jM2UCUZvwedBlFnpJ/Lh4T8HC32SYgvxCLqaDqvmPAZ8BHg/nVtMSoETqNYU0C2If++EaF/FRGogo8Q1pmZlqOpouMpdX6GoEgmbR83CMrimxRGEE0Obwg/VzgFYN+0Sdt4soXoYHaWaBYxWDyew7dIObHH/3P317beMb9bvSc3n9+0bmYwQ/DeLaT1vQvq9b2Om2jAIlmlqOr8F8MXBLJj+keapiDQjfVUZukZoY7CTzqHOQFiCvpwgVTfHDvB6UP8txYReMWZny7A8XYGskga2O3KdEwv0XwCMUm/UxCsGfpCyujTTurPosc1DttSKzKdTR714JZHapgKAdD4hEMzF2D3SlQPxrKLYEG4OvLA9nH9dT3GFvoRDCV2F2D5x6tr7Laorkm6WZP0p+PQQSbpS+sU9eY8BO1EpO0JjDmLtxdlDHw3GQ2cRKUDIkMxWHtDBkVX1Trd9Dmb8n6ucAzW5eH/rhVnQVT5ml0oYMQ6YwrmlO1nZ0Hx+lpJa/TFvAMYhISS+ThK7HKrrDB5U+NAuktag5akq64L1XxumRafMpFqHHRdfmyvC7FbCr+3L2DJySeWjaLFHiM+q4ZXNLfN4S8OQPcMGYT71Ak45OjMU411DswUspKuQHaOrhhylcs9YN7ABvkWtKeNrKcUTqAH7q6zR1ziMGHSmhvWfcfRp/j7iFRDxPIdaa2MPJ2vevUPCHLwF/Ap/aQ7Pr3XdzXWjLwjBRhdYmlWDc+s73cUHIvAwOsraBC+cyRszwU/vrfVfQEroE+wj3rKAbk2Dw0jxl7i6mMISH67tcUd/Hxawq7mYz1mWsmsanvkozJ/w4hsdp0abHKFrlcxQNbJGitbyDbpVuJb+LLGqAUStQU6rXfWofzVzw/WVM0RyIJpwMXYZjvI5t6N7s0wSBORX2EZoLNcGnXqLNY2QSqcozGZcCYwD9H+hkrQQfOUojtssoHHQGuCqVen+PUSZnC0XivotWtmyJFnGohmDkmBGaTmAMe40BQz34yH66DEGJDV01s/a5w0igi1E4sTK7E7QErZMU6WZ/X6UwjoeBw/CRuJWasR6HacxPO1PiUGsyV2GasgjH4yYkVIuPHKx9OULLaXGbveXwkRO1rSOMqkyNwC83ldFWj5jN6dqu/dePv0hh8jdRFu0ainNccDBmNJq5OoCPvFjH7jWa5ni8jt8RCvZwiIKVPEWJaXiMEpnz5xQAM5pb494tbXfN2ziHBlABHzkczovDxAhZ9wYRoI7YhIxcDXWOwiCvom3/Z+xK3BNVZjOg0MkJ+Ij1IqTfxfJueTwewkCqlTAYj/MZOy7sZC0PgaOYe3E4wzWpDMRpSqGRgxRXlXnyEeCBcq0S1zyBiDM4uQ6m3N1Bj56JCBIRroHG5Z0QzytZIwgaQ4Oje1ViV+IaRbqWZse7eJVIqvMCi0b7GVCVKep79AY5npr4nZe5AAAgAElEQVQ4Xms/fIfH6S6i6yhoUixcGyNbY16FCzDTtLWLaczhIsoCXkYpjjtDWcy+u4i9trteE70Q0ExFwj3Wlxg3raKdHjGkqpKP6nNqMk3SihTpNXFszNqMeIWakv32e5xj+xozeMU4DlEYhHU6ZBzP0TZMdp5PJ9J0Lm7PRKGBE3QxFk3NGIErPZ2nHsSFzSCG8AnzIASfJhgVT+Wvc5EcT9OAnVdoOEMIhx2VautR0OxnKExiJpxXO5Bo6sL+xI20Be3ERw+GC9JJ79FlAi4gF5eSBAqBRak/R3OHuaiqJPvEeykMMPbD/i7V97Gtcd+3M62d7AJZQZOGQ1p6sUTkdboNV8En3kmLpfD3WLVIdX1VeNcImgq2naBViF6gMPgpCmOsqcujPI4adMYiI3PwE+8NbUZm4fsqRcfNg6B6A12Pg8x9SJHkmqi2L+akttarNNoPz5AphgpTHY+BoLDjpCfJcPDTFIYpeHmG5qpXg3IcZjJ5EVgGn7isti8AL83JCGz/GC0p7zw2xIXNIDJsdBBDbsRI5TpCUR+1yyMnjiGkSspEsUUvotigf0pbkOOBRSLHQ9h4OtwfF2CU+OMf+ypDEBlX4kYPg35pCfcErRaDMQkJNh6gSSI1g+gaixqQ9qySxD6oiVmkxMXlvYKphnDH+gz12GiyU1SpI8FHLCPmikBLrhtVfqYswNl67QnKnNq22wM6n4KFp2HjqdrObGg3zqPjbEWruHBjwRtB0xz6NEkrlAxtEdsPF/QcbDxB0UxdtGouc+F/wfUYwxI1Ut9NrSW4m9MZSMM6BcH0HZlC9b02JkivUTKdI6gaAtPSccjO7fcAMFzYGESGO56kEI6ov3sURtUc2ma0g3DexRODhk5QEmq+TLeWAeG6yXD9pXDHI3QnJU6y9ynFXWQuxqjeRhXTBaQks/3lFC1hA0WCrWFkA9+h2q00G4T26niNmEN/7H8lmCi/CTsRe5igbRkQVeno7jwOdzwT3mvc6+P7rKBtCzcOXmaKtrCSBry9QDF/vkvT7GICXAR3a6bnHU9RFr9z5jPVsARvoRucFYPV1DRjtOIcjeaMV4DGfMfclHe8QANSX6VhSDKfWlhmdJ+MaC78tqx6GxQcA0Zu+NwvQXQdmpJ2pIGTcMd+yj6dYgyaQGcYaeBJ88mcmEg/b3Bc2BoEwLBw0KxEceJfocVCqELFsFklfIwgkziO0sCao7QAGAuNxHsE8KKKqDQcjH2PxCwjkahiFKO/Gbr8LK1KdYxChMYYx7GQKVoR2Yi2y/QEJvWUCM56re3GwC3HqNaeHKm7jpub8GTafpuafKq9tnGS5qZzTGPswjwl0ebFet0lFFMxFm4d0oLkxuNQcvhd7UzNx34PaAljJmtR+6zJ5BzJJNV4ojdA7UnJHrVH58UgJsfBeBGZrsl3EfPSfKpYQ+7TpSkxkcU+aXZQwMYYkBefGTURBdcCpKO13YoNDaWDaYqp4Zic5bjgGcS9ayibu6jaR0DJVGhoEk9gJ3oZoBusAk1ixIUxTVEVI0N5Fe69JjzfxeJ9Z3N3xkUbGcwgnFeKxwUWg7aCmcMC3HsZ3XoXSiiDmKI2ZMyI9ShfDOPiu0Qpq3SzApRMJJpLFRO4d31432hSOfbGPPiOb6W4MzX/TLU+Wj/a6oKyEQuardeMS9Z1cO9GWkwL4f5NFMGxSAH7HqbFEWjCaUYqKMxjWUFLDtA0iJGmhvmfaX/vXUMXSJYOxjWxmjHaAcChi3043yto9D45IOuCj1pYPLcS7r2crjl9hlJ1zWC1KKBO1LYty3eW48JmEEPYVe2lNKCVmBvU9/RlT9PclA6EbxaR6wHN9nYnayWFrik5qgDdMth1GW0So7ruZEZmEb0a0VMxzhyGFBfl4dAONDNlGK6tbq1dq+j62v2rmqztbD8Fpg7QbPrYL5+jJuK4jWMnibaIgF2XjLU1Q9kU+DVK1GOPVsHKZKf3UVLWLXbyBEVjiGadh96U9ZTFrqvb96x5E7vW0DYIjuP/FG0xHKAEm1nOfjWtLsUiJc9jmhIdacBcnEfHyXGPgU5VQ9vlZjtqeAqJNRSTQ2a9GNqk9ns+nI87hJ2mix2pscXxqv1MCfI07LqS4hZXUKhxxBgM7ztDY1bnOC54DGLnQUrBi7pYgRIRpn2oVBKgjOi+kxttdcG7KKmiC0qg7DiFkA7Czj+mFfCogUxJoFTCjMEvFjwxWOf02EftxSCiGIcwDoIGtXTnw3RNHWi2sAsZGnO4jrblXgR5ZbR6KpTAI5WWFoglEKamsQF2PhV+s80eLQZiLQ2pPwZ8Dfg2LTFpgWaCWFpeZH2JwhiUoM/X69cA/5iyqI4XMG5nLSnPCZr3yoUb1fmn6rzp6YnejiEl4vZ99a8VrvSAnKx/19L2DjFups77zmdoWlfcf8Ngp4n6TuIqy2nRqoZMz4T7fHZMAJuiMEv7Jv0OK+5wEHbuoTGzKVrMRARw58aecx4V4cLWIKCpYnLo6KOOBAGtcKe2mXbgJCUQZ4GmXkIXyYaGdEebW+l+nOYBSFV7McPU+8cBMDl9jMpTIuSxe6DZ7BEUjIQCDXvwvdQWlBIGUGmTRy9HPPrhd00VPRGpjsUKCvEqBU2oiuqxY7Orjv8mWjl29wmNC0dGbFEXx2KeAswOwn0i+Y7VWqq4zGX8vVezxnd1EQlgHqQtfMdPrek1ivYgDvVOWoKXoeuJhlWl8BzxDmiArAuvMpA0U03k43TCn0dz5PeTYUykHWnd93S/EcFKaOX6av2S0byeopnf0VMWzVbB13McFzyDeMQQaLlgnIABZSB86WM0FDsGwmhfH6MRRrTVYiZf3KOAct8jkVEIiEEzU6KmEoE46AJj0d0nyCRz8X9xFU2i4GZ8xMQ0NQuZUTQbVFmNWlRtre8y6qNtyxRiCLea2MnwHBny8VLkZLSTte+lXbufQqw+S4KWyH1/tRrDvhcogVerKElV36LNo1menwPO5JF28cgsjTHG8ThTx/EYLZjMSNqLw3fn7TW6wOYcpTDik6Ht2GeZN+U5j6j5jbu9FykuRwVYzOeBFotQ70kZskzDuB01E5+pqzyC3VWDfCQKR2lWOg+RldaxTPOVeZ3juLCTtZalzPU0fMBSWxKCbjBNBm3w5TTCVc3TP71QhZABQU6S+ITJLGtpm44orWROuhCdOGjAFXSBOxeJNrC/yWQiNqAUUxWMzEQpqcS0kIjvRxiTOKUCc9ACfaL01//fo6jZL1Mklf2M3pxpWsyB46bEHo+/UAuK+JDRiL6vEloGrmenpu2PJK59NPVaE2383tlieuQFCiOQwWhejGuHal4KHKNIz1BMAE0i75Nx+56rKEwo0mHEbMQzCO8mw4JCX9M0YFS3ZWTA0/U5P0Jhvs/QBY8J8+RcyHDVVBwH6WYCeCukTZD3Al/5AU7W2v0oTd3s0YJmlPhOvERvtptI+aCeu5LRIswOYpS+hHN6FqoPf/fDtIWuXSqq7XklS2QOxir0w3VybBdnXPh+xCpO0iZ5DnZ/jcYAIqIe+xGZloV+5yh5K9BCf5doNRpkpm6cKwOMJoGaxsk6HmZa2gcXjhJwEJ4BjVF5vdqHORcWiTVxLeY/yPznaVsSzsPur4d2h+W3rLv6NRp+oGkhjcQxVCqbyXuiPu8lukBt1Ewd38rEdxuPcSlNxRdkjNiZdGOtCrWHYzSNzns1HYYU0+0qCnOepHil/gfgx8McTMLu5xhpGr0BsJiaqSITPEYxlx6F/CDdYLA3OC54E2M20ySzQFlU66co0WFKtAXKoFnGy0X7Eo3gIwjogooL7NraxkFgrqTzjqTveKyDC9X/oy/aWAKlZZTwagUS9/xYOzI2XW1zMJvC7zGsejI8z3Tx6MadoOi6alQRXHWR2JYSDxqRaopVgG5WhqhkZuyeCQqDVvU2TFop7JyKk+jetR/r6hx8M4yZjNf+TVTaMPpQSo4RtBFjgbZQo6ZzEaUe+5+G56vxnYHUq2q/LldpMZgLs0uURa/26fhHzXC+XT8CgmWeczQAs85bDxgmYCrBdC7Mx/auoNCzz6wCZnbAKD5oOEG5T9xO5uS7WAFsHJsaOy54BjEaYMuZaZPp9lmodpScukfbtWktrVCGRGmIrztYRd+yA/gUBaiKHgP7of0f0XIJYxg+Sod5igqpRLGtGC+/lubyu4hm2rjQJujG4kfXqlLKKEAJWNt3QKuQ7DU+fy0tsWcQ2lfNdUx8Z5mPjHqhvptSthfa6lMWlOCZY2RSmNvaPUsL4pLRnaLELviOQZsbmY/e4zuGhQI0IeJ7raFpI/ZnmrKgdtf+GHE4S5HOeyC/QDOXZJYxYlYG5K5aMnnfV63DPmgWOm6J4m06xAgv6k3AULypn8u7Phv6/x3KjmsKroiRQTOXqybYOw5DmWmk54ibnOW44DGI/lsg9+oLKglWU6SXASWqZ9q3qrbuCdGnRGPKRCRgF4yDGfPz1RQWyxwNolvI9iUA7U9VQijEAF1Jr5bgQriIxmBMTrJClExGM2MC+gMYyLScYO1a++6iegslfDl6e2QsxkcM6/1KWu8XtJyEdLqM/yhi8gz0h5Q0YSMXA2A3ikZ07MezMJfRzDEBQZlcBDGh6469klJH/RhlUR+B/slK3zlcp7CwfoRjJAi5lqKuP0XZGHlfvXYtbaeyVRQpvZ8SR+GWhZprubxXSkW76J+GQYxL8R0Uv44BNAY1Ufuj2TUTvutKdU6GFExlA60UvqX6zatYCPMStenU2k2TlT8IGE9QKk79wBaMSbD9UGUOfUhy75GdnboqrhPzNkbELJfMqnbGITgZMgZdPqrAQe3d/jyvd7lNhf9deJGDO0kREBRhh66dfjhcc4xCBJZfUyXMtR8yuYvohtmuphC5XgE3W/GjuWDKchyP6IaNQNtEfcWZ+rx63/bnaH72FXTdxIYqi39YkAVeb+KYj+J7eMh01CYsMPMMpZ7DqfJ9+0t0q1j1KR6QkCcx6psm24CiVR6naCmaYpfRalbOU7wosR7G8vB3WWnLOgvbn6NpX75nFBZmqxJ+17NgZbMFWlGYeRodiFVZ1+JiurEM1jsBtr8y1kffvXoCc/TA1bWSTVc/y/GmTIyU0n8P/BLllR+m1JncCHyeQlJfB/5JznkhpTQN3Eup+/QK8PM552fP94ybDGE1Gs2JWKD4haL/2wXlTknRn390rGFV6BjdKLCTcsfmvcnEG80RgSRo6rWEZX9ME1YFlmtnmhfgDIXwo1SZCPfI/HrASrjJxRftW1VR4y1s15qRurVWUrSu6IEReItquVqMJedUX/WWDOAmAeAeZREZVh13HJfgtceheZJE4V10vqfvYQUwtSGJ/QCNwSzATccoDOEQzeR7loYnKQAcS71g5uKoAZ2k4B3R/ammqNdqLrQnmDpX7r9JrcKxjgwx0q/mADQM6hQjd3I6Ub1rChaZvMLOYsWv0GixasNpBm6yeO5UHfuIj+V6zhwM6Tgyrjc4/tYaRErpcuBfAB/MObtL4i8A/xvwWznnayhT8Yv1ll8Ejtbzv1WvO/ch4S5V9WipahHiEaru1XU54sTfqU8WmV6gVVeO4cjasar1c8Bc7oKH0b6OiLocPAZCKaVlZEpSr19JkQAToT33wZRhOYlRAmVaEpOmgbb+cQoRWZpNl5+ahUT9Ki3oaomysP4ZTaLEzFhL1ekqc8FHD0nEYGJQmBSldlJ3Y6cPXEPbw8NEOxlF1Cp0i0a84duh72oN0YXouKgZ+u5qMetpwUJiLCvDmGbaglQTEiOShgxA0gWpBM+hTeh6s3SdS68yLn9TKKyt3jW9DpqGahsb6vjJhMNvCTqVtkZjZB8naQl7MeI0arhnOd6siTEBzKSUhNEOUKLyv1h//xxlKxQoW+99rv7/ReDDKaXzYKhwe5Wwvaoa57hYfYOV9Xz0zQ9oAT+G6sqdBZVEwNfQ9i2cDO3WSLnbr6AxFglxnAhdKBKuk+VEKi2jm1PGpImTacFJqui0a27fRCNEsRbteqWGmZjiLFW6J6W4Y/MiJeVdRqeWY7Sp/ZTIVlFwgAHcfmX9TeBOZjRuKkQGl2mSS80ixhno7ox2dzTTVLeXU8yBDXD7jXSrRrmYNAWVsjK42N5yWuDcalrhHmlAEFkVX3PIBaXbMMHtV9EAXJ8fGZ0CxDmNniLH+BiNVjTbZDI9Cl5yM60ea9CSci4m9O2X0HAgNegzqT1D74ka3LjX6g2ONwVSppT+O0ox9jngjylb7+2pWgIppSuAXTnn61NKjwA/mXPeX397CvjRnPORsTZvw633enzg2z14pQd3XQH7hmU7MTI8uBzuuAj2VJT5FLDlGrjnxbr13hC2XVG3dKtc939fDy/OwmdfKAP0wCr4zArY/WL5/UgPtl4OOw/ANXVSb74afnM/vK0Ca3deAvMJfv1Aee79F8OOtbDrO6WN/VNw81vLpsObaoDP1rfB7Ufgpyuw+slLYNkkfPpA+f6FtbBzLdz3bPn+5DRsewfsehzWVxVwy9Xw+efh0sp0PnE1bJyDO2o/7t0Au1bAzifK9Y8sL9vi7X6sur8S3HBdsZdvquNx+1vhujNh671L4aFVcM93y+97l8Pt7wxb7wE3vBP+dF/hF/Th1h+CGw/AbQdLG3ddAvtWwI6nSxsProQ7roE9D5fvp/qw5b1wz8NwfdUEtr0Dtp6GW14s77J9IxxIdZ6AB9bBZ66pcSA9OLICtv4UPHQ/TNR3uflq2HYMPvpq+X7nlTA/rPME3L8SdlwMu+q77Z8Ym6cBbL0Wbj8GP113M//kJlg2gE8fLPd8YS3sXAf3PVm+P7kMtl0N39oHC3Vxb7kKPnUYPlLT5D9xVSk4dEd9t3svqlskPkuJBp2BWzfB7ier2zbBDVfBbx6E/6y6IW/fCNcN4eOvAAtw93J4aDXcU9fC3umy9d6TT8DRCcoWiW+BHS/C5qpJ3Pr2Ok/Vq3TXGti3sm69N392kPLNbL23lrIR3M9T+N/vUTSDX3szDKLzjJmU92bYvIlCkdaRjOCTnFYzAlrQjhyzSvfeAIZy+ojsD6q/u2oFvYXqZqpceu93YfMVNPRff7oSRpUTmjmxgqLWLlBsZCW1obBqL2oCgnlK8agp1Wv3PgWbr6OZALr+VlPiBh6pfRCL6IVnGi0aVWzxDt/VvsXArYtpG73W/u59FDa/j4b1HA/XOyeCsKrURogatPZNWsUpVf8E3EjbWlBtRy+G0bSXAGth7+dg8w/X+834tP6DGtQMzcWrqxhaNqPjpba5nKbFOB6aA4KDznGt+rX3G7D5h+q9gr4rKGadNDIHvQx5WTUlrqrveJSmpWjS6NqOGrH9lEYGwFKCZXlEQ3tfhM1XUzxYx4CXIQlO6oYVy/O9AB47O4N4MyDlPwSeyTkfBkgp/T7wIWBNSmki57xEd3u9F6nOo2qSrOZ8cVxR/dH/HwuoqEJOUoj8VbpquQu5Sv+hqlcMiFFNg5GaPXSxC1DVIy3WmAvBNOMtojqrfXya7naBthV90TKAiXAuusS0VaNb0+xMmcOA4rX5+3TrGC7RYu31OMT8g2H4X3U0qvgSpzUWLJQrDjGkqL2XAffT3LgxFsLrJcT1tHoQV9Rrn6Mx2D5drEGgbY5ijlxBSaZaQ9n8RuZhUJYuVt3QYjqChWIN7oYtCCl4aOFbx3ycwa6kgXpG0so0zOaNnosepRZGpmy3t4HCxOcpRYAJ7x0xldU0b5DmUR96PRjGalG9XMZAL4n9fqH2dxUtRsh8EOlQc+z7iEE8D9yQUlpesYQPU5xQfwb8bL3mY3S33vtY/f9nga/k70F9udtiFwblWK4+Lkxte70Wi5BOpLaAY1LPGYpdFkOjZSpqHAbMVIl5d43rH9X6i8FC/fARVINm+0dNRXS8D8nFbYVnF2UMklILmgBWwt1vozCD9TQGCaW2wucoUsM+TlXJAc3nrt28grZ9gAyrTyFMpayYigvtA5TaCUtld2mo/Yyl6NdRGNiKcJ/vHkHQRcpiXFvfxy3+erQS8bZv7Mkpijv4IGVbvoNwtwVjBHEFbKcposqis+IctmsYN+HcMhqdyATs10V13N5d2k4urBr8dvclNIDWha4m4dgcpmgMgrv+7phUT0mKIesy1krj2f6qIV1UNF9p8e6LKXOohigjUetdQ9kHYxiu+X5VlKo7en+RwsuXKErj3cAfAJ9PKf2reu636y2/DfyHlNKTFFn/C+d9SA8eWks3urBHmbjoXTCZxwEdQprIJUTWRbaGVjdiIbfEmzR2f4yAqwziISVwVPG8R/dXBTBTKmrkKP7CibDtqsFkJ22S5qGQaKM2odo6Aw9dSonwuwT4D7TS8NaeiNqV7UQAUvepadxHaYSiCeF7RU0mUdh+Jf6HZNL7GLmC6Tm2tOCtCZrqPkFZJHpUNDdkSsYuyLD0mjinUxQm8RyjLMeHVtEERAxln6BQnh6NGP26IvyVsdQU+XS0gH0jRq5ENonr2XI+66qsfX7IRC9o3oGVtA17jB51HKI7Ww2lajDZBS29RNrsVS1WIPM4ZLWnITw0hHSsko/xPgrBK0rfBgKUaqHfT5Dy+32k5SnvBTb/DCWiwoU4B2lYB0oiFB/QH63k1RZXsr0L+Csa44gushhEImefgb2Pw49cW85lF49ItVhApiXOHKe5YqHr+oOup8XzFSNIi5VIdXXCSNPY+zhs/qv6Lv8LJZhHCShhQyH4CQoRWJz0udCeKq0EKmofXXFGcXrtAiM38t59FYOIGarQpLQRhzEGYTkln+BQeKYxD5olLvalcD56nQYURv9aaWvvl2HzB2jYhGbJLC3s2fFbKOEteSUtPuXi2n7M9lSbkkHJ3NVSxZeOMTIP9j4Cm99BixIV/zBmQ6YUMZmYe6J2DM2NGYPuejQ86wgtTDwWBF6EvYdg8+U0vCl6U9SO3ezaTY+WAd/9/mAQ3//DCfs2XSkYbbbontPlaLQhlMWaenB62CIUxyMa9T+rTcRownp99rnmcujGVIUUk3DC0tinRwMlV9IWkAtbIvQ53uNvqqNfoEjiZ2nMwQWk9DlDkczrKFqCu1+prquW+11mGTUdpW5kcrpUlcZKLxdu9SAwVZ99op5zrAzUkjEMaNWjnqb5/wUX1YimaeH1r9T3FtwzlkU3smHvi7XtzCh9PU/UsXCsLapicJNmUAw6U2M10C66uWWA0DQBGUuma2IJxPpMY2rUct2YWO3E9xbLSJR0/K9B7ySlynUlrjQxLIVp+6FtmaOMoZqL6WTVaqMGe47jwmYQwF79unLwOqBZZFlCFmQzPj0CM9PDhkRbps4JrIOX5mgxFi6KKlH3iicsQlZyyDwmKFWDlL5RakWJHAEhNQjNFgu3TlfzBF7vFViAvZOUrENDpqOZpCQXTzlC0yIk6Bh85eFvBo7FyBSZCuGaqToeEr4bDpvHEKVVDCiaCu2LCbyXZpsn2iKJ5p7jpRa2sf72TO2HTFVmJohsPIcRrzIPF/0wtO3YyWyO0t0WIGpeAsrzFO0swd6VtGjLaJZppsQAO02rJUrU7qncnmNtTfOM1CINvf46pRDtKNYmQ79ZAHsdC/soqCoN9CqNqznoWTvHccGbGPwYZaK1l1WvVN8McYauS8rgGolCLi731PyIgUcpnNd8cWEdp9mknpMzG5UnKDqgLGCLq7iYzqQyqU78pRSJqPtW19lkPa/KKvHrgbGfhPdRk5qgeSxsUzs2ItgxOzKaVBLNNF1MwGfqhnO8DVeOkYRKVfsu4x3XSKyZ+CpNAIwCfGgL0mcvD/8fpnkuTMySCRyt7a6m4TvOV49ijxt5Koitqxaa7e57G1o9oO0pOkGbX8dTU0zB5V+ZmwIrCicFRWSKemIiwO3cqanZb8svKiwsOajZFVMKoDH1AcUDdYpzmhhvxovxn+TYs4cyALM0bi+n15zQTDCiUSmsCi/4FhN7VMOV4n2aypXC/4uwZx/NVSg6rHYAjWvrgnJiNTlUO3u5mUnzFHdUxAkShfAP0SL/xCdWwp7vlL9cRpfRac9ryyu5o4dBTcZ3Xyw2+agal7/57kqWGJlYx3/PvvCexjfYVtR6DCtX4qlhraIV9s0UEHojzUSSCctsNW3MuDxQ2tnzNVpSk31drO3rypQJCfiKWemilMlXZpMUQom276b4ixqAc1UjQ/d8O4yvTML+QFv4F4fvMd9GZhJjMCJty0wM9daklplXetvzHA3M99mOYwrPGlKiqfz9HMeFzSCG0FcF1+3osUghrA20yVHCOvhWC4422QzNxSeB6P+u7sSR1KzE05fjQ1NRJRIltIVdlXAuOu3duKD0vQtaCXyqHUUVXelxHPo+a5GyC7ZBOJozUVp4zj5DI8wqlbM1KE2NV9pPU2oUXETxmGiTHy9/+9Cku9qBiykGEqlpbKRgEr7bsfqua2mLRDesDEdtUHBykYIl5Nr2urF+ROYtTchcfQ9NC/EENU0Dmy6uIPSy2l/HRg1FxqHpU7WefqKVw1Oz8Hm98Aw1GRf/DCWoKdOStuyzeIgayGQqAi1ibFaequPQd/40c2TWp4DTqQGXs8DKYctXOsdxYTOITKnDYGz6BLAKkoOsPz5yQVWuaymgjiBYjHWYKLhlT/UuQU8bMpYqXwespuTYywxsZ3n5JKVbBImU2GIDqnQxfsJ4iAzpNIXBKKllSBIYwKV1LF6jaCYfpLvT1fJw3yDcG80Dx2dA07LsW/SCKGH7oe1e+z5I4fd1FKI316U/1ta7KVEv7oCmtjNT50dMyAg/8Qbbl+AFhO3rRhjoTlxNWxSmS/v+pykax0v1vCCgZkTdhGckdS3uc5yygDSzYlEd55cyrgPqvJgPU0vzd7QhMZYra5/Ny5gBfo7upsxROxy5vHPT5tbQKrhr+i5RaoWoMftcGUkvNyGg+RKCAM92XNgYxEzKvIWmYttT80YAACAASURBVJ2huYwcbFXpOZrNN0EpLpIohTWsTTigTMRMvQcagq/UtfKQeIPMQdVULUZCjqXOPV8lfepD1uU2BIap+tpq21GV15aOrkKJfE19vmX0zFAUexENXwhtyShW0eITJKCYnu2iNXbBsY6hvr1wv++pHb4c+DGKlvAH9TfBRkvZv5WyN4bqu/iRfX4rJRDrqzSTwRoQPVr8BDQNkNqeu24Jom6s7c9QFm309RvAJQOMoLLnVO81ieq+mSOmJ7OKiXbQXOmaLgoEPULQ8RilkxWQXkcBa79Gw9modFKle+oHAN1aoKcopqgaX9z7on7SRAXPDbE2FKAy4/4E9BIs/sAWjOnBDneecjK1U/sUlfMg3ZwGF9hTlECiU9Az9FbOboq1Krf2vupkVM/nauLRMpqKJ8YgyBXjJwSP+tWd5M7gQKfOhCCgUZvWJXRRQEsfPw68CjteoAGXZjdKhKLuLjxoZez935wIzRYXDHRct0nbP7rigkm14xm60alHKCXiz1A0Ao8+ZXH/Na0mYnTN+v9rFAkvo1pDM8WiGTND02aOw45vjY37gILrOJ7OqXUvnO/I0ONCJvym2QrFFDK2RLxCrXRZSYoahYXLeMQPoIsB1b5mx+41SrHgyvzTkGLTqulJ8zK5UxSXsJXHQ/zOjjMUja3OXV5IbSPhPi16czmlrF2GxR9oN+cQNs/RVGX93v6v+jkRrpmmG7WW6zoaVo6daXZXBCdVuaFJvzpJm+eAg7TEF+8d0qI6HegJupvqWqy0F36D17sPo1ouPiJhVcm+2X0dnqcLKirtbW9IS27z+ZfX+6KGohaipnAppBeqHR6BLd+nYg2bZXoypMfpqtLWnLQYi/1yDCwcYwThRbR9MGYpXoaNFKYzSds6T4ZT53/zKUYUPAowi+H4PtOFJi25eKPJoLdCT4eYzlqatjdLY3Sn2jWbNZPiHEBXGPibgs4EOd2xa4FXq8TPFG1zRTEL8rF6/WWUjCajTqX5Gmi1+VD93ffLeaThpknIFzOK2By+QDf/4yzHha1BjMcCRBef/nYoapoSxEIbUIi0qotZpmIi0ES9zImNSVbuvqU0qch/Ns7fhbC6PkctxEmzH9BdyAMKIaynMRjbdFcpVVU9NjFADFqo9US4P0p81fiLaQTapxGWUnqCsjCN4BsAh2mhxjIEaDY9NBxAM8w+avapXl9SP9q9gyodXaza4Eu0EvNqGEZdztSxWEeLDzgx1m6tj5BnKaaH3i6l/Gxtx60MYkCVnqqJcG4q/C/W5dzFWAbo0qemm21aowMazUamZLk+Nbq5cC3AytyAXgXNU3QT4RIl10ew3SpXMqiRyzoVwXaUQrPTNDr8gdYgpuDWf0GJGnycJmm1iWcpxWhrqe8Rwu/kKy0F5SYokqna6lm7GtqgR0kIMF3y9fsDGBhb4aIU2Y7BTQuhrejLh4Z3OKHRleheGC7wGGpbgdBb30WplhVzJqKHpI7ZSNWWyaiJyBhUhZ9iBLaOmFocD99DW7xKqluvomEyMpQZCo7wOIWoNdOMBsyU/BPnJMabWArOMXQP1CXaHhL2Z6qem4Bb30l30apRunC1zTUtnAuD6GJcSwxcs41Y2k9GLR6hubC8zkss3zcegOS9zoP/D+gegq5HGQG5vZdhKKhtcRu1S/9ULfrWDXTnPGq2CscnQt/F0M5xXPAaxI0HKeqx5cGcvLrT9ShvQcbhYtROz+F37TpLi4kUx4CrCFStLNfdeLoi5jFQSZvOYBTR6x7NpVe9Lr3oiow2rxuhzIRnJspEihdoigxLwQ++RtM6TLpSkqsmj2en2hexhyVaFfDa9siVGO1eaHa3kjbDjdr3Evqq+tuBcP8BCjGq1gumuTgFW1fQpJrv/zLNre08Rc9BBX1vNGzaOAJoRWjj+/seMgRo9nt0HUetUVoaX9QxKK8HnIQbj9LFCRh7jn103k0uq+dSDHJzUffKNcMY46IJJK0vhnbn4Ub7KxMeaQe5MdLIHCIDOctxYTOIAdz2H4E/p0QW6paK2oHSQTu6ejkSvbYA9EQs0TwBLnbVygjyudhq1Nxtx2gS08CjyJgMZ42Tp1axRKlDoXdgLrRvdqpah4skSigJHLjN6tcS37W0atNKUbUT21CjiOi/7toV9dpYTyC6weIWhiHp57aXaaDvO+r/xymougVbJD61khQ+0Ije8XPBxaQp32FA181YNcDbDtINq1eTWaTlgcRAqxh9auzGRbweUNSE1SyBrsZHOLcEt9WqXiPmYk6F7cpc4qdHSflPlBwRaMJhgba/qYFeCrhoYlhntdLwbUdodChji/M3U56Z1CDVls5xXNgmhpxXnzK0heZGNAYdSYyVQ+fBsEmGTMmXELCbouVsSIDRdzykbcGmX9/4hrrYJxZgSa1gKbQxAxyotL+CYuvpho1JQRJcBK/UYiSuaFIp/QT2XFBX1d9MoZ4L7cnEFikMVsJdVa+J7yWhjGsPMfhMost1/N8H3A48AOykqfDrKRjCt2iSy2ArCTYChjLDWO1IFXwaeD/FZHplrD+Rkdp/x0/wWmkZzTHjDyIYGa9Tm9KmV/NyPJ2nyfCMGEKuKSmtQTeitd7TOwXDYfVoiF9NUjSqXMfwOUhVqGTbp/Z/PORdPE1Pl8JFOj5Jo2/n3bk+y3FhM4gB3LWONmEyA9VeJ7UGO3UWHLR8hJW0XYzjwjSzTnVMF6GEUaXkXetoErFKsSVoBUS1VZU2EzBUvTWTMoX267uN8gPqPaNAoViparL9f9dlNF/7kALubaz9sCBstHVNdIsaRjSh5mkLP9ElmKjZVHUXynV3XUIDF/+CUnthJUWivUTRKizC6gISPI6aTVS/Taoy6jWaQ/vppjZXhnnXZfV7zNngDcZ0MnygaROOlwzY+VBSx5oU9sXxEoztwV0bQ98i3rQY/o84kQFnEbQk/G5o+Ivl97y8ju9LNAYzTcEqfF4P7lIj1AW9gmLq2YcZSo0UaSBxXg5wYTOIDPuUPqrAl1OIyXBZJ2G62HLZwB45ukSp5NHOjFI5peIUVjtRvR4As7DPEOg+pagItGAkE8ei9FMCSkgSwGJ4tii35pI2tFqI2olI/yrYdxFlEb5cr52hFG2JuQLHw/vLCF2MQT3vJCRR39eALglHpqDEqX3aJ86xn7LTySSlMtRq2h4WBvEsK33nMC2JbBCeE92A0RsTzYyXaZpH0GT2aTL12zkSTTuIKr7Mwf+d06jRyYzdTDfGw0QV335WprJPjUpNwLmwX/127etwEUpbabGCuMspmtIJmgv9eILJXK5fAxyF9Eq5fmIBcg8G07AvmrLmLMVtF3xP6WGRZnae5biwMQhgx0HKAOqu0jOgRHDwU9USBG6U0PS6hO+CWJbKYK8FVue2GDdS3GhTjEqq7XiZGq5aObDayJC2z8A0LShJ4FFX2ypaAtFM+NsPfzVvdMWto5VGq4Dsjq/StCIXbAztjpGPUUJHt6K1F104Ufquo+VFCBIq/S5ltGB3PEdT6V2Ultu3fL1ajsFTagmaSBKti8RwdZkAdLGL6OKui3bHd2luZeM6BD0NZzZHw/m1oMyK8FdTYZFWmcuFrVn3Sv2cSk3LGJZrd+ynqO+vUgSXC1wa1LSRMcajvksW7xA4vRT4SUqU5ZrcaGx9+Zvruy0tg1zfYYdZwY6bTHEptAttU6chTcM4y3FhaxAwIvasSjeeeGQ1qai6y92XgDQs12yghTPPAVO5Rb8p1QQho7mhrS/DsPp0BJai+yy6GyOBQ7NhZWrTFCb1Ml3iUZ2FLnqvBqCkyeG8STiaM7Yz7s5yocVUcs+7mW8/3G8wmfartr9jHcPC11I0hSfpaHYjxixYuJYWcBTR++hSVLNS64t1HaJXoG4rNxrXOOZ+nwzffS+FTASr43Z3Ptc8EbWoydzMJLURPTSad2doeT0rKQzeEG/n0v5oSsX2qPf+Oc3U0xR6geYBoozdcBFYTJBzM1kiRjPoweywG0YvrdrOWY7zahAppd9JKR2qZes9ty6l9EBK6Yn6d209n1JK/0dK6cmU0rdTSu8P93ysXv9ESulj53sulBd9UIltDQWJUwbgxOi686WVaqqcAKeLKjdSLVfQEnVcsLrJZAoT8KA7QMmFI0HHwCmlhabGOIg1Fc5pa7sAXbRRuk+GZwEPmngmI1Cy2b4grCp2xBBkFLHYi0FDLjTvd6w30PYAdZ/KYdnrgosphJ9rH/4a+AoFj3iFBpYOKUzQ1G8DqkbRfnQBUMfO8ZaxuPhc/MvhwY20lH1N0eiWjZ6F+BFEVEtKNK2J0K8IULrVwFR97+uBnwDWwYOGXbs4FQ4nabVMBDWh6ykyWlPJP0/b2eslCtOKeSJ6qRZT0aylo9nMg7rNh6nr2ZketrFdQ2NYMu9zHOdN1kop/b3a3XvrFnuklP4N8GrO+TdSSv8TsDbn/CsppZ8CPg78FPCjwL/NOf9oSmkdxYP/wdrlrwMfyDmP75jZffZUyv0NMPDFJWw5f1xccfA93D0JYBZ68zBcQ0nyeppWXvw0ZTHohoyEdAb6C5SMzkhwLriIxHuP10VJraSN8RrGVqgdLQttWXgk2NL9fgW2JFynLhKntnTEMsYDdVQvZZRLlOS245TFfZLCiNbUdvbTQqsH0F8Dg3dTNAW39BNYcxzcp8IgH7UYx8W4lujWjcVxBKWjieG7Tpa+9ftFOHa8LJExONa2Y1+GY9csUlTthXBujsbkYv7IBuBimHwfLH4QuA8m3gFLmyhBbN+gBIs5Lj5/Fa0YkBiBjEevnCaJoeyraAF0a2hl+hQwalJVgPQHMJikJatJE9HkjEA/9f83UzAm5/wXtGqDHnEbvc/R3V7v3lyOPZQ9MjZSeO0DOedXK1N4gGJhnfuYgD1HaMFNShODk2qqcVqCZBhxjJFwEVRJPVxPGfQXYeRzVmpYaDai+JQ29jxBI/ZItP5vHEZUv6ltRfesjCIi5THFOzK9qJXU+hV7/oQueDlBS8iSueg3F+fQrbk8tLmRtkemY3W4jsUsraDLS/WzjBYEthz2fIOS1xHrF/RCW1Zn0pyz1sGQhtMYrKapAN15i27DyFBlXD3Ycz8NKI6am39dnFHrlBlJS0p6mYbCx+xHaUp8Y0N5v8WvUGq4H4CH7gB+hwIY/yLwz2kZuDKv47TYBk1XGYXvaT8NtY+1MUxAi2MmWFoX+57nGGE8aRVdfGs8FcAs1++Tm3NDzll442XKsEHxMbwQrttfz53t/OuOsa33uC7D3hfgrtWwbwl21H24HpyFO66GPc8CS3BqgrL13pN1S7cM266ErSfhllpHcvvlRVB89nkgwQOz8JmLYfcz5fuRKdj6Ttj5OFxTQ3RvvhYuXSqVnAHuvKKM7a+/UNq8/yLYsQF2PVqeuX8Gbn433PcwbKrq69Z3w+0v1a33gE++BZb14NPPlu9fWA87L4b7HittPrkCtr0ddn0N1tdJ3fIBuHwO9j5QnvOJd8HGU3DHc6WNey+HXZtg597y/ZE1cOtm2P3ndes94IafgO1fh5tqybLb3wnXnYSPPw304e4N8NAk3PM0MIS9s2VLtz37qpmc4YYPwZXzsPfPyjW3Xg43LsBt9d3uuhj2TdYMR4r6fcdllclSML4tb4d7nqtbJPZg2/Ww9RDcUre4234lHJiAzz5dvj9wcd16b0+5/sgMbP0IvPUE7L2vztNPwbYn4KP1OXduhvke/PpXy/f7r4IdPwy7vlQJcCXcfDPc90XYVDc42vohuP078NP7y/dPXlO3SHyhvOsX3gY7+3DfV8r3J1fCtpvg2qOw9/5yz5aX4FMH4SOP1Xm6GDaehDteBSbg3kvqPD1cfn9kRdkWb/c3C0wAcMP7YPsBuOmx8pzbN5b6PR9/qfx+9wZ4aGWhdahbJF4K71yEvfvL+r9hEna8VBPaenDrRrjxJNz2THnuXZfAvl7JEN78ulXYju+pHkRK6Srgy8HEOJZzXhN+P5pzXptS+jLwGznnv6zn/xT4FWALsCzn/K/q+X8JzOWcf/Ocz51OeXeCLZvoIvbRLRjjGhLF/lqXYRHSaUqU2pWUYJunaWXeBH70dYtwCzJpyw5g9z7Y8i6auqa0VEpFThzV4RR+jx4F6MYeRLNIfOFFmt1ZwczdfwNb3ktTv6Gr1q+gIdbRv94LbRv/oKkGDXexHoHqrdqNJlcq53bvgy3vpGE1EYvJFLxBwNex0eRR6qndmdUYIyXVMiKwaSh70FZ2/wFs+Zkwrqre3hcjJw0SU4uw/eO07N9EkdLup9GnaKm/RBGBj1K0jUdpmuBJ2H0/bHlPbdP7jKM4GsZBT8V6uvUkc7hPOp6vfXOMehQcyPmuJkpaLG5OTsPug7DlSkY0mobVdRq2/+vkKgn6P/13X/b+YEppY875QDUhDtXzL1KSdT021XMvUphEPL/7vE+ROdhLB0e1PgJYpkhP5hYApIr4KkW9W8kov4LXwr0ugJjYpI26VDSTkc0Izc0pOKq54WJRxZVYjXnwWeM+dtVx/z9E258xPGPLdXQrRbsgdB0aWVcX8qgvbr4zHX6L3ovqRu0Ag7men6HUHjA35TXYspnGHFzsmkdTFBxDj88ldV6O0iJOBZtlnrGoi+/re0QTQ5Oovv+WfxTeYyncJ1OD7lhpljk2gs4R97BfxnMYOPdMHftjoa0qaLZsKeMyihvR5BlQ3JUvhGdZYUzvgRjVOIAaMSLPSffjwq1+//Bb6AiFPO4evpaiu5+s77EKWErhptcff9s4iLiN3sfobq93S/Vm3AC8Vk2RPwJ+PKW0tno8fryeO+9xT0wAUrrFLMxe+C2H80uQzds/TSnK8SIdQkgRrPJwgYgP9OCe52keChdddJ1Fu38dxf60LoHFRgyKEmHXPReDqyRGXVExpPok3PNY6NsxWmXmaOcrXSIm4vZzh2k7SQlszlEWtIFTa2iReG42ZNmyg6W9ex6mhUq7wOyvGJAfU7RlJOIryyn1DdbSGE0MyXZsxRYmw3PqmNyze6wP0BgKNK0th3uNjRh3raqBxKjT5ZQAMMPlD1MYnRrRPKTNff79s3QB7IhnZApjjWHfChLzdYwbcb7si5W/LHKkN8NQ8VlK7E/1fv27A7RCOWo4A1rG7klIl0y2eV0OafLcFsR5NYiU0k6K9F+fUtoP/M/AbwBfSCn9ImXPpo/Wy/+Q4sF4kkJStwLknF9NKf2vQLWQuTPnPA58vv7IcL3p0bqZZAbzwGSiN5UZRrdhBKd8Q5Hik5RJqphEjqCNhK0UdXKXwfUCVuPgpIvefqli176P+gNdbUXpJcHoZTAqc0Cr+SA4uwjX671QTZRZ2r8JujH40BZmDK+FVpNCwHSaYhroUnYz5I3AVopP/kiZi+utUCQRQgPW9NZcRtnc94/pFEZNS5B1w+pmfYmuOzjGgxDG2aNqCte7L7wqdBz7qAlFphGZyThIF13RMdjtCVoo+xC4hgLSnob8+IB3P1/fd1OC07mVBIgRmnoyNtBlmGoJ0kBMoJIhqA1PA6/VMdSsm2r3XG+chXOgkFukaDKHIZ1ZhOWQNwEbIe+npP2f5Tgvg8g5bzvLTx9+g2sz8Mtnaed3KFjv9344qarl0BZvD8i55DzMJ0bl3Po0m834fRe227Gl0K4uOk0XF2p0XdqXSDSqoCm0B6+XWvH3aFLYZtQevG+WYoQdoYXb+h4TtJBmx0Sz4QxFwunn1s8vdgNNpZ2jEPI0jcHEeP9X6+8vUBb6+2n7c/YpqJkajVhGzEp9FXiIbs5Jv9nLJIpoMXHMuZbRO8cyXmiSPZoGMg7HTubpdXGOluiWi49Yher7ZRTperD+fRttofp+T9PKuDnPK4Gr6rh+M1xrX2ZoWZrW7JSW1Qo8ZB4DurS5yKikaYfRLdV/hrm5PmVKamXVlT6M2FmmaIznOC74SMptG2mTLTeWy15NWRDu6hQX5hSF67vbsYSriaB7SanrBEUXWWU4295X2zEsN0ooiS8SnRMXATFHOqrkftc2jurjXGirMq1t76rXKdWfouw1ehXw+5Rdr9WKNlIWsHETSkfNl1p0ZaTCn6abA+BCe3v97Z0U/W8Btr2VsoC05xNFDZ4Mz7OykRGEMHIRjhjEesoCNLxZJrGOtgeGYyWzoJ3b9hN04yu8PwaHRbpYCr8Jlkbm7oIybsCoWVP0ZTju7Vnd29t+sp7/Vm7PSDQgPbolr6a7p6qAZgSb1QxtKwDyWXrTTb4ETOTiDRL9i1qlNDUPPRmELuZDFE31HMffFoP4T3ZsFUzUbnZxalutBfq5cH7BJTm+BBwZgIO6UFS1DgKv1hCBz5Ww9RTNrx0JyiMSYVRZI5HGMHHvicCcM2ERGPsQzJGt7oU5AcveuhyuS6Xdt1MYxkU0iTBJ2/kp5hdAk26TY+dkLmI8aykS/iiF2daI1K1iGS42QdjqBUj+5tg7DofohnyfpjAxaAFTC7ToQ/szzkyribd1P935ilhCHNM43s6Fc6a5aPuHKViV1++n64W4jm7S1QJsfZzGUC9LcE2vq2nGKE+zhHu9LsOKcQl61KhtGlmpUINCJ5rP9d23GqHar88xo7YGYg01HTXTF2iJf2c5LngGcYvcVcBO4s3Aw9Q6CL1WK2CBhuZr30Uzwe9TlISXGA6t+qVLrNbtu+W50MYwXKfU8rcYpBNtagkkYhK98JsahPe4ULSf62+3HIX+jyRYB4vPL8JDGXYBvwV8kcJYfoYilQ9Tysn7XPMD1CZW0sKejeSUqfgB+Bqkr1Fg6BrbcIvmh9IpqvWJklELrUZB9JBQ32sTxf28kW5dDiMBncMYGQkNROzDLd+hzWfU5qIKHXGJce+GZl00WaCb6RlzWwYUrcb2Aebhlu+G6wYZerkTIt8BSJ+jCqlhV/Psh4/fp8Pvvp+VqNQQVlG026VCH6P3HdLct6+GcwL9AxoNnOO4sE2MyO1fq+f6lFgHt7E7BSkP4RTkYa8MfI8ihVQVldzR3nRxGrkITZWLaLdENK4hRD+7dl80LYI7rhMj4bN1y1nLQomnShtVYHGRJRi8mGEAg79YLBJigQKinaB5TX6Ikhdxuv6/LwBbhlkLFEZ7VRtZU64u0jxJ2wxYxua7SaieU/Xth2esrc88Gp7XozCI5TRvTMQLZGyOlxrGEt3qUCn8FjWJ6DXxr0zDhRgxDaNQNZl8J7c28L3Hk95S+G5uysHc+qU73lyhCOqOM6YIpOqNiOYxof+a3NeWv0ntwijjpdDHGSClQgBnQjvQBMFZjgubQQDb1wCVKQ/ldlO5TWyt35iWgGVDsvntPcqGI6doE6P9HTENaxEo9UyeWc0ISNr+Q/W3uNAjIBa1i/ERdfKjaRSlRDRJoiYzCNdMAr8E299FkUBWJxI/OUlTS/dRtAfdm7PAeyA/QXe3p2FoO8aUGMIuU5ZZJgoDOgrbN9AWse88oETAPFPPWT9xklaENeIRU7WvF1EYRGSujlfU1ByPIHW3v5/uAhsfuxiU5riPY0Rp7LzXRoa90G0vXT1Fmuoz/Ks5WITt7wZWQvqnkOZg+O9omEUEph37yGT9bhXv5+mmm1t0d4KGtTjvA0pWUw9yH7a7y5mmmIxzkRFO0fGkjTbqOftxYTOIHhyoXH2oFM3hU9W63KcwBgmiEnh+jaYFyFGVHBKR51UHRfODWnvACEsHPxJvBEXz2HPi4h+3eaOGYX8yrUKVkaPUZx6EAzIzF4lE/0PAJkhrILu5rQk/j1Iktdv7raSZazIGP47fW2mBTacp2ME6ijlwCA5E0G2G4h49SNsxyzJ3MogorVwwG4FHKAwv0y1O4z0Bf+lI/zqfB8zSjVqdf21n3M3cC+1JUy4i5wQKljNHwwYqvkKGfGqBPJVgCBOb+hxaPYT5TH6QUtZwLjxP7ElMLGbxxuSxGEAmfiNzNr6lB70+DGUgxrJUwXcgajRqaZbNH1A2y1GoplTqoPxAF4zJ8NkDtKSVHiSRcVVCA3uU6EsUonuBEWKetL8dQAc4ujR1j66nTRzl/GcfomsHRxMl4g9KmSgZCPeMA2eRMfndcFzVSAvI/Al8dgfFB7+cruZwJbAa8lcoadfHaWHNpylZhi8BS/0u/iCAaBzHVZS0uhfrGAoE61M/BWyAzx6sbZuReYiWESljGtTxNIzbjXKo4/ZCvfdttECzcXBRRigDiIs7wWcfDOfHF7/z4ffotp4Mv0/RfaaL8wiN0ajqn6JoOy8Dz2cYwtLJIdt/r6ru36DgYpYhUNIr0SfHPs5xomhrB2j0YNSrcSqV4Q6lwfg+VXv87FGaUIlh3HX8eo7/MmBZbhr4OY4Lm0FEUKsyhlzNu070ZI/GJKxapCTPlNL4ExRbTSkn6q7kTLTdnCOBjoNc9isSXwSbCL/FI3okxiVevN7+2O4yiovRsnK/QmEISu5JSAOYOk0xLQ7Q9pJYqNcKuq0ZtE10JVzB0TWUArQfo3mEFujWxng7hUHZV+3emBcTsYeoAfSL+j0K3nmKYrL8fdrGOL6zbuyJ0I7jFDEEtRXxBsfT36PGqDs3ZuRG74tj71ya9q7LUe+XtCWudSI3l2QUOku0DGG1h2m6uFYM7Y5xEGoVmpwRB4OmXSaah+MgLVZDTcTnUr4vhR3SejJ/tcazHBc2gwAeCIVgVdmygA90cwAEJC3VplahujldATeJbFx7cBfxSKzAA5vqs3rhr3YxvJ4hRNNCoo6MITIW24i2totsikKQzwHr4IFLgP8bOAS9fzIBn5gpRH4C8mJqBBKBwqO02hKmhguUjTMKpeAhut4BY06eBB6GB1bRiDPRdteO4KVJWGG3qezCNRL2OEW7MQ06YjoSObxe6lcG/8C1tDBitQTHViAzmpMx0tW5NIVcbU5JD83UE+gz7Rs6C/uBTXRNTDVZ51yGHzUkn+OYTY797dM2mu7RGJLzdYqyy32iYGYzda0sLqqq+wAAIABJREFUQG88fV1txLmAUoXqJG1rxLMcF/zu3rNXwqkqBXqZEjmp9qAKK+eepIE3us4WaIRStYgRccjN/ViL0msrgc7Ow6mogkJ38URACxoRRhwiagt+15vRp0U4DsIHGrA6BbNn4JSS4e31/f+GxgAsLqLNqsRz4ayjLVztY/ufKebVGYo5EuMm5ilaRWVGs0dLev0IjJymVbZSupnRCU3qutjs7zLabuoyGxeMDE1MKCa7VQxldgZOGVRm9XDHXWagBhNMxtF8+fsirWJWPNejMVXNsESJhXiR4j1aqPMiM3cMzAqdoTDQgzShJhPwf9340AoYWfpQLGeEhyRYzK0/Jn7NlW6eWqJEVq7OXQzEMZmozxiksqCWcc5szgteg9j9PCMX3NBQZwEaJ3qyunmi6qfmIAijTaYqGKUZdFXNaPsOYff/RxvcGIgTAbQU2lQrUWWNTMHnRWmhzR6rL9lGANF27wr3Pk8J6Y12Zkxsim24EJVysUyeFLAEPFv/v4SWydmn7YpezYPdT1BK98mEj9IWvqDZCxQ3rKnTakRKRLWMdfWZSujo95dBRUbmZxZ2/180OvCI8SQxxmTcjIvYjxobNPMzBkOZxanX6zVKbbRLy5js/sP6m4ltYhUrKRsPj0etjsfCSCu+h3RjqPXIE5FgOrd9M3zmErAadr9KTRTMrS21rndQtMAYyq4r/xzHhe3FcEKvpHF4aMlXoepytjxXj0LIbkrrxLjQTGiJKqWLKIChnajK6KUQ9/Be6GoVUYuIEmzco9GjAHRHKNJFTcEJjRI305BxiSWmSK8BbqCE8D5fz5ujYMiwi89nKFm0hWWcp0Pbmjted5yRyy8r1WsSU+pBVgpW4k59Smiw43iapg3IrGsC2Ahnchyj9yG6Bl30Cgrt/BiuHhlzxIY81KD8f0hLfDIQSW0h4iGHKebX45Q8E8HvIc0FeZSW8zOkmIcz4Vnj3pjILDzvuy7V8XVsBrnRlEC1GIcxGNK3QOnlqQCSFkZWQKzK5X3OY2Jc2AyiD0f6lEmJaHQEfWIZNwlC95D7VbzRQp+kZWgS/o+TVq8/4sYlZlr6W3RdQlfSRSBTAo9SUZAshr3GoivxGbVvR2Ry0Y72+2FaReoIPkVsINq8cZHYzz5tXw0DuAQiT1Ik1zwcUfqESuPZBRWkXp6kMOpo8tRydL1JGBpkJSNy7qJZFplDNB1OlApgna3oxjU6xycy7nhIL2IfurcjPUVTrB/+p47nBjiiN8fM2/GtDKRBaAs0jjl0hYF0PoTeacrmvb6f/bO+h6bpK4XXRgGV5io2tZBb/ROZ3rGuAn2248LGIGZT5h31i2qfXBAas9CNZuabZoax5ydpqho008MFsoxiY2vXSVg+zwlTuhjuG7WJmKYLXakV8QqlxIDC9QcU+13cZBVd29EgnXhO80ktw/aN3IuLX+KXGMVh1MB8h1hDQAbaB1ZDeoKSGq86P+7KlZE47mag+rvXWph1kVYhSizDXaEiBiEDdU+RyNDVDGP8RPQ4xXgX50NGEbU6zdLoQRnPexAIPEzTUHxXgXGZZTTbFAhRi5JBSFualPbHaFbK/725GrvgNgGOkWD7CrqZuWq5k0WrA0ro+xz052Gg2/o0TPTqazz3g4pBDGHnk3Q3LVUd1V1mIs0mSpXLPs33rl2v5IXG0aOLCdqgR/dlXTw7v0QDoJzQCHaO28xRA4iMwj5ILMdpO5BLzPql+2MfYOdX6OIMxkhE8yie17MzHb5HKStB+t3FMqSotvPAuyFfQVmk1S7f+TQNTYfmNRDHkcnITDVhlP7VDZgEJ5WImoyOm9hBZGKBKe38j+E9IoNwjKOZprYQQVnoMmyfvZKm2fg8GWT0UtQ53/mXdDEo5yDGxmgaRcA1MjyPyLjO1LiHGP7tx8pUx9OIGe40dqMHzEEeVsZex3JYhWmaAyYSS2+kVY0dF7aJAVxj0tJ8KuGimZbpJ86QKTbWDCNC7C3B8Awt5VtUXvNDCRxVb+guoPr9mqPhN8JvEprYSFQbJWwJM4b9RkDKdmJbJ2hgXlDdrzEkeYKulBK/iOBqBF9jCDeExCKa/Szzje8icHkRLXx7Gq7RlZwpklVmLRh6ODxPd6P4w2R9v+W0fSJdeFEyupjtewyHr+98jTu+R4YUXaHDsf+V5pE5+Ns46Gx4uViJKdIGJgWT9pqToe8eUYBEky1qnYHGRsxFzURmPQj3OKdqiJPAmfoyfbhmEdJ0ZQoKwoC35Yq15VhNXbo/y3HBMwigLszcJEw/FcBGohQ5Pslo4IYTFMI+TplcCcRAkuh5cJPbcds/Tko8Hxe0Czaq1PY5Suqoxka1l/CsuCDO0M009VpV8BgmPRl+jziM/Y8qtUzLtrWPJyiL/bXwHkOaZ8NrPATlZJq6U6O9Dc30kwnovjOeQEwoPtMxkWlI8NFE9Bql/0RoJ467fe+P/T4MH+eG0K6LWjPmNN15Gsc1Ivjr98ggokkYr41xGmItaq/S+3oKkK1JKg0vUQrT5sIYgAIK51TKpg8Gzd0qgxXAPlOEbep3Fa/x48LGIJalvGkj7F9PeYtTtBc9QytzdoYGENU8jDRHQdoD0xhhDxKErrWLaBGGflxwS7DpJOyfDee9RjVboCvG0kcCje1FlTiF63O4LxKWiPokbDoO+1eM3R8llYvItPeIeUTbWyI24lKNYpbGICoanoaUKlBBwm4awv4Rs6ZrlsmEJfwBxaRw3E8CixVZ11xSarqPh+q0ZlMM8gpYwqYB7N9IQ/rtyzjIGTXCqHnF2qYykcEbtGNdC81XzdM6X5tOjs2LmFX0ADleHjI0acP7BhS0MW4GbFCZ/bI6laXrKlazaR72T9J2LqvhASO6MqRdzasHUwNYePlNYBBn2Xpve0rpO3V7vftSSrEE/q/WrfceTyn9RDj/k/Xck3U3rvMfCbbpg65hoj173acwDLd5i8FNCxSXm7UPVFEzbZMU6AKaLuBxyd+Dbd+hmSG6RZ1cJXE0CabDteIFMcahF34bj6+Ifvxo/56GbY/SyqlLoNCAyfn6ftEF6hEDsBw/MQMXuv761YwCmLKRmQKaM7BNWzdKeZ8fTSDzSqAtyClaHkBcyHqlnAv7GOfFRVR/2/YtXr+Yo7yTmUQcIl4XGXc0CXO4XonuWI3jOstg235abMH4/EVTSdqJoLF98VwUEDKBuQo4RjDUfqwqWgBD2BYTy/qhHcF6AXvnZhIWHPOzHN8LSPnvef0uWA8A1+ec3wN8F/hVgJTSu4BfAN5d79mRUuqnlPrA/0mpe/QuYFu99rzHRy3/Xm2+oYxBsEgA0r+CmLr7tCddqKrkgmpRpfOIqmiqm7FEnCL6sKO0mAjnJJjpcK3XjFcbmn6DdibCb5U4P/oUDXNQddQbcXrs3cNC6mAnznjERyR4JWP01riQzAUZwkcPjY2Fh16kceZ0hiIBo1kEzWS5iG7auQw9uhqjel/79tFH6ca1vBGjGIydjxpE1OoiyBjvj9fJ+Ma0po8+w+sFRoy/cW4jg4mBanGOHBPnONMNfhM/M0fmcsi1APFHj1IC2CLWEr1yYmlR+LxZDCLn/Bd145x47o/D1z3Az9b//0vg8znnM8AzKaUngR+pvz2Zc34aIKX0+XrtY5zrUE2Ve1c/fZosdtcIFJK7OsgxyWgZRSJaC3Ei3OMznKhoy0IXl4iE4v/jwTyE+6J3BBozcwFrUkSkPoXfIuFERB+60jCCXVESRXs9LrC4WKCZaLogJyhmxoAWhaqqPKC54FbQ8j60a2NBWHNBevVa3ymGcCtVVfUFLe2r+ETECzxsQ3Mmaon22YUZtY8IFI5rG3F+FRiaCHG+PMa1IMHo2JZ9GT/n+Yg/xLmJDGsC8unwW/VSMKRErAa6yAKpsVCNZppj5PzM+KCzwwzfiwZxvuO/oRQ+g7+Drfc6xwDuvISuDTVbMJgk4BJR4ZihKbEMKIFWUdJENQ+6XDSHT528O2+kC25BdzGPS5goqR37yXCfx7j9Gd1kfbpEneHOD4bnSvgx5oHQXvS7S4SxP0O60tXxO043p2BIA4CrOXHnxtCWWppM2udMh3M+z12mlGja86rNxjT4/nGxaKKcaffd+aM0fCACv1GjiFrCOLX7m7iDQmJ8MY8LB9urdHPn+2hzF7ElhVakU++NUtx2pVuPaAI7l0a6zgEnU8vRyIk7rZs6VYRoxyzyexyLIbBwbh3hTXkxUkqfqq/wu2+mnbE2byPszfmbL8Gn+3UvwWNlv0FS2YL+jvWw52lgWBK6/sFG+O3DdS+NDNs2wdYjcMtr5Z7tl8CB5fDZfUAPHrgIPvPDsLvu73hkBrbeDDv/AK6pkvLmm+Gnn4JPP1SuufMmmO/Dr+8uv99/Dez4AOz6fPm+fyXc/LNw3+8VUJEEW38ebv8m/PR3yzWf3ALLBvDpB8v3L1wLO98J99V9I59cA9u2wq7/F9bXPSW2/CP4r74Ln647i3ziR+qej4+W7/e+re75+Ofl+yOr4dYPwe4/Cntz/kPY/i24qe6Befv74bqX4ePPlfG6+xJ4aKpuFJRg76qyL+Sex6qwm4AbroN/+jx8+uVyz61vgRtPwW2vlO93rYV9U7Cj7rX24HK4YxL2PFl+P9WDLdeWDZGur7jStqth6zzccqjMy/ar6zx9p7TxwOXwmffB7i+FefrH8Imvw6fr3ps3/xxs21fNjgx3bqnz9JU6T2+HHTfCrnvrPK2Cm38e7vtCnSdg68/B7V8v800PPvmhOk8PhXl6O9z35fKMJ1fBtn8A//Ib8Olv1nn6z+FTX4eP7K/ztBk2zsMdD9d5urbO05+Vd3lkLdz692D3l2G20u0N74ftz8FNFTC+/XK4bh4+XrGfu9fBQwnuOZ7hBOydgds3ZP7Ni/DpBIMe3Hgl7HgZNlfBeOvFcGOG22rdyrtW1b1ujy3+3e/NWc/918A/Az6ccz5dz/0qQM75X9fvfwT8Wr3l13LOP/FG1531uctS3tuDzZfRvAwCMaL1U3QrWS/V7262o/ppjr6FSbQVV1FsYDfOUYKH+IO998Dm2+hqYlGqDML36OaK10Y1XzMmRkQSflcSxfDxDHt/DzbfHJ7hEf39UT1WGkbpFYOCDH0+He4dzypU0gBpoph2e5+AzVfS4v2nKOXmDtKkfPTA2B/f3T45JonmUlSSj2faxiChKq33fg42/zJdABSaJhK1h3G7PNEC5hyzeM34X+fQuRKjGcLeL8LmnwljFcZspJ1EsHLcDIpm1YBWnWtY58e4C7VeA7XO1HM1RH/vd2DzFeGZ/TCHasn2bZHmDfm7jqRMKf0k8D8CPyNzqMeXgF9IKU2nlK6mlNT8a8qOCtemlK5OKU1RgMwvnfdB9s6J1pb10Je+GM4L9KmS6QZU/ZVYxtV0fyNcE+MSot06rsbGPmpmxEXbG/vr9TH3/41s2zdCtsfRfQ/ty8hsYh+jOSGRRvzCNiQqTaITjIqlZDEdx8D+z9EqeOmOFDuyLLuh7oLHhslL+JGZR/MmvvM4oCZjiJ6qcezH63xfz8dF6fUReI1mRoxhsJ34G3TNyzh38dnQpYtxBhbNEhmYTG+R4q1wc2doCWuLtNJyPYoHSjwnZu5mqkmWuszpHMd5TYyzbL33q/XRD6SUAPbknP/bnPOjKaUvUMDHJeCXc86D2s4/p+zH2Qd+J+f86PmeDXC/lZrnoHeIEj4aiVfvRYxBWKAbXq0P3oARA3eijR/TYONEp6Kejur/RUakjRht5XECiJMfmZKEEKVrvD+6q6ptev9VYWAm2vkOoUVQzXZF18efAd2FMaC5SZcgLUC2jqSVm87A/RakXU6LwKSOqxvoRMno+M7WOaDc3xvAcIqyW5SJWzO08PmI7g9ozL6O5/1vozHFmm/QmVOPcW0gekfUqKK2FXEm25IBRAFV57IzL1GLcHwjuDqOLalZyFAEd434lRHPV6Byngbenkr0yQznE3kqc/8qRgx+9Mr90OUJSsjAdIaFHiwOz6siXPCBUus3wZE1wEnoL8BwBeQJ2rZyEn+cSGsVvJZKBWwLcrgN3SyF6QwoWolb2al6juUsrD9ZM/Yigfi/xAavBwwjGBQXcQyMihIjno/SrDKU9aeL/T3qQwTBopSKQGvUWFRJfYbAoGncEq5bBkzQUu31bADr52tmqS63OA8SsH2yD/9/e+cea/l11ffPPue+Zu68H7bHdvzIg8ROQohj5wExCrQJMcWkAamNhZQ0RaLtQFTk4oraUkSpQKJuqRpoeAlSXGDAaTMiUZmWQHECJU7GThx78nA8dmxnxuPHzHge9zX3nvPb/WOt71nr9xtfD7Zn7Gv1bunqnvM7v8f+7cda3/Vda68tpJD3m8Cfm1P9yTuS90KRGSN3tQu1bXNwWNnH1xOemEw0Zq9Q19zKdZTw0X/1E+ncHM2Z3mvbPBzuqlpdq3Bn0nWke6lO6mfF9shUk5mWo4EdlfWPwlDCZADb+nBYgWlaPKdcHjkMQEFoCu67/+W6WKvCnoOMAoFqZmK1ICYHoAwxPmERONqHNTUmvRpFE+ZpIgAI2qhBtqnDyj272t9P8xJkGzbLW9l62R2W3bESaNkbkrVf5i0K7PkM7ck/kc7PZkh28WXI3tVmWdtl/7sEl4SK4k5cGO05SKAXmRINsaxb6kvL4xUPoP5SXSqxilNtLt8/hMDNdVK7Ans+FXVqrdXQpNF7dUPRc3t1PU/qq4zEMoqR0OnHvfd8mhBiQnZCB92cD9nMySaI/mR26d0LoyXyo7rNQnk6ya8pYKyw5xHauUJkZijGJLs8u2hombKyBYQ61aVwM4Sa8+ipgTV4s61ahtEIkqiyV6WxlM9QnZ7dgd16ZFsyD5guyTigXSedlyF/HqxdFKG66PcsTDKvoOMZPem+mezsCpB8737nXpmTUTKUx/y3nMNA9xsSiWi0PmAdps3FNeRl4FrRqdyO6peuQFPJbabJkd+B1H7a4CYvitO7C75n8yHHsOR+zBOny4NkAd41YSY61+r5Y53r87W5LXWOEKliQ5bS/dKYqgr0Uz0n/cHNeNRZO4kLxQhBylzLK3KXKStbQFQ40B3Q0sqLwFgiW/pYgx2nnYhF13clufYoVDRiXkqczYhirsvTBkie6M8UxFQ694E2w501cEYhWVDk3xqP91cdxtNvuv+A9rOzQMuEb/b565jaSJM8P0soxzmCA8ropMxeiv0Xz6PFVTITNqf3Ew+UhF/Rb3C650UIJbdhbo+86lFL5YVU8rt3Cd+uoO6ahmWZv65CKF4P9Z0UVH4O7fNbnFVGlmqDrskMgXQ20M6mJkG+BQ6MAeNL1ubaRGe8jHi5njx0nkipNESezGXKyuYg1pTKpYRdmYNnZGLkvTrlzch7Cuiz1virI6TlJrFG30Rb23ThfhY2GSYKCg4750uDa+BkG1Wkna7PE0S/63vXC6GSNYru1w2qyc/XNbloUinhqwa0vBUHiSzYi0RWLS2130agjDWEoNjix08SxONhwk1ZCDZ+AaaW4NQYVK3bEAwW7yA4vyYd04TOSKGbyRraK0eVcEWoQW0oQjUfVz1UNCayIukSsWr77Blo0l/uQ40vHRd6EP8glKw2FRmsPgVb2q3d0gqx7kW8ziyUaWzbB5l7IpIzyX3k5cpBNLD7Mdp+3YplTK5EHgN1qJKGZsncp02QZZtS52VNmhlmh6+7P+m/dyGkrsswVM/M8FA2cJcBl3buQutngrU9D6TSANSgzMJjZF7RFlT6y9F12dWpc2X/KumqbFdxCz5Ydx/ABvIF2D4Z4iDk8fAFRvSIzZTzmhRFZnoE5MK4C4duG6l0UZv30+4/7bRX1swyHzcTrtf1xBjReRlZ6R5Z+EjAZpNO/euKYven073UBzmpsty9GwkXb+6nvCZIKCx7bRQh6UKk7wqnKmzdBePuQ0SkqaeUi8V2JeJe9B5rgIlnsqmjvKBIynNeqi1hHbGtYDXWJiZ5heRSgYUaE8ZXuo2uHWCbyvSITsqwccjpk8aZ34tPpvNyjEVGFHngaIBI8+aBS7p/1ko6Dm2I6u1AgYtnOT0eoNJmxPPqyi4hqefl8xMJOmpbhS9f7L+JzPVJfHGDoYSvE0l6sndEfTLLKHVcGWD7mWRILT5CJp7qoPpLyEqw5cV5eL+ov4TiNPFExIkfyQvgNBEFr3NcSUYPXWHVEKg1mYMX5xXC+f00DiDMt25+ylwkFPROGhfaCe2Y/R9K4Gmyuzl3sfgLXzQ3lHCQcB555gr06unI9RnKyhYQmXPwLEQUYr8Bsb19oKn2X8ElA9rp1PLk7hPBJFqinJ+Z7fguSsg+a4hBnd2ZmXTSQNNvXQKycHquzcy3dImsXJeMIPIAzUSe2icf02TKGjKvQtSk0ua/qqPqJffw49A/AcMGMzeO+jlbiHUWjkpqTgwD4crM75DbVXVX4hzfxLkVPZhRRfZAKcmLJqfMCLkNN2Drc0TeScDk+I3lvFILneNdU1HtWwnkkE1GCPK2SxJKYGVU2BBbGmZTw2M+SjUk0e97Y+idcrTwODBbw4yZqLFt49SzUwwrnoPYtgMOS2MLhiqcWgNcnbQF01oLBKQ7RhBmInXWYgP6Ar9+ATNblEdQE9ufte0EHJ4mxGmewJlfyDyE/udErPpc07l5QOpYRhLJBNh2DA5nEyqTnfoP7YkDMQGyCaSU6UcISCtNvYhxBhIMEs6uEbcdgcNXAFuhnIR6v58rk+8SbBI+ke6/2Z+dcxZoUxm5NnPWbkVgCmVsTe3tcSvb5j0uJHuregRXIbNKrr+jhNnXpGfJdhfvofbKIdtZUchs9T4YxadoLKjt85jJPITGwByReX0cE8hPE4Fnaq8+sUBL/Iy7hHsNNH173PaByT02pHfL3pAc4yHCsvAyTlpbYaeSmGRNnCZf0WQYZwTBRhNrDQbPcuNIog6Ircc8IUsrxkGlDzu/mp6p5+VoPAihkbVLhrq1c26XzZb27qd7kf43sPPedr0yP9ESAl0iLCOIjCKUmwBCQGVom2M9hNbWw84ZLO/k08nGzTEMc5hwUDtuar9XIb2v2j+73zqCkSVagVqq4857Uj1zfYfpuiPY3hSPEh4Utd8Y8KpiwquHCZCjtPmrmu4pIjhvtdDAzvsIoZ6D1+YJhSWzLXNgG4g0clrpCoHwhAayApAJe6ld23gW9NoU/rm8OOKLFMCma/LWDj0iufOzlJUtIApcLz+uJL+EhSfdrHkC5AATRVQ2WMOsI2IfxEOI4e6aFdCa6NfvT+eQ/meycBJLna/G7wqKLEDUSV2isptxKCOFAtc/5PdraE+gDM/z4BbPku1vPbePTYz1hKbRRFj09pKwkJBwZvz6Y5j9+zVsv04JWD1vlkhoo/olkrKqfj1i4xdBamlrtV9ya44Srfr51z9Ee0I2nesXMA+LOCRF0b4FeBOwA9hc7b9M2ZylLPNSQn0KuU8czvWPcLqw1vlZ8EognyK2OthCmxPSe2ahoomtmJM5b38tUlwCqFwv5Cx0AJFNSqhHaf1kApWXM0kJPpkK9KvDvwK1hmQFGIOy5MJiWFxSV8NbG7HrxMqLrBljtMajtSWfJllGLHSOdZGAyL0n0/nQJjR1LizvI8+TpHbOyRxGnlCqR/7eNYX0X8JUdTqKIYFMmuWJuATMFxew1SbVALiXECbZVFqLaS6ZgZoUipbUWgkJQwkHCU4N6owU9F6C+2rXHBAl9JTbcAZbQKaM01pxOo15XiQAnsDQyUy6n7gTIU2ZXxKwQkrP1IeqX66/fpsk0vk96vWTSddFoJmjycmE5WqWF0iJeqT4tO5IAi8LJhGz4vImgeGzUwwrG0EAN18II/LlOOap8E1NeiKthlgmHYDxOiJeimCeGjBPFkFaufA0KDTBJTQK3PyDhGbILq+uSSBpLoGQtVq2P7uMeRe9dMlN14g3fy9tmJrPl/aWAMlCqWsaCZFppabeR/eWWbQWC0YbeNvvBQ7AzZf7M9bRRkKCstuIgXustDNla3Wh0J6+i3xTSe0/mjAiGb3+N7+DtuknM2BIJH6V61bcxBhmcswQSEeII99fCWEhhGbj12RSGrj5bZxubmbUiN/rOLGF5KLfSytgM3HZo73PyFHahD1Ekma13yzc/Fpgqti7dT11EqhCIKrbWchJ+dKVagk72IBJXr2k/2+0+1Lx38Fe2pn3KsieB7DCco8Tm8NAdI4GpDRu8Tr0058mfldSd3mKrP3h9NbOQiRzBBI8nfpP1XQs5xrUs/JgyHXJ7tIsXLL9qehI7Sg1yqnZWBjvOmxgPQhTxwkBIs0kZHABYXevwZDHVix/mHJWChF0BaHqkeuY0ZQQi7ddq1+ysB1gNn1+l0UM1m8FJqH3RmAjbPy+PmUdRlJrQsk+Fz/S7cdKy2U6JRSVj+sPwqXbYKjhPgxtznl9Xuf/lR1KXhfVRfyYXL0icbU6083DqRlsz80+sYWiUJBMEvEe59MmtpcpK1tANPBRJSHR5M2xCmpEae+cCARCS4ENWklzDRilV1NDyeWZ7ccKH/0cbdYb2to428xds0CDXHXVuU3nf57kEgqK8/D3+ujf0p70z/SXickchNUVGLJxs5mgdx8QMfxqMwVMnYKPPunXaDMZiAmcg8ROYZBXngZpRGlzJfaR90OCRmtlBrQ5BvWna/iPfjG1tex8eWeEOPQu4kR22JBv/hJ4Ck5+p9peEkI9EIhF60hyu2Wzx8fJR+8i+j57K7JAzh6n+XRfCZ3vw4SEBJyUzslif7peykPjn/j+0QPAIyXMI6ERBUVtoz0O9I7PUlY2B6EGkAmgcNuCDSrZg9CGTD1icGvA6FxBY8FFcRI9bCCtwwaonp81swbBRoIlzjxEV8NogGS7PwsFoRQIswDCLMnSPdu3XcY/3yMLngx1VfRdk0gcgJ6he0mTTxBIQ5mdtIuZ+kHtPsAZZfpoAAAgAElEQVQ0oybHVm+2I5j2lkCdSvdWbgkho7yuQ//1vjKfuh4tTUD1gTwMleAL1gCvKHBXjS0Px6A50TB+0TgMCktlsR0/k81JOp+7cRISGjl+QXEcEmzZRJUQeMrb5ht+3PuzLBZqv5odLQJeZrKul7CZLIwCn+ZrjD+ZgHl7hcxH6PiTLFtWtoAocPs0o8lUwHcOIuxCSf1JAp5BDHYFVOUJpsg6fVejQ9hvMmd6cPuVhKDIa0B0jeqS3YhL6XOehBIm0twaVDlOIUNuTYA+3P462iaDNHN2f0kjlM7//CzVOQs2fZdpcaRzvSIal+B2Eb/K/aABPEYgKvWThIjs5fWE3a8JkTViA2Xo3SVNLmELgRaB2y/yaxRVqD7X5FP99V5P1TBFnbTuX7mOpa/P2HflxVBfzdFOUd8Qi+TUhkO4/dL0jjktQF4fk983B2IJ3Tzabr86WcOVLgEnRDSJZYUaVOj1LFJtCm6/EDgPE+BSHOMYySk+ZgeW5+1+Iv7jQZYtK1tA9GDXdkaQswp2acWhBrtgrbSRBEfW1BCSFSI4RfEQIg9PYoNIE2Mcdr2eGBRLGGmkAZA1dDYpSPXLrs+slbKW17mZjc9ZsXqw6w3EZMxka9ZM4+majCRy/cTDaLDqPTZjHMJhYh+LgY9RDeq1sOs8Ik9EDliT8FK7Pm3tXaaweIkh5go+QJuUlQDfAKyD2sMCsHJb6hkJWew63++l1bbZRQwp8xKhJTfB9OvGWWwa6iU9miMD0+JHiaA6uQKzshHRmcOsXRDt2kHsa6p6QjuSVygij43Ep4wQoQScFI4m+AnCI9H3Sk0VWDDhwCtg1xECIRfMZJG5IWG7Hbja33eeiINZpqxsDqIHu79DMLU514D/PnpxwUpNnGzTS8uIVMtiUZ0hFCA2PQWn7P4kbUJQUD6nqCvpGgjtDG0bVLZ6tllJn/VbDr/2Sb77U+n9NNAyB5InE+lzhrcSiBrMEqAbMZ/864jFTQ7tq5YOb7Hju79NIAINcmXsyt6aCRMsNbs2FeKcEZjsahjFVIyQYuZuoGU37/4So4VJo1R52VOkc09iQuBR4Jswd9cS4xc0DB9corl3wdyOJ4iVqmoXeXrkCp/FhN7T6fgi7P4b2jzHQrrPYvqcTQMJBBG2Qh4SsNlUuYwwiyHiOdbU2C38GOzen35/FfBGQtBNEbtrHcMCrSDyfSxTziggnmnrvfTbvyql1FLKNv9eSikf8+317i2lXJXO/VAp5QH/+9CZnmsXEYNemjKHv0rCaiJqQCnMWkRNwXzga9J12mlaA0kp7NRROfAoa8g8aLOtCe2Jq+N5cmZ4n2F9JhH1HmKtx9Jx/Zbv0etco0Ek7acBl6MmlVIuk1zKxLWP8PsLWUizFmy7eb3XJiIjOIS5kttLzLsmx6wfV7IYmVd9r5fI1Q2EO1QCTegwL4CTEJjBJvlhv4+4BMH6IWY2HYY6D3P/p1L/F7bg7G6CI5HAV910X/EW4m5yIJN4D7X5IP2u+I88FjLRfYJYxi2BoraT2fItTONvYoQWRv2hnBvagHo9JuDngIf8OerPjYSS1U5sZ7Ah/i4mxn8Ffh24LR8spbwCeA8ml1Wuwyyc1wBvA34DeFspZQuW7PZqf+W7SymfrrU+zRnK/swHqAO0ZkLRe7J/pUU1SdRoYAExDdbIV2ID6QixIUwmD7UYxt2l+zfTnsx0Put7RiyqQw7oyQKGdM/a+dP9FtM5Q9svo4WMMtGZOQYJquw+FcLRyswcOSmEdonX9yGCRJR3qME057CyXxyK1rI84uevIzaZFSGpftGkE7LSegGhh0zm5veRgCNdj91/vyIJdXwyXduk41O0OZ+jBGLQ5N6E7dhWCI2vumczTCWZCvun0nnZywEhLIfEpK7YuBNSbAiyfZCOacHZABuLQjqPpntJSU74XJEZkt9fpPsU5t5sMOQwzwuPg6i1fh5r0m75T1jq+wxo3wfcVq3cCWwqpewAfgj4bK31qAuFz3L6fp/P8HC44Qr/nAfTGBENpgaRO1BBQxCNo4U/k8BVwD8GrqC9ehHag+s4I8h6w4/QNguyd0ATkPSbWkQDRsezh0HPzOd3UcRS+hvCDT+WzpWQyK5coYKMLjJxJrgtaKvrhthkEZml/J3KnZBXJa6FG+RD/zaWVEaaPeWuHK2Rket43shHsM+cILienBxm3p+rjNkQfJL61n+74Yr0bGVWyjEpeneRlEp1p20ZxVn4b1WJZWSq6K+LEvKkL3DDG1I/SZBkBKrJ2kWnmcfK7ylSXosMtxKrZRWZKp5ELuExuOFN/l39NQ5F0Zvuneu9BVvtLRL3XMRBlFLeBxystX6189NFnM2t9wrs2Ufbx680cceIhs1aci3WGHntgdLLjWEsrjJZryd4B2mxAYYunmQEO/d8kuhsMeQpTmJUh4wMvP4jjZJ5iGxiZP4iR0Dm+3kn7vkjwn6FgOd6fr6frtMkmceEnjIL6R76/Qjwt8RaA03ik97WA4yDqLDHd8BqcTXQMol6a6Ao76QLstrzNt9IkMRCgjMEUtDkEWGnRXgSyg7f99xDTDxFRea4ihyTIVes0hHKo6LPg3QdtJFZ9oyoL5cY7bS+5yuMBFNRm2b3pszF3K4DFwC6v4STBMY6qFv9t83A5cS4niAyoSlj14S3hxBX5pd0/43Q9KG5j0AnZ5AAz9mLUUpZC9yMmRdnvbS23huDa2dh713wa5fAN9bAx+8HKvz1ZrjplXDnl4HiW7pdDZ/4GrzB7dwb3gTXPQUfPGj3vvW1cOiz8Kv/GZiCz66HX5qCO+62ex6egOveALu+Dq/2SL/3/wi84SnbxQlsn86Fcfjlz9v3z7waPn417PkTe+ZoS7c/gYt9+77rPgA7v5y23vsBmGo8AAtzo+66Enb/d7vH/k1ww4+bQNjmK/Te9RPwpidh7x/Y9xvfBTvm4KYv2TW3vR72XAa7PmO/79viW7p9BqZ98L79Krj1a3CtJxrZ+Uq44oRv6Vbgt7fDFw7DJ+6z9tg7BTsvgTu/GWb82y+Gq+ZgrxuWH97hW+85r/Fr6+AbU/DxR72fpuCmjXDnITt/dhze9U74xHfgDY/YsRu2wnWn4IPftnreOgOHNsGvHgAW4bOT8Evnwx3ftvMPj8N1r4K3zsDeffac978GbngM/pG/yy9eCgtj8Mv77ffPbIaPnwd77vZ+moL3f5eR4Be7Nr3ulbDzMFzvm9Dc/GrvJ3/u7efBrm2w23d02T8FN7wW3nEC9vpK2x94Ndx8EN7tC9VuvMi33jti73bbZtizAXb5u++bhA9fBHc85JZAhbe/CW496FvvNbBzA1zxAHzkYe+nS+ALF8In7rQ237sZdr4R3nkU9v4tDAu8/fvg49+Aa44DffjwdfCOe+Gn/hgYwK9dCN9YCx9/gLO79V4p5Y3AXxJr9S7GLJq3Av8WuKPWusuvux94l/5qrf/Mj/9WPm/Z524sdW+Fa76X0FSS6ku0swZJSksjyBRRMJAQxSRmYlwO/AG28EjLcjPhJ823Efb+DVzzE7TtNWkzhXFnFCMNKMQhyJlZ+4woslmhmZgJL7eH934CrvlQ53zdL6Mbwe75dB+RbDpH2lWaNmtOoap1hF/d81aWCfjSQ3DNpf7cvCeG2lBZokQCC1YrR+V64LXAN4n9HkqBLTVG1WswMu4pjBPRmhHZ6MDeh+Caq4nQbmlE9Z3eSyaFIguzl0h9ngnNvEdKDnLTWBNH4W2496twzWW042CkxUXKKuhLZqE0ew451+riaYKIFcGoUHiRkup7oaJjsHcPXPP92MLFSeur0b53azDEMYNxSRNYW1fgr5bPB/G89+ZMvz0MXF1rPVxK+QfAzwA/jJGUH6u1vtVJyrsxBgDgy8Bbaq3PxG3EvTeWOv02T1acoZ46Sx2t73kNg6CYJqDITA3QHRi5dggbQHJx5RBVN1mmp2F2E0YSZUEAbS4i10FF9RbZqEGr6wbp93xNNkV8Ek8vGlJqBe7kyEox52Kos9tRiWlPpTo6NwC0I+60sjGHST9i1/dOwZrx1CfZk6LBTGoDvfeMnydPylZCEKlf1FeTwJsxAbEAfI4QdrKd1xgymk0k7qjvFRwnZbKQPicbf/TeEqjQguyjSNvuKtN1RPasoW26O7uYrpeAH8LYIgy0BkV1kILIfIQTk70JbI3RBmL16zpGLuZRPTSW9DcH0zMwm8fFGOF1GcdIZSWlEe9jjODzTxjjW+99AXhtKeVAKeUnn+X0P8Pk/X7gd4CdAC4I/h22HnAv8ItnEg72cLjlAG3NmrV2lspjhLstx7mrs3LI7hE/dzMxoPQ5Tzhnl2/Zh9nByjisgZzXFuhPkzDzE6Tz1HGkdxC/oWv0l12hDdzyfwmBmAeXnrlALF3WSsHjxMq/3H4izHLCVAlKeVCGULQkfNb+mnG4RVGWqqfCrTMiEgpbwr0fBLt+ioj2y5Mev9cbCZt8Q2pXIUH/fssBgnvI2nsL0YcSlGr/LMAl5IUspdG1cbHQVHZtLmDens3Rhrc8TnBceTesKRhICAt55BWbaiv146TriZ4/XwTlFgJ5NARC7qX79OGWb9IeH0LZEjASVPh99I7PUv4uXowbaq07aq3jtdaLa62/2/n9slrrYf9ca60/XWt9Va31jbXWu9J5v1drfbX/feJMz9VLvPsgbTieWfmsQUnf1WjQHgzSMD1MO303piU3Y423iVhEJNb+FLz7EDHYDhNsveB7JiKzy7RLSmZvR69zXXZ5ZgI0CYh3P5yOZw/GDJHbYc7/RJLJxFLbCWprUm4CXo+ZEhv8mATOMahHMSEDFEcT7z5BaESRb25jj9q8D0UCW2RaF0rLbMg7nF2AoTt5U5R8+ALa/b8I71bchOogoXDI+0krHTtu0jKkbYLJdMweCsXFSNAqSc4MlHu9rT0w6t3H/Pqcd1NEpSuDkVdnHW30m5cBKLWexuFa/y6zWSZ0Xi0qpDvh41TeIBHA+DM3ESSs5oA8OM9SVnaotSS8tFMWBln6qoOzq1NaJ7ua8nLxuzCD53xCgvcw/8p3iAVEmuRanDWWflMnKotP1k55oKgzMqKQNiSdkwWCPkNMZk0ODXAJAcWBzKdzS+e+OTpPk1sD8jLMjfYYET0o6K+2WY+FPsttmU2xDYQtLe02gOqCpbWycg2RQEaBQGLzG+gNoP4NVPEfm7DozkdS20kQ9zBf2FEikXGOh1kg9t9QXs15LLKzEklrNL40nnp+vsKd9Ux/v3qUtusaYnm49l45RQsd1BzoV4k1H5roS1gY9CuInBGKv1lDTPjk5Ri5YEv6U/vL/X8K8xq9AsP16qOuF26ZsrIFBHDjlVB6WABL1+YWvJZUBGsA5SSQfZ1DWkVgaWB8APhzzDDaSAxiXdeDGy8jVgVmzS17X5NN5JYGoEilHJugTs5oI2uRbBbl5DADuPHNmHbM0Xk58k+DJV8vAaHBnt3F48Tinc1EngIJlEH6LOKxgRu3E+7mPm7jF+jXsLHzBNDamAGxaEiTSD79AZZjcQEzSRaAL/pz3oEhFNXD3aI3bsc0+5w/75X+XeHDUhhz/v88fz9NUpmuihTdwIisLH0XiMqSNe31fIxYr3ESmIAbX0uQq5mYXsTRTQ+GTUxIoR3xO+JAjgPfg/Evf4EJxgOpX/XczFelPr3xPbTjL8TtaJl33rJSQuRljSCACxdd+kJA+ix1dVzQS7BJUZSZPJIWUNDV5cBroBzDliRrnYKWN7sG3tENKsmrC/XbAkE+dW1b8SMSFqTr+4TwkUaQttUzfaLuOExE1kkASCBJQGjCaqJKQ+tZs9hgl9C4EBuU5wMPEBM3r2ocEKRcgR2qo4TPWmCxBuLImZolYPrE7wrx1XqPhlGMxUjTP+nPmwM+ReTv0HqaCjuO+zmaaPdDWXDmfhORJKZgk389I7J1VM+1/lyZNC486wZ/nrJJX+D1FXOmDNPrYMdT/pvHRYyUkdp/sokJvoHIkal3laCfwYLPHvP6PUxo/C7HJv5I/bwEO455O0rhuBeOyzEXgRSkEHZWosuUM3IQL2npwc+5H3sUhCIkoUklqCgEIUi2hYBSOa2Z+ItZ4K8w7fFdmPbJezvKhFgHN2nDHV1/BNNyx4lVcYKPIrKELk5gg/0gpg0ewQbAQSxk9mEMFTzh5z2B2bcSVCIPl+CmB4j1AFqAcyHBuGswa9K+Bhsc4hYEeyVcZN8/DvwWRjG7ti0StIr/F+IZwk0KUhv360WijRGLlZTyTDyF4K/ccol9HwlEIayCaV2Fwh9LbaxJvAg35bZYY/eqmmzZ3a1xcgAYc+ktU0noc6nzHAlwEdhHva+EDM5nZL7c9DihjbMbNGfbUpvLtXiqH5M7mxpKey/hqfZSGwr1qG80LivctNfrrLZd732j1IKqSzZJXtYIQp3ksK9m957QgLScYG0Pm7jbCFdR9ifL/l7E/PAfw3ZK/jYBw7cSngAJDRFKMm0kpNRpgpj4b9oTIXsmxEJvxRDOgVQvmQpriQ5dwgao6i+kIOGQFwjpuTKnlGVbcQRzDptV7wnM5v0c8Bk/9oRftwnqE+md5OnQuxbCBZj2hiw95w7mMTj/WLRbWUq2f47BUHHitAyxHdzXNtYWj/vvQoxP+/PP9zoIGaQwbxrMjs9E3nwxATxeoFfbQk+hyL7fBgNsDPUwE+xt3ld3QJn1cZjXmBQs9+ZEDXQqwS4TUTEiIz5saM9S4qMN2JidTvfMbky1fUaFEm4SahAqXwJiDBO0QnwaL4X22FmmrGwB0cBtWhAEARWlKXVsiE0qQc2jBCRTWrPszcixBweIhlIswOswuO1p127bzumEjjpaATYbMBTikZ4t00MDSddNYcvWXGD13gvNkwQS+AKxxmGzv8sBuG0bp9vNR4jQW5GVGpD3Eyv7KpaTQW2wiGlEeWdEQGZhJL973rF7DG4TQSbPhAul2sdWe45XQ0XihyQcsjCXiaN0+C7sqrwK01avsQYG03gcRYFBtX65CG7bGte1yOCnC4xVu8YFHlR7z601bP48Bi7E3JffwQTFU37fBwjFMwZV7fwgjPdgqcBtk9jel1RoevTXNtSJQnPSB4yE0EwxYkOBTpr42kN0k7ePtmcQv0B6N5HpOeDNlcttVxJCcj2mAMCErPpUrluIjNrPUla2gOjBnvNoS0/ZtxluyWWWpeMR2gFFgmI589RkPGcUU3ESuIeAygPYszndI6MBDcoe1sEZakPYmtJUqs+7gB/EhMA7obkEG5CfxzSfMjJDuCRPwR4RVHK/9TEUJDJOnS/oem96x+wa3kBAVsF81VtcihDZJsI74hN7T3d3L3kPGlCKtLIEVQhLi6+0dlcBXLoHVqcyBfVJWm7JgYTQBmBYra7b7Po94p2U3Ur1qdVm7rAGoqyYgLmgxkpGTTYXRjxMEKSkNtpPIE8pEWDJ+2bPNBaT3QDzDcMe9E9W5x+KJf2dxSohtCiOYg2BKOV5EAkv80PPLZjHaRIzVYUyXYjseSUxFrZjHp6TxHYM4jtyTMgZysrmICrsupd23IAYaDVY9j1r4giGa2KWuF8r9HmQjkmrzBHp1jwzz65vEoNPvMYY1llrCPv/6+le2YMiUk/1/GtgP4wdA34b+BfAH2IT+mECti9gGu2k3WrXtwl/PITAkUbPnh5pJiEsn0x9EWgz2MTR8mxFEC6VWODWw6D86/x6nzi7nor+6OXnahJVbGGW/PYqIi1lBqhd/HOdS981gbIdL7veA7p2PU6gKL9HmcdXQbqmlnDrAdM1iEtpVHFGMrHkLhbK0k7nIkwVEZqg/K7D/lnI9BQM5XFZqGZ+zKbrhIS1YGwi7tVasKb+lUnW8/NfTzuhkJdd/9PPuRhTWKeIfU/UR0K22kzqDBJgZSMIldxoAEvQd45npGGzfVdpCwU1itx/alTBRXELui57FHILyZSRi+pJbH/ECWge9/tMEzkU8rJhsAG2HUvK8gswWGDk9is9h+GawJLyinhbZ/UqAwu6aSawjVvV6ePAXIG52tbo+k3I4QSUxq/Dn7GdMCMma6Sbu8iveQhj8Q/S3h4OaDTZpzGY/hhh/sjFOKC9T6UGu4SSgr30zlrXIc+PuxNHMDsTbgO3/bPXpKsdlbJ/DuOaZNdrzIgzONGH+cYyNakfcuwIfuxpQturzkoPOJV+W0N4O6bc9JI3p2DoTGSuAqEU+AXtADopuwcJhCliNAfDXQS8gRAOipdRm+RgNgnxZykrG0EA+9SIqumYfR+qMb0vi15Wf7J/JbXzugHlIZBpojBarWHQNW6r7ZMtLnOmh6X0cijeNIyWJPdEdDoCKMpfqYk6C0U+b8VQVLe9pck0IKXZjwLrYd9al4HeFlXxGif9b7G2J5/btEXPnzH0XXsEX6MBLwEh19cpf65Qy4Go1z4RZCJ7N2H5NR6n7SlRm+k530PkK1Bf5rB4/NxLvf5PFjhUIvZEiMOR3r5JKP0ai5sGxhFUf9/SL/abeJxsEuYoRp/UpamRX2EzFlzkXoQija4cqZpsDeyjMCaea6v/ftLbYyMmya0nQmko2jG/tzwLGTGr7zQmZjAhLG9IisTddx5GDivFnsK1tbRbgkjoO2+atExZ2bt7byyVv+9fHDpWDc5sJmRNoGxTpPOkTbKGUir8ITYRMuGoAZ47Rh2RU6bhz1XQ0pgjm40EcyyPA1gnvREjDxeJZDZKTiPtIp+89s2cJfaPVCATBE+gTWrkY09Qn5NYXoaKBZupjXq07XA9U8FZ2lT2GCFwFTwl+K1FUbqnBmROoSYN5/Zz/xFXxpk3mCfiG3pE4twjfp9pIuhnHjjhs0dbJ1Y8G3mhrKlmqvShDMao44Noi0UMTXSE+GgiZk7qu/37XcUEr1yOMi8nvV+8DXpL0AwwAVK9T8cwDkIotefabBrjURTWr7wZ4jckLLSATTxMjhIVosj82mbg+30MHCI2iNKCRCGSfnrmLPDxl+vu3gXu+ByjRSdVEFINlE0JTepnWiOhP72tFi8pYEYh1SK9JBwcpdxxH21SDmwzk3ki/6FP2qFs3LwkGEM4pU/wC6rPVkwQaNJpEKuumohvspwBpZiZMTKttIM5wJO98O8L4m7EdhjLE7V4PTTxZe+Lz4AgKiEgdLXf7zhEDGa1x3y6h0jMpXR/h/dDCV0JkC2YzawclWPAV4CnSvSbVqJqok00MA13POrXzMczq4T8GNRmEEvcB6lOM0TOyy2Ea1NxIWDrje8G1lZrX5lrea2Dc013HIGmDwyLKRsl5Wkw4UI1063BSCChKCFEoZHMzxDvMYraHaR6yKyQR2MAd/wOZg7OEh6RPA7H058UolDoMmXFcxDTgtqKGlMRETdizwnBIWGh/SuySytPPHVGRgjSztPYZHna6zCOQV8tBpLpADZgZLdnLQQRwCN7/FHC9pvFOlSEpjpSrkPlTFxj959ecgQlt5vOk6uq18SEHBTTWEs9mGpMSCiWQMSkYjyy92MYzxtpVUU/+gSbBps0S5wufFWXHHItsnQRE4jSZvIokY6d0HOKScO5xojS9cRkx/5Pi5lXbtJ1NTgiJbqV1q0YhFIMxElgwaW+4hC0glRaXKHh8hDJfFXfuNCc1gQeq+Eu1mrhY4RpM4ERpVojoVgJcRbZ/S5UADG21TdKTSBU5+7O6UUMeWkne6EFjSspQbXHGcwLPXrlFtVOKbectCqK9BN0lEYSAsgSWBBaE0nuSwgiUhv4NulPob05Ucks1gEzwPnV4h5kF8o1NU5kz85rMfRMH+BlnlHkXtEEE8zMnEsfi/QUKy0UALGcW/akou4qRoiBud+m4/1K35ZMjDSLNErFSM7ZEkFHslUlpNUOaluhhmxLi7334z0JBrWNgsDO83sdgzERlLrXGBYo1W+C6JsjXLuZtBbfclFqb4iM2z3CYyNPF37P8WrvPCBcsUI48qAIUQpFiHNJHMSoHbNbUun41c5yq0uxqQ1lZgmhah9OCKSpsSkkpjaVW14BfVJ2QhsyS6RM9D6kuuQAv2coK5uDOK/U/o/BUMtfFdSzRORXVONqQskVlqWxBAVEo4vJViOvJ+C/pLfb0v3GobFsa+1zeITY0GQMG4hbsYCbx4mBItZbsHwNQY4K/stVqd2cZggX7g773D8IQ2lYvUsf+kNDtyPEpPc7VWBztWzNcufp2lOEXzybbYK0Q0KwVCKj8gz0p2CoQTtPxFWID5DG30/AcglziOxPi24y9RyiK87jSM/QkCaftPdxYln+MeivgeGYfR55RGbSNbKxpQg2wNipHoMLGnNpLgJTPVjjfIa2DdRO231godBz7qBR/SUsfVL3T3k9mgKTlV7Fkr6c8JcbG9LrQZmAodyb4sIUFKaJLCEptCKTUEVoQ0hNAq9CfzsML/B7bMPG3VFMqQk1SJHp+5CXMQfRh1u/Svjkp7DBuBnTGFnTqvO67K8QQJfB1oTJK+XErmetdwr+w0PEJJJGOIYNWHknxtPvSi4qLmADNqizpoFwbSmJq+oqk0MD8VHg3h63PpLeLaGdodyIFxLQsY+566agfi+WQVQaVsSfoKzMHU1mtUOOSXiMkeC59clUV7kd54pNEJ3/NK0ov1GdRaa6UK/rzWU7SjjbBzY27b02JMgVyDUBXAK3VszsG0vXj5cgE2cxIam+bWBANSGiujdNBODJq6QktotArTSnnIBUeyz4fX1M3jqHIdzNlTKEibX+jlPVpbc1zVBtKiJYf+p7Ceal9Pyu90a/DYhENj7hb/0zIvAr80Aa35nklNlyhrKyOYgeXPsop/vPRepNEKGwkoyC2/JkZHtSMK5DIFIwtxBYw6ojsGe/U+sOxPYfJziKY+n+6/3/Pdik10TUHhCC/ycIQbGIaS55MCQISfc/BfQbrvU4it4QGgVe6Z5gmkLCStr8CAGzNeA8hr/nvNkITQhyysyRpgQ0ZLsAAArwSURBVFH8waxdd+0CRu6pDLAQaLHtcpVKQ0poa0DLTFkyU2u0RmNQjEeQMNDaFZFz2PM539rs2qcwTSlidA2MHakMJ9yVuwRscpt/ptiErdVD8XsWxy3UNlr854Ok514z9ZuPhRG/NVVH4+haKaIFe+7CwNu9EkpAXphMNMqLlvNOjBar+Tna6yKbcCPhRaCAHlx7CBtbmwkvlbxu6kchEj1bpu8yZWULiFz0wnnnbQ3AvOjE3Z2l+iBRI0JEM6qTJAikddWQs4S0FSxfj01WdeBC538l0utLE2/DBnMPY+YLpuUfJTSWigaq6qoJVfw+IkeBRlpBAknvqQklU0FC509pDSQhpGY8PUPxIUuYyZXdldLcvfSsWTzxS89mkZDEhQRiWyA269UEUbv37Po6V2BjdeK1Rn6HeUK4jBl3UqU5v0XwJI6qyjjUWR8K0owSbJWY0EJN4038JnJ5iBE0nki3eCBdFRGqSZoVy0YMLUnwzfoxBXNNFROeamvFjohvkKaXIlswL1XVdz1X413tJ86pO3b1/1i6Vu0hIYH3nTidZykrW0CMwU5loFbqr63YoOhjHaPJntdd4JI/x52L+FFjafLkKEsRjpoEE8Ac7LwIQw1ygWkfxiFwkffJMSIGQTkRH/LvbyK08xOcjmDkqssuQ0FaeRDGYeflhCuwIRK2SAuJvBPDLtedJoEGnFj7JUfAMtVEdGnwKjoxw/wJS4UvFMJSE5yHC4Oy5Ip4nGDx8f7SmoMenhu0wMkaC4/EAQwKvWGlKfZudTsRSq9+UVwE2EKxocvvoc3Jkfu5pv6CGBOqizS6vB4zwBpotqQ+kekhQXeYUWDUzguxKFMJwHXpmdRYlKY+yIuw5M0h6lph5Kkqxb9LGMgUcdOi4J6tHuy8lnA9ZwJV7a1kScO4/0jhLlNWNgfRwBWPEkEz81ioqdZNyPuQfMG6rmVnqUM08QV51Xj6ExnWbx+7Ql6KU5gQOEpojG1OAEoLjGHC5LDXVyHYP4ot0LoUC545hXkMVA/tqagUdhfR9mUP4YonCE2/kfAMJLa9J3Qh74baQPESSl/m2qaRWaAcjRKsCuDB3r1MYRGgJ+EKIalFIteGXJZPYcu15S3S/WSm6Tte73VN5ATdTNjP643sG5GMyi/ZeNvWwhUTfv550W7DMSyLtIK3xI8UIh+FtLgg+DzBSyx624ooVqzMWAnBvYBBmgb4FlzxFdpJcjI3JY0+ckUTvEKTfsvckvgG58jKkOAcfIWxEMfIxVDgCpG42axTW+dI0vxZ5tMyZWULiAIf+WvMRheJll180v4iv7YRsFENkDtA37NQEHSTAJDvW+nBl+AjWuGn8Oi86OhBgpDTIFB2aPyaz2CbDZ7AbVM3/uVB0sBcSsFBIlE1aMbgIwqH1m7WCTFp7X/jkZtlI7F12zjmWbk0va9PrpojN0cDt1h+fdn2x6E+hS11noCPnCDWWMjMk60uDSWPkNokJwNei8UgSLBr3YVcy87MD+aAfs9QRkOYfpNAqXxEO24tlQhvJvX3BDBRgozOrsvG6wAjlNTrQ38GygzhCdiOCeqlfsyWU8Caof027uNDSCB7hQaYEhj2bNhKmIq8zShHCk5j1O9TGxcCC4SLWOPDzWmZjB+5jxAwQnB6X52XI0E38TI3MST9su94SGQagmB1NWGyj1niNbsFM0mTS8Ei+o4RAzH75XcwWpw1ur8CciD80w2jTX9b8H8/tjJz6DamBpIg9SksiCYTedJ0su8rNnmXMFtZwlArDnuMIuNGYdUi0L5JQN8cQafvCoqqBdZXOF7DfJNrdi2xKnHSyVItOVYkpLYzVN4OuQzlTpY7d7LGZNI9Bul8tf+8k4xzxRZlaSxIAE8CS3UUHTu+BEvy1PSAmRrmkSa+FMigxkTuQ+OTp9/AcBZDilsLHKrmKsoTeYI2aoUIoFLeTLDgqYlq/ZEX4eXvCqITcSjXd3cRovggH8NlkBCEzBQhgoyYNQ67QVNCk89SVnYcRCnakeHwmc49x2XbCqgDrNajW1br0S7Ptx6X1lq3P9MPK1pAAJRS7louiOP/pzqs1mO1Hi9FPVY2B7FaVstqeUnLqoBYLatltSxbXg4C4rdf6gqwMuoAq/XoltV6tMtZr8eK5yBWy2pZLS9deTkgiNWyWlbLS1RWBcRqWS2rZdmyYgVEKeW9pZT7Syn7Syk/f46f9YpSyl+VUr5eSvlaKeVf+vFfKKUcLKXc438/nK75N163+0spP3QW6/JwKeU+f95dfmxLKeWzpZQH/P9mP15KKR/zetxbSrnqLNXhtemd7ymlnCil/OyL0R6llN8rpTxZStmXjj3n9y+lfMjPf6CU8qGzVI9bSynf9GftLqVs8uOXlVLmU7v8ZrrmLd6f+72uf4dF1mesx3Puh+c9n2qtK+4Pi/V6EMvZNAF8FbjyHD5vB3CVf16PrRe8EvgF4Oee4fwrvU6T2FKyB4H+WarLw8C2zrF/D/y8f/554Ff88w8De7DYurcDXzxHffE4Fqh9ztsDS7t6FbDv+b4/tlzuIf+/2T9vPgv1eA8w5p9/JdXjsnxe5z5f8roVr+t1Z6Eez6kfXsh8WqkI4q3A/lrrQ7XWReCPgfedq4fVWg/VWr/sn08C38CWSy1X3gf8ca31VK3121gg9VvPVf38eb/vn38f+Ifp+G3Vyp3AplLKjrP87L8HPFhrfeQM9Tsr7VFr/Tyxh3a+/3N5/x8CPltrPVprfRpbCfPeF1qPWuuf11oVXH0nFpy/bPG6bKi13lltBt+W6v686/EsZbl+eN7zaaUKiIuwlQsqB3j2CXvWSinlMuDNwBf90M84pPw9QdtzXL8K/Hkp5e5Syk/5sfNrrZ4NgsexLBPnuh4qHwB2pe8vdnvAc3//F6Nd/imGCFQuL6V8pZTyuVLKtal+B85RPZ5LPzzv9lipAuIlKaWUdcD/AH621noC+A1si5zvwdK1/McXoRrvrLVeBVwH/HQp5fvzj66JXhTfdCllAluo/kk/9FK0R6u8mO+/XCml3IIt0/pDP3QIuKTW+mbgRuCPSikblrv+LJQXrR9WqoA4iO1rpHKxHztnpZQyjgmHP6y1fgqg1vpErXVYa22A3yFg8zmrX631oP9/Etjtz3xCpoP/f/Jc18PLdcCXa61PeJ1e9Pbw8lzf/5zVp5TyT4AfAX7ChRUO6Y/457sxe/+7/JnZDDkr9Xge/fC822OlCoi9wGtKKZe7FvsA8Olz9TBnln8X+Eat9VfT8WzPvx/bVROvywdKKZOllMuB12Bk1Autx3QpZb0+Y6TYPn+emPgPYUnkVI8POpv/duB4guJno9xAMi9e7PZI5bm+//8G3lNK2ezw+z1+7AWVUsp7gX8N/GitdS4d315K6fvnV2Lv/5DX5UQp5e0+xj6Y6v5C6vFc++H5z6fnwqi+mH8YQ/0tTBrfco6f9U4Mtt6LpZy9x5//34D7/PingR3pmlu8bvfzHJnpZ6nHKzGG+avA1/TeWKK9v8RS9P4FsMWPF+C/eD3uA64+i20yjWXe2JiOnfP2wATSISwbwgHgJ5/P+2McwX7/+/BZqoeyemiM/Kaf++PeX/dge3Jdn+5zNTaBHwR+HY9efoH1eM798Hzn02qo9WpZLatl2bJSTYzVslpWywooqwJitayW1bJsWRUQq2W1rJZly6qAWC2rZbUsW1YFxGpZLatl2bIqIFbLalkty5ZVAbFaVstqWbb8P13MT9wwanAzAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gt13tXtM2XB7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "7c8ff8b9-a66a-48a6-e313-a6fd376a8091"
      },
      "source": [
        "pred1 = Image.fromarray(prediction[0]['masks'][0, 0].mul(255).byte().cpu().numpy())\n",
        "\n",
        "plt.imshow(pred1)\n",
        "ax = plt.gca()\n",
        "ax.grid(True, color='w', linestyle='--')\n",
        "plt.draw()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zchsuEsx2dXB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "d832254b-d95e-4d93-98af-6aeeb7e68823"
      },
      "source": [
        "pred1 = Image.fromarray(prediction[0]['masks'][1, 0].mul(255).byte().cpu().numpy())\n",
        "\n",
        "plt.imshow(pred1)\n",
        "ax = plt.gca()\n",
        "ax.grid(True, color='w', linestyle='--')\n",
        "plt.draw()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "52RAVvnG2h1F",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "4d4807db-5aa4-4190-de97-c408129195ea"
      },
      "source": [
        "pred1 = Image.fromarray(prediction[0]['masks'][2, 0].mul(255).byte().cpu().numpy())\n",
        "\n",
        "plt.imshow(pred1)\n",
        "ax = plt.gca()\n",
        "ax.grid(True, color='w', linestyle='--')\n",
        "plt.draw()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IpfhYZ-q2qIN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "4e5c09b8-b866-46e5-e123-fce90abbe786"
      },
      "source": [
        "pred1 = Image.fromarray(prediction[0]['masks'][3, 0].mul(255).byte().cpu().numpy())\n",
        "\n",
        "plt.imshow(pred1)\n",
        "ax = plt.gca()\n",
        "ax.grid(True, color='w', linestyle='--')\n",
        "plt.draw()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}