3D_Segmentation_eval.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "3D Segmentation_eval.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "mount_file_id": "1RtZZ2TJjmhvABH4gvjYObBYuq_jkdewp",
      "authorship_tag": "ABX9TyPFJXZPsr7FCWV1HVcOQuPW",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Meddebma/pyradiomics/blob/master/3D_Segmentation_eval.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ltF1K3dyrHpd"
      },
      "source": [
        "**Setup environment**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_8RlsQ_Sqvdf",
        "outputId": "489c0d9b-e03b-4540-8d85-a6ee88ec8f9c"
      },
      "source": [
        "%pip install monai\n",
        "%pip install 'monai[nibabel, skimage, pillow, tensorboard, gdown, ignite, torchvision, itk, tqdm, lmdb, psutil]'\n",
        "%pip install matplotlib\n",
        "#%pip install -q pytorch-lightning\n",
        "%pip install pytorch-lightning\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Installing collected packages: itk-core, itk-numerics, itk-filtering, tqdm, itk-segmentation, itk-registration, itk-io, pytorch-ignite, itk\n",
            "  Attempting uninstall: tqdm\n",
            "    Found existing installation: tqdm 4.41.1\n",
            "    Uninstalling tqdm-4.41.1:\n",
            "      Successfully uninstalled tqdm-4.41.1\n",
            "Successfully installed itk-5.1.2 itk-core-5.1.2 itk-filtering-5.1.2 itk-io-5.1.2 itk-numerics-5.1.2 itk-registration-5.1.2 itk-segmentation-5.1.2 pytorch-ignite-0.4.5 tqdm-4.61.2\n",
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            "Building wheels for collected packages: future\n",
            "  Building wheel for future (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for future: filename=future-0.18.2-py3-none-any.whl size=491070 sha256=8cada70e9d14e2b7f6bfcb3f30a2e84fcbcbbb9ff61391ecda38e065ef1458d9\n",
            "  Stored in directory: /root/.cache/pip/wheels/56/b0/fe/4410d17b32f1f0c3cf54cdfb2bc04d7b4b8f4ae377e2229ba0\n",
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            "Installing collected packages: multidict, yarl, async-timeout, fsspec, aiohttp, torchmetrics, tensorboard, PyYAML, pyDeprecate, future, pytorch-lightning\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.5.0\n",
            "    Uninstalling tensorboard-2.5.0:\n",
            "      Successfully uninstalled tensorboard-2.5.0\n",
            "  Attempting uninstall: PyYAML\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "  Attempting uninstall: future\n",
            "    Found existing installation: future 0.16.0\n",
            "    Uninstalling future-0.16.0:\n",
            "      Successfully uninstalled future-0.16.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow 2.5.0 requires tensorboard~=2.5, but you have tensorboard 2.4.1 which is incompatible.\u001b[0m\n",
            "Successfully installed PyYAML-5.4.1 aiohttp-3.7.4.post0 async-timeout-3.0.1 fsspec-2021.7.0 future-0.18.2 multidict-5.1.0 pyDeprecate-0.3.0 pytorch-lightning-1.3.8 tensorboard-2.4.1 torchmetrics-0.4.1 yarl-1.6.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GN-NZFY7Iw73",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad1c267d-e5ba-4f21-9686-7406a2fb72fa"
      },
      "source": [
        "# Copyright 2020 MONAI Consortium\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#     http://www.apache.org/licenses/LICENSE-2.0\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License.\n",
        "\n",
        "from monai.utils import set_determinism\n",
        "from monai.transforms import (\n",
        "    AsDiscrete,\n",
        "    AddChanneld,\n",
        "    Compose,\n",
        "    CropForegroundd,\n",
        "    LoadImaged,\n",
        "    Orientationd,\n",
        "    Lambda,\n",
        "    RandCropByPosNegLabeld,\n",
        "    ScaleIntensityRanged,\n",
        "    Spacingd,\n",
        "    LabelToContour,\n",
        "    KeepLargestConnectedComponent,\n",
        "    ToTensord,\n",
        ")\n",
        "from monai.networks.nets import UNet\n",
        "from monai.networks.layers import Norm\n",
        "from monai.metrics import compute_meandice, compute_roc_auc\n",
        "from monai.losses import DiceLoss\n",
        "from monai.inferers import sliding_window_inference\n",
        "from monai.data import CacheDataset, list_data_collate\n",
        "from monai.config import print_config\n",
        "from monai.apps import download_and_extract\n",
        "import torch\n",
        "import pytorch_lightning\n",
        "from pytorch_lightning.callbacks.model_checkpoint \\\n",
        "    import ModelCheckpoint\n",
        "import matplotlib.pyplot as plt\n",
        "import tempfile\n",
        "import shutil\n",
        "import os\n",
        "import sys\n",
        "from glob import glob\n",
        "import numpy as np\n",
        "import statsmodels.formula.api as smf\n",
        "import statsmodels.api as sm\n",
        "import pandas as pd\n",
        "import seaborn as sns \n",
        "\n",
        "from monai.utils import set_determinism\n",
        "from monai.transforms import (\n",
        "    AsDiscrete,\n",
        "    AddChanneld,\n",
        "    Compose,\n",
        "    CropForegroundd,\n",
        "    KeepLargestConnectedComponent,\n",
        "    LabelToContour,\n",
        "    LoadImaged,\n",
        "    Orientationd,\n",
        "    RandCropByPosNegLabeld,\n",
        "    ScaleIntensityRanged,\n",
        "    Spacingd,\n",
        "    EnsureTyped,\n",
        "    EnsureType,\n",
        ")\n",
        "from monai.networks.nets import UNet\n",
        "from monai.networks.layers import Norm\n",
        "from monai.metrics import DiceMetric, compute_hausdorff_distance\n",
        "from monai.losses import DiceLoss\n",
        "from monai.inferers import sliding_window_inference\n",
        "from monai.data import CacheDataset, DataLoader, decollate_batch, NiftiSaver\n",
        "from monai.config import print_config\n",
        "from monai.apps import download_and_extract\n",
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "import tempfile\n",
        "import shutil\n",
        "import os\n",
        "import glob\n",
        "\n",
        "print_config()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n",
            "pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "MONAI version: 0.6.0\n",
            "Numpy version: 1.19.5\n",
            "Pytorch version: 1.9.0+cu102\n",
            "MONAI flags: HAS_EXT = False, USE_COMPILED = False\n",
            "MONAI rev id: 0ad9e73639e30f4f1af5a1f4a45da9cb09930179\n",
            "\n",
            "Optional dependencies:\n",
            "Pytorch Ignite version: 0.4.5\n",
            "Nibabel version: 3.0.2\n",
            "scikit-image version: 0.16.2\n",
            "Pillow version: 7.1.2\n",
            "Tensorboard version: 2.4.1\n",
            "gdown version: 3.6.4\n",
            "TorchVision version: 0.10.0+cu102\n",
            "ITK version: 5.1.2\n",
            "tqdm version: 4.61.2\n",
            "lmdb version: 0.99\n",
            "psutil version: 5.4.8\n",
            "pandas version: 1.1.5\n",
            "einops version: NOT INSTALLED or UNKNOWN VERSION.\n",
            "\n",
            "For details about installing the optional dependencies, please visit:\n",
            "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zZPvqOR0IPkn",
        "outputId": "74ce29e1-6b38-4081-d1e2-51010802440c"
      },
      "source": [
        "eval_dir= \"/content/drive/My Drive/Spleen_AI/CH Data\"\n",
        "\n",
        "images = sorted(glob.glob(os.path.join(eval_dir, \"imagesTs\", \"*.nii.gz\")))\n",
        "labels = sorted(glob.glob(os.path.join(eval_dir, \"labelsTs\", \"*.nii.gz\")))\n",
        "\n",
        "test = [{\"image\": image_name, \"label\": label_name}\n",
        "            for image_name, label_name in zip(images, labels)]\n",
        "val_transforms = Compose(\n",
        "            [\n",
        "                LoadImaged(keys=[\"image\", \"label\"]),\n",
        "                AddChanneld(keys=[\"image\", \"label\"]),\n",
        "                Spacingd(\n",
        "                    keys=[\"image\", \"label\"],\n",
        "                    pixdim=(1.5, 1.5, 2.0),\n",
        "                    mode=(\"bilinear\", \"nearest\"),\n",
        "                ),\n",
        "                Orientationd(keys=[\"image\", \"label\"], axcodes=\"PLI\"),\n",
        "                ScaleIntensityRanged(\n",
        "                    keys=[\"image\"], a_min=-57, a_max=164,\n",
        "                    b_min=0.0, b_max=1.0, clip=True,\n",
        "                ),\n",
        "                CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
        "                EnsureTyped(keys=[\"image\", \"label\"]),\n",
        "            ]\n",
        "        ) \n",
        "val_ds = CacheDataset(\n",
        "            data=test, transform=val_transforms,\n",
        "            cache_rate=1.0, num_workers=2,\n",
        "        )\n",
        "val_loader = torch.utils.data.DataLoader(val_ds, batch_size=1, num_workers=2)\n",
        "from monai.metrics import DiceMetric\n",
        "from monai.transforms import Activations, AddChannel, AsDiscrete, Compose, ScaleIntensity, ToTensor\n",
        "dice_metric = DiceMetric(include_background=True, reduction=\"mean\")\n",
        "#post_trans = Compose([Activations(sigmoid=True), AsDiscrete(threshold_values=True), KeepLargestConnectedComponent(applied_labels=[1],connectivity=1)])\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loading dataset: 100%|██████████| 20/20 [00:58<00:00,  2.90s/it]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "JDsfyw6ErGF_",
        "outputId": "167b54de-dde6-48db-fea0-18b95b3dbb39"
      },
      "source": [
        "post_pred = Compose([EnsureType(), AsDiscrete(argmax=True, to_onehot=True, n_classes=2)])\n",
        "post_label = Compose([EnsureType(), AsDiscrete(to_onehot=True, n_classes=2)])\n",
        "post_trans = Compose([Activations(sigmoid=True), AsDiscrete(threshold_values=True)])\n",
        "\n",
        "device = torch.device(\"cuda:0\")\n",
        "model =     UNet(\n",
        "            dimensions=3,\n",
        "            in_channels=1,\n",
        "            out_channels=2,\n",
        "            channels=(16, 32, 64, 128, 256),\n",
        "            strides=(2, 2, 2, 2),\n",
        "            num_res_units=2,\n",
        "            ).to(device)\n",
        "model.load_state_dict(torch.load(\"/content/drive/My Drive/Spleen_AI/best_metric_model_ch_500.pth\"))\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    metric_sum = 0.0\n",
        "    metric_count = 0\n",
        "    #saver = NiftiSaver(output_dir=\"/content/drive/My Drive/Spleen_AI/CH Data/mask\")\n",
        "    for i, val_data in enumerate(val_loader):\n",
        "      \n",
        "        roi_size = (160, 160, 160)\n",
        "        sw_batch_size = 4\n",
        "        val_data = val_data[\"image\"].to(device)\n",
        "        val_output = sliding_window_inference(\n",
        "            val_data, roi_size, sw_batch_size, model)\n",
        "        # plot the slice [:, :, 80]\n",
        "        plt.figure(\"check\", (20, 4))\n",
        "        plt.subplot(1, 5, 1)\n",
        "        plt.title(f\"image {i}\")\n",
        "        plt.imshow(val_data.detach().cpu()[0, 0, :, :, 80], cmap=\"gray\")\n",
        "        plt.subplot(1, 5, 2)\n",
        "        plt.title(f\"argmax {i}\")\n",
        "        argmax = [AsDiscrete(argmax=True)(i) for i in decollate_batch(val_output)]\n",
        "        plt.imshow(argmax[0].detach().cpu()[0, :, :, 80])\n",
        "        plt.subplot(1, 5, 3)\n",
        "        plt.title(f\"largest {i}\")\n",
        "        largest = [KeepLargestConnectedComponent(applied_labels=[1])(i) for i in argmax]\n",
        "        plt.imshow(largest[0].detach().cpu()[0, :, :, 80])\n",
        "        plt.subplot(1, 5, 4)\n",
        "        plt.title(f\"contour {i}\")\n",
        "        contour = [LabelToContour()(i) for i in largest]\n",
        "        plt.imshow(contour[0].detach().cpu()[0, :, :, 80])\n",
        "        plt.subplot(1, 5, 5)\n",
        "        plt.title(f\"map image {i}\")\n",
        "        map_image = contour[0] + val_data[0]\n",
        "        plt.imshow(map_image.detach().cpu()[0, :, :, 80], cmap=\"gray\")\n",
        "        plt.show()\n",
        "        for val_data in val_loader: \n",
        "            val_images, val_labels = val_data[\"image\"].to(device), val_data[\"label\"].to(device)\n",
        "            val_outputs = sliding_window_inference(val_data[\"image\"].to(device), roi_size, sw_batch_size, model)\n",
        "            val_outputs = post_trans(val_outputs)\n",
        "            shape=val_images.shape\n",
        "            vols = [(b[:,1]==1).sum() for b in val_outputs]\n",
        "            vol_label = [(x==1).sum() for x in val_labels]\n",
        "            value = compute_meandice(y_pred=val_outputs, y=val_labels,include_background=False)\n",
        "            hausdorff = compute_hausdorff_distance(y_pred=val_outputs, y=val_labels, include_background=False)\n",
        "            metric_count += len(value)\n",
        "            metric_sum += value.item() * len(value)\n",
        "            print(f\"shape:\", shape)\n",
        "            print(f\"val_dice:\", value[0])\n",
        "            print(f\"Hausdorff:\",hausdorff[0])\n",
        "            print(f\"Volume segmentation:\", vols)\n",
        "            print(f\"Volume label:\", vol_label)\n",
        "        #print(f\"Shape:\", shape)\n",
        "        #print(f\"Volume:\", vols, f\"mm3\")\n",
        "        #saver.save_batch(val_output[:, :, ...], val_data[\"image_meta_dict\"])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x288 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "shape: torch.Size([1, 1, 208, 243, 254])\n",
            "val_dice: tensor([0.8960], device='cuda:0')\n",
            "Hausdorff: tensor([5.], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(61722, device='cuda:0')]\n",
            "Volume label: [tensor(51109, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 257, 301, 241])\n",
            "val_dice: tensor([0.9451], device='cuda:0')\n",
            "Hausdorff: tensor([194.9897], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(72541, device='cuda:0')]\n",
            "Volume label: [tensor(39505, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 251, 303, 234])\n",
            "val_dice: tensor([0.9485], device='cuda:0')\n",
            "Hausdorff: tensor([9.4340], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(70902, device='cuda:0')]\n",
            "Volume label: [tensor(63166, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 208, 247, 214])\n",
            "val_dice: tensor([0.9111], device='cuda:0')\n",
            "Hausdorff: tensor([130.5144], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(52858, device='cuda:0')]\n",
            "Volume label: [tensor(22918, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 191, 241, 231])\n",
            "val_dice: tensor([0.9092], device='cuda:0')\n",
            "Hausdorff: tensor([136.8722], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(55671, device='cuda:0')]\n",
            "Volume label: [tensor(44010, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 206, 239, 241])\n",
            "val_dice: tensor([0.9109], device='cuda:0')\n",
            "Hausdorff: tensor([40.1622], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(57599, device='cuda:0')]\n",
            "Volume label: [tensor(51601, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 209, 252, 341])\n",
            "val_dice: tensor([0.9495], device='cuda:0')\n",
            "Hausdorff: tensor([150.0766], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(85932, device='cuda:0')]\n",
            "Volume label: [tensor(48847, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 222, 302, 296])\n",
            "val_dice: tensor([0.9187], device='cuda:0')\n",
            "Hausdorff: tensor([125.0840], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(89392, device='cuda:0')]\n",
            "Volume label: [tensor(43028, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 197, 264, 384])\n",
            "val_dice: tensor([0.9554], device='cuda:0')\n",
            "Hausdorff: tensor([2.8284], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(101376, device='cuda:0')]\n",
            "Volume label: [tensor(44519, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 203, 253, 246])\n",
            "val_dice: tensor([0.9514], device='cuda:0')\n",
            "Hausdorff: tensor([3.7417], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(62238, device='cuda:0')]\n",
            "Volume label: [tensor(44746, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 222, 250, 216])\n",
            "val_dice: tensor([0.9607], device='cuda:0')\n",
            "Hausdorff: tensor([30.2324], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(54000, device='cuda:0')]\n",
            "Volume label: [tensor(50147, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 114, 217, 300])\n",
            "val_dice: tensor([0.9488], device='cuda:0')\n",
            "Hausdorff: tensor([5.], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(65100, device='cuda:0')]\n",
            "Volume label: [tensor(155157, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 225, 255, 224])\n",
            "val_dice: tensor([0.9392], device='cuda:0')\n",
            "Hausdorff: tensor([112.9690], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(57120, device='cuda:0')]\n",
            "Volume label: [tensor(48556, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 193, 278, 231])\n",
            "val_dice: tensor([0.9260], device='cuda:0')\n",
            "Hausdorff: tensor([35.9166], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(64218, device='cuda:0')]\n",
            "Volume label: [tensor(30015, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 228, 251, 234])\n",
            "val_dice: tensor([0.9699], device='cuda:0')\n",
            "Hausdorff: tensor([5.0990], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(58734, device='cuda:0')]\n",
            "Volume label: [tensor(263472, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 211, 252, 278])\n",
            "val_dice: tensor([0.9560], device='cuda:0')\n",
            "Hausdorff: tensor([6.7082], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(70056, device='cuda:0')]\n",
            "Volume label: [tensor(252714, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 194, 240, 238])\n",
            "val_dice: tensor([0.9567], device='cuda:0')\n",
            "Hausdorff: tensor([5.7446], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(57120, device='cuda:0')]\n",
            "Volume label: [tensor(239205, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 250, 262, 316])\n",
            "val_dice: tensor([0.9594], device='cuda:0')\n",
            "Hausdorff: tensor([121.7662], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(82792, device='cuda:0')]\n",
            "Volume label: [tensor(401099, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 182, 241, 218])\n",
            "val_dice: tensor([0.9484], device='cuda:0')\n",
            "Hausdorff: tensor([7.0711], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(52538, device='cuda:0')]\n",
            "Volume label: [tensor(107901, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 187, 218, 211])\n",
            "val_dice: tensor([0.9272], device='cuda:0')\n",
            "Hausdorff: tensor([27.3861], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(45998, device='cuda:0')]\n",
            "Volume label: [tensor(183902, device='cuda:0')]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1440x288 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "shape: torch.Size([1, 1, 208, 243, 254])\n",
            "val_dice: tensor([0.8960], device='cuda:0')\n",
            "Hausdorff: tensor([5.], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(61722, device='cuda:0')]\n",
            "Volume label: [tensor(51109, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 257, 301, 241])\n",
            "val_dice: tensor([0.9451], device='cuda:0')\n",
            "Hausdorff: tensor([194.9897], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(72541, device='cuda:0')]\n",
            "Volume label: [tensor(39505, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 251, 303, 234])\n",
            "val_dice: tensor([0.9485], device='cuda:0')\n",
            "Hausdorff: tensor([9.4340], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(70902, device='cuda:0')]\n",
            "Volume label: [tensor(63166, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 208, 247, 214])\n",
            "val_dice: tensor([0.9111], device='cuda:0')\n",
            "Hausdorff: tensor([130.5144], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(52858, device='cuda:0')]\n",
            "Volume label: [tensor(22918, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 191, 241, 231])\n",
            "val_dice: tensor([0.9092], device='cuda:0')\n",
            "Hausdorff: tensor([136.8722], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(55671, device='cuda:0')]\n",
            "Volume label: [tensor(44010, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 206, 239, 241])\n",
            "val_dice: tensor([0.9109], device='cuda:0')\n",
            "Hausdorff: tensor([40.1622], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(57599, device='cuda:0')]\n",
            "Volume label: [tensor(51601, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 209, 252, 341])\n",
            "val_dice: tensor([0.9495], device='cuda:0')\n",
            "Hausdorff: tensor([150.0766], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(85932, device='cuda:0')]\n",
            "Volume label: [tensor(48847, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 222, 302, 296])\n",
            "val_dice: tensor([0.9187], device='cuda:0')\n",
            "Hausdorff: tensor([125.0840], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(89392, device='cuda:0')]\n",
            "Volume label: [tensor(43028, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 197, 264, 384])\n",
            "val_dice: tensor([0.9554], device='cuda:0')\n",
            "Hausdorff: tensor([2.8284], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(101376, device='cuda:0')]\n",
            "Volume label: [tensor(44519, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 203, 253, 246])\n",
            "val_dice: tensor([0.9514], device='cuda:0')\n",
            "Hausdorff: tensor([3.7417], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(62238, device='cuda:0')]\n",
            "Volume label: [tensor(44746, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 222, 250, 216])\n",
            "val_dice: tensor([0.9607], device='cuda:0')\n",
            "Hausdorff: tensor([30.2324], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(54000, device='cuda:0')]\n",
            "Volume label: [tensor(50147, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 114, 217, 300])\n",
            "val_dice: tensor([0.9488], device='cuda:0')\n",
            "Hausdorff: tensor([5.], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(65100, device='cuda:0')]\n",
            "Volume label: [tensor(155157, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 225, 255, 224])\n",
            "val_dice: tensor([0.9392], device='cuda:0')\n",
            "Hausdorff: tensor([112.9690], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(57120, device='cuda:0')]\n",
            "Volume label: [tensor(48556, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 193, 278, 231])\n",
            "val_dice: tensor([0.9260], device='cuda:0')\n",
            "Hausdorff: tensor([35.9166], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(64218, device='cuda:0')]\n",
            "Volume label: [tensor(30015, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 228, 251, 234])\n",
            "val_dice: tensor([0.9699], device='cuda:0')\n",
            "Hausdorff: tensor([5.0990], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(58734, device='cuda:0')]\n",
            "Volume label: [tensor(263472, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 211, 252, 278])\n",
            "val_dice: tensor([0.9560], device='cuda:0')\n",
            "Hausdorff: tensor([6.7082], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(70056, device='cuda:0')]\n",
            "Volume label: [tensor(252714, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 194, 240, 238])\n",
            "val_dice: tensor([0.9567], device='cuda:0')\n",
            "Hausdorff: tensor([5.7446], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(57120, device='cuda:0')]\n",
            "Volume label: [tensor(239205, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 250, 262, 316])\n",
            "val_dice: tensor([0.9594], device='cuda:0')\n",
            "Hausdorff: tensor([121.7662], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(82792, device='cuda:0')]\n",
            "Volume label: [tensor(401099, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 182, 241, 218])\n",
            "val_dice: tensor([0.9484], device='cuda:0')\n",
            "Hausdorff: tensor([7.0711], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(52538, device='cuda:0')]\n",
            "Volume label: [tensor(107901, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 187, 218, 211])\n",
            "val_dice: tensor([0.9272], device='cuda:0')\n",
            "Hausdorff: tensor([27.3861], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(45998, device='cuda:0')]\n",
            "Volume label: [tensor(183902, device='cuda:0')]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gktgU4uaxt+l2vwo46S1Xp5T6XeAb6XVgfxellHTevgq9FXs3n7YsC5/Ph9banO+6LkoptNbm/xv/dhyHjQnDjbcrzdBvujWtdXab7+Oq4hIgoII6RHSbN0uIwVWhILEpxIBpUqOtW+rKl7xuEptCXIVBjk0Zb14dGW/efrTW1xSb25UwmmTzcniLwJGNF1BK/QDwA9t0/7c8v9+P67porUmlUvh8PlqtFtVqFcuyUEqZgOx2u+bneti2bYLedV1c1zXnhcNhbNum1WrRbrfx+XxmGzqdzvU+XNFbRnK7XTEuYXNshohwRH3gJmyaEIPpWf0HEptCDJi/1p+5WXclsSnEVRjk2BTvJuNNsRXblTC6Iq31R4GPgmR8PZZlEQgEAEilUrRaLWq1Gvl8ftPlNgbWjXS5QGw0GuZvpRTxeJxAIIDP52N1dZVAIIDf76dWq+E4zrZsn7g5NsZmQg1JbAoxICQ2hRhMEptCDCYZb76bjDfF1dquhNESMLXh9K7+/8QGfr8frTXBYJBAIEC1WqXT6eC6LsvLyzu9eZektaZYLG76X7vdNhnniYkJXNcll8sRCARwHId2u70Tmyo2k7gUYjBJbAoxmCQ2hRhMEptbJONNcb22K2H0RWCfUmqGXvB+G/A3tum+bjnRaBStNZZl4boujUaDWq2205t1XRzHMZnec+fOAb2ywlQqheM4dDod6vU6rVZrJzfzTidxKcRgktgUYjBJbAoxmCQ2r0DGm+JG2ZaEkda6q5T6IeDP6C11+Bta6ze2475uBZZlYdvvPNWtVutdDcBuR41Gg0ajYZqk2bZNNps182Lr9fpt/xwMEolLIQaTxKYQg0liU4jBJLH5bjLelPHmdtm2HkZa608Bn9qu278VKKWIRCJ0Oh0sy6LZbO70Ju0IrbXJCOdyOWzbJhqNEo/H6Xa7NBoNCeSbROJSiMEksSnEYJLYFGIwSWz2yHizR8ab22fHml7fzrzmXLZtU61W5c15gW63S6lUMssqZjIZ2u02WmvK5fIOb50QQgghhBBCDC4Zb16ejDdvHGunN+B2YlkWkUiEQCCA67pUKhUJ3svQWqO1JpfL0Ww28fl8jI6ObiqnFEIIIYQQQggh482rJePN6yfP1A0Si8UIBAKUy2XTvV1sXavVotVq4fP5yGQytFottNaUSqWd3jQhhBBCCCGE2FEy3rw+Mt68NpIwuk7BYBClFK1Wi1qtJhne6+Q4DisrK/j9foLBIKlUimq1KjtFIYQQQgghxB1Hxps3low3r44kjK6Rz+cjFouZ5fvEjdXpdOh0Oti2bcouS6USnU5npzdNCCGEEEIIIbaVjDe3l4w3t0YSRtcgmUziui7VahXHcXZ6c25r3W6XcrmM3+8nFAoRCARoNBq4rrvTmyaEEEIIIYQQN5yMN28eGW9enjS9vgqBQIBMJoPWWoL3Jut0OlQqFZRSZDIZEonETm+SEEIIIYQQQtwwMt7cOTLevDipMNoin89HMBikXC7Tbrd3enMuyrIsXNfF7/dj2zbBYBDXdbEsC601nU4HpRTdbhfLsnAcx/z/VlGtVqnX60SjUTKZjJQNCiGEEEIIIW55Mt4cDDLe3EwSRleglCIUCgFQqVR2fFts2za/4/E44+PjBAIB4vE4Pp8Px3GwLItut4vWmmazSbvdxnEccrkcrVaLer1OOp2m3W4TiURwHId4PE4+n6fb7ZpO8d4yhIPWWM1bQhJgdHSUUqm046+NEEIIIYQQQlwtGW/KeHOQScLoCrw3eLPZ3JH7DwQChMNhkskkIyMjTE1NEQqFSKVSxGIxlFJorVlcXOTcuXMUCgVKpRL5fJ5ms4nW2pQybgzERqOx6X6UUiilCAaDRKNRbNvGcRy63S6O42DbNu12e6Cy3ZVKBdd1icfjhEIhcrncTm+SEEIIIYQQQmyZjDdlvDnIJGF0CUopIpEI9Xr9pmY8/X4/Pp+PZDLJ1NQU4+PjjI6Omjcp9JYCDAQCaK2p1WqcPn2al19+mdXVVTqdzjVtr5fZbTQam4JbKYXP58OyLLLZLLt372ZycpKVlRWKxSJLS0u0Wi263S6tVuumZ4fr9bopGRwfHyefz9NqtW7qNgghhBBCCCHE1ZDxZo+MNwebJIwuIhgMAlCr1W7K/SmlSCaTpFIpdu/eTSKRYGRkhEwmQyAQADC/q9WqmQdar9f57Gc/y1tvvbVtb1qtNd1ul263a5ZzHB8f54EHHiAcDgOYDPO5c+dYWlri/PnzNy2IvB1GtVql1WqRzWYpFArvymgLIYQQQgghxCCQ8eY7ZLw52CRhdIFIJILrutv+BozFYsTjcfbs2cPQ0BDj4+P4/X7i8TjhcNhkUb0SvXq9TrPZxO/34zgOy8vLvPHGG7z11luX7J5vWZYp7fMkEgkikQi5XO6auu4vLS3xqU99ioMHDzIzM8P4+DhDQ0OkUikOHz5MsVikVquxuLjIqVOnWFpaolwu35QO/51Oh+XlZRKJBEopms2mLIcohBBCCCGEGBgy3rw8GW8OFkkYbRAOh+l2u9s2b1IpRTgc5tChQ+zZs4fdu3cTjUZpt9sEAgFCoRC1Wo1CoUAikaBcLrO2tobWmlKpRKPRoN1uUy6XOXPmDNVq9V23v5HrupvewD6fj8OHD9NqtWg2m5RKpYuW9AWDQTqdDpFIhGg0yvr6Ot1u15zfaDR45ZVXOHXqFNPT00xPTzM7O0soFMLn8zE9Pc3Y2Bj3338/uVyO5eVl5ubmzJzX7Qxm13UpFosMDw8Ti8XI5XID10RNCCGEEEIIceeR8WaPjDdvHZIw6otEIliWtS2lZUop0uk09913H3fffTezs7N0Oh2CwSC2bVOv16lWq/j9ftbX11lZWaHRaFAqlTh58iT1eh3Xdc28Ty8obdsmFouRSqUYHh7Gtm0ikQgA5XKZs2fPks/nzXY4jsPc3BxKKeLxOOVyGeiVH3a7XXw+Hz6fzzwfWmuGh4fZt28fr776Ko1GwwSy4zgUCgWKxSJvv/02p0+fJhaLMTMzw/DwMKlUCsuymJiYYHZ2loMHD9Jutzl//jxf+tKXyOVyFAqFbcvIrq+vEwqFyGazrK+v35SMsxBCCCGEEEJcjIw3Zbx5K5KEERAKheh0OnQ6nRt+29FolAMHDvDQQw8xNjaG3+9HKUWn06Hb7Zq/19bWOH36NIuLi+RyOZrN5qY3nWVZ+P1+/H4/Y2NjjI2NMT4+TiqVIp1O4/P5cF3XXK5cLjM1NcVnP/tZCoWCuY1KpWKajXnB45VDXuxNvrKywuOPP042m2VhYYETJ06Y24DenM56vc7rr7+ObdvMzc0xOzvL/v37icfjpFIp0wk/kUgQj8cZGxujUqlw8uRJFhYWmJ+f35YGZt5KA2NjY5w7d+62zvwKIYQQQgghBpOMN2W8eau64xNGkUgE27ZN9vNGCQaDzMzM8N73vpfZ2VmTre10OtRqNVZWVvD5fLRaLU6cOMHp06fJ5/Mmo6qUMp3YvYzuvn37iMVihMNhLMvCsixarRZ+v990q3ddF9u2icfj7N27l4WFBV5++WX8fj/33HMPxWKRxcXFLT+Oer3O2bNnueuuu0gkEkxOTnL06FEWFxffFfDdbpfV1VVyuRwnT55kdHSU+++/n7GxMWzbxrZtQqEQoVCI0dFRxsbGTBb4zTff5M0333xX2eP1ajabrKyskE6nKZVKt23mVwghhBBCCDF4ZLx5eTLeHGx3dMIoGAziui6VSuWG3m4kEuHIkSPcf//9ZDIZotEolUqF9fV1crkcq6urnD9/nkKhQK1WM6V34XCY6elpZmZmGBkZIZvNMjk5id/vB3oZ2U6nQ7vdxufzmaZk3nKMlmXRbDZptVrE43Hq9bop+YtGo8zMzPDZz372qh5Lo9Hg2WefNVnr3bt3MzQ0xBe/+EWOHz++aa6pR2vN2toa6+vrLC0tMTY2xuTkJNPT0wwPDxMMBs0ODeDee+9lZmaGqakpXn31VZaXl6nVajcsQ9vtdimXy8TjcSqVym0XxEIIIYQQQojBI+PNK5Px5mC7YxNGXlB4ZWQ3UiAQMOV6ruuysLDAmTNnyOVy5rdXluctcXj33Xfz2GOPMT09TTQa3dQQrdFoEAqFKJfLaK1RSpmlGJVSRCIRWq0WjUaDQCBAq9UiEAiglKJWqxEMBtm/f79ZGvFqOY5DpVIhn89z9uxZHnnkEd73vvdhWRZzc3OXDDatNcVikWKxyNmzZzl+/DiTk5Pcc889pulaLBaj0WhQr9fZs2cPiUSCY8eOsby8TD6fv2GZ+G63S61WY2hoyDR2E0IIIYQQQojtIOPNrZPx5uC6IxNGfr8f27a3peEYQLFY5Mtf/jJnz54181ULhQLVahXHcbBtm2w2Szab5fDhwzz00EOk02mCwSCWZdHpdFBKmTmf3m04jkMikaDRaKC1xu/3EwqFaDabKKVMCZ7rurTbbWq1GgBTU1PMzMzwyiuvXDRDuxWNRoOvfOUrWJbFfffdRzwe59FHH2V8fJzjx4+ztLR02dtuNBosLS2xvLzMa6+9ht/vJ51Om1LGdDpNIpEgGo2SyWRwXZeRkRFKpRK5XO6GlPd1Oh3y+TzhcPiOWAJRCCGEEEIIcfPJePPqyXhzMN2RCaNwOGze3NtBKUW1WqVSqZisr8/nM0v/7d27l127djE6OmqWPvT7/TQaDarVKpFIhG63S7fbpdPpmHLAcrlMLBYjGAxSr9dN4BYKBQKBgAn+RqNBs9kkn8/jOA4HDx4kkUhcU7bXk8/nsW2b973vfaZDfq1W4+677zYlfwsLC1fMpDqOQ6PRoNFomGzu3NwcU1NTjI6OMjU1hd/vZ3h4mEQiQSqVotVq8aUvfYkTJ05c9xKUjuPQbrdJJBKXXOZRCCGEEEIIIa6VjDevnow3B9MdlzDy+/2buq5fK6UUoVAI6GUzvVK98fFxoNftvVarEY1G2bdvH0eOHOGee+4hm80SDofRWhMOh2m32zSbTQqFAqFQCL/fj2VZdLtdLMsiGo1SKpXodrvE43GT7fW6vHc6HaLRKO12G8dxcF3XZIiHhoY4cuQI6XQapRR33XUX+Xz+mhp9eR32Q6EQ4XAYgGQyidaa2dlZlFJYlsX8/PxVZ1Lr9TonTpzgzJkzHDt2DNu22b17N4lEglAoRCaTMVnaxcXF6y7r9OaYRqPRGzp3VQghhBBCCHFnk/GmjDdvp/HmdSWMlFJngArgAF2t9XuUUkPAfwPuAs4A36K1LlzfZt4Ytm1f91KGSikCgQCRSIRarYbrumQyGSYmJtizZw/VapWXXnqJRqPBvffey4c+9CH27t3LyMgIwKYMbrvdptPpEAwGabVaNJtNkyF2XZdGo0E4HDZZ4VQqRSgUYmVlxVzWsiwAs8yhbdt0u12CwSCpVIpkMonrurz22mvU63UeeOABisUix48fv2TJnc/nQykF9ObH7t27F8uyTCd+L1gTiQTNZhOfz8eDDz6I67o0m02Wl5ev+nn1dkq5XM504Q+FQnS7XRKJBJZlYds2SilSqRTNZvO6Atl1Xer1OrFY7IY3oRsEt1psCnGnkNgUYjBJbAoxmG612JTxpow3PbfLePNGVBi9X2u9tuH0TwCf0Vr/klLqJ/qnf/wG3M91iUQiANc8pxLeWbpwdHSUY8eO4TgO4+PjPPHEE0xPT1Or1Xj22WdJp9M888wzHDlyhGw2a0rivGUIAVNi12w2zbKFXrOvaDSKbdtYlkWpVDJzR72dhrcEopep9DLHAJZlEQqFaLfbppv9Jz7xCd566y0ApqenefLJJ4nH46ytrbGwsLCpIVo0GuXBBx9kcnKSarVKNBplbGyMoaEhtNa0223a7TbxeBylFM1mk2QySafTYc+ePaysrLC6unpd8zU7nQ5ra2t86Utf4oEHHmBkZMQ8f97rNzw8TK1Wo1qtXvNr6rourVYLn893W3Wy3+CWiE0h7kASm0IMJolNIQbTLRGbMt6U8eaFbofx5nZMSftG4H39v38TeI4dDmCvode1vtBKKYaHh3n00UcJBoO89dZbBINBHnnkEQ4cOMDMzAxLS0s8//zzaK35nu/5Hqanp+l2u1SrVbTWJvC8jLGXfdZaU6lUiNADjpgAACAASURBVEQi+P1+wuEwjuNQLpexbdsEQjAYpNFoUKvVaLfb5rY2PjavZNHbEQSDQRYXFzl58qQJ9rW1NdbW1jh06BCVSoWhoSGUUjiOg8/nY3x8nGw2SyaToV6vmx1HJBIxyyhCr6wvGAzi9/vx+XzUajUzb3Z4ePiineGTySSJRILz589v6bXwsufnz5/na77ma3jmmWd45ZVXOHbsGOvr64yNjbF3714WFxdZW1u7pte33W6bneXt0JTsCgYuNoUQgMSmEINKYlOIwTRwsSnjTRlvXsqtPt683oSRBv5cKaWBX9NafxQY1Vqf75+/DIxe7IpKqR8AfuA673/LrrWcTCnFnj17ePzxx3Fdl6NHj7K+vs709DQPP/ww6XSafD7PCy+8QCAQ4MMf/jDT09O0220zb7TRaJg3itdgzHEcIpGIyQY3Gg2i0Sh+v592u22ykMFgkE6nQ6vVMgHa7XZJJpOEQiHy+bwpM/S211sGsdls8tprr9HtdvH7/czOzrJ3717uvfdeVldXqdVqZLNZYrGY2a5YLMbw8LBZBtKb/+qVDNq2Tbvdxu/3m+UV2+02rVbLzKv1+XzvCt7R0VEee+wxUqkUb7/9NsePH6der1+xMVqn02Fubo5ischTTz3Fe97zHiYnJzl69Chnz54lEonw1FNPsbS0xJe//OVrarTm7fwcx7nuEtIBckNiM0TkZmyrEHcSiU0hBpPEphCDScabMt6U8eYOut6E0eNa6yWl1AjwF0qp4xvP1FrrfnC/Sz/YPwpwqcvcCF7W9FoaTYXDYR566CEOHTpEqVTixRdfxHEc9u3bx7333svY2BhnzpzhC1/4AvF4nKeffprR0d7+KhgMmkysF0ClUolGo0EikSAQCBCLxWg0GliWZZZA9BqKeXNGQ6EQoVCIVqtlmnK1Wi2TcfW6sHuNx+CdpmiJRIL9+/dj2zZTU1OMjY0RCAQIBoO0223S6TSdTodwOEw8HqfVahGPx81z5c0t9fl8poN+IBBgaGiIer1OqVQy2dJkMml2kqFQCKWUuR2lFDMzM4yNjfHGG2+wtraG3+9nZmaGcDjMK6+8csUSvXw+z6c+9SmmpqZ4+OGH+fqv/3qOHj3Kq6++yhe+8AXuv/9+nnzySb7whS9QLpev+vX2SgVvIzckNhNq6Nbt0CbEYJLYFGIwSWwKMZhkvCnjTRlv7qDrShhprZf6v1eVUn8IfBWwopQa11qfV0qNA6s3YDuvmdfQ62qNjY1x4MABDh48yNraGi+++CJra2vs37+fhx56iHA4zBtvvMHzzz/P7t27+aZv+iYsy8Ln85n5nM1mE9d1sSzLzBut1+umI32326VYLFIqlWi326yurhKNRk0p3sbyOy/TqrVmdHSUYrFIrVYjHA6bBmSWZaGUwu/3o7WmXq9z5swZ8vk80JtP2mg0aLVaDA0N0Ww2iUajpNNptNakUimTcfaaoDmOY5qCeUspOo6DZVmmDDEcDmPbNn6/H6WUCWqfz2fKIOfn5xkbG+P48eOsra2RSqXI5XLs2rXLlE16jdMWFhYu+pq0Wi1OnjzJ2toaR44c4ZFHHiEWi/HCCy/w4osvcu+99/Loo4/y1ltvXXX3fG++7608v3SjWyE2hbgTSWwKMZgkNoUYTLdCbMp4U8abW3GrjjevOWGklIoClta60v/7g8A/Az4BfDfwS/3ff3wjNvRapNNpisXiNV3v8ccfJ5PJcPLkSV5++WWq1SozMzM89thjhEIhXn75ZV588UWmp6f5uq/7OjKZDI1Gg2azaTKqXpOrVCpFIpGg1WoRCATQWpsM7+Liosn6Oo5Do9Gg0+lgWRZaa7P9iUQCgHg8bpb+y+fzuK6L3++nVquZ5fuCwaDZWXg7ES+TG41GGRkZoV6vo7U2l/XmlHrbnM/nWVpawrIsOp2OWU4xm82SSqXQWhOPx8282UajQS6XMzskx3E2BcL58+c5evQoExMTFAoFarUayWSSfD6PbdumEdrw8DDFYvGyneSLxSKf+cxnOH/+PI899hjf/M3fzHPPPcfRo0eZnZ3liSee4Ny5c7z88stX9frfinNKL+ZWiE0h7kQSm0IMJolNIQbTrRCbMt6U8ebtPt68ngqjUeAP+3MNbeB3tNafVkp9Efg9pdTfAs4C33L9m3n1bNu+psxdLBbj4YcfJpvNcurUKV555RVqtRozMzM88cQTZLNZXnzxRV566SVGRkb4yEc+wsjICO1228wVhV55nWVZJpPrLYlYLpdN2Z/WmkajwdLSkmlM5gWklzldWVkxXetzuZxZ/q9UKqG1Nhlmx3FQShGPx03wJhIJvuEbvsG8MavVKoC5bjKZNEsuejsNy7JYXl7m9ddfp1gssra2ZhqMRSIRdu3aRSKRIBgMMjExYZZfLJfLnDx5ktXV1YvO39Vas7y8bJqUeZnnSqViLu9lfW37ym/LbrfLG2+8Qblc5oknnuD9738/L730EqdOnaLT6bB3714OHz7MK6+8ck078VvcQMemEHcwiU0hBpPEphCDaaBjU8abMt68E8ab15ww0lrPAQ9c5P/rwAeuZ6NuBJ/PZzrGb1U6nebhhx9m165dnDhxgjfffJNms0k2m+W+++4jkUjw5ptv8md/9mdMTk7yrd/6rczOzprSPdu2TWd5b35iPB43c0u93/l8nkwmg9/vZ3R0lPn5ebrdLu12m0AgQKFQoNvtMjo6ajrK5/N50/yrVqsRDAZNl3ZvPqTXyKzT6ZgdijcnFHrzXKvVKrFYjFgsZm7H7/dj2zbdbpdKpcKJEyc4ceIEtVrNPH/e3NVSqQT0dpDj4+MkEgmy2Syu67KyssLKygqNRuOiz683VzYajZLL5fD7/SZ4Y7EYH/rQh6hWq9RqNfMaXm4n7DgOZ86coVwu86EPfYjDhw9Tq9VoNBqsrKwQCoU4dOiQ2RldjVu1iz0MfmwKcaeS2BRiMElsCjGYBj02Zbwp4807Ybx5vU2vB5I3X/NqXoBgMMjMzAzpdJpGo0GlUiGZTBIIBHjkkUeYnZ1leXmZ5557jmQyyTPPPEMmkwF62Uyv2Vg4HEYpRaVSMQEdCARwXZdarWZKBOv1OplMhlQqxdDQECsrK3Q6HWKxGKFQiEKhQLFYNNetVqsopVheXqbZbDI2NkY6nTZZZm/+phc8gUCAZrNpAsbLII+NjZkgdxyHUqlkAmVlZYXXX3+d119/3ZTsXcgL6E6nw/z8PD6fj7m5OWzbNpnjy+00fT4f8Xh8Uwmh3+9n7969hMNhPv/5z5tlHJPJJJVK5Yqd5PP5PM8++yxPPvkkjz/+OCdPniQcDgO9sr/777+fY8eOkcvltvx+EEIIIYQQQoiLkfGmjDfvlPHmbZkwikQiJmu4FbZts2/fPiYnJ1lbW6PRaDA0NEQ+n2d2dpbJyUm63S6f//znWVtb48Mf/jB33XUXrVZrU7fzer2O4ziEw2Ezr7TRaJididfQy+M1BxsfH6dSqVCpVEzH+XA4bDKuXkOzaDSK4zisr68TCoWo1WqkUinTACwajW6aR+otnxgKhUzXeW85v0qlQqvVYnV1lXq9zsLCAouLi5TL5ava8TmOc1VLC2qtTZY3EokQj8e55557mJqaYnV11WRm/X4/qVRqy5nalZUVPv/5z/P+97+fffv2cfbsWSYnJ2k0GkQiER5++GFeeOGFy85VvdCt1pBMCCGEEEIIsf1kvCnjzTtlvHnbJYwCgQCO42y5NNCyLO666y4TkNAr68vn86RSKbM04NmzZ1lcXOTpp5/mySefNMsNdjodlFIopQgGg9RqNfN/6L0RvYDwssJeZnd9fd1sRzwep16vmyUPA4EAoVCI9fV100BsfX2daDSK3+831/WCvdPp4LougUCAYrGIbdtmx9Jut2m321QqFaLRKEtLS5w9e5b5+XlKpRKu65rrbyelFLVazTQ3O3ToEJOTkwQCAc6dO8f58+fNNg8PD9Ofr7xl586d46WXXjJzfzudDtPT05w6dYpwOMyRI0d46aWXKJfLV7wt13VNeeWgB7EQQgghhBDi5pDxpow376Tx5m2ZMLqaDGQ2m+WRRx6h3W5z7tw5Zmdn8fl8rKyssGfPHjKZDNVqlaNHj7J3714ee+wxU+bnzYn0moH5fD5z/955GxuKQS8r7M0H7XQ6+P1+szwhYJYrhN7OJRgMUigUTGf5TqezqfzRmxfqbVOn0yGZTNLtdk15odcNv1qtcuLECU6fPk2xWLyq+bY3guM4vP3222it0VqzurpKPp+nWCyilDLP48Zyw6uhtWZubo5IJMLhw4dpNpvUajWmpqYolUrEYjFmZ2c5ceLElt8jgx7AQgghhBBCiJtHxpsy3ryTxpu3VcLItm2UUlvOXIbDYSYnJ02WNZvNMjQ0RC6XY/fu3YyMjGDbNsePHycUCvHUU0+ZN5fXXd4LGq9ZWLPZNB3zXdc13d4rlYq5rOM4BAIBkskkzWaTbrdrssxegFuWZZZF9B6Tlz32TiulTHd6rTXtdhufz0c0GjVZZ6+LfTgcZmlpiXw+T6PRuGzwJpNJbNumVCqZRmc3ysbX5uzZs8A781SVUgQCAVPiOTExYXamW9XtdnnzzTfJZDLs3buX9fV1JicnyWQylEolpqamsCyLY8eOXbJZmsfb0Xg7FyGEEEIIIcSdS8abMt6808abt1XCSClllvK7Ep/Px8zMDMlkkvX1dbTW7Nu3D+hlJmOxGLZts76+Trvd5qmnnuLgwYNUq1Xq9TrhcNhkbX0+nynV8zqze9tTqVTodrvU63WTCU4kEibYvIywF7jeTsDL+nplakopE/yRSAToNQJbXV0lFAph2/amZmjFYpFisWgCJpVKAb0scigUwnVdJiYmsG2bXC5HuVw2b9aZmRmmp6eZm5tjbm7uqjLoV+PCoNBam3m6XgO3cDhsOupvVbvd5tVXXyUejzM5OUk+nyeRSJidw+joqFmW8Uq8nbMQQgghhBDizibjTRlvwp013rxtEkZeMGy1rCwejzMyMmLezKOjo2SzWebm5hgeHiYUChEOhzl+/DhDQ0NMTEyYwA0EAmb+qJcVbLfb5sX2+/20221s26bdbtNqtdBaMzw8bP7vBaU3j9S2bcLhMM1mk1AohNYa13VxXRetNYFAwJSreffpLUtYqVQ4ePAg7XabWq1mMpTeUoLeT6PRYGFhgWw2y+HDh3nPe95DLBbjzJkzPP/885w9e5ZOp0O9XicUCrF7926q1Srr6+vUarWrmnO68Xm4Ft1ul5WVFfNYYrGYyY57c3gvtz1ra2u89tprpFIpGo0GnU6HbDaL3+8nGAwyNTXF8vLylnb4Pp8PrfVAlwoKIYQQQgghto+MN2W8udGdMt68bRJG0WjUZFqvZHh4mH379pn5maOjo6TTadbX1/H7/ezatYt2u83a2hpDQ0PcfffdtNttU47nleCFQiFzn97vbDa7qZt9IBAwnePb7bZZfq/b7ZqdxMZsppd1jUQidDodkxluNBoEg0GUUrRaLTMv1e/3c/78eSzLYv/+/bRaLUqlEvF43GSLw+EwkUiEQqFAJBLh8ccf573vfS+RSMQ0LltbW6NQKFCtVpmcnKRSqbC6umrOv5pVAAA++MEPMjMzw8c//vErXjcSiVyybFFrTSgUMh37u92u2YFtdOHcT6018/PznDlzhtnZWUqlEu12m1QqRbvdZmJigl27dnHixIkr7pgcxxnorK8QQgghhBBie8l4U8abd+J409rpDbhRMpnMlrK9fr+fQ4cOMT4+bpp0jY+Pm0AKhULmzVSpVMzc0rGxMUZGRnAch1qthuM4VKtVHMfBtm2z1ODo6CgzMzPEYjGzg/Bu23EcM8e00+lQKBTodrtYlmWyyF6ZoTcH1Qter3TQdV3a7TblctmUBlYqFV599VUajQapVIput0ur1cJxHCzLIpvNUqvVqFarfPCDH+Tw4cPE43FisRjBYNAsgwi9+aSzs7PUajXefPNNFhYWWF9fv+qO9rFYjOHhYbLZ7BUvW6/XLztns9Vqkc/nTdM2r7O9J5FIsG/fPgKBwKb/t9ttvvKVr1Cr1RgbG6NSqRAOhxkeHiYQCHDgwAHGxsZMOealaK2veBkhhBBCCCHE7UvGmzLevBPHm7dFhVEikTBzIq8kHA4Ti8VMJjMejzM8PEyr1SIajZrAW1hYIBwOMzIyQrvdxnEc0x2+2+3i8/nMkoaxWMyU8HlBmE6nWVlZodvtmlJB6GWGh4aG2Lt3L2fPnqVWqxEIBAgEAqRSKfL5vCkj7HQ6RCIRAoGAKW2r1+smkwu9UsdkMsny8jL5fJ6ZmRnS6TRra2tmW+LxOJ/73Ofw+/1MTEwQjUYJh8MEg0HTXT+RSNBoNGi1Wnz605+mUqlcsUnX5XzqU5/ihRdeuCFvfK95m8/nIx6Pm8x3sVgkHA4zOzuLZVmbuv17O5xCocDbb79NOp0G2PStwOjoKPfffz+NRoNCoXDJ+/f5fCSTSQqFwrYvBSmEEEIIIYQYLDLelPHmnTrevC0SRt1ud0slbEopE7zr6+sAzM7OmvI9L5u4trZGLpdj3759puzMcRxTmhcOh0kkEliWRbFYpNlsmrme+XyeoaEhLMtidHSUXC5HtVo1gRiJRLAsi06nY5Y+7HQ6pgt9PB5Ha021WjUNw7zSOK9M0CsNdF2XRqNBLBaj3W5z6tQpHnzwQXM6FAoxNDTEmTNnmJ+f57777tu0pKLX2d6bQzo8PMzCwgJLS0tbei4vt8NsNBrXtQO4GC/LHgqFSCaTJnPrZe5HRkZMVrzZbFKtVnFdl9OnT3Po0CFSqRTNZpNoNGqy6uPj44yMjFx22Ufv/REMBm/4YxJCCCGEEEIMNhlvynjzTh1v3vIJI2/O5FY6q0ejUXbt2kWz2aRYLDI+Pk4qlaJYLJrywEqlwqlTp2i326TTaYLBID6fz5QfemV++XzeZIlrtZopsZubm6NQKODz+eh2u7iuazrN+3w+6vU6nU7HZIsTiQSdTsfMJY3H45TLZWzbJhAI4DiO2Xmk02mTDd74RvT7/QQCAYrFImtra4RCITKZDKlUCsuyeO6557Asi3vuuYexsTGCwSDNZtMEcrvdZn5+nqmpKYLBIOvr65RKpStmN73SQq+j/nbzntPV1VUAJicnsW2boaEhxsfHeeCBB3Bdl2q1yiuvvGLmi5ZKJU6dOsUTTzxhnj/XdSkWi+a68/PzlwxOr4QxmUwOXAALIYQQQgghto+MN2W8eSePNwdzotxViEQiW8r2BgIB9u/fTyqVMlnTTCZDMBgEMJ3d8/k8zWaT/fv3m9I813XNb8BkarXWNJtNUx6otTbL85VKJVZWVsw8SO+F73a7rK2tEQ6HCYVCAASDQVqtlikt9OanekHtZaIbjQau6+Lz+UgkEoRCIVKpFFprU+rn7Yy8ear5fJ7l5WVGR0dRSplssWVZxGIxstkshw4d4oknnsBxHBKJBI888giTk5PvmrvpPY/pdJrR0VEymQyxWOyqliC8Hhu7x+fzeRqNBtFolEgkwurqKqdOnWJxcZFyuczMzAyZTAbo7XRPnTpFLpczO51sNovruqysrJhlHS/FywRXq9WBbUYmhBBCCCGEuPFkvCnjzTt5vHnLVxjFYjFyudwVL+eVwNm2TbVaJZlMmnmU5XKZRCKB4zicPn0agJGRERMIoVCIVqtl5izW63UCgYApk/PmM3rd6r0AsSyLZrNJJBKhUqmYeZFKKSqVCsPDw0CvVA96ZXWWZTEyMsLc3By2baOUMvNYtdZEIhEcx6HRaNDtdkmn0wwNDZFOp01n9kgkQjAYpFqtcu7cOXbv3s3o6Kgpj6xWq4yMjLB7924ikQgA6XSacrnMW2+9xd13382BAwd49tlnOXnyJK1WC9u2yWQyZDIZE0Re1tjLxG63jUsmtlotcrkc+/fvp9Fo8NprrzE/P2+avI2Pj3Pw4EGq1Sr1ep3l5WVOnjzJkSNHzGsaiURYWlpi3759TExMsLCwcMVvDm7WYxVCCCGEEELsPBlvynjzTh5v3tIJI6+E7kpPqGVZTExMEIlEWF9fZ2VlhWw2SzKZNHMFA4EAi4uLLC8vs2fPHoLBIIlEwrxJE4kErVaLRqOBbdsmoGu1Gq7rEolEaLfbm7LDrVaLZDIJYBqa+f1+KpUKpVLJZHkDgQDJZBKtNbVajUgkwsTEBKVSCa21mR/q7UACgYApE/TmTbbbbTqdDvl8nrGxMWzbZm1tjfPnzzM1NcXIyIjJYFuWZea2hkIhk9k9cOAAKysrprywUCiwurpKq9UiHo+TTqfNTml5ednc58blBW8G27bpdrsUCgWKxSI+n49CoWB2JqVSiWq1CvS68Hslfvl83jxXruuSyWRYWlpieXmZVCrFrl27ePvtty87V3bQMr5CCCGEEEKI7SHjTRlv3unjzVs6YeS9WFeilCIUCpkl/aLRKKOjo9RqNWq1GrFYjGq1yrFjxyiXy4TDYYrFoumOrpRieXmZYDCI1hrXdTeV6lWrVUqlEn6/n2g0ahqG+f1+s9RhJBIhHo8zPj5Os9lkaWmJxcVF6vW6CSovC+ktYegF6MZSPW/+qhd4XgO2VqtFu92mUCiY5RGr1SpvvfUWhUKBRx99lPHxcQAqlYqZhwqYBmrJZJJTp04xPz/P3Xffzfr6OiMjI6ytraGUol6vU6lUqNVqW5rDu128HbbjOJw9exbXdYlGo2QyGdbW1kxTuOPHj2+63vz8PLlcjkQiYXaU6XSaer1OMplkZGSEhYWFS84bdV2XRCJx2YZlQgghhBBCiNuDjDdlvHmnjzdv6YSRN3/zSrzg0FoTDofJZDIkEgkqlQpaa4aHh5mbm+P06dMkk0my2Sx+v9+UAnrlf91u1zQlcxwHpdSm+Z9eltdrVOb3+83fzWaTQqFALBYjGAyya9cu2u02S0tLNBoNksmkWT7Rmw/qNcvysqxemaDf70cpZTq1ex3wbdumXq+zvr5Ot9vl/PnzRKNR0uk0yWTSzL+cnJwkk8mYDva1Wg2lFCsrK5w6dYpiscjRo0fJZDKk02lmZmZYW1tjcXFxU6nkTrMsi0ajYZ77er1OuVy+5OXr9Trnz58nnU6b90M2m6XT6ZBKpeh0OoTD4Uu+p7wdoxBCCCGEEOL2J+NNGW/e6ePNwXglrsHGuZZX4i1P53ViD4fD1Go1crkcw8PDaK2Zn58nn8+b0jlv7qeX9fWyvPV63WRi6/U69Xodx3HodDqm+7xlWbiuS6fTwbIs04Ts3Llzpqt9pVIhEokwNjZGOBw2ZXZaawKBAMPDwyZL7d330NAQ8XiceDxOIpHAtm1TIheNRhkfH2doaIhOp0OhUGBlZYVEIsHMzAzxeJxkMkk6nTbNw6LRKI1Gg2azSS6X4+WXX6ZareI4DrVajUKhQCgUIhwOU61WTbmk17htp3nlmBMTE1QqFVPqNzQ0dNFSPm+H6b0+Xpbb20Fms1nTuOxSut0u0Wh0ux6SEEIIIYQQYgDIeFPGmzLevMUrjK60DB/0At1rPra4uMjo6Cg+n4/l5WUzBzGXy3H69GlTeudl9rygsm2bSCRCOBw2JX+hUMgs79fpdEzzL6+k0Js76rquCRbolfgdP36ceDwOvJNFbLfbWJZlstKu6xKPx6lWq6akzes432q1TGDH43F8Ph/BYJChoSEcx2FtbY1CocC5c+eYnZ3F7/dTrVapVqtMTU0Ri8VoNBrE43FKpRJLS0v89V//NZ/73OdMQzToZUjn5uYYGRkx2WGvVHAQuK7L0tIS7XbbNGh78MEHqdfrzM/Pc/78+U2XV0qZzHk4HKbVahEKhVhdXWV4eJhMJsPQ0NCm1/5ClUqFdDpt5qwKIYQQQgghbk8y3pTx5p0+3rxlE0bhcJhms3nFy/l8PlKpFH6/n1qtRrFYpFqtUi6XefDBB0154PLyMvBOsAYCAROAlmWZuaJeqaCXce50OmZ+KWACPBwOA5jLbJwD6jgOlUqFaDTK2toaqVTKzGv1bsvLWHoliV7QeQ3RvFI4b2nFRx55BL/fz1/91V9RrVbN0oixWMxkoqFXVuc1bjt37hwLCws8//zz/Pmf/zmFQoG77rqLer1OqVTCcRxKpRLT09McOHDAdMu/XBnezdbpdFhZWSEYDGLbNkePHqVSqVz0mwCtNblcjnK5bEpAI5EIPp9vU4M57zm6GNd1TTnmIHWvF0IIIYQQQtw4Mt6U8SbIePOWnZIWj8c3ZScvJRAIkMlkGB8fJ5FI4Pf7SafTJtNaqVRYXV01L5i3jGClUqHdblOpVHAch3a7bbK63tKAXmleIBAgGo2aJmfeMoCRSATbficnp7Wm0+mQTCZxXZdarUYymaTVapnO917AdTodM5+1Wq1uWm7Ra4blZX9brZZZss/bCSwvL+Pz+cwcWegt9ei6LsVikUqlwtzcHM899xyf+MQnOHnyJE8//TQ/+qM/ylNPPWXe2EopstksBw8eZGpqamAynRs5jmN2OoVC4bKB1Wg0TBd7by5qPB4nFothWRbxeNw0Z7sYrfW7GsMJIYQQQgghbi8y3pTxpudOHm/eshVGW11uLhwOMzQ0RDgcJp1OU6vV8Pv9jI2NMTQ0RKPRoFgs4rquKRn0MnqO4+C67qYMqteIrNlsmgxsLBYDeuV/3lKC0JvDuLGxmFfS2Gg0SKfTNJtNbNum3W6Tz+dN13sveEqlEul02izPl0gkTObYywzbts2rr77KuXPnqFQqBINB9u7dS7PZJJ/Ps7y8zPDwMD6fz8xdLZVK2LbN66+/zl/+5V8yPz/P8PAwTz/9NPv27ePYsWOm7NHv9xOPxzfNt/Sem0Hp3H41vMc/MjJCqVTC5/MRiURwXRfHccwc2lqtdtnbuBUfuxBCCCGEEGJrZLwp481rcbuNN2/JhJEXYFthWRbdbpd2u00ikaDdk4JGhQAAIABJREFUbps3v9///7P3pkFyXNe95y+zsrKyqrLWrt4b3ejG2li4gJAoCiRF0qYsS17okMWw5fA47OexP/h5vsyHmbEjvITjORz2LOHxOBxhe15orAg/y4ssP8myZHGRQJEgAJLYG02svS/V3bVvWbnNh2Zeo0mAaIANoAHeXwQCjUJV1q2sey7i/HHO/4RxHEe41yuKInonG40GkUhE9HkCxONxMepPVVURMJ7nkUqlME0Ty7JIpVK0221gVWVtNptUq1XR/wmrarGu6/i+Lw6SarUqygl1XScajaJpGsPDw5w+fZpQKIRhGBSLRbq6ujAMg4WFBWZnZ5mfnycWi7Fv3z6efPJJuru7mZ2d5Yc//CFjY2Ns2bKFlZUVDMMAoFarcfbsWd59912hME9OTqKqKufPn6fZbNLf30+73Safzwtnd1VVSaVS2La9qUoF14vrukxPTzM0NESr1cLzPBzHEap6YBr3YXieRyQSEd+xRCKRSCQSiUQieXCQ+abMN2+XBy3fvGlLmqIo/1VRlLyiKGeveSyrKMr3FEW5+N7vmfceVxRF+b8VRbmkKMppRVEO3IlFB2roekzIEomEKPsLRgDOz88LxbjVaomNeO1joVAI27ZFH2Wr1RKHgKZp+L5PIpEQfaZBqdno6ChDQ0PiecEBEBwYiUSC/fv3s3XrVlECWK/XicVi5HI5FEWhUqmIns5QKCSc9a9cuUK1WiWZTGJZFsVikStXrrC8vIyiKPT19TE6Oko6nUbXdfr7+3n++ecZGRnh6aefJpVKiTLCdrtNq9USIwuLxSJf//rX+au/+iuOHDmCruscOHAARVEYHx9nbGyMiYkJPM+jUqlsGiOy2yFQ0NvtNqVSCUCYwF1rGHcjgvLNe81mjE2JRCJjUyLZrMjYlEg2J5sxNmW+KfPNj8KDkm/C+jyMvgJ87n2P/a/Ay77v7wBefu/PAD8O7Hjv168Bf7Exy/wgNzKJupZrxxMGwR6MC4zFYsLBvNlsAqv9p8EYwUCRjUaj5HI50b9Zr9fp6uoSfam1Wg3DMAiHw2zbto2HHnqIZDIpehaDsr/A9Mr3farVKoVCgUqlQqFQoNlsUigUaLVaRCIRYYIWiURwHAdVVeno6GBxcZGLFy9SLpdZXl5mYmKCCxcuYNs2iqLg+z6NRkOUvQUGaX19ffzwhz/kq1/9Kt/73vdYWFigXq8zODhIT0+PuC+XL1/m7bffFqMcX3/9dVZWVlhYWOCll17i9OnTYnzj9fo2gzWvh2BcYnBv7iaBiZxhGFQqFer1ulDTU6kUXV1dH1qCGhjTbQK+wiaMTYlEImNTItmkfAUZmxLJZuQrbMLYlPmmzDdvlwco37x5S5rv+4cVRdn6vod/GnjmvZ//P+D7wP/y3uN/46/W1L2pKEpaUZRe3/fn2UCCMr6b4fs+lUqFdrtNLpfjwoULQtULXOdd10XXdWFoFhiFBWMMS6USuq6jaZoopYtEIkSjURKJBJlMRvSFrqys4Ps+09PTOI6DpmlYliWU4larJczNAoVR0zSxjkajgeu6wvE+EomgKAq1Wk2MMDx16hTnzp0TirRt28DqpgqCOpfLYVkW5XKZUqkk+ijPnDlDu92mWCwyMDDAzMwMy8vL4p5eeyj6vs/Kyor4c6CMXu97CPpqd+/eTalUYmZmhnq9ft2+y3g8zvDwMFu2bKG/v59EIiH6epeWllhcXGRmZoZyuXzH+jbb7TbNZlMEYjQaFeZlruuu60Apl8t3ZG23wmaMTYlEImNTItmsyNiUSDYnmzE2Zb4p882PwoOSb8Ltexh1XxOUC0D3ez/3A9PXPG/mvcc+EMCKovwaq6rwLRP0aa6HwEG+XC6TTCZZWloiEomQzWYxDINarSbUy2g0SiQSES71QbCrqireT1EUCoWCcKgPXM4bjQYTExNcunRJmIPVajWh4LbbbXRdF6ZkAel0ek1vqqIoIiiDUXqB+ZmmaXied123/lAoxNNPP83nP/95+vr6RL+qZVki0I4ePYpt27zzzjucOnVKBOx6DkNYDdhkMkl/f79QxGOxGPF4nFwuh6ZplEolduzYwfT0NFNTUxSLRRGI4XCYL3zhCxw6dIhUKkVfXx+pVIparUaj0cC2bZaWlrhw4QL/+q//yvj4+LrWdatYlkWr1SKdTlMul0VJZTKZFGrwegiFQuv6n4e7zIbGpkHszq1UIvl4IWNTItmcyNiUSDYnMt+U+abMNzdBvvmRTa993/cVRbllac73/b8E/hLgVl8fbOj1EI/HhRN5EIyGYZBMJtF1nZWVFWFoFlwzMCe7NnCDDRsEWKVSIR6PMzQ0hG3bXLp0iXa7LcysQqGQGKPXbrexLEuUwwW9pvF4HNM00XWdSqVCIpGg1WoJ1XFlZUWUG+bzeQqFwnVVUEVReOKJJ/jyl79MLpfDtm3C4TC2bZPL5XBdl2effZaLFy9y+PBhXNf9gLobXDcoNQzYunUro6OjzM/P09/fzyOPPLKmrLDZbBKLxYRSPjs7i6ZpDAwMAKsO/cE9HBoa4oUXXmD37t3iPvi+T0dHh1Ccfd+nr69PlCJevnz5VrbGutA0bc336TgOpVKJRqOBaZpomvaB+/B+gpLCzWzEthGxmVSym8OeXyJ5gJCxKZFsTmRsSiSbE5lvynxT5pv3jtsVjBaD0j9FUXqB/HuPzwJbrnnewHuPbSipVOq6JWs3IpPJ0Gw2sSxL9H86jiN6N4PNHIwj1DRNONzX63VRThj0pWYyGWq1GoqiUK/XWVpaolAoiLGA0WgUy7KwbZt6vS7UWlVVRXAF1y8UChSLRTKZjFB84/E4juNgGIYoJ2y1WmL0XigUIpFIiGvHYjGeeuopenp6aDQaeJ5HvV4XB5PrugwMDPCrv/qrNJtNLl++TKFQ+IBaqSgKmqYRDofFa377t3+bkZERLl68SDgcXnO/YLXcznVdHMcRZmpBqeTg4CCapnH69Gna7TZPPPEEw8PD9PX1YRiGmB6gvDde0nVdarUa27dvx3VdqtUqKysrt/Rdrxff94UJmaqqwmQuEong+/6asZTXYzMZkb2PexqbEonkhsjYlEg2JzI2JZLNicw3Zb4JyHzzXnO7gtF/B34J+KP3fv+Xax7/z4qi/B3wOFC+E73eQQndeohEIrRaLVqtllDxgrGG7XZ7zbWCAHRdl0KhIJRBRVGoVqvE43F0XadcLosvfWJigkqlIkzKgvUF17Esi0gkQiQSET2mwSHheR6FQgFArC9QGzVNo9VqEQ6HqVQqFItFEomE2GRBT2u5XGb79u08/vjj5HI5arWa6JXM5/PU63VarRawqn4/9dRTzM3NEYvFhPN8LBYTjvrpdJpsNsvu3bv51Kc+xdDQEL7vMzIyQq1Wo1arAQi3/qWlJebm5piZmREKue/7RCIRwuEw2WyWwcFBZmdn2bNnD9FoVPRxBvcjcOcPhUJkMhnxGXO5HB0dHRsewKFQSKjVwUjJ4H8CghGaNysTvJVRm3eZexqbEonkhsjYlEg2JzI2JZLNicw3Zb4p881NkG/edAWKovw3Vg3HcoqizAC/y2rg/r2iKP8JmARefO/p3wY+D1wCGsAv34E1o+v6usoEA4OsarUq+jiDDXZtIAVfhGVZWJaF4zjCiEpVVVRVFQ72wRefTCaxbZuFhQXhEh8ooa7riuC/1mjL9/01/bBB0AQHS7CJXdfFtm3Rm1qtVkUvrKZpNBoNGo0GmUyGVCpFd3c3sViMSqUiDqRwOEw6nRaO/cFn3L59O9lslnK5zPDwMLlcjv7+fjRNI5VK0dHRQW9vLz09PXR3dwu1u9Vq4fu+KKkrl8ucP3+et99+m0uXLuG6Lh0dHezbt4+RkRFhtub7Plu2rP4nwNatW+nq6sIwDFzXxfM8cZhGo1FisZhQgVOpFIlEYl3lerdKOBzGNE0qlcqaw63Vaol9oev6h/5Dce09vVdsxtiUSCQyNiWSzYqMTYlkc7IZY1PmmzLf/Cg8KPkmrG9K2s/f4K9+5DrP9YHf+KiLuhmGYawx8roRwRhCx3FIJBLCOKzRaGAYhhjJF4/HqVQqhEIhLMuiUCiQSqWE2hv0PgYu8bqui4BMJBJC4Q3KCIPSsVqtRj6fp91ui80ZuMjXajWi0SjxeJxarUalUsFxHJLJpChPC4VCokyuVquJsYwB15a0vfvuu4yMjACIgyAej9NoNEQApNNpMpkMo6Oj5HK5NUZgwdi/XC5HX18fsViMcrmMYRg4jsPMzAz5fJ6ZmRnm5+e5ePEi4+PjNJtNMpkMTz31FC+++CIjIyMoisLc3BzNZpNarcaVK1fYs2ePCJJwOCzKK23bJh6Pi7GMtm2Lft+Ojg66u7uZnZ29oQv+7RAOhzEMQ5QHBodnIpGgs7OTSqVyXaO3awmmDNxLNmNsSiQSGZsSyWZFxqZEsjnZjLEp881VZL55ezwo+SZsgOn1vaBara7btb5er1Ov10mlUgBrNk5g9hWou8GIwaGhISqVilBYg77SWCyG4zg0Gg2hCgZ9h0Fg12o14Rgfj8fXlCgG5l/BBi6VSlSrVfH+Qb8rrAYnQLFYZGZmhkKh8AEFstlsCvX3m9/8JqOjoxw8eBDDMGi1WnR0dIhrzs/PMz8/j2ma/Nmf/RljY2O89dZbQnUORiwqikK73cY0TdLpNFevXuWll17i/PnzQvVNpVJMTExgWRYPP/wwv/iLv8hjjz3G6OgorVYLXdcxTZNqtYplWfT09OB5Hh0dHVSrVUzTpFarMTs7SzKZpLOzU5iVBf2c1WpVqLHRaFSUJm4EwQGraRrxeFyUccZiMfE93ex/FHzfF6r+vXaul0gkEolEIpFIJBuHzDdXkfnm7fEg5Zv3pWAUbPL1EGy6cDgsTLuCX4DofYTVsq+lpSV6enqE0hmMJ1QURain4XBY9KQGAdxoNMQXCohyQM/zxPi84NAIygsDA6+gp1PTNGzbFkZklmWxvLzM5cuXr1uuFqw3cL0/deoU7XabPXv2oKoq09PTwjl/enqaer1OPB5nYGAAwzB44oknROCUSiWmpqYYGxujp6eH0dFRJicn+epXv8rFixfZsWMHBw4cIJvNMjk5yfLyMgcPHuSXf/mX2bt3Lz09PcKA7VpH+larJe6xrutYlsXi4iLFYpF8Pi/uZzqdFk7+tm2L17fb7XUf1usll8uJ/tGgxzUoJw1U/5upy8FBE7jtSyQSiUQikUgkkgcDmW+yZr0y37w1HqR8874UjIJexfUSlOQZhoGiKPT29gKIHsLAcMr3fcrl8hqn+qC/tNFoiBK2WCyGaZo0m03hSB+LxYRaGgR+QNCLqmkauq5TLBZFoMZiMWHGpWmaUIVd12V2dpZ33nmHlZWVD3ymQLVst9tMTk7i+z7d3d20Wi06OzsB+Jd/+ReOHz9Ou91mcHCQSCRCZ2cn+XyegYEBotEokUiEZDKJYRjMzMxw8eJFVlZWMAyDS5cu0Wg02L9/Pz/yIz/C9u3bWVlZ4a233kLTNH7913+dRx99VEwBaLVadHV1CTU0cMAPTNGCKQCVSkW48S8tLdHR0YHjOGQyGXEPkskknucJ1X2jUBSFdDotSibD4TDFYhHP8zBNk1arxfz8/Jrv70aoqrph65JIJBKJRCKRSCSbA5lvynzzdnnQ8s37UjC61fFylUoFwzAol8sAQpkMSgWDw8D3fRYWFoSjfOBiD4hSMF3XRWDX63WhZKqqKnpCg17PcDgsHNLr9br4wh3HESZYoVBIbPJqtYqu62iaJgIzUG3fTxD0wboBlpeXOXbsmAie48ePU6lU2LlzJ77vc/nyZT7zmc8Ixde2bVRVFf2nqVSKdrtNo9EglUpx8OBBRkZGSKfThEIhZmdnOXLkCOfOneO5556jt7dXOOj7vo/jOBQKBfG5W60WV65coa+vjytXrjAzMyPGPQ4MDKCqKn19feL1gVN/vV7H8zyi0SiKoty0v/NWCHpnr1XYI5EI9Xqd5eVlTNO8qbldwGYIYIlEIpFIJBKJRLKxyHxT5pu3y4OWb96XglGgnK5XlQs2RaBMJpNJFEWhUqmIPsrAqCtQdnO5HK7rilKwwJk+FAoJtTYwNgvGIgZGVoAohwteEzwvUDGDwHddl2q1SiQSIR6PU61WRalhu92+4eYN+i+vxXVdxsbGKJfLdHV1Ua1WqdVqTExMsLKygqqqmKYpDqJ6vS7uZVDuls/nWVlZEaWSuq4zNzfHhQsXOH78OHNzc/i+z44dO8hms0QiESzLEg77jUYDz/NIJBKUSiWOHDmC53mcOHGCqakpTNMkm82yfft2HnroIQ4cOCBGHgalnKZpCrf+6enpDS0RTCQSJBIJarUa7XZbHJbhcJhIJCJc/m9GKBQSpaMSiUQikUgkEonkwUHmmzLfvF0etHzzvhWMght/M1RVpauri127dlEqlVhaWiKfz2MYhvj9WoOrUqnEwsICXV1dwpQsUIYDN/XAtb7RaIg1BGZl8/Pza8b2BYFhmiblcln0SZZKJRHkwcYNftm2zdLSEjMzMzf8jB9WIjkyMiKc+dvtNjMzM2KN8/Pz1Go1TNPEMAwAcQ+q1apw1T979ixjY2McOXKEcrlMo9EQh8m1B1CgmGuaRrlcJp/PU61WKZfLvPTSSxw+fJhSqcTy8jK+71MoFJienmZubo7FxUUcx+HFF18UJnG1Wg3btikUCiiKIiYLbBTpdJpoNEq1WsV1Xbq6ulAUhaWlJeFgXyqVbnqd4OBdT7BLJBKJRCKRSCSS+weZb8p883Z50PLN+1IwCsy71kOr1aLRaIiRgqZpipF9ExMTZLNZUa4XOMDn83kx4q9er4vxgoEaG41GhQJsWRbtdluYWgUlc5FIRIw/tCwLz/NEn6rruqiqKoI1Go2K6wTlgouLiywuLt7yaD/f97l48SKpVIqf/dmf5Qc/+AFvvfUWsFqaePr0aT75yU+SSqXwfV8cRrOzs7zxxhvk83lCoRDj4+PU63WmpqY+sEmDsYetVksYrAWH1/LyMq7rcuLECb7zne8wMzMjAnzLli2k02kxRnB+fp6rV69Sr9fJZDKizDIoI7Qsi3q9fkuf/2YYhiGmDYRCIYrFojCLC957PUEZ/E/AvVZ8JRKJRCKRSCQSycYi880bI/PND+dByzfvS8EoKElbD4GxWKPRIJvNEovFGBgYIJPJUKlUqFQqpNNpHMdhYmICgKmpKT71qU+hKIoo6YvH4zQaDQAKhQLJZJKOjg6Wl5eBVaMxWD1cri05DA4Ay7LWfOHJZFK8xnEcVFUVrvqWZVEul9elaF+P+fl5YrEYjUaDubm5NX93/PhxstksX/ziF9myZQtzc3MsLy9z6tQpUc7n+z6zs7Nks1k6OztZXFxccw3XdXn55Zd55plnePrpp4Uqm0wmicfj1Go19u/fz6FDh3jzzTfJ5XKUSiVM06S3txfDMMRYQdM0WVhYEM79kUiEWq1GqVRibm6OSqVyW/fgeoTDYWG+FrjrB+Mdu7q68DyPYrG4btMzx3Fu+YCVSCQSiUQikUgkmxuZb344Mt+8Pg9ivnlfCkaKotySAdT8/DwrKytivGCz2SSVSrFjxw7efvttbNsmm80yOzuLbdvk83kWFhYYHh4WDvlBaaCiKGiaRrFYFH2dgVKZSCTE8+v1ulB94/G4cG0P+ifr9TqGYdBsNtco2KZp0m63Rd/q7TI7O8vf/u3fUqlUUBSFaDRKq9WiVqvx0ksvcf78eR555BE6OjoYHx/n2LFjLC0tiftrWZYYUXije/raa68xOjpKf38/uq4zNDSEpmksLi7ieR6f+9znSCQSdHd38/3vf5+ZmRlKpRLpdJq9e/fS1dXF448/zp49e0Rfa61Wo9VqUa1WmZiYEIfmRmAYBv39/aRSKWEOB1CtVmm326L/eL0Hp2EYWJa1btMyiUQikUgkEolEsvmR+ebNkfnmB3kQ8837UjCq1WrEYrE1vaAfRqVS4cyZM3R3d2OaJqlUSiiLgcHY8PAwc3NzLCws0Gw2GR8fp7+/n1AoJNTlSCQiyg3T6fQa4zLXdUXJXBCogZO7qqq4rksikRCbJDDdUhRFBG8Q0HNzc5RKpdtWfGG1NLLVamGaJgcPHmRwcJBms0l/f7/47HNzc5w8eZK5uTkKhYJ4beCy32w2b1gu5zgOx44d4/Of/zzZbJZEIiHKH/v7+8lms+RyOZrNJlNTUyIYM5kMzz77LLt37yaTydDf3y/KLoOe2kKhIMYN2rZ92/fg/aRSKRRFEWMc4/G4MI6zLAvLsiiVSus+NCqVihSLJBKJRCKRSCSSBwyZb94cmW9+kAcx37wvBSPXdYWB1nrwfZ+pqSmmpqYYHR0VfZSmabJz505UVaW/v5/du3eztLSE67pMTk5Sq9Xo7OwUmzhQmYMe0VgstuZx27aF631QJphIJMTrg9JGy7LQdZ1Wq4VhGMKhPighDPob19s3eyN0Xef555/n0KFDDA0NsWfPHnK5nFAqFxcXOX36NK+++ionT57kzJkzwmjsZu7tQe/q97//ffbs2YNlWWiaRiwWQ9d1DMMQ6u6bb75JqVSi2WwyMjLCgQMH2LVrF1u3bl1ziCmKgmmaeJ4nlPmNNPkyDEOUIrquSz6fF99BIpGgWq2yvLy8rqBUFIVYLCZGZ0okEolEIpFIJJIHA5lvrg+Zb67lQcw370vByLZtYfh1ozGA76fZbHLy5EkGBgbIZrMAQp2cnJwEYHh4mNOnT1MsFllcXOTo0aM899xz+L4vjKpisZgwyXIcB0VRSCaTQoWORqPYti1KBU3TpFar4boujuOQSqWEE36grGqaJkry2u02tVpNONzfDsEIvueff57f+I3f4ODBg2K047V0dnayd+9ePv/5z3P8+HH+4i/+gm9961vYto1pmjiOQ6vV+tB7+u1vf5tPfOITHDhwgFAoJA7XICB37txJMplk3759/Pu//zvDw8OMjIyQTqeB1V5cRVFotVq4ritGOwaBvZGoqioOhMBELni83W5z4cIFrl69uu7r3Yk1SiQSiUQikUgkknuLzDc/HJlvXp8HMd+8LwUjz/NwXfeW1cD5+XneeOMNYNUwKxQKkcvlaLfbVCoV+vv7efLJJ3n11VepVqucP3+eoaEhtm3bhqqqxGIxURao67oIUkVRCIfDwsU+HA4TCoVEv2gqlVoTxNcqyM1mU2yCUChErVZjZWWFQqGw7gAOyt1KpRKapjE8PEw2m+U3f/M3eeaZZ25o2BaMO0yn0zz33HNkMhl0Xef48ePs2rWL+fl5xsfHCYVC4lB6P1evXuVrX/saHR0duK5LJpNB0zRs28YwDLZv387u3bt57LHHeOqpp5icnCSRSIgDsVwuE4/HxecPxgwWCgWq1eotfb83I1CjgxLJYHoAwMLCAuPj4+v+ByEYwbjRa5RIJBKJRCKRSCT3FplvrkXmm+vjQcw370vByLIsYQB2KwRj/kzT5Mknn0RRFFKpFKZpcvr0aeLxOKOjoywsLHDs2DGq1SpvvPEGvb29xGIxms0mtm2LYAbEJkgkEvT09DA9PU2j0SAcDpNKpfA8j6WlJQqFAuFwWJTRaZomStE8z0PXdUKhEI1GQ4wKXA+pVIoDBw6wfft2jhw5Ihzjv/SlL/Hcc8+tMWvzPE8o1aqqrnFnD4VCHDx4kD/6oz/i8OHDfOMb36C7u5vh4WG6urqYmpri7NmzLC0trVGBXdfllVdeobOzk1/91V9F13XRLxuLxcT4R4DR0VH6+vqEst1oNMRBGIlEhBlbpVJhZWVlw8vvDMMQh204HEZVVXzfx/d98vk8pVLplq5XrVY/chmnRCKRSCQSiUQi2VzIfPM/kPnm+nkQ8837UjAK1N5IJHLLJlC+73P58mVyuRx9fX1i5N6WLVtYWVkhFArxyU9+knw+z+TkJPPz85w+fZpHH30UwzCEWVag9gaoqsrs7KzYhEF5YLvdplqtYhiGUGYDg7DAzCzYBKVSidnZWfL5/Lo2hmEYfPKTn2Tfvn2oqkpnZyeFQoGuri6efPLJDzj7B38OlNXr9eVu3bqVXC7H2bNnGR8fZ2RkBICHH36YwcFB5ubm+MEPfrBm/GCj0eBrX/sajuPwxS9+kd27dxOJRNA0TZQMRiIRXNcVKntg2mbbNrVaTSjfiqJQq9W4fPkyzWbzlr7bDyOTydDb20soFMJxHDRNEwdN8F3fSjCqqoqu69Tr9Q1bo0QikUgkEolEIrn3yHxzFZlvrp8HNd9c/6zATUagkN4OCwsLHD58mFKphKqqZDIZtm7diuu6XL58mXA4zFNPPSUU23feeYfp6WmxCS3LEoqppmmEw2GhQgdrCkoELcsSgR+NRnEch1qtJszKgoAvFApcuXJFmHWth507d/LpT3+azs5OcrkclmWRTCb5yZ/8SXp7e4HVAyswTQt4/5jIQPUMME1TuMonk0lSqRQAXV1d7Nu3jx07dnxgLY1Gg7//+7/nH//xH1laWkJVVaFwe56H7/skEgm6urrEe+q6TjgcRlEUoQAXi0W+853vcPjw4Q1TU0OhEH19faKfODhcVFVlZWWFsbExFhcXb+manuetu5xQIpFIJBKJRCKR3F/IfFPmm+vlQc4378sKI4BisYhpmrf9+lqtRqlUYmBgQGyeWq3GO++8g23bPPbYYzzxxBO8/PLLVCoVfvCDH6BpGtu3bxclb6FQCEVR0DRNbMJCoUA0GsXzPDFm0PM8qtUqlUpFjDgMTMECxbBardJsNqlWq+sqfUwkEjz00EOYpkk4HMZxHEzT5OGHH+bQoUPiee12W5Qj6rp+3WspiiKM3YL+1mD0YBBE8XiccDiMbdvs27ePS5cufaCEz7Isvv3tb7N161Z+7Md+jJ07d6Ioiji0NE1D13VxoFiWJe5LcA+++c1v8jd/8zcbpqQqisK2bdt49NFHcRzvFxDBAAAgAElEQVSHfD6P4zjiPU+dOsXY2Ngt9ycHfcMSiUQikUgkEonkwUPmmzLfXA8Per553wpGgWN8YAp2q7iuy9zcHP39/cIQK51Ok0qlWFhYoNlsirGHJ06cYGlpiWPHjhGPx9m6dSuGYdBqtcRG9DwP0zRFz2K73cZxHBqNBvF4nEQiQaVSET2WqVSKarUq+jx1XcfzPNrt9ro205YtW+jv7xeKrmVZRKNRtm3bRiQSEc8LRgiGw+HrXuda5/Vrf47FYqiqiqqqhEIhotEotVoNgGw2S29vL5VK5QP3vlAo8Nd//dcsLy/zwgsv0NfXRy6Xw/d9bNtmZWUF3/dpt9ssLi5y9uxZLl68iOM4TExM8Oabb25o8OZyOR566CH6+vqYm5ujXq/T0dGB53miNPB2RimGw+F7Xh4okUgkEolEIpFI7gwy35T55s34OOSb961g5Lqu2FC3g+d5jI+P09XVRSQSIZPJsHPnTvr7+5mdnWViYoL+/n527dpFqVTi8uXLTE5OcvToUbLZLNFoFMuyCIVConcycGqPRCJrgqjVaokgVVUVRVHE5s9kMlQqFebn55mfn1/XplBVlb1794pxgqFQSJieBWMDYbVsr9FoiEMjeK7jOITDYVzXFYqu4zhUq1XS6bQw5zp//jzpdJqenh5hGBYonaOjo0xPT193vQsLC3z1q19lYmKCz3zmM/T39xONRslkMjiOQ7lcJp/Pc/z4cY4dO8bCwoJwkb/d0Y7vR9M0enp6ePTRR+ns7ARW+0pTqRS6rqMoCgsLC9i2fVvXTyQSNBqNDVmrRCKRSCQSiUQi2VzIfFPmmx/GxyXfvG8FI0D0Kt4ulmVx+vRpGo0G27dvp7e3F9M06e/v5+rVq0xOTpJOp3n88ccxDIOJiQneffdd2u02hw4dEhtT13VhqAWrwWBZFu12m3g8TjQapVKpiB7LoNSu1WqJkX6lUolSqbSuAO7u7mbXrl1i/KBt25imSU9PD9lsVjwvKIkbHBzE8zxs20ZRFFGmGKjlQY9soDarqoplWeTzeaFqB4dCu90mFAqRTqdJJBI3XG+9Xuf1119nenoaTdMoFAqkUilx7VKpJA6YjXR+D4VC6LrOtm3b2LZtG8lkkitXrjA5OYlpmuzYsYN0Ok2j0RCmcbeKqqo0Go3bUoolEolEIpFIJBLJ/YHMN2W++X4+bvnmfS0YbcQXv7i4yMrKCsvLy3z2s59F13VisRg7d+7k/PnznDhxgnQ6zfPPP49hGFy8eJHXXnuN1157jccff5xsNktnZ6cowfM8D03TsCyLcDhMNBqlVCphWZYImGsd34PSueXlZQqFwk0PpFAoJMoAA2OvZrOJpmnE4/E1SrPneYTDYWHYpigKoVBIlDIqiiLGC168eBHDMOjo6ABWS/0mJia4ePEimUwG0zSxbZt6vU4kEmF4eJj9+/eTz+dvuJGr1SoXLlwgHo9TKpWYnp7+qF/Xh2KaJkNDQ3R1ddHT04Ou65w7d46rV6/SbDZ56KGHSCQS2LYtFPzbCUJVVTdF8EokEolEIpFIJJI7h8w3Zb55LR/HfPNjLxjBqjK6sLDAiRMnRHB0dHSQyWSYn59nenqa4eFhfuu3fouenh7OnDnDH/zBH/D666+TSqXYs2eP6E0NyuBqtRqmaQrFNHC4D0bsWZaF7/siUIrF4rpKHrPZLI899hgdHR20Wi0ikQie5xEKhdYoubBqHBa8TxC8gPj7AMMwSKVSaJomntNsNmk2m5RKJWFy1mg0iEajaJpGq9Wio6ODaDT6oSq14ziEQqE7sulDoRCGYZBMJgmFQmI9rusyPT1NtVqlXC7jOI74fEtLSywtLXHu3Lnbdp0PjNQkEolEIpFIJBLJg4vMN2W++XHPN+9rwSjYbBsRyOVymRMnTnD16lU6OjpIJpMsLy/TbDZxXZeTJ09Sr9fJZDI8/fTT/Pmf/zm/8zu/wz//8z9z9epVBgYG2LlzJ6Ojo3R2dhKJRAiHw2LTBqP1PM9D13Ucx2F5eZnFxUUmJiZYWVm56QaPRCLCqX5lZQVVVTEMQ5ieFQoFxsfH+exnPyv6TLPZ7A0NyALC4TAjIyOiVBBgfn5eqM+WZRGPx4nH42iaRqlUIhKJMDQ0RGdn54cG8PsV7o1A0zQGBgbo6+tjy5YtuK5LpVIRUwGazSbFYhHbtuno6GDXrl1kMhl83+fq1aucOXNm3aMkr0fQlyuRSCQSiUQikUgeXGS+KfPNj3u+eVPBSFGU/wr8BJD3fX/fe4/9HvA/AkvvPe23fN//9nt/978B/wlwgf/J9/3v3oF1A6uBG2y4jaBer1Ov15mfnwcQGziRSFAsFvmTP/kTfv7nf55HH32Ubdu28Yd/+IcUi0VefvllxsbGuHTpEqdPn6avr4/u7m7S6bRQIwPFVVEUPM+jUChw/vx5FhYWKBQKN1V7VVVldHSU0dFREbSBc7rv+4RCIRYXF2m325TLZbq6ugDWBG9g3FYqlYDVEsJIJEJ3dzehUGhNeeHCwoLoWa3X64RCIUKhEPF4nGw2S7vdxjRNDh48yNzc3If2Zt6u0df1iMVi/Mqv/Aq/9Eu/xOHDhzl//jxnz55lYWFBqOmBCgysuS+WZTE2NvaRghcQfbz3ms0cmxLJxxkZmxLJ5kTGpkSyOdnMsSnzTZlvfpzzTVhfhdFXgP8H+Jv3Pf5/+b7/v1/7gKIoe4CfA/YCfcBLiqLs9H1/41ym3sed6O17/zU9z6Ner/Pmm2/y8ssvs3//fl588UW+8IUv8OKLL1IoFDh16hTtdpsrV65w9epVYrGY6MVMp9NCcfR9n3K5zPLyMvl8nnq9flPFWlEUBgcHeeKJJ9i1axew2vMZKI+WZWHbNpcuXcL3fV555RV+7ud+bs36L126xLFjx5iYmGB5eZlWq0W73WZgYIBHHnmEZ555Zo2B2cjICI8++iiKohCLxUilUti2jeu6NJtNIpEIqqqyY8cOBgcHuXTp0gZ+A9cnEonwC7/wC/zu7/4uFy9e5Pz585w+fZrl5WV0XSedTpPL5TAMA1VVcV0XXddpt9visPyoTvMfZbTmHeArbOLYlEg+xnwFGZsSyWbkK8jYlEg2I19hE8emzDdlvvkxzjdvLhj5vn9YUZSt67zeTwN/5/u+BVxVFOUS8EngyG2vcBNQq9VQVVWUwl24cIE//dM/5bXXXsMwDEZGRojFYoyNjbG8vIzv+0I9Dvo3A7VX13Vc1xW9nushm81y6NAhRkZGcF2XVquF7/tifGIikaBarZLL5RgbG+Pw4cNrAvjChQt8/etfZ3JykqWlVZG+2WxiGAb1ep0zZ86QTCb50R/9UfGaL33pS7RaLY4ePYrrurTbbWF2FoxqVBSFjo4O9u3bx8zMDK1Wa6Nu+QdIJpMcOnSIz33uc7zxxht84xvf4MSJEzSbTUzTxDRNurq6xKFpWRaNRoOlpSUmJyeZn5//yEpvsI7NMN4QZGxKJJsVGZsSyeZExqZEsjmRsSnzTZlvrl3HZsk34aN5GP1nRVH+B+At4H/2fb8I9ANvXvOcmfce+wCKovwa8Gsf4f3vKoEKHIz3A/je975HrVbDMAzS6TS7du0inU4zMTEheg6DIA3+fCulZYqiYBgGAwMDmKaJ53lC3fU8D9M0aTabonxv165dDAwMMDo6uuY6pVKJmZkZVlZWsG2bSCRCJpOhWq1SKBTo6OhYUx4IsHXrVorFIlu2bCESiYjDJxQKkUwmsSyLYrGI4zhkMhlyuRyLi4t3pHROVVVGRkbo7+/ntddeY2ZmhqWlJaGqw2rfa61WE+p5s9nEsiwRwBsRvOFwWEwZ2ORsWGwaxO7wUiWSjxUyNiWSzYmMTYlkcyLzTZlvynzzHnO7gtFfAH8A+O/9/n8Av3IrF/B9/y+BvwRQFOW2662CTR6ooHeSvr4+tm/fjud5qKpKoVCg1WrRarUolUqUSiX27dtHT08PFy5cYGlp6bbWpCgKyWSSbdu2sW/fPrZt2wZAo9GgUqlQr9eF0ZllWeJwicVibN26lZ/4iZ9Yc71MJkNfXx8A+Xwe27aJxWIYhoHrupimKQLh2tf83u/9Hq+++irj4+MUCgWy2Sy+76OqKuFwGMMwcByHvr4+XnjhBc6dO8frr7/+of2lt0OgblerVeHM39fXJ8zcWq0WKysrTE9P02g0iMfjwKqxXKFQ2DCH+Ww2u6nU3huwobGZVLKboxZSIrn/kbEpkWxOZGxKJJsTmW/KfFPmm5uA2xKMfN9fDH5WFOWvgG+998dZYMs1Tx1477E7RvDl3ung1TSNXbt2sX//fmBV/RseHubo0aOMj4/jeR6lUomjR4/S39/P3r17GR8fJ5/Pr9tVP1B3t2/fTnd3N4ODg8BqiaKmaZimSSwWo1AosLKygqIo2LaNpmnEYjFyuRzPPvssW7duXXPdbdu28elPf5rXXnsNx3HI5/O0220SiQSJRIKf/umfFr2q1zI0NMSXv/xlyuUyi4uLnDhxgtdee41SqcTo6CiJRIJoNIqiKKJ3NhwO88Mf/nDDN7rv+4TDYWKxGOFwGF3X0TRNBGjQWxuYkQVmbxtJuVzeVGrv9dhMsSmRSP4DGZsSyeZExqZEsjnZTLEp802Zb36c883bEowURen1fX/+vT/+DHD2vZ//O/C3iqL8n6yakO0Ajn3kVd6EUCiEaZrCjf1O4DgOb7/9NqVSiccee4ze3l7RS6ooCpOTk9TrdRzHYXJyEsdxePjhh5menmZqaopqtXrDayuKQjab5ROf+AS7d++mo6MDwzCwbRvf99F1XZS/hcNh9u3bR6lUwnVdtm7dyvPPPy9U4euhaRpPPfUUjuPwD//wD7TbbSqVCgMDA7zwwgv8+I//+A1HIeq6TmdnJ52dnezdu5fh4WFeeeUVIpEItm0LQy7f90mn0xw6dIhQKMTRo0cpFAof+b7DaolgMIIxHA4LRTdQxvP5PIVCgXq9jud5Yj0bjeu6GzJS806y2WJTIpGsImNTItmcyNiUSDYnmy02Zb4p882Pa755U8FIUZT/BjwD5BRFmQF+F3hGUZRHWC0RnAB+HcD3/XOKovw9MAY4wG/cjWkSjuNseFna9SiVSrzzzjvMzs7yxBNPsHv3bpLJJE8++SSWZXH58mWi0ag4UKLRKDt37iQUCjE+Pi7GEQbqpaZpRKNRdF2nt7dXlPy1221KpRKNRkO4rwfKakdHB7t27ULTNHp6etizZ88H+kGvRzgc5plnnuHUqVN861vfYmVlhYcffpjf//3fR9f1G76uWq0yMTFBLBajv7+fQ4cOsXfvXk6dOsX4+DjlchlN09Z8rk984hPE43HeeOMNFhYWPrLy6nkemqaRTqfp7+8Xpmi1Wo1WqyX6dYvFIq7rbrirvKIoRKPRDelL3Ujuh9iUSD6OyNiUSDYnMjYlks3J/RCbMt+U+SZ8/PJNWN+UtJ+/zsP/74c8/78A/+WjLOp2aDab6LouVNI7he/7LCws8Morr9BoNBgZGSGXy7Fnzx5RPtbR0cHQ0BBbt24lFAqRSCTI5/MUi0WSySTbt2+nq6uLZDKJYRhMT09j27boFy0Wi5TLZVzXxbZtqtUq6XSaSCTCJz7xCZ5++mni8TiKolCtVnn11VfJ5/MMDQ1x4MABYZL2fnRd59lnn+XVV1/l2LFj7Nmzh/n5eQYHBwmFQtd9zR//8R/z+uuv8/DDD/OpT32KQ4cO0d/fzzPPPIOmaXz9619H0zQ8z6Pdbgs1dtu2bRiGwdmzZ7l69SrlcvmW7nM4HMZxHHzfR1EULMviypUrqKqKYRg0m03K5TKWZXHu3DkKhcIHjN82imCk42YZbRhwv8SmRPJxQ8amRLI5kbEpkWxO7pfYlPmmzDc/bvkmfLQpaZsKRVHI5XJUKhVqtdodf79yucxLL73Evn372LNnDyMjI/i+z4ULF/A8j3g8jq7reJ5HT08P+/fv59VXX6Wrq4uDBw+Kv49GowwMDOB5HrVajXa7TSgUoqenB1hVMS3LolqtEgqFaDQazM3NYZomp06d4p133sF1XVKpFAsLC5imyWOPPXbDdff19fHFL36RL33pS3z6058WCvX1aLVazM7O0tfXRzQaZWxsjOnpaR5//HFSqRTHjx8nn8+LHlXTNOns7MT3fZrNJo1Gg0gkwo4dO8jn88zPz9+0JzPoT2232/i+TzKZZGRkhL1795JMJoHVANU0Ddd1OX/+/Lqu+1EwTXPTmY9JJBKJRCKRSCSSu4fMN2W+eafYzPnmAyMYeZ4nNvlGl4jdCNd1OXv2LKVSiaeffpre3l5hEBaJRHAcRyih3d3dJJNJUUYXiUTwPI96vY5hGCiKQjweF4Zivu8TiUQwDINEIkGr1cK2bTKZDIcPHyYcDlMul/E8T/wK1NEbEQTEgQMH2Lt3L6qqMjU1RaVSIR6PfyCQl5eXSafTAHR2dlKtVmk0GrzxxhtUq1UsyyKZTJJIJLBtW6yz1WqRz+eFYpvJZEgkEiSTSQqFgvi7a9E0TQRlb28vlUqFZDLJE088wcDAAIqi0G63aTabVCoVJiYmePfdd4VafidQFIVEIkGlUrkrJagSiUQikUgkEolkcyLzTZlvbjT3Q775wAhGsNr/mEgkyGQyG2aAdTNc12VycpJ/+7d/Y//+/XR1deG6LqVSiY6ODhKJBOVymXA4zODgINVqlbm5OVE+WK/XKZVKZDIZVFVF0zQsyyIcDmPbNp7n0Ww2Ralhb28vjUaDWq0m+kzn5+fxPA/Lsm54cPm+z9WrV/F9n2w2Szgcpl6v83d/93eEw2Eef/xxdu/eja7rotxuYmJClP0Fa1teXmZgYIBUKkW9XhfB6/s+tm2LdXZ1deF5njA30zQNRVHI5/McOXKEmZkZGo0GzWYT0zR55JFH8DyPd955h3q9ztatW8X9rNfrTE9Ps7KywsLCAktLS5TL5Q8cAhtNUBq42YzHJBKJRCKRSCQSyd1H5psy39xI7od884ESjGC1tzSbzdJoNGi1WnftfZeXlzl58iSPPfYYnZ2dlMtlFhYWGBkZQdd14vE4g4ODFAoF0um0KJ9TVRXTNIVq6bouqqoSCoWIxWLU63Vc1yUSiQiV13VdGo0GlmWJ59frdWZnZzl37hyPPPLIB9Tbc+fO8a1vfQvHcfipn/opAGzb5vz586TTaXRd59SpU5w4cUIYqHmeR6PREGWEnufR2dmJruuk02ny+TytVgtNW91GxWJRlNIZhiECPxwOo6oqAL29vTz66KOYpimM2fbu3cuuXbt46623aLfbKIrCjh07UFWVsbExUZoYfN67RTqdplKpbOoAlkgkEolEIpFIJHcPmW/KfHOjuB/yzQdOMHIch0KhIAzJ7ubNLxaLHDlyhP3799PR0SFMsmKxGJqmsWvXLpaXl8Xzg1GGoVAI13VFj6llWTQajTVqaTabFcEafKbADd4wDEzTZGpqiitXrgjTsgDP8xgfH+fdd9+lVquxY8cO9u3bh6IopNNpOjs7icfjTE1N4XkeuVxOlDfWajWq1SqKomDbNj09PbRaLaFK67pOq9XC931SqRSdnZ1UKhVUVaXdbosADhz4dV1neHhYlCS2Wi1GRkZotVpcvXqVzs5ODh48SKVS4fXXX2d2dhbLsu7adxigaRqVSmXT9pJKJBKJRCKRSCSSu4/MN2W+uRHcL/nmAycYwX+UCnZ2dpLP5z/ymL1boV6vMz4+zq5du0gmk1y5coWRkRHC4TC1Wk0op6qqYts2qqpiWZbY8LFYTFwrMDMzDEOUCqqqKtRT27ZFKSGslrTV63X+6Z/+iaGhIXbv3s309DTf/e53abVadHV10d/fL97DcRx27dolXOBt2yabzaKqKul0mlqtRiQSobOzk2Qyieu6uK6LZVnYto1hGMCqWZlhGGiahu/7YiyhaZq4rovjOLRaLeFED6CqKsPDw8RiMRzH4fXXX8d1XYaHh1laWuLkyZPU6/W79r1dSygUIpVK3bUyU4lEIpFIJBKJRHL/IPNNmW9+FO6nfPOBFIwAGo0GmUyGjo4OlpaW7up7l8tlpqeneeyxx9A0jVKpRDweJ5lMCld613Wp1WooikI4HCaZTGLbNoVCAU3TiEajInAjkQiwaooViUSwbVsYZAWbTNM0UqkUqVSKZrPJiRMnuHLlCs1mUxiDZTIZod4CQuFttVosLi4SjUZFP67jODiOQ39/P9lsllAoRLvdXrMWz/NwHEcEdvBzLBYTn8/zPAzDIJ1OiwMHEOsJh8OcOXOGqakpVFVlcnKSpaWlO94veiM0TSMej1Ov1zflWEOJRCKRSCQSiURy75H5psw3b4f7Ld98YAUj13WZnZ0ll8thmuZdGX0Y4Ps+8/PznD9/nr6+PmKxGL29vQwODuK6rgigICCD/tFgjKGu66IU0HEcofR6nofrumiahmEY1Ot1LMsSPZ+GYaCqKq1Wi1AoxOLiIgD9/f2izM6yLOHsv7i4SKFQwDRNVFUV7vmGYeA4jjAcU1WVeDxOJBLB930syxIqb6D0NhoN4vE4lUqFSCQiTMd83xfBWq/XhfqeTqdpNpssLi5y8uRJoYbXarV7GjjxeBzXde9qP7JEIpFIJBKJRCK5v5D5psw3b4f7Ld98YAUjWA3i5eVluru7iUQiFAqFu7Y5fN8X/Z29vb0sLy+LntEgCEzTFL2aQR+mqqpiTGKz2SSVSqEoigiGWq1GV1eXGGmoqiqxWIxIJIJpmqKUMAjGwEQsUIqD9/d9H13XabfbhMNhcrkcjUaD+fl54vE4vu8Ti8UwDINYLIZlWWKtgSpaKBSIxWIkEglR4hg8R1VVEokEkUhkTR9ttVoVh1atVuPUqVNCtb7XZl+ZTAbgrh72EolEIpFIJBKJ5P5E5psy37wV7sd884EWjGB1UywtLTE0NISqqne1XNDzPBYXF3EcB1VVcRxHjBKMx+Oiz9TzPDzPE6MDW60W09PThMNhsana7TbValUE4NLSEqqqEolEyGQyOI5DNBoFED2mmqbhui5TU1NomkYymURVVbZu3YqqqnR2duK6Lu12m0QiIdRaXdepVCrkcjlCoZBQi8PhMKFQCNu2cRwH3/dFyWEoFCIUCgkTNU3ThKN9UHKnaRrhcFiozlNTU7z77ru02+279p3cCMMw8H2fcrl8X5QGSiQSiUQikUgkknuPzDdlvrke7td884EXjGB1Q09MTJDNZkkmk2J04N2iWCximiYzMzOoqsrg4KAY2ReNRkVZnm3bxONxFEURBmqTk5PiOp2dnWIkouM4wqAMEM7xwUjB4PO1Wi0ajQbLy8skEgkeeughYUI2ODhId3c37XabiYkJMYIxCORg/GGgRsOqcVkQiMHhUCwW6e/vFyWNQSlhoP5GIhHRI6ppGvPz87iue0+NxgIURSGXy4nD/X4KXolEIpFIJBKJRHLvkfmmzDdvxP2eb34sBCNY3XjFYpHR0VFqtRqTk5N3LYg9zxNBcenSJWq1GvV6nf379zM4OCjKAYNe0UgkQiqVIhKJUC6XOXPmDIZh0NnZKZTUwNir1WqJErug9A5gbm4OXdeJxWIkk0kGBgaYn58XPaCAKC8cGxsjFArR1dVFOBxmYWEBz/PEmMLAab/ZbK4ZvdjV1cXU1BRvvfUWly5dorOzk46ODrq7u7Ftm0ajIXpIbdtG13WKxSKtVouTJ08yMzNzV+7/hxGLxdB1XXxmiUQikUgkEolEIrlVZL4p883rcb/nmx8bwQhWN9H4+Dg7d+6kr6+PhYUFUU53N7AsSxiUaZrGpUuXaLVa7Nq1C8uySCQSNBoNVFUlHA5TrVbxPI9EIgEgxggGpmBB6V5gslYul4VZWCQSYWFhAcMw2LZtG6ZpCuf506dPk06nGR8fF6MUo9EojuNgWRbd3d00m03m5+dJpVJomiYc6j3PEyZpwTjE4FDp6ekhkUigaZpw3q/VasLV/vz58ywvLzM5OcnMzMw9D5hMJoNpmszNzd3zflaJRCKRSCQSiURyfyPzTZlvXsuDkG9+rAQjWO3NHBsbY+fOnezfv5/p6WnK5fJd62tst9uih3NlZYXl5WVs28Y0TZLJJLFYjHa7TblcBiCVSjEyMsLY2Bjz8/PEYjFisRjFYhFN08TIxECddV0Xz/PI5XKUy2WOHDnCysoKfX19LC8vk8lkxGbN5/NcunSJxcVFOjs7OXHiBAMDAxw8eJB0Oo3jOBiGQbvdRtM04SgfKL/Be2maxu7duzFNE9/3aTQatNtt8vk8uq7TaDTI5/O8++67TE9P33Nn+mBEZCaTua+DVyKRSCQSiUQikWwuZL4p880HKd/82AlGsFqyd+HCBXp6ehgdHeXChQssLCzc1TUE5l2+73P06FEGBwfp7e0VKmk0GiWZTOI4jhh1CKtlfYEqHHyWa0cdqqpKpVLBtm1yuRx9fX18+9vfZsuWLczMzPAzP/MzlEolTp48ieM4FAoFLl26xOTkJI7j0NvbS71eF2ZnrutiWdYax/1KpUKxWERRFObm5ti9ezc9PT1iPGK9XqfRaHDp0iVUVWV6epqrV6/SaDQ2Rc9mLpcjmUxy9erVe646SyQSiUQikUgkkgcLmW/KfPNByTc/loIRrG78ubk5bNtmaGhImIS1Wq27toZgMzuOw9TUFN/97ncZHh6mt7cXXdeFmZeqqvT399Nut6nX66TTaTHiMOgjzWQyWJYljMzK5bIwKnMch0qlQjqd5vz589RqNbLZLPl8nunpaYrFIolEAl3XsW0b13WFg3s4HBaHiOu6GIaBYRiEw2FhQNbV1YXrutRqNeFeXy6XuXz5MjMzM6I08l4TiUTo7OzE8zwmJyfv++CVSCQSiUQikUgkmxOZb8p880HIN5XNcGMVRbmni4jFYmzfvh3P87h8+bJQV+8FQS9mKpUSZYMdHR2Ypkm73UbXdfr7++no6CUleAsAABzASURBVBAlgvV6nVAoRDKZpNFo4DgOi4uL1Gr/f3v3Ghvnded3/HueuXCGnCE55IgiRdIiZYmRdfPaUhzHGzeVjTjJwojbYLFOAnTTNGiKNkF3gX3RJAWaFkWAbVEkaLJFkzQpEqNBdh07ztq5ILJSx06DWLJki5ZkRxJJU6R4E6nxcDg3kjNz+oLDWVKWJYoSOQ/5/D7AgA+fufCc4fkR+h+d50yavr4+hoaGMMYwNTVFNpvF7/cTj8fJZDLLlkZGo1EaGxuJx+N0d3fT3NyMMQa/3080GmVycpJAIIAxhv7+fiYnJ9m5cyddXV3MzMwQCAS4cuUKo6Oj9Pf3V2aR3SAYDNLT04PjOJw5c8aN4T1prT1U7UZcrd402feZh6vdDJGqOWqfUjZFXOaY/TUpmzDVbse1KJviZW7OpurNf6B6c224vd601q4qm55dYbRUNpvl7NmzdHd3c//99zM8PMzIyEhVglwoFJiZmWFmZoZIJILjOBhjqKuro7a2lmQySbFYpKenh4aGhsomZYFAgPn5+crSvVQqRTKZJJlMks1mmZ+fx1pbCfriRyIWCgV8Ph/pdLpyLevExASjo6NEo1FSqRQNDQ0ApNNp0ul0ZRmg3++nWCxijOHSpUtMTU0xMTFBMplc183drsdxHBoaGmhvb2d0dLSysZuIiIiIiMh6UL2penOj0gqjJYwxbN++ne3btzMxMcHk5GTl+sxqWlx25zhOZbldTU1NJZTRaJRiscj8/DyBQACfz8fs7Gxlqd/iJls+n4+Ghga2bNlS2UysWCySyWSYnZ1lbm5u2eBe/BmO47zrzK3P58Pv91eWELqJ3++nvb2dcDjM+Pg4yWSy2k26Hq1iEHEhrTAScR83r2JQNsXL3JxN1ZvXp3pzdTZSvakVRreBtZbBwUEmJibYu3cvu3fvZnh4mAsXLpDJZKo2QBc/JnCpxY84BEilUpXjpUv+jDHU1NQs24F+y5Yt1NXVceHChcpyxEAgUAlrKpWq/KzFn3G9GdJrtc0N4vE4O3furCyTXM9rhUVERERERK6melP15kajCaNryOVyvPrqq0xNTdHR0cF73/tezp8/z+joKKVSyRUbaq2EtRa/309jYyM+n49wOFyZ/Zyenq5sZLb4uMUNzdwYyJWqra2ls7OTeDzOyMgIg4OD1W6SiIiIiIhIhepN1ZsbhSaM3kWpVGJwcJDh4WG6urrYvn07O3fuZHh4mLGxMTKZTLWbCFD5qMTFMF4dvsWd7rdu3UpdXR1zc3OkUql3/CGan5+/7jW0i8F2Y8CNMQSDQeLxOFu3bmVubo4TJ04smxUXERERERFxC9Wby6nedCdNGN1AsVis7NDe0dHB9u3baW1t5Q9/+AOJRMI1G1otvXZ0qUKhQDqdJhAIMDk5CcD09PRNz1ovvrabwus4TmWH/9bWVgqFAsPDwyQSCVe1U0RERERE5FpUby5QvelOzo0eYIzpNMa8YIx5wxhz1hjzF+XzTcaY540xF8pfY+XzxhjzDWNMnzHmdWPMvWvdifWQSqV444036O3tpVAocOedd7Jr1y7q6+txnBu+jWvmRkEslUqk02mGh4cZHR1ldHR0Uwxun89HT08P9913H/v37yeZTHL69GkmJyc3Rf9WQtkUcSdlU8SdlE0Rd1I2F6jedBfVmwtWssKoAPyVtfZVY0wUOGmMeR7458CvrbV/bYz5IvBF4N8BHwV2lW/vA/5n+eumMDU1xdtvv01DQwMtLS0cOHCAqakpRkZGyOfzVd/h/nr8fj/19fWVjzTcaAKBANFolM7OTsLhMJFIhLfeeouxsTGy2Wy1m1cNyqaIOymbIu6kbIq4k7K5hOrN6lG9+U43nDCy1o4BY+XjGWPMm0A78Bjwj8sP+wHwGxYC/BjwhF2YinzZGNNojGkrv86mUCwWSSQSJBIJduzYwZ49e9i3bx9DQ0OcOXOm8hGDblJTU0N3dzfxeJxUKsXAwADpdLrazVoRx3GIxWJ0d3fT1NTE9PQ0iUSC48ePu2aJZjUomyLupGyKuJOyKeJOyuY7qd5cX6o3391N7WFkjOkC7gGOAVuXhHIc2Fo+bgeGlzztUvncsgAbYz4HfO6mW+wyAwMDjIyM0NbWRnd3N4cOHSKRSDA5OUkymXTFRlihUIiuri7a2trw+XyEQiFKpRITExMkk0mKxaKrguDz+Srt3LZtG6FQiNraWgB6e3u5cuUKhUKhyq10l7XKZojaNWuziBcomyLupGyKuJPqzXdSvXn7qd5cuRVPGBljIsDTwF9aa1PGmMp91lprjLmpXa2std8BvlN+7Y3xuYHvYnZ2lsHBQYaGhujp6WH37t3cddddXLx4kdHRUXK5HNPT0+s+6Iwx1NXVsW3bNnbt2sW2bdsIBoPkcjkikQgdHR0MDQ2RSqXIZDJMT09XJcimvOt8Y2MjoVCIWCxGLBajpqaGQCBAf38/ly5d4sqVKxvmIybX01pms9406Q0XWSVlU8SdlE0Rd1K9+e5Ub956O1Vvrs6KJoyMMQEWwvtDa+1PyqcnFpf+GWPagMvl8yNA55Knd5TPbXqlUolz584xODhIa2sru3bt4s477ySfzzM2Nsb4+HhlR/XZ2dk1CbQxBmMMkUiESCRCS0sLLS0ttLa2Mjc3x+DgYOUjAQOBAOFwmHQ6zezsLIlEgnQ6TTKZJJfLUSgU1mSp4+LPrampobm5mdbWVqLRKMYY5ubmSKfTZDIZTp8+XfnDp+Bem7Ip4k7Kpog7KZsi7qRsrozqzZVRvXn73HDCyCxM7X4PeNNa+7Uldz0LfBr46/LXv19y/gvGmL9lYfOx6fW+nrShoYHOzk7GxsaYmZlZ1w23rLXk83kGBweZmJhgy5YttLS00NnZSWdnJ6VSiStXrjA1NUU+nyefz5NMJsnn81hrKzOuKx2wxhgcx8FxHBobG2loaMDn89HY2Eh9fT2NjY34/X5++9vfcunSJdLpNMYY2traKjPBTU1NAKTTaXK5HJcvXyaZTFIqlZiZmaFUKpHL5cjn8xSLRay1lfZd3c7F/wlY/BoKhQgEAgSDQUKhEDU1NTQ2NlJbW1uZ1c1ms5w7d47Z2VkymYxmdldoI2ZTxAuUTRF3UjZF3GkjZlP1pupNrzA3eqOMMR8AfgucBhbXj32ZhetKnwTuAC4Cf2atTZQD/zfAR4As8Blr7Ykb/Izb/ts6ePAgjz/+OJlMhrGxMV5//XVeeeWVqm0O5jgOTU1N7N27l1AoRENDA8FgkJmZGVKpFI7jMD09XTmempoCFkK1+DtyHAefz4ff78fv9xMMBonFYkQiEWpqaqirq6OxsZF0Os3k5CSFQoHz58+TTqffdemf4ziVsMXjcTo6OvD5fBhjiMViBIPBykxsJBJhaGiosuRxceZ66R+d+vp6/H4/oVAIgKamJkqlErFYrDKDnEgkyGazlT8oS/8geNhJa+2hm3nCemSz3jTZ95mHb6ojIpvJUfuUsiniMsfsr0nZhLnxI5dTNkXWlpuzqXpT9abX601r7U1nE1YwYbQe1uqaUr/fz+7du4nH49x///3EYjFeeOEFent7KwOpGptZLYbN7/dXZl5DoRCFQoF0Ok0oFCKfzxMIBMhms8zMzODz+SgUCkQiERzHAaiEwnEcJiYmuHTpEtZacrncdUO7Eo7jVK7pDAQCWGuJx+N0d3fj9/vJZDKV+2dnZyuzzqVSqfKHZnZ2llQqxVtvvYW1lkKhwOzsLLlcztWB9fv9tLS0EAgEmJmZob6+nn379nH06FHy+fxa/dibLkrXg/7hK163mgmj9aBsipettihdD8qmeJmbs6l6U/Wmm1Sj3tSE0Qp1dnZyzz33AAvL14rFIhcvXuTcuXPMzMysVzOuyxhDKBSivr6empoaSqVSZaf5xesrE4kEmUymam30+/00NzfT3NzM3Nxc5Q/hyMgI8/PzVWvXaoXDYT74wQ9y8ODByvWtg4ODnD59muPHj6/HMlMVpSIupAkjEfdxc1GqbIqXuTmbqjeXU725/qpdb652wmjFn5K2WQwPDzM8PIwxhubmZvx+P/v27eMrX/kK1lomJyc5ffo0iUSCqakp+vv7172Ni7O2uVxu3X/2ShUKBSYmJpiYmKh2U25aPB5n+/bt1NTU8MADD3D33XdTKpUYHx/niSeeIJfLMTU1RSqVqnZTRURERERkA1G9eXuo3nQHz00YLbLWVq7bHB8f5+jRoxhj2LFjB3v27OHw4cO0t7czNDTEwMAAvb29XL58mXw+jzFm2eyruJPP58Pn8+E4Dnv27OFDH/pQZUlmMpnktddeY3BwkCeeeILLly/f+AVFRERERERWQPXm5ueFetOzE0bXYq2lv7+f/v5+fv7znxMOhwkEAkQiER599FF2795NS0sL+/bt48qVKzzzzDMMDAyQTCZ54403SCQS1e6Cp9XX11d+R7FYjIMHD9LS0kI2m6VQKPDiiy/y/e9/n/n5ebLZ7FruRyQiIiIiIrKM6s2NzYv1puf2MLpV0WiUvXv3snXrVkKhEA899BB33303ly5dYmZmhsnJSc6fP8+JEycYHh4GoFQqkclk1vXjFjcjv99f+aNqjCEajfKRj3yEQ4cWtheJRqPU1tby0ksv0dfXRz6f58yZM5XN2VxO+6SIuJD2MBJxHzfvk6Jsipe5OZuqN2UlNnO9qU2vq2TxYwcB9u/fz8MPP0xHRwfWWu644w4OHjxITU0NzzzzDK+99hrWWlKpFL29vQwMDGyKWce1EAwG6enpYefOnWzZsgXHcbjzzjs5fPgw1lpOnTpFLpfDGMOrr77Kc889V/nIxPn5edcH9hpUlIq4kCaMRNzHzUWpsile5uZsqt6Uq3mt3tSEkQs1NTXR2dlJXV0djuNw8OBBHn30UVpbWxkdHeXtt9+moaGBuro6rly5Qm9vLy+++CJDQ0OV1ygWi2QyGfL5PLlcriofy3g7GGOora2lvr6ecDiMMf8wXmOxGA8++CCHDh2itbWVZDJJPp8nHo8TDofp6+vj2WefZXJykmKxyOXLlxkeHt6Qu+Nfh4pSERfShJGI+7i5KFU2xcvcnE3Vm6o3vV5vasJoA1kcvLW1tXz0ox/lrrvuIhAIVGYpu7q6uPfee9m+fTt+v7+yzO2tt94imUwCC9e/zs3NkUgkSKfTDAwMkEgkGBsbW/ePP4zFYjQ1NdHV1UVLSwuNjY1EIhHm5ubI5XLU1dURjUbZvXs39957L62trZVPBDh9+vSy9o6Pj/P73/+es2fPVv5YuWGMrgMVpSIupAkjEfdxc1GqbIqXuTmbqjdVby7yar252gkjbXpdBYsDMpPJ8NRTT73j/traWhobGwmHw5XlhwDhcJj9+/ezY8cO9u7dSywWIxAIUCgUyGazywJTW1tLNBolGAwSCoXw+/3LZlmXmp2dJZfL4ff7CQaDwMLSR2MMjuMse+zSGehMJsP09DSlUolgMEhtbS01NTUYY0in0/T19XH69GlOnTrF6OjostdYfH4qlaJYLN7yeyoiIiIiIiKqN1Vv3j6aMHKhbDZLNpu95n2nTp264fPb2tpobm5m27Zt1NXV0djYSCgUekcYV2N2dpbJyUlSqVRlqV4qlbrl1xUREREREZG1p3pTVkoTRpvQ2NgYY2NjnDlzptpNERERERERkU1E9aZ33PoUoIiIiIiIiIiIbCqaMBIRERERERERkWU0YSQiIiIiIiIiIstowkhERERERERERJbRptc38Mgjj2CMYWpqiosXLzI1NVXtJomIiIiIiMgmoHpT3EwTRjdw5MgRtm3bxje+8Q3C4TDnzp3jN7/5DUeOHGF+fp5isVjtJoqIiIiIiMgGpHpT3MxYa6vdBowx1W/EDdTV1fHAAw/whS98gfb2dlpaWvjd737Hk08+ycjICCdPnlSYXaCnp4d8Ps/ly5fJ5/PVbs7NOGmtPVTtRlyt3jTZ95mHq90Mkao5ap9SNkVc5pj9NSmbMNVux7Uom+Jlbs6m6k25XTZqvWmtXVU2tcJohTKZDM8//zxHjx7lPe95DwcOHOCRRx7hu9/9LslkkpMnT5LJZPj617/OxYsXmZubI5fLVbvZm1ogECAcDtPY2MhnPvMZurq6aG9v55vf/CbPPfdctZsnIiIiIiKyIqo33Uf1plYY3RK/308wGGTHjh18/vOfp66ujg984APE43GOHz/O008/zfj4OCdOnGB4eLjazd0UIpEIhw8fJh6Pc9999/Hxj3+cYrHISy+9xI9//GN+8YtfMDs7S6lUqnZTb5ZWMYi4kFYYibiPm1cxKJviZW7OpupNWanNWm+udoWRJoxuI5/Px/79+4nH4zz44IN88pOfpFgskkwmyWazTE1N8a1vfYv+/n4KhQKJRGJDLWNbT8FgkKamJoLBINFolM9+9rMcOHAAx3FobW0lHA7z3HPP8bOf/Yzp6Wl6e3s3+nupolTEhTRhJOI+bi5KlU3xMjdnU/WmXM1r9aYmjFzIcRwcx+Hxxx9n//791NXV8cgjj7Bz506SySQ//elPuXDhAplMhmPHjvHaa68xPz9f7WZXheM49PT0cPjwYerr6+no6OBjH/sY7e3tDAwM8Mtf/pJsNkt/fz8/+tGPyOVyWGtxw/i9TVSUiriQJoxE3MfNRamyKV7m5myq3lS96fV6c80mjIwxncATwFbAAt+x1v53Y8x/BP4lMFl+6Jettb8oP+dLwGeBIvBvrbW/usHP2By/hRvw+/20t7cTjUaJRCJ86lOf4oEHHsBaSy6Xo7a2lmAwyODgIEeOHOHll1+mUChUZoenpqaYm5urdjdWJRQK0dzcTCwWw+9f2Drr/e9/Px/+8Ifp7u5mfn6eVCpFJBLB7/fz0ksv8dRTT5FMJkmn0wwPD2/2Td5uuihdj2zqH77idauZMFI2RdbWaotSZVNkbbk5m6o3VW96vd5cywmjNqDNWvuqMSYKnAT+CfBnQNpa+9+uevwe4EfAfcA24CjQY61913ffKwG+ns7OTh566CGamprw+Xzs27ePhx9+mLa2NjKZDGfPnuXs2bOkUikAstkso6OjXLhwgePHj5PJZN7xmou/29s5M2qMwRhTOb5aOBzmwIED7Ny5k127dlFbWwtAU1MTe/fu5a677qKmpobx8XGOHDnC2bNnAUilUhw5csTL196upihd82zqH77idaucMFI2RdbQLRSlyqbIGnJzNlVvqt70er25Zp+SZq0dA8bKxzPGmDeB9us85THgb621s8Bbxpg+FsL8+9U00CuGh4f5wQ9+UPk+FAoRjUYJBAIAtLW18dhjj3HPPfcQCASw1lIoFKivrycWixGLxXAcp/L8mZkZJiYm6OvrY2RkhImJCSYmJkin06sKs+M43HHHHbS1tbFjxw5aW1tpa2ujqamp8phSqUQymWRycpJ0Oo3f78dxHPL5PK+88grf/va3GRoaAmB+fp6ZmZkNfR1otSmbIu6kbIq4k7Ip4k7K5vpQvSmrccMJo6WMMV3APcAx4I+BLxhj/hw4AfyVtfZtFsL98pKnXeIagTfGfA743Kpa7QH5fH7Z4B4dHeXkyZPLHuM4Du3t7ezcuZPW1tZlAYaFPwKdnZ3s37+/Mnscj8eJRCLXnK29llKpRDabZXJykpGREQYGBhgYGODo0aPvWLJXLBaZmJjgwoULjI2NbeolfW6zVtkMUbum7RbZ7JRNEXdSNkXcSfXm+lG9KSux4k2vjTER4EXgq9banxhjtgJTLFxn+p9ZWEb4L4wxfwO8bK39P+XnfQ/4pbX2qeu89iSQKb+eF8Xxbt/B2/1f7Pt2a+2W1bzAGmdzBji3mnZtEhqb3qVsupuXx6eX+w4L/a9bbS5B2VxjXh6fXu47KJtu5+Xx6eW+wy1mc0UrjIwxAeBp4IfW2p8AWGsnltz/v4Cflb8dATqXPL2jfO5dWWu3GGNOuPGTaNaDl/sO3u7/rfZ9rbMJnPPq7wY0Nr3ad1A23c7L49PLfYdK/7tu4fnK5hry8vj0ct9B2XQ7L49PL/cdbj2bzo0eYBbWkn0PeNNa+7Ul59uWPOyfAmfKx88CnzDG1BhjuoFdwPHVNlBErk3ZFHEnZVPEnZRNEXdSNkXcayUrjP4Y+GfAaWPMqfK5LwOfNMb8EQtLBAeBfwVgrT1rjHkSeAMoAJ+/3o71IrJqyqaIOymbIu6kbIq4k7Ip4lIr+ZS0/wdca8eqX1znOV8FvnqTbfnOTT5+M/Fy38Hb/V9139cpm17+3YC3++/lvoOy6XZe7r+X+w7Kptt5uf9e7jsom27n5f57ue9wi/1f8abXIiIiIiIiIiLiDTfcw0hERERERERERLxFE0YiIiIiIiIiIrJM1SeMjDEfMcacM8b0GWO+WO32rAVjzP82xlw2xpxZcq7JGPO8MeZC+WusfN4YY75Rfj9eN8bcW72W3zpjTKcx5gVjzBvGmLPGmL8on9/0/TfGhIwxx40xveW+/6fy+W5jzLFyH//OGBMsn68pf99Xvr+ryu1XNjfp2ARlU9l0N2VT2VQ23cfLuQRlU9l0Ly9n08u5hHXKprW2ajfAB/QDO4Ag0AvsqWab1qif/wi4Fziz5Nx/Bb5YPv4i8F/Kx38C/JKFjd/uB45Vu/232Pc24N7ycRQ4D+zxQv/LfYiUjwPAsXKfngQ+UT7/LeBfl4//DfCt8vEngL+rYtuVTbt5x2a5P8qmVTbdelM2lU1l0303L+ey3Cdl0yqbbrx5OZtezmW5P2uezWp38P3Ar5Z8/yXgS9V+49eor11Xhfgc0FY+bgPOlY+/DXzyWo/bDDfg74EPea3/QC3wKvA+YArwl89XMgD8Cnh/+dhffpypUnuVTeuNsbmkP8qmsumqm7JZ6Y+yqWy65qZcLnsvlE1l0zU3ZbPSH0/mstyXNclmtS9JaweGl3x/qXzOC7Zaa8fKx+PA1vLxpn1Pykve7mFh5tMT/TfG+Iwxp4DLwPMs/A9H0lpbKD9kaf8qfS/fPw00r2+LKzbV7+EmeWJsLqVsKpsbhCfG5lLKprK5AXhiXF5N2VQ2NwBPjMulvJhLWPtsVnvCSAC7MMVnq92OtWSMiQBPA39prU0tvW8z999aW7TW/hHQAdwH7K5yk+QmbOaxuUjZVDY3os08Nhcpm8rmRrOZx+VSyqayudFs5nG5yKu5hLXPZrUnjEaAziXfd5TPecGEMaYNoPz1cvn8pntPjDEBFgL8Q2vtT8qnPdN/AGttEniBhSWBjcYYf/mupf2r9L18fwNwZZ2bumhT/h5WyDNjU9lUNjcYz4xNZVPZ3EA8NS6VTWVzA/HMuFQuF6xVNqs9YfQKsKu8i3eQhY2Xnq1ym9bLs8Cny8efZuF6y8Xzf17ewf1+YHrJcroNxxhjgO8Bb1prv7bkrk3ff2PMFmNMY/k4zML1tG+yEOQ/LT/s6r4vvid/Cvzf8ox4NSibCzbl2ARlU9nckDb92ARlU9nccDb9uFykbCqbG8ymH5fg7VzCOmVzvTdjuvrGwk7l51m41u7fV7s9a9THHwFjwDwL1xB+loVrBX8NXACOAk3lxxrgf5Tfj9PAoWq3/xb7/gEWlgC+Dpwq3/7EC/0HDgCvlft+BvgP5fM7gONAH/BjoKZ8PlT+vq98/44qt1/Z3KRjs9wfZVPZdO1N2VQ2lU333bycy3KflE1l05U3L2fTy7ks92fNs2nKTxQREREREREREQGqf0maiIiIiIiIiIi4jCaMRERERERERERkGU0YiYiIiIiIiIjIMpowEhERERERERGRZTRhJCIiIiIiIiIiy2jCSEREREREREREltGEkYiIiIiIiIiILPP/AX0lUDXqJUJEAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x288 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "shape: torch.Size([1, 1, 208, 243, 254])\n",
            "val_dice: tensor([0.8960], device='cuda:0')\n",
            "Hausdorff: tensor([5.], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(61722, device='cuda:0')]\n",
            "Volume label: [tensor(51109, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 257, 301, 241])\n",
            "val_dice: tensor([0.9451], device='cuda:0')\n",
            "Hausdorff: tensor([194.9897], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(72541, device='cuda:0')]\n",
            "Volume label: [tensor(39505, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 251, 303, 234])\n",
            "val_dice: tensor([0.9485], device='cuda:0')\n",
            "Hausdorff: tensor([9.4340], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(70902, device='cuda:0')]\n",
            "Volume label: [tensor(63166, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 208, 247, 214])\n",
            "val_dice: tensor([0.9111], device='cuda:0')\n",
            "Hausdorff: tensor([130.5144], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(52858, device='cuda:0')]\n",
            "Volume label: [tensor(22918, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 191, 241, 231])\n",
            "val_dice: tensor([0.9092], device='cuda:0')\n",
            "Hausdorff: tensor([136.8722], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(55671, device='cuda:0')]\n",
            "Volume label: [tensor(44010, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 206, 239, 241])\n",
            "val_dice: tensor([0.9109], device='cuda:0')\n",
            "Hausdorff: tensor([40.1622], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(57599, device='cuda:0')]\n",
            "Volume label: [tensor(51601, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 209, 252, 341])\n",
            "val_dice: tensor([0.9495], device='cuda:0')\n",
            "Hausdorff: tensor([150.0766], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(85932, device='cuda:0')]\n",
            "Volume label: [tensor(48847, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 222, 302, 296])\n",
            "val_dice: tensor([0.9187], device='cuda:0')\n",
            "Hausdorff: tensor([125.0840], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(89392, device='cuda:0')]\n",
            "Volume label: [tensor(43028, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 197, 264, 384])\n",
            "val_dice: tensor([0.9554], device='cuda:0')\n",
            "Hausdorff: tensor([2.8284], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(101376, device='cuda:0')]\n",
            "Volume label: [tensor(44519, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 203, 253, 246])\n",
            "val_dice: tensor([0.9514], device='cuda:0')\n",
            "Hausdorff: tensor([3.7417], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(62238, device='cuda:0')]\n",
            "Volume label: [tensor(44746, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 222, 250, 216])\n",
            "val_dice: tensor([0.9607], device='cuda:0')\n",
            "Hausdorff: tensor([30.2324], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(54000, device='cuda:0')]\n",
            "Volume label: [tensor(50147, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 114, 217, 300])\n",
            "val_dice: tensor([0.9488], device='cuda:0')\n",
            "Hausdorff: tensor([5.], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(65100, device='cuda:0')]\n",
            "Volume label: [tensor(155157, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 225, 255, 224])\n",
            "val_dice: tensor([0.9392], device='cuda:0')\n",
            "Hausdorff: tensor([112.9690], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(57120, device='cuda:0')]\n",
            "Volume label: [tensor(48556, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 193, 278, 231])\n",
            "val_dice: tensor([0.9260], device='cuda:0')\n",
            "Hausdorff: tensor([35.9166], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(64218, device='cuda:0')]\n",
            "Volume label: [tensor(30015, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 228, 251, 234])\n",
            "val_dice: tensor([0.9699], device='cuda:0')\n",
            "Hausdorff: tensor([5.0990], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(58734, device='cuda:0')]\n",
            "Volume label: [tensor(263472, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 211, 252, 278])\n",
            "val_dice: tensor([0.9560], device='cuda:0')\n",
            "Hausdorff: tensor([6.7082], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(70056, device='cuda:0')]\n",
            "Volume label: [tensor(252714, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 194, 240, 238])\n",
            "val_dice: tensor([0.9567], device='cuda:0')\n",
            "Hausdorff: tensor([5.7446], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(57120, device='cuda:0')]\n",
            "Volume label: [tensor(239205, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 250, 262, 316])\n",
            "val_dice: tensor([0.9594], device='cuda:0')\n",
            "Hausdorff: tensor([121.7662], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(82792, device='cuda:0')]\n",
            "Volume label: [tensor(401099, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 182, 241, 218])\n",
            "val_dice: tensor([0.9484], device='cuda:0')\n",
            "Hausdorff: tensor([7.0711], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(52538, device='cuda:0')]\n",
            "Volume label: [tensor(107901, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 187, 218, 211])\n",
            "val_dice: tensor([0.9272], device='cuda:0')\n",
            "Hausdorff: tensor([27.3861], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(45998, device='cuda:0')]\n",
            "Volume label: [tensor(183902, device='cuda:0')]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x288 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "shape: torch.Size([1, 1, 208, 243, 254])\n",
            "val_dice: tensor([0.8960], device='cuda:0')\n",
            "Hausdorff: tensor([5.], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(61722, device='cuda:0')]\n",
            "Volume label: [tensor(51109, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 257, 301, 241])\n",
            "val_dice: tensor([0.9451], device='cuda:0')\n",
            "Hausdorff: tensor([194.9897], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(72541, device='cuda:0')]\n",
            "Volume label: [tensor(39505, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 251, 303, 234])\n",
            "val_dice: tensor([0.9485], device='cuda:0')\n",
            "Hausdorff: tensor([9.4340], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(70902, device='cuda:0')]\n",
            "Volume label: [tensor(63166, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 208, 247, 214])\n",
            "val_dice: tensor([0.9111], device='cuda:0')\n",
            "Hausdorff: tensor([130.5144], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(52858, device='cuda:0')]\n",
            "Volume label: [tensor(22918, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 191, 241, 231])\n",
            "val_dice: tensor([0.9092], device='cuda:0')\n",
            "Hausdorff: tensor([136.8722], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(55671, device='cuda:0')]\n",
            "Volume label: [tensor(44010, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 206, 239, 241])\n",
            "val_dice: tensor([0.9109], device='cuda:0')\n",
            "Hausdorff: tensor([40.1622], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(57599, device='cuda:0')]\n",
            "Volume label: [tensor(51601, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 209, 252, 341])\n",
            "val_dice: tensor([0.9495], device='cuda:0')\n",
            "Hausdorff: tensor([150.0766], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(85932, device='cuda:0')]\n",
            "Volume label: [tensor(48847, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 222, 302, 296])\n",
            "val_dice: tensor([0.9187], device='cuda:0')\n",
            "Hausdorff: tensor([125.0840], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(89392, device='cuda:0')]\n",
            "Volume label: [tensor(43028, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 197, 264, 384])\n",
            "val_dice: tensor([0.9554], device='cuda:0')\n",
            "Hausdorff: tensor([2.8284], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(101376, device='cuda:0')]\n",
            "Volume label: [tensor(44519, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 203, 253, 246])\n",
            "val_dice: tensor([0.9514], device='cuda:0')\n",
            "Hausdorff: tensor([3.7417], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(62238, device='cuda:0')]\n",
            "Volume label: [tensor(44746, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 222, 250, 216])\n",
            "val_dice: tensor([0.9607], device='cuda:0')\n",
            "Hausdorff: tensor([30.2324], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(54000, device='cuda:0')]\n",
            "Volume label: [tensor(50147, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 114, 217, 300])\n",
            "val_dice: tensor([0.9488], device='cuda:0')\n",
            "Hausdorff: tensor([5.], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(65100, device='cuda:0')]\n",
            "Volume label: [tensor(155157, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 225, 255, 224])\n",
            "val_dice: tensor([0.9392], device='cuda:0')\n",
            "Hausdorff: tensor([112.9690], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(57120, device='cuda:0')]\n",
            "Volume label: [tensor(48556, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 193, 278, 231])\n",
            "val_dice: tensor([0.9260], device='cuda:0')\n",
            "Hausdorff: tensor([35.9166], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(64218, device='cuda:0')]\n",
            "Volume label: [tensor(30015, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 228, 251, 234])\n",
            "val_dice: tensor([0.9699], device='cuda:0')\n",
            "Hausdorff: tensor([5.0990], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(58734, device='cuda:0')]\n",
            "Volume label: [tensor(263472, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 211, 252, 278])\n",
            "val_dice: tensor([0.9560], device='cuda:0')\n",
            "Hausdorff: tensor([6.7082], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(70056, device='cuda:0')]\n",
            "Volume label: [tensor(252714, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 194, 240, 238])\n",
            "val_dice: tensor([0.9567], device='cuda:0')\n",
            "Hausdorff: tensor([5.7446], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(57120, device='cuda:0')]\n",
            "Volume label: [tensor(239205, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 250, 262, 316])\n",
            "val_dice: tensor([0.9594], device='cuda:0')\n",
            "Hausdorff: tensor([121.7662], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(82792, device='cuda:0')]\n",
            "Volume label: [tensor(401099, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 182, 241, 218])\n",
            "val_dice: tensor([0.9484], device='cuda:0')\n",
            "Hausdorff: tensor([7.0711], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(52538, device='cuda:0')]\n",
            "Volume label: [tensor(107901, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 187, 218, 211])\n",
            "val_dice: tensor([0.9272], device='cuda:0')\n",
            "Hausdorff: tensor([27.3861], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(45998, device='cuda:0')]\n",
            "Volume label: [tensor(183902, device='cuda:0')]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x288 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "shape: torch.Size([1, 1, 208, 243, 254])\n",
            "val_dice: tensor([0.8960], device='cuda:0')\n",
            "Hausdorff: tensor([5.], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(61722, device='cuda:0')]\n",
            "Volume label: [tensor(51109, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 257, 301, 241])\n",
            "val_dice: tensor([0.9451], device='cuda:0')\n",
            "Hausdorff: tensor([194.9897], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(72541, device='cuda:0')]\n",
            "Volume label: [tensor(39505, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 251, 303, 234])\n",
            "val_dice: tensor([0.9485], device='cuda:0')\n",
            "Hausdorff: tensor([9.4340], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(70902, device='cuda:0')]\n",
            "Volume label: [tensor(63166, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 208, 247, 214])\n",
            "val_dice: tensor([0.9111], device='cuda:0')\n",
            "Hausdorff: tensor([130.5144], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(52858, device='cuda:0')]\n",
            "Volume label: [tensor(22918, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 191, 241, 231])\n",
            "val_dice: tensor([0.9092], device='cuda:0')\n",
            "Hausdorff: tensor([136.8722], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(55671, device='cuda:0')]\n",
            "Volume label: [tensor(44010, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 206, 239, 241])\n",
            "val_dice: tensor([0.9109], device='cuda:0')\n",
            "Hausdorff: tensor([40.1622], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(57599, device='cuda:0')]\n",
            "Volume label: [tensor(51601, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 209, 252, 341])\n",
            "val_dice: tensor([0.9495], device='cuda:0')\n",
            "Hausdorff: tensor([150.0766], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(85932, device='cuda:0')]\n",
            "Volume label: [tensor(48847, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 222, 302, 296])\n",
            "val_dice: tensor([0.9187], device='cuda:0')\n",
            "Hausdorff: tensor([125.0840], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(89392, device='cuda:0')]\n",
            "Volume label: [tensor(43028, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 197, 264, 384])\n",
            "val_dice: tensor([0.9554], device='cuda:0')\n",
            "Hausdorff: tensor([2.8284], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(101376, device='cuda:0')]\n",
            "Volume label: [tensor(44519, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 203, 253, 246])\n",
            "val_dice: tensor([0.9514], device='cuda:0')\n",
            "Hausdorff: tensor([3.7417], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(62238, device='cuda:0')]\n",
            "Volume label: [tensor(44746, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 222, 250, 216])\n",
            "val_dice: tensor([0.9607], device='cuda:0')\n",
            "Hausdorff: tensor([30.2324], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(54000, device='cuda:0')]\n",
            "Volume label: [tensor(50147, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 114, 217, 300])\n",
            "val_dice: tensor([0.9488], device='cuda:0')\n",
            "Hausdorff: tensor([5.], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(65100, device='cuda:0')]\n",
            "Volume label: [tensor(155157, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 225, 255, 224])\n",
            "val_dice: tensor([0.9392], device='cuda:0')\n",
            "Hausdorff: tensor([112.9690], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(57120, device='cuda:0')]\n",
            "Volume label: [tensor(48556, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 193, 278, 231])\n",
            "val_dice: tensor([0.9260], device='cuda:0')\n",
            "Hausdorff: tensor([35.9166], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(64218, device='cuda:0')]\n",
            "Volume label: [tensor(30015, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 228, 251, 234])\n",
            "val_dice: tensor([0.9699], device='cuda:0')\n",
            "Hausdorff: tensor([5.0990], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(58734, device='cuda:0')]\n",
            "Volume label: [tensor(263472, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 211, 252, 278])\n",
            "val_dice: tensor([0.9560], device='cuda:0')\n",
            "Hausdorff: tensor([6.7082], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(70056, device='cuda:0')]\n",
            "Volume label: [tensor(252714, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 194, 240, 238])\n",
            "val_dice: tensor([0.9567], device='cuda:0')\n",
            "Hausdorff: tensor([5.7446], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(57120, device='cuda:0')]\n",
            "Volume label: [tensor(239205, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 250, 262, 316])\n",
            "val_dice: tensor([0.9594], device='cuda:0')\n",
            "Hausdorff: tensor([121.7662], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(82792, device='cuda:0')]\n",
            "Volume label: [tensor(401099, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 182, 241, 218])\n",
            "val_dice: tensor([0.9484], device='cuda:0')\n",
            "Hausdorff: tensor([7.0711], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(52538, device='cuda:0')]\n",
            "Volume label: [tensor(107901, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 187, 218, 211])\n",
            "val_dice: tensor([0.9272], device='cuda:0')\n",
            "Hausdorff: tensor([27.3861], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(45998, device='cuda:0')]\n",
            "Volume label: [tensor(183902, device='cuda:0')]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x288 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "shape: torch.Size([1, 1, 208, 243, 254])\n",
            "val_dice: tensor([0.8960], device='cuda:0')\n",
            "Hausdorff: tensor([5.], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(61722, device='cuda:0')]\n",
            "Volume label: [tensor(51109, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 257, 301, 241])\n",
            "val_dice: tensor([0.9451], device='cuda:0')\n",
            "Hausdorff: tensor([194.9897], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(72541, device='cuda:0')]\n",
            "Volume label: [tensor(39505, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 251, 303, 234])\n",
            "val_dice: tensor([0.9485], device='cuda:0')\n",
            "Hausdorff: tensor([9.4340], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(70902, device='cuda:0')]\n",
            "Volume label: [tensor(63166, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 208, 247, 214])\n",
            "val_dice: tensor([0.9111], device='cuda:0')\n",
            "Hausdorff: tensor([130.5144], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(52858, device='cuda:0')]\n",
            "Volume label: [tensor(22918, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 191, 241, 231])\n",
            "val_dice: tensor([0.9092], device='cuda:0')\n",
            "Hausdorff: tensor([136.8722], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(55671, device='cuda:0')]\n",
            "Volume label: [tensor(44010, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 206, 239, 241])\n",
            "val_dice: tensor([0.9109], device='cuda:0')\n",
            "Hausdorff: tensor([40.1622], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(57599, device='cuda:0')]\n",
            "Volume label: [tensor(51601, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 209, 252, 341])\n",
            "val_dice: tensor([0.9495], device='cuda:0')\n",
            "Hausdorff: tensor([150.0766], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(85932, device='cuda:0')]\n",
            "Volume label: [tensor(48847, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 222, 302, 296])\n",
            "val_dice: tensor([0.9187], device='cuda:0')\n",
            "Hausdorff: tensor([125.0840], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(89392, device='cuda:0')]\n",
            "Volume label: [tensor(43028, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 197, 264, 384])\n",
            "val_dice: tensor([0.9554], device='cuda:0')\n",
            "Hausdorff: tensor([2.8284], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(101376, device='cuda:0')]\n",
            "Volume label: [tensor(44519, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 203, 253, 246])\n",
            "val_dice: tensor([0.9514], device='cuda:0')\n",
            "Hausdorff: tensor([3.7417], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(62238, device='cuda:0')]\n",
            "Volume label: [tensor(44746, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 222, 250, 216])\n",
            "val_dice: tensor([0.9607], device='cuda:0')\n",
            "Hausdorff: tensor([30.2324], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(54000, device='cuda:0')]\n",
            "Volume label: [tensor(50147, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 114, 217, 300])\n",
            "val_dice: tensor([0.9488], device='cuda:0')\n",
            "Hausdorff: tensor([5.], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(65100, device='cuda:0')]\n",
            "Volume label: [tensor(155157, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 225, 255, 224])\n",
            "val_dice: tensor([0.9392], device='cuda:0')\n",
            "Hausdorff: tensor([112.9690], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(57120, device='cuda:0')]\n",
            "Volume label: [tensor(48556, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 193, 278, 231])\n",
            "val_dice: tensor([0.9260], device='cuda:0')\n",
            "Hausdorff: tensor([35.9166], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(64218, device='cuda:0')]\n",
            "Volume label: [tensor(30015, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 228, 251, 234])\n",
            "val_dice: tensor([0.9699], device='cuda:0')\n",
            "Hausdorff: tensor([5.0990], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(58734, device='cuda:0')]\n",
            "Volume label: [tensor(263472, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 211, 252, 278])\n",
            "val_dice: tensor([0.9560], device='cuda:0')\n",
            "Hausdorff: tensor([6.7082], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(70056, device='cuda:0')]\n",
            "Volume label: [tensor(252714, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 194, 240, 238])\n",
            "val_dice: tensor([0.9567], device='cuda:0')\n",
            "Hausdorff: tensor([5.7446], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(57120, device='cuda:0')]\n",
            "Volume label: [tensor(239205, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 250, 262, 316])\n",
            "val_dice: tensor([0.9594], device='cuda:0')\n",
            "Hausdorff: tensor([121.7662], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(82792, device='cuda:0')]\n",
            "Volume label: [tensor(401099, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 182, 241, 218])\n",
            "val_dice: tensor([0.9484], device='cuda:0')\n",
            "Hausdorff: tensor([7.0711], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(52538, device='cuda:0')]\n",
            "Volume label: [tensor(107901, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 187, 218, 211])\n",
            "val_dice: tensor([0.9272], device='cuda:0')\n",
            "Hausdorff: tensor([27.3861], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(45998, device='cuda:0')]\n",
            "Volume label: [tensor(183902, device='cuda:0')]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rmAGNTaUKTJwYSZOQBXgqjU2lpqGQY1PzTXWpM8bMKDbnq2BUx/jT4bUBu8euICIfBj48T8+v1FJzegGe46JxCeNj00eA3XLDAgxNqcL0lHlIY1OpAvNr87OFeiqNTaWmoZBjUyk1M4vW9NoY81VjzE5jzM7FGoNS6nxjY9ONd7GHo5TK0dhUqjBpbCpVmDTfVGr25qtg1A40jLlen7tNKbV4NC6VKkwam0oVJo1NpQqTxqZSC2S+CkYvAmtFpFFEPMD7gEfn6bmUUlOjcalUYdLYVKowaWwqVZg0NpVaIPPSw8gYkxaRPwOewDrV4TeNMYfm47mUUlOjcalUYdLYVKowaWwqVZg0NpVaOPPV9BpjzP8B/s98Pf5y43a7cTqd9nURq4m5MVZD/2w2SzabJZ1OL8r41PKgcalUYdLYVKowaWwqVZg0NqdP8001E/NWMFITq6ysxOv1kkgkiMfjJBIJAGpqaqiurrbXczqdGGPsoB0ZGSESidDR0UE2m8XpdOL1enE6nbjdbjweD9lsllgsRiwWW6yXNyGXy0UwGMTr9eJyuUilUiSTSRKJBCMjI4s9PKWUUkoppZRaFjTf1HxzLmnBaJ5VVVXR0NDA4OAgZ8+etYMtk8ngcDhwOBwkk0laWlpoaWmZ1mM7HA6cTiculwu32002m8XhcODxeCguLgaguLiYYDBIPB5naGiIvr4+u4o8F3w+H/X19RQVFZFIJEin04yOjpJOp3G5XCSTSdLpNIFAwB6riOB0OvH5fBQVFVFUVITL5cLn8+Hz+RgeHubUqVMkk8k5G6dSSimllFJKLTeab2q+OZ+0YDSHHA4HIoLX6+Utb3kLJ06coKuri9HRUbvKGYlE5uS5MpkM0Wh0wmUiQiQSQUQYGRmhr69v3PRCn8+Hw+HA5/Ph8XjweDz4fD6y2SzxeNzeCLhcLlyuNz8iTqeTRCLBwMAA0WiUdDpNMpmko6MDp9NJNpvFGEMmk8EYg4jYUxsHBwfHbThExN6A5V+H0+nE4XBgjMHpdLJt2zaqqqqIRCLs37+fbDaLiOg0SaWUUkoppdQlR/NNzTcXmhaM5kBZWRnGGMLhMB6PhzfeeIOf/exn9vJ4PL6g4zHG2B/yVCp13vL8eOZqet5MHicf6JlMZsIxAhw4cMD+u6SkhNWrV+Pz+Xj11VftaZajo6MzHrdSSimllFJKFTrNNzXfXCxaMJqFkpISampqSKfTxONxOjs75+0DJSL4fD772Mx8w7J8EOQrqNlslkwmQzKZHHeZC2Ofc6ENDQ3ZAe1yuSgvL0dEqKqqIpvNcubMmQUfk1JKKaWUUkrNF803F47mmxPTgtEMbd++nfb2drq7uxkaGiKbzc76MUUEEcHj8VBeXk55eTkVFRUUFxdTWlrKihUrCAQCpFIpMpkMHo+H0dFRe2pi/jGy2SxDQ0OMjo4yMDBAa2srsViM4eFhBgcHSSaT0zquND8ut9sNYE/h8/v9GGPw+XwABAIBu4I7ODhoV3LzzzVXx7Km02laW1sBa2PicrlYv349nZ2dDA8Pz+kxs0oppZRSSim10DTf1HyzEGjBaBoCgYB9rOWBAwdm/UHJN97y+/3U1dWxevVqGhoaCIfDOJ1ORISioiL7OFW/3080GrWn14kIqVTKrsSKCC6Xi3Q6TX19Pel02p4qGI1GiUajDA4O0traSktLC/39/QwPD583Rc/j8VBSUkIoFKKiooKamhpWrFiB2+22Nx75hmE+n2/csaf5qnM8Hicej9td9IeHh+nv76etrY1oNMro6Cijo6Oz3vDlj9EdGBgAoLGxkba2Nj0lpFJKKaWUUmpJ0XxT881CowWjKQgEAjgcDkpKSmhvb5/VYzkcDsLhMNXV1dTX11NVVUV9fT3hcBiwgjp/GsP81L7i4mJcLpd9SkSv12svywdV/rSIY5uIZbNZvF6vXZUtKiqitraWNWvWkE6n6erq4siRI7S2tjI0NMTg4CBut5vLL7+cG264gfLycnvKIVgVW4/HY29cEomE/Xe+IVk+oLPZLIlEwm5IZoyxO/TnO/h3dHTQ1dVFV1cX/f39s9og5u978uRJSkpKSKfTFBcXMzg4OOPHVEoppZRSSqn5pvmm5puFSgtGF+FwOPB6vYyOjs4qeEOhEKtXr2bDhg3U1dURCoXGVUvzpyhMpVLEYjEcDod9P5fLZVd586cRzHd3d7vd9vr5rvJ+v98eez7wPR4PqVTKru663W6amppobGwklUrR29vLiRMnGB0dZc2aNVRXV9uPLSL2satgbWT8fr89NTG/LF9lzU8TzAe5McY+vWH+fnV1dWzdupXh4WG6urpobW3lwIEDdHR0zPJ/zDr+FKwNbyAQQESIxWKzflyllFJKKaWUmkuab2q+Wci0YDQJh8NBU1MTp06dOu8UfReTn/oXDofZvHkztbW1lJeXEwqF7EDKV0qTySTxeNw+PjOZTOJwOMhkMni9XtxuN4ODgwwMDJBIJOwKr8/nQ0QoLi62K675ymoymSSVSjEyMmJXYvPHWr797W9n7dq1hEIhhoaGGBkZwe12U1RUxOrVq0kkEhw7doyenh4SiYTd4d7tdtPX10d3dzcul4uKigq7ipsP2GQySSgUwhhjP3dJSYn9fuar0vljUx0OB36/nzVr1tDc3Mzu3bs5duwYhw4doqWlhVgsNmlH+6nId9Ovrq62pzYuRgM1pZRSSimllBpL803NN5cCLRhNoLy8nFgsxokTJ6YVuH6/n6qqKhobG1m7di319fV2pTN/yVdE81PnUqkUHo+HRCJhH185PDxMX18f8Xgcl8tlV5z9fj8+nw+3221XcaPRKH19fXZjMZ/PRyqVor+/H4fDwdmzZzl9+jTxeJw/+IM/4NZbb7UrwhMpLi5mxYoVEy577LHH+NKXvkQymSQcDlNTU0NZWZm9EcpXoiORiD1FLxaL4fV67WNd0+k0LpcLp9NJKpWyNzr5rvzbtm1j3bp19Pf38/rrr3PkyBE6OztnFchdXV04HA5KS0uJRqNz1sVfKaWUUkoppaZL803NN5cKLRidw+Px2NPwpmPVqlVs376d1atX25Xd/IfW4XDYldGxTbdExJ4qWFJSQmNjI8FgkOHhYfbv38+RI0cYGRkhHo+TTqcZHh5mYGCAZDJpd4jv6uqym5CBVUVtaWnB5/NRXl5OW1sbHR0drF27lne/+93jgjeZTOLxeM57LfkKbr4bfd5VV12Fz+fjwIEDBAIBjDG0trYSj8ftBm2JRILBwUFGRkbw+XwMDQ3h9/tZv349oVDIfuxQKGS/Lx6PB7fbjdPpJJPJ4HA4qK+vp7q6mq1bt3Lq1ClaWlo4c+YMAwMDM2oulu/kn2+WppRSSimllFILTfNNzTeXkuX1amYhP+UuHo9PuXGViFBaWsqePXtobm4mEAjg9Xrtyu7IyAjGGMrKynA6naTTaZxOJ/F4nGAwyFVXXcVll12GiOB0OvF6vfZxpWfPnqWlpYWRkRF6enoYGBggGo0C2MeO9vb2UlxcjM/nY3h4GLfbjcvlIhqNIiK0trbS3d1NKpWipqaG0tJSe+yjo6N0dnYSj8cpKyujoqICgPb2dvu4Tp/PR0VFBbW1tXbztMrKShKJBKOjo7S1tRGJROwO9PnlLpeLkZERuru77Q2Yx+PB5/ONG29+ul5RUdG4MwLkG7CNjo7icrlYt24dq1evpr+/n97eXk6dOmWf0jBfNZ6KsVMEPR7Psqr8KqWUUkoppQqX5puaby5FWjDC+qB6vV67edVUOJ1ONm7cyJ49ewiFQvap/dLptN34CqwmYqtWrcLlclFeXk5tbS1NTU3jqrRjRSIR9u/fz7333mtPjRsdHSWTyRCNRu2Ayevr60NECAaDxGIxampqCAaDdoOzgYEBRkZGOHLkCHv37qWuro7i4mL8fj9NTU0MDw/bG5B8BXpwcBCfz0cwGKSiosI+deFrr71Ge3s7Pp+Puro6PB4PgUCAyspKuwv+Sy+9hMvlIhAI2N3281Xo/PGu4XCYQCAAWMffDg8PE41G8fv9lJaWUlVVRSgUoqyszG625nK5qKqqYsWKFaxZs4Z4PE5fXx+HDh3i7Nmz9Pf3T+t40eUQvEoppZRSSqnCp/mm5ptLlczm1HJzNgiRRRtEPvjy1dSpKC0tZceOHWzZssU+5WC+2prvQF9WVsbWrVupr69n48aN9n2NMUQiEeLxOCtWrBg3Ze3YsWPcc889/OQnP6Gvr49YLDZuSuGFOJ1Obr75ZsLhMI8++iihUIg777yT/fv3873vfY9kMkl9fT1/+qd/yu/93u/R2Ng45debyWR44okn+PrXv87TTz8NwOWXX05rayvt7e1UVVWxe/duGhoauOeee8hkMvYxol6vF8A+XWImk7GPs02n0/h8PjweDx6PB2MM2WyWbDbLypUraW5upri42D4OdezUy/yxqOl0mr6+Po4fP87hw4c5e/bslF9XgdlvjNm52IM4V0jCZrfcsNjDUGrRPGUe0thUqsD82vyMiOmXxR7HRDQ21aWskGNT802L5puXbr5pjJlRbF7SM4yCwSBOp5Ph4eEprS8iNDQ0cOONN1JdXY3L5SIej9vd5ePxOG63mx07dvC2t72NkpKS8yq7+Q92NptldHQUEeGb3/wm3/rWtxgcHKS9vX1GDbfyFVmfz0ckEsHtdnPmzBleffVV+/Ha29v54he/yP3338+VV17JW97yFnbu3ElTU5PdSX4i//Iv/8KXvvQl+vr6SKfT1NbWctVVV3HmzBlGR0c5c+YMpaWlHDx40N7gGGPsqYMXkn8PXC4XwWCQbDZLIpGgqamJz3/+8/T19fGrX/2K5557jkQiQV1dHSJCNpvF7XZTXFxMSUkJVVVVrF69mldffZVXXnllyhs+pZRSSimllJoPmm9qvrnUXbIzjIqKivB6vfT3909pfb/fz9atW7n++uvt40LzH/r8FDaXy8V73/teNm7caE8RBGva36FDh+xTBh46dIi7777bPnYzX72cayJywcfNd9IfK9/cLJFI2LdNNL6LPfZsBAIBKioqePzxx9mwYYM9tfDFF1/k0KFDpFIp0uk0kUgEEcHtdmOMIR6Pk0gk2LdvHy+++CKRSGRexjdPdBaDUgVIZxgpVXgKeRaDxqa6lBVybGq+qfnmuWO41PJNnWE0TU6nc8rBW1JSwlVXXcW2bduAN6uUY0/bNzw8THV1NeFwGBEhmUzygx/8gNdff522tjYefvjhKTc3mysXC7CJAnOqUyXns9A4MjLC6dOn+eM//mM++clP8s53vhOv18s111zDNddcgzGG06dP09bWRm9vLydPniSRSNgbpD179lBZWcmzzz5LW1vbvI1TKaWUUkoppSai+abmm8vBJTnDKH/6wYsRETZv3szOnTuprKzE6XTap+LLnwoxf4xkT08PZWVl7N69m/vuu49YLMbRo0fp6+tbgFe0fNXV1dHY2MgjjzxCaWnpeVMuR0dHGRgYoKWlhVdeeYW2tjbi8Tg+n4+2tjZefPFFjhw5skijnxadxaBUAdIZRkoVnkKexaCxqS5lhRybmm+qyVwq+eZMZxhdUgWj/GkJ4/H4BY91FBFKSkq49tpr2bJlCz6fz254lUgkMMbYU+gcDgciwtDQkP1hmcrGQU2Pz+dj9+7d/PVf/zVVVVVs3bp13HJjDJlMhkcffZSXX37Z3tCOjo7y1FNPcfDgwWl1tV8EmpQqVYC0YKRU4SnkpFRjU13KCjk2Nd9UF7Pc8009JG0KnE6n3fxrMiLCpk2buPrqq1m9ejWZTIZUKmV3U3c4HMRiMfvD4HA4OH78OK+//jpnzpxZqJdyyYnH4+zdu5e9e/dSVFTEZz/7Wa677jouv/xyALuR2bve9S5SqRSHDh3C4/Hgdru56aabcLvd7Nu3b5FfhVJKKaWUUmq50nxz6dJ8c2KXVMHI4XAwNDR0wXU2b97MjTfeSDAYJJlM4vV6iUajiAgOh4NkMmk3HEskEhw+fJhf/epX45p2qfkVi8W488472bJlC01NTWzatIkvfOELgNUsbtOmTRw7dgyn08nIyAhut5srr7yS4eFhjh49usijV0oppZRSSi1Hmm8uD5pvvumSORblgZ4AACAASURBVCStvLycvr6+SZtnORwOrrjiCq6//nrcbjc+n49UKoXD4WB0dJS+vj5cLhcVFRVEo1H6+/t59tlnOX36dEFPPbsUuFwuSkpKcDgcfPCDH7T/z/x+Py6Xi5GRETKZDLFYjFdffZVDhw5NudnaAtLDXpQqQHpImlKFp5APe9HYVJeyQo5NzTfVbCyHfFMPSbsAr9fLwMDApMErImzZsoXrr7+eYDAIWB8Kr9eL0+mkuLiYWCyG3+8nlUrR1dXFo48+SiwWW8iXoSaRTqftZm9333233QyuubkZt9ttX4qKirj66qtpaGjg+eefp729fZFHrpRSSimllFrqNN9c3i7lfPOSKBhdzKZNm7j++uspLi62T12YP13e+vXryWazfP/736elpYXOzk5++ctfavAWsIGBAV544QWCwaD9fwrWhtzr9dLY2EhtbS0///nPeeONN0gmk4s8YqWUUkoppdRypfnm8nIp5ZuOxR7AfMt3L59sGl91dTXXXnstK1asIJPJMDIyAsBb3/pWrr/+ehobG2lqaqK5uZne3l6efPJJBgcHF/IlqBkYHBzkiSeeoL29HRGht7eXgwcP0t3dTUtLC36/nxtuuIErrrgCh2PZh4FSSimllFJqHmi+eWm6VPLNpTvyKQoEApOedtDv93PdddfZx5smEglCoRA33XQTN954Ix6PB7CmEJ49e5bHH39cm40tIZFIhOeee47Tp09TXV1NUVERZ86cIRqNcurUKaLRKNu2bWPNmjV4vd7FHq5SSimllFJqidF889J1KeSby/qQtHA4TH9//4TLnE4nl19+OfX19QwODlJWVkZlZSV79uyxT52XF4lEeOKJJy54ekRVmGKxGPv370dEqK6uxul04vV6KSoqore3l3A4zJ49ewgGgxw4cGDSjb1SSimllFJKjaX5plru+eaynWEkIhf8zwiHw+zYsYNMJoPb7aampoa3vvWtbNu27bx1H3vsMfbu3Tufw72g+ZzCtpSnx01VNBrlN7/5jX38aHl5ORs2bGDDhg04HA6i0SiVlZVUVlYiUpAndlBKKaWUUkoVEM03F/+xC8VyzjdnNcNIRFqAYSADpI0xO0UkDHwfWA20AL9jjBmY3TCnb8WKFQwPD0+4zOFwcM0115BOp/F6vaxZs4bdu3ezdevW89bt6+vjwQcfXLTT4okIa9eu5ejRo/Py+E6nExFZ9qdqHBkZ4ejRo+zatYv3vOc93HTTTbS0tPDss8+yb98+jDFs3rwZEaGjo2PSMxwsFYUcm0pdyjQ2lSpMGptKFaZCjk3NN6dG882lnW/OxSFp/84Y0zvm+ieAnxljvigin8hdv2sOnmfKPB4P0Wh00uM/Gxsb8fl8pFIpysvLqaurY/v27ROu+8UvfpFHHnlkPod7QcaYWQev2+2mtrYWv99POp0mkUiQTCbtpmupVGrZBzBY0wWHh4epr6/H6XTS3NxMXV0dK1euZO/evZw6dYrR0VFGR0cnnVq6xBRcbCqlAI1NpQqVxqZShangYlPzzfE037Qsx3xzPnoYvRu4Lvf3t4FnWOAAzmQyZLPZCZd5vV6ampro7++nsrKSmpoarrzyyvPWM8bw8Y9/nHvuuWe+hztvampq2L17t91oq7S0lHQ6TSQSoaurizNnznDgwAHa2tpob28nlUot9pDn3aFDh8ad1tDn8/G2t72NQCDAQw89xNmzZ6mvr7ffp2Vm0WNTKTUhjU2lCpPGplKFadFjU/NNi+ab51tu+eZsC0YG+KmIGOBfjTFfBaqMMZ255V1A1UR3FJEPAx+e5fNP9LgUFxczNDQ04fKKigpGR0dxu91UVlayfft2Kisrx60zMjLCpz71Kf7xH/9xSVZCw+EwN910E3/0R3/ElVdeSVFRkT0VMD/1LR6P09HRwfHjx3nhhRf4xS9+wb59+4jFYpNu/JaD0dFRDh48yI4dO+zjR51OJ7t376a/v5+enh5EhP7+fqLR6FJ+L+YkNn0EFmKsSl1KNDaVKkwam0oVJs03C5Dmm5NbbvnmbAtG1xhj2kWkEnhSRI6MXWiMMbngPk8u2L8KMNk6MxEIBCYNXrfbTXl5OcYYNm3aREVFBeFweFzjqUwmw6c//Wn+4R/+Ya6GtKBqamq48847ee9738uqVavOW55/rYFAgDVr1lBbW0tjYyOrVq2iurqa559/ntOnTy+ZYypn4qMf/Sjvf//77dNYgvW+7Nq1i5deegkRIRKJ0N/fz8jIyCKOdFbmJDZDEl6+HwSlFofGplKFSWNTqcKk+WaB0Xzz4pZTvjmrgpExpj3371kReRjYBXSLSI0xplNEaoCzczDOKbtQhdblclFcXEw4HKaystL+d6y77rqLL33pS/M9zHkRDAa5/fbbuf3222loaJjSfQKBAGvXrsXv9+NyWR+HRCJBZ2fnRe65dE1WxS0vL+fWW2/lm9/8pt3FvqWlZWEHN0cKMTaVUhqbShUqjU2lClMhxqbmm5pvXsxyyjdnfI47ESkSkWD+b+AdwEHgUeCO3Gp3AD+c7SCnqqys7ILHRbrdbgKBAM3NzWSzWTKZjF31i8fj3HnnnfzTP/3Tkq12bty4kTvuuIO6urpp3c/hcFBfX8/27dvZvXs3u3fvpr6+ftmeAjGZTPLOd76TwcHB85atW7fO3qCVlpZSVla2CCOcnUKMTaWUxqZShUpjU6nCVIixqfmm5ptTsZzyzdnMMKoCHs5NOXMB3zXGPC4iLwIPiMiHgNPA78x+mBfncrkYHh6+YMW3oqKCkpISysrK7ONKwTqG9NOf/vSSbjjm9Xr5wAc+wLp162YUeA6Hg8bGRpqamujo6GB4eBiPx8Pp06fn/Lhap9N5weN+F8LTTz/NXXfdxT333EMg8Ga/Ab/fz/XXX09LS4t9TOnIyMikZ0AoUAUVm0opm8amUoVJY1OpwlRQsan5puab07Fc8s0ZF4yMMSeBbRPc3gfcMJtBzUQwGARgYGBgwuUiQjAYJBwOk81m7c7tDz/8MI8//jhf/epXF3K4c87j8bB9+/ZxH8bpCgQCrFu3jgMHDhAKhWhubiYej9PZ2TmnVXCv10tdXd2iBjDAj3/8Yz74wQ+ye/fucbcXFxezcuVK4vE4IsLg4CDt7e1LpiFdocWmUsqisalUYdLYVKowFVpsar6p+eZ0LYd8c7ZNrwtGIpG44BscDoepqamxm5B1dHRw+PBhEokEL7300gKOdH6sX79+yseRTsbhcFBaWmofZ+vz+Vi/fj0Oh4O2tra5GCZgdY4/fvz4nD3eTLW3t3PbbbfxwgsvUFdXZzdoq6yspKmpiUgkQigUwu/3s3fvXrq6uhZ5xEoppZRSSqnFoPmm5pvTtRzyzWVx0KDH4yEcDl9wGldxcTHV1dU4HA5GRkYwxjA0NERLS0vBTv+ajukeRzqZYDBIbW0twWCQbDbLihUrWL9+PdXV1Tidzjl5DmMMyWRyTh5rttrb29m5cyc/+MEP7F8LPB4PTqeTTCZDKBRi9erVNDU12VNKlVJKKaWUUpcOzTc135yppZ5vLouCUSqVore3d9LlIoLb7aa0tBQRIRqNYozB7/eTSqWWbNOxsSKRCJFIZNaP4/f7CYVCOBwOEokEqVSK4uJitmzZQkNDw5wFcSHp7u7mQx/6EF/+8peJx+P09fVx+vRp3G43yWSSs2fPEo/HZzX9UimllFJKKbU0ab6p+eZsLOV8c1kcklZfX09ra+uEy0QEp9NJdXU1xcXFgBXwDocDEZn0lHdLTUdHxwU79k9VOp0mk8mQzWbxer04HA4ymQwNDQ0Eg0Eymcyk7/VSFolE+NKXvkQgEKCyspIzZ84AEAqFCIfDuN1unE4nIrIsNvhKKaWUUkqpqdF8U/PN2Vqq+eayKBil0+kJb883uxocHKSiogKn00k8HieRSODxeIhGo/T19S3waOdHa2srLS0trFy5clan5kun0wwNDRGPx+3bamtrcbvdhMNhuxJ89uzZuRh2QRkaGuLv/u7v2L59Oxs2bKC4uJhYLEYgEKC2tpaTJ0/i8/kYHR1d7KEqpZRSSimlFojmm5pvzoWlmG8u+YKR2+2ms7NzwmWJRIJYLEZRURHV1dUYYxgdHSWdTtsNpwpNOBwmHA5Pu0lXOp3m2LFjXH755ZSUlMzoVIdgnfKxs7OTRCJBIBCgvr6ehoYGSkpKSKVSVFZW4nQ6eeqpp+jv75/RcxSy3t5eTp48OW4j6PV6aWpq4ujRo+M2bEoppZRSSqnlTfNNi+abc2Op5ZtLvmAUDofp6emZdKpfX18f27Zts09vGIvFACu4L3Qc6mJ517vexZ49e/jYxz42rftls1kOHjzI1q1bKS8vn9Hxj9lslo6ODtrb2wkEAuzcuZONGzfS0NCAy+XC4/HQ0dFBdXU1HR0d/PKXv5z2cywFIyMjnDlzhkQiYU8t9fl8NDU1XbB7v8PhWDZTTpVSSimllFKab+Zpvjl3llK+ueQLRpFI5IJvmtPppLKykrKyMs6ePYvX6yWTyTA4OEh7e/sCjnRqWltbERG2bdvGgQMHpny/bDbLkSNHeO2111i1ahVr166ddtW3r6+P/fv3E4lE2LVrF1dffTXr168nGAwiIogIFRUVVFdXk8lk6O3t5ciRI9N9iQXNGGNv5Pv6+ggGg3i9XkpKSqitraWiooJYLDbhKTW1WKSUUkoppdTyovmmRfPNubHU8s0lXTByuVwXPV2ez+ejurra7kCeTCZxOp0F263+F7/4BR0dHfT09EzrfvnTNuarjqlUCqfTics1tf/ikZERfv3rX/P6668TDofZs2cPGzdutBu35QWDQYqKivB4PJw4cYK777570mN6lyqn00lpaSkA0WiUQCBgn/KwoqKC9vb2CQNYKaWUUkoptXxovvkmzTfnzlLKN2d24GGB8Pl8uN3uC65TVFRERUUFqVSKVCpFMpnEGMPg4OACjXL6jh8/ztDQ0LTu4/V6+cxnPsMtt9wCWMfaZrNZIpHIBTdUxhj6+/t5/vnnefLJJ8lkMlxxxRUTBm+ew+GgtraW2267jdWrV09rnEtB/owGfr8fv9+P0+kkk8ngdrsJBoP4/f7FHqJSSimllFJqnmm++SbNN+fOUso3l/QMo1gsNumHMz8VcOfOnZSUlJBOp+3/iEgkUvBd1/PT+6Y67ezP//zPue2223A6nfT19dHX10dxcTGRSIQTJ05QX1+PMQafz4fH4yGRSDAyMsKRI0d45ZVXePHFF+nr6+Pd7343V1111aTBm+dyuWhubuaOO+7gb/7mb2b9egtNPB63dw7GGDugS0tLCYfDDA4OFuQvBkoppZRSSqm5ofnmmzTfnFtLJd9csgUjh8OBx+OZtIu4MYZAIMDmzZsJBAK4XC4aGhrYsmULL7/8Mt3d3Qs84qlzOp3s3r0bh8PBvn37LtopfdWqVXzkIx+xpwOWl5eTTqdpa2ujp6eH559/nlQqhdfrxefz4XK5iEajnDx5khMnTnD06FF8Ph+7du3iLW95C+Xl5VMaZzAYtJueFWJDt5nKB2a+Wu71eslms2SzWcrKyvB6vfZGUCmllFJKKbX8aL75Js0359ZSyjeXbMHI6XROGsAiQlFREc3Nzfh8PhwOB16vl1tvvZWamhquuOIK7r///oL4D5jIhg0buPPOO6msrOQrX/kK3/ve9y5YXXz/+99PbW3tuNtcLhfl5eV0dnYSCoV45plnAKtbv9/vZ2RkhKGhIXp6ekin06xZs4YbbriBlStXTrl5mYiwcuVKdu7cyeOPPz7j11to4vE4AwMDBAIBu9KbD1i/309NTQ2Dg4N0dHQs9lCVUkoppZRS80DzzTdpvjm3llK+uWQLRqlUatLmV06nk6qqKjZu3GgHcE1NDQ0NDTgcDq644grWrFnDvn37FnjUU3P77bdz3XXXUVpaSnNzM4888gijo6MTrltaWsrll1+O1+slnU6PazoWCATYtGkTAwMDjIyMkEwm7dMVptNpRIRQKMS6devYtWsXGzZsoKSkZFpjra2tZf369Tz33HMMDw/P6nUXipGREVKpFB/96EeJRCKcPn2adDpNOp0mHo9TVlaGz+db7GEqpZRSSiml5onmmxbNN+feUso3l2zTa7fbPWkV1O12U1RURGVlJR6PB4Dt27fblUwR4aGHHlqwsU7Xd77zHVasWGGfovEDH/iAvWzLli1cd911rFixAoDLL7+ca665BrA2XGAdhxqPx3E4HIRCIRobG+0Kb74Jm9/vp7q6mq1bt3LVVVdxxRVXUFdXN+Uu93mBQIDVq1dPO/ALmcPh4Ld+67f47d/+bd73vvexYcMGMpkM6XTa/mwFg8Fpv1dKKaWUUkqppUHzTc0358tSyjeXbMFoKm9evvt4KBSyAzlPROZraLPW19dnb5xcLhcf+tCHuOaaaxAR7r77br7xjW+wfft2wAqgQCBANpu172OMwePx2Lf5/X5SqRRtbW0MDAwQjUYpKipi1apVrF+/nqamJmpqai56BoCJeDweGhsbCYVCc/cGLLLy8nJ27dqFz+cjHA5z5ZVXUlRUZE8pTafT9lTB/EZTKaWUUkoptXxovqn55nxZSvnmkiwYeTyeSafMAfbp6PJ/h8NhAoHAQg1v1owx415f/vUaY7jrrrv4D//hP/Diiy8C0N7ezokTJzDGkEwmSaVSZLNZRASHw0E2myUajXL8+HFOnz5NW1ub/cGsqqqioaGB1atXU1paOuMAXr9+Pc3NzeM2qvmN51SJSMGcMnHz5s1cddVV9vW1a9cSDoeJxWKkUil8Ph9+v59AIDCj90wppZRSSilVuDTf1HxzPi2lfHPx5zjNQDKZvOByp9OJiBAIBPB6vaxatWrC0/aJSEGcqu5c0WiUT37yk3zuc58jk8nwwAMPsH//fgAOHjw4bt2enh6efPJJSktLWblyJcYYeypkJpOho6OD7373u4yOjrJ582ZWrFhBaWkptbW1rFy5kqqqKjwej32/TCYz7SpmfX091157LZ2dnbS2tlJaWsqtt95KJpPhpz/9KcePH79ow7d169bh9Xqn9bzzJRwOU1lZaV/3er2UlpbidDoZHR21N46pVKogPz9KKaWUUkqpmdN8802ab869pZRvLrmCkcvlIpPJTPrGFRUVUVVVRXFxsR2gE1UeHQ4HgUCAWCw230OetnQ6zf3338+qVasIh8M88MADk64biURobW3l4MGDJJNJwuGwvQE7e/YsTz31FE8++SQbNmygqamJlStXUlNTQ1VVFWVlZYB1DOrYJmX5KvlUK7bFxcVs2LCBLVu28Pa3v53bb7+djRs34vF46Ozs5Kabbjpvw3Ouo0ePTv0NmmcTve6ysjIymQw+n8/uZJ9IJMhmszidTjKZzCKMVCmllFJKKTWXNN8cT/PNubeU8s0lVzC6UPCC1aQrH7z5zuwTnbbPGDNp1/tC0NXVxec//3ne8Y53cPz48QnXcblcbNq0CY/Hw/Hjx+ns7MTlcuH1ejly5AhnzpxheHiYhoYG6uvraW5uprS0lLKyMkKhEOFw2P4A9vb22tVen89HcXHxtCqwW7du5ZVXXsHn81FeXg5YgVBbW8t73vMeDh8+TDabnZP3Zj4FAgFuueWW825vaGjA4/HgdDpJp9Mkk8lxAQxo0UgppZRSSqklTvPNN2m+OfeWWr655ApGF5uS1d3dTUdHB01NTWQyGWKx2IRvrDHmotPWFltPTw8PPfQQl19+Oa+99tp5UyPXrFnDX/zFX3D48GF6enp44403OH36NP39/fT29lJZWUlzczPhcJi6ujpWrFhBbW0tDofDPk3f4OAgxhhSqRStra0kk0lCoRB1dXXTCuCVK1dyyy238NOf/pRYLGYfo5pOp7n66qvx+XyMjIzM6fszH0KhEL//+79/3u2rV68mHA4Tj8dJJpMEg0GKi4sZHBzE4XBQXl5ONBpleHh40acNKqWUUkoppWZG8803ab4595ZavrmkCkZTmYrV2tqKw+GgtrYWwG7OtVQlEglOnjzJlVdeybFjxzh79qy9rLi4mIGBATZu3EhzczPRaJRDhw5x7NgxWltbKSkpYfPmzbhcLkpKSnA6nfY0wEgkQjweZ2hoiFgsxtDQEIcOHSKbzdLc3EwsFrOnEE6FiLB161YcDgddXV04nU6Ki4sZGRmhtraWj370ozz99NMcOnSo4DecE50RweFwEAwGicfjuFwuioqKcLlc9iklu7u7KS4uxuVykUqlFmHUSimllFJKqdnQfFPzzYWwlPLNJVUwmsoUrPw60WjUDt6lMDXtQiKRCCLC+973PtxuN6lUio6ODhoaGrj55psJh8P4fD4ymQzbtm3j8OHDPPPMM5w9e5ZYLEYoFLI74WcyGdLpNIlEArfbzfDwMH19ffT393PgwAHcbjf9/f3s2LGD+vr6aY3T5XKxZcsWurq68Pl8uN1uQqEQIsKnPvUpbrnlFr72ta/x4IMPFvT0zInE43GKioro7u62A9zn89nV3Uwmw9DQ0GIOUSmllFJKKTULmm9qvrlYCjXfXBIFI4/HM60O4fmO4olEAp/PtyQD2OFw4HQ6SaVSZDIZfv3rX9PQ0MBtt91GSUkJhw8f5r3vfS81NTX2fdxuN16vlx07dtDT00NPTw/Hjx+nrKyMiooKioqKyGazdHd3E4/HicfjnD17llOnTtHe3g5YH8o33niDVatWce211067g73L5aKurs4+ptflchGPxwkEAuzYsYP29nYeeeSRJRHA2WzWPh65u7sbh8Nhn9awtLQUr9erM4qUUkoppZRa4jTf1HxzMSyFfLPgC0YOh4N0Oj2t4/TyFc2RkRESiYTduGyqXdgXU77r/I4dO9i6dSvf+MY36OvrI5VKcf/99+Pz+di+fTsDAwOTnrrR5/MRCoWIRqMcP36c2tpaUqkUJSUlpFIpenp6aG9vp7W1FY/HQyQSAbCnC6ZSKX7+85/z8ssvs3Pnzmm/hvz77HQ67SAwxmCM4aWXXiroAksmk6Grq4vq6upxzeu6u7vtqaZut5t4PE4mk5nSrxBjX79SSimllFKqcGi+qfnmQlpq+WbBF4xg4tPO5bvRT9ZgLJVKkUwmicfjtLW1EYvFJvzAF5Kmpib+5E/+hHA4zNve9jYaGxtZs2YNH/nIR+yq9f3338/JkydZv349g4ODBIPB8x5naGjIbo7ldDp57bXXCAQCuFwu/H4/IkJbWxtOp5PKyko8Hg/xeJxsNksqlaKyspKysjKeffZZGhsbWbFixYxej4iQzWbxeDyk02leeeUVfvKTnxR0tXdwcJB//ud/5rOf/ey42yORiD2lsrS01P7cORyOi/6iYIyZ9LOqlFJKKaWUWlyab2q+uVCWWr5Z8AWjfMX33Ntqa2sZHh6e9Di+ZDJpd01vbW1lcHBwXAC/+uqr8zrumYjFYpw8eZK3vvWtrF+/HoA77riDl19+ma985SuAdWzjSy+9hM/n4+DBg9TW1o6bxpdIJNi3bx/Hjx+nr6+PQ4cOjXuPnE4nfr+fUChEIBCgs7OTrq4uhoeHAevDNjAwwG9+8xtGRkaorq7mlltuIRQKzeg15RvHHTx4kO9///v09PRM+b4lJSVcd911vPDCC3R3d8/o+acrlUpx5MiR827v7u6mt7cXt9uN0+nE6XSyatUqnE4nBw8evGBjtaXya4NSSimllFKXGs03Nd/UfHNyBV0wcrlc5wWvy+VizZo1bNiwga6uLg4cOMDo6Oh5981X4iKRCMlk8rzO9R/5yEfmdewz0d3dzde+9jUeeOABHnvsMRoaGmhvb+eNN94Yt97w8DBPPPEE5eXlBAIBVq1aRVFREQMDAzz77LP8+te/5qGHHmJgYOC858hkMkSjUfsDNTg4eN7pB/Pv16FDh/j5z39OUVER119//Ywr5p2dnXzhC1/gwQcfnNY0uaGhIZ555plFn1IYj8dJJBL09PRQVFSE0+m0q+fBYHDcaxIRvF4viURi3O2FXOVWSimllFLqUqT5puabmm9eWEEXjJxO57gX7nK5aGpqYsuWLdTW1lJaWkp/fz/Hjh0774ORfyN9Ph+9vb3jAnj//v1Eo9EFex3TNTQ0xM0338zOnTs5evQoHR0dE6730EMPEYvFWLlyJX19ffYGrbe396LP4XA4CAQCZLPZ8wI4b3BwkAcffJBYLIbP52PXrl2UlJRMu3oZiUR44oknZnRM5WJ0gj958iSHDx9m06ZNABw7doz+/n7785RMJkmlUoyMjIz7HDmdTsrKygiFQgwMDEy4AVVKKaWUUkoVBs03Nd/UfPPCHBdbQUS+KSJnReTgmNvCIvKkiBzL/VuWu11E5H+IyHEReVVErpjN4MZOu3K73axbt47m5mbC4TBFRUX4/X4uu+wyNm7caK8XDAaprKy0T+cXCAQwxvDYY4+RTqc5deoUX/7yl4nFYrMZ2rwbHh7m6aefnjR4wXp/Hn30Ub7+9a/z3e9+l5/97GdTCl4Ar9eL1+ulvLwcj8cz6XpDQ0M8/PDDfPazn+WHP/whXV1d9jGU51bRJ+P3+6moqJjSuoXg1Vdf5d5777WDs7+/n0gkgs/nw+Vy4fF47ONlk8kkgUAAgBUrVrB792527drFunXrLvi+zoXFjE2l1OQ0NpUqTBqbShUmzTcXh+abi2ep5JswhYIRcC/wznNu+wTwM2PMWuBnuesA7wLW5i4fBr4yk0E5nc5xHcPB+sBt3LiRQCBgdwv3er00NTXR2NiI3++nsrKSO++8kz/8wz+0K72ZTAaXy8UPf/hDTp8+zaOPPsrp06dnMqyClK/YTreamq/aejweqqqqLrhuPB7nueee42//9m/59re/TUtLC4lEgmw2O6XnXblyJR/72MfsD3qhS6fTPPzwwzzzzDOcOnWKvXv3ks1mCYVCOBwOO4B7e3t5/fXXGR4eJhQKcdlll9HY/m4n9gAAIABJREFU2EgoFCIcDuP1eud7qPeywLGplJqSe9HYVKoQ3YvGplKF6F403yxYmm/OvSWUb168YGSM+QXQf87N7wa+nfv728Bvjbn9O8byAlAqIjXTHZQxZlwncJfLxYYNG1ixYgUej4dsNsvg4CCDg4O4XC6am5tpbm7miiuuIBaLEY1G2bJlC6WlpRhjcLlcDAwM8Pd///c888wzwMSd8JcCh8OB3++f1WO43W7KyspobGxkx44dNDU1jWtkNhFjDG+88Qb33Xcfjz/+uF2Jnsr76HQ6ufHGG7nmmmtmNe6FFI/Hefzxx/nxj39MR0eHHbjZbNb+NSGdTtud6PNB63a7Aet0leFweF7HuBixqZS6OI1NpQqTxqZShUnzzcKi+ebCWAr5JkxthtFEqowxnbm/u4B8ybAOaB2zXlvutvOIyIdFZJ+I7Bs3IIfDfhPySkpKqK6uZsWKFdTX11NaWmpXD3t6ekilUlx22WV4vV4OHDjAqVOnqKurw+/325XhVCrFD37wA1paWpZ0A2JjzKxOl+d0Olm3bh0rV66ktraWlStXsmnTJkpLS6d0/yNHjnDfffexb9++ab2Pa9as4bLLLpvpsBecw+Fg7969/OhHP0JEKCoqIh6Pk0wm7YZknZ2d9g7C6/Xy/7N330FynOeB/7/dPd3Tk2dnc17kRSQIEoEQSZESo2TJTJZF6fyTfXRJvhPvZN9dlfyruyr6Slcl+8qSz7JNOZxUMo/+SaJJlSnKpAKDjqIIEUwAERZEXGBzmJyne7p/fyz7FZYLkoiLAfB+qlAkZmdnenr76a3nwfs8b7lcplgs4rouhmHQ29u74Lx6A8wuoPMamxbvPY1fkqQzImNTkhqTjE1Jakwy37xIZL65OC6VfPNsC0aCO7dG7IynS7mu+/eu617ruu61Jz+uKMqCLeNisRidnZ1omkYoFCIWixGPx+nunrs3FAoFsVzNcRyy2SzT09NUq1X8fv+8gVveD+JS5bruafdynsrq1at5++23efXVV0mn0ziOQ2dnJwMDAx9Y9YW55XOHDh1i9+7dTE9Pn/b7mqbJJz/5SZYvX37Wx76YHMehUqmQy+VEhbdSqaAoCpZlMTk5STKZBKC7u5uVK1dimiYnTpzgxIkTZDIZcZ2efF69LRIXw/mITZ0Lv8xRkq40MjYlqTHJ2JSkxiTzzcUl883Fcankm2dbMJrylv6981/vJzkG9J70vJ53HjttbW1t8/4eCATo6+sjkUiID2+appggHovFaGpqolarMTk5SU9PD6ZpMjQ0xMzMDIAIem9rOtd1F6XfrxHt3bsX27ZJp9M8/vjjPPHEE4yMjLBp0ybWr19PKBT6wNeIRqMUCgWefvpppqamTvu9r732Wn77t3+bW2+9lVWrVjXsMk1VVVEUhWg0iuM4BAIBqtUq+XwemPuFYVkW1WqVQCDApk2bGBwcxDRNKpUK5XKZZDJJoVAgHo/Pq/Dqur7gXzTOswsWm5IknRMZm5LUmGRsSlJjkvnmJUrmmx/sUso3z7Zg9EPgc+/8/+eAJ096/P95Z3r9NiB70lLC03Ly1nCqqtLb28uKFSvE9HmAer2Opmnouk57eztdXV0MDg4Sj8dJp9PEYjFmZmbIZDLouo5pmkSjUWDuBLquS09Pz1l+9MuH67q89dZb/PCHP8R1XTZv3sytt95Kc3Pze35PNBrlrrvuoqmpiX/+53/mj/7oj9izZ8+8HuD3EgwG+YM/+AP+5E/+hM9+9rOndbO4GEzTJBKJiBu+pmlUq1Wx9M/n84ldDxKJBK2trSQSCYLBIKFQiObmZkKhED6fj+bmZmKxmHjtarWKbdsX8uZ1wWJTkqRzImNTkhqTjE1Jakwy37wMyHzz1C6lfPMDm9sURfkucBPQoijKKPAQ8KfAY4qiPAAcBz71ztOfBj4GHAZKwO+dycEEg0FKpZL4u6ZpLF26lPb2diqVilha5fP5xAkIh8MUi0UikQi9vb384he/wHEcCoUChUKBYrGIoijEYjExFCoQCDRstfFimJqa4nvf+x533303S5Ysoa+vj+HhYV5//XWSyaS4cS5ZsoQtW7YwOTnJt771LfL5PK7r8txzz/Ff/+t/5dOf/vSCir3HdV3S6TQ/+tGP+F//638xMzMz72fdSPx+P9FoFMMwiMViOI6DpmniXwu84FYUBdu20XWder1OIBBgYGAAx3Hw+XyUy2VmZ2cJBAKoqorjONi2jd/vR1GUM95p4N0WMzYlSTp9MjYlqTHJ2JSkxiTzzcufzDfnu1TyTTiNgpHruve/x5c+eornusAXz/Zg3l01bGpqoqenB7/fT6lUQtM0XNcVf1RVpVKp4DgOlmXhOA7pdFosDVRVlXK5TCwWIxaL0d/fj2EYhEIhDMM428O8LBWLRaampgiHw0SjUZYtW8bAwADlcplyuczIyAhTU1M89thjC3pyp6en+dKXvsR//+//nb/9279lyZIlrFmzRlTpXdclmUzy13/913zjG98gk8lcpE/5wTRNo7+/XywP9JaW+nw+arUaPp8Py7JEH7NpmriuS7lcRtd1EfgAuVyOYrFIa2sr09PTlMtlYO4G4W0TeS4WMzYlSTp9MjYlqTHJ2JSkxiTzzSuDzDfnXEr5JpxGwWix+P3+BVPQg8Eguq4TCATE12q1mujz8waWWZaF67pMT09jWZb4fm9olGma+Hw+dF3Htm2x7EvTtHOaAH85cV2X3bt309raCsyde5/PR7VaRVVV3njjDdFT+V5SqRSf+tSnGBwc5LrrrqO5uZnu7m78fj+vvPIKP/vZzxo6eAFxA6vX61QqFUzTFAPuIpEIlUoF27bFIDjXdalWq2KyvW3b4nt8Ph+qOtf1efLgMcuy5L84SJIkSZIkSdIikvnmxSXzzTmXWr7ZMAUj7yR5vOVXmUyGXC4nBjd5ld5SqUQoFMI0TcrlMlNTU4yNjVEoFMT3eyfPcRzC4bBY1hWNRhkeHqarq4uRkZGFB3OFSqfTDA0NsXLlSnRdx3Eccrkcpml+YPCe7MCBAxw4cABFUcTNslgsnpcK54W2YsUKTNOkWq3S2tpKS0sLgPgFMD4+TrFYpKenh0gkQrFYJJPJsGzZMur1OpZlYRiGWKbq7Zhw8uCxQqFAR0cHExNyFIIkSZIkSZIkLQaZb158Mt+89PLNhikYnVyp1XUdv9+P67qEQiHK5TKapmHbtliOpWkapVIJ0zSp1+sUi0Wmp6dxHAfDMBgcHGTdunWYpimCtrOzk+npaaLRKLZtk0gkZACfpFarsXfvXqrVKoODg7S2thIKhYhEImf1eq7rNmzf6Kn4/X6CwSCWZWGaJn19faiqSr1eR1EUscRP0zRuvPFGHMehWCySTCbFDc4LaMMwCAaDdHd3U6vVSKVSVKtVSqUSrute0lttSpIkSZIkSdKlRuabF5/MNy+9fLNhCkYnc10XRVFoaWnB5/Ph9/uxLItisYht26iqKgI3mUyK6u7SpUuJRqNomsa2bdvw+/0ioC3LQtM0DMMgHo9jGAaBQEBsTSfNqdVqDA0NkU6nWbFiBa2treRyOdEHeTkbGBigVqvR1NQkJup7vzi8JX+VSoVoNCq21vSquV6vqd/vZ2ZmBsMwiEQiBINBsWPCydfZu5fDSpIkSZIkSZK0OGS+efHIfPPSyjfV8/Iq55mmaQSDQTo7OzEMg3q9Lipv09PTTExMiCVYtVoNRVFYvnw5H/3oR/n4xz/Oli1b0DSNQqFAPp9HVVU0TSMWi4nX3rZtG9Fo9H239LsSKYpCb28vq1atIpFIEAqF0DSNjRs30tXVdbEP74KzbZve3l5isRiKoqDrOrVaDdu20TSN1tZW2tvbKZfLYglgV1cXtm2L7TdDoRDxeFwsX63VahQKhXl9pZdSJVySJEmSJEmSLicy37x4ZL55aeWbDbfCSFEUFEXB7/fT2tpKc3MzjuNQr9fFsqtoNEowGMQwDHRdJxQKiepuKBQiGo2SyWREz1+1WiUYDIoT6/f7+S//5b/w7LPP8rWvfU3cEK508Xicm266iQ9/+MNs2rSJjo4OFEUhlUpx5MgRhoaGePTRRxkeHr7Yh3re9fT0sHr1ar7yla+QTCZ59NFHSSQS1Ot1otEopmmKLTWnp6fp7OyktbVVbF0YjUZJp9NYlkUoFBI7Kti2TTabFf3P3lLY87HFoSRJkiRJkiRJZ0bmmxePzDcvvXyz4QpGAIFAgKamJnw+H47joCgKPp+PSCQy7zFvu8JAIDBvAJSqqhiGgaZpKIoyb0J9U1MT8XiccDjMb/3Wb9HZ2cl9993H9PT0xf7YF1U4HObuu+/md37nd7jqqqtIJBLzvr5ixQqx7eFf/MVfnPYNr6mpiVKp1NDLC1VVZWBggK985SusW7eOQ4cO0dXVRSAQAKBcLovp/a7rsmLFCu69917a29v5xS9+wRtvvAHMDSqLxWIUi0UKhYKo6nqf35t0D3PnJZlMLv6HlSRJkiRJkqQrnMw3F5/MNy/NfLPhWtIURRE9pbquUyqVRF+oYRjEYjFR4YW5ypn3dW97OVVVCQQC+P1+YrEYuq7jui71eh3HccS2iIqicMMNN/AHf/AHF/lTX3zbtm3jM5/5DNu2bVsQvACJRIJrrrmGu+66i3vvvfe0XrOlpYXf//3fp7u7+3wf7nnV0tLCQw89xOrVq4FfD8ErFotEIhFxzem6Tl9fH9dffz1tbW0AmKaJbdvil0yxWBSPFYtFarUapVKJZDI5r4/UNM2L8lklSZIkSZIk6Uom882LQ+abl2a+2ZAFo2AwKJYGessDvYnhtm2j67o4ad4QMdd1xYlXFAVAVIMdxyGfz5PP59F1nWQySb1eF+/54IMP0t7eflE+byNYuXIld9xxB9u2bRNVzlPx+/1s3bqV22+/nXA4/IGvGwqFCIfDDb/88t/9u3/HRz7yEdHzaVmWGFqXz+ep1+u4rovP5+O6665j3bp14hrLZrOk02lqtZr4ZeJtrxmJRGhra8NxHKrVqnh97xeKJEmSJEmSJEmLS+abi0/mm5duvtlwBSPvw3rTwA3DwLZtTNMUVV/vD8ydbK+vz+vj8/l84mR5J1TXdeLxuHjuyRdVa2sr3/nOd+YNibpS+Hw++vv72bhxo5jU/n50Xef222/n5ptv/sDnzs7O8tBDDzV0D+ott9zCl7/8ZRF0MDdd3jAM8a8K3v93dXVx9dVXi+cWCgUymYzoc25paSEajaKqKqFQSGzRWSqV5m3j6Q3QkyRJkiRJkiRpccl8c3HJfPPSzjcbboaRtzTQ41XRyuXyvH5SL4C9SlytVhPB6fP50HVdVIy9begURaFery84oQAbNmzgjjvu4F//9V8X9fNeTFu3bhXL3yKRyGl/nzfM7YMUi8VzObxF8Z//83+eV+W2LIuxsTGxS0IwGCSfz+O6Ltu3b5/3vd62hYqiUKvVUFUV27YJBoP4/X5SqZSoHOu6Lv6VwdtRQZIkSZIkSZKkxSXzzcUj881LP99suBVGmqaJgHVdF9u2sW0bv98vtjv0qsL1eh2fzyf683K5nDhZ3mt529S5riv+JBIJfvKTn8x7366uLm644QZR5bvc/c7v/A5f//rX+aM/+iM2bdo0b2nlleLzn/88H/7wh+c9Vq/XmZmZoV6vU6vV2L17NwcOHODGG2+ko6Nj3nPL5TKO49Dc3Cx2VQgEAuTzearVqrh+C4WCCHaYq/jKljRJkiRJkiRJWnwy31wcMt+8PPLNhisYebyg84LWdV1RUbMsS1Rxvf4/Xdfp7+/n9ttv5xOf+ARdXV1iSrht27iuK3ocDcMgk8kseM8vf/nLrF+/frE/6qJrbW3llltuoa+vj87OTkKhEFNTU6d9URUKhUt+hUw8Hmf79u0LemhnZ2eZmJgQU+eLxSKbN2/mqquuWnCD834hVCoVAoGA2B1B0zSCwSCKoojJ9dFoVAwh875PkiRJkiRJkqSLQ+abF47MNy+ffLMhC0ZenyjMbUHn8/lE4HoDobzJ84ZhUKlUiEajfPzjH2fdunX09PRw880309vbi23bVKtVKpWK2GrPO5FTU1ML3vvBBx+87HtLm5ub6e7uxu/3k81m2blzJzt37pxXlXwvrusyPj5+1sv/7r33Xq699tqz+t7zRVVVHnzwQT73uc8t+NoLL7xApVIR/+rQ3d3Nli1bFiyhdByHVCpFOp0W22pms1lKpRKVSoVyuSx++RQKBfEvFDA3sf7kHlZJkiRJkiRJkhaPzDcvLJlvXj75ZsNlrY7jiCV/Pp9vXrVXVVVRUbMsi1qtRq1WQ9M0otHovO35otEoN910E36/XwS8N/2+Vqth2zZvvPHGgvf/9Kc//b6T2y8HR48e5Zvf/Cbf//73+frXv85TTz3FT37yE3bs2DFvmv+pVKtVXnrpJXbs2HFW733XXXfxiU984qy+93wJBoN86UtfWvD4/v37eeutt0QVN5/P09vbS1dX14Lnlkol9uzZQ7VapVgsMjk5ieu6RKPReZXfTCYjrj+P198sSZIkSZIkSdLikvnmhSfzzcsn32y4odcwF8Re1c3rJ9V1nXA4TKFQEMHsDRVTVZVYLLbgdaLRKCtWrGBoaGjelHrHcTAMg9nZWarV6rw+Ur/fz9NPP82NN964KJ91sbW3t+O6Lj/4wQ948sknRY/uyy+/zD/+4z9SrVbZsmULTU1NKIoiLjTHccjlcuzcuZPvfOc75PP5s3r/3/3d373o7VjPPPMMLS0t8x6r1Wo88cQTDA0NsWHDBtHT3NLSQk9Pz4LX+NnPfkY6nRZ9pdFolFqtRrFYxHVdqtUqjuMwOztLNpud973etStJkiRJkiRJ0uKT+eaFI/PNyyvfbLiCkTdgzOsnrdfrVCoVTNMUS61M0xTDyur1OtFoFJ9v4UdRVZWNGzcyPDxMOp2mWCwSCoUwTRNFUZiZmeHYsWMMDg7O+76BgQFuuukmfv7zny/Sp14cGzdu5LbbbuPAgQOMjo4SDoeJxWLk83nGx8f56U9/SjKZ5ODBg2zdulXsDmCaJsVikV/96lc8+eST7Nu376yP4YMqyhfazTffzMDAwILH9+7dyxtvvIFpmjiOg6ZpmKZ5yq0fR0ZGmJqaEr3Kuq7Pq+p6N8Xx8XFOnDixIGALhcJFPw+SJEmSJEmSdCWS+eaFI/PNyy/fbLiCkXciCoUClmURi8XE1oa2bZPP59E0TZwUb7vDiYkJkskkzc3N814vFArR19dHqVTi4MGDtLe3EwwGgbllXqcaRtbb28vtt99+WQXwtm3b+MIXvkA2m0XXda677jqi0SjRaJTR0VFyuRyHDh1i//79PPvss9RqNZLJJOVymVQqxejoKENDQ8zMzFz0iu25uOOOOxZUcC3L4pVXXmF6epre3l4OHz4MzO1k0NTUtOA1Dh48SDqdRlEUQqEQruuSz+cpl8tYloWiKJTLZY4fPy5ufl7AejsqXMrnUJIkSZIkSZIuVTLfvDBkvjnncss3G65gBHNV33w+z+zsLIlEAsuyKJVKxGIxUYHzqmpen2k6nT5lAIfDYXp7e5mZmaGtrQ3LssjlckSjUfL5PN/97ndZvnz5giVj/+k//SdefPFFnnnmmUX73BfKxo0beeihh8hkMhiGwUc/+lFWrVolqpCu65LL5fjmN7+JqqrUajUsy+JHP/oRhw4dol6vXxZbwd9666384R/+4YLHjx07xiOPPIKqqoyNjRGJROjt7aWtrY0bbrhBPM91XYaGhhgaGiKfzxONRsWAO9u28fl81Go1FEVhenqamZkZsWzQ423XeXKPqSRJkiRJkiRJi0fmm+eXzDfnXI75ZsMNvQYol8vk83my2Sy1Wg1d10X/qN/vR1EUUVnztpIrFAocPHhQbG14sra2NiKRCEuXLhUV5Fwuh2VZTE1N8dJLLy34HsMweOCBB+b1m16KIpEIDz74ICMjI8RiMe655x7WrVsnton0+Xzouk5zczN/+Id/yH333UcsFuPgwYP09vbS3t5OIpG45IPXMAw++clPin8hONmjjz7K7OwsjuMQCoXo6OhgzZo1DA4Ozlt6+vbbb/PLX/6S8fFxMajMq/J6S1a9gD127JjYTcGbUK9pGoFAgHK5vGifW5IkSZIkSZKk+WS+ef7IfHPO5ZpvNmTByDM+Pi6WYnkVMu9k6bqOqqrzLsSZmRnefvvtBa/T0tJCJBLB5/NhmiY+nw/LsigWiziOwyOPPHLK97/33nv58z//8wv6GS+066+/nnQ6zW233cadd94pdgTwnFx5DIfD3H333fzxH/8xuVyOSqXCvffee1kMZEskEnzxi19c8PhLL73EP//zP1OtVmlqaqKvr4/+/n4Mw5jXa/zmm2/ywgsvkEqlCAaDYtlqMBgUy/5qtZoIYl3XSSQS8yrlfr//lL3PkiRJkiRJkiQtPplvnjuZb865XPPNhi0YFQoFarWaqPpaloWqqpimKSptALZtU6/XRS/qwYMHSaVS815LVVVuuOEGDMOgVCqh67qo0AFMTU3x8MMPn/I47rjjDuLx+IX9sBdIIpFg3bp1PPDAA/T39wNzyy+npqZwXZdCocCBAweYmJgQF5nP52NwcJB/82/+jejhLZVKF/NjnBc/+MEPFkyKr1arPProo+RyOVpbW+nu7sbn8+G6Lm1tbQwMDOC6Lvv37+eNN94gmUyK7TW9XyInV3q9QJ6dncW2bYrF4rwe3JaWFqamphb9s0uSJEmSJEmSNJ/MN8+dzDd/7XLNNxu2YORdYDMzM2JSfTabpV6vYxiG6Cet1+vYti22RCyXy+zbt49KpTLv9fx+P9u3b6e7u1tU41pbW0kkEqiqyksvvcTExMSC41i+fDlf/epXF+tjn1dr1qzhxhtvJBqNisdUVRVbGIZCIdra2mhubl5wcd92223ceuutZDIZMbTtUrVly5YFk+rr9TpPPvkkb775JoVCAZgbEBaPx9m+fTu33XYb4XCYVCrF66+/TqlUol6vi+02g8Gg+MVSLpfFtoa1Wk0scS2VSuKXRCAQEFt3SpIkSZIkSZJ0ccl889zJfHPO5ZxvNmzBCCCTyZDP5ykUCqLCBogtEL0g9vo+a7UahUKB48ePc/jw4QUna2BggI0bN4qlhV5/aiKR4NixY/ziF7845XF8+MMf5tprr72wH/Y93HnnnaIn8Uz4/X5WrFjB5s2b0TRt3te8vkpFUWhubj5ln6W3XHDlypWXfAD//u//Pp2dnfMeGx8f51/+5V/IZDL4fD6i0SjxeJyuri62b99OJBLBcRxee+01UqkU9XqdUChEIBDAdV1qtRrVapXp6WkqlYrYRcF1XaampsQvG5g7z+3t7Qv+JUKSJEmSJEmSpItH5psy3zwfLud8s6ELRgDZbJaxsTFRlfMqb5qmicqvt0zQ6xOdnZ3ltdde48c//jHHjh2jVqtRr9dRVZUNGzawdOlSLMvCdV1M0yQUClGpVHjkkUc4cuTIgmNYvXo169evX1AVXQyvvPLKWVUJDcNg06ZNtLa2vu/z3q/HcXBwkMHBQfL5/Bm/f6O46qqr2Lx587zHbNvmq1/9Ki+++CK2bROJRAgEAvh8Pu69917C4TAwd+4nJiYwDIN6vU65XGZ2dpZsNks+n2dsbIzx8XExDK9SqVAoFES/qRfA3i+YS32QmyRJkiRJkiRdbmS+KfPNc3G555sNPYHXcRySySTBYJD29nYCgYC4mF3XZXJykmAwKE6QV9ms1+ukUilmZ2fZt28fhmGwevVqrrnmGpqbm9m2bRuZTIZKpUKtViMWi6FpGnv27OGv/uqv+PrXv76gyvrtb3+b559/nuPHjy/qOTjbKmE4HGb58uVnVS326LpOS0sLBw4cOOvXuJgURWHLli1s3Lhx3uM///nPeeyxx8S1FI/H6e3tZcuWLWK7y3w+z7Fjx8hkMqTTabFjgqZp2LaNZVlMTEwQi8XE9oaFQoGRkRGmp6epVqvi/Wq1GqlUSrajSZIkSZIkSVIDkfmmzDfPxZWQbzb8CiPLssjlckxPT5PL5VAURUyf97aa8waWVatVXNfF7/cTjUbF9oilUokDBw7w4osvMjIyQktLC9u2bSMSiRCJRGhqaqKjo4NEIsErr7zCCy+8cMpj+dKXvrTIn/7sRSIRcTGei56envNwNBdHJBLh4x//+LzH9u7dy9e+9jUAsa3hhg0b2L59OzfccAO6rlMul3n11VeZmpoSgWuapqjsqqrK+Pg4w8PDFAoFFEVB13VKpRLFYvGUQ+vy+bxcYSRJkiRJkiRJDUbmm2dH5ptXRr7Z8AUj27ZRFIXp6WmKxSKKoojHvJ5S0zTFQLJ6vU6xWKRSqaBpGpFIBL/fT71eZ3p6mp07d5LNZunv72dwcBDDMGhqamL16tWsXr2azs5O/umf/olDhw4tOJbPfOYzxGKxi3AWzoyqqvT394tJ9eeit7eXlpYWTNMUlfVLxZo1a7j99tvF3zOZDE888QSWZbF27Vpxjq677jruuusu8bOdnJzk8OHD1Go1sTWh67qEw2EMwyCZTDI0NES5XCYajRKJRLAsi1KpJHZGqNVq4n29bTglSZIkSZIkSWosMt88czLfnHMl5JsNXzACyOVyVKtVRkdHKZfLYjp9pVJhdnaWSqWCZVnAr3skVVXFtm1qtZoYDlUul5mamuL48eP4fD42b97M5s2b6ejowO/309HRQTgcJplM8swzzyzY3q+9vZ1vfetbBAKBRT8HZ2L79u309fV9YD/p6YjFYqxdu1bcBC8VoVCIz3/+85imCcxVd5999ll2797Nhz70IbZu3crVV1/NPffcw+c///l5wfv888+j6zqGYZDL5cRrWpZFMpnk0KFDjI6O0traSktLC5ZlkclkmJqaYmZmZsGyTm+6vSRJkiRJkiRJjUfmm2dG5ptXTr75gQUjRVG+rSjKtKLO8trTAAAgAElEQVQoe0967E8URRlTFGXXO38+dtLX/l9FUQ4rivK2oii3n/pVz4w3WCyfzzM7O4vP58MwDBRFIZlMip4/70SpqoplWSiKgmEY6LqOpmkUCgWKxSI7d+7k7bffJhgMcuONN/KhD32IlStX0traSldXF5qm8fLLL/PYY48tuGi3bNnC9ddffz4+1gVTqVRoa2s7L6+lqip33XUX27Ztu2QqvqFQiI985CPcc8894rGnn36a//N//g+rVq2ip6cH0zS57777+OxnPys+VzKZ5Oc//zm1Wk0sNVUUhWq1iqIojI6O8uqrr7J3716CwSBLlixB13Wmp6cZHh5mdnZW/GvDYmiE2JQkaSEZm5LUmGRsSlJjaoTYlPnmmZH55pWRb8LprTD6DnDHKR7/C9d1N77z52kARVHWAJ8G1r7zPQ8riqKd4nvPmBe8hUIBVVXx+XwEg0Gq1Sq7d+9mcnJSTK93HAfXdbEsS/wwNE2b14f61ltviSrc0qVL+cQnPsHWrVvp6+sjFosRDAb55S9/ye7du+cdR29vL7/7u79LU1PT+fhYF0Q4HOaBBx44b693yy238Hu/93sMDg6et9e8UPx+P9dccw333HOP2J7xqaee4tvf/jb9/f00NzczNTXFnXfeya233kokEgHmgveNN96gVCrh8/lEf6jXB5rNZtm1axc7d+6kWq3S2dlJd3c3mqZRKpU4dOgQ2Wx23tLARfAdGiA2JUla4DvI2JSkRvQdZGxKUiP6Dg0QmzLfPH0y37xi8s0PLhi5rvsicLqj038T+J7rulXXdY8Bh4Et53B8gtcTOjIyIibH+/1+urq6SCaTPP/882QyGQAxqd1xHCzLolAoiCn1XkUulUqxY8cOEcRNTU2sWbOGzs5O+vv7icfjRCIR/vVf/5V0Oj3vWDZs2CAukIux9eH7aW9v584772TJkiXn7TV9Ph/33Xcfv/mbv4lhGOftdS+EJUuWcPPNN/ORj3wETdP4v//3//LEE0+wYsUKli9fjmVZDAwMsGXLFvx+P4VCgZ/+9Kc89dRTjI2NievEtm0KhQK5XI6xsTF27NjBjh07ME1TDC0LBoNMTk6yc+dOpqamyGazFIvFRfusjRKbkiTNJ2NTkhqTjE1JakyNEpsy3zw9Mt+8cvJNOLcZRg8qivLWO0sIvfJnNzBy0nNG33lsAUVRPq8oymuKorx2um9Yq9XI5/McPXqU2dlZXNclFovR3d3N+Pg4e/bswbIsNE0TywNVVcUwDJqbmwmFQiiKIoL4rbfe4vXXX8e2bQA6Ojq4+eab2bZtG4lEgkQiwfT0NH/5l3/J9PS0OA7DMAiHw2zevFksKWwU9Xqd22+//Zy2NzwVTdPw+/0NG8CaptHR0cH69eu588476evrY+fOnXzve98jEAiI6q+u67S2tqKqKslkkscff5wdO3aQSqWoVqs4joPjOGiahs/nE8sCX3vtNXRdZ+PGjaxZs4ZgMMjIyAg7duzg0KFDKIrSSEsoz1tsWlRP9RRJks6OjE1JakwyNiWpMcl8U+abDeNKzTfP9qf8TWAZsBGYAL52pi/guu7fu657reu6157u99TrdVKpFKlUit27d5PNZjFNkxUrVtDW1saBAweYnZ0FEH2kPp8PTdOoVCrouo7f7ycUColK79GjRxkeHhbvEY/H+chHPsJ9993H6tWr6e/vZ3R0lL/5m7+hUCgAc1v/bdy4kUgkwvbt27nqqqsapvJbLBZPe+CVtzXk6cpkMui6fraHdkEFg0FWrVrF/fffz8qVK3nqqaf4xje+QTqdFsPlDMMgHo9z5MgRfvCDH/Doo4/y2muvUSwWKRQKjI6OMj4+zujoKG+++SYvvPACb775ptjBYMOGDaxZs4Z4PE4mk2HPnj1MTEzg8/mo1+uUSqULspXhGTqvsanTMEUwSbrUydiUpMYkY1OSGpPMN2W+2VCu1HzzrPZec113yvt/RVH+AfjRO38dA3pPemrPO4+dF67rkk6nicViZDIZ3n77ba666iqCwSDr1q3j5ZdfZu/evbS2torBUV4/qaZp1Ot1YrEYjuOg6zq1Wo1UKsUbb7zB2NgYGzduJBqNYhgGq1atYsmSJTz33HOUSiWGh4f5n//zf/LHf/zHBINB7rvvPnK5HM899xzt7e1s2LCBPXv24DjO+fq4Z0RVVRKJxAdeQKVSiUwmwy9/+UsOHDiAruuEQiFWrFjB9u3bUVWVQCCApmm4rjvvxhSPxwkGg2QymUYojAiaphEMBlmzZg3t7e38zd/8DcPDw8TjcXp6etB1Hdd1qdVq1Go16vU6hw8fZnh4mGw2y+TkJI7jkM/nOXbsmNjtIJfL0d7ezooVK+jp6aG/v59wOMyuXbvYs2cPuVyOSCRCuVxe0EvqXXuL7WLFpiRJ70/GpiQ1JhmbktSYZL4p802ZbzZGvnlWBSNFUTpd15145693A95E+x8C/5+iKF8HuoAVwM5zPsp3GR4epr+/H7/fz+joKEuWLGFwcJDZ2Vn27t3L8uXLWbNmDZqmYds2hmHgOI5YAmbbtlgKZhgGpVKJI0eOMDw8zNVXX82GDRvEkq/rrruOt99+m/7+fg4fPsyf/dmf8cUvfpG2tjb+7b/9t5imyTe+8Q0ymQwtLS0kk8mLsh1gc3MzrusyODhIS0vLgq9ns1lOnDjBP/3TP5HJZMTkf+98jI+P89xzz6GqKlu2bKG7u5v+/n46Ozup1Wo4jsNnPvMZTpw4weOPP04mkzmjavGFoKoqqqrS1NTEpk2b6Ovr4yc/+QlTU1OEw2ECgQCqquI4Dqqqis9RrVZxXZdwOEy1WuXQoUOinxTmtjOMRqO0t7ezadMmmpubUVUVRVHYu3cvQ0ND5HI54vE4pVKJcrk877i8G6D3LwSL6WLHpiRJpyZjU5Iak4xNSWpMFzs2Zb65kMw3r8x88wMLRoqifBe4CWhRFGUUeAi4SVGUjYALDANfAHBdd5+iKI8B+wEb+KLruhfkah4dHRUT6gFaW1tZt24dyWSSF198EYD+/n50XReB6vV+elPJ/X4/lmVRLpfRdZ16vc7u3btRVZV169YBc0sNI5EItVqNJUuWcPToUR5++GE++9nPsmLFCu6//35c1+Wv//qvGRkZIRwOi+rhYonH42iaRjgc5qGHHqK/v3/e18vlMj/84Q/ZsWOH2LrP2wayXq9Tr9eZnJwkmUyKi8/v9xMOh+ns7KRer5PP5+np6eGhhx5i+fLlfOtb3yKTyTA2NnZRqr+GYZBIJDAMgyVLljAwMMDk5CS5XA6/3y8q2d7uBT6fj1qthmmaOI4jvpZMJqlUKszOzootMpubm1mzZg2xWIyWlhY0TSOTyTA5Ocn+/fsplUoEg0FRPT+5yu9VyBcjeBs1NiXpSidjU5Iak4xNSWpMjRqbMt/8NZlvXpn5JoDSCEu9FEU5q4Pw+Xy0tLQwMDDAsmXLaG1tZXx8nBdeeIFQKMQ111zDwMAAgUCAeDwOIJa+ZbNZ8Vi9XqepqQnXdalWq2IbO2+Zl7d9nd/vZ2pqimPHjuE4Dh/72Me46667GB8f55vf/CYvvfQS2WyWXC6HZVlks9kF1cDzRVVVdF2nu7ubcDjM1VdfzUMPPURPTw+O48wbiOU4Dslkku9+97vs2rVLPF6v19E0DcMwSKfTDA8Ps2nTJv79v//3rF27ltnZWUzTJBqNiufCXC/q4cOH+du//VtefPFF9u7du6hVbl3XWbFiBeFwmI6ODmKxGD6fT1T3Q6EQgUCAcDiMbdv4fHN1UU3TyOVy4vx4ywN3795NKpXC5/PR3d1NR0eHeN1SqcTRo0fZt2+f+Lm2tbWRzWbJZrPzbl6KorB27Vr2799/NktFXz+T/urFElUS7lbloxf7MCTponnWfVzGpiQ1mFfc58i5qcYY5vEuMjalK1kjx6bMN8+czDcvr3zTdd2zis1LumAEc8uxotEovb29rFu3jqamJmZnZ9m3bx9TU1N0dnbS2dlJKBQiEokQDoeJRCIoikIgEEBRFGq1mlgClk6nyefzRKNRIpEImqYRCASwbRvXdVFVlXw+z+TkJKlUiu7ubmzbplKpkE6nqVarpNNpMpkMhUKBiYmJBf2G58o0TbZt28bmzZvx+Xz8xm/8Blu3bj2t6fl///d/z9DQEGNjY1QqFaLRKLZt4/f7ueWWW7jlllvo7Ow8reNwXZddu3bxxBNP8Mwzz7B79+4LFsiKooifYXNzM4lEgubmZnw+H4lEAsdxyOVyqKpKPB7HMAyCwaC4YauqKn5OPp+PQqFArVajWCwyOTlJNBqlublZ9B5XKhUmJiY4cuQIY2NjYpK93++nXC4vqHL7fD46OzuZmZmhUqmczUeUSakkNSBZMJKkxtPISamMTelK1sixKfPNMyPzzcsv37xiC0YAoVCIUChEZ2cnV111FfF4nHw+z/DwMEePHkXXdarVqlhGt2rVKtra2ojFYoRCIdEHmkqlmJ2dpVar0dfXJ/pWveVltVoN27ZFH2EymRS9iL29vTQ1NYkbQqVSoVQqMTY2xokTJ85LEDc1NbF27Vpuuukmbr/9dvr7++nt7f3gb3yXqakp/tt/+288++yzqKrKhg0b+OpXv8rg4OBZH9vhw4d5/PHHeeaZZ8QSzfPF5/MRjUZpaWkRk+fb29sxDENU6SuVCqFQiI6ODur1OrquEwgExP87jkOlUhG9xJVKhXq9TrVapV6vi4Fr2WyWI0eOUCqVSKVSpxWMiqKwdOlSLMvixIkTZ/sxZVIqSQ1IFowkqfE0clIqY1O6kjVybMp88/TIfPPyzTfPtmB0VkOvG43XE3rkyBHy+TxXXXUVy5YtIxwOEwwGmZqaYnh4mHQ6jeu6nDhxgvXr17N69WoURaFarXL48GGOHj1KuVwWlcLe3l7q9TqFQmHeICtvIn44HMbn81GpVMjn89i2jaZplEolfD6fuKC8QWcA1WqVXC43b4CX67riAvJ6Er3/qqrKxz72MT71qU+xYsUKmpubaWtrIxgMvuf58I71vbS3t3PDDTfwgx/8AIBPfvKTrFq16gPPs+M4onr67m0dly9fzpe//GXuv/9+RkZG+NznPsfIyAiO45xTFdg0TTo6OgiFQvj9fhKJhKjcV6tVsQyztbWVeDwu+owdxxE33HK5LM6/z+dDURQqlQozMzMUCgXGx8dF9bdWq53Rsk5N08Ty1Ndff/2sP6ckSZIkSZIkSY1J5pvzyXzzysk3L4sVRjA3lMo0TTFcbP369XR2dhKNRtF1nampKYaGhhgdHcW2bXRdp7e3l3g8TqFQYGRkRAwOi8VirF+/nq1bt4q+Uy/gvGnktVpNDDDLZrOUSiV0XUdRFDKZDMFgEFVVqdfrJBIJNm/eLG4oq1evxjAMMQTtyJEjJJNJli9fTm9vL+3t7fMC0OfzoWnagqA5meu6jIyMMDQ0xOOPP87DDz+Mruvv+XzHcXj66acBWLduHQMDA+97fo8fP87//t//mxdeeIE77riDBx544H2XEnoT4nft2sX/+B//g6mpuZ0xC4WCmHjvDUIzDGPBtoDeDa2pqYm2tjZUVcU0TXRdx+fziWV6+XxeBLd3frwqvVd993Yp8G60s7OzjIyMiBvn2caAaZoMDAwQi8V49dVXz3WLS7mKQZIakFxhJEmNp5FXMcjYlK5kjRybMt+U+eaVnm9e0S1pHm8yfaVSQdM0Mcl+yZIlBINB8vk8o6OjZLNZZmdnyeVypFIpbNue9zptbW2sXbuWwcFBwuGw6Es8ufc0l8vNW57mVRp1XceyLDKZDH6/n0gkQldXF/fffz/XXHONGIh1vj311FP86Z/+KS+//DKmafLkk09y2223nZfXtiyL73//+3zta19j165dKIrCrbfeyj/8wz/Q19d3Rq81OjrKW2+9RSqVEj2gTU1NopdTURQcx8GyLFKpFK+99hoHDx6kWq0SCoXQdR3XdRkbGyOXy9HU1EQwGMSyLOr1uugltW2bQqFAsVgkm81iWRaVSuW8TdiPxWKsXbsWwzDYsWPH+djyUSalktSAZMFIkhpPIyelMjalK1kjx6bMN8+dzDcv7Xzzim5J83gBpqoqjuMwMTFBPp/n6NGjrF69mq6uLtasWSN+uJlMhkOHDjE2NkY2m8VxHBKJBBs3bqS/v18sCTQMQ1RgvcpuIBCgWq2iqiqVSoVyuUw+n8eyLOLxOJZlieWEtm3zyiuvsGbNGiKRyHn9zAcPHuS73/0uTz/9NKOjo/T19eHz+XjooYd44oknWLJkCV/4whdoamo66/f48z//cx599FHGxsaAuWrsT3/6U377t3+b559/nkAgcNqv1dPTQ09Pzwc+z3VdUqkUmqZx+PBh0uk09XqdWCxGPp8nk8lg2zbZbJZ0Oi2m+HtVeK+v90JIJBJcffXVFItFfvWrX52PYpEkSZIkSZIkSQ1O5psy37zS8s3LqmAEiGVa3vK4QqFAoVDg+PHjJBIJli5dSltbG11dXSxfvpzW1laKxSLJZJJKpYKiKHR3dxMMBsUPplar4bqu6B89efK5N81cURQikQjFYpFisUg8HkdRFBH4b7zxBsVikQ996ENs2LCBQCAwbyvC01Uulzl8+DDf//73+fnPf86JEycIhUIsW7aMlStXMjw8jGVZaJrG888/T29vL0ePHuWjH/0od999N4ZhfOD5m52d5e/+7u+Ynp6mubmZf/zHf+T48eO4roumacRiMXK5HK+88grXX389P/7xj2ltbT3jz/J+6vU66XSao0ePipuo4ziMjY2JHlDv5uktCUyn01iWdd6quu8WDAZZuXIlAwMDDA0Ncfjw4UXd3lGSJEmSJEmSpItL5psy37yS8s3LqiXt3Xw+n/hTKpVEsAUCAXp6eujr66O5uVlsi+e6rviBeD2OlmVhWRaFQgG/34/f76dYLKIoCpZlkc/nyWazpFIpALGELRqN0t/fLyqtgUAAwzCo1+t0dXWxbt06Vq1aRSwWE8sQ30ulUiGTyZDJZPjlL3/Jv/zLv7Br1y4mJibo7e1l2bJlogrt9VJ6/arRaFR8luuvv55PfepT73njKJVK/OxnP+Pv/u7vGBoawjRNXNcll8uRy+UwDINYLIZpmsRiMSzL4tixY1x11VX82Z/9Gddcc81pbbX4flzXJZlM8vrrr/PEE0+wf/9+stksmqaRyWTEtpF+vx/TNHEcR/QCXyiGYYhlo5VKhV27dpHNZs/328i2F0lqQLIlTZIaTyO3vcjYlK5kjRybMt+U+abnSs03ZUvaKdi2jaIoRKNRgsGgqAgWCgXefvttTpw4QSQSIRKJMDAwQCQSET2JMHcD8AZYaZqGZVli+WG1WqVarVIul7FtW0w994ZnlUolmpqa0HWdSCSCpmnouk6tVmNkZATLskgmk+IGYpqmuNl4gZ5KpSgUCkxNTTExMcH09DSTk5PUajU6OjpQVZXly5cTi8UIBAI0NTWJoVveZzdNUwTwj370Iw4ePMhXvvKVBefKdV2+8Y1v8PDDD5PNZmlqasI0TbEtpGVZBINBTNOkWq3S3t5Oe3s7r7/+Os8//zxf+MIX+I//8T9y880309fX975T89+tXq8zMzNDPp8nnU5z5MgR9uzZQ6VSmdcL7PXpdnV1kc/nyefz52X7yPfi9/tZsWIFnZ2dhMNh9u/fz9jYGIVC4YK9pyRJkiRJkiRJlwaZb8p881xcCvnmZV0wgrkBWjMzM+i6TjQaFcOoLMuiVCpRKpXENojeUj8v2KLRKG1tbWJauuu6Ymq6pmlomkYoFCIYDBKPx8VUdK+XcWRkhHQ6LW4MoVBI9Jqaponf76dSqTA+Pk61WkXXdXGjKJfLYrq7qqpYloXjOJimyZIlS1i6dCmKoqDrOrquEw6HaWtrI5PJYJomqVRKLJesVCqiX/bHP/4x09PT/OVf/iWmaQJzwfvII4/w1a9+lXw+TzAYJBKJ0NPTQyKRIBaLoaqq+K/f70fXdZqbm8W2hyMjI/zsZz9j37599PT00NXVRXt7O4lEQpybYrEozp3rumJAWLFYJJ/PYxgGtm0zPT0NzAWQpmnkcjmKxaKY6H/06FHS6fRZTYl/93R8j9eL7PP5aGtrY2BggEQiQaFQ4M033xTXiiRJkiRJkiRJkkfmmzLfPNnllm9e9gUjQEycT6fTmKZJJBKZV7Wt1+uiSnqyTCbDiRMn5j0WCARwHEdcBF6F2KsSa5qG3+8XF4Nt2xSLRTFQy6uETkxMiMD0AtfrYa3X67iui9/vF9vwqaqKpmkYhkGtVhNbKpqmiaZpot81GAziOI7Y9s/b3m96epparUZnZyePPfYYq1at4j/8h/+Aruvs3buXv/qrv8Ln89Hc3Ew6naZQKBCJROjv78c0TfH+3oXuBVcgEKCvr4/29nZxbjVNI5VKiZuTaZrk83lxzNVqVdwAi8Uik5OTZDIZurq6qNVqDA8PMzs7y8zMDLlcjnA4zODgIOVymT179pxWIHmV+VNdCx6/308oFCISidDc3EwkEqFcLuPz+Th8+DATExMXrD9VkiRJkiRJkqTLg8w3Zb558rXguRzyzSuiYORxHIdSqUSlUiEcDouL8UwGSZXL5Xl/r9Vqp1wy5vP5RKXY7/djGAbhcFj0r6bTaXEzCAaDGIaB3+8XW/V5Q88CgQAzMzNiiV40GhUV3FqtRi6XQ1EUDMPANE1KpRKWZYnqqbdE0bIs0Rc6ODgoblqO4/CrX/2KaDRKX18f+XyeYrFIrVYTAVsqlVBVFcMwxDAwr3INEA6HxftEo1ER3N4NslqtYlmWqGr7fD4cxxEV31QqxYkTJ5iZmSGVSjExMSGOzxv2lsvlSKfTpwwoRVHw+XxYliUe0zTtlAGs6zotLS10dXXR0dFBPB5ndnaWiYkJJiYmOHLkyEUfLCZJkiRJkiRJ0qVH5psy37zc8s0rqmDkcRyHXC437zHTNEW/JCAGkLmu+4FL0byBXyc/z9tK8WQ+n08s5+vv7xfbA1qWhWmaIiCKxaK4iLz/+nw+arXavG0Xvcqr4zjU63Wq1SqhUAjXdcnn8ySTSUKhEAMDAwSDQfH6oVCI6667jkAgwPj4ODMzMyxfvpxDhw7R0tIijt0LthMnThCPx4nFYuKm4FXHT142GY1GxWP1el304tq2LW58+XxeVKEBZmZm2L17N8eOHUNVVcLhMOFwmGq1ysTEhLiZnOpn4PULG4Yhbmr1en1B8GqaRnNzMxs3bqS9vZ1AIMDx48d55ZVXxPl+941ZkiRJkiRJkiTpbMh8U+abl0u+edkXjLxKazabfd9KXqVSEV/3prUrytwgccuyRPXx5GWE3laKXuX0g3jLEMvlMjMzM+zfv581a9bQ29srele99z+5UlwoFEin0+LiU1VVVIRPnsSfy+VwHIdoNIqmaeTzefbt20cikSAQCFCr1VAUhaVLl7JmzRpg7uI9evQo1WqV5uZmADo7O9m/fz+zs7MUCgV+8Ytf0NbWxubNm2lubhaV38nJSXK5HLFYjNbWVmzbxjCMeZXXfD4vhrLZto2qqmQyGSqVCqOjoxw5coTx8XECgQCWZZFOp0XV+lS8yjPMVZq9nuGTeVXz5uZmWltbWbZsmaiUP/fcc5fE0j9JkiRJkiRJkhqfzDdlvnk555tXRMFo06ZN2LbNgQMHmJycfM/nehfNe1087w7S93reu3lb/737BlIqlXjttdfYu3evmALf1dUlBpQZhkE8HhfL66rVqtgu0euT9W4o3jK8yclJmpub8fv92LZNvV4XVWKAQ4cO4ff7mZycJJFI0N7eTm9vL0899ZSoPJumydKlSymVSmSzWUKhkOhlzefzmKZJoVCgUCiQSqUIBAJUKhVxU7Ftm2q1imEYlEol6vU6lUqFbDZLNpvl+PHjzM7OkslkxGeYnp5+36A6eWtGv99PrVYjm80u+BnE43ExRCwajTI8PMzbb7/NkSNHFvQMS5IkSZIkSZIknQuZb8p883LONy/7glGhUODVV1/lnnvuYf369Rw4cIADBw4wNjZ2xlPPveqqN9zrdL//g55XqVQ4fvw44+PjDA0NYRiGmJ4fCAQIhUK0tbWJKfmGYYiljJqmUa/XyefzzM7OMjY2JqbC27ZNW1ubWFroLaHL5/NMT0+zZs0aotEora2tjIyMoCgKLS0txONxlixZQjwep16vMzg4KHpfXdelUqmIafne82FuGWO5XJ73XoA4lqmpKXbv3s34+Di2bYsBa+/mVdq97R6953iVde+zn/z8pqYmli5dyuDgIOl0mqGhIXK5HLOzs6f1M5IkSZIkSZIkSTpTMt+U+ebl7LIvGAFks1keeeQRmpubufvuu1myZAmVSoWRkRGOHTtGOp0WF5u3DZ5pmsTjcbFsD349xAx+Pf08EAgQi8XE0K2JiQkKhQKVSkX0cL5XJdMbHFYul0Xvaj6fF1/3Jq8risKhQ4eYmpqiXq/T3d0tgtQ0TXw+H6VSCdM06ejowLIsMZG9r68PTdPEVP2tW7eiKAovv/wy0WiUrq4uqtWqqObm83lOnDjBsmXLWLt2LYlEgs7OTtG/6lVyA4GAqCq7riuWJCqKQjAYpFqtEolE0HWdYrFIsVhkdHSUsbGx96yUe5VxQByvd/M5VdCapkkoFGL9+vXiJvL4449jWdYlNUhMkiRJkiRJkqRLl8w3Zb55uVIaobdOUZRFPYhQKMTatWtFJdZxHDHlvVKpiAvAtm1yuRypVOo9X0tVVVpaWhgYGBA9nrZtE4/HSaVSopcymUySyWQWBLPXIxmJRIC5CnWtVhMDyxRFoVgsimPt7e1ly5YtrFy5kqamJnHcXjXUG8DlOA6BQADbtsXFDnNV2Wq1SiaTYd++fbiuSzqd5siRI2LrRW8gVyQSIRKJ0NraSkdHBz09PeJYY7GY2LLQdV0xPT8cDov3LRaL5H+dnIgAABDjSURBVHI5pqenGRoa4vDhw+Km5vP5xJaPqqqiKIq4YXnLDU91rkOhEJ2dnWKQWy6XI5lMsmvXrnO5JBrB667rXnuxD+LdokrC3ap89GIfhiRdNM+6j8vYlKQG84r7HDk3pVzs4zgVGZvSlayRY1PmmzLfvNLzTdd1zyo2r8iC0bt5S+8cx6FcLp9ztdAwDLq6ulBVFdu2SSQS9Pf3Y9s2lmVhWRbT09McPXpUBIuu62K5n7cEEeaCSFEUKpUKlUoFVVVpa2ujq6uLVatW0dbWJiqrJ09sV1UVXddxXVcMYvOqx7t37+att95ieHgYx3Foa2vD5/Nx9OhRstnsvCWN3haCqqoSj8fFEDLTNLFtm0wmIwLP7/eLYWIwN4CsXC6LAW+KolCv16nX6+I1vRued25OJRwOMzAwQEdHB+FwmGKxyKFDh8REfe+mcImTSakkNSBZMJKkxtPISamMTelK1sixKfNNmW9e6fmmLBg1MC+YvF7Jjo4O1q5dSzgcplKpiKn0qVSK6elpUW32LnqvYqvruhja5TgOkUiEUChEc3MziUSCWCyGYRhi+aD3x+fzYVkWoVAIgDfffJMTJ06I5Yitra1omsb09LRY8ge/Xi7pVWZhrlJ7Mm85YCAQEFsxlkoldF0Xj3s3Dphb8uf1mJ5MURTRp2oYhqgwh0IhTNNkcnKSl19+GUVRRMBfZmRSKkkNSBaMJKnxNHJSKmNTupI1cmzKfFPmm1d6vnm2BaMrYobRxfbuwVnDw8MMDw+Lv8fjcQYHB1myZAl9fX2oqkqlUiGTyZDNZsnlclQqFdHP6lVKva972/yFw2HxmsFgkEKhgG3bRCIR0aNZrVZJp9OUy2VRHa5UKmIpoa7rovLqDR0zTXNeFdyrisdiMVzXFdtAnlzR9aqw3k3C5/OJSm8gECAYDIrKsWmaGIaBqv7/7d1fbFxlfsbx78/j2J7x+M86Do5DEBACxhERbFjIikarikptiYSyvalyAaXVStx0pV2plZrd3KzEzbZSc7FSWUHZlUi1KirNVotQkcxGQb2IwnqzIcRJ8GJoSBZsB2cSx38y9oz99mLOnI4Tj+0zeHxee56PZHl8PLYf3nmfoPPqnHfqSCaT4S74Z8+eZXR0tAqviIiIiIiIyMag802db1aLFow8cOPGDU6dOgUUStPZ2UkymSSdTrN7925yuRw3btwIC3n9+nVGRkbC+zmLZSm997V4qV/xfs2pqSny+TypVCp8/tzcHHV1deE/Lrlc7o7VXiDcJK24ylosayaTWfC84sZnyWQSgLa2NlpbW0mn0+ElgclkMly5/vTTT8P7d4s7zC91/66IiIiIiIhEo/NNnW9WSgtGnpmenuby5ctA4V7XwcFB6urqwkv1HnvsMZ588kkAvvzyy/DywtHRUcbGxpibmwvfYtA5x9zcHJOTk2HJstnsgntGi2Uqlrm4MpzP58PilkokEjQ2NpJOp+nq6qKjo4Pm5uawvKlUitbW1nCn/Gw2y/DwMP39/XzxxRfhpYLFDd9qZXd5ERERERGRuOl8U6LQHkbrWEtLC/v27Qt3pN+6dSvZbJZMJsMnn3zC5OQkUHibxeJbGRZXdYubftXX1+OcC3eNTyQS4T2wpfenJhIJ2tvb2bZtG11dXbS1tdHU1EQ2m2V8fJyZmZnwssYzZ84wPj4e59CsR9onRcRD2sNIxD8+75Oibkot87mbOt+sjM43Nw7tYVSDJiYmeOedd4DCfaa9vb3hZmD33nsvDz30EE1NTeHbJE5NTXHr1q2wbHNzc+HlfMWNxoo7zzc0NJBKpcJNx4r3n166dImhoSESiQTZbJYLFy6E/1CIiIiIiIjIxqDzTdGC0QYxPz/P+fPnw6+bmpro7+8P3zoxkUgA0NPTQ1dXF1u3biWVSpFMJsNd8NPpdLgSPDk5yenTpxkaGgp/Zz6fZ3JyMtwMTURERERERDY+nW/WJi0YbVDZbJZsNnvH8eHh4fCtE5dSfItEERERERERkVI636wNWjCqQSqniIiIiIiIVIPONzeOurgDiIiIiIiIiIiIX5ZdMDKze8zshJldMLPzZva94HiHmb1rZh8Hn78WHDcz+4mZDZnZh2a2p9r/ESKL2bJlC9u3b487RtWomyJ+UjdF/KRuivhJ3ZT1aqOfb8LKbknLA3/nnPudmbUAp83sXeCvgePOuR+b2SHgEPAPwDPAg8HHXuCnwWeRquru7mbfvn10dXUBsGnTJq5du8bRo0djTlY16qaIn9RNET+pmyJ+UjdlXajB883lF4ycc8PAcPB4wswuAncDB4A/Dp72OvAehQIfAI66wk2Lp8ys3cy6g98jsmq6u7t5/vnneeKJJwDYvHkzV69e5e233yabzdLf389nn30Wc8rqUTdF/KRuivhJ3RTxk7opvqr1802IuOm1md0HfB14H+gqKeUI0BU8vhu4UvJjfwiOLSiwmb0IvBg5sdSM9vZ20ul0+HVPTw+HDx9m69atTE1NkUgkmJqa4siRI5w8eRKAW7duMTExUXObrFWrm02kqpZZpBaomyJ+UjdF/KTzTVlLOt9c3ooXjMwsDRwDvu+cu2klb5PnnHNmFmnEnHOvAq8Gv7s2RlsW1dDQwK5du+jp6aG+vjAlzYznnnuOvXv3curUKcbGxgAYGhripZde4sSJE3FG9ko1u9lqHeqmSIXUTRE/qZsiftL5plSLzjcrt6IFIzPbRKG8v3DO/TI4PFq89M/MuoGrwfHPgXtKfnx7cEyEnTt38uyzz9Lb2xsea2xs5NFHH+Xhhx+mr6+Pvr4+ZmZmOHbsGK+99hrvvfdeWGBZSN0U8ZO6KeIndVPET+qmrBadb66uZReMrLC0+zPgonPuSMm33gJeAH4cfP5VyfHvmtkbFDYfG6+1+0nb2tqoq6vDOUcul2N2dpZ8Pr8hLlszMxoaGqivr6e+vp66ujvfaK+np4eDBw/y1FNP0drayuzsLNlsFuccyWSSzZs3Mzs7S19fHy+//DJTU1Phz2YyGa5fv74hxqra1E0RP6mbIn5SN0X8pG5Gp/NNnW+ulZVcYfRHwPPAOTP7IDj2QwrF/Q8z+w7wGfCXwff+G9gPDAHTwN+sauJ14JVXXqG3t5cdO3Zw5swZTp48yblz58hkMmV/JpvNMjMzw+TkJDdu3ODWrVvk83lmZmaYnp5e1Xx1dXWkUikSiQTNzc0kk0laWlpob2+nsbFx0VIWNTc38/TTT9Pb28vjjz9OKpXi2rVrDA8PMzIywtzcHFAo+tjYGBcuXOD48eO8+eabzMzMLPhdKulXpm6K+EndFPGTuiniJ3UzIp1v6nxzrZgPg7gR7yl95JFH2L9//x3HOzs72blzJ7t37+aBBx6geG/u9PQ0N2/eZGxsjJGREcbHx5mdnQ0LvZqv06ZNm2hra6OhoYH29nba29vZsmUL27ZtC4vtnOPKlSv09/dz8eJFJiYmFv1duVyOy5cv89FHHzE4OEg+n1+1nDXmtHPuG3GHuF2rdbi99idxxxCJza/df6qbIp553x3npsvY8s9ce+qm1DKfu6nzTZ1v1jrnXEXdjPQuabJyAwMDDAwM3HG8sbGR1tZWOjo6aGlpqeh3NzY2ctddd5FOp6mvryeXy9HZ2cn8/DwtLS00NTWRyWTIZDKMjo6SyWSYn5+P/HcmJycZGxtjfHycXC5XUVYRERERERFZXTrflLXgyxVGXwJTgI87TXWiXFEoVzTFXPc657bEHeZ2ZjYBDMadowzfX1PfKFc06mblfH9NfaNc0XQCzT72Erzupq+vJ/ibTbmiUTcr4+vrCf5mU65ovlI3vbjCyDm3xcx+6+Nl/8oVjXJF42uuEoO+5vN17JQrGuWqmLoZkXJF43mu++LOsQQvu+nr6wn+ZlOuaNTNyvj6eoK/2ZQrmq/azfK7TYmIiIiIiIiISE3SgpGIiIiIiIiIiCzg04LRq3EHKEO5olGuaHzNVeRzPl+zKVc0ylUZn/P5mk25olGuyviaz9dc4G825YrG11xFvubzNRf4m025ovlKubzY9FpERERERERERPzh0xVGIiIiIiIiIiLiAS0YiYiIiIiIiIjIArEvGJnZn5vZoJkNmdmhmLNcMrNzZvaBmf02ONZhZu+a2cfB56+tUZafm9lVMxsoObZoFiv4STCGH5rZnjXO9SMz+zwYtw/MbH/J934Q5Bo0sz+rYq57zOyEmV0ws/Nm9r3geKxjtkSu2MdsBdnVzcWzqJvRcqmbq59d3bwzh5e9XCJbrPPM114uk03djJZF3YyeK/Y55ms313Mvgyzq5p051M1ouWq3m8652D6ABPAJsANoAM4Cu2LMcwnovO3YPwGHgseHgH9coyzfAvYAA8tlAfYD7wAGfBN4f41z/Qj4+0Weuyt4TRuB+4PXOlGlXN3AnuBxC/D74O/HOmZL5Ip9zJbJrW6Wz6JuRsulbq5ubnVz8Rxe9nKJbLHOM197uUw2dTNaHnUzeq7Y55iv3VyvvQyyqJuL51A3o+Wq2W7GfYXRk8CQc+5T59ws8AZwIOZMtzsAvB48fh349lr8Uefc/wCZFWY5ABx1BaeAdjPrXsNc5RwA3nDOzTjn/hcYovCaVyPXsHPud8HjCeAicDcxj9kSucpZszFbhrpZhroZOZe6ubrUzUX42sslspWzJvPM114uk60cdXPl1M2lc5Wj/2eu316CurkodTNyrprtZtwLRncDV0q+/gNL/wdWmwP6zOy0mb0YHOtyzg0Hj0eArniiLZnFh3H8bnC53c9LLqOMJZeZ3Qd8HXgfj8bstlzg0ZgtwpccRepm5byZZ+rmqvAlR5HP3fRmjpXhxTzztZeLZANPxqwMX3IUqZuV8WaO+drNddZL37KAulkpb+ZZrXUz7gUj3+xzzu0BngH+1sy+VfpN55yjUPLY+ZQF+CnwAPAYMAz8c1xBzCwNHAO+75y7Wfq9OMdskVzejNk6oW5Wxpt5pm5uWOuim77kKOHFPPO1l6BurgJ1Mzpv5piv3VQvV4W6GZ0386wWuxn3gtHnwD0lX28PjsXCOfd58Pkq8F8ULs8aLV4+Fny+Gle+JbLEOo7OuVHn3Jxzbh74V/7/srY1zWVmmygU5RfOuV8Gh2Mfs8Vy+TJmS/AlB6BuVsqXeaZuripfcgDedzP2OVaOD/PM116Wy+bDmC3DlxyAulkJX+aYr91cp730LYu6WQFf5lmtdjPuBaN+4EEzu9/MGoCDwFtxBDGzZjNrKT4G/hQYCPK8EDztBeBXceQLlMvyFvBXVvBNYLzk0riqu+1+zL+gMG7FXAfNrNHM7gceBH5TpQwG/Ay46Jw7UvKtWMesXC4fxmwZ6mY06mb5DOrm6lI3V87LXkL888zXXi6VLe4xWwF1c+Vin2eL8WGO+drNddxLUDejUDfLZ6jdbroq78C+3AeFHcR/T2GH7sMx5thBYcfws8D5YhZgM3Ac+Bj4NdCxRnn+ncLlYzkK9xZ+p1wWCruv/0swhueAb6xxrn8L/u6HwSTsLnn+4SDXIPBMFXPto3AJ4IfAB8HH/rjHbIlcsY/ZCrKrm4vnUTej5VI3Vz+7unlnFi97uUS2WOeZr71cJpu6ufIc6mZluWKfY752cz33Msiibt6ZRd2Mlqtmu2nBD4mIiIiIiIiIiADx35ImIiIiIiIiIiKe0YKRiIiIiIiIiIgsoAUjERERERERERFZQAtGIiIiIiIiIiKygBaMRERERERERERkAS0YiYiIiIiIiIjIAlowEhERERERERGRBf4Pvb7L9NteYNAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x288 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "shape: torch.Size([1, 1, 208, 243, 254])\n",
            "val_dice: tensor([0.8960], device='cuda:0')\n",
            "Hausdorff: tensor([5.], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(61722, device='cuda:0')]\n",
            "Volume label: [tensor(51109, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 257, 301, 241])\n",
            "val_dice: tensor([0.9451], device='cuda:0')\n",
            "Hausdorff: tensor([194.9897], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(72541, device='cuda:0')]\n",
            "Volume label: [tensor(39505, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 251, 303, 234])\n",
            "val_dice: tensor([0.9485], device='cuda:0')\n",
            "Hausdorff: tensor([9.4340], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(70902, device='cuda:0')]\n",
            "Volume label: [tensor(63166, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 208, 247, 214])\n",
            "val_dice: tensor([0.9111], device='cuda:0')\n",
            "Hausdorff: tensor([130.5144], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(52858, device='cuda:0')]\n",
            "Volume label: [tensor(22918, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 191, 241, 231])\n",
            "val_dice: tensor([0.9092], device='cuda:0')\n",
            "Hausdorff: tensor([136.8722], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(55671, device='cuda:0')]\n",
            "Volume label: [tensor(44010, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 206, 239, 241])\n",
            "val_dice: tensor([0.9109], device='cuda:0')\n",
            "Hausdorff: tensor([40.1622], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(57599, device='cuda:0')]\n",
            "Volume label: [tensor(51601, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 209, 252, 341])\n",
            "val_dice: tensor([0.9495], device='cuda:0')\n",
            "Hausdorff: tensor([150.0766], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(85932, device='cuda:0')]\n",
            "Volume label: [tensor(48847, device='cuda:0')]\n",
            "shape: torch.Size([1, 1, 222, 302, 296])\n",
            "val_dice: tensor([0.9187], device='cuda:0')\n",
            "Hausdorff: tensor([125.0840], dtype=torch.float64)\n",
            "Volume segmentation: [tensor(89392, device='cuda:0')]\n",
            "Volume label: [tensor(43028, device='cuda:0')]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.7/multiprocessing/queues.py\", line 242, in _feed\n",
            "    send_bytes(obj)\n",
            "  File \"/usr/lib/python3.7/multiprocessing/connection.py\", line 200, in send_bytes\n",
            "    self._send_bytes(m[offset:offset + size])\n",
            "  File \"/usr/lib/python3.7/multiprocessing/connection.py\", line 404, in _send_bytes\n",
            "    self._send(header + buf)\n",
            "  File \"/usr/lib/python3.7/multiprocessing/connection.py\", line 368, in _send\n",
            "    n = write(self._handle, buf)\n",
            "BrokenPipeError: [Errno 32] Broken pipe\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.7/multiprocessing/queues.py\", line 242, in _feed\n",
            "    send_bytes(obj)\n",
            "  File \"/usr/lib/python3.7/multiprocessing/connection.py\", line 200, in send_bytes\n",
            "    self._send_bytes(m[offset:offset + size])\n",
            "  File \"/usr/lib/python3.7/multiprocessing/connection.py\", line 404, in _send_bytes\n",
            "    self._send(header + buf)\n",
            "  File \"/usr/lib/python3.7/multiprocessing/connection.py\", line 368, in _send\n",
            "    n = write(self._handle, buf)\n",
            "BrokenPipeError: [Errno 32] Broken pipe\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-02ca22bcd286>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     54\u001b[0m             \u001b[0mvols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval_outputs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m             \u001b[0mvol_label\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval_labels\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute_meandice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_outputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_labels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minclude_background\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     57\u001b[0m             \u001b[0mhausdorff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute_hausdorff_distance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_outputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude_background\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m             \u001b[0mmetric_count\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/monai/metrics/meandice.py\u001b[0m in \u001b[0;36mcompute_meandice\u001b[0;34m(y_pred, y, include_background)\u001b[0m\n\u001b[1;32m    143\u001b[0m     \u001b[0mdenominator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_o\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my_pred_o\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m     \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_o\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m2.0\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mintersection\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mdenominator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"nan\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_o\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    146\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m  \u001b[0;31m# returns array of Dice with shape: [batch, n_classes]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sQttUi_InYHQ"
      },
      "source": [
        "print(val_data[\"image_meta_dict\"]['filename_or_obj'])"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}