TSG.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TSG.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/PranY/FastAI_projects/blob/master/TSG.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "8AsiVn76e1tI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!pip install fastai"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "fhVrdL4bJ5Nv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!pip install torch_nightly -f https://download.pytorch.org/whl/nightly/cu92/torch_nightly.html"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8Fdj_T_DfQFp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "! pip install kaggle\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4YJmIolMZQQi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "5925d0a2-1a49-4f7a-8849-c38592fdd311"
      },
      "cell_type": "code",
      "source": [
        "! pip install tqdm"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (4.27.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "zUIFajUkgN_7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SFOgSEpNiXtp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "! ls \"drive/My Drive\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HPhnV4GYkKx1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "! cp drive/My\\ Drive/kaggle.json ~/.kaggle/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SnGsEGD2kerM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "! kaggle competitions download -c tgs-salt-identification-challenge"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "G991UyQlCVss",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 766
        },
        "outputId": "6ef7b540-ee89-48e8-ce54-585901e96faf"
      },
      "cell_type": "code",
      "source": [
        "! python -c 'import fastai; print(fastai.__version__)'\n",
        "! python -c 'import fastai; fastai.show_install(0)'"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0.7\n",
            "\n",
            "\n",
            "```text\n",
            "=== Software === \n",
            "python version  : 3.6.6\n",
            "fastai version  : 1.0.7\n",
            "torch version   : 1.0.0.dev20181019\n",
            "nvidia driver   : 396.44\n",
            "torch cuda ver  : 9.2.148\n",
            "torch cuda is   : available\n",
            "torch cudnn ver : 7104\n",
            "torch cudnn is  : enabled\n",
            "\n",
            "=== Hardware === \n",
            "nvidia gpus     : 1\n",
            "torch available : 1\n",
            "  - gpu0        : 11441MB | Tesla K80\n",
            "\n",
            "=== Environment === \n",
            "platform        : Linux-4.14.65+-x86_64-with-Ubuntu-18.04-bionic\n",
            "distro          : #1 SMP Sun Sep 9 02:18:33 PDT 2018\n",
            "conda env       : Unknown\n",
            "python          : /usr/bin/python3\n",
            "sys.path        : \n",
            "/env/python\n",
            "/usr/lib/python36.zip\n",
            "/usr/lib/python3.6\n",
            "/usr/lib/python3.6/lib-dynload\n",
            "/usr/local/lib/python3.6/dist-packages\n",
            "/usr/lib/python3/dist-packages\n",
            "/usr/local/lib/python3.6/dist-packages/IPython/extensions\n",
            "```\n",
            "\n",
            "Please make sure to include opening/closing ``` when you paste into forums/github to make the reports appear formatted as code sections.\n",
            "\n",
            "Optional package(s) to enhance the diagnostics can be installed with:\n",
            "pip install distro\n",
            "Once installed, re-run this utility to get the additional information\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "hwV2u7KopL2s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "8357c301-f67d-4365-a41f-e83be171d5e3"
      },
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "depths.csv  sample_data\t\t   test.zip   train.zip\n",
            "drive\t    sample_submission.csv  train.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bTA1X9BiDNDx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# ! rm -r train/\n",
        "# !rm -r test/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "K7QtIKklrmoO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "! mkdir train\n",
        "! mkdir test"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SVAT2vovswkX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "! unzip train.zip -d train"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "j9-tPyGotFOt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "! unzip test.zip -d test"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9kZ7vuw2tj6M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "89d06d6b-85a9-4e2e-c77d-3b1728cd2f94"
      },
      "cell_type": "code",
      "source": [
        "! ls train/images | wc -l\n",
        "! ls train/masks | wc -l\n",
        "! ls test/images | wc -l"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4000\n",
            "4000\n",
            "18000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "te_nGHkRk22k",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "%reload_ext autoreload\n",
        "%autoreload 2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "m84l1Nn3Zb3x",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm_notebook"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "fIdOZ7VKlxJI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from fastai import *\n",
        "from fastai.vision import *\n",
        "#from fastai.docs import *\n",
        "import PIL"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "I6qepqUkl6i7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "ff5abab2-51b9-443f-8d81-4158d89e905a"
      },
      "cell_type": "code",
      "source": [
        "# Loading of training/testing ids and depths\n",
        "train_df = pd.read_csv(\"train.csv\", index_col=\"id\", usecols=[0])\n",
        "depths_df = pd.read_csv(\"depths.csv\", index_col=\"id\")\n",
        "train_df = train_df.join(depths_df)\n",
        "test_df = depths_df[~depths_df.index.isin(train_df.index)]\n",
        "num_workers=0\n",
        "len(train_df)\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "metadata": {
        "id": "iZmeuBOOayus",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "PATH_X = Path('train/images')\n",
        "PATH_Y = Path('train/masks')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tiAse2rE-qdD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# def resize2d(fn:PathOrStr, sz) -> Image:\n",
        "#     img = PIL.Image.open(fn)\n",
        "#     img = img.resize((sz,sz), PIL.Image.BILINEAR)\n",
        "#     img.save(fn)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ra_r_ie5DKlk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# for l in list(PATH_X.iterdir()):\n",
        "#   resize2d(l,128)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "OsEOW641RLAX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# for l in list(PATH_Y.iterdir()):\n",
        "#   resize2d(l,128)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RDSkQnvTq_mL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Reducing mask images to {0,1}\n",
        "\n",
        "def FormatMask(fn:PathOrStr) -> Image:\n",
        "\n",
        "    img = PIL.Image.open(fn).convert('L')\n",
        "\n",
        "    # Let numpy do the heavy lifting for converting pixels to pure black or white\n",
        "    bw = np.asarray(img).copy()\n",
        "\n",
        "    # Pixel range is 0...255, 256/2 = 128\n",
        "    bw[bw < 128] = 0    # Black\n",
        "    bw[bw >= 128] = 1 # White\n",
        "\n",
        "    # Now we put it back in Pillow/PIL land\n",
        "    imfile = PIL.Image.fromarray(bw)\n",
        "    imfile.save(fn)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Lrr2fRq1sS_s",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "for l in list(PATH_Y.iterdir()):\n",
        "  FormatMask(l)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-ckUjeO7HbDL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class ImageMask(Image):\n",
        "    \"Class for image segmentation target.\"\n",
        "    def lighting(self, func:LightingFunc, *args:Any, **kwargs:Any)->'Image': return self\n",
        "\n",
        "    def refresh(self):\n",
        "        self.sample_kwargs['mode'] = 'bilinear'\n",
        "        return super().refresh()\n",
        "\n",
        "    @property\n",
        "    def data(self)->TensorImage:\n",
        "        \"Return this image pixels as a `LongTensor`.\"\n",
        "        return self.px.long()\n",
        "    \n",
        "    def show(self, ax:plt.Axes=None, figsize:tuple=(3,3), title:Optional[str]=None, hide_axis:bool=True, \n",
        "        cmap:str='viridis', alpha:float=0.5):\n",
        "        ax = _show_image(self, ax=ax, hide_axis=hide_axis, cmap=cmap, figsize=figsize, alpha=alpha)\n",
        "        if title: ax.set_title(title)\n",
        "        \n",
        "def open_mask(fn:PathOrStr)->ImageMask:\n",
        "    \"Return `ImageMask` object create from mask in file `fn`.\"\n",
        "    x = PIL.Image.open(fn).convert('L')\n",
        "    return ImageMask(pil2tensor(x).float().div_(255))\n",
        "  \n",
        "def _show_image(img:Image, ax:plt.Axes=None, figsize:tuple=(3,3), hide_axis:bool=True, cmap:str='binary',\n",
        "                alpha:float=None)->plt.Axes:\n",
        "    if ax is None: fig,ax = plt.subplots(figsize=figsize)\n",
        "    ax.imshow(image2np(img.data), cmap=cmap, alpha=alpha)\n",
        "    if hide_axis: ax.axis('off')\n",
        "    return ax"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "p8wSwunvrcMc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "d85abdb1-6991-4b6d-c044-f3744a7173a6"
      },
      "cell_type": "code",
      "source": [
        "img = next(PATH_X.iterdir())\n",
        "open_image(img).show()\n",
        "open_image(img).size"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([101, 101])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EhBo4iHogEZfjhs/oT31xcaEaiE0JhJdXq5U2A+EZ1SeTsikym81yXDmenblE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\ngBISaiAJUEJCDSQBSkiogf8HvpaVGBFvh9oAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f26aae3c8d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "4t-HzDCZdrWy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "93a624d2-b0b8-480e-817f-d2251d150066"
      },
      "cell_type": "code",
      "source": [
        "def get_y_fn(x_fn): return PATH_Y/f'{x_fn.name[:-4]}.png'\n",
        "\n",
        "img_y_f = get_y_fn(img)\n",
        "open_mask(img_y_f).show()\n",
        "open_mask(img_y_f).size"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([101, 101])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAANAAAADFCAYAAAAlv3xcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f26aad52d30>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "un2CkaDYNDRs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "7ee3c9ea-87bb-472c-c024-fd958e1f77ea"
      },
      "cell_type": "code",
      "source": [
        "x = open_image(img)\n",
        "x.show(y=open_mask(img_y_f))\n",
        "x.shape"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([3, 101, 101])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kxoOfjkLflzW4C6qmMvhLSHdBwQ1xWxWOQKXhwrfWiHp2/xAsb5h8niiWGn3f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\nTE8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f26aae3c940>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Auwc1e_3e3uE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "5211e84c-1623-4e7d-d8b5-905f18178443"
      },
      "cell_type": "code",
      "source": [
        "open_image(img).shape, open_mask(img_y_f).shape"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([3, 101, 101]), torch.Size([1, 101, 101]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "metadata": {
        "id": "ru9zCSgPiew6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def get_datasets(path):\n",
        "    x_fns = [o for o in path.iterdir() if o.is_file()]\n",
        "    y_fns = [get_y_fn(o) for o in x_fns]\n",
        "    mask = [o>=1000 for o in range(len(x_fns))]\n",
        "    arrs = arrays_split(mask, x_fns, y_fns)\n",
        "    return [SegmentationDataset(*o) for o in arrs]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "GrF3zqPpjeX7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "4855aef8-366d-47f7-96ee-e059ce263f3b"
      },
      "cell_type": "code",
      "source": [
        "train_ds,valid_ds = get_datasets(PATH_X)\n",
        "train_ds,valid_ds"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(SegmentationDataset of len 3000, SegmentationDataset of len 1000)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "metadata": {
        "id": "gAv3-V6eji52",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "outputId": "437b41ac-d7fb-4b03-9f9d-d3278a4330ed"
      },
      "cell_type": "code",
      "source": [
        "x,y = next(iter(train_ds))\n",
        "x.shape, y.shape, type(x), type(y)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([3, 101, 101]),\n",
              " torch.Size([1, 101, 101]),\n",
              " fastai.vision.image.Image,\n",
              " fastai.vision.image.ImageSegment)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "metadata": {
        "id": "Jr61LBMGj-Cm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "size = 128"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QeAfNBTjkGTu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def get_tfm_datasets(size):\n",
        "    datasets = get_datasets(PATH_X)\n",
        "    tfms = get_transforms(do_flip=True, max_rotate=4, max_lighting=0.2)\n",
        "    return transform_datasets(train_ds, valid_ds, tfms=tfms, tfm_y=True, size=size, padding_mode='border')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JZW6O9yXkTIM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_tds, *_ = get_tfm_datasets(size)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qu3RaRefFs7-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "outputId": "18879a1a-2ea9-4179-cd22-04c71d397acc"
      },
      "cell_type": "code",
      "source": [
        "for i in range(0,3):\n",
        "  train_tds[i][0].show()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qTyxG4DYCkcQe4YptE5nEYWLUBi9ggb0TPecOXdIXZyyKIXBPo9HQq1evshg0\n6gG7fu/qnKtBDmXzba+qEtNqMZ5j6SOvVQac7wvQy69Op9PkdfdNytXfWq2WTgHwo6W/+OILdTqd\nZEvc398/ycIkcc8Zzuswc6ZivV5P6++zjQL2HdgXu+9KPnDsGK67xqhY3hWdghjPviv3+/0n6Irv\neEy0R8rimIthGHzLPdu+S2OEutPKvcJ+CA+54Sxa3sfpuyA/jGG9Xk+Gc7PZfBKst9lsnpyGRV89\nMSpWcMHDzkKqGiN26ejL8jCY4XCYDGUpL9bnAa6NRiNtUuTb+/h3Op10XiQhJySTRT/RarVKC38w\nGGRhQoAKnrz2nCZyVCaJXmkYwMMvXN3BseYdcqPZd0ivIMg1N86bzWZWiZ72+AKI6hdRw66SIKF8\n0VArzFUOCj3AACAsXqxOUlafCsZiAZLz7kyCY5CFTYhGzJXwcSMdwSUJTORFLgaDgc7OzjJnIgvc\nY+C8YDhj6BG2p6enaTdnzPjnkRPMC20+PT3VxcVFYnp2/sVikcaIjTOG96AWci1C8gAubC4xdsyp\nGO6FCh2go6tbDokifj3/wH0i1Op1u8RzCSCKtblxDwTqksTPNgeCdjsBu8HVJtQdPwaBMBhpCzdy\nHonvbtgkXvkE6cb3AQ9QyXC4VeWLeAlPdlXag+RwlTDq+6hSvpPTZq71+32dn59nUhJ41c8s9P6j\n7jUajez8w4eHh8yOIkjSw0m8jY3G9gi/L7/8Mkkk+saZiIyRq20epOlHeLjdRDCnV8ivCriEjsok\nFCyQdkeUuS4LIuEe3xh/hUiNXnoMQ2mX3OXGPepIrLzodYVhKn/34+OjPn36lH4HRXK0hbPcHbmT\ncnQNXw4qIX4BR8A8ng2KDlbsGg9KpHok78ZwhkFpB4eUSk/PXuTayclJWpAwhxvBLG6Pi6IYBecf\ndjqdzMAmkpjys8yR+7OY27OzM7169SqNNbXPUDcZE0cgV6tVFl2BTUr/sVcjSLSPjsokl5eXadck\n5IJgOGnnhffdwfMvpO2ksJuzU0U4UVLyAvsidSbB+UWBOKiKSa6vr58EYdIuTmdyb7LvljH61qMC\nYh/pv0skGMTDa1zacKhmPEeRUAzeCwLELo0RH9OX/YDOh4cHPTw8ZJIMg9vLznKGpYMZsaSrRwZI\nSuVto2T3hdzpdJ4EPsbI7cgY3OMaiYfyOAK2j4pNUqjQATqqJCG0gP/jF4lZYq5KSPnujpo0m80S\nclSr1bJibOv1OtWBcgjYUQ+kzGQyyaRNDHnnXb5jeduAV30HJHjRi1xEnxAqSwyV8W/Qnhi8SMCg\nlOfhOwoXgzJjGdFut5va57sKUAerAAAgAElEQVTyer3OJMl0On0yJq4iet+5NplMnqhHMQwFhMzV\nH77Pezih2CUJDmifDw9xknb+HpdsjJWfWLCPjsokrhIQAOh6uxu80s6X4A5GHHR+bJmDABBM4o5J\n6Wnaq+vJBBO6sU+b9jnBPG3V8znQ57nPo275FottX3wZ7fWgS97DGLnd5c/EzDugZL7lcUyuppHW\nLOVH5vn4ud1EPzxQ9OHhIfMPMTcxXs494IPBIPm1mHd8LaPRKIvsjSoiKqlvUjGrkcDJqiS5SEdl\nEvdAo5OyUKXd7ucH0sfOkDgzGo2SJCHCNi5298zG1F0mzO2LOJHYMR4Gwi7Gu6pqylKpxXc8Sn3G\nbzm6ht4cQYmY9++Zgu4o9LGKUgupxFjjb3CPO+PvDMgYcA/gAr4MbB1f8Cxqnw/66b4tt9nwz/j8\n12q7OsOsGz93kTZC/j2vXUA/nUk+W3Tr06dPaYdxFQbxTseYpNvb2ydqCh12p1us6Yv4dcbBUHWV\nBrXF47J8l2Tnd6Zgl6qKMvZ347x0JonlUiNyRXs8IzOm/UZG5r4I+VZFJdRqu3NVYC6XZPz0CAKP\nfvZ2g2QRezedThNyFmsOMAZ+DYcooM3l5WVCqlgj1NNyNC+ONZmTVUa5j6uUQ+nPFYIohnuhQgfo\n6JLEDTd2ZVebXP1AZLu4Bzd3NQn4ld1usVikgDZPFnLDnN3eoUJ2RXY+t3U8dsxFdVVUMob2ycnJ\nEwkU88GlvFqJR/MiRd2fgC2F+sFO6hB0DMvwa56sFX0HDibwu8fY8R4PQQGKd1CgiqL0c5+OtA1l\nQUKjEk4mE93e3mYBoK4m04/YxhiVDMUg0H109FrAHrlLaDQNBm2J4dRuOLohjKimXJA7qjgDnmtk\nOXrsFFG2ju7EYhEY7VG/9vgybAvXlRuNRgq0q3rO9XMIJva4JhaFM4kjQK72+Xg5uuXefWdOvuFj\n64UouOYMiIcdJyQlTn1u3FCnbfzjPQAeOJWJhvCAT07nxS7jXQ5WMD8+JhzZ4aotP11F30dHT9+t\nKhMUw9fdcYZezCCxKAkzkXYFsiHu98XNNc+NhqJXPOrQPrmRGVgMsY3suA5dO9P6wmHiFouFJpPJ\nk+OlXZf2ajGMXXQ4uqHOM7VaLYWZQPP5PAvniRHOLD437rvdbqqnSz8iBB7n0RnZkUAPEwFscWgf\ne8TtNsYyIoK+kfCu+H0gfel5Jik2SaFCB+joZU7ZJU9OThJs6RzvId/syB6+jkrk4jyeVuWnsToq\n5mH47OL+7vguhy7dtoh6fPyWqzXsYjg2I06/Wu3OcsfeisX2sMv4fvS3cD3Cmzj08HW4FFkul8m3\n4WiapCwGDXWGHf/k5ES9Xi97t48D7/aAQ8jtObe3eA8oGWgnjsx9dcb4bryOehjRLkcpnwtLOTqT\neEKTi3lpF+vkxi2e1BiDJe066jkm0tYYjHZCjAvzRe86tTOftFOn9sV3EQ/m58/HjEhp55jzQnTR\nmAaidkZi0/BkscFgkMV7ca87aj0I00/9cj8JMWAeYez9R2VyFYtIYj/VC/XHbYLYDzYSZ3xf+MR7\nzWaz9G7POPVNySmqr7TJgzCh/zVMEoPQ4oJ0OwIfRtXE+cKlQqD7RIgy9QBJnxgkSPTexoGlPdHD\nHZmEMja0kfaxA7MoXUrQD2+TVz5k4Q8Gg+S8c2NX2oV3RH3fo5v5VrPZzMLX3eiV9MRew4g/OTlJ\nIUXtdluz2SwLE2FMPFRms9kdM8fYMjY+1lx3JqGNHhYfwQHI3+c2ZNU9/p7nkLijZyZ6qHsMC4+h\nJW4U+j0xorPb7WblMYlLcqkESlNljEPr9VrT6TSTNuSmeKi4qxEsvE+fPmWRscCZLi09vAW1DcnA\n9waDQWIC1M/hcJjFvQFV0w8Y0qFzl2Z818PJHfr28qAgTDzX6/V0fn6e1bDykj6OOkWkLYay8H/e\n4wyK0c5mEtdEnKsIubtKHOFfd7a6w3kfFcO9UKED9NlIEvD+6FtwQ5pCDH7N1SMoOpN8B3UD2u0G\noE7POSFbzzMcqQR4d3eX7nGfTKPRSDFp7hTkHo5Woz3RuVmv74o6IFVQt9DJvYAFxi2hG8ClHheH\nmsI9SD83gN34d9vBYXqgddrDuzzi2kNrPHTG58znIQaYeoUZ4qv83phjEkN5nPZJErSRKiO/io6e\nvsvEzGazys76gBD75AwAQ7g+i5j2e6qiYDmjUdpNgN+LmsKigEnc487kuq2DncC14XCYVC5UqWho\nE7vkjFSv17OoYpyfnsBEWiwMMB6PU/u8AotXRmGRrtfrTP3zuDLIFym2F34H/h5juliEEQGEoo9I\n2oE0zvwwiatC0SbhWrRNmAtvd7Rjq2LaquizYRI66h2OgWkYoB5mgO4ZvcceBEftKjew0bWdSTDc\nPQ+96hTdWq2WFTPz2lQYxET5Srsyp47meWox76BfPrkewk54O+2VtkGf+3LnYy0siI3AF4nbhI44\nekUXIHiXLjjp3N5AcjhQ4KAMc+a7OdLN55X58c1un6EenYs+RjH4FADEGfezlSRu3LGAfSGz27j4\nJz3VsW4mzRe8x+LEyuT8jOgGO6JHAZ+enmbfB12KHncGnGqFbhR7sbYIlXo72f2pKeUh3bTZVR9p\nmy5M3oe0i1NzCexM6mPviw71s17fFYMj3s3b12g0MlCE+eE+1MyqSGnf/OKCZ0PyuQcVew6Niv2g\nTXHRxzwlGP4lkqQY7oUKHaCjShLP1EOnjvq+i3+XJOwUVWoK5MYd3l33y3jpTcS/17VFreK9nU4n\nxTY5lBzzIqLdRJXDRqOh6+vr1DckBf3YbLaVFoGXSS+NvhSXEjjpfMcnmcuL07l6wXdRL3kPsDBg\nSqygSFs8WDBKCWwU36FRt2KAapSAPodx53+OXJK4FPGf7l7AjmSeDtFRmeT+/j5jEgYvGtk+Idgk\nLnYx5ty+8d+ZINQgSankjjMJJXbie1B3Op1OxkTSzph3RuFkW/pxd3eXdO73799L2mUCuuGOvu/q\nlpTr3UQmOAP0+/2MIbkHAxzVjwUB8uWGMhuJR/QSueB+mtlslpLfpKcJXqitfg0m8XHjdy+z6mPo\nRnYMlKxy8jqTRPWL+x3t4t8+h6PT0ZnE0S0mzXdO3xmAjD01lQXg0CR6su/2GNLOJM4Am80mIVJe\nH9Zzsz2swyuWu2TbbDYpg453cxR0t9vVzc2NJKWCEyxSYN2I3Plil7Z2gsPCpBPD7D6Ojjh5X9H3\nHThptVrq9/uZvYWdgo0C02AH8W5ftFWLO0L50i4OLAIRTtgMVd71fTZJlXEfbV0HevzaPio2SaFC\nB+izid1i53f9GpHMroX/wHcu8ki8+jnIBYQDMMb8xGw0gvvY3VarVSo2x3cbjUaqYigpoU+8dz6f\nazwep8hV7ysFr3mXlBdTAynz8wejMxH1Cgg65twDo7pt5/4Sfo+RxUisWq2W+jaZTJIUlHYFBF11\n8jAYKVcRfW4dAkbKuE2GBKhSo6JkcDW1KpdkH0zs9k7003y26tarV68yTB6D1BOmfLCHw2GaEPfM\n9vv99Ly0C0P3iXOjU9rp1+5gQyVx/8JsNstq8+K78Jgjj7B1L75H+LKwYHRKffo9bBJen7bX6yV1\ni7PYPegR4MAXIPo9zI7tFw3nWCyDOCwvvEBgKM9hD8XgR1flUK1c3YoeeNrNWDkYIVUHl7rT+CUG\nd6SobnkbP9so4C+++CL9/+zsLA2c1/6Vdout2+2mMIiIXLj3nPJBMXvPT4ji727MscB9wt03AXPi\nwYYeH3eHb4KaeIYf/6/VdlVF3Nkn5Sd9eYSvV1SJRz7w0yXDcrk9UZZIZGkLHHDaLu2h5hnvIVfj\n+vo6MRc6u58ZAoP55uJt8zmrkhxSfjyCF55zRorOXygyyXMME22ZyCSMsf+tio5eC5iB4wwLj6p1\nB6O0C8GI4dOoTq7iUPtXUopbcsecF1eQtpPCMcUe6+NV65mQVquV8ilA3Fxt4rDNCC6sVquMSTzC\nld3Vw/lRP1yyRTSGkBTGgxBz4s6krQRyJjk/P09jzJhRCf/jx49Zkbput5vePZ1OnxTngJGiRK6C\n5B3tArZ3NTGG6T/HJIeuOUWnoTPJS6gY7oUKHaCjShI/YB6VwVUgj7mSlHwP7mBD+ngGG8F8PNdu\ntzUajTK9M4ZTYDB73kO73c6OcEBtaLfbqYROo9HI7BB8KYPBINO3saX8rBUHJaSnCV0Qux6+pOVy\nmfpK2U8/VgEQwNW9i4uLpN72+/1kawFJ39zcJFWT5zj41YMxSRxz6RrjzWJEb4SHuc8lBWMcgykj\nVdkkVRA08+XzFuPU4j376KhM8v3332deUESw67f+u6tTru/G0HRsG3fCOWIk7UoQeWAig80igUFi\nHjYIk3+ftkUQQcoDNd2fMZvNnni8sZ245sgRCWCuloxGoyzpCYMcxE/a5qGfnp6mRK3NZnvExP39\nfTprBbuF03S9/9F5GjebOB/0uSouz9/hc4QNE1Eyf3eVHbLPkHeUKyZqeXu8oso+OjqT0DgPmvOB\n8h1pPp8ndCXCxO4oJMDPGYJFyDWM68g0Xh+Ws0+qghmjx98RLeBXR9J8QUhKMDIL2Re+2xe+AIjw\nRZpwj6Ss7ycnJzo5OUlI2snJSQYAPDw86ObmRjc3N8m7Tzmhs7Oz7GCfuABjtHWEblmMzhRxHp1B\nPCo6MkmVkc7PQ4a7I1cxCNWfc6BgHxWbpFChA3RUScIuJu2g01jdOzoTgS+5j8rjnNbKMx6qTviH\nQ4ybzSZDrhqNhk5PTxNsKm2lS8xn4H1+3LOkzCmGbh9hUle3ptNppm6xa7t/B3+Fq6SoW+ySnCrl\nyFWn09HJyUl2ZuJkMkmBkx8+fNAf/vCHpKrR18FgoOFw+CTzsEoiuHrlEtKzEOlrhNL9WVdlY94L\nVCVJ4t+rHIqubsWcF2ypOLdVdFQmOT8/z+BG4qt8cQMpSrvz+KLBDVP4WYsedeol/d0mkHLsHNvG\nczaiP4OF7sct8w3/lqsOGI2eLIZx6+f4Rccgz7ohTZSCq1cwhaQMMOD7d3d3ev/+fdqUbm9vdXd3\nlwEJw+FQp6enmSqHGuW/01aPHPBF6osu1hhz3wTzGgtH+PMvdRoeUrf4njNJjHz4bJnk66+/ziJs\niV5134V3DlvDdWCSmWIoOM9LuePOd+loJ+CQdJsgok0MuDsq/R2EkXsYOJLPJyfe48zB7goIQLgN\nUsSNa84m9MSw29tbjcfjlIf/6dMnvX//PqGJOGMbjUaSGsPhUJ1OJ9Xe8u/FEJCYZRilLeNK31xi\nMo+QO2H9PQ6kRNoXxBj/7+kELn29Ty+hozLJ27dv00Ay0e4FhkncKcdCi7t9zOmOKoHv9NLWcJ5O\np5njjpAKT031GCg3/mNFRkdO8Jx7GDy/cz8IGMzmJYXY3VutVqZKsQA5FVfaMpJLv9FopA8fPuj6\n+lofP36UJH38+FH39/eZN3wwGCS4mvcwThEVgmCImPcRw9wjqMCcuUbgzMC9kUkiVUX67pMAz6Fb\nh5gsUjHcCxU6QEeVJF55kLI3Vfqu68RAqx6FS0yR+xc8vosqg9JOFSPv3fPZCbvwXZLdm2c3m01m\n2FapeNEYxFHpejpqG05BfBKeCHV+fq7T09P0Pe83755Op7q+vk5OQbc/sEEIcKSv3W43gSAeTBnr\nE0eQgP55BG005LG14rxFGJmxdLvxJaEiVVLA6Tn7xOfqp4SlHJVJJpNJUi2IrHUbIHplGWwPZ/e6\nV372R8Ty42TyfogF4ampqD+oZF7Qwe0HLxfq6pYbxScnJ5kqCfjgiz6m5lJEHOJsSJAxxu2Pf/xj\nYgiieD2amXgyNqXT09PEfNH5F2syV3mw/TxE5igyP/8gZxpHsdxP5vanB0XuQ7X83TGmzVFRNlvf\nyCLzP0dHP8QHIy6WzuFn1SAtl8tMMsBcHgbitgw7m+/y2Dceqs7OzmKgYJszyXK5THq8pBS2DpQK\njO2HCuEABa7mWq1Wy3JFpBwVmk6nWVDmzc2NHh4e0j9pKyU+fPjwpIKJn9fSbDYTo0pb5iMy2SOn\nJWXpy0RXu2SMdkuVp5sxj+iSz2t8jne6LXJIavD96FyMz/Fet3f4d4gBpWKTFCp0kI4uSaqy5dgF\nIpLCLhF1ygj5RmeS675cw9+Cb8UDGaFer5clPfFdzwMn59yDKwmU5Dn0bbdvoqh3R52rkiRCSVtJ\nQragh7hPJpO0Aw8Gg5Sl6ZLM/UioVL5r0z4KUvicxJoDnmRW5UxkzJFI7NgeyxWfWy6Xmf+rSs2L\n/4deIkn4rqRUvNyly3OS5Og2CcSC2Gx2AYYwiRvyiE6PzEWNiT6IGLvlBSROTk6Sri4ppdZ60YV2\nu63hcJh9q1bbVg9xZsO/IOUJVrTn7u5O9/f3WWIY/fP6vMDY+Dfu7+81nU4T0xDJHH0Cw+EwxWld\nXFwkb7u327Mg3dHKzxjoKe3g3eg3qWJ2X5TR3yPtGIP3YF+63VIF01YxxXMQ7j7D3RkC28dh+s+W\nSaTd7h/D36U8g03aDU48W4+QDi8r6gMOk7gPguBGL5+zXq91fn6efc8jADAmOZRU2g6uV4gkJXi1\n2pUk/fTpU3YoJv32xCgWHsa5tEsN8DHynH76ClolbY3yePKXSyfaHA3nqiMT2KAiA7gzjnGNEsAB\nD2xBr8yCr8n7v08C+O/PMY9/v+p53xhgkM+eSVB5pF2Gm4cq0JEo7t0JVgWvev4370FKxNO0vFgB\nE+koDEwg7RaEOzNBmiJq5lKCMJDxeJypHOTP8H0YxbMnHbpsNpvq9/s6PT1N+SyDweBJjS3QPXeC\n+mbjY81zDnI4I7vaxO4fYXp/HnIjPGoIXsE+SqkqZoMY//id+Ht0NLpTkW+hbu0DIJyK4V6o0AE6\nujPRdym4G0nAkdTsNuSOuLqF+uFOQOBc3+UoJI3kIL+c3TVmREq7nA83pGPOx8PDQ4qu5R5sKd7j\n34o7lhdCQA3xpC+PeF6vtydfnZ2dZWcWupRAhYjnk7juzz1uf0m7GLeqog7e3mjwxhxyrrufwuFu\nfEjeLmBzD+95CQQsVUuTqp+ubvHvJZLkqEzy+vXrtAA4C4RkKEnJ2eUOOAbP1SUWmGf5ecQtk+F+\nkpubm0z9YYG45348HmsymSRDGrTHkbjJZKLRaJTp/7TRFyHM7KqTLwqYA7TM++alkmJKsaSMkanM\n6KhgRHf4f1wkjI/bhg6AuJ/BkciYgsz7fPE6kkicnhvOMZnOme/QQq5ipip7xMcDNfYlUcZHr7vl\nuiy6spfwlHJDPp5PAkN4YefJZJKVBsIYbrfb6bl37949cWCyi8YzA/1UKxyOrlNzZAFtZMH7iVXk\nxdNG0Cd3vrGQ/DQsn1wWPsAANJ/Ps/wWmMzb5A5A6WnpUZgkpud6G31xOZjhkQQe8u9oo9cT4ygI\nrzLjhcZ9zqtsjJdSbDfvdpvkJUxSbJJChQ7QUSUJOeTSVtxyBBhEkKLr26PRKDtumR3LdxmquCNJ\n8DWghkjS+/fvszwRdmjPKNxsttmLOO5QUYgXk3a7oiM3QLRIBIIGT09PMyektwdp5LkiSAw/ni5K\nBewclyyoKa7K+Y6Jmlelk8cAzehgRZJEODWiVN4mEEgvVs6YefKYhxZ5fw45E6voOdQrZikeoqM7\nExmUwWCg2WyWlRmKGW2LxeKJU84rMTK5t7e3GZSJiubGPcY2g+WwqQMFHgOFGvX4+KhXr15J2jG6\n21EwiJ//hx+HicKX4QGO2DIOILgqhWfdbRlJmT1Wr9ezE6N4tzsTeUdcfHjT3RnoRjr3OXM5RO2/\n12q7gNN4PB7jGvNZnEHdHjmkbu1zLlb5d/z+l8ZuHZVJfv3rXydfwmAwSAfYePKUDxy1otxrPZ1O\n08T6fT5AGKAkP0n5kQ1S7kzzhXx1dZWlGJMw9Pr163TParVKTMI9/X4/k2TRjsBA9pB/rrnkcCcr\nerRHHOAQ9Z2x2cxPEY6ebd9JI1WFkEeJVOVdj/p/9O94cXQWaafTSRUtu91uGid/T6Qqh+M+FMwZ\noMoB6u3+bJnkl7/8ZWKSbrebHHAxwtedYBEaRIq4IwpnlRuXxDLF02arHFz+nsFgkEkWoEsvTufq\nF8+DltFuV+Mg98pTDJu8Du+/q2iod+69dhQNNRPQgTb6oq1Cu5ziBuOoYXQiMj9x4caIhxhNjQoK\nk6CyuRr3Ugh4X9ujgzcyBM7RQ1QM90KFDtBRJcnvf//7LHo2OrMcooQQ2zExyqUCVVXc3sBG8MJz\nPM97/Sx1/uZ56OzGnvUIbOrHmmHwYvAjSRyUwE5Aak6n0xQY6RIgBmp6kCb3uN1G/olXiAGCjfnq\n/tNDePapab4Tx/67Uxjy77lkR/XsdDoZuMEZ9Lznpxjprj5V2SeeX0T7X/r+ozIJSJW0sxscUQE7\npzOU7/SFgmj3Mjtk9Ln3lnI5XOMYBzcU+/1+tpAlZYY8MUjuvHNDW9r5VjxbkTZ42is6stsf0ZjG\nwPYj41BLot3mxStogweBujoIGhjbzrdizJf7LqoCAn1jcUbyOfJ5ZBxbrZYuLy8lbUEJZ+R90cWo\noIfUJL8nxq5VPf/Z2iSXl5dpByZQz3dOFgUDBYwYzwOskiT1ej1b3IPB4El4hksAaTtQfkCPlOe5\nRKkh7RZydPj5AnSbwJEjKc9x4F0Q11yS0B8/jcv7xWJw5vIi3TzDhuEgSPw+G5fn68TI4Ag/u+c+\nSmT6DoM3m80UXkOOSlWo/KEdf5/0iEzizO5MzD376Oh1t5gAkptcLHudLUkZE/lgsih9MTkEDGLk\nSBFh2l72hgQnN+yiNPBUYJ5zX4IfDuoSkLbxHKoV/SdGC+Od7/NdniUuyxc8qJePUYxv80XgCWCe\nqMbC8bGlQj/tiOoZFBdkrZZXZ2TMJSVPu6cqMM/PqU3xW05VMPE+Jom/73snVAz3QoUO0NGL03nS\nTzyA0o1vKc90izpx/N2L1VHcwaULUsLVDXR935X85C3InZKu5vE73nu/n365g83VHbe/3OfhgYrx\n3Efa6DYHxeri0dY+Zq5yej8iJIz6FdOqN5tNNi/x3UhR94s4JI4t6H2N9JyKFaWASx3g9yqbI/b/\nJfCv9BnUAmYiMXYJu5DyNFMpR2S8o55EJO0WtwcqxixHKXecRbSM93hgHsznoACIE0TwXAwDYTHE\ngtCuktEfKDIJB3rGBVir1bKzSDjHBAIwiIiWL3aABP9+VXgLKl9Ud6NKGNU2Bynok0ccxzH7Kf4R\nb6Ojg3Esq5yJL6GjMolPkqNCrm/7T+73AYchJGW6fJxsfo/hHL4DSU/La7qzDmcXoSk8599zB2jM\njfBvAxVX7XhxM/C2R7uB73vkNGPpiJdLP/+Ol1RlgfsicycgtFgsMmnntg2LPXr1IxNsNpsUYSHt\nIiei536fQb5PksR7q376e6B9jlWp2CSFCh2ko0oSj9TlDEGHWJEangSEs8ulh0ftQq42OYzs91Qd\ndeY2kauAfD+qIPG9LgnceQWc6kiPi32XEBGpcbutyo8QkasIVRPy4sgUv/v7onqHjeaO1yobYl/I\nB++OEoo5HI/HqTxrjMk7BAFXSQf/WxVy5T8Zt+feAx2VSdy49YNeXHVxR5mrH7E+sKtuMb7LVaLI\nAA6zYm9EZnNR7CoPv9MO3sskVDGEQ8EeF+W+C3+ntxE1z9vDeLi+H5PH4rnz+GxiOLrHaPEuCmjs\nG0c31P39zuy00e2v1Wql0WiUKt8DSjiT8dP/X6WevoTi3P+vYRKv6OF2g+vAfk9c/FK+OF0vdn8D\nz8Z7fAFSjNolyXOBe34eyD6nlDMEE+OF7twAZ5OI7XYid9+lq9tm/IxhMIyh210wnC9KJKszCcdY\nMC8ghN43MkMZj6qsx4iIkRbhbYqoXaQqJoljXkURsPFnPnsmcaPbC8y5RIghD1Hd8SQgj3L1AY2q\nhbT/IMm4AzrzugfZ3+WMEtNfvT08K+1UQJjEE5QcuYrvYYw8Viuqf3GXpC/+bRA3H1vmwBEvSRkC\nFePCaEvcNOLG4VKUPnikslfq30fPMcghQ/4lhv4+KoZ7oUIH6OiSxO2GGFOD2uS7FjugSwnsCJck\nrgPz7oihR3UsxkrxDo+z4rloN7mRjjSJOr+3ESPck8CQXK6WeF+RrO4nctWHvkZpy7MuzdjZfUeN\noSG8K/qkXJLFMBV/Pqqerlqu1+uUVMa9EUjZZzfEa1FqVrXD+7/PRt1HR2USnyQWgy9eOubJOjjy\nfHKiwek517w7qk5S7jnfbDYpMDIyW5VDk+e8uqSUH2tWFfYdF6ob5dHRGCdwX+2u+HtUP6P6BIro\nqqX3EfKxpe/RIel9iv/3OfIobSpeeloEDBLj1vapTVUMU9UGf+6nomTQ0ZnEURd2Nw+ncCPZD+xx\ngxUG8wUbdzHf6aWdN9/hWvdo81yMeAV+jmcwukc9hnHwfZdkMIhHARO57KHqjvgQFu8LI8KkbhN4\niq8bxSz2uCG4J5zvV0Hg0cnnv0c7iL5Rs1jaFvSj756+7DUA/L37mHAf7WOw/ywVm6RQoQN0dEni\nempERkCAIk7uu53vyNGfArlqFB19qGUOqToKFFER3u/Xov2DKoXqEOFQf9alDu2O4fkQUsWljaup\nTo4mgVo5tLtcLlM1fq65tIEiikfQo/enCl3z5wjbdySPNnvOD206RFG6xDF1G/Ql7zpERzfc3ShF\n/48L3CHIaBPwDs/W47pDmPV6PcP8yV2JMUfRWVcF9UYH23q9O4yTaw7vco8b81H9c4PYczz8J22M\n1ej3jW00VH3snrOtfMHT3zgG8TsOHsRF7GfR8y2YywEIZ+R9i/eQKhXVz5fSZ2uTrNfrLDdcyj3c\nMEBEV6KBh3PNJYnr0ujWziRxZ39uN46IlDvP8B24b4PdPQIK0ej2iXQmcedd9DfQV8YNVOwQuuSg\nRZQq3n//ZhVz8c+/504bfnoAAAGkSURBVBpBtOEkpUo1VYXHfQOoAiXifLyEXsokh5yQ0FGZxBcE\nUbFeiCDGbrVarRTjVRUWEsNSosHpEGP0CrPT+zFp5IF4pCzvBXCITkk34B0Jwth3eNm/H9NwnWIb\nXaJGx6ajZs4UHobCInd1h3fGg0Vd+nnUAt+k6FxMEXbi9C9OCKbGWIS7fdE6Q74EgYrjFdWxKtj4\npVQM90KFDtDRbZJD8J5LCLLkHN6Mf5eUsgl9V+90OtkOHHcxyNUr2ldVLsd3JVcb0eFdBcPW4egE\nrkWHZhyT5wIFY5+hGHAJxQr2vCuqYB4FzO9R2sSceop4MNZVSWfr9TpV58dPEv0whyiqeVX9rPpb\nBBJ+ihSRpNpPfaBQoT81KupWoUIHqDBJoUIHqDBJoUIHqDBJoUIHqDBJoUIHqDBJoUIHqDBJoUIH\nqDBJoUIHqDBJoUIHqDBJoUIHqDBJoUIHqDBJoUIHqDBJoUIHqDBJoUIHqDBJoUIHqDBJoUIHqDBJ\noUIHqDBJoUIHqDBJoUIHqDBJoUIHqDBJoUIHqDBJoUIHqDBJoUIH6P8BA4L5wMyXQUgAAAAASUVO\nRK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f271153b908>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9Q+3vqTPaECPR2ZnEYyDEsowAK+LL3Qk7uEU/fdXphFPdEwLRmPZ+eZ0pJuNw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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f26a7d6a2e8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1yW10NgGZhO8+seYjk9gp9tFIDBw7I9dol+M91CKel7TZAV/HxBif+grdPC2F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\nK/Q/LNhHDk8gYNoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f26a7f123c8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "EfLtvFaPGPWC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "outputId": "d531888d-20dd-443b-f984-9710a1898466"
      },
      "cell_type": "code",
      "source": [
        "for i in range(0,3):\n",
        "  train_tds[i][1].show()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMkAAADFCAYAAAD+BNZ+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f26a7f4a710>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMkAAADFCAYAAAD+BNZ+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f26a7ed75c0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMkAAADFCAYAAAD+BNZ+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngkggiASCSCCIBIJIIHwA5N4LhWVDNLMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f26a7d6a898>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "p6JtvMnukW8e",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "32ae0a56-caf2-4109-94dd-bd2646cc6a31"
      },
      "cell_type": "code",
      "source": [
        "_,axes = plt.subplots(1,4, figsize=(12,6))\n",
        "for i, ax in enumerate(axes.flat):\n",
        "    imgx,imgy = train_tds[i]\n",
        "    imgx.show(ax, y=imgy)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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INPcuqFgUFDuvpgaSBjkm/8LwAuiArSgCHFiAQbXca91fKSOsDNnATjNnuB1vC5J2Buab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\nGke9aa2DirHJFABK1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7Aj5GCO/uQcyXfPwLD+7jfY6T3rWFCCQcGO3p9+b5JA5JGL1c9t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\nJ7iz3e8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f26a7e143c8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "YMKfrjdGkW4R",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "default_norm,default_denorm = normalize_funcs( mean=tensor([0.4850, 0.4560, 0.4060]), std=tensor([0.2290, 0.2240, 0.2250]))\n",
        "bs = 32"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AIsMvBShkWvx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def get_data(size, bs):\n",
        "    return DataBunch.create(*get_tfm_datasets(size), bs=bs, tfms=default_norm)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VS3c3dIhpjUE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data = get_data(size, bs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "aoLQZ2O5p_Uv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#export\n",
        "def show_xy_images(x:Tensor,y:Tensor,rows:int,figsize:tuple=(9,9)):\n",
        "    \"Shows a selection of images and targets from a given batch.\"\n",
        "    fig, axs = plt.subplots(rows,rows,figsize=figsize)\n",
        "    for i, ax in enumerate(axs.flatten()): show_image(x[i], y=y[i], ax=ax)\n",
        "    plt.tight_layout()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mX-QczCXqDkB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "outputId": "214f51a9-93e7-4bfa-b537-4370e80cc90a"
      },
      "cell_type": "code",
      "source": [
        "x,y = next(iter(data.train_dl))\n",
        "x,y = x.cpu(),y.cpu()\n",
        "x = default_denorm(x)\n",
        "show_xy_images(x,y,4, figsize=(9,9))\n",
        "x.shape, y.shape"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([32, 3, 128, 128]), torch.Size([32, 1, 128, 128]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BTB4dHcXp6WmcnZ3FyclJhQRSZ3h/fx+3t7cxHA7LYhqCpLwYLvdVJnT8L/etv+fv1Ckt\nOauSMwQO3rxY5LdUmd+jcU/2WZAMrtNECltlfts/oi4an1zHH7HFCmwWkgT+ZjUQm7RwYkwlyKQv\nIV9gAu+ziIMiSXCDfUdE8QF8Fx/oshq+A+GzGECfmGfktC79YLURfDIuce3grvEAfHCfcN6cFs7Z\ngd+yu51645ubm+j1esVIMKKIKBEERZ/+jFfVGjQxEAw2Ykt26HiTG74DCGLIXmnsFyoh6g8GbCPP\niwwy+fGLeolMbLMzsFrI3/w+okrOIqpF/N9TFp16tCqYU+D0F+fJoJpVTyuhTAyn138EIsi4U6cC\nEUQRPDw8rHXQJiK8sBenxU36DG4R1bR/RNXBMGmpiyKtlu0lYksaCUyY/E4fmHB4/Lge18K4XhDQ\nIzqFeNgROHhar9cl3RexXcW32Wyi1+uV9+5T0oxOTdMvnU6nkGsrM0TCBkEax0Z9YKxdl7NrIhjx\nvD7wJUXQ48WiCtfvPT09xXg8juFwGMPhsKx4PDw8jMFgUFKslL6QUmWeg4mk3xlnxtZpNCuHzoaA\njeAm5O3u7q6oeJA3UmxWAyGBR0dHJfjCviC4kEDSwlad7OSMS+6775G7HMRmx8p4GZ8jqqU1dSTQ\nRNDK5S5bv98v9wYZp79ns1mlhIi57gyW/SLKL/jkeZfr/rAXMAK8RVGmf73iGBzCh0EeWXgSsU0Z\nc31W5ux3aOxQEBEFu8DZxWJRdntoNBrl+p2t4JgQ2s1mU0oXuH/6IAesvLedMffrAg7/zXYOGbZf\n4do4l1VGjvc93Ns5EXRKbX9/v0xugx0GwICbmNmZOjXGAK9WqwqZAfQ88Tk2srUN3aSTCeLtXjhm\nTiHj2Fyj6Lw/n/fWOTmNm43BpC8DmqMBKz/ZALKqyOeZYFyHo5oMkHXRtZVXKzxWTH0spyF30RgX\nSEur1SoqoBcJeQJaxvcY8t7OPCKe9bt/J0qmP0zCIraF1y6K9ypwnDNzo9/vV5Qx6g2bzWYlcjU4\ncs1cD4ojwOnV4tTsWfXMAYzJJ/cB0J+cnBRQ51q49lyDit0acG1PXC/jRmTNGKFIMN9QASDqu7Y9\nz1M7QEgEY2C8aLfbZXuhw8PD4lQfHh5iNBrF3d1djEajeHh4KAsw+v3+sxQrAQW24BpCLxRipbcX\ndmR8w0F5AR5O0SRwNBpVVEUIyGAwiLOzszg7O4vT09NCWhuNRrH9u7u7ChHMamAmgRkPv4eVdepf\nJoI04zLjZz8B6WMXCqeGTaSycvl7N2rqcomECZ5xLqc1HYziA3OgS2BKaQ0EyaTKNdERUcFM/mac\nsu/Gjr2AMfer5xW2ybxiJTqlCs7uOGDGxp0hwV6Yl4y5SRqYAza6/xyoO2uWfTx9krOEBFEOSvyd\nPB8zsXyp7ZQI3t3dxdPTU1l5Q4oWAtbtdisOwos1MOSsqpAWYZsI1zHx3awSmqBguHVpP0egOGrU\nhjwReFHb1O12C8mwukY0lg0np75tKDmVVmc8dYTEZMT9lkmkAc/nz+esA1zXnLgv/L3fik5+j+YU\nP/dH3ZMJUERUlGqnTpmUHAsAy2Q6OxkcuPsOZQ7ig3qT018cl+id1bA4ZY4FuTJ4W+nFDtfrdan5\n83larVYhcaiCXnHre/S9ZcDqdrvRaDTi9PS0FJPjGPk/QZJtENIG8SMlaaWi0WgU4lAXtbda31Yb\nopK5xmmXjXGk7pMVuaRsmUcmXXVqIDV/qG+TySSWy+qelKiq3kuS/vN5PAbUfFEbZcdlEoijBaew\ntfF4XEkNo+ChBhG4kBI+Pz+vEEGIOwR3OBwWosuxckrYOFhH6P4R4vfSdxgzbN84bRWQNCnvXW5T\nl77eRSOdT32uSS0kPmIrNuSFOLme3CQQe3LK10Qw9y324/kLfhGk5JczMFbUrBrSOD7v1+t15dq6\n3W5Jhfu+XCcJpsAbTEhbrValL4xN4Be4bPyOqNaj1inQ/lwdEfR40H9ZUKLlY9S1nRJBAJoUEi9L\n697gOJMMAz8/AbFms1kcoOumXEOUZWtHARH10r/fA5IMGs2qiwEVpdM1Ya4F8PnsaDmmiWAmVxFV\nxcqTNAMgn81ExNFVjkY8UW20fp/TwVk1o0/r+uz3bgYa+s+qiNUYE3PGxsfIiqkJG59zRAwJoKHG\nkqJtNpuVDdVN6DMYWzXnXCaCHBMShL1RckH0mBeYZNKU0+DYs/c+zCnfzWZTgrlut1vuib4+PDyM\nk5OTEix5Ra/rkTxXPS8MyE4vub/39/dLfU9Ol+yqQXTYOJ86PJMlMI+fWQ00WUItm8/npV8pbWg2\nv63Qzos1jCOcA6dlfHN/On3vgJX5QHoRImjiZgX46OiobBfz9u3bePPmTZyenpYNwElJ57SwU8J1\nKfRM7jI+8VneG/deehkvjV1WAk0CqZXzVim5lGOXzfMYEkQwSIkBAdbBwUFF/ODeeU8WZTAYVJRZ\n40/OmjAevgbwIm/Vhs3ZTufzeUX9N97Yh5kcMVa+ZjZ6ZmyYhyalENmIb4EomEggc3BwUGp0SY3n\nDIkxG3JoESTbK/dlLOPv9vsR2+DHxNPj41anmrrtlAii4EVs93VrNBqVzS4pTGajZ5NBnLh/j/jm\nRFAFiczM1DNBypsm+5UJGg6Yie6Ot7xswkiaAGOA3FoZdCRglZCWiaAjc5M3DM+qXl2ES7Mq4FrA\nrFLmyPslVfAl0M39aODYVcvgBKnJtZGO+jwumfz+FhHEgWP3TjNTq8iYkZbDVhkjbIrrdwqY67Yt\n1SkRvm+ntPmcbRlnEREV9QNbI/XIfdBXrGRGVcwAf3BwEIPBoKTmqfkBzJ2mcr0iafGIbZROKtSK\npQNGFE3Gb9e2xz2gBpLuJHXl0gQUFsoYUEAhdxCl8XhciDl1rtgU279ApLL6YueSlRHmcK7htDqG\nY/NWMy5pgHhCAs/Pz+P9+/fx4cOH+PDhQ7x9+zZOTk7K9VL3iNLpzaON5cYd23R+RTyvq85BcF0t\nt/HMwbV9gAkg94vP8dM0OO+uyxKm02mZRxY3uJfFYlEhJgS+kCArb6i7eU9PhJY6cu4+dW2pfXsW\nOKzYEVQcHR1VxiSXlnCtFlWMcy7rctbKi0ZcmoKKiqrIptVOI7sUwBhjv2FibIJnzDYOux9zSQ/f\ny9jNHLXfwae/1HZKBC1xWtYkKn14eCjpIx4Dl2vw3KH8Dfn74OCgsprJEaxJG+fzCtKc0nRE7M/V\nHdPkyU4fY2bF3UvAY3XHxK1uUvE5ftoI/LdM0iw1+35QwRgH34+dbCaWHgeO7zSioyLGYpfNxBYV\nhjHOqyetgrguxv3siZ/7moZDsc1tNtv6TAcojmAJcGwXXFPewgh1zv3NddZFifk6+T1H6yhL3iyW\nhQNezQsY8n+rddii052bzaYohZ5jTtFgi643XS6XpaTEG3Q7TYmNUQ/KdTgi30UzCfTiDVSI7BT3\n9/fLVi0EJrPZLIbDYdzc3MT9/X0pK+AJHV4pzNM9OE8OKh28uMY6okqobb9ZpcZeUDld1xrxTUUZ\nDAaFBP7000/x6dOn+PjxY1xcXASPPYNMjsfjZwtNnGZlDH19Dm7qSlgiqovrXsL1HLhEVBdzQWAg\nT9gvL6eF6zJQu2zgAf7Vm9SzgIffmedO4ZosMfe8XRSfMbmpI4GZwGdlK2Kb4eI94kuj0YiTk5Pi\nSyCCridcrVZls2zOYTJIFtJ7ZhrXHRz4miGC2EnE1oeyQ4IzGbZTfItty9hswh3x3K9zLGM6vtoK\nov0E5/2tLNxOiWCv16tsgwHgI6k/PDxEq9Uq0eXT01MlVezVhq574m84dK8Gdl1HXv5tspZrdAwy\n2bisnDl9hjPCyTOxqIvEoJhMMP2I53sG+r2l8ay4AVgRzzdNdvOxchqY7+bv2wnkSe3rNWg6GnLK\naddEMCu5WSGgdhCnwyTkPuyQPP4+Ribe2ZHiJHEYkD+AOmL7BBCrjY7yqE303yFtdeqJ1WrO5XF2\nWgzg5X6sgnQ6ncozj6mXdd+i4vF35hTAvdlst8eB6JmEGnwhivQF2QKnjLG5Vqu6ITYRPIEn6fRd\nNStILN6I2BJWK/04GRwIfXZ/fx83NzdlpXDENzxl4QUr3iFV0+m0skgkBys5yImISn87WIDYREQF\nuyGC3uoIG+Da3r9/H58+fSok8M2bN3F8fBzNZrN8bzKZVLacsS3XZUlM/nJa19duvMpq4EtEkGZn\n+1Ja2C8LD/Tnj0AESXGyeIhniGNjOYDM/gBsJBhFvfZ8M7k2IbeCZwxznRuqeBYseO+UMs3qHXgI\n0XUa1djgMeHztnsCVWMSc8RENat1xhbuYblcln7g+lDoLCC4/2wrdUSwTizKfWUbt3Jfaxf/oP38\nj7Tj4+NCjpyGwklCGiCDLLoA1F3YT/qnjhSiuFhezqu6TN6Izl0/k9NuKBvZsCKqadC8GtB1I09P\nT0Wm915KdWoS53TLCtxvpQDrDMbkxCTP9+C6xZdUQU/0DJgm4F6FvctmMpcBm7Fl/CKeq3ncG33l\n4msTyYjnTx/AkfqnUxj8HRUrp2NtK15F6/HBMVv5jPjmxImqPUdQt526IF3Jebhnal17vV4Jhoiw\nUeU4J8ci0ic9zvdQxDivgR5nAqEBtLlfaiQJsvgbwA0YsgiIMaU+aFfNi0R4Woef7et5nWv42KPv\n5uYmrq+v4+7urqTiT09PS73d/v5+SQdbUUOpNRmKeL7dVE5h5oAmorpFktOLXmQC/nJtEMGPHz/G\n27dvy2PkIJEsMmEjagSA7DRfIoDOrmTcqyOBOS0Mzvt7DvLARJf+uPwnCwz0E+divu6qMTZWa3OG\nISIKdjiItH91v0Vss3HGFnxyRJTg1j7GL/sfCFxWUU2swF7jnYNeq4QmkA5csE8CJNdIO7sHvmcb\ncOmEyWidOFPnq13i42s0icaGTERp3LsDZh+f/1l5fKntlAheXFxU6iny3ksA5mKxKESQJz5kIujH\nx63X61IojYF7xaEnrFd4maRAAh4fHyvqYjbgiOe1YgYQALLdbpenLOQ6Qq6JCZaPwzloJls0T9iX\nImUbnA2FCc11evKaoLh+IZ+Lz9tg3dd5G4XvydS/R8sLQNyfRGtW/9y/EdW6D1Qmb0tkddcEj0mK\n0gdQmZSS9qQGhc+gkqH2QDgBxEwEvfjCY2/ggyBxfDtS7DUT2c1mUxYkENgAzFYAHNkbEOlf0paP\nj4+l7IOxWa1W5VoajUZxuixyIYrf29ur7GFnm6SPAX/I9cHBwe9kZfXNi0Tocz/RI2JbH5RXbU8m\nk7i9vY2rq6u4vr6O2WwWzWYzjo+Py8KLfr8fq9Wq1OuZTNkePFYR8cx27ZztSKwoRmxXZxq/cfbU\nLJIS/vjxY7x//z7evHlTeZZw3cbRKFZ5scVLCmCu98rK/EtqYB0J5Ht5XnG/DpBzyUsOKinZ4DnO\nu2xsOO7FC3mOOuvluYM94g/pPz7remUrX8x3iE0mZFbcjINecBIRRbH0tTp1DDZhxyai9nk5oDfW\no8i5JpLr4rj8zudsIybTHJ9+ioiK8mh1kXNZaW21WpVsEe0lTM1ijO/T36trOyWCZ2dnZb8qy+r8\nzsCyognSxpYTfuDz3t5e5bFDgIwBAaWHFBED6iX/VgutsjCoWQo2sFoSr0s7tFqtyqo3ABQigEyf\nC5Vzy9GKVZ/8yvefgS1/LpPQTAJzdM17rsuEwUQQQLeB7rLhrIhU86Sif61uRFRTtPQlRAWAg9Bk\nB0ofQgStXNQRbu+daTtkSxZAw3YYUVV8ARqntrC3rATYQebUjRUjnq/Z7XbL4gPO5afu+AkREDWv\nQnTZBOdzbZXJpAk5Dol+gQgyv50V4Ppdg7fr1DBqoFPCWT3BjrzNz+PjYwyHw7i8vIzLy8sYj8ex\nXq9jMBgUte309LQEnd430MGMcSnjg4M6qyRZYbAa6ICac0VEwWirge/evYvz8/PyBBoCguFwWMgt\nT0hBxbSyxs+MbXVE0ITOxCZjuO0lpzOxQ5PoiOeBoImG+ymTwH6//z9sXd9vbJzsPms0GiUz12hs\nt2SinyBxJoL2J9hKu90uvpkVxxHVR1kah0xQjC9Z6WIsmLf8Pyu1/OQY3tLL/ioHtx5Lq4xgkwMb\nB6sOlDi/50UuGfK15zllX2EbdlbNgpMDDxPBLDhYgf1hiSAFny60ZYNpVJD5fP5s/yi2lWG7BDrO\nDyN/eHgoD4Om9sbbxOAkIrYy0mcyAAAgAElEQVSrISGhLvq148KZRDyPqDGCHFliPE4b5pVFOMWI\n7aQx2ML0+SzXYoWO72ZCl18mHDmacHMkhWHlVEtWyerSyTZYT/zvGeXv0Qw2dcBD/+ZtNFCf2fwc\nsMvOxWDCuFMfl/uez+VxabVaZVVtduSMg2tjHD37ejK4Ol3CfKtzmMwV20BEFKeGssbfrbihoEOk\nHQxkwPdcYn57PvEeYGP+evUmfyft6z0ivRiFJynsspGyXS6Xlf3WmOfcNwQx4ltK7/7+Pq6uruLy\n8jKGw2E8PT3F/v5+nJ+fx4cPH+LNmzdxeHgYi8Xi2WpkOxlIj1fJOzCBPHvVum3DShHBXl1KeG9v\nr6wSRq2EBLJQaD6fV+7r6uoqhsNhZaubumswkfHP/HerWzlANnHMtYEOcgk+bINZLbVTNwazIIEF\nj0dHRzuzu4goAQh7TGJ33hQ5Iiob7dMHVgaNk/irzWa7gt9lItgTdh0RxW4iqlvaZGUPG8PnERg4\nZcxnuQawlL/xGdutfZqDdZoDZogguGGFzmq1SwoyGTMRzPPcKWCu1d/j3k3S7VdzuYHtnePx95fa\nTokgygagfXh4WMANcFytVuUB5tPptEJQHNkwufncdDqNo6Ojolzwk324TNZIT2QySKFyVoWsyLlz\nGQADraMnJh0g5AiJZuncgxnxfAuW/F07yqwoOsKtKwZ2ZGPDsUFaocjg5/NnxdKTru6+dtEcgWWl\nwfWBEI2I7QO/c8Qb8XwXfJ+D/2dFgeYUtIulW61WKYNwdG3AsfPyY+ac4jK4NpvNsv+VSRcBE2Pu\n1KxBrdlsxtHRUQH7bP9e3QpgQQLZBNtk04r+ZrOpLEZxlE6fspCLe2OlnktAVqtVuY7NZlO2H+n1\nepXFK7tq4/G4bIFjNRBbabfbhVDjJGazWdzc3MTl5WXc3t7GbDaLiG911u/fv4/379+XRRez2ezZ\ns33pQ2zXGMt7+gaVjiCmroEVYCY4yXixYS/7BV5cXMTZ2VmxHVSoyWQSV1dX8fXr17i8vCx1j17c\nElF1br+lAOa5WEcCnRa2iuw6bWMjpCVjHX8zQXUghV9jSx8eb7arhv9w5onxYkxzXzBfsvpkRZG5\n7OMyhyO2pSlgBiSRY+Mn2u3tjgHgB4SMTJ3Hy8EkJS7UDfM58Cyi+og4E0ErdTTuxZkMzwnmCfdF\nkIk/4b0zcFwP36E/XK/p0gLGLBNKX1POCOWALauRdW3n28cwkV3rA4iwGS1bCjhf7gGxzAt5m81m\nZcsZJuBgMCibsuIokI8ZdGR8HBKE0GqGiZSjGDqaWjvA1Vt8WOFA/csEzPeUJe+XCFid9OvP018m\ngj6mVbw6xQq1h/c5aqlTBWnZKD1Zdtk8OVHfAI9cI0KzKmgb9NY7JlMAmmtrcmmC60z8WfqKPfcg\nbHZ4gAnqEqQxq9WMWavVisFgEKvVqqQorZAYuOxI7eDZrNjfoW+cTnZQxZwE7CE5XuQFsXAhOItw\nGAcDJgBKf3EdgOPe3l6lBvjo6Kj0wy7bZDIpZBWbo0HWWJHdbDbLAhHq5yaTSUREDAaDePv2bdmC\nhQUiEF+v3EVF9R5oEEA/9chq+Etz1Gp3XiASsV0dfnR0VJ4ecnZ2FsfHx9Htdss5ptNp3NzcxJcv\nX+LLly9F6aRutC4orlP/6kigCZ8JWiaRmQg6YDO2Z3w1ITKe8z/3d/ZDu2wmcFmYwA85IAbTIupX\npXo8mP+tVquSGbKfcP/aF+KbnBmw6OF1BASO3W634qsIJp29sxBCy6qdx9Hk36SRY9aJP5logZ3G\nNZf70M8O1J29cRbJ/p7zcQ6rkVlJ5bNZAHqp7ZQI2nBwrgDk4+NjiR5JYVEsn6M+pzyd5kUFGI/H\nZRIeHR1Fv9+vKISs1rNUy/UcHh6WWigMAVkWYmpj9SpZ/gZJsFrE/duJ0kwADTSOfqzW+VgGzbo0\nhusWbKAYOveWax5MXgwMmQy+FCX7WLkfdtUy4YG4m7hagXb/OyI1efPnXVfqled1AGdSbiIYEYWk\nQgR9fj5L6pNo2CBogtZut6Pf75f7hkhlNYQiZ6fEOL8fc5brWfksc8Ob7PJUADZHtgPhWnLJhFcQ\nMmb0CWBuZcLkwUoGquH3ouLfq7FAxGQvYrtBuDFps9lUHrc2Ho9LIf3Z2Vl8/Pgx3r17F4PBIBaL\nRdmImcV1HNfqlINg8I0xNfbUNY+NnbML7UkPHh8fx+npaZyensbx8XH0er0SZD09PcXt7W18/fq1\nkMDb29vyvGRjkHEkp4KtCGZ1sI4A1qmBTo1jV9g7wdlLLxNU+tp4Qj0tZHDXi0WMUcwJ16HmEhTs\n04E93yWQy+UW4IdX2mZhg3NnjOV3lDiwEn/78PAQ/X6/2LOFC4seuWbYiruD27pttGjZB0RESdFy\nD5A1frd/zJho35t9J3bktLP7Cru0bZqEwjeyffpevxcA75QIcnFWBZ2iXC6XJYqHqLCNBBEsxuSl\n6V4iz15dbKo6mUxKytgvjvfSXoSkAhlQUoYu5rT64GiIz5u4OiKJqEaYLwGxBz4TxzqQsmJZFxkB\ncjY4GtFDTqtkkvSScTnKsSN3FLnL5iiJsfbKTdIaEHgDWSa/vkerVxzfapz3/QPYMoEncNhsNgUA\nbfMQOgcwzBmPPddhx4mTjoiizOS9wEzamQMAK/vcYUd2pr4vq+NOHaII0hee767/4XgEf07lREQh\ndQbIiKg4De4F0tVub7e32GVrNBqlbszpeK7TJSwPDw/lecKj0agsKDs+Po4PHz7Ex48f4+zsLFqt\nVnm2L5tHmwAT2EIA/ZPzQcY9JrYH21TGO/CCeYQCdnx8HMfHx+VJJ5vNtu7bKWFIoFeqR1TxJ6t5\nLxFA4+xLJLDuZZzKqTi37wW7Pj5jiY+BcO+ymXwZo8kqRDxPIULs3J/GIFL9FhN4RTzf45Z+M/GO\niIra778zp8Flj21W5OAN2KQFJv7PnANzsq2ZbFlR4x4c/BLYZz+Y1UgTwTq7arWqexKbeJvQZbHF\n2O+xM35z7O/h3k69MSmOOnWA9yiEOILr6+uSPs5E8ODgoBRJW11gQQor6SaTSZmUOVqjCN67hzO5\nGRwGyluiQDy9AjovLc+Tyc0KRiZ5WfmzgeZmomiZO0dDvKwOcp95YYcVKE+W32omR0w8rmfXjf5B\nsc19klNLBhivKAdMTbA5fsTzlY4QK0DNYAYgE8WuVquiiPkaOYbr9FDMHRH7WiDeHDti+7xfjmc7\ndf0Q6h+Pk3INZaPRiF6vF81mswK4rkGllsabRFNTA2lbLBblOcQm5A6+IqqKBvZkFYfPuQTCtUKk\nmnfZ/Hg9gtxWq1WcKisvGX/21ptOp7Fer6Pb7cabN2/KXnzdbjfm83mllhrss724FpD0GuogJC2X\njtjZRERlHnh3BauBLCY6OjqKo6Ojcq/NZrOU+XgLHJNABxFWVl4ici+pg1kVrCODxqZMAupetDqV\n0g4erKRvrcDuesU6z3POalwmQ9wPNsS8dmbL6n9EdS9T+xAHFaTZfV4LCh4LB34OCE2svCDRJJTd\nDCDkJmJgXkSU+m/zDxNQ4zs4bN6RF0OaXOPzuE5Kdoxh3DP96f7if/xsNBqlr701mFVrk29s0fb6\nUtspERwOh6XzrG54lW+z2SwTqdlsxq+//hrj8bgAnAeEbWZ42oFz4svlshSrU58CMEIGSR9DCiGg\ndKBTnBFRCtNzms9kEHWSz1larosa6sAoR+Z+1YGUW5aQrRjhhCwbM2FynaCNO5+njthlwkgfcY+7\nTtHlVKMVStKl2JiVLj9NwGmUPBZMbqdZ6D9vVUH/ADKuD8GmI76BAwoa14v9bjbb2pOIKEoj15Gj\neI8hCqVrI/k/TxWBbHa73RgMBkVZY6EGjj6DM+dDpaeOkSi92+2WVf486QDVkRW/VhdsuzibiK2t\n8btX2qJ4QkJQ5nfZKE+xygmxJxAF9P2UDdTjk5OT+PDhQ/z0009xdnYWzWazYB97E5oE5lXCdSuG\nI7YrOU3ujHc4KMi792+M2NokK2TBUwIWUv13d3cVEoiCaWXRNmpHnImeiUkdEcxkkM8YQzlXJn7g\nYE7p0Ty3qPP0/M4Lc+jvXTYePchcNy5lTHd6F0WOvjOZsW+z76Bl7Ad7CTaswtJMHJ0eJkBG2Ydg\n4XchZFaonZkDS1AFua+cNcvqnYUaX5+DFuzGfepSMoig/YX7KvvquuDDG33XETvOmUvLIqJy/bnt\nFBGvrq7Kja1Wq7IFBsBtokYk0+/34/b2tgygc+DUBeJgcuoWg4745iyn02m02+0YjUYFhKkjhBCS\njquLPAESJncmhN6f0JutmqAy2I6ebAgGIKtLNkKTqmxIBnOcg8mmiSDfzXUMvtZMTPP53XwO7yVn\nhWpXrS5lbVIfUU1pOLKzTdFfeZK5Zi0inql/2bnRn5ADO13GHTAlZU1qBtUc4ggYen54bLk/rzqD\nPFkxdtqaWifqvLDriCh1T6junJv74nq41729vbK1E+oVKUarnCYl9I3VjGx/VmPIDqBi0H8R27m2\nq3Z0dFTZ4B7VApzrdDpFORsOh0UxWywWZePoP/zhD/H+/fvodrtlOxqTQCv5kBKISF4cEfF84Ucd\nEbSykQkj44KdgKOs1N5svq0I91NRrASyOCQHn3WvTPpeIoAmgSZ82UFGVPd3c4rRZNCEkOZ+jtiu\nXq1TYE26d9VITbu+ORMcfjooNOHlfhzkg49e6e35b4U6jw32E7H1GXV9v1qtii1hrw5M/DkwYrPZ\nlKCd6wBDCbaxH6dk68SVjKE54+b+cO29A6ycJaOvrdCaZFuR5NwE8A5MTP7qiPhL5Wa0nVrl169f\ny0aWj4+PpWYGdYRVwzgOAP74+Dim0+kzNQbw7Pf7Zd9BlIYMNq6FabVaMR6P4/DwsKRhDGQUdTtd\nnFdvMoAUYBssvQIZtdBEy6TObD8TwYioGIOJVl1E5585BekoH2UwR8dOazPh6uohfB6az2EnY8Vr\nl82AhB05eouoEl9eBhtS/QCZiToOgWPkyU3LpDCi+jghFhZg3xyPdCwqIuA8mUye1YlwLN6TZjEx\nZ0W4a+usFBCQofxxnFZrW1BOfdd6vS6OcDabVaJz1H6+Ayij3uHQsRlA1A7WTiwHLpCqVqtViCz9\n91sF079XY+xQXFHRIMc4r+FwWAjTfD6Pdrsdp6en8fHjx/jpp5/i9PS0qGwoq/Q1tu0Nvr1Lg4vo\nUVvYb9GKou3HRNCBHeezGuiUcKvVKscfDodxc3MTNzc3cX9/X8FlE/o6MliXGq6r8csEML9oObA1\nCapTPkkBZjzmOpkzdVvS5BrEXTUCEIiP79mkjTlnBdBZCM8p7Me2AYZYicOvcUwHwbx4wISDDF+7\nty9jLMAIkzGveMaeseO6LdDoA19nHU6DIdwX9x0R5YlPCATcm+dPnbgSsQ3KyYjYHzkNz9iBgbZd\n/+TYHPeHJoLX19cFvKbTaXE0OcrEAZycnETEt0Fm9VzEtpYE59Hv9ytPKhmPx5VNqW20OFvSaSws\ngRB6MQlEFWeGYbpuwNdLnY9XPLF4xQOE8dSBIS1HBvwtRyP5e/47fWXSYaLj6B9D92dcD5EjYxs4\n1+rorm4/xl02rt0RumtLTJzskLLiyt8ZdwAgF0I71QERAyDtZE3WssO1ykd/855zAjaOcomWuVZW\nGAOegA/NK0BNBFHHHURxv81ms6RomLf7+/tFyYK04kggexHb/bgiorKvlmt8rTix1VTEtsiaY3kL\nEOzX6taPkBqmD1E5IU9sqcPikOvr67i+vo7xeBybzSaOjo7iw4cP8fPPP8e7d+9if3+/rCTmyUu2\nyZwSNinx4h6CNG/aH/Hy87WNARHbsobDw8OyQIT0txeI3N/fx3A4LE8OYUV5He79I2rg94jgS8fI\ngW5EfRYG3M5k0HVwWYiACNpv5WurS+f9ni37CosixifwAkHGNcrOjNi/8V3jlT9HP3srLXwkx6WO\nH7XPmTiyEfSjbcb1lzxogoAT4cXZEO7V42EVjyDeQYn9G8fztkksuAFXXVrm2kD7xhzIElCZUHKN\nYK0DnJzByqQQTHgpa0fbKSLe3d1VniVMasRRqR1rp9OJXq9XDMSOjA5dLBbR6/VKGpbnmY5Go0IG\nqUmCnDn6oJaQdNV0Oi0Fv6iSGDAEkf9bNeR6UQ9xyL1er9yfJ4yfsZzl3gw6ObWbgc5GG/E8as2g\naFJnQIzYptFy2ptrz6qmCaEjQu6vLr2yi8Z1GAiYOFy3SXHut+xsHO1nxSUiKqQO27LN4mxMBHP0\niYLECl/X8LhGx8QOJ++0rBdM8DeiWR/P/3e6FpJMypXvENQxR5ijpF8ooG+3289sndIQrjvXBKL0\nsWqZeptmc/s0EsbA10DJSEQU8vsjOGNA/+DgoNQkkxIejUZxeXkZX79+jdvb2xLcvnnzJj59+hQf\nPnyI4+Pjgm1swAwegpeMq5/Jjl1CArE7siZerMG14szr6gIzCTw6OipEkHIe8P3u7q48Rzg/OcRO\nN+NZnmMvKYOZCOYUsHGK310m4P+bCHqLHC9IqMvWfE+9/BGIIJjHvVJOQnrSq3IRX3gSDPiA35zP\n5xWyB6nMD2MguAY3EVG63W7BBoI06lwpf4H87e3tlb1U6WurgHkbm0y8IrYbtRs7ORZ9kwWRRmO7\nAANfzfFbrVbpO1R8E2kCXoQCX5fLW7ge2wnnsZ/meviOVUnbKN/3/WWFM7edEkHAh0LnbrcbBwcH\nJcqlYzEgBponI8CIc1oNAyOCnc1mpXDZj6FzypY0FATBaoS3wKgDWUhi3qPLKiEDTKTi6BKj9P5Z\nBiUDFy2DWq47cLMamMGWPsvn8aSgGSABcUdRTiW70Y++L69q3UWDfEVUCStEwQ7Ek9Ig7yDF6W5/\nhj7mGExWghlPWgDTpNqgjbLm6BuSw+INwPHg4KA4dew2IgrZcETOiwjUizMge/wkwozYFmJD9qzK\nQcIge9wrhNK1jNgZK2YBMx8Lkki/M5dQ/ubzeSlxsGPgmbVE5pxzl405w+phlLPV6ttTlK6vr+Pr\n169xdXVV6phPT0/j06dP8fPPP8f5+XlEfHtCCc/m5ZF12CRjaizyVj/0G/iI43ZAaCUw1zg7SLcS\neHJyEicnJzEYDCppbvY3zI8L5XzMk5eUwJeUway6vaTc0zJu5r8Zx7wQwQFwVgbpizpCmAns99Jz\nv0fLwTrEbbFYFDHDW6kRoDjgQoHG5iK244Y93d7eFv8+nU4rtjeZTCobm6M2LpfLMl8bjUaZGy4p\ncbbGGRD61RgD0eX/zAvmiXEdNQ+yZ1+f93mtq/Ozwoc/wO9Bqp058qIhzmkSngUWn8OYaX8MX6Ju\nHOylP74nvux8Q2kijPl8XgBrOBzG3d1d2RSVAWK/LEvzEdUUHce1kkJ6t9/vl/0EHbG4ds/KHJPc\nKglRBnK0N2nFkbEvFy9qKixzO8fPoFFbaCXIg+coIKt4WeWjGZzyK6eBTeo4h68TZ28C6Wux8Rls\naC9d4y4a98KYmPRxL55orh15CeCzWmuQgryQ/uDpM3V7cBGdc1yXMDSbzRK8TKfTouxACkj7Eixh\nywaq1WpVUhDYJ0oa4Ml3AGpSJXWKqaNrFPB2u10WgkRst32hD+bzecXG2+12SY9C5BaLRSETfgKH\nwRgbQ/XkWBBtnLdTPbtWZax2+J4fHh7KJstfv36N+/v7WK/XcXR0FD/99FP88ssv8fHjxzg8PIzJ\nZFJUQzIr3LtLV5ylYBzpN0iAS2Yiqs8Zt9rgYDliq7D0+/04Pj6Os7OzOD8/j5OTk+j1ekUxmU6n\nlS1wMgnMgWqdGvg9pc0qp32A/UCdGlhHAJn7ORWOn3KK2IFwxrQ6ZfNHsD0CLHDAGTXGk9pOB7nY\nAntAXl9fl7KPVqtV2fKIBUE8IcZZtU6nU+a1s2gEquv1utjuYDCo7HdJs7roMhkHFYxhTsc6gDFu\nu57PGRH6yXbf6/UqxN+2apzHX2OjfJZ7sCrrcoz8GQsyfN7BrH0zx6hTu39YIkg6ChACzJvNZlEF\nvV3Hu3fvCsCYrBFNODqkM50SQ47u9XqVwmjIIH8jXexIIGI7CBHbuqZ2ux2TyaSyJxcKYX6CiVVC\nG7EB3Abkc3N+G6VVI0co+Tv89P9zGtkAyKT3hMt1RihLXq2V60EitoBcl2LcZWPycG0mqJ40dgJM\nyOysnIrivZUCpysioqT0rBI3m9tHqi2Xy0IOuAacD7YPuHIuFJf1el0WkGDDfM+pDSJFUrhWObh/\nANIpdKLVrBg7HU1d2Hg8Lt+lnx2lo24C/iwO4/eIqNiOI+WcliatHrHdgHa9Xhc1gv79EVZuQrDB\nBwjTcDgsT9oYDofx9PQU/X4/3r9/H3/+85/jj3/8Y5ycnMRisYjr6+v4/PlzXF9fl3FnXuJI69RA\n7h38cgBMdgVcwYb4jAOiOhIIEWSLIZfXsLK5jgTWBahZVbcK+BIJrJvHttWX3jv4y0TQiydMCvm7\nVSKn3+ru60cIgPv9frk/1wIb47Ed5hXjTu0qdjocDosPvLi4iKOjo2g0GsWPEoBRJoC6aBxFMbaf\nYdU5K+gJXqzgoQaCS16ggf/i8/gc43Kuc/ex+Z3vYMcsjgObGHewCXwn0PYuB+Aq37MN2pdbYaT/\nuV/mJrbJnDem5pS4y0B+WCJ4cnJSGDcGZwXj/v6+Ush8d3cXp6enZSAc0eS0bF0U6TQTj7Hzyl6U\nwbwptNWyPHj8j+vkHAZ6JgFqoZ/O4AjWknHdoFl1y2kVR8N1RNCTj/Pl9An3giFzTRjpYrEo9+I6\nxxwhZ7UyqxRMrl02q6u59shlBk4JuV/dd/S9yb3rV7zCnMlMn7FS1OkX0pzMDUoXcOLL5TImk0kB\nOH43uWQuEcwY/HFYOFPA2EXi3KNJLvdo8o+9ojDyncViUbZv4Tz8HxtxjY1VSUfsVkdN3jNR9zWv\nVquSlppMJiWTYIVzl+3g4KCylyDbqlxdXZWnbMxms9jb24u3b9/GL7/8En/5y1/i/fv30Ww2Yzgc\nxq+//loIo9NYqCfeKDoHyOv1uhL8QvLsiLCVujpqxrrX65XHyEECj4+PSyBCwEMa0dvS5OxNHRHM\ndpdfdTWCVtyyKuJ54Ffd362yuLaX310X5/vIGJHvJWdJfu+GakefI2Asl8ui6mUFkLlEmhdCz+LH\nTqcTo9GoYMfT01NFdMEm8SURVRWLYDHvQQkmRmzTpwSc4AHNAolJPOogNst5wUWuBdxyIIHtmDTi\nE8BeL1IxLsE3HKRgj9hSVu5sP/YTzjpZWMA/I4LxOjw8rFwDvvt7trdTIvj+/fvSiQAFgAM44hhZ\nRUfk4X2Mms1mZVNoomxe3vYFlry/v18cPJPbTwYBKD3pcw0hLwwPYuQVy2xcnVVCK4Nm7oC1AYaf\nOVr2/WdF0NEHn8nkzxGqoykmnGuu7EC811juB0c+Pq4VUybnLpuJINfo+o2sGLgWhuaxy4W+ruMg\nYuQ7NPqJyXt/f19JzTabzQqhcwqCeUFfQqgAOtebcr/YKONsgo8iBKhY7QB8GEePtcGa941Go7Kn\nIGTEm05jJ6gBXBMga9LisYAIAvLMSat83Oft7W0hrSYJHoNdNBQPHFN+0sZkMolWqxVnZ2fxxz/+\nMf7lX/4lfv755+h2u3F7exufP3+Ov//976WGMGL7NJa6khSUCYIU8DWrgbYVOyyTwGaz+izhs7Oz\nuLi4iIuLizg5OSl1XE9PTxUSaLIZ8TxFFvHytjGZCOb5ZnvJqj6/ex5nPP8eMfRn6xaLMD/8vi4l\nnO91Vw08IhBkTjkARNjAB/vZ1fQpPhVfmGtUKQ/AHyPO5CyblTuEAsqsGEMfnyDTRJLv141xLhUA\nG7yALJf2tNvtCvnLZQTYAAqqMyhcC3bpnSRMJJ2V4zpyGYNFHB8T7MTmEaAQoUjnM/fdxy+1nRLB\njx8/lkiBur3pdFrIDM7k+vq6EjUjNXvy93q98oBziCLAgRqHMXqDaAYcVdJF0RBFq5X+rD8PMeL/\ngCtKDo4QMuiUDdeU6yUinj/BwyCFsWTgsSHldERdqiJHGcjdTBAMMGKrFHCvBsisBObjcc+7rpOJ\n2O6fGLGdYC5gtvpB3xoU+J6/Sz2bAYmxhFTbsfF7p9MpCwYittFtrs8EhIkuidhtb1wbgMd3IGCA\nELZLCo+/R2ydNOOMgo3D5f7swAFQwNYqqpWHukVZ9JHT3/STlVVsLyJiNptV+pu+45iz2Szu7+8L\nENpZ77qBX83mt70X7+7uyibLo9Eo1ut1DAaD+Omnn+Kvf/1r/PLLL3FychIPDw/x5cuX+D//5//E\n169fYzwex2q1qqiAL60SZkztCF0TbRJk+3XNMgrE4eFhUQIvLi6KEshj5HjcHYsK6h4dB1blMcGW\nvqcEmgxadQGvnHqLeL4a2JiVlaTfShG7Txwkc+1WlnJq+0dQBKkrjoiStYqIkgnzhvAPDw9xf39f\n6vUJTik/uLi4iMFgEBHbrduWy2XZ9Pzi4qKy7RpKPXPaZA6c8W4DLk+4u7srmMjOByZTHMtkzLjC\n3xBq/NQkzumsmccQscClLPgPK27ZDnMgS+P7tnPbR54TDjAIxCOikEJUUnA6YuvTnKHKXMBtp0Tw\nw4cPBcBGo1F5nBKTmwvHyQHuLP5w3VCv1yuRy+npaRncVqtVNjiFgHFsK2l26i60zwXDJkBWxzBw\n19EwAKge1MqMx+Ni9BiPo6GXyJIBzcaViR2ffel/WR10Ks4TyyQu12hlcLWiwHm5B0gChvk9g/y9\nmtNTBgRKCwAGACxiu5CEfoLYOhXnVIcbAOU+RVHzXl0GEn5CbgAKlxQ4hYfdARRZGbZdtlqtmM1m\nZZx4vBtjzTnX63VJxWQSiD3YDgE/nKvnF6BppxFRfc4nx7bdsbAk4ltNIMppnrutVqtSoE7K2Oo1\nqeZdNjIWftLG1dVVSX25l7YAACAASURBVPPu7+/Hmzdv4k9/+lP88ssvcXFxEavVKi4vL+Nvf/tb\nfPnyJe7v74vzeYkE1qW6nPnw4gfXiDIGVkQitsr10dFRnJycxPn5eYUERkRlhbC3iclqoB3v91TB\nTAhzOriuPtDqXEQ1gK5L9WYiaDJsomgl8CUC6+v0+6wO7qrd3d1VMOb4+Dgito9gZSFbRBRVFyLP\n/VPzfnR0VMSUyWQSt7e3sV6vK/6W8bCK5fHANzgTEBFl/jImbExP4Mr44suNf85COQNhEg8Ztu15\nsRT9Qy08fwc/6MOI6qM6fb/Mt6zwZbvJqjH3DB6biILB3CP/AwMitvWNYK2Dm5faThHx3bt35fml\n7PU3HA5LHV2n06nUp6CA8BQQ1zMcHBxUVEUPTL/fr2xr4I0eDUBMWJg2g2Bna7BAVXl4eKjUHAIc\ndtB2RKTuXCdoNQrlMuL5E0JyVJn/z0Qw6OaII6ctACunzzMRsZrl79phcH11iqO/5+vdZWPCc+8m\ngpnckpJgYtUReOyRyDmiuiLSx3OaEkBxeg+VEbvkWFYZIqLioOy0GC+rQVbxHh8fK8Xe1KTxmaz0\ncgwIBNcLyJj4A2YRUZw19oB9oU7auds2AEFqYADA+Xwed3d30ev1KqSW4zoYo9+enp6K0kGf77JR\nQzcejwsJJCWMc/706VP88ssv8f79+2i1WnF7ext/+9vf4u9//3vc3NwURcOPpgMzXU8UsSVCLltx\n5oOXbYdmB8RKTkjg2dlZHB8fl5XvbC3CptGsEgY3rWzktJfP52uvC9ZfetWRrDo1sC7NW0cCHdD4\nfyaBJoDGEWdAXJ+560ZqlrmBzUCQvE8kNgo5xIZY8c53ISLgFVup4UdJGWNHqGwWTcgk0K+Xl5fl\nOevwgIxr9HXENztZLpfFd7bb33ZkIJgn+OQYBO2eI8wDMJe5ZN/mrayykOBAJCIqNdp81u89T7kO\nSJ7vk3PnYJ45y+e82b8JJTb+PZ+7UyJ4fn5ewIXCbj/ndzAYxP39fWW7F8ggEUxElMiC/QGn02nZ\nc3C9XpdURgYvHmxvoHHqDlkVYHBKwLL14+PjMyJI3VXef4sB4lgYAcfBIb8kE9dJyWb9jjz8eRyt\nwdj36vSmVwabDDv6NrBBUCKqaZ9MWpkgJo67ahAL7sNqMIoYhIeaUu/159VcgIP73v0bsX3cj9UR\nzhsRhRS50JcNeYlgnZrJ0ScOywoPAUaupzV5hwg+PT0VAgzg8/2I7cbUROb0l/f6pHwA4KGWzGlb\nbILzOPiI2C724HhcU6vVKsrDfD4vm8tm5cc1gV49PZvNyvzedRCyXH5bgXl9fR2Xl5dxeXlZVgn3\ner14+/Zt/Pzzz/Hx48fodrsxm83i8+fP8be//S2ur69LhuHw8LCyIM2ZBBMgbAR8yVtl5dIWq63Y\n2/7+fgwGg9p0cKPRKM95hwSSTiSQz4GiSVEd8cyp1ZwWriOCNONm7oNcQ5aJoD9fdwzjMdcJVjqY\n99Y9kMEfgQgyHxiH0WhUwTuXPTGm+ZFvYAK4CG6x0pj+IH3Liz7K5RpW6LAFxmNvb6+sTGfuut/x\n8YxjTo1ynRAmglDXOjOmBDLr9bpkHSGoXBf3DKY6aPX84V6MRznTg8IYUd0o2oo2868uM0TfOWMX\nUcVBi1LfU6N3SgRZGQRbp7DUO9TzTEqKjik8ptkIeAGoGABO9fb2tgJibFaJMyJtzKR1CgkDd22I\n5eaDg4Nni08oyDYh9Pc9MDhlVETfWyZ2Tp1DMBzh5lSLpW7O/RIRzADmNJP7yuBm5c8OhHuoi8pX\nq1X88ssv/3PG9RvNTiWrHpBEADIHBqzksuLmFKRTHv4O4Mo4Ax7Yf6vVKpE24AJwRWxTF1zjZrMp\nC5qsbrjmj6Jmxp/vAfiO0AGPiO1YelNgR7jYIONJH2DHq9Wq8jQext4F6tiiV8ujlqO27u/vl9Qw\nweJmsyl9ZEceEeWevEktBBxg33VqeDqdxs3NTdkH8Pr6uhBVbxx9dnYW6/U6rq6uyuIQgl/qu/r9\n/rMMRyZWOAaIfMYkYxPfYTwJcsiqsE0MiwGwe5TA29vbsvWXnxCFY6vLGHBOfhqzXiJ/TgtbIXdg\nEFF9bneuA6z7m4lLfrlvIMsEHLlOk/Sp696sFu26MS/YaQAiBJaw0BFMY56CCXkbNLCTsSaFC36C\nifxuzIUkmnBTQ7u3t1fx1a7tjHheP4dfNnFz2RPnaTabFb9pn0WQz31wTw6s+XvE1mbBQgcUOYsW\nERUSmTN5zq44rW3C7PS6s0S09Xpd2QWCc3wP93aKiHa61B6w5Bwy2O/34+7urtQQjkajGI/Hz/Zy\nwnjpMGptGJxWqxU3Nzdxd3cXd3d3ZeNTE5+6LRc8iPzuHL2XbfN0BqeFecKDV9pmsmaSZPnXhhVR\n3bIEw7QyycsTwdF9RFQMLSugTo261sjgTEQEuGVC6Z8RWyC2Egpp+V//63/93iZXaY7OHOX7XiET\nmSBB8Gy/jDO1b+4H7hkyhWLGGKPy4FytqJoIQoI4v22ec7bb7aI8Yg9ZGeR4gKDrWVBG2SLEz8je\nbDaV6NNEkGPy9BGUqhyx29bpZ4I27hfyR1+zcpB5zf1zfsgO/cM1es7ZfnfZ7u7u4vLyMr58+RKX\nl5e1zxJmR4XRaFT5HGmtXq9XFp3YAdInDv4INOo2zs/bI1ltAQ/YJubk5CROT0/j5OSkOGrqyG5v\nb+P29rY8Rxj7wy5puYTA7yOeb85rQpjrAk0CjYu0OjXPjtUp4JdI4EvXHrElGV7pipJkIgiOZJK6\ni4Z9+ClGPInCNe/YhwNhbIJsWh4L7g0fY/LlEqk6Yk3/MOfPzs4KuYNcE3iiUkI08cVgAjZvpdFZ\nDP7PPTGm3I8zIrYLjmV/6EDe5ItrMUGzPdvPZzLIdeH7qdn09WK/CBn4e/sYz+nfajslgkQYEVEU\nGCIrVtd2u904OjqqEEEXIT88PBTlBqCj0dGkklutVuUYZ2dn5Rze98iEMG/zYjDIJIjBc2qMSQAh\ndN2VJ4PVRQOXFSEMMBerOnJwpOGX5eR87dyTyYqVJAMlk8l9wmTPhDCDMBOM1y5b7ssMTgZt1464\nD1DGTKScQomoKqKui7HE79Q7m6g2Go3y2YgodhgRBYQ3m03ZPgRb4bsmsJB8r5DLQMTYoAxZhYGo\nPj09VUhgXv3nLWharVZR/Jl/jpTpH/qS8/A3jg3RYS+z1WpVUQ/syKlf4l4hTblmc9f7CJISvrq6\nKos+nBL+6aefYjAYxNPTUyGMd3d35f7Ya80L5lxukZ2rsxNWA10PZmXFqU7wl1XClNWYBA6HwxJk\nj0ajUj5QpwRGVOud3exg61LE+ZUxLAfW/8irjvDl6/HLQZrrivNesXlPWxP1XTbmCcHdYrGIs7Oz\nZ2VMzBlIH/MQspTVzojnKX7wqt1uVxaHcR1OWzogBzv6/f4zW+B4HBvbB89sMw5SF4tFETdarVbZ\negs7Aj8huw6svSNDthdszYEV/ZDTvMwrsNnHsrpXNw+yb2J+ObAmFW3V1HPje7a380fMWTK3OuD0\nIyuUZrNZWZHGY5VY8t5ut2M4HMZkMinGBVteLpclgmCxCavajo+PC/nzvkf52cEmP77+rIRloOJe\nnLKzZMtA4Uxdt+AI1kDNcR3tEAVAODG0nJrlvSeWAZTzeWUWx65LHZkIZnA2IJswWGHbVTMRdMQe\nEQU46Ctqq1y4C9EAWFyjR9+bfGQQsVKLs95sNmWTYacJOJ+jRZyolT7bnNMHEd/AE5tmTB1sZKLO\n2DNmWWV2iQNA6Ch7f38/jo+PizrIddEf3Lf3vcKO9vb2ylOFqE37+vVrSdkApAQpPqa3KCKtTR0k\nfeY5vIt2dXVVVglzjxcXF/Hzzz/HH//4xzg/P4/NZlMeN3dzc1NJl3tvNhMMOx0r8l7UBqH3KuGI\n7aIM+t+b+x4dHRUSSMkNaUVI4HA4rGwV8xLZ+0daXWo4p4NNAGl1wcFLxO8l1e9718M8zhkUP1o0\nP0nKaUAHQLtq9AuBG37SQgTYAGacnJxUtrcCMyKiosqBFVkl5L7BlRwQgjmel+AjczzXwXNsgnBj\nmUkl2G5S5KCEcxBg4u+9EIOMhK+HxVr0h0WA9Xpdgm1fVw62TAK5lmzTfJ4+5vMuQcImN5vtAhKL\nHCaeL7WdEkGrLnRAJlXIw6SoSBkPBoOyOIQFFiZ+fB8SllN3RMiTyaRC+gaDQYlGeDnC8948vs7M\n5iOqaUSuyY7MhAyWT7qwLqrgmI56mGhW2nIRbJ0S4+s0QEI6LJnj+C3v5+gvE0FH/379VhT+ezVH\nf665A8gMjDwhwSTc94wzNokm+jUB4ne+48jWaoEBi+YAKRMx9zvX5rQM5OHs7KzyPFA+4wUhqH9W\n2h0dR2x32qcPvBWJt5bo9/uVAm6rlCa4XLtLEXxtg8GgKKiAIupYRFSKwnG+m82mHNtpKytnu2qo\nZ/P5PNrtdpydncXPP/8cf/7zn8uzhKfTaVENSR0zjn4+MXZQNyc9b12baocVUV30QJmMSSBpYZPA\n2WxWIYH39/eVmsCMg3Xte0pcVgNzOrguJezAGTvj5/8rKbQ6Y0fOvMJv5CdHgSmuMXNgtqvm7AR2\nwXVyr6iYtoN+v18IkRU45iHfh1DRdxAn8BUsMEkyJuSgnOPgiyK2NXYmahBKMJ3zcU9koBxkOIti\n4tfv90sARdbn4eGhzBGIoO+D5uu0zeHrnUXi+8ZX32tWQh1Q2A/DLSK2WVDPEY/5S22nRDADRsQ2\nD07DWRweHpZFIBRIU7dE3RDGSAFsnTSfl8dTe4SS4CeUHB8fF+C1Ssj+RI6gXU9ocufBhLQ5gsco\nuVdL3XVkyv1iIDSBtIr4EtDR71m1s1LlMYr4ZuQsZMlL/01q85iaZNI8YXbRTAQZU8AF27B6ibLV\naGy3EADwIqKS5iBNYKJj5cqkjmMAZI4o3Y+uzXT6hvHB/gA4Kyh26p1Opyic2J1raSG9zEGeB07U\nDAB2Op2yTxj/41pYpd/tdmMymVS2Q3LxeXawTon72v3ISCsaOBZvI+XV2yYTJgPcw67a3d1dzGaz\naDabcXR0FD/99FP85S9/iT/96U9xdnYWy+Uybm5uyvNcvaLc/eG+yLXEzGuInxcC+LMOSrBrZ0cI\nuqnRIlC4u7ur1ASSZswKbR0O1AWEdtovKYJ1BJDvZhKYXw7UHBjXqXSZuPq6sPe6TbxzutTO3hmf\nXTayGMxdBBHmEUqmt8DBtzKPHLBGbGs9rbozHnwmkxz3O9tQkSFAeZzP54VQ43PBDp/X9uR+txCD\nf+RasOWIqJD1Vuvbgr2I7fYvFnHIBBFY4cONcVZBLeg4U5nFHK7DfjL/tM1ayTcJNwkkIHZw9lLb\nKRH0oEZUyQFGw6RyygIn7MJnE8Gbm5ti8CgUOHoXmlL8/vj4WNQdpP5+vx/j8bgohIAikZ/Bk+Pn\naCaDIQ7Vk8GDaqC0wsj/6qJZzlMXIdP8vo6AZVD0MYjwIBCQED/CzPdp8pKvh9+zSraLxlh4YQTK\nbV3qDIKCY4mI8jgt+skr7lgFTIRt4MPOvHkzalmW/DOgcC4XdgOg9C1ggzJBRM+qWWoAuT62UiCF\nwkbT2CbkktKKiCh1YlmRjIiistppbjabouC7lsfqN8DII5NQWDhmXXCFbQKA7Xa7BCsm4vTXZrOp\n1BHvovEM5m63Gx8+fIi//OUv8de//rXsGQgJ5BFyjK9TjVYFrDzQGDvXDGWyaCUQW4TcEAwTdOMs\nedoEJLAuHVwXEJrw1WUJuKZMBjMhzBkH5ocJIOq056uJYCaELymCnCMH6t5hIT/jPvsBCw4/Qm00\nWSfIDrhDqps55wVZlFY58I2IkjkxkeF4pIFpDohtJ1wP1+b+Y9yc6YvYEi1nRDh/Jju+Br4DnoEl\nVgbx68YVVuUz/pRXjEajGI1G5dnwb968icFgUGoM69K5YJJrtY1/OVDJc9u2iv/yrgwcx8Sde3pJ\ngY/YMRHE+ZhMWGHK5MEpAkji4eFhLBaLApJEzmwxQ9qLYtf9/f0YjUZlo1Pq9TjHdDotj0aaTCbF\ngR4fH5fXYDCoFFQvl8sCBHS4CV2OcDMR/B4I+bNOdeQ0kM9hsLSyxPl9fLc6gF6tViVStLrgVYEv\nRTC+V18nfbfL5rSC0wk4S++tFhGFYDj17nQ4T2tgUQMLhBhXgCAiCjnC+bpY2gXODhSy43IKerPZ\nVICKwmjOw6O/HJ0yfzzvOKaVlfV6XZ4RzPYtjjYjthuqAnQ5AueaWBhBnztyzgo2cxtiyLWa4GZF\n1ekRl2vgVDz2u2yMz9u3b+NPf/pT/PWvf40//OEP0ev1YjgcxtevX8sCEYJUp++cEsrqf0QVKxzQ\n5GAkk0D63OlAFi8xJ8bjcdl5gS1ivE1IVjloL6mBdbhnHPutdHBW21yHbUJYRwxfwl9fe1YCIUwI\nEt4zELKTMy2+rl0TQQsYXCtkzCufmW8mdPZFEVHZRoU+BEMp5YiIit/Gdn1M+tZZDfejfRHE1ETL\n2QIrexY1TMJ9XBPBrOByXWyLw2cZx/F4HL/++muMx+NKORXBc0655/tg/r0UHBn36Xf+79IEyjWy\nIpjT3+YMue2UCFL87QHlwjMRNKGxgcG+vaJpf38/xuNxRYb1qiFek8mkYpg4QpMdNqeG/Y/H48q2\nMxSW+kHvjoYNihnYrLp4ctg5Y5w5sswTyg7Y53BtjYmpoxATyYjtROB6MToTBKcm+Q7XQcsTmv//\nCEQQgLPDYRK75s1bKPA97MQ1oxBHVDqToIht0b4BgL8TJXrC47AYb64tp+MNfCY/gLrrlABnzgnY\nc1/cA3OBz3FO9qBjV347NQMe9+x9M5+eniqbtZL+sRrOe1SAiO1CKKurpCv9GYM61+Vnb7K/YFbO\ndtE6nU6cnJzEp0+f4s9//nN8+vSp1EFeXV3F58+f4+bmpuwZ6PmLDWXM8M8cNLpQnPmHczAeUhMG\nEYQERmwfN8Zm0SaBeR4xVlnxqyOBmYDZsf+jamBWAvMTU7IqajKYFcl8LcZxcMtEMG815rFxzfCP\nogiCPahce3t7ZX75iRn2vxFbMsf9IQBA7hyURkRlHhKgmZjV+Tv7H4I5j5kzEyZrnCP/zfWz2ATH\n9/noFx/Ddcy2uVbr257EZ2dnEfEN97xd0t3dXcnCgL0m3CaCzMWcGTQf8We59oht1sX7qdpe6UPm\npjMldW2nRJCVN2bfriuIiGcgQMsdiTrAxKQOxwSTzW09iXl8jhUQ1xCiRkwmk/Ks4Ol0Wupm9vf3\nYzabVbad8R5LNoSI6opTqzs2BK45pwhdf0Zxq2s2DFY+v19OsTEhDN7uX/rYpDJfs51qBnWrAwZ9\nnNAuG/eatyJZLpel7tR70GWHErGtYTER5DOuKTFBoU8ZX0geQEx/eaGKFUKPmYMEgyljTX2f07pO\n5zhIqgNzbAs7BOyazWbM5/MYDodxcHAQEdW0DKkk0sAQGPY8RKHELgxyOTDzKmwUBwMgwQi2zT3s\n7e3FYDAoaWH+v2s1MOLbs4bfv38fP//8c3z69ClOTk5itVrF1dVV/Prrr3F5eRmj0SgWi0UlhZQD\nwBwYR9TXy/nzWQUHB71IxOnBRqNRxo7dGqgJdO1RXcYjk8CseLyU1bAdv0QCsVHPT89BL4zxvM0K\nab4GZ6DytTgLlJ8akgkO9psDul3bH+IJCmCv1yv+zxmOiOoiDfc76jBj42DDY57JFH/LixnBAYsj\nzGEwgXIRsJFrse/jPRjH2Lfb7RLccz34H3AVrMtpVpMx8NyC08nJSckGcV8R263xsuhiH8mcsQ+3\n7bimEbtkvnl3BLc8Tnzf/6trOyWCrMSJeL4lCQMG+TChiKgWSeIgIIKkvcbjcSV1yQonJNWDg4Py\nPEwXU+NcABAGmtTYbDaLs7OzomxMJpPKymLXJWIQgBrXjhrDfXuvIvoj4jmwWxn0whdHtXUE0CTZ\n5DKriD4v1+a0O2PDRMpEMCuUNtTswHbZstJs8GIxhTdrzvuuRTyvvaTvnWbz6k5Hd9iXJ7VTpnZg\nJqnYEONBw84NQIeHhxWV29sWeXyomWNcnGpkn0JAGWCmvpa9Am0v3JtTOaiRzGcAGkLn1A73A+HG\ncfua6SfGiD5nHjebzXL/ZB68lccu2/n5eXz8+DE+ffoU5+fn0Ww24+7uLn799df4/Plz3N7e1u5z\nhhqYA7TvYUVWXbKqlRVBMhtODT48PJQnh7gmMJNAMC6r1Z7vWQnMhDYrgXVk0PcKCfRGyFkRtKrk\n0gf3pa/RfZWzK+6vvLsC18N7E0GrkLtsjL/7lvpv94tLsGxX3CPzmnt1H0RERb0DtyBoeU9B8NDn\ndBrZqfUc9DC/8wIM9klkIanT2V6bwHfAGkpaOLexO9cQ+qk+i8Uibm5u4ubmJsbjcSUtjJ+tu3bP\nE/rC/e2UuYmguU/GBI9DXVBWaxf/rwb1/0ejyNLye5bjcQ7Z8eXJazWM3/f29kqBeEQUxs+G1US+\nFHyS2iN6sFICILLYZLlcFieYo2q2ziC1UgcerofgujNQWHnzgGZw97YuTIw6UIVsZELNNZnocXwi\nR9Kg/rwjPV+zpfccwWfD31Uz8cLOHI3hXBgPtlQxYJh001eoKFaGXbvBeYiqnXoGsHBm2H3Et7Eg\nVYgKYXCx3diRZdUPcIl4vjjJ8wfAyWoz89F9wd6ETq8A9LaPvM0EnwcocTpE/aQymAOUXfiavVgG\n52GF1I/989zZZXv37l18/Pgx3rx5UzIKl5eXJSXMU1UYv4ht+juTwIjvP03DKomJjBfdGaOw2Yht\nucBkMimb+EMCGT8TJpMizutUXb7GugxCVhc5brZ3CIlLGpijWZ3PRCz3jQmmMdPndf/RZ/ZXXI/n\no9PzdQrkLprJa1bHID/GRfAQIsbc5p5dQhKxJSD8zjwGy9brdUwmk0LeuB76yauG8bfUyUZUa4Eh\nlZPJpGLDjUajKNeLxaL4aauAXiBjzKMEChsHvx2Ec29kXfhOxDdsPjk5qajQVhppthfPWQs9nN/2\n6X622h5RTY9zn57LP2xqmE16HfmaqSObZvm+Dszt/Py7i9Yjoig1EDXqYah/4nnGfiYnqgwDxGRB\nYXQU0+l0yt5brCCi9sIbjhqEeTQdYJVJiSMSDN4k1bUxvl6MiglPKt7G5763wuD+e3h4KKtc7Uwc\nleSoP6t+dWrFLhtpfRO5iKoybbCzkoba5s1kI6rRsOtFHWXX9QNpYRMtHD+KogmqASBHhYw9n4eY\nArbco4MMiChBUsRWYYyISjqs3W6XvTiJ1rNaksFrufy2lyD70LkEAnWQuWtlIadser1eUcaoa6J0\nw2kj+sLP1q0jwrtq79+/j3fv3kWv1ytKwufPn8tj5OgvsMU2E7El5Q6G60iug2tSWZkEch6Tm4jt\n9lCz2aw81clKYCZMVu3s2OqUQONbVgNNAP2ybzC2gXnsIGEymFPBJmQZo3wN7pO6zErOjtQFty73\n+ZGCEPDAgRb3hIKey06svjqTFPG8NjwTQYJGFqRERPGr7fa37aGwUQgnAd39/X18/vw5Hh4e4uzs\nrKwHIANByng8HleCUWdwcn3harUqj/N01gSsx76dxvcYMq6Pj4+VdQnNZrNgNcEdK/7p3zrfblvk\nXJBIz4nsXzMBt8/Cr2OvDkpeaj8EEcxqlEEmT7A6iTWnKNiuIqL6lAgGqdvtlj0JIYKQv8lkUlk1\nTA2hi5AxPjt3Xq1Wq+zZNhgMnm3QenJyUh6inRevMBGsxhi8cpTJZMAxel9FIppsxHbwnCenZXK0\niBrqtLv30csGmxUKfvrad100zT5pVqmwJQO/V2NZes/pNdQnvu/I22kR+txEB4dFP9JXRJwANyUM\nRNMR1doYE1dsAxt7enqK6XRaAgMAE4BHkeT8/M3jTX+1Wq3Ks4A5p2un/AD6TqdTFq5A0OgHAhnO\n5XkbUS2OXq2+bW1D31nZyfa9WCxKPe/j42NxNFYUdtXevXsXx8fHERExGo3i69ev8fXr17i/vy99\n4bISKy9OkeasiJsVNtcDumTF9mmFEeeJIpNJIMfPqpltMbc6Elh3vT6m/UImXa4L9DOUMxF08F5H\nAu1z6oigyaBJrwOW7JSzIvsjEECaa5a9zZIVQLc817inPD/pT/scr5IGt/DHjA/iiYkQ14YSzR6V\nkEYTR8SbVqtVea45vp7sHyqjFTOui8Cm0WiU3UZMBLNat16vYzweF1wmcwSuOXMWsQ3c6AOLWwR3\nnN+KYA6oTc6dKfQYgBe2Wae5X2o/BBE0KPll58nN0sk2upxj53M4IzrH0fN6va4QQRj8eDyuLPwg\nFUwaG9UFdY2ONjnj8XU8loeaRD8N5fT0tKSQWd3I8Q1UGUAzkJlMQARRQepqZHIEUpem8Xu+AwGG\nHFrCxwCtKGQVw0rXj0AEITYmtPRNtkkcBPeLTXHPOTXstDmgxb3bYToSJYplzAEPtqABWEn7syeg\nQdT1IY74IQHz+byMQ64lIYAy8bDj29v7ttk6i2MAJTsP7h81FIAElFETHczxmXw817JybEDaqSHu\n3ypzs7l9+gjRO7WI3Ocu28nJSTSb37aqurq6ii9fvsTNzc2zPQOxN5qzDpmQeE5bCWTswKA6FdzO\nhrnpJy9BplGEIA51hM2k3AFnnWrGtb5EAvOxuU87y7zLg5+lbNyrU1+MVVyHS3Ws+DhrkglfVrj5\nn++Rfv4RWiavTiU6wPD9OUDL/ZCJOliGILHZbAoJ4jjGIBaGENxib6zG5dnV7M6RFcRccuKtp8iq\nEID6GrKy1mw2K48SZVzpB2qpSW9jexyXNQ/UDVrBt81CpE1KPVecFs7BnoMWY3gWKjzHuV/uq679\nEDWCrnlylGoS5wnMDdqgDTLuDD+PM3cq6sXh4WFZDMITREjjjsfjki7miQuTySQits9ZNBgtl8ui\nKhJ9OzJxhM2iSlwk3QAAIABJREFUk6Ojo6L8+F5NREzWGPBcMwMQ1j1YnolkxZHvmVBzfBQjXq4x\nMFjyWQNnJqicxyrYrlfPsXkqKW87mIjqZtg0FC3XyljNov/sZJ0OZoJbOWVMc5QIkDEOrMTlkVDU\nnzJXNptNpSiaa8uEzqkD5oVtrNFolFS3VXrGtt/vF/IMMWCMmUuQRfcPNXyQXc4H6YiIcg8AO32M\nDbJS0QSJVLZXc9sGUQchuWyEvcvGllP39/fx5cuXuLq6KvVMYJZJOZE/JDAHKVnhcirJ5Bxbz0Gm\n+xn7Bg+n02lJ7WUSaEJq2zLW0uoIYMTzLbVeUgQzXuWyGJPBuvpA+pFzW2X5nhLoNLCvw048ixC8\n9z3Sd7tWB90HzDVKXLhHB+85q2ShJpctOA3MiywE6jIr4cETiJ53AjDG9nq9km3haTx7e3sFTyOi\n7AbiYBu/C6GCS3je2JaMywRLtnlnMrg+rsGLSdfrdUyn02fZIgJSFrLlIIn+zYs/GSvbkG2Rezam\nW62OeP6Y07q281XDljIZQBg0wOAbMpkzsYioKjpMXhyKo0BPUu/Hw8vPj+z3+5WtYyB3SMpch1Nb\nTiEDKJwHufvu7i4uLi5iMpnE+fl5HB8fV1Zm0hc4e5rBPteqAPh1K+Ys1btu0BPBIGHZ21up2HHY\nQUFObYQulrUqUKcM/N4NkAcMPeGtHpisWaUGDFBJI7aBgcHSyhZjkpXTHHlayse+XQNqG/E1uQ7Q\nBN5pDuaU076eSxFRsSsa9zEYDEqtH6vy+Tybr3c6nUImnp6eKsqxnaaVUNsfCi3/4/q5V4AVYgcp\nYGEY89/nWS63m75/r2j692rj8Tiurq7i8vIy7u7uih0RNLo43hkSSEpWCTIJidiqvOAa2GrctMPh\nb8x5Ako7xDrVLs97k6XvpUaNZZkEZjvl2IxlVgPzK2NfzqQ4MHLfulTHqkodCXS2JosM2VfZ8e+6\nPTw8PBMpCKgiqnv8Gct8z/Qh44GdeC5jS4gp1Oq6XpV5zPPJIU4QSTAUX0StIXaDX4Uskm72JvpZ\npfV4GidN7HlQBPcZEaU8IiJKjXKeg/h/+0MTRtRPf96BihVKkz5jsX2pVUZnSz33sO8sbLj9EBtK\nR2ylYhMryKBrtNxBZvR0nmVmy/k2YJNBSJqBADJI2hgFbzwelyeUIFvb8XBcDJJJ0Ww2C2lgk2qe\n1XlzcxMXFxdxdnZWjJdrRrEyGQQsvY8VkwcnyX5pjvJNzKzGOeJzioO+RBWgTsMKbESU/vNqOiZY\nxPMicdr3jPL3aLYNbInrttxvcLAzgaSQzouoBiJOdZEmAFAcoXEcO1CTJFKnrvk0SBNEuY9NuAEg\nzuFgi3NBhDm2x5HvQPZ6vV7pM+wrIsoiKYqvWVnvOjfukTlNn5HyjdiuQHaKksgfwGdMCHxWq1XZ\n4Jhr4Ric8+npqbLn4y7bbDaL6+vr+Pz5c1xdXcVkMikpLLae8vWb8DuwMCZGVJ/kgwOlj7zwBMx0\nqYiditN6rkWqS9/mzISDbpPATAizgpmPWZcS5hrr1EAHvn7vOePA1dkjY3/dJtFunutWY+2sTQKd\n3foRSKCv0WUgEVHmuW0johpoWI3FdvBpznow3qR4R6NROQ9jYiULP4nNMme9TRHfXa/XxWez8DJi\n+8ha1PbRaBQRURQ+jgXGRTzf39K10RZkGo1Gqb/n/vyYUAsIJpVcMwtX6E+Ph5VABxxW+DwP3HfY\nO+PoEjvjgudoXfshnjXMhGKi5lSHVT13DJ3iyY5xe/ViRPWxMnR4Vm5wUiaEqILz+bzyAPabm5sY\njUbFGTI5vNcgCwC4V4BsPp/HaDSK4XAY19fXcXl5WdmXEHAiRY0DrLs+P+IoG4EjWisjJmlOC9cp\nghwbJcWy/2ZTrWeywUHiIRmcz/exy+aFLjSTp1zT4ujRTol0pe3MEzqrKD6/I1/3kZ2yyamjW4M1\nY0stjesvn56+PdGDtIbHiOOZFEZEGc86Ish4R3wjNNzr3t5eHB0dRafTKY9wXK/X5bncXKsVfvoS\nlZNFXnzegQurGumbiOrWRzz1x44BInNwcFAh7OyNuKvmVcI4SLAGDDABi9g67ojn+3GCiTQwM89N\n+s57Slq9qSNRPo8JWlbtrPZm0pcJkB1dJoJZCbSS5sAf26h7GaNMaPI1cF6vpM7BdVZXuL86tS//\nzffwIymC9hOMuzebJ4hz0Gn/4V00Go1GSftGbDNB2JdLC1w/DXaa5Ewmk8qc9rPU8VXT6TQajW/K\n+fHxcamVzgvOwDuXMVgAcmYKuzPJd+bGtXbci48LRlt9c9mElT6uj/fZr1icyIo7tsP10Y8eK+6n\nDhe+l4XbKRF0xztaiNgSN4w1E0GDotNpTgV4jyA+WwdsJicZIIjSHx4eyoPY2TJkOBxW2DgKGotF\nqBmwWsPE43M8YJ6VxN6mhTpFF8hmIohy6WJwk2CDtg0jg7UJuKNFjNEqo/eUsrMx0XN0SMsKwC4b\nQE/LEr2BCnth7AwkOB9Hb/SDSQvAaCXadS1ZncmRXi5yRrWMiIpz5zPeV48oNquf3INVJmqG2AqB\nCNnjTKq33+8XcOp0OjEYDEqqjqLrbrdb+gjSmNO2mUCgqvM/zo8NOpMA+HOfXggCwDYajTKPcXq7\nbFdXV3F1dRV3d3cldc5c9wbkNAfLxkLbnJ2cMQIcsMLKHpDYnMkVwZ7Pk7HEhC2rgfy0o80t4/hL\nqWHfd0RVSfpHiKAD3EzQsgrop0K51jmi/pnw2R/5/k0CM8bsmghCyJjzEK2IKKle7CPPTeYeaWA2\nlWfugSEsNALbwAFwxZkR/Ay7czjLxnmxZ+Y2e/W+f/++rBR+eHiIXq9XqzZyXgc3HNvEC6xwUI8N\nuA7VZTaNRqP481wuxbxxXT6ft8Dl2nHulf87MML+vFiV6+T6+T3XEHO/L7WdE0HfYAY6p5I8ybjR\nl5Qu14REbElO/juGb1KSVUJqKPxAbgzLj+dhkvMoJqRp16mQpuPFlh4og5A+IieTvTowBsBQEqjR\n8iII173kCNeRCs2OhD7wg+ftUFBxvHGyZfwMwlkB2GVzHVyd7WUg9z3xHX4HfPJ3qN2IiAIiNKu1\njpwhfY1Go7JogmMZmHjvzXQbjUZZNYd9z2azigoIkeL79AP1Nfv7+yXShmyiyPHZTufbzvqAHPbK\ngpbFYlFJcbpv6U+rsfQHRNWE1cGZU+wcD2fkyNzj1W63y7yaTCYVkrWLdn19HXd3dwXMmcPMMwck\nBnFH/VbNrBpkjMBBuDQFZ2aVzSSqjmxmwmYHxfXxM7/8f6uBdQTQ95KxKqvEdQpgLnXJ2JZJoGvC\nSctbIcrE73vONCuCJsi+/l025pQDVq4TQkc5hlUs5pYzYCjHBKau1wSHvPAJUoadMw4RUTJk4/G4\n4BDZN2eVIiK63W68e/cuPnz4UPbUZaWxA3m+gx9fr7cLNozj2KLr/iCF9Av+jr5jRxLS3nd3d8We\nyKbQbwTo/5e6N21uZMfRtiHJq1bbtZ2tY3oi5v//o/kyEd3n9KnyIlnyquX54PdiXglTruqZ7sp6\nGaGwLUuZTBIEbtwAQf+do0i21dZ5dvIimjAzJcRMaPBMmQHd55C5dQ4E8+85tm6w6MVlQ87k22vz\nC8ORE4cJOTHpWbnSL4BU9hyHw2GLuWBBsCGEPELnrhA6ZmcxQkYo8v7+vtRzyzSzGQCHf8hlnEwm\nrZfDxuQawsbYa3dIGMHjO7XwD1v2YZAQRpRHVoYGSNmAdNUAuwawZozNduDF1ZrZ7Kw0uY6ZFp6b\nMCv3Z+7NDvozGH/k32PtJHm+g+eNcnaIENDEM9LsHTM+9q6dNxgRpSwDHi794vo4Rj5BxWCNtR3R\nlLgBuNBPjzGfn0wmhTlw3o1LNtj5Y51ERMuT7qrd3NwUVpID6sfjcWuTjHWVmw0Xc1Qrv8W8Aj5c\nZ89sjxnpHA7Maza/MmNn0FYDgxF1IFjLOTRAyWFhRyZqIDAbPuu1WjSFl3e8mzHPTF7teWrN44Hz\n3HV+Ks8Fi54djpzjZ1bd48iYu54e+eQRLyWSzs/PyysTGf1+v4DEXq8Xq9Uq/va3v8XV1VUBgh8+\nfIjZbNaqhoAe8saSiKZQNvdAN9SiEOgs9E1Ec3KPn9XXYb0io+Tzw34Cfu3IIsPUOeR6hJ1ZyzjW\ngMEacMvyz3cdoUEX5vxar7l9rVMgaABi7ykzVrzvzzlsmdk+szT8DwNjBYtA51BHVqoZeDFpFKa2\n8kRoVqtVOZzdBampVbhYLFqCSE4DYMEG00rTigdmFFAKK+gQNh6K81+g462wc9iEXVyj0ahVe4z7\nZmaC98ziOizthdG1IY54XZwToIZMAqj8TLxvGbSizB423zMDc3p6GhHtHCVAmxUvuTrb7bbkzplt\ndLjYIRUYXBSVmXLmFQckotlYwZh4buwRPz8/x2g0ao0BShKZpR8oQrMFZkfNShgEMAZs7OCzZn+O\njo5iMpmUMeLYPZyXvH7M1rL+c32+790MAu20DQaD1okIWadFNOyBnbWc/0SDtfG5qzk8ZgOTc8Js\nuA3QGF/fKzvjOZTKNX3tt0Cm11HW6QaBZjGzQxfRBrMZBDqS4gL/BgfOw812qtZqdox+Ahq6bKen\np8VGMZbWebabGUwRwQBUPT4+xnw+L/oPMDidTuPs7Cx++eWX+PTpU/z222+vQr22D6R8/Pzzz7Fc\nLovOI1UKAGU2rN/vF8fZuhG93e/3S+iaF7m3XIMQ+Xa7LfdlLEww2aGkzA41gtG5PB+6looo6EVs\nvWUTecKZo9/gEuMgM/e18LMdMpxg7Lrvta91CgSz92aAwe/ZQ41olBcTEdHe1clkmslAmLyTzIPu\nJOgsrKZ5Myg0QsfbIgR8fn5ePHGAIEVaF4tFzGazGA6H8eXLl7LDKRtEjEINcLEADg4OCvikxE0O\nMzsfhpBh9jwMesfjcbx7965V5xCK34vLc0iYnOcAqNLyd7ps9AV5YJxtvOzd0ayUMrOWZZfrkWAc\nEa1Cpw4/MD8wBgAXV753eNThPFi3XBcsIgp44ljFk5OTAjDxKHk+y5e9TwAER7w5TDGfz1sAF0BK\nCCRvcrGitdOBh0wOpncyG6gQtvaJA9kLNzjGaHgsuy4ozXOMx+OSG3x4eLh3s4adWday13gOAQGe\nfeqGzwi2gaitR4PAfQBtn+O+LzLDdXOUoBYeth6yTcisyL58wHy/fUwgpwtNJpNWfUyeKzt8mRnk\ncx43v+916pI8XbbRaFTSUOz8sm4z8YAzsds1x10iX5SE2W63xbZMJpP47bff4i9/+Ut8+vQp3r17\nFx8/fmzV+8TxZc4Y88lk0roegJPP7Ha7WK1WcXl5WaoEZDIionGWzPKZuCByADmyXq9LygjPijxa\nzgGZrF3GAgfZMovejGgfN5hlEx2cI3V8lkiUHeqIhvVz/4wNDJBZFz8sEPROH4xjDcFmpsyLPTOF\nWQEZzTNBKI4M7EzLZlCYGR8LVFakVsS5rhXKebVaxfv37+P9+/fx+fPnuL6+brEmsB3UYrOg19gC\nDCgCw1mIri1kz6PGBnoch8NhLJfLAjLI1wBE0tesoBlXFi5APM9l17ky3tm23W5fAcGs8JETe2cR\nUWSKBR3RgB3GBEbQ4Jh5Qj5hePDUIxpGAhmMaHb88QzOjaG0EQwxCt515MzYmuX182y32wLqzSax\nOcTe/+3tbas/GBT6n9enFWCv19S+pBkoWn4IkQNc6Vt2khxqZ9yPjo5arGTXjCDMgkEgMpiPofI6\nJZxmAIOBdYjUIBBjTW6U2ZOIRn8i28yPHWODwFpahwFgftnw0XK0YF9Y2CDQeia/9oWFs26HQXUq\nDWwgkQ87aTwb/WJ8DP5qzTocQOBi/122yWRSNjcgF/1+P5bLZQsEoS8YP8aeFJb5fB5fvnyJ+/v7\nYlfG43H85S9/ib/+9a/x4cOH4qh4/ec5wm4dHR2VHfPoJ9Y4oA05XS6X8eXLl+LUAVC5Ps4vRBE6\nifnDJsKmo4esj+lnZsVZf6PRqMgdzxjRONPWS+hinhngZ3Y1Ilpr3diFvuPcRkSRR2+ci2jjIedc\n40jua50CQcfV/Ypohw+84DFUBn75u7xvocvoGIPDtWzgAY78bTBlQ2aGhvtY6J1QmxOc7+/v4927\nd/Hhw4f46aefSt6QP4cnz+TncJzDOTlkA1jLuUYOebq/Hjs8MjOZZ2dnJck3osnfdKjGY0ASr3eJ\nGQS6dlwXzcWgYZDwjnOo2MCpNu4GvxHt2ox8v9/vt+SI8cLgAHhgq3q9Xiu/NCJazhJAiTnOOazD\n4TA2m01hiOmbd+V51zIMO/fgcz5dYrlcFnm5v7+Py8vLeHh4KKFol53gGVBiNvLcn79xNvgOgA/9\nkHcbApapXwbjaUBjdpr5RbF3vVEJ5gQWiv5hmGuREkCgzyunpqPlMYNAh4QN9LguzY6mQ/Y5vSOz\nCjU2cB8QtK7JKQEZDOY0mH2s4D4QaB1tNtApNM6ndljYITgTEDVGxaAwO5E5JAz46rIhR05FgX3O\nlQp2u13rhJvHx8eS2/78/Byz2Sw+fvwYs9ksLi4uYjabxS+//BLn5+ctuSYVhcgVDN92+xKSxSah\nF9GrfPf5+Tk+fPhQUrEuLy/jzz//jMPDw5jNZuVZ0OHoADO8lo+8MSMiykEMtmt2WI0h0OMGWOht\nE0SO7lFqyyQT8h7RTkOwXba841Agp3ze0cqItiPC3yYTaq3zI+bobEQ7WZ2/eUgWe0R7QwmTkxMk\n+Z+BIJNC4zo5/OAXQC+D1Rw25nr+DMKWlZUXHQqC2oMOJeNFOtxm42/h4XkzAGYBu24YO6AMDj0m\nhDEBrRQFpUabx9hgNyv6+/v71rFBfNaeTVcNrzjiNaORDYw9s4h2igLjzDNbHpBXM7L8z0XHHQ6D\nrbLRxStmXms5l94Y5HAvoVfAG4oXcMh8oCANBHFGMA7UGuv3X/JvSGcYDoex272EbZxykcMS3vji\nUz5ccxOvGwWOkrWhoD/Ip9nSiChrxM4hQJANLl02gAhMhkO4Xs8R0ZKLyWRSku+p2Yg+YTwy6PAO\nUK6X2f+I1/l7eQdvzdHex9LZ0c7Xt+40ACTPyvfFEPqeXqe1exoIco9c/SEDQebC6QgRUZj6DIDN\nLtFHdJx1sXfS/iihYdYFYBBHgvXpFBenVRA+vbq6ipubm5hOp/Hp06f4+PFjvH//Ps7OzgpThq4h\nGkbe8snJSdFlES+yADCkL3Yi0T/IBEDPpW+oKwgQxHl8fHxsbTTZbNpF7skzxj7adkc0hAnjA0Bl\n3rEHBpQuGg1zyDF3Bm6AYp7ZYA/AhvwyDug9jhhF7pFBh7KzQ2J2fV/rHAg6xIixrDFvBjYR+8t5\nZAVnpVTz6jIVm5UMHkYOj4D8WTj2orhXzSD6nr4HBhGD4Nwv13qqAUHTx7BZCCWAp5Yobi/HbCNG\n5fb2tiyw+XzeKknjUDtA0GN0cPByBJkLbiLMP8LuOYcdmGMrPRsW74SNaLw+Fq09sewoeHEColGy\nGAdvQoKJdn6KQWle/Ch0yhpZJiMaho2xf35+LmBut9vFcrksypprmlXyiSQ4Lvxu7xrw6Lp43BdG\nk+vzPUDv8fFx3N3dtaIAVpiwEhHR2kjBnBCaYv54do8FBiKDiy6ai15Tc5EwtxnoiKZo9mg0iouL\ni3j//n3MZrNSvoLvATbQH8wdDiDX8qtmHGpAzU6vv5OBYHaeLIP89PX35SBm8OX1tQ987mMEWVsu\ntQUInE6nJTTsHfw1NpDmPthG+T36YUaQuXh66vZ4w+vr6zLGhCxxwgAtLjF0cNBUHqBkyYcPH+LX\nX3+N3377LX766adyGAIRE8qnGZitVqvCupHOglPo9WhWDxA/m83i9va2OO70NeIF2C4Wi5Zj6GgB\nuoE+kDLFXK/XzcYZR9Ds2G82m3ItIl18htJDEVFSqdD1BtYGfRHNWsS+uKqES71FtDd01cCco5M0\n5LfG/tda52cNR7SZObxkDFg2DhmR47VZ8aBEa4CwxuJlJo1rMtkoZr8Q4hz2RBGYuYlo78ishWfw\nTFyHzYDPytehEXvDZgV4sSBqhsEAnAVJaRhYGg4KXy6XxehnIUeIDRA9D2YDHUruujHGgCMYMrw6\n5ox+5/yliCZpl2eksaABy+SMRDTKjnFwyCA7HXi+Dp3SB8DWbrcroS2DRz6PsnEqBmuI5wWA0ghF\nMDYw2/f398VLdZgVkMma9rFPKDhkzGE/2Eyeg/Fix/1sNmuFd8xwwSySG2l2DABFriu7BPNzdtHY\nCZlztZAB9AvjSz7hxcVFXFxcFB2Jrri7u2sdBQkQdKgrAzDrpYh26LYG0LITXQNkXwsN5z7UQsI5\nDI0+rd37W+6F7O5jAzk7G0cCvWQjmh13p3w4fEezLgb8eRNQl+36+joimnIrbC5jzazX61LVgnUF\nkzYYDGI2m8X5+Xn89NNP8eHDh5hMJq3STMvlMq6vr1/tfl+tVmVjxnw+L3X3HKXL4U521eNkEl7F\n8T08PCxRDtt/fjL+XBumnM+cnp6WazsStF6vy/hENHrH10T3GDD6O+gZ1xAEw9An9BDygs0wyEXP\nWndmVj+Hmfm8cRF2el/rfLNIjovn5gf2wsyg0H9HtGvmZNbPaNxerr09Fr6v55wZ7ml2BWXAoqkp\ntcx0WtGirLKxq71qzBV9Nwh0mAgGysASYTQzSR1EdmbBWCB0eEEYaPqBx8h1zRR4I4wXbVcNgGcv\n0uwfDdmg9hMsE6DYISxClg5J4VEja/4ccoiy9PUimnNQfUJNlkN2B9NXe5dcD4D29PRUNvz4HigN\nlKHl3wwkzFNEFCcI44kcWpkynmbCUeQG2zgWfBYAirLlxAA8bucRTSaTmM1m0ev1ijeOHDrEaGbR\nRruLxvOzPs26mhk+ODgoxvDs7CzOzs5KmJ+cTdYpqSUuCGz2IYNAOwQRr0/DyJ/PDGItPLsvNGyd\nZ4BWCwkbFNLczwwC97GBXoM5PzBvEhkOh6+cVt/Ljredbxt32xF/x858Hpcu2s3NTTw/PxcWNKKJ\ncDDO4/G4FD3ms8PhsLCoZgB7vV4rzYXiyo5MwQKy9u7v78smM+YNWbeMoMvQf7e3tyXigO5xtNAg\nipAw68F69vn5OW5ubsppKBmD4KhZlnGWzRgTGSES4fSTiCaSE9HOJbU+9JpySgbfQ84yMWZskesD\nOzoC+PPvtdZ5QWmXl2BQHF5gwg0SM3AzCwcANE3Kvfg/36mBRRtBv4/AWIk5X8EhAa6N8CCwDo1F\ntA8At3H36QJ+XvoTES0htZCZamYR5J8GOjk8TYIu3uD19XUpPeHx5Xm9mD1eDgP7uy5+22VjLuiH\n8zfwwixnLCK8aIc/d7tdOWJos2k2XdhLcxI2hsMOiZk5s9aEf+mfC496XZBOYCbZa8cg3hsvxuNx\nYT9QcpkxBwDaQen1ejGbzcozG8Dag2bMuJbr5fV6vRbLbAMKMIbFgW3g2ZC32WwWs9msVXLl6emp\ntWMRZh/l3rUTwnzXQCAlLZjn0WhUzjjHwHB60fX1dczn8wICmR/rDmSsFuI1GMxAbR8ItJ78GhPo\ncd7HCGZmkJdl2+zcPhbStsDPzJjmjSK82OjEtR3VYT3kVzbu1oPIWw04dg0CI5pcWeflAh7QNaen\np3F+fl7W6Gw2i59//rlsGASksR4jmjOGYaOzjFOnzzLBevRatyOEDYXFxKbh0PJ5ziAnAmAHA6AK\no4hjDSHiSiEwcpRkQh5OT0/j4uKiVGNAxrCn3APHFfmGkefz4ArWu8GbSalMACFXJgqcDgMhg8xi\nfxzt4v/7WqdAkAXHICKUZhRstPiOlZLpVQY9g74MFjOotLfJT3ucXAvFaINtg5uVH2Ax574gBFYO\nFgRfz0bZHm8tHGJBMhDLIQoLhJWdgSAFVo+Pj1uFsSOiLFZvfvDYOc+Q+9tL57pdN/qDvLlsh+eX\nMbLHiAdLfpxzMgH7dkqYbyuGDJ4wMIwrL8ba7Jw9QTxf5IF+WFGwU9xHFgLEUNgYMIMAng1l5zIs\ndmS4JiFavG07QAA+sy44PVyHuSDUAmvDmnR+YL/fL4n+ZqVRhsPhMFarVSttIa//LprZEoPAfF44\nbCggEIdjsViUpH1yp7zr3Y7mPjYwg78aG+jP0mq6MYPB7KTX7lcLDxsY2hHJjOA+IBoRrbWRcwMN\nAmECGW/nYFkf1HL8uJdDcb5/Hgu3rmXv8fGxgBTnAQKsptNpvH//Pi4uLuLPP/+My8vLODk5if/8\nz/8sxZSdu47eMQMKQ021AUcM0J/YF3QPTo9P1coFoK2TIhomEz0NK2gZ4/85SmPnAl1F1IBoFzIQ\nEcWBdu1gOwYQAXzPYeKIhpiyI+poY16Xdojt2DCG5Fgj63bQHfExefNW6xQIOi8DY8NCNIMCymZA\nItrAh89FtBUVg+/QFp/x4GTGkQWclVxmFjBcmZqlZaOavWF7tlyjBvoYF4dx+KwVZQaY9kpzyM6C\n4t1tj4+PhYFAWTpMvF6vi5HKz21m0cwni4/6XXjiXTazuF7UyGFElAWZPX0reIwXRysZ0GXjZwCO\nPDncS7/IZYPed6FpGgseWWTuzcgCwMkpQ1ECyACXDv2jtFF6ToC2M+G+81n3ByDqOl18jjFGqef1\nhsdLOOrk5KTkB9nocG3nX3FtdlFT8NqOR44WfO/mHFmMEwymTwIy67Hb7UpI7fLysrCBuVC0Dco+\nZi+inWtac1KtayPqJz7tY+cyQ8f9av1yiJiXDZnTc3z/rJv9TFzvLSYwF9x3pMXgz6DHYNtOUGZA\nLdPW44xvlw2dEBElXcjVIHDqRqNRfPr0qYzNer0u5aMIAVPqyLnO/O5IEHOBUwgoxwZwXUrBMMbI\nvYEdenE4HMZ0Oi02mDQL59X5mFQDRPRcJlrQOay/3W5XnATYwMFgELe3t2WjoR0lbAngOKLR0yZ8\nHGFCViFrkdBOAAAgAElEQVQQsl3C3rqfDgvzHmvQuCCn9rzlhHSqEcmHMw0fES0gCCrOZTGsyBjc\nTMO7cKMNT2YzUE422lw3olnYfN7eDZPlMGAOU1jx8TKoxHPJSZ+0HBbODKbZTz8T14elcb/9jDbw\nbFjxUXWLxSJub29jtVqV/CvT9whuBpTUfYpozlQFZHYNBDOA3Ww25fnsqSFTdjgMqCPaoMwLFGDG\n/JghY7wimlNY+C4gZjAYtGo5ci9+2sHBS+Q+9BOWCdDHdwGCrDW+YyOHfOCVo+z9N2sMw+f1iELl\nfoBF+tXv94vBiGg2uJg9BMR6bdtQ8J5THlxMlrlD0bo0TVfNYHcwGJSTLgCBDmfCOAMCr6+v4/Ly\nMm5uboq8mqFCt70FAg0Wa/op68D8si6pvXJkJOJ1WZqcCpNzBbl3ZgO9Bmv34x4urF47RYRd9g5B\nEsWwDnMJL2+0Yx3nsaGftefnebtszutmvdj+2SGGKXx6eorr6+syH8gjc0HYFdaadYY8unxMRBTn\nBt3Apo3xeNzSed7NPJvNYrdrdv9zUhL6i7XOpimcP9YRMoVsIFcwx5vNpjyvHRF0tb9HONlE0/Hx\ncYk+4Ihbl2bZMNPHNewogYcM5LheziUHfNMnbLrzqSPiFa5w61QqR6NRCakBCL11v6ZgItqJzRFt\nD8w5h7tdO8fQg2zgZ9DmCbEwZC/PigfjaAXmvqJcHZ4yk4eAOv8r9yNf083Pwd98JoNT+uzxI1Gf\nlxXn2dlZWVw2PAY1GGrnhLCQEc7T09OWIvZpEl00lBSgAzBvRg3F6MXoULqZLAwQcuBd1bA1zLOB\nDZ4r8wBw92acx8fmrEoDCPp5f39fPHm8y4iG+XapEvpnsEh40htBvN4yCIiIwgx4TOy5DgaD4kEz\nHhHNSQ2uIUiYl/FgzWBM1uumxAP5h4RxHBJmDAlhIa+73UuNw36/H9PptHNWhjl0KCqXZkIHoBvZ\njQkIpOxPXos1p9O6jfsbCFqfek1nhxNZdsQhM4ER7SoQvl92ULPzy9/MD05rBoDIge9fYwNhVH2K\nCEwU421nx7nVgBtAIDrNLCf9rNmL/PJcdNks+5xqA/MO+3V9fV3Cpf3+Syj4H//4R6zX61IEHYCB\nzoclo37e+fl5y6kgipTtNg4jzKHD7WaaiQBwDdYK+AFwuFgsCluH/kOvOq/atVPJT3Zkxpt8zHrT\nb67nfHDGD53lmonWnyYf+D96kd9dosZzxvOYCYSA4dnQkblm41tOSKdAcDKZtNgVhGufgjFgc1Jx\nRMOKMYEGUNkbNgULQDNopPF/7s3PPKEW2izkvifKDI/Cn0V4zNrVgKAVUe6nlaq9+gxwa2NgQORS\nCyTiOzySQ59mjXK+UkQUz5yQAMWEu2ywAPbUDbBs9Hhe2r7QFCAOQ2RGEcXEvQBeZsswFC7R8/z8\nXPVqkScUBqH30WhUyqVgfPJuWSsLl6ZBpg1gGSs7GsgOa4u5Z3MGCpID7s0a0teIKGxLTnMwqwDI\noxAvjPV0Om2xhTAPEVFyBlerVdEpDnl3XcKD+WBenWZhQ4ku40gv5wUCAmvMXmanI17XUM36wDoh\nR0Qi6rt1a4561tX5fr6X+5p/2uk2CPRmKKdpWAfmcjGEhbP+cdgTR3ZftQXnOmfj7GfbN748G+PT\nVaOP/X6/AGTkj7F0ZYnRaFTKEwGy3r9/HxHNpjCckvv7+7Kz+N27d8X5s91izJlH21V0lsunMD+E\nexl31jepETwTjDqbJHCymHPLOZ/B/iNrrDnGwVEWbCS4waw9ehW9zz12u10Bz3a80V3WizwvTocd\nIzs66El0L4QL5IVPa0JP/7BAEGrXNKoVC4MRUQ+JGczYq7Pi4HNuNYYQj8SMnT8bUc/bM3DL4I4X\nk2CFadDJ5FuJZvBmhsYts3w59JKNSw75WIG6n3g3VvwIqPvqcYCdcukKM1yEBBDmLhsL3saJfnpn\nYMTrkHwODxtsO0kdhecwh4GcQyU+iN1zB0AH3CCzNSVA3hOf45oYIcstJR18ljXPbABsBtLskseG\nlze5cG9CTFwf2URO+LyVOXNxcHDQqrPH85I3OZlMyrVgd7iXDbfB4mAwKLXEumqcqWpAgmJnXtGL\n1Em7ubmJ6+vrkrOZU0veWvcZ1NVAoF85bYLfvwUE1pzVGlj1q9b3iPYxlg5ZOkmfe9J3s4EwXQaB\nrDVk2uziWyW3MOLZoGYAbX3g590XMv/eDeeJncHT6bSl27E1LnCOPnc9QcCPc/LM/pN3aB2IPslg\nx8xqRJTdyJ5/7In1mA+kYC4PDw8LwZSLhJP/x/vUH2Q+rdPN2OG0OxyM/ajNu9Om0ENOzUEGbO8j\nmvp/2FizmPzf64S+Iv/YBbOkjnK91ToPDSMQOQ/LSp+dTjVmK29YcO5DxOvj6NwMgmrh3Yhmcmrf\n539WhgZufg9qOwNE7mWgi2BnEFrrh3/PYDCzDFkZW4lxb4MaPw9jaU/cjXlDCO0Bopwdpu86PJeZ\nXfrMwjH4zsws4+B6UlkRogSc74Kjw/MDBNm9OBgMinJz8WdCJ4y7PUlk/eDgoOQVuqxCRLS+G9GE\ndQn54xkDLhz2A9TZsJtdJKTOcwOEnehtZcTL5WNw+tg5SLhqu922cuF86sDp6WnJG1osFiVMHBHl\ns8wNYJScpLzx5nu32WxWxsjhIMsVXv1qtYrFYlFeAOIMAh1Wzc5fdmRp+wCaQSnfqzF0NTD41j1q\nDFmNwaytM5eicupJDn3lcjEAQsLBGXgDEmC0YL9qp7Pg6OcIjKNTmRniWfc58t+7zWazsqZPT08L\na8XZ1GbwGWuetdfrlXxB1/JzXiu6BzsQ0YQvzcwzr8gmTibOo+0/oGa73b4q0g/rd3h4GMvlslUh\ngc8xj7ZH1qO1vD6zkjwPp0AxDnyPa+S8cfSfcxLRrw5dm5QydvCasPxYpvg7R4kiokVsWX/XWueb\nRQB6IHt7nRFNUUYmMuL18UEWVIwKE2qFmEPNXMsMTwZPnkADs4jXhaqtCK0sM6PH53JSqr/LIsvs\nS/5ZA4feIp+fJb/8vF9jCjLApd/un8OgZr9sYLoOj0S0d5LjjLCAeP7aYqXvyNnT01MJ9QGEHLKk\nhMJms2mxrB4bDFav1ysMRDZwZpTzGDvZPqIJPeLJkg+D4uz3mzItGECvp4gmtIeCB/whc5wggscM\n0+JzgR8eHlolFzDcsJA8F+zDyclJTKfTYqwBqpyPjLMIiD4+Pm4dD4X88cwGHT4KquvzXqfTacur\nxzACOgjrcAa583M9BsyXi5ubud2n/Gvr3O/t03HO2aoBwH0g8C1G0GDQ+g55rW3ecL3EvE5yfiAv\n5MWJ9hkE5lfODaQZyGY202vUeu+tsNz3bJ8+fWrNCSwfc8uO116vV8Zms9mUvD+fmczYkkI0Go2K\nY0OlAWTLERZ0VmZWDw8PC6jbbreteXYDiDHGOI5Ok4F0+fLlSylwTWUEimkDNu3kM5fIEHNvpxsd\nBlMMWM5OgHW9a4bijLJ+kUd0b0STH8uayHY3Ilo21uyrIy/u11utU+k8OTkpQoYit9LBmHhDSUR7\nIbKwnd+FoTPKZoJqwCknIFs5cb+Ids4g1wDM2RuoAdoM2vgM/eM+/q7ZOu6fm711fjqXgf4xZjVG\nNe8ANlNo2t6CmcclGw+Pl738fYzi927ML8YAY5MXmlkAFBRzg3LB60MO8Sa3220pzULqQQZx5MS4\nvh7n7gJgLCOZgfb8IWswIoBbWEae26De4X5CJzRkwoqT+cPIsQZgXOgvCpzkcgzLev1ShiKiCVPR\nb87TjYhyagbFkskJZNzNXNpQm1EzMOBlPdJVGw6HZZ0ajNvI1jYrYByYc4NAdFZ2HiNe5+tF1EHa\nPvCYw79fYwH33SezmDUghVw6XGmgZnDmdYQuc94lQMU71s3AeuxhxGAEfR/nBmZG0zbITLn1pyNW\nXTevffL+eAYidM5lA+hwzOFoNIrFYhGr1aqc6uNx3u12ZQx3u+ZM8Zxn7fQW1nNElF27HHvnNBJk\nIiJaAI65GA6HJWKA84kseE0tl8vChnJNF3EnHAugcnQkoiFq0GV3d3ctGXDaAc9K5IX+OvoY0VSe\nQI5NmGA7jV8crWL+eBaTBxGvCaRa67yOoKl8MzIYYBYUgknieFYgDAYDTc5RVnS0WujDbItDhNkQ\n1zzniHb9QQTdoZQMBA006GvOTahRx25vhWtsNPf1mfF3fpcVmGnnzC56fN8yJu6fdxN32QyyWaTe\nKZfBukOtZtYAkVawBuUGKjZwEdGSsYh28vVutyu5ZGZJDCDct36/X5KtGVvYJQwbTBuyaWXinBzk\nFJAKmDNzavBKeIaCsJvNppxcAEBmvO/u7srJNRhjNoxgWKhZScmiiMbpYMy22+2rIxMBh87nQjGj\nYF0eqKsGU+tQZAZ9rl/HekHpY1yd75R1Bc06B4NSW6/79EPWVWYFa5EQ2ltMoAFUBrGWb2/eACDn\nqgQAYzOkyGZmS3k2+p5LxGQ20OFnwFwmIcy8WOfbCeZ9fnbZDDBwUAeDQZydnZUC5n/++Wcrfw5g\nRXkX8rtns1k5a9gsHbJr0JKdBjvhDoN63ZLLeXh4+CpqGNHYLuYVncL1tttt2ahIsxNrfeEogx0p\nno33Mz5wTqE3qnLCCewfG/hgL23P6S/jmsmYTJCRc8hnbZfR4Q6BZxBea50CQR4+s1JeQCgv70TF\nk8sKBaXIQMIq1pSRBROWI4c+ItqgNHvbvjZ/Y5DMinki/DvKASXj93gu39fslFsGsgaDBoIWKvfH\n3kkNCNqgWpHXgGEeC4MfwBbK9dOnT/8SOfrfNBul+XxeFhgUPfMIEEZJ4t16ceLAIL+EJb3YkS/L\nGeOOIjI42G63sVqtymI2eDYQdDLxw8NDWQMOMcKQwdQh8wYXNacG8IRXDYjiHt5Vx45lGIHlclmU\npMPpGNqjo6NSOma3eznxhNy5x8fHVjh0NBq1FB5zwO5Ze8ted2ZcHQ4kN7OrhmHgxaYdwAcAhDWD\nLFpfZjawphdy4zo15y3idZpHZgIzKMz/p+XowT4AWItGcC2XcqkBZYcGmWcTCwDBnPpiWeH6XLfG\nOrJOrENrTKb/78+/BbK7aDilPD8gBBBNGP3x8bGAIO8qBhR9+PCh7Dg22EEfMm52dA1W8kZQRygA\nzMgH8oMuQ6Y4oMAllyCNIIIODw9jPB4XHQlwtJ50DmhEu0wSABG9TngZts2EAvID7iC68vz83Kox\niGxSP5GxPjw8LHqeZ/f6tD0FiHr8Ito1ac1E16ICbp0fMWfGyYbXsfMMqsy0YFTYhYdQZIDGoBAy\ntQKrMVXOCbDH54k3KMwThtLN4JJ72kgiOJ4wqOesTHP4JwMu58wAAnmZseI+fC+HgDPDgOedQbdD\nIPaOvbBZbHj2LoLbVbOsufhpzqvkmdgJB9hxfhGsluuSsYhZ9J5vxoUQPoykjV3EC1MJUDQrFPG6\nlqZBA8+SDRoOlGtMoejYkMW17JAACBk3wjAcb9br9Qpr6BAnrB2hDsbJydURL143Zwb3er0Chh8e\nHkp43PlE7BZ2uIU5iWjXiIQxtZLtuoblYrEop/Uwhi5YbP1kEJhzAvMadEPW3lL+bwGVGujblx+Y\n13KNDczgyWxaZqlruXt5A4cBskEyQHAfCHTfa2xgDQTyTBnQOpzNy2OWne4fAQgC1gBsrBkKROPE\noucBgtgTh16ZJ4gVwLvHwPnOzE/e4OaUFI+3yRynaTHvbEyxjDHGPqgClg49ZsD79PQUNzc3JYJh\nRxn9Dbj3SSo8j52z+XzeKjvD82D7drtdC1yzCcVAGL3FfR1mdtQmN5NVjF/Et4WFIzoGggZ4PIC9\nCHKteHjQPg/pHA8UgPNAaJn1wLux4mGR57Al9za6RrEgpAZaVqj2LMwCZQVqT4TfTWGjhDJgtvLJ\nHntEu3B07bMOxZjFzHNiOt/KNnvEZg0RbAMPHwPUNRA08PM4YYxZhHZMCJOioLxhgmfC03YtMxaw\ngTdycHR0FKvVqhU+oN3f379SgigSgBn/W6/XxVsdDAZF+RBew9nhvmyYwEMGNFgGbfCYb+RyOByW\nuUUp9/v9Vg0r/ubZ7amjUGEcJ5NJ9Pv9UpiWz8FGHhwctELmFKS2wmXeYHRZPzwjwLHrGpYUhSb0\n7RyoiHb6icHHvrxAhz35fkT7DNyI/Wkc+0BgBn01AFhjYfndIK3Wfz/DPjYw7+R1zjjPYBC4L2+S\n58pEQgaDtc0oeS6yU54JgH0s6o/QVqtV6Q864erqKi4vL4u+g5Fi3dhObDabFjhisxz6Mm+6Yx5h\n1/xZUsLYPEWppMfHxxJCNdBGF0c0u4yJgkTUj/ajHwbp2D4cBv5+fn5uFWofj8dxcXFRdDoN0GUQ\nyLMRKqcagh07f4ei9xFtptTjhJ7HLjEvDmvz3KQr8byeL6/bfe2HAYLZq7eyIHEUY+LvkFcFGLSw\nWSmZ5eLeXhAYvFwM2YbR37XiRECzko1oAIeZIAOh7Hnz/GamvAgQJgO8LPgZYNtT8GcBtwYpLCRf\ni75lheuwDn0ynY1H6Er9eXy7alD9efMKY2Bq36wSnhxAA2eEubW36FCAizZjYPBAYdXMcEVEAW70\nAebS402uDZ63N1QhT/f3968A/+PjYwmh8RxOW/D84kFzfa4Lc4JXzvMAamxAYCAAv4wzzwUzenl5\nWZ6b8UbGSEBn04jHm/GEreR3b5wBcHlDTBft8+fP5ZxgGFI7ZwZQ2eB4XjIjVQMbmYXKOiE7rhHt\n6gpmAr8F5LzFBGbGzon5jiDgTNRyKJ0z+S0gMwM1G+ccqTAbmDej5GfyuNeuXUs1+hEAoWtoAiCu\nr68Ls8dmL1hzs+uEgdEt6DfrvKOjoxK+dfUC5/65bipgkr79+eefcXl5GcPhMD58+BDT6bQVUdjt\nmrxFy6XLw3hu+Kz1M7gBPU+uM7rN8g3DibNrUGjSieuy69lEEWPtMWFzB31Ej1sn8qzGD5k0cxSQ\nNeGxymlM+1qnQNA0cERTXRymwKEdDAtCl8EVNX4AZDAmNBY0wujBsWcNK+h8RNOrHszM6GXPPNO0\nTDLPkRVERu2ZcXR+BD/dsmLaBwTzmNfCJrlfBo8O7eRwdb6Xk98pG7KP3v6eDafC1entvSFDKBIf\nAeaE4qenp3JoOgYMhnm9XheQhOLCW+Z3e454zmacnQsFiELRAAQNsvjpHbIAQcv/ZrNp9c0Gmefl\nWdkRCMjjmQmnAHoJidjpYlxcZgFjEdH2himaTHiG9Yxi864734+/2SzD2ofBPTw8bIEIh5m7aJ8/\nfy4y55B/ZtD8M+fUOQ3Dzes5og4EcxSD70VEa91n4FcDgZlx5OdbbGB2JN0HZPZrbCD3qYWECZnX\nGMEMBB2xcH6mc8a8bjxmjBf6zM4dNsSOZtc6L6LJUfd8M7bYE+aEscSuoXci2hUuPn/+XBw0Nosd\nHR2VNBrLOJ8j5YV8aBh/coPRLU9PT6X2IVEM2DQ29wHm2SVsB8TrhrnGrrs6AyW8yGEHALKRjQgP\n5yk7hE7Ki1NzAHOweOQle906vYFrWkaQNVe0MHnDy5tonFNu576WPuLWOSOYQxNZMRrY2WChNKBM\nMTJZWLm2GQgPSAY4KBYWsJNaa953bYGjBO3hZ+MIGDSiz33KII3vOffPn/e9zGLmZ7ZXUWMBshfL\n53yfDP5sBGjMC8rVuT1dN8aVem0ABdP4LH68QQyNj6ciNAHY8EHfm82mnGbAHFqmYL0wTAbIBnh4\n6wA0OwKZgQYwDQaDUpD49va2BW6pWcgasuFGeZJ/Qw6PWUMbTdYNIDgiWmeR9npN8WoUNyDcSu3h\n4aGcmuE+AfiQP4AzDHNEtEJSMBowg5PJpKX8CWV12SgMnb18DEhm/Go/MSZZl7FmawCQn5kJ9PeR\ny6wHvuYg+lqOwGQg6FI+PCf3zLmBgALC51/LDcyMYHZSrUt9L78Amjx7dnC91jI4Zu15o08GlV23\nbK8Gg0E5+922Dx0S0QBuR58AG/f396VO33g8LmWezs7OYjgctphsM1gPDw9xc3PTCh2vVqtyjCK6\niPXLdwzoVqtVYfsXi0Xc39+XjWUA0dFo1CKIvF68yY+oB9Ge5XIZ2+1LiR3Xojw6OiqOdUQUvOH3\n/D9YQIAfoDeiSTszG+71aSYzf485coSRa6Hn+D9r8K1ISOdVLhEylJzDWxikDGY8IIRzYU9A8Fyz\nBoD8flZaDjnBwBlUWQnUNgDUwJI90xw+zovL98uAKX8mf87gwEKJsfEYsMi5hr1aK323HN7IID5T\n0DlU4jyobIS+d+PZzEAgR4wxnhxhESfpozCenp5iOBzGarUqC9rKzvKCbDNGKBCHODE2BuXOa7LX\n6dAEicc8Dzk35KGhEACmDgnTHKb1+Zy73a7kMXozCMDUhbJJAPcGF3J/WE8u74J84vUCHPmfj1Pb\n7XYxnU4L6GQzDdewF97v92MymcTp6Wnc3t62cgm7ZgS9szAiWuAubwKxLsthyRqj57XP3/7doN//\nz+xfzm2uMYK0fD/r0xpbl9NKuLZBIMXEMxuIDNVAoDeJZEYwjxVA0Kygy/Wgy9/Sddb3BoL005El\ndHDXei/PFekT6BQ2VdlR49lwXoio3d3dxXw+L7oPHYSu9IadiOaEkYeHh7i9vY2bm5vi+D08PMTV\n1VX88ccfsVgsSpUGs7eOtKzX67i5uSlglML9lIoh75icPfQ4zxPREDvIEN9lDd3f35eoCPJpFjqi\nAXuZ5PG6ZZydf04/DAbJ8zNBZvtNVMh51pmRtlwjh+7rvtZ5+ZiIZhMC6BraFuaFEFZE+zgVvtvr\n9YrBYGFzrUy1moHLStKT580aDhFE1Ldj5zAJ98rhW3uWLCgUqxW8wZ3fy322J2Cg6H7yWT5n5Wuh\nrIV++G4WzBxiMJitMYnuZ9fKMKI50DyiOWPSNeaYHwMjF3cm1wXvkdMvUAhce7PZlDIpNg6wW8g7\nsgK173mPaLM1u107nSGiyUk0s44xZU2g7Bz2yXNjuXNuHmF9G7+Tk5MSejUjD8jZbDaF5cNj5rPO\n0WGc+Ju+epccynw2m8V2uy1hJOsGF41m3siRxPs/PDyM6XT6b5evtxoGxWDGYV/CcX7VjM3X1pHX\nvF92QmtOnB24fSAwO335vmaXMxjMOXzc41uAoNNlnAPqU21qQPBrIDDvFka/7Rsrg+WIJtXDQBC9\nwjh9y5z9u5tZvogX+zmbzYq9A/TgoGIrWD+WBcYJvQLzxZziCOIAw+rWakLe3d3Fly9f4u9//3t8\n+fIlzs7Oylq3DBoYLhaL+PLlS0lTubm5icfHxxiNRvGXv/ylyNdqtSpyY4YaebGzD4CinxFN+s9q\ntSrjkdlS623Aoe28N6Uwds6FtgNl8ioiWte0406zPPrlNWubXWudF5S24eJ0AoSQRYUytJH2Yo9o\n8tFMi/p7VogGlQZHVrp+L4MpAy4YDof17AGatcOA+56+h4Gewx85BMT/+ZkBKM9ZU9p+hnw/95Pn\nzcaDz9SSoC2E2fgg5FmpdtW88xBjApMGoMCQOKTlBHTCj2dnZ3F/fx/X19fFWwPYw3I79IuStOwD\nknA88hhGNErcspHHlvsacMKa87sNtJU1Mu0+s64IL5vpAWzZqLCmeEYbWK5rwM148xxOxeBZ+ZsQ\nDcDa/WYuDg4OihO52+1aYfr1eh2z2SzOz8+/j5DtaTYeyFcGgzmsaSDotZid3dq9/FnrtgxM8hp+\nKyxc0yG+B/0F2ObwrZ15G8a8QSSX1uF+XBdDnmsHOnRu25GZO1c0AASwVu3QeYxsVHF4kGs7eyYd\nuE4mBr53m0wmr/Qya4TyWDiKi8WiZS/QgzhWlHXa7XbFIYx4GZPlchmLxSK2220sFotyZjiAE2DD\n5w8PD+Ps7Cz6/X78+uuvMZvN4ueff47pdFruTxSAHMPFYhF///vfYz6fx+PjY3z+/Dmurq7i4uIi\nRqNR/PbbbwWUMh/ZlvX7/XKyGbZ8Pp/HYrEoMkB6wnbb5PsZWyCPfJ50Ilq2e9ttkzLltemfjkJG\nvMgPqUvWDTTkEr1Iy0TPvtYpEMSDwPhhcMj/sQIyk2VlYCBY82Y9AQ6/RbQNK7/XgGBuZtP4jMFg\nzWOuKc6ap+7PZyYgT7z74mtn9i5/hu9lJZ6b++VxqSnH2vMZqGSl3DUQRFmjhACDx8fHMRqNirKx\nF1lbgIPBy4kY1KPq9XrFk+QZHQr0QjeIx2CYybYcsgYAN87hiWiDcwDVdrttJXujpDDEPIfDVxFR\nwsooNpKhe72mphj39iYVe7u8XIB1t9sVQMj4kJvD3+TmYHgwLrvdrhXOJowS0ZTVIbQDMEQxEpbZ\nbrfx8ePH1kkDXTQD8ZzQnvP/aq+vrZ/a//JazNfIzEENEGaAvu/ejqzk3ECHCzGKDhk6N9BsoPWr\n2UbnHGYgmB17OwS+X61+oAGcbYvti9duzsvKIWHmoOtWq6HpVAwDQ+vqfv8l1eLi4iKurq7KBjmi\nGl6bj4+PcXt7G1dXVy3G1XnDTreJaJzb0WgUk8mk5PHCFkZEcRIARH/729/ijz/+iKenp7LeuTdh\nXVcQyPPKfJO2wtwTxZhMJiWthXmk3I13vANsjUsAoBEN4cR9kXdvJuR/EW2b7hxaUuYYP5rtCOPo\naKM/t691DgRZ0AA1dtM5F4EH4kFgBCOiZSgQhIgm1MTk1FizGqtWS8SuKUyMpJVNLZTCJGZDnQEB\nLXvvGYSYlbNyykAsA0H3i+aQt4XV/bMB4vr+u8Y48D0r4Rzm6rq5ph5gkPAGP1mEEW02OCKKUwFg\nOT8/j/fv3xcPkmbngM87d8ReYo3Cz3ODQmDHG54oRghGEmbNu9X6/aYIa2Yy6JfzcJg3wq0oN37C\noiBn5AgR+kXmUXbIm3OJAN8YepS+S9vgOds4MO4wajwXaSHkLZqZPD09jfPz82p45Xs2mFQDQe8E\n9vyjYnMAACAASURBVLqxE5UBXAZ8NT3C7xkA1tiEGhDcxwTmZn2FfGeQm0PCEVGcCIBZ7cg9ZNsg\nwsw9cpTDzr4P/TbzuK9QdWYEs52wU2fHx5tDckg4X6urVgPznmeAF3rR+hrnmKPcer1eqemJDsMx\nxDF2KRn0SCYY+PvXX3+N//qv/4qffvopDg4O4vLyMv77v/+7pXPROzB04/E4Tk5O4sOHD9Hr9eLu\n7i6Ojo6Kw+eoBXOAfqJcFzmOzD3FtU9PTwtY3O2atJbHx8cWe0qFBVoN2CG7/p9liNA7zY4/Mgeg\nzRFHkwbGGc4ndm5irXUOBEHSGEYEKaIpt4JSM7izIsBAsPhyjNwTEtHk6BgkMpAYQgOaHBY1G+j/\nYyANvDII9KRlcMH1a4whys+TaVYpg2Vfj3FkXLKnQMsGJgNg9wmQxPMYpGfgl41E1+GRiHbIkZ8A\nCJSL2eYMrP07O9TevXvXclK8wAk7OOnfQN/1+eyA1BTGZtMcMQR750K4OETb7bZVwBWFzHcxYDhP\nKBmvCdjRiGgVNd0XZnC9voj2MZLIweHhYSwWi1KaBgbT/bfXTTPLB3iHXaLW6G63K7sgs9EAqC8W\ni3+pLP2zLQNBA8AckYho66Damsy/07KDlgGhv/sWCMx6lGtaNvP6/hoIrLGBAEDYQNifWiUJg8Aa\nG5gBNTo45yHmo+vMPGY2xWygx8IMeLY9npuvMbnfo+VQdUTbySXMCsBhzfE9b6ohRMr/XYMRMDkY\nNBvUIqIwg5YRVwZAF1BTlP70+/2YzWZxdnZWdt3/+uuv8de//jU+fPgQP/30U6k5CJgjfJ3BEfYK\nubm+vm4xgbCJPgAhM9ieZ1KKvAa8kZSxs+zzOfSw7aSZZthPHHWepQbw7JhENJv/Ihpgua91CgQJ\nGUAx5/w5kDsPbJDnPCXyROwxWIkZhTu3g8E1U+awhZVnzcP25yKisIK+t3fLZiDItehbZvXouxV1\nVuAGjgZk+doGrwaqHu98bT+3r+swb0Q7nyEDVwP5bOi6bB63iIbhsyzyOcsQxoS5ZV4495JrIF8O\njeIpY4gi2nUZAWTZwDPvznezAlksFq9qUjmfKjPLdiYODg5a5Qes6DC4rCszjjwnfWXMYFYYs4OD\n5pxmM6zr9boY7YeHh5YB2u12rY05GBQ7EIR6eNZe72WTyWAwKJUGXLsS1pCwUZct58nZMcigL7++\npdV0lX+v6ZDsQNfA4L77uN9mAzMrmFNcLFP7cgMNBBkr5waaVd4XGjYQzCHhXKg6gzyPj3W6QR7r\nIwPBPBeM1Y/Q3A8XeCfPHrb98PCwOFKME8cjnp2dlTXN+eKs4bu7u8KUOXUK2+3SVNyTiODBwcvx\nkV++fGnlFRKWfffuXfz8889xenoa7969K6cSscfg8fExfv/99/if//mfMneuIWoAZh233W6Lk4g+\nQX8TRbHzwt+WRz7n8CxOMWFw8luRfbPGyAvsqh2STAJFNDaVzyGDdoZMUu1rnW8WIfwDQrdXiWBB\n51thUUCaSt5MqOvWuf6avR4v4rzo83dyy9fgczAeOZyNEOdSArTcN4Mz95PvWbG5H/nz2YCYZcie\nrin6PC4ZnNJn53nZ+7XXbg/Iwu57ddnoKwDeOYG9XlPrzs/OHHqXMaBnMBgUMMh1KXHicOzj48tx\nck4vwCvm9Ayaxx9l6nJJg8Egrq+vC1NHwVNkBYVihYEDBLOH90veCvJxcnLS8koNGGE4Cf3MZrPC\ntrjMwm63K3XF8Na9GxHlv1wuyxwwjowXn0eOyDsDSNipIwydy3fweUBzl8019AyOas7ntwDAGhsY\nsf9IOX8vgxw7iWYDfZ/sxH4tJGxQmJ1yA7O3dgqzBrm2QSBAsLbZxoaVNYsjlnMDnTecn9HjxfrI\njlPWg3aKf5S0GBxcN/oIWw/ocsmV9Xodl5eXZQ3N5/PW5pqbm5tYLBax2+3KDlkXor+9vY2IKCHV\n09PTGI1GJV/3+fm57NoFaLJhw3Z/u30JB3/8+DHevXsX5+fncXh4WJw7dPhoNIrxeFw2nqILAXX8\nDvNpgI8sugrDbtdELU0UIKuAyeFwWKoW2AGprbGI9obZiHahaG+cs/ybFGLNOfpYizAcHx+X3PWq\nXPyfpOr/2MyGELq0ElksFqWYKF4JhgYv8uHhoZVr6ERNg7p9+R4YtYhoGU3TrfzMStMTCPjJRhxj\nb8DBhPn67lNm6nhlRUqrGQsbgMxAIkBcB0WePY5aWMgMKNcyuHVfnQeXwWfXQBAWqd/vtxJ/3WcU\ngefb+XgwUMwneWrT6fQVA4dMR0TZmebkXxby1dVVWbBmXM0uu5AzThFKklQL5qFWsy+vOTtQAC/L\nh9kSxgG2kyKv1Pdz+BjwCyOIUSC5m7I7nCIAmBuPxzEcDsuzMUb52vlkDkJZOVmf8cBw7QNO36v5\n5IsM/LLe4fd96yXrgZpeeAtQmgnclxdYa5kNzEDQG0UMzmwcWUM5NzAXeGetOgpUA4I5NOyxzUDQ\nx8r5NJHa2DFOGQRm533fuOcx6rL5/u4j6R/oOPSVHUp2ApOHu1wuWyzhfD4vRZyp4QkotNNLSgjr\nFf2BjsIZJtQLmNtsNjGfz+P4+Djev38fw+Gw6JiI9vGdgKaIBsR5Qwe6j2gO+gUm9PHxsYA5M5jb\n7bbIGvacXEM22KFPOZIP+4COQ4+j7zwfBoHIP3OEA8S6sfObHRjbH4iIGqtP67ygNEaSSbMneXJy\nEre3t7FcLl+V1vDOL9cwMhCEsfECZOEanBgEPT09tcIXGJHMwtmjoNUWGZ/LIR+DC/fFHua3NHv7\ntbAS45sBoftoJWVFTXOYIzOLCK+BpoGlxzGPX9cN5X14eFhyWFjgzAmf8SK2QXGo3dfCKPE+yg92\n+OTkpNS3MsgGtHEth1SQe8s0oVdv2mCtREQrTOvUBRQISh1WEhlAcTpclGXU+Y/22Pk+DD/gGmAA\nWNtsNnF7e9t6tsFgEBcXF0WJweYNBoNXxz2hKGFBmR8ba4f7vMGny+Y6gRlw1By62u9fA2j8/hYQ\nNIix47cPCOY+Zt2xjw3MEZZ9IDDnBjo/ivtkIAjr5NNK9m0WsYHNLGAtqpF1pvVWtgc0O9MZBFof\ndtWm02lVLnBSDw6aHfa2Q+g5cv9I9XBUwM/peSfvGuDiCBOpJtZXAEHGirnEiWRHcq/XKxvzTLhs\nt9uWs0z/HUVwBREIptvb25jP58U5oAwV8kbeMSltOOJePzjlTvXIQBC547ktP9aFjsCAhwCzGVdk\nAgaHHxDI2tnXfoizhi04ufp8rUL5drstDKG3fRsEMaAOgyFYGKwa2DMQxJOoAUEEOXvyWYEwyTn8\ng/LJDJ89dC9Ge+p8bp/H6bwA3jddbOay9n0DVBtPAIjZABsFs5j+6f/bO+yysQDpC94bYM0y6ZCm\n84HIh2EevWB9eDteonNITk9PS5kMjF2v93LSCMrJRZUdHgMIYQT53vPzcyvdAiWNos9gH3DEvOI0\n+PMOv7IWMNS9Xq+c0ZllnX7QuA8hFAz+crlshTYODw9LeB0wSYjaeZzT6bSMEUfgYRRY64AN5i0i\nijHosrnshEHGv7J53dVAIPfcBwZrbKDXPT9ZJ3lTSN7By7Ny7bxpIx8n55Ix3Msh4dPT0yJ7Pv7L\nbKvvmRl1A8Gc05d1qfWYP+dxqY1vTbd2rffevXvX0s22XcwJEQDbWwDdZDKJzWZTGH7Ill6vVwCh\nWaiIl3V8fn5exvn5+blUPfC1AaFsHMGBtC3lO3d3dzEYDGI+n5f7+gxjGDOTIOgeR0bcd/KHKVzv\ncD8ybrDL+IxGowImHX7FOTdYjGiiUVwvRxG9Bhkv59gCbtHbfM+EF99xGJv5qLVOgaDDpA7LZdDi\nvzGSEVG8Exa4dyZGNDXObKQNEiOaBc0AZiYMoGBDR7OS9STkBeb7RDQ5NQZ1vG+v2SUJCGs5Z8KA\ny+Nl5eWFaUWdmTv3K3vB/LTxYkEyTv5sjeHwd/zdrhpFRDEI5MOMRqMyPs6zAPh5UePhoaBIhGbj\nSPbaAJUYLAyYGbezs7NWyNa5bigVwi8kRw+HwwKser2m1p/ZOcJnKEuuxTPhhLmQNs8UESVvCMBF\nKJzdvaxlPsuazYe4o3Tn83ksl8t4enoqzOlutyuA/P7+PiaTSekLjbE/PT2N2WxWTiFZr9fx5cuX\nODk5ifPz89hut2WjCM8KOPWGly6anUs3r7WIej3A2nu1999iGbmHgSAv6xevURtiA0KzgftAYGbm\nHM0xCNxXN9DrxiCQF+soh4dhtB1F8nqyobe+zno068M8P3wvA8Ias7hv/r5Xe/fu3Ss7lsGH2VPG\nDODy/v37Eh7l+LY///wzrq+vI+LFFpAKQvkVdAsOnzfFMR6AqHx/9I6jGhFRABchWYCgnWffA2fe\nqSIOW/OTiERExPX1ddzd3ZUUFdg40tKwY2dnZ6W4dkTD+Jt5NIljjINNR8bR/QZ8EdHCFyZz8jo3\nuKUfYKQflhF0Lk/E64Xj95xzYgDGZEDH+hpOTHfIy4JP86AbjRv4ZIYrKwjeq4UK8veYIPfDrCbX\nyWEUl9fh+gBR5365bwiRd1nbWPOZWq6LPY4MBDEOVvJe3DXmtGtFSJtOp2VHGLkhHJkGEHGeIyyW\nWVqUkyn/fr/fqjEF+4A3xhgQxop4mUPCEex4jYii1ACKyMR0Oi0gEDaOnbeELna7XamPhTKlLI7n\n1CFtTuYwI2yFyvPiyZ6dnZWTCkiutlMD8xPRsPCPj49xdXUVX758ibu7u2JMAISEfKnVlcMgEVEM\nC/pgu92WsA4yyprh85bLrmUQmaIZAP4rmMEMRnLL4M8gwP3Zdw3rwwwE94HA7ODW2MC8gzezgaRX\nGAgaBOadw8ggz1sDglle7QBmEFgbg2wb9jnBtmldNoBJxGu5I6rA+8/Pz+UM3+32paTTeDwuEYeI\nKHOxXC5LqanZbBaz2ay1XpkvUjxMbDjVKyJaINQ7mnGgI6I4sD6+knk3+48MkstnNg49aXxxcnIS\n//Ef/xEnJyfxxx9/xB9//NGqMmAiiX56RzHPenh4WELc+cW90NsAa/qLzkR+cNAto5ycZGBrUgog\naLl/S/46BYL7FiUPY9BnZcNn+v1+8Th4j3ASR+FgiA2QIhojyCCadq6xbhmk1pREBjoZCLmfRurO\n2+Ll8GlNcUe0y8W437WQk8eGXAPnZmbFl9k9K37/n8+4Wej9mQwYu2xs6CAvDQBnNo1Fy/hjoJwX\n411fm81LkdKbm5uSgEwYhGvBXhmcYyyZO45rGgwGZRcdGywioiRi4wHbeySkg8Iza0ESN/16eHgo\nIIm5R6ECYr0rl4bhnU6npXAz12Lt9fv9skOX8V2vX2p1XV5etg6qp38O7ex2u1Lx//7+vjwncus1\nu143J59ERMkRAoA7aRzj0mXLDKd/5mYA+7VWAyD7rlljA80I7Yt++Hez3PtetU0ieadw7Tg5nArr\n/cwGOixsAGhdb7YnA0ETENaZtchKDQzXbME+h/dHcYKvr69bc++fhOVxvHBuASbO14xowNjZ2VnR\nGaR18HlAkTf1DIfDWK1WpW4fdhD2HpuYCQV2eDOWADzmk3p+OKybzaY4yuhm9BlACqdsMHg5IWo6\nncavv/5aQO/R0VFcXl5GxAvoRbc4MkSuOH0wRoGVRgfB/kEgPDw8lLA1awmCIkfqItq1lT0X3jTj\n53JOpPVObp0zgngG1PiJaFguG1qMHQaBz3Ceob1PwmcwKWZwaoycE0dzPl4GgiiJvPitOB1SzuCT\n+/inaWkEmfe4F0LC52vhBo8bn8sL3rlYBoJWbBnMZgPA89ZCCv6ev/+jsDE0cl2oG2VPzZsZ+B3D\nQYFiHBDKHzjsQbFkZAxmyuwtiz9vBoloNpagdABYKADnR+G8EOpgfcCwOTfQ+bbIOBtNkD/WIdfA\nSwccI6P2UCOi9AvZ3Gw2sVwuY7fbFXB9f38fl5eXcX19HZvNphgJWmaf5/N53NzcFIDo8AhrFgPP\nszBflJ569+5dmTc+OxqN/t3i9WbL+uD/4hTVAAc/a6CkBgLze1+7X9aFOe3EcuawMOkURDcAA8vl\nsoAQ715nrOzAAgJJjfBmkQw8eV473DkszD1y5OdrIDAzohkQ/ivm9t/R/vjjj1dzbztou9XrvZRh\noV6gbR+66fn5OabTaSkPdXp6GvP5vKSPMOeMIUAOcIn9QVfQJ3Qe88v9vB8gor3PgO879QWH3bvF\nAUVPT0+lBiF6ajabFTnjtBLyBXu9l0jR7e1ty7GPiJLSAHs4HA5b+tFyYQYZuSVSY9LLNjWvUTtK\nvLDt3M8pRTj8+9oPAQTtuZuFqwHCDMIGg0HJFzBoBJ2z+CPqORt4Hpm+zdux7Z0a1ZuFYJKsGJik\nzNAhJH5Gg72svGFKzPo53GCh8fMYxHhhGnx6bHxtg759BiWHk91qrKHDNV02jBb9tzOAQWF+PGZP\nT0+xWCxKPh4LDRBC3t18Pn8V/ncKA8ARrxC20XJjEEmfer1eYUYIo3rBe30QDgYkmukk3IDicJkZ\nhxBQ2mZLIqIARO5H6A6whWKkzmdExHw+L+ePovx8Xa/p7fYlIfzy8jJ++eWXlqH2jjv6yrjQB+bn\n+Pi41GxknruWPRuIr/Xln3GcvgYCuV+NCayFhmv3tg6thYXzZj/PkZkfF3SulYzBwDulxWHhfXmB\ntahPZgKtDw3q/Htm+DyuBsGZGMj6uPbqsv3+++9v/p/xRvedn5/HbDYrJVHImUPXQSqcn58XULRY\nLFq7Xj1+ZmMd3XLol/XhYyYBqMypyRDGnL4TdXFdVJx38lKfn58L+Dw8PCyRCzvJx8fH5bl4Vqoc\nkPc8mUwiIkr0kfQhdJztOgydU9jILQSoAWQBht6kmFm+iPbmVIC82VyIhB8aCFoxMHCwGxHRWthG\nyxmgsfPSdcuc67RcLlslGxB0kjUz0GHAEFImyGyhwR2TwysbNf+dhZfv89mI9i7qzBLy+QwE7eX6\nVcuLgUXiuazUt9ttS4G7317U2XjQL//Mz8d9+dllY84d6qVsQk7wZeHxDA8PD0UheAwI1cJAcY2I\n9o48DBnAxAVMDRAfHx9beS3MEewIDJ5ze2x0YdxwoFBQTjuIaMCvvdUaew7ThkPi8LRDts7N5bg3\nQtx3d3cFpHqd93pN3o7ZSANOg9JswFF0BqOj0ajkovF5HMSum1mjiG8rCr2P/fPvtc/l9jUgmK+d\nr/8tbGBmBM1kYJDzMW/ZqeE+5O/C1NRCwjmUC+hi7bK+9+UH0sd9Di8tg2Az7gaPmW17y2H+nq3W\nB68lwBxEyNHRy/GZR0dHLTaN/Lr1eh2LxaLopbu7u7i6uio7+K1nttttcQDQi1xvtVq18kodyrVD\nTTUGUmWwvYAqp+ogRyZA0Ckwk4vFoug7ikGTioJcEfXBVnK95XJZQuEAPGobnp6elvtlts654c7Z\n5h7IFADV4XCvw7xeDZjR9Y+PjyXv/YcFgggSE4NSYbBr4UsnH/N9CwaDCtsAfQvww8tgsRIyyhRs\nLWTixYxxpl9OerVXmkMHCLrj/FzTyB3BQqgyqDQ4pj8Og/Oy8LtIpfMgPQe+N8o+A3D3u2akrFj8\nXkSbCe2yAfo8PigjL1x7lvR7vV6XYrQ2iAbwgESzG7vdroSkAWaAJuZrt2uStvFeAUOMqe9xf38f\ny+WyOCeWPYxnr9drpWBENHW16F+//5JD6Gf2ZhnyADk6yiUb6JPlbbfbxWg0iul0WuqBAmpR1qxJ\n1rgdODZG2diTb2mAYWeJflAM9uzsrLAB/A6Y7bJ9CwjMSr/2/9pna//3fb722tffDL497pkRzPmB\n/X6/pY8yEPQJH5ZHHBTYawBgLhlTy0XMTnCuHejQsMdrH4NqJzwD4cxEYlu87q03u2yz2azqSPlv\n20TbQTb0MO4RUcL78/m8lMqidil6tNfrlfEH8HP9p6enwjZGRNkwFxEtwBjx4gxOJpOCEdBlRLFc\nDBr9EdHgBpx09A5RNmz2aDQq+asAS+xxrv2JfSAEzMaa3W5XNt1FREv+YfuMF3xeM8+KU+778Te2\nOKeQmUyyM+7C1m+RL50XlDb7kcNxZlzMpuF97Ha7whAweTaSVkgYJ4AV97IxjGjnlJgFtELISoSJ\nRqCYbP7nftt7ttLiszynF2FEcz5hZuto3N/Agt99EgYAJpcl8VgyDrwcPjbA2wf4/jdG6ns3hy9Q\nQt5la3CTwbMNGt6jT7dxyyEK5JsddA5xwFixuYQTdWCZMxsLIIVlOz09LZ68w8F+BsI1zqPh7/F4\nXMKq6/W6hOQcwiY0RN+trAj5UcqJY6QiooxBRJuB4TqWE7zZ9Xod5+fnhZGAIZ1Op8W7Rn6n02m5\nz8PDQ4zH4xiPxzGfz1uOC/37/0Pbx/j9b1oN9NnI+3O1+/M3eoz1kdnAvFEEuc7RH4NAHyfn0L1D\nfa4fSN6Yc2AtP8i117ed4Vr5GL/M6Pm5/XsGwzn8Zln+EfQdDQCXiQ/bmcPDw1Ibr9frtfLf0E2A\nFU4XAQDZcev3+60i4d5kYRCN80bqCN9HHswKo+fQleiBiIZkIN80ojnO0fqZcjToBefnE43YbDYF\npJqgwmFlDwK6H8BFpMQNIIpOtZwwFoBD5D+TL+h8rzvk25/nms45/5qjF/EDMIJO6DSKdhgWY2mQ\nhaHDC7EHCcjyGZTULOv1euXkh36//yonJaJdww8wRbzen2Ngec+sYAZLTBT5BggiwujP8IxWOi5I\nnL1PlHoGgywUh0byGZvQ/fQhGwr3n3GuAUCPRw0I/gjesNt8Pm8xcgcHB62q8MiWHRKDaMYMgASI\n7PV6RfGYPYTedwjFSob7cEwRDFzOs4mIck0ziTa4m82mVeLBsoySGw6HrZxTwCDhZucxooAcskAe\neQaDLD4L2LVSYozMiJIfxFonF6nf75cd0ig+rzmcu+FwGLPZLHa7XfzjH/+I7XZbWP+IaAFizgXt\nutkQ5JYZuJozVbueP+P1zN+Oauy7v8FU7g+/ZxBk4FdjA+1ce8cwL4CanS0bPJ/sABOILnR+Hv3e\n7XYtEOiXgWAtnSaPP3/XGEGzgn5WA8p/dh7/3W1fSRNeAC7rMOYOYELdvogoOhFbMpvNyryw3q6v\nr1tEzcHBQUyn08K4Mb6fP38u9QgBe4Rb6Q/9RLeSk+dnw9ahF6mWwL3pHwSS5YwcQWTk+fm5pJxZ\n36/X61epN9h2g21AM2FexsA2lefxPbAVjhDyPLxvIIjswQ7y2dqeg1rrHAgiZCBxP5DZODM0EQ3l\n6qRzjDBgycmmGF8QO8JwcnJSjJG9OIcM8+85N8xgjAVTU8SmgD2p/OS5HOZCwKz8LDRc215wVnAO\ny1gB4+GRm+NxzR52VnA5F8efc/sRlF+tcUC60wLIYQG8GHCzQNl4wOLf7XYlSdjeJSFRFIcPJn96\neirXAbAgA4B27lcz5IBE+m6mmf4w3yhjy3FEFEVqIwgrj8OCgYXJI19oMpnExcVFnJ2dlTOBMbQO\n0TCmZtDt8ERECcc7SZsUBY6xQn5Q1hFRxtwhHJ6Xa1OgFkYRdhWHs6tWA2H7nKd/Zv1knZP/l19f\nawZCmQnLjE5mB2thNOTP5/zms34dHclAsHa2cA7LZtDp3aJmBs0Keh342e1c1YCgn90st/MDfzQg\n6PIxPI9fOH04U0TTCPVSZQAWEAcyj2VEFGaONQmY3263ZQ4BNAcHB6X24Hq9LjadkC27kg8ODso1\nDO7pOy2nR1nX2lkfDAaFyRyNRi1G0Cky6GVsBHbe+hPQxbnNES/yiAODjbDdpLqD5wRbjK4jJQaZ\n47n87B57rs/a4LpvpWN1vlmEhYGSyMnFBoIYvIh4VRYjoqnb4x3IoHlA48PDQ6vcQA4/I1DE1x1W\nteBZ4P2dvPgj2nmHTFhEQ2U7v4W+AgCtBFF+mbnz9S0s+X+AYICJz/nEy7HCrykx/sc85eer5SGY\n9fxnDNG/s7FjzICP/jGPBjXMMXWv7Nk5L8ZsHPlphEVgwTabTTkiEdmOaAqsWxYBNXaQUGzMh3Oz\n6LdTAmDO+D3LK9edz+eFxWN9oQS3220p8DydTuP9+/dxdnYW/X6/hJLJeaEf9JOxiWjCJKwNnybg\n5/Oubq7tJGwUMkoXOY6Iwh6iMM0IErLusu1bA5mF+lbg8C1ryZ+psX377vUWGORlFtBh0qzHrUfN\nJJkN5F6Zec65gW+Vi7ERNwisgUFe2VmxDnRqTI0NNMNjm1bTnV036oh+ja0EtFAPFT2JfkAPMv6s\nOXbrU75pMBjEhw8fCnN2dHRUro0uImT622+/FT3FOGPTAYaQIuhN5tFMt51P9CnOH0DT9VftTGTi\nBJtIYW30iNNjuI8doojGMUNmnaONrcEuQAxhf2ASceQdesZWQwagJ9G5PK+JAY9Brf0QQBDFBDqm\nww5lsCAZJA9ARLQSKXMeixemw8d5wqxMEBaMJ6U6XOrDoMdMXESTB2WPwcoggwD6Z6VRy0HJ/ax5\nsfsMA8A45zWcnJwUZtD9qDGAzttxKMQL6K3787NrIMjiICzp+YJyZ25I/gXIRURrLlAOjBHMITWn\nUCDIgT1Oe2sO07OQAZQRjcPBd5EN5pP1cHh4WPILKXbKhinkEIMIqN3tdnFzc1MAH/Pv8g3Iz2g0\nKsVWHWZDYfMsgMD5fF7kiPXopGxvxOE5eW6elZyfg4ODEg4yq0HSOmveSeCWZeS9y5bl3+v5LTC4\nD8zla9cAXe2e38JWZd1ZCwvnkDD6ivuaoauBwAzEfO1aWDiXjLEO9fqt3c/OfC1f0M/Kc+T3c0jY\nBtYy6XFm/XcNBl2yqdYAXpvNJm5ubmKxWJSdsL1eU4g5ohlrnNKIiC9fvsTt7W1Mp9OYzWYxHo8L\n0CEi5SM1GZP379/HTz/9VHKSHf1AN5AiBbNHKJa5BESSqhPREEbMp4FmRLySV29AcUUGmFFHNzrX\nWwAAIABJREFUOyj8jC4yO4yuNKDlfZ4F0JajnNlW2JFGHyLHxk/gFvqJPWZs3nKAOwWCeAxeJGZj\nIprQUl5QzrPLCtJKz+Fj5w1kYOXwLNeH8mZDCcoHA8jkZKbG4MkgiZYXYk0Z+zNmS2heLH5+38vP\nxu/2WvIuP+h+vmuGj+/7exnoug++TgaAGcB20eiT2UDLH94qIQ2zH4AvgzY+69wYAAy7X5ERlJB3\nz5Gj6BA9r+xIWLYcHsYgwTiiLABnvl4uOdPv90vuDzICcDo4aApIcy1A5GbzUluLcE4+2H0+n5fj\n+jKzyu5e+mBFCFPjEgoAOBSzUzBQmPQPMMAzo0OQ3R+t7WORvgXUZYBn9j2DyK+BwX19Y14cETAQ\n9CuHavexgXaqrTezg5N3C2cgSL/tOOT7ZQCYWUHrLMuidbnXpHWBQa91W40R7NoBdv8sR/4dG+lU\nFuetsY56vV7RWY6AEFbGPpCfj14lPcVMrk8dgT3EWSR1zHYnImI8HsfFxUXc3d2VslawYjc3N/H8\n/Byj0SguLi7i6OiolIchBQZ20bl4ROuQI2/GW6/X5Rg9nH+em/7BjIJh6KtTtYhYMm6WGxNEODMw\nrxHtzbXoR88taw0gazD9VusUCLLrpmYAsiLxg5h9YzcPC9beiRVhzqPK/49olzih2bibSXRouAYE\nfR0/QwZ19i79eQMs6GkWaESbLc2GwcJkpcbCyzmIflkhZiHiO96QY/rdDGFuGQT+KEAwolHY9lpZ\nTAAR5gmFFdGctoEMGmhxHZdSwINzagDjQJ0uZN/zxhjnEgbe+c28RrTPzSbZGmBnL5Nnh81jN6e9\nbpcuoi+ca2z53O12rZpaKFwfCO9QrzfbZGCG3DEOGHIAL2uZOQIko2AtjziCzuvsWvZoNYeQn98C\nAmtrzQ7YPjCY75lBSk0vZofZkYoMAs3AZhDoHaiZEeRejjrk3cJOk/GOYfRWZh+/tlnEObnZ2crg\nMOdC+pn9/X1j/CM0SpxEvHYO+Alxgp5jbpwrbKDMeOL8Ij/r9Tqur6/LRjpsL3NMDqJTPlarVZlr\ndKmdzMFgUMAkuu3s7Cx6vV4hMk5PT2M8Hsfd3V1rvgCaPIPPP0ZeTQRAHKF7IqKlf7JtdTqLI0Rm\nNyOandk5JaxG7GCD0N9OPyPNxYQTes8EGqDVZybn1jkjGNEUjuY9HpwHcx4fRhYg6KO3EDCUj5N4\nvcgRVjwNK0F7l/zt63tgvYBMk3tCfD0LhBnNmhLlehFRhJH+WLHX2AArr1oow2OFEc7MHoJJX7me\nr0XfMmOVvc5a69ozNjBHTryzmv+zu9bsICwW423DyLgRouC7zB/eXWZqSQi294cM8T3WiN/DiFqe\n6DsKfTqdtpQz32EeAW6sDRQGyhUlZCbz9va2KEg2Me12u1Y4jnGcTqet/EiDZ4wwaSHZC+Z/GPCD\ng4MCOJ1fw5FjAA3KT3h+yC16SyF+7/Y14GBGPaK95vMaM4DLYHIfsMt6LUdZ/P3MiGW9Ynad/jlM\nCwsICASYOSxro1rLD6zVD+S7yBfgLoPBWm5g3liH/sos/74oip2/PGa1ee4aEJJHu88poLGh0jVM\nI6JlW2lE2waDl/N6cUwXi0Up38T56RAIAMF+v19Cz9hk79R1WBX9NZ/PSz7zcDgseY/eTHJ+fl6Y\nv9FoFKPRqDDJzDMg1HqPkkZEZrhvlhfnhnp8WIN2JNDP2PO8hmxLnF6Wd997xzKOL0DW8ms2lj46\n2lRrnQJBFDJGDqrV+XsGTwZHNoAYcis0fqJYiJUzISg8Ky9T1dkrjmi8QvpFy0yXJyUrRu7vRH33\nmevxf+csGGzla9can82erClrnotxNxA0CPf1LMBmXmrsRI1561oZRkRrR25Ee4OBFYUZvF6vOd7N\nz824kJdhpQF972s4GZp74A1HNLW+HJ6usUdeF7AuAJ3drqmZORqNWuFahzUAZN6ohfdoBtjh4aen\np7IjEJZ0MBiUEzwAgtvtthxzF9EwCpSDoc84OiSSs3PbsobcosyXy2VhGZ+fn2M8Hsdg8Pqs5Iho\nsfkA2C5bTbe41UBDjQnM0YDMBnqN1+7x1it/zro0s4COaOwLCe+rG8g6ycwbbCBHyvlEEe8YdhQi\nh4ZrIWCzgd7pauc1g98MAh2atg7IQHzfvHbZAGIRr+WMn+gydBO6DrYdvWQSA5t1cnISX758id9/\n/z3Oz88LYzcajVq2x/oP5pF5sK1jIxHRFMK+V1dXZaPZ8fFxyVmeTCaFLaaPsJY4hrZzNJzbu7u7\nuL29LXl2k8kkJpNJKWTPczL3tZQv7mtbYLwR0eAary2eHb1l1juiqcXqa9mhjmhOYHLqFjrxrbJZ\nnQJBdhjhgeVcQMftjar5HIuawagZS4Q1IgoYzLsuIxr2xcrQQuv/ZSDF9w2eDPCsKPJz5FCsX/TT\nLA7P49CEAWTt+Q2uc1FqxiV/x0JdA6zZCPFZzwPXtYFyGL/LZuOD/Dk0QtgBTxXQw/swTU41gJ3I\nLAP34H3LDorVyo7wB9/PDDmfsUJgxx4gbrvdlo0dVgr01WCWvvNMKKN+v19YPQwzbCjPhSwSwrUT\nQ+iCMDPPx98wdxT3NmsEI+Fwby5LAzCljz5NiM8BDieTSbnXW55xFy0DL7cM+JwOklNDvK6YV8va\nPpD3NSBYA4G1kHBe+55Tl6ty2RjWhOXaYWEDwVrBYgyugWcGgX6hSw0CMxDlJ7K9r3wNcl8bzx+1\nkeoRsR8IWoZcSzUiXukXcvrsuD09PcXV1VUcHR3FdDot65Q0DaeKoIfu7u5eFc/HzkRESV1hI543\nwUU0KVbUFLXeW69fThO6vb0tR8o5LSAiCnuJPAAap9Np2fDCMyBnlAezc42uRA/S50yggEOMOxxK\n5pm4J30ya80cYQsgvKznHV1BJ9Za54ygFbsHs9frFaExK+iFy8J2fl3E62PiHBLNoIpBzWAohz08\nkTWlaeDKT97PiJ9nNChk8v2e62AhUKB9kqitjGq5T2YFDQRzTqIXBc+S+127R2YhDBIdsmFc3urr\n92wZCLKQYKJRYsgfskYoA+ADgMrMKTKUDSSNMeKaKAAbUuYfoxjRACDmE0YcZco89npNQXWcBSuJ\nrIhyuBoF4vxDUgmGw2GsVqsYDAYlVGeFZocFJ8xhGRd79hq0oWYekFOMDewhhsjOHYWoYQN4rsfH\nx3j37l1ZKw5rddEyc0Dbx6i/BQAzGKyBkCx/NrDZOeNvZNFAsAYGs7Mc0Ti0zIlDwrWTRJxKk8PC\nBoEwgnYIuN8+JrDGCtqO1Nat+4GudZjaxaztKJssqLU8v1009EiNNHFzCJiNhIwzVTRItyAnj1OE\nPn36FJeXl68ct36/CVt6TJkfO2m2Rc5Z3Gw25Rx3NnMYKzA3zA9zc3R0FBcXF0VX3tzcFMd0u93G\n1dVV3N3dvWKasbc0rzn/dAoVes4RG3QOTrvZae6VU2fAK6wRbA/OLTgDtm+73RYbQPTl4eGhyPFb\nNrdTjQjIyewaaByvK6Kdh2flwYDQrCDNwJjCNopnMFEQKMBaUnAtDGKjH/E6T2+fws1MYUSTt5Y9\nabxoDDwg0KxIRHu3rsfDwNb5LSx+j5W9ckCvmS0/kw2Mn8//y6yhn7vL5jAEuaaEOVEmk8mkgEDv\nLMQocSoNLxQLz+jcOhsYK0czyTTLDoDM4WJCMPRzs9mUOnqEPe0pY9htzFEyXDsngBsE2jgPBi/F\nsjG4jAUnpeTwDmON3DIeDotQ2zKi2UDDeBvE8vwobFhFjNNqtYrb29v49OlTAYSDwaCUqen3X04Z\n+BEMcnYksxy8BQK/BgS5DnPu6/M7fag5chFtZsxyu+/lNZ1BIDqMVz5NxEDMLBzAz0DQ4TI7YGYD\n39otbDbQjpH1WAaBZiYBgk7gNyOZGUJatgddNexpZv/ye+g025bNZlPGgSLP4/G4xZgCxp6fn+Pz\n58/x559/ll23OYrFunWpK3QTus8MWURDlNh57PV6LdDq8ikG/IBPXs4fvb29bdl25tRkEuPiEDPz\n7s1vBmLMN/2DHbTTAqPnPEL6QLoPa5O+MwYuqg6GcaFp3+8t2esUCDIYzkUwwEO4MJwZ4HkTBf/n\nJ4qBgc0CiEEiX8Chg4ho5YQYFPK+WUYUJcbJAGgfM+RJ8bMjDCgyKHPvKHUoxR4F77tl4Glw6zH1\nmDj0jCKwgffY8bnsCe8DeozZWzT192gsZl4wVi6XgpfF/1lofI4wiQ9Sd64lLGhE28gBdng/Il4Z\nbRryCGPJeyiiwWBQ8vUIsdobRmbsbTLnBpkuVm7F5t11eJUkY+OIRDSloACjBpoPDw/FSNB/8nV5\nDuTYssTffM85hYSLl8tlYTBvb2+L4UB/mC2AwSQXs6uWQVduXwN9+4Ag37XDWXMwvtY3/+7owD4A\naKcP/ezcQANBH2+JfrZTztoz+GIjEGwg88l3a9GTWt3AnCdox9ny5lxAs5L5nGOTCDzDW3ObneIu\nmtn4t1rW+5zPy85bjwPr7ePHj3FyclKOcyOicnt72wJNu92urGMDwl6vVxw15DiiDUwjGjCLngJg\nMR/0qd/vF51NHjHfR5eZEUY/8zywj4PBoHXqCOwousVRPVJ0AGn0E1kjX9kYhb6bWY540ans8nYu\ntm0ueIA1gCx6vtGVP+xmEQ8GioRFivEgB8FG0oApK0sWmr1SDLKp/Igoiz6iYRoIl1kh2JPJ75lt\nhFVBETi8wv0dbnUIxcySvQXnvKzX6xb4BFzwvNm7dstMFd5sprXNHJraR8A8dihIlDj3qbGeEdG6\nf9cKEbng9fj4WMAORsiLk+cGAPssTJ6PcUSunW5AGMWhdhtSg3t75g4xcH/vrIUJWywWsVgsWsws\nQJX7uSSCGRG8eAqOEvKGrRuPx63cosFgUHYBHhwclBM+MsPDRg6UtXMd2UySC8T2ei+7DX0MJJ4t\nY8R7GP7Dw8MSIjo6OirOISAVFqfGUP9ILbOAX2P//H/aPgCy7303r13+3gcG/Z6dW/S5Adk+NtBz\nyn2cH2gQSH6gddJut2vpygwCnYuY2UCPL8+K7ss7lnN4GmcxomH1cQIdIs/z+lZo7nu1b5EDGusy\nhzZdL5QTmrAZZ2dnERFl7X769Ck+fvxYwB7lZPJJQL3eS6mq6XQaEdHKv+NlByQiipMBq0aOH4DN\nEUAcVCJpjowA2tA/2G1SUZ6enopuXK/XRYYjopSjsQPmEDO6LqLR3V5j6FlAJbput9sV+UVPso8C\n5x4QbRuOXWFO6XfeHJlbp0CQQTeoy/V7mIS8KcIAyoPAdSPazIMbE4HXh8eBsCB0DqVaSRgI5vfN\nFLo/zjfMDKfzW0xlZ4BmAAhYjmhAIN+j5dBtVuwoUoeFzW5mIGilfXp6WhadE7dtsDwXnvMfITSc\ngeDd3V0Bgn4mlBCL3HlmjBGfxYPMLKtDqxFNAi/Xgda345BZZrN7zD2KcLlcxmq1Kuf2krO33W4L\nEOz1mt28Ee3SNNwLRQS7R59zvs1gMCiGEePGrmcrURK8XVrCHmxEU9/T+YRc20oSY2tHAlYBOXQ+\npOuOjcfjkteYdUGXLbN1mfWrObn7QKGNy9eAbmYQI/aDQIObt9jAiCbv16ktBoBmA0mrsMyzpgy+\nKP1hBgq5xSjmkkX55BKDwBwWpllm3wKB9MOhaX7iqHtcPCc/ghPCjtQ8//veQyeybniG9Xodt7e3\nJaLn/F0cQ2wjTqjnhbE3s3Z8fFx2F3svgHfeRrRLtRjU+/7oqYgX3cQJT5PJpLB41s25nqDxBteI\naCI0j4+PcXNzU/puoof1AQgkT8/9ZL15D4JTz/LGED4PnsnrLjPbjA9sacQPfNawmQsbCDxKL2DT\noRHt41QcAjbQYOAdBnBOCELEpJl5QzFQi4jJNbPBy2Fj76DKk+PvuplBMriln35ugy2eHUXoPLbs\nsdcAYQ1A8j8/i5lahGsymcRu15QoMdPp/AqumduPAgQjogBBPDlYThYv8uBQRkQUEDiZTIoMoYiQ\nTYM65t3hTYwfCx85w+CNRqOiLMmntTwTPsCwOqfWcjQYDEpYA4XgkGyWVedBefMJDtTZ2VkMh8Oi\n1JfLZVFczi8lh+/h4SHG43GRce7FOCJjEVHYH4NP5waxAcT1Hg2qcdQc3rOB6LrV+mDdVGOsDCJq\nzCBt31r3fWqg0i0z+N+aF8j10UdfCwtbx9nxBoCxAcFsoHWMAWDt9VZ+4D4QWAsJZzYQ+WZdZjbQ\nrxoY7LJZ79FqYDAiCus+Go0K0PM5t7e3t7Hdbst6Qwc8PDwUsIyzSMSCc4bRnZSQQw7QIUQKBoNB\nkaFa6BPmEDtpUshOPFED7s3/cXLpp6/lFB50f0QTNUNfOdJ4eHhYxgvwSzoRetjYw1jAIA49C4uI\nbJooyOlatus8B/1HPve1ToEgyNyxeiP7bHQxjvYIzaRlABTR3sRgls2MnXMUI9rAjLCWF7aVYd5U\nYlbQk2Nl4zwo98/sIN9F6fAsEY3HzSYSFK0XCcJvhpJntEHx+NnA2FPhc8yF80XG43FhfAyoLaBc\nw4uzayCYqXJ2nHojDkAkon3KCs7C4eFhzGazMk7Pz8+xWCxazHYOm9MYLxQeSsqytds1uTmMK2NM\niCKiyXd0uRjWkGUWueH6KCfAGfkrADZCLOT4EAYZjUalXheKFtmgHAT3wkFDNv0ewJnrolhhBXq9\nXsnn89rEEBGeQc4BtShiA1kXL+b+XbWaIa6xfP7bn8u/R3xbKZjaPXNf8nczEKwBHa7HGskbRfbt\nFs5A0GkZnGdtIIge5rtmmWr3ybUKc0qE9bOBYD7NJJ9z7A0FEU1KyL7x+dpcfM9mQJMdhvye9bXB\nzmbzcoQlYCOi2eSFQ8o1vamL+nxm/vge65mTjbB7FIlfLBYlT56xdqoAuaU4GWYujQeyPkcf5iNo\neSan5NhZmc1mhfk8PT1tRTW9sQS5BqQiOyavuK435zjySJk9bDL9MVPKOkZnPz83JzZldrPWOgWC\n9vjtAeewiClbFBJCmb1aDwiDZlaDz2BMGXgDO4NSX8/hE/42Mwj44kWzJ2G2xyyG6Vy+Y3aR58GY\nRkTLO8tA0GNmT8TPz3NmT4IQpD9nZmmz2RTwgoElAZZrGJDSzK517Rln5Qe7dnd3V447MvDGs+P5\nkJ3JZFJy/1arVSvVgGdFpsy+OQxlBttGA4Xn79OsxCKisLSc4kF/+b8BJJ/n+Wy8AcLcD4cDNrDf\n7xfGxqwfR86Nx+Po9/uxWCxaoWKzz8gkQM6GmtA2Sng+n7eKYjv8gwJnPd3f37dC9TZcMJf83mXL\nYC4zfTkk7M/6+7QaeKsBkdr983u+3tfYwBoQJELxVskYO54R0VoLBoG8hsNhq0iw71MDnDVGENmr\n6XWn9uw7zcTH23lccXTzCz3p92rs6/du5Iy91TKDyfgxlqwjp5MYnJuEwY6Z6YZo4JXTB8wyj8fj\nWK/XZUMcOtTltMz6OUpmhhC2Dl1BBGK9XpeNIMwnupLvw1CCV7B/j4+PMR6PC4jkZChsL9egWoLT\nkSw/trHICmvBGz3s0EMEoE9p/X6/3Me45GutcyAIaHCOHwvJgC1Tm1nYMuJlceNxOEcvM2BMXqZp\n6ROfMdXKZNI3jKKVSi0si+LJHobzobgO3+FzKBfT9Gb9HN51ONv34rkQuvwcFk4WrxlKv8wm4YVl\nIJjZSIP7LltmZZlbG5XZbFYUPfJj1haFMBwOo9frxXK5jOl02trg5M+jJFFcjE1OnIcZc/6NjW5E\nk19Lv/C+yQ/kfi41ENEGGw4JPz8/l5AOSpaNKDClhDi80361WsX19XVcXV2V4576/X7c3d0Vpc0L\ntoA1yM5e7r1er8t4Hh8fx/X1dStHeDgctkJSFI7t9XqFHcUwsRbs3DC2b+2e+x4NXfIWEPxWEBjx\n+vSPiGjpkn0gcB8jyPf3hTtrIBCZQ+5y/cC8acOOIjqQcCwAcDKZlPzAHBbOQLAWEvYOYYeE7Wxn\nIPjWBhFHpZgvj4VBoPVuXmtdtgwKshz4b97DeXLUgs/mNCoTJIB7bJOZ14gm/EkKDfNjOWLjmucR\nuXMuHWQEjBi2EXmJaDawsCa4B06tZR7QD+ninMfBYFCiRi7HZhIl62qvccYO8EpkxYy1iaVerykD\nAzC0jDOv5GNGxCtnnRqP+1qnQNDAw4vTwlLzoKzgeHiDNAMshIFJR3hQJL6mwaJDEBlkOm+Av820\n8D2HZX0fjKGNFp+rKRUbe+epGUBENCFCC5jzGc2ksoAYL65HPgaLjhefOzo6KiEbFkdmGA0efV8b\njG/xUv6dDebO422F5/MmmSvPM4sRT204HMZ0Oo3379/HfD4vuTCMJcoO+TCYokQQ+agRjZOUZXi3\n25W1kQEioIf+OT8lolk3dmzy95Cd7XZbqu0/Pz/H2dlZAW4Y9vv7+3LckzfbMF72viOisHKEkFC4\njEO/3y85YrPZrCRj0+fj4+Mit6enp3F2dlbK+NjBcD1HxoHnzflhXTQbg30G2IDlLQCRncx9Icna\nNfZdt8Ysfo0NxODm3bs1ds6RCAyYdwlzrBdsoFnfGth0mRgbSDOBjvL4GZ3ek9nA2mkm6GxkzWHQ\nmtPv8fsRgOB8Pn8T/PE3P3le0pBwyJwOg41xOpXBr6MZVCHgu+jULO/eK+C5ss4ye46TGdFEOKyr\naSZYsHc49MgBQA/gaHYNOfD4AHaXy2UBbBEN6GZsMx4gNQhnYTAYFD1oAA3AhIjYbrdlxzXPlhlz\nF15HF2cc49Ztif3/rxkEYizJLcisQgZKfmWvwzs1t9ttMSR4DBHNQHJfMzVmaWhMGoAne9uAMhp9\nzR6WGSNaTekaxPGZzFIeHByUcCYKyruY+T7PB91P/1kcz8/PZRdqLvGAws7hG0CJQTHCW2Nzf4Tw\nSESUDS4Gg4CG3e5l6/5qtSqLifIpZhpWq1Usl8tiLE5OTuLi4qIVIri8vCz5NBHtsPzT00s1/eVy\nWarqQ/lbbnF0kEXkxLmtgCn+j1LO13Bagr3QiCgyQekc+oXMsJsY47tcLuPu7q6UkMDY0jcSsG38\nee7NZlPOIv1/7J3LciNLkp4dAAniQoKXqjpV57SmzUZaS6aN1nodrfUSWszbzKIfYbZjprVW3TOj\n6VOnihdcCRKAFmV/8MufEQmQVaeAVqWbwXDLjIyMi/vvv3tEctNUsZxi+TRuFAZX+XJGNP8UftHY\noyPJ+UswsC9hnbwuztK9FAQSrDk42cYwlvRqLizMctWmMt7Srw7UfCEc2UDljYkJHI1GcXZ2lthh\n9T3TGHIv6XcHgXQAnPUs5QbycXKe+10nDgw1f5wp2oeMx+OIeJ4eUHoXgNCcJcEgEUAmU0uAJ+ZP\nwF4sIBe56V1tLOfXnQ7mZ8qJpB2WHlHUQeNC+uvx8THtc8hQN5le3jf1pMYqozoRkcaZ8qMFimUz\ndG3u1Upwqfmj8SLsIn3maV0RTyBTc1LjVMcJ0HJbnLpxu/d9BHNsYMQT1SkgSIaBICmi+ng6DR5n\n0nQ9ZyrEUtA7VMMLkVNZ05DLgLoH78LJr7JonHOKnDkUUr6spzq91+ulJz0I1PL+PUyhScWkfbWH\nGDFto0M20AG2vCdNTN6X2oYsU45B2KconOuMIJ0QbSaqvBoBFRk4hUW1irbdbsfV1VUcHx8nY3J8\nfBx//etfK0+GYRgtItLO+gwFiB0kkGY+jJQOgY1YEoJGGWfNC425iCelojJpzBUmv7+/j16vF4+P\nj3F7e5scqNlslr63Wq2k5DRu2u0vj0OiQSZo7fV6cX5+nkIsZEg3m03c3t7GZDJJQJzKnKDEDSwB\niov6b99AkHqDuq/0m0R95+85EOhgTee70ECUmMCcI55jAxmudUaQ4IxhYRkrhoXFBmr/SuonMkU5\nEMgQoq8SZttSRzI30IEgmUDpU44hj4b4dXKAcJ9Cm8OxkfvMPhbY0KIxMfFM/SFJQRKFLBqBuvYA\nZPs5ONe1ZW+0aEjXpk4UU+Zkg/SGIhkREWdnZ5XFfMqBVnREOpTstVJXtBm19CtxB7f7Yr74ZrNJ\ndacDzzQjd/A1Vwi+eYzmu8av2qXVaqV0GgHX3Gpxyl6BIHPwIp57pGokomQqKCZCKkYvJUuFRQDE\nPAENlhzrqDKcEYyorirWQKQSyN2L7lfnU3LKl0BO51MBcTm5yqTHm1Pc/I15klyZLCXr2zuwblT+\nTP5ne8gwaGDSqBwCEJS35kpMyk5tq8mt+9YWJGI9rq+v03/aSubq6iqFt8Tsffr0KQFLCQGbFB7z\n/MhSR1RXl0oJMn1BeaPMMXFW1vtGyjbiafEWjafGhvYqlPOllXyr1SrlcQksap6S1efc7HS+7NQ/\nGo0qq4yn02kyEALOWpXNrRjUdpzDbFO++1irc9i+lzgzV+etO9NBIB8Rz/SGz3u/Zt01fD7kXjmW\nkQDN8/Y8XEudwvHOkLDYQKWfiH0SwCCY8O1h+GLOFfWz7sUXiPBZwjkQqPun/vdXDgyqbQ8BCArw\nSEqf+V365uzsLOkk6iiBOfZpRKT+Pzo6qoAwgmdGsbRog4BbLKGApxbXeYhf9STLR/3darUqC1IE\n5qSrGDVTHyl6EfG0Glf350BQx8h5V/qP9L0wDO0799OUMH1LtrXdbldYWBEBfKiBGFDdk6KEnU4n\nRW3qZK9A0L1zAgYZKObeka7VRFZDszGYHE9lKwBIYCZvgdenYmQogJPZwR8TXenx6n4kOQXBz3yR\naXSjLwXKPMASw8B31cc9ETKh9KDZ3mSzpOzJHtJ70vXd0xOQ2LdClLfmL+aJqK7MldSYUftoginU\n+e7du3jz5k2MRqO01163242//OUv8fHjxwpQVnhAik3srvZopMFkiCsHyFlXbWsglk3/k81l+RFf\nxoUWiTC/U7/p8XVqJyZk65zb29vodDqJ1aHiUj0UqhiNRmn8RlQfH+egMSecIxH1TBoz31CDAAAg\nAElEQVRl3+NOsks96u5D/zsIpNObu1YdW1p65UAgHU/pErKBDAszxYS6MZdqMhqN4vz8vLJamONf\n1yH4cxDIY+tyA3NAkC8BQRluB7/Micu9CA53GZvfQwTSclKqm4CPmEAdp7EmB02ATudoDmsxmKJv\nzOsnIFc7M21Kdk4rgmn7GapX+2rsqZ/Fpul/bcVCUohzxgGe7K2cEOVJSxiF4/jg73QiiBnoIMsJ\nV9sIAGueaa7od+VR09mX3iYxQdxQJwfBCEZUlZqMVsRTcicZQB1LmlVgo9VqVUJwAiY0qhFRWa0j\n1kLHUaESqJLdi6iGeZknww5hCEvHkp7XIFZdCTRp6OSdyDuTUVdCswwqFTQBK9ta9WEuH7e9cQND\nVorhY+aGke0kC8tJwe1w9i3ctoUOgJS/WGKFSLWoQ+FujTkxV/J237x5E71eLy4vL+Pq6ir1z+Xl\nZfz5z3+O8XhcyRlUG4lVEyPiRlPbpWglMLcz8DGr/uDKM413rWjWKl3uwyXFOB6P02OUNP+0ephA\nmMy96qY2jHh6JqjmnBS7tlxwxa92jYjsHOF8yAFBni/huYcQFt5V6gAb26YEAt0RlOQA6EsAoAPM\nEiPoIVuCsoinRUkak3IeuFCEYWEaTmcFHQT6AhEHwhx3AoK+UMSdbOplAsC664sUOAQWWlIHREv/\nkcXidjFifWnDGOLUNlQRT2yuUk00jqXH+PQwkg45AkdEke8QcHx8nBxhOtg8ttVqJXt5dnYWEVFx\nojW2mKdHZ6Pdbie9KZsskEbygwv7PLeUWIH6i/pUbS6WU2NUx2iHhvV6nXZMUFtFPH8SClPkcrJ3\nRlBCT425LhFPjURAopvmJOWNkqmjEVC5MnwCXAJZziyQ2vXwiySnED1ErTJ0XER1Q+ycF8/cBykd\nMTUaeFztpvuUAmIYRv9JdF/0bDVgucjEFSG3QNEkoJKUqI1pyJ1l3KdwsQiVD7dHUb6IQJG82tVq\nlZKjBQQVNv3w4UM8PDzE5eVl2gz3/Pw8fvrpp/jpp5/i3/7t3+Ljx4+Vla0RkRaIaBGEALrG/ng8\njm63m1g4bcLc7XZjMpmkMaX+pycd8RSOvbi4iJOTk7QamFs56P6n02nc3NwkllL3KvZXTo720tIK\nNi3u0FxSSEMGXfOP7EpEnrHy+eziY2gXRtDH6L7la8EBgVkOsDlbylepLJ8PJYAY8XyRSA4I5vYN\npP5lbuBoNKosEuGqzRwb6EAwBwIJxGhjpNtyjKAvDuGYdD3voNQXqjgI3Lfeew0jKUePxIFAkPYK\n5dhSOFbOIdkpsVuyGRpnAoOco7TVtFckJwjMpZNGo1FFfzDy0Gq1kpMhXeV9qvGj6IV+Y+hXOlG6\njsw106U49nR9jmNG5Jw4EejU7hGDwSCNOTG7q9XT8+SZX05QK11cOy4OxVNppJFGGmmkkUYaaeT7\nyv5jdI000kgjjTTSSCON7EUaINhII4000kgjjTTyg0oDBBtppJFGGmmkkUZ+UGmAYCONNNJII400\n0sgPKg0QbKSRRhpppJFGGvlBpQGCjTTSSCONNNJIIz+oNECwkUYaaaSRRhpp5AeVBgg20kgjjTTS\nSCON/KDSAMFGGmmkkUYaaaSRH1QaINhII4000kgjjTTyg0oDBBtppJFGGmmkkUZ+UGmAYCONNNJI\nI4000sgPKg0QbKSRRhpppJFGGvlBpQGCjTTSSCONNNJIIz+oNECwkUYaaaSRRhpp5AeVBgg20kgj\njTTSSCON/KDSAMFGGmmkkUYaaaSRH1QaINhII4000kgjjTTyg0oDBBtppJFGGmmkkUZ+UGmAYCON\nNNJII4000sgPKg0QbKSRRhpppJFGGvlBpQGCjTTSSCONNNJIIz+oNECwkUYaaaSRRhpp5AeVBgg2\n0kgjjTTSSCON/KDSAMFGGmmkkUYaaaSRH1QaINhII4000kgjjTTyg0oDBBtppJFGGmmkkUZ+UGmA\nYCONNNJII4000sgPKg0QbKSRRhpppJFGGvlBpQGCjTTSSCONNNJIIz+oNECwkUYaaaSRRhpp5AeV\no31e/H/8j/+x4ffNZhP39/dxf38fy+UyHh8fo9PpRLvdjlarlY5rt9vpt3a7HZ1OJ70eHx/Ta71e\nx2bzdIn1eh3L5TKOjo6i2+1Gt9tNZam8zWYT6/U6VqtVOl9ltFqtOD4+jm63G0dHR9FqteLx8TGW\ny2U8PDzEcrmM9XodnU4njo+P4+TkJCIiFotFeunY+/v7mM/nsV6v4/T0NE5PT6Pb7cZsNovZbBb3\n9/epDkdHR6kdjo6O4vj4OHq9XgyHwzg5OYlutxudTie1je7j8fExptNpPDw8pHvQe6fTiW63Gycn\nJzEYDKLX68Xx8XGs1+t0L+qL9Xqdrs22Vl1UrspmHSIilbVYLGI+n8fj42NERPzjP/7jU6d+Z/nf\n//t/bzSmWq1WzOfzWC6XsVgsYjqdpvouFovUDvzOPr2/v4+Hh4dn405jiZ81th4fH2O1WsXDw0P2\n3Farlcap+lqvwWAQ/X4/jUP1fcSXflitVukaej08PFSux2v69VUH9aP6W985F9frdbou54mfxznG\nYyLi2Rw+OjqqjHm/pusClsmypR/8+q1WK/7hH/5hb2Pvf/7P/1nRe9RlEo6XOuG89naKiGzbeT/5\nMRoHOob10m9elo7Rd+peXq/0e058zGiMcax5Hb0sv57a9PHxsTJXVqtVLBaLaLVa8csvv8R/+S//\nJf7zf/7P8X/+z/+JP/3pT/FP//RP0el04g9/+EN8+PAhzs/Pk848Pj6OVquV5txyuYzlchmnp6fR\n6/VSm7bb7Xh8fIz/9b/+197Gnmyu5rfaV+2ieav21O/US2z7TqdTOSYinh3DvpFea7fblWNUH+8r\nHwPUoavVKvUhdd39/X3MZrNkX9+9exf/7b/9t/jv//2/x3/9r/81/vSnP8Wf/vSn+Od//udot9tx\ndXUVp6en8fj4GP/yL/8Sb9++jb//+7+PX375JWES6vqzs7MYDodxfHwcNzc3cXt7G5vNJs7OzuLy\n8jJarVayI7Klq9UqIiLOzs6i1+slnb3ZbNIxwg3tdjvu7+9jPB7H/f19tNvtePfuXdL9o9Eo2u12\nrFarmE6n8ec//zl+++23eHx8jMvLy7i8vIyTk5PUdpwDJb23VyDoCkYDTZ+pZHRDalAqdRkPTcaH\nh4c0qB0IuuKjYqHxiHga0FRYnCDqRAE21kXKQeBiPp+nDhcIaLfbCdQ5CBN40713Op1KfVgvDiDe\nqyZSp9NJk8bFjeg2KRnkOikp8H3Kzz//HA8PD7FYLOLh4SGm02ksFouYTCYxHo8TGJdTope+EyCq\nv9SvBH+u2Lwt6MxwvGoMnZycRL/fj+FwGKenpzEcDqPf76cxw/No1KggVT+9/HsJiBKglYCZ9ydB\nDR2HHBDUO8d46RyfyxLOTTdUDgy9Doci1HvfQ3JgkXXZ5dyX1Dd37C56YJdjHMTm+p7/1dVfgEhA\nYr1ex+XlZbx58yaurq7i7u4u6W/aCzn/ufF2dHS0FfAekpT6qs526Bg/J3fMtu+lOnF+uw33uS1d\ncnx8nHTg7e1tfPz4Mebzebx58yY+fPgQ/X4/7u/vK9dZrVYxHo/j9vY2zs/P4+joqEKEyN6vVqtE\nJh0dHSVb8PDwEEdHR+l+dB5tt3StgK/0tfDA0dFRrFarNKbW63XM5/OEL2TbVReKnGjVQfUo2f90\nXvGfPYobS/cKI6qdL4MoEKjG4QByUFka8A4G/VwheRl8GU96UvJ4xEA6YJCxPTo6il6vF71eL9rt\ndlIyugcO6pJXzkGvgUWDeHx8XPHS6R2ofA02BxJ+vRyb4+2ba9Pc533LcDiM8XicvCpN/ru7uxiP\nxzGfzytMnwMpAkSCKbVdrk1yDLPGTURUWF2BwF6vF/1+P7HGBIJUMFIM7HuCwbp6a7yRCdK48rrq\nvxwDlQN1BJA+76jQnRXkOTzeGbJtBspB4CECwe8l3/K+v+Vc3hUIO5O4qxBE6Hvp82q1SkBwMpnE\n6elpvH37Nt68eROz2Sw7zxW14RjTuKf+PURxUEXZBgBLzmCdw1ZX7i51zdWZ+khC53q9Xsfd3V38\n9ttvcXNzE8PhMN6+fRunp6exWCySrVW0b7FYxN3dXUwmkxgMBqmu3W63QgApEnd8fJzGxXK5rNyv\n6qB6aIwp2umOqyJ1ApRikQUEu91uGn/S5TmsIDykujAqmJO9AsGcoXQqOqIK6CLimcGQQVRnCMHr\nXF0rx75oEPEcvjtzQ/aFITQXgVJ1lujlh4eHVO9ut5vCsiqP3odYoW63m+7BgbHqqRfvhcbV2SlO\nJt6P3lWuwAk9XBp31oGT0ZkwZ8P2bYyn02nc3t7G58+f4+bmJq6vr+P29jbG43FMJpPUVxpPDD8I\nVDkIZGi1JA7C/bPaXCFhsoGnp6cxGAxiMBjEyclJ6hs5JPxMgKj6s54ObukAqG+873MG1BWys4h1\nQJDluBKjAmXbUQ/wdzprKrd0jX2Pvd9Ddr0nNzzfqi2+haEvlVtXx9x/237TWJBh9rH58PAQ8/k8\nptNpXF5exvv37+Pdu3fx6dOnCtlAvXZyclJhbFiu9HHJVuxLckDupWPCz/Xf9N0d1Nz5OftR6kvX\nPWrj3Fxfr9cxnU7jt99+i19//TW63W6cn5/HaDSKX3/9NQGmk5OTODs7i5ubm5jP5zGbzRK2EODr\ndrvJpnu6VkQkPapzZHsF6FTf5XJZieJxvIgVJE5RiLjf7yc9LrJCaWS5/pNtXy6XhwsE3WASiHh+\nH0GgGorMA411bvBwItYN9Bxjo8+sJ3MqRMOqzjLGHkpUftzJyUnK/1Ku18PDQxwfH8dwOIxer/eM\nxWDYMZczQW9Dg8LbkPfkk0jncEARXDLkrQHsnrZ77OwTZ0P3bYz/9V//Na6vrxMQvLm5iclkkkLE\n9/f3KQTkeSkOojwvNSKyY0jf+U7l5Qqn3+/HYDCo5AeKEZThiYjKRNfYe3h4qPQL2QvOrzqWkmAu\nFxrOvRwAOhBk27BNCAJzoHEby6ByaHR5P7lxf2hSZ/x+b6GRLhkMjt06o/J7MGB1bGAd8MvpGjqt\ndMD0eblcJkYwIuKnn36KDx8+xP/9v/83bm5uKkCQOkJj3YkF1eeQmcHS/MqNB84hH7N18/Rr7j/n\nELJN6YS67liv17FYLOLm5ib++te/xi+//BLD4TDOz88TUIr4wvhdXV0llnA+n0e/36+wvO12O+nb\n+Xye9LCYX7J9ilSy/kwV8Lq7DqXTylxuOSPT6TTm83nCFR4h4hhdLBa1TshegaBYDBo03ah+101F\nPA+DkoKXkfYJyPLdMLrQayFS53m8DuskNoUdJbTOxQbKDVRojwtPOp1OnJ2dVRJJ1SakgenpKM+N\n9626kcXSvTH8SMNMgEBvinUjKMhNfLJhTMr23LlDkH/5l3+J6+vrCgiczWaVBT0ESWwfX2Dh9ygh\n8MixuBHPlQEXiIgN1EuMYK/Xi263m8YAE99zCzQcvKlurKN70wL8fOXK8nt0AMiQWWn+eV2dfaTi\n3AWsbBtjhwgEc4Dle9Vzl+vkHOKcuJHL9SPL2UUflBikklNPkFAC/nVjeLVaxWw2i/F4HMvlMt69\nexcfPnyIi4uLGI/HzyIFm80mMTwiBdyxltRFC7631IG8EuAmeH7tPPPIUB3Q9M/e/573TwKD1394\neIjxeBy//fZbvH37Nk5OTuLy8jLl5ovV7ff7z8gAsbztdjuRNiJ3CN5E2Dw8PEREPLOVEdX0H+pH\nnU87zf98IY5SmkRerFartC4h4sn2k5TiokKXgwKCakTedERUGqfb7SYaliid4TEOiJLRcAWh4ziA\n3Hg6m0IwxYUeDB/qfT6fx2KxiM1mU0lAJQN0cnKSGB/lAChEoYFCpocegpQQFSDDGO4tOcOTM878\n7EyP2szbVG0hwMzQY0mh70P+8pe/pJzAu7u7Sk6g19fBIAE2nRcHuj5+1Na5MCvTBaSQyAgqP1Bh\nYRocAkJf2Z17afypDFc0TEvQS2OrND8IBL0OOXbP26kEAnW8pxa4QeXxHM+81r7H3K7ykvruCha9\nfXKfd2XbSuLzuwTCXlIWwUIOSNaVXzomBwD1e6fzZeeJ2WyWVn1++PAh/vCHP8Tbt2/j48ePlaiV\n7JXCiiQQRAwwcd+B4T4l1/d189T/L5VZF/FxnVp3fZbpY7YEFN2hJCkym83i5uYmFotFHB8fx8XF\nRZycnCRg1W63482bN3F3dxefPn1Kdnyz2STMcXJykhhBkTPSd8rf0+4htLHSX8wHp00l1mF6GJlF\n2QbhmsVikcgLxyPOHgoblWSvQFDImUDQQWDE0+obJdC74dKE9BXF+j9nnEveZM7j1DXck9FLoI1A\nQi/9LiDYarXSaibd19HRUfT7/Xjz5k28e/cuJbFqAYOSUtvtdszn82cAq91uV7aRYb10Hf2nQedA\nkMIJpbb2MJ8rCE5wD5+W+nWf8q//+q8pH3A6nSYAX8rzU7+TbufiGiaPRzz3niU5r5UOwcnJSQKC\ng8HgWW6gwsIKQWmCr1arCgvnn/Xy3FaBQtVNCkrgz/NVXDgPSos9cmxgHWDWcXpnGNtD2TqfQrae\n59EwHJLkANPvAVpz8/wlx0scpPl/X1v3Xc8v1W8bAC2BQM3F9XqdGMHJZBKdTid++eWXePfuXfz5\nz39Oi8zEBEr/K+1Hc1N6kDpANm9fkpsDdSCwZPNKALHUd+4Y5By0nINS18c5HUK9o0id7LcWgczn\n8zg+Po7z8/MYDAbJpm42mxiNRik8rMVBEZFsQ0QkQDaZTBLmEFBT7vhsNqsAN+p517PM9RbRQIdc\n53S73bRtTafTqUQb2QatVusZSbFtXh4EIyjx0C4HCI2S/pOiFzDy/DkqqlJIwQe5hIrBDTyN23q9\nTjQxtxHhu8LComd9ZfPx8XGMRqN4//59/PLLL3F2dhbT6TSur6/TQCDFq3ftfRcRiUEiGPScQnof\nnu+luvAeyTBGxLMFIt6uMrrOiBJoEFTvU3799deUY6GFIcyvLCkuLqphmgDvyZ0Qdz5yoVuywlpJ\nrtXB6lv9rv0vyZYohFECgJo/BEkMZVM5+XkOAn0+qDwpujoQ6JJT+CUm1p0xluH10HlSqnov1eNH\nkZwRzx2j9xJALTmCr73ma2UXx9JZqpwjq+M0hpWDNZ1OY71ex4cPH+Knn36KwWAQ0+k0sTvS/yIB\ntAME9Zyc9c1ms3cgSMkB+ToQXXLYfF7t4tTswtTngCHndg7IU49RBwm0a0eIk5OTOD8/j7Ozs8qu\nCfr99vY2EQSyoUzNksPOFCKBQeXtSaeTESQwZb1pXxjBE+g7OjqKk5OTOD09jZOTkxS6Fh5wXOL2\nye22y16BYC5/QkYxIiq5bTJKMmA0CIyHkx6lAeaKSA4ODkiey0blefpP9VWOnjxCMkU5QETGTmUK\n6Z+fn8fl5WWcnZ2llcIyZvP5PCaTSRrQ+r5cLtNkcHDHwcawHUFJyRv09sgpDQc/7Au1B4EHF1Ls\n2xhfX18/W3VV8n5VZ180sgtD5X3NRRH8rBCsg8Fer5eUjtIimA6hMZUL/x4fH6fQlJg/9YGz6gxF\neHiXY6UExhzYchxuAwLeznrPgcCcE8HrlBg/nbOLUtyXlAzpPqQOEGyTr5nbX6sXcsBG7zkd5sdq\nLiyXy5SDtVwu4/379/Hhw4cYjUZxfX2d5l1EPNv71cczoz+HFBr2uVZK4eC4dOBC2RXwO3ng12N5\n/E99kwOBOYc7Z6/0wIaLi4sYDodxeXmZ9oxUiFW52RFReaCCiBdGbeSEk8Vrt79sCD2ZTFIaDwkE\n2XDZfukthrF1X8oZl00YDAYVcoiYh/ZEulIiTFWSg9o+RjfR7XYrhoA3yYkV8QTG1BE5I5wz8Lq+\nGp15HA4C6GmwDswVIOhR2ep4KQ0+yUMMjzalFAgQIOj1enF6eprYRq1OEiicTqcxm80SE9Ttdp/l\n4RG4EgBLGEqn18KJRNDrfcfBKyXHnfU9kZfX3Lfc3d1V9nYsKTwfP5zQJVDi33Mgiewd80Q1Rvgi\nANTxrKdfJwcKpYz4JJiI6iIVDym7M0RQRkDoYLfOoOQMCMvh99L16voqorqVBI/XPDwkILhv0Pct\nr19nyEvX3BX4lUCG63SWnwMSPm68bM75+Xwed3d3MZ1Oo9Vqxfv372M4HFZsheYVo0JigTgeI/Jh\n2X2It487ILl+KwHs0nGlfq0Dj6UyXceUnJRt/xMIrlar6Pf7cXl5GdfX14kpnEwm0Wq1UmSNoVU9\n+Uv6cTAYVBYNMYVKEUHpdWIGpRK0219Suqjf6Czou4PBiKhE6zzETDslsClwWJK9AkHvLN2wQlhq\nYCpv/U4GgI9w4QTMMQ2c6NwMWufmQloRTxtDkvViCFjHkJoVK6PtYLgZ8Pn5eXoEER9RJGDpCaNS\nItyKRmBL7ZJj8njPNIwK4areYoxcQartcoZag03l+v56BM/ute3bACoRnNS+xMdQDpj4YhJ6oiqj\nDgD6ixO9DgTmgBnHdum6urYfT/ZQ78wlJYCnwsutkNb7S0Agz+dYzr28PF6nNN7JmNKhPAQpzYPX\nsGKvAVd1Ze0C6Fzq+rbuP/+ccxRyuvwlQv3Da+XAg97n83nKE1R4WEBQc02RG6UAaZ9YjbWI6kb+\nX9s330JKjFzuOB8HtCN14rYid63XtsWuQNXHi1i/6XQaj4+PMRgM4vLyMgaDQdo79vb2Ns7Ozip7\nBspOKm+00+nEYDCo7PghcCc9enR0FNPpNO7u7tIOIcIcsvGtVivZfoE+bVodERUdSwc/4mmxiXS4\nrhHxlM+42WwqDn2dE7JXIKibivjSWWTFuAybEyriyeC1Wq1Kgr9TwlT4ZLpUFlG8ynWDzut5ONlD\ncwJTzFdU57bbX1YkaQBplVm/349OpxPL5TLu7u7S8nQydBLVWeAzB0A0MOSFMKSmxGWVo7CyrzZW\nORS2Acuk90IQ6JR1RJXy3jcQlNJ2FpVCMMH7Zd/n2ovjyIGYA0EPCTsIJGNMEJMDp3UsGetGhlh1\nIBtNhRPx9GhGZ3NLgIGKvwQESwDIwR9D8F42y2HdeO9izA8RCH4ryTlu36LMXeZoqc9fK9vYoZeW\n5fMyN0conLv39/dpV4HFYpFyyriPqnS70mEEBjiHNDapLw5NcnYvd4z/7wSBjuP/LiWgX5KS88d6\nsRy+yLAx5K99ey8uLpINfnx8jM+fPyeA1+/3kw7SXnwCggJfAmPr9TqBu3b7y/ZwykG/u7uLwWCQ\nFq/I7oiIUHuIEOCOG2IO9TuJF0aT+Pxilqt7oa3OyV6BICl2VZr7lTmwU0dzktE400jmJjiNAFcB\nMRm+pBh0rueS6XfmL3pZR0dHMRqN4pdffonBYFABqjJy8/k8bm5uIuILSFFugUBVDmgozEcgxzBf\nRFQSYcXS+XfdhzMpBN5qAx1PZlTHMnmVwPsQvGAXzwuMeLpPJetGPM8N9Fw1TXwJAaCHZ7cBQYaG\nS0xgRHV7HuYrloxwjpFQPVkferM5A+9KNmcUeE13mihkFvSuc3xBjgPdkvj/NAaHDgRfw6bp/28J\nLr52rm5jmCTbwIIfkytrG4AoCfVzbixrzDw8PMTd3V3c3NzEdDqNq6urtEWMdKHmzGKxSDpVekXh\nON3foaTFSHJsn37P9UndfPdjXKfuAvZKzmFEPYj2/nT8wDJWq1UKA7darRiNRtHv9+P4+Dju7+/j\n+vo6Rer0eLnNZpNsm7Zy48JVXZ9AcDgcpvSju7u7BA51LBd0CjOIaGIeIfcIPDk5SbmFInWOjp4e\nU6vxxkjXYDBIdaybm3sFgp6vJE/KDZaMhMCIjKM/GovHE9BERMUIiLUTWyApDfRSOK7dbifgyhBB\nq/W0uOXk5CQuLi7i559/jr/7u79LbOdisYjFYlF5moUGwf39fdo+hF4AtxWhsWR9ORGogESLq02k\ntNgmpKm5gpYAxg01N1TWZGH7e1uqnfftGaueOYZA7UHgRQDooIRGKQc8Stu6SJkQBBIAEgTqOhrr\nvpG1gyYuWHLQ6MxvLiWCgN8NA9uK/znw43vpfAqBoN9PDsjymvrM99L9cc7vQ0pK2e9tF7C0a/kv\nnXPs328tOed+m/h8K52/rSwHJQ4emB4j0HB9fR2z2Syurq7i6uoqzREd2+/3k2Mpx5jEBFeK7lvv\nubwEuPuYyAH7XcfLtuN87LuuyR3vOpx2m2WIqdtsNnF2dpbCqo+Pj2mluGy3ROCL6VkCirqOHuyg\nnR4Gg0Hc39/HeDxOKT9y+jkHFD1zwEr2stfrxXA4TEBQtlzMpJjpiCdGkPvbCvOU5CAYQQIYGg+K\njuFTDsQ+eW5bxPNQHVlExenF/LhS0PXo1alOvJ6zKWSJWq0vYQMxgf/xP/7H+OMf/xgRX3INbm9v\n4/r6OikQLge/v7+P4XCY6rfZfGFLT09P4+LiIuVN6lge5y8CNikk/s579zxIhtvVfm6s1ZbKD1Tb\nsF1zBnzfItYyomoMKA5wHBBGPH/GMpWQg8DcAg6CvhwAZBsSHJHV9QnPvuF+jty2SHX3c3LgLAfG\nckCY84RsK9lAV9i8P7Ku24Bg7tpeNwfnfB2i7AqKclLHlmxjUl4ru4DFnE5+6XUd+OX+36Vc9Tsj\nHXr38aj3yWQS19fXid05Pz+vpCWJqaGD7bqTRMahSs72EUC5lBz7OqCmd48O+DWc1MnNa1635Mxz\nrlNfC+ytVqs4PT1N2649Pj6mhZjaBqjf7yf7x0WhSoOKeHrsreyxGMHRaBT39/dpc+qISKDNU15a\nrVYiWmhrZrNZLJfLODs7S7a21WolINtqtRLApOPBnTtU58ViUez/vQJBD6nJC3OWj6BMQJAGgwq/\nNOFkmMmQsBM8P4Idonp5GFoonGHjiKeBISD4/v37+Lu/+7v4wx/+kMIN6uSIqN/JqbYAACAASURB\nVHSaBpM2idSGlL1eLy4uLtK1lYyqXBa2lQYCmSN5A/SU+K4yee+6FleRuqH2jaNL3hjLfSk78XsI\nNzNnW7hiKYFrem8ax7l8wBwjmNvfzxdrlELBDsKd7csBRb4clDHMRSdAUrr33O+53D53AlxRsx1f\nCgTr6uVpDWrLQ2AEXXIsyy7z41uCvDq25Vuc7/XZBiBZ7rfQFQ7ycuPCjxXzcnNzk/IEh8Nhcoi1\nL6yYfI07kgUEjYeg93KyDejpmK8dU3V9vm38+PyoE9fBYvNkz7j12tnZWXpak4CdwOBwOEw5dsfH\nxzGbzZKtVQ6fdDu3vFsulzEajWI0GsVyuYybm5tKVE/kEoGdSC4fj4wIynFfrVZpE2yV6Wl2m81T\niFjnHSwQ9IFF4+YDiCBQHUsw5/mELJvAhF5bDgRIcqwkO0UdTwArBUD2RxtUXl1dxWg0St4IBwLB\nn17a/005DEpoFVDgdVVfDQb9RtAgECCAqjKYU8j9jggSBAK9j8g6MS/GcwlVF37fNysjDy2XS5KT\nEuvk5xHk5MCggz5/cexoPDqo8i0LnKkkKGQI2VfJy0CRTXaW3Nug1BYO4lgviXvoMvIOJB3QshwH\ngiUGMsdCeh32KXXsGMFPaVz+3qDiNWXvCiRLdXeHIfd76Xq58vjbrsDavz88PMT19XVcX1+nEN1g\nMEgMoQy7gASBYMTTY1AFIA4BCObGVI7V29a+fqyXuSvQ9+/e9wydbnM2BHpyEQD1iSJwi8Ui3r59\nG8PhMHq9Xkwmkzg+Po6Hh4eYzWaJ7RUYlE7n00boSCu8LNuiqN5vv/2WjlEOuHTvbDarAMGSXVLK\nkM5TJHC9flqsqjZQO+ieZf8FdHNyUPsIEkzpfzUIjaQUv28bUwrtEew5i1gHAp1tEHhk6E6GmQ2t\nwchQ6Xr9ZUGInmGpl54VKDC42WzSOzeu1NYiGix8IoaYOk4EMi00rGpHMY0Rke4nB2r4kpLzbVfI\nHuZCwqwL232fIg89Ynd2QqJ7yhmOEggshYHrQKDGHEFeKUxbAoN1wIx9q/PIRnJu5Axzjg30MHXO\niKvvc0DQmUCyez6eSm2icrxv/Pr7kl3AwC5g8CXlvUReykZuYyVzoIOfX8oMerkqY5f6bGtHlqHX\n7e1tfP78OeVlaVNpGWPl+Uoncg6IxNDYJHNzKJKb3yUQSAc/d0ypfXdxXFyvUr/wunXlsP+ox7w8\n5fkdHx8nIBgRKdqop4/I5speiiUmwygASCD3+PiY9gK+vLxMupW2l4RURKSnkvA+FS4eDofJ6Vit\nVpVdLxz4RVSfAqYxqbrl5GCeLJJT4GogGlN5aQJPRP/bPEmGzNSJvorQjQoNpRgvhpfW6y9JmAJm\n3MsnItLvd3d38fj4GJPJJD59+hSfPn2K6+vrtH+RmEHl/3U6nZQ3SFo5IiqPH9OCFw2qXK4g70ch\nbT68mvdSmmzMB1Q9c6F1AmSvg/qEVPq+RN5cxBPjLNmVbcl5qA4G60LB3LLFF4bQe9X3UoiUdcmB\no1LdOd/oWbo37aFqL6cUnnY20EGo1z0XTta5/C1njHL3rt+9vofAynyNlNgvb5/vVZeXXMvB2jYA\nQSmN5V2u+dI5zXOm02l8+vQpOeinp6cJTAgIav7KHpBs4BYjhwQEc3MhB/IiXt5fJSC5S512+X8b\n4CQz5uSEMITy+fQsd/WPSKb5fJ4WeZDJa7W+5Oh5faQrOTa0WFTYIFc/4hwRXRwvrVYr5TJKtzEd\nSxiImEUsNdPW6ubPwT1r2BkjsiukReWN6ZgcEIyoGnWGNSOqmzKSgWE+FkGiMyUcMNrgebPZVPLl\n9Jiijx8/RrvdjslkEp8/f45Pnz7Fb7/9Fre3tzGfz1O9mIzKJ1/QuxSI0CNulKjKPYloGDlplNNC\nejrHPkk06HRffC6vtws9GNWB7+oPeVj7FI4dBxnuYbkhIQNRGm85MJgLA+cAoLPizgjyOhy7zsLl\n6uP1draNrKCUUK4NckDfgaA7Fe4gSLwsb0+J91Wujyg5sPhaMPF7Su6+Kexj/pY7zsFgnRF29sXL\n8rp9KwDtDBPr863K/Rb1Ubsvl8v49OlT2hFC24BsNpvEBGm+aP5y7PO/Q2CjS+1cNw8l32r+lMaU\nj8dcPbbV0YU2W/UnWNMj5UgqaWeP6XRa2dJL53LbNIVehRdarVay3Xo+sGyB7CztLfU7d01RCLrb\n7VbyGBmZk77VfdJGcDUxbVpODo4RZKcJWCg+r0YXgyZalIbUlSEHFoEWmQ5Sx8ynY8fqGIJHDSax\nfrofeRudTiftD6hjx+Nx3N7ext3dXdrPiB4kDavCzaKBuYm0gGC73U51UJs6cFB7+BYmZGIinudt\nCahrsIlO5zM1peToiVARStQXTK7ep2isuWLLgT7/r/R7DgTq5aHg3OrgHBh3ZaEx6Uxdrr91nIxQ\niQFgG+RYpVKb5EAqQ9EONHLAzP9T+QJ9uXM5R1wcfDoIdKB86OKArg4o8/jcfzlxQF73/yEK+5vv\nEfWAM9eOdSTC9fV1LBaLyubuBH2aj7QxdN4VPtw3EJTk2iA3P73dckDS2/n3HjN1QND70XWydIYI\nJT2+dTgcprrTvi8Wi5jNZpVUKpXPBZlavBERaUeP2WyW9vijnlQKmW+5Rp0unCPGWSuDhU0IPmWn\nCQTX63U25agkBwUE2QmeZB/xtCJHwCiiygaqHE3AiOdhYS5qYLhZ53HxA8ugUdWAEuATMNU9KNzb\n7/fTKuHJZBLj8Th9JgCk0WN7CHwJgDGEq40k6cHwmbl+v6y72p5sH0ELJ5MYQw08hUfYHvRIWH8X\nhaWlTPcpJaDqoMdZLI2FEjOn/zl+fdNosoK5fEACFvYhr89FPKqjpzQQBOpecuHXHBDze+F1HFwx\nF9GBK0FdDsA5KMyxXyUgqLI1V/0+cr+XAOT3lG2sjI/BunL4/i0k5yxsO/6QJFf/Xe7H57D3g6I+\nshdyfrXVl5w9jj3at7r0ikOQki7IyUudDR1Xcmb0G+dqyYEspXz4tfyz9Bd11f39fcxmszg6OkqL\nMbW37tHRUSJiBOi4V5/KEks8nU6TXdZ+f9PpNOUZyklgfj3xjDCHxpL2NJzP5zEajVL7ub6V7Y+I\nVH9dR/sjCkTWpSXsPTRcQu/cSkNAhGygs3oRVYZC5el3GlVNZD6omR1D5o2TV9dTfQhKCay0R5G8\nDC0M+fz5c9zd3cVsNqt4i8w9yLGTAptE9dzwUp4mlZjOVRvrfiMisam6Bw1grspWGRKG1UtKzQ0c\nDZXqIJr9ELfwYBvmxmUJKLrXSaDmTw3xJ4bkQGBuoYeEIJN9EVF9yo7GllaaEbQTbHrZDmKZN+ie\nsDscnlYhh8GVeI5B8DalePg6IpIS1DyqWxzyUnDzPSRXHwfFPO73Alx116yTbUC19F/ud+qJb3Gf\nzhDWHafrlupFxn0ymcRoNEr6Uvo+4unhCGRrmELD0OC+pQT0SiDwW8ydnIOna+auv+1YHl93PdfN\nKleh39vb2xiNRgnoyc6enJxUyB4tHtE2QcQAq9UqZrNZrFar6Ha70ev1ku2V/lUbEqjpXVE2EjwK\nS89ms0oYmKliYv14vxFRGZe6l21RuL0CQRohGS7Pq2LHqVM4ucSK1BkbXYtGkqDHJ6/KIFgh6Nxs\nNpUcPm6nwtVI8iCm02n89ttv8fHjxxiPx5XnB3J3cl2Hk4Y5kQIW7XY7eZ/OeuhemDPoSaN6vA7D\n4AKV6odcqJpt50BQn50d5CQUCOTml/sSgjYHgG4YNEbIijFM4ADQmUBnBD29gGCKjHQOBPr8UDkS\ngjuNE53j5RIU6p5zC1x4DYY46BxwpSTnmdefeVI+RtzxkjCNgQAzB2ZLTuAhgsGIfE5UzqFyFmYb\nCHsp4Ni1jXJ128YgvVRe0085wPwStkrv3tZ0/m9ubuKXX35JT3dge2leKywsW0UWyeu3D9kGjKn3\nX1uen79tvOocjqU60LfLGM1dl+FhAbDb29sYDAbR7XZjOBzG9fV1RETKu5c+1nYzEfEMB6ivBf5O\nT0/TedKpit6pLJIxKnu1WiUWT7+JaRQ2EeaQHnagqXbiugLanpIczPYxRO6ex6ZG8OfYkq2gUaBh\n13UYrhIz5Y9U8/3wVA4NuzpT4IwraInuB4NB2pV8Op3G9fV13NzcJISvvYl4784ysV7L5TILIgTo\ndH++slf3KjZIdaQx1zUFLnN5X2pXPguaXreHtx1cOUXtDM73ltwKWQd1NAhyCvROQ5EDgqVFImR1\nmYrA8c09/yQch+wfAic3hrl74jUZKtHxHOu5hSxeZ764v6Hq4+FkF857Zx4d4ObCRLwey5SeIIjU\nfNm3E/JacQOY+/49gMYuhtidRGceSyzgtwKBpXYg4M05sn592aNOpxM3NzfRarXSJsAEL2SQfH4o\njCx9vm9hexH4lkAYpc6x8u+7Ol+7jNldwKH3Z86xlz6SDb+7u4urq6vodDrpuby51DIRP9Ld3m4K\ny/percIJjOLoN6b/KG9wsVhEr9erPGAiIlLZxBybzaayxyHbhY4+U7JKcjBr2WXoaDRbrVYFBPrq\nWeZIlXK2IqoNI1AiUBXxfK89glFdg7mKAlOM7QuwKWdgOBzG6elptFqtFNol2+i5Vw4COcjIEq3X\n62dgsAQCW63qohLVQaFptru3nzNVVBo5NpD15b3oRSAU8XzF+PcWjS8HgwR0EflwiSsAD6Pmtopx\nEKh+5Wbi6jspE4JsjUHWJ5fHmlOIZODUlxHVldN6dyaQc4QsoOajP76O84zX9bHhANVzVHWM2opA\nkG1TYiQIAPWZ9/K3KruAsByrWCcO0l4qrz1vlzp+SyaXoCEHSHPXkU3qdDrpSVAyzhxTshPS9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urXtXKJx9UmKnCOg9z4bAeZ/CMLfakU4FmT9ONIJFAg/PL1XbemiVjJTaROOT6QY0JiUQyHHO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X1h7fB9PAfHJqx/zfOGTMCcqOfSA2/Qc3o958oBvGdZFoxJliuxcfT4D54xGI4MhACy2LhF\nO3iTTpZlQSax3mo2m/bixQsbDAY2Go1yc4n+4j4AQo4QKQBjJ4BnJKjnz3PiKJ9jE2qtVssZ6pBv\nzJusr/S0EJcvDuKiz0yeyzUlqMCoeNd8QLP8riRmXAWAqtS8JGxewBwSVYXigS1MJlvpCgZZuXl5\nKuirgi0W+CpI2frh35kBVaArMMZvmhSLB8pjEbHQ0AXIbYi9H5Our6/N7KmSim1iiC1uzB//Dy8G\n/mOlwt4rBlI6z7qTUo+kUK8YA2+sAeUN9t6hnfrIObVaYVwwb8AQwme00cvl4sfbcbvZUGHS7zwu\nqrDRf4wpH9ekXsxYfcegfr/vgjoPfMS8HkzKQywb1RPhEYN6lBe7zrs+Zrinyipqj3p9ABrwyDik\nycBgBvBnDzZSPNrtdo4neQMfgwVe52b5jYfaNx5z7ifLcI0GsbF9LEKuHQMRrA0OdzKwBbE80JAr\nA0HWl2YP84k5Yw89rmXnhgIrzlXc7x83Y+I+9nCyPvIMVDZ2kWPKnkIY9P1+30ajUWi7Gl14qWEL\nikWOUB765ckshHt5UwhA4P39vTUaDzudsdGFwbUaNZ5+idHJA0FehJh4ZgC9lz0i/JsuSp1U/Y2t\nQFW0jLw1Fs+gC21WYc7gAUKOPX5sSbFVgFAwFvB+v88BDXYNq9LE71iAKsjVK2hmOcXN98SYnBku\nZn2cAgg0M/v555/N7Ck4Vc+n11eeYyxoCLoi/mLrj4nDVBC27AnkvDq1wDEv4E0NB4BvOG2CPS0c\nvsF1ALJ8PZ8txoKHc0r5xV5MtqB5HXAfYoCC54CFGY8thCrvlOfzxnhsjw0Ge72e+3sMCHI4zeyp\n906jFTxmLGdiXsHUmvQMSs/bp+Vy2zxQ6Hl/PE8QlDR/hoxB2gPSYqBMAQh7vV7wGLJRopuu8F2N\nMX15POiNA//m6aBjEqclAPioN4nTMXjOFWiwN43/r9fzzzBmQ5kdNKgXY8rlon2cew4gyGUwvyDC\nyGuADV6WQVmWhXxJBpOoC4CQ+6nzWas9HhWjujNmKDAvqMzCeOAIuslkYnd3d+Gxf/X6Q65mv98P\n5xIDBMJphA04uhZPGggyxYCgkloBKYHiIXlPSWv4xbO+uX0q2LT+WJ/UMoCAZtCJycQuYbOHR1Ih\nPwCAAUyBhQYwafaoyL3z5bgdrGC5zfpZvT66k5b7ivqLPD3Hpp9++skV3hBgLLT4GhYErJQgbDzj\nhDdtINSPOdH7eF40v4m9Fd748n2wms0sCGf2iqmHTdcTytpsHh5t6HnKUbaCVgZ/3uaQmAHHn7Vv\n2l8FgVgDvGEKbeX1yVb8sQgH7XoASGVg7LOCQLxnWZbLIYqFh705iIE6TybH7tc2xl6ecaVyiNvM\nAFmjLHyQtIJA7LLkvEJ9Gg8bQ+p9jn32DDIdDy/dCDL6WMSnUkAmYd2rEcFA0CzvXFBAx+DP7OFo\nON2UwWOo/Gj2yC/cNg5bY9wZLEJ+mllIx+J2QhZi3vnIG0S5NMKy2+3s4uIitAFPSPFyF/W74gRd\n25BbLJ/w2cwCCJxOp+EIM6Q1AATiaWU4CxFtRh1KMXzC9KsDgnqtKhMzyzEpD74H+Bj0per3rFp8\nVxCq7yrkOaTLXkAwNYAfFg6+Q5hx+JcFIC843WXKXjooRM65UItM+8dgh8OJbOFw37xyvM/Hon/8\n4x8uEETb0Q9+/BAvYix0LEAeOx5nWJnwXvCucwAsT2ioYGIBzYAQ13B7UD4saBVQKF8VLwQrcmCQ\nD6MKja1fBsLsXeFQtvKCZ3Sxp1EBi7bTA4HsOeA8G8wNK6AyMuZLEhsQPI+YP+UHtvj1v1j4KQak\n9P/YdR6/KMXKiIFBbavXdi5XozPKx7wWsc7wwnEo8ALGvNSaA8ygj40wBYuejEPb8c7tZ948JvFZ\nrwBaZo+PdzPzHQUqe3jN8txAjyGEifIAujabjU2n0wDa2Cg1e5QN/LQiboM6efA7H2mDMjkiwmCQ\nPYN8MDPXn2WZjUaj0BY4Qxj4qVHJTiaVMWoYqDENPY8zbG9vb208HttisQhtAAgcDofW6/WCbgKw\nLTKuUnRUIOgNVtE1+C1mxepvnudPwyQeCMVkl7FaY8pFhZ7nJsZ3gDLsDuJcPH5UHB9szB4euJjh\npu90Oq53BkwHRa678ngDCAtHLDb+nUEOhz1UecRA9DHpw4cPuTljRcZWGsaJQSBvnjB7BNa8yxFj\nBoGLxHcIp+VymQu9MqhURZfykCmw43mCpaz91DVTtD7Um65pE54C1fAaj6ECPwWaMWHKZeAangv1\nWDLvsnJIhUi+BkG5mT1NL+GxwthjvcZAFvcxZVDgnRVpKjISqw9UBCS5fk+OekCK+648ApmjPIz5\nZoNAj51hOcggUE9CYE8Sp04wEPQiKd44YzzUa31M4rMnAU4ZaGEcNfVDd9+ybNT1hGsA5OAlxLXz\n+TyXCmOW30SHdc3GHI+38i3uR5nsuOAcUN4IiLlH7h2HVAHYa7VayKmEccHpLQwgea699cQ8gH6x\nzkcoGC+EgzknECCw3+/nHAgaAVTjhvsVo6MCQV08z7lfBYlnFZd5aZlqqXvCFcSL3sxyDKnWFIMr\nMBSH4ObzeTgmBpYIPw+SzxFEblqWZeHh2awgO51OTuAxk2BRoN1s6bLnj4EhFgsYi+vjDS/4rMD3\nlADhcrl88hvnakIg8lNT2OOAfjPwwtixAOJQEMAJNt0okMI17EXQNjG/MkBkxcohDAYIXL7ncdG1\nwJ8VsPH1KQACYi847mPw5u1OZlLw4oFHtrJZGaihk9o99zVIATMLbJYLKZDFY6+eGzXglAd4LM3s\niTdWlRjIk7cxoxu/6f2xsrRf6gn0+E7XA+YfaTMq7zxZyN49ldMKAnmeUmPB/eH2Yz0fk/hoJcgV\nlj9or5kFOaKbyjgawE4Cs7xXu15/eDIWGz7wCLIjwyzvMWPjV2WjpnapUarGMMscls/enHLOHh7m\nMBqNQjgdaT0YJ0TTUD63T1MC1HkAEAqnAPIBAQLh4ex0OjYYDEI4GJ5Alm/eo0bxH89rKi3hZELD\nZvZk0rz/Yt9BnsfD+8zvXCYGmBVXzIJVqxrXMePjfhU0+B+LBtYBwsBw+Zo9PlsQIBEM1Gg0gmsb\n9WdZFpgYOWkon8McOKW83W7bfD7PKVKMAdrnHRPDygkMD3c/GF6BIH8+Nhhkb56ZBWDbbDbDQd4I\nuQNUw5rmXWxYiB6gAy+YPY7jfr8PnjrPu6AgMHYWHupHP1hBqTWsApEVNod6sR543tRS1/CwghXU\ngb7E1p2WB0EJ3mFAhzUC8ow1KG++zzt6h5+teizCgbApD6rKLZU1CtIUCCoPKFgEsQJLeYXNfCCo\n5RQBV1xbdA97Qr0QZOwes/wpCM1m03a7XVDiCgA4HFpmDHGt13/uEz6fmkfQA9VqyENfKO+wHOH1\nBuAFMMX8BY8W/m+1WjYcDp/oFHgP2VOJstkjrEYMf2bPP8thtIGjYmxEbTab4JHDLnQ80AHjwRv8\nmBe1Lh5LfTQn+JMNU+QDjsdjG4/HwRNYq9WCFxCHXSP9C7INKWCxo87UG3iyQJAXFCbHA4F6jwcs\nzPwk5xjTxMpmJlHlo+5pDDCsJM9SjHlL1BvKoGK5XD45nBhAcbFY2Hg8DhsMGBhCySGHACCGd6hy\nPTh/azqdBisIDI/wNLuxeYFzyJMT9BmgKBj2Ph+LcFaU2WMOCw4UxbZ8JJ7r85x5TDiXDmPNnlQG\nRWYWBKIHAFhxcM4dgyQGSwgPqHcIQN9TYmpJM+gCxbw1qkQUPOh3Bi6oO1auAg81nmJeTa5DwSPm\nRs/pPDYQnM1mySgB+mD21FjVecPY8n3qzYp5H0EesGLApXJQSduqc68AVl8xTyTeua+xstVI4nvN\nLABCHm8FxWxgMBjU63Tc+LMH3E8JCKKtusbZE7her202mwWPF4eFmTcgn3TMeA1nWZbLRTSzoF/m\n83mIaLEcgCxl3Rfz8Jn5jhbUrbmKkPUIu3K4f7lc2nQ6tdvb27Arl6NCapA3Go0wRuAPdoqwPgVA\nhn6fTqd2d3cXXpPJJJwT2Gg0wgMl8KSxwWAQeBjH3vCRXZrzynPNbY7RUYEgKyeQAi2mGMDC9Zh8\nthrUCvWIrRxuj96jQFCFl4I+zZ0o6hsWEiwXBh7srkeYGBYz745bLBZ2fn4emKjf7+cYmS04eAyR\n/8A77rrdbjiHq9/v5xJsMUZYZN5Zbar0TwH8Mb169crMHtvVaDTcMeD+sWufQyY4voKTkflcMs07\nUhc+CHzBoRYOvUAQeF4jbpOXBK9C08s703XA7fIUt4JCDuvyO9qqYEeJBToMFh47b83zuIFwPTyA\nLCh5p/uxCI83ZDCiIceY980DTzqHkD3MZzFvAVPM06b8kTKQuSxuK6eQaEiff/OAIG8EUPCvdfAa\n1TaoJzkGatU7qH1S484bMwWpp7JrGHKZvVi6dvgpRhrB4vlhIMjADH3c7Xa5Z+IivFqr1XL/efJE\nD7pmMMqyj2WDOoGY5zgkzrn27CWs1R7y/qbTqV1fXwcPHNo+HA5zmwdRPutGdghwH1hvj8fjJwAQ\neZTQycPh0C4uLgIQ7PV6YW4wL2rksezHXHG6hB5ZxnQSHkH2BnqATQVPysPmUQwEKoj0ylABy6RC\nhq/jcJbnYWEh4nl1OCkVC0jzOpArCGWH8DGsLSi/LMuCm5uVNcrPsiycZfT/qXvT3jiSJUvbkqQk\nrlJVNW4PuoEeYP7/7xrMAH2rJFLcRTLfD3qf4BMnzYOse6crsh1I5BaLL+Zmx46ZewCCzs7Opj24\nRsCiY3Pe0+/7UP7jP/5jNi5sE+DHquVj2WijQd3j4+OUi+l9yfzux2MRFq4aywHfO0Bm42ZAk55z\nhvF9DUoCxE5OR/dNAwwD7M/IqVd2dqDHYJl6daxWd262yWxghoU78L1Gubm5mX3PvljSazkWI8CG\nPGR4L18c391jpI+zeFw64JT5UrnS28bW82wJmI4YQRgVy6L1XF6nc8iTlc375PnWgyOQOsp9Xauk\njWJuZs6Zdb3bX/VKSlTVDgA3m3d/f7/z1AzuDZmQC1hG7G6nN0w8cGzK9MvLy8TSeT9JR9Iod3d3\n9e3bt52c5cPDwzo/P5/AHtc9OHjdXNog1gwrEbjv37/X1dXVtDUM0byDg4Mp6nR2dlZfvnypX3/9\ntT5//lxnZ2f18ePHCbxyX+uKLs3EaTZ7HRp2ycmXE9WgceQ9jwzZn7n3ZrPZMRQdoEPwEE7T5ghk\npzCYUG6nhT7va2V2fn4+e1IDiabeeJrcPxaYEA5DoJh0THwE15Q8oPDs7Gyiq22UM7/DEzNZ030t\nCQS9EXEXCk4PFFDh1cAYXANBg3RWqGVib9Wr7HEPlwSIHG/glIoRJdvNI4+V51LHEqSx7I4dXdds\noEGgX7TfDKjljH7KUF0335MN8yrRZBTWLCzSqupDXF2bXJL5GAGbHJMsee+l+yyVLo/MejLZPwwl\nc+fDhw/1/PzcsoJLoDeBF0CQVwJMX7sLfadzYZDXXSPrkP8l0/VeYP1Xlc7uOcRp3WR9CPmQttZ2\nBFuGrXMOIQDTzJmdErOWJhwAVnZyAJS+n4v7HN1CGhRhXTuNfL68vNyJmpiUoU5E2WxPbV+JTJDf\nDwBkk/PtdjsjG1gckhG9bLdJorRRHSHzltztBSM4+o9B7di2Llz0HgWfgLP7fQlM5n8WEM5dAoBZ\n0nv0Mb4ejNXFxcUOu2HGwwLt/Kjb29spd5DJR1ucJ7HZbCYKGVCUDE0+gi2ZQocR9rX827/9W1W9\nKkEUXbcymOPcD0dHR7M221FhXLwB993dXZ2cnMzClMmkMNGTIUtjZK/Xc8BKESDoc5Ip6kKvVbup\nEdyrYy87UOgyYgHdn+4Dr/LMVet5fAKm7PsEgWszgZTHx8cdFq9qvDuB+ziPpSyxXQlwLNMpO6P7\nc26WlNkcH2T36elpAoJOc+hAWzJBqR8THPJ9xIBiSEfXSrIhjzObaaPb6W7Pzy5asrYTAlgxOLKd\nqaqJHKh63QGDxXObzWaaUw430j953c3mNbJB24lSkcO+2WymrdI4DpasA9jWtSZWUj/x2b8/Pz9P\njxwkxIoNZeGG363juC+Eikkf7Knr4/0LSd/yvoXYdc5noeLZ2dm0WMWLQ/x4RAPBfKcPuz4blb1g\nBEeeKJ3Vecz/jCc7AoGj6y1d00K4pLDSuPLZCsOrttKTPzg4mEK35+fnMzBR9boSEQbv+fl5YgNh\nCa+vr+vs7GxaAWuF4H7IdtjIsqLJShRj4DGyAnhvX/6V5V//9V9n7TX48/YFVfM8PU8q+qbzuGAG\nzQj6WacAMTs1XTg0x8TGvmouu2m4MrfRYMv5ciiWbh55jEclHZ78b+TsWG6c49KFcw0Cu/BwAskR\nE7jdzp+nvEax9+7S9R3yVbUL3NwXHJNzszOK6FVfE6aH3/ye4V6X7XY7OUUudg48ziNZcbsSWC3p\n9w7Idf2Kns3fPM99LRvWXKDV6fbuu+0Y7wCRtUqyw17UZiDhPQY3m81sj1ra6JxHAyDPVcuww8VH\nR0cTuISZo78AijBu7l8KdTP4Sgehc4rQER8/fpzlcbMor+p1f7+bm5v6+9//vuM8//rrr3V+fj4L\naft+HJ97F3q7GYNb7/DBjhUATeqGDfdz23NeAxptl97rhOzNhtKjSibb0oW0ls7vPONOueb/nbe9\ndP3O2HWeor1Ov9JztFEjbILQAAgBetTXOQ94PwcHB7NEVTwNP2PS90WgunAGBqMLk6R37kk7Gtc1\nyy+//DLVA4XjbVFsmAwmqvpQbbJ1VTVjt05OTibFkMn7KA2DGD538sp7eqqMnWUkV3NnUnHm3y2V\n0fygmAlxXphZVrM+dniokxlTA0GDn7x/gqNkhZIRW9sRMRDo2LdOrwHgOr1nGUijOHrvnGHPWdfj\nrbkMeCOcaB1NnWhDXivHjYJsdP1iuUlwayd0ZCty7uZvnT4fyU3aJ4+Z27Ddvu5h9/nz58X+/K8s\n9DEy4LwzgxoDXsuMAR72g/5GX6Xzhd06ODiYgTw7ROyZi37yNQ0C/c41ExAii5nPaV0DEKSeRHl8\n34eHhymf1+c+PDxMYJB8cvdXkgCObK6x5BMAACAASURBVLifqSfRKIAx/9EXRJRIC7NOzHC+nfYE\nwXsPBG3oOo+T9zR+nWEaXaf7b2RMOiDYffZvHbBzuDjvNzrHgvj4+Dh7NJGFHQH69OnT9Lvpbq5h\nQUaI7+/vJw8vc2XsofiB1lU1Aw2uC54IE9y5FV0/529rlPPz86kemdxbNQfiyZ6kjNjAWgGh3Mzy\n5SpO58Hl7vAZNvXnLgRH4jU5MP8IEOzktQMd3TgmEHQCuJ9okKygDaivm0bIrEIaW+rfOXgYCz6v\nvXLz9PT0Xfos+512GBjbyHVgiXcb0DQQ3MOlAzhVywvv3JbOsUjd2DmTHcPWOcrvAfSdvh3977Z1\ndsIymgBpZHDdJ8y7x8fH+vd///fFev9XFkcZnp+fJ7aqqqYVvScnJ7Owb1Xt6DHPT7c9Ix1mnw3s\nPK+32+3MhnAsfQdQ5Tj6HH2SOsj6xyxn6j9e/J47XxBhu7m5mepCCJuxvLi4mHLFDeByaxeDwHSW\nse/UM+2Dn5dN5M82PnMGPSbWB0vzZS9Cw++Z0J1y/DNg4j0swEgR+xqdMsnOToXHuSmwWS8bNXtO\nptw98Aw+AID7mb0iRMz9kgpPRcb1eJyOQYSNEHUDLJL7Y6OVffSPjNt/VTk+Pp4+++HmVa8KqwMp\nafAoHk8bOK4HEE8G0AnKI3Bmlsu5fRyTjgEgkEUvBvN5jQ4ILgGTpbmR3nguDhiBQOSe1Z7IIudn\nX2ehPlbyJKU7ubrqdZ+vNcvZ2dkOY7XU9wmu0oF2WkaCxyWHOIGV/zcI9Fy2nHeA0sbHpQNlXXQh\nPycjt+RAd/KXzBNtSuaEd4MT9CthON+vYwo9XoxZzlvvGrBGMThzepEZZ+eguV22cwk4qna3gfN4\nGFxZ5skXtUxUveatc46Lt3DJMfA9rXdMsMD+Ua+Xl9cV5n5owKdPn+rbt29TruDDw0NdXl5OjL7H\n1MSKIxyObGBLTDxYNrHL1mXeDQQgmESAdaWvmTpjidnfCyD4VlkyQC4jBZGKanR9PlsA89zOk04l\nZ2G0srHHk96jr2Whcahys9nsMEnU0d6P9zXiXM4xI5JKvqpmxje9JOrI/expdUxqjl3XX2sVJvNm\ns5kmZlXN+n/k4Y/aRN8wDplraKPA+CVz1i2M6FhFywhKz0CQPFCDfQMmX79LsE9AQnGb6T8r8Q4I\npkG2kiLp3OdhHDJknfMnGZvO27fTVLUfjCBOVtfnHSBMh8DFSn4E4JOZs4GnpE7z79nnCYYcgrOu\nSF2bwLRjAjvw112r0/EdCMzIBPIAMzW6Xsf0de13sRzmPDbLs2ZhDDyvDIroIx6DSYTHDlWXMtTp\nd+dWVr3uDNBFPHAEcxwsxwbklByLHB/rnQ64o4urXtlE9gskuvL9+/fZxtk8hYQ59/DwMD3Ni3sl\nq2d77GJnzoAwWUUWmzi1CADbPT/aY2nbNCp7AwRzgK38/d79Npqonfe2VDpQZsEeecHd/x7wBEl5\nbipwI308Cg+ujSiTKylx53LkpLJR6eoGEHx5eZlR3lbSZnfcboOhzih147lGAQwcHLzu+5TgfzT+\nlE75dQaJc1Cqzv1DzjGiHTDowrmMm71fh4WtHJbkLQFhBwSpXxaPv2XjLVbH94W9g1E2i5cgDtnK\nOWaZzlzLjrld8oz/iuIth7qxTj3g8ama6xnSMHyur0dJ/cL85Hqjkro1QZsdGQP/NM6ug6890tku\nI72R53Vh5jSuyfC7JAjs+qYDoFyP6ydjn4BwzUKUxyAQAJFgh5Bn6gkDDkC186G5jlOMttvtjO2i\n70m94Vo5t21T3N8p2zl2actHcsy10MnOZUaHHh8fT9uxMYb39/dTnXmggHeasAPv/EDsaxcl6YAg\ni+bYbsb60TKf89D91sl6lr0FgjmY/u5G+n/eR15dTtxR6cBZCmMqijzGA2ul6zbYSGV4x2wPYVzn\nFyDEGALug1AkJWyGysbY3qHrYsPE5Nhut1Oo0StSXS/qYaFOwLQ2CKx6ZUXclwmO0nBU9SsvE/yM\nHJBOPqwAkROzQiiTDHkwdmYDYQTNBCb70zk7Od4jI8l753V3n9Owuw6AWxuhNKBvOWIdEMywd8pf\nBzT+6mIjl0DQczfnKO32Z+sRA8KRnku9ST06B8f1HYFALwTKvTc9H1Kfd/ehLvye86Abyw5EZns4\nD6e5u95Ir3f169jCkeyNHKu1imUGh55tYapew53OJXcEyawuY0X6EZsj2xam44VsolfTuan6ObYZ\nzvc1bL/yPtZTdp5GDDvOo/cURJZPTk5qs9lMj8TjCSAGseTsEd2xffUCuC6Ck7nTmcKTC+hSP3qX\ni4y80I+UvQaCVmgjYEfJhqSiW1IKbyn+9DjTe04vrvN2838Lu+uZ4NbCnPdPQLLZ/AxjkgNG3oDr\n1NUHwGAPxJMp2cGDg4NZiI29BKtqJnzOp0CpcK/R+HX9sEZxPTrmzVsLpMJholEyFy5DUtynM7C+\nXiq4lD0DR85HoRgAOhw8urfbb/bWMss9U9FybTPFHQDM9rtdDglzjTSermfO72QMOlneJwPsYkbE\n+sl60E6BHQ9/hmGmnQcHP/PZ+J1jOye2G6+qXlY6EJgLgfzKp4WM5kLqum4cU/d2eiRflHRsOp06\nkveudE4IdfB1OyfSY7BmcZg0N3QmkuT+ImyKjuJ8z1UzVcihZdiOCbJjZ9BPHTFAtQ3NHO4OyDsC\nwxywA2CiI3P4CLtut9sp595RI36DmKFtdi64B78T1vXegVzPEZx03mm365l60XPPdseOFLr9PTK3\nKhBEaVmZ29vwJPUE5PeOaemAoM+v2s1V6ZQIvzvcQjHTlgrW97LC6BRXXq8zVv4NJu7jx48zEMnC\nkq4/zDw5dGNvzp5ssojewBIgSp90xtnAwGO0b4UJjJKyYrKH6BBl1avCsYxliCyZKn+2geB6Bs9m\ngLKP03gDxm2M07NMI0sx6zKSvRzT/NwBwTR2NiyZ+2rZgQUwKB3Npw5sJoOY3/fFAamqHXBTNY8a\nuA/clgS4o9BjguEEnZalLo8u+ysZQWQsmcAODGZqANd0m7L+HRhcYol5p44w6CYZRtdzJIYyIiRS\np6cT0qU0uH7Jjq1RCAvbliBvaTtTDu20Ak5gw3A8kC1ATQJiR6aen58nIAQIrPq5kI8+pf8S0CQw\n8r25V84Xn5ehVxhNnn+c211ldIF75LzLBR7eRBpbA4jz40yd050ym/PXQNLnVM2dzHTeF+Xin5Cp\nf7ok24E3MCo56VMZJABMcFb1dniy+y+ZhTTeHqAO/Pnavh6/vQcsIUA8CJv9ho6OjqbnAacn2uVt\n2WuiTZnUajCYwC+ZL/qEicx7Kg6PwT6U29vbqprLA0DQ3m6CCY+7DWMa1QSBntidJ2tw2OWOUZKN\nSSbQoW6Dya4dHUvROQ8dEFzKx/K9+WyFmVvppNxmPQy4u3tm+yjZZo/DmsUGrgO7lGyTGdUODHYg\nJ3VW57CNgODSuZbBfGXiumWSe7h+bpdzzVJG0qHpwFvnmHdAzf3na+U1l67rMRkBb/cbc3fN0jlT\nnnue99gIM85VNZEP+ZSL4+Pj2TxNguHl5WV2jPPq6EMDr842pl7McevGJ+tgPeRN9dk30Mxgptmg\na+wMcB+2erm+vq6rq6u6urqaonYJAtk82v04cuQ93zIVyMRD9kuGnJfs794AQXsgbxV7MPl7sgcu\n7wGBo9/TIBt8ut6dsrCh8jFV8/D46MU5TJ7T09O6uLiok5OTOjk5qcvLy7q5uZk9TLtqd3d8K+VU\nqEwQmDD614+i63IZWMhipsDMGB6nyz4Awqurq9ZrzHGu6nPTkmFNliCN1ojN6Jybjr2wx44yyLAC\n4RNYcxR5VZ/ykPKeRmsEAkcMj+/jsEyndDsjn3OhY5fT6eoAld+z7V27/+qSDqzb1ekPn5fgugOB\nHSjpQLXHtwM3HtfUd9YrmXKSdU+dm8bYG4jnM1+71aUJ5pfG0/JowJlzdDQ3sz0GQ51z53mac8n9\nuGahDgmwHdrlf9rEWNhuGUBCGiAHXA+bwj0yp42FJAAlAKSZLe7X1TnbNZo/1Nf6CLBJ/at+Mp08\nC/jl5WUn9cY5fQbILy+vq4m/fftWv//+e3379q2ur6+n/EEW8lXVbHGcgXPq8Zxr1AMWtWoui7SP\nYmyyRLKtDgTfMzE68JbnjTzBvEZ6un+2LHmOacTSKFn5jM4ZeaRVr5Pt9PS0Pn36VJ8/f67z8/M6\nOzurb9++1eXl5QQmON6AJbcS4RgDVQsTQALgAYWOADoEk8YFgWZ/wzR6a5dv374N2YY0mPneAZKq\neTiiYzAoS4bext1GsOo1TyfHdRSCM7M7MnxW5K5f1svHGrB1xyQ4McuTytigxX3TAQ1k8fn5eTYO\nWTpjMAIRaxSPY6czHMrpHMKquawkM+j+TF2T83WJzU0w6funoTIrYUY9wSFzxFEIP1LQL44bsT+U\nBHEJ+tNRyZLndv3uvhyBQPdLpuEAFtZ2QtDpnn+O4kA4MI5d3yY5YcbKuX0eczswCcq7710/paPe\nOdJp+z0e2+3rgirq7vNg9R4fHycwSF1OT0/r5OSkTk9PJ/tr2Xh+ft182mDw5uZmYkLpG0fVmB+0\nm2u6bmYRefxcLuBB1zoXkeukM9eV1YFg1etWHiOA9tbkGTUwFUPn+XXX6EDae++fAIFXp6j+DBD0\n+YeHh3V2dlaHh4eTUALyOA7vLY0pRsgeWsewMFmSaWAV03a7nTaqdlusGBDeNNr7AAb/+OOP2aN/\nDG4ShACEadOoDUzKtwDOiFHLkAXXMctAeQvsWBH6HB9nw4TRTqDkfsm5iQLL+o9CwNmutwB4hh87\nljJLN3ez3faY1yiZT9nJRjJ4frc+ScDWOR95naU+qtpd2NNdswNjzpMa9T1ykhuq+1FcZgNHYDQN\nfoK+EVPX9UfXptHc6trSgY6qebRoBGL/6oLs5FM1mBO2D9ZfnJt95yhQ6jQ7q+mUdnsJcl/fqwP1\n/J9RGOTKxzvM7WiK9ep2u52e4sU1WQCDU8I1iXKRp29768UnlmXuTUoXYPLk5GSm26rmziGMKTmE\nRIDICUyHKu3FZrOZGMeRvqTsBRCsqgmx25PojFh+7koqrKWXr5fXtXLOvLoEfKnEO2+De9or8bkw\nHt39LWwIJKFhgDQ0PMd5IjAB7Llvt9vJ0CJUmccy8l5yL7xsD0bcT+3YJyD497//fZqwVkTJoObq\nLJQAE9ChIoPATC5Oir/LJ0ywZJrfjIJfz8/PO9ehLM2DNK75u+cmxzmfKtmojumzosrf3wKBCb5z\n8UGGMykJeGws3M41S+d8jfQIBiH1jY0Q7aJYr70F+Fw6gJnvVbt5WhmK7gBkyokNJ0Aw5d5sOHoz\nx9Vt8X1Tzmhf6uyUo9RTb9mPtAeM75JNWbPYWUVnG0BwTLbT8y51pDcxdo61GbAMV1bNnx5iG+IV\nwlWvOYnWFQcHB5Pu43ppw3JVre1agv6np6c6PT2dUplcX+piYGySwGwedfv48WOdnZ1NsnZ2dja9\nLi4upucUZzjdc54tbFhI4oUhgN4ujaJqvjr8LRBYtSfbx1BsEKrmkyYVende57V6wvKbjSWlU5Sp\nLNI7tXG3ke9YHwYvGTQMPOe7nlYugDznAW42m4miJtn19vZ25sW4DUww19/ABM+t6tUzZHUTuQkW\nWoMX51sYDOaTSfYBBFZVff36dVrebwXTMVFeDZk5edkXNnZ53Y6dNTBHQaen3DET7nvOT4ZjdJ5l\n2GOWQA9AOGL7OgDYLQJJozxieTyfctGBZc/KP0sHqhIIrQ0EUfhW1l29DRhHbaLwOdvbHduBvJHu\nTDCHTGDEyUfO8e9kIl82XrmReCcjZja6fhgBzk5nZx9nX9medKyo39MBNljKe6xdcncIgwrqByjM\nOdblqXm7KmTj/v5+ui5kQMcqp0Px8vIyy0lPAJZsV9V8KzezjtzXc825rNa9AD+eEMIxHH91dTVt\nfO8t09gzk7aRvw8jfnJyMs2Pjx8/Trn95+fndXFxMQHPtMv0NUAwn9PepVR4Lpk0cD8u6b3VgWDV\nnN5P8MQ7A2pBGHm3b3lxnSdctatEk1FwB48mv72mVCjJfozulXVjgrHE/fr6eqKWmZSnp6d1fn5e\nnz9/rh8/fkzPJKQYjGUOBm2wAmBSJTVtVhGFmwrY/eW+yDFds1xeXs6W9XcKo0sUpg+c/9MBQXuP\neLlMbnvR9n7NriyxOgaByEbneHRGLY07JRWyDTRKJsN2ZnDeAoFLADTBT8fIWhF6niSYfA+QWrt4\nVaJDQgn6q8bKu9MdOBwJdrK/PC5Vu6vLfQ/qZR1NQQ7N6o0WfjhSYVlJx6ADW/SP6+X6eVyxIT4+\nmSfeiUIlKHBfdfOl63cDhwSDtg1r672Hh4ed7zwx4+DgYGJmrbsODw+nXG8YM0CRt6Chn+h/b5ky\nYpOr5o7RW0Cws7+2a9wPwJrgMCNVriu2LWXl6Oiobm5uputS56pXzPDhw4c6OTmZ2vLp06dJ5mkL\nLOHZ2Vmdnp7W2dlZnZyczOQPOcNW0O8cQxvJ1+c97Vine5dSYlYHgp78BiAGeqnA3WkuViA5if37\nqFCHBIAGMp2i7CZ91jk9a7cVZevrejKgvHnUjFeMOuR7fHxcZ2dn9fj4OAN2bn8CNws0Qm5AnM+v\n9WQxg5Thvuyjjr1Ys1xfX8+AoFnZDEvSB58+far7+/udbVo65ixZCBSQGcXcmsDy0TkhCeZ5XwKA\nOZdG8u+624BjKLzfVhr5ZHk65i9ZeNcrHSj311v70fE+ApT5eR+M8fX19YxVtjzZONn5TRkZ6aoO\nCNsZ8DikA7OkHzu2Lfdhu7+/n8nK/f39DmuRK4FdUlek/KaOzVSBrOfR0dFOuDn14VskQdV85aX7\n133s+mS/W+6W7M9fURzCfHp6msaIQmQJAGPw1/Vf57SSP3dwcDBde+TouI/Iv8bBBUB2oU/PB67p\nyJSBkYvHAZlyyPj5+blOTk7q8fGxLi4uZvvJ+to8Ys7j+/Hjx6r6GTbmeeLYBu796dOn2d6Bp6en\nO6QCQNC/mwCwQ+65ZfDO/Q4PD2e6eVRWBYJLApXsHIUO6ZR5em9WfBaI9Dj8uz3jzuPkPAtwFwro\njK+NPOfa6KfC9qRDmO7v72cGsqqmXAQEkARRg5NcwOA8QrfVdbdBNlOGJ51AcASEurK2MWbzUG+X\nY1bFnqMZMNi8HN+OAaPQj7mb/NPT7t5R3SvZHubAW2xRzqElcGiZwIhj3O/v76f+6ox7xwh24TTq\n5jmV4A+lmnLn8zqDnQa6k1mDwTXL5eXlLOcn99/LkGJ+7n4zw4UhsUxXzeUUQ5lJ5p1utDNqwwwA\n9MtgMGXFjoL1zgiwV803Ek4AmMxbtrNjsJOZzj71nHE/jPR9Fwq2HrecjuzWX1l8/wybd/JAe5Nd\nAlQw5x0pwuHdbF4XG7ovOifHuhbQCeBi9a5Dw2ljuzBzAnwDKY7xs5SpH0RArtLt9rZ8fn6egPJm\n87oRddWrg0o/c21HlUw2pMNhee5kOuV75MSMHC+X1Z81bHBESSCGoquas4HZUSMB6IyGz+Mz97GA\nck+jbCuMLhxstqhq/oxGA0Dqkm3Ca3MdXdfb29vZPZ6enurk5KSqXnMR8ATM8JAPl6HFnKCjfvJx\nlKOjn/shwUCSM+GxXRLAtUoHWBijkdwwGckB4TeD624lHLJhUOl7Z85hGpVksawYupBa1TjNoTN6\nVu4YdHbEz3cbd5iDzPfqQsFdHZLhQX6YHyR+d+33Z1+PPoQRMju0T0DQBqADhAkuOvBneelAUbbV\nQHAE3l06Z8H6JNlAh4czb8l6wNfNVACPYTrfncx0DnjV6zY2BoEsDOsY+85Jcj/yPYGoP1uP2yl2\nFGZtRpA6mqU6Ovr5yDfsQQdI+O3p6WlKbYDtQ7cBZrxbBPbB9+a6KRPWHU9PT3V7e1s3NzezPG7X\nKcG6P2cEDhl4eHioT58+1cPDw4yZQ+ekk+L78Ci6l5efC2FwjknRcm6hZbIjuSwviR+of9qmjl33\nM4jdftszh9NHZXUgWLUbssUYW3AQRAMqznFnZXyf61tozPr5/vwOGGSAbGyoC8rBSmsUvvJEcj0S\neFHvnKz+r6qmx+D4dwAFHgeewNPT0yTk5IJYmNwe7pt9Y8OBce1AhYFLgsF9LZ7o/p7AAyWRkyn7\nJ8NQZhitXDz5YXY5tguHprOzlIOXStKGLY1p1WtY2CE9A0CDQIAiSrXLEbQSc78mYPPvORctPwlm\n8rpuCzLYsQHWIWuWu7u7aUwM/rpw8YgdHIVJk9Gvmq9+NRDqgLtLNwecK9qlCJidYGyqXvdATSPr\nNiQITgfA+qXTuTakVa9g0AtXOiclbc+SwzIC3/R1OtnoAc+NNYvtxsHBz1DldvsaSuUYy46BIk/J\n8Dyr+rmo8OzsbBrDHz9+TPbIus/9hKxQH0cX0D3ey4/6IFPZrk6PmAF/eHiY2Epv43J2djbZzAwn\nW5f6OlU12wKJbdyc04dM25lA7rxAJHU19ffcfH5+njlZZtlta9LGcK23CJm9AYKj35O1q5oDFSP/\nEUOSxjHva+OQg5IKl3u6o318/t61Lb0sio3VqC+6PA3qfH5+PvNKLMAIoT0jVkJ17FDmEnIsytZ5\nFQmk/zsUJmvVrkdppZ+5avbkqnYfF2gDm5MzDVF6imaJfK+O5fC9koVcMmxpzDzGDvU5HMwrwyK0\nOe/pe2cdbOBH7I7ZGO5BeW/7MiRsYL22jHqekZMFUOrqnoBnxAIm49npu3SaM4oyKsl658IPyzXj\nAcgd6eLOIUpGcOSk+Xxfx/915MAoYpTOhZmbTj59P4MbgI2BoMN3/L9WYRwMXrfb7aR3zDJV1eSs\nWF84skT/weizndnj4+PEtjn33M6YbbiBuvUQ9TABZL1g2Uqw5esC2Ox83d7e1vn5eT0+Ptb5+fns\n4QeAOPoF9jCjbK6HZd/pLF4ZTJ0sP3k+oNiMOnqiA4LIrueSyQPGsQPQlL0AglV93t4I8CTqNwhM\nw5zX7O6bL8pms5nCVcne8D+Dl9dMVsLnjBgbmLQ8xm0wGOR3MwgnJyc7oY4EdN65P5lNK/NUZjZQ\nBicZ8jDI7fpgyeD8VSUToNOgMJm7RQvuVyfhYgjMiFJstDrGwfcxS2RljYynIjRDmIbOcoAMU0fL\nBooy874y6dpOQBpyrtPJRNX8kYdp+P3uvnI7+ex+dFhxlG+XBnztks8YNViw7HVMe0YjOhDYtTGd\n6dShlE5XWvYwSh5rA6nUJ2/puwRVCdgT1Gb98np5Xrarsy8JajguDetS31rezUJ14bx9KB1zbD0A\nAPPcsQ0BsDkKQT8AHAFKhF4T2ANMnJZgwF5VU8oRxWOAXnJbuK7xQkZOeDqWGTXOJ6TNog6voibq\n9vHjx2lXDmwA17QuI1fQOZOdjLkttN9Ab4mFN7DP1BKD4r0HgkugIMFQKrGq3T2GRt5tB6p83c5j\nrJrvFYixy4kPYBwBt6yXz1tSlAkOfT0m4uHh4cTemEmib5wTlKtA8QQtuFzbApp95jZ0Ycn0RqjL\nPpXT09PZGBoAdiG3DgRb2RuwVc2dhA6o2NtMJijDfFzXTkIHAhOEpZwlI0cbMuTnV+YSVdWk7K1g\nuXcCUgoKuwOCXZgtDW7OfQPbDkCbUV1iydYov/zyywxsO4EdA/zjx4+hbqjq9zA18BoV/9eBrO58\nO59mgx1F6QBrB6DSGUowYhnwtbLuI+e6c7S6unT6uXNeOgDDcRlWB0R5kRW5k5mOs1bJ8T84OJiY\nsGyT+62qpsUb6J+0h/xHSNgyTAg668J9uZdBGYAujzerSF0S6HfjRvsyTE8f+MkdJnwAcicnJ/X9\n+/e6ubmpm5ubKVWGa1iODGapNwDZ+pv2oTtfXl5mQM96OVl4A7zc89bEToeHsqwOBCkJojqQlKVj\n85IN6e5jJeCSg9hRrkyUDx8+zBRHAkfu44HoFM1SHREuf66qmVL2QhAek+PjUjGZ6fEkQoiqarYY\nAoHLnLX0blx3t5+J/R5h/CvLxcVFVfUpAEtg0G1H8bOVAOd9+PBh8ta4tnNT8nFBBn8jYz5iLyyr\no8R0zyM7LAkiu3wq2kCeD+8+PwHgKNyYxjmB4BIj6HePmdnari/dn/sif7/99tvOaluzr2YHMgSf\nIUwDpiV9uQSURtfp7stnjrPMus87prK7f9ZtBN5Gc2LUzm4O2zlwWK5qV/db5qzHMdToRS9EYZEV\nr2QCnRO3VklAi74CdKXOYr6To2abwUIQA5S7u7t6fn6eNpGmT6rmT0fyZ9ctx+/l5WWmF5LJHLHO\nvHcLepze4ggIOtuO5adPn+rs7Kw+f/5ct7e3MyB4c3NTt7e3E6hEp3VADyAISE5Q6qhArrLPhXhm\nQmmf93T0tmQmC5bImFWBYCr69BRHA805ndEZgZN8WRi5HtfMhHcDhRHjwbFmclIgujoasI5YHK6T\nNLzriwAZCDrp1h4qyolJjgB7J3Z7VlWvXpEfc0PfMGnTYCwloq9tlH/55Zcdg2EjMQKD9AsTl+18\nfJxpe+5B/3pLAvqyY/6SiTXYcp8m+FpiBLmO75HKyCCE8wkp2HCbQUhlMzKmKdPvBYJcw/dmfJJF\nXcoFHDljf3U5Pz+v4+PjnVC8QYOZ2nRMs3/Tie5KgrwEbQnSzcgaIHjcRoAvX5Y/63bryQ4I5n0S\noGaxLs1rjHIvKelQdex2F+5NMG8QyDgCGqnXPhTaA5irqtn4et/Y5+fnuru7q6enp+nxmpAH6RCn\nA1n1qisPDg6m/vD93CedHFhGDUT9XwcGfW3q8PHjx1m4F+fY43VwcDAtJmFfXuzo6elp3d7e1vX1\ndV1fX+9sQ5a6y/XgKV2EjInQbbfb2bZdCQS7HFxkutt1IJ/2Qqh5pBuq9oARHA2eFUhnFGxk3gMC\n+ezfu89J+zO4T09PExDsgCeDpwQYiwAAIABJREFUdHT0+oDtVFoZVh61IcOsnJvH8t1hCYSsqmZs\nIGDQQmaliXJ0Pzi0Rt4Ey+TpE7cljUcCl7UNsMuvv/7aslL2OhMU0j4rv2RVjo5eN7FNoJyPqzMI\npL/8OQ2VQ/yZpN8BQkrKYoZ0k4myUk2W1O3legkIff8EK772aEFHgpbOOaIuXR5gOmH0H5+dc7RG\nYQ7hXPEUggwJmZk1UE/DUNVvM0VxX6Th5f88LoF31ZzRHTHl+RrdZwQYu/SBzM3tStfmkTO3BATN\nwPJ7sn7d3onWtXYEPZ+qahZyXKNYhyE7RDS8n53DjQcHBxOQAySSu/fy8jJ7/Ch5gV70BuFC/wKa\nOsctx97MV8pt58i4JJECkD06OpoxgPznVBieFLLZbGZsL+CLdy+qo11V1cobT+nyxtOAQMCa9+G0\nXrb+om8dWRqRFp0+6MpeMIJ8HgHBNGQJoPKa9kbSAI3qkEDThpfS0dl5DqzgiJHoAF/HomR9bdwA\nGL6ujTrHIVgJBJ1fkP1uw2Jwk/lsHIPg8T37w4Bln8qvv/66o4isjKpqx0BVzUNjyTDh9TmHA4BM\nPyYjwdhxbQBgB2jcn124oGP0rPhTrq38DOitkDu2zQxm1Zyh8n24r4uV88hIpxOYBtXH2UC4rV29\nOpC8RslwKu0345IgsAvb53ibMbSO7HRhGtGO0evYmQSAIyO8BEp5TxlI4AYYsUO6VHyv1Gsdo2g5\ncH92ifkdAEwm18abuZf2y872GsX3x/Fl/m82m9lWRvxvBorzrDt8HjmQODnISNXrBtZV830MPbbY\nT+69xPB3zDHFOhkd2jkU1MNzDP0Oe/fjx48pCuZcaDuzHz9+nD3hA+CXjyflmsxLmDpHmOxMOKTr\n+5GzyB6GHX6iP60nRmUvgKCNQ8cEevJyfALC0XX9bqXoYxNwdYAtz18CqQhSBz7TKPFbAsFkXnjn\ndxtHG3gDQahn5wciqDasFpxkjPK+rtOImrcHxjFvCeJfXb58+bLDQFGofwdIAGNpZLxCLA10Z5AM\nAhl7X3ckN8nk2WtMVq9qviWR5aQL23Nfh9G8Cs2PQkuAmS8D/47RSyWe4cXsd87vWICcfxna68Lp\naxYcLjsX3qIjw8IjNrADgt3nrnQAL/Vux9KMQHvKWNbBcyyv6T0TOyYwGcdsU/c9dfqoP7p55RX0\nzr8284cuzdBdzp8O9K9ZrOcODw9nY0YfMSYGE5vNZie/7fDwcHJ4aSeAifw6zzl0luXt5eX1STUA\nTa6DTk17bx05YnhTZ1Bn3hkfjqmaP2SANsF+vry87DxWDtnkkXrofs6FtTs5OZn2GUSfGifQx9hv\nOxi21dbHHRBM+bZdeSs3dVUgmB5TZwQ8oTs2LEsqgDy/81w5tvPgfFxOGoS7q2fnwWQ70nhmO1J5\ndvXoDDyT0xteOnEZAcz+NtDx9jIAOV6U0R5GVbuh7K7P1ywXFxczZgulVDXPiTK7ZGXmEJ4n6uHh\n4c7YulgmRuDYk7qT/wztJkDwfWwEM3Rsp4N3lLiZARvmZGY60NHNM+qC8u7Yn2QDqZOBxMhJNDga\nMWj7wgjCqHRyknoQwGidw3kJtju9kNfPqEPn4PFfOjAGaak7qIPvnU4J17KTMdo7sQsbpg7ks393\nf/g9nW5+S71pIOidFvzO/5xv8EddaV8yrmsDQZdk0P077TAh8PHjx9l3+p7+PDw8nBhB2l71utqY\nfQHppxxD61A7epalql0SxnJve5ny4LFHD7n9nXPlsfO9/LJM0w/OqXdeONcyjkkbTn8ZCMKQcn0v\nZjk+Pp6uY5m2g/OW87sqEGQg7FWmN0qxELyndEAOoe2u0QFST9yRUXbHWzA5L88Zeexp/LI/PGmy\nDQkOPHm6UKINa4YpAYKAHfrMytJgz0Aw29FN2A5kr1GOj4+nehgY5bjY6OYWKxnydp9WzY1Rjnsa\nIRcrsJHMdayXjTzndiA/QWDH0Iwed+Y6Jeh0WQIWHQC0R58gciQzNugGfbn9TQLBtR0RHmrvcaEd\nKYcU60Tmv+dTgqE0ViPHs3N8OxlKFthMxeiePi/lqtvvMXOSuxSErH+2z23wKxnhJWfK8ylXdZrx\nMXNmhiifDuM+XDtHsJuTntcJvJxuRN1zLlmWYMU2m80Ou5qOcwKz7XY7y6G2XaqaO+h853zsXupe\n2pTjbvmsmu9pOLKXI6eH85EHh4KdVmUAnE6ddbsdEPry5eVl9pQSZMmpE+7zkYM0KnvBCCYA9H+U\nDF8l4KCkB+rfRh2SHiuTozN0o7CU789E6kBgp5BT2aYnnso2QWcqNgtbvmjPKBTH9QiBVL2uuCJX\nwu3mPH7n/gmSO8O2ZiHEYRbJSsIA0OELALLZt3Qa0mhWveYgVe3upA9tn0qV0hm9ZLhG4CG92A4E\nmvHrNrQeecQpl9mGLOlkjZiotxRXAuoEgLllh0Hz2iCw6ucelrkIwjmlOVZmKKr6FdQ26Fmyv0Zj\nlKB0ZHTTEfWYWS9nmDdXOJrxyzCz79s5UV27ujZ24M/5bR34S/nmlXrPgDXzqHNDeM5ZmxHsnCwK\nbXl+fp4eg2i9hc40c+qoUIJdAyrrHctz6jkvSvH/3JviceT+yFnaa9psnZl9wVh79e7d3d1sj0XL\nZs4L6kwo2M8wtkPD8d2KYOsz6y47ScmQ2wExg+o0o27cs6wOBDsw2DEAnbLpPOERwFgyAKlY7bUZ\nELrOo/yVvNdIWaXB7sCVjSXHI3SALq6HYDnB10qs6/cuJEedEUZAECvC0vDQB6awAYL2BPfBAGdJ\nAMH3BLG5ISyT1/KCosJrs1OTBiyZhgQBXekMnD/7Pt25I5mzkU62JpWOFVUa2hEQ5T1fHQhMoOPP\nvlaCJIO+fCXA3wc5NFMAYHh8fJxWMzqfx47JiCXsdGc6w8xJdEnKhD93spUlgSD3wOh14Mib3dpo\nZ33z3il3S0Cvc5ISBHZGmFcaWPdr5oe5jR0T5Dw42rG2A5zpBGbEsHXkqI3mdcqKF1ggz8m+UWzL\nMhJF3zCnLVfWFVyXcDO6OJ0r2yjLVdU4WvLw8FC3t7d1c3MzLewgJG4Hmn5IuUlWMG0q8uiFnLnI\nqGMLsy20gXdkkWM7h2Wp7C0QdFliGBjIEQjze4LJUZ3M7HTC8xaQoiTKTyWWXoYHMT3lziu1skwg\niOIfGY5u5R9t5vpMDq5POCtBBHtNvby8TCAaAJnPrdyXkvk/yR4ZrHFcTliDYBhRFGIaZTMMaZxG\nIMXfRyBwZMz9n+WA+njscjNSh7U4B0VGXbp7jOTNpZvfWXe3Peer70ffJVs7WkizDyCw6mdb8uky\nMPB8zidR0PfWI1yLlxdndaBwNG4dqOqch5HzmgxFBwANflPnpMxajrrIQr4S5Hll5yjM67CfZYx6\n2cFPXdwtpjIIdC7YZrOZAYC1C9u7VM1Dw4AI54dnH5kldljcdiJtnuXUiynsCGAr6B+fX/W65Y6Z\nPW/Xw3HdhvIJ3mwbuRd18v3sDJNGlMym6+pxpu6wclzb+urx8bHu7u7q9vZ2tptHht2pe8eiu6RD\nnee/ZXtXB4IdCOxAHSU90c4zTSOSCi0Rdf6e4Mv3Si98iclMYz1iBPN7As0OYFTtbiybFHJ6E4CW\nzGVJNtIA10qUSdsxkrTDhsvGmPrtSwHYdXs2uc25gaxDISjPqpr6g363gbTHuOTk2FHx/57YKT8j\n8JdMEiUdnfRgzdhY2eR1RvXsfu/my5LTl21y37h9yeiaBUzGx0Z4bYfk/v5+BpJI+v706dPOqkHL\npxcpwNYzzskAJBC08ataDqvmWLsYPFTNF4l17Jjbab3CdRLUVfVPeErHqQN4Hn8zwQkkOwBoNjOB\nrcNvoxQKA17a6vk8mh9rFvoibZ31nqMVHqOjo6NpxSrXch/7mvQjn70IjeNHDmJV7ei0nO8wghR/\nTtvJ/wagzvVmXtmBPzs7q6Ojo1mfJGNngGeAm0CQOext3RIMGjwfHBwsPoQgx6abA9knXVl9+5ik\nMd8yDKNrcGyWRMej+3ONEYORynXkdY9A6Ig18X1sJFNpdsAxFaSBYNU8rxKDX/UTkHkT0EwWttdr\nFiiBiBWoE1RNf+eq2n0p+WivzBUyyHDOWQLfjl2hLMmL/0+l6c/cp7uf/6OgdJJB4X5V8wfOLy3c\nSMbGpQMaHJu/v3X9pfnrdlnpJhPUedEOzeW91ix//PFHnZ2d1enp6aTUmW8vLy/TYpLcvsQsIb+N\n8rCqdndloHSMsuXE8pcvs2KZRmBQ1DEYyDb3SaA2AoAJ9szq+ZXOQMpFglvrulFf0R/0G8a9atde\nuS/5PcPRa7OCdsbNxDGvql6jP4Rd6T9/Ruc7CsX1re8M4ABszEX3i39PZ5G6brevjKXrXTXWjYxF\ngkHajg3ATnmxC/V/fHycHplHv5gt5XrYPPoYht4MYW7pxpNoeCa1WUlsJk84OT09nTak9sIdry7O\nNKb3RkJWt8xveQEJAkeGdomhWGIYloBeMpbpKS4Z6vS+u7qkFz8KN/s+I08ZwXaOBAUlDRtoIOj9\noqiXWSImH/fyhDdFnd4RHpIVx4hlWKOYdclwkidugml7tglosiQbk/JjgFLVs3vJJqasdzKYRpQ2\nWDm7fp1D4zp0cmtgkGCWe1mmnY7guWTw674ezVfLma/vY/nNzI+V+9rl//yf/1MXFxd1fn5eZ2dn\nk7dvB4y8JDshCQ7JkcKIGBAuAY5OJ6aerdp9NjR6gUdlWYd027+kMXfoj/cOCHZAP8P/CfgSKHah\nX9pkx9gOQs4BdGrVT6NumeLpEC8vr0+TMivTgR3e1yyWf+t9M38GWwaB3lIMoPL8/DzcaqrqdYEG\nhfOStU2Z8bh4DlvGrX9oj/VhhlkdXXt+fp4BMvLYIUusn56enqZ8v47gQPdVvaZTGXzSxpy7BoL8\nR/+iDz5+/Fjn5+d1cXEx0xXuRwCgxy4fo/dWWR0IJsgbKaf0YrOMWIW3AKUH0gKVCi0VWxrMLszy\nlqFNkJmG08I4AoGmf53z50lyeHg4CTLC6WdJWiFQb/ZB4rq+J7+5ztTPdXLY6j1eyV9ZMhyc4Lzq\nNUfU/egyGi+Ps2Uqw0wGdpbvqnn+JwXww2feR46Iw2DZNt/D44aMoJDyulZ+ls3OQevYwOynbG83\nv/OYbk7hfVtWsz9GjM9fXf73//7f9fnz5/r8+XOdn5/X+fn5zNv35tJOvEdevakxYSVvdtyxYSNQ\nT7EONMg3OMXYoztslJL5o7j/Ox2WDkvHyHcg2OFy65sEgVW7jvYoCpLsk9tAfxAi5D7Op81UG8AI\n9aEta5bUKZkH7HFJAMY44Qxst9sJvHjjZQN+gzffh/Gq2k0Rsz1xve2cHxwczHIUPV4ONzN2HhvA\neoKm7B8D4+Pj48kOuO7oNTumnGsW0+yjH03oBz3QRzzejnccxpOTkwlscw/k0P3k+WJcs1RWB4JV\nc+o9jcsSOLRhWgKA3XeKDXYqDisNL8/uQCAG1O3JAUgj1LGAnSedBjvBFkKc16IdrGTFSwMY4tEz\nOQxgaTNAyArCdWfcrPSSku7atHZxnkfVLjtbNQ8d5XEuHWNMyevmtZCbDF+NgEvKWgd4bFxHJYG7\nDcFSqMwgL5WM/0uQnLlhrkeChc5pcNtgZmijGe8O/HD+vgDB//t//+/00PqLi4sJEHbhH89F2nh8\nfDwBoePj45lBSZBkZyDncNWYFU7gxP3JVzIQTMemar7Bt++fv3fgzykZ3sw5cydzcYjvgWG2Lq+a\nh0bt0FS9AhcDGb+cLsN9eKTk4eHhxASaHc/2rR0azvubvaP9ntcJegx8Dfoc/r2/v5+BKJ9fNV+w\n5vcEc1yvajdvFH0CA5fsfzqujBWgrUsFSj1s/QgIc+oV+twylJEPXw/gmdtbmTGGDWQbmgSBjFXu\nimDH2rig06VdWRUIGjB1zEBVj9BHLIRBYV5zdF6GtLiWjZlDHg7lZf2yTS4GBB1LtMQEWkGmADvc\nkEYjQ3IGed4upDMSBo7poaXCt6DbAGUbu/5bqzgUkmNjmfBjjgzc3GdVu8/MTHlL54JrAmoSxCTL\nasBoMDgCVSnPCYI8ZmYBYTlSNl1/f3efmUWyLI+cmwSA6dm7LDFJvPt63COLx2it8ve//72ur6+n\nbSpub2/r4uKiDRf78WvMSad3EKY1aLKhSSOX4dIcy3QAfD/SRRwWxpE0wEoAkPLocepy/Jae7evn\n+loPWtdXvc8p6UBg1nFkM5L1I2euY4Qsj+81zH9VYUwPDg6mcKmZLPRk2sLUPwl6k5l1VMqL0Y6O\njqYnY6Rd63RE1ZxAcR0S2BnUIv9eyFH1apet/zIVAVDF/oDeWSHBoevn8TYjnDmjdtrpj/Pz8ylq\nwOPpnHvIOQaClkmPSTrsXVmdEewUxIgdyMm+NFnfukd3T0p6xqlQLPyj++S1ql5XMPF7ZyTz2img\nnmAWWK5J6ML5gmZiHOqBWsdzSKFMUJTtqXrNgXHoJs8HIOwTEExmZFSvbmyS0TB7R0GG/bv7n8+E\nOTo2i+8oaRtz38dsXspzOh1uA8rWMmb2OxlxrpMMdhrYpZQK9386IB0Q9DwYAcElXdExlhni/6vL\n77//Xh8/fqy7u7sJCN7e3k5J43d3dztgsHvKC/3qfMKR05j9RTF47xyGBFJe6ewtQFJvMR42ijij\nHi/qa/Dn/Ckv6HLemmW3c+ItuynHDmWbSaEk6KOkPvUcz/Ncr7Qna5YEBKz+hdF8eXmpu7u7mT2o\nmrOBHk/bx5Sxjq0ymCT0ycbLLy8vO0yZHQr3dQIf62HLVZ5DWzqbhBxaT3shhomUXB0PKLUeSlYx\n2e7c2QD7DRP45cuX+vLly7SobLP5mQJzf3+/0+6cvwbKVcsL8ar2gBE0Oh95ACOQ99b/eex7jkt2\nKA287zu6fncfG33fawQC6ZPR5Mi8mBRS/veqxGQIqUvH0nRty4KQO3QDqExGMQV17ZIhkm7cOyaT\nvsj+d0mmzp9z/M3upZE06LGi9Xh1jCD36dgRKxDqSjtQjHz2Km8DD289ZIDyngUD1HMJ0GV7OsBo\ndisVdZZkutYGgldXV3V4eFgPDw8T+HNolwRyh4kBhAZhBmle3IUhzD5KhqpqN/rh3zsdluC+auxc\n+/oGTZ5DGEWAX66khAVM5q8LJ3I/g7+Uxw4EpB7MPnG7u9QbO1e8m9m33l1b9+X90wb5d3RB1dwB\npL/8yLOqXQaqk1E7Dplfudn8fJ5xB5icQ5f1x8FI8iQL9aK+GeZm7r28vG6xZraN31goxL0dHk4g\nnFvdmPH2VjSbzWZi94+Pj+vs7GxiBU9OTqaUImzskhy5rmbll8peMIIdAMzf/kxJBZGsTP7evbo6\nJp1c1TMVHcOY9ajq2aYEwRamZAX9MgAY7YbeJXXbeHZ9kOPi+uNBWYkj1CjK7McESWsV5/Nk+1Px\n850+yJCXnZmq3Y17c4xteOyljuTfoY5ubLr68/9oTO2FosBob3q/XBsZykeFmbFK4zuS7wSBGd5N\nuUtG1CyDXznHDP48rmuWh4eHybvPrWFyjzHCQn7lno8GPTnWlsWOPUuQzLEJwDsnsXNeuK7/8xij\nr1jcYibU7c4nLlT1i/hcbLBHzHSCvXxP0JIMufvIfZUMd85L5s7ahVQX19+LWUgV4TjyUm1XOqBt\nIOW9/XycFyzaIX1+fp5FqehDgyZYMDs2VbtRFuuEqleH3/LgccAOUP/b29v68ePH1Fb6wpuF52Ik\ny0Da5VzolE8Ucf0+ffo0MaQnJyd1eno6fT86OpqumfPVc90pJGZ03yqrM4K8d15rKnlKdsCIBfDL\njFx6q0uM1VtgtMvv6urM/fLern+CJAxgJpSOQj/b7U+6Pr0oBDq9WU/KBBHUx5+zXQi1t7KwMLqt\n7st9AYIshqHN6RVX9SvCMWg59hT61QbG4WCO8f/cq5sTyTxaiXKfjtHJ4vO4B78bHDmc5+8OD+XT\nSHIPuc74uj8tt9mXbptBRXr02WfZdvdxgoM1CzlRT09PdXt7OzN2d3d39fnz5yk8TIjYzy8lZMzc\n7uZ3ygMlZaTTeQbflnUvyvDvydZV9fLrVb+wfqx6zn09AROd7DnHLB1rg/4Rc+f2d4xf59zYcc4+\nwRHP0Hun49cuADyXh4eHWf0PDg4mh8NzEjBnFo/vLy8vdXNzM0tRMHj0BtKAxJeXl2llK9ejr7fb\n7SwHj/7znn/o7pR7OwTOUfSYIvPO7wN83t/fT/2QOY9dNI7z+Y4MZ/pWEifICSAQ4MfLK/JhBDsn\nN50w9FuOdepJl9VdlASBnWfVKfolQ+f3ZGLyWhnqSCPjzu8AoT2ULmdpVHe/8rqpRBMEphJ2CCg9\nkE+fPk3/dXViMnkBgwEox/gapr6zbvQp1xqxDWsXQtg2Gl29PC7u67fGOpko7kV/d+yVz00vM9mM\nHE/ubWcn/0v2wsrD4NXMEXPCIK8Dg86ZMYNiWejAAQYDkGGmkjqnsht97vqRNmSe2Jrlb3/7W1VV\n3d3d1fX1dT0+Ptb3798nMHh7ezutKAYMsqksm00fHx9Pc47xYuEXJZ3bZAA7kM67WW/3vw1gpqw4\nvzidJgwkINBMoENkZnvNPiUDnWyzX8n+2tHv5D4Zru5RZfSFwYt1v/Wy547n4J+NbP1XlJR9t6fq\nNc8cMGF9kePgz4wdDHeCnOPj45l8oWe499HRz+3KuD9MHGHQlKWUc5x6jymsGL9nbmhV7YB+j9mS\n/XJ96LcuV95EjucNc4V6wgDyIhXEIDUdYtfLLDTXTKd5US7+vCj9vyudYUrP6i0wlYbG71WvyjA7\nkXPSI8xwoevXGW0D2Hx5cvk9P2c/pEduQcqXhRIgeHR0ND239OHhYcbSUPJz7oGFULsPEPqcDJ0X\nbOBgL+y9VPV/dWGiekU1SsAOwQi8jNqRY2xwxcQ3I8vxDsEmaMIz7dgbyzAhGjPEmUeXssk1LKs2\njDaQXejXx3YGdMQIdgrW/e1jMyzcMfA5Br62dcU+AMH/+T//Z1VV3d7e1h9//FFfv36dQCDh0qur\nq2mPQb/IHQIQnp6eTsYRUE6oDZCd4M+Mb6eHUvd021zY4cwta2ywMy8qH61l4O8cLDsabFtjEGim\nz7Lk9iUrmLJmmbAMd0x2zqt00vPRY1Xzjc29yfSaJetAv6etSvtl+9g5VQkWq2pyThhLjzO6kb5C\nTlg4Yn2IXcvNpLmfU1qyvrbd/Gb9wCp4WDjYwM5+WZZoH3Zxu93uzIvEMe5f14eQMCCQ/X3pI+xu\nbvLtscI+ML6MFePzFvmyF4xg1WtjbABT0ScSNtirGoeKzXj4e3qQo3yS9OSSkemM7JL313nhrpv7\nw0AwqensI4Th8fGxPnz4MAOBpteZQFa+tBcBxLtzf3gpfdVPhsyTHLDpyYV3g0D+mb2N/isL4RCP\nG0rb44Pi6fo8PcYu/PT8/DwpJL9S3jq57gw1JYENOSSuK0AA0NrJZBrMZEdGeX9vGWD/n6C6Y3Hc\nxuyrzgGyvuj6JkPd+1T+1//6X1X1Uwb/8z//s87Pz+vbt291dXU1rSC+vr6um5ubury8rNPT02l7\nmdxmhs2oCSV1z4u2jHVjRMn5kGAuvzuc7Q2teVKD9/zzb/x+f38/M4anp6dTLhahcPKyDAI910bs\nX8p0Nzer+lXw9IVlL0Ht/f39jj62jjA7Q95dd/+/unS2kbBj2rScozmvAT/b7Xb2qDaIBYBIsrcw\niLCBljl0lXUi96F/q171su9h3ZJAkLZ27LBlFTtWVTt7ZnrbpExNwP7mtjPWec6V5HcvEPH2NNTd\n4Nf1TOcYGUsyI52fUdkLRjBfncefVLA92jSYBoRJ8/r/kQLplEbWo2Mzs75dnfKa+bljoDL8MmJF\nmDAHBweTAjYVznVzxac9bj9K5/7+fpa7wURHIN3PhKXwCO1lAQQJBexDiMRh71R+BtbdSu1UNkt5\nWfSlPTR7sGnYRoxffnfoI8Ffsje004qCa6WX7/DYUiiu6zszUPyf89TzpLtGGt+lnNhMeXD/eA6+\nxSD+1eU//uM/quqnMfvy5Uv98ssv9fvvv9ff//73+uOPP6bNpgGFV1dXdXV1VWdnZ9MG1IBB3hMM\nLoH3ka7rgGCGs2zoAIE3NzcTGKTOuQUMC8kMlp6fnyc9gQN5cnIy21jb+7Z1DpPTELK9+bkjCnLu\nVdVsHpnpzKeZ0E95DRMB6eCvXbpIhsGEGdqMjLiP3D4W/7DQYrvdznTJZrOZARWDNofe6XPq6Do4\nHaWqplBw1W5Os4mfBGPJEpvRpN3kPB4dHU3gz0ANW+n8QrOD3ufSbQT0OUrU5VvTtpwrdkS6XEXu\nRTvoq/eU1YEg751hsFLqigef0hnS0f+pLJYMXcdSJgjLNlhhJTB0H/g82tp5oskGuj1WQHhb3Qph\n/gec4Y1793LAi58dCcNIIqxDA15ZRq5HThwE1PsyrVkAzO4bj7sntJ9iYAVmwASj6OtVvY4P9yNs\nl4osDUbHdKF06Odkos3YPD4+7oy75wsgLXOj/MSZDgRa4SV49bxIJqaT0Q7sdexTx4RnmI6SYJcx\npt/3AQj+9ttv0+fT09P6/Plz/fbbb/Xbb7/Vf/7nf9bXr1/r999/r+/fv0+5dOQRXl5eTmxgMoT5\nmLoujP9ngKAZ2ASFzvXL183NzQQMzBQmK4TTeHp6OgO1AMFkX+zIp1G3Y9u11aHGUbHuzMUrCf6q\n5k+R4Lo4XpbPfSo4wC7oZcKOgBb60PYMB9nF8xRnEGBPv3Nfr1rmP+5h2XK+Hv3IOFK3tKn0teuX\nURh0N9fg/pzP/w8PD5NttC1LZ8v9g7OA3KBzLCdVrw7409PTrC4Jiu3kpw7s0sWqarou7X1vWT00\nnKUDX+nxm7npyoiGtwJJNiRBYNXrQpBUjgkGXW/fy3Xu2pmf8z6dIXRyONdm4vE7goLyRSBc74OD\ng2l/InYyPz8/n4Dg09Ou7R+uAAAgAElEQVTrEyYQVoQfIMjnk5OTqponBvvFedDb+5AnmKFH/26F\nZGNghWSKf1Q8rowT/Qp4TPCEIs1rVL2GcfjsY+w1Pj4+zlhgX8O5NFZQGNFcAJIgEIXne/t32mfj\nnQx7yvgSEDQbNWLEDQSTlbWXbPC/ZuEZ3lU/mQ3A4C+//FL/8i//U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vetjGyU5zv1Zj7x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\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8HidbtbW1VZhnkXmnZptVoA4Ec843Bu3RTnMc+owNXbde/U/JKoYpZ4Pct7odiQxcvKYD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\nQ8OBES0TGPi5W1t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mwAij4rU3HrFHZtdZR35wvBhBX4XH/d3h85mvcHRn5uL66eygly54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\n4n87tWLe2OwIQgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f26a6fef2e8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "078kN90HqFsu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "c62cfcf4-f88b-44fe-b7e9-cf118f3e8e8b"
      },
      "cell_type": "code",
      "source": [
        "head = std_upsample_head(2, 512,256,256,256,256)\n",
        "head"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (0): ReLU()\n",
              "  (1): StdUpsample(\n",
              "    (conv): ConvTranspose2d(512, 256, kernel_size=(2, 2), stride=(2, 2), bias=False)\n",
              "    (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (2): StdUpsample(\n",
              "    (conv): ConvTranspose2d(256, 256, kernel_size=(2, 2), stride=(2, 2), bias=False)\n",
              "    (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (3): StdUpsample(\n",
              "    (conv): ConvTranspose2d(256, 256, kernel_size=(2, 2), stride=(2, 2), bias=False)\n",
              "    (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (4): StdUpsample(\n",
              "    (conv): ConvTranspose2d(256, 256, kernel_size=(2, 2), stride=(2, 2), bias=False)\n",
              "    (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  )\n",
              "  (5): ConvTranspose2d(256, 2, kernel_size=(2, 2), stride=(2, 2), bias=False)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "metadata": {
        "id": "6NxhlABM40kW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def dice(input:Tensor, targs:Tensor) -> Rank0Tensor:\n",
        "    \"Dice coefficient metric for binary target\"\n",
        "    n = targs.shape[0]\n",
        "    input = input.argmax(dim=1).view(n,-1)\n",
        "    targs = targs.view(n,-1)\n",
        "    intersect = (input*targs).sum().float()\n",
        "    union = (input+targs).sum().float()\n",
        "    return 2. * intersect / union\n",
        "\n",
        "def accuracy(input:Tensor, targs:Tensor) -> Rank0Tensor:\n",
        "    \"Accuracy\"\n",
        "    n = targs.shape[0]\n",
        "    input = input.argmax(dim=1).view(n,-1)\n",
        "    targs = targs.view(n,-1)\n",
        "    return (input==targs).float().mean()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "XSs-O38tqna0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "metrics=[accuracy, dice]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KEKgzwPzqu-D",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn = ConvLearner(data, models.resnet34, custom_head=head,\n",
        "                    metrics=metrics)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WCP9RO4mXevu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "5fda2194-ea35-4bfd-ade8-50593018ade1"
      },
      "cell_type": "code",
      "source": [
        "lr_find(learn)\n",
        "learn.recorder.plot()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch  train loss  valid loss  accuracy  dice\n",
            "1      6.040244  \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9YhQXn9mXvQeP8uu/rGTvwaNOl+WIsILcGDPCGFNqjLm9lXXuM8Ys6rTKRETa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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f26a6903160>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "6twqbUqCx0kV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "91ab788a-ec49-4661-a4fe-bffee730c3a0"
      },
      "cell_type": "code",
      "source": [
        "learn.loss_func"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CrossEntropyFlat()"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "metadata": {
        "id": "P45JLd-zi7te",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "lr = 1e-1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "wSH29xMWpcQs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2438
        },
        "outputId": "120231ee-f2a5-4f6f-82ff-6b64fd2237bb"
      },
      "cell_type": "code",
      "source": [
        "learn.fit_one_cycle(10, slice(lr))"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch  train loss  valid loss  accuracy  dice\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-68-56c1c61458d6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_one_cycle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/train.py\u001b[0m in \u001b[0;36mfit_one_cycle\u001b[0;34m(learn, cyc_len, max_lr, moms, div_factor, pct_start, wd, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m     17\u001b[0m     callbacks.append(OneCycleScheduler(learn, max_lr, moms=moms, div_factor=div_factor,\n\u001b[1;32m     18\u001b[0m                                         pct_start=pct_start, **kwargs))\n\u001b[0;32m---> 19\u001b[0;31m     \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcyc_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_lr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mlr_find\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mLearner\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart_lr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e-7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend_lr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_it\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mAny\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/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, epochs, lr, wd, callbacks)\u001b[0m\n\u001b[1;32m    135\u001b[0m         \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_fns\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlistify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m         fit(epochs, self.model, self.loss_func, opt=self.opt, data=self.data, metrics=self.metrics,\n\u001b[0;32m--> 137\u001b[0;31m             callbacks=self.callbacks+callbacks)\n\u001b[0m\u001b[1;32m    138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_opt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFloats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m->\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(epochs, model, loss_func, opt, data, callbacks, metrics)\u001b[0m\n\u001b[1;32m     87\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m         \u001b[0mexception\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     90\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexception\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(epochs, model, loss_func, opt, data, callbacks, metrics)\u001b[0m\n\u001b[1;32m     82\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'valid_dl'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalid_dl\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m                 val_loss = validate(model, data.valid_dl, loss_func=loss_func,\n\u001b[0;32m---> 84\u001b[0;31m                                        cb_handler=cb_handler, pbar=pbar)\n\u001b[0m\u001b[1;32m     85\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mval_loss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_epoch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_loss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mvalidate\u001b[0;34m(model, dl, loss_func, cb_handler, pbar, average)\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0myb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprogress_bar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpbar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleave\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpbar\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m             \u001b[0mval_losses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb_handler\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     48\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_listy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0myb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0myb\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m             \u001b[0mnums\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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/python3.6/dist-packages/fastai/basic_train.py\u001b[0m in \u001b[0;36mloss_batch\u001b[0;34m(model, xb, yb, loss_func, opt, cb_handler)\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_listy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mxb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_listy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0myb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0myb\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m     \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m     \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcb_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_loss_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     90\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_modules\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\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[0;32m---> 92\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torchvision/models/resnet.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     46\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    475\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 477\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    478\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    479\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    311\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    312\u001b[0m         return F.conv2d(input, self.weight, self.bias, self.stride,\n\u001b[0;32m--> 313\u001b[0;31m                         self.padding, self.dilation, self.groups)\n\u001b[0m\u001b[1;32m    314\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36mhandler\u001b[0;34m(signum, frame)\u001b[0m\n\u001b[1;32m    272\u001b[0m         \u001b[0;31m# This following call uses `waitid` with WNOHANG from C side. Therefore,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    273\u001b[0m         \u001b[0;31m# Python can still get and update the process status successfully.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 274\u001b[0;31m         \u001b[0m_error_if_any_worker_fails\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    275\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mprevious_handler\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    276\u001b[0m             \u001b[0mprevious_handler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msignum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: DataLoader worker (pid 508) is killed by signal: Bus error. "
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "8dEes3iDpexL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "f26324e7-cd7b-4fc4-c4a8-82e5560b8a95"
      },
      "cell_type": "code",
      "source": [
        "# memory footprint support libraries/code\n",
        "!ln -sf /opt/bin/nvidia-smi /usr/bin/nvidia-smi\n",
        "!pip install gputil\n",
        "!pip install psutil\n",
        "!pip install humanize\n",
        "import psutil\n",
        "import humanize\n",
        "import os\n",
        "import GPUtil as GPU\n",
        "GPUs = GPU.getGPUs()\n",
        "# XXX: only one GPU on Colab and isn’t guaranteed\n",
        "gpu = GPUs[0]\n",
        "def printm():\n",
        " process = psutil.Process(os.getpid())\n",
        " print(\"Gen RAM Free: \" + humanize.naturalsize( psutil.virtual_memory().available ), \" | Proc size: \" + humanize.naturalsize( process.memory_info().rss))\n",
        " print(\"GPU RAM Free: {0:.0f}MB | Used: {1:.0f}MB | Util {2:3.0f}% | Total {3:.0f}MB\".format(gpu.memoryFree, gpu.memoryUsed, gpu.memoryUtil*100, gpu.memoryTotal))\n",
        "printm()"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting gputil\n",
            "  Downloading https://files.pythonhosted.org/packages/45/99/837428d26b47ebd6b66d6e1b180e98ec4a557767a93a81a02ea9d6242611/GPUtil-1.3.0.tar.gz\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from gputil) (1.15.2)\n",
            "Building wheels for collected packages: gputil\n",
            "  Running setup.py bdist_wheel for gputil ... \u001b[?25l-\b \bdone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/17/0f/04/b79c006972335e35472c0b835ed52bfc0815258d409f560108\n",
            "Successfully built gputil\n",
            "Installing collected packages: gputil\n",
            "Successfully installed gputil-1.3.0\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.6/dist-packages (5.4.7)\n",
            "Collecting humanize\n",
            "  Downloading https://files.pythonhosted.org/packages/8c/e0/e512e4ac6d091fc990bbe13f9e0378f34cf6eecd1c6c268c9e598dcf5bb9/humanize-0.5.1.tar.gz\n",
            "Building wheels for collected packages: humanize\n",
            "  Running setup.py bdist_wheel for humanize ... \u001b[?25l-\b \bdone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/69/86/6c/f8b8593bc273ec4b0c653d3827f7482bb2001a2781a73b7f44\n",
            "Successfully built humanize\n",
            "Installing collected packages: humanize\n",
            "Successfully installed humanize-0.5.1\n",
            "Gen RAM Free: 11.6 GB  | Proc size: 2.1 GB\n",
            "GPU RAM Free: 10883MB | Used: 558MB | Util   5% | Total 11441MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "p7PWri2bi3uP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}