Final_Report.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "###雲の画像から今後の天気を予測するAI\n",
        "\n",
        "雲は10種類あり、そのうち雨雲と呼ばれる雲は2種類あります。webアプリなどにすることを考え、ユーザーが撮った写真に対してそれが雨雲か否かどうか判別するようにしました。(最初は10種類雲の種類を判別するAIを作っていたのですが、スクレイピングで得られる画像の数に限りがあり、精度があまり向上しなかったので2値分類に切り替えました。)\n",
        "\n",
        "流れは以下のようになっています。\n",
        "\n",
        "1. Bingの検索結果をスクレイピングして10種類の雲の画像を取得する\n",
        "2. 雨雲とそれ以外に分け、教師データと評価データを用意する\n",
        "3. CNNを用いて学習させる\n",
        "4. ユーザーがアップロードした写真から、この後雨になるのか晴れになるのか予測する\n"
      ],
      "metadata": {
        "id": "ZhyokshZn8eo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###1. Bingの検索結果をスクレイピングして10種類の雲の画像を取得する"
      ],
      "metadata": {
        "id": "zArYov-GpQTQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install bing-image-downloader\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "clouds = ['Cumulus', 'Stratus', 'Stratocumulus', 'Altocumulus', 'Nimbostratus', 'Altostratus', 'Cirrus', 'Cirrocumulus', 'Cirrostratus', 'Cumulonimbus']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_IdiARLNFAmm",
        "outputId": "5205dcd8-f47a-4d61-cda0-23a7ff37d9fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: bing-image-downloader in /usr/local/lib/python3.8/dist-packages (1.1.2)\n",
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "雨雲は2種類でそれ以外は8種類なので、不均衡データになるのを防ぐため、雨雲に関しては150枚取得する"
      ],
      "metadata": {
        "id": "P4fdS2TkpeTh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from bing_image_downloader import downloader\n",
        "for cloud in clouds:\n",
        "  if cloud =='Cumulus' or cloud == 'Cumulonimbus':\n",
        "    query_string = cloud+' cloud'\n",
        "    downloader.download(query_string,output_dir=r\"/content/drive/MyDrive\",adult_filter_off=True,timeout=2,limit=125,verbose= True)\n",
        "  else:\n",
        "    query_string = cloud+' cloud'\n",
        "    downloader.download(query_string,output_dir=r\"/content/drive/MyDrive\",adult_filter_off=True,timeout=2,limit=50,verbose= True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9v-EEF-WFL7e",
        "outputId": "762791d8-b565-457a-fbcf-600700cfd75f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "[!] Issue getting: http://2dh8s42c4g0b4fkujgfp4w7a-wpengine.netdna-ssl.com/wp-content/uploads/2013/06/cumulusclouds.jpg\n",
            "[!] Error:: <urlopen error [Errno -2] Name or service not known>\n",
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            "[!] Issue getting: http://images1.wikia.nocookie.net/__cb20060103165814/uncyclopedia/images/8/89/Cumulus_cloud.jpg\n",
            "[!] Error:: HTTP Error 404: Not Found\n",
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            "[!] Issue getting: https://upload.wikimedia.org/wikipedia/en/thumb/0/06/Cumulus_Cloud.jpg/320px-Cumulus_Cloud.jpg\n",
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            "[!] Error:: <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1131)>\n",
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            "[Error]Invalid image, not saving http://upload.wikimedia.org/wikipedia/commons/thumb/5/50/Cumulus_cloud_forming_anvil_shape.jpg/220px-Cumulus_cloud_forming_anvil_shape.jpg\n",
            "\n",
            "[!] Issue getting: http://upload.wikimedia.org/wikipedia/commons/thumb/5/50/Cumulus_cloud_forming_anvil_shape.jpg/220px-Cumulus_cloud_forming_anvil_shape.jpg\n",
            "[!] Error:: Invalid image, not saving http://upload.wikimedia.org/wikipedia/commons/thumb/5/50/Cumulus_cloud_forming_anvil_shape.jpg/220px-Cumulus_cloud_forming_anvil_shape.jpg\n",
            "\n",
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            "[!] Error:: HTTP Error 410: Gone\n",
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            "[!] Error:: <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1131)>\n",
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            "[Error]Invalid image, not saving http://teacher.scholastic.com/activities/wwatch/images/cumulus_sm.jpg\n",
            "\n",
            "[!] Issue getting: http://teacher.scholastic.com/activities/wwatch/images/cumulus_sm.jpg\n",
            "[!] Error:: Invalid image, not saving http://teacher.scholastic.com/activities/wwatch/images/cumulus_sm.jpg\n",
            "\n",
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            "[Error]Invalid image, not saving https://thesignishere.files.wordpress.com/2013/12/cumulus-clouds.jpg\n",
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            "[!] Issue getting: https://sp-images.summitpost.org/526553.JPG?auto=format&amp;fit=max&amp;h=800&amp;ixlib=php-2.1.1&amp;q=35&amp;s=c2024e3a7b1561706f179bf3c996ec6a\n",
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            "[Error]Invalid image, not saving https://everipedia-storage.s3.amazonaws.com/ProfilePicture/en/Stratocumulus_cloud__53f839/Above_the_Clouds.jpg__80539.jpeg\n",
            "\n",
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            "[!] Error:: HTTP Error 404: Not Found\n",
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            "[!] Issue getting: http://farm1.static.flickr.com/253/451572161_d5db9c6ac7.jpg?v=0\n",
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            "[!] Issue getting: https://wordlesstech.com/wp-content/uploads/2013/07/Altocumulus-clouds-illuminated-by-Sunset-2.jpg\n",
            "[!] Error:: HTTP Error 403: Forbidden\n",
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            "[!] Error:: 'ascii' codec can't encode character '\\xa9' in position 44: ordinal not in range(128)\n",
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            "[!] Error:: HTTP Error 404: Not Found\n",
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            "[!] Error:: HTTP Error 404: Not Found\n",
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            "[!] Issue getting: https://photos.travelblog.org/Photos/198236/614135/f/6147177-Altostratus_clouds-0.jpg\n",
            "[!] Error:: <urlopen error [Errno -2] Name or service not known>\n",
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            "[!] Issue getting: https://preview.redd.it/0m2iggckne211.jpg?auto=webp&amp;s=afde96be17947c2dba827f237548fc02607e9be1\n",
            "[!] Error:: HTTP Error 403: Forbidden\n",
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            "[!] Error:: <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1131)>\n",
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            "[!] Issue getting: https://weatherreport.com/images/cirrocumulus cloud.jpg\n",
            "[!] Error:: URL can't contain control characters. '/images/cirrocumulus cloud.jpg' (found at least ' ')\n",
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            "[!] Issue getting: https://images.creativemarket.com/0.1.0/ps/7344996/1820/1213/m1/fpnw/wm1/t90jixr4efxshpkmwffzxe7hcb8rksk4rb7wwn4k5elq06q975zdoysqxptowovk-.jpg?1574370989&amp;s=edecc070735988f5f702eaf276c99772\n",
            "[!] Error:: HTTP Error 404: Not Found\n",
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            "[%] Downloading Image #33 from https://i0.wp.com/petiteprepper.com/wp-content/uploads/2015/04/Cirrostratus.jpeg\n",
            "[!] Issue getting: https://i0.wp.com/petiteprepper.com/wp-content/uploads/2015/04/Cirrostratus.jpeg\n",
            "[!] Error:: HTTP Error 404: Not Found\n",
            "[%] Downloading Image #33 from https://cloudatlas.wmo.int/images/compressed/2635_main_cumulus-humilis_clouds.jpg\n",
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            "[!] Issue getting: https://nickpiesco.com/knowyourclouds/images/cirrostratus_full.jpg\n",
            "[!] Error:: <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired (_ssl.c:1131)>\n",
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            "[%] Downloading Image #49 from http://www.metoffice.gov.uk/binaries/content/gallery/metofficegovuk/hero-images/weather/cloud/cirrostratus-best-photo.jpg/cirrostratus-best-photo.jpg/metofficegovuk:heroMedium\n",
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            "[%] Downloading Image #50 from http://profhorn.meteor.wisc.edu/wxwise/web101/sm-clouds/cirrostratus.jpg\n",
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            "[!] Error:: HTTP Error 403: Forbidden\n",
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            "[%] Downloading Image #9 from https://cdn.britannica.com/58/150358-050-254AA162/Cumulonimbus-clouds-farm-Montana.jpg\n",
            "[Error]Invalid image, not saving https://cdn.britannica.com/58/150358-050-254AA162/Cumulonimbus-clouds-farm-Montana.jpg\n",
            "\n",
            "[!] Issue getting: https://cdn.britannica.com/58/150358-050-254AA162/Cumulonimbus-clouds-farm-Montana.jpg\n",
            "[!] Error:: Invalid image, not saving https://cdn.britannica.com/58/150358-050-254AA162/Cumulonimbus-clouds-farm-Montana.jpg\n",
            "\n",
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            "[!] Issue getting: https://sp-images.summitpost.org/526553.JPG?auto=format&amp;fit=max&amp;h=1000&amp;ixlib=php-2.1.1&amp;q=35&amp;s=df777dafcdfccc9bb15fd746698dca38\n",
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            "[Error]Invalid image, not saving https://upload.wikimedia.org/wikipedia/commons/thumb/9/93/Fly00890_-_Flickr_-_NOAA_Photo_Library.jpg/440px-Fly00890_-_Flickr_-_NOAA_Photo_Library.jpg\n",
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            "\n",
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            "[Error]Invalid image, not saving http://farm1.staticflickr.com/171/483193255_87ce56fa41_z.jpg?zz=1\n",
            "\n",
            "[!] Issue getting: http://farm1.staticflickr.com/171/483193255_87ce56fa41_z.jpg?zz=1\n",
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            "\n",
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            "\n",
            "===============================================\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. 雨雲とそれ以外に分け、教師データと評価データを用意する"
      ],
      "metadata": {
        "id": "NwgTk5pJuidf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import glob\n",
        "\n",
        "from tensorflow.keras.utils import load_img, img_to_array\n",
        "\n",
        "#ディレクトリ(path)名称\n",
        "dir_name = '/content/drive/MyDrive'\n",
        "#画像拡張子\n",
        "file_type = 'jpg'\n",
        "img_list = []\n",
        "y = []\n",
        "#不均衡化が避けられないため、考える雲の種類を限定する\n",
        "clouds = ['Cumulus', 'Stratus', 'Stratocumulus', 'Altocumulus',  'Cirrus','Nimbostratus',  'Cumulonimbus']\n",
        "\n",
        "for cloud in clouds:\n",
        "  #glob.globで全画像のpath情報を取得する\n",
        "  img_list_path = glob.glob(dir_name + '/' + cloud+' cloud/*.'+file_type) \n",
        "  #読み込んだ画像を格納する空のリストを作成\n",
        "  img_shape = (256, 256, 3)\n",
        "  #for文で1枚ずつpath情報を取り出して、それをload_imgで読み込んでいき、リストに格納していく\n",
        "  for img in img_list_path:\n",
        "      temp_img = load_img(img, grayscale=False, target_size=img_shape)#PIL形式で画像読み込み\n",
        "      temp_img_array = img_to_array(temp_img) / 255 #PIL⇒配列化 + 255で割って正規化\n",
        "      img_list.append(temp_img_array) #用意したリストに追加する\n",
        "      if cloud == 'Cumulus' or cloud == 'Cumulonimbus' :\n",
        "        y.append([0])\n",
        "      else:\n",
        "        y.append([1])\n",
        "  #学習画像として、リストをさらに配列化して、(n, width, haight, ch)の形に変換\n",
        "X = np.array(img_list)"
      ],
      "metadata": {
        "id": "PmFYl8M-XC8P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape)\n",
        "print(len(y))"
      ],
      "metadata": {
        "id": "sC8Xgfo7ZswK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "863ee66f-1a38-42d1-8e65-8f0e8f6d5b93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(459, 256, 256, 3)\n",
            "459\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "u, counts = np.unique(y, return_counts=True)\n",
        "print(counts)"
      ],
      "metadata": {
        "id": "MDiPe2-eZImZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa81f659-ae97-4aee-d4b7-c691f9f4a3f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[229 230]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = np.array(y)\n",
        "print(X.shape)\n",
        "print(y.shape)"
      ],
      "metadata": {
        "id": "xq1hhNVudDDU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "82dec8b3-1a8f-40df-d3b7-f074e822e0e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(459, 256, 256, 3)\n",
            "(459, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "画像を表示してみる"
      ],
      "metadata": {
        "id": "FBl3rRNAuaeg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "\n",
        "id =  40\n",
        "plt.imshow(X[id])\n",
        "ans = np.argmax(y[id])\n",
        "plt.axis('off')\n",
        "plt.show()\n",
        "print(y[id])"
      ],
      "metadata": {
        "id": "2vWmnpEa_1bz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        },
        "outputId": "4ee94094-45f1-45fe-a332-01ace5249111"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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0P3GnI1zdefCIrA+z6e9+3vEFwgmTdVOzeBSIEtb0QWNySiglTp/BgDAzjaDkHc5RkQjs8DuHc3AQ1gblQKdiKK0ZEc3yXfP2Sn8k3c0lI64psBMGeiwpTxqd4rQpoMzQfgmBu+OimWkYrKypm8ZcGAUrB0ZLil+b+dZIwSxtpuJxrTRKhn1EM4UhCZfk8Nk9ZzpTNGmhSwGEQij/MKzqIfEyLY42ZYyI/kqylSRIiKwN1iWFU4TWwhKvLaB1DjzaBPXdBRAJq+GiiAqrNhYRrufB3WXjIxFa+qbNwzVQUZoqp6UxumcqI4Sm28CR8DOTIVR501iWRlsWIqiXyERhWXLe3DmtSQRZ2j6HHtfp3RjbmNS/bUSgqY/OZgM88rQQFtDTnxzDIqda8Jmym84gDFIBu+7GgwtbCaWlq/KlhXOG8GBGGURS1b3u3+2wcWcDHd47wNlg8WgmukiBk8KJB3/GD5aqzFuurvRP57MdMSK7XCMRca2wrElMoMGEe5IQbyS0U2Jy98cqfy3vEU2t50CYmEUiKGOVa1WZgaWwiMFPjcR4i9yjVK40hncZywGY+E75q8+VQKaYCQVFQ9iDsaQTiBQxo0k8WwxfRUmThqiRYjGXEAgho7OeEDMs0KqhdBtwSiUmGRRj3geZklHMjZZBp6UJpsqdnXhy7tydItIaynyb+dcJztQz8OLpvzHh4RghUJ65XM952GTQmiJuMb/mbCacVFgXTV+2UNV+DHMetsE2IlI83DAbQSpI+l75lm6hbItwUOhgwMzjp51PyqCjHmSICBRKuidHt+3Lwtrzsgclxu7juB5OWP5iPbuQkbeACRGRdWb6JBPNO4mgvlcWGkLwDhD1EVKVWHTjCHWhoqNYXGe/w4Op193/K31RcabyU9SFLrA4yVopAKG5mGIB7tHWuEJZIwirEuKelAWRyFeWpWvh/5RlbtJQcU5ZVDCG4cnoCSuoB6GsYMZjSDv/1f16RCKXxJ6V4QrIFcl3qdBsomUnAkjDjOaSxHJYVA7DH8K6XzPGr2kQ1nW+tkxS/doa0sJSP3lYuFsjhTaAa+8hPE1QbXMpNYm89Oh+iHrG2IweVtV87Etmy18KbqOhaIU4d5IsytqKhPV9uHbur4OHvrH5yPxjpT0iI1wKoFMsoEiF9CRQDA/KqEpZUMVshEBnsFQQVrPdqlJmv4KgP3m8WThv1qDg1xOZ41uujqUsJUwIWtpaYnA8yehCJNNjVCZG3a9T1nRapqMZTOH2UgIpxHIUYPbfhR0ap5JQSa0lMoVWcrKKuORE9BB2FhBTkJMZUwKiGiRzkZllqqicQJIe2POaIpnzDNOgVHYqqW/5+2kJmHtlm0SDRwU0KRAtV51IRkjZIXw9o5NMm1Io9X4+v5ll9iI+4yTXM30xS80fub6iCkb0FaJ6pOa/EZHddVGWQ94WAq4GdVFnQOnZeaGpcBnwonX6iHsRDgQMDdbZqHyhJ8+1J3kgF/qEhm44C0iiAs1cbgWtSPQAE9I+bIPLtXPpg6170vxG+rMhWGYFow/soyQW4FVIkBY1l7e40z0ZX9MehQYUL9qm73L1OccX5DlzZZTvXjF0I+z0oqB2yHvqhFKY7BUqcjxnrZC0nuKP3yhYXKyiUu2aq6tSECrMCO7RuvLa9UJNBLSVXZyr/MrdkjGTlkpjUJqTECUEsgRWCMuhqrS2sGTks9xNc0/ye/mPOYn4TOyYOKKNJhG0MSpRktfSNsP/FQyK5yCsaMKZgsVSiIDdK2A+J4+4I+FvxQI1A13CSm199wdEJBfiPpahG1PxSljDUJoBW29Oa1jA/PxIixEoIg+HpTXeuYvxe+jOi+vCNsICavnFntUkNjLEEJNbFSKbj8gH57wlHxNt8ZluFkQHbbnUnMEhCEYwgR4ug+uWPucIqxiVLhF9HVa5TSZPeWQgqmDrIF9zw0RYLOasKlwaAh4c6E3C99zh7Gfk9g/HF+Q511gIly1oe+KzZlNEoWlEKjFouyeEKK5WYTgm8CtYCsSJ+mO4Oi0fR+oOO3TNVSKWQvlZR1nQXdiH6CPf1AnIGlG7xphBJEctkHEyaoEMDuWpTdOCtaDutUTXKhERLog+o4dew1+JjFh0rg3TspgrbsbVDRL++QDNWsUZDJL9nhDL2tCqevG8511okfBGospP0jqFPyYt1E2UrTVay2oR1X3otCxlXHNZAh4uTVjXxtoafQxOqjy9XWmqkzeq3tIiO2Y1qUGCcFHWdYFL57Qo5waDxtKC9B70bactDeshLHiEJjaSGJAWyxJR6ax2CjguRGVRWVxLPV4sqmv3yZkNi9zpSVLvPSCpWfB9B0k+yLyqmyVB3tnwWc1CRWfZRc7Nwn1AkBFob4/SHpHfTx5fEK1t8NYN/vwVPFzSGVBYFL9tu8VsC5Vfiuyyxiq2AVpQMm1ARlVFx+Oc6LzmAcpWWqVqQ2dEll34DoL1k88ZAu6+W16Zi1cTNjrqpVYywpmPqRKTXtzOnj7ozFAKDIvFvHkPnZDWpsCGu+/+EsJixpDGdXQWd9qawRVVbk5C76GlHWdL/7CsVSBsx6Wl95BaPQNUmumTsF57WVrB8lVIAST95iTui7M0meCnUjzrJCHkYpGAs0tr3KwLT24W7h/i/Sc3K4sIfTReVe1iasJuAzTciGVpLCn4L4mKGck60UXD4jQB0+AFXT0U8aww8rSWI2DqcAtfVxX1iNAuLcZ0GyPWS0tFn9qqA/eXjWtB2q0n6ydJ6YyEtQMTD9JB1JJN6B/y5ixmvEoCwlprMtMrg5ZBOGezMEQyg0HHrMNnH28UTt0GLmvMqqxwlvjGonvlrAKtpVDuVxNvETiq4BD5RF1IzMKMzh69ISEEbhINyqc4aplYdDFXkirxIJBHjVRwuKID5U9acFCF6ZZNyBP8nYShudCr6r+JhO9tQemCEfnEup8JK9N3Suux2YCRQY9hmGgku9tAR+LOJborNBOWFn6Ju+8VJA6uQpE3zKNKo543OgpkvSWakeAqZJbpCUgK5bBQNKoZoU0f2HzsFXiZcmnSEIl84c0Kt2fltC5cNuN2WUCUZVGaGt0WrrZN+OxEjhCiE4GjmZrwPOfCwxggSSn08Ckn0ykheK3rblED6rbPe1QHRWT2JMpWsNeNPnbChw3n2jv3V5vVJRGZjfrMrfcsso5o8cixrbor9yCr1/JshFuAR7EgHtB1IFwZLASZQj0rXqaB2uf08443CqfdrOF8PTlD7+mMpVBVFFeS3lc+YrYhcSCqW9fUeBbvpTbzKZhH4TzcaT7wfqSQK1SkxWc6J/k1aRmmdY2nmL5SubSV+CdRuklQ++qbFT/rxOIvq6XAZoZr0K40YXsUC4ePZiXiklY6J3QQ9ZkV6BlmaNMs5g1CuPeElQTLx9qC+5iMpOKEYM5p0R2WHp62WotI1oy2jANEcChTRWkZQ3nEZ1q2F3Gc7jrvvQJVJw3ebAR+lqiT7ANV4XyK/HNUyOb3VMMVynNY+qCLRgCqlksRZIrH2lrmRFPR1zIYnhU1WaRcgRi84CrcnheaaqRpcizw8p9jfsaIouzrdUsou7H1qCrZci62VA4913ElJ0qRSyFE0pXIuEdA8vh5nbA7l2ymYfb0QJ3w86XzC0rGLIOojqwpDGUt9ww9KRXpWpaT6zsMVgmhLAtXZV8B/3chrJBine/RiLDL7oz41uG8/sr8zoSFOl1Rl0qt7lBYROYtRw5UsIyyiu/Je8GzUn9kXjQqOoYEWjAxxCPJXqGQNL4T4sY1dkK0904TDcFuZXcDQrpbeL/uWWsYEUhjh1GzzGz6O3sgKsgw6ftJaPlW6Q6NNEPQ/aJwvPcxSRKaPOBn54Wo2ixfVrn0kSkNQ56cWJfwF7vZ3hpGSjDzPjwstAMPBa8JSxME9ThfuGOelj+tuYU/GEGivQtCkCOUu9PC7c0SVk8dGRUe2bm1wwbXzbi/dq5blHr1JK+P9D+rL1GVpGlF4EVyDLIkkAgaRdAulG+xgUohS7olVqtnRmdztU5E+NnHF0RrU7U1AkI8UiHsWmAY9AE6wjpOwQC2LeHkrumgVmudqFIpFXk94Li5upn+1/7OQRynfMvxz6k8pn1NaCGZd/Vc4FXbeOTTFl+04hleltSD/VGiYFYpC6OinXskOhajSgpmTsZimZw2ScreQKRFPaWAJIyUhKek1p9Im4gSLnU9UvgtydqEr2zmQUYncrhI+pQatEotwdRIg0hCuChAUm7XWPjuxsPWMzU3GLpMCt2r+yt245zakumIPdVA1Wua5nVjLtbWuF3hpl0jp5nBm5H/9iWbisWju8E2bI6hEyjgtDRub9awyj4Qk2lxKyAUzB/j4brxcN249C2sdR9sNtgOaaNaUJHy2teiJzyt9IgdVtpIsv9CkuC93g1hJ4N7j4Tz0UL9yePNwnmq1IjPhRcYqsLBMq2IJIMiwoxJhaGgLEybNKOtB+w9n1/2RQ75+wKMnWZLLNg95DJfZK+7eO2hDwIrObjofr6FnebVRA9VJYdDo0xrAA3LMqOEXRqQruIvEaGMxl4haBFYKpldJMbTkF27I/OZdrpgWL3hSUtL/9kdZIAvlVTPxerFqLFZluYdWosF0Zqi0rDh0xetwWtJn1vE6YRQ3Yiwrkpr0ZpGelHZYiHfnc/0rbNtRmtj6vISzqiEqSkIJSASQr82wVfhyWllvb/y6up0cZqPWX5lsiOAtKvBu6VSRMKyLpzTdSr6YFnePqpXzy7YWx/RxWAL4axcpaXLURZPcwImfbJHhz1FQWzmTAufN7f0NYNH25AkT4SsDISdupqj8garCT8Nt3YGeWRqg0g4hYV0CUfIiwEksAuvT4RbXp372KXic9XGEcPa40/OwNOMjR8e9EB+3lcekxWEz2JnTU14RL+I0H2wmNDaklzcOJ8TXNGwvjuM7zhrpmOWzEKW5bW8ZngBxcsFRLN1SPHVqpFWKJvodhE5nVkQrHvk1onIcTNnaDz7bCViVVsY+dtB+ndIJvMNX+KJZpuV9N2bxuKP9ifC0iQUiYeiaVqVI43T0lhakOTNnaZt5gRnACdnbz7/YdZFomvA3SnyvUtrDBsILZQDexrdkWSbHU7g0JaFZVmjSYdFrpLKhVrmJXuIdQhl+pajM3pEYB1j71YQRBRxSThKVPe4MyQYx4hEat/TbEi0ggnf0hNcWpTD5dzV749rN98smPDTdEIo4fSY6ujiZGUiKKLaTkCYGDBOMTXFlNJ56hmorXPJzuD5jF8OUAGQlidJSzzfeA0WZ45Tj2/XOTX8oZH+pkv0o62sqLnMwmIwFtGs37RcdLU4sylYsWOcWUe5u9aWvXDafHhRZj2oeXVikN3/soLfA3PdXWgp8n2M3T6EpZFjsUS6IXK6wYEVeg/annv0G2oZ4FvMwBZ6+vtL8yiObvG94UHjO63KzXnh5rSwNMU1YNwYY/p3R6u5z8RAtbox+Jzzpzcn3j6v3G/GxZyWqa8i6rfWkO5zHOu5m0Rgak2yx+jOQwYje/cs97JsdRJd78J6Rj7TbExCvZU/TyIUivwR97qq0sWTUWWICYsHGQJLHjCa7J8ssqZNNDmf+Z/w+MK+tfOQvc7wJ+T/kSE8QlVJqJn27EBZ8uMZ0hd61AazckbT+h4EvJgrKsUYYCcmHK1uKos873QF5y07qxdvNY7ZKwinJa9SpKKXKbYWXFnIhSQyz1lVIF4BMg4dFWQPLC0J1OZdSvFi83wa5IJYNLKnNnIWauCPdZ6P0ELopRQYy5qF0La+OaqGitMs79Vb1psyIW50Z1C2pNdp1nhGb3FNYdFY3ONxCVfkauN88YD6KORgFuPx7ls3/PjlA+8/vySUZPp2RazfuxYWf1iSABLpExXNvrQdMcLvrQ576QMP92hR2cfsfoeHUjQiPRNK1OdaCKcqc9+t2r6CSpAaSBCociC0e632Mc9TwaVHgnOYq887vqD7nnFsQ+nH9MbrJ08Tzm485hMeZTYkZGcL7UvXd7T76Ivlgx6tb0JA0V0gX6cJznv0dOrju+X/xa0m+TwXdkMmZXiforjxgoyq0eyqhK+g5hBmY7AStmhHWUypQt6hnDqW2UiLHCI8EnIVZdGGmrHZTjAvBSvm7wAAACAASURBVGBkkXJ1Rjz4nSG0BxRVMDth3HBHRlSBmLfZbMoFbk8LS5soHxEml3Vpwo1EdNcO7CVLvVic1EkRJCK85X5UlLjaRJ7WxmltJbto6e5U6UKEL9qiaFfoAcNPBDli5p2JahUTw0cI4BjV+jItuiV3dlS0V6Z1dy8BS99W2HsyZWbgdlEuplxGklPY3ZQQyr0lidc8ODze0WB3oXa1/PnHF3dCmGqkhOQgCEdBdaItxkzXHoUlo7El3AJiiktlFI0ZzJGDNCfMi1xFTXpqgISa+30cLcfhp/gU6WwWuL+bpyrma3UjKJXh9ay54Kd1S9NVxtGJms0K4CBFni8CfS3SgdNYctzMDENp2N6wew53WQ0YPnLosiaxKddRiESn4ivksfN4/TANXkMdPGNKABPWD+e6DVZtdHVulixB05YQ0WltL+Ea2eIy4He15chAydTM2SEgr9Na+JZX61P6Xz5co3NdlsgVlXKqYY1It2q4TY0Ksmn6tgPSeodF9LnVwfD0B9nTVvF7zLBKlLZVK9TIrlXqIy2pNFp2WPTh9KFcxyCCU5XbjMDSINNsHNGh74arRqWU9KO1+pPHm4VzbfGJUi82mEHkSW09mEuqmu14TSG67FWaJAudtSZBgvJG5lFVUgcUsSGVQpHo6287KAoEJAu5j0caTHkEIOv1gJ8DSUvEbF2yfz074Hl+vvwoySqTlM6wwMUtTg2ZdLLCoGbZWyh6cCJDcGUWJVfurqpOQqjj2aqwWcpS5+d2Sp9MwRw4C3FvDSbB/9ESsLIQktUpu2LY+kAVnpyiz+vduXFV58VDRjSHsW19QvyAlJl/9Mj1RTeI8qPjvJrWThVQZ8s2mWYRqArSQpzDhlflAf2wXc4qDZaskSSU0RhOF8ttdZyrh3UcY+xd1X2PHte4lm9pppPYsHjQBpfhOJq5352uWdNZNiDgcChY9YGmf7vLBfPmnWK07Uv4CwznFwhnazwKthQ7Z8Dez6eErxIPr8PL1IGHm/Gy75lu8UdCc3j6spyzrKKCKcm0yJzqDCTNmtGDiEnCJThAaFB1ei7sGVFMYbPp49T3MnLnnugga/eS2NDE5/RJQW6YbTvi8XMLAuscu86aCt2NxXjkW5UlaKqcsinVlsGGSct0B6KnzszXVi4zzzN9bNk5si2xlaSyiER+m2R5PFpq3l8HN6dI77QWyMLcuA7ZFUX5tVTqYM/lBqIP4ZcWhH8ZDTdhG1FStbaGi3KzLIhEB/cre/1mz7pIJUnzHiiktpcwKuhTvXwOXfKmb+lJpo9xODfHVbABwbQlugcWnzr/odFjKJZUIKgZ+mCH4MH8yWxEafNysD3Wzfx9ykD+/w0C+sWwVpM364e8omrxyNgBSErCTwhm/dihQkG/KaD+2ufrXL4AG/teLKEhqu5vjlAJ5VRJ+ykUSaZGQ30EIq7HEKmqp7x6AELDOXkuvnmfZQT3Aa3keMiIYa0ivZLX3skKQuQKg4I3Zk7UiFSEyb6oK7o3PCLES1txjL753HhtQuwpB05zR9xmnrTGSCQWfvU3khTOfbxzbkTnTmubDwzlstnMy6q0g28Wx+wyR8DuaK9ziHaaTN06AEY1tRIerh1bo/zs1OJ67hFMOiUh46pR9KACklUwlYMsEsQM+lAd+aqbAVGfmWPRRDi1li1NBF+gDWhbKISKkqsoSy6SkQpRHLaq6Swc5uAms8tE93AnvNZnIs5oVyIFfXYU94gT/pPHFwtnaoUYnQVsY4/cpGDONgL1muyyUl0NjsGk+b28yWnv2YWh6HUkzC1BcU9K3diX1gE+PpJvjzSCpbUdqbZqt6+iB7tFcOQIfuNxdu9tDklCJPE9SDO8BMOnP2pmM1EeZUQjc6AN8cqB+TxfFOZm5NUimR3BGA0SvFcIoCLgdacxlup7wjze2eEvEnQHyb9lhn531BFCG4ouXCjZny0X6jIpidmdIRdvWTlN380txz2Vsrgg5pl7lUwRRXCnm3FelXOP36OWP0gKw3K3Mw3Y6O6saPq1IQQjx7LP+7Ao6bKDD5oBqeVgFXueY1FFV0U1cvdDIigYBQC10VFY39hfxSYM7qRSzXRKjIw98rkpnCRkhL8WrNfbn3t8QbTWmcVnUWcU/+ySubLyQVMLoMy+Q2UVRQ+CUxavLN0xwJQSVrnSCt0Vq+gRK+h1YS+I7WCRVJ/sy/RJLbLZuURraWaZGBEsGlL0NRgKtUNXpYMqt1kOfnlqnkpAvPwKz1Ku+MZI2LdkeRpkAMM9awN3DqoklzYgXZQkefp6zKfPZ5uIIUF7amtP+D3bcuZTCEXbSyE63r9HTnR1R1o2vqYirNmMOq9f5IVovkXWa2a5eqUV2C23Z78gEZKGF9HhalB2XuHJWOnDuPRqrVJQPFtzEsJLIoQqgn/cNT4hrPm0eMXWarkuN4utAckxEAky/lkb1qO867w0DLhsUdDQPaxzlajVvA2vlBGIhaK1uVJqkVUV72Hp+r6m/LAeXz++wOfM5beeorrEjdoU1y0FpXycDNK4tAL3aQFL8Dy/fxDEEuD5IG1/iDKf1YOI9DMT2u4FLa+pngMMnZ/XsCRLKhGXuK21dIN6+JkoQ5L5kZZqye0LD2qBIr+X1ZFagC3TCR5pkijhSh/TwVCwkTmz+FxPvTL9TYtNgdQi3jvceeh9h6jp90ewJZ69/CFNwW6pVHeLGO07VAvT75B97z0UwZm2KE/XJYQ7I8arSPraZJ6zZXvIffcwT2sgqdSsgk5kczMJi7hZlonpEmV0OM9ubxE2HrbBujrebaZ2okeu0tXwrhjGGIrqQC22tTDZmUg9fXWrFiO+j0PlOoGpXIBDUGxXfWax4dFme7VKbaEQCsETOmcaKkfVy+/wWoq1adTRasYa+qLjp+i+J3BaWG5PjLHhmwFrENoPchWT00M1tjyt12qqPVEOlJictrjT6dBRj1HwMEKoBZNL8kioDVXsWGfcUbDO1+NMLVg6ovQp67FAi0u7CjOiJxKgtrY8zPhAfNaZHfsqcBSLO+B/6PeAYZqwPSh1GeRwi2EwkgmzR3Mb0dpiqIF1xghl17JtZmxk5FOoImRf5WtEJHaJsSroKKQfOnVmjErVFs7eufl5Ezi37AQogi6a/rBPJTJTF2WpKSQCxWqK8Ze8l9h9WhVu2olzU17cX3m1dc4n5XoRbs4LlxeXCYqayuyOoIOI8haaklqiSjOjS/BW5UDFOy7/kdau2pMOG7lW4rMrSyghhMsWO4Zdu2c1S3YLTMG8ZPBHLDi1WwVH5880AvPqtXh8CudPc7xZOF9e4dygZTW7Z0JdW1jV6NQ8JzbSCCM/U/7PLh7p2B2CSQREnOY9ClZjc5zwTxJHhVV1o7rrRalMVFxMobb2GpGh4FXBrozqZeqgk/tsToFnTp7WMyWkLoUR1DI9FOHue5JYiAHu0XZfxDnNjKPjlnCxKitUcKJR8VVBZzVFWEHL65ePU8GYCoJpWntLNEBepyyBppsw/dE085boo9p5SCKbG4mcqpmwnhrnNbZHiGqPjKqLMHoEtCqnWSED8yyQnstSZr52XRbeees2KlfMWdeF83nl7tJpKjy9veF33n/BNvbecUrwayF6KqkWfZSJzgpRNG+MQ49kqRQ4JQ9pJT1iAaNgdiKMGMOFKKYOhtHIbf3cH1tj9RBMCEWt+2qmpYvgcbG4FxwfmUKcQvvYkn7W8UbhlAfDzy3Yx9HcNHKd1mNwNMzOzHxMv2eEECnsfWpJITsIVy2nyg0JSTrOdWTshPqaEE3HJSHtju8zCnmYiFjpmU7IxV3/IpgQX8i2SAypliVl7RL2TfD6CCykrxhC4tnNT7z2v5CpacMCgbvFSq6O455lUgn1Rk56S/+tYbHloOW2fr4v+2qzIokwnLTI6UbUxDbfu71XIKhb3gtTt06mTflvqsLtqfHW7Yl1UbbNuGxbNnu27LKXFjN9qEf0S9/TEk3DKn7t7TvuTiuX6+AyBttoqAovLxv/0/d+zP/8j57zb/78mXNVzCTs1qw4iftVRHZqHOlaVLuqQlzK7utNxk8hBiEg7VwmOzVz7kBttVnSHgAqgrxYKno895bJSPu02Hvj9KJ3FsJ5fAhvOr6AhCDRu7Z2fWFjlhsQ2nsnqJQFS0xStDXN3gClYkvAZsLH9t9FaR49P2e6xXdYpBko8dpjJRzGeZ5ZDO5JlJA276t6+6QKCb9IwqK4F1x1XMJvM3SnI3pA0rLtR7qHezBsqn0JBe0IJoph06erUSKJ0S2ji0FwGLsvmZFc99geYPGjEsy7yHvwqqBwf4ScyvedakUKCcR9m8dWCxBNms9r/B7BHc1aznUGo1Sic9519Hn+GWUWeXTuo/chkiKsVZETvNwfPb/wpx+94O27M3/zOz/mr/29H9G3wQ/fOfEzd7k3qJAtOPdgGCospgwpWMoj5ma4EdGW1VJBZO5pz/1SbkFmC0UzvBJjtnXLXcJysyLb+82GkB322syHnh6W+aNA4q5OP89CfknLye3Kzc2Zt28XPrbOxbcKawa03bP7VAnZ/PtgwWJSDu8dNUYV+UlWdphFh/IhcX4bUe1RwkNUEKRZS6YQR/wC4kEPJD+Dz0mqznocUiGVg8qxDUK47DP+mAodAh0yu0MTmdffc3z7tGS7CtlzgA3BTVN4lT6CzBCdzEPoYn/KUERNiyhQFiCjuxV887jPGQf3LIUaHZMlt76LZ9jS3K2LZqDKZ9mueVDzrj3rE7vyvQ9f8ktfu+Wtm4WH64ZI1nQeLGURHuulSEXtHN++GffXLUvOogH1sqxsw/hrv/cj3n8J7QrffWE0Gos6X7/TTFPkOSXrJOdcZKEB1R5m7zpoEpsyjYw+4+zbX4hMAFeR5/6oqqbQxY4KqudscJotrabRPMr3yGj93mjQpwwU3/nxUdDgywpnd25PjXfuTlwejHfaEz4Ehl3ixItm2bpNAUszeRBCyQCR7e+XUBAUtiKcC7GXhdoI5v8IFk/lDKsGMwZXo2SnxkHI/kQ5EI+uo4iGHziHRuc8hVubNLouWZlCRGlDMPetBGtjItiJ7rhkn1aSEF6CHd8qml8Jc1AWA1p5ZehhNj2zDO4MQC2fUyQq/LWh2jJX6MzAg1TptoG3zGwlM8aTncSeOrhZYV1adrurkqyE2UJsVHvtXIfwnY863/5ZZV1Xnt3Bi4eNvkXbkNaC5tYSXg6zg4WvEQhf63IdnJfO7enE3Wnh/mL8zT95zg9+2PnGs6f88JMrv//h4A9fDX7pZPwbd7d0r8ogsmtDKLQ6ebC1ItAodW0pOBzPo/XcS2OpiU9fo0ncc0+YGm1hDPP+aD+T4M/2LAcL33K1gLXujla/JAJ9AXv843VeQCbhyl593vFmn/N+8DYL37i94ZUZl+16WM0JKRvhX3qRDY5aoj6bDIUMeapIckqDRneDcBZJbQduQfEaEnV6oqAZWp/dECz9BAW3dLZby+cvId39UQdcom2ksXd1FxE2N1aNxPpKBHiKbdjyMfaAscwyoVnGxF7RgOojizJD56UkJQRzFCyVsDpaWjYT+CIwcpJFs1tdCmj4n0Fpq/BCVOizK4Z8zuiDXhU3MQe67j2CSusMPMrFlFn6dX/ZOC0nzg2+//E93xkP/MpbZ6498pGSFiKsesBJ2zKxn8EalT1XuvXB1eCG8OkWh+/+uPPv/vlf4L/+Oz9APukMfUI/N15snfsBT04Lw2OnkabCqp3eFEnFEL6g5GZIqUgFaJXSAhFFW+O8rrH2RmyIsGpjmGULzVgy7rBl7raUgOGZOpw+Q+5eHSQExk4qqbhHFaDtMDImRVIpxNKo4oXPPt4onHfvvsWvfvMtdIX3blY+cGNZG2PT9OdSvWNxqiqPmUJxIC6osCzKqsKT1ng5wqlv7pwIKHnSxiqpjTLytjr00WNnK0YmgYsnk1FUDWYNydbYM2ySVQJpraYer9vLbddF0/+MIRy5WFtCUClNTAmjUlsdFKqMnGTRJA7XSWXlORle21NQftoupAWpTEKDK0JLF9/SMrkeIn/IZJ3MrSLykjLTVXHEBkvRYU+bznabVs9I1nAWxc8jWP+9T+757//Bj/jOX/+EX/vV9/gP/8I3eO/pytffupvNmEUkdpJ2Z21RjC0I2zYQjZYoANfr4HLdGKvy/kev+NPnD/ydj+55e+mMjzZkWfjwxcaf+8YNP3+rnLIr/ElDaa5nYdsUY6MJ9NG4Sp8K2aXNyqOAk6EkTsvKsi47NzdrM92jALumKdpoVl1q0fCywTREmsqin20RS/rISfNiBtXaOwrdsZDhUfMbPr85+hcI57N3nnA6NTYfPDuvUQkvzve3jT6EWUv5qNA5L+jsKZAlSm5u2sLT1nh6Wng66+wsd2wanFukNboYls2Rh0hmTzyT7QMd4Ts2G5kylUNXBfYIQQUCUntJDZSEFdWsdJnLWGahVQADB8vWJCL7uJTFFSHbWMj+OsX9qA3R4tVWc+POjMemFpbslj/yu/G4WZ5FwDVE5i6lI30rF0E923xkGxHfb2QuClGZNaPkuTWjrUsioeGxL+k5m013whp9cm88fzGgrXzzrRNPz/DO3ZlntzeRMuqDbRjmA0G4OcFpVbKnFU2C3I4FQvn4+QOntvCDT6/8j7/3KX/0fuehX5Cew6jOLZ3f/NYdb2vnDz7pPFvh7hwFAItEkOh+G1yvI1oqA/c4a89tHQ5dI9a2cHNeWZeGEztU9xHVLsN2Lm71+ynWm3q4BEYMumdN6/C9qzvFkvMD2T/nOZbeAerluBfyYlrZw8J67XijcP7yuzfRiGkIiwdT425ZeOt04n67crXomhbdpnKJV5CmIiAtIMWdNu7WxpN15VbDgpYvch2REmnpG1T17pKQeBNJbTW4dmdocGsXVbr7FC5PP+4wOjlZaZ1KQCq6iLBK9LmuDEfNRvT3yTA9TlDaD9ZTolkWlC+ZfFF0lg0FGf3x+Mcnw58t5erhJmbaJdpguO7WuDVJuFvmNapAISpNlhaF2a1VcVx8zuez6nzu0jJSUeQkU2zmXLpzXmILBgHevjnRdNBuV37l689YW+O79/Drv3DDeV1Yl9ja4ZOXF9b1xCLK/cMloLMKuo3p6wVBXHn56sq6PPD8vvP//NFznv/wgrcFLgZ9oJuzXTvfeutEv0QnwWuD8xLWLvbYVBYzumY0VwM+r0u4NZbzpE24O6+s68JpWaKp1wjW2bDag9NnO8yR3Nmi/RWEdYsG0xdPuOtgWHpPPsd6V37M1E8xq8KoVvzhILDyJYXzL/3L7/H7f/icoc5Na4yrcdMaT08nmsfeHq+GhRXNqKO5hSXNXcBEgv61toApq+YOw7KmcXPOS7I6clDUjMXCevaecNQVG4K3gA1mQY2rLeeagzXl2ncqVa1+k912lp9YHdEXossb6ftF1kKTCyqsZaXYab9Rv1fCFrMSnl0S2SUEfJ+H6iCf8GYWCKQcTQUgNYuzCcVIqI5mPjiJ86awaKgLz0nWjE5GpLlNJaTKo82OIILrNI29LvHs8OfcrY1B9M1ZF+etc+Of/fodf+/7L/jde+XDa+df/Ln3+MVnwvP7e775zh3gPDmfeXl/4eWlc3taaC3mvDXh9rzyzrNbfvz8nvP5zMevOufF+IWnK7/7/EVOXo7Z6DxdVn70svPRiysC3Kwr7z058cHzhyRo7KmmLWHpqbYRJDYqFg34fnNa6MNoTZAewjGSHL+NaGNyHUHR82Hhb3ooUK+It43YezUbpAXFN3LJRzQSOtEfCehBDHk04T/F8Ubh/N0/fs6ld4TgXN4sCw/bxqJwu6ys40oTZWS7iOGpecxi78KkS90SFQGL5O7OKayL7uVf3uDVZeDjOq2TWfmN0HQFEbYUpOFwceeclgQVTrkHyW6Vyp/w6ZuNhI1kjx6XLLy1vg8egrlykj3IEIcyRDMid7SIxxwkBUypVo6Q3N2Ml8W+0eWZJgieeDq+GZAouMoGs/5Ak3Xinv5iTrRW1DAFsnYxy/xJFnDHRayep/ckFQjdFG+wro13F+cbT2+QpvzfHzzn63dn3n138N1/8CEfbD/mP9Mb/qN/+1e4+eQl33/+irs86/11xA5pkl0VlsbTJ2duzys3p5Xbk3P/4YXf+v0f8+e/+YTTcop8dQ94IeawCL/70vj7/8eHnE7OX3jvxAXjV95dHqXwhcqDwp00yF5Q69JYNbjQS8FrJytMYiexMQZXG1xHdOMboychv9JYyXv2PW1iakjWiQpRNXSfpWQla+Vz7uvo8Ksf6aTlz0p96TOPNwrn/af3rOuKt2CfGOHw360nrjKwoZzNo9VgLkJ1494GDyN80XPSb9aCkod7jt2MY6E5ztoimifiM/kbPWka5sapLZwS/w/J/OehsLoic15SkJarmm6ZBSzaN7KNTggCqDRmraMIK48jumWZSMUxXX4JqyXuycAphlFQynyWEiVj5UAv3ptWxe+TTaTTzk/4WSF9CP5taPZ2WAMplBL7sVQEdVXN7vN163EuS81+uzauAx56BIx+9HLw7V96ypOblXNb+be+/R6//f4rfvjiAR3Bqvrb/9t3+N/fW3n2TPmtf/gh/85vvMUvvnXl7duFu/PC7fnMze3KKsrp1FjX2H9ljI1XYvwP33vO915s/PjlJRVpuD8OMJwPv/8pPGnorfDB9cqTvnLpMU4jy7NUAuriS+5GHUiltdgNbRWdBIWmwnXrjN5DIIdz6SMrTXIzqOQ2mx/ZQDZ5tVjtgWL0LF8LjnEFQR9JYohgReprkioAOFONbz70TW+elxYQRZVFhZu1sS4tXl+EJ+vKk3XhtESI+tyicPbcVp6sK++sJ560JXafMuOaTnUJXm3IGnAgBrxJWjqP5anZG1W1YSKsS92PctLooTo7F1BbC2gSFyLeOgi/a5F2EIj4t6A4Ol9TSW+uBi8neaZMhPldgNpjpZz/SLPEFg5hoTNazQ5x6/96mKHgL+gs4IFSCodJ1oJOsn9219vzvtJVj/xfWtmZSvW9QHy3utGx/H5zPnj+wIcvL/zxxxc+ug7+qZ+94+7mxLtPztz98lvwZAUX/qu/8f/y3/7999m68L/8/j2//cOND14M7q+dZW288/SWt57dok15uA6Wprz97IafefvEX/1Xv8Xv/eCeD57nSOjjZagasHR5fuXXv3ZHcGv3OklPJpaz551FYqe0RRvLsnBaD3PaosPCwza4bFsIZm4dj0eHiYf0QSn3Kn3PDePqIwyQ+UxIqJO+Qc3V0WIeJS9z5bX3REQRD6L3JS0nItxvVx4sIodv35xZRLltytVb9MChJYXJWTUWa8vIamxICg+aoWg3hgm9KYtH+mNYDKqJcvGRECvYJ01b+LHqsecj0Z/m1BawasM0WJNDaAJnXcA7G/2RQBWcUyVLpzQpbxkNSjZmlARF1LjSExVIMTwssDAjqoglz5VM8eRC2b2RJDPpnIpSJFXmFFPqJO1gDx7kvis+Azl7kIHJDmJawplXVKVqITXP7WScTQ6cU98Fuyn4GGzW+Bvffc6zdcXlU9Zl5Xufdv7St8781j+653S34E355jsn/rWffcqPXlz4X//gx7x/Fb7zyYXf/NYNbz2945vvxI5fz18Zn7y659nNwvOXF775tSec/vgFP3qx8fHHPROruWgzlqUIthl6s7I2+MbTc7zmtVVDKMJa59U/eGkto9bhEo0eVm8bxqvLlesWu4j13INzTFpe9AI6eXj/WLXPjL1GtwxQzkARHJBMsaJK6VawsaynzbjA3s2PPYPyBv/zjcLpDC7biK7hIlzG4HZVxNdp+qtl4pIpB9cqGlaGCC9750TLzWSzwNhjgVQitzaarSZXTVsIuUTrSrPBRrI3xKN/6mg0M4YYrrrTrFRZvKXjHxAwmnwIY9EZoUQiEnpkM4kXV7Tk9SAQGgKm5C7Xsgvt7KwwfdSyeDGpZc1sTmJC4yLv5vXneWfobxdoh8hdegW2KuCQoDktZC2OCXGdpJ3FYvJ6ZkBaWneJlEcTuA744L5zeqqcF+Fv/dGnPDuv/CvvnfjXv3XD7S/e0R1+7unKt99d+aOPhR++esJv/+PnfOd94Xf+8Yk+hJ9555avPb1j64Z6jPXv/uAl/7Q0Pr40lp4BmirOR9MhH/SP76E5/esrH9nCX/n19/jxpw+8uvTcPCg7POZzuVTT7KiquTyM6PY+nIc+eJV7cU7BrA2MRp+lb6Hsy2LmBlMJoTv5vo+9RC6DUpX62+McGRPI9RQDHXGRLN4LxTrl8ktaztaUE4HXBeh98N7TG/y88OkD/MnHrxIKACKZM9Os7jJMwolfbbeGIyOPMyjxaCAk84+x45Vnhcekz0amGFKD4smModazc0LwFkv2IfmQzQPiDYm9UGYLTI3W+tHz3yseE3BXMuMoUi8erNdBSx48g+qDi9QWCAvOCG8zBWV4KIrJkXWf7J46h5FEfGT+5+ESApWlOgYcZL+jfH3ebiqY4OFqwrJAAEp1D8jGyeK82gbbiPsawzEffOvJyu0Cf/EX71jzcU8tmkt/6+3Gt995yre/bvzJh/f8cIMffrLx6f2V957ecndu3Kxntm6wOf/d//UB/8I3n/FrP/uMly8+5f7j3OA27x8Xcssu+scP/M6fXjmdlHefnnj/04Xrqw6En5jdQ4AkTmjkL69bFE5s2aZEfO+OEIVVERSa+6NYOCebW7J/9kLt+l2I4E90dK+E1R7XkBn0kyl0e+Q2VtagCvSzvacfGdg/ebxROFeF22VhMGITMZwX2wW3gYvwMHo8fK2GFJJqxRF+QMs6v72yoDRPYrLMc3puW+60ZeHkkXcbHmTicqQ9BzCYRIJp42RRAU9aWvewngvkAIdf8iRpZUZA0AVmjix2lgyYuotcFk67zuDQbIGpO2Qsmjdl8ETT4oNIy/xrobZIcMfGvCF+jtNcA3Xgp1CA+QAAIABJREFU7Huy+B4wOljrUjARAdxLx0SORdjHmRQkc7KGU1u0zGi4BE06BCkaSz85x/N968nK12+UtUV6JXahtvmsd6vz9Inxq+895VffXnjowtfvnCfnFXPj7mbh+f3G9z94zn/61/+Qj67Kv/+X4ReeNf7WK4fangLZCyELcTw3vvs7H/Af3w7+g3/p3XSLfFc+6ugI2D/cebjvOe7GqbXJqrqkBaxtAC3jHqHkq6PhiO3lh0/B7SPWlZjnbgWV/wQYj1DIrkrh2A1hIiCreAEgRbo8VGT9kwrneT1x2QYnWeiyMcbgRy82HrbOZWxso+MWpnpVZ5N0lCWMtjjcrpECGZeHHNBYhKIa1m9irejyJgrrsnDf+665kBycQqGOypIdTHL/yopuevgQSxPOrgwikBBBg+BdLpr21nPQxWYgRmhh5RWW6oCQMPfY0sPRFNC09tkHlaaIZEvpqjIJtcVwYaCo7URzUpmMDCRFXWL5i7L/TNimGtsmzHRTBRnys7MF5wxYxbV0MrgcGUkibzKJ8UJsXK6r8PZtuAPD4Te+tvLOjc78oE4imHC1yBf/c+/dMLzz/Gq0Bd57+wmXrbMN43xaaTL46FPn0w8ufPRw4j//b/4Y3YxZ31vCWW1Y0bhzdy7PH/i7f/uH/BcfX/nL/8xT3jtFT6FumrW0ydrp0fW9k93fM4AUu1VnLnPkPpy2s4K6jey5G39fPSCs5WvWg9eb4jh3Ipu+RgqoZ/bAYVcylEa2XTuXqa3PfVnLeX+NHZkufbBZpATut43Ldcvdf3O7OfaghbixIKzaIrrowd08L2vmPYVVWjTrVc3CZCAFadGGZpeFyJnCNvosFyttVUGW8CE1WUBKI1rwjxJaD03aPJp2hXWtFpbhD5rE9qeafszc06RVOqVaRpZwahRXZ/8aQfZ7QfeIsPuuMXNeKpCF8Bjewv5M1GLdYaq7BwNIIqiD7lCW2VApjgoUHSG4e7aEcoGWDJ5UBLM1iwprE04tlFsjSA2LhsuheZeG8tDh+SUCLi863CyNDx6EK86vfVMYQ0h+ADfnxifX2NOTVaL30LAZEg8k4jslm7xPQMy5acIvP2u8tQaaa9napZtnwMezA73MngAP14EKbGPkVvLBNuq5xfzVs32mp+/qcS4bA7fB1YNWqlR3vxFu1jQSBwHMAfYMMJW7Eg+Qa2DOhdfkH6zuZx9vFM7L9Rph5t65WOw9MYbt+3CmH+koD8N330U1LU6kUNRJwkFZnwhuNDhUFni29ohrV9MkcViktg6viGQIt9fDZ5AJkoInIabVIW+zgBkt51ym5YwuD7E7FAQ0zRyWHzxLUSQFY3+GuFCRGGoiPJVO1Y+KRPDCiMVX/NHSmJL+aez8LFNQmZ/QmadVklta0LZFcKtq16tMare65PUzbOSxJYKTVSlThoNjrErUjGpcY20SPnwqZk1E5A6vNuP9l8Gy+cMXxj//3i0/uN+QRfk/33/Jn/uFd3m4ds6nBXP49V94yte+dsNH7/fYSSDXS8hhPrPEU8t89vj5c1+/5Td+/obbBVateVYuo3PpnTH2/TQ9fcQxotpk2zYu25Vt2+ij5+s2q3mkfFEb+DBsZJd4izWJO937jHFQsNaPplMPr7H7kZVqyXEjUZ9LBsHewKuFLxDOrXcexmBzmzdb5F/zfSA7EbTZiEhr3hmjSh/I9iAtl7vsDInqHAdVHSHZvdtyW71Gz4dQjw4CAVMNkQUrXzEFe04Ssu8Q5RkASIWlyTVdcrA2i+0EGJVLKwsKjWLZxPnCZ5UZ/a8WIFNQVadwpo1N9JLFyVLEdubPqYC1nqO+u6dy9vRIm1Fh1eDBLmUJ89qPC3irM8OUYHabu/uzzN8922cevptMruBGROT15dV4tTmbw+0S9nQM4bs/3vi7f/AJP743/pO/+Et8+nDl3Sc3fPOtG053N0i7x7tlCqXmXjlaEccpEsnp3Pj2Lz7h7VPwsavCpXr7rNoYvSc7LZT6llvJX7fOw/XCdRtcekRrr73HemZvbFbujeQ55z6mldQszytdteo0uN+zHaDRo6Gf55/wST0bBIRwVorts443C+fYW9tvlg/v0a8z0Fr4VeLCjcx0a0CEHqZHawcFEsZCOPwSvuFMN5CWdozwY7NGc2kRYVwkaG9hUQPK7fVzka9ccpE3DcFvGumY2Ao8ks49l2Lsw5lBKhtsQ3HvEcRxIk7hnmSG3deU1HpeVj5/OhFhlgxKLegkPR+Lbes7Ael9tk2MdZJ+ZVrAlufz5PqSPWelVUBoJ05May7Z8Hla0f+PtDfptS3bzoS+Meaca+29zzn3xo3iFeHy2c9p/Gw9Q5oqMU6lMpUIkYBSiOxRiDZNJOjRywYNfgAtRIsGTZQSlSwLhGSRRka2wVjp4tnGjudXRMS995yz91przjlofGPMtU8UN+zILUWce889ZxdrzVF94xvf2B1WRFF1ZcGYa1XImJnXwkzGEIBUZJ+BXvOsdRO82jruZsW6Gi4VeKwd379f0X6w4X/7wwV/9ydfYZoy7vKCf/SdR3xnzbApA1vFkF5w24QlYHEROfg1ScDdN97Ct75+9FSW2VZzY1JnW3WfjlGNdYTcYL1sG3ubvjul+v6UBhu/F5KZ5mBRBdXuxdPdUWM+MdKwPBtfnlrj1WNEW2FmZXL1GeGEis9+vNE411odsm6oMGzjidjiUL/RUxiJo2YMKw055iR9bjKN7CXSQtcCMgIs6lvHeu/IkqCJli2WkL3XWRtRN1gGd1LROhIEyUJXzpA0lsl7qpEz5fkd9VVVJGU6q61CVAkmVKK13QxVQvZC/ARFc5mRP5nPD0LGgPGQMImbGSWheVpoAeLwSoyhbZhfT3cEIcoVaeqIyExHVd144dfODVcDcPPXVRF0SRglnYfpmCqtxsQgDXJ8RNqdwcToma6OHoGtD84Nr1b2mf/soeMHNaHnCZIazr3gv/jHH+Hf/9kTzpjwv/z+GZfXG6Nlzp46+DMGDfPCQ5vuCnpvUFH8a9+8w93kTlx0B2Uk9HPbKDHCsfZmJBm0jt48yPQAciLvgQt5BYWPGwF6o7zrZuzBm4828rZ/kjngnuqTteP4uz013JHKWhwAhIToZz3eaJzd2fjVeHFirV1XQTNOF4oAph1dCB7w4u3DxVm5npzp1H7jk1zVGoa9kIYC1lHUpzRE3MMXTL2havedFR1AHoAJx736EINGtDrM5S4l6nKmgsPQzJBaRqobHlvbIxELBIjuXB8ClaxLs+cJoWTeAlQRT9AFOxWPcg1uMIKmHDKPHibvc0TNHb2MSD0Mjx8LCZ42K9N+Tfx9zfokrVZHkffRMWfZOOEARj2hyWgzk0aU9PsnUcPHhxE0Z3B9/1yd8gYgKT5+bPjuBbDXlbObf/6AD17P+C+/e4/TWwf0c3ftKQCzBtVnj6BL5eG/zfjnf/4Z/vTVgtqpfnDIsUvUnrR/BFSH1+jl9o7zUt0wm0tbBtGg+QpARiuKde387Z1ny+83a1dspE8YGWzndF8bpl19T65/3p/jqvaE+MB826PoJx9vpu/h6rkAb5qGCTnFTgjvRx6/R3rhvXCUdSwcBdG2kZ6AYFGHw/3CwyzKzVb05E7LEtYT67ahwutgj3IJBklcQMRopYA1JPPV5cI2Q04Zp2lCVsHaGlptONcNZoaaMupWvWEPB1tCrWA/3OLUB6atHQEahRpdeP0s8YlJuA8igAa0LtfGiR3MkfTEKEXhuzIJDmW9jqb8t5ISZk2QlEb9yZ2YArPQQCK1jWW0oFWnWRojJrnN8iRVoz8jYLTBSJLfgEMS3GbF7VTw2686Pvho40zmxUmJmoCV6fLrjzZcoU/+YcHmalLgsqGg4f2fOMEmw/u3wHuHjK8/S3jv1DHr/l6uYxSDWSOTTLnmYXPmT/NUNtpx1dPXQGhDh7Y1l8E0gkFkApG8gG6QUXtij5KRqu6pyP69YTRXhhg1qdlTm30SWT/9eLOGkCOmcTg5CxGaoX2vl/xFk3u07k8s/rvkFyaEngzM5e/DCBBt/ARDd6MgGpl8ip/9MvJ1ly1ja9UXowbtjySGZGwcxzuK9elB75pLwbPjASUrWq24XzbY2bC2BNkUmtS1X/kpVXUH5sQjfABUV7GP+0mcqeKooXmNzeqW6XIKg0y8MfEMzGJljNSFKlyOVFaf/peUS11jwa0mheZEgTSNdBjeFnLgTZh+0/EAkgRVgK3R2Ir3baGDVjHoiLUDl2Y4N8N5ZRSYC53ud19daJgrWDuKAYn9WHNamwy1Cj+eWfCVdw946yj4gz/ZMJ0O+KVv3+En5w0vSsd5azgVIsbiPc+nGSSNiUQPXsParpg9CMcT6azPYsIczW1cF+g9z61jqB3YlRE+2WZ3bZx2bZSfMDjshvhUy/cz+EDyyW/sjzcaZzVv6joSxgj49AXNazNgp76J4GpwmG+YmebeWwtAhB6bPytO9BSvZZNHz5Iyih9E5IycM9a6oVUucW1gMS8gx3espAPHuBIEJWVMiSrmz04zShIsW0LtHY/L3i+1KMCGsXnkkIgiGL3y6G3GdTBjASd+HTAOo9fCHknV5QJjkkHGdfO2jI/SBYldU/KIyc3QRRU5Z8BnYksiyXzKCfMhX+kHkbJoXVCN9EEOvsOt02CNoNQpU+mC98iGVHEoRlwa8N0H4MGFvV5vgj85Gz56WIALgLUDVSAlORpLGqVDyztPWb3InRUPapAimN6bAGu4zRt+5IbZ1inHZ7jOIOklVUhgETDV3WrDZat43CrWVj0j48/Glopxf2wf1DY35uqaQO0qqlIriGd3eGc3zOsZzjjfT74aMHZyXr3+0195c9QEvgitNXqSzYzMCXH+LOBkaq+xLA7r/mYkvhHlip/CSM/ED2hyY46aNMUBFdaKKfEgZtf8EQHUx8Z6Lo40xsqC7pEujNNHikRQUsGcFYepYJ4STlPCUtvICoj+AoBCNIOLMPk5o24LRJXC03sQQHAlZXxyf06+fmj+jOMmgJhT/DwDGioJETUlrpMMwywpUaIjZWqw+rXMif9WimJOOqJ+6wRJ1m74cAHuCjDDe50G5Cy+j+Sp5Kd6mhijZFs3fO9s+MN7xSQdP/5sxm+9vOBPfrhhewSwetHaAZs8D5IEJ+wCgTYLmT8oAhwVD0vH4+MKqR23LwRfU4VY5a/Ar40zfXq/PmTRdgM3k60V61adVuoUxb5zGvboB1ij4kHsQGm9YTPOeBocO7iKzldPchVNr63kU98AENxbPI2WnzTIT/3e08ebp1KMsoMC3rwKUroKdkapBRAC3lAD0+Gu9MBisabNPILtKabAMLnQL7XAgpbmKR5k9E3TYLWw35hVBytExC90s904O6H1Ehub0TGXjGlOmJJimgXzJWHKBUlXvicNhJheFd24FC1SUiOnFmlfIqsecSP6DbbrUF4wT0t5HZtnCFzcG97Vf8+jtAaHNQxSE1Kmk5oLDTOlhJSALIlRc1JMWTHn7K8Tm70EHy8dP3zc8JV3ZswZmFw3KIlgyiHUzFBUVIhi86pj64bvXzq+86rh9z42/MtfO+Kn3zvhNz5YUB/gOpK8m7RmYWTMif2vcqXS6N5JDbCVSOghC/7ONw74+z874eP7C5IEqb2PCNj77tTG+QBrcWoBNfQGCns5r3WUYxZAki/VhXnnoXqK61EURglOEw+UV7XliJqfEf3eZD+fGRqv0uEviJ5vJr4n5f4NkKWDbg7f25Cc5AgM6ysyXMxrCydhCiiK5MQDpmb00t29ePT0sgI5BwJrA4Xbah/bCAHWqSWHRk20FuBD2AkfP1yAnjCVhCk771U6TlPBPFGA65gzzscJ57XjuKx4WDLX0mljZe1DmzRrP1C6a7BGdBr9T9joLSaf7ieSq6N+3A+X+FA5xqr7aG5HtFRVFFGkxLUJOWVKcOSMuXCgOCW+l5IUc0mYcqaEqDJiNgNqU5wK8M0XM27nxDUHqgjekIi4Ch3/flBBEoI6a+t4uTR88AD86l+cUfOEX/tgw5+fH7CuYPraDGMlRmM6K1pgSYEi0FMCHhrFyWBAyHJKxzfey/j3vvUM//bP3KLXBX/83YbvPbRR98VQeMShkX1cpbljF4mfL1i0gNzoxnNdAUDNFfTaFcmgG9cJ+tjYMMw4AJ/IXL/osSfl8XvXf9t7uV/aONF3SQwxLx3g6CtcBHpPrfmSnTN2SSj2FewSMxutgJLymAoAwOiQFJMKJheHiomMpILzWgeVKtJGBQkKKfHrzay4PU5YtorLyo91nDIORb11Yrg7FOQE1M769e5QIN1wWVd8fD4720bRjVHUOsUqLYpNJyRkTmz77Cefn/fTJUXcSOMmkclDoKb7DGeSfYTMXMSLXQkaZ450VTNr7sx6M5eMUmikyQ2xqGLOCZODOkUVm+eFKooXR8UhCx6c0TMDyN5q6ha7x/cpm+SaxJfa8SevGv78rFg3xS+9c8Df+8Yt/q/vr/iD7ywEgFT2g+xfYu8lTLkVMhBgqKe6QMqGf/DP3OI//KUX2C4b/uJhQ+3b0ChvjoT2uP7ouF6a1W1XdSAw4KHC+jBaMxINWt+Bn9bNnSKzmgAsge6q/Xb1WfxUf0GEfPqI1PfTdegwKvNa5oqM8FmPN9ecXiAzlsRrdP+qg33CEax+RcUDGhqjhrlxd0BQkD2dc/rASAezCqaScHPYjZMSE5xAebiw0Ed4SoCRTRhZbueCuzkhoePukEYNdix8brY24OlmB6TjrUPBKSte3i+YUkbOCakqCd8BWToMHqQAoqVMayNlzRB0jSVAe5uD+rCMekHoj8oze2rfPe2Csu0DJX+WxpmHLIw6EMYaPDEdjTZKTpiz0Dgz687UwTTcDDdTgibBw9bwcqmYEjCn5C0d6jsVE6RkwUXH2jseK/DhIvidj1b0teFvfPUOf/tn7vDs+IB//Hsf+4nzFRCiwFGDvT8g+F4x6kT0RtBIgPePE775zgnHnPHqfI/LsqB2DDJ7Rzg3G+lnj20CEkBQH5ExwKI4GhjRcqedbj32bFZvmeyRddhJvw6Tewtkf4Txyv6ta+DlE3a8z3d2PDXMSJcFn/f4QuOM3L36G8oSIGTo//BFghjcr9I0jMjpIENiNJiyYl3YTywpDwM9HgpOBxfagkeEIjiljnVz/usnLhP8d9WlGCHA2zcTm9NCTVd2bwyHotiso24dU844ThTCevvZjA8+yjiXgrRVXyHu1GgOqAKOWsbKPwADlEjOeDGrA9DJV5E1jHXS6EEy9eVSHF9NYV4igBzSlICsTM1LGGZOmHLyNsrOq+UkCdURnx8Jli1bx6Xxuh+ngnOtEAHu147bIrhxVYikXD8RBJGg/G1NsPaOF3NCvb/ANOMXv5awto5vvj3hqy8S/vgHGw+Z906RhLVmUKA2wxBkijxPgHQq+LmvTvjWu9yHmXLCsvWRXvMIXUUw+KZxP4siglo7tq1hrW1XMriaMAmQMFQPuvcyG1iTNgv+rBv2p/PQKwu7+pb4Nz43mD59Ip6hkYcPY9zT88+Pyl/IrQUIirDm1/Ea8bT8j2nuBtc/BbydwmI+pYRZFbeFHp6OpoFDSUBIOBwmhSqh/5KYir1zmmAw/OD1gq0pRZmw1yGAL9ptHctW2U6Yyc1dGrcTz4U/f3ZF8G6GtXbcToaSBdMUS24oJAZpKCmjovJGAuho+94UvwgGP5dKwh4s+dQ8Bu0v0Naigikz6kXbpLlIV+9EViXqUaUUadKEw1SQNSMXhXokPUxMYbOTCaZE/ZzbQ8H7b9+gdcP9ZcPl1QJAsdaO80Zd2q/dFjyb9my0GTAnIgDhdAyCx8rm/su14tIFugjeOyl+449e4bwV/GChuIx8RXEoBeePSakbFwUEBk0akVu3V50F/8KPJPyn/9Jb+OpNwf3jivPWBnI+4tKIMEKnB2NJq8q5y61h3Wig1fvdQQIZkynez9x6BZcCUz2vg+0TrrpwpwQZ42NPD3icszDg60h3ZXSfZ2fGdJvP5dWw7a2ePdR/+vEF9D1PC7zP16SNJnoS8X6PM0JwDRljEBOobWyYFDgUxd1xAiD4wcMZU0TdTpnH1jrFv8CF2sfs7QYAt4eMlDpeX7w+cuifQJBgbR26GqakkJRQkrokiEFdpZszqT6i5Y36+8czPvjBxVsT/EyTel9Q1elVNnqanpwgmD0B9BRlxFrXFQYyVVJKyNEaUU9VdTfOMSjQ1VcucHlv8pSW6WvBXJgSp8Ks46ZkH+siIHrIgptZ8PZNwc1xopxj63j3dkYSxe9+/4yPF8PzWXE3GQ4p2hD0t1m9BysYYMjdJA4uURXxrXcEmyn+z++v+NU//Qjf+94Fx6+c8Cs/JfiffnuFPXagZB8aJQPKNHILw7N3J9x/uCCXjF94XvDipChZ8XC+4HsfvnJFPI7vZWf7xOQSHy5D2Rq2alhcTS+0aIN+1zx93TqNklRPkuHFZWuaGUrH1foLro/vn2p72F5PDy7mdRi9NsJrY/zkj7jzHjby+QZ5/fhC46Td802ZCVqi0nUU3I6L+QfdPwBvC3Pr5D+VIDhMGQZDhnnfkherGceAchVoBg6JuzHutw0wwfNjhlnDmnnjcvKpj4GEEgJvBqzVUBJBqewLRgSswUQF1oDzuuJegZfnhldnjhXV1h3cUXTpSJJgXv+SW8x0u8u1pi2FsqeU0EzQXRkwomaIe0Xj2kDJUUZwpuSmhgJfnwcafPG2yTRnHEqGZo6KlawoWXw1Owej744TXtxMeHaacVlW1xhiLf64bDglw2sAry4Nx9uM08TP1DPrOxFvVbnDrQYcC6G/X3z3iP/3h4BNE377u/f4b3/3Y+DjirdvMv6Nn57wcm2w+1jH4Qfv+qD6h361NEhSrAB+9YMz/vUPTzhm7sVMmmG2jF5m6AcrACQfjuhGCRHYUGpvnq62HgoHPqDfKmprYxolggx8FAzeUrnOwTrYFgsZlE8/Ak/gZ/osjOhTmfGIttd//uQvfsm0NgZSGYZ1zB9WEWSz/cBJkOX2N0nuKYYo16SKQyZye14r5pRQpszfb35APNei0fNmb9Vwf6no4DIbCHDZnMNqXHYU1DeSiXkatk5pk6w+yqWCc+u4rFRm+/hhxf3S8fJhxcNCdQdmomQvZUsYo2zGvm30O8cgM64MULi6oSnVxsfIltdb0RyP/qfAG//e5409ILQ5r0u9Pi+F+0EETPdLTkhgH3POQlR6mrjxymuqbsBSG3oH3r2ZsFnFBw8dX3s2Q3xjcykJW+vYqjs7c5G1SjrbQzV8fzH0iS2mX/tgAx4b3ns24W//9Iyvnwy/9b3m6aunOC4iLi8m4JXzrbPi9lBwXi5orype1oLf/N6CH3uWXb6yDWZZVqUwV7TqOsZ73Dr7z2trqJVtkaDi0Tg5jVJbQ62VS4v6vp2ae1BcJf+KF8syxJlIFoAJroAewRNkNZzqdQosn/j+px5hpeqeWgF5MxL8ZuP0wpyCaC5K1bG3BJzlYk40MLPxhOTdGjISsnS8dZrw9u0BqoKHy4pqhtIdyfVeXXgxE47raKexnWtF64rjxL7mpIZlacjeRC8qLiqGkXIHaJgdCV7RXW7C1b4BvH5YcT4zDS0pAZZ9ZMydkgDaAVF6b3MJkej1wgkIUTeYj8ixbtyHoyFk5AQyqCmI6IKcsw9mdKzb5oeUn5Ogj2IqGVNS1G4oyvlUrrMwF/pWHtjWcFsyDOKC3eSfJxieHxSvVsFpSqhbhyQKLz8/TXj5sGJr3leEYmkdH16ApSr+7482fOdlxbtvTfi1f/Ia799N+Ld+5oB3Thyy/+O2wV5kyKsOaBxMQ5oVP/cTE161jnUq+HjxmhQNH64b/sc/WvCtFxO+duQgNJcBk8jfmmHdWJJs1gn+VEbMAHm22nyo2ldK9O5G7BKYvVFx7xqR9awgWfC9aTPkeysuZjSY/hm82N0uec4+w6Y+RTqIGnws1xr/4H9WvOnxBbq1T1+YYzYx3U9vwi1VHaHHSaFDGU70+ZxxSAk3hwlL73iWMopyUDZnas3knCi5yEYp8pxQkg/Lm5Ji1YDHe0oeToWHO0S0Ddd1tXiqJiOq1k61A7GOm0Lq4FITFncMx2kiA6qybrxcVlx0g4OvEB+T2hPTpxETCD1SjxRXPdFgNcX1jJUQKXHRUBJGyN4EKfXxnByFUiQla6ekYPUYFJ1TOonXQlXQm1MfVUcTP3iyj2vHezcz5pyYSUxExOecMfvO1POykSgOH62sBNB+8LhiNcMHS8VbBfh3f+4OP/eeYN0aPjwDNQN/968dgSXhf/6zC3AWQBTv3Uz4Gz+e8PuvGv73P12x1M45Tj8rv/ODBb/+vYRfeZdgWPSSu/VxrVY3ttYo+szWiWtXubxl9a9bo5EGOsuJFE6jMMCF6Jezvq5SzCSKZm2M1kF3QbnPyFWvQMGrg2cB7kSz7GqSZQ+rTwLoOE6f83jzVMooYh38EV9V4OmzeI0SfJ8gnKunf6KCrVec5owf3F8goihJcXc44P7yCDPgxpfcABRvOhwyEjpOJaNZw6WzhuhdUWuHJEVr3pdyQnX1qZRZ2SoR1zIyE1ej5yzqi5sZN1PBy/OKLBsmEUw5u3xFw7Y5GCMKXXhNW+PzCzq4841tmxRR1FPcJOIiWELSt99Esqm6K60DIgnm6uTqtKecKPHZbDdOtlyoDxtiVircppW9HVOEEztENBumkhzEis3exlRXFKUkvF2A0yy4LDydQavkCjxGn/vNlwMVw7Iptk2gk+Lf+bET/v5P3eH9u4KX9494flPw4cOGn/qzFf/Rt1/gh48N/8cfv8ZLZOCm4LvLBf/173LwuV1AGt9li34I3krAr/9/r9EfCv7Fr6TBCoNjF5HStsYsKgzPPM0NZQNuCWOpstXNjbk5KEb9nw7KkRLK7DvBJcoOCHqoJI7QiB23eWKgFjcbq2zwAAAgAElEQVT2Ch+SJ5GRDlr3BVpPQKKrNBjyxuD5ZlHpUVR6b+nq/Rp2TRze5KfOIByJSMJaDVtbuXgGhnlKeFhXAMAxJ5xmMoTeezYDELYOkkBM8dg2NEl4uFTyZI1qBYDzL9u+MLZ3DhLHUJuJjc1mx0nx7FQwJXUU0tgnLA29JzwuDcuasDVzTR5DezRsYCoGM/SeCQiIG6fKqDtjG1XMptJ3KOsYlbEW/TAVtm8KP2O9MiS2WbzuTUDKPtPqN3hKirsDNZSyiNP3PP1yBb7WKoJQWLtgacBaO7oYbksZEaFZx1obahNcts3V7hkxb5Li2Wz4je9v+KPXFe/dJfyDb57w8++fsFYDLOEwKX7y9g7/8F/JWLYLvvHiFn/zm2/jv//dVyCVTrCdN9J9IOx57kwWHJDxaHWgxSYZW11dA4jpbLRDzKjW16vvL6kdq4M+awh5NTrY1ZcVxUgYW6C8voEYULrINaqMZJIImnC2Fw3oKkSKeLCS3dZUBhDG6Z1PwUG7EYdRjO+FgX/JmlN8aqSP0aaO0EYLeGQzG6/R/PUafxlJMmprWJwq9up8weO6UvDXgPtlRVHBaWWjfW3M+aecAZi3bVgD1MZlONHGiMFkRhRe0OYtNTaXU7RlcUiC00zWUMqKk7deamcrojrQss5s55QkuKwLR7X8OmqkXP71iZPq5mr8jh4DmFzWIxxjUpcUMcPdYcY8JRxKwnndsDbOxqZkA91NSTDlhEMmdW/ZKnISTKWgCLyeYro3gKS4b8r211/cbzhXwduz4fx4QS8ZOalr6cDH0tS/x+zi+ZEg28MF+NOPGupm+Dd/9BbfePcWxynj7phQMvD6ccWybkhScSgFd5PgF3604Dsvj/i9Vxu2Dbgt5Pq+2jo1paIB3gwfto6fLBN+8d0Jx9LxsFQsrWOpnZhAZTuqm2DZWFtSB6ph26g1W12LNv6t1bovwb0a++oQru0QQOGq9wqy1DpTZfEsx8Io8WnDGafes7IRqkR2VY/xw/bZz3P9c28wTOCLNISsY5Lww1cTAd5E7Xh6KCIR7tZdxoNrF9aN+1Qefd93luqzigmPSfHyXlFKxTxxquKYGZVLTnh2KPjhq4qbA9M/sloEtRH0AMTpfowGfN39UjptAt0lLwRc2nOYMp4baWyX2nHI6l67Y61E0VQFaurCYbojiK5GiDBGvwgKsqBiomYw2RwgChnQnAW5RL1YMFVu945GukqQ2dk2mZICxt7tlBQ3U8FlXbG0XXcIoHOiXxPAGl6uFTc5Y8reYtoq1sr7VLLiOGdkVSwlo3bAmiE1HuJugGQ6/K/fZafCESc4TgmvH4DX55XXNQlOB8Xf++YNvvVWwX/zOw/oOeMbbwHfX4H/4c8W4B4+kiO4OQFffZ4BaXh2ylCrqN1wqR2PtTFy+td1a1jWDWuljAkNrzGFdcPcHCCKWjNU3H0UH0Gi717eiBk2IwhFbjjLhj3U7ed5EAiGN9aR1QJCzr8vKn5K/fsC45NPfP2MxxcAQobaxZUhPaQPLJOHNNrM8f+Rsgub/YxsBrWEtTu9SoBD4jKkZWt41A23qvjwnrWgCFPEZwfBs3nG198yfPS4ceV8Z/9OBLhsHWhUHZhLhpj6/OX+jkTG6g203pBS9ka34vY4o6QKu2zh0PF62Vxp0Ie+IVRZFxvXurYrtrH4jKqw1aRKBDcJkU9ujIYrFPionINEh8w+6lmByZR9utaQkmLOHASYk2/NUncCYphKQrOMhoosZBF16756njeh9ISffDYjWYcWrodnJKfh3x4n3BwmnJeK08Rt0Jet4/F+w2/+xQMOKeMrpxnvvZPx/C6hOG/zsnI58TxnyLJhzorLVmEgDVJM8R/8c4LbPOGQG/7Rn5zJ6ko8KWVW/K33E771VsJbE4Be8fKyuD5y81SbQm6bO63Ve5a9d/SrWnNziREapUdPcGg6VPUQJ7YHiZ4YRDU4IQH7923/+uQA9b3OtDCFeAR1kYLIiFUSXxQV3Ui+vHEmowGy0e5h36hPK2I+1UC0lisI+FpESQlXN/igct+BmtDzgZGQ/LBVdAjy1qAiqAZ8/Fjx/Lji9sjdn713XFbKUtSNwA88ElL/ltMdOsRDMDiaWRl1rne0BHc1ieFhrT4XyIu6LL4MWLhvdAA+wGiQ9847Zf788bmTG1MQI2ozlJwx5cSdkymjVcN04MB3i3nZpFhqR23OmVW2iE5TJqdYqfKwrRVtJsdYQW6s+k6a4mJXqmzRtC54XCpKZvP8kDgpVFLGaS4QIanh9lhgEJyXipdLx3/3Rx1//esJ33yu+OX3C37qruDDVxfczMmfly2cu8OEy7Ig9YR1q8Ch4J1nE148n7BuDX/ww47f+2GHrYLDsWNZubnsF14ofv5tRa+Ch/OCZW04u0byZWPrpMc2sMo1fNb7SF/X6j1N7182CyJLx9rbrhdkcExgp8cYuke4tuve2c6zffqw3QCffPc6TU3eMvNsI4jun5Maf+rxZY0T4EBrhyH2RKqELouncXCPgT367zVhfJowHC/IA0yCOLXK8IAN2es9PAisT6ib4bIant1ONBLtWNeG3jeUnCDGaQszpmSWjLL+8Og0ZjzVp0N0pDitAW3b8NgYNTUxdJ4rG9zWO7JQAiSofiGvaEZpyG7mWrVEslXFDSTI8XzNkhPmkhllk2LpFdU4sl5EoJlE9kl1bFKec6KAsjiTytNyM+Bhbc4x343x5sjo97isOGjBNClECjQJciJ9TSFIkqHC6LKsHUsznA4Fx2lC0YT/6vf/HO9/NeGdGfj2uxOel4S2Vfz6n79GEsP77z7HXBSpClNcmZCzM8kMvvbdg03K+CePG8rzjP/8F2+R14Z/+HsLEfxccK4dD15zL5XD8kRdaaDRJuFeE6Lhm/8M0WUflna+NaejIoVlF0Hhw9tiNHK4OnzMbl4Z0PVyrd1o5MmXAfAAiJaZCdeKJDWE23zC0f3k4xoRfoN1fgFDqCGpb8mCORdUHb11aT9PbJPs7RtB/Bsj7NDZgdOkIjUWVgGcC923f62VlLopJ6yt4bw1TEXweOFSGpGE2ymh1h1QM8CnGrjLIxl2cMX7qqFmZ53p0+O64bw0zBOBoPtLxauHFcvWuAbCe4DJjc2QiK5ax4aG7s4pgLcctaV6S0Pg5PWMeSqAYaSsKuCkzOQMoS5A6mid6e1c1GdLmepOWTFl9oRDKzclOOldcTrMXPRaO0rJmDJT2bdOCTkDrx8v2KphUu4wefW4eXrOCY9yo1ir4D/71ntYteFmmvHimPBwXgED/ubPfBXff3mPd56vuDseMZcZa6tYAEw5j1V667ZBEwcAbhT4lbdn/Nmy4a9/7YQfu1X8P69f49tfAWq98PdrRagDtsZuVXdAJ5YNRZtncL1t/7t5StrDcYLItvrZg4/NaSfcE9pDAo5d7HpTbmxjS4EDOjFITrAF15gsBcyILxygVArhP/D37Wrz9ZVN7t/5p0lrRcDGugNCQqmSLC585TVUeAD1N+yjj95O2Jv1Op6TxXewSdgX4iB38Itb71g3VgxbN9yA0TN2gBYFDqeM89p3wSWB95X2mjNF1FT2BFMWSFccJ/bQZPL6tTG9fVzJEi6Jc6Uc3wopFVcgNAIA3VG+0J0JwkFyGZFgHh3ngqnQyaUkuDkU3E5spZxmpu1bpWGWxJ4eaXqK3jrEGFkPJbMtI1SpyDl0hRKakxByVpY+ZjjMxfWYWA48Xti+iga8GaPvWjvO64ZSFD/9I7c4TgkPy4b784qbY8btYcLbz05479kM8xRxSgmnueD2kHBeK84rQZ37y4a1CX7/5YJXi+K3Xi74O9+4wbe+csSsgv/4nwU+/PgVPrwPEIeSMmvjhC+v7+5YAfikiRuhl0LmCv49QBjbJ1J2hpdcKXbw5zL23nR1YFPiDF6d5SAUQK7MSnScVQYogkN8TpZ7fPAV9z9fGf911P2niZyU2GCMZ/BzeRJBJLRPov+TSCkYgEoKlXKvRUN0a5f44IGLTdYCr6mdpwkI6kZkr3Xnvib4zbGh5xMfNTxhOIkAbCT5oXbpDxiw9QTrXBqrypnSFxBMZfNah6ixqLqjyjQmVDTppEo6ajzU8/yiFK8DmdYy+k5JcTsnTEEkyDrq5Me1IokMmmLJilUAkY6c+BytG1ICZUlK1LF0Fs0o+5ITifgBjG3dMJdMBNRX2uVQM4RHoWZ4fprw/IYSk6HgkZPi+c2EnICbY3ECDVP8qXC2tIFsnsel4k8+vOAPPtzwv/7FI3o+4DuXihenjNPEuvzZEXj9GhDb0dVqwrraDy7bTpS+QaORcn+OjWwxhtS7nwHAnb1rH3cPAN0nUWLh1RjUN3NdrB3mtOtIpsoIGMEEYIlhAQoxSCTvYVMh3vPF+JV4o09sCoiNfDSghM97vDlywj1PTKCP4loRDiRq5V1bx1/X6zFIzNvKAJHCOGMQGYO9EdHHACGn9DBlZDGcZnXBLgbG2gwPq0tXpCvv5heFC3lk1JtDajIxlakNOM2TryI3zEnx7FBQkuD5nPDqkPDhqwVbtTGy1TtryGWrUFFPC1nnsgWiEEclSb2TYWSHoiiqOJaEQ8k8DJ21IESwVa6YS658kLxtYsZUaS7UDtpqc7lQ9m1N2GJSYS1WfEKGOkJPrwm5DA58hSMUxTQllJKwrBvmiRTGeSJnt7aOx6UhacPtcULJCbU3LBvfb84J85RwfzaoJtzOBVtf8WKe8EeXFd9+R/HLX5+4/lGddtebL49ifb12zv3SadDptwp0X2wc5/gqvu0KEuDXfSpK9igLQ7WG6tmY2D7MIW6gQAy5YycSeC0ZC4fjVVVY2lEBg88SK0Y24b9AhCWKhRXH2WQwGBTBiJr6ZY1T2ONhtyhkkWmkSQNBpjVGSkppEu8v+koFlbjI+iRiJq9jY3U8rxf7p1mBU0k4zRk3c8HaG14/rjhMmQhuEmC5uHNyL9nd43uNSKUAev8y7YJY0ZyWBPSVjKDbOaN3Q/ED21vHK1VIFsxTRimMMq0b+aMAclf0FhMy0cogQER9o4RcuI7iZi44JMGcM/dddtbI57VDweZ6SQk3E1PgeUooSqWGrXbkzCg7nE5hRG6wEVXCKUS1EYc0Nrwlj+Ktsj4Tf544qCll1NoxTTRO68B54bJk8aW9BgYn1v6saxXRf+b9/M2PLjjlCT//YsZ/8stfxTN3Kpt1LOuKdduw+XJdzl2SX9uMHRc1GaXeCDAKslz8vUbLY998jmFI3SNH74IKkmhYfooPzexpr5eSNNCIqpogQtygC6DehlEzTDB/GxFrZaThSRWtfWKrddRbEc0iao549zmg0RcZZ7RveJFCMyji/p4CRKrAsSn/J//g9DAcvpXrHFswjDQuKszV1b3uuGwU+12rYe0d2+piKQo8OxaIqu9T7E/SgxDIKkoub0mKQ0lk7Yhh8aY1ydFMM2vPXNQULYtesXTB/aWiFMGzA1lL5+rbuVyas3qtlHwdg6ZQaTfMpSBnwe2c8Na8f18kudq4cVQtCcfqimKaCu6OGYepgArlBTl71dQxHBwQ6dW+dauUgpSdlSQyRtdEOIo154zeDKUQBZVEbvRWGy4rI/LNMY91j+tGNszss6StNWxbx7JsePTdm4c54+GyoreoYQXffu8ZvjYp/tWfucF7p4LLumFZiL4uPk0SmcWIid0GkFNj8NlsnGG4+BaH/5vrCO8dARmqCTaM18wXVUF86zqF1BRwPrYPacj+PugFIlgYZo92zSLQOCDqLc19uoyvYRL91U9YUoApT+rAz683gb/ErpSAnrvtbBR+L5bdcDlsDjNz79AFPsbFNKFbp/GBfVPpcFpg7H28fuN8vaVW1K44r6wHuwEPC2/su3eUMXm1+Tq33mFGJXBV1pTF5yHnEgJbJLrPJePhvOLhsmGeC966mXBzaDgdFry8X/DqccXbpwOKrPieG9PdzNGuslVMKeOSGtYqqEaplaQyliiJCCdAkmFKGTeFRPfZR8UMNhhEtRl640Y29E5RsnlyFpS5Fi0vzNIazEDFd++ntsFsiXICQ89JhS2iWNxjYkM5QoQSpOSFmBtewqLkqp6XDRDB7TxBEtO3h3PFw+WCWulIcs7ovWNZu++0BN46FfytHwV+/L0bvLidx+haV8FyqWi1uZYw319Oitodpm1c0Vg7CfNwoC0iIXEEAaBoaFd1pcF6Y1YBX1Y8UlZnh0GQ/bN2MawS4COjXzJDc7AR4vrEYdAWGR1Q4dfVyTlbJ6GleOUa1NUnwE+wjMIoBzBkbzTQL57nDC/tRhcZex/2xKixWzOX+5gTjKmkLkOzJp43yz7zEvKHjA/RP716Sk+ns0baJlgbFQ/4tvz94Co6C6croseYM7Vdi9dzSQXznB1oKcip4X7ZiNBmQeusU58fCwTCQXEBkpJe+FoEj1ohxnQzu/OIYYCDq+BlFZwKDbOktB+WBGQ1rGjYGp1YyIiGYlxrja/J2TlX0wNKcaEwAQ9R6xT0btz2RuV7eKQGzr27EJZHG59/3UdRyV1+dBWFZV1wWSumktGm4oJhDQ+XBa113B4miAK1NlzMuJC2dhwmrrt479kBz28mqBJc4k1s6N6z3Fp3bnNEOMckQKCJUdgjH2yg/QPMATGNMD4D0+DegZBxNd+TIgAquNW8gYa5XeecV+flOjoIfMjez2azK+1lkJONRspoghCXsU5Nz09GzuifXr3/HXj6kmnt0o1zll7UdhCZggQFXvbC+qoGjp+nsTkH1lMHptx9tDyCTmWaUJ0bO9LieP/yFHzaGvDh6xXbZr5T0j9teFpvUmfxFXgW0h9pENCPxwMOLjIMwUhzg2VjDv7cKrHUkn1LmbEORRwaExyKOIqJwVaKVsicEo5TcilMAXqHZB8Vi1qlN/RGD1xrx2WtTjjwzEJjvylfRK6uTxjb5LVka53GDB+S70DdGtaNlLZo94gTK6gZJBDhjOfDZcG2cexKIGy/OIgUJPlu5hMiFZw6YqMiJcHNsTj7yMkAvbs6AWl4NE7DtnWXG3FDdIJAYAjd1zwKCCDubYr90QdUszOE4CfPAp31ENA88nUzbqgzpRTptTaQZ7fqdmtgIGkGn2W2AT517/fH1ncTRQ8lQI/IV+ET/sauuikBEn3+zNibubW9owJAUp/h9HlN2wWieZH4GYMgTkTbfFrIBmNf/WKZBH2v04NLoGt7Gi1+MXgA+WLsW4Gzl85ASP7hmCZFO8MbzgCWWiHrropuINc3pUT+iK9yqJV4X4hrWe+4nSef81Mf/MaoMaeiuAV8HI31bvUJ+tZD2Z46SGzfJNRmT4wq+cB0awk1Ne5/8RqwO9LbmgHWXFY0jQkUkvB3R5C8lWLDJQsZN7UOYxtb3jyN6YBHfa6hUAFe3j9iXWMesnOBcjecDglFC0yAxdckNiP6KiJj8Lv42gyEwVkAZu4Au2FtDWtvPke6K7LvYtB9iIhbtEL8jClkr/MclDGXMb1ev6D+2jWM03EPGBt8zc8LMwims20gIMG3Fd8JtA9OWxin79LpvWNzFp25lGw8JLLWq0Bj/jz8g+7t0M94fAEgREbQNdder5xM5M0VnP1s8HEqA02sA5DOglsF2dPbbpw8735AI03WKMj38zWQNRhGvypkJFUdRrdPAFFGuuHjwl5lM9Y2EOAwZ8w5A6BuDdsqJFHDOFidPfIUTWhGgzE3zA00vJtJcVO8bhJKXi6V686bUCEiq4z6itEuBMm8reO1X9eG2T+PBs3MPwvXTZC/O08cFtjqNlhbcWOqbx9PLuWRPSOptXnEaijThKlkYOIqxcvKWjc7oaH3fdg7KWuo3jngLZJ8hCs+Q2QJnMeMdRCQaGXAN3aZo7qkRa7r5iCeb6hGEA92wxQw0+kWCvoYpRHgzsBLGTGMmtCAsVa+Yo+UxD94HbvXhuI9S0mKJAkrxOeodnwFiPEGYU3sr2HCviaMSo1dOG86UORhiFdprJc7O4Ibb/5LGiel7T39VI9m/kKe3Y4doc32AS0fv/ZBFk5phJR+dYNjWe/GHmmAdUhnc6VreJ4rBtFIl71a7dTsMcNYKd89BVlqg6igWwXERp+TkcfbDUb5Eq58Y2rXGtO/Q84QNWhnnVi9vi2axr6XpFyzUHvzBUgeqVVGb5e0Qc8cCHcDEBeG5jVOmSSCfcV91ES7geYUhJCO1QyamIYG4klQhEMDHX3oHbUW0YX1LznJgktrY2McU3S+5FwyzIC1CtDbcGbVZT+22r0OTv7eWNcfCmvTMEzzqBhjdmvl7zK7EC/3PPb591RpRGYdIh2iRvX93nZYFBjZwx4g+OfNXGcZBG6qG5CCka2Zs3rQMIkN5NuEbRITHUiruEToQF5NAWuO77ikKbjUqxoRZlzZB3zfzBUStL9Ri9P/5scXrABkKspCAEM5TuNFnJzAj+u1IyiiJLJLYtr4dz4G8cfTEhhrANYb8G1Qbp3K1CJ1SpRY3JA4xB2D7N47VRJqEtcy7VwOWwWXZRuzvh3do1ly3iZbNlkNUxb0TXg4POXeE3r4ygOujhChoHPviq0apCS8PJPFg8T1CIC4lGf3dI/RSAXU2JX9BnbX0uleX6oJO0QW2jo6DD2JorpsivkBT2ruoGPW1TO5bmONxN7iUpSJWUao43ewvitJ8XDZGCXXjto2vkpENxGgG+YiuLuZfVlUHFfF5vWqiu+e6VQtqJHKOuc0CXe2NBXPFqiVy96hwiyh6d7TNN/qwj/b2ELG3ZodagR/YID065qP76xJbCwQlzalkTdw6qq7llAEjNAzYtYWY/48zxUMHBeQq42nL/fpWjIc4ehNGkZD+nMeX0B8Z9qivTsTwiFw2yly4h+2jWLZb5EX22KMcOpvRk3QxJDMBsrV4MhYp1x+gBXR+A3vGxKdvF5scdBGfTrBEToD2wSxc0R8UPq8Mqq1nj2ljF0aQ5dil7LEHmmtBwIX1L/E1FiAKWWoGu7PdWQuXiZiSnu6VRDr9QSlcMmSamQ7TkowMp7Mp2m4KhCuDLgj01MulPcAQbSgsEVX5XoQnCBQdPTdmSogvjtG9SobMRs9UYBgzVZXvpZcbXULD+mBfi48RmMkz8gW0jE0wZYRNWQNgZlkv9bVeH84TuiOwssR2yKzCUR/r0nb6IfyZ8ZSYyM6WwFPcXk/OWzPqNXQMUf3QTDOV2RxMQt6nZzu+cxui2Ml/ec+PpG3Whil0wPfkNe+0TibC1PlHtS8DtVExoXXhlFCixtsrGFU29FadWw3PrxgZzN2owH33tFEoKHf58tYRZUROGrnSKm98CWwpJBuyOHVrwCrnJh6JWfOWKOUf3ZCOKcf7Kr+dARRBCnDUVOFSoeqDcQypdiGLdgalR7EKWhr46dLYeQKtK5ANswHxelQcMiJs4etezDg7eYYmhPtA5nVGPpmiptq9fG9fqXf6q0HGcniqH+GWkNEoC7D8EOdoVXOTooYUhafauHs5LZ2NtgnSqRkBRQBEmI4AX5aGegyHcTeFqIj0qv7krB18s+KFmY73VAbM4Xm6nu9m4tFkwHWriZUxqYxN5YAj4YhSXhAG1ETCljnAD3A1ghrVNb6LKM8jRvp6W5cFAkTnwvFpzLXJ3XlJ7/PO7sbpn5J4wyicY8b2xOnUpI66BMEpoCaHQyAoWuYWXg796wcVRlvVMD2THhcGWF//yIIhM6TS08RYs6UHEryZXt3wzdeBDMZVL7Y0DXWKrSGx40pSeuGy0ZKmghTrsNUoCpcby9ENmPJEi+JYasV9xdOszyuHfcrpU6SKC4bU2VAsYEIIsnRNLxtbViqUfayewYhYZQ0/OJjTdxa5mQLvzYhovyE+O0XLcaoAh0mGsu7wX5tcgCKDqCNmoyRd8rZDYEZiRk452odlhKm7DX0AEE8NTeMPSfm6GEPx6KKZVvQe8ekjMS1c3WviGDZKu4vDa25yFclIWJtdaDHW6Pc5eZSJJsPV3cYVpjTCT9Z57GtVq4CCRAi6Jy0Yh1pIysEIoruxhM9V5gOji7iPFJRwDOsON+2/354ixEt42e+ZCulwdcEmKdq1lHoWhwgckQPUVn6PKc3c4bwlOd7sXXL4lvRUBqG6DdYXNrEvUoY9/WOMbnyOObRoHkPuHdH6uARuQosAyGdEjtGt06gpwa8XxsUvs+zpOGccuJBnnLCZVmhvv/lslYoFI9bw8vzhqUDzdTTloYu8GzAQYkOLFvDK3D7WuuCdaPsZ+ggMVUUJ7Mzbywp+fSGeW+On706zS4Q4O5zj5B9MBxm+1ROnBNPpUPXVkSQckKu0T4h8DFlBZCxbG3MS4bCYffoEorrg6xijJIAMGtB9yyCM6aC87KnwrOr2DcYVusolpB0V93bugtHN6OwtP996+Tpbp4mX2dkgRZ72EQzQQbXNCYJVx+zyZ69OXfWegjYMZthyRS19K5TNXrwUZeEoQU6up9SDKTN/zqipwSl7/MfbzTObDv3sJtvdBO+wQnq1D34B+GbDz+gfgqyCMwZRKrijsRYfONqQkUZKXoSpwLuPSeAHnw4HvCgR73E1Ir/0I2AVDdCVbGbs3egSvdryPe6Vc5OXly3ViSNKNw3YNm47zOrQLNzYw/TVdtKx+T+6kV4EqazikS0V3ZmS06CmwMPqYEpHPt/faSjIrobk8G5VnRUzXs62QnoUStnTyPNzJfDthHRzI1zXEe/J1Q29ALB68malHRCo7CWCumOKjpEmy2Es+EOG3SImiJYBAMquePpgHUkMU4ZlYxLJYA1F0p8zFtF23ziUoKOF2msMDo2KiXE5rDeGrgsy5iQ9j4ygOgZwAOAGLmwQ7kDTGOZuQbMpCTGdDe2MDoQ54gQNGxD9kFtvpynqB68nkZEwQA3nbtrw/q/dM0ZELOM/Lx3wBLrwZT2iMlyq/tOkZ2xHy8e0ZRoKRCa8EnEFQQEknxFO4CRVlx9Pmq9KhEAACAASURBVEgcp3h07N6JPjEodEyvO0SpBAAwolafrWmGMRXR/IaYR9za4btAV/Q54zRnsoBwzYnlbVnqTkMDfL7P313JXJOXVHAoBT/y9gnPTwVz5jKn19OKjx5Wj34dxzlj23apy7EqziU6LFBtwEWubIzqXTf0I+Jat+G0Iq/iElqnVzaiAjn1MblisKGWbgav1+GevsN6QvRXY78Laxa2lwz7UqRuQhnLSomU0zyjbi4A3Q2lcJaWqbyirg11I32vdePET697FO17VL0Gn8hwEi85ONzAC9iR/eyZqOMfcZwY7awzcm/xK8CTCLgPbOto8osISjes45zKeA34OOW45NfG5+oYI9L2zzdM4C+xyIhMiYbws2wFGLp6GiAyms0kyANEaclFVFzN5EXU7K6I4AbK9y3DMEdNB9Y/TZrXSxhNcvhrxeE0BKvDHEDAUDJvXgeZASv6eJ/Va5sAY4OlFKSD+6XhUjveNiBxGSa6UYO19Y7L1vHq0nC/dE+Xw4cwIr5zM+GybCg5463bGe8+O+DmODHlV/VV8iS4rlvlIQXnJFln8jrUSkkUeE1UG8WTu1+35gp01w3wlHZ5mUCga2c0bMK5WIBOplbKWxIx5gFM3nIJtYJRQyFqrzhk/O9Jrew/2q0R81AyiFJKuLs5QpI7m254XBYyo3rziZVd/jKof815uJvLl4QD7o5BiJ+x2J/avARSJEwIKRwPFSNI9NFHF1dnl857R5uKytVTXYkTybNWJSR7hPboQSnq+u7k8xGABaCEZh/GL7Lb8V/ZOFtnmmbCNwNRFMQEBsdvkBTrQOrI/hHrDiJ5/RjprsSb4loH2I7wZvq83aOBfSamx1666+4h3YchWCrOGBs3rnZycC+tQ9aKpJx+MK+FzZxN0hl1QtzCri4cDy/lS8pFvS1j44g+rjRQenSip0kVmhQ/8c4JX7ub0exKUiQnT0GZxh/nQo1cGLJmXBZq1Q8EOdo6RnpbOKjm+eSUXU7Teaj06l5DqgCJgmFF6c254tBbFdqgmpCduHBZOF8JkFgfvdNuBLjMbMzD7vVr3++Fp6MCjH9XEZL0x88KjnMCdIZAcFkqLssKgMJda+vY3Ai3kMc02xHakaft8ah7RhU7ethfp4FGacPSjN8X2aMXt5ezfJiMMyWbJDQlZZBnONpQ4ms11EOWJ8jey4ayHJmE2dLW+Xk37+kOFN3prej2JlrtFxunONuAolkx9W0+28aQH/1FNYCLcAhNdyOFrZkh73HQDxCuQr+nD/4/kUAdrxrycTeiHBi/IUOmpJmrt3dKZpTWcWkNWBlBVfpoKof37J6Kdm+A8618UkFdcNk6Xl82r9P4Hpat4xwEblo1gRZwV+W7dzNePDsS3MnkyC6bp4twUa1EUGRtJL53mNd/Ifq1I7M7yLG/LxWfffQdIuMQeA1vng5326NmNYNJgzVuKTMQ8X1cNqytoqh6X/QKVMLVe0AgvzKuP1fsEQfIOSHGAlUUSQ2pZCdgAGZpKMzX2kkbjJZIx6Ab1laxGlX5u3+uSRUVGI60gf3yrlzkhMRqjjBWcI1ZQvVxoOnFJ/NwIpxXgcCZbH04Fs+Y/cJeVZ6yz3VCxNtyikkTDsqs4+JZm5r6uSa9NTowA+2VJzf1yePNxHfroNI5UcckND5rDS2lgRwmj5qLRJLh5uPc2lWBSZIDJlGgE/QoidEkbE+v/hwV7WgOa7wvfo8yL+LRjl7XesK6EfVTFWy9Y20kP5i/z7ggDiF5Wtx9Ul5h2h1S9/rNFOeNRIzaGZWre8bq7QYYazYI085lY/RLKmjV8PrMzUi3p4lrBv09HA4T+mXFRZmaBjpLY1SXfRTf5haKBzxjMVrWHaGO0iGwiS6u9+s9RkppdhpsEzRpWNaK87bRsNwpsC9ZSZxvHX4MeM0cuBMnDEd9D9Awo/wZlEtvpuWUnOTPA621oWzVyf6G1WtKLpTaXF/IFRuMv59ToZNrm4snM3vi1JMNhlM4UZJoOmAcViDZhYY+FvH6eSCYxPOUgqrHBN6ddQcEwxAJqlEm5iCCgysuHtTJKZWlydIacQ5jXVvFBR3eMCr2lzLOZH4IPU1pxv7dpOQ/1u5AuPEFUwATbgTZvfg+/uU9viehEyMqxuiQ+FgXBQ7o6djT5A0nw8T/ICDNLjimXVCVgMOldsQIVzNjim4RrF3X1r0gx6gwWEnAHimK7+cEWANvV/3KFrVqN0LrjXquoSawbRVrrXh9XvHi7ggYcJgmHl+P3utGwCQnxeOyYdsaSk5OwCYoo5JGPSeuwuDn3xHJqOv5PqMvaCAaDJjXdub/Tixh3SpaYwQ7DOX3bTgsKsUzahR14TH1vaE+gRJ3ktHa/+L17uBRqzh3PGRN4hwAl61iXTuWtWLdmm8W6+OeUwuK5PraBVJdE2rboBA0bQ5c0ngmpXNbO4ckuiP9EUQGMR+MvhIK7f7mqxu9qiJ1km3USO87lIxJEyaQ2XSTaZAH121SsOzalKsLVbhpL2RbTQw3og7wYWgQ/ZWNk6RghuQgK0dkpNdgpEhg+mEgEDTSdGDwa2MOUbGnjB0u/+BRiqsfnNvFWRtWocK0eZDkZa+frNNhkMzB1+je3hDBWBkXz9MHx43wOkWnbai0a3xuOLiFjpYUsx80Lg/iVAKxOQcIwJ7w3ZxwnBPuzws+THyOxwsX9B6nhMeScZhAqUzwfXbrSF6D3l/IY62tQTuJ6pFNRLoqIiiSUDeDJfEICicX8OAy6vHAbd3GqnsBRm+we1ROidq6h6mg9obUlSqH0h2UcpJ/zkiKUT+nlPdhgoGY+72/UgOIkiV6oIyIAco1nGvjEqPNFfn8XgIdUNawJSccpgm9d5zmgtomPFxWPC4LMQMjj7p4NtZaA3yvq2nmJAzYeqmjHcRJFHK9I9qCkV0Tq0qPyFOa+F/JOKpiEleBzAVHN9gwNusdj8uKx3Vj+tp5ENU6kg8gCBTimeaXMs6AzEWA6qNjyTiiBRgaGqzt+X0F/yxeB3aQMtUlDHonkLcYyvG0yDNDrw/56te1X8zFqai7B389Y3tEzTdJe+3bu1Ck+IpnGiG6Ry8LVCPIuTuDJWh/u3cpyhu4NbqKbnsbBoYhOJ1E8O5NwfNjQU6MiK8eLmPqPyfFR/dnNFdYf/HsBjlTpFp9ygWN2rSxF8REkJEGihxtAREgmcKSi665gfAty5h5XbdGLq+jZUSBw3jojMSorXuYJhJErLlkZ3JUmfUaaYvi42UJU05kXakr3pun1oEpjK/sx5KGx1Kpe/rauoU0EK+pU/IiAxBkqHZMWXGYCk5looKEKNbecCwZrzJ7zc33pmiQY0RgjWexg4Dm2J0SdbljJ00UTQXZOBBRlPWtwXnVYjhNBzw7HHFXJmQNYxXczDMmX1BDABVY141LsWrD4nYUcQXGPnzxe7jtndK/mnHGhujaDatQAmN24EKFaFYDEdkWUdV3mJijYtHKibcQpHgKnBua9HFA4bOUzZnRqvAmm6eOXuyHWsHa+j57555YLVohzukUGRB5H4iTg0FCoKQYvaCop5dKRPl2on7PIQEPXHLFWb7WsTV+mtiGRjExku1hjKbntWP1pvlWnQbXFyytY5oKTsfJRZUNkhLM2HYxELlkgG8cHPD8h1gZ02ymipE6sr2zAegI9QdyV2O72mATCRlJECrjhwJf641pfkowR3VT05GtcHSNAMiUE1JOPrVDGKZ531ONlL1g3XQjGHZZVkZc4WbsZuZjXa58AECSIltGax0zDN0U88Slx1OmGmE3w6Rcs1hSciJIw7ptHKRoDU1kcLFbr3RO3fWV0L0Don6mFCpOuhDKziRl/azGcuCtm1u8d3eHuzJDevNrqLg5BMWz+5InQSncpZpGOce6OwlfL7vjNwDbl9UQMje8gH/D2IYchANCEkYpfB8NQJOGOUAg7PxZM0MTRWqAZm+/+A2MhUVeIZEg/ck35albFs5WrtURPbc7Gj9rjTjICo+8poCyvogIQsSO39NOOtvWgXeOCYdsOJWELMD91rCYwTxdrJ0ME02u79sFrxfDzcTatnUKctXeYc2g0rBsDjEk4NXDwrqnsZeXVdBqQ1aga8K5r26c+nT+XOBUxe5yKrxP0TpotUKac2HdeY35VXdyIXQWZ6ckLgUm+Oir4XtHt8ReJchL5tA1gbyUaai89wDQANPRc459OoyozoutVHuIaZLWGhI42ZMl4Zgz5lxwXnyOpDJy3RwKbg4T2WnN0BuQlcqGKSnV8nNDnwvVGtcVl23nCq3NQa44051c5RpAHrjkycyQc+Ee1FSQjOfsMB/w/tt3ePfuGWYk1FrdEbAUySJ45Zxh9p4TVBLg5QgEsMQzctSESfPYbpbfMNXyBQwhPJEkoXYK08Pqpi8S2i2C0Jw1cTEqV0EIcKo2d8Eaaa9gs45key3VW4MJWR69M8Kp2agXu3i/z1PJlCI1cA8su9feJzUAgNEiiQ6leIl47kbZBShq+Nl3b3F7zHhcOp6fCrba8BzAR+eKx+Z1grFePqZMmZLuy4F6xvMpUcqj7avoRLjFuiVOzT8uC0exXPRra23UPctWcV47ppzgrFxIrIQAFd/MOrYuaNYG1c3AFk+klSEgHZM1FP6iEiDV2vWqVcI0N6uiDioiGBlFMBeumMhKAkPS5G2hHYRiCqhXKHIoITAtFgnCire0wChdsuB0cFX8pHhYOl7dJzycF3R0vLg94fkN78N5aciNrZqSBNqAkg2GaYA6l5JQlg05bdBNkRq5hdY6pDVoC0KC82tj9tgxiHkqmFLBJAmTMqU+lIIXpwMSBC/v28BittZQAW7i9vS1FHVWHYfqs+7b1pPL12gQZN5gf28etoYhIY2IBACr8YA0YxN1NvEJ8o4kaYyOwTwgJRqmqg+oeiSt1iHWoF19tpI3desGc5GqZq6IDmCCQJLPjHaqB4QXHFC/walWPrxrUXATuKK+qwyUVBHgFQn5rXf8ta/e4d3bGYdZ8c6NG3IR3EyK2yL4w48Wqv45h/bnv36H0wR88NEZsdiodsHm0TyAwP+fs3f7sWXL0rt+Y84ZEWutzNy3s8+1bqerXVXtdrvbuDECIYyNZZBlhAUICV7oZwsJGfEH8IB4QbzxyiOPIBBINhJgbAGCBkMLGrvb7aquKlfVuZ99ycy1VkTMy+BhjBkrT7Wr23VS2mefnTt35loRMecc4xvfpTZoNP+czTmXpcDIBmLZhtjco8ctRMWQ2oT4xifOA+4jjH5KVkqzCieGQC7mSj9ELwW7+NqbvCCRmLz66TfXS60uyI5DZPJePaVuu9Lj7kxaJ+DkhAs9UtWYVyh+HyrFyfNr8SLPASltjevdgERLsK4q7IfGEGG3C5SiPLlOPD0M5Jo4DYWlYKnaQbjeT4AYMt+UVqsdJI7SS4ChuXzQA3abqvWF2hiinae2fwdSsLjGfUyMMTEO9ucxRvKaWZo6+toIDW7rTEzJgT3cnmZkNwzMKbIOA4odckVMphaCz4IVVL7kySliyUnqsqxEQFSoYmMm1csMtUkgYTuEATkGbQdx9bmfXo6/+iKDFjsQ1BBR5qYcBILVwaZVFEGkmjjYXpjZWXChmxlJ2wAK9b5SsYjxgAmzbROxB7I1jLrlqLCgPN4PXO0GDlPg6jCiVVkynNfCcSl8cLs4JG7k82f7gadXAykoN/uBu6UZ3L9FoV96qi41y6WyBiWmkVwqIdpcc62V5IuqNmca+S+hqyEMeOimXvY9zYSr9JGH2AluTB5InZAusi1moyhWVJMxVbhQD8HuWUjJNzCzCTGfJOs/e5m6ZksamabBrUGND1ua8WkFG8WtuWzmXjE+eGiwxTMlS/geonkU25hm5NEhsayNx4fkzCZrUUJQAo1pDDzejyy5EjPG4w3mbpFiJQ3JbUCNYJ/cBVBVyUNizYVYt9A+WxApsfNf05C42jlCOw4uPnd9qRtsFxrjaIBkv4AxBZ4+fsRSinsZ20mZtW7C99CMkbTbuM8/5+KMNArBTcu8bxSc8NvLGSWpUF0BY5myVt7GEHBAmofx6OoPS+9XqjpPvzWEuNmj2EOlaCu0kFDtyhPL2whhsOTsYI7ue3d/ayLM2fWdgBB9LNRHJBfitG3wjf0u8f7THc+vR6526cGCshNtGiNIoJSVIURudgPffuuG/RCY18Kcdeu9LYjXSh4ImxudiHFVYwqEXBy0CW53WokxMnuQ75CCxy8AuPGYW2daxeA2LC4erw2GBDf7idZMm0rTrV3YitcNQVVK7l0YaOj3ie0Bao5udkQ6ID4f7QCMuVakGhG9MH1Kru4fZL5BpznTmjIksfQ2n0mHaGGGzTe7w2ibpF0TQ1nzrjEmI3tEgd0QqG3hcD1aLKI7FI5BacGJHzEyDslQ/ICHIQlDiBRnAKU0cL+szMtqEwKs90wOBg0xcL2beHJ9YO9Bw/M5b4nbpTSWWmlUYrOSP4bBg5IHhgCPDlfGEYgrya1BY7TjaSmNSDHA7cssztFLwoVocLTqFtQzdq4ieA/pw1qfFcbQCQegQbwG50KT8w10AxS8uopyUbqXjizKA8S2Nbe/sNlWDyoaXEYZY+B6lwjnTJsrJQhFbAiePDfSKFhsYneqcrMbeHo9Mg6BUipLKdydVo+6r7z99IavPBHeudkzpkhT4fHVyJgSn96txrAJwpIzpdjCrMUAtUE8Yt5Pt5IrGduQcqib6l/XzFo7UnVRlxi5wkqgfsrlaqHD5kdrmyRN2A/Bck1YWXL5AjGhtUaMyftus3zso6Nahe5ImLBeOEogS7W+M+IjC5Oj5WLqkoiaNCxEJFhkw1rMihPxEZuXmrtpz2GXGKJQio/NotEb7TVUxpQYghHEYwgO7DSmKLRkz8o5D8Sg7t9kJ7X5DuuWih1FHjDPbE7bYkIR0hDZjyNPFU7nmWVZaTTWYuX0UtslfBlhN1pMBk15fZo5l2pz7oiBjAjDkGxhDhNNIgXY7a8IITGmM+e8ciqWFhAkMJdCXsPGZ/65F2cjkCSYSl6EijColZl2XLt6wUvDrq43o+JGkuRjC/Gyyhahoaf+Z1ejNNqmMUzBLBaNpGCzqgHx4bDbajRjWEaxwKV+eo5u5NxGcxkwYYQPjIOY7lL8jBA7Gfdj5NnOclfOcyZX5X7JfHI3c7dWrqaBd59H/vQ3H5vx9DBwPq9GnNfK433idq5MyXs/bRSF1VUUpQamOPLoEDmudqLHBtIaUjwQqVkfm4vzkgdPYOt9nDip32eJtdrJtToXdV4Lh93IXKpnigqxXRQ/rTXGMbHz9LBuy9K0o+9tM+I2z6bqBHvnv1bj4OZaad2xvTbfvDBnhHghOPT8FnPOswhDCwC2Z6QLsocYOIxpo0Du+wke7DXmBrXYQmsirKWxd/f9WhvHXDivldNSWYpv6v3nY3hICNYS2Iw8st8lHu8nQoT8aOS8FEopvL6f7XrMK0HsetyvGb0Xrg42nwwhErTY/F6FOIzEFIlDQmTkLhvgcy6N6xSZmAw3CMIQB0gGMu1z5ii2yX6pxbmIuFpEHDM0kMVcgaxU0yCu9+sQuhDVqF7iTWk3Fu694AbT+MmgmNsaIpbyhc/z1PqmISWC50n2AbKqubn1VDNt3bNUyDWTYuLRXjitlaUa7zMEn+E1p4WJENV2y1Ours1cXUvY/YXg/Teuef5ot1lUitogXouVsWtdSb1/Va8GWrdntAdrGiK7aeL2fCQ3G9vgIw9TsxhFbW1Wjo5D2MgHrQuJmz3QrXXHORv7ZJdRSVPWtZJ2lhAW3S3CBvuWdjakyC4NNhNV87MtDzdvT8RecmPNjWmKSIjuc5tZSt1GUaVcZGpNMa8ptQeun+a7NDBFyxEVIOcGQbeetJMqQoD9ZH1dcQZRVdyPydQp2SV+gwhLq9wvhbtz4ThnzqurP7DNplarUkzaaVzYbjp2dRjZjeaSLxJYdwZWPT7s+PT1mSiRXE3CV0rlXjNr62J/Q7rX3Ggl+zgpWCDzPvH5SVmzUyanSFIY0sgEIJU4mLFaEkFC8NbnSyzOFRvCZkx1gNgDB6amN16ibP4+NTQPKMJj34N/vWx+LSK4CZMREdzHy2D1Buoorap9r9pkQ2WTWJmMQm5u2KW6qedr9ZuvkRAM0dwNiYb1Y81HP4MIRPOblRDZ7RISA58dV2IwCtijKfHWzZ7rpXE9jtav1Eotuu3apVZSSlxPA5/eZaxCuRD/A7ZxKcJSlbvzSnGgqqm464CBLSEYA2sIARVj7XQzZnv+O0XOrFXWajzUNRdEjXd6OBiIUb0sS5j5WHeaGD3MtzsxVx9tdfMzi51ILLn4ZucaXWf4LLlshmRNDewRzFvIxibW0tSNleVEeaKX7kqui41Uiue3+JyvhzOpVkr15LOqzK5z7SBit2Y5LZW7uXJcLFU7F08uk7552YERXbFSBXOkD8KzQ+TROFpWa4M1GJXyarDn/jgX7s4L85KptbHWxYypnbyR3F+pqFELszY0WAUxifCqWMs318YoRvjXFpBUbAylzUPxuqn6l1icBvA8tAm0NxxgA4eKl1tKIzVbkOoPIHIhYzvsYG137zVx0EFNh9cnj01BaqNGkFaRKkwyoMke+1qrkwCMyRN8sJ2rUd4M+o++GP17t4tFxzAG5rWytkCMRnV7da4b++idm5Gn1yOnpfDOowM3O5tbjhqYc9kAH2OrWFTCLll8ez/RvIL3YTfczplltdLfem0PLWrmyWMjI5MhheBkA20snSzuM13USkpVN78ebcHFYKnc4j+7M2mi9+RdDN2tTDbtZUybG35wzqcWO/2GwV5jzsVOTY9g7MQCRbcqRl08jVwUMnbL88ZV7uQGsMXXhdUGWkVndllve8yV82pKlU6QAPu7uVSO58rdnFnX4gR/2Z6wjgKHEJhSRIqJGq73A9dT5N3He55fHUxPWhr3q7sg0nh0NW7GbqVYbIRiI6E0WoziGBPKAsHQd0LHupWPzsoPbwPPr2wjWCWwSmIuq5EWIiQqWpy88mUXJ6hFqsvlMyb36kvLne98PBJbf7Au/8CcEGxpq5fCTdwPlp6+7InBDtL0TUAbZGmkonbyOQ1vyY0qVrKkKF7GCEUaKSbbXUtFJTBFO22Ll3ldGzs3YfYxy/2q3IzivEp4frVjTPD05oYp2YVv2jgu9kCtuTLn5gP5wDD0rE6Tra2lOXNHNqBM1fqnHrzUEXBz3WvkwlYiRjoI1P1s7XVaNLuVfU+vdpzDyuB6yOReutYSqJEBWkPChWRus0m7hmASst1kJ/TdeWVZy+by10Xa2d/XstaNZtnBQXutl9J7e0aMsWJzTXrsoEUGzqsht2t3WFB3YXAb0lwrpzVzXAqnNRurp4/dmvribJzmQs4W8dB80B18Xj6kyCg2btqNiV2zfvdrT/c8Pgy8+/iaKSVOKbDPSoy2wO/nbKL8aPPX3KwyCVG4mUZiHAygasHnys3nlgH8Pn2yCp+ehTduAi+y8Loqdwpvh8TcKk+n4NWcIK1uVdbPvTgtkUl9WOorTAzIsLFEoziPcq/ii1CoBCxqFlBx8yVjYoCPZNBtwQYvgQomnrWXKxfKme/GTZWWK6s20ECMht7majzJIRoS14Gg3BphssW1KqSqZDFCwFpNV1pQriaL9QPl2WHHzS7x+HpyCVxlTDaymVcDRESFT25XXp0bz64ib96MnIuDI9o2J/J+Pk7J6HEipozfBQPKTNuoTm4ynnESV+77A2kXI9JoJigIwqP9QIyW+7IfBmbXRQ7xouIBNVJCUVeBBIhW2YDFORymEW2Vu3nl1b2VccOYmKJFRZRiJPjNPV+VUOuDTRULMH4wmjK6urU7a/NslQq7QQjOZDotzfnJ9v0nEVrOCOJOhit3c2Eu1ckEbiJd/H5rs4VZ7VqjVs5GN/iOsVcTsJsib+0mnhwm3rrZ8eRq4ulhz+1p5mo38uQ68LQ2djHyyeuZ3//8aOlwQ+SwTzw5JJ7sRx7vd/zg0yM/+PzIKVczKyvV3fJNa3qugf0ghJ3weVZeHqHkRhnhbgw8T8JHq5FphjgRQmY3fcnYeUqhuoC4H5+qpi5Z6ca6VoMVaVtuBA4cIUKlEd30SJwYrXSurn/P0IOMZKP79Z8oIRj62Xdo+ilsaLAxbpQS3Li6iSN2hswutTvXNYoG5gJLMZXJIIJicqnWCkOUzWx5jAOlFZbcWMpKDIHjUkCFZzcD78fIy1NBpXF3rnxytBJ7F63p7qAXoeelWOm5ExPNFW0ugbNyodKQJmgSO/VUbONrjd7pjylwmBKjf88xJbdOsU0kewbn4FERYNWG3ROjkaia8dj1fuS4ZD59eaSpARhVleuU0GZIrYqJFNZSnMDf6WZdsWuSve6EYOi3bbjq1Yz1fVaW3p8X1lo5ZyVXSypLRFIzZ8emjbutlzT/pm7j0hO/wO7dmp0hJS79i+agP7olzG406d44BJ7tJ9663rGfIrsY2E+RZ48eW4QGPpdvcL3fcdglPr49c14LX3m64+lhZBoHmsIUjzy9nkinlVmUMe2Zi2EzIoG7OTCq8s3ryu8eMabOAtIsT/aj3cCcGp+c4Nu7wNv7yNuPhy+5OOkGReJ0ICMHLP6WNug1KIXIGixXOPm/UXUvH4EkhlC2yKUkVqGIkpxIkHA9nJfJyUtbDb207GqS4D4x9hAE9wEqrTI394StBjblBpFAqWYH8jKbWuQnq/LeDr51GFmrjRDeOyQe76wUWkt2InRkWSu5GFJ5GBP7MXG9gyHCp8fMUgufnSsalKeTshcHtOTiEtFdBlNKdsoUK+N7DycOsjk0ZiJhtoPVlkRtaG3gyLM6QCJeXs9r5rwWDhKJsaen1Y3s0L1tUjALzNujlbIODLAfE7VmundTE1P0rD5DNIBKMVO2hmqmiCkd1F+5gKPdtpEpVtXMzXix51worXGuBialphxXk2llk0m8lwAAIABJREFUVV6dCvdL5TQXy/1sF6sWAdBuKdocGXd/pijeQwfSIDw6DDyaBg7jyOThxodxMHyiNa52E2u2nleCMC8r+yny/PGOfUocl8wwGvniNGdOS2U3JPapckzCTgbGSXk7jHz388ZHq5Xb10OjZjXqm2/OaS/cTBN3rfDiXnghgobA157Bze7L9pxOPbNBq13mPkLxY8EummJSHbXSrbuHB20gaQN7+oMYxb1U2GySADbNYgcQbDTTe1YnFcdo3FwMnYULybiKfbcpCaeCZ2ZY33euykdH4bUqT2LkraFxLPCDY2GXAs92ZuT89DC6bM0G2oYEWxk2BGEagrvLK6dsA/dS4Z2rxI9uGyeEMFracdxAig6aiZuMyZYpc6kTuvuNLUwR0Gp+RFa4NGqFITWGZjPnWhshNHdyUHARcW3VhczO9vXrV5twXDJrrtzLyu1pJrdqiWdiG0T1XpFoRm0XTe+llO29sWpEa/PoR4Pz3MNiQ6JVAquPXObcWKtRDW0+andZ1QKiDNG2hTnnvNmUKBB88WnTL6iQQugZK+J6YSVF4TBFrsZECsIY4eYwcHOYbDOCjWjSK48YAudcmIaBeB24X1eOS2Y/DNzPleO8ci6V2Q23hyFxNQXef3bDi5D5uz9ZiG1Gq/KjZk4X/QzLc+JXvzbw8Qrff6Wsc+MfniqnMPHGo+nLLc5+wuHI7IMJpW+Vlz40qAmx6ZC3M/4H/5yxhDp6aoMtS/Hqp4WVdybh6g+zh+30f4P9zK4Z1GBAyxCEq2ngsEvsk6Gju2TC4BhsdBBE+e658s4+8dYhsKzwe6fA73w28439wO46+mtw1oZcrD6Ko7N9BzeUr/LqlFmyk9m1sR/04gIIbp3iol0MEDJfWi4EDDAwo4NWUTwIybcjFUwxr+ySgDZylW3XD+IZoMEc7Mxl0B/q5git36SqjWXN3B77bK2bmVVSMHlfLmpqonpZiBfHQ/P8tTI9bppMcy/UTXSAWATjFjLkc+4lN4qas97ZPZaaz8LnAnNuHOdidiW1Gvqrjvz2Fqhzgf1zyZHqTkyhBvv+a2WQym6AQxiQIJwXo9/tGZhXK6vnXEgp8vq4spTCkCKlVOacaSrcnU8suVnkRq7MxWIJxyS8+Wjk688P/NJ7O97dv+RvfNcW+1+6uea//3BmXpS7UqFU/s9PM88fC88PkWkKfEhhbsJaH6CtP8/i1L4QYeuN+rLdPi9hu3FFxYnk6lpMe0pbFHfrMyqeSiBwIRREH62I4O59l6g73U7rS9BQwyVIWLm2GwfeuJl4fEjcns02uilkDEV7tEs8Ghuf1sL5vvDJDGsTXs2mEFirLYgYg6OFusnR1lJZ8uUhWlJlzcrdXDgtlbU16ztq42CUXBf0Ko1AaUJSQzV7JWCsHJu/dZvRKcUtOMloct17p5GcKpnEZGflbGBKp/hNowm+6zZSMDxABE9btnFS935dffbZTdLA9ZfNQpi6PPAS82Dfa0yBISZDyJubaVfjOWvva6U7Mtp7XUtz/av1j0u1EnkpbRs5Nb9aJduiMq5wj/vrrQ4X2mdQ1MOerLRvfkgYiyishdfn/tAPnKLwk8/vGWPgajciGMlkzaZV3Y2J+9PKcVkZhsh5Nd7z7XHhXAzFPTpNcuz01Sg82ZtMrZTMLz9P/Oj1yLNDpLbIrxyF33kh3Itdk+OxcsrwLEaurwdqKfydTzN//NHPXoJ/RM/54MORVnEBde9JFLZdrWJIbgq2SBVTsHSLEOsp5XL6Sdgoe+LgSPOHJTrq2MSsT1pTN16qSAgchkDTyDQG3nmy4/E+ugO9/dwXZ+W1o4e/gC2GXz0kfnxqfPC6IimhuTKOdsGrBo4F4lyZ1rLZcFi/hQMPgXltNPXgoqycS+XlIjaywaVftqp4eVYe74QhmZ2KnVL2/kuF89ar2SXcRWNR3a2N02on5bODbUSKUdVs2F8o1TSog0vnSlNohgR2Bb5s4FtP7KqeB2IUwloLaYgkiZQaqLUYyCc4Gb2DSpXDlIxlhVUJpTTLQm11MzlDoUqDZkLzpdhMsrv4LbWyrM3HEHpRCGGC9OKpZj3AuI9njPYr27xXFGKw+WN2Qof4WKpj5HdkM+NWJbfKIMJhtM231tFcEzcLmWi+P/PCUBJrNYLF/VJ5NWfu10yQwGG0zUlVSRFuT4W3H9lG9ew68Rf/2COGKHz6uvDhXeL3P1/Q7XBT9Ky8FOX1cqJE+NEdvPjGlxylXGZmcBEVKRrsgrAtTN/S/EmzU+tifj+oQrCB6+UBDf7lVrpsbKMmtOjgiPhQ3E9lbZ4dqbbDp9R4cthzvQ8UhZd3hbvceLkEfvtl4/dP8HiAVRfeOhz4+/cLCwZsJFFkn1At3DehaOD12cClJVR2qcfSGwskiBWo5quqLEU55sbd6jW/vybriwPahJXIqwzPdnbqr25V0powl8oPjpbmNYhwm1euhpH7s12/Hx4b3342omKZpficb1kz57U5STyyG6yUTMVOt70MHk9oi7M06+G6b89a2hanZ0bREfdW8U3T7qkJvR2EwnrwUG1jnJfMaW2ccvH5o2spnejRw2xPa+XcQ4iy/+zSWLxtCOIGZ/5air/G1qz0bw789A2MYGJ5VA0JdhQ+ijGbVOxwCFUgK1orKa7QBnbJmEpWAFlwVVQDoYJYFTO71/FpWTmulc9P9vs0RQ4pcDUlUNlSzkttrGtBo0nTvvrGgXltfHabebpr/OJz+GBRcg72jAwBXcxhnwF0P/A/fPYlRykP13RfqFax6WbHWLcK13sbdYGrmKpBMcg7ODBjsJILnh09VEcqA2bepE1o0ogko25J3CIVmvepVRtXcURoHM+Fu6Vxuwgfz43v3Ss/PAa4h5dR+du5QTrDYvPRcAj8uXdHnqTK3/p4YFbzsJEonKqSqp0sEipTMg1oCkLwPqx5nxUExmDz3qaBc7PxTAIWFV6eG+8/Gpytg4u0rSf8fBX+v7vGqpm1Cb9yY7ziua1MQ+SjWXlfI7dZuT833phMmXLK1pupChqU2IRYrWKIsVFaIYS0ua6fF9OirrmQW90G+oKNYsYUmYbEEKM7uztfOXgokgj7wco4VWUtxZzySgGn0pXmOk7tpbOZXOdmI5p1LcxrYS269Z8281XTYLa6xdSbdUhjVSvPOyqL2JxUg7vrO4vK/I8FKAyjKdeb2NilEVhz415WVBMSYByE3TiRQmL1WekQbfFog9O6cLusnHPjfjUixM1hTwzRXCb8rKotkEvl9rSQYuYwJYIYNiFBOBblnANPrysvV0VXpVR3m69GvJa58t3vnr7c4rQF6Qu1w2OoAzfe/Ou2rzlO1J0J1GlqmxkIxc2mohiSV8VGIN2Pdgg2Fw1iZWZHCQ0XcedytZFMCnbC3C/Kq6WyZFhV+N4x8MOjoEdgxZTmJUAyBBKF/SS8cRi4CZFfeZRpIRinFTjnyhAM0EhObRIaLRqjJnqBEII5qJVgHrZGqZMtkzJGGKNyNZj7wm1ufLbYwH6KlrD2/nXkf/ms8uO1oUG4VeW2wBiUj05KjYX7UviVHbTaGMRLRGByB7whRUSstI0h4ek0NMXJ68o5w1owfaufHFfjwM1+5DCZkHg3JotG8A1UG8yt0FpliANDEs6LLbIl9+wSK+trVee9NnfcVy8ZrX88r5XzUrzH9NJTxHNY6kUi6L/zwH9Isaor+oZeWqNg81doIOYgHyJMmNSt9PJWKmttSI6k2JjUDMH248AQE9e7kdvjYtfKx05zaRzXyqlzf5vy6piZUttK6mnwqEINvDgXrqdEio0xmo72g9vMR3PgHy7GqPr2Day18r17N50o9kujwpd1QtiQuqYGx/bS1ht0n3ZtfNZ+ulYRzsAe2PlF7TcGrHdD5MLPxIXKdGMo2YDgPsQRDLRpzcjMawvc58apFCqBpQoZ4YNjQ1d5qAh3lAYrLUbhMAVezo1brdwV4QbhXJVzbQQaY6gkUXK1zWOMwo5I2npaK3FTMLpfrtYTR6tcEOA6Bd5/JIg2jll4tcJvfl758WJuft/cNQiRWmCSxO8vjR8ei7nKi/LyDn64VhqNH+0a/zzCkyRchcAQ1E+8gRCF2dyRDXQbYMQXoWMEL5fCLiRTw4iF9Y4peXaLIb1dv9jtYla1krPTRs9r4bwaOp1rYymVOTuwo0ZC6PTF3HM+c/WvK4be+uC2ufFYPylrbWbh4mWseIW2jVG85K7N+l2vaA37iB6KPAwMaSCGSC7Ze3AD+UYufb2qEfVTbIxpZDdGlrXYvq0u5FfzaRIxF//s/ky7FEliG3FUc2G8nSPXkyH9r06Z46q8njNBlffGyGe58NVD5B/cCbtBmbXSjnZR5QFl8+denL5EvdP0nWqTWHsZ20ccmBFXDcGBIGEGpmZDc9P3uYpfqwlqsQXTsNGKUQK9z9zaWAM1okS7QUBDWL32b6qcmrKo6SerGcjY0aa4S5UvTBRc9vX3bysvZ6Wsjd97VfnhU+HXHsOj1JBBjWroO7cQSH7zcqtuPNZfvscBhEBE2QUDmPZJ2IuwVuWTs/L9Wfjtl5VPz7YN/Xgv6Ng4LqBSaUGod3ZKIEBWXt5VGJXvN+VXb4RTEa5i5BCEe02MxU7BnxwrN0PkvUPgyc74mkOILLny8W3hb34Mv/yocZMCjwc1snuyU/ZczO2CYOSNVq1nNCd4ZRjEytKcuZ+Lp0srp1K3cYhF9LEtyrzaibVkC0eyWbCPOuhOETaC6obQXVbYTZcuz6y5STTvey25zMCwgMnADtPEk+s9NcPZHSbEF9hQlDDZNe09ePdg6tENINuB0PyktlxTsQUvNjtPMTJIcFpls7S01ridC60a82wpdn+fD4XxJvK0GGvoEJSbBOezYykCmhSWn73y/ggPob4A/RPhwQjFRwUP6RvtwSyyxyCUmAwQ8iCX5pC7NBv81nBxM+/yGfFy1uabASQgLpZOEp3Gp8wt8HkRvpcFqYKkRkqRuvcNpEi3kOv3GBA+uTU2jz0rAarycYLPdzCgDFQ0mqtpQVhEoVZi8dJb7UTNFdZmp7kqjEnYDYFDMiLG61X5f17CD8+N7943PjsJZLtkL5oadaqKpRSvsFUm2WvnbP1WCYEfnSOf5crLVjkMkT99CHyabYv83inwi1fKG/eFX3kaWV9U3rtSbtfGX/+o8qNFOWrjzz6zUcIuAmJ+PWtpTGNljBNTupAk1lzslGvGd733uMNWDdCZq1r4sLYNJMvFFCy5mG9Q8dK2M3qaI7DdyqWTEMwVvbslWpnXT82EuGmYV1XYhmTSWWOKNTWa4lptdIMYkw2xn3HOlWmw59FUQpWR4LS/CGQP6u2pZs1nx2aLcj0mnl+NaGsESZzXyt1aNtLN/Vx4cVyZi40Lc60MVG5CYz+a7PHR48A/PCc+v1+Je1hFkBjQ+UvqObdVJ74wxRej2OLsi3QbEDsKi4gBIwKFimpgUFdWYGz8lgTU5FND6A58sjGGFNnCe21f8MUrVt5WNcS4tkhswlwEmhBlsGd8B2RHDfKD95KNKUNfC140v5jhu/eVa2nsBIt56EBaNSreOTdaDNvoYqke/e5jn+BI9VIbxyr8jx8rf/szqC2hy6V/t7Zd0J7YWqPlFdrW/UUkTuE4K//TZ17cj4mvpcR//SIzH5WetfijHNAK/8UnM1+7SfybQfg7nxf+50/sLn99l8jAsShLEw4IN1PiyXXk+aORx/uJ43lGgXk2baqC9WFr4fZceDWb/mj2/qzbpFgpa5S9NTcjZnQPoVo3MUC3yuwLsHklZfeZjTy/XQJVijiC79aTCAzujxtiJMXk0i1jAYW5uywqIQaX2SmIP2OErUcOEtBop3TxzSNK4GoczPNoMKH8e493vHlzIGcr0VO0ZmttmEpFAq0FL9+Lc5Wz8ZNV/GAJDFT+zHsjP3nR+NG9okv74r3+uRanyKVYF+GLgYIP/q5/reHj24NaMUeECoz9e+Doq5fFg39tZwFFZ6r079catKgPXoqARHJRVoVZTf5FFVjE5mxjhNFFxUUvr9WdCqwr71WAWVgkGjtwOw5Fovq7tCYyqoURVmwd5c7n1Z6jZeypk49L/t9Xmf/ts4HrIJSk3DXfJFRMiT6ajahWPA3JT/n+/x2NK3I5RQf7mk9PjXkVKAFxJlFZvURq8DrBh8diwT5NyWvju2fhT+XA568bSSK/9gY8vxkZgnFqpyFyWiIiue8JlNo4r4X7pfByLqxVaaIs1cZGpRiqnbP3j8Xof52tU2huoGbf8FKqPqywettk+4zqRayu4nuP2K0Mfl0kRfZpYDcNPLne8daTA0OIPDokcp649/54PybzS6Y6mOMECZ+NrmvbHtsUA0NKTK2Qojm274dEDIH9MFA88GmIwhDNBobWGFI0VDc3jmtmXq3yyG4EpmpsKkLlV9/e8+Zu4H+n8OGi5DVzoW/+vIvTWTFWujrSoWw70XaiYjMkq3KDO91ZDxlCIOqF/xh84fU49O7K12efRlIIl5sVHvR3vqBMQ+okB6thrMds/jpXrIwt8GDWQx/59AdDRtBsCyuJsqeajKwaKGHvzYjRTW1DqUU94LX3KmxsKCFwLvB6hd/8bOE7jyLvjoHfOVbu12iKkyYQbccWBNnyXLi80f46qzfeHR2roDOcO42yPYiTPV124fO5cCeRX38iXFfhb94qtznw+RxYtPL2dePbbx/4ypPRkWcb7yxrvoToqoXm3q92Yp5K3SIb1wZ1re6hpA4KGviCeLxhTIiF1Zhdqjaay+Y64NPd1nu/2Wplk9D5hho9C7Q1U/GkZHYvT68n3n/rhnefHNgle5ZSDNTcuFsjim08pcKrZbXc0W4E4KDQGpxlhXA12Zx0LjZj3Q+R/RA9FKpxbt7jOuJvxnKKVmUfAzk0IhC9XQq+NHoZLmIJcuFqzzvPJ64/O/PqwUb0cy9O3f7Dg4PmwUmEOZJL6BHdtpD7yanYtR588Yk/dOrlazdxEjcSQ2zI3ORiBqbSE6UjVQJFhfsqvKqBQwjcm1TFybjes9UHJ9DDhSn9JdvrV0segKKcGiwtELT4fM9GQjQhNLNmPDuPtrTLgRz7twxGWH+5Ki8X5VdvRuIQeGNSPjz3RSiXF1LtlNg2O39Yv/DR4cXt2toCta998G/cKBoFSfC1a/gL7wx8+1HkL35N+fC37vmd15V7TbRS+HNf3fHO45EhJXZTpFQnNyyF05w9Sc1Su8+5sbhH0SFFphQp2lijoGpZKWM0BH2Kbo/ZKrnBeS7e11YyxRDhBwBidZ2vobwWzWBsrG4e5y6AMbjWwlDmJzc7vv7mNd9864o3DjvUT/IYhTgqNzt7rI9r5ZQbV21wIzijhVbFrCmleGWkW7TlmCJLKUZbVGjFN0jM6HxANlOuKbpOOAgqiaU2UrpwpFVtRj4OgaeHif0U+erTHXcl8yg1XnbU82d8/OP3nFwU/PZhCgp8xkbo/kC2EIPaBU5qlD0LPzIieu/RolhYba94kwNM6p+IvnAtWAZmFX6wwkcnoQ7C2ylSab5awqXEVi6L4Kfeii1MvZTkOyAr46DEWCnq1hguTu4Lk9rIajPbUs1SI4gyJkOPkw+xBxF2UdldJd9hI492ikaxk1z6AvP+sj24OdvM+Kde48MPp8ldWv5+6tq/2Q2Rf+ndiT/+dOLxPjJE4a9+G/793z7zp55F/sSb1/zaezee51IpRbyvzPQoQYuar6a5xCRfSRq7LmAOgTZEcmyMIZFi5WpM7JORH6pT+j67X3hxyqylMS+ZTpwv6o7wze0v15VYgnlWiTjIbrzeaYyeZ2okliEFnj8eee/xFVNIrOUixKdZgtsBuJ8rTS0caYjm/dP52o5DURqINMaYiNHQ9TnbrPLk8+7WgStRDimSg3IuVmkhslnEZK1MyZhKuSjTKBxSRELg8X7iq493ILCfEn/2aqKUwH+2vub521c/c+X9ET1nuDzfCrq5WrfLsy/iD7yrTjC2zyiXEUl11NeX89Zf2pHv30jlkgruD2RQV3BUQ9aawn0OnGegRT68FzdyClC9rN3a4ocPtTwowcFVxDaVHxWmZlrLorRkayZjp2dTodZkbvHVustTNRj+kGwUICgpBnITVlWmaESLFGw3//oe3j8I38/yUC3+sAnzj/7i9YuL9sE92D78geknJlKRIXC4SXzn6cCzQyK5yPubjyP/7jcmfuPXnzAOkXkx+8/WhJTgvGTmbFKqmALznCnFmDO7aBWGEjlMwmEc2Q+F68GM2HZDYBcn8/wNwdlLRuB/62bkg9vZHAtaM08gFeZsrnndeW+co5V8QWg6bNTDIQrPbnZ89fkNpyVTVJli4tlh4Gq0fnVeVlSFYzFV0H4IDMNAbpWlmret+uYfHMMAu2/rmhmTzU9ND2xjoqUpy9yYQttkcigwBVDLE72ZzBxbg+XGjEEZQySrEgczmPvKkz1P9hNDDLzxaE/J1o6kGPgL35j4699PPL36sk4IACH6r9H/nNnY6RIgJFxDZJ4+1G0hpBA9SsEeuyrWm47eb4orVFRc1uULIvlzaw+XshQziTo2OK++xE82U7Mv+qkHdysHe0nrfVvvmbu6xkuyIJHnCXZD3OxBQlQakdIiiwpR4fOlIiHxyVx5tgu8vQeqzdPGCJ8tjUXh+ShcDfZQvFwarwrcOli2ZSL+IcPnP1DpbIgKEJSn1wOvjqt9aivfA8PjxF/7hcQ/+Va02aIIu8nIBX/+63sEOJ0XajODLbjMIs3cWLZLNo6JKQgilRhMw/hkP5nQXBPzarPCp1cDN/uJFOB+zn422iY9xonxWaJUc8d7PdtI5pwjQ4iesaIsU+L+nLhfBnqMfXR/o3cfT7xzPRFvdha0HCyiMbnwey7mmlBQUhh4fSrckblbKmuzqMDgNNLc1GMciyP9zRzXl0p2Nc5SGrerMYUmT1FLwU7dkI1vnVtzIbzy4mwl77Nd5Pn1jhdzYanCe093/LG3rklililrKeRSyEU47AdiFH7tzcZ/+YPzz3wM/vDFGRLECcIO0gi6Gi/QCFQgEcIAMtjJiWdVCIhERjGwJfpYRRFacPd3NWlu9RJY3LDLOJHuaarGt5UQLE5PzUmN3BHdwJbrFr0UbP4g96YXHnD2e6mo22hordVA0OjDdF9DkWC7fBVelciUAj9elBcFflQC39HLbnxbAh9VeFng+QD3xZrDFxp4kgxM2pCIh5vETy1C8TneFxfmA0AIQIR/6xuB/+tO+NFt48PPxdem8Kd2lXd3JgMrtWs1lfeeHFDg1b1NvG3maEdIEBMe51aprbCWYvPkaGZhbVDGJG7+PNBd6PeD+fbsxoGnh4nb88J5tYi/gDF/jG0Fkcjgo7BhMavLKRoFUYC8V077ysv7bBEHCvtd4sk+cDUmrgfDHebatt6xs4vMhsUAqVP2yEO3qClNWebq9YjS5YkBYXX3wlTUDcCVIdjivF+NAdVSd0q0jfG4Guh2NQbO2SqLqynydB8Zg6WkXZ0r33u98kvvPmbvMr4hBsY0OF5REFGePJr4q//s1/mgfPIlF+ewg7AH2dnTLY2t17QnxVGVwb2Emo0ygHkbFFvOpmkwlYqBOgcPgG1NCdKoqIun9VLFdbAoApJ4nQNL8ZIv9OPYXs4bN5Vne/j9F4HaezsXfGsvZ/trFgdRFKhCTXDb4FmDNdjstPoC/bwI3ztZINCtms2JxsAPz8JtNpT2LsPr0CAIVykwKiw18FkTnqQ+k92aRP5AL+yL7gtUrg3w2f5jH014ewf/zjs7/qPfOvKBA1wpCf/qV4WbqW3RgzEG9lPiMA58/PLIaS1e1Pg1wBKzetCwNh9XxEt8vbhYuytzpIM1QTw+vXHOZdNGltq2krthp3pneYkYBa4RaC1a8BTCXBuT77fH1XI/nxwSV0PkaoxMY6RWOC6VFDFuspqxmyWduRhcLYtTm5mBl6asD9qDKOpCBSfrq5KlXgCwphw9uKoYxXebMoBQswFYEhJjqdzsBt5/84qSzd5zSoHrw8hbjyeW1ZD/AOzHRBoGnqWBVu65PWaePYq8sYv8tV979DOX3x9xck6gEdO3ANK5Rv0owhHSyJb1h0KwGzOrZW1KExbfgZpGp03NdJpWlGoJwmrObWF7dgwg6t40ASUMiktUCAelFeFxbHznuvHmPvE8Nj6YAz98bbXuhWLoi6L3eh00akKrcCfCSZWp2fw0VBhj5MMc+PHs73Xw/ngR7hvcAzRzl9MdXB+EUZWPst2UX94F7pryu/eFu3svaSX8wbL14Vr9w8pd7G1UrPT+zqPAs1H4/lH51iP46qPA2zfJRkHClkK95Gwp4E4s7+MJEfMATsmokXO2HnmItiEOMXK1E3tYa99U7PeNFIJyWuz7a1Nn67gzgrjGUrvA3FHOAM2JJ6iZhRMC+wRDFK6mwM2QuJoGUlRELWa+NOPuNmVT15Q+TlK2P6O68WOrn64xCH2EXtRK3OKc2eJf10/NVt0YW80cLgRPr/P3kHJjio3dEGilMia42g1M7nc7pshxLpzmlbU2TqGAC7ivDxN3y5H7s5nGfeXJl7QpMQS5QCxeFXo3vYE4/euifavm/SYKoW2jAu0qEwmggRYKSmBVizlfWzHnd1WuxMjFMTx0F1IQ81cdR3PQQ4U/tq8kaRykciVWEr9/FXj3SnmWGr/1MvgC51Ia9t+b4glHAJyD8v0yMLfGKwWVRCjKSx/sEzyHKjY40+0O7KUpaBZOi/I9oCQDt16/KiwlsWRxJTqXMrV/9P996OT14Eu/eMg2vvYosA9KK41/8V1LPvubPzny579x4NtvHEy1UZo7GKi5tyvQ6obhaWu0YBacyVVCosIUI4MrdCRY/HoQQ6BDTHx6e9r6ye5MsWWjFN1OfrNJsVZhPyTbIrWRhpFP7hZ3l2cTWwc8hycJVzFxGCxFexBTgRQn2htF2ixaggj4AhFxAAAgAElEQVS5VZep+ailOVlFPKai2qIzi1PfJCxwmooxuaordXJtnEuznM8+rio2ux4GS0HLXv6OTSlaCcFQ7d20s80rFx4fJqZhYFkbp6Vwd84MSyNhdprTOPDkauT2nLmdF3bxy7rvhSs/LcsFaxVDrL74ZPUFEC5PVJf0qAMWmuzzoQGVooWk1eIDtbkpmP27faxMEnwXNnGyUskIa5/wauCjAt/awU5M/dFaRUXZJ3j7Gp7N8OL0EBzqC9NfaxOrW1DIwrEI3/VRi6KeRu/vMfvb9iE0TYihEYNtKhSlnYRQC//hP33N+1eB//i3F37n40oolSbpi/1m/+gv7aeUQ39QrSB87VnkN74V+dqNMKBMIbAfA7/xS495dpU2MLqbRivKeS0sS3Xes25BUIMIMUaKetkmjf1gzJlpMotREWEcIjeHwKu7xSlvvUy1E2lj89A1R974RLP/eHI9EcQAqCFETnPmXKy/7wum260ehrCNz1Dc7V2My1sqKXmIFY6uqhHui89h7b3baylVMS+vxpScj10bWjqmINQqLKU6tRBKAS0me7MzJjCkyhhG1my82xDEx8qWwD2KkuJKLspul5jGxJLNWrMHTZXaeP7kihiE02llLZVjrgwNrp+NP3P5/dFlrSeF2fik2j8RbMGJx6KLXn4PCsH6y+4tRMtspa8v3KMqB7XbXBwIGTDyQmpK1EpQI7hXt2q8EuXRUMnVbEeORTgXYXQ3gNoqxd23d9J4cw85RpZ740F+4Rh6SFBo3W6lf0rYRjpiBlva1MY1RQkN3tw1/ty3DvwTbwg/uhX+3qtbfutD4V9474Z/7iuP+cobI//51yv/7d8785/+H0c+cKFuL8G2n9NL7IeEg4cLMyhPrgXN8Fe+EfiTTwytBGFKZq14cxi9RGy0xRhN0cvnUpSlFgbnJXeD7/1uZD9ZzEStlVwFUe8rMXpao3sLWZyE+v0QuiQNRy+r78te8gZDUtfcWNfK9X5kd0jmqRsD52z2m7nZid21wtbBOqqKsW8kCrm0ra80LM8Nyh2x7ebSfXOw6A177wRFi9l0lqrufGAWruoGbt1orBTrRdU3rBicdaR2EkNH941YkGuhtUAuwVK5sTFitx4N4p5DwaRnrTQs3dAok+dcKOWnduV/3MWZDgfKObPN00LwccBq5WIY7ATqA8ooxBEOSbgZA4s2XhxPqFYg+8lhnXYGi0NQF8+KUDWSCGSUoSnNb7jdlMpVhK9qpE6NF9VYSbMKNy1QgpLUTI5rsOH7d24C35TGd/eJ733yYHSoYdtf7G4+PNH68XMpPze3e/97jY2/8ks3/Hv/zBPSENjFwIv8mP/md19zHUce7Yw8frWf+Nf/RODz88J/8n/PvuZi/0Y/tVn8IxYm8MaV8G9/K3HMgX/5FwZuzytrtVnq9S5ys08cpoQ2G+xXrUTMTDlGOM0rqmqEbBNUMg3D9hoO0wAkzosTEWpFJdki8Xi/lMyu5OHL1mZ+T6Gf0n1xqBHcq5o9yeszNLFAqeN5IYrwxtXAi2PmVAsxmiOCtgbi6Lw0sig1CIOawgd06zPNJK15ypo50av3odmBnOr6UgLuPWV/XxzpuYDgZqZWvZ/slz/GwH5n9pq5QqVxPQ6MyXM/1WxnjtnHLkPgED3KEFNH3RyMHplr5fX9if04cLUbud4Jp2UliPDZ6+PPXn8/82+AJ08OfDafvZ5rIJMvRi9RiZi012ehCfaj8s2rxFv7xIeLSWtW9RmaFnso1LgiK7abhxBoYSIH6z52NLKa1aaoRXo3bYRmp2sMAclWvgVXK1ScwdffmMA0mmXFV0T4/RdWuvYFcVkCD04vuJSeDwkB2yq2P3/tJvHrXzXj6WGcSGPgrXHHv/adynFuzKWgROp5YUiJp1cDwslag4d0vIf17T8CCJKo/Bu/eMVf/gXrw4XGrRqz5Xq8gBQ5F3c+sMH+Q4quao9jcAwAYwadl0wMFrvXX4vhMnF7OaVW7udCdItRz5VD8UzPJg+ueUBoW7ZKwzaLpVhJOgZjWpVW2adkJHqA5lKtZrV9EGcHPdi3mnbARzcN6NrU3SfsKaxuEVKL+xg1Ozk9B2ADFW0jMPzEChbZFrYZiIGEwG4MPLkauBoSS24MRRn8RUUnxMzZ3tduiOxEmKaByemBEgK4sXhToRVljY0rr5JCiBzGwMtj+Znr7w9dnI9D4LNpxKKuvH8kgbhpLgF0sHJVrNwNWnk0BJ4fRqpUrt6I/OYnamwcxIR3ISAaacmsBZsIGiZAuZdIkkqgEFUJTV172TzvsnKukYbNJ69jY0uM8LFJcJ/aIZhoeqQaRNidnC+P/4PF8cU/bidlB28eVMFvDsq7k7LUxmM3OzaBsrKshWlIRmYoFgc/12qu9Y0LIGQIxqW8Vr64QKPwa29H/pVfSLx5ZcLp01KZAgyj/8zWKAW/hjCv5scj0igpMKbkMjY7Ecw4q/vyVlL2YCfwDFD33vHVvazmYKC5Ypms/rOa0vNY8BOz8zo6t3ZtypztZDquhSRiFk6tMcbGUv2hDEZ/zG7rGUWpTkSrqsRkpqLFIx8qxqNd3SJVxEjo0mw6UJp57paGp77Za7CrbNtLELYArYf3uEvGLOApuA1mf5bcUgczrzOJG4yOOvfUb5XIvFQQG2cta+G4WDp3VSUws0vJ0fTAYfiSDKF39sr3fIf0q2h9ZnhY33gf2QKHXeRPPAk8HhNPdom7UvngWAjRCNfWjI5GKg+BOOyode1Ph/UQMnLSmWtVMiZ+bmrO573/fCqFx4ORHCKFQQXDHi2O3k56RxNF2QflKi7cyuFnvNO+untpyQZISwIdA1oaZEVCo6XGaamMgz04a3Vrx9xsQK6NRAQ1iVEcgwFP/tBLEkKaiaGxHvcPQLQHqEYz+5ckyuJ2H00b17tAzmawVUpkFd3UIUsu1Ga90pqLC6MLngAPQdy+1KIaltVGLaOnaJuxYfM0N/F4up5wXTdedVUrPbWrTKQ7vnvpqGLC62a/7Jqa9aUQmKOdwMkrHxt3VJsHe0MbaBSxfFgLEjbyRAyRMVWOvvBMFGEsoFEsqEqxBLO1NorWzXnfRjnebwqmjlFPCfPnQILFR+6HhGAZJ1aqBlL0hAIFpVGaSQQJXU2jHMZACnELJF6bCc5zV/BoY46FqykxpcjV/kuOUnYy2IMujU3LqVwoaP3BxoETIIWB/Shc74Tzy8bnSzal/+yYdTz4aDQhGvjFp4+5L5WPb+8vPZ8kzCzTfEfNYNlKpaDKMxTVhUECgyO80gQNFnE3ubeo2VHCj+bAEnZGFwx6+TnhwYL84swCBW52wrtvCPet8nKG8631WkcJvF4ad/eVIVb2I9zPK3dzZl4twuF6qjy9OTBE4S9/Y89v/uDE3/hBY0rKf/WXDnz97bc5rvAf/K0X/Hc/eKjEZtvNf+eu8tn9mVISU+pCZBM7r7WnXdtT0Od0gyt9zhm7Zl4KxuguTaqsrTGSPPzpQgahmYNFweIkqoIFKrXNj9dAINufh9SDju0Uqn7NS7WqolT7WdmVTKoQpBLVgJMc7LUvpdF6ye3IKYAEe02pk2PFQpGe7iaGEPjgfjXgqV02gihuiN2tT3wm3vNZmzaCEyQi7ovcT32BaQjsB6ONdsMyGgypmwFYSS8uiTzVSl0ht0SumXOOjMk26Yb93FyVuSj3y8q4mOdyDIH9OBL/kBX4hy7O350bDMHsFM7tcmfaBSzxLRloLOfG924rIQ48OTd+fF74fF65niLrAPM8M6UDSzB3unevJn797Sv+7st7Pr7DHtBWqVqYKQyhMXgZYvcnENRj75uJpNGAqhkn0/zlNd2CfqDx6dL45g7+QS6UJbjGE8KuMY2F2BJkuF+5LFzspo3R6F1fu1bu0sraEtdj48PbmR+8PPEn37vh0WDSpiBmsjwlmxMmF54/O0x86/mB37tduBnhjScHI4Y/jvyZrwv/68fK3dFPz75HqLCelfPaiGTCPpLCJTi4lMqp2oLpfNTW1ClnEVVDbS1kGBADiFKwuIJ5rQwp2N9Xt7j0HR6EEG22aKnVnoPjJWIQIQ0WImXldS8vvfP0jBUz7eqG1/bMVFFa6KMqO3EtAsL6wj477dhhFkPnr4bAWs3BhaB87fmBBnxyv5i+tIu/faOVYNeL1qxaCGrkAq0+om6bOqpFHxsl6wOvp2Tz1dbjJe0ame+yXcOUjEZoG5FVFpYBZEbVk5fjQSNrMY/fpTSkKDdTshlts+TrL7U4f/JqgcVmXgzSOXiW8xij22x4v0SgZOWDY2Nl5rP7E99/fUts5lynwCrwq+8c+GAp3K2NbzzeM8WBnq9Ym2WmVSonCkPo8evdLeGyL/jwY0MHm++YTSI9o4NmPcE/9ahxX+HUlB+uXgEMyjv78v9z9ma7lq1ZftdvfM2cczW7iebEaTKdlVmV1VBgkG0BAiOBBBdc1IUFSNwg3oAX4HG4RuIBuDKSESrJVZbL2Intqmzq5Omi2d1q5vxaLsaYa0chVYo8kTondXZE7GatOb5vjP/4N3w+VfajI4rjn74X7p64XKZ3BYZD4b/8gefad6QGTkZyf1gaXho/++aJ1xvHJnh++GK42GPsxoFVq9hq509+MvFf//6OmE6czlmX/w7++z+65i8fKn/2y8TP3nfbUSpf9rMrz7noqmAThTC4i19ZqnrrYcWht5I+0YJc4uU7qjdcPWi7txuhdoZkhO5R378VPApec2xoOqR/bGlJh+4aYxgYvaZ5B9eZBp1lNSZxldjaKNJMTGDoaLd9ocizA5/YTFbaqloyEEcaLQpDDOS2hvgGXm0i837i24fZ3OvVBlRNrRUhrdaee1Z3ee3E1jVQcFiWp8d5p+FH28ga6JSbro4+fua03X8OqXKiB2ABZnOYH7x+zInGZ5zNqXBuHdcrT7kSg+ae1u+blUJ1+ifyiljqmyODx28D5alou9vaM6DRMu+OmXc9Qc6EEDW2vDY2MfKHtyOf5Yl/eTfzdoGrjePHVxt+8fYDx7qg3XznLJWI2ptIVXaIc9rWOfTUYl2I29zi0VuuN0etis9FqVwH2PrGnB1fB0dqHmLjRYTPR3i9VZTwV0+Ou8sqBaDycmr88cuJJRWezo3d6IlO1RBOhMEJ+zEwelXdd4R5qTy5TAyr3xD8nVuldv3lN6hfqZYg203kf/oHL/mrH534X/7skR9/tif0xv/+q8RmmzkXz7HBJ7XqIebUROy0mIt8tZClC7Bh+2UJl1WqPkSrsF33fUuuHAyZvijpbOcoXml8wXuQdjnsvBem4PDec7vTxXxNhZvtwM1+4tu7I9mEz7l2i1zoFwWIuiZgLhBrTow9at2kW1UdExDNj1GTMMcpN56WRmqNd8eF3eD46n7hlPrFkrPUdiHCl6YzaTRXxFyeneidCDF4dpPmlQ7R471ntwmMQZPRlZfdLmaIa25rZ51B5cJf7qgAu9gqZy4rUq7Mo2S71BA8V6PjuGQ+ux7prXH/fdFaBjEPm27+N7BeYddb1QPeH9vlAUBgiAEfHOfSoTVKSjy2jg+BwekeaPKez7cb/nXq/F/vTvx0MmMvhRRBFMg5IQRRKpo0wXdHc+qoLgKuNWW+mKKl9k7oSqkSUeR2PbHpcBsbv7Nr/Bs/cBMb16GwDyokPlXhlDsXGp1z/O7Lkf/hD2Gek0UNNHbW2qwR9mNQUvw0BFY3897gnDIPR7XVEBFa9Nw9zpxTQaTx6nbHdjMYl3Tg79wO/PhlYGfz1D/648K5FubDmYdjIjelm41BLGtS1xG1K1CUSqF3f2nlO/XyYBG05RYzaCuWmzJLJ3qMpvdMHhfxCHpL9/a8iriZPNvRvHt2E2/vj+w3etv0rhxbjclrFlBcWZq5wFcz3XKO3jX4yDvFKxQQ0kNlCo4xqurjZhN5PGcOS6Gjs6lIZz96vn3KvDslaldUVvWp5uq3orEiOGfBWCZmC96xHQLRw+cvtgYc6Q07Rk8lgOt41575xzRG7y3WUO1OnalSvFPVTiqVViDlBqwKGGExH6HSG1svfHq9ZUmZ4znRSn82kftti/MPbhx/ee7UozJjtHeCcef5D64H3sXOQ1roySh1G+HNzcBPt44PS+Bnb4sSlZv2+9sh0Lvw6VUg+84/+7rga+HP758UEfQBSAru4Mm9cWgQa8YB2XyCqgjZDLm8g+70gXOo4ZQX8F3wbTWQ0tZkFM+Pp8J1bOyDcBvVOT44xw/3I//tleepev7Xf5X4n//hntbOfLbx/OyxXMjRtEpBGJ22S0483b6Oni/qF9Nb1xasdAZbrXw4Js5LY791ePT3gv2daYj8+NU194eZZVn46cuJ2j2/rAt5UXrb+jMO3nO98Tye1SUdHKlqPIDaukDr1VpnudDuaungFKxw5iK4lMq2eWIM5givK5UOhCjKcXWOq63n0xd7RFROFqLjh59c0VrncErcPR15PCkgdjQngVwwKZqannXQLkigNU/0ehhWe7a8E4YgbIJ2Ie8eZ374auT9IXNMlZQ1MaA24ZuHM+fc1J6zNnO6w0AsA6lELv8fgmfrNZX80+sNx1T47HrinLUV7wjVRf7sIfCH147b0MCpOXTt5oVlc3JpcC714u6xsZ1zyu1yO8Ma/qzxFLVDCI53j2fe7CLz0vnucObV/nvS9/74xUDwnb9qao+47v666O7qkDVy3LuOBPjsWvBSuImRx1NR+J0KznM9DZQY+KffPXL6ulPFczjMsJjY1MHqA6Ti69WrtJO7Y7FZBGx32aCJypMEtaEoVWO7olNTpipqh1/MFsS7zt53Nk7njbD6BHXhxzee240W1e/vR9L5wNXQ+eoha/AOlo+JghXeaayhl6Z7Kyc253T2UdhMa6a1SpCe3hfuT5m5VP7g5bX5u2acBKWI1cbDOXE8JxOuiOVAOsYgjCEgaABP6539FNlP1WhljeAah1ooDnzTMFp1kFOJlthaaXDqkB59uMyQqajdyhgFCBcRzxQjshOWVHlzu2caI4KNorUxDoHjXLg7LLx7TNydM0+pMZfOnNaMTtMINHO665qlU2sjOyM9dKBrgU0xcL3x0BpLFl5MA5vg+fm7mfPSiEH49YcTj6eqqem1YU2aStRWLEIEj67nSodhcPzemz2308BSCz96sSc4z7ePC24z0MTzT95G/s+vG7+7F15vI4/nRjOrllTgvnq2TkOsgjhccPRSSYWL/efq+uCdZzYShO+dzaAt+lzVY+rXD7Pygldg9bctzpeT44+k4qLwbyvMT9p6hto51s6HZQHfuLly3OfMlx8eiQjffGsRb82ElR5SThp17nQxq91XhWoQ6UoO6DxvtIHcHU8dungm1IayVi26jvb5rXdC11uCppaETvSE9rqi0ratqUmxd89o7ppROQXPxlQYV2MhFzWMXnLBoWnJN4NjFz3HUu3bdAymK621XFDiGIXdJhKcJ5XKOXe+vV9Ykv69v/rmiddXA2PU1CsldMOyZGKA692EE52b9uNAu1IHu+OcybZAW+VTarHRLmBFrp2zQAyecRgYg5K7S7eUb8tXCJFLDmhtnfNSmEbPdlCoYwWbvPPsJm87UC2AOWWcg6V47p9m3j+deX9M3M+FVBuD6zyUSs469zVAjCW2vg+5W7FifDunOEI7Zl5sI+fS2E6erw6JUeCcGoel4Ba93Utbye/9skRYJbrdsMvRebyDwTt++umWN/tB51gct+PGvqamoH9YHN8uhc928GKovD1lHM10EcJfPAx8WeDv31S+PalM7CVw5R1ROprr9LwGuhmEt0+ZafB4p6PNxgj4cyqcUyPTuDt/z+J8zI4f7B2v9kJvnp/1hKvwYuP4d18MfL5z/Nl3d3x4vMc1gZooqVw2hxo35qELc1qgJ56J3uvQvF7HfDS7rv+tj0nuntSdZlx2cDRbpXAJee3d/nHQarlY1AYvCiyZ9hB7UDoq7hbxlN759cPC3Vlbwoe58OMXg/rWVI0HvB48L7eKBI9R571TVke255yXVcmisP44KbVLf64z47rfip79Rue0ecmQdFbfTY4YRrwTllz57u5oVLL4HDbb1dVtthtKZ5tVa9hIDXqBqQipKGDX+sp7NdaNgFygb2W2FFGEslTNOfVmwpwoRK8G30vKqqiYkyLRuXF3OPPhtPC4ZE65qLmV92aEti7e182brXX4KHLekGBxuidfnPDlB21vg1d13mNpHBclYojtLpPtcIv1J6NXMswqLGgdeujcbAde7SI/frXDiQJaGOMn18Y2BkqrBBr/xRsl0p9zIYgmyDnnSB3uMvzig/CUPb86O26mzp/EinhF42upthdtbAfHT15OfLIfuZ4Cv3h/5v1xwdWGd4HSlawWnCf+hkXnbyzOf/zlif/8i5H95PjR3tG644t9ZOrC51fCD1zgr58SH97PaiHYK33F9RHwHqogNuw/M2AcrHaa68cvhWk91SroFmgiVHEsTaBnvCh7pIpR/3qnWgBPuFzCxhIydqVD935K56q4LngJ5gLeuZ8Lj0motfJi8gQDHo5Zl9y7Qc2xo4ObaeCcK8EVc2FDFRT2Y6fSmHNhCI7gPVMU3lyNmlh1WvjRJ3vGwVFL5WTpWzvMQkMadM/7xzN3x4XtEBiCFvVuDCSnB0GxFkoVJM8EgNZ0fWHPIOekbXAM5kAnql9YARgxtFYJDZ3zkhnHcHE1dwIxqnqFLsxL5jhnDShKqlc8LpqbkmtjyZ3DuTIv9ZIc1u0Q1m5DOcKq+VwJ7+vXEqoTvnvQAGbvHTFqkraKbpRt07t2C61britKkXTe2/pFZ8CrzcjtLnKzDQzeGYCjXcDjqVw+1zl3onQ+mxyn0nladNZuHR6L8P8c4C+PiccUebzXZ/RH+85tFHIVNu45r7P0xifXI6OH3/3BNV+9P3NaClebwDcPiaV2tlHtPp2D4J+7xN+qOO/E8Rd3mR/vHDEI/9GbkU+vIneHSnCaJLLzkdg6ueTnhZshqHQHNfM3v7wo3eljK0v46KZ8/mOY3AbUymLpjaFXRvTre68QfOudKl05u2IocV05tmbHiCpWmn1u7xyuaQjtedEDQlFYi58vnXNRIIIuLCbwvb0embzesNCVfP4RS0cQcz5XnWCQTnSOV/uBUypsp625A2BvaCUXjW4XHFfbTi6Jtw9nJVYHwTfVX+7HyGwIdG6VJT8feN36OSf6/U+2F0wWPDSiqwkAmh5Zq45yjBolOFpb72xnsDoZpFQMiGksqXBOheOSOS2ZY1Jv23PRWHkNMNJbTZzQP8rkLL0ZGi8X0EblYtV8wU1j2zrZCd60lUMU9kMgFcd5SRd5WOsa6d5tPyo27njv8dIZLU4hlc5xyVxNg1p+Fj0kuxEKsqlbUlWr0N4rj6kRh4E/fej8i0d4Onn8RvAuk7vj88FTekV8YAjaybkiXE2B33kxsg2ex8PCccmU2rmfMyJCypXr0bOf9GLYeMff9us3FmedK98I/GDr+OHO82rXeXtIzEm58I/nhbcP86XFJCgzRX/ZF60f7XHEPr5KtD7+c3+DYK6/H72SrSuibuDoItej8u9RzDkEa7uMPD1Ko3VvELbOOELHedModJ1BVRalH1uyzm3Xk5o6HVLh3anw4VRUST8IpQ1sYmVz5Sx0VZfk4JUNQ19Jhpd9WGsdF7rF64mF1CakRFzr3O5Gvns400XIKfP+odoSXckMUwxcbSK51ovlRxd4sRuZ04z4ztO5cCxFAXU6XhTVXare4NnQ5Clk6GpEtb7cMSjROzrPbooMMajhV60EUeBsit5MprMVX+WUKk9L45iVSZQs4k99Xq3lNFuUlQivZ3e9TDbaOTVFl0UIralAAPP3aZ3UKr0HBueMh9tIrdqBq6/3GmMYvGMcAmOMaoXjnsOwnFNg7GoTmc+Z92uwkj07wOVATtXx1DynpSI9sswZmme38fzR2HBB+HSnxnWdSvRNiRxNmELjBzdbphh5Oi7sBs/NJvDhnFmacLuP7MbIZ/toK6a/vf5+Y3H6DD98OXA9em5G0bYmNw658edvC69IbGLg01c3XPvA+3nm7lwV4SpFC/NClBSrvI9lIXbDXhb/cml5Ecckwsar198BT6oFxHFeTzw0eFfQxDN19St4IkPXHaDrnoC6sXkxqylrgZspZByalBWco3Yh58pdrZqzKI7o9SZJtfC4KKXPO51fpiHwdE52Wz73CCuyDLrrWrLFqzvH4bjw6WZkv1WLilcdfvXtQVOpbZ/mnN4W3jli8GzGqKTporfXaSlE7yilMGcFGM61aUfh9GDqDeaii/JK57RU3GjmXF4X9JtR28b1UMylKLUv6vrh9UY1id/dHTnnoq7wKXNcCoesadW5gRNPrUlbu9ovKxRdI2jMfe3aKVRRbq4+EWr65kTjG8OFSKGtcCpV07RzYTdoEbYCDd2ZDT6wiQO7IfDyemA/RbP+7Fxt/MUHLpXGbtRcl/enxQ5pZfJ4gU0MHJdMa0Ju8JR0MHodOvupc6hwHRLHDLE5trvFzAEaUXSL+vd+dM2PX+3ZxECplc00cF07t8dM694QeserKz08sq34vldxUmGkM7hKbnoqfaiNn9833p8bf/Tphn/w6RW5FqTCh5z4598ceMyZL98/cX5ajXM+mkNFzB6k2e+556HnQlXRzAnpndAU1KkO1NS6M+C4Cp5zKcxdMyrk8nwpNF9QZLajbVqqjV6a3V6A6VjUJpKLG1tK8OjhdjewiwpsrGncV6OSljud6ylysxvVRMvDu6fF7DJUleKcI4QAvZOSRtNpGK0eCN/cHfn8do93IGgxHI+a1FUr3G4HrrcD52VhThoTEL2wHT2l6InrfWdJmmmSm1LUxCL6niwjM1e7zW1dEUPHu4p3Hh/1Nq9V1TazgXk1q4JodXCZU+FwzpxMn3tYKgeLOlgF2aUpU2lNzy6WzLZG/V2cCnqn2yG1IuWl6TVfeX8AACAASURBVC3txNk7plK6lVRQeqXWxpKdSsUM0PNoyNDtbuTlLjKOwdDgrmyy1i8H+DEX3Cx4yQwh8JSLsXpgGhzilPyi8jBYGvxy7rwYCv9g0/m5b3wWOwOdH2wcV17QkDi9GraDYzs4pqjPe60VmrAbIz99s+XuXPiLrzOnufL+qTBIJ0bPb6rO31icf/JHW/7uGzXRPVY45MafvivcfxB+dO34hz/Y8O99OjBG+OpxocuGP3iz51f3J/6345nzwaqmG1K6XpLObkg7Oy9D6Tqv9kZvmos5IGx84MqjRsJWdNdDoOEovVJqIVjxt9bpTswesUGrtPSs53Ou202hfEipKmWqTROfVx7mFKuuUJx6pO7HwM0UDBAKvNwNBIuJuN1FdVrLWvDrqU9rZBR5XXI1W/9K68LjcWFwns2o+SMvNgOHcyFGuTQZc0447zicC6+udF8bvUdcRuyWnEthqZpA3SssvZNPmdKUUbPx69qiQwwcl0LrTnWLrjPngkO/ZrFIsNIqrmjOR6s6Yz6dM09L5mnOPC6Vp1Q5q18MpSvBoVabtas2+WJrntpU7K0oqmomLf6IS3o6q/RMGTj9cvN2ulgmaFnPbj2JJ+e4miLXG3XqW2MZVjuT06ySks3g+OQqUnJXt46oo87ZZGdVxECtqsQBr8S4b7Lj2Cp/7wp+12UeCny+hdfDjIh2YMELo++8uRp4uRtwiKbQVZU3rvYqP3k1ckqVn98tfHV/ZhMdL64iw/83H+f/b3H+j39/z8YLd0vmX71f+PapMomwGxr/8aeeyevSfz94bjcBxPFiM/DnX9/x4WjkAoPQnyVRpqRdDa/WogQDC5Rv15qQzfFsqLqEH72yg+gQxXE7wEOGQ85sRQuiWmFUHKlBsAfFuVXN77CapTZt+NuldKH4ju+aVnw16ZwTnFLXNtETRZlOIqKmVaO6Ak5DYMrFKGPWUqZCaaIxBLXZGds5JxXinpdEa4HkdZ7+4uUG7zSu4DA3ns71cm692E/a7SMXfe3KfpliYFOhtsJsOsaHRSP7SnAMXn/W1iB5TRQbnMq9fKqGBouF/AhzUvbLYc6kXEitcUqNw5x5SjpvnhZd06z5nLWtjgurX203+5DngqnWpVz8pXq//HyrAL3WevEuKk3laq6ta6BmzZdGRY7Rsxm0MAUuVDnDhTknXW94H3haCpMLpCakqjGHqWjHkYoqVJasHcUY4O/eev7O9cDv3Aa2LfNPfnVkGjpXTkEt7+BmI+wH3XW/3AZNNSuduRQTC2jXNkbP9Sbyk5dbHpbGw6mwRGE/BR7Ol/DY3644N95cui1G/fHceDMF/pufBIaW9ESdC2+uI7cbz19/yDwunX/73dE4hsqseb4aP/olBo3SMca4GUTbTeocXWxgN4BgsIWv6151eU6YWyEh+K40trDWu2gCVKtqyb8u6VvT2aaolZ6aiPVuhlGe4BxjFFzwCJ1N8HyyD7zZD0TnzPRLwY612HurfHK9ZYzq79OMSvY06+m/1EY0+VhDHRpCMK4mCpgMwVnBqKOBs9vh64eTJngF4cVuJJXK/Tnx+e2Ow7Kwn6JlSzp2yfPtqbDkxFw1lElaoAfd39VayV61k9EDs5IvpsHZrKog1ly0JVxEOOV8SbF+WgpzrhfaXC5JObPwkRUKF/tizcLUglXfW6PYXQ5lDTUS14l0C0CSiy/vujFvNj83O8hFOt77SxqcPjCe1jOgDoLN/GxLLZSmjuyPS+bd4cgXtxsQGzVqUyNqsGdA2PjImx38V59tKHPmX3594pNBd7i+Norr4B1ThFebSPD6nB3PhRjWHS4XxVQunQ/HhePSWRad6WmO7x4W3h2+Z3GmUtiOCgi8OzX+8XeJ0DuTbEmlU4fG/Vz4cEhIgF89nPnZuxP/+u2jrVXgb96a9gp3Bz2r6oWV1mEE+ouwVl9wMeSz2BsfnCNKI/pA6Y0bH5icRn171Ah6O0RwnnOt5N4IDcR7bXP7M0lAnBa4ggZqOhy9MHhhcrAdHW+2kc+vR/ZTwOFJTU2pUmmUkgnesTHS+24I9CVf5s7edZ0wiWMzaiRDr5UwRWOKVITKfjPZPjFwPi7UuhocZ94dq+kyT5e9cMqZpWQ8wm7wRsB3hHPmlCp3XW/P0TuupmA2jcYsik7Bs14UCAmdpai7gDegTOmKWhTH1JireuHOuTCnRrL8Q1knrq6qlWKt3Arw6frE9n/mQt+oeAKY44IZzCtZ3UgEpRuye7H0170uXT8SgnpK0bt1RDrnDbYWqbaeGqJHCKTU+PZp4TgrsvyU1KAulcppaeSuJHbnRYGmrmFPf/6LdxzOjfvTWY0ne6fQWJpwFnXdA8gFDqVw9I0xNqLTfe5SCufS+esPM794d0bEc29z+1wc57r+jN+jONV9UBfav/9y4L/7SePhWHg6JdWz2UN4SIV5aXzzkPhwVluSMAyUlhWxLdWKcu2vO1Rz42s80/XECtdr69YbiHn0iA33QTrOK1nAiXAzeJbqyCHg6GyHyPU4qprCGC3OLCTUw0XsIWn4rgCSX2eH6PBBF9Cj7dYmU2zEEGi9klNhjCPJzLTWGyKVqgTqDp2mnqsYvY+GtxZumKJ1753b/cQQVU8ogA9iLoSNw5z46kkDeaIXptCZU+JqMyHOU1pnNPuOYoqMF9uR7RT4+rBwzp3UG0+nxJILqRoRe50Li9puLK0xlapcXKfmziI6qjycM4fFLD9Mk5jKSrbX6nUCvav9SWvP1EI9bxtUW1n0NYpeSfkYM0h/Xk11uzwaXY3ZVun0yihqXXfG0JWkEJyufXIlGkMoFb19D/cnXnyyIwbP8Zz58v2RbvrSfqeJ1LWp7rN0hUCGDjU3ziRSciy58zSrdWhHxwFpTT2OxHF/EDZeXcdrF6I4BiOViF1oD+fMNw+J98eF2hsnVf6pE39ZXSK+R3E6BylpFuEY4HdeeH52zsxZ/WTiLNxuHG8Pme+OWduaVPnDmz1308D7lJlTYl7O9CWvDGh7vTsXjt2l9V1vUvW4XROdhJUg3HDV2bLdWjH0oRoFovPsx8g2rPkb5sWKrjYcXOZcjzr0iSjqOXjHFD2D14JSzMiYH62RS1F+6rrqEJvRWuPuqJHkziho3RZ5Q1QAqnenpHyxcFg6IQQ2lkRWa6WUSmyei0mzCIdcOdXCRjxL9TjnebmPpFwvhIGKmi/XpllTGwm82Q2clkrK3YyN1RFv8OptE5t6wcYgxCrkoCzL6EXXKyFwSJUHA31qhVPuZPMxomv725u2yF5WRchzBkkpVdcmNiuu+89qe2et7WZZxs8f61bsCDZC2J+FSzEr4h4YRJPtTknJGikVzibZ2l5tVBEUOuMQeJo16UwEHk8N74ve+4KGP6/tOfCYi0oEaydVPXCd05+pd40NkQT3rhBiZgoqD0s0vp0L7x4TiOfhnFSpU5Wgv7SGDCq72wyaPpY/3iz+NsUpTki1cL9UPpwLT7PqAL10jfOzi+/ulHj3mLk7Z6ITXk0DrzeBH5TK/ZxY2PLLu0fOS9ans1bddZhx1+XXmhyzqvCLcPLC0tSISkN11Mh6Av1hnRBFlf9OHAHNaBTRFYPrejKrDFVV7Nib7J2SGEbvmKJjEC4yKy8wBOXF5to4pcI0BnPwts8XHIc5a0ttycgdbQeDtVsmL6aZ6sI5BXAWMxSepsDgA7VX0qkaiR0OqbI0VfMvtZKqV3lUg2nQU/8x6d4zFSVwU7QrGGNEZCHXQm3NCqxRgzYx2RhOIQsxQIrqgzsEYTcIuelcdFiMXFBVWlbrMxWvtbXg9ACrrdmtpe380rSVTWX14jFXvK7GYCtQpHS7jmtGGGD92PN67bIeEWdZLh5nYUsOPWhKLcxLYQ2MCl6T7Dqq0LneeHIWUunqhd51rl6fKy19VTPNqZFysQWDvoODOYjW1pAuZvsplpDtOJbOvFTuT4Vvnxbm3Ci5MNdKFuPRjpEfvt7yyWYgrIG+fPT8/zbF+f5QOOXKh1l3Wh9OiVOuXA+OF5vAGL3+/lPi8VxIpVmiNQwh8GIaeL0Zab4TxXFIlfslcT+f6eflObt9vdovXW9DebqoftMJzTkOTezNj1q/NtMVU1o4K8hcCgHH6IEGrq+cUnNeQ4vUOZVVjd5p4G3QwNYLhU2/GXJRkXNvjfOs2Y6jFUg1ydwaU56L7hen8HwLOkGjD2ujloYfHc5p/swUAxszK344zBdDqFOqeuO2TO3CKVfuz5nrQ6Z0vRW/PTaeUre2VwvTSaU0U+ZXFWGfl2rtd6N4hyudTXBkFEUuxVOCUAZvKV1afOesLe2Fjtj0Ya3Gi6WvlicKBuVmNiAXg69meSXtsuvU29PcFT4CCgNcltUadKU+QGDSvq7g5CYETe0SZ9OaBuLmplzZtUPq3anViHR7T4XdFNiih+qyVOsEOsHSzvS2bBa8W1k5lkpO7+pX24Rgz8pm9GyiMBe4OxYOc+HxVHhcNIqh1ELunRADu+3IF682fH41sQu6qWitXnyCf+vi/PoxsRSF5XNVmlgulXc1U1rjamzcbD2HJXOytUHrinqprb3j5XYg98ZPXzgLJk18dRz4Mh4pudBLpS/puUAv2FHXm9U9rw1mB6V7CpBMQj52tU8sKP0r18KSFHXLVVcRarykhGs13cKcu7UYVzW6F8y3SFhSI8WibgxOI/ZyTeqFitCTajmdneylVaTpGqQ23aW21PDB2TxkMjOnRbKG65TSyKI5IK9uN/z820feH5PdVKsHkK6UPpwT3MFu8NwvjfezxhOO3vNmrNwvwh9cCaekpPRSCstSzBNWjZtzE83FLB6hEpqnhE5tihQH3+xBXfeT69th5PpabEWiv5ebHjiYnjE349iadC/1Ruu6xlkVKLp6WXlriqJXuz0b2uEEr0i4TpJr0le/0PSc+AvTq4tmjJbcGUf98834y85sVuiduAkm39IE71zVwAxnYFDTA620Zut3RcnVUkVfn3EMXE2BafS8vhq4HT1fflgUZe66ntoMEZp6BEWnaeCvb0a+uJ64GuJz1yCOp+Vv5+/9xuJ8e0z07nh3KvTeOBnAUnvnQ1XStPdwd0wcU2MMShj33uGa8DgnbrcDqXauh8DiGtvg+XQ/8cefXPM4Z3724ZG390/qC9s++kaNnaIYul3HTSiuM1dVpwentD0RIfZGbYVcAkedTihdLRhF7HulE4O/rA2CN8Msr58jCIzR662RCmVyPBW1unxKajs5WOhrqZXu7IRvaohVjPTdBdrc8K4xoSqInCtLLup1a4v31lWj+TQvjN4zxMAxdb45Zc4VKhrPFxwMXi1POsLbY2ExAfrOw6uN50XseApeOu+fjhc7xtSspb2ANeoGN/pCFGXGxOqpNv8E6zBWsrqzQm1NZ+9U1Au2d7UpzbWZ+kRR2dT0Nq6m0Ei90+yBF6Up6cfMcX2tfod2FxpjpEL20MvFmiZ26Gsx2s2bmxCrmseplrYh3f67245YAlQFgHLueF/VH0nsZi2ot29Uqp9uBXS0wHi5m6BIf/CecXS8vhn4dD+wGyIe4XbbKD3TULK8NMd3/UTpDvGBEAOfXI9cT56bMXA4F92rNli+LyA0eMfbY+W4KP38nApLVuh5TrroeFoSj7Pa/mV7oTZOTbeC16FYWSHaDkTjigYnfLHTXeFfxsD7U+J8npWT2+CyI3WCDgnt0vZmVEwta5vllIieHfSe6AYmVQBnwFFvjCGqTk+UKhbd+j2tikw9gZvNUeesVLHau8q/nLAJzlpWnS+9U2odwCll1XSKsNSCs7Om1MacdXZLqRBsNi2lUZ3jnCsPTf/uLz7MHJbKEHW26l1b1m0M1AYfzpWKzsQ3g/DHLwPO6yz6cgr0Wni9n7g7F2bRNkwJ1upaocoNoVVHcmbhWTs1OIaP2iwlOCho121+1CK0YrdVRrMCLVU9g5LNl8V2lkvXr9trM7BHNZgf+92uPJXFZtABwffA5ADXaV7Tx5r9HAXFGXKtnFImmphZRAwpVkJDF6hJs2JSdtQhMo2BJsJ5VteJuRRdxxjjpzV1DUT0+V/n3lKFlDI+BK7GwJvdiHMKHm7CFi8nNtEzDo5f32VeXE/4GDiXhgsqYn8xRQaBsyg7SR0Uv2dxghKIT7nSe1HDpqrWGLV2UpnpqBNdUVoHg19Tj9XA+WFekBVZrWJMfvAhUIFPphHXhE+GzF/HyLvjmZazwXbtI5WKcnD1hhOmlYmDHgTJxLvVCAJx1fatP6g4WtMdVrf5K1fdpQ7dMXghOiWDPyUF9t8dFsb4bM2p7bHjmHSNcrsb2Qz+Mtw3KqUq/3NOaqi8mEtBrer9U8FQQ51xllI55ULK8JiyMXsaxWIanFtfM/2fd45d1H8Etemcgh4yAHP2hqKa43nTmy6vepmmyV4qDFexuDf1jNqGNEtvXGdClXVpMWraWrUCrMbnLa2Yx6sS3VtTp/PeO+e+8nqV41qdmi3TjPiBouIdyF3fy+6E0NV8unTY9uFyYHRDgsUIIBm9xT9a0l1UR91IFbU0+oXSIPjSOZ4XlpJNwaasLe+b8WB0TaMhRipL092rRdTXdhE+ONFE6x/1iQ9npVK+3AbeXAXeHgpfPSyMU2DjHfshsAuO41KpuTNFzaj9XsX5cFYE7noTOMyFkwmP1Wpfl9Kla4hQ7yBG/FbwASQrIroimVpjwticwey6NB6852ZoHJeBNFROHkpRONuGEDbBE522O4PzDLbKKNaCiGgrU7pj6ZVIZxCFDJQu5ozk7i/WFs6YPGN1SAgEB4c5c5wrzdTyIir6Lb3jm64mmlMDYgVcHLvJq5hZlJzweM4qgG4Yhawry2pQgW0XfZ0a2iq6DmPsXBH4+ljIrRFwDEHXCt3IVNej59U2MkVdIaWqWKJznlxhOzjePsx8d1akVWduvREm76je2Cudi39ro9KlMXdHaaqyWAuhocXYmwUCSb+ARWvbuj60tasechVBZ/s6ix2e3mwmuzSzS8G8otBdKEJE9baD6GvpnLAJumuOHtaAklKqIb66h+/BQP6ujLL1TEe4uCbKikHYTbravXS6oc76PMao5mUxOvabSKlNASPnmEvlZjeAcbydUzxi8IGwF262A3fnQu0zn+wHvKi42uJs2A2el5PnnALfHReaWETg9ynOd4fMLsAwOlJW79BTNn+g2owDifXqldA9q4lwaUoWUF2d0xfcKUqaWydLR7wCKtEJLg785IXnp68nulQ+HGb+6uHEhzkzOM8XoyYer05yKzuk1Ea1SzZ3a7+AXjpFOpPXGSpKZSAYIKGnlUeovlOy17A0OnPWVCxouBiUnia6rpAODY2AEKcKhutN5GoK6ozudB+rIhvz63H6UF5PgRfbqEif8VqbKB/UByUEfPFi4twK/+q7Qm6qMFGlht7qL7aBT3aq7YzB41K9kKydEx7OmiHyYuN5GzwlRsYY2AxqXAbCw6y0y1QL0jR7U60kG0m4uAGuM2ftDanmOCA62ymZwNQhzvSQHRM+64HSXKN3h7OZ0mMjgHP41hDvaWJZn6j1ytCVEz3aqDA53T1vYtR1h/W/tTW1mGk2o6oAhDVFrTUugNXKThZRnN7VFSBSvXA1oEucrqg2IeCcMrr2Y2DOmoz3YhvoEoiD7tVz1e99PwbdADrHdXT6PIyO3uB+KDxMgWNpvJo8NWfe5cph1ujAVvXA/V7F6aWqPvMED2nhWArn2i8oXuurblGNuFJv+K6F0FEKU3QeVyvZkDYngtTGJjhejYMlNMHWe66niU+v1QXv/pT4l9888su7Gd/gk6uBN/uBpVQeU6bmyjkXkunvam0sBYiOVDQevPZK6kKsamdSLNDHoXPE+j3moG3aYdaHcQz6OWpFW50mLDYKT04ViENXDeP1blAgrDZSUjtMtWnUh2Jj9hjXm4Fp0OhE7x3Xg/Dd/XLZ7V2NI/tN5Hdf7fjFh5kn22l6dIeqAb5KSFeLCyFGR22Vp6QrFBHYBb1dX10PvLqJvBiCkjacEtnHR8eXD2cTyBtzx27Jiknfazcam833tl4IogqdZq2uCEhTpDM68JaTqjkk3pzctWhjdzbbacvo7UbXfCdbhXVt+xFHCMJNjEhwiqyLIxthqFYVSORqPN7mdafcjey3qlPMokbBIqcyu6Djztrl1LaGDQsv9oFNVDR1CIrctwC1CfuNZwge1xv74Hk6F/ajQ1y8HHyCY/CdT65HlqUw2gHuBRXDt4HDvPD+pIj+GDyj+9tZCL+xOOeceJgLp9I51sJchblVlSjZPOdtbVJbR7qjiM4upTYCetLiBOkV11RSdTUFXNB1xjYGxPR7GDoYRdhGz09e7vh0P4HzhKYtzX7wbLzwKLaw77oHK7buSLWQUMStdsFV9ZmdxV0i4r0tuMUOlsU7ns562wXnzddFyL3hm0caF4+dS5QAasW5RrgtS+WQCudsiVJWoN4Jm0EfRtU8FlwWbrd7KieOqbMfV45m5Xoz8HuvNnz1VHhKnd6r3UzexgXz4On1ctg5KVrAKN2xS+DF9ZaNF0bt97iJjb96awE89gAvrV4O12LAEb1dCraKcVwNjd2g34smS8MowuCxeV3FCMWrPrSKKmGyrVdoSrkLCINTEsTaForRcb0Vb+s6Cw9GyfPoz9mcheNKhyoUqtqniCUAmG3JugZSwYS+192IIqGqqD4Mw7MypsMUB0YXGZzuQ9fi7VUYR8/r3cjGex7OC603bqbINjhKaSyp2fevXzvY6yFoO1tbYx8cT0vh5/cJ5zT7k67yue9VnEuBU4Fj7Szd0UT3iTgNYlmlsSLCNmh0QutCMlZFKoDvqqtG546rzcTf++EtHg10OS5VEUVQl4WlMrhOjHrbBFc4zpWnc7pkUbTeOC5J/XeqkqKriYxzUaRwDWcV6SziLHpcifEObS0vt+eiKF8InsE3hhguvNmV8UPr5iHr2AZnLm7Ch0OitcpSCkeTaa10NW3BVYd56o2eFnJrRAlszbzr7dOJq2liKYX8pKbQ57yybWAbPEtVf6Ho9fsUkUtcea7PAvIlKzB37sLoI6cKhyq8HqqueYraS+am9i25qazA2c61GalArUH65bXW21IPwYrGMkYRJq/x6ZP3Bkh57SCMf1tEPVsVnOlEWedJZfAM5p7v/LOliIqSzNVCVOYXRMw1UGiiuIdCVY3elFhSFPmwtYqtdroeLrhuoKTtqAX2TkeyNfIelPghAj7rLJiqUhKn3pi8R1rn7qQWol/cTho83CAX9bZdi/GUVnPtzjkVxuhJS+Or45GnCq/2jk93urpa+ci/dXHenSuH0ql49WZtSjDWPAtFUIN0RufYeuEqRoYQSb1yWDKpFDWCD0Lwkd4av/tiww+uo6ofiobYgDYFg1eQ435p7LqSuu9PC98+zNwvhVQq0sHRqaIKl2bQfKJxboXUILVKQtHVQkMMvKgdpmYpWU5R1s0U1YUNGDBUOBdCCHgvl/0WRmbS294z2f7x13dn3V8G0VvTKG6pdnLXhUvp67yGBfUsiIPdoNaZhyXhxXNKnZc7z80U+eqQFUQIjlPX7JLc4O2xXMgUrXWTe8klk0NwXEdhKZ3vEtxXx83Q+XBu3J2KkSSUxbTUBqK6CCUeKIiUuuIKCqZUe+gayTsGp2NBdOpvtA+RTVTzayfqkTs7zQ9dzHxaszzXjBs7EGyNQl9Di9xzW2psnebUEZ/eLHfluaCK7Um7e16Jf5y2pihy11Sypr5KPSg/uYlwzoVV8RQMgFqKrs1iULrgUrQjTLVzXArXo1eZZC7solI+dz4o19ye4cNS+HBc+O5YeJgrp9K4CsK7Q+PXTwUXHMEXXm48L6aB9H09hDpm7yFixSi8nEZSazr3WRs6iLAfBm42W17uRnZT59d3R3LSN38/KDgSneOHt5Pdt51q1vbBKdLYUd3h09KYcyOXzNtD5u6ceMzFvHXgehiYQmAaAoNXNubTnHl3PNEl0YrgujCFCHSOSQ2CA8JctQXsosnFwXme0nIJ2dFTXmg4QtOi9GZF0oGldPqgOzAvwiGrn47PXKh3K9yeDBU+5dVQTLmtpalDwo+8gknf3M+kVnhcOsFHdoMqL0oXPqRGDIFT9nx36tynwosBRt/ZR42CeDIGlxP1N7qJwn2D+wrvsi7tTzmbA7ve7LlVci0ahUMnou3XUhupFvL6sDcFtKKs4bOKjLp1DUo34rvud0fvjNYoLJagJazrErGOS2+20LyOGOhM7OwPd61ZxFK4l/Vgs5VLbZpfokQOxQ0aWrCrvFMhPRR06l1VHPqdEJxflwAXN4P9GJiMINJqIyHkvBI5Op0ZdzMyOs/dKfGwKZrhEjpnK85ka0fpna8fE4esTn+9dx6ozFVTxj4cdS/86XX7/snW0XWWUmhmm34zRl6PgULlhXm6zqWxi46baeSTq5EvbiK1V5bNyLjz3O4DpyUp8NIaIQpzqTwunZPFMQUfiEGd4FJqSiiumi7lYmC3nSAVSlXa25v9ltt95PPrSWPhSuPhlHm1G8m1cpjVeOyQCseUENfZ4Pj0dst5Sarip3O92yK94LzoGiDbjBCjBvE2fWydVzqec/qAzKWzGyoTnijqDNi7tkFKUl93jOue9dnUeS46e96f4VfvzlyNgZdb3WN+mBu7wbN0TarKDWquODwfCkxL51gdqVV+bx8IrnFMhaekD8R2cBZfAYuhpy8HOGXh2APdO8T2oZVO7lUF6cBsnNf8kdUHdrO2DtmtgLblhjgufNpc3eUm9GLRBLZe0bSxdpnvam96UIrSH8N68Du5iLRVlC6IVFzQ9rZ3lcaV1i7jCiIMTgXzi33uIPoaiO1M8apc0dtfb1XvHNdjRH0dg2p+nRL/z3NnyXoJ1KZKnNz0OvlkH9lOnqk4Xu8DGwsPXoo2ta0XxsHz9qGo1Yt4civ43rndRkrLzAkec+WYGzi4nb6nqfTSlHuaHZbgRQAAIABJREFUUSBh9MJ2M/Ef/mDH3Xzmu0Plu0Pi1W7k043gR+Fm6nz1WJhLIwxwnDU5qtgJ97QogfrxrMDJ9RQMXDHOpO8471GjPcfV6Nn6yIu9tlPb6LieBm53uiRuTZ3PX+wGTmctTu/gi+uJ2gt358wv3j2yCwP//o9vud54vr2f+dm7mf2gzoL/4stHHhfVnXbUU9Ub4bZQKV1oDCpNFThJY2MJyFejQ3rkblF7ylNSSpaKiz29q8nZSm8LXvhwrDzOMwXd3/4nv3ONkPDAl4+FXdKb3buu7KgGi/f8m5Pnk6jGzb8+JF5v/UXI7MSTq4IjS258e3acspBw3NH4qgyUuEXIq6UARZQNk5q6JmA7zQsUf3EZ6HTnGG3vuAveug5VdSy10huMVTTt2hhFK0khNWVdld7JveG6xspHgWjEjgJa4OKIonYpPQiuKYjknDMCTNccFGMdJKmWIaY3faZpN2Am361VgnhiV+AKGikl/CbieuRxyYjXDpG5aIRE02ev2tpoGwK1qORvt3G8scDblFSSdjV5zqXx7lSQVPny0NgMni2i9HAf2A6eJelNWtaVI+5vhsT/NsWZasP7SOvwYhy4GSKjDzzOhc+uJsbY2A8BJ/Cj11vG0Nl4nbceTwpRdydspmDqDW135lxZ7HYZSycGtXkoczFiuL6pqjwH8YGNF4J0izfonIsimcpI0lb3zTZyyoXWhGkI7OLATz/1/Kc/ecVSCtfTltdXI/Prhd//ZIHueUrqdnd3qsxJw4Ry0TdlzRVpTbWCpQhzbNSpKcfWC5vdwJubDe1x5suHwjFreljt0ETbSOnqSperopCPS+OcFPkspfHlw8wX1yO/ejgSnGOYnb5uUZ3XQ3D8ztbxrnZd5qOG0u9OjW1QIfgYhMeskRUP3fNvFrhv8K5qAfyyRLLA6yHhwgnJMKI3tkfbeJxah5ba1H/NUNJBhH0IvBwjm2AEEBHWLWIxal7DDqZSqbWq0z/VOobKGZ11fVdiSMRS0KTZHQa+622KCNKDaorRqPjcFWEOCNna7CBipanfbxPtHM5NW28vuqcO3nOlEyYhCd/eH1n76M0YWVKhGkleU+7UJzgET+sw58q3jws/fTXhBnPf6I2Nj5AzOTd6F1LrbEe1yBlE2I6Bh1R5So7rjXadm+bofs3zid+vOM8ISfSfY4NN9/zfp85fnhb+szfhIpa9mQa6MSlOqbENjt2g/Nlt1OX5Jipt6/5U+HASlpo116Njrm26dghB1fjdgKLLBsmYOt5p+Ov5WLja6MExBl0MT0HjwO+PlcezGlDdTJ6rXcR12I/e5ibPYE7gn11FvNvz5X3ir9+fOS+COEe2XW53wlzUScAJTE19Xs+1cspqdTEOuus758ppthYOS020G6NWY+WkxmFJFLPpRBp/fXfiX7894Z1nO6qRxzjo7m4TPLebgV2ETwdoRYscUeP85CD2zsZ7cu388uz4Z6fKP18cCWEvFcHzTYUnGWjjnuvpzLYVovdcdTg3pSsG74lduC8JoSkiK46dF3ZD5MV2ZAqBXCqH88KSmhHFVzIB9KKBTmsYhjfEO6Oxeb1W8zfQuU5TN7QYnYnvL5Ixr0oVscJXc0DhbM4ShYa/mJIDFhupER2Wm1I7OIhdy196N9pntyQ2peid5oxz6/5BiREOiPbz1VYpeaB1NXgLngt54WobOZdKnQuHpam4H7WDUdtSWKq6OFxNkU4jOs/LjWeu31eV0gJNOksXzj3Qq3Ci802D3z81Xk4weVhK4eHcOCzaf3uvrmRrGOrN6BmjcFwKUXT3djXqieS9IxlfdxBhJ2rIu/qgdtFZABq1qXlvDI4lVc6p2TzhOOZCqUo4P5wyPng6A+dU2cTKEDytdY7nxCmppYhyZtVB7v6YmC1Ps1SLse9V49erUgQHM+VKxSiMVRlT7QT3p8LZQKui4wRzUaeAslLlmkYnJkspmywCoYljiJrMHb1nP0UQCwIKMEXH13PnkIUbUdbQuv+sTahtFSE3/vQo/B8nITf1FnrfHC3BeztovvQDP9nuuBZNC++tW/iTw+sgye9NjrQkpVyyevYOXG+Cmi+fEz9fss7kxrEcomcM2iH1poV+XhYcOg/zcfvWO7RmhAG5/F5FizSjYJD+PSvOrs/CWokBKNjpbmsv+xcXo3L7TxpkHMdS9bYu2oVJ97SWKa1SStEcFdHLQEyxVFkppopLNDSmcDAbjbXD6r0zp8bdUeMX5qa3+ik9G5DXrp9nDIFt1Od6jN9zz/mhB030coEzjn1Xr5d/Zyf84MqzpKJrBjSDElHUbBRhN8ApqStBMLh7Lgqm1Kqty5v9wCmr6iW3rtKg4rgaoY+O7570VNkOwqutU7Op3Jii1z5/6ZySzkS5KAr7cFqQ3omh8+nVQOud41J1b5UTlc79OVNb52qKvD8Vvn5MvHtUdc0pa3RAaxqHEJzGr49ByQROFHiYS2WpnsfUeEqNU1aP0lQrS1G6WuoNutODpnfbNWqbNw6OVztFazViXXd+SGQITne2DZ6WxLuj44nAhwzbqMwXDSvSdRCiB5TYfvWFc3zXHK995T4JvzJAg9Y5i+Nx3HETPYM4RikEJ9zPHXyklsbr6/D/cvYmvZZlaZrWs7q999nnnNtfczP3CO+izYgMUFahyky6EWKAasyfQOI3MWKKhARSDRADKKAKQSZKQSozMtwj3NzdutueZner+Rh865pHlQqhsoErpJCZ3ebstdda7/e+z0tbFsY5qSEcRZw4q/XsXeO5WK+Y5oWpFE5WLZ9enWANDLMSGffjrCo/YHjK8Dz9968t1Pcr92laWCGi1XnzfncFQIkVTygYajrm6e/xfo3+K28DHbsZU+e4iTEasi+YYvDFEnJ+rzN4o0p90KErJlQqYkw1TZNZ+bYKlNTF51iiYi+PUV88CtXWXXgdM6vGM+dC4y3nvae18Dh+YO28mEA2SiRyYjh1Gl79vNHd5OktE3NmiZq06IKl93C1dhjX6ChLCjEbxqTgq23nOC6wagyHRcPA1hh+dNawaj3rAOvO8uxE+H++m2iDY93Ai9OGP9zPHOdMHwKpLFirR+mYMnMs3B4mnCiRvfOW65OGUwwskf2gd6khZowRbveRIWW+e5i4Py48zInjoswXRB0tXXBsjap7bWMI3jBnWKLwdh+5PyRV6evD0TnLHBNDyixZFTytt9NwbxRwTh0nv3qugC/1eRqGaLgdE1NVCEvJeGd5mAs4YWNAciFKru3V6m5JAodlofWOf3qZ+eno+Iep8J89N/zX32f+r4N/vyQMMFjPW9djMJyR6a3lNqi7aHbCeco8cxbvHSZmSlb3UCqWKek97NnJmuAMN/s9VydrNquOi76hcZl/+dV99ayqjzpVYeVp9KJP/B+t0/c7q/4On3y0P6zbP94J68mhzks1jGR++Deffkqj/4axIGIxVpNHK290LIQS8IyDpo6IUl1QykvVF0ERoQla05EKjDHROeiTHtxtPV0E70hZvdJTTGD1ZGitjoluj4mPvH+aK9F7iydXYNkHLE5jnwb5hgtXuPDCs87TmsJxVuNwqgsz5oL3aka4WnsuV+qTnKoJORW42jS8ODUcl0w7ZW72sd5R9Yf80dlKTQZWWFnLq2XiGCNv7govTnreHBLv9ppel14tYsOS6IPODh+nhWGOzDnydhh5cxj5/HzN5TowZ+E4JYa56H3KW1JtD7sbJx4mJZtPSVjQXGFwjrkmV55tHKvQYY1hN0Qej+pGMuhDjHlis2oMbIo6MxRjIFmMycxF+TldE/j1x6f8Jz/b8na/8DhlWmv53cPC2cqxXwqSRBMNuTDOCXGFDYbGUBlGFclSs4xjVL/yj9cWUxY+axy/Xnv+yxcD/8XvN0zJcuoMWxd4mwv3oh/9KQ3rkjANeCwPFIaSKU5PN30IFCe4WhG/8g5LwnaW8/WWn318Qk6a9W09PN925JyqqqrqdkSqBTHzb9w9n9YloqMPq+OTJ/ZsXWE8WbbmP/7zdWG9/yedrTstbJxnbVR4Dt6zCY6N8xRqQ7j5I2dSHcM4q1/O/JGPuIjjetPTt4qmWaJwJ4t25QRH8foNeK+7vNoGC0kcvdXOGes9KRdOukBw8O4wsW4V8vVBi9OSuTSZnfVsbGHjYO30F2aMzoCWpE6NktUjqmn0St821DJSjfKoi09/EdrrWOgr3yU4x7vdUkPZwvdL4d0QmbI2mA2z0DeOJuj5XqzBWwg+8PPLFkrm9T7wZu+0zFQSb/YLv73d8/s7o7CsKHWYrUemKQtYYUiFIdeRSVHxwhlLU4SQE8XAN48Lc6r+UwO7WSvEjcnYmPDGEp7U6FyBxYCtiqUGjBUg1jvPbz7quexbbBFOW52RfloapiT89mZQvllRbk3Omm1srVbMJASPASyJQgPvDR1NMFx0jj4Z3h0L+3nFM1v4YiP8k174dkr8D4PjjahR/NEUjtbzC5v5dVv4ahRkWHDBISYpoL8OCjtvOV0JnfEEp7UURjIJy9tjnUUW4dOLDcdFvb/q9VWDwFIsJf9r98/6IlRTvDZyWwteLIuxzKixOT8JPKKmivdb63usqv1ht1TpBydqfHfG4ERTIGMVGq2AdRkjtp6mtRSqWItxjkTU65FTw/4qGO1oTUK0RY3xNWRvRJiWRBMsZ+sWrH6/D2PGOkPbGPoGgjf0wWALRGNI2XLW/n/PUv5/HUJGhLOS+bizbENVHIsqceOSNIFQTeTHcYGiv4zLbYPBcIw6lH+6t49GMfnHsSbB6y9gWDL7MSHF0DRo1rOasIMLSiEIsArw+UXP860eVYZZ+NlFhzPCF+crdvPqPYbkX7x85OXtxDRnTYxkJaFPlfR9fMrxFWHAkJXGS0QJ5L2DDoNJiTRM3M4Lvfdsgtfhc85VZbQ6kE4qIsVS6rHuyY5WNWep+UKk3tWFs3XDCcLDMdJ7w6tD5GHSo7W3dcFLxtVdo9QrWBbDE6lAnxAYYmJOtjKNVEB7TMJ/vk18vrGMCe4nYWMtb4t6TDG6eFqX+bSDlTF8c1QjgdID9etaA41XUuF5bez2RkMK19vAqhm5GxOfnTX09pRvHib8svBi23E3NnxzvyOXH4LRUo+hBm0db6tQ2Hqn31NFsU1ZTzIRFYySPLFv4QcMfAFcxZCiI7giTO9PFkLn9HlytRWgsQ5bnmDVNfObVQDCJoxVUsbGhhp6qBZB9KW8wlWXmKZkglPf9aZ1WN+Ss8HZRbOh3iIY2jp+Kaj8/DBnrtftBy7OAkdnOaNw0TqC00hUqYpcKpml6K5pANM5ijiQhDEQgmVY6odcpN6fIm92EykZmuDovLr/b4bEzTATrGfbOtaNJzjDttEcZ99YzlcOc2H45KTlYhXwXscqrhqm2wa2nc6NYsp8ebbCimM/LTyOjncUjsfMWETbt4wukiYopHoqGjUrVHA16vm0/ND/aa0g6akuwdQjmKmjF/lXWrVSNWg/6RlPd6gE7MZIEX3pTFO1yLWeMD2BhvX+3jplE6lf7ge18glLWUzFfBqjTiEjbLzSGLyB687hJHOImWMUirFsrLByhmO9W6+MOqi8MeyKMInGw3QkpGVOZ73lfKWEvyXrS/IJuNUExxcXPf3jTHCOL683/MlHB35/azldNXQhcDctGi6wlqeWMI9a/jrvqnKtIhXG1dFJocuFaKgBAmGmMBUqjUENLal2furqqQsUzaearKp8FHW8OQpdZSRZAer9VURFvIq6xplMY7wGrWvQYFmKGmoMjEtE1g2GGjjISnpXkJxaPk9WSonUikTlGJUM2YhGEiWTywcuTkTwRTjxBbKi64cpQt0tkwhzkdrArG+61us9aVoy2z7oqMTKe5z//Ri5P0Z6r1XgMSlI7OsHdcx0Nmklgtcgtq8PnhU9xn20dTzdW7yxyomRKmeXJwC0DpMv+gbvPUk6hjGzm9e8fDzyarcQs44oto12XcwlMSyZf7g9Mi8w1vYsb9V/q03als5pV2bra5KjaPPXkgpjUnFLbXBPWcJcZ372vXiAFEqKOo6RzG6KNI3DGn1RWaubYut11NEFpzAzgSgodqPOYq0RNFSlL6qnBFLnQCh1ty7skzBlWBnh152wTcLfLoYHsZyZwqFY/vnB8nWM/PKkA5PIi9rvnINN5+i8GvHPe8dZ72ltddag89jn24ZYYIzwy4+3HCO0tnC6anlctnTWcT9ODDliRAt+wxPtoG1orbKHrdMMapZEFMWMBhs4zItWbGRhqSjMORfGBPPTvdbUXQVdpHX9vU/W9F7TSEqzsLRWmUIFlHxhHbHo1zztWk67Fmeozdfa0Na4QKlC6LHAEAv7OXM/6SlR27UN2y5wmDPWFj7ZNOwXvULFLOwnvfZdrD6wK+XUJDZlYVXUB3ukMEWpOH4N/Q45AU4ZM0UYlsSDsay95XGJNN5xiGrfOiz6yyxF1CWTBB8s3x8XvpkjHstJcLhoeL5p8U7/nDPC/RDpO12gcxZOGseMii9PIfklZ8Y5koqtaqvltDMYHJM1XKy1uu/zi8wQhdYbPtoqLeD+uGARfnox8fc3I7+/P2o63zkaH3BO55zrtuF03fN848ipcHNUQ+5xTrU8qLxv3bpYt1rEO824iufogrZjna09h3F5b7IoGTDCw5gIFpYMS9SH5DglumCwztE5wXnHbkrkbChW57fZqBLtK6Fu22qz1sO86AgrQ0LdVNc2szXCkB1TEUYx/DbrUd9iuTaBcyNYScxFF/nDceZF33O59nx00nDWeZakbWhTzKyCNjbPqTDGxLZxXG4CX162GDyPY2acIkNUomAwTwJTYBUa2rari0nN7M6AlMhJsKy94zhFXLU0JknYokyoVHSEpRgVPVw01qmSi1I2vBW89XTOcrntOFk1lAzOw9o2CE8NaOo1iqWwDoEuePpaYOychtuboFgab5U0OaXIw1x4GCP3U2GIysfaBD2mn50GrtaBYczcDZH9pNeqYdGT1d/fzx+2OD+xiTkqtfteoIgStYN1BGtVDKqq2liElDUhMpeIxbA6ero6Lkl1RvoE3Uols4sZay1v58ichIHMUQoimd8BzjqmUvhk6xGjBoBoCt7BcRGsTe+Py7EogCyLLthQIWMGYY6Jk1VgjpneOzZN5nEWrleabJlT0dC3gU8vLbjAplvpvU6gIDQhcLQe4wKbFTzf+tryrDt133jEZLzVh7xvAsF57gusQoMhs24cPzpr+eKqR+qsdNV4chbujtra/fJxogkeaw2HJXOMkTkm/OJpguJOtqEKHVVEWdVqtZKF0GSWZNlTuBkLS9HdNmKJVYmxxfA2qeHcYLkpwtOhDgyvjWGV4blbSKKspZ9d9awbZQyt6kNqrfqiH4aJVBzBUUcmBe8sP3u24nkf+H43sw4611XvLfW4rFzYNjga33E3q6AUDPQerPEEUUX+uOT31XqKwalhamMwFnoxBBMIRvBoCqgJljY4TtuGk75j3TW8OOs5aTzTrJjSxmu7t7NGGVlJXzapCK1xhKBGgVWw9I16ZFdWVfzHKfM4Zl4fotYqGJ0n78eE9w29E/pgOPGWx7QwLInbY2ZKwlyRzHf7D1ycriRiSQzVAwrQWgNkpideKU+2KjQKhKnxJcOjyYSlkMSSqBa8OkAfxBCMkHLkvpaYggomtzFxzMLKe06CY7csvDq0YD0fbSyNwE2aFQptjUbSnHJodSlVPq3Rl4VvPRYNWgevNsDg9KVzcz/SNkqvP8yJY4R1Y7netByiUESNB6uu4XbxnLWOz7YZS2Y/x9pkDCerhp/3LUKmc46T3vJ/fDuyj4VPtz3bIJytGr687PnivGPlTE1aZO6HyN/dLRyiMOaKXal3u4C8pxRkCiIzpqhJoSnQVZCx4joVDCYWHkad4ZZ6rhvEMojBiWEqhq+y5W2BqWRC3ZG0ZsTwtqhwsiWw8sJpY7lYBZo60RgXpaE/zei2baO4kaLjhHXrEAvrynzatI4//fGGn8aWl7eJ394OLDHReMd571l3gSKex6iWztaq8yij46L9PDNG3cWLAoIQdMzXeoezsKqlUV1Qzqx1elxuvOWy77g6W9N6z/laww6Woq4xCiv/VIIkHGPRXtOSWHvHqnEsSd7bB1OC6BWR8jBqtnNMmSVDX5u1U1HH2NW65Wql5MDPL3v+9l1Uh1gUjkloHQo5+5DFOaXEnDTzZyrAeY4Fn5WuZk0t0hFV0pR+pwu1MVpzIMWQzBOsV+9bYxGGpGKAFd6b3FXwsEwCYgQvBWs9bbDcRegWYb0UvNWKAG9RlbRoHtR7w5h0zPNkiLfVm5KruLEkYds4jBR++xg5xkxf4Kyqcqugd78kwmLgEGEbDKsG+mL4qIXnneF+Fj7aeLLAbhbOW88nJ8omPe9bjCu01nGx9nyy7fj01NGHwLoJNB6mOdX4GBzmTBeUX9t6uJ8yh1iYs+BFkyPe6kIexWAlK/axoljGqLG9TVDxqGkMxSiEOopBK9xrvtJavk1wLxrLWjs4QWiBN6KplkFgV7SLJYjgneJP+5XHWa0ujClz0gWs0ThdzD9Q7BvvaHIhNIbjpC6ay5Vn8jCs4WYI5MbRt46fP1szR3hzUKWekhVxklXcipWooHfJqoCXqvI6yzY41sGxDfqC7VtdjMFpe3fwjvN1q/wfq3lTb1ADv3cUdPHNuRYzFfVqb2zQsZ51amK2Rh1qScA4Tjo9Ma6LYx0KD5N+FsNSWHc6P085UrJjQrPMn500/OFxYrAGMYVDKhT7gaMUa6GpnBfvDEMu3GQFBFPXUzE11Fr+aLBsDNFoZd+hXtI1ImOq/QmoGcj8ZNmy9r3nsvWOyxCwVqvFX6xbztaG01ZN3w+DFux4Z/Q+kgr9ospmLGq8z0UT7d7Uhi8RimRaHzgume/2C9/tFlKGzUpYCkzZ0gWjTphSOODYGcOZh6vWcNYZtoEKrtId1VvD841l44OS5axGzlrj+dX1mk9OW877lk3Q9Mh+Wphj4VBV7FTHUhetoTGehzlz6AwHA0JkqZUO4i1k/bkR3Z0sOjGfS2G3JO17sbAKlinBtBhGsTRk2pokORY4Go3wrQ1sraFFA+InVt4fcUspTHNhJ5nLlWdIQt5ngi+kTu2MmzZjjdXRVFKeT8yFxlUvatT6RYuKWktUeNpPrjpV+UVpdXRCFMPmkEnFcowZK0rob7ylOM9i1UWmrh9TX/6GdXCcdIGzLtCvApvWct632nZtjNoNa7LEVuXUGGXNlqrMJ03LMSYVOtetwxd4e0hMRb//67UG910d6V2uHKeh4+0xEWyhSORh1NqLL85XHJbIJlgOS+F+zGSZeXHe8hdmze1S+O4+8oeHiW33gTtnHyzb0LJq4JgKuyGrQPC02POTrxH9P58GwqLtxaOtA6k6BtCXX/1zRXiaAuqC/qNvyumsrg+WH5+u+OKi06JbtB/09lApDBY6Z+kaS9/qoLgPlta5Ch1T61Qs+k23wTHGxM0x8Ye7iYehAJYIPE6wGE9yKpWWJLzFMeF4jtDYzEWnr+39nCoS1GiUzWnCX9EYqvZ5LXHU4h1v3/d6LEnvwO/2I2d9Qx8cz7YtMQurRqkRxsN+tAyjp/FZPapO4VRWZloHTbCY6nVtnGEpOuZ41juctVx0lrECniUrNGtbLWZWHCsLnXlCVevpR9M/NSkkGmAmJqyx3I+KnFGBTSsDW2e42HiCl4qv0c/+CYA2pwVnXS27FZx1rDvh2dZze8x8+1j429czzzaeT09bvrqZeLnUvkw0xvbMWTUMWH0x2KIsK4+SDVeNJ3hP4z2btmHdBtad52odNBlSn7uUqfNtdTltG8dhyYgYWieUVLRg3Sn14XFK3A46fhIRDnOhayxtAL8Ij0aI9UoxLJmz3mNt4qfXgUMUTltPawzf3E18t8+c9ioCfnru+Fg8p14/k09OP3CU8nzTYI3Wr2cmlahNqZyIJ51afkgEPC2wf+OC1RGusmKqHPcUw3NPaXd9vbXOcr0KfHra89PrDufVuH6/j7zezeymqPeb+mdXyZPFYW2gbWBImVVUMcgEi0iNmUXlurw9JN4eFLzsvCct6rGM3nCMBrGOVBw3YknW8A8LmKPwK1MoRhiTvkxaq6mb46JlPW2lr89RUyPDvOgRuVX63rwIxznSec9Hpx3DnEhZ35xv9xOnK8f5qsFQ+Gax5NCSTKTgEO/xJK43Dc9bPYYNsTChQWLq8XfMwql39Kbws1PPulF7393i+D/fjlhj+LZo0DcYZSyVuhiMgVOM1gUEw/2q5ecSebby/OK64cW2YZgKf38z0nn4/lHFsJN63DUI1urnneuLyKCkiZiFUhIxJlZ9C6L19bdD4rvHyIv1wsv7mV3KBKMnnrlojUIUi3MNGx+wxrBPkdPgqdxGHV80LefrTtvOV0Fb1LK20qWku+646KjDGMVXjzFXfqxhN2UOWThdWxyOt/vE3ZRrvSLs50jjLOuV5fOzlozREUosWG857wOfn69IJXN7TDSNhtPbJpDtzFwyr/czP7taszGCv/DsJ3gYlg9bnOedJUajW7tA5xzVV1Y3Pfnhv3rkeF+dYCx4SxcCbSOsTOG4qJMiGU+JmbHCnRUODTiFT1+sGv6dF+c8W1sUZuUZYuLVIXM76dHOAhP69bxYBnHIonfFzutk8X7W40vjIBS4WzLf3C+83iceoqH4FVBIKMBYBCYMB+tJCL3VBzYgtNbwMGeC1Tq6Yc6UYDm6zOOU9ee1WqYzxMjJyvN2n+lby0nSGeXDMXKcEs3G4Y3l7V6N9to0YbjcOCjqx/VoF+nQOFai4GdjAtYVhiLsUmIqlodkuSuGgPDjTkctUQxTsvzoxHHSOaaU+biB/+2NMCEEI/Ty9HWcAsGtcEFhFJ313RTBrzq+vDT85pnh+dZDEV49jppUsYEpZo7LwLPYsG6cIk8xlKynFWc1CTInpRKkF8jwAAAelklEQVR8/mzNF8Db3YKzppIJ4GGKvB0WJvRAlY2wrXT32yWqCGSExuquVsQyF8shK1nBGAuHyD/54pxpXECEx9FQKMwxK5ZkpeDuBd0Fj6P+byyGN4eFfcq0wfH8TJ1tmjHWzK2ImlusKfzqWcfVKnCcVdktRasnpyVz0Tm6YPFbNcJ462it8Plp5u0g3I+ZN/uFHIVV6/jFs45//g8fuDhTlIrYzzTVXW+qE+b9UKn8kYXsSZE3Os+7vOi5wPHZiedq7Xl7GPjsomcSeHk38fvHCWvVV3rIBWMcP9m0/OZ6w/NTp9XpuWiUa4lMxjKFlnWrv8CxFBbvab3jJlvmZGkXw4tOCFZrCm4PyqpZppmYMo9D5iYZJteQnactmWgcpmSys4izeGOZjOGXXVGBIhXiFDn4Qu9hPwv7SUWcYUnMSegaj0hmnBKdszxOkdtD5HKtdQIxCbfjjDeGiGU/Rb66n9kvwrq1vNgGxpixLvB6EnZF13uD5dNWOFQ3zy4bHpZCwtbhuOO+GM6tWiW/2Wf2ceF6Zflko+aNkjLiDLtieUw1RPzEu5LC2sGZgz/ZZG4ny5sM3yXD46Knp03QsHiwliZ4xpSQKeqLoBTaRsuKteyosoKKjlP2c+H1buLzixUU6NuGbaMFxCVLJcg/MZd+cA1YK1y3Dd/uphpEELZB26xnMbVpTgU1oXCYDd/cjPzksuXumBiWqSrzWhGZR4WgBQyvHxZ2U+H1LvJu1BhYaCznrfCwyfSoBmEN9R5v2HaWs87xbNPQGstujIyx8DDrS8hZr72sznHSWMal8Dgs7KfC3X5E8DzsZ0oU7o8ZHwwXffijHth/y8V5WNQnqjKy5bQJXK0LYxSGoqwVov1BPjMG6z3BW/7i41N+ctVzt19oSqY3hj+52vLifIVQeNY6PjtrkGIZslYD9p3natPwo03L97vEQ8ysa0yr9w0PIuyzw3nDUgpjMVx6wBYmcbwVR0lGm7hEd4g3UTgmyFFT/mTDwTfcuRZnDRsbaTA0KN+2s7Axmcl4HmJhYzTu9JCEZSkcbWFOUoPUGl8aogaBxwjjnHTGZmslYBYOc+a4ZHZLZuUD/aAnhkUMVjsIKTheHpWw/t1imYqhM7Cxwp/2mbeL4Q8zHIq6jWYDT4gqEbhoCkuK7IQa1YKbQdM3pyvPLJbTlePVXvk7NUDBqRc+awu/2lo+XnmFjy2ZnQ38j6+Ea2u4OUQKmXmGV8eFmyFxvXY8W3s+OlnxyWnLMFevdVEVOXgdbWSRSsJQy6K1Oi+M9feiz9ZTsa5WGn688XQeWhu4GyeGpdAYpwFsY9no5IMkwjFlRAqb4HkYMt/Z+X3x1FMznDVKTeyD4WafuT1kus7yZog8RG19WyfDviS+vZs5azu8txwPhcu1U5FpZbnuPaYGJ2KR94zinBQG9jgmSlYDyDRnbvaZ/ZJpm4a46A5/NyxY6/nuceH7XcT7DwR8pVKPqFiWomiFX51umCSzj3qB3i2Jm5hpM5x3nufrlhenHT+/6JhypneGMeuR+LIPWBE2rWF70XA+NuzGQt86gnMkcVxv9K44JX2w22JpjOcxFQYce6On6haLc4XWCd4KPZZLDK+i4U1yxEl45oSlGN7lmucrWYtxrWUxINbiCejR1rMYw4ir/FThbbIcjWElKkrsp0hnhM7q7XlKyjYdkxIRWm+YFjUXpKQC9JgLKw/HBIjhx+eFbfBkY7jeKF6zDyqw3M/CQxGFZdU7/JdNZO2F02xoq8NnKXoUj8oyYEXBpqhWMWc4DZYohtup8HyrLKh1sPzjM8NXR3jKRP7ZNvGrkxaTMyur884pFk4cOEn8aRt5PGS+nxLrBr5/mCvrFvYjyFadQneHxBwThULrLKZQTSCG56cdL057dseJh2lhv8BVr8zbMQ+KQK3GgikLKw9njUMKfPM4ah4UcAiPc2bl9GvYGiTQjlFlCWsmVth0Ghg4aTyNF45L4TgU9giHWUdVPgnj+/utahIRxej8w5tF57mTkFoh9PqzYAz3UyImiDlzsdKgxOO8MEbLNBfemsgqBHbHma8fFuYMZ73mOO/HwsMSaa0moAx1LvQhi3NYIs5azeOhGlBEh8+XbeCsUXrYPhXiUrjoAp+fdVxuG6WrReH344DFsBsjrTcMk8WfelrniCmSkrDLmTEVLnqhzOCCAquCs7wdIstseF0aJjF0tuYMnbB28OMu8Muzhr7R5q2/vp357+89QzGMRGwBYxwDlgEqlcGS0fjWKCrI7EVI1r1HSmoW0TFIYY4RM0V8hhG9+3ijUaFUZ2NTKWxR9VTFabU4YoTjAn0XuNo0fH7aEhwMS2axcLpyPO8DxggrlzlJhq8nYQIuXebCVf+uEa6cgrBul4XRtsrKQXBWVfPLleXdrL7emA3H+jN2Xme9v7lekRj59pD4k8uOLzaBJQp/9XJhZ4Rl5XicS/3zqg5/9zhxc1zYOEXBeGcJVrgfC7+9nREjnLba3P2E4jDO1mZqJSbOMb+nWOzizNs93OwL8yLMSU8RuWhL2ZKEf/nqEVvvwhoZsyQpjChB3xhLMBods/V5NKJh+5JVJ9h2liaAE8P9MfHmEDUMXdCaxVErCEtRu55Fn7clC3+4n7R6g4IUjSAuWZijUi/mLOojjoX9MXI7ZPaDsCTD/bgwJmXU7mKm955XQ3w/Oy91vlwQNcHnDyQhbL0uvFznmc5U+UcsJ96CFHqnCRLXGZyxnLQ6JH+yQbVGG5UeR42fXZ+1lFwwzrCfS1XP9IPsG2ERxzzrXHLVCJ9fdXz1UPjdIPRG2DSwdoafbuA3lx2f9A4RPWoiwk9PLC/2wo864doZvh0KQ4EJYcEgRonfwSpjJxgqXdjjUZubmtr0w3GospxtbVzue6y1eFM494KhcJ+DQpysoK2jjnMHFy7zxUlD4wutcxwXqX2neuoIBrZeyeGpFE5aT5TMWb23906r9mLSWvjGKYUhpkzbuff9lhaFos1RuGqd1jnkjMPx5jDj8cxFWDfCXzxriVcNxmpF4sMSdQ5s4XYQGmfZ1/6W26MCq4ZYOE4Z78CVgjjYdjoy+v4x4s40SpazMJrEycrT+pYpRvaVZgjKlnr5ZtY571QYc2KK5b1o/wRDexRNnHijpzVn1XkmRVNApUTmCvbKaOfoHPXaEetROnhDyYXDlCnWYUziZpxI9b6aRIW9+BT7M0JTPMdZNHqXC6dt4PV+YYpgzMTHZ53WMhrHq/uF14+R17tFqRf1Zzhb6aRhXBJ9cZy0QcnzsbBb9PojNWSE0b6dD1qcRVD8voA4VRAap1yx3ukiTbn6PEWPHjFZDnNkiplh0R0xlcKQEm6Bawpto4zaLkDKlochYZzju32iCxpX8g6+PG24WDm+3BZef60PYkPhP3rm+PW1LggjwjBrB4jRZDJ/ufXYomn8s2D5kx66zvG/3kWi83ixHMVwSMKFF37Sw7PGMIrjr/ZCJrMCHrOCpG0w2LB6r9p+1grPG8vaGwyZlwPEYrnNllE8YFm5xMrpfee80RTDTYr0znCYIsU4Ljp9wT2OiwpuRl88Fy10WasTZgHjPHcJjkWRi67vsegxVJMvNZgthTkqhsNg2QbhetNwWBJ9o+S5KSZFakR4N88c58KmcdyMkbPapPXP/u7IIT1y3TjmGPXFkhdsMdqiLYqjDAiPQ+G0s2xarU5onI4whkW4Oy4Epy9lby3WZGIqHGLicVbVXVM8OsppnSVWTk/2hjWGi2AYFMjDVetprFMlVed37JeIawJjhu/2C+sGrlzDHAu/u03spoQ1hcOS8aFhWqLOdK3TMl+pTeZWryQzwnHJLAj7OPPd0RDMwMpbXu4i5+uARyjZMpXEaR84NxbnIInuvNdrR1l5vj8krUssYLzVzqCUa2WjlhZPH1qeK6UoucAZhqQV8ZlCXCKuZvGy6MVbB7qAaC15rFnPMRVq5w9zVjbsm8eZi3XD9TrwcByZRehcIOHef5gfrRs+PmlZcuLdIfHnZw5jDWMytCaTsmM3LGRxvHqcGWJmFSxnm45mGHl1KFz2nv/whedZ7zntPD87cyrRG+HlIPzNTviyM3y+tnTeYJ3luivcj8JVa/lnN0+j2KdGbfhxW/hPXzR4oy+dMVsumsycDW9mDW0PGTZYTMnaSi3C22Pm+13io7XnpA/cHaP6dq2peU8VkKwxnDeGdRYO1mCyYAPcF0Mqhkn0aP7Tje4Yzhlez4ZPGsuLzvMvXisuxADrRoWVPjQcx4S3jpVzIJrg8N7z9nbP/RQRhGebnv/5dwN/2E/85LSh8UIbGi2YmkCsCjOdE/oAF73n9V6jgEb0KJu03ESrEEWrJDat1+ROtGAKKWaGOWnpVNZ8rYjOLBsL587ws8uedw8T523Lt8PEZycNvbPVYG5ZsuWwxOo7LkxJCSWrClR7jIZ9zhxy4TR4Jmtw3iCpKMHfGvZL1BSKiPa5IthSOObCEdgnFYs23nAijmwicyk86xVVurYNwavlM5fCOGWmKNy9J93rGKk4iw+WF+eBLhde7RbeDokxl0p1+IDF+aQ2FrEMBUBdMCICc6okbh0wjzGzz4WXx4WAdhiuG41HPXWIBKcLrBhL6w1X60Z7CvcZZx3nK0/jCylb1sHgTOGkdTg8zaxplP2sx5/bY+RuiNwPkTf7hSLCWe+JduEwqYH909Zy2hrOOkvrhC835n3D1ufG8MXWcHfQbGHKhrU3/GZruHFabfCXRXg7CXcznK/U7vXLlZBzJKO0PG/gMRYWsWws/HzrGGLh7VDYF3WmjEn4br/wMGW2jaMLsGkDoAJG68C7Ojqxevdx1vDcCJ01zMUgtjAcDNkZPm4Kv1jDkpQV/JOtYWUbliQsEpGoo66bMXEYPeugx7skWnXe6MiS17uZ1wfdvayFm8PC7Zz48086fn3Z8Hqf2I2Zu33UY17j2DSOzy9d5f0adtNCLoYxgc0Zb52WPTlFSt4eZ1q/IgDjUghWuNo0/P5uYIr8EEyvIty2M/zlp1s+P93w3+3e8W5O9NbRWsOmcdzNmVD9sXdjxlj128YiOA/FOg4FGhzSOlqj3m7rBQd0xcKoNXzWVfi1UcLHseiu9pgLswheDGfO0HvLxjstNIoa3MAoAUF7P7X7JBe1bt4Nem5NxjIbgwuBjTf8/KJhOOoVYbcokjR+aJHRt/sRZxzZqVMmQS26tUoZqzK+M7Xt2mjBzFIEkmBswWVNsWwbz3nveX6intDHY8KIGtafbWAbFDMxZ2HdGpwtHJfIbsqctp7rvmGMmcccmUS5RGMq/MO7mbnUavXOcuUDP7/Wt2LvtW49FaGxCos2ZJYojLOiKIxR/2bOaqu7GxMiDk/hH5053g4zsxjOusD+ENmGwsv7wlnvOV85HYqjd7UpJeYsNE6PfA54dVhoneFhTFh0VjwsQvCW45QZLFyvA6lkQjXfx5hpV57L3uGAMcPzUhVnK1w3jmmeWZKwCapuZmCJMOSnY6SwXwq/v114tlFT/cmqurDEUsj84Xbg291SbXmOu2Nk7Qq/ON/QmsTPrxr+7++PDFEtUaetx1qhddpLOak0oEQ+IzT+qRjY44wG05MxapQ3wpvHqOF5p/rEkBaK+rjIpdA4w5897/nzj0+YFkuRJ0K84bs9rII2eh2iWhGLFJ5vOqa6S2/XHaEJ4A2TASsFG4IKSLaAFFatx1ZmbpsDq1XDkiKPx5k5WwYyYylsG8eVd1wHz6ECxo9JecYxG6SoSGWyikpFdMKghMnKdGoMyQeCtfSNIUVhP0cMhrNg2RpYygd6a78aZzZeKwE679kLLBhcgZgKj9mwrsAtYxQp0hgt2F0K7KKCtnw1g7u9fjhPx6pt23K+0RzhoSScUVUzpcxQ9G26GzOXfcNxieznrGHeKVGM4W7IvB4WzlYamF2WmbVt+eS05d0hc9YYTCmUDOJU2YxZFUJBmGNmjLVMF4jFEEWBw49LpvfCZeNIpXB/nLg9CK/zwvOTjtY6DnNm3QU+2ihWY4iWlGAUYZLa2pXguBTuJ+iD4X5InK9VeEhJSM5wXXOAWTJSLFMunHvw3mCKYWXhk03hWac4mDeHheMsNEFxo24xLEthswr86bnjr+8yvVXv5lcHvbeed477IbFky+WmFvWIzoBLFrogNFZ9r1/fjEgp/PKjli54nm1aboeZY1Tkx2/fabfoEosiS1qjJxwjDHPGmZbGF87WLVfrlt0w8/3jxO1h4buHiS7oMXdKhWKdojxQVEpaDH//eubmELloGvZpIRtVoL/b64vk9Tiw8y0vTlrOu5a/vxsokhHv8K0nAU3jMZWsgNHaSOss1qgwZRrDZmWxzrEbDIdhwVu49I7P1p7z1qssmIXdpL2pi+iLsbF6lF41TkusalprWApzKRjvWMRSfIt1jrMWOifc7SIPx6yVk6IdL09Yzn/rxRmz4bFknBd8URSEEcu98xxrnVwkMBtl6ny8bjhmHdivDPiscSJjYJ+E4yHzblpYB9i0lrfjwOUUEFObsa3hvNF/wxT9u5ebwMuHmTFFUjbcD5mbXVT7WoLTvqWzRo3v3rGfF+6OC5+deu7nzN2sqm2u6JR9VKuVGKOlQwLTqG3HnasCmIPHSe1YjYOH2bAfNFTbeOETowGAY9S+xfPG8/XdQsnwbhB2i5be3OwiwSv/qPOqTqdiEJvfZ1G9V3W0q4jEiHJoS4E5QmszxujfL7kQcybYH0DpMQlvlsh+Ec6L9meeeMOXW8f9VHicMyXrOGCXC0kWtm3DurV8cuL4X749EqzlcSpQAjEZfvt2Yir6oJ22hqvOkLPHG1XE5yQc5sRQxZvzoomcYCzv9pF4lThpWhANQs9R+Prtwpvdwn4WbsdFxSC0aKhYjbXdx8TfvDrwu3cDQy7MRYuJeq9xwCjC2lswgaEkYnHsxpFPTjzdZs3BePAaoA7OqWgkQjlM2JSRzhGXjClKbvizjzzPO8M0d/y3JfNqP3G+UuNk63TcdndMIEqhv2gVzZJqciqnQq4Lv/OWx6UCyVNBGs2Zrp1w3RpsEu6PM+/GUlNNBpH84WptH9SwXahdJmjr1j7WmBfCYVo47wIfdQ6TE/eTcLcUWqtHSSeWrhgaU+rMtOCt45n3JIG+dXgHQxLGxfBuVEhzLHp538WZlw8TFnhcMo9zomC4WAVOOsumVQbQpka6TleBs87w1U3i1ahdLieNsA4wZWE/V3dOPZZlY5mT0NhCLkqhXzWGOc7cj5mzzrIbC2/HzCyFzjje7DNLjDiv0GjfBw6xcD/rqOm4qJf25T5x2jpSUQjUMRaM89yP+mG2Xki1VmEdApcr2ATDfrbqikSqvbHW8iE0wXHcL0wxsvaBi95xN2aWUvh+HzHWctlYxiWxmzJXfcNppw+2iNLJrVWs4/Ntx7/3o8THJwEphr/+buBvXk/cjRHvDC8fCq/rcxCcfR+0ngWOS2aIBWolQmOgsQFntJ38+kTLaHdj5Oubka9vjrzejXy/j3y7zExZYWPe6mdQRDtpdovhxFmlE1qhD4HzzvLjs5aTzjNH0Xo97/n4JPCr64auaXi9eP4wawGUZsCFBmEZC8tuItrCWVjp8b8IoSR6F7joAt9NMy/WnrNuDVU/SSgm5kkMjDWxcoiCJWmaxWv3jiHR2qd2NBWYvOh46aKBCw/f3yx8s1NOVhGNWM4x6RXwQxbnT9Ytm8ZxzPpGn0xhSMIiBpwlxUJeMsMSeUnmD0NNbIhR5qgz2rvoCi3C2j2lFyxYq/Yz0Yf59qip/W3r6Zxh25lqOq7HUFG17cI62mDpg0KtvDNcrCxfXHg+2jjujsI3d5Hf79IPDVEp8+5YeJwKs6joUkSRH7ejcneD04fp7jhzTuC4lPddLw9zZj9rbYTH8OoxcVyEy7VjKYX7QVGWU9Z5WSqF/azjlUMSyiy44LhqgxLtjBCc1gcqzt9iLZz1DcEpG/YwZYZafz4sUUcRaDBoStqk9WRLG5PhZja8GRWv+dHK0VnDj08866DVeXMW1sESk3ZnPqVQ/oNPTzhMkTmKcm+8Q2Thfo7sZq3l23jLj05ahkXHU3PRTpxYNNGSRev1vFODxlc3C8dF2A0Lf7ib+P5x4eu7I98dR/YR9qJ+VET0+PdUvVAKA4YJ8AjnVntanp+0fHbWs2o0IHB9EuiD44tzzzZY7qZMsyRW1pKcEhuMwDQLw/1BzTRG2K60dnAsQmfgxOtd3FrHJyeBt4fMzby8p3IsqXBcMjkLkxQkw8EUJcWLIaRMsA5jsjahVWiYWMumsVw2cO4LvbPs48LDshCLZo3nIhXz84GLM5eibcvechEcYh2ht0QcxlmOi/JUvRjeTDPBqvvhdi48TEl9t14JCos19GgOr3WG4xy1R6JAFMPNmOmD59yqUts3hlyUPzQmbSIzFk6rULKbFJCUc+KiawkUDqPwap/4ZleYs+HUFZYEMRnezokhSV3UmiudF8O8FKxTCPZ+TtyP8DhnjunpbGB5vc/cDpm7IUGxvBsSMetIY9Nqj8ZJ5xjHzH4RpqjkgnWjvuEShZPGaON1MZwGHZSPEdYVBBWzMMwwVTr+692CMZZfXAaCVSqfqqOJ3aIzzlzgdojcz/B61BLin5waUlJx5svzwLcPkSboTDkYrV+8O2YKagTXSgDDzZQ4Xzk+O3MkGtrZMs6FTGEs8HI3g/mBfpHRkcOUM/uUOKbMt3vDXArbtwMC3E0TD1NmSYnjtJCyimLnm5ZfX6/4fr/w1cMMUkuLALHKRHq+Dfz6coUTCE749nHgsvd8ctpxvXJsWsOLtWdcEtcrNWS42bBZOUwpHBP8zduBwzRzWFTrKA9HTlYtV9vAv/txA5J5+agOrodj4eawcJgLh6iOnt2cOERhKJmhwFB0J52K47IB0ROvHs99y8bBb358yWrj2d2+49OLE3700SUXa8PVM90lbx4G/urbG66ePeO09fxX/9PffNji9JWJGqzFGaWMf9y796OQrx8KgvY1uq6lC2ouuIiZPxwNN7O2bCHauBSsISKKnZwzK++ZsnCIwj4JwSkWY0qVO1S0xntOAlg6Y/DUhq+UmXPFk1jDfhau1g7E8G7UmWvOhpV1HFstAb4MSlGbE3y7T4ioN/e6cwhC9sL9JLw7CphCzpY3+8TLfeFu0O7NWeb3Lp3GGa6i0AQ9xoYqwtyNmZPGcsxwiIZtC5+fObI4fncf+dm557udilEgdN5wJxZjIkMsfLPPDClx0Xh6m7jatCDCWaeGjZgLF5sWb4SHCW5nYS7wH7/wnHSW7x9mto3HGMfz08IcLSuHKuCzZT9ndtOANToyWjdK6j9dOT49a5Tf5CyD0+O0tjyrQ6h1evK4WDleHxf+9zd7bpPwKi6VNgFGFA/qHXy+bmhM4KGxjDFx1Qn/9JeX/PrZhv/m7+54tRSmOVObf8AazlvDv//Jhn/8fE0whijqNuu849mJ56oLxJQUUxMM3ntCk+lHwOqscYqZ/TCxmyNT0R6fOAvFwK8/dvzsasXLh5GlCF/fRd4d1bCgBppENkKuz+BtVEbQEyDcW8M/+sWX/PlPP+J48/D/tnfuvHEbURg9M+RwudqX3pZgGZad2AbsPlWKtP6jBvITUiadUyRF4EKVX5AsyVqtJXFJDjmPFHdbpUjjLeb8BAIfgbkz37loo/jnrOKvj+e8eDDk0QTqyTZXtx3GO4aZ4aejMReXX9kZOx4+nzE52OfN24/Yqv1/4ZwaWUSTI8rEYRaZFTnXrXQJrZO7qWVc2cmjNCUebxgOy4zKiz7DqBKPZ3cogui7tkepjI1c00VpqHfeU2rN3HqmQaG0tPiXTqa2BLlLWlpAOcYDI8OBGDi9cRxMc2n3G6ln9QHwEEeaUVHgXMfQZDLMaAJXtRzGCyVn22xlNDAZLK0jKkVlPV/uPBeNbOpWCqyDR5sG28NdG1i0no0y4856VITeSVi0inxpHD0yGKhsJENaG1UbiX2gLGTCHaN8y6rznFeBeeMoC5FaN9HwddnLevsIi04eLBgCVRe5bCIXtWOvUGQYahtZ2MDIRKJ3HI4LTq4sjY0cbeb0IXJTdZhMngNeVj2FFrGyyaTg0LpIFzQej5xgRKEyyGXj23aZsTfKeLZT8mq35PfPN5xVfrVMVmG0YTxQ7A8ML3eH8sO47dkoI8fbBceTAeNS8/rHLX5+MuXDdUtt4XzZU7UdO+OSH7aGPJwVHE1Kln1gXlkOpzmTsgAv9rp6pWyxnUdnmq2BZt6IGnU00OyNMpqoORgU3NSBqu0oC1n9oYJM4t9bx7x2LFqHD/DNSnGhW+lzuqjkUb9fVbtyTQyR396d8sfJGTGIvFvHiAqRX//8zPE055enM5pmyfXCcXLq8V6zqEUcdts6Pv19yafbmv1ZcW/+VPyPUW4ikfh+3H8DmkgkvispnInEmpLCmUisKSmcicSaksKZSKwpKZyJxJryL8tmGpy6PhtuAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "分割する"
      ],
      "metadata": {
        "id": "9HrtuPL0uj76"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
      ],
      "metadata": {
        "id": "CAjsg_OzciBy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('x_train shape:', x_train.shape)\n",
        "print(x_train.shape[0], 'train samples')\n",
        "print(x_test.shape[0], 'test samples')"
      ],
      "metadata": {
        "id": "UW2AJLaZc-a7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fc922a26-7b3a-4264-9c90-d50de03e8b57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_train shape: (367, 256, 256, 3)\n",
            "367 train samples\n",
            "92 test samples\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = x_train.astype('float32')\n",
        "x_test = x_test.astype('float32')\n",
        "x_train /= 255\n",
        "x_test /= 255"
      ],
      "metadata": {
        "id": "RmP40XTOdt9h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3. CNNを用いて学習させる"
      ],
      "metadata": {
        "id": "71ut1m19uuWi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Augumentation"
      ],
      "metadata": {
        "id": "QcYiAIJVumot"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "# 与えられた画像に対し、様々な処理を加えて新しい画像を生成するためジェネレータを作成\n",
        "datagen = tf.keras.preprocessing.image.ImageDataGenerator(\n",
        "        featurewise_center=False,  # 真理値．データセット全体で，入力の平均を0にします．\n",
        "        samplewise_center=False,  # 真理値．各サンプルの平均を0にします．\n",
        "        featurewise_std_normalization=False,  # 真理値．入力をデータセットの標準偏差で正規化します\n",
        "        samplewise_std_normalization=False,  # 真理値．各入力をその標準偏差で正規化します．\n",
        "        zca_whitening=False,  # 真理値．ZCA白色化を適用します．\n",
        "        zca_epsilon=1e-06,  # ZCA白色化のイプシロン．デフォルトは1e-6\n",
        "        rotation_range=20,  # 整数．画像をランダムに回転する回転範囲．\n",
        "        width_shift_range=0.2, # 浮動小数点数（横幅に対する割合）．ランダムに水平シフトする範囲．\n",
        "        height_shift_range=0.2, # 浮動小数点数（縦幅に対する割合）．ランダムに垂直シフトする範囲．\n",
        "        shear_range=0.,  # 浮動小数点数．シアー強度（反時計回りのシアー角度）．\n",
        "        zoom_range=0.,  # 浮動小数点数または[lower，upper]．ランダムにズームする範囲．浮動小数点数が与えられた場合，[lower, upper] = [1-zoom_range, 1+zoom_range]です．\n",
        "        channel_shift_range=0.,  # 浮動小数点数．ランダムにチャンネルをシフトする範囲．\n",
        "        fill_mode='nearest', # {\"constant\", \"nearest\", \"reflect\", \"wrap\"}のいずれか．デフォルトは 'nearest'です．指定されたモードに応じて，入力画像の境界周りを埋めます．\n",
        "        cval=0.,  # 浮動小数点数または整数．fill_mode = \"constant\"のときに境界周辺で利用される値．\n",
        "        horizontal_flip=True,  # 真理値．水平方向に入力をランダムに反転します．\n",
        "        vertical_flip=False,  # 真理値．垂直方向に入力をランダムに反転します．\n",
        "        rescale=None, # 画素値のリスケーリング係数．デフォルトはNone．Noneか0ならば，適用しない．それ以外であれば，(他の変換を行う前に) 与えられた値をデータに積算する．\n",
        "        # set function that will be applied on each input\n",
        "        preprocessing_function=None, # 各入力に適用される関数です．この関数は他の変更が行われる前に実行されます．この関数は3次元のNumpyテンソルを引数にとり，同じshapeのテンソルを出力するように定義する必要があります．\n",
        "        data_format=None, # {\"channels_first\", \"channels_last\"}のどちらか．\"channels_last\"の場合，入力のshapeは(samples, height, width, channels)となり，\"channels_first\"の場合は(samples, channels, height, width)となります．デフォルトはKerasの設定ファイル~/.keras/keras.jsonのimage_data_formatの値です．一度も値を変更していなければ，\"channels_last\"になります．\n",
        "        validation_split=0.0) # 浮動小数点数．検証のために予約しておく画像の割合（厳密には0から1の間）です．\n",
        "#datagen.fit(x_train)"
      ],
      "metadata": {
        "id": "mCWLj9cRd0wc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rpjou84MjYqP"
      },
      "outputs": [],
      "source": [
        "model = tf.keras.Sequential()\n",
        "\n",
        "# Convolution 1 フィルタ32枚、各フィルタのカーネルサイズ3x3 ストライドはデフォルト (1, 1)、パディングは`same`を指定。\n",
        "model.add(tf.keras.layers.Conv2D(32, (3, 3), padding='same',\n",
        "                 input_shape=x_train.shape[1:]))\n",
        "# 'relu'で活性化\n",
        "model.add(tf.keras.layers.Activation('relu'))\n",
        "\n",
        "# Convolution 2 フィルタ32枚、各フィルタのカーネルサイズ3x3 ストライドはデフォルト (1, 1)\n",
        "model.add(tf.keras.layers.Conv2D(32, (3, 3)))\n",
        "# 'relu'で活性化\n",
        "model.add(tf.keras.layers.Activation('relu'))\n",
        "\n",
        "# Max Pooling 1 (size: 2x2)\n",
        "model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "# 25%のユニットをドロップアウト\n",
        "model.add(tf.keras.layers.Dropout(0.25))\n",
        "\n",
        "# Convolution 3 フィルタ64枚、各フィルタのカーネルサイズ3x3 ストライドはデフォルト (1, 1)\n",
        "model.add(tf.keras.layers.Conv2D(64, (3, 3), padding='same'))\n",
        "# 'relu'で活性化\n",
        "model.add(tf.keras.layers.Activation('relu'))\n",
        "\n",
        "# Convolution フィルタ64枚、各フィルタのカーネルサイズ3x3 ストライドはデフォルト (1, 1)\n",
        "model.add(tf.keras.layers.Conv2D(64, (3, 3)))\n",
        "# 'relu'で活性化\n",
        "model.add(tf.keras.layers.Activation('relu'))\n",
        "\n",
        "# Max Pooling-2 (size: 2x2)\n",
        "model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "# 25%のユニットをドロップアウト\n",
        "model.add(tf.keras.layers.Dropout(0.25))\n",
        "\n",
        "# テンソルを一列のベクトルに平坦化\n",
        "model.add(tf.keras.layers.Flatten())\n",
        "\n",
        "# Full Connection 1 # ユニット数512\n",
        "model.add(tf.keras.layers.Dense(512))\n",
        "# 'relu'で活性化\n",
        "model.add(tf.keras.layers.Activation('relu'))\n",
        "\n",
        "# 50%のユニットをドロップアウト\n",
        "model.add(tf.keras.layers.Dropout(0.25))\n",
        "\n",
        "# Full Connection 1 # ユニット数256\n",
        "model.add(tf.keras.layers.Dense(256))\n",
        "# 'relu'で活性化\n",
        "model.add(tf.keras.layers.Activation('relu'))\n",
        "\n",
        "# 最後の活性化関数は出力を確率にするためsigmoidを使用\n",
        "model.add(tf.keras.layers.Dense(1, activation='sigmoid'))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "id": "lLM_r5MBd8nw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d11301af-43ce-4194-ff84-c6a5db418940"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 256, 256, 32)      896       \n",
            "                                                                 \n",
            " activation (Activation)     (None, 256, 256, 32)      0         \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 254, 254, 32)      9248      \n",
            "                                                                 \n",
            " activation_1 (Activation)   (None, 254, 254, 32)      0         \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 127, 127, 32)     0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 127, 127, 32)      0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 127, 127, 64)      18496     \n",
            "                                                                 \n",
            " activation_2 (Activation)   (None, 127, 127, 64)      0         \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 125, 125, 64)      36928     \n",
            "                                                                 \n",
            " activation_3 (Activation)   (None, 125, 125, 64)      0         \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 62, 62, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 62, 62, 64)        0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 246016)            0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               125960704 \n",
            "                                                                 \n",
            " activation_4 (Activation)   (None, 512)               0         \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 512)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 256)               131328    \n",
            "                                                                 \n",
            " activation_5 (Activation)   (None, 256)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 257       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 126,157,857\n",
            "Trainable params: 126,157,857\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's train the model using RMSprop\n",
        "model.compile(loss='binary_crossentropy',\n",
        "              # RMSprop optimizer\n",
        "              optimizer= tf.keras.optimizers.RMSprop(learning_rate = 0.00001, decay=1e-7),\n",
        "              # Adam optimizer\n",
        "              #optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "fuyK4mj1d_F9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 8 # バッチサイズ\n",
        "epochs = 400 # エポック数"
      ],
      "metadata": {
        "id": "b7T_sdyKeEgX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_augmentation = True # Data Augmentationを行わない場合の精度を見たい方は、ここをFalseにしてください\n",
        "\n",
        "if not data_augmentation:\n",
        "    print('Not using data augmentation.')\n",
        "    model.fit(x_train, y_train,\n",
        "              batch_size=batch_size,\n",
        "              epochs=epochs,\n",
        "              validation_data=(x_test, y_test),\n",
        "              shuffle=True)\n",
        "else:\n",
        "    print('Using real-time data augmentation.')\n",
        "    # This will do preprocessing and realtime data augmentation:\n",
        "\n",
        "\n",
        "    # Fit the model on the batches generated by datagen.flow().\n",
        "    model.fit_generator(datagen.flow(x_train, y_train,\n",
        "                                     batch_size=batch_size),\n",
        "                        epochs=epochs,\n",
        "                        validation_data=(x_test, y_test),\n",
        "                        workers=4)"
      ],
      "metadata": {
        "id": "rDeOa4yeeG6x",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d00e4fa4-a163-466e-da91-c39055d3126b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using real-time data augmentation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-30-d5510f1d0764>:16: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  model.fit_generator(datagen.flow(x_train, y_train,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/400\n",
            "46/46 [==============================] - 17s 167ms/step - loss: 0.6933 - accuracy: 0.4768 - val_loss: 0.6930 - val_accuracy: 0.5000\n",
            "Epoch 2/400\n",
            "46/46 [==============================] - 6s 116ms/step - loss: 0.6932 - accuracy: 0.5014 - val_loss: 0.6929 - val_accuracy: 0.5435\n",
            "Epoch 3/400\n",
            "46/46 [==============================] - 6s 123ms/step - loss: 0.6929 - accuracy: 0.5259 - val_loss: 0.6923 - val_accuracy: 0.7391\n",
            "Epoch 4/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.6923 - accuracy: 0.5422 - val_loss: 0.6909 - val_accuracy: 0.6087\n",
            "Epoch 5/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.6918 - accuracy: 0.5613 - val_loss: 0.6894 - val_accuracy: 0.7065\n",
            "Epoch 6/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.6909 - accuracy: 0.6185 - val_loss: 0.6876 - val_accuracy: 0.6413\n",
            "Epoch 7/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.6902 - accuracy: 0.5831 - val_loss: 0.6848 - val_accuracy: 0.7717\n",
            "Epoch 8/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.6889 - accuracy: 0.5995 - val_loss: 0.6821 - val_accuracy: 0.7391\n",
            "Epoch 9/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.6866 - accuracy: 0.5695 - val_loss: 0.6801 - val_accuracy: 0.7065\n",
            "Epoch 10/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.6858 - accuracy: 0.5804 - val_loss: 0.6762 - val_accuracy: 0.7065\n",
            "Epoch 11/400\n",
            "46/46 [==============================] - 6s 115ms/step - loss: 0.6831 - accuracy: 0.6213 - val_loss: 0.6690 - val_accuracy: 0.7391\n",
            "Epoch 12/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.6804 - accuracy: 0.6022 - val_loss: 0.6636 - val_accuracy: 0.7500\n",
            "Epoch 13/400\n",
            "46/46 [==============================] - 6s 121ms/step - loss: 0.6785 - accuracy: 0.5858 - val_loss: 0.6590 - val_accuracy: 0.7717\n",
            "Epoch 14/400\n",
            "46/46 [==============================] - 6s 110ms/step - loss: 0.6782 - accuracy: 0.6349 - val_loss: 0.6538 - val_accuracy: 0.7609\n",
            "Epoch 15/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.6696 - accuracy: 0.6376 - val_loss: 0.6527 - val_accuracy: 0.6957\n",
            "Epoch 16/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.6717 - accuracy: 0.6240 - val_loss: 0.6415 - val_accuracy: 0.7174\n",
            "Epoch 17/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.6633 - accuracy: 0.6349 - val_loss: 0.6313 - val_accuracy: 0.7609\n",
            "Epoch 18/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.6648 - accuracy: 0.6240 - val_loss: 0.6249 - val_accuracy: 0.7826\n",
            "Epoch 19/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.6644 - accuracy: 0.6213 - val_loss: 0.6187 - val_accuracy: 0.7826\n",
            "Epoch 20/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.6585 - accuracy: 0.6185 - val_loss: 0.6121 - val_accuracy: 0.7717\n",
            "Epoch 21/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.6602 - accuracy: 0.6185 - val_loss: 0.6101 - val_accuracy: 0.7500\n",
            "Epoch 22/400\n",
            "46/46 [==============================] - 5s 111ms/step - loss: 0.6487 - accuracy: 0.6458 - val_loss: 0.6083 - val_accuracy: 0.7174\n",
            "Epoch 23/400\n",
            "46/46 [==============================] - 5s 104ms/step - loss: 0.6407 - accuracy: 0.6676 - val_loss: 0.6102 - val_accuracy: 0.6739\n",
            "Epoch 24/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.6455 - accuracy: 0.6267 - val_loss: 0.5979 - val_accuracy: 0.6957\n",
            "Epoch 25/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.6392 - accuracy: 0.6785 - val_loss: 0.5910 - val_accuracy: 0.7283\n",
            "Epoch 26/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.6319 - accuracy: 0.6349 - val_loss: 0.5767 - val_accuracy: 0.7609\n",
            "Epoch 27/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.6367 - accuracy: 0.6649 - val_loss: 0.5789 - val_accuracy: 0.7500\n",
            "Epoch 28/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.6290 - accuracy: 0.6458 - val_loss: 0.5693 - val_accuracy: 0.7500\n",
            "Epoch 29/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.6211 - accuracy: 0.6567 - val_loss: 0.5602 - val_accuracy: 0.7391\n",
            "Epoch 30/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.6154 - accuracy: 0.6703 - val_loss: 0.5746 - val_accuracy: 0.7065\n",
            "Epoch 31/400\n",
            "46/46 [==============================] - 6s 117ms/step - loss: 0.6194 - accuracy: 0.6703 - val_loss: 0.5691 - val_accuracy: 0.7174\n",
            "Epoch 32/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.6314 - accuracy: 0.6458 - val_loss: 0.5835 - val_accuracy: 0.6739\n",
            "Epoch 33/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.6132 - accuracy: 0.6812 - val_loss: 0.5666 - val_accuracy: 0.7391\n",
            "Epoch 34/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.6021 - accuracy: 0.6676 - val_loss: 0.5648 - val_accuracy: 0.7174\n",
            "Epoch 35/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.6230 - accuracy: 0.6567 - val_loss: 0.5668 - val_accuracy: 0.7065\n",
            "Epoch 36/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.6249 - accuracy: 0.6458 - val_loss: 0.5697 - val_accuracy: 0.6957\n",
            "Epoch 37/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5909 - accuracy: 0.6866 - val_loss: 0.5789 - val_accuracy: 0.6739\n",
            "Epoch 38/400\n",
            "46/46 [==============================] - 5s 105ms/step - loss: 0.6019 - accuracy: 0.6948 - val_loss: 0.5553 - val_accuracy: 0.7391\n",
            "Epoch 39/400\n",
            "46/46 [==============================] - 5s 105ms/step - loss: 0.6240 - accuracy: 0.6649 - val_loss: 0.5519 - val_accuracy: 0.7391\n",
            "Epoch 40/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.6151 - accuracy: 0.6649 - val_loss: 0.5783 - val_accuracy: 0.6739\n",
            "Epoch 41/400\n",
            "46/46 [==============================] - 5s 104ms/step - loss: 0.6155 - accuracy: 0.6540 - val_loss: 0.5638 - val_accuracy: 0.6957\n",
            "Epoch 42/400\n",
            "46/46 [==============================] - 5s 105ms/step - loss: 0.6160 - accuracy: 0.6621 - val_loss: 0.5519 - val_accuracy: 0.7283\n",
            "Epoch 43/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.6173 - accuracy: 0.6839 - val_loss: 0.5532 - val_accuracy: 0.7283\n",
            "Epoch 44/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.6158 - accuracy: 0.6512 - val_loss: 0.5537 - val_accuracy: 0.7391\n",
            "Epoch 45/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.6115 - accuracy: 0.6785 - val_loss: 0.5474 - val_accuracy: 0.7500\n",
            "Epoch 46/400\n",
            "46/46 [==============================] - 5s 111ms/step - loss: 0.5985 - accuracy: 0.6594 - val_loss: 0.5367 - val_accuracy: 0.7500\n",
            "Epoch 47/400\n",
            "46/46 [==============================] - 5s 105ms/step - loss: 0.6004 - accuracy: 0.6839 - val_loss: 0.5732 - val_accuracy: 0.6522\n",
            "Epoch 48/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.5884 - accuracy: 0.6975 - val_loss: 0.5474 - val_accuracy: 0.7174\n",
            "Epoch 49/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.6063 - accuracy: 0.6703 - val_loss: 0.5464 - val_accuracy: 0.7174\n",
            "Epoch 50/400\n",
            "46/46 [==============================] - 5s 105ms/step - loss: 0.5874 - accuracy: 0.7084 - val_loss: 0.5410 - val_accuracy: 0.7391\n",
            "Epoch 51/400\n",
            "46/46 [==============================] - 5s 105ms/step - loss: 0.6371 - accuracy: 0.6349 - val_loss: 0.5362 - val_accuracy: 0.7500\n",
            "Epoch 52/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.5975 - accuracy: 0.6785 - val_loss: 0.5381 - val_accuracy: 0.7391\n",
            "Epoch 53/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5895 - accuracy: 0.7112 - val_loss: 0.5328 - val_accuracy: 0.7500\n",
            "Epoch 54/400\n",
            "46/46 [==============================] - 5s 105ms/step - loss: 0.6059 - accuracy: 0.6676 - val_loss: 0.5222 - val_accuracy: 0.7500\n",
            "Epoch 55/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5865 - accuracy: 0.6894 - val_loss: 0.5265 - val_accuracy: 0.7500\n",
            "Epoch 56/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.5892 - accuracy: 0.6757 - val_loss: 0.5323 - val_accuracy: 0.7283\n",
            "Epoch 57/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.5950 - accuracy: 0.6975 - val_loss: 0.5296 - val_accuracy: 0.7500\n",
            "Epoch 58/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5991 - accuracy: 0.6703 - val_loss: 0.5371 - val_accuracy: 0.7283\n",
            "Epoch 59/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5915 - accuracy: 0.6921 - val_loss: 0.5218 - val_accuracy: 0.7609\n",
            "Epoch 60/400\n",
            "46/46 [==============================] - 6s 127ms/step - loss: 0.5908 - accuracy: 0.7057 - val_loss: 0.5158 - val_accuracy: 0.7609\n",
            "Epoch 61/400\n",
            "46/46 [==============================] - 6s 108ms/step - loss: 0.5852 - accuracy: 0.7030 - val_loss: 0.5178 - val_accuracy: 0.7609\n",
            "Epoch 62/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5992 - accuracy: 0.6921 - val_loss: 0.5431 - val_accuracy: 0.7065\n",
            "Epoch 63/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.6056 - accuracy: 0.6894 - val_loss: 0.5158 - val_accuracy: 0.7609\n",
            "Epoch 64/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5757 - accuracy: 0.6866 - val_loss: 0.5247 - val_accuracy: 0.7391\n",
            "Epoch 65/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5777 - accuracy: 0.6894 - val_loss: 0.5102 - val_accuracy: 0.7391\n",
            "Epoch 66/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5875 - accuracy: 0.6785 - val_loss: 0.4974 - val_accuracy: 0.7826\n",
            "Epoch 67/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5723 - accuracy: 0.7057 - val_loss: 0.5106 - val_accuracy: 0.7500\n",
            "Epoch 68/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5832 - accuracy: 0.6839 - val_loss: 0.5063 - val_accuracy: 0.7609\n",
            "Epoch 69/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.5939 - accuracy: 0.7057 - val_loss: 0.5001 - val_accuracy: 0.7500\n",
            "Epoch 70/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.5858 - accuracy: 0.6894 - val_loss: 0.4931 - val_accuracy: 0.7500\n",
            "Epoch 71/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5722 - accuracy: 0.7248 - val_loss: 0.5184 - val_accuracy: 0.7391\n",
            "Epoch 72/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.5775 - accuracy: 0.6975 - val_loss: 0.4999 - val_accuracy: 0.7500\n",
            "Epoch 73/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.5608 - accuracy: 0.7275 - val_loss: 0.4800 - val_accuracy: 0.7935\n",
            "Epoch 74/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5745 - accuracy: 0.7166 - val_loss: 0.5267 - val_accuracy: 0.7283\n",
            "Epoch 75/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5745 - accuracy: 0.7057 - val_loss: 0.4831 - val_accuracy: 0.7826\n",
            "Epoch 76/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.5553 - accuracy: 0.7221 - val_loss: 0.4963 - val_accuracy: 0.7717\n",
            "Epoch 77/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.5627 - accuracy: 0.7057 - val_loss: 0.4865 - val_accuracy: 0.7935\n",
            "Epoch 78/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5593 - accuracy: 0.7057 - val_loss: 0.5368 - val_accuracy: 0.7283\n",
            "Epoch 79/400\n",
            "46/46 [==============================] - 5s 105ms/step - loss: 0.5637 - accuracy: 0.7275 - val_loss: 0.4821 - val_accuracy: 0.7826\n",
            "Epoch 80/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5549 - accuracy: 0.7057 - val_loss: 0.4786 - val_accuracy: 0.7935\n",
            "Epoch 81/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5907 - accuracy: 0.6839 - val_loss: 0.5233 - val_accuracy: 0.7283\n",
            "Epoch 82/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5679 - accuracy: 0.7084 - val_loss: 0.5093 - val_accuracy: 0.7717\n",
            "Epoch 83/400\n",
            "46/46 [==============================] - 5s 111ms/step - loss: 0.5748 - accuracy: 0.7030 - val_loss: 0.5308 - val_accuracy: 0.7283\n",
            "Epoch 84/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5820 - accuracy: 0.6894 - val_loss: 0.4830 - val_accuracy: 0.7717\n",
            "Epoch 85/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.5612 - accuracy: 0.7030 - val_loss: 0.4759 - val_accuracy: 0.8043\n",
            "Epoch 86/400\n",
            "46/46 [==============================] - 5s 105ms/step - loss: 0.5627 - accuracy: 0.7275 - val_loss: 0.4804 - val_accuracy: 0.7935\n",
            "Epoch 87/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5673 - accuracy: 0.7275 - val_loss: 0.4782 - val_accuracy: 0.7717\n",
            "Epoch 88/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5589 - accuracy: 0.7139 - val_loss: 0.4636 - val_accuracy: 0.8043\n",
            "Epoch 89/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5632 - accuracy: 0.7030 - val_loss: 0.4665 - val_accuracy: 0.8261\n",
            "Epoch 90/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5539 - accuracy: 0.7193 - val_loss: 0.4729 - val_accuracy: 0.8043\n",
            "Epoch 91/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.5433 - accuracy: 0.7384 - val_loss: 0.4830 - val_accuracy: 0.7826\n",
            "Epoch 92/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5668 - accuracy: 0.7193 - val_loss: 0.4833 - val_accuracy: 0.7935\n",
            "Epoch 93/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5442 - accuracy: 0.7193 - val_loss: 0.4571 - val_accuracy: 0.8152\n",
            "Epoch 94/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5379 - accuracy: 0.7302 - val_loss: 0.4754 - val_accuracy: 0.7935\n",
            "Epoch 95/400\n",
            "46/46 [==============================] - 5s 111ms/step - loss: 0.5646 - accuracy: 0.7139 - val_loss: 0.4632 - val_accuracy: 0.7935\n",
            "Epoch 96/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5535 - accuracy: 0.7084 - val_loss: 0.4533 - val_accuracy: 0.8370\n",
            "Epoch 97/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.5513 - accuracy: 0.7166 - val_loss: 0.4531 - val_accuracy: 0.8152\n",
            "Epoch 98/400\n",
            "46/46 [==============================] - 6s 108ms/step - loss: 0.5531 - accuracy: 0.7221 - val_loss: 0.4424 - val_accuracy: 0.8152\n",
            "Epoch 99/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.5454 - accuracy: 0.7275 - val_loss: 0.4741 - val_accuracy: 0.7935\n",
            "Epoch 100/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5374 - accuracy: 0.7357 - val_loss: 0.4441 - val_accuracy: 0.8152\n",
            "Epoch 101/400\n",
            "46/46 [==============================] - 5s 111ms/step - loss: 0.5511 - accuracy: 0.7084 - val_loss: 0.4450 - val_accuracy: 0.8043\n",
            "Epoch 102/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5374 - accuracy: 0.7112 - val_loss: 0.4577 - val_accuracy: 0.7935\n",
            "Epoch 103/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5492 - accuracy: 0.7221 - val_loss: 0.4447 - val_accuracy: 0.8261\n",
            "Epoch 104/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5657 - accuracy: 0.7166 - val_loss: 0.4578 - val_accuracy: 0.8043\n",
            "Epoch 105/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5455 - accuracy: 0.7003 - val_loss: 0.4426 - val_accuracy: 0.8152\n",
            "Epoch 106/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5223 - accuracy: 0.7466 - val_loss: 0.4393 - val_accuracy: 0.8043\n",
            "Epoch 107/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5495 - accuracy: 0.7357 - val_loss: 0.4660 - val_accuracy: 0.7935\n",
            "Epoch 108/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.5265 - accuracy: 0.7248 - val_loss: 0.4401 - val_accuracy: 0.8043\n",
            "Epoch 109/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5377 - accuracy: 0.7221 - val_loss: 0.4293 - val_accuracy: 0.8261\n",
            "Epoch 110/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.5400 - accuracy: 0.7439 - val_loss: 0.4548 - val_accuracy: 0.8043\n",
            "Epoch 111/400\n",
            "46/46 [==============================] - 5s 105ms/step - loss: 0.5267 - accuracy: 0.7275 - val_loss: 0.4877 - val_accuracy: 0.7500\n",
            "Epoch 112/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5421 - accuracy: 0.7166 - val_loss: 0.4526 - val_accuracy: 0.8043\n",
            "Epoch 113/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5320 - accuracy: 0.7357 - val_loss: 0.4367 - val_accuracy: 0.8152\n",
            "Epoch 114/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.5409 - accuracy: 0.7466 - val_loss: 0.4492 - val_accuracy: 0.8152\n",
            "Epoch 115/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.5205 - accuracy: 0.7466 - val_loss: 0.4793 - val_accuracy: 0.7717\n",
            "Epoch 116/400\n",
            "46/46 [==============================] - 7s 144ms/step - loss: 0.5406 - accuracy: 0.7166 - val_loss: 0.4598 - val_accuracy: 0.7717\n",
            "Epoch 117/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.5339 - accuracy: 0.7139 - val_loss: 0.4678 - val_accuracy: 0.7935\n",
            "Epoch 118/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5651 - accuracy: 0.6866 - val_loss: 0.4724 - val_accuracy: 0.7935\n",
            "Epoch 119/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.5509 - accuracy: 0.7520 - val_loss: 0.4420 - val_accuracy: 0.8043\n",
            "Epoch 120/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5192 - accuracy: 0.7493 - val_loss: 0.4749 - val_accuracy: 0.7935\n",
            "Epoch 121/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.5512 - accuracy: 0.6975 - val_loss: 0.4474 - val_accuracy: 0.7826\n",
            "Epoch 122/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5092 - accuracy: 0.7657 - val_loss: 0.4407 - val_accuracy: 0.7935\n",
            "Epoch 123/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5515 - accuracy: 0.7248 - val_loss: 0.4306 - val_accuracy: 0.8043\n",
            "Epoch 124/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.5413 - accuracy: 0.7548 - val_loss: 0.4289 - val_accuracy: 0.8261\n",
            "Epoch 125/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5323 - accuracy: 0.7248 - val_loss: 0.4433 - val_accuracy: 0.8043\n",
            "Epoch 126/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.5277 - accuracy: 0.7493 - val_loss: 0.4276 - val_accuracy: 0.8152\n",
            "Epoch 127/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5315 - accuracy: 0.7493 - val_loss: 0.4308 - val_accuracy: 0.8152\n",
            "Epoch 128/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5374 - accuracy: 0.7384 - val_loss: 0.4525 - val_accuracy: 0.7717\n",
            "Epoch 129/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5240 - accuracy: 0.7275 - val_loss: 0.4594 - val_accuracy: 0.7717\n",
            "Epoch 130/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.5282 - accuracy: 0.7166 - val_loss: 0.4404 - val_accuracy: 0.7935\n",
            "Epoch 131/400\n",
            "46/46 [==============================] - 5s 105ms/step - loss: 0.5355 - accuracy: 0.7384 - val_loss: 0.4347 - val_accuracy: 0.7935\n",
            "Epoch 132/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5285 - accuracy: 0.7384 - val_loss: 0.4643 - val_accuracy: 0.7717\n",
            "Epoch 133/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.5059 - accuracy: 0.7520 - val_loss: 0.4232 - val_accuracy: 0.8152\n",
            "Epoch 134/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.5236 - accuracy: 0.7411 - val_loss: 0.4241 - val_accuracy: 0.8370\n",
            "Epoch 135/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.5429 - accuracy: 0.7248 - val_loss: 0.4802 - val_accuracy: 0.7609\n",
            "Epoch 136/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.5205 - accuracy: 0.7466 - val_loss: 0.4236 - val_accuracy: 0.8152\n",
            "Epoch 137/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5185 - accuracy: 0.7357 - val_loss: 0.4329 - val_accuracy: 0.8043\n",
            "Epoch 138/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5097 - accuracy: 0.7602 - val_loss: 0.4228 - val_accuracy: 0.8261\n",
            "Epoch 139/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.5431 - accuracy: 0.7139 - val_loss: 0.4546 - val_accuracy: 0.7609\n",
            "Epoch 140/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.5384 - accuracy: 0.7330 - val_loss: 0.4567 - val_accuracy: 0.7609\n",
            "Epoch 141/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.5200 - accuracy: 0.7548 - val_loss: 0.4947 - val_accuracy: 0.7500\n",
            "Epoch 142/400\n",
            "46/46 [==============================] - 7s 142ms/step - loss: 0.5456 - accuracy: 0.7248 - val_loss: 0.4217 - val_accuracy: 0.8261\n",
            "Epoch 143/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.5507 - accuracy: 0.7112 - val_loss: 0.4276 - val_accuracy: 0.8152\n",
            "Epoch 144/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5079 - accuracy: 0.7439 - val_loss: 0.4209 - val_accuracy: 0.8152\n",
            "Epoch 145/400\n",
            "46/46 [==============================] - 5s 111ms/step - loss: 0.5338 - accuracy: 0.7112 - val_loss: 0.4233 - val_accuracy: 0.8043\n",
            "Epoch 146/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5092 - accuracy: 0.7466 - val_loss: 0.4278 - val_accuracy: 0.7935\n",
            "Epoch 147/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.5146 - accuracy: 0.7221 - val_loss: 0.4180 - val_accuracy: 0.8043\n",
            "Epoch 148/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5199 - accuracy: 0.7493 - val_loss: 0.4320 - val_accuracy: 0.7826\n",
            "Epoch 149/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.4989 - accuracy: 0.7738 - val_loss: 0.4183 - val_accuracy: 0.8152\n",
            "Epoch 150/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5160 - accuracy: 0.7548 - val_loss: 0.4176 - val_accuracy: 0.8043\n",
            "Epoch 151/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5424 - accuracy: 0.7221 - val_loss: 0.4179 - val_accuracy: 0.8152\n",
            "Epoch 152/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5347 - accuracy: 0.7221 - val_loss: 0.4259 - val_accuracy: 0.7935\n",
            "Epoch 153/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4956 - accuracy: 0.7602 - val_loss: 0.4217 - val_accuracy: 0.8152\n",
            "Epoch 154/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.5357 - accuracy: 0.7221 - val_loss: 0.4226 - val_accuracy: 0.8043\n",
            "Epoch 155/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5217 - accuracy: 0.7330 - val_loss: 0.4393 - val_accuracy: 0.7826\n",
            "Epoch 156/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.5053 - accuracy: 0.7548 - val_loss: 0.4075 - val_accuracy: 0.8370\n",
            "Epoch 157/400\n",
            "46/46 [==============================] - 6s 110ms/step - loss: 0.5268 - accuracy: 0.7684 - val_loss: 0.4374 - val_accuracy: 0.7935\n",
            "Epoch 158/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5165 - accuracy: 0.7493 - val_loss: 0.4137 - val_accuracy: 0.8261\n",
            "Epoch 159/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.5225 - accuracy: 0.7330 - val_loss: 0.3983 - val_accuracy: 0.8587\n",
            "Epoch 160/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.5091 - accuracy: 0.7302 - val_loss: 0.4256 - val_accuracy: 0.7717\n",
            "Epoch 161/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5064 - accuracy: 0.7439 - val_loss: 0.4254 - val_accuracy: 0.8261\n",
            "Epoch 162/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5273 - accuracy: 0.7384 - val_loss: 0.4380 - val_accuracy: 0.7826\n",
            "Epoch 163/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.5211 - accuracy: 0.7493 - val_loss: 0.4185 - val_accuracy: 0.7935\n",
            "Epoch 164/400\n",
            "46/46 [==============================] - 5s 112ms/step - loss: 0.5137 - accuracy: 0.7411 - val_loss: 0.4108 - val_accuracy: 0.8152\n",
            "Epoch 165/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.5022 - accuracy: 0.7548 - val_loss: 0.4603 - val_accuracy: 0.7826\n",
            "Epoch 166/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5130 - accuracy: 0.7548 - val_loss: 0.4282 - val_accuracy: 0.8261\n",
            "Epoch 167/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.5061 - accuracy: 0.7657 - val_loss: 0.4079 - val_accuracy: 0.8152\n",
            "Epoch 168/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.5073 - accuracy: 0.7439 - val_loss: 0.4185 - val_accuracy: 0.8261\n",
            "Epoch 169/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5096 - accuracy: 0.7439 - val_loss: 0.4169 - val_accuracy: 0.8043\n",
            "Epoch 170/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.5257 - accuracy: 0.7493 - val_loss: 0.4142 - val_accuracy: 0.8370\n",
            "Epoch 171/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5050 - accuracy: 0.7439 - val_loss: 0.4253 - val_accuracy: 0.7935\n",
            "Epoch 172/400\n",
            "46/46 [==============================] - 5s 104ms/step - loss: 0.5029 - accuracy: 0.7684 - val_loss: 0.4090 - val_accuracy: 0.8152\n",
            "Epoch 173/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5259 - accuracy: 0.7221 - val_loss: 0.4340 - val_accuracy: 0.7717\n",
            "Epoch 174/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5154 - accuracy: 0.7493 - val_loss: 0.4173 - val_accuracy: 0.7935\n",
            "Epoch 175/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.5384 - accuracy: 0.7193 - val_loss: 0.4213 - val_accuracy: 0.8261\n",
            "Epoch 176/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4966 - accuracy: 0.7548 - val_loss: 0.4343 - val_accuracy: 0.8152\n",
            "Epoch 177/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5175 - accuracy: 0.7493 - val_loss: 0.4367 - val_accuracy: 0.7717\n",
            "Epoch 178/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.5025 - accuracy: 0.7575 - val_loss: 0.4500 - val_accuracy: 0.7717\n",
            "Epoch 179/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.5135 - accuracy: 0.7629 - val_loss: 0.4165 - val_accuracy: 0.8043\n",
            "Epoch 180/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.4933 - accuracy: 0.7629 - val_loss: 0.4104 - val_accuracy: 0.8370\n",
            "Epoch 181/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5195 - accuracy: 0.7302 - val_loss: 0.4218 - val_accuracy: 0.8043\n",
            "Epoch 182/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5249 - accuracy: 0.7275 - val_loss: 0.4449 - val_accuracy: 0.7826\n",
            "Epoch 183/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5008 - accuracy: 0.7520 - val_loss: 0.4093 - val_accuracy: 0.8043\n",
            "Epoch 184/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.5118 - accuracy: 0.7411 - val_loss: 0.4610 - val_accuracy: 0.7717\n",
            "Epoch 185/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.4964 - accuracy: 0.7384 - val_loss: 0.4357 - val_accuracy: 0.7717\n",
            "Epoch 186/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5075 - accuracy: 0.7466 - val_loss: 0.4295 - val_accuracy: 0.7935\n",
            "Epoch 187/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.5055 - accuracy: 0.7548 - val_loss: 0.4374 - val_accuracy: 0.7717\n",
            "Epoch 188/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4914 - accuracy: 0.7766 - val_loss: 0.4349 - val_accuracy: 0.7935\n",
            "Epoch 189/400\n",
            "46/46 [==============================] - 5s 111ms/step - loss: 0.5022 - accuracy: 0.7684 - val_loss: 0.3959 - val_accuracy: 0.8261\n",
            "Epoch 190/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5178 - accuracy: 0.7357 - val_loss: 0.4192 - val_accuracy: 0.7826\n",
            "Epoch 191/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.5110 - accuracy: 0.7738 - val_loss: 0.4071 - val_accuracy: 0.8152\n",
            "Epoch 192/400\n",
            "46/46 [==============================] - 5s 111ms/step - loss: 0.5036 - accuracy: 0.7466 - val_loss: 0.4197 - val_accuracy: 0.8152\n",
            "Epoch 193/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4852 - accuracy: 0.7602 - val_loss: 0.4257 - val_accuracy: 0.7717\n",
            "Epoch 194/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5160 - accuracy: 0.7493 - val_loss: 0.4553 - val_accuracy: 0.7500\n",
            "Epoch 195/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.5070 - accuracy: 0.7520 - val_loss: 0.4075 - val_accuracy: 0.8370\n",
            "Epoch 196/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.4904 - accuracy: 0.7738 - val_loss: 0.4093 - val_accuracy: 0.8152\n",
            "Epoch 197/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.4853 - accuracy: 0.7766 - val_loss: 0.4306 - val_accuracy: 0.8261\n",
            "Epoch 198/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4772 - accuracy: 0.7793 - val_loss: 0.4043 - val_accuracy: 0.8152\n",
            "Epoch 199/400\n",
            "46/46 [==============================] - 5s 111ms/step - loss: 0.4936 - accuracy: 0.7629 - val_loss: 0.4274 - val_accuracy: 0.7826\n",
            "Epoch 200/400\n",
            "46/46 [==============================] - 7s 133ms/step - loss: 0.4868 - accuracy: 0.7466 - val_loss: 0.4096 - val_accuracy: 0.7935\n",
            "Epoch 201/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5100 - accuracy: 0.7493 - val_loss: 0.4145 - val_accuracy: 0.8043\n",
            "Epoch 202/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.4941 - accuracy: 0.7629 - val_loss: 0.4202 - val_accuracy: 0.7826\n",
            "Epoch 203/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4867 - accuracy: 0.7738 - val_loss: 0.4296 - val_accuracy: 0.7717\n",
            "Epoch 204/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.4990 - accuracy: 0.7684 - val_loss: 0.4058 - val_accuracy: 0.8043\n",
            "Epoch 205/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4916 - accuracy: 0.7629 - val_loss: 0.4332 - val_accuracy: 0.7717\n",
            "Epoch 206/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4884 - accuracy: 0.7684 - val_loss: 0.4518 - val_accuracy: 0.7717\n",
            "Epoch 207/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.4956 - accuracy: 0.7684 - val_loss: 0.4417 - val_accuracy: 0.7826\n",
            "Epoch 208/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4998 - accuracy: 0.7493 - val_loss: 0.4081 - val_accuracy: 0.8370\n",
            "Epoch 209/400\n",
            "46/46 [==============================] - 5s 111ms/step - loss: 0.4655 - accuracy: 0.7875 - val_loss: 0.4841 - val_accuracy: 0.7935\n",
            "Epoch 210/400\n",
            "46/46 [==============================] - 5s 111ms/step - loss: 0.4877 - accuracy: 0.7820 - val_loss: 0.4364 - val_accuracy: 0.8152\n",
            "Epoch 211/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5081 - accuracy: 0.7684 - val_loss: 0.4196 - val_accuracy: 0.7826\n",
            "Epoch 212/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4754 - accuracy: 0.7847 - val_loss: 0.4079 - val_accuracy: 0.8043\n",
            "Epoch 213/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4699 - accuracy: 0.7766 - val_loss: 0.4016 - val_accuracy: 0.8370\n",
            "Epoch 214/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4884 - accuracy: 0.7575 - val_loss: 0.4311 - val_accuracy: 0.7826\n",
            "Epoch 215/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.5143 - accuracy: 0.7357 - val_loss: 0.4360 - val_accuracy: 0.8261\n",
            "Epoch 216/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4777 - accuracy: 0.7793 - val_loss: 0.4577 - val_accuracy: 0.7826\n",
            "Epoch 217/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4862 - accuracy: 0.8065 - val_loss: 0.3969 - val_accuracy: 0.8152\n",
            "Epoch 218/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4990 - accuracy: 0.7575 - val_loss: 0.3943 - val_accuracy: 0.8043\n",
            "Epoch 219/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4918 - accuracy: 0.7793 - val_loss: 0.4451 - val_accuracy: 0.7826\n",
            "Epoch 220/400\n",
            "46/46 [==============================] - 5s 112ms/step - loss: 0.4936 - accuracy: 0.7602 - val_loss: 0.4015 - val_accuracy: 0.8261\n",
            "Epoch 221/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.4899 - accuracy: 0.7738 - val_loss: 0.4018 - val_accuracy: 0.8043\n",
            "Epoch 222/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.4990 - accuracy: 0.7520 - val_loss: 0.4012 - val_accuracy: 0.8043\n",
            "Epoch 223/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4573 - accuracy: 0.7956 - val_loss: 0.4528 - val_accuracy: 0.7826\n",
            "Epoch 224/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4761 - accuracy: 0.7793 - val_loss: 0.4661 - val_accuracy: 0.7717\n",
            "Epoch 225/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.5032 - accuracy: 0.7602 - val_loss: 0.4201 - val_accuracy: 0.7717\n",
            "Epoch 226/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5002 - accuracy: 0.7684 - val_loss: 0.3917 - val_accuracy: 0.8261\n",
            "Epoch 227/400\n",
            "46/46 [==============================] - 7s 136ms/step - loss: 0.4925 - accuracy: 0.7548 - val_loss: 0.4027 - val_accuracy: 0.8043\n",
            "Epoch 228/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4826 - accuracy: 0.7766 - val_loss: 0.4213 - val_accuracy: 0.7826\n",
            "Epoch 229/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4825 - accuracy: 0.7875 - val_loss: 0.4035 - val_accuracy: 0.8043\n",
            "Epoch 230/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4877 - accuracy: 0.7766 - val_loss: 0.3907 - val_accuracy: 0.8152\n",
            "Epoch 231/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.4730 - accuracy: 0.7711 - val_loss: 0.4439 - val_accuracy: 0.8043\n",
            "Epoch 232/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.4948 - accuracy: 0.7602 - val_loss: 0.4299 - val_accuracy: 0.7935\n",
            "Epoch 233/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.4883 - accuracy: 0.7602 - val_loss: 0.3990 - val_accuracy: 0.8152\n",
            "Epoch 234/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4839 - accuracy: 0.7711 - val_loss: 0.4142 - val_accuracy: 0.7826\n",
            "Epoch 235/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.4901 - accuracy: 0.7602 - val_loss: 0.4143 - val_accuracy: 0.7935\n",
            "Epoch 236/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4991 - accuracy: 0.7548 - val_loss: 0.4474 - val_accuracy: 0.7826\n",
            "Epoch 237/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.4788 - accuracy: 0.7629 - val_loss: 0.4008 - val_accuracy: 0.8043\n",
            "Epoch 238/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4690 - accuracy: 0.7684 - val_loss: 0.4331 - val_accuracy: 0.7826\n",
            "Epoch 239/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4869 - accuracy: 0.7520 - val_loss: 0.4176 - val_accuracy: 0.7826\n",
            "Epoch 240/400\n",
            "46/46 [==============================] - 5s 111ms/step - loss: 0.4600 - accuracy: 0.7847 - val_loss: 0.4027 - val_accuracy: 0.8261\n",
            "Epoch 241/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4886 - accuracy: 0.7766 - val_loss: 0.4045 - val_accuracy: 0.7826\n",
            "Epoch 242/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4869 - accuracy: 0.7520 - val_loss: 0.4299 - val_accuracy: 0.8152\n",
            "Epoch 243/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4852 - accuracy: 0.7548 - val_loss: 0.4488 - val_accuracy: 0.7717\n",
            "Epoch 244/400\n",
            "46/46 [==============================] - 5s 112ms/step - loss: 0.4950 - accuracy: 0.7575 - val_loss: 0.4401 - val_accuracy: 0.8152\n",
            "Epoch 245/400\n",
            "46/46 [==============================] - 7s 145ms/step - loss: 0.4639 - accuracy: 0.7929 - val_loss: 0.4325 - val_accuracy: 0.8261\n",
            "Epoch 246/400\n",
            "46/46 [==============================] - 6s 115ms/step - loss: 0.4752 - accuracy: 0.7657 - val_loss: 0.3998 - val_accuracy: 0.7935\n",
            "Epoch 247/400\n",
            "46/46 [==============================] - 6s 131ms/step - loss: 0.4784 - accuracy: 0.7820 - val_loss: 0.3977 - val_accuracy: 0.8370\n",
            "Epoch 248/400\n",
            "46/46 [==============================] - 5s 111ms/step - loss: 0.4822 - accuracy: 0.7847 - val_loss: 0.4241 - val_accuracy: 0.7717\n",
            "Epoch 249/400\n",
            "46/46 [==============================] - 6s 113ms/step - loss: 0.4706 - accuracy: 0.7684 - val_loss: 0.3883 - val_accuracy: 0.8152\n",
            "Epoch 250/400\n",
            "46/46 [==============================] - 6s 125ms/step - loss: 0.4666 - accuracy: 0.7820 - val_loss: 0.4006 - val_accuracy: 0.8043\n",
            "Epoch 251/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.4503 - accuracy: 0.8011 - val_loss: 0.4077 - val_accuracy: 0.8261\n",
            "Epoch 252/400\n",
            "46/46 [==============================] - 6s 114ms/step - loss: 0.4935 - accuracy: 0.7684 - val_loss: 0.4038 - val_accuracy: 0.8043\n",
            "Epoch 253/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.5010 - accuracy: 0.7657 - val_loss: 0.4170 - val_accuracy: 0.7826\n",
            "Epoch 254/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.4791 - accuracy: 0.7684 - val_loss: 0.4193 - val_accuracy: 0.7826\n",
            "Epoch 255/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.4701 - accuracy: 0.7657 - val_loss: 0.4227 - val_accuracy: 0.8261\n",
            "Epoch 256/400\n",
            "46/46 [==============================] - 7s 144ms/step - loss: 0.4705 - accuracy: 0.7575 - val_loss: 0.4117 - val_accuracy: 0.7826\n",
            "Epoch 257/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.4645 - accuracy: 0.7875 - val_loss: 0.4659 - val_accuracy: 0.7935\n",
            "Epoch 258/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4749 - accuracy: 0.7766 - val_loss: 0.3907 - val_accuracy: 0.8152\n",
            "Epoch 259/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4887 - accuracy: 0.7820 - val_loss: 0.4003 - val_accuracy: 0.8370\n",
            "Epoch 260/400\n",
            "46/46 [==============================] - 6s 111ms/step - loss: 0.5249 - accuracy: 0.7330 - val_loss: 0.4097 - val_accuracy: 0.8152\n",
            "Epoch 261/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4873 - accuracy: 0.7575 - val_loss: 0.4261 - val_accuracy: 0.8370\n",
            "Epoch 262/400\n",
            "46/46 [==============================] - 6s 114ms/step - loss: 0.4599 - accuracy: 0.7820 - val_loss: 0.3998 - val_accuracy: 0.8152\n",
            "Epoch 263/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4726 - accuracy: 0.7847 - val_loss: 0.4256 - val_accuracy: 0.8152\n",
            "Epoch 264/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4748 - accuracy: 0.7629 - val_loss: 0.4288 - val_accuracy: 0.7717\n",
            "Epoch 265/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4879 - accuracy: 0.7711 - val_loss: 0.3923 - val_accuracy: 0.8043\n",
            "Epoch 266/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4510 - accuracy: 0.7629 - val_loss: 0.4425 - val_accuracy: 0.8043\n",
            "Epoch 267/400\n",
            "46/46 [==============================] - 6s 111ms/step - loss: 0.4903 - accuracy: 0.7766 - val_loss: 0.4001 - val_accuracy: 0.8043\n",
            "Epoch 268/400\n",
            "46/46 [==============================] - 5s 112ms/step - loss: 0.4525 - accuracy: 0.8011 - val_loss: 0.4595 - val_accuracy: 0.7826\n",
            "Epoch 269/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.4947 - accuracy: 0.7520 - val_loss: 0.4198 - val_accuracy: 0.8152\n",
            "Epoch 270/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4624 - accuracy: 0.7875 - val_loss: 0.3895 - val_accuracy: 0.8152\n",
            "Epoch 271/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.5002 - accuracy: 0.7711 - val_loss: 0.4053 - val_accuracy: 0.7826\n",
            "Epoch 272/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.4557 - accuracy: 0.7929 - val_loss: 0.3915 - val_accuracy: 0.8152\n",
            "Epoch 273/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4825 - accuracy: 0.7657 - val_loss: 0.4047 - val_accuracy: 0.8152\n",
            "Epoch 274/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4729 - accuracy: 0.7738 - val_loss: 0.3964 - val_accuracy: 0.8043\n",
            "Epoch 275/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4728 - accuracy: 0.7684 - val_loss: 0.3952 - val_accuracy: 0.8370\n",
            "Epoch 276/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.5015 - accuracy: 0.7493 - val_loss: 0.4257 - val_accuracy: 0.7935\n",
            "Epoch 277/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4799 - accuracy: 0.7602 - val_loss: 0.4045 - val_accuracy: 0.8043\n",
            "Epoch 278/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4427 - accuracy: 0.7847 - val_loss: 0.4394 - val_accuracy: 0.8043\n",
            "Epoch 279/400\n",
            "46/46 [==============================] - 5s 111ms/step - loss: 0.4637 - accuracy: 0.7711 - val_loss: 0.4074 - val_accuracy: 0.7826\n",
            "Epoch 280/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4287 - accuracy: 0.7875 - val_loss: 0.4547 - val_accuracy: 0.7826\n",
            "Epoch 281/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.4652 - accuracy: 0.7684 - val_loss: 0.4259 - val_accuracy: 0.8043\n",
            "Epoch 282/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4523 - accuracy: 0.7902 - val_loss: 0.3816 - val_accuracy: 0.8370\n",
            "Epoch 283/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4793 - accuracy: 0.7657 - val_loss: 0.4076 - val_accuracy: 0.7935\n",
            "Epoch 284/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4841 - accuracy: 0.7602 - val_loss: 0.4064 - val_accuracy: 0.7826\n",
            "Epoch 285/400\n",
            "46/46 [==============================] - 7s 146ms/step - loss: 0.4829 - accuracy: 0.7602 - val_loss: 0.4096 - val_accuracy: 0.8370\n",
            "Epoch 286/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4792 - accuracy: 0.7766 - val_loss: 0.4515 - val_accuracy: 0.7935\n",
            "Epoch 287/400\n",
            "46/46 [==============================] - 5s 111ms/step - loss: 0.4736 - accuracy: 0.7820 - val_loss: 0.3943 - val_accuracy: 0.8152\n",
            "Epoch 288/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4649 - accuracy: 0.7820 - val_loss: 0.4082 - val_accuracy: 0.8152\n",
            "Epoch 289/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4946 - accuracy: 0.7711 - val_loss: 0.3991 - val_accuracy: 0.8370\n",
            "Epoch 290/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4481 - accuracy: 0.7847 - val_loss: 0.4546 - val_accuracy: 0.7935\n",
            "Epoch 291/400\n",
            "46/46 [==============================] - 5s 106ms/step - loss: 0.4840 - accuracy: 0.7684 - val_loss: 0.3724 - val_accuracy: 0.8261\n",
            "Epoch 292/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4695 - accuracy: 0.7820 - val_loss: 0.3972 - val_accuracy: 0.8261\n",
            "Epoch 293/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4724 - accuracy: 0.8038 - val_loss: 0.3959 - val_accuracy: 0.8261\n",
            "Epoch 294/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4644 - accuracy: 0.8011 - val_loss: 0.3980 - val_accuracy: 0.8043\n",
            "Epoch 295/400\n",
            "46/46 [==============================] - 5s 111ms/step - loss: 0.4737 - accuracy: 0.7766 - val_loss: 0.3847 - val_accuracy: 0.8478\n",
            "Epoch 296/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4760 - accuracy: 0.7766 - val_loss: 0.4024 - val_accuracy: 0.7935\n",
            "Epoch 297/400\n",
            "46/46 [==============================] - 6s 110ms/step - loss: 0.4473 - accuracy: 0.7929 - val_loss: 0.4092 - val_accuracy: 0.8261\n",
            "Epoch 298/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4541 - accuracy: 0.7902 - val_loss: 0.4059 - val_accuracy: 0.8370\n",
            "Epoch 299/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4280 - accuracy: 0.8174 - val_loss: 0.3886 - val_accuracy: 0.8152\n",
            "Epoch 300/400\n",
            "46/46 [==============================] - 6s 112ms/step - loss: 0.4571 - accuracy: 0.7820 - val_loss: 0.3834 - val_accuracy: 0.8043\n",
            "Epoch 301/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.5039 - accuracy: 0.7629 - val_loss: 0.4135 - val_accuracy: 0.8152\n",
            "Epoch 302/400\n",
            "46/46 [==============================] - 5s 111ms/step - loss: 0.4602 - accuracy: 0.7793 - val_loss: 0.3980 - val_accuracy: 0.8261\n",
            "Epoch 303/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4557 - accuracy: 0.7548 - val_loss: 0.4080 - val_accuracy: 0.8043\n",
            "Epoch 304/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4597 - accuracy: 0.7711 - val_loss: 0.3888 - val_accuracy: 0.8370\n",
            "Epoch 305/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4495 - accuracy: 0.8065 - val_loss: 0.4192 - val_accuracy: 0.8152\n",
            "Epoch 306/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.4935 - accuracy: 0.7847 - val_loss: 0.4345 - val_accuracy: 0.8043\n",
            "Epoch 307/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4650 - accuracy: 0.7766 - val_loss: 0.4127 - val_accuracy: 0.8152\n",
            "Epoch 308/400\n",
            "46/46 [==============================] - 5s 111ms/step - loss: 0.4681 - accuracy: 0.7657 - val_loss: 0.4079 - val_accuracy: 0.8370\n",
            "Epoch 309/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4655 - accuracy: 0.7657 - val_loss: 0.4164 - val_accuracy: 0.8261\n",
            "Epoch 310/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4695 - accuracy: 0.7684 - val_loss: 0.3945 - val_accuracy: 0.8370\n",
            "Epoch 311/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4511 - accuracy: 0.7875 - val_loss: 0.4122 - val_accuracy: 0.8043\n",
            "Epoch 312/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4539 - accuracy: 0.7956 - val_loss: 0.4184 - val_accuracy: 0.8152\n",
            "Epoch 313/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4487 - accuracy: 0.7766 - val_loss: 0.4203 - val_accuracy: 0.8043\n",
            "Epoch 314/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.4424 - accuracy: 0.7711 - val_loss: 0.3898 - val_accuracy: 0.8043\n",
            "Epoch 315/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4511 - accuracy: 0.7820 - val_loss: 0.4061 - val_accuracy: 0.7826\n",
            "Epoch 316/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4499 - accuracy: 0.7929 - val_loss: 0.4420 - val_accuracy: 0.7826\n",
            "Epoch 317/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4335 - accuracy: 0.8120 - val_loss: 0.4106 - val_accuracy: 0.8152\n",
            "Epoch 318/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.4687 - accuracy: 0.7629 - val_loss: 0.3976 - val_accuracy: 0.8261\n",
            "Epoch 319/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4606 - accuracy: 0.7738 - val_loss: 0.4243 - val_accuracy: 0.8043\n",
            "Epoch 320/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4573 - accuracy: 0.7738 - val_loss: 0.3908 - val_accuracy: 0.8152\n",
            "Epoch 321/400\n",
            "46/46 [==============================] - 6s 112ms/step - loss: 0.4525 - accuracy: 0.8011 - val_loss: 0.4210 - val_accuracy: 0.8043\n",
            "Epoch 322/400\n",
            "46/46 [==============================] - 5s 112ms/step - loss: 0.4675 - accuracy: 0.7929 - val_loss: 0.4061 - val_accuracy: 0.8043\n",
            "Epoch 323/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4815 - accuracy: 0.7711 - val_loss: 0.4298 - val_accuracy: 0.8370\n",
            "Epoch 324/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4612 - accuracy: 0.7766 - val_loss: 0.4045 - val_accuracy: 0.8370\n",
            "Epoch 325/400\n",
            "46/46 [==============================] - 6s 110ms/step - loss: 0.4688 - accuracy: 0.7629 - val_loss: 0.4191 - val_accuracy: 0.8043\n",
            "Epoch 326/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4663 - accuracy: 0.7956 - val_loss: 0.4204 - val_accuracy: 0.8478\n",
            "Epoch 327/400\n",
            "46/46 [==============================] - 5s 107ms/step - loss: 0.4527 - accuracy: 0.7902 - val_loss: 0.4198 - val_accuracy: 0.8261\n",
            "Epoch 328/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4386 - accuracy: 0.7956 - val_loss: 0.4194 - val_accuracy: 0.8370\n",
            "Epoch 329/400\n",
            "46/46 [==============================] - 5s 112ms/step - loss: 0.4564 - accuracy: 0.7738 - val_loss: 0.4304 - val_accuracy: 0.8043\n",
            "Epoch 330/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4643 - accuracy: 0.7929 - val_loss: 0.4217 - val_accuracy: 0.7935\n",
            "Epoch 331/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4966 - accuracy: 0.7520 - val_loss: 0.4237 - val_accuracy: 0.8043\n",
            "Epoch 332/400\n",
            "46/46 [==============================] - 5s 113ms/step - loss: 0.4419 - accuracy: 0.7847 - val_loss: 0.3977 - val_accuracy: 0.8152\n",
            "Epoch 333/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4562 - accuracy: 0.7875 - val_loss: 0.4128 - val_accuracy: 0.8152\n",
            "Epoch 334/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4345 - accuracy: 0.7875 - val_loss: 0.4142 - val_accuracy: 0.8261\n",
            "Epoch 335/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4404 - accuracy: 0.8038 - val_loss: 0.3840 - val_accuracy: 0.8370\n",
            "Epoch 336/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4410 - accuracy: 0.7684 - val_loss: 0.4051 - val_accuracy: 0.7826\n",
            "Epoch 337/400\n",
            "46/46 [==============================] - 6s 110ms/step - loss: 0.4727 - accuracy: 0.7711 - val_loss: 0.4216 - val_accuracy: 0.8152\n",
            "Epoch 338/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4528 - accuracy: 0.7956 - val_loss: 0.3794 - val_accuracy: 0.8370\n",
            "Epoch 339/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4467 - accuracy: 0.7984 - val_loss: 0.4443 - val_accuracy: 0.8043\n",
            "Epoch 340/400\n",
            "46/46 [==============================] - 5s 112ms/step - loss: 0.4558 - accuracy: 0.7875 - val_loss: 0.4226 - val_accuracy: 0.8152\n",
            "Epoch 341/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4630 - accuracy: 0.7793 - val_loss: 0.4133 - val_accuracy: 0.8370\n",
            "Epoch 342/400\n",
            "46/46 [==============================] - 7s 143ms/step - loss: 0.4608 - accuracy: 0.7575 - val_loss: 0.4066 - val_accuracy: 0.8152\n",
            "Epoch 343/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4406 - accuracy: 0.7766 - val_loss: 0.4047 - val_accuracy: 0.8043\n",
            "Epoch 344/400\n",
            "46/46 [==============================] - 5s 112ms/step - loss: 0.4477 - accuracy: 0.7929 - val_loss: 0.4212 - val_accuracy: 0.8152\n",
            "Epoch 345/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4320 - accuracy: 0.7984 - val_loss: 0.4158 - val_accuracy: 0.8152\n",
            "Epoch 346/400\n",
            "46/46 [==============================] - 5s 111ms/step - loss: 0.4355 - accuracy: 0.7793 - val_loss: 0.4214 - val_accuracy: 0.8261\n",
            "Epoch 347/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4674 - accuracy: 0.7766 - val_loss: 0.4139 - val_accuracy: 0.8043\n",
            "Epoch 348/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4481 - accuracy: 0.7847 - val_loss: 0.3966 - val_accuracy: 0.8370\n",
            "Epoch 349/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4373 - accuracy: 0.8065 - val_loss: 0.4208 - val_accuracy: 0.8261\n",
            "Epoch 350/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4574 - accuracy: 0.7793 - val_loss: 0.4116 - val_accuracy: 0.8043\n",
            "Epoch 351/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4641 - accuracy: 0.7738 - val_loss: 0.3939 - val_accuracy: 0.8261\n",
            "Epoch 352/400\n",
            "46/46 [==============================] - 5s 111ms/step - loss: 0.4490 - accuracy: 0.7766 - val_loss: 0.3913 - val_accuracy: 0.8152\n",
            "Epoch 353/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4539 - accuracy: 0.7875 - val_loss: 0.3973 - val_accuracy: 0.8152\n",
            "Epoch 354/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4562 - accuracy: 0.7820 - val_loss: 0.4107 - val_accuracy: 0.8152\n",
            "Epoch 355/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4498 - accuracy: 0.7902 - val_loss: 0.4044 - val_accuracy: 0.8261\n",
            "Epoch 356/400\n",
            "46/46 [==============================] - 5s 113ms/step - loss: 0.4400 - accuracy: 0.8038 - val_loss: 0.4301 - val_accuracy: 0.8043\n",
            "Epoch 357/400\n",
            "46/46 [==============================] - 5s 113ms/step - loss: 0.4495 - accuracy: 0.8065 - val_loss: 0.4192 - val_accuracy: 0.8043\n",
            "Epoch 358/400\n",
            "46/46 [==============================] - 6s 112ms/step - loss: 0.4529 - accuracy: 0.7738 - val_loss: 0.4056 - val_accuracy: 0.7935\n",
            "Epoch 359/400\n",
            "46/46 [==============================] - 5s 113ms/step - loss: 0.4293 - accuracy: 0.8038 - val_loss: 0.3992 - val_accuracy: 0.8261\n",
            "Epoch 360/400\n",
            "46/46 [==============================] - 5s 111ms/step - loss: 0.4716 - accuracy: 0.7793 - val_loss: 0.4253 - val_accuracy: 0.8261\n",
            "Epoch 361/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4485 - accuracy: 0.7902 - val_loss: 0.4226 - val_accuracy: 0.8261\n",
            "Epoch 362/400\n",
            "46/46 [==============================] - 6s 112ms/step - loss: 0.4579 - accuracy: 0.7956 - val_loss: 0.3973 - val_accuracy: 0.8261\n",
            "Epoch 363/400\n",
            "46/46 [==============================] - 5s 112ms/step - loss: 0.4257 - accuracy: 0.8065 - val_loss: 0.4039 - val_accuracy: 0.8043\n",
            "Epoch 364/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4326 - accuracy: 0.7956 - val_loss: 0.4287 - val_accuracy: 0.8043\n",
            "Epoch 365/400\n",
            "46/46 [==============================] - 6s 110ms/step - loss: 0.4528 - accuracy: 0.7847 - val_loss: 0.4001 - val_accuracy: 0.8261\n",
            "Epoch 366/400\n",
            "46/46 [==============================] - 5s 111ms/step - loss: 0.4344 - accuracy: 0.8256 - val_loss: 0.4499 - val_accuracy: 0.8043\n",
            "Epoch 367/400\n",
            "46/46 [==============================] - 6s 111ms/step - loss: 0.4609 - accuracy: 0.7738 - val_loss: 0.4230 - val_accuracy: 0.8152\n",
            "Epoch 368/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4672 - accuracy: 0.7793 - val_loss: 0.4127 - val_accuracy: 0.8152\n",
            "Epoch 369/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4732 - accuracy: 0.7602 - val_loss: 0.4263 - val_accuracy: 0.7935\n",
            "Epoch 370/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4620 - accuracy: 0.7793 - val_loss: 0.4201 - val_accuracy: 0.8152\n",
            "Epoch 371/400\n",
            "46/46 [==============================] - 6s 111ms/step - loss: 0.4216 - accuracy: 0.8093 - val_loss: 0.4004 - val_accuracy: 0.7826\n",
            "Epoch 372/400\n",
            "46/46 [==============================] - 6s 111ms/step - loss: 0.4500 - accuracy: 0.7847 - val_loss: 0.3991 - val_accuracy: 0.8370\n",
            "Epoch 373/400\n",
            "46/46 [==============================] - 5s 114ms/step - loss: 0.4278 - accuracy: 0.8311 - val_loss: 0.4079 - val_accuracy: 0.8478\n",
            "Epoch 374/400\n",
            "46/46 [==============================] - 6s 120ms/step - loss: 0.4468 - accuracy: 0.7820 - val_loss: 0.4104 - val_accuracy: 0.8370\n",
            "Epoch 375/400\n",
            "46/46 [==============================] - 5s 112ms/step - loss: 0.4491 - accuracy: 0.7875 - val_loss: 0.4565 - val_accuracy: 0.7935\n",
            "Epoch 376/400\n",
            "46/46 [==============================] - 5s 111ms/step - loss: 0.4480 - accuracy: 0.7984 - val_loss: 0.3973 - val_accuracy: 0.8152\n",
            "Epoch 377/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4591 - accuracy: 0.7820 - val_loss: 0.4083 - val_accuracy: 0.7935\n",
            "Epoch 378/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4518 - accuracy: 0.7902 - val_loss: 0.4001 - val_accuracy: 0.8152\n",
            "Epoch 379/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4587 - accuracy: 0.7956 - val_loss: 0.4184 - val_accuracy: 0.8152\n",
            "Epoch 380/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4447 - accuracy: 0.7956 - val_loss: 0.4870 - val_accuracy: 0.7826\n",
            "Epoch 381/400\n",
            "46/46 [==============================] - 5s 111ms/step - loss: 0.4649 - accuracy: 0.7929 - val_loss: 0.4142 - val_accuracy: 0.8152\n",
            "Epoch 382/400\n",
            "46/46 [==============================] - 6s 112ms/step - loss: 0.4351 - accuracy: 0.7984 - val_loss: 0.4077 - val_accuracy: 0.8152\n",
            "Epoch 383/400\n",
            "46/46 [==============================] - 6s 113ms/step - loss: 0.4680 - accuracy: 0.7575 - val_loss: 0.3910 - val_accuracy: 0.8261\n",
            "Epoch 384/400\n",
            "46/46 [==============================] - 6s 113ms/step - loss: 0.4410 - accuracy: 0.7902 - val_loss: 0.4264 - val_accuracy: 0.8152\n",
            "Epoch 385/400\n",
            "46/46 [==============================] - 5s 111ms/step - loss: 0.4225 - accuracy: 0.7956 - val_loss: 0.4122 - val_accuracy: 0.8152\n",
            "Epoch 386/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4530 - accuracy: 0.8093 - val_loss: 0.4107 - val_accuracy: 0.8152\n",
            "Epoch 387/400\n",
            "46/46 [==============================] - 5s 113ms/step - loss: 0.4286 - accuracy: 0.8093 - val_loss: 0.4078 - val_accuracy: 0.8152\n",
            "Epoch 388/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4249 - accuracy: 0.7956 - val_loss: 0.4136 - val_accuracy: 0.8043\n",
            "Epoch 389/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4427 - accuracy: 0.8011 - val_loss: 0.4135 - val_accuracy: 0.7826\n",
            "Epoch 390/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4327 - accuracy: 0.7984 - val_loss: 0.4015 - val_accuracy: 0.8261\n",
            "Epoch 391/400\n",
            "46/46 [==============================] - 6s 111ms/step - loss: 0.4441 - accuracy: 0.7820 - val_loss: 0.4261 - val_accuracy: 0.8152\n",
            "Epoch 392/400\n",
            "46/46 [==============================] - 5s 111ms/step - loss: 0.4504 - accuracy: 0.7902 - val_loss: 0.4184 - val_accuracy: 0.8370\n",
            "Epoch 393/400\n",
            "46/46 [==============================] - 6s 109ms/step - loss: 0.4446 - accuracy: 0.7984 - val_loss: 0.4251 - val_accuracy: 0.8043\n",
            "Epoch 394/400\n",
            "46/46 [==============================] - 6s 109ms/step - loss: 0.4566 - accuracy: 0.7684 - val_loss: 0.4198 - val_accuracy: 0.8043\n",
            "Epoch 395/400\n",
            "46/46 [==============================] - 6s 111ms/step - loss: 0.4299 - accuracy: 0.8093 - val_loss: 0.4232 - val_accuracy: 0.8261\n",
            "Epoch 396/400\n",
            "46/46 [==============================] - 5s 108ms/step - loss: 0.4384 - accuracy: 0.7902 - val_loss: 0.4470 - val_accuracy: 0.8043\n",
            "Epoch 397/400\n",
            "46/46 [==============================] - 6s 115ms/step - loss: 0.4298 - accuracy: 0.8174 - val_loss: 0.4096 - val_accuracy: 0.8152\n",
            "Epoch 398/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4452 - accuracy: 0.8093 - val_loss: 0.3931 - val_accuracy: 0.8370\n",
            "Epoch 399/400\n",
            "46/46 [==============================] - 5s 110ms/step - loss: 0.4465 - accuracy: 0.7875 - val_loss: 0.4801 - val_accuracy: 0.7935\n",
            "Epoch 400/400\n",
            "46/46 [==============================] - 5s 109ms/step - loss: 0.4403 - accuracy: 0.7793 - val_loss: 0.4182 - val_accuracy: 0.8043\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J0HqinJ5riWN"
      },
      "source": [
        "検証データにおける予測精度は8割前後となった\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "save_dir =  '/content/drive/MyDrive' # モデルの保存先\n",
        "model_name = 'keras_clouds_trained_model' # モデルを保存する際のファイル名\n",
        "if not os.path.isdir(save_dir):\n",
        "    os.makedirs(save_dir)\n",
        "model_path = os.path.join(save_dir, model_name)\n",
        "model.save(model_path)\n",
        "print('Saved trained model at %s ' % model_path)"
      ],
      "metadata": {
        "id": "g_W9eM5i2GpR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8cc1141a-ee0e-43a5-bd76-a813b910dbd1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved trained model at /content/drive/MyDrive/keras_clouds_trained_model \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = {0:'雨', 1:'晴'}"
      ],
      "metadata": {
        "id": "8ih0r-GyGTXP"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "テストデータの予測結果をidをいろいろ変えて調べてみる"
      ],
      "metadata": {
        "id": "g9H8NuzZu_OS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "\n",
        "# idをいろいろ入れ替えて、正解と予測を見比べてみましょう。\n",
        "id = 10\n",
        "plt.imshow(x_test[id]*255)\n",
        "ans = int(y_test[id])\n",
        "plt.axis('off')\n",
        "\n",
        "target = x_test[id] # 単体の入力データを用意\n",
        "predict_prob=model.predict(np.array([target]))\n",
        "predict_class=int(np.round(predict_prob))\n",
        "\n",
        "print(predict_class)\n",
        "print('Truth: ', labels[ans], '\\tPredicted: ', labels[predict_class])"
      ],
      "metadata": {
        "id": "FxQz06ofHYUW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "965f57df-1263-494e-f6e1-e4ed92e42086"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 28ms/step\n",
            "0\n",
            "Truth:  雨 \tPredicted:  雨\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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Ua9PxPjdCwkWqxwmcVlaMZYqiuuxZcmPh68qTDPIe2Vjn3EMom12GbRn549bzxAe4qfgulrPC+fHdHU97eHrsOBpLj+V1u+ausyirA8FSMEQBWccLSVBKd+i0wGae1Ownf2JCqW1LZslZc5yMqEUXB/kELbnlPvWcG2/ODuEiCVNct033LkGubLqWs3lKKBQFcnEhfjQmJwW0wPEZDEt9+gICOkZ5R8heKlo/H7nojP+O4zuD0adouL9T4xDncLUoLvw/KqQTdcvS0yfGPrG9Kx8847586f2cH9zc0uotvROUCMYIratxpj+vmifEeoJJBMeJmEZQ4+2WCdQoBuKMBR4bWqRlqnBnQjBGNkolAzh5gb0EMqYL5mXRV5NycArhzelZbCbLf1VZVHxhTk6skU/akPHAqliZumc80/N5N2TpctmPE76iy+5dhPa5wGdGtNQqS/RlCOgLKqAXKnLyS9EWTHg4KNlTcwz3COeTrmbbWF6qNAcqrLN8crtHRPucz3OGb0LsomhNhDd4CcuwZdrEon8zarTRSi20Oz1OEkfCL2lbWUyizi3wl5zMJZ+DkiGL7+n6uLO/9AnHJYdZBLB4ftkKp0PLgCIDJt5fPDLv8710LyP18RlHoQRHpTIZp5nydCNvpNtyhbAg1nmVOS+4cmxeJGtpVqZKJ1SeZC5vFDLeDI8AspAcEstZ4awUrLRipRTWOI4O7iwI1icaTEQuJs6d1f752BfCO6r9aapTgl5LA5+eUeBscuKzOcxunpiWZCml/DyFJWWDyyWjrzh3IJkfSWNRLFDnmv1kmUL20MoZFJD7oWWqnsyeHZ9foGFRuMc2yquxvRzAzusUwQedXPbYKPGZYXHlQyx8n2h9YUQVEudUSNsLIz33Ab8xgj3Xe0uzUfiZie88fZOZOdNqWc6n7+maGhCtuOk6nlsH4ZwW3/HiPDJEVCaagl3o/amF2NNJB+FP9GuKqNwE0qQDuKIWG6VzPHKl9HsLNZZGNT+eZYm57oUMJ6/nlqlkwJL2ubDE63l903ui4C2xXvxtCudyYdfht3Jv7sm9oIvoJa+TE7woY3JAsr5R0OYYa5yL8oRFXDh0LSnVYDvFIyEHiIt71qKgxPGfI5lpn9IcSNiEkI/7yXEsKgtNuXRPUf89aOyscN62BmMEoyx7A8aUWnN2hEVs68R619TPmj6bT8sIDaLFSVKaRQVDPmPBiEKxdzSDfSlJOwn3QgnMP5P/XCjmYIAxQrx8mysgThTCpSNMzlnn8M9EWEZfNT47OSVwygRFE3FMcjdgRA8lHBwfHCOyp44fGWk78UNK0hj7sIRsyvbH7Yhxbs/HPd3YlWL0k6JO1UvGTzkPjntE3akhjBTJfco8KqJIiGMuCGM5K5xWFHtr6Q6GYbBoJAVrJnC6aFTyCzLXvuWEkz3l5r/PGG+8Poc54encFwqJ+CXskMkzEyuW80c62zYqiRlOKYX9xNH8RQrfpO34fKI6GVNZGNTsmfjRuWy9LyqJ8e88sptZj3wA0oep5ZUZD40Bu0zhpeonEuGmVipa8bJDMznOXJSRxoxxoqU9m/wQX9Eg5IggO+KnmL5RcP03R+CBhWmfu1+nytxC+nqBhWWnWO454MuCFQbrkqZ0slxf6Uel5ok78sdtXdmkRCsVNMhMy8Y5lUw7Bj/C1zSm0Y0ZLhPmmgZOsmuz4RSKCS5kNWPmUwv9J/22ookQGY12aJZYvcDsselcAUR0MD16RaaPjdYgjuP4u4zjkx52LJ4oP9ElM6WRQ4ackLiOOj0cOx4DkuZdECc4Z5N/PE8XDIc1pxMs4llAM7FhDFO79NclXgxETPhgPp95jUIKSC2WcuDzmuJpGFKOvL920kW6bz9ntj7l1AvAh0REpsVdNk5JY5dMPLXE92SBpZsKWJ3DiZj7OhE2mEDgSf3TtcbFxROhgFdFOy7Wn1uBifAQlEqSmFOWdE5fSYRk940C5qucaGk5P3cZFcmClwpvenNmqdEjPJ32ufB9o+KJiiVezfNPNVoHgTEW0cr/dQ6rHFYErGS0xbGYdiK/lFtwGa9FJOVS65kBWRgbwQeVosAvIRqXVG5JyBQB5Pef4fEvf8DXVMuQw8JMS84fXGa6nPiM2cdBnls+/zHLC03RuVEjLvZdxmdnpRC6+Q798fnRIkerNJvse/zJMrhDRndJgydrvOZiXZPqC3Qx6cuLlBTdzOgdkzByzpdRCZwY63n0OF6fvwnM4aiqil2luGoarDHUQTnuTY8W4TgY7gbonYex0b8rxmdBSCPElvR5irBLpTiF/4uo00mGRvJny3lKbS3A3+jL2jNq8579nNEKTiuQk5yfZweJiD+OcrGMm3Ml6M/EdL7xvCvnyJwoZ8kGan5P1NRzuRyFrbh6NnC09OU8VMlLTscphikFblmzzyOqkj1T+ptFNkxGb4STcb7nTHXG+i70t5w3l+j317PItAhKaSolrJVwIY4HFw1bpbFYnneCcY6ja3h6NDzretqhpDUfgxLxxN/HtEeK9ouL2RVv4ae9yq+Xqzy+zWIVwUULHehIeiybqyXRysq9J77PtHimLqawL+WRxlYLXzHUmUOfQuvKZPBKX+AcuxfrioyDMHODSlImJTJxbhGXsEt2JcpKYfxK4Zz6py6jM/nOLwA7SbBwqjzK+n2Zn7yXK8mlhe/x/SBlh8ZPqrBUo9XMgiwzOiITl0gHB2hYiUIpTWc9dDUG+srRG8ugFNY6aqewztG1HSsHdSW0xguX4CbuxVzBTnNpU6/yMG52QyJXbDFPoWISrC36mAlb/JD5p9NQxEhnOTbT8sKwNmeuJKOT3yZceiIQtfSi2jlEiHXM7yRXiKPyyZ45CWfDw/kZQzOrE7XbpP0z1ZX0TQRNBWEdk93zZxdgVv7bKTdBxoGajsuiGjth2WaK+wwcdrhwwsV4r8RGzxSZzIdX4A4NXDYNK+3otWZtYa0Vl5VPenF9yECrFBeiWFcrfnxzS+uiXI3pi6lDM2E7AfAyFBUHoEQok/5PGT4q/jg2uU+5IISxzdGITPltubzwy3OLxOuMcRYFM/86ZYyCwZbYUgpFwHRcYtWqGJ1kWKaUxDoK6xomOAUjckZ7AXmcUDJvdHLJuVwdJCqKu9KQ5tY+h7Jx3GfPzxsdZfpEZ3IhngriFBJM/ftk+3PUMyErn5rZX4dSwmXT0Gh/9lR37NlIRVMptlWNE8d6u2IwA03vT2XszYH3HmzhruX9vs9eQ5lD2QyuRqU7GYs8huBcvjVsOlYn+HMqzKGtsd+LN6S+p+9BwZwr95++l6qbwqmRi3LMLYXFdPMJS3RNc3MXdkkWDDlWOadjifSJ4ISdAULO6CNxpwJOqVlXBhU8KbkkTV5VTgRe03Gb1jw/L6loIzDO6TU1ndof6xmFePQxl7KelvbYCqTc3Yy2IJRjal5QzsXEjmMR75DwbDytUQS0ANbROcNKwZXW9ALPB8uNOaAdbLuOWjQa4dD1dAjd7QEVgzERwhUoJx51E0Y+3iglpTlP+ndpKvy7YLNxzU5YHPVVsMbRlYjsnQ9halPCWwmWxFyye/8tT3wvIezobqS/BEadvaI9Uq0Qif6BJKJKvsgePvOOlpwXPG053s/MBoyL8zJ9UDLNSwGB5j7dqFlnFjz3r8mFYE7wxEsZfyt1xAuWsc382tjuZJDI+heflKU6wq/hhEVJ9/m6itMCZn05RaWf70ag1opaa3CGWvnra60Rgf3Qc9sNDDi0KKQf2IqjqRSDcSiB3mnawYIKEd+pq+CylkfWG7sFGUTJx8ffkOQ9t2y5fcksZNzIv9zxkg/PljOu0z3CmZnrAhbg1xgln5o5VPNFFVon3XkS1lLel9eaKYRiAiaaazyOccwumjU3XSdNNM2t8eklkTAG6XydOfO66SP5xTiu53TR4uRlFltGoVk6NSJa+ykMGxl1oe7MrZjBt4AgIl125Ohpq6EExSw+42ywDiuK1vp81b3pMNZxRCPO0IhfXhic4mgNOgSLVkrYOzgaC3ZEKSMayGhkFBwVlcpEQYe7cIujlgWasstlUso5AVya7NDuF5DbL+BzposvQFD+fLSWKijlzKRPn8mUQFFr+meaXZQdkEVu5coDuPKFmtSXAvYu0DLr1ynlE6c51jPNEF0S+KVtQqfqz8nMFOTk+tJ8p2tKghRl9weBnkdXc82xUKuMWUb5qMW7Xfo2/nUIg1I0YX6PvcGIP6irI2jbcO7uEJ4y1jA4hwqHmjvrhchnvOVjOiqMrIcRHjBC+3JWEkgLSq1U3rmidQH1MfJJtKrZCCR3Kf40pWjCMmNTX3LL2DIanluHc0sBLgmWx/blGJwxGUUrpaUq7xh/i4wRj5goqV4+AWFaT7ndKerY8spMpyReyOmZ+mSTB5ZokOwzlP4DmSqSeUT6lDafQbLs+9Qyyuy+0YKmXk0QUBG8y/vmsrrEYa1hU9cYY9BKYZ0peEGch9Neh4zW2QZoecT55SAlfiByYSv/jEMXeSEITsoAysZ56fgUEqifJ0zM14nzOXb5jXEQfA2Lgbfz5d53pZz/vdz0NX0gpRVkls9FQnMGG/9h1sHypsJC+cGWVNfs+cmVmcUsuhmZdcLccVKj87Fg3bM7ivoWyxlFtnRv8Tc+P11iyZvLoOqSzT9FmuQ3TpVEHJVZ1Ng/k9Zy4wuW0zYuglwIlSg+tQNaCZUTNBpnS+QTDyOf5y4H8BmmYERfUMSnFtMCR3pH6zcSP99bGyxx/jpBxh0n080DS/scxuGbKNm8LMDmaXkhWFseJ+kKbi8CK+mZERKM1/2LDcYjClMVjHvsptTORr/8jYVOnzCPcw2/8MwCF0v22xJCSBYoD9HD8oSco+uUIxIVU+ZfnV4iyZRZJsBTGY6oq6glAwWzQEesavIC3GIxfQrlcloFtpXiar2l7zuetx0iGhFNfOGFcQM+2cQBlse7BnHw6b7HKIWyCi0G63oerjd8eujCDinf1elxLPmyRTQDLo1LqeyAMRofrsXNGAXeXTJ+mQJh9inqsnvm+ER5oWjtDOqEGYwCVxCWZ5EUvpWEK4rRCc9KjumLxmN4PBGTPaJGBikfyuiKT2Z+aHz1g1IZ2ohHHc77lOiQOci9DzZyYmJmApZ9L3g9NZ+NZVy/i+gjZ6Dcgua1FZa1FLA8L3Xqn09pndEdtz1lz0em9bPguF7X/KNvP+Y3vvEqz2/3/PEnN/zRT/f8q5/e8s6jLdcrxcXlmuOxpe8tD9bC3/3ZV2i05l+8/5w/+KsnPNytee1as9U9P/PgEf/kB3/Fv7lx4GzgJxUUmMblKwFphQAPcrPB9cMYDEDUSlP0ECdZzrlFlBotKtQ0lpnCWkI9J8qXSHyPs6AKBvBKKcDVzKjFtc9T1SxdTJHAdPn+jtxTcVZKvTZWXTL3C6COeW0Z5JzO8dl82ynimdQ5v78UhvsrzizcQp1JkQQhHVPepvByXhatMMEeikOL4XtvXfOPf+sbvPH4AlEVf28w/C+//0P+9QdP+Y+//w4//8aOB9sr7toDN/uON1/e8ODqgt2m4ftP7/ijH37MxVbx+qM12+2Gz5513LUH/vv/+30OqkHT49AYs2CZ0rJchvgy6H9y5HKlNVV0MH8+MyxJFqa3pPterNybhDDuPCezHKWFS30OAxEnzMVrGZ3pt6CwfLXZu0zc8rjFfaEJbpwsjsUDquOYuvx81fhWrVyYJEFYGP2bZV2ylDhRjktB6QITnyovmjyfVz7yXA7dRoYqXK6Mx0beKxPEcxi9uNsiVJ+WK3K6HVSi+IVXHvEf/bvf4mtvP6auKoyxHJ895XtffwX1t4Xf+bWv8fqjS1AKEcvh0OGGlqaq2VzseKPZsKlqRODYdaw2K16Sir//69+k2Wz4Z3/wU374ZI/TOvBr2Uk1UTThSxivyK/j7EfoW+KY8FeCj+yAFMENd5xhySIfebxYQumFcj7xPfc1nQuaVY3aOphGjwhGH2v0zMZ3Z4wi7v/zwj4Sp2bv2cgRRaaVJL8pMt6ki+EeJ+VgYB2Sha4lDa4gaOLxv0tCl+g5MZpLl2XxBxn/FNDp1OxK8WcKSfO0xFEEVaHkBcGKQwfmc5ZwqkVWV05qxrQujYpnZndaQt0AACAASURBVCWCdRZxYMXnJ1l8JFWHRjWORhp21cDfeWfL9777DrvNGqXg88+f8vD6AdvVlq+/+ZiXHj9ktWpwztEPLU1dczxoqkpjrWO7UlQvXdL1BnUH+7sjIo7XX7nmH23XfPvlK/7b3/sT/uLzA9pppDGYQfm5NyEa64JQiU48nEbMEaB8ZnYkumzj+cD+czmxEp6foqRcufspC9utcwNTuH/L5cXT95JVjO51+C1G6KLmnjhLpdbNhJoswyKp/EzDnLRY2ZUlH7W4bfK7zD4A5bs3Y4lKYdliLjc5v29y42wW45fpSXlZ2l1SgGMwLX82Bv3HyPjkECzGoci3y0VlOY7QqIhtRHOTChJtwVxIqFOnM309jboSrpqK/+E//Fu8/dqO3aZGaUXftqxXK1arFatVQ3vsefr5U9quo65rVquGhw8uUds1xhhEhLquEBEGY2nqCjbQ9Ybb2z0vv/qIX944/rObAz/59MA//eMfI7Llg8EgtkcrGBzppD+ZLr9k5j+xi3PzpY9i+WUyrzK5d1ama8mjRr4vQPRiSymyAFuK+/LpdfNrBdQrBXTKwAWMTp/GToyoJTLIaciV1zyNKk9pn19eiMySM/YXqGfqwEYdlF9S84rT78GPzwUmjybm18vRKumIwx2zemaqLdONEPVuCGrISAuhPf/irlFVW+fQSvHbb13wX/ztn+drb2y5fnCF0jXW9DhnuLjYemEbwBhDvdqysRsOxyPGWtquY71apXEzxgKOVVPR9z1VpahqzQHH8+fP2awv+Du/+U3ubj7lP/jeO/zJhzf8N//sD3jaKwZn8KBblg8FF9+H2RGcU/iZhmbk6VGYQeVR7BzqxrlwEySTQapzAnr+aEzl9zf6qOjgBUspxJmE5l2YIGWVP0ZCwpuSM+2ckZnwvv8pOzR5wrl5wnbe25xBooVZynKZi4pLSibVIdFIl3s3c0ryV6aMFmRhsKJlcmWG0AgIljGARBMULqQxk3C+l/PbzsirSBqqjIy67OdpnDxnq2hcnQtzbH0qo6T2smdCxU75fZUexvrLWhTODBDyYxWKle34L//93+RXv/GI43GgrmrPZHVNU9dorVHKz5nWGgcYMyCyYn84cnu7RytN09T+4HLAWkGUo6o0WgmIo+807bHl8kJxtVtx0bxBazq+heUffvNl/ukff8QB8L6hhNiB9aomU05TXDoGUzODki5OziAK8xQTbfxQeciac8HimVsLn6blrHD+va+9zO1g+ezQ04ji7tDzZOi57cHgOy3O8WileW235k+e7H1GRw6t4ublxBCTLWE5jS5/U7CAGne6LJVpfqWvP9jvCcN6Zz47ECzck4TejbtpcroKaJeRMbfYGSJIwhP9cSlmaEqvKsbDZYkPpF1QSkYfsUQfsc0Rt+hghW1QoRIVE5JWC4RgGaNuEEEhDFiUhNyY5NOOlj/BaKVQ1vLNa8133n6Zz9uBP/3rT/n+z77CW483rDc7jL1L/am0pmkarPUHf2s9WhwBjHasmoau6+iHnqap0zhZa3HWUWmNFUHrimZl2R+OHI8dlxdr1KpCDpaXHlzyu7/+Ne6c4vf/4hM+aC1oUM4l2uNSlA8GequaLGOMnRTGcwIpklJnfBerteMwKZX8zJyHRvA/ztc5v/OscP6D33wV5TS3dx3P9j1PDwP/4i+fctsOdF3H1x7tuNqteXOruL5aMfzRJ/zp031mvhesheQfMgILrDDy8tJ6YA5bp5YKlWWnZEwbmWyJpuRfZj8tJT6PJExpyj9K9j1Mdvy4ZD0L2OTvyS03CE5Fixbzk8FM6lQSl+9d1qQUL1aKVlzhNwdoEWx4m7TgBVJl7kb6n3gljBKMs1TieGcN/9XvfIdvv71he3mJ2Vs+v7vhpQdXrGvFp599jtYqWMgxeq6yFwsrpbDWYkMie7Smt7d39F2PMYarq2uaZsUw7DNr62jqis26oe87RO3QWmjqiouLHV9/E/6Ti5p/7xdf4Y/fb/kf/48/4y6foUwJO6FwKWK0fpyqoNhsxjsZ2Il9mfPG6KPPfnM+MHlfTsJZ4Xz3+jFt33O17nn5yvH8puX2tqfvDrz35mu8dFlT1xWH3vC//+BDVnpFLS1DftDwgnCNfovOrk9SuSgh5Yyto9xFeDrqvpEZXTmg0XEr9iF4CZ/1ferES9zRtqRRY1en5pUxeEIQiMLKJdIysZ1G8cTDSCt+spSITw6PdAdZ8t6VX74ACoupAh1GgjMSfEMVz/TVMYdVaPACGM8ojihAK1/fWmveXAv/9T/8Jf7WL79L0zk+fXrAVQdevr7iwW4DFnprqCpv/VarVXCRQETRdS1VVSUBVUpRVRWqEqyt6Y4D7bHDOcfnT57y8NEDYuqciGCMARyXFxv2+yP7uz1XVzvW6walDIodm1qzUhseX97g5Gf4n/6vH/O879J4e/nLDxp3CZ4iUcFl3mYGIPIA0SzYySj0bvpcNqmufGSxnA8IraASywPd0A8O0Y5feu8Bl9uXudyucFjawdE+veUHnzzn8wFQNc7akd1cBGMx84aMAaNARgvqxr5L7ISUnZDMZ4TZ79EKTvdgKtTsbffJDQ4CXrxbMxs9ES8h0cCPvp1LEUsX/LLile+M7RMtWPC5E4SOxlX8MoUzhkoEk2kkEfxr38MGYCX+H2cMKUqOh+0aT49VUFmLcxVOdThqGueXOwYV5kV5teCPnLSIrqkM4I5oNKItlYYH6wt0LVy7nl994wH/4Pvf4NvvPOKnP/6I/V2HUo4H15fs93sO+zu2uy3OOdq2RWtN1zt225rDsefy8oLj4Y4HD0aB8wJqqFRN3/fUtcLhP/e949geqSrFYC2DMYhqcMOAVppKexY+HFouLi5wrqeqhd5odtuKD550fPOlDd+4qvjDzw44VeMG5/1k04/xBpVbOgFnAux1OCujlIX5FhHM4ECVW86iOKedLGl+RlibA6ovHRA6Ho84a6nqGl07tpuGdaNRWlFpHxrYrDXrumK3Eu5cjbFgnQpQzAuhj+p54cwhTBmEyYU0djYTjozh0yjEb4vweeFaEORZRDj6nlEgMyGW0byN7WSwOcwfolR4N8xE+kdJDqBBGEYUVdAqgNI63D4uoEdyrHhhVs73QSudzLkShQ6pjiqMPVXlz3mVNQ0gziDapgDJFn8CgT/FQVNh0NohSmNFo63m33nzIf/pb73DTmu+9tYjjFicNfzk/Q+523fUtadhf7fn0PasmhprXVi7tOyPLYN5zgefGj7//HO++d5bPH50NfJGgLZVVaXlE6UUWntfs+t7rIWuG7AWhh4Ge0AcqFpR1zVKgdKKYehomophGDDGMgwDK61oa/jWKzvuDKwbzV98vufQDfQqzF3GK1FA/TnNnimS9YtxhBjryF/mFRBZdLNyCzwq6Jx3/y19zsvdluPxyLHrEPzxEuuV9ppOa+pqjbM+3P3dV6859BX/z0+fcOtCjqNYCJHAJIbxvY8ZvPRfhcWw1olSrAe+wL1LcLRwdZMvUlq+KcQcJynZ9eTvwbQLgpYxthe3LxUujY/GkMf3XDCwUTjjLimb/S4SF/1H66sDLeIE7QbWWDa14tMBaqDRCis+rG+dRTvvo2pRVJW3DmpoEVfx1lbzvfce8ru/+i6//K3XsUqoxfLTT27pj0fafmCzrVk1DSpsBewGQzv0rI2PtDZNhestP/34M45tj7OGm5s9b7z2eDY/Sim6rkufJezvNKbn9s76gJj1R2Iaaz0/aoVWGmMHqrpCKS+sXddzPLZYO3B9uQXg19+94juvX/Dygwv++Q+f8L/+8fv0g6Swdr5U6Oc8KElnson3xebKOflXZX9cOLZmDn/He5au5+WscDaVRlYr2q6j7XuUCpGySlNVNVop7o4dd/uW3/n2qwiab7/W8Ht/8ZQ/++zgHe2wXBKZMTJqDDaQXS8+R/iQeuPmwhuWAUZhidAiOz/Gc2Ky1MXxG3a0ZnEtLybmJ7c5HKchzqaNvkmQAlOKyxMZfAdUbEOct3RIsqwKn6Fj8Fk7AhjllVMUYBUCBpX4a/HltisnWA3xvRiVRFju0E5Y43h4veKN3YbX1zVrXfNnT+84PB9QjfDRoUWLxqEgBGOsqlkreOei4be/8QqvPLrkqoG3X7qk63r+5F//hGqlWTc1d3cdTaVZNQ2DMRzbjtVqRV1VbFY+QAMwDH6/5uHYcXdoEefYrCrA0ZuBpm6wLhw14hzWWrTWGGMYBkPX+nq0aJQoL+hBOK1z9P1A3w9QecurlGIwlq478uTJc7p2oKkaRPm6XrpaY61lt635/lsPub17xv/2589wugluFsQlEBWOAY0WMC2VJM2Zx0eico2XxENkV64klFlIo3P0pS2nCB62UGPCocPrpkFrQStFPxgGa9CV5sHlGmMcv/DGI27uLG3r+Gnrd7OTXrQ7imgU0LLB7E88RCnXWgtRUpEx0T5hx3S7IGngpFwsnvqylL6uSAiiRHpCRG8EmvF4Lx9NjZbLSoBrOVySvK8uKSYloEKQSof7Brww16JCiN9bX+ssKtBQW8FVDjUYRGmc8i8zfv3C8d3dincfX3C11Wx2FeLgV95r+MuPD7z28BKN8KfvP+MH7z/jrw8VV7sV//gXHvGzr17w6KLhtZcvaKqaZzd3PHl6R9v1OKBSjsvLC4wdEDSX2x23hzuOx56+Hzy8FKjrBmMMq9UKEUXbHv26poPNZs0w9Dz57CkPHlxT1Xp2QoOP3pp0bbNp0GEd1Tlvzbreby87HA4MtaGuK9rOB5Bubu4w1tE0DaLgeNdinaWuK4bBcGh7jDvw86885vd+fOAYw94hyONh9mgYZqdOMjUSUrJSDrSmAcPMbUqifwb2nX95bqXp+wGlNKtmhbEGrQVjBpzAYejYt0f6oWcYLMY6BjPwa+9cs9pu+Sf/6kNM10fzNDL3BGZO+okg0wNICmRRQALJU6/G3/ME7CjAszXR+FdiMGg6qOO91jFKr+ADNEGrDvi1yspJOKfWV25xaFSyelVSFFlEVcbz8wbnQPugjrLgAupQQCOaQVxY74QNjvceXfHh3R29s1xuFb/92pa3Hl+xXftIaQVUUnH5oOHXdxeouuJoHN9/sOE3vv06P/npZ6A1v/1Lb9LUlr7raJSiMwM3+wMOWK19VLWpvE/nEYmla308IroX1hiqpvbQUiuUEqwdaJqG2rikrKKP2XYduloT/XKfNTQwDF7wog+qtaauNcZYjPHWVYYBpRS73Y6+7xkGv7TXdUN2v5/Nuq5pVg2tcRhjuNv3dHbgp5+1dEOHqFWa10p5ruiVQixjqiJBmJb4b+F7NBLTZJi5O7MsBrGcFc6+t17ojCFGJtt+4OZuj1KadhjoOkPb+XsG47zjjvCwNqwwKOVomoonx/hyzwzghu9xa05a88uFuLi/tLUq+mu5clNZ/mfeRhCgER1LskyJqijASsJCYrgmcQ3QC4sNusIH9F2KQysVhdiTrsJ+UeUkGF4f1bWhXjXGErBi0eEEQhWsJuLPdcXBqlJsxdPcATunUW7g8apGieMXX7/m3ceay5Vwsd3SVBq0xQygjaHFofoebR2rukKv4fHPPWJdNxwOtwz9it5p9s/uYBioVAXNgJK4WcChlA8C1pXPd9VaQ3gzWK0VF5s1gmMwHpxr7V9MVClw1lt/58AYy+3NHcNgsGZgt9umnV3RSirloa4XMhPzBjzk7X3Ax5/g6B9s2x4zeHhrndD1hr4b6IY+tGMZBsdgDRjFv/zwCdv1ltt28O2J96krpRj6XGj8USVpJdi5MOtzfhxL4CkhQ2ORD8MnV9y6WM4L59DTDYZ+GHxgB8fBGvbHHmTAWkfXGQZjsC4M3GA5Dgbl4DsPal7aXXG1XfE//9GHtNnBWy5a00jjxKKJxNe7hWvhvNLUY1fEZpJwY+P5M2EwgnBpF/xTpcbQtvML8aThDprOZkItcRFIkh8blzQkwFKHoxaFVYLY4Ne6cQ3NZ5xlQYQQTVUIRnxflQs+qNbBZ3WBTkEreLlWPKwVR4Tn/ZFfurpG1Q5RCuUcX3tpzVuPVqzXNRZHU1W+j43FWuHm6R2VwGZVs21qmkZRKW9ZkJ627xiMoak0zjhQllWtQxaRYKz1WS8RiUA6xV6U5upix+XVDmsMz+/2gMNaaLveZ/xU3h06tC2gOLZtyk98/Pgh19uaullRVRXWGpzz6XqDsQzGcjx2dMNA1/ZeGdQVSkDrirbt0FozDAOH4xGlKw5tx/ObO8zgsA4ObUtnoBscz/aGH/fC0PcBZfkA1AahqjQ3Zjx2sxCiMOkxKjuNg8Qg1nh4WvkSYwnKP1/Tc1/23Npj29EbSz8YrHVYGxxxE6JnzjEEizkYh7ER2npCvv/2Ax5eXHDTDbxxveUvb9qkO3I8P3WKR19tWXgzAO8HN1qryTMOPw4+J1SVL1USL5jj5iFfj0o5beCst4oKyQR0rELht2K5sI1OA06FNLswaToIdKzXxjSyoHUr/CTH7Bwb0vkUglUDK2re2il+7nLHp/sja4S3Vmu++/aa7W5D3/fcHnseX6+oK58iJ9iQnQPWCdY6Hl1vEWupK82qqWhqlXJolXjLXmvlLchKIyLBQnpLJ85HeEUJldJY54NJxjqquqZuGiqtqFY+UKSUput6EIVSFbvtmovtir4fePrsOYP1St1Y4fnNnm++9waPVmtEhGPb0bUDohR93+EcHI8th2PrkYgKmUZKgCG5K03T0LYdXdthjQ9IGTtw6Az7fU/XW24OPX/+6QHjYpqk5xTnHKLDubineG5SkufkTt0xLxJ9nvDEKdsL91pOQztYut6MYezB0Q9+4OKJi855uAvQG0NvfH7ndlvx6eHAj562POv6ZM2irZdMOFNObOEXFpggXvHLMTlYT9DVQ0Yd64jwNgVzRpwfdZcK/m2EvV7got/qb/JLFjaleWnGCLT2JhsVCLLOgyCfSDC2I6GfXthJvmnUwioIq18OiZNTgcA3LtZcrh2VaqiU8PajFV9784Km1jx55tPjmkrQlabRCqX93CilMMEKNVWAi6KpK28RtSiM85CtCil266amVtorDK09DLedP5hLVwF2CtZ4IrveMFjHk6fP6M2Wpmno24FmpWi7nqEfsMbSdz1dU3E4tinI5PBHYFbaj1D0O42x9MOQfM5h8CPpHHRdRxVgNVpR1w0gdF0XaLXJ11MBJQ2D4WbfcmgtT1vDhzcdW7GYquIQ+FqJsB8Mg0B6fypj6DIwagpgzQxK+nea4VWu50fuexFBPiuch9ZhrKE9tmgRBoS2HTwcVAajhMH46FnXDShRGCccO0dTC1eNgucH/s2TO552NkMGkuBBGIEkoEp52GeMTcnrcXt3XAjx8ZXRiuW5ozoMgovMh/NH+KvRQc+PjLHWhaMa/ani0QKL81YwDqk4lfJbIzyx+LVG3471VlT59pTzyy/iFKK8eGsFxg0oK+igta0D0Rot4IxQiUMrqJSiNkJT9Shr2Wxr3n5YsaqFRy9dcLFd+2CSGrjcVFxsV2y2NQZFe+hY72q0Vqy0woXop1IK0SqNYaU1Ct+f3vjxrZuaSsRn6Tjx65MC63WNVjoseziigVGB+Z/e7jl2Paum4mKzpe8H9nd3WGfoe8ON26O1YhgMxgwoXSGiqMQvO1h8vcZaDwfFonVF1w0MxtD3ge90hbXO+5iVRkQllNAPIU9YJCUh9L2h6wx3bcft0fLxsw7THXi7aXhmHW3vsME1acWg0Njg4mjn1yud8/61tTadEOh5NiS4J+MQGMhBPK+ojMYKkgKEaa3uywpnS9dDOyi0AnGWu3bgye3A46sGnLeWXdeH6CU4a2lNj3WK9rDi//yo56Nj3Ak+MruPZpVQIJp8a21IVsgwuyv1TQ5hYx6jdgQ/Lua0hkAQkkH/kNWj/P2iFca54Ht6IVZ4KJwgbbByfpfHmLCsZVwf00KISPpMoaRogPTicaeoRGOUp09JNiaC3+GgLVe18PZuw83R8vWLHUcRDnvh+pUNWqAWzworXfHweoM1Yf3PCAxHbnrLB+8feOOlDY8uN6h63OERxz0moysR6rpGlPYwsx/QtWa93tK2HWC9Na18Eru1hIX5uF0vBr5ssMbCMPQcDnt0VaGDpanqysN7YLvd0vWDn7MKKgHT93RdS9cP3N7uaZqavh84HA50XR8Q2phfaz0htIRlE1HgBvqhx1rLse95vm95fnvg6c2Rm33HXTtQKYVqNHed4a6zVA7ayIjhRHqUoCzUyrGrKh5ozZNu4GnOp5EVk31xIZtssndUipvS9bQN8Ew5K5y3dx13reH2OLBuNFebGuPg5tCyrhVgfAaMjWuClt4Ynt0ZLjbCobOI7ekNBWSM0dRE3sQCJm6NPXE+y0gxLpHkJ3i74D+4UFcegZUsCKUSwIx6Lpyo53yWjMUrIKWkeLNc3FQcht6vQWKJL7tx4tDKYpygQ5DJSAZfYz3K+3+KkKKGX2IYQmBBxFGJwxph33e8fl1zfbFiPRz47puXPNjpkE+qqXVF1/fUClbrFR988pwffXTHbTtw11uMgeuLhkdXMePGM/RgBox11Er7oIoKIxPQf1NXNE1N03gLtTIRzluapqHrBsDHIGJJ57mGQBFaIU6PSkx5+F/F1ERx1JWmH3rquuJqt2a78cnxQz8wGIt0Pc6SllUGE9uUlOaH+PzW1nVo7dFP2/XcHVpu9h2fPrvj+d2Rw7Gn7R2HrmewwqVAW/t0x8+H8D7QcPSKR/SWTSW8VQuXjaI3js96V/DemKEWByHj4ZERszHKGP0Fy/mAUG85Bl+y0t4cV0pxUfu1TmOh0t4HsypoUV3xYGuotdANA4/WDVcHw7NuFJmoeFSKyIr3DyVusA6CFwIlKY6aoGCJ60f/zaVlComCGCxbTKqP6W1A2qWhMwGSLAE6BtXEjceEaBGua0GhGZz1Sx1K4QZFH3ZeWAdaKeoAfVMQwFmcVogZA0AVMagEtRM2yr/sZzCWd65XPLiquF495OpSMVg/BwrojcU6v1vEOhhUw0dPPqV3wmd7i3WWY7vzKKTyfqJSilopdIJlfqy9K6GotLBa1cn3qyphvW44tEeqdGC0C9FUm/w7a/1r+vbH3gcFTdyz6eF8VSnqpvJBGj/VPttMaa4vL7m+WiOivNAphTEWhaMSnZSKT44XjHFJGRhj/G4cB9Zp9m3P05s9T28O3Nx13O5bjl3Hoe3pegBFayx3vaMLwT5t/cE7PjikEGd5vFrxZuOXhD45Op4Mjls39zNjSevqTtIG8akcxn4v/HSynBXOdrD0xluLSnkm9T4BGBsFKEDRsIDnrGO3qqhUhQAXjR2z+uNiVUx784jeW8XcZ44HVIUgQB48sjBGTBgDSQGUegvlRmEdrWV8n7OE/RveUuFsetZH9n3dOrxZ2QZNYoHawqvbil98tGZT+138h94z63ZbUTcNf/TRLf/m2ZFK4FpqlBaOxtJZH2xYicIpmyEHl5ZztDhqFCuBq3XD5UWDVpaDMbQ3huFwZLWp+ODJU7799iM2K5/admg7+r7lcrfiyW0HCL2BD5/1XG/3vPzoklWtQZSHxcozfd91NKsqtK3SCQUq9D3uvRS8D2etF76AKHHWYgLctNbRDz5JpVZBmMSvW1Yu7N3E58XG3FTrHFXlZ2gYBtpu8BlH3YA0FToYAa/0VRDukO7n/MoBeMMRn98fe272LTd3R9resD8O7LsBa4XOCM8OlkPvcM73MR73opSiEaGxGovwtLfc9ZbDADcR2bm44T3yq99AUAVjORR+2sij3j5kzmceJDqTT35WOJ8fBm4OHdebGsRHYMVZWldjjj1X24pKC9ZY2h4Ga1jXTUrYHhx8dHPkWT8gyoFVKdk6Jm/H4IwKAaIgi2TeYlEcWTaPczPsboOvpyTu1pMUwPHRUkXvTBK6lFwQg0XKa09BqJTQRyut4O11zc8+qvjWKyte2jUMznEYHNtacXmxpqk12mme3PRUK8WlhR5DawClqD17Jks9iIf5yglGORDFdqXYVZrLRnPZCKIanj4/MOBY15o//etn/OizPd966xG1eGY/9t5arZqK42AwYevKH/7l56xqzaFree3lRwiOR5c7Li92AByONX6BP+4QCYo2WETEhgV8L3jgI7N++cwvo3ihDUrXOayxSBMO5gpCWVcVOBispTc+sOP/s7RtT6X8qRrWOo7Hjn7wllmHTCMV/OG4amCtpe0Hjl3nl3nEB7cO7cDtoeXZ7YGb/ZF+gOPgOHY91tV8cnR8ehTa4JO2aG6jfTEWqxRGOY7DkR7hYKAVRW16XtmssKZDVxVPesve+uQLcX4rWwpryCiUiWdd8TXiu2CJv6Rwfn7T8vTgHfcHFzVGR1ho0Ur5w5aUcOzh0A7eoOmYfqW5bQf+7Lnh1lrWStFFayjZOdgpIBI343oGUeG7o9z1QRYU8AY26KDM4o0wd4yWKaWSIqjCzgwzCXurABHzII0WoVaCs5afe3nH116qeenBhl3t4bBWirpSVLWjqoRfe7fhYnPNX7z/DOmEX/3WYz676/nBT57z/t5nr/TaW0u/CKDYKcv33rlicDU//mzPw41iV1XcdXB7PPDt165onaXR8Hzf8sbjN3ly29H3wkuXG57eHPnzD57x6HLLZdPxYLfim2+9wvsf37DWwt3BYIae7WbDdrumaaoQvTY+wkk8ncAHlgZjkxB46+IVrTUGY3ziehTQiF5ixpCPfFtqrbHOYQefwN73vYeioc5Y9scjShq2m50PNIVIa6VVCKr5IzLbrqftej9vg+HY9dwe2pBnq0AUz2/3fH5z5Pl+4DYEIe0wIMCzzvDJ0XI3+CwfqxS99VF/g8ZVgKqQfs93rnd87XHD0VXY7sjLu0vEGD5+vuaHe8MHfYs4H3NAPLqKm7PzZUE/pi4TxAweFlHc5XIPrHW0TIx4JQAAIABJREFUg08wiJNQ1WtevoLbdkgL604qlLKsasVq5WOcSoRHes3PPmj5fz8zNA6MCEOwGrUSjHNZuluAmaEPyk01UYjIVvF8lrF3CvEJ4FFw46BItNA+cq1C8KcOSyxO/Gm56YQCotUe29Tit1o9Wle8/VDx+EpzvfZHYtS1F06nNHWlcGgeXVb8gtL84luX1Dju+oY3Hhh+7vUrfvD+nn/+/lNWnWPdNLx9uWJXKR6tB37m3ce8d63588+u+ZOf3PBSY7nerTzDKEOta8T0/PJ7jxkGwyd7x672uzgQxa9/8w3efuSTvZ3WrET47rvXWOf46ZM9rzy8pG5W6EqDWJQottsNx1YRd4UMQ/DjjGEYBp8wYUkM562lt6b9YOmtd1lcyKiy1qJVRa11WG7waZ02CaSvx2QxA28lBWP9EgvOJ74r3YT0Pb9MNwxeITjrOHZHut7QtgPdMNAbx+F45MnzI3etZd9aeqPoTYd1sO/g86Ow0xVrBQfjuOsB5V2ql7XwK69e8ta24mdeeZnrC4uyGz55dsdtV+OcxVhhu+nZ3FherxzrleaZsfzLj24ZVHSboqOSx0/Gkud753z9pYTTGP/att1KqLVCKqHCsTEaUdAb4cNnHc/uBlYNXO8ab1GVZ/B+sFytNBslHKxjpRQrcWyV5c3dioOFjw+Gm95hsWnpwYYjprWIx/H4Rf4h2FuNvy7KR0ddELqY4C6EM1qAeL6f0iFR3fm2RKASF5LRI5yWGIpKUdaVOL59VfHO9ZoH6xUPtyuqVcVgHc4o1qKoiJqzR2lht/M7KdxgkMpyu7doDb/x3gXYDjA8fnhNBexbw8+/+zKvP95xva5Z7XreeVRzsa7p24Hj0GOt8tulVI1WsF6vuLrS9F2PrjXv7RoeXmxo6irAer9UsmoaRISvr1Zo5Zcy/EFUFdaVENbDWo1zJkXCrfUxAeWcF0AHzvi0PB9jEKzJNuI5D9msg9v9MWwJ83ygtfhN4Cas/+J5pG17nrk9da3ouoF+MF54IWQG9exbv2Uxwuj9vuPYG57ftfSDZd/2PL/d01vtlYYxfLZ3fH5waC04V2HEb466tZbDoOgMSGX4+6/u+OV3X+LVnUIqzbHtORwV/bDHYlkp65dXxHK11jzYNHzz1Q0XG4Wymj/85ECljQ84RbQWjYZiPOolx7aS3XhGRs8K59uvbXny+cC60lQ6pEw5H9GMWSTg2Pc2ZJCEFAAF3SA82w883AjfeWnLT246Xt9qVpXmrZ3wyqNr/uKjG5Q5gjV0KDpnEWfpJT8NwBFQKLWTEFwiZOTI+No54vKHGx334GOm7CPCsSIy2ka/vBMizVk4PK5bXa9q3nu85c2rhpeuG5RWPLvruDtalHM8ulpzsal9EApvXZzzBy0jPplgu6rZbtc4Z/ndX3qDy8s1dVXx5HnL/tjz7htXXO62mL7npUvNo51/j+W+VqyN3+rkHP70Ogbc4JHDum6oas3FuuFiu/GZWwiqCsEYJaiwR9IBph/ouwG7ddRNRdu2gPPn9wR3wJgq5NGCWAfGepQRlJyHvjalc7qQB+VCrNw6vI+JD+6ko0jQXrgcDNaPVaV9bKDvB59pZGyKxvYBut4djuzblmPnkxmsc9wde459z+e3e4bBcWwNx0Hohx5jYDAhQ0hXDGFO7wbDrRFuneI4CLUIX9tV/MqblzRq4KYTnn62Z3/oWW8rHmwbKgW69gE6rRqaWgd+U6ybin4Y+K03d2xrx5MDPG0t79+0HEObnbV0iaeiVI0GwEUN9GWE85XrDa6/9REy/EK7xfo9ntYiyvHK9RprHBfbxp/IbQfMAPujoRscu43i63rFmxeKN6434BSDDBgGVo3mzcsVb107fvSs45MWHIrnAb87UWgLtcPvxNfwqNZ0zvFp7+iCqopJ6QobhFlhRILW932xMvqqfs0VxI0L8Q6DEp/tA6SUvl2tWKHZrmocjmf7gZ98cuCmt9Ri2Q+Wq41wtd7inGWwBlFCUylvyYxjs6l59eVL1nUdoGNHVdVcbNesm4rttvHRvsEHTvyOip7OWNZ1g7X+yDQl0JmKwQ00jX+/ZVNVfi+lFjZ1Q1XVgIenQliy0BqlfACq7w13hz1V71PgKu3HToczin0QJixniI9GOpGw0Tm3C9HP9PsfvaWFtjP0TU8f0j7jwV5uMGG5xNL2xrNordis1zhj6TsveF2Ar3fHI13fc9wf6I3l2Br2h5Zj5y3o4djTtgNef/jnvGD6FNJ1pXlZV/z1neHZYDgOigNgHPS1oRrg2w8ecmh9/OTjJ0c+vb1hs6rZPznQyBW7dUVVCZtV7SPqKqwGSNiNowb+7tevWFeK5wfDx89afrQWrzg6y4cdfNhZBrOQBZSs5pcMCF2uatzDHX0fIow2rGk6lyKm21rz1uNNuOZT7p7cHvjgacuuadg1FasKHl/u2DYVt/uWp3vD0+OBi1rzylXD83agNZY1CqkdzzvHla64qOHY+QDShVY8WlVsa+H5YHg2OExIbogJzDEB3lvMCCG8009g7riEMbrm/n8qnVcXfg1rV0+OA3/62YHNpuHjveGDz33A4WAclcBfPxv4mYc1VxuF1v4MvM1Gh8O6nvNw1/Dy40sarX3amlLU9YrNesVm3bBe1Rg70A/4lDlr/Hk4xlApYbNeMQx+V8hgDOIslRY24QzYPPupqqpw9k48jjLk62pCYAWU8vUMw5BOxfPw1kdhI7LADyN1XQFCO/TpXp8JBGi/9BTXH421mN7y9DZYWkeAvX6knTiGwfi9nMrnANfWX7s7tDji1kP/99gZDseBbjDcHTsOnRfINmzEUKIZrKE13idte+8bWisoMTQasJbntgaxrJWjdsLBdLy727CV4f/j7E2+JcmO9L6f3XvdPSLekFMVCoVqNBpDDyKJ5tBsitLREUUeSly0js7RSiv9l9JKW224oRaiRHFGg42hqrIqM997EeHudzAtzK5HPHQ3QNQD8lTmmyLc/dq9Zp9932c8LZlcI7/8cGTOmdwat07C2I2JaYxWp6s6JbCiFCd2BFJqrK2hNKZB+eR24P0MTSrvm9BW3Vp/qnK18OyAqH/N/PjfGJyHncly5iVvJGJLJZ3DqkIKJk9aStk4nLeHif1xZRqEdWluFBx5Gke+PBbOq9UD72PhfhLenQstRH73bqSSOaRIDEpsdkKsNbMbI8TKLg0skmhUQvALUwzkiMEawVdsHlVjpJjyIjwzdBKCn7henV4hwd1mpCD87Jh5+HdfQYRjMdE03rIpUrgfRt4f39NSJMWB6VFZ60Cm8T//6Su+9eoeb6ex3+84HEaGIZGi8241IlJp2qhd5Ctw2O8B2O12AMzLQtPFNxohpmCLPBisX0qxiLkqZhTcPR1iVEoNBL2cqMkBNgNk6rN7ANBaRZwM0AkaRggw+ZgBr4Hq7YTWGucloyj7/Yj6SabFiQNNKaVB6iCTaS5juGyZPWU+nhbevnukETxgjTsboqliSqvkosafzYVSTN5u121Mok8OE8uxMAThH/3gJffTyOfvj5znM01nVt1xfDwz52wbVKmkmz27FBgcpzDtKD4awt0SFEquTjrxEiZFVJTHtTBnmFugkhAXB/dySq/+vg3w+m2DM8bIboIUZbs5HYmq3olOwdI3zpUWjJt4Nyp/9OktRQPHRZh3idaM1fKzD2fuY+B+Snw1VxKFN7tIpvJyFHb7PelpMUS1TXx+KhymkSFYo/kY4Wmt7s9qNUwIbqSlIGJopAKp+9SGQOhzU42LRz9PNwmbn5RG0zPXtD5J61zh1JS0CkGVGo1gsdfM//L3f4/HpyM/e9rx+YcjU8ggysNaGKbEi8PIOA20qux2A4fdSErBhMtii/N0XNhNIw+PR0qDdcmmJmlKjEZAV28OBoEULKjGYXj2vCyowtXfZXuOwZ3iQhVqFUppnM9n0u2BlMyGMoTEuq7+/ZGcM/gGV9GNBtjbJq3U61e31kkHj2BDf3PpvU0lVwNmIubgbhTIQK6W9hqoF8Dtb4qDT62q16zNa9TGnJU1Z5ZcTaZI3LLEvsn83e+95PfPmQ+nwvHpxJdfvaUWZZwGcoYvT+/Ne0gbgxiH3E7hzDgEJFjvFsHlh4K26um09cV7thWCcLMbuD0LX87K0f11e6tKEN9In+tUvlFwBhGqGMAQMfa+NQJNySHBTcBQdruRpqYBbRrIIbHMC3OF41KYUqA0+P7Ht3x7l2gifHgqHFtjLjNjGlg0wJL53v0t9yOcSuapZN6tlUJjisIXp4X3pbE2S62GTkT3hdnTbdWLDvPKA2xrk9jPmALC5gDbLW4OeIVm/dCbIZqmtSmJREzKy0EgBP6rH33CP/uTTyhN+OLdzP/2L37Cv/rFzKkIryflT75nA2C1KTeHPbspMSZXhohxS0s25USpMz/7/K2zZ4IT7pXDfr9ZR4qYljLGyOAgjmrzRrwgkp4F5XXQxhioNZiJVuxNf9wn1pZBqXVbMCEENCWWdWHNGZG4malZXRpNHtdbLh0wgs3x4DxnWrNasjUDiwwBFnaYYVj1fmqVQGvZ6+CwtW3GIXE8z6YZ9u9dS2MtldN5JXsaHKK9PwvKy1Cl908nypp5eJxZWyZqZEyReZ4JUVg97e5pKlgNu+TCoY0MLsBofp+rv4b1gt2yJ7hDhkKgcX+YePz6yFxtNgsOcD5XqPiibM8++Z8fnJ2B03duuSKEb7uByEb1stMrWe4/Z5al8jQrXz+tjCmwVGWXAtOLxBght8TOZTtBCmtWaIGXB7jbReqx8nIaeVhnmiRrmYREkEqSQiMYkLFlRHbqdcvIrptGN67QJj9rvbjH2i0Ro5eFYLvlnsKLKfHZ3cDXc+Wnx8ok8Pd/9wU/+GggRuXHP/gWH796wVLg9uaeP4vCq3/9lv/7J+/409+957/7428zpIEXd3vGadxaNUaTs1PmvFQejycen07U0qVdduILllbGaOhtd0ffZoz4vbdn40CW+/p06l3/XN+jU4hodC8il2hVJxOUnJ0vLd6CsNMql+Z6T9OMausbWUDV7Gqulsym8e090046sCCFtTRSMP5w7JmMmPbUUnsLgtYMVS5HY/nUquTWWIuyrNXBoWY+u74h2HvQjcDy7vFk1jnafQ29VdOMWFFbIwRIBFI0/6MYxU9E9d6r1/zN7oc2qNWuLzdFayYXOOfGWuDd08JDKawkp/Z5NtONpr2roCjhaujBbxWcG/WteeEvxtjouXcnH/e/d2I1CdqQkBv45eNiN1ogpMTLfSQl4YvHzJdPjZHGy5sdbxC+opKrmsZPhFwsl1+UjccZJXof0qececvCHOpsRQ9EGuZZ1HP7TXwt5ljQ9alBG2MI7IbEOTcOAe72Iz/+9ktugzIEkGlg/5N3/MmPXvDjz17x+s68csZd4LQWXhz2HJfMizHw3/7oY370MvLJyz2HNLLfjwzDgKpyOp1RFV6+vDPa3bzy7v0DT09HWruwbFLstbDVyTiVTUJgU8J4XRqCUE3qsy3KZ89QOpHffV6jPdNaLT2009PcBOxkaxQs1ezUvFYbGnwD8NImiHnEtmaLFFfc+Lrz9o0DRb5eSjOy/ulcUG0cjon7/cgQxYn8ZmsC7rKRCw9PM+cls6zVfXGrobNr3hwXeuaEdr6YbcYlF9aaEbp/kX9VjQnW1Da8nk0NKbCfBnbDYEh7U5a1mJZXjDurDacwWu2c1TaVNTfm0piXwNNqxmrSW0hWLW3vrD8X4FcmBPwWwQn4ztcb05dffu1c0F/M0ENbMONkqd+rQ+J2H/n512dmVWopvH04c6yBppExwVM+kWi8PIx2SqDeq1JuYuD7ty/4/z48WhA2OzHxk7qpoWFRBWjUEBBXHPSR8SbPsu3GUMN+8e6aHoz4/dl94MevE5++3vM7n9wybOBK5o8+3fPq9o67Q2CaIspILZl3DzPzkpGycs4rQ2x875M7iAkJC6VOfPnVB/b7HafjiRBGpmnl/fv3DM5Dngbze41psHoyxu10jCakZRgGUkosYIAKeEC71tbpYSLh2anZn02MyevX6r9bCGE1s6zWaKW5QXiltUDOC7W1jVzeXfPMaa/RtLq+1XittZVtI+jzWFTNUKvPYVGvGZsakHOaZw5TshaYRnK1U7qpBfG6Zh4ejjwtC3ltLM0IBqJCropI9M3JQRm19dm9jRQfTdkNntWljdvahbCtCvzrtsprzRQJlql5zY0K59UklLmIA6V2Aq5NOObGFw8Ln68NDZEQqntaSYc5Lj106eXVNwSElmLAhPmtXKdRF8BBtgBg25kEqwdjFPaToCsUhLfHhd2w4xAhDJEP5zOskQB8ejfwcm/GxKgN05nGiY9D4zZDCDte7UferZX/8O5o2juBVd0ZQRXE2jxVHM28An5qR8e8XzUhDA6hTrFxExr/8Luv+NMffWKWjkOyawgBkR3QUK2cl+p6QkOwyzGTZxuTfkgREbulpkGEp6cTtVbO58WDqPL55zMSAuf55JQ3QdPANA3c3e43utw8zwyDK/BpiOgmku4bYwhCSu61+ivoQgdYBDMJG4cEJForlKq0FijF01tVilo9uNWJ9cIeamrpqdYL/xawhe7BX1slhkaL5iLQctuAtdoaDUtpl1KJQTifKsexuJWmIaXraqqVWitzLn4aVXKpdrp63RnjSIdAesrafC6PxWlzja+Be61dBPVde3qtuolespVm9ex5VVIQImpCCU9tv3w48/WxMEQDB4+t8H4xksNpEb7KVgY+qon58X67EWrC5uZf9RpP/wbBKd5+aNI29Ye/nn88r2/E34i98qUWzTVDEGYVHueV/Sjs6sq5RSOCt8qrFllyYzfC43nmmG1I0iHB65uJj+9GvvN6x9PS+NZOOOfAuShvl4U1N94WuwUXkOiyO/XWSA+2oLAf4ePbHQn48bcO/Nd/9AmffnzHOc8cTwtaKzGYEZZdd3CLEnG2VLTPhkAtxh8d0rAxlkIIjOPwzO3A2hcOBuVMLTYaHTUZV4qBaRoBsxidfMJzdOZVcwuPvhH60/Dvke399LbXxoYSdYS4uxYMKNWtKwt5Kc72ucixajU7yX4Cdg+prR2ykRIup7al3sPW16ytuNbSKB21NkouaHVKnAekiBKkspbCkgu5NFOwlEpTA4eyp9h2X0ejEDZTQvWNSrkcHL3XbSVtL21s1RZ7e9ZeCx6Y0Sed+X02M7MOEDU3sBNe7Af2o6P1u5HTuVDenzhmmJtw0kwCXoSAxsRTqVRHO7S6LpmLveqv+/iNrRTlkhf3Oke1P5CLyh56wd821KyLV3PF0hAnMJwynDQTYyI5VPP1aeWTw45hzYQ4kLQRKLy5i0xJudntEEncTMp3XxW+OimlBdLjwFeh8tgqazOp1zafRKyXiRh0P0ggiPLx3Y5/8vsv+ePP7qkSCVRuDnZC1ayImi1jitYm6t0ps04xjmjnooYQKFb8+sNU7/cVWguM08UixOpJQRu0aModgGkavRaUzS3hEmgX8fe65s3s6leJ1V1a1d+bsX6CG3JZWtw/elvmouS/KClKKZZ6trYNhN3E51ev1/1ztNdyDgK11i6LTnvN33umhhIrQnayPKIsy4oGYS2VUhpzKeTcWEvhPC8+J0a2VkZzTWm3LumnKX3NYffGcLW+Fi+hENRkiw33Chbx+2CYyoVnrA4a2YVPSWghkKRSFIbQ+Oh2oDJxQ+CjsXFzNnAJrbwvylnMbLwBEmyjCr6Oqv5lyeN/dnDWXC+FtPZ+xGW3tMUo28BWRTcAoaiylkYIyu0u8mIe+GpRVq0mapbIFK1FIxXud5H7fWIcI1GV3RCIcQd1RdzbJZfCPFe0Ca8m+MWp8R/OC2u1FHbw9xTFkoiojaLCPjZGAi/vDrzaD/zTH3/C3/69l8TYWHPjfFqprXFe7b9RArtpNL+ZLXVvWxP+skAvPb+rZdu/SM7WXO8UtuakcwvetgXkOA4M6boqthO+L7Dev9yeA72O7BkLGzi0eQMFQ7JTitsQ2x4gtdZn1pa9HVOr13vV0tpN44r4hsuz97jV8D47pP9vC+IgRvP0a7DTyAJG/BQUlFKUXA3ssRNZLI3N1Xm+l3TUsob++y79zEsiYetUPL/91aRRr/7SCTvdnDzgPWHpli6XGjuEXuYIMkSam9rFXeAmBdabSBpsIFQrlV+cIQXhJgZyMQZyp432wV4No0X+dR+/NjgbV1C7COKzCMV3PPX6pC8W1UarHfUzXusuOTJ6H/nFWThmg+qTwosxkVuDoNxNgZpXZtnx8hD9QSrjMJpsK9lEY4ZIChFC4iYvaAlIbx8IhioSuB8Tf/Bmz1dPme/eDwQa//brM3/4u3v+9IcvWWvjdC4sa+5LjLIulvYOdmoakb8Hw6UB3xdul0Nd0tZLPShiN37NeWtD9Hp9GAycScleQ66yj+tMBOxUQDrqaYyUrjqBi0N63/n7a/dTN2zUsYsx1tbaaObo372FcjY7yuq2I1t8Ya2Hy+9RZ/QYyby32now0/x9X26GAzL2z+CAV8A2XcVq2OxStNqMz1ur4x1Xm5I2dSJ+ZxI1y0Q68KL29esTqUOZG2rbF7HvMt23t9uzhGFgXQvj4Btia4g0Qhi8drXAs/dVTSqZA3POjEFo48C+VJ5qZamNarmyAWWiz0eNyDcEhB7mQiuVGCJDMsW+pV2XVsq1sLS1xmWvEiTaRSBKbnBeTXvYamU3JPahMbjd/9unTGyBV6HysMAgaj2nfWSK5lXU1NKK90shzyeSNA67yMPctp5sAyKNpMonN5HvvRCO2WZ5vdjBd17c8/LVCx4fHjmdztbjDJE0JEx9H7cabkhxo7Rd0LWrxbKlhrIt/MsJ1dXuF8aObWRuzSE2JGocBv+5DvzotuEZqyfx+PDoNLpgpUCKjqAb2pfc/LlvGL3uRC5C6Ov33fubVrddakk7SdtGJle/p8mnLF3I777YgyLNUsLW3OHCM6wLDZCrzcOID7Up05RQgXmx61irKVNyvZDfz+tqA3ObbfhC1/9arX19Pf1ZqHg9pw4FXmWNzSmG10Am/T1iG4Rg5mMpDRZMQQgS/f332TBs/V6VQNZG0cApB96eM6c186HAQ4VVILZKlMgeq21zMyldFcjXtcJvE5xffHW2BRQLL+4mxhSJEshqPMTrxndKtg11UoA64yaIUOdMVGEaEk8lkwSmIXJzGCm5wKI8FuHPT/BYF24SfPbyBgnCulZubkfGwShnDwX+1S8f+eRw4PM5cV6LGWI5GheBkUKJwr/+6swfvt7zey8U4o7/4rs7/ub33nB6PFGqO5qj7MadAQvafCEFn66Gqy6e38C+SC1AobcntgcuQnNDZOjp4CVoS6kMgwWQtTeMAWRDfPDPARJ4//V7dtOOOLioGdsEbGjsJV0ehrTt/LU2QrDg62uz+wKZl2vZyOWbJYnXsSKyobSmfbVNcQimoe1gj1U4psDBT9ra8QawbKB5GmtAul2X+8+2Zl60XZ625Eyu4ihy4zTPfoq3S3+wp6ranQ8788juccBbayLbafls7ffsxMvdEIQULj7EpRSG0VQ9YwrcTInSa+heliDUpsQUIEROc+XDUvnyqfHzDysPRHIduElKksZQlVkilQjayC1TLMzNbODXwEK/NjjXBuvSGPaR5lObIoEaIq3lnvxTtUETdxq3B5VEESLzquQsrIoLsSP3sTHFCMVSonFQbsQ8W45ZqQRerAXiyH5KqASqwsOq/Px95X0W8mPm7VJp2BwXE1snRK3HVNaVp7hjbfDxqx0hjry63TFEe78xNA67wU+jCNlQyC3j8R22e9Rcb8H9lOxmYOpAF2qAgo0UCZbyUTe9ZA/+DlD0+iz66PRO5OhNsVwMAU1uDxOSWX9IMm5trcWIGn6KWIBX3wR0K0cA8pLJpXI6n4nRZl129LN5XWhcVmsZdY6s0Aw8Cdbewv0QTRIfqdol8BZAVa8c10VRJ36Pw8DqyhYRpTYDCVvrG0ykarOWTrlsGq02m2uqDgC1Dk62C5fXA/DSgzeJ4LUD1QXbNluRJB1Eu9zvTmyJwTKtGN1gunnrSBtma5KQpsyl8O4MX54bP3uqPFUoWgjAWhsSA7F1M/WKEKgEinJp/4VvGJyfPxV2SbjdRbemsHHgKs1PB2t3NEmM0UEhb/h35oXmzIfcOM+VN0PgNgSmceDDorzPRr8a48A4mLD6qQmsyk/fr9yfK4MMDGFg1cC/+WLhJ1+feCzwwEpuPp9SbJhPQJGYSAE+Pgj/+G9+yt/74RtSrTStpHRh1wzDwH6/306pZVlZ19VlU88DsT/wZykt/KW/9/Rxo9SJ0byAzSayB3l8Vs+KK0qef5xPJ4YhMYzRFSSJjsuFEJhnRaT+yvuyJWjMLXNYTylxOs7k4p6wUokhuBds4zr97OCRDWK6qk1rIxdL+3ot2xqs2Zk/zoW9rjXTNq5PCWlgWVbGwcnsVVmWuqXBnY3U7Uiqj2MAy0J8htIlfd1SWs8QvKbtvU0DKi9bau1JDU48lUttTl+vobcKhd1oZY4RKWxq91pMzjdF4d0RHubGY2789MPK52cLyiFG5mZGaEGtvVL8RK80qli6XbAMLXzTmvMnb0989/UNa1bWIaBzpQ7GS10X210fzoW1Ct9+MXAYL2pJO3XgabZCOKXAKI1aIqcM89p4tUvkYIDT3JS7kHgsKwtwapHHAm9uI1OCf/nLR/7isTF7ClYUmgS3XRSQgLTCq13iv/zeS/7od17wxz/4CG2V2qA2MydOg03mBmUYhN00Oorq8urlebD9aq15SQMvH9f11fXg1+v6U/DWlBriGOLz2rRT3C71mXNwQyA5QaHXkq3VbVYl+VIT9/q4n6SlFE+jCykFkARSbWKXsrVyYp+JAgRp2+wSVQ9SdaJ3Tx/9z7IYulpbR3fbX9qwwACgfnSFYIN4azP7kt5kwhMTAAAgAElEQVQjRSKN4g4Wlxr41Yt7tCkfno4uqriATYqTEOzFzLKm38SO3KoFppHS+xnJVYqsG2A1umNgR9htDkxjrUZMKLVxSIlVIqdlJoVAdPOcRY2Q0Vp225xAzdYEqk7/Sx3Jlu6GIN+cvvdYGl+dlYd84ruvdvzw9Z73T4VTMY1mU+GrU2Gu8HIXiBqIY1eoCaXBT99mVoX3q7K0wFyUp6Uw+GIaozAMYkNpgvAyCl+e7OHkVvniSXjMhZ8+ZrI/Demi3xCJ/TIl8E//6CP+5Af3vDzsuD3sqKWQS7HTx8GXZ20F3637vI0YEyI+ZOfZaLZOB7uQt7ev6OX0uwaE7GuX1katammep8ilNGrJ7HaTB1zxk7Ftm8I0TczL2U/NgRijzw6BeV6ZxokQskuz2tYTDWICAREjs8eULK3yeR+Csb/cQZOQrH6r1WaBxhg3f+J+GLdaHZW1RVidPFK3mu8KnHFgaPO4xe7zvNqJ0uOnNv+99KjpizcyjQO3u5H7u1sbl5ESX737wHlZNhCrK3Hx/nbzU6nLkOycVzpvueND9siuW1NWQw/RRNWIbFlFa+oWKuJAUWAphV2y9z6Jcp+U2xR4qIFVC3utFDHjcbD7pUEIenGAHPoG/E1rzhYCX5/PfHKTOAxwyitfPBVO88ouJaZhZJeUrx5O/PxD4jsvBg4U1qqUrKzVBq1+/uHMsSiPvss3TIr2oVl/8l4CA/DTh4VKJLdqg32Azx/OVKmU5jtN1/yBIchZQRpjy/zt79/zN3/vM47zbIZSIkxx3AbdbCmbKLXqpl20oDCUtPrJGMPVqdbrt+tw1eeBed1Guf58T9fiBlpFjsezPZgAvV0SgqGuoFvdWGtmHAYnFsCyLLx//2guCX3Slr0iiKkzUvRWE0YoCMHKmjRONkSoWL+2NWVeFoYhscOGFIk4j9pH/lm8yNbX7MFqo/vqpd7zP3r1xxZ8pLlh2FKKV6+eaXj7Y+3pfAjbWPZSMreHPftxJDvb7LDf8XQ8cprP2yZgmYpnHFysZRCj1gEbj3ojSjja2vuktVa3dHXLTLorRG+XKSU3GkIIyQLWf0YDDKnxnYNwOyR+8rTwyxlyGAgdjVNL79dts3dsYlst3zA4p2D59XduB2KDn71b+Pn7M9PuwHFtjOnM7WAub//P5ytvj8qnd+bNWgnsI+S8IDGwrja2gH6CKMyrotJINfB0Lsyq5uXq/rGqylNVSAMi5VJvefqirTEOA3/4Wvmzf/AD/viHH5v/6YdKh1ygz8bsa8BFx0FQtbqr1ue10nVq2PuTRhqIV1+//NdOCqOhdai9ExcMUAqm1K82iW0/GTI7jpfb39U9feRdfz/TNBFj5Hg8cTqdQYRhiH4i23WM4/OsoIM8feOxt2gN/xiNnCAC61IordkcE9+g7Hs728bbDnaxzzYkpfdMr1sxlmE8701a/1ubay594BHBB0kBubl/gd+/cRyxoUIYwNSsJnzz+g2n80IpZ7Nyad0d0MXgzq0WujqlV/X20Ty3vQ6Hfr9ELBNAoYSLgbX1YYUhmjldcYIGdmVMITHt4X6nfPbijn/x+ROnWQlT4u1SWdWu355Ce+ZTpXrpJ/9VH79ebN2UfbKa5e0J3s+NXZo4nRYaypCEpqaETCHw8w8Lyh6tM4WAivBwbCwKJ9cMipiU2LiFhrCesdN0wPp3OHNGNFBF0VYZPNRswK1dUKqVv//de/7sT3+Xz14fmEaryYYUcCzBwRN7ANaqsBkdGvDGvjXVLcB6H0uthpVLE7rP/+jax75z+7r1xRe206WnuD1ADByy2u56bHu38LxWlSCBZZ39CGrknMk+hTnGuPURUWWIiZguFL9rtlL0Zr/9/LK9rw1IUdC18NQa+2my1LC1beTFZod5tRiNlNCF+GxoZtOL5Kyzga5PUlEzUGnbZDZPdz1zEB8V0fncIg40tX4Sm8fR4bDnNC9+InYusPcpN81mM+7z1alp1YSDcWI1ZZAOXGLYRLU31lTdihMHLMXWVCfkgGcEQoxwMw0MQcit8Q+/c8PTqnw4F5b3jS+Xi0zRhHjee1bLaL4xfW+MgTc3iTRN/Pt3Mw/nxuudMqXRULUGH2YlNQhRWbPy+dPCLipVKo+LKR36XGgLgciolU/fHECFP//qyFqae9SCxGT0u44WilUW4jIxu5XWV9xPN/yv/+gPOdwmpjExDHtEKtM0kt0JPMYO0/WGfNg2iRhNfmX1mj3WGAItmAS7R5pjB+Rc/fle0qpOQQtuHSJili6Gvir73cjN4WA7fcnb3MgYE7Uq85IZx4GYbF5la9VOrauH1lPY89n8g2IIftI6EUAuU8Sa0/HmedkIFU+n07axtHrhivbsQRzdjWKC49p6HShb39Lekz3BIQpJbABv62CR155bBtJZQ1xOh21M/dW/hYuCqVVDhYM4wOe7cVPzxy1V2e0PEB832lsPvE3spRfUHHvS9gzFgjEJTDFc0Fr/OnpR2kxqounmaXZvda2lQnte0kzjwP1hZ2MmcmNKoKLkKnx6SCyt8JQbzV9fnHwgfjh1+dpvHZwftHJYA1+uC58fV1D45SkwhIUosEuRIdrNn7N1vk65cFqBaDMkrDNWEf/7dw6Zf/Y3vsP3v/uK47Hwf/y/X/Cd1zd868XIP//Xv+A/vRcOI0xj5GGufFjN+uGy85mAVUJgEOXlmzuGYMNYO8fTUr5hOzENfGiopmeN92vmTGty9bOOqkq3ZrIPEeOq2sRlYRxMhdJns1xGGLTt+29v94yjBUkazLJknsMWOKqN83k2iZprODe7EE+5+vemdEl3rUaNG2Or6z+PxxOKeiBbzbuu5gXUtZ6qSopGn6t9M8CaD9HtOkSr7+7eHrraLGIMDDExDrbtHuvKWtR7p83vRV/z1i8t7TLAyArES+ocg5lm51bJvklcCwBwlLOpEThiD75mtXHrLamelftJa/xvQduFYJGiuen1e1ybsdaM19B88p2Jv1VtXk5Tm3wg3mup7cIUUq8/o9iclXFwGxdgF4VRC//+GHgsSsOMwcyw3Hr8v8YI4TcAQg0es/K4LORmVLyqwloLY0i0UnkZh01Ss1ab7ixusNynJqOKxMh3X0z8jz/+hL/zo495cTfS1sz3v3PDfn8gxsT3XiSOLfC///OfkhJ879WB01z4i4eVh3MX80JFGNrMf/+3vk2lcDsm8JkafeH2lCd4fSUx0TRTq2zf96vUQ/EdHMJziuJV+0TVfv8wJHbTiMmxzMDs4eGR7O0LABvxnkg+N0QUSi1MY/eW7SmynXjHow2Nrc7e6T5BYH1YO3Hj9jmTW1m/bFlnAy9K2dQr0Q2ls88LGc0r0hhAzQJRwQLLF/3qZlYCJDEuKsHYLIq1RaZxYAiB7KMY1lyRZm2OEAJjMjbSeTGTstaaTf7eatG2Bel1uhmaIM02xNX7t6g6uQVHi4UU0yZnC9LHPipbHdPLg195xl3I0G1WLITVA99OPJVKKVaSESComs+uiPVrEfo4yZ6l4Bs5CtoKo0RkMsOcj28GlrbyiznyrlQG+7We0F9rZX7L4ITIw2JGRhYU+KDY5GqNRqFSQ6K48bGljD2VtB6nejr0wzc3/NFnH3F/Y0bI4+GOkI68f//EMEb+4PvfZplX2t9r/J//5h15Ff6bH7xgPBz4t7/4wC8/ZH75mHlalf/pb3/MP/m73+HFYccwJF+ol4DCwY9tx0TQaP45qtXbIf5A6SoR28dCsBQ2OoDRYQVVpebCOA5Mo/GNDXAJLLp6imc78hCNOJCdWG5Qvsu6xDaIZV19EQWWdWWaRpacrUWgaiMkRFiWhXk+uxuCBfOyLNaeSVaHGmldWZfVmunaNyejImpjSz3Xks0ASy61bq6NrDjbyzaOTsiXEEEryeVUu3G0Zn4L5KSkIVvgi9l7lDUTxkitZ+a1olSCGtfo2h8oqFEu/daYD7Hf8z4gtwvdbLs3QsOb1/ccz2fevl0IPu/mUgZdEGbwHqgExmjzecBq0+AvKnpJ3Rtqa7oaJiFqTKJ1NbZPCFavdq4xCEWFpVZQa79UoGhGZSCGwpCU+zHyULA+c8NQcSIRA8O+UXDm1jY2BR15Q9EKLVr9+LQ21rp2lAC4rlE6IBOJQ+J2GNiPsD/s+fqrD+T8nsNhx8vXt5Rcef945jxnbkflH/zwJdOUeHNjqcj3P/rIyAu5EOLAdz++5e7uYEEQImE0AfM16trT1s0YSwMhCqEZn7WrRK6/twdhR/DsZ6NxVWtltxs53Ew2AVkMmS559XQlEYO1ZHa7nfvFFlIcSIOnUz4yQaq1B8qWdnaVhV42GWcanc9nF0ZHILDmlZx906hxo+Jd851Vy5YSW884k7PXhbUvCEFbQZrSSqW0alpO61sxyOCva7XpNI1mS+KOi0jkvK6Mw8A42DUkMZeLmBJtN258WZp6rfxcpJ2iMg2DjbZfMm8/nMl6Gby7HxMEcRJEomCtno4+XIsOusG4nYI9ffRgFSN09Bp163WjW/0qIWyI7zYpoLkroeMS5mWbTLqGbeLLmgnivVGncq45k1U5rfi4DxuhmAkkUW6j8NkhMHxTQKjXysHfuPoPdI1hxdJcFZBou1s32HUMzBa6wo9eDPzjv/UtPnpzQ4qJGAdSGkkpspsSM/B4OiIy8K03L/i4moxpt5sIKbLbjezdVClF5eb+hmkaPJVo5inkC/FacWAnlaWhudYrfebl4/pnLmyfi/qmNev/7fcT0zQ4ydyMsZZl3lLdcYiWZjUh5xURmMbAkCLjOFqwiFHqJNoCaZo3KlxnGQ1DshNZ7YTsvFkJwuPTmS/fvuPli1um0RQt18wgSydXs9xwdHpw5Usp6qZeRr/s2y0imzywNPOARYyMH5NZSSZ3CrAxD5f21BQTbWoE2SGYrWX//cmbeaeonM8227IPMWrV0roYjRAuqozBRm5kn5kiEszDSLg436m60UbcrgvgGqUWTD/Zn2OQK6+gq+dsz/aSbfUhxq2ZmZwBYmWrlQei2d0Ul1CKpdvzagys4uBY1UZtgQ9z5i+elLXau7pPo1msBng9Ki9HMwb/RsEpXC7MAGo8DbHJvs3pUTZDRRwRtdxbxKQ7sSYOU+Xv/M4Nn350S2vC119/QIRtqnEuRp97/eKWkn0cXavGmQ0+YWxdCSIcJrOJmIZoN9MRw67p7AGKv9fBF5g4gFJbJdSeSl33JJ/XJ7210j81DslsR0Kf+mwIolny24nJKAzJGD/neaHkfnqF7SS+7PT16vUusqvNI8jBrabWH7QNovHFF1/x8HRkHCPDkFhzBa3ba9RWKLlwczO5ZxC2ixe3nVQTNoOSvCG+lkJ1elpujWW1jGmMjXQQDvtxy4SCXCR1ANOUIOjGp47OQlIsbR0i3OwHHodErsW4zKeZVZvXjWYoneuMEhhT5LSstCaE0FjUWiImZ8NrSoEY2B8OnI5PXp3Idi+D9EKk25AEs5ah+yH5d4qJNWxDFpJYD9L8jtRrcm/niE3WC2qk/dLq1rZbiyl2mvsy2eJyW9FmksSPR+F+Z0BcEjHdpyr5GRPttwhOr9a3lq6/pqcKDlOj28V2EzC/cuvqRPgbn97z9/7gW4QEp3mhS7J6mpPXSorCYbfjWBdHH23Hz6USmjDEAaKlVNNuYrfbbTtdr2F6E7sDJuYCYABK49L3644GPe1+Dgw5QLD9tyPAPpJAXMXhDz5J3CxNOpprVED79zQMpg30zMOC9BL0fQG1dnktbRWNkZhGT5EWpmGidLpfTOS18vBwZFlX9tPAtJuoJZNLYTeNTJPZq5TSWFYzry7d6UDNj7WnmDYjU7e2SC5GluhTqZOPfTCuLtv1qdpQKxEYU9oa/p3E35ItvHmdrb2QbdgQfn9rs/aM+vuszktOIbA6JdHYqV5uNKueUoiuMR6YxsEcBGvbSpC+dNVxh+Qc5T4uJFytEei+Svh6auYxpL1BiznQqyG7ze+LmUxbpxaEWK2dEzelS2MI8O2dDbW6m5S7MdqkMiAX5ZzVR1x+g+A03BKfNOUByAaIPasdthsibP1BauFuGvkffvwpr24PtiNL47Df28JOiVIax9OZpVTO80Iadqylet/PdslcG+NozgHRRxGOw7jVwqEZEKMegL1VorhmbiOX146zb8HZ99jtZ1Sf/b2PpLuuSfv3mx4wkoYBoT1DiXtvMobLJlTV6suOJEqwmSPWp2zb6ZAdp/rzP/8Zv/Odj6FBaRDjiMjs26VJmyzdjSzzyjwbKDXsk8ucMtknltWm3n/trSQcnDEWjGOc7q6nlFZ4Oh6520/c7Mdn6Z+1ki5qjnF0hUu9kDVso0qeiloPcS2OSs+LB1dkzTYpzEbSNzpkUGpFkhC191DFet3q1AWBaRg4jK/4+sMHZlaieH2MIc09EO12N3fa3yyeLXvrNbojx623Ya7XtTiSopdSrd+L5vQCUxZVoBivGeUwRnajgUpDjBDET2Sfc9OU0APrtw3OJLIthOA9q8sbvvzSjVSsdHslN8OKpP2OeW7kXJl2yWD4IW7qitP5bF+bRmpbOD0e+XCaOexG9jGiKbCWSv7wyMv7A+zNMOvDwwM5r7x5/ZpuKtaNqS61YmclXTkBXOJxu8HbjXY0s1Tb+bW5Oxxh6/dd6rjoxIfBf0fYXifnvLU9dJpM2ibi7ZC61YEI1iaodVuUx/PCV++fSFH45Rdf8e1PPiKFwNNx5uHpxGE3Mk6Rw+6Gx6cHWm1kjL+5242sq5EfSsl+Gtv1rT42oVteVucRo7YUWz8oWkDwmiokmjaz7Bjjs03qInGzfqC1m+xPV8j01k7OFal2L7PCeS0IxfrILiBXETf16hOuBan4xCyhloZKI4qSZGCtBbwOvr+/Z59tvH2MgfPpiITgTpD2vIPAwPWJ2Tfpq7WsHbu/fHT6okg3/+qf91RZYEyWIXWSvTYxrUpw90qFuQTyUi1Yg91Hoyf+9Z3O33ByWk0gHqTBd3j7mvbvsED00XSjCHdj5DYKNcJPj5X/66df8MPv3m01ZgjBPWss3dpNo/XjcuW8Lkir3O4HG+5zynz+9gM348hHL+9oalOrcl4Yh4GSM4gJgetV896MxtgGtKqY72jb3OrZ2imbi9zVze2nZ22VcTL2+CaBcpOqKIExJgfHdFu05gyRXGRtp1suxU4RLGAe3z9ysx8JwcgTrRYen2bePZ75Tz97CwHu7g789Odv+fabe56ejjw+nIjhlhe3B2pZmNJEjR39tM3F0kzzsS1qg2/B12ht1Fwc9LFAFYnbxgRYC6HZvcl5RVshRdxc+uIZ1Zq1ySRaEDS9jJbv3NxaC+c1m3Gay8mWJbMsGcSmePVkpPo0MlTdpE0uG2YT00LW5moPpwaKPfMUhWk4bOBYiomYAiVXarCNMwXdDNU6Cf76owsyYgik3p3wMmYM4uu2I9Um2BAgJucy06z+zIWmBVETk58rzBVw1z2CuAFd45jhVPlrP35DzZkIQdkHOzXXdjl01M566x1p5cU48GZUdgPshkYcRn72ofB6hLcfzhznwm6amHNlLWdCCDwdM8uc2VXr6Z1mY7V8+q03pATLklnnM6/v99zsJ7MWbNZaGMdkxszL6rrEy67YG93V08TOFOoB1/1KL8wgvyZ6rXlJbw2Rs3ZF7/ulFDcHdnyzUS4LvH+tB/M8r8zz3JtMnM4rP/n5Ww6HicMQuT3sOK3Kf/yLXzCvynmdiSkStXAYEqfTgkjk5YtbhmCsn5TCNoqxg1R9cfaerajzgrVBbU7yr458Nnd+0I015A+dnrrl4oqcjeN6qZVTcvxTL2ZatZnqpfhohdO8cppXllIo2WRm82Itl9KfDaBu2GzE8wvNsqkRLYYQ2EVhsQKQpdnskwHnE4eIRvckDoHbmxsUJYeyufmJlr8y67suzYIfQr3ODGIMoeDg5RBgSEYsuaiXDGi0jMrbRX5ttSrHRXm3mOrzxWRAqgqcSuOcheWb9jnH0LgdhPskZCJfz5XK5UFGgbtoJsuv93A7wH4I1AZfnswl+7MD3AyJf/kf3vL7nxViFPK6cHdzcCCksqy22KYxEoL3Ck0gwMu7m+3im1peH53a1eubnm5dmy93hXwIYFYYV9/jhX5/SKV4IOo1mf1SW+RsU6I3QMkD9K+SiW0j3J1wviwry+Ipl//M23dv+fLrB4aHwGcfv+Y8P/LFhyfefThyXrJR6IpxOeMwEERJ0VorIsrTceb2ZnKXg4s8rGcO/R4Yw4YtlW3V0NqLq93z590d+yCRaybXxuKAkoj0A9EhhcYQrQfZPCMppW3+RLkqj3PmOGfWtXI8Z46LjXgANnJGcieKjZu7XYNlIeqT42IwskGpxW1rqrfxldYKtYVtjIVN6b60arponCtk9Hoj3z76iakwRENiUzCUdxwTMcrGMuqyPFXjhTfTGCJJkAbLXPAOCgVlafD1Co+5UlVYqvpMmr/+49cG522CN2PgkISHIkwxGJNEAjeivN7D/dBAE8cGcxWmCKdVWbPweh/46MYGH/3s7ZHbnfD6xR6qefjc39+wrJXzeWUaR/a70S7YNnqmMWwWjCLiPTphHM2uoxRTguRiDflBzX6k4QNaRaBBn5iVUrKbHQy+j8GlK+p9N7Hawl7vWoxr9v+bLjR05Lan0G6476/RT+l5WUDFHcpNR3k+z/zyy3fEEDidVz7/6pHWMuc1O9im7k4vvPtw5GfTV7y+v3EFg7Lf7xmHQCnKh8cTt7c7RlXWXJDgU8T6qW9NYDtVvR7vTnatOi4gz5Uq6jV6wEoFwVHTZj+jWr0tZS2D4MqNZS3Ma2ZeVmafn/l0WpnXwrq6BWc1JtK6ze00EXP37sGR5D5qsLTGmlcW6+FYB6A2YhxA1W1Q3UneM4YQI+Nu9CnsLgMTcaKBkReIfThycCqdAUfdpjOlwO00mndTMKMy0wcHur2mtQsDtQlzyR7Zl9q9Yem4eR41Vh05l8KlqhWUdr1f/JbBOSSyCB+qci4OnBA5UPnuyz13Y+M22ck3SWIUOBerZT7aV8YhEoNdYC6Zf//z97Ss/P73Pub+5UQKCVXbOW8OB0JwuL0pmhsX+8cLEGGqDiglujXj1W4oAmTmJW9oakju4C2BaXOsU0LrXrcXTxzzw7ogdN0Rr7dfOorXf4fxTS+mVvRH1JxjOgzOnzUO6DIv/Pzzr3l8PIIE1rXwZc6MLvQFGIfRJHUot7cHxiGyrAv3tzfsxuSIbtcbqs2ojDa1a0iJkKx+6+DXJcNoG7hhSKU6IaNnCbYxGJqLuwdaZlNKY87FPuc/k7P16VIcyLkwLwvHOfN0mi0gc2Feiy3enFmzjfCrzbx867YhqJuSW0DaKWz3tNfvDdDSzA0iBHbTwDwvoE6qUHOnyLUypcTim2yuxTZc3+A7dXI3RO53tomel0pWY7kmbX5iRg5j8uctaLiQE8DI9F3BshRDkh3P3VwIbSy0YR43oXJSyM2oifEKv/hLCNR/bnCurVr0I6zNBK33+8hQKqMUJlEGqUhS9qMtsMfVdhQJ5r3yfnENZ0zoqnw4LozRGB7FJ0oZEtiIMfku/pzPuiGq9glPg8wZ7hmzx9Nfm9IcnqUund/7Vzmnd3uP3lrpG0FPcUO8oL1ckQnYHsnlo9epHSU25LK5DlJ4+/7B7Dla3QCotVUnVLiKwtHPwzSwnxK3+50/RNuFT2erQWu1MYcRCO6F2xfQ5d516ZZvLi7IvO4H92AFtpaLqrKbdvSxBLm4S0KMxpcuxVlMZgVzXs2gey2NNVeW0gyNrI21ZLK3u7r1ZW/n9Olgwdtvvd+7jfbwjVmQjQnWWiFGqNUnZmP3WAmE1oilYubNTi3X3mUQduPAq9uRj+8OBIEP58LTfMYdWhhDIol0Jqqnx/009M81RV0csZRm9FHvfRYVmgaWaoN5RZXbIfAkQsmBim7jAIFvrkrREH3c2yXFeTkWDocdKRTGCIgyRkix+TAXe+F8joYWqlnY74fKfVIO5wZilCYtlehFtmqlFDYVyTWlbdtlvMnad6igbIHUuaVbK0WgT/xqrTF4Qz2EQMHrK09DoRKjpVgiIMEUNc3TGaH3NUf6QNjNrydctJTbQg8WfIK6AXRjXgsPp5WH40wTq4kEiKKEkJwQ2u+FEOPAm5d33O4n1sUmO88Cu93E43lhGgfjCff6N5pUqaeEW6bRzMG9aldQdKtGtgC+tv9srbHkwhAih/3ON7PL6kxj3NprnTY458LjaXEAKLOuxSZOt4vlx1ptOnirZvlRvP4NUTa/2U46QByYw4fbitWcNmx4Ibvz/dajVusZixr7SYLNy4kKrRZCNKbYOCRe3+749NWej17csuSVYRgZIpwXM+oaosnX7HiwvmRupintrC1FfBNTs57RRped9T20VDOVO4z2vl5oQ6s5e3RNTMCQ228UnLchsIo4Ad7mQNowK1uc2ZafI1RiARcCp9WcuyWoEZCBXCrvKjx+fuL9MfOtQ3clCJtx8zZzQ2Q70Qwf6FpEXD95ARGuyQHGbLEFPDhTp6Ozu/3ORbNGqo59V8Z2QgN73Bza/V6jk+pNYXthvhhaekFnr8nX0VG+GCJhF1jWzOk88x//0y/54qtHExOHgIa2BXbxDWgcB25v9pZaDZEUMN3lbg8ing4q0zCag6DoJlTuOlPDPZxI4CPZW58Y1mwn7+uhb2i1miPg1vPzE2OeZxgSMYzWDtJMyRdH+YaNrT+fV+Y5czzNnJfivUpb4MXrzI2c4NfcneVFjOZZfU6KBK/rHMWUaP+OzvaqrfF0NoAtSM8A7HQfJWzE9yGYj1AT4yp/dHfDzWHidozc7UcDkRS0FZtM19FnrO3UN5aGUqqTExqodANtDzJV0hCIyaaIScP4stkA0aW60XZVDsk8a2f1wPx1aNBvCs43e2WtwtKEosZLjZh9omrkcYGKD1oLcM4Gfbftzt5uZkoAACAASURBVFmhX5pSgDFGYoLT2tCKu57LNgx260Eh205u2U7YGsB9AXWp0+XDEWQfozek6MJhcdnU4MJbZz45dI5vECkFhuaCbY3G40RZl+Kv2d8jjjg+dyvop71pD8OmdtDaOM2Zp9NKa0YUryghjs7rxLbZWLg57Lg77BijsN+N7CanzjlVTrDFud+Nm7t6FXc2cNodetF6tmbzR2pvsXT0ls7aMUCsqrovrFI9mzF+sE2PztXAJlzNYtdopM5cGudz5jwvzKsRH3KzDaGoBWetuqHDIsKYErMDK63UbUhSU5DW/L7YRt3bVcntXkJroLMn+MaFbdrYjwO70UzUQhD2g51YEwMvbnd8cr8zSqNYnZjVJ1NvG3HHNPD0WjfShuEI3ituG6/BQKVoLb0h2UY6Z1grSIws2VopQSxOEnCXYPBWiwTZrFV+++C8Nfez0pTTqpxqQIK6QZE1kI+5khGyCqWJda/dv+U6dMYo/N7LyN/4nRd8+8W4ARMXJPvSkG7bG3bgYrPCDFsj2XPcS03Sg41L8AF+krnDgLN7ekj1mssGyxrTJ8YJbcqy5m0ce39/XWMYQid5h2ftiORtGglmhNyc9Jyi8PrFDftp5Ok4QxBudpObfjXSkEhpRFQYh8h+MCbVdZsk5+wLvfnPOQ81JbLT8kppXrtby6m0S28z542haqeYdBc6bzuhW+1mhAqTjpUKyyo2LHars698hBos2ZDaUq3/babQVnPmakixJ0Bssz+1UWtxdJZn2RDKFsw2a9V6p2ueqWqaT+hDnITdkHh1dwOh8HQstAq7aeB2vycAL/aDg43ZsjQFKW27/o43qHZ8JCJafTOSbePqE7ptNVjqPA7Re74gBNbWeL/A21NmIbo8rDEOifPSkNAYpaFiRW6Tb8gQQmA/mv9m08qpVJLYSbmq8FjhWJWijeRHvEowASuX03BKgR++2fMH35r45NXOeIVjYvCBPMCGyl5/hA6OdGDrCtm6rvN6cKo/sBj6zbo6ZfWidewP9jqwafZ3q/mUqpVauwjblSfO2x2GgRiTbVK+T3TjbvENI4jtiqqFw37kzatbnk5nl44pU0wsOTuSaW4FKWBbs4iT0QHM1a94gJVSWWt1Z/rOFy7QZHM/aM19jJqltUuuWzB3vrA12rt152UzLH5qqKPUQYRQhVq76Zpu80tiDOS1Dx9S5jWjuK0k9rnqqaGGy0m55mwIN87S0p5ity3lVXxqtrs4tC2rYtO29vUypMh6PjFOOyKWGj2eZ6Y08fr21kysUSc9VBu+pM836p6libhvowNIlzXpXlHSZSA4t9oOjTXDec28O8PbOXBiRAT2QbmdBqIWahLOxYkwPQDlG56cX54rb3aJIbqpUwCVwKqN3OJWmAtWN1kQuODVay87xaCWTCuBwxTZ73fmCySdY9uenUAeqQZ2DCZszaWT2RuoOLPngugKl36mNuuBqUh3SiX28fS+bXR2R6mV4zzz+HTk5rD300qJklC1nXYckw+4tQlp05AYQrD0LrjZlge3wjZaHHAvGmUcInf7HVMqqJMpUkuk2tDYSNEeZlNYV9N4rj7aPvrvW4udUD1FVW8BqKfg57I6NmAesFVthMKy1m3MuSrPxADqfco+sr2fDuqoZBMrY1q7snPs7JgUWEpmXooBPv20VusbZ+0Ty5rNxfFa0lREvhn3YJdoNaI2xJ0be2ulzypV8FLFFrxxuKMHTGCaIuNwIFebFFabzYdVtQMjeH/Ser7QtDrYx7ZRdGDKBAGNXGCzPAEqjRSEyR0Vm7jRtwaWFjg3JQ7wRpuReEZhiM28b1ujkEhiaLIt5W94cv7ioXJalNvRidVDJKCsTVizSWtUpG/2WA/IpVEXPBDVQK6BykReKtrMfXwLLE8VLyfhpd/YSfIS6jZLsgfzxSfWkIW+y7XW/kq2f0+ncnVvISx9nefM8ZhRVW4Oe2uqLza2IKXk8zTt+/swpg7vb6wjdwz0/MhOU7n0x7oSv7eL7IE3FwFfmCI9De1igj5wVYJwdl5qaR3BtgWfi5lMze6yMAQDpIpaTZhLMSL4Flh+Ujliaq2Ty7MQhxyv/VmN4N6pjfZ1aZV5WTmv1ned15Xq9706CNLHNIij3903yTIf24yqKmjd3PB7yRJFrObAAiqIUeUGCUxBeHm7A8R6jU6buxkiH78Y2bnLv1+s9a1DcIPwXuMKtdi4DlXd1DFWo5ufUqlqBadnRwFx1wNlqY3S7HmICASbGVRz4zZBkkIt6kSLyBACezBcQAIa+yHzDYKzEnhcbWLSlJT7KRIxoegYmw1oadhgI3yHkc6VCDRspmRT87z9d18eGULlzesDNzISnTR+ra2ETiO71KOG8lm/sTW2xbG1WdROw661bKGhes2z7T6qBsX/8qsPtFr41uuXLHk124+aOZ26pEqNshfMKtLApW554g59PTj6ls5Vqt3TaD+tDHV+ztmt7pqem9mDNO1j3Isv3ksqEYP97mXJLLluLBRX2VL6yepqjhCEFNt22pRaCXpxd6iOvtRayf0ks2PT2zD+wv5MXAhHb1n1a9Vik8GX1RDapVS6UKK0duVnq4j2QUeuHgI/HdV5yFcLwH//kFKPTZZsapugig6gQRlS4P52YsnFWx0mxr/bp/727VpDVwxdMrRS1WSKa3WGkbOT6JIOc/mwdNtaRmmI1i+vsJZmZU8UzwgaQQu7IMSoBKzPr65okiTEgPPUg79WZP3rY/M3mEo76beqAYq5WprT1FzIIoKEZg/ET4prDHXL6AWWZtaAXz0tHI8LQ0oMg6dWKn4KOkCzTeS6AAidnN1PjP4Arz+a79aSLgLxWgoaotc89vPLuvL27TtOxyM3twfW1WZFajNrlN7XNLd1I5hvs0jUmCitNVKLZiTt7/aZNE27o0Ovrfpu7SyZarVjLtUf9JXDXz+lvPHe8a9cCtkR4M4jrn78NHxYXzMheAye3kYb6zdgQEcMlppXfPRAPy26llK6J5y7PAQzAO9myqbe0C2dr57+mgG1neIiRlhp2tNgnISv27/7/JLgtbxudX9/ujY7pztvxGBDZ6vaib86D/h2GjgM5vlbWr2qCcX/b0HbVJ1Dbafecc6cc2HJ1d0OdXPLkCB+7zvhhe0ZSjA5mGfIW1mgrXVLEIYNf2Crb/q9Sn6tSxNmP32/UXBu2OfVfEyhkUIkN3MO6zVnD0a3ANt+vn9lDMohGfp6zo2b0oEJwZg+bCdoJxar2IKEPp+jXQXo/8/Zu/1IkiVnfj87F/eIvNWluzhXDilyh8uVuAuC0gKEJEgQ9kH/hP5Tve6bIEESBIirpbQE58KZ6UtVV2ZGhPu5mB7Mjntkz3QPpxOY6aqsyEgP92PnmH322fftX4oXa8HGmLIroFe11sCUbEFeamUphSlPTHnidK60ft4W4VD/bq1t7tO1VSi24A/zZGngshKDzfBNkrdTZUynsJ1Quy2eMoLT6xlHXYsTxkeqKh5wwyZiiEdrZwv0FAYjxs1m1cWuxRHoNqiJbD3O6TAbOb7rBpgVzBRpWL6P/qhp81iF1WRIjgAiZjLsG0ir1SQ7ms1gOoHNWijdQafOtsjH5iRjpXg9JD54n2APTt0R9xAyGUWasZGG+TKu8GxZDf65hkiaBagBTsq5Nc6LSYael8ZSLGsYsqRgsSVY6mo/Zif8AK/EUKqxqO3EbK4kGHwyyq85xUyK0Z4P9hkjpn/UG9QeOTWh9e+oITTUqDdx5W4fPnSD0jcFb+zKxd2srxGwkao0FZ5qoD1X/p9fPJJQUrQGcfBd3urLnZhgKdmOEv6ur/20sZ37/eOFv/+Hf+Lt69d88f4rXt/f8KMfvKaspnx+XhYuy2rk8jyRc/Jg3A1sh3dmKQXV5N8LvoN6zaRs6en1tchYIKO+Gz3GPhTy+jaEPEgDXXkhdm11YjMd4O4EAnEwBHu4YwPoXTeXbAVqsd9ZtXPjjKHkUxprLSzFALXWjE1zWevGDdY+6n5Taxjiyf4kXeqzbeSF2iqqLmylA+SxtTECEvCUUy19N4QJgpBiJkTbGOeYCGonugCHKTO4z1NOBJSP58ZTNX3b7z088O4+XaH24n1IC3jLtDoQ0Na4lMbj80pRpXjvdTwLcxaPrgXlSbzzo4dTdhjordiOo34y2z8P7jUQBg5hrmLF622JgTko52JW9HYvlPotBL7fo1vrcLuDL/gOkhCI4o5VYSMtWyo0dsWXinZLD/zq0SQEf/X4yDwlUo68fX2PYjt/ismoVjFuU/K787K8gLG/dpV2szTwd3/3j1w6PJ0q85x5fL7wjz/7krVVghqx/DgfqWU1tk40Z+neBmNnyDc2ggtVx7jb4gHbrqyeosrVJhHcDbn76Tc2J/XaxVTw6tUCbxuTx+pQC87qNZy5U49erxNA/ZQOMZBz4OFwpGq3dLs1JJrez1qqe6A43/XSKF0sSNd1U7PzQxHUSoxSTcZxLDxwOqXf59qb1eZqYJOO392D6/yoD7WPUsIzicEKIhFCIkRjneUYmFNkzpnLupJi5PXtkfNqVnuvH26IdI6nTM6F9+eV53Lhe/F+m5ttTQnaUd+kLO20D1W99m7NTXBbNUwAQ4FDDFt7xkqakba/JMdsf5a9lBmVf/c/Gf/XfX9ENqqi9s4zkXMXTgUWDeg1Af4PDc7ri1KvKTdNoW41ifjpGPfj0qbYYVuYBs/3DTGsMfMffvXM5VL4m58mbm9mwmzDwVGG8Y8gTQ1pFEs22DaBr8/juepZqMzHA1IKt4eJnAKlNh5PF45z4nicScnMhMKUyYqp0/sBf133pXTdBw1OcysIME0mddxRtA0Cu6W+Mnp0bbdOyDFRhwN1VxetssZ/a9buKM37f55mLsOVrENKluu1ZulbSpHjYSZHG587TJlltQWXZq8Fu6XLKys5Rz4+n51SJ+7x2Z2CZqeLOY8Z8Ia3ekZOItigtLThPm2bSMWej2LjZM1Nfcf6YOAEfThzWdnT1ZzNggpJbGZyzsLdDHeHI0EC98fEnAyNOqRAIHJ/NKZYqzZ19HQp3B+McSSxm/GVb5jWwrFgaV1prsVreEnYWkeb5lUQBxaFhhDFNH9EGxEnsmAprWjYShl1YWfrwwpIpHXlYzFQ9NzgyYfWqwYKzgXA0+Tfedj8M4JzBOg3//0lMHP935fUNteIaUoiUFE+P5nY1/e+uPBnkxAOiZQi01V6OVTyrk/P3/WlKoBr3eTEnAM3x7wN3YYQXW92WOeNE0E2FtBAIr/++wawM/43mEFyVSqMKYotWxDr88kAJLh+rdeezjcdxkZr8TrI6XalNJo25pjIKVlaG415NaXAYYocpsQhJ5t57cl6pK78btQ+88Vca2EdMiOO8ooI2Qfjrw54Pz3NAmEQ2ztiQtNeM45+4O4Cplsw9IHFiGytmIE+DzR/nFYpWg/3/jjxcEy8vj14cz4QRDnkTKNZfalwn2wEcX734Ca8ptI3xpY7wTOQ7swkK3TtOjrHOaGInZ6lbu2jwZKqwU7T7bmr9TE1RhTTb9FRcoqDYKi1RUgUVdYeuNRKcUsSs5w35NcG1hs3yTbyU/3mwITfi9Zey1fsQbeTvMWhd7vjAxQRf8iEqwD1/29Yinap8MtT4f/8+SNv7iY+fTvRamVVJU+Z8+VCLW0fsh2nmF/H1gv09+3a+OzzjzyfV25vbHpENBAx+4DJBbnGidv9hBtMkNFn3bMF+9wdpZV1/2y9AZ1J7WTegpe9sQ9sr48y+nuGCBeXcdzaCt3qy1I7l3Xoy+qGDOY5M03Z9JlyRkSZp8kWqSuvqwMR29iXkxTsJPMTKzhBopt26hSHyJRQQnPSgm8gOhQBx45vAdlaueppO5OIEZx6pbzIVbSLyamE5G0Ws53PKZFC4M3dDa9ukrXp3GEOt0VU7cwhbyNWgjJPiSkmVq/nO+ZuJ17ymOSJ4yWjlxwCU0rkDrVXSgdoDqCxrYfWlJis+7xUA29ah6qRqqaCkKWTXWuk+Weu3UE14Nw7heizweM+dFQCNznwV68PfP828KvHyt9/6DwcfveB83uD83Wu3B4yH1d4XLthypuso+yprgg1TKS+0mlMCgXzJ4FAHx6Unp2H7sBJ7/zRpw/82U/e8XB3MLGuIKyl8cvfvOd0Wnjz5o4UMx8en/jk9cMOQAFDUEr86TXXIhI6Za2oI7B5EuYpbe0H29WtzWKzn9EHrsd7sgXnaJoD1GIk+O7K85Nrw+LthdYGidt+NudEw07DZS0bX7fV5oCQN8R7p5bq9uZKd8HFu2nikDJBOsfDgSnPqCpzgvvjvI1dgRKTkHughk6KkdLtPQZCOhDaGDpI3E7IFN1zc6SAqDG743hiPoVRTQ6zD+JH183EeMhfIqP631FrPGAiEUmABo5T5PaYOeTAw81EqysSDyCW5QwWsL/hxuiyz2KLPYrYqdZ1uwfj2eITTIrSzIvegDYHwVQFiQYyRrKrpttJWaoLBnia2lQ4V6UoxGji2YcIQuRUO0mMyloNFjZaibjKgQqi1jX4i09m/vrHt7ydbULr+bLyJpz5mx+8/m7B+T/9t39OrWf+j5+v/C8/+8pt4PwmYDS9cVImCik2fvrJLf/9f/6Of3h/4t///Ynz2ki9YPMPgaaRLpbZvz1m/uoHD7y6swmNROZ0PvN4Wvjs84+U1jkvhWnOlib2r3h4dYsqfPjq7E7hatS6ZMqAN3Pk9mbm6E7PFnw7SXqMUq1rZVmWDZmt1VbX4PpaMjBSz/1UHONtUsVg/ZR807kaFHbEsDalNzstL9XUBdfVVANKU5ZSDfzxEyhEIfbuJkgBSYlSO1OK9A7rujqv1/59uFL3bifAcZ43GcumxnnV7jxnZVN0UM/NbGB9z3wG+muD/UafzNFOn2m2jW6AWc2Ds/oJNU5pVfM97aNdggUCYk33KUWOUyLHyJDdlJT4/PmZN4eZOO/DBPa8HGhBNzvGfuX96TYsbOma6uZS0NVKqcelseJgVfcDJWW0b3ArXaBo5NICzxV6GKQQ5/UKVLWSzO6rUVSlO91PjEI4JFQ1CMmNkP71uzv+yx9OvLqzPvPjRfnR2yOf3GYO+bv2OaXRNfGj151/ej/zj48Lw5k3BDcexfpC97czOQb+8id3/Ph7r/nRD95ykF/wv/3DB0o48v65cjtHpDc+LtZHejhM/PEnpmAu3YR3D9OBX/zmA5dSfEFW3uTE27f3HCeTNXk+n7ksC0LneLwx6tjjiSkl5mwTHznbzY1hIsUrS3V2EMks3q9qsau6c8Dpgz3S+p5Gl2oNZZM+6aTswNcYj2rDBtDAplIq53VlWQfhoIFEu+5ig8mtW4928o3G6qmKtsJxvjMurtfLp8tKrYXL2jhMmSm5ggRuSZ+ibReXaiehXpcd+yD2UO0DB7dwlzEJmJSjSVQGsen+WqvTvy1gupgdo8qYexyjVruyhEQbfMgpUJswT1bTnUv139e4C9H6zNtc71Vw4iwdNWiq+aTLGEodLT1UEYmIWwK23lkKPBflyzVwavYeQRMxdO4cW1gJPj4Gzx2zNfT2y7gGSwLseSrQeyARSKExx0irRqpnSJB0e1Z/chf4yasDb+4ir24njtNE7TY8cpNgus6r/9DgvLuZebiFN3cTtcPH/9g41UGN871RlU/vJv7Hv/4jcui8vXvg7pBQ6fzbv3jHv/zxa85L4f/+xSN/+u6Os078z//7L3hclD/55Ia3DxOxm/ktopyXM189nv0mFFLMLMvCuh5opfH4eKLWRkiRGAwEKctK62YJkKKYf4coaPDTfbR0rgp63dNPq9HYAI9xgm4zk109XXNx5mbkAfET49B3xQUDIyy9HXXgUrvLeDSGbYANj0cbg6pGdYu+QBXrsRICEkx/KbulQymFlC2wT4sJad3f7Kik+mczK4WIFucC6fXGNDYiR183PulufjtYOTLU9apRMY16133KY58HHfREPMCDGud4CoH7m5nDnFgujZtD4pCjE+QtdayKqdaXiiYDbUa5NgabB1libTaDOU9GqSzFOa7NeMoh2H1fW+d5FT5U5alGM9yiWzsNocRGVOGpQO2WeppopxIwnx77G7w9Ju5n+Hgx+8mmcIiNd3eZH9wFPpwqvzwpXy0wibGcDgn+9HXm06Py+nay/U66CRYEC/OY0+Zn8x2CMxNDZCmdt/eFT28TP3ssxN4JdJII37sP/Hf/6hP+9Z++okngclp5XswFO+eJT9+Y38e7t7eeFsHlp3f8h19e+C9+dKRLIsRkygpd+ez9E198+RER27WbVHoPrGvh+fnEZVk5TDPlYmTuPNkA+JQDh3nUjgbygEIc6ZFtKoN1012naDTOW7X29wjgpjuaagJUo+4ZblfdlB5Wy6sG9lW7mhJAKTYgHO3aL64QEMTaB9X7g91TLbAeaVOribpCLYWbeTKVAhcqA1xBcELV5iFHP3RT1BurKsh2wowmuaWAztjB9WKd4D1qx04jhuSpvTO+dPwPT/NH/1KpDnJ1dV2i8WsFDvPE3WFiSnDwfiYh0Ktxc43nC6EJZ4W12QTUWMAqhiZfKixVKD2yNFgN3WJpwvMacFrvVq2uLXDpOPhjVayR1q28ujTjhhevab2j6fXiSIvh1SHw7376mle3whdPhV+8P/P5x5X7KfD9u8ibhwOf3qzk94Uvk/DqZuKgheMh8fYYkQhTFuZs1iPBnckGi81k7b9DcB6n7Pl15c1N4q//5BVf/F9f0FGmmHhzVP72X7zmx9+7Z4qZHicuFIOge6eXwsPDLaVUkjd2p6j8xY/u+OEnr/iz7z9QWuWzX33FfJj4+T99yc9/+ZGGEr0PGFSZZ3h8urAUa4yfl9WwgtbpPXN7PJDdTCi6BV2t1aZJhmqe7Fq31ywXGCNT2GmpQ+JjoKnNNXac2tf7Nq8YpJN6QL1tEkN0oMl6lyqR2lwyshSWtTFFOzHNV8NOJ/UNxMx2ug9ND3R8F8MerCMpdraEMLR3LJU0umNjECiHQPZIDwfCPdym7dRjQ4/t4NvvlwFnu/GviqVvbaCTriRQajc8IoR9ENonSG4OedP4laBUFUoTSjNn7KV2SoEcTQTtJsHDwRDToUJQGpyb8rgoH1dlHdi/ysbtTRidtHSrTc3FUL0F4uR7sbZbF7hoZFXP/gQ2MyDwShl+/Grmv/rjO/7ljw4c8pFP7xa+d5/57P1HzjVySIGbNBFm5U8e4Pu3jVd3mbuYSPPE+bISo1ENY4hcign7IGbHUfveV/+Dg1MI1FZAlftj5u1tJUtnFXh3F/jbf/V9fvA6E1Pi6XQmHSBE55h6r3BdG5fLgohLTrSKauLVTaaL8PR04ouPZ97/7HM+Pp14PldyVmSovHXbjc/nC01tRx31n9W/fq0y2Cl9Q/tijGhv5By3dka7ktvcZhP7/ucRnIMT27xZj1ZnD7VtRMjcDg3SDyKo+3qMVLHXCq26SoErEvROD4I4ihFD9J7dNatqnGz29+iEiOpuYKpGTMgxmAKBN9gt8DpjCmmb2VTdAvW6PbYvjKsaUXalhxGqNlli96M0r+nUUvyxMUjYRIhcAsY8UeecDAjskYZtbEXVVBI6XLoBMCkocxDmKK5OaCfzpSmnBh+WxmnpLN2IBgFhilAYVvDe9kFcJW8QLe1rU3Mfn8dD0PH+7b6PGlO78sevZ/7i+7eudhBdKiVwdzww1c58mNBayJNw2yOvYuZwEBKBmGBNNr8cnbEl3RUDxfSXaml8c2j+PmnMVkEbx6OJ+N6WyJvbzKwL/+7ffMrbt0duDkdiiEYJW84WFB6YIU1cLpdtF/7N+yd+9qsPvH//kbdvHlj/04UYJz48nng6XdzzIhoDo6lD3J2npzNrE6sFosPVXZiyNWtEFdXGUlw0LEazWlcbRzKOrFsLjBOx1W3BjX7dcmWLN06r1syuzWrR5vo79ufk9L6K8VfFBgO9d2meoqNnqRKQ0VssRu3LMVK6tVpSitS12CSMNJPLBILaEHmvVgfNUzIUNQv0RsAa5M25mhBMIPv6s/kJ2BS02SYzdJqCmOGtBePoKdt4li1SOyUvVVkunXOpNgLnnxFRR2MNIMOb+N0VAi49stbOqcDq7zWJodldbTa4NqX0QMjw3KBchFMRntfG2qzOLSo0Z6RJV3pQLv55m8IK1ntkkPbs8w1DwGsCS+9KFWNG2S41tGQTkcYaEnep8emhkQJ7WbVcWC8rUQKv74xVdenCnISbbJvSUMu4rMqUMofDxGUp28hbqbDWwnmplFo2TvcfHJwiwvFwIGUTV373EHj3ySPv7l7x7o/eMGUT+dVuk/TDrmAACgqb5k9T+PkvPufXv/mKJsI/ffaBQw6UcuG8VlqvzGnaapwYLSVN2Qeb2zCItSCTADlaCjJmNZOjlsAmBq1XjfJa3aPSA7Qz1OFcBMtrN9kAJD99fKK/XiN4DlZ0HyVbHByxqRPdEF16p5bmrseVw82NbXrgWq4OIGHxMNDjEIw8kdLoSVraLSSmZGRxpyCxrG37jB12HqsTLdZiEyc9RKRDCpYV0SGJEga6q5Yu1x63zKL27v1Za0F1tWHiWpvXlwO1l63VYUygyNoCX54aTQJL9/pSlWMaygbBpTAV1cjHFb5aO4jVrx562+fCswmbkrL3Mn/1XX/3txcx24n5YuFL2ECiUVtHrSRRUhT+5kd3/OWPrQe5LIs901LMfLh2NAii3VPq0abr2xohNDNTas14zM2kQc8Xwx7OS0OiEEL9rWv7ZwXncZ7IKfnpIEwx8d/89FOH1+24Nouztj3MMUfY1XwzghMQfvmb93x4unA8ZtZu/7YuwfPusXPZ4uvNHmiQIVcoZF+LzWlZr+8OPNwe6Q5bb2nY1Q65ruvVHKh5nnS1hdWq8Uq7iqvFuZdIa863HWkOdnw12Ra78YmtddJdjm3Ug6N+3WlhTmRw09UUAx0zRjqXxsnVXSDZigAAIABJREFU6WtXjtPEUAiw+VhLm1uzYYCmdjoOQsDIBMYwgLGU7G+lq51svJzFVTWd3il2bqdIJOyBvTGcZENHa2ksxYaqSxvUPUFCIgv0METNTFtKfYa0hwNL7/RmEqrVuoAEMaG4QYg3VlPY2jiR0U6xE1nZAZtRh+umOhGM7qAjPWUnJnn/8uuztiKyA0eqm7rBgBBXIn/6MPG3/+I1t8dIa8rlafXJodFaq5SzvT4EYco2a3rdPlOclrlWGwbv2HD3Uhy978xTciuR7xCcU7KxndJWRI01cXe0oInJaiFRkO7kY29mg+v/pAS98fG88p9+/hm1dHJ2G0Bv8QQgKQM332qm2ptZyVebJMlpqMVBUuVmnphzcl6oPYABeFzzcJvXfLbg9hGs1mwx18GL9Zo0yOhleV8sGMtj1Ge1922wHAZA1EGb80tH3w0DIkbfVG0Otvq1dYVlLf6wB8Dkwta+KeQYQa02qtpfIMJ1sVQdR1jNDcxO3optHMtqyG/rZshbRSkN7qfIIe4MH8UUFlu1jQWvDddeKc1JB45siyTrtyoEUVrMdh0iEGyqQwUq0dJQsdCbrCNKILCQtr4qKlvjHuddI9hoFlayiJ+N3UcStxNy74uRxAjr4982JUbZn91WT/r6Fj9VJUBonSqR+wj/w5/fczMllsvqA9rw4WQtr8HJHamzolw8M1FMJHwoQfTWWZfKuZotRO/KabVyYCmdVTsfy2+f6v+s4AQ2sncSG7cJPi0+RdNEaV7T1OZu1OPmhEDrlZgSl7qwFutDjXRLRFhbpTd7KFGgtEqr+PyhMk8H5ilyMx+orWxoqGDpsic52wN4+b/9AViQjrlK3TVa+9C4sSc2WETNT9cOrqFqKUt32F10d0duPj5l7ZeXIIyqcUgNrLJ0ZymFKMb4CTESY2dtFemd1ispTtRN44htMRoZ3SZqGGBba04KGOrjjdXT+dbV50EN+WwqhCTk0DlGUxkYrtZdjW9auxK6je/Vbv3ZpQtNTDM4ZEEle81qoInEkXZbKut322A52dPNIdI1sooXa2wEDxaU1kUfG8eWvW+kFx3HJPszvl7i45CIVwF6/RW2hWHda1FQHwn8Nz888PbVwbxYRCilUFbT5rW128xyUK1veblUdDFBsRS8iYkJCqxFuVwKi+rGPHsugedFKT1yuSjLtyBC3xqcm+AWdiqNOm4MpUbf0c/LhdZgKQs3x5kpGygw5UxV4T/+f7/cmttdhbXWTaxLZHBjrS61nV9JMXLTOlpBDtamGIJNpsinm0rCtUTmDubsrYQxSFy9RjC+6B5AIiYGjX/O8Z4dDHVuexuj92HwGg2oKM3t1J1dgxPkvTVidoWNzSO0QUiRWhfWpdgCR7i5uaHWhd6bj4JZEHQawdHQbe7Q6xvpSnVMcq3mVaKS6FVZmtI0UPDxKY0cEV4fhRzVSgu3STjV6PVx4ZiEVsUAsyZ2DHpd2jqoZBoRRQjBB+w9YerbeeJBqoOqYpzVJnsKrTIC0X54ENf9x7AzzXN7f82IwOv1vM8Mj5Vq/xr9orbNe3v9eJ13T4IiXYDOX74R/upHb4gYHe90MYfyj88Ly2Uxrvb4rA64mdjdyjGDTMGZUo3nc+Vc1aVM7dqfFniuBn6V3o0O+F1bKVuQ+iJOPrI0nKKXWkHg4eaW4hISQdh21RyFLx/PfP7lkzFNgpHHBwhSS7eUwgnnQy9mCD+V2uAmU9X4pZZNifFsCfReGCNe16jXaB0M5QHrEXpPUC3dGAAMOBI7ZuxGG2CaMAHnYsjrQHqb3fzFdU9bV+g2z5piYMrJ6IKoj40JGgYZ2hbFqaw8LQsNdzLTxnktxJCoKtQeaMVSo+zgSXYN39470c2CYzSAZyj2LaVyqZWuk/X5QjIXNrXA6AoiSvExsY+LcmmZpurI7cGU2msDsgEzUTehKwKoTDRnXnXUCRiCStsCpPqwQ99qQSPMj88/ArrrHix6FXwjxLYTz/buqwDb+cD2epdkYQd/rB4cm4G3hHbEYH8fNenPTw/Cf/3nb5lCAwKn04WPbmH41dOZHIUs1k2YUuS0NJa10TE7iC6BS1V6saxBJFJa5csFzsWe4alalmmYwO/Yaf6Q4LST0mqCKdmAcZPO06lwXi7M2SZADmk2O4MY6U351ecfeXo+8Sc/+pR//7/+HR3lkGzWrjY1O3CvZUTFwBmF5qfLmLmv3bxHtHZiNmDKGsqNulr6E9LYzfQqMAeBYASo2dqtgw70tRtiqWzz9NY4taWztQuauoapc2zr4KN2Oyvm2RDU1hrTlFxBwShuMZinqaF8RkYftnEiUAZNUCJop+lE6YnWA7I0PrmbkbDQq01fzG4zb3IuppfE2jE5HSN6t2AUs9IF7UMM2XRuTmvzIWThqyJUIlnMOHip0EJC3KrORvQUpLGmRG9KwMyNIyuEyLM/ByHQfGeuauCQiG6p6aaULnvwbMFytU69LHwRQDYKxrZ5XgepqjrrxzR6RMT8SsRQ6dWbneqlyNcyXBDhNsG//ck993Nkypnn55XTWnm+LJwuxacWTb/44Xa2a+/mhVKXgmDlVgzKXc42UN0Dp9I5V+VS2TSAa99BLt22oe8QnOqImg0ou2hWKfz6N+/5eF4JIjw+XXh4uOGnP/4UgI9PF/7fn39O7cqvPj/x4atnDvNkKmVtpIFt8zfEU89hjRa6kES4mbLLIjoQ4xMgijFwhpRE7JFyWTnO0zYkPQJzH/eyhWLq6LrZM2zP50qZb4wgtaa+mbQXKN02HoXVLjFGjrO1PJZlATUF8tqE87KSopgtno+ZFbc4GHpDBjpFRn+wEagESgMh8eG88noCpYF0brO1H+acyD6s3Hszpk6z08lkYSJVXT24d25Tp3WxwBFYVFiJdITSE8HnG1sfQAcbEKMSmWkswSz/7idIEnlf7ARtIgyf1S0YGZG2F/9bXAwkdUPEd8LDCMzGLnMzfla8Hm2qL4IXZMNGhmSOTayMRPm6z7kHhPj6e3s38/Y+cZgzIHz1+ERTw3DnFDaz5CSBoJ5JYnExu2XGQG0POZDUJHymaIh98UxjAEiqhiJ3+8FvjL9vDU5TYRv1HIAhmx+fzvzm8cL5UjmfzzyczlwuC9qV53Plw9OZy7qgHaYcbYF7a2Vx64FS6yapPwAcEdBgeqUpXhkQoZZO4hPrTck5IQEenxfoK1Majs57UHVPs/QKHFDYUt2NkXN1mNbtxMX9PvrmkjVO4uGyJWA8UAenYrQHmSRAEC5Fyd2EutbixPEx3bKdAEMcyh5WsTCBEBkuVaX5GFRUDM0yAx9RQ48vpRtX1MGfooFFo7cYFJXISQOxwU1Uli48VvG0U5mkc5NsE3u/WosJ7Pd3aQSFP393YO2B5dIJonx+Uqq6LrEIfet2jvjbVSH8UzpJYqcGXiM715o89nq2+nQEkozvb3XjnqaGGLcNdnCrr9Hbr3+NYJ2T8MO7zDFnQDkvq7WCSiUnIYZsvfDaOEzJwc9KU2VZqznstUog0mrnogV1In3rVi7ZgKQ68o61gly/6FscAH9PWov3+RhTGcIhKRoDy1Js8Tblsw8Xns7Fbly38SHrBXq62pTWbTRq2MOpXs0+4oPbdFJM3B4S8yFbOtK71aRqAVNLJUUhx8RXy4XzeeX+ZqK3cRpaf6l4v2+0RnymZwMJruF1kz5UUkgIpsXTEYrbGdTaHOzZFx5qUh54Ko4qx3mymy7iFDGlSyeJ8Th7MPbU7XHm6bxQaidooEmkihBCp9boP48TB4LV3GL7bggzNzkxBzuRl1q4lLZNpCjW/G++moPX2qUkdO5culC6ARGTCPeT8MO7wGEK/OoR3nurICJUabydIw9x4Sdv7hCE8wW+eCx8LhVTLlYLGe0eZzsN0G6W7OtnBJWnrs4nehE+A33uHqwR2VQWRhiP7veYXmlXLZWwobD22i7DtvIKOLKFihC4D5VPjxgtdek8nRYuxYa5x3t/vFSqdu4kUpbK4kQTETGdpGadBhT6auNn74vwYYHVA9QOAO/J+r0JAt+mVPKtwXk5n5lztqa5X5CBObCsKwZcGg2qrmUzch0oLhhYsctx1M3uz1Tc7MgS7ISd4sThkHi4PRo0nW0g1zi2dmeNRC188fERRDgcBIKylJUYrT2wrtXee7QjRstHvSbVwcd0YoHXFDlGT4cadaneRG6u1Tqa5teoheuVqhKj0QmHadBaihmxVlC68UpLJ4WECqQpUUWpGlm7EcKPIRHEdXh8PVUCLUwUXelVKWmhhUjIZoJzLuatYr1IA2PaIG/ofqkxVFoNXIisKpAir2blz9/Au+ORD+eV3i8MZYsmyjEIP3kd+ePXDxyjYQA1wsMx8BONvD8XfnmyNDs4iDeykGve7lZXyvX32bKWF0Um4yzcXxeDoaDDe2f80zhdh3DcsK64/j2GBu8jc8GDPAd4d4Q/fXPg4ZhsMxfhsqw2PRICl9o418qHp5XehYfjhVVMl7esjd4UDcZtLmr15Gk1JYXPV9lG4q4PAhmnqH+I+M1Z7e+pOXEp/xBtlq42Pnx14p9+/Zl5SHS3A3TNmRiCNYP7aDHLRh6v6pL3VwSBjpLEjEvvjzO3x4kUhOOUsR5T5QkIasLBOWfmaKyKFCOTQDpmzpdiPb9uBOPip6Q0T52CEQd62AdzR79y3LTjNBExNy3z1Nit9rruQ8ob04RdG9VSuLANWddmQNab2xvePz6xFEeMsU3gdFkJPp9ZVxtbysHMmmJQ1BUwCZHUC9oDZ7UecliVh1WpMVCLSboM/eCuQlE1z1TdOaYINIk8ZHh9UH72aNzWd3eZpI15DrzNiS4Hnj9XvjpXpih8MnVeH6K5dHWbqVwafH5q/PpZWXtmeAsjxoKy8mRQNnXrTdpzvwrADRjyForsJ+GWro5SR/WKUrk/s62edNmScLUbbV1ivUqj6QYUaeBNrvz0k8y7u0CgO9e1OY9aeH8ufP5cORfhUoU5wvvHwqCirrVb5kJkLT4+h3Ju4q2SUbnb+406WH1D2mAx/Y4jYzEMrVgf5O2N03KGbgFVVmtYZ+/rJa+/Okrond5lW7zDi2LvPRkp+TCZRunNYWaaMs0t8RSTwFjLQg6JaQomf+hmPW/uD3zv3Rt+/uv39L44khsMCXY2jSvZmHIcXq6N2Ulhe5gpuNGup0/NdWRH/SkyauKrdsBYYN24taqWCQygKyUbEzoeMg07yacQPbVJaINI526OSI8EArUWareRrxACCeXdrXGVPz815qD88PWB17eZWhtFA7Xj85F2bc3LgxmfV/Rl+snUeXcDN1l4XuCYlSNGbxwtqnd3ib+ohZ+HlU9vj3xyn5kFQkicl5WPzwuff1w49QSO2tJGujrkRfYQu4rFfeD96vt7eLHd35H2jsC8rkhFoF/9m3qquJH0ZA/XcXoOxHmc3MGf/R8dO3fZrrZ0pbeVSzFU/8Pzyq+fK48lWltLDVw7Pdnv675RNTVAraubcomJfVlduXEE/YpcLsbXzWBnbeT7PzQ4g9+d2ip0Uz7Lh0yUSO3V5xxNMybHYApxyeYVg9hYEMVZOIN0oLpdz5Qzb+5uePf2lro26KYMvw4tVFXmKVNb4yZG99uwgvrN63tCCu6BiafdDQ3CzXRgXVdGW6Q2r4K3U893YrFeYZDd9t7EuCqXsvqMY98EwIzSZxZwgk2iHOeZGIw9tREUxBrTIUZKacw5k2NCJXBeisEtITgBHZ7OnXPz012slZSkcT8JNxGmZBvZ7Zz5o4dECvDkPdhLaZTmfFdsI5m0cRMbi2RySDzExo/fZI7Jlsl/Fho5wBwi0yGarYIaF/fNMfDqeOR2mulqFgtaK61WHpfKUw18qMrSxG0Wbf3tbZdxwnlF+gLxcBzAU+4dHMFbHWFTbIwvgtv1dNG93hRrw22B7iepXJ1M4qfxOJ9FLVvKoXGXbRpmLY2g1upaS+VpafziY+OrZoAYqqjaWNuoZAdGouzfs76vXL/Cak3/3fa9sL/+6n/fKTiNF2p/Pq8rs0x8eH9imjOXZ3deCsLkzfcYbT7RfCsFXGVu2zEA9bohBOHhJvPm/sjtYWKRyuVSCdFI16Va+ntZrc8Ug9nJ28lQ6SjvP3wkiPBwf0Orle66O6urDkQx6t1Ww+n1LTHu70BejbJnLJvabaMQDDm2JrujvKhZjYs4tTBRm9kIJaf/bTYNngL11k3nR22Ds/aHWCWqLsgtajtxL4CQCYjr+MyxMx0Dd8e86fr2trCsK6UZsV29JssKKSmv50BIgSkFHqbA3WxN+dLhkCzFCsmynYFoG0VQOIbIFGFpQlkqVeHjOfDrs/K+TBSj9xCd1eU394qt41Wj7KfjSDXFQTsVA0aGqLd9KLbNc+fb7i2X/YW6/3Ucu1y3Xq5X/Z5OW2tIKNo4N+VofSMUV8RbCl8sga96pqjik21bQF1BgtvOsX3+F3nCy6/9UNhfGbbvffPPfTta670jBb748Mzp8p4vPn+id/Nj7H5DYvB8X/1mqBpRvu8q52PWzaYoOjFE7o5GXjCFPCFPgbZajfp8XnyXbbx59eB3wtrWD/c33BxmksDrV2+4LAun05mukcfTmaenM82J2xYwu5qAAWx+Yrr8vuJizLW43IZZQ9RaiE5y2PifTslLydJNo/nJCz1cgGmeKbXyeGneRx21ttViHy7QJKHqZHp/TK8OCULgtFyIWoGJTiDFSAzKaS3U1rjUzqpCUSHlmUwnSufQlXkKvL2ZmGOkt07K5tXZi6Xrl7VBTNxMVm+1Llvm0LVTsBnYx4vweFLOVfnyonwoZtUXxOl3rr6whcxW3+3J3LZAFQeaRpDY9aqM3iwbe8bSUu8DXp00e04se7B7lOqLc022WjWOQsRbWEmUoI21dM5LZ6HbHG9XPlyUDxW6JqIq/couwbaU3w60a9jL/uD59rgfV6/5+tfXOb9f//r2mpNAq50vH0/88rNHvvzwyJwD59OFELqlZT7ys/ULm80tnspqkhneb6xOANhRb58EqQ2JiWEYg/bNz6J3k0M55sCcjMUTQmQKwvEwcTNPPF1WDocD69r4zRdf8eXTyVg9wzYQ2xyiNO97+eNTPJ2zJbBWd//SsRE35sl9T5xbm4Kp24Wtt2kWCaV3ajckWkLy2rVxWYdyn7eXRKldQAIfCnSNnp6B9krMM2/yhSCdtznx+ibbioiCqNEdgxpTaunKpWANbjUVgdLgkCJTML2d01JAhKmpTQBtFuc2y3luK5BtI8Vq7NKVi6yczp1fPBsZgt75UAWI5vtK2Fbj6Dsamhr2RRmEqLZxmRW8lR+TwCyNmwAER83VZkGN1QS1m3u6lRsmXdqCTe6N6ZW+Jax74JpwtdHnECNoTK0ReyG0FfFnnSI8XjJLOZuUiSSqwmMNbghdHDD0HrsMaGdPW7fhuq/Fl6BbKr/Fhjc0DaMYr7Ir/85p7fNyZq3Kbz77wOPTM2utdmM8Z4miZjrEaOgbB/WyFrNH1zH4e1XviXkUzlNmKYVjnzmvDSVwKcUJ8UoOGUnKcTL92Y4FiihM04E52kI5f3nhvBQOc+L2mHj/DN1rVqPhVW5vZrQHWhkGQsYDjQH/fT4hoy9v1kZhc4g+DbEtf1ijTXQpjefSWZaVKVUOSSAmTkuhyGSu1LVTuzjntNPFZhsDHQ2JNUZ+MF0IKXBelYdDBD9Rk3jvFOXkbaLTpZoej9eppRnRYl0bugpdVjqBgKWud7FxMwdysB7isnZOpXM3NWIIBmZ10xnKwGONfLyMjuMuHSoD6PCTadR54erGjSwg0s20l07EDIJzDBwC5NA32l3wNLGqkDpcnPvbUTdmVqZeGdIiE2eTpAmJ7mQNW+tthKp5zNCY2yM61C08fa/NBtADuPFRpTnajgoxZTRNtNB3MgRXh6Jf93Vb6LdaRC++L9uf9zTZg/Obxzm/PTiXpfDxtPLx+eLuwZYKrLV6AW9ygkqEWrH5RUubLg7xZwdPBGPQiBg173gwYS5VdVNYJ7ojZp2QEzEGjtlG1JZaycke7v3dwY1thfl4pDRTRH//8Yx0MxoaagQpBmqr9GZEayepUOguVKU+qWLuWc3J6tX5vdrspBiD0sPpbHB1VRsfz2Vrl1hfK/kcn1PkqoEKqNCC3Y2bACk0pmgZwULn02NAQ+fVFIlhyGXC5HX42nwOsKmdyhoQidscqbUJfDJHG0USFUFa5yTKTVWS2JlTmo10/eZkJ8lSrSE+ifB6Vj6sjeKzpF/vO3rqY2Cgj9/tC85rKho5VPMKRVFtqEYD/RCqqyvGrT9qLKgodi5mMaZUkI62Sq4nP+HVFAgUqkSUSAgJCRFNmaFuEylMbXmhesEoszCEVoKg0rfvOWRsG3Ka9lT2RRJ79TnlKkBlD8DtFu3fRn26x35mvBebd88fHJy9md9jqc2mv1ujDlFkNU5oKw1x49Vh7rJWWwApRe6OkzXm1U7MJMrdcWZOicMhm2WdYGoC3T54CGJ+IAJTcAMfVW6PB17dzDzczYQ0VO86KShffXzkiw/PqM+V9m7mNiJQVtt92waPidHoup1GKUWCRJZanHK1pyYbyTxZ3Qc+wN3cB7MYOb63bilvypxK41I6RRIFqJqYAqYcoI2ggftk1vA5gGqjhUrpM8cpMIlPWUggJUNrB5HjvFY7gQnEkEhqoJJNiBhRrEjY9IesqhTOCGsZJQiI28OXJoSiFIxNo3Q+v8C5GV6q2vZTg+sMyP8bRryOJNMQ0SBC0G4yIxiVTlKiidB7pGizHndQolj/UVV8s9/GIo3L2juhV0SDBVi3wfHeizGgJBHyTHPDJ0NlK9IXH30zvtE4Ocdm3BQkWlXamttXRN9AtxaZ2Qq+rBxf5rLbPLFHY9erCSs/OS0L+11R9s2J7bcG54ePzzxdFkNeJbCUQvfU0C6lejHuHy6YDODNYWLKifvbI6/vj5zPK2trHA8zEScOO+vGmvumhtBjNKJBCsRo6ZyIkueJ45T45M0DDzcTMSVOpwvqIllPHT4+LQa6BDuhVc1BS8dAtU8FiKcWohBT4JgTN8eZtdi427raUC2OELbamPJEynFjGKGJsq5cVvWgXlGgrQ2apfQSs+3MPoVwiMIkjVA7OQtTtI3FyAyQ40SUzhTz1vmKKMkBua52apZuky4Zaze0IDyXPd1qPW18zUin0ugkUDuxjM+lBB2pqNIcLwhdqSFwcdmV4BuZjkMFDCv4+gkCHlxeWwUTY6O7X6gI28B5iD5eZllG83M2ElEtoNAl0gYI15TQi89PuhZSs9nc5mg2PgyOYxKhd2I5g+MWw027bhCTt3RCIqQJ21pWUp4siQ/RjXibr+9OkIhqc9kdeXFCbvfpRf5w9eXWiiPd3cYqdQcQ//DgfL5wmDM5WWvBrJOMqibB2iWtmRp4yoFXtzPf+/QNUwrklNyduDPHsDkYB3GKVJNtEDfEAF05aPLxtOi1aWKeE69f3XI8zkwpcDzMZiy7mJpZTomcM60V5hzI84G1mcHMFGdHle3mlVo5TBMxmlLczXFCvFF3zInXd0eWZXXNHzFaVh9AlimTm4GuPZiqJst/dzx6I7uzdsxwCJPwvM8ZAQ6hMYXGfexosjlMiYEpWKpee93kP6p2T/MCNKU7sliaje4lEeJkT3itNg52Q+K8dppEQsD5niab0egwJlRwvvP4FKPN4IuteTYxNvSv11YvYJGvp3LD2VxAuqAS0WDZRkfQaAoQEhOxFaSvPrjtAVNN7bGlAzUkZH1CWkfrSm9GNHFBAdPsAeNZp0DQDuVimU4raF1Z/YTs3cb+2tXHCK6m30NAgo35NQkmtRKibZBBXM1BQdvGuhqtlIFhcoXK2lhaoGM9cthbRddZx0B0w+8+ToHfN5USQbsbASXhMGdEZoIoebKh0yhmDvvq/sDdYaLVRkg2fiMkTucLOWamyS4y50ythRAnENcQcrGq82pK8bU15ilzd3/g/jhzc5x4/fpuA0hqNdg/RmvY397MvHnzQKmVHODpvJoPSmmczsVaNTFyONya1KEvfEHd9n5A7zbUbM7NSpSKNLbB8AGKxCAc54nSy2CtcUyR3gIHcVsFf0DT8K9zC/LCRJZGSkKIwsHJG60bZXCptv+6hDVaza2qNMghEqJNwgyxsRwjd8HR5hS3ofUq5pzcXH5kDM+NkAM2d6699eAgj2eo3tUd6+iqjbCfmAOFHifrkG9BAl0ydbgqj7ZLVKI2Qju78bCVMXUtqEukGOkfpF38pGzblJE5au+LfG3rdn0E6xkPC4nmG2ZX8ypV3ULLsIPeyCKENG2fSYeurwjB37jbxIWLXjlpxbOOAYjZfRxxu9VP238tBndu9k5f/I5p7R9//w0N4bP3T5yWSs6ROdpoTc6B45SMNRQTMQfXCHI5DoUUGyFa/SZiwryjZTKMkEZBXKs138cA7jTPvH19Z/3MFFH36ujYhItJf0RETTV9ypGcXIuouPEQShTl5uGOIGpuXRujaNRQgHRiMiezMVhtM6dtE84ybxD1xrmJW01RIVoPMWED4aVVsjkOuNemgMRtkUdp5BS5zdaOiGIZSIiBuixsFge6E0BSSiTfoXMygK2QjF6IK/75ZwkO+DQxJLep6/f0vVdra8ZS/aEosjeZAq6Xvr/2aqeXr52W+Ck7Tsyx1lQE6YYWN6+37N4psjyi5bKxmkx0zZNlEXo723mqtvyNqrgd5ZsIuKprVbVqJ5DabGlv1gaszXxHOzYZtQEywVhsIhH362BsOcZgs/cawmx9vVCXCyHNyJRJMftnvE5i92AbENoGpG2158u69Vqa5Xd9fbs/590Nz8vK69sDh2QUt5wTqJIcIFGsD6hqA8nzFLdjuzV1ZyxDcWsdsvhuQ+ALMQQxsgDGKX24v+XV/ZGbOZNj2Jral8VFmptus6A2vFydKGByjiHaKSQEJh/aFnC1A6O6aR81EUI5AAAgAElEQVRnidHMhmZt77rZDFQP1uac3E0T1xdlijZUncWu3+ZG7XPVah2+FJ1zLMKlRi4X8yC1hWEPbCDIprHEtsMbYSJawz4GgkQkQG8mdjxOgMXrHUrnuQVX2zNebQfXKereAhghJVdjHfY7t9bFCLZxmsi++Lal5QsuXL3pOEnHadzC/loAuiKtoOuZ2gZBhRHfSMq4r6NpIfUGrdnv76PGC6735GixD0IHn0zxWLA/B1On2Ks6QfJEiJl0OJJiQpLZy1tfk22HGaNrFquBlDNhnnwSy10DUEJI+331n2/jfbb9S7f7/CKe/Td909fv0RAS5pyRW+H2mB253P0Tu5qmTlHznZSNo+o3qKuflp1WK7W9fFi2+9lEy2GeaNVogD9494rJJ8xrbeRsWreXpaDenDbKXfW+Vdver/fugYsBDL6Imja0jjlLu1+D+WhlhaVzazUH6rW6ZtDVaTMmasYu3VtDm7jws502tkF2820J4/d4P7Nbb22tjY9nZYpCTtGb9z5+1pVxcl2nk/bwTUgMH5I2vWnbvAqdSzQBscruZflSa+eK6SS7vTqMAPQ/jEx0azHAIJh/bXlcH6p7esngue4Lr3vrTVB0viPUQmuVVhav04yHK9HaIojZRUrv0DKizTeCsNWxotXBLwuuLLbpSYz0Vg0EbCu92thfTIl0vEdyJsbk4ueuZTQ+nQeWva1tYCFPWy1qJsQdaaaxO+7T/sn9tHxZpjPAtLHpbaDUd605LQ2zPmM3FQtSFDfbUfevbGhT1rUSQ3YwxX5pynJlttoYs20GJDU/IbwwjpYQ3N3fcjzMdK8x1rrSUZalsCzFTm5x3w6vu0wjqG8KgR1vPbRmzezmE5XdaHT7gKtzODtuimMPsbTuPpoGDAVfXJu6n1qmYPWWpaU52mB2TsH0lFKg18q52e7euhBSgrpQmxryPWV7QMGMeHaoya8tjNZNG9+xGg2laXW9XOP5lm4h8SorS4ucqrI6r9gE04RhDPTNX147XQMX7FKVAxzagnrUWqNeZYi/+YKVlz8TAOKExglyI/VK6Lf4uW6/P1j/U8W4xajpFA+EWCVY8PaO9IL0ioplcxInE6JWJfhxKcszKRRyFIgZybNnLuIZgx1l/lf2ectxSxwUiraJjziMLgnafdDe97Ct9r6uOEUgqlBEif7CkYx857R2GALt4khiCuEh0EOlNaF3ofTi3onVp84BCbRm4E4pLvbsigPRe2wVk8tMIbCUypTg1e1hTzHVmDDnyyOKmv+mmvFP9fkvkw5x276mm5Vd97S0VrdbGPKWGHI30ilhPCB7j2tr8tFSGRYL0euEMUPZHBwxM1eTrZxTdA+VjkgiBRME6736+JmxdFq3dPmyNnIS43GOByWB3qGXtoFV41ErRicrFYREB5bVNGwHySLSOYrQqFbz4n02p1n6UzWa4QAsfGnu0KKrEuhoD+zAxugfb1XUmA4ZU1JXP7GNa+Gp9ChMNUFIDvJZ6dvl5cLGvVHsd+ynvyDoqBmjbRLmdrg7ouExEw9HRG5s8xDsN4lu17iNSOnYgPy73kLRbXPzwBNBXROI7V7YVe+MqXEaGzYxSoYNLGMvAV4A4V/7+tbgbFfp4j6witcLdhXjRDxdCiEqB/c2WWtBmrUvBoEefOQr7YOxIQrqAXGcZpa2EuNMqYW1FB6fLqxr4fZ2pnc4L8t2fVMaHhX4jF3bvCNbtzbDspZNpbs5/D7MhfDvjR10k9DcxJbtcZkNoN+wEHyGb6i5Nw6HA2Cc4ugPPgQDQ6QZhzgGszpfLsUohcfDhiwyftdWmtiTH2rxwxpwpE9VG6pWYy21cy59A9qus5Hk8EQVC4zuW/tWVnBdYng8eMr1grTOOAXHW+9XOVbadQDDy5Jrq2HDCHbZWhA4MGi12DWxbQ+Gbb8fb8x+LdtVyP7nPV1X66v6z220+uv0Qcas6DVosxPwLYj8LnxDJF2n9uO9bF7z6gRANs3bTeZzZBPf8PWtwVmuenrjg5crcSwzTbUFHkQ4zpkkSnFRrO6F87XKO5iKdhDbTbrGjdO6LpXn00IpnbIWzpeVy1KYp2SCW1qRsLcqrm3Tq+utllJxsy/Tl62VpRTMOLdtyN2oT+tVrWiAT9hge3tUJgpt6aRQQyMZldsXSrDxNgyk6gTrr7VqD1YDDXNU7tW0aGK0kaxttw2YsLG3AAbJI4RoNZancxawHbMuNjS46FDsg92e3TcrlE40YrruC2d8vZzB9wD04NvnNMdZqB60bCfFeFHfauKrwMEzL3n5Pf9N24m4SY/Arj07MhrZX38dUeI7xfVmsQ9Wv/waa+5r32VP369fe52MBgZffL/+31UUjNPvamRNrZ21i2zvb7vdGx3eoN+x5uwuSAzWGjBEtvmQauFyXgnBFMiPB0NFLQgDilsfIA4M7Puxoi7KJJsgcs6Z1jun88q6FK9V7cKt3WJ0qs2IlV05fgRlKc3Q224SnOd1ZakGVvVu/UtDaw3sadq34N4s2xGv5Uaa6Y8lCCknb9Zbr3OcoJe1kARimLbxr9ET7b2bdyUGbgWMCmj1n2USEnxXZVjAq8+t4pM141R1cAuszu9GFi/4ND9428cWQt/qP9nSr+sFPZaF+rob52nYgmJfuWOHF2wMK460F/O26T5Z8iLwGLUk25MX32zGrQ2egY0WWpA9EK43hC2vHO+l/gonPkT2gHvxdXWEj0AdAw0jqL5h9b/cIbjeH3T/DPsdfPGl2ye/fo2v/rLSlguSJ6bD4Rt+/++b52x1OzH71sANfPjqK9MQUkjJQCNh2BZAF/V5x92Sby3DxiAgyXucIZrzbwpM08yUo8tLBnKKfuIGjP5krRsDcCyFrL3Rqk2WrMXcnMrazEW6mCbM0sz/slec7WMLoVZXrNvSWba0ZaTsqmwD2c03pjlnRLvp0W5ZBIh/1hEEhuTbBArdtX26boLT1TVQgxi1T328qgf3dXER6egbgnFBhyqfXd+lmyJBxUsPzEDI6GawV2h74F2FyVUQ2t+HTItgp5cOYAhFupIjZDGyfvTv1WCk/t6FEmy+tDgbSTzb2MYJxyK3y9+CbENRxs8IG2Vw/NNICV/0WbdA5uqd/U9XtfNvfe9qW9q/dwV0bXfuOuxeAmFfP+/Ud7gX/d/t9/r1GjBCvbhKRynXsf9bX79XCQFspAqx0/OzLz5yejoz5WzMHjEYX9uVBR6ugXMFpc8p2UX6LhlDYIqJeYocDtH6kS4ZYm2Ybk7A3cj3dj3de1fW2+wd18G1k3JZCmvtPF1WV1fvXEplLVaLjlMNdedqDzjF0r4gY4E6tVAEcUTYOJwK0skhkLMNhpfufEvtqJrtPdo2TxYzmvWaUsT1jcxSIoEptvvDHMAUMnZpYS0etNhQti0Mu86l+X3ClPjArPa2RXF18iNXKZwv6hd10r6KXiytICawluhMwFFMTEsRWggkGupjaKj9t4oO10TwQXuzKPBNyy38ArIBN2FbpDp2R2BP/b5e7m2sHPm2WnAH2a5P1K+frr/r30T1RaDaHvIyxZXfcT7+1mCAv5d9Git3Qo7EdCC2hVgWvunr90hjmjxkrY2YE6CcLxc6TjrupgCv6DbgbEDLGO6NmzB0DmFLjSVGDm6vYLo9igTrSw1FAVVLnaubBK1lIadoagpOfi6lsZRiAbpaMJrUiPkiXkplqZVSh0DZkJ1UR1vZ5ETAdX18Q+niCt8uYxJQDtPkUxTmBj3nvPXytBvLKceJnDOFaqckgzZmNWNT3U7+GAO9By5WGZqRk/HFjS9K4P3TmTUcqWpE+LFsU7DGypRg9M8UwwTqWEAKOgYG1Z7H+BobTvfW17jn2+LbDh4liTJFEOloDISIWzVavVsUVrX6ug7kVnyhhmuRmv0/4aovMcrCl1mmvkCW8evl6u/X4Ndvz1Dur4yDknf19fWgftEPHqfg107m3/Ue4t9/uYFYeqO9IiHTu81BI4IkFyZonYlGk+9onltbodTOshqLWoA5Z5gm8IVtNRzbB8sxEKKxX1RhnjNB1WlzskkYDgV1VVPLa82a+8EZH6010/PxFkiOQgmKatlSnuZmpKe1cl5We2031e5L6VxWS22bn2KjxhiNeMUmYSKyPZAQLXWcYmLKw6nb/p6cpB584eUo1jZBOJdKFiMeiJj6nqqxmRipuBoKHLaWhlA7SO9c1BhFpdvrWw88lcaqQ/DLTkwj55l42jyCyk/64gPKQ0BLTDLLMMNhcnu1wPoGeAzSgJ9GqkRVsvz/nL1ZsG3XdZ73zWattZvTn9vh3ov+Eo0IkCBAShTFnhYlVkRTJZZdebBLchw7lbKr5FjOk+2XVKpSlZc8OZWUnbLyIEWylMguiWooUipKoiiJpNijBy6a29/T7241s8nDmHPtfSEALGGDAIF79tl77bXnmHOMf/zj/4MQKFRE6ShUuAitS+PTQezeHQkljgn4UkmOJPat/XRyr/BPl6XgCnq8klMTISqRCg25HXPnObV6AC4JMnfWneZNUt1cymQxcPnml2l8OtCXtSrLilFxZ2Avc5N0jUqA0MWtpznYu8zZBz/E5NZzjM88RWHKvtwokJG51Q3zjY+3H7buPDHv4gaZALEmIYrQOZd8RDwtCm0MFtGYMcYQ0jfg0yxicFJ4GC2znV2CVXWyk+9vYCTR6HxPBjCa/uaL2Ja0ThatY9E6Gif9VDll5bTvfBDkVPhfZGs+VOL2JtaH0iaxPQJFL+osAWu11MFWQaFE9UHF7CBGv5FYRZr/E9KDT6d0l4nXCeTpvFDkSqXoCDh0MjYCFT0+RsRPU8jsMYKNGWSTfqADhFgui70NkS4m8CUqTMw8VVnkBYoYWqwWfVyFzIo2yZk7KJNmHXNttFzwQUHfiUzBJ3CHLMkOlfhMCoXp68XsFr1KrHgj6Xs1kKT9sHIyqQzcaGn+h7dOYd+Ylr7Vc974vFz75odsVvmE1Mufpdd7MzbP8ilRMotuQTuZc/TSnzOZzVG+Ybx9ERMzmUOhXUvhGrSF2Jz8jdfMj7cPzlYAoTI11rWSVNIl2QfvPW0nvicyTQHKy020VotUTTYESigkUYxvC2MTWhowxqYPmASSg6CgLrc++jEFBXlQNgioI2mrBL9zS6TTh7BcFFq8RbI5LkhtKC2NUkjVEWJ0hOhxMVIUJdoodO6lKplsyAiFkBhCX6+CxgURL1NRVAV8lBlMcbuWFDBEjdGBDmicvFbtEz8oiVIrayT9jYA2eGXSySbv5SPSTokRnYCkfOr0xAKVTk4t6bBKta5CY3TExoDRAR8VTfB0mCUuk46KiMyAxqiJQX5XZrZ0X5+G/rTTaVg7L1o54bMdRqY7KJbpX+59S7m6nJvpaehqadagdAaB5EQl3Y98aq3+8472SapvVyGcSETHpXWDrL1MNV4J0OWx2D9kyCj2p38uKSSbaVnsP8Pe01/n4ODbYIfEK7epBhsYJUoQOoJqFvjFbWy7h2mO3zL+3jY420YWi7cG5yxag3ORuutYdDJU3CafDudkdioPN8vNF4J7CHmWLiGgJlIYTQbotXZphjD24J1PbHCJZ0WbiLkyYRGTNIfvSfcxarRJ+UgI8u/ppvmYUMNE29JaMyhsMgE2aB+onbQ1AhqsRhmDMoIcyqmeJiq0SidiJjAkhyulU7829U1T+tx5nRhEcpJJSt8RE+NW1Ct8+ixJXzdmOws5f3oHtNTrjIBXER2W7J28oKW8TcBLFA0foyLB6OQUqFBepYFrncjuy4CICNUMJSrxWa18yQbNwbYK1KTlqVaCJJI8QeIdNexKUXnHf+esQyUwKLdI+nSx1wmS38rE9DuID+k6BMxeBifkEzIT2ekJ6CqPn5FjcRnuQrkWVptOaXbWLhZO9oqyfpCR8cHGA/juj1jMwFEzNzXu5Wepth6irHYpY0e19z0G7gjfTniTw7h/vG1wTusGYmTRacK8Tuho4GQ2pwvCP3UJ9cTH5Da1dCtGKSpbUBhDUSaUUQGmAJOel29s2q1kbSmyUFzrfJqq0JAAms53Av7I7Le0ZNQypXEhpb/p5hLVCrlcpQFtIT6Lb6UErMtfsVZJpjGJmCmVRKDTBpI2VXn9JVLZeGkj5J4dIDS8lTlF38msaSSKg7IPPQHbJBlPH3xqYtPfx2zhrvrFmU7t9E4apMVDxAAdOR3N9yT25AWpemXRi7TacrHnucu83HP9F1f+KC/lvkZkeTotKYgqqc4tf1GlEyesLMh8GEqpmd8lphNqeZrGdJ3LrED+Pb/W6gmqVv8Z+0tMfxrJlXCfxnMn2NPXxss/kWTBk8YfQbxUMy0V0BblLbPpFY79IV0LttKMBwrbHXH46nc4f9e7OTX/Aa6dSKtQeZruHdox3D6Zk4GUtpX6r00vZo2F0KfjvREMMaI0FLagKoUUXlrDwOhk76Dp+Uur3oTRs8qYUEZGnyCgtKLUul/g0twXa7qsIasU2DS+I7L6S0v7PG0vgSwUPJU0/X0U4n6IBq8VNq5sGiEStezORuWvNsmspOsPQZDK6CMuJsgik0TTAsgkBIUoNxilKY0srvG4SEEtnF+VTlzvFcFIquzjkk7Wr7P0Xirt/jZhG/1yVlntQM6A0C+3TF8TgneM2e9SDKN8Drr87DeioD2Sk7cnEmKdQ0Itf9zfgOWpJinlyte+ErT5O5LDOG0tOZhXTk2VXjf3PvvXSlGoQiC4Rjbf3vF8ZdNIgxKSjaglFpGeF0MguIAyGoyWfm4tE07W2n6qKab7pqIG44jRoY73Keua0YbCLQxKdXTNhPHiMhu3DtHjdaytCKFjUA4Zlrzl44fIlEizVPqdCYBIiGWMUTiyQXb4gbHJd8OknhZU2iY9UkXtFVZlGU0BOorIktidC/YoAdh1LtVwcqIUVuN8h29ltxpUA4oELMHSUDWGSFWUAlrFmOrYfADJe0mbJydqqi9e5ASSWsSSmDJJ7EvMfc2yd6UkxWldxEVZ/Eqk3IlmuRMbLeALIVJaxdag4uKpdcZFpOkcm+OKq3tTbp4s5HWCzKR2SEN/GY55YS/rrbiyOMXGUBHRIpIVl6ddzgKWfxBSWMgJmv8CkkZO+q+eP7dMDeU+vrEYe2PQqtVYktRRZYZYOvNy0KY6ZtmTVP1L5eDUCZjKtaQKQfrPWq9cR/bnCaimxrhWpqbW1rFlmTKe1LprmiRIp1DJUCpnbiE9B+8AQwyOpq5FvK0oCPjemV2hKUqRhWnmc5yrcV1NLFsKFVkbeobDIRfG5zm7tUOMJdqC6kQ5sWvdipbw33y8bXAqlU1iU96dHKGVsct0Ic3maG3peZheENIuUfnEKk3JdEqaB1WA01AQ0zC0kjGxoGjDsk60RuoyqW0lJRNTUzmJjUnfZEDoeT1BU5G4celLXgajjwqCpG9dOjXlxAliKaHARBHXqmwydEqTND7mxafSZElKA2Mia0SW6TugBBdDq8DFjYL33r1ONV6j7TpiEJnLnY0h68Mh87ql7hwni46D2idEWK8IqiW0j4SCprbL0kEknzorkZT/M0fX6mNFNFXSvAjJ4Vqli1cxn2xR2iO5Tls9UdOGu0qa1zktJ1fEuRHPUjUoBSbpk2kQqcp8rTFiYoeNng7wpiIohfYN2nuiLYnGSjA5D22NlhnBpDGksSsCWq7rhCfdtmhrqapKJqzyDeg8XQzo2BCaVkqMGDHW9KWHd+DqBufmVOWGZE9tzZUXvoxbHHFy/Vk2Cs2r+4pzuyX3bN/D1vA0tihwXYfvHDFK6VTYPBH65o+3n+dUOSyXM4zZggDo5RW1zqyIVC8qSV91mt73UVI+hyZ6OZ+UAhvkz3O7KY9k1T6lG2hcJ4SGGPLpodNkibh6lch7irxIBivky43aJKJDTOLBKfULkr4FLaJdGXSS/mX6bFpS4lUytYxkKTndotS8uTe2TAXvDIDgI2iDVYFzu+sM10doFfBe03kxP5JxKY/R4hptjQShR6ZRVhvcq2TwfjwqZgrhnd9fZkPF/Bv9iZfZLzFtJunMSsedCjlgcpNEqj+9LEbJ8mwxnZrLxFtKBhUzR1dqsvxnfc2nlnzalRcVrCE6dPSYECnSGGKhI7WLKFWgXEv0TvrspgDf9UEZkNMzKoOpKlHF8J2UHW2LSb3rzjl0clcPaRhB60jhI3HR4KOWrkTXYq3G14qiggKHP7kGscQ0junhdcpo0MevsVl0mPEYFSJ3nyk5v7PD9ug0eOjajslsLvfDx3Sw2Ds2yDc+3n4qpRNAReeiPy0K0a1dphPGaEkxtRViekJiewROqSQhkYjYfc2TLNNS4FVImyGbD3lU0iDNSVgGLUj9C8WcpAgQIz6IBXomNuehu5iUDjIXQ4e0iXj6xaqIosQXpO+a67LWL2skaY8ISttFWHWVIgfoKqhBoulFz4Wzm5zZGrMxHnE0W4irlY8sGkfdejrvaHxk2nQs2pgs5IVmTioT7sQmSeNWqfqLb0w10zVkiZawHDvIP4NETo+hB5qA1G+MfXqZJxENQH8i5s9IX1cSvPh9llocxrSmTZqxTdOiTSHfaT8alv7Zv7d8zsI5Ct+ikNrPIfKhlkAMtWhIOWn2B1pASq+oYl/CaCvsJ+09bdNh0RRawLYuOIqiQgHtbCalVGF47k+/wP07CtopQVvqpkV5jR4McM4xUQtpAbY1PnY4PUa5OaEcsB00NAVGjzHVGsW4QCvD/v4xVe83KymxtZZyMKBeLKibd0jfc1lLMH1z4pgVMValug2h1KlIYQ3KJJdjbfqaSymNDoEiCCFe+paCPgoNMC0ISKwSEZiSUtsQVUhsjYBXslhN1j1V0oNrkiq7SohhQBHDnQmDYqmqFg3EIDZwjY90eQSMKNeGpu6CSHkmsoGL0OWTJrUXeqJ7CooQA04UWCEaDIH3nKvY2qyI0fDy1X2ee+2Au3aGRHxSPJfUetpEoo9Ma0ftIgGbcLMlTimfjT4Gc7qqiUSj+/QSDVsGxtbhVcl+o9Cxw2mbFO403iW9I4X4bEZJYV3iwKZ0BYUmRIUyIppF6CRgtcYGnQAk2XjPDAIPbFeURgges1rqMh8j26OSeeu5tQhcPWroYkGrTDprI157CAHTtljfEb0TuRUnjmQWIyOIqa70ISBnhOpVKoIPaJstHQPgaBcBO6jk242RtukwVggVsV4QWwdB6r9Pf/j9fOgDj/If/rf/GVMOGBpFNagoi5a6rmmPp5L2aqgKC0yxtsB1CzSRoih6nWGNorSK0Xid+WzOYr6g60JaY4am6wAYFG+NCP3QYWuVm7hKboQITYm1XFlYrJa6cHWqY/lIxaACpYUwrZC0UOJkCfcGZfGyRnBK6GCxT40g2KS0l09sZRITJteRshsbEpVsJWWK6XVzilYQ2RwahgZq50SWIkqd27QyFRIViZygesjQqyXmSD9Nl0+r3B6RlMVHxWaluHB2h2nTMZ0t5LV84MbelO2NSpzDbAG0FEoxDZ556+kCBBP7TXE5V5KHc+OyLoReWzWDWetl5PGzIwoFx/OA9Qu6asj+tE4lQaA0uQLMOK2WNsEKsJbvoEV0i9vQYY1loDVWBYyR07+wcP+pEfdtlVgTaLpAiJrOB7S1nN4sGVRDZrMFa1XHhY0BUSsOJg2vHNeceOldU9cE14pVhk+IaoKffZT6w4XQj/7JhrtkjunUHgvOp8xL6KZt2xJDoKxK6W0rhasbChVSO0nU6T/5yQ/wn3/jV7jvoceYHt9kOmmYTRfUZiGnXVViNAyqCmMtnkDXtFRWAiyXezEExhsbhBCYThYopSjLAcOhoaoGRALT2QzlI2ujdzgylr888a6RPU4rRWFlsDpLzvetBRK8nJHXFCOrabGKMQVQGkXNhzOiUuC9NN+BXlXAKIVWQpETKUnRYm297gW7dKptfNaP6JNwWcJ5TrAARiYystJnHBdKrB1ipPMSCLVPinvymzLVvpIxLtO6JRDjUx0l9a6mMpEHzm7SdI7JdEGIUrlpE3FRcTRpsVY2lbrpWDSeOiraYPAq1ZtwRyGZT+m8EeYfaZXMZlOQrhvNZrK6YE3jYsm1qaMsRaKj8yzTvxAwEbT2SVF9STjXSuERVFQrw0i3nB1HNorA9tqQtg0snGdYWc5sGKoCMYxyjtaJDQKx4/DYMx4FZtMpRldUKqAN6KGi7QzhpGXeKmLwRB/oYhBT5BD71krsul69oh8nk5sip2kMFNUA1zUJZZfRw6ZryONwzWJBiBEThMARjBhY+dZz85Xv8CejG+xu72KKisXiBNcdCukgROGLK2kltl3Xt1+8d6ytrdF1LtFapUVzfHyESqy0FBk0TUNdN2JTMhrI8Id/hzWnsTqpjZEYQEqMdaxFKZPquyUY0QMArOKHpI1+hW2pAyrNacqPU28PyJZ4ua1idC6aFEFFvAqQWw5p84ipjunTvpT7rSKmRaqhCg0Dq9OVJAnNxPnNOIi0V2I/9pVfchVskviXKtgjs5caqHRgp4rcvTNiZwDHi4bSKrrkp+mi6uU262Q7OKllwGBBQTS2Z1PJxrYkI2TQRqslgFXiGVuoki2hBk6tDeSza4NzLd45dkvNdinE/GnjWHSeJkQchpEOjErDrPUslMUFnxy2Yb2IrJWRgYWBLTm1Jgtq1rTMZ46yFBuKo2lkVIk28aRuWLSBWd1hjaboNAeTRnSFEWuOypok3u1RQVJzZcSGwdctoZMU3fctkNT+ikteUIzZLgFsUaCMRoU8aaPxKx6ywaeUOC3WkEqYznlU1/HkwxcYDQwH+3u89NqLfPiDH+AHXcfJ0RE+xr5lp5TIqGZutjEWpUSCFRSLxQKAqioBIdd3nUuHVaQwBRpFVQpD7Tilt3/r4Cys6cGaXsRIZXQ01Wcxtw5Sm1tl1DPVYnnny+gg0tC3NqLyHGSQaQqXn4to+UjdIwCMj/LeeUPIH1beIMl1KkmjVyUZ5efX1XMAACAASURBVAZldDKTu4XXqnsAQXi6aEObfEIC6bRMhPKc3ebEvd/R03/IjdTsjAz37wwY2MikbmXsTBlaQuIhCzc5O5ZZbRgU0HQtIURGpWbaiEmSTtnKkmu2wghKbYb1UrMxyCNsFoNirUq93yD+p4XVGJ+YUAoG2tAVwqlFGUZGUGvnFbUXhNYA4yJyeqzYKBVrwxJjFGWhqRtPbFq64PCdofNOFmEr7KfD6YJZIwwykZUR247xQKaZOi+2g52P7C9EamVzYOnajk7JPGhIG16vbxuROdIoNfhyfAzQWmz7vCc6aUF571LbK4OaGeaWkiCm/r1CceP6i/z4Rx7G+Bknkwn33HMfW6fPUIw24WRC7ETwfG28JpzvrqOuFwgQ5ambDq0sRSFps7WWrmvJShi9Fpc2jIYF66MhSsOrNw6Zx9E7C06jFVEZ2tb1u5a1NoEhmWNIYsColLol8asovJSQejomEwBUMhMl9ACOMpnTKSvPBbER8DEsdW6UeFuSAz+d0n1arHLi/DcfMenpFMJxE3DHg1U5yIPIfXRhKSf5BuTzzioWMoOFNBallSzmU0OLjoHOpRZCDHRE2s5RO0fbdgSv6XSQ8qAwMi1SGowPVIUmSH5NJKGLpEHdKPdZoSiiZ2A8w8JQJpHlGAQQ6Zzj+KSjTaLew9IwXcirSG9NMbQKtAwfOK+pFzI762IBxjCygTXTUuoC58F1LWVRiYpDkiDNQ+/lsKLtWlyISJ80cd0QYTRRXzQcz2ppwERoECDPR9goFRWezsp0jR2VjIclk9mcphPNJ49wqWMuc1JaGYgUpdR8ru0EOsu1X1xqV2VjJlHSSH6dUZ7zo089wqhy3Lx+wsHhET//uc+zub3D9vYpfve3fp39G1dkAms2E9S1bem6NrURZeSwCy7pNFucc8Qo5s2j0RhQTCaTZLwVqeuW128fs3PhXTQHB+8sOOW0DBRFniAQ+pL0MaVm06nH1XgI2qb8nr614BOrIwSFUp5MIk5DK3c8tFqebBF6xDBPD4Q+/Pr8M12n3OTluZZhk5hwFGmrSOqsWHSy2NuUaOsEODh00rRNQZ/7EPmVU4SKU5kAXF6BiYaN0nHv7pDhihp5QRB7eOeZt45ZLYPg4iEpSvDEUu6TCwySpUWZWkPWSAtkkaZt0GLTIHOxJK+aSOt96i/rpFKvZdA3CAOr7RytC1SFZEJKQ1lY2ugIXWTRBqatbFoj3VJZze5aCbGk9dDGwMAKEcT5wLSRjaYsFZUpUoKj+nrLR5kQ0sYkRzBoutjPurbR0mLxSrM7jGzZwGzeJXUMhQkyCjguLIPC0PmkpJicyKuiSN9RoLCiQ+uDTzPDeRppKRkDGRNJAGGQCSMPqNBxfvMuhtWE23v71E3H5uYaWxsb7O8f8Hc//1/zh//5V5lOTtjbPyJGGYccDoc0TUNpLMYamqah67qVujNQFJbFokZrzXA45OzuNkeLlpdeu819Dz3Ot775dQ4O9t5ZcK4PRE3PBZJOTWJ7aINWkVKJiDJBTjivQ0/ny+lHrl2cApXEGsGndkrs1dMFFRbBLa1FbU8rj1KBCo1WgUXQ+FzNppxSxKySeU+amZPdQaeUOq48Pwj5INVsOROOUUSqBGjIkx+ZYpaR0OVGQL9FSPpX6ZadytA1DZ2W1kNpZWPx3lO3nsmiY96IKoNLdZRRirkTKD/EQOcKsZ8LcsK1QXwrdQzyuaPqM1wfA1YXkt1EEbQOvsMpBcaI4awWenzWUzJa1OBNYWldx6xrcR3MnNhJPHCq5J4zY5rO07SB1w6mcvqiWTQNXinqtmXedkQV2RhUxAh1I/OzrZdTU6vIwinqeYfD45NKu1KyeXdpelZFkZeZBkXAJ4v7hCEYkxQMwFpFLE3qc8bUvI/iz2osxoLvFsxaR4w6yY1qYuyIWmNicsmLHbGbCGZh1iiHA+rZhGe+8wIPP3iBw+MjLj3yEMdHR4zHm2xtrHPz9h6x3OTo4PXefkQrQWaLosQFD1HsGoooA/WbmxsYkwXVPb71rK2NuHlwzGtHjq3du/j6X/0lk5MjbPkO0drNgSWqPOVvUnAm+FxHhjb5fVjNIO2YSoublkg4ql6tTvU7Vza1iSm1WKkHSGNmSnpJWmsIChWT3VuCdxNo1x+KxNQ+0VItiZdFOjXTbqmyg3Hw4oKcfhRYqr6HjDKT62hJv+X3Vd8nzZ4aIVEMtfJMnafxkUwCNypCGvtqnQBARhuiETqj17LptW3sxY7bNmKMZ2ih0lkZP9CopDqRLc1ihOBSL1daA57kOK4U2jkikQYFDWlmVtN0Uo91QVQtprV8F+PCcHqz4tTmqAfBpnVLaQxd11F3DQpLU3fMa2maF9awqB2zukv2h7IwFQEfO3wc0iiI0Ug5oouUieglWBgjc6doXZqYiXlDTQ5pK8CijXKPYkxAmImCLapIqTWHCzkIBlZRGcWsCURvMG6BUR672GN+dBPXtTzx3kf5zvPPc2brYcKG5dG7H+bw4AbHx0f805/+aVSIdF3HYDBgY23M5z7/9/iD3y65/Nx3hXapNUVhEmaSSoq+r6+YTKYAlGXJaDSkCTUnDXTFFg++6xRf+9M/oW1bxmtbRPPWIfi2wWm1Yt66NOUvhbxNpHEDxJDUzFMuWhhxwzJRiv3OJTAmgRtyjiUaoHySJTEgpSRZOkITZd6KPGKkKFTE9imoSkyiPNArpHClVtMa+UvuW58r90BOjwBCkidZXs8ysEMK0uQInVOlXOMqsds7adM1JyDMJhDNkFzLQhq2VhB0XCqGK4VVYmOfywir6VUIhd3lBbkMYsZkkM2t6cSqqLKCOi8aRxsSk0qJMW32Ey2s3ButFAMDpVWoouC+7SHrQysnvfdM644bBwtBcl22aYfJogO6RHVLLQgfaF2kjRIoRgWGpcXqdYx2OGVovRbCSSqLUtnfJzMBTYuQLvJ9VzGiohbCe8/QTZaHRHYqz7gsUBjxtvGezTXL7lgxKDSunXP1RAaZi7DAuinXrt/mZOHZ37vF97//DZ54z/uYXXua4WDA2tq7ee65PR588BKb62u4LjKfz1lfX6eqKkKMfPzTn+HmjddxB3viD4RLA/u6X19Kyyyu1orWtdBBe+xYRMvZuy/QnBzzxd/9AqPRiNFoDVMM+1HAv3Vwzrpc5GoZ0VJLFXi0xkVZVMQ0XZKh7xSIRovfRa79Mk1MZi/ly9FKNgEflv1DIXSvADBKdkjTa57KKR2ikb4n8poueGJYBo5wT3KhiPTr8nukNNaHLADMkhiR3lRnC2LoX2MFEkqoNHjp3OJJRHElI2CSookqX56SQ8sQQN6AQqrnlI80PlIkVGxad0SM1ItR2jUhIgPZEbTRTNpI4zxaB4IP1J1oFEWS+1VRYmyJSpM5pMW9rjXrhWE8KrEEutbhnNRte0c1x4uOpWSop3Vyz61WDEtxyRYlf6lTB0XBB9/zEIvFhNC2/PkPXub07imCn8vmqG0Kw5hqdJXvqPy5UnKiZrJL2nyIwufVGQRSops0LgZYFQQlJlJWlqqEqhxTH11n75UfMGgnmFhzc2+OWT/Fq9f3cV2DMordrW0uv/oKF86d4Rf+8T/ime99m4ODE37mZz5L13oG4zUOJzcZjIZsbGxwdHREVVU89NiTfP8bX6WZT2QUsjBoIySHxXzOoCowppCSw1pCLDHDTdrpnBeee57Lr1xmMF7HFgN0UaKMxep3yBCa1g6jE0XLu4TmJxcvEtMHyELBzsvkPUZjdR7aFfI2Mdmbk+vR2As3KTxaS/CR2DlRJaK1EjTYmCW7SCOkeTmlQkprM1OEXvY/RPqdN23J5EQ1n5zSKlF9LbGUXPybAZmy2ZwoyzhTfvUEfPWLridnqL727YXF0n2xeLGjB0i+mzFKvdf5KCZOIRJTHZovQCfrui6KoHZOq6I2xDRVo22BUqJhq4gMYqAqSulXZj0oJWp9i7ZNFoYBUxZsBAnIRetxQWziffRoY+iix3XyszaC1iWq6Di69j0ODqe0LnJp27K7rXn9uRew67sMdi4wjQodPSoa+lIh3bfIkrebMxOpyQPbg8QeSvIzIQRuz8QxoNAwrApG0XN86xp7N5/FKEfpGkxR8P1XjnjsfT/Or/36r7E5HjIcDDDFAGMHVLpla2uH6dERR4dHzBY1o/GY/cNDzo/HFEVB3TSMtoaEEOi6hg99+KPo0PL897/NbD5jXrcYLYeQKYfUIeCaltFozPVbt7jv4ScIUfP8X38HgMFwTFkO0MqspPBv3mH4ocFZtw4k1tBpWNpoQ9sGYhQZTKtNAhrkhg9s0UuE9LQ/oWxgI6mfJkiiRVytjZFAFNhbFlmIS3v7zDgSlXZJeWxSk/MkFDejrKqPjvS/1Y+v7gyQqPp6V9oziQCRgyzK85RS0CvALc/OfHLmz5qDdFWZLT87IEoR+XXlMhMTJoIKgv62IdJ2kS6k1F2npneSTTGmAGWSaocBnep+lTaKtHkorWRyw3XSqrKaEFqcA6fE9axpO1qXrkeHlL7OQYmC/KINvZfMwBZCfvA5E5FWWBcttw7m3PzBMwQaYjni1MV38cprM4q7H6ebTRjMbjMenaFDS4DmLS33wglp4iVSaMVaZVHBUWmD81JHO2VYeE/XgVIOjaFsZkyuX+ZwcRvvprhGURSaNihuHzecu3iJ3/4v/x8bowHl2o7YySuNCY7HH7iL5y+/yq3rVzk4OuLCPRfZ2dlJbgOejY0NDg8PmcxmDNfG+OmUqiz58Y98guOD2zz/7DPEAIuuhhgZDAdsrm1RVQNu3brNT/ydz6G14ld/5f9mOFiXzTJ9X3nsrywM73/i0jsLzlyTiehWJAbwWoadjTGURWLa+Ejnk7CVTg10JQhg7ocKMppGi/IUSlmgVOiV6kIQC/voI8poNEZ0rxBOZV+fIsOqkjkm18d0Kmqle2Flld4zHZp9wCgiKp/afcqvyA7p+Xfz9Awg84tpBGT1tFQrv9C3gNRyE5AsPCYkOG0UidHgoxWGUvRU2lDo1D82KtGAhIZgzXIAOSZx654QofviVT5vSvvxS2RTGc3CexkaD4q58xzWkim4JJMSomgNyUcWTaTCKkoKYdlEZLg8SO3boRPNMFCON5lu3oPCMTp1kZlOm2LUmLVt6uYEPb1JHGyDKlBq6ZytVeo/IuVNoQK+WaCNoQ7Scmo9zH0LDqpuRhHn1IdXqRfX0ARMLPC+QOuWySKyP/dcu7HP3s1vM17fpKwGGKUZVZaz2xVnNsccTab8s3/xL/jed75O27R86EPv4+yZs+zvHzCbztje2cEaS13XnNo9hesch0fHXLhwno//1Od47cYBV69eYTaTQYBmbx8fb6cDxvD867+F95HRcFPSWFsAmqDF83ZtXPCTn/gwa4P1dxacsvhEy1Up7pBziDE1h4NY7WWebd5pA1JHBZ3n+aQuzFZ8CgGYlMr0NPkiiKLOl/1Quhj6qYN8Mor63BIouiMw4grntC9al6VjDrhVhFiRWbJLyl/+88wWUnoZiCoF4Kqqt/xR7O9Nz5jMzO2wHEhGLRUNkggKc6LoMGEIxvabWf8ysd9WUm2m+g2kn41MNX1IJ75OLKSgBGBqfRDHMZXaGsHjtRZurYIQOyFmpJuV+85ZNTGkzKeNiqgzPU0mkManz6V7IHdIaTCp30c5ou2mjOb7hLVTaGWEphkipdEoHN7lDEkQ7i6ICFwMEGOH9g41uU57+3nQTsonpem6QIwdyhhieYrXrr5O2zYcHeyzsbVFUY4ZlAU7GxV3bVesj4fgGmpVMjne5/DwiFOnT7GxsU7XdVRVxfHJCcFHBoMRk8kxTdMxGo5pFgvqxYLCDpjXjtn0hJv7e2xu7HJ8cixD+dqmE8CCLimrEm1LbFGiraUsLD/yyCU++P4n2FzbYF4v3jL+fsjJmcCLtLhXbb2XRZvCaCvCw1JE0E+9L/NCgET5k3aD1jq1PtImEKDQQi7WRvdE9h5kJdenycGYSJdCqj+ZuLPF0uvC9DSiNLVyx58tZz7I17JSG+fA7XVv8vOV6t9r+XIphU1gSq5RSfdRY/on52sFehkReYkUZOm9lukyLO9EvpY7viwJpBiIyVxqVU0kqOVIn1XS/mnTOJ9kGF5GstCSlZC1coVTLdq6ghuEhBnIZ1sCOnn2N6tkRLW8z8PhLrE+JBxdhvX7iEbGtupOfj92HhUi2iY8I2lK6dChuhnm9mXi7BaWVnjeiLKB6zq6IGlvMRxQGocLc+69cJrOeSob2VovOLM9ZDwq5XdNxc//w5/jm1//GgfHJzz+2OOcPn2atpXgtMYym80YjUY07YLJZMLGxgaD0YjZbM721han77qXJgSmteN4MkMMKwxEkVI1tsCYEmOkdw0aazSf+OiP8tgjD7G5sUkkMhgPeavH20+lpCj0Pcy/XChZPEun/Fmt5oSQUDr5mVVLb0JTpDNKJWFnEpFACyd0bA1ozaJ1TBohNwSFoLB5HZKmT3INqLIf4jLYM5YTWV3c+WcyC9pL8OdTqH/11SqVlZ/JYgsriy6rzqn0s2XgLAvUGJfPX/3ZshfLctPr33X5/3/DNuBNHjql9kqZ9KYhBf3ynuUeY0TE0TatoqoEvHM+MmsMTYiUuYetFYvG03hxA1faymvrpY6S96keztfXO7RJTU1fjoC2myhdsBYOaNlAh4ZOWZyuROFQRY4Or3D1yitcetcTlAZe/96fodyUrp4m/EPhvKMoCmKM1IsaZSwYw2J2yJpVrO+soUIkeMXZU9sU5SCBhArvWy5fvcWFl15mPm/YPzymGo4YVAPm8wWDwZDRaMxkNmMwGLA2XuP27X0GgwHrG5scHx1z++SECxcuMvM1ezcP6NwtCUKT773QXiVQJLMoTOC/+qmPc+n+e7FG0TQz0TaieNPvE35IcGq1REDzrJrR0qIw6ecqoSI2LVGXxJ9lDQmAo5MLmdLCI41B+nY6MYZMFJTyobu2GBaOadMxqwvGi8iVhdgqRCViVCHlbKJho/sFq1cWsQ5d4k/m4eRsVidnukIGwnM03xkKd8bRHY+U4vYz6DlI+2BNtWBKM016f4eS1DEGvEqEf5abSf9a+WVy8IY7A1rOq0BuCOVZVhsFzEIpVIh47/rX0mmgGqOkBtVgiVTA7igyLizKKKZ1oGk1bfCMS4Uyllnd0vpEEFCqB3JWU3yrNdEJMVwZqSEDUCS5l5B6u1oBJqJZxy0cpr7KA3ed4cqiYNZ5BmXFrJ4ym93i4ObT7MUTbFvTnJyIH08U2lwTZY40l1i2kMU9Gg4oyzEnJxOcDxSlpbRW+sLRYZUhBEcXHXdfeoQnn3iK37u5x4WL93DvffdTDdaYHu5xPD1mPFxHzxe0Xcf29jaLk0Ou37rFe9/7foZrZ/if/pf/lR994kH+7S/9S/6Hf/mvqRvf99pzJucTgq6MEFwee89jDAaKW7evU1ibJForwfPe4vFDBb5WGTJ5QQwHRfJnlIvxMWVjUab7ldb0CgEraGtIqu99/chSd+j02pCtSlEajdEjmqZBm0jXzFFK+mQxX1NK92Jo0zXlf8h1FgqU0emETYs4IG2N9Bmccz0BIf9eFpxefSz1bzPtr787d5yesPxhUtJBISPYRsiPQk1UGq1j3yd++/sPPWc4LgkT/QA8MQV9XC4MZOZ15ZYAEZlrFwe4orCUViWBMU/beCaNZ+EcaFi4SNfUvQh1yNeRitEe7Aq+t7QIqY8ZFFgfewdwozOjS4vehOsotnaoOsNoTVFffpbLT3+Xqhyzfvc5rj73l9y3tkG3f42ZaxkPR4zHI9q2w3uf3AHEaU5rRVEMmE2nKBrx29EaExTrow3Wx2OicjKk7TxaWZ54z3v5+Gc/z81be3zkYx/mW3/9Dc6dOU1RaIbVAKuEWDBaq6iqii//2Te5cGaHf/5L/5Z/8Pd+lsODCc89/wIf/rHHGFUVf/ezP8X/+e9/GW1sop6KSXOprXxHWg6Rb/71d3ni8UcEwDKarlvQdS1+ZTr4jY+3V0JoG8pBIQyPNF6jUiPfZBpejImxkqfScw2X6W8ZMFlqx/Tp6MrfHsX+tGVQGUJ0BBWpuzYJPSkwIq4VQ0xhGln2y1Ji2QMl6QMnacncz+y3msTkyOls/j351zvyy2Vgriz2ZRW+PPmyz4ZKa1gp0tRNQCc6oYoyJC7pb9Hfl2Uy/MZgXV5Xfqh8jWnTCH1avWwF9VeYg4koZJAgz/dGVA6bTtLZ3qwqgNVQ+9h7gQal+nvAyv9n0ykIhLohOtcvM68CrUJE1YJPwaywKmDxbI02OTw4ZN9U3HztJdrpEcfdCYfTfcZ2QKErmtCgVEHjoPOJDGMKmYOt5z1zx6iItppFs8BFw3A4YDgs8dEzq2cUtqDrOslCtOP1K1f40y/9Ad/61rdZNC2XHnmUvb196rpha3ObxaLh8iuvc/au8xir+OIX/5D3v+cxBrborRi8E4L77b19zpw9DfmUTF+VSTYTWqte5M6YEa9evsJjjz3MsKpYWxtRFEWvafxmD/Vmpi/58bnPfTbu3HU/O+fvpRwM5URQ4q6lUyGWmTX5VXK6oful1msoiFNV+gQq1RhKicxlZTWVAVsYCiXg0ayVfp/SluvHiyRjmQAXteT/5FMsz3kanRdwElZWMpydIOe+x9nXqCwzg9Ug7I/kIAs9qOXnkZuXNxbZLHSUZnqODx1lGJpE6A9RGDVKiTq8smIh6CHx6+Md9/LNVMjzNefPahEZT5XvRd5oVD5HJYjFrU3G9awV68BCJYvCLFiWNrE26P67i+mDLmmQ6XtGfmF9oLj+8vM8+uC9hLAUsvbtHJe9J1PrTNZNwCqx9NjZ2Wbv9j6T+QztYWNzneGw5OjgIIF/Kn21eWOQf9d6iZSv1gYhBqqyTNlbQraTnpAITMuKCUrjmhZtC85fuMhgOCbESNM6Ll9+lbvOn6EohywWCy5fucb57Q1eev067333IxwcHXHl2k0uPXCBey7eQ72Y873vPcPm5qaM6h0fs7OzQ4gu7+xp2RkCnrXhgK5p2NgcUlYF1hj+2//ul94USHh7xfdXfsD+9RfZvXovZ++5xCOPP8l04cQsyEcKq1kfFGilmC7alKYFhOycFnb6QsX7Iia6nsUYMY6trGFYWoZFkoHUKglIe6ADXTBvEz83SlqrU1ToqJbBmXP+XJsqWehL7w1RUIhB+vaC7r45uJKXZewXewrgmDPZ2INJSoGOnkpFBqXQCbvgcV6a/sRknIvBaDnh2uQ5qrwXooXgN/SnscpbBv0Jmf9Ep5pGspdIpYLwYpXuS4ccxTEFnMxZ6r414oIiuEiTvp98L8IKwtxvAP3XmFLrHJwqMhgUbI8sJyV86ic/mXxhAvX8kJ2R4dqV1yRIQiB7tYYgqWkgcveFu9k7OGJjaxNcy/7xCcPhAFcv6NJEx9ILJ2/83LHB9y2lGNGFokxEcqWFJF+UMtLVJXmRrnXUXU1bdwyGI5568gMsWpmqIYIp/4KP/sSPsmg6BsOK5158hd2NEXe99Aqf+sTHuXnzJi+++DLnzu7y0Lvehescp06d5dFHHiLGyGw248t/9Ed89rM/w8nJMU3TsLuzIwPgMTCdTPn2t77FyfSI6bxZym/8bYOzEFEYbr/+HEe3X+fWy99h/eJ7eOLJD+C9Y1RZhkW+QQXHtReu56pkCCRHJ5Igl/SyChTjAsYDw7ASW8E6SPrlu0jXBcaVYdJ4jhcdg7Jg2rYMtNjj5WwgD9CiUtobSTVwIq4pGdJVSlDg3GckPSe3KHI9uhquy4qU/mc9gJOR2eBY17AxsigCi65lMZsnTmiDL5LwsA9ifqQUShVoQ89+kqkSiaYe+V0JjmWNl3qlClRSgZPADqmNpfs0JqBEezUkO8L8okYiNyq98j5p0SelhBjy5KxsC0qJ8l4mrceQRTsdTSsbqjYWguf46JCdjYrga9nIfMRacS2XOcec7sO8rpnOZqxtrLF3eMDe7QOstQyrgjK5u2UcIM9IriLYX/va1yjLkh/7wAcoy1I6BjGm54vuz3xRE6NiOp0my8qk0KcLoUFqzXQ2I8bI5voGxlpiEL2jwXDIaDhkvqhTtyEyn80xxtK0HU1Ty3yzhsOjQ5RS7Ozs8Oqrr2CtZTQaUxQlxhZoI2oZbdsxGo3R1tK2DV39Dr1S1tfXcV0nCGBdc/3aFQ5u3eDole/xoY/9HTYu3i/T9MHjo6P1MlfY8yMVy4a2VrJgQxB5jKA4szlGKbFe6JL6QZGGaxsfGY1HtLMJw0JzMK0JWrMI8mF0EKEwpU2aks+Dvoo27bSyYIX2liHtOwAgYFlNpi9dsyQLwPLkXz6DmMSnIiH5v0ROTo6YH99mUS+kQX1yDdyMux/7aYIdEl0gGA1FCf21y+vJFIYs2MzSEcZTyO8opyAkCqAMq1utspJLOtFFdT4ExPvzDoJ5+tzy4YFcrwJKeppZBmS1DsoEeC0uPiQ2M6UVpXUXfJ9idl2LD4614Tq+bdg5dZq9mzdo25YyqRUEL6Bh3XbSP1xfY//2HnU9T/3LwMVL93B4sMdisbhDTzd3DLz3FEXBpUuX2NzcxFjb/0wDdV1ji5LQOYbjNZ599ll+4zf/X/7JP/mn2MJSDAYUdkRdz2iahsVCAq5tW3zwTKYTTiYTdk7tMhwMuX7tFuvr64J3hMDa2hrOCUBVliUxwre//W22t7cZDodcvHiR/f19XnvtNZ588km++tWv8uSTTzIajajKis2NDXatxXufBO3e/PG2wTk5ORH00mpsUVJFaFWgnR3wpd/5NTZPX+CJD/wEG1vbnLiCpiM5IOgEGKglbS6KJoxPKG81HlK3EWNk521anyzdjfhtukBX1zStZxEsDmi7gCrE28IAeEcXMzyUdYQg6tzUj5ADLbd4VhoqK1BVn85lvwyjlgyjHNKZgC3aqZFC0/CerwAAIABJREFUOY4O9piWlunRVY5uvMTJ0R6nzz3E9OQYHQN7N1+n2roLow3Dar3nsKr+ZIIia8SmepQIrvMYkwTIokztB+So1JF+csf5pQaT5PoyY+gjAnyl4AfdnzwqpagmYQMq4fl9jZtlPIKk4xDwSrxYiIbOBeqmZlwVOB24efs2v/zLv8yFu+/l4sWL+NNjvvPtb9F0it/97f/C3XffzWg04hvf+Aaf/vSnOX/XXfzZV7/K+558ildevcy5c+d5/bXX+dSnPswLzz/Pd+sTvvzlP6Zual577TXOnz/P/ffdz9kzZ/jud7/LU089xSOPPMK3vvUthsMB169e4/HHH+f3vvh7/PP//p/x9DNPs7W9y9333MP/8e//Az/56U8zmcw4Op4RgV//zf+Ln/v836dra778h3+Ai4oPfvCDfOG3f4cHHnqEP/id3+LU6XO8+/HH+dCHP8pivuDM2XNJKlYxHA45OJxSVVXewnjPe97DlStXODw85BOf+AQADzzwAF/60pc4d+4czjlmsxlNU1M3C3QnB5a179DZOoM1g8EwMR003nVEIpXRzI9u8ce//1tsn76Ls4++ny4M2djelpTPewgmqa2vIJ2JXxtDoOuEf5llGrsYcXWQuVEjDfEuwKxppWaxSzQ4RJFFgZVTLf2g59skACgvvJXGyQrQIS8gSLJciGWFXIHHR7HiCN2Cpm3QdoC1JV4ZFifXmB5e5XDvNapqE7pAMdpgNJsydy2H+9dorjzP6bvuoyofQsdAtAWlLVMtCvggo2RI9tCFiIsG5UDmSFfmTPPpr0T5IUad0ORURviYnhNRYZkyxehW/l33m4AHAbxAriGC8grvWvZuv8Tm1mnK4RZWKdp5ixoUqKgpdEHwMvOzaBzzpuHDH/4ot65foannTKYLJpMZF++5l9FoRFlYHn30UXwINJ3j/gceYHNzgyeffD/j8Yhf/o//kU9/6iP85Kd+kn/37/53uq7jH/+j/4YXX3yB3d1dXnzpZX71//lV/sd/9a84deq0gGHWMKhKHrx0iSeeeB+DtRFXb9zEVEP+6E/+lJ/4iY/wc5//+9y4cY3BcMgjjz7EX/3VN/mFX/h5Hnn0R9g/OOTU7ibWlhwcHHL69C6PPPIwf/aVL/HE+z7AqZ1d1scj2qZjNKiY13O2t9aI0XD95jWRgtElzjmatuWRRx7pT/AbN26ws7PD+558kqIoxCWg6ySVn5zgfC26UfatR8beGsdFtF4aCjpd4KwlDivM2gahHMJwnYnXTFvH5Vdf5etf/m2e/fpXCMosLddDFHGomPmwqj8BWg+Nh0UXaJyAFrWLzFvXa7zO6o5W7KXkYlUeyhaq2erjjanbamtkFdhYfWIGeTLqKn8L7OK96PW0XtG2oup2dOMlXnvxz5juXaY5uQbBc3D7Gid7t2ERqU8OcW1NfXzMZDYlUqLbQKxrtO8woWV+fIvjW1dopnvomAwdjPQEu5imdoLUyemDLFFmhKbokUHqLs1TekQYGrR0G/Pv/ZCHgLMq/U0PKLkArjvmlef/kNcv/znTyRHB1bz48he5ef156vY6dlCiyxKrSlQs6brIq6++SvBCAPmVX/kVnnrqKQaDAe9///v5wQ9+wMWLFzm1u8vJ0QFaKYajES+99DLXr9/gk5/8GCEEXnzxRR68dInRoGJ399Syzo+RBx54gMsvv8z1GzcZjceMxmtEZbh+4xZXb9zkz//8L7m9f8gzzz7HYDDEuY6qGgCGzc0NurYBpXjm6adp6pp6seDpZ57mhRde6E/BzfVNmromxshiPuXo8FBIFUXF/v4B3//+D3j22WeYzebUTYNSitOnz7C+tsZwOGQ4HFIUBS+99BK3b9/mtVdfZW9vL6HMouk7m825feuA69f2uHbt6lt+P297cl69djWxgwSgIQq4MBwOeeKJ9/Layy8l4jugYX39iFee/mvue+SJxKKJPRyeF4tSis57auelBk09GaXSdD4ySG2DqCkI2UBk/aX2Eb/EGAK8ISVYbTfk0yMTFvoxsDcsTpWuM8P0MR/jSjYTlKJu5iwO9ji6+ioHh5cpfIkfDpkeXqdralznGW6cE1cwHJPDm3hv0WHB4mRBOVxjMTnm8NYVYjdnPl+wONKcv/fd6HJA1KIE0L+1FkmTO2vDJZKbB9YzL16uW35X5xZEJLG3lm2X/NyVG4ZSySgJBUGxf/MFlDe89OIXOdl/jcnRdVwz4bhaY3r1u7jjA+LGLuWZOV1XM5lMqeKMDz71Y4T5AdvnTuOd4xd/8RfZ3t7mM5/5DMTAxz72MaqqYmNjnZ2tDboI5+46z97BMaHr+Pznf5ZBUXLz5i0++MEf5/777mc4HrN9+iyjtTXue/BdXLznPiaTE0bjMc4HHnjwEk3T8MILL3Pjxk3OX7ibe+69j8FoDdd17OzsMhyMOHP6HJ/61CcZDCrOnjvLmXNn0QrW1sfce8+9GFPw8MMPs7W+TlmVfOYzn+G+e+/l1Kkd9m/fJkYYjEY898ILfP873+axxx7j93//D/iRh+5jc3MbrRV107CelP5yPfyVr3yFM2fPsr2zw3g8RitF13VsbGxhrQxpd/4dEt/nSQsFEhgRxV7eqsitG9coygG0C7x3hKCYnBzwva99CVVW3PPguyWtigIkqKiIWuOjZ1QYhkZ8UERbVNJIk1C2jfEATWAYDaMicHMm+jTGBUxIQ93aEFRO4ZD3CLEf1I5p3CgflrkVsNr07/HKKD2yN8LaKko9PCwL9g8uc3x4Ax0qZoc30N0GjfOcOv8gt5qnOTo+wBYDqqKkbeY0XYdttIzN4SG2tItDZpNDBqN11rfOMp0cYIoRw7VNogIXU+3Z9/FWTv8+ngIqZtZOROXMIrdBYt50AhEjZHIjY2beCZncaIihQBvxsKxMxTM/+CO2t88wKsZcfukLlO0J967fRVUMCMeHzOo9ttQGVdOiD24xr+eyWc/nDDfP8eh95wkRCgPz2YyPf/RjaCPjZvPpDKM0bdsgoFVkXjfM53MevP8BDg+us7G2QSRy9913o41lbX2ECp6L589z/eYtjFLYakBZSBp55cpVQDMar3Hq7FnWNrbZPn2WnVPn2D51ltnJIad2tnnXQw/xF1//S86du8D58+eZ146NzS12trdYNC1d21DaQsDP3V0a57jv4R9ha3eXs3ed5ejgAJTFuZYvfOELfOzDH+SrX/1Tzp07x3/69d/gX/+bf4NSMJlMqKoKYwzGGHZ2dnj00Uc5TvTDLEpdVRVr62uM10eiEu8db/V42+AsKwFfdPIwjDEKuhQC12/fwhYDQljI6RcgRs/i5ID57Vu4ey+htcWqPAeiUCGwXhrWhkWioN35fm3bMByUjEtN23ZSLxaKAkkrdUx+mmghQaTTQmIsCCNGsTJqtURBlymt7hvTPrUlNBGTDqp8SQMthHCCZ+bmaD+nW5wwGG/STA8YjzdoGk97POfc/Y/x9Df/mEE1YuvMDs1syOR4j2Z6TBht4tyck9s30DGACixO9jk52aduWwajHeaLGYO1XQajDXwCh2TPSqda2pGXjZwkD4OoOCQBIvmIIUrryojxz+WXv8nLz32Ze+75AK9fvc5HP/6zPP3CVzjZv8Xdl55ka7TBX3z1N9HR0NR7lIXmsbP3Mu+GDIZjqqpktpgzHosUZGg7inHBbHJE2zSUZUFhC6w22Kqi6RymKNDW4JwsvMFwQFSapnXJhdyytT1Ga8OgHFKePSM1eBARM9e1aQ7VsL6+xnS2YG9/HxVlUterIIZaqkBFxQd//CPUtQT+Pffew8HBIac2Ruxuj9je3uTChXPcvrlPCEJSqBdz3PoIa+Qah9VQgDZr0NHTNJ2AmloT8RRFiVaRS5cucd9993Fqd4dpHfjQjz4pHiqlbBiitie1pdGaU6dOsbm5ycWLF2maBmOMCLXFiCkMRVnwdg6dby/wZZdu1bmOycBEMdqiKkdYrZicHElwhIBzDd/6i99nsLbJ/Y+8h6IIiUAvJ+PQIK7A2Xwmd7yRfpa1Fuc7rC2Z1IFFF5i1AY+h8x1okxCuJJ0CSRVAFrFXyyXcaxKlPitJAIyYZTEyB1j6q1Zl+3ot6m6+42DvdY5Pjjk83GN9Y53jyYKd3XNMWyjGO3SAmhxQ/f/tnUmXpGeV33/P87xzDBkROWdlVVbWoCohSgJNDBJC0NDgbncfY7cXXnjrjRftL8AX8dbH53hhu1dtsDFgAy0JEKiQVFPWmFk5RUbGHO88PF48kUV70ZzT2jSLul8gM07Efd9773/yXIoiYzgYkM0m5JUgyytcKXDsAClhNpkYNYyUZHGILBMmo2PaQY20Ah1NcL0aeVGAVLiW4vfUxPkTRuj5xCDnn6OaQzLG3tNwngsqKXlw++d89Osfkc5GHOw9RWqPn2YjKj2ke3BE9+gxpAW1eovLV69RxecQky6qsLEdl7LKiWMTrGJbkiw1V9woinFdm8XFNgvNBRKMhjFOM8IwZmtzg6ooGfYHnJ6eIpQiTjMKjMG0LjOCeoNOu4NSitksoygL8qLAq9Up8hitLVbWNvnJj/4366vLc7ikBG0u+rYUeK5LUG/gBjWOD7s0m01cx2FhoUUWDucBAAWLrTbT4cRgj7Uap/0+vZMTRuMp8+TmeVI1z1LM8jwnSRKiKCbPU+I4ptFo8PDhQ9bXVhFSE8cxw+Hw2Sjr+/6z5gtnM+I4ZmNjwxhKz3fOKIoYDE7I8hmO42BZn1GVIuYo999zfMESEpTCtj0Dos+BXFvJ+ZNDIIqKm3/3Ay5euoxym3MDZIltGa5lWZ4xPubZkPMBVErzxnSkIstTBmFOVimKStFwJbk0l9PFmkOlNVFaEBfVnHZmOLfPDLyewSNzMvuz3I2zcdbEMUhpksHk/M3kKIP3VTonSWYcHz8mmoVIZTMaDmk0FxHKwVUOukqM6qIqaTdWyYuQJAmJpiOCpfNYymU8HjAo+rQWOzRbKxwdH7O03KZ3vE/v6IBaow7xiFqzRWAp+r0jHj1+xNq5i3RWzuM6AUr9niRxNpafPdhsaUb2sxHdTAIWB70H3L/9HmUyphKCaKKpeQXD3h62nVAPBOVsiFerEY6HrNQ8Ll59kZu/+illkWPZNgoTL1WWUBQ5juOg5hxrz3FZXFw0aeRFySwMiUvjoDCdThkOh1QVDCcT+v0h+0fHTCZj3nj9dR49esh4MiOOb/HuO+9w5849VlbXcV2PoLJ4svuUaBZyYZahhWIWTkmiCISk3++zvrZCGM64dH6DSZhgS8H/+p8/4EtvvoFlOwS1Oslshm8LTk563PzdTZr1Jr3eKSenQ57s7eFagijOqDUbLDSalEVJnudm9BfGGiZPzcirpOS0d0o4m3Jx8yppmjKdhDzZ3eWdra8xmUOOnuc9Wy+SOObx48dsb28/2/fPoJiy1ERRRpzkv1+t/rHNaTsm26Kaq57PCN1aaOr1GmlSUG8tM5tNDWKRpVjSEJLJZ9z+8Ge89NZ3qLvOPODWnN4FhkZXVhqpzN4olPmndaUZzcylNNdQzH2FyqJAa1gMJOcWIC4khSPphjDLtGEBYSRSnI2yMJ979TP8spy/+Q3qUuIohaMMs8hcj821OAqH9I6fUsmAKOqSl5okL2E6RLquYRxpKKcTVOAwmw5orV+kHB5TlOYLqjdbhNMTHK9GhaCoKpqNFsl0xlK7jaDCtwR2PkZGR+h8gY8/+jk7n37E2rlLvPLmu2xtv2wOO2cBSOWZEFuaa68QnAXgKhS20mRJzOHux4yHPaJUGM6ykCa0J03nmKrEUhWj4ZQXL7/AtN/jxzc/5OKlK4z6PXRRIGxrbix2ZiNjhv6a79FqNKHSjIYjKl9iOzaWcijynP5owmQ6JU0yBqMJ+/tP6Z6esrNzmytXXmA0GpMkKVpr7u7skJeSKBccnRwxme6QJhmOLfnth79kbWmNW5/8jsXOEt3uMbZtU6QRT/eeEM9GnA4m3Hj1VSqt6Z+c8nD3Ce9+/V3u3vqEL964TrMU/OCHP+R7f/k9wiiiLAv2nh5w/PQRL33+ZaIsY6nTmUsIy2f4r65KkjhG64qyzDg9PWU2HbN5fpNBv09jYYGf/PgnvPPO18yeKU2bnRE4TF5Kzs7ODltbW7TbbcqyJM1SfL8O0ngCl9lnDDKSlmeI0swbqzJASOBZXL+6zb37T5HKwXVcgzlGIVoXVGVJmhT89oOf8NrX/7nR+JXz+HhdPTszCqGpKoGU5uIrMY1VUM0TpMF3LBpKkKQ5tu2y1bHxbEUVZgwKY3siqKjEPJelMjuxqwSBa1NUlWkqDb5tSOYm1g20NiZYtqVQlokfLyoLjWY8OEVHY5Rt47ou0TSkXq8ThzPC2YT2ShNle1ja5KD4jTYL65fI8xQpDpCioNZsEkwapEmJriDPMqoqIo1DilzQbi/i+g2kXSPRTd7/4Eco16fUsL/3GERBnky59tIbKOXP06nNj+dshBX6jFwwTzqTgmky5unBLgUVtoK6owmWNUmicX1NnkKRadwANhdXeGHjAo3AZ7tlsb59jZ//YvD3nvbGSziMMqSAzY0NPNdCShiORmR5CVZBo97A9gN2dh4wnEw5W1POnTvHaNRnOIAr2xdpt9uc1Bs8ffqUq1evctw9obnQxPUDJrtPiKIZy8urfPDee3z+C19ASsnJyYnxRCoSNi9s0Gm36J0es7//lJ0Hu1y+fg3f9+medLlzf4frV69xenpKURQsLy2BgA8++CVf+epXcP2AnZ0dRr1D/uVf/WsOT/qMxxMDpZQFShnv21rg0mg2aC00WV5ZoywLVpeXePL4CaNBn7TI+Ma775DnZqLQQBiGz4QfjuNw7do1sizj5s2bvP766zQXGlRVTi3wWWg2nu2pn6k5/+SNLWPJsLhIo1bD9WyQFoEtOYzrPNk7Rkgbx7VpLF/kYDoCS/L5G19k6/rL/Phv/4Zf/Oy/8O7X/xUlxrqfM9vKqmI8GdKoNw3v0HbIkgx/jk+VWlMWglpd4YgUD/BcQaDgaDDlcFqZN1RhlA412yIvzWi8WPcJrApRlQxijZYOddeiKjJKNHGqyXWFkDZN36ZmG1+cKi3ROmM26CLSkEpLovGI/tEjY2KWxcyignq7g7Q8dDpjGIUEbpMkDhn3DtBliWXZ2CiKuKK90GZQDtF5SUXJZBAiLU0yi6gvrBHjUpUOdhSz/+QxQlVYGFf9o6f7zEZDDp7e59zWF7j04itmMpiTOMJohK4yguYqVBLyiP0Hn9I//JAtR9NZbOPaATWlDKUv1yjbZEbmWUW9XiOPYvqnA4LVJRrNgFn/kGoezSeFmAsQwPcs1leX8WyFlorBLGU8K5GOhed4HPf7aH1KlhcEfg0lNI5j4/kuX37zNV66foVrn3sR22lQliXnz1/Atm1euLzN7t4uF86fYzI8xXXP4Xs1brz8Mm+9/Ta941O++93vstBoEMcTtNY0ah7fWH6HWr3J7bs7rG1us3XhGkdPH7F3sM/ayioryx3a7SZJEvPXf/0f2Ll9nxdfvE6t0eRb3xqwEBj9Zbvd5vzGGuPJmO2LF7GlYHv7MlVVIKXAcx3q9RpCKNqtBeI4RkkTVdhptZ/tm2dvymd0x/l+OZ1OuXHjBsPhkOGwD9Jg9EWpkUrg2Z9x59xYWUQIQTSbIKsCoes0O21ajQYnhdl1sipBWR7CsmjUPNKi4Mn9W9y99VtaS4s8vvdrijDlC29+E9cN6HRagEJImERdfvfJj1jsrHJ8csDx0W2WO+exnQZ/9p1/S6tuYVcJrm/T8BRZpdnpJfRD4wAHglKX5lhZFSbezlHEcYx2bcrCyKR8cnRurrN5Uc79dgVKVrgCqspEAni2wLMcnLJJw4Kn+7uEsaKizmR8hKUyqjyj3zshSzVZPCFoLZNEfTqrV/D8gNl4gN9okyUJw6KL52rCaMxCa4nB6YAw7FOi2bzwAmuXXsK2fcaTEb3xHu2NNRYsyff+/fdIk5IkSbh99yY/+fH7COFy8doNqATKliSjHsP+I3SeMhw8xG9dZMWv0X/4Aa7tIwG/XkMAURRh2w6uA0EQEEURQlekcYrteiwuLyMpCPw6WV7Q6iwy7J8aAYPlYjs2W5sXaTSbDEYTHj56zMHRMV6jye3bD/jqV78MCMZRSNN32b58lU9u3aEqC2peQBGHCGGjlMvigsuXXnvZHL1gLsAvCcMYgcJzfQK/Tq3u0qg3qVoJx90I2/cJZ6dYtkWWQ+ApLMfBsRyODvdpL3RwbcWf//mf4XoWSVzMmyVlob5KzXtKnuVILCylqPs1dFWSRxmupbiwtkqRpdi+j8KE8eZpSVWWcyKDEZbbtkOe59i2TZrEhv1TGH9nKc1UpudBTmEYMhnPKLIKJSy0MAnnvV6XSmcEgY9jf0YPocuXLxPHMeEsJEsSer0eJ4MBy+0WibWOxoSSZklGU1ooy0KUFQ6CRqNBWcbkYc7+7g7j0TGXLl3n5de+w3C6x97+PY4OnjId7/LRb8Zsbl5BFnV27t7D9W3eq7d5+0vv8MnOb1BC8e13v80sl5xOM9K5d2qeF3Puq5w7t4Mmx5aCrCiwlDlj5ZVJyMrnnNgzMoPUGUK4lJXxTXWUwLPAaTbYHfUpypLFpTXisIeUS8STCbVaDsqmKmYopag1WgjpoYVGIXHdGkU8A53z0qtvcO93f0c6GzMuNNFsQpZl2J5PkUac7N/H8VsIy2ahtcrj2/+HQRLxn/7zf2Qyzjk6HCKF5MYrLxGFY/r9Y4p0RpFM6e7fMl6yzgJKWczGt1ndvoREEkcxWoCq5LOL4FkUXhRFz/C4qqqoBwHNZot8cECeZRRlxWuvvcL//eA3yPkk4voedx7c53Q4ZjIN0aXG83xGYU6tHjAcDvn+97/Phe3zBBVsnNvk1s4j7DJjY+sSJ7uPCTXceOkaG1sXGPV6hgGlIUpjHj895NzFqzx5+IBOu45j1Xj48BZ3bt3nzse/wwk8Xri0yaR3NM99CfAsycJShzv37hBVNjXfZ39vlzgr2b60xXg4ZmPJZ2l1iYXF63xy89dcunSBhfYSd+4/YmWxSaPRots75u4nqyhlk2ax+bxztlBR5CbyQkpjwq3MylPkpvGFAM/3EUox7A9wHPv3JI+z24ZQ/Pf/9l8py4LbOzexHcn5jU1WV9eZTKb/H63yH9WczWaTVqtlIJIsJ89zxmGMKAvAxAGeGSUXZYGSFrYFQeCx2PTRokLnAsevIyTc/OhDsGxufvILtBasra/TOxlSFrD75N4zraLWGXfv/pZoesJoPCRwfC5d+RyhWGIYFhia2lyxUVUmGl0b7LOscmzLYJQuClto0gKyykQWSGmWd0tWtFwXpQS6MFBhVBZobaErTZomtBo1TnqneLbHJM+RysHSRmVvC4tcGGuO5aUO7Vabw917uDomzBMsr86j3V26h8dIaZNnBVWhsS0PS7mMxlOOu12ktGivX6CzFhHHY6IC9ns9pFA0OgY+OJncY3X5PLt3f4YUmt7Rfaoio1FbYuNChzybMuofkS96lKVJKGNul3EGjp/Jr/J5ZDqYS7ZtOYYPqg1Mkecpn/76A8LTPkenE6qqJMuMCXhlB1huA9eyOb/eYePyK/zwb/+Gw8NDiqrgW9/8Kl5ecG7rAjc//hRRJbz4uat0H94lKyquXzrHV779LXbv3eJsJx2OpwgpWd48x+6D+zQChyjOyPOUb/7Ju+w9uQfA669c5eGdCUkc47qKhbrP9pULnBw9ILBauJbi/b1dpBvw+dqLHB4+ZW1llSuXNnnptbcZ9fd56603qLcX+fKX3iSMx9TqHWwHms2OgUUGA6qqotVq4bgmizSO4vnkYVNv1OcqFM10NiWOYmxLsbS0yq3bnyIwKpWzEN35HocQkizL6A4eEsVjOp0Wy0vrCCRRPPlszfnw4UN8P6DRqBF4AUGtTtBcwLUED/vmkjceDAxZAUx2CmeKelBCsdZqYtkupVT0ej1++6sfs7x+nu7JAYf7e3M6S0UcgeMY9/c0huPjU0ajAaNRzOpih199fIuwgrKUuG6TzuoVpJLzPEQDulvKBLJWVYW2jDaxqCryCiMVkkbc7SiFUVCWFKUin4fhlNpmEI3xXMmgf4ygotNqMrNKRiOX7skezfYyjrIBRRD4iCwhPD1CZAWzcZ+yiBkNj6l3VvGrNsoyLBmpFPlkTL1eZ2V5lXGc4wYNZqMh3b0duif3yUuoCklVCTy/JGhJ4tiYgA5HXRZqTXrdLpPBGBDYMiHqH4HOUGlJliT4lkuUxNiOgy4rY8BVVUaqpRS2shBAUZowxTiNWVGaUV5x9/Exp7OItc1tLmzYhGlJlqUEvs+rX7jBrZ3H2K7PcqfDjWvbfO27f0ExPSCJI8Io4sqFCxzef8R0PKC91GF62kNUFWWRUJWSeDICrShLs48ayVTB9WvXuPVgD8uycG0HpIMrK8LJDIFtoCLhkMQp0+mMvCxwLZMK0GkvkvvrhJM+SZqyvb7C8lKH3Uc2jh1AqWm3FlldXkEITZFnjPt9FjqLzMJTzrcuoasSy5LUajXi2FhhNoRh8wSBwS7jJKGaTPB9nyDwaTYaJn8zilFSEschg0EXz/OxHQdLWniei23ZKMtiMO7hBw7dXsL581ssdlYM8lEufbbmBJiMJwyHfaRQuK7HQmuB1aUVLGlRZgXT0ZBmI8BzbU7SjDPVukk9NrkqZWGehNvLHSJdmqtqnuE6DrbroZSFtKFRC8iLnEfHEzKRk4UaLTSD0Zj3P/g5TlDiWh4Sn6Wlc1jSpxQmRNaxFJYFAkVRmr+RV3ManxQobbJEXXvO+skqkizHdwWWNCR9S1ZYSrP3+CG6yAl8lyQckszGlFnG0uIaWjXo9fZZWmqRT8bkUUjWWsaud4gzCblgluW0pUdjeY3qwad4QY28iCnzBLe2zuHJCUtr5xlLhBjSAAAD0ElEQVQNe3i+zSRJifoptispC43jaZr1Gh0nQCgL26pACmQS0XQaNNY8LKGpBU1Wl1foHhxTZTbT6ZS1RsBekZOlxvzM932EEKRpMmdKVeRFjmM5SCRROOXRzj2WVjbZOzrgeDTkK9/8Z/SffMyrL1+jf3qKa9v86TfeYjqdkpYWeWocHpqtGjeubZFFE9IiY6PTZODY9I4OmE3GaGmRpzHT2ZDKW2A6HlAWBYPBgMXFDlEUcrC/yxdf/yqno4jHDx/guh42PoE/pnu8hy0EOArbcTnpdgmCAKUUrutTVYJazWXrc9dRruT9X77H21/5IsvtGl//8iusLdZxHRvLcpBCcbh/wNoFi1Ik5HlKkaf85sP3ePW1t1FC4Xk+WsNsNjXm0cpEyTebTaRSpHFEHM5Al3iejz//7QopqYqcLMlQUhm7TozhtpQWJQk3dz5i7/Euju3QPd1nZWXFCLFx/8He+4PNubl5nizLSJKI6XRGmqTs7+8Tz0Iy/yKWpfA9j7IouH37Nk01T+tCz/1MxTPSte8H5EVJrSzRyubahYtGAVGUZqEOJJaUFNqlKEZIR5IlubHZSCviqIsbaBzPoh40ycdDNq4sE2cZZWlSm5XSCC2YaEFZGEinKg2DxvYCQ1yIUyotSApNllfkRYylLBQ2p9N9qixDC8l43CWJBbO4JAxnaJESxSHTaMDG+jk8z6Lb7WLbNirPKKUiygpc4bB1/hpFDtE0RSqDAYbhDCU96kGTohI8enAXx1JMJzMqWWBbDpaq8HzIkopXrn4RkQ3JU7BkQZpnVEWJLRR+ow5FSeD5tFot+t1ThOdRlAVocz3MshyljDVIURRYc3GvbdtkSUZJiVQm9BUhWFhoE9QnMB7SXuxw/zcDXnjxc9y4fpWa73Hu3Aat1gIFHtPpDMd1jYhaSbQSFKUhfwe1Grv7u4bt5SgcxybPS0qrYDwZAzAcjlhdXaHVWuDypUt0Oh2kEDi2jcZ4G+vS5vGTBzQaDaZpRFEUuK6H53kEQcBCu8PS8iob630O927Sri/y7/7Nv0BnM5J8xsLiAvVmHYHGcRzWN9b5xXv/A+vexxRJzlF3D2lNqbTN7vH7vPXmX7G6uv1sF0+SiCgM8WtgWTZBrYbr2ERRyGwWUs0FIJZloaTCtn3W17ZYXFoxDKMsnfdOytHJHk8e3Wc8GhGHkir7Kft7+1y5fJnF9uo/2H9/0ODreT2v5/VPV39Qz/m8ntfz+qer5835vJ7XH2k9b87n9bz+SOt5cz6v5/VHWs+b83k9rz/Set6cz+t5/ZHW/wNZXejJbv/EiAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4. ユーザーがアップロードした写真から、この後雨になるのか晴れになるのか予測する"
      ],
      "metadata": {
        "id": "GXP_Np0Fu43T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "from tensorflow.keras.utils import load_img, img_to_array\n",
        "from tensorflow.keras.models import load_model\n",
        "import numpy as np\n",
        "model = load_model('/content/drive/MyDrive/keras_clouds_trained_model')\n",
        "from google.colab import files\n",
        "files = files.upload()\n",
        "filename = list(files.keys())[0]\n",
        "\n",
        "img_shape = (256, 256, 3)\n",
        "temp_img = load_img(filename, grayscale=False, target_size=img_shape)#PIL形式で画像読み込み\n",
        "temp_img_array = img_to_array(temp_img) / 255 #PIL⇒配列化 + 255で割って正規化\n",
        "\n",
        "\n",
        "plt.imshow(temp_img_array)\n",
        "plt.axis('off')\n",
        "target = temp_img_array # 単体の入力データを用意\n",
        "predict_prob=model.predict(np.array([target]))\n",
        "predict_class=int(np.round(predict_prob))\n",
        "\n",
        "print(predict_class)\n",
        "print('Predicted: ', labels[predict_class])"
      ],
      "metadata": {
        "id": "WOXfiEgDF0ZL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "outputId": "45ce15aa-241f-41e6-f8b0-a1234faeeb16"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-1fc982bf-5f6f-4e4a-841d-2b30812d61aa\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-1fc982bf-5f6f-4e4a-841d-2b30812d61aa\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f492d5ff700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving amagumoPAR51064_TP_V4.jpg to amagumoPAR51064_TP_V4.jpg\n",
            "1/1 [==============================] - 0s 82ms/step\n",
            "0\n",
            "Predicted:  雨\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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VgnfUWlCFlAo5K3nN5FI/cRa14REGjNrfa62bN43RWzkl/o2e04AeyxvN6iveebTWG9LA7kHtP92svOtepsVOu4DfKPTuRBrwYkrakV7x7RhNqewxaivMN/Friiz7BTUp7oJsf9SGFLsWg3eFsns0hemWZTMaW67a/conP26iCLbjKbdgkWshkdXt7Du7x+4lo0oFp5uXFufYAKpbY9HzUlw7jp3UwlilquW5Ti1vrUD0oFU2MGhwjpwzg4e17gyrII4idq3ee7wWEG81SQL//OgRCVZGiJXTpXA43DGGyuHoGYISgwEz13nFhfZMqxEEclHWUtFWkywpM44BFJYlU/KVMQamKW7phThPlUpKya7Je4L3nMYIYscJfi/7dc+mGFpbUsF5wRcHUsyLi7MIoVpOOkSH94aop5wgJ4yYUrZnPi8JMO+fUmJdKqUYwEM1mYzBUUshF1PkrhfB2+dUiM2gODEizi9tvx7WtnBnY1y0/C340PJRE9HNA/bwSvyW09EF69aEtc86mmbUvf3zF+K7edquSLJT8m7y4b5ZfmYhiGwAlCnWVt5BobIpqbxWuptQtSPHeptH32yvPWZtQnFbH9buwzpw07Pe14rXnunmwW/v6/U13ebCqlbPbKHuFo630pEV8vfyUvdAj6NnzkoMnlIzDiiaEfEbWIQIQ8m8OVidseD47lj46nFiCIF/lBPRQS6J6C0nywlSToyj4zAEptGjVZmTIrVCKQ0lVsYpglpIOKfMOB5aPgzqhNLyaOlxNxCcEL09x5SLlTXEb/lzi27bc+u1dqtBiti7GYZISpl1zSxrxqOIKLlmQ+G1QskWNkNDznvIWjcDUEtp6Ktdl1BxzuOcET/6q62tzLPXly1//rXt19Fa2PicTjxVlSA7TH8L8ig9L9ofUhcky612ly/dS0mvleoW5jYp7RwFuwaRDZDhRkB/+bo7zaBuxwDZwtGuGN2g3Aq91mrI6BbicnMMtpCxf6+jnv3ynHMUrft1tu8556hVt5j4NkyGluNuIfFNzfTmGrqh2q6rheXeCVnLFlncpg52rF5S6lAYxrIpidEFBu/ILgDKc9HGZBIqxVB5h5XAqvI4wuMhcBrgMHlKKYjubK0OLI0DHKKFj+MYySkTCq3W2O9NSWtiGKJda4W8VuIkpGS1RMRoe1McWdfUQlKPOOPAgpJz4Vorh9EIrxuHdpMFM8r9PddaySVTa7XwWg3xVTUgySS1/d7SCdeMeKkVrZWcrUZbSmnGuJfydNt3k93NSFbWNTOMcZOJX5tE8uthLQYgdJnucH5PPgVQhyXHziN6i+xaANoL7sbsaYpu4rWhp53cYMV9QzBFbz0Km7Z31lLfPilP9BcvrxRAuyHZHFQzEjdfagBSr/33Lxux371QuP6yO5BlMfRurG6vryu0b7nkJ38TMcSz103lJmdvKUJp+WjPE2snZLTPPWyIpDZQohtGr7BSW5hrv6tXavV4B98f4KfsuKSCxxEbX9p38ogoE4UhGpG+xwM1r0RzEcZ+qZ0ZVRmCY4gekUrN1YruYp6w3giuiLAsK9MUeTiOVBQf/BZliOoGrPhS8d4TA+RqRsmqRxYilpJvkG0QreQWPqtWM4xNrnIqrcTS0WB78CJYXlm3l7yBg8E5UlZKzRaeV8PmpeX7ruXXzjUEXnfnA80gZiPvd2/r/X8Dfa/WSs8Xm45tNUUTnj0sfKkgdqNOXBPIloc6QTamTP9eA0uaoPdc9Xbr5YvuDfr59uL/pyDOzZW8OJ5z8snf5caDvn4G7aR09Nk+34kJexhsJaBbb/zL1lGbp7NQr3YD1E5xCzr1cLQfpWhtSbLdlrsxoK+fQ2clhdY1hCrBdVKHUF0AKqPAWYXT4HgcPB8uK4gjYPudhsTdZILkvSPlim/PQLVanksD36qFdsEpYxxAYFkTa8rUkqGXRzCP5BuJHe/RUlmWzGmKeFFSqay5GJOnGZ0hDEguFmre3OtmtNXYVRWhFFPCWmvXtbaLbrXSjcy5edZCrYVSMiINQBNPbsqVUqYUc0pbWau9tBgCMVhOLO0l1mqGa17suyFaxOH/WzznbbH8xedsWVMLGvb9924SduFrYe2ueDfhcPvVO7/xQ2+3Tbhv3NxrgbcHtIetnwAodNDk8/enqNULu+7py+9/zuj03HDzduxGis+Ey/BScfqzKqUSHRT8TnQX2Yjw5iXZnpeAedEbYbWyFJ8wlrZ2Num1YsdA5S461gKlVu6D5/u3E//5QyYk44GeAjy7asitg8Pk+Oo+8ngc0ZJIRRm85bL1Jmy0EM8+qcU6QHrYuaZMKib03hnpvKpCUcYxsK7J9q2dQGL5JCLtOPvzXkuxtKdxbNv/tmcxRE/OmZStRELrmuqGs9ZqNMBmpG47rXoe2EsltFzUyPaVZUmsawOBZGdrVS048QwxEkOwGL0dp9bK5TIzzwtalRgdMG0I7i9tv6qcpYWarudn1Be5Ig57gQC6cx63nFIhe9m4qK6Fq1vm4zqvtrbQcVfWJuLbA6Il9N36d101IWyGoBqaLO2FSj9Iy+HMY37qXYWXx/LeNzheN3dV2csXW+raLW1TpK2u2w/cQ3+xnNL4wC3H7p5VBMmJHAYmLdRuuG48sUe26ylUfAPctAlxFUdu3TXSENqixpoBC7FcU/i7aeDLMfK8rCwVTkE5ULkfHR9W88Dvy8ydB5FAHCp/dxK+fogcQkUZQJO1lBVPRojtXZh3UoKvjDEAVkaI3nFRNg/mRJgGzxCFNZkylZSIIVBrpj/0nKymmlKGFtqqwrJmDkM0zwNbfbMjtVbbdhsl1KPgHaXs/ZzaDLqqeWBapbczjEopDcGvFK2sa+U6J5Z1RatQyTgXaak4WmEcLG+vJTcecAY887Iyz0vLcTNTHcm54Fw1BPkXtl9VTu891MaM6FS6m7DAQxMGocqej3Vr4pyzfW48ZFfzTcC3WH8PnfuB5HOKdOvFdq2yENO3xmh32x0qu+L8wiY3UKC7CRe7B4Sd7P6aaAE9h7a7ukVeNwphu8bba9qUE/j7Lx+Y5zN/XuOm9L3dTlrngvSTqhnEjhKLKrkmxLV+RjWkUbp7Zyfme4SnNXHNiegjS8kcg3J/PyETOJf48ay8X+HLO0/BkYvwL99OPBwDy7oyhIAXK0GICuu6EKah5bPmeXzjv4qzjpY1ZaqWZlwDIXhqqWawdME5YRxie4+G4haFVAqk0jioljtCQ5GHuPForXnaGqsDjUDfZDRIIz+03LGU7tFMAaXxcXMu5JxvpNMMaqlW01yWzHVeTTmxZm9BGIdADK3nFkgpIUSCj9SqpLSYccHQ2h5mL8vygifwue2varY2SpjljFVM6Ry7F+rkgc6GMSF328OBvV2nifDmgaUL7qtw71Yxfz2vNMXsj9R7z+vw8XP31LcXSG33ljTre7PdTk+wn9stoPUmJN4ygV0Vt6j8ZuvXOA6ej3Nm9BPOF3Lra7R9xLzfFmbb86w9emnGQHw001Ir3CC+9tzcRuSoTaFTdYxa+H6Av3sYuB8jU3Rmn9aPDNPEyUO+zDw8DLy9i3gyNQjHwXx5yobgHobAWhJFc2MeFa6rEoIV7a+rdWJ438o1Ym6mFKsZGmgE4TBwva4bYynnTKFwP53IOb8QZLe9Y5Ox2sL7UltOGmVDaztmonUnAnR5ck6QVu/t5BVFG5pbWZaVlAyZTTkzp8TcEGMDzArgmYaRUgrn68IQfePZFqIL1GJykVJmGAbGyfb98cczx+NhU+rPbb9ZSgGa8MsGBvWRGT2M6znh6/IAtJatLWRV0L3O0/femqaFTeV7ONjz7S7wmwd5cZ1dS1qSfnMdt8C9yP5zB2y6Vr0EFIyzXuk8YXlNUdLNJ96wgl4CC8Bew9Wbz2VnNiVVSoYv70Z+WC44CTunVxTY66tV9/y9pwaK1Q1rU9borIWqlxHArHppgE1wjiDwT18e+Sou/Msf7nk8BrQKMVTuhkc+Xlam0ZPuB8QJwxiJCj60NraiRC8E38LsVn7xzhGc5zLPnI73pFxZc0YVjtFI3oLlpr0w372f9yY/fkmUUihVcaqs60qMcfuOc64xfUwpfXMMW73ciTVdF2u+Dt5avyxvrbhW69yUURshopj5LaWwLIt5yuvKmgrXeUG8o+RKKZZ/eoTDYOqzrIl5LeRlJcYjy5pa5m26kVICccxraobcIozz5UIcBn5p+yvqnMZSuan77yjri5+mHD3v7KFqRyVp+ZC2Y/QQtx9z86DdQzlH7aD7a2Xc42F2MoTuSi80xG7nBPeQUzemzw5cwa4sSr9We9kFKy4b8nebvO8GYGt0/pwVvFGmW0iqpTxbWuAlI+x9rhuIJD1tvp080fIrcY3VU7iPgaUKWU0AHTSktpVwijI0T/EQlD++cTyOR0ZfOQ3C3TTgXCHGSK2VGWEtwskrl/OFIIXj4YjWgrjKmgraEFPvnbFmckarsqbCu/cXosDpOBCCIx49k3ek0rzSuuKA6AXnAs4rMOCdGHiUjK5nefvL6EOawa/VykPeO1zVrfzV65fuRjFLo9htiEeLQGpj92ipLGllmRfmJTHPmetirWmXJZHVeLwdnPLBc1nNiK1q+Wh0jnlZAOHnpyvff/1I9MYGevfhzM9PFwSYhmigVSmozp/KTNt+Qzlfe6ieP+7erlu+PSxspZeWiTm/k7n70+0Q80vlZPPA2r3ZbV4nuzJulvITpb1h0dwQxrnZ9/bnazR6iwTErLHz0kG3TxDcXyy7vLqOehOif/J9CyTIqhSEx+j4qa6IhG3/im4K2+/d0YZpVcU7eDN4vjt5fnhe+UuWFnIUjl5YakbwRAdHL5xGCFTexsTj8cjDwXEIjuMY+ObhSC0z/3qtvC+Vf/ryyMFn1lQYTyNpScTotoL65Wr0PxEjsz9dFuYlc5kz9fnMV28fWdfC6TAyDQNjEJacET9yvYops0JrnDWCQQxbbjhN0w7+3RjTWits7K/2bKRuz7m2KQg1Gw+2aqU0dLp7TSeu5X87AcHyTmWeV+Yls+TEmjMp29AwI24YYyjXynV1Rl5HWZbEIso4REpR5mVlSclSwVJ5/3zhw3nFiTKnjKiF/TaU7PPbrypnFasvOiyULRh6G/QmB9tm7jQZ986K3Hu0uIMTN/omPUft3rIrXQNDrA+09a3cfk/6BLx+HF4qF7uX2T77JK+8+b5sfv/FMaoTq8cppKpbHvi54/zScTuN8BZI6ptBUL0eF/j545VcYQrKUuy5llLMC95491or3jnWlHE+tHYueLpcOB4m3FMlq4WY91Fx2WqSQwx4FFcKXzw4ggx8eR+5ixURa7D+8nFEnPCvP57587vMFGHyjjEGG7DlHDE6tAqlLswpk2bru7Q6XuUyV+a18Idv3uClcM2JBy0oVtI4HUbGYeQQHR/OFy6XhIq2cHFnVnnntq4YsHDTe9coeNJyzYrHwJxSKvidEldrRQstfy2k2mXM5CPlxQAgEVKyUDqnzLKurKuxhqpm5qWQq5V5tGAjUrRQHVyviT/95WcejhM5ZVItTEvGizJOIx+eLlzjilZYlpWckxE72jO9Ow58+9U9v7T9JvHdYUisiDWLigrqhdCTZwrR7QOq/NZc3RRWbz1U70rpOdPGFQHxIHWrhyJ1ZxxtkW17cZt47y8P2ELZ28+cyFae2JTmM17vhcKL4BokH5y9VK3F6ot4rH9wp4R9AjopFIGgO6m9swgKytAmuFkuqDjNnDVSRTcuKY2OR632nERwVUmpIg1EkVoI3hGdMPjIXCOjz1QKRweHYCH5E8ph9PzzG+Gr08D3b0YeTyNeVqSxeQ5DIHrH3ej4p+9G7kbPHx5H1pKZoiO0d7Pk1Rq+s5LWijqFYlS2y3WxNq9gbJjDZC1RoXnEGAJfPB6gQlrgECNXnw19hRZ6mqEcWj5XVYne413Ah8AwhBayV5wL1lspSqoFevhLqwmH1pyuiuayGXbVPe+tqvghkK+FlAtr6zJZ1pU1VbSR9Wvpdc1W2y2VeVnbtD8bFBeD57omhug5OHtXa1p4fp7JGcYhMogzb1wybx6Pfzsg5JrwS7sowRJuq1XqPl+HnkfeIKo38v+SnLDnap+EpbApLryuS+5AT/tuizUAACAASURBVO80qfW1Ytyipi8V8sV5bn7v/rC+VjD20DU4h/dirVf6kioh8nnP6DcOLT1o2O7xNHqWpWzwfs9zS93D/60FSZXgPB5wogyHwJ0vvJs9jsroHaODLw6e90vhvZrnOIVA1cTfPUaO3oaV/fd/vOdhityNltumUkkl83gcbXqeKMcRvj0N3A3CFIVcHIc2t1YkMK+OD+eZ4ITjFKhVTICXzHEKBOd4vmaCF6bJ2DJjDGgpDEObK5vyDSDnycmapGvr7+xI6oYPCAwhNvCnKV8T6rV5zV7b1hY1GJe57jXLFuGV1rdZSmvhajxdbR7WiBUN+MlKLQbqaJv345yD0ns5lXXNOBZCEFDX+L7wfDZUd82JeUkoxksP0dn4kwB3x4m/eUxJFevxa/n3npAbymEhr7OODVd1y0e70HYF/OtKGwKtNe1Vuvli24X2Vd64lTqacrbcsVc3PufhtO3zWpF7obwfN7fJbVVaPndTVnmdT7cHhLSul4JuQJmqzTUtObUZRYLIbnScQBBhbfU8weqcIjAaLkfWyj++HTi/qxSF3ChjwQtvp8y/flS+O3i+Pwl3w8B3957v396TVPjjFycOg2eKhuz++cOKljYAjEzwI9M4knPlmENDym36QS7CEBxoIE+BNa0sWakltQHNmcMU+OLuwOESef984e3DgcEHmxlbKqkULtcFxSid9q4reySz16IMEBKgon1KmzQ0HiuZ5dJ6TWu18kVTjB49lWKlqT7yZQtfc241yMzkJ6urFkuhliWRitERVY3xtF9X6z7xnjVlcI1g0169zWsycOy6LohAysq8ZpwXRmcloDZGiw9PF/R0+AVJ/82wdof+pSFcXWu6T5PaItLeFKy9U6MJHDvxdy/QsyXv2+vofaDNU9NLNg0U2ZTlRmm6XultvaXt8sK7Nay057m960NeKWY/bu8p3ZpIaBPdmxXfrl33427/bgYBuZ2GZ/uGBuNb/uRtPlBzqxaUCW8mz89zIalYPtUANjTjEN4eAl7gu4cDT9czoxfuhsxxnCAX/rsvB/74JvJ4gENwPIyeh7uRj5eFY3S8uZ94OI389O4dWvPOL22dNKrCYYxMY7CwvcBlXYjBprGv62pGxAvTaHXO65K4zsJffnqmrpnj6cBxGgjOE4J146SsXNJKqg6tkEpuUYGQmpH1PrKmZCydYu93GKJ1p1gO0d6m/VeqblPdcynUYvl4bfXONTWv6hxlq2Wa5K5rMnqgXw2dnhegtdc12uAubiaQqtpm/LYRNmo0RLs289y57iNovCi1QMoZKcbU8gIhBpyD9x8vv+iE4K8ppTRhdBtwIptAInsIauAHL4GYtv8LRWza1G/6loPu6WBPO4cALb/rdcYOtLy4zk1B99/71W6v88aoqOw4dGfPbNfVdjJh3V9QbQTyDTDa+Ji059EfxK7Ee3i+97+N0eMrfHWA/3JxNhQKcFZStZk5Ar4KoXX8eBXCMFjuppmLRny+8j//hzdc1kTKK2lNqFf+/aPw5YPY7B5gHByjrPzztw+8uT9wvB8RhbkqQTzVV9ZScVmZIpzPV6bDuNV/Sy74AlOMzKkwl2SeS4ToHL5NYR+8dWy8e05o8Hz99mRDs9VC55yVrLmNI2keUS2sjRGWZaEUqwmmlDeygHOCTOa9nfjNuK4pkavtn5vno1QkGAoMxs11YlMU7NU0pV3Ng65pZc0rguP5PG/VBt+Uf4vQ2nvtIFTDlqxM4m3JiI4/oMpaCgHBxYDzrVGgZJ7nzDgMDFKJ3jz+tRmFz22/kXPK5m02nIaueF1B7cJ7O9lL79jk9DbPfJEP9rzsVnn7ni9/bEDOjee63Toz6OX22j/v1/E6b2z/4LYH9EVuekM0+KUyzsY+Yc9Dt58tVHU1cxoDuZgXEefaWJaKo7LUcFMgN4qka0I+l5VjHCm58nAaOQxCyrDUQNZCIvPFaSIXm9lzNw18/TDy9Zsjp+PEdJgQUdZlZU0r98fI8zk161ZZ1qVFCAUbFes22mYulXfvP1hbWYsshtHjRcmD5/5+ZM2JDx8vnA5vmMYIVfDesyQbFKbFpqEvOW1gTGnE8Np6JDs/t6OuMXhThDigtTSqngFFOVdSzwmbYc05W93TN+ZYz9tbLJlSavJic4ZSqninaFFSI1doDNR13d5DN7SdiWSkI5v4MHjzk7kWvLOhYFIL6yoboOrExrZ8fLqQSmWKA96boTZD8vntt2cIdSVs+aVAa0bWZkHdFsZtwt89U7cm/bPuZbqyOnZgB0fCuJHSjqdtaJXAxqyQLUw2w2D5oaMWK753BXmtOLvAt8/aOW7/ZsonL67R5vg0L3wzje816ts7Ijrn1bxlG1bSz43yXBzrZWUaRpJWlGpdGhJwFFK2Z1Gb3YshoBVO0br3XbUa3Td3juhszH9wlfNcuFxtBs7owFF4ODm+fnPk/m7icDpQcazzgiq8PR1xzqb5pbwS/RHBZq+uqxJCBMwrrktiaeUIhyPVjGKhbQyOVJSDVr5+e+LhNOFIiE62UFOjDxZt+d9k8rSWvZ8yxoCNDTEwJ1NamOtbvlpIKRm+UY2a0okF9WY+T4/cOmXPtbnIfRh1KXl7+apqClw92poEUi6UXBvQaKCSlsI2u7S9dycQQuQwGBfao63OqgzjQIyR53lmXc2DS3D4akytXCrXcrVj3B+5tNEn/9+V09FmnEjL/WqbhN5RNNk4s31ZAFteYA8HdyCIjWX0Uvh3xo7vU/ro4a+VbmjfvWUkbUs1bEO7DEW+bUl7mYfKlqM2bUK9tLlCt3e9K+IeWncjQit1vAzd+79f5MwNXZad8b9bdyJLslGV6szAlZrBO1t5qoV8UxD+6csjz9crXxxtlGWp8NVR+OZx5LLO1p2BchX48gFbHMc5puAZ48DpeOJwHBiPEzUrUgqXZWEMtsqW9RZ6rilzGIMtQVE7F7WYRy6Z59kQVe+Vmq0IL97jVLg/TixeuJsmzteVD89nRGzKec6J3jFZMsyrjRWpWxO0gT80cKgb49RqkDZ7R0m5EoJryt46jrA1Vjprxzm31eLtTTZcwvo38N4mEORat0hGNQE2zU/EmEBLzqaoNTeZ1q3RvRvyWgvODbYuStZtoSUnig+O0XuW3GYeIVtvqBNT/J4qvU7Rbrdfzzmb+95CSZFNGdhy0B6Ts4W/vP6Mm9zslUDv/97rgn27pcPdhqJbWN289a1ybYAMu8J8umlbB4VPHk1nL22Fyf6Nzx7nt7fXIXTfktoSel1IvDhGJ3x1N6Cl8Lys/Ms3E28nRz0NVFUejwMP08j3bwZyrry/KmOIzOKQuRJD5RAtLxuDQzRTa0JkxEnA+UrxjhA9p3Bk/ukDT88La0oIyrIG6w4BjjIQg1Kzebhc8jYyc1kLa0qsSWAaCcEoaZaPBX7+aEhyFy7nxKbYgY34aDmbhbK94ZlW4jAaYK0GzHw8XxnHwaKs5g1v30lulLqeVgDQJik4TF69s6kOwTumIXCqE/N1odRCWlLLX9v7qjZgrXNue8h8+5+IyWbK1RxYGwpnpH3XaHnWlZRLwcewNYzkamWtnAtP5wt3h7+RIWQDdM0POmljIQAVZz1ytAfQcqzuMfv8zo1aBFsIKbLvTw9Q+yS9TsHa4k/7e3COgJJx1D5FvYn+Dv7sCmRzFez47dRA6+JwDlXLfRy2fgbi9yX+dte91VG78Kjss3j8jeLXW6JFv7ItR97Bon2WrLTVp9r6mUUYvOebA/zxUbjWgXf/eeYYA3dj4W46oApfnCLfPE68OY0WYv7FQs3r6ih4joNv/YVKrSvLWlp64HA+oGp0svl6xYcB1EKxXCohCEuju12vF47ffk3KhXlNXEsClU1Y13VtZQybVHd/uuc4Wc4/DoHghMu8MD0cLUxU8xAheAyVN8JBzy+3WT6t9JGSLVGIrzw/XynZ1iABwXtbqGgbeKatgV9N8UspaK5oNMUMTeai9201AocWmxvbh38558hrJsaA1mQD07NQxIH0JTF2GfPe7nFJK7WN2XSibUxL2fJlpxYBFGcSH7yzbh4RrjkjzjGvf2Odc88NZVMUsIcgvs2S7WEfcuMlZCMn96X75OZ4prhmZbY8oSvX5nJ7aGvCPUZPqMqSK6Up2y349LKOuZc0eo6oOKpUItYBUVru4sRYO5U+dsTOqTfX+9rDV2VHdtkV8ZNaquy2SbFJ9MadUrJTAkYuOMTKV6fIPzwKd5NSn6/845uRtCw8fnvP4JTjONiiOlPkOEWWUrg7Tvz0vDCnxJvJwmCbll8JohyPEz44hjGaYRoiaOXN4yP/9S9/QTFUV8fQ3msheM+bhwfmeeGpFi6NAJ6yMZosL63M80oInskHnp4vPD1XHu6PUCvTNPLx+crj/QHEooIlr8Q25mSeLc+67ebpw7T6f7VaPXGZVxs3WZWUM84P1MaJzcmI9h0MzMVyRnXtvcc9kkul4JryrrlQ1Uj+h0Ml1wtaM4qzlq7rzNLlsKdMsofCtVbEhTZytbambmvmzrmyppVcTU9CCKwpMQ0DuSbGYWgVCqthr+lvzDnlxtuZZzOuaGiNvR2OvUVP3Y2Ktif/Ih8DUCc3gr+jwO4GcX3NKvICXgoleGqV2+Uxtxe9f3c3Jaacfc4NOGclkdLyVmfObQOaes6qKK9bwfr76jNub1HoW2Ox778/i22JBwyefxsj15J4PHj++W3k79+OeC3MKfHvvjjw70S4rhWnhVECvoKWDNS2kJKtPK3VygXff3Hgej3jxRM8TNExOFu3sicHSls0KE8MMfDzzz9RxHGdZ6ZpJMZg/aRBWAt8uMykWknJ6qFNKjifr/z47okvvrjjNA5cr1fefzyDKo8Pd+Rc+PB05eF+5jgZK2gYArEN/QousKQV761uauud7B6kt1kZPc+Eo3ujnPPG3HTuZcRi/FwbYVLVbZ55O2YbW5KKURHHwcZy1lJIS+Hj87mVSapN6WjKZ2QE3UJnmwlUmqe13Fjb5+uaKBWyNnpmMbbRkjKlFMZhYJlXy6dDeDGP6/X2G8q5Sd4WjvYP+zgIufn75h1vItMu4K/bqVyL8KwfwQS53oyl/NQLKeIDJbX6WEd3tImd3HrnrhyOPiIztpWivPONhmcIMN7tfN4edW73/ykd0QZNsoXpLwnwzdDcKOntXN3SjIYIDK5yPwQeR8fjBNMAlzUwDMJxsvkR98cDtV4ZhiNoZRwHxqFNonO2StfDMfJ8zTzPiawCOeFiIKsnxsFm0paELw7NrQ+yFg5D4O7unv/0px94f7kSzytvThMER5ktF1xzgSosaeE6r23dEceyZOa1MC8m6OMUOJUDBWFeVlJOrEvi49O1rfxltM/eSF1rtkJ/M4AxBJxbqaWlMmqosUhkiNGmC3QQpbGCxmGgBqVcl23/y/XSyifWqCDBb/IZvSHgVqs0EfFeGKeA+CPX60IIgZ8/nrcctvRO6SajVXXnVNOnHpgWeW+rVtMMhi9tOEEtNkx7rURvvyt9iFibcPgL228Mle7uqXsQWqNr3eo3GyK2xXj7iAzvva2iJR1i+RQM8je/+5479HpmD2tFWEpbf6M05s42krPv0xVLDeXtJ2rKMkYYpVBVmdmBhZ3l0y9kS3dfeFT7XF545b65Fr73vLQXpLtB6pe2+QZVztmQ7notfF9GfnpaOc+V794Ecqv5nUZ4HEdyKRxHx2EQpsNAQBB1TNFxGm1MxpIWDqNjvtik8vM88+YYyWklLStOHHldUZS0rlznhZ/Pz/z44czzZcb7iNbC6W4kZVqR3uqR12vip/fPvPtw5ouHu9ZzWfnhxw+cpkAcAiVnfvjLz7x9c48TA46enq6IKPenyWSkgsuZeTWSQS2t/lgdMXgDblabGmDK43k4HllLYlkXtFYeTnekVDDcyrituRooVNS4QzbORPBtaUARCMGj6lq3SZtUEAPOLCbTFBiulqNqZ/vkDGpGvXRNaMK+5Ex0jqyFWBx9KYxxiCzzCl7azCILpWvL/Zd1J1jYchF/o3JuYW07xC1L6DYf+xwY0jWjh2D6yit9fv/+O5sb6jediq0YNQbHUvZpaXzm3Fst8+Y6vBNwnpStSfk2X+Tm0pS2HHlj7HgqqQq41oLE6xD69vzmVz+pgfJSMcEYOroCg+OalKOvnAZlDIJ3wlKtfohTXE28vXvgMIVWx7OxjT5YbfB+shB2Gh3zubAms8zLsnK+XO16SmVeZlCbTnBeFkou3N9NiBM+PF14/6G0KfktFSlGBJ/nhafzzGkcuZzPFIXLsnJ3Gm3qwLIwDpH3H86cz4FxCFQK5+vVJioMtiJY8MYio9ram9mBz4W0lvZ3kKjI1abULWtCjyDiuFwW0rJCFYYp2IjKNVFrYZ6NMOCD36bqyWtZu4niumfsoaX1jo4Ef7G1NitG7Wvvty/43A9pgJtSxIaX1Wr9tdSKOE9ojdQFW4/WV0O9YSdXdGJK/ZW49q8DhMQU8nbhoV6MvQ1rPxVWtjU4/Ku//5JCWxjp9jC15YaCsGRbKCd4t1my199/eQ7zeM45llRZvLfy0Ov7ZH+ZAjwcAs/X1RZgbW030bu2oM9+jl9mJL0Kidv190WKEFuXMwPnBB+ulbdj4f4YCd5xmto6GuLIJfP1472hy53YUK05WDCu5t3kOcQDwyCcz54fP8xoSbw/RI6HASczJSXO88JlWSlqQ7Sm0QAmVeV8mVlz5jovNl0hRutrrPD+45XLkghi5AOtwuW6sq6JN8fI118/4hCmceR8nrlelGVZ8XFgzTYW8nAcWdaVGMxDr8sKTtBq4fI0GofWecdxGpmvCx8/Xnhzd+T5MvPh47PVDMOFVNsgtEY7DqFPS2STIW7ktGol5WQKhC0nUmtt4XIffeI4HUculwvP10R5Qchvx7WD4wQO0aOiHOPQemw9Q3TUBpzVYlPlG6DMGONmFLQ9f9UOYHx+++VmMlqM3VWkCRlVic5vx6wqGPP9pTe1pQIh0NY2EQvjyquQ8LXn7blr74SBFl6qwTWZwOgdU2POeNeX8La2cGvr0w1I2HIYMQqaYXJsxIVtnKfrCb+tWmyhcYEinFxhwupxo/fcS+LgzAv0SeZO7D4d2KQCZ+FuQBmc9rkQjUyxd70Udfz5uRKGE7Uo61KQUrkbheCUMByNHzsOqAofP5wpao3Iy2K80OjZmmVisK4TmkDOa+b95cK75ws/fjjz4/snfnq68P6ysBYlxMAUA0Nw5Ko8PV8paeV6vfDh48yH5yt/+svPaK78/PRsHTrA0/mZMHj+9O4jHz5ccFJJtRj/Ntv9lXW1PLFm0rry/HThfFlArItlvmQ7X55Zl6VNn/C44DgcR3LOvHt6tkghGxJ6Pi8s10ROCfFKiI7TYeIwDkzRENRhjDirlRkoVO1d59LI6k6YhoGpAUJgwFrOidPpSGjTO/aZUgb4OOljTQDxiHqyVnwQI1KIcjzELeLIxUpEim6EhC7vxg2zJvNf2v6KZus9JKtYyJe1WjO164DQp8vAm4VxLzyMl5dW4nWJ4hZM4ebffcFSq4lljq7NyYmuNdoKtcWcPrw8x2dD0ObJTHJa6xJGw3oYAy4v3A2CakACfHd35KdZEBaOsSJu5HnpDdctH6UDUnYC6ysUIslGSWLh9AZNd4CpJu6HwIfnM/EIfgogihd4OETefXxm+PorHh/vyKUwDPfUnPnxhyfOS2rT67zli8NIvT9ymA789OGJYYzkWrg829zUGEZDErPB9+uytpGSSspwPS8wRXKMPJ3P/PDDew6HiTEOXJPVOp8+ntsAa3h6vjINgT+/+0CMnrzMnJ9npuPINE1cLleezxfkOHG+XgFlLckI/a05O6kZeCeBS0ONRYQYbU2U9++e8F6Yl0QIgflqYh0ifDE9Wo+oCqMMzPOC957DNBEHTzfM2sLQvVmhYw4erQvn65WcC94H8uXK8TDydJ3hJvpxToxYL2YI53UlSl/lLTKvCy47dLCqA3WlUzk77VOLlarWxn7q1/ZL22/OEOodKUa+Zssh9/xzV7ROhHfdI92EFl0BtyO/UsyukR1Meo3WtqYQ81BBuOZKUm80MmkT4181rr4OoV8gq86I1/06S83cjfDd/cDohbUoMQxcc+EuVuZr5s2941wCqaTNYPVFgNvlvzRG3vF3j3dcsuDWymVeqe5V47kTxkG4Pww4WRBRzmvhbor4arXKEBxjjAxDQEtmna8spTCnzOkwmMCUwhgd4+Genz6cuVwc87pStY3wwPHzzz8TfMAHm4gex4hW5XxeOF+WNjUOPp6v+BhQH9ukhkrJhcMQAZvK7h5OPJ0vRC/8/P7Cu/cf+fbtPTEGYxSlhVQy87KitXK5JlLNnA4jhzFyGKzJ+eP7Z4Io13Hm22++tC6TXFuPZbYlDBo5IfjVJvadbdHb+bAyDm5LXY6nkVJsbIoXYc3JiC0izA3cseuzFjOttXF0dVtxrKg1FZyvA0/XtcmOoy/p2KMri8asdr3Miy1SlBIpLTgXtjlUxki1d30cR7uXNl3wt1hnvzkJoYdsVdVI6T0X7OHnnnRu3Nbbymf3JD2HtOFQPZzt3vK1MrXv7FrW9lVSVYpEqtp4C9+OB3vZot/yjfozDoE5V6R5fS9KVutfBDgE4YtJeDNWvng88HS+Ih6mZaRoIg6OXKxJel0wsAZuFI393D1UFrgbYEmF6BTcbsS6A60Ih2nkuzeOda5clmR1sbVynDxf3B8NcXRWlJcQyGnl8f6O0yHjHUwo94ej8UO9MF0XxiHw9OHKx6dMHKLlzLbqzjYGMi0LQ7Sp5ePomeqRH969N8XIlnOfRgNzpmiTEaL3FFVmL+QcCRRyhb/89IRT+Obrt6w5E7wtsed9sGPlzGVdSanw1OuwPuJxTGMgeKtFa6tPni8zl8uV82Jll+AcRTxQeTpfLGpz8HhvHTc2JHKba4ETQ4DtWBfOzxemceBwGBs4KaSUuV5nrteZnA2d9eJs3lLLTzs6m9abmimdaaYMY+B+MsbWeZ6Z55UlmYccGhUyxrChv5sXzi+B1c9tv+4527j7XjaBPX/VplWVm9DxcxBZA1t0U5z6Qph/6do+UVD6kumKqK3M7LwzeJ4ditmSbNUtr8wl89UU+E/vbbn0hyh8cYj8cCmcsyK1cj/YrBtFKesVEceHc+ZaMm8PnrtB+ZgCebGZMji3PbyyAT/SBnrpRs11WnicHOnSCM9sPTX2PH3g7cETvSAh4EumuIqKcneM3B8cgzeAaJhGy+XyxDSNIMKyLKzLggDX65XTcOCrhxPLvPLuw5llNXbP6TByOoysOWNsSUGqswWNYmSIC89/ueCc5zJbfbLWwnVJeHGm3KUSo2dZVp6vtjr1aRyMlYNyTYWUbT2R7Kvlj21cZMm54Q+2Vmh2yhgV7xobaJpAbQbs+Xzh6ekZrca68b1DpIXgHkAq83XhMAwMg41rEYRaFB+90fKALMK6JP5f0t68ybEsufL73e1tAGLJrbrYTXGGNEnz/b+EPoDMJNnYiBQ5vVVlZiwA3nZX/eH3AYis7C6SjbKqykQgEAjg+XX348fP+fz1SL/reNxkMnPEOlF9KEVApfu7HdO4VuJAujgH5CL94VYV6Zo9AZbVY7Uh5sR5WgWzsIrGGlYv+59K5oOEmC5sO1Wv1f90WbsF5vb/29smiSmBdNMbfmdecnkBW9a4+dr3Sk5+8fVaJmqhpi1JkRB7uNvn2ErFJPUlmwLC477ntwfNfe9Y18DQaO56TTGaeAxYA+8Gw7vB0unElxlmH5l8piiN9wGlW5wq7G1G97BEoRLeUiO3D1KR0VmhdQblWKN8GK72us5ZIoXVR1SJHNqOnS1EYyBpfErEnGk0HPqeprGoylRJWU7uruvw3tM0jtZZxtNZzFuLeHa0jZNMVEEH76OoEzjLvK4VMcxYJehp6yykeKHPOWMErEiZYS883JQzX19HEorjuOBjoDWFw27HS2t5nSb058Lj3R4fpEcUEepICIG+79h1jhBh1xna1qL7RmQ95gW/hgsFb/EBVccT1kn23vw5m7blx0/vCT7g/YpfxaPEmXShxomBrSIFOJ1GjueFcV1lKTxICdo2QiIYhq5SBtPlkLXG4LRo7F4znq4ymteWa/WRNYyXNk8yuiDtzhimNdQKaQvG67X+XZ3jm9uvkBC4iHlt9gTlpnzcfEnKbebglyMGpbYw2Qb4+k0wXR7z3dnhLYIrINOStCz8liqdWcvjN73cDdpmSmDXtPz9Y2L0Tl5DjvyXB0sOhcE2vB8UY8x8XTXPY2JvM3edoWsbQso8T9L7/HDXE6aMjwGnCit17KPUhaTRGLkwnDWs0TDmQCrQGIUvmZQDFohaoWLkQxP5zQ+feH75is5Ktj20Yegc/WDpuk72ZnNBaWi6jlQK/W7ALzMkmbOppqVvO748PzGuawXlBCyzzuCcqdmvmrcWocPlJODWx/cHPv+PPwoPlYRGiOqTXwnLSkIzzysFxRoz0Uf0nWXoW3rX0JrMbz99oG0tz69nYfyYwmHXo5Ul5sKubympkCtirbUsL59PC65pUIiqgbUtz1+fKaVgtWx6aK1Z17U+RyRGT1GIQLVRtLbFOVfppWLpsC6i3p5LRgXF6Tzh18RPz2c+vtvx4WGPUkLml+f34Ex1Cgv1MHWUmC86t/I5C9W0oCsxX2OtQmtDCBFVpC0QEFChdRUEQA7ob6mh/+HgVLVslQ2M9DZbUiU+2EYZEn0FYe3cgkFbcMnf35qFfhuQ3xutAFU3V4ANU0WIN3v0jVYF9cCgUg2LuGflBKdp5YdDR28hKw00dKXwT58GQoiMQfFlTlgi963BpMKhc2idoKjKBy2UlFi8Z8XiNzBKNTgCjVV8PLQ0pvB0XrG6MAXwS2bNm66MMEXmlER3SSumkHh5faXEhC8KazWP+47GObqmQ1tNqQiuUgZlKrpsA+Gi7wAAIABJREFULP3hnrjOdCXRWsfPP33mPM2UIvo7toI8zmliyLSdo/ggYtm6QJQPziehmf346YHPzyMhRjGpDYWwRGYfL1aEUDAUTOMIQb6vdZqcYegtbWs5vipCCryeF7rWSZlZCsfjzMN9j59FWsWHwK4feLgfWNYIKDGmnRfGaaFpRTJ013UsQVbgSoo8vY4452ibaopUiQDOGrQxBB9ISeayp2niNE7c73rGccHLR0prDblonl5GFu/pG4e2huAjzkkvPC2+jvKyGCRt2N923VKxAKUoRVceckYVmWrkkshGkzc7CaE+Vcrh3wAIVdbadT3nejcbMqT4FtXhUrpuKO9tGfC9wPtLf99q8otbcRH2P6pU8S0BdS7VQc1cstYlWy+xwJwL933LEmWArCnyRsZCqzNLTnwZpQq4bxuGxtHZyGb/ZkzG7Br+/qHjdfE0rWOcZPBcSiErxa51vOs1OUWOayQVxZ2TN2SOisnL4oBoniacFoGv3hk6C52zFC0L3n3b0jeWtnN0fYtt2kv1sZX51loh7mt9OcXHceQ4TihlOJ1fiSGQc2bY9XS9w2SY5rmOXmSkMi8yi6SIVKMRgIA/fH4ipk20auMzQ1HS923CXfMa+PnpRUxjtaZxdckgZR52Pa/TAmiMVUTvMVa4sj6KpJlRoqy/yWY663g5znjE7TqlzLx4cpafeb/vWJfA08uJdw97WtdibSMKhNZKW5MSISfmeWVcZnJRoihBQRnN6ThKn+kcr6cTPz8dGTrJ2jlAyrLDaowAYCHmS3K5rAnmaz+jbiVsskh/+rQhu5kQF5zR9fqsSo789cCEf09ZW1/Wm8XnDYq8edx2/4VH+51A+2vI1O1zvyWT19EDcnVYLW9KLOCUwZd03edT4puxs4UpQkgwbqtHMfOnz4q7prDvZBG5FI01uWZ/+OHQ8NgV1uj58a659HcxJ6Ygw+fZg6kGOWvItUjP/GYnROmQEmsUyt/gLMU0mBAgJNGXQTIjGnTWvOszXdvyYddwnDelvYxzzUWtbxteU5Hz82mk6zpc4yjRQ8ksy4KfF1CZ12lmCkJcDyFwOiXa9p6ubZh9IOVAKaJ0nkumJKHTjcvKdJr46esRVTdHSpLVLG3AGSs+lyVWeUgvQbYW7q2l6Trkmi1oq7i7G4QlA3St4dDvOY0Lq/fVMj7RtB3H04l37x54eLincUb4qCXSBUvMMI4LKUXePez5+O6O3//xC5A5TwtQMM5hrRYut1bEEAg+8vnLC+Oy8PJyBiWWCdMSmdaVh/3AvKy8HM9MqxxiPkZMNixhEXKHk7m0j+Eiu7ldy9stpoTaiPKXJevqzVmbzQJERHFRes/8i+f53u1XSAjbOOU6N78q5F2DJxewF4G5raTdENlNBkSx6Q5dMaOrit/1GOBN4F+GtUq0dawqdFaydWcSSWnGaPBJsulgDb/ZZdZs+NM5cucKHw4ObRVpjjyfV0K07FtD11SzV6V4Pxj2rnDXKcDQasg6IYsZQgH7eoy8rLpaE2SWItbrv7uzfBwKfzoKOV9rS8jgVKJo2Lea47wZ4yr2DZxDpm8M//SpZ9fAHDNd2/B8POMqUtq2HcbainqqKj8K6zKhcsbZPeKaPDLPC+u8chxnnk+TlFdJdhaVUhxfJ5p3MthfRil7U5LdQ4qsTa1LwIeCth3jOAm/tKrKd87gnGZe1lrNqJqxM/Ma8D7QuIkYPT9+eqwHhux8qiIMGmslyz+9jtwNrfwuPvB8XDgcPO39Hq0Vu6FjXQNfn46gNM7IKOL1OIr6vFUsXsSac4oc9gMKOWyUVozTxPE04f3K8XgWrVwjIM82ywxJ5DCDrwBULixrJIVIrhncJrF6mKsn5zbffPPnct3jzVkAuFKk1WLTP67vtTGmattu7dsvKYL/7uAsubK/t1O7BtaGTG2Aw2Wh+pJpr5NGxbel7M0fKuB0e9v607eILiilxTbdFB5b8feIKQKWSCYrqfed7fAxsmsSf39veWzgMBTe9Q0qzUyLiBuf58TQNDTW0rrCb51laMCqSl7XQkTwMTJ6xXlNzKXhaZVxjE/glOLTneW3d47eBf7royUROS2FhKYzmqclyewSOVycAZ8LTinCGphmRaMafnp55XHXs6wL+4c7hl5KWWutvL/1jQshMAw9TdMQo9DYxtcjKUSeThOnaaUoIS34EFjXSNs6coqsa3zjxAXCM22sgbLSGM+qCvvWELz0bcYoccWym06siFttjJu2cazeE0smrAF9mvjx4zusMfz56yuNs9zvBz5/PRNj4O5wIKXEtAR2Qx3tNKaSXERhvWkarLU83h8YpwWKlM9zzHx+nXnctbgiVgt3dz13dwPG1oMnSKk+TQtdYyk5S3AZ8dPcLObHeeFuEBJGLoUYJOt5X/dMYxZSf9XGTVXtf0sm3wZmKUJqvzif1+u7IK3AhUBfNlC0fDsp/I8F58YbpQai0pqilKwssWnCbiXsFk6FtxDx93vM29e1maD+Mohvs6n4Sg4uc+gdFsVxgd627DvNnDw6aVCRWODv71shTtjCw+DYtwZUz7Jmxlm0Qm0OPOwdzimckaF1KlFWfWIix0BIhXnJ+CDjlbMvjFFTtOZhUNw1geA993d36OzB9OybSEiJMSqUSiyr9Ke6JB7ahqcpcrCBvzsY/uHDwGHvOC8zx/PEru/Z7waMgaZtLgjfphZQSsI1jhRl8XqeF47HkTUG1hgIm96PErGwMa4y/9z1TIuvzmly0TlnCUU2MlLO7HctriKOFJgWT2MlcFYfam9acFpUB7QqpBRonWHxGZ/Etu80znIB58K0eHado7OG4izRLxgtu6p9J6OtO6fRyDz0sB8IIXP/MLA/tPz5pxdejmdAAvn15YwF9kPLchwxylYTosRUVkJI/Pz5mXlZuTscUEZ60Vh3M28z1XFaeT6eKFXbJ0YxuA0xVulNucK3wNN1Dk65UgEv5ryXf65Zc7u2t9jIOYuX0C1+81cq27/ec24nthJdnq3VzDXyL2JL6iqwvAkGb2XtL3rV+sBLPN+UvtdzYOPlXutyqxX73vLQKXq1ktF83Gle54VztLzrYdhZplURS+HLcWU/GA5Dy0PfiMiyaunvNc4eWNaViGJoLK1WrCmKPV3JKA1T8Tgti8pryWA6xgitK8RieN9r/tsny7xM7FrNaV4xOjOohZ2DETjNohF0N7TEJfF3dy17k1FJY90D//ibyPsu4CM8Hc+E1fO7T+84jzN9q+n7Xpj9OZNzJAUPKVKUYjyfKQmenl95Pk8UpVh9IngRcLYm4xrDbtcyVfR2XhZMFUK2zpJzkq2UHOnbBnImRRF+LilVZXdNykGwhCzqAfu+pbUw9A3TlPhyOqFUobEGlaWKOR/PhAzkRA6Zf/jdB+ZlQSvN6Xxitx+EfGCFkD4tE60TQazdbqBPmf/vX/+AdZa+FaLBNGUojp9fX7Hukd98uq+l68S8BKbzhA+Jl+OJ03nmv//rz1hrSCle3No2DxWA43mhbJ4qQp8Sjd2cqsxIFfmut23d63KN1u8tXAFRWeYW/w5JOLlS+Wowq2tTuJXG/6ng1BWh22hG19i6GXuwMfi/x6V9+//LHJKaE7+T1m9HKW90eVTG5IhKQcSbEEStMYUPjTBYQvR01rFrkTe9KMiJzokaXe9g37WknGhcRwyShWKKiDKlwdiG0zwLEJQyi4cYHEcfWYvDb5V+XjmOgYhF+8zJT5imZU0Fo8QawCXFkhU7o4gO3nfwrhPCw3H26CR6s+dp5MvzK4ddz+fXM8u68On9QcqlmsFj8KzLQo6JmAI+JKZp4XWc8THSOlGW8yGSg6cUjS5S3tKL1YK1IljdNJbVy9DdDh2qCEnBWsvd3YF5XCgUhq4RlBHh4oboafsOHwSw+vHHjzx/PXGcR7J19IcG0Dy/HFFKMc8rTWPpho6ubWgaA6Ww27U8v5zE0j1HfBCB5qzE+KdNjnVd5WCPK0Pf8PXlWPdbFWmNfHk+4YP4nvxT2zNPC//X//g3rGuwTnrLGDwU8WrZJDe36y9HWeje7CWNluVnUd3L1ervirh+Tz7n4lS2SStcgrO8addKzrWduM2Yfyu39puZ4+0T/2LNqx4e382UfBvQ27jl+6SD29v2NXkDxBbPB1m+dvWHquTpnQWTuWvl5HKNuFL9cNdgTUGrLPotMTC0LVpHolYUVSghyQggX+mFc1CMU+a4BmJxGNeQFtk0MarwMFgomY87izORc9F8nhIxKfZO2Dd3Q0dZAr2FrjE89prOiqCXJRJ9xFM37pWUr8/HkRAano8TXX9kN+zwyyRjgXEkVJjf+8C8Bs5rJKeE1U4uAkR60WgpKxtngXwpYymFxoqfpqpKdCgqclsuF5e2ms6KH2bImb517FrLbt+xeuG7vr5OlQlkUEoQX0FMCxTFfujoW8cwtEKCsA3ee6Z5BsQmUOwXAAzrKj6YsqKVuLsb6FPLNE71eUWjx1rH83Hk+XhGa8N+N7AsK3PM5HWkbayU9dbWJYxyCUytRL0/ZXlP0Kq+D+nynpVapm7t1rfX/u04ZduxvWX+yHTh2zFi/kXS+ZuCc+PyvCUebH3olfC99Yfb92xJsXwD+OgNlZV1+AvdqWyobXkbpNusFCXE+zVGlliwrcLHRNNpVi9ZYucSjRUOpNOF3omjV2tUvVi3Q6TQ9y3jJD2ILorWOZwVbZw/P5/40zlxmjMUzRgdRos4VGc0sSg6Ffm0a2iU5r6Tcmg2mhATTSeHRtTCHNkZ6Gzm7+4czsiGfdPI/M4kUbJDCVNmWbwoghvPz09HtCq8u/PEFDiOMyEEShFn5BgTPlTzHqPrLmUk5c1Vy2CdaOtAQRtDDkF0Y5PoALaNvZRmWmtyjDjnCHlEAftdQ0kFnTRtA7t2x919g7Ga19eR43Gscz2ZK6aQaawRf8rG0LWapmlkD7LAvK5EHy7yNm1rpGpJmWlZUUahTpmP7+7Z7zq6oSPnRNc4Xl4nXo4z94c9X/xLnW1L6/P7P3/F+0AuiaFtuOtbrDWc5xVVqvyN4lLdKS0HUyCI6nvRUr7WYBQJ1IRS5WJYdEFUait2kRrZKHm3faXeRMmFGZRyvlzL2/f9zcH5NqOVaoS7heFWJnAJ3Fsm0O19lz9z3X/cgKMrllRTb3n7/ds7ohSEYliywsTE7BMpBmKMDK0jZui1nL6HhxYlFBiWEDEWetfSWM2u212XZoEYIsYatDa0rThAPb2eKEqRleNlyQytY6naRSFl7nuxcVcqoXXB4BhazbtBY41CqUjbWEpW1ZDW0LdGmCNZkUPElAJFyqjVyyaKLExnwPCnz8/EECghYpxhPC8ohchoxFiXdAXNzUUU8nzdqlA6o7fe5lKCBQyKmAvzvNAP3aWnL6VU5b2I957TONM6x9BbYiqUyXN/6JjnlcP+HTGKxOMw7PHLxLhMhLCR+jPGdmijOex3TNPKz5+f+Pr0zH5oQRmM0Qy7Xe2lNSEsxHXlfJqATN82DH1bhbgMzkoFEEIgVRU7s/WOpUgvq1VFbC3D0IvUi4+VPCFbPcYI39VqxVQCGV0dtDVaw+zlYHMmV6DnmnG3AK05FW4AoI0PsF1TWokM6yVA1Vtq6vf+/B8OTq2v8s/XQJWXqC5RelWnuyK7N8+hNsSKy/NIMrym+Mvzl7d96JsXXiQ4j2sixyJuWCpDjpi0MuuGwbWY2ldZJ9S+WAWL59VTwkL7+FC1Qgv3+wPHKn8hCnCa/+XTgeOU+T/++Yjte5STGWtICVsSd73lv71X3PeKzsjKlM8ZS+GHvWVJAXGqyiSV2VnL3WCIMaDJNE5QUK0AtZGqwXuPM1bWp7SQ3//09YUPj3v6pDFK9lhvP+gQAudlIaQs9LYlkFOmaQxKZdFnTYnWObLRzNlT6gGZ62GwLPOF5NA0Dh8iyyIkgw0lbpzibtdxt98xzjPJB/zq6XTm7354pGkb/vX3n3k+jYQkyryUwtenM1qDD6kaJ3U8vYzEHHm423EYOmEB5QiqwftMVhB8rDNIkTUJcWVe10uFMPQdZV5kN7aCKsZI0G+KBmIfUWhdc6GZisYs9M6KKoJSWCcz3CZYcpmJStYBDVc+ONUlRGUhulBuNJy/Icx873ZtzfJNxtwe+5dbul+dc16CCW5GntIX3GbLbe9zS4yXbRK1/VZvg/RCy/vOyXEpm/V1j1MrMZwpAglDCjgbUKpQaOvqmsEZI8p1jaZrpGcSvcBMOwycR4HynTaM45kQA87KgD0lhSqRvu/QekSHid80hqwg6sxdX/i0lww5tJrOwOi5qAmovKKL9HA6ydqX0RkN1ZnLYFTG198rKVE98POC9wnbyevt24Z1nWU3cI0MjaXrLaUoznNdis6ybuZj4unlzKseQWvmaSWmRNe2PN4PNI1BIzxb71MVOfZCscuV4lgzkbWWtHj61pEShEjlq8p63qcP7/jy/MRw6DmExDAMdM5wmgNai59IiInd0BO9aBVZYyg+0DQNPmViDBirWeYFXRLD0JP7Fr8mbFf9aZDDI4aAVorTuDAvEWeM1Gsp09RMX+qkQtVSsrEd6yoqg61zOGcus0xqwC1J/FpCzKK2v2vROuDMnnnxPJ8TxmhyqYp+FTiSNu1b7cWbeKnX8jbvvBBsSkFre1EFlMf99ZL2V4PT1OfW6irxuAXPllH1zYbJpUZV16C6oK/foFUX2cgboGjbCZWAe8vH3TxMChCEWIs1il3jOHrF7AvjKohj31h2XcvHxz0hZnxleExrEF9Ja0UUKhVMuhraTrNnqVD8//6pp7GJxUemJdL2mh/fN+y7hqbSCH0p+JTxqbAm2DtTtY8KqEznDLuuFfZU1iSVWH3AGkWKgXVNHM8zJUex3lOFeRFH5K5pOBxalhBYvWXyC+McCbng1yDkex84jzPTtBKjMFBSSsyL55URKPz48R4o1eogoLRBfFM04zxR5dFJaZvtZQ77gZ++nPh6Wrjfd9z3jtkrlINd17IbWsZJpCrXNUpVUD/73sI4zZexQ87iAN04g1aaD+93KCUKfUPfCkn96YxtFCkZ0eoxcpzudz05F07jivcBV8WS2taikixUyzVWpEIKCmuE7njoWywa48CvgVQE23C2emoGObBiXbje9S1DZzGqMK1eGFRZUTKkIiXu1pIJgb062pWNUnAFg4Crq3qRIviWiysBmn81QP965tzKAUolJPzlAPvF2IRrYL4J0u25S/nF92wWDtQRzfd4tlZVSpRuOC0jzhYxXs2JtnHs2sSu37PrGoamQXeaNTaM00LJCde1NM6BgnVaMcpWeRNVe67Au53m3eHAyzjzp6+ebmfYdQ373r3RAQqhUBIi4WELu85gtGEJwmm12tJamXUapfFLqHqpjqW6Y4VcaIzl3lnO88p+kPljTKrydxOfjxPH8yh7mErz9esr4ziJ7XyQbRFxvRKBZtdYYhBpzNUHdkNHTJGmbZhr6ZtLZJoWGudomq5u9sOyLBhtiKkw+cT8dEK/v+c0PfGbH+4qQX37TOV3P59HlNI0zgmjappFj7YUMcvVsnzcGM3dvYyIjDUsi2c5n2SZOWWOr6PoMvUtw5Bo2pbPX184nyeZU1ZBZ6McWmeCTrWqkg0PrQQ/OOwEJXbO0jTS6x+PI0tINFYqgRgzzlm61vHh4YBSQoR3TvNyXogpVx0vWWa4DSMBO7c5v66g2/UaBdnk2gLy35spv739SnBS0a0LanP5YZdsugWf3mQwN8UyQQhvX/DtL/D9+7j8nA2a/vZxqYiW7GOvWRZH2/Q8NJqYRcbi011LZzVdY0gl1dJGE0Ng9enitlyAvutlE55SBZ40xigOQ8OuN6zJcr8TbqpzSBbOAacNPmXOa8SiGTqZAx66BqMVKS0ULf4uMisUJbZpVXi/CoJXCnPduSwIkPR6mmlcLyVX8Ox3O76+nIg5M55GtIaQMy+vZ758eabfDeI8bU2F7gtaJxqrZW6YJQA34M1ag9aR4AWEUtowLZ7TuPD+Qdg0OcE0T3SNo+9axnFknGb2fcfT8ysfHw8Y42gaWRWb50X61yxZKZdcbQQTWoltYWutlLLBY5eG/a5jWTwvz0ecsxhtmKeZXCCFjLeJnAqnaeXz1xdRVsiCbLetwyDvraJuLRmND5GhcTTO8ne/eY9BrAX3h57zeeSs5XO62w8YDbuhY5p9pdtl9rtBDu2S6Fsr4Jq2OCNbTDEVfLoKc20Rsc36t4x6ERErm1DBJady21/+e5ZAfn1lrJanm8w8XMcpb/6tGNZVAf6X885fWxGTX+7651ve4vZYqw2dTVhlSFHx57HQ9h1QCC9f0f+wA8OFvZRKrs7R0DjDsq7kxhJDxjXbaawIyZNLpHWWfdfhnOKub8ipY1kDS0g4axgay8tpxEeFVgbjMo21/HC348P9HT4GUk6MKrOrzs8pZNYQcVZK/6/HheQDJW+eIIqvxxUfIi/HiRRb7vYdx9cToHg5j5xPixjhOMfnL0+sIeETdE3LtAhdzjlDLg1aK97d39X3UbMsiywMl0Ku6ulPLzNGF7rOyQ6kFVrZz08nDoeBMSz4daGxmvM8UUgcj4a7XYfRllwK+93A0LpqYltIOWJtI+toqe4ylsTqZan77rCDXHh9OaJQtK2jFHh9fWWtGj25ZJrWYpTmX/7593Vlz1BItQ8toBO6rf0k4t05zSvKGJyLqALv3z+SYmSaJkH0+5aPHx7p2pZShEY3raL5M82LuJv5QIqRvnP4KGzotrGsvuU4Ltgo79G2fZJzJmyl7k2wfk9sYAuoWzBow13+U8F5C+zc9pvbE2+Z7hYU+sVzfCdLbrdvM+PGyNhu39sF1SXycachtagcOZd3xBRxzNzvWl7OJz7dS1kjzk+JrmkwdZ6qrblA6iULVS9lAay01pWyFdBYWisZbT80OB+JWdyc393fVeIArCHz0LX88P5AY0TM6X5tMUrRWU3TSA/cOsdxXjieZqZlpiik/BwT02nidBz59H7HeJp4zWJ76MOKMZbnlxMlK8max1e06fDLmfNypm88fduKfi+whlXGPAWGXYdPkb7pebzb8/npFe8rAb+xmBLZD7J6FkOWMjhCPJ15PYuOUNd2BL/UPr3jp88v/O63DmsNPiZUgX4/0J7ONNbUEltQ0YJYxuet5dj3iI+JLIoPQ0uInhgHfHXnyikznmZyiiirYM0Yo6BoVGNrpnSUIttBWivIkULmcf8glh3189bOUYwjpsJhL+OVqySmAE45RT59eEdKglfM88zjgxhdrasoyitkZ1gIKNWqq4gWcoiiliDz6l8yf/5SkrlG2F++/bvmnFd92np/1aqRkcr3v+/239vn+va5/9qs53sMo8Pg0Kwoo7nb7ZiOR3Zdw85pXFGgW76cA23vedx3sjKlZc4VvED60YrCWpZWU6zTc7qcahu4tb3hpv77Mi10bUNKIhOpdWFwho+PHfu2EQ5qUQyNpnU9Vsss87DfsfpV+kMNzigOu5YUFeswsCwLzX1HWCM+F5Z5xVrNvjecx5njeSGlxBoj0+wZ50lcu4yloHCNCEP3nWU3tDhrUUY0fHtjaRrL8Xzmy9NRfuciFgaP9zshJcRIDEk2ga0oTHTuajWgnWNoGl5ejjinWOcVt+sZx4muc2gNd4c90+yJUWaESlfuaM6UonCtpesaSjXGpUSmsZBKZFkWAWOMoLSL92LtpzS6kd8x6oTJSjxDl4WixJK+cbIttetaSlp4d/++umlD8J6vT0f84uXrtadu25bdbkAbzTzPksGzhQJ2tyeEV+76lueYBFdAqqaCwodQGVZZ1A20EquFyqz6S3vLt/ff0lL/Wnn718vaS58pF+uG2UqW3LDTX8qKbJSmbwGkS5BtPITtueQL9WtVZUjdeHbUhtpqcCXgnKZET8qR//KgMUbIANMoXe///c//k4fDP/Gb93f0XUeprlbei4NxiomYIm3Tiah0zdgi5WhIuTCvkfPsmRZ/cSL2UehmbSO9btdYdn3DbtdWNE9+h661dH1HSSLfOc4zL6czP3898no8s8yBw/COpis85IYTidwqTkcRg1IFQlZ0Xc/X5yM+BqZ55fV0xtiWJRTW4OkaWJYjjVUMzY4fPj7ijMLHXO3QI1opplnGMuN5YV5XPn24J8XC8Txi7/bCPW4dP/18ZJ4ThUQqYifQVOK9UtB3LYXMNMlO53kaWRZD24qIdNc0eBUvVdW2ViaHo2I6T2glKGnJso43TTMpF5yTYItk+s4JGlpE+iOVgqnqfxRIRjEuK2Ne6VrH42HH3lo+vr/j7tChtBEPUR+Ypol3jwfu7u9Yl4WmbWqGXNBG09Q+VWsj92lN37UiihYzI6tsAaWZUNFeQXLzRbQr/4Xk8u3tNhGVvxLI2+1XLQAvQViADanceHk1mICLVMMtI2gLzF/Q/Uq5BPj1wXLK6ooabj9T1SGT05nf7OCuUTgVaFowTQMlYp2gmi8xcT7NdNaRogz6c4pM80xImTWITH5KCZ8UIWUe9nuUURCFEqhTYbOQm1fPsq6AwjUNjQWtJCD7VrwxGmvQReQ3NwEoa+T+iPyuPkQBWtYgfiKnlfu7lhwTjZVf/zhO7HaZpun58nxmPJ9Zdg3eRyiKkGSHNMcVcVRSAlAAx3GhbRx/+vkrnz4+Er3n3acPnKeZrBRfnk50XcfhbmD5Enk9e4ZdS2sVQ99gjIiLtq1BI/21VEsinWmNpZQsJralMM2rLGmrgu16rGkYlxGUEvdqNvRY+vlSpIxNSbw1UwKtBK31QVy3ljVgnanXhmhGoLjYFWxMHkohBC8BnwtjlaM0ZH78dIezrlIbA0+nE/tdz7quKA390FOK9Pm2tjcxKsZJrO0LVHU/RxMCjdOMM8TgxcdFVTe5AiYpolLXTZUNPP1mIgGblIzgNFef07eP+Q8H58arvYI+N7n05r7buecFlao/6ZVHAAAgAElEQVSZtnznZPg1JsXlULip0z/18A97sVyIMZHWQLfrUDjxYUyJHCOvpxOP9wMZzfE0YrRmDlE+XKMgZtZSSPGqKJizLNOGuM2eMs6J+kEpstKU8sJ+t8P7pW5xZVQx0nNoRU7pUhFYJbzaGIIIDa+ecfa0XYuJgd1vP3LoDK3rGedFMkyJJGdkH/HLK+dp4f85zzw9vdD0Lcu6iDhYytfl9iIymE3TcF5WYhFlgb/7+I6vz6+EJMSA3//xK03fcD90HMeF1y9njEn8448/0NqFDx8/EMLK0Le8v9/z5eWEMWLLXnTBaofS4pylENmO/V7mp9t14FwV9jrNWOegki9KJZ8Idxca16K0MKJCkBJXVqdkphhCIqSIUgZjhIKXktj1ifWEAHtNlG2ZnCLz6qHICtg0z3x6vGdd4U8/f+HDw3ueXk/4kHm8H3h8uL+AY1rrKh4t9ds1gGTmPfvI6zjXFkaRi3i85EqalwXs63ThzVweQNU1NSO2DdtO6LbN8jcF5yX4tr9r0YqVsvXGhg/YfENu0Setti2265D2TQByCzDf9KaX2L2CTca2LGWl14lD1/AapAcrUbHElb5RvLvbs8SFnz8/87sfDrx2Dl1g9oFD36BUpm8tIVWL8hyFyqeqbEXdgs9ZFPaWCuFPy4ozFqW8gCdFTv8YS933C3V0IK7fuYINMQRCyuz3e1IeCXFhmic+vv9Aa+0FaB86R+MOhAj/77/9gW1t6ctpxOfEMk4iV6LqNv9WrQA6F0oSgeR1DXhjWWZPGDPj5Ok6g4+Kzz89Ud6/47C747w+YW3PT1+f+Psf/1e+fP6CNmJklGOiaZr6+0rlYpRsthRERS6Fla9PXzHacNj31a2r1FGNxa8RdKJzrpa3so+qjaxlkREh6op8phjpmhYUZGXYKDlLVa0AiPNKyWJJ0XWaNSZy0dAiOr8h8/XlzKcPj+wPB9aYOI2RtH6haHjNcvikJFo+MYpNxTRNpJS4vz9grchajuPM7//8hZ+eXinFcPhhYF5mZu85TQvb1kmMqZZ/v5z9O2MvcZFLujzkNpv+TcR3ikbpbdiqoGwRny9B+22/edvoJoSjKF+rZe81sW6vtvabcNlKKXCdxMjzramINKEP0DtOPvHQas4+VslCRdcbXOxpTMvXlxmnLY+HlrtdizUadEdjNOdFXJS1spJZ10QohVAUyxJoG8lgMYl93fk8YZyilAOlWLrdQC5JLAKTlDjOGtHcKYlxnWmathrfKF7PZ1LOaANNNxBj4rz4qrukGBqDaXq+fn0lLJ5d3+BMldxQmpgy1iio6nzOilJBTAlrLM42NMaSTabvW47TwuenV7q+w5eGw9DiGo2xjlRWtIL/7bcfeRnP/J///V8u20K73UAg0zVX09lSMlonhNidUCqjrAEMSomP2svrCWMMiw+sPuL9KmMdY+v1US9kIKaqK1SJ6MYojHEieGZkBm2VAFHG5DrmSuJQl2U+K9diXeZXBqcVykZyTPz89Ao5M04zIWS6uz2owjQvfHmRSiKVzP3dgbYRJcBpWasHj7lIpCwhsyagJL68nihlswyU2UWI4imakvjmXTJoqq0Z6bI/W+qM9g2PnF/E9X8sOLdeccuI6puv/aXRyds/XzdV3ryo73yPBHd+84ANOX1ZMi4F0jyhjOU4RwKWx87SGLjrpKyalxmtNed55nUSwvnQt7w/dAyNCCvvWkeI0h+uMbLWjY51WWXEETfoS2Q2zpNnv+vwPtB1TrYpisZXzdYQZfC+61us1ZTiRHArJbTSjPPKGjOrjxijOU0Tzu1Ygxj0dM5ynE/4FGlaEXzOJQhP2Ep/bq2UfeAgRZyzOFstCqKn2bekIq7VaxBlwdfTTIwn3t8f+PjuvhoVZXat5cvTMz8/vfLx4yPGaI7nBR/O9F2DdUqU/4BSxM5RPgvpxanbHApkRpiFMZNiYl1XnLNsGxkhRLY5IFArEykLtZLnzqW6d+VSVesVPiqMtkQiqiospgSpnojSHyLvicxmMM5gleLnLy/EWnIWpRka0ZdKBb48H2kbx+v5zONhj7WW19PEtHo+vLvn6+tZRkrryr5rWWvwWlv9U2L6i5KvutpeKqUuywTfAkDX6xpB1f82QOhmjHLTY/4yuP4Cre8N7+n7+rXb30u56ae++Xosimfv6GjBexkee8dvD5aQFLuhmr0mCeaYi5igpkyq2elh30u2scL1jFGACB/F9SmXRM6KMc3Mq6CO4xKZ14izmcNOBIcnrWmLXBwhRrySRV1nFTFqihK1tRAk0/mQeTmOhBDZ7QdS9ATfXjLCGiTjjPNCTIWXceXnryfmkPBRTmyUq2VipOnEPDZV0EUpU238IrkYvBd3LRRiA5jErFYbw+oTfdfQWuG2NkZJP6slIxty3Y5xF1BPFOUqqFeRc61lXS2GhGnclT7YOHIllseKaFqtL1Kpcm1stgapGhgpqq3q5VBIJqMqw0gb+ZkpZfFKqcEuoJsIj+msSbGw34s1g5jlal7PE/ZuEBwzJ5puYOgczg7MPvB8OvGnn59FG0tbPn95wifksxp6CvA6zpRt17OI/tLbef81CLWR5QVxzE71IMobbnpT1qraBvwnSQhbR3hlAH0bUFwEna/sISpL6Bqwqiqrfe+UeNOjfueFbveJno3lrmtpFeyaQkDxZUr87qGlb6oMvtJ8+frM4+Mdf/hyom8snx6htRBSpKuGObnAHALaGlFny2C1ZQmBr89H1pD5nz+/4FwvX/ORp5czwyD80aF1NM4xeU9jDEPrGFeP0ZZQle5SySxLYFo947zSOktMIjc+rV4EnbVmPE+sa2AJnnEKvJzmG/lRkRcxWgjqjTWYyoCyaAFJEMJ733WsUcZBZFn6dlUFPeRMrlXCGCOP+x27Xpy0UdA7TdtuhHRZ5TLGCLAVpc8tUOeWYulgrWVVa900EXUFF7Og1Or2MzQ1y4t9e0qi7L45eYnwlYIiqGyxgqyJUJbGJ+GvbmVwKYDJGK1prFRBmxasT0JnbNumrqoF/vjzMylFmsYxnj0/fLiHIkvr52lliZGU4N/+/FXWEddV9HKXlTlEQoxoY4ghXiYO281UEElXva1SebYpRtYQKkgoS9dXuRMFtd9+S4h/e/sVl7EbEsF2Xy1nb0Wjtz/K/bx9fF2P4gb13bbB3/ys72VUKeblvpLpreKuN9hsUDSYpkfnIGUcjtZpQoL3797T6MzeWfa94x9/88hucFWDR0CakDLaWBafCLGwhkiMkdN55PPTifMU+fy6chpfsRp2jebD/Z7ffOhZuxb9cEcuiuAjttMsfpW+UnuWEHk5TaBhmVaMtkzVfsCGwP1+x9PLKzFljDWM00hKMC+e43nGB3ECE78VTanb9VoJ0KABZx3nccIUxTA4ihENIUohFBE6ckZjrcaZWikk0AFUUcS4YBsRP9Zkuraj7xyH/cDhMMj2RuNEbPqGP7tlgm08YIyuC9MBisi4YN+CHTknyJrLknLNwttz3F6gZsMmtIwd5pgxdd5p6uNjlIrBGI1GYbTwkzfNWGtEeTCljI6FNWdiLPjoMQbCn0UELRaxcGiNYQorPz8d5Xnq/TkhWVlXvSCtq4A0b3pFsYCQO1MyF8CIC1VPPjttxRs0bQoYMX03YW23Xy9rv/OXmyr3TXD9srf8/txnKwM2IOLyvKpwzdbXcloBjdboklHZ87Dv0DqIqFU7EJczp7Nn37fEMnMeT/zwMPB43/LbTw98vO+52/XAnlwKn59eWf1cfT4y8xqZxgXvfYXjE6uPdNbymmXE0h7uOc2JBy9qdM4tKArOavrekdEsIVVN1pU//PErfT8Q48p+6BmnhWUN7AdLaw0hZEzNiLv9QPCR3//5mdmLeWwsUIdRWKswCH3Mao0uwn4Zho7WWrSWXq2vGrJaFWzjyCXhjIBVm/R44wQl/uHTewBeTsKw6jpH33c83u94/7gnhkLbWlZrqslsuXhMpiS96CabcgE+iDhrMVspp2tmQRFiFPetrRKqgb3NttXNdVFKwSopd21ra/sgyKjWGquvbutaKVonfWxMIqRljRVcoBLxcy5ouy3eB04xY52pAnAFp7WYOyWAhHM9MQa8jzhtKDVKRFFms/u4Xut1gxGlNEYJET9n+XxLrrvI8uR1FnqNov88Q2hjd1z+KVe1TH09GcxW8Kq3oM+2Ea+UFMVFiQu0uYxo3r5QOWzqOCbXYNUKUwAyucD90LNvtSxLR6HH7fcHTFmZY+D1uPDhfkfbteQqxbjGhOzyFYJX3B16kdhPieO4ME0BvwZCiHx9FlW4vm84r56EodWZplX84c+vQOLjnSCufSeEhJgiJcCaIkYr/OoZ54XxtLDGxFcnwlyLT4TgeHi4B5WZxlEkNazipy8nAU/qLJFKHWwMONtgVEL0ZAtdZ0kRDvdCScuhcH+/o5RMFxxeC9nCaVf3G3VdABagabfr+Pz1TNMonHH0nROWT9vSOcfgNLSWzhpiIwJfMQZ8kAwek/RTScl2kownNGBRiMxkjJFYMYatZLU1c8h9VWq19l0xxm/E4WRfF5WJylVntK3nM29Hckoym9WiIaWtISeDoxBSRFtdudNgtXiylJr+tJJsq5WhbQstDVNYySlj6xw0UcipeuwASdXsmbcwqYbERZhOGrCNI62ZrDJGF7RyhKo1rMgYI3TB8lfMjH59zqnEQVhWx7aAkmynLyF7fZOub9q1R93KYFX1bC+7nre9af1ZueSqLH9dtpHvz2in0c7QNQVtYBkjsQjIcdc3vLycKQV++8MDIQTWVfHl5cy8elrn6BshB6ScyChS0XifRN6jHkRSaskW/cMOXk8jD/s9jRPrwJ9fzsSUuPcNHxk47AZKhnO1Qrjb93JgKc0YAyjFaVylHCyZVSn++V/+SD+0aDE1ZPEr//OnJ/qmo5BxFoauuViTpyRGtHJKF1JR7PcDh6EDJZKeAlg6WSzPmZRB6YK1clEbY1ApA5YUxTvTWqGvdV1L2woN8bDveDjsmZfArmvQpmWNmfNsQEVsSoSYiEYRo+iwynuWUCpdMgUK9DdopqnuWpfDu/ZAt8jmdVZexHG7tFgt3qAKLoJatzdBTq0cCimCUsL59boa5ULWSsBC5EApF9ZbDQSl6TqL044+Ok6zJ0ZRe4xFDihVRzxCLay7mrWUvrRftW2L/popUyok4nXqcQGT/kYSwrXkvKkxb2pupahy82/NdK8x9w35XanqRH39fuqvCeoC0W+obVGybbG3hR+ahKvQlNWF1ihel8SHB8dabdKH1tFpRT/0TGvi6fnI62nmw/2etdHcDw1O6Yv5UdM4tPb0XYtSsN+1PL8cGVpLSpn7oZVe1stoY1wDX08ru/1A0Zpxnikq8+XLkWkNeJ9onaxvja+TnMpIX+JD5jTOGKv58/OJxhhCuCOlQIqZU5qwWjO0lsf7PYrC0+uZKdfgglpWiqHs8SRi0cMgLluQsa5BrbEa7goV8WqhblEqYbQQBrq2oa1LyUPfsN819G1V5nOa1iohq7/boZ4n9CoZzmhNiEpmhqgqd7IxbOQxSt8sG18CqPJJAW3eYhDb9bFdvBK8QsJPJtTHyDWxAStbFt6+L4WIMWCsxRnNsgYp640iFdHm3Sq1jf+7vabDrufd/Z7VR1rv6vxbtoJ8GIn1+y5lbVbXAL8l2BTJ3qhNq0mQWmEeXqV5cpZD4z8dnFJmbtqc1QatXF8gtUV8Cw7dbJkouCBEBUqVMkxVG9TIPTIO2J7sUupSlbIvBTUxenJGxhVZVNBaozkHIGd+eBh4f+h5PLTMPjC0hrERgeHjeYXB0hpF6zTvDj0voyigD72Uc9Zq3I+Gu0EEkF+nhZAT0znRWY2zGhs1i0+cz54XozlPgcMcIRdCLMxLZF4L0yRLzdloTIk4Y4XXS2FePD5GaDuxw8uiaJ/qKGnoGow1TNNCwJCyhyLZsXXSS57HBaEZOubFsyiP0Y6QhNVEiWIchMYWUcWzxgnLyGihw3USnH3Xcb8f2LW2jmsSu6FlaBshhjcWiuan5zOrksNG10opxHjJ7qKRI/pExsiyt9wqTbIUKObau91kESGb3ASA3oBDjdbu5tqoman+3FJKHcEYAiJ12netKNPvCmPn6Ebpxb0TXd0QogAyN9jG0Hf0XYdtM+nllYdDx92uZQmJ59NU57HX16hRFC3o9VbeXg6bC3d8+3u+0PtkGUC6llzy305CuATa5X4uPeaFPVT/q0p+E6w6J5QytWFOlKJpVGEwMG+n4U158QasLVIeFyBmsf0LMeJTI2VrKjjnOE0rThd+uG/53ccHlnVhI89bZ7DWMq0rT6dX9I8PPJqermnpG8fLcaSUREIxTYHj64S1jtN5JPqVT3c7Pr+eRRQLXUWaE7MPvI6ax32D94mQC5+PE5oFRZT+UVvOpzP3hx3aGKypJrRZXaQdM4rjeSLFyP1hIKDZ71qUyrRdS3wd0SqTUkBZDdqwzrJRo7UW8rj3lci9yjLxFg5ZlAOVNTLwN7q6jhUx1jWGpnHsuoaHfU/jRIOnbRqGtqHrO1wjfeK+C0x9g1GBYBSLV9VeXYIlm1IFpqXElOywUdjMZUNFROPMGzK43N5mzw1oyjmL5MklO0mPJ6OJOmesfZuxll3bshu6KnQNXdei9SuNd8S4Ed4Np/NCCLGW94qubVjmlTkkXNOzWYTM/oyqFR01yEq+bcu4ZEkBxbaS9TZurqV4yaXuDGdS/lvQ2u3duMmOvxx5XHmeilL7UioI+/+z9mbNsWVHlt63xzNEBAK4N0ey2TVZ94NMJpPJTP//J8j0opa6qrqKZJKZN+8AIIYz7FEPvk8AN8nKrCI7zHAHIADEcHy7+/Llaynq5mTd7u+04usuUGugRk9GEetLofs6zW/2abVtwBQ0ucJlTaQIxopH5pw0VRdGL/o7IWfWkFnX5uCsKqdp4nxe+Pa4535vOE1zswlXXC4zj+eZEDPzHMgp8+a45+Fuz7JGPp5og+fMw37H8zTxdJ2YlsDQv2VNkQ/PF67zynHs5ABJCZRA8jEmgq68vT+yho/CTqqVTOY0i0rd4Xhk8Iqnj2fWnad3CqcU3siQv+875jmI0JX3TMuMa/bq2ggxHZ2oNTN0vmUv3S6KijFW0EQj5IF+8M1hzGINWFPZ9z12W0RvrB1rOzlI9qKsZ7UWZzDVMAilSS0TbXaDSjUd3vZebqwgyXYvKhqfX0fmM+rnFqTW6p/cbxv88+KHiWQw3cjyw9C3nyHmT4f9DjsvKNXdRNBKaXKkVtyxr8vCdZpZ1kwqYjG/241crmurNkAVUKWSmv9JKQ1XeV0t8lLCb9ld/FS2JXQh2pe2P/yXl7XyStzWwW7GRdutnQr6FTikm/asqvL5TGXUBa9lH3NnhOicq3h6pvLye6TMaAW6/K+VxuA0Db2Tu/edOBFf5oU75zgMDqe5qdPNa6CUTM4y0yrK8v7DE5dfveWPH0+3FyuEfDMAWkMiNhDn6enEN9/cc3cYebskfvh4Yhh6ujbgt0axxsg//f4d+8Fzuk5YY7lcLqA1azuVcwjs+zu0MXx6PMkSsd7KcjhPK945Hp+eOf7mW9ATn04zD3vP3UHKzW4Yef/xufU5UFPCeXs7EK01aFWbj2eb/VkrCKoSZT3pd9QNCCqloK1qChHim7nrnfBiTYdzBuc7XNdRa2V3dyftCGfWtVH2lbzHUcliusn5RkrRm/hW60NjG+Bv7tOfofQt4FCf01ykz2595W3m3maEiAdKqZWaM8paUhvLxBhv4JPWlt1u1+wc5MFN00TnbUsmYveXYsUYRz86qPD0fOFynSkta5YsUpdag1GyoXOLgfqCGBurBMB7zZJTL85uOUfMlrD4qSrf57dfyJytJmcLws/5tW12gii5yCB4w2Nde+OcrnzhEk4nVI3EpMjFNPGvlpWpVOT/ViWyEnJxpfl/Vgk611QErAKxAKl4XbkfNQ97h7GGGIP4R2ZRbPNW8/h4Ys6a58vKx+cL0+pkZhcz18siOqcFSmOaOOvQFJ5PM/ux8uY4ssZAiJU5iOaPQdA97yxKyQ6nVqCMoeRC7y0lV3b7nrvREUtGO8MSmhAX4tcR1iAqBLXyL9+9I6RWFejC8X7XhMBEYSHljVOqUZs0oxZUVFuL1aq5J1fZjmlZpfP+RhXbPrx3WG+F6OAcpSGHb+7vRI7DeXzXoa1DTvrI/rCnlMRsXgCbSUeMNuRiCEH6LRE3VDf0O2dZ9dvKVL3pEbPZGdTtnH9VobUSF/3qQpdrTtTd1Y0QkdtrYtvFviwr3ntKyVjjcN4wjuMtU8nYxTbhbJEaqbVjDQtlLcQk3OUQRc/XOXGklj82IEpohFU1v53yuRTJtud5y/hVxlYpJTKlMcV+puH8peCE12UGWxP4qixRElgKoLkvq0JPYTBJMmqtOAK6KELMXJaEsg6vOyoVq2VwXABdzQ1Ayq102fZEjYLRiat1rRmKyGgMnQA5IRdqLqJA4GSAHlIbihtYLzOdNzw+X1EMeGfpe8cyL+25yEXjsRhEFyesK8sauL8b+c3XbzhdZtaY6bzl4iK5yBs0rwWrN8McRdeJsa110qPWCp019H3HtCRKjiJZ0l7ljb62htA2GhSXJfC77x+ptcjiuFY4Ixen0aI8Z/RL30ctzVZA3TLN5tHpnHzoxpyx1uCdpfOOsXPcDY5D7+h7T98PEujOYZqsZVUWoxS61+xyxijZSHE24q1hDYklJmopFCSjG6WEUaMUEaiuKQZ8RkThBpaIyHW5ASlAMx0yrzItnwXAVi5bK9OCbU8yJZE+STlRysJu19N5z8uitcMaSzd4lFKsayDGTCk9Hz6dWjsgAGVMlVoNuVZqanrKn00htph4edzbobE9XtVagFw2IbR8e65/cXC+GH8KxPC6V9gYPltf2lZfcTUzqIDOGa8S1lRKUGRVmAOEXPBGBuwUQWtLFYqFU4GMQtVMbQ9tg6d7U3EqE4MwRe5Gx2Arb+52gvo2tHONiaeLWCwcRo9bE/fHPVpLOXe6royDWOU5b1mXCEr6V51Flc0YcKaivJNAy6I2vhs9Zkl0rmK0eGoOw8CaAqYgPpJW+udrzlRV8EZ2YA99RyiFJYamXSTOVighSytkNGGNbDasa4a6cBg8xpm2iqRuLUatLxYEugWosxplWmbVMr6xTUPINtV2ZyVj+M4xdp7d4HnY9zzsB/q+ww09znVobVCtulGNDogxOO9RWeZ/nXf4NTK7gJ4gZRkjlKxwtmtof71hEAKYbNxSOexr1be/U86y0N3QWtVK9D/RsGqZ6XVP+3q0ArKiFnOhFFlA77uu9b2yz9l1nqF/WemyRvR+L9NCzgI4qVm1kV4hlUypwgZCfT4ifJ0BcyO83Myh2tgn5Xxbzn798RcHp3TAL5C3WOy8StXtL10VhsDOVFyNmCrlVygVSMTmEJyRrf2ixMzHW0WsUJCl6ARCqWqnZW1jFVMLJUNIlVAy3kB/14GuOKPpncXoyqenE39894FP54W//803dE6Akm+s5e1xz+5p4scPj6RcSDExlMLd/Y7LvNA5c9vj3MY6+/3AVsbEtqSrNeQ2+vDWsvOWzo9cp4TWhX3nmFcJytqkNQ77gb43nD9dxbS2CvFbNR6nQi5+kfiQQ895c8uMikqoWfRvtG7GrknKY6XwzarPtP7OGkEkjRa3bjlEtZR4TnxhNmWBu95yPIyMQ0fnO6z1GN+BtrcDoyIqEyiD0hbbdwzW4FPC+YibFTFmfMgk2zZJGigi2z4aY03Ta6ptNPailF5rlYG21rfJgm2P/3UykEDcKjZ7k82EppfUJERo5aZWmqoKOZWWHWN7P7eM64lRqivauLCzjqQytTiSkz4xmZaIlJbeHpnvNl8QJHu20cjtGWhKls/lZtt4C8Z6a1X/muDcfkjlM2aQfhlzaER0y6uASxmrGgqlRKApFUdqkvwKjdWaSAMJUqYYg2pPtaJxRBSFjKVooDg6E/GqEFNl8Iov7gf5eVqzpnhjcsyxkszAFC784cdH/tNXR3ZOC4KaC96L9s/T85kUFePYMww9xn5k9D2+M6SQcNYxTRPD0HGZJs7XlRQD4yDl0X5nMReFd5Lx9s5h1cr1OqGVqPI5K/qUT9cLvVeM4wFlFCmI1H/nLSlKoMuF0vo4wBluFEej5aJ+uPOsa8JaBamSiwBwgrS+zAYF3m79vjO30YGxBuvk9e+cxVvD0ESv9+MgXpZWSllle1Rj+dScqFWhrUG38UQtBuOktTCrbKVsh1viRXC5ZFFPMFXsFKt+yRgKdUOTt1KUoqAF22Ys9VNZyY09hJaksElxvgZgUi4taDYvztxU9jqUUkyTILfzEnBWsz/siDGSkqjjT8tCKoW+8xTEWc403mxRAhCl0nxdXpe3bD6yoLShNFX/14H508z5V4xS1G0Eom4fr+hT7e9SRa4j1iBZURkZj1iHL4lsHLkqDIVQFElbdNEUrVBFoU1TwENhapFSR1Vq0RgSVimsEhX1475jdKYxh4R8HasQvx/uRp6fn/n27ZHTeeLj05V/+PUX9N7ydLmy7x2qjszTysdPE3e7ijGBw35PijIeOOxHwhI5HHY3zdt3y0k8TqxjGGDoxJh3W9h+fp4xSuG9QPXaJEIpXJup0HVILIuo/z3c7ShN+qQqIbMLhyLjjcU7jW3bKKVW1mW5lWreGYbOo3olHh9rkHK1gTy2XdBbP2Zaf9l10m9ZLTNfZw1j7xg7+X1a0R63gVYSy4WuUdbLe9GkU3AWXW0bMoqV4TgMTHOgCxLIC6JMDxpDkayIMIm28nO7gLYDxW4spiZ3s5XrsH2PXGyiRiBEDVAsjSb3OjitFTCsaHFZe0GFa6ssLPOy0g8d1nS3121ohkprDG1eK4v3miqKDUVRtL5RCbWMI/587NQXqcztcb12yP733O4Ls6QAACAASURBVH55lEJD3tpTVJ/B3W2WhCYWRS7NI0IbMuJPaa2jamGYlFbuKqXJSlNbr1SbC5nwRuXr3mhSKxsUBVUqtXqua0XXlV0vJZptvNLj2HEYPN/ejzxeVp4OIlT14enMr7+8p++8bHsYzRdvDszryhpkA6XUyjh0pCRGQ/MycXc4iHpfqezHrsH0W9DIyGAcPCkmJifEAK0ExPDOkstKUWIxd51n3n+SbHC9nPjyq68oORMQnxO21SQl9xm8eHzEmFBocm36RFbmeN5ZcushzY3UbXDOtVUzIWx7b/FO+mZRr5Oe1lnZPx07x36/Y9ztUMaijQVtZYFFSe9XVZtBK3U7nEEUhahSvRjn6TvHsorFoBjRblxqbt8vJrobDi+tgTatdK1gsm5Zr7aSFpRWjdEjFUEIIqh2PIxQC2tsrZd6YQ/Zto2Sqbxe8t4AJICUE9MsXjG1bmMfy/HuwHWemqwItzU1rTVGV7KW60AbJcbCSgli+xnQtZXz5c9myBfCwuth0n8wOLcnLBedYhMAavCQ/CIqVStyEWnBqlUbiHvQCm3kwsooUvWwvXG64LRuxkCKoipFaQqyDdE7RaqFJWqRqtAyYwqpMlMYeiEcOP0yiPFWwA83LQwO3t71TKmI+p4xOCNiWhk4NLLAx8eZvusEve0s13nl4eFeuLTNx/Fw6Em1YKz0YEqJW3Zpy7K916SiGPtRwIUk9BWrhAQesmgG/frLAWuP5Bx5e7/jdJkAmINkFaNoK14apzVFKZx3zHNspjud0ABbD9P5bVOjgUqqgUDWYqwgtJ0XsenBe2zT7UlZ0GHfWYZOdF6Vllmn1halHdTUeqNX5Rj1Vi1p6o2ooIyl6zu6aWnAj0WnQlQaRWrMGqG7iTJGy9Z6s2hUbdRiXmiA7aBTGlJULTjkOVatiY3jKy7SW+p4FRypUCi3+ei21rb97E2yM6YsLUitpJhujDIVIt4ZSmeZZpmFGt14wchiQ1ZFDvBWbm+yl6UtHrwuyV+jtyDFRIWfzaT/vszZAlO1KlcyaBWnay1opDKW0l7g7UF5rfEWTM0ENLl5aChjsEbTmYozgpZNsYkgWYchsjMCySdjSVnRKY0ho6piXiJnC34/yFaF03TWtG15z9uHOw5NUOq3P3zk8TLjtWbsRTkv5cQXD3dc15WwZoahE/L4NqvUMtQPIcmCtNXENTNPkRwqvlvFIdnKvFEpWZpzxqC9Jk1ry4IKsqCqvZNycj/2rGGBWvnqYY/znvefTlJWOdHvcdawrBGlCgoZ3RijUUr8SLzv6PttuB/QWoSpaq0Mg78N3J0VuZFD78USz8gi8hpyE1J+2RRRt/dajtzX7YwANjKXew3QlCKqckZrusZiqiG8EApUaa8NyMpfo+W1g8QbRe9cE7AubenBEHMltoPXtIBOWabpRktpKmwbLQ7ia9zAX4wRraEtS27jFREME3ex3JacQ4hc1YzVmsFb1hgJcRG1jNYy2Ub1MzqxvsqOClmVTK9iZEORSyvpX/PMf/r39jLnn4GFfmGUUtmUzGvT4NyCE2jZTKFyBgNKWXJto8qa6XRB14LVlefrxHVO+P0OBQxG0VtF76HmQi6GuKxo3+F1olcLgxUnMFCcL7pJDFa8tyxrIY5SCnfOcNj3mLagqxTsRpFB/Ophx79+/57iek7TzBIid4cDQ1eJNfP27R3GIChdqex3O5Y1yGxu0yZtF3GsmUxkWQ1RZ7zTlFLpO8vxODI4yaDWaHbjwHWZMRoOveOw66FkvBuwtmdZM4fDSK1ia7/vPdoadoOXheE2mwRa4IllnUL4xKXkxvQx7HZD+1xhNw5yeLZSbDd0HAZxXru/36FKZloCpSq58PWLpKk2wlxS+oUMUG+XT6VW4UmDoJCljVQArHXsdoNUFUqjiGhdqGtFeeh7MS2KQQS/jLbcjZbD0DGHFSFmFWJJrLGSi2XXaZwRu8Snc3Mh857OCJK/hkwsGePszeF6GzHV+pLNts9vy+HGCIMrq8w0zXTWcjf25Fo5XxbQMA4DyXvmacI5QdKtfSlVjSrkVvIrxWeSI3I4yevyU6WHG22xfd/PtaC/iNYKG0W1E1ShNnqeRrIptYnmSruiEE5hyRmrNORAroByVAtPnx75zdcPDFZxPxiohWuudCpRbSYg4I7M+iL70YOxdA6Mdk0aUUgGy7Ky68TxqiSho8UoY5G4LKAUnTU463k+XUk1y7iiJmKWk+/+0MvpikGrQkgJ7wwxGZwvuGTIufL2rufxDD8+PmO1EU6q1lgLNXuO9zuGcSTnwuU6o8liSWc0+9FinWJdEl0fGUd/yzzLGqBZ1+soM7Jh6Lg7jJyZ2ulb6AfZEAExSxKHssQ49vSd8F8xzRi4ZabOWx52PYfRMbgep0E7D431YrWl5ETXKHoxrBgHyigqpimsQ81RqqMKSpe2+d+ocusM2mBsh3YRY4R4kbOoQkirYdj3ktFPVxHZHnrHm4cDvbFcJrnIcy5MIdD7jDOW42GH1aC1Zw6Rj4/PLCmz7zzGwPcfzkyxkIomxnwbq1hjCDFTmu5QjKEd2mLYVFXCWJrESuUyzeybr85u6DldrvheeMfRyPjPLAmtsqDnTjJ7bZIlqtTbIXVbC6O29uMlKF+vut1ufyl972X5Vd0i/XUD+/oU2L5QW7A6rVE1cr42DVejGceBY6fZOaH15RRQylAaAbB3BmsyX41V5CGqYgmFeT6JqU4Wlby4iHDWce9YUuI0yXPcrA3ASZ+bkljDUUVhTSt+9fUD0xKFC9pocFtzv71orzfyt5U5pS1jr/nq4ch1mlnX2GzohGS9LCsK2Q/d70e6pyslFw77ns7L7O9pnVk+ZtyzILLWmZua+bIGSoytNyq3190YzdAPdJ1v74MM/43WrKuAI7mIVYKseXXc72VkYBUMTQPXWQGUrHP4zuOcw2gnS9zG3YA+43qqajIfNYhgVUlC9K4FjaXkSI6hgSyOmAI5RYxxOC90P2sTXal4rzje7Rj7DmUs4ziwrmIm1I89NSZ2ux5V2/NwgjJLoIxkKn48cFDw5os3nB6f8J2jRLFhePd4plTF7ujoneU8rVzWiNXQuY6UC8sqln7rGqk1gtJ45ds1XKBWLpeFcUTGTdYQojDAchtLDZ1r2kU0RQgDpM8y3781FnlNOPg5Lu1Pb7/IENKoRlf6qWrBCyDUNuMk3WsF2gn4QOUaquzZ9WBU5TB06BxFv2dN3O13eF0J1hFr5aACX971dM7gneGffveRNSSqlQOiVLEDMEozLZXOG0KwnKaZsXPsvOc0x2aZIKjZ2Hdoo/jx8UzfOY7HPeuyCjhxbIPvhtiJSJdpQ23T1MELl3nhdJrZ7WUHclkTQu9KWK15Pi2ENbE/7KAWBq/p+66ZtWqukzhpTUtgXaW6sE2e02iLsbDvDGLhqUkpUoqU8PuxZzd4tLVQC53V7PuBXMTJWbP5kcBxN/D1F0eM9azr0hguMiYQhoyMAaxxKGOxThyzjPENpCjUHKFE0joLUd/IqposC2RyCqQ1SOZBqqWcEtrptlcZUAr2ux7f9+wPd7dDYNgVUlwl0yhFURrrHduStvWi7WuNw3Q9znnopBWy3Q4/HqEm0jphu4HOWfqu5+7+gK5wvV744ccPXFfL8TCwzJHz4llT4ek6Uy+wrgWnhC/bqQYUUVnWcMNMYvNbSU08eug9GjFQylkOfP0KeX4dpNtSxevg/HNjlL+KIdRWShuELv++AQW3YBUoWdU292l9qa7SrxrjycrirUerwnVa8CpSnCCTUxCCQs0KazK9tRz3nuPgWWNu8HuhVCk7ZWBdpPSqhrAG1tpAm1wIIZFrFT8NZ4gho43izZt7vnv3yI8fnrjbD9RSuVwXjLOvzGVU09tZbytiJWeul5nztHK+zCgN8xrQFAG7lOjaf3i64u3K02XizXFP3znevjnycLdv6PSJnCUwni4LS0yUNTQwRrPfWWYcVcGQOoZhwBrTRiSW/dARcmHXdeJu1jmGfs/QiyXhGsRJbegsxlrJtAj4lpKIWxclvZBzncwwtW5rT3JJaW2hCBsoxYUcV/lKMWgraHxJCWqmlEQtAqnekNCcblpA49DTDyN+2AkVUGBdyehetI8qFWdEH7eUjNeiug7IGpwxaOPa6+dQpoJJkAJOKazv6bpeLCSGUa49o7lfF7oliBv5UjHayLgEKT+1lrW4VCqqZoxxN4J+CIXrdXoB1ZxiWRTWarKrqDXcCK1bnfZZYP4E+PlpMP7c1/9DwYna1nXkzdu2xmVbYOtB5HO6kbIbKQ1bIqquiLdhfwvqNQZCSXTdQKyFuFa0SlAro4bBV86XiWVa6L3l/q7jPC8izWENRkvAXWIkLCthcGgrKHEqntUo5jVw3O0Yu4I2IjFiDOzHDm/F4k9p8L3hfBbTnjUknPPCqQRcZ0RLdk1MS+b5fCKUwnkS8aeHfc/dvkdr+P7HZ0KGXCJKV5Z15e3Dnl3fsd+NiG2eY27uVVbL4ZZKwWpD5wQsWULAOtWUzw13hz0hhBY4mp3V3O86jLEcx55x59qrLcjxdV6gXegbSrnJWcYYUUbjnb8Z+ai2Z6iaDULJEVWhpkhuIldsBIU2h9ZGS9bUUpKrKgirzLvTbZ4IYLse7fwrxFauamNM46jK+KbUAq1MRYsAtTZWWp4qesLVyAJBrQqlq3CNnaezXcv0pXGBK/fHI86cOS3iDoASpfuS25ZO59j1I0tcWZZwYxhtr8swDM1dTjJgyqVdI3I9LGuSrRJhIsArni9KCVnjFoz/dnj9VfucMuekjUr0TRZf0MAWpHpTy5NTWYqoCiWhDDgSzkCOK2uK1JxxqnK5ToxDR6mVlGU7pdeyI/nhJOrpTi3iRK01S5QAHnqx7ltzpkbNu+dn7saOu9FTsowSzueFwzAQkqKESK0wL4kvv7jjcp74f/7779jtBt7e77BWhvTzshKj9I0pJQZ6LueZp+crH08Ll1mMjXLOHDqP7+RiCRGuQUq9w37EW401mvu7HftxoDaLhd3YczqfsUZhrWLXicZsKS0obEffy/3u7/fsdj3eGXbjHdSKs5YvDj33h45rlNlcyQVtZCQA3LZFtnI813JztiqlMAw9senzApSaEUqlzGxrzZArtQRyDKg2g6ZUMBVa0Je2r7iNFH46gN/milbLNFy3Hc6SEiW/bGwY4+Qw1wblvDzO3ISbtRaKprGNdruJi1sKGZOlasvKoIwQF4RZZuiGkXVdSBdYk2yCuMagsjbTdZYv7vd8/BRZo6bEfENzX6Oum15TTiJqNq+B2HCPUkRqs25E2Z8E3UbC/3MB+LIAwM+yEH6+rK3Sf2ml0FUuBLH5rmxGNvIut0wKKF1RJTWY3jD2BtKKcR1FKyKKNWaMtsj2u3hAxqI5p4qaM5fLE/O8Yq3i2/sdsSTmJdPvHSnKRRXXmesl8ePTmePuwNNOSMz/299/TUEWb/NamOaFGCtrCs0PMvJ0mfnu3SN/+6uvePsw4nsBkE7n64twcq1M08IaMtOyEKMY04zO0g8CDsyl8uE0iYtZ/zJH7J0Y5AxjJytaSvRZS5S+46uHI+dpItdJUEat0SazG3rhuu56vO/Z73p2zkqPrQtoiLmyLpFeQT/sWOcVqqiXywXf9l/TSs0VlAzq17DeMIOcQtPAUVjXbUTY24kfUxFudGk9Va2EIMayOck4IrX7b36cIJs9xowvXFKthJurLaVEchTl9BwTIUwMw0FK1kbOz7XeDhO7mSCVQgmLXF2lSoZdF66n9+zffo0yDgpo61utKYryRhuctlhbsdXK6KMY7oadrPHlhOsG9JTI5JtSQymVvvd0ned0vgrophRrEGuPkpuvi4ywyVmIDbUo4Q/X0gBY3TL950Jkt7KWz0vj/3BwbpKFLwTfdjq2n6c20S8aGaH9KpGLyNS0oKv4iJQKMSuU1SjlMNYKDxfJyhnFlCHFTI6F0zmy94nBWaYkmq1LLEzzlVqyaKqmjDeGNawsIXCZZg6d43jwPJ8VY2+baPTK82ni/fsTu51DIz6L//yHd7z7YPnf/9e/x3eePga++/4D1nZorej7jvI8I2LBwussvUVV2VJZQmKZF0ZvOeycBJWz+M5KiQTUkjktC9O6Mq0L0xywttAPO8YlofvKZVplY3/seHN/R9/3ONvMYhUc9yODF6X3ZVl4OAwYpQjrKvzbmDjPC7YBSUPn6YwnpIzzEjx9P9zs70qVxXJQLavmm426VlsWfbmIUnrpJW+jglxu14T0m7mVcE01QCliEIKENoaSEnGZpQVS4jMaliu2VhxDUyCMr7KNqGpcn37g+TzRDyPHhzfgLNfn93x6fATruHvzJUVLFSL8X9psUxzRZPdX9IjGrgnGKcW1KR5u89xcmnB2zqiw2dtLtTSvIvQtUqWvAJ5Xo5Ht85sO7ets+VOW0MsX/gqG0I1swOdRrwCUKJ2BlLillTO6CvEgo+mNYWibHjEkKA7rPdp7QUQR+LzRNClVsxRLtY7h2OP0yjkZTpfA6IVt4nSjdRl1O9mvy9I8Nyu/e//EP/RveP/xytdf7IhJFNxqLoyj5+E44KzhsgR+/PTEs1I8fPee3/zqC+GmKk2MlafTwpsHy9LKwN7LuCHXyhwzdY0ivWgNb/Y9b97seTge2A09UBiHgc4K6htTRNfKm4cjtT6LMesa2s6iwZiE1XCdFo53QpvrnGXfO7wR0MIZ1+ZyUEthKTJ6KjdhLMOny8yXxwPLGjFa8/R84e6wp+87ckqUqm9kA1Vqs/KjgTsyUsilkjdnsDY+KSWTQpLv2/ZJS2mo/YuynuGF0JBzRrc5X2IlhIWwbD1xyz4pYYwlG4NBdh5VSwhaW3KYeX4+83//43corfhf/svfcOw7Hj99RFkj2V03V7YwUxuApZXsc66xtFZEoZ0g/WuOKCUspPyKUpezBF/KlZQD65rEayVk1s3ktzZJ6C0QX8XKFnwbSWH73L8JADVA7OduPxucRSlBXXVjBjU2Caoy1ERVmlQNqrZsWjOaIiyhmilK7PFyMzoVKUCFNh1VazJZiAzWCrmhJKqyws0tVZzCqqOoLJlYGZTV1LqijdjiiSqaqCMYLfKZl0sg68q4c+SccFqG1Md9h9WWsX9Bc9eQ+dc/vufNcc9u1/H2zQOfPp1Y5sCjomWnTekAzvPCFFYpZ3Lhy+OOsRe1urv9jmHw7PuuaaUWpmXFe8vD/R32OjFdLzjb8/37J2rVvPv0zMNxh02JT0+VsTuhMRx+8w2qZnZ9T0iFkDKpCMS/hMgSK28PoiahESOi9yHz7iwmr29jQBTv8iva5SY7IzxXYwy1JFLznZRjt8iCQhFkvuSMxoioV1ZCtEf4r74RGqRaQnRYS24MlUoKEVQkJVkJi1HAl7r9JmMxIWC8LBA8Pz2zuzvQ9QPrfCHOZx5PEx+eLuzHjg/vf+RJiXnTrx4OmJLI0xXjLDmtmJJIMRDTSoqR8xRIqYAxhFgpVVDfJSbWNYpSQi3kWtpmlQRYKpmYBP2WJetCbnPYVAux9d5S4rfArOUmNC2Hl4xnJJvKESql7MuffzZo/73B6dSLdF8uCl0Fkvem4Emspc2/ar3ZNgiIJWs2tVTmNZOyyI6kmnGdotlkUaqgid44waMby2FTDK8hg+0IyH7depl5d53oOsf9zmNMQwc1eGUpVfGbb7/k48cnJiv6sr3TdJ3m8TrRD/c8Pp9lzapWOmvJWXGZFv747pEv3ux5er6w3488nyfipZKS9NIhRFIuYu0QhJwweMswCDvoeDzIRknnRa09J56va6OMGWqRzZGu3/H4/pPIdc6RWOA8BYq3LHEh6wcyhSmsHOkA4SOnVCkVQkx03vLj84XzRZYEvnqz5+G4Z4mV5+vMZQ7c7zz70eOcYVlmQHpUiQyJ1hdq22ZRJz1TbnZ+tWRqkeH9NQacNfRKxiC6LYJTq9jMK8U6z+0wqA1MkUVkaR3LZ2BLzpWkRd/WdY7LNfJ//bf/wT/8zTf83X/+z+T1yvly5ePzhVIr1yXwh/fPWON4e9dzuM4ctKGmGUxPTRmMIeVEDIGQmjeMtaRaODXFi1xKy4RKqhA+J6fnpsYgmbQ0KUvdUNt8e16lUfRel7G3FbHtQ70AZXwGAjUf2l+4/Wxw5trMQKk4cptfFnwJ2BJY1XBrdKkZ0GDFz8MqxTolQrEkZYm1oXzVooz0Y9pYSnOfkpFpC3RjyDkRsmTEZD0frjM+R9aiCPOKUbVRu2RAHFPm8XQlrSt3+4Hz9cqgHF1viaHww8cnvDHsh4E1SNmXYqJmOTXffXym1MrzaSKViht6/vjDexmxWIsyIrGyZkhroPOm/TxPP7jGvdxMfyTLXaZAWAN3+z2FyuPjiVoKMYtt/RpkBHC6LvzwfuLNmwf+5bc/8HE34EzH3W7EaEEGY6nMyyrIphZJj9V47seOoRfK3q+/uGPoNB+erpyXzFoCB4RI75wjpyT+mUUOtRKlZC2URhTPjSDeBvExsa5BSs2W76ZpwhjLbiecZ601GFkzi422qbVmnmcZXbyQrW5ZQuaJCWUMRkO/y/zLb3/PFDLvPjwT07/w9njgx6cL0ypIdAgBe7fDWc3zHNEfTiLg5r2oNeTMEuVQeL5MfDqJ61vfGXJULGlpQNUG4uQWbELMuMmVFih5o95tAddEoEu5iVEDLRmlz3i9r8tdebp/ft7577n9YubsckDA60rVHVpZfF6hZpTOaLPxBrdTAqySWVnJcJkzw92Ay5YpzXx6fqIq2I872T1Et6wr1dB2kqVUQFuG3jNpzcdPT3zZOayu6Fp4Ol/Z9U56pFR42I2UWvn+xw8sqXDcj4Q1cFaAKuQMS1wZOsOaEkrrRmjXWCVarN+9e5RTs2TGQRT6ruuCikKoX0KgVt20ecTOwDnTRIIr12mmFI8ymmWWzZO+65mXlfN54jqv/L+/fU+KmUNvyEpYRHkO9OOey3lisHv63vH+44/EHPj733zLYed4vs6cL7LNH1Okcx5qZuc9nZPHar3ni/uDOECXys45VK2sQWaI1onINoiO7XSZRImuBa6ckJptu2JaVi7XgOtk8+d0vnDY79pYo/lrakcplXWZOJ9PrcWoLQPJ7qM1ltpKwm0skapijitTcHz340f+vz+cMNbyx49X3j0vUN9RK0zTzDTNuK5niZVcMyknFJW3c2ayV6yRXnZdF07XifdPF5YgPXFdIWRaFbdCfemRl2UltHHJFqi1bhzYDZgC6xRFKVi1VHVkftpzbkH3eqy0iVC3Pxo//d8Vl78cnKpEEdsqmayadKWW4fWckQFvK5O0cqiSEG2RiioZcqKzoHIEFN6JJug0L3TOYbuRQmkaN9LvoKQ/rKVirCXmzOV8pTjPogudiUyT9LKXZSHnROcsXWd4owaus6JMCzlnHq8BpRXTupJS5m7oRN38INlTkcVWfBZT2SmEpikje6baiNV4TA2sUKaVfYXeO+52nv1uBATJ3Z6fOEvrVg4HpmXhj+8+Mgc51a3V1CUzr1EAK62wtdDtBkLK/Pjpwsfnif3TxK+/fouxVQxsgWVdSdliTWZwjnldmVexV7itRmmZi8aSWdcq2yHV4H0nTC8tgIn3nhACJQnZoKoq4FBVtyzjrIyCQhRV+7Xpz7ZOE1USJYvdfYhbZ6XkeSHyJDKCyCw5s0QJzpAya6qkVFrLECklYb1nWWQeGlOUBe4qANTpchH3ba142O/wTpNy4jxNrGsSmuWyYLRDafFtDVkqgBDlcW6L1SFnlrD15ZWaRVYlN9S51EpRsiebkziM5yrEkW0Re1Nur8Br1QPYltElx1Z5paQU5s/l0r8gOMGQKWREy9PrgEORq8wrTYqNZrXtrsl36UaUFkK2nOpVGbq+EwkO17dypG2a33Bh6XNKXCk5C0snCNcylULpRtGNWSIhr0zTAlRROSiVmMQPZOgrp+uVJvPCeZrF6bIU+t42xofib775gkzidAn8+OlEauBJLoXz5UpsF+g2QgJBlY3RvLnb8Z++eUPvnVy464W+75qQs6B+ubFLLteZNUZOU2BeVirQdxLIonAgBPLOaNYQSSnx6bTw5vjrVnJJFug6TwiRy+XK8W6PNpolFZ6mhmbXSlWq3V88U5ZWlnduYRz6G1K6DfU3F65tRCZKBHLReWfJKQup3wDKcJlWFEL0t9ZJv0pTnbshltIbgxDLjZYrOcTMvCauS6QggN8aYusBSyvXC2uQ/cxlWW99sWoIcLaiOLjEyI+PJwbvmNbIEhK5KEJOdFYW3EOupBwJIRJTvqkppCSBmZrJkBwE+bbovQWgBGthTYkU86vqUIC1etvm5PZ5+FPmz2tyRoNpb/f7i4OzFEhKVqxyLAQiKha069A1kkMS6pxKQpquClOTDGLbdrrqR1QtKG1ww0heVvGxMJBiFN9F9fJgS8kir4H0cesa6TvPt/1bjruB5fyMdlfW52diCHincVrx/sMzRSn2+wFTNPtxR8qZeV04XyY6Z/n+aWqDcU2uibH3VFX46u0Ra0RX9/F8FRJ2jrIcnkrTe9WipUtFa0dMlRAKfad4fDxjlCKXeuPEppy5XK/Cyz1dURiWNWGU6OlS8o1zKia8hl3fkYsm1cwwDBz2PblkrtfCsoqN4LKsOOd4enpC1QNWV+EUe1nRq6qNCJQSV7M5UNLCf/m7X7GtnwmdrxDXtfVMDfTjpW8SIEtID+IbbghxbQLUuTGbZNQgfVy+BWZufXzKUs4ZndFKevbLtHBZxMe0AEZDSnG7iokpEfMLIWIjjIem6ZtToh86lpB493hhbPby2zzWOMu8JtZcZd8z5oautn6x1Nt4q9TWXzYWUG6AUGw2kTk3F+oWtKUiEi5lA9G2KHsJyD9Pydvy53/s9svqe7WgasY6ke1fa8Vmcd1SzqEMdAZcmZmVpWSFqgnyQsyJnTfM1wAAIABJREFUGFbQht14EFUBCrpCTS/WdBtupbVCt7nVtsJ0vNtj7CYsrNDhQu09J2vk9+vC0/nMvKxY4+h6xzwHUkgYq5iWIBclhZAK//z7D6SS2PU9U5hxCg5jz2E/8PUXd1QFIWY0lfMSRR8GWdNam5nRvM789z9GUi3sO0vnFPeHHTHKlorWgkLPS+R6XQgpsqwQY27LAKrJV1qZ9wE1V3KWeWdskor/+rvv6QzsD7sW/Jlx3DXAJsmooIDVM/d3e7QW/dWSM8476SepnGIiLgvJirzKJrEitD9FLLL6FJvyvNWKUhXzskItaG25zJHrErm3lphFwKvqAkXExjaR7JhlTmuUZs2JNUlWHDrHHDMhVaiZEEWMfK3b/q+sZqWUxK6jKabnNuIgxZsIVwWmeWXRmvMsIxFnnVRlqrS2Qp7PGuIrokCVzBwjJdGC7yXjx5Jv4xPVStuci4wKb/dJAhpVoQmVKgfez6no1dqm0Ru57n9GcGpr0DGjq2pLuo5aVlxL0cYqCoV7HdAqsEwr0ezxTZ4+K4Pqd3gNOq8cxp45qGYNYG5P6qawoAz1VqfL1+y2Sd5KA+0HbNfz9ngnbmLKMowD3numa+Cffvs993cHYdAEERbWxhBiIK4rIWViKQwUjLKi4Lcm7KAYrEKVwhpkz3Ra1/ZGVFCFNWbZFDGGkAv/7buP7L3m2zd3pAxffWH59HRFacXjhye0FUUE2zs+fvfItEbheVpLbyx97znPK9oYeicSJrWsVCo1K6ZQ+e7HZ/rzyhfHQSh2FQ6HA6VUzuczl4vGWintcjnSWcP9XqwF11SwzuGK4cNpltU5r24Hn1GaT+eJTYQaRGBsWVbOc+A0LRzGDoh8fD6z2+3ICMPId9125ZGr9MJGVZQzhFBYVukrQ0ytHFSEHFs/p4SjmjMhBpzzL7IipWX2JIvNpUgZWnLFmkqMGeekHNZKFApLztQe8J7r+Soss+ZhugXfBuDIuKSVsy1TCvlgK2vFOSAVCeRcixAWam0sonK7b7z97JcS9aeZ8+Xff0pa+KuCUzUGjyzZNicUY3GqsnOaiKLmBVdXUir0SjcpD0XC44539LZHl4TTtdlucyNNy615MCp108PdqFH6ZkbzIjasXU+2A5cgJcq8rpyfT9Sa2Y87qtJM08xh16OqOGeFUpDcmXk47pjXxNcPB3Gkvp44TxPG3mGswXvD/JSoVUCiUo0cFFmkNVKtt/KQmrFm5Pm6cD/21KL43fefyDlBNRzvBt59PPH2fse6ruSSsU3AS2tDVeoG3FTEB3McKnmqhCLUsyVk5jjzt796S0lJhJJrbZYPAx8fn/Gd2FCsGTqvG/vIoGhaTU5Kx5QzawgYowghEBqdb00ZkmgeVV3ZjQMhV9R15rokYq7MsXBQFacKD4c9fhN31nJhp3aIpVJZU2FOiZCaVyiKVCo5V2ILyHlZGzIuIKBrwXmzMlCKJUgpn1N5uSa0wjgjDLVGJ7TaALqRKRS+E571a9mQF4abCILltt5W6pbNtq83EbGabmOXEF/61tTKXCEnfB6QP93Z/Ozf/Glh+1f1nFprMgKQWCUWfChN0pDjjHEWZxJziIRYWbNC64XajehhICvJuAZLSZFKxTrNTW5fCcSu2ErczW24bbw0UsILw0WIC+PdA2+Ay/kM61l8LKoSByctvey6yk7h0Hk+PJ853t2z9wWrtej0tOH7/d2OmDLvH09QK/McMMby+PSMMZYUN7txQfoky1e5+KvmsgQ6q0hovnv3if/xwyeOux6t4HS9EqJId1hnXzwjvVg8pCU13xNFLJJltBaE0JVmMowmhMTj84X90FFrkhldQgCUlKllIYwDl+uM05WgRPXQao2zDq8V+2EgJlGzt1YQ1NCAMZVoinyaauQy8kbx5v6O58vCx6dHlBG36C/u94ydeHvmlMHoG0EjpExKlTlG5hjbiEKyVEjSl8YY22ZMEfWH5vGClflhKZV1FcJAjLFxZYUMkBv8aZzwjIUgb0C3ftNsxPNXxrqtHdpK1I1EkBvKCrVhJBKYldJU2uUw2bJkaqBRSo2kkIRdtPFr/2TzhNpYcy+f+492nr8oU6KtReGJqeA7qCWgbM9aDD7OKJVZcuLTNaFMR9fLXp1xDqs0yihU0SjvMWpbM2sO11keqpw4bae+0nRCq7hBmG38LWOCaV5QwOF4L6V2k7SoRS4OqqzyPF8XYlE468m5cJ4WejfSW81x7FnWRfwoVcJ7z/fvH7lMiZzljS5K1O3UxvKotXGAlaiNb71ihSll/vH7d6gq9Lqna71tb/TO8tsfPjZPS0PvnSxEK9eMdjZVQynDndViI0FiTZJpak78/vuPfPvlA703+E4WrFPK3N3t6ZwTkellRalK7z1KCT938OY2Z5yWhWiN9Hax+ZJoAVBEjqNiiyavgqpOIRFrEWsII/pNpRHEdWkMsBhZQyTEzBITIcqGzNoCcBsBicpEltW8IObCsWUj7z1E2ReOKclWTC2sITULh5fsF0LC6CAgmrMNS6xUa25Xf63Cgd6Am5Ry611fWEqfZbXSAB8QAkljCcWcbmX2lvlro0TmxkX+6e3GNtoCcSsQ65/eb4uxvyg4tdYUbbDDDpLoj4qTcSZpiymaaV25zoFT1ByPnloNBt02WjazRm4giQTmi8SitZZ1lZGI9/0tS0IVHm2BVAuPHx6JcWUcR/omq787CMk75BPaCDBRq/S0a0zMYcJ3Rx7uDvzhhx+4s4Vv3nyDN5rx7oAqkd1uT997KW/ePVIVXEMQ7m9+eRNvFCy5UqD1xCiwaMK6DaZTqwbkfYlZFrS91Rx2jrv9iKIwdJ5pbpvdtZKq2EPEkKCKcZIgnKIuOM0L//y77/n112/ZH/acrxecdcSY6JxHacO8RCFmF9FFKlVxXTPWAEU8O3MtopdURUNpXgLTmlCAUxZtDDFEUlE8Xxeuy4JWmmWe0Ie3XGbxPbXGYq0VedFUiaWyRBn7hLwRzl/ocKmhoSGI9McmIQOKGDK5GSTHKLPn2kZj26aJjFokC7psiVEEqa2WnlQYLNvKW5LMlfJNlXCzvUjpc3uEUmsTzn7pTTfDIvmedp9XAf3nesw/t7P5195+Ydk6YzsRe6rGShZJqVkAGFKszLVwToV+HDC+J+SMjQHVddIXlC0QJRtusoUV0FpeTO+7G1/xNduolIQBTqcr0/XMuBvpOlHoVtrxm7/5Wzrv+f3vAjpHIV1X8d3U8hv5/fcfOc8z/8d//TuWaeIf//CBXef58u0RnTJDr9iNPb2zfPVmzzKvXK8L75WIDIvgstAkS3tnahupqHY0vtjuacCLtEoRi0JjNJ11dFYz9h2dc3RONRs+wzgMWGu4XCZO04LvRCIzrBnnDCokatHsek3MmU/PV67XC19/+SXTfKUUxfPpwjD25OqxfmVwE+PYM47Cg+28ZV7hrpf+LlUpXTtrm9aTiG4XpXi+LKwpE2Lmw+NJsIS+5839kRAi82KbHCnc9z1qWShF7r+mTIwbSR+WNbR1M0GGc+OmirCzSJ2GIM7eVr/ovoaQWGNu7c3nq1Zd18l4S2WUytRCM9ENN7mUtREXYk4MSrcVN9ky2Q6J2gCfJUr21m0UJjuZ+RaABShskqsvafCnGkG3mNnW6nJ+6TPV5/f7n0Lfu9XrWvpNU0sTepLl3b5ZlPd942saJ19XRmzjUm7mNy8AUM6Zugnutgcqg3j7WZ8gz9RAzThdOd7tpeGnYp0V9TPjuHt4w/F04vzpvZS/bbCvm9vVHIVG9/379zjX8f2PH/jP337D77//wPl65u/+5v/k8fGMdYax8yzhSsDQGUdxqr2R5aUq+RPEXE7/7U2RN7TeJPwVsgDddx5nDd7C/f2eaZKZ4fEwMvSe3jfyeMiUJNmuAs7q5qKscOIYzGVe2S2R3suBud/v0VpzOc1M58zoLccYG3G9oBnpxo5U1S2TCXJZ6Z2jG3eEEFljUzds9ge1BbExmmme2e161jZqyEm2gNaUm4GslJOheWOuMTdzoCTEirgtNEdq46TK6MwIUNhUBAuQsohrNfjmZXVRqZv2LECpK7U4VKeoVVNKun09l8IaVsEyirx+rwEn+XgZt/g2b89Z0PxUyq0Ufm0bv6GzsK2+fW45/1rBcTMN/ulFszG5fun2iz3n9gB0a7JyrVilRTSpZBGWtlaG7GHBCDue6XzGGYMZelQz5dkmmrfGWMnvSGWzWHt5EkopVBHRKNXg8ulyZRgHuq6TJ280XTfQ9wOT6yhrEAPZzZND1WbQo7kumbJccb7n6TIRY+I8zfzhx0e+eRhIKfLj6ZnHy8Knp5OormiRzNz6mhvhWd2ewe2F3hgutYqhrnYvpq9agfeWzotdxNB3eCec1GHoxKAoW477Ufisc73Z0osb2UtAQSU7xYePnzjsBx7uDhIULaCmWaiK3mqREy2CZopBkDgrbx6YKRWu8yoZv6Hi1Er6/2l70ybZcSRd7wHALSJyOVtV9TbLvde0mUxm+qAv+v8/QXZNunNnpme6p7u66my5xEYSgOuDAyAiMjLzVLdEq1MZC8kgQTjc/XX316Nm+FhnS5jKGq1MGr128Q5BhSyKJkHMCZ2dvWecZ45H1ZTjODGOUxJ2wzj6gr7b5Lb5EDTXxuTYIwlIEnJjozwHow9aoG9BxKVFcVQwCPUZRUjla0b5o3JBeRUu0QyuXDTNUs8ZVXOHIKl3jywVKAZdmGJ4ogWf1YZyGkT5Jebuq5ozhzKUYDilfCWGNggY19I1A841iB8helzT0nYrJDnXyKRdkl1u6Yaah4aUl8vJ6lgmNcJ2u2X2wp9+/AkL3KSuTko21dC0QtsPmKanaSfEj+oXpzQpmzT0FD0EJdQ6TBPTrEL/hx8/8+Hm13RNy+dxx49fdghwSFX8SPbP0iCbTBWaKmmqQZcUFjCJ1Jm0UBirPTENkcY22nG6VXNZaT60Q1k3eEht8kLQEqPZe1xiOA9Rs1kkBPouMagHYfJHgogmBsSA94Zx8uz2x8Iv5L3Hr4eiVVSzp3Q9qZu9qp/mU3GAEnBpyuI4eaZpLtlMc9A6x3HW1LxxUsFUIiz1TQ/HsUqNU+TVmMV/lJSxg7EJQTWasZMEpxT0o8kIWbCNCYm3yjBXyH4JvSUh8D4iVo/NlCI5tVJSrQ3GaCZQFC2wnqtUxJzSV5W71UqkhGtY+gdFiVrLaRbM6LnC678aEMoXooiiNr9pnPLh4FxiPGtYD6sEmGjIxLo2kTq15SZDiMm8XXoulpxak6zzYterQBgcWIdNTUzHceSnv/xE3/2W1TBgjWEWoR1WDFc3eD8zxVl9hiBF05DS6rtWQyhhVNZ41zr+/U8/8fc/3HA19NzvRv7y+V4b8/SW/X6/wPEUhtIC9tTmSimdQ0MgfaNFzU3rSsOdpm0RtBdnYx3r9Yo5CQExsl4NaoaJLcW+NrUatEaD/TH1TGnblmmaeNgeAPWfIjblEAvjpJUbvrHJHNZ2g9bZwtMDWtHjZ59QSmUmiHGbEumzVmo4JoZa59StaWbtATeLMCV0dp7VhB2niWmameaJaczxQa30KONVmXWZkUAFgFKknKtD6jG2AjbEgt6HBGzlhBWNvakZ6tKiH5J1E0I+rxSNPc1aRCFR0xfHSbV/TlrILeJrwaqZ2yVp1QJ+pi2B+2XBWD7/di36KlqbUcrMvSKktm+NmhjGJdoKlMLBtkNKbdIrtC6xxVMn/z7dMvKZQWhjDPcPjxAjq82atm2YxgPjfs/28REJM23XMY0j++NI03Y0rWP2HaAUKtEIEn26fqU+3G63hUbfGsMUIv/P7//M+ze33D0eOCa/tu2H5bpYUgzrBogn115t2gnbMDihbze0jdNWgQfhet2xGtQs7/tO43zW0vc9bczdsy3jcdZQQmoMZE2LiyogbWs1DGXbAn7EqGzlziphmsSI90prGoIQZ2VQaBtbFUIrPcc0K9+ONdrOIgYYj0pY7ayaoV6UOrRrtRLJoP5ZQJPeD+PMMQmlXrsmtM+TTwF7lO0iTcqQO5SRsQf1ZSXNnby0FopPyYpCQSJmRaV1TmnBQuvaYpkJWlTh07VGEUjaOKQnOc9axD7OapYHierrpt+SxMZRTOGUivgksZ1kRUnWkE/N3b8GzX0l8T2bP6a0LpfipqNF0cnHUsHSrBdnU8tvNOM/mzCZi0gn9KVfPF1VpsOR7z+8Z7ff0lrDalixWfW0bUvTdjx8vSP6yDwqx0//5g2fXMf0+BUasGKJ8yGBELDf70ucVVLw2Rrh88Oetuv5dH/HHLRgeD4Jo6SHbTK19tOBXTSnUUkR4Vcf3hFRqn+i8OH9Nd+9f8tq0FYAIfjEKK/Mfo3T1LlV3+GMI8oBEVe0xBy09tAZy2E6atG40+ZRIMQwaqt514AR7XsaTeK+hWhC1cpBS9m8F7a7gzbTXWuigsjCkpBJ3rqhJUS1ddqmwQZXNFeMkeM0M46J4XCak8b0i2+XQw5RzfWsaXIcWc14c7bPwl2VUwAx2rtzmgMukiws3WZ72uHamIBLwm0ShpCVxDQlCp2oHQR8otDMQJghdQzw2nMl02bmdMAsHyKajpk1b3YRnkNzf8n2snCmpACJgIXWxlKXprSinTrM6MRtUnxSnecEJKFmlGsSGTCJ+uQMVKlvxKUBvn1zxeNux2G7JfiZNrH4ffz4CSORoetoDHSdY321obMDh3HH5Ab6qw3z4aCs5yJK3elMIp5yWpWQgv9BhC/3OyQanNGMkxDmkwXECup3UwsiJ9ef/7q2YWiU9d40jqHX2s/NqmO1GrBOm/76oOMBGmLoM2Iogm1s6q7sUjA9FS2LNunteqf3402J7yFaZrVaGYK3OCzBqv/svUdiBxGaTuPAn++2zHOks4YwawVQFJQ02aduYE45mu4ftnz48EFnhXVJ2KKirF6Kn3k8zFoV4kNKoZOFnkQyB5WannnM6jS7nGIHyS9MmjTmRTEJataGUVILSgCb+XQpWWdZizmjnEkqjFqXekzJEz4tMpDybf0iTCGIFmSnRSgLaO1/RrT2k3RN+Vz5ns5fn6YTPr+97HMmexoMErWZbYa/jaij7ErZ0+lErQO2zi2M37WzXu+fX5f3UZiPRx4eHtk/3NN3DdEHXAxEPxPF8jjPWOd4+/Yt796/o29b2n6F+f0fmayB8Yi0uQg8d4XSh9y0LTYGfNSsl8f9riDTl7fL5uyl/Vvn+O79DSKR3X7iZt1ye71hsx4UWBlHgmtoW01+GPq+rMir1ZAAFKVz1DFLghP1eVgDa2B2gclG4jEWv80q9T4iOgEjgnNgOu3svd3vaSbHPM9sHw+KHl9dEVPdqU9lXW3X0riG2Udcq4vScRyJohpznjMboGobZSw4qnk7zinFTZPEF6Er6EiZV0qKlcMTSXOm94KchrHSfWUNGGMgSMCglpqJUTPLkrmc+9/owOjC7lMp2ZRjrwm5jWk+l+a9VIuFZJBuEcyT5BSWUrzXgJ9fEut8mbc2maPWoEnvybyLErGiTYs0eI0CERcmapm8VSA28x5wpnHyaiIiTMcj4+M98bDjw80KaxzjeETCzNpZTS/zATEDTdMktM4yrK/47sN7/uPH/1DQIZlm+siTj2LBiC4s0eqA29RqIoMR9aA659L3p+zm+brzglMWJqBtW+7vH3DA92/f8v27G67WK5qmSWmLmujfu5bglTFACwwsfWvwJjK7lH+cesHo4qFJIf1aS6cetgdC9BzHSPA6YadJWSSMscgMxmuH7dbZKinEaLaStewPR3zIvnJkdziyttqM2PvAioGh75hmDZeEoNUh1qpw+VnrTXeHUYuevZa/5dS3U424PHdMRYqVHIaYsApM8hMreV4eSmIVEEmdxhV5dkAQTShos3ZPyf3GuoIWz6mIGijZR1rgQWkXnx9kkPzd8twzg36tAS+lBdZz6NLf17ZXQilqHthk/llIK3dqXpS0UDb3ctji9CR5dDlxNAVKC3eTTB0LeIR5v2N//5XWRH7z4S3rQek0xkYw0jA4gw+Bjw8jR0uqNdVqB2ss12/f8I+948vnT2zv7oiTYZogoPFoazTvw6BlU0EiKT5S9GMev1LFbhdB1IbC+r2zT03dxlk+PxzYOMtvf3jL775/x9X1WnuPiKLGxsDQKTHX7pCY4UYN6FurFJQ319ccjwft3Ib6722rsbth6IkS6KdcJBwLuZaQckslag6sRAx63V3bMPlJg/QhMO2PSBIKH4R58uxHj+u1WRFicY0merclUL+4HsHPJStonDxT8ArwZPqPlP5WfLDk+1UgbJLFUBRq8ji5xFAnCdgxaU7GIFibai1NxBloXYNHaCRhJdZpskHKfAohW1CoRk2cxzHm1L5YriuDQdGfEm2Hso8+0xik1Go+a3xd2F6qA32FfU8D2DltLSZBillQs7YQpccoMUz92fQdCWquzN1kdiwhCYomtQKEkcEGur5ntWpZN4IxDvGGoWlZNRoT2x4Ck/eMx4mbm1sOx0n9Mmux3cCbN+8w04g0sJXAQbQAW6JJXEeJJsW65AtTVuoYMxihZleMkbZplgvOE0lOEWhjDO9vbxkPB/7xtx/4h999oG1Vo233O6wz9G3L1aZn1bUIwnrVYwl8SlUjbdNwnGeuVj2Ns3y5u2McA7e3A2A5GG194I8TQ+tAOs1bNhM+aOhFQJPO84RhIkpgbrUYOsbA7nhgGj1dp1Qyk4+M48zsBbZHrDEMw5CEK6TyKkmhHVLw3qeMolTJETVwn5HLRSvFJSQVa0DozPeS3FLyNNsm76sm50L5oSl/C/t8NAZvRPEN0bS7eY4pkT0WGhZ9gMnflXPzNM+BuAhnTeuZTO1i6aVK6sVuOt3O7/Fb/E14RTg7q8XUBkosKQudJa8QxV59dsnIyjMLae2j5ovVU6i5PPQtnV1r0x8jDM5yGCOb1cBglZNmPszE4Nk0sH2857PRQXo4THx4d0tLwMYjNzdXMDv8PKnpkfpARmJp6GOsKTw69eCVGKfRa8u5n6DNpWDpVqz8uWpexWnP//T3v+Y37wd9qIlP1diWw36kf9MydD2r1rEbJ1pruB46QoR113CcIm/WK6wVPo5Hvt7tOI6Rt283xKgNibyfla7DzNwMPdZZ4kNAJCIOpknpORW8M3hxiNe4nabBebaHGcRifOpHGdTk248TELm5vsZZW1jzg1CC8orEL8ikD5pVkzNq8tzIfpqOWSrpEiF3sKtmAaD5ypIjytmUrMzH4kqwLI6SFIQTwTYpdzsaglVtFvyEBE3O9/l5kRPbF7M7/6tzqGsfMoNX+TMtU8uAXZnMXBLQv2Z7UTgbo37kwo5nKuFKKj4NDOY0Y+bE1TRLOKIM8JmAAjj0wTmr/tHQKA2meCH6ma5taKykhOkZI5GVs3jjsKJ5m85Yuq7FBiG2PW9bwY8d4fYa1zZM48T+IExzQpOdw2Ewyf+xRlf2XJmv96Jx3tlr9UZBmwXtgFVpAWcd29Hz090d1l3RNY6b9Zo5aOyvbQxX1ytAwbWh61J7+I71sGLoDLNEbAjc7T3HcMNh/0eM007W795eK+erUb/y7n6LFRj6hubtDfe7Q2llPx58Km0yzHjEOULipNK0NY2DTpNSjBijRGrGGYxbWuLNCbnNsesYFKHMfpv3HiEWEzaDU9mczaBK/jyHRMBUYTaNQC2aM6VEJiwgjy/Vs3LGnAhxdldjFIKJEFIYRiISNOkgpPmn5WGxXG+u3SSb4CJFW2bNn62BLLi62CzXVrRhcuEUXc4fLF9RCfuL8vfSlwFojFSBYiknLv6lSQF9UcWehVWH/lSb5olt0gUae2raaEbQDMcDGGUORwyTnxmcsG4BiUxjXqUjx8liW6Eh0DlwvTaGHTbXGtt8ODIkQKBzlrZviDGRVInSfGpPWaetJwSUIiWeIMuaiqV5xWqup9TGyrzSg7VsKorw+x/vaIxwc7VW5r/W8o9//z3j8cjsr/BRJ1jvDEPruF63tE1LFLi7e+R+u+Wf/u1n+kHbHqzXPe+uBvZHz902sOp6vn//hvVqIMTIw8NWJ5IPBKtZRTYBMTHCJJkRTy2BPG8CauZmZ6NrW4x1iY1QmQNiSKgq4NNcUBAkkGtSFxJmHbP8uoRKiguZF+is/aICitUxmfsiE2DneaL/EiJtTpkOSKY2ZilU13uXAvio8CZkVmJBZ7Oglsyf9M97n3iR5MQCzEzvKQe1mse6gxQBrcIqlWxlYOml7ZX0PXXMc7mzPZP0dEknGtAaKYNuKoSzNhmfhk8oSQEShU4icZ4Qg/qaMeBEaAxMsxbhPu6PPB5n+lXLpu9wjcVEy2bds1n1uMYRuGLc77jfPiDWMnQtTeLA0VwnJaCyoqux+g+x+MmLWbOYZc7m5j8xgWWmEmJ9yKu+ZQqecYps51HrHEPkqrf87jffMRplKHRGEzZEtOXe0LeMM/z45YGfPz3wp88H/q9/+hNXveHt21uuNwPXq36hCo1KXzn0rXapTqCEmpmRrg0JgQ0VuJLG38SyuoeYwwdC41JfTGPwGTjJRMss2q902ApKRq0lV0/DECKLiaoJAxlYSwtcTBCQTpgyL5aw1kK5mtFdkygGDMu8yuaoBu2UfcHECk1NC0eMlXDGmNLzcigllkVHRLQz2qyNd7U9xWlVShGC6n0GSZ9IUnVQrljKqO9z26vpe4vJqiZQhYcsDr7kImqjq7XNaX6aUVM78dlp1htw1Q3qALowchwnWhOxpiEkk6nr2rS6axH0bgwcbQfdwFXb0vcNGIcZOqXeMNqEqbcQjeaBNn3HqoFZEstbZ1ODmmXggg9Jq7tqgp0ih1kQbWKMd86VmJ+ESNM0/KcP7/n0cMfnR1LjX5CoNJrv3lzhRYPb665FhTogpufff/yZ//7vP/Hz/ZF//fErR+9Z2543N2v6tqdpW9bGEE3DNCuWGrrhAAAgAElEQVQnbQiRVeu42vREUTqN7WGkcUo9OYUM0tjkO6deJ5BAnOXeurYpwfoQYgJNUpwvLl20JGkS7zVUEdLimjNscpaNMRr2kByiMtVnyQ4VEWjOF/9FKPOEz4t8BpfysQUjwGBc8Z+IKTvIWptK0aqc2LgsJiEEcl/W8n1tkqdnXsDLArPIiWL61u1SAsul7UXh1MRrmyOEScPowGHUaMhNchdNYhOSmZz7RCGocauzC5SMtgWcsVpaNu84CqzaFjEGb13q+Sfqh4SJOcAxCu8+fMdqtWazajFEpUbpN8sq7Sxzf82Xn7/wfuWY40wbtcD5624mmhnbRLrYMKZ0tShC07QECTQp2dxEZRXAaFgpopkzjXO0rqF1TtsyiFKBrIeBECZW3YpVrx3KGutYrRo+frnnt79+T5i1leDQtcxB6xEfH3b8tz985L/+8x85zPDxboeIYzfN/OE/fsIEz4ebf2DoB5pW/bL92HEYJzCGwfRMc0jpfwZrHNZEnI3YaNBiqxTgD4ps5gL39FQ0e8kZSI1+YsIBgs+EWalulYWmowApWRMl7dO3XdG4whIPTmsbkcR0IIKEBlc0TkoVlVQalrSTMWmeJV8/siyUKoQ5A8lCNNp/M5PEYTBiUikYJ5pSpAqZ5NgsovduFl+zpIxHoy7YJQ15LojPfF6P23PbK3HO3MaIRG+RYnCOBKIkk1VEiZbKYKUfzwHdBJ48Refy6qPOwjzukXmiS7m8h0kIrkHz6cZSa3e/O/Lmw/e8/fUP6CPSqg9rHeJUmIxoINpYw/0U2TS6UERrwFiCaxn9hPjA1dUK2Y8cj8dUFZIzbYQGg7FKGgWKWufFqkmo9Thr49e3t9e8vVrzsN0TzRXEoKBF22ob9P3E//B3f8fhOPLx6yObodeURxPpG/DBMkXHcRbe3N7w05cdx3nEuJbHA0Rp+Nf/+MQP373hZjOwai0SDHNwbHd7hn4odY2LVWOWwH71fJSeY3kG6v/YBIiYRE6dNeYS4/NlEdO6xjZ1oA4htctLmghTxwIXv7OEPKL6mks/y3CiBXNZnpr9p4BLvh+hztTJrHmqnSOCTUUEQCnhyn7hku0jJ77yQqW5hH+K1s8CVfmY51tGWzgTugVPW871N/mcXVO5AQLYWo0LFId7yZaIZf/kLyTE6hxhPokPGiFEj4nKktA2DaOkejuBcTeyaSIuaCI1bUfXt/h51iylxqoZChC0ya4gqnGjds368WHLDzfXzE3P/eMjIQi/fb9hnFepdUNM1esei1J1Yp1mRsXUl1JSmphJyf5IWWX7ocHIzLpruPnuBqdsZvRdSzjONK4hSOBPf/mJ281vORx2TP4WLwONbTDG8cfP96w6x+Nx5sevP3IYAyF4TGPp244QZ46TMjx0TnNtGxfZ7/eFAkRzaFPcz+rzmH1cEklszjRaQL5l4mtPFS2izlpENEySjsl1lj7MiGh9bBbgwgHrA85p2ZuztkyiPMaSfT+hgDSq/Z62Z0ccWUGolrdFKCWSYtQpaSGCGE0qaBqtq82JGbEyt5WwS+ktJQrK4l7Rcp4JUT1nFRR9UaaebHUoJpMKZDfwr9acugarqbpkxhgdIFPWCGBZnWOCkCBl1ZgKQqNabSppzSuVs44YRo6axkPf9YhzYCPBOKYwE6Khv7pRWk4EP44Ys8K5tpyvwIJGUxDfvnnDP//hkfHzlmEt7HcjvcxsbgYclp99oGnUp21cC7iUNaQ1mcamhrMCDoexylQnBmwUege3m543m4GbzYrrq7Xqc9njvNWOXoeR2YNxBh8939+8pWkbZh9Y9x0SA//8p08pC6jl8DAqo/y644f3N/zuh/d0jWYf/fTxkS9ft7y7vUrNfPRJzN5r8vlxLIxyOdwRRZHmeqIUf8/YwgmrJmxIccuY6CRnQlz4ndQEVO2oZFpLYB7MwuQf8/xZ5nMGiKKcIrt1TNkkzZexCRG1wnLzZg15WcRUYQ4A16S4blt4gtQkjaX4PsSo1Jwhlc2lc4dKaxaByp3GOIt5Spr5xjwR1ASflL95rEUkxcGXZ/Bcul/eXvY5BSh+Zk6ZUt9PijYsj2S5vIwG6l2WMEMW5xwnNSwCapsGCRMSfeLHbYiuw7Qd3VVH9BNThGAC7WpNP/SM+x2NS0hlMjY0iJ00Q4x0XcObq4F10xCsNsDZ7Xa8eXfFIRrWq4E5KI3k9ZW2PdiOkXHUMqsgls7mjBbR1utIGgetDlk3hrfXazpr2O4mhqFnNbREmYGINRqaaVKq2HZ74LAZ8P4KkZ7OCl3fcLtq+e9/+Atvr3p++vzAFALvb265Ghy/+eENXWN52B7Y7w4c54mIMPQ9u92EsRqaOhxGTTyP2klrjprNE/OEYRFOSWVYMQmMxRAizGFpYZCpIjOqmqszckB+nGaNjRaNkJBe78mgTiyoqiGXhdUg1PkWU1jDQEmc18SXxQw0uSxRFL3N12qtTUCPLg7OuUp7kpjalag6A4G1r5kTJmoNilnoerKZWxaRcHrteU4XAU2fu8rEzuc9sR4vbC/HOWNa9Yo5ASVpPT1Qsq1PldCeLhNeQaaSv2Gcmi7GWnBOCYzbnmiVvc82Dh8apOmxjbYYiNEoNcmqx7gmXaf+bJ0U0HUNfmi5ul7zuE9ZQiGw3R+4e4y83XjWV7ccDwdaZ3g8TiS6VExCmr1of5McNglRwyAi4KxjPQxqavY9Xx4e2Wwim8GyH33qSh1wKEucawz70fPlYcewHrDG8uaqY7+fuN2s+P79LddXG37+vONeDtxeDdxcDfTO8eZmzXGcmFuItufnz/c0zqY+4opM7vZKWTJNyjaXQTh35t9IWf0pC2w2O88nUHpXnikshfiZl6cOldUTMc+dnK4nkjXq5blR01bqvqelZSJarVOfv/6+mPaSQhaI0sawsLxnpse8AJW0vMKt5E/Ofa4eX0seyPsUPKVCd8/N5b9aOG1ZKZJ/kqvQWRz7okUh57QllfryDahSNiURAdBKim7AWodtmmIegdAOHTY4plEbwdpG6NerhQeX2ldJfVYag58j0xy52mwIkzAZwbiGgwcjwl8eDri+U2KqSXuRqmaPCRjS6wz5fmyLRTl8owi70dN3gatVz8FH9iHy509fmOMVNmq1Ztf1Cpq1hlXrmDz89GXH7dtbfIw87CeO88z+sON/+y+/47/+y49s1ivEz/yX3/2G3X6LcZYvXx943B4IYjgcRg6Hic5CW7UnCDEyzZ5jSrlzWDKPHZKycMqCq0h6TgDAGnwMWLSwwIiiptqGogZvlol8ERSRhccnJlcnlUYglYujfzMoRfm7xDelvFcAKfvHOZ82TfioJmMIofQ5MdbQOW2KRVSi8TkExiyccUFr61hjiAF9aia5cYICKdW1JpM7aaezu8/XVY3NmXDm13+Tz+lDzpGUaqWqQKI8xGaJUS0Pq/JILzzAZaVdrl4Qun54ci4RDV1Ya2mapqzwpz1XlvOWh+a11f142IIxRAtD2/Pu7S2H3RYj2tVss7ni83abhNGmeGqeiAaSQWWN+ouNtbTG4BIT2zRPfH7w6RkqSDbPHi8a1F+1htY13FxvQODLw5Y4BXYPW3bXK740hnXT8p9++z0/fX1MPUI8/+f/8b/w5qqlbTZYCQyrgd0Y+PnTHeNx1NZ5TltmOGdprWqe7fbA4TAxJwGTKFU4IuEBRgWPyp+Kqd1hNnN9DKnhrBTBLDHFZybVJe2wfL4IQ4yaAVZz79TPWxFdNTObZkGEz59zCaFk2Ul+aeMaRWiTlvYhNyAKJ2ZsAcfydRfNfX5/C2pcsJLKTSv3m+fuiSAvY1D/PX99vr3MhCBLIPkl+Dc7+Xm/Ot3q5PaevK/NG13ZF215ev48CIDmw174jXrg8quu63hzteZ4vOfmes3ucU/nLG69YhyPBAwPX+8wBhpjIPXKMGnSWmOU8JiU/I/gjOCsvm+i5tOKUd8tBs8s0NgV0q3pGuF609M0juurHj8L26NjDpHRw8e7LbaxmLXl7nHPquv57s01f/fDGz7crFh3LSKGw6hs7uM4FishTxjNtQXvta+KMh2qKW2M0BrLHJKAIokqMibUczEd8wofQ6INydqs8lPPE8Vr7VkjrYs2kkThEQiSTdegjX4zj9DZvMnnqoETVzW1ysnreb/GWYxRtNpaza1WYV60vfex8OjWZmyJMhQtGk7u91u3WimdQqUXxNyYE3P9ue1l3tokM5cPX3zM8wdU9hDNoskrdbm4sq4sZWWGs4djSXV7iTOX7Jxr8ffZ7abvlxq7vFJbo70wv3tzxf1+5P6LkmGvVgPOGu4etwQ/Kt9uYktwyVcRk2lBwTqjQmkSrGFQRne0U1jrjFJYSkMMUcmSp5F3NxstlBah7XqQic0wcDhO/OXnT3y9a5EQie89Ia757u0N1kQ2g2PoO5q24UNj2fs+0U1mgEITtedZSiqaptlpnxNjEtueMcrwHhWZjIIyE8qSx5rTLA114bGUORAL6vmyv5TnwQl/DlX4IQE4mZNXnw/FHJcyZxQ9LqmCUHh7teP0okFLHDPGNC9S8oSEkhEkosn70zQRUuZ/lEiUDP5IMvml4Bb1HF5gnlQXmy1a0cUv4y21lkzIZDaAT2ToJW1Zb6+SShuzSOepltJfPPc76tVUyupYmwQsqGo6iTGayLBoR6nc1qVGNJ/36aR4etNGcuKEAk5d23F7bfDfv2caR7q2RaxjNQcexz2ZqNNgErPCsvo7Y9I/5U5qnaFvHV3jaIxq+75TEufGmpQSF9IEEI6jZ/aBd29J7BEpQmwMxznwhz99pG0bpROxFi+W66srptnz48cv/OrDW47jyN39luNRSZpnrwRVMQXS1WzTqeAThJgiGSnftoTOS48SNVdTEUMaOi2QTlUZsuSc5vG95DfVz+Z84uX0TkkLcH5y+dqElEAPi9AZU+KOOeRzTph1Ho4QkYTaR6aQkPbkFy89NX0RyKwlyzlLwtNLgpMyo0gugYEclXhqvlYKSZaz1hr/Nc38TaTSuuhZTrXj6QM6EaqUtVP7BbolODoDhVn4LuhmPe9iu790M+cLAmgbPZuoL/rVCvqIHUfevWsAw/3DDkalbSRdu67WHhsNbdeQqyWyVnRisE45ghpjlBGw1/S9rjWlY3VjLfvxyLGBcdJOyEjgeDyyWnVsNmv2h5nD9Mhx9OyPE7v/+9/53//Xv2ea1QS7uVqzH0e2+yOPx5Htbs/2MDJHSX1JcqMgOQn+i7HMIcUrVRUlP8tgE59rTiiAJYxQP+9lXE9Z5GIVRjkXylpr1lhATAjUeW5yEa6zvzUFyEKL+XRROH/m1tpCSxKywOb7C0tKYYj+if+q52TRmmdgTYnbf4OZe2ku5gWmdv3qfZ/bXtWcTdPoiesfSn9PTdpTc8ck6St+ZfkfRTDLEUbjhifcFSzAwMuDUjvZS21gvnGXqiyi0ZYI3bBi8jP744GudRoWsRaLBq3Xq4GuUcHMbQycERwpjxjlU3K2pW9butYydA3rodX2fV2LcRa3hek4c/94IMbAD9+9YbcbkQirvuPDuysVqC+PBCNEL/zbv/3EP/zDr7EOTNMgU+CH796x349sH/cEH7RtXtA27bl9gCTNGATtGB2VizZX/etkhYDWduZJjyT29yanXsaSAF7nnoJ2egsxlkhjFtIamFvQ1UUL1fxKOX6ZtfLy3Dg5R8zXIdmieloUvSQ6LNfYdx1+nkuCQ+3/6n7xyTHLLFq0XEazdT5RzNbLWjXLQ/VJ/eb8d+oF5sL39fYywVfyEayAuAwOUTRa1pLnwIACKaGYiRWkcKINybVukvI6q80mZ3dJ9zptGjOO2qSmaRqlkCym1zJA+fpJD9OLBoXaxvHuzRXTPNJ2HU6ElhHTOpw1rPuecZ4K8pd9V2O0KZGSIARso1p0PXRcrQb6VcvVStux902q6Pm8I5qWrmu5f9hye7WhHQzX7S3eC7vDkbZdYyVy93jk6/0jq77lk/nK0HWEMOLnRB+SWutNk2eeg/a/jNoNLYTcqwXtkTmF9ByEmCtvEgaQtZMmFARkTkJkVDtqnWMgc8UaYzAZxKkm2CXkVueBO/VlTaJNqQQsb5l9PmvcnGeUgRyXmCuMNeqmJJPRYoiJlCwnOcg4FoS3nosiUXPh5fT3TxVOPHMMdb4WU9cs7lUWKl3gstv37SCSnI3jc9urBF8iKWFYnv/hiz7gcytJ/qzaJRdlp+F8ciO5IzJQMlDUsReaxunDqzR5/bsZcZUYNbQSlXil7XqsbZB5huBZ9Z2y9RkhhInWCmI0N9OI0X6Z1gCBrnF0TUNrtcPa1c2G27VmBWm9pwqAD5F5Fo7jjBVhve75j5+/8D9efY+I8Ob2Ch8CX+8fmbywHloeHnbEzYpp9hXDHMqdGoTDOHEYJ2Uo96ohMlgiQTVnzqqBxEyQeo+QCqyzFsltBxqXxif5yEvy96nQ1SZn/XzqfXJopFy7MQUUOX+u+diQGPKiLKBQ/R1wRiOjJGYFAIzanSxKeGKiZ2GWs8X9/FqevjeLAJ7vl03gfN8k7Xppnl9YwF767Xp7hVRaM2EwBsfrK8IJeFOFPvINLH8Xsic9Sj/LB9c+qIgUwWzbNpUFRdbrVcoGmcntx/NAPBkQ0aygvmsYxxE/TcQJ9o+PWJlwLoVOYtD2gs4xTTND42i7lDSd7w9L1zquVx031wNv31zz4e011+uWddfyeBwJQVvLX20GDIa7hy1RhGmK/PnjHT/8cMOmE4ah47v31/R9y+cvD+z3I4+HmcOkZt00j2yG1OJd1L+cg3Cc50Q/udBsWGuVuTwJojFGeYtS0D9NUWLwxRJZBDiWeGDtB2k57+I71hMphzfyMYv5qhMyVpQghqdm6VJMXRVWC6VTW5k3ZpkjJ2BK8o18InxON/LE/YoxFjeq9p/PBek1LVY0aPlTzdFlhwvHPfWVi0n7yvay5pRlIApqW6n3xXRI9ZhVbGfZUn2nOf1cOBdYoAYNsr0vqcLA2BKWyZtzTlfdJ2BV2mJU0zRG4jQTVg5rHW2TSZ4CV31Hg3A47BAxtBg2q4Ewe0QC1+s1oMCOlo05bjYDv/vVG25vrnDW8eZq4Hbd0bbKVLg9jISomUHTHBiGnt1+ZJ49bdvw3/7lP/if//NvcaU1YurrEZUOZJ84febJY22DYfGjfFAalJB8y/wwvORwSizeUU7oLu5UJRj5n7VWO3ElZ1EZ3qEwQxhKz8sswMYYjHUsoJ/aQSLJgRFJbTqWypc8eXPfynMBzOatqRZ4qPNySax9FaBSLK4FuCRhGYIstCJCMZcXkOfsNRWYkw/KVmsam3K9xQN7TaBPzf9fur0snM/8Pb0oOfn7RGNW8R6TR+5poLI65aKlFmd7Sb7P7/NAdl1XHvwTs8IYJGjwOTd3LeavBH7YDMxf1WwKXhAT6RrHx09fcU3D2+trbtYdzkUed4Kf9Xevr1a8f3vDJrHevVl33K5bzWltG/bjzOxn+rYlDD2zVwaH7e5I74RpgvvHPRK1r+bxcCxkxiEIx0k7jBkHu+Okw2VMMWFDCNpI2JiiaYJAFKMtHmoNJVI0V42CLtk3muJ2arItxxaUs3Qm032WLC4SGVrSwnGhjcxjHSofq/YFs/atBVakSsXkFPl8Iph5Uua5k1HHMzPykp561tQsQq4nzqc0+cUytU7H7ez1pe9Ovi/j/VeatbVJUXxE4WzATvfNF1I/hAwClZWpVptAAYYubPW5Ll1bTGjrhSOZ/UgYJ6bjSN/3hLjHNEopEnxg6Bp+eHfL7//0F6Z5onGOY+pa9vbasWoib25W9J3FEDkcZhrneHetpWE364FV59j0jqFtUlczw2GaU68RJXEeulYbEDn47u0Nc7R8/rLn8WHkze2GpnU0wLVZMc4BZgU//BShtYhVHzuDGzEuDYdzyz8ftS6nju3lSakJ4aE0PFIW80DrtKA8lnIw7cDVtq26EvLUTCyTrLJUlsUgdy4/5ZotQlehp+dClzOAoM5CWyTvku+X/eBzgKfsH5+f+MtCQCWMlUo405aXjr/0+qV9zq/zbwKEFm2o/zdZGNMdFEs3mS+mPkztBgqnhMmOfB3XzBf5dBzOTYFTbazXIyLPCKZuhR8G8POMxdCuesI8J/Y+LUNqXcNmZekay3E8IgI364Hv3t3Qd5ardYfhlgd34Pvvb3l3e40zhq4xrLqGzdDRNhbXWIZOGH3gMHuOKStF7arIeuhpXMPtmxt+/8c/s1lr17TVqqdtHT/+9CmRIYdUmgZTiJioubqF4BotfRJRRFw1j5BSmSomuSV5IMRASOwDRdCtugUGWzRe1mL5fX6dhbcssMacLJAiKfuoel75GM4+W+hKluSCwkFVZ4lRK5an/upz2q98V31WQJ6zTQ0587yB+ox/eAl4XH5n+c3nfNtvMXNfMWulup2ImKU2UysWKjORZNJma6MI8pKih1X2NSEWgdebe/rbJ87/mWDmNa42dUXqwdDP2rajd21KQQu0KefSz3OayAZpWr5/f8s8eVoLP3/1+NTZOHjP0F3RdS2ta1kNHe9ur8EYDuPIu2vVnKuuoe0UMpMGPlwPHOfAj1/u+Okvn7i5vaFrW642K5q2wVrh/Zsbrjar1KQWjscj0xQSqKEmXS59ExLqmgCPGFRwVDsbRJTc2Eg26WIBQkR0kYqh4pONOvbjlNr82ZQ6aXKxdUpxS6ZvDSA9FcykdUVOFFXR3pVJm5/ruTbNn9fnL/uW+YCOTWUR5GeeBfdUiyefsQhBqb15an6eTz5j8skXjfqc68RTgTu/r/r+vkXj5u0VzRlZ4o9VRuyZwJQfY/EX8oCdgjhmEa40AKfmyun5Es5AyWnkdJDOTedyFZJ/2yY2hkhTuE4NTdtpXDRGmiB4CYzzNgEijre313QN9I2SdhkMrnOsVl3REl2nrRnmGBjnoG3kE/n00DnebDq+PrY0jdPW8iJgWnyEw+HIMLQ0jd7TNHmmyRO8nlsAXyWwhIR8ZoqQLCx10x0fEnqa7jtkIU7CcV6/mF/nJIJQaaNaez5XrV9MVFFtTHXsyZyQp4IDS4FDnXx+bqLm89UTW++n4sL9BRM+C/L5fs9ZaM+d79yqu7TPJYF9Trs+t73adr6ywp+c8El88+yzOjaW6y7TCRZzOJsNlal6em5J/1Um9pN14XzVrcwjo8kIeXUHMNbRDivGw4FhvWKKIA87pslrQbQzHKaoxF1BW7JjNf3NuCYxvzccj56hbbAy0jbgjNA2LdZahgberAe+/+4tbdez34+M2z2u6zRhAMF7IcaJaZrYPh4YJ01s19CNas/coKguQq4BnVCZeTmDR6ktl/Z7ObWt7oRVTMQ82UW0N6lIqdyoF8BLZqSayzFx5oaL+z7379yMrQX7fELX15w/1+urJ/ZTRPRbBOiSksnz5om2eOFc9fW+tP9z+1zaXknfW4LKqU81CzSkQmuwFdRsEKmzM0BiSO0cVMiyhVtfdEbmcpimslYrm0POhD+ejF1t4ubXJwMvsuQgWYME6PsWfGAgEPpG6SpHjUd+/PqVv//+luNBK1a6rmPdK3O7JfVEsfBwnPChxdmZvjVL06cIXWs1Qb7vtHt162ic43HSrJZp3GENTLNnDoHDGNgfJ6bgibiLRcGa2SMnwgeLwOaYL8acIKD18Xns1dzU8+iQ6pgFiRc1Vj2eWh6XEgRSMkPtU9bX/fyEFaVXMUYTzxPZTI3EhqrIW5JZcXpOU85V1+DWdaKnwp594KU+eZk3l83tfNcvhUQuacVLnz+336XtFbRWEPFqUpplVcuATEFw8/2Z+tjnEdbzLfsHp/uea+hzDZ7DK5xMjPObvgQ0ARhn6FYD0+GgwrNe449buslxHGdc27M7eG7WPQ+Pe25uHD4sqLP3wjxFohPtbRk8t5ueToQY4DAL9/uDcvoGT9tY3t3e8qefPvHTlzucS1y7Fm2hF1Lrdq9hlCn4NGcWzTGH8MQHlDON6L2HriumKXDy90QDJRQ9Z1idCAYZkHkqnDU4VH+eNV/J6pFT8xPOBQuUHSH9WmUhnWvK83/6uY7Pcr7T1+e/e7pJWdzzXHtOk9bneOn7l37v+ft/fntFc6bAsNEV1hi7qPoz+/Icfs4kWwW+OXvIJzdau6XVaY1Zvqw1Yy2YBhaqk2qVIwFVp9ebd9PzuaZjfd0Rr2/YvPvA/U8/MgZLCFuaduLnhwPRQJiOvDse2Vz1pfXd0XvGeTGDj23D/dGzGRq8CIfDxHEMyoZgYBg6fDjQNC33j0dMo7mqpAmsjY6CtuGbPHPUmlBjtC3CgqAqKFJ4VlOSeoja4dlYpRixcQGFCqUHpy0R8ljkLdSCAaWpUwaS8gKYzd7F1dDfrRcEWFL46t8ojHvZbM6ZRWlhqMM/+VouZRfl66nPbc6e8en9nQMyy6Q7F66LGjKPSfmtpYHvye/Vc+7k8Cem4KvbK9SYtvSlwFDMnozK5hWnaDZzyWc8vemL2rOsYMvDru3axVRZkhF0HUgrdtozRG2F8HQlSw8/Xa+tbWtSkr2zrG5vCUap+39zfc12N/LT3Z7OBN7dbBjHmbZx+KAVHwc0JthYg3WWxlm2R5dMz5DCNxZvDCZqN2ifSLf2+8OJ1SGiIRjvQ0FhgROTNiRhK2VWOhLJZ0+TJap5V1BV1E/wJybukj55br7WschCOs0CHiEoU/yJwKKtGWIo/r2wJJpThI8CYEl2U+Qp4FQL0RNt/8xfnQu23Jt+F57st2zn7TXyHHw5mydXu5QQ0SXhzOc6u5fT3z6xOy9u3xTnzNcgiQ5SRAqZb/EX808ZUhdoFaD6sp4zC5786tlgnfuOJ++r13UgG0i8pZIspqVK4uwwvR9nGdYr2r6n6Roa1/J1e2Q67Dh8+YgYy/4wMvQd8zyx3WsFRN91WjtqrFasWIOJkaaxS6A/xXkPxyPb7U/KHmAAACAASURBVJY5Rg7jnHxxHbMYItM8M00z4zzTtB0GreDPhdtlTLOwpgZF50RVz8H45wXLNehTh0oumXjnx196ZiICTVP642ShzprRVAvL+fXVYNAlc/a5+XH++6f/dMzq7XxsLimR5767GM98TjhFuCziL5vI9fZNwpm1lMkdxIx9ss9rq8C3bOeDco78ltdQVvUMEuVBLXB8ZXIYk64/3cxzg26bFrGR69sb1QTG0r+74q4xEI+ECN6ryfr1/oAPkb7XzKJcWaOAkTC0DcYKQ98VpPXr3QMfvz4yJ15ZUimaoGGP2QfmkIq/jU2gjy/XWPe7zBZBFppzwfklPs65yXhJ22QBridoXc4nIiXXOZ6BUefjfD72cOqnnpjBZwvKc/d1WYifX0zq487v9fy782s/+a1zrZ5dtmfud3HRXhfQb6MpkYX1Ui9UNZGF0nWY+nWifzBGC51r30TNI313Ktj670QpsmRvLIgZ6bj0W2a5Yf0uYqpVPjfBvSSQ559nU6/pO6Y5sN6s2O/2zLR40XS2x92Bh4cDH++3hGjYrHqszJC1RIz0rdA67XO5P0za6BfD57s9hzFw9EE1Yki8wIkMeQpazmatIySW9RhJWUOSYrNL75KsRS9pOGNMufeXNM9rgM35gnlSzXImrPX+l4T8kmDWQN6l/ZbXqgn1vS3vdXu9tcEv3V4618l1nmlII3XiRP3NQgl6ek9/pXCmMz3jP2bFVJu0Z0APqh2o3meQ53T1yN8tGtMYU6U/UD4vglh9fr7VkH6+nud84Pp9XjgE6CyIcRyjME1H7v3E/HGPBOF+u+NxN2oXNJ9zXjULCYkMjTL2dY3yCh0PR3yIfN0eeNjPHMax5EjFoLZ/iLGgpplVPXgppnpdSfKcsJ1P0PNA/fn9PgHmeCpI+ftLQlyfu/69S9dVv7/0vC6d+3SrhV3N1XPX5FzTXdq+WejOPr+0iFw8X7LQLmvFS+d99nJeF85aa730Q09/dCl0fnpR+tecCG39K1Ilx18GkXKFS/bZ6q2eSOeC+ZIZ4ecZ6zT/9/PdgY+f73m7bnnz9pa7TzP3h4njYc9xmjjMAescc5hS2pqk7s+BcdQ+pW0iAGv2I0HgYX/kOAf2xxFMokBxLmXZqEYE7eQdYyR3hTJ2AYQy40M9Uc4F56JvxOVJd2kSPWdafss5L43za4vJt5mU599DnjfPHX8++V8Lg7x0P/U58+vL8/L533runM9trzIhFL/zFICvLNLnE9QV8IhJwpOuM5ycqaB9+bwsr0Vs+S2Tj8uyXqQ3X1kl6Gf8p1mKlz3OqmNEyD2XxnHm05cHjlNEgufr48T1ZkU0LV/3Dxz2B4ha5UGIxNSVDKNBeYswG9AaVwXHDBCiln9FMYzzDGidJ5jCVpD7RoaUJUQUJFW3QCr7yn5q7XsmMyZr8GydqK97KliXtpe052um6XPnW8b+eWE+P+dL2k7v69wCy8eexiyrI58I9bdc92vffYufWis1fb24YN96US9rzvxD+eGXL0wBQC5dXAZf0rynyEfyX9Ofk7NlgTxd6dJtGRLRkyzn0QOLnC7H6SCcrHC2/r6q0QOm8YgfJ00cB6bDyOdPd8SoCO6Pnz7z6++/Y9V1HI1j9GBCSBQfqska1yJEJAZIjYOU7lFKO/YQhCkYnE3RDqOF1Z6QKkwk0TOm9D0hoZ1LxpVqMC0Py/eW/Z7TB/7UWrn0nJZxfoZB4hdsL2mgXzLpX9Zu9X2dn7Pe53nBec69ee2en7u2J/s981pjovr6Ujnzpe0bAaH8N5PqxoIQnQijSEFRszBrllFe3DO0bZPA1pqwGAT1HakQVb6o7pyoN/MImLNBNjn6lwf01EM1Rouwp3nGjxPH3R4/apMj061gmhE/83UKzGPg4+d7rq5WvHn/gS/jzP7hC3MMBK+piWITUVYM2LSIhCBEYxOLnWbfRKu9JTHaUmLykVaHqkJhn5qEy7/FnIPlGJPHWxb/sI5XvubzXRLM10zYX6JBf4mwf5uQv2yqX3r/0hj8Ndd5fu5vPfYb5RL4hlCKSNJsRV0vD141UCW89XEqMRQBScfnNva1Ri3o0vIBJXQjiXrDGDBLb48657lGg0W0VYIk9aiDdnq9FoOPSn0ZG0fb90SUOPrHn79AUGbw3eMjfp44SsDFwNAZvHgi2ipP+0GChOT3SV5PDOJ6QhKmaEO5SrENMnkwmpzOHEtmzVyTHItcBH/yd4sZZ4ofikl1ibWgvbDaP+f7veYD/q3bS5P59PPFOMzma/78JS32kob8JddVm6aXhPsSlvHc9S+fSFlIX7umVzRnXnVLPgelPtMk5mvJ8a6QENbKZq0deaMCLjFTEJoSC6JMtFjM1pMqUWNT89SsZcC6s14Toh2YRSLOtKqlnU1CnBBeSfsRlPk7lVkFA4jHth2NCTgTGccDjbNMk6GNEZlnvt5tsa4hYjCtlo/FEJfPjCBBCORsGC2MFtHxAfBRiacJyjoQJGp9WNCYZu4DWvO6Pn2I9tTnqrCBZRKw+KL5EZxpHHNm/td/X9teA1e+1ZR9fquR37RQl02bRaWzXTzmW0Cm574/UT7PbM9p4kURLQtJlMvHvLZYvGrW6ulPIXcRwaTOVedwfDFzzxxLtS5PfQfdVwdakk1eT7pikaK8o4IoI0CU1N4tEUjFyPi4Y5wCTWtpbq6xiYJDRJAQsE1TKBglAS7OOYL3mMbRr9bEKAzW0g8drRHMw54YtDiaOLO92+HaFf3mmv3xSAyTMqx7bdbaOIfYiI+aLqdNW12hncyWvAdsevg5iUmigNXUQ63OWOJ6r5luL5ls9eL15LnK+fvXBfRvF7qXt0V7PQcO5f2W/dM3Lxzz7ZbAaybqpXj5k+dTrrOe75VQnu373PYqh9Di51WnlKq0i9MbPRFSamBIMMUXdGlfnYg5onkqmLXQS7lZg5p73nuapiF4z/bLPcevDzS31/TrDTFGXOM0JW6aaJuGaRwxmZ0cFaR5npEQGFYrpnnm8+ev3O9H5sOBTaNcQdqWNtGFhEC/UV5aORwSXWVqiCO2jLQPC39qSoAtRFsiAtYSDVhS0+C0KkWbzfhl8T0XzkuCeWrynX9+ur0GgJwL6Lf4q///bPk6LoFAl4TwFASr77HGLL7VdH9+nF/frLU41+L9dOEYOVVaL5znVc1pjSgliSjJlTUVHaJUvmNGVpPpu0Ciy6DFYrI+veDFxKoqTiQntZti7tq2oW1SyMF7vvz0iXl/ZNU6jAj9MBSA5Hg4MM8zBx85Hmfef/+2JHBn4em6npyw0DQNpu+Js6frHV2IyCoQgP3kMSFwteoY1tccx0lJs+KMiKVpm9KTQ9L9BBGQZORm89RlNnSl0TRiiARMYxOjerp5q8diTFKvFTtdNaZP54suh5dCKPXErKd79oMKZq7+xqumHTzVJL8EKHrOrFNBqBedGix8abHIc8ekeXnu70EufbRlvuURMHmaFa7f02u+dO3LczBORckIDKsNt+/eYUzk7stXtZwMBO/x0wjY0vbiJXK7V6kxRSQlsud45TIpbPEN89VnhJa0QCSzNa9c5ZbzQ8/v5OTdAuDoJ9k8LGZvegD+MDLvDjS9o8cwjyO7Lx85ToHGOsbDhGkbokSurq5KxlBmqHPOJX4hDYnEELi93nDdOjqEebdj3TUMbcPWzbQC8zhhrg3rTc/9wwPGWLq2Se3MY8kSyhM7m6dFOJ887PTAJVaJF9qp2liLExCrmllyArwsgJBOq7QgViKXJ95rJmt5V7kh9R4vaZiLQfhXNO35eZ5PVjg/99P9Lgv3aQro06+XdocF/zC501oC4VIHsdK/hWTflHxhZZg31nJ1fcOwuebN7Q3OWu7u7jCm4f33P3BzteHL7SeapmGaAp8/f0ISwfl0PDJOR8I8PnupL2vOKBhHoaJQYVUOVrCpfVzlBFeoX53u9GSIJR34xJfNGMby2TzPqWLh1KQJs+f+yz2NsWyalt3jllmE1WEPrmW2FrHgTMP1zQ3XN2ttaBR1VTzsD/R9r2VwzuKcJXrNXW3bhvHxnsEamtZirdBZwVoh+pnt42MR5sxSJ7JwxJb7FsFwRrER43InaRUzxpa+kQqppdrGsu4lLqQKNSdp6LIQmEXQF8GMtQNUmufW0pdDTulNOm/58kXN+ZoAngvet2bNXDr+bznPcj7KmFrnEi6RFdDCHpE1Z6liypZEszBGYB2rzZrN9TvW6zV917Pb7XDdBoPh4WHH8TiCwNt3b/n88Qu2GdQQ8jMxGrCO2HTPXu8rjO+J09TmhqF5ac9qP/HoSQp7Sh6EMwc4va8xtoying9esj3KTtM0qRClLWu+x4dH9ocDb66uud8/YnzEtA1HPCYGxBmurq/pVwPGOXz0WBQMyqRW+92OMHv6vsc6w3icGKeZzkaGxrF2lhAdo5+JweMax7of+Munz2xHX8zhIJpsYEQzjYgRa5T1PK/GZRwyCmTS4oeOq7UuCWgsptcJ6q0zKnuoJcE6d4jOwhrTOYqLUJD2enGT5W9tNZ4/kUrgT3CXb9xe09rPa81fvv3SBIEYazIykziULI1buuo9NauT1nUG1/QMq2vapsNPnmlUGlSdw8LeH3h8nEDg6uqG4AN907If94TUCU1EBfS57VUmBBGtT8x+yKIVfHqfKwIWQEePrbVn4j3VL8ipc/myFo9imQHWKIXlMRFC1w94tzuw2+1ZbQZiY7BdT9N1GBEcLd5rgF87GUeGzUB0PV5mZA50qx4Jke3DI85oI6TN1YbNZqBtDNN2jxilBTESiD5grGEYrtiPIXW71uvJ9I8YreWUbNYqu+zJeAC6cNTVMJW1obNGELMU8mYTbPEl5WTRU5ZLWyrOnRiEhbZEtfcy0TKKru/PqkqSZs2cBFD5n/kZvWiK/rLtEqPBt2rFv0p7ZsuCLJiLKVu6aYeF/aPwVOU0UiNgVOP6MDHPE92wYp4nLIZ5nhQESt3HQ/C0Tccf//jHZGUFxmlUFyaP9QvpQq80Msr+zOIXLgW66X7TZKlpDV8zZ8wzglxns8QYMdawXq9SfDMJQ6r7G9YrhrbnuN9jrJqF8/6AiZ4ZgxsG+k1P6xqMtUyHI7v7R4w1tOOK2Xuss6xWKtiN0xDK/nDgcX9gGz03XUOMM3NIxdDTzHa7J8agjZ2yr5roXBCXUvj0M73WNPHyApfBmiKcJ7mFyS/NNspTIbgI8Oib9KlNC0U80ahZSKmPT9qz/p18qvKZWfbmwrP8lu05pPOSf3oJ+b/03Uv7/dJrqgu96/lXGCPMgoNkl+zD+x9oXMP9/V3p1yNC1SjKA4Y5jszTRMmOk8Wtwbx87S9nCEWVcFMb6+nhSznxgh6W1u0pYQAEKy5fR37qiFkmX9a6p26PmlvOWtrGISGAWXyotjH4YHjcPtB1HX57ZB6PzFOg6QZu320ICNvdzLR9oLOOLw8PKcYZcOaBprN8+NU7hqFTXRGF3W7P/n7L3fbA/eMDN1e3fPd2oL9es7vb87g9cJxmZh+ZU7/KfL82+RIqrF7T+SQvbsu15/sTspm7CJ0k0K20WzQnslS2ehLVApfPYcqAZ9O1EuzKB+UXCUUSJHO2/5lJbP826/TC7/5/sV+Op+v2WgJF2usEC1EXLmJTyGwcRyaZ8dOILWmaCyOFsZLmgs6ttnHLc8cqPUsMhZ/p0vY6E0IGeGz2GLMGzQK43KhePMVHRSjUJnmzxi4TqBqUGoRYwJPFFItxaQvgnOVqs2E1DDw+bNnvjxy2IzORN5sNpul4fDjyT//yr5ig9ZWTRK66jvdvb4kxsL6+wbmudNnKHbz3hyOP2x2PU6C3jpvvfsV+nNiFA3/5es88zYTo8SFgjEWMtoSQWCO1MTlrcCmRIK/atVmnZFynuuylKVfMVFjS9+rxLL+5IMVPhPNs/9e2C/OXIrTVczQ8tZi+KVHilev4683pX6ZZn5570bCZR+l4OKrFCIoVJFmorccgOkeapqHgAGmAJLHXX1x90/ZqVUpMNvjJBSerTFPqlhWmQNQigCXGgLWnLcXVpl8m3nNwujGn7Gb1Prko2llonKHtOsI6cNiN/Pkvj3z6cs/D/RYTPNM40q3WtEbbkh+PB959eMsUJnz0CD3b7Y7rqzX+cGC73SMibPqWVQuH8cj99sDnxx3b44iRiCFqh2hIrRFSq/RsRlblXC+BICXBQlu/UlzNsks2cMtonYzbufRmgc0dv17b6kv7lsSEb/08n++5968d90v8z5fGdxmg56/l0u+eHlv2KI2b1eDRckGf3BfrXKpsskU5OdsuC7FUtbhIWshf5mV6tdh6QbTq2rg6BqkhBZeycCxGM3fCzOFwZLUaaJomkWBVgFKqJT4ZlGQ+kEELzkITACInwt402qcEt2Y7emyYcLZHgsfEtGDEmevNhmm/xw4dj3eP/PDb72msYZ5m5mni4SGwe9wTRWiaDjGRYbjmy5ctw7pX8EoCJlGKWFJbexOZTvpNLtdZaDQkAyl2Ea88AbMfUoVYFpMqLXon1kU+Lp/mdEKdaOjqii4F5Kv8rdPvqgXz/HV9j6YsHqe3fn6u8rf42YtP+5wmfW5he04Ynxd48+TS9J7Oigde5Buq3C9UYYlJHMJJuGJQYbOuPfmdmAroMyiXraRcbvjSQvWicGbwxRgIISe4U1S7sPxYVtf5vXWGzWZVGrPG6MGmGF7dhNUYZTJBHe8FnUxDWj2oPGHVnVWt2jjHarPCb/eEEFmte3xqU98Yy6ZrcUaY/UxsGwJCNOCDx8/ap2ToVXvebw9MUff1IfIvv/83+sTGd39/rwNqdMCtgRANwfsTCkj9p+0WLKe+WfYp64lX59AuEyUl99uUf1uju09M0tME9nroVCDOzFZTTdSKKFy/WtyIy0KTT53F2hSqy/KjLOc/10/18mNfMUvPNehrZuxzCQn5wuVsDTql0FyqnZbf0+NNtaDocCvtab1/npsCRCsp00vnwmL5LIuIRkGeZ8PP26uas0ZnJWr6noRAqC9cfxUEotH9rUoqTWPLpFyEfdGgC7RPanpUTvjkWvJkzI0VdPJpU6Gb6zXTOONax59//ITtG8QHAkLbD9y8vyWI0DvH7e0NQ9/S9x1+DkzHmce7B4IPiPfE4BmMlocdDgFGq4ntTUeYR6L3BQrXlL1YzFtj0MSG+qFVD+Apz07mwzm9d13QaqEzTyZDmSDl+WctkT+rTeJlDC/5gxmsWEQqzWZjykRbtkUbFSwiPQspEzP9XiUUNUb13DOut0s+67cI6sVN8jkuuVCn7PW1hVBakizylfZ9RtNKyEhAma8g5IzAok3TOMkLY/CycIZUVG0Esbqa5BicIKVTVL7getxDEE1MsObiwNTaQAX5qYlG+hYu+ywxRpw1OGtprONXv3qHMYbr9Rofl+RzZy1D3yZzVOj7Hucso/e0neHrlz3jOIOzOCv/b3tn0hxJjuX33wPcPSLIZGZWZvU6o2mTdNO3kOmm73+RSaaeMU2XdWVVFteI8AXA0+EB8IVBsrov04eEWRWTDA93OIC3/d/G+6uOsZ9wTmjxXF8deDwPxBQJ40gCQkbn7GetlW5Amc7zXL7TWi185bMi7dKWGJfXFpUygRaNJFm1w0ywmX88OwCXDn3+xX7U70hBd6iF2qSUjtHFdySDWQUdTvU+MwZR7rnWjv4eQvt1aOt8rYEzwFagXBhbAi1MuKq2C5ByKWnLf4UhLrUhVoSfuBT3fGm84edMkBI+I5kGfMwTKYd/5eOcX9NUM1NYFwpP4cgz+SkzgW4jh+rBytXmCycvjZWigheHc55dzvf01y0iXV64orIwG/QomiI776z/SOP43b/8nsaDWHQ+fR8YppEff7zldBo5NAMBR5o6pinnW0qicZlEUsqobVzYFFsAZ+akUiufS60e4baqjmYEVwoMXypJFJscE00yc/F5v1/jyfO4dEBK4EG5oYjAAoGfFeCZAFe2XY6DLoehdN9ehDKgOUBjlqz2j4pwFIKW+VaymWqSmQEVtbKORW/T+ez9OmbwnGnm9Y1FNc37tYrusb1JcaHVXKi5q5kpi5LV35fHG2ptfvu0kIqzCl4XrUZYwCKCJNtCOYh4Dr63CZndqrUAVlGFih3L5llFratSk5K2tuWEuuZk+bsiM4dblsQOIfH+am8+1dZxe//Ev/75R/7zn37L58/vmcYJH2659jfc9Wdil+v4xLDqMVlS0WpkjYr5cxfctbx9UX1RrYdPVXElE2OxiWXhUlpkjSwZYeXsZVNeP3x/6+Escze+svCvLqTs8nuyse3IUyqaU7lGpPRIWbORVLiOlMDDZRZUvl/Ody1SWcmNhutRkGdzeE1i/po1kbzYl++j9d3mc7oGe5bn2545FxZ4abwRW1tiaE2lWVd6h2JnoEvbtGzAcsLbF13YTymRxD0jsfLOZU+qRZS/WwpTF3Bo+ZStCrglVoVcshP2u47DrqUfJp4eB/7P//0BlYZfbnuOp8DT00gUn8P1JGe1GzBmLdwXlc3VJPIKoWS9+UX6izjTTJaEmJcziWSAzJibzdSYwsqOqVrG0saZ33n77ItLvJnbRUlapf5SRc0MZbtzs3B8eYgYaemsCRSNsFapE3J6oreki+WZIc7atmoOh2Rej4XUfuld/5Yx26PrtV3fdya6tTmT99VR0wlted7InuFXVUIwoxgBFes0ZorhIl2scA4kG9B5oYqZuYrXy7qruEogqgVMKb9z8d/lUbYxkiXPc242C95ZNVtfsFjkUooyJL5+ubWK6gTO40jbNUjuVXkO1kPTmbjm+upA03q8bxCUcRgYx5G+HwjR0sfatkEw9b/rWkIITFOgzWlsKajZiLnanrjccKjMr0iAFyTZBkNavftb45J9v2Umq1tVyTX3RV3akzOmkAlZqf7aYtYs51za3M/bMyP+5X5mgnjEyfxRPlz2WUkQmBlXBcjWb7t4znYNyvUvq71LN8rzvxdiXKiwzB3Y6lcWWERaEOhr442UMeNI1iJA6mHWmeoyTiA5ORj7rKg8zpn6mQmscou8wKVjE0XVW1qsOm/QM8nDrE5dlJhV4i9Vn/nuVRVyDhFHCAPH45FxGPj9bz6zv+pQVfZtR0Pk6Xi25dY9wzDyuz984nefPtI2Hu88KgYOHY8W4hcSnM5nrq+vaLIguLo6MI4jqLkfzuPIw/0Dp9MZnBCiMpwHGCcLnsbg+rLUqmpB8UmRBSg3v1VZ97er7b016mHMtFbtuWqeLMXj5uBXPVfrHiYt0jHPsXJPqT9US6vJQuxudb+i/i419/p2CwYuRR3egF5Fha5fWf6+wgNeXrftWVxrMYVjrTW8KggWDG3587Xx66rvZcJRc3YuXm6RMRELkc4vkHKwdwGMit+vFGFme7heWIjKCqrus1ZbLw2RdYROOUZlQUXmOrApKVdXV/zLn3a41tE6oQAKXee5vrmmcc7y88RxuGrpfJYIqiCepMr76yt++znkeRVOnyOk3OzfVWAYE8PQIyqM48D945mf7u65v3tkHEemaSKmaVXmUsntIkpru/y3NUPfcne/+lOZgbhZAi2zYCoxbtW3cu1iH7R8p+7gHHNd7UnVmbFf2NuXxvL5yzDH2vBo9a6ztNyq9r8G2a1rUv6wAHpEpPb+uXyfja9SC9Nca3xVyxDJ6YU8F+Wb8Tpau0BjU4wW4LtKvNaqmpbJbNOAvPcVaZ2RV7NhdcFlymZfchsU4pyjlZ5fNy8W1d4tC1el54pTFxVIaBsPrX0npdz8KCZSUg67ll3nkASH3RW4ZNrCojKDakSSxQs3bYOKrxk9ToM1iEVQZ7Z4jInDztM1OwOT2h23xxO/+/4zf/jt9+ATP3255aef7vLhdnjnSZhPVaNVnJ+mKSfuhtqUtrz6rCVsiAJm1bEc6IolLCWHFlrN+5w/L9qMZGVVZ0Kuyo9gpW14rvWs7bHn45LUL/ef97yI9IUUvXDPZ0S4UdsLsZRpr3mcfZZSWoBeppGt7zMzgWfg34LZFUG0lPxv2SBvEqctsBFTCTDQFNGMsr7lWiklQSRvrnNWQcGM5EVH6q1evyW+DeGX58yLtl5X+2zJZVe3nm+7ULlVM+yfbUtLVcvtD7KJlKIieJsfRUJkFNjN4I0vD1zUrAlJmQIMY0Sahuk8oQK7Xcd//affIV5onCOp8sdPH/nh41fwztoIRquP1A8T//pv/4+n00jkRNO2WdW1h4gacynVI0ohMwDftgZgoQynMylZehPOWeXAlIi5ea9otMOPZjzQY1E1ypYOVodTS5V7rerl8tCuQDknuWWigWCa90MVvJ81tJp7ucQQ1JhwVaYWTYDXZ2Gewxq4mQ+LMa21lLNt1Nzl7nkUlL1P0YiXz1xoSIvzXJhiOWMLq/TF8brNGXMJDlkcvrLAMXMdmRdAssjWmC58Zn8quW8UiZbfMlUCNju22vrPuOZMnJUZFGa6+GzFsZ8t63LM7Hf5rPL7OAVO/ZTb9SlXh462mTlgqWXmt0j2ZoxToJ8Sf/3xJ1x74JfbOzrvefduzx9+85Fd64l5rt4Ju67jv/zpD5yHwPF04sf7B376tx94OJ7wznE+9whzPSQUUgj4ZoeKw7cdbdvSNQ3jNCIiDONEyLGevt3TOHIooqNtLSZUk7mJGmc4wziO1kmttIAo7QdJlo2hBewxsNAI1HF11eGyLR1jAl2GKBaXieJ9S9KE9w6XvdeGwG4Lmi21pXK2nmfjvOaamKXuc6G1ZDB2r1nzoggofp1K/up4JqJfHm8QZ6wqDLli3dJXGJWalbL0Rdl3qCqQvVT5T+ZA7i1HVal9QJfeyK3tsO4LOdtCyHxtbZPOvBiXWiGubVHqvFQtY6AfBo6niTEYUxHXgk4UcMPQ25kLXmIOpVGRhMA//f4z+/2eD+9aOu/x3tF6q8TnxOSDiuC95Nq3Ex9vDrSN59PNL3epRgAAHiVJREFUgSlG2rbjLz98ISqMIXI6D5YGd+iI0danbVtSMuApRpOGcZxyXGguaJWsX0vMrrJd17HrOk5jT9c1dE2Lc2frzaKJxjdoirStxzshhciUrJSMqmaJa4kPnz5/YL/ruH04WWKEGKE756wsTH7m8dQzxcjH99c0uaJDCIFhnHg6HlctFh3rc0BOci8V85drvjSB5n1dnAXmKJ1LQI8smHZJ9yrnasm8X5PUda6yumD+7HWt9u0CX1oQQ1W8MwIi180hE6PpgpYqo2JopMq6P6amzHWT4tsGL87UEjcf6KJairic6G1/n6YRgG7X1vv5AsWrYXjlZY0nZMd9VisqZF2NIvu7LfsWqCj7YarToevo2g4FwhQ5n3v27T7nXzrE5+7OGvHO13vEpLTOW04fQtc0HLoWzSUvv7s5LBC8GWE0UCWr7SK8u9rjgO6m4fOHdwBElO/eH1BMMv37j1+N+FPk3c07HNbUd5gC949njscz4zjiW4/PEUdDiNze3pM0sd8fcM7RtZ7v3h3Y7Xe5AHfD3d0DP99aYydE8J2naVsasTYTkhL//E9/xGHZSeOUeHh8omk62mbHbz8fCHFC1bp8a7LyMV3TIE64eWeSe79vKvE23vP1/oFxSngfa2Eyc0stJaRJtcO+I4SJpFYRw4kwhSmr+zkgXZU2N6tKalpQKb5XAkNKGm4i4J2vGMtu17DzDd1uR98PjGFiGKbccCo8I9ziDbBMFTMzcLlQXhLEKXETpPA3E2dSaw8gkFspGEFIRfvmM6+YLWN1WO2nVic7BgBhhFwADOccTmdVWfD0/RkwNcu4IZkrR9LDEe8dh6sdvmnRmNh1nVXFU629NWsOeCFwWRrnGxvhGffS+mcRK/qUYUnafUvb+GzrWIv12/snuqbh6uaQH6mMY8R5x7Sw0SqTisXGZWYmMDMQ5r9RgDeRWuZEBFrxyL4zkEw8767+yBgCniJ9be4pdny+2RPje8DhvCLS8L///Be+fP1qzEU8/TjSeM/N1Z7P373n9v4RcY7b21sej31uNWESOeEJcaoIpgr89eevXHUt14c9d49P9OPEw6k3TUOEkBl5m4FFRa3sJ8q7w4GuaxifJnzjGfqe83mgH0cmwPlmod4qeGfuKWcM6N3+wG7XcTwe6Se1iv/ecX//xBgslDLGSONg37bsD3sAurZlHK3reEqBtmtxvqHve0IQ9rsdnW8QEd5dXTHFgEe53nc8Ho84lGEKXB9u8jMS0zRWZh+S4jTStQ1Xhz2qwjAMDOOIAP3QA2qhsX8PcaIuG9u+JjhXl4C4Amhl15fU/pIFLJK0EO0LwzlownlnKlUsUSKmiozjhHOevh8rbN51O/pzbwvfNGiy0pgOIYwTZPsILYCT1R9ioa445xa9WoqdtLZji/oEgmSVKanmYthKiFY7KAXhdDxye3ePJhhCYnfbcLXb5Y0KkJT91YGbjzeW+iViWS9Veud5iaz4wxK5ToAXsdIf1Z5TEjkYQq3aXiOKb9yimoKvWo96h2sawhSZpsRfvnzh3378hYSv8b2CFTz+8vMdD49PBHVM02SgTKlAj9bedSKCZneDE+V46jmdR77cHTNoZkc0BAOiHEBKjE1r9Z5CQiSS2o6nKdCS2Dc7hiFw6kemKeCl4fP7G3zj0TQR44SIqcQfrq8Qgat9RyOmdXx6f1XX7uHphHfWjsM6A3ga59m1LSlFmqala5z1WBXhNAzsdx3H44nz0yO//fSJXduwa1umaWS3a8Ht0KBEEvv93lR1R65fFRmGiXEceTr3hBhpm5b9rqNtHH0/8vj0xIduT9ztiJrY71pCjOy6HS+NV4nzx7/+TMISmlM0HcA5x/X1gWkKtd+Id46YTH1zzpmvMxjHKuFYTqSCJsl2NUvhOVIopmjAQPGL5j4qtpnQtR2g1sBWBZfsvk5Mkvu2ySirZvcFOG/3MHeEbOzTuZ5nKfE6DKMRaIpcHQ4WsQOIaxj7gZgLgx2fBk6nwDj1hCTcPSYaEYRE44R92/LweKSfJm7eX7HrOryDEumtmKvGZU3EZXU4BatNlBRc2zClxOl4JiZoW1ObDZnMqXneEXN1wOPxZDbmaMzp1I/8fH/meOoRnKnUxUQhmWmCQMxS2TnGaSJmO68fozVXyuxja/MV95jdJZk5kdysrheGh4A0lhQcYwZjXAYOHb71RNSiq0bLj40kZBz4w81HPn/4lLEIZwhvmGgaIz4LIgkM48jjOdB0noSjazw373bEaNhDI4629SDFC2E+7OPxTNd4GhFu3l3TdR1OreaP98J+1xFiJITI6dhzDiOHXcf1YUfnXZbOSswJIu8OB7zP9W7VUiy990xeeAxWeU8xrCKGSGr+zkoIwxSIKD7OETcKDCHHNmY7pDiZU7LFKmhszPmfYIRWwrAM9oA5zcg2PmYwqWsa5giejACjHPvRNoSZ2J1zOG+LrzLQOmvnTrYBSngcmMvCez8/V0pwdQF2mN0iTcswTuaWADQGuq6BrkGTMu4T0g/0AzgvhJgIKeEETiFxn22Sh37k5v7A+3dXHK5MVSr5n5GEy3ZU4xzDFNAQObRNdkVAHyJ//eWex/PArrG6tZNGpinQeI9zMI3B1qFp+O7je3ZZ2nnx/O67G6abAzFGjv3AOEVCEmIA34hFOKkSkqlgvmkY+55xGmnF4WmJuZxnDAFd5Ny6jC9435A0G221hs0C5VwAhTNhmxYwaiImMwNQaLuGOEQjluuO333/HmKka1uct9q+IXnCFBEH3huwNQW4ezxyHEY+3Fzx4frAzrccpx7fevZdm5Fpb8Ckc9zeHwkJGuf45eGJUz/w4f07Pr2/RnKY4tPxTEqWIOEbz8F17NsWkjLGkX4I/HT3yJQiu6ZB1GzLrmvI+VDGHBKcJ6s9ZRqbaaFT3/+9xDmCCKGgknmEEAxNlRl5sv9ypYSlW0FKdMVsZ6XqhVlU1M7X2HOnGRXOKLEBK9ZjAlUr7CuCl1y7RZVRI603FaaWOzQuQts0uPOEF6luAnGWd2oAk6mQxuWd/dsJnfcIJdRwlgZDSMRkPT7NV7nDidA6YZoCY0x4Ea66hmmK/PjzPS5XE/TOtIWYc0FPw4TiGHPjG2dvmoELsd6hqqRga5kySDeGUFfNIThVHh+O+IMBWFb/SWjFoQla1yBdAyFX0RdovYFshImu6/jn336HxkCYJhQhZqQZgdPpZH1Jsf2LMRpR4nh6OnIe+oqgO2ftLmKUHPBNBUxESmytqyhv61reHTp+++kjd09n3l3v+cP3n4jTSMTTNJ5+DNzePxIna2mw37U0zlvxbsyOvH86c3f/xPGp53yz59PNNUSrzIE64jCizooBxBAIIbK/PnC17/j4/pquczRe0CgcTyckRKYQDTzKGMY4DLTi6Jzw+HTm1A8ZGJwIwTSDMftvQwxmq0ertDeDRunVmrVvEqeT0uh24e+pToNUCTahSHJYRjlWcU8uP1hV5ywDSTkM1v5GlhZKtsXEZdTQFsKpEsVCC1JKBJRIhGBADN4xxphdMka8gtA5QaPSx97sUpFazrAwFZeTtktIYnF8t43L7oPW1M6UGKOVxwy5Lm0M5m9MORa5SFFV5U4kX5fXU3PUlCoxGZoXc/2gap5rSRMrflzTBkoNJtNEU5VKTjx4McaEcvt0pvUmtZJGxpCYpkQ/ZkQTK1Lm24bRRVOxSaQ+8fMv9/z++/d8OHQWxiYuR4QlpukGVeH28UjXeENfc9LC4+M7kmiep4IkxlG5vTvRh4EpKh/3VwzjxBgjoZRdzWfher/jw4cbnvqRUz8wjIG7+xOQuN7t6FrPuR/px9Fs8pRohpbGN/TDiDg4DhO7zlDtDzcHxilyPo94geNx4OCEnTMk/X6KdM4BnuE0ABHfejQKp3NPnCLDMDLFSB+MwBqVTOQR6TqmEDkNAyJq5WqkWg2Mcai2esEKjKkVfyqbEi9/I3HWsiJZHd3GDBYARYWcx7kmwuW1y4mhc3PYMnyWmvnO2RGdOW6IRAf7tjNUWBN772hbKwbdCIh4phBzqcuAbxqGXDokaGKKicaV4ksWjtbkfpgxo7GpEIwIDdanRcbccuE8UJrlxgyVhxizmjIDTzFa4aeYm924/N7FXisRL1t/2drRvlybHJmVZHVtZZhOcJKT4h08HAeD+J1D8blNhu1TmxHeECbads/3N1d45+jaHYKpiGEa6R/PNPsdqlNmIJoRW2F/6Ph4fYVvrSGww9Ti3Xc3iIN+Sjw+HXl3/ZH3V8r19TXHp4HzeTDXgthaFzSx+CdHJ3z95ZZ+GGugvHOOxpmq9XhMDOPAOE04Z644HYPZz95Mqfe7lpurG1oRmCI+u/8aJ4wxcR4n7kLI3d/sRPdToMkJEL88Hu2zlPCYXd/HUH2TJBNPjXechpGUO4WZhmd7UzoArGKAZX7t2Q7nRQFWxps1hOxA1MdUuWmO2gIKUF/gpQiKZ45ZZmI1es31iereGQGV1gUxwin2VRXoNTKmiBeHxyPOQII4BlQiMkVDLbEyJQYaDFkqSkY3FZeUKR9sa3RrB+OUxiq5VqUmxULTQprVlPJf9RUt16aoMVmKk7amwExo838bjirFDDAny4rxqamvQwhM04CK1dAVzA2URDjsWq67jsNux75tTE1Ts3EDqTiEUU1416BO+Xo84UVo28aIpMkgRgInEaLSNBZEURPBNdE18PHDFQ9391wdDpxPZ36+faQfh1pnqYY8JgvAkHxf8ULb7ephHvqBKaYKUEUsk3OaJtwiosR7OLQNuxZkGggqnJLh3VdtZyi52nseGjN3jqeegCVzjClxHgJ97mFSkPCS6ihZSwkRSyEU5qLqmSCX5+C1KKKiabxGK2W8Spy+xMjO9n0+dDWRHwNoTC2Mpcq52CFaGJPzCVu4Eawubao3VA1V5YQcf5jTvyQTrpaFRsiszNQJ840gC3WVMoMsrUoYoiDIaJ2mRI34Ul600qezcD9bSPJ8S5U9qYHmKSZUY2VOihFMaS1YVqBGM2X9tZTCLOupee756s2Omi0suer9ktFJWS8R8wHiaLuOfdfSOEdUa4dIStWODZp4OPeMMdJ4zxCt/4cDrnZZfXYe53PMq8I0GpE0bswlR5XdvuOw2zOFYMCMmCrdtS2fPr7jPERuH86oKG3XMo3B0H8vHNoditAPWRp6xxgmO+wxUps9LSoGpJRIovgFcFcizlSV4zAxRmUMkX6aaBvPSQZzx6jFDXsneIHTMDKpxTKHFIkxIKK5BGomoFj2tBwkMxOmsDjnOidsWDBKmrdQCn3k85M0E2ehjBKyenm8SpzvrvaVOFlQOxlhLNGxXgwUiYuiWmVKCjWZuBzoWQoXSWH3NEdEdjQsDrZdX8oRUl5rwTRK0HXNaJtVxrowC2lUVnsK85/K9QI6ji+uyTZ0y1oA2qGfn+HyO5f1mt95NSny/KtkftkGWYYwrkIDU8rZLrYGQQNjEE59T+MtG6bxHofw2E/4pslMx8akkZgUdLLmTSlSMPTOexqXA+lzdI1zHnX23H6MnPonQA04886iX1IihMQU4WrX8O7QAcIYJqw/ZeJ4GogpsWtb9m1H6y0h4uHJage7xoC1EKaVCQAQXdZCioblLG649QV09Ij3jOee3nv2YhFdTiyGOyhEEcYQOU8x132aQwFLoIT53U2D8t6ij0pQQ1FbF6k9F2nlUmqY8Px8XhqvEmfnc0SQYFUQkApqWDu09eGJyc/Z78kILpbMkNkmNgLM0qqogzEpUTN3rKUDN3ZWtkPKoSwoSmmlV152jiiROrdLYx3oTH1WucdLY2srbr9fNmf5+3IbVkSm6816bWzjNosKtc3WKT+TJrx4+mg2shdw01TXqiYylOAC5xhGkyAOQWJEWp9BOU/jBdoWh5Bb4NB4C8NsmmZlezXe0zSwa98h5NA19nz5esuXn7/Sh4g4R+vgumu5/viB4+mMaoAUOfcW8hcWAfB1/Qryv1nPMVcb8xmtT2pnth8GQ4jFEcNohcjblr7v87zKSFWLOux2TGHgfD4vnsOzudjGFDzm7TPzlp25HK/H1kqq0smpM+mYJ+EkJ72oObcdnsa5XEJTq43UFKieigHMP/OLpfpes8qraqhnDOYHKzKlEoNQe20mTcRk6Vwpf7Y9qM/1e/v7Mr2tNNaFdde01cHY2MwlrU7k0jNeWNfNNS/9vnzWcwawvv6STQ+mPnvvaZuWrmtBhCnGHIxucaxXhz3eN3z56Qtfnx5xKJ8/feLdhw8c9tZ+8fF0ZHe45rDfGbqd+7pMcaJxOdEh5WwdATCXRBDPl19uOZ6OPDw8cfd0op8CLquATeM5noQvd0947wkVgCk61AbtyOdkmXAlOTVIyIH82X1XgLd5NSDGiYQFtMSQrFBbxT1mze4pTLlT+cz4pCR/MAsaO8c6P2CzH4X5/j3j9QJfNVyL2lHL5jAboWbDFXerAr7C/YhYuFv5TpEYfj58RfSbL9EtllzpiqRdGdmZa2qac/iwPUvJ8kunaAEAu65lTJFxiIzDiG98DnEjMwWTvm3bmuKchC5LEsnaQaxpc4WZqNk4zuzOEGbpFSVvXC1hWTZ8zoCw3rhz0D41L1brT7ytR07toayK9z6rcb581WJGd7sa/ZSy7dq1DV4MBW+8ZxgnHsaRMbeoA5hC4nh8yqFxUp8UNfLl55/48vXnemBddjOlkqdZrCbv+Zc//p4/fv8dV/uG//XnH7i9f+BPf/pP/PXHL/z09SvOl++aelrKoFpguWa/bSAUl7jWo4KmpXo2m0MLQ9DWe8Ec18yW+szSsFaAKbtkSo3elHT13frIbJZYitxsT9ZZpRnXWA3VvIdreVpbAdbfX2bm8tqH//1//E8FsZDKIuaAEgxvpFJKEuaFq6k6Nn2v+TAuJ1SIt0gvzK85W0IlX/K51CtIXy2CJY4wTcQY6drO+rSkxC/3D3y9vZ9fNKvFih3qubiYoY6/+fSB1jtc09IPFgjunc99X1JtrKooAfO/poVv08AAzUDZepOLz5T8ns4Zw4rZf1aAgRgNsRZgmIZsU1nwd5HM3jc0OYyv3M/rDCwV5BCR3FhnHv0UmKJ15H56euLp6SmrtmW+MyBVieOZNJ5H+XuMkdY3KImklig9hQnR2U++vcezc1dKpWybMOmyltBlc+I1n3q9vW6fuU3Qf27erL7M+tftE1/TbDZXskXj77/++8UXeD0IoejSYjMqef7mh3MIs0/Oon7E0C6ZFVRbt7RZwFkqFHtMAS/z50a7CxBEZrfDMAZCTNlmVe4eHjme+yzMnbXMjkaKpbasZqO3EEdx6IdkkT5fvt4xTaMhpvk5v/n4HdeHHTFF2rahax1d7lQ2qrI77Nn5OdIlpUSIczFpVevLUl65/qOuhRW+NncLhGB+06939zweT4QcfH99uDZQRgQfrUVEk6sd7NqOc/arnvszXduZBI1Z3XamAoYQiNNECBNTDiI3oozZzBBU48IeZl635c/MMSph5P9NUdFk3w+TgjiEmEM6NW+ugwWRrAGuxf1YPjtu1m95+Iur6zK2sJyjPvv+BdvxwpivWc5Z+RVffeF+L93/+XjTz6marCAXUoOzixSVhcSUvALGOY2z1yruMxuu9sNS2uRQ2Mz9SxSMcDqPnPuhqoaqSkzKqR94OhuK5rIDughn1dxKb9aLKMpwnW+eZxlWcCnVFCcFEo6vd3fcPcxS3znhcDhwOvX048jNu2uLs8zM57Dfc311ZSGBmTBiyigzmonQZfW1VBZQFM/PX78ypkg/9AzjhDhHk6xO0ak3UGLLsZumYd/tsosH+vMZ3QWapmUYR9MSMgJ5Pp8pbRKMccTMJ4xJpjTvyepQF9VMi9myHQLJMmWKii753CjzfMvf8sMrKRaVUHVRGeOVUey4Z3Mpml39bCHJyBmzFwl8dYPN/5/PxtZn/n1bof7Z/d6g4tcAoleJMyg0AuTg5rZpmKaA5s2agkmURjxWVic737NNdPfwwP3TkbZpUE1cH3Z8/u47EMso0JxhLyrV7kPheD7zy909/ZQYxmCB7KV4cyYe56m2jCwO0LxkC5WmLmqxXWcmoElJi3BDc68JLld5CTGX6jKdkWF8zBqB8HQ88ZCsMY0IGQVsSGkGmmK0pN9PHz/w+eMNjW8JKdGfLA718enI8TTQj8HcGFltr0XRVFeqXnk2WIzzU5jdQQgM42C5gsVnWghwYessJckstSTv66L0Bzozt2IjPztYWjWlQnTLSLLKjmuBv/pgoNjIa/S1Pv/CwS5nrCa3lkXRmRU8l74zmb1KLMuFme22y9fl5166xJ7xemje+trL4/UghFzQawyR269fOZ76vJi5lXyuQZOmQOOFjx8/0LUtj8ezpS5pqggeIvzyeOLHX56QFNnvdnz/+SNd23Hue25/+JHjkNVK8hqlmIMbcoB68W9RbMZZxViVJbmgsjxTz1afy+pfS7WnwmC6iOBZ3LPYgiLCFCLDNJGQDNGbr05EeBq+8sOXrwiC71piNN9ZmEZCmHBNC4W5bRDh7WHbvsMy0qgQYIqhLMVsX78gl+r3dI7Xfb4eL33v8ijzvCQZbOfk+V5cuF0pJzIHtpTrdEGc5T5rsGU5l0sur9ek1nJtX37PIqmff+/itciz+/79xKmWhvOXH7/Qth0l4atW4wu2QAkh4Pnplwca55hISMrZFQVhFHAoUxhRFcZTz8P5r1S2K4LTiObqC4hFnMRMrOISMZVCU2sbQ2sFQFgSWn358v6LvbiEypkqntXO8qWserM4SIoxJsk+Pe8k564Cyd5To+K8lC2BOBEAvCOcDHCaxoTmagQpTBQlaQX/rw7Hmk8XYVbCxow5zeqyd8Ykal5QTdmbiaE0ZMoQnNmrC6XzrQP02ti6gmbtZI6UyirPPKu0lNwF3wDd+NWNPstBL/9eM663XFFr0IfVHm990fParw9T2d96ycur8dqHl7/x2sL/85/+mzrvFxKJPOH5eStF8gK33P5b88bEFIlSyQzjKrkLsJQIkLkGzHIzuMDxLOwv/901813F1YJi+LXfa8tRVddtDcsj04LAq+SUNaKpaU6N+jWNUbejoqUya2nbNZVit2bb0GJALzMak5NL1UqoLb1kfVAvRR6V+/yt77Da75Xq/NbYFItcvNeKyMq93ULN1svEeOn3Z0Qqy7suX+Y1e/LCxRe0tV/zXVXl8faHi5T7quRs267Gk25fSnVOhi7TW35eFmKpbpbfy3DLA68RUebE7eXiZslVCXUxl6XaZzZeJCQrBelzYm1xZUjOoCjf04VELJK3dAxbvgurBc68stb0Ke85q7xrQGVxn8W4RBCFSxdJUEAVk96Lw765fjuKur35Iy9x71+lti5Fw2Luy2CNVSvIxb68pcIt1fHleEklBqyKwmaehdhc7sOjG1XykvQkf2d7ptgASC+NtVbz9nUvSfNL4/WUsRRz7do5xWsOGzN7U8ib4hwu9wEpxLkML7uo3y8k8Mzty31nAl23JFh8Let1qvP8KjdUxdLL7H6qCrGEmM2HqHgCVUoxsLV0BSMKzYRbI0ZKHGZ1dNsLyeKljHYuc9PtYdBMjLMNM7/pWroxMyleAiPK8m4YQvnbhYPya0YNPChvKaz2dmsPb9sDXhrPpG3+mfRltbSiDVummYksaW79sZGUSyaxEip/g3Zweay//9K7vqShvDReR2unkAlnOQGt8ZNgi5hynKRlW80ZJXkmdUKFAF2umGDt/2yh3aLKnf2/QPez4jvbQnaZppSLi60Pm1zYWFUlxQmQ6uoRybV3mSN0yAnDxbVQApoNubXcTVBIEcnNVJummcHRuu6ZfN5Q7crcqhTMTGLuLr1ck7wypZK1XiD9IrGWYrveZe758RYgcmGmM2PQxd4oVfqwkIDz+26l0JrRzNfO77jUxZSlBlVnsvpZX11LxE9utrWU9pDXrWAL+d71s8Vsf4UaO0d9Fdv/OYG+GXjxxng9thbzha3veUEdg9qlV7dgxqX7qgU0FwROlRykPGtfku2pKn3r6hVpWW+2um9VpxaHu1QasGsMkZxD4Vx1vNj8A6ob9Uxm+xfVavOVa6ZcVqWowxsQ8eL7b9fQvlf9Dc/UYV2engXRX7j79kxeHL8GLVyNdGnOC6ahsHQfbK3E5QG+bNtu72pvktK8VxXh3bbsql8yRrB69vIdReZ+oitzaH2rt1jW9j2eLeEFQv17xquA0LfxbXwb/3Hj9QYf38a38W38h41vxPltfBv/oOMbcX4b38Y/6PhGnN/Gt/EPOr4R57fxbfyDjm/E+W18G/+g4/8D0GvbL6ADpKAAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "空の写真に対して雨雲を認識してこれから雨が降ると予測できた"
      ],
      "metadata": {
        "id": "ikPkQhrG1wj_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##振り返り\n",
        "\n",
        "精度は8割程度だった。当てずっぽうで当てるよりかは精度は高いが、2値分類なのに精度があまり上がらなかった理由としては以下が考えられる。\n",
        "スクレイピングにはとても時間がかかるので、学習データがそもそも大幅に不足している。\n",
        "データの前処理を一切行っていないので、画像を見てみると関係ない画像も一部含まれている。\n",
        "\n",
        "また、Fine-tuningや転移学習を用いればさらなる精度向上が見込まれるとは思うが、今回はあくまで教材コードを主に使うという条件があったため、CNNで一から学習させることにしました\n"
      ],
      "metadata": {
        "id": "X193IWE4vJQ-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "参考にしたサイト：\n",
        "https://qiita.com/ku_a_i/items/7f71933f6b71cf30a3a8\n",
        "\n",
        "検証写真：https://www.pakutaso.com/20210202032post-33248.html"
      ],
      "metadata": {
        "id": "KZSaRjI6wMpT"
      }
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    },
    "colab": {
      "provenance": []
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}