Viral_Prediction.ipynb

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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "include_colab_link": true
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    "interpreter": {
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    "language_info": {
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        "name": "ipython",
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      "file_extension": ".py",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/helloworld53/projects/blob/main/Viral_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ccDPllcwTVza"
      },
      "source": [
        "# Mini-Project 2: Viral prediction\n",
        "\n",
        "Date: 2021.11.15\n",
        "\n",
        "Solution author: © 2021 Karan Karnik  \n",
        "Template author: © 2021 [Mantas Lukoševičius](https://mantas.info/)\n",
        "\n",
        "This notebook is part of an individual project for [KTU IF](https://fi.ktu.edu/)  [P176M010 Advanced Machine Learning](https://admissions.ktu.edu/programme/m-software-engineering/#P176M010) class led by [M. Lukoševičius](http://mantas.info/).\n",
        "\n",
        "You should only eddit this notebook where it is allowed, comment on all of your improvements in markdown cells and in your presentation.\n",
        "\n",
        "Here we analyze open COVID-19 data from https://ourworldindata.org/coronavirus and try to predict new cases in the world for one week into the future."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9jPQ8KERzqWb"
      },
      "source": [
        "## Load the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ALqSDD7UzqWb",
        "outputId": "2968d349-7766-476d-9f3b-a45aa34070b4"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "!pip install statsmodels --upgrade\n",
        "!pip install pmdarima --upgrade\n",
        "!pip install prophet --upgrade\n",
        "import pmdarima as pm"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NtKwpInUzqWd"
      },
      "source": [
        "Download the data to cache locally. Do this once (a day), and comment out again, not to DoS the server. (adopted from https://stackoverflow.com/a/35997720)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c3g5PM6YzqWd"
      },
      "source": [
        "# #'''\n",
        "# import requests\n",
        "# fname = 'owid-covid-data.csv'\n",
        "# url = 'https://covid.ourworldindata.org/data/' + fname\n",
        "# r = requests.get(url)\n",
        "# open(fname, 'wb').write(r.content)\n",
        "# #''';"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ljjbKc0RzqWd"
      },
      "source": [
        "Load the file to Pandas."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bo8l27eOzqWd"
      },
      "source": [
        "url = \"https://covid.ourworldindata.org/data/owid-covid-data.csv\"\n",
        "# download the file locally when developing, not to DoS the server:\n",
        "# url = \"owid-covid-data.csv\"\n",
        "df = pd.read_csv(url, parse_dates=[3])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e3u5H6AAzqWd"
      },
      "source": [
        "The time series to predict."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "wsCpLS-nzqWd",
        "outputId": "5b158764-8579-412b-eadb-bf4a631d97a2"
      },
      "source": [
        "time_series = df[df.iso_code=='OWID_WRL'][['date','new_cases']].set_index('date')\n",
        "time_series.plot(figsize=(15,5),logy=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f83522a3dd0>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6uX5YBezzqWh"
      },
      "source": [
        "Error measure used to evaluate your solution is Root Mean Square Log Error (RMSLE), square-root of\n",
        "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_log_error.html#sklearn.metrics.mean_squared_log_error."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_y88XSxNzqWi"
      },
      "source": [
        "from sklearn.metrics import mean_squared_log_error\n",
        "def RMSLE(y_true, y_pred):\n",
        "    return np.sqrt(mean_squared_log_error(y_true, y_pred))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oQkj3kyt6e37"
      },
      "source": [
        "Maximum execution time in seconds and days to predict"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fXH0ATh16e38"
      },
      "source": [
        "import time\n",
        "max_prediction_time = 3*60\n",
        "days_to_predict = 7"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "miWM88y5aKma",
        "outputId": "04098d7f-9a5f-4010-e99f-91bd0d84e366"
      },
      "source": [
        "from statsmodels.tsa.stattools import adfuller\n",
        "print(\"ADF\" , adfuller(time_series.iloc[:,0].values))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ADF (-2.3224072092967893, 0.1648430008323422, 15, 647, {'1%': -3.4404975024933813, '5%': -2.8660174956716795, '10%': -2.569154453750397}, 15209.237915355177)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9rx41fM4vYmx"
      },
      "source": [
        "Therefore time series is not stationary with 15% probability"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "imD37pWMvcRz",
        "outputId": "6aa7b6d4-5c71-4849-a148-1fc661b2db10"
      },
      "source": [
        "time_p = np.log(time_series)\n",
        "time_p.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
        "time_p = time_p.dropna()\n",
        "print(\"ADF\" , adfuller(time_p.iloc[:,0].values))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ADF (-4.4879962039532195, 0.00020669734674675941, 19, 642, {'1%': -3.4405768522418607, '5%': -2.8660524488494903, '10%': -2.569173076930542}, -468.0238106881234)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "BUbkA2HXC5wr",
        "outputId": "e9e6e214-762d-4f3e-cbfa-5063eba8456a"
      },
      "source": [
        "from statsmodels.tsa.seasonal import seasonal_decompose\n",
        "b = seasonal_decompose(time_series, model='additive')\n",
        "c = b.plot()\n",
        "c.set_figheight(10)\n",
        "c.set_figwidth(20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/APirmFkILpFIJBKJRPIWE42JjJ2BdnePU2q38P33reD+W9Yz4Q/xxxO0ON3WPkZBXjYXLCwF0i8cRj3qAr/MnpN0W0W+OjbiCRpjTm+QzW1OrltVZWTYOHKzKbFZ6BmXpXNvJ770SDOf0xazmdAzlebl55GbncXiSgct/YlCk+5oKs9PPg4BFpVrQtOQKjRlciiBmjF2YVMpj+3rZ8gVSOuUArDnmCmxWege8xkd59I5mkDNaeqfnBZfu5x+6kvV+ec2lHDXR8/GlmPmy3/cz4gnkG4zkjOUo0MeAuFYxlw8nd5xP4XWbKoK8tIKRy2aKKuLResWqEJTqjDwSDTGU/sHWFzhQAEWVdipLbYyOWNfupw+ItEYf9k/iCcY4d/fu5z3rplvvK+cvmDStgGjxPvp/QNM+kN88v7d/OKvbUnz9Lyz9hEpNJ2pnFShSVEUG3AF8Fjc8A8VRWlRFOUAcCnwRQAhxCHgUeAw8Dxwm+Z8igC3Ay8AR4BHtbkAXwW+pChKG2oG02+08d8AJdr4l4CvvYVPUyKRSCQSyduAI4Nu+ieTO02lQwjBbb/fywd+tS3tnOaeSVbXFKEoCo1ldupKbCmvDv9v2No+xoaGYoqsFkxK+tK5UW+QLJNCUYqg5FSOpmcODBKNCd6zen7C3NoSK13O1I6mWEzQNyHdTmcS2zvGeGxfPy8dHp61zfqgK0Bedhb5eaoDaWV1AQf6XAmB4EPuAEXWbHKzs1Juo8CaTUV+DseGPQzOUgqn87HzFjDqCTIVjlJZkFrA0qktsdI95jfKP2cTmpzeoBGa3DnmY4HmigJ457JK/uXdapPswwPJHfYkZzb7tAYMk7PkIoFaOldTZCU/Lztt17n9WpnpvR9fxzuXVXD5WeVA6tK5Le1jOL1BvnjFIv7yfy/gfz68loK8bDzBiJGXNOkPcflPNvF/H9rH73d001Ru5xzNnVpqV78H0l2YaNOEox2d49y5qZ1gJLWgpgfit41KoelM5aQKTUIInxCiRAjhihv7qBBihRBipRDiOiHEYNxt3xNCNAohFgshnosbf1YIsUi77Xtx4x1CiPVCiIVCiA8KIYLaeED7faF2e8ff6zlLJBKJRCI583BNhXn/nVu59Eev8ZOXWmfNpwA1t+X5Q0M0906mdDVM+EJ0OH2sqS00xprK7RlDwf+4u5f7tnQmdeyaSe+4n76JKTY2lGDSRKSxNKVzTk+IUrslZVByqd2CoiQ6mja1jtJQajNKmXQWlNgMN8hMnj04yEU/fFUuus8QYjHB9545gqLAVDhqdINLx5ArwLyCXMMBt2J+Ia6pMIcG3Hzj8RZ6x/2MuDO7jgAWVTh48dAwLX2TNJbZZ93Pi5vKWKCVwM227bpiKz3jfrrH/SgKzC/KSzu3Sus8N+gK4AtGGPUEWVBqS5hz1rx8AA4PymP+TODokJvvP3tk1s9egH09EwCMz+JoCkdjdDi91BTnkZ+bjTcYSSnaNvdO0lBq48KmMu766DlUOHIxKalL557Y109+rplLl5TRVOGgqcJBfl42Qkw7oJzeINGY4LmDQ+zvc3HT+lrjvVmiOVvHvNOf+SOeAC8fHgagfdSLI8eMEHDXJnWJnUpQ0x+rfcQ7p7+Z5PTjVCidk0gkEolEIjllue33e/nFK8czznlsbx/+UJSNDSXc8cpxntrfn3auNxjhqf0DfPfpQ0Yr9O0d40nzmrWr1PFC06IKB11j/pQdfwZdU3zz8YN85+nD3P6HfRlbUG9rV6MpNzaqZXPFNgsTaYSmUW+Q0hRlcwDmLBOl9hxGNEeTEIL9vZOsrStKmltbbGXANZVy3/f3ThIT8MC2rrT7LDl9ePXYCC39Lr74DrXb4L6ezCH2fRP+BAfSyuoCAD794B7+sKOHJ/b1zxrYDbC6phBfKMKN62uNToeZMJkUPrpxAYDxXkxHXYmNAdcULx4aoqogjxxzamcVTAtNA5NTdGmdFuMdTQAFedlUF+VxSIqrZwSP7+vn7tc7mJiDS6lZez9M+EJpRZYRd4Cb7t5O7/gUFzWVGW6/VOVwB/omjaYOoB7XjtxEB9Rje/u46mev82RzP+9aOS/h+C3U3Kp66Z3+/0WLVCH2+rXT7lSbJYscsykh0+/323v45AO7GfEEaB/1sbGxhEUVqtDryDUzOZX83aI/D3cgwqg3dRme5PRGCk0SiUQikUjeVty3pZMb7942p/bMoUiMFw8P8dKR4bRzhBD8fkcPq2oKue/j61hQYuWhHb0p5zq9QS7/8Wt87qF9OHKzeeAT63HkmNmWovvQvp5JTAqsqo5zNFXYicYEHaPJWUe/fLUdgeDWixp4pmWQp/cPpN3nbR1jlNgsxmKgyJbe0TTqCVLmSF9WVO7IMRxNfRNTjPlCCYsenQWlVoSA3vHk8sJjWiDsE839s7bZlpz6HOhzoShw60UNlNotGYWmUCTGkSEPy6ryjbFFFQ4sZhP9k1OYFNjVPcGQK3NgN8Btly5k69cu4/vvW0FBXvac9vWjG+q446Y1rK1NFkfjaaqwI4TqUrrt0oUZ5+qiVf/klFEuuqA0udRuWVU+R9IITf2TU2z4/ivs7EwWoSWnHnqThnRNFXR0p2qJzUIkJvAEU5fDfeuJgxwacHPHTWu4cX0t+bnq8TyzHG7IFWDYHWSVJs7q5OeZExxNf97bh9Mb5NaLGvnndy5JmKu/V3RBSBeavnTFIl79yiWGEAVqV8USmyWh06j+nLe0Oeke87Gw3M5HNtTRWGbj3SurmPSlcjRNj7WPyOy+MxEpNEkkEolEIjntmUupGsD9W7v4ztOH2d4xzpHB2bOOOpxewlFB67DHyK+YyfaOcdpGvHzkXLW84Mb1tezsGqdtJHH7Qgi+8VgLE/4wD9yynjf++VIay+yc21BsOIzi2dczweLKfGxxnbMWVUx31oqnf3KKh3f18KFzavjKlYuB6U5eqdjfN8nZdUXT5RCZHE2eYMogcJ2K/Fwjo0nPClmTQmiqLVYdHakCwVs1oSEQjvGdpw/x/WePGFk4klMLT5rOV/F0On1UF6nB3qtrioxSoVQcG/IQisQSxEmL2cS59cVcsLCUG9bVsKdrnDFfcNbyttzsLOYVZHYmzcRiNnHdqqqUpaHxXL18Hn/8zEZ2fONyPnxubca5lQW5KAr0T6R3NAEsnVdA55gPXwqx4clm1cX1282db+LZSE4W3drn1WwB37pT9dIlao5SKhGme8zHS0eG+cQF9Vy3qgqAfE0Mck+px4oQggN9k+zsUoXIlTM+c/NzsxNEqXFfmNU1hXzt6iUU2RLz9nShaaajKT/XbHxHxFNiz2EsLgx8Upv/x919hKOCxjI7H9u4gFe+fAkV+Tl4ghHCM0r+vMEIeVreWrvMaTojkUKTRCKRSCSSU45hd8AIFU3F1nYnS7/9PDf/dicfv3cni7/1HI/uTu0i0tneMca/PnWIc7XWzwf6MpfzgLoIBgiEY2nzhX6zuZNCazbXaguCD5xdTXaWwsM7E/fn6QODvHh4mC9fsYiLFpUZC9sNDSV0jfkZiAsSj8UEzb2TCWVzAA1lNrJMCsdntIR+9sAg4ajgs5c0YjGbyM81J2RozGTEHTTKe0B1NKW6Eh+LCZze2R1Nw271sfb3TmIxm5LymQAjC2dmILjLH2bIHeDaVVWcW19slKD8YWdP2seUnBzueaODld99kR88dyRp4RhPh9NLfanqlltTW0iH05dWyNQX3qtnLJTv+/h67r9lPRsaSvCFogjBnAK+3yqyTArrFhSnDSOPx2I2Ue7IUUvnnD7KHTkJgrHOsqp8hFDzfWbyzAE1pvblI8OMemRp0amMEMIQxjM5mp4/OMj3njmC2aRw0aIydX4KYereLV2YTQof21hnjOXnqsePLh5tax/juv/ewucf3ofZpLB0Xn7CNvJzsw1RClQnVaqGDgCF1kShSb9fOldgid2SEAaud6zbql0waSyfzkcrNASyREHNE4hQX2rDZsnK+F0vOX2RQpNEIpFIJJJTji8/up8P3bWNUCT1YnbzcSfBSIyuMR/HR7wUWrP5856+tNsLhKN8/bEWaout3Pfx9ZTac9jf60o51x0I839+v4cupy/B9XR0KNkB1dLn4uUjw3zygnpjAVpqz+HyJRU8NaN07S/7B6gpzuOTFzYkjJ+n5SRtjXM1DbkDeAIRIzBYJ8ecRV2JNcnR1OH0UmKzUF1kNfbBmWbB4w9F8AYjCW3iS2wWJvyhJNfW5FSYSExkFprycxnzBYlEYzT3TrK8Kp/srORTzGKbBXuOOUmwa9WcX4srHPz65nN47SuXsHRePocGUr8+kpPDq0dH+P6zR6gttnLXpg4+99C+lPOEEHSO+mjQwq91sbQ5jbC7v3eSUrslKSMpy6SQZVI4R2vVDsxaOncqMb8wj/19kxwf8SYFgess1coFZ4bgdzp9HBpw8+Fza4nEBH/em/6zTXLymfSHjRK4dILqptZRPvO7vcSE4Jf/sJZqLUx+5nx3IMwfd/dy7coqyuOO9/wZgk2fdmHi4kVlfGzjgiQBND/PbIhSQgjG/SGKbamFJqN0zj/D0ZROaLLlJFzIcM0QkRrKpo93vexucsYcbyCCI9dMY7mdPd0T/PtfDvPD54+yvWNMhoOfIUihSSKRSCQSySnFkCvAlnYn474Qr6TJRjoy6Kap3M6mf7qUzV+9jA+vr2VX13haF8+PXjhGp9PH99+3gjxLFquqC4wyr5k82TzAsy1D/H5HN0eH3IYTJ1W3t5+/0kpBXjY3n7cgYXxtXSEjnqBxpRegd2KKpnIHWTNKdJZUOrBZsjjYPy2sdDnVcpuGFAvUxRUOjs+4Atwx6qM+bq56xTn132JEcx+VO6YXMcU2CzGRvGBwatvIJDRV5OcghCqOtfS7UuYzgZrtUVtsNUqJdHTX2KJKB/m52SwotbF8fj6HB9xywXGKEIsJvvzH/SypzOe5z1/Ipy6s5/lDQyndG8PuIL5QlEZtsblSyxg72JdaOGzunWRVdWHKEh1QBRtdhIoXR091PnlhA63DXpp7J43PkJnMK8il0JqdFAj+zAFVpP6/ly1k/YJi7nmjg+89c1iWk56idMeJ56kcSkIIfv5yK1UFuTz/+Yu4clklxZoAM/M99MqRYXyhKB+JczNBnNCkiUe6QPXfH17Lt69dmvSYqqNJnesPRQlFYkklczqpSudslqyUFwxA/X5xxgWZT/rDxvdPRX6OkScFUGBNFLF03IEwjtxsGsvstPS7eHBbN3e/3sGNd29nf5rPCsnphRSaJBKJRCKRnFI8tb8fIdST33TlcEeHPAlunyuXVRITaplJPEII/vO5o9yzuZOPbqjjgibVPbSyupD2UW/KvJnHNPfAsy1DHB30sLa2iNpiqyGI6LSNeHn5yAifvKAeR27ilV+9tXq7FtothKB33E9tcfKC02RSaCizJ+RUdGpiTH0KoampwkH3mC+hq1yn05fgmii15ySUNsSjB3eXx4lH+pXumYHgeslOuq5z6nZUweqN404C4VhSCVQ8C0qtSYvl1mEP9hwzVXFlUcuqChjzhRhyp8+ZEkKwr2ciYwmX5MQw7Akw7gtx07m1WC1mrl1VhRDw2rGRpLkd2nHcoL0H7Dlm5hfmpcxhcQfCtI9604qTOucsUIO6TydH0zUr5vHJC+oB0jqaFEVhxfwCDsxYWD9/aIiz64qYV5DH165ZwvwiK/dv7eZLjza/5fstefPEuzRTOZq2to+xt2eSz166EItZXX7rZWwzM51ePTpKqT2H1dUzM5e00jmtrG3cF8JiNmGzpC7lzM+b7jqni1nFaUrncrPVTnJuo3QunNbNBKoDNhSJ4Qup30GT/hDnLyzBZskyvvt0ioyOdonP0xtUHU2fvaSRb797Kdu+fhlP3HY+oGabSU5/pNAkkUgkEonklOLxfQOsrinkIxtq2dQ6agRN60z6Qwy6AiyJywFaVpVPdVEezx8cSpj7ZPMAv9rUzkc21PLd65YZ46tqChACWvoTF3jto1729UyydF4+/ZNTDLkDLK50sLjSkZSjopd2XbmsMuk5LNQyKto159GkP4w3GDHKJWbSWGZL6CTXOeojx2xKubBeUukgJqadQL5ghNrbFesAACAASURBVBFPMMnR5EznaPKof894d4guNE34QwnBxLrQNJujCeCOV45jMZvY2FCSdm5tsY3eCX9CePuxIQ+LKuwJjpbl81UR8VB/6o5c/lCELz7SzPt+uZXvPXMk7eNJTgydmsOuXgu0Xl5VQJkjh1eOJgtN7bobL658pqHMRlsKoelgnwshmFVoumFdDe9dXZW29OdU5WtXL+Eb1yzh+jXVaeesqi7k2LCHKW3RPhWKcmTQw3mN6vtobW0RT952Pp9/RxO7uyeM7maSU4ce7cJAkTWbiRRdM+98rZ3K/Fw+dM70ceDINZNlUhKEpkg0xqbWUS5ZXJYUTm+zmDEp046mcV+IYqslrRMwPzcbbzBCJBozHiOdownUCzvxpXOZujaWaBcexrxBojGBOxChxJbDv163jE9f3JgwtzAvtaNJF5oWVTi45YJ6Suw5zNMuNox6pr/z20a8XPJfr9I3IY/70w0pNEkkEolEIjllaB32cGTQzXtXV/HBs2uICXh6RtbR4UFVfIh3NCmKwlXLKtnSNpYglGxpc1Jss/Dv71mecOKul/PMdBI8vrcfkwI/u3E1Zm3+knn5LKl00DXmT3AR6aHWdSnKYqqLrFiyTIaLo1c7SU7laALV/dE/OWUsNrvG1FK4VJ2wVsxX21jrIpkhAsQLTbYcJvxhIincPnrpXEVc6Zx+1fmN1lFWffdFtneoeVFzEZp0R9OgK8DHz1+QkCsykwUlVsJRYQSfC6F29JsZHr6kMh9FgYNpcpo+99A+ntw/wPL5+TywrYsWWWrxlqIf6wtK1ePXZFK4bHE5r7eOJjnKOkd95GVnJRxfC8vttI/4kjLA9JKxlfMTW7PP5LzGUn5245q0i+pTFXOWiVsvaswYYr66ppBoTBjv58ODLqIxYXxG6bx3zXwAntjX/9btsMRgKhTlS480z+mzpXvMT5kjh6rCvCRHkz8UYXvHGO9bO58c87T7yGRSKLJmMx7Xda65dxLXVJhLF5cnPYbJpOCIK4eb8IcyCkf5eaoDyhuMTDuaZhGa4kvnMjqa7Op2nN6QsT+F1mw+dE4NF2sh5zp60Hi8ACeEwBOIYJ8RkF9ktZBlUnDGuXEPD7rpGvPzwqHkMnp/KJI2E0ty8pFCk0QikUgkklMGXeC4YlklC0ptVOTnJARyA8bvM4Oyz1lQTCgaS3AG7e+bZHVNcv5Lsc1CTXFeUue5Z1oGOX9hKYsqHGzUHAVnVTpYVOEgGhOJ5W1OL1UFuSm7UGWZFOpLbcZ8vbSiJo3QpJcbdDi92v++lO3QAaqL8ijIyzYynbpSlNmVaguBVHkhI54gliyTsQCA6YXDbzZ3EokJI5h81Bskx2zCkaJjVvxjKYq6UPk/Fy9MOw+gVhPl9Fbgg64AE/4wiysShSZbjpn6UltSdg3AwOQUrxwd4fZLF/KHT22gxJ7DNx5vkXlObyFdYz4sZhNVBdOOvEuXlOMJRNjdNZEwV+04lyiSNpbZmQpHGZzhTuxw+ii2WTIumM90Vuth6b3q31FvUrCyOlF8m1+Yp3ZlbO6Xx/rfgZ++3Mpj+/p5/tDgrHN7xv3UFVsptlmSPnP3dk8SiQmj22k8RVZLQo7fq8dGyDIpRon3TNSAb/VCypgvREkmoSlXDw+PGI6mTEJToXWG0JSbXmgqtU07mibjhKZUOHKzURRwxT3PQDhGNCaw5yZ+r5hMCiU2S0KXRf1+m1pHE+YOuwO8+xeb+cd7d6bdT8mJZ1v7WMoy6FRIoUkikUgkEsnfhUHX7LkLLX0uSmwWI6+nvtRGpzPxpObIoJtSe06Sy0Z3FnWPq8KLJxDm+IiXVdWpy3LOqsyndXh628PuAJ1OHxc1qVdkP3txIzetr6HMkWOU6cXnNHWO+akvSy0GATSW24y2zb3j6nNPJzTpZUYdoz4i0Ri94+m3ree66G6fTk1YixempksbUglNAcocOQnim+5o0jM39veqApzTE0yaOxNzlon3rZ7Pv1671Ah+TYe+j/prpDvKVqYonVpeVZDUjQvUckgh4ANnV5Ofm83nLm+ipd+VFJAuOXF0On0sKLEmiEcXNJWSZVLY2u5MmNsx6ksom4PpUtKZbcw7nd60QdlvF0rtOdQU59GsvecO9E1SkZ9DRQpn4PvWzKdj1MfBNCWlkhPD/t5J7nmjA5gWxTPRM+6ntsRKkdWS5LDZ0TmW1D1Rp8hqSQgDf+3YKGfXFaUtW4sP+J7wzeZomg4P111T6TKaQCud07btCUQyls4V26cz/XShrDAv9bazTAr5udkJXef0bMSZ2YagumdH48q+9ZK77R1jTGmh5i8fHuaGu7bRMeqjV+Y5veXo526RaIxbH9zNT19qndP9pNAkkUgkEonkLef+rV1s/MFf2duT6H7oHfcnXJ0/OOBm2fwCQ9ioL7XRNeNE/8igm7PmJTpgYLosTXcPtWj5L7pjYCZNFXa6nD5CEbX0Z2fnOADnNqgLgvMWlvKD61eiKAoLSm1YskyG0KS2cPemdR0BLCyz0zPuJxiJ0jPup9hmSSoV0KkvtaEo6iK9f3KKcFQYeTipWD6/gGNDHoKRKJ1jPuYV5JIXFwqrX+lOKTS5g0kiXW52lhEqu6qmkP19kwghGPUmz03FT25YzfVr0+fQ6FTm52Ixm4xA8AN9k5hNCktnuNNAzd3qn5xKWLgJIXh8Xx9n1xVRp/19LtKu/u/QXj/JmycaE2xpcyaVtul0pXDY2XPM1BVbE65uByNR+ib8RhC4zszMsunt+tMGZb+dWF1TRHOPLjS5ksrmdC7SypKaU3TMvH9rF9f/covMcPobicUE33yihXJHLqtrChOCvlPRMeplyB2grtimOppmCk0d4yyvyk/52V9kyzbcRsFIlKNDHtanEKR08nOzEzKaMjua9PDwMBO+EFkmBUduemdqft60iDVrRpNtumOeLiBlushQaM1OyGjyaOXtqZyypfachHxB3WUVisR4fF8/l/34NT75wG68wSiXLC5jwh9KyPyTnFjaRryc959/5fmDQxwacOMJRBh0pW/SEY8UmiQSiUQikbyl7Owc59//chjAWEwB7OgY48IfvsoVP32d5w8OEQhHOT7sYXnVtOhQX2pj3BfCpZ2kBiNRjo94k8rmQC23KrVbDBFDX4ytqk6d/7Kw3E4kJujWSs92dI5hs2SlFD2ys0w0lts5NqwKTRP+MO5AJGVXOJ3GcjsxoV4R75vwp3UzgSr06J259MylTAvwFfMLCEcFrUNezW2SONdwNPmSA8FHPIGEjnM6Ffm5rKkt5MZ1NUz6w/SM+xn1BDN2nHuzmEwKtcVWo9zvQJ+LxZWOlOWHujjR4ZwuhTw04KZ12Gvk1YAqMFbm5xpCoeTN8+9/Ocw/3LOD11qTw71jMUH3uD/lsd5QZqN9ZPr16R7zExNquH08JTYLBXnZtI16aR320KqFXw+5AzRIoYnVNYUMuAK0jXjpcPrSfmbNK8jFkWPm+HBiObEvGOEnL7Wyt2eS6+/cmtQhUzJ3nmju52C/m69fs4RlVfkZhaYvPdrMZT/epAXaF1BkteAORIzcskA4SnPvJOemaZCgClPqd1v7iI9oTCTl1cVTkJeNe0rdvjsQMZyoqdDdQu5AmHF/iCJrdsrMP53CPAuuKTXXzxvM7GjKzc7CnmPG6Q0a382FGeYXWi0zHE2a0JRC+Cpz5CSUzk1OhSmxWcjNNvHNJ1qY9Ie566Nns+3rl3HxojKEmBajJCeefT0TCAFPHxhgmxZtMCSFJolEIpFIJCebUCTG5x/eR02xlSJrdsICSC8VEULw+Yf3satrnEhMsDwuGFgXUDo1YWJ31wShSCxl3gWoooNe6tDcM0l9qY3CNCfjTeXqCb1ecrWzc5yzFxRjzkp9erS4wm7sf6oA7pnouUvtI156xv3UpOk4p9NQZqfD6aVrDtuODwTvdPqSyuzKNHHImbJ0LpjQcU7njpvWcMeNa4xsmCebBzg+4mVRhT1p7t9CnfYaCSE40DeZlEWjowttXXFC04uHhzEp8O4V84wxRVFYX1/Mjo4xmV3zJrjnjQ7O+Y+X+MyDe7hvaxcAbxx3Js0bcE0RisRSCp+NZXY6x3yGo6BDczfNPHYVRWFhuZ09XRPccNc2PvfQPkNslI4mVWgC+NqfDwCwIo2jSVEUFlbYaZ0hND26uxfXVJgfvn8l0Zjgv1449tbu8BnKVCjKf71wjJXVBVy7soq6EiuT/nBKIeOvR4d5bG8/H91Qx5avXcYli8sptiV2WNvXM0komv77qlDLaBJCcGxYLYfMJDTl55mZ8IfiMpfSizt6GLh7Sg3MziRKgSpieYMRI2NKv3861M6mcaVzGbZfmJedkNHk1YSmVC6vMofqaNI/y11TYcocOWxsKCFLUbjzI2t557JKsrNMRubUeNwFlTFvMGWZfiAc5bY/7DXKwiVzQ2++8trRETYdU3Oyht2BtO7XeKTQJJFIJBKJ5C3jmZYBBl0Bvn3tUpZVFXB0aDpb5Niwh4r8HO64aQ3BSIzvP3sUmBZRYHrBqosNrx0bwZJlMoK6Z1JXYjOuQOtB4OloLLOjKKo1fNwXonXYm3ZBALC4Mp9BVwCXPzwnoUnPqTnQ76J/Yiptx7np/bHRMepjX+8kds2dlY6a4jzyc8387OVWJv1hmsoTxaD8PDNmk8KYN9HRFIxEmfSHjU5x8SyfX0BNsZVFFQ5ys03c8cpxsrMUbj5vQcb9frPUlljpGffT6fThDkTSlgnVFFkxKdNh5wBb25ysqC5MyiZZX1/MiCc4pzyVgcmphADetyuvHBlhKhTl5SPDXLK4jA0NxWxtG0uaZ3ScS1HK2VBmIxSJ0a/lpLSPpn9fNJbZODbsYcIf5uiQh93dE2m3+3ZjTU0ht5xfT3PvJIqSuQtfU7k9IesqHI1xzxudrFtQxIfW1XDdqireOD6KPxRJuw1Jav7f80cZdAX45jVnae5L9djsmfG5EoxE+benD9NQZuNf3r2U+YXqRQT9c0kXgnZ3jaMopMxnAjUzKRITeIIRjg55yM5SMn6n1BZbGfEEjfdbsS292zQ+o2lsljwnmA7z1p9rJkcTqO66vgm/4VTKz1CWV2h9ExlN9hzCUTEdTO4PU2jN5gfXr+SJ287nwqbprnbFKUrEv/3kIT5+7y5AzXW69Eev0Tfh57G9/TxzYDApVDwd29rH+Ozv9sxJUDmTOTLoxmI24QtF2dYxhtWSRSQmcKZwS8/kpApNiqJ0KYrSoihKs6Iou7WxYkVRXlIU5bj2f5E2riiKcoeiKG2KohxQFGVt3HZu1uYfVxTl5rjxs7Xtt2n3VTI9hkQikUgkkhOHEILfbO6ksczGxU1lLKl0cGzYY7gfWoc9LKpwsKyqgJXVBRwZdJOfa6Y6zvlTW2JFUaYdRK8eG+XchmKsltQntbXFVgZcU7SNeBl2BzMKTXmWLKqL8jg+4p3OZ8ogNOmB4K0jHrqcPrJMSsZyOKvFzAULS/nVpnYiMTGr0NRQZscfivJk8wDnLyzJGMCtKAobG0vwh6J8+uIGblpfm3S7esU58WRQL0lIVTqnk51lYnlVAZGY4CPn1qUUpf4WFpTY8IeiPLyrF0jurqVjMZuoLrIar70vGKG5d5LzUoiMG7RcrVTlc7GY4I+7e/GHIggh+NBd2/iPZ46cqKdzWhKLCQ72u3jf2vns+ZcruOdj53BhUxnHhj0JZSsw7SZMLR5prj0tsL/T6aPckZNyAak7CN+zugqAh3b0ANLRBGpJ6bevXcpLX7qY333i3IyiwKIKB05vyMgC2tExTv/kFJ+4oB6AK5dWEIzEeL012Z0mSc/j+/q4b2sXn7ig3ih1m9lgQufBbd10jfn59ruXYjFPL6f1sG39tTky5Ka22JpWtDGEKV+I1iEPjWV2stM4amH6/bZHE2mLMjia7BYzedlZ9I77mfCFMgaBw/T7W89RnE1oWlTh4Piwl0l/GEeuOa0TGFRHU8qMphTiVKn23aR/Dk1OhSjIy6ayIDfB7QzTQtNE3IWD3gk/R4c8TPhCPNcySKfTx3eeOszdr7cnbHc2frO5g+cODs1JUDndCEdjPH9wiK8/1sKOjuSLCzpCCA4PuLluVZXhPrtqWSUwt/K5U8HRdKkQYrUQ4hzt968BrwghmoBXtN8BrgaatH+3AneCKhoB/wqcC6wH/jVOOLoT+FTc/a6a5TEkEolEIpGcIHZ2jnOw380tF9RjMiksmZdPIByjWyu1OT7sNdra37hOFUqWxwWBA+SY1eyiTqeP3nE/bSNeLllcnvYx60qsCAG/294NwMWLytLOBTWw+/iwh78cGMCRa2ZFGtEDpksajg556BzzUV2Ul3FRAPDLj6w1HDuzCU3XLK/klvPr+c3N5/DfH16bcS6opW67v/UOvn71WSkzjkpsOUlh4CPaSXaqjlbxrKsvxmrJ4taLG2bdjzfLJYvLKLFZuPv1DnLMJhZVpC8VWVBqMxxNO7XSyvMbk1t/N5bZKbZZUgaCb+8c45/+dIA/7OihfdRL38SUIV69Xeka8+EJRlgxv4CCvGzMWSbOX6j+XWd2kety+sjLzqIiRbllQ1liyHfHqDep45zO+8+u5v+9fwU/+uAqHDlmDg+6KXfkpA3IfztSX2ozXod0NGnvFz2naZ8mDGzU3hfr64sptGbz4qGht3BPzyymQlG++fhB1tcX87Wrlxjj+md2vFPSG4zwy9fauWBhadJ3kV4+pjcwODrkMS5QpEIvfRv3hTg25MlYNgdq7h/Arq5x7f7pxSOTSWFtXSG7uiaY8M/uaFqiNdjY3qFuey5CkzcY4fCg23BDpaPAasEdCBsXmTJmNNkThSbXVDhtR7sSm55FOP0959Tut6d7gp1dE1iyTLx8ZJiuMT8mhaSLL6nwBiO8rpURp2qoAapY/7vt3UxpnVpPJrG4rMm58MVHmvnM7/bw6O5ebvr1dqPD4kwGXAHcgQiragq5ZLF6LvUeLR9xLoHgp4LQNJP3APdrP98PvDdu/AGhsh0oVBRlHvBO4CUhxLgQYgJ4CbhKuy1fCLFdqEWeD8zYVqrHkEgkEolEcoK4f1sXhdZsrl+jdiNbEifUqN3YYizSxq5bXUVBXnbKEoN6TWx4TbO86yc8qdAXBn/c3UtDqW1Wt0RThYP2US/Ptgzy4XNryTEnCzY68wpyceSaOTLo5tiQJ2OJg05+bjYP3LKef3vPMtZlcEuBGuD97WuXcvlZFbMKWKCKcKkEpuntWXD6Qty7pZOn9g8Aasc5YNZOcp+/vIlXvnzxCXczgVre+NwXLuSqZZVcv3Z+xudaX2Kl26nmOW1rH8OSZeLsumQjuqKonevaRpJDkPWF00uHh40MormGmZ6ptPS7AFgxf9rxt2J+AY5cc1L5XNuIV+uKmOywK7ZZKLJmG4HtHU4f9aWpM72KbRZuWFdLdpbJ6Owo3UxvHr1MtlUT9/b3TdJYZjOEAXOWicuXVPDK0REjlFqi8uMXj/HPf9qfNH540IU/FOVTFzYkfB6pDSZyEkrn7t/axbgvxJevXJS0HSMzyB8iEI7S5fSxuDK5uYSO3mhhV9c4A67ArEJTXYlaTry7ayLh8dKxbkExR4bcjPtCGfOcAModuZTaLezSxPr8WYQmfV+beyfTCkE6hXnZCIHR1U7PaLKlyWgCGNUEoUl/OG1HO93RNa6JQUIII5fwr8dGODrk5tMXN7Cw3E5DmY1zFhRndDS1jXhoH/Xy6tERoxttOqFpV9c433riIE8292d87n8P/ry3j0t/9Brto16EEDzXMpjQrTWeWEzw2rFR3rO6ir3fuoJLFpfzH88cSTn/8IAadbB0Xj5fuXIxP7thNcu0Zi2ng6NJAC8qirJHUZRbtbEKIcSg9vMQUKH9PB/ojbtvnzaWabwvxXimx5BIJBKJRDIHItEYn/3dHu58rT3l7SPuAC8eGuaDZ1eTZ1HFkIXldkwKHNWEGsBwNNlzzLz6lUu4/dKFSduqL7XRPuLl1693UFdizdilqlYrdfCFoly2JL3zSWdhuZ1wVGBSFD5+Xn3GuYqisLjCwSO7emkb8XL5WXM7fSjIy+ZjGxfMSTw6kZTaczg66Oa7Tx/mF68cB2DUo54cpgoDjyc3O4t5BZnDy/8Wyh25/OqjZ/OD61dmnFdXYsMTjDDmC7GlzcnaukLjeJpJTXEefRPJIbDbtdKAXV3j/OWAevo37A6c8S2xfcEIwUjqq+0tfS5yzCaa4oLes0wKGxpK2DLD0dQ6nNlp0VBmp13LOZv0h5M6zqVCd9/Uy3ymN0185zkhBM29k6yaUSJ81fJKXFNh7n49tVNBJxyNJeW4nans6Z7gv19t44l9A0nvi/29qvCaqoy3rsRqlM75QxHu2tTO5UvKWVObLHjrzp5Jf5jjw15igoyOpuVVBayqLuBHL7QC09+H6cgxZ1FTbDUcPLMFfK+vL0YIiInZ5wIsqcw3ytpmdTRppbChSGxWR5MuCOk5TZ5AmLzsrJTfiWVxpXOBcJRgJJZ2X3LMavc7PcDcPRUhpImrf9rThxBw/sJSnrjtfP78mfOozM81BKyZCCG45b7dXPuLzdz9egdmrUNfOgfUUe0cZn+fK+Nz/3vwxnEnMQHPtQzS0u/is7/fa5Smz6RvYgpvMMKGhhIKrNl8bGMdAG2j3qS5hwfcKIp6DC8otfHeNfMptlqwZJlOC0fTBUKItahlcbcpinJR/I2aE+ktPQvI9BiKotyqKMpuRVF2j47OLThMIpFIJJK3A3e9ruYX/OKvx3EHprMXfvDsEX79egeP7OolEhN8+Nw647bc7CwayuwcGfIYXZPiF7rFNktC3oXOghIbvlCUSX+In3xodcbsojJ7DlZNiJiL0KS7A65bXUVlwezunSXzHERjgs9d3sRHzq2ddf7JpNRuIahdlT0+4sXlD9M67MVqyTJKDk51dNfY9o4xDg+6OS9F2ZxOdZG6APMFp0OQA+EozT2TbGgoJibUxaYRZnoGL7CfbO5nw/df4UuPJLs3QHU0nTUvP2mhd35jCX0TU/RqgfquqTCDrkDG8sbGMhsdTp/RcS5d6Vw8es7WzG6JktnRO88dH1bLQJ3eEGtmCE2XLynnulVV/NcLx3g0xYIzGInymQf3sPxfX2Dd915OCBc/EwlHY3zz8RYUIBSNcXQw0fnY0u+iIj8nZUlxXbHVcDTt6prAHYjwsTQNEnKzs7BZshj3hYzGF5mEJpNJ4TvXLTPEkdkcTTCd05Sfa5714sWamiKys9Tvy9ncTwBnzZt+/NmEpgJrtlFOO9tc3fGkN2HwBiPY04SH5+easWSZGPUGjUDwTEJWsc1iZGLpIlK5I4dQJIYly8TqmkLsOWaKbBZK7TlGaZ2OOxBGCMGRQdVpHY0JWvpdvHO5mkWUXmhSX98DfW99F7un9g9w75bOlLcJIdjRqV5Mef7QEH/crfpsutKUhx8eVIWxpfNUZ9J0Ke70Z4A7EOaZA4Nsbhulrtia4DwzmRQqCnIYdp/iQpMQol/7fwR4HDVjaVgre0P7f0Sb3g/UxN29WhvLNF6dYpwMjzFz/+4WQpwjhDinrCxzxoNEIpFIJG8Xjg65+dnLrayqKcQfivLYHvXERgjBA9u6+d6zR7jjr8e5YGFpUnnZkkoHe7oneOP4KLXF1rSh3vFc2FTKhoZiHvn0xpRlU/EoikJtsRVHjjltp594ls8v4BMX1PPFdySXQaTis5cs5Jf/sJYvvqMpo+B1KlCldUL6wjuaADXk9fXjo2qbaNOpve86emnVD549iklReN+a+Wnn6iHy8a6mvT0ThKIxPnVhg7EoumbFPGBuGROnG73jfj7z4B4+/3AzWVkKz7QMGqKuTiwmODTgTune0POBtrSpriY9B2hxZepyOFAdTaOeoNFFriFN6Vw8Syod/PADK/nQOTWzzpUk01Ru58iQm22aW291TeLnosmk8KMPruLc+mK+9+wRo1W8zg+ePcrzh4a4blUVMQGbj5+5F9SjMcFX/3SAo0Mevv3upYBabhjPgb7JhDLSeGpLrAy6AwTCUba2O8nOUli3IP33UJHNwoQvxNEhD7nZJupmce2tqS3ipvW1VOTnGN3rMqE7BuciHOVZsowurrNlNIHqaAK1EUOmsmwdXYCe3dGkPvbApPqZ6wlEUuYzgfodXubIYdQTNALEMwlZRfFCkyYivVMLrF5ZXZDwPMocOfhCUaMjo8sfZt1/vMxvNnfy4uEhFAX+9JnzuHp5JZ+/vInsLMUoxZvJYU2sPDbkIRCO8tjePvZ0J2cE/m/pHffjD0UYdE3x1T8d4Jdp3OPdY36G3UEaymwc7Hfz573q+ZjefTdpvwfcmJRpUbOqIBerJYvjcWXnv93cyW1/2MuuromUzr3K/FwGXcnu4ZmcNKFJURSboigO/WfgSuAg8BSgd467GXhS+/kp4GNa97kNgEsrf3sBuFJRlCItBPxK4AXtNreiKBu0bnMfm7GtVI8hkUgkEokkA9GY4Kt/biE/N5t7/3Edq2sKeWBbN7GYYNQTZCocZVGFWo52c4qrvrde1IAQgl1dExldEvE0VTh4+NaNnDUvfdZFPDeuq+H2yxamdEfNJDvLxL+8e2nG7nHxzC/M45oV8055kQngpvW1vPCFi7j1ogayTAqP7eune8zPRbMEpJ9KVBflkWVS6J+c4t0r52V8nfTb+iamT7C3t49hUtQSkiuWVpBlUrheDzOdnP1E+XSif3KKq372OptaR/mndy7mpS9eTF52Fr+asUDpHPPh1YLAZ7Kw3E65I4ct7aqAcUwTmjK9V3Xh8ofPHyU7S0noGpkORVH40Dk1c1osS5J5/9pqXFNhvvvUISxmU0onjMVs4j2r5+OaCieIry8dHja6q/3XB1cxvzCPnV0nboF8KjHpD/G5h/fx2L5+vnLlIm4+bwGl9hyae6eFJk8gTIfTXnbC6gAAIABJREFUl7b75Yr5BQgBm4872dY+xpqaoowXSBaU2Njc5mR39wRN5Y45ifr/8d7lvPyli+f0vaI7muYiHAFGNuBsXecA4zs2P0XXyFTopX6zZTQtq8qn1J7D4/tUz4cnGMGRoQlAqSMHpzc07WjKsP2SOKFJdx9dvbwSRcHIgjO2a1e34/So8zucXoKRGD9/5ThPNQ9wTl0RK6oLuPMjZ7OowqE11Eh2NEVjgtYhD9VFeURigtdbR/nnPx3grk3pS1X3905y35bOJNE3FZ1OH5f/ZBPX/mIzX/1zC1PhqHp+lSJ4XHczffOaswDwh6JUFeSmF5oG3TSW2Q0BTlEUFpbbE1yN3WN+KvNzeeZzF/C99y1P2kZlQd4pn9FUAWxWFGU/sBN4RgjxPPCfwBWKohwH3qH9DvAs0AG0Ab8G/g+AEGIc+Hdgl/bv37QxtDn3aPdpB57TxtM9hkQikUgkkgz8YUc3+3sn+fa1Sym2Wbj5vDo6nD52dY3TrZ3YfP2as9j5zcu5YmlyhtHK6kKevO0CNjaU8O6V896SffzH8+v59MWNb8m2Tydys7NYXOnAajGzrCqfvxxQA8EvbMrc1epUIjvLRI0mXHz6osyvaU2RKjQZZV/+MM8dHNJCrrP5ypWLefTTG1iiLaZOpqNpxBOYU+kBqIunH794jD/u7sUV1yJ8Js+1DOILRXny9vO57dKFlDlyuGl9LU/uH0gQ3/QSiSUpQooVReG8xhK2tTsRQl1M2SxZGZ0Wq2oKue/j67DlmGkqd2Rscy45MZzbUMKtFzXgC0VZXpWfVlRfqgX3HtJCfQF+v6Ob2mIrX71K7a62vr6YnZ3jc1oAe4ORjMfgqcTm404u/dFrPNcyyNeuXsLtl6ku1NU1BeyPE5oO9rsRInU+E8BFi8ooc+Rw9xsdHOx3sVEr+0zHP71zMU5vkP29kxnL5uLJMik45iju6J3nSuYoNF27soqV1QVzKlNtLLdhNikU5M2tE+RcHU3ZWSY+eE41fz06zJArgDcQzvh8y+y6o0kVhDI5mopTOJqWzMvnoU9tSDoPmA4aVz97dQHWE4jQ4fRx5dLKhPkldktC6VwoEmPIFaBn3M9UOMoHz1Ydmf/xzBEiMcFAGpdPMBLl9of28p2nD3PnpnaebO7nA3duZSTNd8C/PX0IS5aJcV+I11tHjeMo/nNcZ0fHOCU2C5ctKeeseflUFeTy/rOrGXBNGYHm8RwacBuB3joLy+0JpXN9E35qS6wsqypIKarOK8hl0BWY9TPjpPUTFUJ0AKtSjI8Bl6cYF8Btabb1W+C3KcZ3A0kyXLrHkEgkEolEkp4JX4gfPn+MC5tKuW5VFQCXLFJzkPb3TVKs5f7UFVszdiurLbHy0K0b3vodlhicXVfEgT4X1UV5c+qWdypxyeJy3FNhY9GcjlK7hdxsE70TUwxMTvHR3+ygZ9zPf394LaC2Hj+7rhghBDlmE0NpTvKFELSPetnbM8nqmsI5O+/mQtuIh1sf3EPHqJqfsbDczs9vXM2yqtSL3C1tTj730D4j/PcHtqO89MWLKLEnZ2y9dHiYJZWOhP39hw21/HZLJ1vanNywTs0U69SyOxaUpnaHnbewlCeaBzg27OHYsIdFlY5ZnRYXNpXxypcuNrJmJG89X75iMa1DHi7NkEW3uMKBSVFdDFctr0QIQUufi8uWlBvi1Pr6Yh7f10+H02e4ZVIRicb4wJ1b8YUivPiFi9OG8v9vCEaiGTt+pmJru5Ojgx4sZhM3rqtJEjh/tamdvOws/vCpDQlu2FXVhbx8ZAR3IEx+bjYt/arolMrhB6pI8qFzqvmfV1Vn4HmzCE2ragr5x/Pq+e2WzjllLr1Z9GYYcwn3BrU8/KnbL5jT3BxzFgvL7UbO4WzoXWML57AvN66r4c7X2nl0dy+eQCTjOUJlQQ47O8eM8PDZMprGfCGt41wQs0mhMC+bDQ3Jr5Pe5W9UczT1asLNu1fO4y8HBpMujpXac4zPXoB7t3Ty45dajVL7S5eU8bsd3YZ7SC8NnMmD27rpHZ9iVU0hP3z+mDG+q2uCd8244PbKkWFePTbKN685i6uWV/KXA4Osrinkpl9vp2fcb2QqgerY29Yxxvr6YhRF4b8/vIZIVHCw34UQqmDUEPeeHveFGHQFkr5Lm8odPLa333hP9E9MsSHDcV6Zn0swEjMcZ+mQlxwkEolEIpHMiZePDOMJRvindy42Fp5FNgsV+TkcHVKDNE2KGsosObU4p04tIbiwqey0KPuL5zvXLeMnN6yedZ6iKFQXWekd9/OrTe30TUzx4CfONfI64ufNK8hlIEXpXO+4n5t+vZ13/OR1/vlPB3jXHW9w16b2Obk95sLvtvfQPzHFt951Ft+85qz/z959x7dVnv0f/9yS94y3HceO4wzH2XsHAmGGFVaB0rJKCy1tofNX2qd9+jwthba0hdLSxW6h0AJllpEEAgkJIXs603HieO89ZOn8/pCs2LEsmz5K7MTf9+ulV+yjW0dHumNb5zrXfV2U1bfy2BrfRV4B7n8rj4hQO+/ccxbP3j6X6qZ2nl5/pMe4mqZ2NhZU9zhRGpUQSWSInbwuxY8LKptIjArtNaOgs07Tyj1l7Ctt6LMTVqfkmDD97J9CIUE2nrx1DjfNz+p1THiIndFJUd425cV1rVQ1tTO5S/bOHM/Sqk8O+18+9/dPjrK3tIHC6hb++IHvejH/iaNVzUz+8bu8vauk78Eetc3t3PLkRv73jT381yu7WJnXvdyuZbkLOp+dk9RjyXVnh76dnm5h2wvrSB8W7jN42+m6We4gbViwjWmZvms5dfWtC8Zx28JRPYIIgRAfGcLk9NheM7D+r3506QS+fUFOv8ZOSY/lJ8snceHEvjuwjkyIZOGYBH69Yj8Hyhv9Bo9y02Kob+3w/r+N7SPQ1N7horndSWVjGwlRIdh6Wa6Y7M1ocmcpHatpIT4yhF9cM4XnvzTPWxOwU0JUSLfi4QfLG2nvcPHrFfuwGXdG11TPPIxPjaa6qZ1WR/flbXUtDh557yCLxybywpfmcdnU4dzpybTq7GbYqbqpnXtf3snY5ChuXpBFRnwEX14y2ts0pbDLcrhXthax4IH3KKlr5dIp7gt/o5OiyEmN9nbf7bp8zl2ywP0zPiGt+/+dzqYona+vtL6VEX6yWDsbp/SVFTxgGU0iIiJyelmVV05qTFiPK785qTHsLWnA5bJIiw3vV20kObXmZceTPiycK6YNH+hDOaky4sIprGlhd3E9i8cm+byqDe4PyifWmCipa+Hih9dggB9eOoG5o+L53XsHuf+tvYxLjeacnL67GPpjWRYr88pYNCaR2xdnA5BXWs+qvHI6nC6fS86OVDWzfFo6OanR5BDN+RNSeGZ9AXeend1tScN7e8txWXBebvcTPpvNkJMazZ6S40unDlc1MaqXbCZw1yFbNCaRX6/Yj8vyX59JBr8Jw2PY6AkidQZXuv4Oz06MJDEqlE8OV3PDHN+dNOuaHfx6xX7mZceTGBXKHz84xDUzR/S7tp0/q/eX097h4hdv7+O83BTe2V3GyIQIJvWSYQTuk+z2DhfPf2ketz21kXWHKrlo0vGAcmF1C3UtDp8FvqeOcG/bcqSGBaMT2HC4isVj/dety0yI4KKJqQTZTb8yryJDg/jRZRP6HPefMMbw+tf6l6H0n1gwpv9Lq202w+fnjex7oMd/XTKBV7cVkxgVwsWTew/CTfJkeK49WInNQJSfmliddd6qm9qpaGjzZi31NtYYvMGjwupmMuLCiQgJ8vm3IikqlEpPtpQxxhtYcTgtRidFEhZs5/wJKRRWt3DLgiy+9/JOimtbumURvbK1iLoWB9+9cDxhwXYeuWE6AP/cVOjtZgjuvw/f+ed2apsdPHXrnG6foxIiQ4gIsXO0+vjFkWfWF5AaE8bvb5zRI5g6Mr57oKm5vYMLfvMhx2pasNtMz4ymlOOBpsTIUFyW/wuGaZ5AU2EvdaA66ZOgiIiIUFTbwt8+PsIfVvvO3mjrcLLmQAVLc5N7ZMSMT43mYEUj+ZVNZAbgxEMCLyEqlI++d26vgZczRUZ8BPtK6ymqbWFpbu+BoeGx4T2uxm4qqKGxrYOnbpvDFxaNYlJ6LA/fMI3IEDvv7i7r87lbHU5e3VbEyj2+x+73tKNf2iUYdMGEFOpaHGwsqOkxvq7ZQUNrR7efqTvPzqa22cELJ7SsX7GnjJSYUJ/Lf3LTYthbUu/9uT5c2URWH52w/vj5md4Oj/2tMyOD04S0GIrrWqlpamdnUS12m+l2YmqMYeGYBN7ZXept2X6if2wqpKbZwQ8vncAPLsnFsuDJjwr6fQwuV+8ZgesOVhFsN+RXNnHLkxu567kt/Pdru3sdb1kWL2w6xqT0GOZlJzBnVLy3S2KnHZ7lcL6yfmIjgpmcHsv7+8o5UN5IZWM78/vxe/EPn5vhXYYr/5nctBi+d/F4bl+c7bfuW06qu4j6wfJGYsODe81QguNFzqua2qlsbPfWYfIlyG4jPiLEm9FUVNPiN6CSEOXOlmpoc3epK65tYen4ZEYmRDDD043tutmZvPONs7zZUCcun3tx8zEmpMV0yyIEd/DySJdA05oDlazaW873Lh7fIxBkjCEjLsK71A/cFyHmZsf7bJKSFB1KWLDNG8jaV9rAsZoWbls4ilfvWtijCcOIuAhCg2wcLG/kWK37Mel+mjqMT43BbjPs8ASue6NAk4iIyBC3vbCWc365mv96ZRc/f3svHx6o7DHm4/xqmtqdPTImwF0HpL3Dxc6iOkYmKNAkAycjLoLOc1p/GUipsWGU1bfS3N7hvSrbWbtoQpcP7qFBds7OSeK9vWW9niy3d7j4y4f5zLt/FXc/v427nttCTVPPltgr89wBqK4BsMVjkwix27z3ddV5UpERf/wD/8yR8cwcGcfT6wq6BYS3FtawcHSizxOy8Z5lKMV1rTS2dVDR0NZjiciJokKDeOrWOTx03TTmnuHByTNdZ/2vvJJ6dhbVMy4lukfr+nsvziUqNIjbn97kLazc1du7S5k4PIaJw2NJiw1nSU4Sb+woxtnLz0RJXQuHK5todTj5xgvbmHf/Kho9J+tduVwW6/OrWD4tncnpsaw9WElydChbjtZ4CzufaFdRPXkl9Vw3y12IeeHoRA5VNHXLUNxZVEeI3dZrNt7S3GS2Ftbyxg73cr2+CnwDp92S49NZWLDdu5zLXyFwgHhPJ7mapnYqG/1nNIG77lJlQxsul8Wxmha/XTI791XV6M5qKqptITspkje/vpifntCNbXisez9dl2TvL2tgZ1Ed18wc0WPfI+Mjui1t68w6vdrHWHBfROn8W1Xf6qCqqZ2RvVwwMMaQGR/hbdDS2VHu8/NH+swUtNvcned2F9d5C6T7e1/CQ+zkpkWztbDnBZKuFGgSEREZwlodTr79z+3ER4bw9j2LSY4O5bE1x1v0WpZFQWUTL24+Rniw3ecH8vFp0Z6xeGsDiAyEzg/Hk9JjvHUkfEmLDaPDZXHVo+tY9ts1dDhdHK5sIn1YeI8ix0vHp1BW38au4p5Xb50ui6v/sI77/p3H1BHDuP+qybR1uPjHpsIeY1fllTE5PZaUmOPHFRkaxIIxCazMK+uRSdh5UnHiFffPzcukoKqZ9Yfcba2b2jooq2/zdqM60QTPz+feknoKPMG07H4UhI8MDWL59PR+tWeXwSvXM/+fFFSz81gtU3ycaKbGhvHnm2ZRUtfK42u7t2gvr29l85EaLupS6+yKaemUN7TxcX5Vj301tXVw1aPrOOfB1Sx44D3+tbWI8oY21h6o6DF2T0k9dS0OFoxJ4Nefmcp/XZLL4zfPxrLgvb09g6+tDif3v5VHaJCNy6elA7BgjPtvUtespp3H6hifFt3rMu6l41OwLHhsTT4j4sIDsgRQAqszIBLbR6Hxzu57lY1t/Qo0JUWHUtHYRkVjG+1OFyP8zH2CN9DURnVTO20dLoYPCycqNKjHEsqU2FCMcWeHd3pp8zGCbMbnkvXMhEiK61po63DXdDpY3khSdGivgbVMT2DKsixvplKWn89bmV0CU4cqmgi2G28HV1/mjIpn85EaDlc2YQykxfY+FmBaxjC2FyqjSURERHrx+/cPcqC8kfuvnsz41BhuXpDFmgOV3iUUz6w/wpIHV/P69mLOHZ/c40o4uDtndZ6Mjow/vTqayZml84Tx3PH+i9N2fojeW9pAQ2sHhyqayK9o9NmR75zxydgMPQoOA3ycX8XOojr+94qJPH3bHG6Yk8mcUfH89eMj3bI9Gts62FpY67ND2NLcFI5UNXe7ug1dM5q6n0xcPCmN2PBgnvvkKHA8E2t0L+3Lc1LdGVp5JfVdOs7p53SoSIgKZe6oeB5aeYCaZgeTeikiPS1jGJPSY3ss43zHsxS0aw2kpbnJRIUG8eq2oh77eWjlfkrqWrnj7GymjIjld5+dTnRYEO/t9f3zAzA/O5GxKdHcvjibSekxpA8LZ8We7uMdThdf/ttm1udXcd+Vk70n5LmpMcRHhvDRIXegyeVyFwLvrYscuAPRKTGhNLc7+7VsTk69SZ7lY8P6yGiK8wSaDlU04XBafpfOgbs7aWVjG8dqOgP5/jKajgexOpfEDe9lyV9okJ2kqFBvRpNlWby2vZglOUk+C82PjI/wdIZzjz9Y3sgYP10fM+LDaW53Ut3U7v093ltGE0BmfKQ3MHWoopGshEifdQA7LRidSKvDxZs7SkiJDuuz1ub0jDifWYpdKdAkIiIyRFmWxfMbCzl/Qop3mdGNczMJD7bz5NoCAN7fV05mfAQv3jmfX31mqs/9hAbZvSfoWjonAyk3LYZvnDeuzwK1Y1OisBm8xY93FtWRX9lEto9gTXxkCDNHxvmsvfTyliKiQ4P4jGcZD8AtC7I4VtPS7cR6f1kDlnX85Kmr6Z4uWCfWuyisbiEmLKjHFe6wYDtXzxjBO7tLqWxs41CFe1nEqETfJylRoUFkxkeQV9LgzWjqq0aTnFmevm0O18/OIMhmmOfpMufLjMxh7DhWi8Pp8m57Z1cp2YmRjOmSMRcWbOfCiam8tavUm5EBsLe0nic+KuCGORnce3EuT906h0unDOfscUm8t7eix/LTdYeqyE6K7JZ9aIzhvNxk1h6soKX9+L7f3e1u+/6/l0/sthTJZjPMH53AmgOVOF0WR6qbaWjt8NuVzRjjDUb3Z9mcnHrejKY+Ak3RoUGMS4nibx+7u3F2Bod6kxQdSkVDG4WewtoZfmo0dWZHVTa2U1znHu+vttTwYcdr/xXVtlBS18pZ43wXmh/ZpTOcZVkcKm/0FuX2JbNLge8jVU3d9uFLVmIEze1OimpbOFTe2O3n15c5o+KxGff+/dVn6jS9H90XFWgSERE5w7R1OPnD6kN86ZlNfOXZzfx21QHySnoWeT1Q3khFQxvndakZMywihIsmpfLunlLaO1xsKqhh0dhEZmXF+8xm6pTjKRispXMykOw2w93nje3zqvbIhEh2/vhCfrp8EhEhdlbvK6ehtcNnRhPAojFJ5JXW09Dq8G5raXfy9q4Slk1O6/azcf6EFBIiQ3h9e7F324GyBsB3B7dxKdGE2G3sKjoh0FTT3OuSnuvnZOBwWry1q9S71MHfSUduWjQ7imrZV9ZAakxYj+WBcmYLC7bzwNVT2PHjCxjrp4vgzJFxtDpc3r8XVY3u5XEXTkrtUaPowokpNLR2sO1orXfbPzcdw24zfPfC8d3Gnjs+mcrG7stPXS6LTQXVzB3VM9Bz/oRUWh0u1nZZDvfvnSUkRoXy2bk9g8gXT0qloqGNDYereN8T4O0s1tyba2eNIDspstdAgAys3LQYjIFhEf4DTcYY7r9qMk3t7uyapH7UaGp1uNhY4O7E6C+jKT6ya0aTO9DUW0YTuINQneM2H3FnBvb2/7Dzs9LRqmbK6ttoaOvwGwzqGmgqqGomJSa0W+fRE3U2/nhvbzlHqpsZ7SdbCtwBvc4sQH/vSadRiZF9BgEVaBIRERkgdz27hUdXHwzoPgsqm7jkt2v5+dt7OVTRyN6SBn6zcj9X/O4jb6p4p7Weot8LT2hnfO74ZGqaHfz9k6M0tnUw188V8E6XTx3OZVOHExPm/4OHyGARGRrkbvWcFuMtxt1boGla5jAsq3vW0bt7Smlqd3LljPRuY4PtNs4Zn8zqfeXezJD9ZY2EBdt8Bo5CgmzkpEaz88RAU3Vzr1fbxyZHkRITyob8KvIr3LWl/AWCl+a6W3C/saOk19coZz5/J6Zw/KR4i+ck+fXtxXS4LJZPS+8xdu6oBIyBDYervds+OljJ7Kw473KmTktykjEGVnVZfppf2UR9a4fPzIg5o+KJDg1ixZ5SwB3UfW9vORdNSvFZM2zp+BQiQ+y8urWYv318hOmZw/wG1Dpf63vfWtJnTR8ZGJGhQdy3fLI369SfmSPjucmTxZrmJxAEcHZOEuHBdp7dcJTEqFC/vzeD7TaGRQRT1dhOcW0LoUE24vwEvoYPC6OotgXLsthypIaIEHuvXTuTokIJD7ZzpKrZW6zb39K5EXERGOPOjj1S1eR32Ry4/0YMjw3jmfXuZdyjk/v+vT9/tPuzoL+srU7GGKZl+M9qUqBJRERkANQ0tfPmzhJ+8fY+Vu/rWbviP9HhdHH381upbGzjyVtns+pbS3jv20t49a6FtDtd3uLBnT46WElWQkSPYsNnjUvCbjP8dtUBwP2hvy8XTkzlkRumB+R1iJxKk9JjaXW4A0K9XfWdNsL9gXpb4fHsjTd3lJAWG8acrJ4/H+flJlPf2uG9ar6/rKFbLTNfx7CrqM5bENzlsiisaenWca4rYwxzRyWw4XA1+ZWNZPdxtfozszJ4/OZZpMWGMTe7759nGZqGDwsnLTaMzZ4spZe2FDFxeIw3Y7Wr2IhgxqfGsOGw++9KeUMre0sbely4AM/y08w43t5V6v0/vvVoZ8ZHz5PVkCAbS8YnsyqvHKfL4v195bQ4nCybnObzuMND3Ev5XtxyjPzKpj6Xzsrp4bNzM8lN67nc2JfvX5LLc7fP7TOQPj41hsdvnkVokK1fS/0TPXWXimtbSR8W7rf74PBh4bR1uKhuamfz0RqmZQzrtS5SZ2e4o9VNHCx3Z7z6y2gKD7GzaEwiL28p4nBlk99C4J37XzI+uUsQy3/gFWCBZxlpf5bOQd/L5xRoEhERCbCapvZutSV82XbM/UF+WEQw33hhW6+tnMH9Ab65/XjRxVaHk2UPr+Glzce6jfv9+4fYfqyOn105uVtr90nDYxkWEdztyrPD6eLj/CoWje15UhAbHszsrDiqmtrJjI/os/uIyOmsc7lASJCt12URsRHBZCdGstVzAt7hdLE+v4qzxyVh8xE8Wjw2iRC7zZvBsb+soddW653HUN/a4S0IXtHYRnuHy283rLnZ8VQ0tLGnuL5fXeSW5qaw/t6l3L10bJ9jZeiakRnHliM13tbsV8/w3W4dYK6nU1V7h4t1B90Bp8VjfC9Fu2LacPaVNZBX4j6p3lpYS3RYENm91BY7f0IKVU3tbCus4c2dJSRGhfhcZufd//R0nC6LuIjgXgNScuYKDbKzwEeQ05cFYxJ5+SsLuO/KSX2OXTQmkQ8PVLCruM7vsjk43mTiQHkjeSUNfS7fzEyIYH9ZI/vKGogJC+pzyfeNczMpqWulsrG9z4wmgCVdloX6qj94ovmjE/jW+eNYNql/Pz9fP9f/3xIFmkREZEg6sSipP+sOVfL/XtxBe4fL7ziXy+Kpjw4z/4FVfPnZzX7Hbj1ai83AU7fOoabZwXMbjvoc19Dq4LxffcDcn63i/rfyaOtw8o9NhewpqeffO0u84/JK6nnkvQMsnza8x4dsm80wJyvee+UZYHthLU3tThb18sFsqadQan+ymUROZ51FZ7MSInrNOAL38rlthbVYlsWu4noaWjt8Zm+Ae9nH/NEJrMoro67FQVl9m99A06R091X7zuVznW2p/RWq7Tzpdln9O4no5O+KvMiMkXEU1bZwyxOfEGQzXO6jNXunednxtDpc7CyqZe3BSoZFBDPRR8F7gEunDCfIZvjXVvcFkq1Ha5mWMcxnoBZgSU4SQTbDf7+2mzd3lHD51HS/P58LRyeQlRDBrQtH+V0OJQIwcXgs41P7zpb63LyROJwWR6qaGT4szO/YnNRojIHvvrgDp8ti5kj/gaZLJqdxtLqZFzYWMiY5qs/fzUtzU0j2BKP6swR64ZhEgu2GtNgwIkP9L5sF91LBry0d22Ppa296+9n13t+vvYiIiJxBKhramHXfSp7/xHdwp6uCyibu+OtmXthUyEtbjvU6buexOq7+4zp+/PoeEqNCWb2vgk0F1b2O33q0hpzUGKZlDGPx2ERe2Hi0Wzv0Tm/sKKG+tYNpGcP40wf53PvyTv70QT4Am4/WYFkWTpfFvS/vJDY8mP++bKLP55ubnUBhdQvFnvoBf/4wn9AgG/OzfZ8onzchhSCb4WwVSpUz3OikSMKCbX12YpueMYzKxjaKalv4yFOkeIGfjlXnTUihoKqZZze4uyGN89NRKCc1mmC7YWdRHa0OJys8NaP8ZTSNTor01pdR3SUJlCumDefGuZmMSorkK0tG+61hNMcT7Hx9ewlrDlSwcHRiryefcZEhLMlJ5tVtxdS3OthXWu/tuOhLTFgw87IT2FVUz5KcJL57UY7f4w6y23j/20v42rlj+vEqRfpnTHIUC8e4/5/3ldE0KjGSh66bRpGnIHhfS8uWT0/na+eOwWX5XzbXKdhu47rZ7g6n/ekcGhkaxPkTUvpVZ/Nk6Du0JSIiEgDFtS189bktLBqTyDcv8P+B8WT7w+pDVDe189S6Aq6bndHrVaSapnbu+Otm7DZDTko0j64+yDUzRxB8wpr7lXvKuONvm4mLCObBa6eybHIqZ/3ifX6zcj/P3j6vx35dLovBDrfEAAAgAElEQVRthbVcNtV9pfizczL58rNbWL2vnKW5Kd3GvrCxkLHJUTxz2xweWnmAhz11ky6cmMI7u8s4VNHE+kOVbCus5eHrp/V6Jarzg8Ynh6sJCbLx7p4y7r14PLG9FLYclRjJ2v93LikxKpQqZ7Ygu437r5rc5wf3aRnuq9PbCmtZe6CSCWkxJPg5Cb9qejp/XH2IB9/ZB/juONcpNMhOTmo0f/ogn798mI/LgtlZcX5riLjrNMXz5s6SPms0ifRXYlQo9105uV9j4yNDGJcSxVPrCgC4bKr/JTdXzUhnZV4Zd/99Ky4LpvextOie88aSmxbNty7I6VeWkrL15GS4aX4WHx2s6leR7CumpRMfGcLB8kaGRfSdGfTN88eRGBXa7+zxO84ezciESHLT+q65BPD7z84YsJ8LBZpERIa4umYHL245xrLJqTS0dvCH1Ye4ZUEWU/voJuFyWTS1dxBst/X5AXB3cR23PbWRsvo2Cqqa+frSsb0WSPxPjr+4roX4yBASo0L9pta7XBZlDa38bcMRkqND2VvqrkExZUTP11pU28JNj2+gsKaFJ26eTavDye3PbOLVbcVcM/N4zYodx2r52t+3MnF4DH/9wlxvu9c7zx7NT9/MY1NBNbO6FAuuaWqnsrGNhtYO79Xc8yakkBQdyrMbjnYLNO0va2BbYS3/dUkuxhjuXjrWXZSyroXvXJjDO7vL2HC4it+/f4i5o+K5fGrvSxxy02KIDgvit6sOUNnYxqT0GL6waJTf9zY11n+auMiZ4srpvdeh6TQ+LZqIEDu/WbGfwuoWblmY5Xd8ZGgQP10+iVuf2khkiL3Pk5SfLp/MB/sqaOtwsmhsIvOzE/o8Qbh21gjaOlykxehnVQbGL66Zyr7SemZlxffZQv283BSumpHOv7YWYQx9fs6YlRXf7e+nyEA4PzeFX39mKhdOTO3X+MVjk1g8tn/Z4MYYbl6Q1e9jiQoN6vYZtD/7HygKNImInIEefGcf/95VwtGqZiJDg1iSk8RvPjPNZ0r7b987wONrD/PAW3lYFnS4LMobWn1m4gB8uL+Cp9YVsP5QFS0OJ7Hhwbz05QW9pv1+uL+Crzy7heiwIO45bywPrTzAxoIa5vtYclLT1M7mIzXkpEYzIs5/dw+H08UNf/6YTZ42zAA5KdE8+8W5PlP9H1uTzy/e3ofNBpZl8dStc7jy0Y/4x6ZCn4Gmu57dQnlDG3+9bQ5zsxOwLIvctBgeW5PP1TPSMcZQ2djGF5/ZRHxkCI/dPMsbZAK4ce5IHnnvIE9+VMCsrHgKKpv4yRt7WLW33LvGvvNqbrDdxvWzM/jd+wfJrzjeQeqZ9QUE2w1XTne3lrbZDL+8dqq3a09cRDCPrDpIaX0r9105ye/7ZbcZLpmcxrt7ypgzKoF7l40PWLBPZCgIttv4w+dmcu9LO2h3unqtb9bVOeOTuWFOJk1tHX1+4J+WMazPdtEnWpKTzJIuhf9FTrVP8/82JMjGrz8zja8sGUNpXSvx/awFIzKQbDbDVX6K4otvA/YJ0xiTYYx53xizxxiz2xhzt2f7j40xRcaYbZ7bsi6PudcYc9AYs88Yc2GX7Rd5th00xnyvy/ZRxpgNnu0vGGNCPNtDPd8f9NyfdepeuYjIyZVXUs/v3j9IYlQoX1g8iiU5Sby6rZi/eeqEdNXQ6uCFjYWck5PETfOzuGl+Fneclc1HB6vYX9bQY/zB8ga++Mwm9pbUc83MEXx/2XhsBr7xwjYczu6Fsts6nPzq3X3c+tRGRsSF86+vLOSLi7MJDbLxzu7SHvuub3Vw7Z/Wc/szm1j8i/f57F82UFTbwuHKJo5WNfcY/++dJWw6UsMXF4/id5+dzg8vncCR6iZuevwT6loc3nGtDie/f/8gP30zj7nZ8Vw5fQQ/XT6JCcNjWDY5jVe3FffoELenuJ5thbV88/xxzM12B8SMMdw4N9ObBeV0WXz971upbXbwl5tmkRzdPaMgPMTOdbMzeHt3KZuP1HDlox/xcX4VtyzIIshmGB4b1q1T1E3zswi223hs7WEAdhXV8dyGo1w3O6PH8hxjDMYYZo6Mo7S+lZEJEd26zPXmgaunsOWH5/PYzbP6vPIsIj2dPS6JFd88mydvnc1iHx0bfbn/qsn89obpJ/nIRE4fY5KjfHY8FZEzx0BmNHUA37Isa4sxJhrYbIxZ4bnvN5ZlPdh1sDFmAnA9MBEYDqw0xozz3P174HzgGLDRGPOaZVl7gJ979vW8MeaPwBeAP3j+rbEsa4wx5nrPuOtO6qsVETlFXtp8jGC74U+fm0lcZAiWZVHT7OCBt/ZyTk5yt+KyL24+RmNbB3efN857RbKzdtFT6wr4WZc6De0dLu5+fhuRoUG88tWF3sBKZnwEd/5tC79//yD3nDfOO/72pzex5kAlV81I538un0h0mDvb56xxSby9q5QfXTrBm2HVGbQpqGziV9dOpbKxjd+uOsCin7+HZbkzdzb+4DxvBo5lWTy25jDZSZHce3Gudz+jkyK5/elN/GbFfn58+USe3XCEn72ZR1O7k0smp/Hw9dO6ZfFcNzuDf20t4rXtRVw3O9O7/R+bCgmx21g+Lb3be3vZ1OH85I09vLCxkKjQEtYdquIX10xhQi9ddj4/byR/WZPPDX/5mBC7jde+tojRSVF8f1ku7U5XtwyzpOhQrp6Rzoubj3Hz/Cx+8Mou4iND+M6F43ud65kj41mZV85N87P67P4hIoERGRrUr8CuiIjIUDVgGU2WZZVYlrXF83UDkAek+3nIFcDzlmW1WZZ1GDgIzPHcDlqWlW9ZVjvwPHCFcecnnwu86Hn808DyLvt62vP1i8BSo+pxIjLI1bc6uOJ3a7npiU/IK6n3OcbhdPHKtiKWjk/xFoU2xvCzKydhWfDIewe8Y9s6nDz5UQEzMrunvcdHhrB8WjovbznWLZPouQ1H2F1cz/1XTe6WvXPRpDQunZLGHz84RHlDK+DuqLbmQCXfvSiHX39mmjfIBHDRxFRK61vZWljr3fbe3nJW76vgh5dO4OqZI7jj7NG8dfdZfHFxNjfOzaSm2cGWo8fHf3K4mp1FdXxh0ahuAZYlOclcNCmVf20torqpnQfe2sv4tBj+9oW5PHLD9B5LxeaOimd8ajRPrTviXY7W6nDyr61FXDAxpUdh7djwYJZNTuMfmwr504f5fG5eJp+ZldHblJERH8HS8cm0d7j4xTVTvFlEIUE2ony0mr19cTYOp4sLH/qQ7YW1/PDSCd2W453o8mnDuXbmCG8XEhERERGRgTYoijN4lq5NBzZ4Nn3VGLPDGPOEMaazHUE6UNjlYcc823rbngDUWpbVccL2bvvy3F/nGX/icX3JGLPJGLOpoqLi//QaRUT8cThduHy0tu/kcll884Vt7CquZ3thLRc/vIblv3fXF+rqw/0VVDa2c/UJhQJHxEWwfHo6r20vpq7ZvazsF2/v42i1uzD3ie46ZwyhQXZufeoT7/iXtxYxKT3GZzHEb1+Qg8Np8ej7hwB44qMCokODuGl+Vo+x509MITo0iMfX5nu3vb69mLiIYD4793hWUWZCBN9flsv/u3g8QTbD+/vKAWhpd/KTN/cQHxnC1T7WzF8/O5O6FgdfeXYzDa0d/OCSXBaN9d1yubMIY15JPRsL3LWeXtlaRF2Lo9fgzXWzM3A4Lc4el8SPL5voc0xX9181hSdvnc2yyf678QCMToriV9dO5UeXTuBfX1nAFdP8XX+B9GHh/PLaqT6DViIiIiIiA2HAA03GmCjgJeAey7LqcS9tGw1MA0qAXw3UsVmW9WfLsmZZljUrKal/leNFRD6NysY27n8rj5k/WcG1f1pPdVN7jzHl9a3c/cI2VuaV88NLcvnwO+fwnQtzaOtw8d0Xd3jrHXUuJ0uMCmFJTs/fWTfOzaTV4eKlLcdYsaeMx9ce5qb5I30Wks1MiOBPn5/J0epmvvb8Vg6WN7DjWF2PpWSdshIjuXbmCJ7bcJSn1xXw750lXDc7w2cAJCYsmJsWjOStXaUcLG+gub2DFXvKuHhyGsE+ilPHhAUzc2Qcq/dVYFkW/++lHewurueX10zx2e1uwegERsSF83F+NVMzhjGjj/bJy6elExsezC/f2cvWozX85I09zM6KY+Fo3/Uj5mUn8PcvzuMPn5vRr2LaSdGhn2qZzVUzRnDbolF9tn0WERERERmMBjTQZIwJxh1ketayrJcBLMsqsyzLaVmWC/gL7qVxAEVA18vLIzzbetteBQwzxgSdsL3bvjz3x3rGi4j8x97dXcrjaw97M4D8aXU4eXVbERf85kP+8mE+s7Li2VVUx9V/WEdpXat33IGyBpb+6gPe2VXKPeeN5eYFWcRGBHPXOWN49a6FTBkRy3f+uZ2jVc38e2cp6/OruHvpWJ8Bm0npsUzLGMbDqw5wx183MT41mu8vy+31GOdlJ/CjSyd4u8bZjLtGUW++vnQscZHB/PdruwH8tmu9beEowoLsPLzqICvzymlxOLlsSu/7Pmd8Mnkl9Xz3xR28tr2Yb1+Qw9LcFJ9jbTbDtTMzPM/T+zF0Cg+x88NLJ7CtsJYrH11HcJCNh6+f7rfm0fzRCUSEKItIREREROREprMmxSl/YndNpKeBasuy7umyPc2yrBLP198A5lqWdb0xZiLwHO7A03BgFTAWMMB+YCnuANJG4LOWZe02xvwTeKlLMfAdlmU9aoy5C5hsWdadnmLgV1mW9Rl/xztr1ixr06ZNgX0TROSM0dLuZN79q6hrcRAebOeamSO4eUEWY5J7dvb6yRt7eGZ9AQ6nxZQRsTx47VTGpUSzqaCam574hMnpsTz3xXnYbYZbnvyELUdqeOWuhd6W910drWrmkkfW4HC6CAu2Mzw2nNe/tgh7L0GSV7cVcffz27huVgY/vGxCn0uuLMvipic+Yc2BShaPTeSvX5jrd7zTZVFQ1USH0yInNdrv2Afe2ssfPzhEkM0QHxnC+nuX9nrc+0obuPChDwG4af5I/ufyiX5bhTe2dfDK1iKun53Rr6wjcHd5+/nbe7l9cTZnj1MWq4iIiIhIb4wxmy3LmuXzvgEMNC0C1gA7gc6e2N8HbsC9bM4CCoA7ugSefgDchrtj3T2WZb3l2b4MeAiwA09YlnWfZ3s27uLg8cBW4HOWZbUZY8KAv+KuC1UNXG9Z1vFiIT4o0CQibR1OQoN6LtUCd5ey7764g/uunMS2o7W8uq2YdqeLSekx3L4om+XT3UvOSutamf/AKpaOT+a62ZksyUnqln304uZjfPuf27njrGymjBjGXc9t4QfLcvniWdm9HteRqiYeWnmAd3aX8tcvzGHmyHi/r6O8vpXkmDC/Y7oqqWvhxsc2cO/FuZw/wXcW0X/C6bL4984SHluTzyVT0vjSWaN7HWtZFp9//BNyUqP5wbJcdVgTERERERlAgzLQdLpRoElkaHtlaxH3vryTRWMTefCaqcRGdO8Edvnv1tLqcPLOPWdhjKGioY1Xthbx8tYi8krquWFOBj++fCJPrC3g52/vZfW3l5CVGNnjeSzL4pv/2M6/trpX+mbGR7Dim2f1GuA68bFqoCkiIiIiIiebv0CTCkyIyJD1yKoDbDxSw58/P9NnUelWh5PV+8r519Yi3tldxvjUaN7fW86y367hd5+d7i3WvPVoDTuO1fGTK44v50qKDuWLZ2Vz26JR/HrFPn7//iEaWjvYV9rAzJFxPoNM4O6C9uC1U7lhTibbC2uZl53QryBT52NFREREREQGkgJNIjIk1TU7eHT1IVocTn765h5+unyy9z6Xy+LHr+/mpc3HaGp3khAZwtfOHcPXl45lV1EdX31uK9f+cT3fu3g8N84dyfde2klCZIh3eVxXdpvhOxeOJzosmAfe2gvAz66c3GPciY+ZMyqeOaP8L4ETEREREREZbBRoEpEh6R+bCmlxOLlgQgp/+/goC0Ynsmxymve+Z9YfYfm04VwzM4N52fHegtLTM+P499cX8+0Xt/PTN/N4bM1hSutbefq2OUSHBff6fHeclc2xmmbe2lnKJVPSTslrFBEREREROdVUo6mfVKNJ5PTW6nCy7lAl+0obSYoO5aGV+xk+LJxnb5/LVY+uo7yhlfe+tQSH08U5D65mbHI0L9wxr9flaJZl8eRHBdz/Vh53nj2ab12Q06/j8FdQXERERERE5HSgGk0iMqi4XBZ/+OAQKTFhXDNzhM8xlmVRUNVMU1sHFQ1t1Lc6uHBiao9aSu/tLeO1bcWU1LUyKT2WG+dmsq2wloLKJkbERXBubjJxESHc/MQnbDhc3e2xP1iWS7Ddxv9cMZGrHl3H/76+h+K6FupaHPxPl3pLvhhjuG3RKK6fk0FESP9/lSrIJCIiIiIiZzIFmkTklLEsi4rGNu57M49XtxUTEmRj7qh4MuIjACivbwUDseHBfPW5razYU9bt8RdPSuXRG2dgjKGl3cl3X9rB69uLSYoOJX1YOE+tK+DxtYe7PSYlJpSzxyWx4XA1P75sAlfOGEFpXSuF1c0szU0GYEZmHFfPGMELmwqJCLHzP5dPJDctpl+v6dMEmURERERERM50OkMS+Q99dLCSLz2ziX/fvZiRCb47iMlxm4/U8NXntlBS1wq4axY9vb6An7+9l999dga1ze1c8shaaprayUyIIL+iibuXjiU3LYbEqBA+zq/iwXf389DKA9xz3lj+943dvLGjmG+cN44vLxlNSJCNo1XNvL27hJkj45icPozdxe7C3f/YdIwrp6dzy8JRgDuQlZMa3e34fnhpLjmpUSyflk5yTNipfntERERERETOCAo0ifyHnllfQFO7k5e3FPGN88cN9OGcUqV1rXywv5wP9ldQXNtKWLCNhMhQshIjuHlBFsnRxwM1lmXxxo4Svv3P7aTFhvHfl01gasYwZmTGERps57erDrBozFE+Kaimuqmda2eO4L295Txw1WSun5Pp3c/MkXHkVzTx8KoDrDlQwZajtXxlyWjuPm+sd0xmQgRfOmu09/vpmXH8664FvLyliM/NG+n3NQ2LCOn2WBEREREREfn0VAy8n1QMXLqqaWpnzs9W4nBaZCdGsupbZ3er5/OXD/PJr2zklgWjemTOnCot7U7yKxuJDQ/G5YJ2p5PsxChstt7rDr2/r5wn1h6mud1JeLCdrMQIbpw7kty0GFwui3f3lPKH1YfYfqwOgNSYMMamRNHmcFHZ1MbRqmbCgu3ceXY2Ny3I4mhVMw+t3M/KvHKmZw7jsZtmkRAV6n2+prYObnnyEzYW1ADwtXPH+C2q7XRZPL42nwff3c+k4TG8cMd8gj3d4EREREREROTU8FcMXIGmflKg6czQ1uHkrZ2lbCusJSLETqvDBcBXzx1DfGRIj/Eul8VfPz7C6n3lHKpoIj4yhFkj44iLDOGX7+zj5vkjeXr9EV768gK2F9YSFxlMUU0LD767H5sBlwXXzcrgvy7NJTosuNu+O5wu1h6s5GB5I83tTprbnXQ4XUSHBbNgTAKzs+J9Hv/qfRW8s6uUtQcrGREXzjfPz2HhmARvoKu+1cGa/ZXc9+Yeij3L1DqlxoRx5Yx07jxrNLERwbQ6nLy05RgbD1dT3tDGukNVZMSHkxkfQWNrBwfLGwH43rJc/rmpkB3H6shOjOQzszNYkpNETkp0twDb4com7nszj5V5ZYTYbbQ7XUSE2PnGeeO4dWEWQT6CQi6Xxd83HmXb0Vp+euWkfhXLrmhoIyo0iPAQFdYWERERERE51RRoCgAFmk4vLpdFZWMbSdGh3kDImztK+PHru6loaCMixE5bh4vwYDstDicXTUrl95+dwfbCWjYcrqK0ro2Jw2N4a1cpK/PKGJMcRU5qNDVN7XycX4XLgty0GP7+xbnMvm8ldpvxBq0ALp86nB9dNoG/fJjPX9bkExUaRHxkCDabIchmsBlDZWMblY3t3seE2G3YbYYWhxOAWxZksXBMItVNbaw54A5IFVY309TuJC4imAVjEtl6pIbiulYSo0LJToqkoLKJ8oY2AManRnPn2aNp63Bit9k8GUllrNpbxrDwYEYnRXGoopGaZgdpsWHEhgdz8aQ07lyS7Q32lNa1cvMTn7CvrIGUmFC+e+F4lk9Px+4nKwpgV1EdL2wsZFxKFJdPTSc2ItjveBERERERETl9KNAUAAo0DX55JfW8tPkYhTXNbD5SS2VjGxdPSuWLZ2Xz5w/yeXt3KVNGxPKtC3JYPCbRu4Tsd+8d4MF393PBhBTe9XQ5Cwmy0d7hwm4z/OjSCdw0f6Q3YLXlaA0/ezOPWxZmcemU4Xzl2c2s2V/JL6+dSlJ0CHtKGrh+doZ3SdeWozX8fcNR2jpcOC0Ll8vC6bKICLFz8eQ05mUnEBli92b7NLd38PO39vL0+iPe15YcHcqUEbGMiItgSU4Si8YkEmS30epw8saOEj7cX8GxmmZGJUZ5gmJRnDU2yWcG0e7iOh5aeYD6FgfpceFcOzODednx3TKTuqprcbBiTxkXT0olMlRl3URERERERIY6BZoCQIGmwcvhdPGXNfn8ZsV+bMaQER/BxOExJEeH8sRHBThdFpEhdr5yzhjuOCu7R/DF4XRxxe8+Yk9JPTfOzeRbF+QwLDyYvaUNhAXbyE6K8vv8Le1OHC4XMWGBzdoprG6mttlBRKid7MTIXgNBIiIiIiIiIqeSAk0BoEDT4GFZFmsPVrIhv5rosCBe2nKM/WWNLJucyn3LJxPXpdbSlqM1fHK4mutmZXTbfqLy+lYKqpqZM6pnXSQREREREREROc5foEnrYGRQa3U42XGsjn2l9VQ3OTha3czWwhryK5q8YzLiw/nz52dy/oSUHlk/MzLjmJEZ1+fzJMeEkRwTFvDjFxERERERERlKhnSgyRhzEfAwYAcesyzrgQE+JMHdWS2vpIEXNxfy4uZj3YpsJ0eHMj4thq8sGcOlU9JodTiJCg3yWYtIRERERERERE6tIRtoMsbYgd8D5wPHgI3GmNcsy9ozsEfWt/1lDXz/5Z3MHhXPlxZn+10SNlg4nC6qGtuJjwzhk8PVPLXuMBEhQYxJjsJmoKi2ld3FdVQ0tFHZ2IbDaRESZGP5tOFcMCGVSemxxEeGEBLUPaAUFqz29iIiIiIiIiKDxZANNAFzgIOWZeUDGGOeB64AfAaaapra+fsnR3G6LCzL3TXMZYHLsnBZFu0dLsob2mjvcJEWG47T5aKm2YHLUwPLZUFxbQulda0MiwgmPMROU1sHAHabIchm8/xrCLJ3/95uMwTZbYQG2YgJC+LJdQUAbD5aw+NrDzNrZByjEiMxBgwGY8BmDOEhduIjQrDZDC6X5TlW9zFbVtfjp9trsqzuY10uiw7P4zuclrdzmssCC3C5LFodTlocTlodTpwW4HndDqdFc3sHRbUtOJzH64ElR4cSbLfx2vZiAGLDg5mcHktOSjSJ0aFMTo9l7qh4EqJCAz/zIiIiIiIiInJSDOVAUzpQ2OX7Y8DcrgOMMV8CvgQQkjqGe1/e6XeHseHBBNsNlY3tGOP+Psh2vGZQSkwYGfER1LW0U93UTmRIEMZAh8uixeGkw+miw+UO+HT+63C6vN+3tjtpaOtgfGo0j98ym6a2Dl7YWMj6Q1W8vasUd3zH8gZ/mtuddLj6V+zd5glO2Wzm+NfmeNCqM+DVeXPfj3dMWLCd8GA7ESFB2D2v2RgIshnCQ4K4aFIa6XHhVDW2kRITxlUz0gkNstPW4cRgCLYbdVUTEREREREROc0N5UBTnyzL+jPwZ4Cp02dY79y71B1c8QRa7MZgbGA37uBL5zKuVoeTYLvNG3AJpFaHk9Agmzco88NLJ/g7fhrbOnBZ7kBSZ4DIdAkk2QwDGuAJDdLSNxEREREREZEzxVAONBUBGV2+H+HZ5lOw3UZqbP+6kp3MukGfZt/GGKLDgk/asYiIiIiIiIiIdDWUW3VtBMYaY0YZY0KA64HXBviYREREREREREROW0M2o8myrA5jzFeBdwA78IRlWbsH+LBERERERERERE5bxrL6Vyx6qDPGVABHBvo4BlgiUDnQByHdaE4GH83J4KW5GXw0J4OP5mRw0DwMPpqTwUdzMnhpbgafkzEnIy3LSvJ1hwJN0m/GmE2WZc0a6OOQ4zQng4/mZPDS3Aw+mpPBR3MyOGgeBh/NyeCjORm8NDeDz6mek6Fco0lERERERERERAJIgSYREREREREREQkIBZrk0/jzQB+A9KA5GXw0J4OX5mbw0ZwMPpqTwUHzMPhoTgYfzcngpbkZfE7pnKhGk4iIiIiIiIiIBIQymkREREREREREJCAUaBIRERERERERkYBQoOkMZozJMMa8b4zZY4zZbYy527M93hizwhhzwPNvnGf7jcaYHcaYncaYdcaYqV32dZExZp8x5qAx5nt+nvNmz34PGGNu7rL9bWPMds9x/NEYYz+Zr32wGmRzstrz+G2eW/LJfO2D1WCZE2NMdJe52GaMqTTGPHSyX/9gNljmxrP9Os++dxtjfn4yX/dgNkBz8rYxptYY88YJ27/qeaxljEk8Wa95sAvwnDxhjCk3xuzq4zl9zt1QnpNBNg+PG/dnrh3GmBeNMVEn63UPZoNsTp4yxhw2x//GTztZr3swG2RzsqbLfBQbY145Wa/7dDDI5uZcY8wWY8wuY8zTxpigk/W6B7MBmhOf44wx13qOwWWMmdWvF2BZlm5n6A1IA2Z4vo4G9gMTgF8A3/Ns/x7wc8/XC4A4z9cXAxs8X9uBQ0A2EAJsByb4eL54IN/zb5zn6879xXj+NcBLwPUD/f5oTlgNzBro92Sgb4NpTk4Ytxk4a6DfH80NcUACcBRI8ox7Glg60O/PUJgTz9ilwGXAGydsnw5kAQVA4kC/N6f7nHi+PwuYAezy83y9zt1QnpNBNg8xXcb9uvP5h9ptkM3JU8A1A/2eDPRtMM3JCeNeAp8R8VsAACAASURBVG4a6PdHc8ME3IkwhcA4z7j/Bb4w0O/PUJgTf+OAXCCHT3H+qIymM5hlWSWWZW3xfN0A5AHpwBW4T5Tw/LvcM2adZVk1nu0fAyM8X88BDlqWlW9ZVjvwvGcfJ7oQWGFZVrVnPyuAizz7rveMCcL9y2RIVqEfTHMiboNxTowx44BkYE1gXuXpaRDNTTZwwLKsCs+4lcDVgXulp48BmBMsy1oFNPjYvtWyrIJAvK7TWQDnBMuyPgSq+3jKXuduKM/JIJuHegBjjAHC0WeuAZ8TcRuMc2KMiQHOBYZ0RtMgmpsEoN2yrP2ecSvQZ65TNSe9jrMsK8+yrH2f5vgVaBoijDFZuK80bgBSLMsq8dxVCqT4eMgXgLc8X6fjjix3OubZdiK/44wx7wDluE8YXvy0r+FMMxjmBHjSkzL8Q88H0iFtkMwJwPXAC5bnEoIM+NwcBHKMMVme9O3lQMZ/9ELOIKdoTuRT+D/OSX9p7vowGObBGPOk5/nGA498yn2fcQbDnAD3eZa1/MYYE/op933GGSRzAu6/6au6XBQf8gZ4biqBoC7Ls65Bn7lO1ZwE1JBc7zjUGPfa/JeAeyzLqu8aT7AsyzLGWCeMPwf3f85FgTwOy7IuNMaEAc/ivnKwIpD7P50Mkjm50bKsImNMtOdYPg88E8D9n1YGyZx0uh73fAgDPzeWZdUYY74MvAC4gHXA6EDs+3Q10HMiPWlOBofBMg+WZd1q3PUwHwGuA54M5P5PJ4NkTu7FfUIYAvwZ+H+4lwQNSYNkTjrdADx2EvZ7WhroufE8x/VAZ0D2XcAZiH2frgZ6Tv5Tymg6wxljgnH/x3zWsqyXPZvLjDFpnvvTcGcZdY6fgvuX7RWWZVV5NhfRPZI8Aigyxsw1x4voXd7buK7HY1lWK/AqQziVeLDMiWVZnf82AM/hTmEdkgbLnHj2PRUIsixrc0Bf5GlqsMyNZVmvW5Y117Ks+cA+3Ovkh6RTPCfSDwGak972ndFlTu6kH3/rh6rBNg+WZTlxL0cZkstOYPDMiWcJjGVZVhvuoJ8+cw2CnxPjblowB3jz//7KTn+DZW4sy1pvWdZiy7LmAB+iz1ynak4CyxoEha50Ozk33IW3nwEeOmH7L+leQOwXnq8zcS8RWXDC+CDcRXFHcbxY20QfzxcPHMZdPDfO83U8EAWkddnXC8BXB/r9GeJzEoSnUCsQjHsp450D/f4M5Tnpcv8DwP8M9PsyGG6DaW6AZM+/ccA2PEUqh9rtVM9Jl/FLOKEYeJf7ChhihadPxpx0eVwW/gu49jl3Q3FOBss8eI5jTJdjehB4cKDfn6E8J5770roc00PAAwP9/gz1OfHcfyfw9EC/L4PhNpjmhuOfuUKBVcC5A/3+DIU56c84PkUx8AF/A3U7eTfc6XIWsAP3idE2YBnuImurgAO4i9p2nkg9BtR0Gbupy76W4Y4mHwJ+4Oc5b/P8Bz8I3OrZlgJs9BzHLtxp3EED/f4M8TmJxN3VbAewG3gYsA/0+zOU56TLffnA+IF+XwbDbTDNDfB3YI/nNiS7Zg7gnKwBKoAW3DUcLvRs/7rn+w6gGHhsoN+fM2BO/g6UAA7Pe+uz009vczeU52SwzAPu1QofATtxf+Z6li5d6IbSbbDMiWf7e13m5G9A1EC/P0N9Tjz3rQYuGuj3ZTDcBtPc4A6k5OHOIL9noN+bITYnPscBV3q+bwPKgHf6On7jeaCIiIiIiIiIiMj/iWo0iYiIiIiIiIhIQCjQJCIiIiIiIiIiAaFAk4iIiIiIiIiIBIQCTSIiIiIiIiIiEhAKNImIiIiIiIiISEAo0CQiIiIiIiIiIgGhQJOIiIiIiIiIiASEAk0iIiIiIiIiIhIQCjSJiIiIiIiIiEhAKNAkIiIiIiIiIiIBoUCTiIiIiIiIiIgEhAJNIiIiIiIiIiISEAo0iYiIiIiIiIhIQCjQJCIiIiIiIiIiAaFAk4iIiIiIiIiIBIQCTSIiIiIiIiIiEhAKNImIiIiIiIiISEAo0CQiIiIiIiIiIgGhQJOIiIiIiIiIiASEAk0iIiIiIiIiIhIQCjSJiIiIiIiIiEhAKNAkIiIiIiIiIiIBoUCTiIiIiIiIiIgEhAJNIiIiIiIiIiISEAo0iYiIiIiIiIhIQCjQJCIiIiIiIiIiAaFAk4iIiIiIiIiIBIQCTSIiIiIiIiIiEhAKNImIiIiIiIiISEAo0CQiIiIiIiIiIgGhQJOIiIiIiIiIiASEAk0iIiIiIiIiIhIQCjSJiIiIiIiIiEhAKNAkIiIiIiIiIiIBoUCTiIiIiIiIiIgEhAJNIiIiIiIiIiISEAo0iYiIiIiIiIhIQCjQJCIiIiIiIiIiAaFAk4iIiIiIiIiIBIQCTSIiIiIiIiIiEhAKNImIiIiIiIiISEAo0CQiIiIiIiIiIgGhQJOIiIiIiIiIiASEAk0iIiIiIiIiIhIQCjSJiIiIiIiIiEhAKNAkIiIiIiIiIiIBoUCTiIiIiIiIiIgEhAJNIiIiIiIiIiISEAo0iYiIiIiIiIhIQCjQJCIiIiIiIiIiAaFAk4iIiIiIiIiIBIQCTSIiIiIiIiIiEhAKNImIiIiIiIiISEAo0CQiIiIiIiIiIgGhQJOIiIiIiIiIiASEAk0iIiIiIiIiIhIQCjSJiIiIiIiIiEhAKNAkIiIiIiIiIiIBoUCTiIiIiIiIiIgEhAJNIiIiIiIiIiISEAo0iYiIiIiIiIhIQAQN9AGcLhITE62srKyBPgwRERERERERkQG1efPmSsuyknzdp0BTP2VlZbFp06aBPgwRERERERERkQFljDnS231aOiciIiIiIiIiIgGhjCYRERERETkjdThdrDtUxVu7SmnvcJEcE0pSVCjGwKGKRlodLsKCbUwcHsuMzDjG/n/27js+ruJa4PhvtmulVe+SJctV7gXZxmACmN5bqAGcQAKBUNIJyUuDvIS8EAJplAChQ2gJDoEABky1ce9VliWrWL1v33vn/bFrY8AYF8m7ks7389Fnd+fOnT3rq+I9O3MmNwWLRcU7bCGEGNAk0SSEEEIIIYQYVDY0dPPkRzW8uq6Rdm+IFKeNVJeNlt4gYUMD4HHZ8Dht9AQjPLF4R7TNaePUifnMO2o4ualO0pLsOG3WeL4UIYQYcCTRJIQQQgghhBgUdnb5+dELa3lnSwsuu4WTxudz5uQCjh2Tg8tuxTQ1nf4whqnJTnGglEJrTXWbjxU1HXy0vY35qxt4bnkdAMkOKyeNz+Prx4xgYlFanF+dEEIMDEprHe8YBoSKigotxcCFEEIIIYRITK+u3cmPXlxL2DC56YTRXDqjhDS3/YDH6fCGeHNTM/6wwYaGLl5Z20hPIMylM0s4Y1IB00szcNlllpMQYmhTSi3XWlfs9ZgkmvaPJJqEEEIIIYRIPN5ghF/+ez3PLqtjSnEa91wyjeHZyX02fpc/zO9f38yTH+3AMDWpLhsXzxjGzLIshmUmMSbXI3WdhBBDjiSa+oAkmoQQQgghhEgsq2s7+fY/VlHd5uVbx43i5hNHY7f2z8ba3YEwS7e38+LKev67rhHDjL6PyvU4OWdqId86fhTpbke/PLcQQiSahEw0KaXGAv/Yo2kE8DMgHfgG0BJr/7HW+pXYObcCVwMGcJPW+rVY+6nAPYAVeFBrfUesvQx4BsgClgNXaK1DSikn8BhwBNAGXKy1rt5XvJJoEkIIIYQQIv4auwIEwgavrNvJXa9vIdfj5A8XT2XWiKzDFkOnL8SOdh9bmnpZsKGJ1zc0kuK08YNTy/nKzBKZ4SSEGPQSMtH0iSCUsgL1wCzga0Cv1vrOT/UZDzwNzAQKgQXAmNjhLcBJQB2wFLhUa71BKfUs8KLW+hml1H3Aaq31vUqp64HJWutvKqUuAc7TWl+8rxgl0SSEEEIIIUR8Pbuslh8+v2b34zMmF/DrcycdVC2mvrSpsZvb/r2BD7e1UVGawR8unsqwTHdcYxJCiP60r0RT/8wrPXAnANu01jX76HMO8IzWOqi13g5UEk06zQQqtdZVWusQ0RlM5yilFDAXeD52/qPAuXuM9Wjs/vPACbH+QgghhBBCiAS0YEMTt764lqNHZXHXRVN49KqZ/PnSaXFPMgGU56fy5NdnceeFU9jc2MMZf3yPhZub4x2WEELERaIkmi4hOltplxuUUmuUUg8rpTJibUVA7R596mJtn9eeBXRqrSOfav/EWLHjXbH+n6CUukYptUwptaylpeXTh4UQQgghhqSm7gAPv7+dNXWdJMLseDF4BSMGWmsWbm7m+qdWMKEwlfuvqOD86cUcOyaHRPqsWCnFl48o5uWb5lCYnsT1T66gsrk33mEJIcRhZ4t3AEopB3A2cGus6V7gdkDHbn8PXBWP2LTWDwAPQHTpXDxiEEIIIYRIFKGIySMfbueeBVvxhgwARuQkc+7UIq44spSMZCmELPrO4qo25j28hHS3nQ5vmDH5KTx21UxSnHF/C7NPpVnJPPK1mZz+x/e44akV/OtbR+OyW+MdlhBCHDaJMKPpNGCF1roJQGvdpLU2tNYm8DeiS+MgWsNp2B7nFcfaPq+9DUhXStk+1f6JsWLH02L9hRBCCCHEXqyr7+K0e97l169sYvbILF6+cQ6/vWASOSlO7npjC1c8/BGBsBHvMMUgsaPNx3VPLKcwPYnZI7I4bVI+T1w9a8Ds6paf5uKui6awqbGHbz+ziohhxjskIYQ4bBIh0XQpeyybU0oV7HHsPGBd7P584BKllDO2m9xoYAnR4t+jlVJlsdlRlwDzdXQe99vAl2PnzwNe2mOsebH7Xwbe0jLvWwghhBBir7r8Ya55bBm+kMHDX63gwXkzmFiUxsUzSvjHtbN58MoK1tV3c+uLawnLG2pxiCKGyXVPLsfU8PBXZ3D3JdO455JpAybJtMtxY3P52Znj+e/6Rm55Ya0sMxVCDBlxnXeqlEomulvctXs0/59SairRpXPVu45prdfHdpHbAESAb2mtjdg4NwCvAVbgYa31+thYtwDPKKV+BawEHoq1PwQ8rpSqBNqJJqeEEEIIIcRe/PyldTT1BHnhuqOYOiz9M8dPHJ/Hd04cwx8WbOHdLS3MO2o4N84dlVD1c8TA8ciH1axv6Ober0ynLDs53uEckqvmlNHlD3PPm1upGJ7BpTNL4h2SEEL0OyWZ9f1TUVGhly1bFu8whBBCCCEOqw+3tXLZ3z7iOyeO4eYTR39uP601b21q5qmPdvDmpmauOrqMn545TpJN4oA0dPo58a53mFWWycNfnTEovn9MU3P5Qx+xuraTV2/+EiVZ7niHJIQQh0wptVxrXbG3Y4mwdE4IIYQQQiSoh9+vJivZwbXHjthnP6UUJ4zL48F5FXz1qOE8/MF2bnh6JfWd/sMUqRjoQhGTm55eiak1t50zcVAkmQAsFsXvLpyCRSm+8dgyGuRnQggxyEmiSQghhBBC7FVNm5c3NzVx2ayS/d41SynFz88az3dOHMOCDU3MvXMhCzY09XOkYjC47eX1LKvp4P++PIVhmYNr1k9RehL3Xn4E9Z1+zv3LB6zc0RHvkIQQot9IokkIIYQQQuzVY4tqsCrF5UeWHtB5SiluPnE0b33/OMrzPXzzieX8e3VDP0UpBoNnluzgicU7uPZLIzh7SmG8w+kXc0Zn8/x1s3HYLFx43yIefK8q3iEJsVeGKeV1xKGJazFwIYQQQgiRmBZta+PxRTWcObmAvFTXQY1RlJ7EE1+fxdWPLOPmZ1biDxtcVDGsjyMVA93ymg5+9tJ6jhmdzQ9PLY93OP2qPD+V/9x4DD94fjW/+s9GwobmuuNGxjssMYiYpmZlbQdvbWqm3RsibGjChonbYaM0Vh8sYpiUZiXjDxus3NFJTyBM2DCJGJod7T62tfQyvSSDs6cWYrUoAmGTiGFSlJHElOL0QTfjUPQ9KQa+n6QYuBBCCCEOh3ZviGXV7dR3+slOcVKWnUwwYtAdiNAbiNATiGBRMLc8l9yDTAB9kcVVbXzjsWXkpbp4/puzD3lbeX/I4JrHl/He1lauOLKUa48dQXGGvFER0NQd4Kw/vY/LbmX+DUcf8vfaQGGamm//YxXzVzdw2zkTuHL28HiHJAYAXyjC88vr+Kiqne2tXgrTXYzISaE0y83OzgAraztYU9tFTzCCzaLITHZgt1qwWRU9gQjt3tBnxkxLspOV4sAR65frcTEiO5kFG5uobvN9pr9FwZWzh3Pl7FJSnLZ++zskEt++ioFLomk/SaJJCCGEEP2pyx/mb+9W8fAH2/GFjC/srxScUJ7HraeXMzInpU9i2NzYw8/nr2NxVTuFaS6eu+4oitKT+mTsYMTg9pc38I+ltSgU91wyldMmFfTJ2GJgCkYMLn1gMZsae3jx+qMoz0+Nd0iHVdgwue6J5SzY2My1XxrBLaeWY7EMjgLoou+09gZ5bFENde0+3tnSQps3RHFGEiNzUmjsCrC9zUsoYmK1KMrzPUwrSWdmWRbHjc0h1WX/xFg9gTBWi0Kh2N7qxW5VjMxJ2ev3nWlqGrr8OKwWnDYrVquips3LP5bW8vjiGnalEY4Znc13TxrD1GHpg6aAv9g/kmjqA5JoEkIIIcT+6vCGWFvfxbaWXpKdNorTk6jr8NPlD5OaZCMtyY7TbqW+w0+7N0S3P8yzy2rpDkQ4a0ohXz2qlNKsZJq6A9S2+0hy2Ehx2kh12Uhx2egJRPj36gb+/kE1gbDB366s4Pjy3IOONxA2uO+dbfzl7Uo8LjvfOn4Ul84chtvR91UWGjr93Pj0Slbu6ODOC6dw/vTiPn8OkfgMU3PzMyt5ec1O/vqV6Zw+RJOOEcPkl//ewOOLazhlQh5/uHhqv/zciYHBMDUvrKjjuWW1NPcEOX5sLi+vaaDdG6IgLYlxBal889gRVAzP/MQ5jd0BMt0Okhz7t2nDodrQ0M2mxm7qO/w89MF2On1hspIdnDetiG+fNIYUp3wPDwWSaOoDkmgSQgghxOdp7g7w9JJaNuzsoqrFy9bm3gM6Xyk4fmwu3zt5DBMK0/b7vJaeIBfe9yEel535Nxx9UJ8mv7+1lVteWEN9p5+zphTyi7PGk5XiPOBxDoQ3GOEbjy1jcVUb91wyjbMGafFnsXdaa370wlr+sayWH59ezjVfGto1irTWPPJhNbe/vIEJhWk8OK/ioOuiiYGrJxDm5mdW8damZkbmJFOU4eaDylZG56ZwzyXTGJvviXeIe9XlD/Pa+kbe3dLCf9buJCfFyfSSDI4alSVLQgc5STT1AUk0CSGEEOLTdnb5+eOblTy/vJaIqRmZk0JJppvpJelML81gdK6H3mCEhk4/xRlJZCQ76PaH6fKHCYQNCtOTyElxRpcyHOSSgyc/quEn/1zHM9ccyZEjsg7o3GeW7OAn/1pHWXYyt58zkdkjD+z8Q+EPGcx7eAkrdnTw6/MmcWFFsSy7GCLuXrCFuxds5aa5o/juyWPjHU7CeHNjEzc+vZK0JDt/vmw6R5RmxDsk0c/Chsnq2k4WbGxm/qp6mnqC/OLsCVw+qwSlFN5ghCS7dcAsqVyxo4M/vbmVypZeatv9vHzjHCYW7f+HJ2JgkURTH5BEkxBCCCEAunxhrn9qOat2dOILG9gsiktmlHD1nDKGZycf9ngCYYOj7niL6SXpPDhvxn6dY5qa372+mXsXbuPYMTn8+bJpeD5Vy+Nw6AmEYzOb2jl5fB6/Pn8S2f08m0ocfqapWb6jgyXb22npCfLIh9VcML2YOy+cLMnFT1nf0MXXH13Gzq4AR47I5H/OGC9v1AeZdm+IV9ftZOHmFhZta6M3Vrj7qFHZfPPYERw1MjveIR6y7kCY2b9+k1Mm5vOLsyfw6AfVXFgxjPw0mak3mEiiqQ9IokkIIYRIXA2dfu5/ZxvvVbZSnu8h1+Oi3RtiW0svjV0B0tx2DFPT3B3E7bCS43EyKjcFh81Cly/MBUcUc9rE/H2+6Q1FTHa0e/nus6vZtLOHS2cOIzPZyfnTi+K+1fM9C7byhwVb+N2XJ3NhxbB99u0JhPnh82t4dV0jl80q4bazJ2CzWg5TpJ9lmJqH39/O717bjMdl4zsnjeHoUdkMz3JLEmIACYQNWnqCpDhtpLvtKKX4qKqN55bX8d7WFpq6g7v7Hjc2h/uvOAKn7fDUkxloegJhnllSy/3vVtHpC3HD3FHcOHc01gEyq0V8li8U4Y0NTby0qoF3t7QQMTXDMpOYMyqHY0Znc/TIbNLchz/Z359+/tI6nl5Sy8SiVFbs6KQwzcUjV81kTF5iLgEUB04STX1AEk1CCCFE/LR7Qzy5uIYVOzroDkQwtcZhtZCV4qChM8Dquk6sKvqJcFVLL13+MOluO8OzkinOSKLbH8FqUeR4nATCBo1dAbY09xAxNBalqO/0U57vIWSYBMMmSkXrJiU7bIzMSaGlJ8jK2g7ChsZuVdx/xRHMLc+L9z/LboGwwTceW8YHla384eKpnDO1aK/9NjV2c+3jy6nr8POjU8v5+jFlCZPM2dLUw3efXcW6+m4AyvM9fGVWCV+ZVTpglo0MRb5QhCcX7+D+d7fR2hvdOv2I0gwmFaXx6KJqUl12jh6VxUnj85hbnofHaZPruZ86fSF+MX89/1rVwNzyXO6+ZOpndhETicswNe9saealVQ28vr4Jf9igMM3FWVMKOWdqEeMKPAnz+7c/VLX0Mvf37wDw3ZPG8PjiGjp9IY4bm8t1x41kesknl4ZqrdnU2EN5/uD+dxlMJNHUByTRJIQQQsTHws3NfPOJ5QTCJuMKUslMtmNRimDEpK03SGqSnePH5nL+9CKKMw58ZlHEMHl0UQ1vbGgkM9lBkt2GRqM1dPvDbG3uJd1tZ/aILMbme5hWkkFZHJbIfRFfKMJX/76UZdXt/Pq8SVwys+QTxz+sbOXax5fjdlr582XTmbHHrkWJQmvNtpZePtzWxvPL61hT18VpE/P5w8VTcdll9ksi8YcMnlhcszvBNGdUNmdMLqDdG+KJxTXs7ApwUUUxvzh7guyidogeX1zDL+evpzTLzd+urGBETkq8QxJfoMMb4vonV7Coqo10t53TJxVw7tQiKkozhlSi9d6F28hPc3LetGIaOv08/P52/rWqnk5fmJ+cMY6vzCrFYYvOqH3wvSp+9Z+N3HH+Z/9+icQkiaY+IIkmIYQQon+tq+/iva2tNHUH+MqsEkbneaht93Hmn96nIM3Fny6dxmiZcr9P/pDBN59YzjtbWjh1Qj5nTy2kNxDh9Q1NvLWpiVG5KTzytZkUpifFO9T98tD72/nVfzYwLMPNRRXFXHHk8EG3vGSgaej088LyOh5dVL07wXTziaM/kbgMRgyqWryMK0iNX6CDzOKqNq5/cgURw+RvV1Yw6wAL/4vDZ119F9c9uZym7iC/PHsCF0wv3p1MEdFd6r737CoWbGwmyW5l7rhcrjq6jCse+ghfyCA7xcHb3z8uLnUDxYGRRFMfkESTEEII0X8Wbm7m6keXYZjRJXGG1swqy6S61UtPMMLLN86hNCvxZhElolDE5I9vbuXxxTV0+cMAZKc4uLBiGN88diRpSQPrP+9vb27m/ne2sbiqnRyPk9vPmcgpE/JkacVh9tr6Rp5YXMP7la1oDceMzuamE0Yn5My4waq23cdX/76E+k4/911+BMeNzY13SGIPWmueXVbLT19aT6bbwb2XT2daiewcuDemqXlrUzPvbm3huWV1+MMGSXYrf7h4Ctc9uYJ5s4fz87PGy+/5BCeJpj4giSYhhBCif6yu7eSyvy2mNCuZx66eiUUp7lmwhZW1naQ4bXzr+FEcPWrg78JzuPlDBpsau8lKdlKQ7sIex4LffWFdfRc/eH4NG3d2M2VYOj84eSxzRsv3RX/zhSL89F/reWFFHUXpSXz5iGK+fERx3AvgD1VtvUGueGgJmxq7ueH4Udx4wugB/7M9GPQEwvzy3xt4fnkdc0Zlc88lU8mSHTT3S3Wrl1+/spGTJ+Tz5SOK+fE/1/LURzuYXpLOT88cL8m6BCaJpj4giSYhhBCibwXCBg+9v527F2wh1+PixeuPIi9Vtj4Wny9smDy/vI6/vF1JXYefc6YW8v2Tx0rSo59sberh+idXUNnSy01zR3PTCbLzWSLYM6kxtzyXB644Iq47Rw41nb4Qb2xoQhOt47epsYdX1+7EFza4ce5obpafk0NimJoXVtTxu9c209IT5JyphVw9p4xJRWkywynBSKKpD0iiSQghhDh0H1S28saGJkKGyevrG2ntDXH6pHxuP2eifPor9lswYvDXt7dx78JtREyTMyYX8r/nTZQdufrQO1ta+Objy0l2Wrn74mkyeywBPb6omp++tJ4rjizltnMmyJvwfuQLRQiGTVp7g1z96DJ2tPt2H/O4bJwyIZ8rZ5cyuTg9jlEOLr3BCPcurOSh97cTCJtMHZbOr86dyMSitHiHJmISNtGklKoGegADiGitK5RSmcA/gOFANXCR1rpDRX9z3gOcDviAr2qtV8TGmQf8T2zYX2mtH421HwE8AiQBrwA3a6315z3HvmKVRJMQQghx8MKGye9f38J972zD7bBit1o4ojSDq+eUcdTILHmDJA7Kzi4/j35Yw4PvVTEmz8MjV80g1yOz4g7VB5WtXPXIUkbmpPDI12aQKzMNE9ZvXtnI/e9W8fU5Zfz49HFDakez/lTZ3Mt972xje6uXxq4A9Z3+3ceyUxz88ZJpDMt0k+K0ke62y9+wftQdCPPSqgbuWbCVdm+QMXkeCtJc3DB3NEeUyrK6eOqXRJNS6vx9Hddav7gfY1QDFVrr1j3a/g9o11rfoZT6EZChtb5FKXU6cCPRRNMs4B6t9axY0mgZUAFoYDlwRCw5tQS4CfiIaKLpj1rrVz/vOfYVqySahBBCiINT1+HjpqdXsmJHJ5fNKuFnZ46XrepFn1q4uZnrzwmDhgAAIABJREFUnliBx2Xjroumyuybg6S15qklO/jl/A2UZSfz9DVHkpnsiHdYYh9MU/PLf6/n0UU1nDWlkP+7YDJJDvn9erDaeoPcvWArTy3ZQZLdyuTiNPJSXYzITibZacMfNjh3WhFFA2TnzsGkyxfm3ne2UdXSy+q6Tpp7gsybPZxbTy/HaZPv+Xjor0TT32N3c4GjgLdij48HPtRan7kfY1Tz2UTTZuA4rfVOpVQBsFBrPVYpdX/s/tN79tv1pbW+NtZ+P7Aw9vW21ro81n7prn6f9xz7ilUSTUIIIcSBW1ffxZUPLyEUMfnN+ZM4a0phvEMSg9SGhm5ufHoF21q83H3xVM6dVhTvkAaUxVVt/HXhNt7d0sKXxuRwz8VTyZAk04Cgtea+d6r4v9c2UZ6fyn2XT5ddOg9QQ6efRxdV89TiHfjCBpfNLOHbJ46WJd0JqjcY4c7XNvPIh9VMKkrjllPLmVGWIQmnw6xfl84ppV4H5mmtd8YeFwCPaK1P2Y9ztwMdRGci3a+1fkAp1am1To8dV0CH1jpdKfUycIfW+v3YsTeBW4gmmlxa61/F2n8K+Ikmmu7QWp8Yaz8GuEVrfebnPcde4rsGuAagpKTkiJqamoP7RxJCCCGGEK01S6s7WFXbwZ/erCQ1yc7jV89kRE5KvEMTg5w/ZDDv4SWsa+ji3zfOYaR8z32h5p4AP39pPa+uayQr2cE3jx3J1XPKZAnWAPT2pmZufmYlhqn56ZnjuXjGMFnStR9eXtPA955dTdgwOW1iAd85aTSjcj3xDkvsh9fXN/KD59fQ5Q/jcdn49oljmDe7VIrj95FOX4gtTb20e4P4wwbnTSv+xPF9JZpsffD8w3YlmWKagJL9PHeO1rpeKZULvKGU2rTnwVg9pX4tIrWv59BaPwA8ANEZTf0ZhxBCCDEYtPUGueWFtSzY2ATA+IJUHpxXQaEsMxCHQZLDyh8vncZp97zLNY8t444LJjNjeGa8w0pIWmteXFHPbS9vwB8y+P7JY/j6MSNkWesAdnx5Lq9++0v84LnV/OjFtby+oYk7Lpgkdcs+h9aaP79Vye/f2EJFaQZ3XzKV4gzZwXIgOXlCPnNGZ/NhZRuPLa7h9pc38Piiaq6YPZzjxuYwPCtZdgA8CBsaurn1xTWsruva3Wa1KM6dWrTfyeu+SDS9qZR6DXg69vhiYMH+nKi1ro/dNiul/gnMBJqUUgV7LGtrjnWvB4btcXpxrK2e6KymPdsXxtqL99KffTyHEEIIIQ7S25ub+cFza+j2h/nJ6eM4b3oR2bLsQBxm+Wku/nLZdG56ZhUX3reIy48s4fZzJsrMjpiIYfLiinoeX1zD2voujijN4LcXTGZUrsz+GgyK0pN44upZPLqomjte3cQpf3iXX583idMmFcQ7tIQSCBv86IU1/GtVA+dPK+I3F0ySZVcDlNth48TxeZwwLpc3NjRx3zvbuP3lDdz+MjisFnJTncwYnsn3TxkrtbU+R6cvxMPvb+fppbUooN0bIt3t4IenjmV8QSo5HidpSQe2q2uf7DoXKwx+TOzhu1rrf+7HOcmARWvdE7v/BnAbcALQtkeh7kyt9Q+VUmcAN/BxMfA/aq1nxoqBLwemx4ZeQbQYePteioH/SWv9ilLqd3t7jn3FKzWahBBCiL3r9IW449VNPLO0lrF5Hu6+ZCrjClLjHZYY4vwhg9/+dxOPfFjNr86dyOVHlqK1HtIJp3X1XdzywhrWN3QzJi+Frx1dxkUVw+QT/0GqsrmH7/xjNWvruzhvWhG/OHvCAb9ZHIy2NvVw49Mr2dTYww9OGcv1x40c0r8XBqOtTT2squ2ksqWXnZ0BXlvfiFIwe0QWk4rSOHlCPhMKU4f8dW/3hnjo/Soe/bCG3mCEE8flkZ3iIMVp41vHj/rCOn39WqPpYCmlRgC7ElI24Cmt9f8qpbKAZ4kuv6sBLooljRTwZ+BUwAd8TWu9LDbWVcCPY2P9r9b677H2CuARIAl4FbgxtlRur8+xr3gl0SSEEEJ81tubmvn+c6vp9Ie5ek4Z3z1pjCy9EQnDNDVXPbqU97e24nZYyUh28OTXZw255TGBsMEfFmzhwfe2k5ns4LazJ3DqxPwh/yZrKAgbJn9+q5I/v11JrsfJnRdO4ehRQ3NXxmDE4L6FVfxlYSUep407L5rC8WNz4x2WOAzqOnz8deE2VtR0sLW5F8PUFKS5mDE8k9IsN3mpLkbnpjCxKI1kZ18s+kpcYcNkVW0nr69v5MmPduAPG5w+qYAb546iPP/APiTs72Lg5wO/Jbr7nIp9aa31oPooUxJNQgghxMe01tz1xhb+9FYl5fke7rpoKuMLB9WffjFIdPpC/P71LQD8a1U9RelJPH/dUaQM8jcTu2xr6eX6J1awuamHS2YM49bTxpHmllktQ83q2k6+8+wqqlq8fO3o4dx62jgctqFTMPnDba38zz/XUdXq5czJBfzsrPFSu2qIaveGeH19I+9VtrKipoOm7gBmLCXidlg5Y1IBJ0/I58gRmXhcA/93ZSBs8OyyWra3etne6mXJ9nZ8IQOLgjMmF3Lj3FGMyTu44vf9nWiqBM7SWm88pIESnCSahBBCiKiIYXLri2t5bnkdF1UUc9s5E2UWkxgQ3tvawlf/vpQMt51jx+TynZNGD9rZTc09AR5+v5rHFlXjslu566IpHCezN4Y0f8jgjlc38uiiGqYOS+dPl05jWObg/P6H6Aym+asaeHrJDlbs6KQk083t507k2DE58Q5NJBDD1DR1B9jc2MN/1zXy8poGvCEDm0UxrSSdrGQnnf4Qp0zI59KZJQnz/50uX5iNjd1s3NlNdasXX8jAZrWQ63GSm+ok1WWnrTfI3z+spqbNR4rTRmG6i1llWRw9KosjR2SR7t730rgv0t+Jpg+01kcf0iADgCSahBBCDHWhiMmjH1bz+OIadrT7uPmE0Xz7xNGy/EYMKO9uaeH55XW8ubEJh83CnRdO4dgxOQN+O+zqVi+vrW/EZrWwvbWXZ5fVETZMTp9UwP+cMY6CNCmCK6JeXbuTHzy/BsPUfO/kMcw7ajj2Af79vyfD1Dy1ZAd/fmsrTd1BRuYkc+nMEi4/sjRhkgQicQUjBstrOnh/ayvvV7ZGEzgWxabGHvJSndx62jjOmVrYr//3CUVM/GGDYNigpt1HuzdEMGLyxoYm3tvaQiBsEAibu/t7nDY8Lhshw6TNG2LPFE9ZdjL/e+5EjuqHJbP9nWi6B8gH/gUEd7VrrV88pIETjCSahBBCDGVN3QGuf3IFy2s6mFmWydVzyjhlQn68wxLioFW19HLN48upbO4lxWnj6jllAy5x2h0I89bGZp5ZuoPFVR+XG7VbFRdML+baY0dSlp0cxwhFoqrv9PPTf63jrU3NDM9y85MzxnPS+Lx4h3VI/CGD1zc08uB721lb38XMskxuOH4Ux4zOHlA/1yIxLa5q4zevbGR1XRdZyQ5Kstz4QwYel43vnDiGmWWZ9AQiX1hAe0+mqenyh2nzBqnr8LO0up0l29tZXdtFyDA/0z/dbefk8Xmkux2ku+2MK0hlfEEquR7n7u/xsGHS2hukJxAhLclOdoqz3zZ86O9E09/30qy11lcd0sAJRhJNQgghhqrFVW3c8NRKfKEIv71gMmdNKYx3SEL0CV8owpsbm/nPmp38d30jX5lVwvnTiyjNSiY7xRnv8PZqaXU781c1sKymg02N3WgNwzKTuGRGNPYkuxWLRZE6CGqLiP6ltebtzc389tXNbG7q4cenl/ONY0YMuKSM1pqX1+zkF/PX0+YNUZSexA9PHcvZU/p31okYekxT89LqehZva6eu00eS3cbGnd3Ud/qxWhSGqZlVlsnpkwro8IVIcdoYkZOMUoqIoQlGDFbu6OSj7W00dQdp94YwzI/zMVaLYlJRGjPLMsn1OHHYLAzLdJOT4sSiFCNzk3HaEmdWXkLuOjfQSKJJCCHEUOMPGfx1YSV/XbiN0iw3919+BKMPsmCkEIlMa81vXt3EA+9WAdGCsA/Oq+CokYm1O9e/VtbzvedW47JZmF6awRGlGcwekcWM4ZlY+ukTazH4BSMG3312Nf9Zs5OzphTy0zPGkZs6MAplN3UH+Mk/17FgYxNTitO45bRyjizLkp8HcdgEwgZPfbSDdm8Iu9XCM0t3sLMr8Ln9HTYLM4ZnMCzDTVaKg6xkJ9keJ7keJ5MG2K53/T2jaQxwL5CntZ6olJoMnK21/tUhDZxgJNEkhBBiKHlrUxM/e2k9dR1+zptWxG3nTBgUu68I8Xm01mzc2UNTT4DfvLKRmjYfN584muPH5jKu4NB2VOzwhnh3awsbdnbT2BUgK9mJqaMFaM+ZWsSpE/e+DFVrzb9W1fOnNyvxhiI09wSZVZbJg/NmDJld88ThYZqav7xdyZ/ersRmUZw5uYCZZVkADM9yM7EoLSHqG2mtaekNUt3q45W1O3lheR1h0+R7J43lqjll/bZESIj9FTZMWnqCZKc46QmEqWn3AeCwWrBZFSWZbtyOwfH7u78TTe8APwDu11pPi7Wt01pPPKSBE4wkmoQQQgwFjV0Bfvnv9by6rpFRuSncfs5EZo/MindYQhxW7d4Q33x8OUuqo3WPLqoo5udnTTigT5q11ry7tZW/f7Cdd7e0YOroG43cVCft3hAKcDtttPQEOXtKISNzUgAIRAyCYZM2b5BVtZ3UtPmYXJzGuPxUslIc3HTC6IR4wy8Gp+pWL39dWMkraxvpDUZ2t1stivJ8D+X5qRSlu0ApFFCe72HWiCwyD6Auzedp6Qny5sYmtrd5CYQMjhmdw6jcFN7Y0MSq2k42N/VQ2+4jGInWrnFYLZw8IY/vnzyW4VKLTIjDrr8TTUu11jOUUiv3SDSt0lpPPaSBE4wkmoQQQgx281c38OMX1xI2TG46YTTfOGYEDtvg2YlIiAPV2BXgsUXV3PvONnI9Ts6fXswF04sYlfv5S0i7/GHmr27g0Q+rqWzuJcfj5KKKYk4en8+EwtRP7G4Xipj8/o3NPPJB9e43z3arwmWzkppkZ3xhKiePz+OC6cWyFEgcVoGwQWNXAA1UNveyuraTVbWdbGvppbE7gNagFGgNSXYrV80ZznFjcynNcpPr+fxldw2dfqpbvdR1+qnr8FPX4aO+I3q/ocuP1h/P/PCFjN3nDctMojw/leFZbooz3AzLTGLasIwDKrwshOhb/Z1oehW4AXhOaz1dKfVl4Gqt9WmHNHCCkUSTEEKIwarDG+LxxTXc9cYWZgzP4M4Lp1CaJZ8OC7HLR1Vt3P9uFe9sacEwNeMLUpkyLJ2idBdWi4Ud7T62tfTiDUbY2txLKGIysSiVq+eUccakwv1K2O4qCCtLf0SiixgmFqUIGSYbdnbzyAfVzF/dsPt4QZqL4owkbBYLealOclNdaK2ju2nVde3upxTkp0b7FqUnMSo3hZPG5zMmL4WIqVm4uYX6Dh9zy/MoyXLH46UKIfahvxNNI4AHgKOADmA78BWtdc0hDZxgJNEkhBBisAgbJq+ua+SVNTvZ2txDVasXreGsKYXceeHkhNrRRIhE0tITZP7qBl5b38iWph46fWEA0pLsjMlLIdVlZ1imm/OnFzGpKE12vBJDRl2Hj8rm3ujsp7ouWnoChA1NY1eA1t4gAKNyUzhnaiETi9IoTneTn+aSWbNCDGD9lmhSSlmB32qtv6+USgYsWuuegx4wgUmiSQghxEDX4Q3x7LJaHv2wmoauAEXpSUwoTGViURpHj8pmekm6vDEWYj9prQkZJoapSbJb5WdHCCHEkLKvRNNBlztXStm01hGl1BwArbX3YMcSQgghxKHb2eVn0bY2lla3s7S6gw5viHS3nQy3g4ipWVPXialh9ogsbjtnInPLc6XuixAHSSkls/+EEEKIvTiUffWWANOBlUqp+cBzwO5kk9b6xUOMTQghhBD7ocsX5u43t/DYohoMU+Nx2agozWDG8Ew6fSE6fCGUgm8dP4pTJ+YzoTAt3iELIYQQQohB6lASTbu4gDZgLqABFbuVRJMQQgjRT5q7Ayze3s7Czc28snYnwYjJZTNLuPzIUsbmeWSmkhBCCCGEiItDSTTlKqW+C6zj4wTTLodWYVwIIYQQn2Gamne3tvDE4hre2tSMqSHFaeP86cVcObuU8vzUeIcohBBCCCGGuENJNFmBFD6ZYNpFEk1CCCGGhIhh0huM0O2P0B0I0+EL0dgVIBA2sFosTC5Ow+Oy8fKandR1+PCHDCxKYbMq7FYLhelJjMnzkOywsrK2kycW1+Bx2Th5fD6nTMjHYoHnltWxvdVLZXMv9Z1+slMcXHfcSE6dUMC4Ag82q+zaI4QQQgghEsNB7zqnlFqhtZ7ex/EkLNl1TgghhjbT1Lxf2cqymg62NfeyraWXug4/vcHIfo+R43GSZLdiak3EiO5Y1e4NfaLPMaOziRiaJdXtGGb0b7TLbmFsnofcVBdnTSnk1An5siW0EEIIIYSIm37ZdY69z2QSQgghEo7WmkDYpMsfpjsQJjPZQXaKc7/ObesN8tzyOp76aAc72n1YFAzLdDMqJ4XZI7NIS7KT6rLjcdlITbKTnmQnP81FstOGP2SwtLqdDl+YUyfmU5Se9JnxuwNhqlq8BMIG2SlORuWmANDhDfHmpmb8YYOzJxeS5rb36b+JEEIIIYQQ/eFQZjRlaq3bD/qJlRoGPAbkEV1q94DW+h6l1C+AbwAtsa4/1lq/EjvnVuBqwABu0lq/Fms/FbiH6HK+B7XWd8Tay4BngCxgOXCF1jqklHLGnvsIooXML9ZaV+8rXpnRJIQQ+09rTXNPkLBhkuNxHpYtwNu9IZZsb2NDQzf+sIHForBZFNWtPt7b2kJ34OOZR1aLYm55LieU5zKxKA2loLk7yNbmHtp6Q7T2htjR7qWmzUdzTxCAmWWZXH5kKSePz8Nlly3NhRBCCCHE0NUvM5oOJckUEwG+p7VeoZTyAMuVUm/Ejv1Ba33nnp2VUuOBS4AJQCGwQCk1Jnb4L8BJQB2wVCk1X2u9AfhtbKxnlFL3EU1S3Ru77dBaj1JKXRLrd/Ehvh4hhEhInb4QW5t7qWzupbk7SE8gjNWqCEc0Hb4QFhWdoFrd5kVrzZg8Dw6bhYipyU52kOSwETFMkhxWnDYLHb4wEVPjtFmo6/Cxo91HuzeMaWpcdgv+sEFzT5BOX3h3DOluO/mpLkbmppCf6iLZYcXQGpvFQll2MjPLMincy2yfTwtFTJq6A6xv6KKhM4AmuvvauoYuFldFl5pZFDht0fFNU5OV4uDUifmUZaeQmmQj1WVnfUM3L6yo440NTZ95DqfNQobbQUmmmy+NyWF4lpuTJ+QzJs/TZ9dECCGEEEKIweqgZzT1NaXUS8CfgaOB3r0kmm4F0Fr/Jvb4NeAXscO/0Fqfsmc/4A6is6LytdYRpdTsXf12nau1XqSUsgGNQI7exz+GzGgSQiS6dm+Il9c0sGhbG1uaevCHDAKRz9YAcjusGKbGbrWQkWzHNMEwNaVZbjRQ2dyLqTVWpejwhTD38Wciw22nNCuZ7BQHVosiEDZJslvJTHEwJjcFl91Kc0+Q5p4A9R1+qlq9tPYE8YYMrBaFqTVag1Iwc3gmgbBBdyBCuttOhzdEfaefDLcDt8NKmzdET+Cz9ZAc1miy6oRxuZwwLo8Jhan7NeNIa01Vq5etTT2AIjPZwdg8jyxRE0IIIYQQ4gv0V42mPqOUGg5MAz4immi6QSl1JbCM6KynDqAIWLzHaXWxNoDaT7XPIrpcrlNrHdlL/6Jd58SSUF2x/q2fiusa4BqAkpKSQ32ZQgjR53oCYZ5bVsdr6xtZXtNBJJYwKs/3kOqyY7NaKMt2MzrXw6jcFPLTXNgPYIeyiGESNjQ2q8IfNgiGTdKS7NgsipBhHvQSMq01SimCEYPtrV5eWdvIgg1NZKU4KM500+kLUZiWxskT8unyhfGFDbKSHWQmO8jxOBlXkEppphulwOOyY7UceNlApRQjc1IYmZNyUK9BCCGEEEII8VlxTzQppVKAF4Bva627lVL3ArcTrdt0O/B74Kp4xKa1fgB4AKIzmuIRgxBC7NLpC7GlqZctTT1UNkd3PPuoqo2eYITyfA/f+NIIzppcyPjC1D57TpvVwq7ySnarBVwfH3NZDr5OkYot13ParJTnp1Ken8p3TxrzBWcJIYQQQgghEl1cE01KKTvRJNOTWusXAbTWTXsc/xvwcuxhPTBsj9OLY218TnsbkK6UssVmNe3Zf9dYdbGlc2mx/kIIEXddvjAbG7v5qKqdmnYvTd0Btjb17i5KDZDssDIs081JE/L42lFlTCpOi2PEQgghhBBCCBEVt0STin6c/RCwUWt91x7tBVrrnbGH5wHrYvfnA08ppe4iWgx8NLAEUMDo2A5z9UQLhl+mtdZKqbeBLxPdeW4e8NIeY80DFsWOv7Wv+kxCCNHXTFNT2+FjZ1eAbS29LNneztamXmo7fLvrECkFBakuclJdzBmdzdg8D2PyPIzJ91CY5to9K0gIIYQQQgghEkU8ZzQdDVwBrFVKrYq1/Ri4VCk1lejSuWrgWgCt9Xql1LPABqI71n1La20AKKVuAF4DrMDDWuv1sfFuAZ5RSv0KWEk0sUXs9nGlVCXQTjQ5JYQQfa6lJ0hrbxC7VdHQGWB9QzfvbW1hdW0n3pCxu1+Ox8mEwlQqhmdQkulmRE4yR5RmkpYkhamFEEIIIYQQA0fC7DqX6GTXOSHEvoQiJttbvWyO1U9q7PKzpamXVbWdn+lbnu9hVlkm4wtTKUp3MywziZJMt8xQEkIIIYQQQgwICb/rnBBCDDRbm3r477pG3qtspa7dR3NPkIgZTdxbFGSnOCnOSOJ7J41hVG4KIcMkL9XFqNwUslOccY5eCCGEEEIIIfqHJJqEEOIL1Hf6WVffxc5OPzu7Aqys7WTJ9nYAJhWlceTILArSXNH6SXkeRuQk47Qd/I5sQgghhBBCCDFQSaJJCCH20OULs2FnNzu7/Gxu7OGj7e2fWP7msFkYnuXmR6eVc8H0YnI8MjtJCCGEEEIIIXaRRJMQYkgzTU1Vq5fFVW0s3NzMO1taCBvRJXAOq4Vxhan84JSxzBmVTVFGElnJDqmlJIQQQgghhBCfQxJNQoghoTcYYfG2NtbUdxGKmGitWd/Qzeq6TnoCEQCK0pP46lHD+dKYHArSkijNcmO3WuIcuRBCCCGEEEIMHJJoEkIMKqapqe/0U9/pp9sfpicQYWtzL099VEN3IIJFgc1iQaMZm+/hrCmFTClOY2ZZFsOzZOc3IYQQQgghhDgUkmgSQgwIYcNka1Mv1W1euv1hwqZGAUoRTSY19bK1uYfK5l58IeMT5yoFp4zP58qjSpk6LB23w4bWWpJKQgghhBBCCNHHJNEkhDjsDFOzvqGLyuZemrqDNHUHaO4J0NQdJBQxsVsVwYiJL2TgC0XwBQ28oQim/vwx81KdjMnzcPGMYYzJ81Ca6SY1yY7HZSPd7SAtyf6J/pJkEkIIIYQQQoi+J4kmIUS/0VpT1+HnrU3NbGnqoak7SEtPgO2tXrpjdZEAPE4bualOcj0uUl02woYmw23B7bThtltxO62kOG2Myk1hdK6HNLcdu1WBBg0kOaykuuyfH4gQQgghhBBCiMNCEk1CiP3iDUZ21z6q7/DTELvvDUawWlTsy4LNougJhKnr8FPX4ac3GE0oZbjt5KW6yPE4OWNyIbNHZjGxMJW8VBfJTvlVJIQQQgghhBCDgby7E2I/+UIRFApfKEJdhx8Al92KNxShNxChNxghGDFwWK0cOzaHlARMnmitMXV06ZqpNWHDpMsfptMX+/KHYvdDtPQEqe8MUN8ZTSp1+cOfGMtmUeSnuUh12TG1xjCjXxFT43ZYKc5wc+SILMqyk/nSmBzKspPj9KqFEEIIIYQQQhwuifdOWIjDyB8yaO0NYrdacNktuOxWAEKGSThi0ukPs7Wph2eW1rJwc8t+j5vssDKpOI1A2MTtsJLstBGMmDhtFgrTXKQm2bFaFL6QgWlqXHbr7ud32a0k2a047Rbg46SQYUZ3VDO0JmKY9Aaj9Yt6gxG8wQjekIE3GK1n1BuMxI5F+4QiJobW6H3UOPo0j9NGUUYSRelJVJRmUJSRRGF6EkXpLorS3eR4nFgtUudICCGEEEIIIcTHJNEkBrWqll4+3NbGhp3dbNzZzfZWL6kuOy67habu4Gdm6Xye7BQn1x83ktQkOw6rhWGZbiyKaCLJacXjtJHstOGyW2ntDfLs0lqq27x4XDb8IYN2rw+n3UogZLC4qo3eYAStwWW3YFGKQNjYZ6Hrz6MUpDhsuJ3RZFaK04bbYaUw3YXbEY0p2WHFYbNgtSgsKvpltYDForBbLKQl2Ulz20lPspOR7CA99thpsx54QEIIIYQQQgghhjRJNIlBJRA2WN/QzcLNzfx3XSNbm3sB8LhsjCtI5bSJBXiDEQJhg1llWeSnuchOcWCY0XMDEQOFwm5V2K0WPC4bpVluJhal7XfipSw7mRnDM/fZZ9cStl0zgrTWhA2NP2wQDBsEwmYslujuaFaLwqoUFkv0HJvFQorThstukd3ThBBCCCGEEEIkDEk0iQEtYpgsqmrj1XWNrNrRyZamHiKmxqJgZlkml80az9zyXEoy3QmVkFFKYVWffOywKRw2CyTJ7mlCCCGEEEIIIQYmSTSJAScQNnhvayuvr2/kzU3NtHtDpDhtTCtJ59ryEUwuTmfG8Ewykx3xDlUIIYQQQgghhBhSJNEkEp7WmqpWL0u3t/P25mbe3dKKP2zgcdmYW57LaRPzOW5s7u5C3kIIIYQQQgghhIgPSTSJhNMdCLOlsYdVtZ0srW5nWXUHbd4QAPmpLi6sKObk8fnMLMuMLjUTQgghhBBCCCFEQhjSiSal1KnAPYAVeFBrfUecQxr0tNY8FcN5AAAgAElEQVR0+MI0dgVo6g7Q2B3YfX9nV4DK5l7qO/27+w/LTOLYsTnMHJ7JjLJMRmQnJ1StJSGEEEIIIYQQQnxsyCaalFJW4C/ASUAdsFQpNV9rvWF/zg9GDIIRE8PQhE0Tw9REDE3E1EQMM3ariew6Zurdt7uO73psmhqrJbrTmdViwWZR2KzRncZ8QYPG7gDBiIlpagytP34NRLe3VyiUAotS5KW6KEh34bJZcdotOKzRbe139bOo6IkWpVBEb00djcXQ0ZhN/XG8xh5xGp9pMwkbmlDEJGyYhCImwdjtrsfd/jCN3R8nlZq6g4Qi5qeuBWQlO8lPc1IxPIPL8kooz/cwoTCN/DRXH11xIYQQQgghhBBC9Lchm2gCZgKVWusqAKXUM8A5wH4lmm5/eQNPLN7Rj+ENDi67hfxUF3mpLqaXZOy+n5/28W2ux4ndKkvghBBCCCGEEEKIgW4oJ5qKgNo9HtcBs/bsoJS6BrgGoKSk5BMnnzqhgOFZydgsCqvVgt2iYrOSLJ+cnWRV0T4Whc1iid1+sq9F8YlZQ2Hj41lQLruV/FQXSXYrFgvR2UkoNBqtQRNdjqYBw9DsjC1D2zXjKhgx0VpjamL9o/eJtZlax2KIxmXZI75dsVrUrhlWFqwq1m6NtjusFuy26K3DFvuyfnxrkwSSEEIIIYQQQggxZAzlRNMX0lo/ADwAUFFRofc8Nmd0NnNGZ8clrn3JSHYwvjA13mEIIYQQQgghhBBiCBrK003qgWF7PC6OtQkhhBBCCCGEEEKIgzCUE01LgdFKqTKllAO4BJgf55iEEEIIIYQQQgghBiyltf7iXoOUUup04G7ACjystf7fffRtAWoOV2wJKhtojXcQ4hPkmiQeuSaJS65N4pFrknjkmiQGuQ6JR65J4pFrkrjk2iSe/rgmpVrrnL0dGNKJJnFglFLLtNYV8Y5DfEyuSeKRa5K45NokHrkmiUeuSWKQ65B45JokHrkmiUuuTeI53NdkKC+dE0IIIYQQQgghhBB9SBJNQgghhBBCCCGEEKJPSKJJHIgH4h2A+Ay5JolHrknikmuTeOSaJB65JolBrkPikWuSeOSaJC65NonnsF4TqdEkhBBCCCGEEEIIIfqEzGgSQgghhBBCCCGEEH1CEk1CCCGEEEIIIYQQok9IomkQU0oNU0q9rZTaoJRar5S6OdaeqZR6Qym1NXabEWv/ilJqjVJqrVLqQ6XUlD3GOlUptVkpVamU+tE+nnNebNytSql5e7T/Vym1OhbHfUopa3++9kSVYNdkYez8VbGv3P587YkqUa6JUsqzx7VYpZRqVUrd3d+vP5ElyrWJtV8cG3u9Uuq3/fm6E1mcrsl/lVKdSqmXP9V+Q+xcrZTK7q/XnOj6+Jo8rJRqVkqt+4Ln3Ou1G8rXJMGuw0Pq/9l78+jNrqrO+3t/c01JKmYgCYQkJIC0AiIEGzAmoKIgBLu1BbsVAWUp+Lr6D/tVW5F+fVevpbaCCgmSRKZ+HWgGSRwhQxUJCVMSEsycqkqRkUpSSarqNz7Tef+49z7Pfc7de5+973N/qV+l9netWqn61a2d83nOc8+wz9775Guub2dZ9tksy7auF/dG1gbrk09kWXZfNprjX7pe3BtZG6xPrqv0x8NZln1hvbiPBG2wvnltlmU3Z1l2W5Zln8yybGa9uDeyDlOfkM9lWfazRRsGWZa9XAUQQvBfz9BfAE4B8LLi99sA3APgRQD+GMBvFz//bQB/VPz+VQC2F7//SQBfL34/DWA3gLMAzAG4FcCLiP/f8QD2FP/dXvy+tHdM8d8MwOcAvPVwfz7eJ9gJ4OWH+zM53L82Up9Ez90E4LzD/fl432A7gO8BcD+AE4vnPgngdYf78zka+qR49nUA3gTgH6Of/wCAMwDsBXDC4f5sjvQ+Kf58HoCXAbhN+P+xfXc098kG64djKs99oPz/H22/NliffALAzxzuz+Rw/9pIfRI99zkAv3i4Px/vG7wIeSDMAwCeXzz3BwDedbg/n6OhT6TnAHwvgBfAsH/0iKZnsEIIj4QQbi5+fwjAnQBOA3Ah8o0Siv++pXjmhhDCk8XPvwbg2cXvzwWwK4SwJ4TQAfB3hY1YrwdwZQjhicLOlQB+orB9sHhmBvlgclRWod9IfeLKtRH7JMuy5wM4CcB17VAemdpAfXMWgHtDCI8Vz10F4D+2R3rk6DD0CUIIVwM4RPz8WyGEvW1wHclqsU8QQrgWwBOJ/yXbd0dzn2ywfjgIAFmWZQA2wddch71PXLk2Yp9kWXYMgNcCOKojmjZQ33wPgE4I4Z7iuSvha66nq0/Y50IId4YQ7ra03x1NR4myLDsD+Unj1wGcHEJ4pPir7wI4mfgn7wLwL8XvT0PuWS71YPGzWOJzWZZ9EcCjyDcMn7UyPNO0EfoEwMeLkOH3FQvSo1obpE8A4K0APh2KIwTXYe+bXQBekGXZGUX49lsAPKcRyDNIT1OfuAyasE+08r5LaCP0Q5ZlHy/+fy8E8CGj7WecNkKfAPifRVrLB7MsmzfafsZpg/QJkM/pV1cOxY96Hea+eRzATCU962fga66nq09a1VGZ73i0Kctz8z8H4L+GEA5W/QkhhJBlWYievwD5l/M1bbYjhPD6LMsWAPw18pODK9u0fyRpg/TJfw4hPJRl2baiLb8A4FMt2j+itEH6pNRbkfeHC4e/b0IIT2ZZ9msAPg1gAOAGAM9rw/aRqsPdJ666vE82hjZKP4QQ3pHl9TA/BODnAHy8TftHkjZIn/wO8g3hHIBLAPwW8pSgo1IbpE9KvQ3AZetg94jU4e6b4v/xVgClQ/ZLAPpt2D5Sdbj7pKk8oukZrizLZpF/Mf86hPD54sf7siw7pfj7U5BHGZXPvxj5YHthCGF/8eOHMO5JfjaAh7Ise2U2KqL3Zu65antCCKsALsdRHEq8UfokhFD+9xCAv0EewnpUaqP0SWH7JQBmQgg3tQp5hGqj9E0I4R9CCK8MIfx7AHcjz5M/KvU094lLoZb6hLP9nEqf/CoUc/3Rqo3WDyGEPvJ0lKMy7QTYOH1SpMCEEMIacqefr7k2wHuS5ZcWnAvgnyYnO/K1UfomhPDVEMIPhxDOBXAtfM31dPVJuwoboNCV/1qfX8gLb38KwJ9FP/9fGC8g9sfF709HniLyquj5GeRFcc/EqFjbvyP+f8cDuA958dztxe+PB7AVwCkVW58G8OuH+/M5yvtkBkWhVgCzyFMZf/Vwfz5Hc59U/v4PAfw/h/tz2Qi/NlLfADip+O92ALegKFJ5tP16uvuk8vz5iIqBV/5uL46ywtPr0SeVf3cG5AKuyb47Gvtko/RD0Y6zK236EwB/crg/n6O5T4q/O6XSpj8D8IeH+/M52vuk+PtfBfDJw/25bIRfG6lvMFpzzQO4GsBrD/fnczT0ieY5GIqBH/YP0H+t3y/k4XIBwLeRb4xuAfAG5EXWrgZwL/KituVG6jIAT1aevbFi6w3Ivcm7Afyu8P98Z/EF3wXgHcXPTgbwzaIdtyEP45453J/PUd4nW5DfavZtALcD+HMA04f78zma+6Tyd3sAvPBwfy4b4ddG6hsAfwvgjuLXUXlr5mHsk+sAPAZgBXkNh9cXP/+N4s89AA8DuOxwfz7PgD75WwCPAOgWny150w/Xd0dzn2yUfkCerXA9gH9Dvub6a1RuoTuafm2UPil+fk2lT/4/AFsP9+dztPdJ8Xc7AfzE4f5cNsKvjdQ3yB0pdyKPIP+vh/uzOcr6hHwOwE8Xf14DsA/AF1Ptz4p/6HK5XC6Xy+VyuVwul8vlck0kr9HkcrlcLpfL5XK5XC6Xy+VqRe5ocrlcLpfL5XK5XC6Xy+VytSJ3NLlcLpfL5XK5XC6Xy+VyuVqRO5pcLpfL5XK5XC6Xy+VyuVytyB1NLpfL5XK5XC6Xy+VyuVyuVuSOJpfL5XK5XC6Xy+VyuVwuVytyR5PL5XK5XC6Xy+VyuVwul6sVuaPJ5XK5XC6Xy+VyuVwul8vVitzR5HK5XC6Xy+VyuVwul8vlakXuaHK5XC6Xy+VyuVwul8vlcrUidzS5XC6Xy+VyuVwul8vlcrlakTuaXC6Xy+VyuVwul8vlcrlcrcgdTS6Xy+VyuVwul8vlcrlcrlbkjiaXy+VyuVwul8vlcrlcLlcrckeTy+VyuVwul8vlcrlcLperFbmjyeVyuVwul8vlcrlcLpfL1Yrc0eRyuVwul8vlcrlcLpfL5WpF7mhyuVwul8vlcrlcLpfL5XK1Inc0uVwul8vlcrlcLpfL5XK5WpE7mlwul8vlcrlcLpfL5XK5XK3IHU0ul8vlcrlcLpfL5XK5XK5W5I4ml8vlcrlcLpfL5XK5XC5XK3JHk8vlcrlcLpfL5XK5XC6XqxW5o8nlcrlcLpfL5XK5XC6Xy9WK3NHkcrlcLpfL5XK5XC6Xy+VqRe5ocrlcLpfL5XK5XC6Xy+VytSJ3NLlcLpfL5XK5XC6Xy+VyuVqRO5pcLpfL5XK5XC6Xy+VyuVytyB1NLpfL5XK5XC6Xy+VyuVyuVuSOJpfL5XK5XC6Xy+VyuVwuVytyR5PL5XK5XC6Xy+VyuVwul6sVuaPJ5XK5XC6Xy+VyuVwul8vVitzR5HK5XC6Xy+VyuVwul8vlakXuaHK5XC6Xy+VyuVwul8vlcrUidzS5XC6Xy+VyuVwul8vlcrlakTuaXC6Xy+VyuVwul8vlcrlcrcgdTS6Xy+VyuVwul8vlcrlcrlbkjiaXy+VyuVwul8vlcrlcLlcrckeTy+VyuVwul8vlcrlcLperFbmjyeVyuVwul8vlcrlcLpfL1Yrc0eRyuVwul8vlcrlcLpfL5WpF7mhyuVwul8vlcrlcLpfL5XK1Inc0uVwul8vlcrlcLpfL5XK5WpE7mlwul8vlcrlcLpfL5XK5XK3IHU0ul8vlcrlcLpfL5XK5XK5W5I4ml8vlcrlcLpfL5XK5XC5XK3JHk8vlcrlcLpfL5XK5XC6XqxW5o8nlcrlcLpfL5XK5XC6Xy9WK3NHkcrlcLpfL5XK5XC6Xy+VqRe5ocrlcLpfL5XK5XC6Xy+VytSJ3NLlcLpfL5XK5XC6Xy+VyuVqRO5pcLpfL5XK5XC6Xy+VyuVytyB1NLpfL5XK5XC6Xy+VyuVyuVuSOJpfL5XK5XC6Xy+VyuVwuVytyR5PL5XK5XC6Xy+VyuVwul6sVuaPJ5XK5XC6Xy+VyuVwul8vVitzR5HK5XC6Xy+VyuVwul8vlakXuaHK5XC6Xy+VyuVwul8vlcrUidzS5XC6Xy+VyuVwul8vlcrla0YZ1NGVZtjfLsn/LsuyWLMtuLH52fJZlV2ZZdm/x3+3Fz7Msy/4iy7JdWZZ9O8uyl1XsvL14/t4sy95e+fkPFvZ3Ff82e/opXS6Xy+VyuVwul8vlcrmeOdqwjqZCF4QQXhpCeHnx598GcHUI4RwAVxd/BoCfBHBO8evdAD4C5I4pAO8H8EoA5wJ4f+mcKp75lcq/+4n1x3G5XC6Xy+VyuVwul8vleuZq5nA3wKgLAZxf/P6TAHYC+K3i558KIQQAX8uy7Lgsy04pnr0yhPAEAGRZdiWAn8iybCeAY0IIXyt+/ikAbwHwL9z/+IQTTghnnHFG+0Qul8vlcrlcLpfL5XK5XEeQbrrppsdDCCdSf7eRHU0BwJeyLAsAPhpCuATAySGER4q//y6Ak4vfnwbggcq/fbD4mfTzB4mfjynLsncjj5DC6aefjhtvvHFSJpfL5XK5XC6Xy+VyuVyuI1pZln2H+7uN7Gh6TQjhoSzLTgJwZZZld1X/MoQQCifUuqlwbl0CAC9/+cvX9f/lcrlcLpfL5XK5XC6Xy3Wka8PWaAohPFT891EAf4+8xtK+IiUOxX8fLR5/CMBzKv/82cXPpJ8/m/h5UoNBwNf37MdgoPM7PXpoFffuO6R6FgBufeApLK71VM+udPq4+f4n1bZ3PbqIfQdXVc+uN+ctDzyFJecc03pzfu2I5Tx01HA+uo6cux51zlhHLOdB56TknHUdTZzfWmfOvDpDWk04lztHKueiui0WzuVO74jl/Nb9T64b57371o9z30bjPOScVTknrSOVs3+UcG5IR1OWZVuyLNtW/h7AjwO4DcAVAMqb494O4PLi91cA+MXi9rkfAnCgSLH7IoAfz7Jse1EE/McBfLH4u4NZlv1QcdvcL1ZsifrY9ffh5y75Gm78TrpDuv0BfuGyb+DX/vpmFffX9uzHhRddj09/84H0wwB+5/Pfxs/+5VdVjqnvHljFT190Pf7wX+5KPguMOG9SfPE6PRvnV3fvx1sMnL/dgPOP1pHzPevMqVlYN+F86yVfU204rJw37H4cb7noevyfG9eH8y0X3YA/+tf14fwvl30d7/2b9eH8rc/ZOf9QyflXX7Fzrld/WjgfObDSiPNbD6wD566c8zNKzv+7Aecf/evdKtuXfWWPifM/X/Z1vPevv6Wy3ZRTs0BpzvlU8tmmnJ+96cH0w7BxPvzUCi788PX44y+2z7nW6+PnL/s6fv1v1ofzv332VvzsX34VK51+8lkr56XX5Zy3PNg+5/UbkHM9+tPK+ZsNOP+XkvOSRpy68fYr91r7M38/V7vrx3nLOnH+9MU34HPrwPnQUyu48KLr8SdfWifOS7+G/+tvdd9bM+dnGnAq+/Oj1+actz54IPmslfO6ex87ujhvVsVc4Dc/c6uZ80+/eI/K9l9eu9vE+bZLv4bfUHJee8/G4fxowfntdeT8/DpwPvjkMi686Hp84Es6zg3paEJee+krWZbdCuAbAP4phPCvAP4QwI9lWXYvgB8t/gwA/wxgD4BdAC4F8B4AKIqA/78Avln8+oOyMHjxzGXFv9kNoRB4qQeeWMafFh/swZVuEuKSa/fg7n2HVM+udvv475//N7XtL9/zGL5wy8PoDwKWFRuf/3HF7Ti01lPZtnJeep2N83f/vuBcTT+/8+5HcbmB8/1X3JZzKmzb+3N3zqmwbeXcUeFcUmx8GnMqnrdz3pbbXkm328r5+5ffhsW1nsq2lfOjX96Ne/Ytqmw34bzi1uJ7q9gQWDk/cOX6cY7GofY533/57c04Fc+bOYfvp4LzrkfxD0rOEAJ+v+RUvp8Wzr/88m7c++ii+v3cKJz377d9b62cv/P3+vmzCedSp6+ybebcuQe7Hl1Uz58Wzmvu2od//PYjBafc/004P3iVfv60cv73Bpw9NedtRyTn1Xfuwz814VR8D7+zfwl/ZuD8yM7d2PXoIg4pxpWVziScmvfTxvnB4fuZbntjTsXzZs4v3IblTl81T1g5L96xG7sfW1L1j5Xzqjv24Z/+LedMOUitnHsft31vrZzDdd86cL5vI3Iqnr/yjn3453/7rp1T8X7ufXwJf37Vveq2XLRjN/Y8tqSyvdLp43e/oB+HqpwpB8xEnIrnLZzLnd6IU/H8enICG7RGUwhhD4CXED/fD+B1xM8DgPcytj4G4GPEz28E8H2Wdv2PK27HStEJqz25M+7fv4w/vzr/Emk8hB/ZuRt7Hl9S2a46MfI/D8Tnr7pjH/719u+qbAMRZ8L2d/YvNedM2F7t9vF7X7it8uc05xdv36d6FgDeb+T8i2t2qW1fbOX8+xHnWuL5KzcY532G762V80t35Jxriu/t719+m4nzQzt2qdoNABfv2NWYM/VefOn2764b597H7Zx79y+rnl9vzveNccrP7318CR+6piFnwnZ9HJKfv/KOfbjyjn2qZ4Gcs+xHDeeHh+9n2vZF68j5JSPn718x4lxLPH9fA87vWL63Rs6r7iw4e+kxsdqfGs6Lhu9n2vaHr7Fxvu8Lt4/+nLD/xdubc6bGofXkXOk04XxU3Zbfe7o4E7Zzzsr31sKpmMvfd/ntas49jy3i4h27i2fT7+eHd9yL+59oyJmw/8Xbv2vmXOvpxtvJODXfWxvn1XeV39t0W37vC7fZOHfm31vN3Pyha4ycl1e/t/Lz/3qbjfN9l+s5d68j53KnZ+a8ZoNw/kWFMzVnLXd6+P2mnMr1kIXzIzv1+5W/uOZePPDECoAGnAn7/zIRp2zbyvmha3YNOVPPN+Hccfdj6rYAGzeiacNppdPH1Xc9iredm5d8Sn3AF+3YhQzAT//AackFwYGVLv7qK/fhDd//LByzMJPcgH/mpgfx4JMro7YIL3oIAR+48h6cdcIWnHvm8cl23/LAUxGn/MJcvGO3nnN5xLltYSZpuzHnGccnbd/ywFO4pgHnW156avLZA8td/NV1e/DG7z9FzfnQUyt46yvSbbFyfuv+J3HNXY+qbAPR99bCOa/gvPEBPPTUiuozr3K+4oztKs4ddz+m7s+SU92fX7kPb3xxM05pog8h4INX3YuzTjzyOP/POnLefP+T2FnlTCxmLtqxC1mm43xquYO/+sp9+KkXn4Kt8zPJMdHK+YEr78FZJ27By5+7PTmWN+W88KWnJtvdlHM0VvDPhxDwwSvvwfOK/kxx3vSdnFNjG8g5p6bK/tRzbpmbTj7/6W/G45CO8+XPTX9vb/rOk/jyPY+Z1glTU2V/yrafXOrgY9ffhze95FQTp3Ze+bOr7sHZJ21txJnazHz4mvXkvN80r9g4n8C168z55pecis1z08nnP/3N+/HwgVUV52CQf2/PPmkrfnAdOC/asRtTU8CbX6Lj/Pj1e82cmu9tznmvmvPGvTmndj304R27MD2V6fvzKznnptk059+Z+zPnfNnpx6k4r7v3cfU64cM7dmFmaqroT/l9e6Lozwtfqud8ZKw/efuDQcAHr7oH5zTlTOxBLrpmHTm/8QAeGevP9ji/uc6cn7h+L97y0lOxMDuVtN2E8/knl5yy7aacb3rJqUnHUVNO/Tik5/zGfTmnehy6Zhdmp/P+THHuX1wbcs7PTCWf/9taf+o4f0DxvS3ljialHj20hmMWZvDLP3wWAHkifuTACj7/rQfxc694Dp5z/GZ0egOx2Nf//upeLK718OsXnIP52WnRdq8/wCXX7sYPnH4cXvvCk/O2CF/qL9/zGO545CB+9fznYev8THIBcfGOXTh202yFk7dt5fxUlXNmWrRt5dxZ4dwyL9uehPP04zdjTcG51Onj1197torzo18uOU9KtmXnPY/hzkcO4te0nDt3mzj//lsP4a2veA6es32TjXN2Ks157R687PTjcMELrJwzas53vcbIqejPT5acFzTjlCadnXfnnO85/+zWOR9+Kud827mnt87Z7Q/w0S/vwQ8+dzvOXw/OHbtx3OYKp2DbzHnDdyrv55Q4Jlo5d9z9KO767qEKZ9qBreV8qMq5fTPWev0k5/IEnNLzVc7Nc2nOj+zcheM2z+KXf/jMpO2HnlrBF771EN76itPxbCtnYv7s9ge45No9ePlzt+NHnm/kVHxvP7JzF7ZvnsW7XmPl3JS0/cmv7s05L7Bxnv+CE/O2CN+ta+4qOZ/XjDPxvb38lvx7ux6cl153H15xxnb8yPPb57x4x25s3zyLd766fc5P3LA3ej/l8dbKefe+gnNOsx7Scz745PK6cXZ6+fd2jFN4vuR87wVKzp0F5/D9lDmvuOVhvO3c03HacWnOj9+wFytd3Xqo0xvg0mv34Nwzjsd559g4teuE47fMjfpTy7l9U3Is/0TJqVgnjHEO+5O3f/Vdj+KefYt47wX69dAYp7DxfeCJZVx+68P4+VeuE+d1e3Dumcfjh89ZB84du3D8ljm8Yz04r78Pq73KfkWwbeW86s59Q07NOqExp+L9nJyTt3/Vnftw76MGzp0Rp2D7gSeWccWtD+Pnzz0dp2o4b9hb4Ux/by+7bg9eeebxeM3ZVs70eFvKHU1KHVzt4pdedQZO2DIPQN5sXHbdfRgE4Fd++CwszOYfMdchK50+Pnb9XlzwghPxolOPyT2tgu1//PYjeOCJFbzn/LOHtqVT8It37sYpxy7gLS89LWn73n2H8KU79uHtrzoD37NlruDkbVs4lzs9fPyGvXjtC08ackovupXzIzt249Qhp3z6YOW89Nr7EALw7vPOwvzstJrze09Jc/7Dtx/Gg0+u4L3nn42FwrbUlpLzQgXnPfsO4co79uGXjJy/YuB8XcE5PyO3peR8j4HztOM24S0/cBoWErYn4VyYnUYIQKcvcF5/n5nzvReMOKVJ5+Kdu3DacZtw4UtPVXO+49VKzuv2AFgnzlsfxkNPreA95z+vdc67v3sIV92Z9+fxLXMurfXw8Rvuw49+70l44bOOKd4hfky0cIYQcFHxvb2wPDUTbJs5r61yTmEQgG6fdsBYOa+4Jed87wXPG423zPNVzjcrOO/67kFcdeejeMerzsT2zSUn/3wzzpNzzhm5LSXne5pwJk4HS85fMnK++7yzsDAzjf4goCt8bz9xw1786PeejBc8a5ua872Fswbg58+cM38/3/QSPec7Xl3hFN79S6/dgywr1glKzh97kY7z8rI/z2+f885HDuLquwrOLe1yLlY4n3/ytuRcbubcuQvP3l5wJmxPxDk7jd4goNca50N4+MAq3nNBep0wxvniNOcdDx/ENXc9indWvrdS/w85zztTxfnJG/bix0vOxBxXcv7aBc8zc+ZzM/9OlJzveNUZOM7KOTONbj+gz9wiWeU8x8CpH2934TnHb8JPvfgUNec7Xz3ilKJULr1uD6Yy4Jd/WM/5+n+n4/zCLQ/hkQOrxTqhXc7bHz6AHXc/VnDOts75iaI/zz5pW3IuN3Pu3I3Tj9+MN37/KUnbVs5Lrs05y/1npz9oj/NbOWe+ji85pffTxrnz7sfwrtecie0lp/DdKjl/WcF5aLWbf29f9KyCM/G9LTjf04Qz8U5U5Y4mpaayDL/06jMxP3So8B/wZ296EG/4/lPwnOM3Y2Gm2JwwX7wddz+KJ5Y6+JXz8hPtPAJGtn3WCVvwuheeNNr4MLYfeGIZ37jvCbzj1WdgbmYqaftzNz+E2ekMv5SqBuwAACAASURBVPSqMyqbKv6FqXLOpzjveiznLCJrFmZlb2iVM2X7/v3L+MbeJ/COV5+JuZmppO0q59C22JYH8IbvPwXP3r45+bnEnCmP8mdvehBnnbgFr632J/N8zJmy/bmbH8Tc9JShP0ec8zOy4/Cau8a/twuJE58qZ8r2d/YvFZxnYHZ6Kmm7yjmfcHgCwGduegBvfLGe88nl7uj9VHA+L+Zkvrff2b+Eb+59csiZsl1yvv3f6zg/e9ODeOOLT8Fpx21aV86Fljk/X+FMObCbcD613B29n7NypI+tP5dx03eerHxv5fG2yplqNwB87qYH8VMFZ8rpVeNMRDR99qYHcfZJW3HBC05K2o45U/PK529+KOd81XNVTskq52h8pp+/uuB893n6eaXkTNneW3C+8zVnVvpT+t6OOOcVnJ+96UG86SWn4tSx/qTtx5zzCs5zTtqK819wYnINsnf/Mm6+/6khZ8r2529+CHMz5Tgk2y7b8lMvzjlT45ad84F15HxwyJlaa5k579yHAyuW7+2IMzUO3ff4Er51/1N456tH39uOkjNlO4SQf29fXH5vbZya9dDzT96K859/YvIzrHLOTOfrIQ3nLyrmlSrnKcem55V6fxo4E7b3FJzvek3OuZCYs6qcqc8whIDPFOPQKcdW+5MZb+P+THB+puD8kedX30/a9p7Hl3DLA5X+VHDOz0zhF6qcwib5sxXO1D7OyvnZCud865wPDTlH41A7nFfdsQ8HV3vROCTPWVrO3Y8t4dYHnsI7X31GwZleJwz7M2G75HzzS07Ds45dGH4u3Psfc2r22S84eRvOO+eE5Nxc40zts2/KOf/LDz03eaAfc5afC8t5Z875K4b1UMmZ2gvvfmwRtz5QHYc8oql1Pfd7NuP4LXPDSYHzWHb7AxxY6eKck7YCwPBF506I9i+uAQDOOWkbACS9oY8vruF5J23F1FRWaQtt+3Gj7f2Lazhh6zyO3zKHuWnZdsyZijrav1S05eTic5mZEr2hVc6Up/XxwvbZSttVztRn2OkNcHC1N+rPGXlCizlTHuX9ix2cfaKyPyNOje0Tts5hewPO1IS2f7GTcw4/FzkUtcqZOsF7vLB9dsW2lrNczHDPr/X6OFTtz0RbYs4FDedJW5FlFU7mnYg5UycE+xc7OHHb/IbjTJ2wWzkfr3DODznpccvOWb6f+ZiYOk209Ofo3S9tp8bySn/Oypyr3T4OrfXGbEvP1zgTEU37l9Zw9olFfyY2mzGnZs46cds8jtucnj9jztSp6Wj+zPt/LjX2VziHmyqlbc2cVXIuKDgX13qjMS4xx1nbsn9prfJ+atcg5fuZ5jxp2zyO3Txb4aSfjzlTY7+Zc2wcapdz/2JnyJmaP+2c8fyp50yPQ4Vt9XpoxJlq91pvgKVOv7LWMs4rqTXLUqU/U7YjTo3tk48p+9PGqV2zlGvt5JpliZg/k5/hyLbk8KxyptYJq90Bljv9im153Io502uWNZxz0rbo/eTG27rtNOcCjt004uQ2vjFnanwerVnsnAttcy6uDTlTTkk759oYZ2rNYuO0rYeqnMlMoG4fK93+2BiXc8p74bO1++ylNZx98vh6SL2mTO2zl9bwrGPH+5OzHXOm9sLD75Z2n13hTO6zjXNWVe5oUmrrfH5BX/nF4zyW5ZWJx27KQ+JSp2wHiueP2TQzfF7yhh5c6Y5sJ7yhI9v58ylnwIGK7ampDHPC6ZOZc7loy8Ko7ZI3tAln9XnJtomzuL7x2M3Kthg5D6wz5zFVzml+ADRzRt+t1MBt4ax9txS2Y07edm/cdmJTTXFa+5P9zAlOa39uDE7Z9iSc01MZZqczYby1cubPH7MwU2mLbkxMcdLvp2xbzbkajeWJiJnJOFO2bZzVsXxmegozU21y5s9vG3LKG4JJOSXbJk5m/uTsU5zq9zNxOmydVyhOzunBrhMS88q2JvOncq3VZJ0wOz2F6anMvh5Scqaiq0ycy/bxVstZ/wzlDeFEnCnbE3FmmMosa600Z5ZV30/Lekj3GY6vh3RjnJ0zPQ7FnPJn3hvbf6Rsj7dFP2eVnHrb6bE/y4Bt88059eOtnnNuegpZZhvLAZ7zYI0ztWaZhFP/7jfn5MdnC+fTtf9MOQ6tnAdWupjKgK1zunXCRP6ExOFlVe5oaqC8kju3YS8W+IXjKHXKdnC1h4XZqeFLm/KGHlztDZ0YKW9o2ZZjK22Rcl4PrnaHtkv7Kdt6zi42zU5jrmhzyhtq4ixfgMrzsm0DJ2FbbIuVs7LZbJ2zYrt8PrXxsXzmm+emMTtdcqYHbjUnsdnU2i7tW2wD8kQccyY/84V4EtG3JW17Zvjnw8q5WuWUT9jNnKsxJz+hmTlXu9gyN42Z6dH7KZ0mmjgj507qBM/ESdgGBCemlXOlNxrLU7atnBXbqefNnCs9bJ2fGecUF1YGzvi7ZbBdPq//3soLTopTHit6etsEZ9J2NH/y6yGak59Xetg2P4PpqUzXltUuwakch9pcJ1g5V7vrz1kZt9ZvPSQ7D8ycK8R4m/rMtZwV22XEVGucK11snZ/B1JBTsWYx2AYqa+3EBTMmztXIdnIcqnOmv7cjZ03K9lhbEtHG+bqvGgDAfy4cp3QIuG1CTm6vZeZc7UWc/D7OzLnaizgVaxYtZ7lHrKyHpUAHE+dK3TYg74W1nCGEsTlOu89Wc9beT/55M+dKF9sWZiNO+lkzZ+T0SpVjqModTQ0knWzGE1TqpLL6pSuf557t9QdYXOtVXvTUxqfelk5vgAFTSCxeKEteXztn3TbXbjNnzemVTzjcTUUmzsh2OkrFxrnU6Y9FP4m2rZyr0YZA8ECbOaPFqXSCZ+asfbdy261wErbFtjThNEY0mTg3HX7Obj8Pz65GP4m2rZyR4zC/ilY7DqXbMm6bfz/NnNFms7TdCidhO2+LMK8oOTu9AVa61Pupb4vIWftuaTY++rZUnXULs3zUrpmzFhWWt7sNzjjiTLOptnPGY3mLnMo5rhFn9L3lONd6fax2B+vHaZjLzZwrPeL9pMcVOycRbWxwkEqc9fcz7QjWcq52+1jrDWq2U5Gyak7DHGfmjJ11gm0zZ+RQS0U0mTit63gjZ6c3aOw4bMbZ0n4lGocOK2e8R2x7/6lcJ5g542wdwfawLeZ1gn4vrOVc6w3Q6Q+IvVBLnKvEeNsWp8G2mTNyqOVzs0c0rZukwnDmk0piQOOeXVyjvZvW00TuRibqZMtqW+RU2jZzMtE4bbSFt62ciAXbh2LHUcq2lXNlfEPQJKJJSlkat80768ycxfNleHYZpcJ+by2chLNObEsTzigagz2taMIZf7c2BKccQm3lPBQ7DoUaBmbOJu+nljNKb5qfmUIQbkwzca7jGHeIiPQQbRs56cjKljgNtptzjp4fBKDHHtQYOM0RTRuMc73WCSvd4fdqaJvlLN/9deJc5/VQjZMZV8yc8Xhb3IDH3ZhmWw8xY6Iw3po51baNnIY5zswZOdSk6Boz50qdU7oBz8TZILJSy9nENmDkVK8TGnDGtg8X5wbZlzWxDYzKzszPTIk34LWyL9M6ghvt+VriJNfxLXG2YTvx/NbKuq/T5wNXqnJHUwNJUUdxqJvmlG3spFJjOz7tFSaoueI2jtK23Ba919fMSZxUJm1rOVe7w9vmqs+LL6O2LewJu85xqLKtjmgychInPtpJpM2IJjPnShfzFU5Niouacx0jfdjPUBi4zZxjpxWHiZM4SQZa5Iwdh61GNBlO762cURq07pRNyRmlQaeK9duiFLhoRv55E2fkUJNP8IycpmgpK+d4GrTmc1m3k+cmnIa52cypjFKZNJLA9Bm2zUmuE1riJGxrNwSadlfToDXjs5qTTRHU29Zu2DWRsibOCebydApSHHEopMM0iJaqpkEnU9BMc3kU5WeOrNSk/ejHcjNntB7SprXrxqENwmlasxg5Y6ekxbZiDVJN907Xi5oksrJFTqYcg2TbxGmZ4xpFeDezneSM0qBTNZ2qckdTA+kimsZfdKlApTaiabRhjyMmJNszyLJsaBugvxiDQcChtR4xifC2ASJKRdrgWW0Xz5c34Mm2xydWgH7RKU7dCd74okAcLK22ayeVku1xbzX3/GAQivRD7aKdiSJrY7C0chK2AXpCozgtJ3hWB6lk+wBzQqAPz+YnqP4g0BEwLRZVNn+GUQi1foxLcK5F77NwUkkVKORsl20321Zy1k6TBEcwxWmJaNIUbly3EzzCNkBzxmnQAMSCwPa2xItTfZpIE9sAvSGkOOVLJmKnl2KOq0Vt6uZmq/NVWicMOSv9L930x9Vy5NtO1H/SfobK9dDQdoKzmu49bIu2jk5ykxyPcbxt81qL2Gzkz9ftd5ty1sYhYV5ZJ9v1KAWZs5oGXdrXR3inaxw2dZA2sQ3Q6wqKs8k6QesgtUT6WJ2v0vg85Iz631pDTT/HGSKaEvNKfa3FPx+newPW9ZDRKWmxrYqqrgZR8OMWxWmJrNQU1Nfapi5SSdsen5sBmjNOgy7tp6Lfant+5ZpF8ifU071t0YypW+qqckdTA0lRR3GVeE2R7GM36QbL2HaW5TemSbbHvhjCQH9orYcQUJtEUl7f2m0VwoJDyxnbLm+GkyIDjh1bhPMRMxSnXHshLoAmR+PEnHKdDoJTuhku4pQigw6t5pzHRhOa/fYR/eCqtZ3iZAduwj7FKW02a84DxSSitR071FI3w9VsCxPUYpQ+kWoLd2uOdGpq/QzLGwrLG9O0Y5zEeSh694FystSOQzJnbRxS1EXSctbHOH4ipjil22e4RZ40htbGIeUYN7xJTBi3xj9DYV4ZRiiNfy6pMVHrCK4vINOOYAsnuZEl7FOcbW42482JxfbM8CYxXX+K64TIuVO2RR2No0g/1daYoPpzWrwBr6fmjCPrgPIUPOH0MnDWxyHZdnwznHkcYubP3LaOk3N6cePtodVefV5R3mo0M1XeJKafmwGaMy5kW7Y9uU6ICx+LazPbWuuYYX+mOekDRh2nOJcvx+NQ2gFTt607eJudzvKbxNRrLeH9jD7Dsi2pGwot9YXqay3dOFTemCbZVnNGaxDAFuGtuemv6VorxckdMFJtpzg1a5bReih9YBx/5tobYVM34HEH4yTnCrUeSs9xlvVQ3bY8N1eLe6du+htPg/aIpnVVamE1M5VhU5neZDwFVy3aok2bxTYgT1C1gVuwbeI01KIyc9ZedD5ixsy52sXsdDa0mYxosnBG0W+j57UDmsAZRb/lz8vOPRNnvGg3FLQDEpyEbUCeiGuFchPppENOTWix1jbjJLHYBuT+1E+WdNqsNhIvZRuA+iTMxLlCONQStm2cVLFhXTRjsi1cJB453ho5ubRZQzqMNm12+LyyrqDIGUW/5c/LY6KJkxgTtekwpX3JdvwuA/SG0MwZpZNaiw2nPkMgGiuETXUtMkRwvtPjkFyEl+LUfuapzxCI38/EOkHLya5BWuKMHWozfNFzMyeRggLQGwgzJ5M2K4+3Ss7I6ZVlWXIeUnMSDlLpNlsz5wTpwUlOYpMMyA5SPefk6cGp97N6MC6WBuEi8chxiNiwJ9agFKcUcVhfa+nGoZJTn9qo2JcZ1iAmznidYIiqTnISzjqA3iOaOZm02VY4o3XC8Ga4NjjJNYhs28RJrhN0UX5JTmJuBjyiad2Uuub2mE2ztXQ1yrsZQkBcJX5+Zoq9GY76kqYmSypEU96wR97QhNNDzcmEolI3vpg5mZNn7YZA5CxsDzkF22ZOYnBNeaC1nLEnPH8+MVgqOQeDgEO1lLKp9jgJ2wA9uHKcqTpH6nRSoi3cjWlx+kTJqb4IQDgdjtNJm3Ja+pPlZByH2shKiZNyHKZtGziJtFnuxjQzJ5E2m7dF5yBN29aN5WZOwqEmznEWTsKhNi9uqm2cXNpsK5xE2iygd5CKnIa0WY6TuzGNbEtis0lxiv1pSCshT7Wl/iTSYSzrBP04pDnAiG1PzkmmzYoRTQ04yXdIuR5K1KLSps2aOcl1gsFBKnFabU+aHmwowptuS902oHdMtGWb49SmzQKpA2a6LXJEk/Yz19um0oObcKrrBYnrBMa2+lIXmbOWNquoE6nmJEo9AFxEk5GTSZtthZPdr7TAabat52TTZpWOw2RbiLRZwCOa1k2pejRxcW+AnvyWO330B8Hg3R7Pqcyf5yeRQ1EKkpQONVqcautd6DmXOn0MQt020A5nvTgp7/Qyc3K2if43c3LebXURVoGTPGFPDJZqzl7OSdWjaYOTicTTbghSaSXxqQlnuzFnrRBni5yG9Jl142wyQWk5yRP2djgXO3TabAj0DXiNFgVkpE8LnLXipC1yEg61/LtljWjSRhymHaSxbTHdO7I9YG7AM3MSdQXztugcpLb5k7fdmDOKgGmVUz2WN+CMbLOcZMRhS5xkxGE7nGQadHFjWpcch4ycnNNLHUEqF7LWpomYOa1znIVzQtsi5xrRP7P8TWKN2rJe6wRDPS8zJxOl0ipnbd1njCKSOKN54rBwNjmkVXKSadAz0+yNaWbOWhq01RGcmJuJentSerCaM7rNsmy79vIaef5ssM+2ckbt7vQHrXDGNWI9ommdlQovi1OnAHoRzkVjcM8fXO1iKgO2zEWhyNoCv6pUq/FBpBXbzAAF0CdEB1bqnPJVtLRDjbMdt6WRbcMGnGvLwZUepqcybJmbHrMvRx0RObJqx2EqckuXf0sPljbOVJFXerOp40zZ3kaGoes4U9FV01MZNsecohNzspNKLacUuRXXo0i2ZbXOKdZHiCbilG1An4J0YKWr5uQ+Q+55sj8TYc7xZhAwjEMW21J/Lts44zToUVu0EaSKkHhDfQRrPRJ9fQSCc8aSqmxLQUoV4dUW+OVsA/y6opoGXdpvI3KLHivszgB5nUDNQ/SaheLUF+FVOEiVtagsFx5w6d758/RYEXPKBfXpQ0BxHFIX62fSoLVRuIkDxmoadGlf2xZ5HKKdJJ02bZOHgDpOS2qOmMLLRG2ynEw5Bq3zNTWXV9OgS/vaGwqbRLLrI0gVtrXzEMEpF2HWc/KpVtZ9mX6tlT9Pj88WzprTS6hbR/ZnqoyI0qHGrbUAnnO+9r21Z9SI64ToULeNCw+k9RD1/lOcyb2w8hAwljuaGmhePAXlCsLyLzqdf01/MY7ZNIup4nrBvC30wJ2n5XFFPvlFe72+DD+ITMIppdqVk0KVk5ugKE5Nyoq6wHM0QZU34FlsAwLnwijtp2w7ZZvsT8GjTH7mhjpXqrBlciLWcXIbHzr9MO0gtRThpQuN85zU+0kP9D2Sk7JdcuqLXzKOQ22xWUU6jIXz2ErabGmferZMD7ZyjhW/TIQtWzm1KS7l+zbWnwZOc/HLRpy6xamVkztNFPtT+X7K6TAxpzHtR9oQUpzMXC6PQ9q5vEFqjsE2IKwTFsY5uRvw6HHIWEsj4SClLgLQpnunOOP+5G7AK9ODrZzaIvb1tVaak67/pX0/9ZyptVbclgUDp32tJY9Dx0zA2WwcUvanIrqGGp/pelF1Tm4up9Og7bUc9WnQdtsAP99Wx4nSvngbtHbOYhyHklNSfREA6TiUn6/aLu2r07016yFtOhS759PaludbKyc9DinnOAOnef40cnJrljI9WH0pgXk9xMxZBtuAPK9Uxa1Z6FuS5XpRVbmjqYGkU9D4hohRgS3dYCltfGPbAB91tNodoNsPai8ufUIgF6iMOecYzuEJOzEAcos87QS10u3nnGQqh+4UXOKMTwjKG/DESAJywUEvOI+pvej0ZElxilFHjHdbe0OEdGMavSGQ60XFnNwJ3nKnj96A7k8x5DZ2qClPB6Ub8JqcbFGcXNpsb8C9n0rnQbLm1mgBWd6Ypj3ZSp0mVm0D/GaTSg/WnSbqrn+N04MlTur0Xoo6ihfhZdsp20slJ5U2K548a+tXjH+3Rpw6p6TkgDkQXVtbtp2yTXHKthtENFWeLW+GU0fXCIt2ipObmxfXekQatGTbFtEUj4nSDXhcFC7AbQip/qTH/iGn2jbNKc4rC+Oc3A14XP1Eri3keoiZP8n0YE3klqHwsZqTSZvN26JcDzFtWSw5DWstQB9ZGX9vZ4ec+rkZEOaVTfF4q+fUjUO6eaX2fhY34FnmLIDebJKczFzOpZMCKWetLsK73p/8DXhcnUhA+N5uqq8TtOnBukgfXb2oeM0i3YBH1k9McSrXCVZOehySL9LRckrreG5doeZcpW8xB/jDS4Aah3Tzp3QznHTAyM4r5L6MmD+J9GCJk18P6d5PiVPuT+2+jOY8RMz7HtG0zpJv8OnRk4i4SVamCVG2mYGbDP0WT8F7yDJg23xcjE3Pyd2EEl/9W9oG+MJw2glKctZxxfVMnOSpDD2hUZxyvSjKocbYJk9w5KijLAO2zukWHCQnU1/IHNHEbAj0Rell2zGnpSZBaV9/Q6Gc4kJvfHQbNvn0qQGn8vTJzEk61BL9qeUk0oNTt8moOZnCt4AQiWf9DNXFL4k0aEMkXmlffwOanLJUt52Ys6gaE8ziNOccT1eTI33isZ/hFA9qdBsC7sZRcs4Sbdc5k7X/qM9cqllI1WpgD2qUtpmaEbxtKt1bnwYNlBEzLXBG6cGAYm5W19Kg06DlGodKTnIcktcsMScXdcQ5jgB+jKulQRtq4gHSZy6MFcr5k18P2ce4etqsnrO8Gc6c2qrlTNnWOoIJTst6KD8YZ9YsTdYJygPjJmstilNb0kS6Aa+N9VCbtmtps4aLdEROylmXGBPtay2d85VMg07VaKrYLgM6pCgiyxxHzZ+W2oyAMW1WGXEoclr3wgRnep+tc5DGckdTA5UFtsib4QybTc5bDRg27MzA3cT21vmZ8XS1mTyHnbrxhT9NlAZXfd0ALSdXSwHgNxsWzrg4bWlfPsHTcdIONca2lXO1h20RZ3k6ODEncbWs1UHKf4Z221rOPE2kHubMpc/QjkP5hKAeiqrfJCdPHxbitFmZU3sKzp3ec22hx7hE9JvB9raIc2GGvtEwhECOFRwnmQqZSHHRcnK1FAB+jLNwkos85sSPSs1JhYprT/CsnFyaCHUznJWzaWqOilNIm9Wm/Yic7Fhh27CbTmRb2LCXkQFU2g/HaV0nkCm8SgcpN69Qdcs0p/f1/qzb5sYhnpOKOEyth2zzp7Z+ItefbJqlYS7n6nlxbeEi8drgpN9PIZ200dpMF3Vk4mxlvNWnzZbPWy4CAPi5vL7W0jtfxTqERNosd/EKPw7JaxZ1yhK5BrGsh9K26/1Jp6tZOMXaucoI0iSnIdLHwhnfkpw/T89x5GeeiNrVrhOsnOU+SzPeWjnte0T7uk+7HorljqYG4lKWVrt9rPUGTNi6buBO1d6gI0O0Yct22wDN2SE4bZOIXLSVjq6xDZbqUzNmoB9yUgO3OmLCFtHETZZSFBHLSSwgAD0n52mnI/FaitwSTx90nJynfa03QKc/IKPltJypaDkqmlF9mtTAdslVFcdpOe23toU/7Z3c9vwsfWPaMD3YymmJ9KnZTnBaxiErJ3E6rL7BJblhp04qtadm9ig/6iaxxpwmxwQ1DtlsW6IxKM6Vbj09uLRvSQ/O26JdQOo5rbatnMnT4Qk55UX4ZLapG9OG6cETcErz5yFmjtOuQYZOZs6hRtgWOWtjRSIKeyxlJcEZO9QM0VLzKc6abfomMY4zHW1OpPAqOZO2qawHbXoTw0ndklzaV3OK0cn6FF7Z+aqzbeW0RGFbOZPRxhNwLszSN6YN04Mn4NTUCK7btu759LZFTvWahehPRY3DuC36tZYU0USvtaib4TjO5JrFkPUyyXpI4ozljqYG4jpvdI1i8wlK/mIQ0RiJiVhdcJKxTT1PDVCAFC5av0ZRU/xSZVviZDyzlG2qLZRtoBh0hJPK8esi00V4VbYlj7KSkztN5DgXZuW2bJ2nJkt60lHbFkPF9baptlC2c/tylMo2MvRfG+ljiK5pYBvgOamTSikdysKpLZbYhJMfh7TfW54zy6JxSNxsEkU+Ext2ffFLabzVceYpv/TiNOaUI5qIhTJnWzjtNXP2lONtinO+3p+WiAnONmBInSO/K/T4TDnrSvtczaWYM30pQX3s52wDlgK/0oEUzamO3GLSgwH5UoKqeEewMUKNHIds6wSJc6qW1m5bJ5jSuIxrEO5z4d/PaTZlJeZMpU1Tts03Tqpt0+OWvB4SONXjEP2Zm+snqm0znA3WQzXOBim85A1Y5PrWZps7SJXWQ9xhSi3dO3kpgfbwkhpvE5zqfRk33vKc1G3QlO3S/iScwwN9tUPNxsmvE7r1dO+E49BiO26LOGcxay2AmD/Z/uT3ZTO1dG8rp7znI/dlioimmeQThLIse5n09yGEm5vYPVLEbXypfE1AnkS2zE1jZno8/5ayXT6vLZZozankUqeo5ynbeVv4CWrr/MwYp1ygkq5FJYdQKydixjYgcGo3sgRnui3aTXK9LeUNeHwhY27B0cexmB17NrZdtp2zXevPhBNTbVs8edZuqqoDd4WTc5AKp2zb5mcwXU1Xk+pFscXAbSey/M19Rk71ZrNr57TWopqAc3wjW/3e2jlrabPmCBh9Cq/ofGVS/kZtIThrNWDoWoFUenAyoklruwknMWcNn18Yfza2XdrXciYjZSeonyg6JRnbAMFJfIYA+BTelW49Pdg8l+tPQVMONY5ztTvANooz+p5zl2mQadApTnVqq91xyM2fa70BtlWfZRyHImecNsusQdg06ETNLSptVj8OJTi10VVEejC31hpy1g4Bucgt/sYsfhziDmr6Y86TJpxU2ixQH4c4zlT0uMkRzB4wRpzm9VCdk6sXxadBt1SLilxrjT6XLRSnYS43c5K2tY7DBCexn8yfH2DLfOXZBuuh2q3XzJ6PSz9MclJlRLQF1SvjM8mpXg/V06DlcciwHpIuXlFGblXH581zaU5pX0alzVo4JdtxW6SUvMV6iQAAIABJREFUz1iNHE0A/lT4uwDgtQ3tHhHiNtVUviYgRTTx6U3x893+AMudfoMUpNHzwxvwmLacfvzmmm2qLdaIJqrGABd11OkNsNKlOHnbwLjTq7wBj/PMxpycd5uyPWyLUL9izDaz4Bhx6sJcqbaUN+BxbTnjhLg/6Q0hx8l5zsn+ZCYRjpNbcIxqaUScwg14MScXMcP3J2+bW5zGttd6fax2B/aaIRX70g14B1d6dk7DyZaZ0xjRpOU8sNLFWSdsrdkG+Eg8LSdXYwCoc/Jp0PobCsub4bhxKObkNoSU7bLtemddgpNKVVbW/pM4D6x0cfY2uj/58ZZyHuhDv4H6Z8hxSmNc3JbUDXg1TmZDyPancCkB/36OP8+nQadqTIzGLekGvIMrXZzEcMb2Kdtl2/XroQSnOrXVxnlgpYuTj5kf+1kqoknLSd86Rp8O8+neqXXC+Pd2eoobh3p41rELYz9Lr4e0EU1UgXSac7VbcJLjkG68nRU4D6x0ccqxm8ZtDyMrteMtzUnOK+w4RHMuGDhTN+DFnFxEE1W3rGy7VCeStB1xDm9JNtgGxudy6Qa8gytdnMpwxvaptVb5vD56PMFJ2tZFHKZu+jv1OOZ7G49DxnUfdxs0ZZtP97ZxZhkfcXja9rg/reMQv+eTnJJVUbdBl7a1NSvLm+G4z+XZESeXZsuvh+z7Mi2npRaVdANerEaOphDCBU3+3TNFbEST8MVYYR0wzOkDZ1sZclsOrtuUm2rxxEc9EQsDN5Oupt088hFN9bSf3D7tmSUHVy6iSdjIak/NrJtkK6cUGSRFNKnaItlWh7kKtoUT2RonN6ElI30qz7IRanxaJvX+WGyn6gXR76d2rEi0hZh0ONtmTtKhRtm2cVIRh1zUkeQI5m0z4xD3TtQc5HKB35iTOwnjrvIW20K0XT8OGW3Pyid49f7kI6C0tf/aGYeYdLUGtgGiP4V6i/z7qZ3L9WN5E9tSLY1qpENpX7vZZFM+jak58hinXWvJG1ktp6WWo3XDLm1k1WstwTYAbCVqHWkvXuEjvLlIAvrw0mSbc9bNyEXsY07+fRbmFXbDTtnm0mF0cxYbzchxFuuErdFBKr9moSIOGU6iNiNQRlYabbPrIaWTmUgPlm7AO0BlJrCOYC5VmV8PmTm1kT4rXRMnnZlg3JcJN/21Y5vnrKbNljfgTTresnOctNZSjrfWtdYBhlNKP2Vr53LzZwv7sjbWWhzn01KjKcuy78uy7D9lWfaL5a9JbW50pXNHtcVp6RB3yTY1+XV69RvwDq52sTA7NRwgR/aFSYRdQNITcS1fV6ijxEYS1AZufsHBcW6ancbczPhXmd8Q0sXYAOGEgIquMqb9sJzMwB3f4MNxSjf48CHUegepvri3zfnK3Zh2cIXpT2mCUkbj8CHUduer2rbAuXluGrPT9e+t5WYwsS2GzeaktsuIJup7a+ZUb2T51FbeNhNZqbUtcMZp0KV9vmhr3bbYFqLt6sK3KU6qOC1xYxrHuSC1hU210jsx9YVvuUWbbLv+fvbo/mzgIGWd7yYHaTu2Kc44Dbq0r1+zNHFi2mzrDzDoG/CotPbSPs+pOwVvxMmkzeqjFHjOOA26tM+nkxo5tRFNwkZWvdZiIn04TtuaRa6JR6+HlJtkdhziHaScbbI/GzgxJ3UEiw7SFtZacRp0aV9/eZHNQcpfXiSkzbLrISpVmd5nmTm18yfrOBQ4rXOzgTNOgx61Zfz5UTopHf2odhwKdSXVTknOcSjYpjipeYVPg+YOx+i2NNmXaTkl22R/Mg7yWBM5mrIsez+ADxW/LgDwxwDePInNI0F87SJ+guLqC/F1OvS2gfpNRZRtri29/gCLa9KpTDy48l9SlpMNodbbBhjOyHb+fN0z2+sPsNTp2znJAngtcTKORqpelJlTHdFk5GxQuE/NSRSzHLVl3HaKs376VK93kD+vd77abfOc9PtZ38iWabO8bd2iXXRKGjm5cav+fto5+chK3cbH4nzlUjmkTRXAcBLfWyrSZ5hOauXURjRRjsMUJ9GWQNwk1ohzwogmidNayFjNyY5DdQepmZMtkm3ZVHEnz7xt6mY4yvla2q/fZpmnzfKfuda5x9dbZC92YDgp24MA9OIDKcIpWdrnOK3Fhi2cVFQQaZut+ylwUt9b4qR6lE5q5FTXUaJqndgjCfrETWKNOInCt3lblM49iVN7wGjlJMby0j6VTmriJAoZc7aHbWHHIf1ai7oZjl8P1efyUdqs0lkrrhNopxc/DunXWiQnux4SONXzip3TPDdbOMn3sz6XD9ODrZzaWo7kOj61Bqnv46gb8Ki9UPk8y8nthdiIJgOntv5wA85jN9PreGrNEmvSiKafAfA6AN8NIbwDwEsAHDuhzQ0va+0iKe3HetMbe6ME8Ty1UKaiqxbX+MUpaVsI6VOfDiYimiyfC7khIKKODq3SnNwNeOzALUU0TcgppRTS/SlxKk+HrZFbxAlBKnJLfQMe61DTc468+LRzj0yHYTj5m4d0jkOJk3s/42eHt1my+dfattCTwiHyZrCUbX0ElJVTv5HlUxy0nKPvrT5smWyL4CCtf29TY5zlFFz5vWXqdCTHW2JzQvanxLk5HuPKhZUlqkHpOGyLk3OoGTjnE5y1dG/TOGSsnyiMz7TjsD6XS7fqAlIKEjGvGNOgLTe9cc9bOfW2uc+F51TbZhyHUtQR65jo6cbbdAqSrg4heSugkZMdn7n+FDg55zs7x5Fp0zYHqbo/2XGL2cgaOFMRwdR6SN2fqagw9fjMr4csUdVkW6T0ffM4RLeF+1y0B6kSp3q/wqR7S5zs4SWbxTIZJ7VmabLnA6j5074e0nJKB6/a9VBqz6fllNZD+nFI/m5RnJyDlGpLrEkdTSshhAGAXpZlxwB4FMBzJrS54SVNInMzU8O/L5UvOHSpOfyijV/M5G1pfhIi5RlztklOLh2mSdiyYSNLvQBUuCjrlRc450lOfRFBa3g265gSImCsEzHlxKI4LSfs3A14/EmIjZNyTCQXM8TgSvanmPOsDS2WOanTRNahZijyyLXFlDZLhhbb0malccvOqQ8VN3OqN7Kys5bkJL+39dBiPsqP3+Bx6aT6NGhrGDo/x5HjkMTJ2SYiK8n04Aac2khZ6RSc709bFC41xtH9qY8gbVIXCaDHZ3bDbkixBujNBpU2KxbhtXIanjdzaudPJg3aUmy48Yad4iQit+YIh1rKNsXJpZOqI2CS6yEutZna+NQ5qbYkbRObKjI92FACI1mbk7s0hhyHaE4+Cld5CMile1si1BIONfbSGNJBql0n8A5PgF5rmdJmLanKQt1P7nkzp9YRbE2DJm8/tEcEc23h5k99gAZvm04ntUd4q9ODhXGLWw/pU4955yuZHmyohyk5Aqm2SOOzdl9GaVJH041Zlh0H4FIANwG4GcBXJ7T5tCnLsp/IsuzuLMt2ZVn229p/J3UeHZ5dTxMZDAIOrfVq3meukBjnreY9rXXbZdvbsM1Ofgxn/PyIkx5cLcWJKc4Fot4Fa1v8DHWbR4lzTuRURjSx/Slxak+1bZyLBCd3Ax7XFn5DWC9MXbbdbJv4nlOc1ERccsbPczfgsacPghOT/szttrWclO3+cByajFPabJo555W2V5pwEt9bipOJOpHGfvp7K9iepxccNKfOdn/4vR1/vrwZTp32I2yUyHFI4uTmFWIjq7Wd4uRr4ulrOvGfORehNKnt+hjHcXI34EnRGAC3IdDN5dIFBpxt7ZxVpkFbOetzObfZnJxT2sjaOcfffZaT/W7xnNwcp30/pYsDtLbLNGiKc1rkZNZDRFkDcuwXOLkDRnqMow8vtZzcDXjmeYWb44iIJrOTmbFN1Yti+5O5AU+KOgHoiFPrOsFyKYF2L1SmQds5uf1KG+s+fdkJM2d0OUJ5Ax6X7m3j1K1ZpAtj7LbH37dOb4DV7oDkzAycfOokM8eZOHnnK2+b4Yye526G4/fCNs6nJaIphPCeEMJTIYS/BPBjAN5epNBteGVZNg3gIgA/CeBFAN6WZdmLNP+W8+IuFoXeYlGe1uVuHyHUb0Ep7VO2AerWFHoju7hGt4WyXYbc1m/wsNtmOdlTmdh2PrjG9rmIpsW1HrYqI5oOlbaJm6E429yLHk+UEidVN2CRaQt3ysZxUqemh5jPkFtwWDiXOr1m/ckVJ445V5n+pFKt2O8KvfE5tKp3vpac8QQFlN8t5v1UOmu5scLy7oucSttLnWLCsXIy3y0qLdfMqdzIHlprwKk8BS/TiflxiHo/6bGftc2OQ3VO0jb1Ga7xnNQpG9cWbsHBcYq2uTkrTkHjbBs5qfmW/cyNnPQYl/iurIPt0n5tzkq8Q/E4x7+fRFvYcYh2Bki243YvrfVJ26V9jrPmrGU2m4tr3QacWtsNONlxKJ7jGE4momlxjd9s8u+nLlpK+t7WxlvmfQPKyCCGUxnRxHFSUUf8GMfMzYYxbkl4P+k1i7yRpdrOrlm084p5jKsfMHKc3A14h1inNL2pXmTmcnKOY9daAqd2ndCQU7tO4NZmEmf8PBd1ZFlTcu9neQPepOuhQ6v29VDMKe6zWdu6eb+8Ac86VmjHRMk25ziM91oc54LESXxvqZvh+LHCPvY/HRFNyLLstCzLXgXgdADHZVl23qQ2nyadC2BXCGFPCKED4O8AXKj5h9yibbnTwxZmwdHpj9+Ytlx0NPU8t/EFgM1z9BcjHlyXmMGVsr3ckTeyse3lNZpzfsbGSRXAKxdiMScX0ZRzjqfOAHRE0zKzmOUWHDknbbsT3SQm9qfEGaerMadsHCd1aspxchsfjnOe4uzk/9bcn0Q6KUBwdvp0fxKec7Y/mVPQ5U6f5pyt3wwncVI34C11ZM7ahqPTN7z7TTh52+PfWztnlgGbammWdFuW1ibn5GxL45CJkzgFX17rkZzc2M9xUid43GKWi2ha5hbKFGdHGoeIsZ/h5EKo2f4UOOPnuYgmjpPsT4GTqtWQ6s+Yk5/L6+MQx8k59pc6woYguhmOm5tL+7Wxv8P0J7No5zipeWWJ+cw5RzDHSd2At9SAcyobfZdKcZvNZXYckji5Ma4+x9k56TmOajfFyUUd5ZycbdrpFT/POZn5MY6fD8k1C8M5PZUNv0vDZ5l1AsdJjeVDztramVtrGcY4Zs4q7VNrFpqTGROZNQtVo4nj5Mchfs6qHzDynNyeguLkNrKpNUtsG5icU9pnWThnpP5U7hFFTnZfVm+7ldOyZrFykp8hswah2sKutYR9NrU/pGxzbVla62F2OhuOO0PbzHjLznEmTtr5yvankTMPAKk/T3Fy80pqzZLSRI6mLMv+CMD1AH4PwH8rfv3mJDafRp0G4IHKnx8sfpYUF4a+tNavDX4AvVFa6tCTfPl8fUHYx6bZafI617wt9YE7dtZwtoeb5HjgZmwvdXp2TrYt9QGqPU6ls451BvRY24CFs36Ct9zpYfPcNHktJkA4g1hO4tS05IwXymbO+kA/HNAm5qQjmpbX+LaoOZlT0CXBNsBwku9n/TQxb7eNc8uc9t1vwkkvwoH4e9uAc1bgpDayVs45JWenb+aknqeu6F3q9BlOYcOu5izHW+791HOGMH4D3shRTzuOKdtb5maQZTQntcEnOQnbnLPWyrlwGDjzuXxCTu67staAUxnpU65BTJzKaBx+4yNx0rbjG/BGY1w7nOSaxcrJOCboOc7IaViz0JyMI5hdmxFjOccpOV+Z8bM/COhWvrflporkJCKalop1AstJbHxITiLSZ5mZ44achjGO5SQPJPWcXN26pbUWOKUDxjY4yYiZvpnTYhswcjK24xvThnPzhJzcmLjU6ds5WQdpfR6ycvL7Mmr/YeRUj3ENOBnb8Y1pIiczl0t7vtqYyMxxVHRVirOWJcNxEjfDiQEARFuk/SQwGSelSSOa3gLgBSGEN4QQ3lT8evOENjeMsix7d5ZlN2ZZduNjjz02/LnkxaUGESoaZ0mYiEkPNBt1UrcdQigWVvTzvKeVO70nXkYzJ9cWYkNAnlQJnErbo5cxPsHjOTnbwPjJicxJnZoyLy5x2i9zEqeDzIBm56yf+HGbZK4tLCdhezAIWO7ybVFzSpE+Zk59NI6Vk9pU0e9+A07Gdvz88loDTub0PrY9GIRirDByqiO3+BO8+Pll5jPk2pKP5TrbQ071Z8icbHERTcymiuRkNo+lfZJTOcaJnORpf9kWbrzdmJz9QcBKtwVO6UTWzKmd45pwKsfy4Ttk4GQiZfPnq+sE2nZpn+RUrkH6g4DV7sDMGdvno6qZNYvEqV6bcWutuu0RpzEK1xDRJM5xyoNUOoKU3lRR86fIKURXcQepVESTuDZTHjDaOOvvZ68/wFqvBU4h0qcVTmb+tHJyz3eYSJ+JOYlxSzpIZTmV64Ruf4BOE052HKrvEa2c3BhK7z+tnPqxnGoLt05gD1Kp/Sdju7RPcorz58h2pzdAp89wElFH3EEqdwMef2AscCrbwnPWnXtWTkqTOpr2AKgXVDky9BDGb8h7dvGzoUIIl4QQXh5CePmJJ544/Pn8DF1ga5E7fSC8oXJkCJ1Tq/W0rhT1grgJisvv5AZu6nkzp/qkkuEkonGGnGzOc902YOPkTliB8ZMTmZOq1cCn5eVtGdmWODnbAD9B6TnrJ36LbXESUSrtc1KnZkZOZZRKE06LbcDIydgGxsetRpzMqXbcluUuv3kUOZmNLHeaSNkGaE7uNLE+ljMRLcQJ3pBTfVLJRG5xkT5rtLOOaovISdTeWBTSYYDxd0LkFOqXxG1n00kZTiqCeF05pSg/AycbXcOMQyKnYS43c2ojSJtwKk9NxYM3jlOKNq7absjJpeZYI4JtnNH3VohQAhhOZh7ivrfaiCbOcUiN/amDVIqTHpvrc7PIKdSLYiOayJQV3difOkjVcxJrSinrwcLJZmC0w8lG+hg5tbYXUxFN6jULxWlrC7cGocbyZaPtxUREE/m9NXKq95/cAYbEyaZx0fvP+jgkcIrrIeU+m4x8TszNxCEQySnUieQivNUpgsT4LO4/yeiq1F6oOSel+r+0aRnALVmWXQ1grfxhCOE3JrT7dOibAM7JsuxM5A6mtwL4ec0/5ApssTVgiAmNi64BmMVPIkqBGkToOgBUyC29IRzeDEeExGsXHHKtG2qy5EP5Y9tcBIRkG7BxbhVOCLSc9OaEOdmiFlaJOjrakFvuBjyOkxroxRowzOmw6JQkF5CMbSUndwPeUqfH9Gd9cyLXgCFqL5jSSVPO13q7ASOnciJuxClENI3ZTjqZac54c1LeDEdtCK2cXO2FeqiwfjGTqrfHFQS2ceqiVLi6ZXnbOU7+1EzfnwLn7Lh97mY4vt6ejZOrjyDPWURagbqug5GTqUPXpD/J6FSt7US9PardAM9JRyfrHGpyrRuOUzk3C/Mnx0nVuSpvhtOuWayc1JolPQ7pOKl1Ale3zMxJrBOkItlkag5bn61+aJDsTwMndTMcuwYVOC21/+h6ewRnS/X2uBvwLNE4KU7q0hjuOw7Qay12ziIifSROS+1cYHxTLa5BDZykk7kBJ1WfrbwBj0pvsnNyDtL6HGfn1DlfuTp05Q14NKduny1FkHJrFvU+W6hDRzn2ufps5Q141PN6RzDvOJwnalaaxnKx3p4uomlSR9MVxa8jTiGEXpZlvw7giwCmAXwshHC79t9zNzPoa/rIm+py8C0l1QCJbUt1AOgQ6h7mZ6YwM10PcKMLPXIhmgIn0/YVZa0TyraUVkC1e1ng5FJcpFMZLSd1A94yd2omOT3Y0MW67YXZqVqdq9x+/ZYAjpMMoRZSHLh6F+qNj5Q+wUzEHCd1GwKXhkCemqZSHMgJSulktqZPdPp2TjGEWndqynKKda4MtglOqj4bwIUi2zm58ZkqfiinNxGcSttcHbrSvpaTqrvDOety2zSnFEGq5SQL/DL12Ur7VOFbLtKj3hapBgx3IivNWcTmUV2/guckb5xNrhMITvX7mYh8JqNwdba5OnT580QdiFQaAnlqauE0zs0Wzlnme8usK8yczPxJp2TrbKfq0JG2GU4+HUq3TpBT7PWcZP1EKU2EsU3VuQLo9VATTi7yQF9vj19rkZ+LoQ4dIM3luo1s6sCYOqSV38/6WotLyacOGCVOq4NU7dxjOXXzihQZYuEsb8Aj1yxWTtNcbuRk10NE5A7Dya1ZtIe6IidZjsOwzxajNrl9c73OVXkDnjrFnhyH5Chs7RhHjbdyvb2noUZTCOGTAP4WwE3Fr78pfnZEKITwzyGE54cQnhdC+J+WfxtvqkMIQjE2frPJF6fVphXYvbid/mC8kBgzEI/aMrLdmJM9BScmYmNEExe23umN34DHDcT589M0pxgSX1+0c+GF+mJ5fBoX96KTnMSzuf06J19wko9oUnNaNj6JzaOVk67nZeTkNpvKidjKSd2Ax/VPaV9bn02KxjFxKm2n0kRITsL2sC1U3TIjJ5/eRjg9zJzUBiK3PX7zFD1+Vp8vJdVnoyO3bCd4XN0yK2dZ+DbmpPoeKE+HqfeTj2iahJPfVPG2tZyLzGZj2JYKp1SHTuRUv59NOHnb2v7kOLUpn8lUDmVEk7zWMnAq10NSHTqRU3k7kHT7YWxbqkNXzvvxjYY85zTNKZZMqM9xJk6tbYUjuMZJvBPD5+PvbaLeIsnJHXaSaVzK/hRTPuucnPN1+HxUz4utQ9eAk4r0sXMyY7my3t6oLRSnbkyUD1IZTmEvtKa2zXAq10NSHTqJky9MrjtIFTkNkT485xTDqdyXCcXdqQAArj6bZNvCya/jx5/v9Qc8pxB1xNVci+dPbs0iRTRpOSlN5GjKsux8APcCuAjAxQDuybLsvElsHimKvaFrvQF6g6CO9FkUQovpFCR9pE8qWgrAWOE5aYKKPbNrvdxJZeXkCzzXTyrtnPzz8Q0+LGd04jfkFAtx6iYRtuicZFu5qSodjTVOdiNb5xwEfiAunylV5o2TKWjMqak2ommU8sk7VNWc0Qneajfn5MLQAZqTLQpJ5F9rQ8XFMFcmbJ1zkHKc2kgfqeYWx6mO9BHHOJqTdUxEkT5iHTqBky/YHU/Edk4p5Hr8e9uAU/wMCYe38gSPq89GFb9MpU8AdU7WoRZ9t1L19gDLZ85EbilPe8W5meDkojaHbSE4pc+Q7E9DpI/2VFuem6cQohvTuNTG0j7JqY3GMUZX8QVhhTUIY5vipNpd2h877e2Wc5aNUx2Nw9WAIWxLdcvmZ/Ib8HqDcQcpzzlFcsoX0kzIqZ4nhM0jx6mcP0VOMupIilKho3a1nKm5OebkUlWHbdGmkjfhJOs/2Ti5Oasf3ZjGrbVGbdFHtORt0c/l2iwWu22ak3NMxOsEKS3PGrXLZncYObnn4xvwuDp05fMUp3z5AjGvGKKOrLYtnPx+ZbqVcYhrOxnRZLWt5KQ0aTHwPwXw4yGEHwkhnAfg9QA+OKHNI0Kxp7V0Bsh5rLqICTrVylK7SIpSoDyz/MAdeyzl3PsmnLHXl5mgBE7L58JyRtE4bXKS12hyG1kyjUsu9Ba3RYyAMXFSp6b585u0V3+nTvCUaSJmzuhkSy6UynOyJz5kepNg29iW8SKvvIOU46TzxvkIGLZApbIGTBPbQJ1TijiknZLGSB+2EGecPmPn1BeolCNI6dNBvW3AkNrK1Wcji1/ybaEKJXNplsO2KOvQ0cUvi+/W7KSctuKXVs74NHFJsC1xaiN9pOvtAZrTUjycj1CjOSXba13iczFwWudm7TzERREB9TVLKiIUMHISG1n13CzWgKHnOG2Ed6reXmxbjjbnOJVzcyNOaf6kIl+1ay1pHJqQU4wMmZBTiPRowqk9YJQ4xfc5arsY6aOds8j6Qvnzcf2n0jbNKY1Dddv68VleD41HbMsRLVRbqDpXpW0yjc/IKfVnnJolZ9TUObV7RK5uWWnbykkdvFk4xX12j3r3deNQilNdh46asxT77Pi9iDWpo2k2hHB3+YcQwj04cm+hM6nmaRVPcKhonD5mp/NivrRtKhS1bntuurgBjxxcdW3hNslUW1LX28e2lzp9zE1PGTi5K+iF/HhldJXIGUXjyNfb05tklpON9NFFwIicTN0AaaGs5ySicVrinJuWajS1wBmfbEkTMcc5M4VZrp6XMpJA5FRGqYgbnyac0YbQyknZHnKqi7sznOIJu9KBzXDK9dmICBiDbSAVvaHlnCY5LbZ5Ti7SRxlxKNaKq59sWiJ95Dp0tO12OImxXIxOnZCzge026tCJnIaIUzOnMgKmvTp09Bok2Rat4zBas6hsR5xyfTYiAkYb+aqZy6OIcI5zLmpL6nr72Ha6Dp2Rk9jISpvqmJNzkM5FUSpyHTqak69bNimnfEgbt0XijKNxxCiFJpzEAaOZUxy3xttu5hT3QuMbdiunnIFBrFlMnLr1kC7SJ+Jsoz6bWKNJv0eU95+Uo95gm+W01Nuj95N8W+jxWeQk331l1JFYn01fo0naZ2s5KU3qaLoxy7LLsiw7v/h1GYAbJ7R5RIiLaNLXDeAHS/JmOCa0mLoxTc5hpyN9tDWa5JNKhlPMv40jJugUB4lTzksebzvLGUf6JPLGgXrUkRQWyRWQjTW60XD0/KLEyeTTsxEwUVtS19sDxKm2krOs/0S1hboxTSxmauXkIpoMnJwzgKvn1QonE6Vi5lTXLuLTJ9i6ZYTtISfp9DJwKk/w5DoADKfSdpJzmuEUT4iiBYfyBC8VGQDUb3zRng5KdeiGnGQaFx+NU4/EE+Y4bR068tTUxslFP1I3w8l16CjHRGIu19ah4ziVUZtSHTqRk6mhlrclGvtb4aQiJhpwEu/nkFMZdcRyKtcscvQ4wynWXdFxUjfDSXXoaM5EZIh6rUVskoU6dCwnYXvIqSxiT9dbTMxxypQ/6mY4eYzTc1I3w4l16JioI/YAg4toaoVzvL6QVIdO5LQpBjnmAAAgAElEQVRE+pg59dE4Vk5qnqBuTJOjU20RTXEtR7kOHcPZQh06kdMQcWqOaFLubeU6dAwnYXvEqd0L0/OKPsI7nfJZn5v5Oas6N0ucZeDKJJyUJnU0/RqAOwD8RvHr9uJnz3jFix/JGUB9MRaZ6yIp253eAJ3+QDwF16eU1TfVYu0ixra8eRznFE8HK4PIWq+Pbj+oN76pnOe8LfGGwLiwUtuW0kQYTnWUiuZFH+cUUzmolD8xRHNkW8wbj9J+Ov28bhnv3IvThNJ1A7Sc9Q17Orom5pTTRKr92YxTXnCMfxfl1FZqoTy5k5njlE4TKSemHBIfbzb5Ezxq8rNwSqeD1WelOnRA/R1K1bqh2qIe48STSsp2wilJcQp1lOiCzbqxX67RFKXmNNlUiQXVxzm5OnSl/XjOArg6dJTDW9hU1+bPwrY4r0T9qdywS3XoSvsUp3yDz/j3XJ96nrZde/eVaVxSHTqg5Bwfy4FEGl/EKdZGUc5ZHKc8fo5sS3XoSvsUpxRJop3L6ym8tvkz5XwlOYX1kP6SCWYuF21r187MGkQcP/WcCwynPu1L2FQb5mZ2raUc46Q6dIDQn+qr3KUDY/3cTL37Uh06lpM5MOb3K3pO7ZpFqkPHcWr3k8N6QUZO7V5Yrp1LH4xLUWH1/adu/pTqIpU34E2yF5YOUuu2y8NLwz5btF3ZTyY4ufIq2nRFShM5mkIIayGED4QQ/gOAXwZwdQhhbRKbR4omLa4neSDnZ6bQ7YfhzXBSLQWg9FhSocX85iQO01PbFhfKNCd/spXfgFfeJCalFJX2tWHr5HXrqaKQJKfOtpw+wXAKA6Dlmvi4LWINmDj0X+CcpziFDd7CzLSJM75GVVM3QN+f+s+QmoiXBUfwfHEDXnnji1T/B+A55aK94ydb0qaK5hRsK9MnOE7JQU5f5a3nFGs0kcUP9Zxsf86O34AnbR5L+1rO0Xdr3HGsH4c0m+Tx58VCqRZOU38ytRfECBjbpqpWIJ+tQ2flnKq1G+Dq0Nk4Y9tiHTojZ60/BadkaZ+8tlh0YsbjEDfG0ZzqdDUxrYDmZDeE0QmuJvQ/5pSicay2TXXoKjfgSXNzaZ+cV5Sbamkur6fwSlEKzebmmDP1udTaok3lYeqzjWzrMhPoYv2JA0YDZ7xOkOoiWTnZsdzSn+YxTtefqstRlFGeHKe+TIV8wKi9dKm0T34uBk7rWkv7GaaiiKh5og1Obo5LrVmq7Qbaqf9Us53g5PbC6lRlwYlZL2ki+BO4NYh2Pkz1J8NJ1n8i1tqUJnI0ZVm2M8uyY7IsOx7ATQAuzbLsqCgGHl/PrEtxGN8QSgMxMBropbzx8vk4SmV6Kht+IVNt4dIngLpH2RpunwotBkZf0iVhsCyfj9syU6R4cLZX48HVYBtIceoiYDhOfZpQA05zSLwuokmqAxBzjk4f9NE47XHGIbcNOAXbQKU/G3By9dnISJ8GnJLzVZ8+wXBKIbrRu2/mFGyrI2A4TsE2MOKUNo/l8zEnV7eMuxZXfcqmSStQcs5H76eUVlDaj0/NeE4u0kc33ooRMEw6jJZTmpsB4jRR7E/qNFGfki2G/nNpP8JnGMLoBjwpfQJgOJn6bOwpuJXTklbQKuf4WG7lZN8JwjZgTZ/g3/1B5QY8aQ1SPh+3hatbxqXZaqOOVKnntbUWP35SnPKYOD5/8pxcpI9uLBcjYEinpDw3VzmXBNul/di2iVMqU1ErOyE4Ja2cxY1p3ej9tHCy9dmosgZSlCfHKUWG1BykRk7lWlusQ8dxCocp8VjOtYXlFMa46k1iku3SfszJ1mfjUniV+zKpPht3+YK05yM5DXtEjpPbZ7cRncpxSrargSujMU45lxs5KU2aOndsCOEggP8A4FMhhFcCeN2ENo8IsddoaotCSt7NyAMtnTwO2xJ5ILfMTTOFwWhPq8U2YOOUIn3y53ObUkQL15bNSc7YuWfkVBYElk5BOU7t1aXlporkpCK3xAgYQ5FPMnJLivSxctZPKrfMMwXtzJx0pI8ccah0vkbROKlIH4pTanfVNlCePBsjmrSFcsXTJJpTbov2NInmlEKLLVeuAkRkSKotscPbcBIm1S2L2yLVDeAimvTFL+UTvOrz0uaRaotcn80etUufghKcTOHb1jiJ08T0WJ4/L9Vno2yLkT5sgV9dW1KRPiRnynZ3nJNPVzT0J7kGkaONq8+nIrfmaye4ijnLxKlzqHGc0rufP98fPpvbVkY0qT7D/PkkZzxnCXXo+LVWarwd59SvWTRjecQprhN0kT5kRJNmLh8eGMuOwzgaR4zCJThbi/SxcsYHUqn5k+CU1sLA+PupiU4upYrCVUb6cJzq720icqfaFqk+W96WPNp82G5FamstcksY4wDqgFHPaZk/Rc6ZKYZTuf9M7Pmqz6cOUqm9k5lTfdmNsM/mLkZKrhOifbZ6zaKfmzlN6miaybLsFAD/CcA/TmjriFI9/JN/GYc3w0UnJ6nIkNIDnfRAElEq0ukgMPJA9voDrPUGCdvW8E8jZ3c8AqZdztx2tz9AR+CMi+vJN2FYaxLQnHLtDd0mOY7GGXHyE1R8msS1hefk2119XvoM8+enal58/hTMxsnXLmqDczwaR6rPRrVF5IyiVFL12Zpxjp9stcs5HgEjpU5V26LjpMbb9jjLcSsduVWvdaPlbLcOna0WVXwSlo7Em66dDktjc7UtqTp0tdNBVb29aOxPRVZ2y4VVgnNmusGcVXxvk3XomP5UFvmU6+2NP5/aEDTi7JX9Kddns3DydQX5OSt/Xs9pnrNMnPWTZ22kj45TuZEl1gnpdz9/PlWHjpqzgHY442ictOOwHi2XHssjTnVxd1ukj1zcffx5afMI5OO5vj5bnVOuQ1efmwEustLKGb+f6YNUSx06YPR+puqzmTi56FTl2J/ipOZy7Xhr50xHwDTntO/LtPOnrg7d08ypjsKW0iwZTnGtRa3jdZHPUh262r4sxUnM5dLYHLeF0qSOpj8A8EUAu0II38yy7CwA905o84jQptlprHR0ERNZluXP19LV6M4rcyFL+6lIggXCdmrBUT4v1XQp26ItZspxcl78TVFbUjWayM8w8XJZbFs546K9LOfcFNkW6XMZ/27xjibOtpZTKgqZF/qrF2HWtiVVGyXmlCI9rJwLs9NYjT5D7nmOM/m9Hb6f8sBNcUrPAhVOTfqEknOqSKet9Sdjm+MU+zMKueXGIY5TO8ZJ4xbLqfzMpXe/fD7m5CZi6xjHc9btl5wr0bhl5+TbvqJccJT1jGqcwjxEzZ/cTWJzBCe3OYk5F1Occ1F/CukTMae0MR3aHnv3899Tbec4U/252tGlOFCc6TEu2iQbOam2Txfp0fU1i5WTmyvqc3N6jNNFp9Y+Q2F85jiT/anc+GxqMmdZ5uZJOA2HnVZOqSh9/f20cUobvJnpKcxOZ3VO5UZWKkpftl3rZI45pXVc+Xzc7qqdqppylu9/6oDRxDl898dt68c4vi2zxY2G5r2Tsi3UXJ5aD8Wc6jlLiFLhOFOOw9HaTJ5XKE79GGdbg0jzEMkpzeUsp7Q200Xhcpzava20rpidzjBNcHL12Wpjf2qtPVfnTM7N6+loCiF8JoTw4hDCe4o/7wkh/MdJbB4p2jw3jeXuqNDf8loPWTY6aSCfV2+qp4tnxidLbpDaPKd3TJQ2aptkwfZyp1cpCNyAU1icVtsghduXz9c4hXYDwEpk28I5lY2iBrjnS0mnbJtmZ4Y2q22ROJejQYdf4Oe2a5zM4FrjXOsnOGeacwrX25dtX1fOsfezAadgG6i+n3bO1Ls/WszIkzzFydVnGz4fbQjtnFzbZ8ZsS+kTLCdjOx7jpDp05fMxp2S7fKa0nf+cb3uNM7EhWI4XpwZOrm4Z9/z6cfIneOWCI+bk+n/zXH2Dt26ciQiY+Lti4kxEhcUbgkacqfezm7ehEWfy3S/HcjsnV59t+HxlvJXSJzjO1BxXSjfGRZzqNQhfh270fMSZWLOM3s/U2D9T5zTaluasmJOrW5Y/X/9ceM5ynRBxst/bmdpYznLO0rYtc5bISTjI+bV21JZEBAw5N2s5k3Nzfc5qj5M7ZNBzpmxPxMnUZyOfFxykLKc0xykPGCfn5OvQkc8L0TjxHjHlmKDnZu04ZJ+zuLplgHHNwnGK++zqeGvnTO0/q7Y5zizLsHlWP5ePOMf3ZWxbZptzcpq0GPhClmXvzbLs4izLPlb+msTmkaKyQOUoXzffyFD1ZcrnxwZuzWmi0tO6KfrSLQspDvWFcjGgCV7cQZVzzc6ZPGE3RIbEnNoNQSp9YtOcnTN+0aWTKoBy7kkRMOMvuplTGFzHOIsNG8cZf+Zy5JaVc6o+oCW+K2rO2bxwY6df4WTqP5XP1/ozdYJXbnySkT4EZ+Jkq7SZuiGC4uTqeZXPaxdWHCd/QjQ1vuAQ6tBxnFKUSm8Qhvn6Un228vnawipxghePidJJWMyZPDWLNz7rxSlsqhcYx6E8DsUbgtRiRrd53DSbF6gcFlbt2DmlE7lqGzRFPmNO/Rhn57SNQ/o5ToqqBmjO9Bg3zimtWer9ST9bPl9bsyjHfk1/xu++9JkAtrVWpz8YFpCVInfK52trliTnuNPLwpkcy2sHNXpOblwpn483SqnoZK1zz7LWWigjn5UHjJtm81o31RueRU5iQ8g76yLO1AHj08zJjeV52w2czCGDNCfWnQEJ21E0hrSuqHEytsvnyzExVbeMPUzR9qdmjKtFnfC216qcwn6yfL4p53LqIJXgTI9xurXWAsGZGm8n5lTuPzUHqdpD2rJcSnljt/TuA0XUUczZ4l5Yu9aKOTlNmjr3vwE8C8DrAXwZwLMBHJrQ5hGh+mk/P1iWz5fP9gcBK1396ZOUHw/UvxiLhhSH1E0YNe/2unImPK0NOLW24w2htDEt27JS4VztDsyc0ilrfYIyfobaja8QuQOMn1T2+oOCU3cqM7whQjyRrfYnvzkxcw4joEb9yQ3E+fMWzvHTxFTNEBtnYTsOtxdOZAEjZ+E4StVna8TZHS1OpRowG5GzFvovnbJZOWtjufKULbWRrXxvU3XoNsdjXCLFoT4OpU9BY87UKdvyRJz8iRxQd3jbOHXjreYzrD4v1WcDbJzUd6X6/6Ta0pQzVZ/NyllGYgLp+mxNOGtplm1zVtIypXFoo3JqbANGzsJ2qg5dGfkcc8oRE7qoanbOSkaSjE77Zc6ZOie7MR3nTB4azc2MR78Jc1w9KszGKc1ZpX01Z+0zTM/lWs56NEZ6bq62Jc05ej9T9dnsnETqefLdt3FW93HJfZmZU79msXPq11qAkbMz4pTq0DXZZ690JuNM7eOq0XJmTmNmgokzsY5b7xpNZ4cQ3gdgKYTwSQBvBPDKCW0eEaq9AMIpKICx9JlkpEft1FQxEddqo9DPzs9MYSqzhUUC4552ibPalmSkR/QCpCfi+rWLUjX8KmfKdr0/5Y1P9UVfSnHW6rSkPxdtKuT8TF5ofjWynUorGeb2KziH7e4qnVja+ghkf9K2J+WUIj3K57Wc9dDilNOrzsmdyC3MFpxRxERqQ1DllE4qqwvIJa1tZcQEVadFKghLcabHod7w+RRnvX/0mw1A4pwZ1nQp28JHTOSc5QIyHW5fbE4qERby+zlKn7Ha1nBqoxnLApXaOnSUc089DiVOHmPOVP2S2oZdiiSYsY9xQGXcEiI94rZoUjMAY4rDetWhIzi1jkNN+kS1LRpObXSquQ4dsTaTNgSb5qYrY5xyrWUYt9S1HJlIguT3tlPtT52DVP297caclgNG3VorWQOGdOwLnLPTlTWIvGEzcxJ1WtqrQxfNK8LhZWlfz5nbXq3MWYDgfCc4k3OWug5dnTO1L6tHp9o45XEoTm21csoHb9VoOfngrc6ZnJtrkey6cUhOhaQdwZY5LrnPjmxb5uZqG6m2jEenCqmQzF5IO/ZLdegAjNV00nLWDzD4tsd1zqRnq+3mNKmjqVv896ksy74PwLEATprQ5hGhuGZMKrR48+y0YVM1/qKXk2U5uVDPa/Opsywb22ym8qnjSILlxIZ9fAFZbmRlztVoIuY59WkFJWdtkld68aVQxPL52sZHvVDu53WuZiwnlQLn7LR6cUo590TOim3rxmdYt4zjNIQWWznrOe/pgVvLWS98nHOy31tDaHFZaN46EY/1p3GDpy2smqxDV0vN4dvCceqdXmnO+LQ3efpUWXCk6tDVU1tp2/EFCalw+5rTKxX6P1e3neIcOqY66Tp02tuepqaysZRfLWd1QyhGwMzOjH0mQHrzWHXASZybZsfnT2kTVuNMpBVQp4miQ43gTNcuGo1bUt0yipOby6em8gsS6kXPlU4SYYwrn9c6sJtwro6NQ/xnznFa5nL9gZTuM6yOQyIn6diXOS2bqtJm2fZUZEh9HFLOWR25bhlVgJ2zXX5eZidmpe3JCJg4GkPrOEzUZ4szE5aFTXWNU7F5BGyO/Uk4Z6ez4S2asShObowrL0io12fTc6YPGKMIJSMnV+eqvi/jDxh5Tn5NCYwuSEgfvNU502utkdMrxVl1qElrlvjCi9FeOOE4rO4/E5E+NYdnwnFYHeOkumXV/WT5PMc5Mz2Fuempsf0kkN5nV9ue4rTus6t7YZmzvs/mxqGYk9OkjqZLsizbDuB9AK4AcAeAP57Q5hGh2JOnC9EchXMCigVkZUDbPDeNqSm+rsNKtz/K7/z/2Xv/MMuuss73u+qcU12nkvSP/IBJmoQOoAkgkEgLau5FE7wmiEoGUML4ODxeZzLOHeZOuN48dq7jJTg6RKMDjs9cZ6KiMDIaEGzBoIEh/PDBgSGhk0CUSORHoBIgSXel011VXadOrfvH3uuctffZe6333WfvOru6vp/n6ae7T61aZ3332uvXu971Lol7/iC3IIh0aKtind3mdKYNXaNz0oU63OlkdUo9JmI6J634i724Tj8weVjnpBU/1rn6E2VphxY/VjDZcYd0usmp0xmvT7nOyaOQderMt/1wPC+tTt/NWRJLA1DqHOTbhHSXJa7TD0we2nn20wNynZqjylqdfv3XrlNoOCw6UhZd4E30cTXqXB9fHKBanAiPCPpGr2BMnyKdCg8Yjc7Qlcjj9HkjZtl7m9t5Vhydi+nMb3a5Mas+nd1MuYGQN6tW59jDOzY2V9KZa5/hxWYVneOyhz19qujM9uX16hz35YBubhbV6ZUbCOksHrOC42d+gzGyqJ4MmSA1ekU8fTJ9XGzxmNt4W1fqDGwwuvRaI2amPiP9rVhn4dgsG7Nc/tL0sWNcWp1FBjXdHES2GT1KLzS+i3V6ay2t4TCuc9wn1q8zt0asVWd+/Rl7VzydQicKa21UZ79Ap3hDKjrX6k7qFOYdj0PXndQZnbNslP4cmP7Wud+z1h6z1n7CWvssa+3TrLX/eZo8twvFHjDllbEwP+kxEQoMBuSNAeEXGgDWNhIjTOJ1JPPekA6W/oIgFlxPq1O82OjpdU4GS5R1rlHXfy/4ZXRBUKQzkre1GAWHi+n0O+4qg4j4aI7yGcY74u5I5zieVyj9nFjnpNFLMrGS6Zy82SbcES/Md5Q6O5lJeFKWmPeGTGfWGBDJuyCIoESnOzMeilsGZIMTS3VmPWC0fZxQZ8RY518Eoda5LitLRmc0CK8u74zOQN7+RRDjuGU6ndGjH74RU7h4jB6fKFoQRCaQvs5TG+Xx2YD8exvbwFCO5YVem+FNIL99xvo4jc7MWB7xDCm6RTK4eMx4hUnHFblOd0FCLG6Zy1+t0/MOCD/DuQo6fWNduNxN6cxfBCHTmVvIanRG5kPugoTKOsUXQSgM+5G88zoli2SnMxbPy+Uv3UgtWq8EF489uc5CL79I3hqdi5n6dGuKkvqcRmf09mCdzsXe+IKE5N0tj1vmylJZp2DOotYp9DbuT6NTuGGs0TnZx4XbvmZO6S5IWB8mca60OnXGPaXO0vAN7r31+qFIHzehU2hPKKP8twUYY54O4N8DuMBa+wpjzPMAfJ+19venyXc7UBTs65m7FkvTLxZ03BoLZJnLZT59urkV95jI78jGBij/bPc5wkWy0PXf7wC1OmPpJ49DKXSeqzQGKHbwYnGugKRuhjb5d0hnlWNfK56LZvB2g6IBSqpzPXYTxvhWFqczuNjsFRz5LNWZvfElbpTsFtRnScc9ceNL7DhMUX3KjJjaIJ+q+hQvHsc6g++tNynodRK367jRK2fckx4RFBydk8Z0mZhYBY4s5MvidMbSSyccVXSKJzMFOmMLHyBpz51Osguv1Rk9DiXVWTg5leqM31QDZHVqjV7iOISBYyL5vGNx6CaNzJFj7Uqd2TYUMXop5yzZo63hhc/kbm/c+AokOp33SHxHVlafhR4wwblWd0KnNB5m3Ltm7AHldEb7xFw4hlicFr/s4bbf9Y79xHSmeQvnIHlPr1DeSXq9TnEMmF5nQmf0GQrjluU3mIHycR8oPpkgjbkm8k4V6izq42Ll9ssChOtzQTGWFx2dC8f0KVoL1Tc2u/TO61C8wVhBp9YLt66xebFAZ3TOMhG3TLqBIY/lGG/7+rEZSNrzcBjXuVioU75G1OsUjs2CzUsgq1N6eqCMqQxNAP4QwB8A+MX0/38P4HYAp72haTQJ9w0TWnf7khep15lDr2MyL5LE02d1fYhNYUNfy72k2mMIZfgvXcwzxOn0B2KJ55avU+rSKb3dYC2jM7IgEOqc786hO2cyzyU28U3SDUfXemp1huJcAZ5OwTEuaXD3CZ2C44dA0oaczvgEUqbTHXEYv+dxY+3Ee1uSfr4zh05OZywwNZDVGXVdHYyvtwfGE4uJtAU64wtZ2QSySGfsaAaQPL9eGocitoDI64zVZ9a4J6zPiNem05k1SsYnHKuDIQZDt5AN68xPOEp1aj1DMm0irHPigoT1+JgFJBOOzkZcZ74sofhsk0cE415++Vh+Ze9t0UUQEp2rgyHmNuIL9uy7FY5DV+yhptOpuQhCYiCdRmd5+5zUGT9WMO6zQmUp0ild+LhTSuILTCJx6Aq9q8RzrfAOu9OpWVQByEzyNRuMsTh0gC7o+eQGRpknQfYiCMlcCwBWBhvCjdRuptyxOHQuHRD3mPAvglDrjMWh8+YJbn4bnrN4OiNx6CYumYgtNjU6Jy5IiF9g4dKP1yvysTwUt6xIp/QSmNjpjiKdUoPacFSf0n5IpjN75FNnmCj1ZuwWbWDEda4Nhthw83ihV2AsDt1kP6RfZ8dOmmhPDq2uDzEYxjdS8xdBhOKzFRq9FM8w+Sys0z/1IDohtT7EutMpHMvLmNbQdK619j3GmJsAwFq7YYwJf+NpQv74TNw9fzwoxM7HA3mXTtlCdmXda+jiHdkNLPSShVZh2gmd8WDg0jgAQFbnCcEiGUg6erXOUxGdFRY+0iMO+fQS10VAp9N/V/q9jljnimAh65c7+Uz6XOJHHICk416fk3nAVNUpcYmX6swHJo/FLVPrzB0rWZwPx7kCNDq7mfoByo1e0+g85XRGLkhoVmd2t7c+nWOj19iToC6dbqIsi3VTFF8mqHO+m9Mp8ZiQ6cwbD8Lx2bI6Y0ey8325+6yIvM5YHLq80SuUt0ufmZxG4tABuVut6tSZOyJaq85ediyPxbkCFDp7nYl+SHwRhEqnM+xHvFQq6oweh5pKZzwOHZDzmBDrDMcty3sFqnRGDIdFF0HExmZAobNgriXVKfKYEB6FzOuMbaRmPNmFG4z5uVZMZ+YYX3SuJdM5eUGC0HAo1OlfkBCLz6bVmfVkD28wTqNzQ7DBOK3OsDfrVugcG2CkOiXx2YCxV6BGZ2z9OXkRhNCjaX2I9Q2BzvnJMUuqM+qdPN+Z1Blon77OmKesr/PUhvRkQtjsM20w8JPGmHOQjubGmO8F8OSUeW4L/GMl1lqRp4879xhz/Xc/k8Yu8ju0WLwgIGmMK5kJoWTxKNTZ62I9jV2i1xk3erh0ap0RS7h/pGykM9JZOp2SsmQt0DKPCanO/FGO2GIQkOvsz3dGsWjGrv9So5dcZ+z4hPuZVKef9+amxcogcs5cqTN/u6J0IK6iU9omRDp7Y52xHTxgCp2RXTOXvrLOSFmS65mTGGd6nfFFsiuLVqe8L5fHoRvpjHhWAtnjFlGX+J62H/KOz0SOT6h1zo8vgogt2JP02Vt2RDvs68OxN6NQZzzw6XijRhafTaszaziOlRvQ6My2CYl3qlSn22HPts+w0bOKTkne0+iMxS1zOpOgqkqdgpghQEWdgksjgIo612VlWcl4wIT7ciDReUKqU+HRAqSbtII4dK7cvs7oYnO0SBbOzdaHos3LrJdK/HY1IHmGkvhslXQKx3KtzsmxWbBJq9GZrm1iniHj9EKd3iZ9zFgHVNcpiVs2lc5ofDanc0OtM77+HG92NaMzu1En1imaa2UvgpAdD1boTNtnLA5dkl6uM7P+FOTt6yxjWkPT/4XktrlnG2M+BeBdAP71lHluC/JWv00bd0MHkp2TmEu8S+93aLFzxi5v9ztRF03PuCMt99pgE1aoM2mMMp3j4zOx8/Hjhi7VKXe3Hzd0jc7VgTeIRGMYCDud0TGhcd7h+u9itYLOZGLYoE7FGfa6dfreNWsbTejM7SbWqjM74YjFOgH0OtcG3mJT4f0oeuaDobdIlk8gJTrXBsPROyCtT6nO7LEC3UAc1ektqqT16X5HErdubWOIWBw6lz57Vl82mY3FoXNl9xfJojFrfTjqL/Q6pcY9aX1uROOzubJrx+aVtL8F4mMWoNOpHculOrNegfXqdBPrUxubXvusf84iMdZV0en3KzGd7iIIqc7xFdeysTzbD4XTi+PQeYtkjc5TG+ONVOlxC6nO1cFQlLdmzFr05lorove2M6FTM5aLxyzBgn1y4002Zq0MZH35NDolfXl1ncLNLqFOd0FC7Ei2S6/VueatyzRrPunpDknelWedpV0AACAASURBVHR6HjCi+lyX6xR71/ibegqd68PNaIwmADmPQ1ns3LWBcGzuKcYsz5guG/e7o4sgpDr9o7CSsXl1sBENr+HKHrt1rvy3AxhjvgfA1621nzPG/ACAfwHgNQA+DOAbVfLcbiwU7PaGGsxC5kUKu8S79H6MiZiLu8t7Lj2yInWJl0S3B+SLqoX5Kjp9jybZIkyuczyBlDzDjM5Qub2JtcRLJa8z5l0DJA3dIB7MdHFCZ3wXdEW4C9ofTWY9nSVxOoCcTnFMAk9nsLOcE+vMTMJHQcwlz0Wm0995jsVnG8cvkemcdLnV1afUkyR2Vh/I3W6h2GG3iJ/Vz98MJ9Y52mWRGvd0OmMeE34fJ9WZ9ZhQ6hTvDku8PPM6BX1c6nkCaHXKxiyZ5+ukzrJ4Qe5nWa9dgSeBr1N43Dseh65ApyQQp1BnfudZMgcR65zP6RR6nUh0Fhm9VPUp0bm+gY1h/L2dmGuJdPrjfnjMcmWR65TFofMvvFDrXI/cOOldeKHTOd5I1egM93HjvNcFRznyOsN9eapzIPROzbTPcBw6IKczMsYVHc2RXuwRi0OXMZBKjOlKnRNzFmHeEp35Cy9EAdWlxoBc/xyKW+bKLtXp16czHkvXZVKdviE45uEN6HRmxnLhXEurU7NJq9a5LtA5r5izZMoi0Jk3eAvnIKL1Z05nKD6by3/VC+4v9WaV6mwqGPh/AfBD6b+/H0kw8H8N4DIAtwF4bcV8tw0ugFniASEYoLzKO7G+gfnu3Chgbll6V8mSa1Fd3u4IqOZGplDakU7PoCYdiNugMzs5Deucr6DTpdfqlMckSD5T6Qx0CtPq3NWdQ1eoUxd7IflMeoOPVOfKwHfnjRvUNDozVz8LY6jJdfrHfsrTdjtzmO9U1LkejluWlKWazqEwyKdap9RA6r+3Up2ZCaRsIJbr9Pu48rSjCxLWhV5EufYZ05m/LVPSPjPBLzU6A3n7F0G4mGganaH4bMCke75Wpyaem1qn6Ki6QuegQZ0D2ZjlLoLQ6nQbdYvz5XGuXPoqOtclMUOq6FyXvbfZDQ+ZTu2RFZXOTAyYgM7RBQlanRtCnfKx3L8IYm1QRWd5Wv8iiEo6A/HZgBp0RhaE+Vg3ZRTplC02FToHQp2j9inXmb0YqTytuwhCrXMwjMZnc+m1OlfWh6M5lPiyG6HOzDxesnbS6MyMcbLNEbHOTB8X1mmq6Ez7LbVOqfFV6bUZ68tbr1N4FLKMqkfnOtbao+m/XwfgNmvt+6y1vwTgORXz3Ha4xYnEApmxEkY8PYBxp5OcwZR7+ox3ZerpuF166QIv6xYr05kswAU6C3YrojpHBjWZTt9wKHXRjVmr/bydTtH1kp4VP1b//k1FoQ7KL4v02A8wPoYQ6qD8vEXxn3JWeSDupaLR2e8ltz5IPD3yxy1iOt2OjzQ+GyDXuZDG3QHiHjBA+lyEOsdXl25Ez8e7vLQ6V9Y3RF4q/fmuWufquiwOnX/cQqrTne2XxKFzeUt1ZjwJYjp7yXuu0el2nuM65e0ze3xG4NXgjSsxjwlg3IakeQPjo+ehcid5jWMArQzkMWDG7vmyHTyJzoUJnbLxU6LTPUNJfLbiIw7h5yKNzwaMJ5yiXdBeVmesTbixXKMzO2cJ91tanW4el/x+/L1dq6IzUpbMuyLUOT4mEp6zuMDkUk+ffm6Mi+pM+zhJfLYib8ZYjC7/BtnQWO4HYJfEdFnw6jPm6QGMdY7ilqmPnst0xuYsLjD56rp3hDMyZgFynf3eHNbWpfHZvLFZoDNzMVJkbHYXQUh15j1DYmsEN9cSxdvzPNm1MdSkOuUnE/Q6V9f9+Gzx9rk2kK8Rfa+wqM5ebl0mNO7FPHeA9N0ayOPQAW7zUqCzl11nh8Zavx+SxrgDxmthyVxrZd2PWxZ/hm7MAiKGfc9DrYzKhiZjjHs7Xg7gLu9nVb2kth2uoUvO3vvnHsUL9nSnamPTCs/qj4OUxc6abmxarKfxEUJpXf5ynTkDjFCnC1Asjhmi1RmJ/wRgYsIhdumMxDoBxoOlWqfwjOxgaDEYOp2RRdhIp8CFekKnbOEjiVuWmVg5nRHvKpXO+axOWYwRoc4070Z0zo8vDojFDHHppTrzbrHR97aqTsG7tTU6N1Q6JfHZ/B1ZtU5JP5QavdQ6IztVAEYXJKh1SmKj5C5IiPf93dGOOaB4bzU6BXHLMm7rWp2KMa4Jna5NNKHTvyChUZ2RWCcufRWdkvhslXUK8q6qUxOHLjtnkekUzVkq6ozFoXPpfZ2SGDC+cS9mIHUXJKh0SjYYe3mdsvZZXWfYkDHSKRzjMkYPQcwYuc7kggRNHDqxzvnxBQm168x4eNet09+oka0RK+kUGiUBvU5JfLbpdArGcmdQU+uUzLU6I+MboNApjBU3di6Q6xTFuMt57EvHLEkcuvwcRJK+qaNzfwzgE8aYxwGsAvhrADDGPAc75NY5YNzQj6/JXjogqbyn1oSLjcEGnlpTTNoGw9FZ/dgOO5C8pE+tSRpAovMpkc5s3vXqHDd0iet/JZ1pWkCu87hIZ2dU94Bc5+gaTeExhKfW4t4bY50DALIJh9Mp8ZhYWR/ieJq3tEM7JXCJr6JzZeDrjE8gNTpXB75O2QRSotNvz0+tDWRGL6FOf2crybsZnc7zTOR50CKdTyl1rqp1hr2IXPoVYfvM6pROODZkbT9nlASkXrtJfx4yprr0K8L+1r8IQtL2xzo1ffl4cRKKz6bX2R3VfawsWp1u0t6kTpe/bCwX6sw9Q8kkvIrOEwJvnGl1Sjcw6taZafuKOGdJf17vnCWrM97fauZa+b48KZtGp66/lc5ZpGOWdK6V12nMeEEZKosbt2ats9qYJde5tiEfy/U6N8TvbdWxuTmdGsO+Vqe+L9fp1MxZtDqlm10rjej0L4LQ6Dwu0jleI4rHLKFO35P9qbUk/lMwDl2qM0QlQ5O19leNMR8FcD6AD1s7+po57JBb54CxO9ryyjoAYN9iL5gWwCj93kBaYOxu7/IOpffPd55Id8znQ4HBvOMzx1bWg+V26VfWhzim0rnRiE4gmUA+1YTO+eTM+1jnfDxvoU7XcY91luft63QTwpBO3+h1bGUd+84oz3tclg0cO5kMliGdfoe2vLIeTDvOe4jllSTvkE7Xea2sD3F8dYCzdnWDca78gVui01nxJTr9ibJE5+KW6RzEn3lPrtOfQCZ5y9p+JZ0L3WCcK99tXaVzRVCfvWo6j0l0enk/qdS5LNC5MKFTNq7o+vJ43gu5MSuqcz6rc+8Zsr5fMq4sTrT9JsasROdulc71qM7kvRWOK0qd7iII3Zgl0+lfBJGMcZL3VlaWiTEr2vbnKuvc0++F41xNjOX16fSPz2xHndk2JNUp68u1ebt+K6pzPj/XlulclowrFcbmFenYXFHnyfWNpL8VlmU8lgf6fqXOxYk5iHTMiut0ff/JU2781M5ZAmXxNoGOrQzi83iFzuxcax17+71wnKteVZ3xtZN/EYRKp2L8XK2ic7U5nZr1ZzM6k7H15PpG7Tr9iyDkY9YQx04KdHqe7MfSvCU6Q1Q9Ogdr7aettX9mrT3pffb31trPVc1zu+EW1a7TOTuw8PXPPR5bGQTTAuOFrFsQhNKPz1+nHVRscpqW5ejJdawNNkWGiVVhWfzjM1Kd0rzn5kxmENHoPLUR17mYHrcYlUUy4RgknU5MZ7/XTRtuPG+nczXtLKU6nzh5KtEZnRR0M5M8iU43+RXrPBnPe6Qz7SwlhiNAq9ObtAfyX6ykc9xxx97bhd6cWufjJ05hXfLe5g2kAp3uvRW3fYXONWGbqKLTN0qL6lOhc81vE4H0/kUQap1DaX3qdCZ9xUDRl8fbp9MpHrPSidhjqc5Q3q7srtyAtH3KjHXj9zbe3/oXXmh0fvupUxgMrUhnZiEb6M+r6MwsZAN5+xdBqOrT6RSMQ1qd0vfWHd9X6RS+t86gptHpxuakLNKNGpnOzMInUBb/Igitzo1Nqc4Nlc7V9U3xGCc1qDmd8rmWa59riU5B+3RjFqCpT+mGlPcMBTrd3CxW7pHO46fEOleFY1y2H9JsvMWfoX/hhUqnq0/tGCcZP9OyS9dlGp2uH5KMzYBOpxubgfp1Ssdm/yIIrc6htD6VOtekY1zqJS8xMjudmjELAL51PNEpmrMIdfoXQRxbGeDsiBFroj4FOsdzkFjecX+lyoYmkh2g5gywe0Fo3T4Zt0C6F+PoyfgA5dKvpOklLzQAPLK8Js57dZCUW6pzVaFzRalztaLO6IDm6jPVedaCwD1buuOTq8+Ypd0NaEdXBqJyA359xr2r3HvbmTMinW5Ak5R7dSCbKPvpj4p2cJJyjuoz5kkw38HqYHOkc3dApx/88pjQiOnrjD+X7linwIgFaOqzq9bpjIHxQUSvc2V9A0eFbR/Q6XR9RVynN+EQ6lwZ+Ds+skl7FZ2S9uz6iu6cwVlC9/yjgkm735cDYc8tl37sLaWtT5mR5OiKQucgSS8eswQLgnx6eX2uivIe1WeqM3gEqZfVKZkoZxbJiuciNXi7+hTPWVbW0evIdK6su7FcPmYB8vaZzEFkGzUanUm5B+h1jDgAu1Rn0j7jiw2Xfq2izvgz7Kbllut0fX9s4eMugpAYDpP0Yy8y6cJnXJ/CMWtlHfOdOXEQXll9JjqPCoweSfrxey4pNyDvh8ZzkETnYkind+HF0ZMC456XNyDo+3tjndIFe5V+aL4r1LkuX1P4OiVGz9XRHKT+/tatJ+e7c7Jjlgqdrm8GZO9tVZ2avlyqc0VRllVPp+w0SNommlhnp3OtXdL6HCjW2euK97bCOjtE6wxNxpibjTFLxph70z8/4v3sJmPMQ8aYB40xV3ufX5N+9pAx5pD3+cXGmM+kn99ujJlPP9+V/v+h9OcHqpTVn4TH3Mtch3byVOJGJ3lJrQW++WT6kgobunT3AfAGKMViI6pz3rnzDlU6v3W8eZ3aRZVYp/BI2aanU9pxS9witQsf31su5v7p8j5xSrYTMtZ5KimL+KidZuEjr0/nRbZvsRe+/rOX1Skp93DTjnRKJhzj3UHpgkDYPnvVdD4paJ+L892KOuO7LP38RLlOnWneJ05tqHR++6n0vY0aGruVdYqMta59Ls6rdErerQ1PZ9yQ0fX6xPhiEND1Q34fJ9F50ukU5L2xafHYU7J+yNcp6eMA4NEndRNI18fFriEGxjolfdxgONYp8VKSGg7z9SnxPJDXZ9oPrW3g+JrsaE5VnfIFgV7nvojO/mj81OqUv1tVdarGZpXODdF7uz7cxOMn0voUL6plm3qAV5/CxePyyWQjTaLzqbUBjq9tiPqh9eEmHh/1Q5INxiSsgdRTdtwPCcM3nEw20jQ6JWPW+sYmnjgh3WDsjnRq50PRDcbUY3/5ZHye4Ot8am1D5CXv65QaMaWb0YBep2v7zehM3lvNnEW9/hR6yqp1norrdBckOJ0aY5B2XSY+xXQy3t+6vI+vJjolfdypjU0cHdWnXKd0XRaidYamlLdZay9L/3wIAIwxzwNwHYDnA7gGwP9njOkYYzoA/hOAVwB4HoDXp2kB4NfSvJ4D4BiAn00//1kAx9LP35amUzM+PiM48zo/drcfbtp4w00XhEujSUEsvd+hyTpuad5Op+jccJq3cxfU6pROOI4qFuxLygmHZDfJ17lpZYsqvyyqCaTQVVyat8a44848O53SzlVcll5HvMuirs/euOOOxi/I16fQnVtTn06ndEGwpDUcKnYfpPVZVafGA0arU2Nk/mZDOt0FCU28t+6CBKknFpDotIJ+yBm9loRGL6fz2Em5q7h4zBrpHAgm1eNFlbUy7ze/LPFYDXPisTyvU1J2qeeOy9vpjBkxF3Lvrag+hQv2iTYhXBBo+rjReyscb7U6JbFrJuZaioWPtO3L39t0cfKkzBvHXZAg2tWe0Cn0DFH0cc7oITWSjOtTMGcZyOYsk++K3MtPOtfSGJkBvz6FcxbBQlY7T3BhCqQeoYC8PvN9xd6+blM3xGQfJ9tglOjMtzdpmIpKc22hx75Wp+ykQbrZpdT5iPg97476Ia3O+DNMveUUY/MjT8pOPUy2T8mcJd1gFHhhApoxq6s+ZvmI0Mis1dnPbEjJdIZoq6GpiFcB+BNr7Slr7VcAPATgJemfh6y1X7bWrgP4EwCvMok58CoAf5r+/jsBXOvl9c70338K4OUmZD4swe0+SCZW7tyjZrEBJC9pzI0O8IwkJwUDcS8/UZYMCrJF1XxnDp1pdEZe2v6oQ5MfQZIvfDpiV+G8To3Ra6Gn0yk1ekkXPtkJZDjtru4c5owub1cWic4qRhKxgVRh3Mnr1Ew4+r3OaDIUSi82elUxegiNde7iAE3bd2XR6pRMfF3ewDbVKVn4KCfKI52CvPM6NUavxXmlzpoNh77OeDDL6vUp09kVj+X6RfI4vlB8UTWXyVtj3DtjvoNdgdthXPrxWK4zqEkWBCtCY527OGArdMbHrOo6Y+NE8zrHbUivU2FQU+rUzFlil524soz6xJo3GLMe+zKDt6aPc+klOjNzlqY2LyVzrQpjlkt/llKndpNW7v2m16mZg8YudXH5V61Pjc4mNqNdernODfFxb01Z/Asv1AZPQV/u0p+1oNMpMb75ZZF54cov6PLz1mwwinRmxjiZzhBtNTS90RhzvzHmHcaYfeln+wF83UvzjfSzss/PAbBsrd3IfZ7JK/35k2n6DMaY640xdxtj7n7ssccmCpn1gAlXBpC8HEvHZI3L3fqwdGw16kbn8j6+tiF2F3R5A8LdJOGiygUmr6Iz1hABoN+bw5OrA5yooFOyIFwR3uAxoVPoGeTqM4avU3Lsx+UNSHfNNkTBZo0xWJzvivP2dcbKDSR19OTqACfXh/LOVfhujT1g4gsf7Xvbz7TPcN5A8lycTrGx7phsQaAJkG+MwaKnU7wgqKBT7KXQkM5+RZ2Sfmih18HyygArFXTWecxS2w/1lf2QrzNW7gVl+/R1xvqKyfqsV2d/voNjKwOsDoaqvhyQLQjG8WXqHT/9vkI6B5HqnGyfMo8JyYLAXQQh72/nRmXR6ozPb8Z5i8rSk9enuyChis5Y/biyuEtdtDol7Vl6HCavU7wgPLYa7ZtdevHlNRP9UNwrUBogf7I+5XOW2JgFJO35ifTyGrHRS9gP+UGyY2WZSy9I0PRxriyxWzhd2Z1OTR8HCNYU3k1isfrs5HSKDVPC9pmtz3r7W19nLK27IKEpnf35Dh4/sY71KjoVJ020OjX9kHSe4HRq+6E619nu4oAq9SmZ3y7Od8aX1wjzDjETQ5Mx5r8bY75Q8OdVAH4HwLMBXAbgUQC/OYsyAoC19jZr7UFr7cHzzjtv4uf9Xnq+U7BrBiQvkjh+RVp5jyzLO7RHFS7RLm9A5kbndEoWm1V1SiaQi/Pd6jr7ssCNj5+IL3yS9B1FnKtxnBZJh+br3NugTmnnKo1f4euU1qcmmKXLGwD2CHSuDoZp+5To7FarT2H7lMeWGudtTFyn81B7QrCDB2R1SgfiKjo17bMJnX5ZNDolC5+t0CnZBZ0oiyKOknTho4kt5fLW6tSWRROPRpu3JsaEMcBugU7p7Yfasvg6ZX1cNZ1zSp2yvl/TPsd9olan9Da2Kjpl9anpb3XzBFVfntcZuNQlKbeLF6TXqXm36h6b+/n3VqFTPWdR6FTXp1Jn6FIXV5bRzWDK9qnp+/VzZ/mYFbu8xpXF6dSP5XKd2rWQ9Hh4FZ3qMa5mnYtT6Axd6gJ4BtIK8wTNe6sfm+V5q3Vq19mKfkg9NgvzDjETQ5O19oestd9V8OfPrbXfstYOrbWbAH4XydE4AFgCcKGXzTPSz8o+fwLAXmNMN/d5Jq/053vS9CrcA378xCnxS/qYIvghkFwVLTViSfPue3nvFrjRnS46u0KdT5xsWqdsIK6ic0+/16xO4aK6kk7hDvt21Lk4321Mp1tAVNEpNahV0hl1iR7n3YTOfqM6FW0/p7MTCL7vym0tFIZDjc7xuyVdbFTp4/Y2rVM4Ua6kU5G3Vqd2jFPpVPdxCp2L8w3rVLy3dfdxvSl0Cr2rGtPZq64zdAlIUpYuNi1Et1kCOp19r78VtU+NzlzeTeqUGvcq6RRuvGl0DjetyNPHpa+kU9s+VTrDl9e4vJvT2WA/lBmzlDqV71bdOvuq9pnVGTuts9hLLvaQXC7l0lfRGYuHCOh09nNzkNp1Vlw7qXUK8w7RuqNzxpjzvf/+YwBfSP/9AQDXpTfGXQzgOwD8TwCfBfAd6Q1z80gChn/AWmsBfAzAa9PffwOAP/fyekP679cCuCtNr8I1GCsIHgwknbH7lph7ad/LW2qBdHnHA4d75RZ2li69dCCuolM6EFfRKV08jssia4yjsqjqU2CV1+Td05U7U59CN+dRfQp3n8TvliLvbLnrr89MGxJ2rtL22c+0T9lALG4Tyva5VTqlLtSN6ex1K+mUHW3tVtIpnShpyuLrlBpIq+is+xmq+2e/LIo2JDuSXa0+pX3cKL2yferqU2bckT5D9ZhVoT2Lx/KpdNY8NldIX0WnZEGgybuvfYa96jqlRukqY7OmTUiP5Y3KIpxXNNUPVc1bagxw6Wc/Z6k2ZsnK3VWVRadznLdsriUfP/28tTrrr0+tTvmccrFC/bj0tets2dg8Si/cSBWvnabq42RjVoh4FKet59eNMZcBsAC+CuBfAIC19gFjzHsA/C2ADQD/ylo7BABjzBsB3AmgA+Ad1toH0rx+AcCfGGN+BcARAL+ffv77AP6rMeYhAEeRGKfUuIYOyBsjAHTnDM7aFXOL1OY9Ti893yk5fwlkdWoWhFqdmrwBnU5pXAdp3n5ZunMGZ0Z1jvPWGEmA+HPpdebQ6xgMhlY14QDkHXfyPTqdms4ViD8XX6dkAukHANYMaL2OwRmRztN/htIFwagsEeNer5NcHLCxadXPUPNc5jtzUZ2Z91apMxpQv9usTpdeq1NjrAV0OmULNnlfDmR1xgZ9dzQHqL8fchckDDdl/ZCvU9IPuXh+812dTlnbH++5SS+CGG5aVb8iL0tS9nnBJSDaPk4zfs53kgsSNhWLDU1ZnDbpZSeavDXt010EUUWnpg1pdWrGZklZfJ11P0M/vehSF+Vcq6/on90FCdoNKUnefllEl7r0dGPWQuaZy3Xq51ryMU6rU98nKupT3cfJDY3SS0AcojFL8VzchRdVdEo9t4BmdGrWiL5OqVHSUb9O7VxLPmdZ6Or6uKpr4cV5pU5t+4zF8otcWAG00NBkrf3pwM9+FcCvFnz+IQAfKvj8yxgfvfM/XwPwE9OVNN8Y5R33Xokb3RQLdumLtL66qV5UaRqMWqeyoWt0ajooad4u/b4zJG6RukVVX9m59nsdDIbxAOlAFSOm4r1VL/D0i5PBMB4gHdAvfFx6kZvrNAsCYfqn1qT1WW1BsFfitjyFTmm/1TadGiMzoNMpPU6qyXv03p6hrc96DaQu0HxyIUUDC5+ea5/K+lT2iZKLA8Y6lWOWQufZon6o2pglSe8ugjhxaqOZMUvV31bXKbnYo6pOzRin1ak1kmh06o078vR1G7An0kfyd+3z5Ho8EDygW1QBY8OEesxSeHkCwvpMdWrLotl4lc5XHKIxS2GAcRckrKwP9WOWqn02Z6yT5D+NTs3GW5t0SmMuOTQ6m5xrSfJ3F16sDuIXxuTzlnlKp+vPmjckAN1GnbsgIZgm+o2klIxXg6DynNVR0nAXtDs+yp2T0WCpzFt68xRQQaei3IBOp6yD8ne15WUXPRM/7wZ0joxeyt172Y5PkrdkwuHrlE045As8YNxhSo+TjspS8wRS33HrJnlj40FzeWvL3YTOqv2QaqKs7uN0O5WasjThWdnke5vR2Zd7HWk8WgBlm1AvHnX9raj+FZN2zaIK8Ps47cRXN97qJu0Njlnqfqh+nZo5S+X22cBcS+0xo5izVNXZpHEHiF924ucvDfUwKkuD/VDsUhc/b0CnU9vfaoxHjcy1vPSxSyP8/LVjlqo+G5xrSS7H8PPX6tTMcZqca2l1audaGp2a8Q3QPRexzsrvrdz7UTS/qbjBKNXpb5AUQUPTFGjdnBerdtw1u9H56et2o/Pz1urU7IRpdcomHDqvI80z1LpourzPmO9gl8A1cVwWueEoSa/zDImnbVpnavRSGMgA6S6OW/jodGo67jN3dTHfjXe7Y8NhvccPk/TO47C5AUqus+ouW70L9qp9nFZnE7v9qsVGxTHrLG19KnVqFj5NeEr6OmOXY/jpm6xP7Y6sqj4Fl4D46ZvwOKy6w94mnbKd6mp9ucbDW61T7eEt1ynzTqw2NksudUnSp32/cizXhKmQeSfq8q6qs8mjOdL4eZq8Xbkll4D46ZtcUzTZx+1eaFZn3QbSrdKp9x6v+72tNjZLLnUBdMZ3tU6NZ2VFI7NWZxk0NE1B1Q5NvcuidEOXsBVuzlqdmp0wrc4mFj4qY13lXe1mddbdcVfVKSm3n38jEw6NZ4h2l0Vh3PHzb2RBoNg10x7l0BjrsumbWPhU06k9riahqnFP1Q8pjmVp85Ys2IDq3lWznkBqFuBAs94blSeQNS+qAOUuuNYYpOrjKs61hPWp8mqo6L0x67HZT9/EBqNmzlK9jxPqVHhhZ72OavYiUhsllfXZq2rElG/U6TdqdMegJYzqs4mjVj35HES98aaYawF+/dd/1ErTD1XdYNT3Q83p1Id6qH/81KyFq4ZvaHKDUVJuP30ZNDRNgd4tUr4I8889NrHw0XmpKHUqyuLrrHtRlUmvdHPdrdIZz7szZ7Cr6+pzthNIl3ZOqVPSQfk6m1j4VPEkmDPJTkuMRuuz4oRDXZ8KndL6nK+gU1ufTej0Y8XF0OrsK46qJmXRTDiSvOdM4qkgTS8pi7sgAZAuIOQLtqQsmglkkndnzqh0Ssrt65S5lssXbElZNAuCYXaqsAAAIABJREFUsc7dIp3yMU5dnwrjTqYsinmCWqcgb3cRBKA9Mq/cqFHolFx24qfX6mxyQ6ruy2v89JK83QUJQMMbjA3qlOTt66x7keynr/vyGj+9ZM6S0anyCtPplB3jSvKWXF6TlEXePt1FEIBuA0u8waiYs/g6Y5edJGVpUqd8PQno5k+aS3r8skhClLgLEgDpSQalgVTxXDSX1/jpJWXxdTZRnzQ0NYhrLFL3Mv2iWtEYFYHBAN0CYqt0aoxeWp2aHZ+9ap1Ko5eiQ9N23KIjZWne2vqU5O2nV9WncsKhqs/Fecw1Wp+ajrv+ibK/qNLoVBsPGjQcNqFTs6jKlKVBndojuRqdTXqzSt9bzQJi0etvVfWp7isa8JhQLAj8fiUWDBrQGzF19ak1qMnL4ntXaHRK26cmfsnoGJe2L1folFyOAej7obExUOFJ0MDCx2/7Gp3Sd6tKP9T02KzS2WA/1OSGlOTyGj+92ojZwAmMKp4h0vrUPEMXgF2aXj0HUQRg98st0ynvy/U6lRtSqoD643JrdEreW3dBAqBbCzc5Nksur0nSy/shX6fmqLJ2rlUGDU1ToDkmAFRYEFRwW5fn7XZB5Qs89USpgQnkgjpvt6st19lEubX5L6jz1ten9phIs/XZwNEcRVBAbd7Z9PLAfdIFgSZv9QS/8nNRBCgU5q0KTrtFOjVHfrU6NW2iiWOWmfSavl+Yt6Z/XqhYbvHCp4JOaduvVp/N9reaiyCkY7mmjtxFENpyN2J8r1gWnU5tP9TcJkMTY7O2j6vaDzXhiacaPxWB4LV5++nrvrzGT68JYq8esxrUqW+figDM2j5O+lw0XipV237NRziBtuqc7djsp29y7VT35TWZsiifS92X1/jpy6ChaQq0uyzql3S+I3aj0++EyC2Weq8TvVvsfHcu6n6XlEVnadXp1JZbb93W6tR6hel2e5vTuas7F+18/LI0c3ROq1PZ0Wt0Vjyy0kQ8kp2is8pzWejNRXdnXFqgis7m4h1oPA7VOtXtU+ON0ZzOfq/TqM66YxZm0ivqv9+TXY6hiUfil6WJ4zBV5ixN6dSMcVuhM2mj9fdDmrlZ9T5Onl6rUzuWN7FJW0XnGU3rbOD4od7Ls6O+vEZs3KuiUxteQ6HzzF1d6izIW6tT5f3WsM66L3VJ0svXZXrPdP04pL3URauzjPgBVlLKru4cjKkyOZU3GKkbnXonRFGWsU6tG7o8vdTdXn+kTJ5+oVdRp7gD7Kp1ar3lNDrlx2Gq6KzfbRkYd66Ssut16o17TbgtA0qdXd3uQxXjXlM6Ne75Wp1VJtZNGBr89I3sJlU44tDkmCUtS9UxS6ezuTFLmr66Tnn6NunUHp3SeG80prOCB4xcp/4iCH14Ba1OhUGtgfgyLv2+RStKW9W4pz2aI6GKzr3bUGeVuZlUp7Yv1x1tbW7TAEjaZ1NjVpWLILa3zvrDpVQZ4zZtc2OztCz6dZZep0WzOsugoWkK3DlW9VlthWtcU7v3i4rFzEhnQzshmomV1htD0xkbY5STPL0xSK+z/gmk06mNuaTSqfauqT/GRGWdirI31fY1OufmUp1N7VT2uth3hihphT5OvgjbCp2bi6KkFfq4VKcg+L67IEHtAaNIb21Dfbl7bzU6tWOc6rk0p1N6OUZlnar0s9fpLkhoUqcx21BnhYsg5oRnDZrWOV9Fp6J9Cm5CH6UFdJtAWp3ycUKvsyuIQefSAjqd0ktdtDq1cVp0OvUnE6Q63QUJTeqUeIUkafV5N6azQj8k11mhjxNe6uIuSGgs1EcVnYq2r9bZVCie+c7oAqMYVb2wy+jcfPPNoox2OrfddtvN119//cTnK+sb+MFLnoaLz42vxPb25/Gtp07h9S+5ED3BCGsBXPqPduOyC/dG056xq4tvHFvFTx68UPTi7erOYfdCD1c992nRtMBY5wGBzj39Hr6t0WmBS8+X6TxTqXPe6by0SZ0XiXU+94LdeJFQ59ePruB133ORyBg4353Dnn4PVwp1njzVsM7zz2pE564KOq+89Ok4cE5c526n83sa1HnwwkZ0rqxv4MpLn9aIzk1r8dzzde/tdd8j0znfncPexWZ07qmg83linT21zn2LPfyg9L1dH6p1Xid+b/U6XyfUuavbUeu86tKn4ZkN6NzcbE7nfHcO+/rzjejc3e/hsbS/7Up1XrAHL3qGQOeCa58XYY/k9qFuYvD8wUuk/a1O5+NPncJ1Up22XTpf/lzhe7uo1/nchnX+QAM6d/e7ePzEOq57yYWzr89OYiCT6jxxaqNhnbvxQoXO17/kIpGRbL4zh7PP3IUf+M7zommBajpf9z316zzLr88mdK5v4IcufTouOie+c6TVOdxEYzp3dZvTuaffU+t8/n7he7uri4c17213DuecuQsvE+o8eWoDP/RcYX0u9PCE5r2toPM6hc5zlTpf3pDO4abF8/fvaUTnVx4/iQ/8wW89evPNN99W9HNjrczVcadz8OBBe/fdd8+6GIQQQgghhBBCCCEzxRhzj7X2YNHPGAycEEIIIYQQQgghhNQCDU2EEEIIIYQQQgghpBZ4dE6IMeYxAF+bdTlmzLkAHp91IUgG1kn7YJ20F9ZN+2CdtA/WSTtgPbQP1kn7YJ20F9ZN+2iiTp5prS0MRkVDExFjjLm77AwmmQ2sk/bBOmkvrJv2wTppH6yTdsB6aB+sk/bBOmkvrJv2sdV1wqNzhBBCCCGEEEIIIaQWaGgihBBCCCGEEEIIIbVAQxPRcNusC0AmYJ20D9ZJe2HdtA/WSftgnbQD1kP7YJ20D9ZJe2HdtI8trRPGaCKEEEIIIYQQQgghtUCPJkIIIYQQQgghhBBSCzQ0EUIIIYQQQgghhJBaoKHpNMYYc6Ex5mPGmL81xjxgjPk36ednG2M+Yoz5Uvr3vvTznzLG3G+M+bwx5m+MMS/y8rrGGPOgMeYhY8yhwHe+Ic33S8aYN3if/5Ux5r60HP/ZGNNpUntbaVmdfDz9/XvTP09rUntbaUudGGPO8uriXmPM48aYtzetv820pW7Sz1+X5v2AMebXmtTdZmZUJ39ljFk2xvxF7vM3pr9rjTHnNqW57dRcJ+8wxnzbGPOFyHcW1t1OrpOW1cPvm2TOdb8x5k+NMWc2pbvNtKxO/tAY8xUzHuMva0p3m2lZnfy1Vx+PGGMON6V7O9CyurnKGPM5Y8wXjDHvNMZ0m9LdZmZUJ4XpjDE/kZZh0xhzUCTAWss/p+kfAOcD+O7032cB+HsAzwPw6wAOpZ8fAvBr6b+/H8C+9N+vAPCZ9N8dAP8A4FkA5gHcB+B5Bd93NoAvp3/vS//t8tud/m0AvA/AdbN+PqwTfBzAwVk/k1n/aVOd5NLdA+Bls34+rBvsA3AOgIcBnJemeyeAl8/6+eyEOknTvhzAjwH4i9znlwM4AOCrAM6d9bPZ7nWS/v9lAL4bwBcC31dadzu5TlpWD7u9dP/Bff9O+9OyOvlDAK+d9TOZ9Z821Uku3fsA/NNZPx/WDZ6HxBHm6wC+M033ywB+dtbPZyfUSSgdgOcCuASK9SM9mk5jrLWPWms/l/77KQB/B2A/gFchWSgh/fvaNM3fWGuPpZ9/GsAz0n+/BMBD1tovW2vXAfxJmkeeqwF8xFp7NM3nIwCuSfM+nqbpIulMdmQU+jbVCUloY50YY74TwNMA/HU9KrcnLaqbZwH4krX2sTTdfwfwmvqUbh9mUCew1n4UwFMFnx+x1n61Dl3bmRrrBNbaTwI4GvnK0rrbyXXSsno4DgDGGAOgD865Zl4nJKGNdWKM2Q3gKgA72qOpRXVzDoB1a+3fp+k+As65tqpOStNZa//OWvugpvw0NO0QjDEHkOw0fgbA0621j6Y/+iaApxf8ys8C+Mv03/uRWJYd30g/yxNMZ4y5E8C3kSwY/lSr4XSjDXUC4A9Sl+FfSiekO5qW1AkAXAfgdptuIZCZ181DAC4xxhxI3bevBXBhJSGnEVtUJ0TBlHUihXUXoQ31YIz5g/T7LgXw28q8TzvaUCcAfjU91vI2Y8wuZd6nHS2pEyAZ0z/qbYrveGZcN48D6HrHs14Lzrm2qk5qZUeed9xpmORs/vsA3GCtPe7bE6y11hhjc+mvRPJy/i91lsNae7UxZgHAu5HsHHykzvy3Ey2pk5+y1i4ZY85Ky/LTAN5VY/7bipbUieM6JPVBMPu6sdYeM8b8SwC3A9gE8DcAnl1H3tuVWdcJmYR10g7aUg/W2p8xSTzM3wbwOgB/UGf+24mW1MlNSBaE8wBuA/ALSI4E7UhaUieO1wP4vQby3ZbMum7S77gOgDPIfhjAsI68tyuzrpOq0KPpNMcY00PyYr7bWvv+9ONvGWPOT39+PhIvI5f+hUg621dZa59IP15C1pL8DABLxpiXmnEQvR8vS+eXx1q7BuDPsYNdidtSJ9Za9/dTAP4bEhfWHUlb6iTN+0UAutbae2oVuU1pS91Yaz9orX2ptfb7ADyI5Jz8jmSL64QIqKlOyvK+0KuTn4NgrN+ptK0erLVDJMdRduSxE6A9dZIegbHW2lNIjH6cc7WgnZjk0oKXALhjemXbn7bUjbX2f1hr/1dr7UsAfBKcc21VndSLbUGgK/5p5g+SwNvvAvD23Oe3IhtA7NfTf1+E5IjI9+fSd5EExb0Y42Btzy/4vrMBfAVJ8Nx96b/PBnAmgPO9vG4H8MZZP58dXiddpIFaAfSQHGX8uVk/n51cJ97PbwHwllk/lzb8aVPdAHha+vc+APciDVK50/5sdZ146X8QuWDg3s++ih0WeLqJOvF+7wDCAVyjdbcT66Qt9ZCW4zlemX4DwG/M+vns5DpJf3a+V6a3A7hl1s9np9dJ+vOfA/DOWT+XNvxpU91gPOfaBeCjAK6a9fPZCXUiSQdFMPCZP0D+ae4PEnc5C+B+JAujewH8CJIgax8F8CUkQW3dQur3ABzz0t7t5fUjSKzJ/wDgFwPf+b+nL/hDAH4m/ezpAD6bluMLSNy4u7N+Pju8Ts5AcqvZ/QAeAPBbADqzfj47uU68n30ZwKWzfi5t+NOmugHwxwD+Nv2zI2/NnGGd/DWAxwCsIonhcHX6+f+Z/n8DwCMAfm/Wz+c0qJM/BvAogEH6bAtv+imru51cJ22pBySnFT4F4PNI5lzvhncL3U7605Y6ST+/y6uTPwJw5qyfz06vk/RnHwdwzayfSxv+tKlukBhS/g6JB/kNs342O6xOCtMB+Mfp/08B+BaAO2PlN+kvEkIIIYQQQgghhBAyFYzRRAghhBBCCCGEEEJqgYYmQgghhBBCCCGEEFILNDQRQgghhBBCCCGEkFqgoYkQQgghhBBCCCGE1AINTYQQQgghhBBCCCGkFmhoIoQQQgghhBBCCCG1QEMTIYQQQgghhBBCCKkFGpoIIYQQQgghhBBCSC3Q0EQIIYQQQgghhBBCaoGGJkIIIYQQQgghhBBSCzQ0EUIIIYQQQgghhJBaoKGJEEIIIYQQQgghhNQCDU2EEEIIIYQQQgghpBZoaCKEEEIIIYQQQgghtUBDEyGEEEIIIYQQQgipBRqaCCGEEEIIIYQQQkgt0NBECCGEEEIIIYQQQmqBhiZCCCGEEEIIIYQQUgs0NBFCCCGEEEIIIYSQWqChiRBCCCGEEEIIIYTUAg1NhBBCCCGEEEIIIaQWaGgihBBCCCGEEEIIIbVAQxMhhBBCCCGEEEIIqQUamgghhBBCCCGEEEJILdDQRAghhBBCCCGEEEJqgYYmQgghhBBCCCGEEFILNDQRQgghhBBCCCGEkFqgoYkQQgghhBBCCCGE1AINTYQQQgghhBBCCCGkFmhoIoQQQgghhBBCCCG1QEMTIYQQQgghhBBCCKkFGpoIIYQQQgghhBBCSC3Q0EQIIYQQQgghhBBCaoGGJkIIIYQQQgghhBBSCzQ0EUIIIYQQQgghhJBaoKGJEEIIIYQQQgghhNQCDU2EEEIIIYQQQgghpBZoaCKEEEIIIYQQQgghtUBDEyGEEEIIIYQQQgipBRqaCCGEEEIIIYQQQkgt0NBECCGEEEIIIYQQQmqBhiZCCCGEEEIIIYQQUgs0NBFCCCGEEEIIIYSQWqChiRBCCCGEEEIIIYTUAg1NhBBCCCGEEEIIIaQWaGgihBBCCCGEEEIIIbVAQxMhhBBCCCGEEEIIqQUamgghhBBCCCGEEEJILdDQRAghhBBCCCGEEEJqgYYmQgghhBBCCCGEEFILNDQRQgghhBBCCCGEkFqgoYkQQgghhBBCCCGE1AINTYQQQgghhBBCCCGkFmhoIoQQQgghhBBCCCG1QEMTIYQQQgghhBBCCKkFGpoIIYQQQgghhBBCSC3Q0EQIIYQQQgghhBBCaoGGJkIIIYQQQgghhBBSCzQ0EUIIIYQQQgghhJBaoKGJEEIIIYQQQgghhNQCDU2EEEIIIYQQQgghpBa6sy7AduHcc8+1Bw4cmHUxCCGEEEIIIYQQQmbKPffc87i19ryin9HQJOTAgQO4++67Z10MQgghhBBCCCGEkJlijPla2c9oaCKEEEIIIYQQQsiWcPjIEm6980E8sryKC/b2cePVl+Day/fPulikRmhoIoQQQgghhBBCSOMcPrKEm97/eawOhgCApeVV3PT+zwMAjU2nEQwGTgghhBBCCCGEkMa59c4HR0Ymx+pgiFvvfHBGJSJNQEMTIYQQQgghhBBCGueR5VXV52R7QkMTIYQQQgghhBBCGueCvX3V52R7QkMTIYQQQgghhBBCGufGqy9Bv9fJfNbvdXDj1ZfMqESkCRgMnBBCCCGEEEIIIY3jAn7z1rnTGxqaCCGEEEIIIYQQsiVce/l+GpZOc2hoIoQQQgghhBBCWsbhI0v0/CHbEhqaCCGEEEIIIYSQFnH4yBJuev/nsToYAgCWlldx0/s/DwA0NpHWw2DghBBCCCGEEEJIi7j1zgdHRibH6mCIW+98cEYlIkQOPZoIIYQQQgghM4VHhAjJ8sjyqupzQtoEPZoIIYQQQgghM8MdEVpaXoXF+IjQ4SNLsy4aITPjgr191eeEtAkamgghhBBCiIjDR5ZwxS134eJDd+CKW+6iIYDUAo8IETLJjVdfgn6vk/ms3+vgxqsvmVGJCJHDo3OEEEIIISQKA9OSpuARIUImcf3qTj1SyuO02xsamgghhBBCSJSQ1wkn/2QaLtjbx1KBUYlHhMhO59rL9+/I/pUbG9sfHp0jhBBCCNmhaI7C0euENAWPCBFSD6fL8WYep93+0KOJkArQlZMQQsh2R7tjTK8T0hQ7/YgQIXVwOnkBcWNj+zNTjyZjzDuMMd82xnzB++xsY8xHjDFfSv/el35ujDH/0RjzkDHmfmPMd3u/84Y0/ZeMMW/wPn+xMebz6e/8R2OMCX0HIRJ4MwohhJDTAe2OMb1OSJNce/l+fOrQVfjKLa/Epw5dte0WxmTn0hYvotPJC6jKjXttqQeSMOujc38I4JrcZ4cAfNRa+x0APpr+HwBeAeA70j/XA/gdIDEaAXgzgJcCeAmAN3uGo98B8M+937sm8h2ERDmdOnFCCCE7F+2O8bWX78dbX/0C7N/bhwGwf28fb331C2gQIITsWNq0AX06eQFpNzbaVA8kYaZH56y1nzTGHMh9/CoAP5j++50APg7gF9LP32WttQA+bYzZa4w5P037EWvtUQAwxnwEwDXGmI8D2G2t/XT6+bsAXAvgLwPfQUiU06kTJ4QQsnOpchRupwamJYSQItp0ScIsjjc3FU5Ee5y2TfVAEtoYo+np1tpH039/E8DT03/vB/B1L9030s9Cn3+j4PPQd2QwxlyPxHsKF110URUt5DSEMSoIIYScDtx49SWZeB4Aj8IRQoiGNm1Ab3Wf3nRMKM3GRpvqgSTM+uhckNR7yc7qO6y1t1lrD1prD5533nlNFoNsIxijghBCyOnCru54KrhvscejcIQQoqBKLKGmqHK8eZq4RrFwIlsZM6lN9UAS2ujR9C1jzPnW2kfTo3HfTj9fAnChl+4Z6WdLGB+Dc59/PP38GQXpQ99BSBTejEIIIWS7k9+JBoC1weYMS0QIIduPKl5ETd5erfECKvNIuvtrR/GxLz4WLV/Ii2irb8Cjh277aKOh6QMA3gDglvTvP/c+f6Mx5k+QBP5+MjUU3Qng33sBwH8YwE3W2qPGmOPGmO8F8BkA/xTAb0e+gxARjFFBCCFkO8N4FqRpmlxME9IWtBvQW22ACVE2Drz70w+PjvuEyhcKJ7LVYwwdAdrHTA1Nxpg/RuKNdK4x5htIbo+7BcB7jDE/C+BrAH4yTf4hAD8C4CEAKwB+BgBSg9K/A/DZNN0vu8DgAP4PJDfb9ZEEAf/L9POy7yCEEEIIOe1hPAvSJG1aTBPSNJoN6DYZ+cv6+3xMmbLyhbyI3nT7varvrAM6ArSLWd869/qSH728IK0F8K9K8nkHgHcUfH43gO8q+PyJou9oEu7qEEIIIaQt8GIL0iRtWkwT0ibaZOQvGweKKCpfyIvo1jsf5Bizw2nj0bltg9R4VGVXp0nDlDZvTfom866Snuxc+K4Q0m7YRutHMy85eWpj4nPGsyB10abFNCFtok1G/iKPJIPiW7LKylfmRbQdYiZxHtIsNDRVRGM80u7qNGmY0uatSd9k3lWfi4Y2Gfe2K23RSZd9QtoN22j9FD3TG26/F2/54AN48489f/Rci4KAA8mNc346Qqpy+MgS5ozB0E4uV+nNQHY6bTLAFHkkXXnpeXjfPUuVy+evBfb0e1jozWF5ZdC69Q/nIc1DQ1NFNMYj7a5Ok4Ypbd6a9E3mXSW9hiY7m6Y92triFdamDpsu+4S0G2kbbYvxejtQ9EwB4NjKINMXl6VbnO/y2ZKpcXOBIiNT27wZCJkFswhaHRpLizySDj7z7Erly68FllcH6Pc6eNvrLmvd+FJlvc35iA4amiqiMR5pXSSbNExp89Z83mTeVT7XdAhNGiaaNBy2ySusyjNsyqBGl31C2o2kjbbJeL0dCPVvfl88i/6RE/SdQ5khs2MM3vrqF7DeCcHWBq2uMpZWLd922ujVjIVFz/DG996Ht3zwgVZ6a7WFuVkXYLtSZiQq+vzGqy9Bv9fJfBba1dHkDeiNXnV8Z9HnTeat/dx1CEvLq7AYd6qHjywV5tHkxLtOw+E0aaukP3xkCVfcchcuPnQHrrjlrtLnF9ITMgRK60hbn9p3SIvmuVRJT8jpjqSNavurnU6sf3N9cdP9Yx5J/80+8vShbMzftJaLMEJmwFaOpdtpo1czFhY9w8GmxbGVgWhdslOhoakiGuPRtZfvx1tf/QLs39uHAbB/bz+4q9OkYUqbtyZ9k3lr02s71SYn3k0aDpv0CmvauNOkQU37bmnQPhdtekK2M1KDgaSNtmXCuh2MIGXBvX1cX9xk/1hErP9mH7l9kLSFrTZk1s12aO+EaNjKsXQ7tf/QWJjvByQ383EjbBIenauI9nytxgVRm7cmqFyVckvTN5m3Nr22U20yMJ82b81RS+2xTE16rfurVmeTBrUmz783HVuMR0zIdkXjni9po224mactx/dC/UJZcG8fvy/e6vggsf57q49asI+thrQttCnQsZaq7Z3vFGkzWzmWbqf2XzYWApjoB8pu4stT1XjXpj6kzrIYWxCsj0xy8OBBe/fdd8+6GKW06QVtA2XW5/17+/jUoasKf6ctt84VLRr6vU6hF5wmrTb9xYfuKOxUDYCv3PLKqXVq6qhKfWrQlFv7XDTptfWpLbsW9ivtpY11U3c7rdIe6qbpvifG4SNLuPkDD2B5dZD53H8OZWXsGINNa2f+fsSeYZWxpipF75RbQOxvSTtqK5q20Mb+SULVueOs+ymy88i3sSsvPQ8f++Jj4s2Iqu+opG1v1/bvKOsHJMamKnODNs39i8rSmzM4c6FbGovKGHOPtfZgUX70aJqSttz4lbfKOte9usrSFp3SvKtY1JsMzNeUR1uTXmFVdkCkOsuOeYSOZTa1Q6LdwWyTF1nbbkskW0OVSwC2YtJXp3u+K/PqYIhOek36LAwBszy+F/JUkgT33rS2dkNNFWL991buthf1sW7hwD4uTNnRkaL3bysDHddJWVsKHZspG7dvuP1e3Hrng9tukU3aT9Ec4I8+/fDo5/m+rC4vVuncY7u2f0dZP+A2JB5ZXsWefg8n1zcwGI5NT1XXJW2a+4diUVX5rqChyRhzdujn1tqj0W84jdmKG7+kC4Qmy7IddVbtVJtekEnz1xqmNGWUpm/KuFO2eNq32MObf+z5tRjUNDR9RFCTvskbJ7U0fYtgm9hu5dbUzVYaDOsyGOTLPLR21Ga2ul5meXyv7PYuhx/ce1ZllLSdWP+9lUctYgbCtt6ONGsOH1kq3c1vY+yVqpS1JYPkGWhCNQA0XpJmiI0NwGRfVofx5y0ffKCxOedWzMOk31HWD+S9leoqc11z/zqM25JNNE2dxzya7kEyrhgAFwE4lv57L4CHAVwc/YbTmK2I1SJdIDRZlu2q03WqriN4U6QBNr0ga9oLoW4PNX9xsLS8io4xmUB3VZ9J2QC5ON8N5tnUDknT8Z+a9CJr022JTbWfaQZyqYt30+2+7smTpm62Mv5NXQaDrY7ZE0Kjya/rPf0ejMFU1x7H2rEf3LtpQ03RewxMxrHIt538773tdZepDVHacoV+r6yP9Wnj7UjTUPaMNM/u1jsfLD3eOOvYK3X2sTdefQnedPu9E1otUNr/xN4pGi9J3Uj7qNB8Tbu+KDrCrS1PKP+mY6NpvkM6pta1Lqlr7g9MP4eVjJGxMvgEDU3W2osBwBjzuwD+zFr7ofT/rwBwregbTmOavPEL0E22myzLdtbZpLFOS5NeCE15qLn/17kQr2IcaXKno8kjgtr0TQaO16LNu4n2M40RSPq7Tbb7poxYmrrZyqNfdXkeNl1mTX8i1ZSva39CXrXeQxO+rQzuXfZ5JvtfAAAgAElEQVQeL/Tmgm1HO9Zoy6sd99ymSSzOxlZ76FQd36YxpN/9taN43z1L4r4pdJRklgaU2DuQXyAv9uawq9cpNQBfe/l+3HD7vYXfVfYMisbtst+tYwMR2LpA/judtno7S40BRX3ZtOsL6fdoqOpBr9GhdVRwv7MVdV/X3N8hPX3gnAj80ASS/syVQYI0RtP3Wmv/ufuPtfYvjTG/Lvzd05YmY7UAusl2k2XZzjqbNGJpadILoU0eakB4cNbWYdMeJ226IUM7uGnLrpk0afOuYgSOlWUaI5D0d5s0fDZlxNLUzbTGSO1Eu44dPkmZp1mca/sTiabYUYbQpoJ2wjdnstcou/I1NQkue4/L9Lq205bNm3ydOzd9/2/HVvf90+zkT2NI/+PPfB3D3GVAoboJLWyefdOHtiyGWr7dr6xvlL4DAHDje+/DYHOsc2WwiZXBJoDyZ7Zf2Wfmvb+LuGBvv5YNxBvfex9gMIoLs9VH85oMQNw2g06b41RKjAFlfZl20/vn33PfRF9RVJ5pqDIP044vVU4vbFU91zH3zyN1wHB1697vt776BXjrq1+Q8c6eJhaV1ND0iDHm3wL4o/T/PwXgEeHvnrY0GasF0C0QmizLdtTpT96LqMOI5X+PpGPQLJ405dZ+3oSHWv7IiN8p5Qdn7fsxzWJFWj+7uuOd+VCsqK2YDEkHN22wZO2kSTvwadqPtCxl797S8mppvAyXv6QNHT6yhLn02UnKrSl7qPzTGrE0dTONIXVWE+1YmacpV1PGD8mGRD7NtBO+raoP7WaLazvTbN5I2oE0/6I692+Zm+UCt+pGTtHiT2NIL1s4VvHayb+3QL3vY5k3WmhH/5Hl5FIc38hURNEzq9JnunG7yPvD/W4dm3xFekJ5xNpRU0ePNLTx2D3QriPceYrmAKFb53yk/aarl5iRaW+/N3U9aedhgH5tUuU7piW0kTSNV6/EuD1nDC4+dMeEJ6TEE+pTh66q3E/kkRqaXg/gzQD+LP3/J9PPdjTahViT3goa935/0rrQm4vGkNhuOqu6eVbxDtEMjtrFk7Tc7vO6PdTcu1I2xLj0oSMjDn9w1r4fVT1liialRfFD8s98eWVQGFBvKxbdVYPiS4IlV5k0aQY+TfuRliW0ix47GlpG/r0tmniEYvBIF3ih8s8ZU2goq3rUyI9Dl39v8hMSTaw1jcdInYv1WB9RVq433X4v3vLBB4JjWlOeq5KjDPk+NuQFVTThu+KWuyb6161Y+EiPaQDZtrN3sTe6qSafXxlFsUDK2kFZuSySZ+XqP1TnWsP+NEeeitpMzCBetFAJHf3Lay17Rp2SRVf+2TkkCxtA9j5OY9yIXS3uuGBvX9ym8+k0fWaRFt847C/y6tpALEubL8uVl54XPB7Z5NEjDU3kW2W+ln9+2vqKladug3ZVjxtpvxzz0gWS/v7mH3++ugyOKvMwR2wtU7aRo/mOaQhtJL3p9ntx99eO4leufUHh70nelZBxO/99eU/IEEXv9zTeXSJDU3q73L+p9A2nIXVPOEJpX/Pi/UEL9TQD9vLqAP1epzA453bWGescpzXWhb4nNDhWWTzFyh2KO5FPr00bMnr56SWDEZDtvKSLZEC+mCgqd75LzddP2U43MDkxqbrz3MRuYZWy1DlpyqP1rpJOrEO76GV6i25FcUje244xeOurX1BqBCrb3St6vldeeh7e/emHJ97DobWjOCmu39vT7+H42gD5DevYbrVkUe7+1k68pTuGTRhgQxObsnJZIHoFr9b45xNqzzFX9qL+O9b28j9v+nh3GTdeXRwgOY/fdg4fWcKJtY2JNL2OEW/e+OTbweEjSzh5ajJ/h1//Wm/LIiPBtEeefON3KKiuX7ayhYomvlTZBsBrXrw/Y4TwKdPn2uTFh+4IliHmsTmtcUPCyvpG6YI6j2v77vukhpp/e/jzmf7d90TM30wl2fwo+lxq4N3T700816Kxx29HZfOIn3/PfYXzsab6nyby1c6Rit7LMvb0e7jilrtUa6N83jekmyJl3vP536+6tipaSxX1y0DSZvwxMPb8Q97/UjTzsJghFZDP8Tatrd2DVXKs12EBvPvTD+PgM88OOkr4MfXK1sj5tWWR51bMs9Onbg+voKHJGPN2a+0NxpgPomBss9b+eK2l2QbUccY6thPvp33fPUuFi54qZdF0vNtZZ6hzjMUQ0FhttW6brnPwJyDTlDsUdyKfXpMWCE/u8umlk4GqR6hCCziJMSiPX95Y2f32UcWzqqn2WaUsTV1NXcW7Srqj5n6/LDhrfjJ4+MhScGHh9zFlz2rT2koLnvz114ePLOF99yyVLshWB8PMQiC08Cwqq2ZRXlb+mHFSskCfxfEC6QKsqBwx4x8w3u3XLDrddxQZEQyA17x47JXk8owthPPtYdp4W1W59vLyAMk+ftspO7Z0RuB20Vgb8718Yt6/wLj+NUfui/rsWNBziY7VwRA3f+ABnNrYFHkJlB21kvyerye0AXDwmWeXeig5gwMwOV7F2l/ofdT2F5L5xd5+D0C2Dz22MkBvzmDOYMJ4n2dobWHso5ChBkDpz/OGmlA9asNQ9ObMhGdCv9eBSeO2+ZTJXlpexRW33FVah2VHITWbfhqq9msho0ro2H2RkUja1npzBifXN0bvmmRjpSzvYyuDoGeL0zjN2iqftqxfduWR1HfHGPzmT76oFgOZdB5WpM21v7LNzVDeX7nllZXLXPZ7UkOlo+gmy7L+scigDWQ3E92/Lz50R/S7y2jCw2su8vP/mv79GwB+s+DPjiM0SE6bvsm8gfqCUU9blqZ1lg1O+/f2J86dTkNoFwoYdzxLy6uwGHcObuds2nKXeeMUpdekBcrfFQNMpJcscqocoXJce/l+vPXVL8D+ku/x00smpX55JWV3eZaldeegr7jlrkzd1tU+3cTIzzv27uW59c7yI5BuF8vn8JElXHHLXYW6ivLW6NR6Olx7+f7SunfGHb8sZezf2xe9t2Wfx94tN2nwyxKbuGqOgeSRLsrL/h/63NW/8370ybflKvlK3qsQN159Cfq9jiitaz8XH7oDl73lw7j9s18PGv9uvfPBwr773Z9+uNR44PK/9c4HYfIPDEk9/8V9j07kGTIyFfWZRbq3Knh1WRv08d/TsvdieXVQ+g7E2pjLX2OAccfj3BhikGgp2tgq68vK6klr8F9eHUTLvW+xNyqbxqMj70122Vs+jBtuv3e04CnaALj28v3Bd8cZX/PtNNT+Yu+jdIPOvSNzRQ0qx/LqAMfXJutosGmxe6E3MkSFGGzaiWMlIUPNWz74QOnPh9Zm5nyhRWfZBiuAwvf21p94EW597Ysm3uVlgeeWw0C2EAZygdUD9R6b34ao0q/F5tdl47jTnv8dSVvrGIMzF7oT70lovgOE+zXn2VL23OpeW0k3V4HyepEamSTrH+k8LHT6oGxzUzvH067ZYuWTIJ2nhQzeebQbT520jy0bF6claGiy1t6T/v0J9wfA/QCOpf/ecWgnFprPm8wb0DW67ayz6cm4dBGmXYBry93kM9e8K0Xl7s0Z7FvsBSf1mnJde/l+fOrQVRPPO58+1sHmn6dkweryLEubn1S6QUljOIqVPZ93Xe8KMN7FcnlrB1vtu1XF0+HGqy8prPu8cSekc9pFu8YoGSuLhrIySRflZf/Pf+76tQOH7sCbvAWq834EJtvy4SNLpQvBophv+ffqTbffiwOpAejyX/6w2ADlFmAdwSLUX1gsrw6iMQoeWV4NTmrzLK8ORMYjiZFhLpVT1mdKDSZNEOsr8++pdqEX+h33e1deeh4AXdtyebox5Cu3vLJ0w0nbZrULGQlr6Y1omnz8xZ9rZ6FYiT6hBXLZ7+Q3fzQLFe0GXSwQsaPMa+nJ1QHuffMP46u3vBJvf91lIoOpBMmRPAAjb7IiDBJv3QOH7sDlv/zhTL/n+uM3pZ6Eb3vdZaP3tuhdlr4rofheZbh2odn0K8IfY55904dwIO3vART2awBw2Vs+jAOH7ph4RrH5dVF/VaTd/Y7k+W1aW2rQC/Udsbz9eUz+GWlCHkjmYpp5zDTjjXT9U1ZP+XmyxkAWyrvqxUMxqs73pPO00HfmN/CuvPS8wjVZr5Pth/q9Dt7+usvwmz+ZGK7d3KfqBmAZMY8mAIAx5uPGmN3GmLMBfA7A7xpj/kOtJdkmaCcWms+bzBvQNbrtrFPbOWp22f1JEBBehGkX4NpyN/nMNe9K2a7bkf/3h4OT+irlin1eNmgBxc8zP3EKGQ7zOosmj/6gFDMc3XD7vZmJU2wh5+edL8veNLD/m26/V23Eyuddlwehc6fPl6WKp8O1l+8XBb8tK0vZrSi7uuMh0PckKEJjlAyVxRE3kSQs9IqH6VD+Wm+Yon7Np8j7MRSzqugIz8+/575S483y6gDHVgaqXcRrL9+P3/zJF0XrRLuo2tPviXf76+b8PX18NdJnSgwmTVDU54Q2E7QLvbLfcVgA77tnCYePLKkMMJoNJs0kP3RhQCh2VAzp83DkY5mEYtQB8h30WBr3Hn71llfiH976I9H31hGbW4Tiqrh3bd9i3EPJ4depK7PG2CTtp0M4j4s8fls4tjLAjX96Hw4fWarkWXHlpedFy7p/b1/dHwLFz7Dsu3wPUn8cz48xRcfz/H4NAG58730TxyHdMwpt5Lly5ueloTmEdHyv4iUj6Q8eWV4tfUbS75OUTTuPqTreaDaR83Pw/BExaZ8fyluyppomVlho7lnW30jnaaHvLOor3nfPEl7z4v0iT0gAlb24pEhvndtjrT1ujPlnAN5lrX2zMeb+2koxI4wx1wD4LQAdAL9nrb0l9jvSs/5V0jeZN9Dsldht0um0Fp1fnjbIZ+wImk+Vc+eaGFFNPnPNu6It9zTliqXXljtfdveOlN00IzkH7QalWHBgYPJMvCu7ZBfLlWXaOFf5vKt4EEpjaDkPmKIJlO/OX6Rhv6A9lb0f+VtRimK8+J4EReTrJxZQv6gsfly0okCWRRS9I2X5A+UBOkNt44pb7oqWI3+0pej2PWBy4RsySJUhjfEUi4ukXVS5+BtlVMkTSN6Nhd5c1AuiLi+4ptD080XvW6xfi/WBoZhLRTjju/sdiRFEkm9ZzERJkG8J0ucBZGOZxGLUAfK4X6HfmYbYGC2JqyKNQaKZD5XFPnrNi/fjjz79sExcCe59CQXrBZLvdvONsmO6Rc8tFg/QleFTh64KxmYCJvs450mYnz+H4sv54/gNt9+L/+f992NtY7PU62x1MJy46bfM89k9o7L31o+VmJ/blY1ZF+ztB8cSIPsuSees0lhyrgzS41fTzPO185iqaNY/rp6K3k1Nnx/Ke5oySy4KCc098+uLR5aTy1+MwcT45NLGYiKG4vitDob42BcfK4wHnNdQNPeTzr+kGCuY/BljPg/ghwG8E8AvWms/a4y531r7wlpKMQOMMR0Afw/gfwPwDQCfBfB6a+3fFqU/ePCgvfvuuwGUGyzKBk5Nem0gMm1ZpHmUlUWrM+RFVEcZpRR1+KHJf5HhCEDpbSsGmAguV/addR51mPaZA/W8K3VocMad2I1l2vTackvrrWzC5r87fllD5N+3sryLgjBKyiEpS2wiWtYmJHl3jMHrX3phqWGlbAGff5aSepHUtVZj1bYTK0vRxKNs8l5UtrraY+wWKWD87gGTE22ffF8YW9iUUdSnhih6FpK25xv/VtY3Sp+/W3T6N7+E0udvtgHCzw0ob7N19rezRNPuQu+kMzp3ShbswGSfIh17JQajfYs9LK8MRnVy99eOFgaGnob8mCZ5drG2VtZfht7LuucsMabVKb1RStOnl33f3n4PZ+zqjtKX3YKVf36S/laCyzvWz/l9V5FxwU/33Rftwd/8w9HMz4uMcHMAwlsz1ajDuFfUj5a945Jnk9+8kfbL0rHP1aP0Zs/8HLBoHuH3T1s9p3f5atc/sfVVyFhogNIb1Kcpc1nZp1lP5W+rLMo/9O74Y4NmTVrEtL8/Sm/MPdbag4U/ExqafgLALwH4lLX2XxpjngXgVmvta8SlaBnGmO8DcLO19ur0/zcBgLX2rUXpzzrrLPviF7+4MK8T51yKJ551DWxn7M5rhgOc8+W/wplPfLGW9McuehmG87vRWT+OfQ9/sjBd02XR5l2l7NK0Wr5++fUY7toz+QNrURy91eLAZ35DnI8ZrGBuczBR9iJNAFQ6tc9Fmr7p91ZDm97bsjrunHoSFx65rVK+pe+fI/e+FeVd9h1ffen/rXqHC/NOx4HO+nH0jz6Ek0974YSuM759P1bPfk7wvSoti/uOwnJuAjAiDVX7iPzvDed3l36f+7nfXkP1XEe/5eeRfEFx2QBE23SVskTfzxQzHMBsDrDZWyxNk28nwXciQD6fKoTaEazF3MYqzv7qR0Vt6dyH/qKwb3n8OT8qbn9J+lcCpuA4ZO47muxvZ0UtfWa+H9lMPdDmuuVpUiTvVPCdKWJzAzCdSu84gPJ+EZP9TOzZhd7f/Lvu9xVmI1lgb3b7mX/XOQ+rc15y4pxL8fizX5GtcwDY3MC5//CXtbcP7XwjplMyHxC/T24NFxjPisb00e+lASBcWY9d9DLRWNAoGv0lvz+3sTp6hzc7C7DdXQXpNnHmN49MPpscVceiUHvMzzPOfOKLsnFYMFdsyzihnY9I5t5l89czv/k5nPu1uyqXRTIHc+WY5pmH5gxVdErXK2XEfl9ah5/4xCdKDU2io3PW2vcCeK/3/y8D2LZGppT9AL7u/f8bAF7qJzDGXA/gegDYtaugk0o5dtHLJjop2+nh2EUvK6wQTfr8yzbctQdPPOsaAJg6b216bd6asmt1ut+RdmKjzkOI2VhNGmAu730Pf3Ky8W9uwHbmMUwXX/my5zszjc4m0zf5rmhp03tb9q7kP3e/m5+sP/6cH8Wxi16WeR8L3xuPzvrxwryLFqX5snfWj5dMTmwy0QEyi4x8uQGMBr3hrj04+bQXThiVuitP4MQ/+u5MuqL3qrwsCEwcS4xMKH4u+fZU1E59itoEAhssrvxOo9kclL4/ANT9Vh7xwjby7LX9rf/eWtMRTe5tpwebX9z5RRwORsY5R+k7Efo+azGc342vX379VAvdUDuCMTCbA9H721k/XliGM5/4Io4eeHmh4S3/7vrlKZtA+t8R6rfcz5vYlKmDsrE53/eEyl7YZxa9M3PdZKNncDJrSC5AMg8oeu5BAu1hRMgYgGRSX2T89vv62LM7cc6lyEaNHGM2VnHRPf9p9P98X2F7i8BwMGHoPHbRywrHMy2SvinfJ81tDkqNXaN2dODlsN3kuEzekFYnmvc2P0YVse/hTxYbyhzGyI0tgTSd9eO48Mht+Prl10++08YULkYff86Pxr+zaaYxMqW/v+nNx8vHe4PVs58Tbe9F/YZk/REaT4qMALF5ovtdnybn5dMiaQs+RfrzcwpNW3TE+h/pHMy9B9M882MXvaz0/fbfszOf+CLWzrwgM++GMTj5tBdi4cQjwbVF0TysjNDvV1mXFyH1aPpOAL8D4OnW2u8yxrwQwI9ba39F/E0twxjzWgDXWGv/Wfr/nwbwUmvtG4vS+0fn8mhdzzTptUc8miyLNm9N2ascZdG4ZYZcn09tbIrO6vtXB/vukWXHJ+rQ2WT6Jt8VQOeSO4v3FsDopgWJm3zoXSw6alHkaitJp9UpjQPQ6xjc+tqsy/Xlv/zhwnfXP35w5aXnlR4L0bioazEAfup7L8KvXPuCiZ9pnmVZfRbFotAcZzAoP9OfP1IROmIcOn4VQnrcssk68ily6S/7vvwxNP/YYNUjTyGk/UUVd/+qvxPrG0P9Vr/XafRI9jTUeWQ8/5zKjhJIj2uGjv866jrW5Igdb3JlmuYIg+bIBxB/PnUf+9/q79sOSI5olsUllOA/vzrWHKcjoaO3PtIxdM4ktx/u31t+XFo6NhTVgeYYpvboU1uo8yjftOEiytJP88xj65Gqc7ppQ8tojgy7MvjfEzo6Jw0G/rsAbgTwXwDAWnu/Mea/Adi2hiYASwAu9P7/jPQzNdqgz5r0Va6lb6os2rw1Za9yRbomgFksSHBs4efnnQ8uFwsKPY3OJj9v8l2RBKjeqrKEgkYWBZ/WBCgPTfDz76MfvFs6KJSV3d3o5v9uLE7DYGjx8++5b1SWUOBY/0aYUOyRpeVVHDh0x8SgU3aOXoO7ZergM88WxxRxAVP99GVtwsXliS1iy7gg/d0illcHowWE/24Bk5cPVEV6e9TS8iqefdOHRrHMVtY3pjIy9XtzWBtsioxB7l13V3zH4qmFgoFOs+CU9hexQMVFVP2dmJ6yMrtLCnzqDt45DdqxOUT+OZVNfPP1WOUCET+vuhba+dguoTJVuTzEEbqprahdxuYJddbhLL5vOxAKgAwkdXfj1ZeI4vb4uA0Qvw/SvFvSQOnT4OLpAPG4dU0imaMU9Rtl7c3FLndj/ltf/YKRkXmay2lCvztNv9EE0xo9NMG7Y+WY9gIcn7r66tB6JP+eaW7um+aZlf1+6LnE1nQ+UkPTorX2f5qsu1f1+1vbwWcBfIcx5mIkBqbrAPyTKhk1eYua9oVu021xbTFiAfHFQFXDkbbsTRpUtOmbfFfqMgQ29d6Grtl2OwWSwTJ2S0jZtdDSQUFzoxsQn7QNrR39jrvdJoZkarm0vIo33X4v7v7a0ZEHUqwsHWOwu98NevOsDoYZ45grdyjf5dVB5oaQsjYh3T0qwk0KJMGmnY6y24Sqsqefdd0OLZB9w+E09OYMNjbthCfYa148+U7nA166K75DE89prhcuwt/RjN2uk58gawKL1jU59inr48renzpvrJtmsVClDqXfJ+33qxj/Qt/h3p29/R5Orm9EF9xF3pixMk1jHAvd1Fa0aC279czNE+puh7F5Sd3ft50oG+PdWB263S1Pmcee5t0qe0/9z/YI20EZFtk5d2wcDXkbS2/01OBfEFHkjawZ8z916KrKY4NkXJmm36gb7QZzk0hu8HP9T2hTp+hiAckzLxvTysaXn/rei2oxImouoKm60e2QbgZIDU2PG2OejbStp8fOHhX+biux1m4YY94I4E4AHQDvsNY+UCUv7aRGk17biTRZFm3ebTFi+eWXdHZNGveaNKho0zf5rtRtCJwmfVFayTXbknclNhGOXYsau0Ev5q3kd/TSq3Hd79Q9ibcA3v3phzMeSKFJ2aa1ePOPPV9lHLv28v2icvuDn7RNhIx6efyJsvR3NM9bMnk+ub6Rebeq7H7H6M0ZnLnQHd1gU+TtaQF87IuPZT47fGSp0BMuNjEpa5t7+j1ccctdKqNBftLroteULSCamiBXNdqU9XFlbaquHexpn4V2/NR8n7bf9z1I89dIlxH7Dslis8xIGRpXpjGOxZ55/hkXGZnq2rF33+frKLuNra7v286EvIBXB0Ps6s5NGJjLwjvUuS7w284Nt987MT/J56c5Ar7fq1eJZ1foxtqF3hxe+cLzS3+upczjtsqR860wlE7Tb9RNmzwTY8/eby9lc8T/v73zD7arqu74d+Ul4JMfBhAtPlGgKoilEHmD1rROibahOpYnqNBSy1inHTs6NoyTMY52FMcOT1HBcTq1DP5ARYmCE0SmRiA4OijWxDx+BEQQEHlQjZKILQECWf3jnpOcd97+ee7Z9+x77/czc+fdd+4+++yz1t7n7L322mvbljiGvCN877QU43/fdWPf7SF94pA6Hhqj6RgAlwB4FYAdAO4DcI6q/sJ78ojgitGUmmHe4jh2W/m2t6Bvco1+Y274theN1Wfq9CnoJ0ZGDE3vtUn5mm6f7lpWFBNXw7cuPCa2iCu+kC19aN51GdriQFXjc4TIMXY9/cWVwV49HkZ9SUtJbFlMebvOAcxeRaaYToDfiFWX9VEWb8wYXMaYNevnrOeExMgxpa1iahO+mHk2UsYIDCVF7JnU8Wz6lUVb8RPbeFeklNWg3nEh+O7TNYg3zdj3I7eQmGxtXm9UcL3fLzrrpMZbqTelrf5JyLm265lio9q8U888eQpf/dEvLUbUJTj0gP29RrHlk8sw98G/NMqiSTiALp4FXZJTvKiY+EJAu+OmtsYX1XofUi7fdfsply/OVd8xmopd5l4rIgcAWALgMfSWmo2NoalL6rNgG7bOR8/udkWoZ0iTtEDcizbUmlv3xihjYpRLX3yzp75rxNxn0/Rl+R/auctZ7lT0687bxCAYM/Me6/padxUvr3XmyVPemTTbjM7512yznlc/Z8PWeetyh9JrKsZwVMo0ZIauOkgIyb86w7Fh6zz+9/HFq6yXTcheWdvaji3f0HLX68ITT+3Z+9uOx3Y7277NoAIsrielN5nL0FQ9xxYrzlZnXS/5+mxSaPDYqmHLZxgH9rU1G3XPA9csl8s7yfRct8XMe8/XbsF56+esZU4VIzCm45dihjf1DHa/y5hiy5dK9mUZTPJfE+jd5CKnJSs+mbuW1pkGgPX8ymdEiFeYTeY3/nS7dTCTk1dGV7i8umx9wJTycXlIm55htvLbNsYwtWVXPCOX51NZvz7xlhODDJYbts5j7ddvwe49C/tTdS/hMu37vnFbtJGpq2dBl+TkmdjES6mfyZ+YFROm811jmdBy+d6lTd7trv55aB13GppE5GAA7wQwBeBqANcX/78HwK0ALvdegbRKTmtgu6baAKvu8bZOiq0DVI8BU/0eK+uYgUUqz6OmdSS0PL7lXtXr1N3nP/TNbXsH8S6vknr516yfw/nXbFuQPkafdWJdX01GhLJzU+0c2boi9Qe5KxB3/RxfR6dcWmYyerlc7OsGVRP1YLIh7uPVTsWFG+9a1JkDgAP2W7pIP+X/tpnDMt96uW3eVr5lhaa64jOoxAbXtZ0Tu4ShnyDIdXyGLROuQUdZn0JiwAC9zrwpULrNEG+LmVeNO2V6vqWInRf7bE0Ve6afTrGPNgYLMev2Yo0AABvuSURBVOVLJXugvWCmdWKD3MfSpG/gknk/oQZS13lXbLRBeGfn4AEOtG+4NN0X0L8B2Pa7rfymd42tTl1wxgleDyBX/Qo1WM6smML512xb1Afb/bQu6qv7QhKUhjRXf3hcGCbje1uY6rKtT2p73rY1IWV7zpebB9nivYW82/uRp8+j6UvoLZX7IYB/BPB+9Lzg3qiq9infMSbVUrGSVIaMlMuzUr/I60FnbR0h2wvqadUoQ4ar8Yd2skKNKfVzqkt06lup+sq9Zv0c1qyfs7qNhpTHFuvBN2A0zSDteGw31l652Chke7HXvVBc+rR1hEPrYmi8o7JzU+ZhMwjUAzeXXmYuyod/SFlMRq+QzqVvtqJuIPEZp+qdCpuOfmfx/jEZeEseq8w21uuWzQPJN5NTrys+g4ptNsz2ghcAn3jLiYtkGPv8C+3A1fVTdnxNHeCY57Jr0HHBGYuDv5uMTALgmftN4P+ejHumhnjqmfKI6fSeetzhi2JKhe425Cp/k3hFKd7DMfkOerAQer0m72Nf3QnxjKtjegf6gtzHkGIysR+dhsg9JtB4Fde9AvETfbGknriNaXdtDoxN97X267csmHTy3auv7dT1GVP+fgbWvmdq6Pt1p2Wir/6uc737mkzajDKDMu7ElCf1tU11uRoPssT1vG1rQso10Ti/cxeWLREsm5Dg2G51msrTZ2g6RlVPAAARuRS9AOAvUNXHo680Bpge7vWdmFxpQwwNoS55MS/Q2LL0e59rv34Lzr9mmzeGUcgMgSvobN1bwffiDDVk+F6+pt/rQaFdxhSTLE2GmvpWqr5yV8tfrwu2ZVx1mfjcqU3eRK717aYZJFf5q50Rlz5NnZaYNhH6gDd5lIS4ZMcEKgwtS93oVSVkNhyIC7IL+DvSTWfSASyKe+Ra7mYzfJXXCa0rPoNKzODctpNIE5rqx0bsAMsmv6liWcfK2U3G50I9Bsx5HoOgidClkvU8QmW2Yes8rtoyb9xJD8CCZX6xLvExA/xYnYSmj8130IOF0Os16YyH1B2fZ1ydFMshU+ffj059crdNPlVpsjtt+d30W1uToCl12cSIVddT07AHpvsyeRZX77UuN1MA9xLXBjNt1CkXbRnCQ/smtnQ27+ZxZxDGnbZowwnCVmfLOJchebe15NA3Ebx7j1qXsgLpnEKWeH7f28tX1acBPEgjkx2bZfPymx/Ahq3z3rRAbzC1Zv0cVnz4OwvO8S3pMHlM2LxaVs5uWpB3bFls9/nlmx8ISrt7j2LHY7uh2PfyLc8p77NsJNVO4Hnr5/CBDQtlcOHGu6zLlUpvhTLvtauPxeSyCUvqHtVOjquR1++zxHaNellcL1RTnbEtQYottym9bxlXNa2vI1C/z5D17fU8lz9zmSXlwvQ+fdbz9XVqq4TI0OZRcuAzFtvvS4NaSP5TyycXdGBCXzb9roOfWTGFm9atwn2zrw/ektd3jklHIZ3CmRVTOGD/xXK06ct3ndC6YpNhaVBxlfeCM07A1PJJSJH+orNOWmR474cm+rER0xYAv3x9MWDK8trk6zM8XnDGCVg+6X4umPIIkZntffatWx7e+y4q31USce1q2av1wjY4idVJaPrYfMtyt1XXQvBdr4xRZyKk7kwFPht9cgHSLYdMnX9TnfrarK3/OCHirfOue20ih2r/0dS/jLl+vzRpd7HltxFT/od27jJe96ot8zjz5Km9bWeiaH8ufYbS5D1QEvNMdRHaN7Glq3sqk+Girbbm6jOGPm9j+8llnOaj1127aDxfPudtfZWdu3Zj7epjF5WrLXmY8Hk0nSgijxbfBcBk8b8AUFU9uO8SjBAuy2bd08P3IojxJAHiPCZiPWBCPX1i05b4YqmUmLZP9+Vfzbs8x7d7RNWQYdsu3ORdUY3b4CuLz7tKsXCL9hA5Vssd4wEQsoyrOhCPWcYSsuyr+qC2BY82pffps+5FFuOh5pOhazYrxCU7JlBhaNydHANPppxJj7mOr66Udaqf2dJhmsmLHWD55Bs6I9dUvuWzxBZw3RXIv+myQNO1Yl3iy7KnmOUPPe567q2c3dTJ0obYpfe2yQqXd5hpCXETz7g6qQPe5hRQF/C3WZ+R2YXvXmPlkHppawxNjFhNPKxMdT2kn1byvOWT1uu6Arj3Q79eSW28a0P7JoP28CSDoS1vxjY87GLqWGioGNczwJQ+pXen06NJVSdU9eDic5CqLq18p5GphuvlVPf0iPU68XV+Yjwm6nnHlqXtvAF/LJWS0gBTEpJ/Nc+ZFVP4xFtOdHo3VA0Zrn0m6l5BVU8sX1lCvKuq5Q65z2q5LzjjhL0zUL70IUas6kDcV+5qnr68q7uPAX7PLdOOXzZ9hrY5ARZZ7V1eFL7ZrJDZuphZOVPav3vlC/qe0RsUqWbSY69jqyvVOtXWbGlKXLNZoTT1LLLJN3RGrh/5hi5rjJ2Zix1Uli7xbdePWJ3YjpcGdl86oN1ZSyCsbsbqx+Ux49omvZ4/gAV1z/Z+9NWHJl6aMW22qRdoKnxtth/vFNe9NpFDrHGnTU+COk3kElt+W10/9bjDF91XGZ+lSnmvqb306uTyng3tmzTtw7TxriZpaKvOt1WXQ+qYK1RM3WHANVYzpU/5DPB5NJEIXB4wwELrYKzXScgMRd1jwrVFdzV9bFl899kk75BYKvW8Q/O3BS2sx4ABFncyfNuFV72CfPdYLYurDKZy22L/2Mpd5m+TTTW9T+b1gTjg3qmsWnZX3qZd51wPNVucLpe3Sr3Nmept3Xusmu/MirigyUBc4ObQl9Ewecu0RYqgxCEzR4OQddO18G0Fr21btjEzck3l64sTVRI7M2eTxTOWLTEuKS5d4tsmVie2d189wL3vHWmTTZPA5CF1M1Y/Lo8ZU3pX/qalAk2868rrxHhk5RojKwRXm42NQ1a/L9eW9kCcHGI9lGJkHavHJs/Y2PK7PJFiNgbxxThMwaj3aVIHmif90aY346DqsitUjC1GpW+jnJKU3p00NLXIzIopbP7FI0aLY0mp3BBDA+Be0mFLW+Zv2r7TlD62LCH3Gbq8CVgcSyX2PgH7Vue+oIW+zrSvPDFeQSZj0MyKqUWukLa0wEId2Xads8nGFljddI+lHE15Vx+qvs56zBIxwD2YdA3uZlZMeQMNz6yYCn7o1vOOeYnkOFAYRlLJsesObj8d0Lbcm1PINrVcQwdubS0LBBYb6qvXaztwZqxOQg3sIZMDddk0qaOhdTNWP7Ed4ND8+2kDMXW9SZvt+hkVQ6gcm2xpHyuHJsad0GvE6rFJ/Yotv6uu2+6rLaMYcRNbX1Lvzk0Wkkudb2v3X1uMylAjckp50NDUMh+ZOQHTLzzUGwsEWGjs8HnXNDGofPANLwvyaIktS/U+Q9K6vGvqXi2+TrHPMyT2Ye3rZLiMcCFeQfVdl0zXKmUZslY89sUTck6/HW7XubF59/OwCxmQ2DzU2p65G6aBQs6Mohz7MRa16d48bLINfZY0mZlzycJ0vVSz1U2M2iE7+ZX5rpzdFCSbJnU0tG7G6if2nRCT/yDawKCXJXVBiBxTxgCplqO8VtsD9iZ6TD1J1ZYXAifH2iemvtD7afA0qfNtGwPb2v1XgCjPZ9s4GljomFBdYtfPfYo6giKTfUxPT+vmzZuD09s8PVxrN2MDZIa6+/o8WgZdlphGmpOV31WWJvomZtpaVgQs1gH1RLrm6HXXGj1BBfAGz7UZClIt5xpGBtHGY/WQ8j0WU5ZQ2TSpo6HlGETfqIn+U+mIbbZHP8+9HMhRj+zP5MmGrfNWZwNTfcmxbpGFxLS10HdJk36EafXJOa98gXOH40G8P0Vki6pOm36jR1MimlhLY2Y/QtM2nbHLpSw5zbq7ysIZofbop84C/vg7vjSEpKSfWehc3L1zZhBtPPVsdUzHMLZO7L90yd60pjh5gL2O1nfybFKOQfSNYvNP6VGQW5vtavIuZQyQQdCPHlPJnP2Z/GiyS+Y4eD0OgpTPtlCPzJh3SVvL/ENWrYTKIYXnKQ1NCcnJSELSQ313T+hSQeqJdEU/AxYOLMJI3cZjBs1NYnWkCB5tmql8fPce4/2FBhqvE2LEKs8tlyBeuPEunLd+DhduvKvVAXhMPimXdTVtsyk8xWPrVpsDt0Eb3FLGUItZWpJ6WRT7M3kRu0smEPc+yWmVR06kbGcbts4HxzeMeZe0vcy/DVIYPWloIoQQQgZEv8YiDiy6J2bQHNtxSxU8Oibf8n9foPGSGCOW7Zwu45Kk9iiIbbOhsomVYUwdaFs//T73UhrUYu8hlczJ8BO7SyYAnHrc4d4NgYC8npm5kaqdlTK3UTcKxbxLcvN2BdJ4ni7pp0CEEEIIiWNmxRRuWrcK982+fsGW62Q4mFkxhQvOOAFTyych6MVUcM1Wm+h317RYmrjp77HE8IyZxbXR5JxUxOooNaGyiZVhTB1IoZ+mz71ysDe/cxcU+wbYG7bOG9OnrFspZU6Gn9hnyYat87hqy/wCI5MAOPPkxcbpnJ6ZQK/sK2c34eh112Ll7CZrexwEscvZQ8tt81ADzEahGP3H9CMGxdrVx2Jy2cSCY/0av+jRRAghhBASQaiXSspd02Jokm/oOU0G0zkNwHObWQ6VTawMY+pATvqJ9VZIWfaUMifDT+yzxFS3FcCNP92+KG1ObbKpd1WqpX+h7Sy23C7ZmoxCsfrPzUM9RXgGejQRQgghhCQgdtYyxYxi03xDz2niEZSTF1FuM8uhsomVYUwdyEk/TYw7McdjSClzMvzEPkti6nZObbKJd1WMZ2Kst1RoO4stt022U8snrfEHc3qXNKFtj3t6NBFCCCGkc0Y10GnqXdNS5Rt6ThOPoNy8iHKaWQ6VTZPZcyCsDjTRT2z7DU0f6xWUsm6llDkZDWKeJTF1O6dnZhPvqhQ7t5WEtrPYcjeReU7vkhwQtazBJwuZnp7WzZs3d10MQgghZOQwBZSeXDYxdLOB40oTI+GoGhbbIMWuc6nKUKaNab8x6Zs8G3KRCyEumrSbHOreytlNRgPZ1PJJ3LRulfGco9ddC5PFQQDcN/v6vvIOpUneucg8Z0Rki6pOG3+joSkMGpoIIYSQNKTsXBJC0hLbfmPTc7BHRpVhrNtNjL+hbT7UIFUvT6hhnhNa7eMyNHWydE5E3gzgQwBeCuAUVd1c+e19AN4O4GkA71bVjcXx0wB8CsAEgEtVdbY4fjSAKwAcBmALgLeq6pMisj+ALwI4GcBvAZylqve7rkEIIYSQwZNToFNCSByx7bfJLoipBoLDONAno0MuS61i2kGTJaGhy9Bil8rGLLXjUtbB01WMptsBnAHgP6sHReR4AGcDeBmA5wG4XkReUvz87wD+AsCDAH4sIt9U1TsAfBTARap6hYh8Bj0D0n8Uf3eo6otE5Owi3Vm2a6iqef9CQgghhCSFuzMRMrzEtt9c2nvT3bMIGSWaxkWKbSP7L12y9xpLZGEg7jKvNnbuc+1KmYthb1zoZNc5Vb1TVU0h3k8HcIWqPqGq9wG4B8ApxeceVb1XVZ9Ez4PpdBERAKsAXFmcfxmAmUpelxXfrwTwmiK97RqEEEII6QDuzkTI8BLbfnNp7012zyJkWAjdvS11OygNWTt37d57bE+xPq6++1zKnfvI4OnE0ORgCsAvK/8/WByzHT8MwE5Vfap2fEFexe+/K9Lb8lqEiPyTiGwWkc3bt2/v47YIIYQQYmMUtgUmZFyJbb+5tHcOUsmoUhp35nfugmKxQadK6nZgMmRVqRu1ZlZM4aZ1q3DRWScBAM5bP2c1lLm8Jkn3JFs6JyLXA/gDw0/vV9WrU123TVT1EgCXAL1g4B0XhxBCCBlZ6NLeLYxVQ/ohtv3m0N5zWcJHSNvELClL3Q5CDFb1NKHL+WKX2pHBksyjSVVfq6p/ZPi4jEzzAI6s/P/84pjt+G8BLBeRpbXjC/Iqfn9Wkd6WFyGEEELI2BEz+03IqJDLEj5C2ibGSyl1OwgxWNXThC7ny8U7kpjpKhi4jW8C+IqIfBK9QN0vBvDf6O1q+OJih7l59IJ5/62qqojcCOBN6MVtOhfA1ZW8zgXww+L3TUV62zUIIYQQQsaO2ICqhIwC3IWKjCoxXkqp24HJ66iKyagVYyjLwTuSmOnE0CQibwTwaQCHA7hWROZUdbWqbhORrwG4A8BTAN5Z7gYnIu8CsBHABIDPqeq2Irv3ArhCRD4CYCuAzxbHPwvgSyJyD4BH0DNOwXUNQgghhJBxg7FqyLjCQSoZRUzGHQFw6nGHG9OnbAd1Q9azJpdBBNj52G6rUYvLWkcDUWXooRCmp6d18+bNXReDEEIIIcRLTMyllbObjJ36qeWTuGndqtRFJYQQ0jIf2HAbLr/5AVRH+pPLJlpdWpYqtl89RhPQftlJO4jIFlWdNv2W265zhBBCCCGkD2JjLjFWDSGEjBY3/nQ76u4kpjhHTUkZ24+xl0aD3GI0EUIIIYSQPoiNucRYNYQQMhyEehGlXhKdOrYfl7UOPzQ0EUIIIYSMEE0GGOzUE0JI3tSXlJVeRAAGHueIsf2IDy6dI4QQQggZIWwDCQZSJYSQ4cXlRVSnyZLoDVvnsXJ2E45edy1Wzm5yLoPje4b4oKGJEEIIIWSEYMwlQggZPWK8iGLjHDG2H2kbLp0jhBBCCBkhGHOJEEJGj9jlcDFLohnbj7QNDU2EEEIIISMGYy4RQshosXb1sQtiNAHteRExth9pGy6dI4QQQgghhBBCMiZ2OVwMjLlE2oYeTYQQQgghhBBCSOak8iJK6S1FxhMamgghhBBCCCGEkDGFMZdI29DQRAghhBBCCCGEjDGMuUTaRFS16zIMBSKyHcAvui5HxzwbwG+6LgRZAHWSH9RJvlA3+UGd5Ad1kgfUQ35QJ/lBneQLdZMfKXTyQlU93PQDDU0kGBHZrKrTXZeD7IM6yQ/qJF+om/ygTvKDOskD6iE/qJP8oE7yhbrJj0HrhLvOEUIIIYQQQgghhJBWoKGJEEIIIYQQQgghhLQCDU0khku6LgBZBHWSH9RJvlA3+UGd5Ad1kgfUQ35QJ/lBneQLdZMfA9UJYzQRQgghhBBCCCGEkFagRxMhhBBCCCGEEEIIaQUamgghhBBCCCGEEEJIK9DQNMKIyJEicqOI3CEi20TkX4rjh4rIdSJyd/H3kOL4OSJyq4jcJiI/EJETK3mdJiJ3icg9IrLOcc1zi3zvFpFzK8e/LSK3FOX4jIhMpLz3XMlMJ98tzp8rPs9Jee+5kotOROSgii7mROQ3InJx6vvPmVx0Uxw/q8h7m4h8NOV950xHOvm2iOwUkW/Vjr+rOFdF5Nmp7jl3WtbJ50Tk1yJyu+eaRt2Ns04y08NnpdfnulVErhSRA1Pdd85kppMviMh9su8df1Kq+86ZzHTy/Yo+HhKRDanuexjITDerROQnInK7iFwmIktT3XfOdKQTYzoReXNRhj0iMh10A6rKz4h+ABwB4OXF94MA/AzA8QA+BmBdcXwdgI8W318F4JDi+18B+FHxfQLAzwEcA2A/ALcAON5wvUMB3Fv8PaT4XuZ3cPFXAFwF4Oyu5UOd4LsApruWSdefnHRSS7cFwKu7lg91g0MAHAbgAQCHF+kuA/CaruUzDjop0r4GwBsAfKt2fAWAowDcD+DZXctm2HVS/P9qAC8HcLvjelbdjbNOMtPDwZV0nyyvP26fzHTyBQBv6lomXX9y0kkt3VUA/r5r+VA3OB49R5hfAnhJke7DAN7etXzGQSeudABeCuBYRIwf6dE0wqjqw6r6k+L77wHcCWAKwOnoDZRQ/J0p0vxAVXcUx28G8Pzi+ykA7lHVe1X1SQBXFHnUWQ3gOlV9pMjnOgCnFXk/WqRZit7DZCyj0OekE9IjR52IyEsAPAfA99u5y+EkI90cA+BuVd1epLsewJnt3enw0IFOoKo3APi94fhWVb2/jfsaZlrUCVT1ewAe8VzSqrtx1klmengUAEREAEyCfa7OdUJ65KgTETkYwCoAY+3RlJFuDgPwpKr+rEh3HdjnGpROrOlU9U5VvSum/DQ0jQkichR6M40/AvBcVX24+Ol/ADzXcMrbAfxX8X0KPctyyYPFsTrOdCKyEcCv0RswXBl7D6NGDjoB8PnCZfhfiw7pWJOJTgDgbADrtZhCIJ3r5h4Ax4rIUYX79gyAIxvdyAgxIJ2QCPrUSSjUnYcc9CAiny+udxyAT0fmPXLkoBMA/1Ysa7lIRPaPzHvkyEQnQO+dfkNlUnzs6Vg3vwGwtLI8601gn2tQOmmVsVzvOG5Ib23+VQDWqOqjVXuCqqqIaC39qehVzj9tsxyqulpEngHgcvRmDq5rM/9hIhOdnKOq8yJyUFGWtwL4Yov5DxWZ6KTkbPT0QdC9blR1h4j8M4D1APYA+AGAP2wj72Gla52QxVAneZCLHlT1bdKLh/lpAGcB+Hyb+Q8TmejkfegNCPcDcAmA96K3JGgsyUQnJX8D4NIE+Q4lXeumuMbZAEqD7HcAPN1G3sNK1zppCj2aRhwRWYZexbxcVb9RHP6ViBxR/H4Eel5GZfo/Ru9he7qq/rY4PI+FluTnA5gXkVfIviB6f21LVy2Pqj4O4GqMsStxLjpR1fLv7wF8BT0X1rEkF50UeZ8IYKmqbmn1JoeUXHSjqteo6itU9U8A3IXeOvmxZMA6IQG0pBNb3kdWdPIOBLzrx5Xc9KCqT6O3HGUsl50A+eikWAKjqvoEekY/9rkyaCfS27TgFADX9n9nw08uulHVH6rqn6nqKQC+B/a5BqWTdtEMAl3xk+aDXuDtLwK4uHb8QiwMIPax4vsL0Fsi8qpa+qXoBcU9GvuCtb3McL1DAdyHXvDcQ4rvhwI4EMARlbzWA3hX1/IZc50sRRGoFcAy9JYyvqNr+YyzTiq/zwI4v2u55PDJSTcAnlP8PQTAHIogleP2GbROKun/HLVg4JXf7seYBZ5OoZPKeUfBHcDVq7tx1EkueijK8aJKmT4O4ONdy2ecdVL8dkSlTBcDmO1aPuOuk+L3dwC4rGu55PDJSTfY1+faH8ANAFZ1LZ9x0ElIOkQEA+9cgPyk+6DnLqcAbkVvYDQH4HXoBVm7AcDd6AW1LQdSlwLYUUm7uZLX69CzJv8cwPsd1/yHooLfA+BtxbHnAvhxUY7b0XPjXtq1fMZcJwegt6vZrQC2AfgUgImu5TPOOqn8di+A47qWSw6fnHQD4KsA7ig+Y7lrZoc6+T6A7QB2oRfDYXVx/N3F/08BeAjApV3LZwR08lUADwPYXcjWuNOPTXfjrJNc9IDeaoWbANyGXp/rclR2oRunTy46KY5vqujkywAO7Fo+466T4rfvAjita7nk8MlJN+gZUu5Ez4N8TdeyGTOdGNMBeGPx/xMAfgVgo6/8UpxICCGEEEIIIYQQQkhfMEYTIYQQQgghhBBCCGkFGpoIIYQQQgghhBBCSCvQ0EQIIYQQQgghhBBCWoGGJkIIIYQQQgghhBDSCjQ0EUIIIYQQQgghhJBWoKGJEEIIIYQQQgghhLQCDU2EEEIIIYQQQgghpBX+H54jFGu4LAcbAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RxJYwJP-zqWk"
      },
      "source": [
        "# My models\n",
        "\n",
        "<span style=\"color:red\">This is the main section which you should rewrite and expand significanlty providing/documenting your models tried and results with them.</span>\n",
        "\n",
        "**Here you define your different prediction models. Each model should be in a function that takes the dataframe up to the training_end_date as input and outputs prediction of OWID_WRL new_cases for the following 7 days. The models should only use the data that they get as input! Training, possible hyperparameter optimization, and prediction should be done inside this function. The execution should take no longer than 3 minutes on Google Colab nachine with Tesla**\n",
        "\n",
        "Give links to the sources of the models and the ideas!\n",
        "\n",
        "Execution of this section should not take long (no training here, just definition of functions ;)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "czjf_-ajzqWk"
      },
      "source": [
        "## 1. A simple persistency model\n",
        "\n",
        "Returns the last week as the prediction for the next one."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZHZbJHCLzqWk"
      },
      "source": [
        "def get_last_week(input_data):\n",
        "    # simply return the last 7 values of the relevant time series\n",
        "    intput_time_series = input_data[input_data.iso_code=='OWID_WRL'].new_cases.values\n",
        "    return intput_time_series[-7:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nugFmOQSy9sT"
      },
      "source": [
        "## 2. PROPHET models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rJdEr_D3y58d"
      },
      "source": [
        "def prophett(input_data):\n",
        "  from prophet import Prophet\n",
        "  intput_time_series = input_data[input_data.iso_code=='OWID_WRL'][['date','new_cases']].set_index('date')\n",
        "  intput_time_series = intput_time_series.reset_index()\n",
        "  intput_time_series.rename(columns = {'date': 'ds','new_cases':'y'}, inplace = True)\n",
        "\n",
        "\n",
        "  #Initializing Prophet model\n",
        "  ml = Prophet()\n",
        "  ml.fit(intput_time_series)\n",
        "  future = ml.make_future_dataframe(periods=7)\n",
        "  prediction = ml.predict(future[-7:])\n",
        "  prediction.rename(columns = {'ds': 'date', 'yhat' : 'predictionvalues'}, inplace = True)\n",
        "  prediction = prediction.set_index(\"date\")\n",
        "  final_df = prediction.iloc[:,-1:]\n",
        "  return final_df.values\n",
        "\n",
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZfTEu9c8_pt_"
      },
      "source": [
        "Weekly seasonal prophet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9h8TMv92BCEh"
      },
      "source": [
        "def weekly_prophett(input_data):\n",
        "  from prophet import Prophet\n",
        "  intput_time_series = input_data[input_data.iso_code=='OWID_WRL'][['date','new_cases']].set_index('date')\n",
        "  intput_time_series = intput_time_series.reset_index()\n",
        "  intput_time_series.rename(columns = {'date': 'ds','new_cases':'y'}, inplace = True)\n",
        "\n",
        "\n",
        "  #Initializing Prophet model\n",
        "  ml = Prophet(weekly_seasonality= 50)\n",
        "  ml.fit(intput_time_series)\n",
        "  future = ml.make_future_dataframe(periods=7)\n",
        "  prediction = ml.predict(future[-7:])\n",
        "  prediction.rename(columns = {'ds': 'date', 'yhat' : 'predictionvalues'}, inplace = True)\n",
        "  prediction = prediction.set_index(\"date\")\n",
        "  final_df = prediction.iloc[:,-1:]\n",
        "  return final_df.values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XqwkboSFVYwh"
      },
      "source": [
        "Daily Seasonality Prophet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jCVj90fhVcxo"
      },
      "source": [
        "def daily_prophett(input_data):\n",
        "  from prophet import Prophet\n",
        "  intput_time_series = input_data[input_data.iso_code=='OWID_WRL'][['date','new_cases']].set_index('date')\n",
        "  intput_time_series = intput_time_series.reset_index()\n",
        "  intput_time_series.rename(columns = {'date': 'ds','new_cases':'y'}, inplace = True)\n",
        "\n",
        "\n",
        "  #Initializing Prophet model\n",
        "  ml = Prophet(daily_seasonality= 50)\n",
        "  ml.fit(intput_time_series)\n",
        "  future = ml.make_future_dataframe(periods=7)\n",
        "  prediction = ml.predict(future[-7:])\n",
        "  prediction.rename(columns = {'ds': 'date', 'yhat' : 'predictionvalues'}, inplace = True)\n",
        "  prediction = prediction.set_index(\"date\")\n",
        "  final_df = prediction.iloc[:,-1:]\n",
        "  return final_df.values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ez_ZXvRsxAIm"
      },
      "source": [
        "## 3. Seasonal ARIMA model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JJSfKlwcVSwY"
      },
      "source": [
        "Using daily seasonality"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PJteV_-2w_Nd"
      },
      "source": [
        "def arimaa(input_data):\n",
        "  intput_time_series = input_data[input_data.iso_code=='OWID_WRL'][['date','new_cases']].set_index('date')\n",
        "  prediction = pm.arima.auto_arima(intput_time_series,seasonal=True , m =7)\n",
        "  return prediction.predict(7)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B6PjODbV6e3_"
      },
      "source": [
        "## 4. Autoregressive models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CVXFMkF86e4A"
      },
      "source": [
        "Daily seasonal component."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "awMUpKDR6e4B"
      },
      "source": [
        "def weekly_autoreg(input_data):\n",
        "    # An autoregressive model with weekly seasonal component\n",
        "    # preservind dates as index may be useful to some models\n",
        "    intput_time_series = input_data[input_data.iso_code=='OWID_WRL'][['date','new_cases']].set_index('date')\n",
        "    # construct the model and train\n",
        "    model = AutoReg(intput_time_series, lags=7, seasonal=True, period=7 ).fit()\n",
        "    # predict\n",
        "    prediction = model.predict(start=len(intput_time_series),\n",
        "        end=len(intput_time_series)+7-1, dynamic=False)\n",
        "    return prediction.values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bxk8zVnS6e4B"
      },
      "source": [
        "# Model validations and results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y0hBFk7fzqWj"
      },
      "source": [
        "## Data split for experiments and validation\n",
        "\n",
        "Using out of sample validation\n",
        "\n",
        "Set the dates:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zigia6H1zqWj",
        "outputId": "a6cb1cef-f0a9-46c4-e839-9bfbc2f72eec"
      },
      "source": [
        "from datetime import timedelta\n",
        "validation_end_date = df.date.max() # you can change this, e.g. to df.date.max()\n",
        "# testing end date will be 2021.11.22 in the end\n",
        "validation_start_date = validation_end_date - timedelta(days=days_to_predict-1)\n",
        "training_end_date = validation_end_date - timedelta(days=days_to_predict) # will be 2021.11.15 in the end - the deadline\n",
        "print((training_end_date, validation_start_date, validation_end_date))\n",
        "validation_date_range = pd.date_range(validation_start_date, periods=days_to_predict)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(Timestamp('2021-11-07 00:00:00'), Timestamp('2021-11-08 00:00:00'), Timestamp('2021-11-14 00:00:00'))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tNm-cZUD6e4D"
      },
      "source": [
        "Generate input and target for validation:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MgiHVzlEzqWk",
        "outputId": "84eabe32-764b-4452-c5fd-89698783f841"
      },
      "source": [
        "input_data = df[df.date <= training_end_date]\n",
        "target_output = df[df.date.isin( validation_date_range ) & (df.iso_code=='OWID_WRL')].new_cases.values\n",
        "target_output"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([481288., 480717., 572076., 518160., 585111., 428588., 350606.])"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B1ZqaIvA6e4E"
      },
      "source": [
        "## Validate all models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-qTHjn9KzqWl",
        "outputId": "93436452-b3ac-4c13-e7ef-194a9f093f50"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "import logging\n",
        "logging.getLogger('prophet').setLevel(logging.WARNING)\n",
        "\n",
        "\n",
        "model_names = {weekly_autoreg, prophett,get_last_week, weekly_prophett,arimaa,daily_prophett}\n",
        "for name in model_names:\n",
        "\n",
        "  model_to_validate = name\n",
        "\n",
        "  start_time = time.time()\n",
        "  predicted_time_series = model_to_validate(input_data)\n",
        "  end_time = time.time()\n",
        "  elapsed_time = end_time - start_time\n",
        "  prediction_error = RMSLE(target_output, predicted_time_series)\n",
        "  print('%s prediction error: %.8f, time: %.2fs (max %is)'\n",
        "      %(model_to_validate.__name__, prediction_error, elapsed_time, max_prediction_time))\n",
        "  assert elapsed_time <= max_prediction_time, 'Training is too long!'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "weekly_autoreg prediction error: 0.10815085, time: 0.03s (max 180s)\n",
            "weekly_prophett prediction error: 0.09319378, time: 2.58s (max 180s)\n",
            "arimaa prediction error: 0.05550455, time: 72.79s (max 180s)\n",
            "daily_prophett prediction error: 0.09313657, time: 2.55s (max 180s)\n",
            "get_last_week prediction error: 0.09072434, time: 0.01s (max 180s)\n",
            "prophett prediction error: 0.09240721, time: 1.87s (max 180s)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ppHyVbK6e4H"
      },
      "source": [
        "## Model validation results\n",
        "\n",
        "The results here are from a simple out-of-sample validation on dates 2021-11-09 to 2021-11-14, using all the previous data as input.\n",
        "\n",
        "\n",
        "| Model, variation | Validation RMSLE | Execution time, s |\n",
        "|-------|---------------|---------------|\n",
        "| prophett  |    0.09240721 | 1.87        |\n",
        "| daily_prophett |    0.09313657   | 2.56        |\n",
        "| weekly_prophett| 0.09319378 | 2.57        |\n",
        "| get_last_week |    0.09072434   | 0.01        |\n",
        "| weekly_autoreg, 7 lags |    0.10815085   | 0.02        |\n",
        "| arimaa |    **0.05550455**   | 72.86     |\n",
        "\n",
        "\n",
        "\n",
        "We see that all except arimaa,models are very fast, the time is used mostly since its an auto arima model which tries out all lower order combination of autoregressive and moving average terms as well as for seasonal terms.\n",
        "\n",
        "The most accurate of the models validated is seasonal arima with daily seasonailty component. I choose it as my best model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Si90j-lYzqWl"
      },
      "source": [
        "# My best model\n",
        "\n",
        "I choose the autoregressive model with 7 lags and a weekly seasonal component as my best, to compete in the \"model olympics\".\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2GlitS8WzqWl"
      },
      "source": [
        "my_best_model = arimaa"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sVIX39mzzqWl"
      },
      "source": [
        "# Testing\n",
        "\n",
        "<span style=\"color:red\">Do not edit beyound this point!</span>\n",
        "\n",
        "This is a dummy testing routine of `my_best_model`, similar to what will be done after your submission. If your `my_best_model` doesn't work, fix it, not the code here (it will be replaced)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "5gAH8fNgDQxE",
        "outputId": "c0720665-bc27-440d-da3a-697eaeacddb3"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from datetime import timedelta\n",
        "import time\n",
        "from sklearn.metrics import mean_squared_log_error\n",
        "\n",
        "def RMSLE(y_true, y_pred):\n",
        "    return np.sqrt(mean_squared_log_error(y_true, y_pred))\n",
        "\n",
        "\n",
        "max_prediction_time = 3*60\n",
        "days_to_predict = 7\n",
        "\n",
        "url = \"owid-covid-data.csv\"\n",
        "df = pd.read_csv(url, parse_dates=[3])\n",
        "\n",
        "testing_end_date = pd.Timestamp(2021,10,25) # you can change this, e.g. to df.date.max()\n",
        "# testing end date will be 2021.11.22 in the end\n",
        "testing_start_date = testing_end_date - timedelta(days=days_to_predict-1)\n",
        "testing_input_end_date = testing_end_date - timedelta(days=days_to_predict) # will be 2021.11.15 in the end - the deadline\n",
        "print((testing_input_end_date, testing_start_date, testing_end_date))\n",
        "testing_date_range = pd.date_range(testing_start_date, periods=days_to_predict)\n",
        "\n",
        "test_input_data = df[df.date <= testing_input_end_date]\n",
        "test_target_output = df[df.date.isin( testing_date_range ) & (df.iso_code=='OWID_WRL')].new_cases.values\n",
        "\n",
        "start_time = time.time()\n",
        "test_predicted_output = my_best_model(test_input_data)\n",
        "end_time = time.time()\n",
        "elapsed_time = end_time - start_time\n",
        "\n",
        "prediction_error = RMSLE(test_target_output, test_predicted_output)\n",
        "print('Test prediction error: %.8f, time: %.2fs (max %is)'\n",
        "    %(prediction_error, elapsed_time, max_prediction_time))\n",
        "assert elapsed_time <= max_prediction_time, 'Training is too long!'\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-58-7543c384f04b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"owid-covid-data.csv\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparse_dates\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0mtesting_end_date\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimestamp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2021\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# you can change this, e.g. to df.date.max()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    686\u001b[0m     )\n\u001b[1;32m    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 688\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    689\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    690\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 454\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    946\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    947\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 948\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    949\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    950\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1178\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"c\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1179\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1180\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1181\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1182\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"python\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   2008\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"usecols\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2009\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2010\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2011\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2012\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'owid-covid-data.csv'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zj4Gac5npZdG"
      },
      "source": [
        "## Information on the hardware\n",
        "\n",
        "Only relavant if execution time is in question: then it is tested on Colab Tesla K80 node."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gpxloV0tzqWm"
      },
      "source": [
        "import os\n",
        "osname = os.name\n",
        "print(osname)\n",
        "if osname == 'posix':\n",
        "    f = os.popen('nvidia-smi')\n",
        "    print(f.read())\n",
        "\n",
        "    f = os.popen('lscpu')\n",
        "    print(f.read())\n",
        "elif osname == 'nt':\n",
        "    # from https://winaero.com/get-cpu-information-via-command-prompt-in-windows-10/\n",
        "    f = os.popen('wmic cpu get caption, deviceid, name, numberofcores, maxclockspeed, status')\n",
        "    print(f.read())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1AiQ9K2mSrh"
      },
      "source": [
        "#from google.colab import drive\n",
        "#drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}