Rainfall Forcast using SARIMA Model.ipynb

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            "Requirement already satisfied: pmdarima in /usr/local/lib/python3.10/dist-packages (2.0.4)\n",
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            "Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (1.5.3)\n",
            "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (1.2.2)\n",
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            "Requirement already satisfied: statsmodels>=0.13.2 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (0.14.1)\n",
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            "Requirement already satisfied: packaging>=17.1 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (23.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.19->pmdarima) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.19->pmdarima) (2023.3.post1)\n",
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            "Requirement already satisfied: patsy>=0.5.4 in /usr/local/lib/python3.10/dist-packages (from statsmodels>=0.13.2->pmdarima) (0.5.6)\n",
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      "source": [
        "!pip install pmdarima"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eNBeYVrDS2gd"
      },
      "outputs": [],
      "source": [
        "from pmdarima.arima import auto_arima\n",
        "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
        "from statsmodels.tsa.seasonal import seasonal_decompose\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px\n",
        "import plotly.graph_objs as go\n",
        "import plotly.io as pio\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "cvupB95pUt7Y",
        "outputId": "687f6496-7b93-49c3-9fcd-3c070da03049"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "YEAR      0\n",
            "ANNUAL    0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "data = pd.read_csv('/content/TELANGANA.csv')\n",
        "print(data.isna().sum())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mpz8QMf5sQPg"
      },
      "outputs": [],
      "source": [
        "data = data.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "base_uri": "https://localhost:8080/"
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        "id": "DNhdk6Is4BzX",
        "outputId": "bdd05cf5-438f-40d4-b0a9-de73939b6b86"
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          "text": [
            "              YEAR       ANNUAL\n",
            "count   115.000000   115.000000\n",
            "mean   1958.000000   953.378261\n",
            "std      33.341666   205.925999\n",
            "min    1901.000000   437.000000\n",
            "25%    1929.500000   800.150000\n",
            "50%    1958.000000   942.100000\n",
            "75%    1986.500000  1080.300000\n",
            "max    2015.000000  1544.900000\n"
          ]
        }
      ],
      "source": [
        "print(data.describe())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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Rainfall(Bihar)\"}},                        {\"responsive\": true}                    ).then(function(){\n",
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          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "figure = px.line(data, x=\"YEAR\",\n",
        "                 y=\"ANNUAL\",\n",
        "                 title='Annual Rainfall(Bihar)')\n",
        "figure.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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          "text": [
            "                           YEAR  ANNUAL  Year  Month\n",
            "0 1970-01-01 00:00:00.000001901   827.7    70      1\n",
            "1 1970-01-01 00:00:00.000001902   630.4    70      1\n",
            "2 1970-01-01 00:00:00.000001903  1283.4    70      1\n",
            "3 1970-01-01 00:00:00.000001904   526.7    70      1\n",
            "4 1970-01-01 00:00:00.000001905   724.9    70      1\n"
          ]
        }
      ],
      "source": [
        "# data[\"YEAR\"] = pd.to_datetime(data[\"YEAR\"], format = '%Y-%m-%d')\n",
        "# data['Year'] = data['YEAR'].dt.year\n",
        "# data[\"Month\"] = data[\"YEAR\"].dt.month\n",
        "# print(data.head())\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "# Assuming 'data' is your DataFrame\n",
        "data[\"YEAR\"] = pd.to_datetime(data[\"YEAR\"], format='%Y-%m-%d')\n",
        "data['Year'] = data['YEAR'].dt.year % 100  # Extracting last two digits of the year\n",
        "data[\"Month\"] = data[\"YEAR\"].dt.month\n",
        "print(data.head())\n",
        "\n"
      ]
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LfWxr/AEgPbrrBt+8ag0atQYESMUYHx8CkYhBjNLf7vh9TW8y46kfc7Ejv7ZXno/03PHyFuH/azV6l5Oge4ICfx94aWMe/vb9Sfx0rLKvD4WQHuNbeQ6LCESKtW6V6vwHl/pWPd7bWYRGrW+zo2TosE0O2F4E+ErnTbbhfI2/j6f32g7vsjUqlgv8z9a1CgEbX+YzLUUFqURkd7wVzb7/GQHAryeqsH5vCZ74IbdXnm+o2ne2AUtf24VDJY09fgzbjD/LAjVq75atUeDvA8XWN7sy2rhDBrAia6nPsIggCvwHqfd2FeEfv57GOzvP9vWhkEHiXC9n/DtKbrgAnM/41/sw8NfojGhpNwLoWuMfq5QjWC6B0cwKyRN+Y+/M4eHC/fiv662M/+FzTQC430mtlwNJ0uGHnAqcrlLj+5yKHn29yWxBbiW3WiSzXiR6u9yHAn8fqLZuxvDlGw8hvtRmMAlL2cMigpBqDfyLaIPvoHK2lvt9Hi1t7tsDIYPGOduMf2+U+gjddbhAuje6+vDPqfT3Q5BMYncbwzBIt+nnbzBZkF3EZX9tA/9YIePfW4F/s/D/h6wXAcT7Klu4i6rSxp79Xs/UtEJvskAhk2BiYggAoKqFMv79mslsEd5wfF1jSIiv8AG+KlCK0EApUsK59nPF1Mt/UOGzpbkVLb3SgYUMfrYZ/7Jeyfg7LvWpb9WDZX3zmq5osr/Y6GyUzQTfnLJmtBvNCAuUYmSUQrhPvPV4y3sh49+qNyHfpsvQwfMoQyGuVVkv5Hr62j9R0QwAGBunFLo/efvikAJ/L6tvNYA/f1LGnwxUtvX9AKjUZxBiWVY4OWkN5n5xUafWGbHxZDVdhAxgnUt9fBV88yo61dqHB0kBAEYzK5TjeP05nWzs5dlO8N1tre/PGhYGkYgR7iOU+vRCxv9YWTNs/6QOU8bfJ1i2o7VseVNbj97Hjlk39mYkKBEXIgdApT79XpVNz1XK+JOB6qxNfT8AJIdzJ9WmNiOaaCPooNDUZoTWpj2rbSeJvmCxsLj1w4O449PD+OZIeZ8ey1D13dFyvLa1AJYeXnjpjGZU29SPtxnMPm2rabGwwvRbPvsuk4ih9Oe6kPnqHOxsYy+vo9RHg73WwH+WTZkP0HHRUNnc7vOLoyPWQH9yUigAILdSDa3NcEbiHWqdSXhPNZpZu78Fd53gA/+4EMRYXyNU6tPP2e6+rtP4bqlxqKhR67D2qxzc9tFBGGnQSa/pyPhzgX+AVIJYJZd9KKKs/6DQeSnancC/tKEN//wtzy7B4S2fHSgVao+PllJGsrcZzRY8+s0J/GvrGewsqOvRY/A1/Qp5x/uFL+v861u5VociBoi2Ph/QkfX3VeDvbHgXb2SUAgzDHd8R62t5ZqfAP8aazdUZLWjwcTLlsPUYLsqIQVyIP8wWFjllzT59zqGo8/uip12t9CazMPgtI14p7AOhjH8/V21zZaY3WYTpa8QzZguLj/aWYMErf+DbIxXYerq2ywh04jt8D/9hkYHC51IiqNxnMClrsj8pnazoPvD/19YzePuPs7jpgwNQ67xXRlGj1uGF3/KEf9sOPyK9o7heC721/eRXh8p69Bh8mU9yWCASw7hseGmj794v+Gx/jNIffuKOcCbCxy09nQ3v4vlLxUgJ494vLSyQqApAgsp+dUAmESPSepy+GNLEs1hYYfN+ZlIoMq1Z/0MldHHtbZ1/j57W+edVaWA0swgN8EN8qL9Q6kM1/v1cVaelHSr38dzJihZc8eYePPljrt2FU7WXl7uGIr3JjNs/PoQXN+Y5vY/ZwgrBPZ/xB2zr/HunFrxWraMheD5UZu06MS5OCYBb/je5WFVjWVboR15Q24p7Pjvq8v6eeOrHXGj0JiGQyq/WUJ1/L7NNrGw5VYOGHgTNfEefxLAAJFoD3dIG3wW15U4y7xEKLmDyWca/mxp/AEiP6djIO3N4mMP79EZLz6L6VrS0GyH3E2FUTDCmJFsD/3O0wdfbKpvtYxRPV7v4/v0Z8SFgmI4hbxqdCRovJloo8Peymk7Bqa/Hhg8mWr0Jz/x8Cpe+sRvHy1ugkEvwzOVjsXBUFAD0qF6uP/jleBU251b39WEA4LI8W07V4K0/zjo9sVc2t0NvskAqFtnVsHZ09vF9xj+vWo2ZL2zHI98c9/lzDVV81nJOWjgCpWK0G83C3g5Hiuq1qNXoIRWLIPcT4Y8zdXj2l9PnfRybc6vx28lqSEQM3r4hEzKJCO1Gc6+0giQdbFdZjGYW3x31vA95R8Y/AEnWjLcvf49C5r1TAM6X+vji/Ntus2/BWcYfAEZZJ/gCXct8eHG90NLziLWNZ0Z8CPzEIkxOVlk/3+S1C/f+qsGHnZ0c4Ut9xNZN3J4H/tb6/nguGRMokwj7VbxZ50+Bv5d1Dk4p4+8ei4XFyvez8cHuYlhY4OKMGGxbOxc3Tk8SlrsGYsa/rLENd312BHd9dsSrV+w9dcJazsGywO/5jut4C631/SnhgcIbGIDz6uVvtrB4bWuBkDHuzrbTtTCaWewqcO/+xHP8gMEkVSDGWLP+thMjO9t3tgEAMCkpBP++dgIAYP3eEny8r6THx6DRGYVJorfPScXYOCVGWlshUmlf7+Jri8dYJ89+ebDM46CJn9qbpAoUSlt8WerjrK2mL3v580F6kE1Q5gi/wRcAslJdZ/x92dKT7+DDl/ikRSmgkEmgNZiRVz14S+o251Yj89mteO7X809OuKvKmvHnV1HP9TjwDxE+F6P0fmcfCvy9jA9OwwL5jAMF/u7YlFuNnLJmBMkkWH/LFLxx/SREBnMv+CjrC38gZvw3n6oBwGXQCmr7vl3iCZsNnNvzahzex1F9P9BR6lPSoPW468fm3Gr8a+sZPPT1MbeCCX5zZ6PWgEbqIuQT5daTUrzKHxnWE9UJF3X++4q4wD8rNRxLxsbg4SUjAQBP/3QKf5zp2WbQVzafQbVah6SwANy7YAQAIJ3vge5G4L+nsB6rPjyA+744ihc25uHjfSXYcqoGJytavLoHYSjIs2b8H1w8EnI/EQpqW3HUww2gfIYzybbU5zwy/nvP1gsXnI44K/UJD/Jdjb9tmQ/DME7vNzVFhbgQfywbF40w6/F0Ft8bGX/re+mkRC7wF4sYTBLq/Advuc9e6+vm/d3FONxLZU3873FaKreq4kmNf5vBhIJa7m+Qz/gDtt2fKOPfL7FsR/umsdYTKWX8u2exsHhtWwEA4NaZyZg3MtLudv6KdyBm/G1LfM70g+yKbWC380w9DKauS72dW3ny4kP9IREx0BktHl+E7bRm7itbdN1mt1iWteszXdgPLpgGG4uFFX4PCaEBGBfPZ/wdB/4syyKbD/yHcdnLO+cOw/JJ8TBbWNy94QgKajx7fR8tbcJH1tWCf1w+DnI/MQCbHuhu/L28sjkfO/Lr8H1OJd7acRZP/JCL2z8+hIv/sxuT/r7FZdBIOjRpDcLf9JRkFZaNjQEAfHXQ/U2+RrNFeE0lhQUiyRr416j1PdqrU6vR4eZ1B3DTumzUahy/33RssrXfOMtn/Ot9cP7tbmMvT+nvhz2PXoA3V2Y6vY+va/xb2oxCwmmSdQosAJs6/8G7wZdffWJZ4LFvTzg813kbX44zPYV7j2zUGtxe6c+tVMPCAlHBMkQFd3So8kVnHwr8vUjdboLOyL24xsZxJy8K/Lu3+VQN8qo1CJJJcOuslC63838EAy3j36g12E1IzPcwMPK2ljajkH1T+vuhVW/CgeKumZDOrTx5ErFI6NThaZ3/7sKOjLCj57RVXK9FU1vHmyUF/t5Xq9HDYLZALGIQo5QLS8unqtQO2+YW1LaivtUAuZ8I4xO4iwSGYfDclWMxNVkFjd6EWz866PbqjNFswWPfngDLAldOjMOsER010OnRHcOPXNEZzThZwd3n7vnDsWpGMhaNjkJGvBIhAX4wWVg8+8upHvekH0pOW8t8ElUBCJJJcM2UBADAT8cq3e73XtncDrOFhdxPhEiFDCEBflDIJAB6NsV0y6kaGM0sjGYWm3K7rk6yLCtkWLuU+vgy499NK09P+GoyK+9oGRfYJ4cF2K06ZCZxGemDJY2DtuU4v99ExABnalrx7s6zPn0+i4UVkpMjooKgslZ98E0UusMnXcbFhdh9nm/7WunFFsoU+HtRlZr7xYQE+AkZCCr1cY1lWbxuzfavmpGMkABpl/vwO9urW3QD6k1q2+kau2mJZ/o48Oez/YmqACwew22Y3nq66wm1yEngD9jU+XsQ+J9r0Nq9+XU3Lr7zVEkK/L2Pb+UZGyKHRCxCkioACrkEBpPF4euUz5xPTlJBJhELn5dJxHj7xkwkqgJQ1tiOlzblu/X863YXI69ag9AAP/z1olF2t42ydkMpb2p3Wa6TW9kCg9mC8CApHliUhqcuHYN3b5qMH++ehe0PzINCJkFupRo/Ha9065iGMr7Mhy+zmpaiQnJYALQGM345UeXWY5RYA61EVQBEIgYMw9jU+Xse+NsG+78e73oMDVoDdEYLGKbjHMHjM/4NrXqvd4cqd7KvoCf4i4eWdqNPWn/zg7v40h7ehIQQSEQMatR6n+4v6Csms0W42HxwMVeS+Pr2QuHc5gsNWgMMZu71GBUs9/i1z++vGm9T5gPYD3rzFgr8vYi/2osOlvs04zCYbDlVg1NVagRKxVjtINsPcD9PgJsCqRlAcxH4+v4F6VzpUn513wawfOA/Ll6JBdZOSdvyauwupprbDELHitSIwC6PkWq9GPDkDZTfoCv3495uDnQT+B+x9pzmO3MU+vDNeqjiT4oJ1gSFSMQIG9JOOCj34QN/vszHlipQipevHg8A+PpQWbfZ3eY2A974vRAA8NiyUV3qn0MCpEJ5X76Lch/+AnFSYmiXWmtVoBR3zBsGAHhpUz70JmoL6wq/usJvSGUYBldP5rL+7pb7lPIbe8M63jeSwnoW+Kt1Ruw727GxP7u4oUsSjQ9YoxRySCX2oYwqUAqG4XroN7V5d49QR42/46m9nrDdIOyLch/+vZSv7+f5S8VCOXLnRIs7WtqN/bojUGWzDiYLC6lEhDvmDMPsEeEwmCz4v+9O+Cx5yAfmkQoZ/MQiYY+Lu6td/PvuuE6BfyzV+Pdv/NTeaKXcpzWGA0V1i07YWOQIy3bU9t88IxmhgV2z/QD3JhUs55aMO7dL7a/aDWbssk6//PP84QC41Z+e9Mb2lhMVzQC4jgOzhodDKhahrLHdLqPO1/fHKOUItC7T2+ro5e9+xn+3NfC/YVoSAK4rkKsSOD5LdeWkeO6Y3Mz4n2vQos0wcC4M+xK/ApNgUxst1Pl32uBrsbDYX8wF/tOddCeZmqLC7BHhMFlYvLG90OVzv7XjLDQ6E9KjFbjK+jvuzJ0Nvp27lXR268wURAXLUN7Ujk/3l7o8pqGO7+4y2qb3/FWZ8RAxXB24O6tufMY/yWZQFR/8nPNwgunveVxXr+GRQciIV8LCAhtP2rdEdtbRBwD8xCKoAnwzvZev8fdGqQ9g29LTu21PzRZWaJLg6G9ksvVz3a3AdnasrBmZz2zB0z+dOv+D9JGO7lLc6hO3h0iE/UWN+PpwuU+ek2/lyQfqiSruv+5c9La0G4VVdNuOPoD9HkdvlS1S4O9FVTYZ/3CbyYEDqTzFm1Z/dBBXvrkX//wtz+ELdnteLXIr1QiQinHb7FSXj8Uv5Xqzl60v7Syog85oQYLKH5MSQ4QTYF/W+fMZ/4w4JQJlEiF7uy2vVriPs/p+nqeBv9nCYq81c3dRRowQ0DnrJtHSbsQZa2eDayZzQWFFc3u3dcZHS5sw7+Ud+Nv3J906rqGOL/VJUHUELxnW2tLOGf+8ag2a24wIkIrtuk10dt/CNADA/46UC4OcOqtqacf6vSUAgEeWpEMkctwVpbsNvtwG8GYAzgN/f6kY91uP6Y3tBdTlxwmTuaO8K92m93xUsBzzrY0WvnZjki8f3CeFd2T8EzzMevI2W8t8Fo+JwrJx3EbjXzuVHHUXgAudfbwY+BtMFtRaH88bpT6A7zb4nqnRQGswI0gmQVqUosvtfD9/Tyf4fnukHCYLi2+OlLu1abu8qQ1XvrkH3/dgLkRPneu0+pQYFoC1F3LvBf/45bRPSrD5jHyskg/83V/tyrWem+ND/YW9AbyoYDlEDGAwW1Cv9c5xU+DvRXzGPypYLpQpGM0sWtqH3glHrTMit5LL1r39x1nc/1WO3XK7bbb/pqzkLi/2zgZaS0/+xLVodDQYhhHeePuqs09zm0HI8vI92xeM4k7q22zq/DsC/65lPkBHjX9ZY5tbXRKOlzdDrTMhWC5BRnwIplhPNs7KfXLKmsGyXInA8EiF0Ba3u9kBO/LrwLJAdtHgbU/nTXwgZtsNhQ/q86rVdn+rfBvPKckq+ImdnzIyk0Ixb2QEzBYWr29znPV/bWsB9CYLpqaoMG9khNPH4ktOnG3wLW1sQ30rN0yML1lw5KrMeAyPDEJTmxFv7/Dt5r6BqqShDXqTBQFSsRCs8PhNvt8cKXe46dvWOZssK68npT46oxk78rlkxKLR0bjIGvjvL7Iv9+mu1l5YdfdikFfV0g6W5coWw7o5Z7mLz/iXe3mDL78iNiEhxG4eC2+ytbPPmVoNWtrci1FYlhUSRW0GM/YXdd8166O9JThS2oynf8pFu6F3Su5KbAbJ8W6dmYLRMcFoaTfi7z5YreAz/nyG3pOL3mPWZMv4Ttl+gFu94hucVHmp3IcCfy/ia/xjlHLIJB3lKUNxg2+utdtGoFQMiYjBDzmVWLXuoHARtCO/DsfLW+DvJ8btsx3X9tuKCR44LT1NZgu25fGBP1dLPzKay6Dn1/RNvTqf7U8KCxBqSi+w7j04fK4JTdZuLGdrra08Ix1n/CMUMgRKxbCw7p3M+TKfGcPCIRYxmJpiDfyddPbhy3wyrTWp/HEU1rm+YOK/v8qW9h61DhxqhFaeNhn/+FB/hAT4wWhm7Wrr9xc5r+/vjM+wf3e0vMs+kMLaVnxlzRw/siTdZQ90vuQkv1rjcLWQD2rGxgULbUAdkYhFeGRJOgBg3Z7ifvf+0W4wCwFDX+EvrkZGK7qswFyQHonwIBnqWw3YbrMy2JnFwgrvB8k2Nf62WU93yxT2FNZDazBbu00pkaAKwLg4rtxnk0175I6OPo5r7X0xxEuYG9BND39PxPso49/Rvz/E4e3hQTKkhAeCZeGyJNdWYW2r3WZgR80hbLEsi9+sJVpNbUZ8e9Q3ZTadOVp9kohF+OfycRAxwI/HKoWLS2/hM/4x1gs5frWhvKm92w3mQhmukxVVbw/xosDfi/gyFD47zb/x1A7BOv+T1kBs1ohwfHjLFATJJNhX1ICr396LyuZ2/Nua7b8xK8npcBNbAynjf7CkCc1tRoQG+AllCCOtS+h91dlH2Nhrkx2NDw1AerQCFhbYcYZ7E3TV0QfgNv2lRLhf7rPLOqmXb9fIB/6nq9QO+xsLJyvrz204H/i7qDFmWVZohcayHSUAg0G7wYwN2ee8ujfEaLYIwaZtjT/DdGzw5X+eZotN/34n9f22xieEYOGoSFhYCN26eC9vyoeFBS4cHeW0PIeXHBYIqUSENoPZ4QVmd/X9thaOisTkpFDojBb8e+uZbu/fm/706WHM/Od2/HjMN52H3ttZhPu/zHG5uZmf2Gtb5sPzE4uwPDMOgOtNvjUaHfQmCyQiBrEh9j3IxSIGepPF7UYXHaulUUJwzZf7/HaiI/AXSn1CnJX6eL/Gv6OV5/lv7OXF+WiIl7OOPrY8rfPnL/745NG207UuS5lPVqjtLhQ+2FXcK+11+dUn24w/wNXP3zwjGQCwbk+J249X2dze7XHz7TbjrK//6GA5/MQMDObu594cK+Mn9joO/GO9/Brp08B/586duOSSSxAbGwuGYfD999/b3c6yLJ544gnExMTA398fCxcuREGB/cmksbERK1euRHBwMEJCQrB69Wq0ttoHCcePH8fs2bMhl8uRkJCAF1980SffD1/qw1+d8TWGfJeUocQ20Jw9IgJf/mk6IhUynKlpxeJ/78SxsmbI/US4vZvafl70AMr4bz7FnZwWjIqCxFoaMdKm1Kcv9nwIHQM6lUV0lPvUwmCyCCPGnQX+AJASzt1WXO969aJVbxI2l822Bv5RwXIkqgJgYbt2k+A2ozUD6OhCMTyi+8C/Wq2zW1Urru+9wF+tM+L9XUVOa9rP1xM/nMRfvzvpdptMd1Q2t8PCAjKJSEhO8PgTD/96OV2lhlpngkImwZjYroGhI3yt/w/HKlFo3a+RU9aMjbnVEDHAQ9b2eq5IxCKkRXG/ez4wteVJ4M8wDB5bxmX9vzpU5vGgMV9pM5iwp7AeFhZ44KscoRmAt5jMFry8OR/fHa3A1lPOs5t8K89RMV3rwAHgGmt3n9/za50G0XyGNT7UX3jPA7gLB/5CwJ0VQrOFFbLIi8dEC5/ny332FTWgUWvgevj3QamPu8O7POGLGv+GVr1Q7jIxwfnfCF966e4gLz7wv2v+MPj7iVHVohNKeh357SS3L2NOWgQUcgmK6rUuV468wWJhhfOY7eoT78bpXJOJPYX1wkq3Kxuyz2HGP7fj0+xzLu/Hl+Hw+xHFIkZYjSp1sbm9oVUvBPTOyhb5i0Nv7XHs08Bfq9Vi/Pjx+O9//+vw9hdffBGvv/463n77bWRnZyMwMBCLFy+GTtfxza9cuRK5ubnYsmULfv75Z+zcuRNr1qwRbler1Vi0aBGSkpJw+PBhvPTSS3jqqafw7rvvevV70RnNwtAhPkj1xVLjQMFn/PkX8phYJb67ayZGRAZBo+M2at4wLalL4OHMQJney7IstpyyL/MBuE2xEhEDjd7UJxuUbVt52rognTvGP87U4WxdK8wWFoFSMaKCnf9e3N3gm13UAKOZRYLK367Fn7NynzM1GrTqTQiUijHSugnYnYx/52mzvgrCO2vUGnD9e/vx7C+nfbKp+GRFC/53hFsa51uieoNtbXTncgV+eAzf2Ydv4zk1RWUX0LkyNk6JxWOiwLLAv7cWgGVZvPBbHgCuU5OjjYaOjBIGedkH6hqdUdgk37lNoTOZSSosHhMFCwu8sNF7F1HnI6e0WSgBMJpZ3PHJYaGXtzcU1Wuht+7D+fWk8178fKkPv6G6s2ERHZ11tjkp7eD/5hIdBFpCuY8bnX0OlTSiQWuA0t8PU6zvE9zjBmBsXDDMFhabcqvR3GaE1lovHusk4x9h02DDW8qbO0p9vIV/rFqN3mttZ/k2niMig6AM8HN6v0xrnf+xsuZu92y1tBmFC4SlY2MwJ41L5jgr97Et87kqMx7XT00EALy/u8j9b6QHqtU6GEwW+IkZIW6wlRoRhFEx3GuJT9I5w7IsPrSuDLi6YDGaLajR8KU+Hc/pTp0//16bGhGIYLnj39WgKvVZunQpnn32WVxxxRVdbmNZFv/+97/xt7/9DZdddhkyMjLw8ccfo7KyUlgZOH36NDZu3Ij3338f06ZNw6xZs/Cf//wHX3zxBSoruaXTDRs2wGAwYN26dRgzZgxWrFiBv/zlL3j11Ve9+r3w2X6ZRCQsg/ki4zAQaHQdralsM8xxIf743x0zcOHoKIyKCcaf5g5z+zH5zS01Pij1OV7eLJQznK/TVRqUN7VD7ifC7BEdmxelEpHQF7+7zj7v/HEWd356GGs+PoTbPjqIVR8ewI0fZGPl+/vx/i7P3zSbtAYh2OucUZiQEIKwQCk0OhO+tC7lD4sMclm/Kgzx6mbDLR+szhpuv4lzanLH1EhbfBZ3YmKosBmND/zPNbQ53VzYuQuNp1OFe6JWo8OKd/cJk2P3FNa79Xfe0KrHEz+cRG5l1175tliWmzrLLw5VNLf3aPqpI0IPf1XXcgX+wrCgRgOd0Sxs7HXWxtMZPuv/y4kqvL+rGPuKGiAVi3DfwhFuP4azDb78BvAElT8ig7ue2J15aHE6xCIGW0/XdDs9ujccLOGDqGjMHB4GrcGMWz486LXX7ymbTOzvebUON1a2tBlRaU1E8Bfbjlw4yvnAP6Aj49+5tAKwaenpxuuXH9q1YFRkl43ktt19+PezCIXM6R6Pnnb1MVmnSq/9MqdL7bs3h3fxVIFSYcaJtzZvdtT3u74wTg0PhCpQCr3JgpPdvCftLKiD2cJiRGQQElQBwiwYZ6+J/BoNiuu1kEpEuCA9EqtmJkMiYrC/qFFIDPoC38ozITTAabLionHcatIvJ1wH/icr1ELS6ZSLlY0atQ4sC/iJGYQHdiTN3GnpeZwv83HRpCDWy0O8+m2Nf3FxMaqrq7Fw4ULhc0qlEtOmTcO+ffsAAPv27UNISAgmT54s3GfhwoUQiUTIzs4W7jNnzhxIpR078BcvXoz8/Hw0NTle3tLr9VCr1XYf3bHd2MsHTb5oJzYQ8H8gsUp5l/p9ZYAf3rtpMn67d7bb2X6g44q3QWvw6jAek9mCGz84gJXvZ3ulfo7PIMwZEQF/qf0Jic90uhpKlF+twfO/5eG3k9XYfKoGW0/XYkd+HXYV1GNPYQP+8etp1Go8Oznw2f7ksIAuGQWxiME8a8s+IfB3UeYDuJ/x322t7+fLfHh8Ju9YWYvdRtzO9f2AdZ6AVAyThXWayeczJuMTQgB43jPcU5XN7bj2nf04U9OKSIUMqeGBsLDAb25MOP3P9kJ8vO8cbl53ELUuLmK3nKrB/qJGyCQi4ee9z0sXp0IrTwd1yrFKOcICpTBZWJysaBECZHc29toaFROMi8bFgGWBf/x6GgC3n8fZRkyHj8H38u/093K40wZwdw2PDMK11i41z/x8qldqjV05dI772c4YFoa3b8jE2LhgNGgNuPGDbJevDXedsrlgajOY8ceZrqVEfBlVXIi/02wjAFxonfS9q6De4ayMczZTeztLVHV0AnOFZTsysLZlPjy+3Gfv2QbhPc1VAN6RePOs1Hbv2QZ8fqAU3x6twJVv7sXl/92DH3IqYDRbui0v6gmGYbxew+1uKRzDMEKdv7MWy7zfrRlvvinEBemRYBguOHa0QZ3fjzFnRASCZBLEKP1xcQb3O3yvBwksdwkbex1chPL4i8i9hfVodjHgzXYzcq1G7zSWq2rpKPOx3SDvTkvPbOuMlIku3s+EwH8wlPq4Ul3NvWiioqLsPh8VFSXcVl1djcjISLvbJRIJVCqV3X0cPYbtc3T2/PPPQ6lUCh8JCQndH69NK09eb5f67MivxV+/O+H2ECOLhe22RVtP8G/KY1xcwXoqJMBPmM5Yq/bez7NGo+emEFpYbO+mQ4E7hI1pDk5cI91o6fm7tdNARrwS/7hiLF5YPg4vXZWBV68Zj5FRCrBsx3O4q6PMJ8Th7Qutdf7t1iDcWStPHr+5t1ajd7hBF+BamxXWtoJhuMDGVnJYACIUMhjMFhwraxY+L2xGs+lCwTBMR2cfB+U+LMvihLU84tLxsQB8m/EvbWjD1W/vQ3G9FnEh/vj6jiysmMq9P/x03HXgrzeZ8X0O18u6vlWPuz8/6nD6pcFkwXPWYPm22SlYOpZ7LbnTOs8dwvAuVdfghWEYIev/2YFStOq5VqzOykBcuXfhCPALR0EyCe6yDrJzF5/xL21ss3udeVLf39naC9MQJJPgREULvu3FvuKdmcwW4fU+OVkFhdwPH66aiqSwAJQ3teOmdQfOe+4An4DhE1C/OSj36a7MhzcySoH4UH/oTRaHZWfnGrtO7eW528/8VJVaWC2dM6Jrq9eksECMieVKND7cUwzAdclNhPX7btQaPDrP8Z2DElT+kIpFyClrxr1f5GDWC9uFANcbU3ttCRt8vVDnbzRbhJIxVxt7eXxbz71nnb+/mC2scG6abw38w4NkworC1tNdy2D4gWv8+xcAYV7PL8ervJa97qzEwQTpzlIjgpAerYDJwjo9n5rMFvxk3XTPr0CfctJemP9eOpcWdffabzeYhTkKszolyGzxgX+dl8rB+m3g35cee+wxtLS0CB9lZd0PL+Ez/tE2v/gIYXOv7wN/jc6I+77MwYbsUnxxwL0R6499ewLjn97s9Zrokw46yJwvhmGEvRPerJEvt/mDPN9NR2WNbThVpYaIARakR3a5nV9Kd1Xqw2dVrsqMx8ppSbh2SiKunpyAKyfF44pJXHeNzhMsu9OxsdfxyX3WiHD4iTuyFN1l/IPlfkIwUeJkIy3fxjMjTomQAPt+1wzDdCn3qbfdjNYp8+Fqg295Uzua2ozwEzNYYj3B+KqlZ2FtK65+Zy8qmtuRHBaAr+7IQlJYIC7KiBW+F1elaFtP1aK5zYjwICmCZBIcKG50uGn3k/3nUNLQhvAgGe6cN1wos8kuavTKxnBXGX+gY8n5xxzupDctNcxhH/DupEUpcMUE7jV757xh3c7q6EwVKBX2mvDdsOw2gPcg8A8PkuGeC7gLkJc25XU7GM6ZXQV1+Ot3J7C7oL5HKwd51dxwJYW8Y7hShEKGT26dhvAgGfKqNbj9o0MOLwzdwbKsEKTcPZ8rqdx2urbL3wW/muJsYy+PYRhcaN2ztPWUfaDEsizO1btR6tPNShxf5uNotZTHZ2oLrO8FrlaQQgOkwuu2wc2sv8XSsUfr75eNxZ5HL8D9C9MQHiRDjVoPi7WcI9KD1Wp38CsI3ujlv/dsA3RGC5T+fkJZpiv8kLYd+XVOS3ByyprR1GZEsFxid8G9cJTj18TZulbk12ggETHCfQCu1HR6qgomC4uPrIP8vM3Va9EWv4L0i5OV2l0F9ahvNSAsUCqUujkr9xGGd3W6EO2uxj+7uAEGswVxIf4uf1ehAX5COZg39jn228A/Opo7idfU2L+gampqhNuio6NRW2sfrJlMJjQ2Ntrdx9Fj2D5HZzKZDMHBwXYf3eEz/naBfy9m/D/edw7N1s3F7gSGTVoDvjlSjjaDWXij8xZHrSO9IdoHLT1tl1b3nG1we7XEEf7nODVFhVAHQQ4f+BfUtjrs66vWdWyempfW9cJhiXUVYV9Rg1vdCHgdv48Qh7cr5H52NdzOevjbEur8nXT22d2pjWdn/AbfbGspCZ/9TIsKEvbIdD4eR4E/v7E3PToYsUo5gmQSn7T0PFnRgmvf2YcatR4jIoPw1Z+yhCxdXIg/MpNCwbJcJssZvof9tVMS8PLVGQCAd3YW2f29NrcZhDaYDy7istOTk0MhETHWOv/zDww6Mv6OT4z8ypDJ+hp1p42nM89dOQ5frJmOP89zfz+PLWGCr3WDr+0GcEftJ92xamYyElUBqFHr8c4fng/1MltYPPj1MWzILsUNH2Rjwat/4P1dRW4PQQI6yioyk0LtLqoSwwLw0a1c++Ps4kb8nt+zTj81aj0atQaIRQyunZKIGKUcrXpTl2w9PxnZnZ8lH/xsz6u1e/9qajNCY72AcvSaSrQGYPWtepfvr5tznZf58PjAn+eq5EYkYoQhW+4m33LKm1Gr0SNIJsGMYWGIUMhw78IR2PPofLx6zXjMHB6Gey4Y4XTidE95K+Nfp9Hjoa+PAeB+Vu4c54goBS6bwCUvXnTSPYxPSM1Ji7Dbe3HhaO48te9sA1ptLqL597QZw8O7bC7mO/nxK4ruOlLahIl/34zPsktd3k/I+Hdz0bPMWna0x0m5D78ieMn4WGQkcLGMs71ZnYd38fiL3gatweH3yv89zh4R7nJfHcMwwkTgSi/sA+m3gX9KSgqio6Oxbds24XNqtRrZ2dnIysoCAGRlZaG5uRmHDx8W7rN9+3ZYLBZMmzZNuM/OnTthNHa8KW/ZsgUjR45EaKjnGSNn+ExftE2pD58VbdAafFpPqtEZ7WrmDp5r7LYOfFNutXBi5zNo3tCqNwkbe11N1OyJjpae3lsitO0xbDBZsLew5+UUfH3qotGOT1wJoQGQ+4m4tpkOVll2F9TDbGGRGhEonCxtJYcHIj1aYdfyrjuNWoNwcTPGScYf6FihEDGuayN5rur8LRYWewodb+zl8W3kjpxr4soeOrXxtCV09qlzEPjbDD5hGEY4dm+29Pw9vxbXvLMPDVoDxsQG48s/ZXXZVMrXrv503HE/9qqWdqFd49WZCVgyNga3zeIG1z309THh5/jvrQVoaTciPVqBq61tFAOkEmH/wvmW+7QbzEIQ5DTj36nzk6f1/bbkfmJMTw3r8bCj9Gj7Db6ONoB7SiYR4/+s7T3f2VnkcV313rP1qFHr4e8nRpBMguJ6LZ795TSmPrcVD319zK2Niwet3wf/d2BrTKwSV1pX93o6ZOhUFXcMwyIC4S8VC6thtvtQzBYW+dV8qU/3nZampKgQLJegQWsQ2vQCHYFWjFLucKOt0t9PuJh3duF6rkGLvGoNxCJGaDHsSEp4oF1ZUlw3tfae7rPjyz7mjYyATNLxvcgkYlw5KR4bbpuOvyxwf4O6u4SWns09f98ymS245/MjqNVwyYm/XTTK7a9de2EaJCIGO8/UYe/ZrqVc2zvV9/OGRQQhOSwABrMFu2z2kPBlZcvGdj0Xzh8ZidSIQGh0JpezITp7f1cRmtqM+PyA88CfZVmbjeauA/9htuU+nZKfGp1RuBC9clIcRltfc85LfRxn/BVyP2Gl01HWnz8nzHZQ2taZNzf49mng39raipycHOTk5ADgNvTm5OSgtLQUDMPgvvvuw7PPPosff/wRJ06cwE033YTY2FhcfvnlAIBRo0ZhyZIluP3223HgwAHs2bMHd999N1asWIHYWO4K9vrrr4dUKsXq1auRm5uLL7/8Eq+99hrWrl3r1e+FLz+xDfzDrANEzBYWTS42kJyvj/aWoLnNiNTwQIyPV4JlO5ZNnfnZJjN51M2pfe44XaUGy3I/B08277qjo6Wn9weySKxBxPYenmirWtqFjZAX2rTxtCUSMcKyvqNBXvxJnl96dWTpWC7AdLfch8/2p4Q7bxUGAEvGxkDp74cZw8LtTnjOuBrilVetQX2rAf5+YkxKCnH49SOjFVDIJdAazDhdpXE5bIYP/M/WartcQPNlTHx5SrL1gsRb5WtfHCjFbR8dQpvBjJnDw/D5mukOS1aWjYsBw3AX0Y5WG749UgELy6108Mf4yNJ0TEkOhUZvwp2fHkZuZQs+3c/1iv7bRaPtAls+636+gT9/bAqZBMH+Eof3iQqWC6UMoQF+wt6UvsAHpHxJijtDidyxeEw0pqWooDdZ8OLGPI++9rsjXCZweWYcsv9vAZ67YhzSoxXQmyz4+nA5rnhzj8vgn2VZu4y/I7blFz0p7+JLEviAhc+UbzldI9QIn2vQQme0QO4nclkPzfMTi4TAz3aVuNTFxl5eR7mP479LPuCenqrqUhrYGd+RBQASugn8PVl1Z1nWrVUHX+D3DJzP5t6XN5/B/qJGBErFeOuGTATKHP99O5IUFojrp3HtNl/YmG/3mqtqacepKjUYBkIjCB7DdJTybLEmo8oa23Cygit5dXQuFIkYrLYmPdbtKXarnE2jMwr7CHIrW5yuFNRp9Gg3miEWMW61XLXtFGXrt5PV0JssGBYRiHFxSoyJ5c4vxfVah6tWfMbfdngdL8FJqVt1iw5narh9cDOHd59c4R/bG5O++zTwP3ToECZOnIiJEycCANauXYuJEyfiiSeeAAA8/PDDuOeee7BmzRpMmTIFra2t2LhxI+Tyjh/uhg0bkJ6ejgULFmDZsmWYNWuWXY9+pVKJzZs3o7i4GJmZmXjggQfwxBNP2PX694YaBzX+fmKRECR4s5ewLS7bz210+suCEbgog59w6LzkoKFVL1zVMwy3U9xb/fH5QGysi+xyT/mipWe5NcOy1PoGsL2bSYTOfLT3HCwsMC1F5bSEArDt7GOfvWZZVljWdxX485m7XQX1TjfW2nJ3v0W0Uo5dj8zHB6smu7wfj8/47ymsx5cHS+1aBe4u5L6PaakqpxcRYhEjZDv3nK3HMetmNEeBUJIqAH5iBu1GszAdEeBWFjrPJ0gWMv7nF/izLItXNufj0W9PwGxhceWkOHy4aqrTi6eoYLmwb6FzuQ/LskKZz9WZ8cLn/cQivHH9JIQHSZFXrcHVb++DycJiQXpklxIpvhRrX1HDedX58/X98aoAl1l4Pus/PTXM62UNnuCzu/nVGlgsLA6X9nxjry2GYfD4xaPBMMAPOZVdhsk502YwYaM1OLxiYjwCZRJcPy0Rv907G9/cmYVJiSEwmll85iIrWd7Ujhq1Hn5iBuOdbLifnhoGmUSEiuZ2oZ7dE3xmcrR16FpmYigiFTJodCZhVZO/mBoZpXB79WShNZCzDfxLhCmpzi8e+BVMZ5sc+Q21zlZLbfHBmp+Y6XaTrSe9/AtrW1FUr4VULMK8kd1nYL2Jz/hXNescloF2Z1NuNd62lq29eNV4IVniibsvGA5/PzGOlTULvw8A+D2Pez+fmBDiMOnBvyZ+t5aA8dn+aSlhXbr68ZZPiocqUIrypvZuk5QAl8jk5wxYWG4GhiP8PrG4EH+hGYgr/GtpT2G9Xakef3F/xcQ4MAyDCIUMEQoZWLbrXBHAdnNv14uNRCd1/ny2PyM+pNuLXdvHrhjopT7z5s0Dy7JdPtavXw+Ae3P++9//jurqauh0OmzduhVpaWl2j6FSqfDZZ59Bo9GgpaUF69atQ1CQ/Ys+IyMDu3btgk6nQ3l5OR555BGvfh9mC4taa0YhulONFz82vF7jm4z/R3tL0NJuRGpEIC4ZHytkhLOLG9Hg5M3ut5PVsLDcyZ0fktO5X3FPdR7c5U38z9YbV7w8PuN/VWY8/P3EqFbrnC7nOdNmMAnLj3wmwxk+e5pfY/8cuZVq1Gn0CJCKMSXFeVCTFhWE1PBAGMwWtzYj890d3NlvESz3cyvbDwATE0MQLJegvtWAR745genPb8Nzv55GaUObTf9+510KgI4yh0/2nYPeZEFIgOPNaBKxSAgqbOv8zzW2QaMzQSoRCRdU/P3caelZ2dyO8qa2LoNrDCYLHvjqGP6zvRAA8JcLhuOVq8d3eyK5xNpV6OdOgf+B4kaca2hDoFTcpUY5KliO/1w3CSKGa7koETF4bFnXJfrMpFD4iRlUtejcmoDqjFDf302m9KrMBCj9/YQsYF9JCQ+EVCzipkCXNeNcQxsYhps/cb7Gxilx1STuQszd9p6bcqvRZjAjOSygS/epzCQVHljETSX+5XiV0+4bfBvPsXFKp5tY/aViocTq9x40HejI+HN/9yJRx+Z3PruZ52ZHH1tz0yLgJ2ZQVK/FWWvpnZDxd1Ei6Cz4AbhVYv6CbtEYx6ultlIjgvCva8fj39dOdPrz43lS6sOXe8wYHgaFi9VRX4hSyCAWMTBZWI/bNRfXa/HgV1xd/+pZKULyz1ORCjlum82dv17alC9k4p2V+fAmJ4VC6e+HpjYjjpQ2CUO7lo1zfhEn9xPjBusEXXcGev1g7YbGN6HoPAOG19HRx72uS8MjuXIfo7mjlWxlczv2W1tsXmZtTgDAablPu6FjeGusw8DfcS9//jw5x0U3H1txg6XUZ7BoaNXDZGEhYjo6+fA6Mg7eHzxlm+2/d8EIiEUMElQdEw6dbdrlM5IXjYvBROvJ64ibGa/u+GpjL9AR+Nd0087TaLa41b7NYmGF2rxhEYGYaQ1UPT3RfnO4HC3tRiSFdQw1cSaN7+zTqaUnX+bTXakNw3ScwG2zMs7wA6Y6T+w9X5EKOf54aD7+b1k6ElT+aGk34t2dRZj78u9CfX93dYv8Bl9+eXtSYqjTLLSjCb78Rc3omGBhw5m7MwYOljRixj+3Y9YLv2Pk479hyj+24tI3dmPNx4dw9Tv78O3RCohFDP555TisXTTSrRr1pWOjIRYxOFHRghKb5//6MNcL+uKMWIfL71nDwvDoUq7mfPWsFIfZOn+pWAh297lou1fe1Ia/fnfC7vltuRreZWvJ2Ggce3KRW7WnvuQnFmFEFPfz4Df1pUUqumwA76mHFo9EgFSMnLJmp/szbH1rzQRebs0EdjY9NQxRwTK0tBuxw8nGXH5wl6P6flv8yt/vHpYftupNQubTtnafTwptPlUDo9mCU1X8xl73S7lsGwHw5xe3Mv4uhni9tCkfLMvtk3GUMXXkionxbgW4ngzR3NRHZT4Al9zgy4Q92eDbbjDjzk8PQ6M3YUpyqPA+0lO3z0lFaIAfztZp8c2RcuiMZuH9nJ/y7ujY51tXSD7Zdw5HS5vBMN3/HG+cngSpWISjpc0uV9xqNTrhGG6dyV2YOAv8z7nxWuxsWafuPt/nVIC1lmXavk+Osa6ede7swyciA6Vih+WTjlp62u6Dc/c9lq/xH/ClPoMF32UmQiHrMimOzzj4IuO/fg+X7R8WEYiLrS0FgY43+F8d1IHXanTCwIiLMmKEzZTeyPi3GUxCFsgngb9NqY+r7Nz17+3HnBd/77ZVX12rHgazBWIR1yqUz2hs8yDwt1g6RnrfMiO52yVz/iRb0tBm11pPKPNJ7/5NgA/8f8+rcziNk9fQqu/Y2Bvr/dKr0EAp1swZhh0PzscHN0/GnLQIsCy3FBsVLENalOvl5nFxSqFFGeC6fEOo87fZ4CvU99tc1PC1ypUt7S77Hdu2n2NZLiN4vLwFm0/V4FhZMwKlYnxw82SsmOp+xjssSCbMLPjZGkS26k3ChfY1U+Kdfu2aOcOw77EL8MgS5yfu6W7U+T/+/UlsyC7F338+5fD2jlae3htA5Gv8Bl/+Z3q+9f22IoPlwnyBf/6W5/LvqVbdEYBcMTHO4X3EIgaXW7OEfLlAZ3x9/+Ruvg8+8D9U0uRRT38+kx8dbD9AcWqKCmGBUrS0G7H3bIMwvCvdwxkNizq19eQDGldZ1iQn/cwPFDdie14txCJGWC3xJndr/Cub23G8vAUMA7v2k72pY4Ove4Fdq96ER789jrxqDcKDZHjj+kldph17KljuJ/w9/HtrAf44U4d2oxkxSrnLDeB8uc+P1r73mYmh3U7VjlDIhG5C63YXO73fL8erYGG5VeYrrSt0R0ubHSb3StwY3tVZ53If/u/2yk5/46OFwN9+/44wvCvE32EywFFLz1NVajRoDQiUioXka3diQjouDM+3rTMF/l5Q7WBjL49fAfB2jb9aZ8T7uztq+20DTn5gxt5OdWsAtynUwnJL5fGhAcJJ9GSF+rwHQ5yuUsPCApEKWbd/9D0RoZBBxHBtBuu1jn+eNWodDpY0oaql+5IdfqNjdLAcEpuNazllzW63f9txphZF9Voo5BKhC4srkQoZlP5+MFtYFNVx2Ynmto4uGZ03TzkyLk6JuBB/tBsdT+Pk8asvqeGBPl265jpxROHjW6di+wNz8eCiNLxx/aRus+RSiciuZMPVeHmHGf8KPvDveAy+Rz7Lup4Uyl/ovrg8A4f+thA/3zML7980Gc9cPhYPLkrDD3fPdOt30Rnf3Ycv9/nleCXajWakRgS6/P6ArlMfO+vY4Ou4n39OWbNwAfl7fq3DTcbdtfLsj/iAQ28tyTrf+v7OVs9KQVyIP6padHjN2krVkR9yKmFhued3tRn2cmvAsD2vtsv7b3ObAWdquNdwd99HYlgAUiMCYbKw2ONgaJYznev7eWIRg8XWc8NXB8uEjmajPGyLygd5h0ubcK5BK0zGdVXqw7/eyhvbhaQNy7J4wbqx+topCcJqnTfxpbbdnX/51YvMxFCvN6VwV7wb03sbWvX48mApbl1/EJP+vgU/5FRCLGLwxvUT7YaHno8bpichVilHVYsOf/3uBADuvOTq/XyOtQSMt3Sce+VGq62lRb+drHL6fv2DdZ7IZeNjMSKSa/fcbjQ77Knfk4z/8MggjIziyn3+tfUMCmpbIZWIunwP/AbfvGqN3YbkCifDu3h8xr+8qV3Yv8GX+WQNC3P7Yo0vI9IazFDret52HKDA3yscTe3lCUuNXu7l/5GTbD9gP5VuS6e2jz8f4wISPkBJDguAKlAKg9nidDiFuzo29no/2w9wy/78CkqNk84+tpNgixy0f7TFn/j4TEu0Uo4xscFgWThdpu/sA+vF13VTE93qosAwTMcEX2tnn50F9bCwXP2/O50I3C33EQZ3ebnMx5XUiCDcfcGIbssYeFNTuGBWLGIwPsH5cQ7rNMTLbGGRW9E14+9OS0+DySL0/5+UFIrwIBnGximxcHQUbpyehLsvGIHhkT3rZLN4TDT8xAzyqjUorNXg60Ncmc/VmQk9bmnJm5gYCqlYhGq1Tshs2Xpt6xnh/1kWDtve8RcDAyvwtw9MvR34y/3EeOrSMQCA93YVOe3Iw/f1vtxJtp83KiYY6dEKGMwW/NppWi5f0pAaEeh046OtnpT7dO7oY4sfWsQfV6xS3qXPendilP4YG8e9T/KrnapAqcuuYTFKOSQiBgazRThfbjtdi8PnmiD3E+FeH7TIBCB0p+ru/NuXZT48IeNvU+rTZjDhYEkj3t15Fte8sw9T/rEVj3xzAtvzamEwW5ASHohXrxlvN4flfMn9xLjvQm4vJX9R52ggpa3gTrNgljho4+lIenQwZo8Ih4WFw4FeJfVa5JQ1Q8QAF2XEQiRihJWyzuU+doPkwj17f+Oz/uutx3DhqKgu5YRJqgAESMXQmyx2paRVfCtPJ2VqMUp/+Im51z7fmMSTNp48f6kYoda/VUflPp5Mp6bA3wv4jL+jK77w88j4b8qtxs3rDuBv35/AR3tLsLewHnUavctsP48v97Ht7lPdosNB68Yy/oXOMAwmWrOuR86zn/8Jaz25rwJ/oPsNvidsTtp8Rt0ZPvC3HQLDv8G5U+d/ukqNPYUNEDHATVlJ3d6flxbNBbH8BN8d1ufypJMEv6qz9XRNl82pPF/ut/AW/nuenBSKAKnzC6dhEUFgGG5YUEOrHkV1rdAazPD3E3eZNNxdS8/TVWroTe5PtvRESIBUeDN/bVshDp1rgljEYPkk18GiO2zr/DuX+/DZfrGIEcqFvjxYbvfaaGk3CpkiV4OP+hvbGvSwQGm3Ezl74sLRUbhoXAzMFhaPfnu8S4vBvGo1Tlep4SdmcLEb2Uy+FKhzuY9Q35/k3oVxR+DvfltPZxl/gOs6FhrgB/6hPC3z4V04yrpyYO1W1V1phUQsEl5zpY1tMFtYvLiJy/bfMjPFa9nqziKCuMdV60xOp3k3txmEQYLubC72FT7pk13ciIf/dwxL/r0TY5/chKvf3ofnfs3DgeJGWFiuY94DF6Zh8/1zsP2BuXYbUL1l+aR4jLCuskolIsxwo90kXwI2PiHErQQW71ZrQ4wvDpZ16VTHlw7NHB4uJFEnd5r6zmvUGqDRm8Awric6O3JRhv2FiqNSPpGIEZIQuTZJ0o5Wno6/Z7GIEY6ntLENbQYTDlnfB2a7ubGX56qX/8f7Stx+HAr8vUDI+DsI/Hs6vbel3YiH/3ccf5ypw6f7S/Hkj7m4/v1sTPnHVkz9x1a0tBsxPDKoS7afx++ot237+OuJKrDWpWrbFylf7nO+df7uto48H9HdtPQ8Vt4R+J/tJvDnl+jibX4W862B/84zdU4Dat6He7iLr6VjYzx6oxEy/tYWhXy5jielJZOsS9IanQl7HAxcAXrn93G+JiWG4ps7Z+A/1090eT9/qVg4mRTWtgoZ+7FxwV0ufLtr6cm/zicmhvikVaUwzMt60pqbFuG10rfpwxzX+fPZ/isnxuG22SmIUMhQ36q32+DPL6WHB0ldXmT1N2FBMiFzOynJ+Qbw8/XkpaOh9PfDyQo11u2xrznmA/j5IyMdTuXu7NIJsWAY4EBJo10Jw2Fr4mVysnurFlNSQhEgFaNOo7cLNpwxmS1Cm05HGX+JWGSX1XZncJcjC60TW9useyKS3FhBSrCp8//+aAXO1LQiWC7BHXN6NtXZHcH+EkitpRTOyje3nebaUKZHK9yaZ+ArfMa/sLYVXx0qR161RiidXTgqCo9fPBq7Hp6Pn++ZjXsWjEBalMJnfwtiEYPHlqULm3Tdeb9YMTUR/7csHa9Yp5K7a+6ICAyPDEKr3oQvbQZ6sSyL763dfC63ubiZYv3bOVTSZHcxzK+CxgQ7HiTnyvBIhbAnTRUoxVwnSThhg69NGbFQ6uOghz/P9rWfXdwIg9mCuBB/j8vbOgJ/+/inrLENb+5wfwo5Bf4eqHJSe+eqxl/Y3Othxv/tP85ybTrDA3HnvGFYOCoKyWEBYBhAZ+QC0rUXpjndTDoiSoFhEfZtH/ld6xd36obAby45eh6dfdoNZhTUciccnwb+Qsa/a+DPsixOWDu9AEBRvXulPrZB+/j4EIQFSqHRm4RNeI7Ut+rxvbX28NZuWnh2JvTyr9HgREULGrQGBMkkbgcDAJd9WGzNTm08YV/uY7Gw+HT/OVS26MAwwJh+HPgD3IVopKL7wNh2gm/HakZIl/t119KTX9nK7KbmvqcuHB1l1/rzmsnON/V6anoql+3ad7ajn79ttv/uC4bDTyzCiincfpMN2eeEr+XLfDzNhvUH/Am3uw2x5yNSIcdfrdNOX91yRlgxMls6ApAr3Vy5iVH6C3sy+KylzmjGsTLudetuKZxMIha6jbkzxbeoXguDyYJAqdjpQC3b2uV0D+v7eaNjgu2yuu4EzPyqwNnaVry6hbtQ/fP84R6XGnmCYZiOltqtjhtsdExc77tsP8Btvl46NhqzR4Tj7vnD8c6Nmdj/2AIc+OtCvH/zZKyeldKrJXoXpEdh50Pz8dJV7gXyfmIR1swZ5nGZpO1Ar/V7S4TVttxKNYrqtJBJRHYrMePilZBKRGjQGuySO+eEVp49u3i7yjpj5erMeKd190JLT7uMv+tSH6CjpWdZYxt2nbG28UwL9/jCLdYa/9hm/FmWxV+/Pwm9kUp9fKJzvSaPz/h37uEPdGT8G7QGtybUAVw2m88mP7o0HY8sScf7N0/Gjofm4/Tfl+DXv8zG93fN7NITvDPbqXSVze04fK4JDIMuXzc+PgSi8xzkdbqa29gbHiRDVLDvNkfxP+NqBxn/8qZ2oZ8uwPWXdlX3VmENhGzHvotEjJD1d9Un/9P952AwWTAhIcTjmuOR1tKF8qZ2oVPJ7BHhHndk4Mu5tpyuEV5bJfVaXP/+fvzt+5MAuJreIA8mOPZnw23q/PlWnhkO9i8kd9PS01vTX51RyP0wL43LGKkCpU7b4PXEJGudf61GL3x/ttl+/qS3YmoiRAyw92yD0AlpIG7s5T2yNB1/mpuKGz0oqeuJqzPjMWNYGHRGCx779gRYlsW+sw2oUeuh9PcT3hvcwZcLfHukHCzL4mRFCwxmC8KDZB51HbEt9+kOH5CMigl2upo1Y1gYwoO4vvHOBoh1h2EYu6ms7nw//IXIJ/vPoaK5HVHBMtycldyj5/eEq1X3dkNHg4RFfVjfD3AXeW/dkIlPVk/Dg4tHYvGYaIcxRW9KUAV4nD3viSsmxgkDvfh5Cnzv/oWjouyaU8gkYkywvm5ty334jL+n9f2822al4os10/HgYufdpfgNvrmVLcLcKT4h7GhqLy/RZnpvT+r7eY5KfX48VomdZ+o8ih8o8PfAr50yqwB3teUq468KlELEcJvtGtvca+n5760F0BktyEwK7TLyWu4nxujYYLcG2PCB4Y78OmGT4ZRkVZd6ykCZRMj89LTcp2NwV7DPlh+Bjp+xowsUvvyDbxNpsrBOOwWwLNtR6tOp3vmCbgJ/ndGMT/dzmVRPs/0AVwfOXxx9foBb2uzJpMipKSqEBPihUWvA/iJuA9jif+/E/qJG+PuJ8cTFo/HaCtclNAMJn/HPr9YIZQ+ONi4nu2jpWavWoaK5HSKGq0X1lVUzudaua+akujVB0l1yv472b/uKGrpk+3lxIf7C65jvfy9M7R1A9f289OhgPLZ0lM9LlBiGwfNXjoPcT4S9Zxvw9eFyfHuUe++8KCPG7QF3ALfBUSYR4WydFicr1EJ9/2QPy5X494ajpU1o0ro+h7iq7+f5iUX4/PZp2HDbNJedeLpj2/bSnSwrH/zw5UH3LkjrdgCXN7ga4rWroA46I1d24YuWx8Q9cj8xbrAOC3x/VxHMFlZYKbt0QtdyZn51nP+bAs4/4y8SMZie6rrLzoioIIhFDJrajKhW66DWmaC1vp5dzaDgX/uHzzWhoLYVIgZC62dPCIG/Nf5pbjPgGWvr5j/NSXX7cSjw90B+tUboxMLT6E3CG5mjq3OxiIEq0P06/7N1rcKGqUeWpJ9XED0qRoGksADoTRa8uYObQtq5zId3voO8TtgE3b4kBP4OMv58Fnh8ghIp4VyQ6GyDb4PWAJ3RAobp+gc7e0Q4JCJuOqWjzkA/HatEfasBMUq5sMnWU3y5T6t11kBPWkf6iUW40Hryve3jg3ju1zzoTRbMGh6OzffPwa2zUrqdKzCQ8IH/geJG6E0WKGQSpDh4kw8PkiJQKnbY0pO/sE2LUvh0JWTGsHCceXapR2/G7ppu09bTUbaft3Ialx3/32FuEI8wvGsAlvr0pqSwQKy1djV59udT2Gidh9K5r3d3FHI/IXHz3dGKjv79HpT0AdzJPj1aAQsL7CxwnfV31dHH1ogoxXl3gpmWqkKsUg6FXOJw6FxniaqO12dqeKBXS+BccTXEa1Mul11eNCbKpwkr0r0bsriBXkdKm/HOzrOoUesRLJc4TIrxpXKHHGX8fbD5nyf3Ewsrz6cq1ULmPTTAz+VFLL/Kyicbx8WHICSg+71CnfGrCvzz/vO3PNS3GjA8MsijJCQF/h7il594NdYrr2C5xGk2quONp/uM/yub82G2sLggPVKYbNpTDMMIWX+9yQIR47zNFt9j/KhNO0xPnBAy/j4O/JUdGf/OXS74jH9GXAhSI7iTjLM6f76+P0oh75KRVcj9MM1aS81n/c0WFjllzfjv74VCferNM5J7PDCF3+ALcCfpnna1WGrdxK0zWqCQS/Di8gx8snrqgCzn6A4fXJisvZDHxikdljMwDCOU+5TUdw78mwH4rszHlljE+CSYyLJmiraeqnGY7efNSYtAfCg3VfmX41Uoa+JLfQZexr+33TozBePilFDruMROoiqgR21E+T0BPx6rwKFz7k3sdYRPDLhqM8yyrFsZf2/xE4vw3V0z8etfZrs1Sdl2deGBRSO7DLv0FWelPmfrWrHV2u66L9t4Ek6kQi5k91/elA+AK0t2tMrGbfLngv1aDReDnW/G3138ylBupVro6NPdxOnO5+M5Hnbz4fEZ/+oWHfadbcAX1s3Qz185zqOVZQr8PfRDTqVdwMlv7HBViycMEekm43+srBm/nqgGwwAPL/HOFEO+uw8ATEsJc7qJkg+ETlS0dNvNpjOd0YyCWt9N7LXF/5zbDGZobCbzWiysUG6UkaDEMGvg5yzjX9Gph39nfF3tlwfLcMcnhzHx75tx+X/34KVN+ahq0SFCIcN1U9yf6tpZmk2LQnem9Toze0QErpgYh+WT4rF17VxcM+X8+8X3VyEBUuFvCXBc388TAv9OLT35PurdDdPqzyYkhEAqEaHd2p7QUbYf4C48rrNOHv5k/7mOHv6U8e+WRCzCP5ePE1bMLp8Y16O/q9kjIqAKlKK+1YCWdiP8raWanppvzXr+caZOGALUWY1aj0atAWIRI6wo+lpUsNztJEOQTIJHl6bjznnDerxS2hOdS310RjNe3XIGS/+9Cy3tRsSH+vt00zhxH7/Jl3+JOyrzAQClv5+QPDtU0oTmNgOarfv7PNk/0xMdE3zVQncdZ608ecFyP6EHP9Cz+n6AuzgSixiYLCzu/zIHAHDd1ASPkwkU+HsgQCpCeVO7XR28q+FdPHdbevJ9ja+YENfjbgud8VNeAa5G1RlhkJfJgtxOI6m7k1etgdnCIixQ6nR6nbcESCUIlnMrKzU2df7FDVpo9CbI/UQYHhGE1AjXpT7l3dQ7L7CW0BTUtmJjbjXUOhMUcgkWjY7CM5eNwS9/mXVe3ShsM/7ze1Dmw/MTi/CvayfglWvG+6wXdn9i27Pf1WAyRy09DSaLsDI1yc0x6f2R3E8sHL+zbD/vmskJ8BMzyClrFkrbujtJEc6YWCWeunQMpqeqcOP0nm0q9hOLcInN++7ExJAerRJOSgqFQi5Bo9YglDR2dqqKe20PiwjslQ2ZPXHH3GF4ZEm6T9roOmNb6rPzTB0W/3snXt9WAIPZgnkjI/D57dN7bfWBuDYqJhgzrTMDooJlmJbivBxtik0/f76DW6RC5vN9QEJnnyq1TQ//7s+9fJ1/oLRjn5anxCLGrtw5PEiGR5eM8vhx6NXugQXW7hz8CGmgI/h0FfBGuNHSc1dBHfYUNkAqFuF+a32pNzAMg5evHo875w3D1S5qKt0Z5NVuMKOwVtNlwyQfTI2JU/ZKttlRS0/+ZDg2VgmJWNRtqQ9fa+ds0EhKeCBWz0rBjGFhWHthGr798wwcffxCvHvTZNyYlexW+0lXRkZzU3qHRwa5tVGbcGxriTMctPLkOWrpmVvJrWaFBvh53D+5v+Hfi67OjHe5tB2hkNmVMcQEdy1tI87dOD0JX6zJEoLHnrCd9NvTzLKfWIQ51iyhs+4+7tb3DzV8xv9oWTNuWncA5xraEBUsw1srJ+HDVVMGZVnkQLb2wpEIDfDDHXOHudyjNtmmnz+/spvcC3MY+Ix/aWMb8q0zM7or9QGAROuxZQ3zvIOfLduLjCcuGd2jBOTg6PPXSy4aH4tf8lvw8/EqPH7xaPiJRajiW3meR8bfYmHxwkYu23/D9CSvvxFlDQsT6oJdmZQUim15tThS2oTVsN8ootEZcfXb+5BXrYFYxCApLABpkQqMiAoSyifGxfXOCScqWI4zNa12G3yFjj7WLDAf2PFL7J1rUB318O/s8YtHe/W4bcn9xNi6di4AULbJA3zgr/T3c1mr7qilp1Dfn+i7IVC95ZaZyRgRFYQZw7qvFV05LQk/H+daEcdTkNPrJiSEYHhkEAprW4We/D0xb2QEfjlRhR35tcLmY1u9Wd8/kPDnX7OFhYjh9matvTDNrkUk6T8yk0Jx9IlF3d6P3wOZW9kiXPT6uswH4EpO40L8UdHcjt2FXE9+dzL+i8dEYXNuNVZO63mJMMDtFzhY0oS5aRF2q4meoMDfA9NTVAgP4uo1dxfWY/7ISCHj72hqL6+7IV6/nKjCyQo1gmQSl8v2vuZskJfJbMHdnx0VJkKaLSyK6rQoqtNiY27H/XprQmyMzQZfHh/4832pFXI/RCpkqNXoUVTXiomdarq7q/HvDb3Rym6wmTU8HH5ibniZq+C9c0tPmUQslOj1xsZeX5OIRW53gpqeqsKwiECcrdNSfX8fYBgG626egoJaDaadRycdfpro8fIWHCtr7tKOtiPj378H9vW2RFWAECQ+cfFonzegIL0jRukvBOB8FUZyL63kjo4NRkVzuzBM1Z3yyYszYnFxhuM9C57487zhiAiS4bbZqT1OYFHg7wGJWISLM2Kxfm8JfjhagfkjI4Wss8tSn24y/u/vKgIA3D47FSo3xsH7SudBXtFKOViWxdM/ncIfZ+rg7yfGV3/KQmSwDGdqNCioaUVBrQZnaloRIBVjTlrPN6l6onNLT5O5Y1+Cbd13akSgNfDX2gX+LMt2W+NP+qcRUQoc/OvCbus4+ZaeWgPXxnJ4pEK4oO1pfeVAxTAM7r8wDWu/PIaFo3q+n4T0XGJYwHn1zAe4jX2zR4RjV0E9rn13H/51zQRhCm+r3iS0MxwV0zsbewcKsYjBV3/K6uvDID4wJTkUFTntQizQGxl/gCun22IdNAa4jv+8bXhkEB5b5nldvy0K/D106QQu8N98qgZtBpOQdXZrc6+DjH9xvRbHylsgFjFYOf38loDOFz/I61SVGkdLm7B0XAw+3FOCT/afA8MA/14xQQiso4LlPd6Zfr6irfV0/GpLQW0r186yU1/31Igg7C9q7FLn39JuFIZuOKvxJ/2XO/2P+ZaeuZVqlNS3IUjmh8oWHTe4q4fTSgeyizNisWxsTK9uqiTe99+Vk3D3Z0ex80wd7txwBA8uSsNd84cjz1rmEx0sR1iQ7yanE9KfTE5W4XubPZe9UeMP2JfTMYzr+K8/ouJiD01MCEGiKgBtBjN+PVGNBuskRVc1/nypT3ObsUurzB+tL9pZw8OF+/UlYZBXaRO2nqrBM79wU+EeW5reb3odRyu5nxO/uZcfHta5r3uqk5aefH1/eJCs33a/IOePPwmUNGiFMp/06GAE+nBwV39GQf/AFyz3w7qbJ2PVjGQAwMubz+D+L3Nw1Lp/her7yVDSedbR+a6quct2ynOkQnZem3X7wsA62n6AYRhcZu0ty5foSMUilyU6If5+kFhPug3ajqw/y7L44Rg3EOwyJ/1qexvf33xjbjX+8sVRsCxw3dRE3D7b+xNIe4q/uq6xLu8ds3b0yUiwr90c5qSlZ8fGXsr2D2bJ4R0tPfmJ1JOSQvrwiAg5fxKxCE9dOgbPXj4WYhGD73Mq8U9rcwjq6EOGkuERQULjjrBAKYJ7acN2XIi/0FZ8ILZHpsC/B/ggnd/sGqWUudxkIRIxCLMOHqrXdEzvza1Uo6hOC5lEhEX9JJvOb3wsa2xHm8GMWcPD8ffLxvSrLih866wGrQF6k1loJ9q5vSPf0rO4QWs39Iav7+/Ljb3E95JsWnoKG3sH8OAuQmzdMD0JH90yFcFyifD+Rhl/MpSIRIzQIre36vsBLgHM/63FutHKs7+hwL8Hhkcq7JZ6XJX58Drq/Ds60fyQw2X7F46OQlA/KT/gB3kB3CaS/66c1O+WsUID/IRe5GWN7ThtrW/tPMk1PjQAUrEIBpMFlda+/UBHD3/K+A9ufEvXMzUanKzgXiOZg6CjDyG8WSPC8d1dM5EaHoggmcTjCZ6EDHSzRnAtcnv7opfvqsWvLA8k/SPaHIAumxCLXGv7tGg3rviEIV7WjL/ZwuLHY1x9/2Xj+0eZD8Bdyf553jBszq3BK9eM79L/vj9gGG56XWljG3bk18JoZhEa4NclkOfnDRTUtuJsXaswH0Eo9RmAS3TEfXyNf621m1ZYoFSYnkjIYDEsIghb1s5Fu9HcbxJIhPSWG6cnISxIhjkjej4joyfumDMMUQq53YC+gaJ/pXIHkEvGx4KvfokO7n5TLr9xl+/sc6C4ETVqPYLlEqE/c39x2+xUfHVHVr+eaMivsvAttcbFhzgsRxIm+NrU+Ve4MbyLDHx8S0/exEEwuIsQR8QihoJ+MiRJxCJcOj7WrW5v3hQaKMWts1L6tAV7T1Hg30MxSn9MT+GGsbiTRezcy58v81k2LgYyCXWW8RQ/MO1gSSMAYHy846EsqfwGX5uWnlTjPzTwLT15tLGXEELIUEeB/3l48aoM3L8wDVdlJnR7X9uMv95kxq8nqgBwcwGI5/iBGfyeXWdTgzu39FTrjFDrTACoh/9QYNvXmTb2EkIIGepobfA8JKgCcO/CEW7d1zbj/0d+HdQ6E6KCZZiW0vMR7kNZ54EZncfX81I7tfTky3xCA/yGbD/3oYTfeCUWMV02fxNCCCFDDWX8ewmf8a9v1eMH66beSzJiIaahOj1iOyI7UiFzOjlvmLXGv1qtg1Zvovr+ISY1nLvwGx0TjAApXegRQggZ2uhM2Ev4jH9Vs04IPi+bMPB2g/cXtoF+RnyI0/uFBEihCpSiUWtAcb22o76fynyGhIvHx+B0lRpLx/WPORmEEEJIX6LAv5fwgX+70QyAqz0fG0fDVnoqWmkb+Lsu4UgND0Sj1oCzda3Uw3+IkUnE+NvFo/v6MAghhJB+gUp9ekmwXAKpzSCsyybEUWvB8xCpkAntVLsN/G1aevI9/KmjDyGEEEKGGgr8ewnDMELWH6BuPufLTyzCjGFhiAqWdTuNtaOlp9Ym4081/oQQQggZWvp14P/UU0+BYRi7j/T0dOF2nU6Hu+66C2FhYQgKCsLy5ctRU1Nj9xilpaW46KKLEBAQgMjISDz00EMwmUy9/a0A4AYKAVzP+RSb/uKkZz65dRp2PXwBFHLX04U7Wnq2dmT8qcafEEIIIUNMv6/xHzNmDLZu3Sr8WyLpOOT7778fv/zyC77++msolUrcfffduPLKK7Fnzx4AgNlsxkUXXYTo6Gjs3bsXVVVVuOmmm+Dn54fnnnuu17+X5PBAHCtvGZAjnvsjkYiB1I2uSHzGv7C2FXqTBQCV+hBCCCFk6On3gb9EIkF0dNeOHC0tLfjggw/w2Wef4YILLgAAfPjhhxg1ahT279+P6dOnY/PmzTh16hS2bt2KqKgoTJgwAc888wweeeQRPPXUU5BKe3fU8iNL0jFnRAQF/r0sURUAsYgRgn6FXAKlv+tVAkIIIYSQwaZfl/oAQEFBAWJjY5GamoqVK1eitLQUAHD48GEYjUYsXLhQuG96ejoSExOxb98+AMC+ffswbtw4REVFCfdZvHgx1Go1cnNznT6nXq+HWq22+/CG2BB/LM+Mp979vUwqESFR1VHTT/X9hBBCCBmK+nXgP23aNKxfvx4bN27EW2+9heLiYsyePRsajQbV1dWQSqUICQmx+5qoqChUV1cDAKqrq+2Cfv52/jZnnn/+eSiVSuEjISHBu98Y6XWpNnsqqL6fEEIIIUNRvy71Wbp0qfD/GRkZmDZtGpKSkvDVV1/B3993wdtjjz2GtWvXCv9Wq9UU/A9wqRGB2JbH/T/18CeEEELIUNSvM/6dhYSEIC0tDYWFhYiOjobBYEBzc7PdfWpqaoQ9AdHR0V26/PD/drRvgCeTyRAcHGz3QQY2foMvQIE/IYQQQoamARX4t7a24uzZs4iJiUFmZib8/Pywbds24fb8/HyUlpYiKysLAJCVlYUTJ06gtrZWuM+WLVsQHByM0aNpmudQYlvqQ4E/IYQQQoaifl3q8+CDD+KSSy5BUlISKisr8eSTT0IsFuO6666DUqnE6tWrsXbtWqhUKgQHB+Oee+5BVlYWpk+fDgBYtGgRRo8ejRtvvBEvvvgiqqur8be//Q133XUXZDJZN89OBhP7jD9t7iWEEELI0NOvA//y8nJcd911aGhoQEREBGbNmoX9+/cjIiICAPCvf/0LIpEIy5cvh16vx+LFi/Hmm28KXy8Wi/Hzzz/jzjvvRFZWFgIDA3HzzTfj73//e199S6SPhAdJMTwyCPWtehqeRgghhJAhiWFZlu3rg+jv1Go1lEolWlpaqN5/AGszmGA0sVAGUA9/QgghhAwOnsSp/TrjT4g3BUglQO/ObCOEEEII6TcG1OZeQgghhBBCSM9Qxt8NfDWUtyb4EkIIIYQQ4g18fOpO9T4F/m5oaGgAABriRQghhBBC+iWNRgOlUunyPhT4u0GlUgEASktLu/2BkoGHn8xcVlZGm7cHIfr9Dl70ux3c6Pc7uNHv13tYloVGo0FsbGy396XA3w0iEbcVQqlU0otzEKMpzYMb/X4HL/rdDm70+x3c6PfrHe4mpmlzLyGEEEIIIUMABf6EEEIIIYQMART4u0Emk+HJJ5+ETCbr60MhPkC/38GNfr+DF/1uBzf6/Q5u9PvtGzS5lxBCCCGEkCGAMv6EEEIIIYQMART4E0IIIYQQMgRQ4E8IIYQQQsgQQIE/IYQQQgghQwAF/oQQQgghhAwBFPgTQgghhBAyBFDgTwghhBBCyBBAgT8hhBBCCCFDAAX+hBBCCCGEDAEU+BNCCCGEEDIEUOBPCCGEEELIEECBPyGEEEIIIUMABf6EEEIIIYQMART4E0IIIYQQMgRQ4E8IIYQQQsgQQIE/IYQQQgghQwAF/oQQQgghhAwBFPgTQgghhBAyBFDgTwghhBBCyBBAgT8hhBBCCCFDAAX+hBBCCCGEDAGSvj6AgcBisaCyshIKhQIMw/T14RBCCCGEEAIAYFkWGo0GsbGxEIlc5/Qp8HdDZWUlEhIS+vowCCGEEEIIcaisrAzx8fEu70OBvxsUCgUA7gcaHBzcx0dDCCGEEEIIR61WIyEhQYhXXaHA3w18eU9wcDAF/oQQQgghpN9xpxydAn9CCCGEDGk6oxmljW0419CG0sY2yCQiDI8MwojIIIQFyfr68AjxGgr8CSGEEDIksCyLiuZ2HCppwqFzjSioacW5hjZUq3VOvyY0wA8jIhUYFhmEeSMjsGh0FDX6IAMWw7Is29cH0d+p1WoolUq0tLRQqQ8hhBAygFQ2t2Pb6RocKGnCoZJGVLU4DvIVMgmSwgOQqApAu8GMwrpWlDe1o3OUNDkpFH+7eDQmJIT4/uAJcYMncSpl/AkhhBAyKBXXa3HJf3ajVW8SPicRMRgTp8SUpFCMjVMiKSwASWGBCA3w65LJbzeYcbauFYW1rThe3oLPD5Ti0LkmXP7fPbhsQiweXpKOuBB/4f5mC4ucsib8kV+HPWcbMCpGgb9dNBpyP3Gvfc+EuEIZfzdQxp8QQggZWFiWxcr3s7H3bAOGRwbh0vGxmJwcigkJIQiQ9izvWd2iw8ub8/HNkXKwLCCTiLB6VgpSwgOx40wddhfUo6XdaPc1o2OC8c6NmUhQBXjj2yKkC0/iVAr83UCBPyGEEDKw/O9wOR78+hjkfiJsvm8uEsO8F3ifrGjBs7+cwv6ixi63BcslmJ0WgQnxIXj7j7No0Bqg9PfDaysmYN7ISK8dAyE8Cvy9jAJ/QgghZOBoaNVjwat/oLnNiEeXpuOOucO8/hwsy2LLqRq8ueMsWJbF3LQIzB0ZgfHxIZCIuemplc3tuHPDERwrawbDAPcvTMPd84dDJKLNwcR7KPD3Mgr8CSGEkIFj7Zc5+PZoBdKjFfjpnlnwswbifUFvMuPpn07hs+xSAMDCUZF45ZoJUPr79dkxkcHFkzi17/4SCCGEEEK8bFdBHb49WgGGAf65PKNPg34AkEnEeO6KcXjxqgxIJSJsPV2LZa/twr6zDX16XGRoosCfEEIIIYNCu8GMv353EgBwc1Zyv2q5ec3kBHxzxwzEh/qjorkd1723H0//lAud0dzXh0aGEAr8CSGEEDIovL69AKWNbYhRyvHg4pF9fThdjItXYuN9c3Dd1AQAwId7SrDs9V04WtrUx0dGhgoK/AkhhBAy4J2uUuPdnUUAgKcvHYMgWf8cVRQkk+D5KzPw4S1TEBUsQ1GdFsvf2ouXNuVBb6LsP/Et2tzrBtrcSwghhPRfOqMZ176zD8fKW7BkTDTevjGzrw/JLS1tRjz540l8n1MJgBsulhgWgNTwIAyLCERqRCBSI4IwLk5JQ8CIU9TVx8so8CeEEEL6J73JjD99chg78uugkEmwZe1cRCvlfX1YHvntRBWe/DEXtRq9w9uV/n5YMSUBN0xP6nYQWEu7EUEyCcTUMnTIoMDfyyjwJ4QQQvofg8mCP284jK2nayH3E+HDVVORNSysrw+rRywWFlVqHYrqWlFUp0VxvRZn61qRV61BnfWCQMQAC0dFYdXMZGSlhoFhGDS06rG/qBH7iuqxv6gRhbWtmJIcig9WTUGwnFqGDgUU+HsZBf6EEEJI/2I0W3D3Z0ewKbcGMokIH9w8BbNGhPf1YXmd2cLi97xarN9bgt2F9cLn06KCAABnalodfl1GvBIf3zoVIQHSXjlO0nco8PcyCvwJIYSQnjOYLPhobwlyypuhM5jRbrR+WP9fImIQFiRDWKAUYUFShAXKEB4kRUp4ECYnh3apbzeZLbj3yxz8crwKUrEI7908GXPTIvrou+s9BTUafLSvBN8crkC7TRvQ9GgFpqeGIWtYGEIDpLjj08No1BowKiYYn66eirAgWR8eNfE1Cvy9jAJ/QgghpGeOlzfj4f8dR161pkdf7+8nRtawMMxNi8C8kRGIDw3A2q9y8ENOJfzEDN65MRMXpEd5+aj7t5Z2IzbnViNIJsG01DCoAu2z+gU1Glz/fjbqNHqMiAzChtumITJ4YO17IO6jwN/LKPAnhBBCPKMzmvHvrQV4d+dZWFhAFSjFbbNTEBYohdxPDH8/Mfyl3H8NZgsatQY0tBrQ0KpHg9aA+lY9jpY2d9nwqgqUolFrgETE4M2Vk7BoTHQffYf9W1FdK65/LxvVah1SwgPx2e3TEKP07+vDIj4wYAL/nTt34qWXXsLhw4dRVVWF7777DpdffrlwO8uyePLJJ/Hee++hubkZM2fOxFtvvYURI0YI92lsbMQ999yDn376CSKRCMuXL8drr72GoKAg4T7Hjx/HXXfdhYMHDyIiIgL33HMPHn74YbePkwJ/QgghxH2HzzXiof8dR1GdFgBwyfhYPHXJaI9LTliWxekqDf44U4cd+bU4fK4JJgsLsYjBG9dNxNJxMb44/EGjtKEN1723HxXN7UhQ+eOz26Z32xXIHSzLQmswo7HVgAatHo1aAzQ6E2YOD0eEgsqKepsncWqfTrfQarUYP348br31Vlx55ZVdbn/xxRfx+uuv46OPPkJKSgoef/xxLF68GKdOnYJczi1ZrVy5ElVVVdiyZQuMRiNuueUWrFmzBp999hkA7oexaNEiLFy4EG+//TZOnDiBW2+9FSEhIVizZk2vfr+EEELIYPfK5ny88XshWBaIUMjw7OVjsbiHWXmGYTA6NhijY4Nx57xh0OiM2F/UiAiFDBMSQrx74INQYlgAvrojC9e/tx/nGtqw7PVdePzi0bg6Mx4M4367z0atAXsK67G7oB77ixtQ1aKDwWTpcr+RUQr8cPdMmjnQj/WbUh+GYewy/izLIjY2Fg888AAefPBBAEBLSwuioqKwfv16rFixAqdPn8bo0aNx8OBBTJ48GQCwceNGLFu2DOXl5YiNjcVbb72Fv/71r6iuroZUytXAPfroo/j++++Rl5fn1rFRxp8QQgjp3qbcavzpk8MAgKsy4/H4RaOhDKCWkn2tRq3Dmo8P4Vh5CwBg9ohwPH/lOMSHOs7+G80WHCppws6COuwuqMfJyhY4ihblfiKEBcqgCpTiXIMWap0JN2Ul4e+XjfXlt0M6GTAZf1eKi4tRXV2NhQsXCp9TKpWYNm0a9u3bhxUrVmDfvn0ICQkRgn4AWLhwIUQiEbKzs3HFFVdg3759mDNnjhD0A8DixYvxwgsvoKmpCaGhoV2eW6/XQ6/vqClUq9U++i4JIYSQwaG5zYC/fncSAHDH3GF4dGl6Hx8R4UUFy/HNnTPwwe5ivLLlDHYV1GPxv3bi0WWjsHJqIkQiBm0GE3aeqcfm3Gpsy6tFS7vR7jHSoxWYNTwcs0aEY1hEEMKCpAiQdoSRO/JrserDg/h43znMGh5Oey/6qX4b+FdXVwMAoqLsd+pHRUUJt1VXVyMyMtLudolEApVKZXeflJSULo/B3+Yo8H/++efx9NNPe+cbIYQQQoaAv/98CvWtegyPDMJ9C0d0/wWkV0nEIvxp7jAsHB2FR/53HIfONeHx70/ip2OVCJb7YVdBHfQ25TuqQCnmpkVg9ohwzBoe3m1XoHkjI3H77BS8t6sYD39zHOPilbSZuB/qt4F/X3rsscewdu1a4d9qtRoJCQl9eESEEEJI/7U9rwbfHqkAwwAvXpVBNd792LCIIHz1pyx8vK8EL2zMx4HiRuG2+FB/LB4TjUWjozA5WQWxyP19AADw0OJ07C9qxImKFtz/ZQ423Dbd48cgvtVvA//oaG6JqKamBjExHbv2a2pqMGHCBOE+tbW1dl9nMpnQ2NgofH10dDRqamrs7sP/m79PZzKZDDIZ7UonhBBCuqPWGfF/33IlPqtnpmBSYteVdNK/iEQMVs1MwQXpUVi/twQKuQSLx0RjVIzCo02/nUklIrx+3URc9Pou7C9qxFs7CnH3BbT605+I+voAnElJSUF0dDS2bdsmfE6tViM7OxtZWVkAgKysLDQ3N+Pw4cPCfbZv3w6LxYJp06YJ99m5cyeMxo5atS1btmDkyJEOy3wIIYQQ4r7nfjmNarUOyWEBeGDRyL4+HOKBxLAAPHHJaNx/YRpGxwafV9DPSwkPFDb3/mtrAQ6fazrvxyTe06eBf2trK3JycpCTkwOA29Cbk5OD0tJSMAyD++67D88++yx+/PFHnDhxAjfddBNiY2OFzj+jRo3CkiVLcPvtt+PAgQPYs2cP7r77bqxYsQKxsbEAgOuvvx5SqRSrV69Gbm4uvvzyS7z22mt2pTyEEEII8dyugjp8cbAMAPDC8gz4S6nEhwDLJ8XhsgmxMFtY/OXzo102CpO+06ftPHfs2IH58+d3+fzNN9+M9evXCwO83n33XTQ3N2PWrFl48803kZaWJty3sbERd999t90Ar9dff93pAK/w8HDcc889eOSRR9w+TmrnSQgZigwmCwpqNcitUONkZQtyK9WobG7HpKRQXDgqCvNHRg6KVo3v7yrCd0crMCVZhXkjIzA9NYxq1N3Qqjdh8b92oqK5HTdnJeFpauFIbGh0Rix7fRfKGtsxLk6Jfy4fhzGxyr4+rEFpwEzuHSgo8CeEDBWtehO+PFiG749WIL9aA4O565AenljEYGqyCgtHR2HR6CivTATtbZ8fKMVj356w+5xMIkLWsDDMS4vArBERSA4LgETcbytj+8zj35/EJ/vPIT7UH5vum4NAWb/dNkj6yLGyZtzwQTY0OhPEIga3zU7BfQvSaGXIyyjw9zIK/Akhg11VSzvW7y3BZ9ml0OhMwucVcgnGxioxJjYYY+OUiAyWYU9hPbacqsGZmla7x8iIV+LS8bG4OCMW0cqurf/aDWbsKazHtrwanK7SYFSMApOTVJiSrEKCyt8r9cWe2J5Xg9s/PgyzhcWKKQlgGGBHfh2qWnR295OIGCSqApAcHoiU8EAkhwciIdQfCrkEgTIJAqUSBMm4/5dKhsYFQn61Bkte2wmWBTbcNg0zh4f39SGRfqpGrcPTP+Xi1xNcm/UElT/+cfk4zEmL6OMjGzwo8PcyCvwJIQNZS5sRVep2AIBEJIKfmIFELIKfiEGNWo8P9xTjx2OVMFm400FqRCBunZmCuWkRiA91HpCfa9Bi6+labDlVjYMlTTBbv55hgGkpKlw6Pg7TUlU4WNyIradrsLuwHjqj4xWESIUMk5NDkTUsHFdnxvu81OZYWTNWvLsf7UYzrsqMx0tXZYBhGLAsi/waDXbk12FHfi2Oljbb9TbvToLKH2+tzMTYuMFd0nD7x4ew5VQNlo2LxpsrM/v6cMgAsOVUDZ744aRwYX3FxDg8fvFoqAKl3Xwl6Q4F/l5GgT8hZCBo0hqw9XQNiuu1KG1sQ2ljG841tLm9sW5aigpr5qRi/shIiDzsvV3fqsevJ6rwY04lDrno4hEX4o+FoyIxITEEeVUaHCzhen4bzR2nohnDwvDeTZN9VjpSUq/F8rf2okFrwJy0CHxw82T4OSnlsVhYVKt1KKnXoqhei5J6LUoatKho1kGrN0GrN6FVb7K7OAgPkuKbO2cgKSzQJ8ff1w6fa8Lyt/ZCxACb75+L4ZFB3X8RIeBKCV/ZnI/1e0vAskBSWAA+umUqksMH599Kb/F64D9x4kS3l2CPHDni3lEOIBT4E0L6s9zKFny0twQ/5FQ6zU6rAqVgAJgsLExmC4zW/4pFDJaMjcHts1OQER/ileMpb2rDz8er8ENOJU5XqTE+XomFo6KwYFSUwz7hOqMZx8qacbCkEW/tOAutwYyJiSFYv2qq1zcP17fqsfytvTjX0IaxccH4Yk0WgrxwgWEyW9CoNWDVhwdxqkqNpLAA/O+OGYhQDK6ZMCzL4tp39+NAcSOunZyAF67K6OtDIgNQTlkz7vn8CMoa2xEWKMUHq6ZgQkJIXx/WgOX1wP/pp58W/l+n0+HNN9/E6NGjhX76+/fvR25uLv785z/j+eefP8/D738o8CeE9DdGswUbT1bjo70ldhn2UTHByEwKQZIqEIlhAUgKC0CiKgABUsfBLcuyPq2tN5ktHm2MzSlrxs3rDqCl3YhRMcH4ZPVUhAd5J3huM5hw3bv7cay8BfGh/vj2zzMQqei6F+F81Gp0WP7WXpQ1tmNMbDC+WDMdCvnA73zE25Ffi1UfHoRUIsKOB+chNsS/rw+JDFC1Gh1uXX8QJyvU8PcT482VkzA/PbKvD2tA8mmpz2233YaYmBg888wzdp9/8sknUVZWhnXr1nl+xP0cBf6EkP6iUWvAhv3n8Mn+c6jV6AFwm0+XjovBzVlJyEwK7fVNst6WV63GDe8fQH2rHqnhgfj0tmnnHWAW12tx35c5OFbWjNAAP/zvzhkYFuGbEpXiei2uspYSzRwehnWrpkAm8f6eBZZlcbpKg0251dCZzPjLBSN82lnHYmFx8X9241SVGrfPTsFfLxrts+ciQ0Or3oQ/bziCnWfqIBYxeP6KcbhmSkJfH9aA49PAX6lU4tChQxgxwn4Ec0FBASZPnoyWlhbPj7ifo8CfENLXCms1+GB3Cb49Ui6U80QoZLh+aiKun5aIqGDvZq77WnG9Fje8n42K5nbEhfhjw23TelQHzLIsPjtQimd/Po12oxkKuQTrb5mKzCTfTm4/Ud6CFe/ug9ZgxsUZMXh9xUSP9004YrGwOFrWjE251dh4shqljW3CbenRCrx/82TEh/qmrepPxypxz+dHoZBJsPPh+QilTZnEC4xmCx755ji+PVIBAFh7YRruuWD4gE9g9CZP4lSPUwP+/v7Ys2dPl8B/z549kMsH14mHEEK8Sa0z4ssDZWhsM8BsYYUPk8UCCwsEySRQ+vvZfehNFny6/xz+OFMnPM7YuGCsnpWCi8bFDtr2kSnhgfjqjizc8H42iuu1uPqdfXho8UhcOj7W7Y4/tRodHv3mBLbn1QIAslLD8PI14xHXC+Up4+KVePvGTNy6/iB+Pl6FQKkES8ZFQ+nvhxCb36+rMiizhUVZYxvyazQoqNHgTE0r9hc1CCs9ADdzYE5aBI6WNiGvWoPL/7sH79yYicwklVe/H6PZglc25wMAbp+TSkE/8Ro/sQivXD0eMUo5/vv7Wby65QzaDGY8ujS9rw9tUPI44//Pf/4TTz/9NG6//XZMnToVAJCdnY1169bh8ccfx6OPPuqTA+1LlPEnZGhS64zYdLIaPx6rRHZRI8ZYA+4lY6I9HuhUq9Hh5nUHcbpK3aNjYRjgwlFRWD0rBVNTVEMmG1an0ePGD7KRV60BAIQFSrFyehJumJ7osj5/U241Hvv2BBq1BkjFIjy8ZCRunZnilay7J37IqcC9X+Q4vT1AKoa/nxhyPzHkfiL4S8WQS8RoN5pRWNvqcLO2QibBBaMisXRsNOakRSBAKkFFcztu/+gQTlWpIRWL8NyV43BVZrzXvo8N2efw1+9OIixQip0Pz6dhXcQnPt5Xgid+yAUAvHz1eK++hgczn7fz/Oqrr/Daa6/h9OnTAIBRo0bh3nvvxTXXXNOzI+7nKPAnZOhoN5ixLa8GP+ZUYkd+ncPJtbFKOW6ekYwVUxLd6jpT1tiGGz7IxrmGNkQoZLg4IwZ+YhHEIgYSEQMRw4BhAK3ehOY2I1raOz50RjPmjYzELTOTB217yO606k3YsP8cPtpbgkprD3A/MYNLMmJxxaQ4aHQmoX1pmbWFKV8Ckx6twL9XTEB6dN+9d/94rBJfHixFc5sRzW1GqNuN0OhN3X8huIz+8MggpEUpMCIqCGNjlZiWqnK4Z6DNYML9X+ZgU24NAOBPc1Lx8JJ0iM/zYqfdYMbcl35HrUaPpy4ZjVUzU87r8Qhx5dXN+Xh9eyGkYhG++NN0TEr0bVneYEB9/L2MAn9CBieLhUVRfSuOl7fgeHkLTlS04GRFi12WdURkEC4dH4u5IyOw7XQtPt1/Dg1aAwAuW3tVZjxuykp22sv8TI0GN36QjRq1Hgkqf3y6etqQDeDPl8lswabcGny4p9jlrAAAEDFcScraC9N8srH2fJnMFqh1JqjbjdCZzNAZLWg3mLn/N5jhJ+YC/gRVgEeBu8XC4t9bz+D17YUAgAXpkXj9uonnlaF/+4+z+OdveYgP9ce2B+b2y58nGTwsFhZ3fHoYm0/VIEIhw493z0SMkrpHudIrgb/BYEBtbS0sFvtsWGJiYk8erl+jwJ+QnmtpN2JzbjW259UiNFCKeWkRmDk8vE9LBcoa2/D4DydxsLgRWoO5y+0JKn9ckhGLSyfEYmSUfd95ndGMH49VYt3uYqH8BOCGTt0wPQkXjo4ShkEdLW3CLesPornNiJFRCny8euqg24TbV46XN2Pdbu4CICpYjkRVABJUXOvSRFUAUiMCvdYGdCD68VglHvr6GPQmCzLilVi3akqPfh4nK1pw7TvcJuVXrxmPKydR6QXxPa3ehOVv7UVetQbj4pT4+o4sn0/zHsh8GvgXFBTg1ltvxd69e+0+z/eCNpu7nkQHOgr8CfFMm8GEradr8dOxSvzhoFxGKhZhaooK80ZGYN7ISAyLCOy1mvWqlnZc/fY+lDe1AwDkfiKMjVViXLwS4+NDMDZO6dbxsCyLfWcbsG5PCbbn1cBifSeNVMiwYmoiRkQG4ZFvjqPNOozqw1VTEBJAGyJJ7zla2oRb1x9EU5uxRxNSy5vacMWbe1Gn0SMrNQyf3jbtvMuGCHFXWWMbLn1jN5rajLhkfCxeXzFhyOxt8pRPA/+ZM2dCIpHg0UcfRUxMTJdfwvjx4z0/4n6OAn9C3JNXrcbbO85iU24N2o0dSYARkUFYNi4GzW0G/J5fZ9eCEACig+WYkqLC1ORQTElRIS1S4ZNNmHUaPa59Zx+K6rVIDgvAG9dPQnq0wuONup1VNLfj8+xSfHGwFPWtBrvbZo8Ix9s3ZNJmSNIniupacfOHB4QJqetWTcF4NyakNrcZcNXb+1BY24qRUQp8dUcWlP6DZxAZGRj2FzXghvezYbKweGjxSNw1f3hfH1K/5NPAPzAwEIcPH0Z6+tBps0SBPyGuna5S4/VtBfjtZLXwuURVAC4dH4tLxsdiZLRC+DzLsiiq12JHfh125Nciu6ixy4qA0t8Pk5NCMTExBGNilRgTF3zeE1abtAaseHc/8ms0iAvxx1d3ZHm9raPBZMGm3Gp8sv8cDhQ34qKMGLx6zXiqiSZ9qsuE1BsmYf5I5xNSdUYzbvrgAA6UNCI6WI5v/zyDJvSSPsN3lGIY4N0bJ+PC0VF9fUj9jk8D/ylTpuBf//oXZs2adV4HOZBQ4E+IY6cquYB/Yy4X8DMMsGxcDG6fnYrx8Uq3lmXbDWYcLWvCweImHCxpxJHSJrQ5qLuPVMgwJjYYY+OUuGxCnNPNtI6odUasfC8bJypaEKmQ4es7sny+wbal3YhguYSWpkm/0Ko34c5PD2NXQT3EIgZPXToGV2fGd6mbtlhY3PP5UfxyogoKmQRf35nVpx2RCAGAx78/iU/2n8OY2GD8dPesXm/L29/5NPDfvn07/va3v+G5557DuHHj4Odnv/Q3GANjCvyJt5U3teH7o9yUwqXjYjAswv0gti9YLCxqNXoU12tR0qBFSb0Wp6rU2FVQD4AL+C8aF4O/LBiBtChFN4/mmtFswalKNQ6WNOJkRQtOVqpRVNcq1NAD3B6BhxaPxOpZ3fdl1+pNuGndARw+14SwQCm+/NN0DI88v2MkZCAymCx49Jvj+Nb63iP3E2HW8HAsGBWFC9IjERUsx7M/n8L7u4vhJ2bw0S1TMWN4eB8fNSHceeHVLWfwpzmptFfKAZ8G/iIRVwvbOYtFm3sJcc1sYbHzTB0+3X8Ov+fX2gWyY2KDccn4WFycEYP40IC+O8hOatQ6PPDVMRw61widsWs/e4YBLs6IxV8uGI4R5xnwu9JmMOF0lQa5lS3YcqpGuOCYlqLCK9eMd/ozK6xtxd++P4H9RY0IlkvwxZosjI6lv2EydLEsizd3nMWn+8+hyjoTgTcsIhBn67QAgNdWTMBlE+L64hAJIR7yaeD/xx9/uLx97ty5njzcgECBPzkfDa16fHmoDJ9llwqdZACu/aNMIsKugnqYbK4CMpNCcd3URCyfFNenZSLVLTpc995+FNdzgYBYxCA+1B/JYYFICQ9EclgAZo2I8KjkxhtYlsUXB8vwzM+n0GYwI0gmwZOXjMZVmfFgGAbNbQb8dKwS/ztSgWNlzQCAIJkEn942DRPc2NRIyFDAsixOV2mw7XQNtuXV4lh5M/ho4JEl6bhz3rC+PUBCiNtogJeXUeBPeupEeQtuXJeN5jYjAG7T6lWZ8bh+WqJQ3tOoNWDjyWr8dKwS+4sbhJPvjGFheGF5BhJUvb8CUNXSjuve3Y+ShjbEhfjjnRszMTJaIfSn7w/ONWix9qtjOGwd5LRwVBT8xAy2na4VNguLRQzmj4zAvQvSMC5e2ZeHS0i/VqfRY0d+LaQSES4dH0t7UwgZQHwe+O/atQvvvPMOioqK8PXXXyMuLg6ffPIJUlJSBuWmXwr8SU8cL2/GDe9nQ60zIS0qCLfPTsUl42NdDiGpUevwv8Pl+M/2AuiMFvj7ifHIkpG4KSu51zYzVTa347r39uNcQxviQ/3xxZrp/ar8yJbZwuKdnWfxry1nYDR3vJWNignG8klxuGxCHCIUQ3eIEyGEkMHPp4H/N998gxtvvBErV67EJ598glOnTiE1NRVvvPEGfv31V/z666/ndfD9EQX+xFPHyppxwwfZ0OhMmJwUivW3TkWQB33cS+q1eOSb48gubgQATEkOxQvLM5Dq403AFc1cpr+0sQ0JKn98fnv/Dfpt5Va24F9bziBRFYjlmXEYE0vZfUIIIUODTwP/iRMn4v7778dNN90EhUKBY8eOITU1FUePHsXSpUtRXV3d/YMMMBT4E08cLW3CTR8cgEZvwpTkUHx4i2dBP89iYbHhQCn++etpaA1myCQi/O2iUbgxK9n7Bw2u09B17+1HWWM7ElUB+HzNdK/3uSeEEEKId3kSp3pcsJufn485c+Z0+bxSqURzc7OnD0fIoHLEJuifmqzqcdAPACIRgxunJ2HT/XMwe0Q49CYLHv8hF78cr/LyUXOj0Ve8ywX9SWEB+IKCfkIIIWTQ8Tjwj46ORmFhYZfP7969G6mpqV45KEIGosPnbIL+FBU+vGVKj4N+W/GhAfj41qm4bVYKAOCh/x1DfrXmvB+XV1TXimve2Yfypo6gn6Z0EkIIIYOPx4H/7bffjnvvvRfZ2dlgGAaVlZXYsGEDHnzwQdx5552+OEZC+r0zNRqsWncArXoTpqeqsP6WKQj0QtDPYxgGjy5Nx6zh4WgzmLHmk0NosXYKOh951Wpc885+VLXoMDwyCF/9KQsxSgr6CSGEkMHI48jk0UcfhcViwYIFC9DW1oY5c+ZAJpPhwQcfxD333OOLYySkX6tv1ePW9QeFmv51q6YgQOq9oJ8nEYvwn+sm4pI3duNcQxvu/fIoPrh5CsQ97PZzvLwZN607gOY2I0bFBOPT1VMRFkQdcAghhJDByqPNvWazGXv27EFGRgYCAgJQWFiI1tZWjB49GkFBvTvEpzfR5l7ijM5oxvXv7ceR0mYkhQXguz/PhCrQt+PEcytbsPytvdAZLbh7/nA8uHikx49xqKQRt3zIXayMTwjBx7dMhTLAzwdHSwghhBBf8tnmXrFYjEWLFqGpqQlSqRSjR4/G1KlTB3XQT4gzLMvi4f8dx5HSZgTLJVi3aorPg34AGBOrxD+vzAAAvPF7ITae9Gyz797CetxoswH509UU9BNCCCFDgcc1/mPHjkVRUZEvjoWQAeXfWwvw47FKSEQM3r4xU5jE2xsunxiHW2dym30f+OoYCmq63+xrMFnw2tYC3PzhAbQbzZg9Ihwf3ToVCjkF/YQQQshQ4HHg/+yzz+LBBx/Ezz//jKqqKqjVarsPQoaC749W4LVtBQCAf1wxFjOGhff6MTy2LB3TU1XQGsy45p19+M+2Aqcbfo+VNeOS/+zGv7ZyE24vGheD92+eDH+p8ynChBBCCBlc3K7x//vf/44HHngACoWi44uZjk2FLMuCYRiYzWbvH2Ufoxp/YutQSSOufy8bBrMFf5qbiseWjuqzY2lo1WPFu/tRUNsKAAiUirFyehJum5WCyGA52g1mvLolHx/sLoaFBVSBUjx16RhckhFj9/dLCCGEkIHJJ5N7xWIxqqqqcPr0aZf3mzt3rvtHOkBQ4E9a2ozYlFuNn45XYu/ZBpgtLBaPicJbKzMh6mFXHW8xmS345UQV3tpxFnnW/v5SsQiXTYhFdnEjShvbAACXT4jFE5eM6ZV9CIQQQgjpHT4J/EUiEaqrqxEZGemVgxxIKPAfmrR6E7acqsFPxyqxs6AORnPHn8qctAi8fcMkn7Tt7CmWZfF7fi3e2nEWB0uahM/HKOX4xxVjcUF6VB8eHSGEEEJ8wZM41aOohUoDyFBR1tiGFe/uR0Vzu/C5kVEKXDI+BhdnxCI5PLAPj84xhmFwQXoULkiPwsGSRqzfW4LoYDnuWziCNvASQgghxLPAPy0trdvgv7Gx8bwOiJC+Vt2iw/Xvc0F/jFKOqzPjcfH4WKRFKbr/4n5iSrIKU5JVfX0YhBBCCOlHPAr8n376aSiVSl8dCyF9rr5Vj5Xv70dZYzuSwgLw1Z+yEBUs7+vDIoQQQgg5bx4F/itWrBiSNf5kaGhpM+LGDw7gbJ0WsUo5Ntw2jYJ+QgghhAwabvfxp/p+Mpi16k1Ytf4ATlepER4kw6e3TUN8aEBfHxYhhBBCiNe4Hfi72fyHkAFHZzTjto8O4mhpM0IC/PDpbVOR2otTeAkhhBBCeoPbpT4Wi8WXx0FInzCYLLjz08PYX9SIIJkEH986FenR1LKVEEIIIYOP2xn/vqLRaHDfffchKSkJ/v7+mDFjBg4ePCjczrIsnnjiCcTExMDf3x8LFy5EQUGB3WM0NjZi5cqVCA4ORkhICFavXo3W1tbe/lZIP2O2sHjg62P4Pb8Ocj8R1q2agoz4kL4+LEIIIYQQn+j3gf9tt92GLVu24JNPPsGJEyewaNEiLFy4EBUVFQCAF198Ea+//jrefvttZGdnIzAwEIsXL4ZOpxMeY+XKlcjNzcWWLVvw888/Y+fOnVizZk1ffUukH2BZFk/+eBI/HauEn5jB2zdkYmoKtb8khBBCyODl9uTevtDe3g6FQoEffvgBF110kfD5zMxMLF26FM888wxiY2PxwAMP4MEHHwQAtLS0ICoqCuvXr8eKFStw+vRpjB49GgcPHsTkyZMBABs3bsSyZctQXl6O2NjYbo+DJvcOPi9vyscbvxeCYYDXV0zEJeO7fx0QQgghhPQ3nsSp/TrjbzKZYDabIZfbt1T09/fH7t27UVxcjOrqaixcuFC4TalUYtq0adi3bx8AYN++fQgJCRGCfgBYuHAhRCIRsrOzHT6vXq+HWq22+yCDx/u7ivDG74UAgGcvH0tBPyGEEEKGhH4d+CsUCmRlZeGZZ55BZWUlzGYzPv30U+zbtw9VVVWorq4GAERFRdl9XVRUlHBbdXV1l9kDEokEKpVKuE9nzz//PJRKpfCRkJDgg++O9IWvDpXh2V9OAwAeWjwSK6cl9fEREUIIIYT0jn4d+APAJ598ApZlERcXB5lMhtdffx3XXXcdRCLfHfpjjz2GlpYW4aOsrMxnz0V6z68nqvDoN8cBALfPTsGf5w3r4yMihBBCCOk9Hk3u7QvDhg3DH3/8Aa1WC7VajZiYGFx77bVITU1FdHQ0AKCmpgYxMTHC19TU1GDChAkAgOjoaNTW1to9pslkQmNjo/D1nclkMshkMt98Q6TXFdW14p+/5WHzqRoAwDWT4/F/y0bRUDpCCCGEDCn9PuPPCwwMRExMDJqamrBp0yZcdtllSElJQXR0NLZt2ybcT61WIzs7G1lZWQCArKwsNDc34/Dhw8J9tm/fDovFgmnTpvX690F6T6PWgCd/OIlF/9qJzadqIBYxWDUjGc9dMY6CfkIIIYQMOf0+479p0yawLIuRI0eisLAQDz30ENLT03HLLbeAYRjcd999ePbZZzFixAikpKTg8ccfR2xsLC6//HIAwKhRo7BkyRLcfvvtePvtt2E0GnH33XdjxYoVbnX0IQOPzmjG+r0l+O/2Qmj0JgDAgvRIPLYsHcMjFX18dIQQQgghfaPfB/4tLS147LHHUF5eDpVKheXLl+Mf//gH/Pz8AAAPP/wwtFot1qxZg+bmZsyaNQsbN2606wS0YcMG3H333ViwYAFEIhGWL1+O119/va++JeIjLMti48lq/OPX0yhvagcAjIkNxl+XjcKM4eF9fHSEEEIIIX2rX/fx7y+oj3//l1etxtM/nsK+ogYAQHSwHA8uHokrJ8ZBJKKyHkIIIYQMTp7Eqf0+40+IK01aA17dcgYbss/BwgJSiQh3zEnFHfOGIUBKL29CCCGEEB5FRqTfs1hYlDa2oUFrQEOrHo1aAxq0BtSqdfg+pxIt7UYAwNKx0fi/ZaOQoAro4yMmhBBCCOl/KPAn/VqdRo9VHx5AbqXz6cnp0Qo8cclozBhGdfyEEEIIIc5Q4E/6rVqNDte/l43C2lZIJSJEBcugCpQhPFAKVaAUqiAp0qMVuCQjFhLxgOlMSwghhBDSJyjwJ/1SjVqH697bj6I6LWKUcnx++3Qkhwf29WERQgghhAxYFPiTfqe6hQv6i+u1iFXK8fma6UgKo6CfEEIIIeR8UOBP+pWqlnZc9+5+lDS0IS7EH1+smU6bdQkhhBBCvIACf9JvVDRzQX9pYxviQ/3x+e0U9BNCCCGEeAsF/sRnduTXIqesGVNTVJiUGAq5n7jLfUxmC3YV1OObI+XYcqoGepMFiaoAfL5mOuJC/PvgqAkhhBBCBicK/InXsSyLd3YW4Z+/5Qmfk0lEmJKswozhYZg5LBxiEYPvjlbgh5wK1LcahPuNi1PinRszEUtBPyGEEEKIV1HgT7zKaLbgiR9O4vMDZQCA6akqFNVpUavRY3dhPXYX1gPIt/uasEApLhkfi+WT4jE2LhgMw/TBkRNCCCGEDG4U+BOvUeuMuGvDEewqqIeIAR6/eDRumZkClmVRWNuKPYX12HO2AfvPNkBvsuDC0VG4clIc5qRFwI/68BNCCCGE+BQF/sQryhrbcOv6gyiobUWAVIz/XDcRC0ZFAQAYhsGIKAVGRCmwamYKzBYWZgsLqYSCfUIIIYSQ3kKBPzlvOWXNuO2jQ6hv1SMqWIYPbp6CsXFKp/cXixiIRVTOQwghhBDSmyjwJ+elUWvAyvf2Q2swY1RMMNatmowYJW3MJYQQQgjpbyjwJ+dFFSjFw0vSsSO/Fv+5fhKCZPSSIoQQQgjpjxiWZdm+Poj+Tq1WQ6lUoqWlBcHBwX19OP2SxcJCROU7hBBCCCG9ypM4lXZXEq+goJ8QQgghpH+jugw38IsiarW6j4+EEEIIIYSQDnx86k4RDwX+bmhoaAAAJCQk9PGREEIIIYQQ0pVGo/n/9s47vo36/OOfk2TJ2453nDixsxMSMiGEsBOgIaWlUKApZbXQHxRaIB2QDigUSGnLKGXTMlrKLGHPEBJCICHDcfZ0nNjx3pKHZI37/XG600m6O51k6U7jeb9eeYHls/W17nvP83yfibw8+a6KABn+qigoKAAA1NfXh/xAicTDarWioqICDQ0NVMORhND9TV7o3iY3dH+TG7q/0YNlWdhsNpSXl4e8lgx/FRgMXClEXl4ebc4kJjc3l+5vEkP3N3mhe5vc0P1Nbuj+Rge1jmkq7iUIgiAIgiCIFIAMf4IgCIIgCIJIAcjwV4HFYsFdd90Fi8Wi91KIGED3N7mh+5u80L1Nbuj+Jjd0f/WBBngRBEEQBEEQRApAHn+CIAiCIAiCSAHI8CcIgiAIgiCIFIAMf4IgCIIgCIJIAcjwJwiCIAiCIIgUgAx/giAIgiAIgkgByPAnCIIgCIIgiBSADH+CIAiCIAiCSAHI8CcIgiAIgiCIFIAMf4IgCIIgCIJIAcjwJwiCIAiCIIgUgAx/giAIgiAIgkgByPAnCIIgCIIgiBSADH+CIAiCIAiCSAHI8CcIgiAIgiCIFIAMf4IgCIIgCIJIAcjwJwiCIAiCIIgUgAx/giAIgiAIgkgByPAnCIIgCIIgiBSADH+CIAiCIAiCSAHI8CcIgiAIgiCIFIAMf4IgCIIgCIJIAUx6LyAR8Hg8aGpqQk5ODhiG0Xs5BEEQBEEQBAEAYFkWNpsN5eXlMBiUffpk+KugqakJFRUVei+DIAiCIAiCICRpaGjA6NGjFa8hw18FOTk5ALgPNDc3V+fVEARBEARBEASH1WpFRUWFYK8qQYa/Cvj0ntzcXDL8CYIgCIIgiLhDTTo6FfcScQXLsnovgUhQaO8Qw4H2DxEptHeI4aD1/kk4w3/9+vW48MILUV5eDoZh8Pbbbytev2rVKpx77rkoLi5Gbm4uFixYgE8++USbxSYoz6yvxXcf/wq9A05N33f5azU486/r0Odwafq+RPToc7hw8RNf4bHPD2n6vvWdA5h372d4ePVBTd+XiC4f7GzGtx5Zj8NtNk3f9x9rDmHuvZ+hrqNf0/cloofHw+L//rMVv35jh6bv2+9w4ey/rcMvXtmu6fsS0WV7fTe+9ch6rD/Yrun7vlPTiJl3f4ovD2n3vgln+Pf392PmzJl4/PHHVV2/fv16nHvuufjwww+xbds2nH322bjwwguxfTs9pHK8+PUx7Gjowaa6Ts3ek2VZvL+rGfVdA9jd2KvZ+xLR5Zsjnaiu78FLm+o1fd+vazvQ2T+Ej3e3aPq+RHR5bWsD9rfY8OneVk3f98PdLejqH8LXtR2avi8RPeq7BvDJnla8se04+jV0Hu1ttuJo5wA+2t0Mj4c8/4nKuzuasL/FhndqmjR939V7W2G1u7B2v3aGf8Ll+C9ZsgRLlixRff0jjzzi9/X999+Pd955B++99x5mz54t+TMOhwMOh0P42mq1RrTWRGTI5UFz7yAAoN3mCHF19OjoG8KQywMAwvsTicexzgEAQEefAx4PC4NBm/a3jT3cnmmivZPQ1HdyHnctZQ8ANHZz+7a5x67p+xLR41jXgPD/7TYHsizamDeN3ZzMcbpZdPQ7UJKTrsn7EtGl3qu72vs0lj1e3aWl3ZNwHv/h4vF4YLPZUFBQIHvNypUrkZeXJ/xL9Faebg+Lrv4hVdc29gyCd1q0aah8m3oGRf9Pyjee6B1wwun2qLq23qt8XR4W3QPq9lw04IWnze6Cza5tihohD8uy6FCpSN0eFse9RpSWssdmd8Jq5zzEYjlE6M/AkAsDQ+q89/Uiw1/L/dNIuitu6ehzqM6f5/dPm1Xbe8jLHC1lT8oZ/n/729/Q19eHyy67TPaaFStWoLe3V/jX0NCg4Qqjz6//twPz7l2NAy2h82aPdfpyXLX0uvkb/qR844U2mx2nrFyDa57frOp6sfLV0nMi3jPNvaR844Un1tVi3r2f4ZM9oVOwmnoG4fJ6HbSUPeL9QhGj+MHp9uDch9bj3IfWw6XC8VAfB7qrmXRX3PDx7mbMu/czPPlFbchrPR5W0F1qHRXRYMjlEQ6pTRrqrZQy/F9++WXcfffdeP3111FSUiJ7ncViEVp3JkMLz68Pd8LDAluOdoW8tsEvXKrdRmwkwz8u2V7fg0GnG5vruuBWkb/q53Wz6uN1a6T9EzfwOfObjoSuF2oISNXQCj5VAyCPbTxxrHMAjT2DaOwZFCJBSvh7/PXRXSR74oevazmZs+lIaLunvc8BhzfVuLN/SNVBMxq09NrBByTabQ44XG5N3jdlDP9XX30V1113HV5//XUsXrxY7+VoRp/DhRZv6EpNxwo+RxvQWPmSxzYuqW3vA8DlrzaGUL5irwmg3f5xe1i0iPYM5WnHD7VtnMxRJXv0MvxFsqel104FmnECL3uA+NZdFG2MT/j9U9fRF+JK/73DspzxrwWBB8XWXm32bUoY/q+88gquvfZavPLKK1i6dKney9GUunafwDyqQnjqlSfZRF6TuOSIaP/UdSrvnzabQyjQ5r/Wgo4+B5xun7FGEaP4QOx0CFf29DnU53YPF/F+GXJ70NGvbXEfIY2f7Amxf1iW9YsYaSV7WJYNiBiR7IkX+P3T2D0Y0pMulj2AdtHqwP2ile2TcIZ/X18fampqUFNTAwCoq6tDTU0N6uu59oErVqzAVVddJVz/8ssv46qrrsKDDz6I+fPno6WlBS0tLejtTY2WkeF6TQI9tlp5v8Qbngo04we//dOu7Dk5FnAw0CrcHpgGQHna8YHY6dDQPeh3KJSivlMf5RuobCliFB+Eo7s6+4fQP+Qz7rQy/K2DLr/31TJPm5Cnz+ESoi8e1j+NUIp6nXRXkOzRSHclnOG/detWzJ49W2jFuXz5csyePRt33nknAKC5uVk4BADAM888A5fLhZtuugkjR44U/t1yyy26rF9rxMKzvmtAMXeNZf1TNVweFj2D2hjggbm1FDLVH5ZlUdumXvkGek20CrcHek3I6xYfiGWP28OioTuE8g3cPxoV2dH+iU/CMfz1kj2BhhvtnfhA7HQA/KNHUqSa7kq4Pv5nnXWWYnumF154we/rdevWxXZBcY54w7u87fIqi7Ikr+3oG8LAkBsMA2SbTbA5XGiz2VGQZY7pGgeH3EK70fK8dDT12tHUM4hJpTkxfV9Cmc7+IaHNIQDUdaoz3HLTTbDaXZp53Xhhye8dOjTGB0cCIkRHO/oxvjhb9no+YiTsH83C7dx+EWQP7R/dYVk2rFQfPlrE7x2tGlMEyh6+QNNiMmry/oQ0RwLy+o+GSFM9ppPuagzYP1rJnoTz+BPhURugfJUEKG+4jcxNR3l+BgBtTr785s+2mDC5jDP2qbuG/oi9/UDoIil+/8wdOwIA0KGx8JxXyc3maO6hAs14oDbAy6Yke3oHfL3053j3jxbGm8vtEeoQ+P1DXlv96ewfQq8o2tzUOwi7Uz5PO1D2aNWZhZc900flwWzizCmtCjQJeYJ1l7Lh3xCwf7SOGGkte8jwT2LcHhZHvBv+hHKuJekRhQeA3/xjCjNRnGMBoE2eLb/ZR+VnCAcOmt6rP7zhxu+dUEVSvPLlhZjWHv+5Y0eAYbgCTa26MhDy8E4HNbKH3zvFORaMLcgEoM3+abU54PawSDMyOHF0HgCSPfEA7+2vKMhATroJLOvfeSUQfv/MqhgBA6NdZxZe9owekYnyPG5iL9UY6U9toN2jkOrT73Cho4/bKz7dFXunA8uywv45qZI7cGhVX0SGfxLT1MMV1JlNBpw2sQiAcncNXrCOKchECW/4D0P5Hm7rw5p9rarWCQDl+b5IA3X20R8+VWN+VSGyLaaQRVJ8uH2e12synM4sLMvio13NIYuyAKDRKyzHFPr2LXlt9cXtYQUv2+KppQBCyJ4u7ntjCjJRkssZUMORPa1WO97d0RRy9gS/T0bmZWD0CO7A0UjRRt3hD43ji7Mxzpuaqhit9sqeyqJMFGUP32m14VAH9jSFbgDSKKG7SPboD+/xF2SPQqoPf2jMz0zD+GJurw1H9tjsTrxT04h+h7Lu6x5wwu7kolJ8lJM8/sSwOewVnlWFWUJurZpUn7GFWSjO5YTncEJeN79cjZ+8uBX7mq2K1/mEZwbK8zmlT5019EdQviVZqCzijCI5z0mfwyV42KaW5yIjjctxjXT/bK7rwo3/rcYv39gR8tpGb9HoqPwMjMyjiFE80NQzCIfX6XC61+mgSvYU+KKNw5E997y3F794ZTs+3q08MZhvxcgZbrzsob2jN7zhNq4oW6hJU6u7Snjd1ReZDmm12nHVc9/g6uc2K9YTAj7d5S97SHfpiUfkdFg0lRvU2mp1yBri/rKHkwHDkT3Prj+CW16twb821Clex8ue4hwLKgu5PW5zuGDVoKMhGf5JDC88x5dkqfOadPHh1UwU816TCENebg+Lw973392o7DkRhOeIDJR7hSeFS/WHT/UZX5yNqiLu4CjnOeE9biMy05CbnuZLFYtQgB4U7R0l5Wuz+3LDy/MzMEqIGJHy1ZNaCadDc68dg0PSqWL8/qkQGf7D8bodbLUBAHaplT35mb66pj5HyNajRGzh08LGl2Shyqu75CJGdqdbqNMYI9ZdEXr8a9v74GG5Zhf875WjSaS7RnkPjhSt1pdG3ulgNOCE8jyhOUko3VURkOkQ6tAnx8FWTvaplz0ZyLKYkJeRBkAbpycZ/kmMIDyLfV4TpSIp8cmXD7dHevJts9nh8obZD7cpF4VK5/hTgaae2J1uHPd60scXZ6OqkPP4yx0c64X6EG6flQzTa8t7QwaG3IqdDnjvWl5GGrItJozMI69tPCAcGkuyMCLLLCg1WeUreGwzh713WJYVlOrhNpvitT7Zk47CLDPMJgNYlvP6EvohTvWpCuG04uVUjsWEEZlpKBmm11Y8kOtQq7zuGnJ5hMNpeX4GRvK6i2SPrghOh6IsGA0MKlXqrrGi2sYhl8evo104+GSPersHgKC7tHB6kuGfxAjh0uIsFGaZFYuk7E43Wr0eErHXJBrCM9QDIE71Kc1N5wo0XVSgqSfHOgfgYYGcdBOKss2o8uY+yqX61ItytAGIisMjM6DEXjOl/eNL1cjw+y9FjPSFV77jvJGiUMabuL6I3zud/Y6IOrP0DDgx4I0shCN7GIYRCjTJa6sfDpdbqO0ZV+zz+MsVhx8TeWwZhhl2xEit7GnptYNlAYvJgMIssyjHnw6NesI7HcZ5dRYfrQ7s7c/Dt/IcU5CJ9DQjctK5LveRdhXj98+xzn7FZhji+hDAdwDQIs+fDP8kRpyqwTCMYroPL2hz0k3Iz0wT8iSjITwPKQhPt4dFi9drOyo/A2aTQTh0UJ62fog9bgzDhE71EUWLAAy7OLxRNOzpUKu817YxwGvCC1FSvvpyRFQfAkBR9gy5PMKzPqYwE4VZlmF1ZhHLnvquAcU2kOJUDQBUIxIHiJ0OxdkWIVrd0eeQnOgu9tgCEOmuCA23bnW6Syx7xIdGcjroyxGR7gJ8B4A6Gd0ldDMs8I9WR5IqNjDkEmYSeVjl1Oogj7+G9Y1k+CcpvYNOdHgnX/IeE6UiKbHHjWEYYfNH2pnluEh4NnTLK9+OPgecbhZGg+89qTuC/vDCU/CaeFN45IqkxPsHwLBTxcTGW+AsCjHiVA2A9k68IHjdvAdGJdnT2DMIDwtkpBlRnG2B0cAInVki2T9i2aOkfFmWlY8Y0cFRN3yRas7pkJuehqJsb552R3C0Okj2DDfNUCx7FAz/ph7/vcOn+tjsLskDCqENtQG6iy+clZIDbg8rpIqN4Q+OfKpYBJPDA/WOUsQocP9oqbvI8E9SeMOtNNeCnHQuv9YXbg/ejIFek2yLCelp3PaIRICKhSfLyhtvvJIuy02Hyci9H3lt9UccLQKAvMw0oUhKKWLEC09fcXj4e0ecOwso59k2BirfPCrQ1Bur3SnIDF+4XV751nf5Ox0AUapYBF7bwDQdOa+tddCFfm9KEN9UwCd76OCoF77aNN+EeV+6T/C9DJI9UUz1OaRQIxKYqpFtMSHXmyZCnX3040iA7lKSPc29g3C6WZiNBpR5nVXDmWEkdjoA4ekuLRubmGL5yx999FHV1/7iF7+I4UpSj8DND0DUHSHYa8Ir3wqv14Tz+qejvmsA7TYHxhZmBf2MEo3dwSffE8rzgq4LDHcBogeAlK9uBIZLAW7/dPUP4WhnP6aP8t1Ll9sjCDwhx38Y7WCbewchbqhwqK0PLMsKRqGYwFQNvkBzyOVBq9Uu7GdCO3jZI+V0kOrMUt/p6ybGU5JjwR5E6HSQkD2S13n3TmGWGRlmrv0sRYz0R+hGFyB7thztlvb4dwV6/H3RRjm5IYfHw/qlWnQPONHZ50Ch15Ehxqe7fPu2PD8D1hYbGnsGMak0R/X7EtHBancKBz7B4+9tRd0z4ER3/xBGeB1YgK+jz+gRGTAauH0iRIwi8PgHOh0Oyzg87U63MDRs9Ajto40xNfwffvhhVdcxDEOGf5QJDHcBUCyS8uVo+64vzrGgvmsgIs8J/wBUFGSgoWtQVvk2BXhNAF/IlLwm+sCyrMjj79sPlYVZ2HasO6hIqrmX6+Dk5zUZhsefN9xG5WegqXfQm7Y2JHhixPBCkheaBgODkXnpONY5gKaeQTL8dUDcg52HT/Xp7B9C76BT6PIDBEcbgeF53Rp7eCcGL3ukvbaBoXbA11mDZI9++OqLRLJHJlrt8bCCx5/XXfzecXg7s4j3Wija+xwYcntgNDAozbGgqdeOQ219koZ/oMef+/8M7G+x0RwaneCdDiU5PqdDptmEstx0tFjtqOvs9zf8A6JFwPAaU/C6S5A9Mh5/Xr5kmo3C/uRlT4u3o6HBoP7AGi4xTfWpq6tT9e/IkSOxXEZKUivhsVUqkjrW6d+VBRAXuYT3AIhzZ8+axA3QCOV1Eytf6oesL202B/ocLhgNjJ9AlCuS4oXn6IIMQVjxBXaRdGY57r3v44qzUOGdpiq1f1xuj9BnWzJiREV2usCnY/CFvQCXBsHLk0Cvf2CONuDz2g7H6aBe9vgMN98cCNo7esCyrGS0WigOD+hI12ZzwOHiDHW+OHI4nVnEqaeTyziPfaj94yd7KFVMV6Qi1YB8xDEwWgRgWI1NAmVPXUe/pP4T1xbxEamyPG9HQ7cHHf2RzzBRA+X4JylSwpMrkuKVr0+AejwsGgJSNYDIQ17dA04Meot5+amdcnm2gakaAHXW0Bv+0DimIBMWk1F4XS5Xsl5CeIo7s3SF2ZmFF4qjR2RgYgm3f6W8tq02B9weFmlGRogwAL7uCFQjog+1bcGyBwhv/5QMI1WM3z9nTioW3k9K+UqlaogLNPtkJn0SsaPd5oBNwunga8nY5zdYid875fnpSDP6zJlIu4qJjfmJpfKGP8uyirqLnA76UNse7HQAILSjViV7hjEHgpc9J1UVID3NgCG3R3gPMVIpzmlGg7BvYx0ximmqTyDHjx/Hu+++i/r6egwN+RsDDz30kJZLSWpcbo/QdlGc6gMAVUWZ6Ohz4EhHH2aM5vK0W212DHm9JmLvV6ThdvEo6hO8ueBHO/rhdHv8hDPgm7Ba7uc14f6/zeaQ/Bkitvg6svjvHbnuCLzHdqxIeBoNDAqzLWi3OdBmcwhdftQgVr656WlYs79NUvnywnNkXoZfWHRUPh0c9cSXZhhs+H9T1+WXasiyrHS4PcLJ4QNDLnQPcNHMk6oKkJFmxKDTjWNdA0EHESmPP1+gabW70NwzKBh/hDbwOdEVIzL8nA58GpjVzrVL5FNv+Ei1OEUV4HRPbXt/2MabkGY4IgMTinmnQ7Ds6R5wwu7kDpNlecERI0r10Qfe6SBOMwR8XekC05zrJaKNwykOF1KcR2RgfHE29jRZcbitL0gWSmU68F+3Wh1o7h3EzIr8sN9fLZpZVGvWrMHkyZPx5JNP4sEHH8TatWvx/PPP47nnnkNNTY1Wy0gJGrq5SvX0NIOQ9sAjVeDLb/5R+RlCZx0g8rZWfI7tqPwMlOelI8tshMvDCkLa79pu37U8hVlmmI3cBM0WyrXVHKG4rsRfWAUWSfE0BBSG80TaVs9P+XrXIBUx8oVL/Q8VgteNlK/muNwe4SA4PsjpEBxu7+wfwsCQGwzjK3IDRB7/cGWPd0/kpJuQl5Hm2z8SubZSqRqATxlTuo/2SEWqAS59h79P4lki8rInMq+tWHdNKOVlT3C0UezcEh9QtJy+SgTjSzNUl+oj5XTg9VbvoFNxAFcgTrdHmPg9ShStltRdAW2oeXh7rTHGukszw3/FihX41a9+hV27diE9PR1vvvkmGhoacOaZZ+LSSy/VahkpwRFhZHV2UIGIEDIVFUlJFdcBkXv8j4sMN4ZhhIcw0HNiszuFsdjik69BlK9JRXbaw3tFAj3+mWaToNjEef6+/RPsdQPC99o2ilIwJsjsncDrxFCerX4c7x7EkNuj6HQQR4z4vTMyN93PgCrO9ub4Wx1+qR0h3z/AmOf3j1Q7YalUDcAni0j2aI9UUwoeoTlFu5TskdFdUXA6tFodsAbUxIU6NDZ7CzQJ7XB7WMGhGai7xKk+vDzpHXCid5C7r2KPf15GGszG8FuZt/Ta4WEBs8mAoiyLT/YoRKuDZQ8/xCu2ukszw3/fvn246qqrAAAmkwmDg4PIzs7GPffcgwceeECrZaQEUl0ReKq8Xlsp5RvoNYlYeHo37egA5RvodeMVa15GGrIt/llngueEjDfNkfP4A6J0H5HylSoMByKbgOjxsEKKjlj5ttkcgpDmCRzexUMtGfWD97hJOx2ClS8fbQzy2Ob6d2ZRi7g+BBDLHn+vrXhWRGC4nWSPfsh5/AFfxFGsu6SKM4HIG1MEphmWevdhoONBKkcbEBVoujwRTZ0mIud49wCG3B5YTIag+1IxIhMGBhgYcgvPPW/3FGVbkGn22R8Mw0Rk+xwXdaMzGBhMKOHSBKU8/kJHsbxA2aNNjYhmhn9WVpaQ1z9y5EjU1tYK3+vo6NBqGSmBXHEdIPb4i5RvV3CONuBTvl39XBGlWsReE8CnfAN72gZOzRQjGG8UMtWUwSG3oPwk94/3MMmH23sHfFGbYOUbfqoYV9fBCu30ctLThBahgcpXLk+SN9ysVKCpOT7ZE+x0GFOYCYbhpoHzPazlPLb+nVnU759AT6yc7GnptYNlAYvJgEJRez+Apvfqia84U153SaX6BMmeCFLFxN3ognSXrOzxdzr4FWiS7tIUcW1RoNPBbDIIzgX+4CgnewCf0zMasqe2vc8v+uPxsEFtqHm0kj2aGf6nnHIKNmzYAAC44IIL8Mtf/hL33XcffvzjH+OUU07RahkpAe91kwqXBhZJAdLt9ABfZxYPC3SGIUADH4CJ/Mm3VVp4Bp7OARripRe8UMwXTeoVE1gkdayL+29xjkUYgsQTSaoYn2MrnuQ8sVS6s49cuDQnPU0wGmMdMiX8kSvsBQCLyZenze8zOdkDRJYqFmi4TRQZbmLlK5Y9gQOeKFVMH+xOn9MhMFUD8EWr+aiA+AA5JjDVR5QqppbeQacwyTlQd6n1+APiGiPaP1oiFPZK2D1AcHMKXncpy54wdFe3/54YW5iJNCODgSG3nwOzo5+bFWFg/AvDAe1kj2aG/0MPPYT58+cDAO6++24sWrQIr732GiorK/Gvf/1Lq2UkBQ6XW7HotVYhXCpVJBU48pyH78wChPkA9Eh7TWrb+/wiB3KpGoAoV5K8blGns8+BfhlPuNT8BzGBRVJy0SIgsnawx7uDjfnxEt01xN45qYjRKCFiRPsnmogHJklxRGLwm5jA/eOTPcHXR1IcHlj3MaYgE2ajAXanx69YV2p4F085tROOGVa7Ez0D0ikwXBRaweng9fgf6xzw24cjMtOQm+4/pCsSjz8ve4qyzUhP45wYcvVpSvtnFEWMYkZD14Bs7YRQ2KtSd8lFi4BIZY+3MNyru9KMBuGwId4//L4ozU0P6ljI76f2PgeGXOHNvwkHzQz/cePG4cQTTwTApf089dRT2LlzJ958802MHTtWq2UkBSve3IVT/7wGa/e3BX2vu39I8OTLnXzFRVJ9DpeQiyh58s0O7wHod7jQ422nxwvAihEZMJsMcLg8grEGyKdqAL5e7NRZI7o09w7izL+uw/ee+EqyY4HgsZXwuAHBRVJR99gG1IcAPo+/OFfSOugSvHOBeZIA5WnHiie/qMXpf1mL5zbUSX4/1MFxXJF0xEh6/4TfmSXQ428yGgR5J1a+cqka3Gu+Q2M4hcWEMkMuD5Y88iXOefALSZkglj2BURiAq9swGRgMOt1otdmVZY9Xb/UMqO/MIhWB9nVmsUleK6m7SPbEhI93N+P0v6zFH97ZLfl9pTRDwGcPHQkj2hjOADjJ/VMafHBUclgVZplhNnEdDVsjmBysFs0bpA8NDeH48eOor6/3+5cI2J3qWzvFCqvdifd3NsPDAve8vzfoVMifesvz0v0KVsSIi6T44rqCLLMw4lqMb4qduk3Ib/7cdJPw+0xGg6DwD7f7BKhcqgYg7sVOXpNo8v6OZvQ5XDjY2of/bDwW9H3BYyuRYwsEF0nJRYsA0fTVMDqzBBpuACT7afP7rDDLHJRiBIgjRqR8owXLsnhlMyerH159EB0B3tSegSHBiVAlc3CsFAp8+2B3utHqTcVQihipjTYOuTxotQVPcpbK05Ya3sVTmksFmrFgw+F2NPYMoqt/CA+vPhj0faXCXoDzoAp52u39itGi/MzwO7NIyh7v3jnePYhBr6PB7nQLKUajJXQXdYWKDa9sbgAAvLy5Hrsbe4O+H8rjH5jqo5TjL9ZdalGru5TSxBiGQbkGB0fNDP+DBw/i9NNPR0ZGBsaOHYuqqipUVVWhsrISVVVVWi1jWHyyu0XvJWDNvlYMeadQ1nX047/f+BtvwqlXxnAD/Iuk6r0et8CuGjzhhrx8m9//90l19pErcAF8XpPeQadsWkoi4PawuPb5zbjhP9viwnv4wa5m4f8fXXPIrx8/ENpjG1gkpcZr4nB5YFN5D6W9Jlye7fHuQQwMcb9HKdQufj3W/ZBjTU1DDxb++XN8ukd/2bOrsVdIh7A5XHjkM3/jjU8xLM9LR5ZF2ukgniPCG245FhPyMyWcDmHKHnHBblG2L1VkgoTXVsnjbzYZBI9xontt73xnN773xFeC0aonH+z07eHXtjRgf4vV7/tKhb08Qmeozn5RtEjagAq3QFNK9hRmmTEiMw0s61sfb9Bnmo3Iywjet+VJEq1u7h3EOQ+uw7Prj+i9FPQMDOGrw1wTGJYF7vtgn58+7RkYEg5jck4H/vX6zgHYnW7h2VZM9VGZKiYu2PVzOpQGd/ZRihYB2nT20czwv/baa2EwGPD+++9j27ZtqK6uRnV1NbZv347q6mqtljEsAqe+6QEvPPlw1t/XHELvgK/NYahUDfH3jrT3K+ZoA+EXuQT20eYJ9Lq53B60WIMfFB6/As0EzrU90t6HtQfa8fGeFsG7qReNPYOoaegBw3B7wGp34e9rDgnf93hYwesmlyYG+LdlVPKaZJiNyPEagGo9J1Jek4Iss5Dzy69PyXATv57IewcA/retAY09g/jftuN6L0U4NPKy55XNDX4F10qFvTz8RM26zn4c7fRFi6RSO8JNFTsuGr4k/n1SHn+lxgJAcnT2cbjcePmbemyv70F1fbeuaxlyebB6r093ebzGmxg1ukuQPe39qO/i7mHg1F6eojB1V2BxJsAdIAJnQYhTNaT2rc/jn9iy5+PdLTjS3o9XtuifkfHp3la4PCxGe9OGNx7pxJp9vlRn3ukwUsHpUJ7P/eyQ24MtR7vgYYH0NIMgZ8SE25iio0+6YFfs8ecPKnLDu8TrBGIrezQz/GtqavD0009jyZIlmDVrFmbOnOn3LxE40tEX+qIYYrM7sf5QOwDg0WWzMbEkGz0DTjy21me81YZI1QB84fajYuUr6/EPL882sI82z8SAnratNq5FaJqREbxrgWg1xS6W7G32ebWkhlBpyUdew+2kygLc893pAICXNh0TBr61WO0YdLphMjCy+wHwhUwPtNgE5SYXMSrOVe91Y1lW1iAL9NoqpWoAydNZY18z9/cGtqPUGpZl8aF3/yw/dzLOnVYKt4fF/R/uF64JVdgLcAeyNCODIZcH3xzpBBB92ROYOiiuEWFZFizLKqYZ8usEEnv/HGrtg8tbCKm37PnqcAesdheKcyz459UnIc3I4MtDHVh3gDPeWJYNmWYIiFPF+lHfGeVotbAnAqPV/l3plFI1AJ/sabPFtkAz1uzz6q5jnQO6/x287LlsXgV+vJDLELn/o31werMfjoSIVANcsxLewfnFAc6OGlMg7XTgU5w7+hyqBrHxDs+ygILdccVZMDBc5gIfPYgH2aOZ4T9t2rSE79cvHlqkB2v2tWHI5cG44ixMG5mL3y2dCgB48etjQq6+mgeAL5KyOz3YUtcFQF75huvxD2W41XqVL7+pR+ZlBPXc5dFqil0s4Q03ILgdpdbwHtulM0bitIlFOGdKCVweFn/+iDPeeI8W14ZMXjTw0YCvDnfAwwIZaUbZw1ux0BUq9OGtZ8CJAb5gN0TEKJTHf1QSFGh6PCz2x4ny3d1oRUPXINLTDDh7SjFWLJkCk4HB5/vbsOEQJ9fVpGqYjAZB1nxx0Kd8pYiW7Kkq4pSvze5Cu82B7gEn7E7uswxsp8eTDJ199sWR04GXPUuml6GqKAvXnFoJgPP689HfgaHQTgc+GlDb3ieknUnVFwHR113BskfacNOqQDPW8LrL7WGFIY160DvgFNJ8LpgxEj87ezwKs8w40t6Pl7/hohG1KpwOgC9iFEr2FGZxe8flYdEt04VKjJzTIT3NKBxM+f2jNk01ljUimhn+DzzwAH7zm99g3bp16OzshNVq9fuXCDR0D+qqfMWGG8MwOGtyCU6fWIQhtwcPfLwfTrdHSL1QStVIEylf3gMvJzxLwgy3N3b7t7TiqSzKhNHAwOZwodXqEIVLpRUvAIxMggmsfspXR69tU88gttdzaT5LppcBAH57wRQYDQw+3duKTUc6RWk+8oYb4BOewt6R8ZoAQEmueq8tr1CLsi1COz2eiQE1IqFSNZKhQLOhe0DoXKS38uVlzzlTSpBpNmFccTZ+dArXje3eD/bC7WFFqRqh9o9/l6ZQskdtZxapVA2Amx8wttC3Z/nrinMssJiCC8MBsexJfMMN0NfwH3J5hBqVC2aMBADcfPZE5Gem4VBbH17b2iDUpo0J4XQQakQ6B+DysDAbDcKAv0BKwujMMjDkm2sTFDEKiDaGStUwGJiE7+zjcntwoDU+9s+ne1vgdLOYXJqDCSXZyE1Pw63nTgIAPPLZQfQOOlWlGQK+rnQ+3SVtJ5lNBiG9VM3BUUkfiWeJDAy50O1NzZbP8U8ij//ixYuxadMmLFq0CCUlJRgxYgRGjBiB/Px8jBgxQqtlDAu3h/WbGqglNrtTOKXywhMAfrd0KgwMp5jfqm6Ey8Mi02yUFYY8lQF5lGrC7Wo8p3IPgMVkFMJsh9v6gvptS5EMvdjjxevGh0pPGlsgGOMTSnKw7OQKAJznjVdsStEiwJfqwyMXagfCC7dL9fDnCZzAGipcKi7QTNRZEOK9A+i3f8RpPmLZc8uiichNN2F/iw2vbqkXoo7jS0J53fz3i5zsyc9MQ5qRO1DyhXtKBM4PESOeBRHq0Mh9z6t8k8Xjr6PT4ataLs2nKNuCkyoLAAB5mWm4ZdFEAFyHqB3HewCElj1luemwmHxmy+gRGTDKRIzDSRXj5UmOxRRUsMvLHj7qFkr2AOKIUWLKnrqOfj8nZzzoLrHsWXZSBSaUZKN7wIkn1h5WlekA+AZQ8kgVhvOEo7vkPP6A/ywIYZ+lm4JmT/CM0sDhqZnhv3btWqxduxaff/653z/+tURBzQPQ0mvHG1sbotr+8/P93jSfoixMKcsRXp9SlovL5nHG2x/f2wOA8/bLeWB5xJXvSl4TPlxqd4buzDLk8ginYyXj7VCbTXF4F49WXpPb/7cTP3lhi5AvGC06+xx+3oLDbeoOjesPtmPL0a6orsUnPMv8Xr918SRkW0zY1diLVdWNAJSjRYCvSIpHqrCXJ5xwu1QPfx6+RuRY5wD6HS7h98l5TQCf1zaW3TWOdvRj8UNf4J2axqj/7r3N/qlhamSPze7Eq5vrZYckRcKeJivquwaQnmbAOVNKhNdHZJnx83M44+2+D/apdjpUBUQE5IozGcZX/9OmImVC0etWKiV7FPaORjUij31+CBc9/lVU7xfAHdb2ibrmtNsc6B10KvwEx46GHqze2xrVtXy405fmIzbSr5g/FlVFWejoG8Ljaw8DCC17DAbGT3fJRYuA8GSPktNhZF46ssxGuLxRNyFVQ2J+iPAzGnT2sdqduOjxr/CoqEFDtNgb6HRQcXB0e1i8vrUBx7vlB/yFS++gExu8aT5LT/TpLpPRgN9eMAUA8PxXR4XucqH2T2DHn7ESrWB5ItFdUo7MiaIakUaJzj+B8HrLanehL0YdDTUz/M8880zFf4mCGuV77wd78ev/7cSv/7czavnFH+z0nXoDjfrl501Cptko5EeHOvUC/g/A6AL5PHtxZ5ZQJ9/m3kGw3kr5QonJi+JcyVB5kuLvxdJr0u9w4bWtDVizvw07jwf3Bh4O+1s4w433HHT0Ofw6MEnRZrXj2he24AfPbMLG2s6orKOpZxDVfJqPyGsCcGk1Pzt7PACo3j9GA4NKkcJVysmNltekNNeCbIsJbg+LjbWdQttGqX3GM0qDzj4f7W7B4bY+PPtl9Fve8fn9pd5CMzXK959f1uGOVbtw9fNbVA8uCgWf5nP25JKg2SBXnToWYwoyhb0TrtPBaGAEI0mKYpWpYh4PK0R2JJ0OEh5/pTRDXva02RxRdwiI+e839ahp6MGnUTa2W60O9Aw4YTQwQmvTULrL42Fx7QtbcP2/t+LVzdHp5OJ0e4S/7YIA2WM2GXDHEs54i1R3RU32hOitzuuug619im2oeXxzaGInezbVdqKmoQfPrD8Ct4oC1HDgdZcge1TYPR/sasZv/rcTlz21MWjGR6Ss3tsKp5vFpNJsocia5+zJJVg4oRBDbo96p0Ox+mh1OO1glXSXOFqtNLyLJ9tiQi7f0TBGB0dNB3j19PTgwQcfxHXXXYfrrrsODz/8MHp7o2tsxRo1D8D2+h4AwHs7mvDPL6UnXIZDn8OFdRJpPjwlOem44czxwtfhCk+5Vp48altbhWpzJu6uEU64tKlnMGYFmi0iT+LWKHvZ+VD73LEjhOiFeICZFDuP98LtYeH2sLj55eqoeIw+8s6fmDd2BEolBOOPF1b5KbxQBVKAf7qPOq+bGo+trx1jIGLly6e8BbZtDEQLr22LV7HvbbJG3TvDe2y/fWI5AHWyh2/buKOhB3e+vWfYz41cmg+PxWTE7d+aInwdruwZlZ+hmNPtKw5Xlj3t3nZ6RgMjqfzFEzTVePwLs8wwG7kCzZYYOR5cbl+ENFayZ3xxFqaU5QLgGisocaxrQMhzv/OdPdgehRagXx3uQO+gE0XZFpxcVRD0/fOmlfq9rmb/VKo0/MWGW6jOLEqGG+Dr7LPpSKdk28ZAfLIndk4rXnf1OVxBaYHDhf99vOypbe8L+RlWH+P2S1OvHTf9tzoqB2Yl2cMwDH53wTTwKmBccZasA5OnONuCLO/AR4aRHsAmXKtSdyl1owN8hn+7zSF8rkqyBxDPoUlww3/r1q0YP348Hn74YXR1daGrqwsPPfQQxo8fH1Yf//Xr1+PCCy9EeXk5GIbB22+/HfJn1q1bhzlz5sBisWDChAl44YUXIv47Qinf7v4hv5u18qN9QkV6pKzZ14ohlwdVRVmYOjJH8prrTx8nKLzJZdLXiFHrNQHUPwByPfx5JhRz6zrcpu7kW5pnAcNwA6C6YlSgKVbq0U6v4cOlU0fmSvYSl2J3k+8g3Nk/hBtf2jbslDEl4QlwnQd+863JALgc2vxMeS86j9hzoux1805AHKbXDfAJUL6lrdLeEX8/ljUifDTKw/oUXzSw2Z1o8PYp//aJ3H0LpXxZlsWeJp8B8NrWBrw8TM/tniYrjnUOwGLyT/MRc8GMMswdy9VpqZE9pbkWZHiLt0PJHt/kcOX9w6dqlOWmwyRxkOANyo6+IeEzUto/BlEkIlYRx46+IcFTu+VodPvsS8qeEBEj8TTUIbcHN75Urbqpgxy87PnW9FLJXHyGYfCHpdMAACYDI0RmlFCru4qy1XdmCVf2lAa0bQxEi5aMzTHUXbyBet60UpiNBtidnpBG6B6R7vqmrgsrRa1+I6F30IkvvZ/3UhndNa08F5fOHQ0AmFyaG/J3Mgwj6K6y3PSgJhJi1Oou66AvJUdq/2RbTILjL1zdFSvZo5nhf9ttt+E73/kOjh49ilWrVmHVqlWoq6vDt7/9bdx6662qf09/fz9mzpyJxx9/XNX1dXV1WLp0Kc4++2zU1NTg1ltvxXXXXYdPPvkkor/jSIey8uWVytjCTFwyZzQ8LHDzy9XDynsT52fLeTgzzEb8+ycn485vT8PiqaUhf6e4SEpq5LkYtZ1Z5Hr48/BFf139Q0K3EqU8SYvJKAjvWHlOxA/W1mPdqnr2qoXvqjGlLMevuFAJfv9cu7AS+Zlp2Hm8F79/e3fEntvm3kFs8xqkS6ZLC08A+M7Mcvz1+yfiHz+crer38kVSobwm4XRmCeV1mygqsgOUUzUAaDL6PFYRIz7UPjIvHTNG5alSvi1WO7r6h2A0MELh5B/f3SPc/0j4UJTmIzcYh2EYPHnFHKxYMgVXejv9KMEwjOC1VYoWAerTNUIZblkWk/A9vvNZKOUb6xojcRpIXUe/6n7zauANtylluX7FhUrwsue7s8oxvjgLLVbOcxtpJzulNB8xM0bn4dmr5uGJK+YgT2KCcyB+0WoF3SXuzBJqAmv4skel0yGm0Uax7InewbGrf0gYNnnCqDxUeovxlQ6OHg+Lvd79s9zbcee5r+rw1vbIBw9+5k3zmViSLUxvl+KuC0/A75dOxfLzJqn6vXy0OqTTQaXs4QcHFmaZkWGWPkhMCFN3xVr2aOrxv/3222Ey+ZSHyWTCb37zG2zdulX171myZAnuvfdefO9731N1/VNPPYWqqio8+OCDmDp1Km6++WZ8//vfx8MPPyz7Mw6HQ7LdaJoK5ct7bKeX5+G+703HjFF56B5w4v/+E5nntt/hwroD8mk+YiaV5uDHp1XJdjkQYzD4UieUJiUCvnD7cJVvptnk9z2lB4XH57WNzQPQIvq9PQPOqHW/GHJ5hL794Xj893i9bt86oQyPLZsDAwP8b9txvLTpWETr+GiXL81HKTTNMAwunVchdN0IBZ86MXpEhmxLREB9ZxZxmzP5cLu/N1CpIxQg8prEMNwuPjhujqLhv0/ksTUZDaqU7+5G7mcmlmTj1sUTccGMMjjdLG58aZuq4thA/NJ8TlSWPSW56fi/M8cjR6ZTRSAT1coelS0ZQxluQPB8AaUDK6CF7PH/m6J5cPTtnxy/+gYleI/t/KpCPHPVPGRbTNhytBv3fbA3ojV8XduJngEnCrPMmF9VqHjtudNKcd4JZYrX8IwrygLDcBGCCoWuLIAoVSxUmqpKjz9PqFQN3nCLZYGm+OC4+WhX1NJh+b0zpiAT2RaT3wweOY529qN/yA2LyYCfnTUeN589AQBwx5u7/CJJ4RAqUs2TZTHhutPHhbwnPHyxbahCYLU5/mpkT+D+US17YqS7NDP8c3NzUV8fHHZuaGhATk7o8HCkbNy4EYsXL/Z77fzzz8fGjRtlf2blypXIy8sT/lVUcF1z+IJGJQHKb/ITRuUiPc2Ip66ci4IsM/Y0WbFi1a6wH841+9vgcHkwtjAT00aGDmWFwz3fPQHLz52E0ycWKV5XonL6qpoHgDcYgdBeEyD2XtvAUNrmuugo39r2PjjdLHLSTRg9IkNVuL2rf0hIS5lWnovTJhYJxW93v7c3onAuLzwDi3qHy5wxI3D7t6bgzxefqHiduDOL0v7h945Sm7OJAcVdIb0m3u+32uwxKdB0uj1+RWzb63uiNudDbLgBUKV8BdlTngeGYfDX78/EpNJstNkcuDECz+3eZiuOdg7ArJDmEym3LJ6In58zAZfOrVC8Tm1LRqX6EJ6JIuWbaTYGtW0MpDzGNSItAYexaB0c7U436jq4DmLTRE6Hhu4BWecTy7LC/pk+Khfji7Px8OWzAAAvbjyGN7Y2hL0OvpvP+QHdfIZLYbYFD1xyIh68bGZQsXkganSX0+0RBm3J6a6Kgky/TmahdFdOehpyYlyg2So6zLTbHII3ebgEyR4VB8fdTf6OitvOnYSzJhfD4fLg//6zLexUXavdiS8P+YZ2RZMfnTIGN589ATeeOUHxumhFGwEp3RXK8E8Sj//ll1+On/zkJ3jttdfQ0NCAhoYGvPrqq7juuuuwbNmymL1vS0sLSkv9U19KS0thtVoxOCj9oa5YsQK9vb3Cv4YGTujx3imlB4APd51QngeA2wyP/3AOjAYGb21vxAtfHw1r/R8qdPMZLnPHFuAXiyZK5sSKUVtgp6Y3vziHM5Thxl0T21w3XuDz3Qui5XUThGdZrl9h6vHuQVnly3vcKgszBc/p9aePw4Uzy+HysLjxpeqwCg1beu3Y6k3zCGzjOVwYhsGNZ43HwgnKh0ZAXBwuv/ZQ9SEAp5TFPbxDeXiKsixCgWYsJmi22RxgWSDNyGBEZhocLo9fjcZw4Ft5TvUe9tUo3z2C7OF+JstiwtNXzkNOugnbjnXjnvf3hLUG/tB41qRiZMuk+UTK+OJs/PK8ySFTO9S21AvX6ybXgEBMrCNG/LPskz3RSdc40GKDh+UiqsU5FhRlm5GXkQaWhTCkL5DmXju6vV2AJnnTKs6dViqkjP3u7d3Y6e21rwan24NP9nLRRrn87OFw2bwKfHfWqJDXqdFdLb12eFguNajIO7E1EKOB8YtOKbWh9l0TuxojlmUFjz+/f6KV5y+uDwGgKlWM11287DEaGPz98tkYW5iJxp5B/PyVarjCcL58trcVQ24PxhdnYVJpdugfCIPCbAt+df7kkGmGvOzpc7gwMCQftZEbHChGLHtMBkZwaMgR68nhmhn+f/vb33DxxRfjqquuQmVlJSorK3HNNdfg+9//Ph544AGtlqEKi8WC3Nxcv38AUBVC+fY5XDji9bTwDwAALBhfiN9dMBUAcO8H+/D0F7WqPJD9DhfWHmgDEBvhqRZfgZ28APN4fIJIrcc/VKoG4AuZxqq6nT9QLJ3BdS+IVpFdoNekMMuM/ExO+dbKeP35VI0TRuUJrzEMgwcumYEpZTno6HPgque+wY6GHlVr+Gg3Z7jNGZMvdJnQg2IVRVKh6kMATpmIu34o7TOAS2crEyJG0Ve+LYLiTcc8b4rUlihEjNweFgdaIle+00X7p6ooC4/+YDYYBnhpUz3ufm+PqtQDLs3Ha7iFSPOJJSUqO7Oo87qJZU/o5yHWvdgDZc+ept6opIWI08QYhvFzPMhFHHlv/8SSbL+Cx1sWTcTiqSUYcnlw3YtbsWafurajG71pPgVZZsyX6OajFcUqdNdxkeGm1BVGnGceSvYAsc3T7h10wu7kbAjeIx4tw39foNNBtHfkMhb2eHWXWPbkZabhmSvnIdNsxFeHO3Hzy9tVpxzyToelMXB4qiXbYhKaECiliikNDuQRy56yvPSQETBxY4pYdDTUzPA3m834+9//ju7ubtTU1KCmpgZdXV14+OGHYbFIn7KjQVlZGVpb/YVVa2srcnNzkZERnjHE54TJCU9e4I7MSxeKUnmuXViJ788dDbeHxcqP9uOCv3+JTUeU+7R/7k3zGVOQ6XeQ0Bo14Xau3zULo4FBaY78/fT3uqn3mkiFSw+39Ql59JHCe90umMGFoxt7BqOi6AOFJ8MwIb22e0T1IWIyzSY8c+U8FGaZcbC1Dxc98RV++9aukEN/1OZIxho14XY1hhvgv3+UahZ4ymV6+Ttcbmyu6xqWocUbbiPz0nEyb/hHQfke7eyH3elBeppBKEQLpXw7+xzCeqYFyIqzp5Tg1+dzXZue/+ooFj24Du/taFJUKPuabajr6IfZZMAiFc0CYoW4M0uPzAAqlmUj8viHYpRCtLGxZxC7hjn3g5c9s8fkY/SIjKh1hgp0OgChI0Z8tEhsuAHc4fmhy2dhYgmXMvaTF7fiuhe3oqFLOa2Elz3nn1AWMqIcS9ToLtWypzjMNFUZ3eX2sNhe3z2sYm5+TxZkmYVU3Wg4rcS1aXxq8fjibDAMVwPXKZGyw3UTk9Zdk8ty8OClM2FggI/3tOCcB7/AvzbUKXr/rXYn1h/0pvno6HRgGManuxSKw9XsnxFZZmHmjJq9U5qbDobh7kfgZ97VP4Qtw6zpiG78VgWZmZmYMWMGrFYrPv30U0yePBlTp06N2fstWLAAH374od9rq1evxoIFC8L+XeJUH5Zlg06ivhzbYCOdYRj85ZITMb+qAH/+aD8OtfXhB89swkWzyvHbpVMFAdXZ58Ca/W1YvbdVaGUVizSfcOBDXt0DTgy5PH65jjx8jq1cOz0evqUnoNbr5l/k0tHnwLs1TVi1/Th2N1qRZmTw3s9PE3pVh4Pd6RYeqvHF2Zhenosdx3ux9WgXRqkII8vBsqyf141nQkk2th7rls3TDkzVEDOmMBMf33oGVn60D6uqG/HyN/X4aFcz7lgyBZfOrYDBwHjzdK1YvbcFn+5tFTrD6G34qwm3qzHcAJ/npDjHolhUzMOHTBu9syCq63uwqvo43t/ZjN5BJ06fWIT//GS+qr8jEN5wK8vLwElerybfGSpUP2kl+L0zuSxX8AwFKt9AxwK/d6qKsiTTcn521gRMG5mLu97dg2OdA/j5K9vx2pYG/PE7JwgGcZ/DhS8OtOOzfa34fD8XaTwzBmk+4WA2GTAiMw3dA0602exClxYxvYNOoUuYkkzJzzSjKNuCjj6HqlQN3mPbO+hEv8MFD8vio90tWFV9HJuOcAe8Z66cq7ooNZBm66DwPidXFuB4dyO2Hu3CGZOKI/p9PIFOByB0jUhgqoaY3PQ0vH3TQjz6+SH868s6fLavFRsOt+Pmsyfg+jPGCc9hbXsfVu9txWd7W7HNOwNAz0g1oC5VTE2qBhBBfZrQi52TEwdbbVhV3Yi3tzeixWrH2MJMfHrbGarkWCCC7MlNx9wxBWAYX2eoYgXHWyiE2jSLSYi+pqcZMXpEBhq6BnG4rS9I9jR508RMBgaTyoLTcpbMGIl3bz4Nv3t7N3Y09OBP7+/FG1sbcO9F04VIqcPlxsbaTm7/7OPSfMYVZ2GyQjcfLSjOtuBY54Cyx1+l7ppQko3Oui5Vdo/ZZEBxtgVtNgeaegaRk27C2v1teLO6EWv3t8HlYXHHkil+85vCQTOJftlll+GMM87AzTffjMHBQcybNw9Hjx4Fy7J49dVXcckll6j6PX19fTh8+LDwdV1dHWpqalBQUIAxY8ZgxYoVaGxsxL///W8AwA033IDHHnsMv/nNb/DjH/8Yn3/+OV5//XV88MEHYf8Nld5uAr2DTnT0DQU9YEKqRsCpl8dg4DqnnDetDH/9dD/++0093q5pwpp9bbh0XgV2Hu/BtvpuiA9yYwoysexk5QK4WDPC25nF6WbR0eeQFHpKI8/F5GWmYVR+Bhp7BoOm6EnBe2zbbHb85IUtWHew3W9KodPN4uHVB/H0lfPC+ZO43+l9mC0mA/Iz0zCvsgA7jvdic12XbP4oy7K4481dcHo8+Nv3Z0oaeO02Bzr7h2Bg/PuaK4XbbXanUJAnF90pzrHgoctm4fJ5FbjznT040GrD7W/uwmtbGjCtPBef7W3zKxg0MMCyk8eoUlKxJDyPv3L6F2/MBI5el4P/29+tacLrWxpwNKAA7stDHfj6cAdOVVGrEIjY439CeS4y0oxCZ6hJMgrrQIsNt7+5Ez8/Z4KsJ503/KeJPLahlO9uBcON56zJJfjk1kI8/cURPLHuMDYc7sCSv6/HZfMqcLx7EBtrueFEPIVZZlx/+jgVn0RsKclJR/eAE+02B6ZI2Ni87CnKNiv25QY4L/iXhxyoKgqdN8wXaNrsLtz0cjU2HekU0it4/vbpASyaKt2jXgmPh0VrL/c8lOVxqWKrtjeGLPB9dM0hVNd347EfzpE8kLEsKwx+kzL85Tz+uyVSNcRkWUxYsWQqvj9nNP7wzm5sOtKFv316EG9WN+LsySVYd6BNSHPlOWNSMU4Zp1+aDyCamq4oe7yF4SF0F/95FmWbZRsQiOF116YjnbjwHxuwK6C7zbHOAby+pQFXLqgM+bsCEcuevMw0TC7Nwf4WG7Ye7ZJt5NDR58AvXtmO808ow9WnSr+n0AZ2ZI6fs3FCcbYge04Z59+hSUgTK82RPcRMH5WHt248Fa9uacBfPtmP/S02fP+pjbhoVjmG3B58caBdOLwDXPH9z8+ZoKvDExDrLukUpcEhn/NwtArd9U1dV1i6q83mwMoP92NvsxW9ARHPJ9YexrKTx4RsUiCFZob/+vXr8bvf/Q4A8NZbb4FlWfT09ODFF1/Evffeq9rw37p1K84++2zh6+XLlwMArr76arzwwgtobm726x5UVVWFDz74ALfddhv+/ve/Y/To0fjnP/+J888/P+y/IT3NiIoRmajvGsChNluQ4S+VYytFXmYa7r1oBi6bV4E/vL0bO4734rmvfBN+TyjPxbnTSrF4ailOKM/VffPznVmaeu1os0kb/rzhNlqFkfnoslk43NanyktflGURDh1rvF7ImRX5uGTOKEwpy8UPntmIT/a0YtfxXswYrfy5B8Knf4zMSwfDMDipsgD/2lCnWGT3dW0nXvN2uLhkzmjJ4la+OKqqKMvPEFHK0+a9dOV56SjMVvbYzB9XiPd/cRpe/PooHl59ENX1Paj2TovONBtxxsRinDutFOdMKcEICQ+p1vjC7fL5nWq9JmdPKcFdF07DgvHK7QF5+DxtPvqRaTbiW9PLcMmc0fh4dwv+s+kY/vbpAbw5vjDs50zsdUszGjB7TD6+ru3ElqNdsob/g58eQE1DD+7/cB/OmVIi+Z5SHltAWfnKpWoEkp5mxC2LJ+J7s0fhj+/twef72/Dfb0TysihLkD1zx46IajeWSCnJteBAq03W66Y2VQPgen6vP9iOc6epS18qz8vAAbtNaKk8rjgLl8wZjXOmlODypzfiYGsf3t/ZpKrQVEzXwBCG3B4wDPd8nFzFDUCraeiRjaoe7x7AI58dhIcFVlUfx1USBuPx7kHY7C6kGf3rYXjDv66jHy63xy8q29HnQIvVDoYJ3nOBTCzNwSvXn4J3dzTh3g/2oa6jH3UdnO5KMzI4ZVwhzptWikVTS3V3OAA+w1/R469y/1QVZeGhy2aqrpcaKYo2NvYMwmRgcPaUElwyZxSOdw/i3g/24R+fH8al8ypCHlgD4euL+HTHkyoLsL/Fhi1Hu2UN/+c21OHr2k7saOjB9+eOlpzLIRWpBrj9s/ZAu6TuEmRPiHRkg4HBD+ePwbeml+GBj/bjta0NeLumSfh+SY4Fi6eV4txppVgwrjDszyQWhBrixe+dbIsJuRnK5vTPzh6PUfkZuHTeaFXvXZ6fjpoGYKM3JbwsNx0XzR6Fi2aX4xevbMfB1j7868sjWH7eZLV/joBmhn9vby8KCrjT/8cff4xLLrkEmZmZWLp0KX7961+r/j1nnXWWYm6T1FTes846C9u3bw97zVJMKMlGfdcAatv6cOp4n9Fnd7pxyPtQqM3HP3F0Plb9bCHe2NqATUc6MWfsCCyOE4EZSHGO1/CXKc5Ra7gBXDehuWPVeYIMBgZXnlKJ9Yfacf4Jpfje7NF+uboXzRqFVdsb8eDqA3jh2pNV/U4e3jvOC895lZzyPdBqQ8/AkOQE2xdFXZle39ogafjzRuYUCcMNkFa+vNdkmky0KJA0owHXnT4O3z6xHE+v54rFF00pxYLx8SEwxYQKtw+5PGj1HgpCKV+jgcG1C6tUv/fiqaX499fHUJRjxiVzRuP8E8oEhTexJBtvbGtAdX0P1h1ox9lhtqzk989IYf8UcIZ/XReumB88yOp49wA+8xZH1rb3o7q+R5h4K2a/aPiSGEXlq5BmKMWYwkz86+p5WL23FR/tbsGk0hycO63Em1Kkv7EvJlSqWDiyZ0JJdlBPbSWWnVyBFzcewxkTi3DxnNE4cXSe8Pn835nj8ddPDuDh1QexdMbIsHLZ+UNjUbYFZpMB44uzhZSm3U29mDMmeF/895t68MHO17c2SBr+vOwZX5ztd3gYlZ+B9DRuDk1D96Cf11FIEyuUThMLhGEYfHfWKJw9pQT/XH8Ezb12nDm5GGdOKlY9x0ErAjuzSLX/DGf/XDxHndEGALMq8nFyVQGGXB58b/YoXDizXEhVc7jceP6ro2jsGcRLm47hujAja8GyZwT+s+mYbI2R3enGq1s4h1X/kBsf7GrGZfOCMwn4/RNo+POHSKnGFOHKnoIsMx74/om4/OQK/HdTPUbmpePcaaWYMSpvWCmSsSCU7hIfGkPJzZKcdFx/hvr7fMmc0djbZMWcMSNw8ZzRWDC+UHDELD93Em54qRr/2lCHaxZWSaZAKqGZ4V9RUYGNGzeioKAAH3/8MV599VUAQHd3N9LTQ+dbxgsTSrLx+f62IOV7sNUGt4dFQZZZeBjVYDQw+MHJY/CDk8dEe6lRhevM0itb5BKO1y1c7rxwmuz3blk8Ee/saMK6A+3YerRLyBlUg9hjC3BKeFxxFo6092Pr0W4sDvAKig03APhodwvuGXAGtST0pWr4C8JR+RnISDNi0OlGfdcAxok8cj6PbXi1CmV56bjrwhPC+hmtEcLtfQ7J/PeWXjtYlku5KsqOboSiNDcdn9x2hvS6ctNx9YJKPL3+CP726QGcNbk4LKNXaMfofd59Bb7SESOx4QYAr29pCDL8ewZ8sxymjPSPGgh52gHK12Z3CilMcmmGUjAMg/NOKIs4R10rikOkisVS9lyzsArXyBw0rzm1Ev/aUIejnQNYVd2Iy05Sn5IZKHsYhsG8ygKs3tuKLXVdQYa/3enGq5t9kZndjVbsaeoNut9yssdgYDCuKBt7m6043NYXYPjzs2fCi5jmpqdF5G3UEr4zy6DTjXabA2ML/U0ej4cV6seivX/S04x4/f+kawktJiNuWTQRv3lzJ55YV4tlJ4+RnYwtRbPQCtYre7w1RnxnqMAD3Ac7m/166b++pUHS8Ffy+APS0Wq10cZA5owZIXnAjSdCDfEK59AYLoumlsqmg55/QhlOKM/FniYrnvqiFr+9ILw6Wc3K7W+99VZcccUVGD16NMrLy3HWWWcB4FKAZsyYodUyho3QHSFA+fry+/VPzYkFvl7s2j8ASowtzMJl3tDZ3z49EFale7OoOJNHMN6OBXtOeMNtwbhCTCnLwZDLg3d2NAZdJ9VVA/Aq32LpWRByXRGSAT4f3emW7sxyXDR8Setn5//OHI9siwl7mqz4eHeL6p9ze1hhNgB/0J89Jl+2M5TYcPvJaZwh+f7OJvQHdBXi03xGj8gIyiOWU7787JBR+Rlhe34SAZ/HP0S0UeNIaZbFhJ+dxRXX/X3NIThc6iezNwdEGwHlg+P7O5vRPeBEeV46vuU9qL2+JXiolpzhBsjvH6EVo46d42IFwzCKXtuOPgeG3B4YGHVdwqLJxXNGoaooC139Q3helOqrhhYhxz9D+C/fGWp7ffD++ffGowC4w6rRwGDrse6gfdBms6Ojz1ubVirtdGjutft1Qmu3qU8TS0RCe/xDDw6MBQzD4FfeQ/eLXx8NezK7Zob/z372M2zatAnPPfccNmzYAIOBe+tx48bh3nvv1WoZw0YuT9tXXJd8hhsg6qct4fFnWTamXrdQ/PyciTAbDdh0pAtf1yq3SBXT0utvuAGQ7ccuNtyuPrVS8Ja8HjDR0u50o9Y7JEdR+YoOjn5pYmF6/BMBvjMLIO050evQCHBh5x97DfGHVh/0KxxXorPPAZeHhYHxGaZZFpMQ7g4cBCc23O5YMgVVRVlCyF2MouHm7YgVqHz5qZmBbTyThZJc5ZaMvj7aoeeCRJsfnTIWpbkWNPYM4jUJQ1yOFlF9EQ+farj1WJffzAKWZYUUwytOGYtl87no8Ns1TUHDACMx/FNGd0nsH35wIF+noyUmowG3LuYGpD29/gh6B6Tb1Urh6yjm2z8nyeiumoYe7DjeC7PRgJ+fMwFnebtGvbHNf7/yTofKoixkmP3TRbmOWJxTQdwZindYVRVlhRWxSBRCTe/VU3edNbkYc8eOgMPlweNrD4f+ARGa7vS5c+fie9/7HrKzfSkOS5cuxcKFC7VcxrDghWer1QGr3fegRpqqkSgIQ7wkPP49A04MeCvy9ahPKM/PwA+9yjAcr7+S121XY6+fUhUbbounluB7s0fBbDQIIXeew219cHtY5GemCWF8MVL9tA+0cGlihVlmyZ9JBnxFUtI90QF9Do0A54HPy0jDobY+vLejKfQPwBctKsnxb197kkQ//0DDLc1oEAq8Ar22SoZbXmaaED2RUr7JGC0CVChfHfdPepoRN5/DGW//+PwwBofUef2bJQy36aPykJ5mQM+A0y+dq6ahB7sae2E2GfCDkypw2oQilOelo3fQiU/3+lIP+x0uHPP21w+MNgLSTger3YljQppYsusuCdmjo+EGABeeWI7JpTmw2V3454Yjqn7GZnfC5j34Sxr+ARGjf3tlz7dnjkRhtkVISXtzW6PfIFEl2QP48vwP+8kePlqUrLLH22a93yE5e0BP2cMwDH553iQAwMub69HYrTxbQ4ymhv/x48fxxBNP4I477sDy5cv9/iUKeRlpQviHV75Ot0d4aJL1AeC9mlKdWfjNX5Rt0a2w9Gdnj0d6mgHb63uEacehkPK6VRRkoCTHAqebRY13Qq7YcPvRgrEwGQ0YkWXGuSdw+XdvbD0u/Lww7rxMOuVLqp/2blGObTKmiQHKqWJ6pWrw5GWk4afeoqtHPjuoaqq2lOEGACd5vbZb6nzKN9BwA4DvzxkthNzFRh7finGahOEGABNKglPFfFMzk9NwUwq3Dwy5hNxlvYy3y+dVYFR+BtptDry06Ziqn5GKNqYZDZhdwe0fcVvPf2/kfueFJ5ajMNsCo4HB970RxzdEEcf9LTawLHdQkuoMJpY9vHNEnCYWDx3AYoFScbjeTgeDgcFt53LG23Mb6tCpMCiKh08xzEk3+eXy87Jne0M3hlycDOvoc+D9nVxU8RpvC89zppSgKNuMjj6H0K0KkK8P4ZE6OPo6GSan7CnIMsPAACwLyeFleh8cTx1fhFPHF8LpZvHMenUHR0BDw3/NmjWYPHkynnzySTz44INYu3Ytnn/+eTz33HOoqanRahlRIdBrW9vehyGXB9kWE8YUaB9u1gKlcLvaHv6xpCQnXehN/OCnB/1C5VI43R5BEYiNN4ZhhGFMfMhUbLhdLiqI4tN93treKEQHQnlNxOF2XvmK60OSFaVUMTUjz2PNNadWoijb7C3UPB7y+sD8fh4+VYzvDAUEG24A9zzxIXc+Xczl9uBgKydTQu0fPjXM7nQLijjZUzX4ziximrx7J8diiqifdTQwmwy4xZuy8eQXtaqmQQsdxXL993yg7Gm3OfCB13C7+lRfp6hL53IRow2HO3Dc6+kLJXsqC7NgNDDoc7iE91caOpksKOkuvQ03ADj/BK6jTf+QG0+rMN5avPMfAmXPhBKuM5Td6RGcSa9tacCQ24NZFfk4cXQ+AO6AyXcnEqenCT38y+ScDsEe/1CzixIdo4ERoqyB+8fp9gjPkZo25rHil95cf3Fr1FBoZvivWLECv/rVr7Br1y6kp6fjzTffRENDA84880xceumlWi0jKgSefHmP27Ty3LhrRxUtikWGW2AqTTg9/GPJDWeICjX3KBdqttscYFnAZGBQlOXvHfMV+HJeWynDDYBfyH21N+QuV9jLM9arfPuH3ILXeG+Sp2oAITz+OnvdAC4//8azJgAAHl1zOGShppzHn+8MBQDbjnXLGm4AgkLuRzr6MeTyIMvMzQuRItDpsN+bJlaUbUZpbuQTO+OZbIsJ6WmcqgpUvvHgdACAi2ePwji+UHODcqEmy7KSHn8guMD3tS31QYYbAFQUZGLhhEKwLPC/bdxBNZThbzYZMNbrmOL3z94IO7IkEuo8/vo57MQpGy9+fVRwKsjRLPTw99/zfGcogKsxcrk9QgQqSPZ4Uw3XHmhDm80esjYNCI5W9w46Ud+V3GligDji6H9fWnrt8LCA2WgIGqioJXPHjsA5U0pU16cBGhr++/btw1VXXQUAMJlMGBwcRHZ2Nu655x488MADWi0jKgQ+AGqmZiY6xeLOLAFFSPHgNQGAEVlmoWPKQ6uVvf7idmiBhzW+yK76WDdarXZZw81oYPB9r+ft9a0N3NRMmeFLPGaTAWMLfcrX6fZgn7d3clLvHxmPv8fDoplvp6fz/rli/hiU5aajsWcQr25WLtQUBuhI1GSc5J1Rsflol6zhBgSH3H1TM+UdCBNKuAMlnx4knv+QrGliDMOIhsD57594ODQC3kJNb8rGM18eCZqyKcZqdwk1UYEHR3FnqIauAby0iW8oEDwX4jIh3ec4PB42pNMBCG5OkRK6S6EdbLzorjMnFWOeykJNXyvYYGOTT/fZXNeNz/a1ornXjsIsMy4IGOo1oSQHc8bkw+1hsaq6UahNy8tIk21Hzts9x7oGMOTy+KWJSc28SRbkaox42VOeH2xDaM1yr+xRi2aGf1ZWFoaGuND3yJEjUVtbK3yvo6NDq2VEhcCQV7IXuAD+nVn+/NF+PLv+CN7d0YTNdV041MYZrnorXwD4yelVyDIbcbitDwdabbLXtUoU9vJMKctFjsWEPocLf3x3j6zhBgCXepXvhsMd2HqsG72DThgNjOKQILHXlk8Ty0niNDHAF27fdrQLD356AC9tOobP9rbiq9oODLk9MBoY3Qub09OM+L8zuVx//rAnh5zHH/Cla2yq7RSm4koZboEh970hQu2ASPl29sPhcquempno8Mr36fVH8OS6Wry1/Ti+ru0QUg30NtwA4NszRmJccRZsdhe+PNQuex1vuOVnpgXVRIk7Q93/4T60WKUNN4Dr5Z2bbkJjzyC+PNwhO3xJjFh3DQ65BR2WzB5/fu8c6+zHAx/vx4tfH8XHu1tQ09ATNwdHhmHwi0VculhI2SPoruA18wW+W4914fmvjgIAlp08BhZTcO3d5d6I4+tbGgQjfurIHFkHQlluOrItJrg9LI529id9fj8P73T437bjeOzzQ3hjawO+PNSObd6MgHiQPdNH5eHcaeoHUGrWf+mUU07Bhg0bMHXqVFxwwQX45S9/iV27dmHVqlU45ZRTtFpGVOCFZ33XAOxOd0qESwGuZVd3fQ9e2yrtDdVbeALcUJlp5bnYcrQbB1psskpQyXAzGhjMGTsCXxxsx0fe3u5ShhvAhdxPHV+Ir2s7ce/7ewEA44uzFIucJ5Rk49O9rTjc3odcb15yMqeJAcA478Cgpl47/vF5sEerLDc9rMmnsWJ+VSEAYH+LFSzLyipB3+RMKeXLed12HOcUo5zhBnAh92fWH8HaA21o7uUMfiXDrTTXgmzvofRox4BI+Sa/7Nl6rBur97YKaXVi4kH2GAwMTq4swJH2fhxoseHbJ0pf16wQLQKAeWMLsPN4ryB75Ay39DQjvjtrFP6z6Rge+vQABobcMJsMwrMmhdjpsL/FCg/LpafxxnEyMjo/E2lGLr3yyXW1ktfEw/45qbIADMMVkbbbHEKUNBC5NDGAy7XnO0N9U9cFo4EROt4FsvTEctz93l4c6ejHf7/hUoKUZA/DMBhfnIUdx3txuK0vJRyeAFDlTd3ccrRbcsZGPOwdALj57Al4TuW1mhn+Dz30EPr6OO/C3Xffjb6+Prz22muYOHEiHnroIa2WERVKcizIsZhgc7iw7kA7+hwuWEwGjC+WF7jJwKPLZuPj3S1otdrRYnWg1Wrn/t8bTgycQqoXU8o4w39fixUXYZTkNUJHHxnle3JVAb44yHntlAw3gPOcfF3bKRh6oQaZiL1uZq+xm6zFUTzTR+XhuWvmYU+jFS38vrHa0dLrQPfAEJaeKP/5asn4kiyYDAysdheae+2S7WlZlhUOjlLKd0xBJkpyLEJOsZzhBvhC7tX1PYIiDal8S7Kxo6EH+1us2N+c/GliAPDbC6bixIp8NPUMorXXu3esdrT22mEwMDjDWyitN3y0hk/5k0LJcAOAk6tG4DnvQCclww3gZM9/Nh0TZM+k0mzFA7R4+jM//yFZh07y5GWm4dWfnoJv6rpEe8eB1l472vscOHtySVDfej3IMBtRVZiFIx392N9iRXGO9J5WclqZTVxnqI1HuHk2500rlW2xnW0xYemMkXhj23HVumt8SbZg+AuF4Unu8b/m1EoUZplR3zWAFu/+4e0eh8uDxTLTdbVmfIl8pDgQzQz/cePGCf+flZWFp556Squ3jjq88q1p6ME7Ndzk1ikjc+PCYxlLRo/IxHWnjwt6nS/2jRflMdmrfA+0yCtfJeEJ+EKmgLLhBvhC7lY7181DreFf29YHeMsQkj1cCgDnTCnFOVOChaTHw8ZNtMNiMmJccRYOtvbhQItNUmn2DDiFdnklEnm2fGeoD3Y2hzTcAM54q67v8f6scqoPwHltdzT04OPdLRhye5CTntxpYgBXv3PlKdJRt3jaP5PLuOf4QKtV9poWhVQNwNcZClA23ADuQD1tZK5fG2El+Bz/jr4hfH24w/s7kl/2zB1bgLljC4Jej6e9A3C660gHFzE6faK04S/XUYznpKoCwfC/akGl4vtdflIF3tjm62Im18qTh9dduxp7hTqjZPf4p6cZhZTeQOJt/6hFU0u1p6cH//znP7FixQp0dXHtyqqrq9HY2KjlMqIC/wCs2c/1jE/2HFslGIaJG6Mf8BlOSoZ/4MjzQE4cnYf8zDRYTIaQhhsfcucJ6TXxhts7+4dQc7wHQPKnaigRb4KTN972y+wf/tBYlG2WPRCe6fVAXzBjZMihdktPLEem1+M4tiAz5ATMQNmT7B7bUMTT/uFlT0PXoGxbz1Ae/6Jsi2CMX7uwKuR78h1agNCyJ9tiEt7Xp7tI9sQLvNNKTvbYnW5hbsXIXGm5cuakIgCcEX/KuODDjpi5Y0cIXchC1aYBvlSxLw62w8NyTRtKknTopBribf+oRTPDf+fOnZg0aRIeeOAB/O1vf0NPTw8AYNWqVVixYoVWy4ga/APCe/6SPVUjkZjkFZ7NvXbZMeihPP7paUb874ZT8e7Np6maRswXSgHKXTUAroCv3Pu+Qy4PLCHycgltmSIoX2mvbYuVb6cnr/C+P2c0Xrj2JDxwyYyQ78eH3AGu1iMUJHvilxFZvraqco6HULIHAJ69ah5W/exUnFylbLgBwEXeKeIA7Z9EZ4rgdJCWPby3PyPNiNwMaQfB3LEFeOOGBXjhxyeFdAgwDCN0h5pQnB1yAGfw3kldh2cio1mqz/Lly3HNNdfgL3/5C3JyfIbRBRdcgB/+8IdaLSNq8CdfnlQIlyYKuelpGJWfgcaeQexvsWL+uEK/73s8rGJXH55Q3g8xJ5Tn4tfnT0aa0dd6UInxJdlo8hoAU1MgTSyRCBUxEgw3BU+XwcDgrMnquywsP28S3B4WPzk9tIc3cF+S7IkvJpflotXajgMtNsm6pxYV+2dkXoZsNDKQ/EwzVl48A/uarcIcACXGF2fjy0Ncmk9OugkVBfFRnEj4ZM+h1j643J4gvSA+NCoZ9Sep2Ac8V54yFvVdAzh3Wuhc9TEFmTAbDRjyTjdP5WhRIqOZ4b9lyxY8/fTTQa+PGjUKLS3Kw5biEbHyNRkYTCpVX1hBxJ4pZTlo7BnEgVZbkOHf2T8El4cFwyBq3SwYhsFNZ09Qff2EEp/yJcMtvuDD7YfbuFarZpO/8m1R4bENl5F5GXjo8lmqrq0YkUHKN46ZUpaD9QfbZb22fFcfuVSfSLhk7ujQF3kR667pSTz/IREZU5CJjDQjBp1uHO0cCDrkqzk0hkuWxYT7vxc6Mglw8yoqizKFCeOkuxITzdyMFosFVmuwIDx48CCKi+OjI0M4VBRkCgbBhJLQITJCW6aMlO+uwQvP4mwL0nTytIsFOoXa44tR+RnIsZjg8rA40tEX9P3mEPUhscZkNKDKmxqWnmbAuGL1kSki9kxRyNPud7iEJgDRPDiGg7/sIcMtnjAYGCFVVSriqNRNTCtIdyU+mlk93/nOd3DPPffA6eRyrhmGQX19PW6//XZccsklWi0jahgNjJCXncqFmfGK0F1DwusWC49buIhTxchjG18wDKPYGSoWXrdw4ZXv1JG5MCZogVmyIt47fMczHr6jT7bFhJz0NM3XBgR4/El3xR1TSvn9E6y7hInhcaC7ctNNGB0Hw6uI8NHM8H/wwQfR19eHkpISDA4O4swzz8T48eORnZ2N++67T6tlRJWZ3kmu/MAeIn7gvW4HW/vg8UgrXz2F5+SyHFhMBuSkmzCpjDy28cZkhX7sLSHa6WnBzArOYAsnl5fQhgkl2TAaGPQOOoW9wtMagzSxcCnMMmNUfgaMBgazx+Trtg5CGkH2SDkd4kL25APgB46R0yER0SzHPy8vD6tXr8aGDRuwc+dO9PX1Ye7cuVi0aJFWS4g6ty+ZgjMmFWPJ9DK9l0IEUFWUBbPRgD6HC409g6gQ9TnXO1UD4AryXr7+FFhMBsUZAYQ+TBkpHzGKRY5/uFx9aiVKc9PjZngM4cNiMmJcURYOtfVhf4vNT87EQ6oGwzB4/tqT0Nk3hLGF1E0s3uDTVBWjjTrqrnOmlOCpH82lQ2MCE3OP/8aNG/H+++8LX5922mnIysrCE088gWXLluGnP/0pHA5HrJcREwqyzFh64siE7eWazKQZDcKwmsBcW154lurcf3ju2BEUao9T5Dr72OxOoT+7noa/xcTNjgjV85/QB7lUMd5jq7fsmVSagwXjC0NfSGgO39KzvmsA/QGzINR0FIs1DMPgW9PLdN/DROTE3PC/5557sGfPHuHrXbt24frrr8e5556LO+64A++99x5WrlwZ62UQKYjPePP32sZDjj8R3/BdupoCZkHwh8bcdBMyzWR0E9IIBb7NJHuI8CjIMqPY223uQKvv4Oh0e9DexzlJ9XQ6EIlPzA3/mpoav3SeV199FSeffDKeffZZLF++HI8++ihef/31WC+DSEGmyORKtlpJeBLK5GVwsyAAf+UbD2liRPwzRWb6czykiRHxj1TEsc3mAMsCaUYGhVlmvZZGJAExN/y7u7tRWurLQ/3iiy+wZMkS4euTTjoJDQ0NsV4GkYJIhdtZliWvG6GKyRIRIzLcCDXwe6e2vQ9O77wFID5y/In4R8rw5zv6lOamU3oxMSxibviXlpairq4OADA0NITq6mqccsopwvdtNhvS0vRpa0YkN7zXra6jH3anGwDQO+iE3ckpYspRJJSQ6q4RD101iPhn9IgMZFtMcLpZHGnvF14XJobnUsSIkIdvR72vWex04CLVJHuI4RJzw/+CCy7AHXfcgS+//BIrVqxAZmYmTj/9dOH7O3fuxPjx42O9DCIFKc21ID8zDW4Pi8Nt3CAm3uNWkGWmoWuEIlJet2by+BMqEM+C4Cf4OlxudPQNASDjjVBGkD2tvlkQzUIPfzo0EsMj5ob/n/70J5hMJpx55pl49tln8eyzz8Js9uWnPffcczjvvPNivQwiBWEYBpNL/Y23eBi+RCQGU4QhcD7l20JpYoRKJgdM8G3z1hZZTAbkZ1KUm5CHnwXRM+AUatJaKE2MiBIxb0tRVFSE9evXo7e3F9nZ2TAa/b2sb7zxBrKzaYARERumlOXgm7ouoUCTPLaEWsYVZyHNyKDP4cLxbm4WRHOctIIl4p/AiJFY9tDgI0KJ9DQjKgszUdvej/0tVpTlpaM5TlrBEomPZpN78/Lygox+ACgoKPCLABBENOEHMfG5kvEw8pxIDNKMBoz3jqcXIkZW6upDqEMcMQJEqRpkuBEq8A0R9I9Wk8efGC6aGf4EoQeBnX0Ew42UL6GCKaI8bbvTjR5vT386OBKh4NMMG3sG0TvoJMONCIsppf6pYtRRjIgWZPgTSQ0/iKnN5kB3/xCl+hBhMVnUj51XvJlmI3LTaXgXoUxeZppg5B9stQlOByrOJNQgrhHxeFihIxQdHInhQoY/kdRkW0wYU5AJwN94o1QNQg1TRvoiRpSjTYTLFJHxRh5/IhymelN9atv60GK1w+VhYWCA4myLzisjEp2ENfwff/xxVFZWIj09HfPnz8fmzZsVr3/kkUcwefJkZGRkoKKiArfddhvsdrtGqyX0RNxWj8KlRDjwhtuRjn7Ud3H92MlwI9QiRIyarRRtJMJiVH4GssxGDLk9+Lq2EwBQkpMOkzFhzTYiTkjIHfTaa69h+fLluOuuu1BdXY2ZM2fi/PPPR1tbm+T1L7/8Mu644w7cdddd2LdvH/71r3/htddew29/+1uNV07oAW+8bTvWDZvDBYCUL6GOstx05Kab4Paw+PJQh/c1ihYR6hB39iGPPxEOBgODSd79s+4AZ9uQ3iKiQUIa/g899BCuv/56XHvttZg2bRqeeuopZGZm4rnnnpO8/uuvv8bChQvxwx/+EJWVlTjvvPOwbNmykFECIjngu2vwhluOxYRsC+VoE6FhGEboriEY/nkUaifUwaeK7W+xoc1GM0SI8AjUXbR3iGiQcIb/0NAQtm3bhsWLFwuvGQwGLF68GBs3bpT8mVNPPRXbtm0TDP0jR47gww8/xAUXXCB5vcPhgNVq9ftHJC58qk/vIHVkIcJnStD+IY8/oY5xRdkwGbhZEB4WMBkYFFKONqGSYNlDuosYPgnn9uzo6IDb7UZpaanf66Wlpdi/f7/kz/zwhz9ER0cHTjvtNLAsC5fLhRtuuEE21WflypW4++67o752Qh8qCzNhNhkw5PIAIOFJhAd/cOShVrCEWswmbhYEP0CwNDcdRgMVhhPqCJI9pLuIKJBwHv9IWLduHe6//3488cQTqK6uxqpVq/DBBx/gT3/6k+T1K1asQG9vr/CvoaFB4xUT0cRkNGBSqW86NAlPIhz4cDsPHRyJcODTfQDaO0R4TAkw/Gn/ENEg4Tz+RUVFMBqNaG1t9Xu9tbUVZWVlkj/zhz/8AVdeeSWuu+46AMCMGTPQ39+Pn/70p/jd734Hg8H//GOxWGCxUDg2mZhcmovdjVzKFqVqEOFAXjdiOIj3DxluRDjkZ5pRlptOE8OJqJJwHn+z2Yy5c+dizZo1wmsejwdrVBl5pQAAJr9JREFU1qzBggULJH9mYGAgyLg3Go0AAJZlY7dYIm4Qe07IcCPCIdtiwugRnMI1Gw0oyDLrvCIikfCTPZQmRoTJZNJdRJRJOMMfAJYvX45nn30WL774Ivbt24cbb7wR/f39uPbaawEAV111FVasWCFcf+GFF+LJJ5/Eq6++irq6OqxevRp/+MMfcOGFFwoHACK5oXA7MRz4dB8a3kWEizhVjGQPES5i3VWSS5kIxPBJuFQfALj88svR3t6OO++8Ey0tLZg1axY+/vhjoeC3vr7ez8P/+9//HgzD4Pe//z0aGxtRXFyMCy+8EPfdd59efwKhMX7hdvK6EWEypSwHn+1rpb1DhM3IvHTkpJtgs7vI8CfCho8YFWaZYTGRo5IYPglp+APAzTffjJtvvlnye+vWrfP72mQy4a677sJdd92lwcqIeKQ424JZFflos9pRVZSl93KIBOPsKSV46otaLJxQpPdSiASDYRicN60MH+1uxqyKfL2XQyQYC8YVIcdiItlDRA2GpST3kFitVuTl5aG3txe5ubmhf4CIS9weFi6Ph7wmRETYnW6kp9HeIcKHZVk4XB7aP0RE2J1uWEwGSjMkZAnHTk1Yjz9BhIvRwMBoIMVLRAYZbUSkMAxD+4eIGNo7RDRJyOJegiAIgiAIgiDCgzz+KuCzoaxWq84rIQiCIAiCIAgfvH2qJnufDH8VdHZ2AgAqKip0XglBEARBEARBBGOz2ZCXl6d4DRn+KigoKADAtQkN9YESiYfVakVFRQUaGhqoeDsJofubvNC9TW7o/iY3dH+jB8uysNlsKC8vD3ktGf4q4GcC5OXl0eZMYnJzc+n+JjF0f5MXurfJDd3f5Ibub3RQ65im4l6CIAiCIAiCSAHI8CcIgiAIgiCIFIAMfxVYLBbcddddsFgsei+FiAF0f5Mbur/JC93b5Ibub3JD91cfaHIvQRAEQRAEQaQA5PEnCIIgCIIgiBSADH+CIAiCIAiCSAHI8CcIgiAIgiCIFIAMf4IgCIIgCIJIAcjwJwiCIAiCIIgUgAx/giAIgiAIgkgByPAnCIIgCIIgiBSADH+CIAiCIAiCSAHI8CcIgiAIgiCIFIAMf4IgCIIgCIJIAcjwJwiCIAiCIIgUgAx/giAIgiAIgkgByPAnCIIgCIIgiBSADH+CIAiCIAiCSAHI8CcIgiAIgiCIFIAMf4IgCIIgCIJIAcjwJwiCIAiCIIgUgAx/giAIgiAIgkgByPAnCIIgCIIgiBSADH+CIAiCIAiCSAHI8CcIgiAIgiCIFMCk9wISAY/Hg6amJuTk5IBhGL2XQxAEQRAEQRAAAJZlYbPZUF5eDoNB2adPhr8KmpqaUFFRofcyCIIgCIIgCEKShoYGjB49WvEaMvxVkJOTA4D7QHNzc3VeTeqwem8L/vzRfrRaHcJrpbkW3LFkCs6dVqbjygiCIAhCPav3tmD5azvABrzO5xA8dPlM0mtExFitVlRUVAj2qhJk+KuAT+/Jzc0lw18jPt7djF+9fQgsjDBYMoXXOxzAr94+hCezc/Ct6SN1XCFBEARBhMbtYfG3tVvAWDIhlSzMAPjb2gZcdPJEGA2UTkxEjpp0dCruJeIOt4fF3e/tDfKMABBeu/u9vXB7pK4gCIIgiPhhc10Xmnvtst9nATT32rG5rku7RREpCxn+RNxBQpIgCIJIFtps8voskusIYjhQqg8Rd5CQJIjY4/aw2FzXhTabHSU56Ti5qoDSDIhhQ/sqmJKc9KheRxDDgQx/Iu4gIUkQseXj3c24+729fpG1kXnpuOvCaVQ7Q0QM7StpTq4qwMi8dLT02iVTWBkAZXncISlW0IGM4CHDn4g74kFIEtGFlE788PHuZtz4UnXQs9XSa8eNL1XjyR/NSWkjjYgM2lfyGA0M7rpwGm58qRoM4PcZ8VLwrgunxUwm0oGMEMOwLEsVkiGwWq3Iy8tDb28vdfXRCF6JANJCMpWVSKKRDEonWQ4ubg+L0x74XLaGhj9Ub7j9nLj/+5LlniQDybSvYokeslDuQEa6NLkIx04ljz8Rl3xr+kg8+aM5QUKyLMEMxlQnGbyAyXBw4QmncH7B+ELtFhYmyXRPkoFk2Vex5lvTR+LcaWWaHVhDdchjwHXIO3daWUofyFINMvyJuEVrIUlEl2RQOslwcBGTDIXzyXZPkoFk2FdaYTQwmh1+6EBGSEGGPxHXaCkkieiS6EonGQ4ugSR64Xwy3pN4YTipU4m+r5KVWBzIKMUu8SHDnyAIWYYj5BPdC5joBxcpEr1wPhnvSTww3NSpRN9XwyGeDeFoH8goxS45IMOf0JR4FpKEP8MV8onuBUz0g4sUencXGS7JeE/0JhqpU/Gwr/TQLfFuCEfzQEYpdskDTe4lNOPj3c047YHPsezZTbjl1Rose3YTTnvgc3y8u1nvpaUMbg+LjbWdeKemERtrO+H2SDf14oV8oHeVF/Jq7hmvdORULwNOScarFzDRDy5y8IXzZXn+6y7LS4975Z2s90QvQqVOAVzqlJycEKPnvtJDt0RDRsYa/kAGIEgOh3Mgi+Y+IfSH2nmqgNp5Dh9qKaY/ar1T0WzNl8htWfnPIZS3LFFbFCZi9C3Z74nWbKztxLJnN4W87pXrT1GdOqX1vtJDtyRa+9LhRiZisU+I6ELtPIm4IpUL8uLFuAonTBvNPOpEbssaD+kLsSQRC+eT/Z5oTSxSp7TcV3rplkSrNRluhzxKsUsuyPAnYk6iCcloES/5n+Eqx2gL+URuy5rIB5dkhe5J9Ej01Cm9dIta2ffV4fa4kXnDOZAl+j4h/CHDn4g5qegtiKdCqHCVYyyEfCJ6l3kS+eASTeIlegXQPYkWid6NRy/dolb2Pba2Vvj/eCr6DZdE3yeEP2T4EzEnFbwFYqOoKMuCP74bP6lN4SpHEvLBJPLBJRooRa/CNcCjdYBI9XsSDRI9dUov3RJKRkqRyN1vEn2fEP6Q4U/EHDWGZGmuBR6WxTs1jQnnvZMyipTQOrUpXOVIQp4QoxS9uuGlauRnpqFnwCm8ruTZjJf0t3hG68hKIqdO6eWkUJKRciR6PVuk+ySeIoUEB3X1UQF19Rk+St1dWCAs4yGekDOK1PD3H8zCd2eNivqaAom0EwoZaUSo7iVSyHVToc5eodHzmUtUA03PzmHhOn144qlLUri/P5zrSYdoRzh2Khn+KiDDP3ykhMPqvS1BQiDQ4OeJVGjHUohJ/Wy4RpEYLYV/pMoxUY0BIjqobeMXSOBhMtHaH+pBvB6MEkEGxMuB6VBrHx5bezjkz6h1+sT674rl74/X/ZysUDtPQleUhMmG28/xy4X/5Rs7AAQb/pGERcMVYsMVeqGKZuUIN/wcDeEcaZhWrzzqRDA2IiHR/q5IiyID09niobNXPH/28dryOFE8tnoWe4tl5MbaTlWGv5r0y0gbRKjd57FsQBGv+5ngIMOfiCrhCJONtZ1osUbHGAhXiEVD6EViFIWbIx9N4ZwonVASxdgIl2gWyGrFcIsi+WdE785e8binxAZah82h+8EokHjqTKaGeCj2jlbNQaSGczhDGmNpmMfDQZ+Qhwx/Imro1S8+3PeNltCLxCgKpxAqFt2B4kE5KpFoxkY0vGvhFshqSSTdS8Twz4jaZ6XD5oh6gb9We2q4uc9q0KrlMXlsIyNajREiMZz1GtIohd4HfUIZMvyJqKFXv/hw3zdaQk9tt6IHL5uFjj5H1I2BZPOaJJqxES3vGoCgOhdeWT/+w9kYkWXRLRIQSfcSINizqeYAYWCAP32wT/g60sOPHq11w4koDKchQDjOhuGkNpHHNnKi0SUpXMNZ7yGNgaRCC+9Ehgx/Imro1S8+3PeNltBT493543dOwMIJRSHfazjGQLJ4TeLB2IhFfmwktSD87735le3wiN5Ej0iAnCHDRynUeDbVHCA8AS9G4pHX4vAcuEe6+4dw08vye0F8eFM6iCihdV0QeWyHx3DTKsM1nONhSKMYmgUT35DhT0QNvfrFh/u+0RR60fDuKHlrorVONWvQO8dcb2MjVvmxw1lvNIzhaCBnyEh16pLb+3LPioEJ/juB8D3yWhyepfaIgZE+yMgd3sIl2nVBaqJI5LEdPsNJqwzXcI63IY00Cya+SQnDf/369fjrX/+Kbdu2obm5GW+99RYuuugivZeVdEQiTKJhOIf7vtEWesP17mjVHUiOSLyD0TooBBY4qiEWxkYs82OjuV49U56kDJlw937g9R02h196TyBqPfJaHJ7l9kgoo344Rj8QPSdCOFEk8tjqS7iGczwOaUzkwXDJTkoY/v39/Zg5cyZ+/OMf4+KLL9Z7OUlLpMJkuIZzuO8bC6E3HO+OFt2B5Iik8DFaHVLkvKdyhlI0jY3h5IFH27sWLrFOeQr3UBfu3hdf/05No6qfCfWZx/rwPNyDRbj8YelUFOVYYuJEUBNFIo9tMPE8UVkvp5uav0FOt8dDlDlVSQnDf8mSJViyZInq6x0OBxwOn/fRarXGYllJiV794sN933jyRsSiO5AaIimmjVaHlHC9p6GMjVh2VInUgy/uUPOHpdNw08vhFciGIhYpT1q3vYxWSkmsD8+RHiwiWVNZXjquWVgVkREUyecg96zrLSPjyTDUqx2sWqeYXk43NUjpdj2jzESKGP7hsnLlStx99916LyMhkHoY9eoXP9y0A72ESbS7AwHqhGS4KSvR6rqjxnsa6PlXMja06qgSjgdfqkPNT8+owrs7mlUVyKoh2ilPerRSVbv3PSyr2OYz1odnLYpYo+FJj3RPyEWR9JKR8TR3Qe8Ww2qdYokypFHPKDPBQYa/BCtWrMDy5cuFr61WKyoqKnRcUXwS6mHUo83bcNIOxMTauxD4++U8wuF2BwLUC8lwU1ai1XVHbTqCmnSHcJRItPLAI+1Q88z6OsnCSqkCWa1Snnj0aqUaylPJArC7PLjin98Ir0eakx7u4VmMFkWs0fCkDzetTEomxJthqGWL20RrMRwvziw59IwyEz7I8JfAYrHAYrHovYy4Jpkfxlh7F+R+v5RHOFxjIJz7Em6aRbS67qj9PUU5Fnx31ijZ74erRKKZBx5ph5o/fbAPG24/x08RSylrvkUk/7PitQDRSXkSo2crVbnPMs8bDZGbcRBuTno4h+dA1EZ5xPc+1OFtOAcROSKdu8CjR5eecOptAG1b3Gr5XETL2RTPQxr1ijIT/pDhT4RNMj+MsT7QKP1+OY+w2s8w3PsSbkFYtPKx9RrcFu088Gh2qJFS1k8a5MP2504rw8bazpCRA7VGkd6tVAM/y6IsC375xg4AzqBr9chJV3OweGyZ/7Mb6vA2nIOIEuEeSvk16dGlJ5IJxlq2uNXquUiVVBa9osyEP2T4E2GTrA+jGsP5j+/uQU56WkReOjW/X8ojHOp3ig3PcO5LuAVh0Wrxp9fgtljkgceiQw2PUu/80x74XLJWIBC1RlE89G0Xf5YbazvRYg1fxsQy1SGSg4XS4U3rotBIo0ixYjj1NmJi6WzS4rlI5uh5IHpFmQl/UsLw7+vrw+HDh4Wv6+rqUFNTg4KCAowZM0bHlSUmyfowqjnQtFgdIfONh/P7wzkwReItA/zvSzjGTLRa/Ok1uC1e8sDDMRICIwFyRoKU0Q+oN4rirW/7cGRMLFMdEqmBQLhRJC2Ny2i3R42VsynWz8VwoueJ2OVGrygz4U9cGv7htM/Mzc0Nec3WrVtx9tlnC1/zhbtXX301XnjhhbDXl+ok68MYyUElHK9MNA9Mw/GWBd6XcIyTaKVT6DG4Te888FgaCUqoMYpi0bd9OIZJPMuYaDUQ0INoHkSGc39j1R41Gs6mcBsvDCdSEqkzKB5Tg9TsB72izIQ/cWn45+fng2FUpjq43SGvOeuss8CyWo1eSX6S9WGMxIgIJ8wcLWMmUgNQ6b6EY5xEy3jQenAb/5565oHH0kgIRSijKJqfzXANk2SVMfFANA4iw72/sYoGhyPDpQxVuTqZaDRekCISZ1A8pgaFsx/0iDIT/sSl4b927Vrh/48ePYo77rgD11xzDRYsWAAA2LhxI1588UWsXLlSryWmNMn6MEbaCk9tmDlaxkwkBmC070u0vJhaD27jfyae8sDVMlxjSY1RFI3PJhqGSTzImERMpdCCaNzfSJws0SxOljJUlepkhtt4QY5wnUFaNtZQu/8j2Q96RJkJHwwb567wRYsW4brrrsOyZcv8Xn/55ZfxzDPPYN26dTFfg9VqRV5eHnp7e1WlFqUK8RhuHC68EAPCb4X39x/MUmw/qfT7eXGnRmm+U9OIW16tCWttiX5feOSUUbwZabFYz8baTix7dlPYP8cbReEUjUeK28MGFR4PZy16yZhklG3RIFr3l/894dTbhCpOHu7UcCVi9Qyp+RzE76tWBrxy/SnDcqio3f/Rft7535kIMj7eCMdOjUuPv5iNGzfiqaeeCnp93rx5uO6663RYEcET78NCIkHOu6AGtR7V4Xov1HqJ1AzASiTicWCcHLHI644kIqV1BC7aBex6yJh4TKWIF6J1fyOtt4mkOFntnAAlYlU8HG5kS4vGGuHsfy0aVsSrjE9k4t7wr6iowLPPPou//OUvfq//85//pGm6cUA8Fa5FC7me4q3W6OQbD9eYUZsydM3CKs2N/Vh5ZcgYUzflNjBdQetweCwMEy1lTDzPKIkHT2g0768WaXqRdj6TIxa1CeF8DrEueg93/2vRsCKVZLxWxL3h//DDD+OSSy7BRx99hPnz5wMANm/ejEOHDuHNN9/UeXVEshJobPzxO9HNNx6OMRMP+c9SxCo9Ip6NMa0JZSTIGUVaGYfx3I1HDfE6o0Tu2frOzJFBRaexTEmK9v2NxAmiVnZGa06AmFjtW7WfQ6yL3sPd/1o0rEg1Ga8FcW/4X3DBBTh48CCefPJJ7N+/HwBw4YUX4oYbbiCPP6EZ8VZgFG/riaW3Jl6NMb0IZSQEfgZa5qsnYjce8aHoUGufqp/RckaJ3LPV3GvH0+vrgq6PpYd0OPdX7vAZi4hOtOcEaLFv1XwOsXb6hOvB16phRarJ+FgT94Y/wKX73H///Xovg0hx4q2mIV7WE2tvTbIOjBsOw/V6xso4jNdolByRpoJoFbGIxICNpYc00vurdbF0NOcExNu+jaXTJ1wPfrSed5Lx2hKXhv/OnTsxffp0GAwG7Ny5U/HaE088UaNVEUT81TTEw3pi7a1J9PQRvdArfB5v0Sg5htPdRauIRaQGbCw9pOHeXz1ytyMxEOOlTkYNsXL6ROLB17JhBcn46BCXhv+sWbPQ0tKCkpISzJo1CwzDSA7gYhhG1QAvgiBiR6y9NYmYPhIP6Bk+j5dolByReNL18PwO18MZKw+p2vur1+EzEgMxVJ1MvBELp0+kHnytGlaQjI8OcWn419XVobi4WPh/giDil1h7axItfSRe0Dt8Hg/RKDki8aTr4fkdroczlh5SNfdXr8OnGkMycE6AUp1MKhGpBz8ZG1YkK3Fp+I8dO1by/wmCiD+08NYkSvpIPEHhc3nUHnZuPns8Jpbm6Ob5jXSaeLx4SPU6fEY6J4Dg0CNiRzJeO+LS8Bfz4osvoqioCEuXLgUA/OY3v8EzzzyDadOm4ZVXXqGDQYpCU/ziB628NZEoo1TeJxQ+l0ftYWfhhGJdvb9Kz5Yc8eQh1fPwSYbk8NAjYhfvKYLJAsNKJc/HEZMnT8aTTz6Jc845Bxs3bsSiRYvwyCOP4P3334fJZMKqVativoZwRiETsSeSDhGpbABqhdadOxJtPXrAF1YC0geyVB2K4/awOO2Bz0Meijbcfk5cyIl46eMfLvHwOZPsJ1KBcOzUuDf8MzMzsX//fowZMwa33347mpub8e9//xt79uzBWWedhfb29pivgQz/+EGuQ4SSIUMGoHbEi5KNZJ8kK7T/pUm0Q1E8TO6NhET7nAkiEUkqw7+kpASffPIJZs+ejdmzZ2P58uW48sorUVtbi5kzZ6KvT93AleFAhn98wHuP5IrFpLxHZACmHpHsk2Qn3o1DvaBDkTbQ50wQsSUcOzXuc/zPPfdcXHfddZg9ezYOHjyICy64AACwZ88eVFZW6rs4QlPC7RBBY8BTE5oCGUw8d9jRE8op1gb6nAkifoh7w//xxx/H73//ezQ0NODNN99EYSGnvLZt24Zly5bpvDpCS8LtEEEGYGqidxtLIrGgQ5E20OdMEPFB3Bv++fn5eOyxx4Jev/vuu3VYDaEn4XaIIAMwNaE2lgRBEAQhjUHvBajhyy+/xI9+9COceuqpaGxsBAD85z//wYYNG3ReGaElfHtCueAwAy5vlG9PSAZgahLuPiEIgiCIVCHuDf8333wT559/PjIyMlBdXQ2HwwEA6O3txf3336/z6ggt4XtaAwgy6qR6V5MBmJqEu08IgiAIIlWIe8P/3nvvxVNPPYVnn30WaWlpwusLFy5EdXW1jisj9IAfylKW5++lL8tLD+rQQwZg6hLOPiEIgiCIVCHu23lmZmZi7969qKysRE5ODnbs2IFx48bhyJEjmDZtGuz22OdnUzvP+COc9oTUSi51oTaWBEEQRLKTVO08y8rKcPjw4aDWnRs2bMC4ceP0WRShO+F0iKBWcqkLdRIhCIIgCB9xb/hff/31uOWWW/Dcc8+BYRg0NTVh48aN+OUvf4k777xT7+URCQIZgARBEARBpDpxb/jfcccd8Hg8WLRoEQYGBnDGGWfAYrHg17/+Na677jq9l0cQBEEQBEEQCUHcF/cyDIPf/e536Orqwu7du7Fp0ya0t7cjLy8PVVVVei+PIAiCIAiCIBKCuDX8HQ4HVqxYgXnz5mHhwoX48MMPMW3aNOzZsweTJ0/G3//+d9x22216L5MgCIIgCIIgEoK4TfW588478fTTT2Px4sX4+uuvcemll+Laa6/Fpk2b8OCDD+LSSy+F0WjUe5kEQRAEQRAEkRDEreH/xhtv4N///je+853vYPfu3TjxxBPhcrmwY8cOMAx1YyEIgiAIgiCIcIjbVJ/jx49j7ty5AIDp06fDYrHgtttuI6OfIAiCIAiCICIgbg1/t9sNs9ksfG0ymZCdna3jigiCIAiCIAgicYnbVB+WZXHNNdfAYrEAAOx2O2644QZkZWX5Xbdq1So9lkcQBEEQBEEQCUXcGv5XX32139c/+tGPdFoJQRAEQRAEQSQ+cWv4P//883ovgSAIgiAIgiCShrjN8ScIgiAIgiAIInqQ4U8QBEEQBEEQKQAZ/gRBEARBEASRApDhTxAEQRAEQRApABn+BEEQBEEQBJECkOFPEARBEARBECkAGf4EQRAEQRAEkQKQ4U8QBEEQBEEQKQAZ/gRBEARBEASRApDhTxAEQRAEQRApABn+BEEQBEEQBJECkOFPEARBEARBECkAGf4EQRAEQRAEkQKQ4U8QBEEQBEEQKQAZ/gRBEARBEASRApDhTxAEQRAEQRApABn+BEEQBEEQBJECkOFPEARBEARBECkAGf4EQRAEQRAEkQKQ4U8QBEEQBEEQKQAZ/gRBEARBEASRApDhTxAEQRAEQRApQEoZ/o8//jgqKyuRnp6O+fPnY/PmzXoviSAIgiAIgiA0IWUM/9deew3Lly/HXXfdherqasycORPnn38+2tra9F4aQRAEQRAEQcQchmVZVu9FaMH8+fNx0kkn4bHHHgMAeDweVFRU4Oc//znuuOMOxZ+1Wq3Iy8tDU1MTcnNztVguQRAEQRAEQYTEarWivLwcvb29Ie1Uk0Zr0pWhoSFs27YNK1asEF4zGAxYvHgxNm7cGHS9w+GAw+EQvrZarQCA8vLy2C+WIAiCIAiCIGJASqT6dHR0wO12o7S01O/10tJStLS0BF2/cuVK5OXlCf8qKiq0WipBEARBEARBxISU8PiHy4oVK7B8+XLha6vVioqKCkr1IQiCIAiCIOIKPtVHDSlh+BcVFcFoNKK1tdXv9dbWVpSVlQVdb7FYYLFYgl7PyspCVlZWzNZJEARBEARBEOHgdrtVX5sShr/ZbMbcuXOxZs0aXHTRRQC44t41a9bg5ptvDvnzfP0zn+tPEARBEARBEPEAb5+q6deTEoY/ACxfvhxXX3015s2bh5NPPhmPPPII+vv7ce2114b82c7OTgCgXH+CIAiCIAgiLrHZbMjLy1O8JmUM/8svvxzt7e2488470dLSglmzZuHjjz8OKviVoqCgAABQX18f8gMlEg++hqOhoYFqOJIQur/JC93b5Ibub3JD9zd6sCwLm82mKs8/Zfr4Dwe+j7+a/qhE4kH3N7mh+5u80L1Nbuj+Jjd0f/UhJdp5EgRBEARBEESqQ4Y/QRAEQRAEQaQAZPirwGKx4K677pJs8UkkPnR/kxu6v8kL3dvkhu5vckP3Vx8ox58gCIIgCIIgUgDy+BMEQRAEQRBECkCGP0EQBEEQBEGkAGT4EwRBEARBEEQKQIY/QRAEQRAEQaQAZPgTBEEQBEEQRApAhr8KHn/8cVRWViI9PR3z58/H5s2b9V4SESYrV67ESSedhJycHJSUlOCiiy7CgQMH/K6x2+246aabUFhYiOzsbFxyySVobW3VacXEcPjzn/8MhmFw6623Cq/R/U1sGhsb8aMf/QiFhYXIyMjAjBkzsHXrVuH7LMvizjvvxMiRI5GRkYHFixfj0KFDOq6YUIPb7cYf/vAHVFVVISMjA+PHj8ef/vQniBsO0r1NHNavX48LL7wQ5eXlYBgGb7/9tt/31dzLrq4uXHHFFcjNzUV+fj5+8pOfoK+vT8O/Irkhwz8Er732GpYvX4677roL1dXVmDlzJs4//3y0tbXpvTQiDL744gvcdNNN2LRpE1avXg2n04nzzjsP/f39wjW33XYb3nvvPbzxxhv44osv0NTUhIsvvljHVRORsGXLFjz99NM48cQT/V6n+5u4dHd3Y+HChUhLS8NHH32EvXv34sEHH8SIESOEa/7yl7/g0UcfxVNPPYVvvvkGWVlZOP/882G323VcORGKBx54AE8++SQee+wx7Nu3Dw888AD+8pe/4B//+IdwDd3bxKG/vx8zZ87E448/Lvl9NffyiiuuwJ49e7B69Wq8//77WL9+PX76059q9SckPyyhyMknn8zedNNNwtdut5stLy9nV65cqeOqiOHS1tbGAmC/+OILlmVZtqenh01LS2PfeOMN4Zp9+/axANiNGzfqtUwiTGw2Gztx4kR29erV7JlnnsnecsstLMvS/U10br/9dva0006T/b7H42HLysrYv/71r8JrPT09rMViYV955RUtlkhEyNKlS9kf//jHfq9dfPHF7BVXXMGyLN3bRAYA+9Zbbwlfq7mXe/fuZQGwW7ZsEa756KOPWIZh2MbGRs3WnsyQx1+BoaEhbNu2DYsXLxZeMxgMWLx4MTZu3Kjjyojh0tvbCwAoKCgAAGzbtg1Op9PvXk+ZMgVjxoyhe51A3HTTTVi6dKnffQTo/iY67777LubNm4dLL70UJSUlmD17Np599lnh+3V1dWhpafG7v3l5eZg/fz7d3zjn1FNPxZo1a3Dw4EEAwI4dO7BhwwYsWbIEAN3bZELNvdy4cSPy8/Mxb9484ZrFixfDYDDgm2++0XzNyYhJ7wXEMx0dHXC73SgtLfV7vbS0FPv379dpVcRw8Xg8uPXWW7Fw4UJMnz4dANDS0gKz2Yz8/Hy/a0tLS9HS0qLDKolwefXVV1FdXY0tW7YEfY/ub2Jz5MgRPPnkk1i+fDl++9vfYsuWLfjFL34Bs9mMq6++WriHUrKa7m98c8cdd8BqtWLKlCkwGo1wu9247777cMUVVwAA3dskQs29bGlpQUlJid/3TSYTCgoK6H5HCTL8iZTjpptuwu7du7Fhwwa9l0JEiYaGBtxyyy1YvXo10tPT9V4OEWU8Hg/mzZuH+++/HwAwe/Zs7N69G0899RSuvvpqnVdHDIfXX38d//3vf/Hyyy/jhBNOQE1NDW699VaUl5fTvSWIGECpPgoUFRXBaDQGdf5obW1FWVmZTqsihsPNN9+M999/H2vXrsXo0aOF18vKyjA0NISenh6/6+leJwbbtm1DW1sb5syZA5PJBJPJhC+++AKPPvooTCYTSktL6f4mMCNHjsS0adP8Xps6dSrq6+sBQLiHJKsTj1//+te444478IMf/AAzZszAlVdeidtuuw0rV64EQPc2mVBzL8vKyoKap7hcLnR1ddH9jhJk+CtgNpsxd+5crFmzRnjN4/FgzZo1WLBggY4rI8KFZVncfPPNeOutt/D555+jqqrK7/tz585FWlqa370+cOAA6uvr6V4nAIsWLcKuXbtQU1Mj/Js3bx6uuOIK4f/p/iYuCxcuDGq/e/DgQYwdOxYAUFVVhbKyMr/7a7Va8c0339D9jXMGBgZgMPibIkajER6PBwDd22RCzb1csGABenp6sG3bNuGazz//HB6PB/Pnz9d8zUmJ3tXF8c6rr77KWiwW9oUXXmD37t3L/vSnP2Xz8/PZlpYWvZdGhMGNN97I5uXlsevWrWObm5uFfwMDA8I1N9xwAztmzBj2888/Z7du3couWLCAXbBggY6rJoaDuKsPy9L9TWQ2b97Mmkwm9r777mMPHTrE/ve//2UzMzPZl156Sbjmz3/+M5ufn8++88477M6dO9nvfve7bFVVFTs4OKjjyolQXH311eyoUaPY999/n62rq2NXrVrFFhUVsb/5zW+Ea+jeJg42m43dvn07u337dhYA+9BDD7Hbt29njx07xrKsunv5rW99i509ezb7zTffsBs2bGAnTpzILlu2TK8/Kekgw18F//jHP9gxY8awZrOZPfnkk9lNmzbpvSQiTABI/nv++eeFawYHB9mf/exn7IgRI9jMzEz2e9/7Htvc3KzfoolhEWj40/1NbN577z12+vTprMViYadMmcI+88wzft/3eDzsH/7wB7a0tJS1WCzsokWL2AMHDui0WkItVquVveWWW9gxY8aw6enp7Lhx49jf/e53rMPhEK6he5s4rF27VlLXXn311SzLqruXnZ2d7LJly9js7Gw2NzeXvfbaa1mbzabDX5OcMCwrGo9HEARBEARBEERSQjn+BEEQBEEQBJECkOFPEARBEARBECkAGf4EQRAEQRAEkQKQ4U8QBEEQBEEQKQAZ/gRBEARBEASRApDhTxAEQRAEQRApABn+BEEQBEEQBJECkOFPEARBEARBECkAGf4EQRAEQRAEkQKQ4U8QBEEQBEEQKQAZ/gRBEARBEASRAvw/9KTemZD8Z4QAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 800x600 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "data[\"ANNUAL\"] = data[\"ANNUAL\"] + 0.01\n",
        "\n",
        "result = seasonal_decompose(data[\"ANNUAL\"], model='multiplicative', period=24)\n",
        "fig = plt.figure()\n",
        "fig = result.plot()\n",
        "fig.set_size_inches(8, 6)\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fKNCXoeIEDnE",
        "outputId": "2cc0d2c1-7b45-41b7-d1f5-757d8314babe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(4, 1, 1)\n"
          ]
        }
      ],
      "source": [
        "model = auto_arima(data['ANNUAL'], seasonal=True, m=52, suppress_warnings=True)\n",
        "print(model.order)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Yh43dnxuSLBJ",
        "outputId": "64a89ac5-b27f-491f-8351-eb84fb2942c7"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "  <div id=\"df-b31d9982-e6f6-4902-a54c-32fc03677551\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>YEAR</th>\n",
              "      <th>ANNUAL</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1970-01-01 00:00:00.000001901</td>\n",
              "      <td>554.31</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1970-01-01 00:00:00.000001902</td>\n",
              "      <td>863.91</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1970-01-01 00:00:00.000001903</td>\n",
              "      <td>887.61</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1970-01-01 00:00:00.000001904</td>\n",
              "      <td>492.61</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1970-01-01 00:00:00.000001905</td>\n",
              "      <td>811.51</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>1970-01-01 00:00:00.000002011</td>\n",
              "      <td>890.51</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>1970-01-01 00:00:00.000002012</td>\n",
              "      <td>714.01</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>1970-01-01 00:00:00.000002013</td>\n",
              "      <td>1118.61</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>1970-01-01 00:00:00.000002014</td>\n",
              "      <td>705.71</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>1970-01-01 00:00:00.000002015</td>\n",
              "      <td>622.91</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>115 rows × 4 columns</p>\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "                             YEAR   ANNUAL  Year  Month\n",
              "0   1970-01-01 00:00:00.000001901   554.31    70      1\n",
              "1   1970-01-01 00:00:00.000001902   863.91    70      1\n",
              "2   1970-01-01 00:00:00.000001903   887.61    70      1\n",
              "3   1970-01-01 00:00:00.000001904   492.61    70      1\n",
              "4   1970-01-01 00:00:00.000001905   811.51    70      1\n",
              "..                            ...      ...   ...    ...\n",
              "110 1970-01-01 00:00:00.000002011   890.51    70      1\n",
              "111 1970-01-01 00:00:00.000002012   714.01    70      1\n",
              "112 1970-01-01 00:00:00.000002013  1118.61    70      1\n",
              "113 1970-01-01 00:00:00.000002014   705.71    70      1\n",
              "114 1970-01-01 00:00:00.000002015   622.91    70      1\n",
              "\n",
              "[115 rows x 4 columns]"
            ]
          },
          "execution_count": 106,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-oHHHGc7Dv7w"
      },
      "outputs": [],
      "source": [
        "import itertools\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "# Assuming 'data' is a pandas DataFrame and 'ANNUAL' is the column with time series data\n",
        "data = data\n",
        "\n",
        "# Define the p, d, q parameters to take any value from 0 to 2\n",
        "p = d = q = range(0, 3)\n",
        "\n",
        "# Generate all different combinations of p, d, and q triplets\n",
        "pdq = list(itertools.product(p, d, q))\n",
        "\n",
        "# Define the seasonal p, d, q parameters to take any value from 0 to 2 and set the seasonal period to 52 weeks\n",
        "seasonal_pdq = [(x[0], x[1], x[2], 52) for x in list(itertools.product(p, d, q))]\n",
        "\n",
        "# Split data into train and test sets\n",
        "train_data = data[\"ANNUAL\"].iloc[:-12]\n",
        "test_data = data[\"ANNUAL\"].iloc[-12:]\n",
        "\n",
        "# Define a function to fit SARIMAX model and compute the AIC\n",
        "def sarimax_grid_search(data, pdq, seasonal_pdq):\n",
        "    best_aic = float('inf')\n",
        "    best_pdq = None\n",
        "    best_seasonal_pdq = None\n",
        "    best_model = None\n",
        "\n",
        "    for param in pdq:\n",
        "        for seasonal_param in seasonal_pdq:\n",
        "            try:\n",
        "                model = SARIMAX(data,\n",
        "                                order=param,\n",
        "                                seasonal_order=seasonal_param,\n",
        "                                enforce_stationarity=False,\n",
        "                                enforce_invertibility=False)\n",
        "\n",
        "                results = model.fit()\n",
        "\n",
        "                # Compare this model's AIC with the best AIC found so far\n",
        "                if results.aic < best_aic:\n",
        "                    best_aic = results.aic\n",
        "                    best_pdq = param\n",
        "                    best_seasonal_pdq = seasonal_param\n",
        "                    best_model = results\n",
        "            except:\n",
        "                continue\n",
        "\n",
        "    return best_aic, best_pdq, best_seasonal_pdq, best_model\n",
        "\n",
        "# Run the grid search\n",
        "best_aic, best_pdq, best_seasonal_pdq, best_model = sarimax_grid_search(train_data, pdq, seasonal_pdq)\n",
        "\n",
        "# Print the results\n",
        "print(f'Best SARIMAX model: order={best_pdq} seasonal_order={best_seasonal_pdq} AIC={best_aic}')\n",
        "\n",
        "# Predict on the test set\n",
        "predictions = best_model.get_forecast(steps=12)\n",
        "predicted_mean = predictions.predicted_mean\n",
        "\n",
        "# Compute the mean squared error\n",
        "mse = mean_squared_error(test_data, predicted_mean)\n",
        "print(f'Test MSE: {mse}')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MIUh3zTWGcQU",
        "outputId": "542fd2ad-a7ec-49fb-897d-e3bb22e88a22"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/statespace/sarimax.py:866: UserWarning:\n",
            "\n",
            "Too few observations to estimate starting parameters for seasonal ARMA. All parameters except for variances will be set to zeros.\n",
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                                     SARIMAX Results                                      \n",
            "==========================================================================================\n",
            "Dep. Variable:                             ANNUAL   No. Observations:                  115\n",
            "Model:             SARIMAX(4, 1, 1)x(4, 1, 1, 52)   Log Likelihood                -429.962\n",
            "Date:                            Sat, 20 Jan 2024   AIC                            881.924\n",
            "Time:                                    21:03:54   BIC                            905.323\n",
            "Sample:                                         0   HQIC                           891.111\n",
            "                                            - 115                                         \n",
            "Covariance Type:                              opg                                         \n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "ar.L1         -0.3273      0.267     -1.224      0.221      -0.851       0.197\n",
            "ar.L2         -0.1150      0.302     -0.381      0.704      -0.707       0.477\n",
            "ar.L3         -0.0753      0.242     -0.311      0.756      -0.550       0.400\n",
            "ar.L4         -0.3414      0.190     -1.793      0.073      -0.715       0.032\n",
            "ma.L1         -0.6968      0.236     -2.950      0.003      -1.160      -0.234\n",
            "ar.S.L52      -0.2439   4961.471  -4.92e-05      1.000   -9724.548    9724.060\n",
            "ar.S.L104     -0.5932   2788.575     -0.000      1.000   -5466.100    5464.914\n",
            "ar.S.L156      0.2103   4081.794   5.15e-05      1.000   -7999.958    8000.379\n",
            "ar.S.L208  -8.213e-05   1704.905  -4.82e-08      1.000   -3341.553    3341.553\n",
            "ma.S.L52      -0.7004   4329.252     -0.000      1.000   -8485.879    8484.478\n",
            "sigma2      1.774e+04      0.090   1.97e+05      0.000    1.77e+04    1.77e+04\n",
            "===================================================================================\n",
            "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):                 1.55\n",
            "Prob(Q):                              0.97   Prob(JB):                         0.46\n",
            "Heteroskedasticity (H):               1.00   Skew:                            -0.25\n",
            "Prob(H) (two-sided):                  0.99   Kurtosis:                         2.41\n",
            "===================================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
            "[2] Covariance matrix is singular or near-singular, with condition number 1.04e+25. Standard errors may be unstable.\n"
          ]
        }
      ],
      "source": [
        "# from statsmodels.tools.sm_exceptions import  ValueWarning\n",
        "# warnings.simplefilter('ignore', ValueWarning)\n",
        "\n",
        "p, d, q = 4,1,1\n",
        "\n",
        "model = SARIMAX(data[\"ANNUAL\"], order=(p, d, q),\n",
        "                seasonal_order=(p, d, q, 52))\n",
        "fitted = model.fit()\n",
        "print(fitted.summary())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SCGDBWL0dvw7",
        "outputId": "06f1a60f-3a11-4a69-9197-3243597f18ee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "           ANNUAL\n",
            "YEAR             \n",
            "1901   746.259120\n",
            "1902   665.353724\n",
            "1903  1076.719314\n",
            "1904   919.206913\n",
            "1905   869.876808\n",
            "...           ...\n",
            "2096  1084.437579\n",
            "2097   964.204418\n",
            "2098  1312.831846\n",
            "2099  1188.137162\n",
            "2100  1373.633011\n",
            "\n",
            "[200 rows x 1 columns]\n"
          ]
        }
      ],
      "source": [
        "# Use the same number of steps in both cases\n",
        "steps = 200\n",
        "\n",
        "# Generate the range of years starting from 1901\n",
        "forecast_years = range(1901, 1901 + steps)\n",
        "\n",
        "# Create a new DataFrame with the predicted values and the corresponding years\n",
        "predictions_df = pd.DataFrame({'YEAR': forecast_years, 'ANNUAL': fitted.get_forecast(steps=steps).predicted_mean.values})\n",
        "\n",
        "# Set the 'YEAR' column as the index\n",
        "predictions_df.set_index('YEAR', inplace=True)\n",
        "\n",
        "# Save the DataFrame to a CSV file\n",
        "predictions_df.to_csv('predictions.csv')\n",
        "\n",
        "print(predictions_df)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9h8GKluwGqrY",
        "outputId": "76366d68-496c-4f20-f45f-4f04ad97ceee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "           ANNUAL\n",
            "YEAR             \n",
            "1901  1007.007295\n",
            "1902  1057.593866\n",
            "1903   765.952539\n",
            "1904  1007.818469\n",
            "1905  1150.781600\n",
            "...           ...\n",
            "1986  1096.945792\n",
            "1987  1172.356627\n",
            "1988  1094.179762\n",
            "1989  1064.897318\n",
            "1990  1142.906165\n",
            "\n",
            "[90 rows x 1 columns]\n"
          ]
        }
      ],
      "source": [
        "# predictions = fitted.predict(len(data), len(data)+200)\n",
        "# print(predictions)\n",
        "\n",
        "# # Generate the range of years starting from 1901\n",
        "# forecast_years = range(1901, 1901 + 90)\n",
        "\n",
        "# # Create a new DataFrame with the predicted values and the corresponding years\n",
        "# predictions_df = pd.DataFrame({'YEAR': forecast_years, 'ANNUAL': fitted.get_forecast(steps=200).predicted_mean.values})\n",
        "\n",
        "# # Set the 'YEAR' column as the index\n",
        "# predictions_df.set_index('YEAR', inplace=True)\n",
        "\n",
        "# print(predictions_df)\n",
        "\n",
        "\n",
        "# # forecast_years = range(data[\"YEAR\"].max().year + 1, data[\"YEAR\"].max().year + 91)\n",
        "# # forecast_index = pd.Index(forecast_years, name='YEAR')\n",
        "# # predictions = pd.Series(fitted.get_forecast(steps=90).predicted_mean.values, index=forecast_index, name='ANNUAL')\n",
        "# # print(predictions)\n",
        "\n",
        "# Use the same number of steps in both cases\n",
        "steps = 90  # Adjust the number of steps based on your requirement\n",
        "\n",
        "# Generate the range of years starting from 1901\n",
        "forecast_years = range(1901, 1901 + steps)\n",
        "\n",
        "# Create a new DataFrame with the predicted values and the corresponding years\n",
        "predictions_df = pd.DataFrame({'YEAR': forecast_years, 'ANNUAL': fitted.get_forecast(steps=steps).predicted_mean.values})\n",
        "\n",
        "# Set the 'YEAR' column as the index\n",
        "predictions_df.set_index('YEAR', inplace=True)\n",
        "\n",
        "print(predictions_df)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "1fby9lJBGx1h",
        "outputId": "2b54afba-0f68-4199-fe13-2c123591ba43"
      },
      "outputs": [
        {
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Data VS Predictions\"},\"xaxis\":{\"title\":{\"text\":\"YEAR\"}},\"yaxis\":{\"title\":{\"text\":\"ANNUAL\"}},\"legend\":{\"title\":{\"text\":\"Data\"}},\"width\":1000,\"height\":600},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('82bd9b84-9e42-49b2-b384-2d825c061354');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = go.Figure()\n",
        "\n",
        "# Add training data line plot\n",
        "fig.add_trace(go.Scatter(\n",
        "    x=data.index,\n",
        "    y=data['ANNUAL'],\n",
        "    mode='lines',\n",
        "    name='Training Data',\n",
        "    line=dict(color='blue')\n",
        "))\n",
        "\n",
        "# Add predictions line plot\n",
        "fig.add_trace(go.Scatter( x=predictions_df.index, y=predictions_df, mode='lines',\n",
        "    name='Predictions',\n",
        "    line=dict(color='red')\n",
        "    ))\n",
        "\n",
        "fig.update_layout(\n",
        "    title=\"Training Data VS Predictions\",\n",
        "    xaxis_title=\"YEAR\",\n",
        "    yaxis_title=\"ANNUAL\",\n",
        "    legend_title=\"Data\",\n",
        "    width=1000,\n",
        "    height=600\n",
        ")\n",
        "\n",
        "pio.show(fig)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "thzSCYKW8ShL",
        "outputId": "1eda4c9a-278e-42a6-8879-2c16e399a7d1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<statsmodels.tsa.statespace.sarimax.SARIMAX at 0x7de281b71f30>"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "01_z3Vg38NOi",
        "outputId": "bd740746-8f4d-4fa3-e40c-564c48931def"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<statsmodels.tsa.statespace.sarimax.SARIMAXResultsWrapper at 0x7de281b73dc0>"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "fitted"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "twfNQLIt6RFC"
      },
      "outputs": [],
      "source": [
        "import pickle\n",
        "Pkl_Filename = \"/content/model.pkl\"\n",
        "\n",
        "with open(Pkl_Filename, 'wb') as file:\n",
        "    pickle.dump(fitted, file)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Tt7_j5S7WdM",
        "outputId": "77476ebc-6608-477b-998f-a7941b5841b8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<statsmodels.tsa.statespace.sarimax.SARIMAXResultsWrapper object at 0x7d44d810aa70>\n"
          ]
        }
      ],
      "source": [
        "with open(Pkl_Filename, 'rb') as file:\n",
        "    saved_model = pickle.load(file)\n",
        "\n",
        "print(saved_model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mrUJhFwS8tyM",
        "outputId": "54bd9e35-84af-4aa8-ebe8-d1585165e658"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "115    1007.015556\n",
              "116    1057.901908\n",
              "117     766.098066\n",
              "118    1007.994982\n",
              "119    1150.969285\n",
              "          ...     \n",
              "311     800.161994\n",
              "312     664.618723\n",
              "313     735.962653\n",
              "314     931.701012\n",
              "315     918.277224\n",
              "Name: predicted_mean, Length: 201, dtype: float64"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "saved_model.predict(len(data), len(data)+200)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Z-ZOmlE9OqL",
        "outputId": "2e50a018-a92d-46fc-aa44-4f8f5d078d76"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: streamlit in /usr/local/lib/python3.10/dist-packages (1.30.0)\n",
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            "Requirement already satisfied: folium in /usr/local/lib/python3.10/dist-packages (0.14.0)\n",
            "Requirement already satisfied: branca>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from folium) (0.7.0)\n",
            "Requirement already satisfied: jinja2>=2.9 in /usr/local/lib/python3.10/dist-packages (from folium) (3.1.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from folium) (1.23.5)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from folium) (2.31.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2>=2.9->folium) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->folium) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->folium) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->folium) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->folium) (2023.11.17)\n"
          ]
        }
      ],
      "source": [
        "!pip install streamlit\n",
        "!pip install folium"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uyHCBge0e97W"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}