ML_LAB6_21171.ipynb

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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NUmubMa8rOr3",
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        {
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          "text": [
            "Mounted at /content/drive\n",
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"drive/My Drive/code_ques.csv\")\n",
        "df.head()"
      ],
      "metadata": {
        "id": "s9ekR7cIy5Od",
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          "height": 255
        },
        "outputId": "d320f63b-55c8-4b82-8f26-ca9b83bd38b2"
      },
      "execution_count": null,
      "outputs": [
        {
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              "          0         1         2         3         4         5         6  \\\n",
              "0 -1.355365  2.348287  1.030452 -1.503548 -0.380958  1.095197 -2.063201   \n",
              "1 -1.403411  2.131454  1.117223 -1.542901 -0.351417  1.042730 -2.025140   \n",
              "2 -1.388594  2.358305  0.994477 -1.480365 -0.382565  1.104627 -2.042682   \n",
              "3 -1.352139  2.155489  1.027737 -1.424550 -0.316464  1.111701 -2.039937   \n",
              "4 -1.411875  2.337363  1.050743 -1.506995 -0.321872  1.118751 -2.068671   \n",
              "\n",
              "          7         8         9  ...       759       760       761       762  \\\n",
              "0 -1.657276  0.780015  0.224140  ... -2.330601  0.542652 -1.660974 -2.150713   \n",
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              "4 -1.603735  0.730069  0.242844  ... -2.279187  0.451709 -1.607142 -2.184134   \n",
              "\n",
              "        763       764       765       766       767  score  \n",
              "0  0.545611 -1.614411 -0.133085 -2.134145  1.184289   10.0  \n",
              "1  0.514854 -1.625416  0.014533 -2.156347  1.088794    8.0  \n",
              "2  0.582454 -1.606852 -0.164269 -2.139201  1.158317    7.0  \n",
              "3  0.419316 -1.635483 -0.074271 -2.112344  1.094625    5.0  \n",
              "4  0.521283 -1.604708 -0.055002 -2.056900  1.290956    6.0  \n",
              "\n",
              "[5 rows x 769 columns]"
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          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in df.columns:\n",
        "  if df[col].isnull().sum()>0:\n",
        "    print(col)\n",
        "    print(df[col].isnull().sum())"
      ],
      "metadata": {
        "id": "5JxVUNK-y5QN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b5c39dd-1088-44ef-c6ef-2e6481c625c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "score\n",
            "4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "AUeVc5W9y5Tz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Final_score'] = np.where(df['score'] > 5, 1, 0)"
      ],
      "metadata": {
        "id": "IgxM7TPry5V0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape\n",
        "df.dtypes"
      ],
      "metadata": {
        "id": "vmYb2DNsy5Xv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "919edec6-9e1e-421f-a630-2479fa90ef2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0              float64\n",
              "1              float64\n",
              "2              float64\n",
              "3              float64\n",
              "4              float64\n",
              "                ...   \n",
              "765            float64\n",
              "766            float64\n",
              "767            float64\n",
              "score          float64\n",
              "Final_score      int64\n",
              "Length: 770, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vector1 = df['1'].to_numpy()\n",
        "vector2 = df['5'].to_numpy()\n",
        "\n",
        "plt.scatter(vector1,vector2)\n",
        "plt.title(\"Scatter Plot\")\n",
        "plt.xlabel(\"Embeddings 0\")\n",
        "plt.ylabel(\"Embeddings 1\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "0ck8VcaMy5Zd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "5c4acf5b-a642-4420-f006-787a7e1e4746"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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OnKqqNW8n95gMzExy+nqWz21ZWdiV/B+l9pF2uMo6BRoGNURBTK6N89BHW+1uk5YYhU0H7NekkYOPqUt3Ytuf5dh1uKLeNsUWlYidvZ4tecjKlfwfy/Zx2QVtcZV1CkQcfiIKcoM6JOOBvul273cU0Fj66Jc/FQMamYSLPSYP9E2HTvCCft+RU9hYUIqSCveWTeCyC9rhKusUqNhTQxTkak0Svt7unZNQcXkVZq/Zh3fXFQoPI81eW4DZawuQEBXq1mtz2QVtOFtGg8N95M/YU0MU5Fwd1nHVgg1Fqgv9AXUzolyhQ11ekEmSONVbA2pWWSfyN+ypIQpy3h6WOXnWteDEFTrUnWSrzptwx/xfzLcz98N1gbbKOpOZyRKDGqIg581hGT0Ak9deDTBGhuLkmXM4ecY6kCqxSFxmYKNOIK2yzmRmssXhJ6Ig1z09AXER7uWriPJWQNO3TWN8PPoKhDcKUbxfHnyyrYNDzsllAOz1dfjLKutMZiYlDGqIglyIXod7e9mf/WQpISoMzWIMHm6R+/IOn8Kvf5Q5nDElkvvBZRfqk8sAAKgX2Mh/+3qVdWfJzBKAiV/uxIZ9x/mZNjA6SZIazCdeUVEBo9GI8vJyxMbG+ro5RF5Ta5LQ9flV9YZpbL09sgv0emCsijozviDn0oh4fUQ2hmU3r3c7hy4cU/P+eDuvZWNBKW6ft0loW36mwUH0/M2cGqIGIESvw0s3d3QYrDzYNx2Ds5LN///u+kL46yWPmmYp5X7IQxe2z1NcXoWxH23FhAFtML5/mwadcDqoQzIGZiY5DVY8HRwqBUxqkpRt86uYWBzc2FND1IAonYASokLx/LAOGJyVYt5G6YQvu6VLc/RqnYiCY6cxe22BF1rtGh2AJGM4Xr2lE45XVptPYADQ++U1Tqe5J8WGY9qNvMJ3xN53RQ4R3E3UVv6+huGK9AR8k1ci/Dzyd2HykEzMWMHeuUAkev5mUEPUwDi6Uq01SQ5P+PLJ4aen+2NzYZnwEICn2Q5HyX/HXZgdJUs2hmNEt1TMWr1P+Hk5g0qZmu+KvZ4QR99FZ8G1VrQKwMizOPxERIpC9Dq7aySpKbwmz5LxZmE/JRMGXIpPthy0akdcZChO2JnqLRrQABcTTmMMoeiR0ZjDFBbUfFcsv29yILM6vwRLcg9ZFV20XBhV7eKmrmKV5ODCoIaIzNQUXrNcLNOX3b1tmkabe46OnqpCYrQBT36Wq7itK+08eeYc7njvFw5T2HClSF9OXjGmfb0LJRXVitvK+S+PD2jj1WCZi6IGD07pJiIz0YJqRccrAdQlk865swuSjb4rxPbMVzutpu3uLq6we9J0B+ufWFNbpC8nrxhjP9rq8LORp2Mv2FDkfgNd4C9Vksl17KkhaoDs5TLIQ0ol5VUOezVmrd6Htkkx5hkyMYZQbCg4hg9+/gOVNbVe2w+grielx8zvUVZZ49HX4TCFNWffFTmnpnt6AmpNEiZ+uVP4ub251IYlf6iSTO5hUEPkJ7w11dTZFFx5SMkR+eRuMqHebBJfcCWguaVLc6zKP4LyqvPCj+EwxUWWw49KidrAxSJ96/ccc1ojyVPk2X0zVvwuFIBRYGNQQ+QHvFUIzt6MEttaHo8PuBSzVu+1+zzyyf3hRf5dpM8evQ7439ZDLj+ewxR15OFH2+9uksV3NyevGE9+tt1nbZx8w2UYnJUCvV4nFIBRYGNQQ+RjooGGu5yVlrccWklLjHT79fyZu5Xzj5+qRq1J4kkQjov0eWtatiPHT1Wh1iQJBWAU+FinhsiHtKj1IUq0tPziMT0AwG9q0PgrzoZyzNl325uiDCF45W9ZGJyVworCAUr0/M3ZT0Q+pKbWh7vUTMF1tlIzcTaUM86+295UWV2Lhxdtw+iFm801loZlN0dP1h4KOgxqKOj580rMrtT6cJWaKbgiKzU3dPK3aPqyfI99p/z5u+uMP+Ydfb/7GG6ftwm9X17DYDRIMaeGgpq7Cbie7qpWW+vDHWqm4AKOk0AnD2mPZ77K89mMFm+5q0cLfLjpoN37PTkbKtBXEffn6dHFGuerkf9gUENBy90EXG+cVETqwsRFhsJkktxOTFUzBVdmLwkUAJ75Ks/ltgSCZGM4urSIdxjUyFbll2ga1HgredyT/GUZDUdYcyj4cPiJXOKsW9zX3ebOZvoAjocN5JOK7QFZ6zwKR8M8MrlMvxZd5nLvS5JNBeAkY7jdE6W8VpRlDsLmwrKg76UpP3sOB8vOCG37/oYitz8b+TezZNshPLNkp8vfXX9h+d32R1rmq5H/YE8NqeasB8Mfus1dXWwPUDf1WYsrPHvDPLaKy6sw9qOtGN0rDQMyk1weCnM0BdeSo6E3f8yX0NqZmlrMWr2v3krfStz9Tij9ZuwJpAKAgzok4+2RnTF+8Ta3p9F7SkP4LjckDGpIFWfd4g/0Tce76wp93m3uTgKuOwGRPc5yc+RAY9OBUoz7eKvDMvHvbSjCexuK3AoUHa3UDTgPXBOjDKpfM5i5E2i4WsslUE7Gg7NSMBs6vy3U6M+5P6RewAw/zZw5E926dUNMTAyaNm2K4cOHY8+ePb5uVoPirAdDAjBvff2ARr4fEOs212Loyp0E3PnrC4QeW1JRJdTOnLxi9H55DW6ftwmPfZJrd/ZFiF4HvU4nvO6Np6YUOxt6m7kyH09+7rsKsd528sw5XN8hSWhbtYGGo9+UM4F0Mh6clYwJAy71dTOs6FAXqHNphOASMD01P/74I8aNG4du3brh/PnzeOaZZ3DttdciPz8fUVFRvm5egyBSd8JR/CFyNSs6dOWs50PtTB9ZzXkT1uw55nAfZTOW70JZ5cUARKmdahM+1ZwUPTEUJpKL9M66QrdfJ9BkNBE7xqgNNFyp5RKo6xSN798aizf/4ZEV1F3FpRGCT8D01OTk5OCee+7BZZddhk6dOmHhwoU4ePAgfvvtN183rcHQqrvb3vOIJueK9HyI1FlROqB9uLEIojW2LQMapXa6kqys9qSodbKjLwqmJRvDEW0I8eprqtWzVaLDYoSuXvWr/U0F8jpFIXodpt14GXTwfa2jhKjQgJhBRuoFTFBjq7y8HACQkBBYVyuBTKvubqXnEQ0AVu44LDwrSWSmj+1QV2Fppcv7ZRuouFIt2NVKvp4OOD1l8pD2+Onp/vjX3zp59XVF6QAkxRoAHTC4Q5LdYSIJwIhuqaqfX+1vytEstUBg7zfpbZNvuCxg30NyLGCGnyyZTCY8/vjj6NWrFzp06GB3u+rqalRXX+zqrKio8Ebz/JIWReREaqrodYAkQdWQDyCenPvs0jxVs5KcLbZnO9QVE+7eT8IyUHElWdlRLRlHPBlwuioyVI8z50yK98nfhXt6pSNEr0N8VJhmr6slCUDVeRPumP+L+Ta9TnmYddbqfVi8+SCm3Sh+whQZJk2ICsOzQ9ojyRgRFOsUDeqQDJMJPk0cTooNnHwkUicge2rGjRuHvLw8fPLJJw63mzlzJoxGo/lfaqr6K6lgIJqo6oyzIR0dgDF90u3eD9jvNhcNAGyHfCzZG4pRqrNib6jrdNV5oXY4U1J+VjhAWJV/xOpvNVezWic7arnm04NXZSjervRd8OeZPLbTuR0NT5ZUVGOsiuRtkWHSF27qgJu6XBI06xTVmiTMWJHvs9dncnBwC7igZvz48Vi+fDnWrl2LSy65xOG2kyZNQnl5ufnfn3/+6aVW+g+ti8g5G9KZNDhTqLib7bCPllOEnZ0gRYa63DVjxe84UVmNZjHOeyBW7izG2Zpaq/djYGYSfnq6PxaP6YH7eqUBUB8oukKrgmnJxnCM798Gc+/sgmSBQn+BNJNH5Dsy8cudwrP2XCmIGMh8vdDljZ2SgyI4JGU6SRJNi/QtSZLwyCOPYMmSJfjhhx/Qpk0b1c8hunR5sKg1Sej98hq7BxB5COCnp/ur/pE7G85ydL/SsE9SbDiqztei/Mw5h93wpZU1Ttu2eEwPh7VCNhaU4vZ5m5w+j+3wj14HjO6djuU7ih0OwcmPBYAereKx8cAJp68VE94Ipyx6iWxnUnm7oOGY/27BqvyjLj9+9ohs3JDdHIDY0Kf8XXX2vgaSj0dfgV5tEoW39/Q6Y/5iae4hPPZJrseePz4yFF1bxmH17/ZnMd7XKw0D3ShgSd4nev4OmKDm4YcfxqJFi7B06VK0bdvWfLvRaERERITQczS0oEb05O0sCNCSvSnOjvJH5EPOWyO7YMaKfKc5PbNv74zBWSl22yB6UP3PrZ1w4kwN/ig7g5YJkbirZxrCGumFi6XpAESEhuDMuVqnr6X0WAD1ere8cdKbuTLf7Wnbel3dUOSkweK9PvL7CmjXY+ZLw7NT8O/bsgPipOnNgEr0uOQOe3lPtgJpgVB7GkowHHRBjU6n/CEtWLAA99xzj9BzNLSgRvTk/fqIbAy7cFWtBXs/Mmc9R/YkG8Pxz+vb48ipKqzbdww/7j3ucHsd4LDbXotgLyevGM8s2ekwx0cLyR7qSbOn5rwJbZ/9RrOg4sG+6gMb0eUCAkEgnDS93QvoT71yShcPgcQflqTxFtHzd8DMfgqQ2MuvuFNV11WOfmTGiDCXTlbJxnA88sk24QOgBOCZJTtx9pwJSbHaFeazNKhDMs6eM2HCp7lO26NmFpMtV0rvK30GCVFhGJ6d4rTLfeFPyhWhXTVvfSGevLYdwhqJpe+Zl4soKMW4RY6Xi7AUZQhBZbX6HjFPs1dk0V+urn2xGrjIDL/IsBCcqXH+ebrz2wI8U8DSHWq+F8GwkrsnBFyiMIlzNpNF65kzzpKSV+eXuPS8Ww+eVH3gKqs8hwmfaluYz5botNAhWe4dWFapeN/sfQZllTV4f0OR05lv3/3u2mdkj0mqK2ioRoheh15tEvF/3RxPBAAufoejw7xXvE/NaU+pyKJWsxHd5e5K9u6wlxwt/+REAhpAm2FKf1mtW833wpefnb9jUBPEtDp5ixD5kS3JPeT267jC1cJ8zogGja+P6IwH+6bD9m22M6Jaz9Lcw0IHp1qThKlLdzk90Bc7nPmm/ZXqur1iy05YyskrxrtO8nrklo7o1gJHTjlPINdKkjEcD/ZNF97e8qSp9WxEd7hSHFJLgzok15vh58tzsC/LCqj9Xvj6s/NnATP8RK6RT971ZhtpPO4q8iMrqzyHhKgwnKis8epYuiuF+ZRYdg0nRhsACbi+QxLe31BUrxvcNmicNDgTT17bDh9uLDInHo+8oiU6P/cdqs4rF6iTlVbWCA1BPfbJNhw5Jb6ujlKX+8DMZvj1D+eztdTYXFSGWpMkHDyrWeTxgb7pSEuMdK+BgtIbR+LFm7PM35HOLeIx7et8lFSInQxLys/iX9/uUVU80pPcWcleKyF6HbqnJ+CJz3I99hqifFVWwNkFodL3wh8+O3/FoKYBUHvydoXoj2d4dgoWbCjS7HVF2VtMUy7M54yzBFadzroom1LQGNZIj9F9Wpn/rjlvchrQyJy9vzNX5mP5DvGrfHvvx7290jHzm93CzyPi7DmTqrwg0TomEoB31xXicRdWfxadHWOpsPQMTlTWWAXFJhPw9Jc7rKbj2/Pj3mPCV9femI1YdFxsSRBPn+w9WbdG5HP29QKhanpd5O+FL/IlAwWDmgZC9OTtKtEfz8DMJFzeMgHjF2/1SVezK1cuIlO45X0Z3SsNAwTrX6jJNXH0/tacNzkdqrHH9v0Ia6THg33TNV+JW837rnal8sWb/0B8ZChOnFFOKtYBaBZrwL9vy8bx09U4fqoaM1b8LvwaliYvzcN1HZLMVanHLXI+tV/2Ve5hoe28cXWdk1eMWav3OdzGWyd7T+yv/MubfXtnxEcZsDq/BO8pXEz5wwKhrvS6aDHZIVgxp4Y0caKypl7OiCXLpOT4qDCfjZ2rvXJRMxSiQ92Jq6T8LDYXljnNg/mj7IxQGyLDQhwenJ75cofLw3kJEfUrHk8anIkH+6Zrml2j5n1X+xmVVFTbDWiAusBnSMdk6HU63JCVgsQY16tXy0OBar4Xann66lpuuwhvnOy12F/bJsq5cYOzUtAzozEmD71MuLq1t7nS6+LNfMlAw56aBsoqPySqbhXi46erXRqaEr1ilX9kWl6ZjemTjv7tmqGk/OyFpQmU83VcvXJR0zUuoe6kN+Gz7QCc14tomSCWCzK4g/0ci5y8Yvxvq+sJ2LuPnEKftk3q3S7nAI2ctxG//nHS5ecH1M+wO1FZ7dLwkBL5ed7bUIT3NhQh2Rju0mralo6eqvLIkImnr67l3/yG/ceF2v74gEu9crIX6XWIu9ATZy93Te6RcTS87o1heFe42uvirXzJQMOgpgFylh+ipniTyBWrXOVXfr7EaG3WeYoMC0HXlvHmYbWIsBDF2hfuXLm4E4A5qxdxV880vLDyd4cnbx2AF2/OUrxPzRW3PX+eqN9bZBnwtk+OdTuoGdEtFct3HDYnVx+vtB881wXI4jWJnLF9b0vKqzBr9T7ERYbaXZLDGdFcFLUk1AWwmwpK3brIUOJKUUNvJWA7qlsj7/XMmzsCgNsncE8Pw8vU1JsR2X97xy5/DdR8iUFNAyOSH6KmeJPIFatJAuIvLFiZk1eMaV/vUttsRWdraq3a6YkrF3e6xp3NaAlrpMeYPo7zVx7om263cJ0WvQW2vUVaVvSNNjSCSZLs5m7YBs+eHNKRyc8tF/N0pXjb4s0H8e/bsjVsVf0eJUtxEaG4t1c6xvdv7dLJSnRZD1veTDIV/e0GwgnclSq/7hy7vBWoBQoGNQ2I6ElDzfRSNUlurh5c7VFqp9ZXLs66hkXa6GhGi7yEwLz1hVa9CiJrJ7k7jKfX1fUWydz9fN67oysiI0OxOr8En/76J05XO54RZBs8e3P15vKz53FDVjJ+LTohPCVbVlJRDUhw63shu75DEr7JK3HYW3fy7DnMWr0XC34uxEs3d1RVmdiVQNFXSaYiv11/P4G7U+XXm70u/lLR2hMY1DQgavNDnE0vrTVJOC5YFyUx2oC/f75d86twpXZqeeATKekuwl4AUmuScHXbZri0aQxy/zoJQIe0xhcXz3TE3SvpMX0u9gK500sinwSvvqwZVtmZZaLENij1dk2N5TuKcUPHZCzfqb7g3fHKavP3whXyjKxtB08KP+bkmXMY+9FWTBjQBuP7t8Gq/BKnPQJqA0VfJ5n6e9DiiGi9mf7tmuG3P04oBhTe2P9gXy+KQU0D4spJw95jRIcp5BMeJHj0KtyTJ0R7XcNq2AYgtSYJs9fsx4INhVbrG8kHF5G1kkR6kaIurKFjeb9SL5CrvSSWJ0EAqnN8LINSNUFaQlQYTCYJ5Wddy4uRuRLQAEBilAF6vQ739UrDZ7/9iVNV4utOye/Z7d1bOJ1WrWTW6n1YsKFIcV0s2x4Btb+Lhp5k6g7RejM9Zn6PssqLFbC9GVCI9iQFck8Og5oGxJUre6XHqBmmkFB3wjteKV7p1hVajf/b+zHbdg3LSa9HT1djxvJddlfrVurKz8krxsQvd+KkwjTk4vIqjP1oK94e2RmDs1IctlUkwfDft3VC/3bNrCoZK/UCiZ784iJCrU6mlifBjQWlLgd9R09V4YasFKdBWlxEKN66owt6tGqMVfkldvfdk3k5cZGhePLz7VbDVrbFFx2R37NqwcKLSuwt9Cn3CPxzSR7O1tRanTwdGd8vA71aNwmok5e/Ef0N2X4mSkNTtsehri3j7fbuiBLtSTKZJMxY8bvNgriheG5oBzSOcTzDzB8wqGlAuqcnICnWUJcTIEBpKq6rwxSeSjrUcvzfUbeso7HuiFC9eRjC2cyFnLxijBUYshi/eBtmQ4fBThbDFE0wtKxkrET083lrZBfoL0zLt30f3OktaxoTLhSkvfS3jujVOhGA432/sVOy5gUEZXXBqHVQIRrQ1G1bt7GnfhO2pQUcTY+Xfz8TBrb1yxNUIHH187QdhlUaVrT9DF3p3RHtSXp40bZ695VVnsP4T6xv99chKwY1DUiIXqeqy1tpXN2VMfrpy/Lx41P9NEmstH1ue+1Uy1G37NiPtiIuMtSqZ8XyBy0aWKiZgm2SgIcXbcVcvfMZaFokGIrWyuiR0dju87p6ULcMntXOAnG075FhjVwa3nEk2tDIaQK0M0cqqvHQR1vx5ohsJESF2u3l04qjgAZouEXatHZCsFdMiRxQzF6zD6+t3lfvN6hUmkB0hqpM6yF6uVdZzvHyl+8Qg5oGJi0xSmi70b3SFH8san8Y8o/1tz9OuJVYGR8ZCgmwCiy0Gv8XWWHcdqjI9qAiEli4krciusChuwmG7tTKkHVPT6g3PCXC8nlrTRKMEWH4x6B2KDtdjYSoMCQZI5zW+VDa9/H922Dx5j9Vz26yR6sARH5vH/0016uVtW2v9o2Robj3ynQMzExy+thAzrHwhlqThBkr3KsZBQALNhQJD+urXQDVUz2Ds1bvw+LNf2Lajf7Ra8OgpoER/WIPsHOgc/WH8U1eMa67LAlDXJxtMvv2LuiR0dgjB1ZXgg2lg4qzwMKVKyVvLnDobp2fEL0O9/ZKE+4diYsMtZqi7Gj4z5XPOUSvw5QbMvHwovqBtJq8G/mVb8puLjyzS4S3lwoxScDfujRHzq4SVFbX4uSZuqnin2w5aP58lSqNr/n9CJbkHrIK6Px16MFXtCpHoOaCwNEMVaUg1N3yFI6UVKjvOfIUBjUNjLsLobn6w/jvxj/w341/uNJkAHVTaNX2RoheXbraLat2VWVXA0JvTnV2dyhrfP82WPBzkWIStEwH4NFr2uDRa9pY5Rq5Wt/Dnpy8YrtXz/FRYThdfR41Asm6clBnjAjTNKjxhS8UltSQhxHG9EnDF1sPCfVGyY+Z6wcnMX/g7m9Uh7qeM0e/G9HXdnRxoEV5CkfU9Bx5Che0bGDcXQjN0eM9SW1AkJNXjN4vr8Ht8zbhsU9ycfu8Tej98hrk5NXvJXK3W9byoFJrkrCxoBRLcw9hY0Gp1aKWckCo9n3zZmVX4OJwzrDs5ujpIIfG3mNfurmjw318a2QXTBh4qdWQk7Phv+nL8p0uEGpJDpLsXT2XVdYIBTSTh7THT0/3x6AOyU4/P3mNoqRY735eWpm3vkj18NrEL3eq+lyClZrfqL3j7r1Xprv92va+9/LFAQDMubNLXZkNjVle5PkSg5oGSB5msP1ii65Ya+/xnqCD/QUR7QUQzn7YtoHNmt1H3Gpj05hw1JokvL56L7rOWGU3kLIMCEU1jgrzemVXd8nfD9sVkZON4Zh7Z5d6M7pEZ2WIHixrzpvwzJKdmlyJJsYYrAqjObsgeOnmjtgwsT8+vK+7Bq/u/06eOYfZa/b7uhk+J3LBEhcZiseuaY1msdZr38nH3TZNo1W9pu2xUfTiYGBmEn56uj8Wj+mB10dkY/KQ9qpe1xlvF9G0xeGnBsrdYQbbx6/fe8yt1aKVOOo5stfFOnlIJmascF6LQe4irTlvwns/uT71Ny4yFCcqa9D1+VWKXcdKCcVz7uyCiV/sFBo/H5adEpAJmQMzkxBjCMXGA8cB1PX89Gil3OujZqkNZ3LyivHMkjzNZhTJAavl7+StkV0wY4XjvKNGIQ3nenHBz4Uur0sVLEQqj588cw6vf78fSbHhmDDgUqQlRpqPuwDQ++U1wq+ndGwUvTiYtWoverVONB/va00S5v9UqFmujbd7lm0xqGnAtJgxY/l4rYMaewmqjvIvlJJCLdnmwXy4scithM30xlEOX1N+6meW7MTZcyYkxYbXnfDDQ3HH/F+cPr/IzBR/oxRwfrH1L7uJpaIHQWfbabm2mJxbdqKyBr1fXmMdwMSG4/+6paLWZIJlwAYAGwtKcfRUFfYdOa1BKwLDyTPnvJbM7s9EK48fqajCa6v3Ys6dXczvmdrClUrHxpLys0KPnb12P2av3W+V7C2Sa+MsD8dXa4bZYlBDmtAiOtcBeNzmCsb26k+ki1WEfNX/R9kZl9sLANv+PCm0XVnlOUz4NBeA3KPU3mnCtb1hN3/mSsKvu8nrgPYrfEsAbshKxrhFCvtSUYXXv784w+uLrX/hxk7J+Hp7sdcW5PQ3vh5y8BdyD/amA6UY9/FWxd5YpR5j0fcvMiwE8+66vF69qJy8YkxeuktVW21/k0oBWUJUKG7Kbo4BmUno2jIec34owKzVe+s9lz/VPGJQQ5pwd7qgoymilt3/x09Va3LikIOwlgmRbj+XWiXlVRi3aBse6JuOd9cVulwXxt+IlmG3nR2hRY0cT6zw/d5PhULf5eLyKo9VLw4Uvh5y8Ccheh30Op3D4WXbHmPR9+9MTS12l1TgeGW1+cJvVX6JUJVypTZY/iZFUhIeG9AGbZOiXS774A0MakgT7qxm7agipejCmWrJ1T/v6pmGF1b+7tWaIfLB5OvtxQ7zMwZmJpmHMwKh4JmahF/boYpBHZLx5ohsTPpqp9XikKIHS0/0FHBSj5jGUWHo2jLe183wK2rzxNQUrpyx4nfz/yfFhqPqnPhiqrZsf5MiKQlaVDD3JAY1pBm1q1k7K+ClZY6ErRkr8nFdhySENdJjTJ90r19pyweT+Kgw/PR0f2w6UIqNBaUAJPRslYjys+fq5XJ4quCZVtVi3Un4nbkyH/PWF1oFEjpd3RCQyP6yp8B3SitrcNUra/3mSt0fqM0TU1u4UqZVtWy1FwXu5mN6EoMa0lS91awvVCU9frravLK1ZdepvZOnqzkSOgAJUWEodbIOi+XVyaTBddN0bU+qssZRYRiWnYLIsEaYvVbb6atHT1XVW8Bu9toCxW1FC9GpCVIcFepSeo2a8ya7K34nRhnqba/EdruZK/MVg0pJqvtM9DqYPyN7RIY/Y8MboaLKvXWb3BXeSI8qN1bn9lfuFEkMRq7kiYkUrvQUe0GYVYVpFcdvX2JQQ5rTIop3JUdC/nkNy07B+wKVXy2vTiYNzsST17bDhxuLUFhaCR2AzqnxSI67uO7QxoJSzYOaouOVigvYKRFZ70VNkKI2qVepN+WFlb9jTJ/0uqBD8PhmurBKda1Jws/7juNdJ71k89YX4slr25mDJyXO8nIkAH/regmW5opVzPWUYAxoANfWIgpmruSJyYUrPdU7bY+9WljOhv79damMhlNMgQKKKzkSchEr0WnQtlcnYY30GN2nFZ4f3hEzhnfEzV0vsaqo62pFYHuSjeFYvPmgqgOYo0J0IkUHa00SNuw7jle+3Y0nP98uXMVX7k2x7ckyScA76woxc2U+jp+uFtqHRxZvw8yV+ej98hrctWCz0/03ScCHG4ucPq+9opC6Cx/Ygg3qK+YGkmRjOB7sm16v6KG3WNZBsa2m3RC5UuRUfkxCVJi3mqlYC8tZRW7AfjFTX2NPDfkl0THpyUPaIzHGYNUdWmuS3J4irCREr8PkIcoLJLqi4uw5VNa4luRnG/SJzDya+OVO4aJ/lsFT15bxmLfeeW/KB/eKVdE9efac6hwm0an3lkX/Co5V4pu8kgaR8Dt5SHvc0ysdIXodnry2HXrM/B5lToZgPUWpDkpD5UpS7aAOyThbU4sJn233ShttLwJrTRKmfb3L6cWGv/bOMaghvyQ6Ji0fyC1pMUVYSU5eMZ5brq4WhCOuBjRA/aBPZOaRq4vliRQoNEnA7pIKj60CLDL1Xs1MOTn36tkh7XGw7Kxi7Y1AYrmcw29/nBAKaMb3a42eGY3x8Me/ofys9rlG8qKXjmY3NgSuDMc39dL6YXodcKLSuod19pr9KKkQ63VVu6ivN3D4ifySuwtvuru+lS25O1b0x+4p9tbC8lTxs6Yx4cK9JH+eOKt6bSsRel3d1Htblmt/vb56n9PucksS6mbtJBkj8NiANhjdq/7zBxLLIFfNd6H8TI1HAhpLs1bvQ6+XlBeTpYvk7/OMZbswftFvXnlNkwSMW7TN/Nnk5BW7FOD7U/FF9tSQ37I3RVy0dolW9RTcqVYbYwjBqWrXe2QsOQrmtJ7SbDlEl3+4XOgxLRMiMahDMt4a2UWxEq+rxvRJr5ckrFX9IvlgPCAzCe8JJJf7I9sgV/S7MHvtfnir86SkgrOjlMizi1bll+Cr3MM+GTKUADz1v+3o3boJpi/Ld+k5/KmkAoMa8mvuBia+mokl0yqgARwHc+5WdLYl4WLwJFKg0LI3JT4qTJM26HW4OLPKgpb1i+SDsdbvnzfZBrldW8ZDrxMrHujtfCN/y7/wJU8VFnXFqapadJj2rerH+ct6T5YY1JDf83Whp1X5JT57baBuJfC3bu9Sb70XS+5UdFZi2TMiUqDQsjfF3a7oyNAQTBh4KUZdmVavh0arNZ5sD8Zav3/eEhcZWu+23/44oWmwEhUWYpX/5er744/5F77iycKi3uZvy7kwp4bIgZy8YqGaN56gu/DvpZs7olebRKcHDrt5RLEGGCPUXb/UnDdhrMV0zUmDM/Fg3/R6wxV6HfBgX+veFFe7ouX9/c//dcKYvq0U69JotcaTBGBEt1Sr2+y9f/7s5JlzGPvRVqzccTFfRev8hsqaWiREheG+XmmYPKS92ydif8q/8AWtF1/1lYSoUL8cTgyonpp169bhlVdewW+//Ybi4mIsWbIEw4cP93WzKEjJBx93JMUacLr6PE67MAwVFxmKmTd3VHXQsDdc5+qid5bDBZYFCpUqCstEhnIiw0IQGx5qVeZdJFdKy16zWav34ZMtf1q9puX7V1JRhRnLd3m0tk0jvQ7nBbpVog0hDr9D4xdvxWx0xuCsFI/kN5yorMGCDUW4V4OE6qYx4eZckpLysyirrEFCtAFJsc6rjPvrekNqeGLxVV+YfMNlfhfQAAEW1FRWVqJTp0647777cPPNN/u6ORTktDj4TLvxMphMcKm2zQkXy6UrDdcN6pCMuXd2wcQvd6qa2m07XCAXKFRiuYRCr4zG+N/WQ3aHKv5zWyfVuVK1Jglf5R4WbjtwMbl6SFYylu+oP/vGsnqybXuaRhs8XqxPJKCJj2iEE05mKJkk4OFF2zBXr8PAzCTN84PkmiRLVb7/luQhvxOV1fXWNZM5qn6tZjkPfxYsPVVJXpp2rlZABTXXX389rr/+el83gzzE367E3D34TBjQxnzAffAv9Ytmal3Yqn+7Zhh39Vl8/utB7D1aKfw4kffB3oKUEaEhOGORj2F7IlKTW7G5sEz17JAkYzgmD8nEjBXKPW6WhQmnfZ1v1XMUF1E/X0WJbeAmmqQrqkdGY3yTd0RoW/n7ojY/SKTN8jT4hKhQnKg851LAdGOnZIxbtM3uY4sVluhQu5yHv/OnmUL2OPre+GNysKWACmooePnjlZg7B59kYzjG929j/nvS4ExEhIbgte/F147SMrFSKegQZfk+KAWe/8r53e6ClGdqanFDVjIGZjZzO1AVDTIHd2iG6zokm19PvDChda+MSOVlAPjvvd3RqJEeq/NLsMQDa0tlNIkBIBbUyN8Xe+UQbMmfxBv/1xnf7z6CJQI9MTdlN8f7G4pUJQzHRYbixeEdMWOFWC6JHJzJ/++oUnagzajqnp6AhKgwn1V8dkanA16/NRuPfpZb/74L//W35GBLQR3UVFdXo7r6YrG0iooKH7aG7PHXKzFXpvk6+tGnN4l2qR3u9hjZWwVbhGUNFKXAs1lMGI6ednxwXrmzGP+5LdvhgpQiRIPMu3qmo2dGY3MA9o0Hi74lxRpwZZtErMovwfsbijyS/NkzozG+2PqX8FDo6vwS9MxobM4Pmr1mHxZsKFIM0pKM4bixUzJe+OZ34ecfkJmEbukJqqYjv3V7F+j1OqHtbdc3cxaQuhr4+6pnOESvw3DBRXd9YUjHZNzYpTnCwvQu1wjzpaAOambOnInp06f7uhnkgMiaRb66EhNZ+TkuMtQqR8XRj97Vnp/EaINLjwPq8lycrdvkyI2dkhGi19kNPI+ccn61KS9IaZuLo/akIrp0Rvf0BKzccRjPLs3zeE7M7d1bALDfm+AOeX96tGqMqUMzhRO9l+QewjND6oLqVfklDleBvyErGe+uKxRqu+X7G3Ihd2dTQSnGLdpqt1fLvA8ZjbF8h7p8HDXBvNrA39c9wwMzk9wOasb0ScO89fafw9BIj6FZKfjf1r+EnzMyLASvj+gMQLvipd4W1EHNpEmT8MQTT5j/rqioQGpqqoNHkLeJDA34sraFs6rGan703dMT6gVBQtw4W4qs2+TI19uL8eS17dw+aa/fd8wqqBE5qSgFPSJretkbDvOEtMQoj8xmkfdn8pBMbC4sQ/V5Ex6/pg1e+36f08eWVZ7D5sIydL/Qm+Loc3vvJ/GABrDugQzR69CrTSJe+ltHPHQh4HK0zpraoF7N9mq29YeeYTlAd/V7M2FAGzw24FJ0bVm/xyzaEIL7e7fCI9fUDX9vKDgu3Nv8n9s6WR2/fF0jzBVBHdQYDAYYDK5f5ZLniV5h+XLGgLMrFjU/esmFCON4pfh6U7aBwLq9x1S/nqXi8roFLd09aW86UIYN+4+jR6vGWJVf4vSkAsBu0OMoyDSZ4LWABqg7mar5bqrp8buxUzJmrLDez8gw68Rre46eqhIKtkS/jo56IEWXMxE9kVv2CH2bV+IwiVlt0qq/9Axb9gLLr61GWmIUALHeFJGkcV/nL2opoIKa06dPY//+i4mWhYWFyM3NRUJCAlq0aOHDlpGrRK+wfD1jQKvlFsqr1C8eKLrvniq7LrqgpSNV5024Y/4vSIoNR9X5WocnlUlf7lSczm4Z9Pz0dP96B3IA6PbCatVtiwwLwdka5TY5e5yciCxiwoA2+GTLn0I9ficqqxVnCYkENACQGGXQ7EJgfL8MTBjY1uFJXuTkankid/ZeTx2aiVX5JUJriKlJWvWnnmHRZG4llscEZ8cme6/TOCoMw7JTMDAzKSCGlUQFVFDz66+/ol+/fua/5aGlUaNGYeHChT5qFblDTZ5EoHPlJKO0IrcST5Zdb5kQqdlzWU6ZViLBfn0eOej555I8nK2pRZIxAjdkpZgPxhsLSlXPKNGhLvfgTE2t6vL/el3d64qstaTXAQ9d3Rrj+7dx2uNXa5LQ++U1bn2WT36+Hbd312aovVfrJkInPJHA39mJPNkiyHP2Huh1wOzbO6vqXfC3nmHbYDAxyoAnP9+OIxXaHg8DNT/GFQEV1Fx99dWQpEAvLk2WnCXjAv49fVANV3qbRPbdk2XX4yJD0bpJtN9MQZVrpUz4bDsA625zV05EchCl1IvizOnq8+ZeGmfDOCapbk2mnhmNnZ74tcjRKamowqzV+xAXGYryM/Zryuh1dVPvvXlBYVW12U5F4Y0FpUJDZ/FR6tIL/LFn2DYYnHajZ46HgZgf4wqu/UQ+Z3fNImN4wBXWcqR7egKaxYgdhI3hIZgruO+eLLt+8sw5jFq4xW5AI6/XpLSwojfIQ1I5ecVunYhaNI7C5CHt1b9+RRU27D8utK3WvQRhIeInNtst5c9tTJ90u/cDnrugkE+wN3W5BKP7tMJNnZujp8WCrZ7qUZF7hu3tkQ7ivaNaqDVJ2FhQiqW5h7CxoBS1JqnBHA89JaB6aih4NYTu0RC9DtOHXeZ0am7XlnH47MErhffdl0nUck4IAJeTHt0hv9b0Zfn48al+SIo1oKRCPLFa9tyyXag+b1L9uClLd+JUlViei9a9BOdqnb/TJ8+cw4QBl+KTLQftJvF2bhHvcHbfxoJSr/8mPdWj4k89w85mAAb78dBTdFIDGs+pqKiA0WhEeXk5YmNjfd0cusDflkfwpFqThNlr9uGtHwpQY3MS1QG4v086/jkkU/nBdmwsKMXt8zZp2EoxCVGh2DRpgLmonruJympzWmxNGFA3hXXWaufTnr1JHsb56en+5u+1o+98rUlCtxdWaVZj5/UR2bghK8Xhb0ypPavyS3xWy0XOK3KWa2f5nqrh6zo19nLg5D1hj0x9oudvBjXkU74+uHiT0r6Gh+rRtlk0bshqjlFX1l/xWoSzE4AnLR7Tw2qcvtYkOS3IZk+yMRzXd3CvKNnoXml4z48qtSqdpES+8zOW7dJsP+SaJjL5M9p44DiAumGgHq3qPkM5sCk6XukwOJwwoA3G92/j0YsP+cQPKPeouHvi99XFlPx7tRf8uxuwBSsGNQoY1PiXhnS14sl9rev92Y9Zq/e63D5Xe0lm/V82kmLD650Y1MzG0umA/97THVe2ScTmwjK3ep0SokI9XkVYDdtps/Zq9Nh+D7TufbuvVxoGZibhRGUNnvmq/krtUWEhCG2kV1UYMik2HNNu9OzFRzBe9Ih+trYXDA0dgxoFDGr8R0O6WvHkvmpVmybZGI6mMQZs/6tc1eNsgwjLE05OXjGe+t92oZyTyUPaY3SfVpr0OiVEheFEZY3Xe61sRYWFoNKirkxSrAFV5012AwfL7wEAp++Du8N1WtDB8xcfwTY8vTT3EB77JNfpdq+PyMaw7Oaeb1CAED1/c/YT+YSaIlj+rua8Ce+tP4ApS/Pw3voDVrkytSYJCzcUur2vSrMk5N4QdwOa8f0y8NPT/XFDlvoTk22viOVspEEdktElNV7oeeQCf3IiJ1B/Ro6o4dkpbj1eK5U2hfJKKqod9oTI34OFG+oqIjt7H3wd0MimL8tHrTtrcTghz5Qalm09QypQ+eO08mDC2U/kE/5WBMtVL6zYhfnrrVdnfmHl7xjTJ11xVokj9vZVqTdGvurX4lQiF1cbdWU6Xly5263ntCw1bzIBP+4Tm+5cdLwSS3MPoWlMOAZmJikWaIs2NMLpaucVmWPCQ12u1OoPZqz4HfN/KrS7JIQ/8fXabIGoIRUc9QUGNeQTwXC1Mua/W7Aq/2i9202SvP6QujWIlPbV7uJ7LkxbtiUfPLu2jDdP270hKwnLdpS49bzyie7ZpXnCj1m37zjWXQiA5CEsy6UQEqMNOH/ehFELtzh9rte/34cbspLxj+va4se9x/BVrrrVof1BcXkVxn60FRMGtMGPT/XDlqIyjPtYffK1t/j7xYc/cTStHBf+njykfb0eKU8OwwXTEB+DGvIJf7haceeHvGz7YcWAxhX29tWTlYJlN3ZKxlWvrLXqCbDNBXGVqxWIbVdLzskrxt8/366qt2L5jmIs31Hs0uv7k1mr92Hx5j9xe/cWfhvQAP598eGPnC0XMWPF79DrdapmzLl6PLP33JOHtEf8hTXERJ7PXwIjJgqTz3h6yqaz13Z1VkWtSUL2c9/hlAuLU9pytK+erD+TfGEV6HfXFdqdifO3Ls0RaWiE1PhIzF9/AEdOud87JEoO9CYPyRRa1JB8I5gS+n1h5Y5iPLyofjFOy+MCAKcz5gD7q9pbHldsAw97C6cqcXR89MYsNc5+UsCgxv/4Ysqmu9OrtQw2HO2r6CwJe2wXWbScXty1ZXy9HhpLlicrd6dZu8Nf1pwiZd6Y/RSsRGdFSpJkd7hZB8BoZ30v2+OZ0rHW2UKsjp5P5q3SHKLnbw4/kU95uxy4oyEdyyTXgZlJdtugVf7A5CHtcU+vdLuv426X/ujeaejfLknxfXW2YKDlTBx5ZpJakWEhOOPmMBYDGv/2QN90BjQuEp0B6ogE2J1RZ5u0r9TjqWbSmtLxUYvjqdY4pZt8zptTNrWYSu5usCEvmucooAGcL77nzPIdJeienqD4vooGZjNW/I7/bvzDpdfX6zgcEey+3l7s0encwcwbydWWSftafEq2x0d/LM3BoIYaFC2mkndPT0BSrFhg487qx+7WbHF0MPFkYqcOdUX5RKZfOxITHqJNg8hjAqWWlD/yZnK11j2e8vHRH0tzMKihBkWLqeSr8ktQdd7xsMrAzKaYe2cXJBmtnyfJGK5qjFmeJWGMDBXa3pa9g0n39ATEuficzkgAOqYY3X6eU1W1Pi+gR85xOrdrnPXEyj26SbEGv/sdyMdHfyzNoVlOzfbt29GlSxfU1ro/FZTIU9ydSu5sTaOI0BD865YsDO1UV9VWi3yhgZlJmPZ1PgD1U3rtHUxqTRLO1ZoU71PDXql+0aJ7znBgw/9xOrdrHNWrsezRBWC3po232R4f/aE0hy1Ne2oa0EQqClCOhnScDQ2J1I0xRjTC4I4Xe2HU5gspLYewubAMJRXqroblqzylg0lOXjF6zPweldWuX4BcdWkTRBsa+fwgS74VHxnKyrdukHtiHfXo2ttGSyLXWUrHR3eOp54iPKX75ptvdnh/eXk5fvjhB7/uqeGUbpK5MpVcdCr35CHtkRhjUN0zY69Ngzsk4b0NRULPATieSqlm9WwiEaN7pWHAhVXIWavGNSKF6yy3OX6qGjNW/K7Z6789sgvio8Is6tfUYMYK8eNjQNapCQ0NxcCBA9GsWTPF+8vKyrB8+XIGNRQw1FbAdKVujOgP21GtB7UBSFxEKO7tlYbx/dtY7Y+zuhhE7vB0fSl/5YtKuiKr2UeE6nH2nPMh5u5p8fhs7JWKr6Fmvzz9Pmge1GRlZeGxxx7D6NGjFe/Pzc1F165dGdRQ0HKl6J5IASotgo1oQyOE6IByiyrH0YYQ3N+7FR65pg0AYOGGQk2v7ogseaMSuL/xRfFQy9d2VJE96xIjtv9V7vR5rs1shnfvvlz7BmpM9PwtnFPTtWtXbN1av5yzzGAwoEWLFupaSRRAXKkbIx9spi/Lt1vPw1mtBxGnq89bBTR1t9Xite/3of2UHHR9fhUDGvIoke96MJGDCtvfrrx2WU6eZ9cec5aPc0OWWFDVLS3eE83zGeHZT3PnznXYC9O+fXsUFqpblZgokDhbXdceywJUPTMa17vf01Nia86bUHPe/ZlORM44+64D/rPwoTv8pZKuo4rsNedNeHHlbofHKR2AUVeme6x9viAc1BgMBk+2g8gv2R6AB2YmOVxd15EN+48rHsD9aUqsTgdwEiO5a8P+44pBiy+Ha7SkppKuveBOK/IMS1thjfR4oG863llnv7Phgb7pCGsUXOXquPYTkR2ODsDyQo9HT1XhaEU1XljpfGhn9tr9+GLrX/UO4M5qPXiDHGa9dXsXFJef5VBVAHn4qgw0jg7Dhv3HsGaPNvWB3DV77X7z/8u/GUB5tWl5uEbOxQmEnhxvVtJ15/2YNLjufX93XWG9vJsH+qab7w8mXKWbSIHoyrMrdxzGU//bjsoaseEdc/AwsjPiowwWUyirMW7RNgC+KbBlebUsMrOC/EdcRChOnlVfmNHbHC1wKhdpmzwk0+FUYn8JeEQnDSwe08OtnhqterZqzpvw4cYi/FF2Bi0TInFXz7SA66HRfPZTMGBQExh8feByNhtJPgDfkJWEeeuLXHoNvc56hdxkYzhu7JSMpbnFqgvtuUtptXB7MyuIvE3+Vj7QNx1fby/2i6ErZ4G/fIz46en+Lh+7RC+sGgoGNQoY1IipNUnYVFCKjQeOA6gbr+3RynE1XK0CEX8Yc3dl6ra75HfqsWva4LXv93ntdeMiQ/HbswMVPyulzyI+MhTV5012r7hdkRAVis6pcfil8ITbi2AGAn8odx8sfHmCdzal2p02iV5YuRM0BRqvBTUVFRVYs2YN2rZti/bt27vzVB4XCEGNr3spcvKKMfHLnTh5xro7Oy4yFC/d3NGj1ST95crElSJ7WtABMITqUSVQMAsAkmINOFJR7dYJcsKAS/HYgDZ271f6Pm4qKMUd7/3i0uvJB+NXb+mE73cfwVe5hzVZQbhxVBi6pyfgm7wSt5/LkyYMaIMFPxfV+335Ox2ApjFhqKmVcMLP2u7LE7zaY5/o8d1bw1uBRPT8rTpR+LbbbkPfvn0xfvx4nD17FpdffjmKioogSRI++eQT/O1vf3Or4Q2Zr3splucexvhPtined/LMOYz9aCvm2gQW9gIR2+Q/JZY/8MQoA6Z97fspkoDvZiNJgHBAc0NWMm7ISnZrobu4yFCM79/a4TZKMyt6ZDR2K7F56tBMnKo+hwUbitzusbAs0f/19sNuBzXhKoJKteIiQ9GmabRfBDTXd2iGS5vFYOHPRSg/67h3TP61jbyiJWat9l4voihvzjSy5WhKtS01x3dvJiIHG9WZQuvWrUOfPn0AAEuWLIEkSTh58iTeeOMNPP/885o3sKHwdSGnF1bk2w1oLFkW1nJWq8F2e0s5ecXo/fIa3D5vEx77JBd3vPeLw1wSywOXp7lSZM+bwhvp8fqIzm4vdPfSzR1dChAdLWLnzJCsupOAs4VBRa3MK0H39ASsyi/BjOW73H4+TwU0AHBPzzS3Z5VFhYW49fhoQyPc1ysN7ZJi8fr3+50GNMDFYm5piVFuvbY9Wv3OfHWCF1m0Vu3xXfTCyp/KQfgL1UFNeXk5EhLqVmXNycnB3/72N0RGRmLIkCHYt8//ovhA4E5woIWZK/Mxb71Y4UTLwGLTgVLhWg2W7P3ARXjjwOXOSdsberW+eOAc1CEZPz3dH4vH9MDrI7IxYUAbJMVa15SyPcYmG8Pr9bip5WpA9dP+49hU4Ph7o0ZxeRXumL8JYz/airJK3/eA2BPeSI8oQ4jL+61D3RBbpZu5TKerz+P9DUVOe1wSosIw67ZOWDymB356uj8GdUj22Ak0yRiOt0d2dvtCwl9P8K4c351dWOlQ9zvmCun1qR5+Sk1NxcaNG5GQkICcnBx88sknAIATJ04gPNw/v1T+zpeFnGrOm4QDGtlL3+Tj6rZNsfDnIqHtLQMRRz9wEd46cMknbVeK7Hnatj9PotYkmQMb2yGi8f3bWHWHd20Zj9/+OKF5npZl1/uG/ccwe22B08ecPHMOPx/QtpbKpgOe771zV9V5E15Yudulx8qf1LDsFLyvYrV2d5RV1iDJGIHu6Qnm71JCRJimSc5xkaF46/Yu6HGhd0Ov17k0nCrn1PjrCd6V47uj6uXy92Hq0MwGkySshuqg5vHHH8cdd9yB6OhotGzZEldffTWAumGpjh07at2+BkGL8VPROgS2iWp5h8qhtgNo+18V2P5XhfD2loGIq+sc+eLAJZ+0/W0hyLLKc1i4oRCJMQbFIEUpD8ZTuQbya3VPT8BHmw4K1UvZUnjCI20JFglRYVbJ00kX8i6MEWFeC2oAYHV+CZ74LFfzoF7+pr50c0f0apNovt3ehYRc7uDdC5VxA+0E7+rx3d77keSjaeyBQnVQ8/DDD6N79+74888/MXDgQOj1dSfOVq1aMafGRe6On8rDR5bByQsrf8eYPtYVI5US1SJD3Rujd0QpEHFl+MiXB64QvQ739ErH/J8K/aoYnWWQ5Y1kcmezNkL0OtzbK00okXRzkf/3rPhKfGQoNk26RrFnrdYkebXy9HseCqAcnZQdJd52bhEfkCd4d47vahKRqQ7r1PgBdwo5zVyZ73BtjwcvlMK2N0vJk3SoPw3blRow/rA2jC/eP1GenPJea5Iwe81+LNhQaNULo/SZ1JokdH1+lV/M7glkjvKdcvKKMfZCbRRP0aFuDTCtU/jG92uNXq0T3Top+7rkhSu8UaivIfBYnZonnnhC+Yl0OoSHh6N169YYNmyYOZlYa2+99RZeeeUVlJSUoFOnTnjzzTfRvXt3ocf6a1ADuFbIqea8Ce0mf+Pw4KPTATunXoeBs370am6Ivbo2Ij/wZrEG/Pu2bBw/XW33wOWLg1tOXjGmfZ3v9Yq/IjxxYLRXs0h+PaD+99IbJ11/kRAViueGXoYXvtmt2W/L0ecoF8Uc8+GvmhY/9IbkBn7S9mShvobCY0FNv379sHXrVtTW1qJt27YAgL179yIkJATt2rXDnj17oNPp8NNPPyEzU9vFsj799FPcfffdmDt3Lq644gq89tpr+Pzzz7Fnzx40bdrU6eP9OagB1NepeW/9AaFcj4hQPc56cKqqko9HX2E1Xm7J3R+4L+r5yEHUd7uKseDnPzzyGlqwLMblTuAnGpzERYTirTu6WFWcfm7ZLq/mfnib7fdUfp9Lys+irLIGZWdq8JZA0rQjtkXVlL7zDtvoR6ut2/bYnq2pxYsr81FUegZpjSPxzOBMRLg5VT0Q+LoOWaDzWFDz2muvYf369ViwYIH5icvLy3H//fejd+/eGDNmDEaOHImzZ8/i22+/dW8vbFxxxRXo1q0bZs+eDQAwmUxITU3FI488gokTJzp9vL8HNYC6E9GUpXn470b/OsGK9hi4+gP3RdXhlTsO49mleUJThkN0QK0PTyavj8jGsOzmbh1AnZVoV5IQFYbh2SkYmJkEk0kSqjg8YUAbfLLlT+GkUH+REBWGu3u0RHqTKPNvFMDFQpLRBjz5Wa5b1Z7lzxHw7NCnDnW1g5bv8FwdrIGZTTHv7m4AgDH/3YJV+UcdbhPMAnH4zF94LKhp3rw5Vq1aVa8XZteuXbj22mtx6NAhbN26Fddeey2OH9du6mZNTQ0iIyPxv//9D8OHDzffPmrUKJw8eRJLly51+hyBENSoIdpT4y1qAwu1P3BfrIfiLGfJ3ywe0wPlZ2vcCvzcXfsqKTYcVedrUX7mnNMcAgCK3wG1PRO+FBcZCgBWw3RxkaE4eeacy1Og5Z4aVwJMUXodMLp3GpbvKPH4+/z2yC5YkvuXYkAjayiBjScFc9DksWUSysvLcfTo0XpBzbFjx1BRUTfNNy4uDjU17q/nYun48eOora1Fs2bNrG5v1qwZdu9Wrv9QXV2N6upq899y+4LFXT3T8MLK3zVP6HOV2pkISlOPHfF2PZ+VO4pdCmiiDSGorK71ei9DsrGuJs1Vr6x1a7kJdwscWuYcidTYUPqs/HU6vRKlnKPyC7cZLwQ3smRjOM6ecx7wWfb+eCrgMElweZV5tZ74PNdpteZV+UdxtqbWo0NRwXzS5/BWHdVBzbBhw3Dffffh3//+N7p1q4uqt2zZgr///e/mHpTNmzfj0ksv1bShrpg5cyamT5/u62Z4TFgjPcb0SfdpT8KQjklIT4yC5WrenuLN9VBqTRKeXZrn0mN7t0nEt3lHvL4a84huLfDbHyfcDvy0KHCoQ11vhaGRHiUVFy8s1AS+IXodEmMMTrfzR3IAGREagrdGd8HxyotJ76vyS4SLqgXL2j6iy0/c/8EWjO/fxiPBRjCf9N1Zgy/YqA5q3nnnHUyYMAEjRozA+fN164Y0atQIo0aNwqxZswAA7dq1w/z58zVtaGJiIkJCQnDkyBGr248cOYKkpCTFx0yaNMlqtlZFRQVSU1M1bZevTRqcCZMkee2Ky9Y3eSXmnqLZa/d79CDhzfVQNheWubx69C8HyvBA33R8/tshTVagFpWWGKlJ4CeXaHenHooE4MSZc/j4/iug1+lcvjJelX/E+UZ+Sg4g9XqdOT8GUFdUzV9L/3vKhoJSbCgodfs4Ytsjc6KyBuMWBedJ39kyDN5cDNgfqA5qoqOjMW/ePMyaNQsHDhwAUFd4Lzo62rxNdna2Zg2UhYWFoWvXrvj+++/NPUImkwnff/89xo8fr/gYg8EAgyEwr/TU6N8uyWdBje3QlycPEs5OtlpWHXbnCvnEmXN4d10h7rmypVdnSqk5ATraVi7RrsXU7OOnq61O6Gqs3HHYowms3qL0XRItqqZFgBmI1B5HLIOYouOVWLz5oFUPoV6n3GsaDCd9Xy6z449UBzWy6OhoZGVladkWp5544gmMGjUKl19+Obp3747XXnsNlZWVuPfee73aDn/jT13UnjxIeHM9FC2ukJduP+z2c8j7GXUhT8feNpbBnJrAz16OwaAOyZgwoI1QhWBHXH0fa00S/vmVa8N//sbeeyCSU+boOx/M1BxHRJLKHeUdBvpJ35vD8oFAdVBTWVmJl156Cd9//z2OHj0Kk8l6rFTuvfGE//u//8OxY8cwZcoUlJSUIDs7Gzk5OfWSh4Od7YkoMdq/eqM8eZDw1noo3dMTEBcRKrSWkRIJdWs0JUSF4URljcsnI6PCzBpLSsGcfBK0R97WWY5BWmKUi62+WETRJElYmntI9dDT5sIynPBAZWK9Dni0f2u89v1+zZ/bllY9h/a+8/GRoR55j2ThjfSoOu/d+laWHB1H5GPgqvwSzWoibdh/PCATh705LB8IVAc1999/P3788UfcddddSE5Ohk7n3S/A+PHj7Q43NQRKJ6KkWAPiIkPtzqjwFU9dGXhjPZS6tYzSMWv1XreeZ3h2ChZsKFJ9lR0XGYpRPdPwxvf7HD5OKZgb1CEZD/RNr7cemF4HjOmTjkEdkoUSC109CMr7WnXehDvmX6xXoyZPwlPfHZMERBlCERkW4tGqvCI9h85m4tje/+NT/azWhJJnumk9NHVNuyb4pbAMp+30DHqb7XfBU9P9Z6/djy+2/hVwicPeHJYPBKrr1MTFxWHFihXo1auXp9rkMYFep8ZR4TlJ4f99zbYqaqCpNUnoNP1btw7uct0Y24OwvTom8intrZFdMGOF4wN346gwbJx0Tb3V2J0VKBR57riIULx5e2c89b/tDovIRYaFwNBIb9VjEGczjdn29UXyJNytleOu8FA9WiVGIb/4lEuPdxbAOeslE52pY686t63Y8BBUVPlHkKLW4jE90D09QfOeGSWBumyBq1XaA2mKu8eK76Wnp2PlypVo37692430tkAOakQKzxkjQxHeKMSqToi7FVr1OuCa9k2Rd6jC6rX1Dha8c7Z+TaD8iABgxrJdLq1WbPseKO33qvwSuycuY0SY0EndNnAU+Z4kRIWhVHBWlrMicnPv7GLVa5YYZcCTn293uD6WyDpAtSYJ3V5Y7dXZY7JoQyO8dFNHjP9km9D28ZGheGF4B8RHGYS+186Czgf6puPddYXCxRNFei786WJHjWRjOCYPyXQahGspUBeYVDtlPdCmuHus+N6MGTMwZcoUfPDBB4iMjHSrkSROJMP95Jlz+Hh0F+j19afQdm4Rb3dxQiVXX5qIPm2a4K6eaQhrpLc7RVJ+bZmjbvdA+xEBwIDMJNVBjdJ7YJsUWmuSYIwIwz+ua4uyyhokRBuQFHvx83pu2S6h17Ltmhf5nogGNMDFInIRCsM1ciVded9qTRIWbih0uuCnSL5ViF6H7FQj1uw+JtxWrZyuPo8py8SSlKMNjfDLMwMQotdhc2GZ0+1Fpt/OW18/oLG83zZ5Vh6O3VRQinGLtirmgcmPDW2kR40P82TUSjaG4+FF3l0gNVATh9UMywdzXRvVQc2///1vFBQUoFmzZkhLS0NoaKjV/Vu3NowVer1NNMfgeKXyFNqBmUmY9rXzE2VSrAHTbrys3hdaaabGHL14wm6g/ohEptTa9lo5S1p2FNzJCbyiXey2eS9a56LIu6WUf1J+5pz5swOgKs/BWeCTk1fsk4BGJrLOFwA0CtFhdX4JZqz4XShYFwk6HfWd2zvhhuh10Ot1DhPbJSCgAhoA2HrwpObPeW1mU3znYLkGWSDOFhKZURfsdW1UBzWW6y6R97ib4b65sMyqboM9/74tG71aK6+ubUv0yiCQf0Qi08hn394F8VFhbg09yMHdWyM7Cy8LkKyQ/Cf6PUmICsWJSvcSy+XPbuKXO1UnqZedtv9dlL8vgeDkmXN4eFH9YSp7wbpWJ0ql5xF97riIUJSf9a9JBd50RXpjoaAmWGcLBXtdG9VBzdSpUz3RDnJCpMdA6SQnE+7pcXCyUSJyZeDpH5Gn83S0mkYuEtyJrgYOKA/xic6EmDykPcYt2uZ2roU87KnWXyfO2L3Pk+sdOaM258gee8G6VidKpecRfe57e6XhtdX7FIP0YA505O/+pU1jHJZrCPbZQsFe18bl4nvkXSJFuM6eq8Wq/BLFk6zanh4tAwVP/oi8laejxTRykeBONKAZ3StNcf9ECxQO6pCMOXqdz1bCXvDzH7iiVWPFfSgpP+v19gAX358ZwzpokpiqFKyLXJw4Ci4cnXBFA9rx/dugbVKMYpDeoXmsw5W0A5X8np49V4u7Fmx2uB2gXRFPfxTsdW2EgpqEhATs3bsXiYmJiI+Pd1ibpqzMebIcuUbuMbCX8GuZ42B7slBTy0DrQMFTPyJv5+moXVXclpZXPgMyldc7A8R7lkQSTD1FqRdDDqTX7fVNLo3l+6PXQ5NlIgDrz13k4sRRQAPYP+GqqbitFKSfqKzGOIWhtGAQd6FQobNeRa2LePqjYK9rIzSl+4MPPsCIESNgMBjwwQcfONx21KhRmjVOa4E8pVtWa5LQ66Xv7ebHOJqOKFLLAIDD6aauBAryNGNnPyI1UyhFpi57a1qmaK+WaO0VR1WI1eyXmt62nLxizU7iashT0j1VUE3E9R2ScHfPtHrvT05esd0LCDVDNUr1mlzZX9ELC1cuSpz9ngAgJjwEpwKg1o38CT4+4FKkJUYiMdqAJz/LdZhTGBcRirfu6IIerRoHfA+NyO/e1bo2vqTplG7LQMWfg5aGwFnCr6P8FGdX8AMzk9D75TWaJ/R6Ys0mf0l2U3MCEc93ycS4Re6/V2p6lgZmJtktmmeP3D53qlkfPVVlt8fNW5rFGhTfJ7k3Y/aa/ViwodCqJ0v+nGasyHfpindQh2T0b9cMPWZ+b7cWj5zf8+yQ9kgyRggPd7oyVCqSx+RPAY38GwPqz7qz7W3ZWFDqdJLEybPnoNfpAj6gUToeJUSF4vlhHTA4K8V8m7eWm/EFoaCmoqJC+AkDtQckULibn+LogLexoNRjgYLWPyJ/SHZTO/zlbOhBAjCiWyqu65Dk9QPO5sIy1Qm/SRYnFlcXXEyMNuDvn2/3aYLql1sPYfINl9kd0nlsQBuM799a8Tej1yvvu0gA+tsfJxwWF5RrCiUZI1T/3tQOlQZSUuiEAW0wvn8b8/vqLIDzh2OFN9g7HpVV1s3Qe/Cvk5g0ONN8uzeWm/EFoaAmLi5OeI2n2lr/ieaDkRb5KfYOeJ7+8Wv5I/J1spur09TtBXeyWav34ZMtf2Lq0Ez89HR/rx1wRD/T8f1ao02z6HrtUdonkarTkOCzmU6yiqrzQsUA7fXmuBqA+tPJVrwUgHsLtEYZQjCyeyo+/+2Qw0Va5R5Ay23s9YA6C+B8fazwBkfHI9k76wrR6ZJ4DM6yvtAKxGnbjggFNWvXrjX/f1FRESZOnIh77rkHPXv2BABs3LgRH3zwAWbOnOmZVpKZJ5O8vPHj1+pH5OtkN3eGvyyHNZQWzPRFQULRz7RX60SHQzX2Ek/t9WIcr1RXQsBTjp6qcnnGn6vBuj+dbEVmZSXFGjDlhssUh0aVyNtc3yEJGU2i0LNVInpkNMaq/BLMX19k93HGyFC8dHNH83taUlGFstPVSIgKgzEiDLUmSVVw7+tjhTeIlkGYvDQP13Xwv3pgWhIKaq666irz/z/33HP4z3/+g9tvv91824033oiOHTvi3XffZc6Nh4kOYbgikH78nsjTUUOLq+xPthxUvN0XBQm1+OxD9DrzwoPyyV1p8UzLXoyNBaXa74wLio5X1kuUVTPjz5Vg3Z9+byKzsqrOm6DXK/fKyUtmWPasKPVWifQoRISGmL/35Wdr8K+c3W7NxPT1scIbRI9HpZU1AVtUT5TqBS0jIyOxfft2tGnTxur2vXv3Ijs7G2fO2C+q5WvBMPtJ5mz2hKtTsAMtK95X60mJzmSyt1K5u4/3BHc/e3ufxeQh7e0u9igy68bT4i9M97Xlje+8v/3enM34ktuk1DMFwGlvlZrvffnZGk1nYgbi2nOi1Kxq//qIbMWldPyd6Plbr/aJU1NTMW/evHq3z58/H6mprvUQkHqDOiTjp6f7Y8KASxXvl4cwcvKKVT/vnDu71OU7WEgyhvtdQANcfB8Wj+mB10dkY/GYHvjp6f4eb6d8lW3v2k4HbSo8ezN50Z3PXj452wYnJeVVGLdoG8rP1mBYdnP0zKg/ZXZEtxba7YSCgZlN7d7naBhFvn36snzU2ksOcpP8njeL9Y/f28DMJIQ3ClG8z/L9AICeGY2tPlO5t8re5wyIf59LKqoc5qzJ7VDzufjqWOEN3dMTkBAV6nxDBHbukAjVFYVnzZqFv/3tb/jmm29wxRVXAAA2b96Mffv24YsvvtC8geSYJ4YwtEzo9fQSBoBvkt3c7dL2p3wKS6589q4mTXurNs3fulyCm7Kb11uConFUGLqnJ+CbvBK7j/XeOjjW757KDnTNyDks9rj7foh+n8tOV3tkJmYwJsYCdfv1/LAOiuuQWXJ0oRUsVAc1gwcPxt69ezFnzhzs3r0bADB06FCMHTuWPTVe5slaLe78+OVAZnV+CZbkHrI6kQRLdy/g3swXf8qnsKX2s3fle+it2jRyQPXT0/1xXYdkq+9laWWNw4DGkqd6zFbuKMbDi+oXPTxSUe2T1es93YMo+r1PiArzaDuC0eCsFDz410m8s65Q8X4dAj93SIRLaz+lpqbixRdf1LotpJI/DmE4u/r2xcweT3K1VyuYkhfVfg9FkkW1YhtQlZ+twfsbilS/tid6zFbuOIzxi5WvrH21er2nexBFv/fGCLGgJtiHUtSaNDgTnS6Jv9ArebEGUjBdTDojFNTs2LFD+AmzsrJcbgyp429DGCJX36IHa28MW2lFbc+GvG/V5014fEAbLN580KriaSBV9aw1STh+Smxatvw99MUq3PKUbbXBlKd6zHLyip0OFXhv6Osib/Qg2uvhjAlvhBdu6ohBHZJRa5L8tifT3w3OSsZ1HYKvqJ4ooaAmOzsbOp0OkiRZFeGTx30tb2PxPe/xpyEMNScMZwfrYJ6loLRvSbHhmHBhnZpAOgCJ5sTYfg+16DmcPaIzGscYsGH/McxeW+B0+6Yx4aqDKU/1mMm/FVHe7Gn1Vg/ioA7J+O2PMsxff7HXrKLqPB77ZBvyDtVVvg2WnkxfCNbcIRFCs58KCwtx4MABFBYW4osvvkB6ejrefvtt5ObmIjc3F2+//TYyMjKYKOxl8gEIQL1ZON7+4bty9a10sHY0i8aV2Vz+xN6+Hamowmur98LQSG931oi/sbcvtpS+h6I9h49f06ZebkWyMRxz7+yCG7JT0D09AT1bJSIuwv6sD8tZaGqDA0/NQFL7W/FFsrinZ0DOXJmPeevrDwOapLrKtzNX5gfcTEzyD0I9NS1btjT//6233oo33ngDgwcPNt+WlZWF1NRUTJ48GcOHD9e8kWSfvyxM5srVpO3B2tVZNIHA2/vmyeE7Nb1y8VGhuCm7OWLCQ7Fh33Ecr6xGYpQBSbHhOFLhuIfxkWva4JFr2ijuh0gvkW1AJRocjO+XgV6tm3isx0zNb8VXs1U8uS5QzXkT5q1XTmaVzVtfiCevbRe06xOR56hOFN65cyfS09Pr3Z6eno78fPEuVdKOP/zw1VxN2hsW85eVtz3Bm/umZvjOleBHtKchJrwRyirP4b0NRXhvQ5HVfXGRoeZgztnQgu37ITpzyjawFx2unTCwrUd/O2p+K74cYvHUEMaHG4vsrgkmM0l1243u06pBD6WQeqqDmvbt22PmzJmYP38+wsLquoZramowc+ZMtG/fXvMGkhhf//BF1o6xpHSw9sfZXFrx1r6JrBwuB8Cr8kvwVe5h1bMkRNt4quq83fvKL1SsNdosWuish1GklyguIhRv3dEFPVpZD+V5Ml9ETXAo8lvR64DZtwfnEMsfZWJV50W3I7KkOqiZO3cuhg4diksuucQ802nHjh3Q6XRYtmyZ5g2kwCCydgzg+KTpb7O5tOSNfRMZ4pr05U5M+3qX1WwrS8XlVRj70VZMGNAG4/u3UTwxa/H+y+0Jb6THx/dfgeOnq62CAXtBgkgv0cmz56DX6RTb7onhWrWJ7SK/ldm3d7ZaTTmYtEyI1HQ7Ikuq134CgMrKSnz88cfm4nvt27fHyJEjERUVpXkDtRRMaz/5K6UDfOOoMAzLTsHAzCSHV7DyOkDOhgd+erp/wI2pe2Pf1Kz/IiIpNhxTbshEfFRYvTV+HO2LWrbrWzkKEqrPm/DYJ7lOn9PZ+jZa5RzZ6xkTWZ/I1Vl+gVTuQEnNeRPaTf7G4RCUXgfsnnE9whqpXsmHgpTo+dul4ntRUVF44IEHXG4cBS938nuCqSCdLW/sm9bDciUVVfWq3con3clDMhUr4brCst3Ohs8et7PWmS1nvUlaDNe6m/ztym8lGModhDXSY0yfdLuVbwFgTJ90BjTkEpe+NR9++CF69+6NlJQU/PHHHwDq1oRaunSppo2jwCSysJ09wTyN09P75o1huZILw1PPLFEuyBljUF4M0RG53c6CBKBurbOkWIPLC4lqSU3ytz1qfivBVO5g0uBMPNg3Hba7q9cBD/ZNx6TBmb5pGAU81T01c+bMwZQpU/D444/j+eefNxfbi4+Px2uvvYZhw4Zp3khqWPxhNpeneHLf1CZru0J+3pNnlZOAT1XXQqcDRAa1bWfBiQYJEwa0wWur9/m8N8+bie3BWO5g0uBMPHltO3y4sQh/lJ1By4RI3NUzjT005BbVQc2bb76JefPmYfjw4XjppZfMt19++eX4+9//rmnjqOHy9WwuT/LUvokma3uaaEADWAcgoif/tMQov6jN5M3E9mAtdxDWSI/RfVqZ/645b8J76w8wyCGXqQ5qCgsL0blz53q3GwwGVFZWatIoInKNvdk9ycZwnD1Xi/Iz57wW7Oh1sJsMqhSAqAkSemY09nlvnjeXKQnmcgeyuirDhVbfmRdW/o4xfTgcReJUBzXp6enIzc21qjIMADk5OaxTQ+QH7A1xrcovwUMfaZPcK8IkAZOHtEdijAGJ0QZAAo5XVtsNQNQGCb7uzfNmYnswlzsA6gIapcRhedkEAAxsSIjqoOaJJ57AuHHjUFVVBUmSsHnzZixevNhckI+IfE/phD+oQzIeH3ApZq3e67V2HDp51mp4wZFAnP3mrWVK/GnxWq2pWTaBQ1HkjOqg5v7770dERASeffZZnDlzBiNHjkRKSgpef/11jBgxwhNtJCJBzmqYpCV6t6DZ+xuK0D09Qfjk7i9rmanhjcT2QAz4RKldNoHIEZfq1Nxxxx244447cObMGZw+fRpNmzbVul1EpFJOXnG9asFJsQZMu/EyczDg7eEJV2blBOLsN28MhQViwCdi37FTmm5HDZtLQQ0AHD16FHv27AEA6HQ6NGnSRLNGKXnhhRewYsUK5ObmIiwsDCdPnvTo6xEFkpy8YoxVyJcpqajG2I+2Yu6FOjjuTvs2hOhQXSv+SFdn5fg6X8ZfBWLA58y+ktOabkcNm+oBylOnTuGuu+5CSkoKrrrqKlx11VVISUnBnXfeifLyck+0EUDdopm33norHnroIY+9BlEgqjVJmPjlTofbTPxyJ2pNknkYA4DdAnaOVNdKeOya1kiKVdfjE8izcvyNO8Ut/VF4qFjBRtHtqGFTHdTcf//9+OWXX7BixQqcPHkSJ0+exPLly/Hrr7/iwQcf9EQbAQDTp0/HhAkT0LFjR4+9BlEg2lRQarXStZKTZ87h1rk/43TVefMwRrNYg0uvV2uSsGFifywe0wPjrs4Qekygzsohz2vVRGzNQNHtqGFTPfy0fPlyfPvtt+jdu7f5tuuuuw7z5s3DoEGDNG0cETm38cBxoe22HjyJDtO+RdYlsfh6fB/EhIfijvm/uPCKOnNvQff0BHy57VBQzsoh73hmcCY+3HRQaDsiZ1T31DRu3BhGo7He7UajEfHx8Zo0SivV1dWoqKiw+kcUbAqOqSt6ueOvCtw4ez2On652vrECy1wXR8NZgT4rh7wjIiwEAzMdTzYZmNkUEWEcfiLnVAc1zz77LJ544gmUlJSYbyspKcFTTz2FyZMnq3quiRMnQqfTOfy3e/dutU00mzlzJoxGo/lfamqqy89F5I9qTRI2HShV/bgdf1Vg1yH1QX58ZCh6tKpf/yZYFyEl75h3dze7gc3AzKaYd3c3L7eIApVOkpyv1NK5c2fodBevtPbt24fq6mq0aNECAHDw4EEYDAa0adMGW7eKVyw9duwYSksdH5BbtWqFsLAw898LFy7E448/LjT7qbq6GtXVF69GKyoqkJqaivLycsTGxgq3k8hfbSwoxe3zNnnt9eY6CFKc1cghcuZsTS1eXJmPotIzSGsciWcGZ7KHhgDUnb+NRqPT87dQTs3w4cO1apeVJk2aeHQquMFggMHgWjIkUSDw1qyiZIFaKA1tGjaDOO1FhIVgxnBOBiHXCQU1U6dO9XQ7nDp48CDKyspw8OBB1NbWIjc3FwDQunVrREdH+7ZxRD7iqVlFycZwjOjWAmmJkTxhK8jJK1ZcNDSQi+ARBQOh4Sd7Tp8+DZPJZHWbp4Z17rnnHnzwwQf1bl+7di2uvvpqoecQ7b4iChS1Jgm9X17jcjE9W+P7ZaBX6yYMYhzIySvGQx9trfd+y+8W84iItCd6/ladKFxYWIghQ4YgKirKPOMpPj4ecXFxHp39tHDhQkiSVO+faEBDFIzcLaZnq02zmKAo6OYptSYJ05flKwaQ8m3Tl+Wj1tliRkTkEarr1Nx5552QJAnvv/8+mjVrZpVATETeZ29NIFewSJ5jmwvLHL7Hri4LQUTaUB3UbN++Hb/99hvatm3rifYQkQuU1gQ6UVmD6ct34UiF83o0LJInRjQxm8tCEPmG6qCmW7du+PPPPxnUEPkZpdlH13W4GOgUHa/ErNX7oAOshk9YJE+caE8We7yIfEN1UDN//nyMHTsWhw4dQocOHRAaGmp1f1ZWlmaNIyL32AY6bZNi6g1TJXHWjjBnq5yzx4vIt1QHNceOHUNBQQHuvfde8206nQ6SJEGn06G2tlbTBhKRdpSGqTjTSZycmP3QR1vZ40Xkh1RP6c7MzET79u3xj3/8QzFRuGXLlpo2UEuc0k0UPHxZ/I51aoi8S/T8rTqoiYqKwvbt29G6dWu3G+ltDGqIgoM/BBWsKEzkPR6rU9O/f39s377drcYREblKLn5nO7W6pLwKD320FTl5xV5ph5yvNCy7OWv7EPkJ1Tk1Q4cOxYQJE7Bz50507NixXqLwjTfeqFnjiIgsOSt+p0Nd8buBmUkMMogaINXDT3q9/c4df08U5vATUWATXZV88ZgeLH5HFEQ0XaXbku1aT0RE3sLid0TkiOqcGiIiX2HxOyJyRDioGTx4MMrLy81/v/TSSzh58qT579LSUmRmZmraOCIiS3LxO3vZMjrUzYJi8Tuihkk4qPn2229RXX1xDZkXX3wRZWVl5r/Pnz+PPXv2aNs6IiILjlYlZ/E7IhIOamzziVXmFxMRaUJelTzJaD3ElGQMx5w7u7D4HVEDpjpRmIjI17jcAxEpEQ5qdDpdvSURbP8mIvIWpVXJiahhEw5qJEnCPffcA4PBAACoqqrC2LFjERUVBQBW+TZERERE3iYc1IwaNcrq7zvvvLPeNnfffbf7LSIiIiJygXBQs2DBAk+2g4iIiMgtLL5HREREQYFBDREREQUFBjVEREQUFBjUEBERUVBgUENERERBgUENERERBQUGNURERBQUGNQQERFRUGBQQ0REREGBq3QTEWpNEle8JqKAx6CGqIHLySvG9GX5KC6vMt+WbAzH1KGZGNQh2YctIyJSh8NPRA1YTl4xHvpoq1VAAwAl5VV46KOtyMkr9lHLiIjUY1BD1EDVmiRMX5YPSeE++bbpy/JRa1LagojI/zCoIWqgNheW1euhsSQBKC6vwubCMu81iojIDQxqiBqoo6fsBzSubEdE5GtMFCZSIZhmCTWNCdd0OyIiXwuIoKaoqAgzZszAmjVrUFJSgpSUFNx555345z//ibCwMF83jxqIYJsl1D09AcnGcJSUVynm1egAJBnrAjciokAQEMNPu3fvhslkwjvvvINdu3Zh1qxZmDt3Lp555hlfN40aiGCcJRSi12Hq0EwAdQGMJfnvqUMzA7YniogaHp0kSQE5teGVV17BnDlzcODAAeHHVFRUwGg0ory8HLGxsR5sHQWTWpOE3i+vsZtUK/do/PR0/4AMAIKtB4qIgo/o+Tsghp+UlJeXIyGB3eLkeWpmCfXMaOy9hmlkUIdkDMxMCppcISJquAIyqNm/fz/efPNNvPrqqw63q66uRnV1tfnviooKTzeNglBDmCUUotcFZEBGRGTJpzk1EydOhE6nc/hv9+7dVo85dOgQBg0ahFtvvRVjxoxx+PwzZ86E0Wg0/0tNTfXk7lCQ4iwhIqLA4NOcmmPHjqG0tNThNq1atTLPcDp8+DCuvvpq9OjRAwsXLoRe7zgmU+qpSU1NZU4NqSLn1DibJRSoOTVERP4uIHJqmjRpgiZNmghte+jQIfTr1w9du3bFggULnAY0AGAwGGAwGNxtJjVw8iyhhz7aCh1gFdhwlhARkf8IiCndhw4dwtVXX40WLVrg1VdfxbFjx1BSUoKSkhJfN40aiEEdkjHnzi5IMloPMSUZwzHnzi6cJURE5AcCIlF41apV2L9/P/bv349LLrnE6r4AnZFOAYizhIiI/FvA1qlxBevUEBERBR7R83dADD8REREROcOghoiIiIICgxoiIiIKCgxqiIiIKCgwqCEiIqKgwKCGiIiIggKDGiIiIgoKDGqIiIgoKDCoISIioqDAoIaIiIiCAoMaIiIiCgoMaoiIiCgoMKghIiKioMCghoiIiIICgxoiIiIKCgxqiIiIKCgwqCEiIqKgwKCGiIiIggKDGiIiIgoKDGqIiIgoKDCoISIioqDAoIaIiIiCAoMaIiIiCgoMaoiIiCgoMKghIiKioMCghoiIiIICgxoiIiIKCgxqiIiIKCgwqCEiIqKgwKCGiIiIggKDGiIiIgoKDGqIiIgoKDCoISIioqDAoIaIiIiCAoMaIiIiCgoMaoiIiCgoBExQc+ONN6JFixYIDw9HcnIy7rrrLhw+fNjXzSIiIiI/ETBBTb9+/fDZZ59hz549+OKLL1BQUIBbbrnF180iIiIiP6GTJEnydSNc8fXXX2P48OGorq5GaGio0GMqKipgNBpRXl6O2NhYD7eQiIiItCB6/g6YnhpLZWVl+Pjjj3HllVcKBzREREQU3AIqqHn66acRFRWFxo0b4+DBg1i6dKnD7aurq1FRUWH1j4iIiIKTT4OaiRMnQqfTOfy3e/du8/ZPPfUUtm3bhu+++w4hISG4++674Wj0bObMmTAajeZ/qamp3tgtIiIi8gGf5tQcO3YMpaWlDrdp1aoVwsLC6t3+119/ITU1FT///DN69uyp+Njq6mpUV1eb/66oqEBqaipzaoiIiAKIaE5NIy+2qZ4mTZqgSZMmLj3WZDIBgFXQYstgMMBgMLj0/ERERBRYfBrUiPrll1+wZcsW9O7dG/Hx8SgoKMDkyZORkZFht5eGiIiIGpaASBSOjIzEl19+iWuuuQZt27bF6NGjkZWVhR9//JE9MURERAQgQHpqOnbsiDVr1vi6GUREROTHAqKnhoiIiMgZBjVEREQUFBjUEBERUVBgUENERERBgUENERERBQUGNURERBQUGNQQERFRUGBQQ0REREGBQQ0REREFBQY1REREFBQY1BAREVFQYFBDREREQYFBDREREQUFBjVEREQUFBjUEBERUVBgUENERERBgUENERERBQUGNURERBQUGNQQERFRUGBQQ0REREGBQQ0REREFBQY1REREFBQY1BAREVFQYFBDREREQYFBDREREQUFBjVEREQUFBjUEBERUVBgUENERERBgUENERERBQUGNURERBQUGNQQERFRUGBQQ0REREGBQQ0REREFBQY1REREFBQY1BAREVFQYFBDREREQSHggprq6mpkZ2dDp9MhNzfX180hIiIiPxFwQc0//vEPpKSk+LoZRERE5GcCKqj55ptv8N133+HVV1/1dVPMak0SNhaUYmnuIWwsKEWtSfJ1k4iIiBqkRr5ugKgjR45gzJgx+OqrrxAZGenr5gAAcvKKMX1ZPorLq8y3JRvDMXVoJgZ1SPZhy4iIiBqegOipkSQJ99xzD8aOHYvLL79c+HHV1dWoqKiw+qeVnLxiPPTRVquABgBKyqvw0EdbkZNXrNlrERERkXM+DWomTpwInU7n8N/u3bvx5ptv4tSpU5g0aZKq5585cyaMRqP5X2pqqibtrjVJmL4sH0oDTfJt05flcyiKiIjIi3SSJPnszHvs2DGUlpY63KZVq1a47bbbsGzZMuh0OvPttbW1CAkJwR133IEPPvhA8bHV1dWorq42/11RUYHU1FSUl5cjNjbW5XZvLCjF7fM2Od1u8Zge6JnR2OXXISIiorrzt9FodHr+9mlOTZMmTdCkSROn273xxht4/vnnzX8fPnwY1113HT799FNcccUVdh9nMBhgMBg0aaulo6eqnG+kYjsiIiJyX0AkCrdo0cLq7+joaABARkYGLrnkEq+3p2lMuKbbERERkfsCIlHY33RPT0CyMRw6O/frUDcLqnt6gjebRURE1KAFZFCTlpYGSZKQnZ3tk9cP0eswdWgmANQLbOS/pw7NRIjeXthDREREWgvIoMYfDOqQjDl3dkGS0XqIKckYjjl3dmGdGiIiIi8LiJwafzWoQzIGZiZhc2EZjp6qQtOYuiEn9tAQERF5H4MaN4XodZy2TURE5Ac4/ERERERBgUENERERBQUGNURERBQUGNQQERFRUGBQQ0REREGBQQ0REREFBQY1REREFBQY1BAREVFQYFBDREREQaFBVRSWJAkAUFFR4eOWEBERkSj5vC2fx+1pUEHNqVOnAACpqak+bgkRERGpderUKRiNRrv36yRnYU8QMZlMOHz4MGJiYqDT+eeikxUVFUhNTcWff/6J2NhYXzfHq7jv3Hfue8PBfee+q9l3SZJw6tQppKSkQK+3nznToHpq9Ho9LrnkEl83Q0hsbGyD+7LLuO/c94aG+859b2hc2XdHPTQyJgoTERFRUGBQQ0REREGBQY2fMRgMmDp1KgwGg6+b4nXcd+57Q8N95743NJ7e9waVKExERETBiz01REREFBQY1BAREVFQYFBDREREQYFBDREREQUFBjUBoLq6GtnZ2dDpdMjNzfV1c7zixhtvRIsWLRAeHo7k5GTcddddOHz4sK+b5XFFRUUYPXo00tPTERERgYyMDEydOhU1NTW+bppXvPDCC7jyyisRGRmJuLg4XzfHo9566y2kpaUhPDwcV1xxBTZv3uzrJnnFunXrMHToUKSkpECn0+Grr77ydZO8YubMmejWrRtiYmLQtGlTDB8+HHv27PF1s7xizpw5yMrKMhfc69mzJ7755huPvBaDmgDwj3/8AykpKb5uhlf169cPn332Gfbs2YMvvvgCBQUFuOWWW3zdLI/bvXs3TCYT3nnnHezatQuzZs3C3Llz8cwzz/i6aV5RU1ODW2+9FQ899JCvm+JRn376KZ544glMnToVW7duRadOnXDdddfh6NGjvm6ax1VWVqJTp0546623fN0Ur/rxxx8xbtw4bNq0CatWrcK5c+dw7bXXorKy0tdN87hLLrkEL730En777Tf8+uuv6N+/P4YNG4Zdu3Zp/2IS+bWVK1dK7dq1k3bt2iUBkLZt2+brJvnE0qVLJZ1OJ9XU1Pi6KV73r3/9S0pPT/d1M7xqwYIFktFo9HUzPKZ79+7SuHHjzH/X1tZKKSkp0syZM33YKu8DIC1ZssTXzfCJo0ePSgCkH3/80ddN8Yn4+Hhp/vz5mj8ve2r82JEjRzBmzBh8+OGHiIyM9HVzfKasrAwff/wxrrzySoSGhvq6OV5XXl6OhIQEXzeDNFJTU4PffvsNAwYMMN+m1+sxYMAAbNy40YctI28qLy8HgAb3266trcUnn3yCyspK9OzZU/PnZ1DjpyRJwj333IOxY8fi8ssv93VzfOLpp59GVFQUGjdujIMHD2Lp0qW+bpLX7d+/H2+++SYefPBBXzeFNHL8+HHU1taiWbNmVrc3a9YMJSUlPmoVeZPJZMLjjz+OXr16oUOHDr5ujlfs3LkT0dHRMBgMGDt2LJYsWYLMzEzNX4dBjZdNnDgROp3O4b/du3fjzTffxKlTpzBp0iRfN1kzovsue+qpp7Bt2zZ89913CAkJwd133w0pQAtgq913ADh06BAGDRqEW2+9FWPGjPFRy93nyr4TBbNx48YhLy8Pn3zyia+b4jVt27ZFbm4ufvnlFzz00EMYNWoU8vPzNX8dLpPgZceOHUNpaanDbVq1aoXbbrsNy5Ytg06nM99eW1uLkJAQ3HHHHfjggw883VTNie57WFhYvdv/+usvpKam4ueff/ZIl6Wnqd33w4cP4+qrr0aPHj2wcOFC6PWBe/3hyue+cOFCPP744zh58qSHW+d9NTU1iIyMxP/+9z8MHz7cfPuoUaNw8uTJBtUjqdPpsGTJEqv3IdiNHz8eS5cuxbp165Cenu7r5vjMgAEDkJGRgXfeeUfT522k6bORU02aNEGTJk2cbvfGG2/g+eefN/99+PBhXHfddfj0009xxRVXeLKJHiO670pMJhOAuuntgUjNvh86dAj9+vVD165dsWDBgoAOaAD3PvdgFBYWhq5du+L77783n8xNJhO+//57jB8/3reNI4+RJAmPPPIIlixZgh9++KFBBzRA3XfeE8dzBjV+qkWLFlZ/R0dHAwAyMjJwySWX+KJJXvPLL79gy5Yt6N27N+Lj41FQUIDJkycjIyMjIHtp1Dh06BCuvvpqtGzZEq+++iqOHTtmvi8pKcmHLfOOgwcPoqysDAcPHkRtba25LlPr1q3Nv4Fg8MQTT2DUqFG4/PLL0b17d7z22muorKzEvffe6+umedzp06exf/9+89+FhYXIzc1FQkJCveNeMBk3bhwWLVqEpUuXIiYmxpw/ZTQaERER4ePWedakSZNw/fXXo0WLFjh16hQWLVqEH374Ad9++632L6b5fCryiMLCwgYzpXvHjh1Sv379pISEBMlgMEhpaWnS2LFjpb/++svXTfO4BQsWSAAU/zUEo0aNUtz3tWvX+rppmnvzzTelFi1aSGFhYVL37t2lTZs2+bpJXrF27VrFz3jUqFG+bppH2ftdL1iwwNdN87j77rtPatmypRQWFiY1adJEuuaaa6TvvvvOI6/FnBoiIiIKCoE9WE9ERER0AYMaIiIiCgoMaoiIiCgoMKghIiKioMCghoiIiIICgxoiIiIKCgxqiIiIKCgwqCEij5o2bRqys7M1f96ioiLodDpz1WElP/zwA3Q6nXkNqYULFyIuLk7zthCRf2BQQ0QAgHvuuUdxBe1Bgwb5umma+b//+z/s3bvX183Ajh070KdPH4SHhyM1NRX/+te/fN0koqDAtZ+IyGzQoEFYsGCB1W0Gg8FHrdFeRESEz9fZqaiowLXXXosBAwZg7ty52LlzJ+677z7ExcXhgQce8GnbiAIde2qIyMxgMCApKcnqX3x8vPl+nU6Hd955BzfccAMiIyPRvn17bNy4Efv378fVV1+NqKgoXHnllSgoKKj33O+88w5SU1MRGRmJ2267DeXl5Vb3z58/H+3bt0d4eDjatWuHt99+2+r+zZs3o3PnzggPD8fll1+Obdu21XuNlStX4tJLL0VERAT69euHoqIiq/tth5/kobEPP/wQaWlpMBqNGDFiBE6dOmXe5tSpU7jjjjsQFRWF5ORkzJo1C1dffTUef/xx8zZvv/022rRpg/DwcDRr1gy33HKL3ff4448/Rk1NDd5//31cdtllGDFiBB599FH85z//sfsYIhLDoIaIVJkxYwbuvvtu5Obmol27dhg5ciQefPBBTJo0Cb/++iskScL48eOtHrN//3589tlnWLZsGXJycrBt2zY8/PDD5vs//vhjTJkyBS+88AJ+//13vPjii5g8eTI++OADAHUrO99www3IzMzEb7/9hmnTpuHvf/+71Wv8+eefuPnmmzF06FDk5ubi/vvvx8SJE53uT0FBAb766issX74cy5cvx48//oiXXnrJfP8TTzyBDRs24Ouvv8aqVauwfv16bN261Xz/r7/+ikcffRTPPfcc9uzZg5ycHPTt29fu623cuBF9+/ZFWFiY+bbrrrsOe/bswYkTJ5y2l4gc8MgymUQUcEaNGiWFhIRIUVFRVv9eeOEF8zYApGeffdb898aNGyUA0nvvvWe+bfHixVJ4eLj576lTp0ohISFWq6x/8803kl6vl4qLiyVJkqSMjAxp0aJFVu2ZMWOG1LNnT0mSJOmdd96RGjduLJ09e9Z8/5w5c6xWrp80aZKUmZlp9RxPP/20BEA6ceKEJEl1q6AbjUartkVGRkoVFRXm25566inpiiuukCRJkioqKqTQ0FDp888/N99/8uRJKTIyUnrsscckSZKkL774QoqNjbV6DkcGDhwoPfDAA1a37dq1SwIg5efnCz0HESljTg0RmfXr1w9z5syxui0hIcHq76ysLPP/N2vWDADQsWNHq9uqqqpQUVGB2NhYAECLFi3QvHlz8zY9e/aEyWTCnj17EBMTg4KCAowePRpjxowxb3P+/HkYjUYAwO+//46srCyEh4dbPYel33//HVdccYXVbbbbKElLS0NMTIz57+TkZBw9ehQAcODAAZw7dw7du3c33280GtG2bVvz3wMHDkTLli3RqlUrDBo0CIMGDcJNN92EyMhIp69NRNpiUENEZlFRUWjdurXDbUJDQ83/r9Pp7N5mMpmEXvP06dMAgHnz5tULSkJCQoSewx2WbQfq2i/adgCIiYnB1q1b8cMPP+C7777DlClTMG3aNGzZskVx+nhSUhKOHDlidZv8d1JSkvodICIz5tQQkccdPHgQhw8fNv+9adMm6PV6tG3bFs2aNUNKSgoOHDiA1q1bW/1LT08HALRv3x47duxAVVWV1XNYat++PTZv3mx1m+02arVq1QqhoaHYsmWL+bby8vJ608IbNWqEAQMG4F//+hd27NiBoqIirFmzRvE5e/bsiXXr1uHcuXPm21atWoW2bdtaJWUTkXoMaojIrLq6GiUlJVb/jh8/7vbzhoeHY9SoUdi+fTvWr1+PRx99FLfddpu5Z2L69OmYOXMm3njjDezduxc7d+7EggULzDOCRo4cCZ1OhzFjxiA/Px8rV67Eq6++avUaY8eOxb59+/DUU09hz549WLRoERYuXOhWu2NiYjBq1Cg89dRTWLt2LXbt2oXRo0dDr9ebe6SWL1+ON954A7m5ufjjjz/w3//+FyaTyWqIytLIkSMRFhaG0aNHY9euXfj000/x+uuv44knnnCrrUTEoIaILOTk5CA5OdnqX+/evd1+3tatW+Pmm2/G4MGDce211yIrK8tqyvb999+P+fPnY8GCBejYsSOuuuoqLFy40NxTEx0djWXLlmHnzp3o3Lkz/vnPf+Lll1+2eo0WLVrgiy++wFdffYVOnTph7ty5ePHFF91u+3/+8x/07NkTN9xwAwYMGIBevXqZp54DQFxcHL788kv0798f7du3x9y5c7F48WJcdtllis9nNBrx3XffobCwEF27dsWTTz6JKVOmsEYNkQZ0kiRJvm4EEVGgqKysRPPmzfHvf/8bo0eP9nVziMgCE4WJiBzYtm0bdu/eje7du6O8vBzPPfccAGDYsGE+bhkR2WJQQ0TkxKuvvoo9e/YgLCwMXbt2xfr165GYmOjrZhGRDQ4/ERERUVBgojAREREFBQY1REREFBQY1BAREVFQYFBDREREQYFBDREREQUFBjVEREQUFBjUEBERUVBgUENERERBgUENERERBYX/B8giRn2LoTIcAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression()\n",
        "X = df[['1']]  # Independent variable\n",
        "y = df['score']  # Dependent variable\n",
        "model.fit(X, y)\n",
        "y_pred = model.predict(X)\n",
        "mse = mean_squared_error(y, y_pred)\n",
        "print(f\"Mean Squared Error: {mse}\")\n"
      ],
      "metadata": {
        "id": "eL3g9ikly5c8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4453bf6-7937-426d-c9f4-002cee7136c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 10.747590793555236\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into features (X) and the target variable (y)\n",
        "X = df.drop(['score','Final_score'], axis=1)  # Assuming 'embed_0' and 'embed_1' are your target variables\n",
        "y_column1 = df['score']\n",
        "y_column2 = df['Final_score']\n",
        "\n",
        "# Splitting the data into training and test sets\n",
        "X_train, X_test, y_train_column1, y_test_column1, y_train_column2, y_test_column2 = train_test_split(X, y_column1, y_column2, test_size=0.2, random_state=42)\n",
        "\n",
        "# Create a Decision Tree Regressor for 'embed_0'\n",
        "regressor_column1 = DecisionTreeRegressor()\n",
        "regressor_column1.fit(X_train, y_train_column1)\n",
        "y_pred_column1 = regressor_column1.predict(X_test)\n",
        "\n",
        "# Calculate the Mean Squared Error for 'embed_0'\n",
        "mse_column1 = mean_squared_error(y_test_column1, y_pred_column1)\n",
        "print(\"Mean Squared Error (score):\", mse_column1)\n",
        "\n",
        "# Create a Decision Tree Regressor for 'embed_1'\n",
        "regressor_column2 = DecisionTreeRegressor()\n",
        "regressor_column2.fit(X_train, y_train_column2)\n",
        "y_pred_column2 = regressor_column2.predict(X_test)\n",
        "\n",
        "# Calculate the Mean Squared Error for 'embed_1'\n",
        "mse_column2 = mean_squared_error(y_test_column2, y_pred_column2)\n",
        "print(\"Mean Squared Error (Final score):\", mse_column2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eAAtYtPwqNqm",
        "outputId": "a7ecdd5b-00e3-490c-f209-de7a934e7d9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error (score): 5.705975833990019\n",
            "Mean Squared Error (Final score): 0.321578670869451\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn_regressor_column1 = KNeighborsRegressor(n_neighbors=5)\n",
        "knn_regressor_column1.fit(X_train, y_train_column1)\n",
        "y_pred_column1 = knn_regressor_column1.predict(X_test)\n",
        "\n",
        "# Calculate the Mean Squared Error for 'embed_0'\n",
        "mse_column1 = mean_squared_error(y_test_column1, y_pred_column1)\n",
        "print(\"Mean Squared Error (score):\", mse_column1)\n",
        "\n",
        "# Create a k-NN Regressor for 'embed_1'\n",
        "knn_regressor_column2 = KNeighborsRegressor(n_neighbors=5)\n",
        "knn_regressor_column2.fit(X_train, y_train_column2)\n",
        "y_pred_column2 = knn_regressor_column2.predict(X_test)\n",
        "\n",
        "# Calculate the Mean Squared Error for 'embed_1'\n",
        "mse_column2 = mean_squared_error(y_test_column2, y_pred_column2)\n",
        "print(\"Mean Squared Error (final score):\", mse_column2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eN3Jw_2tr5-E",
        "outputId": "2e306b1e-e673-4082-f9f3-17015187b41b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error (score): 3.9552340425531916\n",
            "Mean Squared Error (final score): 0.18178723404255318\n"
          ]
        }
      ]
    }
  ]
}