Version2_Crop_Production_Prediction.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
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      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"ICRISAT-District Level Data.csv\")\n",
        "df=df.drop('Dist Code', axis = 1 )\n",
        "df=df.drop('State Code', axis = 1 )\n",
        "df=df.drop('Dist Name', axis = 1 )\n",
        "df=df.drop('State Name', axis = 1 )\n",
        "df['Year'] = pd.to_datetime(df['Year'], format='%Y')\n",
        "df['year'] = df['Year'].dt.year\n",
        "df=df.drop('Year',axis = 1)\n",
        "# df['month'] = df['Year'].dt.month\n",
        "# df['day'] = df['Year'].dt.day\n",
        "# df['hour'] = df['Year'].dt.hour\n",
        "df.tail()"
      ],
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              "    WHEAT AREA (1000 ha)  WHEAT PRODUCTION (1000 tons)  year\n",
              "47                 166.8                        376.83  2013\n",
              "48                 118.0                        362.00  2014\n",
              "49                 171.0                        552.00  2015\n",
              "50                 186.0                        636.00  2016\n",
              "51                 125.8                        471.60  2017"
            ],
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              "      <th></th>\n",
              "      <th>WHEAT AREA (1000 ha)</th>\n",
              "      <th>WHEAT PRODUCTION (1000 tons)</th>\n",
              "      <th>year</th>\n",
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              "      <td>2013</td>\n",
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              "      <td>362.00</td>\n",
              "      <td>2014</td>\n",
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              "      <th>49</th>\n",
              "      <td>171.0</td>\n",
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              "      <td>2015</td>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train , valid , test =np.split(df.sample(frac=1), [int(0.6*len(df)), int(0.8*len(df))])"
      ],
      "metadata": {
        "id": "nFJTeqgCxUYt"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def scale_dataset(dataframe):\n",
        "  target = dataframe[dataframe.columns[1]].values\n",
        "  features = dataframe.drop(dataframe.columns[1], axis=1).values\n",
        "\n",
        "  return features,target"
      ],
      "metadata": {
        "id": "6FkMMt7txmKO"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, Y_train = scale_dataset(train)\n",
        "X_valid, Y_valid = scale_dataset(valid)\n",
        "X_test, Y_test = scale_dataset(test)"
      ],
      "metadata": {
        "id": "vWuVrTWaxx10"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import r2_score\n",
        "\n",
        "# assuming X_train, y_train are obtained using scale_dataset function\n",
        "linreg_model = LinearRegression()\n",
        "\n",
        "# fit the model to the training data\n",
        "linreg_model.fit(X_train, Y_train)\n",
        "\n",
        "# make predictions on the test data\n",
        "y_pred = linreg_model.predict(X_test)\n",
        "\n",
        "# evaluate the performance of the model\n",
        "score = linreg_model.score(X_test, Y_test)\n",
        "print (\"X Test :\" , X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "azF7QB73x05z",
        "outputId": "9a34572b-261d-4517-8216-81b76bbc0cda"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X Test : [[  37.1  2002.  ]\n",
            " [  31.   1966.  ]\n",
            " [ 126.24 2012.  ]\n",
            " [  78.5  1994.  ]\n",
            " [  56.   1972.  ]\n",
            " [  70.1  1975.  ]\n",
            " [  48.7  1979.  ]\n",
            " [  47.6  1980.  ]\n",
            " [  68.1  1990.  ]\n",
            " [ 171.   2015.  ]\n",
            " [  48.7  1982.  ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print (\"Predictions :\" , y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BPLwyqrBwcY2",
        "outputId": "cbf7cb2c-e48f-4ae6-c654-f288e0c869ef"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predictions : [ 88.4171495  -40.42900636 366.26939113 178.13808415  47.43460543\n",
            "  95.7954372   48.99727106  49.06232951 136.90936098 499.50143128\n",
            "  58.32731228]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(\"Real Results : \", Y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g4n1JmaxwlMy",
        "outputId": "8b6f8794-6a7b-4b80-d09d-f0d424d45d2a"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Real Results :  [ 56.1   19.   260.68 196.8   49.8   91.4   58.2   55.6  157.   552.\n",
            "  78.2 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Score:\", score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fslJvcRZwoW1",
        "outputId": "d1de14b8-f7fe-42d6-f5d8-67fb7d08c1ea"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Score: 0.9167220444212094\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(df['WHEAT PRODUCTION (1000 tons)'], df['WHEAT AREA (1000 ha)'])\n",
        "plt.xlabel('area')\n",
        "plt.ylabel('Production')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "GhFLbs_vyZIy",
        "outputId": "09028f6b-ea5c-41b6-c552-f9012a0bfc55"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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3XpwQy9U9QpxdmqDQLiIiIiIick55JRaW7MpmYUImWw/mY5wYpW42weVd2jAmLpwRPULx925648+OFlt45KtEViflAnBl17a8PCmWYF/nz4WXKgrtIiIiIiIi/6Oowsry3TksSsjkh5Rj2B1G9X2XdGzNmLhwRvUKo62vhxOrvDCrk3J4aH4ieaWVuLuaeWxUDLcN7NgoO9m3ZArtIiIiIiIiQHmlnVVJVUF9TfJRKm2O6vt6tfNjbFw4o2PDm/zIs/JKO3//fi///TEdgJhQX16f0pduoY1rRrxUUWgXEREREZEWLf5wAe9vTGPFnhxKK+3Vt3cJbsXYuHCuiw2jU9tWTqyw7uzOLOSBz+JJyS0B4M4ronhoRDc83ZpGs7yWSKFdRERERERapLRjpby8LInvd2ZX3xbR2osxceGMjQsnJtS32WwVdzgM3ttwgDnLk7HaDYJ9PXjlxjgGRbd1dmnyGxTaRURERESkRcktrmDeqv18+tNh7A4Dkwlu6NOOWwZ0oG/7gGYT1E/KKixnxhcJbErNA2BEzxBmj4+ltY+7kyuTmlBoFxERERGRFqG4wsp76w/w3oY0yq1V2+CHdmvLI6NiiAn1c3J19WNxYhaPLdhJYbkVLzcXnh7bgxsvbt/sPphozhTaRURERESkWau0OfhkSzpvrE4hr7QSgLj2AcwaGcOAzkFOrq5+lFhsPL1wN19uPwJAXIQ/c6f0JaqNj5Mrk/Ol0C4iIiIiIs2Sw2GwKDGTOcuTOZxfDkCnNj48NKIbI3uFNtvV5u3px3nw83gO5ZdhNsG9Q7tw/1XRuLmYnV2a1IJCu4iIiIiINDsb9h9l9pIkdmcWAdDW14Npw6O58eL2zTa82uwO3lyTwhurU7A7DNoFeDF3Sh8u6Rjo7NLkAii0i4iIiIhIs7Ero5AXlyaxYf8xAFp5uPKnwZ24c1AU3u7NN/4cyitj2uc7+PlQAQDj+oTz7Lhe+Hm6ObcwuWDN93etiIiIiIi0GIfyypizPJmFCZkAuLmYuOWyDkwd2oWgVh5Orq7+GIbBVz9n8PTC3ZRYbPh6uvLcuF5c36eds0uTOqLQLiIiIiIiTdaxEgtvrk7h4y3pWO0GULXKPOOabrQP9HZydfWrsMzKYwt2snhnFgCXRgXy6o1xRLRu3q+7pVFoFxERERGRJqfUYuNfP6Tx7rpUSiurxrcNim7DIyNj6NXO38nV1b9NqceY8UUCWYUVuJpNPHh1V+65sjMu5ubZXK8lU2gXERERqQO5xRUkHi5kaEyw3jSL1COr3cFnWw/z+sr9HCuxANCrnR+zRnbniug2Tq6u/lXaHLyyIpl/rj+AYVR1w587pQ+xEQHOLk3qiUK7iIiIyAUwDIMFO6rOkxZV2Hjr5ou4tneYs8sSaXYMw+D7ndm8vCyJg3llAEQGevPQiG6M7h2GuQV8WJaSW8wDn8VXd8S/6dJInriue7NusCcK7SIiIiK1lltcwV8X7GLFnpzq27zcXJxYkUjztDk1j9lL9pJwpBCAIB937r8qmpsujcTdtXmOb/s1wzD4aMshnl+8hwqrg9bebrw4IZZreoY6uzRpAArtIiIiIrXwXWImT3yzi+NlVlzMJuyOqgZYndu2cnJlIs3H3qwiXlyaxNrkowB4u7tw16BO3DW4E608WkaUOVZi4ZEvE1mVlAtUndt/ZVIcwX6eTq5MGkrL+J0uIiIiUkfySyt54ttdLE6s6tbcI8yPB4ZH86f/bsfd1Uy71l5OrlCk6TucX8ZrK/axID4DwwBXs4nf9Y/kvmHRtPVtvuPb/teapFwe+jKBYyWVuLuamTUyhj8M7NgijgLILxTaRURERGpo+e5sHluwk2MllbiYTdw7tAtTh3bhh5SqVcBObXzUhE7kAhwvreTNNSn8d3M6lXYHAKNjw3jomm50bOPj5OoaToXVzt+/38uHm9MBiAn1Ze6UPsSE+jm5MnEGhXYRERGR31BYZuWZRbv5ekcGANHBrXjlxrjqbs2puaUAdA7W1niR2iivtPPvjWm8szaVYosNgAGdgpg1Koa49gHOLa6B7c4sZNpn8ezPLQHgjsujeHhkNzzVL6PFUmgXEREROYc1ybnM+iqRnCILZhPcPbgz04ZHn/IGOvVo1ZtrnWcXOT82u4P5248wd+U+coqqxrd1D/Nj1qgYBke3wWRqOTtXHA6D//vhAHOW7aPS7qCtrwevTIpjcNe2zi5NnEyhXUREROQMiiusPPfdXj7fdhio2vr+8qQ4+nVofdpjfwntLWf7rsiFMAyDZbtzeGlZEgeOVu1UaRfgxcwRXbk+rl2LO7OdXVjBjPnxbEzJA+CaHiHMnhBLoI+7kyuTxkChXUREROR/bEw5xsNfJpJRUI7JBLcPjOKhEd3wcj/z9tTUE6FDK+0iv23rwXxe+H4vPx8qAKC1txtTh0Vzy2WReLi2vC3gS3ZmMevrnRSWW/Fyc+GpMT2YfEn7FrXLQM5NoV1ERETkhLJKG7OXJFU3f2of6MWciXH07xR01ufkl1aSX1oJQCettIuc1b6cYl5amsTKvVWjyzzdzPzxik7cfWUn/DzdnFxdwyux2Hhm4W7mbz8CQGyEP3Mn96GTPvyT/6HQLiIiIkLV6t/M+Qmk55UBcMtlkTw6qjs+vzEL+uTW+HYBXni7662VyP/KLChn7sp9fLn9CA4DXMwmbry4PdOGRxPSQmeN/3zoOA9+Hk96XhkmE/xlSGemDe+Km4vZ2aVJI6R/WURERKRFq7DambMsmX9tTMMwINzfk5cmxnFFdJsaPT/1RIdndY4XOVVhmZW31qXw/saDWGxV49tG9gxl5ohudGmhf15sdgf/WJPKvNX7sTsM2gV48drkPlwaFejs0qQRU2gXERGRFmvHoePMmJ9Q3QjrxosjePy6Hue1VVdN6EROVWG188Gmg/xjTQpFFVXj2y7tGMisa2O4KPL0Ro4txeH8MqZ9Hs/29OMAXN8nnGev74W/V8s7GiDnR6FdREREWhyLzc7clft5d10qDgOCfT2YPaE3w2JCzvtaakInUsXuMPj65yO8tmIfmYUVAHQNacUjI2MYFhPcYhurGYbB1z9n8NTC3ZRYbPh6uPK3cb0Y17eds0uTJsKphybWr1/PmDFjCA8Px2Qy8c0335z2mL179zJ27Fj8/f3x8fHhkksu4dChQ9X3V1RUcO+99xIUFESrVq2YMGECOTk5DfgqREREpCnZlVHI2Dc28vbaqsA+rk84yx8cXKvADpByYnt8S93uK2IYBqv25jDq9fU89GUimYUVhPl78vLEWJY8MJiruoe02MBeWGblvk93MGN+AiUWG5d0bM33DwxSYJfz4tSV9tLSUuLi4rjjjjsYP378afenpqZyxRVXcOedd/LMM8/g5+fH7t278fT8pWHFgw8+yOLFi5k/fz7+/v5MnTqV8ePHs3HjxoZ8KSIiItLIWe0O3lydwj/WpGBzGAT5uPP8Db0Z2Su01tessNo5fLyqcZ1W2qUl2p5+nBeXJPHTwXwA/L3cuHdoZ34/oCOebi1vfNuvbU7NY8YX8WQWVuBqNvHg1V2558rOuLSwGfRy4Zwa2keNGsWoUaPOev9f//pXrr32Wl566aXq2zp37lz948LCQv71r3/xySefMGzYMAD+85//0L17d3788Ucuu+yy+iteREREmoyk7CJmfJHA7swiAK7tHcrfru9FUCuPC7ruwbxSDAP8PF1p08q9LkoVaRJSj5bw8tJklu7OBsDd1cztl3fkL1d2wd+7ZZ/RrrQ5eHXFPt5dn4phQFQbH+ZO7kNc+wBnlyZNVKM90+5wOFi8eDEPP/wwI0aMYMeOHURFRfHoo48ybtw4ALZv347VamX48OHVz4uJiSEyMpLNmzcrtIuIiLRwNruDd9cf4PWV+6m0OwjwduPZ63sxJjasTrbrpuaeOM8e3KrFbv+VliWnqIK5K/fzxbbD2B0GZhNM7BfBtOFdCQ/wcnZ5TpeSW8K0z3ewK6PqA8Ipl7Tniet6/OboSJFzabS/e3JzcykpKWH27Nk899xzvPjiiyxdupTx48ezZs0arrzySrKzs3F3dycgIOCU54aEhJCdnX3Wa1ssFiwWS/XXRUVF9fUyRERExElSckuYOT+B+MMFAAzvHszfx/cm2Lfu5kL/0jleW+OleSuqsPLuulT+9UMaFdaq8W3Du4fw8MhudA3xdXJ1zmcYBh9vOcRzi/dQYXXQ2tuNF8bHXtDxG5GTGm1odziq/jK4/vrrefDBBwHo06cPmzZt4p133uHKK6+s9bVfeOEFnnnmmTqpU0RERBoXu8PgPxvTeHlZMhabA19PV54a05MJF7Wr89VwNaGT5s5is/PRj4d4c/V+jpdZAbgoMoBZo7prtvgJeSUWHvkqkZV7cwEYFN2GOZPiCPGruw8IpWVrtKG9TZs2uLq60qNHj1Nu7969Oz/88AMAoaGhVFZWUlBQcMpqe05ODqGhZ/9U69FHH2X69OnVXxcVFdG+ffu6fQEiIiLS4NLzSpk5P4GtB6vmIA+KbsNLE2MJ86+fbbtaaZfmyuEw+DYhgznL9pFRUA5A57Y+PDwyhmt6tNxu8P9rTXIuD81P5FiJBXcXM4+MiuH2gR0xq9mc1KFGG9rd3d255JJLSE5OPuX2ffv20aFDBwD69euHm5sbq1atYsKECQAkJydz6NAhBgwYcNZre3h44OFxYY1nREREpPFwOAw+2pLOC98nUW614+Puwl9H9+CmS9vXW7hwOAwOVM9o96mX7yHS0AzDYN2+o7y4NJm9WVVHSEP8PHhweFcm9ovA1cWpE6MbjQqrndlLknh/00EAuoX4MndKH7qH+Tm3MGmWnBraS0pKSElJqf46LS2N+Ph4AgMDiYyM5KGHHmLy5MkMHjyYoUOHsnTpUhYtWsTatWsB8Pf3584772T69OkEBgbi5+fHfffdx4ABA9SETkREpIU4cryMh79MZFNqHgCXdQrk5YlxtA/0rtfvm1VUQbnVjpuLqd6/l0hDSDhcwOwlSWw+UPVnydfDlXuGdOaOy6Pwcm/Z49t+bU9mEdM+38G+nKqdNrdf3pFHRsa0+BF3Un+cGtq3bdvG0KFDq78+uWX9tttu4/333+eGG27gnXfe4YUXXuD++++nW7dufPXVV1xxxRXVz3nttdcwm81MmDABi8XCiBEjeOuttxr8tYiIiEjDMgyDz7ce5rnFeymx2PB0MzNrZAy/H9AwW1NTT5xn7xDkg5tWH6UJO3islJeXJ7M4MQsAdxczvx/QgXuHdqG1j0YZnuRwGPx7YxovLU2m0u6gra8HcybFcWXXts4uTZo5k2EYhrOLcLaioiL8/f0pLCzEz09bWkRERBq77MIKZn2dyNrkowD069CaOZPiiGrTcNvU//1DGs9+t4eRPUN559Z+DfZ9RerK0WIL81bt59OfDmFzGJhMcEOfdky/pisRrbV75NeyCyuYOT+BH1KOAVWd81+c0JugVjpyK7VX0xzaaM+0i4iIiPwvwzBYsCODpxfupqjChrurmZnXdOXOKzrh0sCNn6qb0AXrPLs0LSUWG++tP8B7Gw5QVmkHYEi3tjw8IoYe4VrA+l9Ld2Ux6+udFJRZ8XJz4Ynr6rdfhsj/UmgXERGRJuFosYXHFuxkxZ4cAOIi/JkzKY5oJ82IVud4aWoqbQ4+/ekQ81btJ6+0Eqj6c/TIqBgGdm7j5Ooan1KLjWcX7eHzbYcB6N3On7lT+ujPvDQ4hXYRERFp9L5LzOSJb3ZxvMyKm4uJB66K5p4rOzu1k3Vqded4vYGXxs3hMPhuZxZzliVzKL8MgKg2Pjw0ohujeoVqxfgM4g8XMO2zHRzMK8Nkgj9f2Zlpw7vi7qr+FdLwFNpFRESk0covreSJb3dVN8jqHubHK5PinL6Ft7DcytFiCwCdNO5NGrGNKceYvSSJnRmFALRp5cEDw6OZckl7NVA8A7vD4K01KcxdtR+7wyDc35NXJ/fhsk5Bzi5NWjCFdhEREWmUlu/O5rEFOzlWUomL2cS9QzozdVh0o1jpOrk1PtTPE19PNydXI3K6XRmFvLg0iQ37qxqn+bi78KcrO3PnFVH4eCgCnMnh/DIe/DyebenHARgTF85z43rh76U/4+Jc+hMrIiIijUphmZVnFu3m6x0ZAEQHt+KVG+OIjQhwbmG/cnLcm5rQSWNzOL+MOcuT+TY+EwA3FxM39+/A1GFdaKNO52dkGAbfxGfw5De7KbbYaOXhyt/G9WRcn3Y6OiCNgkK7iIiINBprk3N55KtEcoosmE1w9+DOTBsejaebi7NLO4XOs0tjk1di4c01KXz0YzpWe9VE57Fx4cy4pisdgvTh0tkUllt5/JtdLEqo+pDj4g6teW1yH9oHauSdNB4K7SIiIuJ0xRVWnl+8l8+2VnVpjmrjw5xJcfTr0NrJlZ2ZOsdLY1FWaeNfG9J4d/0BSiw2AAZFt+GRkTH0aufv5Ooaty0H8pj+RQIZBeW4mE1MuyqaPw9xboNLkTNRaBcRERGn2pRyjIe+TCSjoByA2y/vyMMjYvByb1yr67+m0C7OZrU7+HzrYV5ftb+6KWLPcD9mjYphUHRbJ1fXuFXaHMxduY+316ViGNAhyJu5k/vQN7JxfkgootAuIiIiTlFWaWP2kiQ+3JwOQPtAL16eGNfouzRX2hyk51WNzeoSrNAuDcswDJbuyublZckcOFZ1TKN9oBczr+nGmNhwzGadwT6X1KMlTPssvrqb/uSL2/PkmB5qzieNmn53ioiISIPbejCfmfMTqsPvzf0jeeza7k3ijfOh/FLsDgMfdxdC/NTYSxrOjwfyeGFJEgmHCwAI9HHn/mFd+F3/Do1iqkJjZhgGn/50mL99t4dyq50Abzdmj+/NyF5hzi5N5Dc1/n8ZRUREpNmosNqZsyyZf21MwzAg3N+TFyfGNqntvCm5J5rQBbdSZ2lpEHuzinhpaRJrko8C4O3uwh8HdeKuQVEaOVgDeSUWZn29kxV7cgC4oksb5kyKI9Tf08mVidSMQruIiIg0iB2HjjNjfgIHTnRev/HiCB6/rgd+TSx06Dy7NJSMgnJeXb6Pr3ccwTDAxWzipkvbc/9V0QT7KnDWxLp9R5k5P4GjxRbcXcw8PLIbd1wepWME0qQotIuIiEi9stjsvL5yP++sS8VhQLCvB7Mn9GZYTIizS6uVX0K7xmhJ/TheWslba1P4YHM6lTYHAKN7hzHjmq500odFNVJhtTN7SRLvbzoIQHRwK16f0pce4X7OLUykFhTaRUREpN7syihk5vwEkrKLARjXJ5ynx/YkwNvdyZXVXmpuVWhXEzqpa+WVdv6zKY2316ZSXFE1vu2yToHMGtWdPu0DnFtcE5KUXcQDn8aTnFP1984fBnZk1qgYPN0a70QKkXNRaBcREZE6Z7U7+MeaFN5cnYLNYRDk487zN/Rq8k2fDMMg9cT2fm2Pl7piszv46ucjvLZiP9lFFQDEhPryyKgYhnRtq94JNeRwGPx7YxovLU2m0u6gTSsPXp4Uy9Buwc4uTeSCKLSLiIhInUrOLmbG/Hh2ZRQBMKpXKM+N60VQq6bfaT232EKJxYaL2URkkLezy5EmzjAMVuzJ4aVlyaSc2MHRLsCLGdd05fo+7XDRuesayymqYOb8BDbsPwbA8O7BzJ4QS5tm8PeOiEK7iIiI1Amb3cG76w/w+sr9VNodBHi78ez1vRgTG9ZsVgpPbo2PDPTGw1VbbaX2th3MZ/aSJLalHwcgwNuNqUO7cMtlHbSN+zwt3ZXNo18ncrzMiqebmcdH9+Dm/pHN5u8dEYV2ERERuWApuSXMnJ9A/In50cO7B/P3G3oT7Ne8Olyrc7xcqP05xby0LLl6/Jinm5k7Lo/iniGdm9wkBWcrtdj423d7+GzrYQB6tfNj7uS+6jchzY5Cu4iIiNSa3WHwn41pvLwsGYvNga+nK0+N6cmEi9o1y1Wuk1uYOwerc7ycn6zCcuau2M/87YdxGGA2weRL2vPAVV01L7wWEg4XMO3zeNKOlWIywZ8Gd2b61V1xdzU7uzSROqfQLiIiIrWSnlfKzPkJbD1Ytb13UHQbXpoYS5i/l5Mrqz9qQifnq7DcyttrU/nPxjQsJ8a3XdMjhIdHdqNLsK+Tq2t67A6Dt9emMHflfmwOgzB/T169sQ8DOgc5uzSReqPQLiIiIufF4TD4eEs6f/8+iXKrHR93F/46ugc3Xdq+Wa6u/5q2x0tNVVjt/HdzOm+uSaGw3ArAJR1bM2tUDP06BDq5uqbpyPEypn+ewE8H8wG4LjaM58f1xt9bxwqkeVNoFxERkRo7cryMR75KZGNKHlA1Q/rliXG0D2z+ndRLLDayCqvGcXVRaJezsDsMFuzI4NXlyWSe+P0SHdyKR0bGcFX34Gb/wVZ9+WZHBk98s4tii41WHq48e31PbujbPI/hiPwvhXYRERH5TYZh8PnWwzy3eC8lFhuebmZmjYzh9wM6Ym4hY6nSTmyNb9PKQyt7chrDMFiTnMuLS5JJzikGIMzfkwev7sqEiyI0vq2WCsutPPntLr6NzwSgX4fWzJ3cp0V8UChykkK7iIiInFN2YQWzvk5kbfJRoOpN85xJcUS1aVnN2FKOVgWxzm1b1uuW37bj0HFmL0liS1rVtm0/T1f+MrQLfxjYUePbLsBPafk8+Hk8GQXluJhN3D8smnuHdsbVRc3mpGVRaBcREZEzMoyqbb5PL9xNUYUNd1czM6/pyp1XdGqRq4apuSea0GmclJyQerSEOcuSWbIrGwB3VzO3D+zIn4d0JsDb3cnVNV1Wu4O5K/fx9tpUHAZ0CPLmtcl9uCiytbNLE3EKhXYRERE5zdFiC48t2Fk9Szouwp85k+KIDmm53a7VhE5Oyi2qYO6q/Xy+9TB2h4HJBBMviuDBq7sSHtB8pyc0hANHS3jw83gSjhQCMKlfBE+N7UkrD8UWabn0u19ERERO8V1iJk98s4vjZVbcXEw8cFU091ypLam/hHZtj2+piius/HP9Af5vQxrlVjsAV8UE8/DIGLqFttwPtOqCYRh8tvUwzy7aQ7nVjr+XGy+M7821vcOcXZqI0ym0i4iICAD5pZU88e0uFidmAdA9zI9XJsXRI9zPyZU5n83u4OCxMgC6aHt8i2Ox2fn4x0O8uSaF/NJKAPpGBjBrZAz9O2k++IXKL61k1leJLD+xs2dg5yBeuTGOMH/tWhABhXYREREBlu/O5rEFuzhWYsHFbOLeIZ2ZOiwad9eWvbp+0uHj5VTaHXi6mQlXkGgxHA6DhQmZvLIimcP55QB0auvDwyNiGNEzROPG6sD6fUeZOT+B3GILbi4mHh4Rw51XRLWYqRQiNaHQLiIi0oIVlll5ZtFuvt6RAVTNk37lxjhiIwKcW1gjk5pbtTW+U5tWChMtgGEYbNh/jNlLktiTVQRAsK8H04Z35caLI1r8UZG6UGG189LSZP69MQ2o2sHy+pQ+9Az3d3JlIo2PQruIiEgLtTY5l0e+SiSnyILZBHcN7sSDw7tqRNUZVJ9n19b4Zm/nkUJmL93LxpQ8AHw9XLlnSGduv7wj3u5661wXkrKLmPZZPEnZVWMUfz+gA4+O6o6Xu/7uETkT/c0jIiLSwhRXWHl+8V4+23oYgKg2PsyZFEe/DhqndDZqQtf8peeV8vKyZL470dPB3cXMrQM6cO/QLgT6aHxbXXA4DN7fdJDZS5OotDlo08qdlyfGMTQm2NmliTRqCu0iIiItyKaUYzz0ZSIZBVXnc2+/vCMPj4jRCtdvSDmxPV5N6JqfYyUW3li1n4+3HMJ2YnzbuD7tmH51V9oHeju7vGYjt6iCmV8msn7fUQCGxQTz0sRY2rTycHJlIo2fQruIiEgLUFZpY/aSJD7cnA5A+0AvXp4Yx2XqfP2bDMMg9WgpoBntzUmJxcb/bTjAe+sPUFpZNb7tyq5teXhkN52rrmPLd2fzyFeJHC+z4uFq5vHrenBL/0g18hOpIYV2ERGRZm7rwXxmzk8gPa9qZNnN/SN57Nru+HjobUBN5JVWUlhuxWSqOkogTZvV7uCznw7x+qr9HCupGt8WG+HPrJExDOzSxsnVNS9llTb+9t1ePv3pEAA9w/14fUofugRrpr3I+dC/1iIiIs1UhdXOnGXJ/GtjGoYBYf6evDQxlkHRbZ1dWpNysnN8RGsvNelrwhwOg+93ZTFnWTIHT3yA1SHIm4dGdGN07zCt+taxxCMFTPssngPHSjGZ4O7BnZhxdTeNkRSpBYV2ERGRZmjHoePMnJ9Qva17Ur8InhjTAz9PNydX1vRoa3zTtynlGLOXJpF4pBCANq3ceeCqaKZcGombxrfVKbvD4J11qby2Yh82h0GonyevTo5jYGftYhCpLYV2ERGRZsRis/P6yv28sy4VhwFtfT2YPb43V3UPcXZpTVZ1EzqF9iZnT2YRs5cmVTc/83F34a7BnbhrUCcdD6kHR46XMf2LBH5KywdgdO8wnr+hFwHe6r4vciH0t5WIiEgzsSujkJnzE6pnH1/fJ5xnxvbUG+YLpBntTc/h/DJeXbGPb+IzMAxwNZu4uX8kU4dF09ZX3crrw7fxGTz+zS6KK2z4uLvwzPW9mHBROx07EKkDCu0iIiJNnNXu4B9rUnhzdQo2h0GQjzvP39CLkb3CnF1as/DLjHaF9sYuv7SSN1en8NGP6VTaHQCMiQtn5jVd6RCkJoL1oajCypPf7OKb+EwA+kYGMHdyH/18i9QhhXYREZEmLDm7mBnz49mVUQTAqF6hPDeuF0GafVwnyivt1TPtO7dVCGmsyipt/PuHNN5dd4Biiw2Ay7sEMWtkd3pHaHxbfdl6MJ9pn8WTUVCO2QT3XxXN1KFdcFWfAJE65dQ/UevXr2fMmDGEh4djMpn45ptvzvrYe+65B5PJxNy5c0+5PT8/n5tvvhk/Pz8CAgK48847KSkpqd/CRUREnMxmd/DW2hTGvPEDuzKK8Pdy4/UpfXjr5osU2OtQ2rFSDAMCvN0I9NExg8bGZnfwyZZDDHl5LXOW76PYYqNHmB8f3nEpH93ZX4G9nljtDl5ZnszkdzeTUVBOZKA38+8ZyLThXRXYReqBU1faS0tLiYuL44477mD8+PFnfdyCBQv48ccfCQ8PP+2+m2++maysLFasWIHVauX222/n7rvv5pNPPqnP0kVERJwm9WgJM75IIP5wAQBXxQTzwvjeBPt5OrewZijl6C9N6HQ2t/EwDINlu7N5aVkyB050949o7cVDI7oxJjYcs1m/VvUl7Vgp0z6PJ+HE3z8T+0Xw9NietFJjP5F649Q/XaNGjWLUqFHnfExGRgb33Xcfy5YtY/To0afct3fvXpYuXcrWrVu5+OKLAXjjjTe49tprmTNnzhlDvoiISFPlcBj8e2MaLy9LxmJz4OvhypNjejCxX4QCZT05OaNd59kbjy0H8pi9NIkdhwoACPRx575hXfhd/0g8XF2cW1wzZhgGX2w7zDOL9lBWacfP05UXxscyOla9M0TqW6P+SMzhcHDrrbfy0EMP0bNnz9Pu37x5MwEBAdWBHWD48OGYzWa2bNnCDTfc0JDlioiI1Jv0vFIemp/ITwerRikNim7DixNiCQ/wcnJlzdsvneN1nt3ZkrOLeWlpEquScgHwcnPhj4OiuHtwJ3w93ZxcXfN2vLSSWV8nsmx3DgADOgXxyo1x+vtHpIE06tD+4osv4urqyv3333/G+7OzswkODj7lNldXVwIDA8nOzj7rdS0WCxaLpfrroqKiuilYRESkjjkcBh9vSefv3ydRbrXj7e7CX0d353eXRmp1vQGknth6rZV258koKOe1Ffv46ucjGAa4mE1MuaQ9D1wVrSMhDWDD/qPMnJ9ATpEFNxcTM6/pxl2DOukIgkgDarShffv27bz++uv8/PPPdf6m5IUXXuCZZ56p02uKiIjUtSPHy3jkq0Q2puQBcFmnQF6eGEf7QG8nV9YyOBwGB06eadeM9gZXUFbJ22tT+c+mg1Taqsa3Xds7lJnXdKOTPkSpdxVWOy8vS+ZfP6QBVdMTXp/Sl17t1NxPpKE12tC+YcMGcnNziYyMrL7NbrczY8YM5s6dy8GDBwkNDSU3N/eU59lsNvLz8wkNDT3rtR999FGmT59e/XVRURHt27ev+xchIiJSC4Zh8PnWwzy3eC8lFhuebmZmjYzh9wM6anWrAWUUlGOxOXB3MRPRWh+UNJQKq533Nx3krTUpFFVUjW+7NCqQR0fF0DeytZOraxn25RRz/6c7SMouBuDWyzrw2LXd8XJXzwARZ2i0of3WW29l+PDhp9w2YsQIbr31Vm6//XYABgwYQEFBAdu3b6dfv34ArF69GofDQf/+/c96bQ8PDzw8NA5HREQan+zCCmZ9ncja5KMAXBQZwCs39iGqjc5UN7STneOj2vjgog9L6p3dYfDV9iO8umIf2UUVAMSE+vLIyBiGdGur4yANwDAMPth0kL8vSaLS5iDIx52XJsZyVfcQZ5cm0qI5NbSXlJSQkpJS/XVaWhrx8fEEBgYSGRlJUFDQKY93c3MjNDSUbt26AdC9e3dGjhzJXXfdxTvvvIPVamXq1KlMmTJFneNFRKRJMQyDb+IzeOrb3RRV2HB3NTPj6q78cVAnBUYnqe4cryZ09cowDFbtzeXFpUnsP/Fz3i7Ai+lXd2Vc33b6/d9AcosreGh+Iuv2VX1gOLRbW16aGEdbXy10iTibU0P7tm3bGDp0aPXXJ7es33bbbbz//vs1usbHH3/M1KlTueqqqzCbzUyYMIF58+bVR7kiIiL14mixhb8u2MnyPVWdmWMj/HllUhzRIb5OrqxlUxO6+rc9PZ/ZS5LYevA4AP5ebkwd2oVbB3TA001bsRvKij05PPJVIvmllXi4mvnr6O7celkH7W4QaSScGtqHDBmCYRg1fvzBgwdPuy0wMJBPPvmkDqsSERFpOIsTs3j8m50cL7Pi5mLi/mHR/HlIZ1xdzM4urcVLVRO6epOSW8xLS5OrP6jycDVzxxVR3HNlZ/y9NL6toZRV2nhu8V4+2XIIgO5hfsyb0kcfGIo0Mo32TLuIiEhzll9ayZPf7uK7xCyg6s3yK5Pi6BHu5+TK5KTq7fFaaa8z2YUVzF25jy+2HcZhgNkEN17cngeGRxPmr5nfDWnnkUIe+HwHB07sKLl7cCdmXNMVD1ftcBBpbBTaRUREGtjy3dk8tmAXx0osuJhN3DukM1OHRePuqtX1xuJ4aSV5pZUAagJYBwrLrby7LpV/b0yjwlo1vu3qHiE8MrIbXYK1qtuQ7A6Dd9en8uryfdgcBqF+nrxyYxyXd2nj7NJE5CwU2kVERBpIYZmVZxbt5usdGQBEB7filRvjiI0IcG5hcpoDx6pW2cP9PfHx0Nul2qqw2vnox3TeXJNCQZkVgH4dWvPoqBgu7hjo5OpanoyCcqZ/Hs+WtHwARvUK5YXxvQnwdndyZSJyLvpXSEREpAGsTc7lka8SySmyYDJVbUV9cHhXNdtqpFJzTzSh03n2WrE7DL7ZkcGrK/aRUVAOVPUGeGRkDMO7B6vBmRMsSsjksQU7Ka6w4ePuwlNjezKpX4R+LUSaAIV2ERGRelRcYeX5xXv5bOthoGqr9ZxJsfTroFXGxuxkEzqdZz8/hmGwdt9RXlySRFJ2MQChfp48eHU0Ey6KUINFJyiusPLUt7/s8OnTPoDXp/ShQ5COfYg0FQrtIiIi9WRTyjEe+jKxeqXxDwM78sjIGLzctbre2KVUz2hXaK+p+MMFzF6ylx8PVG299vV05S9DuvCHgR31e95Jth3MZ9rn8Rw5Xo7ZBFOHRXPfsC646cMTkSZFoV1ERKSOlVXamL0kiQ83pwPQPtCLlyfGcVmnICdXJjX1y0q7ViN/S9qxUl5elsT3O7MBcHcxc9vADtw7tIvOSjuJ1e7gjVX7eXNNCg6j6u+guZP7aIePSBOl0C4iIlKHth7MZ+b8BNLzygC4uX8kj13bXc3MmhCLzc6h/Kpfvy7aHn9WucUVzFu1n09/OozdYWAywfi+ETx4dTQRrb2dXV6LdfBYKdM+jyf+cAEA4y9qxzNje+Lr6ebcwkSk1vQOQkREpA5UWO3MWZbMvzamYRgQ5u/JSxNjGRTd1tmlyXlKzyvDYYCvhyttfT2cXU6jU1xh5b31B3hvQxrlVjsAw2KCeXhkN2JC/ZxcXctlGAbztx3h6UW7Kau04+fpyvM39GZMXLizSxORC6TQLiIicoF2HDrOzPkJpB6t6jg+qV8ET4zpgZ9Wtpqk1F+dZ1dn7V9U2hx8siWdN1anVM+w79M+gFmjYnT0w8mOl1by2IKdLNlVdUShf1Qgr03uQ3iAl5MrE5G6oNAuIiJSSxabnddX7ueddak4DGjr68Hs8b25qnuIs0uTC1DdhE5b4wFwOAwWJWYyZ3kyh/Ormip2auPDQyO6MbJXqD7YcLKNKceY/kU8OUUW3FxMTL+6G3cP7oSLWb8uIs2FQruIiEgt7MooZOb8hOqxVtf3CefpMT1p7aPGW01ddRO6YDWh27D/KLOXJLE7swio+mBq2vBobry4vTqQO5nFVnUk570NaQB0auvDvCl96dXO38mViUhdU2gXERE5D1a7g3+sSeHN1SnYHAZBPu48N64Xo3qHObs0qSMnjzm05JX2XRmFvLg0iQ37jwHQysOVe67sxB1XROHtrrePzrYvp5gHPotnb1bVhyk394/k8dE9NFpPpJnS37oiIiI1lJxdzIz58ezKqHqjPKpXKH8b14s2rdSsrLkwDONX495aXmg/lFfGnOXJLEzIBMDNxcQtl3Vg6tAuBOn3udMZhsGHm9P5+/d7sdgcBPm48+KEWIb30JEckeZMoV1EROQ32OwO/rnhAHNX7KfS7sDfy41nr+/J2LhwnedtZrKLKiirtONqNtEhqOWMLTtWYuHN1Sl8vCUdq90AYFyfcGZc0432gS3n56Exyy2u4OEvE1mbfBSAK7u25eVJsQT7ejq5MhGpbwrtIiIi55B6tIQZXyRUzzy+KiaYF8b3JthPb5Sbo5NN6DoEebeIM9ulFhv/+iGNd9elUlpZNb5tcNe2PDyim85GNyKr9ubw8JeJ5JVW4u5q5q/Xduf3AzroQ0ORFkKhXURE5AwcDoN/b0zj5WXJWGwOfD1ceXJMDyb2i9Ab5WYstYV0jrfaHXy29TCvr9zPsRILAL3b+TNrVAyXd2nj5OrkpPJKO89/v4ePfjwEQEyoL/Nu6kvXEF8nVyYiDUmhXURE5H+k55Xy0PxEfjqYD8Cg6Da8OCFWM49bgOomdMHNM7QbhsH3O7N5eVkSB/PKAIgM9OahEd0Y3TsMs8aENRq7Mgp54LMd1b8n/3hFFA+N7IaHq5rNibQ0Cu0iIiInOBwGH29J5+/fJ1FutePt7sJfR3fnd5dGanW9hWjOTeg2p+Yxe8leEo4UAhDk484Dw6OZckkk7q7N/yhAU2F3GLy34QCvLE/GajcI8fPglUl9uCJaOyBEWiqFdhERESCjoJyHv0xgY0oeAP2jApkzKU5NuFqYk6G9SzNaad+bVcSLS5OqG5h5u7tw16BO3DW4E6089FawMcksKGf6F/H8eKBql8/InqG8ML43rX3cnVyZiDiT/qYWEZEWzTAMvth2mL99t5cSiw1PNzOPjIzhtgEdtVW4hSmqsJJTVHW+u1NbHydXc+EO55fx2op9LIjPwDDA1Wzid/0juW9YNG19Nb6tsfkuMZPHvt5JUYUNb3cXnh7Tk0kXq4eGiCi0i4hIC5ZTVMGsrxJZc2IF8qLIAOZMiqNTM9waLb/twImzw8G+Hvh5ujm5mto7XlrJm2tS+O/mdCrtDgCuiw1j5jXd6Nim6X8Y0dwUV1h5auFuvv45A4C49gHMndyHKP1aicgJCu0iItLiGIbBN/EZPPXtbooqbLi7mplxdVf+OKgTLlpdb7Gaeuf48ko7/96YxjtrUym22AAY2DmIWaNiiI0IcG5xckbb0/OZ9nk8h/PLMZtg6tAu3HdVdIsYNygiNafQLiIiLcrRYgt/XbCT5XtyAIiN8OeVSXFEa4RSi9dUz7Pb7A7mbz/C3JX7qrf3dw/zY9aoGAZHt9H26kbIZnfwxuoU3li9H4cBEa29eG1yHy7pGOjs0kSkEVJoFxGRFmNxYhaPf7OT42VW3FxM3D8smj8P6YyrVrWEX3eObxrbkg3DYNnuHF5allS9tT+itRczr+nG2Lhw9WRopNLzSpn2eTw7DhUAcEPfdjxzfc8mfSRDROqXQruIiDR7+aWVPPntLr5LzAIgJtSXV2/sQ49wPydXJo1Jysnt8U1gpX3rwXxe+H4vP58Ifq293Zg6LJpbLovUHO9GyjAMvtx+hKcX7qa00o6vpyvP39CbsXHhzi5NRBo5hXYREWnWVuzJ4dGvd3KsxIKL2cRfhnTmvmHRmkstp7DaHaTnlQGN+0z7vpxiXlqaxMq9uQB4upn54xWduPvKTlqpbcQKyip5bMFOvt+ZDcClUYG8NrkP7QK8nFyZiDQFCu0iItIsFZZbeWbRLx2ZuwS34pVJccS1D3BuYdIoHcovw+Yw8HZ3Iczf09nlnCazoJy5K/fx5fYjOAxwMZuYfEl7HrgqmhC/xlev/GJTyjGmf5FAdlEFrmYT06/pyp8Gd1bTSxGpMYV2ERFpdtYm5zLrq51kF1VgMsHdgzrx4NVd8XTTtmE5s193jm9MjdsKy6y8tS6F9zcexGKrGt82smcoM0d0a3IN81oai83OK8v38d6GAxgGdGrjw9wpfdTJX0TOm0K7iIg0K/9cn8rfv08CIKqND3MmxdKvgzoyy7mlNLImdBVWOx9sOsg/1qRQVFE1vu3SjoHMujaGiyJbO7k6+S0pucXc/2k8e7KKAPhd/0geH90db3e99RaR86e/OUREpFnZevA4ACF+Hiy+/wq9SZYaSc2t6r7u7PPsdofB1z8f4bUV+8gsrACgW4gvj4zqxtBuwY1qF4CczjAMPvoxnecW78VicxDo487s8b25pmeos0sTkSZM72RERKRZeWhEN9btO0pOkYUvtx/h9wM6OrskaQKqx705acu5YRisTsrlxaVJ7MupqiXM35PpV3dl/EUROv/cRJRW2nln3QEsNgeDu7ZlzsRYgtVzQEQukEK7iIg0K11DfHl0VAzPLNrD84v3MrBzEF2CfZ1dljRihmH8akZ7w4f27enHeXFJEj8dzAfA38uNe4d25vcDOqoPQxPTysOV1yb3YXdmIbcN6IhZH7aISB1QaBcRkWbntgEdWZ2Uy4b9x3jgs3gW/OVyjXiTszpaYqG4wobZBB3beDfY9009WsLLS5NZurtqDJiHq5nbL4/iz1d2xt9b49uaqkujArk0Sn00RKTuKLSLiEizYzabmDMpjhFz17M7s4jXVu7jkZExzi5LGqmUE53jIwO98XCt/5XtnKIK5q7czxfbDmN3GJhNMLFfBNOGdyVcc7tFROR/KLSLiEizFOLnyezxvbnno595Z10qQ7sFa/VLzij1aMM0oSuqsPLuulT+9UMaFdaq8W3Du4fw8MhudA3REQ4RETkzhXYREWm2RvYKY2K/CL7cfoQHP49nybRB+Hlq27GcqnpGez01obPY7Hz04yHeXL2f42VWAPp1aM2sUTFc0lEfJImIyLkptIuISLP21JgebEnL43B+OU9/u5tXJ/dxdknSyKTW04x2h8Pg24QM5izbR0ZBefX3eHhkDNf0CNH4NhERqRGFdhERadZ8Pd147cY+3PjuZr7ekcHQmGDGxIU7uyxpRA6c2B7fpY5W2g3DYN2+o7y4NJm9WUUAhPh58ODwrkzsF4Gri5oiiohIzSm0i4hIs3dxx0DuHdqFN1an8NcFO7m4Y2vC/NXwS6DUYqteBe/U5sJDe8LhAmYvSWLzgTwAfD1d+fOQztw+MAovd41vExGR86fQLiIiLcL9V0Wzbt9REo8UMnN+Av+9o79mKAtpx6pW2YN83Gnt417r6xw8VsrLy5NZnJgFgLuLmd8P6MC9Q7tc0HVFREQU2kVEpEVwczHz2uQ+jJ63gY0pefx7Yxp/HNTJ2WWJk/1ynr12q+xHiy3MW7WfT386hM1hYDLBDX3bMf3qrkS0briZ7yIi0nwptIuISIvRuW0rHh/dg8e/2cVLS5O5IroNMaF+zi5LnOiXzvHn14SuxGLjvfUHeG/DAcoq7QAM6daWR0bG0D1Mv6dERKTuOLUTyvr16xkzZgzh4eGYTCa++eab6vusViuPPPIIvXv3xsfHh/DwcH7/+9+TmZl5yjXy8/O5+eab8fPzIyAggDvvvJOSkpIGfiUiItJU3Nw/kmExwVTaHUz7LJ4Kq93ZJYkTne+M9kqbgw82HeTKl9bw+qr9lFXaiYvw55O7+vP+7ZcqsIuISJ1zamgvLS0lLi6Of/zjH6fdV1ZWxs8//8wTTzzBzz//zNdff01ycjJjx4495XE333wzu3fvZsWKFXz33XesX7+eu+++u6FegoiINDEmk4kXJ8QS5ONOUnYxc5YlO7skcaKUGs5odzgMFiZkMvzVdTy1cDd5pZVEtfHhrZsv4pt7L2dg5zYNUa6IiLRAJsMwDGcXAVVvohYsWMC4cePO+pitW7dy6aWXkp6eTmRkJHv37qVHjx5s3bqViy++GIClS5dy7bXXcuTIEcLDazbSp6ioCH9/fwoLC/Hz0yfkIiItwco9Ofzxw20AfPzH/lzeRaGrpbE7DLo/sZRKu4MNDw+lfeCZz6BvTDnG7CVJ7MwoBKBNKw+mDY9m8iXtcdP4NhERqaWa5tAm9S9NYWEhJpOJgIAAADZv3kxAQEB1YAcYPnw4ZrOZLVu2nPU6FouFoqKiU/4TEZGWZXiPEG66NBKAGV8kUFhmdXJF0tCOHC+j0u7Aw9VMeMDpIwB3ZRRy67+2cPP/bWFnRiE+7i5Mv7or6x4awi2XdVBgFxGRBtFkGtFVVFTwyCOPcNNNN1V/CpGdnU1wcPApj3N1dSUwMJDs7OyzXuuFF17gmWeeqdd6RUSk8Xviuu78eCCPtGOlPPbNTt68qS8mk8bAtRQnO8dHtfHB5Vfj/w7nlzFneTLfxlf10XFzMXFz/w7cN6wLQa08nFKriIi0XE3iI2Kr1cqNN96IYRi8/fbbF3y9Rx99lMLCwur/Dh8+XAdViohIU+Pt7sprk/vgYjaxODGLb+IznF2SNKDU3KomdF1OnGfPK7HwzKLdDHtlbXVgv75POKumD+HpsT0V2EVExClqtdJeWlrK7NmzWbVqFbm5uTgcjlPuP3DgQJ0UB78E9vT0dFavXn3KXv/Q0FByc3NPebzNZiM/P5/Q0NCzXtPDwwMPD/3DKyIi0Kd9AA9cFc2rK/bx5De7uaRjoOZrtxAnm9CFB3jxxqr9vLv+ACUWGwCDotvwyMgYerXzd2aJIiIitQvtf/zjH1m3bh233norYWFh9baV8GRg379/P2vWrCEoKOiU+wcMGEBBQQHbt2+nX79+AKxevRqHw0H//v3rpSYREWl+/jKkM2uTc/n5UAHTv0jg07suO2W7tDRPJ7fHv7fhACfb8vZq58cjI2MYFN3WiZWJiIj8olahfcmSJSxevJjLL7/8gr55SUkJKSkp1V+npaURHx9PYGAgYWFhTJw4kZ9//pnvvvsOu91efU49MDAQd3d3unfvzsiRI7nrrrt45513sFqtTJ06lSlTptS4c7yIiIiri5nXJvfh2tc38FNaPv9cf4A/D+ns7LKknh04VrU93jAgMtCbmSO6cV3vMMz6wEZERBqRWo18i4qK4vvvv6d79+4X9M3Xrl3L0KFDT7v9tttu4+mnnyYqKuqMz1uzZg1DhgwBID8/n6lTp7Jo0SLMZjMTJkxg3rx5tGp17nmrv6aRbyIiAvDF1sM8/FUibi4mFvzlcm2Nbuam/HMzKbmlTB3amd/174C7a5No9SMiIs1ETXNorUL7Rx99xLfffssHH3yAt3fTP/en0C4iIgCGYXDPR9tZtjuHLsGt+O6+K/B0c3F2WVJPHA4DkwlNDBAREaeo19Det29fUlNTMQyDjh074ubmdsr9P//88/lX7EQK7SIiclJ+aSUj5q7naLGFPwzsyNNjezq7JBEREWmGappDa3Wmfdy4cbWtS0REpFEL9HFnzqQ4bvv3T7y/6SBDurVlSLdgZ5clIiIiLVStVtqbG620i4jI/3rq2118sDmdtr4eLJs2mEAfd2eXJCIiIs1ITXPoBXVc2b59Ox999BEfffQRO3bsuJBLiYiINCqzRnWnS3ArjhZbmPVVIvqMW0RERJyhVqE9NzeXYcOGcckll3D//fdz//33069fP6666iqOHj1a1zWKiIg0OC93F+ZO7oObi4nle3KYv+2Is0sSERGRFqhWof2+++6juLiY3bt3k5+fT35+Prt27aKoqIj777+/rmsUERFxil7t/Jl+dTcAnlm0m/S8UidXJCIiIi1Nrc60+/v7s3LlSi655JJTbv/pp5+45pprKCgoqKv6GoTOtIuIyNnYHQY3vfcjP6Xlc1FkAF/8aQCuLprnLSIiIhemXs+0OxyO08a8Abi5ueFwOGpzSRERkUbJxWzi1Rvj8PVw5edDBby1NtXZJYmIiEgLUqvQPmzYMB544AEyMzOrb8vIyODBBx/kqquuqrPiREREGoOI1t48O65qXvvrq/YTf7jAuQWJiIhIi1Gr0P7mm29SVFREx44d6dy5M507dyYqKoqioiLeeOONuq5RRETE6cb1acd1sWHYHQYPfh5PWaXN2SWJiIhIC1DrOe2GYbBy5UqSkpIA6N69O8OHD6/T4hqKzrSLiEhNFJZZGfn6erIKK/hd/0j+fkNvZ5ckIiIiTVRNc2itQ3tzotAuIiI1tSnlGL/7vy0A/N/vL2Z4jxAnVyQiIiJNUU1zqGtNLzhv3jzuvvtuPD09mTdv3jkfq7FvIiLSXA3s0oY/XhHF//2QxiNfJbK0/WDa+no4uywRERFppmq80h4VFcW2bdsICgoiKirq7Bc0mThw4ECdFdgQtNIuIiLno8JqZ9w/NpKUXcywmGD+ddvFmEwmZ5clIiIiTYi2x58HhXYRETlfSdlFjH1jI5V2B8+N68Utl3VwdkkiIiLShNTrnPZnn32WsrKy024vLy/n2Wefrc0lRUREmpSYUD8eHtkNgOcW7yH1aImTKxIREZHmqFYr7S4uLmRlZREcHHzK7Xl5eQQHB2O32+uswIaglXYREakNh8Pg1n9vYWNKHrER/nz154G4udTq83ARERFpYep1pd0wjDOe3UtISCAwMLA2lxQREWlyzGYTcybF4efpSuKRQuat2u/skkRERKSZOa/Q3rp1awIDAzGZTHTt2pXAwMDq//z9/bn66qu58cYb66tWERGRRifM34u/j6+a1/6PNSlsO5jv5IpERESkOanxyDeAuXPnYhgGd9xxB8888wz+/v7V97m7u9OxY0cGDBhQ50WKiIg0ZtfFhrN6by5f78jgwS/i+f7+QTgMsNkdBLXSODgRERGpvVqdaV+3bh2XX345rq7nlfkbLZ1pFxGRC1VUYWXU3A1kFJQD4O5ixsPNzNa/DsfTzcXJ1YmIiEhjU69n2ktLS1m1atVpty9btowlS5bU5pIiIiJNVqXNweq9udWBHaDS7qB7mB+uZs1vFxERkdqrVWifNWvWGTvEG4bBrFmzLrgoERGRpiCnqIJXV+xj4OzVTPs8/rT7X5vcB1d1kxcREZELUKv97fv376dHjx6n3R4TE0NKSsoFFyUiItJYGYbB9vTjvL/pIEt3ZWNznHrKrGtIKw4cLcXmMJj1VSIf3H4pZq22i4iISC3VKrT7+/tz4MABOnbseMrtKSkp+Pj41EVdIiIijUqF1c7ChEw+2HSQ3ZlFp93vajYxdVgX7h3ahfS8UkbP+4EN+4/xweaD3H55lBMqFhERkeagVnv2rr/+eqZNm0Zqamr1bSkpKcyYMYOxY8fWWXEiIiLOllFQzotLkxjwwioe/jLxjIE9JtSXb+69nGnDu+LmYqZLsC9/Hd0dgNlLktiXU9zQZYuIiEgzUavu8YWFhYwcOZJt27YREREBwJEjRxg0aBBff/01AQEBdV1nvVL3eBER+TXDMNh8II8PNh1kxZ4cTu6AbxfgRXiAJwmHC6m0O3Axm/jzlZ2576oueLi6nHaNP/xnK+v2HaVHmB8L7h142mNERESk5appDq1VaIeqNyMrVqwgISEBLy8vYmNjGTx4cK0LdiaFdhERASirtLFgRwYfbkon+Ver4wM7B3FD33ZsSs1jwY4MALoEt+KVSXHEtQ846/VyiyoYMXc9x8us/OnKTjw6qnt9vwQRERFpIuo9tDcnCu0iIi1bel4pH25O54tthymusAHg7e7C+Iva8fsBHckoKGfWV4nkFFkwmeDuQZ148OquNZq/vnRXNvd8tB2TCT7542UM6BxU3y9HREREmoCa5tBaNaJ79tlnz3n/k08+WZvLioiINBiHw2BDyjE+2HSQNcm5nPwIu2OQN7cO6MjEfhGYTfDcd3v5fNthAKLa+DBnUiz9OgTW+PuM7BXK5Ivb8/m2w8z4Ip4l0wbj7+VWHy9JREREmqFarbT37dv3lK+tVitpaWm4urrSuXNnfv755zorsCFopV1EpOUorrDy5fYj/HdzOgeOlVbfPqRbW24b0JEru7bFbDbxw/5jPPxlApmFFZhMcPvAKB4a0Q0v9/M/l15qsXHtvA2k55Uxrk84c6f0/e0niYiISLNWryvtO3bsOOM3/MMf/sANN9xQm0uKiIjUq5TcEj7cfJCvth+htNIOgK+HKxMvjuD3AzoS1aZqZGmpxcYLS/by0Y+HAIgM9OblibH071T7be0+Hq68NrkPk97ZzDfxmQyNCeb6Pu0u/EWJiIhIs1enZ9p37tzJmDFjOHjwYF1dskFopV1EpHmyOwzWJOXyweaDbNh/rPr2LsGtuG1AB264KIJWHr98fr05NY+HvkzgyPFyAH4/oAOzRsXg7V6rz7hP8+qKfcxbtR9fT1eWThtMuwCvOrmuiIiIND31utJ+NoWFhRQWFtblJUVERM5bYZmVz7cd4r8/pnM4vyqAm0wwvHsItw3oyOVdgjCZTNWPL6u08dLSZN7fdBCoGu328sRYBnZpU6d13TesC+v2HSXhcAEzv0jg4z/2x2w2/fYTRUREpMWqVWifN2/eKV8bhkFWVhb//e9/GTVqVJ0UJiIicr72ZhXx4eaDLNiRQYXVAYC/lxtTLmnPLZd1oH2g92nP2XYwn5nzEziYVwbATZdG8tfR3U9Zga8rbi5m5k7uw7Wvb2DzgTz+9UMadw3uVOffR0RERJqPWm2Pj4qKOuVrs9lM27ZtGTZsGI8++ii+vr51VmBD0PZ4EZGmy2Z3sHxPDu9vOshPafnVt3cP8+MPAzswNq7dGZvHVVjtvLI8mf/7IQ3DgDB/T16cEMvgrm3rveZPfzrEo1/vxN3FzDf3Xk6PcP3bIyIi0tLU6/b4tLS0WhcmIiJSF/JKLHy29TAf/ZhOVmEFAC5mEyN7hnLbwI5c0rH1KVvgf23HoePMmJ/AgaNV3eMn9Yvg8et6NNgotimXtGfV3lxW7s1h2uc7WDj1ihrNfBcREZGWp+73/omIiNSjxCMFfLApnUWJmVTaqrbAB/m4c9Olkdx8WSRh/mdv7max2Zm7cj/vrkvFYUCwrwezJ/RmWExIQ5UPgMlkYvaE3oyce5x9OSW8tDSZJ8f0aNAaREREpGmocWgfP358jS/69ddf16oYERGRM6m0OViyK4v3Nx1kx6GC6tvjIvy5bWBHru0d9psr1TuPFDJjfjz7ckoAuKFvO54e0xN/74ZZXf9fbVp58NLEWO54fxv/3pjG0Ji2DIqu/635IiIi0rTUOLT7+/tX/9gwDBYsWIC/vz8XX3wxANu3b6egoOC8wr2IiMi55BZV8NGWQ3yy5RDHSiwAuLmYGN07jNsGdqRvZOvfvEalzcGbq/fzj7Wp2B0GbVq58/wNvRnRM7S+y/9Nw2JCuOWySD768RAz5yewbNpgArzdnV2WiIiINCI1Du3/+c9/qn/8yCOPcOONN/LOO+/g4lK1smG32/nLX/6iRm4iInJBDMPg50PHeX9TOkt2ZmFzVPVLDfb14JbLOjDl0vYE+3rW6Fp7MouYMT+BvVlFAFwXG8az1/ci0KfxBOO/XtuDTal5HDhaymMLdvKP31101rP4IiIi0vLUqnt827Zt+eGHH+jWrdsptycnJzNw4EDy8vLqrMCGoO7xIiLOV2G1syghkw82H2RXRlH17Rd3aM1tAzsyslcobi7mGl3Lanfw9tpU5q3aj81h0NrbjefG9WZ0bFh9lX9BEo8UMP6tTdgcBq9MimNCvwhnlyQiIiL1rF67x9tsNpKSkk4L7UlJSTgcjhpfZ/369bz88sts376drKwsFixYwLhx46rvNwyDp556ivfee4+CggIuv/xy3n77baKjo6sfk5+fz3333ceiRYswm81MmDCB119/nVatWtXmpYmINDjDMDicX479/D9DbRZOhvXPth4mv7QSAHdXM9fHhXPbwI70auf/G1c4VXJ2MTPnJ7AzoxCAET1DeG5cb9r6etR57XUlNiKAacOjmbN8H08t3M2lUYFnnCkvIiIiLU+tQvvtt9/OnXfeSWpqKpdeeikAW7ZsYfbs2dx+++01vk5paSlxcXHccccdZzwL/9JLLzFv3jw++OADoqKieOKJJxgxYgR79uzB07Nqa+TNN99MVlYWK1aswGq1cvvtt3P33XfzySef1OaliYg0uMe/2cXHWw45u4xGoV2AF7dc1oHJl7Q/7y3sNruDf244wNwV+6m0O/D3cuPZ63syNi68SWw3//OQLqxNPsq29OM8+Hk8n/9pAC7mxl+3iIiI1K9abY93OBzMmTOH119/naysLADCwsJ44IEHmDFjRvU59/MqxGQ6ZaXdMAzCw8OZMWMGM2fOBKCwsJCQkBDef/99pkyZwt69e+nRowdbt26tboi3dOlSrr32Wo4cOUJ4eHiNvre2x4uIM930zx/ZfCAPD1cz7q412/7d3PRu58/vB3RkePdgXGu4Bf7XUnJLmDk/gfjDBQBcFRPMC+N7E+xXs7PvjcXh/DJGvb6BEouNh0Z0496hXZxdkoiIiNSTet0ebzabefjhh3n44YcpKqo6d1jXYTctLY3s7GyGDx9efZu/vz/9+/dn8+bNTJkyhc2bNxMQEFAd2AGGDx+O2Wxmy5Yt3HDDDWe8tsViwWKxVH998jWIiDhDqH9VsHzw6q7cc2VnJ1fTtNgdBv/+IY2XlydTaXPg6+nKU2N6MuGidk1idf1/tQ/05umxPZk5P4HXVuxjcHRbekec3/EAERERaV4uaEnn6NGjJCYmkpiYyLFjx+qqJgCys7MBCAkJOeX2kJCQ6vuys7MJDg4+5X5XV1cCAwOrH3MmL7zwAv7+/tX/tW/fvk5rFxE5HyEnVoNziiqcXEnTknaslMnvbub57/dSaXNwZde2LH9wMBP7RTTJwH7ShIvacW3vUGwOgwc+30F5pd3ZJYmIiIgT1Sq0l5aWcscddxAWFsbgwYMZPHgwYWFh3HnnnZSVldV1jXXu0UcfpbCwsPq/w4cPO7skEWnBQvyqGqQptNeMw2Hwn41pjHp9PdvSj9PKw5UXJ/Tm/dsvIczfy9nlXZCyShvJOcVc2bUtJhMcOFrK7CV7nV2WiIiIOFGttsdPnz6ddevWsWjRIi6//HIAfvjhB+6//35mzJjB22+/fcGFhYaGApCTk0NY2C8jenJycujTp0/1Y3Jzc095ns1mIz8/v/r5Z+Lh4YGHR+PtIiwiLUvoiZX27EKF9t9yKK+Mh75MYEtaPgCXdwnixQmxRLRuGp3WDcPgeJmVg3mlHMorIz2vjPT8Ez/OL+NoseW05yzdnc0z1/dyQrUiIiLSGNQqtH/11Vd8+eWXDBkypPq2a6+9Fi8vL2688cY6Ce1RUVGEhoayatWq6pBeVFTEli1b+POf/wzAgAEDKCgoYPv27fTr1w+A1atX43A46N+//wXXICLSEIKrt8efHtikisNh8PFPh3jh+72UVdrxdnfh0Wu7c0v/yEa3Fd7uMMguqiD9WCnp+VXB/FB+adX/88oottjO+Xx/Lzc6BHkTGehNhyBvhncPOefjRUREpHmrVWgvKys77aw5QHBw8Hltjy8pKSElJaX667S0NOLj4wkMDCQyMpJp06bx3HPPER0dXT3yLTw8vLrDfPfu3Rk5ciR33XUX77zzDlarlalTpzJlypQad44XEXG2k43ocosrcDgMzBrzdYojx8t45KtENqbkAdA/KpCXJ8YRGeS81XWLzc7h/HLS806E8fyyqh/nl3Ekv5xKu+Oczw/18yQyyJsOgd50bONTHdA7BPrg7+3WQK9CREREmoJahfYBAwbw1FNP8eGHH1bPSy8vL+eZZ55hwIABNb7Otm3bGDp0aPXX06dPB+C2227j/fff5+GHH6a0tJS7776bgoICrrjiCpYuXVr9PQE+/vhjpk6dylVXXYXZbGbChAnMmzevNi9LRMQpgn2rjutY7QbHyyoJaqXjO1C1lfzzrYd5bvFeSiw2PN3MPDIyhtsGdGyQDzYKy60ntq3/skp+cit7VlEF5xqY6uZiIqJ11Wp5xyBvIoN86HAimLcP9MbT7fxHo4qIiEjLVKs57Tt37mTkyJFYLBbi4uIASEhIwNPTk2XLltGzZ886L7Q+aU67iDjbxc+t4FhJJYvvv4Ke4RrxlVVYzqyvdrJu31EA+nVozZxJcUS18amz72EYBrnFlqpz5XmlJ1bLq86WH8or5XiZ9ZzP93F3+SWMt6laJT+5rT08wAsX7ZgQERGRc6jXOe29e/dm//79fPzxxyQlJQFw0003cfPNN+Pl1bQ794qIOEOInyfHSirJLbLQswWf7jEMg69+zuCZRbsprrDh7mrmoWu6cccVUbUKwVa7g4zj5dVB/JdQXrWlvdx67nFqbVq5n1gt96nazh7kTeSJcB7k497oztOLiIhI83Peod1qtRITE8N3333HXXfdVR81iYi0OCF+nuzOLCK7BY99yy2q4LEFO1m5t2oqSFz7AF6ZFEeX4FbnfF5Zpe3EavmvGr6dWDXPKCjH7jj7hjKzCcIDvKrOk/9qC3tkYFVIb+VRq8+2RUREROrMeb8bcXNzo6Ki5b6pFBGpDyEteOybYRgsTMjkyW93U1huxd3FzLSro7l7UCdcXcwYhkF+aWX1CnlNxqT9moer+ZdGb0E+v+rM7kO7AC/cXc0N9EpFREREzl+tlhDuvfdeXnzxRf7v//4PV1etQoiIXKiTs9pzi1tWaD9WYuHxBbtYuju7+rabL4ukqNzG/Z/tOO8xaSdXy092Zu8Q5EOwr4c68ouIiEiTVavEvXXrVlatWsXy5cvp3bs3Pj6nNgb6+uuv66Q4EZGWIsSvqmN8c15pr7DaOXK8rHor+zvrUsk9wyr5fzYePOPzT45J63ginGtMmoiIiLQEtQrtAQEBTJgwoa5rERFpsUJOzGrPKTr3Vu/Gri7GpHU4sUquMWkiIiIi5xnaHQ4HL7/8Mvv27aOyspJhw4bx9NNPq2O8iMgFCvE9Gdob90r7hY5J+19tWnnw8qRYurRtpTFpIiIiImdwXqH9+eef5+mnn2b48OF4eXkxb948jh49yr///e/6qk9EpEUIPbHSnldaicVmx8PVeavKdTEm7ZSz5UHeBHi58++NaWzYfwyAriGteGVSH3pHaCa9iIiIyLmcV2j/8MMPeeutt/jTn/4EwMqVKxk9ejT/93//h9ms7rsiIrXV2tsNdxczlXYHR4stRLT2rtfvVxdj0qpnl//GmLTVSTk88lUiucUWzCb405WdmTY82qkfTIiIiIg0FecV2g8dOsS1115b/fXw4cMxmUxkZmYSERFR58WJiLQUJpOJYD8PjhwvJ6eo4oJDe92NSfM50ZX9/MekFVVY+duiPczffgSATm19mDMpjosiW1/QaxMRERFpSc4rtNtsNjw9PU+5zc3NDav1/M4wiojI6UL9PE+E9po1o7M7DLIKy6uD+Cmr5jUck9Yx6JeGb3U5Jm39vqM88lUiWYUVmEzwxyuimHFNNzWTExERETlP5xXaDcPgD3/4Ax4eHtW3VVRUcM8995wy9k0j30REzl/IiVntvx779r9j0qq2sJeSnl/GkfxyKu2Oc14z1M+zeqW8IcaklVhsPL94L5/+dAiAjkHezJkUx8UdA+v8e4mIiIi0BOcV2m+77bbTbrvlllvqrBgRkZbo5Ji07enHAXj2uz0s35Nd4zFp7Vv/skruzDFpm1KO8dCXiWQUlAPwh4EdeXhkN7zdazVdVEREREQAk2Gc6+1gy1BUVIS/vz+FhYX4+fk5uxwRaWYudEyaj7tL9dnyyBOr5CfPmDeGMWmlFhsvLk3iw83pAES09uLliXEM6Bzk1LpEREREGrOa5lAtf4iI1IFzjUlLzy+lwnrubextWnlwrOSXs+yvTY4j8kQ4D/Jxx2RqnPPLf0rLZ+b8BA7llwFwc/9IHru2Oz4e+udFREREpC7oXZWISA2dbUzawbxSMgsqfnNMWrvWXnQIPPuYtB8P5DHlnz/SqY0PN/Rt3BM5yivtvLwsmf9sSsMwINzfkxcnxjIouq2zSxMRERFpVhTaRUR+w+7MQv780c/Vq8ln4+Fqrg7itRmTVt2IrqgCwzAa7er69vTjPDQ/gQPHSgGYckl7/jq6O76edd/YTkRERKSlU2gXEfkNmQUVZwzsce0DmHJJezq18amTMWmhJ0J7WaWdEout0YXgCqud11bs470NB3AYEOLnwewJsQztFuzs0kRERESaLYV2EZHfcHWPEL7680AWxmfw/a5sjhZXnT1POFzA4fwyRvYK5brYMNr6evzGlc7Ny90FP09Xiips5BRVNKrQnnC4gBnzE0jJLQFg/EXteOq6nvUyNk5EREREfqHQLiJSA/06tKZfh9Y8OaYnW9Ly+C4xi6W7sskvreSTLYf4ZMsh2vp6cG2vUK6LC6dfZOtarbqH+HlSVFFCdqGFLsG+9fBKzo/FZmfeqv28s+4AdodBW18P/n5Db67uEeLs0kRERERaBIV2EZHz4GI2MbBzGwZ2bsOzY3uyKTWPxYlZLN1dtQL/weZ0PticTpi/J9f2DuO62DD6tA+o8fn0UH9P9ueWkFNUUc+v5Lftyihk5vwEkrKLAbi+TzhPj+lJax93J1cmIiIi0nIotIuI1JKri5nBXdsyuGtb/jauFxtTjrEoMZPlu3PIKqzgXz+k8a8f0oho7cXo2DDGxIbTM9zvnAE+2PeXZnTOYrU7+MeaFN5cnYLNYRDk485z43oxqneY02oSERERaakU2kVE6oC7q5mhMcEMjQmmwmpn3b6jfJeYxaq9ORw5Xs676w7w7roDdAzy5rrYcK6LC6NbiO9pAT7Uv+pcfK6TQvverCJmfJHAnqwiAK7tHcrfru9FUKsLO68vIiIiIrWj0C4iUsc83VwY0TOUET1DKa+0szopl8U7M1m1N5eDeWW8uSaFN9ek0CW4FdfFhnFdbDhdglsBv3SQb+iVdpvdwTvrUnl91X6sdoMAbzf+dn0vrosNa7Sj50RERERaAoV2kVpKzi6mxGKlX4dAZ5ciQKnFxvGySnw93Wjl4YrLBYxeq0te7i6Mjg1jdGwYpRYbK/fm8F1iFuuSj5KSW8LclfuZu3I/MaG+jIkLx92lapZ7dpGlwWrcn1PMjPkJJB4pBKq65T9/Q6/qrfoiIiIi4jwmwzAMZxfhbEVFRfj7+1NYWIifn5+zy5EmIO1YKde+vgGLzc7Op0fg46HPv5wpu7CC6974gWMlvwTdVh6u+Hqe/M/tV1+74fer20/9vyt+J37cysMV1xMBuj4UVVhZsTuH7xIz2bD/GDbH6X8V//DIUCJae9dbDXaHwf9tOMArK/ZRaXPg5+nKM9f3ZFyfdlpdFxEREalnNc2hShoi58lmdzD9i3jKrXbatHLHy83F2SW1aHaHwbTPd3CsxILJBCc/hiyx2Cix2MgqrP21vdxcTgn+vw71v76t6gOBkx8GnHq/u+uZg7+fpxsT+kUwoV8EBWWVLNudzXeJWWzYf6z6MVe8uIa+kQFcFxvO6N5hhPrX3cp36tESHpqfwM+HCgAY2q0tsyfEEuKn1XURERGRxkShXeQ8vbMulR0ngs7g6La1msUtdeeddan8eCAfb3cXFt8/iPAAT4orbCf+s1b/v+hXt5Wc/LGl6v6i/3lshdUBQLnVTrnVTm5x7beqe7iaT1vdb+XhSmsfN24b2JGYUD8CvN2ZfEkkky+JJLeogkv/vqr6+TsOFbDjUAHPLd7DJR0CuS4ujFG9wmjrW7vGcA6Hwb83pvHysmQsNge+Hq48MaYHk/pFaHVdREREpBFSaBc5D7syCpm7cn/111d2a+vEauTnQ8d5dcU+AJ69vhdRbXwA8GjlQpsL6HZutTv+J/SfGuqrAv/pHwb8+kOB0ko7ABabA0uJ5ZSt+yfZHQYvTYw75bZgP09C/DzIKbLwr9su5nB+Gd8lZrEt/Tg/Hcznp4P5PL1wN5d1CuK62HBG9golsIZz09PzSnlofiI/HcwHYFB0G2ZPiKVdgFetf65EREREpH4ptIvUUIXVzoOfx1efPTabqlbaxTmKKqzc/+kO7A6DsXHhTLioXZ1d283FTKCPe43D8JnYHQYlFTaKToT5XRmF/G3xHoorbADEtQ9g6tDoMz43xM+TnCILhgF/uDyKP1weRWZBOd/vzGJRYhYJhwvYlJrHptQ8nvh2F5d3acN1sWGM6BGKv7fbaddzOAw+2pLOC98nUW614+Puwl9H9+CmS9trdV1ERESkkVNoF6mhOcuS2Z9bUv11XPsAWl9AqJPaMwyDxxfs4sjxciJae/HcDb3qPHxWWO18vzOL1t7uDI0JPu/nu5hN+Hu74ePhwneJacxduQ+LzYGXmwszR3TjDwM7nrXDfdW58sJTxr6FB3jxx0Gd+OOgTtWr798lZrI7s4j1+46yft9R/uqyk8HRbbkuLozh3UPw9XTjcH4ZD3+ZyOYDeQAM6BTESxNjaR9Yfw3uRERERKTuKLSL1MDm1Dz+tTENAD9PV4oqbFzZVavszvLVzxksTMjExWxi3k198fM8fXW5tkotNj7Zcoj3Nhwgt9hCKw9Xdj59Ta0+FNiVUcgjXyWyO7MIqNqO/vcbev9mYA7xq9ran3OWWe3tA73585DO/HlIZw4cLWFxYhbfJWaRnFPMqqRcViXl4u5qptLmqH6Ol5sLs0bFcOtlHdSHQURERKQJUWgX+Q3FFVZmzk/AMGDCRREs350NwJBu57/6Khcu7VgpT367C4DpV3flosjWdXLdgrJK3t90kPc3HaSgzAqAm4uJx67tft6BvcJq57WV+/i/DWnYHQYB3m48MboH4y+q2Si10BMd3M8W2n+tU9tW3HdVNPddFc3+nGIWJWbx3voDlFvtpzyuS3ArQvw8qLQ78DRr4oGIiIhIU6HQLvIbnlm0h4yCctoHejE6NpSvfj5CoI87se38nV1ai1Npc3D/pzsoq7RzWadA7rmy8wVfM7eogn/9kMZHP6ZXN48DiAz05s3f9SU2IuC8rrc5NY9Hv07kYF4ZANfFhvHUmJ7n1e395Ni17KLz61rfJbgVEa29cD3DSvrOjELu+ehnfNxdGN4jhDGx4Qzq2gYPVwV4ERERkcZMoV3kHJbtzubL7UcwmeDVG/uwOikXgMHRbbTF2AnmLE9mZ0YhAd5uzJ3c96xnwmvicH4Z765P5YttR07ZRg4wOjaMF8b3Pq9t94XlVmYv2cunPx0GqlbLnxvXi+E9Qs67tpOhPbcGK+0n5RRVMOurRNYkHwWgb2QAcybF0amNDzszCvkuMYvFiVlkFJTzbXwm38Zn4uvpynWxYfx1dA9aeeifAxEREZHGSO/SRM7iWImFx77eCcDdgztxScdAnvx2N6BRb86wft9R/rn+AAAvTYgl1N+zVtdJyS3mrTWpfJuQif3EJICTPFzNPDWm53l3VV+6K5snv91VPc/9lssieXhkTK3P2p98bdk1CO2GYfBNfAZPfbubogob7i5mpl/TlbsGdar+UCM2IoDYiABmjYxhYUImj3+zixJL1ai6T386zMDObRgTF16rWkVERESkfim0i5yBYRjM+moneaWVxIT6Mv3qruQUVbA3qwiTRr01uGMlFqZ/kQBUBeJreoae9zV2HinkH2tSWLYnG8M4/f7ObX34x80XERPqV+Nr5hZX8NS3u1myq6rPQac2PsyeEMulUYHnXd+vhfhWhfaCMisVVjuebmfewn602MJjC3ayYk8OALER/rwyKY7oEN9THme1O1i5J4dPfjrEhv3Hqm9v6+vBTZe0Z2Sv8//5FBEREZGGodAucgbztx9h5d4c3FxMvDa5Dx6uLqzblwlAbDt/glrV/HyyXBiHw2Dm/ASOlVjoGtKKx0f3qPFzDcPgp7R83lyTckpY7dM+gJTcEkosVTPTJ/WL4Jnre+LtXrO/Eg3D4Itth3l+8V6KKmy4mk3cc2Vnpg7rctaAfT78vFzxdDNTYXWQW2QhMuj0bvOLEjJ58ttdHC+z4uZiYtrwrvxpcCdcXczVjzmUV8anWw8xf9sRjpX8cj5+UHQbbu4fyVXdQ3D71eNFREREpPFRaBf5H4fzy3h20R4Apl/dje5hVSuv606cFb5SXeMb1H82HWRt8lE8XM28cdNFNQrFhmGwNvko/1iTwrb040DV3PTr48LpEe7HW2tTKbHY8HZ34fkbenFD34ga15OeV8qjX+9kU2rV3PPYCH9mj4+lR3jNV+h/i8lkItTPk4N5ZWQXVZwS2vNKLDz57W4W78wCoEeYH6/cGFf9+/Rcq+o3XhzBlEsiNaNdREREpAlRaBf5FbvDYMb8BEosNi7p2Jq7B3cCwGZ3sGH/idCu+ewNZldGIS8uSQLg8dHd6Rbqe87H2x0GS3dl8481KezJqpqN7u5q5saLI7jj8ig+33qY5xbvBarC7pu/60untq1qVIvN7uDfG9N4dcU+KqwOPN3MzLymG38Y2PGU1e26EnwitP967NvSXVn8dcEu8korcTWbuHdoF6YO64Kbi1mr6iIiIiLNlEK7yK/8+4c0fkrLx9vdhVcm9alu5LXjcAFFFTYCvN3o0z7AuUW2EGWVNu7/bAeVdgdX9wjhlss6nPWxlTYH38Rn8M7aVA4cKwXA292FWy7rwB+viMJic3DfpzuIP1wAwO8HdOCxa7vXeCv77sxCZn21k50ZhQBc3iWIF26IPeO29bry61ntx0sreWrhbhYmVB3R6Bbiyys3xtEt1Fer6iIiIiLNnEK7yAnJ2cW8vCwZgCeu63FKIFubXDXqbVB02wsaMyY19+yiPRw4WkqInwcvTog9Yzf3Cqudz346xD/XHyCzsGpF2t/Ljdsv78gfBnYkwNudJTuzePirRIorbPh6uvLyxFhG9gqrUQ0VVjvzVu3n3fUHsDsM/Dxdefy6HkzqF3Fe3eVrw8ej6gOFT7Yc4t31BzhabMFsgj8P6cwNfSP46ucj/OE/W7WqLiIiItLMNerQbrfbefrpp/noo4/Izs4mPDycP/zhDzz++OPVb5gNw+Cpp57ivffeo6CggMsvv5y3336b6OhoJ1cvTUmlzcG0z+OptDu4KiaYKZe0P+X+dfuqtsYP0db4BrE4MYvPth7GZILXJvch0Mf9lPuLKqx89GM6//4hjWMllUDV6vLdgzpxU/9IWnm4UmG188Q3u/jvj+lAVfO5N27qW+OV5y0H8nj0653VK/eje4fx1NgeBPvWbtRcTe3PKeaFJUmsTqr6oOjk9+8Y5M3IXmEkHinkH2tSqx+vVXURERGR5q1Rh/YXX3yRt99+mw8++ICePXuybds2br/9dvz9/bn//vsBeOmll5g3bx4ffPABUVFRPPHEE4wYMYI9e/bg6Vm/b66l+Xh91T72ZhXR2tuNFyb0PmUVNbe4gl0ZVeejByu017sjx8uY9XUiAH8Z0pmBndtU35dXYuE/Gw/yweaDFFdUdX6PaO3FPVd2ZmK/iOrt7geOlnDvJzvYe+Jc+5+u7MTMa7rVaPW5qMLK7CVJfLLlEAAhfh787fpetRozdz5yiyp4beU+Pt96GMcZRtLllVbyzrqqsG4yVe36+N2l7bWqLiIiItLMNerQvmnTJq6//npGjx4NQMeOHfn000/56aefgKpV9rlz5/L4449z/fXXA/Dhhx8SEhLCN998w5QpU5xWuzQd29PzeXttVRj6+w29T1tJXb+v6qxwr3Z+tPXVqLf6ZLM7mPZZPMUVNvq0D2Da8K4AZBWW8976ND796RDlVjsA0cGt+MvQzoyJDT+lEdzXPx/h8W92UVZpJ9DHnVdujGNoDTv+L9+dzRPf7iKnqGrL+e/6RzJrVAx+nm51/Ep/UWqx8c/1B3hvwwHKKu1nfVxxhU2r6iIiIiItUKMO7QMHDuSf//wn+/bto2vXriQkJPDDDz/w6quvApCWlkZ2djbDhw+vfo6/vz/9+/dn8+bNZw3tFosFi+WXc6BFRUX1+0Kk0Sq12Jj+RQIOA8b3bceo3qefdT55nn1IV416q29vrK4a0dbKw5V5U/qScbycd9al8tXPR7Daq5afe7fz596hXbimRwjmX/UXKKu08eS3u/ly+xEALusUyOtT+hLi99s7bo4WW3h64S9j1KLa+PDC+N5c1imoHl5lFZvdwfztR3h1xT6OFlvO+jitqouIiIi0bI06tM+aNYuioiJiYmJwcXHBbrfz/PPPc/PNNwOQnZ0NQEhIyCnPCwkJqb7vTF544QWeeeaZ+itcmoy/f7+X9Lwywv09efr6nqfdb3cY1V25h3TT1vj69FNaPm+s3g/ArQM6MGd5Mt8lZlZvFe8fFcjUYV24okub05rA7c0qYuonP5N6tBSzCe6/Kpr7hkX/ZtNAwzD4cvsRnlu8l8JyKy5mE3cP7sQDV0XXuLP8+TIMgzXJubzwfRL7c0vO+rgLXVW32h0UlFkpLK/keJmV46WVFFXY6B8VqFV6ERERkSakUYf2L774go8//phPPvmEnj17Eh8fz7Rp0wgPD+e2226r9XUfffRRpk+fXv11UVER7du3P8czpDlak5zLxyfOLc+ZFHfGLdDxhwsoLLfi5+mqUW/1qLDMyrTPdlQH9JPHFQCGxQTzlyGdubhj4GnPMwyDT346xLOL9mCxOQj29eD1KX0Z0Pm3V8gP5ZXx2IKd/JDyy/GH2eNj6dXOv25e1Bnsyijk+cV72Xwg76yPuaRja+68Iqp6Vd3uMCgoOxG8yyopPPH/gjJr9e0F5VU/LvjVfSUW2xmvHxfhz7dTr6ivlygiIiIidaxRh/aHHnqIWbNmVW9z7927N+np6bzwwgvcdttthIZWNYbKyckhLOyXbc05OTn06dPnrNf18PDAw0Nnk1uy46WVPPxlVbOz2y/vyMAubc74uHW/GvXmqm3J9cIwDAa9tJqiil9CpslU1a39z0M60zP8zCG6qMLKo1/vZHFi1Zb2Id3a8sqkOIJanfvPtt1h8J+NabyyfB/lVjsermamX92VO6+Iqrdf4yPHy5izLJlv4jPP+biLO7SmlYcr76w7wOwlSRSUWykst2KcoTFdbfi4u3DHFVF1czERERERaRCNOrSXlZVhNp/6JtrFxQWHwwFAVFQUoaGhrFq1qjqkFxUVsWXLFv785z83dLnSRBiGwePf7OJosYUuwa14ZGTMWR+79sSotyu1Nb7OORwGK/fmcPd/t59y+40XR3DPlZ3p1LbVWZ+bcLiA+z7dwaH8MlzNJh4a0Y27BnU65Yz7mezNKmLWV4kkHCkEYECnIF4Y35uObXwu/AWdQWG5lbfWpvDuugM1evy29ONnva+Vhyv+Xm609nGjtbd71Y+93QnwdiPA253W3m7VP7bZDT7YdJDvd2VhGOBiNvG7SyN5YHg0bX7jQw0RERERaVwadWgfM2YMzz//PJGRkfTs2ZMdO3bw6quvcscddwBgMpmYNm0azz33HNHR0dUj38LDwxk3bpxzi5dGa2FCJot3ZuFqNvHajX3Oenb5WImFxBPhrq7ns5dX2vn0p0McOV7OQyO64eVeP+enGyOb3cHinVm8tSaV5JziU+7bOGsY7QK8zvpcwzD41w9pvLg0CavdoF2AF2/8ri8XRbY+5/essNp5c3UK76xLxeYw8PV05fHR3bnx4vannY+vC5U2B+9vSuPv3yf95mNH9QqtDtsBZwziVQHd3fW3dwGUVdp4b30a765Pre5Ef3WPEB4ZGUOX4LN/CCIiIiIijVejDu1vvPEGTzzxBH/5y1/Izc0lPDycP/3pTzz55JPVj3n44YcpLS3l7rvvpqCggCuuuIKlS5dqRrucUVZhOU98swuA+4ZF0zvi7OeXN+yvWmXvEeZHcA06kNdEWaWNj388xLvrUzlWUgnAxH4R9Aj3q5PrN3ZLd2Xx9++TOJRfdsrt3cP8WHzfFedcKT9eWslDXyawcm/VkYWRPUN5cUIs/t7nHse29WA+s75KJPVoafXznr2+Z539mv6aYRi8s+4ALy797bC+7qEhdAiqmxV+u8Ng/rbDvLpiH7knOtHHtQ/gr9d259Ko03sBiIiIiEjTYTKMujot2XQVFRXh7+9PYWEhfn4tIzy1RA6Hwe///RM/pBwjrn0AX90z4JxnmB/4bAffxmfy5yGdz7mFvibKKm189GM6/1x/oDqsm0ww85pu3Du0ywVduykwDIPXV+1n7sqq7vCBPu7kl1b9PAT5uLPkgUHnDNFbD+Zz/6c7yCqswN3FzBPXdeeWyzqcc5W8uMLKS0uT+e+P6UBVN/a/Xd+Tkb1OH+t3oax2B7OXJPGvH9J+87EvTujN5Esi6+T7GobB2n1Hmf19UvWuhfaBXjw8IobrYsPqZReBiIiIiNSNmubQRr3SLlKX/vtjOj+kHMPTzcyrN8adM7DbHQbrT5xnv5Ct8WWVNv67uSqs550IqQAB3m7Mm9KXwXW87b4xqrDaeeSrRL490YTtj1dE0TeyNfd+8jNQ1bn/bIHd7jB4e20Kr63cj91h0KmND2/8ru9Zm9OdtGpvDo9/s4uswgoAplzSnkev7Y6/17lX5c9Xel4ps5cksWTX2UdMnnRZp0DeuaUfAd7udfK9d2UU8sKSvWxMqepE7+/lxn3DunDrgA54uLac4xYiIiIizZ1Cu7QIqUdLeGHJXgAeHdWdzudocgawM6OQ42VWfD1cuajDuc9Ln0mpxcZ/f0znvf8J61A1Wuztm/u1iFnZx0os/Om/29mefhxXs4nnxvViWEwwI1/fAFR17h8aE3zG5+YWVzD984TqkWw39G3H38b1opXH2f/aOlZi4ZlFe1iUUPUBQYcgb14Y35uBnc88HaA2rHYHK/bk8ObqFPZkFdXoOe/e2o8RPUPr5PtnFJTzyrJkFsRnYBjg7mLmD5d35N4hXX7zqICIiIiIND0K7dLs2ewOpn8eT4XVwaDoNtx6WYfffM7aE6Perohug9t5jAErtdj4cHM67204UL39+9cm9Yvgb+N6nbX5XXOyP6eY29/fypHj5fh5uvL2Lf0Y0CmI3//7J/JLK+ke5sesUWc+drBh/1Ee/DyeYyWVeLm58Oz1PZnYL+Ks270Nw+DrnzP42+I9FJRZcTGb+OOgKB4c3rXOfq7T80r5bOthPtqcTvFZZqD/r+tiw3j2+l4E+lz46npRhZW316byrx/SqLRVTdAYGxfOQyO6tYgPgERERERaKoV2afb+sSaVhCOF+Hm68tLE2N8cCwawNvnEqLcabl8vsdj4cPNB3lt/gONlVqDqzPrJjhFuLiaeHtuT310a2SLOGa/fd5R7P/6ZYouNDkHe/Ou2S+gS3Ip31qVWH1F446Y+p23jttkdvLZyH2+tTcUwoFuIL/+4uS9dgn3P+r0O55fx2IKdbNhftSLfI8yPlybG0qvdubfQ18TJVfVPfzpUff2aaO3txnPjejM69sLPz1faHHyyJZ15q1OqPwjqHxXIY9d2J659wAVfX0REREQaN4V2adYSjxQwb3VV87O/jetFmP/Zx4mdlF9aScKRAuC357OXWGx8sOkg7204QMGJsB7VxofObX3YsP8YFpuDMH9P3rr5Ivr+xliy5uK/P6bz9MLd2B0Gl3YM5J1b+xHo407C4QLmLEsG4OkxPU8L4pkF5dz/6Y7qWeW/6x/Jk9f1OOtKud1RNYt8zvJkyirteLiamTa8K38cFHVeuyPO5OSq+vxtRzhWYjmv547oGcJz43rT1vfC5qEbhsHSXdm8uPT/27vv6KiqNYzDv5n0nhBSCYQAIQmQhCoiIggoAjZAREXF3lCaSrFgFxSxK9arWFEEVFBQFAGRXhNKQu8klJDeppz7x4SBmNBLAr7PWqybOXPmZA85F/PO3vv70th6wFFtv36IDyO6JtApIfQ/8eGPiIiIiCi0ywWs2GJj8HcrsdkNuidFcG1y5Am97u8N+zAMiA/3O2rIzyu2OJfBHxnWH+xQn/SMPGcV8Tb1gnnnlmbU9D29AHc+sNkNXvxlLZ/9sxWAns1rMapnIh6uLuSXWBkwYQVWu0G3xHD6tKpd7rUz12by+A+ryC604OvhyqieiVxzjJ9XekYewyalsHJHNuCYeR7dK4mYmqfeQu1UZ9UPCfBy4/nrGnNtcuRpB+pl2w7y8q/rWFb2AUZNX3cGdW7ITa1qH7OAooiIiIhceBTa5YL1yow0Nu0rINTPgxeva3LCQWrOoaXxlcyy5xVbymbWt5BT5Ajr9Wr68EinBrStX5OBE1ayYLOjmvf9l9Xj8S5x/4mQlV9iZcC3K5iV5qgF8HiXOB7qUN/5dz7yp9VsO1BIrUAvRvVIch4vsdoYPT3NGfQTawXw7i3Njtq/vMRq472/NjFu9kYsNgM/D1ee6J5An5a1T2jbQ2VOZVY9xM+DfXmHz+0UH8qonomn3ft96/4CXv0tjV9THdXovdxcuLddDPe1r3/MAnwiIiIicuHSb4FyQfpn435nEHzlhiSCTrAQmN1uMGd9xf3sucUWPv9nK5/OOyKsh/gwoGMs1yRHkrIzm+ve+4c9OcX4uLswpncy3RLPfD/w6mh3dhF3fb6EtIw8PFzNjL0xmauTDs+S/7hiF5OX78JsgjdvauqscL7tQAEPf7OC1F05ANzVNoZhXeOO2q5s2bYshk1KZePefACubBTGC9c3IewUgvLRZtW93V0oLLUd9XXJtQMJ8nZj/ibHBzN+nq48c01jejWvdVqz61kFpbz95wa+XrQNi83AbILeLWoz5MqGp/T+REREROTCodAuF5ycIguPTVwFQN/Wdbg8rvKWYpVZvTuHAwWl+Li70DK6BrnFFj6bt5VP520mt9hRMbx+iA8DOsVydVIkZhN8s3g7z/28llKbnXohPnx0W4tjFk67kKzakc09XyxlX14JNX09+Pj2FuX27m8/UMhTP64G4JGOsbSqWwOAn1ft5onJqeSXWAn0duO1G5Lp3Cis0u+RX2JlzIw0vli4DcOAmr4ePH9dY7o2CT/poFzZrLrJBJfUD8ZsMpG6K6fS0J5YK4CezWsxLWWPs0jhZQ1DeKVX4gnVSTiaYouNz/7ZyvuzN5JXdn+1bxjCiG7xxIf7n/J1RUREROTCodAuF5znfl7DnpxiooO9ebJ7wkm99lAgS4oK5P3ZG/nfvC3OsN4g1JcBnWLpnhiBi9lEscXG0z+uZuKynQBc1TicMb2T8PP8b/TK/jV1D0O+d7TSiw/345N+LYkKOtx6zGKz88iEFeSXWGlVN4hHOjagqNTG89PW8O3iHQC0jA7i7ZubERlYefD9K20vT05JZXdOMQA3toziiW4JBHqfeAu1o82qh/h5cGPLKGr6evDVwm1s2ldQ4bXx4X4M6hzL7uxiXpmRRrHFjq+HK091T6BPq9qnPLtutxv8tGoXY2akO99bowh/nuiWwKWxZ66nvIiIiIic/xTa5YIyPXUPk1c4lmK/fmNTvN1P7hafumo3AAs2H3DuTY8tC+vdysI6ONqMtXv1LwDMJni8SzwPtK/3n6jobRgG78/exJiySvCXx4Xw9s3NKnxY8cbM9azakY2/pytv3tSMLfsL6P/NctZn5mMyQf8ODRjUObbSPf8H8kt4ftpaflrp+HnUqeHNqJ6JtG1w4oH2aLPq7WJDuOWiOoT4efDKjDQWb8mq8Nr6IT4M6tyQpKgAhv6QwqKyc9o2COaVXknlPpw4WfM37ufl6etYvSsXgIgATx67Mo4ezWqd8r58EREREblwKbTLBWNvXjFPTEkF4MEO9WkRfeIt1nIKLbzxx3o2lO2XBkdYH9g5lm5NIsqFqbnr93H7/xY7H398e0s6JVS+tPtCU2q1M2JyKpOWO1YX3HFJXZ7qnlAheM/fuJ9xczYBMLpXEv9s2M/In1dTbLFT09eDN/s0rXRG2TAMfly5i+enruVgoQWzCe5pV4/BnRvi5V75XvcjHW9W/aZWdQAY81s6P5d9QHOkOjW8GdQ5lmuTI5mwZAdd3/qbwlIb3u4ujOiWwK2t65zyBzPrM/MY9es6/ipbzeHn4cqDl9fnrrYxR21rJyIiIiKi0C4XBMMwGD4plYOFFhpF+DOwU8MTel12YSn/m7eFz/7ZSl6J1Xn8vVua07VJeLmwbrcbvD97I6/9vr7cNf4rgf1gQSn3f7WMxVuycDGbePaaRtzWpm6F87IKShn8/UoMA65OiuD3NRn8WDZjfmmDmrzeJ5lQv4rF1XYedOx/P7RFIT7cj1dvSCIpKvC4YzverHqnhFAKS2y8+9cGxs/fRqnNXu71tQK9eKRjA3q1iCIzt5g7PlvCvI2O0N86pgZjbkimTvCpza7vzS3m9Znr+X7pDuwGuJpN3HpxNI90bEDwf6AVoIiIiIicHoV2uSBMWLKDWWl7cXcx80afpri7HrvNWnZhKZ+WhfX8I8I6wN2XxtA9qXzl99xiC49+v4qZazPLHU9/8aoz8waquU378rn78yVsPVCIn4cr7/ZtXq66/iGGYTD0h1Vk5jqC85KtWWTmluBiNjHkioY82L5+hSXgNrvBFwu2Mua3dApLbbi7mhnYKZb7LquH2zHa5ZVa7fyxLpNvFm13BmxwzKr3aVmbPq1qU7uGNyVWG+Pnb+WdWRudlf8PCfXz4OGODejTqjbuLma+W7KDF39ZR36JFU83M8Ouiqdfm7qntGy9oMTKR3M389HczRRZHMXtrmoczrCu8afVT15ERERE/lsU2uW8t/1AIS9MWws4+oPHhR+9cvvBAkdY/3z+4bAeH+7HIx1jeebn1ezPL6VjfPlq8+kZeTzw1TK27C9fqOznh9setT3ZhWT+pv088OUycoutRAV58b87WtEwrPK/4y8XbuOPdXudjzNzS4gI8OTtm5s5K8cfaUNmHkMnpbBiezYAF9WtwaheidQP8T3qeA7Pqu9gf34p4JhVvyw2hJvLZtXdXMwYhsHUVbt59bc0dmQVlbtGsI87D3aoz60XR+Pp5sKenCKGT0p1tvtrER3Ea72TTylcW212vl+6kzf+WO/s5d6sTiBPdkugZSV/ByIiIiIix6LQLuc1m91gyPcrKSy10TqmBndfGlPpeQcLSvlk3mY+/2crBWUtvQ5VBr+yUThr9+SyP78Ub3cXWtY9vBd+6qrdDP0hxTlTesh9l9U7oWXb57vvlmznySmrsdoNmtcJ5KPbW1LzKEu60zJyGfnTmnLHOieEMuaGZIJ8yld7L7XaeX/2Rt77ayMWm4GvhyvDu8Zzy0V1Kp3VPtFZ9UMWb8nipV/XsWpHdrnrBHq7cd9l9ejXpi4+Hq4YhsGkZTt5duoa8oqtuLuaefzKOO66NMZZdPBEGYbBrLS9jJqe5uwlHx3szbCr4k+pPZ2IiIiICCi0y3nuo7mbWbrtIL4errzWO7lC4MsqKOWTvzczfv7hsJ4Q4c/ATrFc2SjMef6hGdZL6gfj4eqCxWZn9PQ0Pp23pcL3dDGbGNE1/iy/s6pltxu8MiOND+duBuDa5EhevSHpqAXTikptXPXm387HrmYTI7olcFfbuhXC6vLtBxk+KYX1mY5g2zkhlBeub1Jpv/MTnVU/ZNO+fEZPT6uwjcHPw5V72tXjrkvrOqvc7811FC48tDIguXYgY3sn0yD06LP8R5O6M4eXfl3Lws2OKvNB3m4M6BRL39bRx92qISIiIiJyLArtct5atyeX12c62o6NvKZRuZnWrIJSPi4L64VlYb1RhD8DO8dyRUJYhXA/O90R3NrHhbIvr4T+3yx3tgLrFB/Kn2mHl3wvf/qKC3rWtLDUyqAJK/m9LPgO7BTLoM6xR33PdrtBwsgZzse+Hq58c2/rCisRCkqsjPktnfELtmIYUNPXnWevbUz3xIhy1z7ZWXWA/fklvPnHer5dvAOb3XAe93Z34c62dbm3XT1nb3fDMPh51W5G/rSGnCIL7i5mBl0Ry33t6lXafu5YdmQVMvb3dGehPXdXM3e2rctDHRoQ4OV2nFeLiIiIiByfQrucl0qsNgZ/txKLzeCKRmH0bhEFOPp7f/T3Zr5csM0Z1htHOmbWr2gUVmnwzCmysLxsT7W/pytXv/M3mbkl+Hq4MvKaRgz9IcV57oT7LibAy40bP1jAzoOFTH6oLeEBFSuhn68ycoq554slrN6Vi7uLmTG9k7iuaa2jnn8gv4QWL/7hfBzo7cbcoZfj/6+e7bPT9/LklNXsynbsLe/VPIqnuieUWzZ/srPq4Jjh/3TeZsbN3uRcSQHg4Wqm3yV1uf+yeuUqtO/PL+GpKauZsSYDgCa1/Bnbu+kx6yBUJqfIwvt/beSz+VsptToq0fdoVotHr2x4Wj3cRURERET+TaFdzkuvz1xPWkYewT7ujOqZyIGCUj6eu5kvFmxz7j9vUsvR+q1zQugxZ8b/2bjfOTv72MRVWGwGsaG+jLu1OQ9+tdx53u1torm4XjAzVu9h8VbHLPzBwtILJrSv3pXDPeOXkpFbTA0fdz66rcUxC6ct2HSAmz9e6Hzs7+nKin+tQsgqKOWFaWuZsmIXAFFBXrzcI5HLyirPn8qsOjhqGUxavpPXf19PRm6x87i7i5lbWtfhoQ71CfUv/3P5NXUPT/24mqyCUlzNJgZ0iuXBDvWPWaH+30qtdr5cuI13Zm0gu9BRif6S+sE80S2BJrUCTvg6IiIiIiInSqFdzjtLtmbxUdle62Fd4yuE9cRaAQzsFEun44T1Q2asznB+bbEZdE+M4NUbkhg3exMbygqKebiaGXl1IwAeOCLIJ0T4n7H3VZVmrs1kwLcrKLLYaBDqy//6tTpqX3Kb3eDtPzfw1p8bnMfcXc0sfepwYD+0BP25qWvJKijFbII728bw6JUN8XZ3PaVZ9UPmrN/HqF/XkZaR5zzmajbRu2VtHunYgMjA8nvjswpKGfnTaqal7AEcBQjH3phM48gTD9mGYfBL6h5enZHO9qxCAGJDfXmiWwId4kIu6O0SIiIiIlK1FNrlvJJfYmXI9ysxDMfe6VdnpDlDX1KUI6x3jD+xsA6OJdk/r9rtfPxktwTuaRfDrLS9vPvXRufxP4a0x9XFXO7cN/s0PTNvqgoZhsEnf2/h5enrMAxoF1uTd29pftT92Bk5xQycsIJFZfv9wRG2fxt0mbPg2u7sIp76cTWzyuoAxIX58coNSTSK8D+lWfVD1u7OZdT0dfy94fBrzSbo2TyKAR1jK/2Q4bc1GTw5JZX9+aW4mE3071CfhzvGnlRxuCVbs3jpl3WsLKtEH+LnwZArGtK7RdRJ74EXERERETlZCu1yXnlx2lpnz+38Eiv5JY4ZzxHd4rk87sTDOsBf6Xu587Mlzsef39mKDnGhbNlfwN3jlzqPv31zM2eYHPDtCufx65sdfa/3+cBiszPyp9V8u3gHAH1b1+HZaxsfdYb7r/S9PPr9KrIKSssdH3ODo5+53W7w1aJtvDI9jYJSG+4uZh7p2ICuiRFMWr6Te8YvOelZdYA9OUW89tt6Jq/YiVFWY85kgmuSIhnYObbSnu45hRaenbrGuSw/NtSXsTcmn1Sbvs378nllRhq/rXEU5PN2d+G+y+pxb7t6+Hjon04REREROTf0m6ecN/5Ym8mEJTucj13NJh7qUJ/+HRvg4Vp5K7LK2O0Gb88qv7w7IcKfDnGhFJRYuemjBc7j1yZHcm1yJAA/LNvpPP7eLc1P561UuZxCCw99s4x/Nh7AZIKnujeqtD0bOML9a7+lO9u/1a7h5fzg5NrkSHo1r8XGvXkMm5TKsm0HAUiOCqBTQhiLtmQxduZ657VOdFb90BjbjP7TWVDwkKsahzP4ioZHLR43Ky2T4ZNS2ZtXgtkE97evz6DOsSd8jxzIL+GtPzfwzaLtWO0GZhP0aVWHwZ1jK+yTFxERERE52xTa5bywelcO93xxePa7SS1/Xu2VTKPIk9tTnlNoYfD3K51Ltw+5qVVtDMNg6A8pZOaWAI6Z1Zd7JjrPeWziKufX3ZMiTuVtVAvbDhRw5+dL2LyvAG93F965uRmdEsIqPXdHViGPfLvCuTS8X5toMnKL2ZFVRO0aXjx7bWPembWRd2dtpNTmqKLu6+HKzoNFvF4W1k9mVh0cHxKM+S3dWbfgkE7xoQy+ouFRC77lFlt4YepaJpZ9uFIvxIfXeifTvE7QCf29FJXa+N8/Wxg3exP5JVbn9xzWNZ6GYSdXXV5ERERE5ExRaJdqzW43+Gbxdp76cbXz2MBOsTzSscFJ7ydetyeXB75axrYDhbi7mhnRNZ6XflmH1W7QIS6Ej+Zu5pfUPc7zv733YnzLlkF/u3i78/hHt7U4zXdVdRZvyeL+L5dysNBCRIAnn/ZrddQPPqan7mHopBTyiq34e7ry6g1J5BVbGb9gGy5mE3e3jeHmjxaSnplX7nWHti2czKw6OPbX/7Ymkwe+WlbhuckPXXLM8D13/T6GTUphT04xJhPc3TaGx7rE4el2/Nl1m91gyopdjP09nT05jkr0TWr580S3BC6pX/O4rxcREREROZsU2qXa2naggGGTUli4+XDRs7duanrMvuFH8+OKXQyfnEKxxU6tQC8+vK0FOw8WYrUbxNT0YefBIkZNT3OeP+yqeJJrBzofj5ic6vz6ysbhp/aGqtikZTsZPjkFi80gKSqAT25vWely72KLjZd+WceXC7cB0KxOIG/f1AyLzc7V78wDHEH32alrK7z2ZGfVD1mx/SDDJ6VW+AAA4P729Y4a2PNLrLz0yzrnhyrRwd681juZVsdoVXekvzfs4+Vf01i3JxeAWoFePN4ljmuTIzGbVRFeRERERKqeQrtUOza7wWf/bOG139Mpttidxx+9ouFJB/ZSq52Xf13H5/O3AnBZwxDe6tOUIB93vl7kCKWxob488OXh2d1L6gdz/2X1nI8//2eL8+vP7mx1Km+pStntBq/PXO+sht+1STiv39gUL/eKs9Cb9+XT/5sVzhB7f/t6PHZlHHbDoNe4+RX2lx9ysrPqh2w/UMgrv6XxS8qecseTaweSnpFLscVO1yaVb0WYv3E/j/+Qwq5sx/76Oy6py9Cr4vB2P/4/a+v25DJqehpz1+8DwM/TlYcvb0C/S+qe0Oy8iIiIiMi5otAu1cqGzDyGTkphxfbscseb1wnkwQ71T+pae3OLeejr5SwtK472SMcGDOrcEBezCcMwmJ3uCGy/r810vsbLzYXXb2zqnGU1jPIzypfHhZ7K26oyxRYbj36/yrns/6EO9XnsyrhKZ5EnL9/JUz+uprDURrCPO2NvTKZD2ft9fOIqVu/KrfCa9g1PflYd4GBBKe/M2sj/jvhA5JBJD15CVkEp936xlMgAT5Kjyu9hLyy1Mnp6Gl8scHzoEhXkxZgbkmlTP/i43zcjp5ixv6fzw3JHJXo3FxO3XVyXRzo2IMjH/YTHLyIiIiJyrii0S7Vgsdn5aO5m3vpjA6U2O74erkQGerI+M98ZpE9mD/uSrVk89PVy9uWV4Ofhyut9mnJFo8PF1tZn5jv3Lx/pzZuaEh5weMn4kcXQvrz7olN8d1Vjb14x936xjFU7snFzMfFyj0R6t6xd4bzCUisjf1rjrI5/cb0avHVTM8L8PTEMg/f+2ugs7nbInW3rclfbmJOaVT9k0rKdPDd1DbnF1nLHezWP4rnrGuPr4cqQ71cC0KVJeLmK9ou3ZPHYxFVszyoEHG3qnuiWcNwWbPklVj6cs4mP/97sXL3RPTGCoVfFER3sc9LvQURERETkXFFolyq3elcOQ39IYW3ZkuyO8aHc2bYu95T1Sn+yewJ1a55YsDIMg8/nb3UWmIsL8+OD21oQ86/Xz07fW+G1fVvXocsR+9UNwyi3z71dbMhJv7eqsm5PLveMX8qu7CICvd344NYWXFyv4kz0uj25PPzNcjbtK8BsgoGdGvJwxwa4mE3sySni6R9X88e6w39Xh5bLn8ys+r+NnpFWLrD7eboyumeSsyJ/qdXOzLLVD4eWxheV2hjzWzqfzd+CYUBkgCev3JB03J+JxWZnwpIdvPXHemeP+JbRQTzRPeGEq8qLiIiIiFQlhXapMiVWG+/8uZFxczZhsxsEervx7DWN6Z4UQc/351NitdO+YQh9W9c5oesVlloZMTmVn1buBuCa5Ehe6ZVY6R7n14/oHQ7QINSXp7o3KnfsnVkbnV9PuO/ik317VeavtL08/M1yCkpt1Kvpw6d3tKrwoYVhOKryPz91LSVWO6F+Hrx1UzPa1A/Gbjf4cuE2XpmeRn6JFTcXE/0vb8BDHRrg7nrqYR0gq6C03OM29YJ5vU8yEQFezmPzN+0nr9hKiJ8HLaKDWLbtII9PXMXm/QWAoz3fE90T8Pd0O+r3MQyDmWszGT0jjc37HK+LqenDsKvi6dI4rNJ+9CIiIiIi1ZFCu1SJ5dsPMvSHFDbuzQegW2I4z13bhBA/D16fuZ7UXTkEervx6g1JJxSwtu4v4IGvlpGWkYeL2cQT3RK4q23dSl+7ZX8BJdbDBe7cXcy8fVOzcoXZDMMoF+wrm6Wujj7/ZwvPT1uL3XAsc//g1hYEepffq51bbGHE5FRn8bcOcSGM7Z1MsK8Hm/blM2JSKou3Oir2N68TyCu9kog9A33K567fx2MTV7EvrwQ3FxOPXRnHve3qVdhfP2N1BgCXx4Xw6m9pfDx3M3YDwvw9GN0r6bh1BVbtyOalX9exeIvjPdTwcWdQ51huvqjOaa0QEBERERGpCgrtck4Vldp47fd0/vePY5lzTV8PXry+MVeVLYNeuSOb98qqnL94fRPCKmlJ9m9/rstk0HcrySu2UtPXg/duaUbro4TsUqudy1+bXe7Y8K7xFXqVj/39cGCf9GCbk3mLVcJqs/P8tLXO4mw3tozixesTK8yMr9qRzSPfrmB7ViGuZhOPd3EEZ1vZ3vW3/txAqdWOt7sLQ7vEcVuburicZuuzYouNV2ekO4vO1Q/x4a2bmtGkVkCFc602u7Mw4PdLD++j79m8Fs9c3ZgA76PPru/IKuTV39KZusqx0sLD1czdl8bwQIf6x5yVFxERERGpzhTa5ZyZv2k/wyelOouI9Wxei5FXN3LOBBeV2hjy3UpsdoPrmkZydVLkMa9nsxu89cd63i5bxt68TiDjbm1xzKD/4i/le4t3iAvhzrZ1yx2z2w1nezSAFtEn1vO7quQWW3jkmxXMWb8Pk8nRY/7+y+qVW2VgGAafztvCKzPSsNgMagV68c4tzWheJ4iUndkM/SGFtAxHj/QOcSG81CORWoFeR/uWJyw9I4+BE1Y4r33rxXV4slujStvNAczbuL/cEvqavh6M6plYrojgv2UXlvLurI18sWAbpTY7JhP0bBbFo1c2JPIMvAcRERERkaqk0C5nXV6xhdHT0/h60XYAIgI8eblnYoVlzqOnr2Pz/gLC/T15/tomx7xmdmEpAyesZE5Zn+3b20TzVPdGx9xz/cOync6Z6EPG3JBcYQn96BmHi89NeeiS47/BKrQjq5C7xy9hfWY+nm5m3uzTjKuahJc752BBKY//sMpZUO6qxuG80isJN1cTL/2ylk/nbcFuQJC3G89c05jrmkae9p5vwzAYP38rL09Po9RqJ9jHnVd6JdH5GOF79a4c7vhsifPxdU0jefaaxkdtxVZitfHF/G28+9dGcoosAFzaoCYjusXTOLLiLL6IiIiIyPlIoV3Oqr/S9/Lk5FR2l7VXu6V1HUZ0jcfvX8uV/96wj/FlgXpM76RjLoNevSuHB79exo6sIjzdzLzcI5GezaOOOY7Vu3J4ckpquWMf3NqcED+PcsesZa3nDmlWjSuML9t2kPu/XMr+/FJC/Tz4tF8rEv/V03zJ1iwGfLuCPTnFuLuYefrqBG69OJr5mw4wfHIKO7KKALi+aSRPX92IYF+Pyr7VSdmbV8zjE1OcH6i0bxjCmN5JhPpVvgLCYrPz3l8befvPDc5jfVrW5pUbkio93243mJqymzG/pbPzoGP88eF+DO8aT/uGISoyJyIiIiIXFIV2OSuyC0t5ftpaJi/fBUCdGt6M7pXIJfVrVjg3p9DC4xNTAOjXJvqYbbwmLdvJE1NSKbHaqVPDmw9ubVFhP/q/ZRWUcv+Xy8oVnwOc++iP9MK0w8vnpz1y6TGvW5V+XrWbxyauotRqp1GEP5/e0bJcBXab3WDc7I288ccGbHaDejV9eOeWZkQFejP0hxRn3/XIAE9e6pHI5fHHLu52ov5Ym8nQSSlkFZTi7mrmyW4J3N4m+qhBOi0jl0e/X8Wa3bnljr9wfeUrLRZtPsDLv65j1c4cwFGc7tEr4ujVIuq0996LiIiIiFRHCu1yxs1YvYenflzD/vwSTCa4q20Mj17ZsNLWawAjf15NRm4x9Wr6MLxrQqXnlFrtvDBtLV8udMzGXx4Xwpt9mh1zRh4cM+cDvl3BruyicseHXhVX4VyLze6c7QcqLZRW1QzD4O0/N/LGH45CeZ0Twnjrpqb4eBz+u92bV8yQ71Yxb+N+AHo0q8UL1zdh7vp99PvfEufPpV+bujzWJQ5fj9P/Z6Co1MaLv6x1boGID/fj7Zub0fAoVeetNjsfzt3Mm3+sx2JztPtzdzGzN6+EHs1qVdjmsHFvPqOnp/HHOkeROh93Fx5oX5+728Uc9b4SEREREbkQ6LddOWP25ZXw7M9r+CXV0UqsQagvr/RKokX00ZeYT0vZzU8rd+NiNvF6n6aVFijLyCnmoa+XsXx7NgADO8UysFNshVZhlXnt9/XO8HqkKxuFVzg28qfVzq+nD2x33Gufa8UWG8MnpfBjWR/6e9vFMLxrQrkZ5r837GPwdyvZn1+Kl5sLz1/XmHaxIQz+biUzy6qyO34uiWeswN7qXTkMmLDC2Q/97ktjeLxLHJ5ulReb25CZx6MTV5FSNlveOSGMl3s0ocf78wHK7cnfl1fCm3+sZ8KSHdjsBi5mEzdfVJuBnRpW2NogIiIiInIhUmiX02YYBj+u3MVzU9eSXWjBxWziwfb1ebhjg6MGN4DM3GKenOIIyv0vb0DT2oEVzlm4+QAPf7Oc/fml+Hu68uZNTekYf/RiZkf6NXUPH8zZVOF4VJAX9UN8yh0rsdr4dvEO5+OEiGMvuT/XDuSXcN+Xy1i27SCuZhMvXN+Emy+q43zearPzxh/reX/2JgwD4sL8ePeWZizZepArXp9DXokVNxcTD3ZoQP/L6+PhevSfy4my2w0++nszY39Px2IzCPXzYOyNyUfd3mCzG3zy92bGzlxPqdWOv6crz13XmJbRNRj50xp2ZRfh7e5C+4YhFJXa+OTvzXwwZxMFpTYArmgUxrCr4mkQ6nvaYxcREREROV8otMtp2ZNTxJNTVjMrzVGZvFGEP6/ekHTcpeWGYTD0hxRyiiwk1grgkY4NKjz/6bwtjJqehs1uEB/ux4e3tSA62OcoVyxvQ2Yej01cVelzlRUrGzHpcJG63wdfdkLf41zZkJnHXeOXsCOrCD9PVz64tQVtGxyuDbA7u4gB365g6baDgKPY320XR/PUj6tZtCULgKa1A3mlVxJx4ZUvVz9Zu7OLePT7VSzYfACALo3DGNUziRpHqfS+eV8+j01c5VwtcXlcCM9e25gpK3YxfJKjRoGL2cSQKxry88rdjJ2ZTmZuCQDJUQE80S2B1vWCz8jYRURERETOJ9U+tO/atYthw4Yxffp0CgsLadCgAZ999hktW7YEHOHumWee4eOPPyY7O5u2bdsybtw4YmNjq3jkFzbDMJiwZAcv/7KOvBIr7i5mBnRqwP3t6+PmcvS2a4d8vWg7c9bvw8PVzBt9ksu9pqDEyrBJKUxLcSyz79GsFi/3SDxqb+9/yy22cN+Xyygsm6EFR8GywhIbeSVWOvyr1VyxxcbkFbucj4+2D7sq/L1hHw99tZy8Eit1anjzvztalZtpnrk2k8d/WEV2oQU/D1de7NGEXdlFXPfeP5Ra7Xi5ufB4lzj6XVL3jBVq+yVlDyMmp5BbbMXLzYVnrmlEn1a1Ky02Z7cbfDZ/K6/OSKPEasfPw5Wnr26Er6crt3y8yFlr4OJ6NegYH8oPy3Y6e7pHBXkx9Kp4rk6MOKGtECIiIiIiF6JqHdoPHjxI27Ztufzyy5k+fTohISFs2LCBoKDDe6RfffVV3n77bcaPH09MTAxPP/00Xbp0Ye3atXh6Vt5iSk7PjqxChk9O4Z+NjlnWprUDGXNDErEnGHa37C/gpV/WAZQtdz78us378nngq2Wsz8zH1Wzi6asbHbP6+L/Z7QZDvlvJlv0FzmMmEzzSMZanflyNu4uZS+qXn7F99PvDM/J/DGl/Qt/nXPhq4Tae+XkNNrtBq7pBfHhbS+dMdonVxujpaXz2z1YAkqICeKB9fd6dtZG1exyV2NvF1uTlHonUruF9RsaTX2LlmZ/WMGn5Tuf3fLNPU+qFVL5cfduBAh6fmMLirVnO8dx9aQwf/73Zee9EBnjSs3kUK3dk8/KvaQAEeLnxSMcG3NYm+ows4xcREREROZ9V69D+yiuvULt2bT777DPnsZiYGOfXhmHw5ptv8tRTT3HdddcB8MUXXxAWFsaPP/7ITTfddM7HfCGz2Q3Gz9/KmN/SKbLY8HQz89iVcdzZNuaEZ3GtNjuPfr+SIouNS+oHc8cldZ3P/b4mg0e/X0VeiZVQPw/e79uclnVPrljah3M388e6veWOPdC+PqVl7d5axQSVq7ReWGp1Fs4DqsV+aZvd4KVf1vG/f7YA0LNZLUb1SnQG2G0HCnj4mxWk7nIUcuvbug7urmYe+XYFNrujEvvIqxvRo1mtM9azfPn2gwyasJLtWYWYTPBQh/oM6tyw0lUVdrvBV4u2MerXNIosNnzcXXikUywZOcXcPX4pNruBu6uZ65tGUlBi473ZGzEMcHcxc3ubaB7u2IBA78qX2YuIiIiI/NdU69D+888/06VLF3r37s2cOXOoVasWDz30EPfeey8AW7ZsISMjg86dOztfExAQQOvWrVmwYMFRQ3tJSQklJSXOx7m5uZWeJ4dt3JvPsEkpLCvbN31xvRqM7plE3Zontsf8kA/nbmb59mz8PFwZ0zsZs9mEzW4w9vd03p/tKBrXqm4Q793SnFD/k18pMWd9+cCeHBXAkCsacvf4pYBjP/uRBny7wvn17Mc6nPT3O9PyS6wM/HYFf5bVCHj0ioY83LGBM3z/vGo3T0xOJb/ESqC3G72aR/HHuky2HSgE4JrkSJ65phE1fc9MZXWrzc57f23i7VmOfu+1Ar14/cbko+4v35FVyLBJKczf5JhJbx1Tg1Z1a/DR3M1kFZQCcEn9YIJ9Pfhp5W5Kyj5MuSY5kqFd4s7YqgARERERkQtFtQ7tmzdvZty4cQwZMoQnnniCJUuWMGDAANzd3enXrx8ZGRkAhIWVryYeFhbmfK4yo0aN4rnnnjurY7+Q5BRauP69f8gvseLr4crwrvHcclGdk95nvHpXDm/MdPQXf+66xtQK9CKroJSBE1bw9wZHW7Y729bliW4JJ7QvvjJdm0SwcLNjOba3uwtv3dQMq81gYVnBtCP3s+eXWJ2z8h6u5pP+AOJM251dxN3jl7JuTy4ermbG3pjM1UmRgKMP+vPT1jgr3MeF+REW4Mmn8xyz8REBnrx4fRM6JZxYZf0Tsf1AIYO/X+n8oOba5EheuL4JAV5uFc41DINvF+/gpV/WUlBqw8vNhS6Nw9i4L593/9oIQN1gb2LD/Fi27SBZBY6fx0UxNXiiW0KlnQNERERERKSah3a73U7Lli15+eWXAWjWrBmrV6/mgw8+oF+/fqd83REjRjBkyBDn49zcXGrXrn3a471Qldhs5JdYAfhlwKUnXMH9SMUWG4O/W4nVbtC1STg9mtUidWcOD3y1jF3ZRXi5uTC6VyLXNa11WmONDj48U9ureRR1a/rwV9peSq12IgM8iT1i+fuDXy1zfj1zcNXuZV+1I5t7vljKvrwSavq68/HtLWlWx1G7YUNmHv2/Wc76zHxMJmgQ4sv+/BLSMx0F2267OJqhV8Xh51kxTJ8KwzCYvHwXz/y8hvwSK34errxwfROub1b5z2Z3dhHDJqU4P3ipG+xNsK+Hs5+8r4crjSL82Z1T5OwVXy/EhxFdE+icEHrGlvCLiIiIiFyIqnVoj4iIoFGjRuWOJSQkMGnSJADCw8MByMzMJCIiwnlOZmYmTZs2Pep1PTw88PA4M8uH/wtq+njg7mqm1GrHxKkFrLG/p7Nhbz41fT14qUciE5fu5KmfVlNqtRMd7M2Ht7UgPvz0eqPnFVsY+dMa52OLzbH0es76fQC0jzscEHOKLM6Q6efpSp3gqluWPT11D4O/X0mxxU5cmB+f3tGSqCBvDMNg4tKdjPx5NcUWx3vx9XBlw958wBF8X+mVRKuT3Pd/LDmFFp78MdVZub9ldBBv9Gla6bJ1wzCYuGwnL0xdS16JFReziRBfD/bmlbC1bLl+bKgvVrvhLEZX09edgZ0bclOr2qe8mkJERERE5L+kWof2tm3bkp6eXu7Y+vXriY6OBhxF6cLDw/nzzz+dIT03N5dFixbx4IMPnuvhXrDMZhNRgV5s3l/AzuzCkw64Czcf4JOyZdwvXt+YMb+l8+3i7QB0ig/l9T5NK11yfbKe+WkN27MKnY+nrtrN01c3Yna6Ywl8h7jD+9nv/WKp8+tfB7Q77e99KgzD4P3Zmxjzm+Me7xAXwjs3N8PP0438EitPTUl1zlYfkldsxdVs4oH29Xm4YwM83c5cdfWFmw8w5LuV7M4pxsVsYlCnWB7sUB/XSsJ1Zm4xIyanMivtcA0Bm90gI7cYgCBvN7zdD3/A4Olm5t529bi/fX18Par1PzsiIiIiItVKtf7tefDgwVxyySW8/PLL3HjjjSxevJiPPvqIjz76CACTycSgQYN48cUXiY2NdbZ8i4yM5Prrr6/awV9gagWVhfaDRSf1urxiC49+vwrDcBSBGzdnM6t2ZGMyweDODXn48gZnpAf3jyt2MXnFLswm+O7+Ngz9IYUt+wt496+NbD1QiKvZ5Gz1drCglMVbDs38elRJ8bNSq50npqTywzJH+7Q7LqnLU90TcHUxs3pXDo98u6Jc27pDkqMCGN0riYSI01uV8O+xvPHHej6YswnDcGwxeLNPU+fy/CMZhsGPK3fxzE9ryC22VnjexWzC292F3GIrBwstmEzQu0UUQ66IIzxALRhFRERERE5WtQ7trVq1YsqUKYwYMYLnn3+emJgY3nzzTfr27es8Z+jQoRQUFHDfffeRnZ3NpZdeyowZM9Sj/QyLCvICYNdJhvbnp65lV7bjNQs3H6DEaifAy423bmparijc6dh+oJCnflwNwIBOsbSqW4M+rWozenoa48oq0resG+Tc833n50ucr/354bZnZAwn42BBKfd/tYzFW7Iwm+DZaxtze5u6GIbBFwu28uK0dZSWLe0/5FTa652ITfvyGTRhpbN93I0toxh5TeNKZ8P35ZXw5JRUfi/bl14Zu2GQVxbmL2sYwoiu8Wf0AwYRERERkf+aah3aAa6++mquvvrqoz5vMpl4/vnnef7558/hqP57fNwdt0p2YekJv+b3NRlMLJtJBiix2mkU4c+Ht7U4Y7PbVpudgd+tIL/ESsvoIB6+vAHgKEL32m/pWO0GcLhq/L68ElbuyAagVqAXkYFeZ2QcJ2rzvnzu+nwJWw8U4uvhyru3NKNDXCg5hRaGTlrFb2sqBuJLG9Tk5R6JZ3Tf/aFq7y9MW0uRxUaAlxujeybSNTGi0vOnrtrNyJ9Wc7DQcpzrQkKEP090i6ddbMgxzxURERERkeOr9qFdqofl2x1tv5rUCjih8/fnlzBicmq5Y72aR/FSjyZndB/2239uYMX2bPw8XXnzpqbO/dchfh60i63JX+mOInSH9rPf/r/Fztf+8GCbMzaOEzF/034e/Go5OUUWagV68b87WhEX7sfy7Qd55JsVzhUJhwR4ufFU9wRuaBF1zArrW/cX8M3i7RSV2hh5TaPjFng7kF/C8Mmpzkrul9QPZuyNyUQEVPwA40B+CSN/WsMvqXuO+/4iAjx59Mo4ejSrdUZXA4iIiIiI/JcptMtxFZZaSdnpWD59cb3g455vGAYjJqdyoMAxK+/mYmLkNY25tXWdM9rea9HmA84e4C/1SCQqqPxMdExNX2dorxvsQ2ZuMev25AJQr6ZPpSH1bPl+yQ6emJKK1W7QrE4gH93WkmAfdz6Ys6ncioBDuidG8Oy1jQnxq7zLgd1uMGf9PsYv2MrssvdoNsEjHRsQ6n/0rSFz1+/j0Ymr2JdXgpuLiaFd4rn70phK6wrMWL2HJ6esdv4cj8bXw5UHO9Tn7ktjzugHMiIiIiIiotAuJ2DZtoNY7Qa1Ar2ce9uPZdLyXc5Z3DB/D97v24IW0RWLmp2OnEILg79bid2AG1pEcW1yZIVz7MbhIPzbmgze/nOD8/E39158RsZRarXj5mI66ocRdrvBK7+l8eGczQBckxzJmBuSKCixcufnS5zt6A4J8/fgheuacGXj8Eqvl1NoYeKyHXyxYFu5SvnuLmZG90o8amAvtth4dUY6//vHUcW/Qagvb93UlMaRFVdOHCwo5dmpa/jpX5Xr/83VbOKW1nUY0CmWmr5qoSgiIiIicjYotMtxLdrsqLTeOqbGCc2Uz1id4Tz/3VuaH3W2+FQZhsGIKSnszimmbrA3z17buNLz5h4RiN+Yud7ZOzw+3O+MVDJfvv0gfT5cwIPt6zPkyrgKzxeWWhn83UrnPvUBnWIZ3DmWhZuzGDhhBXvzSsqdf0vrOgzvGo+/Z8X2d2t35/Llwq1MWbHL2bP9kBA/Dz68rQXNK6n2DpCWkcugCStJy8gD4LaLo3miWwJe7hVnxf9Ym8mIKans+9fY/q1L4zCGXRVPvRDfY54nIiIiIiKnR6Fdjmvh5gPAiS2NB3j66gSuSY6ge2JEpT2+T9fEpTv5NTUDV7OJt25qVmml8+0HCtl8RMu0Q4Ed4Iu7Ljoj48jMKcZiMxg3ZxM3tqpdbnl+Zm4x94xfSuquHNxdzLxyQyLXJtfizT828M6sDRy5Gr5eTR9G9Uyk9b/+fi02O7+tyeCL+dtYvDWr0jEk1grgo9tbVLrU3243+Hz+VkbPSKPUaifYx51Xb0iiU0JYhXNziiw8P3Utk5bvrPDckZrWDuTJ7gm0qlvjmOeJiIiIiMiZodAux1RUamPVzmwAWtc7saAWHexDdLDPWRnPpn35PPPzGgAe6xJHcu3ASs+bs34vABfVrcH+ghI273ME+OSogGPu+T4Zh5aWW2wGb/6xgdd6JwOwelcO94xfSkZuMTV83B3V8oO8ueXjhSzacjh8u5hN3H9ZPQZ0ii23F3xvXjHfLtrB14u2OWfjXc0mOieEsS2r0Lkv/5rkSF7tlVTpjPne3GIe+yHFudrg8rgQXr0hudJVD7PT9zLk+1VkHWPvep0a3gy7Kp5uieFntC6BiIiIiIgcm0K7HNPy7Qex2AwiAjypc4batJ2qUqudgRNWUGSxcUn9YO5rV++o5x4qztY+LoQPynq1A3zcr+UZG0/tGl4EeLmRU2Rh8vKd3H9ZPbYeKGTghBUUltqoH+LDZ3dcxKb9+XR7++9yoTixVgCv9EqiUaSjh7lhGCzffpDx87cxffUeLDbHVHyInwe3XFSHbokRPDd1jTOwP3ZlQ/pf3qDSAD1zbSbDJqWQVVCKh6uZJ7sncNvF0RXOzSu28OK0dXy3dMdR32OgtxsDOsZy68XRuLue+VUTIiIiIiJybArtckyLypbGn+h+9rNp7O/prN6VS5C3G6/f2LTSiufgKLo2f5Nj3NHB3uSVWAEY0LEBoX5nZpYdwGQy0aSWP/9sPIDdgCvemIvJ5OhVfmmDmrx1U1M+mruZD+dudr7G083MkCsaclfbGFxdzBRbbPy8cjfjF2xlze5c53kto4O4/ZK6XNU4nG0HCrjni6VsO1CIt7sLb/ZpWmmhusJSKy/+so5vFm0HHP3S376pKbFhfhXOnbdhP7d+uuio783d1cydl9TlocsbEOBVcY+9iIiIiIicGwrtckwLy4rQneh+9rNl3ob9zvD7Sq+kYxaSW7r1IEUWGyF+Hs4icJ0TQistFne6mkQG8M/GA87HhuEoKHdvu3rcPX4pK3dkO5+7pH4wo3omEh3sw46sQr5auI3vlu4gu9ACgIermeub1uK2NtE0qeVYev/nukwGTlhJfomVqCAvPunXkvhw/wrjSN2Zw8DvVji3AdzbLobHusTh4Vp+6XxBiZUnp6Ty4zEqw1/fNJLHusRVaKEnIiIiIiLnnkK7HFWxxeYMnf8uknYuHcgvYcj3KwG49eI65WaZP5izifSMPBpH+pMUFUjjSH9mpzv2s0cGeDItxRFOB1/R8KyMrXYlWwbaNajJte/OI6/YMcPv7+nKU90bcUOLKOZt3M8L09byZ9peDnWkq13Di9sujubGlrUJ9HYHHMvlP5izmVd/S8Mw4KKYGozr25zgf7VWs9kNPpq7mbG/O3q9h/l7MLZ3Uy6NrVlhXAs2HeDmjxce9b20qRfME90SSIyq2AZORERERESqhkK7HNXy7QcptdkJ8/egbnDVzLoahsGwSSnszSshNtSXJ7s1cj5nsxu8/9dGcoutTFmxC8C5PB1g1c4cwFGErbJ+5Kdr24ECZ1G8Iz349XLn112bhPNYlzjmrt9H59fnlKtof1nDEPq1iaZDXCguRyz1L7bYGDE51fmebmldh2evaVxhT/nu7CIGf7fSWdzuqsbhjOqZSJCPe7nzikpt3PrpIpZtO1jp+2gQ6ssT3eK5PC60yrdAiIiIiIhIeQrtclSH+7MHV1mY+2rhNv5Ytxd3VzNv39ysXKV0F7OJl3smMuDbFc4WaoZR8Rp/pe/jqjfnkhQVQGJUIIm1AogP9ytXsf1kLd6Sxf1fLsVmr+QbAqF+HtzeJpqM3GKueWcehaU2APw8XLmhZRS3XRxdaY/zzNxi7vtyGat2ZONiNvHMNY0qLSI3LWU3T0xOJbfYire7C89e05jeLaMqnDdjdQYPfLWs0jHW9PVgyBUNubFl1FlpzSciIiIiIqdPoV2O6mT7s59p6Rl5vPjLOgBGdI0nIaLiXu6rkyKx2OwM+X5VpYH9kLSMPNIy8vh+qaMPuavZRFy4nyPI1wokKSqAhmF+J1QhffLynQyflEqpzU5SVAApZTP6R9qbV8Jrv693Pm4Y5svtberSo1ktfCrpKw+wakc29325lMzcEgK83Hi/b3PaNii/zD2v2MKzPx/up54cFcCbNzUjpmb5Fnv5JVaSn/u90g8VvNxcuO+yetx3Wb2jjkVERERERKoH/cYulSq22Fjh3M9+Yv3Zz/T3H/DtCkqsdjrEhXDHJXWPem6PZlFYbQaP/5BS7rjZBDOHtMfH3ZWUndmk7sohZWcOqbtyyCooZc3uXNbszuVbHC3P3F3MJET4kRgVQFKtQBKjAogN9XXOQtvtBq/PXM+7f20EHMvRO8aHMnRn+e97pK5Nwrm9TV0urnfs6vs/rdzF0B9SKLHaaRDqy6f9Wlbodb9s20EGfbeCHVlFmE3wUIcGDOwci9u/Zsnf+2sjY35Lr/A9zCbo06o2gzs3PGO96kVERERE5OxSaJdKrdyRTanVToifB/X+NYt7LoyenkZ6Zh41fT14rXfycZfn925ZmyKLjZE/Hd5j3qNZFPXLlqCHB4Q7C9gZhsGu7CJSd+aQsiuH1WVhPqfIwqqdOWV74R1t0zzdzDSK8KdhmB8TlhzuZ/5gh/o8fmUc75UF+MoMvSqOhzo0OOa47XaD135P5/2yXvId40N566am+HkebrNmtdl5Z9ZG3v1rIza7Qa1AL97o05SLYsp/mLJpXz6dxs6p9PtcHhfCiG4JNKyk/ZuIiIiIiFRfCu1SqcP72c99f/ZZaZl8Pn8rAK/1TqLmvyqmH02Df+0Rt9ntlZ5nMpmICvImKsibrokRgCPI78gqImVXtiPM73SE+bwSK8u3Z7N8e3a5a4ybvYlxZUH7SK/1TmZHViFv/bmBCYt3cM+l9Y665D6/xMqgCSv4Y52j2v397esxtEt8uaJ02w8UMui7Fc7vf33TSJ6/vgn+R4T6olIb/b9Zzqy0vRW+R+NIf57slsAlDSpWkxcRERERkepPoV0qVVX72ffmFvPYRMdy87svjaFDXOgJv3b2+n3lHv+4cjf1QnwZ0Cn2uK81mUzUCfamTrA3VydFAo5Z8N/WZJSrBn8801J2061JBJ5uZrZnFfLd0h3cdnF0hfO2Hyjkni+WsD4zH3dXM6/0SqRHsyjn84ZhMHn5Lkb+tJqCUht+Hq682KMJ1zWtVe6cn1ftZuCElRWuXyvQi8e6NOS65FqYzaoILyIiIiJyvlJolwpKrDaWb3e0B7v4HO5nt9sNHp24iqyCUhIi/Bl6VdxJvX5OuiO0u7uYuaV1HT6fv5XXZ67HzcXMgx3qn/R45mzY59wnH1PTx7nPfNXObHq+P7/S18xO38fs9MMfHrz95wZ6t4gqV6l+waYDPPT1Mg4WWgj18+DD21rQrE6Q8/mcQgtP/JjKLyl7AGhVN4jXb2xarid8ekYeN320gIOFlgpjGN41njsuqXta1fFFRERERKR6UGiXClbtyKHEaqemr7tzT/i58L9/tvD3hv14upl55+ameLieeOjcnV1EemYeADddVJtnr21MiJ8HY35L55UZabi5mLinXb0Tvt7n/2zh+WlrsRuOLQIf3taCQG9H//PmdYJY8mRnbvt0EWkZeQR4ueFiNpFVUAo4Krqv25NHqc3OwYJScosszgD91cJtPPvzGqx2g6SoAD66rSXhAYeLwi3YdIAh369kT04xLmYTgzvH8mCHBs4l8zmFFsb8nsZXC7dXGPOdbevySMdYavyrT7uIiIiIiJy/FNqlgkVlS+PPZX/21btyeGVGGgAjr25Mg9CTK5g2p2xpvIermf6XO4q/9b+8AaVWO2/9uYEXf1mHq9nEHW1jjnkdq83O89PW8sWCbQD0bhHFSz0SK+xLD/Hz4Nt7L+a2/y1i9a5c5/FWdYOY+MAllFrtpGfk4eFmJtTfE4vNzvNT1/LlQsd1r02O5NUbkpxhvtRq5/WZ6/lw7iYMA+oGe/PmTc1oWjsQAJvd4PulOxgxObXCmC+KqcGrvZKoWwUFA0VERERE5OxSaJcKFm45tJ/93CyNLyy1MmDCCiw2gy6Nw7j5otonfY1dB4sAuO3iaMKOaGc2qHMsVrud9/7axLNT1+LqYubWSvaYg6MH+sPfrHB+ADDsqngeaF/vqB9cBPm48/U9F3PHZ4tZUVYobs3uXOx2A3dXM4lRAQAcLCjloa+Xs2DzAUwmeOzKOB7qUN953Y178xn03Qpn+O/TsjYjr2nk7KG+bFsWT/24hnV7ciuMYdKDl9AiOqjCcRERERERuTAotEs5pVY7y7Y59rO3PkdF6F6YtpbN+woI9/dkdM+kU5rdf6BDfRqG+3FVWVu3Q0wmEwM6xZKZW8IPy3by1I+rcXMx0adVnXLn7cgq5O7xjsJwnm5m3uzTlKuaRBz3+wZ4ufHl3a2567MlLN6aRWGpjW1ZhcSUzXqvz8zjnvFL2Z5ViI+7C2/e1IwrGoUBjkJy3yzezgvT1lJssRPo7cbononO77s3t5jR09OYvGJXhe/7/HWNue3i6HNe2V9ERERERM4thXYpJ2VnNsUWOzV83IkNPbv72W12g1dnpPHt4h2YTPB6n2SCKtmPbRgGxRY7OUWWCn9y//X1Tyt2lX++2EKxpXzrtxGTU7k8PpRQP8eM/PLtB7nvi6Xszy8l1M+DT/q1JCkq8ITfh6+HK5/f1Yoh361iXUYuNX0d7+HPdZkMnLCS/BIrtWt48cntrYgLdyz7P5BfwrBJqfyxLhOASxvU5LXeyYQHeFJqtfPZP1t4848NFFls5b5XUlQAPzxwyVHbyImIiIiIyIVFoV3KWbTl3PRnzy22MODbFc5K692aRLDtQCG/pKSyK7voiEBuJbfIQqmt8p7rJ8pkAn9PNwK83GgU4U9QWVG5n1ft5rGJqyi12mkU4c+nd7QkIsDrpK/v7e7KB7e1wDAMwNHH/dXf0jAMxzaD9/u2cBaIm52+l8d/SGFfXgnuLmaGXhXHXW1jMJtN/JW+lxemrmXz/oIK3+P3wZfRMOzk9vqLiIiIiMj5TaFdyjkX/dm37i/grvFL2LzvcDD9JXUPv6TuOebrXMwmArzc8Pd0dfyvlyOE//vPv4/7e7nh5+Farl+5YRi89ccG3vhjPQCdE0J566Zmzn3kp6rEamf4pBR+XLkbgL6t6/DstY1xczFTbLExenoan8/fCkCDUF/euqkpjSMD2Lq/gBemreXPtL0VrjmgUyyDO8dqKbyIiIiIyH+QQrs4WWx2lm49tJ/97BWhe27qmnKBHSDI240mtQJoFOlPvZo+BHi5Hw7e3o7/9XF3OSPBtdhiKxes77k0hhHdEpxt1U5VZm4x932xlFU7c3Axm3j2Wse+c4C0jFwGfrvS2ZauX5toRnRLwGY3eGVGGp/+vaXCaoJAbzemPXIpUUHeFb6XiIiIiIj8Nyi0i1PKzhyKLDaCvN1oeJIt105Gn1Z18PZwpUGIL40j/WlSK4CIAM9zMpN8IL+E+79cxtJtB3Exm3jhuibc0rrO8V94HKt2ZHPfl0vJzC0h0NuN9/s255L6NbHbDT6fv5XRM9Iotdqp6evOmBuS6RAXws+rdvPyr+vIzC2pcL0XrmtM39bR5VYHiIiIiIjIf49CuzgtKmv1dlFMjbMaFq9qEs5VTcKPf+IZtiEzj7vGL2FHVhF+nq6M69uCS2NrnvZ1f1q5i8d/SKHUaic21JdP+rUkOtiHvbnFPDpxFX9v2A9Ax/hQXr0hiczcYm78cAFLylY1HKlV3SDG9m5KnWDNrouIiIiIiEK7HGHhZkcRurO5n72qzNuwnwe/XkZesZU6Nbz53x0taXCaqwnsdoMxv6czbvYmwLEv/o0+TfHzdOP3NRkMm5TCwUILHq5mnrq6Ed0TIxj7ezrfLt6O3ah4vWeuaUS/NnU1uy4iIiIiIk4K7QI49rMv23qocvyFFdq/XrSNkT+twWY3aBkdxIe3tSDY1+O0rplXbGHwdyv5Y52jcNyDHerz2JVxlFhtjJicyreLtwPQKMKf1/sks3hLFpe/NpucIkuFa7WIDmLMDUnUCzm7LfZEREREROT8o9AuAKzelUNBqY0ALzfiwy+MtmI2u8HLv67j03lbAOjRrBajeyXi4epyWtfdfqCQe75YwvrMfNxdzbzaK4nrm9UiZWc2gyasdLZru++yerSLrcmgCStJy8ircB13VzOPXxnHXZfGnHYRPBERERERuTAptAtwuD/72d7Pfq4UlFgZOGGFcyZ8yBUNeaRjg9Mudjd/034e+no52YUWQv08+Pj2ljSpFcD7szfy+u/rsdoNwv09eaxLHLPT93Lbp4srvU5y7UDG9k6mQahm10VERERE5OgU2gWLzc6U5buAC2M/++7sIu4ev5R1e3JxdzUztncy1yRHnvZ1v1y4jed+XoPVbpAcFcBHt7fEaje45eOFzg89OsWHUremD0//uJoii63CNdxdzAy6Ipb72tXD1cV82mMSEREREZELm0K78Om8LaRn5hHk7UaPZrWqejinJWVnNveMX8revBJq+rrz0e0taV4n6LSuabHZefbnNXy9yLFP/bqmkbzSK4mZazN5YkoqecVWvN1daNugJmkZufyZtrfS6zSp5c/Y3k2Ju0C2H4iIiIiIyNmn0P4ftyOrkDf/WA/Ak90bUcPHvYpHdOpmrN7DoO9WUmyxExfmxyf9WlK7xum1TssqKOWhr5excHMWJhMM7RJP34vr8MTkVCavcKxO8PNwJcjHnZlrMwFwczER4utBRm4xdgNczSYGdIrlwQ71cdPsuoiIiIiInASF9v8wwzB48sfVFFvstKkXTK/m5+csu2EYjJuziVdnpAPQvmEI797SDD9Pt9O6bnpGHvd84ejr7uPuwls3NSPIx43ub//Njqwi53l5JVbySqwAXNqgJnnFFlbtzAEgPtyPsTcm0zgy4LTGIiIiIiIi/00K7f9hU1P2MHf9PtxdzLzUo8lpF2mrCqVWO09OSWXisp0A9GsTzdNXNzrt/eIz12YyaMIKCkpt1KnhzQe3tmDGmgzenbUBuwFRQV5YbQYZucUAXFS3Bq1igvhuyU7255fgYjbRv0N9Hu4Yi7urZtdFREREROTUKLT/R+UUWnh+6hoA+l/e4LzsEb55Xz4dx84BwGyCZ65pTL9L6p7WNQ3D4P3Zm3jt93QMA9rUC2Z413ie/DGVFduzAUfruOeua8ykZTuZsTqDG1pEMX/TAd77axMAsaG+jL0xmaSowNMai4iIiIiIiEL7f9ToGWnszy+lfogPD3SoV9XDOWl/rM3kni+WOh9/ekcrLo8LPa1rFltsDP0hhZ9X7Qbg9jbRNIrw55aPF1JQasPP05UXr2/CdU0d2wjubBtDdLA3wyelsjevBLMJ7rusPoM6x+Lpdnq94EVERERERECh/T9pydYsvl3sqIT+co9EPFzPr4D5+sz1vP3nBufj565tfNqBPSOnmPu+XErKzhxczSaGXNmQNbtyGT45FXD0r3/9xmSighyF7XKLLbwwda1zWX69EB9e65182pXqRUREREREjqTQ/h9TarXzRFkQ7dOyNq3Ps77sN4ybz9JtB52POyeEnvaS+JU7srnvC0ebuCBvN25vU5cv5m8jI7cYV7OJwVc05IH29XExO/b8z12/j2GTUtiTU4zJBHe3jeGxLnGaXRcRERERkTNOof0/5qO5m9iwN5+avu6M6BZf1cM5YVabnQZPTi93zM/TlbG9m57Wdaes2MmwSamUWu3Uq+lDfIQfb8/agGFATE0f3uzTlOTagQDkl1h5+dd1fFPWrz062JvXeifTqm6N0xqDiIiIiIjI0ZxXZa1Hjx6NyWRi0KBBzmPFxcX079+f4OBgfH196dWrF5mZmVU3yGpsy/4C3p61EYCnr25EoPf50ZP9QH5JhcAO8EqvJAK8T62tm81uMGr6OgZ/t8oR2EN8sBsGv6ZmYBhw80W1mfbIpc7APn/jfrq8MdcZ2O+4pC7TB7ZTYBcRERERkbPqvJlpX7JkCR9++CFJSUnljg8ePJhffvmFiRMnEhAQwMMPP0zPnj35559/qmik1ZNhGDw5xTGj3C62JtcmR1b1kE5Iys5srn234s/yikZhdG0SfkrXzCu2MHDCSmal7QUgyNuNnQeLKLXaCfJ2Y1TPJK4qu3ZhqZXR09P4YsE2wNHqbcwNybSpf35tKxARERERkfPTeRHa8/Pz6du3Lx9//DEvvvii83hOTg6ffvop33zzDR07dgTgs88+IyEhgYULF3LxxRdX1ZCrnSkrdjF/0wE8XM28eP350ZN94tIdPP5DivNxRIAne3KK8fVw5YXrTu09bN1fwD1fLGXj3nznsYOFFgDaxdbktd7JhPl7ArB4SxaPTVzF9qxCAPq2rsMT3RLw8Tgv/m8jIiIiIiIXgPNieXz//v3p3r07nTt3Lnd82bJlWCyWcsfj4+OpU6cOCxYsOOr1SkpKyM3NLffnQpZVUMqLv6wDYGDnWKKDfap4RMc3fFJKucD+6g1J5BY5wvWwrvGEB3ie9DXnb9zPde/9Uy6wA7i7mHmqewLj77yIMH9PikptvDBtLX0+WsD2rEIiAzz58u6LeKlHogK7iIiIiIicU9U+gUyYMIHly5ezZMmSCs9lZGTg7u5OYGBgueNhYWFkZGQc9ZqjRo3iueeeO9NDrbZe/nUdWQWlxIX5cW+76t2T3TAM2oyaRUZusfPY30Mv55mf11BQaqNldBB9L6pz0tf8cuE2npu6FpvdKPdcwzBf3uzTjEaR/gAs23aQxyeuYvP+AsBRYf/JqxPw9zy1vfMiIiIiIiKno1qH9h07djBw4EBmzpyJp+fJz6wezYgRIxgyZIjzcW5uLrVr1z5j169OFmw6wA/LdmIywcs9E3Fzqb6LK4otNuKfnlHuWNoLVzFzbSaz0vbi7mJmdK9EzOYTXxZfarXz7NQ1zgJyR7rjkroM7xqPp5sLxRYbb/yxno/nbsZuQJi/B6N7JZ12/3cREREREZHTUa1D+7Jly9i7dy/Nmzd3HrPZbMydO5d3332X3377jdLSUrKzs8vNtmdmZhIefvQiZR4eHnh4eJzNoVcLxRYbT05x9GTv27oOLaKDqnhER7cjq5B2r/7lfBwZ4Mk/wzuSXWjh2Z/XAND/8gY0CPU74WtmFZTy4FfLWLQlq9zxmr4ejOl9OJCv2pHNoxNXOZfN92xei2eubnzKlelFRERERETOlGod2jt16kRqamq5Y3feeSfx8fEMGzaM2rVr4+bmxp9//kmvXr0ASE9PZ/v27bRp06YqhlytvD97E5v3FxDi58HjXapvT/a/N+zjtk8XOx/ffFFtRvV0dAl46dd1HCgoJTbUlwc71D/ha6Zl5HLP+KXsPFhU7njnhFBG90qipq8HJVYb7/y5kXFzNmGzG9T09WBUz0SuaBR2Zt6YiIiIiIjIaarWod3Pz48mTZqUO+bj40NwcLDz+N13382QIUOoUaMG/v7+PPLII7Rp0+Y/Xzl+4958xs129GR/9prGBHhVz1njcbM38cqMNOfjMTck0bulY6vCvA37nUv7R/dKwt31xJb2/74mg8HfraSg1OY85ulm5qnujejbug4mk4nVu3J4bOIq0jLyALg2OZLnrm1MkM/50bteRERERET+G6p1aD8Rb7zxBmazmV69elFSUkKXLl14//33q3pYVcpuN3hiSioWm8HlcSF0Szy1fuZn2+3/W8zc9fucj6c+fCmJUQEAFJXaeKJsaf/tF0ef0NJ+wzB476+NvPb7+nLHG0f689ZNTWkQ6ofFZue9vzbw7qyNWO0GNXzceen6JnRNjDiD70xEREREROTMMBmGYRz/tAtbbm4uAQEB5OTk4O/vX9XDOW3fLdnOsEmpeLm58Pvgy6hdw7uqh1RB8nO/k1PWwg1g2VOdCfY9XGdg1K/r+HDuZiICPJk5pD2+x2m1VlRqY+ikFKau2u08ZjLBfZfV49Er4nB3NZOWkcuj369izW5Hi7+uTcJ54fom1PS98OsbiIiIiIhI9XKiOfS8n2mX8vbnl/Dyr47l5kOuaFgtA3tOkaVcYN/4Uldcj6hqv3pXDp/M2wLAi9c3OW5g35NTxH1fLCN1V47zWLi/J6/3SeaS+jWx2uy899dG3vxjPRabQaC3Gy9c14SrkyIwmU68Er2IiIiIiMi5ptB+gXlx2lpyiiw0ivDnzrZ1q3o4lQrwcmN0z0QsdoPbLo4u95zVZmfYpBRsdoOrkyLolHDsonDLtx/k/i+XsS+vxHmse2IEL/VoQqC3Oxsy83hs4ipW7XQE+s4JYbzcswmhfmeuhaCIiIiIiMjZotB+Afl7wz5+XLkbswlG9UwsN3td3dx0UZ1Kj//vny2s2Z1LgJcbz1zT+JjXmLx8J8Mnp1JqtQPg4+7Cc9c1oVfzWtgN+HDOJsbOXE+p1Y6/pyvPXtuYHs1qaXZdRERERETOGwrtFwhHT/bVANzepi7JtQOrdkCnYNuBAl6f6Sgi92T3BEL8Kt9rbrMbvDojjQ/nbnYea1o7kLduakp0sA+b9+Xz2MRVLN+eDcDlcSGM6plEeIBm10VERERE5Pyi0H6BePvPDWzPKiTc35NHr2xY1cM5aYbhqHhfbLFzSf1gereIqvS83GILA79dwV/pjqrzZhM83DGWRzo2wMVk4tN5W3h1RholVjt+Hq48fU0jereI0uy6iIiIiIiclxTaLwBpGbl8VDbr/Nx1jfHzrJ492Y9l0vJd/LPxAB6uZl7ukVhpyN66v4B7vljKxr35AEQFefFmn6a0rFuDbQcKeHxiCou3ZgHQLrYmo3slUSvQ65y+DxERERERkTNJof08Z7cbPDE5Favd4MpGYXRpXD17sh/LvrwSXpi2FoDBVzSkbk2fCuf8s3E/D3293Fl1vmfzWjx3bWN83F35YsFWRv2aRpHFhre7C092T+CWi+podl1ERERERM57Cu3nuW8Wb2f59mx83F149tpjF26rrp4/ouL9PZfGVHh+/PytPD9tLTa7gZ+nKy/3SOSa5Eh2ZBVy/5fLmL/pAABt6gXz6g1J1bLNnYiIiIiIyKlQaD+P7c0t5pUZjp7sj3WJI/I8XAo+Ky2TqascFe9f6ZVUoeJ96s4cnvl5DQAXxdTgjT5NiQzw5JtF23npl7UUlNrwcnNheNd4brs4GrNZs+siIiIiInLhUGg/jz03bS15xVaSogK4vU3dqh7OScsvsfJUWcX7e9rVIzEqoMI5tWt40S0xnGa1g7jr0hgyc4u5/X+L+XvDfgBa1Q1izA3JlS6pFxEREREROd8ptJ+n/krbyy8pe3Axm3i5RyIu5+EM82u/pbM7p5jaNbwY3LnyiveB3u6837cFhmEwcdlOXpi6lrwSKx6uZh7vEsedbWPOy/cuIiIiIiJyIhTaz0OFpVae+tExQ31X27o0qVVxhrq6W779IOMXbAXg5R6JeLm7HPXczNxiRkxOZVbaXgCa1Qnktd7J1A/xPRdDFRERERERqTIK7eehN//YwK7sImoFejHoKDPU1Vmp1c7wSSkYhqMKfLvYkErPMwyDH1fu4pmf1pBbbMXdxcyQKxtyb7t6ml0XEREREZH/BIX288ya3Tl8Om8LAC9c3xgfj/PvR/jBnE2sz8wn2Medp7s3qvScfXklPDklld/XZgKQFBXA2N7JxIb5ncuhioiIiIiIVKnzL/H9h9nsBiMmp2KzG3RPjKBjfFhVD+mkbdybz7uzNgIw8ppGBPm4Vzhn6qrdjPxpNQcLLbi5mBjYKZYH2tevUFleRERERETkQqfQfh75csFWUnbm4OfhyshrKp+hrs7sdoMRk1MotdnpEBfCtcmR5Z4/kF/CyJ/W8EvqHgAaRfgz9sZkEiL8q2K4IiIiIiIiVU6h/TyxJ6eIMb+lAzC0azxh/p5VPKKT9+2S7SzZehBvdxdevL4JJtPhfekzVu/hySmrOVBQiqvZRP/LG9D/8ga4u2p2XURERERE/rsU2s8T3u6uXNesFukZefS9qE5VD+ekZeQUM/rXNAAeuzKOqCBvALILS3nm5zX8tHI3AHFhfoy9Mfm8rIgvIiIiIiJypim0nycCvNx4uUcipVY75vOwcvrIn1aTV2IluXYg/S6pC8AfazMZMSWVfXklmE3wYIf6DOgUi4fr0du/iYiIiIiI/JcotJ9nzsfl4jNW7+H3tZm4mk280iuR/BIrz09dy6TlOwGoH+LD2Bub0rR2YNUOVEREREREpJpRaJezKqfIwtM/rQHggfb1ycgp5o7/LSEjtxiTCe5rV4/BVzTE002z6yIiIiIiIv+m0C5n1ejp69iXV0Konwe7sot49zNHu7eYmj681juJFtE1qniEIiIiIiIi1ZdCu5w1Czcf4NvFOwDYm1fClBW7MJngzktieLxLHF7uml0XERERERE5FoV2OSuKLTZGTE4td6xODW/G3JBE63rBVTQqERERERGR84tCu5wV78zawJb9Bc7Ht10czfCu8fh46JYTERERERE5UUpQcsbZ7QYf/70FgFqBXrx6QxJtG9Ss4lGJiIiIiIicfxTa5YwzmeDedjHYDXioQ338PN2qekgiIiIiIiLnJYV2OeNMJhOPd4mv6mGIiIiIiIic98xVPQARERERERERqZxCu4iIiIiIiEg1pdAuIiIiIiIiUk0ptIuIiIiIiIhUUwrtIiIiIiIiItWUQruIiIiIiIhINaXQLiIiIiIiIlJNKbSLiIiIiIiIVFMK7SIiIiIiIiLVlEK7iIiIiIiISDWl0C4iIiIiIiJSTSm0i4iIiIiIiFRTCu0iIiIiIiIi1ZRCu4iIiIiIiEg1Ve1D+6hRo2jVqhV+fn6EhoZy/fXXk56eXu6c4uJi+vfvT3BwML6+vvTq1YvMzMwqGrGIiIiIiIjImVHtQ/ucOXPo378/CxcuZObMmVgsFq688koKCgqc5wwePJipU6cyceJE5syZw+7du+nZs2cVjlpERERERETk9JkMwzCqehAnY9++fYSGhjJnzhwuu+wycnJyCAkJ4ZtvvuGGG24AIC0tjYSEBBYsWMDFF1983Gvm5uYSEBBATk4O/v7+Z/stiIiIiIiIyH/ciebQaj/T/m85OTkA1KhRA4Bly5ZhsVjo3Lmz85z4+Hjq1KnDggULKr1GSUkJubm55f6IiIiIiIiIVDeuVT2Ak2G32xk0aBBt27alSZMmAGRkZODu7k5gYGC5c8PCwsjIyKj0OqNGjeK5556rcFzhXURERERERM6FQ/nzeIvfz6vQ3r9/f1avXs28efNO6zojRoxgyJAhzse7du2iUaNG1K5d+3SHKCIiIiIiInLC8vLyCAgIOOrz501of/jhh5k2bRpz584lKirKeTw8PJzS0lKys7PLzbZnZmYSHh5e6bU8PDzw8PBwPvb19WXHjh34+flhMplOemy5ubnUrl2bHTt2aE+8VEr3iByP7hE5Ht0jcjy6R+R4dI/I8egeObcMwyAvL4/IyMhjnlftQ7thGDzyyCNMmTKF2bNnExMTU+75Fi1a4Obmxp9//kmvXr0ASE9PZ/v27bRp0+aEvofZbC73QcCp8vf3180tx6R7RI5H94gcj+4ROR7dI3I8ukfkeHSPnDvHmmE/pNqH9v79+/PNN9/w008/4efn59ynHhAQgJeXFwEBAdx9990MGTKEGjVq4O/vzyOPPEKbNm1OqHK8iIiIiIiISHVV7UP7uHHjAOjQoUO545999hl33HEHAG+88QZms5levXpRUlJCly5deP/998/xSEVERERERETOrGof2k+kjbynpyfvvfce77333jkYUUUeHh4888wz5fbJixxJ94gcj+4ROR7dI3I8ukfkeHSPyPHoHqmeTMaJpGIREREREREROefMVT0AEREREREREamcQruIiIiIiIhINaXQLiIiIiIiIlJNKbSLiIiIiIiIVFMK7WfAe++9R926dfH09KR169YsXry4qock58DcuXO55ppriIyMxGQy8eOPP5Z73jAMRo4cSUREBF5eXnTu3JkNGzaUOycrK4u+ffvi7+9PYGAgd999N/n5+efwXcjZNGrUKFq1aoWfnx+hoaFcf/31pKenlzunuLiY/v37ExwcjK+vL7169SIzM7PcOdu3b6d79+54e3sTGhrK448/jtVqPZdvRc6ScePGkZSUhL+/P/7+/rRp04bp06c7n9f9If82evRoTCYTgwYNch7TffLf9uyzz2Iymcr9iY+Pdz6v+0MAdu3axa233kpwcDBeXl4kJiaydOlS5/P6vbV6U2g/Td999x1DhgzhmWeeYfny5SQnJ9OlSxf27t1b1UOTs6ygoIDk5OSjthp89dVXefvtt/nggw9YtGgRPj4+dOnSheLiYuc5ffv2Zc2aNcycOZNp06Yxd+5c7rvvvnP1FuQsmzNnDv3792fhwoXMnDkTi8XClVdeSUFBgfOcwYMHM3XqVCZOnMicOXPYvXs3PXv2dD5vs9no3r07paWlzJ8/n/Hjx/P5558zcuTIqnhLcoZFRUUxevRoli1bxtKlS+nYsSPXXXcda9asAXR/SHlLlizhww8/JCkpqdxx3SfSuHFj9uzZ4/wzb94853O6P+TgwYO0bdsWNzc3pk+fztq1axk7dixBQUHOc/R7azVnyGm56KKLjP79+zsf22w2IzIy0hg1alQVjkrONcCYMmWK87HdbjfCw8ONMWPGOI9lZ2cbHh4exrfffmsYhmGsXbvWAIwlS5Y4z5k+fbphMpmMXbt2nbOxy7mzd+9eAzDmzJljGIbjnnBzczMmTpzoPGfdunUGYCxYsMAwDMP49ddfDbPZbGRkZDjPGTdunOHv72+UlJSc2zcg50RQUJDxySef6P6QcvLy8ozY2Fhj5syZRvv27Y2BAwcahqF/R8QwnnnmGSM5ObnS53R/iGEYxrBhw4xLL730qM/r99bqTzPtp6G0tJRly5bRuXNn5zGz2Uznzp1ZsGBBFY5MqtqWLVvIyMgod28EBATQunVr572xYMECAgMDadmypfOczp07YzabWbRo0Tkfs5x9OTk5ANSoUQOAZcuWYbFYyt0n8fHx1KlTp9x9kpiYSFhYmPOcLl26kJub65yNlQuDzWZjwoQJFBQU0KZNG90fUk7//v3p3r17ufsB9O+IOGzYsIHIyEjq1atH37592b59O6D7Qxx+/vlnWrZsSe/evQkNDaVZs2Z8/PHHzuf1e2v1p9B+Gvbv34/NZiv3jxxAWFgYGRkZVTQqqQ4O/fyPdW9kZGQQGhpa7nlXV1dq1Kih++cCZLfbGTRoEG3btqVJkyaA4x5wd3cnMDCw3Ln/vk8qu48OPSfnv9TUVHx9ffHw8OCBBx5gypQpNGrUSPeHOE2YMIHly5czatSoCs/pPpHWrVvz+eefM2PGDMaNG8eWLVto164deXl5uj8EgM2bNzNu3DhiY2P57bffePDBBxkwYADjx48H9Hvr+cC1qgcgIvJf0L9/f1avXl1un6EIQFxcHCtXriQnJ4cffviBfv36MWfOnKoellQTO3bsYODAgcycORNPT8+qHo5UQ127dnV+nZSUROvWrYmOjub777/Hy8urCkcm1YXdbqdly5a8/PLLADRr1ozVq1fzwQcf0K9fvyoenZwIzbSfhpo1a+Li4lKhAmdmZibh4eFVNCqpDg79/I91b4SHh1coWGi1WsnKytL9c4F5+OGHmTZtGn/99RdRUVHO4+Hh4ZSWlpKdnV3u/H/fJ5XdR4eek/Ofu7s7DRo0oEWLFowaNYrk5GTeeust3R8COJY37927l+bNm+Pq6oqrqytz5szh7bffxtXVlbCwMN0nUk5gYCANGzZk48aN+ndEAIiIiKBRo0bljiUkJDi3Uej31upPof00uLu706JFC/7880/nMbvdzp9//kmbNm2qcGRS1WJiYggPDy93b+Tm5rJo0SLnvdGmTRuys7NZtmyZ85xZs2Zht9tp3br1OR+znHmGYfDwww8zZcoUZs2aRUxMTLnnW7RogZubW7n7JD09ne3bt5e7T1JTU8v9h3LmzJn4+/tX+A+wXBjsdjslJSW6PwSATp06kZqaysqVK51/WrZsSd++fZ1f6z6RI+Xn57Np0yYiIiL074gA0LZt2wotZ9evX090dDSg31vPC1VdCe98N2HCBMPDw8P4/PPPjbVr1xr33XefERgYWK4Cp1yY8vLyjBUrVhgrVqwwAOP11183VqxYYWzbts0wDMMYPXq0ERgYaPz0009GSkqKcd111xkxMTFGUVGR8xpXXXWV0axZM2PRokXGvHnzjNjYWOPmm2+uqrckZ9iDDz5oBAQEGLNnzzb27Nnj/FNYWOg854EHHjDq1KljzJo1y1i6dKnRpk0bo02bNs7nrVar0aRJE+PKK680Vq5cacyYMcMICQkxRowYURVvSc6w4cOHG3PmzDG2bNlipKSkGMOHDzdMJpPx+++/G4ah+0Mqd2T1eMPQffJf9+ijjxqzZ882tmzZYvzzzz9G586djZo1axp79+41DEP3hxjG4sWLDVdXV+Oll14yNmzYYHz99deGt7e38dVXXznP0e+t1ZtC+xnwzjvvGHXq1DHc3d2Niy66yFi4cGFVD0nOgb/++ssAKvzp16+fYRiO9hlPP/20ERYWZnh4eBidOnUy0tPTy13jwIEDxs0332z4+voa/v7+xp133mnk5eVVwbuRs6Gy+wMwPvvsM+c5RUVFxkMPPWQEBQUZ3t7eRo8ePYw9e/aUu87WrVuNrl27Gl5eXkbNmjWNRx991LBYLOf43cjZcNdddxnR0dGGu7u7ERISYnTq1MkZ2A1D94dU7t+hXffJf1ufPn2MiIgIw93d3ahVq5bRp08fY+PGjc7ndX+IYRjG1KlTjSZNmhgeHh5GfHy88dFHH5V7Xr+3Vm8mwzCMqpnjFxEREREREZFj0Z52ERERERERkWpKoV1ERERERESkmlJoFxEREREREammFNpFREREREREqimFdhEREREREZFqSqFdREREREREpJpSaBcRERERERGpphTaRURERERERKophXYRERERERGRakqhXURERE6JxWKp6iGIiIhc8BTaRUREBIAZM2Zw6aWXEhgYSHBwMFdffTWbNm0CYOvWrZhMJr777jvat2+Pp6cnX3/9NQCffPIJCQkJeHp6Eh8fz/vvv1/uusOGDaNhw4Z4e3tTr149nn76aQV+ERGRE+Ra1QMQERGR6qGgoIAhQ4aQlJREfn4+I0eOpEePHqxcudJ5zvDhwxk7dizNmjVzBveRI0fy7rvv0qxZM1asWMG9996Lj48P/fr1A8DPz4/PP/+cyMhIUlNTuffee/Hz82Po0KFV9E5FRETOHybDMIyqHoSIiIhUP/v37yckJITU1FR8fX2JiYnhzTffZODAgc5zGjRowAsvvMDNN9/sPPbiiy/y66+/Mn/+/Eqv+9prrzFhwgSWLl161t+DiIjI+U6hXURERADYsGEDI0eOZNGiRezfvx+73U5BQQG//PILjRo1IiYmhnnz5tG2bVvAMTPv6+uLl5cXZvPhHXdWq5WAgAAyMzMB+O6773j77bfZtGkT+fn5WK1W/P392bt3b5W8TxERkfOJlseLiIgIANdccw3R0dF8/PHHREZGYrfbadKkCaWlpc5zfHx8nF/n5+cD8PHHH9O6dety13JxcQFgwYIF9O3bl+eee44uXboQEBDAhAkTGDt27Dl4RyIiIuc/hXYRERHhwIEDpKen8/HHH9OuXTsA5s2bd8zXhIWFERkZyebNm+nbt2+l58yfP5/o6GiefPJJ57Ft27aduYGLiIhc4BTaRUREhKCgIIKDg/noo4+IiIhg+/btDB8+/Live+655xgwYAABAQFcddVVlJSUsHTpUg4ePMiQIUOIjY1l+/btTJgwgVatWvHLL78wZcqUc/CORERELgxq+SYiIiKYzWYmTJjAsmXLaNKkCYMHD2bMmDHHfd0999zDJ598wmeffUZiYiLt27fn888/JyYmBoBrr72WwYMH8/DDD9O0aVPmz5/P008/fbbfjoiIyAVDhehEREREREREqinNtIuIiIiIiIhUUwrtIiIiIiIiItWUQruIiIiIiIhINaXQLiIiIiIiIlJNKbSLiIiIiIiIVFMK7SIiIiIiIiLVlEK7iIiIiIiISDWl0C4iIiIiIiJSTSm0i4iIiIiIiFRTCu0iIiIiIiIi1ZRCu4iIiIiIiEg1pdAuIiIiIiIiUk39Hy0sj5eEg848AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(df['year'], df['WHEAT PRODUCTION (1000 tons)'])\n",
        "plt.xlabel('year')\n",
        "plt.ylabel('Harvest Production')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "OteXnLiVy5R0",
        "outputId": "8da450b9-5769-4b76-fbf7-b2ec0b280ed1"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "rf_model = RandomForestRegressor(n_estimators=200, random_state=42)\n",
        "rf_model.fit(X_train,Y_train)\n",
        "\n",
        "# Step 4: Make predictions on the testing set and evaluate model performance using mean squared error\n",
        "y_pred = rf_model.predict(X_test)\n",
        "mse = mean_squared_error(Y_test, y_pred)\n",
        "#saving the model so as to deploy it later\n",
        "import joblib\n",
        "\n",
        "joblib.dump(rf_model, 'rf_model.joblib')\n",
        "\n",
        "print('Mean Squared Error:', mse)\n",
        "print('Accuracy',rf_model.score(X_test,Y_test))\n",
        "Y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0uWmTiAeXwUF",
        "outputId": "f3593d89-b8d6-4963-fdbe-dc45b6c3de5c"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 2396.0393998340746\n",
            "Accuracy 0.8890372165741323\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 56.1 ,  19.  , 260.68, 196.8 ,  49.8 ,  91.4 ,  58.2 ,  55.6 ,\n",
              "       157.  , 552.  ,  78.2 ])"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ICUv6VfF2FCG",
        "outputId": "27ac0758-01c6-415b-82ec-ea1e0c351385"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 82.46,  41.83, 362.87, 196.15,  65.67,  80.99,  71.56,  67.32,\n",
              "       117.29, 440.64,  71.53])"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install streamlit\n"
      ],
      "metadata": {
        "id": "0SNR9CVf9mfs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile app.py\n",
        "import streamlit as st\n",
        "import joblib\n",
        "\n",
        "# Load the RandomForestRegressor model\n",
        "rf_model = joblib.load('rf_model.joblib')\n",
        "\n",
        "# Define the Streamlit app\n",
        "st.title('Random Forest Regressor App')\n",
        "\n",
        "# Add input elements for user interaction\n",
        "input_value1 = st.slider('Enter the WHEAT AREA (1000 ha) ', min_value=0, max_value=700, value=100)\n",
        "input_value2 = st.slider('Year ', min_value=1966, max_value=2017, value=1966)\n",
        "# Add more sliders for additional features if needed\n",
        "\n",
        "# Create a button for predictions\n",
        "if st.button('Predict'):\n",
        "    # Make predictions using the model\n",
        "    prediction = rf_model.predict([[input_value1, input_value2]])[0]\n",
        "\n",
        "    # Display the prediction\n",
        "    st.write(f'Predicted WHEAT PRODUCTION (1000 tons) : {prediction}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ufv9l3jE8fKN",
        "outputId": "b9298dc0-8f21-4d9a-daa2-c46eca0aff73"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting app.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pyngrok"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C7ehz0RO_-EL",
        "outputId": "d1e1c45f-9a04-4aaf-8cb1-74ff6e16c8d7"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pyngrok in /usr/local/lib/python3.10/dist-packages (7.0.0)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from pyngrok) (6.0.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "!ngrok authtoken #yourauthtoken"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5o0NrmDYABoo",
        "outputId": "7585e4bc-711c-44cd-f5bc-89ab4fa3d1af"
      },
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NAME:\n",
            "  authtoken - \n",
            "\n",
            "USAGE:\n",
            "  ngrok authtoken TOKEN [flags]\n",
            "\n",
            "DESCRIPTION: \n",
            "\n",
            "OPTIONS:\n",
            "      --config string       save in this config file (default /root/.config/ngrok/ngrok.yml)\n",
            "  -h, --help                help for authtoken\n",
            "      --log string          path to log file, 'stdout', 'stderr' or 'false' (default \"false\")\n",
            "      --log-format string   log record format: 'term', 'logfmt', 'json' (default \"term\")\n",
            "      --log-level string    logging level: 'debug', 'info', 'warn', 'error', 'crit' (default \"info\")\n",
            "\n",
            "ERROR:  accepts 1 arg(s), received 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pyngrok import ngrok\n",
        "url = ngrok.connect(addr=\"172.28.0.12:8501\")\n",
        "url\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BekMYhH3AEyn",
        "outputId": "fdbc255d-b9e1-4aee-ad72-975b6c40258b"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:pyngrok.process.ngrok:t=2023-10-15T11:11:02+0000 lvl=warn msg=\"ngrok config file found at legacy location, move to XDG location\" xdg_path=/root/.config/ngrok/ngrok.yml legacy_path=/root/.ngrok2/ngrok.yml\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<NgrokTunnel: \"https://a7bb-35-233-244-49.ngrok-free.app\" -> \"http://172.28.0.12:8501\">"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!streamlit run app.py\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nlZvZJWD-NHZ",
        "outputId": "c70caa79-1f4f-460c-b327-cf738992c4ea"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Collecting usage statistics. To deactivate, set browser.gatherUsageStats to False.\n",
            "\u001b[0m\n",
            "\u001b[0m\n",
            "\u001b[34m\u001b[1m  You can now view your Streamlit app in your browser.\u001b[0m\n",
            "\u001b[0m\n",
            "\u001b[34m  Network URL: \u001b[0m\u001b[1mhttp://172.28.0.12:8501\u001b[0m\n",
            "\u001b[34m  External URL: \u001b[0m\u001b[1mhttp://35.233.244.49:8501\u001b[0m\n",
            "\u001b[0m\n",
            "\u001b[34m  Stopping...\u001b[0m\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/bin/streamlit\", line 8, in <module>\n",
            "    sys.exit(main())\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/click/core.py\", line 1157, in __call__\n",
            "    return self.main(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/click/core.py\", line 1078, in main\n",
            "    rv = self.invoke(ctx)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/click/core.py\", line 1688, in invoke\n",
            "    return _process_result(sub_ctx.command.invoke(sub_ctx))\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/click/core.py\", line 1434, in invoke\n",
            "    return ctx.invoke(self.callback, **ctx.params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/click/core.py\", line 783, in invoke\n",
            "    return __callback(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/streamlit/web/cli.py\", line 233, in main_run\n",
            "    _main_run(target, args, flag_options=kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/streamlit/web/cli.py\", line 269, in _main_run\n",
            "    bootstrap.run(file, command_line, args, flag_options)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/streamlit/web/bootstrap.py\", line 430, in run\n",
            "    asyncio.run(run_server())\n",
            "  File \"/usr/lib/python3.10/asyncio/runners.py\", line 44, in run\n",
            "    return loop.run_until_complete(main)\n",
            "  File \"/usr/lib/python3.10/asyncio/base_events.py\", line 636, in run_until_complete\n",
            "    self.run_forever()\n",
            "  File \"/usr/lib/python3.10/asyncio/base_events.py\", line 603, in run_forever\n",
            "    self._run_once()\n",
            "  File \"/usr/lib/python3.10/asyncio/base_events.py\", line 1871, in _run_once\n",
            "    event_list = self._selector.select(timeout)\n",
            "  File \"/usr/lib/python3.10/selectors.py\", line 469, in select\n",
            "    fd_event_list = self._selector.poll(timeout, max_ev)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/streamlit/web/bootstrap.py\", line 69, in signal_handler\n",
            "    server.stop()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/streamlit/web/server/server.py\", line 396, in stop\n",
            "    click.secho(\"  Stopping...\", fg=\"blue\")\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/click/termui.py\", line 634, in secho\n",
            "    return echo(message, file=file, nl=nl, err=err, color=color)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/click/utils.py\", line 318, in echo\n",
            "    file.write(out)  # type: ignore\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/streamlit/web/bootstrap.py\", line 69, in signal_handler\n",
            "    server.stop()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/streamlit/web/server/server.py\", line 396, in stop\n",
            "    click.secho(\"  Stopping...\", fg=\"blue\")\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/click/termui.py\", line 634, in secho\n",
            "    return echo(message, file=file, nl=nl, err=err, color=color)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/click/utils.py\", line 318, in echo\n",
            "    file.write(out)  # type: ignore\n",
            "RuntimeError: reentrant call inside <_io.BufferedWriter name='<stdout>'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Experimental (Second Stage)"
      ],
      "metadata": {
        "id": "bDl-IkFQ8Vni"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Lets go one step further towards neural network and implement this same stuff using neural network"
      ],
      "metadata": {
        "id": "a3Ic_WXI6Qzw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "normalizer = tf.keras.layers.Normalization(input_shape = (2,),axis =1)\n",
        "normalizer.adapt(X_train)"
      ],
      "metadata": {
        "id": "KKN3-3Sqx54m"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=tf.keras.Sequential(\n",
        "    [\n",
        "        normalizer,\n",
        "     tf.keras.layers.Dense(1)\n",
        "    ]\n",
        ")\n",
        "np.set_printoptions(suppress=True, precision=2)\n",
        "\n",
        "# Assuming 'predictions' contains the predicted values\n",
        "predictions = model.predict(X_train[:10])\n",
        "\n",
        "print(predictions)"
      ],
      "metadata": {
        "id": "dyTeVLjqE3Hp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91b5faae-36c1-4cbc-c1c7-89076afdb99a"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 53ms/step\n",
            "[[ 0.57]\n",
            " [-0.47]\n",
            " [-0.18]\n",
            " [-0.2 ]\n",
            " [-0.76]\n",
            " [-0.53]\n",
            " [ 0.18]\n",
            " [ 0.88]\n",
            " [-0.59]\n",
            " [-0.32]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=.01),loss =\"mean_squared_error\")"
      ],
      "metadata": {
        "id": "N9D7Huh4G_A5"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4eRbAEmeSe_w",
        "outputId": "a2b813a2-cf18-4e35-e4c7-6fa38213e557"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " normalization (Normalizati  (None, 2)                 5         \n",
            " on)                                                             \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 1)                 3         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 8 (36.00 Byte)\n",
            "Trainable params: 3 (12.00 Byte)\n",
            "Non-trainable params: 5 (24.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    X_train, Y_train,\n",
        "    verbose=0,\n",
        "    epochs=1000,\n",
        "    validation_data=(X_valid, Y_valid)\n",
        ")"
      ],
      "metadata": {
        "id": "oRUUHdo6wRMC"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hist = pd.DataFrame(history.history)\n",
        "hist['epoch'] = history.epoch\n",
        "hist.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "b7a1c6Tp24pC",
        "outputId": "ad3f557d-1eef-44d7-fd2e-2e4c77e896a8"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             loss      val_loss  epoch\n",
              "995  40333.355469  15280.681641    995\n",
              "996  40326.429688  15278.287109    996\n",
              "997  40319.511719  15275.893555    997\n",
              "998  40312.593750  15273.500000    998\n",
              "999  40305.675781  15271.106445    999"
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              "    .colab-df-convert {\n",
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              "        document.querySelector('#df-25ad355e-722e-446d-8347-7035b928820b button.colab-df-convert');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-25ad355e-722e-446d-8347-7035b928820b');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_loss(history):\n",
        "  plt.plot(history.history['loss'], label='loss')\n",
        "  plt.plot(history.history['val_loss'], label='val_loss')\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel('Error [MPG]')\n",
        "  plt.legend()\n",
        "  plt.grid(True)"
      ],
      "metadata": {
        "id": "pVrT6ij7xwLZ"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_loss(history)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "6boGg6Iey3eL",
        "outputId": "187de2a6-7104-4ea8-a5ed-64dd62eada45"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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Bg7F3797mOE2iJmvno8b90UGY+JtOeOfJPvgm9X4cfe0hfPenYVjx+/6YHh+NUb306NTOC3IZUFxpwZ6zV7Fq93mkf3UET7y/Gz1f3Yz7/7INEz/Zh3c2n8A3hy7hZEEZLFZbS58e0d1JJrNfImuJVxNHmh955BEIgoCMjAxcuHAB33//PcaOHQsA+NOf/oT169fjrbfewvfff4+cnBz06tULZrO5OarWwMqVK5GdnY17770XX3zxBQYOHIg9e/YAAObPn4+jR48iKSkJ27ZtQ0xMDNavX9+s/WkV/+u5b98+fPDBB+jdu3eDfZMmTcLrr78uvtdq6xK01WpFUlIS9Ho9du/ejfz8fDz33HNQKpV46623AADnzp1DUlISJk+ejNWrVyMrKwsTJ05EaGgoEhMTAQCff/450tLSsGLFCgwePBjvvfceEhMTkZubi+DgYBC1Ngq5DFFBXoi65hJftcWK04XltZPPjeIk9MIyU727+ArF9iqFHJ3aedVe3rMv0tlV74MwX09e4iNqIzw9PfHEE09g9erVOH36NLp164b+/fsDAH744Qc8//zzePzxxwEA5eXlOH/+vEs+t0ePHli1ahUqKirE0aYffvgBcrkc3bp1E9v169cP/fr1w+zZszFkyBB89tlnuPfeewEAXbt2RdeuXTFjxgw888wzWLlypdjX5tDioam8vBxjx47FP/7xD/z5z39usF+r1UKv1zf6s1u2bMGxY8ewdetWhISEoG/fvnjjjTcwe/ZszJ8/HyqVCitWrEBUVBTeffddAPY/pF27dmHx4sViaFq0aBEmTZqE8ePHAwBWrFiBjIwMfPzxx5gzZ04znTmR63kqFejZ3hc92/s6bS+uMIsPMnYEqpMF5eLjY+yPkKm7lODj6XHNxHP7pT5fDRfqJLobjR07Fg8//DCOHj2K3//+9+L26OhofPXVV3jkkUcgk8nwyiuvwGZzzQj12LFj8eqrryI5ORnz589HUVERpk6dinHjxiEkJATnzp3Dhx9+iEcffRRhYWE4fvw4zpw5g+TkZFRVVWHmzJn43e9+h6ioKPz666/Yt28fxowZ45K+XU+Lh6aUlBQkJSUhPj6+0dC0evVqfPrpp9Dr9XjkkUfwyiuviKNN2dnZ6NWrF0JCQsT2iYmJmDJlCo4ePYp+/fohOzu7wa2RiYmJ4mVAs9mM/fv3Iz09Xdwvl8sRHx+P7Ozs6/bbZDLBZDKJ741GIwDAYrHAYrE0vRA34Dieq49Lzu7mOnurZIgN1yE2XCduEwQBF0uqa+dIlSO3oBwnC8pw7nIlyqobX6hTr7MvidA1xBvdQrwRHWJ/vp/aQ/qV/ru5zq0Na+0ejvoKggCbzeayUOFOw4YNQ0BAAHJzc/H000+L5/DXv/4VEydOxL333ougoCDMmjULRqNRPFeHa9/fiKNGnp6e+PbbbzFjxgwMHDgQWq0WTzzxBN59911x//Hjx/HJJ5/gypUrCA0NxcSJE/Hiiy/CarXi8uXLeO6551BQUICgoCA8/vjjePXVVxvth81mgyAIsFgsUCicl3Bpyu9Hi4amtWvX4sCBA9i3b1+j+5999llEREQgLCwMhw8fxuzZs5Gbm4uvvvoKAGAwGJwCEwDxvcFguGEbo9GIqqoqFBcXw2q1NtrmxIkT1+37ggUL8NprrzXYvmXLFqdLiK6UmZnZLMclZ22xzhEAIryBBG+gJgoorAIuVcqQXynDpUogv1KGYrMMBqMJBqMJO07VLYkgh4B2GiBMKyBUKyBMC4RqBQSo7c/pu562WOeWwlo3Pw8PD1RXV6O8vNxt831c7dixY+L3joGAgIAA8d9cB8dIlKNNTk6O0/sbKS4udmobERHR4Pg2mw1GoxEajabROcgVFRUA7FeFrmU2mxutv9lsRlVVFXbu3Nlg3nNlpfT1tFosNF24cAHTpk1DZmYmPD09G23z4osvit/36tULoaGhGD58OM6cOdPsM+RvJj09HWlpaeJ7o9GI8PBwJCQkiDP7XcVisSAzMxMjRoxo9jUo2jLW+cbKqi31RqTKcbLQPjJVWlWDgiqgoEqGg3XPNYZWpUB0cN2IVLcQb3QN8YFOJWOd3YT/TbuHxWLBd999B09PT3h7e1/33zS6PYIgoKysDD4+Pk2ec1ldXQ2NRoOhQ4c2+POREvYcWiw07d+/H4WFheJkM8A+sXvnzp1YunQpTCZTgyG0wYMHAwBOnz6Nzp07Q6/XN7jLraDA/qgKxzwovV4vbqvfRqfTQaPRQKFQQKFQNNrmenOpAECtVkOtbvjIC6VS2Wx/OTXnsakO69y4AKUSQ3y0GNKl7uYIQRBQYDTVLYdQOz/qdFE5Ks1WHPq1FId+dV4SIchbhQCFHDmys+gR5ovueh90CfaGVtXiswXuWvxv2j1kMhnkcrnTytptyerVq/H//t//a3RfREQEjh49elvHd1x2c9S5KeRyOWQyWaO/C0353Wixv6WGDx+OI0eOOG0bP348unfvjtmzZzcITEDdEGBoqP1uobi4OLz55psoLCwU73LLzMyETqdDTEyM2Gbjxo1Ox8nMzERcXBwAQKVSITY2FllZWeJTl202G7Kysm574S6iu51MJoPe1xN6X08M61YXpmqsNpy/UiHevedYXyrvaiUul5txGXKc3P1LveMAHQO06BriY58zpbd/jQrygqoJ86WIqOU8+uij4uDGte6W0N5iocnHxwc9e/Z02ubl5YXAwED07NkTZ86cwZo1azBq1CgEBgbi8OHDmDFjBoYOHSouTZCQkICYmBiMGzcOCxcuhMFgwNy5c5GSkiKOAk2ePBlLly7FrFmzMGHCBGzbtg1ffPEFMjIyxM9NS0tDcnIyBgwYgEGDBuG9995DRUWFeDcdETWNh0KOLsE+6BLsg4frrSRSaa7BsYsl+DJzN9QhUThVWIGTBeW4XG7CL1cq8cuVSmQeqxv19ahdWsERorrW3tHXMUDLR8hQqyQIQkt3ocX4+PjAx8enpbvRKFf9ubTa8XCVSoWtW7eKASY8PBxjxozB3LlzxTYKhQIbNmzAlClTEBcXBy8vLyQnJzut6xQVFYWMjAzMmDEDS5YsQYcOHfDRRx+Jyw0AwFNPPYWioiLMmzcPBoMBffv2xaZNmxpMDiei26NVeaBPB19cDBEwalR38f8+r5Sb7POkCmpXPK8dmSqrrsGpwnKcKixHBvLF46g95IiunSNVf2QqlOtLUQuxWq0A7JOKNRpNC/eGruWY7H27I16tKjRt375d/D48PBw7duy46c9EREQ0uPx2rWHDhuHgwYM3bJOamsrLcUQtJNBbjThvNeI61z3YWBAEGIzVyBWfw2cPVacK7Y+Q+fmiET9fdJ7A6aP2QNfax8Z0C/EWw1Sgd8P5h0SuJAgCdDodCgvti8dqtVoGeBez2Wwwm82orq6WPKdJEARUVlaisLAQfn5+jU79aYpWFZqIiBxkMhlCfTUI9dU4zZey2gRcuFrpNCJ1sqAMZ4sqUGaqwf5firH/mvWlgrxV6Frv8l7XEG9Eh/hA53l3zLOg1iE4OBgKhUIMTuRagiCgqqoKGo2myYHUz8/vhjd3ScXQRER3FIVchsggL0QGeSHxnrq/BM01Npy7XNEgTImTz8uvYPeZK07HCvP1bDBfqkuwNzyVt/d/o9Q2yWQyhIaGIjg4mAuKNgOLxYKdO3di6NChTbrMplQqb3uEyYGhiYjuCioPObrp7cEHfeq2V5prcLqwvO4yX0E5ThrKYDBW41Kp/bU9t0hsL5cBEYFetaue182XigzyglLBO/no5hxL2ZBrKRQK1NTUwNPTs8XuxmNoIqK7mlblgd4d/NC7g5/T9tIqC05dM/E811CG4koLzl2uwLnLFdh8tO5OPqVChs7tvOtd4rOHqQ7+Gsh5Jx9Rm8DQRERtkq9GiQGRARgQGSBuEwQBl8vNtRPPy5zu5qswW+sebnyo7jgapQJda+/k61pvZCpEp+ZEYKK7DEMTEVEtmUyGdj5qtPNR474uQeJ2+8ONq5zu4sutXfm8ytL4yuc6T4+6Eal6I1P+Xip3nxYRuQhDExHRTchkMnTw16KDvxYPdq9bv63GasMvVyudJp7nGspw/koljNU12He+GPvOO9/J185HjW4hPuI6U7yTj+jOwdBERHSLPBRydG7njc7tvDGyV6i4vdpixdmiigaLdf5aXIWiMhOKykzYdfqy07H0Os8GQSo62Bs+DFNErQZDExGRi3kqFYgJ0yEmTOe0vdxUg1O1I1KOFdBPFZTDYKwWX9+fcg5TYb6eiK4XpLrWhikvNf/6JnI3/tYREbmJt9oD/Tr6o19Hf6ftpVUWnC50DlInC8pQWGYSl0XYcbLI6Wfa+2nECeiOUNUl2BtaFf9aJ2ou/O0iImphvholYiMCEBsR4LS9tNKCk4VlTkHK8YDjiyVVuFhShe/qrTElkwEd/DW1c6Z80DlQg4IK++XCu+Up80QtiaGJiKiV8tUqMTAyAAMjncNUcYV9WYSTheXi5b5TBeW4UmHGhatVuHC1CluPOx7l4YF3j2ShY4BWHJGyX+LzQad2Xlz9nKgJGJqIiO4w/l4qDO4UiMGdAp22Xy43OY1K5RqMOHaxGJU1Mpy/UonzVyqReaxuwU65DIgM9BInoDtCVacgb6g8uPo50bUYmoiI7hJB3moEeatxb2f7GlMWiwUZGRsxaOhwnLtaLV7ec4xOGatrcPZyBc5es/q5Qi5DZKDWKUh1DfFBFB8lQ20cQxMR0V1MJrOvDRUW4N1gwc7CMlODIHWqoBxlphqcKarAmaIKfPuzQfwZD7kMUUFetWGqbhX0yEAtPBimqA1gaCIiaoNkMhlCdJ4I0XniN9HtxO2CIMBgrHYKUo7vK8xWnCosx6nCcuBI3bFUCjk6tfOyj0oFe4ujUxGBXlDwuXx0F2FoIiIikUwmQ6ivBqG+Gvy2q3OYulRaXTsaVRekThWWo7L+c/nqUXnIax9y7C2uL9U1xAfhAVqGKbojMTQREdFNyWQytPfToL2fBg90Cxa322z25/KdcqwzZSjDycIynC4sR7XFhuP5RhzPNzodq36Yig72Rpdg++W+iABe5qPWjaGJiIhumVwuQ3iAFuEBzs/ls9kEXCiurLdgpz1UnSkqh6nmOmFKIUdUkBe61IYpx+hURKAX7+ajVoGhiYiIXE4ulyEi0AsRgV4YEVMXpqw2Ab8WV+JUQXnt/Cj7qNSpgnJUWazIrX1eX30echkig7wQHVw7MlUbpqKCuM4UuRdDExERuY2iXpiKj3EembpYUmUPUIVlYqg6XViOclMNTtd+/229Y8llQERgbZgK8UZ0sA+6BNsfoKxRMUyR6zE0ERFRi6t/me+B7nVzpgRBQH5ptX1UqqB2VKrQfsmvrLoG5y5X4NzlCmypt2inTAaE+2trR6XsYapriD1M8UHHdDv4Xw8REbVaMpkMYX4ahPk1vJuvqMwkhqmTheU4XVCOk4VlKKm0IO9qJfKuViLrRKHT8dr7aWpHpWpHpmofdKzz5LP56OYYmoiI6I4jk8kQrPNEsM6zwaKdVyrMOFVQjtOFZbWhyn7J73K5WXzQ8fZ6DzoGgFBfT3SpDVL1Q5WvlmGK6jA0ERHRXUMmk4mPk4nr7PxsvqsVZqc5U47vC4wm5JdWI7+0Gt+fuuz0M+181OKdfF1qJ6JHh/ggwEvlztOiVoKhiYiI2oQALxUGRQVgUFSA0/bSKot9VEq8o68cpwvKcKm0GkVlJhSVmbD7zBWnnwn0UtlDVIg3OgVqcaVUhoFlJoT6e0Am48KddyuGJiIiatN8NUrERgQgNsI5TJVVW3CmqEJc+dzx9dfiKlypMOPKuav48dzV2tYKLDu2A35aZd2CnfXu6gvRqRmm7gIMTURERI3w8VSib7gf+ob7OW2vNNfgTGGF/TJfYTlOGow4dK4QV8wylFRasO98MfadL77mWB7oEuyNLu3sE88drw7+fKTMnYShiYiIqAm0Kg/06uCLXh18AQAWiwUbN27EgyMSkVdiEhfrdISqX65Uoqy6BgfzSnAwr8TpWGoPOTo5glQ7+8hUl2BvRHIV9FaJoYmIiMgFPJUK3BPmi3vCfJ22m2qsOHe5Qlyg81RhOc4UluPs5YrrPlJGIZchIkCLzo5V0GtfXGuqZbHyREREzUjtoUB3vQ7d9Tqn7VabgAtXK8UgdbqwHKeL7IGq3FSDs5crcPZyBTLrLdwJ2Nea6nzNpb7oYG/4846+ZsfQRERE1AIUtc/UiwxyfqSMIAgwGKvFkan6rysVdWtN7TzpvNZUoJfKHqauGZ3S6zw5Cd1FGJqIiIhaEZlMhlBfDUJ9NfhNdDunfcUVZpwuKhfnTTlGpi6W1N3Rt1e8o8/OW+2Bzu280KX22XyOV8cATkJvKoYmIiKiO4S/lwoDvQIwMNJ5eYQKUw3OFtnv6Dtd71LfL1cqUW6qwaFfS3Ho11Knn1F5yNEpyMvpUl90iDeigryg9uADjxvD0ERERHSH81I739HnYK6x4fyVCqdLfKcKy3G2qBymGhtOGMpwwlDm9DNyGdAxQFs7IuVTbxK6F3za+DP6GJqIiIjuUioPObqG+KBriI/TdqtNwMXiKpwuKnO61He6sBxl1TU4f6US569UYutx5wce63WeiA6x38VXfxJ6oLfanafVYhiaiIiI2hiFXIaOgVp0DNTiwe7Ok9CLykx1d/PVu9RXVGaCwVgNg7HhM/r8tUqnZRGia5/VF+Z7d01CZ2giIiIiAPZJ6ME6TwTrPHFflyCnfaWVFnFkqv4yCb8WV6H4Oiuha1UKp1EpR6iKCNRCqbjzFu9kaCIiIqKb8tU2/oy+KrMVZ4rKcaao9jJf7cjU+csVqDRbceRiKY5cdJ6E7lG73IL9rr66MNXaF+9svT0jIiKiVk+jUqBne1/0bO88Cd1iteGXK5U4fc0dfWcKK1BlsYrbNh91XrwzzNcTnR0hqt6dfb7qlr/Mx9BERERELqdUyMVRpPpsNgH5tYt3nqm3CvqZonJcLjfjUmk1LpU2nDflq/FAT50co9x5EtdoNRcU3377bchkMkyfPl3cVl1djZSUFAQGBsLb2xtjxoxBQYFzIs3Ly0NSUhK0Wi2Cg4Mxc+ZM1NTUOLXZvn07+vfvD7VajS5dumDVqlUNPn/ZsmWIjIyEp6cnBg8ejL179zbHaRIREbVpcrkM7f00+G3XdphwfxTeerwXPv9/cfhp7gjkzBuB/5sSh7+M6YVJv4nCg92D0TFAC5kMKK2qgU1o2b63ipGmffv24YMPPkDv3r2dts+YMQMZGRlYt24dfH19kZqaiieeeAI//PADAMBqtSIpKQl6vR67d+9Gfn4+nnvuOSiVSrz11lsAgHPnziEpKQmTJ0/G6tWrkZWVhYkTJyI0NBSJiYkAgM8//xxpaWlYsWIFBg8ejPfeew+JiYnIzc1FcHCwe4tBRETURvlpVY3Om6q2WHHKUIrsH75voZ7ZtXhoKi8vx9ixY/GPf/wDf/7zn8XtpaWl+Oc//4k1a9bgwQcfBACsXLkSPXr0wJ49ezBkyBBs2bIFx44dw9atWxESEoK+ffvijTfewOzZszF//nyoVCqsWLECUVFRePfddwEAPXr0wK5du7B48WIxNC1atAiTJk3C+PHjAQArVqxARkYGPv74Y8yZM6fRfptMJphMJvG90Wh/QrXFYoHFYnFpjRzHc/VxyRnr7B6ss/uw1u7BOjc/BYDOgZ44q3F9nZtyvBYPTSkpKUhKSkJ8fLxTaNq/fz8sFgvi4+PFbd27d0fHjh2RnZ2NIUOGIDs7G7169UJISN0aE4mJiZgyZQqOHj2Kfv36ITs72+kYjjaOy4Bmsxn79+9Henq6uF8ulyM+Ph7Z2dnX7feCBQvw2muvNdi+ZcsWaLXaJtdBiszMzGY5Ljljnd2DdXYf1to9WGf3cHWdKysrJbdt0dC0du1aHDhwAPv27Wuwz2AwQKVSwc/Pz2l7SEgIDAaD2KZ+YHLsd+y7URuj0YiqqioUFxfDarU22ubEiRPX7Xt6ejrS0tLE90ajEeHh4UhISIBOp7vJmTeNxWJBZmYmRowYAaWybS9h35xYZ/dgnd2HtXYP1tk9mqvOjitFUrRYaLpw4QKmTZuGzMxMeHp6tlQ3bplarYZa3XDZeKVS2Wy/NM15bKrDOrsH6+w+rLV7sM7u4eo6N+VYLXb33P79+1FYWIj+/fvDw8MDHh4e2LFjB/72t7/Bw8MDISEhMJvNKCkpcfq5goIC6PV6AIBer29wN53j/c3a6HQ6aDQaBAUFQaFQNNrGcQwiIiKiFgtNw4cPx5EjR5CTkyO+BgwYgLFjx4rfK5VKZGVliT+Tm5uLvLw8xMXFAQDi4uJw5MgRFBbWPVAwMzMTOp0OMTExYpv6x3C0cRxDpVIhNjbWqY3NZkNWVpbYhoiIiKjFLs/5+PigZ8+eTtu8vLwQGBgobn/hhReQlpaGgIAA6HQ6TJ06FXFxcRgyZAgAICEhATExMRg3bhwWLlwIg8GAuXPnIiUlRbx0NnnyZCxduhSzZs3ChAkTsG3bNnzxxRfIyMgQPzctLQ3JyckYMGAABg0ahPfeew8VFRXi3XRERERELX733I0sXrwYcrkcY8aMgclkQmJiIt5//31xv0KhwIYNGzBlyhTExcXBy8sLycnJeP3118U2UVFRyMjIwIwZM7BkyRJ06NABH330kbjcAAA89dRTKCoqwrx582AwGNC3b19s2rSpweRwIiIiartaVWjavn2703tPT08sW7YMy5Ytu+7PREREYOPGjTc87rBhw3Dw4MEbtklNTUVqaqrkvhIREVHb0moeo0JERETUmjE0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCQBQxMRERGRBAxNRERERBIwNBERERFJwNBEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCQBQxMRERGRBAxNRERERBIwNBERERFJwNBEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCQBQxMRERGRBAxNRERERBIwNBERERFJwNBEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQtGpqWL1+O3r17Q6fTQafTIS4uDt9++624f9iwYZDJZE6vyZMnOx0jLy8PSUlJ0Gq1CA4OxsyZM1FTU+PUZvv27ejfvz/UajW6dOmCVatWNejLsmXLEBkZCU9PTwwePBh79+5tlnMmIiKiO1OLhqYOHTrg7bffxv79+/HTTz/hwQcfxGOPPYajR4+KbSZNmoT8/HzxtXDhQnGf1WpFUlISzGYzdu/ejU8++QSrVq3CvHnzxDbnzp1DUlISHnjgAeTk5GD69OmYOHEiNm/eLLb5/PPPkZaWhldffRUHDhxAnz59kJiYiMLCQvcUgoiIiFo9DymNvvnmmyYfeMSIEdBoNDds88gjjzi9f/PNN7F8+XLs2bMH99xzDwBAq9VCr9c3+vNbtmzBsWPHsHXrVoSEhKBv37544403MHv2bMyfPx8qlQorVqxAVFQU3n33XQBAjx49sGvXLixevBiJiYkAgEWLFmHSpEkYP348AGDFihXIyMjAxx9/jDlz5jT53ImIiOjuIyk0jR49ukkHlclkOHXqFDp16iT5Z6xWK9atW4eKigrExcWJ21evXo1PP/0Uer0ejzzyCF555RVotVoAQHZ2Nnr16oWQkBCxfWJiIqZMmYKjR4+iX79+yM7ORnx8vNNnJSYmYvr06QAAs9mM/fv3Iz09Xdwvl8sRHx+P7Ozs6/bXZDLBZDKJ741GIwDAYrHAYrFIPm8pHMdz9XHJGevsHqyz+7DW7sE6u0dz1bkpx5MUmgDAYDAgODhYUlsfHx/JHThy5Aji4uJQXV0Nb29vrF+/HjExMQCAZ599FhEREQgLC8Phw4cxe/Zs5Obm4quvvhL7VD8wARDfGwyGG7YxGo2oqqpCcXExrFZro21OnDhx3X4vWLAAr732WoPtW7ZsEUOdq2VmZjbLcckZ6+werLP7sNbuwTq7h6vrXFlZKbmtpNCUnJx800tt9f3+97+HTqeT1LZbt27IyclBaWkpvvzySyQnJ2PHjh2IiYnBiy++KLbr1asXQkNDMXz4cJw5cwadO3eW3J/mkJ6ejrS0NPG90WhEeHg4EhISJJ+7VBaLBZmZmRgxYgSUSqVLj011WGf3YJ3dh7V2D9bZPZqrzo4rRVJICk0rV65sUgeWL18uua1KpUKXLl0AALGxsdi3bx+WLFmCDz74oEHbwYMHAwBOnz6Nzp07Q6/XN7jLraCgAADEeVB6vV7cVr+NTqeDRqOBQqGAQqFotM315lIBgFqthlqtbrBdqVQ22y9Ncx6b6rDO7sE6uw9r7R6ss3u4us5NOVarW6fJZrM5zRWqLycnBwAQGhoKAIiLi8ORI0ec7nLLzMyETqcTL/HFxcUhKyvL6TiZmZnivCmVSoXY2FinNjabDVlZWU5zq4iIiKhtkzynKT8/H0uXLsWbb74JALj//vudrgMqFAp8/fXXaN++veQPT09Px8iRI9GxY0eUlZVhzZo12L59OzZv3owzZ85gzZo1GDVqFAIDA3H48GHMmDEDQ4cORe/evQEACQkJiImJwbhx47Bw4UIYDAbMnTsXKSkp4ijQ5MmTsXTpUsyaNQsTJkzAtm3b8MUXXyAjI0PsR1paGpKTkzFgwAAMGjQI7733HioqKsS76YiIiIgkh6b3338fxcXF4vtDhw5hwoQJCAgIAAB8++23WLx4Mf76179K/vDCwkI899xzyM/Ph6+vL3r37o3NmzdjxIgRuHDhArZu3SoGmPDwcIwZMwZz584Vf16hUGDDhg2YMmUK4uLi4OXlheTkZLz++utim6ioKGRkZGDGjBlYsmQJOnTogI8++khcbgAAnnrqKRQVFWHevHkwGAzo27cvNm3a1GByOBEREbVdkkPThg0b8Le//c1p27Rp08RlBYYMGYK0tLQmhaZ//vOf190XHh6OHTt23PQYERER2Lhx4w3bDBs2DAcPHrxhm9TUVKSmpt7084iIiKhtkjyn6fz584iKihLfjxgxAl5eXuL7bt264dy5c67tHREREVErITk0WSwWFBUVie+/+uorp8tXxcXFkMtb3bxyIiIiIpeQnHK6deuG3bt3X3f/999/j65du7qkU0REREStjeTQ9PTTT2PevHk4fPhwg32HDh3C66+/jmeeecalnSMiIiJqLSRPBJ8+fTo2bNiA2NhYjBgxAt26dQMA5ObmiuseOZ7nRkRERHS3kRyalEolMjMzsWjRIqxduxbbt28HAERHR+ONN97AjBkzuBIqERER3bUkhybAvnr2nDlzMGfOnObqDxEREVGr1KTQ9Pnnn+Obb76B2WzG8OHDMXny5ObqFxEREVGrIjk0LV++HCkpKYiOjoZGo8H//d//4cyZM3jnnXeas39ERERErYLku+eWLl2KV199Fbm5ucjJycG//vUvvP/++83ZNyIiIqJWQ3JoOnv2LJKTk8X3zz77LGpqapCfn98sHSMiIiJqTSSHJpPJ5PTYFLlcDpVKhaqqqmbpGBEREVFr0qSJ4K+88gq0Wq343mw2480334Svr6+4bdGiRa7rHREREVErITk0DR06FLm5uU7b7r33Xpw9e1Z8L5PJXNczIiIiolZEcmhyLGZJRERE1BZJntNERERE1JZJHml6/fXXJbWbN2/eLXeGiIiIqLWSHJrmz5+PsLAwBAcHQxCERtvIZDKGJiIiIrorSQ5NI0eOxLZt2zBgwABMmDABDz/8MORyXt0jIiKitkFy6snIyMCZM2cwePBgzJw5E+3bt8fs2bMb3FFHREREdDdq0lBRWFgY0tPTkZubi88//xyFhYUYOHAg7rvvPi5ySURERHe1Ji1uWd/AgQNx/vx5HDt2DAcPHoTFYoFGo3Fl34iIiIhajSZPSsrOzsakSZOg1+vx97//HcnJybh06RJ0Ol1z9I+IiIioVZA80rRw4UKsWrUKly9fxtixY/H999+jd+/ezdk3IiIiolZDcmiaM2cOOnbsiP/5n/+BTCbDqlWrGm3HZ88RERHR3ahJz56TyWQ4evToddvw2XNERER0t+Kz54iIiIgk4OqURERERBJICk1paWmoqKiQfND09HRcvXr1ljtFRERE1NpICk1LlixBZWWl5IMuW7YMJSUlt9onIiIiolZH0pwmQRDQtWtXyRO9mzIqRURERHQnkBSaVq5c2eQDh4SENPlniIiIiForSaEpOTm5uftBRERE1Krx7jkiIiIiCRiaiIiIiCRgaCIiIiKSgKGJiIiISIImhSaLxQIPDw/8/PPPzdUfIiIiolapSaFJqVSiY8eOsFqtzdUfIiIiolapyZfnXn75Zbz00kt8TAoRERG1KZLWaapv6dKlOH36NMLCwhAREQEvLy+n/QcOHHBZ54iIiIhaiyaPNI0ePRp/+tOfkJ6ejmeffRaPPfaY06spli9fjt69e0On00Gn0yEuLg7ffvutuL+6uhopKSkIDAyEt7c3xowZg4KCAqdj5OXlISkpCVqtFsHBwZg5cyZqamqc2mzfvh39+/eHWq1Gly5dsGrVqgZ9WbZsGSIjI+Hp6YnBgwdj7969TToXIiIiurs1eaTp1VdfddmHd+jQAW+//Taio6MhCAI++eQTPPbYYzh48CDuuecezJgxAxkZGVi3bh18fX2RmpqKJ554Aj/88AMAwGq1IikpCXq9Hrt370Z+fj6ee+45KJVKvPXWWwCAc+fOISkpCZMnT8bq1auRlZWFiRMnIjQ0FImJiQCAzz//HGlpaVixYgUGDx6M9957D4mJicjNzUVwcLDLzpeIiIjuYMIt+umnn4R///vfwr///W/hwIEDt3qYBvz9/YWPPvpIKCkpEZRKpbBu3Tpx3/HjxwUAQnZ2tiAIgrBx40ZBLpcLBoNBbLN8+XJBp9MJJpNJEARBmDVrlnDPPfc4fcZTTz0lJCYmiu8HDRokpKSkiO+tVqsQFhYmLFiwQHK/S0tLBQBCaWlp005YArPZLHz99deC2Wx2+bGpDuvsHqyz+7DW7sE6u0dz1bkp/343eaSpsLAQTz/9NLZv3w4/Pz8AQElJCR544AGsXbsW7dq1u6XwZrVasW7dOlRUVCAuLg779++HxWJBfHy82KZ79+7o2LEjsrOzMWTIEGRnZ6NXr15ODwdOTEzElClTcPToUfTr1w/Z2dlOx3C0mT59OgDAbDZj//79SE9PF/fL5XLEx8cjOzv7uv01mUwwmUzie6PRCMC+LIPFYrmlGlyP43iuPi45Y53dg3V2H9baPVhn92iuOjfleE0OTVOnTkVZWRmOHj2KHj16AACOHTuG5ORk/PGPf8Rnn33WpOMdOXIEcXFxqK6uhre3N9avX4+YmBjk5ORApVKJwcwhJCQEBoMBAGAwGJwCk2O/Y9+N2hiNRlRVVaG4uBhWq7XRNidOnLhuvxcsWIDXXnutwfYtW7ZAq9VKO/kmyszMbJbjkjPW2T1YZ/dhrd2DdXYPV9e5srJSctsmh6ZNmzZh69atYmACgJiYGCxbtgwJCQlNPRy6deuGnJwclJaW4ssvv0RycjJ27NjR5OO4W3p6OtLS0sT3RqMR4eHhSEhIgE6nc+lnWSwWZGZmYsSIEVAqlS49NtVhnd2DdXYf1to9WGf3aK46O64USdHk0GSz2RrtrFKphM1ma+rhoFKp0KVLFwBAbGws9u3bhyVLluCpp56C2WxGSUmJ02hTQUEB9Ho9AECv1ze4y81xd139NtfecVdQUACdTgeNRgOFQgGFQtFoG8cxGqNWq6FWqxtsVyqVzfZL05zHpjqss3uwzu7DWrsH6+werq5zU47V5CUHHnzwQUybNg2XLl0St128eBEzZszA8OHDm3q4Bmw2G0wmE2JjY6FUKpGVlSXuy83NRV5eHuLi4gAAcXFxOHLkCAoLC8U2mZmZ0Ol0iImJEdvUP4ajjeMYKpUKsbGxTm1sNhuysrLENkRERES3tLjlo48+isjISISHhwMALly4gJ49e+LTTz9t0rHS09MxcuRIdOzYEWVlZVizZg22b9+OzZs3w9fXFy+88ALS0tIQEBAAnU6HqVOnIi4uDkOGDAEAJCQkICYmBuPGjcPChQthMBgwd+5cpKSkiKNAkydPxtKlSzFr1ixMmDAB27ZtwxdffIGMjAyxH2lpaUhOTsaAAQMwaNAgvPfee6ioqMD48eObWh4iIiK6SzU5NIWHh+PAgQPYunWrOFG6R48eDe5Qk6KwsBDPPfcc8vPz4evri969e2Pz5s0YMWIEAGDx4sWQy+UYM2YMTCYTEhMT8f7774s/r1AosGHDBkyZMgVxcXHw8vJCcnIyXn/9dbFNVFQUMjIyMGPGDCxZsgQdOnTARx99JK7RBABPPfUUioqKMG/ePBgMBvTt2xebNm1qMDmciIiI2q4mhSaLxQKNRoOcnByMGDFCDDe36p///OcN93t6emLZsmVYtmzZddtERERg48aNNzzOsGHDcPDgwRu2SU1NRWpq6g3bEBERUdvVpDlNSqUSHTt2hNVqba7+EBEREbVKTZ4I/vLLL+Oll17C1atXm6M/RERERK3SLU0EP336NMLCwhAREQEvLy+n/QcOHHBZ54iIiIhaiyaHptGjRzdDN4iIiIhatyaFppqaGshkMkyYMAEdOnRorj4RERERtTpNmtPk4eGBd955BzU1Nc3VHyIiIqJW6ZZWBL8Tng1HRERE5EpNntM0cuRIzJkzB0eOHEFsbGyDieCPPvqoyzpHRERE1Fo0OTT94Q9/AAAsWrSowT6ZTMY1nIiIiOiu1OTQZLPZmqMfRERERK1ak+c0EREREbVFkkPTqFGjUFpaKr5/++23UVJSIr6/cuUKYmJiXNo5IiIiotZCcmjavHkzTCaT+P6tt95yepRKTU0NcnNzXds7IiIiolZCcmgSBOGG74mIiIjuZpzTRERERCSB5NAkk8kgk8kabCMiIiJqCyQvOSAIAp5//nmo1WoAQHV1NSZPniwubll/vhMRERHR3UZyaEpOTnZ6//vf/75Bm+eee+72e0RERETUCkkOTStXrmzOfhARERG1apwITkRERCQBQxMRERGRBAxNRERERBIwNBERERFJwNBEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCQBQxMRERGRBAxNRERERBIwNBERERFJwNBEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQtGpoWLFiAgQMHwsfHB8HBwRg9ejRyc3Od2gwbNgwymczpNXnyZKc2eXl5SEpKglarRXBwMGbOnImamhqnNtu3b0f//v2hVqvRpUsXrFq1qkF/li1bhsjISHh6emLw4MHYu3evy8+ZiIiI7kwtGpp27NiBlJQU7NmzB5mZmbBYLEhISEBFRYVTu0mTJiE/P198LVy4UNxntVqRlJQEs9mM3bt345NPPsGqVaswb948sc25c+eQlJSEBx54ADk5OZg+fTomTpyIzZs3i20+//xzpKWl4dVXX8WBAwfQp08fJCYmorCwsPkLQURERK2eR0t++KZNm5zer1q1CsHBwdi/fz+GDh0qbtdqtdDr9Y0eY8uWLTh27Bi2bt2KkJAQ9O3bF2+88QZmz56N+fPnQ6VSYcWKFYiKisK7774LAOjRowd27dqFxYsXIzExEQCwaNEiTJo0CePHjwcArFixAhkZGfj4448xZ86cBp9rMplgMpnE90ajEQBgsVhgsVhuoyoNOY7n6uOSM9bZPVhn92Gt3YN1do/mqnNTjteioelapaWlAICAgACn7atXr8ann34KvV6PRx55BK+88gq0Wi0AIDs7G7169UJISIjYPjExEVOmTMHRo0fRr18/ZGdnIz4+3umYiYmJmD59OgDAbDZj//79SE9PF/fL5XLEx8cjOzu70b4uWLAAr732WoPtW7ZsEfvmapmZmc1yXHLGOrsH6+w+rLV7sM7u4eo6V1ZWSm7bakKTzWbD9OnTcd9996Fnz57i9meffRYREREICwvD4cOHMXv2bOTm5uKrr74CABgMBqfABEB8bzAYbtjGaDSiqqoKxcXFsFqtjbY5ceJEo/1NT09HWlqa+N5oNCI8PBwJCQnQ6XS3WIXGWSwWZGZmYsSIEVAqlS49NtVhnd2DdXYf1to9WGf3aK46O64USdFqQlNKSgp+/vln7Nq1y2n7iy++KH7fq1cvhIaGYvjw4Thz5gw6d+7s7m6K1Go11Gp1g+1KpbLZfmma89hUh3V2D9bZfVhr92Cd3cPVdW7KsVrFkgOpqanYsGEDvvvuO3To0OGGbQcPHgwAOH36NABAr9ejoKDAqY3jvWMe1PXa6HQ6aDQaBAUFQaFQNNrmenOpiIiIqG1p0dAkCAJSU1Oxfv16bNu2DVFRUTf9mZycHABAaGgoACAuLg5HjhxxusstMzMTOp0OMTExYpusrCyn42RmZiIuLg4AoFKpEBsb69TGZrMhKytLbENERERtW4tenktJScGaNWvwn//8Bz4+PuIcJF9fX2g0Gpw5cwZr1qzBqFGjEBgYiMOHD2PGjBkYOnQoevfuDQBISEhATEwMxo0bh4ULF8JgMGDu3LlISUkRL59NnjwZS5cuxaxZszBhwgRs27YNX3zxBTIyMsS+pKWlITk5GQMGDMCgQYPw3nvvoaKiQrybjoiIiNq2Fg1Ny5cvB2BfwLK+lStX4vnnn4dKpcLWrVvFABMeHo4xY8Zg7ty5YluFQoENGzZgypQpiIuLg5eXF5KTk/H666+LbaKiopCRkYEZM2ZgyZIl6NChAz766CNxuQEAeOqpp1BUVIR58+bBYDCgb9++2LRpU4PJ4URERNQ2tWhoEgThhvvDw8OxY8eOmx4nIiICGzduvGGbYcOG4eDBgzdsk5qaitTU1Jt+HhEREbU9rWIiOBEREVFrx9BEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCQBQxMRERGRBAxNRERERBIwNBERERFJwNBEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCQBQxMRERGRBAxNRERERBIwNBERERFJwNBEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCQBQxMRERGRBAxNRERERBIwNBERERFJwNBEREREJAFDExEREZEEDE1EREREErRoaFqwYAEGDhwIHx8fBAcHY/To0cjNzXVqU11djZSUFAQGBsLb2xtjxoxBQUGBU5u8vDwkJSVBq9UiODgYM2fORE1NjVOb7du3o3///lCr1ejSpQtWrVrVoD/Lli1DZGQkPD09MXjwYOzdu9fl50xERER3phYNTTt27EBKSgr27NmDzMxMWCwWJCQkoKKiQmwzY8YM/Pe//8W6deuwY8cOXLp0CU888YS432q1IikpCWazGbt378Ynn3yCVatWYd68eWKbc+fOISkpCQ888ABycnIwffp0TJw4EZs3bxbbfP7550hLS8Orr76KAwcOoE+fPkhMTERhYaF7ikFEREStm9CKFBYWCgCEHTt2CIIgCCUlJYJSqRTWrVsntjl+/LgAQMjOzhYEQRA2btwoyOVywWAwiG2WL18u6HQ6wWQyCYIgCLNmzRLuuecep8966qmnhMTERPH9oEGDhJSUFPG91WoVwsLChAULFkjqe2lpqQBAKC0tbeJZ35zZbBa+/vprwWw2u/zYVId1dg/W2X1Ya/dgnd2juerclH+/PVo2sjkrLS0FAAQEBAAA9u/fD4vFgvj4eLFN9+7d0bFjR2RnZ2PIkCHIzs5Gr169EBISIrZJTEzElClTcPToUfTr1w/Z2dlOx3C0mT59OgDAbDZj//79SE9PF/fL5XLEx8cjOzu70b6aTCaYTCbxvdFoBABYLBZYLJbbqEJDjuO5+rjkjHV2D9bZfVhr92Cd3aO56tyU47Wa0GSz2TB9+nTcd9996NmzJwDAYDBApVLBz8/PqW1ISAgMBoPYpn5gcux37LtRG6PRiKqqKhQXF8NqtTba5sSJE432d8GCBXjttdcabN+yZQu0Wq3Es26azMzMZjkuOWOd3YN1dh/W2j1YZ/dwdZ0rKyslt201oSklJQU///wzdu3a1dJdkSQ9PR1paWnie6PRiPDwcCQkJECn07n0sywWCzIzMzFixAgolUqXHpvqsM7uwTq7D2vtHqyzezRXnR1XiqRoFaEpNTUVGzZswM6dO9GhQwdxu16vh9lsRklJidNoU0FBAfR6vdjm2rvcHHfX1W9z7R13BQUF0Ol00Gg0UCgUUCgUjbZxHONaarUaarW6wXalUtlsvzTNeWyqwzq7B+vsPqy1e7DO7uHqOjflWC1695wgCEhNTcX69euxbds2REVFOe2PjY2FUqlEVlaWuC03Nxd5eXmIi4sDAMTFxeHIkSNOd7llZmZCp9MhJiZGbFP/GI42jmOoVCrExsY6tbHZbMjKyhLbEBERUdvWoiNNKSkpWLNmDf7zn//Ax8dHnIPk6+sLjUYDX19fvPDCC0hLS0NAQAB0Oh2mTp2KuLg4DBkyBACQkJCAmJgYjBs3DgsXLoTBYMDcuXORkpIijgRNnjwZS5cuxaxZszBhwgRs27YNX3zxBTIyMsS+pKWlITk5GQMGDMCgQYPw3nvvoaKiAuPHj3d/YYiIiKjVadHQtHz5cgDAsGHDnLavXLkSzz//PABg8eLFkMvlGDNmDEwmExITE/H++++LbRUKBTZs2IApU6YgLi4OXl5eSE5Oxuuvvy62iYqKQkZGBmbMmIElS5agQ4cO+Oijj5CYmCi2eeqpp1BUVIR58+bBYDCgb9++2LRpU4PJ4URERNQ2tWhoEgThpm08PT2xbNkyLFu27LptIiIisHHjxhseZ9iwYTh48OAN26SmpiI1NfWmfSIiIqK2h8+eIyIiIpKAoYmIiIhIAoYmIiIiIgkYmoiIiIgkYGgiIiIikoChiYiIiEgChiYiIiIiCRiaiIiIiCRgaCIiIiKSgKGJiIiISAKGJiIiIiIJGJqIiIiIJGBoIiIiIpLAo6U7QDdxKQeKHz9ETP5VyH/IBbwCAE8/QOMHePrXfvW1b1Pwj5OIiKi58F/Z1u7yKcgPrUY0ABR+e+O2Ku96geoGXz19G27zUDXTCRAREd0dGJpau5AYWH/7Es4dP4hOoQGQm0qB6lKgqgSoLrF/NZfZ25rL7S/jr03/HKX2BkHL98YhTOl5e+dIRER0B2Boau1C7oEtoCuOGjciYtQoyJXKhm2sNfYg5QhR1cUNg1WjX0sBU6n9GJZK+6vsUtP76OF585Gs6wYuDSCTNf0ziYiI3Iyh6W6g8AC8Au2vprJZ6wKXpKBV/2spAAGoqQbKDfZXk/uuuvlI1vUuL6q8GbiIiMhtGJraOrkC0AbYX01lswEm4zWjXFK/lgKCFbCagYoi+6vJffe4eeC63qiXWsfARURETcLQRLdOLrcHEI0fgIim/awg2OdfNTVoOb63WQBbDVB5xf5qKpm8QeBSqHXoU1gK+bZ9gNa/br8jfDleGj/AQ930zyQiojsaQxO1DJkMUPvYXwhv2s8Kgn3+ldTAde1lR6sJEGxAVbH9VWw/rBxAJABkb795Hzw86wUpv4ahqv57p/2133N5CCKiOw7/5qY7j0wGqLzsL9/2Tf95S1WjActaWYyTh/eha8dgKMzldYFL/FoKVBtRN4+rGigvuLVzUHnfYujytV9alHNdWiIid2NoorZHqbG/dKFOm20WC04WtUeXEaOgaOwuRcA+cd5U1kiYKq13CbH0+vstFfbjiMtDXLyFE5ABnrpGQpXfzQOXxs++vATncxERNRlDE1FTyBW3Po8LAKwW+2hVdcmthS6rCYBQt+2WzsFD4iiXH+dzERHVw9BE5E4K5a0vDwEAlurrhKoSaaFLsN7eBHrgNuZz1V5aBEe5iOjOxNBEdCdRetpfPiFN/1lBAMwVNw5VDfbVfq2/EOptzufy8NAgEWp45L1RF7LU9S831n6v9m1km84+l42XF4moBTA0EbUVMhmg9ra/bmUC/a3O56oqsa/nZS63d6OmCp6oAq6U3OJ5KBoGKXFUS+c8qlW/Xf1tcsWtfTYRtWkMTUQkzW3P56oBTEZYyq/gh6yNuH9AL3jUVNaFK3Gh1NLrbDPaLy8KVqDqqv11q+rfvXijcOVZL5DVHw3j8xaJ2iSGJiJyD4WHfeV5pQ9KtZEQIn8DXO8uxcY4Li9eG6Sqay8dNtjWSLuaKvuxbuvuRdQ9/qfRy4q6enO5rh0Nq92m8uGyEUR3IIYmIroz1L+8qAu7tWPUmG8yomW8+TYIt/f4H/vJ2MOT2lfCKNe1oav2vYfqFj+biG4VQxMRtR0eKsAjCPAKurWft9kAc9l1wpUjYJVcZzTMWPcIoPrLRtziyhHw0DQymqWr99UXcqUXwq+cgywXgFdAvf217bkyPVGT8DeGiEgqubxutKepj/8B7JcYa6qvuYRYcvPLivW31U6oR00VUF4FlBuu+3EKAP0BIO/DxhsotY2ELV3DcNXofl/7Y5A44kVtCEMTEZG7yGR1K9LfyrIRgDihvtGAZTLW+1oKW1UpLl88iyAfFeSmMvvdjyaj/dmNgP2rpfKGweumPDxvHKykBDIumEp3CIYmIqI7iWNCvTbgpk2tFguyN27EqFGjIK8/6d5qqVs+wilo1f/aWBCr99XxSKCaavurovA2zkl9C6Hrmv28o5HcgKGJiKitUSglB6/rcox43Wroqrd2F6ym25xYD/sdjbdyibH+ew9PLpxKN8TQRERETdeEEa/rslnrhaiyBpcXbxi4HF9NRvuxrGag8rL9davkypuHLrVP7fc+te91gEIDjfmyvU8KPy6eehdjaCIiopYhVwAaf/vrVtW/o/FWRrscXyHY72y8hecyKgEkAMDRNPsGlU9tqKofsOqFLCnblV5cy6sVYmgiIqI7l9MdjbfIZrNfKrzeSJbTttoRMXFOWBkEUxlsVSVQCDX245nL7K+y2zkx2Q0CliOQ+TayXefcXqnlJUcXYmgiIqK2TS6vXe9KB9xC9qqxWLBx40aMShgOpa1aDFPOActY9734teyaIGase1wQBPtImakUMN7GuckU9YKXrpHgpWs42tVYew81wxcYmoiIiFzDQw0ovW998VTAeS0vU1ltcLo2YF1v+zVfBZs9gFWX2F+3Q5zv5RjZ8m0keN3oEuTdsa4XQxMREVFr4Yq1vIB6z2q8Jkw1CFhljY+MOcKYufYa4y3O92rAw/PmlxSvt12hgYe18vY+/za1aGjauXMn3nnnHezfvx/5+flYv349Ro8eLe5//vnn8cknnzj9TGJiIjZt2iS+v3r1KqZOnYr//ve/kMvlGDNmDJYsWQJvb2+xzeHDh5GSkoJ9+/ahXbt2mDp1KmbNmuV03HXr1uGVV17B+fPnER0djb/85S8YNWpU85w4ERFRc6r/rEaE3vpxHBPtr3c5scH264yMNVjXq+nLSygB9PMbCOB3t34+t6lFQ1NFRQX69OmDCRMm4Iknnmi0zUMPPYSVK1eK79Vq55Vjx44di/z8fGRmZsJisWD8+PF48cUXsWbNGgCA0WhEQkIC4uPjsWLFChw5cgQTJkyAn58fXnzxRQDA7t278cwzz2DBggV4+OGHsWbNGowePRoHDhxAz549m+nsiYiIWjlXTLQH7Ot6mW9wOdHp0mO9ifbmcnG7YCqDRd6yi5i2aGgaOXIkRo4cecM2arUaer2+0X3Hjx/Hpk2bsG/fPgwYMAAA8Pe//x2jRo3CX//6V4SFhWH16tUwm834+OOPoVKpcM899yAnJweLFi0SQ9OSJUvw0EMPYebMmQCAN954A5mZmVi6dClWrFjhwjMmIiJqgxQet728RI3FgpyMDIS5sFtN1ernNG3fvh3BwcHw9/fHgw8+iD//+c8IDAwEAGRnZ8PPz08MTAAQHx8PuVyOH3/8EY8//jiys7MxdOhQqFR1k88SExPxl7/8BcXFxfD390d2djbS0tKcPjcxMRFff/31dftlMplgMpnE90aj/fYGi8UCi8XiilMXOY7n6uOSM9bZPVhn92Gt3YN1dg+LxQLIZM32b6wUrTo0PfTQQ3jiiScQFRWFM2fO4KWXXsLIkSORnZ0NhUIBg8GA4OBgp5/x8PBAQEAADAb7AygNBgOioqKc2oSEhIj7/P39YTAYxG312ziO0ZgFCxbgtddea7B9y5Yt0Gq1t3S+N5OZmdksxyVnrLN7sM7uw1q7B+vsHq6uc2Wl9MnlrTo0Pf300+L3vXr1Qu/evdG5c2ds374dw4cPb8GeAenp6U6jU0ajEeHh4UhISIBOp3PpZ1ksFmRmZmLEiBFQ1n/oJrkU6+werLP7sNbuwTq7R3PV2XGlSIpWHZqu1alTJwQFBeH06dMYPnw49Ho9Cgudn6xdU1ODq1evivOg9Ho9CgoKnNo43t+szfXmUgH2uVbXTkoHAKVS2Wy/NM15bKrDOrsH6+w+rLV7sM7u4eo6N+VYd9SDbX799VdcuXIFoaH22yfj4uJQUlKC/fv3i222bdsGm82GwYMHi2127tzpdM0yMzMT3bp1g7+/v9gmKyvL6bMyMzMRFxfX3KdEREREd4gWDU3l5eXIyclBTk4OAODcuXPIyclBXl4eysvLMXPmTOzZswfnz59HVlYWHnvsMXTp0gWJiYkAgB49euChhx7CpEmTsHfvXvzwww9ITU3F008/jbAw+/z6Z599FiqVCi+88AKOHj2Kzz//HEuWLHG6tDZt2jRs2rQJ7777Lk6cOIH58+fjp59+QmpqqttrQkRERK1Ti4amn376Cf369UO/fv0AAGlpaejXrx/mzZsHhUKBw4cP49FHH0XXrl3xwgsvIDY2Ft9//73TZbHVq1eje/fuGD58OEaNGoX7778fH374objf19cXW7Zswblz5xAbG4v//d//xbx588TlBgDg3nvvxZo1a/Dhhx+iT58++PLLL/H1119zjSYiIiISteicpmHDhkEQhOvu37x5802PERAQIC5keT29e/fG999/f8M2Tz75JJ588smbfh4RERG1TXfUnCYiIiKilsLQRERERCQBQxMRERGRBAxNRERERBIwNBERERFJwNBEREREJMEd9RiV1syxdEJTnmEjlcViQWVlJYxGI5fob0ass3uwzu7DWrsH6+wezVVnx7/bN1oCyYGhyUXKysoAAOHh4S3cEyIiImqqsrIy+Pr63rCNTJASreimbDYbLl26BB8fH8hkMpce22g0Ijw8HBcuXIBOp3PpsakO6+werLP7sNbuwTq7R3PVWRAElJWVISwsDHL5jWctcaTJReRyOTp06NCsn6HT6fgL6Qass3uwzu7DWrsH6+wezVHnm40wOXAiOBEREZEEDE1EREREEjA03QHUajVeffVVqNXqlu7KXY11dg/W2X1Ya/dgnd2jNdSZE8GJiIiIJOBIExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUNTK7ds2TJERkbC09MTgwcPxt69e1u6S3eUBQsWYODAgfDx8UFwcDBGjx6N3NxcpzbV1dVISUlBYGAgvL29MWbMGBQUFDi1ycvLQ1JSErRaLYKDgzFz5kzU1NS481TuKG+//TZkMhmmT58ubmOdXePixYv4/e9/j8DAQGg0GvTq1Qs//fSTuF8QBMybNw+hoaHQaDSIj4/HqVOnnI5x9epVjB07FjqdDn5+fnjhhRdQXl7u7lNp1axWK1555RVERUVBo9Ggc+fOeOONN5yeT8ZaN93OnTvxyCOPICwsDDKZDF9//bXTflfV9PDhw/jNb34DT09PhIeHY+HCha45AYFarbVr1woqlUr4+OOPhaNHjwqTJk0S/Pz8hIKCgpbu2h0jMTFRWLlypfDzzz8LOTk5wqhRo4SOHTsK5eXlYpvJkycL4eHhQlZWlvDTTz8JQ4YMEe69915xf01NjdCzZ08hPj5eOHjwoLBx40YhKChISE9Pb4lTavX27t0rREZGCr179xamTZsmbmedb9/Vq1eFiIgI4fnnnxd+/PFH4ezZs8LmzZuF06dPi23efvttwdfXV/j666+FQ4cOCY8++qgQFRUlVFVViW0eeughoU+fPsKePXuE77//XujSpYvwzDPPtMQptVpvvvmmEBgYKGzYsEE4d+6csG7dOsHb21tYsmSJ2Ia1brqNGzcKL7/8svDVV18JAIT169c77XdFTUtLS4WQkBBh7Nixws8//yx89tlngkajET744IPb7j9DUys2aNAgISUlRXxvtVqFsLAwYcGCBS3YqztbYWGhAEDYsWOHIAiCUFJSIiiVSmHdunVim+PHjwsAhOzsbEEQ7L/kcrlcMBgMYpvly5cLOp1OMJlM7j2BVq6srEyIjo4WMjMzhd/+9rdiaGKdXWP27NnC/ffff939NptN0Ov1wjvvvCNuKykpEdRqtfDZZ58JgiAIx44dEwAI+/btE9t8++23gkwmEy5evNh8nb/DJCUlCRMmTHDa9sQTTwhjx44VBIG1doVrQ5Oravr+++8L/v7+Tn9vzJ49W+jWrdtt95mX51ops9mM/fv3Iz4+Xtwml8sRHx+P7OzsFuzZna20tBQAEBAQAADYv38/LBaLU527d++Ojh07inXOzs5Gr169EBISIrZJTEyE0WjE0aNH3dj71i8lJQVJSUlO9QRYZ1f55ptvMGDAADz55JMIDg5Gv3798I9//EPcf+7cORgMBqc6+/r6YvDgwU519vPzw4ABA8Q28fHxkMvl+PHHH913Mq3cvffei6ysLJw8eRIAcOjQIezatQsjR44EwFo3B1fVNDs7G0OHDoVKpRLbJCYmIjc3F8XFxbfVRz6wt5W6fPkyrFar0z8gABASEoITJ060UK/ubDabDdOnT8d9992Hnj17AgAMBgNUKhX8/Pyc2oaEhMBgMIhtGvtzcOwju7Vr1+LAgQPYt29fg32ss2ucPXsWy5cvR1paGl566SXs27cPf/zjH6FSqZCcnCzWqbE61q9zcHCw034PDw8EBASwzvXMmTMHRqMR3bt3h0KhgNVqxZtvvomxY8cCAGvdDFxVU4PBgKioqAbHcOzz9/e/5T4yNFGbkZKSgp9//hm7du1q6a7cdS5cuIBp06YhMzMTnp6eLd2du5bNZsOAAQPw1ltvAQD69euHn3/+GStWrEBycnIL9+7u8sUXX2D16tVYs2YN7rnnHuTk5GD69OkICwtjrdswXp5rpYKCgqBQKBrcXVRQUAC9Xt9CvbpzpaamYsOGDfjuu+/QoUMHcbter4fZbEZJSYlT+/p11uv1jf45OPaR/fJbYWEh+vfvDw8PD3h4eGDHjh3429/+Bg8PD4SEhLDOLhAaGoqYmBinbT169EBeXh6Aujrd6O8NvV6PwsJCp/01NTW4evUq61zPzJkzMWfOHDz99NPo1asXxo0bhxkzZmDBggUAWOvm4KqaNuffJQxNrZRKpUJsbCyysrLEbTabDVlZWYiLi2vBnt1ZBEFAamoq1q9fj23btjUYso2NjYVSqXSqc25uLvLy8sQ6x8XF4ciRI06/qJmZmdDpdA3+AWurhg8fjiNHjiAnJ0d8DRgwAGPHjhW/Z51v33333ddgyYyTJ08iIiICABAVFQW9Xu9UZ6PRiB9//NGpziUlJdi/f7/YZtu2bbDZbBg8eLAbzuLOUFlZCbnc+Z9IhUIBm80GgLVuDq6qaVxcHHbu3AmLxSK2yczMRLdu3W7r0hwALjnQmq1du1ZQq9XCqlWrhGPHjgkvvvii4Ofn53R3Ed3YlClTBF9fX2H79u1Cfn6++KqsrBTbTJ48WejYsaOwbds24aeffhLi4uKEuLg4cb/jVviEhAQhJydH2LRpk9CuXTveCn8T9e+eEwTW2RX27t0reHh4CG+++aZw6tQpYfXq1YJWqxU+/fRTsc3bb78t+Pn5Cf/5z3+Ew4cPC4899lijt2z369dP+PHHH4Vdu3YJ0dHRbfo2+MYkJycL7du3F5cc+Oqrr4SgoCBh1qxZYhvWuunKysqEgwcPCgcPHhQACIsWLRIOHjwo/PLLL4IguKamJSUlQkhIiDBu3Djh559/FtauXStotVouOdAW/P3vfxc6duwoqFQqYdCgQcKePXtaukt3FACNvlauXCm2qaqqEv7whz8I/v7+glarFR5//HEhPz/f6Tjnz58XRo4cKWg0GiEoKEj43//9X8Fisbj5bO4s14Ym1tk1/vvf/wo9e/YU1Gq10L17d+HDDz902m+z2YRXXnlFCAkJEdRqtTB8+HAhNzfXqc2VK1eEZ555RvD29hZ0Op0wfvx4oayszJ2n0eoZjUZh2rRpQseOHQVPT0+hU6dOwssvv+x0Gztr3XTfffddo38nJycnC4LgupoeOnRIuP/++wW1Wi20b99eePvtt13Sf5kg1FvelIiIiIgaxTlNRERERBIwNBERERFJwNBEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREzUQmk+Hrr79u6W4QkYswNBHRXen555+HTCZr8HrooYdaumtEdIfyaOkOEBE1l4ceeggrV6502qZWq1uoN0R0p+NIExHdtdRqNfR6vdPL398fgP3S2fLlyzFy5EhoNBp06tQJX375pdPPHzlyBA8++CA0Gg0CAwPx4osvory83KnNxx9/jHvuuQdqtRqhoaFITU112n/58mU8/vjj0Gq1iI6OxjfffNO8J01EzYahiYjarFdeeQVjxozBoUOHMHbsWDz99NM4fvw4AKCiogKJiYnw9/fHvn37sG7dOmzdutUpFC1fvhwpKSl48cUXceTIEXzzzTfo0qWL02e89tpr+J//+R8cPnwYo0aNwtixY3H16lW3nicRuYhARHQXSk5OFhQKheDl5eX0evPNNwVBEAQAwuTJk51+ZvDgwcKUKVMEQRCEDz/8UPD39xfKy8vF/RkZGYJcLhcMBoMgCIIQFhYmvPzyy9ftAwBh7ty54vvy8nIBgPDtt9+67DyJyH04p4mI7loPPPAAli9f7rQtICBA/D4uLs5pX1xcHHJycgAAx48fR58+feDl5SXuv++++2Cz2ZCbmwuZTIZLly5h+PDhN+xD7969xe+9vLyg0+lQWFh4q6dERC2IoYmI7lpeXl4NLpe5ikajkdROqVQ6vZfJZLDZbM3RJSJqZpzTRERt1p49exq879GjBwCgR48eOHToECoqKsT9P/zwA+RyObp16wYfHx9ERkYiKyvLrX0mopbDkSYiumuZTCYYDAanbR4eHggKCgIArFu3DgMGDMD999+P1atXY+/evfjnP/8JABg7dixeffVVJCcnY/78+SgqKsLUqVMxbtw4hISEAADmz5+PyZMnIzg4GCNHjkRZWRl++OEHTJ061b0nSkRuwdBERHetTZs2ITQ01Glbt27dcOLECQD2O9vWrl2LP/zhDwgNDcVnn32GmJgYAIBWq8XmzZsxbdo0DBw4EFqtFmPGjMGiRYvEYyUnJ6O6uhqLFy/Gn/70JwQFBeF3v/ud+06QiNxKJgiC0NKdICJyN5lMhvXr12P06NEt3RUiukNwThMRERGRBAxNRERERBJwThMRtUmcmUBETcWRJiIiIiIJGJqIiIiIJGBoIiIiIpKAoYmIiIhIAoYmIiIiIgkYmoiIiIgkYGgiIiIikoChiYiIiEiC/w+VTbqIH1XAGAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FmUi_Xbqy8EW"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}