Gold_Price_Prediction.ipynb

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      "name": "Gold Price Prediction.ipynb",
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      "source": [
        "Importing Libraries"
      ],
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      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn import metrics"
      ],
      "metadata": {
        "id": "2ovY3QOWwxqh"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Collection and Processing"
      ],
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        "id": "p1RpsVHphkdd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading the csv data to pandas dataframe\n",
        "gold_data = pd.read_csv('/content/gld_price_data.csv')"
      ],
      "metadata": {
        "id": "LyY54xCwhHNL"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#print first 5 rows in the dataframe\n",
        "gold_data.head()"
      ],
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      "execution_count": 3,
      "outputs": [
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          "data": {
            "text/plain": [
              "       Date          SPX        GLD        USO     SLV   EUR/USD\n",
              "0  1/2/2008  1447.160034  84.860001  78.470001  15.180  1.471692\n",
              "1  1/3/2008  1447.160034  85.570000  78.370003  15.285  1.474491\n",
              "2  1/4/2008  1411.630005  85.129997  77.309998  15.167  1.475492\n",
              "3  1/7/2008  1416.180054  84.769997  75.500000  15.053  1.468299\n",
              "4  1/8/2008  1390.189941  86.779999  76.059998  15.590  1.557099"
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              "      <td>1/2/2008</td>\n",
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              "      <th>1</th>\n",
              "      <td>1/3/2008</td>\n",
              "      <td>1447.160034</td>\n",
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              "      <th>2</th>\n",
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              "      <td>77.309998</td>\n",
              "      <td>15.167</td>\n",
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              "      <th>3</th>\n",
              "      <td>1/7/2008</td>\n",
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              "      <th>4</th>\n",
              "      <td>1/8/2008</td>\n",
              "      <td>1390.189941</td>\n",
              "      <td>86.779999</td>\n",
              "      <td>76.059998</td>\n",
              "      <td>15.590</td>\n",
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      "source": [
        "#print last 5 rows in the dataframe\n",
        "gold_data.tail()"
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            "text/plain": [
              "           Date          SPX         GLD      USO      SLV   EUR/USD\n",
              "2285   5/8/2018  2671.919922  124.589996  14.0600  15.5100  1.186789\n",
              "2286   5/9/2018  2697.790039  124.330002  14.3700  15.5300  1.184722\n",
              "2287  5/10/2018  2723.070068  125.180000  14.4100  15.7400  1.191753\n",
              "2288  5/14/2018  2730.129883  124.489998  14.3800  15.5600  1.193118\n",
              "2289  5/16/2018  2725.780029  122.543800  14.4058  15.4542  1.182033"
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              "      <th>2285</th>\n",
              "      <td>5/8/2018</td>\n",
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              "      <td>124.589996</td>\n",
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              "      <td>5/9/2018</td>\n",
              "      <td>2697.790039</td>\n",
              "      <td>124.330002</td>\n",
              "      <td>14.3700</td>\n",
              "      <td>15.5300</td>\n",
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              "      <td>5/10/2018</td>\n",
              "      <td>2723.070068</td>\n",
              "      <td>125.180000</td>\n",
              "      <td>14.4100</td>\n",
              "      <td>15.7400</td>\n",
              "      <td>1.191753</td>\n",
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              "      <td>124.489998</td>\n",
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              "      <th>2289</th>\n",
              "      <td>5/16/2018</td>\n",
              "      <td>2725.780029</td>\n",
              "      <td>122.543800</td>\n",
              "      <td>14.4058</td>\n",
              "      <td>15.4542</td>\n",
              "      <td>1.182033</td>\n",
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      "source": [
        "# number of rows and columns\n",
        "gold_data.shape"
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          "data": {
            "text/plain": [
              "(2290, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
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    {
      "cell_type": "code",
      "source": [
        "# some basic information about the data\n",
        "gold_data.info()"
      ],
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        "id": "tIC6WHc3kFBQ",
        "outputId": "a16c1175-443e-4c43-ec42-216128ba537a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2290 entries, 0 to 2289\n",
            "Data columns (total 6 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   Date     2290 non-null   object \n",
            " 1   SPX      2290 non-null   float64\n",
            " 2   GLD      2290 non-null   float64\n",
            " 3   USO      2290 non-null   float64\n",
            " 4   SLV      2290 non-null   float64\n",
            " 5   EUR/USD  2290 non-null   float64\n",
            "dtypes: float64(5), object(1)\n",
            "memory usage: 107.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking the number of missing values\n",
        "gold_data.isnull().sum()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "--o2juzOkUWp",
        "outputId": "801c01cf-be33-4b29-ee66-040e2a70b942"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date       0\n",
              "SPX        0\n",
              "GLD        0\n",
              "USO        0\n",
              "SLV        0\n",
              "EUR/USD    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# getting the statistical measures of the data\n",
        "gold_data.describe()"
      ],
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        },
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      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               SPX          GLD          USO          SLV      EUR/USD\n",
              "count  2290.000000  2290.000000  2290.000000  2290.000000  2290.000000\n",
              "mean   1654.315776   122.732875    31.842221    20.084997     1.283653\n",
              "std     519.111540    23.283346    19.523517     7.092566     0.131547\n",
              "min     676.530029    70.000000     7.960000     8.850000     1.039047\n",
              "25%    1239.874969   109.725000    14.380000    15.570000     1.171313\n",
              "50%    1551.434998   120.580002    33.869999    17.268500     1.303297\n",
              "75%    2073.010070   132.840004    37.827501    22.882500     1.369971\n",
              "max    2872.870117   184.589996   117.480003    47.259998     1.598798"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4036a1d2-3db8-4e57-9a3a-ef6e85fc2a3e\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SPX</th>\n",
              "      <th>GLD</th>\n",
              "      <th>USO</th>\n",
              "      <th>SLV</th>\n",
              "      <th>EUR/USD</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2290.000000</td>\n",
              "      <td>2290.000000</td>\n",
              "      <td>2290.000000</td>\n",
              "      <td>2290.000000</td>\n",
              "      <td>2290.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1654.315776</td>\n",
              "      <td>122.732875</td>\n",
              "      <td>31.842221</td>\n",
              "      <td>20.084997</td>\n",
              "      <td>1.283653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>519.111540</td>\n",
              "      <td>23.283346</td>\n",
              "      <td>19.523517</td>\n",
              "      <td>7.092566</td>\n",
              "      <td>0.131547</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>676.530029</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>7.960000</td>\n",
              "      <td>8.850000</td>\n",
              "      <td>1.039047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1239.874969</td>\n",
              "      <td>109.725000</td>\n",
              "      <td>14.380000</td>\n",
              "      <td>15.570000</td>\n",
              "      <td>1.171313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1551.434998</td>\n",
              "      <td>120.580002</td>\n",
              "      <td>33.869999</td>\n",
              "      <td>17.268500</td>\n",
              "      <td>1.303297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2073.010070</td>\n",
              "      <td>132.840004</td>\n",
              "      <td>37.827501</td>\n",
              "      <td>22.882500</td>\n",
              "      <td>1.369971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2872.870117</td>\n",
              "      <td>184.589996</td>\n",
              "      <td>117.480003</td>\n",
              "      <td>47.259998</td>\n",
              "      <td>1.598798</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4036a1d2-3db8-4e57-9a3a-ef6e85fc2a3e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4036a1d2-3db8-4e57-9a3a-ef6e85fc2a3e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checking Correlation"
      ],
      "metadata": {
        "id": "hsup8mJ16b-r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "correlation = gold_data.corr()"
      ],
      "metadata": {
        "id": "I0tfpFgU6KZV"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# correlation Heatmap\n",
        "plt.figure(figsize = (8,8))\n",
        "sns.heatmap(correlation, cbar=True, square=True, fmt='.1f', annot=True, annot_kws={'size':8}, cmap='Blues')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "b4VAi3YK6kuY",
        "outputId": "d35155ce-52b7-44db-ea24-839846e5ae87"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f192b2d5090>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# correlation values of GLD\n",
        "print(correlation['GLD'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "25kuIPp37do9",
        "outputId": "a122ef8e-aa14-4e34-e2b9-57cee597d21b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SPX        0.049345\n",
            "GLD        1.000000\n",
            "USO       -0.186360\n",
            "SLV        0.866632\n",
            "EUR/USD   -0.024375\n",
            "Name: GLD, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking the distrinution of the GLD\n",
        "sns.distplot(gold_data['GLD'], color='green')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "SWXD_Qyf91PM",
        "outputId": "192f720f-43db-494b-f0e6-1ea3ac868e34"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f19267f9750>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splitting the Features and Target"
      ],
      "metadata": {
        "id": "BEZRW3oF6yMU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = gold_data.drop(['Date','GLD'], axis=1)\n",
        "Y = gold_data['GLD']"
      ],
      "metadata": {
        "id": "vY2rdSwt-SjD"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ysSvh_627RRo",
        "outputId": "47bb8c2a-8023-4858-d480-7665adcbe98a"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              SPX        USO      SLV   EUR/USD\n",
            "0     1447.160034  78.470001  15.1800  1.471692\n",
            "1     1447.160034  78.370003  15.2850  1.474491\n",
            "2     1411.630005  77.309998  15.1670  1.475492\n",
            "3     1416.180054  75.500000  15.0530  1.468299\n",
            "4     1390.189941  76.059998  15.5900  1.557099\n",
            "...           ...        ...      ...       ...\n",
            "2285  2671.919922  14.060000  15.5100  1.186789\n",
            "2286  2697.790039  14.370000  15.5300  1.184722\n",
            "2287  2723.070068  14.410000  15.7400  1.191753\n",
            "2288  2730.129883  14.380000  15.5600  1.193118\n",
            "2289  2725.780029  14.405800  15.4542  1.182033\n",
            "\n",
            "[2290 rows x 4 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l5rtDXae7V0Y",
        "outputId": "d5a94e6e-1726-4e41-e94f-195760696f92"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0        84.860001\n",
            "1        85.570000\n",
            "2        85.129997\n",
            "3        84.769997\n",
            "4        86.779999\n",
            "           ...    \n",
            "2285    124.589996\n",
            "2286    124.330002\n",
            "2287    125.180000\n",
            "2288    124.489998\n",
            "2289    122.543800\n",
            "Name: GLD, Length: 2290, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splitting into Testing data and Training data"
      ],
      "metadata": {
        "id": "zdfNO_L57i8p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)"
      ],
      "metadata": {
        "id": "bj0iXHV27cUQ"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Training: Random Forest Regressor"
      ],
      "metadata": {
        "id": "K7UZ8BexD6Ig"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "regressor = RandomForestRegressor(n_estimators=100)"
      ],
      "metadata": {
        "id": "kv4tjWnKD4dw"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# training the model\n",
        "regressor.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aWDFY5swHBC3",
        "outputId": "40e7145d-006b-4ae7-c9dc-62bb805186a4"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor()"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Evaluation"
      ],
      "metadata": {
        "id": "mWmQvR5hHOjL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# prediction on Test Data\n",
        "test_data_prediction = regressor.predict(X_test)"
      ],
      "metadata": {
        "id": "_hsLjl7VHNEe"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(test_data_prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b4C0u_GTHv2i",
        "outputId": "2a72f315-0498-4d30-c6c3-a646e7cbe7e9"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[168.53299931  82.14179972 116.23759986 127.50070033 120.73290128\n",
            " 154.61099788 150.32299757 126.17369991 117.43069883 125.9231012\n",
            " 116.72810102 171.65890075 141.75909826 167.74339856 115.0811001\n",
            " 117.64140081 138.21440308 170.23860074 159.62630273 159.14829945\n",
            " 155.23120003 125.41110028 175.92489985 156.93720399 125.17860046\n",
            "  93.74919971  78.22480012 120.59820012 119.18199975 167.52729981\n",
            "  88.13820052 125.17740015  91.41520103 117.62540049 121.13839899\n",
            " 136.42280125 115.43150101 114.83930092 146.21379928 107.43740076\n",
            " 104.66200262  87.11649789 126.49040035 117.70130035 152.90919942\n",
            " 119.52199975 108.40169997 107.94999817  93.28140084 127.06609762\n",
            "  74.49640077 113.57589938 121.48200014 111.30809913 118.96919913\n",
            " 120.73999972 159.01730063 167.14210113 146.76719695  85.83579837\n",
            "  94.49860053  86.76629882  90.57749999 119.00080083 126.48510089\n",
            " 127.3690001  169.63330038 122.2459994  117.47889916  98.4052003\n",
            " 168.41330143 143.2420992  132.3096023  121.1822023  121.26369937\n",
            " 119.63430049 114.32570156 118.38420053 107.25840113 127.96190081\n",
            " 113.8707993  107.36650011 116.72130054 119.7114986   88.90980067\n",
            "  88.16199852 146.51160275 127.46319986 113.06380035 110.12839848\n",
            " 108.16269931  77.44779899 170.00640205 113.96989901 121.51719925\n",
            " 128.08730159 155.00099838  91.72089973 136.58850148 159.61300394\n",
            " 124.96790084 125.29060027 130.61680196 114.68420138 119.9106003\n",
            "  92.18469973 110.26529904 168.10739967 157.19589904 114.07939925\n",
            " 106.56220133  80.0821996  113.25110024 125.79250057 107.31329936\n",
            " 119.26070034 155.38430258 159.87829932 120.20989974 134.4839022\n",
            " 101.36329994 117.57399773 119.25220005 112.9470008  102.8132995\n",
            " 160.26379831  99.2792006  146.87070025 125.44580097 169.939699\n",
            " 125.60359923 127.40259732 127.44850188 113.73949912 112.8681005\n",
            " 123.44899923 102.23839901  89.16900001 124.38349966 101.19779946\n",
            " 107.27099905 113.82070028 117.11200112  99.32859938 121.72220045\n",
            " 163.84939876  87.29439888 106.61890015 117.31510069 127.77230123\n",
            " 123.97750043  80.64499912 120.39340089 158.25449845  87.94519959\n",
            " 110.26419938 118.889499   172.97449883 102.96189912 105.10260026\n",
            " 122.58390051 158.48929826  87.71529838  92.97960039 112.89800031\n",
            " 177.26689964 114.33150033 119.25979993  94.58950085 125.51260021\n",
            " 166.45040114 114.89690091 116.80510139  88.2768986  148.5333006\n",
            " 120.49589898  89.51520021 112.10929965 117.05060079 118.63540119\n",
            "  88.07179922  94.12040003 116.9780003  118.58280209 120.43270091\n",
            " 126.74049845 121.92149992 148.35440059 165.92180071 118.50799974\n",
            " 120.66560143 150.02240074 118.22249925 172.83109916 105.14239938\n",
            " 104.92160108 148.57430067 113.33710084 124.77410124 147.40209983\n",
            " 119.57980145 115.28890022 112.45279962 113.67410136 140.43710162\n",
            " 117.88739774 102.92760014 115.82320113 104.04850173  98.81730042\n",
            " 117.4475006   90.67270012  91.79640014 153.40309894 102.68859971\n",
            " 154.70810084 114.28210102 138.4455011   90.05759842 115.46189961\n",
            " 115.20290002 122.94220049 121.84959977 165.29690136  92.93249948\n",
            " 136.25270127 121.28359942 120.78780014 104.74180014 142.00520284\n",
            " 122.14959898 116.52280043 113.64960091 126.84539799 122.79499948\n",
            " 125.7226997  121.21420055  86.89949935 132.68270199 144.34430253\n",
            "  92.61789926 158.75809919 159.36410345 126.17539894 164.96229967\n",
            " 109.12479974 109.76740092 103.71979853  94.28320063 128.02220295\n",
            " 107.07940053 162.03459954 121.80350029 131.94090066 130.60840169\n",
            " 159.81609981  90.09759907 175.47750237 127.70850039 126.71669879\n",
            "  86.33429928 124.64549888 150.69609765  89.60170037 106.81909993\n",
            " 109.00439985  84.24359886 135.96290043 155.00710196 139.82600342\n",
            "  73.70070001 150.88010169 125.89230015 126.80010006 127.55729892\n",
            " 108.60599964 156.00420021 114.43790137 116.93480171 125.31769981\n",
            " 154.20810172 121.29529994 156.37839892  92.95600054 125.5125012\n",
            " 125.61820036  88.00160034  92.13109929 126.03950011 128.40450399\n",
            " 113.37680093 117.58759743 121.00289993 126.95539849 119.95100121\n",
            " 136.67160111  93.90129909 119.65200031 113.14870089  94.29719965\n",
            " 108.92129968  86.99469909 108.69269957  89.51169977  92.51909999\n",
            " 131.73330324 162.34330012  89.17400006 119.61810054 133.4601021\n",
            " 123.97470022 128.19740219 102.04249841  88.93919877 131.73140086\n",
            " 120.40960049 108.50920025 168.31820161 115.10880048  86.64539918\n",
            " 118.77650061  90.94229997 161.82539969 116.25770031 121.70760025\n",
            " 160.48189825 120.0938995  112.94519938 108.4803987  126.66410028\n",
            "  75.73180059 102.99299973 127.8471025  121.75639946  92.6514\n",
            " 131.94720089 118.22290095 115.87909994 154.45340264 160.2412006\n",
            " 109.90239996 152.9414977  119.34350113 160.40420094 118.41640025\n",
            " 158.19069938 115.05849957 116.34890019 148.79819858 114.75660071\n",
            " 125.92329899 164.8888988  117.64839993 125.03619952 153.08980399\n",
            " 153.44140254 132.24690087 114.79600033 121.249502   125.0914008\n",
            "  89.75950045 122.96999997 154.62440172 111.66020023 106.5121003\n",
            " 161.54390107 118.43279931 165.71499998 134.05580127 115.00120001\n",
            " 153.01819868 168.48269942 114.63849992 114.23160132 158.45169892\n",
            "  85.09969947 127.0676009  128.04820117 128.79979983 124.29940067\n",
            " 123.84030063  90.49990073 153.50429945  97.07869984 138.04219975\n",
            "  89.2666994  107.40080018 114.94800026 112.66740126 124.22679894\n",
            "  91.331699   125.48470153 162.48369857 120.13409884 164.92480086\n",
            " 126.80369829 112.25350027 127.48099887  95.03079884  91.06689956\n",
            " 102.83549896 120.88449988  83.70569946 126.22819997 160.82110499\n",
            " 117.14630082 118.36300005 120.04210012 122.70359962 120.0783013\n",
            " 121.37320034 118.28660054 107.26009979 148.02019982 126.16729902\n",
            " 115.78790097  74.04369965 127.79710069 154.09380076 122.22550009\n",
            " 125.68860079  88.86140066 102.7248987  124.80080041 120.26649994\n",
            "  73.44090086 152.12349992 121.1181004  104.53099994  86.11439769\n",
            " 115.46619951 172.13999819 120.00220056 160.81019854 113.27129951\n",
            " 121.11820006 118.64960119  95.97389988 118.96909998 126.04320053\n",
            " 118.54549965  96.17960102 153.67070165 122.20800013 147.54420009\n",
            " 159.71460195 113.7057002  122.52789935 149.84879812 127.36890014\n",
            " 165.36580083 135.31720002 120.08929982 167.22689874 108.31089935\n",
            " 121.78599841 139.15400102 106.41449898]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# R squared error\n",
        "error_score = metrics.r2_score(Y_test, test_data_prediction)\n",
        "print(error_score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kRPn-5N8H30e",
        "outputId": "f1719b87-0664-45fa-e418-a84c4fe40ad6"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.990002370781027\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Comparing the Actual Values and Predicted Values in a Plot"
      ],
      "metadata": {
        "id": "olgSUz5YKIQG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y_test = list(Y_test)"
      ],
      "metadata": {
        "id": "PKHm7ZBgIUkP"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(Y_test, color='blue', label = 'Actual Price')\n",
        "plt.plot(test_data_prediction, color='green', label='Predicted Price')\n",
        "plt.title('Actual Price vs Predicted Price')\n",
        "plt.xlabel('Number of values')\n",
        "plt.ylabel('GLD Price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "qVBqmt0UKbVg",
        "outputId": "023b5193-b82f-4144-c49a-30bdd7700c60"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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nCnzGjUK5NMWNQgY3rolv/PUbuGfVPR1ojI7paRTgZgrmMqfQLDSFdsb9V/0qAKC7ZNcMWqmXkpUpyM6n2qg2faxOi6I+C7B86DHgXf+E5QvPzPy7Xz/6awxVO2NEfvUrdETcnOohqTRFhFM/8BGwoGWmcO65wO87V4cxxFSOG5iWRiF0HwXpTKGewhSiDrB1BUl2xF2lrpb3ZSJqZvLTKIV2aaCmFnjbg4BhtMFzIaq9z2f65f1r7sd7b3gvPv77j3ekZe/75EqccGoHxPkpril0EpddBrzhDdm2LX+1jJOvPjlkCpVSpaljfutbwNve1tRPO4blW5dPCEOQmOIcqzPI4z5yagoNaRTEAta6UeisyyZb5yKvSXNtmZhRox+w8DyyMiAZftyxqKpzXiQ+5Dz7I34CDO0OwN4TeTtEZHtn8NEvLAV2fQzAqZm2/93Tv8P+7CMAgEqz7qMjfgKsOwLAEc39PgXh85rjMXnpf720I21xYVobBbvQbLiPHCGpVVH8qBPuo7QKrU0hYzNbSwzjB+lcbplkeEF03TMaY59x416iUkda1jTe8WHxwX6DIo/h9GMK+MRB4kM2owAAPuODtaY1hZT7MR0wrd1HNqYQBBmZgnAf+SGzaJ/7qB7oORDtcB1ErrLkfW3Y2PqxTMNqw81P34xbn2nOCa/tP+Nl9wN+H72pZhRSsONkNE+N9kn3UVeT7qOJwlS+n9OSKciwS9soP6YppLiPwu3b4D5KYwqt1NYPzzVFU2BtSAwzDasNJ151Ij9eE5PcNMNmVq/l9/HZ5TumUZjMTrdaBbZvB+bPn7QmZEYQagr5urahIV7lF7t2oFEqdgBv4PRkCp50H8U7l5j7yJmnIIyC6ADbMRuY9OPXfXu2dCueqsxuriZC5Uz4GYxCa/tnEcvLaIzXb+D3cfOmHc0o8L+TObK8+GLgiM642FsGY8A9igbrQ7iPckYf/e1vfLraAtPUKMgO3ObmMDs0V/RR1TfdR61Duo9cTKGVvjZylSV3Lu04n0aHjUIQBLmF5oYYQRKb+kZhxcCK+P2aRG/DX7fegLWvsfv12zEYAni59dHR/L+75hrgNUo+l3Qfech3nx/Zdhfwpc4P49t1vTqJaWkUkjQF0yg0AsUoULSuJjKao+3b5z4yNQWJlphCBj8/0J4OveNMgbHc10IKzd4U8ZiuW2fPUH9o3UN4yfdfgov/djGAiCFMJlO4qfcUBC+7LtO2p/3Pafj+376fadu1a4G77uKf580DXvWq/G175hkG7BklewZoLgDkvq0TU0FPtmrjRmBwcEIOmRsdMwpE9DMi2khEjxvLP0lETxLRE0T0TWX5eUS0jIieIqLjO9UucSwA2aKPNKG5PB5+rPm6+6gdmoLE+o0uptB8xxBYmML4OGDOCph0jOHheIIWY8B55/G5eiU6bhRUNpPxsjd8yRSmhlGQnaGJ5QPLAQB3P383RkeB1WuyMbypgmueuAb/8od/ybTtwoXAscdG3x99NP/xHmfXAmdG1iQQxn90LMAnPsGf2SyY6GSye+/Vz30qoZNM4ecA3qouIKLXAzgJwKGMsYUAvi2WHwTgNAALxW9+SNS5MJFEppAkNJfHwo+1RvujjySGx0ymwNupdrZ5H2Kb0Hz22cAxxwDLl0fbJXXop58OnHACsHp1tGzpUuDrXwdOOUXZRxtdajYwxnJnTUdMYWq4j7IU2T3pJOCHPxBMoQ1aTydh3o+bFz2Jx1cmz8u6Ta1c3zOgDbqyYgv0iculpnDLLQw/+AHw3e9m20/a4/TgugdBXyYs2dTEDFcKwl7irf+KR2df6NzummuSy590Eh0zCoyxOwFsNRZ/DMDXGWNVsY18ak4CcDVjrMoYexbAMgBHdaptSUzBdJ9oHVwSU2ijUZDF9iSk68BXhPFGI18nYetEH3qI/1VpbBJTuOuvPjBrteb2GK6NAOeXsWWX34TL2skUNm3ibpYf/zha5iuaQtbrLnUamiJGIW2ObwaGP/+ZAV4jXDIl4YiIO/GPL8Pf/fjFyb896r+AD4lR/ufmAh96tXWzxA7bOLx0H8n30hEnEkMaC7/68asBAL976nfZdpgFb/yCc9VppwGHHtq+Q+XBRGsK+wM4hoj+RkR3ENErxPIFANTpulaLZTEQ0VlEtJiIFm/atKmpRiS6j/yE6KOeaGgjjUIrLh0VVaWqhGuuBrWzdSXVuWDLU6j2LQdOODscRQPJmsLAyz8L/Nte2DQajQDXjq4EPB+bDj7f2s48+MtfgFe+Uh9FSxbz058q56LcN8rotgumGFNoNADMWxpbHtYSYgz4+08B7+ElzKdyXLsTXSPJ60/4FLCXUvxx94fwaotdMN/JJISagowKzDhWS2MK0frWBn+ZheZP7o/RhT9o6VjNYqKNQhnAXACvBPAZANdSzipvjLHLGWNHMsaOnN9k4HToPrJ06GaYqiY0H/fl8GNdDPXMvAYXajX+gH7pS/b149XoOFXH/M+qAco7R7TsSFU3xLNH/hNw1A/x9ODD1mPEcPBVAICxejLNb9YoPPwwDw0cUmrWyV2pT0kQ5Bea5X2kKSI037H9CiVjN4L2Ohx9ibJmahqFdsfS3HtvfFnS82R2sgHJQpX5jEKaMP3wI3z9ww8nbpaKLMbd9wHs/Azwtk+0drAmMdFGYTWA6xnHfQACAPMArAGwl7LdnmJZR5DoPnLUPpoZ7MWjHAJ+yWQugZ9RaB4TcoTLxzmqdLROpqAYrGqa/8EAs4U2kjREUdvVFzBmIHq5N1AN68wSwZUV9YYP9G3SOvwgYMCxX0W9e120fxZEx8380k8tpnD90GcS18c7j8k3Csn9ZufalzTwMjt9JpkC1YBXfQc+stXwShtkbBvkGwwOdj6kdHS8A2VucmCijcJvAbweAIhofwBdADYDuAnAaUTUTUQvArAfgPs61YjkMhf26KN5XXsC/RsAxi9ZqCm0SWhW3UE1R4evZgrX6hmdpQLMIjTLjqfkRY+B2qFv3W5USq1ww6WxJwvS1rvwUP1q4F9ejNF6JOg/uf0B4A3n45lDTw+XBQGL7lNG91HEFKaGURhn9hLemvtIwVRwH7XLVQokdMKWXIGksilxpsDfnaGFFwPH/zv+RtnCY1PPLRyDdN59NDw+uRWKOxmSehWAewEcQESriehDAH4G4MUiTPVqAB8QrOEJANcCWALgDwDOZow117NkaxuAbEKz1BTmVvYAyjWgxB86mUsQ0tSMD4uLzqqCdtVkChSPPjLF6DRY6TFJiq0yhWi7zdsi9qL+3K1nyKTA5pjC9mAD0D2M0VpkFMLr7EWZTX4QwGf5kteaTWrqFHJ3Ljn648WL3W7KVpDsYsl3PnkS1ZJdtPpxmXQfVXj0RM1hfE1kTvhPOc0NGyKvgH0H6ccYmWSj0DEHK2Ps3Y5V1gkUGWMXAnDHaLUReQriyUJqO3ftASg3u24yhZQRa9pDpwraLqagdrauGeFcUIXmkRHgzjv5Z0DvoNTzV7UDNUIprdNv1ihIA6t2Pizs/D1tWR7xkbdpajEFcozHXAXw8jCFV4jwDWkYfvMbLtS3OnmM7zNUjB4jenby3Y+Bgehz2ruR6D4yvkuhWbpGs0qW6UQh2/ntthsP8+bvV3MYGhPvnT85Rf2mZUZznuQ12cHN695DXy6ITNaM5lB6cDxb6oMvi+3F9sGaZwqq++iss3i+QbUW73DV81cT99TJ5V31oKJ9NGcU4tc0ukeq4fKDIOzks++bX6/SFBGaXc+Lk0E48hQefRR47rnkI/3jJx/ArcOtj7faWSZ+61bV8CdvmzWYAwCYcB+xnEYhe95L+v5ciYlZMSJDEf32T7aVBVPlDZlQ5ClzITvAXXsXGNs1ZxRcUGssZWMKzbuPlopISFkRNQjsQrPqSlPzIuydPiGcGa3JCRXk75iFKajXN2BMMV7ZXnrphiKUOj5daBYQ8xKbHmujo8kyll3b/NivAW/4Ihhj3N3xkSPFis832VqOdmoKWwYjFpr2biQLzab7SLxH0ihkfD7SxzFicDIBpYtGquLaBJPDFKalUQiZguVNMzs82fnv1m9nCllHMakPvtbhpzMFV6E+5/GVMhcBYyIRjy9TR/6q8VB1juFaFHPeUDQFW//aNFMIAsDT49LlvvjLHWkWsmnZNQVhFIhNCdHWyRRc7iNXRnOpKoIflA7kDV8EwJ+5W25puaEhrPc1/0RiAIDttcgfmWZs8jxPUlOw6WWJv0sZKLRr2t0sz6vUFGiSmMK0dB+VxHwK8kbf/fzd2DrGwy1Niiw7zHn987TlMsQxK6VOy6xUO+C6n84UmnYfgWHji78LfKEPmLkudmwtQU5ZPqKIv6maQtNMwY/9PrBoCgFjuY9RC4T/i4KmmcK6dcBVVzEc89nvYMmzA+k/sGDrVmD33QHfz+k+cnW7X+wBznqFdZXvM5x0UhONdCB58qR813SwGiWCmoEL5u2xPW+MMdyw9IbY/B/M091HWTvxjMFHbZgeNflAt90GrFydzBQ6zXSnpVEImULAO4hjrjgGr7rs9QDctY/6uvUbFLqP/GzRR2mDHVU4rjnmU2iX+2hgryv5hx4+WlO1A/UY6rVQt0kLOc0yyY69jXGhObC454IgyF1fqeFHI8hm3VtvfjPwns//BXf3/TvedNHHmtrHX/4CrF8P+PW8r15CR7DbI9bFfsCAQ34Z7aHFvqSd7qPhesQUTKNgPj42Nn7fmvtwyrWn4LnAmNBeaArwZF5K1pTmtNXpeTFBAGDvu4CZa7Md04Ljjwc+fa5gCoGdKbQ2ZW46pqVRKIWT7LCw0396kJdojJXOFusr5VKYuAZYRNGU6KO0flIfodtrH2nuo6xFXcLjq3kK7qJ/6ouvurTUFzfNZdZq9JHa4UcuPlWzYIrQnFFTYK0bhZXPBWECX60UjXTzdLbRpUlxH7UhT6HhB8Ap7w+/543YMmFlxU3uUh34VGtmvTEjVygIcN/StVi/JYpjHa3zzw3o2fXMk/u1u4+WbloaegVUpDP+9BBo3wdwxrHAxw5x7ybNnXXUJWEpcHIwhXYK/jZMS6OgRh+ZSWAmRZb+/UqpBCiZvKGmkEdonrlWeWh1aB1wjCm0nqegibcwDZ/CDpQOUxOdVabgNArxduZBdE2jc5Pt9kz3Uc4Z71Sm0KymUHvDOcCp74otz/OOhpfRMYgIk9dyZjQvW8YZiArzPrVaqNAdYNDavh5e/7CxTj/X719/L46+dgHe9t3/iLYJ05iM6yKZAtmfj4N+eBAOu/SwWHsy5ymkGQUA6NuSbWc2nPBJ4M2f48didqaQJxqrGUxLo6BGH6W9ODUxaudMITIKkaaQzSiM1ceBTy/A+FvOtK5XO12XpqCOcBt5mYKiKTAyjYKdKeg6h919ZHuXWo0+spfaMEJScx6jzrimwMDwmXOba1/94CuiL5ogn90qREYh36uXZsj2249rFSriz3ZrI0zb78PE8pyGVn3m3vqbV2rrzCCKW1b9CgCwvhrVeJdMMXZc4TYK/1pey1XbV8WWpbnGsvjxc76SqXAyhTa68WyYlkZBYwrGA2h2aLKDdjGFrKOv8bqYavOlN1jXqy9wwzHzmkobW8lTMN1HaievubECOzvQ5nXIMKVpVgRhRFc8GkodoTHGcusW8pquWRfgkh+0d6SVZ+QWnVpasmM+pmCD+Wy3yhSsnVGT/VOSUY/VH+OV9tHD5iq/l0bB3I98zvMxyfTTkCGpGZiCgRufvBEX/OWCTO1Q4RVMYeKgMoX4i2NGHynuI40pGP7vFE0hbRShMQXnHM3pEUouBBndR+oxXExBE6MtHU2rmoJqpHQmFoWk5q2vFBnaAE33ZMo9VkeoefSdrEwhi/so/ZlK9tXnhW203Kx7O+kZibVbTJzTYHVlG4dRkKG7Xt48hRSmkGEfrjlOTr7mZHzlzq9kaocKYoWmMGEgzX2kum3qluQ13vmWSyXoCVQ6U0h7+NKSrbREMQdT8FsQmqNOJsV9pDxwGjtwRCLZXQqtGQX13MIyF6Ree6bcp4yaguxQvACg9o608ozcwmY7BhEuN0w40VLgR9dJfUwW3AfM1afqMiO0Wu1M7KGhze0ryf0Zm+hKVDqVWekA8OTTfIlycmkAACAASURBVJvB7cnXPnueQtoGcofuTaoZilTmqXnlOaKPOs0UpmXymqdGHylRNZtHN1uMAn/zyiVPG90FyOc+ih50+9OnJoQ1YkwhHn2UNyQ12X1kH/nrEVF2pmAzAE0zBXFNVZ+yHMFp9ZlYFEGUWWiWGc3ULqNgN55pCC+jIxnNltWtovzVMhbOPhqPn7MItZqyzYePjrcrlojZqtDcvgFAHqYQCKbgI3rmx8ZNTU+iOSuVOskOyYAH9/OWtx5ZWhs82JlCp+dAn5ZMweU+2jI6EHsZZXkErimoETBm8lpy55QmDGt+febQFNrlPkpgCuqL7yp5oRsOi1ujyQK38vw0pqCkDUXbsdwvRkMIzUFlCPhYNM/h/fcDjz/eVHOjfTfDFByGST5/ScLtE4N/AwCM11KeqTYLzTam0WwiVdL9izEcaRTyuI/k18wF8bKeR4JRyKnzmTDde8S6cMPSuAbZ6n1Mw7RkCqrQrHaI27Y34kwhkExBdx/lLYiXlmyldcCsA9FH2nScSe4jR0iqYjzTfP5NJ6+JdtUVFmRjBAELNMMzNsan7Tz4YPe+pfuoOm8RMCuKPjlKzASerU+wawrNCc3JTCGO+PZj1eROyHxGOiE0N5u9njRwiDEFWQ5bYQp1V/SR8T2z0MyQ/ApneECyMIUkI2p29h48nHLtKZbtCqbQdnhKfRnVfbRlwGIUxIi8q1wyOqZ8yWtpGoDqMnExBb8d7iMgNkrVyko4kte0zw6twba/PAgs9aTs7iNdU/jlL4GXvzy5Rr/P9Jo4IUpVoJRtdi7XPc7zkqYxhedW8eWrV6cnr41V7c+JhJkp3KrQ7Co3IT7lYg1Jg6QntyzBtU9cG36XTCFQBkuRwUs+ZmamkHH0nbS/LO9kEgM020CeY+BQMIX2wyU0j4434jOvBUqegtIpSP93Vp9qWrSM5pLpBFNQNIVk95GDKTg1hfx+ZlfnYROabe65gAXRxOwg/HXr9ah98hMYGl2Jvj67OCcjWKKaOAJf7AEG9wQQj11PhhJ95Jx0yNKOFE1h1Sq+fPNmADOVFZbtx2r5jEKr5RFsnVGogSCfkJ1kSN9x42v1Y5DUFKLzlb9PdR+1KSRVDdQwUfNr6Cp1tcwUTIZEjoFDwRQ6AE9xH+mT2/jxMhdSUyi3Fn2UpzSE+vCr0ITmDJrCZZcBa0UZlvBRpABJeQqa4Wm4jEV8eXU8mnMhrfNxPdTyBa9Z3UeGpiCPQcBmfwUwcx2GxqNKriak+8g0iACA2asT25uGpjQFVzcUti89JDWNKdTq7WUKaZpCnhFsHt3J5j6KNAXjmE1qCrZ8G209sxuF25bfhu6vdWPR6kUZmYL7WYldX89VGLMISW07XEJzrdGIuT6kUSh7evQRi0UftU9oDgymENY+0kbuyQ/g+vXARz/KJ9MBjDIXpvvImkGcLfooy+x1JlydqC2iS3Zk8egjVYyWxsR9jaPt7cc+7ufH4fTrT7euC6HcYrWUdZ6RW8QUkg1jlpnXRlOMwnhNf0aadetJpLHCPEwh38Q5SUYh+b3KpSkkrQ//6u2+bfltAIC7nrsr07zpSdfIvCaslF4YsxOYlkZBdR9plUfrFvcRi9xHWseU0yiE2zncBqGPtdHtZArqS53mjmIMwLylWL+JZ4OqZS7MDklzDTmE5tTPuz4OzF8a24cNLn0lTF5LyWjWNAVG4TZJc0zI+xVzHwnc8dwduPKxKxPbTaqmoNzGppLXXPMjgCn/x5erSMtqN5lEq75omw6gGqtOMQXmicgxUqKPQqE5+VlrW56CHJgZx/NEGf6ABdmYQo5Mbnh2ratgCh1AyZM3ksVmPDNdH5H7yIPmPhIPh0toZozh3/74b3hw3YN8P1mnsPS7wg5M2ZtorzpyT34AhxoDwCcOwrbXncH3oFVJ1R8q9QXVmIJDw0jLaE5zH7lGicyiKURzK+tzNKtRSfJ3tYRrHI0y2zXKUq5TU3kKjmsgl8fe+/yawlhN71Q6EZKqPyN53Gg5mIIoIslUpuCMPtKRNVUsa5VU83lfs5o/l6vXBqmDg4AFMaOS1AZG6aHpncC0NApRSGpgaArx6KMGqwNBCdyOqPV39DwFc0AyXBvGRYsuwrFXHMv3kzKyl+uJleHyN7McRqEW8Elxanv+n9ZOfhDT16y6ALIIzfYM6HBZi+4j9VrZqtAGLFCYV9SGJPou3UdWTaEJqO4jM0ksCWkhqdHIN919NJ5iFEYNo9BqZ5KsKbB8QnMOV1boPiKbUcjOFJJE3tSZ18Rfc8Cz/Ble+uaZZelMwQ/8XEJz4DAKRe2jDkANSTXr+5gPts8aQFBCqQR79JFTNJXGgv8m7UaG61nJ2XFpJShSjAxB1GkS7hI1ozlW+8jRyTuT1xyGI9pHivvIEa1jcx+FTEEtcxEwTVOI8hsmkilEyMMUIhuX5j5KFlABYLyepino61tmCikZzbncR3lqV0mmQLboo5T3QDUKSeGgGY2U2W51gJlFN0wUmo3rF1DhPpowqEKzyRTMTt5nDYBxpmDVFByls5nhC093H4nOLyg7HZx6SGryqIRERVfpQ9eMnae3xRZNBOidnbaNstw28kkbBTrdR5YyF7borgDqRDkUtiGbptCKUbBrCrmZwtxnQmMdP4RrX/k1hWrdNAqtGUR7nkLk7soluOdpi8gjYQpTiEpnpxmXbEwhNYza4sIFAFKm9k1jCrW6n3gc85q4mELhPuoAPMvMawB3yZg3xhfuo1IJUC9XLPrI0BTMmx/t1yE0Sx85K1s6rvgDmTolZsgMDKbA1zrapj9wuvtI+czsRiQ6dvJD6+oQ5IhfL+Ud1xS0kFTleEk+3dAf3bZieM1pCmvZg8Cn9gfK9lGgvPe2Ua15Xc1O34TJJFoVmu3RR5EwnmcEmytnoiTnXba4jyhlcKR8TjJEcR3PgDg1s91y8qeA+amaQq3hJ7IVM2TYZRQKptABqEZBfaG50GxGH9UB5nGmYAlJdRVmk+Jt6D7KmLyWpCloQrOjvHa4P3le0iio+zRcEU73keYmUsXowLq9rZ02uEJHmcUoyOivpIJ4eZiCc4SeBWrp7CY1hU3s6ZRjuJKkWOz80txH1YYpNLeoKVg6ozCEluXVFPLfB3XWQj+rUTCq67qQbqTsTCGMPkK6plD3k91HJsN2uY+KOZo7AJf7qOHH6V2guI9gcR/ZJpYH1BGuaRQcpbP9SGg2HxybyOUqrx21W/xKJMAklbnQ/PMOQ+AsiGd5QJuNPrJNshNWjDUEQ81ISU0hS/SRYzrUVpBH+JOF+ZwImUIcZmnmVKZgaAqtls625qQoTCGfppC/Y9OYgp/NKKiGvBWmEJUuN40Cd9OaQSs21Or5hGbmCk1vMQkxDdPSKJBLU7C5j+CIPqJkTcEcCaW6jxKZgjJK8SuirVXH2XGEnbhn0RSaSF7zLS4dvn38RUsbBbrcLVamEKhMQUyyo4T2MRZ1VonuI8kQHAlBrSCP+6gRJBuFcEBghieCxTSEVKPQZqZgY0SqptCpkNQQigEIy2ukGQW1iGVCZxqkPLPyvpghpXLQF7BAK+RoQ72RT2h2zefeahJiGqalUfC05DVdU4hFH6EOOQ2nNiVkSvSRGTWT6j6S7ibENYVQ5EIAOfvbeGM8cX9hXkIGptBwMoV0dtBUSGpKmQvNKPg64wL0PAWARXkKCS9lpCm0aZSlJq9Z7u2p152Kc/90bmx5GlOQ99oWfRRjCimdkGk0OqIpKO3NVRCvmfLqikGPNIUMSZwCie6jlKg0010sId1HDCw1mKTuB4ksOi40O9xHO6qmQEQ/I6KNRBSrVk9EnyYiRkTzxHciou8T0TIiepSIjuhUuwC99lFDC0m1RB8pRkEvc8G389M0BTm6Tat9JNZ7VqYgR8VB2KmlGYWw4yXJMhLcRw6NwCVsu8RoibTY8YYrJFWK99rUpHJyHHtBPNWXnfRSpnUe2aDe42Sh+bol1+Fb93wrtjzNKLhHgSxWr7/WSKl95Lc3JNVWH0ga5NyaQjNMwWuEIb3hYM5RHyhqYHTPkphMGlPwmcMoeFFGc1ruUD1NaM7KFHbg6KOfA3iruZCI9gLwFgDPK4v/HsB+4t9ZAH7UwXaFQjODqSlEtY8O2/QNsU0jHJ3bmYLLHaQ/ZOlMQRqXcqzTjsJKg3DduD+WuD/TuCWWznaEpGolsjWmEG1v9zO3xhRUET1yH3kIY/gVpsAQRL/LlNHcJjRZ+yjNKITGzpK8ZjKFNKNQrRvJa024HbSCdwnJawxBPvdRM0zBa4RTkPphMmLyfc1ahsMcyJi3NFAHZmqTlOijtHe8luo+MtY5y1zsoEaBMXYngK2WVRcBOBf6cPgkAL9gHIsAzCGi3TvVNnK5j5SMwz1244bAR0MJh4xrCkGKpiCPlZasI9d7rGzpEALlWHxd1U9mCuaoQ2cKZoKe3X3kEp1bL3ORHH2kJ81ZQlJZlLzGEGSqfZSHKVy2+DLQlymcijUNbTUKztF2PA6+mtK+djAFlzsxXN909FEzTMFHvS4F32zRZKpRS4oSM41UzCi4mEKYp5Ah+qiRIjTHjIIrIGMHdR/ZQEQnAVjDGHvEWLUAekH71WJZR+Bp8ymoI+NIaC57fKqJIEVTCB9ulhx9lNZxRElaJZh5BJpREB16LUgzCglMwYD6oKvbuTp/l+FIWqainpK85juF5mj/YWekMAWnsWEsV37CuX/mWsBI3V2KW72c7TQKzoAEYrFQ3rzuo2bEXX3A4GYKAYJcvu5mwyrlbHNhu1LcR1kzrk1NIR4JlGwUzFkcbcgrNLuQJ7ChGUyYUSCiPgD/AeD8FvdzFhEtJqLFmzZtamofoaYAplG+ehAVxCuVRKgZ1cPs4JANBKWIKbhCUo2RR9Y8BQ9xpiA7NJUpyNpGLsRS5hN9mQoLyMkUmslTyBN95FuMgu4+igROm+AL5Bc15bHihtSuKeRxm9RZctSY73AfwRJ9lOrDNo1CEyPM1DpXinHOpyk0p/GMiXLg4XvnGE2H7cvIFJjxjLgmKDKfJclgGYLU3KHUPIWMRvuxrfdb525uFyaSKbwEwIsAPEJEKwHsCeBBItoNwBoAeynb7imWxcAYu5wxdiRj7Mj58+c31ZBQU2DMiHSJoo/KnigTgUYkNIdGoRJzH5lCczyjWR7HoUGIh82zaQrKiydfgjpLYQrG8RPnhnWMBm1uHL6N3d2UtEw7XoqmYGMKIIpYl5K8xpSSF66RWiPlZTWh1rNxod69AQNjA6K97WMK4X2K3a548prNvaU2pea3HpLqCjCImhXpPLlqQDXJFMZNppACbR6RFpiCa6bFSJ9Mr32UV2h24YuLP2Sdu7ldmDCjwBh7jDG2C2NsX8bYvuAuoiMYY+sB3ATg/SIK6ZUABhlj6zrVFtd0nI0gSl4L3UcKU5AZzRSUnXRSwgxJTXsh5QvnkSX6SGMKHGlGIRbznNl9ZBeUXb5la5G0tOijFPeRLU/BizGFSFOQL5rrpWx2VBozJoqLcLxvOeZ/axex/+wdnGv+7XB9IO91fJ3JFEz3EACol8A0Cs0wBW1ui8TpOPMxhbRnxAWZxZ3ZKDgY3R+W/cHYzmQKhsYga2eZ77vybqYNPuq+nzifQqdrGmVFJ0NSrwJwL4ADiGg1EX0oYfPfA1gBYBmAHwP4eKfaBUTzKTDG9NnMgigkNWQKVBcJZYDKFJBRaI72nfwQy/Ula56CRYBFPk0h6YXNIiK7puy0GcWkBx9IMAoUZwrRfNX2MhcMQXg8p1Fo0n2U9pLLtrUjo1neHtfcw2akHD9+3ChIIRaIwnnD7ZvwRas6hlVTCPMU7EbB9djljj6S+TnSfZTx9/pgJmrM3//67/XtTKNgagpiP6YxU/W+tHc8jSnkKZfSSZTTN2kOjLF3p6zfV/nMAJzdqbaYiDSFIDYqDd1HoaZQ45VLEXUWxCL3kWuOZlNoThsFyIfXQwlxpiBFbSVUE8maQi6hGfYXX8tfcISkNqUp5HAfRZ/1EsiqS02+aK6Q1GbdR1l/p7aXCEgKQmnArin4PkO5HNVxYmYILcXzFHwWP5DaibdDU1CvadK9ZgisRqfRYKhU4qVd8o6Kye8G80ZDo9CM+yiJ0ZlG2HyWXO6jqCxJkFq5OE1T6HRSWlZkZgpCKH5BwHO4j3xFaK4I9xHzaiDGS0tIUYksmoLaad345I1YO7RWO2Zq6QfVfRTLU5CjEaWtlOI+Ml7gRKbgEpddyWvMbixs621wh6QK95FynrIgnp6ZqmaGRqKza6TWNveRA1pn0z2IsQR77SO5Rr4fjkjNY8eZQsPCFKo1NXAie+nsl1/+cpzzh3Niy9WJi6ysUMlots7M5ujo8moK5PcAiGaby8oUtDyLxDwF49oaQnNU+M/tPkqrfZQWfdTp/IOsSDUKRPRqIloC4Enx/VAi+mHHW9ZBqEKz3vEpIamCKTCvGnMfqQlm5oswWh/FydecHApBectclGzRR2HdFcUoeCnuo9ioLUlTsCejOXMW0txHTWsKFuMnjYKahKRqCqRqCvZOPDdTcLqP7MUMtZf5vDmxuZG1bZ1GQdeNAkv8vakp2NxHaukLs85S0sDgwXUP4uK/XRxbrvrWrVVSUzQFV+6InzoPgg6PdQOIrkFWo6I+N3mYQlahWdW2MmkKSe/hDsQULgJwPIAtACByDI7tZKM6DVJDUrVJ66ObVilFTMEzmILH4pqC7EhMcU8tmJWEJKE59LUr7oLASwlJzcUU8iWvBWlhik26j0I3mUVTUEdozMxTSGMKzWTPokmmAGB43G2wnUZBFGuTHUPMfYR4HLxttFytNUJBvG7oF3lqE0mkuo/UkFRLp+aaZS9NdzLhBZwpVFtyH2VzoQJxNhsGlpiZzywy5mmMNG/p7MlCJvcRY2yVsai5t2yKoOQKSVWE5opgCvCCOFNgZYv7KNqHDakzr0mmYHEfweI+YqU2Jq+5ah+5EtZgX560TEWegnjRObPQYAeM6SPUlDIX7dIUTN1IwrzWMpbeum1GpmCWb2DEUDOYkO06q5qCWV69mbl905mCEpJqua+uY+Y11CXGjYKMPsosNMP+bJuIawrZ3EdqYEDac9bwkzOadxj3EYBVRPRqAIyIKkT07wCWdrhdHYXqPtLdIpGmIN1HABRNgf9OZQrRQ+JgCmbMu6NKZ5g0ZwtJtbiP0oxCrpBU2JmC5lpTXsK7vK/gf1f8r9jeNnrMpp+YsGonwkXCk++iDih62aPktXaEpAYsyBx9FLVRP5+RcXcuQpCmKchOJuY+iucp2MI6VReT6V5qhinUfPuzEbUq2X3kGgDk1RRKhvuoKaaQYBRjmkJepoBAe0dsqPt+4nSwO5L76KPgkUELwBPKDsMERgp1AlqVVNlhBJ4wCjL6KArM8gym4CEyCuZ8Ci73UVah2cYUmBl9VO8FytWUhDTz4c2mKbhEZPMl/tQfPhXbJmxvi3kK6rXyobiPKHoBw7hxpcyFq/PPwxQavo+qCBAaT5kDOWyjcQ1GE4yC75yMXY5EheGLTcXIYhnMNheMWn/HFKKbSl5TOsc//pHhLW8xWqUUxLM9Y85Z9vIaBQj3UagpZA1JZdbPsfYYRjiW0ezIUwgL88EPs+9daKTkKUwVppAaksoY2wzg9Aloy4RBm09BPvR+N3wWJa91KUbByhTCyWt0TWFgu/7SDw6K3csOizlcEIr7yFnmQjyY1OgFq4xhaGwcs/p67fsz3UeJQrPqr1dGVtpczL42hOCMxj5aTXvh1c6bMRaVxVbzFEqybZH7SLIspriPoArNbdAUag0fvs/bMzruTl7Tz8cwCtUEpuAwCtJQSmPOzAqZltpHTmGXEUAMDbSevKayk99s+AZwxJ1gbGs4EZ7KFGwjcdfoPK/7qBIahbp23DQ0KzTH359oQKIi0hQSqqSK+8Hdmy8AoZmI/puI5ijfdyKin3W2WZ2FWjo7fDj97gT3kcxTECGpFqFZdhhjNf1FHBvN5z4qZ9AUyOfRwdtG3C6kdriPkpLUNqwrWY/D95ddaFZf2vToo8jvbk1ec7yU4Qg7SCfG47VG+N6OJ2gDKky2NFpLMgr2PAXZYYfXuWTuI84U8rqPXJ1iYjkP1Y1y4I1A7wDGxpTRd3j/7CGpaZFmWVEm4T5q5GMKTGO+2UNSTX0qqmBgGmZhzFE3Bjvqj/lz5/tB4nlPFaaQxX10CGNsm/zCGBsAcHjnmtR56KWzxUjf74LPGmHn2VWOjIIcFZMiNMPj0RbmCxXzJ4sRVZpfO9QUvFLccBhMoSSMwuBwQpRLjozmrVsDnHYaT7pyhaSaI7uhweaZgtpR6PM8xN1HMl4/UJLUGFOT1yIB2nWNw3kI/K7EdgHS3SEMfEJoqQrTGI0pTME0xq76/3JEHbpjykaFVorX1klzH5mitusZSCoRbgspHRiqoV4HvvQloF5XjbOFucQyg4HrbhzO3QFWPG4UZAJfmm4VHU8pc9FCSCpT3JUqIqbQ0NyU2vmxKCy9ndFHzWhEWZDFKHhEtJP8QkRz0cFM6IlA6D5CNJ8CMW4U5I2pKO6jMlXEJ6kp8HUNdXo90ZG7RMa0G+4zTvuJPMB8cEKjwB+6EuMuo20j7rDUWGeU8AANjfi45hpgyRJ3DkIg2hc2Sfh3bEXG8jAFtaOyCc1hEhdT3EeKpgBFU3AxBVlEDUHFul6FmqyVlSmYHdxILWIDpp7hcuOZ7iOUjXBSxGf2Sos+CmBmNNvvi6mDqbBFdG0ZHMO9i0fw5Uc+gNXbNobtszIFw6j85y8W4dSHZ+IZ70bnMW3oIhF9JOadzswUVCaaQ1OIC80RS1UhWaKPhjaYUZ8jyRTanafQbFHBNGTp3L8D4F4iug68V/xHABd2pDUTBC15TdxsL+hGwCJLrrqPSiK7OZoSUlh+Pxodyd/F/Mks2yQ7/AZ7fCYnB1MI28Y4U9g+mo0pMJYyqhAvRE+PmykEQcAfbrGtFN+b0xQUBuIzVMJLLTvGuFHQ3UdKSCpFnZHrGo/LLF8/3ShU6744T5tRyBiSWtWNQqWkHtflPpR5Co5rR/GMZrtRaITt9E2j4Oh0qgnzMthG9Fu2j2LF4CrgsF/AH+PjRQZ7lVRzdP7wxvv5hxkbnce0oavEB0JyDomsTEErnR1jLYqelcYUyM4U5LPKB5TR88Lvlbjvwig02pyn4DMfJZTSN8yJVKbAGPsFgFMAbACwHsApjLFftr0lEwjP5j4KuhGgEb44lXJ0acqezhRktdRaw1deTDtTiJLXUoxC4ANBiT+kMU1BhsPxv2WkGwWtBLafIjLOWQm84Qsxd5hvuo8CRWcR4wlrmYu06CMnU4j7bX3VKIRCs+qbTY8+Cjv3LO6jetSpmhnELsRciLWIwZmlJlydQsNwH8VAtjmAHdE+TCZnZnMfDY26mYJNE9g6NBotL4+JlriYgv57NaovD3o8bhSqWYyCHx1Dvd4x9oz4syeR1SjIZz1AQ3v+bEyBM9n2MYVmy7ekwWkUiGiW+DsX3BhcKf6tF8t2WNiE5pJwHwUsABhpmoKsgxRqCuKy1eqRUZAPmCvyxDQe1vXMEzM5Gdt48hi8U+gCf0G2JxTZUUd41VqQSFux9z3AsRdi6ZYnog6dkUVojh4XT4xQbJP3pImIrqqsYfSRVnYjHnoaIMpTYBSxtYalQBygMIUM7iPOFOTn5pjCaD0y1rFwWEeceljzyDlajGsKNuOrGo6AsgnNI+P5NIVtI2PRviriXCk5T+HZZ3mxwPVrmzMK3WXuPpJMIZGNsujdTdIUtKg70ygYHa42p4m6j9B9VNcLVmqaQkamkFNn6ZT7KIkpXCn+PgBgsfJPft9hoTGFcHKbLs4UhFEol+JMIYw+kjkJWoErl1HIlqfAj1vi+1Y6DnX0IEdHXWLUNDyWxBSifYyO1zOJUl7QzbcLPIB5euQGdKbgkZsp5HEfaaMjQ1AHgIBknoLJFKJQ1TSmEBaJy8AUOO3PxxRM2j+qMgU/G1MIfIbVq4Enn47Wzx44LtrAwhQCFsQiqtRKqqZRcD0DwwlMwdZRDQyPWkM27RnN/NrffDP/fs9d6YbZhp6SbhQSmUKgMgW3pqBHwSW7j+Kl8vXfBayhMVyNKSBiCsnJa8q6IN0t1Gz5ljQ4jQJj7O3EHW6vY4y9WPn3IsbYizvSmglCOJ+CIjSXGDcKDAxgXlgKA1CZgrxcQjiyuI/MkFSE7qPkjjKAHzEFRVPQH1ze1m6Pu4+Gq9lCUsfrjWT3kUA1ZD4EME/37QeBMi1pxBRsHU3zQnPcfRRVC9Uzmm2ls10viXQfyXyTJNTqfhhOmNl9ZGoK9cgomBPhuDoFnwU45hhg8+boenRBKUxM8Xr9TAwkVKi6g5kAp173p54C1q/nn5OYgs19NDhqMwrM6v6Qv+/uFtsprh1X3ocNPRWhKQhR3Awh1aAwQsYYzjx7CDP3XhGrfaSdW6rQnKwpBGhoTFX7vRZ9lJQvpKzLYhQm2n0EQM5zcEtHjjyJ8JTSE3K05VEFAfzQjaMyBSkUhslriGsKzGkUONJuoC+Oa2oK6oMrO8jukjAKSYXXFCPEQyvTjcJ4XTAKxplCYDIF5XEpIUFozmEUZCE4APYaT6GmEBgZzUH4m1SmUJfieBb3UX5NwWQKY4r7KJYA57g2vs/w/PPQ7n231x9tQPF6/Qws1nno7iO3pnDg22/FAa97GACwbShBU7Bc040jG6xMwTZAkM9vlyBpgWoUguyupL7QKKQzBVL2G7AAP+1/MYY/9JJYlrIWGp2WvGYEe6j75+sNptCIMwWeKJvRfcSmIFNQ8CARvaIjR58kxDQF5qGEcuQ+AqGsCs1CHOvt4b/rqihMQfFtA3GjEJa5SImWCJgPYqUYU9AeXDGa6RWRGMPVaET664evw+X3/Xe0P2VUNFbN5j4aq9VC9xlYSXcfscDhPorvd/tQgE9+u4/ErAAAIABJREFUEnC5SLUZ3XyGUz99D5Y8XY0J6uo5q/kIMabAkpnCwKCY0jODUaj7kVBbNWfLcYxsY0yhEd2XUXMU7mAKAQuAl94KHP1f4TLJCOXvhkZ8rR2BhSk8tzoyamZWtOzAxsYAvPcEbH/P4Xj0UeAfT83HFC7f+m7cvfm35hlYq5DKe13pCoDDf6o/EzmMQm9FuI+EUYjXhlLAov3+bfmTQP9mAMDGTfr9TGYKdvdRbEa8sPaRHn2kMgVShGaTKWju4R2BKQgcDWARES0nokeJ6DEierQjrZkgSKbwyNon8Ff6f0CpAY9K3H0kRspc8OXoEkzhwAP57+bPj5gCM9xH4/UUoVl0CsPDwLZt0XomhNxopja+P80dIDrL3grvLEZr0Yj0vTeeio/c+sH48cAnJsniPhqv1zSmoOcs+Ir7THEfWUY+owt+j0vWno616+wPrdqJ/t+DK3HdrNfgmG981MoUpF+cRx9FrCw8LiVrCs8/D5x/QaQbpUF1H9WTplBTYBqj8UZ0X0bGzdG93Sg0/ADBu9+mLZOMEADgBbj5dhHGyaT7M24ULh54I1CSHac9JPWhJwfCZYsXw5I9rbfLhrue+6v2nU+yYxG+xYh58fjVwElngr3qm8qPshuF/i4+EJIaTZJuRep+X3ZD+HHFhi3advqAS9/fWbe+H+/43HXRelfyWhj8UNcGMxpT0IyCm5HkZQqTITRLHA/gxQDeAOBEAG8Xf3dYSKawcderomUog4VCs64pyDpI0qUkjUrD95XO1mUUCAMDwL2LxEPi+QgCYPY5x2Cnr+wdbuWLUZ80RnK/ty/7W7iNzIbtKwujkFFTGK3Wk6OPBMZrNf6QMw9kCs3GqDSsfeR6MA+5Euu2b7KuUl0SdTGt6PadbrcKzfKc+ctkE5qTNYWVKwEcyDuGEjJoCo387iOTKVT9iCkMj1Vx/l/Ox5ZR0SG5mELAws5DoresT3bYOOBq/oHJZ0TXeUwwz568dveSp8NlY9gKnPlq5z5co1GqztG+M0f0kR8EeHTDo2h4o3zB7OejfWSIBpOQTEGG+NomIUrb7/Ob9dwIjdmY+5v3FH7Xe2r4lVmeTSAawATU0N4XXVMQ7iMWdx9p7mH1XVKehX1m74Py2O6x80mb6a1ZJIWk7kJE3wPwA/BKqQOMsefkv460ZoLgUdwNwN1HwpIzCsVoAKiU9eQ1LfrI0BRiRoER/uHc28Be+5/i4NwoBHvdDcyOpqkI4IOkpoDoYfn8tT8HxuaANhwWdoR9YtSkCpom1AdsZDy5oqpEtVHXhGatFj1MpiAmIUrY7/C4Yz7iQG8bAPhd25QIjzg7gsIUAoUp8Jc1zjAklg09Chz2CwBAKQNT4AXl+OdaVqZgGAWVKfzluT/hq3d+FWfdfJY4C4emEAQx91R/JdIUKiN7A/Oe4l/CDoMljyhjRoGf2IPP8/10je6DB8b/x/17uJlCf2mOscSuKVz78C049NJD8bvl1/IFXUPhOsrhPuoW0UdbttVwyCFmdI8OMhK6ZozvDwBYO6gbhYYfYHQU+PznU4RrwOk+UjUFdeB1+53RdvK98S3uI2mYxhvjumYk72vgYeU5K9FVXRBrkqsCbatIYgq/ADAC4L8AzADw/Y60YBJQKlmMAlUwPucR3F++COge1oxCd9kQmj0Zd6wInuJmVxvx2kdLZv6XtkgTV+WvtTyFaKS/fvx5zBr/O8zs7g/9qP3dfAQ51sgmNI9Ua/ndR+BCM2MMn77lfGwvLddGpSWvFDuOieExh1FQfjM4KpKfurclMwWmRh8pyWuUzBQ2DUadUImyMIUoJNWc1MY5yY4pNCuaQuDz63T90uvxgxsWa0xhtrdH+JnHtev776tETMFrzIxWSKaAZKZguoVWjS3FQ+sewqqR5QCAntqCXOHDGgLdwLpCUu9b+TgAYM3ISr5AKd9BOdxHvUJoXrOujsceS/anm/udVdoVALBpRGeuvh/g298G/vM/4WRw0U6TheaAatpA7HOX/l+0kRJ9ZB5HGt3eC3vxnWffp/zG0/6WLMUnXFOdtooko7A7Y+zzjLE/MsY+CeCQjrRgEmBjCpqoB2hGoUswhd1ncArXW+IvaF1hCtXZT+BPT98RMwrECGV0a8tsFl66AsIJXsTDUscwujADYF7YQXaVKkBQwnjdbRTUUdtYtZaJKYw3hPsIFLqP7lv+DL67+KvY2H+7RmlL5A5JlRhxJfIpncf2MaXwm5fkPlJrH7HMeQqDw9FouUzpTIHXvOefs2oKpgut6kf3pVaP1n3i0VdoncK7Zn0HH9mFBwdExjhCf1f0TKosLdQUWKCJtV3oR2VIiRY3jMIVaz6DIy4/AnU2LtoNjFeTOxb3xEXx+R6syWuiUiv5PbF1WUKEJSqlMhCUMDJWB/a9HfDc98YMKOinXQAAm0bjTGF4WP4o+Tq4ktfC5d64vu6kM9UWAbAzhYYfTeO5vRFpHpGxFwNRshmFSdAURJnsuSKDuWR832HheXGj0FMyjUK0jWQK33vr9/DjE3+MQ/rfBID79FQXy1uuOs7KFCqe/kLYLLwM+TQ1hbo3jB6aCSIKmYJHBDR6Me5nq300Wq1m0hRqjTqghqQiwLqN0cuv0nISHbR6/rTyOOBbG4DruO97JIP7aLg6GluvUnnmWUJSVU1BYQo299Hmoe3h53IGplBtRHMcx91H2aKPVE1hqDqsrVMFTY+8UJtp+EHMKMzsjtxHVqOAQLsnJ/ZdiF4/Yh/SyMbaG8bW16JsbwdclUUbiOdfWENS5TzRlpLxiSzHQFe5DAQVjB74E+CDr49caRaYTKGfcaMw2DCMQhAgvMXNMgVZUrs0FnvHrnrsKnz8lo+H0Ue8848zhW2j+jPCDySuDdOLcOq/nXimMBs8e1n+mwXgQbwAMpotRAG95X7tu8YUKvyG9Hf148wjzgyL5dUb8ZmUbGWIu6jX2CZ+M5lkCoam4JeG0OPNAMELH0iPCOT3YM2GMbgqXaijtrGamyl8ap+f4Gdv+h0A7vqKpqP0EDBfG42Q0mmFL4Ny/u844CRgZBdgnPubR0VhuEsWXQr6MmF7lXfQqrtleDx+ApqYF5aajtxHANNq0YRMwWIUtowMhp8rXkamID4nlZTW2msyhSA6p20jI+bW4SePvHCAEmo5Cmb2OJhCuCfdfUTk8VkBUxAmXFENwzWlfX6843G5j3xmskB7SGojtl0EylFseae+GSC/K1MhPbMD7aJ+dNOMmCHx/QCjwTbgne8HKm59jjc2RVMoVZVES473XP8e/GjxjxBWNbBkNPtBgNWbhmCCTPeRhSlMuKbAGNtXyWA2/+3QGc0AYqKe6r8FgJKSvNZT0V+0svCnm0wBsJQhZoRugynYxDsp5JqaQlAeRm+ZG4WwQil5QL0HG7aM46MftZ+eOnodq7uNwoLel2Lh/IMBcKMgM7pJJK+pbVVHpeHLoOw3DOMV5SS2jw9jyaYl+MLN3wMAPLx8DV+tuHlSmYLqPvKSmYJvqX20bSxiChUvQ55Cww/3HS9AZ4epKdQUo7BmkzEKVDoFIgqvmW+JPprdqzAFdVRN0bXXssyJMkVYyesUUA1Dtcho2kRrp/vIZApg1pIncjsbx8qSNyIxd+aMzO4mkyl45OHNu70H2PkZbXnDD3Bf19eBQzPU93QwBfW7Tw7Dok3dG3cfrdpoMQrQ3Uc2ozAZmsILGwmRHoDhPqroNyRkCpY5V2uB6T4K0FXK4D5SM5rBqS1jDKwyjH5hFGQHSURg9V6gPI577jH2I42JxhSqsBWuA7jx6xXpprVGLWwHUAJDgPGa4j5SOo1wtMn0ke+b3gR0lbmG8r0VZ2LhDxdiKOCju+1DovNWo49qcaOgMQUvPkJjClMARTOv2bJcB8fzMYW63wj3ndUoLPdv1/fBlDyFhsEUytE6z4ui3HxZmlzBnH6H+0gK7ob7iIisbgYTcka2gGoY8aNkGZvwazKFl1U/wJcj/pxbQ1KTmEIOoXnezBmZQ1jNa+CRh7fsFp9RuBEEiUxGQ2jMDaOghqFS/FkGgKB7KwCpKcSZwtrNSe4jN1OY8JDUFz50ozCjW2cKavJat4MpNCxMoW4yBQrQZWoKNveReMFLClMYrVUBz8eMrpkGUyCg0QOUxzBzpr6fMdGJq531eAJTKHke+rqFUfDr4qGlkCnoYaWK+yisXqowBc/Dn/4E/Orn3ChsrvOQ26A0qrVJK9bXSGYK0PIUEH7WChFakt4khuuKppCFKfg+ZGmNemwi9vh496lNz+DJ4GZ9H4hGjCN144VXBM0SeWHQgx8EMKObZvc53EfKqFXtWIkoU4RVXQxcmFfDiJ/MFEyjMKfENQvTKLjKXPiOOakBu5/chfmzZ2ZmCjajoJatkQgCZmWXVijX/OJFF+PEq3iqlvr+NzzTVcjBKvwZ9y2lsx99LMD6rTb3kc4UbHqYOatduzB9jQJLNgplzX1kMAVPZQr6TbYxhZ5yulEI8xQQdRIbBvjDMqNrBjx4YTKSR54wCuMxoyBr4ycZBarNCD/rRoGHrvJ2cA1Dra+kMgU5KldHStKg9XcbI/JAlsRAeG4SY/UUoyBCGFVfLC9zoTIFt9A83Ig6vd7u9E6o3vBzMYVHntoeW9ZQmMKoMAoHBO+Mbae6j2zJa3P6VaOguo8UpqAVKSSUM7mPxMDBq2GcRdfHJvyatY+km9Wcq4EhwG9vsrlF3bpMHqawy5wZ8Fg60wPsRqFSsrnGgjA6Kn2nkVE454/n4Oanb0YjaGjPaoMMo1DVX06fxTWFd54SYJ3FKJSq8/gHUe5ij92mkPuIiP6OiN4l/h3ckVZMCkxRT3cfaUahy2AKpYgpxEruxoyCj56yHpKqTjzy5xV/xvVLrxcCbynMgQgCho3beIcyq2cGer3ZQDd/eIiI++33eADDY/pDPTRaDX8vMVbXk9dKjVnRZ89DXzc/v5pfE528SF5jgRZBpI5WmWXULzu4/h79fMPfSN+/0tGM+RajYMlW1XQG033kyDbl+4867S7qj603wcsb++JzulHYPBin/g1SjALjroNj5v5TbLuSZwjNpqbQFwUo2JkC05YTeZmYguwImVfDOFT3UbzjNHUCGSYbm6vBZ/jLX2wVc+vyBGLIwxTmzOzKwRT07UpU0mZSlPCDIDSQSVDfHbVT/+69F2nisu+NAhsXAoN7AvUeUE1P8LNlNIMCPLzUMAr3fBozV76br/b5M/CifaaAUSCi2UR0O4DfAngPgNMB3EhEf5ET8CSBiH5GRBuJ6HFl2beI6ElRQ+kGIpqjrDuPiJYR0VNEdHxLZ5UFBlOY3aszBVJClHq6TE1BSV4zo4+YEYZJQSwvQmUKb/7lm/EP1/6D6PQiptDwA2weGhZtm4GZpZ3D33geYafeOUD/JqzY+QfavofH40whFJBl+wPdKPT3dIl21SOhWTCFkapqFGxMIS40z+g1jILMVBYdjDr6HG+kRB8JaEaBBYam4GYKI/4gdq4dAXYBQ4XsxkpFrdEItZssRmGLCHTfc/jkcNn4jCejz+UNAIB95u4BE54pNCs9503HrUKlonT4Tk1ByTK3aQoD8ZiQsCP0aqiS6j6yhD06jUKcEZuuEcCiPSjIEiklUSplF6Y9I6PZ5T5q+EFi+wD+Tvzw/h8p36Nn4rN/PhcbZ98araNRwK8Aj50OEEOlYRiFwAczQ3M/cjgWP2IYhYfOCJNDvYB7Gcre1NAUvgoeerofY+ydjLGTAewH4H5km6P55wDeaiz7E4CDGWOHAHgawHkAQEQHATgNwELxmx8SUfsnH1URG5W5mUJvt50pPLT1TqyafY22boz0rElGQayTs0V0NGgEXtCl5Sls3s47nJ36ZmJ217xwW488LPrydwEA2+feoe1HZhGrop9pFLrZ7OhcPI+fT+ChFtRER6MwBaW+ktoBhSGpaoilJ42CQfM9KUoz3HYbsPSp6MWqBinuI7lMYQ9+wEJDA2JOpuD7QBXbMaPMjaCqE7nAs04dTMFSJVWGnO7k7WXfXzeftGBu/8zYOq4BSGYYaCG/J75uT20w4dQUSGUKBM94bWYNHAv87C69TdIolGqolZUQT0tlTjMhULpZTaYACqy5CFGnG19ny9JNQlajYIqyLvdRKlPwy/j6Hd/FJ249O1wU9K9xb04jYY4PyEclmK2vZ3XEmMLM9WiUt+nLGKG/T9RZE0zBZhQmI0/hTQA+xxSnsfj8H2JdIhhjdwLYaiy7jbFQ2VkEYE/x+SQAVzPGqoyxZwEsA3BU5rNoCuKFW30U8M2Nmv8W4J2lRK/BFOQDdvvGeN2Y0fJa4zB+TKyzCUSDvY+gb/hgLU9h6zAfQezUPwNzeyKmQCDsv/P+ONx7H4LdF2F4OHrhhsdreGT9IxgPIv9mtaG7j7qhMAVp/IIy7il/DdvKT4EzFs4UxmoRU/C0kNQ4U5Ad3EwHU/ADH8cfDyxfEXW2NZbffbR6TZQFCgoAB1PYuBHATsuxS6+sG5M+qUut7icwhfjvB0Ti0YyueKcPACjxfczp742v8qJoM99S5kLNlfFsmgLTxWmVeUi8ZK9+XPDp3bRlYThpqYFG9+ZwOSuNY+OIWTROf1Zl7gSLMYVI8Fdhlu9Wkcd9BOjsPQkmA/HIQ7ls1xTM0FoNrIRb7jDyIrrj/n8JvzSKvj7CMa8tARTE2EmD1e1Jcrs8rn0980wPrzxaGAXBFF4050XW9ncCSUahpnTgIcQye6pqPpwBQHKvBQBWKetWi2UxENFZRLSYiBZv2mSvwpkJctQ3sgswOl8L/wMMptBjD0n1178sttt6t2kU4i4mW6G1emULjtjtyLBjZYxhYIR3OHNnzsC8fsV9JF6Ol806Cpi5Hk88vy5c99y253DYZYfhvzdFo5uqrwvNfZ7OFPgH/vJu6rknFJoD+DwCKjyuh8HPDaJ3+EAwBDjj27/BlrEt2nrAYhSESBfSXS/FKFjdR8o1XHgd0CvKP1NUf8pkCo+sWAvMWotD5vHpQNYNG/fGgsHhumLE0t1Hg8IozOoWRqFhEUODEmb2x5d7FIWkBpYyF2rmvc4U7O4jAoXlRyR6y/143ev0ztQUfw/YzpNdgp4t2PXbu2rrTKPQ390NBF68s3e4j8ICeBYWYSvdkARWcmfwa/u1uI8qnoMpqNfirvOAHygddFBy++1viOYuQU0MKL1GlGvkBbHr3Ai4e3Zu9XAc3KN4yHd/UNvutH+KnosS40bhvGPOw5eP1muoTQZT6CGiw4noCOPfywGkO2cTQESfB9AA8Ou8v2WMXc4YO5IxduT8+fNbaQUAoLerBytXAnNnujWF+XP1kUdFyVOIta9UA4Z3ib6TH8u0jc/jzPHht79cy1OQ6e9zZ/RjlxmqpsBv24JZ3E/9+rdHo5n7Vy4BoCcX1Qz3UV9Z1xTioJApjNcjo1CiMmZ1z0K5sRO2dj+IK0b+ESvn/DxqlzAKPRV7lEhoDBWjUIclttvCFGI6g8xApWjyHbnNZTc9iO0jNdy14n4AwKv25kZhff1JpGHT8NbQiDUyhCtur0rdRxgF3/JqNLpDd4AKjzwtT8HMWna6j8ADCeJCc9x91F/pDwcaEqYf/ZU7nYQDh6IsyGojuufms1splwFWihuFUg045v/FzhE9XLOwRUTbYu+TEJRSso4FTMG85JWsmoIfGB338G7ApoXRd1ayl4Yf3BP7Dr4flU1H8u91ZUDJvNAwmyykIdxH82tH4ej+d0cr5uvPZcmLkljLLHIfnfiSd2nbTYamsA7AdwF8x/j3bbGuKRDRB8HnZDidRcPXNQBUp+yeYlkHwV+4Gd092GcfYO4Md0jq3Dn2kNSwXv3K12nrvaF9lG9BLIJj+6h9xHPAbntq0Udy7tzZM7qw+2xVU+Bt33X2TgCAsQ8cHq57ZvPy2H5rQVXTGGZWIqZQsrwsBA/EePKaOrVkGT1ifQmsL87S5Mi27JWt/nebUWhYskADy4gwaaataAIUH//95/vw0YdejhO+9i08tOF+ICjhdQccBgDoru/m3AeqM4CgjE1j68NFWWLYh6vDgF8O6/3LbG4Nfjdm9MVHqp5Hie4jz1M7fKNjD3iuhuou4rWU4kbBrPVllmP4u30XaPsfHI9cJCZT4PpTOTZXA/q2APtG+pY3Iq61F90bE6r76Ii1P4qtNxF4GY2Cca1K5KFicR/5AdMNpKmpsJI1eAFrjsaVVwILZuzNvytGQQszJt2h0mA1gJi4T+6ut1zywntfQhTOnhSw0k4klbl4vesfhECcF0T0VgDnAngHY5rf4CYApxFRNxG9CFzQvq+ZY2SG6LRkRzdvttt9JGdek5APmMzW3HmWblB6qnsrhwlgJrgNj9mNws6zZsBT8hRkpENPdxkL5iqagng49thpp9g+1o6t4B/GIzZQN9xHM7tSmALjtY8Y05mCjN4x6Xm4L/Gg85DZ+Ig5NAqlqEPxLVmgQTke5plU756FoncDNy35AwBgy+gAnth2P2jTwXjRnvz+3P6ZS/H53e/CQUuujO8k6EK5Oh8DjWi8I0VIxhgaQcNaOnukPgKqzwjXkVpSelTcM78b3RWLUUhxH6lZ9eY1D8SUpKlMobs/do/NEexRBy4I5x0HgHVbozBe0yhUvBLvPBNmawOA7pru/bVpC6S4lL552pnAH7+duE+XUegZ3xs3nnZjtF+LcbUaBT/QDaRx/RGUYu8uAFzwwWPxqlcBMzzuqSg1orwfYgr7M41CUA/vmWdl6GIfSgBCWTEK5gDOn2LJa9elbUBEVwG4F8ABRLSaiD4E4BIAMwH8iYgeJqJLAYAx9gSAawEsAfAHAGcz1qFZqaMWAgDKJI2CmdEcPVhmXaRQUxAvl/nCzoQSfkh+JIoOcl19u6OK3c4z+zSmIEM3u0ol7LlzVJhWGoUFc+NGYbMvjEI1YgM8qih6AWf3REahYmEKUmgOEKDqRw+2vFYuo6CNbIP4iLnWaAB7LAb2uD9c5pcsmkLFkvbvmJwGAMaqopYPfDwxyGeqW9D3YqzF/dil8QrIwJMD952Nr531Wvz1snfjnYP/p+2Dggp6/PkYYnGjcN7/nofKVyvx0TGA0cYwPH9GOBqnIDKGNM6NAvnd1jh51U2Q133U8Hl+RkxoNp/Frv7YCNMMJ335QTtpxmT9gGoU9NewXCrx0NVSgkALoI/topX1NqcGBYABtjL8/MbXl7Hxxk/HdzS0O3Af18d8h1HoHz8A7zjgHeH3+HX0Qnavwg8CLYrq7W8r4T5lKEqsbGUK7z2KT5vaI+ZKLzO9HIm8lqYYL4VmInK4bTnKnhcazIpSTLNs/KZTTCGfUy9CahgAY+zdlsU/Tdj+QmQLdW0PBFOoCEvcXSnjF6+7G++/47UAdKaw12w93LBiGgVjdDajpxcb5GEghGa/Alz7P8CHX6llCavo7lLmUwiiCJtKuYR5c6LORo4i9t4lbhS2l4X7SPXbB3r00ZzeWYDod20PJynRR6p/uUsYBZOeh+1SjYLfHZMda40GcNYrtGWBxSjY4JqxDACw2yNiGx9r608AJR462PAHcNBOh8c2nzMHOOwwwg3PRsso6EIfzce2kmIUhGvhkvsu4fv34vEVY/4wypgRdu4eq4QtLdd2Rh2AF3SjUrZcZyKEwV+Wmde062nRFMDimkLJ6Pzm9M2AZ0wqZXbQfX161NKGbQlMoVQSrsVk9JdnY9CfiYbHAwJsBnW4a5n23SoRficKDggs1x+wGwHzu+36NwxNYe5cD694BYDfiwXChRpi40J4fi9euvNLACCsVFBBH7QYPTmwM9rLBxlMK3xpg+cRGhgX++7Rlmvtn2K1j9KL8095CKPgRZ3t+457DXDVjcBNl2tGwYwRDt1HYQVI/UWcO0spayGjj5iHr5zPrb7LKABRR8AYC296d6WEeTtFLiw58lswLx4GKWk6iVBIBCXUDaYwpzf63f9v78zD5ajq9P85Vb3dLftC9gUCJIQkQiTBIJtsKpsIA5FhcUDAUdmGGRic0egPN1RkG0HcANHAiGwioChBYAAlLAbIRUAIEJYQEpKQ9d7bfX5/VJ2qU6eWru6759b7PPe53dVV1adPVZ33vN/tRPkUkL6jeVvFb6vqq1iloCdSRZQk2LQlbKOXaUkhwafg7UOZLTiD0LotDutNGRcdKmqSoajkGZwbRXvRJ4V1H7QhhDYwRrThrdUbg+YjbVZeLDujnLDLFCLMF3oIacUY4CE4CJiDSEe5Epm8ZvoUZk6YHLJfB3IMOoqh87+73icF09Gaz9kBBRCHPaaNJV/x+z6KFJQTOgmLF8NTTyXvY/ab+T7epxBUCmbfCWkHHO3HTf4C5Wt9latIwSIPFdd0qkWAmSazDtq8ZNak9b9ty6LdJQW9blqvKwUhxG+JHvwFMDxie/+Ckmdm5cy/OzLUjrqJXSjCqLiOSPNmmjCmxBPqfrfc6CNpsfeHS3AXgYSwULO0PAWlFAr5HI0NdmiffD5BsKn2d5TokEGfwlAtkcq80SCoFNo085G6QWN9CnpcfSXsU1BLbwaQ3wrtJed/AtKQQkWU6bCdAU2126xbpWDGvItKgaHFkUihpdZMegQWCba2W2BFJGwB7HwPW1fs6xG1bs5p7pjCRqDc8E6s+UhxclSVVN3sE+lTECYphB3NH9llJzZsNRf60SrfbhviHaugL0wUZT6qphQOGnMcv/zs/2PsV3/vlwaM8CmMz89kZftzoe06Tjgh8WMg7EMwr61t2ZGk/M47FTZvbUOl7YSeBUMpmOTakGtwYiitduhogMKmgAqQtqEUaEcKh/w3t8U7zXO25S3UpK/FElIKCUuSdgZJlJ/k9Un2CPULOBfbnDEOGwZr10IuFz/gqhus4vkUgt04Y+IobntW+yZZBmkzuMkZVDdti78hbG3mqJKnCjmbnPYVSfZIBTUzE+UG2mXQfDS8JVkpODqTNryLAAAgAElEQVTBXWRHK9uhFk+PSzbXfQqWDJPCWxvfCG0DnOiNaqRQbWF1gtEebRXndT4XfYvbxgMmZJ4xLaM8s1oAagWzmCUgd5q+GeGdzz/vqNxU3gFkflOk+cISwlvwqSzDVVLthOgjTynofgchsAzz0eSxLTy/IqjGArPj9jAprN2skYKpFGy7aiG703f5Mo35RopCU2mGD+IfX3qVYY1DGPqdsAm0Vph9E+lTiCDlC7+1Ao5b7jfRCisF3adgOocb8iXY6o4DrnoSWP55DCKsSCcHxhIWm8xCkB0FL1fIEsKL+tPL7ocCBnra0Syl/HPSX7e0piehlIJxs7z2GqxZ44SdnrXnWfz51PBPVU4r5bAz462Pmn5E4H2FDpCWRwqb26orhXK54s0EinkbvZmpMjvdAcwqlyjLoPlohEYKUfHbyi5arlTo0EihYFeJPtJuWpuw+eidrSuim1qOKFTXEZ0VraNAc+B9e94Pk21XpGBHD2CmOUbIPLuMiS5V4R8UrR5v+szVvulM8wvsMmqq9zqqzIKjFHxzofk42nHJa6gyJoZPISJ5TYjw/aIrhXw5TArvb/bNOqb5yJkQJVegUQqnwdauj9F3U4ZOZkgpWBsIgMfOgzt+DkBjLhjgMaEYXY9TlQf5xoHfYOaomYFyIeD0c2RAxXFBGWI6o0UVpdBYcJ7nstjmXQfnGrj75QxSEIoUbLa0ByeGqvAdOBO1rR3OGFG0E3wK3aQUYklBCHGUEOIL2vu/CCFecf+O7ZbW9CTcdHNzBtHc7KgFIQTXHH4N+07aN3Sosk+qGZfpaG4sFuFb6+CxcwEn6kBIi6HNzoXf3B5PCrqNWdkzC0Y4YyCaZNMIIqHKbMsSHQTNRyMHRZDCjx/3tgkEOdumXClT1ip+ltwbNM5Jpj80xVyYFN7reC36uHJjeGNbcMCPMh991LqQoRv861MuaqTgklkhjhSMB8ySBfaYOjlyX7+h0Uph3vh5vvlIO+3uk/0oNPMaOvsKb+CPjD6qx6egD2xPnwpEEL82QKs6WPr5129JMB/lbEQVn4Lqi+a8phSM9aLj5jUTWy/j83ufyjNnPsPLZwcd0c+e+wh/O+tvoWMU6V380Yt59vPPMvzdTwc+t2NCUk2EVbMIJE2aSkGRQkW0eaa/gFIwUKbNIQVsthjriIiKrgiEVyhSRThB+Dr2OCng5BPcpb0vAh8G9gc+3y2t6Ul4pFC7rz1ECsbMqZCzmTh6MGxySgaUaQ+Yj7ZEkYKbOKMvuuL5FHImKfht/viLK6Ib6T6EVqVImW2eUpgsD2De9PHebsqO+rP/N8+Pq8cibxUpi22BCAq1olqso1lrl1luHGBdZWXkcXYlQikYpKCu18w3L/PbLnLk9GO1QVspnDjzUShCRebZa2etvkxEcbgAnjw92Dw1ymlKYdIEvw+i7jPbMtdTMIgqyadQCecpWNr5ePgifn70z0PngaDTtyTCSkGtpQ0x0UfVlIJLZl7pjxrw2mvwwx/C7B1mM6ZlTOCzwaXB7DJ8l9AxIcfy6wfCdX5sqSWsSFI2YaosJ0hEIwXjnmkqOgO2QwrqWCvWvFsR7WA5pGBWB7YqQTPRkMpOAIy0dta+v/ejjwpSSt0I/IiUco2U8nWgemH6Po9o81EaqEFaxiiFYj7Hiy/Cgo/oDmnLMWVUrMhy0R4paMlMHbIDKha2EVKozyDvuaMJsS1YjVFHjpLjU3B/75Pn30Mpr4W3uoPVZz/rlwcQ0qLBaqFsbwwspuIrhermo1mN4ernm3kvtA0gL8O3k2iPVgq77+Lva1t2LEEpUiik9CmMeOULTByi5Ze8vk/kcQozxuwUeG9F+BSKBQuuewKueiHS0ambj5z1NEylEHQi6yhXKm4kS3CfnHtt9ppf5tRTne2RSuHdGQDs9sG57rF++z5oT44+ilp3wfxdoJX+6EJEzcLNfnv/feCtDzth4O4xUQEVJsx+kiK4Xoo52DcVnedBWm2eyUoQDgtWqAg3o9my2aHto8HfVQkmqc1u/zz89BFmFg6PbZ9J2F2FpJ4KeICklF/U3nam6FDfgDvzTCMrTeQNUjBnGMW8TbHoDgo4SkGo9ZfLJc9eGIA0lIKKPpJ2SGqbM4akEMEhuR3YYr3jmY/MY3VSVDNAgaDBbqaS20hF+LNvVcrB+71t5rrW/u10zPgvwsZgcbVtVqBort+GiDlGzlQPbjtsy/bC/yxhxVba7CDZfBSws1/dyohVxwcf5lW7Rx6nMGVy0IfgD87+efO2zfI/zmXFk7uEzFWqDboylKE8hWq1j4LOaSEEtsqhCThIje/OtTGYiex6s+TKf9/baD9sa/cnAmbto4JtV61uqvp2RHOEz6CTCJCjCgM17un331evnHvetlIqBXMwF+WA+ch8zptLmvlI+RQSSlhULOe5zwmbLx58BHxrHfbb85xzy2A5i7ZtFryxgKLmWjNJqTfMR38RQnzO3CiEOJPuLkHRI3BvmDrMR55SsGKUQsF57xXGku3eoC/KJbaVk0hB8ylUypFmjLCTNP6GH988mUrLG3ywyV3f2RggArMPj1wsmvLNUPggYFNWCkNFe4i24FpL+rknjx4G/7M88HnZis5JKEasiGY6kaVbVsFZq1bVWLJjVYsqMRCvFLTfLS0vuuvfd/453HJrQE1FHq+ugTZD1P+D07fTp8OkSaHDvTao+09KGQoAT/QpVJyqpKajOSdSkALOPdraCjNmhM/fVvbNS/UpBef7xgyJ8Xd1AkFScJSASZhr1dzDDSaxhRWp1AB4aw/vpacm/vbPQHWl0FJyzEfS0nwKMjrSCXxSsC2bgw6C8ubBtDQ5N55eziJnW6i1rQqaa85Ut71hPjoP+Ky70tr33b8HgVOBc7ulNT0J94bJ1bGWj6kUcsJMbnNnsso0QIcnL61yA1sjFpYJKYVKxc1viC6kFjw2fua286gpYLfz5vq3AudX0ElBVwoOKWwMkIJSCspkk+sImq30h2byZDz5Xg1FK+xoDoQzlvOec9QWfrG9JPNRWSmFmDyFQD9Iy4vuunThqbz8208zZ/fk9YC9wclTCmHzUTUVqvsUyhV/WVHvc900ZPzObdtkaJEdSwtJrWjXLS5rPdgW/72+8EwkKVRRCuo3TRjWvQYFVUrF/C1+bqirjpMczatmeS89pXD7LxyfkSgHfAohUmiIMB+J+GxlqUhBKFOxH84eqHFkCebPd17vuSfa9l5WClLKd6WUH8FZgW2F+/d1KeXeUspVccf1GwiVp1APKbizOzVQGedQs04183BK5jqvbVliW9lY4Pv1j9B8l1PQS/cpxJKCmXiVQApzJjnO09UdK9zzJ5CCUjNYNBebnUVYtCqmKtpCBTLlpaEUtIdh2jS4eXG6KiolO6wUGvRt5SKq2mZeK04YqxTKeS9nIU4pBPpBUwoAO+4IxZjy3/7xQaUQ5VOIrivlQ48+qshKKBdD9yWZkS9zPlShvSNcEE+FVQaUQkSoT9SaAwp6wbxQ6WxbI+KISrhOW53tkztV2r46cm1OYISMqssN/sTPsiL7AGDMcD0Sz/ldr77qlLyQVJKVgksKTnFAf0IVN6ao9SBymkkzmhQsFi6EN96AfbXgR9O3aEaGdRWq2k6klA9IKa9y/x6otn+/gZKW9ZCCmlZa7VCxQjZNNcDY3gPa7g24tiyxTRqkcPMd5N50ym8HlUJHZPhfOBs3fvCdu9NkALYUVwTOrxBIkHLJRQjBoKJjvqnk/Zj1hkLRbZtzM5aIVwoABx1QPyk05bXKk1rFVWeQr6IUykXPfFRME31UsZlurJdUtKspBRVt5Ps3AISsRSn4s8pvf0eG6jsFo4/MR9VZ6SzkU1BKQRvMoyKfTDINkIJsZ+tW+Nd/hc2bw3kK3iTEzCUx2j1uaNebj3SUKg7pbKlsCGw/SK0LqT3jcbk9k8f695m6fydPhnFjbBDlAFGbCmBQo08KQgtJjXVq2+EKCIoU8iJoPgIYP54AzJ/QG47mAYE02cEmPPuk3Q4yPAtRp/RK6Mp2b0ZnyxJtGKSgOZP1KqmVSpxSMH0KCeajkVOdGd2wF93zV1cKFrYfOaLVp2l07exlt9ywvoKb/nsVSoV0pGBWoQVoyPvx2XrF1WIu75uPhB1evatiufX+k81Hun32m9+0uOKK4Of5qqSgfmu0sxOqhztbQvg+raNPCVXkDJQNiQiXVLX59f29xEqSlUJSEbky7dx0E1xzDSxvDRbqy+c0dSZtLyItcC63b0c2dq9SUKWrtxBc4/juu3Vnc/Izrk8+9KAL27KRpqPZOM/gRvcetf3n20rIU9DP7b1271+9xlGcqjF/Rm84mgcEokrqVoMXySCcGvhRdfZBIwXavRlljgbazSUotQxMfT2FsixHOvVCJRoS4sZbik0UN+3k5y2Y0Uda+QV1HlvkGdLoPixFnxRU7oFaNEhfwc1pV/B2yudF9Xh/nIVgTOQs30wUIgVvHzscW17JOeUJrGSloA/iBx1oUTLGtqpKwTAfiQifQqxz04VtW3iXsrgxtI5EwHxk2qlHtsKgN2IL4tWqFPTnoENsZkOHGz4syoHotkLOxlKTEGlRyodJQflCRjRWVwqPnfYYr50bndRYDYPzzvnbRJAUikWnEq7XngRSaC6ElQK4s3lDKYRIoUn77d4znFwWG4IJs0opBArfxUwmwkohI4VuQWhQSYHAwy7D5iMF377b4Q24Q5pKbK0ElcLO0yxuusltj1b2oBLjUwiZjxKUgmUJhrXNDrwPtDHC0ZyjwFBFClomaiGvHOfOzTiomKwUcjlSVdRsLkREH2mDsl5HyZn5u9FHdoRPQbrLRdrVlII2AEQ8hFEZ2TrMshbeAkN69FFEvaPAOSzBIOkvyERDMGQ3YD4ySeHkg6GwObBdCOGv9VEl+ijJp/DBoCf4t1UjYeGRMPiNwDUMKAUsr1Ko+bsgqPbiMH/8fCYOnlh1vygMLzmk0JFbl7hfUplqPcEuMFgLGywz+ijYZzopeEohZu0GHXpwS5RSMH0H3neYpNBNS85kpFBHSKptC19Sp1AKFTdPAeBD04fBqOcD+/35QZtPfMJ5ra/RXKYcqQLMOGgh46N8crbFuJwfYWHeWDopqBmgLfIMbTYyitEyud2bcUgpSAqhLGGLcATS1qC6AGguhklBr16rV1wNmI8ilIJwlQIuKRRTRB9F2YCrkYK6tv5gEL4HqjmaLSH40IxBcKu7EpxRKydoPoqZPRqO5qNnHA6rd+WY0Rd526OVQrz5yMMuv3X+NLVXyPt5CkJazNlhDiPsqcFzRZBQd2B0i2M+ksX3E/dLmrkPakhWCsE8hRhHM8Egjcgxpezfh0HycR3Nwr/fqpq01zkxzplS6CbUYz4C/AclQSl4pCB8m+PXDvzviDZYoWOklI7CiFAB5kOXFDduW4Ixg/xyAUmkoJuPhreEScGMbBnakKwUgLBS2Bw2KbSUIkhBizLSi+sVdaUQFX3kKgVVlTOOFHQTXNRDXKoWfWSEpPr3gH+uNI7mYcPge9+IzkgP5ilEn0sEnNGCww8cztLPtvKdf5uhHVubUghBGuYj/Hv/3hPv5YjBwXtaHzzLXymnDk2uFWOHuD6LKhV2k3w7gxuifQqWZYFIjj4K5iP4SjFyTNHW7tY/t12CtbU1WxKvxU8egx87aWKZo7mbUE/0EeCbdRIG5FLOkc8d9gfegLvbqBmh/QJ2fS36qGL6FLxM3uBD3rTeCWYet+o05xwf+OUaLEswdFAyaXj7KvORyAeK5ikML41ymuGSwrCm4GAW+fAZpGa3hUlhcEOU+UhbVEhbsKeU1+sJ2aEKtaKSD/RZvFLw2xrV7qrmI2+ACPoUdNUYVS472AZn31GDY0ghyXzkfbumJtw27bln0CkZaT4ySCZpdipMn4KnFJxz5I1nKJR0pyZQ5eqmxFowujlddFPSMz640Q9yCAzWnvkoOd8DYIfCjn6VVBFT+6isK4Gw+UjPdYqzPACwcj5sGgVSZEqhu5CmJkokNKUQt1TkyLwj89qaXgHN5mhCn1HqC7mHSEFFBxltnrD8cvjZw/z6pB/zl2PW0tju2mhdEkmaserrRqikJFvkGdqkKYVnF8KVL7FDoxMjp37v8CYzTyEiyqUSnCWWKuEHeVBDRPKa5lPIoZmPCsGM5vBqWbl0pGAlm4+qKQXPh+CaBf2BoLbkNYAxQ31SsGSeuxfe7ZwpBSmYyWtRiFQKNZACmqoo5PXoIzd4wo4nBX0/fWDsCkT5M6KQGH1U0u4zbXKgSKGiK4WIycPfzvobz53zl0BIamSyoGYC1cu5e6SgKYVUpfErduZT6C7EpaRXhWZDjEueGV2c4r02QwCDbQg/2OVKhYrpU3BnbKEIIlGE1/dBCMFeuw/VjvFt73HQT6UrhUa96EpHEdbu5NVhUUqhpSHoSIy0JRtKodkKL9rnxXvrvymnm4/8tpTyfkZzJClgBfosbuW1AClEPOwqJyMOJglE+hSqKAV1jnEjfFKY3X4mn9z5k+H2ximFNKQQqRRS+BRc6EohH6h95JKCqRTMdihSiFiNrzPI2RZf3e+r3HvivYn7JZFCQyGoPP1jVGhvh7YtfJ5Zo2cxvHF4YD2FqCKbOinkAvXG1EQsvYo6+2xg26BUQRz1oHvO2o9Qt09B3ejSCpUCUJg7fST8oxEKm6va/f3XweijSFIwHvI5c+CvfwVlhRAYM7laScHKB0I5d5xqc/7/wG67uc1wJbUeHmqey9tWyQVK+rTYIzDT4ZsbwoNFIPRUJJiPQr9NBJVCTK5Etegj/Xui4JfKjjcfVQtJtaKUQsy1soUduTiumbyWFiaZmuoz+B3aIKaFvYoYpRAePN2+KRe7dHH3fM5m0f6Lqu6XnhQ0k6JlQzm4ml+iic3tIyvGfKSHVevPo9+HOVIsLoiUsGkTXNm8hoO/U33/ejDglUI90UegOXelFVjVTMf8+YLiFkcthDNS9TaEH2xHKXQE68wYRfMUrrgClizBy8oVRhmCtMSnzAJ5K+8V9QNobLD513/191OkYFaejCQFIzJqcD6sFJqKUaSgm498JeEM8kkhqSKVUmjM+X6MyOicKhE08dFHfm5GWqUwqNTsR1SlWKsibnucUog+Lj5PwYQZ7OAnDKpJh/l5tFKIWre7M0ir8pMG81KcUlCFBS3fiZ38HOnmo/B+ul8sUJnY7aq8lX5+rg7vpnp4GSnUrxSqm4+EgCFiordfGgR8CpQDUSLCKJqnUCrB/vv77y2NsCD9w+Ol3Fv5wCzXHJCU+ci0mUeNSZYxoAwrhX0KjaWwrVlXCs05PxOpsZgPkJ1ZjFAYSiEuq3pU4w5w243wyH/QXAj7NGLr6bgwq6R6ZS4QzN5wMZBCKQhlerK8irNxpr64gc1cTyEtzElKXLlnCJNCNaUQ51PoalJIuxZK0jPeUIxWCl5lgZRKwdIczZGTDKn7Lvz2qNvMJNYkqMO7KfgoMx/V61Nw1m/FSZRKEMV5HLt7tdWqFAI+BVlG6DO6GJ9CqG2mIzAl8XnJa3Y+UEjONDXkCw4pNDWYpBDhaDYGlBFNw0MyuVQIt6+omW/0wa5BdzTHmY+0vi5GnNtpK7DsJGefiLGqUoUU4gviwdLvfp31679OsYpfNeDX6BhMe3F9KkWgQ3c012I+Ms1UieYjw/RpG0rBHJzNwVNgIQEhu9bRnJoUEqwBAfOR1ifqtdSUQrL5yL0PEJFjii2jzUf+tvRDsWpGphS6CfUsxwn4SiHBfAROJA/UrhSklEiC0UdeUb1qSVE1OJp1+CGGeWMGGjx+x2nO3Th1cgrzUcD8JRjRPDS0T1SSl6cUKsHkQH3mn7Oq+BQqVqwJR3++o9wHcX4iBd98ZDia3Yqrw8NWshACNuyyqxRi8hFi8xS0vqnFfGR+Txp7udkWEedoDikF9z6UXawUUi6QlehT0JRCMArQdTTb6UhBNx8F1yhxn1ktWEL/HnXJanE0Z6TQzajXfOQ/KBYyYQDJWRE13xPqAQkj+kgfDNSsu9rDb5mO5rRqyB1MC3beaYe6oc0IH9u5G80BOSq+2tJ9CtsGMXRQeGCIap9a5Mb6+7GBGbDzEDvv83ZE7SPdp1DJxS4QX238bLbD5KXDN7fEO5qrQf9dtnRMWLFLnarvq8RHDdWSSWwqj0RSiFEK3iTCqC9l1ubywrG7WCmkndAlKgXdfKT1gTcu5NKRgjLZCrPMhavudULUP1eCNO2k0fkOhxgyUugm1LNGM2hp7VWUQs5TCuEbJQpBn0JH0Cbv3XhVzEeGUkhLCir/wMsmNtaN9prvkqAtbL66y+3JbdHbv20QQweFp+VRD+3I/CS4fgkLVt0YUgqqPHUuV8V8lNDP1dJTjvvIXnDz7TS8fEL4w0f+w+sTfW1e9f1pEVAKrpkx1ncQkQcB9TuaQ0ohyaeAuW+yeTLO0azPlrsCaZ/dxDyFKkoh7Xn0kNTA/ezmZthCdzRXL4dfDbadkUK3od7oIz+TOT4kFRxTjLOXrhQSBit9zV4RDElVA2x4Jmacw1AKZsZpHBQpeNnEXi2k4PEqk9ISFotOOJri+plO+6KUAhoJbB1MS3N4nyhS2Glqjh/95/7c8ZtiwG7eWNST18LFx4QT/xFofxSqPYNjxoBsPZqZU0YFts9d9y1e++l3QnkK3sSghmdbv455d8CMdTQbjm0FsyBeWoTWU0gx4Hltce3fIsanYJ7Lc0h3NSmkNB/V42iOIoAkxREoiKdHF5WdyLmcVqolynxUKy6+GA44oL5jq6HbSEEI8TMhxLtCiOe0bcOEEPcLIV5y/w91twshxJVCiJeFEMuEEHvEn7lr0e1KwQr7FJJzFnyfQsh85A52cRnUCnokjHPOWknBvYFjzEcfGvMhAIY1DPOOhOgbPGBH3jbIKadtILLMRD7HGWfAsGFBsmks+dFH+Zwd4aDzlULS4kPqmW+oUsizMR+sATVqpMXEiT55q+uqopVEzGpkUdAH8ZxwSSHOp6CuoXF+fbJRy9og5r5JKkMYRFRw26quSy5XRSmotUR6SykkDOZNpWilEOkMTvi+IClo6q3ikoLQM5rD5qNasWgRHHJIfcdWQ3cqheuBw4xtFwF/klJOA/7kvgf4ODDN/TsDuKYb2xVA/T4F36acFL7okYIuzxNIIaAUjJBUZUqqtmB3SCnUaD4q5AylYPTRDz/xQx477TEmDTFWpI8qp6BIoa0RHr0g8nujBjO9PIWfKCYoFix8paCZj5StXfcpJCgFhWoO4eZ8sAaUT7TBmbsfrVSf+Shv1acUdGLpTPJaEkzzUcF22qrWlA5FH5mln5W5T3StT8HMk4lDElk2lqr4FDQklcQR2kQskJzmhuHqvz2Y0VynVOhGdBspSCkfAtYam48CbnBf3wAcrW2/UTp4HBgihBhDD6De6CNvNlolJLXgOpqtCDNQFILRRx1aSQH/O7d1dEQeq1CtNk0cFLmpyB8RoxQa8g3MHz9fP9JtXxieyeD5f4IXPhX5EMQpBQWPgMt5d4bvO5r9B1CLwkmhFDa4KziOqFJTraUUVArCCpKCrxRUK2ox4WjmI08pRN+PJgn55+gan0KSCdQ0H5VyihScSrQF23Q0h0NSoevNR2mf3aQS5npbO+VT0B3NuhnKnRTppeCr5a/0NnrapzBaSvm2+/odYLT7ehzwhrbfSndbCEKIM4QQS4UQS1evXt3pBnUmTwFUDHZ1n0LgYU5QCl70kXR8CsHoI+d1e0dKpaANnmkQUgoq3DKlWSJqpqoGOz/Jqx5ScI8xBvm87mjWHL4eKSSQr1r/9pxzYncB8NaqVlDmmvAgXYdPQfvdavYdd2/4g3+Co7mW6KNQ8ED8xCZECvkgKYSS12IczTmri/MU0oakpiYPzXwUlWuQcB51HSxhBUtwS8d8pJRg3Ln7EnrN0SydaWnNFjUp5XVSyrlSyrkjR3Z+Ddi0N5YJvb5QkvlIRfLouyQNVmqAVHkKAYXhqoZqpCCqZJzGQZkDCkZNo1wVU0PTKse4OaQQtsV4D4M72I1ucEfj5cf4548qSFeMMB+piqtSUwrqt3m2dktTcfH9vMMOzjU59dTYXQD8tarV2YWpFJz33qBag09BdzQrP065I04pBMnPg76Uay1KwZgJJykFc+0FpRQqwlGsphknvFys08aC6FqlkHbGndbXoq+UV7P5SC9zoUeVuUqhoJFCWrNXb6GnSWGVMgu5/991t78JTND2G+9u63bUbT5KqxRcUtBXcEoarIQRfaQrBWVKqmY+susMSVXMVTKUQjVH9Q7Lvgs/WMHIhh1Cn3kPQ8XmmGNgz11Hwzc3cMzIi719qioF5dStBNvlOJqDvzWgFLogYf/IucGYB6HNCJ3vUD6FSuB9GuiDeMl2ZpTtHdH3UpxPQWokVEuZi5D5KKFmgvmbVAVZpRTC0UfRSiHfS0ohrVLW94tSBUlKQa+BFTgPSilE1z7qi+hpUrgLOMV9fQpwp7b9ZDcKaT6wXjMzdSvqjj5CI4UEpaDCO2WAFNJFH0lRDjy8O75yKbw/hamNs+MOB8I+hVrNR16JCZmOFKjkYP2kyI+UWWT33S1+8xsYOhTkthaOOUabTUVVKdUyl726MlKFQrrHWRopeMujpjMfpcU+M6ew7cv+MpmmUvBCUuvwKei/u+jOvre2b4vcN2q9BueLu0opJJiPDLLxzEeW61MIJa+ZPgWXxK2+rRT07PeoMO7E5DVtkhDMP3GVgq37FPp2daHuDEldDDwG7CKEWCmEOA34NnCwEOIl4CD3PcA9wCvAy8CPgX+NOGW3oF7zkaWTgm4FM8wHBbfaZyUlKVgBn0KHVpES7rp6AWfLVzhwn/BSmcFzGD6FlL/RI4WQUqj/NlGDXahUs+5kjcpoLoR9Cn7FVf93eaYK6Q+a6vebxfjqhW5OUw9/2NFce2yh3geqKkzyXk8AACAASURBVOzWchwpBE2CHrQM59qij9L7FEzzUWMhSAohpRATkur5TboIqfMUUloDdJKJUtdJkyu9dHY+opikTgr1jjk9hW6jLCnlwpiPPhaxrwS+0F1tSUKXRB/JBFKo0XxkGz4FvZTyxIlOmexqCFWxTJunIIJKQeA4faodP3MmLF8OLeEVPD1SMGfQ+sCR2nyk+k3LU/BNJhopdKH5yIQI+RTqD0nVzSylXAnaoK2jd5RCEqmZ5iNFCijzkTHImSGpwlOsXXs9qpUmV6inHEYUKSSdx6+SKqJX8ssVUVbmLPqoj6NLlIKIf6DUrDtgPkoYrPQ8BUQ5dtGVxLZ5s3L3YUytFJzf4UVMpfQp/Oxn8MADMCnCglRSs0Mr6ByvtvKZrk48aW4qBdvyzyM1n0IXKwUdZmKg9wh10nzU4JpktsUoBSvGp6C/r0UpmERfi6O5seReUzuGFEKDYveQQjGlwzZ0f93wp8j9dAdwVOXexIxmPfoo4nlTkyPIfAp9HoUu8SlUAp8Ezq9IQdunJp9CypLbOvwqlrWFpKqpTM54eKsphaam+JR7ZX/uqLQH2xiID0++Db1KpMaCPcKKCtXsXqXgz8YVURlKoYaQ1IBSKDjmhfZKW/S+xkpvChVtPlKL6jUH7lpCUpsKQaI3Z76mYvHyFLp4MExfOtvY77V9Y/bzf2dzU415CuoeNaukutt1E6ROPlOGTgFg6sgxfGb3z9BSiJDbPYy+7fHoAeRSSlATfnmBZJ+CUgoB81HCQK/7FDCij9LCLFhWq0/BNgbaWqJaTDQUirAVOirBiCl94KgaX2+aj9TIK6SmIiwvhc5TCt1CCi5pV1TTgrWPalIK2iAzd06RG++HPfaKVgoKAhGI49Zn+J3JaE5MXjOuf1Mp6BswHafm9fTKYdRQHjoN0pZvMcnyrTctxv4ovJ/efU2N4Xs+WSkI77++X6C0ejkHdkfgefzNBefxndt25eJjP4llHUNfwIBXCl0SfZQQkqociFL4g2LSYKVuqBVbn0JaHXU9SLbpaK4x+shPzKoxpDUCyizSIeNJodpYpgjYK67ntktWZEgpCNm9pKAe/opX68h0NNfnUxjU6EapNESTgk8E5gzfv/dq8SmY1zTJp2Caj5oNUqi6Al+tJdxTIm2pDtPGv8Po6v00uDkcPpsckupPoAIF7/DJQlVM1ZVVPmfzX/90eE2Jh92NAU8K9Tp9rNiQ1ODFVQvA68SRZOtWg8696y+D4oZOKQVRZ0iqefPXvWQpfky7aT7S6xlVg9pXDfKDnloEwNDSCM2n4LZZ+L+/O0hBqSiPFMyCeDWQgp7kNbTBWb+hKd8Ut7uDkPmoPlIIm49c02ElHNlmKsXmBlMpJN8f/mI8XXs90ipYc4afRlFNHB5OjE1KXtPbFGU+soTwki+z5LU+jnrNR8IbeKqYj/JRjub4m8K88WopXKbgOafdpqQuB5BTBc6CztTOkELJVUqm+cgbEBMWHFLwScHpy9Fvng6LJA25hmifgttnXb3Sl96WcsWzHwGdL4h3+M6H84NDf8ClB19a5ajgjL5u85FxTfeYPA2AYwdfyr/PXRT4zPQpVFMKJtTx9dzLSUhrPqqnPP6EoaND2xJDUpVFUwiDJH0FoQIlin08T6Fvt64HUK9SsPEXz0lSCh4pCJ8UxuR3ZT1/jjyvKSPT3viBY7yHz2lX2t84ZWqFZe/BztOCA21nZL/6/WVpOJo9pZCCFDwnnrPvPffAzTfDuHFhU5eQwvv9DWJw3e2OgyKhSiVoPqIOn4J+rS1hce78c6seI60guQaUQg0mCJPozzlqP6aOXs4n99qV11dt4LtLF2ltC+7b0hgkBT18OBrqPupin0JKkqnnGR/RFC7Zkkwu/uAf9Cm4/4VAVPJI+n6eQqYU6hzwVClcgQUi3qegFgbXlcKjX7mc3TaeHbm/Kes7RQpuqGza35jLy2AbvIzmTiSveaRg+BTUORMqmXr7ugNx3u3zqVOdRUaE0EwI3jXwo48a7SF1tzsEqZkB0MpauO9n7zALgDnWialPWUu3ehMPEQzt1Ukh8TptHQQvHJW47xHzp2NZglIxeE0K+eC+LbWaj2pc7Ckt0iqjenKRokxTac5jCStSKQghfKWQmY/6NtImwJjQSSHJfFTKK0ez/zAPbSnxzx8+MvK8pl24c47m2pTC5CGTAWgquHZttymdUwpO+0Ok4BW5q/771MPfYIUHeW927PWv8B7olnwXKgWvyqvzX5mPlGnk0wdN4o45kju/EZezqeGGP8Efv1Wfjd0gBV2lJvkUXvyX9bz9gzu890kmwZIx828pBv0cplKoyadw66/guicS9+9q1JugaiJprIgLSfVNnwKr4jqa+zgpZOajOqVcgBQS8hRKEUoB4md1pkStSyl4PgXXR5DyN/78qJ+zcOZCdh2xq7NBRR91xqegSAEzT0GZe9Kbj5oiSEE5fhXpCgQVy4ngaSl0ISm4MfmTJzgFzjxS0By/Rx0VPiwKi6/YneVPHEg9pmU9ig2gIv37KokUpk0Lvh/SGN83eokRgLFNk3hys/9+cJPpU6iWZ6JFHz2XgjS7GF1FCmkczXEhqbpS6OsZzRkp1HmBlCnDEkGlMG/VjwP7eeYjw8QUFzlhPtj1OOdsy3bz0FylEDEzuf/jb9BurOA2qDiIY2cc673vEkdzIcZ85PkU0igFQEJLPkIpeB4+33y0tfIBAENKXe9T+JcD9gdg0uQKvAjjxtY+4MyZbXHCEXU2wPAplHVHcw0+hWk7hB2pCuYgP3HwRNBIwSSNqCVWgwhGHzU2pm5ml6AqKTy7EHZf3Knz6CGpQbOWb3ZUyZf1Wid6Cn27dT2Aei9Q3g6bj05t+A1/vjpoU65VKZg3XqlYv08hbrlEgIP2Gs/H946ubOrD9Sl0YqZVdAeQiqkUvHLY1UlhU7szyA8qRpmPlE/B7V8p2FrZCMCQhq4nhVHNTqjiiBHONR9bBymkmXGa8IOb6jMfmdh14qjYz8zaRTuNCN4npi0/8HOWnhE6nze5sG2eew5eeSV1M7sE1SY1Pz/8V5z4UkSexg+XwS2/8c+T9By4XRKa7EmfLCzyULHCy5X2MQx4pZDL1XeB8p75yEa6A96oUYKiEQWplIL5MJsLkSiYD/bIiCiIashZNg4H1eZTiENnarUMHeTOjoqmozl99NGG9vfBhiERpOBlX1vKfGSxVTqkMLyp60jhjfPeoKhV+RxSctoydcjUms9VjznDU6OGUtDzX+KW8ozCzuOHpd53lx0mwrPp9t16azhVWJmP8naO3XZL/bVdBnWvPXnGkzz37nOhz089NWaxpXd3d/5cJF43Nfib2dxaqKolC6nu997GgFcK9Y53avEcSwhvGhdlEmoshh3Ncfs67QluHzc4vHBNNag8BTVg1Et8Cp1xNO842Zl3TJwSPZgl5WwobCqvA2BYY4JSaHOSrlo6ptKmSKG56+rIjB80npFNfkLT3hP25vbjb+f7h34//Ulk7crrhS+8wMrzVvob1k90/zsr2NUbklqLs3P6+LGp9zUnRaA7mrtmQLxg7wtoyDXUfNweY/bg5Nknp97/6aed0GeFpBm+7mg2PwGHFGzyqQIrehsZKdRLCmolJSE9M01UjHopIk8B4s1HplKYOKx2Usi7tltpOw7X+sf02spkRGHSEMf08NkPBR9G5c9IsxDOlsp6AEY0x/sUBm3bjYXidh6+8BrahEMKw5qT153oLI7e9Win5HWNqEUp7DJiF8YNGufdW6Pa59Fw/TJO2vHfAdhxp/pIITVuvJ/RI51rdNDUg+o6ha4UugLfPeS7bP7y5uo7JmDPMXvynYO+k7jPnDlw/PH++0SrnzIfxSwwJIRwzEf9QCn0fdrqZtRghg2gkCuAhAodnl03aqDPqxCTlKRgDhhTR9VOCjsWFsAWqJTec76rzvtQdIFPYUTjCDr+uyOkjNrcJUVTkQLvAzCyZWjoM78fJb/6ytEAdLikMDxqgYfehBQgZE1mHu9Q9x4rFmHVq7vzwyce5hf3uA5v5zLX5FNIjVcOoqEB5FdlwH9x34n3MX7Q+FSn6K4qqZ3B0jOW1nxMmu5Vz8z5My7nkOkf4YyfXeZuB1vkU2Xw9zYGPCnUi4JVgLJDChNWf453h/yOHRs/FNqvqcG5CXJ5M/oouvaP+WBPHR3vEIzDuMJ0eOnjNFech7bu3DPZeaUA0WG17R4pVD93m+WYj0YPDiuFmUPmwd9OYtqm//K2tby2kG0zvsH4obX3XfdCOVzrJ1nPRk0wiQ7Sk0Itmd760s26g/nQnQ5NfQ7V1r6+DGU1JHVvoBoq8P3jzvE+cY6V2ORT3e+9jf59lXoRxZxLCrKDf557FE+eJ5m3PLxfc5MzADQ1xygFgxTMWfnoEbUvdm7bwC/vYegE533dpKBqJ3XDDK/iLiOZaxsR2P7iF18MkciYx37Bq+O/yfRP7RQ6T6mQh9tvZNAB/rbmJ77Oe7f/JyNPrlJcrpfQmegTNVn3Snjr0UcpLvTK81b6yYkp0DXio2t9Cn0ZSSIwJzJH83YNteZqhTLnnANvvgnTp4f3G1QcxMjGkVz1iasC29P6FEq1m6y9OPDx6dR9PLogeS0OkxpmwH2XMemJmwPbpw2fxtShwYieI/bYC26+gwnjwnOYefPgwgvhxhv9bcf/kwXtTYwMF7rsXaxwmKsepaAcuMWiH+IIUE6ZvKYwbtA4L3KqKt7as7ZGxkCZj/q7UkiC5zswtyuDAI75KFMK2zFU+YoKZYSAsTEBGjkrx7v//m5oe9ysLuAsvOke+Grtbfv0p6G5GQ6qzy/ooSt8CnGYOVOwcMoXufDClbS2tnLvIfcC0NraGtr3zDPh9NPh3XedPxOnnAIffADq0JNPhpNOgjfe6PJmdwr3fuZyEB289upLNc/AT9xzVw7a5V4GFQfT2trKnNwc7j3kXpoLzRw70kk4tLespbV1fZe09S8ff55cLvp6lEolxo8fTz6vrYT3+Dnw9h6R96vVB30KXQ6v8p2Z7+B8IKUkb+VTRdv1NjJSqBOFnCKFjip7RsPPUwiODp6ppvVoePnjdZ27sRE+9am6DjUQrE7alcjn4RvfWElLSwvDh09m09ubAJg+NkJubSewLIe8pk+vzyyzYdsGWgotCCF4b/N7iHWC4Y3DWbN5DQDThu9CYzFf5Sydg5SSNWvWsHLlSqZMmeJ/cN/lscd0dfRR30T0BRUaKZy1/9H85fXJPdim+rA9X6VuRUmtvVw3KUT7FEYOaWTxR5/jmY6ptJ/fqSZ2GbprdrN161YmT55c0zoA/Rk77QTbttVvpx9UHBTeqE1Me6IXhRAMHz6c1atXhz7be+/Yg4C+X/OnO+AHBEgu+OQxQN9YcjMJGSnUCbXMZkXURwpJq0adcOBunHBgXaftFnSHUlAYKIQATgBAV9f9kUSUZ+hmRF2zLVuILfCnzEf5HvYpPPLZR1j6Vu2hp/XAUwSh66H6quevU73ISKFOlAqdMx/5zsa+Oyiai9tk6DtQSXNN+SbWblkLdFWkUJ3tSQiI8M1HPXsfLZi4gAUTF/TId+lmoqjt/QlZ9FGd8NdJqFMp9KGFuuOhYqy379vkjjvuQAjBCy+8UHXfyy+/nM2b68+mvf766/niF78YuX3kyJHMmTOHGTNm8OMf/zjiaLjrrrv49re/TXOhmd1G7saoJj0Xo2/eU/0++uj9KdX3cSFjhEIl9EHfxfb9tHcjGhQp1OtTENE+hb6E4cOdto0Z08sN6WYsXryYffbZh8WLq5dP7iwpJOH444/nmWee4cEHH+Tiiy9m1apVgc87Ojo48sgjueiiiwBoyDf0C/Ob6O8+hWufhh+8lryPdxmilUJvmPnqRT+l7t6HMh+ZNY3Swu4H5qMhQ+Ct1dDS0v039Pe/MoEXlzfSUnuuXizmzIHL44NiANi4cSOPPPIIS5Ys4YgjjuBrX/saAOVymQsvvJD77rsPy7L43Oc+h5SSt956iwMOOIARI0awZMkSmpub2bjRKatx6623cvfdd3P99dfz29/+lksuuYS2tjaGDx/OL3/5S0aPjl/DQMeoUaPYcccdee2117jwwgsplUo8/fTTLFiwgFmzZrF06VKuvvpqVq1axVlnncXzf38egOuu/Qn777svN910E1deeSVtbW3MmzePH/7wh9i9GA7a75XCtsHOXwKEVIN/6BNne6YU+gHWdy6zq6GzPoVOrHvcU4izk25PuPPOOznssMPYeeedGT58OE8++SQA1113HStWrOCZZ55h2bJlnHjiiZx99tmMHTuWJUuWsGTJksTz7rPPPjz++OM8/fTTnHDCCVx66aWp2/TKK6/wyiuvsNNOTgb3ypUrefTRR7nssssC+5199tnst99+/OqPv+IXv/8FM2bsRmtrK7fccgv/93//xzPPPINt2/zyl7+ssVe6FlYv+RS6CkdGr5wbhJekFqMU+tEz1CvULYQ4Dzgdh1ifBT4LjAFuBoYDTwInSSnbuq0RV/8drPa6ksPAJ4V6fQoipvZRX4JqY09I33/7upNpNnfs3G7/Lh2LFy/mnHOcOjUnnHACixcvZs899+SPf/wjZ511Fjl3djtsWPr1B8AZyI8//njefvtt2tragjH9Mbjlllt45JFHKBaL/OhHP/K+87jjjouc6T/wwAPceOONPLvmWWzbZsjgIfzvzYt58skn+fCHPwzAli1bGDWqd2tAeUoh3z+Vwh13RPgKYmGQQg8+Q12FHr9KQohxwNnADCnlFiHE/wInAJ8AfiClvFkIcS1wGnBNd7Xjon9rZHlEraK08JfZ3P6jj7ZXrF27lgceeIBnn30WIQTlchkhBN/97ndTn0O36W/dutV7/aUvfYnzzz+fI488kgcffJBFixZVPdfxxx/P1VdfHdre1JSyVpFwZqSnnHIK3/rWt9Id0wNQgQr9VSkIUT2yy1cExgexZqW+i96yYeSABiFEDmgE3gYOBG51P78BOLo7G/Ctb8Gdd9Z/vFo8xyyJnRZxtY8GKoo0UxIRyVndiFtvvZWTTjqJ1157jRUrVvDGG28wZcoUHn74YQ4++GB+9KMf0eFWc1271gn7bGlp4YMPPvDOMXr0aFpbW6lUKtx+++3e9vXr1zNu3DgAbrjhhm5p/8c+9jGuucaZN5XLZTasX8/HPvYxbr31Vt5164GsXbuW116r4iTtZqgB01zbeXtC3ATKMwj0I/NRj49MUso3ge8Br+OQwXocc9E6Kb3V3VcC46KOF0KcIYRYKoRYGpVV2VPwV1TrZEZzH8bZ884GYMKgCd3+XbuP3ZWZY3bu9u/RsXjxYj5l1AP59Kc/zeLFizn99NOZOHEis2bNYvbs2fzqV78C4IwzzuCwww7jgAOc4nbf/va3Ofzww/nIRz7CGC1Ma9GiRRx33HHsueeejBgRrATbVbjiiitYsmQJJ3zsBE467CRaly9nxowZXHLJJRxyyCHMmjWLgw8+mLfffrtbvj8tvDyF/hp9VAtCY3//8ykgpezRP2Ao8AAwEsgDdwD/DLys7TMBeK7aufbcc0/ZW3iq9T3JIqS4aGhdx/91+VuSRUgubuzilkWDRUgW0SPflRbLly/v7SZsF3jizSfkE28+IdvaO3rsO2u5dtPO/5xkEfIfb2zsxhb1Lna/8IuSRchPX3pFYPsuFzi//cTLru2llkUDWCpjxtXemK4eBLwqpVwtpWwHbgMWAENccxLAeODNXmhbajSW3MJjVp1KoR/4FDL0L/TVnAXlU+i3eQpp4AWM9P88hd4ghdeB+UKIRuHcxR8DlgNLgGPdfU4BOmHx734o85HYMLGu4+tZkjFDhiT0TUrwQ1IHgk8hbD3qf7WPesOn8Bcch/JTOOGoFnAdcCFwvhDiZZyw1J/2dNtqQUtDCW75DY2/ub+u4zOlkKHL0UdvJX85zu1YKcQ4lOOT2voueoW6pZRfJZwh8AqwVy80py7YNtB6DLLOFR/tnq599PyxUNjUs9+ZIQOu+ahikcv1UdbqAsSGpHpk0bPt6Qy2Xz3XzVBVIT/zmfqOj1tPodvw6187/2/qma/L0PPoq3kllrBA2vTTNIVUiHPneMajfsQKGSnUiXwe1q6Flpb6jo9beS1DhrrRR28lgQWV3HZNCrHrJvTDjObM29kJDB0av7BINQir/9wk2zNs22bOnDnMnDmT4447rlMVUE899VRuvdXJvzz99NNZnpAy/+CDD/Loo4/W/B2TJ0/mvffeC20/ct6RzJ41i1mzZnHIIYfwzjvvRB7/iU98gnXr1tX8vZ1BsxwLG8YNCFLYHmofZaTQa3Bvkj5c+2ggoKGhgWeeeYbnnnuOQqHAtddeG/hcZTTXip/85CfMmDEj9vN6SSEJS5YsYdmyZcydO5dvfvObgc+klFQqFe655x6GDBnSpd9bDR/aeh7iumd6dRGg7kbcT+uPIamZ+aiXoJYmtN/ftZdb0jdw7n3n8sw7z3TpOefsMIfLD6tSO1vDRz/6UZYtW8aDDz7If//3fzN06FBeeOEFWltbueiii3jwwQfZtm0bX/jCFzjzzDORUvKlL32J+++/nwkTJlAo+HW/999/f773ve8xd+5c7rvvPi6++GLK5TIjRozgpz/9Kddeey22bXPTTTdx1VVXseuuu3LWWWfx+uuvA866DQsWLGDNmjUsXLiQN998k7333jvVjHPfffflyiuvZMWKFRx66KHMmzePJ598knvuuYf99tuPpUuXMmLECG688Ua+973vIYRg1qxZ/OIXv2D16tWR7egMclaOXGVgDjWen6f/cEJGCr2FQcUWuOkeGjbsBddW37+zOOIIcMv+Z4hAR0cH9957L4cddhgATz31FM899xxTpkzhuuuuY/DgwTzxxBNs27aNBQsWcMghh/D000/z97//neXLl7Nq1SpmzJjBv/zLvwTOu3r1aj73uc/x0EMPMWXKFNauXcuwYcM466yzaG5u5oILLgDgM5/5DOeddx777LMPr7/+Ooceeiitra187WtfY5999uErX/kKv/vd7/jpT6tHat99993svvvuALz00kvccMMNzJ8/P7DP888/zyWXXMKjjz7KiBEjvNpO55xzTmQ7OgMh2M5NRzCiPMv5L6YFP4gpqd2XkZFCL0FK4OWPY/VQDbi77uqZ76kXtczouxJbtmxhzpw5gKMUTjvtNB599FH22msvr9z1H/7wB5YtW+b5C9avX89LL73EQw89xMKFC7Ftm7Fjx3LggQeGzv/444+z7777eueKK8H9xz/+MeCD2LBhAxs3buShhx7itttuA+CTn/wkQ4cODR3b1OEkUB5wwAHYts2sWbO45JJLWLduHZMmTQoRAjhlt4877jivLpNqV1w7mpubk7oxEY2NkLbQa3/FjPZTuP/aOez2H3MC21sqkwBoFukWWOoLyEihl6CsANuznbU/QPkUTOjlqqWUXHXVVRx66KGBfe65554ua0elUuHxxx+npGKda8CuE0ZRsAssWbIkUHxv3bp16ctud0E74nDOOXDUUV12uj4JgYB35oTyEeZsOY9Hf7sTHzqr/3RA5mjuJag0hcHJq/xl6AM49NBDueaaa2hvbwfgxRdfZNOmTey7777ccsstlMtl3n777cjV2ObPn89DDz3Eq6++CsSX4D7kkEO46qqrvPeKqPbdd1+vQuu9997L+++/H/qOeiYWBx54IL/+9a9Zs2ZNoF1x7egMRo+GefM6fZo+DRGTpGYLG144mj4bLxyBjBR6CYMHw2WXwQMP9HZLMlTD6aefzowZM9hjjz2YOXMmZ555Jh0dHXzqU59i2rRpzJgxg5NPPpm99947dOzIkSO57rrrOOaYY5g9ezbHH388AEcccQS33347c+bM4eGHH+bKK69k6dKlzJo1ixkzZnhRUF/96ld56KGH2G233bjtttuYOLG+WlsmdtttN7785S+z3377MXv2bM4//3yA2HZkSIYSVvl8cPv55ztrhdeb5NobEP0pftbE3Llz5dKlS3u7GRnqRGtrK9OnT+/tZmSoA9m1C2LjRvj6152/LrS8dRuEEE9KKSPXvs18ChkyZMjQSTQ3w6WX9nYrugaZ+ShDhgwZMnjISCFDr6I/my8HKrJrtn0jI4UMvYZSqcSaNWuyQaYfQUrJmjVrujRkNUPfQuZTyNBrGD9+PCtXrmT16tW93ZQMNaBUKjF+/PjebkaGbkJGChl6Dfl83sv0zZAhQ99AZj7KkCFDhgweMlLIkCFDhgweMlLIkCFDhgwe+nVGsxBiNfBanYePAMJLWA1MZH3hI+sLB1k/+Nge+2KSlHJk1Af9mhQ6AyHE0rg074GGrC98ZH3hIOsHHwOtLzLzUYYMGTJk8JCRQoYMGTJk8DCQSeG63m5AH0LWFz6yvnCQ9YOPAdUXA9ankCFDhgwZwhjISiFDhgwZMhjISCFDhgwZMngYkKQghDhMCPF3IcTLQoiLers93Q0hxM+EEO8KIZ7Ttg0TQtwvhHjJ/T/U3S6EEFe6fbNMCLFH77W8ayGEmCCEWCKEWC6EeF4IcY67fSD2RUkI8VchxN/cvviau32KEOIv7m++RQhRcLcX3fcvu59P7s32dzWEELYQ4mkhxN3u+wHZDzAASUEIYQP/A3wcmAEsFELM6N1WdTuuBw4ztl0E/ElKOQ34k/senH6Z5v6dAVzTQ23sCXQA/yalnAHMB77gXvuB2BfbgAOllLOBOcBhQoj5wHeAH0gpdwLeB05z9z8NeN/d/gN3v+0J5wCt2vuB2g9OffSB9AfsDfxee/+fwH/2drt64HdPBp7T3v8dGOO+HgP83X39I2Bh1H7b2x9wJ3DwQO8LoBF4CpiHk7mbc7d7zwrwe2Bv93XO3U/0dtu76PePx5kMHAjcDYiB2A/qb8ApBWAc8Ib2fqW7baBhtJTybff1O8Bo9/WA6B9X9n8IXk68ygAABY5JREFU+AsDtC9ck8kzwLvA/cA/gHVSyg53F/33en3hfr4eGN6zLe42XA78B1Bx3w9nYPYDMADNRxnCkM60Z8DEJgshmoHfAOdKKTfonw2kvpBSlqWUc3BmynsBu/Zyk3ocQojDgXellE/2dlv6CgYiKbwJTNDej3e3DTSsEkKMAXD/v+tu3677RwiRxyGEX0opb3M3D8i+UJBSrgOW4JhJhggh1OJb+u/1+sL9fDCwpoeb2h1YABwphFgB3IxjQrqCgdcPHgYiKTwBTHOjCwrACcBdvdym3sBdwCnu61Nw7Otq+8lu5M18YL1mWunXEEII4KdAq5TyMu2jgdgXI4UQQ9zXDTi+lVYccjjW3c3sC9VHxwIPuKqqX0NK+Z9SyvFSysk4Y8EDUsoTGWD9EEBvOzV64w/4BPAijg31y73dnh74vYuBt4F2HPvoaTh20D8BLwF/BIa5+wqc6Kx/AM8Cc3u7/V3YD/vgmIaWAc+4f58YoH0xC3ja7YvngK+426cCfwVeBn4NFN3tJff9y+7nU3v7N3RDn+wP3D3Q+yErc5EhQ4YMGTwMRPNRhgwZMmSIQUYKGTJkyJDBQ0YKGTJkyJDBQ0YKGTJkyJDBQ0YKGTJkyJDBQ0YKGfoNhBBSCPF97f0FQohFXXTu64UQx1bfs9Pfc5wQolUIsaST55msV73NkKGrkJFChv6EbcAxQogRvd0QHVrmaxqcBnxOSnlAd7UnQ4bOICOFDP0JHTjr5Z5nfmDO9IUQG93/+wsh/iyEuFMI8YoQ4ttCiBPdtQSeFULsqJ3mICHEUiHEi25NHFU07rtCiCfcNRXO1M77sBDiLmB5RHsWuud/TgjxHXfbV3AS6H4qhPiusf/NQohPmr/HVQQPCyGecv8+EvFdpwohrtbe3y2E2N99fYgQ4jH32F+7dZ9w+2G5+5u+V7XnMwwY1DLDyZChL+B/gGVCiEtrOGY2MB1YC7wC/ERKuZdwFtn5EnCuu99knMJwOwJLhBA7ASfjlLf4sBCiCPyfEOIP7v57ADOllK/qXyaEGItTZ39PnFr8fxBCHC2l/LoQ4kDgAinlUqONtwD/BPzOLb/yMeDzOFnVB0sptwohpuFkp89N86NdRfVfwEFSyk1CiAuB84UQ/wN8CthVSilVuYsMGSAjhQz9DFLKDUKIG4GzgS0pD3tCujWLhBD/ANSg/iygm3H+V0pZAV4SQryCUzX0EGCWpkIG4yy60wb81SQEFx8GHpRSrna/85fAvsAdCW28F7jCJZ7DgIeklFuEEIOBq4UQc4AysHPK3wzOQkIzcIgMoAA8hlPueSuOYrkbZw2BDBmAjBQy9E9cjrMozM+1bR245lAhhIUzACps015XtPcVgs+AWfNF4szUvySl/L3+gWue2VRf88NwlcCDwKHA8TgVO8Exla3CUTsWzmBuwvvtLkqqmcD9UsqF5gFCiL1w1MixwBdxqoNmyJD5FDL0P0gp1wL/i79EIsAKHHMNwJFAvo5THyeEsFw/w1ScldZ+D3zeLbmNEGJnIURTlfP8FdhPCDFCOMu/LgT+nOL7bwE+C3wUuM/dNhh421UwJwF2xHErgDlu2yfgmMAAHgcWuGYwhBBNbvubgcFSyntwSGd2irZlGCDIlEKG/orv48xwFX4M3CmE+BvOgFrPLP51nAF9EHCWO3v/CY6v4Sm39PZq4Oikk0gp3xZCXIRTflkAv5NS3pl0jIs/AL8A7pRStrnbfgj8RghxMvG/6/+AV3Ec3q04Kgop5WohxKnAYtcsBY6P4QOcviq57Ts/RdsyDBBkVVIzZMiQIYOHzHyUIUOGDBk8ZKSQIUOGDBk8ZKSQIUOGDBk8ZKSQIUOGDBk8ZKSQIUOGDBk8ZKSQIUOGDBk8ZKSQIUOGDBk8/H/pYSH+nQzdYQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_iZxkZE7MK6v"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}