01 Basic_and_advanced_statistics.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Basic Statistics\n",
        ">Basic statistics involve calculating measures like mean, median, standard deviation, and count for each column. Here’s how you can do it in Python:"
      ],
      "metadata": {
        "id": "hr7FBKUugjpc"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YJo2lAVTgEAV",
        "outputId": "7a96ac60-6bdb-4bed-8b6b-0090b55d10b6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "           Height      Weight       Index\n",
            "count  500.000000  500.000000  500.000000\n",
            "mean   169.944000  106.000000    3.748000\n",
            "std     16.375261   32.382607    1.355053\n",
            "min    140.000000   50.000000    0.000000\n",
            "25%    156.000000   80.000000    3.000000\n",
            "50%    170.500000  106.000000    4.000000\n",
            "75%    184.000000  136.000000    5.000000\n",
            "max    199.000000  160.000000    5.000000\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load the data\n",
        "data = pd.read_csv('/content/bmi.csv')\n",
        "\n",
        "# Display basic statistics\n",
        "basic_stats = data.describe()\n",
        "print(basic_stats)\n",
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Advanced Statistics\n",
        "> Advanced statistics might include correlations, regression analysis, hypothesis testing, etc. For example, to calculate the correlation matrix and perform a linear regression analysis, you can use the following code:"
      ],
      "metadata": {
        "id": "_hp-XKVaicpQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.api as sm\n",
        "\n",
        "# Correlation matrix\n",
        "correlation_matrix = data.corr()\n",
        "print(correlation_matrix)\n",
        "\n",
        "# Linear regression (predicting Weight based on Height and Index for example)\n",
        "X = data[['Height', 'Index']]  # independent variables\n",
        "y = data['Weight']  # dependent variable\n",
        "\n",
        "# Adding a constant for the intercept\n",
        "X = sm.add_constant(X)\n",
        "\n",
        "# Fit the regression model\n",
        "model = sm.OLS(y, X).fit()\n",
        "print(model.summary())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n7JRVw9-iQXr",
        "outputId": "bafa4007-34cd-466f-ec04-4ef3ca205df0"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "          Height    Weight     Index\n",
            "Height  1.000000  0.000446 -0.422223\n",
            "Weight  0.000446  1.000000  0.804569\n",
            "Index  -0.422223  0.804569  1.000000\n",
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                 Weight   R-squared:                       0.788\n",
            "Model:                            OLS   Adj. R-squared:                  0.787\n",
            "Method:                 Least Squares   F-statistic:                     924.4\n",
            "Date:                Sun, 24 Mar 2024   Prob (F-statistic):          3.34e-168\n",
            "Time:                        03:38:11   Log-Likelihood:                -2059.8\n",
            "No. Observations:                 500   AIC:                             4126.\n",
            "Df Residuals:                     497   BIC:                             4138.\n",
            "Df Model:                           2                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       -120.8344      8.740    -13.826      0.000    -138.006    -103.663\n",
            "Height         0.8186      0.045     18.174      0.000       0.730       0.907\n",
            "Index         23.4041      0.544     42.998      0.000      22.335      24.474\n",
            "==============================================================================\n",
            "Omnibus:                       20.541   Durbin-Watson:                   2.012\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               22.166\n",
            "Skew:                           0.514   Prob(JB):                     1.54e-05\n",
            "Kurtosis:                       3.078   Cond. No.                     2.24e+03\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 2.24e+03. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-2-bd0435a05e4d>:4: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  correlation_matrix = data.corr()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Multiple Regression Analysis\n",
        "> Indented blockIf you have multiple independent variables and want to see how they collectively affect a dependent variable, you can use multiple regression analysis.\n",
        "\n"
      ],
      "metadata": {
        "id": "SZDbPiQkijfh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.api as sm\n",
        "\n",
        "# Convert data to DataFrame\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Prepare the data for clustering (excluding the 'Gender' column)\n",
        "X = df[['Height', 'Weight', 'Index']]\n",
        "X = df[['Height', 'Index']]  # independent variables\n",
        "y = df['Weight']  # dependent variable\n",
        "\n",
        "X = sm.add_constant(X)  # adding a constant for the intercept\n",
        "model = sm.OLS(y, X).fit()\n",
        "print(model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yd80SB0Bij2L",
        "outputId": "6ae03694-74bb-4851-a3fb-cf8e7933325b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                 Weight   R-squared:                       0.788\n",
            "Model:                            OLS   Adj. R-squared:                  0.787\n",
            "Method:                 Least Squares   F-statistic:                     924.4\n",
            "Date:                Sun, 24 Mar 2024   Prob (F-statistic):          3.34e-168\n",
            "Time:                        03:44:36   Log-Likelihood:                -2059.8\n",
            "No. Observations:                 500   AIC:                             4126.\n",
            "Df Residuals:                     497   BIC:                             4138.\n",
            "Df Model:                           2                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       -120.8344      8.740    -13.826      0.000    -138.006    -103.663\n",
            "Height         0.8186      0.045     18.174      0.000       0.730       0.907\n",
            "Index         23.4041      0.544     42.998      0.000      22.335      24.474\n",
            "==============================================================================\n",
            "Omnibus:                       20.541   Durbin-Watson:                   2.012\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               22.166\n",
            "Skew:                           0.514   Prob(JB):                     1.54e-05\n",
            "Kurtosis:                       3.078   Cond. No.                     2.24e+03\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 2.24e+03. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Principal Component Analysis (PCA)\n",
        "> PCA is used for dimensionality reduction while preserving as much of the data's variation as possible."
      ],
      "metadata": {
        "id": "GE-0XU20io6q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA(n_components=2)  # reduce to 2 dimensions for example\n",
        "pca_result = pca.fit_transform(df[['Height', 'Weight', 'Index']])\n",
        "print(pca.explained_variance_ratio_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9kS2EmrKipoO",
        "outputId": "0827925f-da6c-434c-cf8e-5910d8129ff0"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.79615335 0.20360479]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "8UnDfYw-k1UL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Time Series Analysis\n",
        "> If your data has a time component, you can analyze how a variable changes over time."
      ],
      "metadata": {
        "id": "qsv1BgbZjjJa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming you have a 'Date' column and a 'Value' column in your dataframe\n",
        "df['Date'] = pd.to_datetime(df['Date'])\n",
        "df.set_index('Date', inplace=True)\n",
        "df['Value'].plot()"
      ],
      "metadata": {
        "id": "fiCWwAE-jipq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Hypothesis Testing\n",
        "> To compare different groups or check if a sample comes from a particular distribution."
      ],
      "metadata": {
        "id": "OtlPdkC2j9m5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy import stats\n",
        "\n",
        "# Example of a t-test\n",
        "# Assuming 'Group1' and 'Group2' are two columns or lists of data\n",
        "t_statistic, p_value = stats.ttest_ind(df['Group1'], df['Group2'])\n",
        "print(t_statistic, p_value)"
      ],
      "metadata": {
        "id": "EhTBBZDNkA04"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Let's plot"
      ],
      "metadata": {
        "id": "ox2kKuTJk_G_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Scatter Plot\n",
        ">A scatter plot is useful for showing the relationship between two numerical variables."
      ],
      "metadata": {
        "id": "4jPxC51Zk8kw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Assume df is your DataFrame loaded from the 'bmi.csv' file\n",
        "sns.scatterplot(data=df, x='Height', y='Weight')\n",
        "plt.title('Height vs. Weight')\n",
        "plt.xlabel('Height')\n",
        "plt.ylabel('Weight')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "HLwm-ZX2k0pw",
        "outputId": "b709a20d-babd-4874-ae01-2a042752a7f8"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Box Plot\n",
        "> A box plot is useful for showing the distribution of numerical data and its quartiles, highlighting outliers."
      ],
      "metadata": {
        "id": "oL2J_Jz9lE9N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data=df, x='Index', y='Height')\n",
        "plt.title('Height Distribution per BMI Index')\n",
        "plt.xlabel('BMI Index')\n",
        "plt.ylabel('Height')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Mh-Fs5Qyk40T",
        "outputId": "46629635-d452-4e5d-dc72-c919fce2fa14"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Pie Chart\n",
        "> A pie chart is useful for showing the proportion of categories in the data."
      ],
      "metadata": {
        "id": "53dBes05lKOd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "index_counts = df['Index'].value_counts()\n",
        "plt.pie(index_counts, labels=index_counts.index, autopct='%1.1f%%')\n",
        "plt.title('Distribution of BMI Index')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "tIeSyTzLlIQd",
        "outputId": "d190d420-f635-44eb-aca1-35ad4d2909e1"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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CCOEiRUEIIYSLFAUhhBAuUhSEEEK4SFEQQgjhIkVBCCGEixQFIYQQLlIUhBBCuEhREEII4SJFQQghhIsUBSGEEC5SFIQQQrhIURBCCOEiRUEIIYSLFAUhhBAuUhSEEEK4SFEQQgjhIkVBCCGEixQFIYQQLlIUhPBhjz/+OIqitPrq3bu31rGEBzNoHUAI0bn69evHzz//7PreYJB/9qJt8tchhI8zGAwkJCRoHUN4CWk+EsLHbdu2jaSkJHr06MFll13Grl27tI4kPJiiqqqqdQghROf46aefqK+vp1evXhQVFfHEE09QUFDA+vXrCQ0N1Tqe8EBSFITwI9XV1aSlpfHyyy9zzTXXaB1HeCBpPhLCj0RERNCzZ09ycnK0jiI8lBQFIfxIfX0927dvJzExUesowkNJURDCh919990sXryYnTt38ttvv/GPf/wDvV7P+PHjtY4mPJR0SRXCh+3evZvx48dTUVFBbGwsI0aM4I8//iA2NlbraMJDyY1mIYQQLnKlIHyO1e6kxmKjrslGbZOdWouN2iYbtRY7tU1/b7fYabDa0SkKBp2CXrfnvzoM+n2/b/mvQa8jLNBIbKhp75fZRIBBWmCFb5GiILxOk83BrspG8ioayatoYGdFw9//30hJbRPNdmeXZQkPMroKxL4FIykiiJ7xZjJizRj1UjiE95DmI+GxSuuaWLOrmu1lDa3e/Itrm/CWv1qjXiE9OoSeCaH0ig+lZ3wovRJCSYsKRqdTtI4nxAGkKAiPYLU72VBYw+pd1azaVcXqXdUUVFu0jtVpAo06MuPM9IwPpW9iGEPTo+ifHI5eCoXQmBQFoYmiGgur8qpZvauKVbuqWF9Yi7ULm308UWiggWHpUQzPiOb4HtH0SwpDUaRIiK4lRUF0ibomG0u3lbNwSylLt5VTWNOkdSSPFxFsdBWJ4RnR9IoPlSIhOp0UBdFptpfVM39TCQs3l7EirxKbQ/7UOiI6JIDje0Qzpm8co/vEExZo1DqS8EFSFIRbrdtdw+wNRcxeX8z2sgat4/isAL2OEzKjGds/gdP6JhAVEqB1JOEjpCiIDttQWMNXKwuYs6HYp28Oeyq9TmF4j2jOzU5ibP8EQuUKQnSAFAVxVOqabMxcU8i05fmsK6jROo74m8mg49TecZyXncwpvWMxGfRaRxJeRoqCOCIrdlbyxfJ8flxXRKPVoXUccQjhQUb+79gUrjohndSoYK3jCC8hRUEcVmWDla9X7Wba8ny2ldZrHUccIZ0Cp/aOZ8IJ6YzIitE6jvBwUhREm37bXs7/lu1i3oYSrA7/HkPgK3rGm7lyeDoXHJNCUIA0LYkDSVEQB5i/qYQ3Fuawele11lFEJwkLNHDRkFRpWhIHkKIgAFBVlZ/WF/PGghw2FtVqHUd0kT1NS9eP7MGw7lFaxxEeQIqCn3M4Vb5bW8BbC7fL/QI/N7JnLPee0Yv+yeFaRxEakqLgp2wOJ1+t3M3bi7eTV9GodRzhIRQFzuyXwF2n9yIzzqx1HKEBKQp+xuZw8sWfu3hn8Q4ZaCbapNcpnJ+dzB2nZZESKfcc/IkUBT+yaEspT87ayA6ZfkK0U4BexyXDUrnl1EziQgO1jiO6gBQFP5BX0cCT329k/uZSraMILxVk1DPhxHRuHJlBeLBMo+HLpCj4sIZmO28szOHDpbl+v1aBcI+okAAeGNub/xuSqnUU0UmkKPiob1bv5vmfNlNS26x1FOGDhveI5pl/9KdHrNyM9jVSFHzMut01PP79BlbmVWkdRfi4AIOOm0dlctOoDAIMOq3jCDeRouAj6ppsPPvjZqYt34VTfqOiC2XGmXn2HwNk8JuPkKLgA/7YUcFd09dKF1OhGUWBi45N5cGz+siNaC8nRcGLNdsdvDRnCx8uzZWrA+ERYswBPDyuL+cPTtY6ijhKUhS81MbCWu6YtoYtJXVaRxHiAKf3jefFCwfJVYMXkqLgZZxOlXeWbGfSvG0ynbXwaMkRQbw2fjDHpkVqHUUcASkKXiS/spE7p69h+U7pWSS8g0GncNfpvbjx5B4oiqJ1HNEOUhS8xLTlu3hq1ibqm+1aRxHiiI3sGcsrFw0i2mzSOoo4DCkKHq7J5uD+r/7i2zWFWkcRokPiQk1MuiSbEzJkSVBPJkXBgxXVWLjh05X8tbtG6yhCuIVOgVtOzeL20VnodNKc5ImkKHiolXmV3PjZKsrqZJoK4XuO7xHFq5cMJj5MZl71NFIUPNC05bt45NsN0rtI+LSEsEA+mjCUvklhWkcR+5Ci4EHsDidP/7CJKb/t1DqKEF3CbDLwxqWDGdUrTuso4m9SFDxEdaOVf/1vFb9tr9A6ihBdyqBTeOr8/owf1k3rKAIpCh5hS3Ed132ygl2Vslay8F83npzBfWf2kvEMGpOioLFftpVx46crabA6tI4ihObOGZTES/83EJNBr3UUvyVFQUPzNpZw89RVsiqaEPsYmh7J+1cOISI4QOsofkmKgka+W1vIndPWYJfpTYU4QI+YECZPHEpadIjWUfyOFAUNTF+ez/1f/yXTXQtxCFEhAXxy9TD6J4drHcWvSFHoYpN/zeXJWRuRn7oQhxcZbGTqdcfTJ1HGMnQVKQpd6M2FObw4Z4vWMYTwKlEhAXx+3fH0SgjVOopfkKLQRV6YvZm3Fm3XOoYQXinG3FIYsuKlMHQ2KQqdTFVVnvh+o4xSFqKDYswmvrj+eDLjzFpH8Wk6rQP4OikIQrhHeX0zl77/BzvK6rWO4tOkKHSiNxfmSEEQwo1K65oZ//4f7Cxv0DqKz5Ki0ElmrNwtN5WF6AQltS2FYVeFTAvTGaQodIKFW0q5/6u/tI4hhM8qqmli/Pt/UFht0TqKz5Gi4GZr8qu5+X+rZKSyEJ2soNrCNR+voEHWLXcrKQpulFvewNVTltMok9sJ0SU2FdVy2xdrcMqHMLeRouAmpXVNXPnRMiobrFpHEcKv/LyphOd+2qR1DJ8hRcEN6ppsTPhoOfmV0r4phBbe/yWXL/7cpXUMnyBFoYNsDic3fraSjUW1WkcRwq89MnM9v8vKhR0mRaGDnp61kV9z5A9RCK3ZHCo3/W8luTKGoUOkKHTA16t28/HveVrHEEL8rbrRxjVTllPTaNM6iteSonCUNhTW8OA367SOIYTYz47yBm7630rsDlnR8GhIUTgKNY02bvpsFU02+aMTwhP9tr2CJ77fqHUMryRF4UipKmXzX6WqVm4sC+HJPv0jjx/XFWkdw+vI1NlH6peXYf4TWKL7cW3jLfxaJUsF+oO61T9St/pH7DUlABhjuhFxwniCMoYAYKsqomrhhzTv3ojqsBHU/ViiTrsBfUjkIY9rryunetEULDtWotqbMUQkEn3W7ZgSswCoWfY1tX9+BUD4cRcQNuyfruc2F26hcu5bJFz5MopO3xkv2+uFBhr48daTSI0K1jqK15CicCTyfocp40BtGbGsmsJ4J+Iu/pOXpXEw0dkac5ahKDoMkUkA1K+fT+2yr0mc8CqG8HiKJt+CMa47ESMuA6D6l89w1FeQcMV/UZSDX5A7muopmnwrgd0GEjp4LLrgcOxVhRgiEjFGJmItzaX407uJvfDRlivUr54k4cqXCYhNR3U6KPr4DqLPvAVTYs8u+zl4o+zUCL68cThGvTSMtIf8lNrLUgUzrnYVBACluZabSh7ju6wfMenk/oIvC848jqCMoRijkjFGJRM58kp0AYE0F26huWAj9ppSYs66g4DYdAJi04kZdwfWohya8tqeGLH2jxkYwmKIGXc7pqReGCMSCOp+DMbIRABsFbsxxqYTlDaIoPRsjLHp2Cp2tzx32VcEpvaTgtAOa/KreUlmLG43KQrt9cNdUFd40IcG5n/GipRJ9AuV/tH+QHU6aNi4GKetCVNyb1RHS/dHRW907aPoA0BRaN69oc3jWHKWEZCQRdm3z5H/+mUUTr6VujWzXY8HxKZjryrAXluKvaYUe2UBATFp2KqKqF/3MxEnXdF5L9LHvPfLDn7NKdc6hlcwaB3AK6z/quXrEEJLV/B98E6eT7mb93Z366JgoitZy3ZS/OndqHYrSkAQcf94iICYbuiDw1GMgVQtmkzEyVeCCtWLp4DqxFFf1ebxbNXF2Fb/SNjQ84kffhHNRduomv8eit6IecBojDGpRIy8kpJpjwAQcfJVGGNSKfniISJHTcSSu4qaX6eCzkDUmOsJTO3fRT8J76OqcPeXa5l920jCg42Hf4Ifk3sKh1NXDG8d39J81A6qouO3lOu4PGckqqp0cjjRlVSHDXttGc7mRhq3LKV+7VziL32egJhuWHJXUTn3LezVJaAohPQ9GVv5LgISexJ9xs0HPV7ei+djSsgk4YqXXNsqf36X5qKtJF7x34M+p37dfBq3/U70GTdT8P6NJF75Mo66CspnvUTyDR+iGOQN71DOGZTE6+MHax3Do0nz0eHMvKXdBQFAUZ2cmP8uK9LfJT2oqRODia6m6I0YI5MwJWQSefIEAuK6U7fiOwCCuh9D8g0fkPLvz0i9dSoxZ9+Fvb4CQ0RCm8fTmyMxxrS+qjRGp+KoLTvo/o7GGmp+nUrUmBtpLtyKMSoJY1QygWkDUR12bFUF7nuxPur7tYXMXCM/p0ORonAoKz6CnHlH9dTooiX8bH6ECxNK3BxKeApVVV33E/bQB4ejCzRjyVuLs6GG4Mzj2ny+KbkvtsrdrbbZKgswhMUddP+qBR8QOvR8DGExoDpQHfus2+F0gFM6O7THI9+up6hGZjRuixSFtlTthDkPd+gQhroCXqy7j7cy/3RPJqGZqsVTaMpfj72mBGvZTqoWT6F51zpC+o4CoP6veTQXbG65CbxhIeXfPk/o0PMwRqe4jlHyxYPUrvze9X3Y0PNoLtxCze/TsVUV0rBxEfVrZ2M+ZtwB57fkrsZWWUDo348FJPTEXrkby/YVLTendXoMUcmd+0PwEbVNdp74TkY7t0XuKbTlswuP+irhYAqSx3J+/iWUWaXN1xuV//gqTXlrcTRUojOFEBCbTthxFxLUvaV9umrRFOrX/4zTUo8hPI7Q7LGEDj0fRdl7X2n321djHjDaNZYBoDHnT6oXf4ytqhBDeDxhQ88nNPvMVud22popmnIrsefeR0B8D9f2urVzqP7lUxS9kajT/0VwxtBO/in4likThzKq18GvyvyZFIWD2TgTpl/p9sNaIzK52X4788qj3H5sIcSRSY8OZs4dIzEZZDT4vqT5aH/N9TD7gU45dEB1Du8138vTPdruuy6E6Bo7Kxp5Z9EOrWN4HCkK+1v4LNR2Xu8ExdbI5YXPMC/ra0IMjsM/QQjRad5alEN+ZaPWMTyKFIV9Fa+DZe90yamy8mewIuE/DIuQ2VaF0Eqz3cnj38mV+76kKOyhqjDrzlZzG3W2oPL1TFPv46607V12TiFEa/M3lzJvo3Qd30OKwh5/TYPdXd91VGmu4ZaSR/kmaw5GndzzF0ILT3y/gSabNOeCFIUW9mZY8LRmp1dQGZz/MStTX6O3Wdo3hehqu6ssvLEgR+sYHkGKArTcR6jJ1zoFYSXL+MH0IFcna59FCH/z3pId7CyXmY6lKDRWwi8Hn3xMC/qGUh6pfJCPs35BUaQ5SYiuYnU4mfTzVq1jaE6Kwi//haYarVO0oqgOTs5/mz+7f0BKYLPWcYTwG9//VcT2snqtY2jKv4tCVR78+b7WKdoUW7iQRWGPcn58qdZRhPALDqfKa/O3aR1DU/5dFBY+Aw7P/iRuqM3nlfr7eDVjpdZRhPAL368tJKe0TusYmvHfolCxHdZ9qXWKdlEczZxX8F9+yZxKdIDt8E8QQhw1pwqvzvffnkj+WxSWvgyqd80/n7p7Fr9FP8OoqPYv+iOEOHI//FXIthL/vFrwz6JQnQ9rp2md4qiYqrYy2X4vj3XfpHUUIXxWy9WCf95b8M+i8Our4PTeZhjF2sDEoqeYnTWTEL13Xe0I4S1+XFfEVj+8WvC/olBXAqs/1TqFW/TOn8byxBc5Jtz//nCF6GxOFV792f+uFvyvKPz+OtibtE7hNsHla5mh3M9t3WReeCHc7cf1RWwp9q8PXf5VFCzVsGKy1incTtdUxe2ljzAja55MqieEG6kqfLjUvz5w+VdRWPM/sPrmaEUFlSH5k1ne7Q2yQixaxxHCZ3y/togai/fegzxS/lMUVBWWf6B1ik4XUfw7swMf4oqkzls9Tgh/YrE5+Grlbq1jdBn/KQo586HSPy4D9Q3FPFn9AB9m/a51FCF8wv+W5Wkdocv4T1H48z2tE3QpxWlndP7rLOvxEYmBVq3jCOHVtpc18Pv2Cq1jdAn/KAqVuZAzT+sUmogv/Jkl4Y9zdmy51lGE8Gqf+cnVgn8UheUfeN2UFu5krNnJ65Z7+W/GGq2jCOG15m4oprTOd7qzt8X3i4Ld2tLryM8p9iYuKHiBRZnTCDfatY4jhNexOVSmL/f9VRF9vyhsnQ0WmUBuj/TdM1kW+ywjo6q1jiKE1/n8z3ycTt8eC+T7ReEv75z4rjMFVm7mY/u9PJy+ResoQniVgmoLC7f49qJXvl0UGith21ytU3gkxVrPtcVP8EPWLIL0Dq3jCOE1pi7bpXWETuXbRWHDN+CQ7piH0i9/KsuTXiE7zDdHegvhbr9sK6e2yXdHOPt2UfhrutYJvIK5bBVf6x/gptSdWkcRwuNZHU4WbPLdJiTfLQqVuZD/h9YpvIbOUsG95Q/zRdZC9Ir/dt8Voj1mry/WOkKn8d2isH6G1gm8jqI6OT7/fVakvU2PYN/vjy3E0Vq8tYwmm2/ei/PdorD5B60TeK3I4l+ZF/wwlyQWaR1FCI9ksTlYtKVM6xidwjeLQm0RFK7ROoVX09cX8lzN/bybuUzrKEJ4pNnrffNDk28Wha0/Ab49wKQrKE4bZ+x+ld8zPibBJL24hNjX/M2l2By+d//NN4vC5h+1TuBTEgvm8EvEk4yVSfWEcKlrsvNrju/9m/C9omBtgNwlWqfwOcaaHbxluY/neqzTOooQHsMXeyH5XlHImQ+OZq1T+CTFbmF84XMsyJpBqEEm1RNi3sYSn5sLyfeKwtbZWifweT3yv+bP+P8wPLJG6yhCaKqiwcqqXb414abvFYXtC7VO4BeCKjYw1Xkf96Zt0zqKEJpallupdQS38q2iUL4N6gq1TuE3lOZa/lXyGN9l/YRJ53u9MIRojz+lKHiw3MVaJ/BLA/M/ZUXKJPqFNmgdRYgutyqvyqfuK/hWUdi5VOsEfiu0dAXfGx/g+hTfnlZYiP3VNdvZWFSrdQy38a2ikPeb1gn8mq6xnAcqHuR/WYtRFN/55CTE4Szf6TtNSL5TFMpzoL5E6xR+T1GdnJj/LivS36NbkEyqJ/yDFAVPlPer1gnEPqKLFrPA/AgXJkihFr7vz1zf6ZbqO0WhYIXWCcR+DHUFvFh3H29mLtc6ihCdqry+mdxy3+ho4TtFQWZF9UiKw8q43a+wNPMzYgN8dwlDIXylCck3ioLdCmWbtU4hDiFl94/8GvUUp8X4xj8cIfa33EfGK/hGUSjdCA6Z2tnTBVTn8F7zvTzdY4PWUYRwu5V5vnFfwTeKQtFarROIdlJsjVxe+Axzs74hxOCbyxkK/7SzosEnluj0kaKwRusE4gj1zP+S5QkvMCzCdwb9CP/mVPGJm80+UhTkSsEbBZev4wvu56607VpHEcIttpfVax2hw7y/KDidULJR6xTiKOmaqrml5FG+zpqDUSejoIV3214qVwraq90NdovWKUQHKKgck/8xK1Nfo7e5Ues4Qhy1HLlS8ACVO7ROINwkrGQZP5geZELSbq2jCHFUtpdKUdBehbRH+xJ9QymPVT3AlKylMqme8Dq55Q2oqnf/3Xp/UZArBZ+jqA5G5b/Fn90/JCVQ1tsW3sNic7C7yrubs72/KMiVgs+KLVzAorDHOD++VOsoQrSbt/dA8v6iUClFwZcZanfxSv19vJqxSusoQrTL9jLv7oHk3UXB6YSqnVqnEJ1McTRzXsFLLMn8nGiZVE94OLlS0FJDqcx55Ee67f6e36KfZVSUb8wxI3xTUbXcU9BOvbQ1+xtT1RYm2+/lse6btI4ixEFVNnr31awUBeF1FGsDE4ueYnbWTEL0Tq3jCNFKVYN3t154d1FokKLgz3rnT+PPpJc4Jty723CFb5GioKV6Wf/X34WUrWGGch//7pardRQhAKhrtmNzeO8VrJcXhTKtEwgPoGuq4s7Sh/ky62f0ivf+YxS+w5uvFry8KMiVgmihoDI0/yNWpr1FVoh39/4Q3q+yUYqCNhrLtU4gPExE8W/MDnyIK5IKtI4i/FilXCloxOrdIwdF59A3FPNk9QN8kPmb1lGEn6pq8N5uqd5dFGzSTCAOTnHaGbP7Df7oMZnEQO/91Ca8kzQfaUWuFMRhJBTOY0n445wdK02NouvIjWatyJWCaAdjzU5et9zLSz3WaB1F+IkaizQfacMmSzeK9lHsTVxY+AILM6cTbrRrHUf4OLuMU9CIFAVxhLrv/pZlsc8xIqpG6yjChzm8ePU17y0KDhs45ROfOHKBlZv41HEvD6Vv0TqK8FEOpxSFricFQXSA0lzHdcVPMCtrFkF6h9ZxhI+RoqAFRa91AuED+udPZXnSKwwMk0n1hPvYvbgoGLQOcNR0UhSEe5jLVnFL1ncYNx9HWWQSDmcN9qYymutKcdq9t2uh0E6q5VggW+sYR8V7i4LivRc5wrMURHXjEXKYsqKMuEoLFWNuIKf5BJrrbQSHNRFkrkNvqMZhLcdSW0JdeRG25iatYwsPpmRkah3hqHlxUVAABfDeyzShPRWFx7r1pLFmK0UDepP43Z/EfvUM0YEhVJ9+Hdt1fakoDALigJ4A6IJVouKbCQqtR6+vxmErp6m+hNqyQmxNvjt2Zv6mHNbtLqasrh6DXk96dCTjBvYmLsx8yOdZrDZ+WreFdQXFNFptRAYHcd7gvvRJjANgVV4BP/y1GavdztDuqZyb3df13MqGRt5b/Ce3n3YigUZjp74+d9LpvfdDq/cWBWhpQpIbzqIDpvU/jWU1mwFYmtrA//29XdfUQNR3k4jUG6gdM5Hc0CFUlrf8rSkoNNYF0lgXCMQALZ8K9UFgjmsm2FyH3lCD017xd7EowGrx/u7TO8oqOTEzjdSoCJyqyo/rNvPekj+558yRmAwHfyuxO5y8u3gZ5sAArjzhGMKDAqlqsBAU0PIG39BsZfqKv7hk6CCizMF8+MtyMuOi6ZsUD8DXK9czbmAvryoIAIpOioI2pAlJdMDuqG683Jzn+v770O1cFBSEatn7aV9x2Amf8z7ZvE/9yReTl3AKJSVt91ay1Jmw1JloKRYZAOgCISK6meDQegzGlmLR3FBCbXkhzQ3ec4P7upHDWn1/ydBBPP7dz+yuqiEjNvqgz/kzNx+L1ca/R5+A/u83yqiQYNfjFfWNBBmNZHdLAiAzLpqS2nr6JsWzelcBep2OASmJnfSKOo9O7733PL27KOiM4JAbgeLIqSg82i0LS80217YmxU7ToD6Y/lh30OeYF0+jH9PoMXQsuzLPpqCo/edrajDR1GACooEeACgBEB5pJTi0HqOxBtVRQVNDyz2LpvraDry6rtFka7lyCg4IaHOfjYUlpEVH8PWq9WwoKCHEFMAx3ZI5pXcGOp1CTGgIVruDgqoaIoODyK+sZlj3VBqtNmav38pNo47vqpfjVnKloBVTKNhkUjxx5KYOOJ3lNZsO2L4xK5DBfxz6uUHLf6LX8p/o3nc4u7MvIa/YgHqUsxo0NwTQ3BAFRAHdWzYaISzJRkhYPcaAWlRHOc2NpdRVFGGprT66E7mZU1WZuWYj6TGRJIaHtrlfRUMjOaUWjklL4tqThlFe38DXq9bjUJ2c3q8nwQFGLhk2iM//XIvN4eDYtBR6JcQyfflaTsxMp6KhkY+WrsDhbNl/UKp3XDUYjG0XSk/n3UUhMBzqi7VOIbxMfnQ6r1oOvqbzrLgCBrfzOAEbf6fHxt9JTe9H0fAJ5JabcdjcM+eN1WLEaokEIoG0lo16CE2yYQ5rwBBQA85KrH8Xi8aaSrect72+WbWe4po6bj51+CH3U1UwBwZw4bED0ekUUqLCqbE0sWjLDk7v13LjfkBKAgNSElzP2V5aQWF1HecP7s/zPy7ksuMHExpo4rX5v9IjNorQQFOnvjZ3MIUc+ua7J/PyohCmdQLhZVQUHk7tgaUm56CPrwsoRUlLQc3b3e5jGnduoNvOe0iKT6Ns1HVsr4nB2tQ5o6RtFiNVlgggAlex0EFYop3gsHqMptqWYmEpo76ykIaqCrdn+HrVejYWlvKvU4YTERx0yH3DgkzoFQWdTnFtiwszU9fUjN3hxLBfLx27w8HXq9Yz/rhsyusbcKgqGXEt9ytizCHsqqym3983oT1ZoJuKwptvvsmLL75IcXExgwYN4vXXX2fYsGGHf2IHeHlRCNc6gfAynw04g1U1Gw+5T3H/ROKPoCjsYSjJI3Haw8SHx/w91qEblvqu6R1nbTJgbYqgpVh0c203xzsICW8gwFSDQhXNjaU0VBVRX1Xe8jH+CKiqyjerN7C+oJibRg0n2hx82OekR0eyelchTlVFp7QUhvK6BsICTQcUBICfN+bQKyGWlMhwCqpqcO6T0amqqF4y0Vygue0mtfaaNm0ad955J++88w7HHXcckyZN4owzzmDLli3ExcW5IeXBSVHoJM/90szXm21sLncSZFA4IVXPf8aY6BXTulfC7/l2HlrQzLICB3oFshP0zLk8mCCj0saR4c0/rbz4WzPF9SqDEnS8PjaIYcl7j3vnnCamrLESEqDw/OhALhu4tzvflxtsfPKXje/HH/4ftK/Ji+nBa5bth93vt7Qm/tGB8+hqyg8Y61BXrc38+narnpqyMCAMSHVtD4l1YA5vJCCoFkWtxGoppaG6mPqKMtQ2bpB8vWo9q3cVMvHEIZgMemotLQP4goxGjIaWv7/Pl60hPCiQswb2BuCEzDR+zclj5uoNjMhKp6yugfmbchiRlX7A8Ytr6liTX8Qdp48AIC7UjAIs27GL0EATpbX1pEZGuOtH06nccaXw8ssvc9111zFx4kQA3nnnHX744Qc++ugj7r///g4fvy3eXRRMntt8tDjPzs1DAxiapMfuhAcXNHP6Z41s/JeZkICWN/zf8+2c+b9GHhhh4vWxgRh0sLbEia7tesC09TbunNvEO+MCOS5Fz6Q/rJzxWQNbbjETF6Lj+y02pq6zMfeKELZVOLn6OwtnZOqJCdZR06Ty0IJmfr7S/wqCU9HxSEo6TW00G+3r+7Dt/DMwELWpY6OWDzXWQWsOm56a8lAgFEh2bQ+OcRAS0YgpsBaowt5URkN1MXUVJfy+fRcAby9qfSf+4qEDGdq9peBUNVpQlL1/wBHBQVw3chjfrdnIf+f8QnhQICdldeeU3hmtjqGqKjNWruPc7D6uMQ9Gg55Lhg3i61UbcDid/OOYfoQHB7r/h9EJAs0dKwpWq5WVK1fywAMPuLbpdDrGjBnD77//3tF4h6So3nI9djA/Pw5LX9E6RbuUNTiJe6mexROCGZnW8kd//AcNnNZDz1Ontv8P/bgP6hmapOeNs1racp2qSuor9fx7WAD3jzDxwq/NrCpy8MWFLW/88S/VMWt8MEOT9dzwvYXeMTruGO75N+rc7eOBZ/JS3aGbjfb16fzemP5c7/Yc7Rnr4Il0BifmsEZMQbUoShW25nIaa4qoKy/B6fCu19IVrnvjI8Jij76Jp7CwkOTkZH777TeGD997M//ee+9l8eLFLFu2zB0xD8q7rxSCY7RO0G41zS3/jQpq+RRV2uBkWYGDywYYOeHDBrZXOekdo+OZU02M6HbwX4vVobKy0MkDI/a+qesUhTE9DPy+u+Uf5qB4Pe+ttFJlUdlR5cRiU8mM0rF0l51VxQ7eGucdn7TcKTc2g9cbD99stK8tPYMY+Kf7s3RkrIOWnHYdtZVmwAwkubabIp2Ywy2YgurQKZXYreU0VhdTW16M0+EZV0VaCI6I1DrCUfPuohCefPh9PIBTVbl9dhMnpurpH9fS9rqjqqXd9vHFzbx0monsBD2frLUx+pNG1t8UQlb0gSMiyxtVHCrEh7RuX4oPUdhc3lIUzsg0cPlAI0PfryfIqPDx+UGEBMBNPzQx5bwg3l5h4/U/rcQEK7x3diD94rx35GV7OBQdDyd1o7n2yIrCj3HFDOykTODesQ5aUh066ipDqCME2Nut1BSuEhJhITC4Dp2uCntzGZbaEmrLi3DYvHf94vYwhYRg6OC0HDExMej1ekpKSlptLykpISEhoY1nuYd3F4WwFK0TtMvNPzSxvtTB0qtDXNv2TLd+w7FGJg5uGegyOFHP/Fw7H6228dyYo3+zfnxUII+P2ntF8MSiZsZ0N2DUw9NLmll3Uwizttq58lsLK6/33v7U7fHJgDP4q3bDET9vlakIJSUJdXdhJ6TaqzPHOmhJVRXqq4KprwoG4oGWG8/GUJWIcAtBwfXo9FXYreVYaoupKy/CbvWN2QlCIqI6fIyAgACOPfZY5s+fz/nnnw+A0+lk/vz53HLLLR0+/qF4d1HwgiuFW360MGubnSUTQkgJ29sFL9Hc8v99Y1t3y+sTq2NX7cHfFGKCFfQKlDS0vg1U0qCSYD74sPrN5Q4+W2dj9Q0hfLTaysg0PbEhOi7qZ+Tq75qoa1YJNR3izrYX2xGXxZuNh7+x3JbSgcnEdnJR2KMrxzpoSlVoqA6moTqYlplnewFgCFEJS2omKKQOvb4Kh817pykPcVPT0Z133slVV13FkCFDGDZsGJMmTaKhocHVG6mzeHdRMCeAzuCRM6Wqqsq/f2rim812Fl0VTPfI1m/a6REKSaEKW8pbF4CtFU7GZh781xKgVzg2Scf8HXbO791yeepUVebvsHPLsAOH1auqyg2zmnj5dBPmAAWHE/Z8CN3zX4f3djM4JIei5+HEZJprdxz1Mf7o1sQ5bszUHlqOddCWQmNNII01gUAsraYpT2huWdNCX7PPNOUF2DrYO6yzuKsoXHzxxZSVlfHoo49SXFxMdnY2s2fPJj6+cwfveXdR0OlaCkPtkQ806mw3/9jE1HU2Zl4STKhJobi+5V043KQQZFRQFIV7TgjgsUXNDErQk52g5+M1VjaXO5nxf3vf4Ed/0sA/ehtdb/p3Hm/iqm8tDEnSMyy5pUtqg01lYvaBbZgfrLIRG6xwTq+Wx07sZuDxxc38sdvOT9vs9I3VERHom1cJkweewbrajvUe+i4il3MDAlA1aNbwpLEOWlJQaKwNpLF2T7HYd5ryJoLN9XunKa8rpra8UPNpykOj3dcB5pZbbun05qL9eXdRgJYmJA8sCm+vaPkHPOrj1n+gk88LZEJ2yxv87cebaLLDHXOaqLSoDIrXM++KYDKi9l5VbK90Ut6492ri4v5GyhpVHl3UMngtO0HH7MuCid+v+aik3skzvzTz2zV772MMS9Zz13AT46ZaiAtpuQnti3Lie/F2/dYOH6dGacI6sBfGFUd+T8JdPHmsg9YsdYFYXGtatDVNeTlNDaXUdeE05ZGJnt+sfSjePU4BYMbVsP4rrVMID2HXGbh8wIlsqD34hHdH6tG8wfSfutwtx3IXbx3roDVTiJXg0AaMxupOnab84if+Q0rvfm49Zlfy/iuFaO9dC1W430cDTmdDB5uN9jU7oZT+bjuae2g11mHu6s/57s8PGNX/n1x44s2H3X9FzgKmzH+GgekncP0ZT7m2/7x2Oj+vmQbAadkXM3rQRa7HdpZsYtrSV7n7H2+i17m3u3TLNOUBtMw8u3ea8vBkW8uaFgEta1p0dJryqCTv6BXZFs2KwvPPP88DDzzAbbfdxqRJk47+QDE93ZZJeLet8b15p36LW4/5p6kAJSkBtdDzpmjvyrEOeaWb+XXTLJKjerRr/4q6Yr79410yEga02l5QsZ0fVkzhxjOfAVXlndkP0TtlCMnRPXA4HXzxyyTGj7zD7QXhUJobjTQ37pmmPL1lo75l5tmQ8Pr9pikvpLGmqs1jBYaYCQ7z3DnZ2kOTorB8+XLeffddBg50w/AgKQqClmajh+NjsdXlHX7nI1Q+MIVoDywKe3T2WIdmm4UpC55l/Mg7mb3qf4fd3+l08PH8ZzlryFVsL1qHxbq3Lb+kOp/kqB70Sm5ZtSIpukfLtuge/Lx2GpmJA0iL6+227B3ReubZQ0xT3lhKfVURDVUVXn8/AaDL14yrr6/nsssu4/333ycy0g1dt2KyZK1mwfsDz2BTJxQEgGXp3tHrx7hzA90+v4cT/3qePvFVBAS659P2tKWv0r/b8fROObZd+/+08lPMQRGc0PusAx5LiupOac1uKutKqKwrobR6N0lR6ZTVFPLHltmcPfRqt2TuTNYmA9WlEZTld6OsIJuaqtNxcBWh8beSMuASreN1WJe/m958882MGzeOMWPGuOeAxiCITHfPsYRX2pLQl/fqDlxa011mhu+ADk5b0JX2jHU44feHGRBdSJD56BsEVuQsIL88h3OHXduu/bcXreP3LT9x6ci7Dvp4QmQa5wy7hjd+uJc3friXc4+7loTINL745RXOP+56Nu1ezjPTr+H5GTeQU/jXUefWgs1qICzG8xcAOpwubT764osvWLVqFcuXu7k3R1xfqDz6QUrCe9l0Rh6Ki8beSVcJAFU6C/b+WRhWd17h6QwdHetQVV/KV7+9yS3jXsBoOPyaw03WRj5Z+DzjR96JOajtdvWT+p7DSX33Dgv8Y8scTMYgusf35alpE7jnn29RXV/GR/Of5olLP8Oo9571jqOTvH/amC4rCvn5+dx2223MmzePwEA3z9QZ1xc2z3LvMYVXeG/g6WypWdfp59nWO5Q+qzv9NJ3iaMc67CrbSp2lmv98daNrm1N1sr3oL5Zs+JZJ185Gt88N4fLaQirqinl39sOubXt6vN/63mk8cvHHxIbvnWEVoN5Sw08rP+X2c19hZ+lm4sJTXF9Op53S6t0kR7fv5rYniEoOOfxOHq7LisLKlSspLS3lmGOOcW1zOBwsWbKEN954g+bmZvT6o2wDTRzkppTCm2xK7MsHtV3z6X1uQhl9uuRMnUdx2Amf8z7ZvN+usQ69ko/hwf/7oNW2zxa9SHxEKqdlX9KqIADER3Q7YP9Zyz+iyWrhwhNvJtIce8A5vvr9LU4ZeAGR5lh2lW3B4dybx+F0tLkKnCcKCjUSEu79a5V0WVEYPXo069a1/kQ3ceJEevfuzX333Xf0BQEgZWgH0wlvY9MH8FBMFPb6XV1yvl8D87k9Pha1pKxLztfZ2jPWITAgmKSo7q22BRgCCTGFubZ/suB5wkNiOO+4azEaAg7YPyigpTll/+0Am3avoLRmN1ecch8A3WJ7UVK9iw27llFVX4ZOpyMuIvWA53mqmNSOr8vsCbqsKISGhtK/f+thQCEhIURHRx+w/cgPHg/h3aCma94ghPbeHnAa27qg2WhfFYPSiJrrG0Vhj46OdaisL221/GZ7We3NfLn0da4e8wi6v3sPRppj+b8Tb+GzRS9i0Bu5YtR9BBi855N3YoZ3j0/YQ9NpLkaNGkV2dnbHBq/t8eUE2PBNx48jPN6G5AFcbmrArnbtHEDXVvTn9PfWdOk5u5rNx9Z16Ern3TGYlF7eu+LaHppOc7Fo0SL3HSxlqBQFP2DVm3g4Kgx7fU2Xn/vbyFxONxjA7rsT0vnNug5uptMrJHQP0zqGW/jOqC+5r+AX3ho4hpz6fE3OXa5rwNEvQ5NzdzV3jnXwB7HdQjEE+MbStr5TFBIHgRf1ZxZHbl3KQKbUbNQ0w/bevtFu3F57xjoc9/OdZIdtIzTCewbxdaXEzAitI7iN7xQFgwkSs7VOITqJVW/i4UgzDlXbpox5iRWanl8re8Y6DPn+XxxrWkNUjFw57Csp03c+LPhOUQDoMUrrBKKTvDFwDDvqtV9MaXFQHrqYaK1jaMY11mHGDQxTlxAf7xtNJh2iQGJGhNYp3Ma3ikLGqVonEJ1gbeogPq7RbvWz/VVmp2sdwSOYF0+j37QbGd4wi+REwDdXdj2syIQQAs2+06zmW0UhZSiYfKMHgGjRbAjk4fBgnB40snVld8/J4gmClv9Er89vZkTJZ6Qn2f1u0uJEH2o6Al8rCnoDpI/QOoVwo9cGjGZnQ4HWMVr5JmoHdGQEvo8K2Pg7Pabexogdb5GV2IDe6FtvL23xhbEJ+/K935o0IfmM1amD+cyDmo32KNU14OzrH11Tj4Zx5wZSP7+XEWufo098pdvWdfBEOp1Ct75RWsdwKykKwiM1GYN4JNzkUc1G+9rRJ0LrCB5PX7qLxGmPcMJvDzEgusAnxzokZoZjCvad+wngi0UhOgMi0rROITro1f6nktdQqHWMNv2cWKl1BK+hq60g9qtnfXKsQ9qAGK0juJ3vFQWA3mdrnUB0wMpux/K/6vVaxzikhUE7UaJ9q9mgs+071mGIabVPjHXoPrBjRWHJkiWcc845JCUloSgK3377rXuCdYBvFoW+52mdQBwlS0Awj4TqUdFsnsZ2URWoGZSudQyvpDjshM35wOvHOkQmBBMRH9yhYzQ0NDBo0CDefPNNN6XqOO8v1QeTOgxCk6DOc5sfxMFN6n8q+VXesTbvqh4waoHWKbzb3nUdzmRX5jkUFIOHfx5w6TH4wEWDjtTYsWMZO3asG9K4j29eKSgK9Dnn8PsJj7I8bQifV3XtGgkdMTNqJ+h8859QVwtaPtvrxjpkDI7TOkKn8IIf/VGSJiSv0hgQwiNmxeObjfZVoK/F2dt71g/2Bt4y1iEsJpDYbr6x0tr+PPMn7g7dhoM5XusUop1e7j+KgsYSrWMcsTwf66PuKTx9rEOP7I43HXkq3y0KOp00IXmJZd2HMr3Ks3sbtWVBcrXWEXyap4516HlcgtYROo3vFgWAgZdonUAcRoMplEeDVK9qNtrXz8G5KJERWsfweZ401iG2Wyixqb7ZdAS+XhRSh0Jsb61TiEP4b7+RFFpKtY5x1Byo1A7qrnUMv+EJYx36npjotmPV19ezZs0a1qxZA0Bubi5r1qxh165dbjvHkfLtogAw+HKtE4g2/Nb9OL70ot5GbVnbw0/njNaQVmMdDEYdWcPc13S0YsUKBg8ezODBgwG48847GTx4MI8++qjbznGkFFVVvfO6vb0ayuG/vcFp0zqJ2Ed9YBj/7JFFkaVM6ygdlmaP4MWXKsDH/yl5OksXjHXodVwCYyb27ZyDewjfv1IIiYGeZ2idQuznpb4n+URBAMgzVKP2kq6pWuuKsQ59R7iv6chT+X5RADjmSq0TiH382uN4vvKBZqN97errv0t0eprOGusQHhdEUpZvrZ1wMP5RFDLHtEx7ITRXFxjOY4FWrWO43aLkWq0jiP24e6xD3xP94z3EP4qCTg9DrtY6hQBe6DuCEku51jHcbk7IDpQwWQrWE7ljrINOp9DreN8dm7Av/ygKAEOvAUOQ1in82pKME/jWx5qN9rArTuqz5b6CJ+vIWIf0gTGEhJs6MZ3n8J+iEBwFg2Qwm1Zqg8J5IsCidYxO9VeGZ03FIA7uaMY6ZI9J7YJknsF/igLA8JsB6VOuhf/0OZHSpgqtY3SqmTG7WmboFV6hvWMdEnqEkZgZ0bXhNORfRSEmC7JO0zqF31mUOYLvvHRuoyOxw1AFmelaxxBHwbx4Gv2m3cjwhu9JTqTVZ8fs07pplksL/lUU4O+rBdFVaoIieNJYr3WMLpPf33dnz/QH+491iIgPpscg//qd+l9R6DEKEgZoncJvPNfnBMqa/GeR+yVu7pq6orGRf+3O5+ScHPpu2czPdXWtHn+jvIxxuTs4dusWjt+2lavzd7HWcvh7NyU2G/cWFjJ821YGb93Cebm5rG/a+7yPKisYkbONETnbmFzZutlvrcXChTtzsfvwCO49Yx3OHFSMovOvJkH/KwoAI+/VOoFfWJB1Ej/4QbPRvn4y56KEmt12vEank16mQB6JP/jaIOkBATwUF8+36d35tFsayUYj1+3Op9Jub/OYNQ4Hl+3Kw6AovJuSyvfp3bk3Lo4wXUub+pamJt4oL+elxCReTEzitfJytjY3AWBXVZ4oKeax+AQMPn7/xJCQQNTZZ2kdo8t5xuTkXa3POS1XC8W+2T3SE1QHR/Gkvhbafm/ySVbFQcOg3gQvXeuW4400mxlpbrvInB0W3ur7+2Lj+Kqmhi3NzQw3HPyf94eVFSQYjTybuHfKhpSAANf/77Ba6WkycXxICAA9Taa/twXyUWUlQ4KCGRDk+927o6+eiLLPz8Vf+OeVgqLAqAe0TuHTnu19PBXNVVrH0MT6TG3m+reqKtNrqgnV6ehtartP/YL6evoHBnJ7QQEjcrbxz525fFld7Xq8p8nETquVQpuNApuNPKuVrAATu6xWvqmp5rbYmC54NdrSR0cT8X//p3UMTfjnlQJA73GQmA1Fa7RO4nN+7nkSP/lZs9G+vovJZ1gXnm9RfT13FRbQpKrEGgx8kJJKZBtXCQC7bTa+qK7mqsgoro+OZn1TE8+WlmBUFM4PDyfDZOL22Fiuzc8H4PbYWDJMJq7O38VdsXEsbWjgzfJyDIrCg3HxDAkO7qqX2mVibrwRnR9cDR2M/xYFgFMehKkXaZ3Cp1SFRPOUzr/nAdpqrICMNNie1yXnGxYczNfp3al2OPiyppo7iwr5olsa0W0UBqeq0j8wiDtiW3rV9A0MZFtzM9Oqqzg/vKU56pKISC6J2Dv527c1NYTodGQHBTEudwfT0tIpsdu4q7CQeT16EKDznUYHY2oqkRf77/uC7/wmj0bPMyB5iNYpfMrTvYZR6afNRvsq6B/XZecK1ulICwhgUFAQTyckoge+qqlpc/9Yg4EMU+u28oyAAIrauDldZbfzVkU5D8XF81eThfSAANIDAjguOAQ7KjttvjXBYeytt/rlvYQ9/LsoAJz6sNYJfMacXiczt2qD1jE8wtLURs3OrQJW1dnm48cEBZNrbf1GvtNmJclw8Hshz5eVcmVkJAlGI04VbPt0RXWoKg4f6plq6tuHsLPHaR1DU1IUMk6BnmdqncLrVZhjeQb/GY9wOLPMOShuaGtvcDrZ1NTEpqaWLqEFNhubmpootNlodDp5payMtRYLBTYbG5qaeKioiBK7nTNC987YOjF/F/+r2nv1dmVkJH9ZLLxbUU6e1cqs2hq+rK5mfGTEAef/raGBnVYrl/7dlNQ/MJBcq5Ul9fVMr65Gpyh096FP1XF33oXi411tD8e/7ynscfozkDNfluzsgKd7DqFKrhJcmhUHlkG9CPz9rw4dZ0OThQl/3/AF+E9ZKQDnh4XxWHwCudZmbiusocrhIEKnp39QIJ+mdiNrn95H+VYrVY69TUMDgoJ4LTmFV8rKeLuighSjkfvj4jlnv+6tTU4nT5eU8N+kJHR/v1EmGI08FBfPQ8VFBCgKzyUkEugj9xOChx+PecSJWsfQnO+v0dxesx+EP97UOoVX+qn3KO5t3qF1DI9zX8Fgjv1kudYxRHsoCunTpxM0oL/WSTTnGyXeHU6+F4JlScUjVW6O41nVt2c/PVqzYgu0jiDaKeLCC6Qg/E2Kwh5BEXDKQ1qn8DpP9TyGamvbPV382YaAUpR0/5mH31vpIyOJu+surWN4DCkK+zp2AsT10zqF15jV+xQWVG3UOoZHKxzgH0s4erO4u+5EHxGhdQyPIUVhXzo9jPsvshDP4ZWFJfC8WqZ1DI/3q4ZdU8XhBQ0eTPgFF2gdw6NIUdhf2nA45kqtU3i8JzMHUWP175HL7TErdAeKn06X4PH0ehIee9Tvu6DuT4rCwZz2JJgPPlWxgO/6nMqiqk1ax/AKjTobTQMztY4hDiLysksJ7N1b6xgeR4rCwQRFwFkvap3CI5WGJ/K8o1jrGF5lU1ag1hHEfgwJCcTeeqvWMTySFIW29D0P+pyrdQqP83iPgdTZ/Gd5TXeYFV+odQSxL0Uh6dln0B9inQp/JkXhUMb9F4KitE7hMb7pO5pfqqXZ6Ej9FVCCkpqsdQzxt8jx4wk54QStY3gsmebiUMxxLc1IX12jdRLNFUck86K9yO3HbdjSQPmP5VjyLNir7XT7dzfCjt07b0/JNyXULKvBVmlDMSgEpQcRf0E8wRltzyukOlVKvyml+vdq7DV2DBEGIkdEEnturOumYvlP5ZT92NJ7KvasWGLG7l04pnF7I4WfFJLxaAaK3j03IUsGJhGXL4PZtBaQlkbcPXdrHcOjyZXC4Qy4EAZeonUKzT3evV+nNBs5m50Edgsk6Yqkgz5uSjCRdEUSWU9n0eOhHgTEBLDzpZ3Ya9te57PshzIqF1aSdHkSWc9mkXBRAuU/lVP5c8uEfU35TZR8U0LqTamk3pRKydclNOW3TDinOlQKPy4k6aoktxUEgN/Smtx2LHGU9HqS/vO83y6e015SFNpj3EsQma51Cs181XcMv1Zv7pRjhw4MJf6C+FZXB/uKGB6BuZ+ZgLgAApMDSRifgNPipGl322+ylhwLoYNDCc0OJSA2gPCh4Zj7mWnc0TJmoLmomcCUQMx9zZj7mglMDaS5qBlouYII6RVCcA/3rib2Xeh2lEMskSk6X/Q11xCUna11DI8nRaE9TKFwwYeg87/WtuKIFF6ye0azh9PupGpRFbogHYGpbffoCcoMomFjA83FLW/0ll0WGrY1EDogFABTiglriRVrhRVruZXm4mZMKSaaS5up+qWKuH+6f4Gcep2V5oFZbj+uaB9T797E3nKz1jG8gv+9yx2tlCEw6n5Y8LTWSbrUo937UF+9RdMMtWtq2f32bpxWJ4ZwA+n3pGMIbftPN3ZcLE6Lk20PbGv52OOE+AviiTghAoDApEDiL4hn54s7AUi4MIHApEByX8gl4aIE6tfXU/ptKYpeIfGyREJ6hbjldWzpFcwAmTS1y+mCg0l++b9+vZrakZCicCRG3AXbF0HeUq2TdInp/U7jd40LAoC5j5mMJzNw1DmoXFxJ/lv5ZDyagSHs4H++NX/WUP1HNSk3pBCYHIhll4XiqcWuG84AUadGEXXq3p5lVUur0AXqCM4MZuv9W8l4LANblY38t/Pp+WJPdMaOX1T/GFfEgA4fRRypxKefwtSjh9YxvIY0Hx0JnQ7++Z5fTLFdGNmNl635h9+xC+hMOkzxJoIzg0m5JgVFr1C1pO11oIunFxN7ViwRx0cQmBpI5ImRRJ8RTdmsg8/VZK+zUzqzlKTLk2jc0YgpwYQpwYS5jxnVoWItds8axCtNRSjJiW45lmifyEsvJeyss7SO4VWkKByp8GS4cDIoeq2TdBoVhUfTe9Jg98zJ3FSnitPW9hrEarN6wF+2olNaFi8+iKKpRcScHoMxygjOlh5IrmM5VFSn+9ahKh0o4xW6SuDAgcTff5/WMbyOFIWj0ePklvmRfNS0/qexrHprl5zL0eTAkmfBkmcBwFpuxZJnwVphxdnspHhGMY05jS3bd1rY/eFu7FV2woftXToy9z+5VPy8d6Gf0OxQyr4vo25NHdYyK7UraymfU37QHk716+uxlliJGt3SlBTUPYjmombq/qqjclElik7BlOi+XkPL0mTJ166gDw8nZdIrch/hKMg9haN1wi1QtBbWTdc6iVvtjurGy815XXY+S66Fnf/Z6fq++POWeZUiTowg6aokrEVWdi3dhaPegd6sJ6h7EN0f7E5g8t7eR9ZSK/a6veMWEi9PpPTrUgo/LcRe2zJ4LWpUFLHnxbY6t9PqpPCzQlJvSm25kgCMUUYSL0+k4IMCFKNCyrUp6ALc99lpZsR2zgkIQLW6p0lKHISikPTiCxiTDj72RRyarNHcETYLfHgaFK/TOolbqChck30qy2u2aR3Fp/1vbk+MK2Vxos4Sc+u/if3Xv7SO4bWk+agjjEFw8f98Zn6kzwecLgWhC2ztJROxdZbw886TgtBBUhQ6KjINLvoYdEatk3RIfnQ6k5p2ah3DL8xOKNE6gk8KHjKExKd8915fV5Gi4A7dR8J5b+Kty3iqKDyc2gOL3aJ1FL+wzFSAkiiLOLlTQFoaKW+8LjeW3UCKgrsMuhhGP6p1iqPyvwFnsKomR+sYfqV8YKrWEXyGPiKC1HffQR8RoXUUnyBFwZ1OuhOGXqt1iiOSF9OD1yw7tI7hd/6UrqluoRiNpLz+GgHp6VpH8RlSFNxt7IvQa5zWKdrFqeh4JCUdi0Omde5q30XmgkF6hHeITkfi888RPHSo1kl8ihQFd9Pp4MIPIWWY1kkO69MBZ7Bamo00UaFrxD5AZk09aopC4lNPEj7OOz6AeRMpCp3BGASXToMEz53+LDc2gzcapSBoKadXqNYRvFb8Aw8QccEFWsfwSVIUOktwFFwxE+L6ap3kAA5Fx8NJ3WhyNGsdxa/NTTz4BH3i0GJvv42oK6/QOobPkqLQmUKi4crvILa31kla+WTAGfxVu13rGH5vaWA+Snzs4XcULtHXXUfMjTdqHcOnSVHobObYlsIQ7RntxzvisnhTmo08RuXAblpH8BqRl11G3F13ah3D50lR6Aqh8XDV9xCl7UIfDkXPw4nJNEuzkcdY0b3tKcDFXlFXXUn8ww9pHcMvSFHoKmGJcNUsTa8YJg84nXW1MibBk8yMkK6phxPzr38R/8ADKIp3zhjgbaQodKXwZLh6NiRmd/mpc+J78XaDTHbnaUr19Tj6Zmgdw2PF3Xsvsbf+W+sYfkWKQlcLiYEJs1rmS+oidp2BhxMSsDplDn9PtKNP+OF38jc6HQlPPkH01RO1TuJ3pChowRQKl82APud0yek+GnA6G2pzu+Rc4sjNS6w4/E7+xGAg6cUXiLzoIq2T+CUpCloxmOD/PoZjruzU02yN78079Vs69RyiYxYH5qGL8Y01OTpKCQ4m5Y3XZaSyhqQoaEmnh3Nfh5Pu7pTD23UGHo6Pw+aUydc8mapAVXZ3rWNozhAbS9qnnxA6apTWUfyaFAVPMPoR+Md7oHffAvEA7w84nU11O916TNE5Vqb7d9dUU1YW6dOnEdSvn9ZR/J4UBU8x6GKY8AOY3bP4ypaEvrxXv9ktxxKd79uoXNDrtY6hCfPJJ5P2+ecYExO1jiKQouBZUofCdQsgYWCHDmPTGXk4Lhq70+6mYKKzFevrcfbxv66pURMmkPL2W+jNIVpHEX+TouBpwlPg6jnQ59yjPsR7A09nc12eG0OJrpDbJ0LrCF1GMZlIfOYZ4u+/D0Unb0OeRH4bniggGC76BE55GJQja1LYlNiXD2o3dVIw0ZnmJ1VpHaFLGNO6kT7tCyIu+KfWUcRBSFHwVIoCJ98DV84Ec0K7nmLTB/BwTBR2VZqNvNH8oFyUyAitY3Sq0DPOoPtXXxHY27NmDhZ7SVHwdN1PghuXQsaph9317QGnsbV+VxeEEp1BVaAmW9tJEzuN0Uj8gw+S8uok9Gaz1mnEIUhR8AbmWLj8azi17eakDUn9mSzNRl5vtQ/WBENSIumffeq2hXHefvttBg4cSFhYGGFhYQwfPpyffvrJLccWUhS8h6LAyHtapuAOTWr1kE0fwMPR4dJs5ANmRuW1rPPtI0LPPJMeX39N0KBBbjtmSkoKzz//PCtXrmTFihWceuqpnHfeeWzYsMFt5/BniqqqqtYhxBGyVMEPd8P6GQBMGjyOD6vXaRxKuMu0b9NRNnn3Qkj68HDiH32ky6ariIqK4sUXX+Saa67pkvP5Mt/5SOJPgiLhwg/h/z5mXeZJTKnZqHUi4UZ5fb17HiTzqFH0mPV9lxQEh8PBF198QUNDA8OHD+/08/kDKQrerN/5JF3wMad2O/xNaOE9FibXaB3hqOjMZhKfeYbUd97GENu5a0+vW7cOs9mMyWTixhtv5JtvvqFv376dek5/Ic1HPmLuzrk8s+wZKpsqtY4iOkiPwhdvB6NWe09xCDnxRBKfehJjUtLhd3YDq9XKrl27qKmpYcaMGXzwwQcsXrxYCoMbSFHwITXNNUxaNYmvt32NU/XvCda83Ue/9ce8eI3WMQ7LEB9P/P33ETZ2rKY5xowZQ0ZGBu+++66mOXyBNB/5kHBTOI8Nf4ypZ01lYEzH5k8S2lqb4eGT4xkMRF19NRk//qB5QQBwOp00NzdrHcMnyIrhPqhfTD8+O+szvs35lkmrJkmTkhf6NjqPExUFPPBCPnjoUBIefQRTVpYm53/ggQcYO3Ys3bp1o66ujqlTp7Jo0SLmzJmjSR5fI0XBRymKwj+y/sHotNG8ufpNpm2ZhkN1aB1LtFOeoRq1Z3eULTu0juJiiIsj7p67CT+na5aRbUtpaSlXXnklRUVFhIeHM3DgQObMmcNpp52maS5fIfcU/MTWqq28uupVluxeonUU0U4vbTmGbl//qXUMdGFhRF97LVFXXoEuMFDrOKKTSVHwM6tLV/PqqldZWbJS6yjiMM6uz+TK17VbKEkJDCTq8suIvu469OHhmuUQXUuKgp/6teBXXlv9GhsrZOCbpzKoOj5/04RaV9fFJzYQ8Y9/EHPLzRjj3bMSoPAeUhT83Ly8ebyx+g121HhO27XYa/LS/oT8sqZrTmYwEDZ2LDE33YSpR/euOafwOFIUBE7Vyfxd85myYQp/lf2ldRyxjzuLszl+8opOPYcSGEjEBRcQNXEiASnJnXou4fmkKIhWVpWsYvKGySzOX4yK/GloLcMexXMvlnbKsXXh4UReOp6oK67AEOXd8y0J95GiIA4qtyaXjzd8zPfbv8fqtGodx69Nn5EC23a67XiGxESirrySyIv+D11IiNuOK3yDFAVxSOWWcmZsncE3276hsKFQ6zh+6eWNg0mZubxjB9HpMJ90EhEXX4z55JEoeg8fMS00I0VBtItTdfJH4R/M2DaDRfmLsDltWkfyG/+o68n4N46ul5ghLo6ICy8g4sILu2yyOuHdpCiII1bZVMn327/nq21fkVuTq3Ucnxeg6vnfGwbU+ob2PcFgIGT4cCIu+j9CTzkFxSATF4j2k6IgOmRN6Rpm75zNvJ3zKLV0zg1RAR8v6UvQr4foGabTEXzssYSdNZbQM86QG8fiqElREG6hqiprytYwd+dc5uXNo6SxROtIPuWewmyGfnxg19TAQQMJP+ssQs8cizE+ToNkwtdIURBup6oqa8vWMjdvLgt2LaCgvkDrSF6vly2Gp14qBqOR4CHHYj75ZELHnCbjCoTbSVEQnS6/Np/fCn/jt8LfWF68nDpbF0/b4OWSQpIYnjScW20nEX7s8ejN0o1UdB4pCqJLOZwO1pWv4/fC3/mt8DfWV6zH7rRrHcujxAfHkx2XzTFxx3BC0gmkh6drHUn4ESkKQlNWh5VNlZtYV7aOv8r/Yl3ZOnbX79Y6Vpcx6Az0juxNdlw2g+IGkR2bTUJIgtaxhB+ToiA8TmVTJevL17OufB1bK7eyvWY7+XX5Xr/udIQpgsyITDIiMsiKyCIrMou+0X0JNMgaBcJzSFEQXqHZ0UxuTS47a3ays3YnebV55NXmUdxQTEVThccUDLPRTEJIAgkhCSSFJNEjogcZERlkRmQSExSjdTwhDkuKgvB6DqeDcks5ZZYyShpLKGsso7SxlDJLGfXWehrtjTTYGmi0N9Jo+/vL3kiz48CF3hUUFEVBQUGn6Ag2BhNqDCU0IBRzgBmz0UxoQKjrKy44joTgBBJDEkkIScAcYNbgJyCE+0hREH7L4XSgoroKgKIoWkcSQnNSFIQQQrjotA4ghBDCc0hREEII4SJFQQghhIsUBSGEEC5SFIQQQrhIURBCCOEiRUF4hOeee46hQ4cSGhpKXFwc559/Plu2bNE6lhB+R4qC8AiLFy/m5ptv5o8//mDevHnYbDZOP/10GhrauQSlEMItZPCa8EhlZWXExcWxePFiRo4cqXUcIfyGXCkIj1RTUwNAlKw1LESXkisF4XGcTifnnnsu1dXVLF26VOs4QvgVg9YBhNjfzTffzPr166UgCKEBKQrCo9xyyy3MmjWLJUuWkJKSonUcIfyOFAXhEVRV5d///jfffPMNixYtonv37lpHEsIvSVEQHuHmm29m6tSpzJw5k9DQUIqLiwEIDw8nKChI43RC+A+50Sw8QlsL3EyePJkJEyZ0bRgh/JhcKQiPIJ9NhPAMMk5BCCGEixQFIYQQLlIUhBBCuEhREEII4SJFQQghhIsUBSGEEC5SFIQQQrhIURBCCOEiRUEIIYSLFAUhhBAuUhSEEEK4SFEQQgjhIkVBCCGEixQFIYQQLlIUhBBCuEhREEII4SJFQQghhIsUBSGEEC5SFIQQQrhIURBCCOEiRUEIIYSLFAUhhBAuUhSEEEK4SFEQQgjhIkVBCCGEixQFIYQQLlIUhBBCuEhREEII4fL/Yc964LzbfswAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8qA4dp-TlOtq"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}