ML_08_Ridge_Vs_Lasso_Vs_Elastic(Regularization).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ML_08_Ridge_Vs_Lasso_Vs_Elastic(Regularization).ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMXxz+b/lqYEIhE9F6jDKJr"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Bias**:\n",
        "Biases are the underlying assumptions that are made by data to simplify the target function. Bias does help us generalize the data better and make the model less sensitive to single data points. It also decreases the training time because of the decrease in complexity of target function High bias suggest that there is more assumption taken on target function. This leads to the underfitting of the model sometimes. Examples of High bias Algorithms include Linear Regression, Logistic Regression etc.\n",
        "\n",
        "# **Variance:**\n",
        "In machine learning, Variance is a type of error that occurs due to a model’s sensitivity to small fluctuations in the dataset. The high variance would cause an algorithm to model the outliers/noise in the training set. This is most commonly referred to as overfitting. In this situation, the model basically learns every data point and does not offer good prediction when it tested on a novel dataset. Examples of High variance Algorithms include Decision Tree, KNN etc."
      ],
      "metadata": {
        "id": "hs-FoAbhnPGD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "OVTNRnupsiEc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Implementation**"
      ],
      "metadata": {
        "id": "MuOkkaR9s65B"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ki1wuOtmjGXc"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import datasets\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We are going to use the Boston house prediction dataset. This dataset is present in the datasets module of sklearn (scikit-learn) library. We can import this dataset as follows."
      ],
      "metadata": {
        "id": "kQ4HRwDTt-Xh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading pre-defined Boston Dataset\n",
        "boston_dataset = datasets.load_boston()\n",
        "print(boston_dataset.DESCR)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LItPFRDJtWAt",
        "outputId": "08332fcc-17c5-43c1-e4b0-d19e620676ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ".. _boston_dataset:\n",
            "\n",
            "Boston house prices dataset\n",
            "---------------------------\n",
            "\n",
            "**Data Set Characteristics:**  \n",
            "\n",
            "    :Number of Instances: 506 \n",
            "\n",
            "    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n",
            "\n",
            "    :Attribute Information (in order):\n",
            "        - CRIM     per capita crime rate by town\n",
            "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
            "        - INDUS    proportion of non-retail business acres per town\n",
            "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
            "        - NOX      nitric oxides concentration (parts per 10 million)\n",
            "        - RM       average number of rooms per dwelling\n",
            "        - AGE      proportion of owner-occupied units built prior to 1940\n",
            "        - DIS      weighted distances to five Boston employment centres\n",
            "        - RAD      index of accessibility to radial highways\n",
            "        - TAX      full-value property-tax rate per $10,000\n",
            "        - PTRATIO  pupil-teacher ratio by town\n",
            "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of black people by town\n",
            "        - LSTAT    % lower status of the population\n",
            "        - MEDV     Median value of owner-occupied homes in $1000's\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "\n",
            "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
            "\n",
            "This is a copy of UCI ML housing dataset.\n",
            "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
            "\n",
            "\n",
            "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
            "\n",
            "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
            "prices and the demand for clean air', J. Environ. Economics & Management,\n",
            "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
            "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
            "pages 244-261 of the latter.\n",
            "\n",
            "The Boston house-price data has been used in many machine learning papers that address regression\n",
            "problems.   \n",
            "     \n",
            ".. topic:: References\n",
            "\n",
            "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
            "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n",
            "\n",
            "    The Boston housing prices dataset has an ethical problem. You can refer to\n",
            "    the documentation of this function for further details.\n",
            "\n",
            "    The scikit-learn maintainers therefore strongly discourage the use of this\n",
            "    dataset unless the purpose of the code is to study and educate about\n",
            "    ethical issues in data science and machine learning.\n",
            "\n",
            "    In this special case, you can fetch the dataset from the original\n",
            "    source::\n",
            "\n",
            "        import pandas as pd\n",
            "        import numpy as np\n",
            "\n",
            "\n",
            "        data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
            "        raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
            "        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
            "        target = raw_df.values[1::2, 2]\n",
            "\n",
            "    Alternative datasets include the California housing dataset (i.e.\n",
            "    :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n",
            "    dataset. You can load the datasets as follows::\n",
            "\n",
            "        from sklearn.datasets import fetch_california_housing\n",
            "        housing = fetch_california_housing()\n",
            "\n",
            "    for the California housing dataset and::\n",
            "\n",
            "        from sklearn.datasets import fetch_openml\n",
            "        housing = fetch_openml(name=\"house_prices\", as_frame=True)\n",
            "\n",
            "    for the Ames housing dataset.\n",
            "    \n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can conclude from the above description that we have 13 independent variable and one dependent (House price) variable. Now we need to check for a correlation between independent and dependent variable. We can use scatterplot/corrplot for this."
      ],
      "metadata": {
        "id": "3Mfy8D_wulk9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate scatter plot of independent vs Dependent variable\n",
        "plt.style.use('ggplot')\n",
        "fig = plt.figure(figsize = (18, 18))\n",
        "\n",
        "for index, feature_name in enumerate(boston_dataset.feature_names):\n",
        "\tax = fig.add_subplot(4, 4, index + 1)\n",
        "\tax.scatter(boston_dataset.data[:, index], boston_dataset.target)\n",
        "\tax.set_ylabel('House Price', size = 12)\n",
        "\tax.set_xlabel(feature_name, size = 12)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "BvEC_nAuuKMX",
        "outputId": "16d6d3fb-91ea-4ac8-fb7e-fcafdb20ef4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x1296 with 13 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can observe from the above scatter plots that some of the independent variables are not very much correlated (either positively or negatively) with the target variable. These variables will get their coefficients to be reduced in regularization."
      ],
      "metadata": {
        "id": "GbtQD43VxmYX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset into Pandas Dataframe\n",
        "boston_pd = pd.DataFrame(boston_dataset.data)\n",
        "boston_pd.columns = boston_dataset.feature_names\n",
        "boston_pd_target = np.asarray(boston_dataset.target)\n",
        "boston_pd['House Price'] = pd.Series(boston_pd_target)\n",
        "\n",
        "# input\n",
        "X = boston_pd.iloc[:, :-1]\n",
        "\n",
        "#output\n",
        "Y = boston_pd.iloc[:, -1]\n",
        "\n",
        "print(boston_pd.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wqomLQBGxXbL",
        "outputId": "a7748303-6275-4549-968d-4f2ccec1bc7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
            "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
            "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
            "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
            "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
            "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
            "\n",
            "   PTRATIO       B  LSTAT  House Price  \n",
            "0     15.3  396.90   4.98         24.0  \n",
            "1     17.8  396.90   9.14         21.6  \n",
            "2     17.8  392.83   4.03         34.7  \n",
            "3     18.7  394.63   2.94         33.4  \n",
            "4     18.7  396.90   5.33         36.2  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split( X, Y, test_size = 0.25)\n",
        "\n",
        "print(\"Train data shape of X = % s and Y = % s : \"%(x_train.shape, y_train.shape))\n",
        "\n",
        "print(\"Test data shape of X = % s and Y = % s : \"%(x_test.shape, y_test.shape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uyVCxGtYyagh",
        "outputId": "0fbf51e2-a627-4ffc-e847-54960c13c64a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train data shape of X = (379, 13) and Y = (379,) : \n",
            "Test data shape of X = (127, 13) and Y = (127,) : \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Multiple (Linear) Regression**\n",
        "Now it’s the right time to test the models. We will be using multiple Linear Regression first. We train the model on training data and calculate the MSE on test."
      ],
      "metadata": {
        "id": "frtn6Vw3_DOz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " # Apply multiple Linear Regression Model\n",
        "lreg = LinearRegression()\n",
        "lreg.fit(x_train, y_train)\n",
        "\n",
        "# Generate Prediction on test set\n",
        "lreg_y_pred = lreg.predict(x_test)\n",
        "\n",
        "# calculating Mean Squared Error (mse)\n",
        "mean_squared_error = np.mean((lreg_y_pred - y_test)**2)\n",
        "print(\"Mean squared Error on test set : \", mean_squared_error)\n",
        "\n",
        "# Putting together the coefficient and their corresponding variable names\n",
        "lreg_coefficient = pd.DataFrame()\n",
        "lreg_coefficient[\"Columns\"] = x_train.columns\n",
        "lreg_coefficient['Coefficient Estimate'] = pd.Series(lreg.coef_)\n",
        "print(lreg_coefficient)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "16Im8Fj60ZPg",
        "outputId": "fa2c0ad4-27e6-43e4-d485-71098581df68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean squared Error on test set :  20.03794243697047\n",
            "    Columns  Coefficient Estimate\n",
            "0      CRIM             -0.093300\n",
            "1        ZN              0.046633\n",
            "2     INDUS             -0.002578\n",
            "3      CHAS              2.288815\n",
            "4       NOX            -15.119353\n",
            "5        RM              3.344039\n",
            "6       AGE              0.010563\n",
            "7       DIS             -1.456231\n",
            "8       RAD              0.292966\n",
            "9       TAX             -0.011025\n",
            "10  PTRATIO             -1.029501\n",
            "11        B              0.010685\n",
            "12    LSTAT             -0.609747\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let’s plot a bar chart of above coefficients using matplotlib plotting library."
      ],
      "metadata": {
        "id": "89S5zVNAJTTd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# plotting the coefficient score\n",
        "fig, ax = plt.subplots(figsize =(20, 10))\n",
        "\n",
        "color =['tab:gray', 'tab:blue', 'tab:orange',\n",
        "'tab:green', 'tab:red', 'tab:purple', 'tab:brown',\n",
        "'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan',\n",
        "'tab:orange', 'tab:green', 'tab:blue', 'tab:olive']\n",
        "\n",
        "ax.bar(lreg_coefficient[\"Columns\"],\n",
        "lreg_coefficient['Coefficient Estimate'],\n",
        "color = color)\n",
        "\n",
        "ax.spines['bottom'].set_position('zero')\n",
        "\n",
        "plt.style.use('ggplot')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "h2uIYeO3JauR",
        "outputId": "f16e2c3e-3e2c-45cf-ae15-2f684ed446d0"
      },
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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jt4s14WjZsmVr/QJmzf/X3XjjjZk7d24OPvjgeo3I28Crr76arbfeut5jwHoJSFuQ+fPnZ+TIkcVjXnvttTzxxBPZddddN3jMO97xjvzhD3/I/fffn913331Tj9ktrV69Oj/4wQ/y4Q9/OIMHD+66ferUqZkzZ046OjrqON3m4c3sYtSoUXn++ec3etyUKVNy8cUX55JLLsnNN9+cFStWbIpRNxul79lFixblvPPO6/rf3Llzu+577bXX8sgjj+Rd73pXxo8f3/Ufrf369cvuu++ef//3f8///M//5K677vLcpCIdHR155JFH1vkZMH78+Nxzzz1ZsmRJevTokYEDB9Zpws3H/fffn3HjxmW77bZLv3798tRTT+W5557LiBEjih93yy23dH1/f/vb336LpmVLs77v1XvuuSfjx4/P5MmTu34+bMiIESPe0M/jt4MlS5Zk+fLl2WmnnbLXXnvljjvuWOv+ww8/PD/72c9ywAEHrPXyU9gUXnvttZx33nn5/Oc/nx/96Ef5wAc+UO+RYL3ePuervs2t+Y/RxYsXZ7fddiueyTF+/PjceuuteeWVV/LhD384N9xww1s46ebpF7/4RYYNG5bx48evdXtzc3N22mmn3H333XWabPPxZnbR2dlZvL+hoSFJss8++2TcuHF56KGH8sADD2Tu3Lk57bTT3han2q952ekav/zlL7vefvDBBzN27Nj07t07e+65Z66//vocdthh6dGjR6ZPn55nn302jzzySG666aY8/PDD+ehHP1qPL4FuaM1fYJctW5ahQ4d2vZx5jXHjxuUXv/hFBgwY4Oyj/9Pe3p599903yetnKazvP9jPPvvsvPDCCxk3blwOP/zwJF7CRnU29L365JNPpn///hk8eHCamppyySWX5KWXXhI83oC/Pqty/PjxufrqqzNx4sSu+x966KEMHDgw8+fPr9eIbMH++iVs8+bNy5VXXpnTTz+96+/EsLlwBtIWZPvtt89TTz213vvW/Mfol770pTz11FMbPKU5SXbaaafMnz8/L774YrbbbrtajdttPProo/nd736XI444Yr33T5s2LTfeeONbPNXm6Y3u4oknnui6dkqvXr2yatWqrvtWrFix1l90Bw0alEmTJuVf/uVf0qNHjy3qL26l79mS9vb2PPzwwznnnHNywQUX5KWXXsqjjz7adf/w4cMzZcqUHH/88bnvvvs25chs4db8Bfbss89O8vpLsf5aY2NjRowYkZtuuil77rlnPUbcrKz53vvRj36Uc845JzfddFPuueeeDBs2LE8//XTXceecc04++MEP5uWXX67jtGxJNvS92t7enueffz7nnHNOTjnllLzyyivFnwNPP/20a5n9n/b29tx5550555xz8p3vfCdPPvlk/vKXvyRJli1bll//+tc55ZRT8vvf/36LuyYjm5edd945L774Yl588cV6jwLr2PJ/jf82Mnbs2Pz85z/P3LlzM3ny5CSvX3T4r//C2r9//3zoQx/KDTfckClTpmzwsQ4++OD06tWr1iNv9lasWJEf/vCHOeaYY9KnT5/1HjN06NAMGzYsDzzwwEZfQril29guOjs7c+utt2bp0qUZN25ckmSXXXbJXXfdlUmTJmXlypW59957c8ghhyR5/bd973jHO9KzZ88sX748K1asyKBBg97Sr6mW3sj37N9asWJF/vjHP+bLX/5y15lYd9xxR+6+++6MGjUqTz75ZMaOHZvk9Qty//VLLuGN6t27dw4//PDMmjUr73vf+9a6b7/99ssuu+zijIYkv/vd7zJhwoS1/nXIiy66KO985ztzxRVXZNy4cV3XQVq5cmW9xmQL9tffq+9973tz77335vTTT8+gQYPS3Nyc2267Lddff33e8573rPVxa34eL1++vOvn8dvZggUL8uqrr+acc87puu2WW27J3XffnQMPPDDXXnttWlpass022+TQQw/NNddckxNPPNHZIdTE888/n87OTj9n2SwJSFuQhoaGfPrTn861116bG2+8Mb169UpTU1MOO+ywtY5797vfnV/96lf5wx/+sMHH2m233Wo9brfw29/+Ni+++GKuvvrqtW7/25eyTZs2LRdccMFbOdpma327+OlPf5rrr78+K1euzKhRo/LFL36x62Vshx12WGbPnp1bb701SbL33ntnzJgxSZI//OEP+fGPf9wVMw855JAt6porb/R79q/dddddGTt27Fov49t9993z05/+NIcddljmzJmT2bNnp1evXtlqq61y9NFHvxVfClugESNGZPjw4V3/wtga22+/fbbffvs6Trb5aG9vz9SpU9e6bY899sjdd9+dT3ziE/nZz36WZcuWZfDgwendu/da17S45ZZbctddd3W9/+lPfzpDhgx5y2Zny7Hme7WtrS2DBg1a6xctY8aMyXPPPdd1ke2//Xn82c9+9m3xsvCNaW9vX+ci93vvvXe++c1vZuedd86SJUsyadKkJK//zL3tttsyb968tV7iBtVY87LUxsbGrFq1KkcffXR69PBiITY/DZ0buxjJZsqpo9Vrbm7OwoUL6z1Gt2V/lbO7ynX33S3bf+rGD9pCDZrTvV/q2t2fe/XU3XfX8a3H6z1C3fT43Jh6j1Cx7v68q7fuvL/xDy+o9wh11f7O7nsJju78vEuSD9zw9r3493XTrqv3CJtM6XrJsiYAAAAARc5ZBQAAAKpS77NwuvsZXN2BM5AAAAAAKBKQAAAAACgSkAAAAAAoEpAAAAAAKBKQAAAAACgSkAAAAAAoEpAAAAAAKBKQAAAAACgSkAAAAAAoEpAAAAAAKGqs9wAAAABsGu3v3K6un7+5uTkLFy6s6wxAbTgDCQAAAIAiAQkAAACAIgEJAAAAgCIBCQAAAIAiAQkAAACAIgEJAAAAgCIBCQAAAIAiAQkAAACAIgEJAAAAgCIBCQAAAICixnoPAADA5qvH58bU7XM3Nzdn4cKFdfv8AMD/5wwkAAAAAIoEJAAAAACKBCQAAAAAigQkAAAAAIoEJAAAAACKBCQAAAAAigQkAAAAAIoEJAAAAACKBCQAAAAAigQkAAAAAIoEJAAAAACKBCQAAAAAigQkAAAAAIoEJAAAAACKBCQAAAAAigQkAAAAAIoEJAAAAACKBCQAAAAAigQkAAAAAIoEJAAAAACKBCQAAAAAigQkAAAAAIoEJAAAAACKGmv1wD/4wQ9y9913p7GxMUOHDs1nPvOZ9OvXb53jTjjhhPTp0yc9evRIz54909raWquRAAAAAKhAzQLSHnvskaOPPjo9e/bMFVdckWuvvTYf+9jH1nvs2WefnYEDB9ZqFAAAAACqULOXsO25557p2bNnkuQd73hHFi9eXKtPBQAAAEAN1ewMpL82Z86cTJ48eYP3n3vuuUmSadOmpaWl5a0YCQAAAIA3qKGzs7Oz0g+eOXNmli5dus7t06dPz957750k+fGPf5zHH388p556ahoaGtY5dvHixWlqasqyZcvyla98JZ/85Cez2267rXNcW1tb2trakiStra1ZuXJlpWPzfxobG7Nq1ap6j9Ft2V/l7K5y3X13j++xZ71HqJsx9/2u3iNUpbs/9+rJ7ipnd5Wzu+rYX+XsrnJ2Vx372zR69+69wfuqCkgbc/PNN+eGG27IWWedla222mqjx8+ePTt9+vTJIYccstFjn3322U0x4ttac3NzFi5cWO8xui37q5zdVa67727Z/lPrPULdDJpzY71HqEp3f+7Vk91Vzu4qZ3fVsb/K2V3l7K469rdpDB8+fIP31ewaSPfee2/+93//N6effvoG49Err7ySl19+uevt++67LzvuuGOtRgIAAACgAjW7BtKsWbOyatWqzJw5M0kyduzYHHvssVm8eHEuvfTSzJgxI8uWLcsFF1yQJFm9enXe9773Za+99qrVSAAAAABUoGYB6aKLLlrv7U1NTZkxY0aSZOjQoTn//PNrNQIAAAAAm0DNXsIGAAAAwJZBQAIAAACgSEACAAAAoEhAAgAAAKBIQAIAAACgSEACAAAAoEhAAgAAAKBIQAIAAACgSEACAAAAoEhAAgAAAKBIQAIAAACgSEACAAAAoEhAAgAAAKBIQAIAAACgSEACAAAAoEhAAgAAAKBIQAIAAACgSEACAAAAoEhAAgAAAKBIQAIAAACgSEACAAAAoEhAAgAAAKBIQAIAAACgSEACAAAAoEhAAgAAAKBIQAIAAACgSEACAAAAoEhAAgAAAKBIQAIAAACgSEACAAAAoEhAAgAAAKBIQAIAAACgSEACAAAAoEhAAgAAAKBIQAIAAACgSEACAAAAoEhAAgAAAKBIQAIAAACgSEACAAAAoEhAAgAAAKBIQAIAAACgSEACAAAAoEhAAgAAAKBIQAIAAACgSEACAAAAoEhAAgAAAKBIQAIAAACgSEACAAAAoEhAAgAAAKBIQAIAAACgSEACAAAAoEhAAgAAAKBIQAIAAACgSEACAAAAoEhAAgAAAKBIQAIAAACgSEACAAAAoEhAAgAAAKCosVYPPHv27Nx4440ZOHBgkuSoo47K+PHj1znu3nvvzXe/+910dHRk6tSpOfTQQ2s1EgAAAAAVqFlASpKDDjoohxxyyAbv7+joyKxZs/KlL30pQ4YMyYwZMzJhwoSMGDGilmMBAAAA8CbU9SVsjz32WIYNG5ahQ4emsbExkydPzrx58+o5EgAAAAB/o6ZnIF133XW59dZbM3r06Hz84x9P//7917p/8eLFGTJkSNf7Q4YMyaOPPlrLkQAAAAB4k6oKSDNnzszSpUvXuX369Ok54IADcuSRRyZJrrrqqnz/+9/PZz7zmYo/V1tbW9ra2pIkra2taW5urvixeF1jY6M9VsH+Kmd3levuu1tW7wHqqDv/uSXd/7lXT3ZXOburnN1Vx/4qZ3eVs7vq2F/tVRWQzjzzzDd03NSpU/P1r399ndubmpqyaNGirvcXLVqUpqam9T5GS0tLWlpaut5fuHDhm5yWv9Xc3GyPVbC/ytld5eyu++ruf26ee5Wzu8rZXeXsrjr2Vzm7q5zdVcf+No3hw4dv8L6aXQNpyZIlXW/feeedGTly5DrHjBkzJvPnz8+CBQuyatWqzJ07NxMmTKjVSAAAAABUoGbXQLriiivy5z//OQ0NDdl2221z7LHHJnn9ukeXXnppZsyYkZ49e+ZTn/pUzj333HR0dGS//fZbb2gCAAAAoH5qFpBOPPHE9d7e1NSUGTNmdL0/fvz4jB8/vlZjAAAAAFClmr2EDQAAAIAtg4AEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAUWOtHvjCCy/Ms88+myRZsWJF+vbtm/PPP3+d40444YT06dMnPXr0SM+ePdPa2lqrkQAAAACoQM0C0sknn9z19ve///307dt3g8eeffbZGThwYK1GAQAAAKAKNX8JW2dnZ2677ba8973vrfWnAgAAAKAGanYG0hoPPfRQBg0alO23336Dx5x77rlJkmnTpqWlpaXWIwEAAADwJjR0dnZ2VvrBM2fOzNKlS9e5ffr06dl7772TJN/5zncybNiwfOhDH1rvYyxevDhNTU1ZtmxZvvKVr+STn/xkdtttt3WOa2trS1tbW5KktbU1K1eurHRs/srUSpEAAA7BSURBVE9jY2NWrVpV7zG6LfurnN1Vrrvv7vE99qz3CHUz5r7f1XuEqnT351492V3l7K5ydlcd+6uc3VXO7qpjf5tG7969N3hfVQFpY1avXp3jjjsura2tGTJkyEaPnz17dvr06ZNDDjlko8euuUA3lWtubs7ChQvrPUa3ZX+Vs7vKdffdLdt/ar1HqJtBc26s9whV6e7PvXqyu8rZXeXsrjr2Vzm7q5zdVcf+No3hw4dv8L6aXgPp/vvvz/DhwzcYj1555ZW8/PLLXW/fd9992XHHHWs5EgAAAABvUk2vgfTb3/52nYtnL168OJdeemlmzJiRZcuW5YILLkjy+tlK73vf+7LXXnvVciQAAAAA3qSaBqQTTjhhnduampoyY8aMJMnQoUNz/vnn13IEAAAAAKpU05ewAQAAAND9CUgAAAAAFAlIAAAAABQJSAAAAAAUCUgAAAAAFAlIAAAAABQJSAAAAAAUCUgAAAAAFAlIAAAAABQJSAAAAAAUCUgAAAAAFAlIAAAAABQJSAAAAAAUCUgAAAAAFAlIAAAAABQJSAAAAAAUCUgAAAAAFAlIAAAAABQJSAAAAAAUCUgAAAAAFAlIAAAAABQJSAAAAAAUCUgAAAAAFAlIAAAAABQJSAAAAAAUCUgAAAAAFAlIAAAAABQJSAAAAAAUCUgAAAAAFAlIAAAAABQJSAAAAAAUCUgAAAAAFAlIAAAAABQJSAAAAAAUCUgAAAAAFAlIAAAAABQJSAAAAAAUCUgAAAAAFAlIAAAAABQJSAAAAAAUCUgAAAAAFAlIAAAAABQJSAAAAAAUCUgAAAAAFAlIAAAAABQJSAAAAAAUCUgAAAAAFAlIAAAAABQJSAAAAAAUCUgAAAAAFAlIAAAAABQJSAAAAAAUCUgAAAAAFAlIAAAAABQJSAAAAAAUCUgAAAAAFAlIAAAAABQJSAAAAAAUNVb7ALfddluuvvrqPPPMM/nqV7+aMWPGdN137bXXZs6cOenRo0c++clPZq+99lrn4xcsWJBvfvObeeGFFzJ69OiceOKJaWyseiwAAAAANpGqz0AaOXJkTj311Oy6665r3f70009n7ty5+Y//+I+cccYZmTVrVjo6Otb5+CuuuCIHHXRQLrroovTr1y9z5sypdiQAAAAANqGqA9KIESMyfPjwdW6fN29eJk+enF69emW77bbLsGHD8thjj611TGdnZx588MFMmjQpSTJlypTMmzev2pEAAAAA2IRqdg2kxYsXZ8iQIV3vNzU1ZfHixWsd88ILL6Rv377p2bPnBo8BAAAAoL7e0MWGZs6cmaVLl65z+/Tp07P33ntv8qHWp62tLW1tbUmS1tbWNDc3vyWfd0vW2Nhoj1Wwv8rZXeW6++6W1XuAOurOf25J93/u1ZPdVc7uKmd31bG/ytld5eyuOvZXe28oIJ155plv+oGbmpqyaNGirvcXL16cpqamtY4ZMGBAVqxYkdWrV6dnz57rPWaNlpaWtLS0dL2/cOHCNz0Ta2tubrbHKthf5eyucnbXfXX3PzfPvcrZXeXsrnJ2Vx37q5zdVc7uqmN/m8b6LlG0Rs1ewjZhwoTMnTs3r732WhYsWJD58+dnl112WeuYhoaGvOtd78rtt9+eJLn55pszYcKEWo0EAAAAQAWqDkh33nlnjjvuuDzyyCNpbW3Nueeem+T1f53tPe95T0455ZSce+65+fSnP50ePV7/dF/72te6rnX00Y9+ND//+c9z4okn5sUXX8z+++9f7UgAAAAAbEJv6CVsJRMnTszEiRPXe9/hhx+eww8/fJ3bZ8yY0fX20KFD87Wvfa3aMQAAAACokZq9hA0AAACALYOABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQJGABAAAAECRgAQAAABAkYAEAAAAQFFjNR9822235eqrr84zzzyTr371qxkzZkyS5L777suVV16ZVatWpbGxMcccc0x23333dT5+9uzZufHGGzNw4MAkyVFHHZXx48dXMxIAAAAAm1hVAWnkyJE59dRTc9lll611+4ABA3L66aenqakpTz75ZM4999xceuml632Mgw46KIccckg1YwAAAABQQ1UFpBEjRqz39p133rnr7ZEjR2blypV57bXX0qtXr2o+HQAAAAB1UFVAeiPuuOOOjB49eoPx6Lrrrsutt96a0aNH5+Mf/3j69+9f65EAAAAAeBMaOjs7O0sHzJw5M0uXLl3n9unTp2fvvfdOknz5y1/OMccc03UNpDWeeuqpnHfeeTnjjDMybNiwdR5j6dKlXdc/uuqqq7JkyZJ85jOfWe8cbW1taWtrS5K0trZm5cqVb+DLo6SxsTGrVq2q9xjdlv1Vzu4q19139/gee9Z7hLoZc9/v6j1CVbr7c6+e7K5ydlc5u6uO/VXO7ipnd9Wxv02jd+/eG7xvo2cgnXnmmRV90kWLFuWCCy7ICSecsN54lCTbbLNN19tTp07N17/+9Q0+XktLS1paWrreX7hwYUVz8f81NzfbYxXsr3J2Vzm76766+5+b517l7K5ydlc5u6uO/VXO7ipnd9Wxv01j+PDhG7yvRy0+4UsvvZTW1tYcffTRGTdu3AaPW7JkSdfbd955Z0aOHFmLcQAAAACoQlXXQLrzzjtz+eWXZ/ny5Wltbc2oUaNyxhln5Fe/+lWee+65XHPNNbnmmmuSJF/60pcyaNCgXHLJJZk2bVrGjBmTK664In/+85/T0NCQbbfdNscee+wm+aIAAAAA2HSqCkgTJ07MxIkT17n9iCOOyBFHHLHejznuuOO63j7xxBOr+fQAAAAAvAVq8hI2AAAAALYcAhIAAAAARQISAAAAAEUCEgAAAABFAhIAAAAARQISAAAAAEUCEgAAAABFAhIAAAAARQISAAAAAEUCEgAAAABFAhIAAAAARQISAAAAAEUCEgAAAABFAhIAAAAARQISAAAAAEUCEgAAAABFAhIAAAAARQISAAAAAEUCEgAAAABFAhIAAAAARQISAAAAAEUCEgAAAABFAhIAAAAARQISAAAAAEUCEgAAAABFAhIAAAAARQISAAAAAEUCEgAAAABFAhIAAAAARQISAAAAAEUCEgAAAABFAhIAAAAARQISAAAAAEUCEgAAAABFAhIAAAAARQISAAAAAEUCEgAAAABFAhIAAAAARQISAAAAAEUCEgAAAABFAhIAAAAARQISAAAAAEUCEgAAAABFAhIAAAAARQISAAAAAEUCEgAAAABFAhIAAAAARQISAAAAAEUCEgAAAABFAhIAAAAARQISAAAAAEUCEgAAAABFAhIAAAAARQISAAAAAEUCEgAAAABFAhIAAAAARQISAAAAAEUCEgAAAABFjdV88G233Zarr746zzzzTL761a9mzJgxSZIFCxbk5JNPzvDhw5MkY8eOzbHHHrvOx7/44ou58MIL85e//CXbbrttTj755PTv37+akQAAAADYxKoKSCNHjsypp56ayy67bJ37hg0blvPPP7/48T/5yU/y7ne/O4ceemh+8pOf5Cc/+Uk+9rGPVTMSAAAAAJtYVS9hGzFiRNdZRpWYN29e9t133yTJvvvum3nz5lUzDgAAAAA1UNUZSCULFizIaaedlq233jrTp0/Prrvuus4xy5Yty+DBg5Mk22yzTZYtW1arcQAAAACo0EYD0syZM7N06dJ1bp8+fXr23nvv9X7M4MGD81//9V8ZMGBA/vjHP+b888/PN77xjfTt23eDn6ehoSENDQ0bvL+trS1tbW1JktbW1jQ3N29sdDaisbHRHqtgf5Wzu8p19929nX9N0J3/3JLu/9yrJ7urnN1Vzu6qY3+Vs7vK2V117K/2NhqQzjzzzDf9oL169UqvXr2SJKNHj87QoUMzf/78rotsrzFo0KAsWbIkgwcPzpIlSzJw4MANPmZLS0taWlq63l+4cOGbnou1NTc322MV7K9ydlc5u+u+uvufm+de5eyucnZXOburjv1Vzu4qZ3fVsb9No3SZoqqugbQhy5cvT0dHR5Lk+eefz/z58zN06NB1jpswYUJuueWWJMktt9yywTOaAAAAAKifqq6BdOedd+byyy/P8uXL09ramlGjRuWMM87I73//+8yePTs9e/ZMjx498q//+q/p379/kuSSSy7JtGnTMmbMmBx66KG58MILM2fOnGy77bY5+eSTN8kXBcDmadCcG+v2uf1WCgAAKldVQJo4cWImTpy4zu2TJk3KpEmT1vsxxx13XNfbAwYMyFlnnVXNCAAAAADUWE1ewgYAAADAlkNAAgAAAKBIQAIAAACgSEACAAAAoEhAAgAAAKBIQAIAAACgSEACAAAAoEhAAgAAAKBIQAIAAACgSEACAAAAoEhAAgAAAKBIQAIAAACgSEACAAD+X7t2bAMACMAwTPx/dPkgKwz2BZ2jAkASkAAAAABIAhIAAAAASUACAAAAIAlIAAAAACQBCQAAAIAkIAEAAACQBCQAAAAAkoAEAAAAQBKQAAAAAEgCEgAAAADpbNvrEQAAAAD8ywMJAAAAgCQgAQAAAJAEJAAAAACSgAQAAABAEpAAAAAASAISAAAAAOkCSTA4VBVl6pIAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can observe that lots of the variables have an insignificant coefficient, these coefficients did not contribute to the model very much and need to regulate or even eliminate some of these variables."
      ],
      "metadata": {
        "id": "a_YsbxZiJjsE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Ridge Regression:**\n",
        "Ridge Regression added a term in ordinary least square error function that regularizes the value of coefficients of variables. This term is the sum of squares of coefficient multiplied by the parameter The motive of adding this term is to penalize the variable corresponding to that coefficient not very much correlated to the target variable. This term is called L2 regularization."
      ],
      "metadata": {
        "id": "6SMmY3smJrqf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# import ridge regression from sklearn library\n",
        "from sklearn.linear_model import Ridge\n",
        "\n",
        "# Train the model\n",
        "ridgeR = Ridge(alpha = 1)\n",
        "ridgeR.fit(x_train, y_train)\n",
        "y_pred = ridgeR.predict(x_test)\n",
        "\n",
        "# calculate mean square error\n",
        "mean_squared_error_ridge = np.mean((y_pred - y_test)**2)\n",
        "print(mean_squared_error_ridge)\n",
        "\n",
        "# get ridge coefficient and print them\n",
        "ridge_coefficient = pd.DataFrame()\n",
        "ridge_coefficient[\"Columns\"]= x_train.columns\n",
        "ridge_coefficient['Coefficient Estimate'] = pd.Series(ridgeR.coef_)\n",
        "print(ridge_coefficient)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RUxHqzirJzCk",
        "outputId": "a7ed1d8a-09ae-4083-d94b-2ada64d6b4d1"
      },
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20.710904256531357\n",
            "    Columns  Coefficient Estimate\n",
            "0      CRIM             -0.088545\n",
            "1        ZN              0.047364\n",
            "2     INDUS             -0.032906\n",
            "3      CHAS              2.215909\n",
            "4       NOX             -7.919773\n",
            "5        RM              3.388613\n",
            "6       AGE              0.003768\n",
            "7       DIS             -1.345675\n",
            "8       RAD              0.273007\n",
            "9       TAX             -0.011377\n",
            "10  PTRATIO             -0.953798\n",
            "11        B              0.011069\n",
            "12    LSTAT             -0.616693\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plotting the coefficient score\n",
        "fig, ax = plt.subplots(figsize =(20, 10))\n",
        "\n",
        "color =['tab:gray', 'tab:blue', 'tab:orange',\n",
        "'tab:green', 'tab:red', 'tab:purple', 'tab:brown',\n",
        "'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan',\n",
        "'tab:orange', 'tab:green', 'tab:blue', 'tab:olive']\n",
        "\n",
        "ax.bar(ridge_coefficient[\"Columns\"],\n",
        "ridge_coefficient['Coefficient Estimate'],\n",
        "color = color)\n",
        "\n",
        "ax.spines['bottom'].set_position('zero')\n",
        "\n",
        "plt.style.use('ggplot')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "puQDN0DlJ5hw",
        "outputId": "9e59cf34-43bf-4897-f684-e06772ed1751"
      },
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import ridge regression from sklearn library\n",
        "from sklearn.linear_model import Ridge\n",
        "\n",
        "# Train the model\n",
        "ridgeR = Ridge(alpha = 10)         ###### alpha = 10\n",
        "ridgeR.fit(x_train, y_train)\n",
        "y_pred = ridgeR.predict(x_test)\n",
        "\n",
        "# calculate mean square error\n",
        "mean_squared_error_ridge = np.mean((y_pred - y_test)**2)\n",
        "print(mean_squared_error_ridge)\n",
        "\n",
        "# get ridge coefficient and print them\n",
        "ridge_coefficient = pd.DataFrame()\n",
        "ridge_coefficient[\"Columns\"]= x_train.columns\n",
        "ridge_coefficient['Coefficient Estimate'] = pd.Series(ridgeR.coef_)\n",
        "print(ridge_coefficient)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FYlQwhuYKI2y",
        "outputId": "0b64ecbe-68da-45a3-8051-3c88743717d8"
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "21.939326819286816\n",
            "    Columns  Coefficient Estimate\n",
            "0      CRIM             -0.086260\n",
            "1        ZN              0.049211\n",
            "2     INDUS             -0.058603\n",
            "3      CHAS              1.599440\n",
            "4       NOX             -1.506251\n",
            "5        RM              3.196244\n",
            "6       AGE             -0.000261\n",
            "7       DIS             -1.246514\n",
            "8       RAD              0.264611\n",
            "9       TAX             -0.012093\n",
            "10  PTRATIO             -0.897242\n",
            "11        B              0.011349\n",
            "12    LSTAT             -0.640890\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plotting the coefficient score\n",
        "fig, ax = plt.subplots(figsize =(20, 10))\n",
        "\n",
        "color =['tab:gray', 'tab:blue', 'tab:orange',\n",
        "'tab:green', 'tab:red', 'tab:purple', 'tab:brown',\n",
        "'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan',\n",
        "'tab:orange', 'tab:green', 'tab:blue', 'tab:olive']\n",
        "\n",
        "ax.bar(ridge_coefficient[\"Columns\"],\n",
        "ridge_coefficient['Coefficient Estimate'],\n",
        "color = color)\n",
        "\n",
        "ax.spines['bottom'].set_position('zero')\n",
        "\n",
        "plt.style.use('ggplot')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "0ALYoaylKSOv",
        "outputId": "9dea677b-0ac6-4b43-c4f9-b3edb0aa703f"
      },
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Lasso Regression:**\n",
        "Lasso Regression is similar to Ridge regression except here we add Mean Absolute value of coefficients in place of mean square value. Unlike Ridge Regression, Lasso regression can completely eliminate the variable by reducing its coefficient value to 0. The new term we added to Ordinary Least Square(OLS) is called L1 Regularization.\n",
        "\n",
        "Code : Python code implementing the Lasso Regression"
      ],
      "metadata": {
        "id": "6j0fdoL0KfTF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# import Lasso regression from sklearn library\n",
        "from sklearn.linear_model import Lasso\n",
        "\n",
        "# Train the model\n",
        "lasso = Lasso(alpha = 1)\n",
        "lasso.fit(x_train, y_train)\n",
        "y_pred1 = lasso.predict(x_test)\n",
        "\n",
        "# Calculate Mean Squared Error\n",
        "mean_squared_error = np.mean((y_pred1 - y_test)**2)\n",
        "print(\"Mean squared error on test set\", mean_squared_error)\n",
        "lasso_coeff = pd.DataFrame()\n",
        "lasso_coeff[\"Columns\"] = x_train.columns\n",
        "lasso_coeff['Coefficient Estimate'] = pd.Series(lasso.coef_)\n",
        "\n",
        "print(lasso_coeff)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j6BbSqxEKmIl",
        "outputId": "800bf11b-2fb2-47ac-fe63-9417fcdf8881"
      },
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean squared error on test set 27.939936201454064\n",
            "    Columns  Coefficient Estimate\n",
            "0      CRIM             -0.048317\n",
            "1        ZN              0.047336\n",
            "2     INDUS             -0.000000\n",
            "3      CHAS              0.000000\n",
            "4       NOX             -0.000000\n",
            "5        RM              0.595037\n",
            "6       AGE              0.026198\n",
            "7       DIS             -0.657321\n",
            "8       RAD              0.247073\n",
            "9       TAX             -0.012978\n",
            "10  PTRATIO             -0.811580\n",
            "11        B              0.010382\n",
            "12    LSTAT             -0.830168\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plotting the coefficient score\n",
        "fig, ax = plt.subplots(figsize =(20, 10))\n",
        "\n",
        "color =['tab:gray', 'tab:blue', 'tab:orange',\n",
        "'tab:green', 'tab:red', 'tab:purple', 'tab:brown',\n",
        "'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan',\n",
        "'tab:orange', 'tab:green', 'tab:blue', 'tab:olive']\n",
        "\n",
        "ax.bar(lasso_coeff[\"Columns\"],\n",
        "ridge_coefficient['Coefficient Estimate'],\n",
        "color = color)\n",
        "\n",
        "ax.spines['bottom'].set_position('zero')\n",
        "\n",
        "plt.style.use('ggplot')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "A9T6Rek1LbKd",
        "outputId": "c36dddcf-12b1-4ff6-b23f-c814c105acd2"
      },
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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AJCcAAQAAACQnAAEAAAAkJwABAAAAJCcAAQAAACQnAAEAAAAkJwABAAAAJCcAAQAAACQnAAEAAAAkJwABAAAAJFdf9ABsucb+fHbRIxRm5vkHFz0CAAAAW5CqBKA5c+bEb37zm+jo6Ihjjz02TjrppGrcLAAAAABVUPFLwDo6OuKGG26Iyy+/PH7yk5/EY489Fq+88ko1ZgMAAACgCioOQP/4xz9i0KBBscsuu0R9fX2MHTs2nn766WrMBgAAAEAVVPwSsNdffz0GDBjQ9fGAAQNi4cKFld4sAAAAfCwN1+1d7P0XeN+LJ80r8N6pBXWdnZ2dldzAE088EXPmzIlzzjknIiJmzJgRCxcujLPPPrvb9VpaWqKlpSUiIqZOnVrJXQIAAADwMVR8BtDOO+8cy5Yt6/p42bJlsfPOO7/nes3NzdHc3Nz18eLFiyu96x7huuuuK3qEwkyaNKnoESrys5/9LL797W8XPUZZiv7NRtFq+bcbtXzcRUQsP+bYokcoTP8HHyh6hIrU+rFXpFrfXcfPXyh6hML0On9k0SOUrdaPu6LV8v6a5i8teoRCte41sOgRylbLx11ExHH3H1f0CIW5d/y9RY9QNQ0NGz8PreL3ABo5cmQsWbIkli5dGu3t7TFz5sw49NBDK71ZAAAAAKqk4jOAttpqq/j6178eV111VXR0dMTRRx8dQ4cOrcZsAAAAAFRBxQEoIqKpqSmampqqcVOw2Rx99NFFj8AWyHFHURx75bM7iuC4q4z9UQTHHT1dxS8Bg1p1zDHHFD0CWyDHHUVx7JXP7iiC464y9kcRHHf0dFU5AwjYvIp+E+RSqRRtbW2FzgAAAMBHJwCR2ty5c+Oee+7p9rklS5bEKaecErfeemucfPLJceSRR0ZExK233hpDhw6Nww47rIhRN7tLLrkkpk2bFsuWLYsrr7xyo7v4wx/+EC+88EL06dMn3nnnnRg2bFicddZZ77mdDZ588sl4+eWX49RTT43XXnstbr755njrrbeivb09Ro4cGaeffvpm/1o3pRUrVsRtt90WixYtim233Ta23377mDhxYvz617+Oyy67rOt6d999d2yzzTZx2mmnRUTE+vXr44orrogxY8bEF77wha7r/f3vf4+77rorOjs7Y/369XHkkUfGEUccsdm/LmrTd77znRg8eHB0dHTEgAED4swzz4ztttsuli1bFhdccEGMHz8+Pv/5z0dExKpVq+KKK66IsWPHxqmnnlrw5MWaO3du/PrXv47vfve7scsuu0RExEsvvRS33357LF++PPr27Rt9+/aNCRMmRENDQ9x9993xxBNPRN++fbtu47zzzovtttuuqC+BGrOxx+oG06ZNi9122y3OOOOMrs+93/PxhAkTYscddyziSyjM6tWrY/r06RERsXLlyujVq1fXY/Hcc8+NyZMnxymnnBInnnhiRESsXbs2fvzjH8c555wTn/jEJ2L9+vVx9dVXx+mnnx7Dhw8v6ssgmQ2P6fr6+ujo6IhTTjklRowYUfRY8B4CEKkdcMABccABB3R9PHPmzJg1a1Y0NjbGDjvsEDNmzIixY8dGff2W/VDo16/fB+7ihBNOiIMOOig6Ozvj4Ycfjh/96Edx0UUXfeje/vznP8e4ceNi//33j4iIxYsXb5L5i9LZ2Rk33HBDjB49Or7yla9ERMS///3vWLly5Yf+v/Pnz4+BAwfGnDlzYsKECVFXVxfr16+PP/3pT3HhhRfGjjvuGO3t7bFs2bJN/WWQSO/eveOSSy6JiHf/sfjoo4/GZz7zmYiI+MQnPhHPPfdcVwCaM2dODBo0qLBZe5LW1tbYfffdo7W1NY4//vhYuXJl/Pa3v40vf/nLMWLEiCiVSvHUU09FW1tb159WPeqoo5zqT9k+6LH66quvRmdnZ8yfPz/efvvt2Gabbbr+v/99Pp4+fXpceumlW9TPMX379u3a3YZfrmx4LD766KMxfPjwaG1t7QpAffr0iQkTJsStt94a5557bjz44IMxfPhw8Yeq2vCYLpVK8cgjj8Sdd94Z3/rWt4oeC95jy3m22EQmTZpUtdtasGBB3HPPPXH++ed3+/yyZcvi+uuv7zqb4I477ojtttsuTj/99Hj88cfjoYceikmTJnWdefHCCy/ERRddFO3t7fGLX/wiBg8eHPvuu28cdNBBVZu1Fi1dujTuvffeuOCCC6KjoyN22GGHGDp0aDz99NNx+OGHFz1eofr16xcjRoz40F3U1dXFuHHjYt68eTFv3ryusLMxK1as6PabyQ3/cMpi4cKFsdVWW3U7Q2fXXXf9SNGmtbU1jjzyyHjsscfiX//6V4wYMSLWrl0bHR0dXb8Frq+v7zobAT6u4cOHd4uu22yzTQwcODAWLVoUu+22W8yePTsOPvjgWL58eYFTFu/tt9+OF198Mb75zW/G9ddfH8cff3w88sgjMXr06G6/vd19990LnJLM/vex2traGoceemi8+eab8eyzz8Yhhxzynv9nw/Px3LlzP9Lz8ZZiQ/i58cYbuz0XH3zwwfHEE0/EAw88EDNnzoyLLrqowCnJ7u23345tt9226DHgfXkT6B5kyZIlMXTo0A+8zjvvvBMvvfRS7L333hu9zp577hnPP/98/O1vf4v99tuv2mPWpPXr18eNN94YJ554Yuy0005dnz/22GPjwQcfjI6OjgKn6xk+zi6GDx8er7322odeb9y4cTF9+vS49tpr4y9/+UusWbOmGqP2GB/0mF22bFlMmzat67+ZM2d2XfbOO+/EggULYt99942mpqZobW2NiHd/q7nffvvFD3/4w/jd734XzzzzjGOTsnR0dMSCBQve8xzQ1NQUs2fPjjfeeCN69eoVO+ywQ0ET9hx/+9vforGxMQYOHBh9+/aNl19+OV599dUYMmTIB/5/Dz/8cNfj+5e//OVmmpZs3u+xOnv27GhqaoqxY8d2PT9szJAhQz7S8/GW4I033ogVK1bEsGHD4qCDDoonn3yy2+Unn3xy3HHHHfGZz3ym28s3oRreeeedmDZtWlx88cVx0003xXHHHVf0SPC+nAFUIzb8Y/L111+PffbZ5wPPpGhqaooZM2bE2rVr48QTT4z7779/M07aM911110xaNCgaGpq6vb5UqkUw4YNi1mzZhU0Wc/xcXbR2dn5gZfX1dVFRMRhhx0WjY2NMW/evHj22Wdj5syZcckll2wRp6oPGDCg6xT1iHdPU9/g73//e4waNSq23nrrOPDAA+O+++6LiRMnRq9eveKMM86IxYsXx4IFC+Khhx6K+fPnx5lnnlnEl0AN2vAD6PLly2OXXXaJvfbaq9vljY2Ncdddd8X2228fBx98cEFT9iytra1x1FFHRcS7Zwm83z+4J0+eHCtXrozGxsY4+eSTI8JLwKjMxh6rixYtin79+sVOO+0UO++8c1x77bWxevVqweIj2HBWY8S7PwvfcsstMXr06K7L582bFzvssEMsWbKkqBFJ7L9fAvb000/HH/7wh7j00ku7fiaGnsIZQD3I4MGD4+WXX37fyzb8Y/J73/tevPzyy/Hss89u9HaGDRsWS5YsiVWrVsXAgQM31bg1Y+HChfHXv/41TjnllPe9fPz48fHAAw9s5ql6po+6i5deeqnrvUN69+4d7e3tXZetWbOm2w+q/fv3jzFjxsQ3vvGN6NWrV6ofvD7oMftBWltbY/78+TFlypS4+uqrY/Xq1bFw4cKuyxsaGmLcuHFx7rnnxty5c6s5Mslt+AF08uTJERHxyCOPdLu8vr4+hgwZEg899FAceOCBRYzYo2x47N10000xZcqUeOihh2L27NkxaNCgeOWVV7quN2XKlPjc5z4Xb731VoHTksnGHqutra3x2muvxZQpU+LCCy+MtWvXfuDzwCuvvOK9vP6f1tbWeOqpp2LKlClx/fXXx6JFi+I///lPREQsX748HnnkkbjwwgvjueeeS/eehPQsI0aMiFWrVsWqVauKHgXeI/+v4WvIqFGj4s4774yZM2fG2LFjI+LdN8397x84+/XrF1/4whfi/vvvj3Hjxm30tiZMmBC9e/fe1CP3eGvWrIk//vGPcdZZZ0WfPn3e9zq77LJLDBo0KJ599tkPfQledh+2i87OzpgxY0a8+eab0djYGBERe+yxRzzzzDMxZsyYWLduXcyZMydOOOGEiHj3t2177rlnbLXVVrFixYpYs2ZN9O/ff7N+TZvSR3nM/q81a9bEP//5z/jBD37QdSbUk08+GbNmzYrhw4fHokWLYtSoURHx7htK//dLFuGj2nrrrePkk0+OG264IT71qU91u+zoo4+OPfbYwxkFEfHXv/41Dj300G5/nfAXv/hF7LXXXvH73/8+Ghsbu94HaN26dUWNSWL//Vg94ogjYs6cOXHppZdG//79o1QqxeOPPx733Xffe96fb8Pz8YoVK7qej7dkS5cujbfffjumTJnS9bmHH344Zs2aFZ/97Gfjtttui+bm5thxxx3jpJNOiltvvTW+9a1vOTuDTeK1116Lzs5Oz7P0SAJQD1JXVxdnn3123HbbbfHAAw9E7969Y+edd46JEyd2u97+++8f99xzTzz//PMbva199tlnU49bEx577LFYtWpV3HLLLd0+/78vBRs/fnxcffXVm3O0Huv9dnH77bfHfffdF+vWrYvhw4fH5Zdf3vUysIkTJ8bNN98cM2bMiIiIT37ykzFy5MiIiHj++efjz3/+c1eMPOGEE1K958hHfcz+t2eeeSZGjRrV7WVw++23X9x+++0xceLEePDBB+Pmm2+O3r17xzbbbBNf+tKXNseXQkJDhgyJhoaGrr9wtcHgwYNj8ODBBU7Wc7S2tsaxxx7b7XMHHHBAzJo1K77yla/EHXfcEcuXL4+ddtoptt56627v6fDwww/HM8880/Xx2WefHQMGDNhss5PHhsdqS0tL9O/fv9svSkaOHBmvvvpq15u1/+/z8XnnnbdFvKz6w7S2tnb7q68R7/488tOf/jRGjBgRb7zxRowZMyYi3n3Offzxx+Ppp5/u9hIxqMSGl3XW19dHe3t7fOlLX4pevbzYhp6nrvPD3sxjE3HqZeVKpVK0tbUVPUbNsr/y2V35an13y4859sOvlFT/B2v7paK1fuwVqdZ31/HzF4oeoTC9zh9Z9Ahlq/Xjrmi1vL+m+UuLHqFQrXvV7ltY1PJxFxFx3P1b7ptX3zv+3qJHqJoPer9gWRIAAAAgOQEIAAAAIDkBCAAAACA5AQgAAAAgOQEIAAAAIDkBCAAAACA5AQgAAAAgOQEIAAAAIDkBCAAAACA5AQgAAAAgOQEIAAAAIDkBCAAAACA5AQgAAAAgOQEIAAAAIDkBCAAAACA5AQgAAAAgOQEIAAAAIDkBCAAAACA5AQgAAAAgOQEIAAAAIDkBCAAAACA5AQgAAAAgOQEIAAAAIDkBCAAAACA5AQgAAAAgOQEIAAAAIDkBCAAAACA5AQgAAAAgOQEIAAAAIDkBCAAAACA5AQgAAAAgOQEIAAAAIDkBCAAAACA5AQgAAAAgOQEIAAAAIDkBCAAAACA5AQgAAAAgOQEIAAAAIDkBCAAAACA5AQgAAAAgOQEIAAAAIDkBCAAAACA5AQgAAAAgOQEIAAAAIDkBCAAAACA5AQgAAAAgOQEIAAAAIDkBCAAAACA5AQgAAAAgOQEIAAAAIDkBCAAAACC5+qIHAAAAAIp17/h7C73/UqkUbW1thc6QnTOAAAAAAJITgAAAAACSE4AAAAAAkhOAAAAAAJITgAAAAACSE4AAAAAAkhOAAAAAAJITgAAAAACSE4AAAAAAkhOAAAAAAJITgAAAAACSE4AAAAAAkhOAAAAAAJITgAAAAACSE4AAAAAAkhOAAAAAAJITgAAAAACSE4AAAAAAkhOAAAAAAJITgAAAAACSE4AAAAAAkhOAAAAAAJITgAAAAACSE4AAAAAAkhOAAAAAAJITgAAAAACSqy96AAAAAN7VutfAQu+/VCpFW1tboTMAm4YzgAAAAACSE4AAAAAAkhOAAAAAAJITgAAAAACSE4AAAAAAkhOAAAAAAJKrKAA9/vjjceGFF8bpp58eL7zwQrVmAgAAAKCKKgpAQ4cOjYsuuij23nvvas0DAPzXbVsAAANuSURBVAAAQJXVV/I/DxkypFpzAAAAALCJeA8gAAAAgOQ+9AygK6+8Mt588833fP6MM86IT37ykx/5jlpaWqKlpSUiIqZOnRqlUuljjMn7qa+vt8cK2F/57K58tb675UUPUKBa/r5F1P6xV6Ra393S2HLfp7GWv2+1ftwVzf7KZ3fls7vK2N+m96EB6Pvf/35V7qi5uTmam5u7Pm5ra6vK7W7JSqWSPVbA/spnd+Wzu9pV6983x1757K521fL3zXFXGfsrn92Vz+4qY3/V0dDQsNHLvAQMAAAAILmKAtBTTz0V55xzTixYsCCmTp0aV111VbXmAgAAAKBKKvorYKNHj47Ro0dXaxYAAAAANgEvAQMAAABITgACAAAASE4AAgAAAEhOAAIAAABITgACAAAASE4AAgAAAEhOAAIAAABITgACAAAASE4AAgAAAEhOAAIAAABITgACAAAASE4AAgAAAEhOAAIAAABITgACAAAASE4AAgAAAEhOAAIAAABITgACAAAASE4AAgAAAEhOAAIAAABITgACAAAASE4AAgAAAEiuvugBAADYdHqdP7Kw+y6VStHW1lbY/QMA/58zgAAAAACSE4AAAAAAkhOAAAAAAJITgAAAAACSE4AAAAAAkhOAAAAAAJITgAAAAACSE4AAAAAAkhOAAAAAAJITgAAAAACSE4AAAAAAkhOAAAAAAJITgAAAAACSE4AAAAAAkhOAAAAAAJITgAAAAACSE4AAAAAAkhOAAAAAAJITgAAAAACSE4AAAAAAkhOAAAAAAJITgAAAAACSE4AAAAAAkhOAAAAAAJITgAAAAACSE4AAAAAAkqsvegAAakf/Bx8o7L5LpVK0tbUVdv8AAFDLnAEEAAAAkJwABAAAAJCcAAQAAACQnAAEAAAAkJwABAAAAJCcAAQAAACQnAAEAAAAkJwABAAAAJCcAAQAAACQnAAEAAAAkJwABAAAAJCcAAQAAACQnAAEAAAAkJwABAAAAJCcAAQAAACQnAAEAAAAkJwABAAAAJCcAAQAAACQnAAEAAAAkJwABAAAAJCcAAQAAACQnAAEAAAAkJwABAAAAJCcAAQAAACQXF1nZ2dn0UMAAAAAsOk4AwgAAAAgOQEIAAAAIDkBCAAAACA5AQgAAAAgOQEIAAAAIDkBCAAAACC5/wt/iEa/nhfbJgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import Lasso regression from sklearn library\n",
        "from sklearn.linear_model import Lasso\n",
        "\n",
        "# Train the model\n",
        "lasso = Lasso(alpha = 10)\n",
        "lasso.fit(x_train, y_train)\n",
        "y_pred1 = lasso.predict(x_test)\n",
        "\n",
        "# Calculate Mean Squared Error\n",
        "mean_squared_error = np.mean((y_pred1 - y_test)**2)\n",
        "print(\"Mean squared error on test set\", mean_squared_error)\n",
        "lasso_coeff = pd.DataFrame()\n",
        "lasso_coeff[\"Columns\"] = x_train.columns\n",
        "lasso_coeff['Coefficient Estimate'] = pd.Series(lasso.coef_)\n",
        "\n",
        "print(lasso_coeff)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VX4kht7yK2l7",
        "outputId": "a21f205c-81ad-4fa5-893b-8c83403ff983"
      },
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean squared error on test set 37.44884867787057\n",
            "    Columns  Coefficient Estimate\n",
            "0      CRIM             -0.000000\n",
            "1        ZN              0.026480\n",
            "2     INDUS             -0.000000\n",
            "3      CHAS              0.000000\n",
            "4       NOX              0.000000\n",
            "5        RM              0.000000\n",
            "6       AGE              0.000000\n",
            "7       DIS             -0.000000\n",
            "8       RAD              0.000000\n",
            "9       TAX             -0.007403\n",
            "10  PTRATIO             -0.000000\n",
            "11        B              0.009508\n",
            "12    LSTAT             -0.598031\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plotting the coefficient score\n",
        "fig, ax = plt.subplots(figsize =(20, 10))\n",
        "\n",
        "color =['tab:gray', 'tab:blue', 'tab:orange',\n",
        "'tab:green', 'tab:red', 'tab:purple', 'tab:brown',\n",
        "'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan',\n",
        "'tab:orange', 'tab:green', 'tab:blue', 'tab:olive']\n",
        "\n",
        "ax.bar(lasso_coeff[\"Columns\"],\n",
        "ridge_coefficient['Coefficient Estimate'],\n",
        "color = color)\n",
        "\n",
        "ax.spines['bottom'].set_position('zero')\n",
        "\n",
        "plt.style.use('ggplot')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "37fMf2SQK-9X",
        "outputId": "ca9918ee-4530-4b7e-d346-f560c30c059f"
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "AkcofhU4Kq_o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# import model\n",
        "from sklearn.linear_model import ElasticNet\n",
        "\n",
        "# Train the model\n",
        "e_net = ElasticNet(alpha = 1)\n",
        "e_net.fit(x_train, y_train)\n",
        "\n",
        "# calculate the prediction and mean square error\n",
        "y_pred_elastic = e_net.predict(x_test)\n",
        "mean_squared_error = np.mean((y_pred_elastic - y_test)**2)\n",
        "print(\"Mean Squared Error on test set\", mean_squared_error)\n",
        "\n",
        "e_net_coeff = pd.DataFrame()\n",
        "e_net_coeff[\"Columns\"] = x_train.columns\n",
        "e_net_coeff['Coefficient Estimate'] = pd.Series(e_net.coef_)\n",
        "e_net_coeff"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "6blr9qjQLvWQ",
        "outputId": "8eebc811-7188-4d20-ce30-35422531c92e"
      },
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error on test set 27.01562011699508\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Columns  Coefficient Estimate\n",
              "0      CRIM             -0.068805\n",
              "1        ZN              0.050690\n",
              "2     INDUS             -0.022234\n",
              "3      CHAS              0.000000\n",
              "4       NOX             -0.000000\n",
              "5        RM              0.809540\n",
              "6       AGE              0.025026\n",
              "7       DIS             -0.760328\n",
              "8       RAD              0.274832\n",
              "9       TAX             -0.013674\n",
              "10  PTRATIO             -0.813510\n",
              "11        B              0.010374\n",
              "12    LSTAT             -0.809071"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-31a90aec-f21d-48b6-a284-4a4afdd815af\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Columns</th>\n",
              "      <th>Coefficient Estimate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CRIM</td>\n",
              "      <td>-0.068805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ZN</td>\n",
              "      <td>0.050690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>INDUS</td>\n",
              "      <td>-0.022234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHAS</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NOX</td>\n",
              "      <td>-0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>RM</td>\n",
              "      <td>0.809540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>AGE</td>\n",
              "      <td>0.025026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>DIS</td>\n",
              "      <td>-0.760328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>RAD</td>\n",
              "      <td>0.274832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>TAX</td>\n",
              "      <td>-0.013674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>PTRATIO</td>\n",
              "      <td>-0.813510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>B</td>\n",
              "      <td>0.010374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>LSTAT</td>\n",
              "      <td>-0.809071</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Observations:**\n",
        "* From the above analysis we can reach the following conclusion about different regularization methods:\n",
        "\n",
        "* Regularization is used to reduce the dependence on any particular independent variable by adding the penalty term to the Loss function. This term prevents the coefficients of the independent variables to take extreme values.\n",
        "\n",
        "* Ridge Regression adds L2 regularization penalty term to loss function. This term reduces the coefficients but does not make them 0 and thus doesn’t eliminate any independent variable completely. It can be used to measure the impact of the different independent variables.\n",
        "\n",
        "* Lasso Regression adds L1 regularization penalty term to loss function. This term reduces the coefficients as well as makes them 0 thus effectively eliminate the corresponding independent variable completely. It can be used for feature selection etc.\n",
        "\n",
        "* Elastic Net is a combination of both of the above regularization. It contains both the L1 and L2 as its penalty term. It performs better than Ridge and Lasso Regression for most of the test cases."
      ],
      "metadata": {
        "id": "j-p3RFTSL4lP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "zjX4iRPXMGeT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#finding intercept\n",
        "print(f\"intercept: {lreg.intercept_}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XlT25tSEAnZT",
        "outputId": "3c47bf11-5d3a-4932-ae76-5e37de9fbfcf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "intercept: 39.09354119930165\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Evaluation Metrics for Regression Models**\n",
        "\n",
        "# on test set"
      ],
      "metadata": {
        "id": "bev3a_SRIcVd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " # Apply multiple Linear Regression Model\n",
        "lreg = LinearRegression()\n",
        "lreg.fit(x_train, y_train)\n",
        "\n",
        "# Generate Prediction on test set\n",
        "lreg_y_pred = lreg.predict(x_test)"
      ],
      "metadata": {
        "id": "PGFYr8rwnh4Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# MSE \n",
        "from sklearn.metrics import mean_squared_error\n",
        "mse= mean_squared_error(lreg_y_pred, y_test)\n",
        "mse"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bdpKQoFeD3mT",
        "outputId": "97ef8e07-c29c-4664-8a4f-f2fb1867c77d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20.03794243697047"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# MAE\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "mae= mean_absolute_error(lreg_y_pred, y_test)\n",
        "mae"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4-yC3C_nKXAf",
        "outputId": "c7e47b62-0fc2-4306-f6e5-0c5a737aba67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.36869789511014"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# R2_score\n",
        "from sklearn.metrics import r2_score\n",
        "r2 = r2_score(lreg_y_pred, y_test)\n",
        "print('r2 score for perfect model is', r2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "71VbjLaFLY0t",
        "outputId": "7cebcb0b-118d-4f61-e672-970209326a43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "r2 score for perfect model is 0.726962018495928\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# RMSE \n",
        "from sklearn.metrics import mean_squared_error\n",
        "mse= mean_squared_error(lreg_y_pred, y_test , squared=False)\n",
        "mse"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-WjkU77ko_ET",
        "outputId": "780835db-e15a-480c-881a-0879810aba4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.476376038378643"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# on train set"
      ],
      "metadata": {
        "id": "1GO7eYvcnmxC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " # Apply multiple Linear Regression Model\n",
        "lreg = LinearRegression()\n",
        "lreg.fit(x_train, y_train)\n",
        "\n",
        "# Generate Prediction on train set\n",
        "lreg_y_pred = lreg.predict(x_train)"
      ],
      "metadata": {
        "id": "kPsgrwt8MAYo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# MSE \n",
        "from sklearn.metrics import mean_squared_error\n",
        "mse= mean_squared_error(lreg_y_pred, y_train)\n",
        "mse"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "brblyu-TnsYI",
        "outputId": "deaf1ce5-5cdf-4b51-f082-d4635e7b325d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "22.834803357044354"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# MAE\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "mae= mean_absolute_error(lreg_y_pred, y_train)\n",
        "mae"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BvC8vWePn1lP",
        "outputId": "8aa328ee-e3ca-46a0-a636-ebd60b7fc5af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.3384189126064103"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# R2_score\n",
        "from sklearn.metrics import r2_score\n",
        "r2 = r2_score(lreg_y_pred, y_train)\n",
        "print('r2 score for perfect model is', r2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hwaS2ZV3oD5H",
        "outputId": "66dd1155-563e-4844-d125-13bfc85ce621"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "r2 score for perfect model is 0.6254231641118793\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# RMSE \n",
        "from sklearn.metrics import mean_squared_error\n",
        "mse= mean_squared_error(lreg_y_pred, y_train , squared=False)\n",
        "mse"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tr5KhJiVoJj0",
        "outputId": "a116bf72-6b48-4c92-810f-4bd9e8020e26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.7785775453626735"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# addiing y_pred (prediction values) as a variable to the dataframe"
      ],
      "metadata": {
        "id": "4Zg48TUyu2V3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting Linear Regression to the dataset\n",
        "from sklearn.linear_model import LinearRegression\n",
        "lin = LinearRegression()\n",
        " \n",
        "lin.fit(X, Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OI4avl3MvIok",
        "outputId": "e87aea91-dbd6-416c-8518-aa3173b2f787"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = lin.predict(X)"
      ],
      "metadata": {
        "id": "HkX66MDdvK6x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "boston_pd['y_pred'] = y_pred"
      ],
      "metadata": {
        "id": "J8DTwXEVvaE5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "boston_pd.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "LfoLVL8QwHJ1",
        "outputId": "990642a7-1642-406d-a301-d277221f7024"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
              "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
              "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
              "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
              "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
              "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
              "\n",
              "   PTRATIO       B  LSTAT  House Price     y_pred  \n",
              "0     15.3  396.90   4.98         24.0  30.003843  \n",
              "1     17.8  396.90   9.14         21.6  25.025562  \n",
              "2     17.8  392.83   4.03         34.7  30.567597  \n",
              "3     18.7  394.63   2.94         33.4  28.607036  \n",
              "4     18.7  396.90   5.33         36.2  27.943524  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-58621099-b327-4f6a-8504-055f4e39df61\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "      <th>House Price</th>\n",
              "      <th>y_pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00632</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.31</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>296.0</td>\n",
              "      <td>15.3</td>\n",
              "      <td>396.90</td>\n",
              "      <td>4.98</td>\n",
              "      <td>24.0</td>\n",
              "      <td>30.003843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.02731</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.14</td>\n",
              "      <td>21.6</td>\n",
              "      <td>25.025562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.02729</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>392.83</td>\n",
              "      <td>4.03</td>\n",
              "      <td>34.7</td>\n",
              "      <td>30.567597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.03237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
              "      <td>33.4</td>\n",
              "      <td>28.607036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.33</td>\n",
              "      <td>36.2</td>\n",
              "      <td>27.943524</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-58621099-b327-4f6a-8504-055f4e39df61')\"\n",
              "              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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-58621099-b327-4f6a-8504-055f4e39df61 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-58621099-b327-4f6a-8504-055f4e39df61');\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",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Polynomial Linear regression(Plot the best fit line)**"
      ],
      "metadata": {
        "id": "mjMVrPvjphoM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting Linear Regression to the dataset\n",
        "from sklearn.linear_model import LinearRegression\n",
        "lin = LinearRegression()\n",
        " \n",
        "lin.fit(X, Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MEK5qT7hon6I",
        "outputId": "f48a2380-54fb-4538-ec24-fa5871b7e20b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "\n",
        "def create_polynomial_regression_model(degree):\n",
        "  #\"Creates a polynomial regression model for the given degree\"\n",
        "  \n",
        "  poly_features = PolynomialFeatures(degree=degree)\n",
        "  \n",
        "  # transforms the existing features to higher degree features.\n",
        "  x_train_poly = poly_features.fit_transform(x_train)\n",
        "  \n",
        "  # fit the transformed features to Linear Regression\n",
        "  poly_model = LinearRegression()\n",
        "  poly_model.fit(x_train_poly, y_train)\n",
        "  \n",
        "  # predicting on training data-set\n",
        "  y_train_predicted = poly_model.predict(x_train_poly)\n",
        "  \n",
        "  # predicting on test data-set\n",
        "  y_test_predict = poly_model.predict(poly_features.fit_transform(x_test))\n",
        "  \n",
        "  # evaluating the model on training dataset\n",
        "  rmse_train = np.sqrt(mean_squared_error(y_train, y_train_predicted))\n",
        "  r2_train = r2_score(y_train, y_train_predicted)\n",
        "  \n",
        "  # evaluating the model on test dataset\n",
        "  rmse_test = np.sqrt(mean_squared_error(y_test, y_test_predict))\n",
        "  r2_test = r2_score(y_test, y_test_predict)\n",
        "  \n",
        "  print(\"The model performance for the training set\")\n",
        "  print(\"-------------------------------------------\")\n",
        "  print(\"RMSE of training set is {}\".format(rmse_train))\n",
        "  print(\"R2 score of training set is {}\".format(r2_train))\n",
        "  \n",
        "  print(\"\\n\")\n",
        "  \n",
        "  print(\"The model performance for the test set\")\n",
        "  print(\"-------------------------------------------\")\n",
        "  print(\"RMSE of test set is {}\".format(rmse_test))\n",
        "  print(\"R2 score of test set is {}\".format(r2_test))"
      ],
      "metadata": {
        "id": "V_P4ZlCospfo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "create_polynomial_regression_model(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nJNF3ehN0NMB",
        "outputId": "afdd71a5-66af-4e41-a8cc-ef689b79461b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The model performance for the training set\n",
            "-------------------------------------------\n",
            "RMSE of training set is 6.245255967547597\n",
            "R2 score of training set is 0.5345477802082016\n",
            "\n",
            "\n",
            "The model performance for the test set\n",
            "-------------------------------------------\n",
            "RMSE of test set is 9.904162355696043\n",
            "R2 score of test set is -0.13807665283277948\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "\n",
        "pre_process = PolynomialFeatures(degree=2)\n",
        "X_poly = pre_process.fit_transform(X)\n",
        "\n",
        "pr_model = LinearRegression()\n",
        "pr_model.fit(X_poly, Y)\n",
        "\n",
        "y_pred = pr_model.predict(X_poly)\n",
        "plt.figure(figsize = (18,8))\n",
        "plt.scatter(y_train_predicted, y_train, c = \"green\")\n",
        "plt.scatter(Y, y_pred, c=\"orange\")\n",
        "plt.plot(y_pred, lin2.predict(poly.fit_transform(X)), color = 'red')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "nnM9-inN152I",
        "outputId": "3b8661b5-11a3-4e24-c561-4d896b26e66c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f16b981df10>]"
            ]
          },
          "metadata": {},
          "execution_count": 144
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x576 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "\n",
        "#create_polynomial_regression_model(degree):\n",
        "  #\"Creates a polynomial regression model for the given degree\"\n",
        "  \n",
        "poly_features = PolynomialFeatures(degree=2)\n",
        "  \n",
        "  # transforms the existing features to higher degree features.\n",
        "x_train_poly = poly_features.fit_transform(x_train)\n",
        "  \n",
        "  # fit the transformed features to Linear Regression\n",
        "poly_model = LinearRegression()\n",
        "poly_model.fit(x_train_poly, y_train)\n",
        "  \n",
        "  # predicting on training data-set\n",
        "y_train_predicted = poly_model.predict(x_train_poly)\n",
        "  \n",
        "  # predicting on test data-set\n",
        "y_test_predict = poly_model.predict(poly_features.fit_transform(x_test))"
      ],
      "metadata": {
        "id": "cu7z0b1lCESq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.scatter(y_train_predicted, y_train, c = \"blue\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "RbqvsEnMAVzZ",
        "outputId": "68e81b46-5ba1-4901-bf1d-d8fbffe5f980"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f16b978a3d0>"
            ]
          },
          "metadata": {},
          "execution_count": 146
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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IbygCJ6Y8Qo5yaMiMI0dKMo5YN2zwo62tMCE3Osrs2UE4nWE4nRY8/PAAr8Qhpu9KkQ/0ljtOKIuoA5+YmEBzczNCoRBYlkVdXR0eeeQR9Pb2Yvfu3fD7/aitrcVTTz0Fi4XGA8J4iK0oTCenpNOXnU4Wv/mND48/PgMjI4md66MDgt/vwL33Ci8yEnPQYvKBFlka+ayx6x0Tx3FpG+hxHIfx8XEUFRUhFAph8+bNeOKJJ/CHP/wBixcvxl133YVf//rXmDNnDlauXCl6wu7ublkGRvI2vbL20QNkt7ZIsTudo4n+7cMPizA0lKosLlkS6WaTfLxkfTneOSefN9kJR/avhNuder4HHhhRRNeVaqNcotdbreOrhVGf7+rqat7tohq4yWRCUVERAIBlWbAsC5PJhHPnzqGurg4AsGzZMnR0dChoLkEoi1ghqGgku3z5GO/+X35pSdHDperLfBr5pOOTnpWSCUoWr+KDNPbcIknzCIfD+MUvfoGrV6/i3nvvRVVVFUpKSsAwkYfMbrfD5/Px7tve3o729nYAwPbt2+FwOOQZaLHI3kcPkN3aImb3c88xcLsTnaLbXYA9exx4661JZ7ZtG3D2LIeLF00Jn/V6GRw5UoKzZ4vx/vtBzJ0L+Hz8Px+fr0j0GvLZE4/Tqdx9cDiAgwej/7MAqMj6mNHrnc01yAVGfb6FkOTAzWYzXn75ZQQCAezYsUOWDNLQ0ICGhobY/+W+vhj1lYfs1hYxu93uGQBSHabHE4LXOymNlJYC77wTkTxOniyE15u4z8WLJtx9N4OlS8dRWMgCKEYydvsYvN708oeQPQBQWBiGzzeBzs4hyZGy1jp09Hrb7eUASlL+LuUa5AKjPt8ZSyjxWK1WzJ8/H19++SVGRkbAspEHxOfzwW63Z28lQaiEnMm8qORx440hnj0mo/HPP7ekNA+WugowXYrh+LgZbW3Fkmt9K1EnPFOogXFuEXXgQ0NDCAQCACIZKZ999hlqamowf/58nD59GgBw/PhxLFy4UF1LCcPh8TBoairH6tUzJOVTq0kmjkYsj7u7uwDz5wdl6cvRa+J2W2C1pj++VC05Ux1aifujtsZOpEdUQunv78e+ffsQDofBcRzuvPNO/O3f/i1mz56N3bt34+DBg5g7dy7uueceLewlDILeVgBmsuRaSsOC4WEz9u/vE/w7MClvfP01g/PnCxJSCqdP5/Dd707A47GkyDWA+GSmx8Pg5MlC3r+l21fJ+zOVV0LmGlEH7nK50NLSkrK9qqoK27ZtU8UowvjosRehXEcT7/T59HBAPJ+az1HGMzxsQm+v8Iuwx2OGx8NfbjZ6bD67xGzT4/0h5EMrbwhVMMIKQDkTf9ddF8LIiCllQQ6fBBN/3MuXzejqEo7gAaT9e1dXAdas4S83y+eExWyLYoT7Q4hDDpxQBb3XaZYiIfB9xmplMW8eC5crxOvwxSLuTBCKjIWcsMPBikoher8/hDSomBWhClpkJ8RPwv3kJ4ysSTgpE398nwkEGLhcoYSiVWLHVQK+yFjICS9dOi6pDG4m9yd6zVeuTF3YRGgPReCEKqhdpzk50hXrbJOMFAkhE5nB7Rb/SUWj+N5ek6i8EoUvMs6mmUEm9yf17SLzQl+EMpADJ1RDzewEqZNwQjq3FAkhE5mht9fEu724mMX3vx+KOUoAaG4ug9drwdjY5D7TpoXBMMDoqLjWnu0gKff+0MSn/iAHThgSKdFxOp1bSvSaSYRbWRlGV1fq9ptvDsWKYfHr5BwAEyYmIo67pCSMm24KwuVK75S1TOGjiU/9QQ6cMCRSomOhiLG5uQxWKwe7PQyWDWLmTI53UjJdhCsU2c+Zw+LMmVS7XK70dgGJkfvIiBkuV+bOWY2l9TTxqT/IgRO6Q4rzkRIdC0WMJ04UYnx8UqJgmCD27eN3cHwRbraRvdRWa5lGtmotoprqDYT1CGWhELpCal2P5CXca9akps4JRYzxzhuQX/40nRYsZWm51FZrmUa2apV4jf9u9fVhWjavAygCJ3SFnImy+Og4UmVOPEovLAynOHBAXrQrpgWL6dJ8dlksHEKhSRklm8hWTa06+t0i15smLnMNOXBCVyjpfPg07EDAhLa21BKwcqLdbLXgeLt8viLY7WNobAzgwAGrIimXpFVPHciBE7pCaeeTHA17PAzOn7dkpeMqoQXzRbJ1dcpEtKRVTx1IAyd0hdorOJ1OFrt2DWD27CDKysKYPTuIXbsmV1VKKbGq9xKqerePUA7RpsZKQ02N9Y0e7BZqApwOqXYLNeHdtWsAr78+PSVDxWqNdJavqwvyHU7W9/n6awbd3WZMTJjBMMCCBRN45RUGpaX0nGiFUe0W6shDEgqhW5QOLTweBg8/bE9Zvu52F+Dxx2ckVBqMEggwePzxGXj77T4cOGCF221Bb68JlZVhzJmTyfLzSdrairFiBYff/pa/XCxBiEEOnNAMKfndSuUwJ5+rsTGA9evLBWuP8Dnv+L/9+Md2BAKTckpXF3DmjLhtYsWtLl820VJ0ImPIgROaINUxi6URRh1zciS8bVukIbHHw6C5uSxFCmlrK0xwwHIR2je6snP//n7e7yzULSceWopOZAo5cEITpOZ3p0sj5BsEopHw2bMcXn65AOvXl/NGvNk4bzE++qgQa9dWwO83x94sAKTtlhMPpfcRmUIOnNAEIcd88mQhVq+eEXN86dII08kRFy+asG6dsESSDUVFYYyNCUsswaA5Ibe8s7MA8+aFJNUFv+46zvDpfWrUXSGkQQ6c0AQhx+z1MrEotbOzALt2DQjmMK9fX572HENDYtFupOKfXOLLvUrB7S5AIJA+Q3fatDCWLRv/NgvFuM5Ob82rpxqUB05oAl9+dzJudwEOHLAK5jCL1RApKxP+e3FxGJk47wjy92NFfNfChUHs39+PuXMzNEknqFV3hZAGReAEgMnXYJ/PAru9POPX4HSv0/PmhWKRKcsC/f2pEXNPDyNYS4RvhWGU2loOL788kKKBm80cbLYw/P5MnXdmiJ0vX3RvqhGeW8iBE4q1yhJ6nd61K9WxWq3yl8zH1xCJZqHMnBmGy8Vi2zYL+vvDsUGCZYGREWB8nOEdKNQmFBJ+uZXSzd4oWjLVXckt5MAJxVplCR2Hb3IxEGDAMBxYVl4FPqHo3O93KN4NXg1mzw7yDoxG1ZKp7kpuIQ2cUOw1WOg4QpOLLGtCcTGLBQsmeOt1eDwM1q6twPe+V4Xvfa8Ka9dWCHZB37KF0b3zLiwM49AhfoeciZYspW6LVDI9FtVdyS0UgROKvQYLHaesjMXQEH+sMDrKwOUa5+16s3r1DFy5MvmItrUV46OPCvGDH4xj69ahBCdx8WLmGvesWSGYzRy6u9UdAOrrxwUdm9xBVMmIPdtjadmXk0iEInBCsQqAQsfZs2cAhYVhwf1OnixMifhaWkoTnHeUaM51tEtPNEr/5JPMHLjVyuLIkT4cPuzDypWjae3MBquVxdatQ4J/Fxr8PvmkgPfNQ8nsD8okMS7kwAnFWmUJlWqtqwuivn5ccD+vl0lpmybWNzK6hH3NGjva2ooRDvM58PTVsBgmkrnS0lKK9evLYbVyePfdvpgc4HAoJwPMm8emvZ5CaZYTE5EBa/XqGZKuTybZH243/4u40HZCP5ADnwJIrXG9d+8A2tpC2Lt3QNDZpDuWx8PECkYNDZnR1RVZ2u7xMNi6dQg1NSFBG5MjPil9Izs7p2Whe3Ow2Vg880xFQv/NRx+dgUDAhJ07B7B0qfCgIxexwSA6iM6ezZ8rf+WKRdL1yST7o7eX/+1FaDuhH8iB5zlSmwRLPdZDDyUe66GHJo8l1uz3d7/rw8qVo7BY+CPj+OhxwwY/qqqEHb400jkgE3w+CyYmEn8C4+OTEk1jYyDtoCMHn88kaRC97jppUpOSjS8qK/nPOXOmOnISoRzkwPMcJfXN5uaylIm+7u6IlAFIa/a7f38/7rlnjPdzHo855qCcThY33ijsPF2uIBYsmJD9HaQSXRUqNuhIpbOzUNIgmu7NI15qUjL7Y84c/n1cLsok0TvkwPMcJbXSzs5pabdLea33eBicOcOvrXZ1FeDuuyuxbJkDixdX4tQpoVKsHHbtGsDWrUOqTToCgNvNxAad++8fFfxcdXVQNFJP1uiFBtENG/yorhYuORC/X1T2OnSoL63sJYbabewI9SAHnofE69SXL/PfYjVWygk5gsbGQMyehx+249o1Yd2aZc3461+noaurIGGRTyIm/PSndrS0lOKOO9SLwr/4oiCtZFFYGMbKlaM4fNiH3/0uMvlZXi79ura1FeG737Xg/vsdMVnF6WRjGTEWC//gpPQydcrlNi7UE1MlcmU3X06vxcIhFEpc8Sj0A01n99q1FQllU6OsXDkaa2hw+nRk5eXQEIPiYhZVVWF8/vk0gSyR7KmuDsLrZVK07OTPmEwm3rREMR54YCSW4yylV2d0niBeaioqYjE2Ju50k+9LU1M5jhwpSWtTrqDfpbZQT8wpAp/mHQqZMHt2EE5nWHKTYD62bh3CuXMFCY6wpiYUy2+Oz0IBgKEhM3p6svgyEujuLkBVVRA9PakO3OFgsXTpeEwKiFwbBr29ZthsHAYHTfD5mLTt1OKjXSkLVqIRdLyjj7ZzE8uYSS5foNQydSPWWCGkIerAvV4v9u3bh4GBAZhMJjQ0NOC+++7D8PAwWltbce3aNVRWVuLZZ5/F9OnTtbCZiCP5xymUuztzZhiHDvVlda5oJolQFCrW/1Etamo4WK1cwmpMvreMeOfr8TD4+c9t6OpK/xPIRGric/TRIlwfflgkuCoVSB0wovuli/rTYdQaK4Q0RB04wzB4/PHHUVtbi9HRUWzcuBG33347jh8/jttuuw2rVq3C0aNHcfToUTQ2NmphM/EtfD9OoSp/UT032x9tuihUbPGNWpSUhPHuu0Fs2hRKcHRARIZIjjz5ZA4+sp3I44t8AfDKIlGSB4xsl6krVaiM0CeiDryiogIVFRUAgOLiYtTU1MDn86GjowNbtmwBANTX12PLli3kwDWG78cZqfIXBssmRnkjI2ZFf7R8zknK4hs1+PTTAgBsSoQtFHm2tJSmdd7Tp4exYsVYVlJDutK6//3fBbznVyPzg+p15zeyNPDe3l5cunQJN9xwAwYHB2OOvby8HIODg7z7tLe3o729HQCwfft2OBwOeQZaLLL30QNa2O3z8d++khIT/Dx+wOcrErVJzO5Ll4Dnn2fQ3m5OaDV29mwxXn89iLNnOZHCUpm1NUvH6CiDf/1XE/bvn7T7uecYuN2JTsrtLsBLLzlEmy0sW8bh4EELLl2qwHPPMfjmGxO+8x0OW7awkjvoCJ1//347OC5VQiku5vDv/85hwYIK0WNfuhSpvijFLpeLwalTqdudzuyeT/pd6gPJDnxsbAw7d+7EE088gZKSxFdAk8kEk4n/R9HQ0ICGhobY/+XOABt11liu3ZlMNNnt5QBSX8dtthD8/tQIz24fg9ebPgJPtjvertLSMD7/3ILu7tTo7eJFE159NYR33ulHS0spfv/7IoTD2mWpXrmS+Gy53TMApNr5xz+avq3LkppNE+XMGQ5/+MPgtxOPk8c4dSqMXbsGcOCAVfQ+CZ3/9GnA6039rYyORq7fLbekvz+TkX2iXUKa9rp1DE6dsqdMhK5b54PXm/kb01T5XeqFrLJQQqEQdu7ciaVLl2Lx4sUAAJvNhv7+flRUVKC/vx9lZWXKWTvFyHSiSShLga8DTqbZC3KaJMS3Q/vjH2dheJjvU5lG3+kj9+98JzEbtrSUP4d6fDwyqFRXBwVllCtXLLxNKNzuAvz4x3YEApPOU+g+CclJ6XplfvhhEZqa0rezk6NpRwdfuz0Mlg1i5kwOLleIslDyCNEQieM4vPbaa6ipqcEPf/jD2PaFCxfixIkTAIATJ05g0aJF6lmZ52S63F1oAUZdXVCRhRlys0riJ+DkLGiRhrDztlpZbNki/XzDw2bRxTJCTSjinTeQfkUl3+rMMf4qAt+e0yxaq0aqph1fA+fMmUJ0dRWgr89EzjvPEI3Az58/j48//hhOpxM///nPAQA/+tGPsGrVKrS2tuLYsWOxNEIiM7KZaPnkyAQAABZLSURBVBLKUlCiyL6crBKXK4gVK0axeHHlt84vjNSoWXn9u6QkjN/8xoe5c22Ivhl7PIzgsn8A+PLLSGW/rVuHYLWW8maFpGtCkQzffXI6WcyfH0xZPDQ6yqC4OIzRUeFjp8sSkVqFkLJPpgaiDvymm27Cb3/7W96/bd68WXGDpiJ6bQwrJauksDCMO+6YwOgo8LOf2THpoPkclHLOWyhTJBp5er3Cg4/Xy+DIkZJYVohUGaqkJMy76EfoPvn9/E765puDcDjC6OychsFBM4LB1OsiNHhLXdxD2SdTA6qFogP0WkyIz66amhBWrhyNSTPvvtsHj4fBJ58UQenoOh0sCwQCJqxfX46mpnJcuhTZLkf2iVYclCpDvf12n6z7JDQAulyRAllnz/bgoYf4JRyhQUFq3RK9BgWEslAtFJXINAsl0xV3SiGUheJ2W9Dba0JlZRhz5kzaJ1QfRWtqazm8804v1q8vF6hiyC/fLFkyLmuFqpz7xDcJnLxC1O934N57TWk/kwlSzp0NU+V3qReEslDIgatEPtkt5Ax27RrAo4/OiGV25JrZs4MYGzOnlU+SUbswVLLDb2wMJKQhbttmQX9/vyqDt5pBQT4930bAsA7c73dg06aQ4QrxGPVB4bNbqCpepIhUdrVPiovDuPnmELq7zbh6VdvaakpGpFLgGwijbw5GeKbjyafn2wgIOXB9hE4CeDwM7ruvQJF2YETmCE2I9fZm53BnzQrh2LFreO89L+bO1c6BWa3pGzdL6SGaCXz6/MWLJur+TmSMrsvJtrSUpizLplQo7RGaEOO47CYta2snO7VLyXhxOFh897shTJ8eXRGaWfTPcRB8k1Ozeh9lhhBKo+sInB54fcCXjWIy8Stv06aFJbc5mz598nN85+CjqorF1q1DOHx4MhNDqJO7ENHCXnwo2UM0GcoMIZRG1w6cHnh9EE1dW7lyNOachaLvZcvG8e67fSgqEr9Ho6OTx4hPj1uwYIK3LG40f3vNGjsAxPpBHjrkQ21t4oBSXR3EypWjKCnhH0yE6qarGTTwDVK1tVzO00UJ46JrCWXDBj/Oni1OKdRPD7z2OJ0srFYubcaJyxXE1q1DcDpZfPSRF83NZejomIb+fjP4Uvj+/OdpCTXK41ePRjMoTp4sTMkqcbsL0NxcBquVi01uv/56EK++GkrJuFi8uJJ38U1vL/8ApGbQwNegYds2C0pLKSAhMkPXDtzpZPH++6mF+o02Y58vCEWnZWVhLF+euCoy2s0dAL73vSre1L7xcTPWrLHz6stRZ7569QzefU+cKEwYTM6e5fDOO/0px6msDKOrK9XmmTP5I3Ol2pgJkVziIJIVocihiSmIrh04AMydC5qw1AlC0Wld3Xjae7RgwYTgYh+3uwD33eeA1RpOWSSU7pzJbwLRbI5kO+bMYXHmTOr+LldqM+Jo2dx580KYNy+E4WGzKkFD/LlcLgbr1mXfKYmYmujegRO5I7lGeWNjgLebzLlz6du1bd06lDZrpL+fQX8/g64u4MyZxKwPvoi4sDDMK+Xw6dRSImq1Vy3Gk3yuU6eAU6f430IIQgxdT2ISuSO+HGk0B3/9+nLU1qY6mStXLAlZGsl51ABw+LBPcraI212A++93xPZNrv0RaciQCp9OLaV2iJqZJ8loeS4i/6EInIjh8TDftgKbgcuXzbwNDQIB/jG/p4eBx8OgubksRZ+ORtSHDvkkN4iIrxh48KAvpd/l+fOWlBWNQjq1WGndbDNP5HRTUjPLJZOuToSxIQdOAEhu1SXfmZjNHJYv58/4iF98Fc3COH68EP394ufhW7ildDZHNpknchf+qJXlouYCJEK/kIQyxYnKHfff75AUGVssHKqrE6WQ6uogPv10Gq/zjhKNMKPR8KJFE5JtFGqYEM0D37t3QLCpr5Rl8dmU85UriahVOpikmakJReBTGLk9LwHg6lVLrCZ4NEsjEDChrS39MT79tABr11bE8sSFmh3wkWl0KjUq5YvopcoPciWR5HM5nRasWyceJYvJI7RqeWpCDlzHqK1pyu15GeXKFQvuuGMC+/dH6mivXj1DdJ/xcTPa2orx+ecWHD7sk1T7BIj0u8w0OpXTVizTFnSZSCLx54rkgYs7b7GBiFYtT01IQtEpfFkgSldilNPzMpn4yE6qMwaA7u4CPPywHY2NAUm1T8LhSH433/eOl0d+8hMm9pno9ra2It5jRnR+ZdCim5IUeUSvXZ0IdSEHrlO00DTlON5k4iM7qYWoonR1FWD9+nLs2jUQS+8rLua3ZXSUv1N78gB38GDk/6dPF8S2C2XM9PYq99hLbXGWDVLkES3sIPQHSSg5JJ1EooWmybfIxWLhEAqlLxNbWBjG118zWLu2AkCkee9117Ho7mYQDEpzjtF+lFEp4f77HejsFP5uydKH0AC3bl15SvpjMjNnKtvDJFP5RSpS5RG17SD0BznwHCGma2qhafJN3v3sZxa8+moI771XLOjIx8fNOHOGr++kPKKD0enTBfi//xMfmOIHL6EBbmhI/DguV0iihZPkMsda7foshHEhB54jxCbYtPrR8hVXuuWWAXz0USEGBrKP9hmGA8vyDwRVVSxOny7AP/yDQzTqj34+itAAV1bGYmhI+C3AYuHQ2BgQPVc8uc6xziZLhshvSAPPEWISiVRNU4n2X3yTgUVF2csMDgeL22/nz/cuLAxjwwY/1q0r53XeyQ0jkgcvoUm7PXsG0urxoZAJBw5Y5XwNXeRYJ+e9k/MmAIrAc4YUiURM01QiMhQqruRwhHH1qqRDCLJ0aaRmCV81wPr6cTidrKDkUVLCYeXKUcGIM10+dXT7hx8W8UbjcucRKMea0CvkwHOEEhKJnDxnuceQ26YsmcLCMAIBE37602He77l16xAAYcmjokJ8Qk4onzq6vampHEeOlKTsJ3cegXKsCb1CEkqOUCLtS4nIUOgY/f2MYDuy9ESkj+jCneR0weTvuWfPACyWRLnEYuGwZ0/22RRK5UZTjjWhVygCzyHZpH15PAwuX+Yff6uqWMlZE0LRZTSH2mTiUF4eceRSik8lt05LTheM2t7UVB6z7ZVXfPjVr8owNMSgrIzFnj0DqKvL7g0AUG7yL5PjUGVAQgvIgRuQqG7Nl+/scgXR2BiQrI3zSTnxcJwJ/f0Mb5NhqcS/EQjp9ocORWyLOr4dO5RxfErlRss5Tq6zVoipA0koBkSohsns2ZEuMgcOWCVnTcRLOclSRjyBQKqkEu38XlaWXmqJ14rT6fZalA/QAj1krRBTA3LgBkRIt3Y6w3A62Ywq5O3dOxCTSoS46aZggpZ9+LAP+/f3Y/nyMcF9kvOu09mWL46PslYIrSAJxYCIZUVkmjWRrvkwEGkEzCcjpJNhQiETfvpTO5YuHceGDf60tuWL46OsFUIrKAI3IGJZEZlmTWzdOoTZs/lllHT7R2WY8nJ+BxVtj7ZmDX8Vwuix88XxUdYKoRXMli1btmh5Qr9f3kNcUlKCkZERlaxRj0zt9ngYvPCCDW+8YcXHHxfi1luDsNkSnarNxmHFinH4fGbY7WEsXDiB1tbB2ASZ2N+FsNk4rFlThCtXxlFSEgbDcLjhBhZ1deOi+9tsHH7zm5K0tUgGBxkEgya0tg7y2nbrrUG0txdicHDyGC5XEK2tgynXIBk9PSdyrr+e7JYD2a0tpaX8MiJJKDpCTvaCWFZEptkXc+cCe/cOJKTBSWXmTA5dXek/43ZbBG1TsuZHrtP4qDIgoQWiDvzVV19FZ2cnbDYbdu7cCQAYHh5Ga2srrl27hsrKSjz77LOYPn266sbmO0qsrFSCTNPgXK4QOjunpT32+fORWitCx1HC8VEaHzFVENXAly1bhn/6p39K2Hb06FHcdttteOWVV3Dbbbfh6NGjqhk4ldDLJJ6UbBC+IlpSGjsEAozqWSX5ks1CEGKIOvBbbrklJbru6OhAfX09AKC+vh4dHR3qWDfF0GIST0r1QrGBRChfG0BCeQChSU21ByS9DIQEoTYZaeCDg4OoqIh0YykvL8fg4KDgZ9vb29He3g4A2L59OxwOhzwDLRbZ++iBTOzetg04e5bDxYuTy9Frazls26bMNbh0CXjssYKE4589W4z33w9i7txJu10uDqdOpe7vdEbseO45JqWvpNtdgD17HHjrLRYHD0a2/eQniP2b7zhKEn+9XS4mrf16Yio933rAqHYLkfUkpslkgskkXIy/oaEBDQ0Nsf97vV5Zx49UmZO3jx7IxO7SUuCdd5iUSbzSUhZKXIJNm8px8WKiRn3xogmbNoUSqvqtW9ePU6fsKRUE163zwetl4XbPAMDXZDgEr7cv9v9165i0x1GS+Out5XmzZSo933rAqHZXV1fzbs/IgdtsNvT396OiogL9/f0oKyvLyripjlYZE1KkhUuXIhryjBkcWDaImTPDcLkSbZLTozEXnWSogw0xVcjIgS9cuBAnTpzAqlWrcOLECSxatEhpu6YMWmZMiDlej4f5VmKZjNIZJoh9+xI7wMipZZ6rdDpK4yOmAqKTmLt378Y///M/o7u7G//4j/+IY8eOYdWqVfjss8/w9NNP43//93+xatUqLWw1BNFJwpUrLZJanMnNmMimhZrYCsGWltIEfVzIFiVqmWeCEu3jCCKfEI3An3nmGd7tmzdvVtwYo5MaTZeIRtNyMiayidajMk06aUSOLVpHuJTbTRCpUC0UBckk/1hO6mCm+c3xaX+dndPQ1VWAvj5zii6s51oklNtNEKmQA1eQTPKP5RQ+yjS/Warz27DBj9ra9N3gcwXldhNEKlQLRUEyiWDlZExkGiFLdX5OJ4v33w9i06aQotkbSmTZ6PntgCByBTlwBcm007xUPTnT45eW8jdqmD5duIEDl774n2SU0q4z/e4Ekc9QOVkFiS8jWlXFYMGCUUllXKWUkE0+vpwysb//fTG++iq12YLPZ8Zf/lIQO5/Hw+Chh8pw6tQ0dHVZ8MUXBWhvL8SKFeOi5VyFeOEFG06fLkrYNjjIwOcz4777hDv5JCP23Y30nMRDdmuLUe2mcrIaEY2mIyu+xKNquRFqJtkffj//VEd/P/PtxGZBTMYRSiPMNONESe2acrsJIhGaxMwxWmRXCOnHyedTY6KQtGuCUA9y4DlGyGm63cotWpFS5rWnh1HF2VJ7MYJQD5JQcoyQ0/ziiwJ0dhbG/p/NopX4TJeTJwvh9aYOBtGMk7NnixNklGydLdUlIQj1MHGcUvkG0uju7pb1eaNWD5NqN58GbrWyCARSnewDD4yo0q3G5QrGBge/36F4GqEW5PtzojfIbm1RtBohoRx8EarbbUFnZ6oDV2LRilhEHO2JSRCE/iEHrgOSsyuamsp5e0sqNfFH2RwEkR/QJKYOoYk/giCkQBG4xkhZVm7EiT+tmlIQBDEJOXANkbNox0gyB5V6JYjcQBKKhuRrSdR8/V4EoXfIgWtIvpZEzdfvRRB6hxy4huTrsvJ8/V4EoXfIgWtIvmaX5Ov3Igi9Q5OYGmLE7BIp5Ov3Igi9Qw5cY4yUXSKHfP1eBKFnSEIhCIIwKOTACYIgDAo5cIIgCINCDpwgCMKgkAMnCIIwKJo3dCAIgiCUQfcR+MaNG3NtQkaQ3dpCdmsL2a0PdO/ACYIgCH7IgRMEQRgUZsuWLVtybYQYtbW1uTYhI8hubSG7tYXszj00iUkQBGFQSEIhCIIwKOTACYIgDIouqxGeOnUKhw4dwpUrV/CrX/0K119/fexvR44cwbFjx2A2m7F27Vr8zd/8TQ4tTeV//ud/sH//foTDYSxfvhyrVq3KtUmCvPrqq+js7ITNZsPOnTsBAMPDw2htbcW1a9dQWVmJZ599FtOnT8+xpZN4vV7s27cPAwMDMJlMaGhowH333ad7uycmJtDc3IxQKASWZVFXV4dHHnkEvb292L17N/x+P2pra/HUU0/BYtHfzzIcDmPjxo2w2+3YuHGjYex+8sknUVRUBLPZDIZhsH37dt0/K7LgdMjly5e5K1eucM3NzdyFCxcStj///PPcxMQE19PTwzU1NXEsy+bQ0kRYluWampq4q1evcsFgkHv++ee5y5cv59osQc6dO8d99dVX3Pr162Pb3n77be7IkSMcx3HckSNHuLfffjtX5vHi8/m4r776iuM4jhsZGeGefvpp7vLly7q3OxwOc6OjoxzHcVwwGOQ2bdrEnT9/ntu5cyf3X//1XxzHcdzrr7/OffDBB7k0U5D33nuP2717N7dt2zaO4zjD2P2zn/2MGxwcTNim92dFDrqUUGbPno3q6uqU7R0dHViyZAkKCgowc+ZMzJo1CxcuXMiBhfxcuHABs2bNQlVVFSwWC5YsWYKOjo5cmyXILbfckhJ5dHR0oL6+HgBQX1+vO/srKipiWQTFxcWoqamBz+fTvd0mkwlFRUUAAJZlwbIsTCYTzp07h7q6OgDAsmXLdGc3APT19aGzsxPLly8HAHAcZwi7hdD7syIH/b3zpMHn8+HGG2+M/d9ut8Pn8+XQokR8Ph9mzJgR+/+MGTPw17/+NYcWyWdwcBAVFRUAgPLycgwODubYImF6e3tx6dIl3HDDDYawOxwO4xe/+AWuXr2Ke++9F1VVVSgpKQHDRJo/6+15jvLmm2+isbERo6OjAAC/328Iu6O89NJLAIAVK1agoaHBEM+KVHLmwF988UUMDKR2cFmzZg0WLVqUA4uIZEwmE0wmU67N4GVsbAw7d+7EE088gZKSkoS/6dVus9mMl19+GYFAADt27EB3d3euTRLl008/hc1mQ21tLc6dO5drc2Tz4osvwm63Y3BwEL/85S9T3uz1+qxIJWcO/F/+5V9k72O329HX1xf7v8/ng91uV9KsrEi2r6+vT1f2ScFms6G/vx8VFRXo7+9HWVlZrk1KIRQKYefOnVi6dCkWL14MwBh2R7FarZg/fz6+/PJLjIyMgGVZMAyju+cZAM6fP49PPvkEZ86cwcTEBEZHR/Hmm2/q3u4oUbtsNhsWLVqECxcuGOpZEUOXGrgQCxcuxJ/+9CcEg0H09vbim2++wQ033JBrs2Jcf/31+Oabb9Db24tQKIQ//elPWLhwYa7NksXChQtx4sQJAMCJEyd09zbEcRxee+011NTU4Ic//GFsu97tHhoaQiAQABDJSPnss89QU1OD+fPn4/Tp0wCA48eP6+55efTRR/Haa69h3759eOaZZ3Drrbfi6aef1r3dQOQtLSr7jI2N4bPPPoPT6dT9syIHXa7E/POf/4w33ngDQ0NDsFqtmDNnDl544QUAwH/8x3/go48+gtlsxhNPPIHvf//7ObY2kc7OTrz11lsIh8P4wQ9+gAcffDDXJgmye/du/OUvf4Hf74fNZsMjjzyCRYsWobW1FV6vV5cpVl988QU2b94Mp9MZe/X90Y9+hBtvvFHXdrvdbuzbtw/hcBgcx+HOO+/E6tWr0dPTg927d2N4eBhz587FU089hYKCglyby8u5c+fw3nvvYePGjYawu6enBzt27AAQmTi+++678eCDD8Lv9+v6WZGDLh04QRAEIY6hJBSCIAhiEnLgBEEQBoUcOEEQhEEhB04QBGFQyIETBEEYFHLgBEEQBoUcOEEQhEH5/2aOoIJQew8/AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(y_test_predict, y_test, c = 'red')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "Ep8KmAdFCoO4",
        "outputId": "fc7f3797-5b4f-4a80-e10b-36688454240a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f16b96f4390>"
            ]
          },
          "metadata": {},
          "execution_count": 147
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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NRmEKhTSjVR5YqSTQee1aJKcdd+1Ll74cnSvsriLOnx+TG3cEAonbobIO3Ig6cubcCWAAJ41omQceb2jA7PZ2OFJ4jzh/fsKUyHR1NcYbGuKm2ytJpQ482+t4M+dOYUyhkCa0zAMXHj6sGLynVqxQnHSjlMoQPR74jxxB4eHDisFbLC3F9KJFmFqxIq4OPLxTfPnGjdd3jI/Kt4dHwiG3W/H9WmPOncI4AidNaJkHVlyb5KabcGXXLgCA58UXEfT5YhaaUkplhGbNQun27XB9/LHiNYO33Sa77kmi0S6A+F3kBQGBfft0HQkz505hDOCkiVTzwIlyuErnkhwOFDc3wxkIANXVGGlpiZQVljY2wtXfD7GwEML4+JfvcbmQ9/nnwOefJ2y/66OPUNrYGGlHuH35b78dl1OPHu0qjYT1nBTEtVsozCFJkpTNC05NTWFQ4SETXefxeCzXR3Ij1WBlJaaXLYMzEIgJ0nKvna6ujqnznrtuHZwTEwmvOV1djZHf/Aal27fHnCtUUIDpr34VzoGB68E7BUrnnOna6tWAJCH/+HHZ3+m5imGy/otmxc9StlmhjyoqKmSPcwROmgivJ1K0dy+ES5cQmjMHrv/7P8x+663Ia8Kph0Q53JGXX4ZYVYXpW2+9vgxsAnn9/SjdujUuSDsnJiBWVwP5+bIjb7GsDBAEOAIBOK9dU3XOuHMkGO3qPRKe2ddcrzx3MYCTZqLXEyltbETe+fMxvw8HaTU5XPHmm4EkARwAhCtXFM+llGq4tmYNRl5+GeUbN8qOoJXOGRZdYWLULvJcu4UAVqGQThIFaTU53PGGBoiFhUmvIxYXyx53+nwQPvsMoYKCmOOhggKMNzRcf69SOxTOKXo8MRUmXMWQjMYATrpIFKSTrb8t+Hwo3b495mFkaPZsBOfNi3vPyIsvxp0r/OAy/9QpOCcmIDm+LEp0TkxcP7fPp9gOuXNOV1dj8PXXIymeyP/PjZHw0B/+EPc7Ir0JO3fu3JnNC4qiiIkkD6dyXUFBgeX7aPr225Hf2QlhdDRyTCwshMPlQl5fHwJPPw3H9DRCbjemamsx2toaCX4l//3fmHXiRMz5HMEgpJtugujxILhkCRx1dRh64QUEly3DtXvvhdPvR8jthiRJEEZGYt87o23C6Cicfj8mH3oo5r3hdsw858z2WYkdPkt6s0IfFRUVyR5nFYoJWeGpuBqRUsH+fuR9+GFMVYlS1QQAxdx09Hulo0cxKPOhTvbeML0rRczCLp8lPVmhj5SqUJhCId2E0wtidXVcSWCimYPJtkfL6++HoHDjqHZrtdCcOapeR2RmDOCUlkTTy+Nem+LMQbnc9EyOCxdUv1dyxn/M8/r6EraZyAoYwCll4YkkBe3timtyR0t15mC4umN60SLFNkgLFyZ8b3RlyLVvfjPuda4vvuDaIWR5rAOnlCWbiDOT2tX6Zk6vl+bNk52IE8rPh5jg2fvMGunyjRtlX8e1Q8jqGMApZammRBLNHIzekzLvzJmYXHlIEGTPd62uDsLixcDgoKp1sRXXF/f5UL5xI9fTJstiALcwoxb1T2cxJbmZg3knTsD9gx/E1HtHc4oiJMSWAU5XV+PKrl0og/p1seXuAGYucsX1tMmKkpYRDg4OYt++fRgZGYHD4YDX68V9992HsbExtLa24vLly5g7dy62bduGOSqe7LOMMDk1ZU2pLGikNblri4WFEG+9NeGO7tFfOKGiIuR3d8etRSInJAgI3nFHzLk9Hg+CN/LwM01s2BD3ZRG59qVLcPp8smudyL3P6qxQImc0K/RR2otZCYKARx99FDU1NZicnERTUxO+9rWv4dixY1i+fDnq6+vR0dGBjo4ONNyYokz6SzUPraWYlMiNGm9hfBxCb6/iju6Czwf3978ftz6KGk5RRLC6Oj4op5DKib4DKN+4UTa3zpw4WU3SKpSysjLU1NQAAGbPno3Kykr4/X709PSgrq4OAFBXV4eenh59W0oxjF7UP9Ua7+Lm5rSCd9isv/41rlwx3XWxuZ422UVKOfCBgQGcPXsWS5YswejoKMrKygAApaWlGI2aMh2ts7MTnZ2dAIA9e/bA5XLB4/Fk2Gx7U9NHQnU1IDPj0HUjvZCWs2ch7NwJx4ULkBYuvF7psXhx4rb6/bLHZ/n9Me1wnT6tqgkzc95hzitXUNDejtl/+Qum29vhWrsW2L0b0vvvw/Hpp1++v6YGrt27E/dBuu+zIP57S87KfaQ6gF+9ehUtLS3YvHkzCmas8OZwOOBwyO9i6PV64fV6Iz8Hg0HT55uMpioHvnUr3MePx+fAt26FmEb/RnZ0j94Y4fjxpDn1UrcbBTLHr7rdGIlqx/xQSFU7HFAO4gDgGBuD6z/+A8HeXgwWFUF47bX46paiIiBRH6T7PguyQn7XaFboo4w2dAgGg2hpacE999yDu+++GwBQUlKC4eFhlJWVYXh4GMUKS3CSPrRe1D/dnLraGu+pFStiNndIJNlu9M6JietT6W9sqZZOzp/raZMdJA3gkiRh//79qKysxAMPPBA5Xltbi+7ubtTX16O7uxsrV67UtaEUT8sglG5OXe0XyZVdu5D/9ttwTk5q0l6lqfR2ZFS5KJlf0gB+5swZ/P3vf0dVVRWefvppAMBDDz2E+vp6tLa2oqurK1JGSNaVyYM9NV8kYlUVhg4fjqv7lhwOONJYEFNpKr3dqK11p9zE5WRNyIicnBZ15TNHiuMNDSg8fDhm5AggZrQ+ee+9KHvqqYQbGIcEAU5RjGmX0nKydlPa2Ki61l2OFfK7RrNCH3FTY4vT+zY605y63BfA7NdfhyMYjPwcHjnODDyX77oLRXv3wtXfD+HMmZgReniX+MLDh2PaVXZjKr3dGV0uSubGAG4B2bqNziSnLvcQNDp4A8oPRaOvGz1jMvpLZGTVqrTaZXWsWadEGMAtwMhZl2opjRTjXqfioahZ/p/MQG2VD+UmBnALsMJttNqdcKw4ckw1faVlukvrclGyFwZwEwn/w3f5/Sh1uyP/UI2+jVYTkJRW/ItOo1hx5Jhq+kqPdBfvSkgJq1BMIlEVCABTrTyodO2Z+etIFUqCkWO6o9VsVQ6kWgWSadWI1qxQYWE0K/QRq1BMLlme26jb6FTy73IjxUQPH61Q45xq+soK6S4lnDCkPb37lAHcJJL9w9fiNjqdD5NeAUnw+eD+z/+MW5fbbA9nU97P06JVI1b4MrWabPQpNzU2Cb3/4ae6EXG67VKzW33kgy2zJjcgv3SsUeR2uZ+Zy4/+f3aMjyNYWZnw9WaU6E6L0pONPuUI3CT0LhfTc7GqyMNXmYk4ciMOubZECy8da4YRYLIqENlnBBUVmFy/Hs6xMctUjVg59WNW2ehT0wfwXMnLRQeKWX4/rkZVoWhBr8Wq5AJYNLkvCbU142ZJpyRKX8l+MZ4/j+m778bQgQPZaJ4mrJr6MbNs9KmpA3iu5eXCgcLj8cSspa3JuXVarCrZaBqI/ZIQfD44z51Lek2595qRXUaunDCkvWz0qalz4MzLJaYm3xymJpebVhtUjKbDXxKJct+h2bMTvtes7DJyDd9pTWzYgGurV2NiwwbbDpSyJRt9au4RuE1GN3pI9e5Erxl9yWZgRn9JJBqth4qKIJaVxeybKfcFozTZySh2GrlywpD29O5TUwdwu4xu9JDOQ0k9ShHHGxriAliooADTX/0qxBuBLJIvTzBadw0MYHL9ekzffbfiF8zML60CGJ9S41R3MpKpA7idRjdaM+LuRGnUL7fcq+xdQJLRunNsLOGDP7Mu6sWRKxnF1AHcLKMbM1bCGHF3ohRACw8fVhXA5L6QoyVrO1NqRLFMHcAB40c3Zq2EMeLuJFEAVfMlF/5CLm5uRn53N5zXrqXUdqbUiGKZPoAbzcy37dm+O1EKoKE5c+K+5Ga9+Sau1dXhyq5dMW0Sq6owfOCA4sYNiaTypWXGuyYirTGAJ2Hm2/Zs350oBVAAcV9yzmvXMPutt+A6c0b2biWdtqud7GTWuyYirZm6DtwMUrltT6Uu2wiZtk+prtUZCCi+R+u6/XDgD771FkZeflk2IHP+AOUKjsCTUHvbbvZRX96JEyh/9NGY3d/TaZ/cyDlZdUm271bMfNdEpCWOwJNQO5vKzKM+weeD+wc/iAnegHbtG29ogFhYqPj7bD9k5MNOyhUcgaugJl9r5lFf0d69MSsERtNiXe/S7dsVzx/Kz4djfByCz5e1OxHOH6BcwQCuETOP+hLNgMy0fUrT40NOJ5yhUORhZv4772D61lsh3nyz/tUyJpk/QKQ3BnCNmHnUp1j+V1Cg22JWzlAo9ueJCeSfOgWcOpWVZwNGzx8gygbmwDVi5tXc5FYiFAsLMXTokO6LWckxy7MBIqvjCFxDZh316ZlSkLvzCBUUxD0wnckMzwaIrI4BPEfo9eUi9+Uw3tCA0u3bE270YIZnA0RWxwBOGZP7cggHdbl9Ms3ybIDI6hjASRfRQT2ddU+IKDkGcEpJOotEmfXZAJHVMYDbQLZW3jP7cgFEuYYB3ARmBmDs3g0UFal+b7KgqlWAN+vSukS5igHcYHIBWHr/fQivvaYqyCYLqlqOms28XADlrlxe+z1pAH/llVfQ29uLkpIStLS0AADGxsbQ2tqKy5cvY+7cudi2bRvmzJmje2PtSC4AOz79VPWoNllQ1XLUbOblAig35XpaL+lMzDVr1uDnP/95zLGOjg4sX74cv/3tb7F8+XJ0dHTo1kC7y3RUmyyoajlqlpvRyZJAMpKZVwHNhqQB/LbbbosbXff09KCurg4AUFdXh56eHn1alwMyHdUmC6qZnH/mBhAATLtcAOWmXE/rpZUDHx0dRVlZGQCgtLQUo6Ojiq/t7OxEZ2cnAGDPnj1wuVzweDzpXNaedu+G9P77cHz6aeSQVFMD1+7d6vrJ44F09CjEnTvhuHAB0sKFkHbuRNnixZmd/+xZ5D3ySMz7Zr//PqbffBM4cgTA9Q9PWUr/s9rh50gdu/eTUF0NHD8ed9xVVaX6/9vKfZTxQ0yHwwGHw6H4e6/XC6/XG/k5GAxicHAw08vaR1ERhNdei5no4tq9G4NFRYDafioqAm48n4gIv1fm/IEdOyAmOX/ps8/ipqjgDVzPzQeffdYUFScej4efIxXs3k/C1q1wHz8etwqof+tWiCr/v63QRxUVFbLH0wrgJSUlGB4eRllZGYaHh1FcXJxR43LdzIkuHo9HffBO4/xq5PqtKVlDrq/9nlYAr62tRXd3N+rr69Hd3Y2VK1dq3S66wagSKVackFXk8kzfpAG8ra0N//znPxEIBPDjH/8YDz74IOrr69Ha2oqurq5IGSFpT/D54P7+95F3/nzkWN7//i/8//M/mgTxRF8O2dygIpfreIky4ZAkScrmBaempkyfbzJaOCdX9thjmP3WW3G/n1y/HsMHDmR0Dbn62enqavkZnDremqpphxwr5C3NgP2UnBX6SNMcOGXHTb29KR1PhZoJPtm4NeX0fKL0cUu1HGWWh5RmaQeRFTGAm9jUihUpHU+FWR5SmqUdRFbEAG5iV3btQrCyMuZYsLISV3btyvjcZpkWb5Z2EFkRc+AmJlZVYeiPf9TlQaKW9bOCz4fi5uZIbn5qxQpc2bVL1blyvY6XKBOsQjEhKzwVDxN8PpRv3AjXF1/EHJ+uqNCs3FGOlfrISOyn5KzQR0pVKEyhUEaK9u6NC94AkHf+fM6sCEdkFAZwyohSFQnAShIivTGAU0aUqkgAVpIQ6Y0BnDIS2LEjrlIGuJ4DZyUJkb4YwCkj4UqZyfXrIXo8ED0eTK5fr+sDTCK6jmWEpJrSolNiVVXGa7MQUeoYwEmVXN88lsiMmEIhVXJ981giM2IAJ1W46BSR+TCAkypcdIrIfBjASRUuOkVkPnyISapw0Ski82EAJ9VyefNYIjNiCoWIyKIYwImILIoBnIjIohjAiYgsigGciMiisr6lGhERaSPrI/CmpqZsX9Jy2EfJsY/UYT8lZ+U+YgqFiMiiGMCJiCwq6wHc6/Vm+5KWwz5Kjn2kDvspOSv3ER9iEhFZFFMoREQWxQBORGRRWVuN8NChQzh58iRcLhfmz5+Pxx9/HIWFhQCA9vZ2dHlrefMAAATOSURBVHV1wel04rHHHsOdd96ZrWaZzunTp3HgwAGEQiGsW7cO9fX1RjfJcIODg9i3bx9GRkbgcDjg9Xpx3333YWxsDK2trbh8+TLmzp2Lbdu2Yc6cOUY311ChUAhNTU1wu91oamrCwMAA2traEAgEUFNTgy1btsDlyt1FSMfHx7F//36cO3cODocDP/nJT1BRUWHdz5GUJadPn5aCwaAkSZJ06NAh6dChQ5IkSdK5c+ekp556SpqampIuXbokNTY2SqIoZqtZpiKKotTY2ChdvHhRmp6elp566inp3LlzRjfLcH6/X/r3v/8tSZIkTUxMSE888YR07tw56dChQ1J7e7skSZLU3t4e+Uzlstdff11qa2uTdu/eLUmSJLW0tEjvvPOOJEmS9Lvf/U46evSokc0z3EsvvSR1dnZKkiRJ09PT0tjYmKU/R1lLodxxxx0QBAEA8JWvfAV+vx8A0NPTg9WrVyMvLw/z5s3DggUL8Mknn2SrWabyySefYMGCBZg/fz5cLhdWr16Nnp4eo5tluLKyMtTU1AAAZs+ejcrKSvj9fvT09KCurg4AUFdXl/N9NTQ0hN7eXqxbtw4AIEkS+vr6sGrVKgDAmjVrcrqPJiYm8K9//Qtr164FALhcLhQWFlr6c2TIvVRXVxdWr14NAPD7/Vi6dGnkd263OxLcc43f70d5eXnk5/Lycnz88ccGtsh8BgYGcPbsWSxZsgSjo6MoKysDAJSWlmJ0dNTg1hnr4MGDaGhowOTkJAAgEAigoKAgMnDK5X9bwPXPTnFxMV555RX09/ejpqYGmzdvtvTnSNMA/vzzz2NkZCTu+KZNm7By5UoAwJ/+9CcIgoB77rlHy0tTDrh69SpaWlqwefNmFBQUxPzO4XDA4XAY1DLjnTx5EiUlJaipqUFfX5/RzTElURRx9uxZ/PCHP8TSpUtx4MABdHR0xLzGap8jTQP4L37xi4S/P3bsGE6ePInnnnsu0klutxtDQ0OR1/j9frjdbi2bZRkz+2JoaChn+2KmYDCIlpYW3HPPPbj77rsBACUlJRgeHkZZWRmGh4dRXFxscCuNc+bMGbz33ns4deoUpqamMDk5iYMHD2JiYgKiKEIQhJz+twVcv6MtLy+P3PGvWrUKHR0dlv4cZS0Hfvr0afz5z3/GM888g/z8/Mjx2tpavPvuu5iensbAwAAuXLiAJUuWZKtZpnLLLbfgwoULGBgYQDAYxLvvvova2lqjm2U4SZKwf/9+VFZW4oEHHogcr62tRXd3NwCgu7s7cpeXix5++GHs378f+/btw5NPPonbb78dTzzxBJYtW4YTJ04AuD6AyuXPU2lpKcrLy3H+/HkAwD/+8Q8sWrTI0p+jrM3E3LJlC4LBYKQ8Z+nSpfjRj34E4Hpa5W9/+xucTic2b96Mu+66KxtNMqXe3l68+uqrCIVC+Pa3v43vfe97RjfJcB9++CGee+45VFVVRe7cHnroISxduhStra0YHBy0XvmXjvr6+vD666+jqakJly5dQltbG8bGxrB48WJs2bIFeXl5RjfRMJ999hn279+PYDCIefPm4fHHH4ckSZb9HHEqPRGRRXEmJhGRRTGAExFZFAM4EZFFMYATEVkUAzgRkUUxgBMRWRQDOBGRRf0/suXplfTJi1wAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualising the Polynomial Regression results\n",
        "plt.figure(figsize = (18,8))\n",
        "plt.scatter(y_pred, Y, color = 'blue')\n",
        " \n",
        "plt.plot(y_pred, lin2.predict(poly.fit_transform(X)), color = 'red')\n",
        "plt.title('Polynomial Regression')\n",
        " \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "ionYcWqqEHGo",
        "outputId": "bf823328-7134-40a4-ae63-bc4d1fee9052"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Correlation heatmap"
      ],
      "metadata": {
        "id": "VAGoU4qyzfjd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "correlation_matrix = boston_pd.corr().round(2)\n",
        "# annot = True to print the values inside the square\n",
        "plt.figure(figsize = (18,8))\n",
        "sns.heatmap(data=correlation_matrix, annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "-QE5QipAyYOm",
        "outputId": "e252b0b0-aeb9-4e34-e881-f757c03958f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f16b9794c10>"
            ]
          },
          "metadata": {},
          "execution_count": 149
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x576 with 2 Axes>"
            ],
            "image/png": 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zZiedP/iVAc2qFotZcICEjCwC3BxzXwe4OnDr7gz+vX67mMCzXx5g4tYTxN8td4MgDxqX9aLDJ3vouHY3LSv4UNnbxWq5F0bt64ty6552lXgLtU/+dvUXx45d0R8+AIDh8gXsGzUFBwdU7h7Y1Q9F4+dX5Dk/LK8AH4t2lRyfjFegT4Gxr26YwTtH1pCVoePw3XYVe/46oR2bANCkawu8gwr//2INKndvTCl5xytTalL+41XZyqg9fTGetVy2rPYtAyYTjkNex2ncW9i17WWVnB9WaasrAO9AH5Ji8+orKT4R74D8ZXpuwots/vhHsnX59yWd+3XlrV/eZeSSMbi4235/IYqXEtEJv5/4+HgmTZrE0KFD8fT0vO8S81atWhEVFUVUVBStWrUqNE4UPZVGTbf3RhO9dhupMbce/IHiRKXC4ZlhZH/7sa0zsSqVVoO9pyu/d32dk3O+oMmqsbZO6cFUKhx6DyX7u8LrSh1YAYeeg8ja+F6hMcWZSqOm+3ujObJ2G6nXLNtS7SdbEVivMgc/+tlG2f1z+u0/kDb+JXRfrMLxyb4AKCnJ3BnzPOlTh6H7dAUuY2aAk7ONM/133uk3j4lNh6G111KzZV0A2r7Uka/nrmNyy5F8PXcd/ReNtHGWD2ZUTMQkp7P6hVYs7NGIOVuPcSfLPHMX6O7EpsFh/DS8PZtPXiMpI+sB31Z8PF7Jj5/7t+LrPs1oXt6b1yPMs3MxKZlcvp3BtgGt2DagNQevJ3M01rYrFv4ph7AOaKvXQPeteZlvztHD6A/vx3PpB7hNfp2cs6cwldAbJi3r9yavNB2K1t6OWnfb1SevfUDYS515ffMiHF2dMOQUj0HWQqlUOHQfSPbmtfm3aTRogmuR9cXb6FZMQ1u3OZqqxWsw8mGVirq6q1LtYAIrBnJw2/582379bCtjHh/BpC7jSUm4Tb+Zg2yQYTFlUormXwlTPNZePkD58uU5cOBAgdv+uib8zp07zJw5k8OHDxc6y121alWuXbuGvb09ZcqUue/vjIiIICIiAoC5E4f9twKUYiH9wqnXJwyA+BOXcAvKGyV0C/QmPb7gk5SOCwdz+0o8R9dss0qe/4RyOwk7r7zZALWnL6bb99zUysEJdZmKOL+6GACVuxdOo2ahWzHLfHO2YiR4YAcqvWiun9vHLuFUJm92zjHIG12cZf3o4m7jFFRwjC42mdgt5tnklOiLoJiw93FDn5RW1MUolJKahN09s6lqL19MqX+rq6CKOI9bBNytq+Gvo/toDkrMeVSePjgNm0nWp0vNlx/YUGi/cOo/n9eW3Mv4cOPuNrdAb9IKWaLcaeFgbl+O58gnlm2pYqs6tHi5B18+Ow+j3vYnNMrtRNQ+/rmv1T5+FqsY/i5n3w6cB5vvAYIhB1O6uYNnvHwO5WYsmqByGC+dK9Kc76dt30483iccgMvHL+BdJm/f5xXoQ0p8wZdGARiyczi+/RAhHZpwZs8JWvRuy8bZ5hPvwz/vo9/CEUWb/AP4uzkSn5Z3fePNNB3+98wgg3l2vG4ZT+w0asp6ulDR25WY2+nUvWd1jb+bE1V93Tl6LZkONe9/zLUGfxdHbt4zo38zPRu/u8vo/+LpZJf785O1y/LuXvOlOTsv3aJeoAfO9ubTplYVfTgRf4eGZWx7TbiSmIja75525euHkpS/XdmFNMLp+b6kvjYWcvKWOes2foZu42cAuL42E+ONa/k+a03t+nbm8T7tAbh8/KJFu/IO9OZ2fOE3mDRk53Bs+yFCOzTh9J4TxF+MZVm/uYB5uXP9sIZFm/wDmO4ko/LMO16pPHzyH68CK+A04k3zdjdPHAdMI2vdfEwpSRgvnTYvQwcMZ4+gLlsF44U/rFqGe5XGuurUryvhz3cA4MKJC/iUyasvn0Bfkm9alql6wxpUrl+VD/asQqPV4OHjwayNbzLr+RmkJqbmxkV8+StTPplhnUKIEqNEzITXrVuXnJyc3E4xwNWrV0lKymsM7u7uvPjii3z//ff3/a4XXniBPn36PPB3hoeHs3DhQhYuXPjvE/9/4NiGCD7tMp1Pu0znwrYj1O5tvhYtKLQK2WmZZCSk5PtMq4lPY+/mxM5Zn1k73YeiXP0TtX8ZVD4BoNGibdIGw4l7RjmzMsmY+BwZ0/uTMb0/xstni2UHHODy2u3sDJ/GzvBpxP1ymArPmm9a6NWwKoY0Hdl/q5/shBQM6Tq8GlYFoMKzjxG/zbwsLu6Xw/i2qg2AS+VAVHZam3bAAZSr51D73VNXDR/PX1dT+pDxxkAy3hiI8crZ3A44Ti44jZhN9o9rzSc3Nha9IYL1Xaezvut0zv96hDoP0ZZaT3waBzcnfptt2Zb861Sk44JBfDd4GZlJtr92H8B48SzqwLKo/QJBo8W+RTtyjuy1iFEHls39WRvaHGO8eRhC5eYBKvPhSu0fhDqwLMrNOOslX4DIT7cxp+sk5nSdxLFfD9H8qTYAVA6thi4tk9RblvXl4OyYe42kWqOmXrtGxF80ly81IZnqzc1tq2bLuiRcse2AUJ0gT2KSM7iRkkGOUWHb6Ru0qRpoERNWPZDDMeZj8O3MbK4mp1PO04Wbd3Rk5ZivzbuTpSf6ehKV/nbjM1upE+BGTGomN+7ozOU6f5O2wZbLXu9dnr7r8i2CvcxLSAPdHDly4zYGRSHHqHA0NoVgL9uvxjCcO4umTDnUAYGg1eLQph36/VEWMZoq1XAdO4E7s6diSr3n71KtRuVmvtmeplJltMGVyTly2Jrp57Pj01+Y1XUSs7pOIvrXg7R8qi1gbleZD9Gu6rdrSNzdduXmYy6bSqWi+8tPE/n5dusVpADKtfOofYNQefmbj1chrTGevucyqaxMMmb1J3PBcDIXDEeJOUfWuvko1y9iOBeNOrAC2NmDWo2mch2Um7YdMCmNdbVtwxYmdX2FSV1f4dCv+2nT2zwwXi20OplpGaQkWA6G//rZLwxvOpDRrYcx8+mpxF6OZdbz5s72vdePN+3UnGt//u3mvv+fKUrR/CthSsRMuEqlYuLEiaxbt44ff/wROzs7/Pz8GDBggEVckyZN2LRpE2fOnCn4i4DQ0NAizvbfmfTGQg5FnyAl5Q7te73EqMF96d29k63T+kcu7zhG5bAGDN69lBydnm0TV+Vu67t1Hp92mY5roDfNx/Yi6fwN+m4xj/YeW7+dPzZG2ijrAigKWV+twHnsvLuPvfoVJe4q9t37Yrx6HuOJ/MuO7uUybz0qR2fzQbZBC3TLp+e/s7oN3Iw4RkD7EDrsfxuDLpvo8R/lbguLmM/OcPPdnI9P+YSG744wP6Jsx3Fu/nYMgKtfRtLw7eG0i1yEojdwdOxKm5TDgqKQ9fVKnEe/CSo1Oft/RYmPwb7bSxhjzmP8o+AVNAD2j3dH7VcG+y59sO9iHpjTvT8DU3pqoZ+xlkt329LQ35di0OnZek9b6r9lHuu7mttSyzG9SLpwg/4/m9tS9IbtnNgYSdtpfbB3dqTHCvMlA2mxSXw3ZJlNypJLUdCtW47L1MWgVqOP3Ipy/QqOTw/EcPlPDEf24tDxSbT1GoHBgJKRRuZK8yCotlYDHJ8ZCAYDJpNC5pq3MWXYdgDoXn/sPEq9sFDm7XoPvU7Pukkf5G57fcsS5nSdhL2zAy+vnozW3g6VWsWf+06x6/NfAdgw5SOef2Mgaq2anOwcNkz9qLBfZRVatZopHesz8qt9KCYTPetXoKqfOyt+P0PtIE/aVguiZbA/+y7f4qmPf0OtVvFKWB08nezZF5/Ash2nUAEmoF+zqlTzLwZPUsBcrsmP12DUj9EoJuhZO4gqPq6sOHCR2v7utA3248vj19h1JRGNSoWHo5bZ4ebBkfAq/hy6nsyzX5r3KS0r+NAmuBhcP60YSV/5Dh5vvgUaNVm/bsEYcwXnvoMwnDuL/sBeXAaPQOXohPu02QAYbyWQNnsaaLR4vGW+DMeUmUHaknnF6uZGJ3YepX5YQxbueh+9LptPJuU98nPWliXM6joJB2cHxq6ektuuzu47SeTddtWsR2va9e0MwNFtB9izaYdNypFLUcj+4WOchr5x9xFlv6HcvIZ9xz4Yr1+w7JD/nS6DnN2bcRq7BADj2SP5rhu3pVJXV8DRHUcIDWvMe79/iF6XzQcT8y5ZW7LlbSZ1feW+n+87tT+VagdjMsGt6wl8NK14PbLWlkwlcOl4UVCZTCXk9rM2lJNY+HMBS6rlDfM/mqo0GNb14e50W5L89oPt72xdFNo/nX9mt6Rbubl01tWwljceHFQCTdpn+5v/PGrL3yg+z+R+lEzp6bZO4ZHL3FK8bkr6qLx2phgMTjxiy58pPoMTj9LYTRpbp1Ak0kzF7wk1j8Kmqz/aOoVHIvvi/Sez/i2HKs2L5HuLSomYCRdCCCGEEEIIUcKVwKXjRaFEXBMuhBBCCCGEEEKUBjITLoQQQgghhBCi6Mk14YDMhAshhBBCCCGEEFYjM+FCCCGEEEIIIYpeMXoKgy1JJ1wIIYQQQgghRNGT5eiALEcXQgghhBBCCCGsRmbChRBCCCGEEEIUPXlEGSAz4UIIIYQQQgghhNXITLgQQgghhBBCiKIn14QD0gl/KMsbvm7rFB65sUfn2DqFItEldKStU3jkxmGydQpFovePpW8nvLHhNVunUCS6/a6xdQpFImJxTVun8MjNnH7B1imIh5SMn61TKBIzPVJtncIjN/JrN1unUCTea51s6xSKhMbL0dYpiPuR5eiALEcXQgghhBBCCCGsRmbChRBCCCGEEEIUOZNJnhMOMhMuhBBCCCGEEEJYjcyECyGEEEIIIYQoenJjNkA64UIIIYQQQgghrEFuzAbIcnQhhBBCCCGEEMJqZCZcCCGEEEIIIUTRk+XogMyECyGEEEIIIYQQViMz4UIIIYQQQgghip4ijyiDUtgJP3jwIJs2bbJ4LyYmhsGDB7N69WoGDhxIly5dAFizZg1VqlShbdu2NsgUwmb3JTgsBIMum18mrCLh5BWL7VpHe7qvHItnRX8UReFSRDS7F35lk1z/jRnzl/F71EG8vTz54bMPbZ3OPzZ69kiatmtKti6Lxa8u5cLJC/liln69GG9/b7Kz9ABMeXEqKUmpPPFSN3r2747RqJCVoWPZlHeJOR9j7SLkU+fN/gS0D8Go03Ns3EpS/7iSL8ajfjAh745A42jPzd+OcWrGeovtlUd0o86sl9hWexj65DQrZX5/o2aPpEm7JmTrsnmrkLpacreu9FnZAEx9cRopSan0HvoUnZ/vhNGokJqUwtKJb5NwI8HaRbBgF9oU56FjQK0me/vPZH37hcV2h849cOjyJChGTFk6Mla8hXLtKio3d1wnz0FbtQbZO34hc9W7NipB4cbPeZkW7ZqRpcti3iuLOXfyfL6Y9zYtwzfAh+y7dTW+z2ukJKUwdtYoGrYMAcDByQEvHy861+5h1fz/LupCHIu3HUNRTDwZGsyg1rXyxWw7dY2Pdp0CFVQP8GThU80BiEvNYPbmw9y8k4kKFe+98BhlPV2sXYRC9XyjP7XCQtDr9Hw1cSU3Tl3JFzNk/RTc/T1RazRcPnSW72Z+gkkxUb9rMzqOfxr/qmVY3nMm1/+4ZP0CFKI0luuFNwZRLywUvU7PmonvE3Pqcr6YV9ZPx9PfC7VGw7lDZ/hs5mpMikL5WhXpO28Yjs6OJF6/xarx75KVrrNBKSw5tWyM9+RRqNRq0r7fSuonluc/jg3r4f3aSOyrVSZh8jwyI3bnbvMaPwTnx5uBSo1u/xGSF62wdvqF6jtrMA3CGpKty2bVxPe5erLwv6FXVk/Fv0IAUzuOB6Bp1xY8+cpzlKlajlk9JnP5j4vWSvu+tPWb4NT3ZVCr0UduIXvzlxbb7dt3x6FDT1AUTFk6MtcsQ7lxFQB1+co4D34FlZMLmBTSZo6EnBxbFMOCplYjHJ8eDmo1OXu3od++qcA4bUgrnIZMJ2PxOJSY82hqhuLQYwBo7cCQQ/YPn2A8d9y6yYsSodR1wps2bUrTpk1zX0dERLB7924aNGiAh4cHW7ZsoUOHDmi1ti16cFgDvCoF8snjEwgKrUL4vAF80XNWvrjDq37m2r4zqO00PPPlNCq1rc+VyBPWT/hf6NW1A/SNGuoAACAASURBVC/07sG0uW/ZOpV/rGlYE8oGl6X/YwOpFVqTcfPHMKbHuAJjF4xdxLkTlh2JHT/s5H+f/QxAiw7NGfn6cKb2nV7ked+Pf/sQXCsHsqPFK3g2rEq9RYPZ03Vmvrh6iwZxfMLHpBy9QLMvJuPfrgEJO8wHEMcy3vi1qUfm9VvWTr9QTcKaUDa4DAMfG0TN0JqMnf8yY3uMLzB24dhFnP9bXV04eYGXu/1MdlY2T/TtxpDpg5k/aoE1Ui+YWo3z8PGkvTEBJekW7m99hP5gFMq1q7kh2bsiyP7lJwDsmrbEedBo0me/hkmvR/f5GjQVg9FUCLZVCQrVol0zygWX5bnWfanTsBYTF4xnWPfRBcbOfnkeZ0+cs3hv+ay8E+mnBz5JtbpVizTfBzEqCgu2HuXDl9oQ4O7Ei6sjaFOjDFX8PHJjrial8UnUGdYNbIe7kz3JGVm522b8cJAhrWvRokogmfocVCqVLYpRoJptQ/ALDmRh21eoEFqV3vMGs7xX/v3Fp6PfJftuh63fyvE06NacY5v3Ef/nNdaPWMbT84dYO/X7Ko3lqtc2lIDgIKa2HUPl0Gr0mzeMN3tNzRe3cvSy3M71qJUTadKtBQc3RzFg4Ui+mr+BcwdO0/qZdnQZ1pPvl220djEsqdX4TBtD/PDJGG4mUuaL98mM3EfOpbzBbEN8ArdmLsGj/zMWH3VoUBvHkLrceHo4AEHr3saxcX2yDtv+3KlBWEMCgoOY2GY0VUKrM/DNYczqNaXA2Madm5GVaTkYcv1cDO8OX8yg+SOske7DUalxGjCOjAWTUJJv4TZ3JTlH9+Z2sgH0e39D/9tmALQNW+L04kgyFk8BtRqXUVPJWLkAJeYSKld3MBSDWVKVGsdnR5H5/nRMKYk4T3oHwx/7UeKvWcY5OGHXtifGy2dz3zKlp6L7aDam1GTUQRVxGj2XjBn9rFyAYk6uCQdK+TXhsbGxfPPNN4wZMwaVSoW7uzv16tUjMjLS1qlRpWMjTn+7B4C46Is4uLvg4u9pEWPI0nNt3xkAlBwjCSev4BbkbfVc/63GIfXwcHezdRr/SsuOLdj+bQQAZ6LP4urugrf/w/+/z0zPzP3Z0dkRk8n0yHP8pwI7NeLa1+aZgpSjF7Bzd8bhb39zDv6e2Lk6kXLUPJN87evdBHZunLu9zpx+nJn7Bdi+OLnMdfUbAGejz+Li7vqP6ur4vhO5M65njp7FL9C3SPJ8WNpqtVDib6DcjAODAf3uHdg3bW0ZpMv7+1I5OOXVR3YWhjN/YNLrrZfwP9C6U0t++WY7AKeOnsHNwxWff1BX9wrv1Y6IH3Y8yvT+sZM3kinv5Uo5L1fsNBo61alA5J+xFjHfHb3Ec42r4u5kD4C3iyMAF2+lYlRMtKgSCICzvR1OdsVnXLxOx0Yc/s68v4iJvoCjmzNufp754v7qqKq1GrR22tx9XcLFWG5dirNewg+pNJYrtGMT9n4XCcCl6PM4uznjUUCZ/uqAa/5WpoDgIM4dOA3AqT3HadSlmXUSvw+HujXIuRaL4UY8GAxk/BKJc9uWFjGG2JvknL8Myt8OSCYTKgc7VHZaVPZ2qLRajEkpVsy+cA07NGXPt5EAXIw+h7O7Cx7+XvniHJwd6TykBz++943F+7EXbhB/KTZfvC1pqtREuXkD5VYcGA3o9+/ArpFlXVkesxz566ClrdcEY8wllBjzagBT+p1i0UFTV6qOkhiLKSkejAYMR39HW79FvjiHJ/qi374JkyHvmKtcv4QpNdn8c9xVVHYOYOOJv2JHUYrmXwlTajvhBoOB5cuX069fP3x9806qe/bsyebNm1FsXFmugV6kxSXlvk6LT8Y1MP+O+C8O7s5UDg8lJuqUNdL7f8830JdbsXmzvbfiEvEN9CkwdtLSCXz4ywpeHPeCxfs9+ndnw561DJ02hA9et/1SOMcgb7Ji8/7mdHHJOP5tUMcxyBtdXHLu66y4pNyYgE6NyIpL5s5p2y+rv5dPoI9FXSXG3cKnkLqauPRVVv7yQb66+kvn5ztxKPJwkeT5sFQ+vhgT85bDK0m3UPvkHxhw6NoLjw+/wGnACDI/Ln7LzgviF+hLQmxe2RLibhU66DFt2Wus+3UVA8a/lG9bQNkAgsoHciQqushyfRgJaToCPZxzXwe4O5GQZjlzdTU5jatJafT/5Df6rokg6oK5A3c1KR03Rzte/TqK51b9yrLtxzEWo5MIjwBvUu7ZX6TGJ+MRWPCAydANU5h15EOyMrI4seWAtVL8V0pjubwCfEi+p0zJ8cl4FbIPfHXDDN45soasDB2Ht+wHIPb8dUI7NgGgSdcWeAfZdiASQOPvizE+b79uTEhEG/BweWWfOEPWoeOUj/iKChFfodt7mJzLxeO45RXoTXJsYu7r5PgkvAPy//09PaEPWz/+Cb0u25rp/Stqb1+UpHuOWcmJqL388sXZd+iJ27LPcOozDN369wHQBJUDwGXyIlzf/AiHJ56zTtIPoPbwQbmdV0/K7URUHpZtSl2uCiovP4ynDhX6PdqQVhivXQCDochyFSVXqe2Ef/XVV5QrV46WLS1H4wICAqhWrRp79uyxUWb/nEqjptt7o4leu43UmOKzDFjA/LGLGNphBK/0nkC9pnXp0Ds8d9tP6zfTr/VAVi9Yw4tjC+70lRQaJ3uqjevFn4sLviaqJFg4dhHDO4zk1d4Tqdu0DuG921tsb/9kO6rXr8amD78p5BuKl+wtP5A64gV06z/C6dnStdRt9pj59Asfwqgnx9GgaX06P93BYnt4zzAif/7d5oOpD8OomIhJTmd1/zAWPtWcOf87zJ0sPUZFITomkVc7NODzIeHcuJ3OT8ev2Drdf+XjfguZ03QUWnstVVvWtXU6j0xpLNeyfm/yStOhaO3tqHW3TJ+89gFhL3Xm9c2LcHR1wpBTsjsM2vJlsAuuwLWOfYjp8DyOTUNwCC059VehdiX8KwZyZFvxHfj5N/TbfyTt1ZfQbVyFY6+7g6tqDZrqdcn8YB7pc8Zi17g12jqhtk30YahUOPQeSvZ3Hxcaog6sgEPPQWRtfM+KiZUQJqVo/pUwpXJ9xKlTpzhw4ACLFi0qcPuTTz7JsmXLqFUr/w10/hIREUFEhHk5cv7xvH8npF849fqEARB/4hJuQXmjam6B3qTH3y7wcx0XDub2lXiOrtn2iDIRBenRvztd+5hv2nfu+Dn8yuTVvF+QL4nxSfk+k3T3PV2Gjh0/7KRGSI3cZex/2fljJOPmjSnCzAtXaWAHKrzYDoCUY5dwLJP3N+cU5E3WPbPeAFlxyTjdMzvuGORDVlwyzhUDcK7gR5sdi+6+783jv85nd5cZZN9KtUJJLHXv352ufToD8Off6so3yC+3Xu5lWVeR1AipQcTdZeyhrUPpM+Z5Jj4ziRy9bW8IY0pKROPrn/ta7eOHkpRYaLx+9284j3jFGqn9K0/170mPF7sBcObYn/iXySubf5Aft+Lzly3x7nuZGTq2//AbtUNq5S5jB3MnfOn05UWc+YP5uzkRn5q3zPLmHR3+bk4WMQHuTtQt64OdRk1ZL1cqersRk5ROgLszNQI8KeflCkBYzbKcuJ7EkzY8/2zZtwPN+pj3F9eOX8Lznv2FR6A3qfHJhX0UQ3YOp7YfoW6HRpzf80eR5/pPlMZytevbmcf7mAcSLx+/iPc9ZfIO9OZ2AfvAvxiyczi2/RChHZpwes8J4i/GsqzfXMC8NL1+WMOiTf4hGBMS0QTm7dc1/r4Ybha+H7yXS7tWZP9xBpPOfP8FXdQhHBvUJjv6ZJHk+iDh/TrT9nnzQOKlExfwLpM3o+8d6EPyTcu/v2oNaxBcvwrL9nyIRqvB3cedaRvnMP/5162a98NSkhNR+9xzzPL2Rbld+IRRzr6dOA8cDx+BknwL49kT5mXoQM6xA2gqVcdwyrarnJTUJOy88upJ7eWLKfWeNuXghDqoIs7jzOdEKncvnIa/ju6jOSgx51F5+uA0bCZZny7FlBhv7fRFCVHqOuHp6emsWLGCcePG4eTkVGBM2bJlKVu2LEeOHKFq1YJv7BMeHk54uHlWc+kX+ZdD/hvHNkRwbIO5gxbcLoTQ/h04+9M+gkKrkJ2WSUZC/muWWk18Gns3J7a9tvqR5CAK99P6zfy03nzjkGbtmtJzQA92/hhJrdCaZKRlkpxgeaBUa9S4urty5/YdNFoNzds34+ge84GjbKUy3Lhivm6rWfumXL9yw7qFuevK2u1cWWvuvPiHhxI8qCOxP+zFs2FVctIyyf7b31x2Qgo56To8G1Yl5egFyj/7GJfXbCPt7DV+rZt3I5j2h5azu9N0m90dffP6zWy+W1dN2zWl54DuRP4YSc3QmmSkZTxEXTXNrasqdaowbuEYpr00g5Qk6w8o/J3h/FnUQeVQ+weiJCdi/1g7MpbOtYhRB5VFiTP/Tdk1boESd90WqT6U79b/yHfrfwSgRftm9B7Qi4gfd1CnYS3S72SQ9Le60tytq9S7ddUyvDmHdx/N3V6hSnncPNw4edj2l+bUKetNTHI6N26n4+/uxLZTMcx/srlFTFiNsmw9GUOvkGBuZ2ZzNTmNcl4uuDnakZatJzkjC28XRw5eTqB2mcIvSbKGvZ9uZ++n5v1FrbBQWvXvyLGf9lIhtCpZaZmk3bLcX9g7O+Dg4kTarRTUGjW12oVy+eDZgr7apkpjuXZ8+gs7Pv0FgPphDWnfvwsHfoqicmg1MtMySf1bmRycHXF0cST1bpnqt2vIuYPme864+biTlnQHlUpF95efJvLz7fl+n7Vln/oTuwpl0ZYNxHAzEZfObbk19eFumGmIT8Dtqa6g+RJUKhwb1Sf18++KOOPCRWz4hYgN5rpq0K4RHfp3Yf9Pe6gSWt1cVwmWEzC/fbaN3z4zT7r4lvNjwifTi20HHMB46SzqwLKo/e4es5q3I+ODeRYx6oCyKDfNxyxtSHOM8eafDScO4fDE82DvAIYctLUakL3V9qvRlKvnUPuVQeUTgCklCW3Dx8latzgvICuTjCl9cl86jVtI9vdrUGLOg5MLTiNmk/3jWoyXTtsg+xKgBKxis4ZS1wnfvn07d+7c4eOPLZeItGrVyuL1U089xeTJk62ZmoXLO45ROawBg3cvJUenZ9vEVbnb+m6dx6ddpuMa6E3zsb1IOn+DvlveBODY+u38sTHSRln/M5PeWMih6BOkpNyhfa+XGDW4L727d7J1Wg/lwI6DNG3XhA171pKty2bJhKW52z78ZQUjOo/C3t6OhZ/NR2unQa3WcHTPUbZ8sRWAngN60LB1QwwGA+mp6Sx+xfZ3iE+IiMa/fQjt9r+DUZfNsfEf5W57PGIBv4eb76b7x5S1uY8oS9hxjITfjtkq5Ydy8G5drdvzifkRZROW5W5b+csHjOw8Gnt7OxZ8Ng+NnRa1Wk30nmi2fmE+KRo6fQhOzk7M/NB89/qE2Fu8MWiWLYpiphjJXPUObrPeMj+i7LctGK9dwemFQRgunCXn4F4cuz2FtkEjMBgwZaST8U7eyanHqo2onF1QabXYN2vNnVkTLe6sbkv7fjtAi3bN+DrqM7J0Wcx/Ne+kZt2vqxjQcRh29vYs+2IxWq0GjUbDod1H+Onzn3Pjwnu2I+LHnbZIPx+tWs2ULg0Z+fnvKCYTPUOCqervwYqdJ6ldxou2NcrSskog+y7e5KkVv6BWq3glvAGezg4AvBLegOGf7sIE1AryonfDyrYt0D3O7IymZlgIU3a9Q44um68m5e0vXtmygLe7TsXe2ZFBqyeisbdDrVZxYd9p9n1uHmiu26kxvWYNwNXbncGfvEbsmSt83G+hrYqTqzSW68TOo9QPa8jCXe+j12XzyaS8e5DM2rKEWV0n4eDswNjVU9Da26FSqzi77ySRn/8KQLMerWnX17yy6Oi2A+zZZNsbHgJgVEha8D6BKxeAWk3aD9vIuXgVz1H90Z86R+aufdjXqU7A27NQu7vi3KY5xlH9uPHUUDK278axaQhlv/kYTCZ0ew+h27Xf1iUC4PiOI4SENeSt31eg12Xz8cT3c7e9uWUpM7pOuO/nG3VqRr/ZQ3DzdmfC2ulcPX2ZJf3m3vczRU5R0K17D5fJi0CtQb9rK8qNKzj2HoDh8jkMR/fi0LEX2rqNwGhAyUgj80PzDLIpM53srZtwm7sSTCZyjh/AcKwYLMNXFLK+Xonz6DdBpSZn/68o8THYd3sJY8x5jH8UnqP9491R+5XBvksf7LuYO+q692dgSrf9IH+xIZ1wAFSm4nDb5mJuaYVHMxNenIw9OsfWKRSJLqEjbZ3CIzcup+TcEf+feM+u4MsvSrKNDW3/bN2i0P2wxtYpFImIxa0eHFTCzJx+wdYpiIeUjO2fhVwUZnqUvs7G6ykl80kvD/Je6+Jx1/hHTePlaOsUioTb+1tsncIjkbX70yL5XsfH+hbJ9xaVUjcTLoQQQgghhBCi+DGZisGz4IuBUnt3dCGEEEIIIYQQoriRmXAhhBBCCCGEEEVPrgkHpBMuhBBCCCGEEMIaSuAzvYuCLEcXQgghhBBCCCGsRGbChRBCCCGEEEIUPVmODshMuBBCCCGEEEIIYTUyEy6EEEIIIYQQoujZ6JrwY8eOsXbtWhRFoX379vTq1cti+7p16zh16hQAer2e1NRU1q1bB8Bzzz1HhQoVAPD19WXy5Mn/OR/phAshhBBCCCGEKJUURWHNmjXMmDEDHx8fpk6dSuPGjSlXrlxuzIABA3J/3rp1K5cvX859bW9vz5IlSx5pTtIJf0jDuibaOoVHrkvoSFunUCS2Rq+0dQqP1C91p+OvzrZ1GkXixzU9bJ3CI6X74CsmHvezdRpFIJmI+U1tnUSRqDn2R1un8Eg941aHOZN9bZ3GI/f6okTmLm9k6zQeqdFj9/N2WIqt03jkko+qWJzqZes0HjEDH00pa+skHjn9nhT673a2dRqP3Gc9FLp9X7rOm35+0sHWKTw6Nrgm/MKFCwQGBhIQEABAy5YtOXTokEUn/F5RUVE8++yzRZqTdMIfgnTAS47S1gEHpANegpTODjjSAS9BSmMHHCh1HXCgVHbAgVLYAadUdsCBUtkBB0pdBxzMZfr9fVtn8YgU0XL0iIgIIiIicl+Hh4cTHh4OQHJyMj4+PrnbfHx8OH/+fIHfc+vWLRISEqhbt27uezk5OUyZMgWNRkPPnj1p2vS/nxdJJ1wIIYQQQgghRIl1b6f7v4iKiqJ58+ao1Xn3L1+xYgXe3t7cvHmTOXPmUKFCBQIDA//T75G7owshhBBCCCGEKHqKUjT/7sPb25ukpKTc10lJSXh7excYu3fvXlq1apXv8wABAQHUrl2bK1eu/Lf/B0gnXAghhBBCCCFEKVWlShXi4uJISEjAYDCwd+9eGjdunC/uxo0bZGRkUL169dz30tPTycnJAeDOnTv8+eefhV5L/k/IcnQhhBBCCCGEEEXPBjdm02g0DBo0iHnz5qEoCmFhYZQvX56vvvqKKlWq5HbIo6KiaNmyJSqVKvezN27cYNWqVajVahRFoVevXtIJF0IIIYQQQghRQtjoOeENGzakYcOGFu8999xzFq8LuiN6jRo1WLp06SPPR5ajCyGEEEIIIYQQViIz4UIIIYQQQgghip4NlqMXRzITLoQQQgghhBBCWInMhAshhBBCCCGEKHo2uia8uClWnfC+ffvy6aefkpCQwMsvv8zAgQPp0qULAGvWrKFKlSq0bduWDz74gNOnT+Ps7Ixer6datWr06dMHHx8fi+/5S2RkJBcvXmTw4MHExsayatUqMjIyMBgM1KxZk+HDhxd52TS1G+H47EhQq8mJ+gX9tq8LjNOGtsJp+Ewy5o9BiTkPLm44DZuBpmJ1cvZvJ3vjiiLP9Z8aPXskTds1JVuXxeJXl3Lh5IV8MUu/Xoy3vzfZWXoAprw4lZSkVJ54qRs9+3fHaFTIytCxbMq7xJyPsXYR/pEZ85fxe9RBvL08+eGzD22dzkPzaBtKxbmDUKnVJHwZQdz731tsV9lrqbJ8HC71KmO4ncb5EUvRX78FgFOtigQvGoHGzQkUEye7voYpO8cWxcgn6vQVFn+7C0Ux8WSLOgzq2MRi+5Jvd3Ho/HUAsvQGktMz2bN4JABxyXeY/eVv3Lydhkql4r0RPSnr4271MhTkhTcGUS8sFL1Oz5qJ7xNz6nK+mFfWT8fT3wu1RsO5Q2f4bOZqTIpC+VoV6TtvGI7OjiRev8Wq8e+Sla6zQSksRV28yeLtJ1BMJp5sUJFBLWvki9l2+jof7T4LKqju78HCXnn1mZ6dw1OrIgirXoapnRpYM/X7mrVgMmHhj6HTZTHx5ZmcPHHGYruLqzOb/rcu93VQmQC+3/Qzc6YvpmmLRrwx7zVq1qnGmCGT2bJ5u5Wzf7CoK4ks2XUWRTHRq245BjUJzhfz67l4Ptx/ERVQ3c+NBV3qWz/RB4g6e43FP+0z7yua1mBQuxCL7Ut+2sehC7EAZOUYSE7PYs/c/py9kcT87/aQnq1Ho1IzpH0InUKq2KIIBdLWbYLjC6PM5xe/byV7y0aL7fZtn8C+fU9QjJiystCtX4YSGwMaDU4DJ6CpWM382b0RZP/8pY1KUbBn3xhInbBQ9LpsNkxcwbUC9oMvr5+Gh78nao2GC4fOsnHmakyKCYC2/TvTpl8nFKPCyR1H+X7h59YuQqFKS7v6y9DZw2gU1phsXTbvTniHSycvFho7fc1MAioEMrbDaACCawczcv5o7BzsUYxGPpy+kvPHz1kr9UKNnTOa5u2aka3LZsErizl38ny+mHc3LcUnwIfsrGwAJvSZTEpSCgBh3dsw8NX+mEwmLpy+yNyX51s1/2JLlqMDxawTfi8PDw+2bNlChw4d0Grzp9m3b1+aN2+OyWTi559/Zs6cOSxdurTA2HutXbuWbt260aSJ+cQuJsYKHT6VGsc+o8l8dxqm24k4T12O4cR+lLi//W4HJ+za9cJ46Z4TuBw9+p82oC5TEXXZSkWf6z/UNKwJZYPL0v+xgdQKrcm4+WMY02NcgbELxi7i3AnLHdiOH3byv89+BqBFh+aMfH04U/tOL/K8/4teXTvwQu8eTJv7lq1TeXhqNZXmD+Xs87PRxyVRZ8tiUrYdQne3cwrg1yccQ0o6x1uNxrtnKyrM6MeFEUtBo6bqe+O4OHY5maevoPVyxZRjtGFh8hgVhQWbIvlw9JMEeLry4pKNtKlXmSpBPrkxk3q3yf35y13HOHt3YAFgxqe/MqRTE1rUrEhmtt7ikRS2VK9tKAHBQUxtO4bKodXoN28Yb/aami9u5ehluZ3rUSsn0qRbCw5ujmLAwpF8NX8D5w6cpvUz7egyrCffL9uY7/PWZFRMLNh2nA/7tCLA3YkX1+6kTbUgqvjlDXpcTU7nk33nWNfvcdyd7EnOyLb4jg92naFheV9rp35fYeGtCa5ckTZNniC0cX3efGsGvTq+aBGTkZ5J17Z5d1z9328b+eV/vwEQez2OCS/PYNjLA6yZ9kMzKiYW7jzDyqcaEeDqyItf7qdNZT+q+Ljmxly9ncEnhy6z7tmmuDvakZyZfZ9vtA2jorDg+yg+HNaVAA8XXlz+A23qVKRKgFduzKQeLXJ//nLPSc7GJgHgZK9h7vNtqejnQUJqBi+8+z0tapTD3cnB6uXIR6XGse8YMt6ajCn5Fq6vf0DOsb3mTvZd+v070Ef+DwBtSAscnx9J5rKp2DVpA1o70mcOBXsH3OatQb9/B6akm7YqjYU6bUPxDw7kjbZjCQ6tRp95Q1jcK//5werRb+fuB4etnECjbi04vHkv1VvUoUGHxszrMgmD3oBbMRlghdLTrv7SKKwxQZXKMOLxYVQPrcHIeaOY1HNCgbHNO7dAl2E5KNx/2kA2vvMlRyOP0CisMf2nDWTGc/mPedbUvF1TygWX44XW/ajdsBavLhjHiO4vFxg79+X5/HnCctCgXHBZXny5D6N6jSU9NR1PH09rpC1KkGJ7Tbi7uzv16tUjMjLyvnEqlYonnngCT09PoqOjH/i9t2/fzp0xB6hQocJ/TfWB1JVqoCTEYUqMB6MBw6FdaOu3yBfn0KMf+m2bMBnumWHUZ2O8eMryvWKkZccWbP82AoAz0WdxdXfB29/7oT+fmZ6Z+7OjsyMmk+mR5/ioNQ6ph4e7m63T+EdcQ6uSdSWO7JibmHIMJP+4B69OTS1ivDo1IXHTTgCS/7cP99b1APBoE0Lmmatknr4CgOF2erEZxTx59SblfT0o5+uBnVZDp0bVifzjUqHxW4+co3Oj6gBcjEvCqCi0qFkRAGcHe5zs7ayS94OEdmzC3u8iAbgUfR5nN2c8/PIfwP868dRoNWjttLntJyA4iHMHTgNwas9xGnVpZp3E7+NkbDLlvVwo5+WCnUZNp9rliDwfZxHz3bErPNeoMu5O9gB4u+R1ck7H3SY5I4sWlf2tmveDdOgSxrdfbQYg+vAJ3D3c8A8ofKAguEpFfPy8ObjvCADXr8Vy9vR5lGLSpv7uZHwq5T2cKefhbK636oFEXkywiPn+5A2ebVAed0dz+/F2Lgad0785GXOL8r7ulPNxN+8rQqoQeepqofFbj12k893Z7op+nlT08wDA38MFb1cnbqdnWSXvB9FUroGSEIvpVhwYDeQcjMQutJVlUFbecVbl4Ah/HWdNJvNrtRqVnQMmg8Ei1tYadGzM/u9+B+By9Hmc3Vxwv89+UK3VoLlnP/j4ix3ZtvJHDHoDAGlJd6yU+YOVlnb1l6Ydm7Hz2x0AnIv+Exd3F7z8vfLFOTo70nNoLza995XlBhM4uzkD5v8m30wq8pwfpHWnVmz75lcATh89g6uHKz7/4Pz2iRe68f26n0hPTQfInR0XmJejF8W/EqbYzoQD9OzZk/nz59OuXbsHxgYHB3Pjxo3cGe7CdOvWjdmzZ1OjRg3q169PWFgY+puLYwAAIABJREFULi4ujyrlAqm9fFBu582+KSmJaIItl2Gqy1dF5eWH8eRB6Ph0kebzKPkG+nIrNq9st+IS8Q30ITkhOV/spKUTMBoVdv8fe/cdHUXVBnD4l91N770RSgIECCVU6SQQqkpXQQQUsIACgiBBULDQFQuCimBHBZUPUOkgSJUaqnRCgDRIL5uy5ftjQwoJgsqWxPc5h3OyO3fW9zozd+6dW2bDbla8/23x972HP8zAp/ujsrZm8mMvmyTu/xobP08K4ktuagUJKTg2q3PnNFod2sxcVB7O2AcHgF5P6LevYu3pSsra3SQsWWPK8O8oOT0bP/eSByK+bk6ciE2sMG18aibxKRm0qhsEwJXkdJztbZn46S9cT83kgdAgxvduh1Jh/meT7r6epJY6XqmJqbj7eZJxo/xNfOJX06nVpDYndhzl0Pr9AMSfv0bTbi05uvkgLXu1wcPf/L3HyVl5+LnYF3/2dbbnRHxamTRXUg2VleFfGaYXPNehPu1CfNHp9byz7SSze7dgf2zZiqq5+fn7EH+95JxLjE/C19+H5KSbFaZ/uF8PfvnfJlOF968l5+Th62xX/NnX2Y6TiRll0lxJywHgyZUH0On1PNs6hHY1zX/OlZacmYOfW0kvo6+rIyfiKj6X4tOyiE/NolXtgHLbTsQlU6jVEWQhvapW7l7oU0vyoUu9gTKkXrl0Np17Y9N9IFYqFTnzJwNQeOh3VE3b4vzeKqxsbFF/9zH6nCyTxX43br4epMWXXEdpiSm4+XmQWUE5OParV6jZpDandsRwpKgc9An2p3arevSePIjC/EJWz/qaK8fvPETalKrKdXWLp58nNxNKjtXNxBQ8/TxJSy5bxg+Z9ARrl64hX122V3/Z60uZ+fUbPDXNMGVuSr9JJon7r3j5eZFcpn57Ay8/L1IqqN9OXTgZrU7HzvW7+Oq9bwAICq4GwOI176NQKvj8na84sOOgaYIXlYL5a5t/wdfXlzp16rB79+67pr1bD+qtYaaRkZG8++67tG7dmtOnTzNt2jQKC8v3Mm/dupXo6Giio6P/WfB/h5UVto88Q/5Pnxr/v2Ums8fN4+muzzFhwEs0atWQrgOiiret+/JnhrV/imVzljNk3ONmjFJUSKXEqVV9Lr7wHqf7voJ7jweKe8krk02HzxEVXqe4ka3V6Th6MZ6J/TqwYtIgrt/MYF1R73FlsnDYW0xo9TQqG2vqt20IwGcvLybyiR689vM87Jzs0RRqzBzlvdHqdMSlZrNsSAfm9m3JG+uPkplXwKrDl2gf4otvqUZ8ZdW7fw/Wrl5v7jDuK61eT1x6Lp8ObMGcno14c+spsvIsc/TWvdgUc5GoxrXKPZC7kZnL9O938PqjHVEoLGPqyr0q2L6O7CnDyPthGbYPG6ZLKGvVA52OrAmPkTV5KLbdB2Ll7W/mSP+ZRcNmM6XVs6hsrAktKgeVSgUOrk7M7zuN1bO/ZtTiCWaO8u+patdVrQa18Kvhz/5N+8pt6zm0F8vfWMbI1k+x/I1PGbug4mmNlujNsXN4MuppXuj3Ik1aNaL7wK6AYYRatVqBjBs4kTfGzOLlBRNxcjFup1+lodMZ518lY9E94QD9+vVj4cKF1K9f/y/TxcbG0qiRoWFgY2ODRqMpnh+enZ2Ns3NJb5mHhwedO3emc+fOvPTSS1y9epXg4OAyvxcVFUVUlKGhmPVcj3+VB11aCtbu3sWfFW5e6NNKDbWxtUcRUAOHifMBsHJxx37MTNRLZhoWZ7MwvYc/TK/BhgXzzh07h3dASd68/b24mVh+GFFK0XfqHDXb1/xGaHho8TD2W35bu4Pxs8YaMfL/roLEFGwCSqZh2Ph7UpiQWmGagoQUUCpQujigSc2iIOEmWftPo0k19JCkbz+CY6NgMnefMGkeKuLj5kRiWknPTVJ6Nj6lertK23jkHFMfiSj+7OvmRGg1b6p5GYaZRjYO4XhsIv3KzxQxic5De9BxcBcALh+7iEep4+Xh50FaBdfVLZr8QmK2HKRp15ac3n2cxIvxLBz2JmAYmt44splxg78HPs52JGaWzANMylLjU6onCAy94w0DPLBWKgh0c6SGhxNxqTkcu57K0asprDpyGXWBhkKtDgcbJeMjG5o6GwAMG/kYg4YOAOD40VMEBPoVb/ML8CUpoeIe1vphdVEqlZw89meF2y2Rj6MdSVklQ6+TsvLwdiw7LNbHyY5Gfq6G4+bqQA13R+LScwnzczV1uHfk4+JIYnp28eekjBx8XCuuEG+MucTUfmWHdGfnFTD2s4280KMFjWv4GjXWv0OfdhMrj5IpGgoP77L1i9sU/vEb9kPHowasW3dGc+IgaLXos9LRXjiFqmZdCm8k3HF/Y+s0tDvtisrBK8cu4h7gBZwFwN3Pk/TE8r2Qt2jyCzm25SBNurbkzO4TpCWmErPpQPFv6XU6nDycyU41f29/Vbiueg17kK6DuwNw4fh5vEqNuPLy8yyu990S2qwetRvXZume5ShVSlw9XXlr5RymPzaVyAFd+HTGUgD2/LKbF+aNM11GSuk3vA8PDekFwJmYs/iUqd96czOx/AinW9+pc9RsWbOd+uH12PTjFm4k3OD0kT/RarQkXE3k6qVrVKtVjTPHzpomM8LiWXRPOEBgYCCBgYEcPny4wu16vZ7169eTlpZGeLhhpdMGDRrw+++GeUQFBQXs27ePsLAwAGJiYtBoDL1C6enpZGVl4eFx73M8/gndlbMofAKw8vQFpQpVy05oju8vSZCXS86kx8iZNpycacPRXj5jsQ1wMPRcP9djDM/1GMOeTXuLe7XrN61HTlZuuaHoCqUCF3fD0D2lSknrLg8QezYWgMCaJcP9HujSimux102Tif+Y7JgL2NXyxzbIBytrFR592pO2ueywqPTNB/F6JBIAj4faFDeyM3bE4FC/Bgp7G1AqcGnTAPW5a+X+G+YQVt2XuBvpXL+ZQaFGy6bD5+jUKLhcusuJqWTm5tGkVkkvT1gNX7Jy80nNMsyBPHDuKsF+xi0L/sr2rzcys9dkZvaazNHNB2jbPwKA4KZ1yM3KLTcU3dbBrnieuEKpoHHnZiRcNFw/txYgsrKy4uEXBrJjhflX3A4LcCcuLZvr6TkUanVsOn2NTnXK9rpF1g3gUJxh+F9abj5XUrOp5ubAnD4t2fhCDzY8350JXRryUKPqZmuAA3y1fCW9Ih6lV8SjbF6/nQGPPQxA0xaNycrMuuNQ9N4DerJu9UZThvqvhfm5EJeey/WMXMNxO5dIREjZefmRIT4cumYo99PUBVxJyyHQ1bJGLYQFeRN3M5PrqZmGsiLmIp0alF8T5nJyOpnqfJrUKMljoUbLxC+38FDzOnRtXL58MSft5bMofQKx8vIDpQrrVhEUHt1bJo3CN7D4b1XjB9AmGcpvXWoyqvpFK8Tb2KEMro/29gVjTWzn15uY3etlZvd6mWObD9C6f0cAajWtgzort9xQdFsH2+J54gqlgoadm5FYVA4e23yQuq0NdT+fWv4orVUW0QCHqnFdrf/qVyb0HMeEnuPYv2kfkQMMU0frNg0lJyu33FD0jd9s4KmWw3mm3UimDniZ+MvxxYuvpSal0rC1oSOtcbsmxMfGmzYzRf735VpGdnuWkd2eZdemPXQf2A2ABs3qk5OZU24oulKpwLVU/bZtVGsunTWs4L9r4x6atjVcX67uLgQFVyM+znwPuCyK9IQDlaAnHKB///5MmTKlzHdff/01P/30E/n5+dSpU4cZM2YU93w/9dRTLF26lA0bNgDQsWNHGjRoAMCxY8f4/PPPsbExLPzzxBNP4OZm5BULdTryVi7BYdysoteAbEaXcAWbh4eivXIebekGeQUcZ32JlZ2DoQHfpA3qD6aVX1ndTP7YfoBWnVvy1e7PyVfns+Cld4q3fbxxCc/1GIONjTVzv5mNylqJQqHkyO4jrP/WcGz6PNmbZu2bodFoyM7IZv4Ey19xfPKMuRw8epz09Ey69H2CMSOHMuDh7uYO669pdcROW0bot69hpVRw4/ttqM9dJXDyIHKOXSR980GSv9tGyAfjabJnMZr0bC6MXmjYNSOHhE/WEbZ+Pughffth0rdV/FDM1FRKBdGPRDB6yRp0ej19Wjegtr8nS37dR4PqvkQUNcg3HjlHj2Z1y6x+rlQomNCvPc9+uBq9HuoH+TCgrfkadqUd/+0IjSObMXfnhxSo8/lscsmrCWeuX8DMXpOxdbBl3LJoVDbWWCmsOLPvJDtWGBaReaB3ezoPNYzgObLpD3b/sN0s+ShNpVAQ3a0Jo7/fg04HfZrUoLa3C0t2nqaBvzsRdf1pG+zDvstJ9P9kKwqFFRM6N8TNghcjAti+ZReRXTvw+6FfDa8oG/tq8bb1O1aVWRX9oT7deXLQmDL7N24axtKv3sPV1YWo7p2YED2aru36myz+u1EpFEyJrMeY/x0xXGNhgYR4OrFk3wUa+LgQEeJD2xqe7LuSQv+v9qC0suLFDnVxK1pcz1KolAqi+7Zl9Kcb0On09GkVSm0/D5ZsOkSDat5EhBkWaNxYtCBb6bJi87FLHLmUQHpOHusOGlZAfuOxCOoFelb43zIpnQ71ikU4vjTXUL/YtRFd/BVs+w5HG3sOTcw+bLr0QdWgGWg16HOyUS8zjLor2LYWh5GTcXprGWBFwe5N6K6VfwWYuZz87SgNI5vxxs4PKFAX8FWpcvCV9fOZ3etlbBzsGL3sZVQ21igUVpzdd4pdRQ8d967aztD5Y3h109toCjV89dJic2WlnKpyXd1yePshWkS24ONdn5KvzmfRpPeKt7274QMm9Pzrnu3F0YsYNfMZlEolhfkFLIleZOyQ72r/tj9o0/kBvtvzNfnqPOZMXFC8bfnmTxjZ7VmsbWx4+9t5qFQqFEoFh3cd4ZcVhulGB3YcpGWnFnz122fotFqWvLmUzDTLWRzQrCrBIsymYKWvDMtRm9m/HY5uifr9WjUP+4ajH5k7hPvuSGPzL1BiDI2/iLp7okrm+WfM39g1hsWv1zZ3CEZRb2LVmpsN8Odbne6eqBKyCqxh7hDuu4L/Va5REfcq+jfzjSgylnemWM4UhPtp8BzLeehyP6Vp1XdPVAn9fn2buUO4L9QrXzfK79o/NsMov2sslaInXAghhBBCCCFEJVcJh44bg8XPCRdCCCGEEEIIIaoK6QkXQgghhBBCCGF80hMOSCNcCCGEEEIIIYQp6KURDjIcXQghhBBCCCGEMBnpCRdCCCGEEEIIYXwyHB2QnnAhhBBCCCGEEMJkpCdcCCGEEEIIIYTx6fXmjsAiSCNcCCGEEEIIIYTxyXB0AKz0enkccTdr/B43dwj3nZKqedh9FPnmDsEomh1/29wh3Hcnm00wdwjiP+60xtncIdx33lqNuUMwinwrK3OHYBQpSqW5Q7jv6ulzzR2CUSTp7Mwdwn3X2PeGuUMwCoWyatZxaxzZau4Q7gv15y8b5Xftn5pvlN81FukJF8LCVcUGuBBC/NdVxQZ4VVUVG+BCmI30hAOyMJsQQgghhBBCCGEy0hMuhBBCCCGEEML49NITDtITLoQQQgghhBBCmIz0hAshhBBCCCGEMDq9rmounPd3SSNcCCGEEEIIIYTxycJsgAxHF0IIIYQQQgghTEZ6woUQQgghhBBCGJ8szAZIT7gQQgghhBBCCGEy0hMuhBBCCCGEEML4ZGE2oBI0wtPT0/niiy+4ePEiDg4OuLm5MXz4cN555x3eeeed4nSrVq3Czs6O3r17A6DVannmmWfo3LkzQ4YMKU53+PBhVq5ciV6vR6PR0KtXL7p27WqSvDR6axi+XcLRqgs4Mv5jMk7Elkvj2rgWzd5/FqWdDUnbYjgx/avibcEju1HryW7odTqSth7l1JvfmSTuuwl7a3hxvmLGf3THfIW//1xxvk5N/7LM9uDnHiRs5hNsavAMBalZJor8zlwjmlLjzRFYKRQkf7eVhA//V2a7lY2KkA/G49goGE1aFuefe4eCazcAsK9fg1rznkPpbA86PSd7vYw+v9Ac2fhbps9eyO97DuDh7saabz42dzj3zLlTU6rNfBorpYKU77eQtOSnMtutbFTUeHcCDo1C0KRlEfv8AgquJWNlrSJozhgcGoeATs+1mcvI3n/STLkoqyrmCapuvm7X/M2hBHYOR6POZ9+EpaRVUCY2mfIItR5pj42rI6vqjDJ9kPcgdNZwvLs0RavO5+S4j8iqIB/OjWvR8IPRKO1suLHtKGenGcp234cfIGTSQBzrBvJHj+lkHrtk4ujvrCres1q9MZRqRefc7glLST0ZWy5N0ymPUHug4ZxbUbfknGvwTE/qDo5Ap9GSl5rFnolLybmeYsLoK+Ya0ZSape7D8RXch2vfdh/Ov3YD22reNNn5AepL8QBkHz7H5ehPzJGFCjV6axg+Reff0bvUBRV2NiSXqguGThpAjSGRFKRkAnB6ziqSt8WYMvwK2bdtgceUMVgpFGT9bwMZn60ss92uWSM8Xh6NTZ1gkqfMInfrruJt7i+OwqHjA2ClQL3/MKnzlpg6/Luya9sSj0ljQKkg+38byPzi+zLbnYcMwKlfL9Bq0aalk/L622gTks0UbSUgC7MBFj4cXa/Xs2DBAho0aMCiRYuYN28egwcPJiMj4677Hj9+nICAAPbv349eb3jiotFoWLp0KVOmTGHBggXMnz+fsLAwY2cDAN8u4TgF+7G1zURiJi2jybwRFaYLnzeCmJeWsbXNRJyC/fDp3AQAr3YN8Ovegt+6RLO908uc/+hXk8R9Nz5F+dreZgLHJn1Ko3kjK0zXaN4Ijr30KdvbTCiTLwC7AA+8OzUit6gRa3YKBTVnP83ZIW9xPGI8nn06YF+nWpkk3oOj0KRnc6zd8yR8+jPVpw8zbFAqqL1oPLHRn3Ai8kX+HPgq+kKtGTLx9/Xt1ZWPF75l7jD+HoWCoLee5eLw1/mzywu49+6AXZ2gMkk8H+uKNiOb0x2fI3nZOgKmDjd8P7gbAGe6jefCkBkEvvoUWFmZPAvlVMU8QdXN120COjfBpZYf69q9xB8vL6fVnCcrTHdtyxE29pph2uD+Bq8u4TjW8md36xc5PelTGsyv+EFBg/kjOf3SUna3fhHHWv54dQ4HIPvMVWJGLCRt3xlThn1XVfGeFVh0zq1u/xL7piynzV+cc788WP6cSz0Zy889X2Vd11e48usBWkwfbOSI74FCQa3ZT3NmyFscu8N92KfoPhxz+30YyLuSxImuL3Gi60sW1QD36RKOY7Af29pM5Nhf1AWbFNUFt7WZiONt59/FpRvYEfUKO6JesYgGOAoFnq+MJWnMK1zrNwrHHpFYB1cvk0STmMyNVxeQvWF7me9tmzTALrwh1wc+y/UBT2MbFopdi8amjP7uFAo8powleewrxA8YachfrbL5Kzh7gcQnxpDw2DPkbt2F+/hnzBSsqEwsuhF+6tQpVCoV3bp1K/6uZs2aeHp63nXfPXv20LNnT7y8vDh37hwAeXl5aLVanJ2dAbC2tiYgIMA4wd/Gr3tz4lYZnvylHbmAtYsDtj5uZdLY+rihcrIn7cgFAOJW7cK/RwsAag2P4vyidegKNAAU3Mw0Sdx349e9OVeL8pX+F/mydrInvShfV1ftwq8oXwBhbwzjzze/BQsZneLUtDZ5sQnkxyWhL9SQunY37t1blUnj3r0lN3/4DYDUX/bh0r4RAK6dwsn98wq5p2MB0KRlV5onfi3CG+Hq4mzuMP4Wh/A65McmUlB0rNJ+3oVrt7LHyrXbA6T8aLjxp6/fg3M7ww3erk4QWXuPA6BJyUCbmYND49qmzUAFqmKeoOrm63bVujfn0o+7AUg5chEbV0fsbisTb23LS043dXj3zLtHC+J/+B2AjMMXULk4YHNbPmyK7lkZhw1le/wPv+Pd01C255yPJ/digmmDvgdV8Z5VvXtzLhadczeKzjn7Cs65G0cuoq7gnEvc+yfavAJDmsMXcPD3MG7A9+D2+3DKHe7DN4ruwyml7sOWzL/U+XevdcGrpeqClsi2YSiFV+PRXE8EjYacjTtwiGhbJo0mPonC85fLD0PW67GytcbKWoWVjTVWKhXaFMsqF20ahqK5Fo/meoIhf5t2YB/Rrkya/EPH0OflG/4+8SdKHy9zhFp56HTG+VfJWHQjPC4ujlq1alW4LTExkcmTJxf/27JlS/G2goICTpw4QYsWLWjXrh179uwBwMnJiRYtWjBmzBjee+89du3ahc5EB83e3x11fGrx57yEVOz93cunSag4jVOwH56tQ+m4/g3a/+9V3MKDTRL33dj5e5AXXzJsTZ2Qit1tN3A7f4/b8pVSnMa3e3PyElLJPB1nmoDvgY2fJwWl8lSQkIL1bXkqk0arQ5uZi8rDGfvgANDrCf32VRpuehv/MX1NGfp/juE43Cz+XJCQgrVv2Yd01n4eFN5Ko9WhzcpB6e6M+s/LuHZtBUoFNkE+2DcMwTrA/DfOqpgnqLr5up2Dnzu5pcqP3PhUHPzc/2IPy2Tn70FeqSHJeXco2/NKl+3x5dNYmqp4z3LwcyenVJ5yEv75OVdncCeu/3bsfoX2j1V0H7a5x/swgG11HxptfpsGP72Jc6v6Jov7buxuqwuq71AXLH1dGc7RkjTBI7oRsX0u4e8+g7Wro/GDvguljxfaxJJRIdrkm6h87618zj/+J3kHjxG0dSXVt65EvfcQhZct59oCUHl7oUksGVquTb6B0ufOnYFOfXuQt+egKUITlZzFzwm/Ez8/PxYsWFD8edWqVcV/HzlyhLCwMGxsbHjggQf46aefePLJJ1EoFDz33HPExcVx/Phxfv75Z44fP87zzz9f7ve3bt3K1q1bAWht/OzclZVKiY2bE7/3eg23piG0XDqOLa1eNHdY/4rS3oY64/uy/7HZ5g7l/lEpcWpVn1O9XkanzqfeytfJOX6RzN0nzB2ZuE3Kyq3Y1Q4i9Jd3KLh+g5zDZ0Bb+Z6kllYV8wRVN1+icqmS96xSgvu3w6tJMBsGVLJpSbcpSE7jaMtn0KRl49gomLqfR3M8YjzabLW5Q/vXYr/YwtmFq0EP9ac8QtjMIcRMWGrusP4xVVAA1rWqc7WbYQqE3yfzsG3akPyjlrvmx19x7NUF2wahJI6aaO5QLJveQoYRmZlFN8KDgoL4448//vZ+u3fv5uzZs8WN66ysLE6ePEnjxoahjdWrV6d69ep07NiRF154ocJGeFRUFFFRUQCs+eLxfxR/rae6UnNIJABpMZewDyh5imt40p5WJr06IQ17/4rTqONTiV9veLKWfvQi6PTYeDpTkGL6BWFqPtWV6kM6G2KJuYRdQMkTQfvbekbgVo9+6Xx5kpeQikMNXxyqe9Np+7yi7z3ouHk2u3pOJ//G3ef9G0tBYgo2pfJk4+9J4W15upWmICEFlAqULg5oUrMoSLhJ1v7TaIoW6knffgTHRsHSCDcSw3EoeeJu4+9JYVLZBYUKE1OxDvCiMLHoWDk7ok0zHJ/rbywvTldn9TzyL8ebJvC/UBXzBFU3XwB1n4wipKisT425hEOp8sMhwIPcxLQ77WpRgp7qRuAThrI9M+YidoEl+bi91xvK947bBZRPYwmq4j2r3vAo6hadczdjLuFYKk+O/n//nPPvEEbjcb3ZOGBW8bQ3c6roPlxwj/dhAE1BNgA5Jy6RH5uIXXAAOccvmi4DpdR6qis17lAXtL9DXbD0dWU4Rw1p8ktNRYxdsZ3WX082Zuj3RJt8E6Wfd/FnpY8XmqSbf7FHCcfO7cg/8Sd6dR4A6j0HsWvSwKIa4ZobN1H5+RR/Vvp4o00uv3ChXatmuI58nMRRL0Gh5S/Ga1aVcOi4MVj0cPSGDRtSWFhY3CMNcOXKFVJS7rxqZ25uLmfOnGHJkiUsXryYxYsXM3LkSHbv3k1eXh6nTp0qThsbG4u3t/cdf+vfuvz5Fn6LeoXfol4hYeMhqj/aAQD3ZrXRZKnJv21uVn5yOppsNe7NDHMdqz/agcRNhwFI2HgIr3YNAHAM9sPKWmWWBjhA7Odb+D1qKr9HTSVx4yGCivLl1qw2hVm5FearMFuNW1G+gorylXXmKpsbPse2luPY1nIceQmp/N7tFbM2wAGyYy5gV8sf2yAfrKxVePRpT9rmskOL0jcfxOsRw03V46E2xY3sjB0xONSvgcLeBpQKXNo0QH3umsnz8F+Re+w8trX8sSk6Vu4PdyBjy4EyaTK2HMBzoKEC7tarXfHcYis7GxT2tgA4d2gCWi1556+aNgMVqIp5gqqbL4BzX2xlQ9dpbOg6jasbDxM8sD0Ans1CKMjMtei536Vd/Xwz+7tEs79LNMkbDhHwSEcAXJvXRpOVS8Ft+Sgoume5NjeU7QGPdOTGxkMmj/tuquI968yXW1nXbRrruk0jbtNhQorOOe+ic66iud934hFWgzZzR7DtqYXkpVjGejO334c9K7gPp20+iHfRfdiz1H1Y5eECCkP11ra6L3a1/MmLSzJtBkq5/PmW4oXUSp9/7s1qU3gPdcGgRzuQUFQXLD1/3L9nSzLPmL9+kX/qLNbVA1EF+oFKhWOPCHJ37runfTWJydg1bwxKBaiU2DVvTIGFDUcvOHUWVVAgqoCi/HWPQL1zb5k01qG18Zj2IskvvoYurXKU98L8LLon3MrKikmTJvHFF1+wdu1arK2t8fb25sknn7zjPgcOHKBhw4ZYW1sXf9eyZUu++eYbnnzySdatW8fSpUuxsbHBzs6OMWPGmCAnkLQ1Bt8u4XTd/y4adT5HXyxZrTNy62x+i3oFgGPRn9Hs1mtRth8jqWjlyyvf7aDZu8/Secc8dAUajoz7yCRx303y1qP4dAmn8/730KrziSmVr45b5/B71FQATkR/Xvy6l+TtMZaxouedaHXETltG6LevYaVUcOP7bajPXSVw8iByjl0kffNBkr/bRsgwnEd5AAAgAElEQVQH42myZzGa9GwujF5o2DUjh4RP1hG2fj7oIX37YdK3HTZzhu7N5BlzOXj0OOnpmXTp+wRjRg5lwMPdzR3WX9PquPbqUkK+nml47dXKbeSdu4rfxMfJPXGBzC0HSFm5hRrvTaDB7x+jSc8i9oW3AbD2ciPk65mg01GYlErsi++aNy+3VMU8QdXN123it8UQ2KUJvfe+g1ZdwL5SQ0V7bpnFhq7TAGg6fRA1+7ZFZW9Dv0MfcOG7HZx4Z7W5wi7n5tajeHUJp/0f76NV53NqfMlrC1tvm8v+LtEA/DnlMxp+MBqFnQ03t8Vws6hs9+nZknqzn8TG04WmK14m6+QVjgyaY5a8lFYV71nXtsUQ2LkJ/fcYzrndE0vOud6bZ7Gum+Gcaz5tEMH9DOfcI4c+4Py3O4hZuJoWrw7G2tGOyE/GAZB9PYXtTy00R1ZKFN2H6xXdh5OL7sPViu7DaUX34dofjCe86D58vug+7NK6AdUmD0Kv0YJOz6XoT9CmZ5s3P0Vu1QWj9r+L9ra6YMTW2ewoqgsej/6MpqXqgrfOv7BXB+PasAZ6PeRevcGxycsr/O+YlFZHypwP8ftoDigUZK3ZROHFK7iNGU7BqXPk7tyHTVhdfN+dicLFCYdOrdGOGcb1/k+Ts2UXdq3CCfzxU9DrUe89iHrnfnPnqCytjtR5i/BZPBcUCrLXbaTw0hVcnxtOwelzqH/fh/uLz6BwsMd7/qtA0WrwE14zc+AWTN4TDoCVXi8D8+9mjd8/G45uyZSWsqzrfeajyDd3CPdds+NvmzsEozjZbIK5QxD/cac1lettAPfCW2v+ocTGkG+hr6T7N1KUSnOHYBT19LnmDuG+S9LZmTsEo2jsaxmv2bvfFMqqWcetcWTr3RNVArlvV/zKy3/LYdIyo/yusVh0T7gQQgghhBBCiCpCL3PCwcLnhAshhBBCCCGEEFWJ9IQLIYQQQgghhDA+mRMOSCNcCCGEEEIIIYQJ6OUVZYAMRxdCCCGEEEIIIUxGesKFEEIIIYQQQhifDEcHpCdcCCGEEEIIIYQwGekJF0IIIYQQQghhfGZ6RVlMTAyff/45Op2OLl260Ldv3zLbd+zYwddff42HhwcAPXr0oEuXLsXbVq9eDUD//v2JiIj41/FII1wIIYQQQgghhPGZYTi6Tqdj+fLlTJ8+HU9PT6ZOnUqLFi2oVq1amXRt27Zl5MiRZb7Lzs7mxx9/ZO7cuQBER0fTokULnJyc/lVM0gi/B10Gpps7hPtuwNqquTLh2uW9zR3CfXey2QRzh2AUDY+8a+4QjGJ6i2nmDuG+m/lhS3OHYBRdH//Y3CHcd3FvP2juEIzCytnF3CHcd0nz9po7BKOYle5m7hCMoJD3J/uaO4j7btR8tblDMIp0fb65QzCK9eYOoBK7cOECfn5++PoaruO2bdty8ODBco3wisTExNC4cePiRnfjxo2JiYmhffv2/yomaYQLIcR9UhUb4EII8V9XFRvgQpiNGV5RlpqaiqenZ/FnT09Pzp8/Xy7dH3/8wZ9//om/vz/Dhw/Hy8ur3L4eHh6kpqb+65ikES6EEEIIIYQQotLaunUrW7duLf4cFRVFVFTUPe/fvHlz2rVrh7W1NVu2bGHx4sXMmDHDGKEC0ggXQgghhBBCCGEKRpoT/leNbg8PD1JSUoo/p6SkFC/Adouzs3Px3126dOGbb74p3vf06dPF21JTU2nQoMG/jldeUSaEEEIIIYQQwvj0OuP8+wshISEkJCSQnJyMRqNh7969tGjRokyatLS04r8PHTpUPF88PDycY8eOkZ2dTXZ2NseOHSM8PPxf/2+QnnAhhBBCCCGEEFWSUqlkxIgRzJo1C51OR2RkJEFBQaxcuZKQkBBatGjBhg0bOHToEEqlEicnJ8aMGQOAk5MTAwYMYOrUqQAMHDjwX6+MDtIIF0IIIYQQQghhCmZ4RRlAs2bNaNasWZnvHnvsseK/H3/8cR5//PEK9+3cuTOdO3e+r/HIcHQhhBBCCCGEEMJEpCdcCCGEEEIIIYTR6c3wijJLJD3hQgghhBBCCCGEiUhPuBBCCCGEEEII4zPTnHBLU2kb4Y8++igPPfQQw4YNA2DdunXk5eXx6KOPAoYXtv/yyy8A2NvbM3z4cOrVq8cvv/xCXFxc8Yp3u3btYvfu3cUr3hmLsn5z7AY+CwoFhXs3UbDlhwrTqcLbYT9qGjnzx6OLO4+yXlNsez8JKmvQFJK/5jO0544ZNda/a8zro2nZuSX56nzenvgOF05eKJdmwar5ePh4UJCXD8DUIa+QnpLBgKf702NQd7RaHRkp6bwz6V2SryebOgtl7Dkdy/yfdqLT6enXJowR3VqW2b7gp50cPH8NgLwCDanZueyePxqAhNRMXv9uG0lpWVhZWbHouT4EerqYPA8Vce7UlGozn8ZKqSDl+y0kLfmpzHYrGxU13p2AQ6MQNGlZxD6/gIJryVhZqwiaMwaHxiGg03Nt5jKy9580Uy7+numzF/L7ngN4uLux5puPzR3OP9Z7xnBCI8MpVBewatJHxJ+KLZdmxJfROPu4oVQquXzwDGte/Qy9hd3o9pyJY/6avYZr64F6jOjStMz2BWv3cvBCPHDr2lKze9ZTxKdmMfGLzej0ejRaHYPbN+SRtv/+HZ33y+z504nq1gl1rpqxo6M5fux0me1OTo78vPHb4s8BgX78sHIt06Nn89acqbTr0BoABwc7vLw8Cale9rUp5rDn8g0W/HYanV5P34ZBjHggpFyazWcT+HjveaysoK63M3MeNBzP5386wPGEdJoGuvNBv5bl9jOXPecTmL/xiOH8axbMiA7lz6FNJ+P4ZMdJsIK6vm7MHdiWg5eTWLDxaHGa2JuZzB3Yls71q5ky/Duyb9sCj5fHgEJB9v82kPH5yjLbbZs1wmPyaGzqBHMjeha5W3cVb3MfPwr7Dq0ASF+6gtzNO00a+90MmvEUjSKbUaDO5/NJi4k7dblcmvFfTsO1qOw7f/BPVry6HL1OR1CDmjwx62msbW3QarSseHUZscfK109MaU/sTRb8ftZwXYUFMqJFrTLb152O593d5/BxsgXgscZB9G9oOM/e33OeXZdvAPB0q2C61/UzbfB3MWzmSMIjm1OgzufjSYuIPXnpjmlfWjYVn+p+TOk2HoBHXhpM866t0On0ZKZk8PFLH5CenHbH/c3l2defpWWkob678KWFXDx5sVyauSvn4uHjQX5RfXf6E9PJSMkwdaiWzcLqJuZSaRvh1tbW/PHHH/Tt2xcXl7KNnMOHD7NlyxbeeOMNXFxcuHTpEgsWLGDOnDn07NmT6Ohozpw5Q1BQEN9//z2vvfaacYO1UmD36BhyP5yGPv0mDpPfQ3NiP7rEq2XT2dpjHdEH7eUzxV/pszNQf/I6+oxUFP41sH/+TXKmDzNuvH9Dy8iWBNYK4KkOI6jXtB7jZr/AuN4vVph27rh5nD9+vsx3F05e4IUHfyU/L5+Hhj7IqGkjmT1mjilCr5BWp2PODzv4+Pl++Lo5MWTB93RqFEyIv2dxmskDOhX//d3OGM5cu1H8efrXmxnVvSVt6tUgN78AKysrk8Z/RwoFQW89y4UhMyhMSCH057fJ2HKAvPMl56DnY13RZmRzuuNzuD3cgYCpw4l9fgGeg7sBcKbbeFSeroR89RpnH5oEessvRPv26srjA3rzyptvmzuUfyw0IhyvWn4siJhA9aa16TdrJIv7vlou3Yrn3yc/Ww3AEx+9SOMHW3Ps532mDveOtDodc1bv4eNnH8TX1ZEh762mU1hNQvzci9NM7tO2+O/vdp3kzPWbAHi7OPDVuL7YqJTk5hcyYMEqOoXVwMfV0eT5uF1Ut04Eh9SkVXhXmrdswoJ3X6d750fKpMnOziGyfZ/iz9t2rubXdZsBmD61pLwb9exQGjWub5rA/4JWp2futlN8NLAVvs52DFmxh061fQjxdC5OcyUth8/+uMgXg9vgYmdNam5+8bZhLYLJ02j56XicOcKvkFanY876Q3w8NBJfF3uGfLqFTqGBhPi4Fqe5kpLFZ7tP88XIKFzsbUjNzgOgZS1fVo3uAUBGbj4Pf/ArbUIspAGkUOAxdSxJz01Bk3STgBUfkrtzH4WXSv7faxOTufnaAlyHlT0v7Tu0wqZ+beIfew4raxv8lr+Nes9B9Dm5ps5FhRpGNMWnlj/TIsYS3LQOQ2Y9zZy+r5RL98nzC8krKvue++glWjzYmoM/72VA9BP8/P4PnNwRQ8OIpgyc+gRvD5pp4lyU0Or0zN1xho/6NcPXyY4hK/+gUy1vQjzLvuqoe10/oiPqlflu1+Ub/JmcyfePt6ZQq2fUT4doV8MLJ1vLqMaHRzbDr1YAEzuNoXbTuox461le6zulwrQte7QmLzevzHe/fLKGH975DoDuTz5I//GP8dk0y3pw3iKyBYE1AxnVcRShTUN5YdYLTOgzocK0C8YvKFffFeJ2lXZOuEKhICoqil9//bXctrVr1zJ06NDixnlwcDCdOnVi48aNKJVKRo0axfLly/nmm2+IjIzE19fXuLHWrIvuZjz6lETQatAc+R1V4zbl0tk+NJSCLT+g1xQUf6e7dgl9Rqrh74QrWFnbgsoyCl2Att3asOWnbQCcOXoGRxcnPHw87nn/Y/uOFz8t/PPIGbz9vIwS5706eSWJIC9Xqnm5Yq1S0r15XXacuPPT3A2Hz9GjeV0ALiakoNXpaFOvBgAOtjbY21ibJO67cQivQ35sIgVxSegLNaT9vAvXbq3KpHHt9gApP24HIH39HpzbNQbArk4QWXuPA6BJyUCbmYND49qmzcA/1CK8Ea4uzndPaMHCujXn8GpDz1Xc0QvYOzvg7O1WLt2tBrhCpURprUJvYQ9JTsYlE+TpQjVPF8O11bQ2Oyro0b9lw9EL9GhqOM+sVUpsVEoACjRai3r+07NXF1Z99z8ADh88hqurM76+3ndMH1K7Jl7enuzbe6jctv4DH2T1j78YLdZ7dTIxnSA3B6q5OWCtVNA91J8dF5LKpPnf8as8Gl4DFztDGefhYFu87YEaXjjaWM59CuDk9VSCPJyp5uFkOP8aVmfH2etl0qw+fJHHWtbBxd4GAA8nu3K/s+X0VdrV8cfeQvJn2zAUzdV4NNcTQaMhZ9MOHCLalkmjiU+i8Pzlcg9OrYNrkHf4BGh16PPyKDh3Cft25h+FcUt4t5bsX23omb909DwOzo64VlD23WqAK1VKVNaqUtnUY+fkAICDiwPpSebtWT2ZlGG4rlyLrqs6fuy4dOPuOwKXUnNoFuiOSqHA3lpJHS8n9l65aeSI713zrq3Y9dNvAFw4eg4HF0fcfNzLpbN1sKPXqN6sWVR2NKi66Bga0tha5EP+1t1as62ovnv26FkcXRxxryCP4h7odcb5V8lU2kY4QPfu3dm9eze5uWWf2l69epXg4OAy34WEhHDtmmEIcWhoKIGBgZw4cYI+ffpgbApXT3RpJYWlLu0mVq6eZdNUC8HK3RvtqYN3/B1VeDu0Vy+ARmO0WP8uTz9PbsSX3ERuJtzA08+zwrST3pnIRxsXM2R8xe/g6zGoOwd3lK+YmlJyejZ+7iWNNl83J5LTsytMG5+aSXxKBq3qBgFwJTkdZ3tbJn76C4/N+5aFa3ahtZAVIG38PCmILzkHCxJSsPYte5ys/TwovJVGq0OblYPS3Rn1n5dx7doKlApsgnywbxiCdYB5H5b8l7j4epARn1L8OSMxFRe/ih90jfwqmlcPf0x+Th4n1v9hqhDvSXJGLn5uJT0+vq6OJGfkVJg2PjWL+NQsWtUJKP4uMS2bR97+gR5vruDJyCYW0QsO4B/gy/VricWf468n4R9w5we7/QY8yJrV68t9Xy0ogBo1qrFr536jxPl3JGfn4etc0gD1dbbnRnZ+mTRX0nKIS8vhye/2Mezbvey5fG+NCXNJzlTj5+JQ/NnXxZ7kTHWZNFdSsriSksXw5VsZ+ukW9pxPKPc7m07G0bNhdaPHe6+UPl5oEkv+32uSbqL0ubfy2dDobomVnS0KNxfsWoaj8vUxVqh/m7uvB6mlyr60xBTc7lD2vfjVNN45vIy8nDwOrzdcQ9+//gUDpw5l3t6PGPjKMFbPX2GSuO8kOTsfX6eSh1W+TrbcyMkvl27bhSQeXbGPSb8eIzHL0GNc18uZvVduoi7UkqYu4NC1NBKz88rtay7ufp5ljlVqYgruvuWP1SMvDebXT9eSry6f70cnD2HRvk9p17cTPyz8zqjx/hNefl7cSChV3028idcdOo4mvD2BRRsWMXjcYFOFJyqhSt0Id3BwoGPHjqxfX75C81fy8vK4dOkSWq2WzMzMCtNs3bqV6OhooqOj70eof83KCtsBT5O/+tM7JlH4Vce2zwjyvl9k/HiMYO64eTzbdTQTB0yiYaswogZ0KbO9S7/O1G1chx8+/tFMEf59mw6fIyq8DkqF4TLS6nQcvRjPxH4dWDFpENdvZrDuj9N3+RXLl7Jyq2EI+y/vEDhjFDmHz4DWMh4uiLKWD5vLrFZjUNmoqN22obnD+cc2xVwkqnGt4msLwM/diR8mPcK6qYP4+dA5UrIsY8js39VvQMW93f0GPMi6tZvQWciDu7vR6nXEpefw6aMPMOfBcN7cfIKsvEJzh/WvaHV64lKzWPZkZ+YObMMbPx8gU10yMu1GlpoLyRm0qe1vxijvn7x9h1HvPoD/l+/jPfcV8o+fBp3W3GH9I+8Nm8WkVs+gslFRr6jsi3iiG6ve/IIpbUez6s0vGD5vtJmjvLuOtbz49ckOrBrShtbVPXlti2H9lTY1PGlf04snfzjA1I0naOzvitJSprvdoxoNauJbw49Dmyp+QLxqwQrGtnmaPWt20m14LxNHd/8sGLeAMd3G8PLAlwlrFUbnAZ3NHZLl0emN86+SqdSNcIAHH3yQ3377jfz8kqdq1apV49KlskOIL126RLVqhsUtVq1aRYcOHejXrx9ffvllhb8bFRXF3LlzmTt37r+OUZeRgsK95GmZwt0LfUbJE0Ns7VH418Bh/DwcX/8cZc162D/7GorqdQCwcvPE/plXyfv6HfQ3E2//eZN7ePjDfLRxMR9tXExqcireASVDL738vUlJTCm3z63v1Dlqtq/ZQWh4aPG2pu2bMnjsIGaMmElhgXkrcT5uTiSmZRV/TkrPxsfNqcK0G4+UDEUHQ695aDVvqnm5olIqiGwcwp9XLaN3qCAxBZtSvdc2/p4UJpU9ToWJqSU93EoFSmdHtGlZoNVx/Y3lnO05gcujZqN0cSL/crwpw//PaTO0K+PXz2H8+jlkJafjGlAyasHVz4PMxNQ77qvJL+T0lsM06NrcFKHeMx9XBxJLjSpJysi5Y2/2xlJD0cv/jiO1/Tw4csl8ZeGIp4fw2+61/LZ7LUmJNwisVjI/OCDQl4T4pAr3C2tYD5VKybGYU+W29RvwIKt/MP9QdAAfJzuSskp62ZKy1HiX6sG7laZTiA/WSgWBrg7U8HAkLr3ikQ2WwMfFnsTMkgc3SZlqfFzsy6TxdbGnU2igIU/uTtTwdCYuteR+sPlUHJH1DNsthTb5Jiq/knuwytcLbfK9D1POWPYt8Y89R9Jz0WBlReGV63ffyYgihnbntfULeG39AtKT0/AoVfa5+3mSfpey79iWg4R3NSwG2GZABEc2Ghp8h37dR60m5p1G5eNkS1KpESVJ2fl4O5a9rtzsbbBRGc6vfmGB/Jlccv6NahnMysfb8HG/5uj1UN3dAXPqOqwns9cvZPb6heWOlYefJ2lJZY9VnWahBDeuzfu7P2HGj7Pxr+XP9O/fLPe7e9b8Tque5adsmsNDwx5i0YZFLNqwyFDf9S9V3/Xz4mZi+WstJamkvrtjzQ5Cm4SWS/Nfp9fpjfKvsrGcO8k/5OTkRJs2bdi+fXvxd3369GHFihVkZRkKr9jYWHbs2EH37t2Ji4vjyJEj9OnTh6ioKG7cuMHx48eNGqPuyjkU3gFYefqCUoWqWUc0x0sNOczLJSd6MDkzniJnxlNoY8+g/uQNdHHnwd4R++deJ3/t52gvWUav6s9f/szoHs8zusfz7N20j65Fvdr1mtYjJyuH1OSyBa9CqcDF3TA/X6lS0rpLK2LPxgIQEhbC+LljeW3ETNItYPXIsOq+xN1I5/rNDAo1WjYdPkenRsHl0l1OTCUzN48mtUp6RMJq+JKVm09qUQ/dgXNXCb7D0DlTyz12Htta/tgE+WBlrcL94Q5kbDlQJk3GlgN4DjQ8sXXr1a54HriVnQ0Ke0NFwblDE9BqyyzoJu6/fV9v4f1eU3m/11RObT5E8/4dAKjetDZ5Wblk3Ugvk97GwbZ4nrhCqaBe56YkX7SsByVhQT7E3czgekqm4do6eoFOYTXKpbuclEamOp8mNUuGdCelZ5NXaJiGk5mbz9HLidQstaCWqX326Qoi2/chsn0f1v+6lUcH9wOgecsmZGZmk5RU8cO3/gMfYvWP5dcxqV0nGDc3Fw4eOFrBXqYX5udKXHoO1zNyKdTq2HQ2gYiQskPsI2v7ceiqoaxPyy3gSmoOga7mbRT8lbAAD+JSsrielm04/07G0Sk0sEyayHrVOBRreDtHWk4+V1KyqOZe8hB244k4ejYqf86aU/6ps6iqB6IK8AOVCsfuEeTuvMcFGRUKFK6G6VfWdWphU6cW6n3mnRK24+tNvNFrMm/0mkzM5oO07m9YCDW4aR3UWblk3Fb22TrYFc8TVygVNOrcnMSLhgcJGcmp1G1tWAG/XtuGJMeatxMjzNeFuPRcrmeoDdfV+UQigsuuH1F6ePrOyzeo5W54UKnV6UkvGpVx7mYW529m0aZ6xVP/TGXLVxt4pddEXuk1kUOb/6DDgEgAajetizort9zq5lu/2cTzrUYyvv2zvD7wFRIuJ/DWIMMio341S+pSzbu1Iv7iNdNl5C/88tUvjO05lrE9x7Jv0z66FNV3Q5uGkpOVQ9pteby9vtsqqhVXzl0xedyicrCMlUX+pYceeoiNGzcWf27RogWpqalMnz4dKysr7O3tGTt2LG5ubixcuJDhw4djY2NYeGXUqFF8+OGHLFiwAJWxFjzT6chb9REOz78FVgoK929GlxiHzYNPoI07j/bEnedu2nR8GIV3ADY9B2PT0zC3RP3hdPTZ5m+wAhzYfoBWnVvyxe7PDK8oe2lh8baPNi5mdI/nsbGxZs43s1Baq1AoFBzdfZQN3xqO19PTRmHvYM+rH08DIDn+BjNGzDRHVgBQKRVEPxLB6CVr0On19GndgNr+niz5dR8NqvsSUdQg33jkHD2a1S2z+rlSoWBCv/Y8++Fq9HqoH+TDAEsZEqzVce3VpYR8PdPwirKV28g7dxW/iY+Te+ICmVsOkLJyCzXem0CD3z9Gk55F7AuGFcWtvdwI+Xom6HQUJqUS++K75s3L3zB5xlwOHj1OenomXfo+wZiRQxnwcHdzh/W3nPntKKGR4by88z0K1Pn8MPmT4m3j18/h/V5TsXGwY/iySahsrLFSWHFx32n+WLHVjFGXp1IqiO7fntFL1xuurVah1PbzYMnGgzSo5k1Ew5oAbIy5SI/w2mWurUtJ6Sz8eR9WgB4YFtGYOv7mrYDesmXTDqK6deLgsa2oc9WMG1Pyusvfdq8tsyp6n349GTTw6XK/0X/gg/zvp783rcqYVAoFUzqHMeanA+h00KdhNUK8nFmy5xwNfF2JqO1L25pe7Ltyg/6f/45SAS92qodb0YJmI77fx+XUHNSFGrp/sp0Z3RvRtuadF6szSZ6UCqJ7NWf01zvR6XX0aRpMbR9Xlmw/QYMADyLqBdK2th/7LibS/8P1KBRWTOgajlvRgnPX07JJzMyleQ3LmTMNgFZH6twP8f1ojuEVZWs3UXjxCm6jh5N/+hzqnfuwCauLz8KZKFycsO/YGrfRw4gf8DRWKiV+nxnKc31OLjenzbOoqUYnfjtCo8imzNq5iAJ1AV9MXly87bX1C3ij12RsHGx5YdmU4rLv7L5T7FxhePPAV9GfMGjGUyhUCgrzC/lq6id3+k+ZhEqhYEpEKGPWGl6T1ycsgBBPJ5bsv0ADHxcign34LiaOnZdvoFRY4WprzetdwwDQ6HSM+NHwgMTJRsWs7o1QKSynHy1m+2HCI5vz7u8fka/O55NJJVMnZ69fyCu9Jv7l/oOih+IfHIhep+Pm9Rssf8WyVkYHOLj9IC0jW7J813Ly1fm8O6mkLrRowyLG9hyLtY01b37zJiqVCoVSQczuGDZ+u/EvfvU/qhL2WhuDld7SltC1QFkvVN65KXcyYK3l3Gjvp7XLe5s7hPvuzKhN5g7BKBoeqTyN+Xs1vcU0c4dgFDM/tJx3Pd9PQY9bXkXv34p7+0Fzh2AUVs4ud09UySTN22vuEIxiVnr5Fcwru/cnG/ctOuYyar55px8YS7q+/MJvVcH6OMt5WPtvZI17yCi/6/yBZUzpuldVoidcCCGEEEIIIYSFqyQLkBqbNMKFEEIIIYQQQhifDEcHqsDCbEIIIYQQQgghRGUhPeFCCCGEEEIIIYxPesIB6QkXQgghhBBCCCFMRnrChRBCCCGEEEIYnbyYy0B6woUQQgghhBBCCBORnnAhhBBCCCGEEMYnc8IBaYQLIYQQQgghhDAFaYQDYKWXgfl3Nb/GE+YO4b4bFX7V3CEYxaRj3uYO4b4bqys0dwhG8b3CwdwhGMVbh2aZO4T7bn7zV80dglEM80k0dwj33cokf3OHYBQpCp25Q7jv0tGYOwSjGGedbe4QjGJodpa5Q7jvdi3uae4QjMLKt4a5QzAKu3ZDzB3CfZE5sqtRftdl+Raj/K6xSE+4EELcJ1WxAS6EEP91VbEBLoS56KUnHJCF2YQQQgghhBBCCJORnnAhhBBCCCGEEMYnPeGANMKFEEIIIYQQQphC1Vvi4x+R4ehCCJsHCzUAACAASURBVCGEEEIIIYSJSE+4EEIIIYQQQgijk4XZDKQnXAghhBBCCCGEMBHpCRdCCCGEEEIIYXzSEw5II1wIIYQQQgghhCnIwmyADEcXQgghhBBCCCFMpsr1hD/22GNUr14dnU6Ht7c3Y8eOxdHRkeTkZF544QX69+/PoEGDAMjMzOTZZ58lKiqKkSNHGj22LjOHEhwZTqE6nw2TlpJ0MrbMdpWdDX0+GodbdR/0Oh0Xth7l93krAWgxqieNB0Wg02hRp2axYfJSMq+nGD3mu7Fu2gqHp8eCQkH+ll/J++nbMttte/TGtmc/0GnR56nJWfI2uqtXsHJ2wWnKG6hqh5K/fSO5S983Uw7u7PEZI2gU2ZQCdQHLJ31I3KnL5dJM+HIabj7uKJRKzh38k29eXYZepyOofg2GznoGOwc7bl67wdIX3ycvW22GXJRw7tSUajOfxkqpIOX7LSQt+anMdisbFTXenYBDoxA0aVnEPr+AgmvJWFmrCJozBofGIaDTc23mMrL3nzRTLu6u94zhhEaGU6guYNWkj4g/FVsuzYgvo3H2cUOpVHL54BnWvPpZpVooZPrshfy+5wAe7m6s+eZjc4fzt3SbOYyQyCYUqgv4ZdInJFZQDg74aBxu1X3R63Sc33qE34rKwVtCe7Zk4Mcv8tlD00k4Uf66NCW7Ni1xe+l5UCjIWbuerC+/L7Pd6fGBOPXphV6rRZeeTuobC9AmJgPg9cEcbBs2ID/mJDcnTjNH+H+p8+tDqRUZjkadz4aXlpJcwbHq/dE4XGsY7lkXtx5l11zDsarWKpTIGUPxrh/ELy98yLn1B82Qg4o9PGMYoZHhFKgL+D979x0dRdUGcPi3u+m9ZxMIGEJvITRpAoEEMAgEQZHem3TpCFKkCVLUT0BEpSqgYqH3Zgg9lNADoaX3nk22fH8sbliSUJTsBrzPOTknM/PO5r2Z3Ttz5965+8uEVYXqCFMLM3qsGINTeXc0KjXXDp5n72fa4+pQxoUui4Zg7WRHTlomW8auID022Qil0Pf+zP7U8PcjL0fB+gkreFDE+WrkumnYuzkglcmIOHOdzTPW6Oq9ln3b0aJPW9QqNeGHzvPbwk2GLkIh1m/Vw336UCQyKalb95K0+me97ZYNaiL/eAjmVbyJGreQjD0hAJh4uuG1YjpIJUhMTEjesJ3Un3YZowhFmvDpGJq2bkRujoJZY+dz4/LNQjEmpiZMmj+Oeo390GjUrFj4LYd2HsWvkS/j54ymYrUKfDxsNgd3HjF8AZ4h5EYUi3acRq3W0LlBJQa0rKW3ffGO05y5EwtAbp6K5Kwc/prZwxipPlXI5Qg++3Evao2azm/5MbB9M73tMUlpTP/udzKyFajVasZ0bc1btSuRmpnN+BU/cyUymo5N6zCt19tGKkHp9Spdb5Wk164RbmZmxuLFiwH43//+x969e3n33XcBcHNz4/z587pG+MmTJylbtqxB8qrg74ujt5xvW4zHw8+HwLn92Bg8q1DcmdU7uR96DampjG4/TsO7ZW0ij1wi/spd1r8zA2VuHnV6tabl1O78OfJ/Bsm9WFIpVkPHkjFzPOqkBOw+/4a80yGoH9zThSiOHkCx508ATBs2wWrACDJnT0KTl0fOpu+QlfdGVs7bWCUoVq2Wfrh7ezC15Sgq+FWiz7whzA2eWihu5Yilusb1hysn0KB9Y05vD6HfwuFsmb+em6eu0uy9Vrw9pBO/Ld1caH+DkUrxmjuUiJ4zyY9Josr2z0nbf5rcWw90Ic7dAlGlZXK1+TAcOryF59S+3B2xGOfubQC43mYMJs72+Kz/hBvvTABN6atEq7Ssg4u3nMUtx1HOryKd5w3k6+AZheI2jfgCxaPj1mvlWGq3b8TF7aGGTvcfCw4KpEeXjkz79HNjp/JCfPx9cfKWs7LFeDz9KtJubn/WBs8sFHdy9S7uhV5Faiqj54/T8Gnpy+0jFwEws7agYf92RJ2PMHT6hUmlOE4aTfzISajiEnBft4KcY6EoIwvqwPwbEcT1GY5GocC6SwccRg8hadpcADI2bCXTwgKbzu8YqwTF8vb3xfENOd81f3TOmtePTZ1mFYo7s3onDx6ds97/qeCclR6dxO7x39BgaJDhk3+KKi3r4Owt5/OWH+HlV5HgeQNYEfxJobjj3+7kTuhVZKYyBm76mMotfbl55CJB03oStu045389ToXG1Wk3qRtbP1pphJIUqNHSDzdvOTNbjsbbrxLd5w1iUXDhmzprRizTna+GrBxPvfaNObv9BJUb18A3sD7z3p6IMk+JrbOdoYtQmFSKfNaH3O/3MfmxiXj/upyMQyfJiyg4Zymj44mevBSngV30dlUmJHP3/Y/Q5CmRWFlQYedKMg+eRBlv/JslTVs1wqtCWTo36U7NutWZunA8/doPLRQ3YEwfUhJT6NKsBxKJBDtH7TGJfRjHrDHz6T38A0On/lxUajUL/jzJqoFtcLezoufXO2lRzQsfdwddzMR3Gup+/+nENa5HG/+4PEmlVjN/426+Gd8Ldyc7esxZQ8s6VfAp46qL+Xb7cdo2qMH7/vW5HZXAyOU/snvxGMxMTRgR7E9EVDwRUQlGLIVQ2r3Ww9ErV65McnLBh9vc3JwyZcpw+/ZtAE6cOEHjxo0NkkvFwHpc+fUvAGLCbmNhZ421m4NejDI3j/uh1wBQ56uIC7+LrdwJgPuh11Dm5gEQHRaBjYeTQfJ+GpNK1VDHRqGOiwGlkrzjhzBrqH+nkJxs3a8Sc0v4u92myEV57TKavDzDJfwC/No04MS2IwDcCbuFla0V9q4OheL+vqCRmcgwMTVB86hh6u7twc1TVwG48tdF6r39pmESL4ZVnUoo7saSdz8OTb6SlO3HsW/TUC/Gvs2bJP1yCIDUXSHYNq0NgEUlLzJOXAJAmZSGKj0Lq9oVDVuA51SjTT3ObTsOwP2wCCxtrbAt4rj93QCXmsiQPXbcXhX169TC3s7W2Gm8sMqB9bj0q/b4RIdFYGFnhU0R9eC9UO1nR52vIvaxehCgxfiuhK7ajlJh/LrDrEZV8h9EoYrS1oHZ+w9j2aKJXozi3AU0CgUAeZevIXMruIhTnAlDk5VNaVSxjf45y7yYc9aDJ85Zf5+b0h8mknj9Qanr8ajWph5hj+qIB2ERWBRRR+Tn5nHn0XtQla8i+spd7B+9B90qleH2iSsA3Am9SrXAegbMvmi+bepzctsxACLDbmFla43dU85XT9Z7zXu2Ye/KP1DmKQHISEo3UObFs6xdmbx70eQ/iIV8Jek7j2HbWv96LT8qHsWNu6B54gHTfCWaR2WRmpkikUoMlPWztWjXjF0/7wEg/PxVbO1scHZzLhTX8YMgfvhyIwAajYa05DQAYh7GEnHtNupS9rn6W/iDRLyc7SjrZIupiYy2vt4cufag2PjdFyNp51v6OmLC70Th5eZIWTdHTE1ktHuzBkcu3NAPkkBmjrZuz8zJxdVBe062MjejbuVymJu+dv2cL4+6hH5eMa9tI1ytVhMeHk79+vX11jdt2pSQkBASExORSqU4ORmmMWsrdyQ9umD4eEZsMrbujsXGm9tZUTHAj3shVwptq92tBZGPeoWMSeLsgioxXresTkpA6uxSKM48KBj7VT9i2W8Y2d+WvmHnRXF0dyb5seOVHJuMo7zwiRLgo/XTWX7uO3Kzcji76yQA0bce4temAQANghrj5FH4/2JIZnJn8qITdct5MUmYuuuXx1TuRP7fMSo1qowsZI625FyLxD6wIcikmHm5YVnTB1NP45anOHbuTqQ9dtzSYpOxkxf9GR+4fgozzq1CkZXL5V2nDJXif5qt3EmvHkx/jnqwUkBd7oZoH3+Q13wDO09nIg5dKPFcn4fM1QVVXEFPhyouAZlr8Z8N605vk3vitCFS+9ds5I5kxOifs2zkTz9WPgF+3C/inFWa2Ls7kvpYz5u2jii+XBZ2VlRrXZeIR+WKuXaPGu20NzBrtG2Aha0VVg42JZv0Mzi4O5HyWP2eEpuEQzH13qj101h87lsUWTmcf3S+cqvgQcWGVZn0+zzGbZlF+do+Bsn7aUzkzihjCsqUH5uIiXvR5+Ci93fBe/vXVDy2jqTVv5SKXnAAV7krsdEF101xMQm4PXF9YGOnfT8NnzyIjfu+Y+HqOTi5FP8eLU3i07OR21vrlt3trIhPyyoyNjolk+iUTBr6yA2V3nOLT81A7mSvW3ZztCMuJUMvZninFuwMvUzg+GWMWP4TU3q2M3SaryyNWlMiP6+a164RnpeXx8SJExk8eDBpaWnUrl1bb3udOnW4fPkyJ06coEmTJsW8Chw4cIApU6YwZcqUkk65EIlMSoevRnDuh72kPdAfylK9c1PktSpw+pudBs/rn1Ls+p20YT3IWfcNlu/3MXY6L93SPnMZ13AwJmamVGtSE4DvJ32Nf692fLL9MyxsLFHmK42c5T+XtOWAdgj7jiWUmTmIrHPXQfUK3nJ8wnd9FjKv4YeYmJlQ8dFxE0oPiUxK569GcuaHvaQ+SACJhIDpPTkw1/jPqv4TVm8HYFatMukbtho7lZdOIpPyzlcjOP/DXtLuvz7DL6UyKR98OZITa/eQ8kDbcNo1bxPeb1Zl1M75eDeqRlpMEmr1q1MfftVnPpMbDsXEzJQqj+o9mUyKlb0Ni4I/Ztv8DQz6epyRs/z3lLGJRHYYwe2AQdh3bo3MufDIgNJKZiJDXsadS2fC6dVmIJfPXWHszBHGTuul23spkoCa5ZFJX82myO5T4XRs6sv+JeP4emx3Pv7291I7QkEonV67sRJ/PxOuUCiYN28ee/bsISio4Jk0ExMTvL292b59O8uWLePs2bNFvk5AQAABAQEALPqp1z/Kxa9PALU/8Acg9tId7DydiXq0zVbuREZcSpH7tV04kJTIWM59v1dvffmmNWg8siM/vT8PVZ7xG3WapERkLm66ZamzK+qkxGLj844fxGpY6T25t+rdjubdWwMQefE2Tp4Fd92d5E6kxBY/EZ5Skc+F/WfwC2zA1b8uEXs7mqV9PgW0Q9Nr+9ct2eSfIS82CbPHeq/NPJzJj9MvT35sMqaeLuTHJoFMiszWGtWjO79Rc77TxVXa9hmKyGjDJP4cGvcOpGH3VgA8vHgH+8eOm73c6amTJikV+Vzdf47qgfW49dflEs/1v6hen0D8HtWD0Y/qwb/ZPaUebL9wIMmRsZz5Xjt009zGAtcqXvTaPB0AG1d73vtuPD8PXGK0ydlUCYnI3AuGl8vcXVElFK4DzRvWxa5/D+KHfgT5+YZM8YXU6RNA7e4F5yxbj4JjZSt3IjO26GPVZuFAUu7Gcv67vUVuN7ZGvQNp8KhcDy/ewcHTib+f2tfWEUWXq/OCQSRFxhLy6D0IkBGfyqZhywEwszKnZrsG5KYb/pGCFr3b0vTR+erexds4eroA2uGyjnJnUp9R713cfwbfwAZc/+syKbHJXNh7WvdaGrUaGydbMpMzin2NkqaMTcLksR5iU7kLyrgXn4xWGZ+M4tY9rBrU0E3cZmjv9etMcM8OAFy9eB25pxt/j2V093AlPka/zkhLTiMnO4dDu44CcGD7YTp2b2/IlP8xNzsrYh/r+Y5Lz8btsZ7xx+25eJepnYz7qF5x3BxsiX30CABAfEo67o76j4D9dvwCKz/STijnW9ELRb6SlMxsnO2KLq/wmFfnvmWJejVvPz0Hc3Nz+vfvz44dO1CpVHrbOnToQM+ePbGxKdkhZGHrD7Au6GPWBX3MrX3nqNFF+7y0h58PioxssuJTC+3TbEJXzG0tOTh7o956txrlabNgANsGLiW7FDyvBaC8dR2pR1mkbnIwMcHsrVbkn9Y/yUk9yuh+N63fGHXMQ0On+dwObdjDrKCJzAqaSNi+0zR5tyUAFfwqkZ2RTVqC/vEyt7LQPSculUmp3aouMbe1t1n+nthGIpHQYWRXjmzab7iCFCH74i3MvT0w83JDYmqCY4e3SNuvPyw2bf9pnLtqG7MOQU11z4FLLMyQWpoDYPuWL6hUehO6GVvohv18ETSVL4KmcmXfWeq9+xYA5fwqkpuRTcYTx83Mylz3DKhUJqVqKz/ib5eemwqvm3Pr97MmaBprgqZxc99ZanfRHh9Pv4ooMnLILKIebDHhPcxtrdg3e4NunSIjh2V+w/i62Vi+bjaWqLAIozbAAfKuXse0XBlknto60CrQn5xjJ/RiTCtXxGnqOBLHz0CdUrispcmF9QdY//bHrH/7YyL2Pt85q+mjc9ahWRsLbSstTm7Yz1dB0/gqaBpX953F71Ed4eVXkdyMnEJ1BEDg+PewsLVix5wNeuutHG2RSLTPGLf8sBNntx4t+QIU4eiGvcwPmsT8oElc3HeaRu82B8DbrxI5GdmkFzpfmeueE5fKpNRsVZfYR+eri/vOULlRDQDcvD2QmZoYtQEOkHP5JmZveGJa1h1MTbBr35yMgyefa18TuTMSczMApHY2WNarQd6dqGfsVXJ+XvsbPQMH0DNwAEd2HyfoPe2w5Zp1q5OZkUlSfOGbC8f3naBeEz8AGjSrR+TNu4ZM+R+rUdaF+4npRCVnkK9UsfdiJC2qFZ4AOTI+jfQcBb7lXIt4FeOr4V2G+3HJPExIIV+pYs+pK7SoU1kvxsPJjlNXteefO9EJ5OUrcbK1Mka6wivqtesJf5y3tzflypUjJCSEqlWr6tZ7eXnh5eVl0FzuHLpABX9fBh9bgjInj90TVuu29d01j3VBH2Mjd6LJqGCSIqLou1M7e27Y+v1c2nyEltO6Y2ZlQccVowHIiE5i26ClBi1DIWoV2auXYzvrc+1XlB3cherBXSx7DEAZcZ380yewaP8uJr71QKlEk5VJ1vIFut3tV29GYmWNxMQEszebkT5rgt7M6sZ06fB5avvXZeHR/5GXo+D7iSt022btWsysoImYW5kzes0UTB5N/HI9NJwjm/YB8GbHZrTqrT3Rnt97ir9+PmSUcuio1DycsRqfDbO0X1G25SC5Nx8g/6gH2ZcjSN9/mqQt+ym/fBzVj61CmZrB3ZHambdNXRzw2TAL1Gry45K5O3aZccvyFNcPh1HFvw6Tji4nL0fBzxO/0W0bs2sBXwRNxczKgr5rJuiO2+3Qq5zadMCIWb+4iTMXcibsEqmp6bQO7sWHA3vTpUNbY6f1TBGHLuDjX4cPjy3VfUXZ3wbtms+aoGnYyp1oNiqYxIgoBu2cB8DZ9fu4sPmIkbJ+CpWalEVf4frlZ0hkUjL/3I3yzj3shvYj79oNco+F4jBmCBJLS5wXamfgVsXGkzheO2O/2+rlmLzhhcTSEo8dm0mZ+zm5J4senWVodw5dwNvfl0HHl5Cfk8eex85ZfXbPY/3b2nNW49HBJN2Kos+uR+esdfu5vPkI8toV6PTtWCzstc+KN/moC2sDDP9415NuHL5AFf86TDi6jPwcBb88VkeM2jWfr4KmYSd3otWozsRHRDHy0XswdN0+zm45QoVG1Wg76QPQaIg8fZ0/PvnBWEXRCT8cRk3/usw5+iV5OXmsf+x8NW3XIuYHTcLMyoLhayZhYmaKVCrhRugVjj+6OXxi6yF6L/qQGXs/R5mvZP34r41VlAIqNbGzV+L1/VztV5T9so+8iPu4jOlF7uVbZB46hUWtSpRdMQOZnQ02/m/iOroXd4KGY+5TDrcpg7Tf4CGRkPzdryhKSSM25GAoTVs34vfQzeTm5DJ7XME10ab939MzcAAAX85byZyvpjN+zmhSklKZPW4+ANV9q7L4+3nYOdjyVmAThkwcQLeWpecxPxOZlCkd32T49wdQa9R0ql+Jiu6OrNgfRvUyzrSsXg6APZe0E7L9fUOrtDGRSZna622GL92EWq0huFkdKpZx4+vfDlPjDU9a+lVhfLc2zFm3nY37TiGRwJyBnXTleXviF2TmKshXqjgcdp1VH/XSm1n9v+7JuRT/qySaV21aYCNYVP6fDUcvzQbVKT09mS/ThIuvXyU3Sl16h6/+G5ulr98d47ln5xk7hRKxqF7hr3l7HfRxizV2Ci/dljgPY6dQIpKkr99VWyrGf6ysJIw2zTR2Ci9d70zjjgwoKce/fj2/w1riXt7YKZQIi6Y9jZ3CS5HUoUWJvK7zduOMTPqnXuuecEEQBEEQBEEQBKGUeP3uqf4johEuCIIgCIIgCIIglDgxHF3rtZ2YTRAEQRAEQRAEQRBKG9ETLgiCIAiCIAiCIJQ80RMOiJ5wQRAEQRAEQRAEQTAY0RMuCIIgCIIgCIIglDjxTLiWaIQLgiAIgiAIgiAIJU40wrXEcHRBEARBEARBEARBMBDREy4IgiAIgiAIgiCUOGP1hF+4cIEffvgBtVpN69atCQ4O1tu+Y8cODh48iEwmw87OjuHDh+Pq6gpAt27dKFeuHAAuLi5Mnjz5X+cj0Wg0mn/9Kq+51O7+xk7hpWt/TGLsFErEgfkNjZ3CS3d9xjVjp1Aiqq5oYewUSsTSEeeMncJLN+ncp8ZOoUR4VWxv7BReuojBVY2dQomQ2FoZO4WXLvPwQ2OnUCK+jixr7BRKxJTJTsZO4aUb81mssVMoEemafGOnUCK23Pvd2Cm8FHH+JXP95374aLHb1Go1Y8aMYfr06Tg7OzN16lTGjBlD2bIF9VV4eDiVKlXC3Nycffv2ceXKFcaNGwdA79692bBhw0vNVwxHFwRBeElexwa4IAjCf93r2AAXBKPRSErm5ykiIiKQy+W4u7tjYmJCkyZNOHPmjF5MzZo1MTc3B6BSpUokJyeX2L8AxHB0QRAEQRAEQRAEwQBKajj6gQMHOHDggG45ICCAgIAAAJKTk3F2dtZtc3Z25tatW8W+1qFDh6hTp45uOT8/nylTpiCTyejUqRMNG/77kbeiES4IgiAIgiAIgiC8sh5vdP8bx44d486dO8yaNUu3bsWKFTg5OREXF8ecOXMoV64ccrn8X/0dMRxdEARBEARBEARBKHEataREfp7GycmJpKQk3XJSUhJOToUfM7l06RK//fYbkyZNwtTUVG9/AHd3d6pXr87du3f/9f9BNMIFQRAEQRAEQRCE15KPjw8xMTHEx8ejVCo5ceIE9evX14uJjIzk22+/ZdKkSdjb2+vWZ2Zmkp+vnewvPT2dGzdu6E3o9k+J4eiCIAiCIAiCIAhCiTPGV5TJZDIGDBjAvHnzUKvV+Pv74+XlxZYtW/Dx8aF+/fps3LiR3Nxcli5dChR8FVlUVBSrV69GKpWiVqsJDg4WjXBBEARBEARBEARBeJq6detSt25dvXXdunXT/T5jxowi96tSpQpLlix56fmIRrggCIIgCIIgCIJQ4jTP+Dqx/wrRCBcEQRAEQRAEQRBKnDGGo5dGYmI2QRAEQRAEQRAEQTCQV7on/PTp03z++ecsW7aMMmXKABAREcHGjRtJTk7G0tISBwcHevbsSbly5di6dSsHDx7Ezs5O9xqzZs3C2tq6xHM18W2AZZ+RIJWRd3gnij9/0ttuFtAB88BgUKvR5OaQvWYJ6qh7SF3csV2yDnX0AwCUEVfJ+W5Zief7IsbOGUnjVm+Sm5PLvHGLuBl+q1DMVz8vxcXdGUWuQrtP90mkJqUyetaH1G1SBwBzS3McnR1pV72jQfMvSsjtOBbtv4Rao6Gzb3kGNKlSKGbv1Yd8c/w6SKCymz0LgxvotmUq8nl39QH8K3syta2vIVMvlm0LP8rOGoxEJiVp837iVvyqt11iZkL5ZeOwquWDMiWDuyMWk/cwHompCV4LPsSqtg+oNTyctYbMk+FGKkVhIdfvs+j3E6jVGjq/WZUBrf30ti/+4wRnIqIByM1TkpyZw1/z+hOdnMFHa/eh1mhQqtR0b1aT95pUN0YRitRmVh98/H3Jz8ljx4RviA2/q7fdxMKMLitH41DOHY1aza0D5zn82Ra9mCpvN6DrqrF8/850Yi5HGjD7Fzd9/lKOhZzGydGB3zeuMnY6L2TuZ9NoHdicnJxcxnw4jcsXr+ptt7ax4o/dG3XLHp5yft26nU+mLqBbj2A+mTORmJg4AL5f/SM/bvjFoPkXRVbFD/OOA0EqJf/0AfIPbys6rlYjLPtMJvuLCagf3gZA6lEe8y7DwdwSNBpyvpwIynxDpl8kmU9tzNr2BqkUZdgR8kO262038W2OWUB31BkpACjP7EMZdgQA8x6TkJWtiOr+TRSbPzd06k9l1qAhtiNHgUxKzs6dZP/0o952q/fexzKoPRqVCnVaKumLPkMdp32/Sd3csJswCZmbG2g0pEyZjDou1hjFKFLQzD5UelQP/jbhG2Ku3NXbbmphRrcVo3Es745GpebGwfPsf1QPtpvRC+/G1XVx1i52LKg9xNBFKFbI3UQWH72OWq0huGZZBjTwLhSz72Ysq07eRgJUdrVlwdu1DZ/oc/pgZn9q+dclL0fBDxO+5v6VwuecMes+xt7NAZlMxq0z19g04zs0ajVe1d+g17zBmJqboVKq2DRjDXcvRhihFPr6zRqEn389FDkKVk74ksjwO8XGTlwzDfdy7kxoM0Zv/TuDO9F7en8G1elNRkpGSaf8SnjW14n9V7zSjfCQkBCqVq1KSEgI77//PqmpqSxbtozRo0dTpYq20XT9+nViY2MpV64cAO3bt6djRwM38iRSLPuPIWv+RNRJCdjOW0X+uROoo+7pQvJCDpJ3QHtBYFKvCZa9PyRr4WQA1HHRZEwdbNicn1PjVm9S1rsM3Zr1pkbdakxYMJYhHUYUGTt75DyuX7qpt+7LWSt0v3ft35lKNSuWaL7PQ6XWsGDvRVZ1b4q7nSU9fzhMi0oe+LgW3Ly5l5zJ96E3WdunOXaWZiRnKfRe4+uj16jr5WLo1IsnleI1dygRPWeSH5NEle2fk7b/NLm3HuhCnLsFokrL5GrzYTh0eAvPqX25O2Ixzt3bAHC9zRhMnO3xWf8JN96ZABqNsUqjo1KrWbAt8p+iYQAAIABJREFUhFVD2+Nub03P5dtoUeMNfOSOupiJnZrofv/peDjXoxIBcLWzYv3oYMxMZGQr8umyeCstapTHzb7kb8o9i4+/L07ecla2GI+nX0Xaze3P2uCZheJOrt7FvdCrSE1l9PxxGj4tfbl95CIAZtYWNOzfjqjzxr+QeR7BQYH06NKRaZ+WrgbOs7QObE6FCuVpXLcddev78tmSTwgK+EAvJiszm4C33tUt7z3yC7u279ct/7FtN9MmzTVYzs8kkWLeeQg5q2ehSUvCcvQilFdOo4l/qB9nboFZs3dQ3btRsE4qxbz7WBQ/fYE65i5Y2YJKZcjsiyaRYPZ2P3I3LkCTnozFoE9R3jiPJjFKL0x55SR5e9YV2j0/dCdKUzNM6rY2VMbPRyrFdsxYUieOR5WQgNOqb1CcCEF1r+D6Iv/WLbKHDQGFAsuOnbAdOoy0ObMBsJ86jayNG8k7dxaJhSWaUjROtFJLX5y95XzRcjxl/SrSYV5/VhdRD4Z8u4vI0KvITGX02zSNSi19uXXkIns+Lbjx9WbfNnjUKG/I9J9Kpdaw8PA1Vr5bD3cbC3r+dJIWFVzxcbbRxdxLyeL7M5Gsfb8hdhamJGcrnvKKxlWzpR9u3h583HIUFfwq0XPeYBYETysU982IpeRm5gAwbOV46rdvxJntJ+gypRfbv/iZ8CMXqNnSj65Te/H5B7MMXAp9dfzrIff2YEyL4VTyq8zAucOYHjypyNiG7RqRm51baL2zhwu136pDwsP4kk5XeAW9ssPRc3NzuX79OsOGDSMkJASAPXv20KJFC10DHKBq1ao0bNjQWGkCIKtYFXVsNOr4GFApyQs9hGn9pvpBOdm6XyXmFqWigfM8mrVtwp5ftBeTV85fw9beBmc3p3/0WgHBrTjw+6GXmd4/Eh6djJejNWUdrTGVSWlbvSxHbsXoxWy7cJdu9SpgZ2kGgJO1uW7b1ZgUkrNyaVzBzaB5P41VnUoo7saSdz8OTb6SlO3HsW+j/7mwb/MmSb9o//+pu0Kwbaq9425RyYuME5cAUCaloUrPwqq28W+WAITfj8fL2Y6yznaYmsho61eRI0/0lDxud1gE7fy0uZuayDAzkQGQp1SVqo9c5cB6XPr1OADRYRFY2Flh4+agF6PMzeNeqLbHVZ2vIjb8Lrbygs9ei/FdCV21HaUiz3CJ/wv169TC3s7W2Gm8sLZBrdi6+Q8Azp+9iJ29HW7ursXGV/B5AxcXJ06eOGuoFF+YtFwl1IkxaJLjQKVEeeEvTGoUPo+ate1B3uHf9Hq5ZZXroI65p22AA2RnlIoHAKVlfFCnxKFJTQC1CtWVk5hUqffc+6sjr6BRFL7INjbTqtVQRUehiokBpZLcQ4cwb9pMLyb/QhgotA24/KtXkbpq35+y8uVBJiPvnPa9qMnN0cWVBlXb1OPCNm09+DAsAgtbK2xc9evB/Nw8Ih/Vg6p8FdFX7mInL3wNUqtjYy7/GVryST+n8Ng0vOytKGtvpb3OqCznyG39htpv4VG87+uFnYUpAE5W5kW9VKlQp00DTm47CsCdsFtY2Vpj/8SxAnQNcJmJDBNTk8fOuxosbKwAsLKzIjUuxRBpP1WDwIYc+/UIALfCbmJtZ42Dm2OhOHMrC9oP6si2r7YW2tbnkwFsWrCuVF1flAYaTcn8vGpe2Ub4mTNnqFOnDp6entja2nLnzh0ePnyIt3fh4TyP27lzJxMnTmTixInMnj3bILlKHV1QJxVUruqkBKSOhXtJzQKDsV2+EcseQ8lZ91XB/q5ybBasxuaT5ciq1DJIzs/LVe5CfHRB2eJjEnCVF90DPG3pJNbuW02/sb0KbXMv446Hl5xzIWElluvzis/IRW5nqVt2t7UkPkP/4utecib3kjPpu/4ovdceIeS2dmifWqNhycFwPmpduo6TmdyZvOhE3XJeTBKm7s56MaZyJ/L/jlGpUWVkIXO0JedaJPaBDUEmxczLDcuaPph6lo5e/vi0bOQOBT0H7vbWxKdlFRkbnZxBdHIGDSt56tbFpmTy3uc/0+7TTfTz9y0VveAAtnIn0qOTdMvpscnYuhc++f/N3M6KSgF1uRuifUxAXvMN7DydiTh0ocRz/a/z8HAnOqpg+G5MdCweHsXfgAvuEsSfv+3WW9e+YxsOhfzOmnXL8SwjL7Fcn5fEzglNakF9oUlLQmKvX19Iy1RA6uCC6vo5/fUunqDRYDHoEyzHfI5py2CD5PwsElsnNGkFnylNejIS28KfKVm1BlgOXYB51zFI7P7ZDWVDkrq4oI5/7PoiIQGZS/H1s2VQEHmnTgFgUtYLTWYm9rM/xWn1GmyGDgNp6bkstHN3Iu2JetBOXnw9aGFnRZXWdbkTov+4lH0ZFxy9XLlz4kqJ5fqi4rNycbe10C2721qQ8MSIunspWdxPyabfltP02XyKkLuJT75MqeHo7kTyY8cqJTYJhyJuhgCMXf8xS86tITcrl3O7TgKwefZauk7tzWcnVtJ1Wh+2LdpkkLyfxlHuRNJj101JsUk4uRcuU7fxPdjx7R/k5ejf8K4f2JDk2CTuXbtb0qm+cjRqSYn8vGpe2eHoISEhBAUFAdCkSRP++uuvQjHTpk0jJyeH2rVr079/f+D5h6MfOHCAAwcOADDlJeb9NHn7fydv/++YNmmNRefeZK9ciDo1mfRRH6DJTEfmXRnr8Z+SPrG/Xs/5q2D2qPkkxiZiZW3JvG9n065roK4HHSCgkz9Hdh5DrTZ+j8nzUKnV3E/OZE3Pt4jPyGHAhuP8PLgVu8If0MzHHffHGvGvuqQtB7Co6EWVHUvIi0og69x1UL0ax+lxey/cJqC2N7LHLjLljjb8POE94tOyGPfDXgJ9K+Bsa2XELF+cRCal81cjOfPDXlIfJIBEQsD0nmyf8I2xUxOKEPzu24wcOlm3vG/3EX77ZSd5efn07vc+X65cQNeO/Y2Y4XOQSDDv0J/cLV8W3iaTIfOuRvYXEyFfgeXQOagf3kYVcdnweb4g5c3zKMNPgEqJSd1WmHcaRu6G+cZO66WxCAjEpEoVUsY+emZVJsO0Vm2ShwxCFReP/cyZWLRrR+6uXcZN9B+QyqS89+VITq3dS8qDBL1ttTo04squ02jUr1ZXmUqj4X5qNt92rU98Zi4Dfz7Dz72aYPuoZ/xVtbzPPEzMTRm0fDRVm9Tk2l+XaNmrDVs/Xcv5Paeo374xfT8bzrJenxo71WcqX90b9/Jy1n/6Pa5lC26+mlmYETyiK/N6zzJeckKp90o2wjMzMwkPD+f+/ftIJBJdw61FixZERkbSoIF2gqz58+dz8uRJzp0797SXK1JAQAABAQEApHb3/1f5qlMSkToXfDilzq6oU4q/o5kfegirgWO1C8p8NJnaoX6qyJuo46KReZRFdedmsfuXtHf7dqJjz/YAXLtwAzfPgrK5ebiSEFu4bImP1mVn5bD/94NUr1OtUCN8ycdFXNAZgZutBbHpObrluIwc3B67Yw3a3vGank6YyqSUcbCmvJMN95OzuBiVTNiDJLaejyQnT0m+So2VmYwx/jUNXQw9ebFJmD3We23m4Ux+XJJeTH5sMqaeLuTHJoFMiszWGtWjSUSi5nyni6u07TMUkdGGSfwZ3OytiE3N1C3HpWUV25u9JyyCqe82K3Kbm701FeVOnL8TS6BvhRLJ9Vnq9QnE7wNtXRN96Q52ngU9j3ZyJzKKGZ7XfuFAkiNjOfP9HgDMbSxwreJFr83TAbBxtee978bz88AlpX5ytldF/0E96Nm3KwAXzofr9V57eMqJiSn6+b/qNasgMzHh0mMTt6WkpOp+37T+F2bMnlBCWT8/TXoyEoeC+kJi76zXi4y5JVJ5OSyHaZ9jl9g6YNFvGrlr56NJTUJ156p2GDqgvH4OaRkfozfCNRnJer35EjsnNBlPfKZyCuoSZdhhzAK6Gyq9f0ydmIjU7bHrC1dXVImFz8Fmdeth3as3yWNHQ772mkKdkIDydoR2KDug+OsvTKtXJxfjNcIb9g6k3qNrrqiLd7B/oh5Mjy26Huy4YCBJkbGEPqoHH1erQ2N2zFhbIvn+U27WFsQ9NsIuLiMXV2v94eZuNhbUkttrrzPsrSjvaM391GxqyO0NnW6RWvZuS/Pu2uvkyIsROD12rBzlzqTGJhe7r1KRz8X9Z6gT2IBrf12icZeWbJ79AwBnd4bSZ+Gwkk2+GG36vE3rD7Tz4Ny+dAvnx66bnOXOJMfpl6ly3SpUqF2Rr/5ajcxEir2zPZ9snssPM7/FzcuNRbuXa/f1cGbhzqVM6zSRtIRU/utexV7rkvBKNsJPnjxJ8+bNGTKkYJbLmTNnUrt2bf73v//h6+urey5cUQqeb1Ldvo5UXgapqxx1ciJmjVuR9T/9SXik8jKoY7UTxJj4NUL16HeJrT2aTO0zdVI3D21cXEyhv2FI29b9wbZ12mcgG7d+ky79gjnwxyFq1K1GZnoWSfH6lZRMJsXGzoa0lHRkJjKaBDTi7PHzuu3lfLywtbcl/GzpGCpWw9OR+ymZRKVm4WZryd6rD5nfqYFejH9lT3ZffUCwb3lSshXcS86krIMVCx6L++PSPa7GpBq9AQ6QffEW5t4emHm5kR+bjGOHt7g7eoleTNr+0zh3bUX2+Rs4BDXVPQcusTDT3uzKUWD7li+oVHoTuhlTDS837iemEZWUjpu9NXvDIpjfq/DESZFxKaTnKPB9w123Li41E3trCyxMTUjPVhAWGUuvFsZ7jODc+v2cW6+9MVWxVR3q923D1T9D8fSriCIjh8z4wifuFhPew9zWih2T1ujWKTJyWOZXcAHTa/PHHJz3o2iAv0Q/rPmRH9ZoZ6AOaNOCAYN78Puvu6hb35eM9Azi4xKK3K9zl/b8/utOvXVu7q66+LZBrbh1s/jZdw1F/eAWUhcPJI5uaNKTManTDMWPj30rR242WbP66hYth32KYsda1A9vo06K1Q5BNzUDlRJZhRrkH9texF8xLHXUHaROciQOrmjSk5HVaITit6/1YiQ2DmgytZ8zWeV6qBNLx83Gp8m/fh1ZmbJI5XLUiYlYtGpF2lz9HkSTipWw/Wg8qZMnokktqEfyb1xHYmODxN4eTVoaZn51yb953dBF0HN6w35Ob9DWg5X96/Bm3zZc/jOUsn4Vyc3IIbOIBkzr8dp68I/Jawptc/HxwMLemgfnC39rizHVkNtxPzWbqLRs3Gws2HszttDM5/4+buy5EUOnGmVIycnjXkoWZexLzyi7Ixv2cmTDXgBq+dfFv287Tv8ZQgW/SuRkZBdqbJpbWWBhbUFaQipSmZRarepx6/Q1ANLik6ncqDo3T16lapOaxN81zgz9+9bvZt967eNCfq3q0bZvECf+PE4lv8pkZ2SRGq9/E2j/xj3s36i98eNa1o3J33/MnA+0N8CH1Ouni/vqr9VM6zBezI4u6HklG+EhISF06tRJb92bb75JSEgIY8eOZdOmTSQnJ2Nvb4+trS1du3bVxe3cuZPjx4/rlidOnIibWwlPoKVWk7P2S6ynLgKplLwju1E/vItF1/4oI2+gPHcC8zadMalVD5RK1FkZZK9cCIBJNV8s3usPSiUajZrs75ahySo9H+LQg6do3OpNtoZsJDcnl/kfLdJtW7tvNf3aDMHUzIylPy7CxESGTCbjzPFz/Lmp4EI0oFMrDvxx2BjpF8lEKmVKG1+Gbw5BrYZOvuWp6GrHiqNXqe7hSMvKHjSp4EZoZBzvfnMAqVTCuFY1cSjFk6agUvNwxmp8NszSfkXZloPk3nyA/KMeZF+OIH3/aZK27Kf88nFUP7YKZWoGd0dqZ6k2dXHAZ8MsUKvJj0vm7tjS8xV5JjIpU95txvDVu1BrNHRqWIWKcidW7DlD9bKutKz5BgB7LtymXZ2KSCQFd1/vxKWydHsoEkAD9GlZm0oezkX+HUOLOHQBH/86fHhsqe4ryv42aNd81gRNw1buRLNRwSRGRDFo5zwAzq7fx4XNR4yU9b8zceZCzoRdIjU1ndbBvfhwYG+6dGhr7LSe6cC+o7QObM7JsL3kZOcydkTBjMAHjm/TmxW9Y+d29HxvqN7+g4b2ou3brVCqlKSmpDHmw6kGy71YajWK37/FcvDMR19RdhB13APM2nRH9TAC1dUzxe+bk0X+8e1Yjl4MgOr6uULPjRuFRk3e7rVY9JwMEinKC0fRJERh2rIL6uhIVDfPY9KwLSaV66JRqyA3C8UfBV+VZ9FvBlJnTzCzwHLsV+RtX43qdikYYq9WkfHlchwXfQ5SKbm7d6G6exfr/gNQ3riO4sQJbIYNQ2Jpif0s7Tw46rh4UqdPA7WazJUrcVyyDCQSlDdvkLNjh5ELVODm4QtU8q/D2KPaevC3iQX14PBd81kZNA07uRMtRgWTEBHFsEf14Kl1+zi/5Qig7QUP3156JmT7m4lUymT/qnz423ntuatGGXycbVgRGkF1Nzta+rjRpLwzofeSeHd9CDKJhLFvVcbh0WSwpc3lw+ep5e/HvKNfkZeTx9qJBTe4Ptm1mDlBEzGzMmfkmsmYmJkikUq4EXqFo5v2AbB+yjd8MLM/UhMp+Yp81k81/uNUYYfO4edfjy+OrSLv0VeU/e2zXcuYHDTOiNm92l7FSdRKgkSjEf+KZ/m3w9FLo/bHXs+hIAfmG3cm/JJwfcY1Y6dQIqquaGHsFF66pSNKQWOjBEw6V/qfzfsnvCq2N3YKL13E4KrGTqFESF6xuRqeR+bhh88OegV9HVnW2Cm8dFMml/5J+v6JMZ+Vnu+Ef5nSNfnPDnoFbbn3u7FTeCnu1GpTIq9b4fK+EnndklJ6psEUBEEQBEEQBEEQhNfcKzkcXRAEQRAEQRAEQXi1aDSv52jcFyV6wgVBEARBEARBEATBQERPuCAIgiAIgiAIglDiNGpjZ1A6iJ5wQRAEQRAEQRAEQTAQ0RMuCIIgCIIgCIIglDi1eCYcEI1wQRAEQRAEQRAEwQDExGxaYji6IAiCIAiCIAiCIBiI6AkXBEEQBEEQBEEQSpxGLXrCQTTCn8vEUBdjp/DSHVhU1dgplIiqo/8wdgov3Xzz2sZOoUQE9lhl7BRKRFgVL2On8NJ5VWxv7BRKxIOIncZO4aXrXHeUsVMoEbmabGOn8NK1lr5+dQXA6OoPjJ3CS9d1QYqxUygR37/x+n2uAEysNcZOQRCeSTTCBUEQXpLXsQEuCIIgCILwsmjEPRJANMIFQRAEQRAEQRAEAxDD0bXExGyCIAiCIAiCIAiCYCCiJ1wQBEEQBEEQBEEoceJ7wrVET7ggCIIgCIIgCIIgGIjoCRcEQRAEQRAEQRBKnEb0hAOiES4IgiAIgiAIgiAYgJgdXUsMRxcEQRAEQRAEQRAEAxE94YIgCIIgCIIgCEKJExOzaYmecEEQBEEQBEEQBEEwkNemJ7xbt26UK1cOlUqFTCajefPmtG/fHqlUypUrV9i+fTtTpkwhNTWVVatWkZSUhFKpxM3NjalTpxoszw9m9qeWf13ychT8MOFr7l+JLBQzZt3H2Ls5IJPJuHXmGptmfIdGrcar+hv0mjcYU3MzVEoVm2as4e7FCIPlXpSQiBgW7b2AWq2hs583A5pVKxSz98oDvjl6BSRQ2d2Bhe82AiAmLYvZ288Sl56NBAlf9XiLMg7Whi5CsWYtmIx/wFvk5OQyYeQMwi9d09tubWPFzzvW6pY9PN357eedzPl4EQ0b12PmvElUrVGJUYMms2v7fgNn/2z1Pu1NmVZ1UOYoCB23mpTLdwvF+E5+D+/3mmFmb83WSoMMn+Rzmr9oOgFtWpCTncOo4VO4dPGq3nYbG2u27/lRt+xZRs7PW/5g+pT5zF0wlaZvad+TVlYWuLg441OuvkHzf5JF4wY4jB8BUilZf+wiY91mve02Pbpi0ykIjUqFOjWV5DmLUcXGA+Dy5QLMa1ZHcSGcxI8+Nkb6TzX3s2m0DmxOTk4uYz6cxuUnjpW1jRV/7N6oW/bwlPPr1u18MnUB3XoE88mcicTExAHw/eof+XHDLwbN/0VNn7+UYyGncXJ04PeNq4ydzgsZMnso9f3ro8hRsHz8Mm6H3y42dsZ3nyAv586IwBEAeFfzZsT8EVhYWxL/MI7FoxeTk5ljqNSfasTs4TRs1RBFTi6LPlpCRHjh8+iSrYtwcnNCkZsHwJSeU0lNSuOdXu3p1LcDKpWa3Kwclk75gvu37hu6CIUEzuqNj38d8nMU7Jiwmrjwu3rbTSzM6LxyNI7l3FCr1UQcCOPIZ1sA8OvZirp9AtGo1ORl57J76nck3Yo2Qin0mdZriPWwUUikUnL37CTn5x/1tlt0fh+Ldu1BpUKdlkrmss9Qx2vrBqsBQzFroK3Xs39aT96xwwbP/3kMnT2UBv4NUOQoWDp+aZGfsYVbFj56LyoAmN5rOmlJaYZO9anMGjbEbvRIkMrI2bmTrE36x8rq/feweqe97pyVtnAR6jjtsXI/fBDlHe21sCo+jtSppeO89V94/xmLmJhN67VphJuZmbF48WIA0tLS+PLLL8nJyeH999/Xi9u6dSu1a9cmKCgIgHv37hksx5ot/XDz9uDjlqOo4FeJnvMGsyB4WqG4b0YsJffRxcqwleOp374RZ7afoMuUXmz/4mfCj1ygZks/uk7txecfzDJY/k9SqdUs2H2eVb1a4G5nSc81B2hRxRMfV3tdzL2kDL4Pucba/q2wszQjOStXt23676cZ1KwajX3kZOflI5GUng+lf0AzvCuUp0WDd/CrX5u5n08nuE1PvZiszGyCWha8v3Yc3MyeHQcBiH4Yw/iR0xkysp8h035unq18sfOW82fT8TjX9aHhgn7sfWdWobiH+89z44f9dAz53PBJPqeANi2o4PMGDesEUq+BL4uXzaZtq/f0YjIzs/Bv1km3fPDoNnb+uQ+A6VMX6NYPGtqbWrUL30gyKKkUx0mjiR85CVVcAu7rVpBzLBRlZEFdlX8jgrg+w9EoFFh36YDD6CEkTZsLQMaGrWRaWGDT+R1jlaBYrQObU6FCeRrXbUfd+r58tuQTggI+0IvJyswm4K13dct7j/yidxPrj227mTZprsFy/reCgwLp0aUj0z4tvZ+hotT3r4/nG54MaT6YKn5V+HDeCMZ3+qjI2MbtmpCTpd/AHrVoNN/P/Y7wU+EEvh9Il6Fd2LhkY5H7G1JD/waU8S5D37f6U82vKmPmj2JUxzFFxi4Y/Rk3L93SW3fo98Ps2LgTgMaBjRj+yVCm9jZuo8HH3xdHbzmrWozH08+HdnP7sS54VqG4U6t3cj/0GlJTGT1+nEaFlrW5c+QSV/4IJWzTIQAqBtQlYHovtvRdZOBSPEEqxWbEWNKmjUedmIDDF9+QdyoE1f2CelB1+xapo4eAQoFF+05YDxhGxsLZmDZohIlPZVJHDAJTU+wXfUH+2VNosrONWKDC6vvXp8wbZRjUfBBV/Kowct5IxnUaV2Ts4jGLufXEe7HUkEqxGzeGlI8moEpIwHn1KnL/CkH12PW18tYtEgcPBYUCy04dsR0+lLRZcwDQKPJIGljKbvL/B95/gvG9lsPR7e3tGTJkCHv27EHzxBR8KSkpODk56ZbLly9vsLzqtGnAyW1HAbgTdgsrW2vsXR0Kxf3dAJeZyDAxNXlsFkENFjZWAFjZWZEal2KItIsVHpWMl6MNZR1tMJXJaFujHEdu6N8933b+Dt3qV8TO0gwAJ2sLAG4npKFSa2jsIwfAyswUS9PSc08o8G1/ft2yHYCws5ews7fFzd2l2Hhvn/I4uzpxOvQcAA8fRHP96i3UarVB8n1RZdvW484vfwGQdP42ZvbWWLgVfi8mnb9NbnyqodN7IW8HtWbrT78BcO7MReztbXF3dy023qfiG7i4OhN64myhbe92bc+2X3aUWK7Pw6xGVfIfRKGKigGlkuz9h7Fs0UQvRnHuAhqFtlck7/I1ZG4F5VWcCUOTVTpP9m2DWrF18x8AnD97ETt7O9yecqwq+LyBi4sTJ4s4Vq+K+nVqYW9na+w0XtibbRpx6Fdtw+xG2A2s7axxdHMsFGdhZUHw4GC2fKU/WqOMdxnCT4UDEHY8jCZBTUs+6efQpE1j9v96AIBrYdexsbPGyc3pGXsVyM4s+GxZWFkUusYwhkqB9Qj/VVufR4fdxtzOGusn6nNlbh73Q7WjudT5KmLD72Ir15Y777ERCmZW5mgwfplMKldDFR2FOlZbDyqOHsKsUTO9mPxLYfCoHsy/fhWpi7YuMSn3BvnhF0GtAkUuqsjbmNZ70+BleJZGbRpx8FftjfunfcZKO9NqVVFFRaGK0R6r3IOHsGim/3nPC7tQcKyuXkXmWny9Xxr8F95/xqTRlMzPq+a1bIQDuLu7o1arSUvTH7LTtm1bVq1axezZs9m2bRvJyckGy8nR3Ynk6CTdckpsEg7yok/+Y9d/zJJza8jNyuXcrpMAbJ69lq5Te/PZiZV0ndaHbYs2GSTv4sRn5CC3t9Itu9tZEp+h3xtyLzmDe0kZ9P3+IL2/O0BIRIx2fVImthamfLQ1hG6r97F0/0VUpajBKvdwIzoqVrccGx2Hu4dbsfEdOrdjx297DZHaS2EldyT7sfdidnQyVvJX7+QP2scAoh4WHKvoqDg8PN2Lje/cpT2/b9tVaH1ZL0/Kly/L8aMnSyTP5yVzdUEVl6BbVsUlIHMt/gaQdae3yT1x2hCp/WseHu56n6uY6Fg8nvK5Cu4SxJ+/7dZb175jGw6F/M6adcvxLCMvsVz/65zlziTGFLwPk2ITcZY7F4rrNaE3v6/+DUWOQm/9/Zv3adRGOxyzWftmuHgU/x42JBe5CwnRBeVKiEnEpYhyAUxcMp5Ve1bQc0wPvfUd+3Zg/V8/MHjaIL7+ZEWJ5vs8bOWOpD9Wn2fEJmPrXnx9bm5nRcUAP+6FXNGtq9sngGHHluA/9QP2z1xfovn9hK3WAAAgAElEQVQ+D6mLC+qEeN2yOjEBqXPx7yGLNkHknT0FgDIyArN6DcHcHImdPaa1/Uplo89F7kLCY5+xxNhEXORFl3Hc5+P4avdXdB/d3VDpPTepiyuq+MfOWQkJSJ/y/7Zs3x7FqYJzlsTMDOfV3+C0cgXmzZoVu58h/Rfef8ak1khK5OdV89o2wotTp04d/ve//9G6dWuioqKYPHky6enpheIOHDjAlClTmDJlihGyhOV95jGh4RBMzEyo2qQmAC17tWHrp2uZ3GQ4Wz9dS9/PhhsltxehUmu4n5zJmr7+LHy3EXN2nCU9Nw+VWk3Y/UQ+CvRl06AAolIy+fPiXWOn+491fLcdfxTRsBNKn85diu7t7tylPX/+sbfUjl4oitXbAZhVq0z6hq3GTqVEBL/7Nr/9slO3vG/3ERrUbk2rpsEcPXyCL1cueMreQknzrl4Bj/IehO4NLbTti4nLCerTnuU7v8DSxhJlvtIIGf5z80d/xuDAYYzrMp5aDWsS2CVAt+3Pddvp06w/axZ8R8/RPZ7yKqWPRCal01cjOPfDXlIfFDSczq8/wKrm4zm8cDNNRwUbMcMXZ+4fiEnlKuT8qh2NkX/+LHlnT+Kw5GtsJ39C/vUraF6hev1Ji0cv5sM2HzKp6yRqNKxBqy6tjJ3SP2YRGIhplSpk/VQwcibh/W4kDRlK2pxPsRs1EpmnpxEzfHGv+/tPKDmlZ/zvSxYXF4dUKsXe3p6oqCi9bTY2NjRr1oxmzZqxcOFCrl69SqNGjfRiAgICCAjQnnQHb9Z/vvRFtOzdlubdta8TeTECJ8+CO+6OcmdSY4vviVcq8rm4/wx1Ahtw7a9LNO7Sks2zfwDg7M5Q+iwc9o/zehncbC2JTSsYmheXnoObraVejLudJTXLOGMqk1LG0YbyTrbcT8rE3c6KKu4OlHW0AcC/ahkuPUyis59Bi6Cnz8BufNC7CwCXwq7o9bLJPd2Ji4kvcr9qNSojk8kIv3ityO2lReV+Afj09Acg+cIdrB57L1p5OpEda9zHG17EgME96d1X+zz+hfOXKVO24Fh5lnEnJjquyP1q1KyKiYmMixeuFNrWuUt7Jo+fXTIJvwBVQiKyx4Zoy9z/z959hzdVvg0c/yZp073pZhXKXi1lg0BL2QoIKEs2KIgsARkqKMhQVAScICAIKvhTUWQju2XJEMteZbWl0D3SleT9I9gSUxCUJKXv/bkurovm3Ce9T5+cJ+c5zzjeaG/fMYmza1Qf18F9SXzpVcjPt2SKj2TwsL70G9gTgBPHYozOK/8AP+Lvc17VrF0NlY2N0SJ7KSlFUyPWrPofb7490UxZ///UeUBn2vfpAMCFk+cp41/0OfTyK0NSQpJRfPX61QmuG8yyqOWobFS4ebkxd+1cpvaayo1LN5j+wpsABAQF0DCioeUO5G+6DHyGTn06AnD+j/N4BxQdl7d/Ge787biAwmPVZGnYuX4X1UKqFQ5j/8uun3czdvZoM2Z+f/UHRBLS21Cfx5+8jOs99bmLnycZ95mu1nHeUFKuJHBkefEjt07/cpD27wx+/Ak/It2dOyi9i0bJKMt4o0syrQdtQ8Jw6N2ftNfGGNWDmu9Wo/nOsAaB82tvor153fxJP4SnBzxN+z7tAbhw8gLe95xjZfzKcCfB9BiTbhV9Fnev3021etUKp4qUBLo7t42mRKm8vdHdvm0Spw4Lw3nACySPHmtUVro7hmPWxseTd+IENlWqoI2z7sKApfXzV1LIwmwGpbInPD09naVLl9KhQweTxb5iYmLIvTuHQ6PRcOvWLcqUMd8wud1fb2Vmp0nM7DSJE9uO0KR7KwAqhVZBk5FN2m3j+bZ2jvaF88SVKiV1IsJIuGS4iZCWmEzVJjUBqN6sNomxCVhTrUBPriVncjMlk3ytlq2nrtGqqvEdzPBqgfwea7jITsnO5WpyBmU9nKgV4EFGbl7hQm2HryRSydvV4sdwr1XL1tKp9fN0av082zbtpEevZwAIbVCXjPQMEm+ZVsAAXXp05Jcft1gy1X/l/Fc72Nz2dTa3fZ3rW45Sqadh2JdX/crkpWeX+Lnf91q+dA3hLboS3qIrmzbu4Pk+zwIQ1rAe6emZ3LplegEA0L3n0/x4T8/qX4KrVMLd3ZUjh4+bNe+HkXf6LLblA1EF+IGNDY5tw9HsjTaKsa0ajOfU8dyZ8Ca6lJJdbiu+/IbIp7oT+VR3tmz8jed7GxbIq9+g3t3zqviyerZHZ9b/YFxW984fb98pggvnL5sv8f+HNq7ayJiOoxnTcTQHth4s7HGrFlqN7IwsUhKNG3abV29iYMMBDG0+hNd6TCLuyk2m9jI8bcTNy7BAp0KhoPeY3mxebTytwJJ+WbmBER1eZkSHl4naGl3Yq10jtDpZGdkkJxrfDFeqlLh6GL6PVDYqmrRpTOy5WAACKxZ9xzVu04gbscY3+S3l2KodLO/0Oss7vc75bUep3cNQnweEViY3I5usYurzlhN7YufiwPa3jRfI86hYNH0nOCKEFCtfWwAUnD+LKqAsSl9DPWjXKoK8g1FGMarKVXAeM4H0t6eiT7vneJVKFC53y69iJWyCKpF/tGSsK/Hrql8Z3XE0ozuO5sDWA7Tp0QYwnGNZxZxjf/8sNopsxNXzlltQ+GHknz2HqmxZVP6GsrJvE0FulPF3lk2VYFwnvkrK1GnoUovKSuHsDLa2hv+7uWFbpzYFsbGWTL9YpfXzJ0qWUtMTnpeXx6RJkwofUfbUU0/x9NOmqwNfvnyZZcuWoVKp0Ov1REREEBwcbJEc/9x1jDrhoczes5g8TR5fTfqkcNv0TfOZ2WkSakc7XvlyMjZqWxRKBecOnGLPGsMqzqumfEHvGYNR2ijJz81n1dQvLJL3/dgolUzpWJ+Ra/ai0+vpGhJEsI8bn+6KoWaAB62rBdKssh8HLt2i+6dbUCoVjI+sh7ujHQDjI+vx0td70AM1/D3oUb+SVY/nXju37yO87VPs/X2j4RFlo98s3LZp9zqjVdGf7tqeQb1fNtq/bmgtlqz6CDc3VyLbt2L8lJG0bd6dkiLutxMEtqlHl+gP0GryODB+SeG2jttns7mtYbXf0Dd6U7FbM2wc1Dz7+yIufrubPz/40VppF2v71t1EtmvFkT92oMnWMOblokcO7tr/s9Gq6F2f7UjvnsNN3qN7z8789EMJmU6g1ZHy3mK8F72LQqUk85fNFFy+iutLg8g7c46cvQdwH/siCgcHvOZNN+ySkMidCYbPqM+Sj7CpWA6FgwP+v35Hyjvvk3OwZFwA7Ni2hzZtW3Lw+FY02TmMG1X0dIgd+340WhW9y7Md6PfcS0b7D3vpBdp3jKBAW0BqShpjX7bc4yX/rUkz5nHk+ElSU9Np0+0FXh7anx7PtLd2Wv/o951HaBDegKX7vjQ8omzigsJtizYvZkzHB/cAt+rais4DDN/B0Vui2b6uZDym8dDOwzSKaMiq/SvI1eQyf8IHhds+3/IpIzq8jFpty7zVc7CxVaFUqji2/xibvjHcROg6qAv1W9SnoKCAzLRM3htv/VXvL+08QeXweozY+wH5mjw2Tiyqz4dsms3yTq/j4udJ89HduHPxJkM2Gp4ucHTVdv74bjdhA9tRsUUtdPlactKz+PVV615bAKDTkvnZR7i98z6olORs24T2WiyO/YdQcP4seYeicRo6AoW9A67TDCOYtLcTyXh7GqhscHt/MQD67Cwy5s82LJJVwhzZeYSG4Q1Ztm8ZuZpcFtxzji3evJjRHUdjq7Zl1upZ2NjYoFQpObH/BFu+KWE3/bVa0j9aiMf780GpRLNpMwWxsTgPGUz+uXPkRkXjMnIkCgcH3N++W1Z3H0VmU7ECrhMngE5neCTnmm+MVlW3mv8Hnz9rehLnb5uDQl8SlvYs4YZX/PfD0UuqRbOrWzsFs6g+5mdrp/DYzbGra+0UzGJs5pOxmNijOF6tnLVTMIuGJeCZweZw/aLpqIgn3bP1rTM82txy9E/WnPKH0UZZOhdrGl6z9A29HXDKydopmMXyiiXzSRr/lY1T6WzalNm8x9opPBYHA8zTKdUkrmR1Ev2TUjkcXQghhBBCCCGEKIlKzXB0IYQQQgghhBAllwxHN5CecCGEEEIIIYQQwkKkJ1wIIYQQQgghhNnJI8oMpBEuhBBCCCGEEMLsdNZOoISQ4ehCCCGEEEIIIYSFSE+4EEIIIYQQQgiz0yPD0UF6woUQQgghhBBCCIuRnnAhhBBCCCGEEGan01vn9544cYIVK1ag0+lo06YN3bp1M9qen5/Pxx9/zOXLl3FxcWHcuHH4+PgA8NNPP7Fz506USiWDBw8mJCTkP+cjjfCHsGhGkLVTeOzefP2itVMwizPvtLJ2Co/d/rduWzsFs7j2fmdrp/DYffpO6Syri8NdrZ2CWTxbf7S1U3jsfjq22NopmIUuOc7aKTx2+Us/sHYKZjF0nYe1U3jsvh+otnYKZjHuaydrp2AW6fp8a6dgFmutncATTKfTsWzZMt544w28vLyYOnUqDRo0oGzZsoUxO3fuxMnJicWLFxMVFcWaNWsYP348N27cIDo6mg8//JCUlBRmzZrFwoULUSr/24ByGY4uhBBCCCGEEMLsdCjM8u9BLl68iJ+fH76+vtjY2NCsWTOOHDliFPP777/TunVrAJo0aUJMTAx6vZ4jR47QrFkzbG1t8fHxwc/Pj4sX/3tnpjTChRBCCCGEEEKYnR6FWf49SHJyMl5eXoU/e3l5kZycfN8YlUqFo6MjGRkZJvt6enqa7PtvyHB0IYQQQgghhBBPrB07drBjx47CnyMjI4mMjLRiRg8mjXAhhBBCCCGEEGanM9P7tn1Ao9vT05OkpKTCn5OSkvD09Cw2xsvLC61WS3Z2Ni4uLib7Jicnm+z7b8hwdCGEEEIIIYQQpVLlypWJj48nMTGRgoICoqOjadCggVFMWFgYu3fvBuDgwYPUqlULhUJBgwYNiI6OJj8/n8TEROLj4wkODv7POUlPuBBCCCGEEEIIs/un+dvmoFKpGDJkCLNnz0an0xEeHk65cuVYu3YtlStXpkGDBkRERPDxxx8zevRonJ2dGTduHADlypWjadOmvPrqqyiVSoYOHfqfV0YHaYQLIYQQQgghhLAAcw1H/yf169enfv36Rq/16tWr8P9qtZpXX3212H27d+9O9+7dH2s+MhxdCCGEEEIIIYSwEOkJF0IIIYQQQghhdtbqCS9ppCdcCCGEEEIIIYSwkCe2J7xXr16UL18enU6Ht7c3o0ePxsnJqXD7pEmTCAwMLJxUD/DJJ59w+vRpHB0dycvLo0qVKvTp08foAezmEnX5Fu/t+BOdDp6tV54hTauaxGw9c5Mv9p8FhYKqPq7M69KAuLRsXv3xMDq9ngKdnj5hQTwXGmT2fB9F1xkDqREeQp4mj7UTP+PmqViTmGErp+Dq445SpeLKkbP8+OZy9Do9dTs1pt24nvgEB7Co65vc+POy5Q/gH0TF3mH+nrPodHq61S7LkIamf/9t5xP4/OAlFEBVbxfmdqxr+UQfQrXZA/FuE4pWk0vMmM/I+DPWJMalbhC1F41EZa/m9m/HOff6SgB8n2lM5Yk9caoayKEOb5D+R8koq6grt5m/6zQ6vZ5utcsxpHFlk5ht5+L5PPoCCsXd8ukcCsCoHw5zMj6V0EAPFj3b0NKpP1DE2/0JCg+hQJPL5glLSIyJNdpuY6+my2djcKvgg16n49KO4+ybtxaAso2qET6jP941yvHrKx9zftMRKxyBKVW1UOy6DAWlkvzDO8jf9WPxcXWa4DBgMtkLJ6K7cQkApX8F7HqMBDsH0OvRLJoEBfmWTP++Xnz7JRqENyBXk8tHExZwKebSfWPfXDYdv/K+jGo7CoCgGkGMmjMKeycHEm/cYv6Y+WgyNZZK/V95Y86H7I06jKeHO+tXf27tdB7a/qN/8u7Sb9Hp9HRv+xRDn+tktD0u8Q7TF64gJT0TN2cn5kwYhl8Zw2NoRsxYwJ/nLhFaowofzxhrjfTvq7SeVwCD3hpGaHgYuZpcPpu4iCsx9//emfTlNHzL+zKxnXH5PD28K/3fGMywkP5kpGSYO+UHUgXXQ915MCiUFBz9jfx9PxcfV7Mx9n0moPlsCrq4y6BSoe7yIqrAyuj1OvI2foUu9rSFs3+w3jMGUye8PnmaXFZM/IRrp66YxIxd+TpuPu6oVCouHDnDmjeXodfpKFezIi/MHo6tnRptgZY1b35J7B8XrXAUxkrb56+ksMbCbCXRE9sIV6vVzJ8/H4CPP/6YrVu3Fk6Yv3HjBjqdjjNnzpCTk4O9vX3hfv3796dJkybo9Xo2btzIzJkz+eCDD7CxMd+fQqvTM3fbST7v3QxfFwf6fbWHVlX8qFzGtTDmanImyw9c4Kv+T+FqryY5KxcAb2d7VvV/CrWNiuy8Anp8uZNWwX74uDiYLd9HUb11CN5BfsxrPZ7yocH0mD2URd3eNIn7etRCcu9eWA74bBz1OjfhxIYDJJy7zsoRH9JzzjBLp/5QtDo983ad4bPuYfg629Pv24O0quRNZS/nwpirKVksP3KFr55vhKu9LcnZuVbM+P7KtAnBKcif/U3G4RYWTM33hnGo4xsmcTXfG8rpCUtIO3qR+t9MoUxECHd2niDz7HVODPmQmvOHWyH74ml1eub9dorPejbC18WefmuiaBXsQ2Uvl8KYqylZLD90ia/6NDUpnwENKpFToOWHk9eskf59BYXXw6OiH8taTsA/tDJtZw9iTde3TOKOLNnI9QNnUNqqeP7baQS1rsuV3SdJj0ti84QvaPhSJ9M3txaFErtnX0Sz5C30aUk4jHmPglOH0SfeMI6zs0fd4mm0V88VvaZUYtdnHLnfLkQXHwuOLqDVWjL7+2oQ3oCAigG82HI41UKr8fLsUUzoWvzCLk07NEOTZdzAHv3eGJa/s4yYQzG0fb4tPV7qweoPVlsi9X+tW6e29O3RhWmz3rd2Kg9Nq9Ux5/M1LJk1AV8vD/q8OovWjUOoXD6gMOaD5et4JqIZXds059AfZ1i08gfmTDDUd4O6tycnN4//bd5jrUMoXik9rwBCwsPwC/JnbKuRVAmtytB3RvBGt9eKjW3UoQk52Tkmr3v5l6HuUyHcvpFo7nT/mUKB+pmh5Hz1Dvr0JOxHzKXg7O/ob980jlPbY9u0I9rr5wtfsgkzPPtY8/FEcHLFvv80cr6YCnq9JY/gvmq3DsUnyJ/XW4+mUmgV+s0eztxu00zivhj1ITl3rwVHfDaBBp2bcGRDND2mvMCGhd8Ts/sEtVuH0nPqC7zf+y0LH4WxUvf5K0F00gYHSslw9KpVq5KcnFz4c1RUFC1btqRevXr8/vvvxe6jUCh4+umncXd35/jx42bNLyY+hXIeTpR1d8JWpaR9zUB2X0gwivnxj6v0CgvC1V4NgKeTHQC2KiVqGxUAeVodJaO6LVKrXRi//7gPgGvHL2Lv4oiLt7tJ3F8NcKWNChtbG/R3vzgSL8Vx+3K85RJ+RDEJaZRzc6Ssm6Oh7Kr6sfuScWX6U8xNnq9XDld7WwA8He2skeo/8u7QgLjv9wKQdvQiNq6OqH2My0rt446NswNpRw13oOO+34t3R8NzFLMuxJF9qWSVVUxCKuXcHSnrfrd8qvmz++Ito5ifTl7n+ZAKxZZP4wplcFKXvHuRwe3COPXDfgDij1/CztUJp7+VVUFOHtcPnAFAl6/lVkwszv6GXrv0G3e4c/Y6el3JqTGU5auguxOPPvkWaAsoOLEfm1qNTOLU7fuSt+sno944VdUQdPFXDQ0FgOwM0JeMWWWN2zVh5w87ATh3/BxOrk54+HiYxNk72tNteDfWLv7O6PXAoEBiDsUAcHzfcZp1am7+pP+jBiF1cHN1+efAEiTmwmXK+/tQ1s8bW1sbOrRsxK5Dxt/9l6/F07huDQAa1a3OrkMnCrc1qVcTJwd7SprSel4BNGzbiL0/7AbgwvHzOLk64V7MuWXnaE/nYV34cfE6k20Dpg9hzdyVJaKtqiwbjC4pAX1KImi1aP+MxqaG6QgsdZtehh7ye8pK6VMW3WVDPUFWOuRkoQyoZKnU/1FIu4Yc/NFwg+ry8Qs4ujjhVsy14F8NcFXhteBfW/TYOzsC4OjqSOqtFEuk/UCl7fMnSp4nvhGu0+mIiYkxeuB6dHQ0zZs3p3nz5uzfv/+B+wcFBXHz5s0HxvxXiRk5+N3Tc+3r4kBihvEds6vJmVxNzmTg1/vov2ovUZeLGhIJ6RqeW7aLDp9sY1Dj4BLTCw7g5utJalxS4c9pCcm4+XkWGzt81RTeOvo5OVk5nNx0yFIp/ieJWTn4uhRdePm62HM7y7in+2pKFtdSshm09jADvjtEVOwdS6f5UOz9Pcm5WVRWOfHJ2Pt7msbEF93QyokzjSlJEjP/Xj4O3M4srnyyGPTtAQZ8E03UlduWTvOROft5kBFfVFYZCck4+5l++f/FztWRypGhXIs6ZYn0/hWFqyf61KJzQ5+WhMLNeCqQMrASSvcyaM8eNX69TADo9dgPm47D2Pexbd3NIjk/DC8/L+7EF32mkhLu4OVnOsXphYn9Wb/kJ3I1xp/Pa+ev0aRdEwBadG5BGf8y5k34/6lbSan4limqy3y9PEhMSjWKqRpUjh0HDJ+93w4cI0uTQ2p6pkXzfFSl9bwC8PDzJCmu6NiSEpLw9DX9Puo1oS+/Lv2ZPE2e0esN2jYiOSGJq2dizZ3qQ1G4eqJPK6rX9WlJKFyMj0fpH4TCrQza88Y3iHQJsaiqNwClEoW7N8qASijcSk5d4eHrSfI914IpCUm43+dacNyq1/ng6JfkZOVwdNNBAL57+yt6Tu3Pu9Gf0XPaAH58b41F8n6Q0vb5K0l0KMzy70nzxDbC8/LymDRpEsOHDyctLY26dQ1zcC9duoSrqytlypShTp06xMbGkpl5/y9R/X1uT+3YsYMpU6YwZcoUs+T/d1qdnmvJmXzZtznzuoQxc/MJ0nMMd0H9XB34fmg4v7zUhg0x10nKMh3y8iRYOmAeMxu9jI3ahuBmta2dzmOj1eu5lprN0p4NmNuxDrN2nCIjp+TMqfv/TqvXcS01i6XPN2Zu5xBmbfuzVJWPQqXk6cWjOLZiK2nXSv4NhvtSKLB7ZjC5G1aYblOpUAXVIOebBWg+nYZN7SaogutYPsd/KahmJfwr+HNg6wGTbQsnfUSnAZ35aONCHJwdKMgvsEKGAmDCkOc4GnOe58e+xe8x5/Dx8kCpfGIvkwxK8XkFUKFmEL4V/Diy1fjGvtpeTbdRPVn34bdWyuxfUChQdxxA3pZVJpsKju1Cn56M/Yh5qDsNQnv9XIkatfAoPhowm4mNXsRGbUP1u9eCrV9ox7pZXzG52UjWzfqKge+OtHKWD6dUff6ExZW8cZgP6a854bm5ucyePZstW7bQqVMnoqKiuHnzJqNGGRa90Wg0HDx4kMjIyGLfJzY2ljp1TL90IiMjC/fRrCh+DsjD8nGxJyGjaB7grQwNPi7Gw9p8XRyoHeCOrUpJoLsTFTyduZaSSW1/j3vex4HgMq4cu55M2+oBWEuz/m1p3CcCgOt/XMY9oOiuu5ufJ2kJyffblYLcfE5tP0rttmFc2P+n2XP9r3yc7Ll1z6iFWxk5eDsZDzf3cbanjp+boezcHKng4cS11Gxq+blZOl0T5Qa3I/AFQ1mln7iEfWBRWf291xtMe8ftA0xjShIf57+XjwZv52LKx9+9qHw8nbiWmkUtP9OhctYUMiCSun3CAUg4eRkX/6KycvHzJDOh+OF57eYNJSU2gWPLtlokz39Ln56Mwr2o50bh5mXUK4SdA0q/8jiMeMew3cUd+0HTyPlqDvrUJLSXTxuGywIFZ4+iDKyM9qJ16pDOAzrTvk8HAC6cPE8Zf+/CbV5+ZUhKSDKKr16/OsF1g1kWtRyVjQo3Lzfmrp3L1F5TuXHpBtNfMKyjERAUQMOIkrVAYGnh6+XOrTtFddmtpBR8vIzrAB8vDxZMM1w7ZGty2BF9DNe7Q2RLqtJ0XgG0G9CRNr3bAXDp5AW8AoqOzcvPi+Rbxt9HVetXo1LdYBbvX4LKRomblxvTv3uHFTOW4lPOh/c2f2TY19+LeRs/ZFrXSaTdNh4BYSn69GSjUQoKNy/0Gfccj9oepU857IfMMGx3dseu32vkrnkPXdxl8javLAy1Hz4L3Z04i+VenNb929Oyj+E6+cofF/G851rQw8+L1H+4Fvxj+xFC2jbkzP6TNO3Rmu/eNtwo+n3jAQbMG2He5O+jNH/+ShIZnW/wxDbC/2JnZ8fgwYOZP38+7dq148CBA7z//vt4ehoaEjExMfzwww8mjXC9Xs/mzZtJSUkhJCTErDnW8nfnWnIWN1Oz8HFxYOvpm8zpEmYUE17Vj82nb9KtbgVSsnO5mpxJWXcnbqVrcHNQY2+rIj0nj+M3knihoenqz5YU/fV2or/eDkCN8FCaD2zHiV+iKR8aTE5GNhl/q2DUjnbYOTmQcTsVpUpJjYhQrhw+a43UH1ktP1eupWZzMy0bH2d7tp5PMFn5PLyyD1vOxdO1ViApmjyupmQR6FYypgxcX7GN6yu2AVAmMpTyQ9qT8FM0bmHBFGRkk5doXFZ5iakUZGpwCwsm7ehFAp5rybVlW6yR+kOp5efGtdSsovI5F8/cTsbnc3iwH1vOxtG1djlSsvO4mpxFoFvJu7A+sWoHJ1btAKBSRAihA9ty9pcD+IdWJjcjm6xE0y/u5hN7YufiwNbXvrR0uo9Md/0CyjL+KDx80KcnYxPSgtxvFhQF5GST9dbAwh8dRswi99ev0N24hC4pwTBU1lYN2gJUlWqRv3eDFY7CYOOqjWxctRGABhENeXrg0+z9ZQ/VQquRnZFFSqLxDZPNqzexefUmAHzK+jBjxQym9poKgJuXG2lJaSgUCnqP6c3m1fSYm9kAACAASURBVJstezD/T9SqEsTVuFvcSLiNr5cHW/YeZt7EF41iUtIycHNxQqlU8uX3m3g2soWVsn14pem8Ati2ajPbVhnOgdCIMNoP7ET0L/uoElqV7IwsUv92bm1fvYXtqw3fUd5lfZi8/HVm9jYsOPpi2KDCuMX7lzDtmQlWXZ1ad/MSSi9/FO7e6DOSUdVpRu73i4oCcjVkzytapNZ+yAzytnxtWB3dVg0oID8XZeU6oNOaLuhmYbu/3srurw03f+uE1yd8YAcO/xJFpdAqaDKyTRqbdo722DvZk3b3WrBORBgXDhvWNUlLTKZqk5qcP3ia6s1qkxibYPL7LKE0f/5EyfPEN8LBMK+7fPny/PTTT3h6ehY2wAFq1qzJwoULSUkxnDhff/01P/zwA7m5uVSpUoUZM2aYdWV0ABulkint6jJy7QF0ej1d65Yn2NuVT/eeoaa/O62r+NMsyIcDV27TfelvKJUKxofXwt1BzYGERD7ceQoFhjtHAxoHU8XH9Z9+pcWc2XWc6uEhTNnzEfmaXNZO+qJw2/hNc1nQaSpqR3uGfDkRldoWpVLBxQOnObDG0Nio3b4B3d4ahLOnK0OXv0bcmViWDphnrcMxYaNUMjm8Oi//dMxQdrUCqezlzKcHLlLTx5XWlX1oVsGLA1eT6L4qCpVCwbinquLuoLZ26ibu7DhOmTYhtDi0EK0ml1Njix4t1OS3eRxsY5h6cWbycmovGonSXs2d305w5zfD4kQ+HRtSfc4g1F6uhK55jYyYqxzrPdcqx/IXG6WSyRG1ePmHw+h00LV2WSqXceHTqPPU9HWjdbAvzSqW4cDV23RfsReVEsa1ql5YPkO+O8CV5Cw0+QW0/2InM9rXoVlF73/4reZ3eecJgsLrMWzfB+Rr8tgycUnhtgGbZ7Oq4+s4+3nSdEw3ki7cZMAmQy/X8ZXb+fO73fjVrUTXpeOwdzPMFW/2ag++irTM1Jr70unIXb8Uh+Ez7j5K6Td0t66jbtcH7Y2LaE8/4DFqmizy923AYYzhiRjas0dN5rday+87j9AgvAFL931peETZxKIG0KLNixnTcfQD92/VtRWdBzwNQPSWaLav227WfB+HSTPmceT4SVJT02nT7QVeHtqfHs+0t3ZaD2SjUjFtRD9GzliAVqejW2QLgisE8snq9dSsUpHwxiEciTnHopU/oFAoqF+rKq+P7Fe4/8DJ84i9EU92Ti6Rgyby9phBNK9fAqZVldLzCuD4zqOEhoexcO/n5N19RNRf3t20gMmdxlsxu39BpyPv1+XYD3wdlErDEPPEG9hGPI8u7tID//YKJzfDfnod+vRkcv/3sQUT/2d/7jpGnfBQZu9ZTJ4mj68mfVK4bfqm+czsNAm1ox2vfDkZG7UtCqWCcwdOsWeNoZNg1ZQv6D1jMEobJfm5+aya+sX9fpXFlLrPXwnyZE6kePwU+vtNihaF/utw9JLozbevWzsFs5g5ueQsVPK47H/rCZ7n+wAtZvlbO4XH7tN3SmdZjeiVZe0UzKL32rx/DnrC/HRssbVTMAtdsnWH3ppD/tIPrJ2CWQxdV/ouK5cPKHk31h+HcV+XvrICSNeXnnVf7rX26nprp/BY/M+/3z8H/Qs9462/oN+jeMJXHBFCCCGEEEIIIZ4cpWI4uhBCCCGEEEKIkq10jr94dNITLoQQQgghhBBCWIj0hAshhBBCCCGEMDtZmM1AGuFCCCGEEEIIIcxOp7B2BiWDDEcXQgghhBBCCCEsRHrChRBCCCGEEEKYnQ7pCgfpCRdCCCGEEEIIISxGesKFEEIIIYQQQpidPKLMQBrhD0GfmWntFMRDUgRWsHYKj12u4o61UzALhYurtVN47JKUt6ydglkoXBytnYJZ5OizrZ3CY6dLjrN2Cmah9Aywdgri/zGFs5O1UzCLO7rSeX2Rrsu1dgriAWRhNgMZji6EEEIIIYQQQliI9IQLIYQQQgghhDA7eU64gfSECyGEEEIIIYQQFiI94UIIIYQQQgghzE4WZjOQnnAhhBBCCCGEEMJCpCdcCCGEEEIIIYTZyeroBtIIF0IIIYQQQghhdrIwm4EMRxdCCCGEEEIIISxEesKFEEIIIYQQQpid9IQbSE+4EEIIIYQQQghhIU90T3hGRgYzZ84EIDU1FaVSiaurKwBvvPEGI0aMYPDgwbRr1w4AjUbDa6+9xrRp0/D396egoIDJkyczYsQIqlSpYtZco64mMX/feXR6Pd1qBjAkrKLR9l/OxLEg6iI+znYA9KpTlu61AgH4KOoC+64modfraVzOk9eeqopCUXJWNeg6YyA1wkPI0+SxduJn3DwVaxIzbOUUXH3cUapUXDlylh/fXI5ep6dup8a0G9cTn+AAFnV9kxt/Xrb8AfxN1NnrvPfLAXQ6Pc82qsaQiBCj7fN/OcCRi3EA5OQXkJyZw/5ZAzl7M4k5P+4nMzcPlULJsDYhtA+pbI1DuK9a7wzEt00IWk0eJ8Z+RtqfsSYxbnWDCFk4ApW9mlu/neDUGyuNtlca0Zlab73A1povkpecYaHM7y/qQjzvbTlmKK/6lRjyVE2TmK0x1/hidwwooKqvO/N6NuPIlVvM33K8MCb2TjrzejYjokZZS6Z/X8/MGEC1u+fV/yZ+TtzfzitbezV9Px2LZwVf9FodZ347xtZ3vwPAPbAMPd57ESdPVzRpmawd9ynpCclWOApjqsp1UbfvD0olBcd3kx+1wWi7Tb2WqCP7oMtIAaDgyDYKju8GwK7va6jKBqO9dp7c7963dOoPNOrtkTSKaESuJof3Xv2AizEXTWI+WPcenj6e5ObkATCl31RSk9J4+oXOdB34DFqtjpwsDR9OWci1C9csfQgm9h/9k3eXfotOp6d726cY+lwno+1xiXeYvnAFKemZuDk7MWfCMPzKeAIwYsYC/jx3idAaVfh4xlhrpP+vvDHnQ/ZGHcbTw531qz+3djoPTVUtFLsuQ0GpJP/wDvJ3/Vh8XJ0mOAyYTPbCiehuXAJA6V8Bux4jwc4B9Ho0iyZBQb4l03+gQW8NIzQ8jFxNLp9NXMSVmPtfI0z6chq+5X2Z2M74M/f08K70f2Mww0L6k5Fi3e8sZVBt1G36GurAP/ZScGiT0XZV7eaow3uhv1sH5h/7De3JvUUBanvsh81Ge/44+TtWWzL1fzT07RcJu1tWiycs5HLMpfvGTl32Bn7l/Rjb9hUAJnzyGoGVDNe8Tq5OZKVn8WpH69cdo2a+TOOIhuRqcnlv/PtcKK5u/34+XvfU7ZP7TiU1KbVw+1OdWvDWkumM7DSK8ycvWCz3kkxfcpowVvVEN8JdXFyYP38+AOvWrcPe3p4uXboAsG3bNqpUqUJUVFRhI9zBwYE+ffqwfPlyXn/9dTZs2EC1atXM3gDX6vTM23OOz7qG4utsR791R2gVVIbKns5Gce2r+DKlVTWj107Ep3IiPo11vRsDMPiH3zl6M5UGZT3MmvPDqt46BO8gP+a1Hk/50GB6zB7Kom5vmsR9PWohuZkaAAZ8No56nZtwYsMBEs5dZ+WID+k5Z5ilUy+WVqdj7k9RfP5iJ3zdnOi3aD2talWgsm/R33tSl6aF//92fwxn45IAcFCrmNW7NRW83UhMy6Lvwp9oWq0srg52Fj+O4vi0CcG5kh87m47HvX4wdd4dyv5OpmVV590h/DFhKanHLtL4m8n4RNQjcecfANgHeOLdqg7ZN25bOv1iaXU65m76nc/7h+Pr6kC/pdtpVS2Qyj5uhTFXkzJYvv80Xw2NxNVBTXJmDgANg3xZN7IDAGnZuTyzaCNNK/tZ5Tj+rlrrELyC/Hi/9auUCw2m2+whfNptukncvqUbuXzgNCpbFUPXvE7V1vU4v/sPOk3rx/Ef93Hsh31UalqTDq/1Yt2rn1nhSO6hUKDuOIic1XPRpydjP2wWBeeOob9z0yis4NRB8rasNNk9/8BGCmzV2NRvY6mMH0qj8IYEBgUy8KnB1Aitztg5oxndpfiLx7lj3jW5CNu5fhe/rt4IQNO2TRg5/SWm9n/d7Hk/iFarY87na1gyawK+Xh70eXUWrRuHULl8QGHMB8vX8UxEM7q2ac6hP86waOUPzJkwHIBB3duTk5vH/zbvsdYh/CvdOrWlb48uTJtVsm7yPJBCid2zL6JZ8hb6tCQcxrxHwanD6BNvGMfZ2aNu8TTaq+eKXlMqseszjtxvF6KLjwVHF9BqLZn9A4WEh+EX5M/YViOpElqVoe+M4I1urxUb26hDE3Kyc0xe9/IvQ92nQrh9I9Hc6f4zhQJ12/7krn0ffUYy9gOno714An1SnFFYwZnD921g2z7VHd3185bI9pHUDw8joGIAL7d8iaqh1Xhp9kgmd51YbGyTDk3JyTIuqw9GvVf4/0FvDCE7I9us+T6MRhENKRsUyIAWg6lRvzpj547hlWfGFBs7Z/S8YhvYDk4OdB/yLKePnTF3uk8UGY5uUGqHo0dFRTFgwACSk5NJSkoqfL1Zs2YA/Pzzz2zfvp2+ffuaPZeYW+mUc3OgrJsDtiol7av4svvynYfaV4GCPK2OfJ2OPK2OAp0eT0e1mTN+eLXahfH7j/sAuHb8IvYujrh4u5vE/dUAV9qosLG1Qa/XA5B4KY7bl+Mtl/A/iLl2m3JlXCnr5YqtjYr2IZXZferqfeM3n7hEh7u93RW83angbWj8+bg54ensQEqm6UWBtfi1D+P6OkNZpR67iK2rI3Y+xmVl5+OOrbMDqccMd3uvr9uHX4cGhdtrzRzAmVnfgN5yeT9IzM1kynm6UNbT2VBetcuz+5xxo+7Ho5fo1bAKrg6G88bT2d7kfbafvk7zKv44qEvGfcka7cI4fve8un6f8yo/J4/LB04DoM3XEncqFjc/Q0+kT5VALkWfAuDygdPUaBtmweyLpwysjC7lFvrU26DToj11EJtqD5+X7sop9Lkl53z6S7N2Tdn+ww4Azhw/i7OrE54+ng+9f3Zm0cWmvaN9Yd1oTTEXLlPe34eyft7Y2trQoWUjdh06bhRz+Vo8jevWAKBR3ersOnSicFuTejVxcjA9z0q6BiF1cHN1sXYaj0RZvgq6O/Hok2+BtoCCE/uxqdXIJE7dvi95u34y6uVWVQ1BF3/V0AAHyM4Afcm5PG7YthF7f9gNwIXj53FydcLdx7QDws7Rns7DuvDj4nUm2wZMH8KauSspAacVSv9K6FMT0acZ6sCCM4dRVQl96P0VvhVQOLmivRJjxiz/nUbtmrDrh50AnD9+DidXJzyKKSt7R3u6DO/G94vX3ve9mj/dgn0/W/8GXvN2zdj2v+0AnDn26HU7wOBJA/nu07Xk5eaZI0XxhCuVjfA7d+6QkpJCcHAwTZs2JTo62mj7oEGDWLNmDd27d8fZ2fk+7/L4JGbl4OtSdEHi62zH7axck7jfLiXy/LeHmLj5JAkZhovNev5uNAj0oO3y/bRbsY9m5b2o5Olk9pwflpuvJ6lxRTc50hKSCxsCfzd81RTeOvo5OVk5nNx0yFIpPpLE9Cz83Is+E75uTiSmZRUbG5eSQVxyBo2CA0y2/XktkXytjnJermbL9VHZ+3uSc09ZaeKTsff3NInRxBcNW86JTyqM8W0fRk58MumnrT9U9i+J6Rr8XB0Lf/Z1dSAxXWMUczUpg6tJGQxctoP+S7cTdcH0ps/WmGt0rF3e7Pk+LDdfD1LjisohLSEZV7/7j36xd3WkRpv6XIwyNLzjz1ylVgfDhXit9g2xd3HE0d38dd2DKFw80acVff706ckoXEyPSVWjIQ4vzcWu51gUro92wWMNZfzKcDuuaGTI7fg7lPHzKjZ20gcT+HzLp/Qba3zzt8vAZ1i1fwXDpw3jk+mfmjXfh3ErKRXfMkV/e18vDxLvGV4JUDWoHDsOHAXgtwPHyNLkkJqeadE8BShcPdGnFt3U16cloXAz/vwpAyuhdC+D9uxR49fLBIBej/2w6TiMfR/b1t0skvPD8vDzJCmu6NiSEpLw9DWtE3pN6MuvS38mT2Pc0GnQthHJCUlcPRNr7lQfisLFA316Ub2uz0hG4WxaB9pUC8N+8EzU3V5G4fLX8SpQR/Qmf9f9G6/W5OXnRVL838qqmHqwz8QX+HnJT+RqTK+BAWo2qkXqnVTiY63fOVPGz+vh6/YPJ/LF1s94YWy/wteq1A7GO8CbQzsPmz3XJ43OTP+eNKWyER4dHU3TpoYhw82bNycqKspo+4kTJ/Dw8OD69ev3fY8dO3YwZcoUpkyZYtZc/9KyojcbBzZnXZ/GNCnnyfQdhh6ua6nZXEnJYuug5mwd1ILDN5I5FpdikZwet6UD5jGz0cvYqG0Iblbb2un8Z1tPXCKybhAqpfFpdDs9mze+283bz7dEqSwdE19UDmqqjO3Gufe+t3Yqj0yr03MtOYMvB0Uwr2dTZm44TPo9F2u3MzRcTEyjabC/FbP895QqJb0XvUL0V1tIuW4Ycrlp9hqCGldn9MY5BDWpQVp8Ejpdyf+KKjh/DM2icWi+mIr28p/YdR1h7ZQemzlj3mV42xGM7zGBOo1q07ZHZOG2X1ZuYECLwXw5dxn9xph/dNbjMGHIcxyNOc/zY9/i95hz+Hh5oFSWykuKJ5tCgd0zg8ndsMJ0m0qFKqgGOd8sQPPpNGxqN0EVXMfyOf4HFWoG4VvBjyNbjW/sq+3VdBvVk3UffmulzP4d7cUTaD6fRM6K6eiunEbd2TBVz6Z+BNpLJwvnij+JKtYMwq+CH4e2HrxvzFNdW7Lv57333V4SzR09j+GRLzGu+6uFdbtCoWDEjJf4fOYSa6cnSrCSMfbyMYuKiiI1NZX9+/cDkJycTHx8PP7+/iQnJ7N582bmzJnD22+/TUREBBUqVDB5j8jISCIjDRdJ2Ytf/k/5+DjZcyujaBjlrcxcvJ2M5wm7O9gW/v/ZmoEsjDYMB951+TZ1/NxwvDtMtnkFL04mpFM/wHpzwpv1b0vjPhEAXP/jMu4BRXcG3fw8SXvAAlAFufmc2n6U2m3DuLD/T7Pn+qh8XJ1ISC3qzbmVloWPW/EjD7acuMzUZ5sbvZaZk8fo5Vt4pUMD6lbwNWuuD6Pi4LaU72coq9QTl7G/p6wc/D3JiTcuq5z4ZBzu6R239/ciJz4Zxwq+OJb3ptXOd+++7knLbXPY1/ENcm+nWeBIiufj6kBCetFw3lvpGnxcHYxifF0dqF3WC1uVkkAPZyp4uXAtOYPagYa/xbZT1wivHoityroNiCb929KwTzgAN/64jHuAJ39NhHDz8yQ9ofiLr2fnDiPpSgJRy7cUvpaRmMqaER8BoHa0o3aHhuSkW3eOnT4j2aiHTuHqaXpBqSk69wqO70Id2cdS6T2SLgOfoVOfjgCc/+M83gHehdu8/ctwJyHJZJ+ku69psjTsXL+LaiHVCoex/2XXz7sZO3u0GTN/OL5e7ty6U1Q33EpKwcfLeDqEj5cHC6aNAiBbk8OO6GO4OjsiLEufnozCvUzhzwo3L6MRJ9g5oPQrj8OIdwzbXdyxHzSNnK/moE9NQnv5tGEYOlBw9ijKwMpoL1rvu7ndgI606W1Yx+fSyQt4BRQdm5efF8m3jL+zqtavRqW6wSzevwSVjRI3Lzemf/cOK2YsxaecD+9tNtSDXv5ezNv4IdO6TiLttvGoDkvRZ6QYje5RuHiiz/xbHZhTNPKu4OQebMOfA0AZUBlluarY1I9AYWsHKhvIzyF/z/8skntxOg7oRNs+7QG4ePICXv5/K6u/1YPV6lcnuG4wX0R9idJGhZuXG7PWzuHNXtMAww3lJh2aMrHzeMsdxN90HfgMnfoaFqE898e5h6rb7xjV7TupHlqN6G3RBFWryIffG9at8vT2ZNbymbw5ZLoszkaJmdFodaWuER4XF0dOTg5ffPFF4Wvr1q0jKiqKnj17snLlSp599lm8vLwYMGAAy5Yt4+233zbrauO1fF24lpbNzXQNPk52bL1wi7ntahnF3M4qapjvuXKbIA9Dw8/PxZ4fT92kIKwCej0ci0ulb71yZsv1YUR/vZ3orw3zZGqEh9J8YDtO/BJN+dBgcjKyyfjbF5za0Q47JwcybqeiVCmpERHKlcNnrZH6P6pVzptrd9K5mZyOj6sTW09cYk7fcJO4K4mppGtyqVfBp/C1/AItr67cztNhVWhbt5Il076v2BXbiV1hKCufyFCChrQjbn007vWDyc/IJjfRuKxyE1PJz9TgXj+Y1GMXKff8U1xZtpWMs9fZVruoV7LNkUXsa/+61VdHrxXgybWkDG6mZOLj4sDWmGvM6dHUKCa8elk2x1ylW2glUrJyuZqUQVmPoqHZW/68xpjIupZO3cTBr7dz8O55VS08hKYD2/HHLwcoFxpMTobG5LwCaDvhOexdHPlx8lKj1x09XNCkZqLX62n9cld+X2f9+XW6m5dRevqhcPdGn56MqlYTcn/6xChG4eyOPtNwnKqqYejuxBX3Vlb3y8oN/LLSsLJ744hGdB3UhV0/76ZGaHWyMrJJTjRuKChVSpxdnUlPSUdlo6JJm8Yc22+YYx1YMYCbsYbjbNymETdijdc0sIZaVYK4GneLGwm38fXyYMvew8yb+KJRTEpaBm4uTiiVSr78fhPPRrawUrb/v+muX0BZxh+Fhw/69GRsQlqQ+82CooCcbLLeGlj4o8OIWeT++hW6G5fQJSUYhqDbqkFbgKpSLfL3bijmt1jOtlWb2bZqMwChEWG0H9iJ6F/2USW0KtkZWaQmGjdat6/ewvbVhhuQ3mV9mLz8dWb2fgOAF8MGFcYt3r+Eac9MsOrq6Lr4Kyg8fFC4lUGfkYJNjUbkbvjCOMjJDbIMN7ZVwaHokgzDsvN+LepRVdVujtIvyKoNcIDNqzaxeZVhdfewiAZ0Gvg0+3/ZS9XQamRnZJPyt7LaunozW1cbyta7rA9vrJhe2AAHqNcihJuXbhbesLSGn1du4Od76vZug7sa6vb61cnKyPrnuj2yCUf3HSMrI5vudZ8rjPvg+/l8MWuJNMDv0pWOQaL/WalrhEdFRdGokfGiJI0bN+ajjz6iatWq3Llzh4gIQ89ggwYN+O2339izZw+tW7c2W042SiWTW1bj5Z+Po9ND15r+VPZy5tNDl6jp40rrIG++/eM6e2LvoFIocLO34e1Iw2OWIiv7cORGMs9/axhq1ay8F62CvB/06yzqzK7jVA8PYcqej8jX5LJ2UtEXyvhNc1nQaSpqR3uGfDkRldoWpVLBxQOnObDG0ANUu30Dur01CGdPV4Yuf424M7EsHTDPWoeDjUrJlG7NGLl0Mzqdnq6NqhHs58mnW3+nZllvWtcyjJrYcndBtntv3mz74zLHLseTmpXDL0cMq5fO7NWa6oHFzyGytMQdx/FpE0LEwY/QanI5Ma6orFrumMveyKkA/DllReEjyhJ3niDxtxP3e0urs1EpmdIpjJFf70Gn19E1tBLBPm58uvNPagZ40rp6IM2C/ThwKYHuH29CqVQwvm0I7o6GG143UzJJSM8m7J6bKSXBuV0nqBYewsQ9C8jX5PK/e86r0ZvmsLjTNFz9PIkY/SyJF2/yysbZABxYuY3f1+6mUpMatH+tN+j1XDl8lp+nFzMU1dL0OvI2f4V9v8mgUFJwYg/62zexbd0DXdwVtOePYdOoPTZV66PXaSEni9yfix4TZT/oTZReAaC2x2HcYvI2LEF7yfqjaQ7tPEyjiIas2r+CXE0u8yd8ULjt8y2fMqLDy6jVtsxbPQcbWxVKpYpj+4+x6RvDxWjXQV2o36I+BQUFZKZl8t5466/MbaNSMW1EP0bOWIBWp6NbZAuCKwTyyer11KxSkfDGIRyJOceilT+gUCioX6sqr48smgs5cPI8Ym/Ek52TS+Sgibw9ZhDN65f8KUiTZszjyPGTpKam06bbC7w8tD89nmlv7bQeTKcjd/1SHIbPuPuIst/Q3bqOul0ftDcuoj195P77arLI37cBhzGG3jrt2aMm88at6fjOo4SGh7Fw7+fk3X1E2V/e3bSAyZ2s12P6r+h15G1fg93zEwx14J/70N+Jw7ZFN3QJsWgvnsA2rC2qKiGg06LXZJG38UtrZ/1Qju78nbDwBny2b4nhEWUTFxZu+3Dzwod63FiLLi3Z94v1bxj/5dDOwzSOaMTX+78iJyeX+a8W1c1fbP2Ml9qPRK1W8+6auXfrdiXH9h8vrNuF+CcKfUlYirWE+6/D0Uui6R88ufOKHmTWIuuvAv247Xix5FwUPU6RC6paO4XHbubU0nmX+40hpfO2ddelD/eUiifJxt9MHztYGig9TRfAfNLlvjvB2imYxdB1pe+ycsXLJeOxsI9bv09KXx0IkK4rfuG3J91vN7ZZO4XHYkH5F8zyvuOvFf9ov5JKVlERQgghhBBCCCEspNQNRxdCCCGEEEIIUfKU/Ge1WIb0hAshhBBCCCGEEBYiPeFCCCGEEEIIIcyu9K0a8e9II1wIIYQQQgghhNnJI8oMZDi6EEIIIYQQQghhIdITLoQQQgghhBDC7GRhNgPpCRdCCCGEEEIIISxEesKFEEIIIYQQQphdSVyYLTMzkwULFnD79m28vb0ZP348zs7ORjGxsbEsXboUjUaDUqmke/fuNGvWDIBPPvmE06dP4+joCMCoUaOoWLHiA3+nNMIfQvamU9ZO4bFLxtvaKZhF3k9brJ3CY5ek8rN2CmZx691oa6fw2KXiYe0UzCJz1y1rp2AWbZTlrJ3CY5e/9ANrpyAekt3k0llWLb6bbu0UHrv809esnYJZ5OjV1k7BLLJ0edZOQTyArgQ2w9evX0+dOnXo1q0b69evZ/369bzwwgtGMWq1mldeeQV/f3+Sk5OZMmUK9erVw8nJCYD+/fvTpEmTh/6dMhxdCCGEEEIIIcT/S0eOHKFVq1YAtGrViiNHjpjEBAQE4O/vD4Cnpydubm6kp6f/698pPeFCCCGEEEIIRtlp4QAAIABJREFUIcyuJC7MlpaWhoeHYTSju7s7aWlpD4y/ePEiBQUF+Pr6Fr727bff8r///Y/atWvTr18/bG1tH/ge0ggXQgghhBBCCPHE2rFjBzt27Cj8OTIyksjIyMKfZ82aRWpqqsl+vXv3NvpZoVCgUNz/YeYpKSksXryYUaNGoVQaBpX37dsXd3d3CgoK+OKLL/j555/p2bPnA/OVRrgQQgghhBBCCLMz14zwvze6/+7NN9+87zY3NzdSUlLw8PAgJSUFV1fXYuOys7OZN28effr0oWrVqoWv/9WLbmtrS3h4OBs2bPjHfGVOuBBCCCGEEEIIs9OZ6d9/0aBBA/bs2QPAnj17aNiwoUlMQUEB77//Pi1btjRZgC0lJQUAvV7PkSNHKFfunxd+lZ5wIYQQQgghhBD/L3Xr1o0FCxawc+fOwkeUAVy6dInt27czYsQIoqOjOXPmDBkZGezevRsoehTZokWLChdpq1ChAi+++OI//k5phAshhBBCCCGEMDvd/adbW42LiwvTp5s+XrFy5cpUrlwZgJYtW9KyZcti958xY8Yj/04Zji6EEEIIIYQQQliI9IQLIYQQQgghhDA7ndmWZnuySCNcCCGEEEIIIYTZSRPcwGKN8F69elG+fHl0Oh2BgYEMGjSIuXPnApCamopSqSxcDn7u3Ln069evMN7b25vRo0fj5ORU+H6TJk0iMDCQcePGsWvXLjZt2gTAjRs3CAgIQKlUEhISQmBgIJcuXWLo0KGA4Rlyv/76KwAODg4MHDiQ6tWrm/34bcMa4TRiNAqlkpwtG9F8/43Rdvtnn8e+Q2fQatGlpZK54F10ibcAcBzyEuqGhlX4sr9dRd7eXWbP91H0nTGEOuGh5GnyWDbxY66dumISM37l67j7eKBUqTh/5Ayr3/wSvU5HuRoV6D/7Rewd7blz4zZLxi0kJ1NjhaMwZlO7IfZ9Xwalkvy9m8nd9J3RdnXrp1G36Qo6LfqcHDQrP0QXdw1UKhwGT0BVoYph3+gd5G781kpHYarRzP6UjQihQJPL/vFLSI6JNYkJnfwcwT1boHZzYk3VYYWv13yxI1X7tEZXoCUnOYOoV5eQdTPJgtkXz6FZAzxfM5RV5k+bSVux1mi7Xf06eE4aibpKJW5PmU32jn2F2zzGDsPhqUYApC5ZQ/a2PRbN/UGenzGYWuGh5GlyWTXxU64Xc169snIabj7uKFUqLh45y3dvfoleZ/h6az2wA60GtEen1RGz8xg/zVtj6UMwoW7YCJdXRoNKiWbjRrK/Na4HHZ97HodOndHfrQfT33sX3S1DPaj08cF14muofHxArydlymR0txKscRgm2r7Vn8rhIeRrcvl14hJu/e28srFX8+xnY/Ao74NOp+PijuPsftfwOQ3tF0H9AW3Ra3XkZeeweeoyki7EWeEojKmqhWLXZaihHju8g/xdPxYfV6cJDgMmk71wIroblwBQ+lfArsdIsHMAvR7NoklQkG/J9O+rtB7X/bwx50P2Rh3G08Od9as/t3Y6j6zl2/2pcPc7a8erS7hdzHdWk9eeo3qPFti5OfFF9aLvLOcAL9oueAk7V0cUKiX/x959h0dRvAEc/17NpVfSQDCh15BA6EoSOkhRLCCGLgQpihB6VTpIURBFfhYQxY6iSC9K772X0EJ6IeWSq78/LiQ5kkBQ7i7gfHx8Hm733eSd7M3uzs7M7t7Z33F9xwkrZl+UvG4oql5DTd+/XRvI/eO+a4vwe9cWBoy5atRfLMIQex1F01bYdXg1P076TCCZU6Mw3Lhi7SKUKGp6FKERoeSqc/ng3Q+4crpobnO/n4uHtwe5ObkATOw1kfTkdOo0rsPgqYMJqBnAnKFz2L1ht7XTL9a77w+naUQTctU5vD9yDhdOXSoS8/GPi/H08SA3RwPA2z1Gk5qchm95HyYuHIO7pxt30zKYOnwmiXcSrV0EoQyzWiNcqVQyf/58AD788EP27t2b//n7779HpVLRpUuXYuOXLl3Kpk2beOmllwBTQ9tgMHDu3DlycnIIDw8nPDwcMD2lburUqfkN+ntPrwM4cuQIW7Zs4b333sPFxYWrV68yf/58Zs+ejZubm+UKL5XiNPQd0ieMwpCUiNuST9Ec2IP+xvX8EP2VS6SNGAS5uag6dcWxfxQZc6ajCG2CvHI10oYOBIUC13lL0B4+gDE723L5PoK6YcH4BPgxPmw4gcFV6T1zEDO6jS8St3zowvzG9VvLRxPaqSkH1++h75whfDdrFRcPnKXFKxF0GNSVXxauLbK9VUmkqCKHk7VgLMaURJymLEN7fK+pkZ1Hs387mp2mmzny+k1R9RhC9sLxKEJbglxB5uQ3QWmH88z/odm/HWNyvK1Kk698RBAuAb783GIU5UIq03R2X/7oPK1I3K0tRzn/xRZe2r3AbHnK6RjWd5iMPkdD9d6taDipJ7uGLLVS9iWQSvEYP5z4qLHo4pPwX7OU7F370F4t2Ff6uASSpszHtfcrZpvaP9cIZc0qxL4WhUShxPd/C1DvOYQxy/Z1q3ZYMN4BvkwNG0FAcFV6zhzIvG4Ti8StHLoov14NWj6KBp2acnj9Xqo1rU1Qm4bM7BCNTqPD2bP4911alVSK89vvkBY9Cn1iIh6ffEru3j3orxccB7WXLpEdZToO2nfpivPgKNLfmw6A6/gJZH39NZojh5Go7DEa/+0LSR6PyuFBuAf48knLUfgHV6b9jL581W1akbgDK/7gxr5zSBUyXv9mAoFh9bi68yRnft3HsTXbAajSOoTWk97guz7zrFyK+0ik2L04CPWKaRjTk7EfMQ/dmYMYE26Zx9mpULZ4Af31CwXLpFLser5D7rdLMNyJAQdn0OutmX3JntZyPUC3jm14vXsXJry/4OHBZUyl8CDcAnxZ/dwofIIrEzarLz90mVYk7tqWo5z8cguRf5mXMXREVy79foDTq7fhXtWfLl9F81WzkVbKvhgSKareI8iaN8Z0bTHtY7TH9mGILTgGavZtR7Mj79oiuCmqnlFkfzAe7b5taPdtA0BaIQCHt98rUw3w0PBQ/AP8GfDcAGoE12DYrGGM7FL833reiHlcOmnemE24ncAH735A98HdrZFuqTSNaMwzARV4pXkvaofUYszskQx44a1iY6cOncn5kxfMlg2fMoQ/f9zMhh820aB5MG+Nf5PpI2ZZI/Uyr2ycvW3PJg9mq1GjBnFxpe/BqFatGikpKfmf9+zZw/PPP09QUBCHDx8u9c/59ddfiYyMzG+gBwYG0rJlSzZu3Fj65P8BebWa6GNvY4i7Azodubu2o2zSwixGe/IY5JruDGrPn0XqVc60bcVn0Z4+AQY95Oagv3YFRYPGFs33UQS3DWXvzzsBuHrsEg7ODriWK3pD415DQSaXIVfIMRpNvXU+AX5cPHAWgDO7T9Cgg+3LJgusjiEhFmPiHdDr0B7ciSK4uXlQTkFDTWKngrzyYDSaPkulSBR2GHU6s1hbqtiuAVd+NN1dTjx6BaWrI/beRfdV4tErqBPSiiyP23sOfd6d3sQjl3Hw87BswqVgV6c6upux6G7HgU5H1qadOIQ1M4vRxcajvXStYB/lUQRWIufIKdAbMObkoLl4FfvmDa2ZfomC2jZk/89/AXDt2CUcnB1xeUC9ksplyArVq+d7tWXT8l/RaXQAZCTftVLmJVPUMB0H9XdMx8Gc7duxa37fcfB4oePg2bNIy5mOg7JKlUAmQ3PEdLw35qjz42ytapsGnP7JVK9ij13BzsURx/vqlS5Hw4195wAwaPXEnY7B2ddUfzSFRv4oHewwloGBetKKVTEk3cGYEg96Hbrju5HXblQkTtnudTQ7fjHrDZZVq4/hznVTQxUgOwPKyA2Tp7VcD9Kwfl1cXZxtncY/Eti2Aefy6lZ8Xt1yKOacFX/sCtnFnLMwgtLJHgA7Zwey4lMtmu/DyAJrYIi/XXBtcWAHihDz81WRa4tiKJpEoN1ftkZENmnbhG0/mW4SnD92HicXJ9y93Uu9fcKtBGLOx+Sfw8qC59s1Z8OPmwA4c/QsTq5OeHqX/ronoFolDu85CsCRPcd4vl3zh2wh/NdYvRGu1+s5fvw4FStWLFW8wWDg9OnTNGxYcHG8d+9emjdvTvPmzdm9u/RDVm7evElgYKDZssqVK3Pr1q0Stng8pF5eGBIT8j8bkhKRenqVGK9q2xHN4QMA6K5dRtmgEdjZIXFxRVEvGFnehWlZ4O7jSUpswZDklLgU3H09i419d9UkFh/5HzlZag5v2A9A7KVbBLcNBSC0Y1M8/Er+u1iLxN0LY0qh/ZWSiMS9aJmUEV1wmrsK1atvkvPNMgC0h//CmJuD8+Lvcf5gDbkbf8CYlWG13B/EwdedrEL7KutOCg6+pT9JFla1Z0tu23hYH4DM2wtdXMHwLl18EjLv0n2HTI3uUCQqO6RuLqhC6yP38bZUqo/EzceD1Nik/M+pccm4+RZ/8h++agLzj3xGbpaao3n1yjvQjyqNajBm3UxGfjeNSvUqWyXvB5F6eWFIKFSvEhOReZW8r+w7dkRzwHQclFd4BmNmJq7T38djxUqcBkeBtGy83MPZ1527hepVRlwKzj4l1ys7FweqtA7m+p4z+ctCercm6q8PCB/fgy1TV1k039KQuHhgTCv4/hnTk5G4mh8DpeUDkbp5oT9/xHy5lz8YjagGTsH+7QUowrpZJefSeFrL9bRy9HUns1DdyryTgtMjnLMOLPqZ6i81p9/BD+n8VTS7pti2bpmuLQrOV6Zri6LHQGWrrjjNX43q1UHkfF10tJmicRja/dstmuuj8vT1JKnQOSvpThJevsUf30d+MJKlG5fS8+2e1krvHynnW46E2IL9lRCbSDnf4q+/Jy0ay6otK+n3TmT+sktnrxDWwfQ6q7AOz+Ho7IiLexkYlVYGGDBa5P8njdWuYjQaDdHR0YwbNw4vLy8iIiJKFf/mm2+Snp5OvXr1ANNL011cXPDy8qJu3brExMSQmZn52PPdunUr48aNY9y4cY/9Zz+IXXgb5NWqo/7JNCRbe/QwmsP7cftgGc5jp6A9fwajoezffS/Owt4zGNnoTeRKBTWb1QHg8zHLCH+jPVPWz0XlZI9Oq7NxlqWn2f4bmWN7k/PDSuw69wJAFlADDAYyRr5GRnQkdu1eRlLOz8aZPl6BLzXHKyiQ08v/sHUq/0rOviOodx/E76sllJszgdyTZ00jTp4wH/WexdhGg5ErFVTPq1cymRQHVyfmdZvIz7NWM3CZDYdg/gOq1m2QV69O1nd5U1NkMhR165H5ycekRA1G5u+Pqn172yb5D0hkUrp+NJQjX2wi7WbBxd3RVVv55PlR7JizlubDn4DGnUSCXed+5K7/oug6mQxZQE1yvlmE+uMJyOs0QValrvVz/Cee1nL9R1Xr2pTzP/zFF41GsL7PfNouHgKSMviC4vtotv1KZnQkOd9/hl2XN8zWyQJrQG4OhtsxtknuX5o3Yh5vtXmL6O7R1GlUh1bdW9k6pX9t6rAZvNGqP1HdhlO/cT06vNwWgI/eW05I0yC+2vwZwU2DSIhNxKB/Mq/fBcuwyZzwR4nPzc1l5syZbNy4kY4dO7Jnzx5u377N0KFDAVCr1ezfv5/WrVs/9GdWqFCBq1evUqdOnfxlV69epUKFCkViW7dunf8zkzq0LHXexTEkJSEtV9DDJvUqhyE5qUicon4D7HtEkj5mBGgLhsGp136Neu3XADiNmYz+9s1/lc+/FRHZnud7mg6c105cwcO/oCfBw9eD1LiSH9aly9VyfMshgtuEcnb3SeKuxLKw9/uAaWh6vfAQyyZfCsbUJCQehfaXRzmMqSWXSXtgB/aRb6PGNExMd+oQ6PUYM9LQXz6D/NlqaBPvWCHzomr0aU21XqbnJSQdv4pjoX3l6OdBdtyjDc/ze6429UZ0YWP3mRg0tr9hok9IQl7ozrTcxwt9QtG6VZL0ld+QvtL0cDCv2ePRXr/92HMsrZaR7WieV6+un7iCu78XYJpj5u7rSVpcSonb6nK1nNhyiKA2oZzffYrUuBSObzqY/7OMBgNOHs5kpthuVIYhKQmpd6F6Va4c+qSi+0oZ0gDHNyJJeafgOGhITER35bJpKDuQu3s3ilq1yGGDdZK/T0jv1tTvYapXd05exaVQvXL29SCjhGGvHeYMIPVaHIc+31Ts+rO/7afdjH6PP+FHZLybgsStoBdL4uqJMb3QMdDOHqlvReyjZpjWO7uh6juBnC9nYUxLRn/1rGm4NqA7fwRp+croL5+yahmK87SW62lSt09ravc01a2EE1dxKlS3nPw8yHyEc1at11ryW6Tp+QpxRy8js1Ng7+GM2kbTc0zXFgXnK9O1RcnnK+2BHdj3MV1b3KNoEl5mhqK/0OcF2vc03Qy9eOIiXv4FdcvLz4ukuKJlS867PlRnqdmxbgfV6lfLH8ZeFnTv242uvV4A4Nzx83j7F+wvb/9yJMYVfbBaYl45s7PUbP5lG7WCa/Lnj5tJik9m3MApANg72BPesSWZdx9/p+GT6Mnrs7aMsjGe7wHs7Ozo168fv//+Ozqdjn379rFgwQKWLVvGsmXLiI6OZs+ePaX6WV27dmXNmjVkZJhOojExMezcuZN27dpZsgjoLp5H5l8BqY8vyOXYtYxAs988Z1nlqjiNGMXd6eMxphea2ySVInE2DV+RPRuIPCAQ7ZHSz4O3hO2rNzKtYzTTOkZzbPNBmr0UBkBgcFWyM7JJTzSfm2XnoMqfJy6VSakXEcKdK6bGzr0HRkkkEjoPe5mda7ZYryAl0F+7gMy7PBIvX5DJUTQKQ3tsr1mM1Kd8/r/l9RqjjzdNaTCkJCCvWd+0QqlCFlgT/Z0b2Mr5r7byW9uJ/NZ2Ijc2HaHyy6Y5uOVCKqO5m13s3O+SeNSuRNM5/dnWbyE5ZWCOMUDumQvIK5ZH7m+qW47twsjeta90G0ulSF1NcyUVVQNQVg1Avc92dWvX6k3M6jiGWR3HcGLzQZq8ZBrGFhBcFXVGNneL1Cu7/HniUpmUOhEhxOXVqxObD1GtSW0AvAP8kCnkNm2AA2jPn0dWvgJSX9O+UkVEkLvX/Dgor1IV53dHkTZxPMa0gvJqL5xH4uSExNUVAGVwCLrrMdZM38zRVVv5vONEPu84kYubj1Cnu6le+QdXJjcjm6xi6tXzo1/GztmeLdO/Nlvu/qxP/r+rRNQnNcb2T3w33LyE1MsPibs3yOTI67dAf/ZQQUBONlnT+pA9ezDZswdjuHGRnC9nYbh1Bd3FY0h9K4JCCVIpssDaGOJte+P4nqe1XE+TU19tZW37iaxtP5Grm45QM69u+QRXRpORXfzc7xJkxiZToYXpOOhexR+ZSmGzBjiA/tp5ZD6Fri0ahz/42iKoCfr4QjeGJRIUjcLQHCgbjfDfv/qdYe2HMaz9MPZt2pffq10juAZZGVmkJpjfMJHKpPnDsWVyGY1bNeb6hetFfq4t/fTlOnq3GUjvNgPZtXE3HV82tQ9qh9Qi824WyQnmN8NlMhmuHqbzkkwuo3nrplw9b3qTiauHK5K8kRd9hr/O+u9sc9O4LDJY6P8nzRPxnvCAgAAqVqzIL7/8goeHBx4eBXMja9WqxZIlS0hNTcXd/cFzhRo2bEhKSgqTJk1CIpFgb2/P8OHDH7rdv2bQk7l8Ma4zFoBMSs7mDehvxOAQ2R/dxfNoDuzFcUAUEpU9LhNMTwLWJyaQMX0CyOS4LvgIAGN2FhnzZ5apIbMndxylXngIc3YtRaPO5fPoj/PXTdswn2kdo7FzsGPEynHIlQokUgnn951m55rNADTu0oKISNOd1KObDrD7hzIwz8lgQL3mIxxHzTG9RuTvjRhir2PXrQ/6mIvoju9D2aor8lohoNdhzMpEvdJ0t12z7VccBkTjNGMlIEGzexOGW0VfLWULt7Ydp3xEEC/t+QC9WsPud1fkr+uyeSa/tTU9fbvBxB4EvtgMub2SVw5/yKVvdnJ84c80nNwThaOK8E9HAJB5O5nt/RbaoigF9AZS5izFZ/ls0yvKft2E9sp13Ib0IffsRdS79qGsXQ3vhdOQujhh/3wT3Ib0Jrb7m0jkMnw/XwSAMSubpIlzoYwMFTu94xh1wkN4b9eHaNQaVhWqVxM2zGNWxzEoHVQMWTkGuVKBVCrhwr4z/J13E2vv99uJnPcWkzctQKfVsWrUMlsVpYBBT8aHi3GftwCkUnL+3IA+JgbHfv3RXThP7t69OEVFIbG3x3Wa6ThoiE8gbdIEMBjIXL4c9w8WgUSC7uIF1HmvmrS1K9uPUzk8iKi/PkCr1vDH6IJ61X/DTD7vOBFnXw+aD+9G0uXb9P/D1Mt6ZNUWTqzdSYM+bXm2RW0MWj05d7P4/d1PbVWUAgYDues+w/7NqXmv8tqGIf4myrY90d+6bN5wvZ86C+3f67EfYRr5pj9/pMj8apt5Wsv1ANFT53Do2EnS0u7SqtsbvDUgku6dLdvx8LjEbD9OpYggeu821a1towrqVo+NM1nb3nTOajahB9W7NUNhr6TfwQ858+1ODi76mb/fX0PE3IEED2yP0QhbbV23DAbUqz/CMXpu/utPDbevY/diX/QxF9Ad24eydTfktUNAp8OYnYn6s7n5m8uq18OQnGB6sFsZc2j7IUIjQvl89+fkqHNYNGpR/rqlG5cyrP0wFEoFM76egVwhRyqVcmz3MTZ+Y3owcrWgakz+bDJOrk40bt2YN959g6jWUbYqDgB7t+2nWavG/Lh3DTnqXGaMLNgXq7aspHebgSiUCpZ8Mw+5XI5UJuXQ30f4dY3p3BTStD5vjX8To9HI8QMnmT9hsa2KIpRREmNZehRhGfVvh6OXRWPOlZ2Huz1Oi8JLf5f8SfHLZl9bp2ARYeVs/9q2x21umoVv6NnItICnb18BfH71GVun8NgNfzXL1ikIpWQ39gNbp2ARnwRPsXUKj13viLLX8H0cemxX2joFi0jVqx8e9ATaH7vT1ik8Fu8+28MiP3dhjI1fcfyIyvxwdEEQBEEQBEEQBEF4WjwRw9EFQRAEQRAEQRCEJ5sYgm0iGuGCIAiCIAiCIAiCxZWNp+/YnhiOLgiCIAiCIAiCIAhWInrCBUEQBEEQBEEQBIszigHpgOgJFwRBEARBEARBEASrET3hgiAIgiAIgiAIgsWJOeEmohEuCIIgCIIgCIIgWJxBDEcHxHB0QRAEQRAEQRAEQbAa0RNeCmPOlbN1Co/dZNd0W6dgEeN2eNg6hceujzHb1ilYxMw0N1un8Ni9q8i0dQoWsexaBVunYBEjat20dQqP3YDv3W2dglBKLdZOsXUKFhF17D1bp/DYDWs41tYpWMQ3jRJtnYJFyDyUtk5BeADRD24iesIFQRAEQRAEQRAEwUpET7ggCIIgCIIgCIJgcWJOuInoCRcEQRAEQRAEQRAEKxE94YIgCIIgCIIgCILFiVeUmYhGuCAIgiAIgiAIgmBxRjEcHRDD0QVBEARBEARBEATBakRPuCAIgiAIgiAIgmBxYji6iegJFwRBEARBEARBEAQrET3hgiAIgiAIgiAIgsWJOeEmohEuCIIgCIIgCIIgWJwYjm7yn2uEv/baa1SsWBEAqVRK//79qV69ulV+9+tT+1M3PBiNWsP/Ri/lxplrRWJGfjURN293pDIZFw+d4+vJKzEaDDxTsxKRMwehclCRdCuRFe8sISdTbZW8H8S+WUM8xr6FRCol45c/Sf/8O7P1qpC6eIwZgrJqIAljZ5K99e/8de7vDMTh+cYgkaLef4SUuR9bO/0HenVqP2qHB6NR57Jq9MfcLGZ/DftqAq7ebkhlMi4fOs/aySsxGkx3+ML6tKdl73YY9AZObz/KL3PWWLsIZlzDgnn2/f5IpFISvt1K7NJfzNZLlHKqfPg2jnUD0aVmcCnqA3JvJWJXoRxBuz5EfTUWgMwjF7k27lNbFKFEPab2o254CBp1Ll+MXlZs3Xr7q4m4ershk8m4dOgcayb/z1S3aj3LGzPfRGGnRK/Ts2bySmJOXLZBKQo4PtcAn0mDkcikpH2/ieQVP5ittw+tg+/EQdhVD+D2yDlkbNwDgNzfm2c+ngRSCRK5nJTV60n7doMtilCijlN7UzU8CK1awy+jP+XOmRiz9QqVktc+HoF7JR+MegMXth1ly1zTcaX95DcIaForP87Ry4XZ9QZZuwhmFA0a4Rg1HIlUSs7GP1D/8I3ZetWLr6Jq3wn0egzpaWQumoshIR4Ah/6DUYY2ASD721Vo/tph9fwfpO+0gQSHNyBXncvy0R9y7fTVEmOjV07Ap6IPo9u+bbb8hTe7EjmpHwPrR5KRmmHplEvlaSzX89MjqRRRH506l63vriDxdEyRmCZjXqFG9xbYuTryaY2B+cud/D1ps2gwdi4OSGRS9s7+jus7Tlgx+0c3adZC/tpzEA93N9Z9/Ymt03kkr03tR52889WXo5cVe20x4quJuBQ6X32bd756c+lIfAL9AbB3cUB9N5sZHaOtXYQi5PUb4dB/GEhl5G77g9xfzI+DyrZdULXvhtFggBw1WZ8swHDrOrIqNXCIGm0KkkDOd1+iPbjbBiUoSla7IaoeQ5BIpWj+3ohmo/n1raJlJ5RhXcBowJijJmf1Ygx3boBMjirybWSVqoHRQM7a5egvnrRRKYSy7D/XCFcqlcyfPx+A48eP88033zB9+nSL/966YcH4BPgxPmw4gcFV6T1zEDO6jS8St3zowvzG9VvLRxPaqSkH1++h75whfDdrFRcPnKXFKxF0GNSVXxautXjeDySV4jlhOHGDx6KLT8L/m6Vk79yH9uqN/BBdXAKJk+fj2ucVs03tgmqhql+H2y8PBsDvy0WoGtYj53DZOFDVDgvGO8CXqWEjCAiuSs+ZA5nXbWKRuJVDF+Xvr0HLR9GgU1MOr99Ltaa1CWrTkJkdotFpdDh7uljLzuBCAAAgAElEQVS7COakUgJmvcm5HtPR3EmmzoZ5pG46hPrSrfwQ756t0aVlcrz5UDy7NqfipN5civoAgJzr8ZxqM8pW2T9QnbBgvAP8mJhXt3rNfJPZ3SYUifu0UN2KWj6Khp2acGj9XrqPe4P1S37g9M7j1AkL5uXxb7CgxzQrl6IQqRTfaW9xo+9EtHFJBPy0mIzt+9FcvpkfootNIHbsQjwGdDfbVJeYQsyr72LU6JA4qAj8YzmZ2/ajS0ixdimKVTUsCM8AX5aEjaJCcBU6z+zHim5Ti8Tt+WwD1/adRaaQ0XfNBKqGBXFp5wk2vv91fkzjPm3xq13JmukXJZXiNPQd0ieMwpCUiNuST9Ec2IP+xvX8EP2VS6SNGAS5uag6dcWxfxQZc6ajCG2CvHI10oYOBIUC13lL0B4+gDE724YFKlA/vAG+AX683XIIVYOrMWBGFJO6jSk2tlH7JuRk5xRZ7unnRb3n6pN4K8HS6Zba01iuSuFBuAX4svq5UfgEVyZsVl9+6DKtSNy1LUc5+eUWIv9aYLY8dERXLv1+gNOrt+Fe1Z8uX0XzVbORVsr+n+nWsQ2vd+/ChPcXPDy4DLl3vpocNpyAvPPVnGLOVysKna8GLx9Fg05NOLx+L58NW5Qf8/LE3qgzysDxQirF4c23yXxvNIbkRJznfoL20B4MtwqOg5q/t6LZ/BsAiobNcOg7lMwZY9DfuEbGmMFg0CNx88Bl4f9IP7wPDHpblcZEIsX+9WFkLRqHMTUJx4kfoTuxz9TIzqM9sAPtrj8AkAc1QfXqYLKXTETxXAcAsqYPRuLshsPbM8maOQyMYgj2PQbxtwD+4w9mU6vVODo6WuV3BbcNZe/POwG4euwSDs4OuJZzKxJ376Ark8uQK+QY876oPgF+XDxwFoAzu0/QoENjq+T9IHZ1qqO9GYvudhzodGRt3IlDWDOzGF1sPNpL18BwX4UzGpHYKZAo5EiUCiRyOfrkNCtm/2BBbRuy/+e/ALh27BIOzo64PGB/SeUyZIX21/O92rJp+a/oNDoAMpLvWinz4jkFVyEn5g65N+IxanUk/7ob93aNzGLc24WS+IOpJy759324tKhri1QfWf22oez/eRdwr245lrJu3VtjROXkAICDiwNp8anWSLtE9vWqobkei/ZmHGh13P3jL5xbNTWL0d5OIPdCDBjvG9Sl1WHM+85JlQokUomVsi6dGm0bcPxn02iYW8cuo3J2wOm+faXN0XBtn+lYp9fqiT0Tg4uvR5GfVbdLU079ts/yST+AvFpN9LG3McTdAZ2O3F3bUTZpYRajPXkMcnNN/z5/FqlXOdO2FZ9Fe/qE6WIzNwf9tSsoGtj+uH5PaJtG/PXTTgAuHbuIo4sjbt7uReLsHFR0GtiFnz/6vsi63lP6s2b2V2Xq2vNpLFdg2wac+8nUexh/7Ap2Lo44eBc9BsYfu0J2QjHnWSMonewBsHN2IMvGx8DSaFi/Lq4uzrZO45EFFTpfXTt2CftSXFvIFXKKm0LboFNTDv1m+15jWZUaGOJuY4g3HQe1u7ejDG1uHqQudLNApSpokGpy8xvcEqWyzDRUZQHVMSTGYkyKA70O7aFdyOubX9+SU6hMdqr8fSTzr4T+/HEAjBlpGLMzkVaqZqXMhSfJf64nXKPREB0djVarJTU1lalTi/bCWIK7jycpscn5n1PiUnD39SQ9segJ8d1VkwgIqsKpncc4vGE/ALGXbhHcNpRjmw8R2rEpHn5eVsn7QWTeXujjEvM/6xOSsKtbo1Tb5p48R86hEzyz9TskEgl31/6K9tqNh29oJW4+HqTGJuV/To1Lxs3Xg7vF7K/hqybwbFAVzuw8ztG8/eUd6EeVRjXoEt0Dba6Wn2eu5vrJK1bL/35KX080hb5/mjvJOIVULTlGb0B/Nxu5h+kix66iN3U3L0Cfoebm3G/IOHjOark/jLuPh1ndureviqtb76yayLNBVTi98zhH8vbV2ulf8s6qSbwyIRKJVMqc7kVHPFiT3NcT3Z2C7542Lgn7oNJPmZH7evHMZ9NRVvIjYe7nZaYXHMDFx4P0QvvqblwKLr7uZBazrwBULg5UbxXC/s83mi13Le+F+zPluLr3jEXzfRiplxeGxILeUENSIvLqNUuMV7XtiObwAQB01y7j8Hpf1D9/h8ROhaJeMPobMZZOudTcfT1ILnQMTI5LxsPHg7QE8wbaa6Ne5/fPfkWj1pgtb9imESlxyVw/F2ONdEvtaSyXo687mYXqVeadFJx83YtvcBfjwKKf6bpmLEH92iK3t2Pd67Mtlep/ntt956u0uGTcS7i2GJF3vjpT6Hx1T9VGNclISichJs7iOT+M1KMchqSCa0FDSiKyqrWKxNm174Zd51eQyBVkTCsYaSGrWhPHoWOQevmS9eFM2/eCAxI3LwwpBWUypiYiCyh6fasI64xdm+4gV5D9gWlagP7mVeRBTdEe3IHEwxtZpaqmv1HMBavlX9aVjVsttvef6wm/Nxx98eLFTJw4kaVLl+b3Xha2detWxo0bx7hx46ye48LeMxjZ6E3kSgU1m9UB4PMxywh/oz1T1s9F5WSPTquzel6Pk/wZfxQBFbnZtic32vRA1ag+dsF1bJ3WP/JR71mMbTQYuVJB9bz9JZNJcXB1Yl63ifw8azUDl5XtoX0PoklI5VjoIE61Hc31aV9Q5eORyPJ6TZ40i3vPZHSjQciVcmrk7auwN9ry/ftfMrbZEL5//0v6zB1i4yz/HV1cEtc6D+VK64G4vtgKmWfRXpYngVQm5ZUPh3Hgy02k3kw0W1e3cxPObDiY//yFJ4FdeBvk1aqj/sk0jUh79DCaw/tx+2AZzmOnoD1/xjRf8glSqVYAPpV8ObTpgNlypUpJt6Ev8/3Cb22U2b/ztJarJNW6NuX8D3/xRaMRrO8zn7aLh4CkbI2i+S/6sPdMxtx3vrontEsLDpaBXvBHkbtxHXeH9iJ79aeoukfmL9dfOsfdd/pxd+xgVC/1AoXShlk+Gu3O9WRO7EvOTytRduplWrZnI4bUJBwnLUP1WhS6K2fhCTu2W5oBo0X+f9L853rCC6tWrRoZGRncvXsXV1dXs3WtW7emdevWAPRf+/I/+vkRke15vmcrAK6duIKHv2f+Og9fD1LjkkvaFF2uluNbDhHcJpSzu08SdyWWhb3fB0xD0+uFh/yjnB4nfUISMt9y+Z9l3l7o4pMesEUBx4jm5J46h1Ftmm+n3nMIVVAtco+dtkiupdEysh3N8/bX9RNXcPf3Akx3Lt19PUmLK7lHUZer5cSWQwS1CeX87lOkxqVwfNPB/J9lNBhw8nAmM8U2D/DRxCWjLPT9U/p5ormTUmyM5k4yyKTIXBzQ5eWr02QCkHXqKrkxcagC/cmyYc9+WGQ7nu9pqp/XTlw2q1ul3Vf124RybvdJmnYPY+30LwA4/Mc+es+JsmzyD6GLS0ZeaKSLwtcLXXzJx4oSf05CCrmXruMQWjv/wW220CiyDQ16hgNw+8RVXAvtKxdfD+7GFT/0tcvsASRfi2Pffb3gAHU7N+X3yV9aJN9HYUhKQlrOO/+z1KschuSix0BF/QbY94gkfcwI0Grzl6vXfo16rWmeu9OYyehv3yyyrTW17d2BVj3aAnDl5CU8/Qu+h56+nqTEm9eraiHVCaxXhY92r0Aml+Lq6cqUtTP4YupneD/jzbw/F5u29fNkzh8LmdA1utgRKpb2NJarbp/W1M6rVwknruJUqF45+XmQWUK9Kk6t11ryW+Q8AOKOXkZmp8Dewxm1jadRPS3CItvRIu98FZN3vrp39nTz9SS1lNcW53abnpkjlUkJbteImZ3HWjr1UjGkJOZPswFTz7gxObHEeO2e7TgOGsn9s9kNt29gzFEjqxiA/opte42NaUlIPQrKJHEvhyHtAdfsh3Zi32sEOQAGA7nff0Ju3jqHsYswxN8qcVvhv+s/3Qi/ffs2BoMBZ2fLzCvavnoj21ebLiDrhYfQqk8HDvy2h8DgqmRnZBc5ads5qFA5qkhPTEMqk1IvIoSLecN+nT1dyEi+i0QiofOwl9m5ZotFcn4UuWcuoKhYHnl5X3TxSTi2DyNxfOmGseniEnB+qSPIvgWJBFWDeqSv+dnCGT/YrtWb2LV6EwB1woMJ69Oew7/tISC4KuqM7CLDxewc7LBztOdu3v6qExHC5bz9dWLzIao1qc3FfWfwDvBDppDbrAEOkHn8MqoAP+ye8UYTl4Jn1xZcHrrILCZ18yHKvRJO5pGLeL7QlLu7TwEg93BBl5YJBgN2FX1QBfiRcyPeFsXIt3P1Jnbm7au64SGE92nPwby6pS5F3aob0YBLefsqPSGFak1qcXH/WWo0q2Pz4X3qUxdRPuuPooIP2vhkXDo9z+1355VqW7mvJ/rUDIy5GqQuTtg3qE3KF+ssnPGDHVy9hYOrTcerauH1adynLad+20eF4CrkZKiLHYreatQr2Dk78OvYlUXWeVX2Q+XqyM2jlyye+8PoLp5H5l8BqY8vhuQk7FpGkDH3fbMYWeWqOI0YRfqkaIzphcoqlSJxdMKYcRfZs4HIAwLJXHDYyiUwt3nVn2xe9ScAwRENaNenI3t/+5uqwdXIzsgqMmR7y9cb2fK16RxXroI3Yz+fyHs9JgEwqEHf/LiPdq9gQudRNnuK+NNYrlNfbeXUV1sBeDaiPvX6tuHSr/vwCa6MJiO71EPRATJjk6nQojbnf/gb9yr+yFQK0QB/jAqfr+rkna8OPfDaQoWdoyr/2qLw+QqgZot6xF2NfeDNZmvSX76A1K8CUm9fDClJKFpEkLV4hlmM1K88hju3AVA0aII+799Sb1/TUHaDHmk5H2TlK2JIsP0Qe33MBaTe5ZF4+WJMTUIR2hL1yjlmMVJvfwwJprfGyOs2xpBgKhNKO0ACmhxkNUPAYDB7oJsg3hN+z3+uEX5vTvg9Q4cORSq1/Kj8kzuOUi88hDm7lqJR5/J5dMHruKZtmM+0jtHYOdgxYuU45HkPVDq/7zQ712wGoHGXFkREtgfg6KYD7P5hu8Vzfii9geTZS/FdPhukUjLWbUJ75Tpub/VBc+Yi2bv2oaxdDZ9F05C6OOHQsgn6t3pz+6U3ydryN6pG9Sn/42dgNKLeewj1rv0P/51WcnrHMeqEh/Derg/RqDWsKrS/JmyYx6yOY1A6qBiycgxypQKpVMKFfWf4O+/myN7vtxM57y0mb1qATqtj1ahltiqKid5AzMSV1PhmChKZlIS121BfvEmF6B5knbhC6uZDJHy7jSofvk39PcvQpWVyachCAFya1KJCdA+MOj0YjFwd9yn6tEzblqeQUzuOUjc8mJm7PkKj1vBldMHfesqG+bzXMRqlgx3DVo7Nr1sX9p1hV17dWjXuU3pM7YdULkWbq2XVeBu/fk1vIG76cp75fIbpFWU/bkZz+QZeb79BzqlLZG4/gKpuVSp8PBmZixNO4Y0pN+INrnYcgl3liniPG2h6uI1EQsr/fiL3Yoxty1PIxR3HqRpen3d2LTS9oiy64G89ZMMslnecgIuvBy2HdyPx8m2i/pgJwIGvNnP0u52AqRf89HrbPpAtn0FP5vLFuM5YADIpOZs3oL8Rg0Nkf3QXz6M5sBfHAVFIVPa4TDC9hUOfmEDG9Akgk+O64CMAjNlZZMwvG3Mh7zm2/QjB4Q1Y8tcnaPJe5XXP3A2LGNvxyZxi8zSWK2b7cSpFBNF79wdo1Rq2jVqRv67HxpmsbW96zkWzCT2o3q0ZCnsl/Q5+yJlvd3Jw0c/8/f4aIuYOJHhge4xG2Ppu2XoFZXGip87h0LGTpKXdpVW3N3hrQCTdO7ezdVoPdTrvfDUj73z1VaHz1aQN85mRd74aWuh8dXHfGf7KO18BNOzcvEw8kC2fQU/2yiU4TZ4PUima7X9iuBmDqkc/9JcvoD28F7sOL6Ko1wCjTo8xK4OspaYOG3nNuqhefN10fWE0kP3ZYowZ6TYuEGAwkPPNUhzemYVEIkWzZxOG2OvYdemN/vpFdCf2owjvirxWMOhNZVJ/YXrzksTZDYd3ZoHRiDE1CfX/5tq4MEJZJTEWNyFaMNP/2X82HL0sm+xaBg5yFjAvvehTbp90fTRP9vz/kvxPKbN1Co/du4osW6dgEd9qn756BTCi5tM3RHDo2adzXz2NWhicbJ2CRUQde8/WKTx2wxqWjaHfj9ucBiUPG3+SyTyenHnlj8Lls80PD3oCvFapm0V+7nfXbTvy71H95x7MJgiCIAiCIAiCIAi28p8bji4IgiAIgiAIgiBY35P4JHNLEI1wQRAEQRAEQRAEweLEg9lMxHB0QRAEQRAEQRAEQbAS0RMuCIIgCIIgCIIgWJzB1gmUEaInXBAEQRAEQRAEQRCsRPSEC4IgCIIgCIIgCBYn3o5tIhrhgiAIgiAIgiAIgsWJp6ObiOHogiAIgiAIgiAIgmAloie8FD58RW/rFB67Id872zoFi/h0nI+tU3jstkxPtnUKFrEk2sPWKTx2z884YesULOKvKZVtnYJFvDw71dYpPHY/9FHaOgWLkDg52jqFx0579oatU7CIYQ3H2jqFx27p4bm2TsEiKlZ5wdYpWERidrqtU7AI3We2zuDxKIsPZsvMzGTRokUkJiZSrlw5Ro4ciZOTU5G41157jYoVKwLg5eXF2LGm411CQgKLFy8mIyODwMBAhg8fjlz+4Ga2aIQLgiAIgiAIgiAI/0nr1q2jbt26dOvWjXXr1rFu3TreeOONInFKpZL58+cXWf7111/TqVMnmjdvzooVK9i+fTtt27Z94O8Uw9EFQRAEQRAEQRAEizNa6L9/49ChQ7Rs2RKAli1bcujQodKXx2jkzJkzNGnSBICwsLBSbS96wgVBEARBEARBEASLK4sPZktPT8fd3R0ANzc30tOLn9Kg1WoZN24cMpmMrl270qhRIzIyMnBwcEAmkwHg4eFBSkrKQ3+naIQLgiAIgiAIgiAIT6ytW7eydevW/M+tW7emdevW+Z/ff/990tLSimzXo0cPs88SiQSJRFLs7/j444/x8PAgPj6e9957j4oVK+Lg4PCP8hWNcEEQBEEQBEEQBMHiLPWe8Psb3febPHlyietcXV1JTU3F3d2d1NRUXFxcio3z8DA9VNjHx4datWoRExND48aNyc7ORq/XI5PJSElJyY97EDEnXBAEQRAEQRAEQfhPatiwIbt27QJg165dhIaGFonJzMxEq9UCcPfuXS5cuECFChWQSCTUrl2b/fv3A7Bz504aNmz40N8pesIFQRAEQRAEQRAEiyuLryjr1q0bixYtYvv27fmvKAO4cuUKW7ZsISoqitu3b7NixQqkUikGg4Fu3bpRoUIFAHr16sXixYtZu3YtAQEBREREPPR3ika4IAiCIAiCIAiC8J/k7OzMlClTiiyvXLkylStXBqB69ep88MEHxW7v4+PD7NmzH+l3ika4IAiCIAiCIAiCYHH/9nViTwvRCBcEQRAEQRAEQRAsriy+oswWymwjPDIyktWrV5sti42NZcWKFWRlZaHT6ahRowaNGzdmzZo1AMTFxeHh4YFSqaRSpUoMGzaMgwcPsmDBAhYtWkT58uWZMGECWq2WzMxMNBpN/tProqOj8fb2tlh5ZNWDsesyAKRStAe3ot3xc/FxdZtg33ss2UtGY7h1BQCpXyXsug8BO3swGlF/GA06rcVyfVSR0wYQFB5CrjqXFaOXcv301RJjR64cj3dFH8a3fQeARh2b8uLI1/CvUoFpXcZy7dQVa6Vdantikpi/6zwGg5FudSrQPzSgSMzmi3F8sv8KEqBaOWdmd6hn/URLoe6M3ni3qo9ereHY25+QfiqmSIxrvQBClgxGqlKSsO04pyatAqD66O5U6hWOJvkuAGdnf0/CtuPWTL9Ye2KSmP/XBQxGI91ql6d/Q/P989vZWBbtvoi3kx0Ar9V7hpfqmObwLNlzib+vJQLwZqNA2lXztW7yDzD6/bdp3qoJOepcpr0ziwunLhaJkSvkjJk1kgZNgzEaDXw85zO2/7GL4CZBjHpvBFVqBjIxajrb/thp/QI8xNNUr+43ePpgQsNDyVXnsnDUQq6cLnpcm/PdHDy8PcjNyQVg0huTSE8u/r2ktiCrEoSyUz+QSNEd2Yb271+Lj6vVGFXPUaiXj8MQexVkMpRdBiErXxmj0YDmjy8xxJy1cvYlkwbUQdnqdZBK0Z34C92BDWbrZXWaowx/DWNGKgDao9vQn/yrIECpQjVwJvqLx9Bu/dqaqZdIXjcUVa+hpuuLXRvI/WOt2Xpl+AsoW3UFgwFjrhr1F4swxF5H0bQVdh1ezY+TPhNI5tQoDDfKznn4tan9qBMegkady5ejl3HzzLUiMSO+moiLtxsymYxLh87x7eT/YTQYeHPpSHwC/QGwd3FAfTebGR2jrV2ERzJp1kL+2nMQD3c31n39ia3TeSTvz51AqzbPo1areeetCZw6cc5svaOTA+v+LKgz/v4+/PT9eqaMn5O/rFOXNqxctYT2Ya9w4vgZq+VekkUL36ND+wiy1WoGDBjJseOni8S89lpXxo0djtFo5E5sPL37Dic52XT8GPpWP4YM6Yter+fPP7cxbvxMaxdBKMPKbCO8OF988QWdOnXKf2LdjRs3qFixIvXr1wdg2rRpREZG5o/dB9izZw81atRgz549vPrqq8yaNQswPbnuypUrDBgwwPKJS6TYvTgI9YppGNOTsR8xD92ZgxgTbpnH2alQtngB/fULBcukUux6vkPut0sw3IkBB2fQ6y2fcykFhYfgE+DH6JZDqRxcjX4zBjGt27hiYxu2b0xOttps2a2LN1gyeB79Z0VZI91HpjcYmbPjHMtfaoCPk4pe3+6nZWA5Kns65cdcT83i80PX+PLVRrioFKRk59ow45J5t6qPY6Av25q+i3tIFYLm9uevjkXnvwTN7c/xUStJPXqZJt+MwTsiiITtJwC4suJPriz/w9qpl0hvMDJn53mWvxhi2j/fHaBlgPn+AWhXzZdxYTXMlv19LZFzCXdZ+3oTtHojA386TPNKXjjZ2f6w2DyiCc8EVuDFZj2pE1KL8XNG0bfT4CJx/d/uTWpSKt1bvI5EIsHF3fRKjbhb8Ux7exaRQ3oU2aYseJrq1f0ahjek/LPlGfj8QKoHV2fYzGGM7Dqy2Nj5b8/n0slLVs6wFCQSlJ0HkPPlDIx3k1FFzUZ3/jDGxNvmcUoViqYd0N8suEEkb2B6PYx66WhwdEEVOYGcT8eDhV5J80gkEpRtIsn9bgHGjBRUfaagv3wcY3KsWZju3MESG9iK517CcLPoDTGbkUhR9R5B1rwxGFMScZr2Mdpj+zDEXs8P0ezbjmbH7wDIg5ui6hlF9gfj0e7bhnbfNgCkFQJwePu9MtUArxMWjHeAH5PDhhMQXJVeM99kTrcJReJWDF1ITqbp2mLw8lE06NSEw+v38tmwRfkxL0/sjToj22q5/1PdOrbh9e5dmPD+Alun8kgi2jxPYGAlmoW0J6RhPeZ8MJVOrc3PP1mZ2bR57qX8z5t2/sCG9VvyPzs6OTAwKpIjh05YLe8H6dA+gqpVAqhRqwWNG4WwbOlsmrXobBYjk8lY9MF71A0KIzk5lTmzJzL0rX689/5Cwlo2o0vndoQ0aINGo6FcOU8blaTssdQryp40T9QrylJTU/H0LPgSV6xY8YHxOTk5nD9/nqioKPbs2WPp9EokrVgVQ9IdjCnxoNehO74bee1GReKU7V5Hs+MXs15uWbX6GO5cNzXAAbIzwFh2nisY0qYRu3/aCcCVYxdxcHHE1du9SJydg4r2A7vw60c/mi2PvXybuKuxReLLitNx6Tzj6kAFVwcUMintqvmy80qCWcwvp2/zatAzuKgUAHg42Nki1Yfya9eAm9//DUDq0csoXByw83Yzi7HzdkPuZE/q0csA3Pz+b/zaP/w1C7ZyOj6dZ9wK7Z+qvuy8mliqba+mZBFS3h25VIq9QkZVLyf2Xk+ycMal07J9Czb8sBGA00fP4uzihKd30RN4lx4d+eJDU2PBaDSSnmLqSb1zK47L565gMJTNE93TVK/u16RtE7b9ZGrYXDh2AUcXR9yLOSaWZdIKVTAkx2FMTQC9Hv2pvchrFn1di7LVa6Ye8kLnLKl3BQxX83qLsu5CThZS/0Brpf5AUr9AjGkJGNMTwaBHd+4gsqrBpd5e4lMJiaML+mtFe8NsRRZYA0P8bYyJd0CvQ3tgB4qQZuZBOQWNT4mdqtifo2gSgXb/Dkum+siC2oay/2fTK4OuHbuEvbMjLuXcisTda4BL5TLkCjnFjXRt0Kkph37bbdF8H4eG9evi6uJs6zQeWfuOEfyw1jRa5ujhk7i4OuPt41VifGDlSnh6ebB/75H8ZWMnjmDp4pXk5paNG66dO7dj9RrTNeuBg0dxdXPF19d8xKxEIkEikeDo6ACYHu4VGxsPwODBvZk3fxkajQaAxMRkK2YvPAmeqEZ4p06dmD59OrNmzeL3338nKyvrgfGHDh2ifv36+Pv74+zszNWrJQ+TtiSJiwfGtIKLe2N6MhJX8wtqaflApG5e6M8fMV/u5Q9GI6qBU7B/ewGKsG5Wybm03H09SIktKFtKXDIePkVfUP/yqJ78+dlvaNRl4+BaWglZOfg4F1y0+DirSMwyL8P11CxupGbT97uD9F57gD0xZaMhdz+Vnzvq2JT8z+o7Kdj7mTcO7P3cybljHqMqFBPYvy1h2+dQf9EgFK6Olk/6IRIyc/FxKmic+TjZFdk/ANsux/Pqmn2M/uMEcRk5AFTzcmbv9STUWj2pag2Hb6USl5ljtdwfpJxvOeJiCxql8XcS8fYzv6BxcjH1Gg8ZO5CvN/+POSvew8PryWjsPU316n5evl4k3im4EZQUl4SXb/EXoyMXjOSjPz+i54ie1kqvVCQuHhjTCy4YjenJSJzNj20jFnkAACAASURBVOtSvwAkrl7oLx4zW26Ii0FWoyFIpUjcyiH1D0TiWvLFuDVJnN0x3i04vhkzUpA4Fa0z8uoNUPV7D2W3twqVW4IyogfaHd9ZKdvSkbh7YUwp+L4ZUhKRuBf9eytbdcVp/mpUrw4i5+ulRdYrGoeh3b/dork+KjcfD1JiC76HaXHJuPsWvb4AGLFqIguOrCQnK4cjG/abravaqCYZSekkxMRZNN//Ml8/b2JvF/x978TG4+fnU2J81+4d+e2Xjfmf6wbVxL+8L9s2/1XiNtZW3t+XWzcLOolu37pDeX/zKWs6nY6hw8dz/Og2bl4/Sq2aVfn8i28BqFo1kBYtGrF393q2b/2Rhg2CrJp/WWbAaJH/nzRPVCM8PDycRYsW0aRJE86ePcvEiRPzX5penD179tC8eXMAmjVrxu7dpb8LunXrVsaNG8e4ccUPrX6sJBLsOvcjd/0XRdfJZMgCapLzzSLUH09AXqcJsip1LZ/TY1Sx1rN4V/LlyKYDtk7FIvRGIzfSsvns5YbM7lCX97eeISOn7MzZf1xivtzClsbvsLPVeHLj06g9rZetUyqV5wO8+KPvc3zfqylNKnoyZYupF6tpJU9aPOtF3x8OMn7jKer5uSKTSGycbenJ5DJ8y/tw8tBp3mg7gFNHzvDO1KG2Tuuxedrr1fwR83mr7VuMeXkMtRvVJqL7w98pWmZIJCg79EazcVWRVbqjOzDeTUEVNQdlx77ob14oU6O3HkZ/+TjqT6LJ+WIKhmtnUXYaCIA8JAL9lZP5c8WfNJptv5IZHUnO959h1+UNs3WywBqQm4PhdoxtknsMPuw9kzGNBiFXyqnRrI7ZutAuLTj4BPSC/5d0e6kj6340TW2TSCRMmzmWaZPm2TirRyeXy4ka1JuGjdrxTKUQTp46x7ixw/PWyXB3d6NZi86MHTeDb795sub4W5LRQv89aWw/+fEReXh4EBERQUREBKNGjeLmzZsEBhYd6paZmcnp06e5ceMGEokEg8F0ERAZGYmkFBfarVu3pnVr09y2zOgX/1XOxrspSNwK7kxLXD3Nehmws0fqWxH7qBmm9c5uqPpOIOfLWRjTktFfPWsahg7ozh9BWr4y+sun/lVO/0br3u0J69EGgKsnL+PhX1A2D19PUuJTzOKrhlQnoF5lFu7+BJlchounCxPWvsesHkXnI5c13o4q4jMKekfjM3Io52g+LNbbSUVdX1cUMinlXR2o5O7IjbRsavu6WjvdIgL6taFSr3AAUo9fxd6/oBfB3s8D9R3zC0r1nVRUfuYxOXkxuUl385fHrNlOk9W2f8CNt5Md8ZkFPajxmblF9o+bvTL/3y/WLs+SPQVzcAeGBjIw1HT8GL/xFBXdHSycccle6fsi3XqZ5pudPXEeX39v7s2M8/ErR8Id857g9JR01Nlqtm8wDdfcun4HXXp2smbK/9iTXq/u90LvF2jXsx0Al05eopxfufx1Xr5eJMUV7cVPjjedA9RZanau20n1oOps/6ls9EQa76aYjdaSuHpizCh0XFeqkHo/g6r/VNN6Jzfseo0hd808DLFX0fz5VX6o6s33MSSVjSlHxoxUJC4FxzeJswfGzPsa1TkFI+x0J3ehCH8FAKl/ZaTPVEMeEoFEYQcyOWhz0O4yn2JlbcbUJCQeBd83qUc5jKkljxrRHtiBfZ+3Kfx0FkWT8DIzFD0ssh0tepquvWJOXMbD35N7s9TdfD1JjUspcVtdrpYTWw4R1CaUc7tPAiCVSQlu14iZncdaOvX/nL4De9Krj6l+nDh6Cv/yBb3Efv4+3LkTX+x2tepURyaXcfKE6YGNTs6O1KhZlZ9/Nx03ynl78eW3y+jbc6jVH842JKoPAwaYOhgOHz5OhWf889eVr+DH7Vjz0RT1g2oDcPWq6RkMP/64njHRppvht2/dYd26PwE4dPg4BoMBLy8PkpJK/g4L/y1PVE/48ePH0el0AKSlpZGRkZH/dPP77d+/n+eff56PP/6YZcuWsXz5cry9vTn3f/buO7yp8m3g+DejgxYK3WXvPcsoyCzQspeAykYosjdUtuxllfFDRBAQFcQtCrL33kv2pkIpHbTQkY7k5P0jEAgtQyVN2/f+eHFJcu4k98PJeXKe84xz8WK68dak/H0VtUdeVK5eoNGirVIXw4VjTwOSEkmY0pPE2f1InN0PJfQKSatmody5jv7KKdQ+hcDOHtRqNMXKo9z/O8PL8Kzt32xmYotRTGwxihNbj1K3gz8AxX1LkRiXyMMIy5OaHau3MNSvDyPr9md6x/GE37yXJRrgAOV9XAiNTeTuw0RSDQpbroTjX9xyTlDD4l4cv2OqVGN0KdyOSSB/7hy2SDeNm19tY3fAeHYHjCd883EKvlsPANeqJUiN05EcEWsRnxwRiz5eh2vVEgAUfLce97aYpkg8O388b/MaPLr03MKCNlDe+8n+0Zn2z9Vw/It5WsQ8O8x5z81IirqahtEbFCOxOtNcrStRcVyNiuOtQrZbOOWnVb/RNbA3XQN7s3vTPlq80wyAClXLER8XT3RE2vlk+7YepFpt05zWGnWrcfPKrYxM+V/L6sfV8zZ8s4EhzYcwpPkQDm05ROMOjQEo7VuahLgEYp6rE9UatXkRPY1Wg1+AH7ev3E7zvrai3L2O2j0vqjyeptFYFWujv3T8aUCyjsQ5fdDNG4xu3mCUO1fNDXDs7MHOdEFFXbwiKIa0C7rZiHLvJipXL9PweLUGbVk/DNcsh9Pj/PQij6aEL0r0PQBSNiwjaclokr4IJmXXD+jPHbR5AxzAcPMSGu/8qDx8QKPFrmZDUk8dtIhRe+c3/11buRaG+8/sD5UKOz9/Uo5kjkb47m+3MKNFMDNaBHN66zFqtW8AQFHfkujiEnkUafmb5eDkaJ4nrtaoqdioGuHXn5avbN1KhN8II/YljXfx76xavpbAeu0JrNeeTX/u4J1ObQGoWr0ScY/iiLif/sWgdh1asO6Xp3cliHsUT/nidfCrFIhfpUBOHj9jkwY4wJIvvqZ6jSZUr9GEP/7YQveuHQGo6VeVRw8fER5uuXbJ3bBwypYtiYeHqS0SEFCfS5dMa+r8/scW/P1N6zOULFkMe3t7aYA/phiNVvmT1WTanvCUlBT693+6YnarVq2Ijo7mq6++wt7e1LPVrVs38uRJu0gHmIait23b1uK5mjVrcuDAAcqVK2e9xNOjKCSv+5IcH0x+fIuyHSj3/8a+SWcMd65ZNsifp0sgdd96cgwNAcBw6USaeeO2dGbnCao0rMonez8nRZfMl6OfzjWbsfFTJrYY9dLXV2takx5T+5DLzYVRX03g9oWbhPSYbu20X5tWrWZMwzIM/O0kitFI2/L5Ke6ek88PXaOclwv+xb2oXdidQ7ejaf/NATQqFcPrlbLofc0s7m8/jXfjKgQcno9Bl8yp4UvN2/y3z2J3gGnV2bNjV+K7sD8aR3vu7zxjvg1Z+UmdyV2hMEYjJP4dyZngFTYpx7O0ajVj/Esz8PeTKIqRtuXzmfbP4cf7p5gXa0+HsudmJBq1itwOdkwNNF251isKvX82NSxy2muZ2bQiWnXmuC55YMch6jSuxbpD35OkS2LqiNnmbWu2raRrYG8A/jdzCdMWTWTUtKHERMcydYTp7g/lKpchZOVMXPLkol5gbfoG9+Y9/x42KUt6stNx9bxjO49Ro2ENVuxbQbIumfmjn67QvGjTIoY0H4KdvR3TV09Hq9Wi1qg5vf80m7/b/JJ3zWCKQsqGlTj2nGC6ldfJXRgj7mDX6F2UsOsv/Q1SOec2vc6oYHz0gOSf084/thmjQsq2NTi8O8p067W/9mGMCsOubjuU8FsYrp3GrlogmpJVTBcPdAmk/Lnc1lm/nKKg+3YRzsFzTecXezeh3L2Nw9vvY7h1Gf2pQ9gHtENbviro9RgT49F9Odf8ck3pSijREaaF3TKZc7tOUrGhLzP2LCJFl8LXwYvN2yZuDGFGi2DsnRwYtHwMWns7VGoVVw6dZ++area46q3rZIkF2Z4InjyHY6fOEhv7iMbtujEwqDsdWje1dVqvtGPrXhoH1ufQqc3oEpMYMWiCedu2fb9arIre5u1mdHsnc94R51kbN+2gWbNGXL54gESdjj59Rpq3HT+2leo1mnDv3n2mz5jPrp2/kpqaSmjoXXoHme6G8dWq71n+5aecPrWDlJRUegcNt1VRRCalMso68a/0X4ejZ0YDfsw6c1//iaVj8786KIvZNjV7rqgZOCn9USxZWf0ZmePWKm/a3o+q2DoFq+g4O/PcjulN+alnzlcHZUGqnLZfCPJNS70QausUrOLDPdmvbv/s+NxXB2VBhUq0snUKVhGZ+NDWKViFPiVzjCr6r+rlb2yV9913d4dV3tdaMm1PuBBCCCGEEEKI7CMrrmRuDZlj7KUQQgghhBBCCPH/gPSECyGEEEIIIYSwOukJN5GecCGEEEIIIYQQIoNIT7gQQgghhBBCCKuTNcFNpCdcCCGEEEIIIYTIINITLoQQQgghhBDC6mROuIk0woUQQgghhBBCWJ1RGuGADEcXQgghhBBCCCEyjPSEv4ahP2lsncIbt6juA1unYBWdZ6fYOoU3boF3sq1TsIo+H+tsncIbt29xc1unYBVDR56ydQpWsbJIoq1TeOOGf+ts6xSsIkqJsnUKb1yS0d7WKVjFd36Rtk7hjStUopWtU7CK0GsbbJ2CVRjuXLB1CuIlZGE2E+kJF0IIIYQQQgghMoj0hAshhBBCCCGEsDpZmM1EGuFCCCGEEEIIIaxOhqObyHB0IYQQQgghhBAig0hPuBBCCCGEEEIIq5Ph6CbSEy6EEEIIIYQQQmQQ6QkXQgghhBBCCGF1RukJB6QRLoQQQgghhBAiAyiyMBsgw9GFEEIIIYQQQogMIz3hQgghhBBCCCGsToajm7yyEd69e3e+/fZb8+Pdu3dz/fp1goKCrJrY8xYvXsyFCxdwcnJCrVYTFBREqVKl0sT98MMPlC1blkqVKmVofq+jy+TeVGzoS4ouhRWjPyP0/M00MSO+nkAeL1fUGg1Xjl1k9aTlGBWFgmUL031mXxydHIm6E8my4QtJitfZoBSWtJVqkKP7YFCrSdm9keT1ay222zdujUNgW1AUjEk6ElfMQ7l7GwB1wWI4BY1AlcMZjApxkwZAaqotipGuD6b2pVrD6iTrklk4agE3zl1/YeyEFZPwLuTD0MBBABQtV5QBswZh52CPYjDwxYQlXD1zJaNST1eO2tVxGzMQlVpN3G+beLjyB4vtjlUr4vbhAOxLFiNizEwSt+8zb3Md3gen+jVBpUZ3+AQP5n6e0em/VI8pQVRpWI0UXTJfjF7ErXM3Xhg7avk4vAr5MKbJMADeGdWZaoF+KIqRR9EP+WLU/4iNiMmo1F/pwOW7fLzhKIpi5O0aJentX9Fie8iGoxy7EQ5AUoqBBwk69k/uYotUX0unyb2o2LAqKbpkvhq9ON16cNjXE8jtlQeNRsPVYxdZM2mFqR4sV4RuMz/AzsEeg97AmknLuXXmmg1K8ZS9nx8uQweDWoPuzz9JWPOdxXand9/BqVVLjAYDSmwsD+d8jHL/PgDeu3agv2EqvyHiPrHjJmR4/i+T3fbVE0FT+1KtYTWSdcksGrXwpXX7uBUT8Snkw7DAwQCMWvwh+YvlB8DZxZmERwmMbD4sQ/J+mf5T+1OjUQ2Sdcl8OvJTrqdTprk/zsXNy43kpGQAJnSdwMPoh1SoWYF+k/tRtGxR5gyaw/6N+zM6/XRpq/jh1Nt0bCXv+JPk3yyPLfsmbXBs1g6jokCSjoQvPkG5cxtNiTI49R9tClJB0g+rSD2aOcoEMH3ueBoH1ken0zF84Hj+OnPRYrtzTifWbVptfpwvnze//Liej8bNMT/Xsk0gy79ZSDP/dzhz+nyG5f5vTJw1j70HjuLmmod1q7+wdTqv7cDpS8xdtQ5FUXi7UU2C2jW22H4vKoaJi9cSl6hDUYwM69KSer5lAbhyO4zpX/5MvC4JtUrFd7OG42BvZ4tiiEwsS/WEd+/enVq1anHmzBmWLVvGJ598YrFdURTee+89G2X3chX9ffEumpdx/kMo5luSHjP7MqPduDRxSwbNMzeuBy4ZTY2Wb3F0/QHenzOAH2Z9w5UjF6j7TiOa923Lb/O+z+hiWFKpyfH+MBJmB6M8iCTX9CWknjxobmQDpBzcQcqO9QBoq9YmR9cBJHw8FtRqnAeOI2HJbJTQG6hyuoDeYKuSpFGtYXXyFslH//p9KeVbmgEzBxLcdlS6sbWavYUuwfKCSM/xvfh+wVpO7j5BtYbV6Tm+FxPfS7u/M4xajfv4IYT3G4P+fhT5vvuMxN2HSL0Rag7Rh0cQOSmE3D3fsXipQ+VyOFapwN2O/QDIu2o+jtUrkXT8bIYW4UWqNKyKT9F8jGwwkBK+peg9ox8ftRuTbmyNZrVISkyyeG7D0nX89Knp4lHT91vSfth7rJyQOU4UDIrC7D8O80VQE7xdnOi6+E8alC1Ice885pjgVn7mv689eJFLYQ9skeprqeDvi1fRvEx4XA92nfkBs9uNTxO39Jl6sP+SUVRvWYtj6w/SYWw31i/8iXO7T1PB35eO47rxSacpGVyKZ6jVuIwYRszI0RgiI3Ff9gVJ+w9guP20DtRfvUrUB/0gOZkcbduQa0A/Hk6ZBoAxOYXooD62yv6lst2+eqxqw2rkK5KPgfX7Ucq3NP1mDmBM29HpxtZq9hZJCZb1xaeDPjb//f2JvUmMS7Rqvq+jRsMa5Cuaj6B6QZTxLcPgWYMZ0WZEurEfD/2Yq2evWjwXcTeCT0d+Sod+HTIi3dejVuP0wTDip41GiY4k19wvSD12AOXOM+cX+7aTsvUPAOyq18bp/UHEz/gQQ+hN4j7sB4oBVR43XOat4OHxQ6DY/hyjUWB9ihUrTO2qzahavRJzPp1My4BOFjEJ8YkE1mtvfrxl909sXL/N/Ng5pxN9+nfnxLEzGZb3f9GuRSBdOrRh/PRPXh2cSRgUhVkrf2XphH54u+emy7gF+FcvT/ECPuaYL3/dTtO3qvBuk9pcvxPO4DnL2fTZRPQGA+M/+46Zg7pQukg+YuMS0Go1NixN5iNzwk3+05zwiIgIpk6dyujRo5k2bRpRUVGAqdf68OHD5rju3bsDEBMTw+TJkwkODmbUqFFcvGi6+nfmzBkmTJjAmDFjmDdvHklJSWk/7Blly5YlPNzU8zNo0CBWr17NmDFjOHTokMVnX7t2jYkTJxIcHMy4cePQ6XQoisK3337LuHHjGD16NNu2bXvZR70xvk1qcPDX3QDcOHUVp1xO5PbMkybuycmMRqtBa6fF+PiL6l00L1eOXADg/P4zVGteM0PyfhlN8TIo9++iRN4Dg56Uwzuxq1bbMkj39ARF5eAIj4egaCvWwBB6AyXU1GNpjH8ERiWjUn8lvyY12fXLTgCunLqMs4szrl6uaeIcnRxp+0E7flpk2auMEZxyOQGm/z+4H231nF/GoUJpUv8OQ383HPR6Ejbvxsnfcl/pw+6TevUmKM9VjkYjKgc7VHZaVPZ2qLRaDNGxGZj9y1UL9GPfL7sAuHbqCk4uzuRJZ185ODnSok8b1i36yeJ53TMjShycHCAT/Tic+zuKgu4uFHDLhZ1WQ9PKRdl98e8Xxm86c5NmlYtmYIb/TJUmNTj86x7gST3o/Jr14JMtRhxzPj6uXJyIvW/bEQt2ZctguHsXw717oNeTtGMnjnXrWMSknDoNyaaex9QLF9B4etoi1X8su+2rJ/ya1Hrtur1NenX7M+q0qsu+3/dYLdfXVatJLXb8sgOAS6cukdMlZ7plepGIOxHcunTLfL6RGWhKlEEJv4ty33Rspe7fiX0Ny2Pr2fMLHB2f1t0pyeYGt8rePlPV6c1aNOKn738H4OTxs7jkzoWXt8cL44sVL4y7hxuHD54wPzdmwlA+W7Cc5Mf1SmZXvUpFcrvksnUa/8i5a6EU9HangLc7dlotzWr7svtY2hEH8TpTeyU+MQlPVxcADp29QslCeSldJB8AeXI5o1HLElwirVf2hKekpBAcHGx+HB8fT/Xq1QFYuXIlDRo0wN/fn507d7Jy5Uo+/PDDF77X/v37qVy5Mu3bt0dRFJKTk3n06BG//vorkyZNwtHRkXXr1rFhwwY6duz4wvc5ceIEhQoVMj/OlSsXc+fOBeD06dMA6PV6FixYwPDhwylRogSJiYnY29uzc+dOnJycmD17NqmpqUyaNInKlSvj5eX1qn+K/8TV250HYU8bYg/CH+Dq487DyLSNmZHfTKRo5RL8tfsUxzeaLiiEXb2Db5ManNp6jBot3sIt74sr7YyidvNAiY4wP1YeRKEtXjZNnH1gWxyav4NKqyV+pqk3WZO3AADOY+aiypWH1MM7Sd7w4pOdjObu407UvSjz46jwaNx93Il5bphy19Hd+H3ZOpJ1lj+Gy6cuY8q30+g1oTcqtZoxb6ff05JRNF4eGMIjzY8NEVE4VCzzWq9NPnuRpGNnKLj9B1QqFY++/53Um6GvfmEGcfV5/tiKxtXbLc2Q8ndGdebPL39Ps68A3g3uSr32/iTGJTKj0ySr5/y6Ih4l4pPb2fzY28WJv/6OTDc2LCaesJh4/Ir7pLs9M3D1drPYVzHh0eTxcUu3Hhz+zQSKVC7Bud2nOfG4Hvx+6iqGfzORd8Z3R6VWM6eDbYdvqz08MUQ8c1xFRmJXrtwL43O0bEnykaPmxyp7e9yXLcVoMJCw5juS92eeIbPZbV894e7jTvQzdXt0eDRu6dTtnUd34/dlv6VbXwCU8ytPbFQs927ds2q+r8Pdx52osGd+r+5F4eHjkaZMACM+HYFiUDiw6QBrF65Nsz2zULt5okQ9PbaUB5FoSqY9thyatcOh9TuotHbETXna+68pWRbnQR+i9vAh4X8zM0UvOIBPXi/C7oabH98Lu0/evN5E3I9KN75thxb88dtm8+OKlcuSL78PO7buZeDQ3lbP9/+riAcP8XF/etHRyz03f12zPO8Z8E5T+s9cytrN+9Elp7Bsomm04O2wSFQqFf1nLiXmUQLNalehV9tGGZp/Zidzwk1e2Qi3t7cnJCTE/PjJnHCAq1evMnq0qXFRv3591qxZ89L3Kl68OEuWLEGv1+Pn50eRIkW4cOECd+7cYdIk04mvXq9Pd643wLfffssvv/yCi4sL/fv3Nz9fu3btNLFhYWG4urpSokQJAJycTFfkz5w5Q2hoqLm3PDExkXv37qVphG/fvp3t27e/tDzWMq/HDLQOdvRdMIyytStwYf9ZVn64mC6Tg2g9pCOntx9Hn6q3SW7/Rsq230nZ9jt2tRvh2K4biUvnglqDplQF4icNwJiSTM7xn2C4eQX9+VO2Tve1FS1XFJ/CeVkxbTleBSy/P827t2DFtOUc2nSQOq3qMiRkGB91mWijTP8bbcF82BUtxN9NOgPgs3QuDr4VSD51zsaZvb7C5YrgXdiH1dO/wqNA2p7IH0PW8GPIGtoMbE+Tni34Zb6Np3r8C1vO3iSgQuFsc8V9QY+ZaB3s6LNgKGVqV+Di/rP4d2vCj9NXcXLzEaq3fIuecwcwv9t0W6f6WhwDA7ErXZoHQ5/OH4589z2UqCg0efPitmA+D27cwBAWZsMs/53stq+KlCuKT2Efvpq2HM8C6V+gr9e2Pvt+35vBmf03Hw/9mOjwaHI452Disok07tDY3IOeVSVvXkfy5nXY1W2MY4fuJH5mmjdtuHqRR8N7oc5fCOch40g9dRRSU2yc7T/Xrn0LhvQzTa9SqVRMmTmGYQPTTgsRGW/TgVO0aVCDnq39OXPlFhM+W8svn4zGoBg4dekm380ahqODPX2nf0G5YgWoWTH9ts3/RzIc3cQqc8I1Gg2KYhparCgKer2pwViuXDmmTp3KyZMnWbx4Ma1atcLZ2ZmKFSsyfPjwV77vkznhz3NwcHjt3IxGI7169aJKlSovjQsICCAgIACA3t+/uFf+ZRp1b0b9zqaFHG6euY5bPnfzNjcfN2LCXzxEWZ+cyultx/ANrMGF/WcJvx7GvB6mExjvonmp1LDqv8rpTVIeRKF2f3qConbzQIlJv5cOIPXQLpx6DYelpqvahktnTcPQgdTTR9AUKWXTRniLHi0J7NwUgGtnr+LxzGgDDx93op/bX6WrlqFEpRIsO7ACjVZDbvfczPhhNhPfG0fDDo35cvIyAA5s2M/guUMzriDpMEREofF52vjUeHmgf8GV9+c5N6pD8l8XMT4edqU7cAzHyuVs2ggP7NGchp0CAbhx9tpzx5Y7Mfct50WXrFqaYpVKsHD/UtRaNbndczPx++lper0PrNvLh6smZZpGuJeLE+EPE8yP7z9KxOuZnvFnbT5zi3FtbT9N5Xn+3ZtSv7OpLr15xnJfufq4Exv+4jns+uRUzmw7RpXAGlzcf5a3Ovjz/dSvADj+5yF6zOn/wtdmBCUqEo3XM8eVpydKZNo60L5aNXL26MaDIcMsFp9UHk/hMty7R8rp02hLlrRpIzy77qvmPVpY1O3uz9Tt7j7uPHhB3b70wHLUj+v26T/MYtJ7psaPWqOmVrO3GN0y/XnXGaFVz1Y069wMgCtnruCR75nfq7weRIWnrd+f/IbpEnTsWreLUlVKZdpGuPIgErXH02NL7eaJMfol5xcHduLcdwTPz9BX7oZiTNKhKVQUw/XLVsr25d7v05muj9daOXPyL/LlfzpaKW8+b+7du5/u68pVKI1Gq+HsGdNUxJy5nClTtiS/bvgaAE8vD1atXcz7nQdl+sXZshovt9yEPzPtLiL6Id6uuS1iftt1hCXjPgCgcqkiJKemEhOXgJdbHqqVLYarS04A6vqW5eLNu9IIF2n8py6TUqVKcfDgQcA01LxMGdPwVk9PT27cMM31PX78OAaDaRhQZGQkefLkISAggMaNnRWkBwAAIABJREFUG3Pz5k1KlSrF5cuXzXO8k5KSCHsDJyH58uUjJiaGa9dMq7HqdDoMBgNVqlRh69at5gsDYWFhr5yD/m/t/HYzU1oEM6VFMKe2HqV2e38AivmWJDEuMc2wPgcnR/OcO7VGTaVGVbl3/S4AudxNc01UKhWtB3dk95qMmcv+MoYbl1D75Eft6QMaLfa1GpF64pBFjNo7v/nv2iq1MISbyqM/ewx1wWJg7wBqNdqylTHcvZWR6aex8Zs/GdF8KCOaD+XwlkM07GAaPlTKtzQJcYlphvZtXr2JXjV60rdOEOM6fEjYzTDz4msP7j+gQi3TKtaV6lQm7JZte7eSz1/GrlB+tPl9QKvFuZk/iXsOvfqFmBZsc6xWCTRq0GpwrFaJFBsPR9/2zSbGtxjJ+BYjOb71CPU6NASghG8pdHGJaYaib1+9hUF+QQyr24+pHcdz7+Y9cwPcp0hec1y1Jn6EXb+TcQV5hfIFPAiNesTdB3Gk6g1sOXOTBmULpIm7GfGQR7pkKhfKfPONd3+7hWktgpnWIpjTW49Rq30DwFQP6l6jHqzYqBrhj+vBhxEPKFXLNCS1TO0KRNwKx5ZSL11GU6AAmrym48qxcSOSDxy0iNGWLIHL6JHEjBuPEvu0rKqcOcHOtFquKndu7CpWQH/rVkamn0Z23VebvtnIyObDGNl8GEe2HLao2xPTqdu3rN5EUI336VenD+M7jOHezTBzAxygct0q3L1+N82F2Yy04esNDG42mMHNBnNoyyEadzBd8C/jW4aEuIQ0ZVJr1Lg8nrOq0Wqo2bgmty/fTvO+mYXh2mXUeQug9jIdW3Z1G5Fy3PLYUud9en5hV60Whnum757aywfUpoWw1J7eaPIXQomw3fdv1fK1BNZrT2C99mz6cwfvdGoLQNXqlYh7FPfCoejtOrRg3S8bzY/jHsVTvngd/CoF4lcpkJPHz0gD3ErKFy9IaHgUdyKiSdXr2XzwFA2ql7eIyevhypFzpkUOb9y5T0qqHjeXnNSpXJqroffQJaegNxg4ceE6xQp426IYmZbRSv9lNf+pJ7x37958/vnn/PHHH7i4uDBw4EAAGjduTEhICMHBwVSuXNncU33+/HnWr1+PRqPB0dGRwYMH4+LiwqBBg1i4cCGpj3sIOnXqRL58+f5bwbRahg8fzldffUVKSgr29vZMmjSJRo0aERERwZgxpuE9Li4uFnPereXsrpNUaliVOXs+I0WXzMrgp7d4mrIxhCktgnFwcmDo8rFo7e1QqVVcOnSO3Wu2AlCzTV0adTdd9T655Qj7f9pp9ZxfSVHQrVqE8xjT8PKUPZtQ7t7CscP76G9eQX/yIA5N2qGtUA0MepSEOBK/MM3dNybGk7zpJ3JNXwJGI6lnjqA/fcTGBXrqxM7jVG9YnS/2fWm6jc3oBeZt8zf9jxHNX96zvXjsIvpM6YtGoyE1OYXPxy6ydsovZ1CInv0ZPktmg1pN3LotpF6/TZ6BPUk5f4XEPYewL18K7/lTULvkxKlBLQwDe3C3/QckbNuHo18V8v/8JRiN6A4eQ7fn8Ks/M4Oc3nmCKg2rMX/vEpJ1ySwd/fTfetbGeYxvMfKlr+80tjt5i+XHqChE3Y1kxfjMsTI6gFajZmybmgxYuR3FqNC2eklKeLvy+bZTlMvvjn8509oYm8+aFmRTqVQ2zvjl/tp1kooNfZm5ZxEpuhRWBS82b/toYwjTWgRj7+TA4OVjzPXg5UPn2fO4Hvxm7FI6Te6FWqsmNTmVb8YttVVRTAwGHi1YiOsnIaBWo9u4Cf2tW+Ts3YvUy5dJPnCQXAMGoMqRgzxTp5pe8vhWZNoihXEZPQoUBdRqEtZ8Z7Gquq1lu3312Imdx6nWsDpL9i17XLcvNG+bt2nha91urG6b+uz7w/YLsj1xbOcxajSqwcr9K0nSJTF/1Hzzts82f8bgZoOxs7djxuoZaO20qNVqTu0/xebvTHONS1UuxaQvJ5Ezd05qBtSk28hu9A+w7SgTFAOJyxeSc5Lp2ErZuQnl71s4duqF4dplUo8fxKH529hVqoZRb8CYEEfCZ7MB0JatiOPbXTDqDWBUSPxyAca4h7Ytz2M7tu6lcWB9Dp3ajC4xiRGDnq6VsG3frxarord5uxnd3rHxfngDgifP4dips8TGPqJxu24MDOpOh9ZNbZ3WS2k1Gsb1bs+AWctQFCPt/P0oUdCHxT9upnyxAvhXr8Co7q2ZtvQnVv+5F5VKxbQBnVCpVLjkdKJ7qwZ0Gb8AFSrq+ZahftUXrxUi/v9SGTPTcpiZVO8i/244emY2r07mvY3Rf9Fzn5OtU3jjFrhmjRVQ/6kJsTltncIbt2J+DVunYBVDR2adtRr+iemFXjy8NauaFJr5RkO8CVGKdUas2VKSMeus7fJPfOene3VQFlNuR8Srg7Kg0GsbbJ2CVRjuXLB1ClbhWKWVrVN4I4p7WGdK7fWok1Z5X2vJUvcJF0IIIYQQQgiRNWXFoePWkD2W0RVCCCGEEEIIIbIA6QkXQgghhBBCCGF1RqNi6xQyBekJF0IIIYQQQgghMoj0hAshhBBCCCGEsDpF5oQD0ggXQgghhBBCCJEB5MZcJjIcXQghhBBCCCGEyCDSEy6EEEIIIYQQwupkOLqJ9IQLIYQQQgghhBAZRHrCX0OcMdXWKbxxGldHW6dgFTEGna1TeOPUGpWtU7CKWGOyrVN441TehW2dglU8Mh61dQpWoXXOflfjH2XD3yuAR0r2qy8SlBRbp2AVGjd7W6fwxkUmPrR1ClZhuHPB1ilYhaZAOVunIF4iM84Jj4+PZ/78+URGRuLp6cmIESPImTOnRcy5c+f4+uuvzY/DwsIYNmwYfn5+LF68mAsXLuDk5ATAoEGDKFKkyEs/UxrhQgghhBBCCCH+X1q3bh0VK1akXbt2rFu3jnXr1tGtWzeLmAoVKhASEgKYGu1DhgyhcuXK5u3du3enVq1ar/2ZMhxdCCGEEEIIIYTVKUajVf78F8eOHaNBgwYANGjQgGPHjr00/vDhw/j6+uLg4PCvP1Ma4UIIIYQQQgghrM5opf/+i4cPH+Lq6gpAnjx5ePjw5VNQDhw4QJ06dSyeW7t2LaNHj2bVqlWkpr56apgMRxdCCCGEEEIIkWVt376d7du3mx8HBAQQEBBgfjx9+nRiY2PTvK5Tp04Wj1UqFSrVi9djiomJITQ01GIoepcuXciTJw96vZ6lS5fy+++/07Fjx5fmK41wIYQQQgghhBBWZ62F2Z5vdD9v0qRJL9yWO3duYmJicHV1JSYmBhcXlxfGHjp0CD8/P7Tap83oJ73odnZ2NGzYkPXr178yXxmOLoQQQgghhBDi/6Xq1auzZ88eAPbs2UONGjVeGJveUPSYmBjAdIHh2LFjFCxY8JWfKT3hQgghhBBCCCGsTvmP87etoV27dsyfP5+dO3eab1EGcP36dbZt20b//v0BiIiIICoqinLlLG+D97///Y9Hjx4BULhwYfr27fvKz5RGuBBCCCGEEEIIq8uM9wnPlSsXH330UZrnixcvTvHixc2Pvby8WLp0aZq4yZMn/+PPlOHoQgghhBBCCCFEBpGecCGEEEIIIYQQVvdf7+mdXfy/boTv3r2b69evExQUlCGf12vKB1RtWI1kXTKLRy/k5rkbL4wds3wCXoW8GdVkKADvDO9EQOcmPIo23bfuu5DVnNp1IkPyfhlN2Wo4duwHajWpB7eQsu2ndOO0VeqQo88EEj4ehhJ6FU0ZXxzavA9aO9CnkrxuJYYrZzI2+VcYOm0QtRrVJFmXzOwRH3Pl3NU0MQt/+hR3b3eSk5IBGNV5DLHRptsfNGzdgF4je2I0Grl24TrTB8/K0PxfxrF2DdxGDwSNmvjfNvFo1fcW23N17UDOt1uAwYAhJpboqZ9guBdho2z/mX5T+1GjYQ2SdcnMGzWP6+eup4mZ88Mc3LzczPttYreJPIx++T0hM9KBv64x97stKEaFt+v5EtSyrsX2e9EPmbhiHXGJySiKwrCOjalXqSSx8YmM+vwnzt8Mo02dKozv1txGJXix96f0wfdxPbhk9P9eWg8GLx+PdyFvRjcZZvF8qw/a0n1iL/pU6U5cTJy1U34pu2p+OPcfgkqtJmnzn+h++s5iu+Pb7+LYrCUYDCgPY4mfPxcl4j4ATr37YV+jFgCJa78hZe+uDM//ZbLbvnpi0LSB1GxkqiM+HvEJV89dSxPz6U8huHu5kZyUAsCYLuPMdTtAvRZ1mbLsIwa0GMSVs2l/GzLayOlDeKtRLZJ1SUwfMYfLf6XN6fOfF+Du/bRMwzqNJiY6Fp/83kyY9yGu7nl4FBvH5CEzibwXmdFFSENTvjqOnQagUqtJ2beZlM0/WGy3a9ASe/82YFQwJulI+nYByr1Q0Ghx7D4MTeFSYFRI+n4JhitnbVSKtObPm0bzZo1I1OkIChrBqdPn0sS8915bxo4ZgtFo5F7YfXq8P4ToaNPCT4MG9mLAgPcxGAxs2rSDseNmZnQR0jhw+hJzV61DURTeblSToHaNLbbfi4ph4uK1xCXqUBQjw7q0pJ5vWQCu3A5j+pc/E69LQq1S8d2s4TjY29miGP/IxFnz2HvgKG6ueVi3+gtbpyOyoGzZCFcUBbU6c420921YjbxF8zKkQX9K+pbigxkDGN8uON1Yv2a1SErUpXl+w4o/WL9snbVTfX0qNY7vDiTxswkYY6NwCl6A/q/DKOF/W8Y55MDOvy2Gm5fMTxnjH6JbOhXjwweo8xYmx6DpJEzskcEFeLFajfwoULQAXer2oFzVsoycPYz+rQenGzt98Cwun71i8VyBovnpOrgzA9sNJf5hPHnc82RE2q9HrcZtzBAiBo5Bfz+SvKsXo9tzkNSboeaQlMvXCO82EGNSMjk7tsZ1WF+ixs6wYdKvp3rD6uQvkp8+9ftQ2rc0g2cOZkTbEenGhgwL4WomOHl+nkFRmLV6E0tHdcPbzYUu05bjX6U0xfN7mmO+XL+PpjXK827D6ly/G8ngBd+xKWQY9nZaBrVryLW7EVy7a/uT6OdVaVgNn6J5GdZgACV9SxE0oz8T232YbqypHkxK87x7Xg8q1atC5J1McFFIrSbnoOE8HD8KJSqSPAuXknLkAIbQ2+YQw/WrxA7tC8nJOLZsi3Pv/sTNmYpdjVpoi5cidlAfsLMj98cLST1+BGNiog0L9FS221eP+TWqQYGi+elRtxdlq5Zh2OyhDG49NN3YWUPmpNvAzuGcg/a93+bCyYvWTve1vNWoJgWLFuCdOl0pX7UcH84eQVCrgenGTh40k0tnL1s8N+SjAWz6eSsbf9pCtTq+DBz3AVOH2viisUpNji6DSZg/FmNMFM4TFqE/c8jUyH4s9cguUvf8CYC2ci0c3+1H4sIJ2NUzXXxMmNoPVa48OA2bScLMwZAJet+aN2tEyRJFKVOuLjX9qrL4s9nUrtvaIkaj0TD/02lUrOxPdHQMc2ZPYNDAXkybPg//BrVp07opVasFkpKSgqenu41K8pRBUZi18leWTuiHt3tuuoxbgH/18hQv4GOO+fLX7TR9qwrvNqnN9TvhDJ6znE2fTURvMDD+s++YOagLpYvkIzYuAa1WY8PSvL52LQLp0qEN46d/YutUspzMOCfcFjJFS/WHH37gzz//ND9eu3YtGzduTBN3/vx5Jk+ezOzZsxk2bBjLli1DURQAunfvzjfffENwcDBXrlxh7969jBs3juDgYIu4Xbt2MWzYMMaNG8elS5fSfIa11Aj0Y88vpl6Oq6eu4OziTB4v1zRxjk6OtO7Tll8Wpd+jnJmoi5RCiQrDGB0OBj36k3vRVnorTZxDq+6kbPsJoz7F/Jxy5wbGhw9Mf793G5WdA2gzzzWhuk3rsOXnrQBcOHmRnLlz4u7l9tqvb9WlJb+t+oP4h/EAFj0otmZfoTT6O2Ho794DvZ6ELbvJ4W95q4Xk42cwPu4lTv7rIhovD1uk+o/ValKLHb/sAODyqcs4uzjjms5xlpmdu3GXgl6uFPByxU6roVnN8uw+bXnSjAridab9E69LwjNPLgCcHOypWqoQDnaZ51h6Vo1AP/b+sht4eT3o4ORIyz5t+HXRj2m29fioN2tmf50ZzqfRliqLIewuSrjpWEresxP7WpajFlLPnoJk075KvXQBtYfpYoq2UBFSz50BxQDJSRhuXseuWs0ML8OLZLd99USdJrXZ+vM2AC6evEROF2fc/kHdDtAruCfff/4DKckprw7OAPWb1mHjz1sAOH/ywj/+vSpaqjDHD5wE4MSBU9RvWucVr7A+TdHSKJFhGKNM5xepx/agrVLbMijpmQtWDo48WXBZk68whkunATDGxWJMjEdduFQGZf5yrVs35ds1PwNw5OhJcufJjY+Pl0WMSqVCpVLh7OwEmBaMCgszjZ7p168HH4csJiXF9N2LjIzOwOzTd+5aKAW93Sng7Y6dVkuz2r7sPnY+TVy8znShLj4xCU9X0z2YD529QslCeSldJB8AeXI5o8lknWgvUr1KRXK75LJ1GlmSgtEqf7KaTPFNb9iwIXv37gVMvdgHDx6kXr166cZeu3aN3r17M3/+fO7fv8/Ro0cBSE5OpkSJEoSEhJAzZ04OHjzI9OnTCQkJQa1Ws2/fPmJiYvjxxx+ZPn0606dP5+7duxlWRjcfd6LDosyPo8OjcPNOewXzvVFdWf/l7yQ/PsF+VrMeLfhk80IGhAzB2cXZqvm+DnVud5SYp2VSYqJQ5bYsk7pAcVSunhjOH3vh+2ir1MHw9zXQ662W6z/l4eNBRNjTnsTIe5F4+KTfEB03L5gVW5fSY3g383MFixWgYLECLF63kCXrF+Hn/+L7DWY0racH+vCnPVOGiEg0Xi++mp6zXTOSDrx4/2UmHj4eFsMoo8KjXrjfRnwygkWbFtF5aOeMSu+1RMTG4eOW2/zYy9WF+88N4x3QtgF/HvqLwFHzGbRgLWO7NsvoNP8VVx+35+rBaNy80zYW3hvVhQ1f/k6KzrKRUz3Qjwfh0dy+eMvaqb4WtYcHSuTTY0mJikTt/uILVo5NWpBy/AgA+pvXsK/mBw4OqFxyY1fJF42n5wtfm9Gy2756wsPHnUiLuj0KD5/067/geaNZumUJ3YZ1NT9XskIJPPN5cmTnUavn+ro8fTwtfq8iwiLx9En/uzRx/hi+2bacXsO7m5+7euE6/s3rA+DfvB7OuZxxedxIshVVHg+UB0/LZIyJRJ0n7X6y829NzpmrcOzwAUnfLwbA8PcNtJXfArUalYcPmsIlUbtljmMrfz4f7vwdZn5898498ufzsYjR6/UMGjKO0yd38Pftk5QrW5KVX60FoGTJYtSt68fB/evZuf1nqlernKH5pyfiwUN8nhnt5+Wem/sxltO7BrzTlD/3nSBwwDQGzVnO2F5vA3A7LBKVSkX/mUt5b8w8vvp9Z4bmLoQtZYpGuJeXFzlz5uTmzZucPXuWIkWKkCtX+leXSpQogbe3N2q1mjp16ph7s9VqNbVqmebWnTt3jps3b5p7wv/66y/u37/P1atXKV++PC4uLmi1Wt56K22v7RPbt29n7NixjB079s0X+AWKlCuKT2Efjm45nGbb1tWbGFK/P8HNhxMbEUOPSb0zLK9/TaXCocMHJP/65QtD1D6FcGjbm6TvF2VgYm/O9CGzeT/gAwa/PZzKfhVp2jEQAI1WQ4Gi+RnacSTTBs7kw5CR5MwEF07+KecWjXEoV5qH36Tt5crKQoaGMLDJQD7s+CHl/crTqEMjW6f0j2w6co42dSqz7dMRLB7emQlfrkNRst5V4PQULlcU78I+HNtyxOJ5e0d72g3qyI/z1toos//GoWEg2lKl0f1iWn8h9eRxUo4fJs+ni8k15iNSL53H+HjEVlaRXfcVwOwhc/ggoB/D24+kol8FAjsEmBoLk/vxxbRltk7vX5k8eAbdGvemf7shVKlZieYdmwCwaNoSqr5Vma+3fonvW5WJCItEMWSN72Lq7vXET3ifpF+WY9/SdLEk9cBmlJgonCcuxvG9/uivX4AsdGxptVr69+1Bdb+mFCxclbN/XWTsmCGPt2lwdc1D7bqtGTN2Bmu/yxpzkTcdOEWbBjXYtuQjFo/tw4TP1qIoCgbFwKlLN5k9pCurpg1m57FzHPnryqvfUGRpRqPRKn+ymkwzZrFx48bs3r2b2NhYGjZs+I9fb2dnZ54HbjQaadCgAV26dLGIedJr/joCAgIICAgA4J21bf9xPgBNe7QgoJOpUXbt7DXc8z3tIXH38eDBfcthRKWqlqZYpRIs3r8MjVZDbvfcTPl+BlM6TeRh1NOritvXbmXsyon/Kqc3SXkYjZ3r0zKpXT0wPnymTA45UOctjNOwuQCoXFzJ0e8jdEunoYReRZXHnRx9J5H07aemIWc29nbPtrTq2gKAS6cv45Xv6ZVzz7yeRIVHpXnNk+d0CTq2rdtJ2Spl2PLzNiLvRXLh5EUMegP3/g7n7xt3KFC0AJfOXE7zHhlNHxmF9pnhbxovTwwRaYe0OfpVJXdQF8L7jILU1IxM8R9p1aMVTTs3BeDq2at45n263zx8PNLdb9GPjz1dgo7d63ZTunJpdv6SOa7Ae+XJRfiDp8d7RMwjvF0tL0r+tu80S0aa6rfKJQqSnKonJj4R90x4oadJj+Y07mQ62b9+9upz9aA7D+4/sIh/Ug8u2r8MjVZNbvfcfPT9DL6a/CVeBb34eNMC02vzujPnz3mMbxvMw0jbTPdQoqJQez49ltQenijRab9vdlWqkaNTdx5+ONTiWNJ9vxrd96sByPnhJAx3/07z2oyUXfdV256tadHFVLdfPnMZT4u63YOo8LT135PndAk6dq7bSRnf0hzcepCipYsw76cQANw83Zi+chqTen+U4YuzdXi/HW27tgLg4ulLFr9XXvk8iQxPuyZE5OO6MDFBx9bfdlDOtyybft5K1P1oxvYx3R83h1MOGrZoQPyj+AwoxYsZY6Mseq9Vrp4osS8eeq0/tpscXYeSBKAoJP/4BU/GEzqNmY9y/45V832ZAf17EhRkukBw/PhpChTMZ96Wv0Be7oZZnv9UqVwegBs3TGtL/Pzzej4MHgSYes7XrdsEwLHjp1EUBQ8PN6KiLI/NjOTllpvwZ6bcRUQ/xNs1t0XMb7uOsGTcBwBULlWE5NRUYuIS8HLLQ7WyxXB1yQlAXd+yXLx5l5oVM8f0ASGsKVP0hAP4+flx+vRprl+/TpUqVV4Yd+3aNSIiIlAUhUOHDlGmTJk0MRUrVuTw4cM8fGg6kY2PjycyMpKSJUty4cIF4uLi0Ov1HD6ctsf5TdryzUaCW4wguMUIjm09TIMOposLJX1LkRiXQGxEjEX81tWb6efXi0F1+zKp4zjCboYxpZOpsf3sXDy/prX4+3IotqbcvoLaMx8qd2/QaNFWrY/+7DP/pkmJJIztTMLkXiRM7oXh1iVzA5wczuToP5Xk37/CcOOC7QrxjN++/p2gJv0IatKPfVsO0PRxL0G5qmVJeJRAdITlj5xGoyb34yF7Gq2G2gG1uHH5JgD7Nh/At7bpe5zb1YWCxQoQFnovA0vzYinnL6MtmB9tPh/QanFu6o9uz0GLGLvSJXCbMJyI4R+hxGSe+ezp2fDNBoY0H8KQ5kM4tOUQjTuYVmUt7VuahLgEYp47ztQatXmopUarwS/Aj9tXbqd5X1spXzQ/ofcfcCcyhlS9gc1HztOgiuUJSV43F45cMH3XboRFkpKqxy2Xky3SfaWt32xiTIsRjGkxgmNbj1C/gz/w4npw2+rNDPDrzZC6fZnccTz3boYxrdNE/r58m77V3mdI3b4MqduX6HvRjG050mYNcAD9lUto8hVA7W06lhwaNCLl8AGLGE3xkuQcOopHU8dhfPhMrmo1qlyPv4dFiqEtWozUE8czMv00suu++v3r9fRrOoB+TQdwYPNBmjwesVS2ahkS4hJ48Fzd/nwdUSugFjcv3SIhLpH2ld6h61s96PpWDy6cumiTBjjAL6vW0SOwDz0C+7Bn835adDRdiCxftRzx6f5eacj9eJqLRquhTsBb3LhkqkNyu+VGpVIB0HNIF9b/kHZNnoxmuHUZtVd+VB4+oNFiV6MB+jOHLGLUXk8bs9qKNVEiHk8xtHcAe0cANGWrgqJYLOiW0ZZ88TXVazSheo0m/PHHFrp37QhATb+qPHr4iPBwy4UL74aFU7ZsSTw8TNM/AgLqc+mSaQX/3//Ygr+/aW58yZLFsLe3t2kDHKB88YKEhkdxJyKaVL2ezQdP0aB6eYuYvB6uHHl8h5kbd+6bfrNcclKncmmuht5Dl5yC3mDgxIXrFCvgbYtiiAykGI1W+ZPVZJqecK1WS/ny5XF2dn7pyuYlSpRgxYoVhIeHU758efz8/NLEFChQgE6dOjFjxgyMRiMajYagoCBKlSrFO++8w8SJE3FycqJIkSJWLJGlkztP4NuwOov2fkGKLpnFo58Ovw7ZOJ/gFumv4PxE93E9KVKuKEYjRN6JYOn4z62d8qspCkk/LsFp0AxQqUk9vBUlPBT7lt0whF7F8NeRF77Uvn5r1J75sG/eGfvmpjm5us8mYozPHLeJOrzjCG81qsnaA9+SrEti9sgQ87YVW5cS1KQfdvb2fPLdXLRaLWqNmhP7TrJhjenk5ejuY9RoUJ1vdq1EMRj4fPoyHsU8slVxLBkUHsxdhNfiOaBWE//HZlJv3CZ3/56kXLiCbu8hXIf3Re2UA8+PJwGgD48gcsRHNk781Y7tPEaNhjVYsW8Fybpk5o+eb962aNMihjQfgp29HdNXTzfvt9P7T7P5u802zNqSVqNmXLfmDJi3BkUx0q5uFUrk92Lxb7soXyQf/r6lGfVeE6Z9vZ7VW4+gUsG0oLbmk+jmwQuJT0omVW9g16lLfDGym8XK6rZ0aucJfBtWY+HjenDJ6P+Zt8068RYeAAAYOElEQVTdOJ8xr6gHMx3FQPySBeSe8Qlo1CRt3Ygh9BZO3Xujv3KJlCMHcQ7qj8oxBy7jpwJgiIwgbup40GjJ/Ynpd8CYmEBcyEzTIm2ZRLbbV48d2XmUmo38+Hb/KpKSkgkZ+XRl46VbltCv6QDs7e2Zu2Y2WjsNarWak/tPsfG7TTbM+uUO7jhM7cY1+fngGpJ0ycwYMde87Ztty+kR2Ac7ezsWfvexud47tu8Ev6/ZAEDVt6owcNwHGI1GTh85S8j4BbYqylOKQtJ3n+E0fBYqlZqUA1tQwm7j0KYHhttX0J85jF3DtmjL+YLBgDEhDt1Xpt9pVa48OA2fBUYjxpgodCvmvuLDMs7GTTto1qwRly8eIFGno0+fkeZtx49tpXqNJty7d5/pM+aza+evpKamEhp6l95BpuPtq1Xfs/zLTzl9agcpKan0Dhpuq6KYaTUaxvVuz4BZy0y/Wf5+lCjow+IfN1O+WAH8q1dgVPfWTFv6E6v/3ItKpWLagE6oVCpccjrRvVUDuoxfgAoV9XzLUL9qOVsX6bUET57DsVNniY19RON23RgY1J0OrZvaOi2RhaiMmWQQvaIojBkzhpEjR5I3b950Y86fP8/69eszdJ42wDuF/91w9MxsZevMO7z4v2j5W9oF7bK6b71Vtk7BKgZE2ds6hTfu17VdXx2UBfXskvnv1vBvLC4X8+qgLGbQhax1N4DXFWXIHLdue5MSlMyxuvqbtrVl9qvb3b5Ou9p3dhB/dKmtU7AKTYGs0ZD/p+w8itk6hTfC2amIVd43IfGWVd7XWjJFT/idO3eYM2cOfn5+L2yACyGEEEIIIYTIurLi0HFryBSN8AIFCvDZZ5+ZH4eGhrJokeVq2XZ2dsyaNYvy5cs//3IhhBBCCCGEECJLyBSN8OcVKlSIkJCQVwcKIYQQQgghhMgSMslMaJvLNKujCyGEEEIIIYQQ2V2m7AkXQgghhBBCCJG9GJGecJBGuBBCCCGEEEKIDCDD0U1kOLoQQgghhBBCCJFBpCdcCCGEEEIIIYTVSU+4ifSECyGEEEIIIYQQGUR6woUQQgghhBBCWJ30g5uojDImIFPZvn07AQEBtk7jjcqOZQIpV1aSHcsE2bNc2bFMkD3LlR3LBFKurCQ7lgmyZ7myY5kg+5ZLWJ8MR89ktm/fbusU3rjsWCaQcmUl2bFMkD3LlR3LBNmzXNmxTCDlykqyY5kge5YrO5YJsm+5hPVJI1wIIYQQQgghhMgg0ggXQgghhBBCCCEyiGbKlClTbJ2EsFSsWDFbp/DGZccygZQrK8mOZYLsWa7sWCbInuXKjmUCKVdWkh3LBNmzXNmxTJB9yyWsSxZmE0IIIYQQQgghMogMRxdCCCGEEEIIITKI3Cc8A8TGxrJq1SquX7+Ok5MTefLkoWfPngQHB5MvXz70ej3Fixenf//+aLVazp8/z/r16xk7diy7d+/m888/Z+LEiVSqVAmAo0eP8sknnzBy5Ehq1apl49I9dfToUX766SeL50JDQwkKCmL58uX06tWL5s2bA7BixQqKFy+Ov7+/DTJ9qnv37nz77bdEREQwePDgF+a4ePFiLly4gJOTEykpKZQsWZLOnTvj7u5u8T5P7N69m+vXrxMUFERYWBjLli0jISEBvV5PmTJl6NevX4aU70XfvU8//ZRPP/3UHPfjjz/i6OhImzZtADAYDPTt25dGjRrRtWtXc9yJEyf44YcfMBqN6PV6WrRoQWBgYIaUJT3vvvsurVq1okePHgD88ccfJCUl8e677wKmVUs3bNgAQI4cOejZsydlypRhw4YNhIaGMnDgQAD27dvH/v37GTdunG0K8hLvvfcehQoVQlEUPD09GTJkCM7OzubvbPv27enUqRMAjx49ol+/fgQEBBAUFGTjzNP3pP6aP38++fPnB+DatWusXr2aBw8ekCNHDvLkyUPXrl0pVKgQP/74Izt27MDFxcX8HlOmTMHZ2dlWRUjXk/1kMBjQaDTUr1+fli1bolarLer02NhYvvjiC6Kjo9Hr9Xh5eWWp790TwcHB5M+fn+HDh5ufe1U9aWtxcXFMmzYNMNWNarXa/L2aOHEi/fv3p1evXjRp0gQAnU7Hhx9+yPjx48mbNy96vZ4xY8bQv39/SpYsafV8n90H+fPn5/3332f27Nnp5j979mzzMfM6+2zXrl1s3LgRgDt37pAvXz7UajVVqlQhf/785t8veHE9aktP/m0A1Go1vXv3pnTp0jbNKT3PnxsA6Z4T1KxZkzVr1gAQHh6Om5sb9vb2FC5cmMGDB6epN8ePH09qairx8fGkpKTg5uYGmPaxl5fXv8rt2fOWjPRsvaFWqwkKCqJUqVJp4n744QfKli1rPhcWIquTRriVGY1GQkJCaNCggflk5datWzx8+BAfHx9CQkJQFIXp06dz6NAh6tWrl+Y9ChUqxMGDB80Vz4EDByhcuHCGluP/2rvzoKjr/4Hjz+Xygl1U1BGPMAEP5KjwREFQxwwc02kqNUjScZxQM5XQlBxspK8hXnlMlNeoFZR5Kx55JItoiKioqASKoIgKCgws1/L7g9nPj+VcUnbXej/+gt3PLq/Pft689/16Xx9dDBo0iEGDBkm/nzx5knPnzuHq6opCoeDIkSOMGTMGMzPjLHZNxejv78+QIUOoqqri8OHDLF++nMjIyCbPZ9u2bfj6+jJw4ECgumNCHxore025evUqtra2JCQkMGXKFGQyGRUVFURFRREeHk7Hjh0pLy/n8ePHLX0ajTI3N+fChQu8++67WkkaVHcYnDhxguXLlyOXy0lPTyciIoJvvvmGcePGsWjRIlJTU+nRowe//PILX331lYHOonEWFhZEREQAsGHDBo4dO8akSZMA6Ny5M0lJSVISnpCQQPfu3Q0Wqy6USiV9+/ZFqVTy/vvv8+zZM9asWcPcuXOlRnRqaio5OTlSI9vX11fqIDJWNa/T8+fPWb9+PSUlJVKHkEZMTAwuLi688847ANy7d0/vseqisXKXlZWFWq3m5s2bqFQqWrduLb3un9aT+mBlZSWdU+2Ox+PHj+Pg4IBSqZSS8DZt2jB58mS2bt3KkiVLOHjwIH369NFLAg7a12D9+vXEx8c3GH/t45u6Zt7e3nh7ewMQFBTEsmXLpDr0zJkz0ns2Vo9aW1u3+GfQkJrnmpyczE8//URYWJjB4mmO+toEPXv2xM3NDajuZPT396d3797Sa2rXm+Hh4YDhEueXTVNvXLlyhaioKFatWqX1vFqt5oMPPjBQdC/fv+W6CS9GTEdvYdevX8fMzEz6Ugews7PTGhkwMTHB3t6evLy8et+jb9++pKWlUVFRgUqlIicnBzs7u5YO/YU8ePCA3377jTlz5iCTyZDL5Tg7O2t9uRsbXWOUyWT4+flhbW3N5cuXm3zf/Px8reutSSxami5lryFKpZJx48ZhY2PD7du3AVCpVFRWVmJlZQVUJ8C2trYtE7yOTExMGD16NIcPH67z3P79+/H395calq+//jpeXl7ExsZiamrKjBkz2LJlC7t27cLb25suXbroO/xmc3R01KonWrVqJY1aAcTHxzN06FBDhdcklUpFamoqs2bNQqlUAhAbG4uXl5fWKFbfvn21OvReNQqFgpkzZxIbG0vtbVfy8/OlUSvAKDtUa6td7pRKJZ6enri6upKYmFjva5pbTxqaUqkkICCAvLw8nj59Kj0+bNgwoLo+OXHiBFOmTDFIfH379iUnJ0fn4//JNatPY/WosSgpKTG6mTGNaW6boL56s6Xl5uYSFhbGwoULWb58OU+ePAGqR60TEhKk4/z9/YHqc1q2bBnBwcEsWLCAmzdvAnDlyhWWLFlCSEgIq1evRqVSNfp3+/XrJ5XzoKAgdu3aRUhICOfPn9f622lpaSxdupTg4GAWL15MSUkJarWanTt3snjxYhYuXMiJEyde+ufSFLVarfe/Kby6DN81/S+XmZlJr169Gj2mrKyMtLQ0pk2bVu/zMpkMZ2dnrly5QnFxMe7u7uTm5rZAtC9HRUUF69evJyAgABsbGynWCRMmEB4ejo+Pj4EjbFhzYuzVqxfZ2dlSb3ZDfH19CQsLo0+fPri4uODt7a2XBkNjZS8nJ4fg4GDp92fPnjF+/Higujxeu3aNmTNnUlxcjFKppE+fPlhaWuLu7s6nn37KgAEDeOutt/Dw8MDExLB9eWPHjiU4OJgJEyZoPX7//v06O5b27t2bs2fPAtCnTx+6devGtWvXWLNmjd7i/afUajUpKSl1yqaHhwdKpRKFQoGJiQkdOnQgPz/fQFE27q+//sLNzQ1bW1usrKxIT08nKysLLy+vRl93+PBhzp07B4ClpSXLli3TR7gvpEuXLqjV6jozT8aOHcvatWs5duwYzs7OjBw5UispNzb1lbv4+HhCQ0PJzs7m6NGjDB8+vMHX61pPGtKTJ0/Iz8/H3t6eoUOHEh8fL9WHANOmTePzzz9n5syZWFpa6j2+yspKkpOTpZHSprzoNaupqXrUUMrKyggODqa8vFxKAF8VzW0T1FdvvozduDWfoUZRURHu7u4AbN26FS8vL0aOHMmpU6fYunUrX3zxRYPvFRcXh6urK5MmTUKtVlNaWkpBQQG///47oaGhtG7dmn379nHo0CHee++9Bt/n0qVLWp0SVlZWrFy5Eqie8QDVbcy1a9cyb9487O3tKS4uxsLCglOnTtG2bVu++eYbysvLCQ0NxdXVtdHp+dHR0VhaWuLr6wvAzz//jEKhkGYqaVy/fl2afZKTk4OTkxMzZszAxMQEf39/xowZw7Vr15g+fTq5ubkcPXqUiooKHBwcpONOnz7Nvn37aNu2La+99hrm5uYNxiX8N4gk3IA0iVBubi5vvvlmoyMiHh4eHDlyhOLiYgICAti7d68eI22e6OhounfvLo0gaHTp0gUHBwfi4uIMFFnTmhNjUzcWkMlkAHh7e+Pq6kpycjKJiYmcPHmSiIgIg1bAmqUQGjExMdLPSUlJODk5YWFhweDBg9mzZw/Tpk3DxMSEWbNmkZmZydWrVzl48CBXr14lKCjIEKcgadu2LZ6enhw5cgQLCwudX6dSqUhPT6eyspKCggKjWbdam6ahlJeXR/fu3eush3NzcyM6Ohpra+s6/3PGRqlUSo2bYcOG1ft/9uWXX1JSUoKLiwuBgYHAqzEdXVdubm5s2LCB5ORkLl++TEhICJGRkXWWUxhaQ+Xu77//Ri6XY2NjQ4cOHdi8eTNFRUUNJqevwg1Yas4g8fDwYPPmzVpJeHJyMu3bt+f+/ft6jatmktSvX78mO4df1jV7FdScjn779m02bNhAZGSk9L1rzJrbJqiv3nwZSXjNzxD+f4o0wJ07d1i4cCEAnp6e0nr1hvTu3ZvNmzdTUVHBoEGDsLOz48aNG2RlZREaGgpUJ8/1rfUG2LlzJ3v27EEulzNr1izp8fq+0x48eED79u2xt7cHqtsAUD3qnpmZKY2WFxcX8/Dhw0aTcG9vbyIjI/H19UWtVhMfHy9N9a8tLS2N1atX06lTJ1asWMHFixcZMmQIpaWl2NvbExAQQFZWFvv27ePrr7/GzMyMH3/8kXPnzuHi4kJMTAwrV66kbdu2hIWFGf2MVqHliSS8hfXo0YMLFy7U+5wmESooKCA0NJTExESpF7I2e3t77t+/j4WFhcGnADfm+vXrXLhwQeq5rG3ixImsXr2afv366Tky3eka4927d3F2dgaqv8wqKiqkdY9FRUXStG2ADh064OPjg4+PDwsWLKh3dOFla6zsNSYuLo5bt25JyXVhYSEpKSlSg65nz5707NkTT09PZs+ebfAkHKqTtJCQEK2N/rp37056ejoDBgyQHktPT5fWTMfExDBixAgUCgU7duxg/vz5+g5bJ5qGUmlpKStWrCA2Nlarl97MzIxevXpx8OBB1qxZ06yppvpUVFRESkoKmZmZyGQyadqel5cXGRkZ0khpeHg4CQkJXLp0yZDhvrBHjx5hYmKCQqEgOztb6zlLS0uGDx/O8OHD+d///seNGzeMapNNaLjcKZVKsrOzpf/7kpISEhISGD16dL3vU7OeNFZKpZJnz55JnUJ5eXk8fPiQrl27kpeXx9GjRwkPDycsLAwfHx+9LSGonSTpevyLXrOamqpHjYGjoyOFhYUUFBSgUCgMHY5OdG0TNFRv+vv7G6TDwdTUVIpBrVZTUVEBQP/+/QkLCyMpKYmNGzfi5+dHu3btcHZ21tq8sSGaNeG1tWrVSufYqqqqCAwM1HnGCFTvq2JpaUlGRgbPnz/Hzs5Oq+1Wk729vbRszcPDg9TUVIYMGYKJiYkUe0pKChkZGdJmm2VlZcjlcu7cuYOTk5PU2Tp06FAePnyoc5zCv5NYE97CBgwYQHl5OSdPnpQeu3fvntaaM7lcztSpU5sc3Z4yZQqTJ09usVhfVFFREZs2bWL27Nm0adOm3mO6detGt27djLqB3VSMVVVVHDlyhPz8fKmy79+/P3/++SdQXemeP38eJycnoHoURfNF9ezZMwoLC/Uy/VSXsldbcXExqampbNq0iY0bN7Jx40amT59OXFwcKpWK69evS8fevXuXTp06teg56MrS0pKhQ4dy6tQp6bEJEyawe/duCgsLgep4z5w5w9ixY8nMzCQpKYkJEyYwevRoHj9+zNWrVw0Vvk5atWpFYGAghw4dorKyUuu58ePHM3XqVKMe2UpISMDT01MqW5s3b6Zz5864uLhw5swZbt26JR1bWlpqwEhfXEFBAT/88ANvv/12nYZySkqKdH4lJSU8evQIGxsbQ4Spk5rlrqKigvPnz7Nq1SqpfggODq53nWp99aQxevDgASqViu+//146p4kTJ0rntGPHDiZOnEjHjh0JCAhgy5YtRj+6/0+vWX0aq0eNRXZ2Nmq1usHkydg0p03QUL2pWXPdUhwdHYmPjweqO+Y1u+F36tSJ9PR0ABITE6XvosePH2Ntbc3o0aMZNWoUGRkZODo6cuvWLWmNt0ql4sGDBy8cm62tLfn5+aSlpQHV9WhlZSVubm4cP35c+mw1/9tNGTVqFGfOnOH06dPSZoXNYW5uLi3Lq6qqwsvLi4iICCIiIli3bl2dzTkFQUOMhLcwmUzGwoUL2b59O/v378fc3JxOnTrVWf89cOBAfv3110Yr1jfeeKOFo30xJ06ckBqfNXl4eGj9PmnSJEJCQvQZWrPVF6NmulRpaSkODg4sW7ZMGvkODAwkKiqKo0ePAtXTt/r37w9UT5Hatm2bNFX6o48+0suusrqWvZouXrzIgAEDtKbFDRw4kF27djFt2jQOHDhAVFQUFhYWtG7dWrrFlzHw8/PT2izI3d2dvLw8li5dikwmo02bNsyZMwdra2tWr17Nxx9/LF2TGTNmsGHDBiIiIoxiF+eG9OrVi549e0o75Wr06NGDHj16GDCypimVyjrr9gcPHoxSqWTevHns3r2bvLw8FAoFVlZWWusGa64Jh+bdhkdfNFOBNbcoGzFiBH5+fnWOS09PZ8uWLZiamlJVVYWPj480rdJYacrd3r176dChg1bC0L9/f9atWyftQ9BYPWmMlEplnU0ABw8ezNq1a3F0dOTJkyfSNHB3d3f++OMPzp49a/DbazZF12vWvn37Rt+noXq0qde1tNrrmYOCggy+P0l9ysrKtKZX+/n58fTpU53bBI3Vm5o2Rkv45JNP2LRpEwcOHEAul0vf9aNGjSIiIoLg4GBcXV2lkWrNbRhNTU1p3bo1s2fPRi6XExQUxLp16ygvLwfgww8/fOHZnGZmZsybN49t27ZRVlaGhYUFoaGh+Pj4kJubK7Xd5HK5VhlpyKBBg4iOjqayspLPPvuswePS0tLIzc3FxsaG8+fPM2rUqDrHODs78+233+Lr64tCoaCoqIiSkhIcHBzYvn07hYWFtGnThoSEhFdiU06hZcmqjL1LVxAEQRAEQRAEoQVERUXRrl07pk6dWu/zTW3MVvN+6/Hx8ezdu5eqqipMTU2l+57X3JjNzs4OMzMzcYuy/ziRhAuCIAiCIAiC8J+jVqsJCQlh/vz5dO3atd5jNCP9ixYt0nN0wr+Z8c3dEQRBEARBEARBaEFZWVnMnTsXZ2fnBhNwQWgpYiRcEARBEARBEIT/tMzMTL777jutx8zNzRu8bZkgvAiRhAuCIAiCIAiCIAiCnojp6IIgCIIgCIIgCIKgJyIJFwRBEARBEARBEAQ9EUm4IAiCIAiCIAiCIOiJSMIFQRAEQRAEQRAEQU9EEi4IgiAIgiAIgiAIevJ/XSaCkueC4WcAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "MKNIzss1zGcB"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}