DATA_LEAKAGE.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DATA LEAKAGE.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BmPrlniie4Dk"
      },
      "source": [
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\"/>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yvv1Rvjhe4Aj"
      },
      "source": [
        "#**Data Leakage**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kvZIuFUTe39O"
      },
      "source": [
        "Data leakage is one of the most difficult problems when developing a machine learning model. It happens when you train your algorithm on a dataset that includes information that would not be available at the time of prediction when you apply that model to data you collect in the future.\n",
        "\n",
        "In a more simpler term, data leakage happens when we accidentally share information between the test and training data-sets while creating the model. \n",
        "\n",
        "*“Any other feature whose value would not actually be available in practice at the time you’d want to use the model to make a prediction is a feature that can introduce leakage to your model.”* – Data Skeptic\n",
        "\n",
        "##Why is Data Leakage Important To Know?\n",
        "\n",
        "* It causes a model to overrepresent its generalization error, which makes it useless for any real-world application.\n",
        "Therefore caution must be taken, else when deployed in production for any application will cause the application to fail miserably.\n",
        "\n",
        "* It can make investor make bad investment and cause huge financial cost. \n",
        "\n",
        "* It is practically deadly in the healthcare sector and may make predictions that can cause lives of humans.\n",
        "* It can make wrong predictions on customer behaviours which will make buiness leaders take wrong decisions that can push the business into debt or at worse eventually collapse their business.\n",
        "\n",
        "**Data Leakage** is therefore one of the most important concepts to know as a Data Scientist or Machine Learning Engineer.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o2y5MFgBkpgs"
      },
      "source": [
        "##How do I know if am having Data Leakage?\n",
        "\n",
        "Data leakage often results in unrealistically high levels of performance on the test set and this is because the model is being ran on data that it had already seen to some extent in the training set.\n",
        "\n",
        "It had already memorised the patterns and everything, so why not? It will definetly perform well.\n",
        "\n",
        "However, this definetly misleading, and this model will fail to generalise when deployed in production."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FKTp6J3TphVI"
      },
      "source": [
        "##How did I end up in Data Leakage?\n",
        "\n",
        "\n",
        "There are several **Causes** of *Data Leakage* which includes:\n",
        "\n",
        "* Duplicates\n",
        "* Leaky Predictors\n",
        "* Pre-processing activities"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "am55u0g95GtB"
      },
      "source": [
        "\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BtWTEHhP48pt"
      },
      "source": [
        "**Duplicates**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nnDG40Ct6abM"
      },
      "source": [
        "Duplicate values are a common problem when dealing with real world datasets. You can't run away from it. This normally occurs when your dataset contains several points which are identical.\n",
        "\n",
        "For example if you are working with customer reviews dataset for sentiment analysis, it is very likely to find customers who have written same reviews for a product different times, partly because some product owners ask them to write more reviews so that they will get sales, or maybe the customer just like or dislike the product and keeps writing same positive or negative reviews over and over again.\n",
        "\n",
        "In this situation, you may experience data leakage due to the fact that your train and test set may contain the same data point even though they may correspond to different observations. \n",
        "Which will fail when you use it in production to test new sets of reviews.\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H03ky0Nk_C69"
      },
      "source": [
        "**Leaky Predictors**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m4bYZG3I_Z3q"
      },
      "source": [
        "You may not explicitly leak your data, however, you can still experience data leakage especially if there are dependencies between your test and train set. This mostly happens when you are dealing with data where time is important(like time-series data).\n",
        "\n",
        "Leaky Predictors include data that will not be available at the time you make predictions.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F3_kydDWeIf2"
      },
      "source": [
        "\n",
        "#let's create some dummy data\n",
        "df={'Purchase':['yes','yes','no','yes','no','yes','yes'],'QTY':[2,5,17,4,0,3,7],'Product':['Milk','Sugar','Biscuit','Chocoalte','Coffee','Bread','Egg'],'Discount':['yes','no','no','yes','no','yes','yes']}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8wh1fS9YDWwS"
      },
      "source": [
        "\n",
        "#convert the dummy data into a Pandas Dataframe\n",
        "import pandas as pd\n",
        "\n",
        "df=pd.DataFrame(df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "ZCMtT6TgDgU6",
        "outputId": "b6d5a25f-a259-43aa-bfce-02b50c4eef47"
      },
      "source": [
        "\n",
        "#let's see our data\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Purchase</th>\n",
              "      <th>QTY</th>\n",
              "      <th>Product</th>\n",
              "      <th>Discount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>yes</td>\n",
              "      <td>2</td>\n",
              "      <td>Milk</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>yes</td>\n",
              "      <td>5</td>\n",
              "      <td>Sugar</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>no</td>\n",
              "      <td>17</td>\n",
              "      <td>Biscuit</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>yes</td>\n",
              "      <td>4</td>\n",
              "      <td>Chocoalte</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>Coffee</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>yes</td>\n",
              "      <td>3</td>\n",
              "      <td>Bread</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>yes</td>\n",
              "      <td>7</td>\n",
              "      <td>Egg</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Purchase  QTY    Product Discount\n",
              "0      yes    2       Milk      yes\n",
              "1      yes    5      Sugar       no\n",
              "2       no   17    Biscuit       no\n",
              "3      yes    4  Chocoalte      yes\n",
              "4       no    0     Coffee       no\n",
              "5      yes    3      Bread      yes\n",
              "6      yes    7        Egg      yes"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8mOYihKZID7X"
      },
      "source": [
        "People will mostly buy a product when they a good discount and the product is what they need. If you look at the data we have above, most of the people who got ***Discount*** also ***Purchased*** the product. \n",
        "\n",
        "Let's check the relationship or correlation between the two features below:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w2XzicrRDhbP"
      },
      "source": [
        "#@title\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "le = LabelEncoder()\n",
        "purch = le.fit_transform(df['Purchase'])\n",
        "dis = le.fit_transform(df['Discount'])\n",
        "prod=le.fit_transform(df['Product'])\n",
        "df['Purchase']=purch\n",
        "df['Discount']=dis\n",
        "df['Product']=prod"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "KepzQcq7Ekov",
        "outputId": "fa832ba1-87c3-48cd-fa6c-844fa168dd06"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Purchase</th>\n",
              "      <th>QTY</th>\n",
              "      <th>Product</th>\n",
              "      <th>Discount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Purchase  QTY  Product  Discount\n",
              "0         1    2        5         1\n",
              "1         1    5        6         0\n",
              "2         0   17        0         0\n",
              "3         1    4        2         1\n",
              "4         0    0        3         0\n",
              "5         1    3        1         1\n",
              "6         1    7        4         1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "4muhBPhSGsac",
        "outputId": "f25215af-1b0c-4d6e-eab2-5dbe935f8a18"
      },
      "source": [
        "df.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Purchase</th>\n",
              "      <th>QTY</th>\n",
              "      <th>Product</th>\n",
              "      <th>Discount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Purchase</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.377135</td>\n",
              "      <td>0.474342</td>\n",
              "      <td>0.730297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QTY</th>\n",
              "      <td>-0.377135</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.485363</td>\n",
              "      <td>-0.320256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Product</th>\n",
              "      <td>0.474342</td>\n",
              "      <td>-0.485363</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Discount</th>\n",
              "      <td>0.730297</td>\n",
              "      <td>-0.320256</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Purchase       QTY   Product  Discount\n",
              "Purchase  1.000000 -0.377135  0.474342  0.730297\n",
              "QTY      -0.377135  1.000000 -0.485363 -0.320256\n",
              "Product   0.474342 -0.485363  1.000000  0.000000\n",
              "Discount  0.730297 -0.320256  0.000000  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h3zav53JH9bZ"
      },
      "source": [
        "We can see that there is a very strong relationship between these two features(**Purchase** and **Discount**), about **0.7** while the other feature has very less correlation **-0.3**. \n",
        "\n",
        "Having a high relationship is actually a good thing, for instance if we want to build a model that will predict whether a customer will purchase a product or not, this variable will help us to get a good prediction with our dataset.\n",
        "\n",
        "However, we should also note that discount is normally given based on certain conditions, like festive seasons, customer type, or it can run for a certain period of time.\n",
        "In short, discounts are not mostly available all the time. \n",
        "\n",
        "\n",
        "Considering the relationship or correlation between the two features(**Purchase** and **Discount**), if we build a model to predict wether a customer will *purchase an item or not* based on the given data, the model would see that anyone who has a discount is highly likely to purchased an item. Validation data comes from the same source, so the pattern will repeat itself in validation, and the model will have great validation scores. But the model will fail when we deploy in the real world, since data that will come later on might not have discount.\n",
        "\n",
        "\n",
        "\n",
        "---\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rlu34FSXfRbT"
      },
      "source": [
        "<br>\n",
        "\n",
        "##**Pre-processing activities**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3lxfRwxSfyt8"
      },
      "source": [
        "Probably the most common cause of **Data Leakage** is happens during data pre-processing steps of machine learning.\n",
        "\n",
        "<br>\n",
        "\n",
        "**Approach 1**\n",
        "\n",
        "Most of the time, we \n",
        "* prepare our data \n",
        "* split it into training and testing set and\n",
        "* build and evalute our model\n",
        "\n",
        "While this is mostly done in most machine learning problems, it exposes our test or validation set to the model while training and typically leads to ***Data Leakage***. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MaYa9PMnpd1S"
      },
      "source": [
        "Taking for instance data nomalisation, where we will like to normalise our data in such a way that it has a range of 0 to 1. This means that the largest value for each attribute is 1 and the smallest value is 0.\n",
        "\n",
        "![image.png](data:image/png;base64,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)\n",
        "<br>\n",
        "\n",
        "**Xmax**= maximum value\n",
        "\n",
        "**Xmin**= Minimum value \n",
        "\n",
        "When the value of X is the minimum value in the column, the numerator will be 0, and hence X’ is 0\n",
        "On the other hand, when the value of X is the maximum value in the column, the numerator is equal to the denominator and thus the value of X’ is 1\n",
        "If the value of X is between the minimum and the maximum value, then the value of X’ is between 0 and 1.\n",
        "\n",
        "\n",
        "Now when we normalize our data, this requires that we first calculate the minimum and maximum values for each variable before using these values to scale the variables. After that, we split our dataset into train and test sets, but the examples in the training set know something about the data in the test set, i.e. they have been scaled by the global minimum and maximum value and they somehow know something about every data point.\n",
        "\n",
        "Again, standardization estimates the mean and standard deviation values from the dataset in order to scale the variables. \n",
        "![image.png](data:image/png;base64,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)\n",
        "\n",
        "![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAsAAAAMCAYAAAC0qUeeAAAAnklEQVQoFXWQAREAERBFN4IMIsggggwiyCKDCDKIIIMKe/PNcbtzmDH4/1vPEokRQuBaKwuJvPfcWlPa8rn3vo0xBvb8ritDhIrGmB2EU0pha63S5o2UEgPju04UY2RMqc29c45zzspAVVT/hcEmPyJ5oW/utwMqDKz1B/UiDGCAD89iohLOCKp2InhjO/LK/p4CUzv19xoGE5ivAWE8ICN+cLjEwdcAAAAASUVORK5CYII=) is the mean of the feature values and ![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAsAAAAICAYAAAAvOAWIAAAAhElEQVQoFXWQAQ3AIAwEXwIakIAGJKABCWhBAxLQgAQ0YKHLk43RkTVpKO3TXgE2yzmLtVYAKDfG8P5aCEFijDLGmO69l5SSFlHOjiy+T4FaqzjnVG7WOaa1pgqllKMBeu9yMAEg0oHxN44NyL+jzfj+gZXnssRYiT0gL0dyUZ5f/kd7AQvGU6rorKSxAAAAAElFTkSuQmCC)is the standard deviation of the feature values. \n",
        "Each data point will have a taste of another, whether in train set or test set.\n",
        "\n",
        "Also **missing value** imputation gives same problem.\n",
        "\n",
        "This happens with almost all data preparation techniques."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3yiZETuwtdOJ"
      },
      "source": [
        "<br>\n",
        "\n",
        "**Approach 2**\n",
        "\n",
        "**We can therefore reorganise our process in this way:**\n",
        "* Split data into training and testing set .\n",
        "* Perform Data Preparation on training set.\n",
        "* Fit the model on the training set.\n",
        "* Evaluate Models on the test/validation set."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_MEcI2Phu0Pt"
      },
      "source": [
        "<br>\n",
        "\n",
        "Let's see an example below:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9eZ9iUJfwOOT"
      },
      "source": [
        "##**Approach 1**: The Wrong Way"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1HiRph89wSh2"
      },
      "source": [
        "First, let's try the ***approach 1*** and evalute our results.\n",
        "\n",
        "i.e. \n",
        "\n",
        "* prepare our data \n",
        "* split it into training and testing set and\n",
        "* build and evalute our model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LtvyMWBawwCJ"
      },
      "source": [
        "We will use the ***MinMaxScaler*** function to scale our data into the range of **0-1**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JF9SJhhd2S65"
      },
      "source": [
        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)\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        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)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R2pwDxU12k1Z"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "byV9-Rcuu5Qy"
      },
      "source": [
        "We will use the *sklearn's* ***make_classification()*** function to create the dataset with 1,000 records and 10 features."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B5qtlIRdHAOZ"
      },
      "source": [
        "from sklearn.datasets import make_classification\n",
        "# define dataset\n",
        "X, y = make_classification(n_samples=1000, n_features=10, n_informative=8, n_redundant=2, random_state=20)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r6Mt0gzZvXME",
        "outputId": "71f533f4-42ed-4f42-be97-dcd57b1486fa"
      },
      "source": [
        "#let's see the shape of the dataset\n",
        "print(X.shape, y.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1000, 10) (1000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-BXyRgi7vp-f"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# standardize the data\n",
        "scaler = MinMaxScaler()\n",
        "X = scaler.fit_transform(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iTJq3Xt8xbbo"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "# split into train and test sets with 80% for traing and 20% for testing\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=50)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "imM7YSI5x4Zs",
        "outputId": "2dede9dd-b7bd-4302-8148-2ed795c8d508"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "# fit the model\n",
        "model = LogisticRegression()\n",
        "model.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 204
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Chejjvcx-06",
        "collapsed": true,
        "outputId": "7023cc2f-848f-4263-ecc0-8f195fb18758"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# evaluate the model\n",
        "y_pred = model.predict(X_train)\n",
        "# evaluate predictions on train set\n",
        "accuracy = accuracy_score(y_train, y_pred)\n",
        "print('Model Accuracy on Training set: %.2f' % (accuracy*100))\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "\n",
        "print('Model Accuracy on Test set: %.2f' % (accuracy*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model Accuracy on Training set: 88.38\n",
            "Model Accuracy on Test set: 91.50\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jd65BT2Gyjuh"
      },
      "source": [
        "##**Approach 2**: The Right Way"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uBgMg1mhyzmJ"
      },
      "source": [
        "Step1: We  will first split the data into train and test sets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BRgmqWxs-NqW"
      },
      "source": [
        "X, y = make_classification(n_samples=1000, n_features=10, n_informative=8, n_redundant=2, random_state=20)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZAQ-bFDPyQLG"
      },
      "source": [
        "# split into train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=50)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2YJXRoCszFhN"
      },
      "source": [
        "Atep 2: We now scale our data using the **MinMaxScaler**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V6UQTFUlzBYt"
      },
      "source": [
        "# define the scaler\n",
        "scaler = MinMaxScaler()\n",
        "# fit on the training data\n",
        "scaler.fit_transform(X_train)\n",
        "# scale the test x_text data\n",
        "X_test = scaler.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v4GiDuMczpR8"
      },
      "source": [
        "**NB**: We did not scale the **y_test** since we want it to represent real world dataset and we can use it for testing or validation. Also in most of the cases, you will not need to scale the **y_train** since it will already be in small range and scaling the **X_test* and **x_train** is enough to get going.\n",
        "However, depending on your dataset and problem statement, you can scale the y_trian **BUT NOT before splitting**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9HYHymBhz7tR",
        "outputId": "15a45a38-4e52-4f57-f9e0-fdd3fde0932c"
      },
      "source": [
        "# fit the model\n",
        "model = LogisticRegression()\n",
        "model.fit(X_train, y_train)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 209
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iuLdER_O1WtY",
        "collapsed": true,
        "outputId": "b3ce5d6c-8972-44e0-dea5-3ce1e2cdfd03"
      },
      "source": [
        "# evaluate the model\n",
        "y_pred = model.predict(X_train)\n",
        "# evaluate predictions on train set\n",
        "accuracy = accuracy_score(y_train, y_pred)\n",
        "print('Model Accuracy on Training set: %.2f' % (accuracy*100))\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "\n",
        "print('Model Accuracy on Test set: %.2f' % (accuracy*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model Accuracy on Training set: 88.62\n",
            "Model Accuracy on Test set: 48.00\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8R8kQ3f61stb"
      },
      "source": [
        "Now we can see that the reality of the model is being revealed. Our model is overfitting but since in the **approach 1**, our model has tasted both the traing and test set, it has memorise the pattern and it was still doing well, however, the **approach 2** has revealed to us that our model wont work in production, it will fail misearble so we need to tune the model.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-thdWxJx8XLR"
      },
      "source": [
        "##**Using Cross-Validation**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jJYtcygO8pcr"
      },
      "source": [
        "**K-fold cross-validation** involves splitting a dataset into **K** non-overlapping groups of rows. After that, ***you train your model on all but one group*** to form a training dataset, then evaluate the model on the hold-out fold. You repeat this process couple of times so that each fold is given a chance to be used as the holdout test set. You finally average performance across all evaluations."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oVCwcvmKF9pm"
      },
      "source": [
        "![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABDgAAAGgCAYAAABPHdcGAAAgAElEQVR4AeydB3gc1bn+SSeVJKSRcm/KTbn/S+5ND713m05wo9iB0CH0EIoBS3LHFVdsg02xDS64V9ywMcbG2Lj3ItuyumR1raT3/7xndHZnZ2e1K2lt7UrvPs9oZldnzpw55zenvPOd75xUXV0NbcoDMSAGxIAYEANiQAyIATEgBsSAGBADYiCVGTgplROvtOvhEwNiQAyIATEgBsSAGBADYkAMiAExIAbIgAQOWbDIgkcMiAExIAbEgBgQA2JADIgBMSAGxEDKMyCBQxCnPMRSa6XWigExIAbEgBgQA2JADIgBMSAGxIAEDgkcEjjEgBgQA2JADIgBMSAGxIAYEANiQAykPAMSOARxykMspVZKrRgQA2JADIgBMSAGxIAYEANiQAxI4JDAIYFDDIgBMSAGxIAYEANiQAyIATEgBsRAyjMggUMQpzzEUmql1IoBMSAGxIAYEANiQAyIATEgBsSABA4JHBI4xIAYEANiQAyIATEgBsSAGBADYkAMpDwDEjgEccpDLKVWSq0YEANiQAyIATEgBsSAGBADYkAMSOCQwCGBQwyIATEgBsSAGBADYkAMiAExIAbEQMozIIFDEKc8xFJqpdSKATEgBsSAGBADYkAMiAExIAbEgAQOCRwSOMSAGBADYkAMiAExIAbEgBgQA2JADKQ8AxI4BHHKQyylVkqtGBADYkAMiAExIAbEgBgQA2JADEjgSEKBIxAISHRIwnJRhakKUwyIATEgBsSAGBADYkAMiAExkLwMNEng2Lx5MxYtWtSo7eDBgwkZtJeVlWHp0qXo2rUrfvSjH+Gkk04y23e+8x1ceumlGDBgAAoKChJyLT9wb731Vtxyyy3B7YknnkBubm6zr+eOk8dPP/00ioqKmh2v+x7y8/OxfPlyvPjiizjnnHPAPLP599WvfhVnn322+d+qVauiXvvQoUN48MEHg/dv0/3mm28iXmGG4fbu3Yt77rknIp5169Yl9J7d96/j5K2IVDYqGzEgBsSAGBADYkAMiAExIAaay0CjBY6qqiojJNiBcbz7V155pVkDVw6KufXv3x9f//rXgwNz7/X/7//+LyGCQ7SM9V7vV7/6FRIh3njj/d///V/k5OQ0K8/c9zB79mwjanzlK1+Jmnc2Dd/4xjdw4YUX4qOPPoq4fmFhIc4444yIOM4888yooog7HTxmOY4bNw6f/exnw+L51re+FXcc3jj1XZWhGBADYkAMiAExIAbEgBgQA2KgbTPQaIGD1gocfNvBcLz75gocBHXjxo34/Oc/3+C1r7766ohBeSIh995vMgocFBDsPR87dgx9+vRpMM+892S/U0iaPHkyKioqwqwz0tPTI+KjNciGDRuC17XXj7a//PLLI+KgZUi08Pq9bVdUKn+VvxgQA2LAnwF3m59KecR0p2raUymflVb/50b5onwRA62XgUYLHDt27MBPfvKTiMGpHRRH2ydC4HjqqacirvvFL34RF198MW644Qb87ne/Q8+ePY/rINl7f8kocLgf2Mceewxf+tKXIvLNex/RvtOqYsmSJWF5evjw4Qih6TOf+QxGjRoVFs6dDvfxvn37wHJzX5OWJd7ruM/RceuthFS2KlsxIAaSiQFOg2WfJdr2xhtv4MMPP8TatWuxadOmuNq9eO5v4cKFYdd87bXXcOTIkZjxMz3R0tqY39k2NzWdWVlZcZ3rF/+aNWvMtNwHHngAV1xxBTp06GCmwg4dOjSuFyfFxcV4++23m5UHY8aMCaaffRxamTYm7/zCMk1Mm9896zfVeWJADIiB48dAowWO1atXg/4a3IPTeI5Z+Te3IK+99tqI6z766KPgtJl44k7EmwLvvTZW4IiWBm+8zZ2iwjx56623IvKL16Hgwak87ET07dsXFI6uu+46/OAHP/AN/4tf/AJ5eXlheXzzzTdHhD333HPjKot+/fpFnPub3/wG9O8RTzkqzPGrEJS3ylsxIAbaOgP0LeVtkxv6ToG+Y8eOeO+995o8zZI+t7773e9GXDeelzbf//73I85rKL3R/jdr1qyYbXBz0unmilNwKSL813/9V8y0s28xf/58lJeX+6aP04TZF+OLlmj3Fs/vNn3vv/8+Tj755GbFxes1tn9or6+96mAxIAbEQPMYaLTAwUGzuxH55je/CSrfsZyOJsJPxXnnnRfR4LAhiheCaOJCvOcznLeRbGwDFi0N3nibK3Ds2rXLt+NAiwy+FeJUI3daOA2FzmMpenjTwu/uThbPmzlzZoQVB6cPxZqmUlJSAr9yvPPOO+MSRxpTVgrbvMpB+af8EwNioC0y0FiBw7aZFDpuvPHGJr21HzFihG/bS4vZaAN7WzYnUuBoTjpteo8ePYp27dpF9CFsPvrtOWWWTt398sIKHH7nNeY3mz4JHKr3LAvaiwUxkJoMNFrgeOSRR8IaYb7dj8es0T2Ybgos9CXxl7/8JezabLgyMzONwBFv/BxgZ2dngwIAtwMHDoC/xZsmb2MZr8DBRpmNMK+5Z8+eiLc83nibK3CwE+IWohg/xajFixc3eK+0ovjjH/8Ykc+c/kMrDuYzt927d+OnP/1pRDh2QBrKyy1btuC0004LO4/pbIxQ1VD8+l9qVkQqN5WbGBADycJAUwUO247T4iCeqSX2ftm/Oeuss8LaRRsX93wpYcP67U+UwNHcdNq0c+qst39i75cWwtH+RwFpypQpEXkhgUN1h2VLe7EgBsQAGWi0wMGG2zZE3HMwzJU1jhdQCxYsMG/8uayp3+opXNGDFgHc2GhGSweXSB00aJBZCvW3v/0t/vM//xP/8R//gV/+8pf461//aqZpxGNl4r53HkcTOKwQQPFk+PDhOP/88821eN2f/exnZiUSOuu0byO88TZX4PAz++QSutHyx/2720qH01kuuOAC9OrVK2yaCi0+OK3Fm27y4J3O4o574sSJEaun/P73v5f1RrUqJDcnOhYPYkAMtBQDXoGDg+7rr78+YlnzP/zhD74+rj73uc8ZHxLxpv+TTz7BqaeeGtGe2vaV/YeGfDncf//9EWnjPVxzzTURUy3+9Kc/mbDeJe8Z/uOPP26wj9DcdDI/tm3bFpEm9u348oyWwJwGzak+AwcOBF+g2Tywe/5m+002f/0EDobjPTVms/Ft374d3bp18z2X/RWbFrunNYrfdfjCh9ayNl7tVaeJATEgBk4MA40SONioeEUGNvq0iOAKJ3S4tW7dOuzcubNRVhENFfbrr78e0ZjYRsW7v+yyyyIaEgoMfPvhTbf3XH7n8qgcyDck2HjPiyZw8J5oqcHlVqO9jWBcf/7zn/Hpp59G3GNzBA4u7+pNJ+f2sowaymv7P5bz008/Dc7HbUismDp1asR1aCXit7ysjbt9+/Zh5zBv6EjM/l/7E/PgK5+Vz2JADIgBfwY4WHW3oXwZQutLv/yi1SNXAPvyl78cdg7PX79+ve853ng4BdR9Pe8x22+KC97zYn2nA1SvdUf//v0bHY+9TiLS2bt377B7pcPxVatWmTR5LXFpKfrrX/86LDzzhi++bJq49xM4WIbuMIk69ltFrqE+T6Kuq3j8n1Xli/JFDIgBPwYaJXDQ07V3sP7f//3fZvUSNsCnnHKKmQbBzsBFF10UczqEX4K8vzVX4Lj77rsj3hZ4Ow/u72xsO3fujLKyMt/G0R2Wx9EEjv379/tOqfGez/xkHN7fmyNweDsQjJtORRsSK7z5Hs935hHL2pt2Lkvrdz47gt4VXbi8bFM6bn7x6zdVcmJADIgBMdBcBhojcHBQXlpaavxDeNtCru7mtTbwpo3///nPfx7WjvLFB/1l2fjYT8jIyPBtV73xub83RuDwigvueHjM9t4vnd/+9rcblc6uXbuGhadVq/da7u9+Tsmff/75sHMkcOiZdzOjY/EgBsRAowSOaM6lbCPst+/evXuDppWxIJw8ebIRTiie0OzTew1aXVhhhdYkNj5OoeAKId7w/E5z0x//+Mdmuduvfe1rvmHuuece02mx8dm9N75oAsd9993nG+8Pf/hD0ESUUzl47I3Pfm+OwPG3v/0tIt7LL7/8uEwDee655yJEr2hTToYMGRKRrjPPPDPCH4nNa+1VQYkBMSAGxMCJZqAxAodNG6eQeK0lKAjE8lE2adKksHaRYsbcuXPNNFbbH+CeLxMamqZi0+HeN0bgcJ/nd5yodPr1jd59992oQhDzjxYytCq1S7HS4sMtyLS0wEHrZb8802+qu8SAGBADLcNAowSOv//972ENsbvxjXZMi4i0tLQmV/5cSoyNBxu0008/PeL68+bNM/9nGM6btCDRKoDWAd500Vkm53ky7I4dO7Bs2TIzjcQbjlNarNmkjZN7bzg/gYNmqbxvd1h2WriM3NatW00nhdNgeHzTTTdFCAQ8rzkCB+fruq/NY5ad+z4Sdcw88k7/4b2uXLky7Hp8w0WrHm+6KEIlKi2Kp2UqEeW78l0MiIHWxEBjBQ4OtrnRD5i7jWPb+OGHH0Zt49guXnrppWHn8AVBtBc048ePjxqXX/4nSuBoSjrdAoQ7bVwa1p1HPOZLqmuvvRazZ89u0vRmCRyqf9yM6Vg8iAExELfAwQaOzji9DROtIThN5eyzzwYdbn3ve9+LCMNzqNA3B7hoq6iwYfOL95lnnolIB80+/aZp0ESU01K890bxwRu3N4yfwHHvvfdGxEXHpocPH46Ij7/9v//3/yLCN1XgYD7xPr3p5BsQ770k4jv9r3D6i/d6d911V5jFCB2L/ehHPwoLR4/onLaSiHQoDlVmYkAMiAExkAgGGitw2Gu+8847ES8s3n777ahtHJdm/8EPfhBsF/lywPqk4lKqXgtTvrxgG2+vF2ufKIHDnU6m0ZtO9gPdfYCG0sk+mN/UVns+p+bwZdCECRPMy6t4VqPxEzgoHPFlVjzb0qVLUVBQEFe++vngkAWH6p1Yz6L+L0bEwIllIG6Bw68B4RQLKu4cpPONAxsIesDmYN42VnbPqQhcyaSpBdwYgYOChVdo4ZuUhQsXRr0+naPSj4hNL/e0wmAnw51m9/957BU4aELKlV284YYNGxYWjzvOZ599NiJ8qggcvI/HH388Iv1kICsrK3jPdEjqnWJ04403Bv/vzg8dn9hKQPmt/BYDYkAMhBhoqsDB5c69fqa4elu0vH355ZfDBBH2WzZs2BAMf8cdd4S1rRz8x1rpxH2tRAkciU7n9OnTIyw/vX0mfqfTcr4A4pRhWry678197Nc/9Ysv2m+0IIlXpJDAEXpO3GWgY+WLGBADycRA3AKHTTSndvAtBf1xcLUUPzNECglf+MIXwhpmzk1lY2vj4QA+WmNjf2cnw4ZvjMDht4rI//zP/4QNuG28ds/VVv7yl79EpMlrEmrTZvdegePAgQNmOVj7f7tvqHH+4IMPIq7bVIGDQhOtaex17f62224L5qW950TsWf5cBebzn/982DUpKLmnqVDMsGnhnuFnzJhxXNKUiPtSHKqoxYAYEANtk4GmChxsy73WDNGcg/JFjPdl0FlnnRXmk2rJkiURggkH2PFymQiB43iks7Ky0liqnHbaaWECj7uP4D3mKjV0OOpnaSGBo20+p/E+BwonPsRA22Og0QKHFxI/gaOqqgq02HA3UHx7z8bann88BY6JEyeGXZvpoFUFp9nY6/vtvYNwnsdlY91h3ffEY6/AwaXk6BXcHY5iDzsJ7njsMfOP0zTc4XncVIGD8XJNdm98l1xyie/1bTrce78ydf/f75jL4XqvSQekDEsrGE5Hcf//Zz/7mVlG1y8u/db2KiKVucpcDIiBZGGgqQIHrQBoDeBu62jh6Hdf9B/GqR7usAMHDgwLS+vY3/zmN2Fh+LIoWn/Ce51ECBx+6fRapTQ1nXw5wunEXutZd564j9mXeuCBB8LyiPfsJ3B489Ydj/dYFhyqe7zPjr6LCTGQ2gw0W+CIBoCfQ1Iu+WrDH0+BY9SoUWEdAjZmV199dfDaNg3e/f333x9x3sMPPxx2nrdh9BM4vPNLaVbqvZb9bsUEb7zNETjYAfDGR8dljZkiNGbMGCxfvhyZmZng2xabXu/epp+Owz772c+GXZce5Lm03NixY8N+Z9roUIzWJt749D21KxSVn8pPDIiBVGegqQIHp6icfPLJYe1d//79I9o5tn3XXXddWDhafninxDIfudS9tz0fPXp0RJx+ed5cgcMvnfQLkuh00kKX01a6dOliXu5wqo63P+HOA+/9+wkcFIboeyye7cknn8T+/fvjylNNUVH95ves6TdxIQaSi4HjJnD4+WWYMmVKsAE5ngLHtGnTIjoEnLYR660Hl5l1N6I8fvHFF4NpJrze/3sFjt27d+OnP/1pRLhoy7tRIGBnwRtvcwSOWbNmRcRHfykNTZNxP5h0CMopJLS6+PWvf42bb77ZTEviNB53OB5bgWPLli2guan3PmbOnBnhJZ5h2JnxxqXvyVU5qDxUHmJADLRFBpoqcNBZpdfXFMV/bx5yBbef/OQnYe0lv/sNxtu3bx8Wju3nueeeG9eSsc0VOE5UOm3+0PqXK+fRsoOO6Tt16hQxRYf3f84554RN5fETOFiGNt5E7iVwqE5MJE+KSzyJgePDQNwCBwfoe/fuNcur0iEn31RwBY1oBdOhQ4ewRpkOO2kRYMNffvnlxpM2rR2ibW7ricb44OASsd6BtvXBwQG5HZTbtHAfLX5Od3GH88brFTg43cRrUspzKBq443EfMy+98TZH4GBZfeMb3wiLk+aafh0tdzp4TGuNRx99NOxcpo2dL3Z2vOHtd07/8VsGlh0xb0fuxz/+cUyxycar/fF58JWvylcxIAbEgD8DTRU4BgwYENZ2UuyYM2dORLv55ptvRkxP8fYBGvp+6qmngsvRxyq/5gociUin2ykqLTrZb+RUXr5w4T2wv+LXJ7P3tmzZMuNs1J0f3v6IBA5/jm0eaq/8EQNioK0xELfAQTWdUw44OKVna3oKHzJkSNQGloKCu0GiySFVeZvBdMbJRq6hzb08WDQBgg2bjdPuaWnAaSHu69OskkKCDePdUxT59re/HXYOrRj4NoFhbQPsjpPHXoGDDTgH9d5w3bt3D8bhvbaftUtzBA7G/8ILL0Sk4Tvf+U5YGXjTwe+cQ+xdgYb3csUVV8QUJTitxXvfNDP1mppy1Ri/a+s3VcBiQAyIATHQ0gw0VeDgVFh3G+h1rm7vi85E3eGacsw23sYXbd9cgcPrS62x6eSLFXc616xZg9/97nfmpRatStknodWL7V9Fu49HHnkkLL+8q8lI4FCdEY0d/S42xEDbZCBugYMrk3itAjh9gc6l3PDQxPCNN94Ia4zYKNLJZ25ublhY93mxjhsjcDAumnp6G2NajXjTy7D0TeFdjo3ncsqKN13eOL0CB8PTuaY3HJc6o6jDhtzdmHNVGgpH3vDNFTjYsfGbMvLLX/4SK1asCPq/sGnh9B2+TWHnw5sWCj0NLbFr84jr23tFIm9cXF3lww8/jMhXG4f2bbMiUrmr3MWAGEgWBpoicPAlCdtKd5tHIcPr+2rVqlVhYdzhG3NMgSDa1Febj80ROI5HOjdv3owf/OAHYfd/3333gf1Gm2a/vfeFDS1YmN82rAQO1R2WBe3FghgQA2QgboGD1hQcpHsb4PPPPz+4GgYHy/T/4DewnjRpUrAxagp8jRU4OB2Gg2l3evk2gUKLe7DOZd0uu+yyiHmzFHPcU2psmt3x8dhP4GBj610ml1YMnDfKqSpWVODxn/70pwgLB8bbXIGDzsH69u0bdv827XxrQosMWuC88sor6NmzJzp27Ah2GmwY9/6GG26I2QGx+XPvvff6xmHja+592etorwpMDIgBMSAGjgcDjRU4OH2XfQvbztn9sGHDwvo9HMjffvvtEeGefvpp0BlpQxv7DzZeu6cD74buv6kCB9N52223RVyvuenkEq9/+MMfwuKlr6/Jkyeb+2DfyPaP7H3RyvdHP/pR2Dn0c0Z/ZzZMcwUOv+vauL17+eBQneNlQt/FhBhIPgbiFjhYeMOHD/d1+MTGlgNXr68F/k5R4YILLgg2RE2FoLECBy0S+GbAdgS8e3os51Qb7+82zVxRxc8pqTe8n8DBe3zqqaciRBMb929/+1tjKeF+2+N1TNZcIcB2Em699daIt0ree2joO5e8pQPReMuNSwFz+lK0OGlqGm9cCpd8FYbKRGUiBsRAa2fAK3Dwpc2MGTPMFE5O47TbokWLzDKn9mUK+zu27WM77+1D7Nu3D7/4xS+CYRiW4eLJT7/lWjmFpKioKOr5TRU4Ep1Oij82nfStZvPI7jnlhP5LmF76McvKyjLTl+mM3O/FGl+6uPPWT+Bo165dsJxsecXa875jlYUEDtV/sRjR/8WIGGh5BholcNAqICMjw3fgbhsq754CQGMGyNGgiCVw2AG9+3y+LWBHxd3p8KbP+51CA60QbGPsjo/H3vDRBA460vLzxeE9n9/pr4QWFe7/JUrgoHms37Kx7mtFO6YJLK1YYpmPuvOIy8py6lK0OONdycUdp45bvqJQGagMxIAYaCsMeAUOWmBSxDjllFPCtmh9C/reWLlyZcRgmSu8uV9ssJ0cOnRoRDi/fOaAnsu9u9tWry8K73lNFTiipdOvn+W9Zqx0ciqrX9+Iecl8Y/+BfSK+XKFzevf98vjLX/5yhC8xP4GD53rLK9Z3LhfrvR/vdwkcqge9TOi7mBADycdAowQOFiBFAzYCNBn0Oo90N0RsXDh9paHVQxoDRCyBI1pcdDjKBonOUb1WEu70snHl3FA6/GzIq7f7HB5HEziYHi7/eskll0S1aGB6uHwtBSBvh6q5AofND9sh4brx7DRE65DZ+7L5wPXoG1o1xcbvt+/atWtEp4TxJ8KSx+96+i35KhaVicpEDIiBVGXA2x7b9jGePVeF866+ZvPhqquuCmsbv/vd72Ljxo0xB9X2fE558bbhdGBu/+/dN1Xg8Evnhg0bol7He91Y6aT/DLfFr/ee/PKZYShu+E3L8RM4/OKI9Rt9t3nvxftdAofqNS8T+i4mxEDyMdBogcMWIp1FPfPMM/jrX/8KTvewDcfJJ5+Mm2++GTQt9DrXsuc2Zc/VSZ5//nkjBLDzYbd4HZfSmSeXSaXg4E4vRYY//vGPSEtLMx2NWNYK9rp2/8QTTzToPLWwsNCYttLBqfttBJ2PTZgwwYggzI8RI0YE74lxc65rNCuSxuSfFTh4DjsBLBeuLe91GPuzn/3MWJGwTNnhorVOY67jDktP6TZ/3Pv58+c3OU53/DpOvopEZaIyEQNioLUwwHbL9mni3dPCg9aS0SxWuYqc13qDU0xiOQp156mfk06uehYtjqYIHH7p5BSTaNdwp88ex0onl6OnyMGpJl5/ZdHym8LR22+/DS5Jb69j9xI4VPdYFrQXC2JADJCBJgscXoBordCYBtB7/on8zkE/hQc2iifyurwW53i6546e6Ot7r0fnaH4dBm84fVeFIQbEgBgQA22BAb6l5zSK8847r8HtyiuvNKIGV46j34iG8mbUqFERcfG3hs7x/o9t9d133x0Rz+LFi33j2bNnD6699tqw8NGsS+y1TmQ6+UKJTt8vvfRS44uEIoZ7O/30081LqZdeegk5OTm+98h0s/9Jq9NY5RXr/4MGDYp6DZs/fDHljSdRlsr2GtqrnhUDYkAMNI+BhAkcqVQQbquGVEq30to82JV/yj8xIAbEgBgQA8nFAC06KGBwxRT3Rl9mzbEmVTknVzmrPFQeYkAMnCgGEipwSDgQuCcKXF1HrIkBMSAGxEAyMMC+j/o/bYdFW9Z2nwwMKg1thz+VtcpaDMRmIKEChzI8doYrj5RHYkAMiAExIAbEgBgQA2JADIgBMSAGEs+ABI7qxGeqQFWeigExIAbEgBgQA2JADIgBMSAGxIAYOLEMSOCQwBHTqZYeyhP7UCq/ld9iQAyIATEgBsSAGBADYkAMiIHGMyCBQwKHBA4xIAbEgBgQA2JADIgBMSAGxIAYEAMpz4AEDkGc8hBL2Wy8sqk8U56JATEgBsSAGBADYkAMiAEx0NoYkMAhgUMChxgQA2JADIgBMSAGxIAYEANiQAyIgZRnQAKHIE55iFub6qj7kZIuBsSAGBADYkAMiAExIAbEgBhoPAMSOCRwSOAQA2JADIgBMSAGxIAYEANiQAyIATGQ8gxI4BDEKQ+xlM3GK5vKM+WZGBADYkAMiAExIAbEgBgQA62NAQkcEjgkcIgBMSAGxIAYEANiQAyIATEgBsSAGEh5BiRwCOKUh7i1qY66HynpYkAMiAExIAbEgBgQA2JADIiBxjMggUMChwQOMSAGxIAYEANiQAzExUBxZS32FgZQGaiJK7w6543vnCvPlGdiQAyIgaYzIIFDHRp1UMSAGBADYkAMiAExEBcD23Kr0H3qxygLIK7w6qQ3vZOuvFPeiQExIAYaz4AEDnVo1EERA2JADIgBMSAGxEBcDGzMqcF9b6xHSY0EDg08Gj/wUJ4pz8SAGDjeDEjgUIcmrg7N8QZR8auyEwNiQAyIATGQ/AxsyKnBPRI41HdS/1kMiAExkKQMSOBI0oJRJy/5O3kqI5WRGBADYkAMtDUGHIHjY1lwqP+owZ0YEANiICkZkMAhMJMSzLbWYdT9apAkBsSAGBADqcDAhpxa3P3GBgkc6j+q/ygGxIAYSEoGJHAIzKQEMxU6eUqjBiNiQAyIATHQ1hhYk1WDG4avRkkACKgPpT6UGBADYkAMJBkDEjiSrEDaWkdJ96vBgRgQA2JADIiB1GFg9ZEatBuyCsdqJHCI29ThVmWlshIDbYcBCRwSOKQ6igExIAbEgBgQA2IgLgY+OFKLK4esRrFWUYkrvzSoajuDKpW1yloMJAcDEjjUoVEDLQbEgBgQA2JADIiBuBhYkRnA2b2XSeAQL3HxogFfcgz4VA4qh7bEgMS9qokAACAASURBVAQONVBqoMSAGBADYkAMiAExEBcDiw/W4FfdF6NIFhxx5VdbGlToXjWIFgNiIBkYkMChDo0aaDEgBsSAGBADYkAMxMXAnL0BfOvhWSisRVzhk6GzqzRo0CUGxIAYaDsMSOBQh0YdFDEgBsSAGBADYkAMxMXAzN3V+PrDc3G0CqgKBOI6RwOLtjOwUFmrrMWAGGhpBiRwqEOjzokYEANiQAyIATEgBuJgIIBpO8rx3afew47CWlQEauM4R539lu7s6/piUAyIgbbEgAQOdWjUOREDYkAMiAExIAbEQEwGKGi8uSEfp/f+EGszS1AeqIt5TlvqVOteNYgUA2JADLQ8AxI41KFR50QMiAExIAbEgBgQAzEZKAsAr310BGcNWIMVe4rA7+rMt3xnXmWgMhADYkAMhBiQwKEOjTonYkAMiAExIAbEgBiIyUBxDTBq+V50Gr8Z09YfwrEaICA/HDHzTQOP0MBDeaG8EANi4HgzIIFDHRo1zGJADIgBMSAGxIAYiMlAfi0wcO5GPDNnL0Yt2W6WipXAocHK8R6sKH4xJgbEQGMYkMChDk3MDk1jgFJYVUBiQAyIATEgBlonA9l1QO9pa/DKh9nImL4OFDxU1q2zrFWuKlcxIAZSlQEJHBI41DkRA2JADIgBMSAGxEBMBg7XAd0nLMbiPSV46NVlyJHAETPPUnWAoHRrcCsGxECqMiCBQx0aNc5iQAyIATEgBsSAGIjJwAEA/3plFnYV1+KWQbNwtE4WHKk6AFC6NXgVA2KgtTIggUMdmpgdmtYKv+5LFbsYEANiQAyIgfgZ2APg/kGTURQAbhk8B4eqJXCIn/j5UV4pr8SAGDgRDEjgkMAhgUMMiAExIAbEgBgQAzEZ2FQBPDbiXZTWAPeOW4E9pRI4TkRnXdfQoFAMiAExED8DEjjUoYnZodEDFf8DpbxSXokBMSAGxEBrZWDh3kr0mboapQHgmembsCU3oD6E+pFiQAyIATGQVAxI4BCQSQVka+0U6r404BEDYkAMiIFUZ2D8B/vx2sp9RuAYsOIIPs4sUR9C/UgxIAbEgBhIKgYkcAjIpAIy1Tt/Sr8GMGJADIgBMdBaGaD1xoK9lWaKypgNJVi5KwfV1bLiaK3lrftSXSYGxEAqMiCBQwKHBA4xIAbEgBgQA2JADMRk4LERM7CxDCgLANP21mHRxgOoDNTGPC8VO8hKswZ2YkAMiIHUZEAChzo06piIATEgBsSAGBADYiAqA1XVARRX1uCRl6dgH4DyALDsUC0mLt+MYzVyNKpBUGoOglRuKjcx0DoZkMChDk3UDo0e+tb50KtcVa5iQAyIATHQGAZopbG7sAbpbyzEkTqgoroGW/JrMWja+8iuk8DRmLxUWD17YkAMiIHjy4AEDgkcEjjEgBgQA2JADIgBMeDLQCAQMBYbK4/UYdzC9SisASqrA8grr8Gjw6biACRwaLByfAcryl/lrxgQA41hQAKHOjS+HZrGQKSwqnTEgBgQA2JADLROBihwlNUA03bVYMHGg6io97lRFQjg0RHvYl2xBA6x3zrZV7mqXMVAajIggUMChwQOMSAGxIAYEANiQAxEZaA0AAxelYM1+4tAfxy20z/u/T2YsPpA8Lv9XfvUHBSo3FRuYkAMtAYGJHCoQ6OOiRgQA2JADIgBMSAGojAQQEkN8MjEddhaEL5iykd5wLOvLURFNX8PCR+toYOse9BATwyIATGQmgxI4FCHJkqHJjWBVkWkchMDYkAMiAExkDgGKgM1yCkHug2dgyOeFVMyATw9ZjYOHatBVaBG/Qn1KcWAGBADYqDFGZDAIQhbHEJ1RBPXEVVeKi/FgBgQA2IgkQwU1wATP9iJkQs2oMgjcPA7HY++l1lrHJEm8rqKSxyLATEgBsRAUxiQwCGBQwJHG2MgUF0NbcoDMSAGxIAYiMVATXUVjlQD3V6ei72FAVQE6sL6DLTuWL4rHwNX5RqBo0bti9pXMSAGxIAYaICBoGARqEY1t+MwDpPAcRwy9XgUlOI8Pg9AW8vXQHUVnA4o50rTnFib8kAMiAExIAb8GSgLAKsOluPZGVtR6rHeYPvJNmVfKdD15QU4WIZ6Kw7/uJTHyhcxIAbEgBiw0xnZfnALH4txfNJ8f04SOCRweMCSkBD+oLW+/CgP1BqHcTQ71qY8EANiQAyIgWgMHKoDXnhzGZYcDESdgsJpKpPXHsCzE1fhsNoVtatiQAyIATHQAAPHamAE86pArbF0CR932RewzRM5JHBI4JDA0UYYqAzUYn8ZMPGTHLzw7ka8OIPbJm3KAzEgBsSAGIhgoMfMzXjg1ZV4ed5GlFVzaop/h5PLxnKVlbQZn+LJSWsj4lE7o3ZWDIgBMSAG3AxkzN2GGVsLcbQKqKmu8IxF/duacCGk4RfQEjjayOC2MVAobMMPTSrmDzugxwLAc1M/wYTVB7AhO4BNOdXalAdiQAyIATEQlYHteQEUBeDpfIa3kfTjURUIIKca2JqrdkVtqxgQA2JADERn4NOcanycVYmeMz7BSzM+QkkA4DglkeMrCRwSOBIKVCLhVFzhncjm5Affri3dno3Hxi83y/zRUVxVda025YEYEANiQAxEZYDiBR2Nxmp/nHCVxspDbYvaVjEgBsSAGIjGAC3KKwK14DLjz72xBGuzIh1Yx2pzYv1fAocEjpgdl1gQ6f+JEyKOV14ergLuGrEAu/OqwIqFHdba6oBxOEqno9qUB2JADIgBMeBlgMJFY9olR+hQPnrzUd/FhBgQA2LAzUCVsfxbsj0b/VZk+Tqxbkzb4w0rgaORjbc3A/W9cZ0f5VfL5Nf6rEo8O2ePqUDYAdWmPBADYkAMiIFYDDSlzY4Vp/4v7sSAGBADYoDty84SoNvopSis4lTIxE1TkcAhgaNRb2ea0tnROS0jath85zJ/czccxOTtleAxGxX7P+2VF2JADIgBMSAGxIAYEANiQAycaAbyaoEX31mFFTuOojSGv6fGpE0ChwZ7Guy2cgZYeQybtRprjtaAvjcaU0EorBo7MSAGxIAYEANiQAyIATEgBhLNAH0ELtuehV5TVyO/tmGH1o25tgSOVj64bQwMCts6K64DAB4dPhXZZbWoCtRI4NAzLwbEgBgQA2JADIgBMSAGxECLMsAVuAoq69Bt2DwcqpbA0aKFISGgdQoBrbVcP8oHnho7V+KGGjHVm2JADIgBMSAGxIAYEANiIGkYKK0BHpu0DjsKJXAkTaG01kGx7qv1iDgTPjiA11bu1TOjxkwMiAExIAbEgBgQA2JADIiBpGGAvjcGvp+FjzNLEpYmTVER4AmDSaJIMooiAbzwxhK8n5U4VVTlnIzlrDSJSzEgBsSAGBADYkAMiIHUYoALIEzaXoVl27JQmaCp9BI4JHBI4Gi1DARQWhnAQ0Pewc4EOu5Rw5FaDYfKS+UlBsSAGBADYkAMiAExkIwMlAeARZl1eGflFtDpaCLSKIGj1Q5u9RAn4gFJ5Tiogh4srsEL4+fjEBJTYaRyfijtqhPEgBgQA2JADIgBMSAGxEDyMFARqMWGnBoMm7kKXPkxEWUjgUMCR0JASgSMiiOxlY1TYQQwdMYq5CSowlAZJbaMlJ/KTzEgBsSAGBADYkAMiIG2ygBfyO4vCiDt9QU4UieBQwNziTNioAEGaPK1JLMWby79FMUJMvlqq5Wv7lsdDzEgBsSAGBADYkAMiAExkFgGqqoDKK6swcND3sH+BFmcy4KjgQGiAE4swMrPE5uf9Eo8ZS8wf+MBVFTXSAzSsy4GxIAYEANiQAyIATEgBsRA0jHw6PB3salSFhxJVzAawJ/YAbzyu+H85rrSr2woxcpdOaiuDuh5UWMmBsSAGBADYkAMiAExIAbEQNIx0Gfqarx3IDHjFVlwCPCkA1zCRcPCRbz5Q4Gj79LMhK4rHe+1FS4xZah8VD6KATEgBsSAGBADYkAMtHYGxizbhddXH0jIuFQChwSOhIDU2h+6VLw/ChwvzNmBjUcrXWVMZTQx6mgq5onSrA6CGBADYkAMiAExIAbEgBhILgbe3VyAYQs3IxBo/jhFAocEDtfgN7lAV8XTvPKgwPHE2+uxtaAWVQHGxQqDvjiaX3GobJpXNso/5Z8YEANiQAyIATEgBsSAGHAY4MII6ZNWJGRcKoFDAkdCQNLDmXwVNAWO+159H3tK3Q57JG6I1eRjVWWiMhEDYkAMiAExIAbEQNtl4JNS4LERM2TBoYeg7T4EKvvYZU+Bo+uwBTiYII/EyvPYea48Uh6JATEgBsSAGBADYkAMiIHGMbALwD3930JloLbZ1uay4JAFhyw4WiEDVYEACquATv2n42id24KjcZWNKmfllxgQA2JADIgBMSAGxIAYEAPHk4F9AP45dAoKygOoauZ0egkcrXBwezzhU9zJX7nROU9FoBY7s4/h/tELkSOBQyKe6jkxIAbEgBgQA2JADIgBMZCkDGQCeGbsbBw6FkBVgD4Dmz7mksDRjMxrTsbr3KZDq7yLnXflgTqs25eNZ99ahbxaWXCImdjMKI+UR2JADIgBMSAGxIAYEAMtwcCROiD9zUXYXRCon6bS9HKQwCGBo1kKWUs8ALpm7Aee/jfe25yJfjPWIV8ChxhXPScGxIAYEANiQAyIATEgBpKUgew64KUpy7Ept8ZYojdnvCeBI0kLuTmFqnNjCwCtPY9KaoBZH+/H0LnrUSiBQ42Z6jkxIAbEgBgQA2JADIgBMZCkDOTWAoOnr8THOXWoCNQ1q5wkcCRpIbf2Abju7/iKMMdqgInv78Bry7ehqEZTVMTb8eVN+av8FQNiQAyIATEgBsSAGGgqA7Q4HzV3LT7IqgOn2jc1Hp4ngUMCR7MAag58Ovf4VIJ0MkpRY+x7mzH1wz2g2KG8Pj55rXxVvooBMSAGxIAYEANiQAyIgeYxwLHLa+99gmWHKXA0b+wigUMChwa/rYwBChwFtcDAOZ9g/oZMcLqKKt3mVbrKP+WfGBADYkAMiAExIAbEgBg4PgxwvDJxxWYsyATKJHAcn0wWvMrXVGaAK6d0f2sFVu/Oa3Ylkcr5oLTrORYDYkAMiAExIAbEgBgQA8nNAEWN6Wt2Ysbe2maPXWTB0cre3uvhTe6H90SVDwWO5956H+v25TbbzOtEpVnXEbtiQAyIATEgBsSAGBADYqDtMUC/GzPX7cH0fXUSOPQAtL0HQGUeu8wlcMTOI3GkPBIDYkAMiAExIAbEgBgQAy3PgAQOWV3Ip4IYaJCBnFrgoVcWYEdOWbM9EavSb/lKX2WgMhADYkAMiAExIAbEgBhorQxUBmqwaFMmJu+qQ6l8cAj01gq67qvpbOfUAfeMnIe9BRWoCNQ2KIYon5uez8o75Z0YEANiQAyIATEgBsSAGGgmA4FqLNl6GG/tBEqbuUCCfHDIEkCD31bIAC047h0xH/sLKlApgUOMt0LG1ZFoZkdCTKheEANiQAyIATEgBpKIgaVbDuNNCRzq4KmTLwb8GMg2FhwLcCBfAodf/ug3PTdiQAyIATEgBsSAGBADYiB5GFiy9Qje2CULDqluSaS6qYJIngoitxZ4YMRsHMgvA+e0qWySp2xUFioLMSAGxIAYEANiQAyIATEQzsDyLZl4e2dAq6gIjHAwlB/KDzJQXAO8tWwDco6VoyoQkMAhIVAMiAExIAbEgBgQA2JADIiBpGVg55ECrDsaQEWgrllplA8OQd4sgCSoJKugEkBVoAZVgWRNn9KlZ0cMiAExIAbEgBgQA2JADIiBEAOJGLtI4JDAIYGjtTIQqFLZttay1X2JbTEgBsSAGBADYkAMiAExEMGABA5BEQGFVMSQiqi8UF6IATEgBsSAGBADYkAMiAExIAZSgwEJHBI4JHCIATEgBsSAGBADYkAMiAExIAbEgBhIeQYkcAjilIdYampqqKkqJ5WTGBADYkAMiAExIAbEgBgQA8eTAQkcEjgkcIgBMSAGxIAYEANiQAyIATEgBsSAGEh5BiRwCOKUh/h4KoCKWwqzGBADYkAMiAExIAbEgBgQA2IgNRiQwCGBQwKHGBADYkAMiIHkYSAQSJ60iAuVRQMMBMSq+GiADw2GU2MwrHJqfeUkgUMVkxqnZjBgOjeBAKqaEYcq1tZXsSZbmXJNcWddcQ4ctSkPkpEB1gPO0tasT6vMwDEZ06k06fkJMRBQfao2RQyIATGQQAYSMyZIOoGjqqoKy5cvx4oVK7B27VptyoOkZmDRokV4f+VKrFm7VpvyIIkZ+AhTp0/HmrXrsGbtx9qUB0nLwEdrP8a0ae+KVTGapIyux5q160FOV6xYiSVLlplj1atqV5KZgYWLl4BbMqdRadMzRAaWLl2GfXv3Nvvle1IKHPfccw8eeeQRjBs3TpvyIGkZGPvqOFxx47X4Ybsz8N1bLtSmPEhaBn5y80U46T9/ij91Hog/dBqhTXmQtAyc3aknPvOTK/H7DoPxR5VT0pZTW61H/thpJLj9qdPL+O0Vj+K0M+7BnzsNVjnpWU1aBv7SaTB+dMZdZuNxW312dd/J3/f7U6fB+NU5t2LBggWtT+CgaXd6ejrmzZsHfZQDyZkDdQDqUAvgkZ7dcdpDV+PrvTtqUx4kLQM/7n4LTvrd73FR+n5cmFaAC9MKtSkPkpKB63pswEmnP4kLnj8kVsVoUjLK+vPitGycc99M/MeNb+KStMNJm07V9WrrLks7hJ/dOMFsPBYTYiJZGWBd+sfOQ/HR2rUSOJJzAKxUte4ccASOGtThoV7P4VuPXI3P9uugTXmQtAx8+8UuOOn3f8DZGXk4M70WZ6bXaVMeJCUD7XrsxEmnv4AzXjgmVsVoUjLK+vOc9Er89b7F+MFNM3FueknSplN1vdq689OL8cMbp5qNx2JCTCQrA6xLf9f5FeOaoLm+7JJuioosOFq3NNCa7o4Cxz97PYdvP9wen+/XQZvyIGkZ+E69wHFORi7OSg9oUx4kLQPte2zHSad3x5kvFOLs9OqkTaeeo7Zdj5ybXoYz71uI026ajvPTi8Sp6tSkZeCCtEL8+MZ3zMZj1V1tu+5K5vJnXfr7zqMkcLSmwbLuJfVyoBZ1eLjnczj14fb4Qr8O2pQHScuAFTjOzcg1g0YOHLUpD5KRAStwnGUEjipxqmc1KRk4L73UCBw/vNEROJLxWVKaVMeTAU5LtQIHj8WFuEhWBiRwpN5YWCluhTkggUOiTqoIWxI41KFJ1g6NN10SOMSql4lk/C6BQ5wmI5d+aZLAIVb9uEjG3yRwtMLBsm4p9XJAAocEDgkc6jgkYychldMkgUPPVCrwK4FDnKYCp0yjBA6xmiqsSuBIvbGwUtwKc0AChwQOCRzqOKRKxyFV0imBQ89UKrAqgUOcpgKnTKMEDrGaKqxK4GiFg2XdUurlgAQOCRwSONRxSJWOQ6qkUwKHnqlUYFUChzhNBU6ZRgkcYjVVWJXAkXpjYaW4FeaABA4JHBI41HFIlY5DqqRTAoeeqVRgVQKHOE0FTplGCRxiNVVYlcDRCgfLuqXUywEJHBI4JHCo45AqHYdUSacEDj1TqcCqBA5xmgqcMo0SOMRqqrAqgSP1xsJKcSvMAQkcEjgkcKjjkCodh1RJpwQOPVOpwKoEDnGaCpwyjRI4xGqqsCqBoxUOlnVLqZcDbUHg+GLfDmjOlioCQGtPZ+tYJrYKZ6cnYmvpzk60e2jpdCXH9VNf4IhWvk35vaXLxC/NLZ2m5Lh+6xA4/Mq3qb8lR7mEtxHJkqaWTUfrEjiayqc9r2XLIlxosGniPpnS1XJpkcCRemNhpbgV5kBrFTg+368DPle/8bhJW98Usu6oTyvvs7UKHakucJyVHsCZCdjOSq8Gt5btTFSZNLjvh/d3ttlaOm0tf/1UFzgSx2ogKVg9M7067Nnj/Tm8tjwrLfkcp7rAwXrQXQc19ThZeHDSUYMz07k57UWypK0lOeW1U13gYDk65VobVr62nBuzb/n2n30Q1qEhTnlv/M7fW5qVlr6+BI5WOFjWLaVeDrRWgeOLfTviZG59mrZ9uU9HcGMcX0gFoYNp7OsIORI4knHQUoVz0itxTnp51O3c9HK4t2hhz04rr7cCaen75D1VBNPM43OMdUpLp6vlr5/qAkdDrLoZtcf+rCYHD2elkQemxT5fPK5IkmeoZVlNdYGDAxmn3nHK149DMur3e/hvycEq7+dcU6c69aqTxgqclVbZ5geNqS5w0CrHlGdadB4brk9D5zkWPi1bdzj3Uxnk1XkOxSmfYQkcqTcWVopbYQ60RoGD01H+PPoxPPrea3hm0YQmb08tnoBuM4fiK306Jr1VxGf732ysVE4ZcFtqCDJNsDRJXQuOAM5LP4Z7x1fg5YXFGOazDV9QjBGejb+5ww5fWIyhC8vw1NQ6XJiW3+Id3hsHFmLI/BKMWHDMpL33nBpc3b/ICDn2Dco5LW5p0jKdwFQWOM7PqMKz02p8WSWDXk753csquR28sAbdhhUYAczy0FL7hyaU4+WF5fVpL0HGHOCStKMt/gy1VH7Y66a6wHFBWgn6zGG9egzDPPUlGbS8+vHprlsHLaxD5yEtX6een1aKJ98swcsLKw2rwxaU4blptbgko+XTZplpqX1qCxxVuKZ/AfrOrsCwhSVh7brl0N0HiM5rCfrPLjNxtVQ52Ote1qsAL7wbwLAFpaYPMGRhNe5/rQjnppW2+XpVAkcjBst1dXWNCK2gyoH4c6A1Chxf6NsRd80egSOoRhlqm7wVoRYr8vfiG706JZ3A8fl6aw1Ow6Hlxnd6dsblr3VH703z8NUUEGSaYmWSygIHBYkxq4FyABVRtkoA7s0vXAmAxQeBS9MOt3gn4oFxRSiqce6H6d5VBtw6ojApBrS2E9ZS+9QVOKpwUe9KrDoSP6csez9WCwBkTC3C+eklLc7q6x8Bx1zP19ZyoF2P3S2erpbi0143dQUOZzrcRWn52F8Wu17149P9Wx6Af79V0OI8XJhWhFmfhlgtA8yz2K5XVounzTLTUvvUFjiq0W14Hg6UxmY1Wn1KXtl/OFgKE1dLlYO97nX9jmJtNkBGmeYiABM+qMEFacVtnlUJHPGPQVFWVoaNGzfik08+iXvLyclpxBWiB62trQ3757Fjx7BhwwZs3rw57Pdk+kJByC0KBQIBbNu2zeQh8zIVP0VFRabsS0o4xEncp7UKHA/OHIkS1ALUBpu48bT12fvxzSQUOCgQfKlvR3y9dyf8ecQjGLB+Dg6iCqvy9uCUJE1vU0QN9zmpK3BU46K0XIz/gM9tdTO2KhKNFQeBy9MOtHgn4uFxR8G6FXVVQF0lDpUCXUfk4Nz0shZPm+2EtdQ+tQWOCqw73FxWq01nvM/ULJyfBB3eyWsC5tkhp9z2lgHX9Nja5jlNdYHj4rRcZJeSVdaMTa9bGcVzbx1pcR44iF+4sTzEKoC1h4Gre2a2eNpaqi611011geOO4VkJYLUWOaXAncNbntUb+2ZiUzafvTpTp/Lpm7yqEBemFbZ5ViVwkIs4P1u2bMHll1+OCy+8MO5typQpccYePdiuXbswderUsABr1qzBRRddhBtvvDHs92T5QgHj1VdfRV4eNXnnk5+fj9tvv93kIfMy1T7V1dVIT083+U5xKZGfVilw9OuAu2ePQBYCYFfBvVWgDgHUGc3D5iO7RlWoCwvHc4pRi5X58QkG1ompe0De2GMbR7yOQiludFs6BpvKs1GNWrAs1+XsS1pBprH54Q2f2gJHPsZ+4LzprjK8kbnQVlPfRSeL3PidHQZ3GB5Tnn3vIHBZ2qGYnQjrnM7Zh6Zq+DkpDQ8bnwPGh8bloiJgnyKYt1Ndh+c30oLDuZbjmKz1OCdLbYGjEquPRLJnWbSM2n00VguNBUchzk8/1ihW7YAm2t7LajyObSeuqTPPkqWV1kYso2jX8Ps9/Lqh58kvbKr8lroCh5P/tODgG22+QbZ82j2rJvtuw82q/b97n28sOOKbBuLmoKFydurZUP0WTx3HweH8jdVBVnkPfBbb92qcxV68aWwo/cn2v1QXOLrSgqPMvw/g1/6z7N2M8phWHJlltODIjVl3hfPXcH3l5sV93BADN/Q7jI3ZTl+F9SrTNnFVCS5opMAR7/UaSkuy/U8Ch21p49hbgePSSy81A/V//OMfiLUtXrw4jpijB1mwYAF4vV69eoUF+vjjj3H11VfjtttuC/s9Gb4cPXoUt956Kzp16oSsrKxgkgoKCnDvvfcaUWb79u3B31PhgOLGxIkTgwIXrXgS+WmtAsf/jH4YnecPQ9e5w9HNtXHqyoJd60Ghgx92fHKrSpG2ZFJYOJ53+9zhuG5aP8cHh8fRqF12loNwThP5bP1mpoz0Cy1L6x2kh32vX76Wv1HUsPFwz42/8Tph57h8V3w3ozNG7l6BSud9D6pRh7USOGI2/C3RGNKqoevYWqTNqnFttUifVYPeswPYS3t+12dfMfDSzFxXWOe8HrOABycizkEjPZzXGY/t7ETwvtnp4YoS3Dv54Cztxv+fmU7v7jZ8Tf2KKNGXfrt1ZAUW7wWWH3S2aTuAG4c6Dhxt/NF9cNjrOmk8K70WZ2fwmkxX9Gu2RNk15ZqpK3BU47yMKvxzMtBjlpfXGvSZWYIiGkHUs8qO+bZ8oN/MQqTNqgvj9YVZQMehZfUOPS1v/nuHP7Lg74Wfzmu5hcKRVWc1AnrujyVyZCwAltRzSl7f2clpXvGa/ZNHDlTdz4cV41Kb1VQXOM5PK8PT0xHBHuvZ8cuKUV4dYpUDxCWbypAxszyMU4Z9fhZw4+DYvgNC/PlzyrrC1nmhsKxTa80KE2cHWSU3keywPAYvDbG69CAwdj1wac9434o7cTqs2ueDrPpfryl1W0udk8oCB5m4qm8pnp4GvOipJ8nfnE/KjUhnuwAU7Oasj+SUdeyz04Cr+sVi1a5yFr1OdVi1vLjbf8trw3XclX2KszEZSgAAIABJREFUMGEjQEZZp3LqbPpcGB8ctv2PzorDY+gZcT9PTpqin+vfhiRTeAkcluQ49lbguOGGG8xUC05TiLVxYNycz7Bhw4zFgFfgoIUELTv27dvXnOiPy7lM1zXXXBMhcNTU1ODAgQPYs2cPKiqoM6bGh3ndv39/XHbZZUHLHQkc0Qf8biGAq6d8tU8nfLV3Z3wtuHXCdzK6YOgHM1FaLwpQ4NhfXoALhj+JrwfDhZ9jRAaP0PC5fnTqebMRIOiE9Gu9OxohhGHdAoU7Te5jK2gwHvrQ+FLfDub8r/d2Vn3h9BOGp3DiG1/fDvheRmeM3kWBwxluWIHjW706OcKIJ83u66ficSpbcLDxpYf089OLPVsRLutVWD8lIFQvrc8Gru6x1Zj3e89xpoCEvJXbwZ/dO4O9GuPs8/y0Y2Cn+ez0SiNqsOPhbKEl6/g/po2OUPn2hT4T+P2s9CqckeGEY0ckrAORQceppbgkLbt+O4qL0vJ8pqc4A9NQ2uwg1enwn5tWYebsnpfGgXD4/8KuFxRkkr9zw3SnssDhsFrm4dTh9vK0QzhaEho08i0jp0xd1WNXRPjz6jlyl6PlgHsOusgVN4c/clcW/M05j9w54gKFDOd8ssr0FMG5Bp8Fp4NuhDIXq5b389MLg5zSuehFaYU4z6xGZHlierjKUYhXmz5ncMrn18kT5/lj2jl4IMeeZyOFWE11gYPlRQa8dSS/3z86E4UuMY7+C0YvzMXFaUcjwjsccWWdEA+WhXPrhQiH1RpzPSc8p+KFD8YY5sx01DPh1KsXpNMPTbGpL7k6kROG7DjcWEGE1+Y1yeYlaTm4NO0oLk3LxoXpfo56Q5zyHJ5reSTHdFbKut9hlaK2M9AN3Z+9z9TZp7LAwXzn6ji0ZrN1F+svh9si9J+Ra6wzbQ+ArA6YmR/BqQmfdixMNHZWvArxQCbJFtvvc9NLTbvO+tUpe1ufOryclV5neGQcrAsuSCsy7TG5IVeWKe697DBOlonTBziKi8mqsd4Ifybsc2T3jMcuKetct8Q8U+eYup/psmJg6rDpzRsJHJbkOPZW4OC0kIMHD8ZxRvODUODglBivwNH8mI9fDBQ4aF3iteA4flc8PjFTnKKlzCOPPGLKgPd0xRVXmGMJHPEJHBQFrFWFe0/RY8DqGThWL3DUoA57K/Jx5qjHgxYUNrwVFvzEAAoRPx9wJy4a+zQ6Te6Hhxe/itunDzKOPv/35Qdxau9bzBK1ftNNzG99KYp0wk9fugPnjXkK7Se8gFunvITH33sNN0/qjfYTXjRp+lHfrvhqbzo57WgsOmxa/nvEg7hq1DOYuf8TM72GJPJetucfxhWjn8EFY/6N341+FF/p17nVrKqS2gIHOxw1OCO9LmxjQ39+72p8dDi8LlmXDVzZY78J61hV2PPcb+UqcUnvMtw1uhD3js7BPaNzccfIQtNRad8nDw++WoCX5lbi+XeK8I9Rebiid16wk8OOxQXphbi2Xw7ufSUXj4/PQdrUYgxZWIWM6SV4fEIe7hyZg6v7ZJkOkh3shRryAC7rfQx3ji7CXWYrxO0jy3FxL4opTgeHgknHYWW4e3S+Sd8/Rhfjmn7srJfhkow8dBmah0fH52LgvCo8O6kQ94/JRechOUZkYfpSeeCY6gIHuYxklav3FCCrNFzg4Bs8Or2NDM+OakgAuLJPseGArHK7ZdgxI6Zd1y8bj79RhP7zAnjqrQJ0HZ6Li3sW1VtlBOoHffn424BsPDA2D4+/nou+cyowaGEAz03Kw2Ov5Rmne1f1ysV5Qf8vzuDRChw3DC7DnaOP1bNahFtHVoc5w6MA0mV4aTB9DNuub6FZ1vHynrkmfqbtpXmVeOqtfNzzSg5uGpALDl6dZyI1RY5UFziMWJBRizMzbP0Y2t81OhcFLoGD0/uGL3BEMX9WQ4O4mwYVB+ut+0bl4IZBJUbA7TgoD09PJAcBPDo+B7cMya53ouvUeVwq85L0fHQekosHX83D4xNyMHhhNQYtrMFTb2Tj4VdzcevQHFyWkRsU80J1qrPkbYchJbhzdEk9q8fQaRgHxtZRbxUu6lWG20eUGFadOr8Al/biwLkE7frk4K5ROXh2kpPGJ1/Pwz9G5uLqPjn1Vn+pySnzKNUFDitsnZFei/DNsYwzrmTquwFkte/Mkog6lec54oBTjhQZOg7JD7J69+gCXDOwzIh4t7ychx7Ty9F3TiUeeS0PfxuQh/PM8tgUGCgql+GKjFzcPiwXD7+Wj2feysPg+VWmz/DUGzl4cFweOg/KNQIbufa2xxdklKDL8PL6erXQMHvjoJD4wnOu6Gv7J7m4Z3Qebh/uOCE/P60I1/XLwf1j89D97UL0nVONR17Lwd9H5KJdb9bjsSxUklv8kMAR3p9t8Jtb4KAlQlM/tPp48803jVXAv/71L4wePRrz5883TkxtnJWVlZg3bx6eeOIJM6B+6KGHMHfuXKxatcoEyc3NNf9fsmSJPQWc9sFzDh8+DFpLvPfee3jxxRfx+OOPg0KJO810fjpu3Dg8+eSTSEtLw8yZM4PxeA/oKHTTpk2YPn06hgwZYgb8HPTz/EOHDgWD0xHqBx98gPHjx+PKK6/Eddddh0mTJmHRokWgU1Te0/vvvw9Ouyks5Mxg52MdkdIaZezYsSbNvXv3NlNCMjMzbbCwvb3/Tz/91PxO56W8R97rSy+9hDlz5oTlZ9jJcX5hemitQ4GJU2vWrl2LO+64QwKHa3qGHeg3uK+fAmKnk3DPFVEGugQOTtHZV56Ps0c+bqwe3GHtMS0s7HVMHH07o8O7A/Be4W5kBUqMrwQ7X7KwrhIbSo6g76fz8IsB/wBXdLHn2j0FjlN6dcHNMwZhXvY2ZAZKUIYAaowfjVrjT6MEAeypLMDs7M24edYgfLN3lzChYsjWxTgcKEFpHe02nA/31XW1yK4uRVZ1Kd7N3ozvD/p7g9NcbJpSYZ/aAodtkPm22b1V4KLeZcaRnLt6oMBxlVnlgW+VQ9YaVjxgh48dlNtGVyCrEsivBPIqgR35wHV9DuHdzUBBvXk2pxEcrgQGvYfgYOzitBz0ng+sywIKqhx/HzX0FeZy13e0DFhzGPjnG1XmzU54ByeA+8YU4GgpUFjhbJsLgFuHhVZRuTTtCObsBnLq03eoCujxTh6u7XMEr68F9hQ783ZpRcU0FlUDu4qAIctokp0XtDhxDwBS5TjVBQ6HMzenzjHfLHsFjmVBp7fe8OFv8h56rdhwSla5Ld8HdHm5AB9mAiW1Ib8z+0uAe8ZxQOeIG1f1KcQrq4AteUBxwGE1zKdCHYyD20V7gNtHcJliWoHY5437AMauArIrQ6x+UuBaRSUjYJz2Ls100sW0HagC/vVGFm4YUIAZm534aRNLTrnPrwI25ADPTaUFEjv17uulznGqCxxOfUDOvOxV4p7RR8MsODhoHLGgwAi7keHDy8zyYlkdvQJ4YNRBfJoNVNU5HNAWeGcRcNPLrKMrjBUSl8l++xNgdxFwrCaS1fIa4EAR8M4GmHrQXZ/zXvgGfeo6p85kvco6fck+oF1PZ0ljXocCyPqcUJ2/PR/4+7DDuH14PpbuA3Iq6utz+nCoA3LKgdWsxyeU4dywtiT8npO9bk11gcPJ30hOyWK/mUVhFhxklb9FcmrPd8qOvExZBxytr1NZx/VfCPScloddx9gfdKZhl9UBy/YDVw/k+bTsKEfnoYWYvxM4WAKU1zNt23/2Z0sCwO5CYPQq4Mre9PnBc+uZyQjg2r5ZeD8TyK2EERKZhrErqozVCcPRWuWhN2uDaWO4NcZ/WBaemlxhGC4KOM8S69WKWuBIKbBkL9uFxvrySi6WJXC4e7MxjhMhcKxfvx7XXnutGSS3b9/eWDpcddVV5nuHDh2Cq6LQOaefM9O77rrLpJJORvl/t5NRCiX8jY5NMzIyzNQW+u+4+OKLze/XX3+9ESqYBp7H3/l/e52nn37aTLlxZwPFmOHDh5vpGZdccolJLy0ZeGzPo28KihtVVVW47777gr/b/3O6yu7duxHNySiv8corrwTTQnHEWkpw//bbb0eIFfb+eZ/vvPOOEVTs/dD5Kq/dpUuXZk3hocDx4IMPYtq0aUacYTrpc4Vxy4IjJDY0ZVBOx5yDVs9wVlgxg7lwgSMiTusno17kOKVfFzy9eDyOoizkqtT2trk3akOdmTayNH83/nfYQ2a1k6AlR98O+HK/Tnh4/hjkG5uL+nPsubaFqY+HVhmHa0tx67uDQKsRm77Xt690rm/CuZ4cfq+PY1X+XvxQAkeoUbaNc1LtuSxnha/A0a7HnvqBk6tjUZ92Dqg4OOn2Sllw7i6LvaAUeGtJplnBwnRt6th1oMNc4MnxdFRXhQt6VWD44kqE3hjxTK6EUmG8oTuyA8+rM3ZO2eXAoxNo0h/+VuWf43JRyZ6Q+dQZB2ruVVQuT8s0c3OdIHXGCdm4OXuwYks5zG9c0cKknnvneoyqrBboPavCDETC/YUkVycm2Nnz4Sn1BQ7/vOZqQOyEsrrih+UYEjjCBQ1v/jw+Ibe+cnIqqb3Z1Vi1zyl5J0bKzcDhY0C3lzmgq8QVfYsxdW2Vq/PPK1tuOMTk5rDDtFAc6TiUIpvDKp+Ts9KqMXENhWN+nGvvLuMUsG3BuuHKtL34yK4IgDpzvTcXbMeOvPoqndcxzwc9OTiVNWMqrgOemFTtsuTwzzdvXiTL99YhcNg8t0KH8/2e0dk+AkehERHIRUOiVDgvwKKPs7E9uDAhSePrCGD7UZjpJKxX/zYwB6sPOs+EQc08HeSTvHIfYocxrD0CXPtSgWu6QQAXphdiQdDJaJ0hm9Z9V/c8hLMznKlcnYYWYjcr9PoeSE4ZMHbWVhzhJXhNcmpaAF6PlDrEcsDbZZjfdBebf8m9bx0Ch38e95tZ7KrjHGfi/K0hRlmHcPn5+Rtrgk5pWRMu2VThmkbosEoKlu0BLuhZaiwx7hh2BFvybc1JQlg70tyJzJAffndqecYweR1wRUa4z6Ib+rqdjDrt+ySXk1EKHI9ODBj6ySFj23O0GgOm7Uc+o69zs8qrOKzyL5efvbbXvmD9nCz1ZbzpkMBhqp34/jRX4KDzTToF5Uosr7/+Onbu3Gn8aHDpWVpocOB89913m8RwegQH0S+88IL5nZYW/G6dc9oBvp/A8be//c0IBFzFhOd8+OGHQcsDrmJCq4THHnvMTL+gZQatJpgmiha0rrAfihYDBw40QglFGVqHcPoJN1oz0PqEosLNN99sLDkYnvdE6wmKNkwbrUhoZVFeXh5V4KC4wWtT8Hn33XeNf5OtW7caYYNiB0WYMWPG2GSZvb1/ijYUQZ555hmz/Czvh9YxVkSiNUdTPxRsKGrYjwSO5okaVhTg3ggcH0QKHGeNfDzMQsJ9Di04KFBwudi7F45GNirMSiwsH1bauQhgSyAPh1GNY6ZL4pQc/XzMPbwJp/W+1UxXYZxf7tMRZ77yBDYH8sy5rMwZblNlLqbs/xijtyzFkvzdyDcTT+pfqdORHwrxp4EPBAUOOhctQAAVZu0U53pcG4aCCKffFKEW84p34fuDu8mCw2cAGm9DdfzDRRc4ruqxp8EGnoOTv79SGuxAkIJALVDmeiviDOqAjXnA1em7jSjy0JgjyGY/xnQonEEla5tMvrnmW+767o3zf2fFIb79ubzHwbD0/HNcTr3AQYprfQSOgy6Bg6v8AHmlTteJb6i4csHhauBY/UDZeZqcTs72Y8A16TtS1oqj9QocecaCw+n6ugWOcDb8nhtH4OCZzlZdA5TXOt/YvbXb0r3AVT0zjZn14MXOUM2yyDBFHFgWAkdqYYQ7durth0PJqZutA1GacTtbaMDqXNsrcPBZo9WUTRvjPFb/JpxCYC6tRCpYV9tuOOPhilzABweBa3q3/LKNfnke67fWJXCEDx4bEjhi5Us4L3UoqwoJF6xTyYexCJmfjwvTinBZ2hG8trIOAVN9kYpaM+ik7+gdxUBOnZsdp46jRvHiFPpZCK0yRB8GFDicers2XOCo9+MUEjjqGayDcabKKp1pyq4Fcmrdzw255ipxwPhlZeD0gFj3n4z/b4sCR6xyYJ6EeHG4Kqu2tVjIkodtbM9p+cbvUPu++UaIc+pNh1VywzpuR5HTLpMV0mXrXbbVj77qWBHZNNlVVJxwtaYfEiZwpB3DYxMD9eKLE6qiGiiuciQ/9jn2lwN8Rkx3xFzPuSbTM3ge/YBEvtyx10/mvQSOYGHGPrACBwfcXbt2jbmCSo8ePcIsDyhk0FElrTBKS0Pv7Xjl4uJiIwjQimPv3r3BxETzwWEH+H4CB8WKWbNmBePgAQUJa1FBEcU9RYRiSvfu3c3/3YIAp7owPRQQeD3vh4JN586dzXnLly8P/juaDw4/C46PPvrInM9r+C29ymk5FD+Y51bc4YXs/fOeKP5w+ov9cMoLp8bwfzwvUQ5NJXCcIIGjoSkwfTvg/4Y+gHWlh6zdBcpQiw8L9uOGCen4RY/bcHrPO9F9xURk1ZQGl6JlmIfffz24dCv9btz/7nAU1gshnGCyMHMTfp/+D3w/vQtO7dkFp6V3wQPTh+EgylGCGuSjGp/U5OAf770SFDgunvgi/jljJFYc3WE62WSQ4sb+Yzn458xRuGfWSFw7rS++3l8+OJK5IeSbv2gWHLEEDr6l7hYUOGwt5HRJDpcBcz4FJq8BNmQB/RfDvO05P60YE9c6A0N2otmx2JwDPDx6j3mj3a7HTtwyYA8+2Ot0jpwuUx2OAripV/iymlwmNmTBgXqBw5lbzjy/PO2gebvPLpT9sJtTGADGLivHTT23mms+9uphbMsNDSAYlh2yF94uSMmOOO+9tQscDheNFThCS7fb6aHsZO8sBKavr8P09cDHucBzM4EL0gtwbf9cfJjrDNTIEDu9K/cAdw7Zjat7bDd5/MDoTGzOclglN4xvYw5wY38KDm6Bo+FlYkMChyXVubfMY0C/2aW4IWMrbuy5DS9OzsHRCuc6DEmes8uAO0fkpCSrEjjCRRHbVniXFWZJ8732xqPApDV1mP0p8H4WcMcoDhrLcGPfw9hdP33Ksjr3U6BTv51gnUqH0d3fOIjDpSF2GN+M9Zz+F1ohxW+ZWE4TvLqXsyw4pxaEBI4Qq+R+bxHw3MQ8U6fe3HsHBs/ONdMPnaGlw2pWGXBxeuwlRm0+JNNeAkckq15eHCLqUFoHrNkPTPoIWLgd+CC//gVHWgXuHleOg9VOnck+AK0phs0vwvXpW9Gux25cn7EDA2dkocCCU2+DNGFVXVgdR4FjQwPLxNLJbciCw6bMmTa1ej/wyNhMU4fzGXl3XSDMgoXtCy2XLupN56gNWwYmE6M2LRI4QnVTzCMrcFihINae0xncFgC0LqD4cNNNN2HHjh0R16PjUooKtHawn6YIHN26dQsTMBgXRQyb3rfeestGH9y/8cYb5v///ve/g78xHbS+oOUGVxLxfigq0DcF46Wlhv00RuCg5QXPHzx4sD09bE9fIs8//7wJQ8HIftwCBx2Bej8UkOxUlf3793v/3aTvEjhaXuDgSil3zx2B6jrHNJUV8I6qfFww5ml8oW9nnNS/I07q38E4BO0+/3UUuwWM3B34Rf87jThBC5Lh6xYYKwvGUYU6vHNwHf6z9+3GguSk/jfjpJduxrd7d8Y/3huNf80fh+tecxyO/mSwEwctQWhR8p2enTHKLBPrDDfsKiqn9O4UXGY2zBKlIQEnBf7XOnxweDspiRU4SAId6z07MR8XpRXg/LRytOtbgEv7coUArjhQigfH5mHQnCLM/CRgOhH3T+D88TL8JQP4azrw1wzg6TdzELIhc96ycGBpG3DumyJwsENFc9fLe+WZa/GaZ2bU4rm3ssKuR9n41RUhp3/u66bCsQQOL+fVxnGtuwFk/zmvGrh92FFcaFYUKEX7AeW4sA/f2lXi8l6FeGhcNl55rwzzNtRg6X7gtpfzcE56jcNOOnBORiV6TT0anG7FgR59EnQcGG5R4R2w7iqD6VxblvwEjhzjhyPHzFc3z0U6zPMzcCYnKDofPm/lAeDBsak5aJTAEckpmfDywnL+9CjQYWBOcNWWqwZU4cKezkoq7fvk4qm3ijF2SSmWbKvF3F3Atb0zcUYG8Jd0mLr10vQc4/OAojLjY134/o4a4zzTcugdsFIsiUfgyK0BKBRzqW3W3+T1ip7ZmL3ZEbF5PV6Voso1L9GCI/UGjRI4Iln18uKUMjBjA63KDpnpTxf2LMHVg7n6j+MA9IaXctH9nWMYv7wMK/cCb64DLuuZZ7ixrF6RdgCrXG4IWa/O+tT68HLS0RSBg/FsywM6DTxsnK7bPsd1vfd5HK3XYVMucPVLJSlpxSGBw2kf4/prBQ76oOA0CA78G9pWrlxphAUbOR170vKDA/p27doZZ5qcEsJlU70WHfacpggcDz/8MOybGRsP91bgsI5K3f+j6MH/c9pJtA8H+NnZ2cavxebNmzF58mRjdcLzmiJwUEChNQnPp3NSvw/vY/bs2SbM/fffb/x8MJxb4KD1i9/H3i/9fyTiI4Gj5QWO72V0wau7HUe7LFMa7s85uglf6d8luMIJRQcu73rG6MexrZpGfc7bmn3lBTBTYPp1MCunPD5nTNCCgx0Pihzr8vaj57K3ccOENPxl5GP40YBu+EqfDmZ6yWf632xWhPl8v5APDgoX380IFzgCqMPanH1Ba5HWJG7wXiRwhHdw/Cw42CFencnODf1tOJ7WQ3t7Pp03luNcs4xnSf0SgvytEhdmlOCmgfl4afpBlzm+M23lwdHh0xAaK3CQdb5ZatfraP28c6eTTYeQfDtOKxH74SBg4prwDpUdBKTCXgKHZS2058o87g8HW5NWlRmfCE6ZOkvGhpcvRbkyIyyQT64oxNWIeHxxRjG6DMnByLkHzMCNcVM04ZvsW4aEm1N7B6zxCBy0BGnXn9MHQmbSRiAccxQ5fNDqP7Ri4rMQnu7QfSfz7xI4/MvJywsFrSHzQ8vCOqvzWAshLm3JY8d5I5cBJTPklOHMahV9CnDnsINYtKXOCBusC4nQxkPARWn5QXa8A9Z4BY6pGzgty7HycHirwoVpeei3wBE4LKt85m4YSEe87iVx/fMg2biVwBFZTl5eauuAY9XAA2NyzOpTLEOy6fBqjy2rnK7krM7DlVkYlssLt+tbhHtH7MMHeyw1Tr06b0t4e9wUgYOi3msf0m9Ndj3zXIq2CnR6/vqHoevxaGs+cP1AChypx6oEjvCybPCbFTg4LcS9IkmDJ7n+SR8V69atM8uncvBNCwNu9BdBa4+RI0eGWXzw1KYIHNFECjvg97MeaUjg4NQQTmFhGum4k34vONXGxsd9UwQOCj633HKLiYuCSbQP84zXcFumWIGDeRftY9MngSNxwkSiBusN+uCIYsXw+b4d8B89bsHq6iPBIqfAsTZ7j5kKcu+skbDbfbNG4vmFr+NghbNaDzsyZbVVuOq1542FBn1wXPDq09hrjPCD0YHWF5zOkosqbKvMxZL8XRi+dTGuHPccKK6c3Kejscpw54MEjsgGP9k6ZbHTE48Fh//bNj+Bw92BCHVsnA6O7ezYQSLf6HAayY29duK5yQV4/YM64/RxZz5wrN6PB/nlhzQ3V+Dg4HNPPjv0drlalh/vrcqYcO8OGRCaqTMcZMTOv+RkQAJHZLl4BQ6+Hnj69UNGvHBYdZZS5nGIVS4zW2PM+K9I249bB+3Di1OLMfEjmNVXKGbQKa21puZ+fxFwawIEDjqMPC8jvHPNwSqXHz0SmplqpmlJ4Igs75Z+dpvngyN8ShPrvzuH7HINFq0YZ1mtwZlmSeRq0BHvlWl7cMfLB81Sn9M/AT4+AhwqcVtTOC8/th4FLk6AwDFwPp0yh/vW4NSX7u9K4GhpDuO5fjQno7HOjRA4AOwuADoPyopg1daxFDPYB6BwQGHh6vQ9uGfEIQyaX4H524ANRwFOZWJfwn5YryZC4OCLixfe5XLjoWlZvEdOSRy6yH1FR+C4YWBo2dlYeZFM/5fAYcmJY99cgcNegj4huCzss88+Czr9pDWHHYxz1Q730qtNETjcUznsNbm31/Ab8PsJHIFAwFiq8Dz6waAzUS6TSp8XdAxKkYH+RPj/4ylwWP8htH4pKKArnJAFh9sHifteY92vN2w832XBkTihpCkCB0WFn794C/bUu3NkmXHgx8qazjztVohacKOTT1pThD51uGXaAHypfkWWU/vdgpe3LEYeqkPhGNxu5k2kI3hkI4Dphzfiigkv4Ju9Ogd9cDBNXoHDTlGhM1S3ENJajtuuBUf8AgeZdPxteEUEOwBxpqnc0P8oXpxSjGV7Hadi9MzEc9mRsZub4EQIHHwbuelI+BtLp1NShfY9toFv1e2HaZHAYcssefYUp/yXiQ237vHrbHoFDlpBPDgmywgcfuHPSa8C53F3HnQUgxdUO0tjcvnC+jnh5Ikmz65q07DLZThva6bAwThX7At/Y8k0UuDoNjzH+JuxrMqCI3n4dHOUSIGDC6hcn76pgakdtCjKx9+HZ2PU0hpsO+aIwtRsOWwjp7ZetdzwN0fgCNXV3gFrvBYcadOOBVcOsnlwQVoxHp9Y5eq1OOtjyIIj+XhNlMBBpj45Clzfl1NAot1nJS7NyMZ9Y3KNP6695Y7jZjerllfLaqIEDurCj75FSxFaOYXSR0Fg8EIJHHTp4N1O8v6QDN/T09PNNBILSHP2iRI4mAZOvaBFBx1vbtu2zQgJdLRJsYDLtdL3BD8tKXDQ0oPiC601xo8fb4SXY8fods75uJeFbYrAwWk5ViCJNkWFV+LKKsyXO++8E/b61oJDAkfiRIcTOQBvqsDxCx+Bg5U+nxZno0d9x/yU1XSlme9Kx2TOduuMQTi5fqlZ3u+P+3XDg/NewZaybGO5wXOd9Sos5eE99121RbhjMLQKAAAgAElEQVRpUm98sW/H4GovEjhCDaS7sUyt43gsOPzv08+Cgx2IF2fTSWP4GxKbJ+emV6DjkDysOuB4MnfoZdemxnDMo+15wMrN+WEd40QJHGv2OM5ObXrsnhYPEjj8y9nmUTLsEylwZFUC94ymTwOa/kfeO8WNByZUY1eBdUBrlxV0lnzlEq2fHgFWbaEbZufDOjkRAofp0G9FxCCBAkfXEbkSOHzKy68MW/K3RAocmXX02bLFl1PeI+vb56ZWILPEskoiuTmWIEV1wLp9AWw+HLI2Yr+BAofboq2pAseLU0uMrxh3fvs5eXSmqBSmpNm/pqhE1pFeXsgUfba073Uoou5y2KjCxb2KMXYVjLNkRx5mr4ETpZ2XHHkBYNWOKmw9THktVK8mwoKDV3p4Yq3xYxPGqgSOCGHD6hgSOCyFUfa0RBg0aBAWLVrkG+Kdd94xU1bczklbUuCYNm2aERb8Vn3hDdgpJhQfuNqJ/cTrZJQCzxNPPGHuOZqTUQpBdqnc5557zohCvI4EjtQUNqyI0hSBw05Reb885HWJ1hLz9m3A7XNHoKvZhqPrXGe7fd4w3DrvZdw6bxhumzcct88djl+NfCC4XOvn+nUwPjW4/Ow3enXGtVN6Y+j2JVhasAd7KgqMKGLfS9q36Fxkc1nBbvxw0J2OyFFvwcHlYimm8CMLjsgOgLsRTc7jxAsc/36ntl7giLT8uDC9ECt2Ox0ZMkNy8quABduBtBk16Dg4D5ek5eDhUbvMcpwGrARNUWF36cPdNE+NXB1FAkdqsJtIgSOzHLhjJJ14+gscHQdmGYehFBv4IT9c1WTap8CzU6pw04AcXJaWiX9P2Bt0UJtQgcMz55z1hwSO1OCUZZVIgYPT52hlFmpDnKl19vu9IzORzdFlsC12pgq8+RHw+MQKtO91BFf12I3XPwrVvYkUOJ6YELICsWmSwJE6rCbKgsNt8eNnwXFeegmeejMX+YZVh9aqWmBTDjDuA+DB8aW4NO0IrkrbjRmfOvUu/xrB11MfNsUHBwWOO8aURVgbyYIj0nJDAkeIvwaP6JCUPjfoWLOoiKvIh3/GjRtn/v/Pf/4z6JyUv1FAoA8M98dvgD969GgTNlFTVOxSq0yv3yoq/D+XYWX6pkyZEkzevn37jJ8OTmlxT7dpyjKxK1asCF7D7RzV7/6DCag/YLq4+U3J8YaN57umqCROVGmKwEFx5PsZXTApM7RqDpdknbJ3Lb7RuzO+1rujcR7KJWB5fGrPzvhBehfjO+NbPbvglF6dQd8bX6yfosIVWb7RqxO+k9EZp6Xfgm/16oRv9+qMn790J84a8QRufqs3Bn48G3tRbBoWh5E6bKnKxR9HPWb8cTBNXEVl8LbF9YJISOA4pVcns8oKnZ5aYac17NvuFBX/jlo0C47HJkaagLLTS4eCd44pD3Meyqkgae+W4cqMw2Ang85HOVf3odH7JXA04y21fHBEMuudonKgDOg63F/gIKvPv3MMVVbhBcCljx8dfxSXpGXh/PRi8xaaHfZ/TTgQ9GgkgSMy3+2A128vJ6P++eV1Mup1SuvOSzq8nb7BsdewAse2QqDb4J24OD0P59evXkFuJ6634RzbuURZcPDZcqeJxxI4/MvWm0/J8D2RAseHxoKDTsYj758vF1buDmfwo0NA5/57je8Yx0FuNS5NO4zZW0Ojk0QKHFzann0Xd/okcEjgAKeS0JqgV69eMbehQ4cG6eRgv3PnzkbEoGBBx5q0dti5c6eZokKxgMvILl++PHiOnZ7B/9HvxZw5c8z//Ab4iRY4NmzYgCuvvNKk6fXXXzerp2RlZZkpNXSISr8cXFGGIsLYsWODaeZStxQ3Lr74YrNSzPTp081UHD+Bgyf179/fxHXNNdeY+2N+cOO9t2/f3vxvwIABwfh54Hf/YQFi+Bzxho3nuwSOxA3UmypwUDRI+2g6KGzYz8elh3H6sAeNcPHZ/h3AjX42Lp3QHUP2LMfzq6fg5rf74ZxX/41TX7rNhPveoG646e0+eO79SXht50osKt2H62e9BIoen+nP5V87mpVYKF7c+VZ/OIvSOlfcXlOIM8c8acSSL/TraASOXp/MDAocnOayIT8T3+rZOSim0EqkNYgbRtB5sQtO+v0fcG5Gaq5Y4G7QQ8eJt+Dg2vPs3Iau4XR06HQubU6oc0OqaLJ8VU+aswbM0oJ0lMe31C/NzA8uhclwiZqiIguOyE6nt5yS+XsiLTgcgSPX14KDptejTXckVN9uzgGu7Z9vnDmekc7pI1xiMx+jFuSAU6v4kcDROL4kcPjnV2MEjvN7VWHNoXoA63dT1wOXpR00darDagDtMg6YqYH1L8/NlEAJHP7571cHaopKZF55p6jQgmNlJnBlemZE+8/V1Fh/Hy0PTZPiJKrxq+joNqeeVcep87XpO/BpbohpCRyRee/HqP2Ngs3vO48CZ09YS4ym7tvMFBUO6GmJYS0EGtq7fURwusX/Z+9LwKOq0rSZbrvHaXvVXm27Z3qmZ/6Znv57+3umBdkRURFlUVYFATdQUQT3VjFV2YGwB8K+gwIii5CwIyCLgoAssq8JWSt7Utne/3nPqVP31k1VEpJKcqty6uFwb+492z3nvd/5znu/852DBw+KyT/TkDwgQcBJPMkA+rtYvXq1dxkGYZ2VlSUckaoySHSUlpb6neAHm+Cgjw0SNKrsnj17ih1gWF+SPAsWLMCSJUvE/REjRnjfQqaLiYnxpuOz8bkDERzc5pWEiSqH+Zsdr86aNauaBYkmOEJ7wiwIjs8+RoHHNoKExcWSbO82roHIAG7/2mn2m7haQjeiUukuRAUWf7ULv44d4rXg+FXsEOy4dsKzopETyCrsyjmP300eKciPX08chtMlGdK5aBW/91Rha/pp/GHiCEFyfCu+r7C+uC2mP4avnYJyoQZJiB8suIb/mM585Haxd0QNQOSXBsFBxSm9qgR/nPwCfho5EHdNeQr/FD/A67Mj0LOFynVtweE7yCoLDpIU6kcT0IAEh9OFt9dU+PjWoAXHW6vKwaUrdOrIL+IDpmTgq0z5hVHlKwmOy6atZ8vE1pgldIvgiSQmrInGhJU7tOy8YhAqeomKb/8pZSiUjorgoMLLHxVk9jH7urbn8GfBMXSGxIvVnJpknNWr/tUi4KkZN9DeUSi3NHbkYOS8XKQVG1g1CA65i4CqU20T1gcjzuPzdM9DBTDJZl7WJSrEPt8BvYuK/bAdeImKr4NDhRHz0R9eekSc9ovxdpEl2HPJkINE0YlcoE/sOYFTylUSce+syIbLbcQzlqhke/0l+Juw0qdCj2i5BWw7ZzEGTHXhHLcgMv1uzoJDbxNr7ms7nAfLgqM2eUz5fcllEByCELnMbeWvCay2E9tv52DOjlJh6akgVneCo1DseMU2DWRBpC04Altr+CNBwp7g4LKS5ORk4bSUy03qErZv366w6T3Sueby5csFCTB69GiMHTsWc+bMwaVLl7xxzCf0dUELCfrjIInB5SK8xvLN+XM7V16j5YW/n6ovCQXr7/z58yItmS7r78iRI8J3COsaGRmJlStXIj1daiHc1UTlqxyjMj3PabnBOs+YMUMsEyExwyUnbEOXi6q6748WLWwHOlllm3B5jnmJizm2v+c33+e5qpe/57XGrcvfBP2ePXtEviRrgvnjZP3lqHdwx8sPhc2XfuuEnT4v1HINEhwJn30sFn9QaNPq4UJJNlrPHFvz88f1w89jnkDS+d0oE6SDdAlK10wHXZcRvfMDOLYtw76sC4LcUJO+YlTizf0f4EfRAyVxEdsfYzbPhnDh6IlE3xkHsy/i78kLMWBpNJ77aBqWHN8ldlkRfV0liZI5p3fjuxMGeQkLWpW8uHuh14KD2ZGwuV5ViHMlWUgpvoB/mfaMIFasbRKKf4czwXHQ43yOmGTgZIuTrpqUL0lwFImv1yodyY5ABAeXn/SfmodC9QnRU1ZqETDxo2t4Y+FVTN/kEtsZMj+iSeVLF8/vrczGPc4KL8kxal4WistlfRnvEpccmAgOfsHccUWuOed9kimfnaOTUf8+OM4UGXnJXVSq72JRU3vY6V44L1FJLZSEgupTEhz3O66grR+TaHOfjFmU5cWTGS9tnUXeCZ4R3w3GL2NEj2cDKu9n0oGI5dfw5sIrWLwzF9lk9DxktZR/QGoBMHR6urBKUvktPyDxp+SydcnBgxEXxDvH4hio+H9iWXPOvBTBQayruHwH+C6oskLpGN4WHBkCHxR37Ctu0pSYnAvuLlJbHym8qD4mXgIRHNzmeuEegzgmxii/Dl0C3lueireXpOHjz4tRyMw8jkcVVq/kSXmotkaWBIfMi9GJ+f2p0mmkxJ8kOM7mGfhjPL4r1mdSE0yOCeo5aOmkd1GxHxkXvy5fEArsJ+KVu5rxmrVPrX9b8UJxWBPhzPjJx8oFrpTkLKkEPjpUhDcXXsL7K1Kx/WQVeE3WRBJy/HPvFaBtpNF2veNS8WW6xCjvE1vL9xaho0NuVxwIf0Nn+/fBMSmlzItT5ncim1jV28SGPcFBqDX1j1Yf/n6BrvuL29BrTVkW6+qvPH/XGvpcdkrfEggOkhuK4Ph+zABM3fcxSDyoT89XirNxT20ER3w/YWHxn1NGYmvmaRSq9FVSeSDRQWefYlwQr04VXCjHtCPJuGvCMI/fDC4/6YdfTRiKpK93ivuiEsKSgyRGJQpQIermnYNWyR1ZNmScwO8mPuslN0hQ0K/HH2a+hFxl5cFyTa/tseIb+NOMl7w+O0KR1DDXOZwJjs+vUypQnZXhcDrQPeJcjQpOO2cBhs0uEMq0SsfUY5a7hY8CqzJERbxzpAsr9peLpSkSuXLRFdNRKeeRkzvuUJEhtm2V9eG1pXtLfTz1vzQvHe5yAo5oLcOVIogtNDkJZNndHJex64pSkyQteOAczWRJlPg66esRcRLnLOWtOEAyxVCoQuk8XAmOLo5MpFP7FoJG9uluYcFB656a+2rsIm62KfFkxYvVgoN59Yy/Aa4nJ/YkiiRWqcQzqJzSy4FC/iFiVsBdDoxZkOqzzptYkvnIVMRaD5PTyO4R58F3TuVKVG8OQHBwm1hiXcZleVXgu1Db89vxfvgRHIZj5RFJN5ArCDApnzjRn5Wc7Z2ABeoPEnUrLXg5XwQ8HHEyYB8PSriGU3lq+CXSJDmssMorLP+SNw7rVIGSKmDQlEzvVskkf1OOlnixSn2C5HePaLnkgBYcA6fm4Lz4VqjegDLw3bI+D/3UcCwQr4bnbWF9eifoXVSsbdXcf4//2OUZk2WfEiu8Vlu9rHghqjjmcuz1l5bv+4vzsnGtxBerxCexQR2AeVDE3yCRLfRJ+f6czAJ6T8zx7sDTJ+4ajqdLLYKykGlX7s0zLDgE/so8+GMJZaIM6izUXcz16+B0YXIKn9rA9ClBcEhfS+a4oXCul6hwfNQ/3QLN3AItgeAwT5LpBDRu/0fgIpEiVCIflTjpzsTfZo2p2YLD47CTpMKfJ7+AZZcO4lIlWXe5ZSEVEQYKeeZ5qjQT005sxW8myF1PuBOLuR6/ihqM2CMbcLQoFS4qOqgS1hccT2Q+VSKfs2U5WHh2L/48bRRui5VLU7z5cBeWmAGYd3oXbqBUECMkWjhEML+jldn4nzmvaoKjlolX8w6YbnSKKcW+VKlgKCVjfzq/il/yUQKs9aQFx5A5JaCuqxRpWlq8vLwK7Z35fr6K8wt0MfomZGDbOcBVYSg0CndUMY6kAq/Nv4yZKblC6WLe/DpzkHWKL/GQE2V4YZ4LuRVScWccThoHJ7q8JAgdlW29IpUl3qfStEtsE0vFzZfg6B5xFieLjecoAMSOA9ZnDpW/w4vgKPda7dDU/nKhxIPqU/Yx+7q2vhm9KM+LUzNepGPb6uQIJ2jDZrnwRZok3JTyTaxS5Sb2dpwFnpp2GXsvGnVi3gsP+W5HvPgAxE4rvMdArJkJRL5r+zMM/JG/WOeX4CgWGCfWmQ/fF9bj+Xm1T0Zqa5/muB/OBMczSTlILzXkE93rT0sp8eufyNz2JDiW+MXL2YAY55KqMYvzcCpL4tCK1cwyYO2XwBNxJ3DeZWCVcm7qp0Anp7Rq6+TIxfqj1CEkvojDvalA9+g0UXZbbvM9NV+QKQrLPPLdMj8DzzkGcCxQeTEex4peCQViOYI1vt3/DlcfHCR3Y9aVCD9Xqk9pZ85rtfUJrSXMeOEYW7M8dqOjMxeTUqpwoUDikNQDZSoDdcfUYmDOrgq8PP1LXC4w3p9rFcALC0hOyA8YveLThdUbMaqwtXhfuddCSuLPwLKQlYDQWaxORts7czFhi8xHtcHRHKBnglyOWFs72O2+JjiaeWKri9ctwBZoaQQHCYpui9/Dq9sW4O0ti/DWloV4ftMs/DrhKR8CwksiWHYioSXIN+L74afjh6DzvLfx921LsOTkHuxM/xrb0k5hwVe78PqWhWg961X8MO5xEVdZj5jz5LKZ2+IG4g8zRuHpj6Zi8ucbsezUHuzMOIPV5w5hysGNGL15LtrPeRPfjx8k6uYvH/rj+OXEYRi0egKmHdqIraknsSX1JOYc3YYhH03GLyYO9frsMJcfiufhacFRhvZRbry7pgKJyXne8O5aoLOj+lc580BOZfehiUWYmlLqTTctxY1HpxaDZv+tnWV+SI5ytHZW4KHYHLy+xIU1hyrw6VngyxvA2i8qEfFhNnpNpGlsCXpPyse0zapOuYjZWIHu4w3luE+CC1M2FyAxmSFf3O8xnuu7S4Vixsnwm6urMD25SNRvWnIx3v6g0Pu10vws9zpuIHIjTchledNSijFqEXd0qT7xNaez63n4EBwkN4xlSR2cBYjbUIIZos/zRN+yjzs5aZVTc1/1nZzr7V/2M/FEvNzjLPXbz9Js3y0ci0aszsNHh8tx6CrwxTVg1edVeG2pC91icoXT0ecXFIP4UviR7490RkwMEUvElLpPrBFzqs5815hG3Z+eXIgxyyWOVRweiW3WOXYj39d8gX2+A3wXzPFC5Tz8CA4Dg70muDDpk0JPP+VhSko5np2d652g1dRHteHFmpZY7eAsRP9JOZi0MQ/riNVrwGcXgSV7yvDi/Fx0iSLW3RizJE8QLcTa1JQivL4K6OyU+OngKMRrSwswLblEYJGy8501lbg3Ur5fxN/98QWIXF/pxSrz4btlrRMnohwLzOPD1BQ3usZwBwvD0sWazq5/hzPBMXRmHianGDoAz5+cWZ20svYN399XBV7cQhZNTy7BG5THwkrSeBdUOuKU439HRz6GzsjB9OR8JH8FHLkO7L0AzNxShOGzstEpsgD3Refi/bVloCwkxlin4bNy0dYhCY77ol14fy3Hd3mf2HphvgvtHHKHlED4o85C3UXViUfG5faxSv7y6FxXia6xoYlVTXDo+bVuARu0QEsjOLirCEkBhltjPUE47ezvXcbid/LPOB7nniQauNsJ/741dgDo1+MHUf1BnxjcKvZWWmvEyTj+SAnmL/NgPLlsRWwdGzMAP4weILaQvU3kQ4sN6XQ0UD4yL/k8t8UOxPejGWQ9SObcEt+3TsSN32e2kDvNHSdcCQ4O8FRcaV3B0N5BgoKTq7oooW60d5SY0pZ4CQZO7Kwkh/STUC78E9D5XQdHAbgul8qj3M6Qk015n+VTEVH14rmsl1Kc3KCDvXaeOPK+Uee2kW6huFB5YR78Uk/ixKzYqHPWReVjjmutv4pv92P4EBzsa5Icqs+JVeJA4kJi1bDqMcerfs4+lvhmunYO5iPx4o/I4jWJRRIsJeKLNHHKQGe4TCvvq/fHjNVS4RNEvQNMT/yp8ll/+X55nk2sLTfeQfmM1QkOPlM7B+OpsmRb1O1dNdqwets0z71wJjgoq/h8Amui70tE39Wt7QPhxX8/tRFY5L1y8W50cOSJSWZHZ7Ywx2/jIfEYj1hR+DFkoiE3pTyX+OJ9A6uybMpw83hBTAfCH51GqrKkXFW49/8cdWub5kkbrgSHkCnEqmkcl/1einsifWWvv/4x8FIi8rDixZrGGN+lRSetQLhslHpAe1pnRKrxn/cN/BhYNHDY3iML23vkusSigQ/ilPXhdYU/Jdd96yV1DZbPeMyPuA2Ea9+0Rnl2ua4JDhtMbnUVdAu0OILDRC4okkE5Ia2JROC2rDLIpSYqrTeNZwkK//5mfF+oPGsjBWQ+ksQQcX3ykXmpsgLlJcv0+BnxIWF4TRMcdhn0aqoHt7zkl5XWHnLBrITUnE5OApmO6ZkP0zKNVCT8WXHwmizPNz7zoHIj82A+Ml913VfZkvcrxRd0bi/LdNbJsJxgqnxUmb75yOeT5TIfGVRb+K9/TW1ih3vhRXAYCqTsc9mPN49VA0cSY9XxYu07c3k8V/fVdZkPMSKVcoVXGZfXJH7kddabOJVYtebHvAz8KTwbz67K5rF6XKNu5nh2Pw9ngkNhgkeJVeKtdszJ/jVklj+8+OtXiTWFMYkHKf94zReLqm7yusSuzJPppByXmJX3zFiljK07/vgcZlwb44O/Z7DztXAmOIw+NeOuLjLFjBcl28x4CiS/FIZ88aBwpfAp/1Z1UlhUeao81HXWxbfO8t0z4491lPGtWJPX1ftTs/y1prXb35rg0HNr3QI2aIGWSHAoIuHbcf3AEIg4qPG6J22909M6wpxHfethMyuLGtusgXUNZwsOY4CWX/iMv5UyUdNRpTG+At5cenPe1jzMeVvvmdPVdG7Oo6Z44XMvXAkOA1f17VOVrqF9bc1H/a2ODc2/ZaQPZ4KjYVhVOOIxGFhQ+TEvda6O/vJX93j0d7/lXQtvgkP1p+p39bcdjqpOgbBY2307PEPT1kETHDaY3Ooq6BZosQRHAyfbjTmR13n7J51aBsHRtAOxVp4bp73Dn+BonHbTeGzadm0ZBEfTtqnGcOO0d8sgOBqn7TQmm7ZdNcGh59a6BWzQAprg8D+Z1iSD/dpFExxNO0hrpaj+7a0Jjvq3ncZd07WdJjiarq01rhvW1prgaFj7afw1XftpgsMGk1tdBd0CmuCw30Rekyv++0QTHE03QGtlqGFtrQmOhrWfxl/TtJ8mOJqmnTWeG97OmuBoeBtqHDZNG2qCQ8+tdQvYoAU0weF/Mq1JBvu1iyY4mmZw1kpQw9tZExwNb0ONw8ZvQ01wNH4baxwHp401wRGcdtR4bPx21ASHDSa3ugq6BTTBYb+JvCZX/PeJJjgaf2DWyk9w2lgTHMFpR43Hxm1HTXA0bvtq/AavfTXBEby21Lhs3LbUBIeeW+sWsEELGARHD3zLs8WonmD7n2DrdmnedtEER+MOylrpCV77aoIjeG2pcdl4bakIjl/2+QgdHbl6xw69Y4ltMaAIjl/1+RA813Kh8eSCbtuGta0mOGwwudVV0C1gJji+HddfbJuqtk/VR7mNrG4He7TDT8YNQqs//wXtItP9bLVn3qpMn1ffilC3SVO2yUMRJ9Hq9++gzbhstHWWoq3TrYNuA9thoIOzAK1HbsadfVajozPHdvXT742WGwoDnRzZuKvPByJ0dlCu6rbRbWBPDHRw5uDPA2fi0KFDKCsra1Bo1dAMGiO90+nEpk2b9Axat4CtW6ACwEvR7+D2l7vjlvi++KYOug1siAFi8473BwqC457ILLR2Vuqg28C2GOgecQatfj8Od4/Lwz3OcrRxVuig28B2GGjnLMbdI7fg54+uRXtnnu3qp98bLTcUBmhhdGefVSLwXF3XR40RO2DgHmcFGFgXytI/DUzCoUOfN4jcIDehCQ5bT6F15ezcApLgeBd3vNwd34rvq4NuA9ti4CcegqNtZIaYNKoBRR/lwKrbwT7t8JCH4GgzLhdthdl7uQez+kjCRwd7tEF7RxHuHpmCXzz6EWhWrfvFHv2i+6F6P3RyuPDLPh+KwHPdRtXbSLdJ87VJW2c52jlk+cYSlTAkONxuN8aMGYPY2Fjs2LFDB90GtsXAth070PepJ/GLnh1xx7DuOug2sC0G7nq8O1r922/xl6eS8Jfhi/GX4Ut00G1gSwy0HzoZrX7TE38dvAj/O/QD/M/QD3XQbWA7DPxt6HL86ZF3cGf7sbh76BLb1U+/N1puKAzcPXQxftl+jAg8V9f1UWPELhj4X88YR1n6uy4jsHPn9vCz4CDB8corr2iCQxMbtiU2FPG2bccu9B8+Cv/1mAP/+cwKHXQb2BYDfxg2Ha3+rTW+N7w/bn3qMR10G9gWA3cN7otW/9EZvx+yHv93eAp+P3yLDroNbIeBPwzbhP/qGYfbO0Tgj8PX265++r3RckNh4M/D1uEn7d8Tgefquj5qjNgNA5Slv+3yCnbu3BF+BAfXzWgfHHZemKHrplqgCsCbzkR0fHEHOkWU6qDbwLYY6Pbu12j1fx/BjyIH4TuxfXTQbWBbDPz7uMFo9f8eQof30sTuFB0cedBBt4HdMNDZkYV7Rq7HXX1WoIsjTWNUv6e2xUBXRyr+pc8SEXhut3dJ10fLd4EBZ56Qpf9vwDQcPHhQExxqsqmPugWaugUqAbzhTETrl3bhb5EVOug2sC0GOow7h1Z/6IXvxAzDN+IH6KDbwLYY+Of3n0Krv/bB3ePyPY5Qq9DaqYNuA3thgDv8/G3kVvz80XVoxx1VNEZ1G9gUAx2ceWK3H+74w3ONVXvJEt0fqj8qhCz908DZeheVpp7Q6vJ0C5hbQBEc94zaiTbC2z89/uug28B+GOj83llBcHw3+kncEt9PB90GtsXAv7w/DK3+2gutx9EZXpmWqXpMsSUG2jmL0NrkZFSPe/Yb93SfyD7p6HDhrj4fisBz3S4aq3bFgOFkVG8Ta55v6nPdAk3aAmaCg4q4DroN7IoBRXB8L/pJfCu+nw66DWyLgd94CI42guBwa7mqxxZbYqC9s1AQHHf2Ubuo6PHPruNfS69XJ0eOl+DgeUtvD/389pVVmuBo0mmsLky3gI5kZFYAACAASURBVP8W0ASHfYWkHsB8+0YTHJrUCRViSxMcvu+ulmX2bA9NcNizX/T7Ur1fNMFRvU00TuzZJprg8D/f1Fd1CzRpC2iCw54CUg9c1ftFExya4NAER/X3QssK3Sb1xYAmODR26oudpk6nCQ6N1abGXH3L0wRHk05jdWG6Bfy3gCY49KBRXyHe1Ok0waEJDk1waHnV1HInnMvTBId+n0IF35rg0FgNFaxqgsP/fFNf1S3QpC2gCQ49aITKoKEJDk1waIJDy6tQkVehUE9NcOj3KRRwyjpqgkNjNVSwqgmOJp3G6sJ0C/hvAU1w6EEjVAYNTXBogkMTHFpehYq8CoV6aoJDv0+hgFPWURMcGquhglVNcPifb+qrugWatAU0waEHjVAZNDTBoQkOTXBoeRUq8ioU6qkJDv0+hQJOWUdNcGishgpWNcHRpNNYXZhuAf8toAkOPWiEyqARzgTHt+P6gSFUJvC6njX3ld5FRcvVUJCrmuDQOA0FnLKO4UNw6G3DQwVz9a2nJjj8zzf1Vd0CTdoC4UFwcMBoaLCTomN9FjvVrfnq0hIIDkV01Of4LU2Q2IYgCn2CwyqD6vt388kLX+XUWn+71Kt56xEeBIe1b+vzd/P2gy9WWRfzM9itbs1Tn/AhOEot/Wvu67qcN0/7V8eoqoe5zupayz5qgqNJp7G6MN0C/lsglAmONs5yqNDaWY6GhQqRV2Ah3jQCu42zzOc55PM1TdnN/ey1lR+uBMct8f0QrKAtK2q2rGiq9glVgoPyRwYpWxsqU1s77SBXfccJNWbUJm9awv1QJzgUVhuGU+oOzY9T9SxtnBWiPuqZeL0lYLG2ZwxlgsO3b0NfV/X3PMTtPSKUt3i8aoLD/3xTX9Ut0KQtENoEBwd+N9o5i9DOWYy2fgKvq+DvvrrWxukWin1tg2zj33ejrbNIPAvr3d5RgrZOMv6GkiMHF+Nv871wPg9LgkMsTemPf4zrj1tjGxaYR1NN4BtSDskcWpuE85KcUCY4WjvLPPJHyiElI9VRyVN1VNerH0tAudq8MonKdoWQoaq+7R1yTLDK1eatZ/PI81AnONhnql+r488Y+xnH33117R5nieerevP0A5+D47p890rQzslxX9aZ+o3GamgvUZE6G3VVfzg097WSTf7iGdekhU/zYpXPRFy29WCVmG3n8NVVW6JM5TNrgqNJp7G6MN0C/lsglAmOds5CDJrlxpSUEsxIycN0S5iRnIdEU+Df1jjq73fWAp0dGc2sjJeia2w+Jqe4RT35TFNSSjFoZrkgccxKEAeXljZ4hCPB8U+x/XHf4vcwdtsCvL1lUYNCv48n2pvgiJOWKv8wvi/43LdPftLe9SURU88QqgQHZUq7KDfeWVPhX6am+MpUyld/spdydVJKBYZMzxFKffPKqnKMWlSEaSnFXrnq3Ajc60hvcTLU2g+hTnB0cOYjbkMJpqcUVBvbb2b8T0ipwoAp2c2KB47p7I9XlxZgakqpeJ4pKUV4Z00V7o10NWvdrLhpjr9D3YLj4fEu/1hNLsD05HyY8RpIV52aUoC4jSVgXs3RB6pMYvW+aBfGra3CtJRCgdXJKWV4cUG+V1dVcVviURMc/ueb+qpugSZtgVAmODo6XBi3HsgDUBIglAJQIVAcXt+fAdzvuFTHQaNxvkqSCe+VkI9c07PkA+IZ+awcKDiwqNDSBo5wJDi+G9MfcQc+QjrKUIRKn1CISqhgvefv79U3jtZ7Ql7fiXxd06klOLfG9sOvop/AS5tnI/Lrzbatb12fK1C80CU43OgUU4p9qXWTqZStgeRqDgDnmlx0cBbUQa6qddyNQ9wuPgBQlqq6niwGukecq0O9Gqc+dpHdoU5wdHJk43IhUGzqW9XHPKqxvyacMl4WgDeW5dQRDwqrwdUDOK53dORi/VEDqwUA9qQC3aPT6li38MVrKBMcfN+HzsiqEatmvJoxbD4vAnC5CCKvusuQ4OJUldsz/gYOpgOFnnePeuuifZXo6Mxr8VjVBMdNTGOLiopw9OhRHDlypM4hIyPjJkoIHLWiosLnZn5+vqjDV1995XPdTn9UVVWBQf3Ky8tx6tQp0YZsy1D55eTk4OzZs94+5zmvBfMXygQHBzzH+iqUiQbh//UMVW4cTr8ZhbdxBgwSHH0SXKbncaMcgGN9pfAgrgaWlnoMR4Lj+zEDMHXfxyhBJUCR1YDwyfXjtiUMuHzmp7FP4P6F72L11S+QBTc+SrMvIROIuKjr9VAmODrHlODz6xSqlKduzzTx5mUrJ52xq9PQwVEXhVdNGhtngrbyQBmkJsPnceN8EfBwxMkWr4iHOsHRxZGJdLIAlJ/1Hf9RJiZp7yxLrSMeFFaDrwdQp9nyZbEXqxwODl0HekRdrWPdGuf9sYPOEeoEx1MzUpFBNqBBWK0UeTCvuvdJ8HHKsvvEXcOxdKF8C5lKybpiby7Ux7i61y/8MKsJDoWLOhxPnDiBbt26oVOnTnUOq1atqkPONUchofLBBx/4RDpw4AA6d+6MPn36+Fy3yx8kdmbNmoWsLHLy8pednY0hQ4aINmRb2v1HQmbt2rV48skn0aNHD2+f85zXPvnkE1iJp/o+UygTHMqCg/qNVFuNI9VxkgN8PhWo4CqV3RqfTPSDEXW14OC67podKSlHdjzKuNZjdaEuLTjyhLLFujOQjqOVSkdnXU0SreXUXM9QGoTCkeD4Xkx/xO//CJkoRzGqPKFSHEtA8q7Ki1/imJgoBu9XD3Wx4FCWFMIPRj2XX3CSb86nLnn9IHoA3j70Ia5X0N6qSvzbkHrMtoRMXYmMQPFCm+AoxWep8us3vx5aZaWSp+oYSK66AESudoHLCGqXM2a5VV02+qY3x7We+0vrxtID8iu/kqtfFwEPRXxdh3qZ86tLWeb49j8PdYKjsyMbVwqlZQ6tNMxYZV+TIFA45bGiyjeOip8N4M1ldbXgMOOgpj42x7OeV08nLThc+OQoZbuU9Xym/deBh6KMCW3bWpenWsvi39XLC7VroU1wuDF0RqYXqwp36kisUo4qrPKc19R9daQ8viIsODJvok8VHgJjwKyv+p4HTtM7Pg1H0uU4wbqSu1m6t1RYIdUdW4YDaENXDlxm3fNt3jw0wXETM1JFcNx3330YMWIERo8eXWvYtWvXTZRQPeq6devExDo6Otrn5hdffCEm3YMHD/a5boc/rl+/jn79+mHAgAFIS0vzVolWD2w3kjKnT5/2XrfryZYtWwQZQ1Lr5ZdfxvTp00V4/vnnxXXiICUlJSjVp0B9w5mIe0btvAmB2bzCQwk5OuF6bEYlItYDzvUVcJhCzLpC7D4rBww2FBWdTDcwe3s+Ite7feI61pdj9AdAJ0duDW2gvtpwsKjyeIv21w6SLaewbu2sRGtnFdqI+ExT6UlHb9P+lA43usUVY95BYJEnzD8IDJ0HtHcW1VA3VQ/mqTxZsyyWyeCvLJUmdI7hSHDcGtcfdy94A49vmoGhn8z0BJ4nYvSGOTian4pygV4qQFW4kJ+B5zYwHuP4hq6rIv0SBsKZp8f/xTfj++Eb8f3AoyIm1P1Ak3W1ZS0dgzKdyoP5qLzM+fnL5+fOQVhx9ZCga/gu8rdeExx1eKeb/v1sH+nGyytpOVZukZMViF1XgNxSKU/Zh1TET2UD8euyfOI711Xg/fVA/6l0kEgHjoGeQ31drKu8UvJXyTYelcwLLOfGfAhQliq5OmkP0KXOPjhYR+ZtriPLDPRMoXM91AkOOuB86yNitcoHq8715Vi4MxeF5QZWOUncfrwE0euLfOIS5++tB/pMrmmMtY7/xB3xwL5WGOY5nSwyEC8KlwqrjK+u+cNqOdo7i0F/YAqrCw4CMSnAvZE16SZmvLEuLMMfVs31NKcJjfPQJjjK8EB8Id5aI7FKfDIonfWTI6ViOZUcGSVpsPFIsfe+ikecMw/mVTf5Q5wpGcl+9sVAW8/fZn2VOqsM1F/lkmh/Zd0Xk4e4bQAxSrlKvXXsyso6+uCQ9ZA7Bsny2nj1Y986+ivb7tc0waGQXIejIjg4Qb98+XIdUjQ8CifVtBghwWFe7sElHlwqcfHixYYXEuQcWC9aOZDgSE1N9eZOawe22/nz51FSQg608X7mtqpPKSRpHnnkEdH2y5YtQ2kpOXz5Ky4uxvz588U9xmHchv5CmeCgkKPnZq5b7ejkWu88b+jmuIrF+6o8U0PZSmS+n5pxDV0cWejoyBOBaZieDkulYqIGe+5mYgTFajOOKE+k8VWIZBwOClQu3MJhGEmTTg6XCCyTyj4FeaBt6XifAzm/TDHwnEqPdyByqEHKqBvvqQGKx/bOAlFHlt3Bwa+nvM9t8EJbKQ9HgoOkwT/FDsBtMQPxXVP4XsxA/DZqGJIzTgkrDiKYRMcXGRfxs8jHfeKqdMzHSi6QxCAJcUu83KGFFiMM9P1BR5/yfl/cEt+3WlqVF+N8M17G+ce4fvhOrEyv8rnVQ54oskMRJyo9CZJfOAdh5dXPBdVoJjjM5ImKHw7H0LXgkPKPE18qaVI+Krmai26OK7hRaEwa+eVu9xUu7/taWJmZZXAHR0E1B6NmmSrlEhXocrR3FAqCWckrqcDy656UW3c7K8U55WNHR76Iq+Qqy2G+lLuUrcxPKcD8Kk4lvb0z3ytTKVdZTxLkbYQ8VTKfuwIYcpV5UE7fLXZhKRGyVJaZK2SylLkksI3yVLmhcgx1gkOOswXecZ9jrMSsCy8kXYbLRMbRKiIpORNdHakWfYH9SayaiThOsmQgJiRWJR6JNzm20reMG20cakJWjjYO+WGDuOOOGVyepXBKvYHlSFx59IRIM3bkOZ9B6QD0MaL0Bl9MGTiVmOV7xO1lK9HOwXdElZvr0Tnk1rO+eRi4D4XroU5wUHZRvrFviAVDvrow8eMsYa2r9HlidcLHWd44PnLVmW8hjQ2cthN4lFggLpiO+JMfyOROgcQm+1viheSH3IlQxWX8Dh791pBxVt2xHG29OJO6amdHjmc5onofZDlmmSrfJSlXmTffEbaFLJPPVeqR49byQgurmuBQSK7DsbkJjjpU0RZRzASH2YLDFpWrYyVWr14tCIynn346YAoSOCSfNmzYEDBOXW+ENsFhKMAc2O92VnkDB8MF+3xbgc6ZhiTmeISoZI2ZRrLVVACoYLjRMaoYgxMLMCIpA88lZeLpmdm4PyYH3aKz8MK8bExOKRdh9MIc9JqQ7VEgmJbkRx56xt3A83Nz8PaybEzeXIopKdzpxY0Jm8rwysIs9J+Ugc5OWQ9f6wo3OkUV46mkAjyT5BLhqaR8dB8vt43jwMRBsufEHDydlCvq92xSNh6bykEiD/dG5uDxKel4bxV3YinHlJQyRH5UgOGJGegWlSmYdQ4qaoALBaXGXMdwJDgkeSBJiH8Y3w/fYPBYSdwVOwQbM0/B7aHpuFzlUMZFfD96QDUrCpXGSg58O64/bo95HH+e/jJ6LYvC6C3zMXbbQryybSF6L4vCn6ePwo9iBoptaq3EhMqLeXCJyX9PfR7dF47DgBVxeGXrAry6fSFe2DwbPRdFoE3Sa7gzdghu85AsKi9aqPwx8SV0n/k2tqee8iEc96WfQ8c5b4rwH4kvCMJFlRnqx1AnOOTEXspT8xc9TrjSLATHjitAV8c1oZgKGRyp0vkSDg/E5gmZNSIpEwyPT88XDkj7TEzHuDUlmJJSjuiPi/BMUha6RvOLdTnucVDRLUUXZyYGTsnE6AWZcK7JF7JNydWoj4vw7KwMPByXKUhlyjdFOsjzMvSeXIink/K8cvWJWW6hWCv5QuV+0Iwij8zPgJC7cS4hM7vHyHEgbqOU45OTS/H6MhcGTM5AJ7F0UBLMKq9QOoY6wSEnYHLsVziVY3olnk3K8CE4uNxzRjInlvkGVj06gyTHiDVJMjw6KR/PJuV48JqB3pMK0MWZhUFTsxH1cbHAquOjIjw5IwMdo/ihQ07qSJJ0i8zAk9MzMHZxFuI2GuM/d5l49wMXhs1Ix/3RHI8LBU7N4zHH935T8gX+qAMQswOmF5sc9brROboIQ0z6yfCZObgvOk9MLnuO5zuSjYmbOf67pZ6yIBu9J2SIya56vlDCqKprqBMcEquShCJWpb4qP3bFrSvwITiIVV4juUpyV8ZlGt+PYyQI+k9xebCaieeScvBwQjHudaSBuIj34O+t5bkYODUH7SOLveQvybYHozPwzKwMjF2YgYmbyry6KnXHt5a7MGRaJrpGZgkiQslU9gfPO0YWYtCMYqGLPpvkEnprn0kG+UIs3x9fjGc879FzSVkYMkPKVC4vf2xiBl5flgu+F8Rq/EY3np+TgR6xGWjvqItjavuSHprg8J171fhXsAiOGzduYN68ed7lLbTOoK8Os0NSWmjQeuOZZ54Rk2j6fODfK1asEHWkJQT/Zj7qt2/fPnHt5MmToP+IjRs34rXXXhPlxMTECAefKi6tDiZOnCjuvfnmm8LHRyB/EpWVlTh8+DCWLl2Kd955x1vv+Ph4mJ2cMj2fIzIyElzW8dBDDyEuLg6zZ89GZmYm+EzMY+bMmWAbWH9cdpOQkCDyf/311zFnzhxhpWKNx7/V82/dulVYthw7dgzvv/++SPvWW2+JdmqII9CFCxdi+PDhSExM9Fe8uMYlSvSDQl8jDf2FNsGhBByVC2UaKo/c8tUfwfFkIhULCk/f+GoQJYP8yMQCHEgDsktluJgHvDjzjDDDc5XJZS/8Cp1bBSw+BKE4UEnp7MyGYwPEusTccmm+zXgqsK2LKoDTOUDCVqCrk9vSSsKB6Vl274n5uFECuDwhoxT4+1oIZZxxqJxN2FSE625Zt/RSYNlRoJfzFGbtBlhX+hlhWSyXJuSpRcCaE/TEfr2aQqWeOxSO4Uhw+JvIK6uGf44Zgk1+CI4fRQ+AimM9mvMjufCT2McRcegjHC26gQKU+6zxzUc5jhWmIfbYJ/iXiU8LksOcnud0DvrzCUMx9tOlOJB/VeSh8EWMEV9uVOJCmQurUo+gy4K/+xAV34sZgGVn9+FqeT6Kq/i93/iVVFUgraxQhOjjm/DD6IEBrUis9bL736FOcMgJm1VGloqttK0Ex84rQDfHZSFT2/ALXGQpeFSTPiVbRi3I88pUytZdF4FB07LxRRpQ5PGxK+RVMTByvvTb0c5RKrZEXPIFcC4XKK2SmFPyTcm4zBJgzxUIZZvLFsyTRpY/by9AWank6pEcX6fS3RyXsP2KlKlZpcBlN/DmkusYnJiH3ZcAXmPdWJ6Q41VSjsdurPBMHH2/Wqpntvsx1AkO2b7Vx3+O788l3ahGcCQm53j8WVmxTV2C5IYkORReskskJpJ2Ay8mXcLJbDm+Egdc8nKxEOg1uVCM3WzLflPzkXwWuFog71PiWbF6vRDYdBYYOJV+FFgPpcfIXVRWHwI47hOrxN22i0D3KLmLiiRACnA4w8Dq6Wxg+PSreGFBAQ5cA/LKfLFaWAF8mQm8sqTQYqVilGuug13PQ53gkO1qxZ38O35dbjWCg9eq66kqvew7Wj+s+gK44dFVKeMmbAEmfZIrfHVQZhF/tAPfexXoMZHpaV1cgOFJuUK2ZZT46ozENgNl7eV8YNWXQM/xWT5YpXx9JO4GPr0qMUqssg5zd1FHlU6lqau+uLTCW7fMUuAgxwpnKiLWluGrbKC40ng/+K5ku4FPLwFP2GJ78fq/H5rgMPS8Ws8aSnCQKNi7dy/uv/9+7/KGxx57DN27dxcT5Z49e+LQoUOiHpycc5mHcmpKfw/8+6WXXhL36WSU1gNmJ6NJSUkin5UrV2LcuHHi/oMPPujNg4TD/v37sWfPHrH8gnnyvnKa+sILLyA3l5sMGb+CggJERUXh3nvvFaFXr1549NFHvc/AtCQDSKi43W6MHTvW+zxdunQRJEf//v3FspRATkZZJskS5kXCgMs++Kz8u2vXrliwYIEgR4xaAer5SaaQ9GGbWp+HfkAaawkPl6z07dtX1HHTpk3mqtXrnMIvVH1w1DQQd3Zk1oHgqK6UUoHgVq1UZCjmKejzSoEthy4JJ0pSdeAUrwJ0bur8MF2QDlwiM25NMXLccnDwxqvi0FIGVFEdYjr5owni3F2V6BDJJStkxKV1Ru+EXLGVocylSihR9DGiPFNzQJuZnCOckDEOB7Dd54Adx1wirnC3JspkebzLn8xn02mgc4zxtamm9rPjvZZCcKjJ+68DEBw/jK6+FEWlUUcSH/8+4WmsvnhQ7tBCJBPMfOFVEJeIjSqsvnwYv08YKSxDzHncEfc4ph/ahHwmUul5tAbhELUSF8td6LrkPZDYYD60/Eg5f1j6EWEa88+U36wT2/GTSE1w2PG9M9eJctU/wXHFO1FT8sycjudjF2X6AOdiRhkOXFVSiqCklxngch4weCondKW4Py4PHx8u88hEgocyzer6lGmrhAPJtBJgcKKxFEDVYfmBSo/0lcA9VwT0iDjlrfODEefwudgRQN7nF9QVW78WhLEEO+UphTuPMg7/Z7w3PuDEtK5OoOuvNKtnCeYxPAgO/236XFK6H4LD5WO5U70tpQWHFS/bDqfhrNdvPadi5QKJx1OBzg5O/srQf1ImjqZJ55ASI3QAosZ9dSRqKoUDySOZQJ+ELJ/lBjTVTz5a5hnL5fh+gE5Go6+JMvjFfsBUF87RV7NHP8koAhZtOo0sMdx7ylR6hwerfEMuFAJDE7Pr6CPBf5tWb6+mixceBIf/9opfl+eH4OAyOv/xVT8QL5uPVvjgZceJEs9uLcQadU75YWPL10B7oW+WY0RSGi4UeCRZFdFBfCpd1XNEpUAPYbXhJPBg1HWvvGT5veOu42i6kt9VQiqv2Mvl0VIWkuAYvbzc41tEOku/kF6OxA1XkSe+dfA/riFjeSyF9ZX/H8sG+sSSNK/5+e16XxMcoivr9l9DCQ4u13j88cfFZJwkBH1RXLt2DcyXlhGc0JPwoKUDrSG41IMTeF6nVQL/vnLliqismuD7Izh4jUQEJ/605qBlBK0RmA+XVfDe22+/Lawv6OyTVhUkCEhimJdbsA60/CDpwHQkZ0gYsA60mHj33Xe9xMyFCxeEJcXVq1exY8cOQXKQCCGhwnskBAIRHJMnTwbJkN69ewunnWwXPutHH30k8mG9aK1i/qnn57M88MADwlKE1iR8HqZT/jOszlnNeTTkfM2aNaI9ScCkp3v3aKp3lhRt4UlwZNVAcPj6zjALSRIcvRPyheM8xS3T83q5h2mmGOZQwOOFfOCJaTlCYXhy0jlhVSHTlIk5JL1KXyqWFh1Xi6Va7hlShCg/7QL6TlZrZBXB4fIMCOyZSlEWCQ4O7qynL8HBOBCO1Eoq5S4BaWXA8Swgq0oOV6o8xrtWAmGOyC+c5mcOlXNNcMglKnUhOH4aORBxX6xDodeDB/FXiVSUYZfrAs6jEPkCxVJ0FKISC8/txU/HDwGXpJCc+H5MfwzdmogcSDxTHclFhfALMvurHZh9Ygc2XD8myA+hmlRJ9O/MPov/THgW9C9ComPVlc+RDSo6UoGRJfI9qkIuKkWYeHorbo+qnbhR5Ivdj6FvweFfseRkzj/BwW0s5eQwkDyRBIeUa0QKZWqJh3cjtqiKU9XlF/AHIq8LJ6DTdyq5KRVgxiH3fCIbOJYBpFeYaWN5vu40cJ/T2HWC9TEmrLL8s2IXFTPBcR5fiOFU3mdp+Z4vm/kAzuQBp1wAz6XUJZYrxRu0/xrQM05+YQ/07Ha9rgkO/zi34qXYYxXB8ZTjP/HK8X3qRunPi349lh0AKgQ4eFdO+M4XyPH/y3TAs3+UdyJHLEWtyfHZZUgRHPJTiEFw9PBLcLCwKpRx7C+T7w7z5LtxuVjWT8paiVV+VFm0u7AWgsd/e9gBvy2P4KAVG2VqYLnqixcpmYo8jnX5l8IqsRfxAZfwFeOh+Cxh6SNjE2ly154bFcDRTOBUjsSOvE8EVYE7Yr26iBbHBj56x0uCQ8arFLLbSnC8srxc1EFJzdJyqa8Si9lVUle9XiZlvNQO+D93kwOmJRu+58zlhsK5JjiUlleHoyI4OKnlkhH6Z6gpRERE+FgekBSglcGzzz4LOqo0/0hqDBo0SBAgjKd+nNiTmLBO1NUE30pwMC4n/Lxv/pHk4D2GkSNHIj+fIlj+SGS899574t748ePVZeE8k1YQtPI4evSo97o64RawTzzxhEhn3i0mkA8OfwQHCRASKCRYTp06pbL2HrmTCdub4csvv/ReV8/P5yE5ZHYCykgffvihqBfT0QolmL8zZ84I6w2SMlOnTg1K1hROmuAwhLYiOKQFh9HEbKfsMmDHee7EUoLtZ4HVJ4DOYv1tKWbv4+RP8eXA2TzgtfkX8LDzDB6MuIiezlNY/GkJSj0fwplzZjnwwtx0sW6cQluW7TJ9rZQDlJXgSEx2+bD9HBByKoDFe0rRN/Ys+DVyUPwpbDstFTE5cMivjTOS89CxTts2Gm1ilwFFExx1JzgeWvAe0jyqBfu/AJVYe+kw/jrhefzK8Th+O+4JRO1djYwqbjgrf1dRhB5LIoQTURIIP4sahKXXD4klKIxRgApEf7IE/+IYjJ9EDsKPowbil45BGPvJXFxBiSA/rlYV4tP8i7h73muC4KAj04dWRuLlj2fi86xLnpLk4ZjrGkZsmClCh2XvCsendicu6lo/TXBUlx9jF3k/gXtxQCLhbC6w9ktg8d5S7L0OvLZKLsl7JD4L+zOk3KKiztF009EKDJl0Dt0jzqJ7xDkMnXwRh69JOclMmd/RDKDPeCvBISemquAzguA47VXYH4w477HgUDGk7LyYC/x9RZaQ4z0cX2Ps/GtIK1ZfLSl5gcxi4JmZmdUcltpFbtZUD01wVMcp22v5AV+8EBXFnJBlACsOAss/K8e2K8CTM+ifoAic7H1dJnHIKSO13BWfudEr6pwY/zkmj513EdcK1VRPEhLrgstEvQAAIABJREFUD7uFg0XVR/KLvLLgkBikBUcPz9dzXwsOA6vE/YkM4IXZ18R7QeukmFVpwuRfyXfG4XLVe503s8Wo//ZR9W3KoyY4qveFFS8SEVUoqAIOXQXm7XIL2bojDejhvCAIjmfnF+NKucQf5WqWG5iwNh19os7ggYgLeMh5HrGrbyBbAcdDPC/aq3YNkvUg5kncEVf80a5uudmCw5EPEhwkrc0/Ln1JPgk8Ne0iKHd7RZ3B8r3FgjBUuiqPB68D90bTZ0h1S+umxF19ytIEh7nHazlXBIciCmo7kvwwT65pYcCJPC0VaFlh/XHHEfrhKCuTvDHv14fgoLVGXp6wnfMWQRJD1ZfWI9bfkiVLxP033njDe4s7nRw/fhw7d+70IWpUBJIK3PaV+W7btk1dFtYXXGJCqw+zk1ErwcGdTugjhAQHrTj8/RhHLbfhUf3MBIc/8oV1U88bzGUq9P3BrXlZZxJVfKZg/CjDNMFhDByBCA6aIk/d7ELnqDyxjrVLZD4eGM90cs3ukMRcvL8yHUv3FOHTM8BrK0rR1lmI/40E/uaU4YmpGbiaZyg4VIJeW3jF+wWnvgQHB4P5uyvQNTJTlMUy6TRt+Ix0XM83yuNgtuGLYh+Fqj7Cu7nSaIKjDgRHXD/cGj8AS899Jmw3lMKwN/8yfhf/LFqN74dWE/qi1fi++HnsYCw8u9drWVGESsw7shXf9ywv+bf44dia9bWwtGA+eajAlAPr8eOox4WVh3CIOr4ffjLxSTy5aToGrxyPrnPfxh+nv4jbxz/h9afBLWR/FsltYr/wEVnrUo95t51VTknrSiDYPV7LIziMJSqB5IOV4ODYc70AeHyqdIBIp4vd4ovRMZoKrRv3Redi1NxsTNyQi4++KPf4LcgRO5souUqnjs5V9C8jf1S2v84G+ifUTHBIC46aCY50NzB2UbpYKnO3E2DgZHbi2lQfgrmkHBg11/frZqA2sNt1TXAYY7+5b6wEB7F6OBXoM0E66yZW740rRYcoOQHrHpeF0YtzMOUTF1JOAB8co9+M696xnzoAiYUNpwyLI2rbe89Uea0zWb51wkpbEEFw+LXgMMTpjTLghSQ6+pU4ZXl0drruK0mSqJichPYcT6vR0Js0tjyCQ/qyMOPSem7FC/uZY/WHh6rwcEyq9A0TWYT7J3DHErlld88JdO6Zg9nbCwXRsORz4L6oLKk7OiF0Vu6Wtdu0YSfxv+EYvIQw61EfgoPyme8R094dWenVj3tEXcanpvL4FCeygJ4TuUMW/Yb4f0/tel0THEri1OGoCA76yvj0009x7ty5GgOXctDvhvrR4mHYsGFi4k3LAi4TWb9+PWgR4HLR+Kj6rz4Ex8svv+yzpazKVU34rdYdvM+tUHmfzj39/Ui6sI5cgvL111/j4MGDwrkm26K+BEdhYaFwokqygA5SA/24bIZxaHmiLDXMBIeVzFH5qOdlPwXjR4KKVjasC8krs1PYhuZPlGiCwxCegQgOKsQ9I097ndfRi7T0Ki3NB+W53G6LSiP/JslA4dw5Kh99J2Vj3LIruG4hOF5fdLVBBAcHs3wqN3PSRVltPFvaiQEozpdhJ8Gx+ahUouw6MNRUL01w1E5w0Cnob6Y+h315l30WhLyxc6lYLkIiQYXvxgzAw6vjhHUHcUTl43ypC7fHDBLkxC+insAH1w57F7lQVtCh6BHXFYzfswa9FryP/0l8BXcmDBNWH7eQPBnPLWX7C+sN+gFRRMTPxTaxvgTH+tRj3vsqXrgcNcFhyFT1TlsJDk62lnwqt7RmHOG7w7RtJv/mdcpkqZxzC0Fuy8otOIvwQGwuhk2/gdmbLnmt3ojRS7nAE1NueNLKPKwT1roQHMfSgYfGc5LB7UMl6ULnfC/OSUUGZ56eH82uR80Lza/imuCojlNizooXfuCYsN6YcJrHf25DzECMSKxyy3niWX7x7uAsRI/4HIxIvIJtJ+WyJkKHEPpK+PDI9k7grBPWuhIctCrp6kgTegf9NjBwOVlcsmHdxDL5ztHPF+vJOoZS0ARH9f6y4oXjOLdGHpF0w0sMKKwKHdUjX6mXcptsylEugRH3nKXoEJmHXhNyMCrpEj47rySc/Ei26UTDCQ6SerM+Bbo4JCFsYDVTOII2SoTwg9c7QTrwDSWcsq6a4DD3ZC3niuDgshB+yb/ZH60RmAdJDk6S1QSczj+HDh0qHG2SBDH/6kNwBCIpVHkkVKy/mggOkg+vvvqqqKPZKarKj8f6WHBwZxX6JOGyHfNuLNa6ff7556K9uCxI7YyiCA762gj0U/VrKMHBfuNSGfY783zxxRd9LFMClX8z1zXB4TtoBCI4Np4A7nNcEwOBdKKnBgWZnmQGt1akwkj2+9HY83hjaRYW7qvC/qvA2RwIx0rKnI99RAuOhhIc7D9aaQydToXed61mj+irwsxP6eKa4DAm3KEwka6Pk9FbY/vjrzNfwVeldOho/BYd3obn18/ECFN4ft1MOLYu91pwUDnKQyV+nzgK3IGFDkKf3TFPWG6I70KMIBTzKtDaIwtlOFWShR3Z5zDxyCY8sOA9/DzqCWHdISwyTATHLzTBEVKTiUAKZWAfHDdvwcFdqCj/FCFsKOJSpvJvylTKW25P+4DjAoZPu4L4DYVYfxw4kiZ3q6DJM+UgfzxezgUGewgO9RzWCWtdCI79V6RTPrOJNL/cP5OUiVST3bUmOHzHUNXmzX2sn5NR+SxWvPAz4FOTz1Qb/yVmSXAQp/ygUSZ8xzzoOIsRs65h2hY36NyRZNmNYsN6g1ilLnDyBp2UNpzgiN9QJLaKN7c5J7/vrvVHcLg0wWEzcieQk1Fzf/o7txIclH/ncoABk1IDYJV6q9yqltZv9zpu4JHIc3hpbhqSdpZjxwXgZKbczUTpjUquBoPgoA769upyryNS9UwdHC5MTuFd48dl4r0TaMERemScJjiMfqz1rKEEhyqAy0XoE4MOPJ966inQCkIRHpzwX7pkrJGuD8FB3x/+fjVN+P0RHNwVZcaMGWJST4sTLjnhsgw6PKVjUi6zacgSFVpA1IXgoLUI6+6P4DD7ILE+c03Pa40b6G9arnC7XfohYRvw2ekvJdg/CkRtwWEoaIEIjrmfGt7SlVA2H/llr0fsDbz7Qa4wtUuvkgQGv5hQkWE7q6Pqw2ARHOYvluY60TEZzVvVQKUJjvAnOOjzouPcN3G+ynepYJFwFFbhdeqpnHvSN4eHtxCwJHFx/4eRuC22v9gi9sfRgzDjSDIy4fbsceGxgWUiTyC26cmDPj/WXj+KjvPewg9ifXdE0QSHIWPM72ionQeT4EgvA56ffcPrg8jaFvzKSKfKQ6alI3F7Bb7KkX6O6EWMMo3yVMlUhWFiMRgEB/PbfcH3iyXrxy+eQ2dk4LJpKNYEhz2xHUyCI4OWD87jAfwB8MOCG/dGZuOZmelYfhC4UCKdiqrx34xVNf7zmiQ45C4sxJd1wkqc12WJimNNPtqbnYdHchKZhzHL3T4+EKQFhyY4rLKmuf8OFsFBTH15A+gV67vjie/zudEtMh2jF+Rg3XHgarmhqyq5SjmqZCrxyr+DQXCQFx6zoty7layqFwmBySmGiwSWKQmOfE1wcDJot+B0OhGMbTzZ0cEiOJRgpWUAnX1yl5F169Z5d/6gBQbbkb/6EBzvv/++KsLnWNOE3x/BQaefdFjKwJ1J6E/D7ByVBAiXjTDf+lhwqCUqTL97926fupr/WLt2rSjD7NNEWXA0JsFBImrWrFnCbwrrmJCQ4ONTxVzHhp5TcGmCw1DQAhEcFL4Uwkog+x7doLXE1otyX2+pdlOUy63kqFTQgmPzF5nglm5q4AgWwXEhF3jc8sWS9dMER2gRGlarkvpYcJDg6DDnDZyr9N12m4qPUl6Usq2ucXcVFbJRgQdWReO2mAFiKcu34/vjlxOG4ZXNc3GyJEPEYzqJYYVkg+zgneOFabh75hhBkKhn0gSHIWN8ZUdoXQ8mwZHmBp5LyvCdmJm+rHKC9sryClzIUyQtESxNJ6il5FYAhy4BWw9neNzpBs+Cw59Cz34jwfFkYqYmOEz9ZFc8B5PgoFPGhyNO+hn/SW5UCCLu/bVu4YBWSkV+TpBfpInVzDJgz+kSHL0iMUq9jXI0WATHuA/z0c7h+6W7gx8nj5rgsKe8DSbBcUg4pZXbCld/N93oEp2PJQellYYcySlTy8SYTnxcLwWSj+Ti5DUiVP78ycP6+OBgSaOWutHe4uheExyBOYxWdiM3WB87ERx79uyBw+EQ27cqwJqPJBE4keZEnpN//khw0LrjZnZRCZYFB7dCZdnPPPOMtz7m+t64cQMDBw4UdSZBo37cReXhhx+uk5NROjXlM7OfSPhYfyQZRo8eLeLQyaiK0xQEx+bNmwW5QYJn4cKF1qoF9W9NcPgOeDdPcLjRKbIYHx9V6gy7p0osR/n0AhD7SRUGTsnGvY50DJp4AaezpWLDWMEgODgEMU+rUz0ObJrgaHkEB5eo/GXmyzhWmu4l0oi1qfs3YugniXjSEgZvmoFBm6fjiU3TMWTTdDy+aQZ+PWOEICe+GUd/GvSr0Vf4yvhp/GD0XhOPWWd3YbfrHC6UZqFEqOmGSKIkrUIVks7swo9N275qgsNXzlRXPEPjfjAJDm6dPXxmhmcdePXnH5CQijM5hrwkvUHrjJWHgTHLivFwbDruc1zHW4suep2McjwLhgWHP4WefaYJjur9ZFcsB5Pg4JIm7kzi/1nL8ULSdWSI+aDUJYnVr7Mg/Aq8uLAAD0alih0jFh8wlqkEk+DgFszWummCI3SwGiyCg7gzW/xYMUFL43dXZiGHAs7zXcJdCRxNB6ZtB56enYd7HRKr1GnVz588rC/BMWx2IbjUz1w3TXBogkP4YqiPDw5aOXBrUS5Lyc31/bJHAM+cOVNM5F966SWvBceiRYsEyfDuu+8qjIujvwl+UlKSSB8sguODDz6okeCgdcO9994r4qxatcpbPy6x4U4xffv2xbVr17zXrbuo8Aa3fiXBweUfVisOkhlcHsI2Yxzzbin+nt9bkOeEaRjq44OD1iskaZh+9uzZKC+nyGq8HwWXtuAwBsJABMeklHK0dxpOxpRw5rZtA6e5cC5fcuHsqYoqYPrmXHSPuiZMThmHa3UHTEoT6yM9Y4smOG7yK6B2Mlo3J6P/Ou057M+/4iM0XktZgDuiBgpnoHQuysDdUu6IGoS7HIPwM+dA3B41UFyj/41vcTeWuP7i79ujBoAEBR2F/ih6AO6IHoh/n/g07pk1Fo8ti8L041twvapQLHZRVPGB8jT8OkI6K6UVx08jB2L+2T0+daKTUeXwVB2VxUeoH7WTUUOmKllpdTLKZR5PzvBPcFARd6wtgVsBChBbbL40L0OQxfwCyCUs9N/x1uJrmuC4SVmq+kQ7Ga2OU7aN1QeH1WeLaj8eicP13t1KJGC5TGBwwll0dmaDWBZLWBw3sPxwYxEcxjIXVTdNcPjvW9U+djoGk+DYfx14KNr/EhU6at1/SWGwSlh1fnoR6Bd/Hp0EVkk8uIW/uQ2mDTeDSXAM1QTHTa04aTEWHJyM0x8ELS1qC2PGjPEqk1ziwXScNHMZCn1L0OHnsWPHMG/ePDHJ5zayu3btEmk4wVfLM1gmrTk44efP3wQ/2AQHl+Qo3xOJiYmirrTO4NaxkZGRwjkonXzyeebMmeN9zuvXr6Nfv36CmOBOMbRMIbnhj+BgoqlTpwqipHv37sK6heXS6ej8+fPF8hg+O32BmH/+nt98n+esF8PNEhy0/OH2tSo9ncLW1M9s94b+NMHhOwgGIjhi1hehncWsjgMkFcQRczLAbdrUr9gNjJpDJ09usbUgPf/zy98Ls68jq8QgQrQFh2/b16ZwaIKjdoKDO5f8KGEw1qcdR7nJhmNt6lH8JnaosMj4RjwtM/riBzEDMHLvIiy6ehB//3QFBqyZgNbzXgetQEgw/PPkpzEyOQmTjyVjY/oJbCq5gDZzXhXkxzfi++KW+L64Na4ffhI5CPMOb/XutsL34Chc+OeIx727pPwkciASTxpbejPOJ6lf4TtxA/CPcf2EY9JvkViJD22rG1V/TXBUf7f9EhyJcttN67vf0ZmD2b58GI6nAw/HZ4ndqbhlK53l0floUjKXTskfxzNtwVG97a3tq/7WBIf/troZgqNjdBkOeL6nKT5u1eeVuM9xVYz/dzurxNfqh6MvCYfjyvA/uBYcmuBQmA7FYzAJjj1XgQecV30sJFSb0AIv020sk6LaOn9PlXCMK7fCrhR6a5/or3HStMGmIjjUzlbMr74WHJrgCGyt4W81SoshONTEty5Hq48ITtzVVrG0TOAOKtxFhHnR2Sh36zBvLUtLD+XIk3E42edWqf4m+PUlOEik+PPBQasFLs1gmSybde3Ro4cgHUhskLjgMhbeI3GjfgTHtGnTvAQBn5NkTiCCg35IFixY4C2nW7du3jYh4bNkyRIf3x8sx9/zq/LVkfViuFmCIz09XWwHq9LXdgxkMaPqUZejJjh8FZxABEfEmvxqZnUU8iQunp6VgetKw/Y4ZErc6hZbYTE/Ov96JPqS8FDN9lY/EhxvLblm2SY216usMx7XLEasB8i8szw63UtMdkH5uKOSVJ8lKuaBSg1+dj9qgkMSHNzdRE2kAx2Hr56EbK97WTpnrMDEA+txV/xQsV3sD6MHoOOMsThZdEMsKSHWClCBece2ifvMt03SqzhXylyqBCvH5SgrT+3FndFDhCUIiQmGH8U/jqVn93l3Y2Fey68cws8iDQsOWo/MsBAcB3Mv43eTnsMvY57Av816Ad+Jrf25Aj2v3a6HO8Gh5BgV5J1XgG4OKtS+uzhZ5UldCQ7KJposJ2x2myg6CBnL7Q87COuNEiETR87LRVqRsYyF9ZIEh9wyk3Wg9VxtE9YHI87j83QlmeUEgE71mNb8HNYlKpzQlni2ibXGNaez63nLJDiqW2Ja+6c2vJjjt4ssxa7zxocLouhMPjAg/qwY+6kDcLeKmLW5Ykt3RYJw7D6VLndRIeYZ6utklO+WuU48D2zBobeJtbZVc/8diOCoTab4w4uUx/53tSLBccllEBy0D//sKtAr5rLQZSnfukVlYvl++uUyfpSrKafNTpfL60VwMM+bITh6JWgno2FPcLhcLiQnJwunpXRcWpewfft2A52eMxIUW7duFRYZ3JVj4sSJgihQW6BaE+Tl5YlJPi04SChwFw/uQMLyzfmfPn1aXOOyD38/VV/mZ/2dP39epD106JD1FniPyzRotTFlyhThUJSkBH/MS+Vr3l2EpAmX5LDOc+fOxdWrVwUxw2UobEO2pfXHpS0kTdgeDCQ2ArWJv+e35qfq5e95rXHNf9P/CdtVpa/tGKi9zXnWdk7BFe5LVKhQ8Dkv0SQ6wBdDNcApguNEtkzDdAwRawrEAKDiqSPj90zIxwmxy7JSXSAIiLWHCvHO0muYkZyHs1lKCZff1RmzAMCE9S6TR+lS9ErIE2lVuRwQAhEcjMMBqjaCg5MQxmVenxytEJOH2gZO9Xx2OrZEguOTzFOCOGD/cbp3IPMivh8tHYDWNKm/0zkIH6cd82rdTM9FJHtunEXC/vWYfSgZF8tzDSuPKuBSZT7unf930FEp8/55zBOYc2Gf1zKDmC1BFfZmXcD725Zh+IoJeDN5AVKuHEe+iUzJq3Rj6IZpggQRdYztBxIqEfs+FPVR72MpKpFaWYCrlQWYn3oIPzURIjU9WyjcawkEh8SkIjjo1M6XDLDKjjGLsoQcYjoGf/KYk7zWYrmFG68szEJppYQwMUNZdyUXmPpJDiJWXMOqA8VwCcs55lYl8uSkMa0QGDYj07NNotxqdukBSRarss8UAQ9FnPZOChXBoe6zLG4NLrf/NJ5LERysu4pbXA68OC+7GhlifX47/t0SCA5ign3FjwKJybno6MivFavLPHgh7viraYkK+3X+p+Ue97f0QCSXAJzOAMavzUDEylRsOV7muU9kyTis1/VC+fGC4zEDiT2O0RyrWWfC2+xTgctdB0x14WyegT/G47tlxZciOFReqg2oY9zjLK0W35rebn/zI89dfT4UQX3wsVsd61sfEhz0fsg+YqBuGLuuoJr8seZPgmOTBy9KRtZEcDD+hiNlPk6Z6Qp399dViPzgKuI+zsLes/AsDWRN5I95kwhpH+X24KYcvePS8GW6xChjcner5XsLvFvA+sMf4wydTWvo6j44JqWUeZ+f+VEH75lQKJaAWZ/b7n/zPf7zwFngvNafVcbNXAt7gkOBTB91CwS7BShIwpHg6OLIxKJ9bC3aQEiP5leLgKGJ6X6JCiUwSVj0ScgTpIFKx6Pzo7wA6dxoH1mE8RuLhU8NOTxJJYb/s2QeqajkVQBXhQ9fDhfcdhPYeqwQZNVZviRXXJ7BR9aZaR3rq3wsOGYlZ3qUJcapwhk6GZ1Y3Wv2w1FXQY/aLI3lMa/ko8Xo4HSFpDLe0giOf44ZgpSMU7L/PLrG4fRLgiygz4qaJvrc5rX93DfwRekNQYwIFFRJdJaCa28lKtQhHeUYvWWesMbgMhfm/Z3Y/vg/U0ZgzeUvkK+ciXryINHBLWVJUoichJFHlbAUmXZqO34ZN0QsZRF1jOsnSJO+H8Yhn2+Cp2jvkQpWwSWfJS01PVso3AsfgoOTexXK0MWZhQxq30IVdYv/dwsLjito4/C1hlMyVR1fXcTNNqVc4/GaICJ85bH6ks3jw7Fp+PSKlJ0SNPIloCxVcpUyjcsD83lBSNkylFcAry5IFcsHZX7lWHGgwoNgWf554TTS2BWje8RZHBYWHKp+wGaPBQfzUM9AgoNjCMcSVYuycuDleZJQUfFC5RjOBMeIpDTkl1DYECVyu9SklGx0FL60DNLKX1+tPCB3QJN9XAorXqxp+k64iuPeL+Msr0KIN4UmjvXURC7kKPHHOJL0GDw9U/jpkASHS4zRvMuyWXuO4RzLWabw+TU1R+wsJOsmS+C7Za0TdyEau1yO+youY/eZpC04rG3V3H+PX5fjsZiQ/UlSKn6dC60F8WXIH2s9SfRQpzPjRcljf4Qz5ddzc3MFuSylKVNKrCu5yutUU6/QwbMYq+Ud6pmPJWQJDDLvPnHXxLJBOZy7hUxeuZcEoktgUeKv1Fs3YpA5DZtNa2j6pTGeizrplBQ+tXpj3EIH760tOKAJDiEM9X+6BW6+BSjMwpHg6OzIwdx9klUma8zArzCDE3M9AtoQrmZBS4dhvRKKcDTbN+27a0hAFPsIZXM6mmgv+AxILZGWEhTk6ssRxfbXLiBhQ7YgSmiDxPpQR75WDjwYxy8qbrSNLEXPhALQxkjVmTZP762HWJrC8siKT00pRY4nDgci1vWxBHpRNyYijNs9Og17UuXXAObHvD4+Bo8Fh//nNz+T3c5bGsHxy9ghWJN9Aty6NR+V4rgz57yw4Khtkk8C5HvRA9B2zhv4+PqXuIICsQSF6gNxSYKjGFXIQTkOFVzDyM2zQT8ZZuKE53Q0+vuEEUg8tR3HS9KRizJhxcH0Mh/ivVIQG18UXse4vR/iF3FDhB8Pc150WvqvE5/GgnP7kIEyFAtypEpYpxCbB5GDXzmMJS21PZ/d74crwUGF+lKhlF3sN8qyLVeAro7UgLJRyZGXF+V75RrTUh4PmRFYHndw5uGJ6dnYexnIrZSqL9Vxjlk8cpvY/VeAZ2ZcFUsEKAuZL8OSI8ayPpa/6KCUf+r+iWKIXS1U3e53XMZnGUZ65rXuhNkkW8pLTjA5hrDuKi9XBfD8PFoFhJ5MDWeC4+kkF9JKDaxyzOTYySVOtfVVbXixpidWn5+Xg2MZcnKo5KzCaoYb2HwaGDz+JE5nyTjED13+T9sHdHLmiCUq/PLLMZpjNe+TS+QY3j36hqgzP4L0n1qAk3kG/hiP71a1OjkKMHq5LMOLVfCreEFIfhUPZwuOqHVuZJvkF895TRG+1r5Vf3PJ8joTXgx5nBbASsmNDs5CRG8ox5lc+W6YsUq9NbUYWLgPeGn6MVwsMN6fqxXAiwu4A0qRwFrPuHQcSjf0S+qt/KjYwSGXgPE9s+KPeB8yp9Sbh3oOOvCfsMUX09RrqYtTJ1fxQuWoLThufi6qU+gWCHoLhCvB0d5RhEFT8/HGwlRvGDU/D/fH1Gae6UaXqCKMnJvnTcc8ek/kV4+aBG0FukTl4dnZOZi9vQgbvpSOxzYcA6I+cqHf5Gx0cBSga2wJXl2Y7cn7Ovjl5f44ZYbHPcoL8erCLG/Zry7MFEKeijWFuzBRnZSFsZ48Xl94A8/PdaFrVK7HKqPCM7C50dnpwjOzC/HawnSRH/MaNsNg4ENlsFD1bGkExw9iB6Ht/LfQZ1EkHlsUiT6LnLh33tveJSS1TfJJMDD8YsIQdJ3/Nt7auhgLj+3EhstfYvnpvZj0+UYMWTUR/zXtBeHsk1vC+suT178fNxB/SXwJL26cidg9q7Hw+E5svH4cC77ahfe2LcWQDyfij9NexK3x/UEnpj7khiffb8cNwI8nDEO/FXGYuH89Pr50GMtO7ha+Qbovc+CHpm1l/dUjlK6FD8HhO2nnhPjledl4fWGakCmULcNnF3oJWPWu+js+HO/yyrU3FqZh1Px8IY/9ytVImuyz7FI8FJuDdz/Iw5JPi7Hza2D3OWDJvgqMnp+JB2NzhdPRQdPzhCyV8v46nplTKK3eIuWX+senu/DqIikHGef5+UVeyznWlZOnZ+cWeetHuTo0Mc9TB6MNWFeOIRxLZFlpeG3BDTwUF5pWceFMcDwY68IrC9K9WB27KAcDJsnxz2yVUx2r5bDiZaQFL9Y0tL7gxK9PggsxH+dh9eeV+PQ8sOUEMG9nKUbOzUbXaI7RZRiWmCWw+vrCVIHJoUkF6OjM9YzvRWKM5lhNfPH94hje2XO/rdONrtEFeGGewp/Ub3rE86u5r1WSIs9NAAAgAElEQVRKO2cJeowvgsqL+VG3oI7BDyrWZ7D73+FMcDyakIMxi3K88ofnvHaPR34F6htijjqd6mN/8thMkhCnXHbH937QNBcmbsjDx19WCie5G76sxNTN+Rg2MxudogrQJSofI+a6QFlIrL6yyIXHJmaDuGI+XSJz8dycPLzu1S+z8MRUpV9SV1X4M+uzWejm1XcNuUq9tu/kXO/zE6sj5+ajSxT13tDDqiY4gj5V1RnqFrj5FghXgoMCWA349LavrBvM1/0NGnIwYFqmqRCe+tW5ys9fOt5j3kxPp6Jk1rneket9qZT4DjIyX9bLXDeVb1tv2TI/37R0RkaBL+so8+CgJZ2Umeuo0qlyZFmqLYzBRZVr92NLIzgkQdEf34xnkKQBdy6pbaKv4tJqgstNJNnQH7fGGdvDfi9mgNdR6C3x/UUcf6SEKkvWReZHHx1Mz8DtZm+lc1BPOSqeSmc+ynvSvweXvzA9t6oVzkXjWAe9i4rd30FZPyVbKyBlVV1liSGzlEytSR4r+UWZRmKB221SpjJQQZdylTJSyVeeK9nKc0MuGnEo/2iNx8D6qLqrZ6IcZR6+6c3xmE6mVeXJSYN8FpVfaBzDmeBQfWhgQvar1a+K0bfsM4VROZ7KMVP1pRkv6po8qrIkVkuElYgc/6WFksQy8cJ8FW4kVo2x21y+wrHEq6qj8U7IuOZnM4/9jK/qpMqUdfOtt8o3FI7hTHBIeWL0ucRdYLwZ/aXwKvVVqzxm3/viy8CFsBoWTvDzxbISLimh7mrgRspAM8bM99TyGeM+4xtkhBHX97nUdeMZDEyqvIzxgWnr0g5GHv7ybeprmuC4+bmoTqFbIOgtEM4EhxKWbZzc+opBCdrAAlMpAzKuSqfSKgXFvzClIJZCXwlllmNWZtSAYc6X5zKeFMI8N99XhISssxywVJ4ynlLYeM8syI24/vLzjWtOZ9fzlklwcEtXFfqK7V3NpIG/cy/JQF8aHuJBkR68xzQqDgkTdc9fXuoa4xv1kGSEkYe8p/5WaaxHSaSYyRqjLnWpgzU/O/8drhYcchJF+WaWKVIu1valTSq25nQ8V7LSvzxS8ljKVSkDKZ+krFX1UEq7OW+Zr0yv7qv4PCq5KsuVdZPXjWeTz+UrV1kHazz1t1E/u8pQa73Cn+Bg30hccJxUOJJjrn/MyTFbjdWqb3msuX8VhlQZEqecYCq9gMQYsWjUiXWT8VVdfMd2WXf1fjGtGcuqbvI9ku+mykfF881PThxrfg4rRuzydzgTHBJzZvnF87r0k2//+seLxI25HyVWDVyqv4lNhV9ek7ql9f0xsCXjqnr7viNGnuq+OrLO1Z9Nxldx1NE3T/Mz2PlcExxBn6rqDHUL3HwLhCvBoYQfv/KpUJsCrtLIo5GO6XnNUJYNJcI3Da8bDLa6p9Kb/1Z1st5jnHamOst41vJYhlE/f2WqsszxVJnGPWu+9v67pREcygKDVhjmUK+JvYfs8EnrydfnWoBlKiKOih9HSwvD2kLVjfWtOa/++FZcf/EstcetLS973w9fgqO6fJLyR8qk2mSLkkFS7lWXlf7Tq7zN8dU5FWUGQx7KvK2yrLb7Mn7d61e3/Pw/j7Vuzfd3OBMcbPvq/alwU1ubq/4tFXncXD9ay+DfMkjLIZW3PMp7Rn3MdZZYtubHuCqP2utnzo+6xc09i1Gv5k4X3gSH6k/jaMVFoPY3929gvATqR394MF+z1sd8z4xDczzfsqrXz/e++bnMcev6/Ob0djnXBMfNz0V1Ct0CQW+BcCc47CLwdD0CD2p1bZsWR3DURDboe7WQKc1LgIQzwVHX91XHa7jMa+w2DHeCo7HbT+ffdBgPb4Kj6dpRY7bx21oTHEGfquoMdQvcfAtogqPxhZ0eUILTxprgaN5Je83WGbpu5vbRBEdw3nktOxu3HTXB0bjtq/EbvPbVBEfw2lLjsnHbUhMcNz8X1Sl0CwS9BTTB0biCTg8kwWtfTXBoEsFMItj5XBMcwXvvtQxtvLbUBEfjta3GbXDbVhMcwW1Pjc/Ga09NcAR9qqoz1C1w8y2gCY7GE3J6AAlu22qCQxMcdiY1zHXTBEdw330tSxunPTXB0TjtqvEa/HbVBEfw21TjtHHaVBMcNz8X1Sl0CwS9BTTB0TgCTg8cwW9XTXBogsNMItj5XBMcwX//tUwNfptqgiP4bapx2jhtqgmOxmlXjdfgt6smOII+VdUZ6ha4+RbQBEfwhZseMBqnTTXBoQkOO5Ma5rppgqNxZICWrcFtV01wBLc9NT4brz01wdF4batxG9y21QTHzc9FdQrdAkFvAUVwtB21w2dLNfN2Tfrc2AJLt0XztUWX986g1R964fvRT/psm6q2KdVH3+1kdXs0X3v867hhaPXXnmgzLhv3OEu920TKre+41Z4Oug2aHwMdnAVoPXIz7nx0NTo4c7QO4N1+tfnGOa1j+G/7To5s3NXnAxF4rtvJfzvpdmn+dqEs/fPAmTh06BDKysoaFFo1NIPGSO90OrFp06agT0h1hroFgtkCJDhed87EPaN2o42zQgfdBrbFQKf3zqPVH3rjtpgh+GZ8Xx10G9gWA795fyha/bUX7h6XJ96n1s5K6KDbwE4YaOOsRDtHCe4euQU/f3Qt2jslVrUeoPUgO2KgoyMXd/ZZJQLP7VhHXSf97hADlKV/GpikCY5gTlZ1XroFbrYFDIJjF+5xluug28C2GOj83jlhwfG96CG4Jf4xfCu+rw66DWyDgVvi+0KF37z/pCA42ozL87xPFbjHqYNuA3thoL2jGG1GpuDOPmtAs2qtA2gdyK4Y6ORw4Zd9PhSB53atp66XfofCeomK2+3GmDFjEBsbix07duig28C2GNi+YycGDR+Fv/SOwl+HrcL/DP1QB90GtsRA68GJ+Iff3I2fDX0Ydwx7EHcM666DbgNbYeB20R8P4bdPPIJW//6/+MuTc/H/hi/GX4Yv0UG3ge0w8NfhC/GnR97Gne3H4m9DF9tS7mudROtkxMDdQxfjl+3HiMBzjQuNC7ti4O6hS/C7ziOwa9euBi1P4eoS2y1R0QSHJnVChdjavmMXBg4fjd/3nozfD9+K3w/fooNuA1ti4C9DluIfftMD//30EvznMyt00G1gOwz8H9EnK/G/T01Gq3/7C77zZG/801OP4VYddBvYEAPfHf4Ybn+4C77X6UX8cdgGW8p9rZNonYwY+POwdfhJ+/dE4LnGhcaFXTHwx+Hr8dsur4QnwUHWRfvguNnFEjp+c7RAFYA3nTPQYVQKOjjydNBtYFsMdHv3FG757+fQNSIXnSJKddBtYFsM9Iw4ilZ/bI/vOnvittg++I4Oug1siIEfxDyKnz7fFT/uH4Uujhv/n733gJOqStPGmRmdcWac4MzuN/vNzu7s7Ozut/u5Ozs73393BCUHJY0CQtOAImJCEAkqirmrOjc0sRtoUjciKElyUBAkgxIFJceGTtU5VYd6/r/nnDp1762uajpp3WrO/XG4t2+dfJ/7nvc89z3vsa3s17qJ1s26OzLxDwPeE6G744bGqtZVbYuBro6b+H+Rs7QPjlBManWZugdUD6hdVNqN3Y22To8Oug9si4HOb19Fm3tfxAPOUvw5ukYH3Qe2xUBP50m0+WNn3BFDZ7iR+K4Oug9siIG74ofg52N64aeRU9GeO6ro8U/3gS0xUCt8xPx6wCowdHQW6edky+ek5xCUoZSlfxySpgkONdHUZ90DoegBRXDcP3YX2jmrddB9YFsMdHn7Er5z7xh0cJbYto76HdIyhBjo7TiBNn/shO/HPIo7EyJwR6IOug/sh4Efxg/GPWN64ueDk8QEUssvLb/sioFOjgL8ZsAKEXht13rqeul3qFU7GdVLVEIxVddlNqUH5C4qqbj/xU9xv9ON+51VOug+sCUGur59URAcHZ3Ftqyffne07FAY8Cc47kyMgA66D+yGAYPgSPTuoqLfYfUO67O9sNDZke8jOHitn4+9no9+Hsbz6OQsxH9FzsXhw5+3PiejmuBoylRbpwlFD2iCwxBKWkDbuy80wWHv56PfH+P5aIJDkxl2IzMC1UcTHMY7q+WXvftCExz2fj76/TGejyY4QjGb1WXqHvDrAWOJyk7NiGvLDVtjQBMcxgCqlQl794UmODTBEYhQsNs9TXDYW45oOW88H01wGH2hcWHvvtBLVPwmmvpP3QOh6AFNcNhbUOqBzHg+muAw+kLjwt59oQkOTXDYjcwIVB9NcNhbjmg5bzwfTXAYfaFxYe++0ARHKGazukzdA349oAkOewtKPZAZz0cTHEZfaFzYuy80waEJjkCEgt3uaYLD3nJEy3nj+WiCw+gLjQt794UmOPwmmvpP3QOh6AFNcNhbUOqBzHg+muAw+kLjwt59oQkOTXDYjcwIVB9NcNhbjmg5bzwfTXAYfaFxYe++0ARHKGazukzdA349oAkOewtKPZAZz0cTHEZfaFzYuy80waEJjkCEgt3uaYLD3nJEy3nj+WiCw+gLjQt794UmOPwmmvpP3QOh6IHbleB4wFkFBrsPFOFQx2+rDzXBYX+8fltYsHs5rZLgSBiMOxMi9Xa3rWjLX01waJlqd1mq6qcJDo1VhQW7nzXBEYrZrC5T94BfD9yuBIfdBaRRP7ftSRijrt/sAKwJjm+2f7+t53g7lNMqCQ5O7ElytKIJ/u3eFk1waJkaLvJYExwaq+GCVU1w+E009Z+6B0LRA62D4KjG/c6WCHYZQMxtkXVqFwbWJt/04BP+BIf5uTb+up2zGgzfdD83NH9VH1kn1R67vEOhrUerJTgSI3BHCwXbkwsJJHSM9tq+vk0gnsKd4OC4qORQ69ABlBw1n0Mryxo6HnzT8cKf4FDjt/nZNufaLriQbTDeQ7vUK3T10ARHKGazukzdA349EN4EhxsPOCvQyVGMTo4idG5G6OAsxQPOShtMHist7WHb2Mb7nW7cH82BJHRCO9RlhzfB4UYHZ1mzMEp8E+d2wSpx2dFpvHsdHSVerN6+GFXvSGskOH6QEIEfxQ/G3XGRzQ53ieUuNvfTYSI4SHSI0AQSwc7ESLgTHBwXOzhLmj3+U3a1F+Ns6GVXB0eZ0AHsJu+VbAvVOdwJDjFetoCu2tGnE4YWq9SXO5rePeqqxG6o8GGncjXB4TfR1H/qHghFD4QzwUHF5pmMWqw9AWw6XtOskLIH6Oa4GWLhXImeiSX4yNSedSeApzNq0d5ZEuK6hXYw5eAVvgRHNTo6ivD6qubjdMNxIPlToIsjJ+R4GDqrCGuPsk214t3LOAQ8Mq1QkxzOKrQ2guP7CRH4X/HDMHHfUiz4cicWn2xeaJ/+OkiY2Hny/73ECHw3MQI/SYzEz6c9bvv6NqUvw53g6OgswPuHgI3HPc0a/1efAJ6aWxhymdrJUYL4DVXguE+5Snk/8zOge0xeyOsW6glkuBMcjyYXYHkzsUqcM49Hk/NDjodecS7M3Q2B0U3HPUJvfXNVhfgAE2qshLp8TXA0Yjbr8XgaEVtH1T3Q8B4IZ4Kjk7MAb68HSgFUNTMcygZ6Rl1qxqBBXxl1/WU0xkkoGf5+yUUoAuD2hhJAtJFtDbXQDnX54UxwdHG4sHBf83FaDmDHVaCH41rI8TBmYQGKawysni8DHkt1ob2zPOR1CzVWWxvBQeuFv4sfjrV5p1CMGlSitlnhsbXTcJdNCQ61BOcncZH4t/inMevYNgzeOktYrzSFRLBzmnAnOLo48nCtVMqg5ugA+QBee98VcrnV2VEgJozUaagDUN7vvQH0jr0R8rqFWqaGO8ExIiW32VglJoh35hXq59Ev8Sa+yJEYZb2KASzZ50Ynpz9RWFcvDnXdv+nyNcHR8DkoysrKcPz4cRw9erTBIScnpxElBI9aWVlp+bG4uBjHjh3Dl19+ablvpz9qampQW8upuzyqq6vx1VdfiT5kX4bLwWd44sQJ7Ny5E4cOHcKFCxfg/zya25ZwJjg44EWt96BadAL/DxRqADAE+k3dq8GRbKB31PlGDBpq7aS0bGjndIPBX3A2luAYkFwgFBtVX9aQbWRb/fO+3f4OX4KjClTE0/cRqApzgc4Kq2a8mq+rwff1s6vAg44rIcfDiwuzUVVN8l3W8VoZ8ERqNto7tZlqqyM4EiPw27jh2JJzGjXwgP+aE0asaQDB0VLLQhqTT0IEfpAQiX+Y8hRGrJmOL9xZKIcH43ek4+641udgNdwJjq6OXGTzK4AAY0Nlat14zOKN9+uz4FR+BujzQ4ZvYvzlOL/teLlPp+FrdjgT6BtzPeTy/ptob2PyDHeCY2TKTWSRuRKHGYPmMZ7X5r/N8XgNZJcCzKsxffdNxB2QcB0nsiF0Euo1JDmW7y1EJ4f/x7i6evE3UR875akJDonyBv1/6tQpPPjgg+jcuXODw8qVKxuUd32Rdu3ahSVLlliiHDx4EF26dMGAAQMs9+3yx5UrVzBlyhTk5ub6quRyuTB8+HDRh+xLux9utxvvv/8+hg0bhm7duvme+cMPP4y33noLpaU+KdnspoQzwUFB+s56gMoJhas58GsOhwnzwbbyPuORtmNQaQ4KC47LvkFDOUwyHCgaSzTkbzVo56wxxbcqPeb0kvwwO0Mz8jILZVpwDEguEhYprDvrWwGINloHDemsSpVhzoPX8r6sD695j0RLY8gW/zzt8Hd4ExzSgkPhzf/MZ20+zFg1xyU9u11YcGSasBcYDwof6hzsGarfpeJOos7IL1gaYmnsQhdKq+V7xvpeKgMeT82rQ3BYHQEa2FcTBfP7wfLCHadsQ2skOH4T/zjWur5EEWpQjlpLoCyl+k0cqFANjyWOOc2wW1hw0IpCWD54nXw2xwqCy2uY3penyjvA+fsJg/Gr6U8h/dIBFHJ0oPWsB3hpuyY4gsmCUN6nZdzVUjlOmsdzykzi0Wz7rKhYxuO4ao7vEhYc5o8IhgyU8pBjPe9Voa33zGvp2NSQaea+MOQoZaoal+v3o8VxnktTWD++R2zHgUygT4zVgsPI2/qhheXL38zjf/1lmuts5+vwJjjceCI1F1fKJPbMYzqvqe8Fwqp/POKCeB+RYl2yZMWDxKP/+BocqwrrxIz8UKfwWh8eaMFxNFvq1MQqdZOle+lDzmrBYa2bgUX/+jFe8DoGfsfqq18of9MEh1mbvcW1Ijh69OiBUaNGYfz48bcMJCeacyxdulQQGbGxsZZsaL0RGRmJ5557znLfDn+Q3CDxwvrdvHnTV6WCggJMmDABjz/+OM6ePeu7b9eLTZs2CTKmV69eePfdd0GyKi0tDU888QS6du2KF154ASRtWuKgYHrVmYr7x+70TZhCKRgaU/YDznI8mlKLd9cDjvU1lhC3rhS7z1l7KM8NpH1ajOj1bktcph3/ISyCmcK2rbNGBElkSMHczkEFx4P7HB60c9b6+kwJa7VMRQj1aI+M6wTucwJtnbXe/Awhb22vGw8mVGDhIYD+DDIOAosOAU8sgmnSyLSSXDHqZ+Qn6y3LtZZHFj28mfRwJjjofGvEgto6uHOur0HchmpcpI206bhcBExdm1snftR64IVlEM49iR0zTv3xQNwSqwxmMs6MOSN9rcCoxCnT1Iq8Zdy6uOF7MHBmGRYeMLA6cw/QN7nc4qyX70Vb36TAwKm17rcuz1zncLhujQTHTxOGoMfqGAzflIIRfmHiR3Nx2UMjZTkx88CDowVX68RT6f5lzhgo4sFHXiRE+O7R/8V3kmT4bpKXnDD97ksTkKSQ8YUPjQTpR4O+NBh4j2lZtn/5ggBJGIzfzRqFA8VXRVvUpGPC9nT8OH6wL3195YfTb+FrwSHHM1qLTV7D8d9TR1am7yxGeZUxceTHjR0nyxCzrrxOXC51HTDdbHnGSZ8c/ylXKSclwUG9QMlUjv+S+LDKJCkvRfpoOfYbcpWymHnJ+lvTVYlx/o2P5LjP8X/xISDmY6BrdJFP12AaQ26rDy1StnKCKscB6hqqnupDTF057l++nf8Ob4KjCj0TSzF5tRWrHP+j19Vg05FyQbgpFYDk28YjdXFKnDMP5mV+VlY8VAuH9MY9r64arXBgJguICWLIwIuBG6XfBsZNj7gixO+QGCVWqbdOWO4x6aqBcKreF0n6SawqnKr3KXB55vba/VoTHArJDTgrgoOTd07iG3M01X/H7NmzBcERFxdnKY5LJG7cuIHs7GzLfTv8ce7cOfTt27cOwcHlKlzuQdKjqorDnH2P/Px89OnTR1htfPDBB5b6sv4jR44UJMeyZctapBHhTHBQyKmdHDo6i2AODzquYck+ts44rpYBT6VcF8sFzHF5TUVJ7aLygE/5qBT5S+/qkuUmqcJ1slxn6G+KbwwoNSIv7nZB3xldHTm+MpmG8SQ5UnfAYR26OvLQzZEjAq9lOYbQZ/0Yj22XodL7ZYkDVZXwbE1lgF+D5I4b8qs8vzzZfWCor37hTHDQKoG+KfxxRxw9GFuALzINnPKKX0b+EnVaOCf1TyPxIHf8kdYO/lggvvhulKGjMx8cbIlbM8EllXeSGLVerJags8OFro5ssRxKvhMkK5RCUhc7xBaxrbBKzFnfCbnLkYFT7gZErHKSQGXGLYgafoVlHRXOjQmFWRELr+vWSHCQEOCEWO6iMgR3xxnhn995HEeq5bJYSQp4sCnzhCWOOX6gXVRIMHwvcZDYrYS7tfwsNhL3xAzB3bGDcVf8YGGBoX4PtqMJ8/gu80iMEGm4rOSeWObFeg/GD+MlsSHL8nNy6m3fP82UBAfboQiOl7enizzoGFUQIzb1H9JYciV8CQ4lD+QuKv4ykn+PnncNhZXGM+TX73nbctHdkVVHDtNhubGLCsda6xjLv+W4XS3GVI6tdBwtdQVVF0WKUK5W+XbN4hhOGSfTcGKqSIi64z9/6+ws8ukAXILTWchGyk5VDutH+S/H//be3d8UIcNxppMzX8hz9gPlL39jMPJQeYXPOdwJDj4H7jrmj1XujDZlbZ6wgFBaAH2vTF3HcdGq18q/1c56fHZWnMqxVmKVz5pjNPtN7hJk3SVQ6QDMg2MvLS+INy6nFbqjgzsL8gMF8VwXO8RZZ6Hf5vp0VWm9YeiqzFvhlHVTWFVjvChXYJV6SrFoD3WSQOWFE3Y1waGQ3IBzcwiOQNlzwn+riT4JDi6J8bfgCJRfoHsso7ycr2ngo6KiAo0lX5iG/jWCHcEIjmDx/e+zzgxNOepra2Py27Bhg1iWQmuTQMeiRYvEc3nxxRcD/dzoe+FNcBgKxX20qjAFCvXFwueB0SU0DxyeykmY1wrDFF8JXArkrjFleG5BKV7KyBVh3GIXese70DM2Dy8syMb0rZWYvaMWr31QigHJ+V4FolpMFDkwPDolC2MX5sGxugQztrmx8hgwZ7sbyVuqMOk9Fx6bmY1u0XTGyAHHPHF0o0tsGcZllGBiRj4mZhRgfEYJHk7mwCAdN1IJi5iej/EZRXg5I0fEGzanQgxi3aPzMHx2DpyrizF3pwfzPgPeWObCs3Ny0DM2F+2dpV4lLXyUGvOgFs4Eh/z6V2PBKPFK3HWKq8IhP4Ljc++SKTOm5bVhBUSS68H4MozPKMBLGTkCqy8skuTbgKl5mJiRh3m7geQtlZiwpAh/SXQJxZuKOnHUzZmPoTPyMCHdhYSNbszZUYPlnwMzt1Vi6uYq0MfGkGk5gtBjWUxnfh69EooxPr1Q4JRYfX5RBXrEcbcfqeBwAvBkWgUmZrgEVsdlFKP/1HyxreNDsTl4Zm4eEjaUY8FuIOVTYGJ6Fkam5qJbdK4gZPzLM5dt9+vWSHBI8iACtKhQFhHq/PdRw3C41uz3y4N1N04EjMs0zMuYjEvygpPtX8U/hvZpk/Ds5jl4c/dyJH6+HuO3LUS/pdH4r9lj8Yu4IcF3M0mIAImTv44bhvvmTkT/96IxdksaovZ+KPIa9/ECDFwWi/bzXsGv44fjR/GRlnr8InEYOqe/gSdXTMPXpTk+coMjyMLDWzEww4G+70Xh3nkvthqHo+FOcFBGtI2uRdto6/hPWfnsvFwUmAgOmtCnbC1ER0epsG4wy1brJM6NIbOLMSGjQMgtjrODZ5WgmzMHw2e78NaHhZi/B0jcWIln5uWhUwwtPyjzOFEsR8/YHDw1Jw+TlxVg+hY3luwDFu6B0AWcq0vw7Nwc9I7PEXKQ9ScZouQZ0w9PLRbjPmXqhIwijJxfic5i8icntN3iSvDcIuonSuYX4qG4QjFB7D/FhZeW5GPalkqkHwDi1pbgxYU5GJScK+S4Kiccz+FOcAisOqkD0FrSiteEdSUWgoNY5T3/eExr6KrS4md4KnEidQCeH51RjgcdmXhqbj6i15Zh4V7AuboMj8/KR8dowwE4CYW+CTl4bl4O3lpRjBnbqrD8C2DBbo/A6jsrS/B0ai4eipUf2fzH4y4xxXh6QbnAKLE6LqMUQ2bxQ4Usg4RG7ymGfkJ95Ln5BUIP5ceUwdNz8PoHRZi9vVq8T9FrSjB6fi76J+WBW+GGI0ZVnTXBYcy7bnnVUgTH1atXMWPGDHByPGbMGLz55ptIT09HZqahXdMJJ8mNZ555RkykR4wYIf5evny5qCctSPj7woULffXet2+fuEdHnrTw4G9cRsNlLJMnT8bnn3/uIzMYJyoqSiy1GTduHObNmyecqPoyM13QOejevXsxffp0vPLKKyK/sWPHivQHDhwQMUmSkPTgMo7o6GjQVwktIBISEsSyDvriYJu45GbOnDnIysry1UUVtX//fjidTjBvhmnTpkHlr+Kos2r/J598IsiQPXv2CL8YbOvLL7+MmTNnWvpTpWvomZYmR44cEc5FA6XJyMgQz2X06NGBfm70vfAmOJRiQOWCZIERuI1mIIKD6yDVl2JzfCWYOJHrn1yK0y5pas11vHmVwIS5X2HlCaC4SplgA0UeYOkhiC85nMCStEj6BPgyFyipMcKwDLsAACAASURBVOKph8K+rqwFuPwg9TO1La2cNMov/BXon1woPKerr4f86sRlCbQaYR3J9qduLfb56eDva04DA2O+RPpB4FqJsZ6TeZAOzC4HtpwD+ifd9E5wVb+F1zm8CQ7V1wZG1Ve4LnFlwpGcwgnPJDh6RV3wDvJmfEvygFggYTUirUwoRnzOtE27UQoMmXIZuy8DZbXy6yVxkF8LzNoB3/N/KPomlnwOXCgEKjxWrDI+sUoMn8mnSS1NpPMtijjLJ4lXXm2kvUwnoynGLioPOq5j51W51p314wKGuFXZGDzlOtafBrIqrFh1e4DMMuD9I0DvhNDvaKBkQlPOrZHgMAgJacWglnnw/Lt3h+GLGjPBAay/ccKyHMQcX1pg0NJiEO5IGIwfJkTi/854HumX9uNKVREqwUUu8hBYRDWOl2Xh3UOr8cv4YfhBQl2Hn5ys/2vqGMw+vQPnKl0iD6blITHtgRu1uOwuxJqck+iU/gboc0NZg9w7/XnsL76KClPZ3uSoBZ1Z14odZBzH1uEXMUNMBI2ZrAmv63AnOOS7aZaPhnx9bl5WHYIjdWuBZSmqoQMo+SzPSw/K3SEotxgy9tVi0uJruFho+BygvM2sAB6ZWiKt4BwlGJZail1XgKxyQ7YpDBGDzCu3Ath5BXh8Nrf6Zn1ZJvWAajHObz5eI3xwMD7LOCicjEqfSySmI2cW4myRzEvUoRR4NuUKXlhcgs9vAKUm3YP459+n8oE3V8kv9U2RZ3ZIE+4Eh+xDA58G9iqRuK6wDsGRuE4tSwqEb4lTflDbeJz+kKTPGRIjMz4F5m0vFthUfpGIk33XgT5TWT6tnorx/OJSHLgu9VsVT8lKgT0PkFUGrDsF9J+SJzBuxkH/xBs+HxyMzzos21vhe79IUkxYJutG3NOfyJdZQM/oa0jc4sHXBUClSfdgnMIq6VT3qXmSCDGXF07XmuBQUq8B5+YSHLRK2L59u1hyQqeVERERYhnHI488Iu6RENi9e7eoCZdIcJmHcmpKvx/8W1kM0MkoLTvMTkZJUvAeSYRJkyYJCwSm6d27t8ifZ5IgO3bsEORDz549RZ7Kaeqzzz4L+skwH/QxQXKEPidYh0GDBol6My+VjkQLSRA65XzppZd85TEN20RfHBcvXhT+KgI5GWUZb7/9tsiPZbBN7BPl2DMpKQnsD/Oh2u9wOLBgwQJ0794d/u1hPl9//bU5WYtcl5SUCGKKTl7NBFNzMucAGK4+OOoTeF0cubcgOKwKjcqLrHP/5GJ8JVycUGwDxZXA3hM3hAA31IoaFHsAx4oc8eWEg2/CBjeKq5VJrFeke7iaksMHHdZxmKHi7hGD2aI9tegYYywdkGUXeNdismyPUHAkwSEdoNEEMHWry1sXj5iI7r0IHDxXIuLKYYTlMLAO8mANPj0PdIuTJrKqveF0bh0Ehz/u3OgSV3ELgsM/jfybBMeTaaVCcVCoKyzzYMOBLO89PnXioBaUrmPTuPOKG/wCuPyQdHYm01EKMB5VEKYhZokdiX8qHW+uKEYHp1nhooVHDtyMLg6PcH5mEIhV4DIxEhwShR6R69Jt53Eq0+O9x3JYLs9yKsoSWYtZ28N756DWTnCYyQ5e/0NUcILDP67/3/SL0S5lIvYXXRFEghdOEn4EBKEhoOhBBWqx9Oxe/G7KSIv1BcmTf0t+Fttzz3ix700jIawg6suHDlCPVWTh/nmviGU3rNMfpo3GsYLrRrkylZG2VlYl7uh6/JUmOGz/hfW5edkNIDgCy9ZlB0mG8SCAPNhzIgtXCtXfFHrVQoYdvQZh0k+5OnRGNk7nGfJOyNIA4z/zYw4n84CB0zhxpA6gCI58bDnuFlKR8Qh9QXDEyl1U+HU8cmYBLnAvee+RWwYs3XoGLiFoZd3gYe0pW+ULxP9pwfr0XLXkNXC77awPtA6CI3C/k8wgOaEOXhsER+A0fFbsE+JFYZV42ft1BVzCeJ5PnRiQWN36NdAhmsuVqjF+cQ6u+6ybGI85MBBEalyWYzLvbDkD9I6xbk3fP/E6jvl2UZHj+7K9xb5dVEhwTFwmd1dRWL6cXY30jzNRKrJWWKWjnBrp0NmL2K8KgYHxl2wvY4K9L5rgUEhuwLm5BAd9ZgwZMkQQABs3bhQWBvTnwCUddGJJwmDo0KHCEoKEAe/TGoL3STLwb1p/8FAT/EAEB8kBBhIZTMOtZOkQkxPygQMHgruAxMTE4PTp0+L3devWiTqRUFi/fr2vJ7h8hvViOhITX3zxBa5fvy7qfebMGZB4YBqWRQKDVhzXrl3Dp59+KvJ79NFHhQUGf6NFSbBdVBITEwWBwvifffYZLl++LPLbunWryJtEydSpU331MrefbXnooYeERQytUtheWnX0799f9ButY1rqYH8cPnxYWMWw3Vy+wmfaEgflTOskOPJajOCo9QDcDZOCniJZseXnioAhM7nkpRxPz7qEbP4oYlSJuPxiTSVm10XgfLH0Mi0VDvl18kwhEDGzwKvg8Is8yRUrwcEhpy7Bke8jOFgiv9STCWe9zhUCey4DWdXmCaxESnYVMGZBnliXGUww2/m+Jjisio4iOKiOSFwBNR6gyjsRIxxp4UPcHsoCejkuCLPoiYuue5Vh/lIjEHujAth/BeKLzoUSqeJI1Eis7rsBPOSwKhwvLsxFpcC8LJ8KtJXguCoIDhlFKuvFblkf8W7kAvuvArkeqVopZZzn82XAo/HKgsXabjtjVNVNExyGBYc/oWH+m5YYv532NNZnfSnIC4k5iJ1avqzOw0fXj+G4OxulXgKMv5PKfWvfhxYrip/HRuKdQ6tQLtBOKezBNZRiybm9iD+0FslfbMTWzFMoItqpz3v4btTigzP7cU9spFj28n9mjMK2sovIQrWw/lB14U4qJahFDqpxDW68fnItWJ65HeF63TosOALLh+YSHJwaSrAA7hoptwgdjrMclynDpq7LFX4SujuuY/lhRdNKicd4XxUAn5wBvrgJ5Hu/VktcEVNA/EfK94AiOAqw5XiVb8JKCR2I4DhvIjgo7ytrpMzOq4FY7vhVvtxdjvVVbeBYsOowrUCtu1womWX3syY46uKclr1mvPBpEwt87tTtFVVBbu71pVlCV304yYUjNxUqquHx1AqL4ItlwK5LwEmXtE4m9mQutSDcXnuPFkdGHfonZuJ4tnwvmBvxtWxvSVCCg7nxg0hFjXyHqKN+dhGgvsG08pDCmX8v2o06ViPm8u18rQkO9TwbcFYEh7JcuNX56aefBr/2q+Po0aOCEKClBJdrmA9aP3AZCnfoIHmgjmA+OOojOGjJQJLBfHDXFVVfLosxb3FKyxJFpJidmXLyzi1Saa1BMsT/UIQF86VlijpIMgRyMqri0yqFfcmDZATTk6QItLMKl9XQOoNx2H/qUO3n/XfeeaeOLxMSSPyNac3PQKVv7JnED/NTgVYp5ufU2Pz842uCwxDYFJjKgoNLVMwH+ym7Ath4CkjeWIxNp4DlJ4DOMeVoH12FtH0Ak6gBhSako2d/jV5RF/Fg1BX0iTqD2ZvzxRIVqXQAuTXAmIVciys9YiuCwxD2UpGqS3AUWNh+Di55VcC8T0rRL/YiHoq6jIExp7HpJFAldzkUTeGbn7qtCB3DVMHRBIcVq3KJSqnAnBmrvKYp9eojwPTNpdhxTm01zHXaRVhxDMKig1gl1kgyPJ50Gj2jLuOhqKvoF3sBn5xWpIPMmYsPIuLPWBScsRaCQ34hDE5wMB9JcuRUAtErs9A76gx6Rl3EY1PP40impAZVOzh5iF6jLEas7bazYqPqpgmOhhEc3JnkydXTkeud0lGpzvNUIm7vavyLczh+7RyKf4sfidSjW1GAGqG4EyOXUI52s8b7SIa/dQzFtkJjh7Ri1GLiqlT8KnqoICPorPQfo4dj9qntcKEGuajEGXcetpdexO/nPC8IDvrg+PO8iRi8JBZfFWcpKIpz6r4N6DX3dXSZ/xr+adYo7YPDNNFRmLfbubkEB0kM4/CIDwmHM4HU7W4s2lWBbVdI6MoPHJzsnamS4z/TcUK4aGcJ+kafF5ZsPaMuYUzKWbE8lboED8rfTcfkshTVd9YJq5SJtyI4mBcpleM3gadmXQHL6hN1Fu8uuw7KWlUezzfLgK7OXIscV2Xb/awJjrrjIPGy2UeISVxxnC2qAXZfAGZsLsGyAx5suwb0cV4SBMfz6RW44sUqx/9sN+D4IFPoqA86rqKn4wLeXHIRLgUcL6G39AAsuCHmacEhiRCpS/gTHBOWkSw2Duq+FbXAumPAkMQzQld92HkeC3cUC8JPxWQ8vmtd4wwLZ7vj01w/TXCoJ9mAc3MJDqYn+UCLBy4V8T/y8vLEUgyz49GmEBxPPfUUioupmhoH/WOoyTl3BfE/uKyFv7/66qu+n2h1wUk8600Cxt8ZKX9//vnnhYVHUwgO5kd/GSx31qxZvnLNF4xDPx6M88Ybb/h+MhMcJ06c8N1XF6ybau+lS5fU7SafV61aJZbf0F+Jshph21tqCQxlmLbgMAaOYAQHyYGpG/LRKVou8egcXYKHkmS69k43HptdgEkZNzF9UwG2nPRgwlLpGPR/nMCfveGxmbm4VmQoHFSCXk6/Lr6qUziqJSqNJTg4GMzfVY2uzjzLVp8jZmcjs9gojwPN+iNVwiu7WRiHy7UmOAyc8pkFIjiIBZosj1vEnQK4NtyNbrFF6BwnfXfQ/8wzc114LeMG5u8oxZbTwONz5A5C/xNtYPWV9BviC6WHGdKHB4BnZl20KDiNITiYDQOV/zk7gW7R+QKr6t14efF1UZ4SlHwH0vd5LOWFC05ZT01wNIDgSIgQ1hOfuy6hyktduOHB++cP4u8SRuA7SYPQJmmgCL+NHY61mccFPIijMtQidv9q31av9AHyFUp9BEghqvHG9vfws/ghuEtsFTtIbPH66xkjMXpLGh5eHIW2c1/GP01/Fj9JGiLyoePTOxIH47ezRglfHAqLPE/YkS6WsnDrWi6psTpJDS+/G2ZrE23BYZWpSsa8f1Ca3CsMUE86dA3om+hCe4cc27vGV/qWmPZOKMDzC1yIX5OH93aXYckXwEPRmRAylXI1mj63crDhK7logPnSQmTPuVqxzECV21SCI7MSeC71Ktp6t6TntrQ9onOw8qhRHsukXP3LFBLHhi8nVbbdz5rgqItVRXD4kwhLD3hAP1t8ph2c5XgwiY7FOc5XoN8UF15clIvEtS58+AUwdy/QPcbl01M5JnN56WemDTtJYmw42XyCg/kcuQH0S8j0OlCV2yj3ibki/Iap943nU3nAw1O5k4vyU1O3/XbFrCY4zE/yFteK4CBBQV8Z58+frzdwOYl5NxBaMNCqQ0286UCUPhy4hIROLc1xVVWaQnDQsWigQ5VLcsD/eP/990W96Lsj0MHdSVhHkgVcCsKlJHQC+pe//EWkawrBQSsS9gGXwAQifFQ9tmzZIsogoUDigoeZ4CgqMtkJqkSAr5/5nFryoD8QOltlf5LsIDHV3EMTHFahGYjgoEL9dQnwcPRZ3y4k0vu5dBAqhaw0MeW13HGiRni75vra7nFFGDIjD9EfXENWiZzo8bmRCpyUcc27PVbTCA7WjT4Snp+fLZQWOitVYUCClWHn5HLLcesXI7sOEIHqpQkOK1YDERz8kkcnd71jM30OReml3+ypX/YtFVxuMcctCyWRQEuiXvGFeCIlB2lbMr3LoKSEoQ+PF+ZdtRAOjSE4mAtlTUEtFW9iVTo7Yx3aOd3o5zyFmwSz9yBWlx20KlSBMGHXe5rguDXBwa1f/2POiyjxfQOUxMWQDTPw0zi5w4kkHeT2tE+tm+lFkcTSluzT+EnSMEFO0NJjXfaXAj2EEW09uKxlf84FJO1ZhcFLY3HfnJfxm+Snxe4pJCkkWWF1Vkqno7+bJbeJVVjk+aXt6fhJnOGQ1EwShPO1JjisMlXJE8MHh0QBP3AkfERiQI75xvhv/ZuyjDJV/U5dgB9D+k8pEPJzzznjizdl9ckb9OFhOFRuKsFBn0rdHXQizt3cpA7AfOM2Wy3xSHDQkTn1HNXWcDlrgqMuVv3xQtnnItk1j8tJFImldFOJVT5vqQ9IHUDpB8RE19gSPJrswoT5F0H/Murg2L35lHU8booFB0m9ubshtpdnPQys5ordXlR5PNOKul8yP9KEH1Y1wWF+kre4VgQH/V5wF4+mHFy+QasF+pVQhAOXbHApCP1F0CeH+WgKwRGMpFDlBVoKEozgoOUHt0zlshbWsV+/fsIKhaQEl3+o5SNNITiys7OFTxI6FiXJE+zgMhWWx+U7ytmoIjhIMAQ7VHtbmuBgeawHHa6yDD6j5h6a4LAOGsEIjrUngR4OThrltm5KgVHKgdxmrlqQFTT1HznrutgCc81x4PNMuWSgxOsbQT2zliA4+PyuFwMjZmeJuqn68Nw35ppYjytXBEtFh+s1OSia44XLtSY4rFgNRHBQgUjbSwVC4kEqL8SsoYhz6znep8LInVpGzb2BWR+7sfk0cPQmcL1Umk8TW+poCYKDX2/O5HBHILldrRl3faO+wjnT6klJcGgLjnCZPDfFyejdcYMRsXGasMZQOKMFx5Tda/DCujkYtd4aZu9eixqPgcoDxVfxu1nPiR1VSIhM+mwpmF6YcXhP9MVBvxz5qMIZtws78y8i5dR2dEubjL+OlpYb5j6mT5B/1ARHWI4PZnnC65ZcokL599T082jnrPHqAEqmKrnK+zXi40Z3Rxb6x17Aq+/lYfEej/CJ9VUekFsprSkUj0t5eDpLOSmVst1/wsqxuyFLVJI2losd1sx90NlZgLc+CkRw0O9X+E0aNcFhHf/5rP3xQul41gUMTpZkl8SDQXBIvZUf32qFRQd12oiE83h3ZaHwIcPlqmfygMJq65JR5tsSBAfH9ddXVfv8dCi8dnIWYPo2/mocJDjo8D8csaoJDuM53vKqJQgOFkJLDfq04HapJA44UVY7htC3g3lC3hSCg9u/Bjrqm/AHIjhoLaGWh5CEIcHA7Vu5XSx9XNDhKK0qmG9TCA5ahDz22GNid5b6CA5uH8syAhEcZier/m2ur73+cZvyd3x8vKjXhAkTAlrfNCZPCi69RMUYOAIRHOzP+Z9ZFRElmNUEsoOzBI8k3kTC+jJ8kS19HHC+RpFNRYaKCoNSbphnSxEclwuBx2Zk1VFK+8ZeF8qRJjiM56uem33OzdtFZUSa1QcH8Zb0MUmEvDp4kG12i6Ur9Pg/6+NKnC2R68X5ZY84IVYZKBfMWG0JgoP5n8i0frFUz6FP1Nea4EgM36UOTSE4fhYXiWc+SbMQHMRdCTwoRG2dUGZGpMeDY2U38YeUsbgrYbDwofEfM0Zj440vQf8bxLA4CGIFZq+TR24DS0eii87twZ/nTJD+NBJk3wcjOCZuTwcJGTMZ0hqutQVH4LHB34Ijl5YPzi+DyFR+ia5Ej+g8vLjIhY+OAzeqpZNP2v0quarOSq4Soy1FcDhWF4sJq5KnPHdyFGHiMrfFB4K04NAEh7mf7HDd9F1UzE5G5dh9NEsuAQnerkr0jMnCpKX5+PSC9B1X6tVVKSrNQYlR3msJgoPvA/1y0BeYuX4kBKZvk259VZma4KgSPh7b0HeE3YLT6cTmzZvVs2rWuaUIDnMlSCJkZmZi165dYocTTsppgaH8cISS4KBvCxIbvXr1ElYctFqgRYc6WPdRo0Y1meBQS1TYZi55CXZ89NFHwoKDy3uUc1RlwfFNEBxsI3eWmTJlCmg9EuxITk4WbadFTqDlRcHSBbpPwaUJDkPJCUZwUPhSCJuFMq8VwTEg6Qb2XINwIiq/03CqKbeaK6wFDl8FPtqfI5aosM95tBTBQYeSwzTBUefZ+D8re/7d8gTHu+tR5wuJajvX4Y5IzceJXKl4SwqukhtFCDKOzmp3nQU27L9u8mwuCbvmLlGh+rL/fODtXzXBEb7kBif6TSI4YiPx9PZ5YimJmvRRLnKk5xITGdTflKr0i1Ardj/hDihHq7Px/+aOB5e6cCkLJ+u/n/EsJm9Lx+mKXJEvLTjkPkCmEnjJ3YbgwcHCK/ht8lPCTwfboQkOYyxUMiNczy1pwXG1GugbdSroGNPZmY/EzdJKQ47unMpVickiSYXz+cCmL4px4EKNj3xrSYLj7RUkOKxWGdym09/JoyY47InvliI4SKIduAH08W4rXPfddaN7XDE+PAoUMLJAKLEqSWHu/HMqm7qqC8cuGxYVLUlwjH2v2ud3TtVPExzBOQxNcEiJGvR/7hhCJ5506BnoWLNmjZgw0y+F2vkjlATHsmXLRH0C7frC+nM5Dbe9JUFBQkAdDd1FhQ5EX3vtNZGeO6H4OzFlfiQb6FOEZTgcDl+cb5LgYLlqV5nXX3/dV6ZqH88kd0aOHCnqlZaWZv6pSdea4LAOeI0lOLjOsXNMmXDAJMYL71fGPLfccWXSB270jc9CF0cuhk69hK9dhiVHSxAcVJKY5+DkG3WUL23BYX22ajC117llCQ6qKi9/wC8kdck4YrV7dB6OmnYs4Vwvqxx4bz8wNr0UvWKy0dWRj3FzL4qdVpRQaQkLDk1whDeJUZ/FQlMIjh/HDUavlTGWLWAr4cE7n32AkRtT8MQmhlQRhm+ajcc2z8bQzSkYtpnXKXhkQzJ+Ne1J4YND+dSgY1KSHffEDkHflXGY9uXH2OE6i7PluSjxeL8QmrgOEiXRJzfgFzFy21dNcISDzGxYHVuS4ODyOS6jCzZ2jF94E3nqy4XXgSh3NUn+GHh8lkv4x+A220sOys8flKstSXC8nJ4r/BmY66cJjobhxNxnobpuSYLDvKTJvz0dncV4d0Ue+NGNLC9FIbdt3XMJcK6txqCpOcIZLnfi+ci0h0JLEhxPppUKB+nmummCQxMcaKoPDhIc9L0xfPhw5ObS2M56cOkHJ/Kc0CtLiYyMDHGPE23zEWiCP2/ePBG3pZaocLcV1ifQriysC8kXtbRm5cqVvupdvnwZ/fv3FxYp164ZHnICbRPLpSnKl8e2bdssZAKtIpiv8ldiXsYSqP2+CngvWHcG85If/zjB/j506JBIS/8gH3/8sSUa6zV//nxRL26h21R/LOZMNcFhHQStBIehCU/bRtbZalZHAd3eWY6hswtx0diVGTUe7rhSiB7Om8Inh3Q6Wo0h07NwoUAqNnwGoSE43Bav7eZBxu7X2geHFatWHxwSq/zmMnEZrY0kVqWFkbQ04jKq0RluscWwQjbjv7a8GD2cWejoKEF7YZVUg3FpV8VWh0pWBCY4clApWT0R7UoZt03M9ZlKc8u5nVeVpYhU7Pef564BhlM9hbnAFhy1QScUKp1dz9rJaAOcjCZE4P+mjEGJ+IookVaOWgxdPRW/jBmCH8dH4sdxDIPx09hIseXrP0Q9hv/tHCp+/0lcpLC4uDMhQsTlVrDcFvY3jqHizL9/HhuJ3yWNxJ9TJqD/EiemH9mMAlSBlh3Swq4Wq7NP4Nexj+GOhOAWHNxFhVvafi9B7qCid1GxyiI7voctTXBQRgVqJ8f3jacNOUckf34DiEw6J3wkdHCUiXTdHFn48LjhE0MRHF1Nywn9fSpQvJonrNQ3ImcW4Lyff/uXMuouSdQEh/0xqvAUmOAI9JHC2qZAeDmQSQuOzIBYpR+TL64prHrE8qmdF4D+sefRyUHns+XCKrmH4zo2fy0M3YRgbkmCg8tqqbuotvNsJTikbNZLVG6zJSqckNMfBJdM3CpMnDhRagz8QpeVhSeffFIst+DSDi7L4DajXAbBHUk4GeeE2bxcg8tr6GCT4d133/U5tAw0wW9pgoN+QlgfkhhJSUnCbwitM7744gtMnjxZLF+h01HWm/5E1EHLDvoSYZ25rSrTchlOIIKDaVhvEglcCjN37lwcP35chJkzZ4oy+Bt3mzEfgdpv/p3XzSE4mJ6EE8kV9gGJFi7ZYd3ol4QYYH1JWrXEoQkO64ARjOCI/oiTvxKLUKZgpqB+Li0XN03LByuqgLELuEVXtdi2rZ2zVpjkjZ1/Ha4KY9DQBIe1780DXqBrTXBY+ys4wVENKrfsQzPB0clRAOcmpWBLioMmy71jrgmP69xi8P7oWnR0liJla3EDlqhogiMQTnlPExy3Jji+nxCBX8QMwdeVeb5tYmnBsfjCXvwm/nFhifHdpAh8N2kQ/jpmKGK+3oq12SeRdGwjnlo/C/ctfhV3Jkm/GN1XOOE4/BFWXT+KA6XXsRPZ+Of4kfhe4iCxW4pawvJrx1CsP3VAOiP1LnlZn3safxf/hPCvQQuOuruoePDqjiWCLOFyGMZh3euzaAmX37QPDqtMVe+zvw8OWnDUJTik88aOsVU4eB1imZ/SyVYfoVPy62IrbLVL1cOxV3DgmvGBo/kEh6KpAU1wBH6O6nna/dySBMfuq8BDDuuOZ6r9XRx5wtkt9X4eVFvT93EL42yB1fuctWjnrMSjsV/jqwJDV9UER+PwRcLmv4bMxeHDh5vtPuO2WaKiJs4NOfv7iKA1AR1zcvLP9Jwkq3wGDhwotp81L9XgUpUXXnjBF4dkQ1lZmW+bVHP+LU1wVFdXY/ny5XjooYdE+Tz37dtXkA50jLpp0ybQPwbrzx1W1MF0JDxUu9hWbgMbjODgFrRcnqPKIamgrDZILvA3LglRB/vn2yA42PckVtROMex7VS9a4bBNyleKqltTz5rgsAouK8Fh9GrUahIc8kuMGix45r2Rc3JwjYsXvQf7dOHuWmGW2sFZgQ7OYgxMuox91+SKRxWPBMfk9677bRNbaJlYEn1R6+k0Ml9MWOk4LHVrgfgKz3z0EhXr8zM/m/C4bv4SFZIU6iBehBMvL8Fh7gN+oXl1RbXF6RwtOOI316CrI1tYIzHO03NduFBoKDfMmxYcL86/5tuNhflym9iKaiNeMAsOxf3xvE/74GgVE2P/CXxTlqjcmTAYd8cNhyShvwAAIABJREFUwcsbF6BAfEuUWHLVlAtC4VfRQ8R2sbTCGPXRbGRRMnrndLT0mH50M36QKAmOocsSkEdpyN9r5S5A845/gr9JeExYd/yQ1iDxkYLI4NaxYrcVLvnkri2ntuGvYob4CI6/m/EM9hReUq+UyHDNtSP4V8dw/EfKWPznkknC34d/H4Tj37cnwVHXEtMsJ3ndGIKjfXQldl3wQVPg5kIpMGzKefFhg1YX3Fll+pZKlHghykiK4OB2riSiWW6gL/LBLTiaSnDobWL9n3eo/w5McNwap/544RhLq0laTwZqU1enC+ddBslGC6FDmcDAhCvo4KBlRRl6xuVgzRFY9FDqtNu+VtvEyu1nA28TW+rz/xXMgujWFhxS9GoLjtvEgqOwsBBbt24VTktpWdGQsGPHDtMALS85Kd6zZw8WLVokLDJSUlLA5RkkLgIdnGhzlxL6euDuJUVFReAOJCzfnD+tQXjv2LFjgbLx1Zfp/Y8LFy6I38l0+R/cLeW9994TdaX/kAMHDoCkBA/mpfrBv/6Mt2DBAqxduxaXLl0SJAWtU9iHBQVU1a0HLVy4HIRLXxjWr1/v80VijYmA7fePo+oVqL3+cev7m8tsVq1aJepEwoMOYSsqzNOZ+lI37LfWTnCwfSpc9jOhDzQAKILjlMtIx/TBCA6apz6SXIqTuVYFh2/UlhOViFmZhfSdpcgsUeQGXefJa65qmbK+wLecQJZdCCJc1ZlPuy7BUSgIDsbhACV8cEylxYh1st83JlOYt3LQY1zmtfl4jd4m1q+f/Pvt2/1bEhxUMtQz5/nzbIgtXOuri7TgKKuDl2AEB/H16PRiFFZJrBKHVLILqoAV+8sQuzITy/eXo8inhNf66kQy7t2VBWjr3SaR9Rq7MA/l1Ua9xfuV4hJECX9/0HFNKFskUdgmnveeN8g6c9v4dfRsmZEXiZr3DyqFyoprczq7Xt+OFhyf1+T4ZBuf97obJ25J5tAa4v/MHo39RVd8AxZRV4JqHMy5gIyTu7D23GG44BZOR0UkD3C8Ohd/mjbGl//fOIdhxY1jls/oZajFnqwziNm1Em9sW4LZBzfhKzdzkhND/n+9phTdPnR4d1KhZUYkfjltOD7J/drSFhIhxahBNqoQ/9UWn8+OcCQ1zHVu3QRHDlyVUsYRjxyTU7cW1tlONZAMoeyhDGI6BsqmuhYclEtym9j5O6u8xLH0a8Ax90I+kLKlANPWu3DggrKc44gt41D2ZpYCdFCqtvHmhJVjNMdqlst8BMERI5ccqCUq54qMujHexHqWqKi8GI+6Rf9kTXAEeuahvJewrhjUB/mMGHjNe7eqkz9eOMZKgoNWmdZxkyQarTjXHTE+crAspjlwEZi2LhszNubjpM9HFxEqdQXG238N6BAjyQ3m3T/hBo5lS4zyd2Jr2d4ysdSFvyuCwx9/I9LKAi5RmbZNOuVlXgzUwfU2sVVo9RYcvpFfX3yjPWC2YPlGC7JR5hQkrXEXla6OXGTsY0dTfDNU4VopMCKVX6rrWmKowYCTwAHJRYI0kKJbpneuLgqarkN0BeLWlaNQPFf2KJUYOTmUJUtliY5Hzwt+jao1FXZg+4kS4YCUDiAlwVHgVZRkuczJsd7YeYJf2OdszTPF8ch9z6fWXXP5l5hrOJypiBe3qNXW4xXo4rUGUW0Ol3NrXqLyeSbBQ3VWPvcj2UDvqHN1lBTzs+KSqSfTSkQKmbZK5PDSMncQJd6Nzs5CpO2oFAqURIbEKrHI0vkXa5BZBlzKM+rE+ysO11jegRcX5sDNH7ypxPuVkuOLw69In12Vygpz5Ztx4Hw1+MXS3A5e94k6jfOCZzdwv/xgTZ14/uns+vftRnD847vDcKw6Vz5sPuhaYFPml6CVhnlCXec6IQI/TIhE74x3ccpD7xgeCUvJQQhkCTWbf3t3PjnlzkHf9xzCukPl96P4SPxp9jh87Drr3XZW5sNkJCe4NSzxLQ6RTy2uohyjNs4BfXn4lpyILWcHw7FvpfQN4i1XsB0eTpY9WJF5BH/jHFp/u8Jky9/WTHCMmncTRWQpvPKJl3O35qGTs+AWcsWN5QerRSoljymb+kadDppuYOJVHPHCX1Iq0ssLMafkKsXbEd8SFaK6GhUe6iR5wsqTsoyWmluPl/vKJvw4hveNlRNWEhxDZubjovhWKGUl6/hSRk6dunGCOXFZtRf3rIXcMrZ/st4m1m7jRuK6fEEQKLyRFEhay+dkJSn8682x1IwXoopjbiALDhIc1H2fmVeAi6USpfLdqBFjs8Iq8yC8vrxWI/zJqffnrAsYlEwfW+UCawMSruNktqFfEo3L9xb5LDhobUxdRKgIXr2GcZ5M444/1uXeXNIxYxvfUAPT/HBHXZx6sX+77f63XqLiHWv1SfdAKHugtRIcnMQv2CdZZTLLDPwK83hqoRDQyiTUX1DSIqNfchmO5csvPirt26ulQ1H/+PILTrVYb5u6E7haJi0llFJD4V7igVijO3H+Rbz5YZEYPJgv9x6/UQv0TSoGy5XWICXI99aXcTjQvC22/SwUZqxc7jJ9mxsubxzmcdwFDEzO9Zm5qjr2js3Cnkz5NUDlte44v6Df2nmVysNO59ZKcHSOq8SeGwbeqAjvy+E62iv1DuxUVobPrxTLRxROyZ+NWwbhMDTQs+NWgv2ScrDqGHCzUhJvCqs8F1YDO84BI6edwbQNReKrJ5Ut4uxQHtBrSqXw18G8Ry8sRn6N8Y5xnbr5/eruuIGPr0pnuqwfrUDo0IxfndR7o+rY03EBX5YbeRH3GYe0BYeawNv9/HeOodiDbBSj1hdWZJ24NcGRKJ12/ix2CLoufAMbbp7EldoSkQeJCWKS51LUIhPl2Jb7NR5eFo2fxUYKHx2qX+hjgw5J/33GaKR9vQsnK7JRgBpwKQstNhhIctChaTYq8UneGTy2Zhp+GT8M30+Q28yqvHj+z+ljsK34AlyoQqk3LfNinkuzj+LXCcM1wXGLyZd6t0N1fnpeAW64peyi/OG4OmOb22cxGbxebiF7+MGCspiBsqlX1IWg8pgTmufSXCBRXewx5Coni5yyXSsDFu+pxcDY0/gyx6gTy5hzSFpxsD78gLHuhBz3WWd+yd99A+BYTp2Fk73BM0twqtiQlazfuIy6X/vpVPrFZXJ5IfNiPPbBI8klQtcI3v76J9WhSkfy5zcDVoigluyGqi4tXW70ejf4PYHPiYH6Xew6Pu/6nwXHUjNeOG5yzOXYG7iObmFZEbWmAqfzJQ5JK1BPZeD1GRcQtbIAo2acwsVi4x3I9ADjlshlLMz7kcRsHM429EvqHvTn0dFRJLBKp+Xjl0F8+FPtYhzqLP4fGanXTtlmtJ9YpQ5OXZx6ceC21N83oUyjCY5Qzmp12boHvD3QWgkOei4fMrMYr6bfEGFS+k2MWVSMh+LICFfWIQMMYehG15hyPL+gyJeWefSfSja9fkHbJboII1LzkbS+CB8erMb6E8CygzWY/L4LfRLzRfousRWYmOHCpPQbItA5WM9EKhxkqd3oGluKl9PzfGVz+zcKebLmkpQpR8T0fLyUke9tVxZGLyhA9xhJgBjtqEIXZxGeSSvDK+nZIu7LGbl4MiUPHb1e3c1xw+G6dRIcVeAa7mfTDLwRG8/ML/X5XQn2bIjHBxP88ZKHvknES/1fPbrFuPDcfBembyrAykPV2HCCCngNxi3OQ4/4EmEy3SuhGK+k3/Ti7AZGLypEjziaN8v3oE9CAV5ZnIVXvXFeWFRkeb+orI9MK8XLGdkC66+k5+CZuWqXFemgT7WNzs+eX1Rmwf2wWSzLvkqMqnug8+1mwXFPbCS6LnkLAzOifeGBha81mAQgQcHlKr9OfhLdFryO8ZvmI+XoNiw8uRMpR7Zi8idL0GdJFP526pO4I7EuIUFSgnlwu9ifJA7Fn1LHYegHiXj94wykHt2KtJM74PzsQzzz0Sz0WfwO/n7qSGG18V1vOjO5wWvW5fcznkPk8jg4dn0g6jH7yFY8t3YW/nveRPw0Xvrs8E8Xbn+3ZguOnvEFGLc4V4x/HP9fyijA4On5vi/Qgd5bdW/Y7AJhFfFKxg28kp6F5xeVgzJK/R7ozEnbw1MKMfn9HKRtL8GG4xAheWMxnkjNRycnv1xXY0SKS4zxrBNl45PzStHJKcdv5vFkah44VlPv4NjNMZxjuSQ43OgeW4LRC4uETFX6Td9ERRob8pJjAMcCpU9wXOFSli6x3MHCWGoQqC12vNeaCY4ByQWYuKTQN/4RqwOTqTMazzPQMxF4SckD9USOw6+k54oxl2NvoPjqHvXjQckuxKwuwKJPSwVO6Rg3Zk0RIqbzI2AFusSUYNT8QkxKzwLfg/EZ+RiUTAtNacHRNboIoxaUYpLSL9Pz8NjMPJ+u6o8/YpVYpM6idAhVH+Y5aDp1IOoTUmenDt41htbW4YdVTXDoKbbuARv0QGslOLimVYYatBN+A9TZOrFSAlaemYbxGIfm8SoNr+VaW2t86+Aj0kXXCLKCX0+4VSfP5jSB6qS+Zj8QLct4wFu2UQfWy7wjhmqbub6B6qd+N7ejYW0x19ku162V4GD/1sWFfMb19z2fufGMrXgJhAcrXuWyqHLh5JZYVV9VZF0U3gzsmO/f78WquXzj3THKpkIvlTRZT+M9knU32ifba7yrDWm/f3vs8/ftRnDcKZx9DhYEA0kGBhIODZ3wiyUipi1Y70oYLJyC3h0XCS4/uSuey0hIbAQmN0Q5CRGCtGC5ouyECJGOlh0yH+6Awt8Gid9l/QIvofHlkThIOBRlHj8SO6iwTUwTOF1D22uXeK2Z4KC8amuSj0o+yQ8F9csKKeuUbFWyyJBrhtwy8lFjNOUqJ2z8gt3JQetMSTYr+WnWK1gn+jWS9WReLMMsc3mtZLH5rOpmxDVkq6yTTOf203/Cd9lfayY4JDaMZ0lc+D/PQJgLhJfA8Qyc8neWx/MDTjc6OOkYtFjgVRIPxJbCmj/O5G8qD3O9WZcHTHqy/M0/PdvIe/7vUiDcq7jWujekfaGOowkOG0xudRV0D7RugoMCkttemYMUmoEFoBS8htA2p/MXyHWFLtNRUZHKin9eMr3M25wvr/mbyp/p6v4u48hBR5ZhtM3s+NHcLlUfa34NHTjrts+cdyiuWyvBIZUJ43nK51UfTtWzURgLjpdgz0ni0B+rLFOWK+vkj0XjN+Yr8+A9c/kyjiqXCo/MU8UJjL/Aeal3QrU3fM63H8GhLCgUyTFIkBwNnsh7t10lscCtXRVBooiGBpEmzMNEkvjnIfNi3maCIzAJY5TrXxeZtsHtagTJE4o8WzvBYZY9ViKhflliyCPKMxXql0dKphrxpawz8pLpzXWi7FTjN2Uu9YC6v/OemnAGl7uGDiHbFliGUw7X3w4lu+12bv0Ehxoj1bkhz6kuXiTeGotv5qP0AeJd6Zq8lvWxvj+yXIldVV/GlVg19AO2Qf2uzrIsK75kfqosebbmZ41ff/tCHVcTHHpurXvABj3QWgkOJeDIUKvQOFM3Ix3Tq/xufWZca3x/czxVH3X2z1Pdl+dAy2JkGSqef3nW/NgO+vcw2mP93d4DhbmurZXgkG00ng+flbndt7pu2vNVOLWW5V+2FTfWuKxXY35Xcetrj4rDc/24tjdubzuCQ1hPcBcSBmlJQbKhKZN2Zc3RlLS+NN46+JyHkmgw32tI3VQ7FEnR2PQqnY3PrZngCCyf6sqwwPJIyWM5dgaOE1gGSdll/Bbo7/rknFWec/wPVGdVP3mur37msvzrUl86u/3WmgkO/2ckn1Og527gSj0fK14aN24yrcpHnv3LNHAmcah+J3HBEOx3o57tTXHqx5+RV2Pab62/UW4o72uCwwaTW10F3QOtneAIpZAzylaDQlOEb3PSKidVzcvDaEdT6t9yaVo3wdH0fqpfaag/31ut8W3us/+m829u/b6p9LcdwWHjSbyP9NB1rEM4tXaC45t6v7/ZfM3jtfm6flkeqE6tSf62boJD6WqNf8aBCbCG5tM8fAXCXH33guOR9fh261JfPZv7myY49Nxa94ANekATHA0dCHS85gr95qbXBIfGYHMx9G2l1wRH06w3NBnx7fabJji0TP22ZGJzy2nNBEdz+0ant9d7rAkOG0xudRV0D2iCw16CUQ9UwZ+HJjiC943Gjb36RhMc3+5EXRMjTetvTXDYS25oOR78eWiCI3jfaNzYq280waHn1roHbNADmuCwl2DUA1Xw56EJjuB9o3Fjr77RBEfTJtyaqPh2+00THPaSG1qOB38emuAI3jcaN/bqG01w2GByq6uge0ATHPYSjHqgCv48NMERvG80buzVN5rg+HYn6poYaVp/a4LDXnJDy/Hgz0MTHMH7RuPGXn2jCQ49t9Y9YIMe0ASHvQSjHqiCPw9NcATvG40be/WNJjiaNuHWRMW322+a4LCX3NByPPjz0ARH8L7RuLFX32iCwwaTW10F3QOa4LCXYNQDVfDn0eXti2hz7xh0cBb7bW0WPI3uT903ocCAleAYjDsTdNB9YD8M/DA+EveM6YmfD05AR2e+dycDtaOBPhtbY+q+CHVfdHa68JsBH+I3A1aAZEco5LouU+sTDcGAJjj03Fr3gA16wCA4dqGds1oH3Qe2xUCXty+hzb0voIOzxLZ11O+QliHEQG/HSbT5Y0fcGdMfdyRG4Hs66D6wIQbuih+Me8Y8hJ8PTkJHZxHud1broPvAlhjo7CgQ5MZvBqxEJ0eB1gG0rmpbDHRwFuKPQ+bi8OHDqKqqalZo09wMvon0TqcTmzdvtsEUVldB90DwHpAERwraj92O9s4yHXQf2BYD3d86i+/d+7z4eqOxqt9VO2PgL44jaPOfHfAj56P4YXwE7krQQfeB/TDwk7jB+KvRPfGLwfHo4shDe2e5DroPbImBro4c/P2AZSLw2s7yX9ftdtVPKD/L0NmZiz8NSWmdBIfb7caYMWMwfvx4LFy4UAfdBzbFwCIsWJiBXv0ewz93n4R/7p+Gf9FB94FNMXDvw/H47q+74F/7p+hnZNNndDvLD8pPJUP/2M+BNr/5I+5+pCd+PKC3DroPbImBnwzojZ93fgA/vO9h/OOjb+O3A5066D6wJQZ+/+jb+Ol/D8U9/98z+Lf+M7UOoHUAG2JgvqjTv/afhb9v+zh27PikWdYbNL6wnQUHCY5XXnkFs2bNwtGjR3XQfWBbDBw5egJPjp6M/4qci/8avR9/0kH3gU0xcN8za/G93z+K/35+l35GNn1Gt7v8UDK006gVaPNPj+E/nl+DP4zdhD+M3ayD7gPbYeCPL6zHfwxOwHe698I9owfhxy/ooPvAnhj4xehBaNOtPf66x9v4n9HbtQ6gdQAbYuCAqNN/j/4U/9pzMnbt2tn6CA6yLnqJSvBlEfoX+/SAB8BrzhR0GPsJOjhL0V4H3Qc2xUD3t87gjnufQ2eHSz8jmz4jLT+kDH046iTa3PsG2r/lQkdHJTo43DroPrAdBjo7SvDAqK1oM2gAfhb3KO5KGKCD7gNbYuCe2IFoE9Ee/3vgHHRxZGsdQOsAtsVAF0cu/hTJJSqfa4LDPtNdXZPbrQcMJ6M7hcOets4a6KD7wI4Y6PL2ZXzn3rFiULNj/XSd9HujMNAn6iza/Ps7aPtOIdo53WjrrNJB94HtMPCAsxR/fn4b2kQMwo/jIvG9RB10H9gTA3fHDUWbiC741aBF4C4VStbqsx537YaB9s5i/HFIGg4f/gJVVdXNIjlst0RFW3DcbjRB+LbXSnBU2dYrsd6dQu9OwV1UviO2idW7qOj3wd7vQ5+or9Hm399Cu3cKxNab7ZyUrTroPrAXBmhx1dZLcNwdF4k7EyN00H1gSwz8NC4SbSI64W8GLdS7qOgdVGw9V9HbxIbvnFjXvBX1gJngaMj+zjqO3gc8VBjo+vZFQXB0dBYjVHXQ5Wr8NwQD/gRHQ9LoOBpb3zYGuCzVIDgG23Jiq0kXTToRAwbBsUDspPZtvyu6PC2fG4oBTXC0okmybkr49oAmOLTQbqjQDnU8TXBorIYagw0tXxMcGqsNxUoo42mCQ5MH4UIgaYJDy9RQysrGlK0JjvCdE+uat6Ie0ASHHjQaI7hDGVcTHBqrocRfY8rWBIfGamPwEqq4muDQBIcmOLSsCpX8aa3laoKjFU2SdVPCtwc0waEHt3AZZDTBobEaLljVBIfGajhgVRMcmuDQBIeWVeEgq8KpjprgCN85sa55K+oBTXDowS1cBg5NcGishgtWNcGhsRoOWNUEhyY4NMGhZVU4yKpwqqMmOFrRJFk3JXx7QBMcenALl4FDExwaq+GCVU1waKyGA1Y1waEJDk1waFkVDrIqnOqoCY7wnRPrmreiHtAEhx7cwmXg0ASHxmq4YFUTHBqr4YBVTXBogkMTHFpWhYOsCqc6aoKjFU2SdVPCtwc0waEHt3AZODTBobEaLljVBIfGajhgVRMcmuDQBIeWVeEgq8KpjprgCN85sa55K+oBTXDowS1cBo7wJjjckP1cjfudDBp3rbkPNMGh8R0O+NYEhyY4NMGhZVU4yKpwqqMmOFrRJFk3JXx7oHUQHGrS2NyzXQY6/3bYpV6hrUfrIDhqvASH/zNuzN+hfQ5K0WjnrIYKkrRhG+xRt1DXI/wJjsbgsb64dsGDfx3tUq/Q1qM1Exx3JEagJUJYEAAJkqgxtzcs6p3YcILpp3GRaBPRCX8zaAE6O/LDcKzxl0HN/Tu0ssMYY/3bYZd6ha4emuAI3zmxrnkr6oHwJjjceMBZgU6OYnR2FDUrUNF7wFkZ8kGTdTC3h9ds4/1OZQFQhXZOGYwBJnSC/NusQ7gTHO2dZejUTJwyvX2wWoGOziJfmzo6SrxYvT3wWB/2w53g6OAobZY8pTym7OrgLAu5TOVzYj3McrWDk3K13Fs3Kui3J2ZbK8Hxg4TB+FH8YNwdF9ms8OO4SDCvO70Egm1JA9YvwUro2LaujSA1zG0Id4KjvdDtSnzjZVN0Vo7/ncQ4aw9dtaPTaI+Q9w57yPtQy3NNcLSiSbJuSvj2QDgTHB2cJXg2vRbrTgCbjtc0K6TsBro5skKs6FaiZ2KJpT1sG9vItlJok9xoqwmOED+nxk+GOjqKMHkVsPFE83C64TiQ/CnQxZEb8j4YOqsI647x3asV796SQ0D/aYWC5CBOQ61khLL8cCY4OsZWYvau5svUj04AYxYUhJzkeMBRhbfX1HjlqkfgddFhoIfjuteaSllV3X6Yba0Ex/+KHop3j6/H4pM7mxXmnfoU/7nwJUGWfN+mJMcdCRH4buIgkAz4ZfIT+MW0JyQp00QiwUwq2Ok6nAmOB5xVGJhcgOWHgI3HKYOaqgd48MEhiLxCOb5xfO8V58K8PQB1ErZn7QngrVUV4gNMKOtmh7I1wRG+c2Jd81bUA+FMcHRyFODd9UAZgKpmhkPZQK+oSyGdlNFS45HkIpSY2lIK4J31QCdngaibst64HSeQ4WzB0cWRhwX7mo/TCgCfXlWTs9BOyF5YmI+SGqNNF8qAx1ML0N73ZTy09QulohO+BIcbneMqsf+G8VybKlsLAcSsykdHZ3FI5Spx8N5B6zjxdTnQO+qst17KxPr2w2trJTj+PmoYDniyUYnaZoV81KDnyhjcHR+JO2nJQdJAnL3XNiERfhkzBN3mvYb3b36BDiuiBCFjJ3KiJeoSzgQHZdCTKbm4Xgq4TfpdY2Ur02aWQuQVyvGN+me/xJv4IhugTsJ2UG99b181OjkKQy7vQ9k3LFsTHI2YJLtcLqxZswYrV65scDh37lwjSggc1ePxoLy83PJjVlaWqMP69est9+30R1VVFWpqanxVqqiowNatW0Ufsi/D9Th06JDoez6DljrCmeDgOkzHeilcAbbEPxADwYI17pFsoE/UhQYLZkU0tKQgJcHRP7lADIBANRj4f9R6T5iuOW3ZCUO4EhzECi0u0vfxrbXizvi7YThlrN1XgQcdVxqM1ZbEqDmvFxdmo6ra433HqnG1FHgiNRdcimOOdztehzPB0SWuAocziVU+W3+8NgynTEfiOW5VFmi9FBgDTSUWuFzP7Vuq1xCyd/nBWiFLlVw9Xwb0jfoqSL1aVm4Fbrs9ymitBMfvoobhSE2OVJMI4yaGctSiz4pY3B03xEpwKLLDazkhlrB4l4k0eTKv0qtzMPJE/Z4QgR/GD8YfZr6AmINrcBUVyEc1eq6MBZfWNLkewcoN8f1wJzhGptxENr9YiaMxctUsh4GcUuCplJshkF1WeT0g8TpOZMtXi3KV5MvyvcXgh0c7y7xvo26a4FA4b8D51KlTePDBB9G5c+cGB5IhzT02bdqExYsXW7I5ePAgunTpggEDBlju2+WP06dPIy4uDjk53sENAEmN4cOHiz5kX4bjcezYMfTq1Us8fz6DljooZl91puL+sTvDTiiRKX57PVAEgDScOShW2dxPHCYqIRlnc1xe788GekYZk0blPFEqz9L/hSI12jndaBvtRluTXwx/oSnTc0kJFXEV+Hfw9d4kOPolF6MYkuXngEFWXFhwWAYNw7ljoPzqlm0PZdq/jxr7d3gTHC7M3ye/IhNvxCeDuua00XyQ2CJW/XFKPHx8DejuyPS9r/J5G5iQ/cq/zfgjBgJjry5e1OQxcHz13MYsLEBxjawnsSotOPItFhw0zWV8VUeV1nzP/H4Eq6M5XThchzPBQQuOvTfqYs+MVcpSHjzz610guZoHwLG6CFynLZ+3gQMDD3J5iBmrwZ6vIX8r0c7JIOWwkVdwOffeIYBzC+KU4XQ50MtxzvcOBaqfGYuB3xHDL1KwOtv9fmslOH7jGIbdyEYhalFkCsWCeCPZ5bFQd+XwgL+Z4/I6C9XosTIGP6YFh3eCL5aqkGQQ5MYg4ciUBAfvN3UZC52DMn9zHuqeKledVfk8/+20kThUcg2V/JBHf4RxAAAgAElEQVTj4Ttbi16a4LC813Z5B4enunCpzNABzGM7ZSh1cXXwmvfMcXhN0pgfEkakuExtVMSD1AFUe80yVV4HHs+V/Awc35yG18Zyvn6JWfg8R9aRMpV6+JJ9VXUsOIz8VT2VTsDxoK6OonQG1Y5wPGuCQyG5AWdFcPTo0QOjRo3C+PHjbxl27drVgJyDR1m4cKGYTMfGxloicaIdGRmJ5557znLfDn9cunQJ/fr1E/W7efOmr0oFBQWYMGECHn/8cZw9e9Z3P1wuWP+nn37aR25pgkMKSBICPadU4Km0UjydVoSnvGFkWhGen3cTG4+bhwwguxxwfHgTz6S5fHFVmshUOqIzTKkplNs6a0RoJ4S6FMZtnbW4zwkReF1X+CoyhPE8+HO0B392QgT+zTyZd910bJMb3eIqxLIbx3oPGLgE59GUWj+HeMxD1k3mxzLpdIoTU9bbI+rHcu+LrvWWxzjhrZCHK8HBZ81lG49MK/fh7ul5RRAhrRDPLyzA6WyrNPrKBYybd8EXX+K0ECPTijEoxe1b52rGqYFXqUgorBIP7QRWzbiTeFB4UZj5n2iFVYUbictAeO07tQzvrIXAKbH66mrgoURab1hxRp8xbb07rpjzkRg2sMo6SAXKmt6cJlyuw5PgkPh4ILoSkSmUq4ZMVdeUq3nl6qudtI87chMYO++ywLOKx/OTaRXonVTkc97sj1UpV2WZCn8Sq4YSbX7eRnopg5VcNdIEx+qIRbWCKCZOaRH38hrUsYoz8pfy1UpwWMu8z6nej/DGamslOOhc9D8XvYR2Cyf5wv0LX0XHBZMxatVMXPeUCnJOSd3pBzag0/zJvrgq3X2LJuGX056oQ1woIoIkxHcTI/CdJBl4/T2SFQ0hO7xxzHm0SYoAA/MT+XhJD2EhYrKgEORHQgR+N/NZlKDGZ6BSilphwfGj+EhJvJjSKIIkXM/hbsHRLbYUw+eW4qm04jqyNWOnS3zwUHjkxw/eeyqt0C9usciDeUnZSPnprw9KmWoeXw15JXVnJVcFSSx0SX/5Bq/OQFlslnEyb6bvEluC8StoYSx1AH6Ie3IBHf8r582S0Db0EualZLv8iKJ0lD9HU6euFfqxubxwJTs0waGQ3ICzIjhoNXHlypUGpLBG4VKTxh6zZ88OSHBUVlaC5EF2tp9G3tgCAsRvSj3Nabgsp2/fvnUIjtraWuTm5oJLO7h8JdyOOXPmoFu3bprgqOO4kMK0Ujixo5d8msar0N1xExliSYDxtK+Wce3iTcEwq3jqTLLEEKwyX95jaC9+kwMDFcKujix0deSIHSQeMAl/OaBIxZfxOjtc6OrIFs5L6cC0syNPOAsla03BLhV864Ajd1EpEMo3l+B0diifBsYgwzIZT9WPExKDoXeLNLLcm2JpBOsv68aBUBI1aoALp3M4ExzElsSSxCjxKkMpuscV43OxJMDAKte29ok6I+IojKoz8zGwWmXFgviyXQ0qBvRqTtwRq9JJrYEhNZkjHviFvYsPqzfRzXFT4I/lyXiBscrfOztdAufEKpciyLopTMs2+3Aq6l2JdtFUZFjHci9Wb4p3inkQ18HejXDCangSHOq5EVOUe4Y8VdeUqzdLDYKDlka7rgI9HRcE6abiyTMVXWJV5euPB+KROKDMykc3Rzboq4ayU+Gb+BMYdEhFm7v2UI4q+Uas0pKPWDKwaijhquz29PbvNOSq3EUleN244wHfoXbRJJJrxRgg63hDvFMdHZIMN7Bat0xVtp3PrZXg+H7CYOFo8674wVCByzl+EheJrmmv4WItvzcbx8vbM/Dz2CG+uCoNz8piwkwMfC+RlhuDRPyfxUbiFzFDReA1y+FvjGNO439NkoJxmP+P4wfj57GRoC+NX8QMwU9jI6UfjQQZRxAaJrKCaX4YH4F/nkGCw/iQU4Za9F0RC5IBP2gIyWLK079+dvs73AkOyjl+6LDKSCljk9blC+sMhUhaaiStcwWQqYxPuWqM5e29+iD1PKmryg9dLIdjP2UlJ9zWsZl6oPxIxvwoQ7s6csXYT5na1ZnnXVpIS+XA4z/zoxylXGTg0pQOziJL3ZTeo3QAylXKQ/lhjrpHsVeW30AXoaeUBvwYYmcZGqhumuBQSG7AubkEh38R9ElRUlICMzngHycYweEfL9jf9N1RWlpq8YWh4tI/Bsv39++hfg90VmmYjoRFoCMYwREorvme6oeysjKwbxp7kDRhvdxuGmq13MF67dmzRywJGjt2LB555BFBcmgLDqUwS4aYX6cphM2BAnexH8FxpQygmaCaRJnjS7KBSqobXWLL8OzCUryUkSvCuMUu9I7PRZ/4HLy7qhRrTgDvHwacaysQMV2a5FO5Zr5dHPkYMiMHb64owayP3fjwC4C7CTDNgt0evPVhAZ6YnYMeMUxHBdtQjDkAsuxxGSWYkOESYVxGMR5O5sAlWXEOFIOmF2B8RhFezsjBxIx8DJtTIQaYB2Ny8czcXEzb6sbKY7LcRXuAFxaw7rlisJRf0o3+CySc7XovvAkO9TXDilPirnNcFQ75ERyfiyVTlyyYNvBKokrirUdCGSZkFAosEK8vLCoQysqg5BzEr3cL7C09CLy2vBj9pypHtdUCT92j8/D4rBw415Rg1rYKgVF6Ql9zDEjdUYNJS/MxdEaOdwJJEs3AKjHSK6EI49PzMSGDwYVRi8vRI47LEaTyxcnoiLQKTMxwifoRy/2nSqeTveOy8cJCF1K212K19/1I3VGL59Ny0SPGJernX55dcRmoXuFOcEjl14pV4o9y1Z/gUE5vjTSSFGB8PkP1HPskSpxSbjE8nVaCzs5iPDYzG/M+kzJy/mcevLTEhZ7xBd60JFvK8VBMDp6Zl4fETZVI3VGFVV75RrmavLlC4G/QtFx08lnhWbE6ZHapkJlKrj67uEq0RT07tuvpBeWiXqzbuIxSPJzECUYx+iXlYMKSAlFHyvLVR4G4dWV4IiUX3aJJylGeWstT+dr93FoJDkkekBywBm752nH+q7hgIjj4+W/8jnRBKPjH59/+5AL/poXI76c+hV6L38Lru5Zh1pefIOX0Dry5+wMM+iAef5g1RhAWJFoCEgUJzGMw/mHKSPRY9Cae3zwXb+1ejjmntmPWiW2YvHMpntk8B23nTMSv4od5SRO5jIX5/e2UEXjo/Sg89eFUsSxFaZ1ueBC1fRkezXCix9J38LdTn2w1O6qEO8FBGSHHcEM+qr8T1hV7CQ75MZoER+K6YkFCBNJvlUylXvhESgEmZBQI2UV9cMCMcjzkvI5xGUVY/jmw8iiQvKUKI1Lz0TFGkbpudHIWon9SNiZm5GHalirxQZDyjYEfBxM2VODZeTnoGUe/WspixNAdu0YX4+kFFRifUejVVUswZFahRVftnUT9RNUtD8/NJwlSKj6oPDYrD9Eflfp05Pc/B157Px8Dk3NE3ZQeYXcZGqh+muBQEqkB55YiOE6ePIk333xTLHPhkgcu25g6dSq++uorXy2KiorE8pdBgwaJyTStRrgkJikpScRh3HHjxuGtt97ypaHDUcbhxJvp582b5yvj5Zdfxvbt232kBJe4vPrqq2LJBZe50F9Gfn6+Ly/zBa1F1q1bJ8rmBJ91ZmCemzdv9pEnJBiSk5PFshlaOtBfyejRozF58mRcv34dxcXFiImJEekuX75sLkJcb9y4EZMmTcKzzz4r6s10n3zySZ14vEEfH2zr0qVLRfl0Xvriiy+Keo0ZMwZTpkzBhQsXAqZt7E1anHBZDZ/BmTNnxJl+WDTBYQjZQMKF9/glMBDBcSsniCQp+ieX4kuXckcH5FUCL6WdwqazQIWXW+MwRH8I7x+E2Necg9eDMXlI3QWcLcD/z957wEdxpGnj3nB3vvXepkt7txe+i9/3/X337fnu9tZ4TQYDxjY2NiCJYIMjYMA22IDtNVgzygKRk4RAwiAwBgwYMMEkm5xzThLKOWsU5vn/nqqp6Z6kOKAZUf37tbo1XV1VXf30W2899db7ospuzHLK1ZWy46KvhbvlQMoRoG8Ew9JKxZhKMgmPgQklYq24cuXHdZdcpkJLDj4XB43ztpcLPx1Mw7XvX14EQqPPY+1pIK9a1luWZhemq2W1wHcZQOisXG3B4ZxNbhpDvrDl399tMJw6GhKCBMfTLk5vSRoYszasA5WOkYmVQjEiFjibzsHniFl3cDhDOd+VeZbagXm77HJWmuSENRNrTgF3yuQyA6NknkncVNuBGyXArG8gSAdae5iffXxyAarqDDe+XF/8ygJJTjAdw3By8EuMsn70LROzLgfDZ9/FzmtAoc21bKYhfteeAZ6JZRhc1+c1lx3o58FOcPhqX8pVd4JjbzrQ15LuGOgTI97fG5VvvmO1n8gExi4rx/VihVWJu6JaYGxyiSA4SOi+lFCErVeA7Cp5r5JtygEq/6+oB84XAB+urUFXS7mpLhKzlNOculBlXxFRVK448dwv/CYYSUtdLwYwbeVtjE+twokcoLRBXlNSnd8a18Iv2itlM8lpX20WyL93VILDK6kQJ51ydvdCcEz8JkUQDr7uc/7ucOz5zOoI7Cy8ghI747QYG88rUYcTFZn44MBq/Hk8l7d4khwkN/p/Homvcy+hqKFa+AQxG1oT09VowI3qAqy4cxiPzn8bD5vyeSrxI2SgDrUOWW3UQOKUv99GJfotm9ZhIqoEP8Gh+k93WWFD3KYSB8Eh0SQJDlpduKdVecgjrSa4FJvpKbso4xbsBVK/rUS+yVid145kAf1nkuCoERYdE1dV4VQOUF5n9v/B8u0gFinj8muA7dcBksdqko3yjPrqwNgs0Dk/p3WZP30crTpQIyZEmIZWbu+lNQg/IrzOdBdygGci72LuN8DNMqBWCnPhEYclU78+lweMX14u5Hggy87G6qYJDrNEauK8rQQHrR82btwoLAHoxyMkJEQs43jppZfQs2dP9O/fH3v27BG1oL8H+th45plnRHo6tuT/JCW4cXDNQbbZySgJDf5Gh6QkIpgnr9PigOd9+/YVhAGdlvbr1w/PPfccWDbrwvteeeUVF6egLKegoADjxo0T11UdWA/ey3u4kxyh1QQJDhI36nlIcpCgefXVV0FCw5eTUS6zIcnDvFgvtgvvY3352/Tp08W94sEdf9Tzf/rpp5gzZ45YOsLnND/PgAEDcPbsWfNtLT7nO4uJiRHtl5aWJu5nm7JemuBwFfLeBE3rCQ6SDGWgDwSlzJbbgGOXcsRATakPPHLQ+PtVOcLMjuXN3dWAygbVWVCks4fhznN2F9wp0e0ir7UngO6RhrkhlXlGUZE2RDId7+YaR84w8jm7WUqxcHux6DSYDzuFQzeBM7eoDnHjcJLl8E4eudnF2b5bQK9omlY33ml6a89A+C3YLTi8t2FzCQ5PzJPgGJVYIRQHiSugpLIBO47nOH5TOGgAB2xjFtxEZ0sN+sSUYtM5iRSpX9BBncILUaQwK69y8Gj5sgJdHaGK1XOMT85HjYIY7KCFlJlA7GPJAAe/RL/C/Mod13CzQH0jvFmVJ9HLEhmYZeGeBrG86gmL53Or8gP5+KARHH0s6U0O8GlhJHEqZdvdglpczDdjgcMy4EYxMHR2tjCpHjCjBAdvSOVY3kusKhln4JvXeG9xPfD2skoH6WxgJ+2IRLWAIuy4VsklYJeddSaZyGVhqn6UwZv2X0eWiHpgluUsmyXJjf9Fb4GYkQxkPPqqmyY45HtsDsFByw0uPwlJi8GN+hKH5wsnZBR0xJEIIZ0X/90G/Dx2qPTJEWcQHX0SP8INlBlI4g2OSRGBLuf/7Lvt2JF/Cf828y2nNcjTiz9GPmWnFJsKjvLI3xqAPFTjueRpYvmLk6AJoiUp7nXuOASHIZfUdxm3qdRBcMhXKAkOLvfwTGv+jXrh12dsDt1UysAj16pQQsHEntfRr1OCbT0Ph9VwLT5cVYh82TE7sFML2BVVoXQBJpA6JiO3PRvpKuMHxt3F6VwFQbvQW9MOVLgQHBPTZHQVlc/tPBvS9mSKCUBDL1b6BoEr0I+r5cCgmOZHNTS3SSCca4LDAavmHNpKcGRkZIiBO0mLnTt3IjMzU/jRSE9PR1xcnBg0kzigJQWXf9DHRmxsrCA4pk2bJv4n4cBNDfC9ERzMIywsDN9++63wFXL9+nVMmjRJ5E/nn7w+a9YsYeHAsmklwd9Igqxdu9bZFLTcoIUIB/NvvPEG+PysE3fmyeUzvXv3BokEWktwKQfrd/jwYUHMkKQgwUALiLq6Op8EB8tgRBhaSTAEa1ZWlrD4OHjwoCAseM1isTjrZX5+tiWJoRUrVuDmzZvg8/A+Zfny0UcfudzX0n8YBYdETXh4uNNviCY4Ghf2ZsHmT4KjwS6FOMUvVWoqvxTJV0qAkDlyicroRenIF8oGr9hEumI7cDwL+Oo8REdAiw8pwKUQv14KhM11NelzJzjY5bgSHCUuBAdzrKwHbHbpYftSMbDtAnCzQnq3lriT2lKOmBmlPwZPc0Nz2wXquSY4XPGvCA6hyzgUg3o7UOuYbSYSaQFEDB3IZNSIW2JW5MPPssVAUCoYDQLL6VXAzssSq8SQJNkkeviXs9v9LLdcFK6mCY50QXBIDkQqSmUOqw1ac5zMAnZfA+7WqAEsS5JYTbcBwxLuCIujQMVjY/XSBIcrVtlW7gQHsUq5xTdODBNzxOvGi0Afaxbo74PRTyR++NcusJxZB+y4BOy8BGTUuBIXxPy2a/J+8/tJO2IXOFfysCmCg6o9rZOYH+X64XTg2xvyXIh5B055fiYXGDSD1niezxzov2mCQyKiOQQH/WT8dukHuFpbJCwnpLSyIwfV+KbgKlZdPYjz9UWodpBtxBBTjvkmSSxX4YCdvjP+atZIfJVzDlxOwo203vHyTEQcWIf3v0nBR3tX4eus8yjjAJUAs3N2vB5zz27HI7HSJ0jXpVNxETUoRL2DQBZZifNi1CMfdTiHMvRM+b0gZdzJgmD8XxMcnvKFlr1fn6l1meSwNUiZKmSYQ7bSRn7KimxhhTFwZqGwkpCIoYST1sgXi4GtF4CD6UCxm8UaPdZ8nGaO3FKLgXGZQvaxHJZI+Z12oNwZJpYWHGaCg6lsdUBNvZTzd6ulHCdJQh5Zfg2y/2c/kPwtnfhLnx2BLkfd66cJDgGK5v1RBAcH/M3ZuYyDPiHUxmUhHCxzSYi73wtaCnBpxmuvvYZTp06pWwSJwLLco6g0RnBw0M8lHOaNfjFUnbm0hX4u1EZiglYKvE5rDLVxWQmtOkh+uOfHNLQyGTlypLiPy1/U5ssHhzcLDi6rYbmsM4kT941tpiw5Dhw44Lysnp/3RkREOJfJqAQkbdTzcmlMazaSOIMGDRKWM2wLtWmCw1PAuwsW9X9bCY6LwoJDtbwcCtJEet0JIH5jKdYeB1acArpFSN8YiYeA3HqppHNAea4AeH32JTEo7B2eiafDr2PmpjxUyWhuIuOCemBcskE4uFpwyLJ9ERzGV8R0duTVAgt2lOG5iOt4ypKB58LPY/2JBmECKDsOSYAs/qYM3YQjqOa3pWrT9j5qgsP1ncklKhWO2RsDq9SJrxUCaYft4MwQ19R+uAHCLJVKx+enAMKbg0oqEt/dBobGXQItLnpbsvBc5C1sPVPvUJpkvgy6PSTmqssgzpXggBcLDjPBIXHKuuVUAZ+szET/8Ovoa7mD0LjrOHzbPFCVClfUlyUu5bU3/lpSviY4XLHKtpuUKidJDKRK8uJUNpC4uxoJX5Xi6+vAhDQIB3cvzCjCd1lyaROxSiU77UAlngu/ILBKvL4YdRmHrkmSjvlS2absHTQjywU7JDgoS9XmjeDgsjBjk+nP5wAkr2nh8XT4Tbw6+wZulyjSRSrlBdUQvo9ago9ASasJDvnGm0Nw/DJ6OBKvf+skN2hZkV5bitc3zcffWYfjr6xD8Wj061hx8VuUOGg5xjc5VpaBf58zVlhx0Gnpfy1+FxdstGaSeM2qLEa/uZPxZ5FhwgkqHaH+nWWoIDQKUYc79aU4W5mNRbe/w1/MHSlIkr+MG4HOS6diaGq0IFQUbkmuTN22DE8v/ghPJk3GX8W/on1wBAHx2HoLDjPBoVBgR1GdnLCYtbUCi3fbsPmWnKDgMpPRy6pws0bqqiQlsqqBj1bcEToq+39a401OviF0SpUjZefKw3CRqSQ4SE5IgkPm505wvJdW56KfUBetrgc2nAAGR18VcpyydeH2IrGEVZXHdMeygF4xhoVzoMjM5tRDExzqTTbj2FaCg34zaG3ACCM7duzwKJGEAQfjytkmE/hyMqoG+N4sOEis0LGoeaNFiBrw0yrBfaMvC15XS2B4nRYctMxgvb057uT1MWPGiPtaQ3CwTlyaQguNZcuWuVfJ+T/9abBuH3/8sbNt1PPz98uXLzvTqhPWTT0vLTtaupFwok8P1o1kifmdaILDU2n2JWz8TXAQ1ZHrSEYwDCadNFagZyzrI8NeDZlTjAlLMxG7sRBrDlXjzaVcB15jChELDJuThzslyqRPKuzvp9x1RLiQkQsMCw6JnOYQHOwMEvfRAWChMzwsnVe9PC8HGWVGeRwkbD4po6z4ardA/l0THK7490ZwCAKhAhiXxKVTEqvdIsvRJVLOhFDBGTavCBOXZ2PW5gJ8cRIYupDpDKwyVOz7KVkuCgdngN6Yd9NFwWk5wSFJjNk77OgZUeLEKsN9vpd8VwxglbwkVlMPegvD7NoGgYpXTXB4vid3goOK8c0iCEd3xKVwshxpQ9dIeW+vyFKMWliAyak5mLetCMuPAi/MLHbBDZfbWVZnOs27if+rhUBYQpaLH46WExxAZhUwNilP1EuFBqefpIi1WcL/ErFK2UtLDzpyDlQsNlYvTXBIidMkwRE7BL9d8B6uVeY7l6aQxJi6dRl+FBMmQrs+FD9IRE/533PH4GhZhsAG8VjTUI9Bq6NFVBMucem+dCpuoNR5Pb+uCqM2zMbPo0JFVBY6Nv1+/GD83ZzX8eLKCHRPnIJfLxiPX84cKf1wiMgqdJ4agr+f+xbKTOtUGCa27xdR+KPYUJdQs8FoseFeZ23B4SlTXS04JJYpk5L316K3VVmV2dAjRjoWp076bGwxRicVYVpaNlIP1CF2O9AjogQqRDz7Y/rP2nVd9caSxNh8ru0EB2U+fS89H5PtlOMst19EOva5uUe8UAAMmCn16MZkWCBe0wSHgZ0mzxTBQV8PXP7BGf7Gdi6XMEca4dKT0aNHOwfe9GUxb948HD9+3LmMw70SrSE46HzT26YG/CQH3LdVq1aJwTydfHrbaInCJTV83hMnToCWF1xaQp8ZzLc1BAfzJBlDEoFLU7xtJBboQJRlmC1fFMHBZTVmaxRzHup5WeeWbCQ3kpKSRL1IrrhvmuDwFPC+hJu/CY6LZcAzFs5g0ymojAwgPVkb3vNVqFY6YFJhsKhAPhdfgpEL8hC7LgP5phCLnJGcnJohfHjwOVpjwcHOjE5ExyblOnxrMEQYB7M2vBjryrCTLKE5o3JY6qvtAvV3TXC44t8bwUFD/m+uU2G46wwdLCNZyHsVRuVvjhCcVhIJNnSPKMMLM4rxxqJ8pH6T67JMhT48xi3JcBnEtZTgIFZL64E+EbmQ4eKIVbm/YL2AbCZwbCQ46BgyULHYVL00weGKVbaXO8FB66FFXxcKyyLpwFbKVtW2BkZl1CCJXeZbgx6RZXgxoQhvL8nCum9zndYZHFDSwmLEHMpDow6tITjogK9fHJfzMcw2/RbZxBKvtxNzkCstuwVaq+sAfgvm8oLlXBMcUuA0RXAw5Opzn4Wj0q4WlkD44ei2ZIpw4snraqcz0DU3jzlntlmC5ch6MITswzGheHTh2zhdmSUKViKPS1BOlWYg4fAmhH4WJaKn/G3Cq3g4LhQPxQ/G9+JlWNo/iJWRVOgPhOX9s1uYWBIcT38RJaK8qLTuREGw/q8JDkOeKfniTnAQT4VVwJuLXOWf0lvlfez3pUzl/5wMk5N2legbU4ZhcwvxYcotnDNFdqNc5dJnVS6PrbHgoNhc/C1E+Fopz6Vcpb6ebBjKi2+DVtQvJJR5hLc11yFQzzXBIeVqs/4qgoMD3Dt37jTrHvdEJAmmTp0qfFeoATj9WNBnBqOGuOfbGoKD+XjbVHlXr171uEyCg9fdCQ4O9mnxQXJhyJAhLs5F6ZxUOShtDcFB56JDhw4V0VbYtr42EiqsG5fLqEgviuAg2eRrU8/bUoKDdeGyHPoEUT5PzGVogsNTwPsScP4mOBiOkOZ77AhUWEBJcMg6sZOgwO5qKUZ/6028uSgTs76uxs4rwPlcILNCmvAphYbvlQuY2kpwsOO5Wwa8Mi/bEY5Rdhhsl2cjM0T4UbmGXfo50ARH8zHkC1v+/b1tTkZHJrouUREKxH4qECpiDgkEI1Qnzx+32uUg0VKA/uHX8M6yXCTta8DeGxBOH3OrJFbMWJUEh6uTsZYSHJy9uZoH9LRKh7nmdnw2/JJw/KjkHck4DkrNaYLpXBMcnt+ZO8FRVA+8vzxL+ASSslQRHJI0FmFnLQ1C3vay5GJg1HV8lFYoQhhyWdXlAiMSj8Iq5SGt5EbMUbOXsh4tJTiYD9eid46QZLHCHgmB1xcXINPkpEYTHEb40kAfPAsLCrcoKpQ5TREc9L8xbkcSGhyeAnhPfn0VVpzdi4TjWzDzxBZx5PmME1twOu+Oya4C+DzrJH5pDRMRVX42YxhWph9FJSkQBVzhCNyOSjSgCHW4UlOAvUXXMfP0NhFG9hcRYWKpCYmNHzpIDhIc/6QJjqDtI5RM4dFfS1Qot64WAKEzsx3WxUoOS9kqdQGSG/XCcriP5Q5GzLqNyA1lIvz1yWzgdilQYTeWnhDrzNcfBAf79alr65x+OlQbdLMWY/YOXjU2Ehx0+M+JP5UuWI6a4DDeY5NnbSU41DIHHkl0cFkIrS1IbpDk4N0S65MAACAASURBVIB84MCBOHPmjLMurSE46BDT29bYgN8bwVFdXS3CuvI+RlAZNWqUWLbBaC27d+9Gfn5+m5aokDwgicBnbyzaCZ2Gsg4kOLiMh5siOEg2+Noae15f9/B3FY1G3W8+0trE/D/PfVnMNFaG+zUKrinWhfjd+L1BJ0QaE3b+JjiSvmUkE+lkSRIcsuOQLHSdCN86JCEX83fV4kIxUOLwb8BBHduYR3Wu3oG/CA7OWA53U+jZNs9G3cWRTGO9uLbgUJ19IB39S3DwHcfvNLDKWWfX78SGbtYysXxp+QHgVpUk2jheIxGmcOqOVX8QHMz/dIb3iBMkBOgXQW18Dk1wBBJOZV0oV72FiW1eFBVXHxw5NmDMklyfTo9pTs2lTKOX5GP9aSC7XjqjIzbccarGiZS1/iI4aAnl+u0wNHMlRi7IF/5mFFYZSUhbcAQHydFaguMn0SH46FsZzU69d2KQPi+qQGJCHnnOnf45iEm17yq9gb+1DHNGQXk07g1syT2PctQ5l7w4EztuIplShQZkohoLL+3Fr+eOk/40SHA4LEZ8ERyPRIc6ywp00qm59dMWHJ79gbsFBzFJy7OBMZkeskvJMsrV52JyEL6+XOiHXH7KrpeTI0pXVbqAwjp/9wfBQcvM99LqnVbLqk4kBGbvMJnFAdAER60IMPEQw4QG2m61WrFt2zaFjzYd20pweCuc7cUoI999950z8gf9UiifF+1JcJB06NOnj7BmIKFRWlrqsuSGfi7UkpvWWHDQ0Sodq5Ik2Lt3r7fmEb9t2LBBpGEkF+VbpL0IDndyQxMcnsJeCUse/U1wUPhSCJvL4LmaHR8yIxMn8wBGBpBdBdVwOROeWwPsvQIRHiuz1PCJ4S+CQ4VVdK+bJjgax4h7e7XP//4nOBh1hzMi3p6HA7Q3k4pxuVgOEhVWqcBwycCtEmDrmQas2X1T/C9ATMfOYolK2yw4qL4cum53hjw2108THMGAVSlX/UVwZNuAt5Yov0bG86ulKAyBaNkCZFY6ZKYIY2gTSjixypnKzSeqsH5fhssSFX8RHO4KPfHK74ehkBkSWW2a4AgOcoMD6dYSHH8SHYIpez9Trxx2xww3g7OThHDfuVSEUVDoH4P7Pns2fuUgOMTykrgQ/O85o2HZuxrXbIXCmsNMihgFyVEnI61szDyLv5n9mtNpqC8Ljv5ro6AJDkOemPuZQD33lwUHiQlOalH38/asJDf6xpbhq4tApdBVeQdphwYxwVFSB5xIB1buzsPx6wbh4E+CY2xqDbpYy1zqpwkO3xyGJjic0tD7Cf1WjB07FgsXLvSa4KuvvvIYyJPgoNVAS6Ko+MuCY/Xq1aI+Zt8X5orTCiUkJESkIQmhtuZGUaElCx2H8vmmT5+ubnc5cokMLSSYhpFe1HYvCQ5Vhq+jXqLS/E7r/hAc0uyve2Qltl+RQ0Vlc0pSY8NpYEJqOXpb7qKbtQhhM+/gcqEaWPpniQoZe+YZkuAaNYCdmyY4mo8Xb8rA/fnNvwQHVZX310iLIs/629A7shDncgyrHiou6eVA8nfAq4uL0Muai+6WEkxYfEuQGkoWaYKjZVjSS1Q828t9iUpGJfDqwjxBGiisktzoJIhjm7AyumMihKluXykAZu1oQNicPPSy5Ih9asodp0Nc4lkTHJ5tr9rX/ah9cEgJ19QSFZIJQ9fRLxoRJrcrlfkITYtB56VT8LvkyS77b5dNxv8sn4zHl01Gp2WT8f+WTRJ+MUhufD9uiPCpIf1qDMGfxw3H819EY/7l3dhTeA3XqgpEWFgx/lSFAchDLYZ/Ho8fR4f4tODgEpfn18VKHxxxwUM8NceKQ1tweH7X7hYcTREcXa1liFiX55SXxFhlPbDnOvDxFzV4NjpbTA5y6QqXZSvrf38SHKMSy9DZWq4JjmYaZmiCwyQEvZ3u27cPdIo5fPhw0P+E2hR4VbQQ+sHgwJ5bamqqGNxPmjRJJRdHbwN8tbTCXwTH+vXrBXnBULAlJTT2NzbWLzIyUjwPrRjMkVlu374tltpwuQ3P1eYtTCwjnHCJCvetW7e6WIiwDJIsbDNev3Llisrqni5RcRbi40QTHJ4C3l1hU//7m+CYtaMOXTzCq9IzdTWGLyjGTZPzUFpxxG8qRm9LDrpaKsQaQlp6hM3OBq0tlIrkDwsOTXA0HxMKG4F19D/BMTGN1kalLgoEn5nKzfiVdcIyQynPtDOauKJEEBsc7HBJyxPWBryTlOES1UQTHC3DmSY4PNvLneCgFcQrCzwJDpIcnOGL+arGEWxTovVOOfD6omx0txSAUVcEpi3l+GhFujOqiSY4PNu9MXmnCQ6pbDVFcHBJyNMp01DVQFsKuV2vLUaPRZNB/xwPxw4REVBoIfKjmFARMvbvwofilxFhIjrKj2JChP+NP4wdAlqDMCTsX1uH4m8tQ/GPM1/Dz6JCRbp/iB+Fxxe+hxc+syLp7G4UQ1osscxS1CP+4Ab8JCq0UYIjbNs8J5lCQqU55EEwpNEEh+e37Y3gOJwJPOPDgoPLrE9nGRMcJERIbjwfeQPdrKVCn6Wuykm5bcawR+is7hZtrXEyygkY+g2jg3SzXNIWHNqCQwy26Q+CEUCa2idOnOgcJnOAr8Kq0p/Fxo0bcerUKRw4cADTpk0TRMaAAQNw+vRp5z1cXqOWRZDkYNQVbveD4GD4VTrbJMHAiCn0DXLx4kURQWbcuHEigooa7JutUrKzs8EIMaw3nzM+Pl74HPFGcPBZSOJwKQwdliYkJGD//v3Cx0dsbKxoa16jjxDz5u35zdd5rtqtpU5G3fNx/189M+vgr40KofbBYXQcMpJJmVj/Z27jiC/JOrua1VFAU0Ecm5SPXPYUjq26FpiwlE6eOBsJh0OnMoxPuouiamXjIS04pqS0LYqKJjiMd2fuMIPn3P8EB2PPd7V4YrWbpRhRX8tlU06sAngm8q4IhflEhHTqSR8dC7eXiigqSplvHcGRgb3phjKll6gEO1b9u0RFEBwL810sONR3y+Upyw4SpYoOBs7lAQPiC8SywE5Wu8AsrThW7ityRvzRBEfLMKYJDikJGyU4HD4v/nv+O7hUmSt8ZnBysBj1mP7NKkFOkABheNc/jA3BP88ZjXVll7El7yLmnNuOt3ck4v9bMh4kN0iADPwyFrPObceuomu4jHJsKr2M/zVjFH4QN1iEhyUpwXSPxY3GqYI7sjwHwTHj4JfgQJ/lMb9/dHMyyqUyo7YsEGQJrU4Y0YVpg4HAaKqOHZngiN1UigrVKTt8YnDZipKHvo7eCI5v04G+FtflpOp+TvwV1ho6KPvk1MMQlnCUqZzc4CTHoKhLuMIwf46NclUTHM2XrSRsHgtbjGPHjrXZfcYDY8GhBs7NOXIwbN4yMjIwZcoU9OrVyzkAV/nQsuPkyZPm5GAo1Q8++ECQH0zH+xgW1dsA398WHLSg2LFjB0i6sGwSHX379nUSFwyVq0K4MhqKsjqpq6tDSkqKs85cXkJHob4IDvryoA8PWnyotlBHEiU7d+50+iRRjePt+dU1dVR5aIKj+QJBCWB/Hf1twRG+ngSHwTrT0Sh3rsl+bWEeaG6ttgY7sOooY4lnCAKkm6UUITMycDDDrK5DzDp+tOKuw+ESo7NUY2BCiVNZZ35kvOlTgQo/24Z5LdxeLJxC8bomONoPY/7BalsJjkoPvPgiONjpTlpTKzClsEoLjtm7gKes2cKrOmd4xi4twO0yqQSp4SXt6CYkZYjBpXpuOlZkBAlFgrgPWPtYJMGhVvLyeFD74GhSaVXtG4hHylX64FC44DslidUaJ6PueDGel0usSpCw3QjJSYzl1ALvLcsRFhxdrOVgZJVPv7ShyGbUxyA4VFQpKR9aE0XFXaFn/dx9cLBeOopK8AygW+ODg0QCSYK/jBqGBVf2oMZBObDvzamrxNhdS/HXUcMF0fGXEWGIO/QlSumyUXwkdpQ0VOKF1RHCyoPLS97eMA/FtE0ieBqAyoYafLh7BXjvn0SH4pGYUHHsnfQRbtSQRpFbLmoRsjJaWIyQDGC9/jJmuPD1oeR5DexIuXYAj0a/iseSJ+If5r7p9NnRFIEQ6Nc1weGp67gTHI3JY1rGUX5fyzPkJeflTuYAITPuCN2SEyP9o/Pw5SkZ9U/hilDecdnV6bJ3C44KZ4SUrpZSUBehDqs2OjP3bsFRitk7TLOE2smokxjp8AQHo4rcuHEDHDA3d+cSDPetoaFBEBn0ybF48WIsXbpUMEzu6dT/dDhKMoHWHGSiSAiQ5GAdzPkzKgl/owWFt03Vmfe7bwy/yuv0q+G+MV8SGYsWLQJ9bdCyQzlBZV4qX0VwqPv5O+tMB6UkN3idYXDZhmxL943LYA4dOiSWpTDCDJ9ZleOe1tvzu6dR9fL2vO5pW/I/25x5sw7+2ii4OrIFB59P7bdpEu225ttQqmXnoSw4LhQa9/F+epymcuuZvgbPz6zA2TxjoMd3Q6RzMLdoZynWHq5GntNyo95ZHzL2s7cUgx2BDD9bIwgOOtBTdSZaPQmOEkFwMA27BOGDQ4QFk6SLquOzkZnC4RQ7PaZlXtvO1IOdokoTTMee027ie4++7eGBO5iewbOukuA4mmm8c76r47nA0+E3Gn1PJNxGJlaKJSdmvPgiOIjtF2eXihkcpqd+TeW5vB7YdroGi3cU4avTdajkRbE1OHFYDsC6vgSdrAwxJ8N4jk8uQFWdUW/5fRkz8orgIInCLHk8cN0g68xtwSUdVx3OJJmW38+qI1So3KPAeCp55nwC5bwjL1HJqpC4Ue9UEhwZjWKV72ViaoETT7zXHS/y3RFb9SKvccn5qHDovMQqTzn7uPZIDRZ9XYBvrwFVvCByNfCc5/DtoXBKDBFLxBTL5U6s8R0pvPBb4zenrrOsrRdcFXqmVQQH667S8hvgt6DyCqbjg2rBcd1e6nx/fI+NWnA4lnmQHPlN8ge4W1/miJEiMVAKG44W3kLa2X04XpIuHI4yTwrYetixPvMU/mHGqxDORWND8K/zx+C74luSGLZL3JajHgfzrmPB8W2YdWgTvrx2DPlwEHyONPuLbuBf5o8xCIvYEPw8cihu20iC2EUIW5ZLkoMOTu/AhhFrZzgJkUAnMJqqX8cmOMrEhJeSKexvYzd5WmG6yxbqctTpqNvxXvaxjcljWnF+eVyRDrJ/p6w7nwMk7SzAiv0VuJynrC4ltebg4XD4LtA1yuiPB8Zm4XSuEX2FemvagUp0s0hn/Fwm+15arbNurB/TUGcxTxbymUhocxm4en4eqYPrMLG16PAEh1Q29V/dAv5vAQqSjkhw9LTkI1WYOFPky51WFiMX5nglKlTHIQaBCaWCNFARJni/dQPXJ3oSHLyvi7Ua0z4vAcNtCX1bqOJUOWRHwW6CJENhDXA5n2l4xSYGl3vOV6KHJV8ox/Rw/WJCsUgr61wrcrJsNiJPsPNYvL3AwYrzuey4SiejMz3Dgj0XmYFjmapONpHX9jNVTmsQ9czBcuzIBMdxwe8aWD2ZC/QPv9booImOukYlljvwQoTJdzwpzSZmYzzfK0PEFmP+zirhQFTikOqNJN14pnZGrrieS81a5ks5seFEvUMxkUrOhORc2OqIZZlGfl+5zu+Es/r706Xixboxj8PXa53hls31ezb8Im4I3la2Ab+ZNUdo1RQchIb5WXjeUQmOntYC5Apbar5N+U75jvmun7A0/q7eT81zymLemy7ksYEXow2l8+ZnonOFt385LaKkqfT2r3BKtFwrAwqJHTt/rUVDA/DxKkZnkY7siCFiSarrEl/E2rPhl5zfF781fnOsl9yB7YLgMBR61k8SHLkOiz2mrRXfwDvLcoOSjHvQCA76wuiZOAV36ssMhqoB+GBXivCN0eggO3aI8G0xauNc3EAZhDcOwtJBQBBf/Ff+oYVmPXbmX8K/zxsnSAbhD4PLVGJD0CNxKk7U5goyQml1UpLaRb78utRGmuMiytB3+TRxr3nJCS0+4g99Ka04HHUR5dtZfgM+2Z4qrEEafa4g8dPRkQmO+E1qmZ2UPyQs+JshE73LVlr2Uqej5HOXx97upfwatbBQyEwpD6UkJXT4P//jkf7hzt62oVYAkb/acK0ICJmVhy4W6f/oxdi7OJer4G4TknPNgVKnBQetjamLqLoxD2oK3p2MFmPODj61kr82oYO/mFAqLJu9PUsg/6aXqCjppY+6BdqxBSi/OiLB0cNShKUHJWNM1pj7tUpgxMISp4M6bwKSJMMLCZU4XSRjg6t7P1lP6wgp2F3vk8p4b0s24rbW4WqxvI9imoKdAr20DuAA9versjE1rVg4b2S+HCfkAHhuRhlYriy7XAw+VblcBjmNYT8FKy59K8zdXiPIFJXHmUJgUAJJEjmzrurXPyoH32XJpTBMy7w2nVF5ee8w1b2BeOyoBEf36BocyHLF6qE8rqO906iCQ2Xl5aQacPmIwgvP300DulpcvZSr90kC79nYPHx2TM6g8z45TJPHfBuw/yYwesFNzNgsl0IxDcePJwqB/gk1+J2Vey3GJpeiqF6WTdXE/fvqbcnCznSpLDEPKk17bxB/7hZEdcJa5UKV8RzE6oqjnjPo6jkC/dhRCQ4q1LcqDdnId8p3zHfd1Dt5N7VMzOYprNKKwqc8juAylQq8OLMAX50Hcm3SAk1hlaTH3Urgq3NASPxN7Lws5anK+/PzQA8rrSqkTCSWiCl1nVgzW0jxW+M3RxwzDWXzJq8ERxWGLywRFiAqL34DY5M5a+lKhjTVHoFw/UEjOGiFwagn51DiDOHKMK7j9yyX0UkaGexLgiIEP4sOw6DVMdhRcAV37RWCSKiGXZAVVbCjBHW4aMtH0tX9+I/ZY4UfDHGvI2+ek5h4fOFEfHbjEK7UFgp/HoyAQusLEhr0pUGnojfqS7Dq5hH8btH7+ElUmLACMZMVD8eE4L8XvYuv8i6gEHVgHgxdy2MmbJh2cG2Tz2XOL5DPOzLBEbXJhkKTfOJ55CY6/K4VuzdZwWscSG86a8i2puWxTSybnryqTCxNKbVLmUc9lXuFHbhSCMRvrcFbs8/hWqmU9ez/s+zAxM8q0dUiJ/qej8vFsTypX/I6J/g4qSgtkmuFDkJdxF0/oc7iPllIB/4zdhrymbKVei11cerF3p4/kH/TBEc7Dmp10boFVAt0VIKji6USQ+eWYUpKlmPPxPhlJegbzeUgjQlMG3pGVmLM0hJMScl03j9wZnGjgpbOmbpaKxE2txi/T8vB0j0V2HAKWLyrChOW5qJ/bIm4v3tUNd5PKXTWibOa/eNkpBUqyD2jKvB+SoG4PjUlC5NT8oWQl1ED6tDZUoOwWQV4P6VIpJmckoOxS4vRO1IuczEL/R7WEryRWI4PUrLFs3yQko9XF6gIBK5kiPm+QD3vmARHLTpH1ODNxFIn1oi7N5IqmrS0YcffJ9bAC7FO7DwbTxPQah9KAd+7Hd2tpXh5YRGsX+QjZV8FPj/WgBlfleKtxHz0iSkTStXTcWUO7PAbysS4ZcV4KpozKvL7eSa2GB8sz8GUlGwQq+OXlbp8XyTlXk2swAcpTJOJD1Jy8cZiuYSFypnEGetTL9YGj11W6WyD91NzMHR+kSNkqEobPMeOSnBwQPzOskJMFjIlS7xTvmNlltyY7HgurhgfOuQx8TLBgReFJ/d7ucSEOOkVWYbRiUWIXp+PNUfqseZIAyLXF2PUggL0iKwAneOFzSsBZSm/gcmpmXhjabmwFOL9DDtLLBFTqj8g1npajGUlJG7eWlopcCzySMnByIWUqa6YY13Zh4xbpr7XbPEN8FtwTxsM/z9oBMcfxYbgr2e8in6p4RiUGuHc/++8t0UUlKYG+CQnGOaVVhR/k/Aqnlr6e7y7NRFzjm/BkjO78Ome1Xjl8xn4zeL38JOYMJHWTG6o/OmQlL//JG4oOi16X4SbnbozFXNPfo0Fp3bgo10rEPpZFLomTsHP4obihw4npup+9+M/z3oTg9Ki8OmeNCSKeqQhbG08/u9cSbC4pw/G/zsSwaF8uCkZMWhmESYJvVDqnBNTi/BiQhGeiJAyUKUzHynbOOk2cgH1wXwh29jHNiaPKVO5zJRybEB8MSYuz8Lib8qFrpq0pwpTVxZg8Gw5Cdg9sgxvJlEHyMXklCywTiEJ7L850VeHnhEleCupDJNTckXZ1D2Gz1X6Ja3dqoUuovRZ6gCU0X1iy506hHoe5slylXzmcczSUqGLByNxrAkONcLUR90C7dgCHZXgkOuv5SD+CbGmm+u6lf8A34N7dhrG2m12LrzHVclVQtl8lMq4zJedBxVHmkh3Ecta5CBOzSZKs3v+pkzw5bm8zs5H1ZVlyzobdeA9Mj95TV1XeRh1lc8in0flwzpT4TfyM9KbnycQzzsqwcG2lu9K4k36IPB8n57vRKYx8KRww3x4zde7Na7TokNiVZJsncSgsg6dIhROXDGoMGXkzVkmlUZiUeFcpZHfhrqm8jXXTWKauDbyYpvIGSyVTzAdOyrBYeDUjD3zu2zsnO9ZYkViXGHCO1aNsmSenZ1ylVglySZxLLEu60P8uH8/zEcpyQpfEqPmclVeqn7GPa6485S/Km/XdI21Q+Bce9AIDg7sfxgXIsgFkgxq/2HcYHBvzsCfxITcGaEkRPjEYGjYR6JD8ccxodJHRqwswxu5IcpwOC7ldUZP4W+MesJ8mAfDztKJqKyXJFTU0hRFjpjrymdiXR6OCXXmQTKHv5nTBfN5RyI43GUF+z4lG6WMojzy1lcaskP1q1KvpdxjHsZ19zLU/2Z5ycm+J62VIiw3rSqULkE9QOkCSmaa68W8ZPlGny3LN2Sq53Wl20pZrepjPqqyzP2DlN9NP5c5n/Y+1wRHOw5qddG6BVQLdGSCQwlLWlc84QiBpYS0LwFoKNVUdHkfd6WINy5kjXuVskxBLhVm5qGUapmfkbf6XR2NclUamR/rbJRh1M/s+NH8XEwrOymVj3wOmUfjz2LOJ1DOOzbBwXes3pMM1yYVkcbek1QUXPHEPAy8+Hp3Mo3Cg4FRhXUDZ6pOPKr0sk5GHiqN+3WlBHleZ/6udWOdeb9rWtc07vcE7v8dl+Dw/p4836fnu5F4Ue9XHSVmfL1nA4dM521nfdxxxrxd8/WFVVVuU9dVOimjmbeqv2dZRlrPNgi0aw8mwaGIjcGCXCDB4CQSGlmiYiYHDJJDkR3yqMgHdd18j8u5I3SrIjhYB0W2qCPrJOvmiFJjuof3mfOT+XjWwZwm2M87MsFBueJNpkjZ5kuOeOv/pdxrWh6zPLkr/ZEyU56rPKS1h6oXr0l9UtaH98v0Shaq+4zrsgx1XR6VnusuCz3Tuubnnj6Q/9cEhxph6qNugXZsgY5KcCjh96TVBrn76iR8/a7uk0eVX9NH1/RkxFm++T6jTq5pVRrX697qx/yM+jU+g2ikU/mqcoLt2JEJDvP7bM4sjPndqfeqjuZrTZ3zHnMab/8b+bqm5X3qmrxP4tKcn+tzece7Ob1nft7wH/i/dVSCw/2dG++9ee+k9e/XHVue/7vm7V4fVzloxpz53DUPT7wbaZuXn5HevT6B8f+DSHCIgb7DQoJWEioMbIsJAFMeyrqCebQ0P691MOftRmZ4rad7evf/m5NHgKfp2ASHqzxpiVw1y6yW3EfZJNObZZGSeSRP+LtRL6lnquvqnqauu+oInuWpfOTR/CyyPNfrgS5PVf00wdGOg1pdtG4B1QIdneBQAqflR8mOe7/PXcjfKyHMOijLD3+VoTouf+V3//LpWAQHMXS/cHQv35H/8OQ6W9XY93cvn8c/eXdkgsO7TPRPu927vFuKp5ak9983cO+e3/v7eWAJjgAfzHslMR7wOndsgsP9+2yJ/JH3uvaf7vm19P+Wl9+47PJ3fi19nvubXhMcaoSpj7oF2rEFNMHhS/AFikBmPXzVsaW/My9/EyYtrUPr03csgqP17eA/PLS1Dv7FExU0wz9MoHx/rWsjTXC0rt3uHbZbiqfmpvfvN3Dvnt/7+9AEh+tSD00sBG57aILD+zesZIZr/9l4WnWP72Nz5V9zy/F3fs0tt33SaYKjHQe1umjdAqoFNMHhSwB2RIEc3M+kCQ5fWG2v3/2LJzUDxaOvdbq+FbL2agPv5WqCw3u7tN/7aylWW5K+JWkDq100wRG4A3pNtri+G4PgSGoywlj7yRl/fd8tlymu/ae/6uGvfFr+PMH8DjXBoUaY+qhboB1bQBMc/hLgOp973SFpgkNj7F5jzF/5a4JDY9VfWLqX+WiCw3UQrUmFwG2PB4vg0PLzXsq9e523JjjacVCri9YtoFpAExy6I7nXwt5f+WuCQ2PVX1i61/logkNj9V5jzB/5a4IjcAf0mmxxfTcGwbH0AbDg0PLTH/KtvfLQBIcaYeqjboF2bAFNcOiOpL06gZaWqwkOjdWWYqa90muCQ2O1vbDXknI1weE6iNakQuC2h0FwPAhLVLT8bIkcC7S0muBox0GtLlq3gGoBTXDojiTQOgdf9dEEh8aqL2wE2u+a4NBYDTRMequPK8ERij+IDdG7boOAxMBPosPw0JBu+OXgRHS3FjqikKloZPpohHHVbdHebdHVWoTHwhbh2LGjqK2tbdP+UFszuBf3W61WbNu2TY0j9VG3QEC2gEFw7MMT1jq96zYIWAz0mHYL33t0HLpYywO2jvob0jKEGJAExzR0ml4C6fxNt4v+NgIPA52tlXh8zA48NGQQHokegh/E6V23QWBi4E+iQ/BQSFf8cjCXqBSbHFErB5b6aDjn1m3Rnm3R1VqKx8IW49ixY20iN8hNaIIjIIfOulLB0AKS4FiAzuO/AZUdves2CDwMVAlc9v7kKn7w6Bix/jbw6qhxo9+JgYEB4efx0L99hCen56GztULLVd23BCQGullL8MSYrXho8CD8LCoED8cO0btug4DEwM+jSJOwNQAAIABJREFUQvHQkK741aAl6GmhXKVeoHfdBoGHgR6WAvxnKC04TqC2tq5NJEfAERw2mw1jx47Fu+++i+TkZL3rNghYDCxdthxPvzAc/9JrCv5lYBL+dWCi3nUbBBgGJC4fHRCD7/91D/yfgQsCrH76m9FywxUDj71gxUO/6o9/GTAH/zpwicarlqkBiYH/M3Ah/rnHO/jef3XBTwc+jUde7K933QYBiYGfDeyPh37zG/z4fwbhn16ahr8fZNW7boOAxMA/vjQNf9NpIPbs2dMmciMgLThIcEyePBnz5s3DqVOn9K7bIGAxcPLUGYwa+yEeC12Cx8Yexn+OPaR33QYBhgGJy8ff3Igf/NMg/GbMvgCrn/5mtNxwxUC30Wvx0D+NwmNvfoP/HHtQ41XL1IDEwH+P2Y//GDwXf9Y7HP85biP+3/htetdtEJAY+K9xX+JP+7yPh3p1xi/GDMYj4/Su2yAwMfDzsYPxp/06Y//+/R2P4CDron1wBMMCDV1HO4CpVi5R2eUwpaY5td51GwQeBnp/cgU/fPQtdLcUaozqbzSgMTAg/Bwe+rcP8eT0XOEzRsuTwJMn+p1UoBt9GYzZir95cR16WorRxWLTu26DgMRAr/Ai/GrQUjw0pAt+HjUID8e+qHfdBgGJgZ9GD8KfD++Ko9oHhx5k6xZovxZQTkafGL8Pnaz1etdtELAY6DHtNr736HgxsNVY1d9qIGPgmfCreOjfpuPx6aXoZK0L2G8qkNtQ1+3ef+NPWqvw2zE78cuXNqCztQSdrLV6120QkBhgZIq/HJyMh4Z0x4+jh+IHcaF6120QkBh4JDoMvxjeQxMc7Te01SXrFgAUwfG78Xt1ZAodQSWgMSCjqLyto6honAY0To0oKp+g03R6+68N+PrqCCeBF+HkfrwTOgbuNGYH/uqlDehKh6MCq8Sr3nUbBBYGulmKRAQVhor9SXQo/iBuiN51GwQkBn4cHYo/Hd5dR1HRg2zdAu3ZAmaCg4q43nUbBCoGek67ie89+ja6Wss0TvW3GtAYkGFiP8ETguCwBXRdA/V71/W6931RF2uFIDj++kVJcOg2v/dtrtu4dW3cXRMcATmY10STJ9H24+gQTXC058BWl61bgC2gCY7WdbZaSbn/7aYJjvvf5hrnrWtzTXC0rt003u5vu2mC4/62t8Z369tbExyeA2lNLgRmm2iCQ4+vdQsEQAtogqP1Ha5WVu5v22mC4/62t8Z369tbExytbzuNu/vXdprguH9trXHdtrbWBEdgDuY1yeL5XjTBEQCDW10F3QKa4Ghbp6uVlvvXfprguH9trXHdtrbWBEfb2k/j7/60nyY47k87azy3vZ01weE5kNbkQmC2iSY49Nhat0AAtIAmONre8Wrl5f60oSY47k87azy3vZ01wdH2NtQ4vPdtqAmOe9/GGsf+aWNNcATmYF6TLJ7vRRMcATC41VXQLaAJDv90vlqJufftqAmOe9/GGsf+aWNNcPinHTUe7207aoLj3ravxq//2lcTHJ4DaU0uBGabaIJDj611CwRAC2iCw38dsFZm7m1baoLj3ravxq//2lcTHP5rS43Le9eWmuC4d22rcevfttUER2AO5jXJ4vleNMERAINbXQXdAprg8G8nrJWae9eemuC4d22rcevfttUEh3/bU+Pz3rSnJjjuTbtqvPq/XTXB4TmQ1uRCYLaJJjj02Fq3QAC0gCY4/N8Ra+Xm3rSpJjjuTbtqvPq/XTXB4f821Tj1f5tqgsP/bapxem/aVBMcgTmY1ySL53vRBEcADG51FXQLdAyCow6/s/prvzedc8uUHvdnCYQ6tX8dgp/gcH+vLfm/Hk9YufOe9n8XrAPronbj+wuMurV3GwU/wdESbHqmlTgNHKwa+JR1fSJAvqH2xmmwExx8j54yyBOP7u/f9//tLb+81b296xQY5XdYgiN2CH4Y5589KMiGWEkIqGcOijrHeZIYjdVbExx6bK1bIABaILgJDhs6W6vR3VKG7pbSNu1dLJV40mpr98Hjk9YadDM9D8+ftFbjdwFQt/ZWxoOb4LChi7WyDRglxsvQzVIKDkqIk/Z+H8RlV2upqBO/v66WcgdWzQpxIA1yzfW6t+fBTnB0tVSgRxtkKnFK2dXZWtnuOOV3wm/PLFe7CLlaFRB1a8/vONgJDvaLXYQMKmmRbJX4NHQGyi7qEu35LlTZ1EUUVgNJ3qv6tdexQxIcsUPwR7Eh+FFMCH4cHdqm/UcxoSKvP3AQCI0NwNvz2h9qggO1tbXN3h9qSeL7ldZqtWLbtm0BMITVVdAt4LsFgpng6GItx5spDdh0Fth6ps6x12PrmZbvC78FeofntLOCU4N+ceXYKJ5HPgOf7c0UgM/aXopFoJQbvASHDV2tJfhoHXHaMmxuO1OPr8/UY9uZBmw904CvzgAJe4Aelvx2x8PQeSXYeIrPxLrVY8VRYGBCiYnkqMOTlnqHddW9JRQCBaOqHsFMcHSJqsH8fQCx11K8mtNvOAuMTS4OCJLjky/rsfGM8f0tPwY8Zbnb7t+Qwkt7HYOd4OhqKcWqo8CWM/YWYXXLmXpwV3hddxZ4dXFJu+OB/XzMV7UOnaZeyPv5+4GnIgvavW7thVFVbkckOEhu/Mvc0Yg6tRnLz+3B8nN7W71HnfpK5MU825PAaKxsWm18L34wHo4NwZ/PHoWfxg+DIjwauy/YrmkLDt9jzqC5Yrfbg6auqqLBWGdV93txDGaCo5ulGJ9uBioB1LZxP5oL9Au/1a5KBGfEX0goRZnpWSoATN8MdLMWt2vdlJLRnsfgJThq0cNSgGUHW47TOgBqJ8arAOxOD4zB2bjkIpTXG890oxIYsbAQna1qZpwEh7bgCC7rKxu6R9fgcKbxXlsrW0sARKwrQldrWbvLrs+OApSl6lkuVwLPhF9r93q1pzxl2cFOcPSwFCKjArCZ3q16xy05FgKYuqqw3fFAnYYktsJqNYCDmcAzUVnOuj35gC6v6ogEx8MxIfjN4ok4U5eHGjS0aT9Tly/yYp6BSgiQfPlV1HC8/uVcLMs7iV8vfkeQHYFa39bWSxMcLRitFhYWYsOGDfjiiy+avV+9erUFJXhP2tDQgPLycpeLOTk5og6bN292+T2Q/qmurkZ9fb2zSvx/+/btog3ZloG+se5XrlzBqVOnGt398RzBTHCww7NslkorwCfxthMHvnYj/Ylc4OnwG04loj0UTxIcAxOKUSNerBzW8m/4Zjv4rO1Rp0AqM5gJjp6WfKQc5Is1MOd57gun/F3ex7N96UAfy512x8OE5FzU1pHkZq3qkF4JvLIwz2XG/kFVxoPXgsOGHtHVOJ7ZWqwa+CYZF7MuG5xlb285svpIgyAKFV14vRJ4NvxSu9ervdsl2AkOytVcsgFiM7Cn5GVz+n6m5aTCR6va24KzVvTz28/UOLHKJzqaCTwbaVgbPagytSMSHH8cE4LHF0/CZVsBwK60DTvzeHzxRDBPrwNzLg0x7y30K+GSZyvy+UXsMPRK/girbh5GHmw4W5ePxxdN8l3fttSvne/VBIeSyc04XrhwAX379kX37t2bvZMMactWV1eHlStXIjEx0SWbI0eOiDq8+OKLLr8Hwj+0zjh06BB+//vfIy8vz1klkhovv/wy+vTpA7ZloG9FRUV45ZVXGn3XPXr08MtjsAOdYl2I343fG3TKXjdLCaZtBkodM9tUqNXOmQ/O4JhtjPisJA9UGvPxYB7Q1zRoVI7LzM7yzA7N1HXprMzT/F5dl0cbnrBylw7RfCm10oKjTChbnJHiTnpRWHBYzBYchnNHWT/X8mVZRnne0viqQyD/HswEB2cakw5KayMz7nhOrJLIMmOV/3vDKhXxnRlAb0um83t1xZqymHDFiMSAuuaOF5WW+GweVomTt5OLUVYv60msSguOIpMFhyzHXD8DX6oc85HpvdfRuM+17oH6ezATHLTgOJDlXU4Sq6SzFFZ5pJz1hlVOJUSsK3FacCj55w0P5t/kued7dk1jxqrEUGOWMsqCQ8nVi1XA0xZXCw73+pmxqOrk+n141jFQ8eirXsFOcHS3FOJ2pXe5ynfNPl9tPCdWiWF3GUxtcfIq8xIVJRPV0XjXrjjkdWLRuM62dk1jlnH08+Xb1xd1GlpwsN9n/VlPfov9TRYc7vmbcWpcM38fnvXzhYdA/r0jEhy0tvivJRNxvCEPpWhw2cvQgErYhbwldrlT9lahAeVuaXkv8/ivJe/CpwVH7BCxHEQtCeFyERfSohn/Ox2DOvIiYeL8rYn7uy//GHfrqMHIDuSSrQC/XTzJd32byK+ldb+f6TXBoaRuM44clHNw3q9fP0RFRWH+/PlN7qdPn25Gzr6TkNggocLyzBvzDQ0NxVtvvWX+OSDOb9y4gQEDBoj6ZWdnO+tUXFyM9957DyNGjIA/LFucGd+jk/T0dLz00kvinY8cORKvv/66190fxVNoBivBQUKg34xqjEyswGuJpS77mCXZ2HKaT2dsuVVA+OfZeD2x0CUt7w1dWCOclakOngpKJ2u92Bm9gkqE/K0Bj1sh9iesDfid45q6TykvvMZ0vzXt/L+T1S7ykencFR0bekVXY/omwLrZDstmu1iC89KCBjzpYvYvI2oY9eOgUCpO5jqy7MdFeaw/FTD38lyVMuMZAvP3YCY4uGxjwKwqvOqG09cTSzA6uQQX8wDzir9LhcCEJdcFTs33jEosx+AFdKxXId6pGaeueCB+7U788dxVETbjxY5OEZ5Ylfj2jZtnZ1Zi+kYInBKrU9YDfePoVNLAmbl+PDdjTF6ze/mejPvN6YPpPHgJjlo8GVGDkAU1eDWxzENOjk7MQkGVVLZJblDhPpkNjFtyyyPtqMQqPBNX6nSIa8YCsarw8ISFAzBDrnaKaHBeM79zymHi2CxXlUztJJZCSUyb71Hno5IbhCwlTrl/sAEeVnHm+imZr+53qZ/Vjk5WVcfgxmqwExxPWisxYnGlwKpZTrJPj16XjTKbQcaRhFt3sBBvJua5YbUcIxNr0DfWvJSK/b3s/xVWFQGmMPh4BNApgmkkyaGwwqPEUoMLXhVWJba8y1X2E2+l1DmxGr4ZmLAG6B5hrpvK31U/Mco1ZCrrKuvvvTxznQP9vCMSHCQbfhY/DI8lv4ffLZuMJ5KNvXvSVHz67RrYnHQyieQGfPrtavRImuKSlvc9ljwRP5sR5uHTQhAaDiLie8IHxhA8FD8EPP9BM0kElcf3Y4fg++Y84ofg+/EyHxIdijxxJxN47bnPLIYyDuCirQD/s3iScIzK6+73BPP/muBwedWN/6MIDlpN3Llzp/HEfrpKEsUbwVFTU4OsrCzk5ub6qST/ZXP9+nU8++yzHgQHl9rQooOkB53MBvJGK5Tz58/jqaeewqhRo8AlQVwm5G33x3MEM8EhB1I1wiSe3vrNe29LNlLFkgCjlWhCP2pBtnD4aE7Lc5IlxsDMJpRy/sZdeleXSkxnawV6WvLEztkWc+QVqRBJxZeKI2fte1mynTvvo1d0lkMFSCn4ZsWC0TG6W4rRw1IkFHCeS58GhiLNMplO1Y/nSjnnb/T30MuSg56WXHS1Fot0SlkzlxVs58FMcPCdSywpnFaAWCJOekWX4dhdA6ccOB7PpY+AKyL6Q+NYrfWCBRJadGxa5sRqVxE1woi8IvEnia+u1nL0dGBG4jVLODHtYi0TM5NyMKdIMoP8Ii4NnBaJpQh8RgNX7jjlNWJVDm75XHSWyjIlVjmDanMMHj3LM/I16hCovwUzwcE2lVitcpGpfF+9rTnIrjBmxmlpxCVT/Sw3PdISH554kDJV/q7koA3drCUCq8QDyyEOWA+JUw4U6wXOKXO5LIHyTWKVci5PRPBheoN4cMUInTfSvwEHSdz5bXjWzZCpnYV8lfmxbCnPFVbzHFYpCquuxF2gYtJbvYKd4CBOiDN3Gcn/xyzJQnGNQXDQGmLx9gKBH0bVMe7h/cSqIR95zn6fGFFYZfvxd9mv54oj71MWHKoPJgb5O/FGbBpY5T0FIl9v/bG0ArE5olKp/r9pudpFTH4Qi1KuKrnPclkesc+8ed0bBoLlt45IcHAQT1Lgj2KH4I9juIeInVYYv4gMw8it84XFhtIOaL0xcus8/CIqVFg+MJ3amccfxg52IQpIHHw/Tv72SHQI/jQyDH8aOVQcfxYVKsoiycG9cYKCeQzGwzFD8CfRIfi5M5+h+FlUGB6JDsUfxoYI8sOFrHA8G+v2wgpXgoMWHJ0WTcIjMSGC5OD9wUxqmOuuCQ6F2GYc/UlwcABdVlYGWjWQrPC1+SI4fKU3/658d+Tn54P+L9w3kgy8xkE70za1sc6VlZXintLSUthsNN7z3K5du+aV4PBM6fkL/V4wb+4sryUbn5EECuvY0nu9lUP/JiSXPvjgA7/k560M9VtwExxmBZhKsLGzM1zuRnDcqQReXlgolBRzWp4blhg29IiqxFtLK/B+ah4mpebjneWFeCYmD8/F5ePjz0uRdgxghIDYLbUIm6ucKtYJxaVXRCGGz81D+PoqLN5dhw2nIaKifHkWWHsKiN5YjjcW56FvJBV5OmM0K8ey7HdTyzExtRDvpRbhndQyDEggySIdN1LpGjK7GO+mlor6TUwtwrBFDJVbhL5R+RidmIuk/XZRP5Y3a3s13lmWjwGxeQ5Fx3O2KVgUnOAmONSsm8KoMfvWPbpWrLNW3ySlzzGH01uJU6ZV9/EoFVkq20/FVuK91GInVsctKxYKcuicAlg2VGDtSYh9+rpKvDRLLXOqE8RJn8h8jFqQj8iNlVh+ACBG1b7qGDD9ixKMXJCPHtYioeRTgTdjpX9sGd5LKRY4fS+1EKOXV+GpaEb7kYNThpAdmVgtsMxviVgeOLMQJFuei8nFeymFWHlUlrnmJBD7VSXGLi3E09EcBFR6JQDN5QfyebATHIqsdcVdA7gkIMuN4NiTLpdMuafl/5KgkLh5Jq7EgVPK1Ty8nlSO7hGleHl+LuK31YKRLD47AkxZWYKnY0ucA0fKvv7ReRiTVICZW6sEca1wyuNnh4EPVxZg2NwCdLdKkswdq2HzK4TMJE65v7lc+jtQGCKR/PrSalE/idUKPB9fKIiMl2bmYuqqIqw+CXx5Dlh5BAj/ohivLykU0S0kye36bah8A/0Y7ASHxBdxZpaP8vytxHwUmQgOOiNfuL0E3Swc8LunNyyK+M6Gzi8Tco1Y4B46rwx9InLwVlIh5u2yYf0ZYNkBYMLyIvSMUoQcLevK8UJcLiYsK8CcHbVYfdyQqdQZ2DfTd9GLM/Idkx2uuOlircYrC0qcWH03tQSvJlWju1X5saGVZzlGJxv6yfhlRegXXYQe1mKEzcmHZUM51p6WOkrSvgZM+7wYL88vEGRHoOOxsfp1KIJD+bBwEAtmkkGdM2zs8G3zUGlaaMVz/sZrKp35aCYXxHnsEPw0JhS/njcOI9YnYPaZ7Vh0YTcWXdyNqOOb8GJaFP597jj8NJqWHyHSR4cXa4qfRoXi0VmjMXBlBD7Yk4pZIp89WMh8jm3EW1sWofOSyfir2JflkhM+X5wMgfv3M19Fv1WfwvL1CtOTAHdrS/HehkUYuMKCp9LC8RcxIzTB4SV8bIcPE+svguPYsWOYOnUqXn31VXDpw5gxYxARESEcWSqigQP8d999F4MHDxaDbFqN8P/4+Hihf1+6dEn8/8knnzgH3xyQMw39c3Cgz2UtXFYxfPhwUQbD5TJ/7vv27cOECRPEtTfeeAPTp08XlhVKuTcfKyoqkJaWJgb6b775priHVg28f/369U6ChoRJQkKCWDbTq1cvcDnP2LFj8eGHH+Lu3buC0GGd3n//fdy6dctchHBG+vnnn4s82S7cmY5OXb1t6vlXrVoliJY1a9Zg9OjRGDZsGFhH+v84fvy4s2285dHUb2xrEhzLli1rKmmbrwc7weGrQ+TMhTeC45WFaobQdYaPjrvkXoOBCRW4UCjNrzlDWVADfJB4FjtvAdV2Y0aIChOd10lLjjo8HVWAFceAW6VAjTMdh6tsZUma0aQ7vxr48gI9o2e7EBycKWKYTebLdHK9JYS5KhVwPisd9i3YXi7W6PI6KcovLwLD4y5i0wWg0GmSK8tjyVUNwJEsYOSCAuFjwVebBfrvwU5weG9f6dTxmHDqaHzOtOAwnN4a1jvmPGgBMjKx0okXYpWDzxGz7+B4tnK+K/Okn5o5O+UMHnH+bNRdbLoEZFZInMlUCqv1Aq2kke9WAIu/A/pGF4oBp7n88ckFqKozsHqLTkYXKMKvVoTh5OCXGCVWufo2Zl0ORs7NxL5bQCkrLDYDqyW1wJbLwPPxDIPr/bnNdQjU82AnOHy1aw9LngfBsVc4vU13Ib+83U8yljjga+d+MhOYuKoKd8qkvwElJYmBt5OLhKzi0ryQOYX45iaQbxqsSpmq7pDy9moJEL6hxmFd4SrfVx2RvhdYPvcrVUD/8CvOOvcLvwlG0lLXi+n7aNUtvL+mDhcKpAyVKJV/mS6nBlhxRC51MVvyeXv2QP0t2AmOxtr1rSW5KDRhRhIcJIDNvjZccaLyI4lFiw+Fh88O2mBZm4k8m+Evideyq4EBM2klWSMIiwmf1eF0rpRtRKfcDJwSPZX1wNkC4L3PaMVnWI2wbPbzDAmuyqYMPiSiqEifS5zgCJ1bgqulsm78jijDx8y/DsvGOlwpkt+SO1bvlANzdgNdI7i0MTjlakciOGi14Ws5h7IE+El0KEZ4ITj4G6+pdL6OJCyYbuyuJByvyBREicSFgcoK+u4ou4vXti/Gz6PDhBXHH8SZLClih+AncUMxbs9yHCnNQAXqXEgK5sQ8a2HHLVsxVqUfw/9KeM3pOJTWGS+usCIbtaiD3bTYRkpw4lfcCxv+e8bYJp/J17MG2u/agsMh+ppzaCvBQesEDuI5aKYfDxIPJCAGDRoEEgJ0YMooI9xo2UEfG88884xI//TTT4v/p0yZIq57czK6ZMkSkTYpKUnkyzJCQkKEHwk6w+Ryiy1btog6kHx44YUXMGTIEKfjVNbHfclLZmamcAzKOnPZCZ2Ektxgnfkb92nTpgmSgRYdJBbot6Jnz57imUjQkKy4ffs2fDkZZRnjxo0TefXv31+0C4kK1p/585m5RMS8qedn2TExMaIs9+dhXidPnjTf1uxzWoDwOVn+/v37RTSVb775RkSuoQNVLlHyh5WIqhC73mD1wSGVEe+ddXMJDneP5JJkKAN9IChSosIGnLleIAZqUr3hMqd6lDQAH6/METNCNEWdv7tBkAmyE6H6w3QU4WxlHrnLqxz0bTgDdI9Usz/SLNyIosJ0dpED1+Gyc+fzconLwu3FQgHideZ89DZw7k61UMaMoQLLYh3kc9TbpTf2frHGmnilzAXL8cElOBT55qqMk+AYlVghnNEpXJVUNmD/2QKBGwOrdSgA8NaCW0Kh7hNThq8vSaRIfBAnCi8KtzxKhaS6HojeVC1Mrs1YmZCcBxtvE5vdEUXFIBD7WDLAwa9EoV3Uc+3um7jlcBgPO28mgvmd8Cg3pl+0twHdrQXOAai53GA477gER4FYoiKlmESNJ8HhXSbTGk7iVMq27KJaXFFYEPijqgtcLwLCZmUJq7UXZhTjdKbCKq8SM1L+SmQp7EhZWNYAvPMZsapmvOU3k3akwfFNEGN2XBNhYi878UUykZG0VP04wNx26AbyhAEqEal2lqeeXpJ3sV8DPaztH2K0Nd9FxyY48lyWqJDgWLSdy5SaJjjSjkh5pfBw9GKOmJiQOGDvzQEbcOSGXVhGkOB4dVG+mNwwJBnPiBt1VOfyl+tlwLD5rk6Zu1sL8fUZmwOr0sHkEUZRiZJRVGjNFDq3EDfIWDtwWFgJfH0kXRDd8ttQ34mSrxKv2TZg/LJCp/+m1uClPe/pSARHcwbkbSE4fhgfgr+IHY5ph9aiHPVoUDKLUHDfAZSgAb/fl4YfzwhzWnHQAuRHMSF4bf1sZDo0TnUvszDQLPttopw+Qr7KOod/nvmGIEu4LGZIihWlTC1hKBMr+PKmBiAHtXg8XhMcnKx337UFhwEZr2cZGRmCGOBA/Ntvv3UuD+HvCxYsEIPpgQMHCmsGWlnQV0VsbKz4nQN5/l9QQBUZwkqDg29zFBVFcHBgT3KEPiTop4PWEiQJmP7555/Hc889B6bl77Ss4ACedSIpsXbtWmfduXRm8uTJ4r6JEyeCS08YWYTWJaxzSkoKevfuLUiYixcvigE/63f48GHxG8mNs2fPCnKC0WB8ERysGwkYWrMwJCuXzdAChSRGWFiYuEbixLwpgoPED5939erV4nlIltBChvfxeWkF0pqNy1z4bMyDliEsg+fc2U5sdzqA9bb0pzXlUb4EN8HhOuhTHXBzCQ6VXh29ERwOGSwEOlUbzkRT771UDAyeTR8Z1Riz6A4KRMdBBbhWKCi5dcCuq8CmM8DB29IzunpHTCoUnHmGgiPLVmFiRWZiUOhKcJS4EBzMr7pBDgBYr4O3gC3ngIuF0rO8LE/2LPl1wPhkLlWRDirVMwfL8UEmOLy9I0VwEJNKe+CKP5JZVD6IUeWNn34S+lpuC18FH36WjWImcJAaROz5PGDzWWDzGYDWJBwMyE1i51QBZ7yvCysnVZcJyfmooQ4tNju4BMxsIdXHki4IDplEkiXK4qPIDnxzWWL1RrkcKKp8qPGk1wLDZ7V/KFz1rC09PtgEh3eZ7E5wNNgNqoJ4Y7RPHtedA/pYs/GUJRtrTkmUqoGbkrvbzkvsXClRV4geGQp2xw2grzXLSV7w3XHASpzLrWmCgzLfVi/zzrIB2y8COy4BWbWqPsxJkipnc4FBM9o/xGhLMcr0HZvgyPVKcDTHgsPAi+yHGQ1bDeiIUxJgRQCsX+QIiyGSuVsvKSkskVbaAOy7Ift/RkZJr5J4UhhkPjO/oj8Yg4zjIJ4Eh1zAaRdtAAAgAElEQVSE7YvgKMJ1E8HBunHnt3GzHNh0Fth7Ayi0SyJFlcc8N5+QViKtwUp736MJDsrHBmHV4c2Cg4SEWqJCnxgD06KQbWccFtmHk3y4iVKsvnUUyy/uw8VaTtrJa5R3t1CGAZ9ZhPUF8/mj2BD8auYoXKsrFlYWTEkfIBsyT2Hq3pV4eW0CJu9KwTe5l1FpIjDKUIeXv5wtltGQIOm/8lNcRRX4u9o4SUvnqbTpy0UdzqIK/54wWltwPMgEhxroNnWkdQb9W6jtzJkzwtKASyiqqiiajY3WHRxIMyoKl1+ozZcPDjXA90Zw0OqDyzPMG8kJkgis86RJk5zLSlSaGTNmiGvmaC0kR0g60FqD97tvJDpo5cB8ad2gNqb15mTUG8HBMLqsE+tMUsN9Y7QVFZp3z549zsvq+Xkv666W9qgEJFnU+yEp09KNUWrU/XwWtllqaipoHcMlRbSG4XWSHP6w5NAEh6syrggOEgTmjcpNejmQvL8e76dmIWlfHRKPAFwzy44/+SiQXSsVHw44j+cAw+LOg4M8OjztF34LkWuzhHmq7FKAQs42Juc4CQdFcFBRURuVEm8EhzEAZcoGZNcAMV8Wor/1Dp6yZOLZ8EtYfbgONudSGTl4WPJNObqZFKr2VlpaUr4mOFyxKpeoVDgsixRipCJ+PheYt70cH67Mw8pDwAdrIZY30f/FF2cAkm/sCYij3deAIdFX8JQlC70tWXjaehubTtY5lGyZL+ntoXFXXQiO8S4EB5ogOJiPVLUyK4GJS++gb/gd9LFkYlDMDXx3w6z8y8Fu3GbXyAEtwUp7p9UEhytW+T4mpcpJEgOpkiw4kg7EbizCxyuzsP488GaKXfhpeWFGEfbdBQrr5eCNpFjynjLhfPcpy12B12fDr2DfZQOrlNPnC4BBM7n8z6iDMSMvS/dmwcFlYcYmCZET6cCIhFtCjve1pGP4jKu4UWyQHJTlRdXAm4vzXMozlx3I5w8awUHrx+YSHJJkUIiwo7we2H0VsHxRgogv8rDqLBDmmKAYPDMX5yqAcgfRQPUhYXOBCD3P/p94HZlwEVccS1+ZK/Pfcb7eaZ1JnHCJytdnap2yl0NCTwuOYgfBoeomZScnNwbH3RL9fx/LbYxffA1ZlQbJQaxyCWvviOBc/qcJjuYTHH8bOQI7Sq87AWJDAw4W3kSX+RPxS+tQsf8mfiy2ZJ13khwkQHZkXhBLVRj+leREn7UR4DIWYodLTC4UZOBfra/g51FDhWPRn0SH4N8SRuPrgsvgIpiLVXk4XHIHn1zagj+JDpUkyYxX0GXpZHy8MclZH55k2EowevUs9FnyIZ5InoI/ixmmCQ5NcMjZfDUI9nZ0JzguX74sLAE4mKdvCZIa5o2EgTvx0RqCgz41aIFg3kgAqDp682uxcuVKQVTQYkNtNNGhlcfNmze9OhSlhQcH+8y3NQQH68Swsby/MT8XM2fOFGnot0RtZoLjypUr6mfnkXVXz8uwtS3ddu3aJaxgGO72xIkTLu+KbUuyg/mTfCGZ0tZNExyGEkwFwxfBQbpw+ppcp4NSpusWxXtl+M2XZpVgbGIOfr8yC0m7y/DaUi49qcFvI2RIWYaIGzonF7dKDIWDFheTU+6CUSyMsovFTIx6r80hODhjn7CtEt2sxUYIxQhg+NxcpJvKI/Gy+ZQN3a3K2aTrsweyIs66aYLD9X15Izj4PWeUA68vygT9F9CZXpeIanSOkMsGaG0UNqcA45Jy8emaXKQcBoYuZMQAmxM7/xMBTFqeKSyVFA5J1b4x76bLIK7lBIdUxGO31KOHtdRZHsMZj1tyByWqMIdvmc8O0fmv6zMHy/+a4PB8b+4EB7WQK7nA87FSrtJhJHH6ZKS8t2dkGUYsKMKEpTmI21iIubuBZ2eU4XFrgzP8Mf0JTE+7KyyVSPiLPIuAkARPCw7zgLVpggO4WwW8tYTEhfw2KMPpM8G6Rpan4FotLOM4aPR85kD/TRMc3t+ZOyFGXO27DvSJLnY6zn0ywobOEdKHRt/oEoxcWISJy7ORsLkY8buAXtYcUJZSvv2PFehpzcWGcwaRSzuPQ9fsfiE4rpUCL8++4/wuWGYvawHovFnak3CIahe6xYCZXKLj6vsj0HHK+mmCo3GCw7zsZdiaeOSZLCbyUYsXU8JFSNfvxQ/GQ/GDhVPRXy+cgHx7tXP1CK0sfrPsfRHRRSyR+Xq+k+CgZ67M+nI8vcqKP48aJggQhoz9fvxg/HrxBDy1/Pf4zYJ38U+z38RPY8NEZBjWiU5Qvxc/BM+4hYm9UFuA/14yCT+MCxHXmc78DMF8rn1wqN6xGce2+uAoKSnB+PHjnQNvDp5pMbF3717h08FbNJXWEBx0NOptUwN+b34p6KyT180Eh8qDCgutL0h2cNkLl9ckJycLcoK+PHhfawgORpF57bXXBLFy9OhRVZzHkX5JWIbZ8kURHFwu4k7mqAzU8zJsrb83klOK3ImMjGxz9prgcFVwfBEcZwppon9VKLHSQz+9n6udETK4y2gX0rM7861G94hyPD+zGK8vysGCrTkoqFJmrHKpy+TUDKdTvNZYcFBtKamBiJ4inYcx9CL3Wjwfk4lT2XJGn0Chgs/ZIeWwNBiUGnMdNcHhilVvBAdn/HZdB/pFZDoVcU+cypCCRpQLGYKTA8pBs4owJjEHq7/NdyHa6HRx3BJXR5ItJTgEVuuBpyLyBFFIvEqs2vBc+HlkmHh3YpWDDPP7D6ZzTXC4YpXvzp3g4FTIgu2MVMKIKSpsthFVQspUKVelbK1DJ5LQlir0iSlFyOxCTEjKwldHC4UVE/HF/ux2CTB8juuSEfcBa3MIjtM5QL84OoJUIcRtYonX24k5yDXWu4hlWvwWggmfqq6a4PDEKdvGHS+c4Ji2Os+JU2JTtaE6SgyTlLULXYDyje3bP7YEw+bm46MVGTh91+iPKasv5kCEm1V5tNaCY8Vh5pPrlKcsm+Fso7Yp/zVSVaR1KB2ZU9dQZQbLURMczSc4Fp7ZJZaAqAHCifpc/EP4MGdIWoZt5RKUn0cOxd6MS860XIIyZk8yGDWF4Wf/I/E9ZxQXyld20bToOF+Rg/lHt2BEWhy6LPkAfz/rNTwcSyJjsNjFUhlHJBVBVMQOwfNuYWIv2grw28WTnA5J1fKaYCY2VN01waGQ14xjWwkOFkFnmYzOoYgBDsKVTwcSE7TyMG+tITgYtcTbxqUk3L0N+H0RHCRdFi9ejFdeeUX46eDSDJUPfXnQB0ZrCQ76FKGvEFpB0IeHr40WFCyDdVDLTRTBQZLI13YvCQ6WSR8kLINEh6+Qub7q5v67JjhcFRVfBAcdgtJ831AGDHKDv3GwSKWCYRQHRt3Eh2lFSDkIfHsbuFgA5DrW37KTUJuw4GgjwcH3d7cUeGWua0QW1uXZqAwRflStfNQEh+u7Nt5le/7enCgq3uvnjeDguGvxfoBOb6USTssNA6tUwjkDzhk8pgmZcQfT15Yi7Shw+C5wtUhGDWI+Zqz6g+CgcnQ1jw5zzQ4ZObC14ZnwS7huWj0pCQ6YvjfvbRAY79Czbprg8GwTd4KjuEFaCnEQSFyaccr3yt8YEplEcW9LJobPSkfEhjKsPwWcyIZYKlLiiOKjsEp5eKcEGNFGgoP5HUwHOkcockM+D+v6+uI8ZJrWEdIPjSY4PN93e3+bjKJS3MooKu4EB5ecvDLruiAuJFZdn5cEHPcnLZXoa7mL1+ZnYtbXNdhxGTidDdwuBcrpH0l1/o7BoiQ4DGfKrSU4ojZVoIvVdUkf8/p4vXKWKgvWBEfwWAm0yslo7BD8cXwoNmedc+m/79hKsPjINsw8sQUzjzv2E1sw59g2XCnMFMtPiBDacsy/thd/Fimdjf4iZhj2FVyTy1goFB2Clu6gSYaU0q9HXTH2F9/ArLNfo/OCSfhFZBgejg1x+gNpFsFhJkM6gCWHJjhMgq6pU38QHKoM+pvYtGmTcP45YsQIp58J98gf8+bNE4Nos28M5qEG+N58cISHh6tiXI6NDfi9ERy0jPjoo49E+VxWQx8hDGfLkLF0HsrlNMqKoTUWHHRIymenM0/m52ujxYgiOBhdhpu353e/v7HndU/b0v9p1cKwuyzD25KgluanCQ5XRcUXwbF4L01JDUVEKW9ylrFOzEKGzc7Fsu/sSK+Rzh25JIQKDduYR3Wu3pG/CA7OWLor9Irg4PpdTXC4vmP17gLj6F+Cg8RA7HYzieAe0cKGbhGlGLUgB+tOAdn10t8F7yNOiFF1JG7V5g+Cg/mezjDPWBqDWhICmuAIZJzKutF5c3aFU88VWPGMouL9OdwJjhwbMGaJ4YPI/XukLO4RUYTxy/Kx7ZJ06kirD5JvSp46sepQvP1FcDCf3dc9CbbO1kqMXJAn/M2ob0MTHN7ft/v7vN//+5PgoHuWAeFnfRCudXjS8v+3dyVQUlVn2sliMonZT5YxR5M5M5PMGZM5cyYuEQ2rimhUELWbTRIXjAbRiMQtolJFr6xKszR7RJSouLA2YBQFEQhLDyIgIGtDb/QO3VW9fHO+e+vWW+pVV0N30q+q/3fO6/eq393ef7+7fe+//1+PfpmleHxxBT48BpAQIZlAfJrxn/c8I1BVGO4oguO5pbVqS6KRcbeA9rg2+uWww0aTEBypT3BcMGkoCkote4rsp2g/g+QFTY7azzNoibhv1aik0dFXS3bi++MHq+0itMPRb8FYbD1zAnXgFsAIennhSXArO29Mtxkn0IjJO1crLypfzna6sqUBU/vh1uAw2g+pcBWCw17TCe47kuAwWXGhzIX+rl27ou5Yqclhtqt0JsFBQ6UkH6glsWHDhhiPISwjSQ8u8s+F4CBBwm0njM9tOvEOenYxREJdHW1e/30JDtrvoDvfsWPHYs2aNfGKFfV8QzsibiOncSPFecD+SbyoWBO0eATH1DVhRWKYCYS5GoJj6PMnsbNc9/doCQEteijghOJINbB8ZwvmrjqMoupImIg3lvZuUWH90ejdkKlOo3osH13LCcFh1a2pM39dO57geG4Zoi5d3W6Q+QV65HxthZ/Y0f5LQmqyTTXsPWXAko9CWLhqn3EMp0J1BMHBhemmA84956YuSHBw24A5tAaHbFEx8vHLtSMJDhpGpo2L7kHaK9Lt1I7X3sEK5KwFSurNopCo0O45ORpvPwYser8Ki9YciC7iiOmO0OBgOqt2exMc9BREj0HmEILDn31sRxIcn4WAX4/brbQ0DVb1tRFXjW9W2nCzNwA1ZDDUqo9Y1doT9Fi18QCwYG0x3t/brPpahiJJ11EEx1N/oV0N57YTGpR+5OXGaNtgnprgqJQtKkmgJXCuGhxfmTAYK4t3m+5JXQnLejQrrQtqXpiTpAS3nNRGznI0YXHpDnx3/ODodpbzcwfh8hmjkbVhKfbWl6mwHMv1/MGWTYT0CKMJU3atxXcynEZDheCIdQHrdgnr9VvcxNow5nX71ltvqW0WNJpJYsN9LF++PGYhfy5bVDpKg2PBggWqPNTSMISLKTPLf+TIEdAVLMkHu+FSboHh9hVuP+E2FHN4eVGh+1vGJ6njNrrKeMyXnloYJjc31yT1d9fgoKYK8xw5cqRnuUi0DB06VIXhVpX2HuykhOCwJmhnS3Cova5Zp/HeIbsxL6DoDDDvg2b8ZkYFrgmUoGegGoMnHcNemxX1jtDg4CSJabqN6nHyJQSHVa/OSamf/t+xBAe1hsYsCXmScVcHQ7g+8xT2lOjJNfsO4udAJTClIIT0qaVqok63haNmHYLWWdM9jBAcZ4cZ2aISKy+3Bsex08DdM0pBrQh3++wWCGF4XjGO1FoTaWJ75wkgY1kjbskuAsmW3oEykCRmX8pDCI5Yubtla/9NwvPKB9bgwoFvePYZ9rDJdt+RBIe22bInBqdaJo14+pVyKM+tkS/cJBI2HALGvFyPGzOLlT2MvuOOYeEWM0/oWILj0T9rY7j2OhKCI3m0Nby0Fs6J4CBxk5OGxQc+UmO7We+tL9uPG2c9havnPYarbGe3eY/hsgWP4fIFj+GK+fr609kPRV3Ffm5CmjJIet7EO/DFnHR8N3sobnwliJzClSgo34vdp4tR3UL/K3pdaVaXxxFG3zl/chgNFYJDCI7IMO28tFeDg1oQ11xzDdLT00EXrO7DLPZp6NMs9l988UW1iKZxUvvhtUUjPz9fhe0ogmPp0qUqveHDhysjo/b8STw899xzyn4IiQC6ezUHiY9bb70V/fv3Vx5YzP+9CI7Dhw8rzzK060FPLua9GYd2LWbOnKnKQDsd9OZiDq/3N8/MleXi6WVzxISJd6XRU8ZlfVGbw3RQDF9fXw/j2YUkjtEqiZdWW/4vBIdzQhiP4JiyphHdPdyr/ip4BndOr8QhreCjRE6f9Bmvn1KLRU7e+VWS3iyGTC3BZ5XWhF0IDqfs7ZMzr3sxMuqUl5cNDi4CH3mZ2kbVMZPxHsEaPLy4yWE8lOF/P7ccfQJlIJZJ2NGezCPzjkcm7LoXEYLDKXsvfNr/JwRHrLzcBAe1IH4z3ZvgIFazljdEPEBoDH5WBdyddxK9AxXK8CdtIbB/fXqx5dWE49nhSmCYS6PNbVMhkZFRpiMaHLF1aMe43+87nuDYG9OnUgZXBxuw5lO9/cToGm04DAzMOoDuQW4d4TbBJlwTKMarhU6C4+MT3LJnbX09Vxscj/451sitEBxdlODITUPGpqU4HdUVAna3VOAnzw5TWhk0HmrOC7IG4cfjhuHicUOV69hvZQ5S3lHOpy2P7HR8K3Ow2q5ycXAY/nPCCPzHxHvA7RffyEzHRbm/wS/yHsYtLwawZO+HqLE2sKASTXh49VwHwXHDwmfQ0EzrHTxa8Em4DJfNelR5c6EHFfGi4k2AdBkNDrNwbsv1tttu07MC+mmvqFAaAYxHzQfavdi6dStWrlyJMWPGqAU1SQFuVzEHt34Yo57czvHss8+qR14L/I4mOEgoDBgwQJEYJF2Y544dO7BixQq1nYYGRo0GBzVNzFFSUoIhQ4ao9+FzakPQA4sXwcE4JBD69euntsPQ1sjatWuxevVqkPAhwcB8SLbYD6/3tz/nvamfcyE4GH/u3LmqTDQIS8Ow3EZDLZvRo0ertLl1h+XoiEMIDuckLh7BEVharSYr7kkdv4A9MKfMYVW/PgQ8NJcGSblY1BbVOdkYs7AY1dy9Eqk4ITicsnfL1v1bCA6nvOITHI0g3tzy6xmoRMbqWKv6v86kS9mGiEHHBvQKViN/Xa2DCBGCwyl7t2zdv4XgiJWXJ8Exo8yxRcXIkR4T5m1kX2l6S2B3KTBggl4MkjAmZq8NFmPJRsu1Nscz2aISK3sjV/dVNDi8ZZWIELPLsWdWWG0FNfMxIvb1bcB1gWOR8V9jdWD2YdAzD7XmeHTkFhW2LbeRXiE4ui7B0X9RAMciIzjxeApNGLUiHxfkDFbGP0kmfCV7EC6dPQb/h1q8W3cY03e/g0fefxE/nDZCeVj53sTheGTrErx+Yid2owqfIYTpu9bi21lMg95S0pSLV9rp6JZxP0qUhRmN7Qo0YeSKWUqbRGunpOP6BWNxuolWPni0YH9jBXrOehwkWUim0KuLlyZLMv5PbHBoHLTpr9HgMAvntlztRkCZyfHjx5XmA72RuONzK4bb2CbtVASDQUVyMDwX/CRKvBb4HU1wUJvi3XffjZIY9vLS9ga1HNatW6feIy0tLap9wXhvvvlm9P1I0DCdeAQHNTU+/PBDtaWFeRhCh/fUHqGRUe6Jsh9e729/zntT3nMlOKipQTKH221MWubKuiosLGy37Q1TZiE4nBOc+ARHredEnF8Q75pZjqO2PdmU6Ws7gL6BI4oU4cLyt9OKsLPUSF1fafPgTy+dcLmJrXIsLLmTd9wyGo2sUAvWnoEqzCiohMlOtqg4688+8UyO+47fosJ9154ER7Aaj77iNDpHfE1/D+gbLFIq6n0Cxfjj4moctRmSJFqrAPxh3vGItwst87N1E2vZ4LB7UdFpiQ2O5MBxR9rgUBocJDgC7i0qeotV7gruGreO0jDw+KJStS2FW/76ZRRjQgFQGba04iyC46QDq4kWrDeMO4i/0ZJk5GA6osGRHJiM18//ozQ4fjW+Ae8ccGKQhsbvy/sMHK+pTddv/HEs2AjUN1kfODh271FuYjtCg8MQHPQ6ROPNYTUGeNvgEDexybBoPuctKrlp+EHGUMzfv0ERxCQUeH4aqkDaqxOUh5RvZg7Czyf+DstPRD5qRwyFFjXX4acvjAQNhH4/cyimfbJOE3LsEFuAiqZ6jFg1U6X/tazB+FrWEHw7awh+/9YMlNkIjiKE0Wv6GIuwyElH3/ljUdtEU6f6OIUQsj94DZdOewgDVk/FDyfdZYVPAhsprWFICA4zkrbhygXvwYMH1ZYHLprbctq3VdizINGxfv165YmDW1eqqjh19T4aGxvx0UcfoaCgACRZuD2EHk6Yvz19Givl/+x2L+wpmvK67WkwDEkTPi8qKrJHUVsz+N7Mn15DNm7c6AjDtOKly60q1MTYtGmTej8SH/wfZcg03QdJDG5ZYT48maeb2DBxzPtTMyTeEa9c8cLH+39tba16B27Doa2RnTt3RsmceHHO9v/st8QGhzWRi0dwjFta67lXnF8Rb5x4BtuLrYkLO3CibNtRYM47lVjzcbOahOuO3XTv9GkOTFtVqexzcEKi8xaCI96EVTQ4LJxSRlqD47TDiJzeouJNcBCr/SdVoaxBT8aJRE6y61qA9fuA+etOYf2eZtu2AAurJOMy3qyMaHlo7yej5pWDBhbNYRasxqZC38BR0MOGCWIRHNaE3tS1N8ERa+TRhPf7NaU1OGqtvo51q72oHIvRGHLXUTwNDoMXZ/gQRs4pQ43t+wLzqm4E3t4exksfVGP7USCsIEoUcyTT5SqN2Pawu/N8ebNTc6njt6jEYtr5Ps6265dnqa3BUdoON7Gt48Vdf1MKTkc+OmidIyKSxnFf/ageL22oU4bANZT5RB9EbGk90CdoYactW1QGv1CJA9rgh0kKWoODGNPkBssXX4NDCI7WFqd+eWYIDhoBJVZ48v7OVdPAZ62V80vZabhu/p9Q3Eg9YT2O0+w942+pOIxVh3bgYEMFQkZHrgWoRAjPrX9ZaVQwbWpVXDbvUeyvp/6HhVluRVl75GMs2LVenX89/gmq0RgpJdDc0oR5BzcqQ6W0B2LKefWsMThaX6nsdZi5B/U5atCMQwij37yx0bAmTrJeheCIdk1yk8oSsNvQONv3bE/ctubFTjM1CY4yLPyQUmDXzLMZx+qgXPzFfjG0Jp+aZKjGHvp5U0OKjhtYWuNJcOiJTj2eWFyJMsWCMx6nMnTMpRd3TIELz6LTwI4iMxVvUqX6YG8IvQPaQJjOm4OOVW5O6sctszxP8IvQzIJTES0PbdppH42MTiqKWWDclHlMqc6yjikDplVQ2BDVBnFP0vz+O/UJDiKFtdSM7SXAjeMOxNSpvY5IcNw1uy5CSDBuk8IOXQN62eBg3J7BKkxaWYuqKFaJNo1VjXRNztFw7V5lp1mny79vF0KRKtx6xbQemleKEIsbmX6Z9mUWrIbgYFyWjTjcfKBJGdyzvwfvbxq3BweUWpKV3yubm1t9f3cafvqdqgRHn0A5Ssh2qUPX6ftHqa12NGFd0U6A7ot1Hbvx4q6/GzJL8OYu3XfqSTrBpvNkCvx1BkBhGVAWxU4zaANp7JJTji2FxJLBIdNwExxsa2xzpnzsu1vT4KCGk8E92wDbgrv8yfA7lQmO+/OLUa2+Z+nlFG9nFpQrrYpEdePES7Pqm9hHxYt3c9ZRrI+SuUQa5wB0walxyiuXmiSSea/xrPvrB+bVRPrrkBqbOUbrMHphS09oNBjOvGknadALFfhMERy6HRGzY+IYGR39cmPM+HDrZPGikgwL569npePOVS+AJjzVZLKFfWEzhq+chq9ntk5w8P2+lzMMo9bNRSmIASsNgz4DQ7YObimZve89/GjS3WqryBcixkq/mp2OmxYFUNhYpryw2OMQo0Qg4/PgbJRkxTvVB/Dzqb9Xdj7scv7X3LtQcGqfsyyM3EIN0Sbcu/R5ITjCsXY4Ut4GRwQ/53T5RyySExXMD2VIVMau+jxVCQ5u6ch7HygOASUN+txZAQzNq0b3AI0pWqSG/Z5fuW+ZXIeNJ614xQ3AE681OvzMmzj8SsizT6AE498KYU+51szgZIpLR57lIWDdfuDuqfvw8MIqHI+UiekerAdunlSLq4INSoPjlsk1OGYrM8M+9SbdfmqjkT2CtZi4MoyjkTB0tbjpJDBwkvUVyJStX2YJCg4DJyJhmdZftllpmXDJck1VgqNXVgPWHrbwRrz+9RgXja1/FSeRMCw/7MALsTPyJTgWd/b6JYl2Y3YZ5m8EDtdprJJ843Sc1+J64PWdwKDcfch4oyradlimD4qAGyaR3AiB7pHvm1ujtma525c2VhrGtYFivH0QKIrgjzhcvYf4o0tDZ/vrN+4QtlZaMmCc2RtFg8Mtp878zTrnF+bdFQD7LtY763TZASgDionKNnJBrWd/bPDijs/Fd/8JZXhjp+7H2acSo+xTeX+oFpjzfhg3jT+IZR9bZWKfv6iQ2/qsrVDEksEhn2+pBK4PHI7ikG2Nbc5g+WQIWFIIhXN7uVjWIdNrwLHEhOX2RLYFysceNhnuU5nguGtWldJ0iNZTCJi4ktufYu0TuevKgZcGqL6JfZQ7nPlNY6JDXyjBuweB8kZNvBm8UkPuU3qqWl2PW4L7sPEQwHGb5SImZ2wGegWJ1ZAa5zlGmzkC2xfHcI7lzIv99+3P12JrmYU/pvPgAr6TJp7tZeJYYNJiOI4Pt0ym4dOGuO9i4vvtyjndD+6Yi/PSeibUYLAvrJP1nloAA1dOxj5U4UhTjTp5f9vKKcrQZ6L3ok2Lb2QOxmziaAAAAA2XSURBVJ2vTcba8n040lytXLwaF7G1aMIphLGt7gQCm5fi4szhDoJBkRy5aSDJ0XvOk1h0cBP2hsqVAVFqgph0qNFRjHpsP3MS2Vvfxk8mjlD2PdxGQ0nK3P72ZGw9cxxlCKmyMB2e+1CDe1fPdOSf6P38/Fw0OLrqilre21cSSFWCg4u/W6fUgPtwzfnbmdW4NtNYNY83GQ2hV8Zp3DmjOhqP8X+dW93qpIBG73oEzyBtShUee7EUk1dUI6+gBpNX1OL+2WW4LqtaESE9MkK4N79Cpc0vTPfll+O6HHpa4YSDeddhROQ58x2Rfwo3TTgd8XChJzgDJlVixOxKlcZ9+aUYPqMKvTNiJ209gzUYMr0O9+WXRcKewqDnK6Np+W0Ck6g8qUlwhME93Fw0GZzyynrrEYwlAuwyImauzTqtMGLiEi/X59SpibA9rLknGXdlsAm9gjUYnleFJxedRN6aWoXV3GWnMWJmucIgw1yXXYffzSqNYLUEbD99MknGaYKjb3Y1RuTr58zf3r66BahFUotBeWcUxvmcWB86jR4wYglG2qm5c9bpqAxG5Jdj4JRYTzDmPfx+TUUNDi7g2a/eNZP9V7E6WaesY9Y167y1eumX7exT7XjxiqfJ47Dq2+6bXYGxi4uRV8B+tRbjXqvCsLxydM84g18GWzBwSk0UZ8TasBl1IKZMurdNIVbLo/gi1qLPxzeqtsY2Z9oR+9W0F9hnO9+JHjE4hrDsJizbANuCO6zJ28/XVCY4OObeM4t1rvuoe/MrMWBi27QX3HgZnn/GwosLE6xfYpUfKfpm1+DBOWUY/1q56lOnranFU4vLkTa1SvV7DHfH1Ipon82xeXAe+3o9frNvTH++0vGcuORYrnEUQp+MOgy34e+B/BKwbblxRjKEYwHHBI3VUozIr1T9u5sMccf14++uRnB8OScdFz5/D7rPfQK95jypSIbuc5/Ev0y9B1/KTmyQkwSFJhkG4UdT70OfuU/h4ZWz8cTahXhy7Z/V/YAXA/ivaSPx1YjxUS/SgGkwrW/mDMHl+aPBOCOXzVBpMK07l+TihvnP4OfTR+Gfc9OV8dHPReK40+M7/SzvQdy8cBz+sHI2nlq7EH9YNQfXznsaF026WwgO0eDw1fpYCpPkEkhVgkMP0Jx00OiWPs2EOfFEVNsYMPH01drb6jX467R1vG7BJrXAvFq5iCVxoQ1/6XytMjEcF5JXBamKb0/fGcaU28rXfKlpAtPQpz2+fVJupcVyxKZlD+vv+1QlOFrDqlXnXnXDunXWr8GaE0/OuAarBjecCOuTuNI46hYhMXR6Vvu5cjwn8zo9nY5+ZseWea7Tsspn5essD9/RPDP5Wb9jw7YuE3+ET0WCw+DU9DkW9gwOE8newoKq5/Hsjyw8uetVP6OGnFlAkuA9E8Eq/2f6swZovFrpu/Fj/TZ4dZZZYXa8Pb5O27tMpjymXXiHdcf14+9UJjgMbogT3a+wfhNhVD9348X67R3fPDdXo5HJfpV58v9XRvpUHcaUiVeNxW4BTRx7Pzfl0mkxjOkr47+bTtcZjvl5v4Pf/9/VCA6SAyQWNFGRjs/npkd/u4mDRL+/kJuuPJp8KSdNuWX9cs6gqNcSPmMeidPQXlNIeJyfkw6SFcrzSQ7/f4dVNpvdDZ3mHfhiLk+dhyqLSiNNp8GyqfdMTNqYNPx+FQ2OJF8YS/FTQwKpSnCYScKVwWYbCWAmxIkGeE6qGVafJo1EEwAzoWL4K9WExp6OmVzZ/2flYRaW/EJo8rWujKPjmzysZyRJ+JzvZIgPMxmKzUuHdYZL9F5+eZ7KBIeuz1isti57PYG1sEDcMQ0zsY2Pc4bRGGUcgyGNR4NF04as9E04C2v2Z/reYNFMxC2M83k8/Hnlpd8jObGaqgSHxg0xdrZYdePBwqruu7yxqvu7sMboeKtf1Zgltog3szB0Yk3jR2NVh3M+NzhnG7Oe0623PrkwvNq1GPTqf4lr3U6TD6upTXCwj3PjlHWduJ6IB4MxnYbpV71xqjHEtCNYjfR1uo81uNN9o+4Dzf/MVT9j2eI9t/JgWBNPX73x5x4fdFjOMXT4+O/ix+ddjeCwyI07lCYGiYXP24iCti7yz48QDjo9nZaVttbyaDvBoctgtDoYj2XiadJUhkUdJIed3ND56Xcx5I0tbhuIlra+d2eGE4IjNdbH8hZJLoFUJTjsAzQHdD2on83AruOc/WSAeThPdxqmPObqVVbzzB3XCmsvnz2/2EmLSctZrthwVtr+fJbKBIeRvVVXba8DK87Z4duJB40hN95M2vr/9vQt/JkwboIt9p1axynDO9My4dsuC5NnZ19TleCw5GrVv/W/xPVk1W/isFa6BgdeV5OOfuadvhXP+7lJg/iLPa1yWOGIdZOWucbDv3d8e1qdd5/KBIeRu7N+DBYSydyE4zVRWPtzezz7fWwYq1yxz+z48s6/7fgz+div3mnay+G/+65GcJgFOgkK92n3TmLCxbu647p/x4sX83+PctjTOpsyMaw9Lu/PKr7PiRAhOJJ8YSzFTw0JdAWCIxkHcylz7ASrKxAcUu+x9Z6MMkl9giM16ikZsdWRZe4KBEdHykvS6rx231UJjhiiweeLeylvmjIC+51hvbB161aEPexqnM3/xItKaqy15S06QQJCcHTegC2TpbOTvRAcZycvwVfnyUsIjs6TveC+7bIXgqPtshJcda6shOBIbCdDyAV/yEg0ODphMStZigTcEhCCo3MHbZk0tV3+QnC0XVaCq86VlRAcnSt/wX/b5C8ER9vkJHjqfDkJweGPxbuQKInrQQgO90pTfosEOkECQnB0/sAtk6e21YEQHG2Tk+Cp8+UkBEfn14G0g8R1IARHYhkJjvwhIyE4Ei+shXzwh4yE4OiExaxkKRJwS0AIDn8M3jKJSlwPQnAklpHgyB8yEoLDH/Ug7aH1ehCCo3X5CH78Ix8hOPyxeBcSJXE9CMHhXmnKb5FAJ0hACA7/DOAymWq9LoTgaF0+gh//yEcIDv/UhbSL+HUhBEd82Qhu/CUbITgSL6yFfPCHjITg6ITFrGQpEnBLQAgOfw3iMqmKXx9CcMSXjeDGX7IRgsNf9SHtw7s+hODwlovgxX9yEYLDH4t3IVES14MQHO6VpvwWCXSCBJwERyOuCsopMvAnBvo8cwj/dMmD6BGsFZxKO/U1BjTB8Qy6PVsFvVjyZ5uSvq5r10v34Glc+cAaXDjwDfQIEqtdWx7y/v6t/16BSvzg9nk4L60Xvp41CF/ITZNTZOBLDBiCY4u4ie2EVa1kKRKISKAFwBPBPPQYtRocQHoFquUUGfgSA33H7sEXL/kdrgmU+rJ80nak7zAYGDCuEOdd8jh6PHMCvQJVglfpU32JgT6BMvzq/uW4eOAruDZw0pdlNG1Krl27f70ucAIX3T4T593eG98dPwQXZA2SU2TgSwx8J2Mwvj+0NzanIsERCoVwzz33YPDgwXj66aflFBn4FwNjn0H3Pjfgf/oMx2X9n8Bl/Z+SU2TgSwxccdPD+MqFl+KK/o/7snzSdqTvMBi4qv8onH/hL3HZTX/E5f2fFLxKn+pLDFze/wn8d6/huOh/b8MV/R/zZRlNm5Jr1+5fic+LLr0Z3/7FJfj3/j3x4wE95BQZ+BID/zagJy68/GdYVVCAcDjcrvO89ibQ0fFJcGzZsgXbtm3D/v375RQZ+BYDn+7fj482b8H2wk/wyf5jcooM/IuBT49gxeq/+rd8gh2pmwgG9uw/guWr38Ennx4VmUi78DUGthXuwaathfhkv2BV5kD+ngNu+lsh3tu0ER8f3IddcooMfIyBD/+2GUePHm0XuUFuwncER0cTJpJe+xgwkV8i+YXQFG5Ac7heTpGBLzHQFK5XGG1qJFYFp9JW/Y0BjVd/l1EwJPUT7VelT/XluCdt1GqjFlbDaA6H0RySU2TgTww0hcNobKfmhlm3CsHRQYI0ApVrIkIgFZ83IRyWU2TgTwyEwk0IhRsRVqc/yyjYkXqxMCBYtWQhuPC3LASr/q4faT+6fohTPfcONYYhp8jA7xgweG3PVQgOITiiHV97gNSV4+oFJAdSM9mRq8jCbxgwxKLfyiXlkbbixoBgVTDhxoRffwtWBat+xaa9XAancu3Ka5Wu9u5CcAjBIQRHuzFgBhIZPLpaByrvK5gXDAgGBAOCAcGAYEAwIBgQDPgHA0JwtHtx65/KlIYldSEYEAwIBgQDggHBgGBAMCAYEAwIBgQDXRUDQnAIwSEaHIIBwYBgQDAgGBAMCAYEA4IBwYBgQDAgGEh6DAjBISBOehB3VXZS3luYecGAYEAwIBgQDAgGBAOCAcGAYEAwYGFACA4hOITgEAwIBgQDggHBgGBAMCAYEAwIBgQDggHBQNJjQAgOAXHSg1gYS4uxFFmILAQDggHBgGBAMCAYEAwIBgQDgoGuigEhOITgEIJDMCAYEAwIBgQDggHBgGBAMCAYEAwIBgQDSY8BITgExEkP4q7KTsp7CzMvGBAMCAYEA4IBwYBgQDAgGBAMCAYsDAjBIQSHEByCAcGAYEAwIBgQDAgGBAOCAcGAYEAwIBhIegwIwSEgTnoQC2NpMZYiC5GFYEAwIBgQDAgGBAOCAcGAYEAw0FUx8P/PH/Sm8fCW7QAAAABJRU5ErkJggg==)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V5t1Fx4k9hmi"
      },
      "source": [
        "###**Approach 1**: The Wrong Way"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "du2xvICj-PwR"
      },
      "source": [
        "X, y = make_classification(n_samples=1000, n_features=10, n_informative=8, n_redundant=2, random_state=20)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W7WyT7hH1WpJ"
      },
      "source": [
        "# standardize the dataset\n",
        "scaler = MinMaxScaler()\n",
        "X = scaler.fit_transform(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k9lvqRX39ya8"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import RepeatedStratifiedKFold\n",
        "\n",
        "# define the evaluation procedure\n",
        "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=5, random_state=50)\n",
        "# evaluate the model using cross-validation\n",
        "scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=1)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ueVshGEw981U",
        "collapsed": true,
        "outputId": "cf20330b-21b2-444f-a2cf-c8d808463e7e"
      },
      "source": [
        "# check performance\n",
        "import numpy as np\n",
        "\n",
        "print('Accuracy: %.2f ' % (np.mean(scores)*100))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 88.76 \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dnc_36uYTKBY"
      },
      "source": [
        "###**Approach 2**: The Right Way"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ikS6H6zLSsHw",
        "collapsed": true,
        "outputId": "dfba84af-3d1b-4be1-d64e-79f7b5fd4570"
      },
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "# define dataset\n",
        "X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=7)\n",
        "# define the pipeline\n",
        "steps = list()\n",
        "steps.append(('scaler', MinMaxScaler()))\n",
        "steps.append(('model', LogisticRegression()))\n",
        "pipeline = Pipeline(steps=steps)\n",
        "# define the evaluation procedure\n",
        "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
        "# evaluate the model using cross-validation\n",
        "scores = cross_val_score(pipeline, X, y, scoring='accuracy', cv=cv, n_jobs=-1)\n",
        "# check performance\n",
        "print('Accuracy: %.2f' % (np.mean(scores)*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 85.43\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XWzm8p-nJWE7"
      },
      "source": [
        "Running the example normalizes the data correctly within the cross-validation folds of the evaluation procedure to avoid data leakage."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kveJ2I2EXTzo"
      },
      "source": [
        "With the two accuracies, we can expect the **approach 2** with accuracy of **85** to perform well in production that **approach 1** even with the accuracy of **88**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bY-x9aZ3Tc3F"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}