diff --git a/ml_learning.ipynb b/ml_learning.ipynb
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index 0000000..42bd122
--- /dev/null
+++ b/ml_learning.ipynb
@@ -0,0 +1,1895 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "toc_visible": true,
+      "authorship_tag": "ABX9TyMWfj3aIUO+IWl/rpYhs9cx",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/GEO-HACK/Advanced-Programming/blob/main/ml_learning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "EwnA2KpKIuQT"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### MY FIRST ML PROJECT"
+      ],
+      "metadata": {
+        "id": "bdk0S6fNIvQ7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "df = pd.read_csv(\"https://raw.githubusercontent.com/dataprofessor/data/refs/heads/master/delaney_solubility_with_descriptors.csv\")\n",
+        "\n",
+        "df"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "q3R6PKT3I_sJ",
+        "outputId": "ca007b64-e78c-4515-f5a6-5667830a7f1c"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      MolLogP    MolWt  NumRotatableBonds  AromaticProportion   logS\n",
+              "0     2.59540  167.850                0.0            0.000000 -2.180\n",
+              "1     2.37650  133.405                0.0            0.000000 -2.000\n",
+              "2     2.59380  167.850                1.0            0.000000 -1.740\n",
+              "3     2.02890  133.405                1.0            0.000000 -1.480\n",
+              "4     2.91890  187.375                1.0            0.000000 -3.040\n",
+              "...       ...      ...                ...                 ...    ...\n",
+              "1139  1.98820  287.343                8.0            0.000000  1.144\n",
+              "1140  3.42130  286.114                2.0            0.333333 -4.925\n",
+              "1141  3.60960  308.333                4.0            0.695652 -3.893\n",
+              "1142  2.56214  354.815                3.0            0.521739 -3.790\n",
+              "1143  2.02164  179.219                1.0            0.461538 -2.581\n",
+              "\n",
+              "[1144 rows x 5 columns]"
+            ],
+            "text/html": [
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+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>MolLogP</th>\n",
+              "      <th>MolWt</th>\n",
+              "      <th>NumRotatableBonds</th>\n",
+              "      <th>AromaticProportion</th>\n",
+              "      <th>logS</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2.59540</td>\n",
+              "      <td>167.850</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>-2.180</td>\n",
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+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2.37650</td>\n",
+              "      <td>133.405</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>-2.000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2.59380</td>\n",
+              "      <td>167.850</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>-1.740</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2.02890</td>\n",
+              "      <td>133.405</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>-1.480</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2.91890</td>\n",
+              "      <td>187.375</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>-3.040</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1139</th>\n",
+              "      <td>1.98820</td>\n",
+              "      <td>287.343</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.144</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1140</th>\n",
+              "      <td>3.42130</td>\n",
+              "      <td>286.114</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.333333</td>\n",
+              "      <td>-4.925</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1141</th>\n",
+              "      <td>3.60960</td>\n",
+              "      <td>308.333</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>0.695652</td>\n",
+              "      <td>-3.893</td>\n",
+              "    </tr>\n",
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+              "      <th>1142</th>\n",
+              "      <td>2.56214</td>\n",
+              "      <td>354.815</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0.521739</td>\n",
+              "      <td>-3.790</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1143</th>\n",
+              "      <td>2.02164</td>\n",
+              "      <td>179.219</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.461538</td>\n",
+              "      <td>-2.581</td>\n",
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+              "      fill: #D2E3FC;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
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+              "\n",
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+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
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+              "        const element = document.querySelector('#df-c55ee024-b290-4dea-8e00-23a833af7570');\n",
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+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
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+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1144,\n  \"fields\": [\n    {\n      \"column\": \"MolLogP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.866002824775324,\n        \"min\": -7.571399999999989,\n        \"max\": 10.388599999999991,\n        \"num_unique_values\": 930,\n        \"samples\": [\n          2.0437,\n          1.0428,\n          0.4903\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MolWt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 102.6204995656741,\n        \"min\": 16.043,\n        \"max\": 780.9490000000001,\n        \"num_unique_values\": 806,\n        \"samples\": [\n          132.12599999999998,\n          194.194,\n          226.276\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumRotatableBonds\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.627398498656514,\n        \"min\": 0.0,\n        \"max\": 23.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.0,\n          3.0,\n          16.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AromaticProportion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.343305175882067,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 77,\n        \"samples\": [\n          0.5,\n          0.2727272727272727,\n          0.8571428571428571\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"logS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0965019290656026,\n        \"min\": -11.6,\n        \"max\": 1.58,\n        \"num_unique_values\": 743,\n        \"samples\": [\n          -1.7,\n          -6.62,\n          -3.324\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 1
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Data preparation"
+      ],
+      "metadata": {
+        "id": "nq928m1BJ060"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y = df[\"logS\"]\n",
+        "y"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 458
+        },
+        "id": "oYRmMzpfJ4pR",
+        "outputId": "2cea3d35-cd7c-49d0-ad26-d43f4dd5527d"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0      -2.180\n",
+              "1      -2.000\n",
+              "2      -1.740\n",
+              "3      -1.480\n",
+              "4      -3.040\n",
+              "        ...  \n",
+              "1139    1.144\n",
+              "1140   -4.925\n",
+              "1141   -3.893\n",
+              "1142   -3.790\n",
+              "1143   -2.581\n",
+              "Name: logS, Length: 1144, dtype: float64"
+            ],
+            "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>logS</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>-2.180</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>-2.000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>-1.740</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>-1.480</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>-3.040</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1139</th>\n",
+              "      <td>1.144</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1140</th>\n",
+              "      <td>-4.925</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1141</th>\n",
+              "      <td>-3.893</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1142</th>\n",
+              "      <td>-3.790</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1143</th>\n",
+              "      <td>-2.581</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>1144 rows × 1 columns</p>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Splitting the x axis\n"
+      ],
+      "metadata": {
+        "id": "cJeeHqX5P5g8"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x = df.drop(\"logS\", axis=1)\n",
+        "x"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "IZmjgdjhKZs4",
+        "outputId": "9e4e0044-fa53-4c40-a16a-7c4811d7cfa7"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      MolLogP    MolWt  NumRotatableBonds  AromaticProportion\n",
+              "0     2.59540  167.850                0.0            0.000000\n",
+              "1     2.37650  133.405                0.0            0.000000\n",
+              "2     2.59380  167.850                1.0            0.000000\n",
+              "3     2.02890  133.405                1.0            0.000000\n",
+              "4     2.91890  187.375                1.0            0.000000\n",
+              "...       ...      ...                ...                 ...\n",
+              "1139  1.98820  287.343                8.0            0.000000\n",
+              "1140  3.42130  286.114                2.0            0.333333\n",
+              "1141  3.60960  308.333                4.0            0.695652\n",
+              "1142  2.56214  354.815                3.0            0.521739\n",
+              "1143  2.02164  179.219                1.0            0.461538\n",
+              "\n",
+              "[1144 rows x 4 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-60fef6bb-7c43-41eb-a34f-38969befdf41\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "\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>MolLogP</th>\n",
+              "      <th>MolWt</th>\n",
+              "      <th>NumRotatableBonds</th>\n",
+              "      <th>AromaticProportion</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2.59540</td>\n",
+              "      <td>167.850</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2.37650</td>\n",
+              "      <td>133.405</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2.59380</td>\n",
+              "      <td>167.850</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2.02890</td>\n",
+              "      <td>133.405</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2.91890</td>\n",
+              "      <td>187.375</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1139</th>\n",
+              "      <td>1.98820</td>\n",
+              "      <td>287.343</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1140</th>\n",
+              "      <td>3.42130</td>\n",
+              "      <td>286.114</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.333333</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1141</th>\n",
+              "      <td>3.60960</td>\n",
+              "      <td>308.333</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>0.695652</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1142</th>\n",
+              "      <td>2.56214</td>\n",
+              "      <td>354.815</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0.521739</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1143</th>\n",
+              "      <td>2.02164</td>\n",
+              "      <td>179.219</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.461538</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>1144 rows × 4 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-60fef6bb-7c43-41eb-a34f-38969befdf41')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-60fef6bb-7c43-41eb-a34f-38969befdf41 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-60fef6bb-7c43-41eb-a34f-38969befdf41');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-76036956-834b-43c4-91ff-5f8e37cdd499\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-76036956-834b-43c4-91ff-5f8e37cdd499')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
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+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-76036956-834b-43c4-91ff-5f8e37cdd499 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_f388c056-91d4-40b1-b772-95e92f868300\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('x')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_f388c056-91d4-40b1-b772-95e92f868300 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('x');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "x",
+              "summary": "{\n  \"name\": \"x\",\n  \"rows\": 1144,\n  \"fields\": [\n    {\n      \"column\": \"MolLogP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.866002824775324,\n        \"min\": -7.571399999999989,\n        \"max\": 10.388599999999991,\n        \"num_unique_values\": 930,\n        \"samples\": [\n          2.0437,\n          1.0428,\n          0.4903\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MolWt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 102.6204995656741,\n        \"min\": 16.043,\n        \"max\": 780.9490000000001,\n        \"num_unique_values\": 806,\n        \"samples\": [\n          132.12599999999998,\n          194.194,\n          226.276\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumRotatableBonds\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.627398498656514,\n        \"min\": 0.0,\n        \"max\": 23.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.0,\n          3.0,\n          16.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AromaticProportion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.343305175882067,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 77,\n        \"samples\": [\n          0.5,\n          0.2727272727272727,\n          0.8571428571428571\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### separating the data"
+      ],
+      "metadata": {
+        "id": "Ff0ArK_vQD3x"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection  import train_test_split\n",
+        "\n",
+        "\n",
+        "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.2 ,random_state = 100 ,shuffle = True)\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "x_V3-3VfK7_n",
+        "outputId": "ca2f67bc-39a7-444c-e9b8-66bc351717f5"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(915,) (229,)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## model building"
+      ],
+      "metadata": {
+        "id": "7OoNX-5CQz1V"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Linear regression\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "FYLwgF6MK2vt"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Training the model\n",
+        "**"
+      ],
+      "metadata": {
+        "id": "1YgjDS78S5US"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.linear_model import LinearRegression\n",
+        "\n",
+        "\n",
+        "lr = LinearRegression()\n",
+        "lr.fit(x_train, y_train)\n",
+        ""
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "id": "wTmDjJluPwbI",
+        "outputId": "c3ef44c4-80b8-475f-d0cb-a86d2f672bf6"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LinearRegression()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-1 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: #000;\n",
+              "  --sklearn-color-text-muted: #666;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-1 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: flex;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "  align-items: start;\n",
+              "  justify-content: space-between;\n",
+              "  gap: 0.5em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+              "  font-size: 0.6rem;\n",
+              "  font-weight: lighter;\n",
+              "  color: var(--sklearn-color-text-muted);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-1 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-1 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 0.5em;\n",
+              "  text-align: center;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**applying the model for predictions**"
+      ],
+      "metadata": {
+        "id": "QPYvvtGsTBdx"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_lr_train_pred = lr.predict(x_train)\n",
+        "y_lr_test_pred = lr.predict(x_test)\n",
+        "\n",
+        "print(y_lr_test_pred, y_lr_train_pred)\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-L67vH6RTHnX",
+        "outputId": "c00f5334-43cf-416c-a1e4-c2e4314a32ff"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[-3.05722870e+00 -7.77785827e+00 -2.55016650e+00 -2.01523582e+00\n",
+            " -2.06375990e+00 -9.99672215e-01 -5.94603364e-01 -5.53626003e-01\n",
+            " -5.72200956e+00 -3.94006681e+00 -3.95496755e+00 -2.29737009e+00\n",
+            " -1.48980354e+00 -1.48988982e+00 -4.64510806e+00 -1.90396018e+00\n",
+            " -1.51566313e+00 -3.16424605e+00 -3.70863920e+00 -5.58105660e+00\n",
+            " -3.25038467e+00 -5.04235077e+00 -5.69194881e+00 -2.14339849e+00\n",
+            " -4.35689341e+00 -5.03964756e+00 -3.10383618e+00 -4.40286964e+00\n",
+            " -4.21276272e+00  5.56508349e-01 -1.45537678e+00 -4.41027396e+00\n",
+            " -2.59668773e+00 -1.53336276e+00 -5.55749874e-01 -1.67111795e+00\n",
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+            " -1.60816482e+00 -3.68121117e+00 -4.60929775e+00 -2.45888480e+00\n",
+            "  1.13185484e+00 -1.69279625e+00 -7.09025955e+00 -3.79386016e+00\n",
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+            " -5.30676136e+00 -2.39225449e+00 -4.88290144e+00 -2.61359308e+00\n",
+            " -3.11841945e+00 -2.05580278e+00 -1.64987419e+00 -4.91881901e+00\n",
+            " -3.93774254e+00 -4.26411548e+00 -3.15082845e+00 -3.49352203e+00\n",
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+            " -2.59432621e+00 -5.38480406e+00 -5.44932525e+00 -3.04107137e+00\n",
+            " -1.87252408e+00 -2.25124657e+00 -2.89215707e+00 -3.46087334e+00\n",
+            " -6.06861986e+00 -1.89916369e+00 -1.98035105e+00 -2.45036038e+00\n",
+            " -2.79393037e+00 -4.76010415e+00 -1.72379306e+00 -7.09025955e+00\n",
+            " -2.86880150e+00 -2.70674744e+00 -4.36825704e+00 -3.11841945e+00\n",
+            " -3.85805633e+00  9.78662246e-03 -4.47466933e+00 -6.08708502e+00\n",
+            " -5.19970454e+00 -6.40483191e+00 -4.10155218e+00 -1.07044752e+00\n",
+            "  1.99249372e+00 -3.63698515e+00 -8.50933009e-01 -3.26770298e+00\n",
+            " -4.78343575e+00 -1.48988982e+00 -2.24629276e+00 -4.13247222e+00\n",
+            " -4.36873484e+00 -1.89724815e+00 -1.50596465e+00 -1.16713539e-02\n",
+            " -1.73602998e+00 -2.34521849e+00 -4.54942814e-01 -4.18812419e+00\n",
+            " -1.87770440e+00 -3.70838271e+00 -1.59899899e+00 -3.26143822e+00\n",
+            " -4.17623614e+00 -6.67872053e+00 -1.23069039e+00 -3.14545964e+00\n",
+            " -4.79863925e+00] [-2.83668969  0.84208401 -0.50930302 -0.13058053 -1.6269208  -2.86103037\n",
+            " -2.32208333 -4.16684323 -5.04235077 -2.29737009 -3.15082845 -4.33037208\n",
+            " -1.43700682 -2.59060064 -7.09025955 -2.32734557 -4.19073421 -2.63904005\n",
+            " -3.58104074 -3.75929058 -2.5501665  -3.26968347 -2.78911367 -2.69251051\n",
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+            " -4.17454084 -1.95444152 -3.50503339 -2.7411418  -1.87131835 -2.45270806\n",
+            " -3.07252433 -1.48988982 -3.98778754 -1.35216679 -0.16932277 -6.05237002\n",
+            " -3.29550658  0.30513174 -3.25501463 -5.31993586 -0.4733928  -4.79180882\n",
+            " -5.82486323 -2.70343539 -3.70838271 -2.4987341  -2.12301018 -3.11393974\n",
+            " -2.79842053 -1.38274662 -4.3267103  -3.57778946 -3.42356884 -9.15826626\n",
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+            " -3.99288096 -2.22595859 -2.63405871 -1.80643013 -4.18615661 -0.32535905\n",
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+            " -2.63177729 -2.88308299 -5.15950486 -5.67559475 -3.04604495 -5.17135083\n",
+            " -1.38069437 -4.57622144 -3.72245121 -5.49591484 -3.39797976 -6.025302\n",
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+            " -2.49648251 -0.79480376 -4.64013859 -4.39618439 -2.32058101 -2.61616401\n",
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+            " -4.64502179 -2.31758244 -3.01521178 -5.1577739  -4.68504851 -2.98727379\n",
+            " -2.16925807 -2.65875355 -4.53388983 -3.39620209 -2.97542583 -2.63426164\n",
+            " -7.77785827 -2.34848929 -3.95395012 -3.14810449 -2.99620551 -4.19807234\n",
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+            " -3.63698515 -4.07880131 -3.2950999  -1.75169752 -4.64981547 -2.68788426\n",
+            " -2.81766206 -3.38232674 -2.14927143 -5.44407214  1.93550814 -0.12972508\n",
+            " -1.49498387 -2.50036024 -2.06396429 -5.93855312 -2.53403538 -2.89650533\n",
+            " -1.58868434 -5.73999593 -1.97390964 -4.64393496 -3.96695922 -1.30865188\n",
+            " -1.8711556  -6.01555673 -4.75194249 -2.1532237  -1.26674801 -4.60929775\n",
+            " -1.75320392 -3.16591868 -2.35789126 -2.73044354  1.43453026 -3.29138689\n",
+            " -2.41564138 -3.88412944 -5.4954495  -1.64461506 -2.29089681 -2.32781082\n",
+            " -2.75074329 -3.88113542 -2.85196407 -1.59255758 -0.83248302 -5.41251959\n",
+            " -3.26968347 -1.83271568 -2.12301018 -0.45113096 -5.34942367 -0.69635755\n",
+            " -3.86581957 -3.82343254 -3.71474837 -3.36603645 -4.49530092 -5.04887579\n",
+            " -5.16431192 -6.08708502 -0.63401868 -2.99158591 -7.77785827 -1.48607797\n",
+            " -4.66162003 -2.39395831 -3.75517868 -4.61318325 -4.17747099 -2.199028\n",
+            " -3.01607829 -2.50036024 -1.88703307 -3.62102758 -3.51234807 -3.16279877\n",
+            " -7.09898181 -2.70478582 -3.37625597 -1.83271568 -3.87356837 -1.73865954\n",
+            " -2.88008616 -2.22977044 -6.70156561 -8.03717765 -5.33606465 -4.17657514\n",
+            " -5.01827423 -1.39251992 -1.36030606 -1.94448808 -4.00428682 -4.29865745\n",
+            " -1.3893508  -0.39302341 -1.51156575 -2.34521849 -1.29089314 -0.97595542\n",
+            " -2.41038224 -4.0790451  -2.6818627  -7.27035604 -3.46876907 -0.83629487\n",
+            " -3.25056894 -2.77794109 -2.70606495 -0.88802552  0.33102924 -8.73475591\n",
+            " -6.08789325 -2.49648251 -0.84907925 -3.69568743 -4.37642963 -0.79254201\n",
+            "  1.98868187 -1.96622986 -2.69309202 -2.75566249 -2.80004662 -2.92698197\n",
+            " -2.29452458 -4.40478538 -3.41695228 -2.97735566 -4.53250784 -3.12440723\n",
+            " -1.48607797 -6.64951931 -1.29322696 -3.59615687 -4.84056475 -2.0637599\n",
+            " -5.4954495  -1.13275935 -2.80728543 -2.40237156 -2.68666558 -3.44429049\n",
+            " -2.41959844 -4.95743275 -6.40483191 -5.13905974 -5.05708901 -1.66084991\n",
+            " -2.37974001 -2.05580278 -3.39656384 -1.42271129 -2.77786114 -2.30422683\n",
+            " -5.53938357 -4.30027875 -6.08708502 -1.53182046 -4.03471242 -0.35059383\n",
+            " -3.43858076 -5.34063365 -2.65303434 -0.32225969 -5.89030528 -1.66084991\n",
+            " -7.09025955 -1.66084991 -4.32045753 -3.2950999  -4.00428682 -2.65971931\n",
+            " -2.61898292 -1.48081884 -3.31248863 -4.1285441   0.81081357 -0.74032142\n",
+            " -0.55409165 -1.48988982 -4.2835894  -3.52940291 -3.67078501 -3.11422447\n",
+            " -1.35216679 -2.33929584 -3.735629   -5.99133424  1.68379176 -5.72200956\n",
+            " -1.1504684  -3.78441114 -2.04833812 -4.23871436 -4.3723975  -2.25259394\n",
+            " -3.75430163 -3.79386016 -4.33406929 -0.79254201 -2.72091671 -2.32584014\n",
+            " -3.04247237 -4.57422729 -2.05580278 -2.19679345  0.21720962 -2.33668279\n",
+            " -3.18126918 -5.18323201 -1.1504684  -2.82306619 -3.81016932 -0.62641139\n",
+            " -1.79284653 -3.0572287  -2.68205673 -3.47393942 -1.35730748 -2.35283034\n",
+            " -5.29861673 -3.42242343 -2.33668279 -3.15515412 -0.81254078 -2.32158664\n",
+            " -7.23311502 -4.27331505 -0.41608127 -0.92992939 -2.85102139 -2.38452854\n",
+            " -1.35467792 -5.35250249 -4.61318325 -3.36770115 -4.54753068 -1.21120552\n",
+            " -1.41272408 -0.62894611 -5.46722693 -2.80332864 -4.70222762 -4.20128488\n",
+            " -3.75287911 -2.89485114  0.44470271 -2.75272546 -6.40483191 -3.43858076\n",
+            " -1.33564461 -7.39989571 -2.3552617  -1.97390964 -3.31913336 -0.99788515\n",
+            " -1.49117202  2.54283347 -2.57935977 -1.5372532  -1.46969589 -5.50256395\n",
+            " -2.84523685 -5.79050585 -0.72972899 -4.50417895 -3.41743324 -1.89701519\n",
+            " -3.04694269 -1.10327863 -1.48988982 -2.81732645 -6.11716924 -7.16934656\n",
+            " -1.96622986 -3.91516316 -3.61172065 -6.40483191 -2.05580278 -3.67078501\n",
+            " -5.54561991 -2.93180057 -3.27180065 -2.72328327 -2.53353851 -1.11363181\n",
+            " -2.90610261 -1.10853776 -1.49259585 -1.15572753 -4.70049144  1.99249372\n",
+            " -1.80643013 -8.46728525 -4.32045753 -1.19196129 -2.25004084 -1.94336083\n",
+            " -1.87633593 -9.15826626 -3.65922507 -2.29683578 -0.5325366  -7.5914365\n",
+            " -2.15587374 -1.90396018 -1.78645244 -4.36890615 -1.91112045 -5.28950294\n",
+            " -3.70838271 -3.34373141 -3.82343254 -3.84290966 -1.75395277 -2.82944893\n",
+            " -1.92755736 -3.95353483 -2.51949142 -4.16972153 -0.47784193 -2.80004662\n",
+            " -2.08049675 -1.57187693 -3.79234971 -1.27341971 -1.10845148 -3.87977779\n",
+            " -1.08911411 -2.73044354 -1.76722676 -2.22965253 -3.56321848 -6.67044148\n",
+            " -1.51156575 -0.24983949 -1.72672119 -2.14897281 -4.05818764 -2.37140338\n",
+            " -1.897265   -1.42797042 -3.08706826 -4.24521459 -6.52588866 -4.60929775\n",
+            " -4.67022068 -2.03122625 -3.11841945 -5.04235077  0.60553271 -8.46728525\n",
+            " -4.23480474 -2.75684478 -2.14339849 -1.5231707  -2.67587664 -3.59418929\n",
+            " -5.68837433 -5.02111613 -0.94099449 -3.51200413 -1.41666505 -1.9765392\n",
+            " -2.67587664 -5.87753962 -3.73205662 -2.65599022 -2.46014399 -3.93774254\n",
+            " -8.46728525 -0.43381828 -3.81993282 -3.25038467 -5.64350181 -4.18419436\n",
+            " -6.47162256 -2.29452458 -3.70387135 -6.42099768 -1.53182046 -2.45533763\n",
+            " -1.91317252 -2.137733   -1.31128145 -3.16496704 -2.22556914 -3.81016932\n",
+            " -2.44235234 -2.76845189 -2.73924332 -1.59715854 -2.90610261 -7.77785827\n",
+            " -2.74034339 -7.77785827 -6.53218341 -2.55678026 -2.59432621 -6.06602826\n",
+            " -2.73144347 -2.39319255 -3.79386016 -6.40483191 -1.32841939 -1.62955037\n",
+            " -4.07880131 -2.42494059 -6.04136471 -1.63103729 -5.2602532  -3.86312053\n",
+            " -2.12601292 -4.28342693 -2.6178098  -7.14847318 -1.53350725 -1.08911411\n",
+            " -4.24790282 -4.63837202 -3.79691901 -2.13813936 -3.86772233 -1.2281273\n",
+            " -3.7672691  -4.28538142 -1.43139099 -3.40980857 -2.6111225  -1.41272408\n",
+            " -3.07730828 -3.90594959 -2.35283034 -0.7271857  -4.19010154 -0.69374352\n",
+            " -1.10472591 -1.62166167 -3.90123327 -2.78518496 -3.43585411 -3.92684454\n",
+            " -2.54406861 -1.91317252 -1.77769503 -0.79480376 -4.23915408 -1.51148185\n",
+            " -1.14665655 -2.00855418 -7.03862139 -2.74305517 -2.11738204 -3.42356884\n",
+            " -2.49648251 -2.1372074  -1.88151626 -1.62653948 -4.00428682 -2.04784256\n",
+            " -4.47943132 -2.61216962 -1.69531648 -5.54561991 -7.77785827 -2.13315833\n",
+            " -6.08238994 -0.73579117 -1.90396018 -3.66566374 -3.26337102 -3.5528319\n",
+            " -0.88717007 -4.50417895 -3.93774254 -2.31911758 -1.79407614 -2.73044354\n",
+            " -1.62464083 -3.36099002 -1.68087565 -4.17747099 -4.36658559 -2.68703435\n",
+            " -5.54667039 -2.40682831 -5.07481336 -3.80377549 -2.93847429 -3.69840797\n",
+            " -3.63698515 -2.77930842 -0.77056362 -2.37730267  1.68379176 -3.2158222\n",
+            " -3.70838271 -3.17308636 -4.36923501 -0.44844001 -5.24894879 -6.52225128\n",
+            " -1.80406335 -1.80643013 -1.90447849 -5.52193556 -2.07398557 -3.22132977\n",
+            " -6.70156561 -5.44962012 -1.25610101 -3.35223802 -3.98606913 -1.89546347\n",
+            " -6.08789325 -3.88711135 -3.78407726]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**model performance**"
+      ],
+      "metadata": {
+        "id": "Z1mONxx_UQs4"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics import mean_squared_error, r2_score\n",
+        "\n",
+        "lr_train_mse = mean_squared_error(y_train, y_lr_train_pred)\n",
+        "lr_train_r2 = r2_score (y_train, y_lr_train_pred)\n",
+        "\n",
+        "lr_test_mse = mean_squared_error(y_test, y_lr_test_pred)\n",
+        "lr_test_r2 = r2_score(y_test, y_lr_test_pred)\n",
+        "\n",
+        "\n",
+        "print(lr_train_mse ,lr_test_mse , lr_train_r2, lr_test_r2 )"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "iRSCpr28Umdc",
+        "outputId": "93013aff-6598-43f1-ba84-d34311fc57ec"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1.0075362951093687 1.0206953660861033 0.7645051774663391 0.7891616188563282\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**plotting the results**"
+      ],
+      "metadata": {
+        "id": "dLGrCtYkXmz-"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "\n",
+        "# Example Data: Replace these with your actual values\n",
+        "actual_values = y_test  # Your actual values\n",
+        "predicted_values = y_lr_test_pred  # Your model's predicted values\n",
+        "\n",
+        "# Create a range for the x-axis\n",
+        "x_range = np.arange(len(actual_values))\n",
+        "\n",
+        "# Plot the actual values\n",
+        "plt.plot(x_range, actual_values, label=\"Actual\", color=\"blue\", linestyle=\"-\")\n",
+        "\n",
+        "# Plot the predicted values\n",
+        "plt.plot(x_range, predicted_values, label=\"Predicted\", color=\"red\", linestyle=\"--\")\n",
+        "\n",
+        "# Labels and Title\n",
+        "plt.xlabel(\"Sample Index\")\n",
+        "plt.ylabel(\"Value\")\n",
+        "plt.title(\"Actual vs. Predicted Values\")\n",
+        "plt.legend()\n",
+        "\n",
+        "# Show the plot\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "mRP65MYoXraL",
+        "outputId": "2f833388-96a4-4031-d08f-f92c272b5373"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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l5HK5pH79+klLliyJCmcPh8PSs88+K7Vt21ZyuVxS7969pZ9++km66qqrpLZt2xraRznC2RUUFBRICQkJEiB99tlnUb8//fTT0oknniilp6dLCQkJUteuXaVnnnlG8vv96jpKGHSscP1IANLNN99c6jpKOPvXX38d8/dly5ZJF154odSoUSPJ5XJJbdu2lcaMGSPNnj3bsN6ff/4p9enTR3I6nVKHDh2kiRMnSo899liZ4eySdOj3tKrbWBZGjx4tAdJ9990X9dvu3bulESNGSOnp6VJaWpo0evRoae/evVHPSaxw9lAoJN1///1S48aNpcTERGnIkCHS5s2bY16zwsJCacKECVKnTp0kp9MpNW7cWDrllFOkl156SX1epk2bJg0ePFhq0qSJ5HQ6pTZt2kg33HCDtG/fvgqdrwkTpcEiSREapwkTJkyYMGHCxGEK08fHhAkTJkyYMHHEwCQ+JkyYMGHChIkjBibxMWHChAkTJkwcMTCJjwkTJkyYMGHiiIFJfEyYMGHChAkTRwxM4mPChAkTJkyYOGJgJjCMQDgcZu/evaSkpFRp9WwTJkyYMGHCRPVBkiQKCwtp0aJFVFFjPUziE4G9e/fSunXr2m6GCRMmTJgwYaIS2LVrF61atYr7u0l8IpCSkgKIC5eamlrLrTFhwoQJEyZMlAcFBQW0bt1aHcfjwSQ+EVDMW6mpqSbxMWHChAkTJuoZynJTMZ2bTZgwYcKECRNHDEziY8KECRMmTJg4YmASHxMmTJgwYcLEEQOT+JgwYcKECRMmjhiYxMeECRMmTJgwccTAJD4mTJgwYcKEiSMGJvExYcKECRMmTBwxMImPCRMmTJgwYeKIgUl8TJgwYcKECRNHDA4r4vPcc89xwgknkJKSQpMmTRg+fDgbNmyo7WaZMGHChAkTJuoIDivi8+eff3LzzTezaNEiZs2aRSAQYPDgwRQXF9d200yYMGHChAkTdQAWSZKk2m5EdeHAgQM0adKEP//8k9NOO61c2xQUFJCWlkZ+fr5Zq8uECRMmTJioJyjv+H1YKT6RyM/PB6Bhw4a13BITJkyYMGGiilBSUtstqNc4bIlPOBzmjjvuoF+/fhxzzDFx1/P5fBQUFBj+TJgwYcKEiTqJTz+FpCR4//3abkm9xWFLfG6++WZWr17N1KlTS13vueeeIy0tTf1r3bp1DbXQhAkTJkyYqCCuvFJ8Xndd7bajHuOw9PG55ZZb+OGHH5g3bx7t27cvdV2fz4fP51P/LygooHXr1qaPjwkTJkyYqHuwWLTvh9/wfUgor4+PvQbbVO2QJIlbb72V7777jrlz55ZJegBcLhcul6sGWmfChAkTJkyYqG0cVqaum2++mc8++4zPP/+clJQUMjMzyczMxOPx1HbTTJgwUduYMgWuuAK+/LK2W2LCROXRo4f47NixdttRj3FYmboseglQhw8//JCrr766XPsww9lNmDhMcc45MGMGDBoEs2bVdmtMmDBRxThiTV0mTJgwERMzZojP33+v3XaYMGGiVnFYmbpMmDBhwoSJwxoeD/h8pmPzIcAkPiZMmDBhwkR9QWIiuN1QjuAdE7FhEh8TJkyYMGGivmHHjtpuQb2FSXxMmDBhwoQJE0cMTOJjwoQJEyZM1DfEiWI2UTZM4mPChIkjA//3f+LzzDNrtx0mTFQFbLbabkG9xWEVzm7ChAFLl0IgAH371nZLTNQFDBkCH3wAbdrUdktMmDh02M3hu7Iwr5yJwxPBIPTpI77n5kJ6eq02x0QdQJcu4s+EicMBpuJTaZimLhOHJwIB7fvevbXXDhN1By+8ADfeCMuW1XZLTJioPJo2FZ8tW9ZuO+oxTMXHxOGJYFD7XlJSe+0wUXcwaRJs3QppadC7d223xoSJyiEzs7ZbUO9hKj4mDk/oiY9ZpNYECNIDQvkxYcLEEQtT8TFxeMLhEJKwzwdHH13brTFhwoSJqkE4DFZTszgUmFfPxOGJ5GQhCefmQuPGtd0aEyZMmDh0hELCqdligdGja7s19RYm8TFhwoQJEybqA3w+7fvixbXXjnoOk/iYODwRCsGcOfDee7BqVamrfvYZ/P57DbXLhImaQDAII0fCiy/WdktMVCX0xMcMZ680TOJj4vDEli0wcCBcfz18803c1bKy4Ior4LLLarBtJkxUN6ZPh2+/hfvuq+2WmKhK6ImPWbKi0jCJj4nDE/qoroKCuKspP+XkVHN7TNQ+vv1WfLZqVbvtqAkUFdV2C0xUB/x+7XsoVHvtqOcwo7pMHJ7QJzDMz4+7mtJ3BIOa36CJwxQnnyzqdR0JWbzNcgaHJ/SKj35yZ6JCMN8OE1WGrVvh00/h1luhYcNabkw5FR/9pMnng8TEamyTidpFs2bi4TwSoC/EKkmmWeRwgZ74mIpPpWGaukxUGV5+GR5/HD75pLZbQqWJj4nDGHffLf7y8mq7JdUPl0v7rlc/TdRvJCZC+/a13Yp6D1PxMVFlUPhFnRhX9J19mcRHAiwm8Tnc8cor4vPkk2HUqNptS3UjLU1IsC6XSOZp4vBAhw4icANMFe8QYCo+JqoMCteoEwSinIpP2OtnBT35kjF1o90mqgfhsPb9SEj8tmAB3HEHTJxoDpCHGywW854eIkzFx0SVQeEadYJAZGTAWWfB0qWl5jJJXPY3R7OKY1nFBm8Nts9EzeJIM/fs3ClC2ouLa7slJkzUOZiKj4kqQ50iPt27453+G55d2XDOOXFXM318jhAcacRHOd/Zs+HAgdpti4mqw08/aYrPyJG13Zp6C5P4mKgyKH2tPtVEbUGSoHdv6Nq19DHPn9RA/e7zShU+TjAIb70Fq1dXppUmagxHGvHRv4Qm8Tl84PFo37/7rvbaUc9hEh8TVYa6pPh4i4JsW+/lmJ0/kzvpq7iNKmnSTv3uL6744Pjnn3DLLXDnnZVtqYkagZ74HAH+EcW5OuJTF15IE1UDPaGVJKPvmolywyQ+JqoMdYn4hL/9Di8J/Mwwmtx6UdxZr1/SIl58RRUnPkoEW25uZVpZ/1FcDIWFtd2KckBPfI6AASMnS3e+deGFNFE1iLyXNZDLJxiE7OxqP0yNwiQ+dRS5ufXPL7EuRXWFfBFZTeNEdgXsCbRkN43IxmOpePZChezVBfNeTSMchmOPhW7d6kES2aZN4Z9/tP8P8+RvYa/ugfSaXvuHDSI7mip88UIhyMyMXj5mDLRoAbt2Vdmhah0m8amD8Higc2c48cTabknF0KxwE0/zEK7C2p8ehMtJfFLWLmYPrfiHE/H5K24CUcbPQ3Eh2bVLiBD1DcXFIlXM7t11JHdTabDbBUt77jl44YXD3txlID51YSZiomoQeS+rkPhce60gOKtWGZcvWyb6t02bquxQtQ6T+NRBZGXBwYOwdm39UhImrT6Fh3iWO1aMre2mEPSWj/hIQcFcQtgqNT4cquIzdSq0aaPl1qtPKCnRvteLsdXlggcegHvvPexrWW2+6CH+4zgAwp76cHNMlAvVaOpau1ZMwFauNC5XLA91XtWtAEziUwehV6brhf+EjAZBofT0yp1Tyy2BsL+cxCcg1uvCpkoZspV+p7LEZ+1a8bluXeW2r03oiU+dt6bs3QsTJpSa0+lwgj9goYhkAILFJvE5bNCyJfTvr/1fhWxE6cMiu0qF+BxOgZEm8amD0JP6UgqL11k4wkYWsGsXvPdezaoCYV/EWxqH+IQDuhlTJTyUD1XxUa5JfexU9JG1dV7x2bsXnn8e7rtP+PrUxwteAfj9cBUf05mNlAw4u7abY6KqcNllMG+eYCNeLzRqVGW7Vl4J/ZgTDmsTHFPxMVGt0M+eS6m2UOeQZxUl2eekDjcsf/hhuP56mDat5tpSXudmRfEBCHoqPhgequKjbFcfx+F6ZerSX+C+fYUt+TBGy5mTeYaH6MEqfI7k2m6OiapGYqIw3Vahr1os4qN/x+tjHxUPJvGpg9APIvWJ+PzrFhLsX+6zDMtzcsTnjh0115aSZh34lhH8zDk80uIDGDw45nqKjw/UDvFR7nV9nE3pFZ86b+qK7LXr4wWvANI3/sNlfE531tTpezN/voga2r27tltiIhbx0UcWm8THRLWivio+tzWaQiOy+dJ5hWG5MsbUZC6I7JOGMZJvGcbPTAqMEzHXsaAbAEPeir/ZR7Kpq94qPnDYEx/lgXycx7H+9WctNyY+3nwTvv76yExCLEki43uFiOldd4k6hBaLMHtlZVVZe2IRn6Ii7fvh9MqYxKcOor76+FiCAdqyg+aerYbltUF89EQkOzt+8ENxUhP1+6EoPsFg5XLiHS6mrrqsKgDRF7gc0TA+nwjlrY+pBgiIB8tGGPu/C2q5MfGh9G/6Z+lIwdy50KMH3HZbBTbKzdU60s8/r9IBwlR8TNQq6qvic2rxTJbSh6fzbzEsrw3io7ykzdjHWdJM8mcuirleVvuTWM9RQAyH6HJAPwuqTMdQnxWfeuXcXInEb488AscdB99+W01tqk7oHqhwSd1lpYqiUOeJczVg2zbjZ7kQ+RxXYTh7rKguPfExFR8T1Yr6SnyuLnkLgL7Bvw3La4P4tJv8KAHsrKE7MxmK49knYq4XCkEAUbaiMqYufb9TGXPX4aL41HniUwnFRyk8u3NnNbSnmmHRPYx1OY+PQnzq/PNTDaiUmbwaExiaio+JWkV9NXWdEvwLACfGN0R5N2sykCbsD2AnRD5pAIRyYzPIUAjOZgZt2MG6hv0qfBx9v1MZ4lOfnZvrlalr0CAuaLNM+78cF1x5XuvjvbEEtIdRqgfEp84/P9WA+kB8TB+feoS33nqLdu3a4Xa76du3L//oa/TUA9RXxUcP/UuiTK5rUvGR5ASGOYgQ+3gXsuPCz9hMJ17ndopD7gof50hWfOqVqSstjb8Ke3EvL/BZ58ehSZMyN1Ge1/pY1ssS1BOfussqTMXnEIlPFT6cpuJTj/Hll19y11138dhjj7F06VJ69uzJkCFD2L9/f203rdyor+HsQWzqd/05KC94QUHNleCQgkbiYy2OfSEtAT9ufDgIVGrWWVWKT33sVOqTqSsQEH6hL3EvH7V5DJo1K3MbRfGpj8Tn4wun8z/uA0Dy1t2bcyQrPso7X6F+o5qKlIZCWnBGQYHm0G/6+NQTvPLKK1x33XWMHTuWbt26MXHiRBITE5k8eXJtN63c0HcC9cXUJUngx6n+H4v4QM2ZuyIVH0dJ6bW6hvEzLbOWVvg4h6r41GfiU5/y+OTP+Y8HeYbhfFcukhYIaO9efSQ+JVICmcjkro6y0nBYG1jraBOrFcEguPBWrN/o0gX69DHupAqg73/CYY2Q6k1d9bGPiofDivj4/X7+++8/Bg0apC6zWq0MGjSIhQsXxtzG5/NRUFBg+Ktt1EdTVzgcQXy8Wgyw/t2sMXNXhOLj8hfEjkvWNa5Z7trKHgaoXMegdHr1cTZVnxQf37xFPMPDPMQztMxbY+zRY0BJugn18974/fA5l9KbpSwe8XxtNycmPB7tlazrxLk6kLFjCV4SuDPz/vJv9O67sGSJmEHm5MBJJ1VJWwIB6MUy7uUF7ARU0m+auuoBsrOzCYVCNG3a1LC8adOmZGZmxtzmueeeIy0tTf1r3bp1TTS1VNRHU1cwCMUkAfALZ+PzhA2/Kagx4iO/paqpSwrHThZikGxqPqqrvIrPnDlw8snRlZNrE/XJubk4T1zg4/mPqauPgX//LXV9vTJZHxWfIX8/wlM8gocEcpJqv0+LBT33rOvEuTpwxgxhiryx4IWKb9ywITRoAHZ7lbQlEIBlHMcL3M+NTFTHHdPUdZhiwoQJ5Ofnq3+7du2q7SbVS1NXIKApPo/zOL6g5u9TG8TnQEY3fuMsNju7cxuv82zbSbE7CV3jpEoQn0P18Smvc/PUqbBoEfzwQ8WPUV2oT87NJfkVy9x88CB0YhMP8xT2knryEurQe8s0ruc9mrC/zt4bPfGp68S5OuBxptV2E1QEAuBBBHdso7067hyupq6qoYt1BI0bN8Zms5EVkcY7KyuLZnGcGV0uFy6XqyaaV27UV8XnZBbiwkcmzQznoJ8x15SPzz+n3sW9P9xFl3awcSO0keDBWLf5ECWbmlJ86qJJrD6ZujyRxKcMGSc7G1ZzDC78LJibCbxVZW0JhcS1S0mpsl1GwRYSD8w9vIR9/ma4Y1z1HaySONKJz9Jul9Nt4/cssJ3KKeXd6MQT4cABOPVUSEiAm2+Gnj0PuS2BABwggzbsIoumMU1ddanvOVQcVoqP0+mkT58+zJ49W10WDoeZPXs2J598ci22rGKojz4+wSBk0Qw7QbqyHn++x/CbgppSfBSi0KqV+MzKiu3ik5eiMwNUYkpTFvHJzYU774T//iu9nWV1KsrvdanzqU+mLm9hxRUfF+LmNM9aVuq6FcWwYdCypdGPqKqhEJ/z+ZFjF02qvgMdAo50U1dAErqDPVyBfmfnTti+XaQTf+898b0q2hKAVMRgU0Cq6eNT33DXXXfx3nvv8fHHH7Nu3TpuuukmiouLGTt2bG03rdzQdwI+X/3oFJSX4i/6s4Je2DZvUH+rDeKjtKdlSziBf+jvm0XRnmiTxcquY/iIqwBRa6yiKMvU9d138Npr8Oyzsbcvr+Kj/F6XiE99MnVVhvhsox0A3/d7qUrb8t9/UFhYwVIFFYRNN5haA3Xz5hzpio9fEhnjrVIFXmrlRUtMFJ9V5IDm90mkI/rHU5kf09RVl/qeQ8VhR3wuuugiXnrpJR599FF69erF8uXL+fXXX6McnusyIjuB+qD6BIPwHA/Qgn0AhApLDL8pqCnic+GXF5FPKmft+4SvuIhZDKZ4ybqo9YJBrWQFlSA+ZSk+eXniM14aqfL6+NR1xaeuEx9fUQVNXQckWrAXgJyEllXaFuWeV6fTtD2sPYy2QN1kFUe64tN9y3RAONyX+1mQL1RWoUx8qqhDCBZoL/MJ/GsqPvURt9xyCzt27MDn87F48WL69u1b202qEOor8bmFN9X/a5v42H1FpFKIwxrCb0sAoCg7egAIhYQz9tGs5QP7jRU+TlmKj6KK5OZG/yZJFVd86lKEUX0ydX3e4Gb6MZ8dtBELyhgw/HuzVVPXQWfzKm2L8pyEw6WvdygwEJ9g3WQVR7rik+cS2cPf5Obykwq5w8jxVS3xCedpg4wFyYzqMlHziJz91AfiEwiAQ1ejK1xUu8THIh/U4nQQsAviU3LQE7XeGX8/xUqO5XI+IzPQqMLHKUvxUYhPLH8OfWdX53x8/P7YbE2H+mTqWlPYhgX043ke4EXuIdz5qFLXt+zdo37vteWbKm2Lcq2qk8TqTV32Okp8Cgu173X9+akOKKb1AI7yBUbo0isrqUOqzNRlT2SfnPAyieLDPqrLJD51EJGzn/oQ0h4Mgh1daHixRnz072aNEZ+QTHwcdkJOEabpyYkmPk5fEY3IIQFPpTrfQ1F89OsHg7GdrxXUuI/P0UeLXCGllHqpT4rPgQPicyI3cR8v4u/Wq9T1dxdqocbNc9ZUWTv0pQGqk/gM772Ts/gNAHuobt6cw13xKSiAgQNhUhzfcmtFiY+ug1KJTxV1CF5XGo/xBADJFJmmLhM1j/qo+AT9YWxo2r2e+NRGyQpLSLylVqedsFMoPp7caOKjjD538SqDfD+VSj5iobyKj9cb3blXpNByjSs+W7eKT12EZCTqi+ITDsPx2b9yO69xHCK8rqz2ri5uz6vcIf6pQoaif0aqk/hk0ZTNdALAHqqbN+dw9/H5+2+ReHTixNi/t8sWSTTv48XyKz7HHsv2hK6UULWmrkAAikgG4hMf09RlolqhDJBOuQJEfSA+AV9ELy7LAZJk7OCLimpmdmcNaaausEsQH19ejAOHtcYN5dcKz2rKUnz0qkik6hO5vv7Ya9fCZZeJHET649R45xOLCUoS0vQf6V/8Kw7ZD6YuD1w5OXCx9DmvcSejmEZbtuM/ULqMevAgBOU0Z4p6WBWoKeLj98M+mtOfeTx48tzqO9AhIFLxqeiko65DmRjEIzVW3XNVLuKTkgIrVjC0zTqu4mNuGrYLLr300BsKSPsPcDQi+ENv6iouhtP5gwd5hqC/Gp3Sahgm8amDUIhBRob4rA+mrrBXG7Xf5Gb2NesNxO7ca0L1sYRFp2J12kWiLzDkFlKg73wqU6G9vIoPRBOfSLKgJz4ffACffw4ff2z8rU7MugIBLBeczwzOJgkxJazLpooDBzT/swk8z3baY/9yStz1w2FIPLiLRsgParjqGEq8xJ5ViuJi7t91C88xgQWcwir3CdV0oENDZLm0w8mUAtq9jkdqJB3Tq0jy05wcOEATMu2tICnpEFqoocHiX3mEpwFN8QkERLv+4Eye4WFO2f1VlRyrLsAkPnUQygvTpAmkk4snu7j0DeoAQj5tRL6Hl9jesh9gHKhl/kH2AcmooVYDtqf2ZD79CKQ2Yl2fy7mLl1mdfmr0irrRx0GgwspFeX18oGzFR78v5fIo29e44qOU9xgwIPo33QilpAKoy4qPnvgoCHrjX8i8PHhHuoFxfAiApb6ZuoqLuTz/Le7kNcJYK3dvsrNh9Gj49dcqb56CSOJTl8lzZaCcTzxCV2RPV7+Xl/hIkhYoUZlM8fFgKRRmhc105HymU1AQ3UUneqsx42YNwyQ+dRBeL7RgD09m30QWTbnknf613aQy4bUl0ZmNHM1afLjUzlY/UCtVQ9KfvQ/S0mBZ1WbE1ePNnu/Rn/nkHnUSWX3P51XuYpWtV9R6ijIEZROfWFK8fvCK1cFVVvGJ7DRrXPF57TV45RVIT4/+TddQJdtrXR60YhGfkD8+6zh4EFqiRXVZw9Vj6qq2cHb5IAHs3Mb/MXrbC7EL9JaGp56CadPg7LOroYECkcSnLpPnykB5J+IRlLeOFmVQikksH4nZuJFw5y7MCZ3GxXzBuFV3CieiKoClWITYLXadxna5VpdCfP7leAD2ONtVat+SVPfMmCbxqYMIeoN8yUUM2zURJwEysqMT79U1BCUbm+nMblrRnm04s0XyNz0xUIhP269fEj88/HC1tUcZmx0OUcQYYkdW7U9sr34vjfgcPAitW8MttxiXH4riUxrxiczvU+OKz803i1obsaR03YkO4E+gbg9aMRUfX/wLmZ1tJD71zrlZfmh8uHiFu7h19/0Vt5cfe6z4HDasihun4XBXfMrK0eULC7XUQaB8xKeoCNuWTXRgK0OYychdr8G//5KVBa++emglUGxFYgITTEgFxOOi3B9F1bUGKi4xSRIMGgQnnVS9easqCpP41EEU++z0Zz4TR/0OVO2Ms7qgDMjjeZutdOSM3x8yLAc45xzjNpLNRnVB6UgcDmgu7eUkFpK0b3PUej90m8BNvC3WLcXHZ9Uq2LNHVEnXoywfn8o6N8dTfGosgeFjj8H558O8edG/6RqaTh5Qt4lPdrZGfHwWUak2VArxyd3npbHs39OcvbzV7dALlCqdfo34+KiKjwMfrugDlwfKi2uvvjrWR7ri4w3KtboIViic3YdLdbwnFOL11+Guu+JHj5UHVpn4XJb/Fk/zEOFAUPbFlNQ0JdZgxYlPICBEqX/+qbmI3vLAJD51DMGg1iEGj+4BgF0qI8mLDitWlJl3rnqQk8MzPMidvAqAzSd0Uj3xefBBePRR1DDhXbb2kXupMryx8Hj20IIme5fT9a/3WMgpjNoeXXMpFNJmNE78cTtf5Z4cPGi8vhVxbo6ckZUWzq50mpFKT40oPsEgvPAC/Phj7IJSOuLT0JoH1O3ZelGRRny8FuFoFi7F1OXZKsqueHCTSTMC4UMj6NdfDy1aCAJWI4qPfBA/znpFfOryM6Tgo4/g99+NyyQJ3nlHhK/rUZbiM26rULytSPi95ZBD5B36cWrEJxhUCcW+fdGblPcZs5cI4mOXgjzEsyRRzJ49kEIhJyLC7t3+iocX61UeT4xsIrUFk/jUAUgS3Hor3H23sX9qkKHrdMqhE65bB716wcUXV30by4I1L4cHeY5mZAFg9wmpQ99/Wq3wxBPQoLMIV/McKIq5r6pAuj+LFuzDYQvjSpe9qmP0rKEQTOd8judfbuf1MokPwGadcFSVzs2lmbpq1MensFC7VrF6K11Dm7vESfn9dc+Or6C4GO7mZT6+5FeWpJwBxFF8wmHYvp3AdmHm2kNLwHLIBGX6dMjKgtWra9bU5ceJF5G8s8KsYvFi8TltWhU2zIj6Rnx27oSxY+HKK43L16yB8ePhhhuMy8tSfJp6tgNwKVPwB8sxFMs7ilR8lL5CqQuoYOJEEQH/559l79pWYiQ1yRSxdy9q1CZAPmmRm5UJ/TNel+6vSXzqAHbsgDffFL6kOTkwj/7MZDBtQ1u1lcoR67lli/jcvr162lkawn7jQGL3RxMfBYFUURrCXlA+o3Q4XHH7sD2sJTB0NxDEx+r3RA3O1y25gf/oQzfWso0O5SI+mzbFXl6VPj6Rpq4TSv5kLgNolV91WYTjQt+QWBc+IwNvwxYANHFoJ1WVUSZViZISWEof9vYYwh+Nx/AW48nuECPE+8UXoX17hnx2OQCd2MInXMGwHZU3dYVCWtZon69mTF0hTxUoPpGspBqgHMJiEZ913dS1R3b7iiQYyv+R+db0pUlivUZKWZGKZm724SKErEIGNTNZpBvXn3+K/mfGjLJ3vb7XJTyF5nMZSXwKSebnxNHlaKQR+vM2iY8JA1as0L4f2FpIP/5mMLNwd2jBQH7nkuZzhbNKGVC88GujAwl5jcTMEYhDfDZtYtjmVwljYWmvsWXuV5JgzBho3rxiNmKrJA5sd9tJaCiIj1vyRIVopnr305rdJCAYSlUrPpX18YlUfL7NOZ0BzOPpVefHbmBVoqyTSk9n7fAHAWhs006qLnVseij3PCkJ5rW4mFt4i109zo1e8ZdfAGiQv4PJjGWHsxNX8Bk98mL4OZUTBw5onb/XWzOKT6B7LzqwhbOYVXniU43+dyDea4X4KIGDdfX5UaCYqiPnoPH87/TnE2veeijER2/qiqf4KPvUT9TiYW2Pi3iUpziY2ApANXUpxKeYpErlWdJfE9PUZcIAPfHx/bMCKxJ7LC1J6NiCOQzk98CAcnVESgdfGx2IFDAqPnGJz7ZtNM/fwGqOYWXbsgfxOXPgm29EyaiKRL/bZOJjcztwpQm5PwFPFPmwyMnp7uZlrubDuNeuJhSfWD4+Smez2iqibP6TTTXVilgMLALFThEq18CSW9aqtY6SEriIqfRe8h7NEY4QMdsqh/993e0xrmEy37W9EwDrISQw1Ptd+Hw1E87ut7rZRgehYMrER/JUsFO48MJqaJkGn097dxo31pbVZSgTr2DQeO/iER/9+cQiDfaweBi+ZwSWg+UoYuh2cyC1A3toaTB1tc1czCwG0SRzZczj6ydq8aC0z+fQylb895+R+FTGzG4qPibiYqXuebUuE7WEVtr7qJHE5VWda1PxiTR1OYKC+CidgUp8ZN3/ABllphaRJHj8ce3/ihQ4VYmPy44lUSg+CvHx+7UBSImY68ImbuatuNdO/wKXV/EJBIyd4aFEda21dBfHdnWP3cCqRFmKT14elpyDzGIQ89pdhUsWFepSx6ZHcTE8wWP0//R6jvKtpDEHCOfFcNSUX7Rtji4AuJLkkhVS5YlPZqb2vaYUH/0xbmQiZzKbYM8+FdvJ8SJ3C6mpVdcwHfR9WiNh+a6zz48CfXCC/l2ttOIjaQuDnnLIKcOGccewLVzKF7zCXZyUvh7uv5+XFpzMIGbz9pYhhtWV52Dz5rL97xrsXEEXNhC2iTpJyRSxfr1GfDqyleH5H5fdxgiYio+JuNArPokblgKw2t0HhwOuZxLX+98sV6bj2lR8FOLjx8FbjGdW86uAGIqPTHyas4+WG/8o9Y2cMwfmz9f+r4ipyy4TH0eCVrIiAQ+ZmdClC5x8slhPP6iVlsdHeYFTKGDbxkDUcojmCJEvekV8fNyeXJqQhcUrdqLI4n6pbJPnIUPXkIOZMYjPihWc+tVttGI3/3S9ErfsP1udhLuoqPLO0yUlWlTXyO0vcYAm9Jr2UPSKhSKJ20F/CgDuZKGyWg+hVlek4lMTPj7SqtU8z/1cx7ss5BT+4Ex8SQ0rthOlUGA13VSF+CQkQGJitR6qyqDvf/TverzAA/35xJo/KH6IAGFf+exIShv205R14aMgIwMr4sVoEs40rKscs6QE9u4tfb9jP+jHBrqS6BedVDLiBiWizU6bBXaVq416mIqPiZgoKtKckgEa7xCKz4bE47Db4Q1u5fXwrdEG3BjQKz41HWET9osXdysduIW3mNzyEUDrDFRLnUx8urGOu346s1RC98IL4tMqP6UVUXzWOHqxjF7Yktxw1FG82/IJ3uJm/vpLOJMvXSpeSms5Mzcrg9T9/I/NOQ0oeeJFw/lB1RKfe3IeJItmjNkuLkIfSYSUdioxytnVAvmkDtKQbWOfiv49oPkmJCaiKj7VNXCtXy/MIePHV2774mKN+AQdggRLgRisQx6ND/qF3J+QJBOfeqb4sG4d9/MCl1u0emQVHXRy/pU7pWrqTBTik5yMSpzr0sAYC3rFJxbxqaji47W41e+llVDRQ0++lPdteuNxAPzIMAPR0L+PpZq7QiFcAdEPTx36MQdnLuE3BgOwnybqatZQxZ18TOJjIiZWr9b6FTcemuaILM0bU/pgs2kVoiNNSbGgcAhJqvmCf5nNj6MXy7jCLcJfI0tWRCo+KkpJN6oQwn6i7FeFFJ+h6Ys4jmVYmmRAx4581+NRPmQcf/2lrRMIGP03IonP3LmwVQ6sUzq1gcwmmWIywxmG5RBNfCJNeV6vkQyVVqvLERK9hEdyEw5DO3YAMCj/m1LPu0rQpAmPu57jKR6J3VnJD1crdtOl8D/cTtG76dctKak6IrRmjdhXeaJTYkFPfEIOMdhE+qQBIvY3PZ1sr6L4iIf2UBKI6omP4uNjJ0AaedUX1SUHGoRsToZYZ3ETbxNaWbFowJz/+0z7pxpyKOiJT3UT56qCvv8pj6mrLMVnaMZSckkX23rL0WF/+CEfrezNIzxJf+bxiO9hpG++ZavjKNE+GimiZdQxS3Vw1tkdd7c5hUaD+5DaWrRrEScz8+g7AM0nqSIwTV0mYkJv5kqlgA2pJ7Ca7hSlNMdu14hPeWYEevGkpjuREmsyK+jFjuTuNOYADQt3QDhcNvEphc0oL24rEWhQIcVH6YwUxV4pW7FokbZOMAh7XcaSFcrgvX07nHEGjBol/g+FIJV8TkLkNylZuUXdR2R7FSgveoMGmmqlV31KU3xGBkWK6KHZnxqOkWNrHPN8qxRNmvCy4wFe545SiU9Dcrn3y+Np4swDtPPx+aBzZzihioqCK9dlx46Kl5wCo6kr5FQUnxjv06JFkJvL3z7h35KQfOiKj97U5fWKa7OCnuTRAFduZvwNDwFKOHvQ6mS85R3e5mas8ysWmaY4uX7a5qFyRZRWFKbiI5YpyVNLyySuYs8euvuX04rdnMxCHuIZpB9/oiQsLqAbr8EwoG9jqYqPHIfvw4k1QbBQ/btrT5BLVoRNxcdEFUHv2LyfplzSbhE9WI3bjYH4lOfFiEd89u0TiQ3feaeKGh0DyuDcMNnPAZrwy9p2UFgYTXwi2Uspio/y4jZvLj4rovjoa3Xh99MtvJreLDVcl2AQnu74Ib0RflV6xSdL5GFUZ+yhEJyGNni0mT1ZXR7ZXgUK8UlM1EJ29cQnnnNzKAQJiF6ik3cNgQBqjo2/EgaXee5VgcH+n/icS2j6TYwcNhENV0LalY5t1y7hU7BqVdXk9tETv40bK769XvEJK8QnGJ/MKLPmnAEjaEQ2Nzb/oeIHlRFL8emGUHVbrZtV6f2WBoX4hKwOAjYxKIZKKjgTUup9VZNPWX1XfMpDfMpSfIJBXf9eDsUn7InO3Bz2Bzmt4EcA2rDTQHz0xy9V8ZGJTyEpdNr3F/zvf4xKE8+mlRCORPEMmIqPiSqDovi0bi0+9+8Xny7XoREfPbueMUMc57PPorepKqTu28CDPMOIwFeElMeqpCQ6qmvuXH6bckCt+Fsa8VFe3BYiV175FZ+iIlYVt2cjnXFKPsjM5OEve7CAUwyrKVFXyqxLT3wiO7NwGM5EVwlZ9mkqrTq78qInJEBD2be0PIpPrDB35Tmw1ESxroICRgc/5xKmkrpmUfTvESeaYRcnpbRbf5+qIg+e/nDr1+t+KCyE++4TDltxIElGxSfskrN4xzHfBALaeaQ2cZNDIwql5Eq3vTQfn6C1ekiF4igbsjoJ2uTaZBUlPvL18Yeqp2SFqfjAJwUX0IJ9bKctHskdvUIEPAVa5mYlgWHYH8QeEstf4w5DEsNyKz4y0y8glc5bf4UHHqBfjiBTT/EIp81/FgC7FKiwu5ep+JiIgiRpis9pp4lPhfgoio/ygB+K4rNDuIdUa2bdhvtW8wwPc1HBuxSTpDYoSvGx2XC2aMxuZPtVOUxdCvEpr+Ij+fy0Zzud2YzDbVOjutz4sKC9icGg+NtGe65sM5dh/BTlm6R0ZqFQBPEJBgzr6durQK/4xKoQH0/x0XcQIayCoMnPQY0UrP33Xy4OfyG+x5qGR/TiDa3xiY/e56Cy0F9jA/F57TWRbbkUm5rfL+7dKKZR/Mk3eNKaASBFEp+8PBHqd9ZZ6jOSJmfoPxSuGZXHx6eNHCXuRpXfcSkIeWXFx+YkZBfER1ELyg35+ozf+7CWsrgKcTgqPpJk9AMvS/E5IyhUldOZy97UrmUe35cv79ClJTAMB0LYw1piw9JMXXFJi6z4FJBKKEGQ/KbJYjDRR3X5cFXYd9QsWWEiCjk52sBw/PFwAxPZEmrLczyAy4XBuflQFB+lhEW1diyyz4Rkd1CCHJ9aUhId1YUgAjnIEkg5TF0VVXz0/lAOt02bUiLs4GqTA/Dy5vNZwvFY3U7+5cSo/DnKi+vI3U9PNLukkieoPM7NCQmxiU+8BIb6exfGKkxyiCi5fr45VDt0vZsUq8fu04cPur6g/tsQo6lLP0BUteKzbp1u3y1Evp38Y/vH3VZ5J37hXJwXX8iBNn34mCtZ32SAccX8fFi0CNui+UhYcbmg4d7VvMONjM95ulLtLiqKfif1+Vp2Nz80J6icnNjXV5KJT9jmIGgXz364pIKjjj6EvxrKV9Q3xScQMJL4WM7NEH+gj0UYnIj7VN7Mzb5C0WHYEl1Y5A41HAjiDIsDeXEbFB99/1JqSHvbtnzb7SE+4mrCMvFxBYro3FnL4/MwT3Enr8UUSktKol03FYTDcCKL6cd809RlQkB5WNLSICMDWrKHtuwkhULcblHD5jrrZM7hZ3ytO5W5v7IUn+okPqqzqN0ek/jY7YieeswYOr5+G98xgmcSnxEexDEQDmtEQCE+xcXl6xwV4hPEhtNlURUfQC1NAWL/LX1bOZr1pLnEjuOZuqzeEl7gXj5LuE6cj1R+xae8xEdv6tpOWwAybS0NnaZDqv6CWHrHX0ush6Z7dz5rdi/fcwGgZW+uLlNXPMXnj3+Espi5Lb7Hs0I+HQ7xt7fbIK7mY2Z2jIiNlxsaThIRXSkp4M7ezY1M4mxP5SLpIqtl+3wQ8IX5iKuYykV4rYmV2i+I9+Coo7Q8g3psH3gNPVjJey0fJ+SQFR9vxV5+Szmydx8KFBJRXxSfyPlZLMUH4hOfKGIjSdgRK/txlov4BGTi40h0qhK6FAhybFj4S7zPtTEVH8U3Pa6566ijmNL1aV7nDsKJslm3qIh334W+x2iZmyE2gTvzTGjfPnbGlbA/yGJOYj79seTlRq9QSzCJTy1CGSAyMkSC1EaIqfJBGqmdwVzHWczgHAKJZVfGrV3FR7wRFj3xiTR17d0LX39N+q9f8DPDeJYHtUyCEdB3BBkZmmJUHnNXwKMQH7t46e12wjbRUUQSH0W5ufDARG7nNXxeSf0NtI4sL70d9/MC/9foCXE+UrSPT0WJT2mmrscQx9loPdow8E903lrm+R8qDMnUAn7BQs8+G0aMUEcsjwfy5FDc9LCR+OjvUVWYuvSd7caN2jVfv1M8Z3Zv/FxQxcUifHyc4xP44gvc9qChrSpk4hN0i44/JQUsDvHM2CoZ1aX37wFxX0vCbsbyEZcwlYDFWer2eXnxVc6DB8VvGzZEm+KKExqzmh7kpLRFkomPVEHis7TXOO2faug46ovi8/DD8MYblSM+pZWs0L9j22hP+x1/lNmWIksKWTRBSktTiU9Yl4+qNbtj+vh0EcJoqX4+Svv0xOf00+HYDkbiE0vxWbtWvGfKBFuPkKSjGJFVXGsRJvGpRSiKT0aGUH30xEfpDBTfmPKk0oil+ASDsHu3cVm1QGmg06G+JFJxhOIjn7DUSOTAKSmJb3fWdyxut+YcXC7iU6Il2FNmO5Jb8fMxmroU4jMg8yte404C3pD6Gxh9fEDU/gKwETaE60e2Gcr28SlN8VEcrm3hAIEAbKEDAPOlU8u+AOXAww+LkPNY11PfKVv9PnEiv/4K338vmMeePXTMXsxqjmHbxQ+wrZkgr8rAVZ2Kj9cLO3eK73fMHApAR9/auNuWlAi5fmLJVXDppSQ4giRQEh0XLzO0gEsjPlaH7FdF1RAfJaprOuexk9a02RJ/sJMk6N0bunaNTQhilTeJ/M3phLktLuU8prPhjJsq1PaN7QaziU5aw6sYsXx86hrxycqCZ56BO+7Q+lAFh6r4BEu0BckUY/OUnZl/xtDXaUYWi3qPZ07SeRzHf2y74/8M6yiqiyRpx2wm3Nri8459+2iSt5E08sQNAe0Gye/Ju9zAczwQU/HRZ4iORBgr+YiyJxX2M6tGmMSnFqEQn8aNjcQnm8Yq8Rkm/cjVfFguB8NYis+ePdrLWBOmLovDzg9cwERuINCstTGqSz5hS5MMEinmWFbgXRA7IkffUTidWiHD8vj5aKYuu5o/R1+2Ql0vGJ2jRQkrjTJ1eYppxzbSnB6G8CsXps1GkoxRC4eq+Oh9fGYzkBbsYZRzOsGgFpXkDVVNJNAXX4gZYKzCr3riYwn4o7MufvopU7acRDfWsveW59jWRvjL1ERUFwhzVzAg4dDVOorngKwPZQfosuobSkjigYURBXLlhvqcmqnL6lQUn8o5lEeaurxe8HtCtGcbrdmNrRSlyuMRSu3Bg1rAgx56MhjpO9Honxk8wpMcXzSX/Q278hPncaBJ9wq1PRhEq+xeDVERyiCckkKNlDypDJQ+NBw25v+C+D4+yn2RpNIVn8jaXJK/bK9hRXVq2BCK3I1ZxnEcTOtgWEdRfPTHU7hMXMfkV1/l/b+O4mGeRkqSV1YGE92g0ptlMSfgyn5j+fCEQppaZCkpm9zVFEziU4vQm7oiFR9lFvSgT2Qctq1bXeq+JCm24qOYuaB6o7oscpST1WHnOR7kJibi7dorpuJjbZrBifzDCnrhGHd5zP0p7bfbRfI/hfiUR/EJSjbW0ZUtls7qsvCtt/M/5yNIDRqRkSGvF4we1BS1KNLU1WrTH2yjA6/sGMlvDOEPy5lGGZfyOTfrJXPlHJUki3pT10a6sJeWZAT2EgxCG0SdnN6hJWVfgHJAId2xCIOSJXwhJ/HmmHnGqWswGLdkRSzn5qqO6gJBfLYt0Q4SwB7XuVIfyo7NhlVW7KLSAijExx5D8TlEU1fTpuLT54MW+5dzDCKLsqWUqu96whjrGkaqYHo0W/oLT/IYxxfMqbSakrF7mdrO6mAk+kG8rio+eqLw99/G38pSfCL7gqhJjjdIQKmwTpyEmhFQSE1amtZnFBXBxYgIzDAWVfHRH08hPnH7f11UV1HHY0WRxKkigSp9+6qrOQhEkadQSJv8xVJ8pBIPLRAzAEtx1TvJVxYm8alF6E1dkT4+yiwobClfyQq/J4QzVIJLNuUonYje7lqdM6rFrUfRj/ksHPK44XixiI+lSQZFDmG7ssSJ6lJeUuUFVyo4l0fxKWnRiW6s48zkf9RljkcfZMSqJ5m+pIVaFFGYukpXfJQQVSXhnWS1qb9Hjp2BgNF0V17FJylJ2x7EdVOUqaKg29DZ3MkrBpWpMvD5tME0FvEJdjuWJ3mEidxIUdAdrfjIDbIRIi17C609G9X9Qs0oPlv/1AomTmZclFlJgUHxcTiwO8X9s0SmBQiFIDWVEofwpTMoPodo6mor/NTxelGLzgKl2q/1k5hYJorSFB/lwZLsDloHtnIFn9Bk+W8VaDmcO/ce7R+LpULblgd64lNXFR/9NY5UfMoiPqVlZQcIpGfgJMBszgTKp/iMmHUT8+jPMdlz6Sxt5AGeo/HPH/MnQnGVsJCfJ0UcX6oQ8bGkpYmAk169xG+vvgrTpgEiCi3ykdXvM2ZWdd3Da/FUIu16NcEkPrUIvakrNRU204lNdOIAGeosKGTRvPfjIjsbR4fWlJCElwRe5q6Yik8wyCEPmnGb4GjOAvpR1PpokmxeGpCDP1dzbrbZMDA9b5JgMta8gzEdfSKJT0UUn8hyFQq6dIEOHbQoh2AQ9tjasI126jqKmSeyM1Mz/dpsjOMDxnrfIZgfLd3qtyuvj08k8fEWh3DKg/UEnjV0oj5ch1zjSX8NY+0rcExvHuNJPuEqQaD1I6vPpza0P3/RYXAnrpp5qeF8YhKfefNE0a1KIBiEFAo4K2M5AH/9BVn/CeKzhD7cyKTyEx+3kggy4n266irIz+erUV8BRsXnUE1d7dqJT5+v/MTnUBQflfg4nPTIn88nXEXP2a+Uv+Fo12c0X8PQoRXatjxQiE+jRnVX8dFfY73TMJRNfCLPJZ5ZW/HlK4/i0/LACvozn3RLHkeF1/EcD9J+1iS8COZoI0xRbkA93lgmk0sDuuX+HdVOA3SZm2NWJ5E70liKT5nER3cRTeJjAjCauux2OD9pDl3YxA7aqbOgkLUcis/ixVizRC/7LtfxM+fGVHyg+mZVemXnDcut5NAI96TXjIqP7oS9ibLiEwzGlAWUdrpcwP3388jPJ5FMYbkUH0O5CgX79okkMHl5qsN4IADnNFxEB7YRtImXO1LxAbkzk3s0yWZjIjfyRmg84Zy8qGPrO4KyMjdHKj7KtQoWazdpGD/h8cBVfATAblodcs1IvVkoFvEJhaAVu3iPaxk9//YoxUeZnSqVmxP9Wh4fSYKk7B1M5Abu4FUxaB84AAMGwDHHlJJMJD6SD+6ggDR+O9CbpomFrFsH638TxGcXIuW5UmIkEgZTl8OBTSE+ccxMCslITQXpmB60YQcn8m+F2wxxFB+fPjtlfAZbluITi2Cr0Mcxy6zC6q8Yq7DKxCeAvVqKqVaX4rNgAbz3XtUUlC8tWV9lFJ9AAF5/XfD/SOJTnsyAloDYqTvVhcUum2E9JdzAJACKSMIrJzn0+2Ey15BOPtf8eEFUmw2QH9QcGuKyBUVtoxdfjIqHrwzx0RO6PbQs8xxrCibxqUXoTV2gZYoFbRZULlOXPJj8yhDu4DXmMNCg+NzDi3zPBSIfUDURnw5ZC7mN12mzdS4hOU1+uMRnJD5ffCHIz9VXY0tJxCPPVGLJOGqBUts+eOEF2uxbzDn8Ui7Fx77iP1bTnU/ydA6sl18O3brBjBmGSDm1JIVNLhbojc7PE6n4KJ2V4g8Uq92gdQQtCjfQas4nQGzFJ9L5MFCoDVIhbJSUaJmbbYQOmfjoyWNMH5/92fRlMdfyASdt/8LYi/t8ai0ohfgkeLVw9vx86BJexw28y9V8JDit3sv3vvsq3N52O/5Uv986dBMAqQWC+BSnNCeNPLL2xL4okYqPw1V6BmyF+KSkgC3ByS7akEmzSg2kCp9vIi4TPh/Y/FWn+DjwYyUUTXxkfzvJ4cSSIN4xZdAsL6wh+T3AXuW+gcGgFn1U1T4+V18N119vTHRZWZT2npWVwDCW4jN7togQu/tukLbv4DuGcx4/sZIe5FN2uhJbUNzDhHQXFtkM6/Dk8zwTAEgnjz0Fwjlf/8omeQ6qbYiCz6eWD1hBT+wOC4wfL97T/HwhV599NqCZug4c0MRbvx9asIcerIwd1SWH2xeRxH+2E8s8x5qCSXxqEXpTF4hZpoIoxSdQyrRLHlh20RqPnENHr/gMYSYXMJ0W7K02B+eee3/hde6g84ppBHVp8g3Ex2oV2nZKSpnZm5V29g7/py7rwNZyKT5SQSHdWUv7kC5xhXJBPR6DqUvpqGbd9C2DmcnesIj9jOzMVIXAZleJj7+4dOLj8UAyhYz85AIa330V43kLn0+bocczdQWLtF7TgoTHo2XwthOsduLj/nwy0xgNgC3kF/VUzj9fNNRiISSbA7MQXrtubx4Wwni9gsO2QcSbd2Ej/rwS4yg+ZYqYllcA+vISV/QV/kSFpLCZjlxe+A55NCC8Pnb10pIS8V68e8YX8NZbqqnLGqn4vPACDBlC19XCnyElxZhtvDKqh3KfFTOn12skPiX21BhbCegVn1jEJ+Txs5M2LKdX1CBrCWh2YotbVnwqSHwUH6ifOI/w15VL4BgP+kR3Van4SJKmcJeSEL7ciCXCKO5OlVF8lElbTg6ED+YynB/YS3N6spKZKaPKbI9NrsmVkObEKueYsvvEgxLCSgi7apKL1c9HLlu7Fj6+ZxUEAuRaG7KN9oYSP+TkGOTEIHYCAdEVHHusCPH3+2EPrVhJTxx7tkcdU1F8gtjNzM0mxEtqUHz++os5W9vylTzgqM7NKvEpZbSTiU8hKYxkGhfyDT6f8OfZtSPMIGYD8GCEv0hVwqIMTg6HoT5QVJFSGYmJwokbiKn4KO1c0HCYqhJ0Z025FB+lvIdCGgHtZfZ4DKau3/L7soQ+eDv1YBaDyQ8kqr+p+wuhs+Vpio+vSKxksWiz1kjiM4avaLB/AwD38BKfcZmq+sRzbg4VayOZEz+eggBTuQQQs7rqNnXp5WmHXAeIH34QBGbMGDxnDuNxHuN7hgNgkSTSycPnE6RKIT4JeEnYv8NIfJ5+Go47rkLt1WcRbuPdSP/+8CwPMajtZgrShamrMCu2/0AoO5c8GrD22Ith5Eho1oxpjGSu4yzjisuXw2+/kZgr0kakpIA9L5uXuJvneOCQiE96uvjUKz7fMZxlbYfH3VZ/yWKZumx7d9GMLHqwGk+JUY5SiI/F6VCJjz1QQVOXThGTtsfITHcIUEhJaqroF6oqgWFenvZOVUU/F+s9U9T5yvj4KMt8PgjLZUX8OKP2EQ/2kDipxAYu1f/MGRDER0k94PNpxXBz5QSjWc17xjzGgw/CQ282Y+Xl/+ODlDsBi5gUKhK0zn7crZOf3iwnGIStW+WxZZfxOjTcHp2aROlLnPjrVHl2k/jUEkpKtBchIwPIzKS5fydNEEk7lIH0o6YPMJqvONjttPg7k01dEhamMZoPuAafT/ChNsEt6mrNyKw+4hPS8viE7Vq2WI0vSHDZZUJGzc8nKQne4SaWjX5WeBxHwODcfMopgCA+5VF8VOJj0Tn5KMTH6zUoPj3DS+nDUhLdwus7skgpiM4sK/0o3mI86zsNi1J87PbokHQQ73l7tol/jjqK9mznWFaqxCfS1KUcszjkZjmis3IQwF9knKpVt+KjjzCxh6OnjoWnncsTPM6fzsGqfbYJ+6OID4BUWKTZ/Pr1g4ceMtROKxf0J7xxI/feK74OGgRSomCNxftj5Aj5/nsefa0hd/OSGskndT+G0UzjIddLESclZJXcoBbObi8p4G5e4WbeqtQ1jyQ+Xi/stLZjGiNZwCmlkqmyFJ9gWHTdxSTi9Rmjrn44/TVOZgGbOp+DLVG8i4qZpLywhbRnoKoTzymTF8XvzeUS2bXtnsJDYj96B/eq6OdikZHmzcVnJPFpzU4SKS5V8TEQHyWIQulLylDiJUl7F5MbOtWIQ1dQvFuJePiNs+jEJvLyxDFasofebQ4y9fZFMY+RkyPUmgX97uOVxIcB2Z1HmYkpxMfhwOJ0qOehV6wDfo10K8lfDe2WiU8iHm7Je6r0k6xBmMSnlqDMul0u+TmTRyNFBVHGhhXpA5jGaIoat4u/s3vu4b/rJ/Ibg8U+8eH1Cv+eE3SOmS58akmGqobiE4DdTtgRTXwSpBL4/HPhOGezkZgIE7mJfwZOgI4do/bn90MDcgSh6C6Sr3VlPbnZZU+9lU4lXIbiI/wkRAPbLvuea3mPlBLxskfO4rY1PYlbeIt/jr85ivjYbBrxiVR8WiGnfO0qqi8nU1Sm4nPQ1YIxiOgiBwFVWQJ4ikeqn/joFB8bYZG1efRoeEVEBhnyD8lJapqSpZq62qJTCIqKYPhwcbF/q1hItYoI4nPeeaJUw//9H1iTBKMpyY6h+FxxBQAvcS/Hbf8G5s6NXxdKllhyfNF5fOwEK6z4SJI20OkVnz9cQxnNNF7i3lL3WZbiox84IyfS+1K6sIiT8Tdoii1BPJjWUMVs3N91ulc7VhUTH31EF4i+bjLj2HIgFd5+u9L71Tu4V5fio2RBNpTUyd3ITtqyhY7lUnz8fu3+dWYz6zmK0dnvlNoWr1ckAiwiiaRGbpX46HEWv9OIg+Tni2N4SMSb2BBbkjuqzaBdo6KiiICQSMUnKcnQZyrPm99v9Edc1TCi8C9Q1LgdK+kBgCtoJjA84qE3c1ksqNMghfgoHXS5Slb078/qU25gJccCQlb0+cRzqyc+ViQC+dUTUmhQfBTi49OIT1pQnuY5nZCUpM7Ai+O8Cz4fzOV0PlnbBwIBAqeewcdcRbCwpMzZkeIIXhbxCfi02P7OnzzMe1xPW79wno0kPmrJChulKj6Rzs2xiI/S8cfz8dGXrHDiN/gSfc/wGiA+EVPdVatELo+774YpU5C2bqM7q2niyIVx41g34kH20iKm4qPeYIUd/vQTfPVVxWSryGJdmZl0GdCcxIEnY0sW99WfG+NB0t2MUV+MgjvvlN8rKXp2KjONg34tc7PNqTmUxyQp+/fDzz/HTcegLFZ8fIJB8Uw8z/2soysnb/wo7imXpfgoA2c6+YRzjbHWerXU07wDFzGV946bGPdYsfBbq3G8xu3iWNVEfBTFJ3nTMq7gM/HPITgh1oTioxAf/W/HZf4ifiOrwooPwFFsJM1/gNKQny9IUqqliMTeR5HVuDv9mcdLnYyEKQGPweTndoRIK9mHC2/0+Xg8jOFLHDu3qMqN04nGSLfJanVBAa/vGM6XjBH+hkHtPEKF2nhyINQwqt2BhFS+5CLRllBxlUTbVQVM4lNL0IeyA1HER1F8enj+YTRf4d6xodT9FRdrdl4bYfwlQYqK4HiWGNYL5VdP9kw1IZzTwe7UbnzGZezv3E8jPgH5hBs3BouFpCSh6KRsWQ6bNkXtz+8XNZaOKloK+flY587hZutEikgp03FRMXUZiE8M5+aQXxvNlFpeSgbqSFOX1VtCBvtJCBZyp+sdzmM6uU2OAqIVn6IiMehVVvHxlwTJoimt2EV7tqnEJ4gNsBxyeHFZPj5EJlPTJzHZsoWMZ+9gNT0YGf4a7r+fTVc/w2Y64/NBzoGQds6AtUT3vEkSnHceXHRRhVI6G3LuPPqo6JAzM2HnTuwNxOw0XFxS9mDncJC4cTkSVraF2xjPXW5PtldTfGwuzaE85nU67TQYNkxN8KaHXoVRiA9AcUGIFuylKxtI8sS325ZX8QGwZxoLSR2/+iPu5BUyirdDw4Z8xUUsanRu3GPFQiCg9SdhX9VGREQRn3Xa5KyixVT1qGrio/QBStdhscT28QkHw1HblOXjo0RGKrCEYrAsHZRXMDVVxIiEklKZT3/WWbob1nPjVRWfJ3iUZavsXHF/C/qyOIpTdihYzpdczLgP+hkj1xXXg1WrxKfNxun5P3AOvxheW79fpN7IoQH5pFLkjVahwmGtZEUiJXUmV9NhQ3y2b9/ONddcQ/v27UlISKBjx4489thj+KuzTsMhIDKiSyE+2YgFiuIzOvMNvuIiMv79OfaOiorg889psPJP1VEOxItVVAQlJOKzJajLg7lVUEMgBvSKz4pmQ7iCz1h3+ni1I0gNyIqPPJtITIRreZ/r3u4tHF4j4PfLDnEATic2mzaAlOXn47e62UEb8tzNtIWnnQZ33gkDBqiKj0KQQCM+BAJIUrTic9Lq99lPU0bMuI557sH8xHkUytmn9YrP4sWinffcA54SidZyqQmF+CTgJS87qNbysRHkqJyFOPGpx+ywaSYlJPEtF7KfpmrYvJ0Q/Zhf/YqPfIAPuZpjmx8wZoL0+8GnJcgDYzjygRwbDckhK1mYL23eIvjsM7jkEvj6a03CjJntLDbWp/Xlf9zH7Bu+grvu0ohy69Y40kWnmkSxMZdPQYE6Ol3Nh2KZw4EzQVNxDIOjzDQOeATxSU3VFymVCAZiTFWVUTDGNFYhPlarZjkAeCr/NlXdiCqboUN5FR+AUJHR1jVo5Su8wt1k5G+udMRUh5wl9EAMfFJ1+/is0ZxiFaffyqBCpq7vv4dPPy11FeU969UL+veHK6/USJB+WMm3pKvf45m6IhWfyEzNUQk1I6CQXyXlifJKLg8ew2Bmquspio/Ph2ouB+HvEzkUHlUoJsXbMk4gEBR+Yg4HcNttomzFnXfCmWeKk0f0x4WF0JV1nMtP+HxQlNqCq/mIN7iVjrv/JBL27ExGISYGSRTXGeITTdHqKdavX084HGbSpEl06tSJ1atXc91111FcXMxLL71U9g5qGJE5fOL5+IRtil0mzouxeTNcdhnnJTXBp5tph0p8FBUlMpSZjL00yP++aE1GMJNwQfUoPp82vou3s8fw8OkdcckTYL9f6whSYhCf/aWEs/t8OuIjSzSNE0tIOniAkpK2pbZle4/zOJ3zGHocnK4sPPdc8Qc4PpLb59ENPLIpzE6AUKiUcHarTVWMlMFNr/j8+afoMGfOFL93Yy1/fLKbDt21mVnx/mICAdGDXcv7XD35FtbwP9YH7wZA8shlR+Q8R/p8QRO5kWCw9LptZaEs4lPY5ww+nmLndwaxL9A4Ko+P0mlLDgd4vaQf3EMHwOfrSHY2FJFCZtsTabpmC3ZvEdKSNVimThUpjJOSxPS1AsRnadoZzOAMPjwJQVAefVT8MHAglkaNmD7DwVZPBzIzoU0beaOiIrjwQpb+fpCSAtmu6nDgSND8dnw+VJMrFguSxUJWiWbq0ociCnUwortUCE9kyCLG5JUOhyBA4TC4dUVySxvsylJ89ANnuMh4LRV/HpvbQaJUzIX8SvddFuDCuMeLxLNLh5CGeC9D4aotWRHp4+NYpRGfYIkfW4xtyoNyKz7hMIwYIb6fcII6KYlEwC/xPcNJ3ermjK2TISmJp2T/XD2JyLWIfmwBJ2OJY+ry+1ELJvt8kHXSBfTCx+uOe7gp8IbmIxkHhQe8/M65uHKc4PmW9LCHW/kMd7aTF7mRFY0H0jN7tqr4pKTo+k8E8dkbcYhuHkF8NqcdT1B2y3M40MpVgMjhs38/NG2KkwAF+RLr6AbAT+sW4U/vyzB+4nre470DbsDo55O0az2nIjJHJ1GMx2NUQGsLhw3xGTp0KEN1qdU7dOjAhg0beOedd+ok8YkydbVoQX7TzuzNagFoxEeyll6yomjTPpKBgsTmBIt1HbWs+AAkptrx2FIoDhYYcsRUJTbau7GabjzQFlxOCSd+QoUhgkExsqT65BOWe7ukpNLD2SMVH377jbW7hrKCnhT7Y5QUj9gWiJ1+vaCAKT+1xoKfj4t3spfm2AjRQL7gSnbSSFOXPnPzoPBvOMjEtud0oI2B+OySBZ7Nm8FqtZBNW+jXFpIlihMaketxU3zQi98viE8XNmINh2jCflYpHZM8LTqNv3iDW9hdfLPaFjtBvIeg+OjTKKjnFoHcAcO5Rw5VT44sWaGr1WVxOODTT+l7/fW8xjBu9/6o3sqs0y/mnjXHMU/qTyh/jehokpMF08jPj+/cFQOqySFcAhdcIJK1tG0LEyZAcjJPT4V//4Wb9WUrWrSAb75hXC84ccW7YpnDgcOtma8MVpVduygpDLMxVQzyKSlAWBuChToY0V3qHb8ioCc+IN7nkhKtBhtoZHr9etEfnHqqtn1Zis/BZjoTRwSJtMvEx+JykurdzzeMwrMukYoQH6VMR2c28uHtnWlWxvoVgcHUFQhgWb1S/S3SBFQRaIqPhM9XClnTM8l//41LfBwH9nIB02E/cN4B+OknnM7odBcHLY34nYGspRvHlaL46L/7AxYCOPE7kiFQtuJTlO3lfOZAEWCz0SB4gP9xOzmFDXiRGwk6RP/lxktenlBhI4nP3xGXtllATJS32rRizjH7TN3C4nytnb6wA5sfihCSpt0bPanWj1t1SfE5bExdsZCfn0/DhtEOV3UBqqmrYViMRu+/z+y3N/IbQwDNfCDJio8Uw76RlQX3XCZy+OQktAAs3Jcxmcv5lLxQikp8kpPh8uPXk0wxWe1PqpbzUV5sux1OOfgjPtyc+8qZ6qCV7JNHRNm2V54EhmrGXacTOnTAikRX1scMm4zVFkOtLo9HZNz64w8SgwUk4KUk5KIle2lGFpZkYTJxEFDH9ut4l37MNxAfbDbuLnqCT7iKlA1L1HOOJD76RIUJCYDFwjtPZtOa3ez0NVVnhBmIByGbxmq79fWcbuEtMi3NOZW/xOEjMzf//bdwFt6yhVhYtw4mTdLIQ2Fh7LwjeijLnuJhXiweb9y3z6c6P1ucDkNUl88HvbdMYxLXk9FY4mXuYSl9CObrHkRFYqmA4pNYfID2bOXYH58REjzAxImqDUmpfh6rbMWIvW/xLjeIfxwOLA6N+ETOyguLrYAFi0X2u0pO5jj3WrqwgaA1ovAbwD//iM+LL476SenglQmM8j4nxFB81p1wJdv7X85372lSnKcgwI28w8vcRYPcrVH7L3GkMZ9+AEjFEYpPWNwfq9uJM1EutFrBemPK+tWRudlAfNauxaK7EYfiSJ2ZCe9wIxvpEl1cSwfpoNbfeBatiLue15rIAk4W//zxB1xxhcoB9NdkqfNkruEDXuOOuM7Neh8f0D3+8g7tUqD0KL+Duh06HNjlDOQNyeVa3qPrwb8JWWw4CFBQEKGYI3wNI++jMyyexRxvorbMiegsPvgAHn5Y7oh1xCcvQKFMdIpsaSStW8JdvCq29ccgPkHtpOZwpkl8qhubN2/mjTfe4IYbbih1PZ/PR0FBgeGvJnDgAFgIc/3kvsKZ7JtvSEvVfAVUxacUU9fGjdA4IHL4HHSIBBN/tB3LFC6nIJhIp1XfsYpjuPDP23C6xa2uLpenkwtminDwPeuxuOQQ2oAW1fXX6Y8IZefZZwExsKjEJ04CQ4Pi0749XoubBLzYd20rtS1t//2axZzIZese1hZOmwatW4vQahnBEu1iWFzGejSddv3Bu9zAfPqLDkln6gpZjeUt9IqPvjrDAObyLBNI/Uv4Z+kLlSr3oYlMfA6QoREfn7F38BcH4pesePll4SwcJ1T83nvhxhvhjTfE/3ozly2O064l+wCt2MWtvMGN0juGBHZFOTrFJwbx6XFwLtfzHi12/6NynNAhEp9Ltz/LVjqStm+92MfNNxsKZzZvEsKB31ioNDcXJIltodbaModDVWeifHzQlJXkZDnS0mpli/NoNtGFkBSjq1Qq/sbozWMpPhCp+IgbOaLoUy5nCu/cupYV8jh859LLeYfx3MWrtCiMDmwIBoX/HkQXf1TyvdjcDhwJcqZqqWIe8XZJcaiveuKjvO6NGgFLNTPXl4yhuGufSu83K1PiRibRmc10XvZV3PX8WVrdGP/f/8Zdr8TVgH4sYMKpYtLBt9+SJIlnWX9N0j372EE71nF0qT4+euE05Z/ZfMZljCiZwhY6cJBGpSYx9OSJhzVgdYLFojreA7zH9WxtcjIT7gnyLjfg8Yj2udAe8Jbsidq/MywaebBYy6vlcCDekVtvhWeeEYxdSZwFlOT51We4RErAka11eC5//Gq6/3ACD/NMnclhWOeJzwMPPIDFYin1b/369YZt9uzZw9ChQxk9ejTXXXddqft/7rnnSEtLU/9at25d6vpVBSXst8mOJSLhzhNP0DRP6+AiFZ9YHq1eLzRHPHjbfMJEptjNvV5w5u/nGNbQsHhX/PwlVYRL897iPa4nffV80KXJV6uzO6xiiicrcIbMzTk5UWXj/T6JtXRjX4Ojxahhs5HrkIse6QtexYA7Zx8n8i/NSnRKhTIC6aAvBhp+6FFGWr/lL/rj90PTXO2ZCoU0R1TJphGfoFcLZ1cmRXo/19OZywSexz3rR8BIfJT7MER2TPyIseq1iiwoGfQE1JIVUcRHuW4xzC3KsUCk4PH7NaXxLcaTRVPcufuitmn78q3sog1pyFWb//c2DyOcGwIhi5oZWHI4DcTHUyKR4RWh7O6GiZzm/oeurEOS/cp8jsoRH0UZKWrbXaTnffNN7ccXXuDdyXYmcqOR+PTrB6mpJHh0z8pNN6n+OAbFZ88eGDqUhneIvD8pKdomymWNORtvpjMARbADA/F5/XWm5J2DGw9utHtbZEs3bDPa9ykjR4pEcH1yf1eXp/r2Rx0/MXMrg5klHywO8UnQFB87oQpV7lQUn+8YQcfJD5V7u/LAoPjYbNCtG28k3MfFfMmB4aX31/EQDmOU/LzxR9jiRm24i5d5hxv54cYZcddTiMLmZqeqz3mTg6IImP52e4PR0X/Ks6U8P5GKj3v7ei7jczYl9aITW3iMJ0slmCW5YodBuQ6invgABJyJhlfL4CpAtHNzKIT6LB4o0vpGhwPB+tu311bW9c2B/BLxLAHpu1YZ3mNXMIb/aFBTDqFq6rFVBeo88bn77rtZt25dqX8ddJl/9+7dyxlnnMEpp5zCu+++W+b+J0yYQH5+vvq3S7FVVDMOHBD+HYCYYhYX0+7tewHROUUqPrFMXT6fRnxWZQvFp19gLufzA87iXMIl4mWxuF2M2P1//MgwWiys2ro7CpQU91anHatCfIJ+Y60uHQymrnA4yoPTH7DQm+X878q1KptTCIc+oiUWVLuyTXfQGMQnIXsX8+nHb5yF7YzT+MU5gt20JhAwSrRGU5dda0cMxUcPJazb2qYVACd+eit/cwod9syL2cnFU3w6HFjEv4gCf1HER6kWGKf+lbLu7t1ajViA8bxDI3LovDLG8xDxrOXnhHiGh7Egse2hD9jYbxwvcg85ae3VAcGND7ungNaSID7JqxYyI6evSLgoKz73PJ4MDzwAkydDz54x2xsLiuOn1WmPJnjyi5JICfv3y8sCARH5VVTEnEB/AopvTvfukJjIHPfZzOBsjfgcPAgzZ5KyUBAJPfF50Psoz/CgyEAdCb2X5p49hp8MxOeOOzjDM4PRfK3OlofxI1+2vc8wqLRgL1u2wO4fl5Ee0swxTdgf5efTcIfm57Yk3Vh+wyabumwJTlF7SUF58yCEw1jlfuh4/iNt3SLtt9mzRZDAww/H2dgISYKdO42cy0B8rrwS1qzhhYbPA5UfGHNyoH14s/r/rvRj465blJDBq9zFeN5h/c7EuOuFvX5aspsGoWw45hgAMrJEYIH+/b04V+TSsREmFJQM56E8S/qoLoCARxcgIKNU4pMnflTKARnuKyA5XAbio1fM83sP4HMuNezf54NHeZKbeJvlJV0A4XytOGAbsuknJ3PZKB8WwmrUI0CDvWuQSjSCmVAK8Qlhw4nPVHzKi4yMDLp27Vrqn1Medfbs2cPpp59Onz59+PDDD7Fayz49l8tFamqq4a8mcOAAHIWs8AwaBAsXEpj0IRaLBZdLU3z+bT+Gq/mQDceOidqHXvHZ4hWKz/h/x/IDw2leuFGNDrImuOlQtIph/EzK3tLzARmwciWcdBL8/nuZq9oU4uPS6gPZglqtrjN+e0DMuDeI4yclgQ83Hza5X5i/IgY0ZVBSrgNoxEcfhh4LCvGR9GwrRpkEe1Ee/VjAySzEajXm4gnpBKhQCLan9eQjrmJ/+76EbGJFhYDpfXz0UIiPpbUgPmm7V3MKC0nM2xdTeVOIzzZ7Z77nAnV5Uolmn4oiPpvlzv7jj6N3iDbWOfDzv/+hkoN/OR5AkJcIWCI08bz9WmO9Xlg+4Hbu40UONuwMCQmEk0Xv3pQsNXmhrYeI/EiimECO6BBXbkumcOBwGDtWOCeXE6rjZyzPS7m3T6JYu6Zbt0IwiJSUxKZgOzbTSSxfvx4aNOD6lr9wAdO19WVnOL9bF9El407PMzzIc7GjIf/5R4vJjpgwKR18E7tGYCQsLORkfmUI+2hOKGR0/mxlFWZry++zDPtqSlYU8VGiuv7gdHbajPfwji4zGMQswk2bGzP8ljcPQsR6Fr/uYX3jDfjlF3j++XLt6u23xa1WHs9QyFiZXYE7wYKFcMzCv+VBZiZ0RCi8vzOQNRmnx11X7zi+cWP8fTbYuYLdtObZ3/qIdAwPPkhhh+i6V46QxmiUYtLKsxWP+IRk4hO2Ow2qUDz4CsQOJbvoaJRiuwqO3fItI6eM4Freo7hY7Ksle5hw6Q42T5zNBJ43tNnrhW8YxURuYptXTJoNr5de8UlMxOp2Ahb8+RpzCQeCBjNrYrgwSlRUnu/+zKeYJLyeupHBsM4Tn/JCIT1t2rThpZde4sCBA2RmZpJp0L/rBpSXX1V8jjoKmjShQefGTJoEH36oMe+dTU/gY65mT/Pjo/bj9cLDPM31TOI/hG087NRsWpL89lkTXARcgqlbiyuQx2fECJGY5qyzylxVkcatTjvWBI34KH3o0cunCodU2faizE6eT39eROfoRxsianXJUAu2llfxsZeu+CgvbQgblsWLuDj8OV1ZRyAAG5L68BenMpmxhEKwqPkIxvIRm06+kpAtvo+PHmoiv1aC+NjS5NlSURE+HyRgNFEoCRV/Tr6IEXzPQVkRs3m1nvphnq5QHp+Htl2DhAU/LnavK+Cjj8RyJTO0krDRgIgDtHz7ISYzll4sw+uNQUpl1acLG2mIbFo6+mhAJGy88ZSVpJPLQk6O54NdKhQFI1aafiX7YxLFWrNl03e401FIWFX/KL7/3tBulfjIrMLv1HL4KFC2VbKBG5CcLMpUQxTxUQa5TiFtomEnyB28ztn8ylL6EAoZn+WWFkF8kmTlaTuCHDZhf3RIuzyKxSpZ8Y+rP7MZhD0lwWgSKa/iY7HwSvqTLJH7FIu+svvcuRXa12o584KSC09vpdYTn1GeTwljo8u955evjRHQE5/NdCrVpB9esYpz+YnRfMXIubfGJXEKuQxZHXDNNfDMMxR1EQV2DSRFdy2U50S5/8qzFGnqUszkJx+YzpLwcbzMXaUSH09hEB9Owi4xgVOcmxU4gl46rvyeXixXTV1ZNKO4URtVHYpUfCJhID56xUdXsqKgSKMMNk+x2oeu5WguY0qUYpfV4WRuQGQNtxOKqjtYWzhsiM+sWbPYvHkzs2fPplWrVjRv3lz9q2tQOmiV+HTpov523XVicqGgtJIVXi/MYSDvcT27Eb5JFnkEtgT8WOSn0JboIuCWiY+nAnl8VNtB2bBJ2uCkFEa064iPuyQ6jw/Ej2p25h9gA1245T1Nsl7UbATvcw2FSWUE1+pDzBREEJ8/OF21zQexw1tvManoMs7hF/x+WJ1yMqfxF9cwOapkRVgmPmF/bMXHIkfSRhIfR7q4B+5QEfn5YlC9vckX6naKc6Ba00dOSGn3i85lMScymWsqlLm5W8kS9fsF/MC8ecKpvhliQqD46xgQQYYabvqHsXzEv5xA27fvw713K23ZToJNbGu97lp2XvEQr9whD/7t24twcgTxWbbaQT7pBHCyf956oRhsKL/yqDgBK4USDUjSssKqM9qtIgrK37azcd1ZglBEEZ8inQ8SRg4etsjEJxDnois+gXriEwwi7RfOVB38goQV21L4QVbxxvEB/3I8l25/hqAuN0Hj0H7ceGi4TuQ9mcJlQOmKz2Bm0T7b6KCrnzRYE1xcw/vcnPRRnFjlGHA4eC3lESbwHBDxjJSnjLgOyrOsvOeRldnp2hWOPpqW0m5j4yuIrCx4gsdoy3be4FaaZK2Ku27q95/wE+fxHBO45OCbSFOnxlxPzVelywAfqzSNmuMLjfiUpfiE5bD9xFAhvaRldGRLqaf+n+1E3PiY/vJmdcdDmaEqtwoS8KimLpDD2m0hmrMXtzdPXc/ng6HM4Azm4JSdoA2Ttwjic9Xi8XzFaEKFJbyAcHa2eTXiM4/T2E77KNe9gvQ2TGac+n91lUyqKA4b4nP11VcjSVLMv7oGhQzcyat4P/4SBg6Mu27T4i2cw8802rU86rdYrF0xM+Hzqb4i9mQ3wQTxBto9FVB8lIi4u+8uc1W9qUtKTeN7LmBZ6/PlQqB+nD6ZcOny+AAkFWXB8uXRscgeD13YRKODmhb9ZfcnuY732Z9hTNMeCdUfyhbb1PXrac9wJn+w2yle7hA2dVBQoroiQ76tfi+JFGMLB5je6mYu5gv+yzhbHCZC8WnbFponF5KOHFLbsiUAdpn4JFNEZib4cTGr0cXqLC7RnwegysHnp/9FRzazKSyyICsqTUUUH3tYO5FLrF8C0JhsOshV4w1mDBmWOAewEyJ99Xwue/MkttOeNl753tx/P20+eZounSUhVfbrp4aaJ1OklvwBaDz1DeEj8vnn5T6HUhUfnalLbbbc+/pd4pkfkzxDzCi++w68Xv5ekYQHN6FcWUaRiY/XFk18FKfMmIrPPfdo2X/1ZvUrruDq+5pwLCvE+9irFz+3vpFcWcFrShbH8x9NPdsNxAeEs7st6CebRsxHJPaJ5eOjf0CH7pusLff7uejAm1zPJJzWIHaXjclcwxT7VbFlyTjQl6yw6hWfChITRY2KJD4NGyJerI0bYf16vK60Su1fQWYmhLGRQ0PWcAzP/3Js7MyPoMpOvyJHBq5aFdPZXlV85IkO+/bRfM3vJFJsbGYM4qOQHCVrd6Tioyh9Ppt4fu0ESz11JTo/LV3OsJzoYCZD+QuRVTlg1/L4lJSApbCA/+NWzv/rHlrfOYq9tOQCj0bwfF6JGZzDHAaSJvdTcU1dSUn02vkDo5mGozBHLUFh9xUTDobx48CDmFhGXsZwGII4VD+7UEHdKFR62BCf+gRlxr6ObjBmTKn+Dids/5qfGcbxC/8v6jfLgf1cyhROkTNjArItVszScoKp7KIV1oxGhBPiJ5kqs6Hl6DBtcpVzm8tOqFETRvA9b5z4KcEgNEJWe6xWtVS1ovg8V3gz9O4dVe9IMSMp/jT6ZpQ16fThJptGKtkDRP6gHj3AZmNLZ0FYFNNSyGI3EB+/HxJKDjKP/syTw9mvXHs/xSRzws+PszGjH19yMVsdolZXpOLTtCmc3FY4uxZa09SR1JJiJD4gZmShZNHpJ/hFB/TK/svx4eRsy69spSM+v+jsTuSfqJIVRbc8AIAUw4cJwC5pvelgfqMBOWpHJ04+hpIhKz638Trt2EZ+ojF6SXE2trkjFITx48Wg8vzzao/fkBymcCmTuB4XXrKLKx7V9YdzKG8xHt9RMRxWdaYu9bmQR5iATVyT3OTW8O67wrnZbichXIIbn5a5W2YVJdb4io++rpuKKVPE57JlcN992nJZQXiOCSw7+lJYtozPerwg2kI6zyKipKzhIMHUhqSSTz7CJpLBAeZ3HstXjOFv+nFlj2UM5de4pi4AZ0B3LYuLeSz7ViZxIy5HOKZiXOZcMBCgi3clHRDKmYH4VLBeSjzFp1EjhL1fboyi4loqGXaqzJtsaSlkK9GiesatgyVPNGI1x5BLOpZwWETWRkK+xorCS9++nPjQWfRieQTx0UU9yZOTMhUfmfj4ZeKj9DvxoNx/xXSm9DeTGceFfMO6k68BNMXHVlzArbzJyUveUDdKCmkPkS9fa4xCWgzEp3Nn0V82agTNm6v9cKDYryYsdPiKmNXvcRqSQybNeIzHo17rpMwtXMg3OOTxIZhvEp8jFvq+I0a2eyPkFWLNwpN3r2cKlxukRKtsZrL4fTwaeow27MJ/5/2EEuWH1VsBxafUFMgaJAnull5iBN8i9T7OYEoIBoXCAIhpnjwzVhSfA+HY2ZslpR6UXTt2osVDKvllZnf9qedDZJDNvKHPagszMoSzdjBIduveAOpsW6/4OPETCMC5+96nP/Ppz3xCQUmrq2SzqZ1OrJIVIIiP+5jOtGU7tx0zR/tBp4JkZUEXNnBu0ZdgtbKQk/AHxbWxh7w4Cai2eWXm6STALM4yPD+v7x4JQHFiBrGg5GMBof6cZplvID6xfHz2nTicSVzPX/RnB+0MJQssfh82OTOwQrLxeIST9ZYtopNt2VI910bkcClfcD3vEcROZmHFic8U1zhu4S18fU+L/rFJE7L6nMPvDNKuS7duMGoUeR2EP4byrAEGJ/qAR95AHpGKrNHOzWHZxydm5nTlmYjzEgexCwvrypWM2vEyF/KNpgIiE5+QhUJSmcBz/Hru/zGDs3ms9WRu5m38rlSyW/ViP02jFB/9fXPoiY9u9HQkOrDZ4Cx+4yzfT+DxcPCgKOtRqoiblcWfeT35iLHiWGVkFS4NkYqPoU6X8k9KCkHZFE8s02s5ULAzj8+5hOddj7KddmJhHOJjyxfEJ4eG7JD9qGIRH9XUpRAfuezMMaxWeackaYrkwzyF3yEetsiorkjF58P2T9KAHL7qKeoUlqX4HHVgPj9wPl2nPAKA0yExjg84hQX8wjkc7HgCoCk+SiLIkM2pJmd1hkpU0hso1BqjlMYxdPNut+gvs7Ph7LNV8jdI+o2XuUes7y9Ww+b/xwM8zhOUFBiflVZrZvINo7RrWmQSnyMWoRD0Yhl38gq2RX+XvrJaUTO687EUid6wEK2ntifKfiGSX305k5MhnCjWsQUqMKP6V/YdePLJUlcLh2EeA/ieEVhbNFOJj98bJhiQNMVHSTKEpvjEy96sEJ+wXWMU9/51Hvmk035p6SH5ZfG1S6aezwEac1zWDPJJpdiSEqX4OAPaCxoKhLFIGvHp6FnNCL6lefYqZVEU8enc1cZO2rIr4zjth7Q0imyphLCRmQlnM4OnN1+M5/jTOIWFrKIHAK6QGC1GFXzA89xPazlSCkQHqSc+mcXivsZzWleIjz9D+NxcNiSbrs3FzG8HbVjU9eqobTaedzc3MonlCIJoD2qdpCUQQ/H55BMxQ7zrLm0nGRnM6fcIbzEegBISCGFnb17FiU8sly0VHTuy9KmfuYF31fXCF13Cyke+ZstpYuBW63EBWCyE5G5PJT733AOhEB+eKJww9cQnZJFNXbF8fOKVrJAbeg8vkegKweLFXLnyHm5gkmE1azik7uIdxrPz/FvZQyt2yPkik5O1tkQqPis7DOdbRL0pZ0h3LeWL4MeBw2nBbocfuIDvgufB/v2sWCFSG0yfHn06KuQHzIMbG0HeuLfyKT7iKT4G4tO4MfYkpdOoHPFxbN/EJUxldP4HbEX2T4lDfByFwtSVSwONJJVH8ZFD2ruzRm1mKATbaM9CTmInbaLy+MRTfPK8bvJogNctUiIoJvZ4aFC4k/P5kdS1CwHR33zAtbzLDaRSgCVRqDYJeCguNvaf1pToAACF+ISwqubc0ua3iuKTTh4AQWy82/11AgGtZAWA76DRohBJmiPrytUWTOJTCwgGhS3/Fe7GMmli6Ssrik8s4lMsHjI98QndcDM38g6L6asuS0oShTvtBHhuyNzyN1TOskynTqWuph+IHQ5wO0L4cfDTDBtJ3oMxiY/bLZyAK0J81JxG/lJ6COKUrNDB7cunMQfZYj+KdPI5teFadWVVctZHagRCqgOjxW5j0K7JfMtIBuwRpo5IU1fzjCAnnSDW1/sIcs89jBqUz528RlaWVq5CapyhtluSwCFnVD26+D/u5wWW0ZtmctqCyHD2xkXb+ZxL+KjbizHPVc3A20iYEkYPzOGzt4TqsJtWqjSvh6Lcj+YrXuBeUoNaKI414FP9huwJ8rZKzYjp0+H998X31FQWn/sk73AToHWOlSE+qf5smpCFU4pN2iPNOT/8INIEKS5qBsUHkGTzlaH0idVKgdcZtf4NHWbRi2UUNYvxDigHHDQIOnYU5xQOq8ubs4+nX0+G669X/9fDGg4S3pfF+1zD65Y7aJLqpSfL2bczoLZj+M7/4xXuFGxFh6zE9nzJRQC4QtGKjx8nTqfgZMrARkh7dqKCCiQJFi4Upkp5JT9OwtiMXEQ2VRORNDYe4vn4NGqEllSqUSOatRHXvrK1uhL3Cqff3IYdyyQ+zmJN8VGJj8I2ddif2I73uJaNXYaJBTrFR7kmwSA8zwTO40d+Y3CUj088xUe9/krJilIUn2AQkH3x7LKi73RZCMpq5BV8SmqhiAhUTF1a/+kyEB+lbwwViRsjzFy6yuxxoKheqXJS0wWcwl5bawYueJKv0FKt+HOMxEfS9aPfcwEF1Ey6mLJgEp9aQCiky+Gji+iKBUspxMdaIh4yZVCxWiHxovOYxI1s5Che4U4WWU7CMfMnnIl2QhVNP6945pURRhQIwHC+4xI+x55/EGeCDYucAM0a8PEdI/h+co7Bj8diicjeHFm2wh9t6lKJT7xK9TKGrH6Z2ZxJ9+WfxfxdyYWhHMNmI8q5OdJhUY3csNnUNin5biIVnwHbPmLIHV3Z+uxUXn/deGwl511mpo74ZGjEJxDAkN0XhJSshFVbkQgGNCeNi9c/xqV8wcb8JjHPVUkzUNy7nyjX0b696ilZQGrMW2vLO0gDcjiPH7mXlwy/KRFmEIP4gCGRX3KyMOsB+B3JJCRAkSSzigoUKf24eCRZNCNlbmyZwuEQkWoBv7gum9YFAUkdywyKDxCSo3Qi80EpTdITn63Jx7KCXvht0ekQ1Iu3d6+IJNu923BeLdiLQ6eWRREfKUj4YC7XMJkbpbc59s83WE5vVgTkVADJcOaGt7mT13Dt2mzYVl+ywh2OVnwCOHA6BSnUO2grxKfIOD7Bjz/CKaeIytwR2XYNSsSyZbBiBbRrF309YuC83e/wJWM4Oet7II7i06gRLXo04ifOZXHimeXarx6SBCn7xfXxturENmTH3K3RNc4AXCWCyKe11Sk+MdKebGvYh+t5j4UDhB+dovj0YBV+n3jW1NxbtCeT5iTsFw9dpOLj8Ri70SG7P2AS19MlewHZ9qbk0DBu31xQoJWfUBR9p1O7Py9zD5bEBPZu8XASiwT/9srEx+HEmqwFACjHUBQfxcyl7DMeQnKfqZjJPSTg90P7vQsYzg/qeoHcCMVHfpY+5kpG8D3bU+MnlqxJmMSnFhAMRuTwKQ2lEB9biTBvKMQnKUmsrijv3VhLX2kx5OZWrmSFEgJexuw8GITXuIPPuQzHrq24XMaIEAkr4bQGEJFaoLRCpd6gnc10xNNEc/xWCEdZik/zvHWcyR+kFcSW6MMOY7SKzQaMGsUzXT/lE67E79dKVIDI4mxViI/dBgoZCwRoQA4dPatpWCInKyTMCXNfxLJ5M+2de6LSB+mJj+L7lPLxm+ykNbf5X8TniyY+DrRaXWB0tLXI8kxmVuxXuXPDHOwE2P/g6yKqaeRI1W5yNr9yzNZoMjHw0VPIoRHHIeoovczdnIHwVXL4tYFdNXXpSzf066d+bVm8kdOZC0DQnUyHDtpgXRHFRyFvtlhRXcEg/c9OIoyNJL8Y0IZ9cSkSVm7ibSBa8VHyQQW9QaHQnHACXHUV1vzcqPXtmlgSDWWhssGuXeJheu011mecyhdcKpbLk5vGaOQ+iyYUWVIJ+TT/rQ4ThYP0ao5Rd+tJEaTSdtCYWqLFvv8YzdcAuCWP2hQlaakXd5TiE/QZiY/ByVlJUrpzp/p+ufHyBRdzydcXajJgu3Yid5E+s2gpGJHzPmP4mpE57wFa8sK0NEQHcOyx0KULrQd24Tx+Ylyw7Gz7kcjOhlY+kcPHeXRHjfjEUnwkiW8GvsNtvE6L45oxmXE0ceQSmhydADTKxHrMMYQdTjLIVo+nrKNcY2VSFqn4RJoq++TP4Xreoyi5Kecdn8kwfo5LfPLztSzMSo40pxNDn2BJTiKxoRuwEApp5XjCdie2VE3xUU10xdHEp1TFx25UfIbwGxdsfRV7wJhEKpgbYXIPGUm0mbn5CEYoFDuHT0w4SiE+ck4ei/x2JSUBGzcyxD6bDmzRitS5XKQEcvicS7jr7wvL39C33hKfscpe6xAMCqkWRFRXJPGB2P4ZpRGfZc6+dGYzS56frS1UC7aWTnyUa2V1xHY6lRxi9nJM0SJmMpjn8sdDr1782fpy/uN4sftQbMXHold8ggHOZzpv/9WD219qTQ9WMoSZpOzbKEwCsolDxfLl3DFzKJMZy4EDmuJDQgKt2U0TKYuSEq2QZViWoMcxWQ1bVdqjnqskBqTzir8gb3te1LmGwhZC2LHZNQdlJbkgQMv9y6K2URxnlbBVD27+ZABOfDx+y0F+6XQbb3OT5pehJ7Qnnqh+Pef503ieCQBIycl07Aj/0Ye/Rv8f3HFH1HHjQTHXRUWRAdjtansVUqY8c0oepEjis7nFaczhDDwhpzDZLFkC06aR7RPvkV4hujD7XR7kGZyZO4mCwiLkPE1kZYmNb7+d97u9qq13wgmGzfaTQTOyeKDZR4RjZCH/F7F+cjL40oWS58g1Ep/jNn/FlXxKDg2YwHPqQBsqFITSQ4Kq+KhJGH1aLSlJiigPobRx8GCD0//FfEmPzd9pvjeBgKhGf8EF5VLtjvEJ8jzQ9wsQEZ00ZoxQj/7v/+jaVajA2dkVSh8GiOoknZAVsU6dWEs3Pk+5Ifr9A7BYmNf2Ct7gNtofk0SxNZUDgfSYXZzF5yWdXK2wrNuN/1hxnfp4hW9mIABfMZoGsu+LUuomlqnLsG/FOd3uiJkbSI/8fAx9ORgVHwBrotvw3PoKZcXc4cRyTHc+tVzJH5yhdp35yS25hTd4gsfUbUojPt9cM4MkipjJEHXZxbtfNDrWoytIrJ6nLoAEzMzNRzKCngAZSqRTGUVRM9uexM28yZyuN0f9poSmJ2QIxSc1FXj1VX72DeJyPtOUA7cbp0PiEqZySuZ35Q9JjVP/KRKBgEZ8rC5HFPG5ibfpNfFGmDfPsF1SEmylAzvH3CMqbusQK3OzqviUYepSnG8tcYiPkt26SWAPg5lFn4DmMKgcW6k9BhAOhlmdeCJfM4qS1kepPYQlGDCoMyezkLORix5efHFUNmqKi+mydSanMh9J0hSfcAfhP5JGPsXFwn4+2zKQ/GSR/6cDW+nOWnU3BsVHdrq+jM/Z93e0tK+mNLIhRjufD846iyUn32q4VnpYI4iPEz8SVgI4KQq6mdTtdW7mbRxJ8gVzu0X5hiVLDGmPlUhCAFuqID6b6cz3rW+F88uXoTcc1j1bsRQfIJwgt1N2SLfJRV4DFnGfI3OYTh7xEwOZw353G9FugD59KCgR+9cTpUsyX+EZHsa1b3v0gXfuFCpP167if0XOANY6e2nr6XKi/MHpLLKJ/DyhkEZiD1o0/7eNdFHbEWwgiI+7wDgyW4PiBXmX6/mFc9WZtK99V87mF25gUpSpK+Q3+ocZzF26F04xASr3X+zYJ4jdQw/Bl18Kf64oe1nZKC4IcTRrSUkyFiVOTNT84ZTyc+XF5s1astBwm7bsoRX3pkyE22+P3QaZr6WmimBP0Ir36tFv/Qfk0pBhX12pbXv9nYxlMr+FBwGi72uKdm+UvinS1BUJhfhIDmeZxEdv6opHfBI8OTiuuYIPLCKs/U//yXRkM4smTIfTT+fGhI95m5vVY+S7m/IWt/A+WlHYUhWf5BRKSDJUfE8IFeEIlk58FKZ9Pe/hx8EZK16Lf5AahEl8agGGZGhl5MgpaNGVt7mZpc3PjfptbpMxjOMDQheO5tpr4fHHtf058RteFrVcApTfv0Iv05Ti5yOSFGq6cCTxGcJM2syYFOUQmZgIe2jFqitfhNtuM/wWi/io7SkrkU+o9IESWfFxyb4RIYsdtm2jX/YPnMA/BALC8XcpvRnPW3gSG/Flk1sZw9dknzRMLSxoDfkNxKcP/zEQWaEaNCj6uLLPVJPEIvr0gSYWubftKIhPOnkUFsK1fMCI5N/Z17QXILISK7if5w0OyYriA7B/S4TMHAzySclIvuBiUv78URCUk04CNBJpiUF8lEzJysB3Hy+q2VpjlqwAoRj06WPYj5QkzvciprLukc/pKPIwVqhsRSCgPVs2d+nER5l92gLinpw22M0HH4i6qHooKY+8XkRJFoC+fVXrm574hC2l+JU1by7UHsVpPy9P+K0sWEB63jYm8CzZR/UzDMAj+YZrGnwLYChZUWBNh169yLE05DcGA3I0ZoYwdSUWGWUQZeBUngU123dSA37lbH7nrChTV8gXLJv4uFwEGjblee7nTW4x/r57N7yoc6KvYEXRQACuWP8Qa+lOz7kRzm/79rFmWyJeXKxdG3v7eNi0SeSLArA1FgpyXJP+/v0cvflHjuM/kpIE8XmOB2hx/bla3TsZGjnR3rfwiJF8xFh2BFsiSeKc9GQg0tQVr/SjLSyud5OCzbz6X3+mMbJcpi61f48wddktISyffcZo6SsAMvMT2EpHgm066DdTjxHr1pVGfJTf9tKCLbLzeEK4GKdMfG7kHU5kMVta9Ddst6HrBYzjA9bQDQdBbD4znP2IhcGpsoxEPqWVrFiXcBwfMo7iY0/mvffkUhfyaOTCZ1B8HMku/MqAGZUGNg70YbqlGGf1ik8k8bGHfCQhP+zJyYbtlAEm1sSxb+6vLKU3R70+Xl22r93JfMHF7Eku3S/KWgbxKW7WkX85noNh0UmGLTb45RcmLB7OvbyI3w+vOe+jD0t5h/FRJStQiU/A0Omdy890Zy2SxQJnnBF9YPn806xFLFkCjb77QCTW6ykc/tLIV6+Fy6VFUijX703LLbzA/aoJB8CqIz4Ht0fcV7+f4aFvuZgvsSQliV4vJwdyctTCitYYeXwiFR+Am3iHKVzKDTOGk56/g0Zkl5nX0iKfbwAHrXs3pkMHUZ8sdeV8reZTGTCaUWP3zFKC0PhdQdnUFZTNq8luxo2DJhF+34rflceDpviceKI6H9CbDMJWOY9PMMR338GAAULoMUCJdMrLE6pmv348sO4qnmcCC1+cD40b8/1NM+nLIgpJ4XTm8ien8diBW9S+IGSxw8KFnNluGwWIhJZJSaiNTyk2Kj4KYR3E74zgW/X1VAY2i0U8q3Y7PMYT3MTbBJq3MfQjhvnPMtnk+c03BJq3YQLP8ywPaX2GzxftqFIG8YlMlFhcDFdl/g+Abu/fBRdeKMyuv/8ODgeusAcXftauip5kTZkC55xjrPWlYPNmYTZ++5F9WDqKQbmhZ48oixI5yfvnHx5cdD4TuZGkJHF5h/IrGf/8ElWxVG+OUqB/5pVgBH0foFzgshQfhczbCXBM7nxO5J9Sic9jPMHQM/3wyitqO8byodbWBumA5h+oZHp2uQBJIs1RQjq56pzRlp1Ff+bRlXUxzy0Sxy6YyGTGsoeWqu+fjTDJAXFDViadwr+cSK6Ubthub9PefMg45jrOEudrEp8jFwF7Av2Zx+hGc8p0Ekz2HWQAc2m7/9+o37war9EQR/FxuXT5FsorUSvE5/vvo0iLHrEUnz8ZwEzHueRJaWpkT+Q+lNlQaPsu0fHqHF7T/AfozXIS9mnmm1Wn3sSlfMG/zUs3kyiKRTzis270o5zIv3wavkwcPyJzc6ySFeFgGJCw2WB7x0Fcw/u8E77BoPi0RISUWnr3NlZgVKCcf3GxsOFccAFcdx22tsLcmUa+ykndbvh96Escywq+k/O1hOVcGvrBS80vBOTtjiA+upOwNJMjr3Jy4NJL6bVAOP4qyQj1iFR8QPiMXMoX9Nn9A1MXtWMb7cv0b9Vnqm7XToxxLdnDJ9v6I51/Qekby9A/W5EVqRVIiaKdCvFR8g5JztgNvP69E8imEa22zxdJ2gD69o0Z1aUoPuFgiMmTBa/57TfEtb3tNrjzTuGx3qqVYFQyOSiUw3YVkpXVczD/0JcgDpo6cjiNv+jqX6Gqv0GLA9xu3E00iSA5GWwtxH1L9RkVH4WcnswivmUkngI558za9YxlMgPtf2KxiEjPT7iKidxEoGFTg3Br6AZ0D5X+2VcmMDGJTxmeqpHCbJTQvGmTpgLrHqaNa6LJ+DPPwIwZUQne1d34cdHiuGa4ksT9+qHkLGGC/Dei38zVcvgoxCdeLh+N+BhrdXVnNbfxOqFF/0YRHyXfk9cLrdhFz2v6cA/RqSaUZzrkLLtkhbjsFpIbOAyKzy+cqxaytTZMl/cbxEaQXtJSnmUCrf/4BP77j+0HklhBT/UYzdf8zjwG8H9oSntpik+rTX8wlo/oxlpDv5AcFAzLnirOIzJmQXneAk6xjUl8jmAEsTOf/vyTdIaxxk8MtNy5kLmcwdj/on18uu6fxxB+JdWr6xR1ik8+aRQ5G0BCgpH4lFfxUXquOOUQ1PPRzcpxCB+fG3iXkc6fWGPvqSk+EV6mCvEZ9nw/OO44rZQzmg8DroqXrAhIdjy4sbhiT2GUF9yKeCsli7FWl98vzmkaI/mNs3Bm7+W1bRcgYaXlzA/IaXEMk7mGPzk9KgJrXeJxxiqzeijER5IMg4a1gZjhp5NHUaFEEUms2tuQcFIKqzhWzdN0jGUNfViihqoCfNLmEfV78V7jwGSIflNCzouKtPwpxPHxkZdN4gY1MipfViEUBHCUSXwU37NPuIrEP36mXTto3UV0kFI5o7oCAZjGKD7kamwt4xSnlYmPIrurCRfjPLcJ3lwakUObvYvEjW7aFFq3jm3qkhUfAkF1Fh8IIIjAG2/Aa6+JxI27dsETT6jvVoEk7plCfNxuGMXX5NCAVzLF82GTguQdcyrN2ctVzWcBorKKgqQkCJ12Bj1ZzpUp3xvOIfK++fPF82SfP5fJXMOtkmZK0kemxTV1jRypfg0VFNOerTRjX+nEpwzFJ/Ln4mLYYBH+UMWDLjDk8dHLDZvXGhmAz6eJMUqVdwWSJIgPiByayjPp1bdbjxwth0+5iY+OETgccBv/x+vcgWXa1wQCmhlqLUdT5G6sutL9wjkkrF3Ki9xHJJRtQq6yS1Yol12vHimX6zb+jyv5GEnOMQRC9enJCibwPE3//MpQz045hj76T39u8aAEhIjUGnZ8sup8YZ8duPDSOWE39/AiLTfNNWzXOGsNQ5lBS4tI5eD0m8TniEW8hK+xoDjo6isAK7hp5wP8ytk03aJzQtYpPsexjHvG5kDPnjidukSH5VV8YjraRCMQEEm0bkn7FBo0iCpZoSo+EcQnTR5Li12yj4RuQFYqQlt15MVhl7ARjMq/EonbW35DIh6yz70q5u/KQKCQtZA1WvF52zeOkXzLWfyOtaQIq6ysWO02QwcRSXz2nX+jyAQcCwkJWun2DRuEk+jChZCRwXpLVzbRGU+ejyRKSA/nqr24Mjs8MzCTJZyAq0DzxJzV+BI+4QoAPPuNhFYhSEFsWBs10I6tC/ONRXw29LmUz7iMzXRiC8Ixp4hkNWEalI/4KIoPAEuFPD54eKJ83GC5Kn0HgzCB57nW+iHWzh1jrhM4ri+/MkQtf7K+6QBmMBR/WuwyHpL8ADhL8kR5i5NPxh+wqKRAb+qSdNXZ9Unr9NJJCJumZsijVL5kVHzar/mJx3iCBuThlOun2aQgAYuTTJqT5xakTpfjk+RkSGyRzkp6sr3EaK+zhiOIT55gbUpmXJ9VO4njLf9xBnMIHzgY39TVW2TpxmbDPn8uW+nIdM7XzKp+f/SEqQzi4/HAufwEwBq6UVgIeyThaR4cdbEhj4/+pSrK8RkiuzZu1C63ItApyM6GJgWb+Jgr6TL1Ca3viUd8IhSfjIz4xEfxFdT7+Nhs8LdcPNa6cL5B8bmcz8hu0Bm/H9qynR6sJh7OZzqt2cm27sOA0hUfnw9u4m3Gz78UfhLX0+mEc/iZVAqYyRDsbVup67vxqsTK4nLGrmfn0ScwFCi1MpFT/Pgy97CX5rjk/du8xfhxcZb/J17kPo7a8rNhs+OXTGQG53B2iZDq7IHyTXiqGybxqQVIBYXcyv9xWfGkMtdViI8+ykhBQlAQClu6biqgU3xAExkqZepS3pIrrijVIzUYhC+5mB9SLofERLXzCQZFf1mW4rM/WY560YVzKIqPRefX0W/6fQRxcPHKCeVqdrwXudMv/8c22vEsDxLEJswZOuLj80ELSUvEJwWCqknJYreR7s1kMMJnYw5nMvPYe0U+ErebM3sejHlMcVJWOatfMvzxh4j8uvtu6NaNE5LWcR4/UZKjDSbtd8zlEZ6kL4vJJV1dri+fEA5rhDaQU2jwq1AKvQZwYHfZNF8UedZ7Lj/x3jERTqbArNHvcQWfkUMjNZTXQ4LBt0jJDFwqhg3TvssP4nkXaQNyQWbZnWCp5SpkFD/5MmfzK3NCAwCYfOIkzmEGBS2Pjr2BvLN/ks8Uz9y0aQYSYFR8ZOfmYATx0TGIW++wkZ4O69ahkoO8kFHx6TLnHY5BPN8lchV4m6SVrFDOT6/4JCdr70hxsTG+4JtWd3AxX6j/BwvlFAjF4lOp+g3wduh65jAQ+7J/4ys+yksbChEqEX1HEDtHsYFzz/SIVNgVNHV5vbCE4xnF19zMW+zfLyLWltAHd8NE7eY2aiTeDYWQ4jdEdumEYFatMvoObdoE7dnGlXyK44dvoolPJDmrgOKjmBMtuo7EYoFVDlGGxrZpPYEArONoVnEMhaQQCgmicgtvqtuoRVP1zaARu2lNKEnc4NIUn2AQTmEBJ2z+QpW+nE6RX+szrqAr63G6reoENQGPzs1BIz76wrwWn7guYUf5FB8lIASgOZl8zJX0c/7LDks7sZ8k8bzbPRHh7PKEvcQmztMVMBWfIxaWnIP8H7cz4cBdZa6r+KnYYhCfxJDoZK2pupn1aafxbpeX+IzLASPxGcTvtMrwwXnnla+h//0nZJk9e2JmNlWgdKbKi+NywZeMoYQEzs/7OK5zs6L4bEyXy2vIETaSpPme6BUfJS9PrIKtepRVq8vpL6IdO5jNQBwEub3rTAPxKSmJiNQIhlTFx2K30W7Hn8xkKM8xgZ84jx/6vQDz54tBLzKEKBJ5eWI95VrI8bTKwKdUTQ5jocPm33iSx5jB2TQkF49FNhPpIoy65y9gN624mTeZGhpluE164mOzEeV3tIGjKLZGe18qA+wxrOJ5xPl4SNAGE8qn+DBqlPBjAvV8u/VyqsrRnB/L7gSDQUglnzR7saEKth7KtZMksUpZZMliF8cP+jTpVTFzRZYfeb/Xm/RjPrs7Doir+Cz4cAN/Bk8hcdTZKjnIDRkVn1BqA3V9j12umycFSVi3lNe5jcvyRM6sSFNXSgpM4Fle4m6Kd2uevauTT+JLLuYAYoNAvjgBqVh8BuzaTF6tN+YvJapLF3GpFJIM4KCIFErCcn0Zhfhce60Y3YcOpTR4PLCfpnzDKP7kdDIz4SYmMsL2I85s4Q+H261JbEqoNn7DPEtPfHJyRKJsBZs3a/WjaNBAvXeqCacMxac04rMxoSefcwn5nY4zLPc6xb21eEoIBGAwsziBf8mkGZI/gNcLD/M0z3M/oFO8Y8DqKl/JCrU/UkrrOLRoveF8L36XTbt6xceqIz4A4UL5nfNFm4NLJz7GH//gDNb7O/D09sv5iKvUQIbIIthKTrV9SR2ZzZmssR3Lb7+VOpzUCEziUwtQHBqVDqk0qIqPFIP4hMULZU/XEYrjjuPnrnfzG4P5i1MZO2UQFBbKik8KRf6ypuk6pKSIUs5Q6uwu6AlwId8wxPsDhEK4XIJAJODF4vXSju1snLktKmeRMptdlWgkPsKhVX5x3br2OuOHYOvxYPZdTOc80jcsjr2CSzMHgjFSy4mfkhJd+CiCaOiJj9JZqWHWNsS1KiNCT5yQ/Mopnayc9VjpVxTi47e6DWQMIKQzuyh4ctPFPMeDLKYvq+lhyNKvhEoHcIimDRpkIL35pEVnKZAkrJ5inPg4htUcjRgQvbgNik+5iA9o5hG5Y7RYLQRlh87fvo+t+EiSqLe1bZsgMbtozf6S5LglCPR9ciAQTcQjoZSBURL1ATEjugB2N+rJAvpR7G4UU/EJW6wEgxKnsJBG25ao55sbFORGGVek1HR1nz6HuBZWQrh2bOQ23mBQoQhxjzR1uVyi2OndvEJglzZaKOROyYStJC4Ml4j31G/XTkRPfPT322Dq+vRT9atUKPoVZWBVB+Q774Tly+Hhh8UArJhO48DrhTe5mQ8YR0t2s0+u2PGQ/X9YxosabjRqpO3njDNY1ewsAjgMVrXIvD56P59Nm6ABMiFs0ACrVdz3uKau0hSfYNCgEP3a4BIu43MyB15u2EXAIZtr/T613tufDKCQVDpu+hWfD3y4ecbxBMyaxTmJfwLGELdHeJJXuJO0oj14HckUkRyX+Oj9iJSXTu8mcSevCcf+rVu5YkQhW+iomboSXOB2q8lQQwVy5KNX7s91qeVLVXCdxpfJQwJp5DG46FtG8g0WefLt8EUqPuI9Wdb8HAYxmwfDTzNmjPDHijRb1iRM4lMLqAjxURSfWKauREk8ZI6Gxlm7yyVmCKfyN203zwartXIlKyAi9jc2pKJivmEU7+wbDuGwoeOxh31kk4HUtl0UMVAUn2W24wUh2L0b9u7F7xcv1j6aYWmkzZTLq/ic4PuL8/gJd2GMrGSAJcIcaLcDxx7L9HMm8gwPlar4WB02LE6NkLRgD02Kt1Wo9hQAixaJz+OPB+DnvH7soA0pO0Uv77clGMgYgGSNJj5KHp+w/CrrJ62BDkeRQAnt2SY6yokT4WMtNf/TPMzZ24TzMkOHCiffX37hrkeT8eHGZRPH2UY7ruGDiis+hYWqb49e7bOliIFjwe8lahS1Hv/9J8qKjR0bHTEYCwnPPkIBKTzNQwQDEp9/66KAFFI8sVMAK5OJt7efI0w4GzfGjOjSHzIUIqbiE8ROnmyGdHvzYNQogo88wV+yH4jy+kiKmRHwOtMoIgmv5FYd0JXosUjFx2KBfHn/gex89bceB+dyId+o+WuCMvFBVnyCDm1ACyuEuTTFR6emKQUug9iZwLM8tP4KcVMyMqBnT4oatY0nvhng8cC1vM84PuR63lVn+TsSdSbIbt207z/+yP+zd9ZxUpXfH3/f6e1gl4alU0rpkBYQA7v5Wii22IFigtjdgd2NUoqKkiqldDdL7bI9/fvjuc+N2ZnZmWUXFn+c10uBmbn3PvfeJz7P55zzOS+eNpMdNDQBH8n4yPh844K5fr0B+KjP2CinETrhea+5kRt5jvn00mJ8DpJO46T9AhQZGJBIzKHPaWBQ1JgqrWSF369hJ0uCE4YMYZmrB7IQqLTLeZtxPIvVCg/fXkgW+2NjfAyDzq7oL9OR4oRatbCmJhPEYmZ8FIUyq2izZPMUVeTTmhQb46OEoKLhTNdUn0tIxJom1iCH1wx8pLSIzSn6YHGxSLVv0UKr+XpE7BjwOQJWGeBjDWV8fD4S1MBaR6aB8cnLo33hArqzSP/M6cThgAv4iDfLLiL48SexNfT663WdkyjAxxRsbLWiKOCzhAEXISYZn9ziZH0ULFyIxwPPcAv12YXy+OPa7yXgCBeQazTpFrRE0H1RVBZpFN/wNaO4cOeTkJPDsp5X8x2nl2N88Pn0Wl1WqxZ3ZMfLG4zhvneaweefR22TZvffD0OH6jo2vXoBUD+wjcZsw3VA8Pheq0vbgt3K08xiCCm+fHGMYdsuAdnpth/pz6+mBcMXsFBGAoWkmp//FULd9Wpep8cetVbXgQOiVoChdIjfJSbLvWSzm3ocx7+cUudP3rVeztecUXGMz6ef6mrGBuBjf2A873Z6ml3BOowdW14bU9Y53b07RCMqwsxsVQKkUEQyRXhLvNgDHlIowpoQvoHulu35EwE4Wb4cEhPDZnQBdN31PTfxLOk7VpizumQhz6CVPAQ4twV9MHgwRbfczwLEe5XARzEAn3WNBpFCEb0cf2tuSxlLFBrjA1BoETsE334d+Fyx7QG+5GxmW4ZyJW+wN62l+ELdyUtWAgyMjzcGAcPJk/EmiIHpw8YIpnHygQ80RD17NtyR8gqrjr8Ipk8nmpWVoQXB3s/D7N4N/9KeSXljxQ+aNFG1AXSTmUuybSUlOtF33nniz2iMDwhs8CMns//im6BDB9P5i/oM4wVuZDVtNcYHYFtxZvnycR4PdjzYbWa2JuAwZAvm57OBZvRBJJgoPh+sWsUDTOB/QaGzE26cSDBvcdq1bh2N8akI+Cgu8bnmNVR/LxnzObXP5gMu0lyAf9YeyV1MYm0z3V0ZDfhsuehu6rCb9Wqyw8V8wCV8AAjgY88QndXlDQmAV+dNm9O8ADzzTGzJPdVlx4DPETA52cUCfLx1GnIHk3mn/r3mcxTqs5bsdADMmcOE6b2YwqWAmo6raut0ZikX8RGBP/+KraFvvaX/PUr6sYwl8WPRXDl+VXOmEdt4iWup9ex95Y6TwKegAJES/Mor0LWrtsAoihkwKSoIDFe3zGgSJEbSfZGThBMPo/iWNkVC60NOUKGMT8AX4C97L35kBIE69bRimXa8JpHImOzPP/WCkKmp2o63yCoWN3+Jm1/pz4asHhrQAxgiFaExx/hYEFvvCb77eJj7TJOnEVBoqgnp6fDKK8y7/iMArBJESr0Tw3ZaCgM68JCSIrRpFgW6cgVvcT8PV8z4GGO6jLm411/P0B/HUZpSh0WL4I03zIfJRa+sDHzeIHaDOGY4UwzVp2XxRQAlIfw72Tf5bUaiZp8oCtSpE9HVNWj96zzLOOpsWmBmfBo25N3x6+jMUjLqJ+KVFdAP5Gt7BOl2AV1gDuBgpgjm9/sNc4EqVml0dWkCn2rfCOTpwMeq1i/7OvkS3uJK9jvrA7Bu+I2cy6cszNYDy3X1aX/krC6DcrNf3Zh5sevuTbcb3niDg3dO5H9Mof2yj6hIYjl0r7Rnl58mbNY/2LKl3LyigT11/Vy1Srg+s7Jg8GDxmQQ+waBIjjTG+Ki3INSVxz0LJ55oOr+8Z7sqiZOaqo/70LIV928bgwcnzb406/A4nAonMYN/XpqD25pIMwzFUH0+lNUrmcBDXOx+G957j2tLnyIbwT5Kr54EPoqj4lpd4WJ8AKwYBrjNBpMmcdmcy+jIMp7lZjqxlMDNIo70+c7vcAkfcDBFZH8tThvEZO5iT3tdaDUa8LGkp7GHOhqzvA8doZeSgLOWCnx84RmfhiVr2E8mm2jCmWfCgAGRr3U47BjwOVz2++/w889QVqYxPpKCjmb+7Lo8wR18mXmV6XOP4uQK3uQmnsWVaoyDEX+XVXSliJsxqysQWk8lkhlHYugsZqAWJOPjU/SR47OK6zZmK9fyCimfvF7u9NLVdfAgcOmlrB00lg9+a6QBn9AwAsngVMj4yGrezvALpSluCAhabFBQQPNNP3Eiv1FcDJtoylpa0omlbGvUm/sTn2QkP+Lu0tMU4xM38DGCgR49NEQiF7dd1GMgv/Lq0K9MwAdgR0obHuI+8ux6arOxZEUqBebJc9Uq3mU0jygq6Hz8cdERbrpJ89lbQ9KiJerwY9GAQ2eWcX7S94A5u6hC4CNX7jp1yu2869eHRx4Rf7/tNnMAq1yc3G60GAog4sxsSRbXSaREq0oNeiXrUEtMhHqoASdZWWC3R3R1Rczqstv59O8WrKMVN92saO6u/K9m41v6Dy5KTeoF1lri+zn0Y0kPwXgEAsTE+BTZVOBzIF/7Tr43W4J4JtK9ktvwBD7nXHIz2mi/9VvKx/jksJnszX/qq71h0BU1aseLXMePnGx2Gb3+Omf8dS+N2AaAtzB6VldpkZnKO7CzTC/4CQK5GNWS+/bl7slp9GOOBn5lfM9xx+ldaOVKwYLs2iW6a2YYV5fxlowWnDGTE/mN9EQ1BkaR6s3TSLvoFLXujzCt5l/IOHQ4YBYncaB9P7yeEHlqnw9/qboRtNrhvvu47+BtGuCTmz0tPtBu4YJ3hzOLIViKw+ur+XzlY3zAzPgA8N13dFsxhaZsYg91WE4n7M1EXKUcOtJ9J/uLEWjHUrJiJe1YbunETkVPny8hEV+HLgxhFtenfWA6bm6ry7iB59nWagiZ5JHJAVPVkyNlx4DP4bLhw0Vw6e7d5Sa7aBapZEWZksDbXMHz3GRec9WBIYGPpGVNwKcgBuDj95uzaIzA5623xAh++21xvjCuO79NtEPGIEh1XaOZGB9gzBiROT97NtzBZGb7+sF772m/9zZqzvecwr+O48udy9hsq8oQ2BIjjOTMWqxEjzMIWKywbh1nvzaUD7iYkhIYzgxas5bldCpXssKYiVFp4DN4MNxzj/ZxkS1dnLMoHxDvK3TCndp5PBN4iP0OveqmcdeXQqFZGmfnDkbzPqfxrd5Grxc2b9YCfK1Brzk/WH3PXuwmV9GJyu/cyWO8WXIBA5mNk7LYGZ9atcwoaf16mDeP687OZdAgAXRGjdLLEUgQUlYWY3kXFa0kU6QxPm4c2B3hg28TEnTgE1QrmEZydQUlH+/3mYEPOjPVtCkU2QXbkHnzaBqd3JE2rDbGjRLs05dhTOd6XqSeezM/MoKPAudpMT5BdS4wJt7Jx1cigU++gfFRgU8ry3qGMhPnTsE6hJPe+irtcm7ncQpbnaC1/UWu54Efu+u+HnngtdfisSZwAy/yOlebgI8vTwzUPYhj9myJruPjLTIjj5LdBVhCgny5xZDZWlyMs6yABEq1fZUExMcdBzk5gjj0egVeWrNGfDeh2fsCBV16KSC6WhJFBLfvMLlu8ftpdOUwfmMAdRP0Z5mdLQoGp8/9QWxQVZMJJaHj0OiaCpSGoCufTwuaD1psGtMpM7tCgQ8uF03XzhCMbgRdJK8X+vE7Lzx4wFQD8M20EL0wtcMZtcVkWx32IE7K8KmgrE7earqwmPou/flEc11nLP+NF7ie7zmVIbWW8reth/ZdCYkkNczgZ4bwp6+L6bh/Gw7nRW4gt5lw/aZYimnW9MhXaD8GfA6XGQps5tVpw3Cm8VSbNys8zB5w05U/aVOwyPS5cYyYOqz6D6vqApELjs0GxYoYhMGC8juL0Lo6Jupg+3ZzEdErrxR/qrEi0tUVMACfba6W/MaJ7EUVkQtdUdAZn5ISdVez5h+u4jX2/rWFVqylt/8PU+5qQf9TOY3veS3ltnLnkrZ3rx4TkpYZfqEs6z+M9qzkLiaJew8RMAwnuy4xoNUKnobNuIlneZAJOgUdL/Dp1cvE98rFzVYsJmSnE3YPupBezGMrYtcmAZe5ZIUOTlMoNL22oJrV5ZP1luSqOm0avR4XZTCsAa9ZSFDtWD5sWBN1sGJLSeBkfuQCPmE2g/mO0yqO8TGW6DDaTTdBnz5YZ07j00/FgrZhgwgpA7OrS/YtIPKW1CDQJoFPGa6IP0+/9XKmMwIAf+16piaGurqQys2hjM+uXYzZcBd38hg2GxQkN2A7DbTDCkkxdQl7o7rMZBj/0JEUawkjmM4gZuvsr+rqstvhzjth9GhooJ6u1KEOFAPwsanA5/J9k5nJMFqu+AaA2n/9yNl8Tp2gngH2U/rZPMntFDQ+Tus7tQkJ/DZ0nKD6MCwWQ5Cwx4Nvfwjw2Rod+HgKzaDAUWIotCWVos8/3/ADPdtS9gHpTbto+2QszZsypJUolLZkiQ58mrZxiuxIFVU4nXAPE+l9bkN46CH9/AYKyJqso9LatWE9LcQ/DIVKpRtYcZZnfEbxNfW/fIHEHevMN+33a9mUfqtdGwMS+Mg5T4vxcTkIKGIZNhWvNpjPB2UkCEkEw6BbnCJ0q3Kt6kZI7XAJlDKKr7nf+gjKIpHZev/cYZSRQM4iEYt4zdpxLOYEjtv0vXa+aIxP8qZ/uJ6XGMYMEhLAbdPn8xISTXN5yOMQf6rxgkogUIkMm6q3Y8DncJmBuilzpTOD4SzPGlThYYn5O/mT7ry92Vz00rtjD0OZSXfHUnNWqWFnXUgyJOuxFcXq4soBs8je0qWi0PSbRhxmXAxr1Yqaqq0xPgYG66vaYxnAb7zHaPFBmFpfxsrFBQUwac8VvMZYnKuX6dSuYTTGUrJi1y6dBYnk6pKnlAApaNFLVsh0doDHuJOvGUWdbX/xW2EXikgi6e85BGrX5Xlu4gMuqTzjEyIiWWwX72Zk7lvsIZurfjmfQL0GLKAX2xG0crZnB21YhbVMBxLP13qIl7gWKM/4yAnYZwkBPoA3QRUcC3jME5GB8dlXqzUrEDFIjlRXuayuCoGPtC1bzMhaooviYrKyRJ1W0DfbRsbH67fwERcwI+2cyDOzYXGR4nvRgI/tgB7M4atV13TNcq4uq57WZQI+u3dzyc7JXM+L2Gzw+gW/0Bi9emkBqSbGx+US2lZbaEzr9SK+yIaPf3tcQQvWMaWdHsT/2GMi+U6O648b3k4HlrN25Dj9HlT1Zw0Uqe+t65d38znn0qJMjwDWSCtDyQrJxGp288367/P2U5tcshOKTDE+iuqK2asI4LN/e3RXl9HtCHoQcgAFPvhAxJXJTRSYdHzk8JAKzh3mvwZ79zKwiWC2/vxTBz6tW5uv63QadHyMO0QDa21L1serCfhs26YdI+OoQovjOhwCWLV/7UYyNv6tff4p57InqYmeqWfRgU8KhaRykNbOzbRmtRb0bXE5NNArx2uoRRJkPZDYkMt5iyez1b5jYHzO5gse9N8H80TQtc8m7tdSKjq6TS1S7Ep3afF/UWN8DO59I/B5j0s4my9IT/JyFa9xnfsp02alwd6l9GMOTr8BEcWbAVsNdgz4HC6TvcqnZ1bEIvsiF29bSFaXsmA+MxnGi/6x5gPU1WgXdUmlkIOL1mhfbXOKiHzb5vWmQ36fWcqZuS+z6AtD2WkjdRBV2QqKUupxBW/yRpuntc8k/opUrkKeVi4OO3dCYVD8pmR/qQ58DKtrqJ86nO3eDTls5YROPujcOexv5HOXAClgtWkNSaCUkhJYTgfu5HFG8S1JBbtICJaQRAkWqxK+ZEWswGfZMvHnt9+aPi5xZLCe5qwKtCabfaRbi7R2yt3hqDm3sIp25OzTJ9t3025kPI+obXHjLzXs3NUJWIu9MgKfNBFMYgt4zcCndWuWtjyb7zgNa6oobAjgSDMrN/sVe0UyLpo4I2AO1pJ9oaQEAgFNKkq6N4zzYoE/iYv4iHtbfBZ5wNSty0Jbb5bRCZ/VyfzkIfxB38g6Pnb9PGWNWmpNMTZNMykh4PFprJ9Rx8ePKGHSvLlZqK6QFBPwcVq8nMvnNGYbDkVdUPFTZE1jAy0oTNbdl6F2MK0x/9KBIqcekGFTF2W3KqZnKRM3YPWoej5Onbpq7N9ENxZhyd2l7cDLAZ8RIzRphaZfPkEudXnSP04HuyUlmupuRisBfPJzozM++621yUTfZEng47MniPHStau5VqGB8ZF9QVYZtyoBKC6mZQfRnkWLdOBz5d/XCEZajVeKmM6uAhovNlzJeh+oXVsE65Y6UgVAV0u6SHdiuBgfWajTXiie4y7qcj6fsqLOYAIeA+NjcHVN4EG++Lspq2lLa1bThlUEM2sRlOA6wsTm88Gz3MSgT67SUx6BpsGN5JHBivQ+4gMD4xMaE+RVU/AVtZ/Y/Xo6u+zzUYGPGhd5Bt8wc1NL7FYxGCwEKCKFtHSF1xjLU9xGaa6u8H3en7cxh/7kbPpVnyNjrRxQjXYM+BwuM7i6XHu3cTlv0Wvf99GPQQc+Mm5Fml8NUC61hjApIRo1xol8u0tM8pbCg6bO123q/bzMdTz+S1f9x0bgc9VVWjwPINSnAPr1A6DYkSGKduaMDm2GptqsJJcHPqCzPhs26GJsZQdKwgKfWj9/RhlOPtp3UthzAZpIWp361ogFYBPWLedf2vMgD4gPLFbtQSVQhrvIa6qzE/T5sRoEDB2BMvrwB/2Yw9tcztJe1+ixEhXZRReJP7t2NX38Rc6ttGItPyF8+K50F6k7VzOOp+nGX3jQZ6WgL3zJCsAUIClrdXkV9RkagM+BLkNowTqubv6T/q6tVnj8cd4Z8TmX8i5JSWjBqI60BDyKzvjILKSo1qQJfPaZCOo3mmR8tm+HJk1ofJ+oqVZYKNYdI/CR3TTqJqF7d0ZlzeVqXqe4fksubziLs/ky8jHqF9fyErlXimzJSIyPXJT8Hv2ZG3V8/Fix2QTwSUE8+4DVRhkuE/Cx2XXg57CJRcOGXvg02v1phTcNOOOh5Ce4kjfYlXUcYAQ+5YXprttzP4voQZ3ZHxsYH4PbSb2nnQdUcTxZdsBu4zaepIFjL8UXX639tsMQ0dfL8sui1jsu81jII5MLLJ9yHp+wnYYspRP76nYIf4A61p24tfcugY8EGG16C/C3ZImM/wnS9o/XRcFYFThEBD6G+lTG9yzwucLuZLO7S7oTra7ywEfOVXjcrKa1xhj5/bqLORDi6pJaPyCOX0MbrE4bQcn4RHF1XcL7tP3jDVPZkMv3TOJrzqRfqSoJYNi8hc6fPrU6ulVlfBx+8SysSS6NhI6q42NgvRp71jM980IGMlvT8knNFIWhAcr26p1C05+z28qn7B1Bi4FzOGZVYgbGJ23zMt7iStau7wZELx+hA5+AWOVkurgGfEJKDtSuzV+nPMBHU1OwWMxEhDchlcZs4as5DemarIOC5H1bAEj3GPI569QRu4tHH4WXXxYj+vLLxXfGTAwIy2Cdmvce33Er2ewDQAnD+IDweefmirmmsTqZuPNK9OA/Y/qmXcGJB3swso9YiqTVjVDIG8RuuT0r2UF9mrKJc7rA8CR90rGXHDQf4PNpldwtditJhbv5g36UkEASJaScC53rR76eyS68ENq2NQu3AWWJmQTRGaTETBdpGxbzNLfyE4MZyk/k1utE7V3LTensx5UsIhOFV9s8y7zVGWQG9AVPMj7+MIyPLz2LDbQgwQIouwQ7prIykhVId5RwBt+IZ5LiEhIF6qX9lhj9XOecU/4zCXxefBECARK3vQdMwe9XKCszbwiLCoPY8GG32QgVgTOaMQmgIuVm6fux4dOYnkgxPnO7j2P8ivM5vlFLmK9fQ9PxwYbdDicsf5t/ELFnXlcKFCsm4GNkmeyqMKQNHw3+ncFEfsW6py8wMmxzm/rWMZ5PaDqtFlws3Jrf2M5mP3BmpoiBs7rVnbzK+BhvRAZOB70+fJbymyh8PhY/v4DuG+eKdqnAR7HZKCCNQi8sXriVfoig8UaTb6DD11exbmcijRZDfxFqYpyeAN2z9Gvtc7Vx2YWlvHw3XBPuRsMwPvn5YMOLVQX0OY9fS/eU51lU2JZt2wTYVCQVZ0hnD1urq0yP/zJOR3LPstXRgqYs1oDPPKUPeaTQtYFZcd5u14FPXlpTuqnq5goBAt4gG/tdwFXv9aF9i3SGJAsWPIVC0tDnlYZsZzuNsNl0d6pWDT7Ewik3A2T4xdzasXQhcLUp1i00/V2KLko3uSMgnoUtJSEm4GM1ZMIGUNiV3JLWLOFSpjCNk7Hbz2MfmSSwE/fO/aCqYWvAx2qF3r0FcKvAg3A47Bjjc5isyC06t7vIG1dWl8UYp2IQZpEBymX2EMYnM5MVZ09gMcczQxmOMl7X/3E6YRuNcXvNr31xc7E4LUvrp39otYqc4zZqWmwUHR/rwQMMYzrtCuZrn7msXrLZxy8MoBkbiJTDaGR8ZKVgf1F4V5c1St0yabt2wQdcxM0LzsdU4tnYXjVbyYYPLw5xjYQEgurCnx6yG8bv19xiFrtV2wGaSlbEaooCxx9fzjUmF245OTpT7OVKY5iCNVT7cndv/qQ7Cxufw/uM1neihInxMYjoBdUJ1O9HBHgtWSJUloNB/D4Rj5Pm1BcNZ7Idv1WfdAOxMD6RTC7KBpZMarEUFpoZH9vWjXhxMGOBISAsjBndoBUVqZUP24ZPW5wjubr21u3ALE5iq7Wp9lk4xqd2IFdjUWQtJyPwMVmmYC2s+Gm04Vfu5jE67Pk5wo+hsW8DD3M/rX7TJSHkPSpJ4lnaVMCjVb82XDxgBD4+qEOu+QJlZbR5aoz2T6tbfQGqiycYhMU76tKZJTzY/xdISqJlj0zcuPhLlQSbPl2M5Q8/1E+bmLuJV7ma+/wPmC6XErJX06xtW9xde7OPLIqKBFnjdpvdcspPP3FR/V/0Z5OsjlWnU7tnU4xPDIyP7Ibrgy3ETagAaYLlYUbxLYFefUzNNLq6JNP2FpcTwMrJK5+kMLEOf9GNPWkt4brruLfbTKZwqa43BHzIRTzMeOy2IEG7Ew92E5NrtEjKze3V+XbkHiGUyP33s+jbXTzE/eUZH1cE4JOsg8BoMXtGMViPxYXTpdCdRfyP9+hqXYyiwH67kNYu2qjPuxL4KDabcO//8kv5oKwjYMeAz2Gy2/fcwa08yYGUHA34BGNJZzcCH0M6jxQwdIcCH8TYaMQ2hvhnoM1M6B07NKi+MCjO4fSFCToLV7IiEBBBOatWAZC29R+mM4Jr/rpCb5+qH+TFzhZLMyyN9IwXo0ngs369votKoAQ3TkosySaAIDVLZNBhONu9G87mCzqu+jRi9oDMVpKTidUKKAprr3qK63gRPyFIxufTA6YdVk0Y0Y6PhmwjuWxfmLS4+Ews0kEmILJQlAULygEsJQzwsantcrjEUDZ6KPcOu5hs9nBbllqmwmbTK6Y7HEzkbq7NFVQ133wjdOSbNOGV1yz8S3ucqfokW9JjIH6bYddXFcBnxAgtDqi1S8SXhQKf0oKQOKVwlpfH/K31ySeNtJ+/YunWDL7h9AoZn6e5Fd824RuN5OqSj9yI+8PF+Dhqp2vfz2hxHVA+7Ov6tPd5hbEcHHo2AEEU7YVpcR5hzJ8kApgdBiZykHsaw5iurYM2j4iXsvtFn5aACPRSJ/iEjk8JidzKk+K7QYMgEEDx6R3HJ2tR2W0MYzqvMJZ6cz5lGZ3Z37o3ACecIH4rQ9ZmzRLP8Bcdk+Dcv5OreZ1r9z7ImXxJfUR8SmokDDtpEmU/zeVrzsTr1fctofFI/a16ynmnHLNqM0RxdTVsyLRhz/Io94YFPo/YHhAU0913y8cFlHdDGl1dEkhoc4bfby51cdxxrKg/lC000RWmgWZs4m4mYbMr/DRlO048rHJ0CvtY/N6ALuJpAD7S/a5ZrVo4GtfFjascQxRQgY9NBbWuoJjP7SkuLSMrWpiiUdrCa0ugHru4FcFmeaziWRQligdZvFEH1lqNySMp0xzGjgGfw2TvOcfwNLdSltVQL3BoqbgzWCMAH+kPcNtDtk+BANm5/9JblVA39manE/ryO20fOE8UGlSt0Kf6ho2qm9u2ibTjp9WAZSPwGTZM5Nq2awd+f3gGyxBrFC1+QQ46Y4xPIiWcxveMPLFQT3s1PAsbvoi1gnbtqrjEgS1RDOJ0DvI+F9Nj2xfi2PPH8TLXmYJ4QdTGMjI+EoCBYNAuuyM7ogZHrCaaqvA66s77mmu0XVZv5vMNp5OcJ8CBtjM0gK2WJcsYyVSSCnZpn/msTvaRTZHDIA7Trx+cdRb+jGzu5jFG5z8nPne7xUvYKq4RwKI9JxCutydrP8FYXuFbTmNlcvfK3+yJJ4o04zPP1IrgtlSBT1GRGfiUFcmMwSjAx+Wijn8XaRRg2ZtLejCfREoiAx/DyivLQURydeXsXsiVvEHjXXrB29BaXTYbGpv2M4OY5L0dKM/4zG16MdfyCplt66AgFjP5LoNRBok/WQU+ZTrw+ch9JtMZQWnj1ozjab6tdblpjJqAjwqqgj6h3JxPBk9zKwpBSr//GVJTsXgFQDi1/t9s7nSaOIfDRheWMJbXqLfmV0DFF//8wzk/j+VeHtFi6qQrywgQg2U66PiSs7mfh1hHC7o/f1HEezXFJG4XfzZONGehtsr9HVn0s129fPGhgc10OmEdLVna7UoYaXAf1qvH7A438QZXhQU+O/faBRhF7O3kHBPaj4yMT6vV37GcDoxBpMQqAR/ZK3/jdh6nS/4vpuONjA+ABwc2mykKIrwZwVsFqZSy/4YyPnl12/Itp7ElXYCrp5XbeIR7sTaoy+23C1UBg0RQOQucOIBz+AwQwKexX1er9tpERy9NEYyPZ7vO+Ciy1I+94k3+4bRjwOcwmbFzx8P4WBMcPMj9PGx/yDwCi4Sry+sMYXw8Hgbf3IFreFX827BDcDqFUFe93z8z1cg5+887AchxG2J3du6E55/XWB0T8DGwSBQW6iJs1vLApz9zmBS4E+bODXt/cg3asgU+5Tz+xxQ+5Tyg/Bi3akyLN2Jm18EdRbqGUYS4IqM+zcV8SOP85abrGctVgHhf8+jNb5yIkppSLtjReL+VNflqr+Y1njp3IYwda9plnc532LwquJIlOwzo74K/b2Uqp9Js1x/aZ75wm6077oAvvqCwh5jl7EGPKEBp1FNBLOjGuJRUlwdvUjqvMZZRfMu3tcdQaevdWwjX9eunAZ/mNp3xMcb4uItkanCUseJyiXIpgDVfLJJluCID7usEI3OQVIoCYqWI5Oo6buWnvMFV9Nz5lfaZ1wv06sWAjGWczycm4JNOvlZSIRT4fPihINZatVawWsUCq80FURg0HfgUaGBXsoAlbU/gWcYx23Uy2O28PvBjLuctlMTyri5jRqk0CfgsKuNT6HaA2iarw6YxJw13/8VdTKL7jq9hxw5a/fIaZ/KVBnhyc83ng/Lifg3YQQs2kJi/k0hm0xMstQQmb2otGDtWpNzb7STs30FTtUxEq+zwjM9fdOPbkW+IivJh7tf4nqVatsej9z2fD1bQDg92nAvnmM5ht8M7XMZn//uBf1qcYUqEsPh9NPznRx7nTrrnfg8bNjBs6xucxrflgI8XOzabKe8lrCleA40bbZ5Ztox6j93IbTzBZbzDVcfN1WoBbuh0JqP4llmtrycQgMmB27mPR7A3qM3IkfDxxybsWM7s6UnsRAQyem0Jmi4PgMcmxpAvUyDIwC6d8dHCEqw2uOYa8bBDa9QcATsGfA6TtVFW04MF+Pce0HaLgSj0tjSrw8oDPMijlvtM7M3GrudxC0+xvM5Q8wGh25MQxmcdakbW2rXaJOoLhmGeQgvHlOP6VcvPD8/4uPQBeovvcb1Kd4hJxsfvh0X04D3+x1K6aO013ZrKtNjwha1rEwyCbbfYJvqTUyMGExiZDNABW+rWf+nPr2Sxj000YR0tcODml5xLOYuvGMBvKM2aYg9RhPbbHBEzyGI1fZFWsPbqDg5HOYC1feAlPMUtbLWr2ScGl5fbJRdHPWMibd40XuYaTi/+qNz1NPXpoNeUKSLNix2rTQ8mbsxWExV+iDgPvv9ezLRffw0NG2rnC3V1uYtjYHwUhVKLGsNgAD4RGR+VpthFvXLBzeWwsto3At6QrK6UFJbTkTW0EddRV40TWEyGXyQJhAKfdu3g9NPF32V3CcagbRFMFe/WGvRDcTEBf1BzcSakipssKwMcDuY2Op93uBxngt4fdcZHAJ82rKIbi8hkv7bQG4HPzsY9eZvL2Jp1PF41k69p4XImcQ8nLH3LpBcjgY/80/jujIwP6Ons1qQIwU8PPQQNGnCvMhHQGZ+9WW1FHb9nntGyIU9LF+6uJmnhgQ+E8XTn5lJ/4x+0ZK1JViwxUa2rhgfHSf2hZ0+8eUU48GDHh9Vhnh8dDlhJe1Y1PZl9STmm7xS/TwOOQZsdFi3iykVXcSPP8wVnU5LdWBO6lMCnzeu38A2n06JoadjHonhiZHy2biXlnRc4my9YSXvW1uqtJTQY64EZn0us49huF+39l/bsy2xFIMEAfFTX6Lq+lzGUmXzfVBe7/bbZOO5iEkV1W4jN8/79ukT7EbRjwOcw2QvFl7OAXrgWzYkvxseQrWK0zc0G8Qy3sKluL/MXVqt5ix/C+GyguQjiPXhQ073YaxMaIo9nTNKPC91+SMYnNN/YCHwMu1YldESFETCEyP7+d7iUBxcNNxVxsmZn8jODWEDPsLujwkKoVSZmS6Vhw/AnRhSvlGrI4sTieTWdeCW/MpA65NKMTbRiHV4cpmBwqxWTqwvAb49RwyeKGRfppmocbSjw2XrWOG7jKVY7OooPDIyPzNowTpJJq/7iGl6le+lv5a4ntUns+MLGQnmxm7pRqj+PE0un8ypXczHvHxrwKS6Gd9SAzGuvhW3bmNZMsDChwMdTHAPjgy7rYDsYH/CR3TqicrP6EIyZdHIsmmI5DNvlx7gLiBLc7PPxqf9svuIMXKX5hpOEN0tKEj4ZQ3LwoFZ2ACDVUUYv5tEmfwGgv0rj+1la+yQeYALbWw/B74fbeJJF9GA/WTTqnAnLl2vAZ15RB1z5u7mCt1naZJRWSkXageOHmPRi9u0Tz0EyPpFcXaADH0tyhAdTVAQ7d1LbJmJ6JPAxMRFq0dHrOv7OOedA+8cuEe/TwCI4nWDBj1JwUCy00qZPZ/yMfjzHTSaAqyhievJhw7lgDixciL+oVGPVQse7qaioN2QH5vdrHSNos5sEDO9lIn99sUUrkOvBgd0O6ct+5XS+I90dEnSu2j6yyGYPv3+0zaSH9U9DUVl9S5ZawkedTGW5IiNG0sRf3X7cJX46sJyWrMXliBAzEGK23ds5iy/5lPP44PwfTMDHZxfv09axHT8xlBVFOhic0eByJnMXpbVzIoq3Hgk7BnwOk8mU4oDHx4YmgzmLL5jZ5c4Kj7PZxA6tnX85Qbc+yMJNcJoZPzRs0x0OIX1elKEu+utUuXXVdbIvaKhYJymVdu3EDLRggX5hYyCvAfgYXV2+lAz+4gT9d1HS2aXVZwcjmUpv5nIic+iSO8Ok+WDt3IEh/MzlvBOW8dm9W6SJAlgaRwY+JCXR2rWVt7nM3G511ZPaQ9KM17Jawe6ycg+P8q6qSh2oJuCjHNeeIczSPi9XssIAfGRNNhMt7jUIqYWYMUsjnJKqD5tY8++4Q9SZGzyY48r+5mpe531GM2bHA7HfXKjNm6e7Wrt1A3RyrlKMD1BmVQXlDooUXzfOyMDn1lsBGMiv2kIdUcDQpgoY+kIYn5UrubXsES7kQ3EdA+MgdZUiAh+LhTMCX3IG3+DwVCxU5EpQOIhe0dcIfLL2r2EefXh270Vw4ABdNn3FAH4xLXr/1DuJB3mAbW2G4vMZCrQCtoI8KC3F6hf9xokH10FB39jtMC3hTB7hXm61P08TNrHnwpu1OUVKL+zYAfvEYzd3pZC4NxmkbEuJ8GDURifZRVsk8GmQcEDU3AoEhHvUaqVlnQI++wwS0+xCu6K+rifhdMLxLObRl9JFBqW0CFldINbkIBYCdjULqqhMz9oMo+PThE10XDyFjuu/Nn1nCfjAZ0DEISUrUlJ0N6VkfOS7j5jO7rewj2wC9c1z2ra6Is5uS92eaCdHgKyxvMK5O57RNrc5G3/BjYPJM7vg2VfAcjqxltaaEGFFZs3bx508zjW8QkICBJP0jazFKmCEWvZOY/9An6IsFsyD/AjbMeBzmEyrkOz2sj+lCV9xFlsa9a3wOJsN/qKrKJa5TfeNp69dSE/mk24p76YwzXohjA9AfnYr8RdVj8fmFRNCkd8wIcnVPiVFBDLLbVfoIpmfr+9wDMBnf8NOdOMvHfxEAD5GxmcwPzOVU7mPh8Pq+ChK9LIVu3ZBLakU26hR+R8YzGYzBEFLaiNR18EAuJpX+YCL6Lj9R7bTgD1kY921HbsdJnEPL3ADAAH7ofp9IjA+tdL5mSFa/IrTXUAjtmJ364vlo84HeYAJ+F1iIjJNnl7JllQAfMLswDTGZ/JkmDYNrFYtUw/U3WxlzegzU10Xck7MzzcTULmeDL7lNNbUM5dsCbUym8r4FMQQ46OCrbn0Lsf4RHJ1BUNdXf/8wwO++7iSN8V16tYV7BWiXEXobZrM4BZ9v+VDdGIpSzqMjvBjcZ7hTOeeU5ZDs2Ym4OPMVq8VLIHVq7lz0Vm8wRjT3kd2b6lxVJ+QGJuyMmaNeIYSVU7CWpBHMoU4LD78jgTu4xGe9t7AFpqIaUAyPkoZx/M3mf2P4xSEGKtJ9cIdnvGxRWJ81MGdaBNzj4zxuXLTvaJszkMPiQjc/HwhjBnBKlJuDtXxAZ2MkBsIf3F04NODhZw3/TI6bPtR+3wqI9mc0BbFyPioHTuDPLLYS4LNS217PiBUj202tMGv+H2EMxOzaLA1zU/mRp5jRRs1AcTA+DzIBK5ceYtGxVkSnDjw4vCV4C0Qnd6H1RTHF810hliUrDBSoyvSRKZfg5QCLuctRqx9TvsuJ38pXViMw1dyjPH5/2iSqg94fKZK3xWZzYam+GmsVD3sk8uYT29aFYWJnZGz3rJlYuEK+Xh/LTPwsfvEQJhceI22Q9BGW6hPOVTPJz+fLbWO50aeY+Hx15a7lsaexMD4SB2fcMqj0qKVrdi9W6QoD+7r1rPRIpjNppes0DJqVHXpwfzMn3TlVa7hIj6iwcGVZLOXbPYJxkdtg9zxysnyUEyes1YtHQTY7UIUTQZrt3l6DFvJYWi+Ouk7HDxmv58HeYBAgpqCbEhLDkrgUxHjEzIR/V1nBIvoXr5/mrjzQwA+RmvUCPr354Ufm2HHQ26uiLV4nhsYyVSWBTowim/5uN/LUU+zKaUj8+lJfnZL5tOTjTSLzPi8/TZfn/gMp/NthQKGkvEx+pqNCQpSwBDQdrIS+ERkfBBlEwD2WOqynE6UpdWJ+FsZrLs+oYMI5DbUQkqoKwZQQrDExGgYgU+Gfx/tWIFr3/bwwMftZn7XG5jIPQB0XfY2haQycM2r5YZ/ejomheDPOYfUrSv4HpEJZtwXfZN0EY3Zwu7+54r7UMe0MfC63I0CiVYBViTjkxFUNzO1aonfGN3mr7wiylXMn286TbRaXZEYH9Dd1v7isojZoUYBQ2nfZ1/OqUzl+zpX6psPu+7qqsdu9lKblgMb8JFXyBnczaSYGJ/aZVt5iWtpMuUB0+edrurBjFY30vY6teajoTyGVkpHfYEyy8/pK8ZToAPAmE09Tzb7GDL9Nuwuq6bU7LKLebReciFvcSXjD95GwCfmrEeWjGQxJ5CWu7ZGKTcfAz6HyWTl8oDbS+bulZzHJzTdu6jC44zAx1emT74yiNVIOepfGpzQhtVLTob7MluJXZu6G5Dy5SkU6Zy1yvgc2O3Gfc3NcPXVwsVVVqaf/+aboU0bdqW25gVuZM1xZ5W7lla/KIYYH2M6e1jgs24d20oy2UxOWFeXTK3NbuCIIhYi7LviQVyEGvSrPiOpLt2IbXRFr4eFz6dNgopdLHTH8Q9N2cQHXMSuPmdHvVYsJrGXZHsA7KUFXM+L2r+DTtWdZQhqln9V1OdkBD6aqysc4+Ny0ImldE9aoT3jqYzkh6lBHu75I/cysTzwMaymyqGor/bpI8qgvPqqeE+LFpFduImGbGf3briZZ7mBF5nKqbGVrACe7fwuvZnPjIs/oDfzeYZbIgOfrCz+7HMz+8mitNQctha6IErtpGCIq0sGO0sBQ0ALEq/Q1YWu+yI3M9HuzxWyhnsdSVzLS9zIcxrwSaEQf76YE0pINA2boZtfZwXH0e3HB8DrpTZic7ORptqJvd7yC6Fit5UDPhkZeoOcQbfmWpZmBD55niS20Zg9F9zMzclv8gnnsdnaPLKsunqxBKuZ8Unzqzo+BuVxQHT+778X5SpWr9Y+rqhkRTTGRxbz9Je4ddbZXp7xkensANtpQLGrltYkk3R4yLwXTM9go01sPA+SJjY3FTA+tdw7uZZXyPrxXdPngwaJemUDBqgfqHOehSCpavkUfQcq2uvyF+MtFB3JrcQBfAzPIKV4F04nnMlXDOYnTb8nq222+Ck+8jaq8VxqVpfFYdN3dDWA8alZyfX/YZNxFkGvjzbrvudW7uLPfy4FouuhRGJ87J4ogOK66wSqrmPeRcoxMPeEGxn83U1anNxCa286yoVezlynnMJHE9Yw+cFSlq3rLD574QVRp8vtFoNbna29Bn08aZnuXWykN43kxBiDq8trTQB/FOCjKGQE87DioyAC4wPRy1VIa+8Tymt9+INBnY9nFKAkC+CVEarc7PNhUXVDsArButkMIpt9HMc/PH7ZcTJXrtIm5xUT8CnYz/PcRCku2tQrYGmqSPPX0I7PR1vfKsqwsLXnObz6ezsOJHbnKvV4RUWHfmv5TBCrTWE5ncT0nfoP65UW7Ag2oI6XiIykYpCtDy3cGJdZLPDaa/q/GzeGtWtpzFZ27WrGmVKDCn1zWBHOkn3PqLoQ7RgJSkpKBD6U9xzaTbd0PYvHvmvL5mATcR28+Hx2/B4BXTQdH9AKz7Zmjeka4cyv2CDoZtCO92lJkAb7TgXCC9i5XHAyP3DWysWwYCi+xj15hWux2WByQ3VhIUjZpu1YKc/4BLWSCD7SSsUg8WJjBw1oxiYoLKTBhjmkstR8YVt54JOWBthqwZYtjLvbxRMfmWu1GAlh+S68XXvxbVYvniu6gratYOU9ER6KejGXRa0xp47xZE8I8Fm+HMaMES8tpFwFhAE+waDwk6vIMRrj47MKMBAoKeNnBpNmLWZAyBxrFDDcndSMRsUbGN4e2CaaNL3bfdyyegwj2jfntNDM0vR09uyoD74/ach21dUl3o/FH57xsfoEeDO6msOayyUGrWFjJJ+pJUUHPr5C8WLcligdNNSMIQcuFw4HTGcEAIPU0ziSHeQpGWQE89i/ag+1WtXCahQwrFdPxFw1bx77davJjjE+h8mMri5dYCW2rK5ywCcYxOkVwCeYHCZl+847BaIYNw5mzNA+ln33r78VatWCJ4V4K7dYn2cVojSFrAFGSgpracVq9XPAvKoYUI5z3w768jv1CnUdILvTQlM2A3BGkyXmFd1gRleXM0MqN5eGjfGR14yk47NrF3zD6Vw5Z3TEchXSvBYxiRSTpK1QFtXVFaoUa1S1xWrFahUxMLItFbERsVgndc0baAhlkeDChk/8XTJTUhvjwAH+9nZkBceR12kgL3E9S+wGIO2L7OqSoCYQAM47j3a2dYzlNXw+fd4MzdA3ZuoFKxBSi8tULZ8ctrB7N+xF7Bwf5H5OKvwCNw6u/W5Y1FNIkBOhi5Yz6dIqLTWzFKGurqIGrfmGM1hKF+5mIkUk06rwL62gpFRuNl5wPiLTMhrwkWN6xJ4pPMJ91N+zNOJvXS44l8/434b7Yc4cU8yHK9nGPlS2YaOouVdCojnpQc4zfh/pJcLNtVupp7mW2bGDaz/tz8V8aLquZDelJSSomyerFRo3JqlpbfoidKPKVKDh8+nhgf0KfuBJbiV7/nca0IhYrgKEkmDHjhSmm+PzksoMri75u0WLhP7UkiXiMxlZSwjwCQZ1BBWDq6vUlQEZGfi9AUbxLWel/VSuALGR8ZFq92dvf5YSErh7/RXsSGnDbwygpFYjSE7mywu+4EMuFM80I51kRcyx9zARm61ixkdjcStKpVQUWLWKkR23mRuLDnysBAjkCSFMj6VyjI/icpqaYpwK8u3iWR1cJ+ZfCXwUu00otf/9NzynxwAdKTsGfA6Tza51LhN4gP3Nu2kdPJpMvTSLJQzwcbs1YShLangXEvPmwZdfCmVA1WRnnTpVSClMmybmhZISKEKcx3dQXwW8XpFyGZDFIY3bObdbiBzu3EmHNV/wOycy8Nf7ta9tSfrI2JzYLmKkp5HxcWXqrq6A7JrGmdceXccnb0cJp/Mdxy15v0KFU1mx3IFHAwHKiOHcxSRmcpLpt1avgS5Xf+xTgU82e7H5IxdNjdXOO09k3l5jrN6o3oMdHzZrEEWNN9FcXVq9KIv2eI3P5e/znqAJm/ihcfmSkFYr3MqT3OOeAPv34/PBLwzg7HMUfpym8AZXlmN8LAbGx1JVMT6gAZ/GbGX3bmiFANBraYUt4MGBV4vHimTnrXmIrTTivvsVttKIS5V3o0orGRkfCXxstjCimYZnMJF7ceLhrp034g/n6tqwgeBXX/NTmnB9RisBEFDEiR3+Mv3iEczpxJTV5T9YxEBm00tZgKLAXkVdmDdvFvcU4urSGB+/n1xLPW7jCV5Iu49VtGVHg+4mhLaBZvqBdrvpPAZSBRDM6maasIo2zKM3Uk1ZThPdSudwK0+TOf1DBnlncAJ/RQc+55wDy5bx08nm+DxXSQjjU7euXkMwNVXo+3TXAb8pxgc0d1fhgFMZrzzCzwwu5zWTwOedqxfAgQMUdD5RPoJyZmR8nL4S9XdBEijD5neba8VZrWw6/iwW0gMAS60M0oL5ADRkBzYbHHjjS+x4eDt4WdjHIhkf7DFsNlq2xJNiyM5VX6A1xaDkvU8ASW8lgY/VaWYCjX+Xbq+STWpQtVpWI9Yg6sNl/0ng43a76dy5M4qisHTp0iPdHADm1D2Xh5jA/qbdIheBiWAS+MhdptFHKpG8yXbuFHQwhM3qas56vuNU7lh6IV6v2PXLHYzG+MyZw6DZ93ImX+k7w9JS+OMPOO00sVg1aADXX6/FHtkM2Q9G4CODFcOZkfFJytKDJhMoY+IjAa2WkzipZHx8eD3la2MFtomgAJ8ryXziMCaF2aZwKY13ilR96+ABTOYu5tLH9Furvzzw8aryBDMZRsdHzol6rVgtdDI2TjbP7rtIl32XMvAqze/HSnrhNvoxh8YleqxDqSuDLTShLCFkxVJv424mMT7wEP5dewgGoQXr9UvjLQd8trQdzjtcygJ6UJiZQ5VZjjhXY7ayb18I8CG2TUJCoFhzqzZie9Q+B2bGJ2IqO5C2bz0X8BH90NV7fUEbAU9IyQqAxo1RzhjFKacquFxw3HGRr9+rwTYcuNksxSijpbO7zMDHsnkjsxnMx2WjAHg94WZu5hncmYL1CHV1aef2+9hpa8xT3MaPDcZwM8/x3IUL4SQB9PNIpwUbWJQ0QDvOuKiZ9HTuvZfhU68ngIV2rGIws0HdIBUXiw2VXLAT587i+bXD+YtuvP5XF/juu8gPBrP33o4He5k6JxkHyAcfiMLH69aJWEODvo1kfGZlnQ//+5/23TT/STwavJe97QaEkjjlEo4iZVKBObg50V/IfHrSb6uIF7QEfXRY/zXX8hLZeWu130v3uZKezoK04dq5bDYhqOrDjtenhC35p22sXBUwPqqlJRh2P+oLtCXYmclQfkk6hcL0RkzmDn7I+l9M5wMgJYWFTgEGSU2NyPiUppnLVmiurqqgxavQ/pPA54477qB+/foV//AwmkmWXF24YmF8AN6xXcVj3IknUw1eUUdnCQk4k8Kc44ILYJNaSyUM8EmglFOZSteDP1FaCvmkMQAhcucvULe/8+czeMFETuV7PYOhtFSc9/vvdVdSfr4GfIx1xYzAZ+yehyLem3GSs9Svy8LRL3Ijz4v2uhTThGYEAj53eQbAnisWPl+9RubjwpjXIkZrO1ZRK18s+FarYNhCS1Yofj8L6c5ia1ftRfoNRTNl0HGVm+F+Tyn4uBzjE/QL4BPAQpM57zGH/lxx8CntmGjZg0Z3HU8/zRI601AtIgkGHR+DWVKTuZx36MUCtrUcdKh3p5vB1ZVCAfUQcSgvcZ0W61VR+rzXaUYtXmv0dxKO8SmX0QU0WPkTH3ERN/Osfm5sFA8/i97M5UHLQ+W62nvvieERwbsrzmFPxItDr14dZUccCnxkVpcs3Pp5xlU8x83sOnUMd6S/zhQuDXF1yX6jl6yQ+4LiYjSaUNaok9lFisPs6jIBn9deo8WMl5jKKTzCvab2FheLec6hjiOpPA3Q7ODSsErhRktJgRas4waeJ4FSdo8aK9ggYwNOOAFuu62cGwrEPBfEwt05H8OUKdokM22a+H7EiPLXDAU+gYOFFJLMyp3p5fSIHA7YTy0mdviYQls6PVlIikckhVgCPgatfIGXuJ6GuSJussWaH7hNLQpLRgbvNR7PPTxKR5aZanUBYWsQSo2lmFRD33iDKzfewzie5t2LZmqoxOGAYczkyjrfk9u0J3cxmc9zbqv4fFojrBTXFyC9VuOkiMBHK1uxWzA+EvhYHDZRB7BZM/HfEbaaBcOqwKZNm8bMmTP58ssvmSZ7eg2wOoFdtGc/tv3ZKHEyPs8n3ElhIZwtg3ZTU3m73ZOsXumndbj5PYKAofxYsjsJgWIOFgWohz4RacBH3fJ4cJgZH7lKuFxiQsjPx6dOxLJqOWCSzL8k90ngibD3ZrWKia6wEFLqp7B/2HW89574rpy3yvC8vKU+jN23uBiSDwrgY40mXiiPtxgylOR5Dx6ku3U9rQJr2UM2vzlO4irPC9RKSWBDgYvMNNivPk5TtfCKgg4rayE8u7vXQF79JoHlCOVmUTxVAB/phrL59d1e81/f5AlWs7vgXEKD6K1WKFEXuuDGjXRmmen7UOVmMLtuqjLEh5wcvElprCpuiwcHl/Ae7zOa7vzJh1wk2ljBJsHrMLt8ZXZOJAsX4xOO8ZFg0+hq+9famQYZdZlPXVxhnoOiVBDLgg5GNeATxXUYCnwCbjVbT80UlfeS16AD79g6sI+Q9yPTpf0+Usr20o1NtLVnMo8WYqEPAT6/KIPZSAPcmY1wGMSETa4utTN0508as5XxPKp9VVIipga5gVDS02Cr4dhIwU9z5sCVV3J6UltG8g/N2EQW+9j/yCvUbR/x8ZSzcCUrAgFYN3UNHSnj1H45QLrpGAl8Bv7+EAz+jbRe/yOZYuG9C5mnHQ7w4GRWrfPpnvYNQ/Z/qlW0twZ8WpCyfKe9vrubFIrYa6lN9gknYJnnZBL3YLGIjVbCB6/zIb/yCefj851WbtxJxqecGn44+/JLhm+cwUe8y452QyUJZ9I/kzgumis2nA08I4PAFzkk59TCYWCmjNPUhiFjuX/JGbSq3ZphwJv17mPvLh89klLAUSQ2zoqiB5wfIftPAZ/c3FzGjBnDN998Q2K47VsYc7vduA0jpKCC3Uhl7ZLND3MKr7B0xgR2yxifGIGP/JmyYb2gd3Ny+KTBPcxaCe+HGwsRBAzlxxL4JAZLyM3XdzMtWcvMcxqLKcEwGZqAj/QL1K8PGzeqO1DV1WWoYRVPSYPUVAF8srMFm53BAaZwKe2nOOGGz0339W9SD/KLbfg85q3R+vUiDR3A3rRi4JNvzwYZCCtnmp9/Zr73LObSmzrsoX4W5O8Eh0p7G2NGfBY72loY7wwSq4UAn7LzL+WaOy/VohcCvgBWhKtLiqxZAnrUd5PFX9GfabxYchzhgI9kfIJFIaKUhGd8skq3MZkXKCQFp/O+Q7o1k/Xty9JZ+7i5t+jon3Ie76uq2CkyLbcCxsfnMqMWvy16BzQyPtFcXZKJseJHIQgEqZMEgw+Rwb9z/+042UVL3yrTdcJZRYxPHfsBMlmL9d8EPB4RJW8cf3vqduRxbienZTt6L5nO44xm2ZphNKAX13/+FhR2BYSL8G+OZ4e7EafxLU82BccG/TwmxscAXuqSyw7qcyMv8CVnUVwspgoT8CH8sSZzu2HdOlJzEklXi5Bewyt40iMzxuFM3ruvzAfFbnC5WLrMyoP7rmUws/Ee/Ai4wHSMBD519v4LS2bjaGBwd4cMBJNaiApKJONoCfiw+M1g1pcgUPD4Wq/y2tln4HxdHC/7jn3xQi7kY/6hA17vaaZ3FwjAZ5zDr/Rn8XMOKpxWVcTdqm4hw3WPmjaVeDwQPJBHU/LIUtKAWuXPEcGUrFoooy+Bc8/F+Y3+uSne54QO/EwHvOoy+mrtCSzbBTOSAbk3kYGlETJ9D4f9Z1xdwWCQSy+9lLFjx9JVVYONxSZNmkRaWpr2X6MKFH8r3T65Y/X5+KPxhYzmXTZ0jk3/pb6yi2ZswLZ2Jbz+Onz0UXTUXgHjYxTfKtulp25vpgkeNfbFCHyGMYML+2wRAYRyeyxdifn5WtC1PcHA+Bia4FGi0wMywDk7GxpsnMPZfMFpfE+T5d+bf+hyMbbzAvrxB6WKGdiuWaOXqyBKnS5pt7Wbxh8ylkfOQElm5ebQgGHj/PdZypXa34PVBXwsFp5JfxCAg7bMcnXbAgZXlyy8agvojI9FLV8RKatLc3UVltfVCMf4pHv2cAdP8DD303LzrHLHVNrsdlIyDGwedk2tWtYdqsjV5XOaGZ+KXF3xMj6amB0KPr+C9c8FjONpBgV/jnqdSDak6Bsu5kPSgiLDJhrwCQ1ulrF+EvicUvIZC+hFxwfOoG/pLBqx1TT+djboxp08zqK2l2puE8XpII2DZJdu0wWwgONZQpvgSkAslhFdXSF9vj67aJMuzlOO8QmNt4sEfGQ8SkDfiGazT+j4hPMBRTB577M3NRGIZtkypk0TLn4Ae0r5viGBT0lQ1coqEoDbq9jLMRPymfTY+z0D878BwK8CH2vQp20+FFUk1J8gTp5uLTTepjaeLQZV5NCajF4vlJDEZppiadSg4ptXJ9Px1kkcv+J97WOHA35mEJtzXfR59WI20pwbVl8b6Szh7dFH4ZFH4MCBiMHNUkpEdilTyYrERP1ZHmEtnxoPfO666y4URYn63+rVq3nhhRcoLCzk7rvvjuv8d999NwcPHtT+27ZtW8UHVcLkxB30elmf3pX3Gc3+JidUcJSw9wpHsYEWJP06FQCfKzk68KmgZEWpAfgUbxG+aR9WfNj1rCCDq2sLTfh1Y2NxArk9biAGYTA/X0MGjhDgMxzhatySHJ2nlnJDjRpBw6tO5nWuBiBgKw+YIpWsWLtWsAMBlJiAj7FkhVzc5MqXiLjHod4feY2ruKb4SbbSiPn7WmjHf1brGp5A+MiV6gI+wB8pIiCh1JqMzVNCJvtx+ooIBsGfnM5k7uB5btSAjzXg1QIkFZVyD4ZJTTEBn5LwjE9oVpSxqr3NGYPseBwmNqpBruFlzuQrTa16H1n8xGDy6kfvQ74EM/CpqHCsBD4VxfiYGZ8ATspQvB4S/pjF09zKWf7IpROimXRT3aC8SG/mkteiW8TfulywiO4Mts+BTz4xuLrE+ytJFnEVSbmb+MF7EufyWTgVCHw+naFQXA495btePaa0nsQbCDCfTBGKWk4hYlZXGPDSNEnE/YUyPmbEFP5YQAc+Ph34zGIoyTm14NRTwx8TxjRXl0HLZ9o0XWk93PW1dHa/OMZSLECKP4xTRD6TuzZdrX1WWKsJsxnISnsnrCrwkYAmkChOnmU5YGqfHJYWh7iGDV+5ec0IhGLSDJU+1h07RJ09Q5vteHHhxlkosrriLq5soLoixfg0SDjAFbzJKVteAr+fpiUraM1qrEpAoB+5uzjCwKfGu7puvfVWLr300qi/adasGbNnz2b+/Pk4Q3wsXbt25aKLLuLdd98Ne6zT6Sx3THWYxvh4ffEmdWmqz949+QAsXpNEmRo4GZXxmTDBlOYpJ64eva2UznORQBlFmwXwseHnOW4k5cvjocOlGpiRi+OuXSIFPiOE8VECAebQjwNkcP+p+uTtdOrMidsandJ8+mmYNUtNLklM1FaiQJj0zUglK9auhft4n50PvcWd/6t4d2i363Eb2m5bHZStWMev9Kd57jYasonpvuE0Yjtuv8N0vDaRViPwcVl19eX0SXezn+d5lHsIBB4lkJnFXYiSJNcliP7twIPfL/qWxSdrBoURMDQAH6W4/CS0gea0D8E29mR9nITWLzpUSy3ZzSZ60oQtps9/YCRPcAcvnQy9oxxflpTFv7TnOFbwD8dR6oie1ZcQxntbEeMTUNWWPywZTdAnBmBFVeMjmQQ+K4JtmU9vrovSXJcL8slgtrcfwdYQ+Fr4n3wWM/CRFqrj4wqU0IRcUg7aNcbHYgQ+derwcfFd7FzzD2N4k3rsJoCV7zfPweHop50nGuMDUBsd+JSVwdW8xpNZ+fx24VZ4X2cfIgIftdH2Ql1H6y9XX4aWzQqT8hjZ5L2XBXXgs3SpzviEa7sW3OwX31lKBPDxhVE9lwt9mYF13tZxJCP/PpuGqTDigGCqJaCxpAkwcvvOcbCoN06nmJO1EIYKGJ+hzGQIP2H/oTecMyr6zRv1QRzm+UrTHioSwCfgiEPAEMQCAPDrrzga6PpuJsbHvp83GUOhJ5mi3Rfz7QaR2jgn6AYcApgVFR3xshWVGrU+n49ff/2VDRs2cOGFF5KSksLOnTtJTU0lOUJpgspadnY22caU5gj2/PPP88gjj2j/3rlzJ8OGDePTTz+lR48eVdqmyljQsO3K2b+YU9hB+oH2QMUR7rLAqXefoMV3FSVrlSXCYjYjl2pAV8OHixCh4cOhpGMS1qCf0p36JHMjL7Bv7vnApSZX10V8QBeWsP2zs8iQCnFZWXD55RRZU/n1jQH8aDmViSP1JpiAjz068OnSRfwHmCbFYBjg884fLVEo4rfdiwDdLamWHaNFOwexlKC5bMsDdOMv8Y8QxgegP3PweNWMiKDYgUrtFYAGwe2U4WIaw+nYNrziblXYKYUfA5DgLzIF2hpFa0FnY6S4o81mYHzCuImsVriUKbgo46fW95G6YCbX8wJ1HrqeL78UZd7OCAU+Sfr7qGrgk9i0DgV4y32uuScquNzOpn3owL906iTa3raCaSgc4xNrcLMjUKa5m4KWyjFfsi9J1jGWkhUgwmAKG7Thdh7HmVWbDkBZanTg02rzTDZxBhum9+Ij/yhxX06nrnXjdocvWeEw6/hEivFZRRvasposvwA+JSUCUO6iPgmp9eH4LKG1M26coHcjxXbIiykwiJ/JYh8DnUugDF28MAbTgY9ed6u4ODbGp9gnjrGWiM2AKYkhpJnFin4fshaW34/O+KiurjotkmGm+sP0dK192js36JOFAh+fD05kDnfwBMHfrq8Y+Bij6g0vz+EQ7ClAxn6RxVrpGoMHD+I0LFvGPpLUVPTFFIrYsa1AC+vRWPUaUqg0buCzZcsWhg8fztatW3G73QwdOpSUlBQmT56M2+3m1VdfrY52VmiN1ZRYaRKANW/enIYxuD6q24IyzsLnZeTGZ3mK91m4/Amg4pRCyfhYiwXwKSZJ86GGJRsGDxYzu4Htkb+9Tb1cTr3dbN1p42nXOhwMUnU40N0e993HzavH8tmcOjzHTZzDF8yb0xQ+fANeekkEqDmd7FwLRW9AWorZFe50wnsInYhmBUsrvEfNDP6GcExFhmc3CRQRKDMU4wyKGB+AVq1iu0yjUoGUnuIWMlqr+hQhE3KZNQmHz6NNmLK+EsB9O8fSjx+4nLdoOTK2WK3K2LCDnwLw2Alf8IhdlESw4hf1okrdNGWHiMc5oTN3MYlNNKWbR8ztOuMTHvj8o2aHlWY3ppi6FJBKLV/kNHgj42NLqFrgY7UpLLd0oUFgJwC7HDlkeXZo6ewVsaPye8neVASU5Nrn8ejZ1eFcXcXtu3Mx77OfWpzI7wB4gzatdlflgY9o8LW8THtWkFR0IVAn7G9dLrDg5zpeIvhIIcXdb+VJbuf42vAI4M0wA59SEsJmdVn8Pi0GzOTq2rmTpnl/48e8GIUqN5tcXW++CT16wK5dLKMTbVlNhs/s6gL1OdeuLbR2br45+kNREYGiwC8MIoES3iocI76rBONTqjI+7gKxcYmF8Sn0qiUrgjCHfthTU1Udbt3ks5UhA6P4mjO7nQyIsTOx+VtsWVHINU3aAWAxFmXNyCgPfKIo0vt8ersjFnc1WgTGx+GAqZxiUueuNPCpVy9ijI+SlooHOw68BHfrKYFWu+o3b99ePOwjrOsTd4zPTTfdRNeuXcnLyyPBgJzPOOMMfv65coF+/y/MwPhoKawxvvyAOrnai/MBXWUZIgCf884TxUY/+kinJ0MsMVVce7WvJUP4mQvUgp2KBD4NG7ImsQu7qI9fpUT3bBEDML/EwVMvOjlwQCwaN/A8HRLWm87vdOoqsN+2vjOm+xQN01cfe1L51UsW3JRxDiBu1Z1fwjSG027COYSVdQ49j5r1s4fa+ioYAnw8VtEWufgaGZ+ARaenq3MMS1n5gMOFxa7HAvh8oKxexUaas4CeODq1ZTJ38RnnaZOnRXVrhGPOjPE7K8e9QX128T6jTXWrQoGPI9lAnTurPjzwt6STtb9fPWQDDrycxEwOkMEJ30TPIgstWVHROzGCnP1qRYRwRISvfmM+5GKTqKUvaCXgjV19PZxJFncU3/IMt5BcsDPib+12EcD+DONIeHS8NqblPQZT0/Cgj5UyJdF0/9KVawn4tNRoqxH4zJjBW0u78gzjTNcNVeg1MT6NGsGmTbxx72beUzPw0j0641NWBjfyHDcduB82GFLDollCgtB4aSLciDZ8pARUVGqsRVKBhcb4lB0U9xzNNa0DHyfY7RRmNaM/c7ivy9Ryv5V9rUhmx1JC47U/sZcsPj8wmD8TTuRHRupgbaSBCo+T8fF6DYAtWg0UaWefDc8+K/4e4uqayimUYDhHvC76Dz8UxYUvvDBijA+KogHCoJo04cWGxaruir/+GhYvhp4947t2FVvcs9fvv//O+PHjcYQIeTRp0oQdspxuDbAmTZoQDAbp3LnzkW4KAJvr9WIyd7Cu2TC91lKMMt5yd+gsE4xPhcAnEBCd9N13zQXrDCYZUalDKP2/lhJ91ycX0IwGoiNvXS0G4G23if9eeaoE+6wfeZ6b+Dm3vQlkOZ2wC6Ekuz8lDpVfw+C2JZRfsCXwMVaqX7sW0jjIcGZg/farmKIAZeFOJ259gU9O5u26egVFjy1R+w3ocRmgZ0olUBpWILCqTN6v0+LFYtfdLsYK4QEs2Gw6UJG474Pzf6A9/7K9XvjA2TMtX3MbT2Bf/Q8Al/AeEycprFylMJZXygOfFH22s9vCSMweon1d+2ru5REG8xMZWeLiSRSTQT52Q6ZPOEvy5rGCdmzbrrCa1mSxL+rvjeNm7171HGGAj3wG2uIDBFHwe6QIaeVeflAxHxdNx0dRwOVStKrvrh0b6M5Ccrxis5GQqAgAr5rPbl4gJfBRAn5+VQbyABM40H04e6jNams7DQUWkEqu4TwWZxRXF4DTybm353DOPa3wtmjD/kxBt0rG52peY8yuh4Xsxdy58PPP0bOzGjaEDRtQ3p3CWOU1OvCP/l0cgrShwMd9UACeF123w113mdXgVZPA5wHPvQTdHhZc9AIQfiqRXreDHvHckijGafOTxX7Sgnmm4uyAJtBJUpKpDIj2/Z130iwjj9t4MizjE81FF7ZxUjkzhPEpJpkf0EFY3MKrF14oigvbbCbgE/qMZNV3CXz8WKt1jqyMxQ18AoEA/jCL6fbt20mpSLXr/7FtaDKYu5jMv63OQlGVm2NnfMTvvmt9BzlsZhJ65lpY4HPAUGQzQuD21fsf5RtOp/7aXwEdTFlKVcbnk084b8NEOrKMBi3EgCvZX8LeK+9i2PsX0YmljJpyOp3uEQPpt4wzIlZIdgbjqGUlt+LvvCMK2oWYfBZBjz5DyIwuQMxgMQhj+e2ibQ8xgdQ8NaDWZuPNpo/yNaMA8NjNwCdgcHVJ4PMs40j+5oMYby5+a1C2EYCz1kw0xZv4fELHR7TLglJcRHfr33RkmTZ57k9tykra43eFj6m4IvgWT3AHSSv/BDCBBWMNM+2z9EQ20Iwd1A+7eByqJaVamci9zDbUUpLFaiuq9aM4nbRDaOK0Zi0WR/SZ1mLRx84qcZiWXWg0Z8FeTuNbRvKD9pkNnw58KhncfHfHH8hkP4Vy3Dmin8flQgM+DX75gIX05KYttwBiyDzGXdpvvXazz072G0vAx2wG8SAPUHjiSL7kbHqlrBCBf4jisHXJ5QAZWpsiprNPnQp33knaghlc9mgL7OtW8cH5gh2Rwc1aVlcgAH37wpAhwj0Wri6Dsb2zZvJKcCxX8xoPnrwQxo+HK6+MeozR5JT3O/3wnHoWBWkiFvD17Hth0qSwbjMJfAJBhbKy6CUr6tUTGEQyPm9wFWl5mwGRzj5i73v8jym4PCpbJTeE6gMsx/gkJlLqTMeN69AZH9B3PiGMD4gSPR6Lk2V0ZG/j2LKKw1kkVxeUBz7hMkSPtMU9ak866SSeffZZXn9dqDApikJRURETJkzg5JNPruDo/78mO57PR0wy9UablzGS5QU5bHF2Zytm9iQsrnn8cf3vEejMTsXz6MqPpG728TBnapOqrVRlfN5/nzGbf2Qu9bGnigGXSAmFn/7GOZ7lvM6lJhHBH5pdz9CQdsm4obb7fidUMCyiXX01nHKKmCDDtF0yIH6PPkOsWRMCfGIwY6p8YtEeUJ+rw6FP2HIB8WPlH47D7crU5L6M2jiWxOrL6pJ2/M6pYBPMjQQ+Fp9eKJMlS5jnOZE1tMLjEQFPFWUP+hQ7BKHLi1fwO2+zjpb6d2EEDF2JFpoj3Ba/V4PclfHVnbLycfozj3YITZkKgU9I/EMwhviFhASxQP/7r/h3hw7lf5OyaTnfMsrkSrLjZfWg67h66qmk1s5hTIVXKm8ljnSMTmgZCBvJjMBHZj1prGMCTOJ6ardII3/9Xna6zAkTRleX3LPK/YXbTTnlZgk2Q11dphifKVNEEeTHH9eAjGTMZHBz2HT2v/6qeGOiUnB7yWZ/8+7wcPfovw8xm01c4tHgeK57DXaq5euiTQ1G12dREWSvmsNuzmH3wg7AT6bfWizQvDm88u81XMAnANgtatmeoI97d15HEkUsKuoHpOrIWvWIlAM+mCIhTGaM8YkJ+OzZAzNnipIekyZpHyuKuMaPvpGcPLCMn3+Gx+NQww61iK4uoMySAAEIFoVhfB58UGT43XAD3HRT5RtwiBY38HnqqacYNmwY7dq1o6ysjAsvvJB169aRlZXFxx9/XB1t/E9YUqCQpuwlIc+lAZ9Yg0O+r38187dAnzCb2LC4xrijinANKbiVXrabTPK0SdXqVhkfw2TozxC7+zrkElBVfktIJKNI1zza1shc2NM4MI7bHYfY3VlnRf1axlQEysyMT7IMzIyRdQwYlH21jAOgkW8T7VkBwIK2lzNw72eUkEgemXRpBYu14/WFSkmofuADQKdOfGS5mIWBHpzsA7tBwFCv5O5FPpr+cyfiwgPu64DyDI3PYkeVy6Evc9lAc+07L/ZyuzRjX6sOBQjjq2u6ZyHN+Fb/oAL3pc0espjG0MDERLEZl2AgXFFR6V50GDLOFtGd4zJa8TOt6FnJJFa5EMisrngYH3uRgEx+A/AB+MR+CauARiFB2hrwCfqo591KMoWkeOsBmWKYhwCf6QwnkRISk5NNi5oxbtYoeihNgodyjE+sCr1er9jwLFkCiCykCmoNhzVFUSu0lwlgV1QEVnx0sq6GDWocUQj4slpF+48v+Z3EC5+k1+pt1GYPxd4DYa/RogV88++JFJBCKoVY1WLRNnzYJHCUmY/tVYShFgkrB3xmz+aJ/I/5jePxeq8xXSdu4FNQAG+8IQZT376mrxwOcT6ZSX4oKhzRGJ/7sl5l/24PT3ZozEuJt5NfYudUOZfk5YmYr927K3/xKrC4CaiGDRuybNky7rnnHsaNG0eXLl147LHHWLJkCbXDFIw7ZsJOWPsxG2nOqJnXasCnoslOmhwgIza+xOPcTieWat9VCHwi7K4CCWKgZgaEe2M5HWnJWr68X63qbpgMPXWFj7oxWzVxv2KSeDx9Em5nKqfyHSmp5uvYbPCumtX1wwkTYrpPzebOFYVWH3us3Fe5tdqxhM6m1FuTqytG4ONz6KuDXNwAHlsyjBy20o85/N3lSnbQkDwENW5kQIJGxifpMAGf00/n6sT3mcJlphifoGLRgIEDj8Z09//zCR7gQZI94Sdvb4iidh76lj4s41NdtbpUM746a7J5kq9orITiIi2LJIoZ15E6dcJ77yRoWEMrLPhxUcpT3KbF2sYkKhfGTt/xMq9yNQlq/IaxwG84czqNjI+qw2IAPlnspfme+bRjRXnXQ2Y9XuYaZte/hIcZzwqOo+6Pb9GDBSz3t4M7RfKBBwc/MoLGbGUsrxKsXUc7V2pqSLB7fr75Iuedx9UP1uMkZpRnfGJdYW02DfSAYHzKxRXFaFqcT1mQooN+6pDLx/92gNatI86Jyclic5f003fU3i7aEU71HKClSo7Ke7Sm6srNtqBau1BmPnbpIhb67783tU3rO6tWcX7Rmwzm57Curit4i6F1lpuDpCOZRKeFheXiqeS7lMDnUDYv0Rifv5P78xNDKWrQmgeTHmc8j+qbKDnIj0blZpvNxsUXX8zjjz/Oyy+/zJVXXmnK8DpmYUwGGPp9fJJ9I9fwMgVtYqNwUyikDrs5bd9b3M6TtGSdOJcSYeKtwIcOEEwUA1XGdRwkjfW05GCiCEg2Ap/CboNg6VJuavGjps1TQiIfuc/i4VvzmcqpYfHGda63aMYG/m57cUz3CYgKvu+9B598Ar/+Wu7r98/+juNZwubM47XPtmyJH/jM7HYve1VdC6MbRWoOJVJSbmIwTvwr6w/R/m5xVZ8A5sIGZwKwubZZ9MwY4+PHqldhxqPFJ0g9kUiBs/4QjZJ8Q+HGioqUVjfjI92rmkUJ/oXyxGYsgMTo3gjn5gIdFNvwEcSCWwXc2f/MZgyv06psecUXCmPd9k/jal7XrxODq6sAsag5i82ursREuJy3+X5/b77ldBId5tWztE4TruNlpjR5QMtQtCc5sePV4qJAjPVu/EkPFpFCoalyuMnNBeWroufnk1Swm7rsLs/4xNpZQia0vWRXivEB8byeZhyt21po/fEDMQUIJyeX1zIKhBEwBMH4NGMDTim3kKpngErVcZPWVZ062gQSKasrUjr7DhqyIbFDbCn9xkE0Y4bpKwlQZHGCcu80DovG+BjDOspliGrpc0eZgOF7snR2BBs9enSlG/OfNrU3KH4fv2WMZCkwKsZkpxs23Mxw3kay7TIDy+WKsHmJAfjodakEgyMLkWqZ4AblZiUzAzplMOQcSJykMz55eZCXLxpgosFVs7usbCprFl+694svinpkEJZWCC1ZEQwKal2jg2MEPsaSFUbGR2ZyJVFMk/ylPMMU0smnJws4sKY5qEGuS5udyZa5jclhK9ZqZHxW1xtAjx1fkZeWQxO/nySLh1IUfD4X3uz6vMh15NtqM94weWq1xWSl9gj0TKgqbUWMj3H9qnbgk6YvUPPpSUad6HWK7HZRdsWmCg3GAnyMa2Ak4GN1lBcwhCCt57/D2XzAGwefAFUPKR6TQfofcz5vczmTKqA2XC54kAnUnjiOtOmf0mHOSybGR7JBLdhAssODcWqX48/t1qUZHMmGdHbgqYR7+aW0J2fylbgeZaaSFeWa98YbcO21IksKNCBUmz2sL4bSkqAGCuLqLA6HNrhdDbMZNCj2Q42Wmgr+XPHu/CXumFTWk5MxPROIzPi0aAHn8an2b1vdLBbRjQNk0hARy2NzhZ/4Qmt1SQSeSElYxsf024rMeH9PPKG518CQhq+SLR3j77aaRWN8+pT9TB824lzVjQbedBw4sVjUDfXRKmB4U0hAktfrpaSkBIfDQWJi4jHgE8GkBovF742okxLJAiFaIa5aybA/ynxyghqtH6VnK8lmv3spCTzI/fT9ogAufcDE+MgBc9+dHuyTfOrvxWDdvFl8Fw5vhAviq9CM2/AwC3ZoyQqvV4Cfd7icp/dfSnpixRo+8jzlSlYAXpXx+Yqz+H3DA/TjOe27zW7FdLycTKszuNmhqCKEVju8/DLbD9zIp5yL3/8pnpyW3MCLpLhgvENUs9YYn2AQW0WMTwjwkcqu8u+h/dNqFe6g/PxD2y1GMmPwqStNPNOnuIXbeIp5wyMcpJrNBmtppTEY8TI+4eJ7QHexNWELbhw48PIHfQh6hfu3sjo+Uvjwd/rxE0N5vALC3OWChXRiZ3OwJ8xSr60DH6PERWiast3iJ4sDJBYGDIyPDnz8tevyUNkjFBikchZzAn97CnA4xMAuB3xatBBBtNIMwKekRPSRjizj2ivcjI1DfBCnU5PS/nR2dizC9mEtI0MHMYFSd0xxMmEZnwjFcVu0gOmG5dPSuSM9WEQixRSr7yKSyKfsd9r0pk6eKRSSFya4+TaeICe/FHZeWXFav3En7DZn0xqn08REcQ+VtWiMzwUHXmQQ37D9q0tZXjiFDTQjaFW1nI5WV1deXp7pv6KiItasWUPfvn2PBTdHM01Lw0e7woUMZDau4v2xHRuiDtukvc74hDU5uKOwH9InLa0MFzfzLF3nPicUAQ1FSu124M03Sbj2Mu33jgxx/Cax3oZlfCTwiUvDwbgahVm9zvryQtbRgpabxKRr1DVLSLLEHE/QdttMUlX3mInxcejPxRkwi6YZtVdSggWspB3z6Yk1qxpQgGpt9gi14IyirdqD1NLZjZWPDcHNHg9m/aYIKOCd5BsZKBW7gYX04IxRQdLTgvzE0LDvbfp0sd5VNvYimhm7qytD9OFYS1bY7dCelSgEUQhWGeNj7BsywNmOl6DcmldWx8eii1FCxZsD2a3LymBrM6HF808jkUWbmKgzPgAOl3laT927gb3U5rNlrTXgY3E5CKqSDkpZmbaRMKqTW502GqnZexUqoqvAJ5u9FBfDjp0K/9ARpVs3cXOyyGi7dtHPI1/cPffEVGw4kqWnm4FPrIxPKPCJJFfQsKE5zs+ZYs6Ig8hlXU46SeRwaByCivhTKAzr6hrHM1y/d4IuuharyUrWqhkBSocOle66gJ45F3peAK9VDCypCefHqsf41BBXV5Vk17ds2ZLHHnusHBt0zHQzMj7jd13LbAaTvnZRTMeG7ipPODGJPn0gYu3WZs2E7sVpp0U857bTr8dJGeN4moV0Z7erqeZCo6gIPvmEU9N/ZzkdxVz08cdCCfqNN2DfPtKyRW+XwKfKGB/jahSG8Ukt2kELNuAoFWKOEvgoSnwBt+lukVWwiSb4DG4UnwH4hNYwCBgA6JCVzzGQX/mHjlgaV19JFDlhb2x9cnngU+qmNrlkKnmQlsZb9cbzMPcJ4GOYQSMxPltcrfmVgRxMrk8BKbhxmgBVuInx+ONhwICqvEPdZB9yOMCWYgY+sZasiPTvcCZfr6JEXo+DDRtxNa/yGydqn9nxElQDyysr2y1dXTfwAmN4HZslemFdpxM6sJwW015AKS3hQR5gRY4APgkJ8AsD8WJjMV3Kx6Y59awuDfgkOLXq3JaCfJq415BOXjngM2IEzJsHTz1VwQ1lCbYwi32UlGiZ2zSQQ+u77wQ1u2JF9PPIxo8aFbtuTRhLT9dBTLCsrFKMz2K6sD8zPC1isYCvnkCFv9NPc4mWkMiZfMm5fIo9Mfy4y8yEL74wFJxXO34yRdF1fOJNwwphfIybgUPV9ZWZc6HnBfBaQ+qdGd3mtWoJLYAG0V3X1W1VJitks9nYuTOy7Pr/d5OLj8XvwxIUk2asWV2hwCe1fjJ//GGSaTBb584CoNxxR8RzJmU68eDkWcbRk4V8WfsaHfgUF0PnzsxV+lJAmujYUn00Nxdq1ZLznLapOFyuLll3KqiWDJDA52bbiyjnn6dlTlRoahmHTTTF4tKv440GfAzKzajtkK6o6rL67UR0Z+c+SeWAT8Lcn8ilLl8WnSRUp5s+zCPcJzBPDMBHTkb3X76DNArYTqOoJSuq22QfSkoC5ZZxZCSU4cTNVhqR+euXUY8NnXzjYXyaNYuccW3JyuR1ruYrztTPjZegX4qQVpbxEce1ZD2vc3VMjM+JzKH3JzfS/s8pgD6uEhLgIOlkkEcPFpYbNkbhSxPj49AR0spAG67kTRPwsbusKAr06hVD6Jw6IUjG5+DWg4znYTr/8nQFB4ZYw4ZirolBhDSaGV1duONnfEqdaZzAYr4f8nzka6iK9slKMdad29hMDitoz9ecyeeci90R4z0YXF3RanXFDASlnl7IDsXYL6qioIE8X2h/89lU4FMaRsCwf39Yvx4+++zQG3AIFvd25bvvvjP9OxgMsmvXLl588UX69OkT4ahjVlKnKS9yHSn1c+i59l0gdgHDUODjyIxRFyOKhU5kaWmGOAHVxy4HoQn4bN0KaPOcZtFcXVUJfLSTqY2TwKu3Mk8MphhrwMhJ34GHoGHtWtdsGH1Xvl6+LZgZHy19vJqBT1otcZ1aqV4NiciaPkFVxyeo7l/k4/J4gMRE7h62mJ9neLkwQjBYB+9ihrOAxhvbAgOx42H6DPHboczEYhka9rjqMtknk5MBpxNLAtQpzaUR29npi67+bbPBTIYyVBWbu8FecYC/fL2R3Fyggz9t4UQAH583Pi2uUDPG7fmxVJh+b9TxqbVnFe35lzRvXSBL189Rx28kxsca9PEhF7HQ0ptxLVsScCawmRyaIJTL3ThNLjObI459cYMGeFu2Y8O65hQWQi3/Ph7mfgKvJcNTt4iyFXv3QpMm4SWypb33HsyaBQcPxn7tMJaeDvkq8FHcbjbRlGUn3UanEZGZhuRk2EBzHrrXzYFCOzwfHUDXapwECyFRKcFqhRy2UmYIjo5Z6qBFCy7su4Vpf6TwYijj4w7gktlxsQIfGbwT4o82TqedOsXYtijmcgnZoFiAT00rWRH3qB01apTp34qikJ2dzaBBg3iqQj70/68VNm7PDbzIKU2hz5q3ALBUoN0hTQKfbzid+3iYF7IrmeNpsKzcFbzPJHKpw208RVoaZlfXCy8wpjTI24zGbk/Xgc/rr0PdumRlPWg6X5UzPm3bwuTJ5b9XTxbK+KRa4hMwlGxbX+aywFsCarD26nZnUjbViQs3luRQxkcfvQkBAQ4vDHwA3rcrL+hSkcmNxpdfwtixgM74yArhfsUKwSDNPGvYhxdfWVuw2diQ2oU/gdERmjagdBrjGM9va64EBuI1qBOnk3/YJysJpmUsrMulx8BEipeQZrNBG1XlGWJ7HTKEJBpWtnpKGcIfnIa+4bPjpfQQGZ/PT5jM3RuuZD698WKPifHZq4KS2ntX8C8d+H7NI8C95VM/xvMAAGLtSURBVNbDUOAjN1h2fLzMtSQ4Fca1gzIXNGUze/qeSfYfX+PBwWl8xzYaU4Yzvi7dtSsF81dwURbgB4e6WCtS6qG5Ko7Zvr0ulR3OFiyAa66BwYPFf5W0jAxYShP+qT+M1Y7OrOA4Vl32BJ3Oj3xMcrLYRBwsdRALrm1aS5SkaB1YTUCdy124OZ+PKSERu/302Bprt5OX3Jh8yis3B8sMgD9W4CNdXCGIRL5PRYkO9mO1sWPF65L6jNI04FMWJsanhljcwCcQrcjcMYtoRv0Va1CmUcf2+Ldmn8DbXMYshvIvHUispFqs0VK8B7iYDwE4my/4oOw5M+Nz11087S/hK04zAx+Ab78la5gZ+FQZ49OuHTzyCOTkhNeYl1kWvhDgo8Sn42Ox6SNRCo4BOOxBbYdlBD4bacqBBH23aFMMwcPVWZ69QK33M38+XHcdYAA+kvFRxL28Na8tAB/l5QK1K3RZSeXf/uveZDpbGcE07btwtbqq23r2hIkToU8fYPFiXix4hv7MASrWuRHp7DbTvyuyW24R2nLR1ldb/l5mcZL5Wnh5v9kEHsy9mjaNjo9wZHQrTcpitxpc78MWE/AxsjGicWblZmmhO3CjOKKFADabWU8mUKZncEqgGUubQs3oLpQaPkooCqsoxkdWjA2llOO09HSYwXBc3YaLeKPNFU8Nxkzr/ite5laeZMevFwCPhv2987RhzHj1JJYl9+UOQ9jCx1zIATKw28MLh4az0GxVacESY/ZGjMDnqqtg4MByiET2ixYtYq7sE9UefDD85xL42MrCuLqKiuD66wX6eustjhQiqsYZ+5gZzaF4qc0BkosNwCfGGJ9lLc7izV/1Ug6xKsBHs4Qs/SQ5bCXF5TUxPkGPBwWh42NydQEkJpabl6IxPnEtoC1bwr33Rv5egkV1hpDAJ95aXcadujFzJ9mvU+xFnfvSijWU4WIbjRnVA6Qz1wR8DjEeIaqddJJIoxo9GnJymJ1+Bn/kd6SHAfgEFCsoCj7Fhi3ow1/mhQMHOGPlKzQkGZstfNKBUZxtCD9pLjMQi/vhBj4WC9wt6+/+sIszij7Qv6tgrNhsZuATa3BzRWK4tjDFTn+jP8sSejIbqJ9e8XXCmdWqZwDFAjKcTl3AUDO7LmAY+lujGZ9dC9bjVFzgqYdDXQmDboNKOw5mMQQ3TrrFuTo4nWIoBIOVEC+UNm6c+HPZsviOCzHp5cnPF2ttOnnUKjwI+ekRUxKTk8GCn9EzRtNny0fieHfkzNse/Rz0aDeDE08ErGaQEwuLZ7TRa+5lJPuwHXgY0MUhJfDxKTZssZ7w+OPFfyEmgU9VxPdEs/kNz+HLdR24/owi1kyZzyaacpdRwHDKlOptQAwW05O85ZZbYj7h00/HGcz2/8Syty8hlx7s+rMJwTiBj80GjdnCdbzEDhqQlHTo2XNJtc3oyZGWwC08zdyTJ/LUBXVRrr0WMKSzy7xWALc7LuATNyHy7rvwxx9wxhl6oJ68dFYD1tJS2/1K4JMcjJPxMYAd43vosFYE0f7ICFIb1GMdOqgxAgGrEfhUp332mcghP+UUSEri7lZ9WbQIvjO4uiRg8Vkc2Pw+sYPPLeDi1eM5mQy+sYbvL35DOq4MBJ2Wdh6dDs7hK87kqSPplw/Z3Vbk6rLb4WI+4CeGcBePUauKPI/GvpHKQdJr2di2P5Fear+rLNnXZdt3nM1LQGyLZDjGJxiB8Snn6nI5eZfR+LAxlz5kFeyHlUtwOjvzK/2p+49g1XyKgwuDH2HFzwdcTO84n6EycAC7WM0QZkUGPrEyOYfoWZA6U3l5InP6al6j5wV3w2WXwdtvhz0mOVnUveu1xSDLEkHHR/5eI7AKzC8wnPJ5NBu65Q3S2MtHeddjBD5FCdl0ZglD+rh5MvbThTX5Kqoiviea5Wa25RvaMrAL3DTlEgDuORpdXUsM9VOimVKdO9+j3OQEag14ecRxPw53AdfFmNLnULx04B/u4AlW0aZKgI/U4ZFmT01gC03Y7AISdL+yBnwSE0VF3RdegIYNTfOXooRnoXJUZeq45Dg8Hnj+eVi8WMQFhACfVVc9y+k/PEv3DBiPHtycFIgP+Bxo1lX7uxEEBRPE9tlFGa6E8oUMpRVnNYntfg7V0tLgvPO0fxpdpqGuLr/VAf4S/G6vSYcp0gRsZHxkgcqba3/MhoN+/Ec6IDEU+FQQD2ezwQJ6kcZB/Nh4pIqAj9XA+BSTREaSFfbD8bnTaMZ+ssoGAPHLGbTK/Z0hCC0qHzYqCvcL6+pSGR+bTWW81PiQcq6uJBeXIhIqTpeFXx0OHA44Dj3exm9z0j6wmkH+X/iJIfGDur17qRPMJZu9mstMW21nzIDbbhPujVisIr2fCiw9Hfoxhx//GclGpQVfo0p7VJDODgoeiwuX1PCKNdAp5GH5sMVFBJfZU0hz78VSbNa38eBgGZ1pFqaOXLx2+eUCCF4cRwWhyphRKVzaURnc/Msvv1R3O/7zpgGfoI+3bFdRAlwXY03XgSte4DluBUTmVU4VuLpCkYorXfhljdWaweDqApH3qx5rBD7JyeFdtRMnwtlnQ+/ecbRr1y4BeiCmkhWS8Unwxxfc7LDqjI1xcZM1zJIoJqlgFw/zEoP5GQce9i7tC6qSc15jsW3aam2KwQlY7SYmlSB+v0Jp3aa8w6XsSWxLb3Ql5kCpR3tA0dgEv1V/vpLxcXsU/Oq0UFOAz3I60CIjTBCZwWQflW2vqlhzI+Cy4icxUTyUMTsfpBMLmbLvWyoDfKSOz1paciPP82UFz9rlgj3U5sn+33PdvAtJ8BaabjIxUQ8Hi1ZjzmEoI+Fw6O99NgPZYm+B3+8Ev3BVxQ18DFo+sgSO1piTToLlMdQ1+/FHeOklePnlOC9utvR0wd4kB4tICBbFnM4O4FZcuIiT0rPZWGVpR9uACLD3KfF1wDK7mLesJWbgY8qsPUQ79VSDdlA1Wh33Vi7gD3KWJJJFH8pwYbHENi8fLqthBNR/12RwpjXg03ZmMU8shh8WkxS3jlVYCwU+mQn04Q/OX3mfECtUzYNDv7ya5k5ioqmSdSSskZQE/fodgnJzmAcUGgQogc/oU/KEQ79p05guY1fESwigmFKJXT4BoHqwiMSSfYznUXqxgBNYTN2i9drvJJXvthymyuwA06cze46VP+mGzwcF7XpyOe/wdpbQa5JAJujxmmqtRWR8rOUZnxqzS1OBTx7pdGI5lh7dov68MkVKYzEjKJ7BMFatViggBQLxaXGVP7E473SGM4PhMcX4eHCyIOsUNtTuJT403KSRyAgFPjYbJFBCCgU6AHA4cDp14HM3k9jgas9xgX8AmMBD8T9DA/D5g77MenAuvPJKfOcYMQKmToV69eK8uNkyMnRNHiexl6wAAXw0i/UhOBz0Tl2hKaHHC3w8DnFxqXYsLTF3E3czkYFb343rfEfSWuUt5CMu4tyPz2AvtfmYC45OxifU/vrrLz777DO2bt2Kx8AOAHz11VdV0rD/mhnVU7v55uPFgtXXGag4+E8xzIqlluSqCYR3OPBiw65S0gkZCfRkNhdtfAQ+HwIIUKBYrTplu0Gtt7J1q4nxCZfRVWkzTkz+8nE0zb54nKV8yLe5Y4DrNeDjTLQSTylnl1+AOAtB847YoueTOhLNwyNo0PEJJKcyjeEcTGhEy5iveoimKFgJaFld8vHI/iBdV4Gy2BifZRkDeJR7uJeJ2gJoVLk/oimoCfGXrDBaVSXaGbW2BvIrACkUYZV10CorYKhKVMh6cbGWrHC7YVHOuUzb0ZHa9fWsnWiC51arcJPJquHyRw6HDg5clGG3q9VxvPpxcZkB+OSTgWtgb6hc0tshm7FkhZPYBQwByoKVAD6EBKzHCXzcak00W6mZ8UnZtYaJ3MuWNZ2B/8V1ziNlAYf5GZuyumqIxd2cTz75hN69e7Nq1Sq+/vprvF4vK1asYPbs2aTFsfD8fzMJfGxBLz8FBrKQnjjycmM72DAruu1V4ecSdmrX3SymC6uUtiipKeSiCouVlrL7w58ZwTSz+ui4cYIrnTyZtDR9YozRuxSbGWfwUFELwHVgJ51YTpZHqISXVTyfhTV3g2a8yHXczUTTBF/URK9W6Ugwz/xG4FPYogsnM40H6r0e34UPxUKUm4NuD8kUauzTgo5XMZk7yHfWiSnG50BiQ6YxgjLFpS2ANY3xceFGIVDhxFldjA8OB7dbyuuTOQJqgdpKMj5BtcFX8BYjmVrh/cn+3W3LF1g9pTzJbRxooqOKihgfX+geN4Txac4GEu1eU13AuBcrlQbORqSkH8mqBHY7WBN04JNsix34lBqAjyc9xngExHj5l+O4hPd4MuWhuNrrcaqurhDgYykTwN9rr3z5jsNt/0ngM3HiRJ555hm+//57HA4Hzz33HKtXr+bcc8+lcePDGe1wdJnMSrEGfVrgX8yTpmFW91Qh8Alm1uIEFtM3YyWB+g3ZTBPxxe7dFHYbxEyGmReQDh2EoF6XLiiKnqBRpYyPcbUNA3wU1WWo+HUdn/rs4NrfzzdU/avYbHaFG3iRx7jbdMnClsdzBl9xPH9HZXzkc6lOCZ9yFgJ86kx/l0JSeXqHCH7+ve/d3MVkchOamBifSADGaoW59KV1o1I6qEGuRsbniAKfWrW48YK9/Et7VtMWZf26qD+vNuBjtfKi4xYW08X0sSxgW1nGRz5cF24mcU+FgbByvb5s3d38768baMk60z0bPcQxAR81xkcC3re5gvbBfwkeyks3MD5dWEyjL54RQc1HyBypOpuVYo3d1VWiAp9hTGfj8Gtjvt7PB7rwKwP4hYHMTolRvFA1CXzsIcBHcYsB6bMdRcDHaW5rTVRujhv4bNiwgZGq+IXD4aC4uBhFURg3bhyvv34Yd79HmSkpybzDpXye+D9skt52xbZqGul26QuuCpNMTUKCoMe3oKZhbd2K1y1o8WgLiAQ+Vcr4GC0M8JFg0eLXdXzqkEvX9Z8KdeMYzXhfJleXA77hDJZwfDnGx1hmQLoTDuuAtunukXBZXabA7x49uKL171zF6xHBWbZvF6N5l0EHv9Y+CxoqPRzRXZrFgi89i/rspBVrw7o9jVaZWl2xmoiRKTV9Jv9d+Rgf/TifUvE5JPApVoNEj+NfnEEdpVbk6pLA5wdO5rNaYzXGR2N5gaDdYWpX3Na4MdtS27OT+pyc9Bv2O28R0hRHyBLSBQJ04WZB2jAhNRxFslgCnxODv9G/azGzGBrXxqaJfz2tWIcTd9z9b+aJj5LDZuZ0vpHdu0UG1p9/guJWdXyOIsYn6CzP+NS0hO+4p7aMjAwK1ZLyDRo04F9Vfjw/P5+SkpKqbd1/yCyZ6VzOO9zi1LMVYp40VeCzh2w+a3hrlbXpvE2PMYX/cYJlCU4n7KQ+Xmzg9VLrufs5n4+PDPCR29dBg8p9ZVELbioBnfHRxAvjaIhxQjOCF+P92hNC3k8YxuewAp+QWl0SDARVhJLm3kNTNqIUF0FGBn+7+vI3XSO2sUnpKt7lUm4vvC/SpY6oOZ16zERFK0m1MT5Aj+AC2rLa9JmMGTFKIcRjS4+/nNt4AogtHkSyOEWK6OOvcg0NNv2hfV+Rq0sWH72Vp3i8yStgs+FwwCi+pcgqKNug3cHUJtcD8Ke1R/w3deaZTDjrX27jKbJS9eyxI2XOjER+py8/MZiZ9S8VgdYnnhjx92lpot8UkcLG3YkEscTVj/wqgD2Drznetyiutpal12UrORQryXz+ObzzDjz3HFgl8HEcPcAn1NUVwHr0Ah8JcE488URmzZoFwDnnnMNNN93EmDFjuOCCCxh8CLVV/uum6a+U6SxGrIxPca0cPuVcnudGCjNzqqxN/Te+zf94j2+3HY/DIVKBd9tEam6dNx/lMe6KCfhUqasL4IEHhB56kyblvtJkAQyMT7yqzRAZ+MjdsqKYgWkutSlz6jFsXbsKMevTTov5koduoTE+RuVm4NzPzmYjzWm1SbgXJEkSadcqBfDaBVZwP2b9+ZoAfM6aewspqFkuFWy9qyu4GWBq8YByn93OE1zIhxTVq1xouzu5FutpAegLZjSTjI9RvdlYxiOaq8vI+Njwae9W9nVbwKN94LKp2Y4xtCmcyXZkJVdSubkKzZmVwon8zlB+IjG14vuxWKB+ffH3O7bfyDI60mhR7CyyBJdPcjs3Hbg/rrYa9bmK1C5fWAgWFfj47Ycxe/RQLSSOKpb+fbgt5hZ17NiRbt26MWrUKM455xwA7r33Xux2O/PmzeOss85i/Pjx1dbQo91s1iCJlJBUlq99VpEom7Q9Lftws1osYWTVhfiYXBlyEtxmbUIj32YgJJU9jMmU9iqPab/99ohfycneEtCrs1eG8Ynk6pKf2+1AZiY9HYsp8Vj5hw7cNFihn/q7unVh8+bqrVZRzjIyWF5/GIt31g3r6pJARvF6YOVKLtg7i3k0x2o9JezpggZV2u6Yd6g1Afh0Xvmh/o8jyPjIBQ1gX0JD/i1tzjeMYg91GJhRuXMaM4BiARka8AnqfVxxhE9nD3V1GWN8GrOVQLA+kKlhEntQAJ+g3YEvOZ159GK9/Tgqwfloe4/MpCMPfDIM76aBfQ/sVzM/o0xqDRrAiC2vcAMvAPCPYb6uyIwLvN8SXwdsvOdPHucz6i1vSZfZs7ibL/hr7knsadVXnO8oYnxK0+pyCe/xv8a/sH1rgIXW3lx2pBsVYjEDn99++4133nmHSZMm8eijj3LWWWdx5ZVXctddd1Vn+/4zZsdLMclgiKGIFfjYbDCad2nENsp8o4DjKjokJlMs+qotJ8s7kl7mj5fmwZVX6qrNEWzMGKE3WN1KoEZTMtLYQX3yA2kEg4LxqVWFri75HOx28b9/7V0o9pT/HRxm0APQqhXPDpvOO+/AJIOrCwl81JcV9HhhwQLuyb2ZqYyMCHwCBuAjdXyk1YQsDL8xriFOxqcqgU9AsUIQLsmZQ6BHLz76TG9LZZmlBrv+4gLE3Om3xAF8AnofNzI+Fbm6vuI0ejGfHxlJ/tIsYC8OB4znYS3NPehwsjFnIH2YR6MsGB3vTbndTPjiBO5w7IM0VbX0CGb6GktyTf5zIGSthF9+gQEDIh7ToAGcqBbGBd21HosZgU8whndqtDr7V3ElT/LvxpNovncBAF33z+S+1m8xaf4AzumVTfe4znjkLJiUzAdcwr52lzB9KyTY4dUj3agQi3l669evH2+//Ta7du3ihRdeYPPmzfTv359WrVoxefJkdu/eXZ3tPOrN6NZ6mnE8wANx1eq6jHd4hPtoUfpPlbVJseqvXy74//rbanW5TKrNYez44+Gbb6Bt2yprUoXmG3MtDdnBLTyN3y+AT7J0h1RhjE+4GJ6awILINvj9aPWMpKsraBcvUfHGpuNjrEPkDtGTqgn3GjQ2vAIkE9reqmV8RDsKE2prm5VT+J5RfE2CrzDaoRGtbu4ymrNRnD+GGB8JfPL94Rmfilxd1/AqlzIFEDW55O/asko/n9OhXadSgM7hIGHHBjI9uWT+pgbMh4nTO1yWng5raMUBMmhQoN5nBboXDRromW6gZ5HGYibGxxpfB/Qnivfq9BRi9euaEnudDfmDfhys1yau8x1Jk33HE2HDWBMs7n1dUlISl112Gb/99htr167lnHPO4aWXXqJx48acdlgDHo4uszstBNSCl49zBxPtE2LeVuesnsEAfhP/iCOOpSJLSikPfIwlKypifI6EGWl8r7fyMT7RsrpAHbw+H7d5JrKRpiygB/0WP1f5hleRGYFPQd1WfMq5rE5V94J2g6srhnT2YBTGpyZMVkGboU1hypcYTVHMi3VVxvj4VWDpsvm0877L//iaM0kp2FG5kxoa+Epaxay5BDNTgrqIXSTGJ5yrC/RyFT6LQ/udcZHH6dTOU6nnZ9S4AMH29OlTiRNVjWVkqPXVyMciqfYo6ewggI9xExAP47PLocdfBuIEPoFEMXc5PUVY/boocNwq/zXAbDY4le8YsO19MtmPQ/Ee6SaVs0MitFu0aME999zD+PHjSUlJ4Ycffqiqdv3nzOhnNwYYxmKKgRmypFRdkI/TVR74ZLh3w913AzUT+Bjb4/WKGJ9HuZcfPsiDyZNjPk8kxqdtW+jVCy65BAgEuK/sXpqymR4sIuvghkO/gUOx9et59u0UdlAfvx92HH8q5/Mp3zQUmTiojA8+b0yMT8AALGoi4+NNTgfghgZfha+CG2JxEERxWUJQZKvese167v++G3vIJpM8wFzSIh6T+j8zGcpfyQMq/L0kKpb4OlBoFe6jWF1d4l0GtRR8n1VnfPJJB+Ax7sTitGvnqfTzk4F/b7wBX3xRtS8iTktPh4v5gA000z+sgPFp2NAMBi1xMD5Xt/6NOxBzULCSjI/LW6iDNKDT+i+5geept2dZXOc7kmazwReczX3rRrOfLB7w3HOkm1TOKo0j58yZw9tvv82XX36JxWLh3HPP5YorrqjKtv2nzGYTi5ADL135i+2WAiC2CsTGXYclteoYH+PqJoGPM1ACagZfRa6uI2H2WT/yB4+ygJ54PE9RWipcEfbsdIgjnMB4X8YF0+mEefPUf/hDlJuPNBpQFFzeIlIQO0HV06UTh0bGxyO+jKbcXJRclzW0ojVraybjowqhJVlKK/ilMLtdF2Csyn6bqAKf4w/+yt7UZmSzT/su1ji9cmbQZIplN2/EfZ8kj8F3sIjW6Xr0bkUChsvoREeEm9zI+BQitW5EyYpDcnWBzvg4nTBkSCVPUjWWkQEraU83/mRex2tok70fmjePekyDBrC5ksDHFLAeJ/AJJgvgk1G20/T50JXPcgN/8MuW54FOcZ3zSJnNJsCjQ30WNTGrKy7GZ+fOnUycOJFWrVoxYMAA1q9fz/PPP8/OnTt544036NmzZ3W186g3u11nfL7hDGaXxP6sjAKGtrQqTOu65x6xLTrtNA34bDdUmn6J62oc8LHm7aMP82jHSs3VBRUy2OXMOLFH9DiGfmE5wmjAkM7u90PAFwCCWjP3dR7Ci1zHGlfnmBgfnzOJLzkLqJmMT9Cl1utSYgM+1cX4fJhxvfb3QEi2TmV1fOQDHsxsOnn/qvDniYliIa/PDuYWdmQGwwhm6ZWCKxIwNGamyWK2QsJCfF6bPdjtHDrjI4HPvn3Rf3cYTAY355HJL2M/hZ9+qhDRlYvxSY59vrVaYRojuIaX+b3RhXG1NZgkNrT2gMf0eXrpLvG96+jJ6rLbzc8wluD9w20xA58RI0aQk5PDCy+8wBlnnMGqVav4448/uOyyy0iKgYb+/26hsvHxoGBjELQ1rQoZn3PPhbw8+PZbXfUXB4EGAvxso1GNAz5y4rLj1YDPrTxJ66evgoUL4z0NFkuU7CxFEYKO0o40GggBPu2/epggFm5aK2T1c4eN5gZeZG7i0JhLVtzLRKz4uJPJ5b470uZ56DFyqc1lRS/E9PvqAj5f1RoDQJ6zDn6bGVXYnJV8UIbNzOi82GLHcnKgE8uYEhjNfTxsut+KXF2muceiu7oasQ2AC1Wx0kOK8QFYskT8+fbblTxB1ZkxqyvW8L/69fVF+y0ux9Orf8zXm7DhEt7iCv6hA6vrDYyjpRBI0oPWV6XpQgIZpYIBOpqAj2R8pAWOZuBjt9v54osv2L59O5MnT6Z169bV2a7/nNnt8DVnsBwhme6Pw8toCr7NqB6QaVwo/A1EkF4TNtc84KM2yIYPj0e4Nk5hKrW/eUMI68RoEqsbXQThLGDYKR9xNGBwj/j9uo6PTGeX78rrBUaP5oyE6bzMtRGbbVd8nMUXnMtnBDGjvyN9qwCN2yVThz00Kl0b0+8juS8P1RJVxslrdZVzYVS2ZIViaGCsC0PjxlCEWMGbsRGbRa+2Hs3VZbHowGcP2SxpIOQNHA5zUHuVuLoef1xc8M47K3mCqjOjjk+sCZ8uF3ySeR21yeUGXojrOeSUraELS8kgL+55M5iWTgeWc3HvjVzXeR6n8D2n8S25riZ6w44Ss9mgFB2oBZUaMJmEWMyv9bvvvqvOdvznzWaDMbxJR5axjM4x1eeRZnEZJqfs9GponVjorFY1Tbq4GIAL+Jh37OdVy/UqbWEYHy2dPY6srtq14dlnxZ/RzKfYcAbV9NIjjQa0khV+fN5guZIVrkAJtSnEUeqC5s2ZqTSnhMiLmAMPXyDESL/nVKEzpVpN0PGRKS1KjKtPdTE+bdwisNQW9FEWAnwqy/jsbdOPrxnFGXwTM/DJyYFcFahkkI/DWwyIFT2aqwt0LaKxvEpSpzM5DwGQnuYqxvAmW1V2VwKoSj+/U08VWaFHeqxgZnziKauT1jCFLQfUoqFxPAcpRHkSM9nkPg5oGvOxNoeFf+lALTu43fALApw+riqqH2N8qtZqwvT2/8IsFvGfrMwej6srkJbBVEbyHpfEJL1eWdPKNezNBUQdnxrH+Nj0zLiSksrX6gJRzP2CC6L/xvieQmvQHHYzLCYBX0CPblYZn2YfPUIudbk2dwKgp8JGXIMMK+TpfBvpUkfOvvpK/FkYm1aOqc5aFfbbe7deDUBm6Q4tW2cqIxnD6ygplXM9+1PSWaYGq8YaCJuTY15QrK7YsrpA78c29JR8h0NPcffgwOEQsjt9+ogimZW2GtF5xHQgAXw8KiANGohirvPohWtrbGwj6LEsN/AiAzfF5+ozsrUyQB/A7qtkEOMRtFDgUxODm2tei/7DZrMGcQVErw7EQf/5GuRwKlMBmFON4VROpwASO17/kcRrRnP5rkdJrmnAx+Dq2rNHkB6VBT6x2FWNp7Nlc5CldGbSsAT6VvkV4jCnk62N+7Jxq1UAH43xEX1JcQogY/V74Pff+Z93DYvois3WOezpZEo1mIXsoIasXXPmVPwbg1UX42O0fbXbsmdbGc9xEz8xlKsqOR6tVn0TFIixInrjxrAXPaDZKIoazdUF+uJTi/0EcQNOnE4z8LHbRSmWP/6I82ZqqFksgtHdvdssL1SR9XQt5WSmAbDPICZYkRmZDaNGVixms8G1vMRL80Qg/Ytcx3x60dytjsujCPjY7TCJu7lfeRhv0MYmZ80LiznG+BxGW+prz1x16YwHBRsn9IpiUg7FJAFQ2LwzU8YtZyqn1jzGx+mk0JpGMUnsULXjKuPqitVWJPdkAb0oI+HIu3+Sk3n/qt8ZyK94sWuMj3R1SZeoxe8lOOVdXg+OYSQ/RA5utulxPYGQqaBGAJ84O191MT6bEoXsxCcDX2PGma/TnT/5iaGHdJ2U/G2M51Eg9vIGOTmwkwbcwPNcwZsm4GNcF8O16W+byCJ9hWu54vdLATHeB/ArAO1YVfPGehXYm2/CM89UmMVuslaOzdrfrQmxP5RDAT52O1zNa9q/L+JDPuRi/rZ2YwQ/Utbk6FJu/pZRnBD8m54s5LeUU490k8rZkZ7K/1+Z9AEvpDufp18V83FG4FOdCXRG9WY1KajmTYYDB/K/0/Lpzxx27gQQxV+Bank4Na1khbGKs1arSwIfVXPEGvAQjDGrS1pNDG6ON8K2upSby6wCUJek1cNmAxtehjONocw0BRjHY8kHtmp/D8bB+AC8yA28zRVhs7ocjvBZio8lP8JtPAHowpUOB2xARwQ1bqxXgY0cCTffHN8x6XV0yszmqhzwMWbtxWI2GxSiM9arEUBnZaAN0xlBMC09rvMdSQsde0d8wxjGamCTDs1++OEHevToQUJCAhkZGYwaNepIN0kzyfLcz0O8WztyBfJQOwZ8zJaZKf7csUMEOdtQAUA10GGj8t9hN3VYQmea//N1lZ8/XjOWrDhQpy1TGcnOVFEsTTI+Vr9XFColuo6PEdzUSMYnzs5eXYxPQHUl2q1+7HZI4yDTOJmZDKv0dYxuxrn1zo7pmNq1zW4s43uVY8IY0Gs0mw2cCLeNBD5OJ3zAxYzmXVqxpkaO9SNhGfX0+BRbHIxPsTNT/0ecDzMS8EkP5mnfHy1ms0F7/mUkU2nGhpoxl4TYfwr4fPnll1xyySVcdtllLFu2jLlz53LhhfEJSVWnyeA3O964OvLhBj5u99EDfLzYaZa6D7ZuhdTUKr/WVbseog576Mwy0vK2VPn54zK/n2seqssesnGVHODfXmM4lanMaXYpABZ1d2oLeEzA56hlfPr1E3+eEr66fKhVV4xP+4PzAeiy+hMGfnsz22ikX9NRuSlUipJupREbs2Kru22xaPWDxbUN99uwoXDrvPde+GNtNj2ex8j4BLHwPqNZR6saOdaPhNVqED6AvCJ7sccHfI4AsUol3LRFhqxKCXxOZSqX8TaOYOyxRkfabDa4jSeZyqlsoAVnHJxypJtUzo4iHBndfD4fN910E0888YSpdEa7drGVhTgc5lOrMLdlFV7/8UD9mI473DE+NZrx2bCBa74eSzfSmbjjc0ChLKkWNKpVLZczBaHbjjAasFhIKswlCcDnK1eywqoyPgL4iA9jZXw20yTid0fMpHZJ6ZFVbpbWYv00tiaeSgIiQcGLrfKMjz2+khXScnJg/Xrx99BrR6sY9Gz+/zgbgYoCdkEbhQZB17ixfoSsbiP9QcTD+BhLVlSG8TGyrhL4ALzNFcxRzgPCRK3XQAvN6qoZk4nZ/jOMz+LFi9mxYwcWi4UuXbpQr149RowYwb9q3alI5na7KSgoMP1XXSZjfJ7gDp7cdFbMx8l+Y7GEz9ioKjsqgE9JCU3X/0Q/fldjfKo34cEot64c6QGsKATU1PWA16+F+GjNat2ad7iUWQwh6BYvMFqtLosFlqop1bupa4oNqRF+efliYwQ+sq8qStXOtXnOOgDsaXg8QcOA8GOttAtClrpowE7quzfFfJyM84H43B92xaf9PWDXGR/Tb2raWD9ClpyuP9h4a3W9zeXcxhPsbhJf+Sa7HZIo1v5tZBUBrMlHV1bXMR2fw2QbN24E4IEHHmD8+PFMnTqVjIwMBgwYwIEDByIeN2nSJNLS0rT/GjVqFPG3h2rGDhBP/RIJdlJSopRXqAI7KoCPQcBwzx5oyDYm5l0DDzxQLZczZd8daeCDnrqOz8egL66lhAROW/kYAJb+/bicd3iBGwnE6OrqyQLSyOcXBpoAZA24VRg6FNatg69ji62SQKCq4yF+byjc5XubdAdb1QAfY/29Xru+ivm4nBz97/FcO2Dox0FDjI/Rwgkf/r806TJPSoK02Csfj1o5kTt4nJ3U50DDjnFdMlTt+AB6vJAbR6VdqkfCQhmf4JGucRjGavzTvOuuu1AUJep/q1evJqDy/vfeey9nnXUWJ5xwAu+88w6KovD5559HPP/dd9/NwYMHtf+2bdtWbfeyNLG39vd4UHCLFnD11fDQQ9XRKt2OCuCjNkhSyvXYxXl5r8KUKdVyOaPcunKkXV0YJhG/H4vPQwJlWBB93/iu9t70KOfwGXPpE5G9sVrBjYsC0vBhN6ni1wjgk5IiOn9F8tqqyfuv6j5bP1O4tmo1TDCd3HcIri5jqYtYs7qg8oyPcfHZW0+UzTnG+ESwRo1gwwZYG7t4IUDdonX0ZS4N2R73s7TZ4GaepRNLqU0uu6nLI9wLCEB0tAU313TGp+a1KMRuvfVWLr300qi/adasGbt2iSq2xpgep9NJs2bN2Lp1a6RDcTqdOKvTf2SwVxs8zO/72/IRF8XVGRQFXn21Ghum2lEBfAzKzYCeyl5NwU8mV1eNAD6iPUGfn9AgH4fVT6IKhPJbduMLumG1RmYJQ8FNYiJIcrRGAJ84TS4OVd1nu9YSrqgmdctYk2tmfOLMWtbMU7shu6lDXXLjQjCVZnzUfnMvj5DUSZQpORbjE8HsdmjWLO7D5Njsyx/s8o4BMqIfEHLJHTRkBw21zz7nHMbzKKUkHFXvphzjEwewP1xW81oUYtnZ2WRnZ1f4uxNOOAGn08maNWvo21eIBHq9XjZv3kyOcbY4giY0QMSCXRPpv6MC+IQwPhrwqaZAn2BNCm4GgioiEcDHrNxs/XkmxZzMYrpQWroYiL44hoKbGufqitOqi/Fh+nTx53ffQYuRAJTh5B4m8mxlg5sTE5hPL87gG4KHEfiElqwwWo0b60eZyQX+NL5n2pbfgdNiPjbcu0xAxLYdjYyPqUhpDQQ+Nd7VFaulpqYyduxYJkyYwMyZM1mzZg3XXHMNAOecc84Rbp0wu90gU18D6b+jAvhoMT4+IKhNDtXF+LzW/nmGMIts9rBj4CXVco14LC+nC3/SFXfQoTM+VnUYqy/QgYe0z9/gLL7AZfFEPFcouKlxrq44rboYH158UQjkvP8+nlr1WEUbJvAgrzG20guSsWQFcaj8NmkCJ54oMvzjKqCpLj5O3BFjoWrcWD/KLGgYNIqj8qrj0oYjAHdlqr0fSbPb4Tf6E0DBh5V8R2yu6sNpNW/1PQR74oknsNlsXHLJJZSWltKjRw9mz55NRkbslGN12oSN/2OETCk9BnwqZ3Y7QZuNMp8NK/5qd3Wtz+rJz+rfa8LG5dcJv3D++TDAARRKV5c64aovqxb7qffkVXwB1LGVRDzXf5XxqfLd8XXXwTXXgMXC5jNP4LQpt2hfVfY52b0lWv29eBpstcJvv8V/ve0JrQC4h0nM+rMFcDmKItxdblUipsaN9aPNDBNEPNlgEL4LjFbXivcYzWk1YO6J1Ww2WEZnrGrsYf/0I9uecPafYXwA7HY7Tz75JLm5uRQUFDBr1izat29/pJulmVXR5e3/rjvyCLYkvEmff40GPpmZKF4vTeuU4semMz7V5OoyLmw1IcXbqNysBMwlKyRyrUMuAKW48NoiP5f/GvCpNsYHtGdss0EKBfRjDt2tf1c6y1Krug0oturvWN80vJ4ZnCT+YfBxGd1dNW6sH2VmdOnEy/iEAz7raQHAX3Q96lxdRqsJ82ao1cAm/XdNDow7eYxfmo85wq0pb0cF46OaVG+ubsan695p7CeTZXQkc8Of1XKNeMxYq2tPVntmM5D8FFWCQX2BFoIA5JERFcBEAz41cbKqyKotxsdgQo5/BXPoz0eB8yp9HqnjA7C7TucqaFl0s1p15WYj2jEGOB9LZz808zn0OShexidcn81Tg6OPNleXzQbp5NGX3+nGohq5iToKp7ej1wJWPTC3JnaGoxH4vMmVPHz5JnjqqWq5zsgNz5FJHh35h4SDu6vlGvFYv/H92UQTmhUsZXaf+xjMbP5teYb4MuRlHSDz/1Vwc7UyPqo1+PVD5iNkKQJU/iEZ09nza8VROrySZqzVpTiPMT7VYT+d+BCbEdHnVmflC+xKk8ztKUw96hif7izid05kET1oU3jkN4yhdgz4HEaTjE9jtpISrD6F6MraUQN8zjiDZ9afQhr5FJNMWd0mUK9etVwqUMPS2V0HdtCELdh9peWVm0O27IfC+BwDPuHNWZqv/d1UziROMwoYOiy+KL+sGjtv25P0RtQcC9rDMz41cqwfRWYsWVEVjM8J/A3AScw6qt5NqHKzYqlG1d1K2jHgcxhNAp+reINLltxSwa8PvxmBj0+di2vkgPv+e7rl/qC5uYzZSFVuBtmBmgB8ZHsUf/laXaSlMT1Fr/QdL+NztGd1VVtws8GMsRsmVe84zejqSvXsO6Q2xWKS7QFQXDraOcb4VJ1ZrfA4dzCBB/DXia0Oo7RwffYrzgRgDa2OOsbnWDr7MdMsYEhbrYmd4Wipzm7U8jmXTxn28x3w66/VcqmAMVPDXgPQgEHH54LvLmAvWRy/5mPxXd263Nn0c+5ElLCIh/GxWs2L4NEIfA4H42MEPofC+Fgd+rE5uxYcUptiu6ABwB9zdVWLdVz9GSfzI8UkEaxdJ65jLZbyQqM38ALjeJoh/HTUAZ+aXqT0KHqcR7/tSWul/T1YAzvDUePqMqg3j2Aa3X97FxZlwYABVX6pYA1jfGS/UQJ+XO6DZLEfW0DX6rHbxU6x46hmvPZNw5iBj81m3nXWwO5ZoR2O4GajCyNwCCKkVru+57RY/6+9Ow9vqkz7B/7N0qZ7Q2lLC9KWnbKVTTvFeYedVpZhUwERQRmZARxFEYV5h01EBH4wiiI4grQKo4IKo4D4sljWAoKAsi8WCkIpi93pljy/P0LSpBtNmuWc5Pu5rl5tkpNz7uT0PLlzn+c8j+NPBZh/0SrThpr+5qku+6mXewV98H+4gUibEhUvL0PbCximCMvJCcI7eNn0mFxUSnwkmLWx4uNEe2L/ivn4h+GGhCs+kk987gelRpnDr+oSEuvjY3E9u6h4rgvw9hK4gmj8GPU4DiKh1qe61GrLfS3HxMcpFR/zxKcOp7rM39+ioAePTF9XxtGh1+EpFEbHmu5nxcd+jO9xJxyDt6LU6uebH6vGizcAQyVITldZqlTSn7JCRm+n/JmP3CzFfwZjH4979ySe+NxvIbxR4vBxfMwrc9I41WV47QpdmWkcH4Vx5OaCAhw4qEQJNNDlGRLC2lZ8vLws97WcGlojp1R8zE51fV5vos3rUamAX9EEACB8/esc14M3WHnKCoAVH3tS3D+g4vAzfAus77dl/v6bj7krt/2iVAIlCmmf6pJh8yZfajWgwv35lSRY/gsONvzOyZF44hMYCAAIQL7DKz67O07BE1iP9vgZ9+L+4JBtWKP4oaY4jVgUCD8oTFNW3G9YzL6+dzr7GRog06qKj9xPdVU3FYM9KYICkYHG+BpDsSV0rM3rsZiywsrB7mwhqkl8zCs+HMenbsxPWap8rN+n5vvFPPGR4EfFA93zCjL9rfNy5NUntmHi40S9Ty3DqzCMNyPFio9Wa/idnS3xxCc4GEKhQCDyHF7xuR7ZBV/iCZxEe4sOqa5yeeF6tMVp7PfqUflUl1kL+Vza8+iI41b18ZH7qS5nVHxKe/RFNDIwHF/X6QNJqQSicBUA4FuaZ6foqpfnHwEAGI6vEXA3w3Q/Kz72o1QI099qX+vfzOoqPnJMfHRePlBAQAGBHH/rrnBzBiY+TmR+3vd6ZFcXRlI12SQ+e/fi8oUybEc/h1d8Kl755GrmXXyUFU91KRQoU5bvMGuu6vLykn/Fp3Fjy9+OoFYD9XEbnXEUMbpLNq/H/P0VDkrazf3cdAh095t78yvT2MfHfuqa+FRX8ZHjfpF6WyLDXFLG7v83fIaROBs71MXBVGae+Eh6HB9/f4TcvzDF0YlP1K2jyEYvXEE0vG9uAdo58FO1FswTn+v1YvF7ZhGK/OubHtcpvaDWG7LWuwhBuAd1bh42DNi7F+jUyXHbUKuBAdiCFIxD2tUkAN/ZtB6VCuiNHWiGS4hvHm/fIKvgpdSZJo1U+nIcH0cwT3w8/VSXecxS7C8owZDcl3G01orn2aVCNhUfAEFBhg+Pfvg/nP/yZ4d92nU9/QmCkYsO+AVe91w/2vZDc5/HSbRFr8LNSOn2IeJxGNda9CxfwGwwEGsqPu7Qx0epBP74R8DfgX2FNRdPIQXjAFgOdWAtlQrYhd74CBOc0hZoFOVDHih9OHKzI1yKf8r0t5ev9TvVXTo3A9JvS5j4OJG4/x9cH3dMQ5tLiTHxKS4GCgoMf0vyoFu/HooRT2JGg49x3SsG9Xu0d9innXkndClc1eWdeQVtcRpBut8rj9wMQFNaYPo7G9pad26ueFWXFBsrKVDrikx/mw91YK2KSaejdb6ysXzbvhzA0BGMbXop1PDytn5sJvP/g8BA53TWdxSpXyEqwZDcl7Hi0ws/4A9H33dxNJUFBpYXDIzzQEmyMTx9GtiwATP6/IhffgHq13/wU2xm9q1eComPsRVU6HVVJj5Z0Q+b/tZB7VEVH2cwP4VR14qPkTM+2HzLyjtQmw/CyIqP/Qg/f8zHP7AIr9n0Xpo/x8en/HoNOSY+Um9LmPg4kcLsP1shwf8GpdJwCsmcJBvD+6Upv+Lf0WrNdOCNN4DCQsdsy7ziI4Gruoz/N0p9GabueAxXEIWo9N2mx48/+y4AIB0xAGpuNGvq4yPFb2lSoDZLfPR1uDLT2YmPWqEr/9urvBphrPio1ZWnTCDraG9dQCzOwAulNrWbFcdXMiY+kmyDH0DqfXxkmEvKV1FQePkNiabxWq1hHB8jSR50xgGHMjOBL74w/P2KgyZ9NfuEUkmi4lPeuzn43g1E4SqOivL+G4qWLfAE1kPA8CnmSVd1OYNFp9U6VHzMPwyc0RTovcvHUqlqAENJHucy45efhQHYiMN4uM6JDys+jiXBXMx9/dYuEZsw2HBDov/Nxn4+RpJsEI1B3rhRfp/DRm6W1pQVxhgUQgeluP8t3uxT1C8qFF/iCXwFwyztnjSOjzOYnyaqy1hczh4mIV/7EADgGDpWOYChJI9zmVELQx+fZrhk81xdRj4+5ReqynHfSL3iI8GQ3JdaXT5aq7G/j9TIIvExVnyMiY+3t+M+PcySHSkMYKi436Iohc40crNpHB8AHV94FMXwRj98D8D2U11MfKpmPrP5T43/bPN6nH2qy/yKUvNtM/Gxn+DMcwCA+rhr0/HjThUfqZ82l2BI7st8ri6p/jfLIvExBpl3v8OmAweA+7X9EEzG++iPLVA0CH/wExxMhIfjCqJQAH8ojBUfs1bW7+RheKMUcThR8aFKeKrLBt7euIMQZKIBjsUMs3k1CkV5nxqnNAXqmufqkuRxLjMqZfk4Prb0l2LnZudh4uNED13ajaT738TlUPFRKqWZrZsqPkYOGrwQALIfaocPMBnfob8kKj7Fi99DDK5gDZ6rsuKjuD/y5HisBsCKj93Vr4+YgDuIRCbU3nU7OIzvsTM7N8fiLE91OUhu8851en7FhJSnuhxHgiG5L/OxewrCYlwXSA3MEx/JHnAxMYZRFvfuNdx2YOIjtSkrLD4kReXExygfAQCs6+Mj9W9pUhGhzEIrnEW90qw6rceZic9vLXviSXyBdviFFR8HudulL0bgc/wh4KRNz3enU11Sb0uY+DiRsWPkacTibutuLo6marJIfFQqQ9WnuNhw24GnuurfPocshOEndIKqrNhh26kt80bkmk8LnERb6H0rD974C9pXWr4i829iHMCw9iaVvouziMWgE2/WaT3G99gZ77VaDWzAkziFduzj4yAqtQLrMQIXNW1ten51p7rkuG+kXvGRYS4pX8bTW14olewHiywSH6P4eOD4cYceWU1/+S/CcBthuI1ihd5h26ktr/f/hUP4DMkYh5dbf4e0NGBNtNkCu3bh64EfY2rhEgDWneqSemMlFS/fewsA4FtWtylMjO+xM77RV9eZ2ljxMR/BmWxjfI9tbTfdqeIj9S9RbN6cyDgGiA+KoFaJByztGubdZySd+Lz+OvDcc4bTXO3bO2wz5p0Uld6ub4GU16/hEfyIKGSg5P7wPRZJSs+emNPsU9yFYThrXs7uOF5m4yfZwql9fNRV/82Kj/3UNfGp7nJ2OSY+Uv8SJcGQ3Jfxg7MxrqHhqe0ujqZqsqn4bN4MbNgAXLvm0M2YH7RS6NxsvJzaGyWmiWQrNizmyas1c3VJ/by81JjPxm0LKSQ+bdsabnfo4PgY3J2jKj6SboerwcSHTJQa8+kPpJnGyybxMX66T5tmSIIcRGF2hCjVrj9czBOfjy/8EafQBtqs8xbLmCc+rPg4jgp1O/XZrp1hfrzGje0UUA2q66TfsiVw8yawerXjY3B3LVsakpWOHW17vvnxZz5lhdwrPlJsS2T4lsqXwmwGcTlczi7pxMcY6NGjhmkrBg50yGaUSolNYORdnvg0KT6HENzGFX2pxSK2VHx4VZf16lrx2b4dKCioPHaWI1RX8QGAkBDHb98TREYaxlQNCLDt+cb9olAY2l5ezu44EgzJfZVGNcMFNAcgj4qPpL9pmH+6O/BydoXUjhCzxEeBqi9nt6Xiw6u6rKesY2d3b2+gXj07BfMAxv2pUEjzg8hdBAfbfuwY21sfH8N++vOfgYcfBkaOtF98ziL1L1FS/mhzO+YjN7PiU0fmgTrwcnYpV3wU1Yzjw1NdznHroc6IdXUQtWT8IJL0lxkPZzz+fO7PJ9u2LXD4sOviqQupT1nBw8CJzOfqkmrFJyio/G9JJz5Oqvhkxg/Gxk/zcNc7Eu86bCtWCAjAbUUoCoQ/VDCMxltx8lR2bnasS75tEX7vCq616u3qUGrNmWMGkW3MKz5yJ/W2RIK5mPvyyb+NxjBchSTVio9KVZ78SDrxMa/4ODDxKWrcAnMwF8k+f3PYNqwyfjxah9zCBHxkqvhU7HRtj4qPFL+lScWYjicRhDzcaR7v6lBqjRUf6TMef8axleSMfXzIxKLRMS+tSIwxp5B04vPCC8CgQYa/HXiqS4rflI2xKI19fOxQ8WHn5tqrr7+FxsiAT1m+q0OpNSY+0seKj/Mw8XEildnl7LoGDV0YSc2MH5ySTnwCAsoDdGDFJ/jqSZxEW3yd39dh27CWsVG5gmikI6bSjmLnZsea+utkZCAabQ4nuzqUWpNiAk+W3DXxkWLFh/m/E6l9yz9ZDBOWSnOceFlUfADg7beBl18GmjZ12CbCj32PljgNlJ522Dassn8/Ntz5J06gNdrgDADguzDLRWw91SX1b2lS0ePWBgBAxJVDAF5wbTC1xIqP9FXs3CxnUj9tzsPAicwrPqr7nZylSBaJz5UrwOLFhhPi773nsM0opHZVV3Y2uhWnQo0C0122jtxs/jxe1WW9cOUtV4dQa84cJZps464VHym2JRLMxdyXl2/5f4Nf1hUXRlIzWSQ+BQXARx8Bn33m0M1I7tuK2eXsRhUbltpWfMzHdOFVXdYL9HP9pLW1xYqP9LlTxUfqp7okGJL7UmvKP01EWLgLI6mZLBIf46f7nTvAb785bDOFrTo5bN02uZ/4+OIejqIzfkRXeBdmWyxS24oPYFkJYOJTS8bO9H2l0+/rQdjHR/qMx5+7XdUlxf855v9O5OUFdMN+BCIP70ZEuDqcaski8TG/nP3MGaBRI4dsRtm7J57AeuhbxuIrh2zBSmaJTysY5ujaX2HqhMBAQzVHiAc3OioVUFpqaKiUSsOPXi/Nb2mScfYskJoKjBrl6khqrX17w5xg/fu7OhKqzqOPAqGhQFKSqyOpO6lXfNwq8Tl//jymTZuG/fv3o6SkBB06dMC8efPQs2dPV4cGwPDPkIZuAKSZBRu1aWP43ayZa+OokfmVXCUl1S9XR23aAAOTn0C7dg7bhHXuJz4+KDLdVfFydqXSkPzk5tYu8QHKk1wvL6C4WNr/ny4XFQU884yro7CKVmvoFqeQWJc1KtepE5CV5R77SOr9BSWYi9lu4MCBKCsrw65du3D06FHExcVh4MCByMzMdHVoAGqeKFBKRowATp0CZs1ydSQ1MG8d2rZ16GbGjgW6dHHYJqxTReJT1azxxoKYNae6gPIGS4qNFdWNO3ygujt32UdSr/hIMCTb3L59GxcuXMD06dPRoUMHtGjRAm+//TYKCwtx8uRJV4cHQPpZsJFCYah0SDlGAMDly8CRI0B0tKsjcR5vbxQpfFCK8n+mqhIfYz+f2lZ8KnZ+lfy+JyLJYuLjJPXr10erVq3wySefoKCgAGVlZfjwww8RHh6OLjV8XS8uLkZubq7Fj6PIpeIjG9HREirFOEmrVujxyD00QbrproqnugDrEx/zU121eR4RUXXYudlJFAoFduzYgSFDhiAwMBBKpRLh4eHYtm0b6tWrV+3zFixYgLlz5zolRqUSaNnScCFSSIhTNkluSKUqn64CqLri8+STQGamocPkg9YFsOJDRPbDik8dTZ8+HQqFosafs2fPQgiByZMnIzw8HHv37sXhw4cxZMgQDBo0CDdu3Kh2/TNmzEBOTo7p5+rVqw59PUeOAOfOucdYDeQaKhWggEAmGiALYVCoKh/Gf/87cOEC0KTJg9cFlDdUTz1lKKIZO7gTEVmLFZ86mjp1KsaNG1fjMk2bNsWuXbuwefNm/P777wi6PwHoBx98gO3btyMlJQXTp0+v8rkajQYaJw6cEBjotE2ROyosxMLTTyIPJYhCBkrhjeN1mPmk4qmu//f/6h4iEXk2TllRR2FhYQgLC3vgcoWFhQAAZYV3WalUQq+XzwirRDVSKJBwZwsAQINilMK7Tg0LpzIgInuTesVHgrmYbRISElCvXj2MHTsWJ06cMI3pk56ejgEDBrg6PCL78C4v7xinrWDiQ0RSwj4+ThIaGopt27YhPz8fvXr1QteuXbFv3z7897//RVxcnKvDI7IPlQq6+4ftIcRjO/rUqWExjgPp72+H2IiIIP2Kj1t9z+vatSu+//57V4dB5FBlSm+o9EVojktohN+QUYfEZ/FiYO9e4OGH7RcfEXk2qVd83CrxIfIEZUpvaPSGkZt1UNXpG1ViouGHiMhepN65WYIhEVFNdMryfj56KCXZsBCR55L6qS42mUQyU6Zi4kNE0iX1U10SDImIavLSkAzE4jQAw6kuKTYsROS5WPEhIrtSeqlMU1aw4kNEUsOKDxHZlfEbVC4CkY8ASX6jIiLPZd65WYrtE6/qIpKZx3/6BwbiLHphF46iKzL59YWIJIQVHyKyq9aZP2AYNqIRfgMgzYaFiDwXEx8isivd/au67DFlBRGRvbFzMxHZlf5+4rMBT+JjPMvEh4gkhRUfIrIrnbp8HJ8/YY8kv1ERkeeSeudmJj5EMqM3G8CQ4/gQkdSw4kNEdqVXc+RmIpIu9vEhIrsyT3xY8SEiqWHFh4js6ttB/8YAbAbAig8RSQ8THyKyL19flMBQ9dFBJclSMhF5Lp7qIiK7UqkABQT0ULDiQ0SSY35VlxTbJ05ZQSQzbU5tQGN8gzH4FP/BUxAKV0dERFTOvMrDig8R1VlE5nGMwVo8jB+hVDLrISJpUSqBzp2BqCigfn1XR1MZKz5EMiO8yqeskOK3KSKigwcBnc7ytJdUMPEhkhlj4jMJK5Cjrw9gnmsDIiKqwMtLmkkPwFNdRLJjTHwA4H/EbhdGQkQkP0x8iGRGeJcnPoKHMBGRVdhqEsmNWcVHp2AnHyIiazDxIZIb84qPgocwEZE12GoSyUxGt5GYgn8B4KkuIiJrsdUkkhtfX/yOegAAPU91ERFZhYkPkcyoVIASegA81UVEZC22mkQyU+/GaSQgDfPxD/yl3leuDoeISFaY+BDJjF/uDUzARxiEb6FTa1wdDhGRrDDxIZIZhXf5lBVSnPmYiEjK2GwSyc39xKc1zmFswXIXB0NEJC9MfIhkRulTPo7PI8V7XRgJEZH8MPEhkhmFhgMYEhHZiq0mkcxYJj4cx4eIyBpMfIhkhhUfIiLbsdUkkhldRCN8gIkAmPgQEVmLrSaRzKg0alzDQwAAvZKnuoiIrCGbxGf+/Pno1q0b/Pz8oNVqq1wmIyMDAwYMgJ+fH8LDwzFt2jSUlZU5N1AiB+OUFUREtpNNq1lSUoInnngCEydOrPJxnU6HAQMGoKSkBAcOHEBKSgqSk5Mxa9YsJ0dK5Fgq6BCJG1iHp7Ci4ZuuDoeISFYUQgjh6iCskZycjClTpiA7O9vi/u+++w4DBw7E9evX0aBBAwDAypUr8frrr+PWrVvw9vauYm2V5ebmIjg4GDk5OQgKCrJ3+ER1dvJnPdrFGU5x9Y67hZ3HQ10cERGR69X281s2FZ8HSUtLQ/v27U1JDwAkJiYiNzcXp06dqvZ5xcXFyM3NtfghkjKVlxKlUAMANChxcTRERPLiNolPZmamRdIDwHQ7MzOz2uctWLAAwcHBpp/GjRs7NE6iulKpAC8Y+q49dnedi6MhIpIXlyY+06dPh0KhqPHn7NmzDo1hxowZyMnJMf1cvXrVodsjqiuV2YVcnfN3uy4QIiIZUrty41OnTsW4ceNqXKZp06a1WldERAQOHz5scd/NmzdNj1VHo9FAo9HUahtEUqA2O2rVglctEhFZw6WJT1hYGMLCwuyyroSEBMyfPx9ZWVkIDw8HAGzfvh1BQUFo06aNXbZhpNPpUFpaatd1kvN4eXlBpZLv+DfmoavAxIeIyBouTXyskZGRgbt37yIjIwM6nQ7Hjx8HADRv3hwBAQHo168f2rRpgzFjxmDRokXIzMzEP//5T0yePNluFR0hBDIzMytdUUbyo9VqERERAYVC4epQrGae+LDiQ0RkHdkkPrNmzUJKSorpdqdOnQAAP/zwA3r06AGVSoXNmzdj4sSJSEhIgL+/P8aOHYs33njDbjEYk57w8HD4+fnJ8kPT0wkhUFhYiKysLABAZGSkiyOynkoFbMVj6I/vcD6oKzq6OiAiIhmRTeKTnJyM5OTkGpeJjo7G1q1bHbJ9nU5nSnrq16/vkG2Qc/j6+gKA6bSo3E57qdXArzD0fStR+7k4GiIieXGby9kdzdinx8+PHzTuwLgf5dhXy2LKCs7VRURkFSY+VuLpLfcg5/2oUgGX0AxrMRonQnu7OhwiIllh4kMupVAosGnTJleHISsqFTAI3+JprENYyW+uDoeISFaY+HiQtLQ0qFQqDBgwwKrnxcTE4J133nFMUGQ1tRoogWHuOS/BKSuIiKzBxMeDrF69Gn//+9+xZ88eXL9+3dXhkI1UKqAftgMAOt/+PxdHQ0QkL0x8PER+fj6++OILTJw4EQMGDKh0hdy3336Lhx9+GD4+PggNDcXQoUMBAD169MCVK1fw8ssvm6YRAYA5c+agY8eOFut45513EBMTY7r9448/om/fvggNDUVwcDC6d++On376yZEv0yMozY7aVr+nuS4QIiIZYuJTB0IABQWu+RHCuljXr1+P1q1bo1WrVnj66afx8ccfQ9xfyZYtWzB06FD0798fx44dw86dO/HII48AAL7++ms89NBDeOONN3Djxg3cuHGj1tvMy8vD2LFjsW/fPhw8eBAtWrRA//79kZeXZ13wVK08TairQyAikhXZjOMjRYWFQECAa7adnw/4+9d++dWrV+Ppp58GACQlJSEnJwe7d+9Gjx49MH/+fIwcORJz5841LR8XFwcACAkJgUqlQmBgYI1znlWlV69eFrf//e9/Q6vVYvfu3Rg4cKBV6yJLg7y+w6ulC/B1/Cq86+pgiIhkhBUfD3Du3DkcPnwYo0aNAgCo1WqMGDECq1evBgAcP34cvXvb/7Lomzdv4vnnn0eLFi0QHByMoKAg5OfnIyMjw+7b8jS7vJLQA7uRFdzC1aEQEckKKz514OdnqLy4atu1tXr1apSVlaFhw4am+4QQ0Gg0eP/9900jGVtDqVSaTpUZVRwMcOzYsbhz5w7effddREdHQ6PRICEhASUlvBKproyDTcts0GkiIpdj4lMHCoV1p5tcoaysDJ988gmWLFmCfv36WTw2ZMgQfPbZZ+jQoQN27tyJZ599tsp1eHt7Q6fTWdwXFhaGzMxMCCFMHZ6NE8ca7d+/Hx988AH69+8PALh69Spu375tp1fm2YwJj5I1WyIiqzDxcXObN2/G77//jvHjxyM4ONjiseHDh2P16tVYvHgxevfujWbNmmHkyJEoKyvD1q1b8frrrwMwjOOzZ88ejBw5EhqNBqGhoejRowdu3bqFRYsW4fHHH8e2bdvw3XffISgoyLT+Fi1a4NNPP0XXrl2Rm5uLadOm2VRdosrU949cJj5ERNZhs+nmVq9ejT59+lRKegBD4nPkyBGEhIRgw4YN+Oabb9CxY0f06tULhw8fNi33xhtv4PLly2jWrBnCwsIAALGxsfjggw+wfPlyxMXF4fDhw3j11Vcrbfv3339H586dMWbMGLz44osIDw937Av2EKz4EBHZRiEqdtTwcLm5uQgODkZOTo5F9aKoqAjp6elo0qQJfHx8XBgh2YPc92ejRsD168D48cCqVa6OhojI9ar7/K6I3xeJZIidm4mIbMPEh0iG2MeHiMg2bDaJZIh9fIiIbMNmk0iGmPgQEdmGzSaRDLGPDxGRbZj4EMkQ+/gQEdmGzSaRDPFUFxGRbdhsEskQEx8iItuw2SSSISY+RES2YbNJdjNu3DgMGTLEdLtHjx6YMmWK0+NITU2FQqFAdna207ftLMY+PuzcTERkHSY+HmDcuHFQKBRQKBTw9vZG8+bN8cYbb6CsrMyh2/36668xb968Wi3rCcmKPbHiQ0RkG87O7iGSkpKwZs0aFBcXY+vWrZg8eTK8vLwwY8YMi+VKSkrg7e1tl22GhITYZT1UGRMfIiLbsNn0EBqNBhEREYiOjsbEiRPRp08ffPPNN6bTU/Pnz0fDhg3RqlUrAMDVq1fx5JNPQqvVIiQkBIMHD8bly5dN69PpdHjllVeg1WpRv359vPbaa6g4323FU13FxcV4/fXX0bhxY2g0GjRv3hyrV6/G5cuX0bNnTwBAvXr1oFAoMG7cOACAXq/HggUL0KRJE/j6+iIuLg5ffvmlxXa2bt2Kli1bwtfXFz179rSI010x8SEisg0rPvZQUFD9YyoVYD77d03LKpWAr++Dl/X3ty6+Kvj6+uLOnTsAgJ07dyIoKAjbt28HAJSWliIxMREJCQnYu3cv1Go13nzzTSQlJeHnn3+Gt7c3lixZguTkZHz88ceIjY3FkiVLsHHjRvTq1avabT7zzDNIS0vDsmXLEBcXh/T0dNy+fRuNGzfGV199heHDh+PcuXMICgqC7/33YcGCBVi7di1WrlyJFi1aYM+ePXj66acRFhaG7t274+rVqxg2bBgmT56MCRMm4MiRI5g6dWqd3x+pY+JDRGQbJj72EBBQ/WP9+wNbtpTfDg8HCgurXrZ7dyA1tfx2TAxw+3bl5SpUVqwhhMDOnTvx/fff4+9//ztu3boFf39/rFq1ynSKa+3atdDr9Vi1ahUUCgUAYM2aNdBqtUhNTUW/fv3wzjvvYMaMGRg2bBgAYOXKlfj++++r3e758+exfv16bN++HX369AEANG3a1PS48bRYeHg4tFotAEOF6K233sKOHTuQkJBges6+ffvw4Ycfonv37lixYgWaNWuGJUuWAABatWqFX375BQsXLrT5PZIDdm4mIrINEx8PsXnzZgQEBKC0tBR6vR5PPfUU5syZg8mTJ6N9+/YW/XpOnDiBixcvIjAw0GIdRUVFuHTpEnJycnDjxg3Ex8ebHlOr1ejatWul011Gx48fh0qlQvfu3Wsd88WLF1FYWIi+ffta3F9SUoJOnToBAM6cOWMRBwBTkuTOWPEhIrINEx97yM+v/rGKX8mzsqpftuKnmB37qvTs2RMrVqyAt7c3GjZsCLW6fNf7Vzh1lp+fjy5dumDdunWV1hMWFmbT9n3NT+HVUv7993XLli1o1KiRxWMajcamONwFEx8iItsw8bEHa/rcOGrZB67KH82bN6/Vsp07d8YXX3yB8PBwBAUFVblMZGQkDh06hD/96U8AgLKyMhw9ehSdO3eucvn27dtDr9dj9+7dplNd5owVJ51OZ7qvTZs20Gg0yMjIqLZSFBsbi2+++cbivoMHDz74RcocEx8iItuw2aRKRo8ejdDQUAwePBh79+5Feno6UlNT8eKLL+LatWsAgJdeeglvv/02Nm3ahLNnz2LSpEk1jsETExODsWPH4rnnnsOmTZtM61y/fj0AIDo6GgqFAps3b8atW7eQn5+PwMBAvPrqq3j55ZeRkpKCS5cu4aeffsJ7772HlJQUAMDf/vY3XLhwAdOmTcO5c+fwn//8B8nJyY5+i1yOk5QSEdmGzSZV4ufnhz179iAqKgrDhg1DbGwsxo8fj6KiIlMFaOrUqRgzZgzGjh2LhIQEBAYGYujQoTWud8WKFXj88ccxadIktG7dGs8//zwK7l+51qhRI8ydOxfTp09HgwYN8MILLwAA5s2bh5kzZ2LBggWIjY1FUlIStmzZgiZNmgAAoqKi8NVXX2HTpk2Ii4vDypUr8dZbbznw3ZGGIUOAZs2A3r1dHQkRkbwoRHW9UT1Ubm4ugoODkZOTY3Gap6ioCOnp6WjSpAl8zC9PJ1ni/iQici/VfX5XxIoPEREReQwmPkREROQxmPgQERGRx2DiQ0RERB5DNonP/Pnz0a1bN/j5+ZmmNDB34sQJjBo1Co0bN4avry9iY2Px7rvvOj9QIiIikizZDGBYUlKCJ554AgkJCVi9enWlx48ePYrw8HCsXbsWjRs3xoEDBzBhwgSoVCrTpdH2wIvg3AP3IxGRZ5JN4jN37lwAqHZwuueee87idtOmTZGWloavv/7aLomPl5cXAKCwsNCm6RdIWgrvTxRr3K9EROQZZJP42CInJ8c063d1iouLUVxcbLqdm5tb5XIqlQparRZZ9+fa8vPzM81cTvIhhEBhYSGysrKg1Wqh4vTmREQexW0TnwMHDuCLL77Ali1balxuwYIFpmrSg0RERACAKfkh+dJqtab9SUREnsOlic/06dOxcOHCGpc5c+YMWrdubdV6T548icGDB2P27Nno169fjcvOmDEDr7zyiul2bm4uGjduXOWyCoUCkZGRCA8PR2lpqVUxkXR4eXmx0kNE5KFcmvhMnToV48aNq3GZpk2bWrXO06dPo3fv3pgwYQL++c9/PnB5jUYDjUZj1TZUKhU/OImIiGTIpYlPWFgYwsLC7La+U6dOoVevXhg7dizmz59vt/USERGRe5BNH5+MjAzcvXsXGRkZ0Ol0OH78OACgefPmCAgIwMmTJ9GrVy8kJibilVdeQWZmJgBDdcaeyRURERHJl2wSn1mzZiElJcV0u1OnTgCAH374AT169MCXX36JW7duYe3atVi7dq1puejoaFy+fNnZ4RIREZEEKQRHcrOQk5MDrVaLq1ev1jitPREREUmH8eKk7OxsBAcHV7ucbCo+zpKXlwcA1V7ZRURERNKVl5dXY+LDik8Fer0e169fR2BgoF0HKDRmoqwkuR73hXRwX0gH94V0cF/YRgiBvLw8NGzYEEpl9VORsuJTgVKpxEMPPeSw9QcFBfEfWSK4L6SD+0I6uC+kg/vCejVVeoxkMzs7ERERUV0x8SEiIiKPwcTHSTQaDWbPnm31KNFkf9wX0sF9IR3cF9LBfeFY7NxMREREHoMVHyIiIvIYTHyIiIjIYzDxISIiIo/BxIeIiIg8BhMfJ1m+fDliYmLg4+OD+Ph4HD582NUhubU5c+ZAoVBY/LRu3dr0eFFRESZPnoz69esjICAAw4cPx82bN10YsfvYs2cPBg0ahIYNG0KhUGDTpk0WjwshMGvWLERGRsLX1xd9+vTBhQsXLJa5e/cuRo8ejaCgIGi1WowfPx75+flOfBXu4UH7Yty4cZWOk6SkJItluC/qbsGCBXj44YcRGBiI8PBwDBkyBOfOnbNYpjZtUkZGBgYMGAA/Pz+Eh4dj2rRpKCsrc+ZLcQtMfJzgiy++wCuvvILZs2fjp59+QlxcHBITE5GVleXq0Nxa27ZtcePGDdPPvn37TI+9/PLL+Pbbb7Fhwwbs3r0b169fx7Bhw1wYrfsoKChAXFwcli9fXuXjixYtwrJly7By5UocOnQI/v7+SExMRFFRkWmZ0aNH49SpU9i+fTs2b96MPXv2YMKECc56CW7jQfsCAJKSkiyOk88++8zice6Lutu9ezcmT56MgwcPYvv27SgtLUW/fv1QUFBgWuZBbZJOp8OAAQNQUlKCAwcOICUlBcnJyZg1a5YrXpK8CXK4Rx55REyePNl0W6fTiYYNG4oFCxa4MCr3Nnv2bBEXF1flY9nZ2cLLy0ts2LDBdN+ZM2cEAJGWluakCD0DALFx40bTbb1eLyIiIsTixYtN92VnZwuNRiM+++wzIYQQp0+fFgDEjz/+aFrmu+++EwqFQvz2229Oi93dVNwXQggxduxYMXjw4Gqfw33hGFlZWQKA2L17txCidm3S1q1bhVKpFJmZmaZlVqxYIYKCgkRxcbFzX4DMseLjYCUlJTh69Cj69Oljuk+pVKJPnz5IS0tzYWTu78KFC2jYsCGaNm2K0aNHIyMjAwBw9OhRlJaWWuyT1q1bIyoqivvEwdLT05GZmWnx3gcHByM+Pt703qelpUGr1aJr166mZfr06QOlUolDhw45PWZ3l5qaivDwcLRq1QoTJ07EnTt3TI9xXzhGTk4OACAkJARA7dqktLQ0tG/fHg0aNDAtk5iYiNzcXJw6dcqJ0csfEx8Hu337NnQ6ncU/KwA0aNAAmZmZLorK/cXHxyM5ORnbtm3DihUrkJ6ejv/5n/9BXl4eMjMz4e3tDa1Wa/Ec7hPHM76/NR0PmZmZCA8Pt3hcrVYjJCSE+8fOkpKS8Mknn2Dnzp1YuHAhdu/ejcceeww6nQ4A94Uj6PV6TJkyBY8++ijatWsHALVqkzIzM6s8boyPUe1xdnZyS4899pjp7w4dOiA+Ph7R0dFYv349fH19XRgZkXSMHDnS9Hf79u3RoUMHNGvWDKmpqejdu7cLI3NfkydPxsmTJy36HJJzseLjYKGhoVCpVJV659+8eRMREREuisrzaLVatGzZEhcvXkRERARKSkqQnZ1tsQz3ieMZ39+ajoeIiIhKHf/Lyspw9+5d7h8Ha9q0KUJDQ3Hx4kUA3Bf29sILL2Dz5s344Ycf8NBDD5nur02bFBERUeVxY3yMao+Jj4N5e3ujS5cu2Llzp+k+vV6PnTt3IiEhwYWReZb8/HxcunQJkZGR6NKlC7y8vCz2yblz55CRkcF94mBNmjRBRESExXufm5uLQ4cOmd77hIQEZGdn4+jRo6Zldu3aBb1ej/j4eKfH7EmuXbuGO3fuIDIyEgD3hb0IIfDCCy9g48aN2LVrF5o0aWLxeG3apISEBPzyyy8Wiej27dsRFBSENm3aOOeFuAtX9672BJ9//rnQaDQiOTlZnD59WkyYMEFotVqL3vlkX1OnThWpqakiPT1d7N+/X/Tp00eEhoaKrKwsIYQQf/vb30RUVJTYtWuXOHLkiEhISBAJCQkujto95OXliWPHjoljx44JAGLp0qXi2LFj4sqVK0IIId5++22h1WrFf//7X/Hzzz+LwYMHiyZNmoh79+6Z1pGUlCQ6deokDh06JPbt2ydatGghRo0a5aqXJFs17Yu8vDzx6quvirS0NJGeni527NghOnfuLFq0aCGKiopM6+C+qLuJEyeK4OBgkZqaKm7cuGH6KSwsNC3zoDaprKxMtGvXTvTr108cP35cbNu2TYSFhYkZM2a44iXJGhMfJ3nvvfdEVFSU8Pb2Fo888og4ePCgq0NyayNGjBCRkZHC29tbNGrUSIwYMUJcvHjR9Pi9e/fEpEmTRL169YSfn58YOnSouHHjhgsjdh8//PCDAFDpZ+zYsUIIwyXtM2fOFA0aNBAajUb07t1bnDt3zmIdd+7cEaNGjRIBAQEiKChIPPvssyIvL88Fr0beatoXhYWFol+/fiIsLEx4eXmJ6Oho8fzzz1f6QsZ9UXdV7QMAYs2aNaZlatMmXb58WTz22GPC19dXhIaGiqlTp4rS0lInvxr5UwghhLOrTERERESuwD4+RERE5DGY+BAREZHHYOJDREREHoOJDxEREXkMJj5ERETkMZj4EBERkcdg4kNEREQeg4kPEbkdhUKBTZs2uToMzJkzBx07dnR1GERkhokPEVnt1q1bmDhxIqKioqDRaBAREYHExETs37/f1aHZxeXLl6FQKHD8+HFXh0JEdqZ2dQBEJD/Dhw9HSUkJUlJS0LRpU9y8eRM7d+7EnTt3XB0aEVGNWPEhIqtkZ2dj7969WLhwIXr27Ino6Gg88sgjmDFjBv785z+bllu6dCnat28Pf39/NG7cGJMmTUJ+fr7p8eTkZGi1WmzevBmtWrWCn58fHn/8cRQWFiIlJQUxMTGoV68eXnzxReh0OtPzYmJiMG/ePIwaNQr+/v5o1KgRli9fXmPMV69exZNPPgmtVouQkBAMHjwYly9frvVrTk1NhUKhwM6dO9G1a1f4+fmhW7duOHfunMVyb7/9Nho0aIDAwECMHz8eRUVFlda1atUqxMbGwsfHB61bt8YHH3xgeuy5555Dhw4dUFxcDAAoKSlBp06d8Mwzz9Q6ViKqGRMfIrJKQEAAAgICsGnTJtMHdFWUSiWWLVuGU6dOISUlBbt27cJrr71msUxhYSGWLVuGzz//HNu2bUNqaiqGDh2KrVu3YuvWrfj000/x4Ycf4ssvv7R43uLFixEXF4djx45h+vTpeOmll7B9+/Yq4ygtLUViYiICAwOxd+9e7N+/HwEBAUhKSkJJSYlVr/1///d/sWTJEhw5cgRqtRrPPfec6bH169djzpw5eOutt3DkyBFERkZaJDUAsG7dOsyaNQvz58/HmTNn8NZbb2HmzJlISUkBACxbtgwFBQWYPn26aXvZ2dl4//33rYqTiGrg6llSiUh+vvzyS1GvXj3h4+MjunXrJmbMmCFOnDhR43M2bNgg6tevb7q9Zs0aAUBcvHjRdN9f//pX4efnZzH7d2JiovjrX/9quh0dHS2SkpIs1j1ixAjx2GOPmW4DEBs3bhRCCPHpp5+KVq1aCb1eb3q8uLhY+Pr6iu+//77KWNPT0wUAcezYMSFE+SznO3bsMC2zZcsWAUDcu3dPCCFEQkKCmDRpksV64uPjRVxcnOl2s2bNxH/+8x+LZebNmycSEhJMtw8cOCC8vLzEzJkzhVqtFnv37q0yRiKyDSs+RGS14cOH4/r16/jmm2+QlJSE1NRUdO7cGcnJyaZlduzYgd69e6NRo0YIDAzEmDFjcOfOHRQWFpqW8fPzQ7NmzUy3GzRogJiYGAQEBFjcl5WVZbH9hISESrfPnDlTZawnTpzAxYsXERgYaKpWhYSEoKioCJcuXbLqdXfo0MH0d2RkJACYYjtz5gzi4+OrjbOgoACXLl3C+PHjTXEEBATgzTfftIgjISEBr776KubNm4epU6fij3/8o1UxElHN2LmZiGzi4+ODvn37om/fvpg5cyb+8pe/YPbs2Rg3bhwuX76MgQMHYuLEiZg/fz5CQkKwb98+jB8/HiUlJfDz8wMAeHl5WaxToVBUeZ9er7c5zvz8fHTp0gXr1q2r9FhYWJhV6zKPTaFQAECtYzP2b/roo48qJUgqlcr0t16vx/79+6FSqXDx4kWr4iOiB2PFh4jsok2bNigoKAAAHD16FHq9HkuWLMEf/vAHtGzZEtevX7fbtg4ePFjpdmxsbJXLdu7cGRcuXEB4eDiaN29u8RMcHGy3mGJjY3Ho0KFq42zQoAEaNmyIX3/9tVIcTZo0MS23ePFinD17Frt378a2bduwZs0au8VIREx8iMhKd+7cQa9evbB27Vr8/PPPSE9Px4YNG7Bo0SIMHjwYANC8eXOUlpbivffew6+//opPP/0UK1eutFsM+/fvx6JFi3D+/HksX74cGzZswEsvvVTlsqNHj0ZoaCgGDx6MvXv3Ij09HampqXjxxRdx7do1u8X00ksv4eOPP8aaNWtw/vx5zJ49G6dOnbJYZu7cuViwYAGWLVuG8+fP45dffsGaNWuwdOlSAMCxY8cwa9YsrFq1Co8++iiWLl2Kl156Cb/++qvd4iTydEx8iMgqAQEBiI+Px7/+9S/86U9/Qrt27TBz5kw8//zzpquP4uLisHTpUixcuBDt2rXDunXrsGDBArvFMHXqVBw5cgSdOnXCm2++iaVLlyIxMbHKZf38/LBnzx5ERUVh2LBhiI2NNV1qHhQUZLeYRowYgZkzZ+K1115Dly5dcOXKFUycONFimb/85S9YtWoV1qxZg/bt26N79+5ITk5GkyZNUFRUhKeffhrjxo3DoEGDAAATJkxAz549MWbMGItL+onIdgohhHB1EEREtRUTE4MpU6ZgypQprg6FiGSIFR8iIiLyGEx8iIiIyGPwVBcRERF5DFZ8iIiIyGMw8SEiIiKPwcSHiIiIPAYTHyIiIvIYTHyIiIjIYzDxISIiIo/BxIeIiIg8BhMfIiIi8hhMfIiIiMhj/H9fVTcsR0Sf4gAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/notebooks/initial.ipynb b/notebooks/initial.ipynb
new file mode 100644
index 0000000..d4387d6
--- /dev/null
+++ b/notebooks/initial.ipynb
@@ -0,0 +1,423 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/GEO-HACK/Advanced-Programming/blob/main/notebooks/initial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bs8h-Svt56-I",
+        "outputId": "bdbe4cc4-4445-41ab-e6e5-79317557ed39"
+      },
+      "id": "bs8h-Svt56-I",
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "\n",
+        "os.listdir('/content/drive/MyDrive/CICICDS')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WizrliiO6tjV",
+        "outputId": "2055ed7f-9f88-4a9b-9ec7-04c559ef9731"
+      },
+      "id": "WizrliiO6tjV",
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['Wednesday-workingHours.pcap_ISCX.csv',\n",
+              " 'Tuesday-WorkingHours.pcap_ISCX.csv',\n",
+              " 'Monday-WorkingHours.pcap_ISCX.csv',\n",
+              " 'Friday-WorkingHours-Morning.pcap_ISCX.csv',\n",
+              " 'Friday-WorkingHours-Afternoon-DDos.pcap_ISCX.csv',\n",
+              " 'Friday-WorkingHours-Afternoon-PortScan.pcap_ISCX.csv']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "d9eb8413",
+      "metadata": {
+        "vscode": {
+          "languageId": "plaintext"
+        },
+        "id": "d9eb8413",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 288
+        },
+        "outputId": "2316808f-a3ee-4549-f433-1fa405ab0830"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    Destination Port   Flow Duration   Total Fwd Packets  \\\n",
+              "0              54865               3                   2   \n",
+              "1              55054             109                   1   \n",
+              "2              55055              52                   1   \n",
+              "3              46236              34                   1   \n",
+              "4              54863               3                   2   \n",
+              "\n",
+              "    Total Backward Packets  Total Length of Fwd Packets  \\\n",
+              "0                        0                           12   \n",
+              "1                        1                            6   \n",
+              "2                        1                            6   \n",
+              "3                        1                            6   \n",
+              "4                        0                           12   \n",
+              "\n",
+              "    Total Length of Bwd Packets   Fwd Packet Length Max  \\\n",
+              "0                             0                       6   \n",
+              "1                             6                       6   \n",
+              "2                             6                       6   \n",
+              "3                             6                       6   \n",
+              "4                             0                       6   \n",
+              "\n",
+              "    Fwd Packet Length Min   Fwd Packet Length Mean   Fwd Packet Length Std  \\\n",
+              "0                       6                      6.0                     0.0   \n",
+              "1                       6                      6.0                     0.0   \n",
+              "2                       6                      6.0                     0.0   \n",
+              "3                       6                      6.0                     0.0   \n",
+              "4                       6                      6.0                     0.0   \n",
+              "\n",
+              "   ...   min_seg_size_forward  Active Mean   Active Std   Active Max  \\\n",
+              "0  ...                     20          0.0          0.0            0   \n",
+              "1  ...                     20          0.0          0.0            0   \n",
+              "2  ...                     20          0.0          0.0            0   \n",
+              "3  ...                     20          0.0          0.0            0   \n",
+              "4  ...                     20          0.0          0.0            0   \n",
+              "\n",
+              "    Active Min  Idle Mean   Idle Std   Idle Max   Idle Min   Label  \n",
+              "0            0        0.0        0.0          0          0  BENIGN  \n",
+              "1            0        0.0        0.0          0          0  BENIGN  \n",
+              "2            0        0.0        0.0          0          0  BENIGN  \n",
+              "3            0        0.0        0.0          0          0  BENIGN  \n",
+              "4            0        0.0        0.0          0          0  BENIGN  \n",
+              "\n",
+              "[5 rows x 79 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-e1ee474e-8eb3-4a20-b195-a1c81c7b71ec\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Destination Port</th>\n",
+              "      <th>Flow Duration</th>\n",
+              "      <th>Total Fwd Packets</th>\n",
+              "      <th>Total Backward Packets</th>\n",
+              "      <th>Total Length of Fwd Packets</th>\n",
+              "      <th>Total Length of Bwd Packets</th>\n",
+              "      <th>Fwd Packet Length Max</th>\n",
+              "      <th>Fwd Packet Length Min</th>\n",
+              "      <th>Fwd Packet Length Mean</th>\n",
+              "      <th>Fwd Packet Length Std</th>\n",
+              "      <th>...</th>\n",
+              "      <th>min_seg_size_forward</th>\n",
+              "      <th>Active Mean</th>\n",
+              "      <th>Active Std</th>\n",
+              "      <th>Active Max</th>\n",
+              "      <th>Active Min</th>\n",
+              "      <th>Idle Mean</th>\n",
+              "      <th>Idle Std</th>\n",
+              "      <th>Idle Max</th>\n",
+              "      <th>Idle Min</th>\n",
+              "      <th>Label</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>54865</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>12</td>\n",
+              "      <td>0</td>\n",
+              "      <td>6</td>\n",
+              "      <td>6</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>20</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>BENIGN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>55054</td>\n",
+              "      <td>109</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>6</td>\n",
+              "      <td>6</td>\n",
+              "      <td>6</td>\n",
+              "      <td>6</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>20</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>BENIGN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>55055</td>\n",
+              "      <td>52</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>6</td>\n",
+              "      <td>6</td>\n",
+              "      <td>6</td>\n",
+              "      <td>6</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>20</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>BENIGN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>46236</td>\n",
+              "      <td>34</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>6</td>\n",
+              "      <td>6</td>\n",
+              "      <td>6</td>\n",
+              "      <td>6</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>20</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>BENIGN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>54863</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>12</td>\n",
+              "      <td>0</td>\n",
+              "      <td>6</td>\n",
+              "      <td>6</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>20</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>BENIGN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 79 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e1ee474e-8eb3-4a20-b195-a1c81c7b71ec')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-e1ee474e-8eb3-4a20-b195-a1c81c7b71ec button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-e1ee474e-8eb3-4a20-b195-a1c81c7b71ec');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df"
+            }
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "\n",
+        "df = pd.read_csv('/content/drive/MyDrive/CICICDS/Friday-WorkingHours-Afternoon-DDos.pcap_ISCX.csv')\n",
+        "\n",
+        "df.head()\n",
+        "\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "language_info": {
+      "name": "python"
+    },
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/src/Lecture4_interfaces_abstract_classes/BankAccount.java b/src/Lecture4_interfaces_abstract_classes/BankAccount.java
index 28d0d07..d9de3cc 100644
--- a/src/Lecture4_interfaces_abstract_classes/BankAccount.java
+++ b/src/Lecture4_interfaces_abstract_classes/BankAccount.java
@@ -1,16 +1,17 @@
-package Lecture4_interfaces_abstract_classes;
-
 public class BankAccount {
     private double balance;
-    public BankAccount(double balance) {
-        this.balance = balance;
-    }
 
-    public double getBalance() {
+    public BankAccount(double initialBalance){
+        this.balance= initialBalance;
+    }
+    public double getBalance(){
         return balance;
     }
-
-    public void setBalance(double balance) {
-        this.balance = balance;
+    public void deposit(double amount){
+        balance+= amount;
+    }
+    public void withdraw(double amount) throws  InsufficientFundsException{
+        if(balance < amount) throw new InsufficientFundsException("insufficient funds in the account");
+        balance -= amount;
     }
 }
diff --git a/src/Lecture4_interfaces_abstract_classes/BaseTransaction.java b/src/Lecture4_interfaces_abstract_classes/BaseTransaction.java
index ed81eb8..0ff9a02 100644
--- a/src/Lecture4_interfaces_abstract_classes/BaseTransaction.java
+++ b/src/Lecture4_interfaces_abstract_classes/BaseTransaction.java
@@ -1,51 +1,35 @@
-package Lecture4_interfaces_abstract_classes;
-
-import org.jetbrains.annotations.NotNull;
-
 import java.util.Calendar;
 
-public abstract class BaseTransaction implements TransactionInterface {
-    private final int amount;
-    private final Calendar date;
-    private final String transactionID;
 
-    /**
-     * Lecture1_adt.TransactionInterface Constructor
-     * @param amount in an integer
-     * @param date: Not null, and must be a Calendar object
-     * @return void
-     * Instialises the field, attributes of a transaction
-     * Creates a object of this
-     */
-    public BaseTransaction(int amount, @NotNull Calendar date)  {
+public class BaseTransaction implements TransactionInterface {
+    protected double amount;
+    protected Calendar date;
+    protected String transactionId;
+
+    public BaseTransaction( double amount, Calendar date, String transactionId){
         this.amount = amount;
-        this.date = (Calendar) date.clone();
-        int uniq = (int) Math.random()*10000;
-        transactionID = date.toString()+uniq;
-    }
+        this.date = date;
+        this.transactionId = transactionId;
 
-    /**
-     * getAmount()
-     * @return integer
-     */
-    public double getAmount() {
-        return amount; // Because we are dealing with Value types we need not worry about what we return
     }
 
-    /**
-     * getDate()
-     * @return Calendar Object
-     */
-    public Calendar getDate() {
-//        return date;    // Because we are dealing with Reference types we need to judiciously copy what our getters return
-        return (Calendar) date.clone(); // Defensive copying or Judicious Copying
+    public double getAmount(){
+        return amount;
     }
-
-    // Method to get a unique identifier for the transaction
-    public String getTransactionID(){
-        return  transactionID;
+    public Calendar getDate(){
+        return date;
     }
-    // Method to print a transaction receipt or details
-    public abstract void printTransactionDetails();
-    public abstract void apply(BankAccount ba);
+    public String getTransactionId(){
+        return transactionId;
+    }
+    public void printTransactionDetails(){
+        System.out.println("Transaction ID: " + transactionId);
+        System.out.println("Amount: " + amount);
+        System.out.println("Date: " + date.getTime());
+    }
+    public void apply(BankAccount ba) {
+        System.out.println("Applying base transaction to account");
+    }
+
+
 }
diff --git a/src/Lecture4_interfaces_abstract_classes/DepositTransaction.java b/src/Lecture4_interfaces_abstract_classes/DepositTransaction.java
new file mode 100644
index 0000000..61fec21
--- /dev/null
+++ b/src/Lecture4_interfaces_abstract_classes/DepositTransaction.java
@@ -0,0 +1,13 @@
+import java.util.Calendar;
+public class DepositTransaction  extends BaseTransaction {
+    public DepositTransaction(double amount ,Calendar date, String transactionId){
+        super( amount, date, transactionId);
+    }
+
+
+    public void apply( BankAccount ba){
+        ba.deposit(amount);
+        System.out.println("Deposited"+ amount + "to account");
+
+    }
+}
diff --git a/src/Lecture4_interfaces_abstract_classes/DepositTrasaction.java b/src/Lecture4_interfaces_abstract_classes/DepositTrasaction.java
deleted file mode 100644
index 81afab5..0000000
--- a/src/Lecture4_interfaces_abstract_classes/DepositTrasaction.java
+++ /dev/null
@@ -1,30 +0,0 @@
-package Lecture4_interfaces_abstract_classes;
-
-import org.jetbrains.annotations.NotNull;
-
-import java.util.Calendar;
-
-public class DepositTrasaction extends BaseTransaction {
-    public DepositTrasaction(int amount, @NotNull Calendar date){
-        super(amount, date);
-    }
-    private boolean checkDepositAmount(int amt){
-        if (amt < 0){
-           return false;
-        }
-        else{
-            return  true;
-        }
-    }
-
-    // Method to print a transaction receipt or details
-    public void printTransactionDetails(){
-        System.out.println("Deposit Trasaction: "+this.toString());
-    }
-
-    public void apply(BankAccount ba){
-        double curr_balance = ba.getBalance();
-        double new_balance = curr_balance + getAmount();
-        ba.setBalance(new_balance);
-    }
-}
diff --git a/src/Lecture4_interfaces_abstract_classes/InsufficientFundsException.java b/src/Lecture4_interfaces_abstract_classes/InsufficientFundsException.java
new file mode 100644
index 0000000..6461c47
--- /dev/null
+++ b/src/Lecture4_interfaces_abstract_classes/InsufficientFundsException.java
@@ -0,0 +1,6 @@
+public class InsufficientFundsException extends Exception{
+    public InsufficientFundsException(String message) {
+        super(message);
+    }
+
+}
diff --git a/src/Lecture4_interfaces_abstract_classes/TransactionInterface.java b/src/Lecture4_interfaces_abstract_classes/TransactionInterface.java
index 5902713..97bf76c 100644
--- a/src/Lecture4_interfaces_abstract_classes/TransactionInterface.java
+++ b/src/Lecture4_interfaces_abstract_classes/TransactionInterface.java
@@ -1,21 +1,9 @@
-package Lecture4_interfaces_abstract_classes;
 import java.util.Calendar;
 
-/**
- * Interface for Transactions
- * Any class that defines a transaction is expected to implement this Interface
- */
-public interface TransactionInterface {
-
-    // Method to get the transaction amount
+public interface TransactionInterface  {
     double getAmount();
-
-    // Method to get the transaction date
     Calendar getDate();
-
-    // Method to get a unique identifier for the transaction
-    String getTransactionID();
-
+    String getTransactionId();
+    void printTransactionDetails();
+    void apply( BankAccount ba);
 }
-
-
diff --git a/src/Lecture4_interfaces_abstract_classes/WithdrawalTransaction.java b/src/Lecture4_interfaces_abstract_classes/WithdrawalTransaction.java
index face5b6..6e2c86b 100644
--- a/src/Lecture4_interfaces_abstract_classes/WithdrawalTransaction.java
+++ b/src/Lecture4_interfaces_abstract_classes/WithdrawalTransaction.java
@@ -1,45 +1,46 @@
-package Lecture4_interfaces_abstract_classes;
-
-import org.jetbrains.annotations.NotNull;
-
 import java.util.Calendar;
 
 public class WithdrawalTransaction extends BaseTransaction {
-    public WithdrawalTransaction(int amount, @NotNull Calendar date) {
-        super(amount, date);
-    }
-
-    private boolean checkDepositAmount(int amt) {
-        if (amt < 0) {
-            return false;
-        } else {
-            return true;
+    public WithdrawalTransaction(double amount, Calendar date, String transactionId){
+        super (amount, date, transactionId);
+    };
+
+    @Override
+    public void apply (BankAccount ba) {
+        try{
+            if (ba.getBalance() < amount) {
+                throw new InsufficientFundsException("insufficient funds for withdrawal");
+            }
+            ba.withdraw(amount);
+            System.out.println("withdrew" + amount + " from account");
         }
-    }
+        catch(InsufficientFundsException e){
+            System.err.println(e.getMessage());
 
-    // Method to reverse the transaction
-    public boolean reverse() {
-        return true;
-    } // return true if reversal was successful
+        }
 
-    // Method to print a transaction receipt or details
-    public void printTransactionDetails() {
-        System.out.println("Deposit Trasaction: " + this.toString());
     }
+    public void apply(BankAccount ba , boolean allowPartial){
+        try{
+            if(ba.getBalance() < amount){
+                if( allowPartial){
+                    double available = ba.getBalance();
+                    ba.withdraw(available);
+                    System.out.println("withdrew partial amount" + available);
+                }
+                else {
+                    throw new InsufficientFundsException("Insufficient funds for withdrawal.");
+                }
+            }else{
+                ba.withdraw(amount);
+            }
+        }catch(InsufficientFundsException e){
+            System.err.println(e.getMessage());
 
-    /*
-    Oportunity for assignment: implementing different form of withdrawal
-     */
-    public void apply(BankAccount ba) {
-        double curr_balance = ba.getBalance();
-        if (curr_balance > getAmount()) {
-            double new_balance = curr_balance - getAmount();
-            ba.setBalance(new_balance);
         }
     }
-
-    /*
-    Assignment 1 Q3: Write the Reverse method - a method unique to the WithdrawalTransaction Class
-     */
-}
-
+    public boolean reverse(BankAccount ba){
+        ba.deposit(amount);
+        return true;
+    }
+};