From 2b3898ccc0c1de6ef7148b21bef7e9e4d8fe69d1 Mon Sep 17 00:00:00 2001
From: anastasia_cher <anastasiia.cher@yandex.ru>
Date: Wed, 18 Dec 2024 09:32:25 +0300
Subject: [PATCH 1/4] Create __init__.py

---
 lab4/__init__.py | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 lab4/__init__.py

diff --git a/lab4/__init__.py b/lab4/__init__.py
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/lab4/__init__.py
@@ -0,0 +1 @@
+

From 6d92c09b7b748f9f25972f3234373ee3207cfedf Mon Sep 17 00:00:00 2001
From: anastasia_cher <anastasiia.cher@yandex.ru>
Date: Wed, 18 Dec 2024 09:33:07 +0300
Subject: [PATCH 2/4] =?UTF-8?q?=D0=A1=D0=BE=D0=B7=D0=B4=D0=B0=D0=BD=D0=BE?=
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MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
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---
 ...\233\320\2404_Text_Processing_&_LLM.ipynb" | 7522 +++++++++++++++++
 1 file changed, 7522 insertions(+)
 create mode 100644 "lab4/PY\320\220\320\224_\320\233\320\2404_Text_Processing_&_LLM.ipynb"

diff --git "a/lab4/PY\320\220\320\224_\320\233\320\2404_Text_Processing_&_LLM.ipynb" "b/lab4/PY\320\220\320\224_\320\233\320\2404_Text_Processing_&_LLM.ipynb"
new file mode 100644
index 0000000..c21a0ea
--- /dev/null
+++ "b/lab4/PY\320\220\320\224_\320\233\320\2404_Text_Processing_&_LLM.ipynb"
@@ -0,0 +1,7522 @@
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+        }
+      }
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/anastasiiaCher/pyad-2024/blob/main/lab4/PY%D0%90%D0%94_%D0%9B%D0%A04_Text_Processing_%26_LLM.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": "mP9q8XdWKnp3"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "sns.set_context(\"notebook\")\n",
+        "sns.set_theme(\"notebook\")\n",
+        "sns.set_style(\"whitegrid\")\n",
+        "sns.set_palette(\"Dark2\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Постановка задачи и загрузка данных"
+      ],
+      "metadata": {
+        "id": "vgYvmbhBkjoK"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "В рамках лабораторной работы поработаем снова с реальными данными, содержательно близкими к данным минус первой лабораторной, - про учебные дисциплины. В этот раз займемся обработкой и генерацией описания и содержания дисциплин с помощью языковых моделей."
+      ],
+      "metadata": {
+        "id": "iUOykV1HkjoM"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Данные\n",
+        "\n",
+        "Расположены по ссылке."
+      ],
+      "metadata": {
+        "id": "L3A-UYCskPSS"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!gdown 1k1drlKxdqLgw_y_iMgSkBuZwa2Gjc6Q1"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CZSb1Pd3kSRI",
+        "outputId": "9ceeea89-89a0-4213-c06f-6db26cf4db8e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading...\n",
+            "From: https://drive.google.com/uc?id=1k1drlKxdqLgw_y_iMgSkBuZwa2Gjc6Q1\n",
+            "To: /content/disciplines_2018_2022.csv\n",
+            "100% 78.8M/78.8M [00:00<00:00, 81.5MB/s]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Из информации о дисциплине у нас есть следующая:\n",
+        "1. `course_id` - номер дисциплины (может повторяться)\n",
+        "2. `course_title`` - название дисциплины\n",
+        "3. `description` - краткое описание дисциплины\n",
+        "4. `up_id` - айди учебного плана (образовательной прогарммы)\n",
+        "5. `up_title` - название учебного плана (образовательной прогарммы)\n",
+        "6. `qualification` - уровень образования (бакалавриат или магистратура)\n",
+        "7. `start_year` - год набора\n",
+        "8. `module_name` - название модуля, в который входит дисциплина\n",
+        "9. `contents` – содержание дисциплины (все темы и разделы не по порядку, сплошным текстом)\n",
+        "\n",
+        "Данные уже частично предобработаны: датасет содержит только те дисциплины, которые реализовывались более одного года. Это примерно треть от всех дисциплин за 2018–2022 гг."
+      ],
+      "metadata": {
+        "id": "A711O2O_kjoM"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = pd.read_csv(\"/content/disciplines_2018_2022.csv\")\n",
+        "df.sample(3, random_state=42)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 288
+        },
+        "outputId": "b22847dc-d479-4c39-9892-eafd2497f115",
+        "id": "f-rkEW7qkjoN"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "       course_id                       course_title  \\\n",
+              "16112      17498  Создание технологического бизнеса   \n",
+              "2457        3002         Теория электрической связи   \n",
+              "1077        2736   Организация массовых мероприятий   \n",
+              "\n",
+              "                                             description  up_id  \\\n",
+              "16112  Цель курса - предоставить возможность магистра...   7198   \n",
+              "2457   Программа составлена в соответствии с требован...   6989   \n",
+              "1077   Данная дисциплина учит планировать мероприятие...   6875   \n",
+              "\n",
+              "                                                up_title qualification  \\\n",
+              "16112  Информационная безопасность / Information secu...        master   \n",
+              "2457    Программирование в инфокоммуникационных системах      bachelor   \n",
+              "1077             Управление технологическими инновациями      bachelor   \n",
+              "\n",
+              "       start_year                     module_name  \\\n",
+              "16112        2022  Наукоемкое предпринимательство   \n",
+              "2457         2020   Модуль обязательных дисциплин   \n",
+              "1077         2018            Модуль «Soft Skills»   \n",
+              "\n",
+              "                                                contents  \n",
+              "16112  Инновационный маркетинг. Корпоративные финансы...  \n",
+              "2457   Математические модели сигналов, помех и канало...  \n",
+              "1077   Особенности организации массовых мероприятий.....  "
+            ],
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+              "      <td>Организация массовых мероприятий</td>\n",
+              "      <td>Данная дисциплина учит планировать мероприятие...</td>\n",
+              "      <td>6875</td>\n",
+              "      <td>Управление технологическими инновациями</td>\n",
+              "      <td>bachelor</td>\n",
+              "      <td>2018</td>\n",
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+              "      <td>Особенности организации массовых мероприятий.....</td>\n",
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+              "    .colab-df-convert:hover {\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
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+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-343a435e-68a5-4ac8-bffc-8519fd579705');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
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+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
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+              "  .colab-df-quickchart {\n",
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+              "    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",
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+              "    border: 2px solid var(--fill-color);\n",
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+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
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+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
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+              "      border-bottom-color: var(--fill-color);\n",
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+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
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+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
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+              "        console.error('Error during call to suggestCharts:', error);\n",
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+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
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+              "        document.querySelector('#df-96801903-824e-4340-befd-62b6eafdf52d button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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\\u043f\\u043e\\u0434\\u0433\\u043e\\u0442\\u043e\\u0432\\u043a\\u0438. \\u0426\\u0435\\u043b\\u044c\\u044e \\u043e\\u0441\\u0432\\u043e\\u0435\\u043d\\u0438\\u044f \\u0434\\u0438\\u0441\\u0446\\u0438\\u043f\\u043b\\u0438\\u043d\\u044b \\u044f\\u0432\\u043b\\u044f\\u0435\\u0442\\u0441\\u044f \\u0434\\u043e\\u0441\\u0442\\u0438\\u0436\\u0435\\u043d\\u0438\\u0435 \\u0441\\u043b\\u0435\\u0434\\u0443\\u044e\\u0449\\u0438\\u0445 \\u0440\\u0435\\u0437\\u0443\\u043b\\u044c\\u0442\\u0430\\u0442\\u043e\\u0432 \\u043e\\u0431\\u0443\\u0447\\u0435\\u043d\\u0438\\u044f \\u043f\\u043e \\u0444\\u043e\\u0440\\u043c\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u044e \\u043a\\u043e\\u043c\\u043f\\u0435\\u0442\\u0435\\u043d\\u0446\\u0438\\u0439: \\n-\\u041e\\u041f\\u041a-3: \\u0441\\u043f\\u043e\\u0441\\u043e\\u0431\\u043d\\u043e\\u0441\\u0442\\u044c \\u0432\\u043b\\u0430\\u0434\\u0435\\u0442\\u044c \\u043e\\u0441\\u043d\\u043e\\u0432\\u043d\\u044b\\u043c\\u0438 \\u043c\\u0435\\u0442\\u043e\\u0434\\u0430\\u043c\\u0438, \\u0441\\u043f\\u043e\\u0441\\u043e\\u0431\\u0430\\u043c\\u0438 \\u0438 \\u0441\\u0440\\u0435\\u0434\\u0441\\u0442\\u0432\\u0430\\u043c\\u0438 \\u043f\\u043e\\u043b\\u0443\\u0447\\u0435\\u043d\\u0438\\u044f, \\u0445\\u0440\\u0430\\u043d\\u0435\\u043d\\u0438\\u044f, \\u043f\\u0435\\u0440\\u0435\\u0440\\u0430\\u0431\\u043e\\u0442\\u043a\\u0438 \\u0438\\u043d\\u0444\\u043e\\u0440\\u043c\\u0430\\u0446\\u0438\\u0438:\\n -\\u041e\\u041f\\u041a-5: \\u0441\\u043f\\u043e\\u0441\\u043e\\u0431\\u043d\\u043e\\u0441\\u0442\\u044c \\u0438\\u0441\\u043f\\u043e\\u043b\\u044c\\u0437\\u043e\\u0432\\u0430\\u0442\\u044c \\u043d\\u043e\\u0440\\u043c\\u0430\\u0442\\u0438\\u0432\\u043d\\u0443\\u044e \\u0438 \\u043f\\u0440\\u0430\\u0432\\u043e\\u0432\\u0443\\u044e \\u0434\\u043e\\u043a\\u0443\\u043c\\u0435\\u043d\\u0442\\u0430\\u0446\\u0438\\u044e, \\u0445\\u0430\\u0440\\u0430\\u043a\\u0442\\u0435\\u0440\\u043d\\u0443\\u044e \\u0434\\u043b\\u044f 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\\u0441\\u0442\\u0430\\u043d\\u0434\\u0430\\u0440\\u0442\\u044b, \\u0440\\u0435\\u043a\\u043e\\u043c\\u0435\\u043d\\u0434\\u0430\\u0446\\u0438\\u0438 \\u041c\\u0435\\u0436\\u0434\\u0443\\u043d\\u0430\\u0440\\u043e\\u0434\\u043d\\u043e\\u0433\\u043e \\u0441\\u043e\\u044e\\u0437\\u0430 \\u044d\\u043b\\u0435\\u043a\\u0442\\u0440\\u043e\\u0441\\u0432\\u044f\\u0437\\u0438):\\n\\u041f\\u041a-3: \\u0441\\u043f\\u043e\\u0441\\u043e\\u0431\\u043d\\u043e\\u0441\\u0442\\u044c \\u043e\\u0446\\u0435\\u043d\\u0438\\u0432\\u0430\\u0442\\u044c \\u0442\\u0435\\u0445\\u043d\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0435 \\u0432\\u043e\\u0437\\u043c\\u043e\\u0436\\u043d\\u043e\\u0441\\u0442\\u0438 \\u0438 \\u0432\\u044b\\u0440\\u0430\\u0431\\u0430\\u0442\\u044b\\u0432\\u0430\\u0442\\u044c \\u0440\\u0435\\u043a\\u043e\\u043c\\u0435\\u043d\\u0434\\u0430\\u0446\\u0438\\u0438 \\u043f\\u043e \\u043f\\u043e\\u0441\\u0442\\u0440\\u043e\\u0435\\u043d\\u0438\\u044e \\u0442\\u0435\\u043b\\u0435\\u043a\\u043e\\u043c\\u043c\\u0443\\u043d\\u0438\\u043a\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u044b\\u0445 \\u0441\\u0438\\u0441\\u0442\\u0435\\u043c \\u0438 \\u0441\\u0435\\u0442\\u0435\\u0439, \\u0438\\u0445 \\u044d\\u043b\\u0435\\u043c\\u0435\\u043d\\u0442\\u043e\\u0432 \\u0438 \\u0443\\u0441\\u0442\\u0440\\u043e\\u0439\\u0441\\u0442\\u0432.\",\n          \"\\u0414\\u0430\\u043d\\u043d\\u0430\\u044f \\u0434\\u0438\\u0441\\u0446\\u0438\\u043f\\u043b\\u0438\\u043d\\u0430 \\u0443\\u0447\\u0438\\u0442 \\u043f\\u043b\\u0430\\u043d\\u0438\\u0440\\u043e\\u0432\\u0430\\u0442\\u044c \\u043c\\u0435\\u0440\\u043e\\u043f\\u0440\\u0438\\u044f\\u0442\\u0438\\u0435, \\u0441\\u043e\\u0437\\u0434\\u0430\\u0432\\u0430\\u0442\\u044c \\u043a\\u043e\\u043d\\u0446\\u0435\\u043f\\u0446\\u0438\\u044e, \\u043f\\u0440\\u0435\\u0434\\u0443\\u0441\\u043c\\u0430\\u0442\\u0440\\u0438\\u0432\\u0430\\u0442\\u044c \\u0440\\u0438\\u0441\\u043a\\u0438 \\u0438 \\u043c\\u0435\\u0442\\u043e\\u0434\\u044b \\u0440\\u0435\\u0430\\u0433\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u044f. \\u0412\\u043e \\u0432\\u0440\\u0435\\u043c\\u044f \\u0437\\u0430\\u043d\\u044f\\u0442\\u0438\\u0439 \\u043e\\u0431\\u0443\\u0447\\u0430\\u044e\\u0449\\u0438\\u0435\\u0441\\u044f \\u0443\\u0437\\u043d\\u0430\\u044e\\u0442 \\u043f\\u0440\\u043e \\u0432\\u0438\\u0437\\u0443\\u0430\\u043b\\u044c\\u043d\\u0443\\u044e \\u043a\\u043e\\u043d\\u0446\\u0435\\u043f\\u0446\\u0438\\u044e \\u0438 \\u043c\\u0435\\u0434\\u0430\\u0441\\u043e\\u043f\\u0440\\u043e\\u0432\\u043e\\u0436\\u0434\\u0435\\u043d\\u0438\\u0435 \\u043c\\u0435\\u0440\\u043e\\u043f\\u0440\\u0438\\u044f\\u0442\\u0438\\u044f. \\u041f\\u043e\\u043b\\u0443\\u0447\\u0430\\u0442 \\u043f\\u0440\\u0430\\u043a\\u0442\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0435 \\u043d\\u0430\\u0432\\u044b\\u043a\\u0438 \\u043f\\u043e \\u043e\\u0446\\u0435\\u043d\\u043a\\u0435 \\u044d\\u0444\\u0444\\u0435\\u043a\\u0442\\u0438\\u0432\\u043d\\u043e\\u0441\\u0442\\u0438. \\n\\u0417\\u0430\\u043d\\u044f\\u0442\\u0438\\u044f \\u043f\\u0440\\u043e\\u0445\\u043e\\u0434\\u044f\\u0442 \\u0441 \\u0440\\u0430\\u0437\\u043b\\u0438\\u0447\\u043d\\u044b\\u043c\\u0438 \\u043f\\u0440\\u0435\\u043f\\u043e\\u0434\\u0430\\u0432\\u0430\\u0442\\u0435\\u043b\\u044f\\u043c\\u0438 \\u0438\\u0437  setters education \\u0438 evention, \\u043a\\u043e\\u0442\\u043e\\u0440\\u044b\\u0435 \\u043d\\u0430 \\u0441\\u0432\\u043e\\u0435\\u043c \\u043e\\u043f\\u044b\\u0442\\u0435 \\u0440\\u0430\\u0441\\u0441\\u043a\\u0430\\u0437\\u044b\\u0432\\u0430\\u044e\\u0442 \\u043f\\u0440\\u043e \\u043e\\u0436\\u0438\\u0434\\u0430\\u043d\\u0438\\u044f \\u0438 \\u0440\\u0435\\u0430\\u043b\\u044c\\u043d\\u043e\\u0441\\u0442\\u044c \\u0440\\u043e\\u043b\\u0438 \\\"\\u043e\\u0440\\u0433\\u0430\\u043d\\u0438\\u0437\\u0430\\u0442\\u043e\\u0440\\u0430\\\". \\n\\u0414\\u0438\\u0441\\u0446\\u0438\\u043f\\u043b\\u0438\\u043d\\u0430 \\u043f\\u043e\\u0434\\u0445\\u043e\\u0434\\u0438\\u0442\\u044c \\u0434\\u043b\\u044f \\u0442\\u0435\\u0445, \\u043a\\u0442\\u043e \\u043e\\u0440\\u0433\\u0430\\u043d\\u0438\\u0437\\u0443\\u0435\\u0442 \\u043c\\u0435\\u0440\\u043e\\u043f\\u0440\\u0438\\u044f\\u0442\\u0438\\u044f \\u0432 \\u0443\\u043d\\u0438\\u0432\\u0435\\u0440\\u0441\\u0438\\u0442\\u0435\\u0442\\u0435 \\u0438 \\u0437\\u0430 \\u0435\\u0433\\u043e \\u043f\\u0440\\u0435\\u0434\\u0435\\u043b\\u0430\\u043c\\u0438.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"up_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 163,\n        \"min\": 6875,\n        \"max\": 7198,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          7198,\n          6989,\n          6875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"up_title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\\u0418\\u043d\\u0444\\u043e\\u0440\\u043c\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u0430\\u044f \\u0431\\u0435\\u0437\\u043e\\u043f\\u0430\\u0441\\u043d\\u043e\\u0441\\u0442\\u044c / Information security\",\n          \"\\u041f\\u0440\\u043e\\u0433\\u0440\\u0430\\u043c\\u043c\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u0435 \\u0432 \\u0438\\u043d\\u0444\\u043e\\u043a\\u043e\\u043c\\u043c\\u0443\\u043d\\u0438\\u043a\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u044b\\u0445 \\u0441\\u0438\\u0441\\u0442\\u0435\\u043c\\u0430\\u0445\",\n          \"\\u0423\\u043f\\u0440\\u0430\\u0432\\u043b\\u0435\\u043d\\u0438\\u0435 \\u0442\\u0435\\u0445\\u043d\\u043e\\u043b\\u043e\\u0433\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u043c\\u0438 \\u0438\\u043d\\u043d\\u043e\\u0432\\u0430\\u0446\\u0438\\u044f\\u043c\\u0438\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"qualification\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"bachelor\",\n          \"master\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2018,\n        \"max\": 2022,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2022,\n          2020\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"module_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\\u041d\\u0430\\u0443\\u043a\\u043e\\u0435\\u043c\\u043a\\u043e\\u0435 \\u043f\\u0440\\u0435\\u0434\\u043f\\u0440\\u0438\\u043d\\u0438\\u043c\\u0430\\u0442\\u0435\\u043b\\u044c\\u0441\\u0442\\u0432\\u043e\",\n          \"\\u041c\\u043e\\u0434\\u0443\\u043b\\u044c \\u043e\\u0431\\u044f\\u0437\\u0430\\u0442\\u0435\\u043b\\u044c\\u043d\\u044b\\u0445 \\u0434\\u0438\\u0441\\u0446\\u0438\\u043f\\u043b\\u0438\\u043d\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"contents\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\\u0418\\u043d\\u043d\\u043e\\u0432\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u044b\\u0439 \\u043c\\u0430\\u0440\\u043a\\u0435\\u0442\\u0438\\u043d\\u0433. \\u041a\\u043e\\u0440\\u043f\\u043e\\u0440\\u0430\\u0442\\u0438\\u0432\\u043d\\u044b\\u0435 \\u0444\\u0438\\u043d\\u0430\\u043d\\u0441\\u044b. 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\\u0438\\u0441\\u0442\\u043e\\u0447\\u043d\\u0438\\u043a\\u043e\\u0432  \\u0434\\u0438\\u0441\\u043a\\u0440\\u0435\\u0442\\u043d\\u044b\\u0445 \\u0441\\u043e\\u043e\\u0431\\u0449\\u0435\\u043d\\u0438\\u0439. \\u041a\\u043e\\u0434 \\u0425\\u0430\\u0444\\u0444\\u043c\\u0430\\u043d\\u0430\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "rjDw-rZIlxO0",
+        "outputId": "d6fd9182-0ae8-4eb2-d00b-ea0994030ee4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(20502, 9)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Немного погрузимся в данные."
+      ],
+      "metadata": {
+        "id": "kGGCPusKmA_5"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# количество дисциплин с уникальными названиями по уровням подготовки\n",
+        "df[[\"course_title\", \"qualification\"]].drop_duplicates().qualification.value_counts().sort_values(ascending=True).plot(kind=\"barh\")\n",
+        "plt.xlabel(\"Количество дисциплин\")\n",
+        "plt.ylabel(\"\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 457
+        },
+        "id": "bUnnyNx5mDcr",
+        "outputId": "0b7b1882-9e4a-44ae-858a-c09018132d1f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# количество учебных планов с уникальными названиями по уровням подготовки\n",
+        "df[[\"up_title\", \"qualification\"]].drop_duplicates().qualification.value_counts().sort_values(ascending=True).plot(kind=\"barh\")\n",
+        "plt.xlabel(\"Количество учебных планов\")\n",
+        "plt.ylabel(\"\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 457
+        },
+        "id": "A1SCdwrAmMXG",
+        "outputId": "bce41e6d-4562-431e-f49d-debc18459273"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Видим, что дисциплин больше (незначительно) в бакалавриате, но учебных планов больше в магистратуре. Это соответствует истине."
+      ],
+      "metadata": {
+        "id": "zv5_0f_wmRfd"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# количество учебных планов по годам и квалификациям\n",
+        "sns.barplot(data=df[[\"up_title\", \"qualification\", \"start_year\"]].drop_duplicates().groupby([\"start_year\", \"qualification\"]).count().reset_index(), x=\"start_year\", y=\"up_title\", hue=\"qualification\")\n",
+        "plt.ylabel(\"Количество учебных планов\")\n",
+        "plt.xlabel(\"Год набора\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 461
+        },
+        "id": "f3iL8_zpmmqc",
+        "outputId": "54ae47e5-e793-4293-bf84-99c0464f1512"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# количество дисциплин по годам и квалификациям\n",
+        "sns.barplot(data=df[[\"course_title\", \"qualification\", \"start_year\"]].groupby([\"start_year\", \"qualification\"]).count().reset_index(), x=\"start_year\", y=\"course_title\", hue=\"qualification\")\n",
+        "plt.ylabel(\"Количество дисциплин\")\n",
+        "plt.xlabel(\"Год набора\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 457
+        },
+        "id": "uVOYDaTNmsMi",
+        "outputId": "eefa7413-db83-4c4c-8546-71b259e8284f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "- Данные по магистратуре начали храниться с 2020 года, по бакалавриату – с 2018, поэтому графики выглядит именно так.\n",
+        "- Учебные планы могут существовать несколько лет с одним и тем же названием (как и дисциплины), поэтому в разрезе по годам уникальных записей больше, чем без него."
+      ],
+      "metadata": {
+        "id": "_QDlo5g1mwVq"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Задача 1 – Предобработать description и contents\n",
+        "1. Выполнить предобработку текстовых данных. Минимально понадобятся следующие шаги предобработки:\n",
+        "  - приведение к одному регистру\n",
+        "  - токенизация\n",
+        "  - удаление пунктуации (можно оставить только буквенные символы в текстах)\n",
+        "  - лемматизация (можно использовать pymystem, он менее точный, чем pymorphy2, но работает в несколько раз быстрее)\n",
+        "2. Построить для дисциплины вектор на основе word2vec\n",
+        "3. Потестировать текстовые представления\n",
+        "\n",
+        "Задач не много, но некоторые затратные по времени.\n",
+        "\n",
+        "Для предобработки можно воспользовать материалами:\n",
+        "- https://colab.research.google.com/drive/1Lxe0_dPxF5_nNC1hnHTRc01rnMmUVCXa?usp=sharing\n",
+        "- https://colab.research.google.com/drive/16EFOsDbaTF2128kkVMqXxx2Xnb7Gr4mW?usp=share_link"
+      ],
+      "metadata": {
+        "id": "Xtyo2G0flBDU"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Предобработка текстовых данных"
+      ],
+      "metadata": {
+        "id": "dIcqmkp4qBjS"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Проведите предобработку для столбца `contents`."
+      ],
+      "metadata": {
+        "id": "AslRQw7oqVX2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import nltk\n",
+        "from nltk.corpus import stopwords\n",
+        "import re\n",
+        "from pymystem3 import Mystem # для лемматизации\n",
+        "\n",
+        "\n",
+        "nltk.download('stopwords')\n",
+        "nltk.download('punkt')\n",
+        "\n",
+        "\n",
+        "m = Mystem()"
+      ],
+      "metadata": {
+        "id": "MFWaZ9w3kjoP"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# пример результата\n",
+        "df.sample(3, random_state=42)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 324
+        },
+        "id": "Us5Gk5I6pjx3",
+        "outputId": "07fc5dc4-1579-4127-d06c-15d3f38b008e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "       course_id                       course_title  \\\n",
+              "16112      17498  Создание технологического бизнеса   \n",
+              "2457        3002         Теория электрической связи   \n",
+              "1077        2736   Организация массовых мероприятий   \n",
+              "\n",
+              "                                             description  up_id  \\\n",
+              "16112  Цель курса - предоставить возможность магистра...   7198   \n",
+              "2457   Программа составлена в соответствии с требован...   6989   \n",
+              "1077   Данная дисциплина учит планировать мероприятие...   6875   \n",
+              "\n",
+              "                                                up_title qualification  \\\n",
+              "16112  Информационная безопасность / Information secu...        master   \n",
+              "2457    Программирование в инфокоммуникационных системах      bachelor   \n",
+              "1077             Управление технологическими инновациями      bachelor   \n",
+              "\n",
+              "       start_year                     module_name  \\\n",
+              "16112        2022  Наукоемкое предпринимательство   \n",
+              "2457         2020   Модуль обязательных дисциплин   \n",
+              "1077         2018            Модуль «Soft Skills»   \n",
+              "\n",
+              "                                                contents  \\\n",
+              "16112  Инновационный маркетинг. Корпоративные финансы...   \n",
+              "2457   Математические модели сигналов, помех и канало...   \n",
+              "1077   Особенности организации массовых мероприятий.....   \n",
+              "\n",
+              "                                                  tokens  \n",
+              "16112  [инновационный, маркетинг, корпоративный, фина...  \n",
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+            ],
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+              "      <td>Цель курса - предоставить возможность магистра...</td>\n",
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+              "      <td>Данная дисциплина учит планировать мероприятие...</td>\n",
+              "      <td>6875</td>\n",
+              "      <td>Управление технологическими инновациями</td>\n",
+              "      <td>bachelor</td>\n",
+              "      <td>2018</td>\n",
+              "      <td>Модуль «Soft Skills»</td>\n",
+              "      <td>Особенности организации массовых мероприятий.....</td>\n",
+              "      <td>[особенность, организация, массовый, мероприят...</td>\n",
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+              "    <div class=\"colab-df-buttons\">\n",
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+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2df1cccd-fa2a-4779-99d1-e681673f8ebd')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
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+              "\n",
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+              "      background-color: #E8F0FE;\n",
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+              "\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-2df1cccd-fa2a-4779-99d1-e681673f8ebd 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-2df1cccd-fa2a-4779-99d1-e681673f8ebd');\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",
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+              "\n",
+              "<div id=\"df-9a62b8c3-82f4-404a-9206-b062c8b8bc32\">\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
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+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
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+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
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+              "      --hover-fill-color: #FFFFFF;\n",
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+              "\n",
+              "  .colab-df-quickchart {\n",
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+              "    width: 32px;\n",
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+              "    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",
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+              "    border: 2px solid var(--fill-color);\n",
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+              "    animation:\n",
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+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "\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-9a62b8c3-82f4-404a-9206-b062c8b8bc32 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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+              "</div>\n",
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\\u043f\\u043e\\u0434\\u0433\\u043e\\u0442\\u043e\\u0432\\u043a\\u0438. \\u0426\\u0435\\u043b\\u044c\\u044e \\u043e\\u0441\\u0432\\u043e\\u0435\\u043d\\u0438\\u044f \\u0434\\u0438\\u0441\\u0446\\u0438\\u043f\\u043b\\u0438\\u043d\\u044b \\u044f\\u0432\\u043b\\u044f\\u0435\\u0442\\u0441\\u044f \\u0434\\u043e\\u0441\\u0442\\u0438\\u0436\\u0435\\u043d\\u0438\\u0435 \\u0441\\u043b\\u0435\\u0434\\u0443\\u044e\\u0449\\u0438\\u0445 \\u0440\\u0435\\u0437\\u0443\\u043b\\u044c\\u0442\\u0430\\u0442\\u043e\\u0432 \\u043e\\u0431\\u0443\\u0447\\u0435\\u043d\\u0438\\u044f \\u043f\\u043e \\u0444\\u043e\\u0440\\u043c\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u044e \\u043a\\u043e\\u043c\\u043f\\u0435\\u0442\\u0435\\u043d\\u0446\\u0438\\u0439: \\n-\\u041e\\u041f\\u041a-3: \\u0441\\u043f\\u043e\\u0441\\u043e\\u0431\\u043d\\u043e\\u0441\\u0442\\u044c \\u0432\\u043b\\u0430\\u0434\\u0435\\u0442\\u044c \\u043e\\u0441\\u043d\\u043e\\u0432\\u043d\\u044b\\u043c\\u0438 \\u043c\\u0435\\u0442\\u043e\\u0434\\u0430\\u043c\\u0438, \\u0441\\u043f\\u043e\\u0441\\u043e\\u0431\\u0430\\u043c\\u0438 \\u0438 \\u0441\\u0440\\u0435\\u0434\\u0441\\u0442\\u0432\\u0430\\u043c\\u0438 \\u043f\\u043e\\u043b\\u0443\\u0447\\u0435\\u043d\\u0438\\u044f, \\u0445\\u0440\\u0430\\u043d\\u0435\\u043d\\u0438\\u044f, \\u043f\\u0435\\u0440\\u0435\\u0440\\u0430\\u0431\\u043e\\u0442\\u043a\\u0438 \\u0438\\u043d\\u0444\\u043e\\u0440\\u043c\\u0430\\u0446\\u0438\\u0438:\\n -\\u041e\\u041f\\u041a-5: \\u0441\\u043f\\u043e\\u0441\\u043e\\u0431\\u043d\\u043e\\u0441\\u0442\\u044c \\u0438\\u0441\\u043f\\u043e\\u043b\\u044c\\u0437\\u043e\\u0432\\u0430\\u0442\\u044c \\u043d\\u043e\\u0440\\u043c\\u0430\\u0442\\u0438\\u0432\\u043d\\u0443\\u044e \\u0438 \\u043f\\u0440\\u0430\\u0432\\u043e\\u0432\\u0443\\u044e \\u0434\\u043e\\u043a\\u0443\\u043c\\u0435\\u043d\\u0442\\u0430\\u0446\\u0438\\u044e, \\u0445\\u0430\\u0440\\u0430\\u043a\\u0442\\u0435\\u0440\\u043d\\u0443\\u044e \\u0434\\u043b\\u044f 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\\u0441\\u0442\\u0430\\u043d\\u0434\\u0430\\u0440\\u0442\\u044b, \\u0440\\u0435\\u043a\\u043e\\u043c\\u0435\\u043d\\u0434\\u0430\\u0446\\u0438\\u0438 \\u041c\\u0435\\u0436\\u0434\\u0443\\u043d\\u0430\\u0440\\u043e\\u0434\\u043d\\u043e\\u0433\\u043e \\u0441\\u043e\\u044e\\u0437\\u0430 \\u044d\\u043b\\u0435\\u043a\\u0442\\u0440\\u043e\\u0441\\u0432\\u044f\\u0437\\u0438):\\n\\u041f\\u041a-3: \\u0441\\u043f\\u043e\\u0441\\u043e\\u0431\\u043d\\u043e\\u0441\\u0442\\u044c \\u043e\\u0446\\u0435\\u043d\\u0438\\u0432\\u0430\\u0442\\u044c \\u0442\\u0435\\u0445\\u043d\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0435 \\u0432\\u043e\\u0437\\u043c\\u043e\\u0436\\u043d\\u043e\\u0441\\u0442\\u0438 \\u0438 \\u0432\\u044b\\u0440\\u0430\\u0431\\u0430\\u0442\\u044b\\u0432\\u0430\\u0442\\u044c \\u0440\\u0435\\u043a\\u043e\\u043c\\u0435\\u043d\\u0434\\u0430\\u0446\\u0438\\u0438 \\u043f\\u043e \\u043f\\u043e\\u0441\\u0442\\u0440\\u043e\\u0435\\u043d\\u0438\\u044e \\u0442\\u0435\\u043b\\u0435\\u043a\\u043e\\u043c\\u043c\\u0443\\u043d\\u0438\\u043a\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u044b\\u0445 \\u0441\\u0438\\u0441\\u0442\\u0435\\u043c \\u0438 \\u0441\\u0435\\u0442\\u0435\\u0439, \\u0438\\u0445 \\u044d\\u043b\\u0435\\u043c\\u0435\\u043d\\u0442\\u043e\\u0432 \\u0438 \\u0443\\u0441\\u0442\\u0440\\u043e\\u0439\\u0441\\u0442\\u0432.\",\n          \"\\u0414\\u0430\\u043d\\u043d\\u0430\\u044f \\u0434\\u0438\\u0441\\u0446\\u0438\\u043f\\u043b\\u0438\\u043d\\u0430 \\u0443\\u0447\\u0438\\u0442 \\u043f\\u043b\\u0430\\u043d\\u0438\\u0440\\u043e\\u0432\\u0430\\u0442\\u044c \\u043c\\u0435\\u0440\\u043e\\u043f\\u0440\\u0438\\u044f\\u0442\\u0438\\u0435, \\u0441\\u043e\\u0437\\u0434\\u0430\\u0432\\u0430\\u0442\\u044c \\u043a\\u043e\\u043d\\u0446\\u0435\\u043f\\u0446\\u0438\\u044e, \\u043f\\u0440\\u0435\\u0434\\u0443\\u0441\\u043c\\u0430\\u0442\\u0440\\u0438\\u0432\\u0430\\u0442\\u044c \\u0440\\u0438\\u0441\\u043a\\u0438 \\u0438 \\u043c\\u0435\\u0442\\u043e\\u0434\\u044b \\u0440\\u0435\\u0430\\u0433\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u044f. \\u0412\\u043e \\u0432\\u0440\\u0435\\u043c\\u044f \\u0437\\u0430\\u043d\\u044f\\u0442\\u0438\\u0439 \\u043e\\u0431\\u0443\\u0447\\u0430\\u044e\\u0449\\u0438\\u0435\\u0441\\u044f \\u0443\\u0437\\u043d\\u0430\\u044e\\u0442 \\u043f\\u0440\\u043e \\u0432\\u0438\\u0437\\u0443\\u0430\\u043b\\u044c\\u043d\\u0443\\u044e \\u043a\\u043e\\u043d\\u0446\\u0435\\u043f\\u0446\\u0438\\u044e \\u0438 \\u043c\\u0435\\u0434\\u0430\\u0441\\u043e\\u043f\\u0440\\u043e\\u0432\\u043e\\u0436\\u0434\\u0435\\u043d\\u0438\\u0435 \\u043c\\u0435\\u0440\\u043e\\u043f\\u0440\\u0438\\u044f\\u0442\\u0438\\u044f. \\u041f\\u043e\\u043b\\u0443\\u0447\\u0430\\u0442 \\u043f\\u0440\\u0430\\u043a\\u0442\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0435 \\u043d\\u0430\\u0432\\u044b\\u043a\\u0438 \\u043f\\u043e \\u043e\\u0446\\u0435\\u043d\\u043a\\u0435 \\u044d\\u0444\\u0444\\u0435\\u043a\\u0442\\u0438\\u0432\\u043d\\u043e\\u0441\\u0442\\u0438. \\n\\u0417\\u0430\\u043d\\u044f\\u0442\\u0438\\u044f \\u043f\\u0440\\u043e\\u0445\\u043e\\u0434\\u044f\\u0442 \\u0441 \\u0440\\u0430\\u0437\\u043b\\u0438\\u0447\\u043d\\u044b\\u043c\\u0438 \\u043f\\u0440\\u0435\\u043f\\u043e\\u0434\\u0430\\u0432\\u0430\\u0442\\u0435\\u043b\\u044f\\u043c\\u0438 \\u0438\\u0437  setters education \\u0438 evention, \\u043a\\u043e\\u0442\\u043e\\u0440\\u044b\\u0435 \\u043d\\u0430 \\u0441\\u0432\\u043e\\u0435\\u043c \\u043e\\u043f\\u044b\\u0442\\u0435 \\u0440\\u0430\\u0441\\u0441\\u043a\\u0430\\u0437\\u044b\\u0432\\u0430\\u044e\\u0442 \\u043f\\u0440\\u043e \\u043e\\u0436\\u0438\\u0434\\u0430\\u043d\\u0438\\u044f \\u0438 \\u0440\\u0435\\u0430\\u043b\\u044c\\u043d\\u043e\\u0441\\u0442\\u044c \\u0440\\u043e\\u043b\\u0438 \\\"\\u043e\\u0440\\u0433\\u0430\\u043d\\u0438\\u0437\\u0430\\u0442\\u043e\\u0440\\u0430\\\". \\n\\u0414\\u0438\\u0441\\u0446\\u0438\\u043f\\u043b\\u0438\\u043d\\u0430 \\u043f\\u043e\\u0434\\u0445\\u043e\\u0434\\u0438\\u0442\\u044c \\u0434\\u043b\\u044f \\u0442\\u0435\\u0445, \\u043a\\u0442\\u043e \\u043e\\u0440\\u0433\\u0430\\u043d\\u0438\\u0437\\u0443\\u0435\\u0442 \\u043c\\u0435\\u0440\\u043e\\u043f\\u0440\\u0438\\u044f\\u0442\\u0438\\u044f \\u0432 \\u0443\\u043d\\u0438\\u0432\\u0435\\u0440\\u0441\\u0438\\u0442\\u0435\\u0442\\u0435 \\u0438 \\u0437\\u0430 \\u0435\\u0433\\u043e \\u043f\\u0440\\u0435\\u0434\\u0435\\u043b\\u0430\\u043c\\u0438.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"up_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 163,\n        \"min\": 6875,\n        \"max\": 7198,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          7198,\n          6989,\n          6875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"up_title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\\u0418\\u043d\\u0444\\u043e\\u0440\\u043c\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u0430\\u044f \\u0431\\u0435\\u0437\\u043e\\u043f\\u0430\\u0441\\u043d\\u043e\\u0441\\u0442\\u044c / Information security\",\n          \"\\u041f\\u0440\\u043e\\u0433\\u0440\\u0430\\u043c\\u043c\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u0435 \\u0432 \\u0438\\u043d\\u0444\\u043e\\u043a\\u043e\\u043c\\u043c\\u0443\\u043d\\u0438\\u043a\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u044b\\u0445 \\u0441\\u0438\\u0441\\u0442\\u0435\\u043c\\u0430\\u0445\",\n          \"\\u0423\\u043f\\u0440\\u0430\\u0432\\u043b\\u0435\\u043d\\u0438\\u0435 \\u0442\\u0435\\u0445\\u043d\\u043e\\u043b\\u043e\\u0433\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u043c\\u0438 \\u0438\\u043d\\u043d\\u043e\\u0432\\u0430\\u0446\\u0438\\u044f\\u043c\\u0438\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"qualification\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"bachelor\",\n          \"master\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2018,\n        \"max\": 2022,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2022,\n          2020\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"module_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\\u041d\\u0430\\u0443\\u043a\\u043e\\u0435\\u043c\\u043a\\u043e\\u0435 \\u043f\\u0440\\u0435\\u0434\\u043f\\u0440\\u0438\\u043d\\u0438\\u043c\\u0430\\u0442\\u0435\\u043b\\u044c\\u0441\\u0442\\u0432\\u043e\",\n          \"\\u041c\\u043e\\u0434\\u0443\\u043b\\u044c \\u043e\\u0431\\u044f\\u0437\\u0430\\u0442\\u0435\\u043b\\u044c\\u043d\\u044b\\u0445 \\u0434\\u0438\\u0441\\u0446\\u0438\\u043f\\u043b\\u0438\\u043d\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"contents\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\\u0418\\u043d\\u043d\\u043e\\u0432\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u044b\\u0439 \\u043c\\u0430\\u0440\\u043a\\u0435\\u0442\\u0438\\u043d\\u0433. \\u041a\\u043e\\u0440\\u043f\\u043e\\u0440\\u0430\\u0442\\u0438\\u0432\\u043d\\u044b\\u0435 \\u0444\\u0438\\u043d\\u0430\\u043d\\u0441\\u044b. 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\\u041f\\u043e\\u043c\\u0435\\u0445\\u043e\\u0443\\u0441\\u0442\\u043e\\u0439\\u0447\\u0438\\u0432\\u043e\\u0441\\u0442\\u044c. \\u0421\\u043a\\u0440\\u044b\\u0442\\u043d\\u043e\\u0441\\u0442\\u044c.. \\u041f\\u043e\\u043c\\u0435\\u0445\\u043e\\u0443\\u0441\\u0442\\u043e\\u0439\\u0447\\u0438\\u0432\\u043e\\u0435 (\\u043a\\u043e\\u0440\\u0440\\u0435\\u043a\\u0442\\u0438\\u0440\\u0443\\u044e\\u0449\\u0435\\u0435) \\u043a\\u043e\\u0434\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u0435.  \\u0413\\u0440\\u0443\\u043f\\u043f\\u043e\\u0432\\u044b\\u0435 \\u0441\\u0438\\u0441\\u0442\\u0435\\u043c\\u0430\\u0442\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0435 \\u043b\\u0438\\u043d\\u0435\\u0439\\u043d\\u044b\\u0435 \\u0431\\u043b\\u043e\\u0447\\u043d\\u044b\\u0435 \\u043a\\u043e\\u0434\\u044b. \\u041f\\u0440\\u0435\\u043e\\u0431\\u0440\\u0430\\u0437\\u043e\\u0432\\u0430\\u043d\\u0438\\u0435 \\u0447\\u0430\\u0441\\u0442\\u043e\\u0442\\u044b. 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+            }
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Проведите предобработку для столбца `description`."
+      ],
+      "metadata": {
+        "id": "P0evNDa4qdf8"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# обработка содержания"
+      ],
+      "metadata": {
+        "id": "HER-PBT8prKB"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# пример результата\n",
+        "df.sample(3, random_state=42)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 324
+        },
+        "id": "8YOO0h_Cp0oy",
+        "outputId": "a8028c39-0d8a-4c52-e6c0-0ebc73f613cc"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "       course_id                       course_title  \\\n",
+              "16112      17498  Создание технологического бизнеса   \n",
+              "2457        3002         Теория электрической связи   \n",
+              "1077        2736   Организация массовых мероприятий   \n",
+              "\n",
+              "                                             description  up_id  \\\n",
+              "16112  Цель курса - предоставить возможность магистра...   7198   \n",
+              "2457   Программа составлена в соответствии с требован...   6989   \n",
+              "1077   Данная дисциплина учит планировать мероприятие...   6875   \n",
+              "\n",
+              "                                                up_title qualification  \\\n",
+              "16112  Информационная безопасность / Information secu...        master   \n",
+              "2457    Программирование в инфокоммуникационных системах      bachelor   \n",
+              "1077             Управление технологическими инновациями      bachelor   \n",
+              "\n",
+              "       start_year                     module_name  \\\n",
+              "16112        2022  Наукоемкое предпринимательство   \n",
+              "2457         2020   Модуль обязательных дисциплин   \n",
+              "1077         2018            Модуль «Soft Skills»   \n",
+              "\n",
+              "                                                contents  \\\n",
+              "16112  Инновационный маркетинг. Корпоративные финансы...   \n",
+              "2457   Математические модели сигналов, помех и канало...   \n",
+              "1077   Особенности организации массовых мероприятий.....   \n",
+              "\n",
+              "                                                  tokens  \\\n",
+              "16112  [инновационный, маркетинг, корпоративный, фина...   \n",
+              "2457   [математический, модель, сигнал, помеха, канал...   \n",
+              "1077   [особенность, организация, массовый, мероприят...   \n",
+              "\n",
+              "                                                 tokensD  \n",
+              "16112  [цель, курс, -, предоставлять, возможность, ма...  \n",
+              "2457   [программа, составлять, соответствие, требован...  \n",
+              "1077   [данный, дисциплина, учить, планировать, мероп...  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8f9a0999-a108-4b5f-be1d-5bbf8e1a3470\" 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>course_id</th>\n",
+              "      <th>course_title</th>\n",
+              "      <th>description</th>\n",
+              "      <th>up_id</th>\n",
+              "      <th>up_title</th>\n",
+              "      <th>qualification</th>\n",
+              "      <th>start_year</th>\n",
+              "      <th>module_name</th>\n",
+              "      <th>contents</th>\n",
+              "      <th>tokens</th>\n",
+              "      <th>tokensD</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>16112</th>\n",
+              "      <td>17498</td>\n",
+              "      <td>Создание технологического бизнеса</td>\n",
+              "      <td>Цель курса - предоставить возможность магистра...</td>\n",
+              "      <td>7198</td>\n",
+              "      <td>Информационная безопасность / Information secu...</td>\n",
+              "      <td>master</td>\n",
+              "      <td>2022</td>\n",
+              "      <td>Наукоемкое предпринимательство</td>\n",
+              "      <td>Инновационный маркетинг. Корпоративные финансы...</td>\n",
+              "      <td>[инновационный, маркетинг, корпоративный, фина...</td>\n",
+              "      <td>[цель, курс, -, предоставлять, возможность, ма...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2457</th>\n",
+              "      <td>3002</td>\n",
+              "      <td>Теория электрической связи</td>\n",
+              "      <td>Программа составлена в соответствии с требован...</td>\n",
+              "      <td>6989</td>\n",
+              "      <td>Программирование в инфокоммуникационных системах</td>\n",
+              "      <td>bachelor</td>\n",
+              "      <td>2020</td>\n",
+              "      <td>Модуль обязательных дисциплин</td>\n",
+              "      <td>Математические модели сигналов, помех и канало...</td>\n",
+              "      <td>[математический, модель, сигнал, помеха, канал...</td>\n",
+              "      <td>[программа, составлять, соответствие, требован...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1077</th>\n",
+              "      <td>2736</td>\n",
+              "      <td>Организация массовых мероприятий</td>\n",
+              "      <td>Данная дисциплина учит планировать мероприятие...</td>\n",
+              "      <td>6875</td>\n",
+              "      <td>Управление технологическими инновациями</td>\n",
+              "      <td>bachelor</td>\n",
+              "      <td>2018</td>\n",
+              "      <td>Модуль «Soft Skills»</td>\n",
+              "      <td>Особенности организации массовых мероприятий.....</td>\n",
+              "      <td>[особенность, организация, массовый, мероприят...</td>\n",
+              "      <td>[данный, дисциплина, учить, планировать, мероп...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8f9a0999-a108-4b5f-be1d-5bbf8e1a3470')\"\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-8f9a0999-a108-4b5f-be1d-5bbf8e1a3470 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-8f9a0999-a108-4b5f-be1d-5bbf8e1a3470');\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-13b355d4-823a-47a2-b485-5969299d15a8\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-13b355d4-823a-47a2-b485-5969299d15a8')\"\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",
+              "    }\n",
+              "    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-13b355d4-823a-47a2-b485-5969299d15a8 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"course_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8447,\n        \"min\": 2736,\n        \"max\": 17498,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          17498,\n          3002,\n          2736\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"course_title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\\u0421\\u043e\\u0437\\u0434\\u0430\\u043d\\u0438\\u0435 \\u0442\\u0435\\u0445\\u043d\\u043e\\u043b\\u043e\\u0433\\u0438\\u0447\\u0435\\u0441\\u043a\\u043e\\u0433\\u043e \\u0431\\u0438\\u0437\\u043d\\u0435\\u0441\\u0430\",\n          \"\\u0422\\u0435\\u043e\\u0440\\u0438\\u044f \\u044d\\u043b\\u0435\\u043a\\u0442\\u0440\\u0438\\u0447\\u0435\\u0441\\u043a\\u043e\\u0439 \\u0441\\u0432\\u044f\\u0437\\u0438\",\n          \"\\u041e\\u0440\\u0433\\u0430\\u043d\\u0438\\u0437\\u0430\\u0446\\u0438\\u044f \\u043c\\u0430\\u0441\\u0441\\u043e\\u0432\\u044b\\u0445 \\u043c\\u0435\\u0440\\u043e\\u043f\\u0440\\u0438\\u044f\\u0442\\u0438\\u0439\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\\u0426\\u0435\\u043b\\u044c \\u043a\\u0443\\u0440\\u0441\\u0430 - \\u043f\\u0440\\u0435\\u0434\\u043e\\u0441\\u0442\\u0430\\u0432\\u0438\\u0442\\u044c \\u0432\\u043e\\u0437\\u043c\\u043e\\u0436\\u043d\\u043e\\u0441\\u0442\\u044c \\u043c\\u0430\\u0433\\u0438\\u0441\\u0442\\u0440\\u0430\\u043d\\u0442\\u0430\\u043c \\u0443\\u043d\\u0438\\u0432\\u0435\\u0440\\u0441\\u0438\\u0442\\u0435\\u0442\\u0430 \\u043f\\u043e\\u043f\\u0440\\u043e\\u0431\\u043e\\u0432\\u0430\\u0442\\u044c \\u0441\\u0435\\u0431\\u044f \\u0432 \\u0440\\u043e\\u043b\\u0438 \\u043f\\u0440\\u0435\\u0434\\u043f\\u0440\\u0438\\u043d\\u0438\\u043c\\u0430\\u0442\\u0435\\u043b\\u044f, \\u0441\\u043e\\u0437\\u0434\\u0430\\u0442\\u044c \\u0438 \\u0440\\u0430\\u0437\\u0432\\u0438\\u0442\\u044c \\u043c\\u0435\\u0436\\u0434\\u0438\\u0441\\u0446\\u0438\\u043f\\u043b\\u0438\\u043d\\u0430\\u0440\\u043d\\u044b\\u0439 \\u0431\\u0438\\u0437\\u043d\\u0435\\u0441-\\u043f\\u0440\\u043e\\u0435\\u043a\\u0442 \\u043d\\u0430 \\u043f\\u0435\\u0440\\u0435\\u0434\\u043d\\u0435\\u043c \\u043a\\u0440\\u0430\\u0435 \\u043d\\u0430\\u0443\\u043a\\u0438, \\u043a\\u043e\\u0442\\u043e\\u0440\\u044b\\u0439 \\u0441\\u043b\\u043e\\u0436\\u043d\\u043e \\u0441\\u043e\\u0437\\u0434\\u0430\\u0442\\u044c \\u0438 \\u0440\\u0430\\u0437\\u0432\\u0438\\u0442\\u044c \\u0431\\u0435\\u0437 \\u043f\\u043e\\u0434\\u0434\\u0435\\u0440\\u0436\\u043a\\u0438 \\u0443\\u043d\\u0438\\u0432\\u0435\\u0440\\u0441\\u0438\\u0442\\u0435\\u0442\\u0430. \\u041e\\u0441\\u043d\\u043e\\u0432\\u043d\\u044b\\u043c\\u0438 \\u0437\\u0430\\u0434\\u0430\\u0447\\u0430\\u043c\\u0438 \\u043a\\u0443\\u0440\\u0441\\u0430 \\u044f\\u0432\\u043b\\u044f\\u044e\\u0442\\u0441\\u044f \\u0438\\u0437\\u0443\\u0447\\u0435\\u043d\\u0438\\u0435 \\u043e\\u0441\\u043e\\u0431\\u0435\\u043d\\u043d\\u043e\\u0441\\u0442\\u0435\\u0439 \\u0440\\u0430\\u0437\\u0440\\u0430\\u0431\\u043e\\u0442\\u043a\\u0438 \\u0432\\u044b\\u0441\\u043e\\u043a\\u043e\\u0442\\u0435\\u0445\\u043d\\u043e\\u043b\\u043e\\u0433\\u0438\\u0447\\u043d\\u043e\\u0433\\u043e \\u043f\\u0440\\u043e\\u0435\\u043a\\u0442\\u0430 \\u0438 \\u043e\\u0446\\u0435\\u043d\\u043a\\u0430 \\u0433\\u043e\\u0442\\u043e\\u0432\\u043d\\u043e\\u0441\\u0442\\u0438 \\u0435\\u0433\\u043e \\u043a \\u043a\\u043e\\u043c\\u043c\\u0435\\u0440\\u0446\\u0438\\u0430\\u043b\\u0438\\u0437\\u0430\\u0446\\u0438\\u0438 \\u0432 \\u0440\\u0435\\u0430\\u043b\\u044c\\u043d\\u043e\\u043c \\u0441\\u0435\\u043a\\u0442\\u043e\\u0440\\u0435 \\u044d\\u043a\\u043e\\u043d\\u043e\\u043c\\u0438\\u043a\\u0438.\",\n          \"\\u041f\\u0440\\u043e\\u0433\\u0440\\u0430\\u043c\\u043c\\u0430 \\u0441\\u043e\\u0441\\u0442\\u0430\\u0432\\u043b\\u0435\\u043d\\u0430 \\u0432 \\u0441\\u043e\\u043e\\u0442\\u0432\\u0435\\u0442\\u0441\\u0442\\u0432\\u0438\\u0438 \\u0441 \\u0442\\u0440\\u0435\\u0431\\u043e\\u0432\\u0430\\u043d\\u0438\\u044f\\u043c\\u0438 \\u043e\\u0431\\u0440\\u0430\\u0437\\u043e\\u0432\\u0430\\u0442\\u0435\\u043b\\u044c\\u043d\\u043e\\u0433\\u043e \\u0441\\u0442\\u0430\\u043d\\u0434\\u0430\\u0440\\u0442\\u0430 \\u043f\\u043e \\u0434\\u0430\\u043d\\u043d\\u043e\\u043c\\u0443 \\u043d\\u0430\\u043f\\u0440\\u0430\\u0432\\u043b\\u0435\\u043d\\u0438\\u044e \\u043f\\u043e\\u0434\\u0433\\u043e\\u0442\\u043e\\u0432\\u043a\\u0438. \\u0426\\u0435\\u043b\\u044c\\u044e \\u043e\\u0441\\u0432\\u043e\\u0435\\u043d\\u0438\\u044f \\u0434\\u0438\\u0441\\u0446\\u0438\\u043f\\u043b\\u0438\\u043d\\u044b \\u044f\\u0432\\u043b\\u044f\\u0435\\u0442\\u0441\\u044f \\u0434\\u043e\\u0441\\u0442\\u0438\\u0436\\u0435\\u043d\\u0438\\u0435 \\u0441\\u043b\\u0435\\u0434\\u0443\\u044e\\u0449\\u0438\\u0445 \\u0440\\u0435\\u0437\\u0443\\u043b\\u044c\\u0442\\u0430\\u0442\\u043e\\u0432 \\u043e\\u0431\\u0443\\u0447\\u0435\\u043d\\u0438\\u044f \\u043f\\u043e \\u0444\\u043e\\u0440\\u043c\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u044e \\u043a\\u043e\\u043c\\u043f\\u0435\\u0442\\u0435\\u043d\\u0446\\u0438\\u0439: \\n-\\u041e\\u041f\\u041a-3: \\u0441\\u043f\\u043e\\u0441\\u043e\\u0431\\u043d\\u043e\\u0441\\u0442\\u044c \\u0432\\u043b\\u0430\\u0434\\u0435\\u0442\\u044c \\u043e\\u0441\\u043d\\u043e\\u0432\\u043d\\u044b\\u043c\\u0438 \\u043c\\u0435\\u0442\\u043e\\u0434\\u0430\\u043c\\u0438, \\u0441\\u043f\\u043e\\u0441\\u043e\\u0431\\u0430\\u043c\\u0438 \\u0438 \\u0441\\u0440\\u0435\\u0434\\u0441\\u0442\\u0432\\u0430\\u043c\\u0438 \\u043f\\u043e\\u043b\\u0443\\u0447\\u0435\\u043d\\u0438\\u044f, \\u0445\\u0440\\u0430\\u043d\\u0435\\u043d\\u0438\\u044f, \\u043f\\u0435\\u0440\\u0435\\u0440\\u0430\\u0431\\u043e\\u0442\\u043a\\u0438 \\u0438\\u043d\\u0444\\u043e\\u0440\\u043c\\u0430\\u0446\\u0438\\u0438:\\n -\\u041e\\u041f\\u041a-5: \\u0441\\u043f\\u043e\\u0441\\u043e\\u0431\\u043d\\u043e\\u0441\\u0442\\u044c \\u0438\\u0441\\u043f\\u043e\\u043b\\u044c\\u0437\\u043e\\u0432\\u0430\\u0442\\u044c \\u043d\\u043e\\u0440\\u043c\\u0430\\u0442\\u0438\\u0432\\u043d\\u0443\\u044e \\u0438 \\u043f\\u0440\\u0430\\u0432\\u043e\\u0432\\u0443\\u044e \\u0434\\u043e\\u043a\\u0443\\u043c\\u0435\\u043d\\u0442\\u0430\\u0446\\u0438\\u044e, \\u0445\\u0430\\u0440\\u0430\\u043a\\u0442\\u0435\\u0440\\u043d\\u0443\\u044e \\u0434\\u043b\\u044f \\u043e\\u0431\\u043b\\u0430\\u0441\\u0442\\u0438 \\u0438\\u043d\\u0444\\u043e\\u043a\\u043e\\u043c\\u043c\\u0443\\u043d\\u0438\\u043a\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u044b\\u0445 \\u0442\\u0435\\u0445\\u043d\\u043e\\u043b\\u043e\\u0433\\u0438\\u0439 \\u0438 \\u0441\\u0438\\u0441\\u0442\\u0435\\u043c \\u0441\\u0432\\u044f\\u0437\\u0438 (\\u043d\\u043e\\u0440\\u043c\\u0430\\u0442\\u0438\\u0432\\u043d\\u044b\\u0435 \\u043f\\u0440\\u0430\\u0432\\u043e\\u0432\\u044b\\u0435 \\u0430\\u043a\\u0442\\u044b \\u0420\\u043e\\u0441\\u0441\\u0438\\u0439\\u0441\\u043a\\u043e\\u0439 \\u0424\\u0435\\u0434\\u0435\\u0440\\u0430\\u0446\\u0438\\u0438, \\u0442\\u0435\\u0445\\u043d\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0435 \\u0440\\u0435\\u0433\\u043b\\u0430\\u043c\\u0435\\u043d\\u0442\\u044b, \\u043c\\u0435\\u0436\\u0434\\u0443\\u043d\\u0430\\u0440\\u043e\\u0434\\u043d\\u044b\\u0435 \\u0438 \\u043d\\u0430\\u0446\\u0438\\u043e\\u043d\\u0430\\u043b\\u044c\\u043d\\u044b\\u0435 \\u0441\\u0442\\u0430\\u043d\\u0434\\u0430\\u0440\\u0442\\u044b, \\u0440\\u0435\\u043a\\u043e\\u043c\\u0435\\u043d\\u0434\\u0430\\u0446\\u0438\\u0438 \\u041c\\u0435\\u0436\\u0434\\u0443\\u043d\\u0430\\u0440\\u043e\\u0434\\u043d\\u043e\\u0433\\u043e \\u0441\\u043e\\u044e\\u0437\\u0430 \\u044d\\u043b\\u0435\\u043a\\u0442\\u0440\\u043e\\u0441\\u0432\\u044f\\u0437\\u0438):\\n\\u041f\\u041a-3: \\u0441\\u043f\\u043e\\u0441\\u043e\\u0431\\u043d\\u043e\\u0441\\u0442\\u044c \\u043e\\u0446\\u0435\\u043d\\u0438\\u0432\\u0430\\u0442\\u044c \\u0442\\u0435\\u0445\\u043d\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0435 \\u0432\\u043e\\u0437\\u043c\\u043e\\u0436\\u043d\\u043e\\u0441\\u0442\\u0438 \\u0438 \\u0432\\u044b\\u0440\\u0430\\u0431\\u0430\\u0442\\u044b\\u0432\\u0430\\u0442\\u044c \\u0440\\u0435\\u043a\\u043e\\u043c\\u0435\\u043d\\u0434\\u0430\\u0446\\u0438\\u0438 \\u043f\\u043e \\u043f\\u043e\\u0441\\u0442\\u0440\\u043e\\u0435\\u043d\\u0438\\u044e \\u0442\\u0435\\u043b\\u0435\\u043a\\u043e\\u043c\\u043c\\u0443\\u043d\\u0438\\u043a\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u044b\\u0445 \\u0441\\u0438\\u0441\\u0442\\u0435\\u043c \\u0438 \\u0441\\u0435\\u0442\\u0435\\u0439, \\u0438\\u0445 \\u044d\\u043b\\u0435\\u043c\\u0435\\u043d\\u0442\\u043e\\u0432 \\u0438 \\u0443\\u0441\\u0442\\u0440\\u043e\\u0439\\u0441\\u0442\\u0432.\",\n          \"\\u0414\\u0430\\u043d\\u043d\\u0430\\u044f \\u0434\\u0438\\u0441\\u0446\\u0438\\u043f\\u043b\\u0438\\u043d\\u0430 \\u0443\\u0447\\u0438\\u0442 \\u043f\\u043b\\u0430\\u043d\\u0438\\u0440\\u043e\\u0432\\u0430\\u0442\\u044c \\u043c\\u0435\\u0440\\u043e\\u043f\\u0440\\u0438\\u044f\\u0442\\u0438\\u0435, \\u0441\\u043e\\u0437\\u0434\\u0430\\u0432\\u0430\\u0442\\u044c \\u043a\\u043e\\u043d\\u0446\\u0435\\u043f\\u0446\\u0438\\u044e, \\u043f\\u0440\\u0435\\u0434\\u0443\\u0441\\u043c\\u0430\\u0442\\u0440\\u0438\\u0432\\u0430\\u0442\\u044c \\u0440\\u0438\\u0441\\u043a\\u0438 \\u0438 \\u043c\\u0435\\u0442\\u043e\\u0434\\u044b \\u0440\\u0435\\u0430\\u0433\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u044f. \\u0412\\u043e \\u0432\\u0440\\u0435\\u043c\\u044f \\u0437\\u0430\\u043d\\u044f\\u0442\\u0438\\u0439 \\u043e\\u0431\\u0443\\u0447\\u0430\\u044e\\u0449\\u0438\\u0435\\u0441\\u044f \\u0443\\u0437\\u043d\\u0430\\u044e\\u0442 \\u043f\\u0440\\u043e \\u0432\\u0438\\u0437\\u0443\\u0430\\u043b\\u044c\\u043d\\u0443\\u044e \\u043a\\u043e\\u043d\\u0446\\u0435\\u043f\\u0446\\u0438\\u044e \\u0438 \\u043c\\u0435\\u0434\\u0430\\u0441\\u043e\\u043f\\u0440\\u043e\\u0432\\u043e\\u0436\\u0434\\u0435\\u043d\\u0438\\u0435 \\u043c\\u0435\\u0440\\u043e\\u043f\\u0440\\u0438\\u044f\\u0442\\u0438\\u044f. \\u041f\\u043e\\u043b\\u0443\\u0447\\u0430\\u0442 \\u043f\\u0440\\u0430\\u043a\\u0442\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0435 \\u043d\\u0430\\u0432\\u044b\\u043a\\u0438 \\u043f\\u043e \\u043e\\u0446\\u0435\\u043d\\u043a\\u0435 \\u044d\\u0444\\u0444\\u0435\\u043a\\u0442\\u0438\\u0432\\u043d\\u043e\\u0441\\u0442\\u0438. \\n\\u0417\\u0430\\u043d\\u044f\\u0442\\u0438\\u044f \\u043f\\u0440\\u043e\\u0445\\u043e\\u0434\\u044f\\u0442 \\u0441 \\u0440\\u0430\\u0437\\u043b\\u0438\\u0447\\u043d\\u044b\\u043c\\u0438 \\u043f\\u0440\\u0435\\u043f\\u043e\\u0434\\u0430\\u0432\\u0430\\u0442\\u0435\\u043b\\u044f\\u043c\\u0438 \\u0438\\u0437  setters education \\u0438 evention, \\u043a\\u043e\\u0442\\u043e\\u0440\\u044b\\u0435 \\u043d\\u0430 \\u0441\\u0432\\u043e\\u0435\\u043c \\u043e\\u043f\\u044b\\u0442\\u0435 \\u0440\\u0430\\u0441\\u0441\\u043a\\u0430\\u0437\\u044b\\u0432\\u0430\\u044e\\u0442 \\u043f\\u0440\\u043e \\u043e\\u0436\\u0438\\u0434\\u0430\\u043d\\u0438\\u044f \\u0438 \\u0440\\u0435\\u0430\\u043b\\u044c\\u043d\\u043e\\u0441\\u0442\\u044c \\u0440\\u043e\\u043b\\u0438 \\\"\\u043e\\u0440\\u0433\\u0430\\u043d\\u0438\\u0437\\u0430\\u0442\\u043e\\u0440\\u0430\\\". \\n\\u0414\\u0438\\u0441\\u0446\\u0438\\u043f\\u043b\\u0438\\u043d\\u0430 \\u043f\\u043e\\u0434\\u0445\\u043e\\u0434\\u0438\\u0442\\u044c \\u0434\\u043b\\u044f \\u0442\\u0435\\u0445, \\u043a\\u0442\\u043e \\u043e\\u0440\\u0433\\u0430\\u043d\\u0438\\u0437\\u0443\\u0435\\u0442 \\u043c\\u0435\\u0440\\u043e\\u043f\\u0440\\u0438\\u044f\\u0442\\u0438\\u044f \\u0432 \\u0443\\u043d\\u0438\\u0432\\u0435\\u0440\\u0441\\u0438\\u0442\\u0435\\u0442\\u0435 \\u0438 \\u0437\\u0430 \\u0435\\u0433\\u043e \\u043f\\u0440\\u0435\\u0434\\u0435\\u043b\\u0430\\u043c\\u0438.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"up_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 163,\n        \"min\": 6875,\n        \"max\": 7198,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          7198,\n          6989,\n          6875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"up_title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\\u0418\\u043d\\u0444\\u043e\\u0440\\u043c\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u0430\\u044f \\u0431\\u0435\\u0437\\u043e\\u043f\\u0430\\u0441\\u043d\\u043e\\u0441\\u0442\\u044c / Information security\",\n          \"\\u041f\\u0440\\u043e\\u0433\\u0440\\u0430\\u043c\\u043c\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u0435 \\u0432 \\u0438\\u043d\\u0444\\u043e\\u043a\\u043e\\u043c\\u043c\\u0443\\u043d\\u0438\\u043a\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u044b\\u0445 \\u0441\\u0438\\u0441\\u0442\\u0435\\u043c\\u0430\\u0445\",\n          \"\\u0423\\u043f\\u0440\\u0430\\u0432\\u043b\\u0435\\u043d\\u0438\\u0435 \\u0442\\u0435\\u0445\\u043d\\u043e\\u043b\\u043e\\u0433\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u043c\\u0438 \\u0438\\u043d\\u043d\\u043e\\u0432\\u0430\\u0446\\u0438\\u044f\\u043c\\u0438\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"qualification\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"bachelor\",\n          \"master\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2018,\n        \"max\": 2022,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2022,\n          2020\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"module_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\\u041d\\u0430\\u0443\\u043a\\u043e\\u0435\\u043c\\u043a\\u043e\\u0435 \\u043f\\u0440\\u0435\\u0434\\u043f\\u0440\\u0438\\u043d\\u0438\\u043c\\u0430\\u0442\\u0435\\u043b\\u044c\\u0441\\u0442\\u0432\\u043e\",\n          \"\\u041c\\u043e\\u0434\\u0443\\u043b\\u044c \\u043e\\u0431\\u044f\\u0437\\u0430\\u0442\\u0435\\u043b\\u044c\\u043d\\u044b\\u0445 \\u0434\\u0438\\u0441\\u0446\\u0438\\u043f\\u043b\\u0438\\u043d\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"contents\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\\u0418\\u043d\\u043d\\u043e\\u0432\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u044b\\u0439 \\u043c\\u0430\\u0440\\u043a\\u0435\\u0442\\u0438\\u043d\\u0433. \\u041a\\u043e\\u0440\\u043f\\u043e\\u0440\\u0430\\u0442\\u0438\\u0432\\u043d\\u044b\\u0435 \\u0444\\u0438\\u043d\\u0430\\u043d\\u0441\\u044b. \\u041e\\u0441\\u043d\\u043e\\u0432\\u043d\\u044b\\u0435 \\u043a\\u043e\\u043d\\u0446\\u0435\\u043f\\u0446\\u0438\\u0438 \\u0432 \\u0441\\u043e\\u0437\\u0434\\u0430\\u043d\\u0438\\u0438 \\u0442\\u0435\\u0445\\u043d\\u043e\\u043b\\u043e\\u0433\\u0438\\u0447\\u0435\\u0441\\u043a\\u043e\\u0433\\u043e \\u0431\\u0438\\u0437\\u043d\\u0435\\u0441\\u0430. \\u0420\\u043e\\u043b\\u0438 \\u0432 \\u043f\\u0440\\u043e\\u0435\\u043a\\u0442\\u0435 \\u0438 \\u0440\\u0430\\u0441\\u043f\\u0440\\u0435\\u0434\\u0435\\u043b\\u0435\\u043d\\u0438\\u0435 \\u0434\\u043e\\u043b\\u0435\\u0439 \\u043c\\u0435\\u0436\\u0434\\u0443 \\u0443\\u0447\\u0430\\u0441\\u0442\\u043d\\u0438\\u043a\\u0430\\u043c\\u0438. \\u0421\\u0442\\u0440\\u0430\\u0442\\u0435\\u0433\\u0438\\u044f \\u0432\\u044b\\u0445\\u043e\\u0434\\u0430. \\u0423\\u043f\\u0440\\u0430\\u0432\\u043b\\u0435\\u043d\\u0438\\u0435 \\u0438\\u043d\\u0442\\u0435\\u043b\\u043b\\u0435\\u043a\\u0442\\u0443\\u0430\\u043b\\u044c\\u043d\\u043e\\u0439 \\u0441\\u043e\\u0431\\u0441\\u0442\\u0432\\u0435\\u043d\\u043d\\u043e\\u0441\\u0442\\u044c\\u044e. \\u0423\\u043f\\u0440\\u0430\\u0432\\u043b\\u0435\\u043d\\u0438\\u0435 \\u043a\\u0430\\u0447\\u0435\\u0441\\u0442\\u0432\\u043e\\u043c. \\u0426\\u0438\\u0444\\u0440\\u043e\\u0432\\u0430\\u044f \\u0442\\u0440\\u0430\\u043d\\u0441\\u0444\\u043e\\u0440\\u043c\\u0430\\u0446\\u0438\\u044f. \\u041e\\u0431\\u0449\\u0438\\u0435 \\u043f\\u043e\\u043d\\u044f\\u0442\\u0438\\u044f \\u0446\\u0438\\u0444\\u0440\\u043e\\u0432\\u043e\\u0439 \\u0442\\u0440\\u0430\\u043d\\u0441\\u0444\\u043e\\u0440\\u043c\\u0430\\u0446\\u0438\\u0438. \\u041e\\u0442\\u043b\\u0438\\u0447\\u0438\\u0435 \\u043f\\u043e\\u043d\\u044f\\u0442\\u0438\\u0439 \\u00ab\\u0446\\u0438\\u0444\\u0440\\u043e\\u0432\\u0438\\u0437\\u0430\\u0446\\u0438\\u044f\\u00bb \\u0438 \\u00ab\\u0446\\u0438\\u0444\\u0440\\u043e\\u0432\\u0430\\u044f \\u0442\\u0440\\u0430\\u043d\\u0441\\u0444\\u043e\\u0440\\u043c\\u0430\\u0446\\u0438\\u044f\\u00bb. \\u0423\\u043f\\u0440\\u0430\\u0432\\u043b\\u0435\\u043d\\u0438\\u0435 \\u0431\\u0438\\u0437\\u043d\\u0435\\u0441-\\u043f\\u0440\\u043e\\u0446\\u0435\\u0441\\u0441\\u0430\\u043c\\u0438, \\u0438\\u0445 \\u043e\\u0441\\u043e\\u0431\\u0435\\u043d\\u043d\\u043e\\u0441\\u0442\\u0438, \\u0444\\u043e\\u0440\\u043c\\u0430\\u043b\\u0438\\u0437\\u0430\\u0446\\u0438\\u044f, \\u043e\\u043f\\u0442\\u0438\\u043c\\u0438\\u0437\\u0430\\u0446\\u0438\\u044f \\u0438 \\u043f\\u0440\\u0438\\u043c\\u0435\\u0440\\u044b \\u0433\\u043e\\u0442\\u043e\\u0432\\u044b\\u0445 \\u0440\\u0435\\u0448\\u0435\\u043d\\u0438\\u0439: ERP-\\u0441\\u0438\\u0441\\u0442\\u0435\\u043c\\u0430, CRM-\\u0441\\u0438\\u0441\\u0442\\u0435\\u043c\\u0430 \\u0438 \\u0442.\\u0434.  \\u0410\\u0432\\u0442\\u043e\\u043c\\u0430\\u0442\\u0438\\u0437\\u0430\\u0446\\u0438\\u044f \\u043f\\u0440\\u043e\\u0438\\u0437\\u0432\\u043e\\u0434\\u0441\\u0442\\u0432\\u0430. \\u0418\\u043d\\u0434\\u0443\\u0441\\u0442\\u0440\\u0438\\u044f 4.0.  \\u0426\\u0438\\u0444\\u0440\\u043e\\u0432\\u044b\\u0435 \\u0434\\u0432\\u043e\\u0439\\u043d\\u0438\\u043a\\u0438. \\u0411\\u043b\\u043e\\u043a\\u0447\\u0435\\u0439\\u043d.. \\u041e\\u0441\\u043d\\u043e\\u0432\\u043d\\u044b\\u0435 \\u043f\\u043e\\u043d\\u044f\\u0442\\u0438\\u044f \\u0438\\u043d\\u043d\\u043e\\u0432\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u043e\\u0433\\u043e \\u043c\\u0430\\u0440\\u043a\\u0435\\u0442\\u0438\\u043d\\u0433\\u0430.  \\u041f\\u0440\\u043e\\u0434\\u0432\\u0438\\u0436\\u0435\\u043d\\u0438\\u0435 \\u043f\\u0440\\u043e\\u0434\\u0443\\u043a\\u0442\\u0430 \\u043d\\u0430 \\u0440\\u044b\\u043d\\u043a\\u0435. \\u0420\\u044b\\u043d\\u043e\\u043a B2B, B2C, B2G. \\u0421\\u043e\\u0437\\u0434\\u0430\\u043d\\u0438\\u0435 \\u043d\\u043e\\u0432\\u043e\\u0433\\u043e \\u0440\\u044b\\u043d\\u043a\\u0430. \\u041e\\u0441\\u043e\\u0431\\u0435\\u043d\\u043d\\u043e\\u0441\\u0442\\u0438 \\u043c\\u0430\\u0440\\u043a\\u0435\\u0442\\u0438\\u043d\\u0433\\u0430, \\u0441\\u0432\\u044f\\u0437\\u0430\\u043d\\u043d\\u044b\\u0435 \\u0441 \\u0432\\u044b\\u0441\\u043e\\u043a\\u0438\\u043c\\u0438 \\u0442\\u0435\\u0445\\u043d\\u043e\\u043b\\u043e\\u0433\\u0438\\u044f\\u043c\\u0438. \\u0418\\u043d\\u0441\\u0442\\u0440\\u0443\\u043c\\u0435\\u043d\\u0442\\u044b customerdevelopment\\u0438 productdevelopment\\u0432 \\u0438\\u043d\\u043d\\u043e\\u0432\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u043e\\u0439 \\u0441\\u0444\\u0435\\u0440\\u0435. \\u0420\\u0435\\u0430\\u043b\\u044c\\u043d\\u044b\\u0435 \\u0438\\u043d\\u0441\\u0442\\u0440\\u0443\\u043c\\u0435\\u043d\\u0442\\u044b \\u043f\\u0440\\u043e\\u0434\\u0432\\u0438\\u0436\\u0435\\u043d\\u0438\\u044f \\u0442\\u043e\\u0432\\u0430\\u0440\\u0430 \\u043d\\u0430 \\u0441\\u043e\\u0432\\u0440\\u0435\\u043c\\u0435\\u043d\\u043d\\u043e\\u043c \\u0440\\u044b\\u043d\\u043a\\u0435.. \\u041e\\u0441\\u043d\\u043e\\u0432\\u043d\\u044b\\u0435 \\u0444\\u043e\\u0440\\u043c\\u044b \\u0444\\u0438\\u043d\\u0430\\u043d\\u0441\\u043e\\u0432\\u043e\\u0439 \\u043e\\u0442\\u0447\\u0435\\u0442\\u043d\\u043e\\u0441\\u0442\\u0438: \\u0444\\u043e\\u0440\\u043c\\u0430 \\u043e \\u043f\\u0440\\u0438\\u0431\\u044b\\u043b\\u044f\\u0445 \\u0438 \\u0443\\u0431\\u044b\\u0442\\u043a\\u0430\\u0445, \\u0431\\u0430\\u043b\\u0430\\u043d\\u0441\\u0430\\u0445, \\u0434\\u0432\\u0438\\u0436\\u0435\\u043d\\u0438\\u0435 \\u0434\\u0435\\u043d\\u0435\\u0436\\u043d\\u044b\\u0445 \\u0441\\u0440\\u0435\\u0434\\u0441\\u0442\\u0432 \\u0438 \\u0442.\\u0434. \\u041e\\u0441\\u043d\\u043e\\u0432\\u044b \\u0444\\u0438\\u043d\\u0430\\u043d\\u0441\\u043e\\u0432\\u043e\\u0433\\u043e \\u043c\\u043e\\u0434\\u0435\\u043b\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u044f. \\u041e\\u0446\\u0435\\u043d\\u043a\\u0430 \\u0438\\u043d\\u0432\\u0435\\u0441\\u0442\\u0438\\u0446\\u0438\\u043e\\u043d\\u043d\\u043e\\u0439 \\u044d\\u0444\\u0444\\u0435\\u043a\\u0442\\u0438\\u0432\\u043d\\u043e\\u0441\\u0442\\u0438 \\u043f\\u0440\\u043e\\u0435\\u043a\\u0442\\u0430: ARR, DPB, NPV, IRR \\u0438 \\u0442.\\u0434.. \\u041f\\u0435\\u0440\\u0438\\u043e\\u0434 \\u043e\\u043a\\u0443\\u043f\\u0430\\u0435\\u043c\\u043e\\u0441\\u0442\\u0438. \\u0422\\u043e\\u0447\\u043a\\u0430 \\u0431\\u0435\\u0437\\u0443\\u0431\\u044b\\u0442\\u043e\\u0447\\u043d\\u043e\\u0441\\u0442\\u0438.. \\u041e\\u0441\\u043e\\u0431\\u0435\\u043d\\u043d\\u043e\\u0441\\u0442\\u0438 \\u0431\\u0438\\u0437\\u043d\\u0435\\u0441-\\u043c\\u043e\\u0434\\u0435\\u043b\\u0435\\u0439 \\u0432 \\u043f\\u0440\\u043e\\u0446\\u0435\\u0441\\u0441\\u0435 \\u043f\\u0440\\u043e\\u0438\\u0437\\u0432\\u043e\\u0434\\u0441\\u0442\\u0432\\u0430. \\u041c\\u0430\\u0441\\u0448\\u0442\\u0430\\u0431\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u0435 \\u0438 \\u0441\\u0435\\u0440\\u0442\\u0438\\u0444\\u0438\\u043a\\u0430\\u0446\\u0438\\u044f \\u043f\\u0440\\u043e\\u0438\\u0437\\u0432\\u043e\\u0434\\u0441\\u0442\\u0432\\u0430. \\u0410\\u0443\\u0434\\u0438\\u0442 \\u043a\\u0430\\u0447\\u0435\\u0441\\u0442\\u0432\\u0430. \\u041f\\u0440\\u043e\\u0438\\u0437\\u0432\\u043e\\u0434\\u0441\\u0442\\u0432\\u0435\\u043d\\u043d\\u044b\\u0435 \\u0441\\u0442\\u0430\\u043d\\u0434\\u0430\\u0440\\u0442\\u044b. \\u041f\\u043e\\u0438\\u0441\\u043a \\u043a\\u043e\\u043c\\u043f\\u0440\\u043e\\u043c\\u0438\\u0441\\u0441\\u0430 \\u043c\\u0435\\u0436\\u0434\\u0443 \\u044d\\u0444\\u0444\\u0435\\u043a\\u0442\\u0438\\u0432\\u043d\\u043e\\u0441\\u0442\\u044c\\u044e \\u0438 \\u043a\\u0430\\u0447\\u0435\\u0441\\u0442\\u0432\\u043e\\u043c.. \\u041f\\u0430\\u0442\\u0435\\u043d\\u0442\\u044b \\u0438 \\u0438\\u0445 \\u0432\\u0438\\u0434\\u044b. \\u041f\\u0430\\u0442\\u0435\\u043d\\u0442\\u043d\\u044b\\u0439 \\u044d\\u0444\\u0444\\u0435\\u043a\\u0442. \\u041f\\u0430\\u0442\\u0435\\u043d\\u0442 \\u043d\\u0430 \\u043f\\u043e\\u043b\\u0435\\u0437\\u043d\\u0443\\u044e \\u043c\\u043e\\u0434\\u0435\\u043b\\u044c. \\u041d\\u043e\\u0443-\\u0445\\u0430\\u0443. \\u0422\\u043e\\u0432\\u0430\\u0440\\u043d\\u044b\\u0439 \\u0437\\u043d\\u0430\\u043a. \\u0410\\u0432\\u0442\\u043e\\u0440\\u0441\\u043a\\u043e\\u0435 \\u043f\\u0440\\u0430\\u0432\\u043e. \\u041e\\u0431\\u0449\\u0438\\u0435 \\u044e\\u0440\\u0438\\u0434\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0435 \\u0430\\u0441\\u043f\\u0435\\u043a\\u0442\\u044b \\u0443\\u043f\\u0440\\u0430\\u0432\\u043b\\u0435\\u043d\\u0438\\u044f \\u0438\\u043d\\u0442\\u0435\\u043b\\u043b\\u0435\\u043a\\u0442\\u0443\\u0430\\u043b\\u044c\\u043d\\u043e\\u0439 \\u0441\\u043e\\u0431\\u0441\\u0442\\u0432\\u0435\\u043d\\u043d\\u043e\\u0441\\u0442\\u044c\\u044e: \\u0441\\u043e\\u0437\\u0434\\u0430\\u043d\\u0438\\u0435 \\u043a\\u043e\\u043c\\u043f\\u0430\\u043d\\u0438\\u0438, \\u0435\\u0435 \\u0440\\u0435\\u0433\\u0438\\u0441\\u0442\\u0440\\u0430\\u0446\\u0438\\u044f,  \\u0440\\u0430\\u0441\\u043f\\u0440\\u0435\\u0434\\u0435\\u043b\\u0435\\u043d\\u0438\\u0435 \\u0434\\u043e\\u043b\\u0435\\u0439, 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\\u0445\\u0430\\u0440\\u0430\\u043a\\u0442\\u0435\\u0440\\u0438\\u0441\\u0442\\u0438\\u043a. \\u041f\\u0430\\u0440\\u0430\\u043c\\u0435\\u0442\\u0440\\u044b \\u043f\\u043e\\u043b\\u0435\\u0437\\u043d\\u043e\\u0433\\u043e \\u0441\\u0438\\u0433\\u043d\\u0430\\u043b\\u0430. \\u0412\\u0438\\u0434 \\u043c\\u043e\\u0434\\u0443\\u043b\\u044f\\u0446\\u0438\\u0438 \\u0441\\u0438\\u0433\\u043d\\u0430\\u043b\\u043e\\u0432. \\u041a\\u043b\\u0430\\u0441\\u0441 \\u0438\\u0437\\u043b\\u0443\\u0447\\u0435\\u043d\\u0438\\u044f. \\u042d\\u043d\\u0435\\u0440\\u0433\\u0435\\u0442\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0435 \\u0445\\u0430\\u0440\\u0430\\u043a\\u0442\\u0435\\u0440\\u0438\\u0441\\u0442\\u0438\\u043a\\u0438 \\u0441\\u0438\\u0433\\u043d\\u0430\\u043b\\u0430. \\u041f\\u043e\\u043c\\u0435\\u0445\\u043e\\u0437\\u0430\\u0449\\u0438\\u0449\\u0435\\u043d\\u043d\\u043e\\u0441\\u0442\\u044c. \\u041f\\u043e\\u043c\\u0435\\u0445\\u043e\\u0443\\u0441\\u0442\\u043e\\u0439\\u0447\\u0438\\u0432\\u043e\\u0441\\u0442\\u044c. \\u0421\\u043a\\u0440\\u044b\\u0442\\u043d\\u043e\\u0441\\u0442\\u044c.. \\u041f\\u043e\\u043c\\u0435\\u0445\\u043e\\u0443\\u0441\\u0442\\u043e\\u0439\\u0447\\u0438\\u0432\\u043e\\u0435 (\\u043a\\u043e\\u0440\\u0440\\u0435\\u043a\\u0442\\u0438\\u0440\\u0443\\u044e\\u0449\\u0435\\u0435) \\u043a\\u043e\\u0434\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u0435.  \\u0413\\u0440\\u0443\\u043f\\u043f\\u043e\\u0432\\u044b\\u0435 \\u0441\\u0438\\u0441\\u0442\\u0435\\u043c\\u0430\\u0442\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0435 \\u043b\\u0438\\u043d\\u0435\\u0439\\u043d\\u044b\\u0435 \\u0431\\u043b\\u043e\\u0447\\u043d\\u044b\\u0435 \\u043a\\u043e\\u0434\\u044b. \\u041f\\u0440\\u0435\\u043e\\u0431\\u0440\\u0430\\u0437\\u043e\\u0432\\u0430\\u043d\\u0438\\u0435 \\u0447\\u0430\\u0441\\u0442\\u043e\\u0442\\u044b. \\u0421\\u0438\\u0433\\u043d\\u0430\\u043b\\u044b \\u0441 \\u0444\\u0430\\u0437\\u043e\\u0432\\u043e\\u0439 \\u043c\\u043e\\u0434\\u0443\\u043b\\u044f\\u0446\\u0438\\u0435\\u0439 \\u0433\\u0430\\u0440\\u043c\\u043e\\u043d\\u0438\\u0447\\u0435\\u0441\\u043a\\u043e\\u0433\\u043e \\u043d\\u0435\\u0441\\u0443\\u0449\\u0435\\u0433\\u043e  \\u043a\\u043e\\u043b\\u0435\\u0431\\u0430\\u043d\\u0438\\u044f. \\u0424\\u043e\\u0440\\u043c\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u0435 \\u0410\\u041c \\u0441\\u0438\\u0433\\u043d\\u0430\\u043b\\u043e\\u0432. \\u0424\\u043e\\u0440\\u043c\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u0435 \\u043c\\u0430\\u043d\\u0438\\u043f\\u0443\\u043b\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u043d\\u044b\\u0445 \\u0438 \\u0438\\u043c\\u043f\\u0443\\u043b\\u044c\\u0441\\u043d\\u043e-\\u043c\\u043e\\u0434\\u0443\\u043b\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u043d\\u044b\\u0445 \\u0441\\u0438\\u0433\\u043d\\u0430\\u043b\\u043e\\u0432. \\u0424\\u043e\\u0440\\u043c\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u0435 \\u0447\\u0430\\u0441\\u0442\\u043e\\u0442\\u043d\\u043e- \\u0438 \\u0444\\u0430\\u0437\\u043e- \\u043c\\u043e\\u0434\\u0443\\u043b\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u043d\\u044b\\u0445  \\u0441\\u0438\\u0433\\u043d\\u0430\\u043b\\u043e\\u0432. \\u0426\\u0438\\u0444\\u0440\\u043e\\u0432\\u0430\\u044f \\u043e\\u0431\\u0440\\u0430\\u0431\\u043e\\u0442\\u043a\\u0430 \\u0441\\u0438\\u0433\\u043d\\u0430\\u043b\\u043e\\u0432 \\u0432 \\u0447\\u0430\\u0441\\u0442\\u043e\\u0442\\u043d\\u043e\\u0439 \\u043e\\u0431\\u043b\\u0430\\u0441\\u0442\\u0438. \\u0427\\u0430\\u0441\\u0442\\u043e\\u0442\\u043d\\u043e-\\u043c\\u043e\\u0434\\u0443\\u043b\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u043d\\u044b\\u0435 \\u0441\\u0438\\u0433\\u043d\\u0430\\u043b\\u044b. \\u042d\\u043b\\u0435\\u043a\\u0442\\u0440\\u043e\\u0441\\u0432\\u044f\\u0437\\u044c. \\u042d\\u0442\\u0430\\u043f\\u044b \\u0440\\u0430\\u0437\\u0432\\u0438\\u0442\\u0438\\u044f \\u0441\\u0438\\u0441\\u0442\\u0435\\u043c \\u044d\\u043b\\u0435\\u043a\\u0442\\u0440\\u043e\\u0441\\u0432\\u044f\\u0437\\u0438. \\u0421\\u0438\\u0441\\u0442\\u0435\\u043c\\u0430 \\u043f\\u0435\\u0440\\u0435\\u0434\\u0430\\u0447\\u0438 \\u0438\\u043d\\u0444\\u043e\\u0440\\u043c\\u0430\\u0446\\u0438\\u0438. \\u041a\\u0430\\u043d\\u0430\\u043b \\u0441\\u0432\\u044f\\u0437\\u0438. \\u041c\\u043e\\u0434\\u0443\\u043b\\u044f\\u0442\\u043e\\u0440. \\u0414\\u0435\\u043c\\u043e\\u0434\\u0443\\u043b\\u044f\\u0442\\u043e\\u0440. \\u041f\\u0440\\u043e\\u043f\\u0443\\u0441\\u043a\\u043d\\u0430\\u044f \\u0441\\u043f\\u043e\\u0441\\u043e\\u0431\\u043d\\u043e\\u0441\\u0442\\u044c.. \\u042d\\u0444\\u0444\\u0435\\u043a\\u0442\\u0438\\u0432\\u043d\\u043e\\u0435 \\u0441\\u0442\\u0430\\u0442\\u0438\\u0441\\u0442\\u0438\\u0447\\u0435\\u0441\\u043a\\u043e\\u0435 \\u043a\\u043e\\u0434\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u0435 \\u0438\\u0441\\u0442\\u043e\\u0447\\u043d\\u0438\\u043a\\u043e\\u0432  \\u0434\\u0438\\u0441\\u043a\\u0440\\u0435\\u0442\\u043d\\u044b\\u0445 \\u0441\\u043e\\u043e\\u0431\\u0449\\u0435\\u043d\\u0438\\u0439. \\u041a\\u043e\\u0434 \\u0425\\u0430\\u0444\\u0444\\u043c\\u0430\\u043d\\u0430\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tokens\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tokensD\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Данные минимально предобработаны. Можно переходить к векторизации. Рассмотрим два подхода:\n",
+        "- Word2Vec, обученный на предобработанных данных\n",
+        "- Tiny BERT – предобученная мини-версия мультиязычного BERT"
+      ],
+      "metadata": {
+        "id": "GmpdQdkLlMi_"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Кратко про языковые модели"
+      ],
+      "metadata": {
+        "id": "HBYPs_iwXAUb"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Вне зависомости от того, большая модель или нет, данные, на которых она обучается и тестируется проходят одинаковые этапы предобработки.\n",
+        "\n",
+        "Перед тем как начать обучение модели, необходимо подготовить текстовые данные. Этот процесс включает несколько этапов:\n",
+        "\n",
+        "-\t**Сбор данных.**\n",
+        "\n",
+        "Модель обучается на большом корпусе текстов, который может включать книги, статьи, блоги, форумы и другие источники. Чем больше и разнообразнее данные, тем лучше модель сможет понять и воспроизвести язык.\n",
+        "-\t**Токенизация.**\n",
+        "\n",
+        "Процесс разбиения текста на отдельные элементы, называемые токенами. Токены могут представлять собой слова, части слов или даже отдельные символы. Например, фраза «Как дела?» может быть токенизирована как [«Как», «дела», «?»].\n",
+        "-\t**Создание словарей.**\n",
+        "\n",
+        "После токенизации формируется словарь, который сопоставляет с каждым токеном уникальный идентификатор. Этот словарь необходим для перевода текстовых данных в числовую форму, понятную модели.\n",
+        "-\t**Преобразование текста в числовые векторы.**\n",
+        "\n",
+        "Токены преобразуются в числовые векторы, которые будут использоваться в качестве входных данных для нейронной сети. Эти векторы могут быть статическими, как в Word2Vec, или динамическими, как в трансформерах.\n",
+        "\n",
+        "---\n",
+        "Обучение языковой модели заключается в настройке её параметров на основе текстовых данных, чтобы она могла предсказывать следующее слово или генерировать осмысленные фразы.\n",
+        "\n",
+        "-\t**Forward propagation (прямой проход).**\n",
+        "\n",
+        "На этом этапе данные проходят через слои нейронной сети, где каждое слово представляется в виде вектора. Эти векторы затем преобразуются в промежуточные состояния, которые несут информацию о контексте текста.\n",
+        "-\t**Механизм внимания.**\n",
+        "\n",
+        "В современных моделях, таких как трансформеры, используется механизм внимания, который позволяет модели выделять наиболее важные части текста, на которых нужно сосредоточиться при генерации или предсказании.\n",
+        "-\t**Обратное распространение ошибки (Backpropagation).**\n",
+        "\n",
+        "После того как модель сделала предсказание, она сравнивает его с реальными данными и вычисляет ошибку. Затем эта ошибка используется для корректировки параметров модели, чтобы улучшить точность предсказаний в будущем.\n",
+        "-\t**Обучение на основе батчей.**\n",
+        "\n",
+        "Данные разбиваются на небольшие группы — батчи, которые подаются модели для обучения. Этот метод позволяет эффективно использовать вычислительные ресурсы и ускорять процесс обучения."
+      ],
+      "metadata": {
+        "id": "z1WjOiUWXEhM"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Трансформеры\n",
+        "Современные языковые модели в большинстве основаны на архитектуре трансформеров, предложенной в 2017 году. Трансформеры решают многие проблемы, присущие RNN, и становятся доминирующим подходом в NLP.\n",
+        "\n",
+        "-\t**BERT (Bidirectional Encoder Representations from Transformers).**\n",
+        "\n",
+        "Модель BERT использует двунаправленное обучение, что позволяет ей понимать контекст слова как слева, так и справа от него. Это делает BERT особенно мощным инструментом для задач, связанных с пониманием текста, таких как вопросо-ответные системы и анализ тональности.\n",
+        "-\t**GPT (Generative Pre-Trained Transformer).**\n",
+        "\n",
+        "Модель GPT, напротив, использует однонаправленное обучение, предсказывая следующее слово только на основе предыдущих слов. Эта модель, особенно в версиях GPT-2 и GPT-3, стала чрезвычайно популярной для генерации текста благодаря своей способности создавать связные и осмысленные сообщения, которые трудно отличить от текстов, написанных человеком."
+      ],
+      "metadata": {
+        "id": "ZQfaxr4BYA09"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Векторизация word2vec"
+      ],
+      "metadata": {
+        "id": "51ucqaP7qpL6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from gensim.models import Word2Vec\n",
+        "import gensim"
+      ],
+      "metadata": {
+        "id": "yzD2Y2fHqyt2"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# обучим на своих данных модель word2vec\n",
+        "# min_count – минимальная частота слова в корпусе\n",
+        "# vector_size – размер вектора для слова\n",
+        "# workers – количество потоков\n",
+        "# window – окно контекста, в которое попадает слово\n",
+        "\n",
+        "w2v_model = Word2Vec(\"ваши данные в формате списка списков токенов\", min_count=1, vector_size=300, workers=2, window=10)"
+      ],
+      "metadata": {
+        "id": "6oQuDr6Bq1XB"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "w2v_model_path = \"w2v.bin\" # будет удобно сохранить модель, если выполняете лабу не за один вечер\n",
+        "w2v_model.save(w2v_model_path)"
+      ],
+      "metadata": {
+        "id": "-cAq3FoSrY3-"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# загрузка сохраненной модели\n",
+        "w2v_model = gensim.models.Word2Vec.load(w2v_model_path)"
+      ],
+      "metadata": {
+        "id": "Fn2KEsktK64s"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# так выглядит один вектор\n",
+        "print(w2v_model.wv.get_vector(\"обучение\").shape)  # размер вектора\n",
+        "w2v_model.wv.get_vector(\"обучение\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "VfNxlY0Jraq8",
+        "outputId": "d074df62-4dc3-47b8-c4e7-444dc8696f04"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(300,)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([ 1.0956907e+00,  1.4233451e+00, -3.6250832e+00,  7.7131558e-03,\n",
+              "       -9.0657723e-01,  1.9586326e-01,  1.5344505e+00, -4.6481729e+00,\n",
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+              "       -2.3857351e-01,  1.4552513e+00, -1.3647692e+00,  2.7503006e+00,\n",
+              "       -3.5059564e+00,  5.1303893e-01,  1.4941009e+00,  3.1286108e+00,\n",
+              "       -2.6847348e+00, -1.1375122e+00, -3.0803838e-01, -1.8903989e+00,\n",
+              "        1.3237225e+00,  2.0954155e-03,  8.6177850e-01,  9.2225140e-01,\n",
+              "       -2.0853949e+00,  1.8333832e+00, -7.4237877e-01, -5.4098201e-01,\n",
+              "       -2.6860518e+00,  7.9029888e-01,  9.0864265e-01, -3.5428882e-01,\n",
+              "        1.5938795e+00,  4.7800976e-01,  1.9034328e-01,  1.2344238e+00],\n",
+              "      dtype=float32)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 30
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# слова, максимально близкие к \"машинному обучению\"\n",
+        "w2v_model.wv.most_similar([\"обучение\", \"машинный\"])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eTxzlxA-rdT1",
+        "outputId": "ea7c3add-f03c-4a41-8d50-08fb15df4871"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[('дикторский', 0.5493224859237671),\n",
+              " ('подкрепление', 0.5253544449806213),\n",
+              " ('нейросеть', 0.5114834904670715),\n",
+              " ('учитель', 0.5069007277488708),\n",
+              " ('аномалия', 0.5064149498939514),\n",
+              " ('семантический', 0.5037545561790466),\n",
+              " ('--', 0.4904346168041229),\n",
+              " ('crisp-dm', 0.489619642496109),\n",
+              " ('байесовский', 0.4880848526954651),\n",
+              " ('сигмоидальный', 0.4741494655609131)]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 31
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# слова, максимально близкие к \"анализу данных\"\n",
+        "w2v_model.wv.most_similar([\"анализ\", \"данные\"])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ndTfiap1rfhR",
+        "outputId": "c9e0f745-0db7-4bce-eacb-5e0ed645fb96"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[('статистика', 0.5160747170448303),\n",
+              " ('панель', 0.4314064085483551),\n",
+              " ('модальность', 0.424674391746521),\n",
+              " ('семантический', 0.4163769483566284),\n",
+              " ('вьювер', 0.40764570236206055),\n",
+              " ('субд', 0.40717607736587524),\n",
+              " ('аудиоряд', 0.40293943881988525),\n",
+              " ('дескриптивный', 0.39662471413612366),\n",
+              " ('pyldavis', 0.3948679268360138),\n",
+              " ('данный', 0.39240917563438416)]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 32
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Семантически получились не идеальные, но в целом близкие списки. Это естественно, т.к. уникальных записей для обучения было передано около 2000.\n",
+        "\n",
+        "Создадим два вектора для каждой дисциплины:\n",
+        "1. По данным содержания (`contents`)\n",
+        "2. По данным описание дисциплины (`description`)\n",
+        "\n",
+        "Учитывайте, что должна быть возможность построить эмбеддинг для нового текста, даже если некоторые слова отсутствуют в исходной модели. При этом вектор не должен быть нулевым. Для этого напишите функцию `embed()`, в качестве агрументов принимающую список токенов текста и модель."
+      ],
+      "metadata": {
+        "id": "ZkKkoFb8ri2v"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def embed(tokens, w2v_model):\n",
+        "    # ваш код здесь\n",
+        "\n",
+        "\n",
+        "# пример вектора\n",
+        "embed(df.iloc[0].tokens, w2v_model)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zbMgvE7Driic",
+        "outputId": "98d5666b-1d4a-4067-f3e6-75f3da0da4af"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
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+              "       -1.00228202e+00, -1.06464195e+00, -3.46156746e-01,  6.27270639e-01,\n",
+              "        7.34158039e-01, -4.11146611e-01, -1.08555818e+00, -6.70471251e-01,\n",
+              "        2.27671698e-01,  3.58476013e-01, -3.03740174e-01,  4.05371159e-01,\n",
+              "        6.46440625e-01, -2.25928962e-01,  8.14153552e-01,  5.15001655e-01,\n",
+              "        6.43403649e-01, -9.11136866e-01,  2.79784888e-01,  1.41376460e+00,\n",
+              "        5.41172922e-01, -9.27936971e-01,  2.42630094e-01, -5.82138836e-01,\n",
+              "        3.10100228e-01, -9.20305014e-01, -2.02172592e-01, -7.33254373e-01,\n",
+              "       -7.67284259e-02,  5.10609925e-01, -1.56563699e-01,  4.34133232e-01,\n",
+              "        1.07237458e+00,  4.41153735e-01, -1.83203876e-01,  4.86719579e-01,\n",
+              "       -9.71264094e-02,  4.77000058e-01, -6.39093995e-01, -2.44859517e-01,\n",
+              "       -3.88560980e-03,  9.40126553e-02,  2.93791622e-01,  7.11750269e-01,\n",
+              "       -6.82483008e-03, -1.56252608e-01,  3.80502552e-01, -3.87075357e-02,\n",
+              "       -1.11809075e+00,  3.97830844e-01, -3.34730715e-01,  2.07063537e-02,\n",
+              "       -1.16870451e+00,  1.08429231e-01, -4.82456774e-01,  7.61385560e-02,\n",
+              "        6.04807615e-01,  1.88425079e-01,  6.91339552e-01,  6.46933794e-01,\n",
+              "       -3.23569030e-01,  5.26470900e-01,  1.11312795e+00,  5.12032621e-02,\n",
+              "       -5.61130345e-01, -1.34176791e+00, -4.88683492e-01,  9.96772110e-01,\n",
+              "       -3.59947681e-02, -4.30267632e-01,  1.22872397e-01, -7.00789511e-01,\n",
+              "       -1.58089146e-01, -6.45145833e-01, -2.38410115e-01,  6.09793842e-01,\n",
+              "       -7.63992071e-01, -1.02998245e+00, -8.10775012e-02,  3.68131906e-01],\n",
+              "      dtype=float32)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 33
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Теперь применим функцию `embed()` для формирования вектора для содержаний и кратких описаний в каждой записи."
+      ],
+      "metadata": {
+        "id": "OeVAenBYm2F7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ваш код здесь\n",
+        "\n",
+        "# пример результата\n",
+        "df.sample(3, random_state=42)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 324
+        },
+        "id": "z9j9i456r9cD",
+        "outputId": "79520fc8-abdf-4552-88ae-6614fe46ca2e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "       course_id                       course_title  \\\n",
+              "16112      17498  Создание технологического бизнеса   \n",
+              "2457        3002         Теория электрической связи   \n",
+              "1077        2736   Организация массовых мероприятий   \n",
+              "\n",
+              "                                             description  up_id  \\\n",
+              "16112  Цель курса - предоставить возможность магистра...   7198   \n",
+              "2457   Программа составлена в соответствии с требован...   6989   \n",
+              "1077   Данная дисциплина учит планировать мероприятие...   6875   \n",
+              "\n",
+              "                                                up_title qualification  \\\n",
+              "16112  Информационная безопасность / Information secu...        master   \n",
+              "2457    Программирование в инфокоммуникационных системах      bachelor   \n",
+              "1077             Управление технологическими инновациями      bachelor   \n",
+              "\n",
+              "       start_year                     module_name  \\\n",
+              "16112        2022  Наукоемкое предпринимательство   \n",
+              "2457         2020   Модуль обязательных дисциплин   \n",
+              "1077         2018            Модуль «Soft Skills»   \n",
+              "\n",
+              "                                                contents  \\\n",
+              "16112  Инновационный маркетинг. Корпоративные финансы...   \n",
+              "2457   Математические модели сигналов, помех и канало...   \n",
+              "1077   Особенности организации массовых мероприятий.....   \n",
+              "\n",
+              "                                                  tokens  \\\n",
+              "16112  [инновационный, маркетинг, корпоративный, фина...   \n",
+              "2457   [математический, модель, сигнал, помеха, канал...   \n",
+              "1077   [особенность, организация, массовый, мероприят...   \n",
+              "\n",
+              "                                                 tokensD  \\\n",
+              "16112  [цель, курс, -, предоставлять, возможность, ма...   \n",
+              "2457   [программа, составлять, соответствие, требован...   \n",
+              "1077   [данный, дисциплина, учить, планировать, мероп...   \n",
+              "\n",
+              "                                               embedding  \\\n",
+              "16112  [-0.32808504, -0.16484217, 0.4222597, -0.21925...   \n",
+              "2457   [-0.4867806, -0.22165154, 0.2573713, 0.9042233...   \n",
+              "1077   [-0.48288378, -0.36358258, 0.26799712, 0.28041...   \n",
+              "\n",
+              "                                              embeddingD  \n",
+              "16112  [-0.6981015, -0.22522101, 0.27788776, 0.152220...  \n",
+              "2457   [0.354802, -0.14068794, -0.2379195, 0.35860807...  \n",
+              "1077   [-0.5632663, 0.11936013, 0.047031883, -0.18054...  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-211dd808-f2a3-40a0-bb29-7ddd5c322587\" 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",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>course_id</th>\n",
+              "      <th>course_title</th>\n",
+              "      <th>description</th>\n",
+              "      <th>up_id</th>\n",
+              "      <th>up_title</th>\n",
+              "      <th>qualification</th>\n",
+              "      <th>start_year</th>\n",
+              "      <th>module_name</th>\n",
+              "      <th>contents</th>\n",
+              "      <th>tokens</th>\n",
+              "      <th>tokensD</th>\n",
+              "      <th>embedding</th>\n",
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+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>16112</th>\n",
+              "      <td>17498</td>\n",
+              "      <td>Создание технологического бизнеса</td>\n",
+              "      <td>Цель курса - предоставить возможность магистра...</td>\n",
+              "      <td>7198</td>\n",
+              "      <td>Информационная безопасность / Information secu...</td>\n",
+              "      <td>master</td>\n",
+              "      <td>2022</td>\n",
+              "      <td>Наукоемкое предпринимательство</td>\n",
+              "      <td>Инновационный маркетинг. Корпоративные финансы...</td>\n",
+              "      <td>[инновационный, маркетинг, корпоративный, фина...</td>\n",
+              "      <td>[цель, курс, -, предоставлять, возможность, ма...</td>\n",
+              "      <td>[-0.32808504, -0.16484217, 0.4222597, -0.21925...</td>\n",
+              "      <td>[-0.6981015, -0.22522101, 0.27788776, 0.152220...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2457</th>\n",
+              "      <td>3002</td>\n",
+              "      <td>Теория электрической связи</td>\n",
+              "      <td>Программа составлена в соответствии с требован...</td>\n",
+              "      <td>6989</td>\n",
+              "      <td>Программирование в инфокоммуникационных системах</td>\n",
+              "      <td>bachelor</td>\n",
+              "      <td>2020</td>\n",
+              "      <td>Модуль обязательных дисциплин</td>\n",
+              "      <td>Математические модели сигналов, помех и канало...</td>\n",
+              "      <td>[математический, модель, сигнал, помеха, канал...</td>\n",
+              "      <td>[программа, составлять, соответствие, требован...</td>\n",
+              "      <td>[-0.4867806, -0.22165154, 0.2573713, 0.9042233...</td>\n",
+              "      <td>[0.354802, -0.14068794, -0.2379195, 0.35860807...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1077</th>\n",
+              "      <td>2736</td>\n",
+              "      <td>Организация массовых мероприятий</td>\n",
+              "      <td>Данная дисциплина учит планировать мероприятие...</td>\n",
+              "      <td>6875</td>\n",
+              "      <td>Управление технологическими инновациями</td>\n",
+              "      <td>bachelor</td>\n",
+              "      <td>2018</td>\n",
+              "      <td>Модуль «Soft Skills»</td>\n",
+              "      <td>Особенности организации массовых мероприятий.....</td>\n",
+              "      <td>[особенность, организация, массовый, мероприят...</td>\n",
+              "      <td>[данный, дисциплина, учить, планировать, мероп...</td>\n",
+              "      <td>[-0.48288378, -0.36358258, 0.26799712, 0.28041...</td>\n",
+              "      <td>[-0.5632663, 0.11936013, 0.047031883, -0.18054...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
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+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-211dd808-f2a3-40a0-bb29-7ddd5c322587')\"\n",
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+              "  </svg>\n",
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+              "\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-211dd808-f2a3-40a0-bb29-7ddd5c322587 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-211dd808-f2a3-40a0-bb29-7ddd5c322587');\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-cf7bd075-b686-427e-9593-c81203636ef4\">\n",
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+              "\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
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+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
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+              "      --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",
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+              "    cursor: pointer;\n",
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+              "    height: 32px;\n",
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+              "    width: 32px;\n",
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+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
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+              "    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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+              "    }\n",
+              "    40% {\n",
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+              "      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-cf7bd075-b686-427e-9593-c81203636ef4 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
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+              "type": "dataframe",
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\\u043e\\u0431\\u043b\\u0430\\u0441\\u0442\\u0438 \\u0438\\u043d\\u0444\\u043e\\u043a\\u043e\\u043c\\u043c\\u0443\\u043d\\u0438\\u043a\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u044b\\u0445 \\u0442\\u0435\\u0445\\u043d\\u043e\\u043b\\u043e\\u0433\\u0438\\u0439 \\u0438 \\u0441\\u0438\\u0441\\u0442\\u0435\\u043c \\u0441\\u0432\\u044f\\u0437\\u0438 (\\u043d\\u043e\\u0440\\u043c\\u0430\\u0442\\u0438\\u0432\\u043d\\u044b\\u0435 \\u043f\\u0440\\u0430\\u0432\\u043e\\u0432\\u044b\\u0435 \\u0430\\u043a\\u0442\\u044b \\u0420\\u043e\\u0441\\u0441\\u0438\\u0439\\u0441\\u043a\\u043e\\u0439 \\u0424\\u0435\\u0434\\u0435\\u0440\\u0430\\u0446\\u0438\\u0438, \\u0442\\u0435\\u0445\\u043d\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0435 \\u0440\\u0435\\u0433\\u043b\\u0430\\u043c\\u0435\\u043d\\u0442\\u044b, \\u043c\\u0435\\u0436\\u0434\\u0443\\u043d\\u0430\\u0440\\u043e\\u0434\\u043d\\u044b\\u0435 \\u0438 \\u043d\\u0430\\u0446\\u0438\\u043e\\u043d\\u0430\\u043b\\u044c\\u043d\\u044b\\u0435 \\u0441\\u0442\\u0430\\u043d\\u0434\\u0430\\u0440\\u0442\\u044b, \\u0440\\u0435\\u043a\\u043e\\u043c\\u0435\\u043d\\u0434\\u0430\\u0446\\u0438\\u0438 \\u041c\\u0435\\u0436\\u0434\\u0443\\u043d\\u0430\\u0440\\u043e\\u0434\\u043d\\u043e\\u0433\\u043e \\u0441\\u043e\\u044e\\u0437\\u0430 \\u044d\\u043b\\u0435\\u043a\\u0442\\u0440\\u043e\\u0441\\u0432\\u044f\\u0437\\u0438):\\n\\u041f\\u041a-3: \\u0441\\u043f\\u043e\\u0441\\u043e\\u0431\\u043d\\u043e\\u0441\\u0442\\u044c \\u043e\\u0446\\u0435\\u043d\\u0438\\u0432\\u0430\\u0442\\u044c \\u0442\\u0435\\u0445\\u043d\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0435 \\u0432\\u043e\\u0437\\u043c\\u043e\\u0436\\u043d\\u043e\\u0441\\u0442\\u0438 \\u0438 \\u0432\\u044b\\u0440\\u0430\\u0431\\u0430\\u0442\\u044b\\u0432\\u0430\\u0442\\u044c \\u0440\\u0435\\u043a\\u043e\\u043c\\u0435\\u043d\\u0434\\u0430\\u0446\\u0438\\u0438 \\u043f\\u043e \\u043f\\u043e\\u0441\\u0442\\u0440\\u043e\\u0435\\u043d\\u0438\\u044e 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\\u043c\\u0435\\u0442\\u043e\\u0434\\u044b \\u0440\\u0435\\u0430\\u0433\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u044f. \\u0412\\u043e \\u0432\\u0440\\u0435\\u043c\\u044f \\u0437\\u0430\\u043d\\u044f\\u0442\\u0438\\u0439 \\u043e\\u0431\\u0443\\u0447\\u0430\\u044e\\u0449\\u0438\\u0435\\u0441\\u044f \\u0443\\u0437\\u043d\\u0430\\u044e\\u0442 \\u043f\\u0440\\u043e \\u0432\\u0438\\u0437\\u0443\\u0430\\u043b\\u044c\\u043d\\u0443\\u044e \\u043a\\u043e\\u043d\\u0446\\u0435\\u043f\\u0446\\u0438\\u044e \\u0438 \\u043c\\u0435\\u0434\\u0430\\u0441\\u043e\\u043f\\u0440\\u043e\\u0432\\u043e\\u0436\\u0434\\u0435\\u043d\\u0438\\u0435 \\u043c\\u0435\\u0440\\u043e\\u043f\\u0440\\u0438\\u044f\\u0442\\u0438\\u044f. \\u041f\\u043e\\u043b\\u0443\\u0447\\u0430\\u0442 \\u043f\\u0440\\u0430\\u043a\\u0442\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0435 \\u043d\\u0430\\u0432\\u044b\\u043a\\u0438 \\u043f\\u043e \\u043e\\u0446\\u0435\\u043d\\u043a\\u0435 \\u044d\\u0444\\u0444\\u0435\\u043a\\u0442\\u0438\\u0432\\u043d\\u043e\\u0441\\u0442\\u0438. \\n\\u0417\\u0430\\u043d\\u044f\\u0442\\u0438\\u044f \\u043f\\u0440\\u043e\\u0445\\u043e\\u0434\\u044f\\u0442 \\u0441 \\u0440\\u0430\\u0437\\u043b\\u0438\\u0447\\u043d\\u044b\\u043c\\u0438 \\u043f\\u0440\\u0435\\u043f\\u043e\\u0434\\u0430\\u0432\\u0430\\u0442\\u0435\\u043b\\u044f\\u043c\\u0438 \\u0438\\u0437  setters education \\u0438 evention, \\u043a\\u043e\\u0442\\u043e\\u0440\\u044b\\u0435 \\u043d\\u0430 \\u0441\\u0432\\u043e\\u0435\\u043c \\u043e\\u043f\\u044b\\u0442\\u0435 \\u0440\\u0430\\u0441\\u0441\\u043a\\u0430\\u0437\\u044b\\u0432\\u0430\\u044e\\u0442 \\u043f\\u0440\\u043e \\u043e\\u0436\\u0438\\u0434\\u0430\\u043d\\u0438\\u044f \\u0438 \\u0440\\u0435\\u0430\\u043b\\u044c\\u043d\\u043e\\u0441\\u0442\\u044c \\u0440\\u043e\\u043b\\u0438 \\\"\\u043e\\u0440\\u0433\\u0430\\u043d\\u0438\\u0437\\u0430\\u0442\\u043e\\u0440\\u0430\\\". \\n\\u0414\\u0438\\u0441\\u0446\\u0438\\u043f\\u043b\\u0438\\u043d\\u0430 \\u043f\\u043e\\u0434\\u0445\\u043e\\u0434\\u0438\\u0442\\u044c \\u0434\\u043b\\u044f \\u0442\\u0435\\u0445, \\u043a\\u0442\\u043e \\u043e\\u0440\\u0433\\u0430\\u043d\\u0438\\u0437\\u0443\\u0435\\u0442 \\u043c\\u0435\\u0440\\u043e\\u043f\\u0440\\u0438\\u044f\\u0442\\u0438\\u044f \\u0432 \\u0443\\u043d\\u0438\\u0432\\u0435\\u0440\\u0441\\u0438\\u0442\\u0435\\u0442\\u0435 \\u0438 \\u0437\\u0430 \\u0435\\u0433\\u043e \\u043f\\u0440\\u0435\\u0434\\u0435\\u043b\\u0430\\u043c\\u0438.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"up_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 163,\n        \"min\": 6875,\n        \"max\": 7198,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          7198,\n          6989,\n          6875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"up_title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\\u0418\\u043d\\u0444\\u043e\\u0440\\u043c\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u0430\\u044f \\u0431\\u0435\\u0437\\u043e\\u043f\\u0430\\u0441\\u043d\\u043e\\u0441\\u0442\\u044c / Information security\",\n          \"\\u041f\\u0440\\u043e\\u0433\\u0440\\u0430\\u043c\\u043c\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u0435 \\u0432 \\u0438\\u043d\\u0444\\u043e\\u043a\\u043e\\u043c\\u043c\\u0443\\u043d\\u0438\\u043a\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u044b\\u0445 \\u0441\\u0438\\u0441\\u0442\\u0435\\u043c\\u0430\\u0445\",\n          \"\\u0423\\u043f\\u0440\\u0430\\u0432\\u043b\\u0435\\u043d\\u0438\\u0435 \\u0442\\u0435\\u0445\\u043d\\u043e\\u043b\\u043e\\u0433\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u043c\\u0438 \\u0438\\u043d\\u043d\\u043e\\u0432\\u0430\\u0446\\u0438\\u044f\\u043c\\u0438\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"qualification\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"bachelor\",\n          \"master\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2018,\n        \"max\": 2022,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2022,\n          2020\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"module_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\\u041d\\u0430\\u0443\\u043a\\u043e\\u0435\\u043c\\u043a\\u043e\\u0435 \\u043f\\u0440\\u0435\\u0434\\u043f\\u0440\\u0438\\u043d\\u0438\\u043c\\u0430\\u0442\\u0435\\u043b\\u044c\\u0441\\u0442\\u0432\\u043e\",\n          \"\\u041c\\u043e\\u0434\\u0443\\u043b\\u044c \\u043e\\u0431\\u044f\\u0437\\u0430\\u0442\\u0435\\u043b\\u044c\\u043d\\u044b\\u0445 \\u0434\\u0438\\u0441\\u0446\\u0438\\u043f\\u043b\\u0438\\u043d\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"contents\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\\u0418\\u043d\\u043d\\u043e\\u0432\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u044b\\u0439 \\u043c\\u0430\\u0440\\u043a\\u0435\\u0442\\u0438\\u043d\\u0433. \\u041a\\u043e\\u0440\\u043f\\u043e\\u0440\\u0430\\u0442\\u0438\\u0432\\u043d\\u044b\\u0435 \\u0444\\u0438\\u043d\\u0430\\u043d\\u0441\\u044b. \\u041e\\u0441\\u043d\\u043e\\u0432\\u043d\\u044b\\u0435 \\u043a\\u043e\\u043d\\u0446\\u0435\\u043f\\u0446\\u0438\\u0438 \\u0432 \\u0441\\u043e\\u0437\\u0434\\u0430\\u043d\\u0438\\u0438 \\u0442\\u0435\\u0445\\u043d\\u043e\\u043b\\u043e\\u0433\\u0438\\u0447\\u0435\\u0441\\u043a\\u043e\\u0433\\u043e \\u0431\\u0438\\u0437\\u043d\\u0435\\u0441\\u0430. \\u0420\\u043e\\u043b\\u0438 \\u0432 \\u043f\\u0440\\u043e\\u0435\\u043a\\u0442\\u0435 \\u0438 \\u0440\\u0430\\u0441\\u043f\\u0440\\u0435\\u0434\\u0435\\u043b\\u0435\\u043d\\u0438\\u0435 \\u0434\\u043e\\u043b\\u0435\\u0439 \\u043c\\u0435\\u0436\\u0434\\u0443 \\u0443\\u0447\\u0430\\u0441\\u0442\\u043d\\u0438\\u043a\\u0430\\u043c\\u0438. \\u0421\\u0442\\u0440\\u0430\\u0442\\u0435\\u0433\\u0438\\u044f \\u0432\\u044b\\u0445\\u043e\\u0434\\u0430. \\u0423\\u043f\\u0440\\u0430\\u0432\\u043b\\u0435\\u043d\\u0438\\u0435 \\u0438\\u043d\\u0442\\u0435\\u043b\\u043b\\u0435\\u043a\\u0442\\u0443\\u0430\\u043b\\u044c\\u043d\\u043e\\u0439 \\u0441\\u043e\\u0431\\u0441\\u0442\\u0432\\u0435\\u043d\\u043d\\u043e\\u0441\\u0442\\u044c\\u044e. \\u0423\\u043f\\u0440\\u0430\\u0432\\u043b\\u0435\\u043d\\u0438\\u0435 \\u043a\\u0430\\u0447\\u0435\\u0441\\u0442\\u0432\\u043e\\u043c. \\u0426\\u0438\\u0444\\u0440\\u043e\\u0432\\u0430\\u044f \\u0442\\u0440\\u0430\\u043d\\u0441\\u0444\\u043e\\u0440\\u043c\\u0430\\u0446\\u0438\\u044f. \\u041e\\u0431\\u0449\\u0438\\u0435 \\u043f\\u043e\\u043d\\u044f\\u0442\\u0438\\u044f \\u0446\\u0438\\u0444\\u0440\\u043e\\u0432\\u043e\\u0439 \\u0442\\u0440\\u0430\\u043d\\u0441\\u0444\\u043e\\u0440\\u043c\\u0430\\u0446\\u0438\\u0438. \\u041e\\u0442\\u043b\\u0438\\u0447\\u0438\\u0435 \\u043f\\u043e\\u043d\\u044f\\u0442\\u0438\\u0439 \\u00ab\\u0446\\u0438\\u0444\\u0440\\u043e\\u0432\\u0438\\u0437\\u0430\\u0446\\u0438\\u044f\\u00bb \\u0438 \\u00ab\\u0446\\u0438\\u0444\\u0440\\u043e\\u0432\\u0430\\u044f \\u0442\\u0440\\u0430\\u043d\\u0441\\u0444\\u043e\\u0440\\u043c\\u0430\\u0446\\u0438\\u044f\\u00bb. \\u0423\\u043f\\u0440\\u0430\\u0432\\u043b\\u0435\\u043d\\u0438\\u0435 \\u0431\\u0438\\u0437\\u043d\\u0435\\u0441-\\u043f\\u0440\\u043e\\u0446\\u0435\\u0441\\u0441\\u0430\\u043c\\u0438, \\u0438\\u0445 \\u043e\\u0441\\u043e\\u0431\\u0435\\u043d\\u043d\\u043e\\u0441\\u0442\\u0438, \\u0444\\u043e\\u0440\\u043c\\u0430\\u043b\\u0438\\u0437\\u0430\\u0446\\u0438\\u044f, \\u043e\\u043f\\u0442\\u0438\\u043c\\u0438\\u0437\\u0430\\u0446\\u0438\\u044f \\u0438 \\u043f\\u0440\\u0438\\u043c\\u0435\\u0440\\u044b \\u0433\\u043e\\u0442\\u043e\\u0432\\u044b\\u0445 \\u0440\\u0435\\u0448\\u0435\\u043d\\u0438\\u0439: ERP-\\u0441\\u0438\\u0441\\u0442\\u0435\\u043c\\u0430, CRM-\\u0441\\u0438\\u0441\\u0442\\u0435\\u043c\\u0430 \\u0438 \\u0442.\\u0434.  \\u0410\\u0432\\u0442\\u043e\\u043c\\u0430\\u0442\\u0438\\u0437\\u0430\\u0446\\u0438\\u044f \\u043f\\u0440\\u043e\\u0438\\u0437\\u0432\\u043e\\u0434\\u0441\\u0442\\u0432\\u0430. \\u0418\\u043d\\u0434\\u0443\\u0441\\u0442\\u0440\\u0438\\u044f 4.0.  \\u0426\\u0438\\u0444\\u0440\\u043e\\u0432\\u044b\\u0435 \\u0434\\u0432\\u043e\\u0439\\u043d\\u0438\\u043a\\u0438. \\u0411\\u043b\\u043e\\u043a\\u0447\\u0435\\u0439\\u043d.. \\u041e\\u0441\\u043d\\u043e\\u0432\\u043d\\u044b\\u0435 \\u043f\\u043e\\u043d\\u044f\\u0442\\u0438\\u044f \\u0438\\u043d\\u043d\\u043e\\u0432\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u043e\\u0433\\u043e \\u043c\\u0430\\u0440\\u043a\\u0435\\u0442\\u0438\\u043d\\u0433\\u0430.  \\u041f\\u0440\\u043e\\u0434\\u0432\\u0438\\u0436\\u0435\\u043d\\u0438\\u0435 \\u043f\\u0440\\u043e\\u0434\\u0443\\u043a\\u0442\\u0430 \\u043d\\u0430 \\u0440\\u044b\\u043d\\u043a\\u0435. \\u0420\\u044b\\u043d\\u043e\\u043a B2B, B2C, B2G. \\u0421\\u043e\\u0437\\u0434\\u0430\\u043d\\u0438\\u0435 \\u043d\\u043e\\u0432\\u043e\\u0433\\u043e \\u0440\\u044b\\u043d\\u043a\\u0430. \\u041e\\u0441\\u043e\\u0431\\u0435\\u043d\\u043d\\u043e\\u0441\\u0442\\u0438 \\u043c\\u0430\\u0440\\u043a\\u0435\\u0442\\u0438\\u043d\\u0433\\u0430, \\u0441\\u0432\\u044f\\u0437\\u0430\\u043d\\u043d\\u044b\\u0435 \\u0441 \\u0432\\u044b\\u0441\\u043e\\u043a\\u0438\\u043c\\u0438 \\u0442\\u0435\\u0445\\u043d\\u043e\\u043b\\u043e\\u0433\\u0438\\u044f\\u043c\\u0438. \\u0418\\u043d\\u0441\\u0442\\u0440\\u0443\\u043c\\u0435\\u043d\\u0442\\u044b customerdevelopment\\u0438 productdevelopment\\u0432 \\u0438\\u043d\\u043d\\u043e\\u0432\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u043e\\u0439 \\u0441\\u0444\\u0435\\u0440\\u0435. \\u0420\\u0435\\u0430\\u043b\\u044c\\u043d\\u044b\\u0435 \\u0438\\u043d\\u0441\\u0442\\u0440\\u0443\\u043c\\u0435\\u043d\\u0442\\u044b \\u043f\\u0440\\u043e\\u0434\\u0432\\u0438\\u0436\\u0435\\u043d\\u0438\\u044f \\u0442\\u043e\\u0432\\u0430\\u0440\\u0430 \\u043d\\u0430 \\u0441\\u043e\\u0432\\u0440\\u0435\\u043c\\u0435\\u043d\\u043d\\u043e\\u043c \\u0440\\u044b\\u043d\\u043a\\u0435.. \\u041e\\u0441\\u043d\\u043e\\u0432\\u043d\\u044b\\u0435 \\u0444\\u043e\\u0440\\u043c\\u044b \\u0444\\u0438\\u043d\\u0430\\u043d\\u0441\\u043e\\u0432\\u043e\\u0439 \\u043e\\u0442\\u0447\\u0435\\u0442\\u043d\\u043e\\u0441\\u0442\\u0438: \\u0444\\u043e\\u0440\\u043c\\u0430 \\u043e \\u043f\\u0440\\u0438\\u0431\\u044b\\u043b\\u044f\\u0445 \\u0438 \\u0443\\u0431\\u044b\\u0442\\u043a\\u0430\\u0445, \\u0431\\u0430\\u043b\\u0430\\u043d\\u0441\\u0430\\u0445, \\u0434\\u0432\\u0438\\u0436\\u0435\\u043d\\u0438\\u0435 \\u0434\\u0435\\u043d\\u0435\\u0436\\u043d\\u044b\\u0445 \\u0441\\u0440\\u0435\\u0434\\u0441\\u0442\\u0432 \\u0438 \\u0442.\\u0434. \\u041e\\u0441\\u043d\\u043e\\u0432\\u044b \\u0444\\u0438\\u043d\\u0430\\u043d\\u0441\\u043e\\u0432\\u043e\\u0433\\u043e \\u043c\\u043e\\u0434\\u0435\\u043b\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u044f. \\u041e\\u0446\\u0435\\u043d\\u043a\\u0430 \\u0438\\u043d\\u0432\\u0435\\u0441\\u0442\\u0438\\u0446\\u0438\\u043e\\u043d\\u043d\\u043e\\u0439 \\u044d\\u0444\\u0444\\u0435\\u043a\\u0442\\u0438\\u0432\\u043d\\u043e\\u0441\\u0442\\u0438 \\u043f\\u0440\\u043e\\u0435\\u043a\\u0442\\u0430: ARR, DPB, NPV, IRR \\u0438 \\u0442.\\u0434.. \\u041f\\u0435\\u0440\\u0438\\u043e\\u0434 \\u043e\\u043a\\u0443\\u043f\\u0430\\u0435\\u043c\\u043e\\u0441\\u0442\\u0438. \\u0422\\u043e\\u0447\\u043a\\u0430 \\u0431\\u0435\\u0437\\u0443\\u0431\\u044b\\u0442\\u043e\\u0447\\u043d\\u043e\\u0441\\u0442\\u0438.. \\u041e\\u0441\\u043e\\u0431\\u0435\\u043d\\u043d\\u043e\\u0441\\u0442\\u0438 \\u0431\\u0438\\u0437\\u043d\\u0435\\u0441-\\u043c\\u043e\\u0434\\u0435\\u043b\\u0435\\u0439 \\u0432 \\u043f\\u0440\\u043e\\u0446\\u0435\\u0441\\u0441\\u0435 \\u043f\\u0440\\u043e\\u0438\\u0437\\u0432\\u043e\\u0434\\u0441\\u0442\\u0432\\u0430. \\u041c\\u0430\\u0441\\u0448\\u0442\\u0430\\u0431\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u0435 \\u0438 \\u0441\\u0435\\u0440\\u0442\\u0438\\u0444\\u0438\\u043a\\u0430\\u0446\\u0438\\u044f \\u043f\\u0440\\u043e\\u0438\\u0437\\u0432\\u043e\\u0434\\u0441\\u0442\\u0432\\u0430. \\u0410\\u0443\\u0434\\u0438\\u0442 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Tag Along.. \\u041f\\u0440\\u0438\\u043d\\u0446\\u0438\\u043f\\u044b \\u043e\\u0446\\u0435\\u043d\\u043a\\u0438 \\u043a\\u043e\\u043c\\u043f\\u0430\\u043d\\u0438\\u0438. \\u041f\\u0440\\u0438\\u043d\\u044f\\u0442\\u0438\\u0435 \\u0440\\u0435\\u0448\\u0435\\u043d\\u0438\\u044f \\u043e \\u043f\\u0440\\u043e\\u0434\\u0430\\u0436\\u0435 \\u043a\\u043e\\u043c\\u043f\\u0430\\u043d\\u0438\\u0438. \\u041e\\u0441\\u043d\\u043e\\u0432\\u043d\\u044b\\u0435 \\u0441\\u0442\\u0440\\u0430\\u0442\\u0435\\u0433\\u0438\\u0438 \\u0432\\u044b\\u0445\\u043e\\u0434\\u0430 \\u0438 \\u0438\\u0445 \\u044d\\u0442\\u0430\\u043f\\u044b: M&A, IPO. \\u0414\\u0438\\u0432\\u0438\\u0434\\u0435\\u043d\\u0434\\u043d\\u044b\\u0439 \\u043f\\u043e\\u0442\\u043e\\u043a. \\u041a\\u043b\\u044e\\u0447\\u0435\\u0432\\u044b\\u0435 \\u043a\\u043e\\u043d\\u0441\\u0443\\u043b\\u044c\\u0442\\u0430\\u043d\\u0442\\u044b \\u0438 \\u0438\\u0445 \\u0440\\u043e\\u043b\\u0438 \\u0432 \\u043f\\u0440\\u043e\\u0446\\u0435\\u0441\\u0441\\u0435 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\\u041f\\u043e\\u043c\\u0435\\u0445\\u043e\\u0443\\u0441\\u0442\\u043e\\u0439\\u0447\\u0438\\u0432\\u043e\\u0441\\u0442\\u044c. \\u0421\\u043a\\u0440\\u044b\\u0442\\u043d\\u043e\\u0441\\u0442\\u044c.. \\u041f\\u043e\\u043c\\u0435\\u0445\\u043e\\u0443\\u0441\\u0442\\u043e\\u0439\\u0447\\u0438\\u0432\\u043e\\u0435 (\\u043a\\u043e\\u0440\\u0440\\u0435\\u043a\\u0442\\u0438\\u0440\\u0443\\u044e\\u0449\\u0435\\u0435) \\u043a\\u043e\\u0434\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u0435.  \\u0413\\u0440\\u0443\\u043f\\u043f\\u043e\\u0432\\u044b\\u0435 \\u0441\\u0438\\u0441\\u0442\\u0435\\u043c\\u0430\\u0442\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0435 \\u043b\\u0438\\u043d\\u0435\\u0439\\u043d\\u044b\\u0435 \\u0431\\u043b\\u043e\\u0447\\u043d\\u044b\\u0435 \\u043a\\u043e\\u0434\\u044b. \\u041f\\u0440\\u0435\\u043e\\u0431\\u0440\\u0430\\u0437\\u043e\\u0432\\u0430\\u043d\\u0438\\u0435 \\u0447\\u0430\\u0441\\u0442\\u043e\\u0442\\u044b. \\u0421\\u0438\\u0433\\u043d\\u0430\\u043b\\u044b \\u0441 \\u0444\\u0430\\u0437\\u043e\\u0432\\u043e\\u0439 \\u043c\\u043e\\u0434\\u0443\\u043b\\u044f\\u0446\\u0438\\u0435\\u0439 \\u0433\\u0430\\u0440\\u043c\\u043e\\u043d\\u0438\\u0447\\u0435\\u0441\\u043a\\u043e\\u0433\\u043e \\u043d\\u0435\\u0441\\u0443\\u0449\\u0435\\u0433\\u043e  \\u043a\\u043e\\u043b\\u0435\\u0431\\u0430\\u043d\\u0438\\u044f. \\u0424\\u043e\\u0440\\u043c\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u0435 \\u0410\\u041c \\u0441\\u0438\\u0433\\u043d\\u0430\\u043b\\u043e\\u0432. \\u0424\\u043e\\u0440\\u043c\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u0435 \\u043c\\u0430\\u043d\\u0438\\u043f\\u0443\\u043b\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u043d\\u044b\\u0445 \\u0438 \\u0438\\u043c\\u043f\\u0443\\u043b\\u044c\\u0441\\u043d\\u043e-\\u043c\\u043e\\u0434\\u0443\\u043b\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u043d\\u044b\\u0445 \\u0441\\u0438\\u0433\\u043d\\u0430\\u043b\\u043e\\u0432. \\u0424\\u043e\\u0440\\u043c\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u0435 \\u0447\\u0430\\u0441\\u0442\\u043e\\u0442\\u043d\\u043e- \\u0438 \\u0444\\u0430\\u0437\\u043e- \\u043c\\u043e\\u0434\\u0443\\u043b\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u043d\\u044b\\u0445  \\u0441\\u0438\\u0433\\u043d\\u0430\\u043b\\u043e\\u0432. \\u0426\\u0438\\u0444\\u0440\\u043e\\u0432\\u0430\\u044f \\u043e\\u0431\\u0440\\u0430\\u0431\\u043e\\u0442\\u043a\\u0430 \\u0441\\u0438\\u0433\\u043d\\u0430\\u043b\\u043e\\u0432 \\u0432 \\u0447\\u0430\\u0441\\u0442\\u043e\\u0442\\u043d\\u043e\\u0439 \\u043e\\u0431\\u043b\\u0430\\u0441\\u0442\\u0438. \\u0427\\u0430\\u0441\\u0442\\u043e\\u0442\\u043d\\u043e-\\u043c\\u043e\\u0434\\u0443\\u043b\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u043d\\u044b\\u0435 \\u0441\\u0438\\u0433\\u043d\\u0430\\u043b\\u044b. \\u042d\\u043b\\u0435\\u043a\\u0442\\u0440\\u043e\\u0441\\u0432\\u044f\\u0437\\u044c. \\u042d\\u0442\\u0430\\u043f\\u044b \\u0440\\u0430\\u0437\\u0432\\u0438\\u0442\\u0438\\u044f \\u0441\\u0438\\u0441\\u0442\\u0435\\u043c \\u044d\\u043b\\u0435\\u043a\\u0442\\u0440\\u043e\\u0441\\u0432\\u044f\\u0437\\u0438. \\u0421\\u0438\\u0441\\u0442\\u0435\\u043c\\u0430 \\u043f\\u0435\\u0440\\u0435\\u0434\\u0430\\u0447\\u0438 \\u0438\\u043d\\u0444\\u043e\\u0440\\u043c\\u0430\\u0446\\u0438\\u0438. \\u041a\\u0430\\u043d\\u0430\\u043b \\u0441\\u0432\\u044f\\u0437\\u0438. \\u041c\\u043e\\u0434\\u0443\\u043b\\u044f\\u0442\\u043e\\u0440. \\u0414\\u0435\\u043c\\u043e\\u0434\\u0443\\u043b\\u044f\\u0442\\u043e\\u0440. \\u041f\\u0440\\u043e\\u043f\\u0443\\u0441\\u043a\\u043d\\u0430\\u044f \\u0441\\u043f\\u043e\\u0441\\u043e\\u0431\\u043d\\u043e\\u0441\\u0442\\u044c.. \\u042d\\u0444\\u0444\\u0435\\u043a\\u0442\\u0438\\u0432\\u043d\\u043e\\u0435 \\u0441\\u0442\\u0430\\u0442\\u0438\\u0441\\u0442\\u0438\\u0447\\u0435\\u0441\\u043a\\u043e\\u0435 \\u043a\\u043e\\u0434\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u0435 \\u0438\\u0441\\u0442\\u043e\\u0447\\u043d\\u0438\\u043a\\u043e\\u0432  \\u0434\\u0438\\u0441\\u043a\\u0440\\u0435\\u0442\\u043d\\u044b\\u0445 \\u0441\\u043e\\u043e\\u0431\\u0449\\u0435\\u043d\\u0438\\u0439. \\u041a\\u043e\\u0434 \\u0425\\u0430\\u0444\\u0444\\u043c\\u0430\\u043d\\u0430\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tokens\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tokensD\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embedding\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embeddingD\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 34
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Оценим, насколько семантически близки краткие описания дисциплин и темы в содержании. Для оценки воспользуемся косинусным сходством.\n",
+        "\n",
+        "$$K(X, Y) = \\frac{X \\cdot Y} {||X||*||Y||}$$"
+      ],
+      "metadata": {
+        "id": "zr_fKP9NnPiT"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "В `sklearn` функция для расчета косинусного сходства генерирует матрицу результатов, мы будем сравнивать всего два вектора, поэтому напишем свою обертку для функции из `sklearn` так, чтобы она возвращала одно числовое значение, а не матрицу."
+      ],
+      "metadata": {
+        "id": "PU-tUBu8Rhrq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics.pairwise import cosine_similarity as cs\n",
+        "\n",
+        "def cosine_similarity(vec1, vec2):\n",
+        "    # ваш код здесь"
+      ],
+      "metadata": {
+        "id": "uI1RtLu2sLDL"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# посчитаем косинусное сходство\n",
+        "df[\"desc_cont_sim\"] = df.apply(lambda x: cosine_similarity(x[\"embedding\"], x[\"embeddingD\"]), axis=1)"
+      ],
+      "metadata": {
+        "id": "J6mF6AjHsNhh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Оценим по графику распределение сходств для всех дисциплин. Должен получиться график, похожий на гистограмму ниже. Пунктиром обозначено медианное значение."
+      ],
+      "metadata": {
+        "id": "mQoZEGihAe_o"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ваш код здесь"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 457
+        },
+        "id": "9WKbW-J0sTWK",
+        "outputId": "89adfa4d-f9a1-441e-c22a-814a739fc4dd"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Векторизация Tiny BERT"
+      ],
+      "metadata": {
+        "id": "KADw-ECz-Oxq"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "В качестве предобученной модели воспользуемся Tiny BERT – https://huggingface.co/sergeyzh/rubert-tiny-turbo\n",
+        "\n",
+        "Она априори менее точная, чем базовый BERT, но строит эмбеддинги в разы быстрее."
+      ],
+      "metadata": {
+        "id": "B2bG4kR1DS8U"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# загружаем модель из библиотеки huggingface\n",
+        "\n",
+        "from sentence_transformers import SentenceTransformer\n",
+        "\n",
+        "bert_model = SentenceTransformer('sergeyzh/rubert-tiny-turbo')"
+      ],
+      "metadata": {
+        "id": "PscCFOor243l"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "> Если вы выполняете эту лабораторную локально, то возможны конфликты версии `sentence_transformers` и `tensorflow`, обычно решающиеся даунгрейдом `tensorflow`."
+      ],
+      "metadata": {
+        "id": "JndiZCXbEYzq"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Прежде чем формировать эмбеддинги, объединим список токенов в строку."
+      ],
+      "metadata": {
+        "id": "R9NSC7miErnM"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ваш код здесь\n",
+        "\n",
+        "# пример результата преобразования\n",
+        "df.sample(3, random_state=42)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 324
+        },
+        "id": "j_Pif01p3OWD",
+        "outputId": "77c0ad61-d588-468e-8d7d-4a709b0d761a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "       course_id                       course_title  \\\n",
+              "16112      17498  Создание технологического бизнеса   \n",
+              "2457        3002         Теория электрической связи   \n",
+              "1077        2736   Организация массовых мероприятий   \n",
+              "\n",
+              "                                             description  up_id  \\\n",
+              "16112  Цель курса - предоставить возможность магистра...   7198   \n",
+              "2457   Программа составлена в соответствии с требован...   6989   \n",
+              "1077   Данная дисциплина учит планировать мероприятие...   6875   \n",
+              "\n",
+              "                                                up_title qualification  \\\n",
+              "16112  Информационная безопасность / Information secu...        master   \n",
+              "2457    Программирование в инфокоммуникационных системах      bachelor   \n",
+              "1077             Управление технологическими инновациями      bachelor   \n",
+              "\n",
+              "       start_year                     module_name  \\\n",
+              "16112        2022  Наукоемкое предпринимательство   \n",
+              "2457         2020   Модуль обязательных дисциплин   \n",
+              "1077         2018            Модуль «Soft Skills»   \n",
+              "\n",
+              "                                                contents  \\\n",
+              "16112  Инновационный маркетинг. Корпоративные финансы...   \n",
+              "2457   Математические модели сигналов, помех и канало...   \n",
+              "1077   Особенности организации массовых мероприятий.....   \n",
+              "\n",
+              "                                                  tokens  \\\n",
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+              "1077   [особенность, организация, массовый, мероприят...   \n",
+              "\n",
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+              "1077   [данный, дисциплина, учить, планировать, мероп...   \n",
+              "\n",
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+              "2457   [0.17525887, -0.29766303, -0.02009153, 0.81608...   \n",
+              "1077   [-0.35568532, 0.014744152, 0.8801875, 0.582805...   \n",
+              "\n",
+              "                                              embeddingD  desc_cont_sim  \\\n",
+              "16112  [-0.0076024113, -0.12759566, 0.81738883, 0.547...          0.628   \n",
+              "2457   [0.71655047, 0.0473859, -0.46722662, 0.2992459...          0.454   \n",
+              "1077   [0.19902095, 0.32024252, 0.5574031, 0.7525074,...          0.704   \n",
+              "\n",
+              "                                             tokens_text  \\\n",
+              "16112  инновационный маркетинг корпоративный финансы ...   \n",
+              "2457   математический модель сигнал помеха канал связ...   \n",
+              "1077   особенность организация массовый мероприятие п...   \n",
+              "\n",
+              "                                            tokensD_text  \n",
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+              "1077   данный дисциплина учить планировать мероприяти...  "
+            ],
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+              "    </tr>\n",
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+              "      <td>Программа составлена в соответствии с требован...</td>\n",
+              "      <td>6989</td>\n",
+              "      <td>Программирование в инфокоммуникационных системах</td>\n",
+              "      <td>bachelor</td>\n",
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+              "    </tr>\n",
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+              "      <td>Данная дисциплина учит планировать мероприятие...</td>\n",
+              "      <td>6875</td>\n",
+              "      <td>Управление технологическими инновациями</td>\n",
+              "      <td>bachelor</td>\n",
+              "      <td>2018</td>\n",
+              "      <td>Модуль «Soft Skills»</td>\n",
+              "      <td>Особенности организации массовых мероприятий.....</td>\n",
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+              "\n",
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+              "\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
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+              "\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",
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+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b84f0056-9d31-4920-8425-1d334ced90c7');\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",
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+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "      border-bottom-color: var(--fill-color);\n",
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+              "\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",
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+              "        console.error('Error during call to suggestCharts:', error);\n",
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+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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\\u043f\\u043e\\u0434\\u0433\\u043e\\u0442\\u043e\\u0432\\u043a\\u0438. \\u0426\\u0435\\u043b\\u044c\\u044e \\u043e\\u0441\\u0432\\u043e\\u0435\\u043d\\u0438\\u044f \\u0434\\u0438\\u0441\\u0446\\u0438\\u043f\\u043b\\u0438\\u043d\\u044b \\u044f\\u0432\\u043b\\u044f\\u0435\\u0442\\u0441\\u044f \\u0434\\u043e\\u0441\\u0442\\u0438\\u0436\\u0435\\u043d\\u0438\\u0435 \\u0441\\u043b\\u0435\\u0434\\u0443\\u044e\\u0449\\u0438\\u0445 \\u0440\\u0435\\u0437\\u0443\\u043b\\u044c\\u0442\\u0430\\u0442\\u043e\\u0432 \\u043e\\u0431\\u0443\\u0447\\u0435\\u043d\\u0438\\u044f \\u043f\\u043e \\u0444\\u043e\\u0440\\u043c\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u044e \\u043a\\u043e\\u043c\\u043f\\u0435\\u0442\\u0435\\u043d\\u0446\\u0438\\u0439: \\n-\\u041e\\u041f\\u041a-3: \\u0441\\u043f\\u043e\\u0441\\u043e\\u0431\\u043d\\u043e\\u0441\\u0442\\u044c \\u0432\\u043b\\u0430\\u0434\\u0435\\u0442\\u044c \\u043e\\u0441\\u043d\\u043e\\u0432\\u043d\\u044b\\u043c\\u0438 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\\u043e\\u0431\\u043b\\u0430\\u0441\\u0442\\u0438 \\u0438\\u043d\\u0444\\u043e\\u043a\\u043e\\u043c\\u043c\\u0443\\u043d\\u0438\\u043a\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u044b\\u0445 \\u0442\\u0435\\u0445\\u043d\\u043e\\u043b\\u043e\\u0433\\u0438\\u0439 \\u0438 \\u0441\\u0438\\u0441\\u0442\\u0435\\u043c \\u0441\\u0432\\u044f\\u0437\\u0438 (\\u043d\\u043e\\u0440\\u043c\\u0430\\u0442\\u0438\\u0432\\u043d\\u044b\\u0435 \\u043f\\u0440\\u0430\\u0432\\u043e\\u0432\\u044b\\u0435 \\u0430\\u043a\\u0442\\u044b \\u0420\\u043e\\u0441\\u0441\\u0438\\u0439\\u0441\\u043a\\u043e\\u0439 \\u0424\\u0435\\u0434\\u0435\\u0440\\u0430\\u0446\\u0438\\u0438, \\u0442\\u0435\\u0445\\u043d\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0435 \\u0440\\u0435\\u0433\\u043b\\u0430\\u043c\\u0435\\u043d\\u0442\\u044b, \\u043c\\u0435\\u0436\\u0434\\u0443\\u043d\\u0430\\u0440\\u043e\\u0434\\u043d\\u044b\\u0435 \\u0438 \\u043d\\u0430\\u0446\\u0438\\u043e\\u043d\\u0430\\u043b\\u044c\\u043d\\u044b\\u0435 \\u0441\\u0442\\u0430\\u043d\\u0434\\u0430\\u0440\\u0442\\u044b, \\u0440\\u0435\\u043a\\u043e\\u043c\\u0435\\u043d\\u0434\\u0430\\u0446\\u0438\\u0438 \\u041c\\u0435\\u0436\\u0434\\u0443\\u043d\\u0430\\u0440\\u043e\\u0434\\u043d\\u043e\\u0433\\u043e \\u0441\\u043e\\u044e\\u0437\\u0430 \\u044d\\u043b\\u0435\\u043a\\u0442\\u0440\\u043e\\u0441\\u0432\\u044f\\u0437\\u0438):\\n\\u041f\\u041a-3: \\u0441\\u043f\\u043e\\u0441\\u043e\\u0431\\u043d\\u043e\\u0441\\u0442\\u044c \\u043e\\u0446\\u0435\\u043d\\u0438\\u0432\\u0430\\u0442\\u044c \\u0442\\u0435\\u0445\\u043d\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0435 \\u0432\\u043e\\u0437\\u043c\\u043e\\u0436\\u043d\\u043e\\u0441\\u0442\\u0438 \\u0438 \\u0432\\u044b\\u0440\\u0430\\u0431\\u0430\\u0442\\u044b\\u0432\\u0430\\u0442\\u044c \\u0440\\u0435\\u043a\\u043e\\u043c\\u0435\\u043d\\u0434\\u0430\\u0446\\u0438\\u0438 \\u043f\\u043e \\u043f\\u043e\\u0441\\u0442\\u0440\\u043e\\u0435\\u043d\\u0438\\u044e \\u0442\\u0435\\u043b\\u0435\\u043a\\u043e\\u043c\\u043c\\u0443\\u043d\\u0438\\u043a\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u044b\\u0445 \\u0441\\u0438\\u0441\\u0442\\u0435\\u043c \\u0438 \\u0441\\u0435\\u0442\\u0435\\u0439, \\u0438\\u0445 \\u044d\\u043b\\u0435\\u043c\\u0435\\u043d\\u0442\\u043e\\u0432 \\u0438 \\u0443\\u0441\\u0442\\u0440\\u043e\\u0439\\u0441\\u0442\\u0432.\",\n          \"\\u0414\\u0430\\u043d\\u043d\\u0430\\u044f \\u0434\\u0438\\u0441\\u0446\\u0438\\u043f\\u043b\\u0438\\u043d\\u0430 \\u0443\\u0447\\u0438\\u0442 \\u043f\\u043b\\u0430\\u043d\\u0438\\u0440\\u043e\\u0432\\u0430\\u0442\\u044c \\u043c\\u0435\\u0440\\u043e\\u043f\\u0440\\u0438\\u044f\\u0442\\u0438\\u0435, \\u0441\\u043e\\u0437\\u0434\\u0430\\u0432\\u0430\\u0442\\u044c \\u043a\\u043e\\u043d\\u0446\\u0435\\u043f\\u0446\\u0438\\u044e, \\u043f\\u0440\\u0435\\u0434\\u0443\\u0441\\u043c\\u0430\\u0442\\u0440\\u0438\\u0432\\u0430\\u0442\\u044c \\u0440\\u0438\\u0441\\u043a\\u0438 \\u0438 \\u043c\\u0435\\u0442\\u043e\\u0434\\u044b \\u0440\\u0435\\u0430\\u0433\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u044f. \\u0412\\u043e \\u0432\\u0440\\u0435\\u043c\\u044f \\u0437\\u0430\\u043d\\u044f\\u0442\\u0438\\u0439 \\u043e\\u0431\\u0443\\u0447\\u0430\\u044e\\u0449\\u0438\\u0435\\u0441\\u044f \\u0443\\u0437\\u043d\\u0430\\u044e\\u0442 \\u043f\\u0440\\u043e \\u0432\\u0438\\u0437\\u0443\\u0430\\u043b\\u044c\\u043d\\u0443\\u044e \\u043a\\u043e\\u043d\\u0446\\u0435\\u043f\\u0446\\u0438\\u044e \\u0438 \\u043c\\u0435\\u0434\\u0430\\u0441\\u043e\\u043f\\u0440\\u043e\\u0432\\u043e\\u0436\\u0434\\u0435\\u043d\\u0438\\u0435 \\u043c\\u0435\\u0440\\u043e\\u043f\\u0440\\u0438\\u044f\\u0442\\u0438\\u044f. \\u041f\\u043e\\u043b\\u0443\\u0447\\u0430\\u0442 \\u043f\\u0440\\u0430\\u043a\\u0442\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0435 \\u043d\\u0430\\u0432\\u044b\\u043a\\u0438 \\u043f\\u043e \\u043e\\u0446\\u0435\\u043d\\u043a\\u0435 \\u044d\\u0444\\u0444\\u0435\\u043a\\u0442\\u0438\\u0432\\u043d\\u043e\\u0441\\u0442\\u0438. \\n\\u0417\\u0430\\u043d\\u044f\\u0442\\u0438\\u044f \\u043f\\u0440\\u043e\\u0445\\u043e\\u0434\\u044f\\u0442 \\u0441 \\u0440\\u0430\\u0437\\u043b\\u0438\\u0447\\u043d\\u044b\\u043c\\u0438 \\u043f\\u0440\\u0435\\u043f\\u043e\\u0434\\u0430\\u0432\\u0430\\u0442\\u0435\\u043b\\u044f\\u043c\\u0438 \\u0438\\u0437  setters education \\u0438 evention, \\u043a\\u043e\\u0442\\u043e\\u0440\\u044b\\u0435 \\u043d\\u0430 \\u0441\\u0432\\u043e\\u0435\\u043c \\u043e\\u043f\\u044b\\u0442\\u0435 \\u0440\\u0430\\u0441\\u0441\\u043a\\u0430\\u0437\\u044b\\u0432\\u0430\\u044e\\u0442 \\u043f\\u0440\\u043e \\u043e\\u0436\\u0438\\u0434\\u0430\\u043d\\u0438\\u044f \\u0438 \\u0440\\u0435\\u0430\\u043b\\u044c\\u043d\\u043e\\u0441\\u0442\\u044c \\u0440\\u043e\\u043b\\u0438 \\\"\\u043e\\u0440\\u0433\\u0430\\u043d\\u0438\\u0437\\u0430\\u0442\\u043e\\u0440\\u0430\\\". \\n\\u0414\\u0438\\u0441\\u0446\\u0438\\u043f\\u043b\\u0438\\u043d\\u0430 \\u043f\\u043e\\u0434\\u0445\\u043e\\u0434\\u0438\\u0442\\u044c \\u0434\\u043b\\u044f \\u0442\\u0435\\u0445, \\u043a\\u0442\\u043e \\u043e\\u0440\\u0433\\u0430\\u043d\\u0438\\u0437\\u0443\\u0435\\u0442 \\u043c\\u0435\\u0440\\u043e\\u043f\\u0440\\u0438\\u044f\\u0442\\u0438\\u044f \\u0432 \\u0443\\u043d\\u0438\\u0432\\u0435\\u0440\\u0441\\u0438\\u0442\\u0435\\u0442\\u0435 \\u0438 \\u0437\\u0430 \\u0435\\u0433\\u043e \\u043f\\u0440\\u0435\\u0434\\u0435\\u043b\\u0430\\u043c\\u0438.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"up_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 163,\n        \"min\": 6875,\n        \"max\": 7198,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          7198,\n          6989,\n          6875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"up_title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\\u0418\\u043d\\u0444\\u043e\\u0440\\u043c\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u0430\\u044f \\u0431\\u0435\\u0437\\u043e\\u043f\\u0430\\u0441\\u043d\\u043e\\u0441\\u0442\\u044c / Information security\",\n          \"\\u041f\\u0440\\u043e\\u0433\\u0440\\u0430\\u043c\\u043c\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u0435 \\u0432 \\u0438\\u043d\\u0444\\u043e\\u043a\\u043e\\u043c\\u043c\\u0443\\u043d\\u0438\\u043a\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u044b\\u0445 \\u0441\\u0438\\u0441\\u0442\\u0435\\u043c\\u0430\\u0445\",\n          \"\\u0423\\u043f\\u0440\\u0430\\u0432\\u043b\\u0435\\u043d\\u0438\\u0435 \\u0442\\u0435\\u0445\\u043d\\u043e\\u043b\\u043e\\u0433\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u043c\\u0438 \\u0438\\u043d\\u043d\\u043e\\u0432\\u0430\\u0446\\u0438\\u044f\\u043c\\u0438\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"qualification\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"bachelor\",\n          \"master\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2018,\n        \"max\": 2022,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2022,\n          2020\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"module_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\\u041d\\u0430\\u0443\\u043a\\u043e\\u0435\\u043c\\u043a\\u043e\\u0435 \\u043f\\u0440\\u0435\\u0434\\u043f\\u0440\\u0438\\u043d\\u0438\\u043c\\u0430\\u0442\\u0435\\u043b\\u044c\\u0441\\u0442\\u0432\\u043e\",\n          \"\\u041c\\u043e\\u0434\\u0443\\u043b\\u044c \\u043e\\u0431\\u044f\\u0437\\u0430\\u0442\\u0435\\u043b\\u044c\\u043d\\u044b\\u0445 \\u0434\\u0438\\u0441\\u0446\\u0438\\u043f\\u043b\\u0438\\u043d\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"contents\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\\u0418\\u043d\\u043d\\u043e\\u0432\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u044b\\u0439 \\u043c\\u0430\\u0440\\u043a\\u0435\\u0442\\u0438\\u043d\\u0433. \\u041a\\u043e\\u0440\\u043f\\u043e\\u0440\\u0430\\u0442\\u0438\\u0432\\u043d\\u044b\\u0435 \\u0444\\u0438\\u043d\\u0430\\u043d\\u0441\\u044b. \\u041e\\u0441\\u043d\\u043e\\u0432\\u043d\\u044b\\u0435 \\u043a\\u043e\\u043d\\u0446\\u0435\\u043f\\u0446\\u0438\\u0438 \\u0432 \\u0441\\u043e\\u0437\\u0434\\u0430\\u043d\\u0438\\u0438 \\u0442\\u0435\\u0445\\u043d\\u043e\\u043b\\u043e\\u0433\\u0438\\u0447\\u0435\\u0441\\u043a\\u043e\\u0433\\u043e \\u0431\\u0438\\u0437\\u043d\\u0435\\u0441\\u0430. \\u0420\\u043e\\u043b\\u0438 \\u0432 \\u043f\\u0440\\u043e\\u0435\\u043a\\u0442\\u0435 \\u0438 \\u0440\\u0430\\u0441\\u043f\\u0440\\u0435\\u0434\\u0435\\u043b\\u0435\\u043d\\u0438\\u0435 \\u0434\\u043e\\u043b\\u0435\\u0439 \\u043c\\u0435\\u0436\\u0434\\u0443 \\u0443\\u0447\\u0430\\u0441\\u0442\\u043d\\u0438\\u043a\\u0430\\u043c\\u0438. \\u0421\\u0442\\u0440\\u0430\\u0442\\u0435\\u0433\\u0438\\u044f \\u0432\\u044b\\u0445\\u043e\\u0434\\u0430. \\u0423\\u043f\\u0440\\u0430\\u0432\\u043b\\u0435\\u043d\\u0438\\u0435 \\u0438\\u043d\\u0442\\u0435\\u043b\\u043b\\u0435\\u043a\\u0442\\u0443\\u0430\\u043b\\u044c\\u043d\\u043e\\u0439 \\u0441\\u043e\\u0431\\u0441\\u0442\\u0432\\u0435\\u043d\\u043d\\u043e\\u0441\\u0442\\u044c\\u044e. \\u0423\\u043f\\u0440\\u0430\\u0432\\u043b\\u0435\\u043d\\u0438\\u0435 \\u043a\\u0430\\u0447\\u0435\\u0441\\u0442\\u0432\\u043e\\u043c. \\u0426\\u0438\\u0444\\u0440\\u043e\\u0432\\u0430\\u044f \\u0442\\u0440\\u0430\\u043d\\u0441\\u0444\\u043e\\u0440\\u043c\\u0430\\u0446\\u0438\\u044f. \\u041e\\u0431\\u0449\\u0438\\u0435 \\u043f\\u043e\\u043d\\u044f\\u0442\\u0438\\u044f \\u0446\\u0438\\u0444\\u0440\\u043e\\u0432\\u043e\\u0439 \\u0442\\u0440\\u0430\\u043d\\u0441\\u0444\\u043e\\u0440\\u043c\\u0430\\u0446\\u0438\\u0438. \\u041e\\u0442\\u043b\\u0438\\u0447\\u0438\\u0435 \\u043f\\u043e\\u043d\\u044f\\u0442\\u0438\\u0439 \\u00ab\\u0446\\u0438\\u0444\\u0440\\u043e\\u0432\\u0438\\u0437\\u0430\\u0446\\u0438\\u044f\\u00bb \\u0438 \\u00ab\\u0446\\u0438\\u0444\\u0440\\u043e\\u0432\\u0430\\u044f \\u0442\\u0440\\u0430\\u043d\\u0441\\u0444\\u043e\\u0440\\u043c\\u0430\\u0446\\u0438\\u044f\\u00bb. \\u0423\\u043f\\u0440\\u0430\\u0432\\u043b\\u0435\\u043d\\u0438\\u0435 \\u0431\\u0438\\u0437\\u043d\\u0435\\u0441-\\u043f\\u0440\\u043e\\u0446\\u0435\\u0441\\u0441\\u0430\\u043c\\u0438, \\u0438\\u0445 \\u043e\\u0441\\u043e\\u0431\\u0435\\u043d\\u043d\\u043e\\u0441\\u0442\\u0438, \\u0444\\u043e\\u0440\\u043c\\u0430\\u043b\\u0438\\u0437\\u0430\\u0446\\u0438\\u044f, \\u043e\\u043f\\u0442\\u0438\\u043c\\u0438\\u0437\\u0430\\u0446\\u0438\\u044f \\u0438 \\u043f\\u0440\\u0438\\u043c\\u0435\\u0440\\u044b \\u0433\\u043e\\u0442\\u043e\\u0432\\u044b\\u0445 \\u0440\\u0435\\u0448\\u0435\\u043d\\u0438\\u0439: ERP-\\u0441\\u0438\\u0441\\u0442\\u0435\\u043c\\u0430, CRM-\\u0441\\u0438\\u0441\\u0442\\u0435\\u043c\\u0430 \\u0438 \\u0442.\\u0434.  \\u0410\\u0432\\u0442\\u043e\\u043c\\u0430\\u0442\\u0438\\u0437\\u0430\\u0446\\u0438\\u044f \\u043f\\u0440\\u043e\\u0438\\u0437\\u0432\\u043e\\u0434\\u0441\\u0442\\u0432\\u0430. \\u0418\\u043d\\u0434\\u0443\\u0441\\u0442\\u0440\\u0438\\u044f 4.0.  \\u0426\\u0438\\u0444\\u0440\\u043e\\u0432\\u044b\\u0435 \\u0434\\u0432\\u043e\\u0439\\u043d\\u0438\\u043a\\u0438. \\u0411\\u043b\\u043e\\u043a\\u0447\\u0435\\u0439\\u043d.. \\u041e\\u0441\\u043d\\u043e\\u0432\\u043d\\u044b\\u0435 \\u043f\\u043e\\u043d\\u044f\\u0442\\u0438\\u044f \\u0438\\u043d\\u043d\\u043e\\u0432\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u043e\\u0433\\u043e \\u043c\\u0430\\u0440\\u043a\\u0435\\u0442\\u0438\\u043d\\u0433\\u0430.  \\u041f\\u0440\\u043e\\u0434\\u0432\\u0438\\u0436\\u0435\\u043d\\u0438\\u0435 \\u043f\\u0440\\u043e\\u0434\\u0443\\u043a\\u0442\\u0430 \\u043d\\u0430 \\u0440\\u044b\\u043d\\u043a\\u0435. \\u0420\\u044b\\u043d\\u043e\\u043a B2B, B2C, B2G. \\u0421\\u043e\\u0437\\u0434\\u0430\\u043d\\u0438\\u0435 \\u043d\\u043e\\u0432\\u043e\\u0433\\u043e \\u0440\\u044b\\u043d\\u043a\\u0430. \\u041e\\u0441\\u043e\\u0431\\u0435\\u043d\\u043d\\u043e\\u0441\\u0442\\u0438 \\u043c\\u0430\\u0440\\u043a\\u0435\\u0442\\u0438\\u043d\\u0433\\u0430, \\u0441\\u0432\\u044f\\u0437\\u0430\\u043d\\u043d\\u044b\\u0435 \\u0441 \\u0432\\u044b\\u0441\\u043e\\u043a\\u0438\\u043c\\u0438 \\u0442\\u0435\\u0445\\u043d\\u043e\\u043b\\u043e\\u0433\\u0438\\u044f\\u043c\\u0438. \\u0418\\u043d\\u0441\\u0442\\u0440\\u0443\\u043c\\u0435\\u043d\\u0442\\u044b customerdevelopment\\u0438 productdevelopment\\u0432 \\u0438\\u043d\\u043d\\u043e\\u0432\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u043e\\u0439 \\u0441\\u0444\\u0435\\u0440\\u0435. \\u0420\\u0435\\u0430\\u043b\\u044c\\u043d\\u044b\\u0435 \\u0438\\u043d\\u0441\\u0442\\u0440\\u0443\\u043c\\u0435\\u043d\\u0442\\u044b \\u043f\\u0440\\u043e\\u0434\\u0432\\u0438\\u0436\\u0435\\u043d\\u0438\\u044f \\u0442\\u043e\\u0432\\u0430\\u0440\\u0430 \\u043d\\u0430 \\u0441\\u043e\\u0432\\u0440\\u0435\\u043c\\u0435\\u043d\\u043d\\u043e\\u043c \\u0440\\u044b\\u043d\\u043a\\u0435.. \\u041e\\u0441\\u043d\\u043e\\u0432\\u043d\\u044b\\u0435 \\u0444\\u043e\\u0440\\u043c\\u044b \\u0444\\u0438\\u043d\\u0430\\u043d\\u0441\\u043e\\u0432\\u043e\\u0439 \\u043e\\u0442\\u0447\\u0435\\u0442\\u043d\\u043e\\u0441\\u0442\\u0438: \\u0444\\u043e\\u0440\\u043c\\u0430 \\u043e \\u043f\\u0440\\u0438\\u0431\\u044b\\u043b\\u044f\\u0445 \\u0438 \\u0443\\u0431\\u044b\\u0442\\u043a\\u0430\\u0445, \\u0431\\u0430\\u043b\\u0430\\u043d\\u0441\\u0430\\u0445, \\u0434\\u0432\\u0438\\u0436\\u0435\\u043d\\u0438\\u0435 \\u0434\\u0435\\u043d\\u0435\\u0436\\u043d\\u044b\\u0445 \\u0441\\u0440\\u0435\\u0434\\u0441\\u0442\\u0432 \\u0438 \\u0442.\\u0434. \\u041e\\u0441\\u043d\\u043e\\u0432\\u044b \\u0444\\u0438\\u043d\\u0430\\u043d\\u0441\\u043e\\u0432\\u043e\\u0433\\u043e \\u043c\\u043e\\u0434\\u0435\\u043b\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u044f. \\u041e\\u0446\\u0435\\u043d\\u043a\\u0430 \\u0438\\u043d\\u0432\\u0435\\u0441\\u0442\\u0438\\u0446\\u0438\\u043e\\u043d\\u043d\\u043e\\u0439 \\u044d\\u0444\\u0444\\u0435\\u043a\\u0442\\u0438\\u0432\\u043d\\u043e\\u0441\\u0442\\u0438 \\u043f\\u0440\\u043e\\u0435\\u043a\\u0442\\u0430: ARR, DPB, NPV, IRR \\u0438 \\u0442.\\u0434.. \\u041f\\u0435\\u0440\\u0438\\u043e\\u0434 \\u043e\\u043a\\u0443\\u043f\\u0430\\u0435\\u043c\\u043e\\u0441\\u0442\\u0438. \\u0422\\u043e\\u0447\\u043a\\u0430 \\u0431\\u0435\\u0437\\u0443\\u0431\\u044b\\u0442\\u043e\\u0447\\u043d\\u043e\\u0441\\u0442\\u0438.. \\u041e\\u0441\\u043e\\u0431\\u0435\\u043d\\u043d\\u043e\\u0441\\u0442\\u0438 \\u0431\\u0438\\u0437\\u043d\\u0435\\u0441-\\u043c\\u043e\\u0434\\u0435\\u043b\\u0435\\u0439 \\u0432 \\u043f\\u0440\\u043e\\u0446\\u0435\\u0441\\u0441\\u0435 \\u043f\\u0440\\u043e\\u0438\\u0437\\u0432\\u043e\\u0434\\u0441\\u0442\\u0432\\u0430. \\u041c\\u0430\\u0441\\u0448\\u0442\\u0430\\u0431\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u0435 \\u0438 \\u0441\\u0435\\u0440\\u0442\\u0438\\u0444\\u0438\\u043a\\u0430\\u0446\\u0438\\u044f \\u043f\\u0440\\u043e\\u0438\\u0437\\u0432\\u043e\\u0434\\u0441\\u0442\\u0432\\u0430. \\u0410\\u0443\\u0434\\u0438\\u0442 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Tag Along.. \\u041f\\u0440\\u0438\\u043d\\u0446\\u0438\\u043f\\u044b \\u043e\\u0446\\u0435\\u043d\\u043a\\u0438 \\u043a\\u043e\\u043c\\u043f\\u0430\\u043d\\u0438\\u0438. \\u041f\\u0440\\u0438\\u043d\\u044f\\u0442\\u0438\\u0435 \\u0440\\u0435\\u0448\\u0435\\u043d\\u0438\\u044f \\u043e \\u043f\\u0440\\u043e\\u0434\\u0430\\u0436\\u0435 \\u043a\\u043e\\u043c\\u043f\\u0430\\u043d\\u0438\\u0438. \\u041e\\u0441\\u043d\\u043e\\u0432\\u043d\\u044b\\u0435 \\u0441\\u0442\\u0440\\u0430\\u0442\\u0435\\u0433\\u0438\\u0438 \\u0432\\u044b\\u0445\\u043e\\u0434\\u0430 \\u0438 \\u0438\\u0445 \\u044d\\u0442\\u0430\\u043f\\u044b: M&A, IPO. \\u0414\\u0438\\u0432\\u0438\\u0434\\u0435\\u043d\\u0434\\u043d\\u044b\\u0439 \\u043f\\u043e\\u0442\\u043e\\u043a. \\u041a\\u043b\\u044e\\u0447\\u0435\\u0432\\u044b\\u0435 \\u043a\\u043e\\u043d\\u0441\\u0443\\u043b\\u044c\\u0442\\u0430\\u043d\\u0442\\u044b \\u0438 \\u0438\\u0445 \\u0440\\u043e\\u043b\\u0438 \\u0432 \\u043f\\u0440\\u043e\\u0446\\u0435\\u0441\\u0441\\u0435 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\\u043f\\u043a- \\u0441\\u043f\\u043e\\u0441\\u043e\\u0431\\u043d\\u043e\\u0441\\u0442\\u044c \\u043e\\u0446\\u0435\\u043d\\u0438\\u0432\\u0430\\u0442\\u044c \\u0442\\u0435\\u0445\\u043d\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0439 \\u0432\\u043e\\u0437\\u043c\\u043e\\u0436\\u043d\\u043e\\u0441\\u0442\\u044c \\u0432\\u044b\\u0440\\u0430\\u0431\\u0430\\u0442\\u044b\\u0432\\u0430\\u0442\\u044c \\u0440\\u0435\\u043a\\u043e\\u043c\\u0435\\u043d\\u0434\\u0430\\u0446\\u0438\\u044f \\u043f\\u043e\\u0441\\u0442\\u0440\\u043e\\u0435\\u043d\\u0438\\u0435 \\u0442\\u0435\\u043b\\u0435\\u043a\\u043e\\u043c\\u043c\\u0443\\u043d\\u0438\\u043a\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u044b\\u0439 \\u0441\\u0438\\u0441\\u0442\\u0435\\u043c\\u0430 \\u0441\\u0435\\u0442\\u044c \\u044d\\u043b\\u0435\\u043c\\u0435\\u043d\\u0442 \\u0443\\u0441\\u0442\\u0440\\u043e\\u0439\\u0441\\u0442\\u0432\\u043e\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 66
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Чтобы не генерировать несколько раз одинаковые векторы (в данных много неуникальных описаний и содеражний), оставим только уникальные, а затем присоединим их в исходную таблицу. Уникальных записей в разрезе описания и содержания около 2500.\n",
+        "\n",
+        ">`tqdm.auto` позволяет в удобном формате добавлять прогресс-бар к некоторым методам `pandas` – например, `apply`, и получать метод `progress_apply`."
+      ],
+      "metadata": {
+        "id": "butHX_I6E834"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from tqdm.auto import tqdm\n",
+        "tqdm.pandas()\n",
+        "\n",
+        "# ваш код здесь"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 130,
+          "referenced_widgets": [
+            "4c2f90393e754b99b9ecf7a928e68e64",
+            "3f367c0f31b24a5cae775733d94e210b",
+            "bb1944e5fa8947bebeb35cebe0341aaf",
+            "4c8a51e701254c3998ff1820428156bb",
+            "4f8392a7f13b47b1a79c6d9f2d72a17a",
+            "9c436921130e423a8452c90a63d69ba9",
+            "ad32d5389f99438d98f3768304471fb4",
+            "1ddecd8002f744d4961fd8d772ac5415",
+            "aa831150a9c643039ed358fbc3f93a99",
+            "692ccf0e93e7425db6afb8d8a3600cd7",
+            "3cd2ced22f4d49afba1550ec6867229c",
+            "b06204f73a9644a2928ecd343b31b35c",
+            "948227983048496b95f0b2972e174bc4",
+            "049fcfe0793846b896bafad2a78eb845",
+            "cc05bea318d14e13be8ebff467cc9187",
+            "d6a18652c6b3476f9b563220714f519c",
+            "b6b1b387f16047919abc8e250c2fc5d8",
+            "18d3652f585e4ce791adf7bc07bf5758",
+            "1dad4f06896145a4bc23b5fb80ca5115",
+            "c0c8c1a340104e60853e64bf40215ea7",
+            "8857dee2b47b4e10a24f521d9cc2d7aa",
+            "adacd35e96eb4bc584dad28690b450df"
+          ]
+        },
+        "id": "abTWJEeL3elm",
+        "outputId": "e1d4084a-ce9e-46ff-dace-6f602a86a1bd"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(2221, 1) (2392, 1)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/2392 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "4c2f90393e754b99b9ecf7a928e68e64"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Contents vectorized!\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/2221 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "b06204f73a9644a2928ecd343b31b35c"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Description vectorized!\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Теперь присоедините полученную таблицу в исходный датафрейм."
+      ],
+      "metadata": {
+        "id": "lKk2aXq_F8mH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ваш код здесь\n",
+        "\n",
+        "# пример результата объединения\n",
+        "print(df.shape)\n",
+        "df.sample(3, random_state=42)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 341
+        },
+        "id": "pmXxf3eJ4o0a",
+        "outputId": "b27335f7-c3d1-400d-ed22-c83d98e24524"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(20502, 18)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "       course_id                       course_title  \\\n",
+              "16112      17498  Создание технологического бизнеса   \n",
+              "2457        3002         Теория электрической связи   \n",
+              "1077        2736   Организация массовых мероприятий   \n",
+              "\n",
+              "                                             description  up_id  \\\n",
+              "16112  Цель курса - предоставить возможность магистра...   7198   \n",
+              "2457   Программа составлена в соответствии с требован...   6989   \n",
+              "1077   Данная дисциплина учит планировать мероприятие...   6875   \n",
+              "\n",
+              "                                                up_title qualification  \\\n",
+              "16112  Информационная безопасность / Information secu...        master   \n",
+              "2457    Программирование в инфокоммуникационных системах      bachelor   \n",
+              "1077             Управление технологическими инновациями      bachelor   \n",
+              "\n",
+              "       start_year                     module_name  \\\n",
+              "16112        2022  Наукоемкое предпринимательство   \n",
+              "2457         2020   Модуль обязательных дисциплин   \n",
+              "1077         2018            Модуль «Soft Skills»   \n",
+              "\n",
+              "                                                contents  \\\n",
+              "16112  Инновационный маркетинг. Корпоративные финансы...   \n",
+              "2457   Математические модели сигналов, помех и канало...   \n",
+              "1077   Особенности организации массовых мероприятий.....   \n",
+              "\n",
+              "                                                  tokens  \\\n",
+              "16112  [инновационный, маркетинг, корпоративный, фина...   \n",
+              "2457   [математический, модель, сигнал, помеха, канал...   \n",
+              "1077   [особенность, организация, массовый, мероприят...   \n",
+              "\n",
+              "                                                 tokensD  \\\n",
+              "16112  [цель, курс, -, предоставлять, возможность, ма...   \n",
+              "2457   [программа, составлять, соответствие, требован...   \n",
+              "1077   [данный, дисциплина, учить, планировать, мероп...   \n",
+              "\n",
+              "                                               embedding  \\\n",
+              "16112  [0.1883952, -0.20439114, 0.25310403, -0.139506...   \n",
+              "2457   [0.17525887, -0.29766303, -0.02009153, 0.81608...   \n",
+              "1077   [-0.35568532, 0.014744152, 0.8801875, 0.582805...   \n",
+              "\n",
+              "                                              embeddingD  desc_cont_sim  \\\n",
+              "16112  [-0.0076024113, -0.12759566, 0.81738883, 0.547...          0.628   \n",
+              "2457   [0.71655047, 0.0473859, -0.46722662, 0.2992459...          0.454   \n",
+              "1077   [0.19902095, 0.32024252, 0.5574031, 0.7525074,...          0.704   \n",
+              "\n",
+              "                                             tokens_text  \\\n",
+              "16112  инновационный маркетинг корпоративный финансы ...   \n",
+              "2457   математический модель сигнал помеха канал связ...   \n",
+              "1077   особенность организация массовый мероприятие п...   \n",
+              "\n",
+              "                                            tokensD_text  \\\n",
+              "16112  цель курс - предоставлять возможность магистра...   \n",
+              "2457   программа составлять соответствие требование о...   \n",
+              "1077   данный дисциплина учить планировать мероприяти...   \n",
+              "\n",
+              "                                          bert_embedding  \\\n",
+              "16112  [-0.018844858, -0.022349093, 0.018487176, -0.0...   \n",
+              "2457   [-0.013909067, 0.008230449, 0.056430105, -0.00...   \n",
+              "1077   [0.017201137, -0.004298017, 0.012148312, -0.02...   \n",
+              "\n",
+              "                                         bert_embeddingD  \n",
+              "16112  [0.027150301, -0.029449247, 0.022479674, 0.021...  \n",
+              "2457   [0.028882269, -0.013100322, 0.014136976, -0.00...  \n",
+              "1077   [0.035781205, -0.0101707755, 0.0017927813, 0.0...  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-70c8992a-81c7-4932-942a-1b6bfb909059\" 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>course_id</th>\n",
+              "      <th>course_title</th>\n",
+              "      <th>description</th>\n",
+              "      <th>up_id</th>\n",
+              "      <th>up_title</th>\n",
+              "      <th>qualification</th>\n",
+              "      <th>start_year</th>\n",
+              "      <th>module_name</th>\n",
+              "      <th>contents</th>\n",
+              "      <th>tokens</th>\n",
+              "      <th>tokensD</th>\n",
+              "      <th>embedding</th>\n",
+              "      <th>embeddingD</th>\n",
+              "      <th>desc_cont_sim</th>\n",
+              "      <th>tokens_text</th>\n",
+              "      <th>tokensD_text</th>\n",
+              "      <th>bert_embedding</th>\n",
+              "      <th>bert_embeddingD</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>16112</th>\n",
+              "      <td>17498</td>\n",
+              "      <td>Создание технологического бизнеса</td>\n",
+              "      <td>Цель курса - предоставить возможность магистра...</td>\n",
+              "      <td>7198</td>\n",
+              "      <td>Информационная безопасность / Information secu...</td>\n",
+              "      <td>master</td>\n",
+              "      <td>2022</td>\n",
+              "      <td>Наукоемкое предпринимательство</td>\n",
+              "      <td>Инновационный маркетинг. Корпоративные финансы...</td>\n",
+              "      <td>[инновационный, маркетинг, корпоративный, фина...</td>\n",
+              "      <td>[цель, курс, -, предоставлять, возможность, ма...</td>\n",
+              "      <td>[0.1883952, -0.20439114, 0.25310403, -0.139506...</td>\n",
+              "      <td>[-0.0076024113, -0.12759566, 0.81738883, 0.547...</td>\n",
+              "      <td>0.628</td>\n",
+              "      <td>инновационный маркетинг корпоративный финансы ...</td>\n",
+              "      <td>цель курс - предоставлять возможность магистра...</td>\n",
+              "      <td>[-0.018844858, -0.022349093, 0.018487176, -0.0...</td>\n",
+              "      <td>[0.027150301, -0.029449247, 0.022479674, 0.021...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2457</th>\n",
+              "      <td>3002</td>\n",
+              "      <td>Теория электрической связи</td>\n",
+              "      <td>Программа составлена в соответствии с требован...</td>\n",
+              "      <td>6989</td>\n",
+              "      <td>Программирование в инфокоммуникационных системах</td>\n",
+              "      <td>bachelor</td>\n",
+              "      <td>2020</td>\n",
+              "      <td>Модуль обязательных дисциплин</td>\n",
+              "      <td>Математические модели сигналов, помех и канало...</td>\n",
+              "      <td>[математический, модель, сигнал, помеха, канал...</td>\n",
+              "      <td>[программа, составлять, соответствие, требован...</td>\n",
+              "      <td>[0.17525887, -0.29766303, -0.02009153, 0.81608...</td>\n",
+              "      <td>[0.71655047, 0.0473859, -0.46722662, 0.2992459...</td>\n",
+              "      <td>0.454</td>\n",
+              "      <td>математический модель сигнал помеха канал связ...</td>\n",
+              "      <td>программа составлять соответствие требование о...</td>\n",
+              "      <td>[-0.013909067, 0.008230449, 0.056430105, -0.00...</td>\n",
+              "      <td>[0.028882269, -0.013100322, 0.014136976, -0.00...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1077</th>\n",
+              "      <td>2736</td>\n",
+              "      <td>Организация массовых мероприятий</td>\n",
+              "      <td>Данная дисциплина учит планировать мероприятие...</td>\n",
+              "      <td>6875</td>\n",
+              "      <td>Управление технологическими инновациями</td>\n",
+              "      <td>bachelor</td>\n",
+              "      <td>2018</td>\n",
+              "      <td>Модуль «Soft Skills»</td>\n",
+              "      <td>Особенности организации массовых мероприятий.....</td>\n",
+              "      <td>[особенность, организация, массовый, мероприят...</td>\n",
+              "      <td>[данный, дисциплина, учить, планировать, мероп...</td>\n",
+              "      <td>[-0.35568532, 0.014744152, 0.8801875, 0.582805...</td>\n",
+              "      <td>[0.19902095, 0.32024252, 0.5574031, 0.7525074,...</td>\n",
+              "      <td>0.704</td>\n",
+              "      <td>особенность организация массовый мероприятие п...</td>\n",
+              "      <td>данный дисциплина учить планировать мероприяти...</td>\n",
+              "      <td>[0.017201137, -0.004298017, 0.012148312, -0.02...</td>\n",
+              "      <td>[0.035781205, -0.0101707755, 0.0017927813, 0.0...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-70c8992a-81c7-4932-942a-1b6bfb909059')\"\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-70c8992a-81c7-4932-942a-1b6bfb909059 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-70c8992a-81c7-4932-942a-1b6bfb909059');\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-2748a095-71ed-4c39-be2e-338967919fa0\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2748a095-71ed-4c39-be2e-338967919fa0')\"\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",
+              "    }\n",
+              "    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-2748a095-71ed-4c39-be2e-338967919fa0 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
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\\u0441\\u0442\\u0430\\u043d\\u0434\\u0430\\u0440\\u0442\\u044b, \\u0440\\u0435\\u043a\\u043e\\u043c\\u0435\\u043d\\u0434\\u0430\\u0446\\u0438\\u0438 \\u041c\\u0435\\u0436\\u0434\\u0443\\u043d\\u0430\\u0440\\u043e\\u0434\\u043d\\u043e\\u0433\\u043e \\u0441\\u043e\\u044e\\u0437\\u0430 \\u044d\\u043b\\u0435\\u043a\\u0442\\u0440\\u043e\\u0441\\u0432\\u044f\\u0437\\u0438):\\n\\u041f\\u041a-3: \\u0441\\u043f\\u043e\\u0441\\u043e\\u0431\\u043d\\u043e\\u0441\\u0442\\u044c \\u043e\\u0446\\u0435\\u043d\\u0438\\u0432\\u0430\\u0442\\u044c \\u0442\\u0435\\u0445\\u043d\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0435 \\u0432\\u043e\\u0437\\u043c\\u043e\\u0436\\u043d\\u043e\\u0441\\u0442\\u0438 \\u0438 \\u0432\\u044b\\u0440\\u0430\\u0431\\u0430\\u0442\\u044b\\u0432\\u0430\\u0442\\u044c \\u0440\\u0435\\u043a\\u043e\\u043c\\u0435\\u043d\\u0434\\u0430\\u0446\\u0438\\u0438 \\u043f\\u043e \\u043f\\u043e\\u0441\\u0442\\u0440\\u043e\\u0435\\u043d\\u0438\\u044e \\u0442\\u0435\\u043b\\u0435\\u043a\\u043e\\u043c\\u043c\\u0443\\u043d\\u0438\\u043a\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u044b\\u0445 \\u0441\\u0438\\u0441\\u0442\\u0435\\u043c \\u0438 \\u0441\\u0435\\u0442\\u0435\\u0439, \\u0438\\u0445 \\u044d\\u043b\\u0435\\u043c\\u0435\\u043d\\u0442\\u043e\\u0432 \\u0438 \\u0443\\u0441\\u0442\\u0440\\u043e\\u0439\\u0441\\u0442\\u0432.\",\n          \"\\u0414\\u0430\\u043d\\u043d\\u0430\\u044f \\u0434\\u0438\\u0441\\u0446\\u0438\\u043f\\u043b\\u0438\\u043d\\u0430 \\u0443\\u0447\\u0438\\u0442 \\u043f\\u043b\\u0430\\u043d\\u0438\\u0440\\u043e\\u0432\\u0430\\u0442\\u044c \\u043c\\u0435\\u0440\\u043e\\u043f\\u0440\\u0438\\u044f\\u0442\\u0438\\u0435, \\u0441\\u043e\\u0437\\u0434\\u0430\\u0432\\u0430\\u0442\\u044c \\u043a\\u043e\\u043d\\u0446\\u0435\\u043f\\u0446\\u0438\\u044e, \\u043f\\u0440\\u0435\\u0434\\u0443\\u0441\\u043c\\u0430\\u0442\\u0440\\u0438\\u0432\\u0430\\u0442\\u044c \\u0440\\u0438\\u0441\\u043a\\u0438 \\u0438 \\u043c\\u0435\\u0442\\u043e\\u0434\\u044b \\u0440\\u0435\\u0430\\u0433\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u044f. \\u0412\\u043e \\u0432\\u0440\\u0435\\u043c\\u044f \\u0437\\u0430\\u043d\\u044f\\u0442\\u0438\\u0439 \\u043e\\u0431\\u0443\\u0447\\u0430\\u044e\\u0449\\u0438\\u0435\\u0441\\u044f \\u0443\\u0437\\u043d\\u0430\\u044e\\u0442 \\u043f\\u0440\\u043e \\u0432\\u0438\\u0437\\u0443\\u0430\\u043b\\u044c\\u043d\\u0443\\u044e \\u043a\\u043e\\u043d\\u0446\\u0435\\u043f\\u0446\\u0438\\u044e \\u0438 \\u043c\\u0435\\u0434\\u0430\\u0441\\u043e\\u043f\\u0440\\u043e\\u0432\\u043e\\u0436\\u0434\\u0435\\u043d\\u0438\\u0435 \\u043c\\u0435\\u0440\\u043e\\u043f\\u0440\\u0438\\u044f\\u0442\\u0438\\u044f. \\u041f\\u043e\\u043b\\u0443\\u0447\\u0430\\u0442 \\u043f\\u0440\\u0430\\u043a\\u0442\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0435 \\u043d\\u0430\\u0432\\u044b\\u043a\\u0438 \\u043f\\u043e \\u043e\\u0446\\u0435\\u043d\\u043a\\u0435 \\u044d\\u0444\\u0444\\u0435\\u043a\\u0442\\u0438\\u0432\\u043d\\u043e\\u0441\\u0442\\u0438. \\n\\u0417\\u0430\\u043d\\u044f\\u0442\\u0438\\u044f \\u043f\\u0440\\u043e\\u0445\\u043e\\u0434\\u044f\\u0442 \\u0441 \\u0440\\u0430\\u0437\\u043b\\u0438\\u0447\\u043d\\u044b\\u043c\\u0438 \\u043f\\u0440\\u0435\\u043f\\u043e\\u0434\\u0430\\u0432\\u0430\\u0442\\u0435\\u043b\\u044f\\u043c\\u0438 \\u0438\\u0437  setters education \\u0438 evention, \\u043a\\u043e\\u0442\\u043e\\u0440\\u044b\\u0435 \\u043d\\u0430 \\u0441\\u0432\\u043e\\u0435\\u043c \\u043e\\u043f\\u044b\\u0442\\u0435 \\u0440\\u0430\\u0441\\u0441\\u043a\\u0430\\u0437\\u044b\\u0432\\u0430\\u044e\\u0442 \\u043f\\u0440\\u043e \\u043e\\u0436\\u0438\\u0434\\u0430\\u043d\\u0438\\u044f \\u0438 \\u0440\\u0435\\u0430\\u043b\\u044c\\u043d\\u043e\\u0441\\u0442\\u044c \\u0440\\u043e\\u043b\\u0438 \\\"\\u043e\\u0440\\u0433\\u0430\\u043d\\u0438\\u0437\\u0430\\u0442\\u043e\\u0440\\u0430\\\". \\n\\u0414\\u0438\\u0441\\u0446\\u0438\\u043f\\u043b\\u0438\\u043d\\u0430 \\u043f\\u043e\\u0434\\u0445\\u043e\\u0434\\u0438\\u0442\\u044c \\u0434\\u043b\\u044f \\u0442\\u0435\\u0445, \\u043a\\u0442\\u043e \\u043e\\u0440\\u0433\\u0430\\u043d\\u0438\\u0437\\u0443\\u0435\\u0442 \\u043c\\u0435\\u0440\\u043e\\u043f\\u0440\\u0438\\u044f\\u0442\\u0438\\u044f \\u0432 \\u0443\\u043d\\u0438\\u0432\\u0435\\u0440\\u0441\\u0438\\u0442\\u0435\\u0442\\u0435 \\u0438 \\u0437\\u0430 \\u0435\\u0433\\u043e \\u043f\\u0440\\u0435\\u0434\\u0435\\u043b\\u0430\\u043c\\u0438.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"up_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 163,\n        \"min\": 6875,\n        \"max\": 7198,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          7198,\n          6989,\n          6875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"up_title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\\u0418\\u043d\\u0444\\u043e\\u0440\\u043c\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u0430\\u044f \\u0431\\u0435\\u0437\\u043e\\u043f\\u0430\\u0441\\u043d\\u043e\\u0441\\u0442\\u044c / Information security\",\n          \"\\u041f\\u0440\\u043e\\u0433\\u0440\\u0430\\u043c\\u043c\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u0435 \\u0432 \\u0438\\u043d\\u0444\\u043e\\u043a\\u043e\\u043c\\u043c\\u0443\\u043d\\u0438\\u043a\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u044b\\u0445 \\u0441\\u0438\\u0441\\u0442\\u0435\\u043c\\u0430\\u0445\",\n          \"\\u0423\\u043f\\u0440\\u0430\\u0432\\u043b\\u0435\\u043d\\u0438\\u0435 \\u0442\\u0435\\u0445\\u043d\\u043e\\u043b\\u043e\\u0433\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u043c\\u0438 \\u0438\\u043d\\u043d\\u043e\\u0432\\u0430\\u0446\\u0438\\u044f\\u043c\\u0438\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"qualification\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"bachelor\",\n          \"master\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2018,\n        \"max\": 2022,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2022,\n          2020\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"module_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\\u041d\\u0430\\u0443\\u043a\\u043e\\u0435\\u043c\\u043a\\u043e\\u0435 \\u043f\\u0440\\u0435\\u0434\\u043f\\u0440\\u0438\\u043d\\u0438\\u043c\\u0430\\u0442\\u0435\\u043b\\u044c\\u0441\\u0442\\u0432\\u043e\",\n          \"\\u041c\\u043e\\u0434\\u0443\\u043b\\u044c \\u043e\\u0431\\u044f\\u0437\\u0430\\u0442\\u0435\\u043b\\u044c\\u043d\\u044b\\u0445 \\u0434\\u0438\\u0441\\u0446\\u0438\\u043f\\u043b\\u0438\\u043d\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"contents\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\\u0418\\u043d\\u043d\\u043e\\u0432\\u0430\\u0446\\u0438\\u043e\\u043d\\u043d\\u044b\\u0439 \\u043c\\u0430\\u0440\\u043a\\u0435\\u0442\\u0438\\u043d\\u0433. \\u041a\\u043e\\u0440\\u043f\\u043e\\u0440\\u0430\\u0442\\u0438\\u0432\\u043d\\u044b\\u0435 \\u0444\\u0438\\u043d\\u0430\\u043d\\u0441\\u044b. \\u041e\\u0441\\u043d\\u043e\\u0432\\u043d\\u044b\\u0435 \\u043a\\u043e\\u043d\\u0446\\u0435\\u043f\\u0446\\u0438\\u0438 \\u0432 \\u0441\\u043e\\u0437\\u0434\\u0430\\u043d\\u0438\\u0438 \\u0442\\u0435\\u0445\\u043d\\u043e\\u043b\\u043e\\u0433\\u0438\\u0447\\u0435\\u0441\\u043a\\u043e\\u0433\\u043e \\u0431\\u0438\\u0437\\u043d\\u0435\\u0441\\u0430. \\u0420\\u043e\\u043b\\u0438 \\u0432 \\u043f\\u0440\\u043e\\u0435\\u043a\\u0442\\u0435 \\u0438 \\u0440\\u0430\\u0441\\u043f\\u0440\\u0435\\u0434\\u0435\\u043b\\u0435\\u043d\\u0438\\u0435 \\u0434\\u043e\\u043b\\u0435\\u0439 \\u043c\\u0435\\u0436\\u0434\\u0443 \\u0443\\u0447\\u0430\\u0441\\u0442\\u043d\\u0438\\u043a\\u0430\\u043c\\u0438. \\u0421\\u0442\\u0440\\u0430\\u0442\\u0435\\u0433\\u0438\\u044f \\u0432\\u044b\\u0445\\u043e\\u0434\\u0430. \\u0423\\u043f\\u0440\\u0430\\u0432\\u043b\\u0435\\u043d\\u0438\\u0435 \\u0438\\u043d\\u0442\\u0435\\u043b\\u043b\\u0435\\u043a\\u0442\\u0443\\u0430\\u043b\\u044c\\u043d\\u043e\\u0439 \\u0441\\u043e\\u0431\\u0441\\u0442\\u0432\\u0435\\u043d\\u043d\\u043e\\u0441\\u0442\\u044c\\u044e. \\u0423\\u043f\\u0440\\u0430\\u0432\\u043b\\u0435\\u043d\\u0438\\u0435 \\u043a\\u0430\\u0447\\u0435\\u0441\\u0442\\u0432\\u043e\\u043c. \\u0426\\u0438\\u0444\\u0440\\u043e\\u0432\\u0430\\u044f \\u0442\\u0440\\u0430\\u043d\\u0441\\u0444\\u043e\\u0440\\u043c\\u0430\\u0446\\u0438\\u044f. \\u041e\\u0431\\u0449\\u0438\\u0435 \\u043f\\u043e\\u043d\\u044f\\u0442\\u0438\\u044f \\u0446\\u0438\\u0444\\u0440\\u043e\\u0432\\u043e\\u0439 \\u0442\\u0440\\u0430\\u043d\\u0441\\u0444\\u043e\\u0440\\u043c\\u0430\\u0446\\u0438\\u0438. \\u041e\\u0442\\u043b\\u0438\\u0447\\u0438\\u0435 \\u043f\\u043e\\u043d\\u044f\\u0442\\u0438\\u0439 \\u00ab\\u0446\\u0438\\u0444\\u0440\\u043e\\u0432\\u0438\\u0437\\u0430\\u0446\\u0438\\u044f\\u00bb \\u0438 \\u00ab\\u0446\\u0438\\u0444\\u0440\\u043e\\u0432\\u0430\\u044f \\u0442\\u0440\\u0430\\u043d\\u0441\\u0444\\u043e\\u0440\\u043c\\u0430\\u0446\\u0438\\u044f\\u00bb. 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     \"column\": \"bert_embedding\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bert_embeddingD\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 72
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# размер вектора Tiny BERT – 312, что близко к размеру векторов w2v\n",
+        "df.bert_embedding[0].shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "22wbtRgA6GPN",
+        "outputId": "93efe9cf-0756-4957-b611-26dd3b0932f6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(312,)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 73
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Повторим эксперимент из предыдущего раздела – оценим, насколько схожи векторы описания и содержания для одной дисциплины, сгенерированные BERT."
+      ],
+      "metadata": {
+        "id": "HR9T8I9UGR5c"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ваш код здесь"
+      ],
+      "metadata": {
+        "id": "FfbkDKOv6ell"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Постройте график. Вероятно, он будет похож на тот, что ниже.\n",
+        "\n",
+        "Здесь снова пунктиром обозначена медиана."
+      ],
+      "metadata": {
+        "id": "Ca-d_31TGekm"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ваш код здесь"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 457
+        },
+        "id": "EGwTsf2B6lbC",
+        "outputId": "f5993cbb-6267-46b0-fdee-badef979310b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Теперь сравните распределения сходств, полученных при использовании разных подходов к векторизации.\n",
+        "\n",
+        "В результате должно получиться нечто похожее на гистограмму ниже. По ней видно, что вектора Tiny BERT дают более узкий диапазон сходств. И более высокие значения. Это можно объяснить тем, что:\n",
+        "- модель обучалась на более широком контексте и не улавливает тонкостей разных дисциплин\n",
+        "- относительно стандартного BERT качество модели сильно ниже (его можно поизучать по ссылке в начале раздела)"
+      ],
+      "metadata": {
+        "id": "6VAi7FmpGnbl"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ваш код здесь"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 457
+        },
+        "id": "HFxuE6gK759h",
+        "outputId": "0350e367-0cb6-41c6-bf67-3942ba183804"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Задача 2 – Сгенерировать description и contents\n",
+        "\n",
+        "Задача 2 – исследовательская. Надо проверить, как с работой преподавателя справится большая языковая модель. В исходных данных расположены описания и темы из содержания, написанные реальными людьми. Теперь используем LLM для той же задачи. Также рассмотрим, как разные техники промпт-инжиниринга будут влиять на результат."
+      ],
+      "metadata": {
+        "id": "HuKMRrSq-rad"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Техники промпт-инжиниринга\n",
+        "\n",
+        "Под техникой промпт-инжиниринга (или промптинга) понимается практика разработки и оптимизации промптов (запросов, подсказок) к модели, которые помогут получить максимально близкий к ожидаемому ответ и эффективно взаимодействовать с языковой моделью. Это ключевой элемент в управлении работой и поведением LLM.\n",
+        "\n",
+        "\n",
+        "Наиболее популярными являются следующие техники:\n",
+        "1. **Zero-shot** (Обучение без примеров) – модель применяет общие знания и правила для решения новых задач.\n",
+        "2. **Few-shot** (Обучение на нескольких примерах) – модель адаптируется к новым задачам, используя небольшой набор примеров.\n",
+        "3. **Chain-of-thought** (Цепочка рассуждений) – модель генерирует пошаговое рассуждение для решения задачи, что помогает в понимании сложных задач и улучшает точность ответов.\n",
+        "4. **Tree of thoughts** (Дерево рассуждений) – подход, при котором рассматриваются различные варианты решений или идей, ветвящиеся от основной мысли.\n",
+        "\n",
+        "В этой работе воспользуемся первыми двумя подходами.\n",
+        "\n",
+        "В колабе слишком мало ресурсов (если пользоваться бесплатной версией), чтобы хотя бы запустить какую-нибудь интересную современную модель через модуль `transformers`, и тем более ресурсов не хватит для дообучения. Поэтому для тестирования воспользуемся API/serverless API."
+      ],
+      "metadata": {
+        "id": "2k4LJKqcZJb9"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Тестировать запросы будем к LLM mistral-nemo-instruct-2407 – https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407\n",
+        "\n",
+        "Это дообученная instruct-версия модели mistral-nemo-base-2407 (12В). Модели категории Instruct способны генерировать ответ, следуя заданному им шаблону поведения.\n",
+        "\n",
+        "Обе версии (base и instruct) мультиязычные и выпущены MistralAI и NVIDIA в июле 2024."
+      ],
+      "metadata": {
+        "id": "6DJB8iYkJhic"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        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)"
+      ],
+      "metadata": {
+        "id": "mbE4L-QIZQbx"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### zero-shot\n",
+        "\n",
+        "Техника промпт-инжиниринга, в которой мы, не давая модели никакой вспомогательной информации, ожидаем от нее ответа на запрос (промпт) в свободной форме."
+      ],
+      "metadata": {
+        "id": "rKMJjn2_YhTZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from huggingface_hub import InferenceClient\n",
+        "\n",
+        "client = InferenceClient(api_key=\"hf_XXXXXXXXXXXXXXXXXXXXXX\")  # ваш токен с  HuggingFace"
+      ],
+      "metadata": {
+        "id": "aoQDB8eYRyZD"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Базово запустим модель через serverless API, предоставляемый HuggingFace. Это самый простой способо интегрировать модель в колаб. Для использования в реальных приложениях он не подойдет."
+      ],
+      "metadata": {
+        "id": "pAbEg5GfKws5"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "messages = [\n",
+        "\t{\n",
+        "\t\t\"role\": \"user\",\n",
+        "\t\t\"content\": \"\"\"Ты – преподаватель в университете.\n",
+        "                  Перечисли темы и разделы для дисциплины 'Язык Python для анализа данных'.\n",
+        "                  Разделов должно быть не более 8.\"\"\"\n",
+        "\t}\n",
+        "]\n",
+        "\n",
+        "completion = client.chat.completions.create(\n",
+        "    model=\"mistralai/Mistral-Nemo-Instruct-2407\",\n",
+        "\t  messages=messages,\n",
+        "\t  max_tokens=500\n",
+        ")\n",
+        "\n",
+        "print(completion.choices[0].message.content)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GpLmXvCWC9r-",
+        "outputId": "25db772e-4dff-4871-ca90-03c1ac28eeb7"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1. Введение в Python и его применение в анализе данных:\n",
+            "   а. Основные концепции и синтаксис Python\n",
+            "   б. Преимущества и области применения Python в анализе данных\n",
+            "   в. Введение в Libraries (библиотеки для анализа данных)\n",
+            "\n",
+            "2. Основные типы данных и переменных в Python:\n",
+            "   а. Числовые типы данных\n",
+            "   б. Строковые типы данных\n",
+            "   в. Списки и кортежи\n",
+            "   г. Словари и множества\n",
+            "\n",
+            "3. Условные выражения и циклы:\n",
+            "   а. Если-иначе (if-else)\n",
+            "   б. Циклы (for, while)\n",
+            "   в. Ошибки и исключения\n",
+            "\n",
+            "4. Введение в NumPy:\n",
+            "   а. Измерение матриц и одномерных массивов\n",
+            "   б. Арithmetic operaciones (арифметические операции)\n",
+            "   в. Agregade functions (функции сложения)\n",
+            "\n",
+            "5. Производительность с помощью Pandas:\n",
+            "   а.很显然的数据结构 (явные данные)\n",
+            "   б. Серий и DataFrame\n",
+            "   в. Обработка отсутствующих данных\n",
+            "\n",
+            "6. Визуализация данных с помощью Matplotlib и Seaborn:\n",
+            "   а. Matplotlib - основы и функции\n",
+            "   б. Визуализация распределений Seaborn\n",
+            "   в. Картирование многоваловых данных\n",
+            "\n",
+            "7. Обработка языковых данных с помощью NLTK:\n",
+            "   а. Кора根據 (окончательные Parlance)\n",
+            "   б. Аннотация текста\n",
+            "   в. Сегментация текста\n",
+            "\n",
+            "8. Интеграция Python в рабочую среду:\n",
+            "   а. Jupyter notebooks\n",
+            "   б. Расширения Visual Studio code и PyCharm\n",
+            "   в. HLA жемчужина коллекция Python пакетов.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Можно получить что-то адекватное, а можно – что-то такое🤣\n",
+        "\n",
+        ">**Текст промпта имеет значение!**"
+      ],
+      "metadata": {
+        "id": "V72MKzqTT7E7"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "![image.png](data:image/png;base64,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xleRdQKTt1593H355UQ79NulVljXzTb7HGVudvDos9PdV1/POFpntXQm+f/3v/95xSiWlHrzzTfdrLPO6gYNGuSXMLAlDSwe206bNs3dcsstPo+99dZbjjy77LLLuo022sjnXfNn2xtuuMFbOhkyZIhDoerpp592K6ywgttjjz2i5/O1u+SSS/zyWIRxyCGHeGUtuxbrQw899FDErqvbZZdd3P333+9/uOP/Jz/5iVtnnXXMe+HbZ555xj3yyCPupZde8opgyy23nI9zjTXWKIsLhbOrrrqq5PbKK6/4/bFjx7q///3vJXd2Fl54Yc+qzDE6GDlypGcTdx86dKi/Ju4eHqd9Jlx74403Op4/HHke9913n1+i7O233/bLWWyzzTaO+20ryZL3LC3ku3vvvdc9+uijPs+yLNSuu+7qunWr/Ekif99zzz3+WbJs2hJLLOFWXHFFr4hiChcWfri96aabnCkUmjsKgZtssokdVt2SRvI879i4ceP8s0Qpccstt3QoKCXJ5MmTvcWsF154wb+XWA/iWWy99dZeGTHpGtxQFCG9TzzxhHv//fcdS8/NO++8Dqs9pJdwipI7nrg/Cn9GaDttuHVJaQWX25/4bymau54a4Q782S6u+0yVn03Jcxvt5CmDwiTVWx6E17DPs+A7/O6777rPPvvMl5kLLrigW3fddd2aa67punfvHr/EH2e5jrz6wAMP+Hfjo48+chMmTHCLLbaYW2qppdzaa6+dWEaHkaMoS5lLGbvMMsv48pXyjvKC/ER6K+WjrHzC+LPsZ3nH0pYHPDvKSMqKHXfc0Y0aNaquMuixxx5z9j3gWZhQFlEGhvKzn/3MzTXXXKFTaT8N20anlUSvPWRGXYQygmUMd9qqvP5TujHtiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiEBBBLpMmTLlu6GrgkJUMGUEUFy5/vrrvRuDGgy0VpOpU6e6888/3w+IDB482A0bNsydeuqp/hIGL7fddttql3eYc0+OuNHd9s/TfXrW2nhH99B/rmyVNpbxGbbl/i4aSWp1DofTj9zMTZ4w1g3dcHvXe4653Z3Xnd3K3zIrre9+secfS+6P/fdf7s5/n1U6tp2tdz3OLb/axnZYtkVB5vpLjvNu2+55krv+0uPdtG+nlvlZdInBbueDznYzdft+gPLmq05xzzx8c5m/Sgd7HHaR679YshLB8Wd/6/5+7QyLK0/f0sPNN3cyj3jYKJ8ceOCB7tNPP42f8scnnHCCO/zww8vOffzxx375nYcffrjMnQOsA5133nmt8hj5DmWqmWaayaFgYbLaaqs5FAhsUA93BtcZZDa5+OKL3cEHH+wPDz30UPfnP//ZTpW2LKlz0kknlQ2cl04GOyjivPfee+6AAw5wp58+I28Fp8t2Uag5+uij3QUXXOAHa8tORgdwI04bcIbLwIED494Sj1lu5vbbb291DnaHHXZYK3cUe375y1+2cjeHLM+Ea1FYQVlm9913d6NHj261tBhKFVdeeaVD+a1oSZv3/vWvf3nFJdJx9dVXu912280rPYXpWmuttdxtt93msFYVFxj+9re/baWAgr8NN9zQK09VGjCef/75/eB/GCbPOsy34TnbZ1AaRazrrrvOnMq2c845p3vttdfc7LPPXuaO0glKWuTVuCy66KJeQWqllVaKn3KffPKJV8JBQSZJGHAnTZWUZeyaix882933+p2uZ7ee7vLdbjLnVtt9zjrKPf/2q67XrLO5u0++0iuX4em+5x7x1lbCCy457HS3VP/FQ6ey/T2v+IWb9NUkt9bi67kD1i0vc8o8ZjzIUwYRZdryIEwm3+Pf/e53jm9zklCu/eEPf2h1Kst15AHulaUCkwTFRMqZpPKEspmy9G9/+1urS4866qiSBajLL7/c/eIXvyjzk4dPWUApD7K+Y1nKg6xl0P777+/Ll3pu7b///a9XWg79ZmHbqLSG6Sa7D1rnazdtunObbzCTu+APjVNcC9OlfREQAREQAREQgY5PgDYqwoQaiQiIgAiIgAiIgAiIgAiIgAiIgAikIaCe6DS02sEvM61tcBKllc4gKK3M1rufW36VYd5SyivP3OfGffqhe/juq93sfeZyq6+/XdXbHDXyBTd61Ouua6Q4sdCAZdyccy/kJk8c69576zk3veV7RQoCmW+hxd3Ka/3Mhzd16lfuhcf/UzXs+Ml//e1on9alV1w3GkDv6V566h43cfyn7r03n3MvPX2PW2HI91YaFl96iOs5y2w+iK+mfFFSYll8mdXdPPMPKAu6T995yo7Dg2dfnqE7NkvPLm7euepTWmH2PwoLCAP9WH9gwHP8+PHuqaeeciimjBkzJozGK3DgDysQCLP3sQaCVZFrr73WL0nFgDsWHpKsoGANAsUprJcwuM4APYJCFrPXn3vuOW+RhTRgxSUuKK3MM888brvttvOD71gAoFPrzDPPdCgXoExSlGy//fbujjvu8MFh6YLlYbDmceedd3rrGeeee66beeaZSwPOKB+Ygg0XMZuee5xjjjnczjvvXJasJZdcsuzYDogHKzQIA8/1WErCAkKeZ0Jc//jHP9i4DTbYwGG55PXXX/dWO7DccMQRR3hlIizeFCVZ8l4Y9w477ODzwVZbbeWtUqEY8uGHH3orEexzPhSUZBg8RshX5J8+ffq4ESNGOKwh8Kxww4JLkvD8WHYN4b2ATy1BSQEFJaxWIFiuWH/99b01BdzIR1hf+fbbb8uC4t3DH0oEvJcoGJBfsKCARR+UWVDu4h3iXQgFhSxTWkGJB8Wwueee21+DQs8HH3zgLbKE1+TZHzd5vL980PyLlJRWPp/ypTvjhr9796UXXcK98t6bfv9/k763NJEnzrzXZi2D0pYHlk4sn/EO8Tx5XjxbrBtNnDjRW6zCukmozJf3OvIdZQd5B4UsrApRNqLQwnvHFqUvFO6wqBTKySefXFJaYZCAcuWbb77xZXutZcuy8gnjT7uf9R3LWx6QzjRlEPUwU07DkhJKMwgWyn70ox/5ffuXZMkmL9v2TKvdB1uMCC0wXxf3wegW9+zLkfaKRAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQATamIAUV9oYcJrgGZRiWROEAbLZZputNOCaJpyO5pclcvY87GLXb+4FfdKGbrC9+/vpezuWDXrk3mvcautsEymlVM6KH777ius71/xu271Odgv0/15pYOL4MY5zoQyIliDih0z5YlJqxRUUTPY84u/ewgthDN1wB3fmMVu7b76e4l588q4yxZWlBq/j+CEo0pj1lRVXH+6WXmk9717Pv8/GzVBcWSTCU8H4TFkwDJ4xgx5hUBMFEAZTQ2FwD2WAUG699daS0spee+3lzjrrrJKVE5ZpYaAUCypYD0hSXGEAjdn8DNjbAB1LnzAgz2ArA7sM4jJ4n6R0hUUMlgoyyyZYs0A5AEWCs88+2+2zzz4lCyhhutPuM5hsSiso93CftgQNg9A77bSTZ3bOOed4yxgsw4G1mVNOOaUUFRxQIoBv6F7ykLDDskf8EJR36lFcyftMLBnHHntsKU/gRv5AIQhLOTwPlCCKkKx5L4ybvIPSyQILLOCdsRLBMigwR4EqVFxBsccG3VFawaKBDRajxEA+xoIL5SZ5i6Vb4hI+P6y21KO4glULU1rZd9993WmnnVbKQ4TPO4BFH8tXuJFWwucdQOHp7rvv9lZxOIeQF1EUmzRpkuN5XXTRRTNORP9RMsI/gtKK7XuH6B8WhlimC2WrosSUUWaftVcpyAtuvcKNmzjeLb7gom6f4Tu4A8+bYYlq3OSOobiSpQzKUh4YEPKUKaZgvcjebzvPUkAo/sUl63UoZJ144om+XEJpKZQjjzzSW2yjXEfxLlRc4b3EuhSCJSbyD3kQ2XPPPX26UYhNkjx8ksKr1y3rO5a3PCB9acqgLbbYwvFDqKOZ4grfEZTvqkkRbNsrrUn3MWDhGYorn/5vRh0lyY/cREAEREAEREAEREAEREAEREAEREAEREAEREAEREAERKAoAsVNwy8qRT/QcBj0RNmAwdj55pvPrbrqqp2GxEpDNy8prXBTs8zW262+wQwrK5PGf+ZGvvZkzXvd4ldHlSmtcEGfvvO6pVesX0GkZiSRhzW+W5bI/M42e1/3o+XX8ocT/lduwcT85N2OmzBjUIhBonrksssuK1lTYTmguNIKYWy++eZuv/32KwuOJSIQlho5/vjjS0oruBGGLT/x6KOPeksouIeCRReEJVL69evn97FEgbCEhSkiYI0gSVCOMaUVzqOIYFZWPvroI3ffffclXZbaDcsDyIABA9wZZ5xRplzAcke2fBLWCHjnGil5nwlpR2EIhYlQsMpggqWOoiRr3gvjx7KN5RXcGaDHIg4STysWL8xCENaATGkFv1iRYZCfZ4oYS3+Q8x+KKgj5Na60gjvvwIUXXliyxIAb+RclIYTBdRQIQll++eXdT3/6U++EshllvgmKX7YUDUo8ceEeYWT3Gj+f9vibyLLHF19N8ZfNHi0VhLz07hvu/x7+j+d61PYHuL6z9/Hu/DMll5JDg3aylEF5yoNQ+Y+lyuLSv3//xOX/sl7Xu3dvv9xYXGmFeLH6gWUhhCUIQ6EcQ1kOoXwzpRWOUczbcccd2U2UPHwSA6zTMcs7VlR5kKYMqvN2Er0Vwba90pp0AwO/q5NEn0r3+ZdJPuQmAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAsURqGzmorg4FFIdBFh2xQa7hg8fXqZUUMflHdrLksut2Sp9Sy47tOR60ZcuAABAAElEQVQ2fuxHpf2knXkWGOgG/mjlpFOFu/VfbLlWYfbpN693++K7pTVaecjp8M3UGQH0nLk+xZXXXnvNX8BAJssQ1CssU4KsueaaiUv5oOyCRRWEpU3ia1KjnGKCNSBm8LM1sfOVFFdMOcH8s2XZFBPiLEJsuRUUAVDQCRUEbH+WWWZxU6ZM8ct9FBFn1jDyPhPiXWmllVpZ4lh44YVLSfr0009L+3l3sua9MN411lgjPPT7lt54Wo0PnszqQXgxCjAsj0T5WVT+GT16tJswYYaFEaykhFZVwrjj+5bvcH/88cfdW2+9Vcp7lu9sySKsy7BM1uKLL+6DQREMRS6z1IPyCvfLsl1tIVOnfVfoRIHPFCkAfRtZiTnl2gui9Dq33bqbuR/3H+Te++R7i03fTitfEqkt0lRPmFbG4LfeMsieS5byAKUPk7333tsrhZDfauWJrNdZXJRNWBdiCTby49dff+1PobiBYKEHxTuWFEKwrGQSlqnmtvHGG3urVnYcbvPwCcNJs5/1HSuqPEhTBqW5r7jfIti2V1rjaee4Z2DgKfqcSkRABERABERABERABERABERABERABERABERABERABESgTQlIcaVN8dYXOAP99957r/eM5QtmcXcm6T1H+ZIH3Fuv2ft55RwGdCfWsGQy13ztxyMprd179PSP49tvo2nHbSD95ujiPh3b4kZ99L0FhmrRMCCOLLrooqksMLCsBRJau/AO3/1jWRwT82vHbFH2MMFqC5LkxqBrkiTFi2UBLGdgaShubSMpjFpuLOXAshkIimAsl1NNGEBtpBjnJDakq9YzwU/StSwpY1wZ4C5Ksua9MP6k9JpCQjytxofrk67D3RiFfnHPKnaPXB9X3qoW5ptvvlk6fcUVV5T2K+2Q90xxBT8sBcNSVuRfrCzww+ILSwexnNKSSy5ZKajU7rP1nNV179bNTf32Wzfpi8/dNf+9yb09epSbf8553N6bbu/Dmzzli1K4fXt9b32l5NiAnaTyJsnNyqC85cGvfvUrb7UJi1B33nmn/5FXBw8e7JUGsWTSvXv3ViSyXkdALPnD8lafffZZq3BDh2+jZ2eKKyi4IFhasXcp9IsVtyTJyycpzHrcsr5j4TuepzxIuta4xcugeu4nyU9RbNsjrUnpx+2973TXok+069O7ki+5i4AIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiEAxBKS4UgzHXKE899xzfvY0gTAQ9cADD5TCs5nWODAQwjmWi2Aws1mk+8zfKzxYmrtEIyFdo/uYFg2+TflyhqKBnYtv+8wxT9ypzY5nmqn9X4k553CR4kpk5eSD+hRXsHSC9OlT/2Ayg5xm7aFXr16J/LDgYpJkNaVLl+8twti+bbku3Ldwwm1oncXcyctYL2Cw0Kxc2Lks23HjxpUuw/LAsGHDSsdJOwsttFCSc7u4FfFMSGjSwHlb3UCWvBdPS5r0hnmiVr5NyrPxuOs5tnvEb5p3zPIeZbgtg1ItvrgiykEHHeSGDBni/vCHP5QUGbGkwe/KK6902267rbv44otrWvuoFmd4rl/vOdyY/411r30w0j3++nP+1JHb7ed69phhZuHzQHEFvx1BwjLG9m1L+sJ9ju2ZsJ+lPGDpnscee8yxJNvNN9/sxo4d61jW6ZFHHvG/U0891SuarL/++kRRkqzXsRSXLftFGFtvvbVf6sfyId//G264wceDsp8JaUJQWEuSShZi8vJJiqset6zvWFHlQZoyqJ77SfJTFNv2SGtS+nF794MZeaxvny6u6/ef/0re5S4CIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACuQi0/yh9ruR2zovDAagnn3yy4k0yEMLyAUgzKa58OXmCm3W2ciWLqVO/9kor3EuSlRPcTbo2QJnE4m6P7Y8HdXWvjZzmxk9scZM+j3gk65WUkjJgwADHEgS2tFTpRJUdBi7nmWceN2bMGBdfjsUuC2f4L7jgguZc2JbwWRIlFCwj2Az3pJnlod969rFCw+A1lnxQDGB5j44qHeGZTIuWiAkFRaJqkiXvVQuv1rkwH5JvWU4nLpZvQ79xP2mOuUeTNO+YXceSNDvvvLMzq0QWVj3bVVdd1d16661+GS6UI6655hp3xx13+Hfkn//8p1ds2WuvveoJqqaffrPPUFz5dPwMZa+frrqOW+1HK5Su+2jsJ6X9OdtAcSVt3islJsVOEeUBVqHOO+88d8455/hyl+dz1VVXeYUiLIDsueeeflmo+LuT5bqzzjrL3x1LDY0YMcLNOeecZXdbKT+alTYUa6hPYG0plFBRJHQvgg/hUd6G9RjK4HgawnjtXcGt0j2F/m0/fMfbqzywuNNui2KbNt6i/LNsmFmB+9Fi0lopiqvCEQEREAEREAEREAEREAEREAEREAEREAEREAEREAERqEygfHSjsj+daUMCzKxGqSDpFw70M/MWP7Y0RhsmqdCgx44Z1Sq8cZ9+UHLr02/e0n6z7oQz/adN+zbVbaw++PtBoXqsrtjyJQz4YYWnXrHBzXBJk/DaN954o3RofksOBeyEy0NYcCNHjrRdt/DCC5f2k3ZsSZD33nsv6bR3w6qLvR9vv/12RX+1TtjzRAmhLcU4N+KZjB8/3mHFJPwxKF9Nsua9amFWO2d88FOLUei3Wpi1zqE0YPLUU0/Zbs2tLfvDIH5SXq8ZQOCBcn+zzTZzKKvcddddpTNY/yhKFl9g0VJQc/Sa3R285W6lY3aeeP15f9wtUmZaZJ5iLRNlyXtliavzoKjygOhQxFhmmWXc7373O/fKK6+4DTfc0Kfi448/dtXKpHqv+/zzz52VwTvttFMrpRUiI94ksbITZaAXX3yxlZcXXnihlRsORfE59NBDy8qR1VdfPTE+c8z6joXveHuVB5ZmtvZdYL/Wt6EotsSVRdKkNSn8jz9tcV99PePMkMFqKiQxkpsIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiECxBNQbXSzPTKGtvPLK7oADDkj8MZvbZIkllvB+9t13X3Nqiu3zT9zZKp0vPvH9YOz8Cy/Z6nyzOczWq280sDnDWsX4sR+lSv7Qlb9/DV947fvlHyoFYtZ2WGrmlFNOqeTNTZo0qezccsst548Z2Hz++RmD0qGHK664wh+y3ER8GZPQX9b9q6++utWl1157bclt+eWXL+0n7TCDHcESBcoBlWTttdf2p2677Tb32muvVfLm4FdJ5ptvPn9q9OjRJYswlfzmcW/kMwktJNg9JLnZObZZ814YRpr9pZde2i+NxjWWP8PrH374Yffuu+96p2WXXTY8lXmfJbNWWGGG5RGWbqlkEWLy5Mll+XDo0KGltJ555plV46+W9+IXcl+2TJItCRP3k+V4k9XWK122+lIruTl69S4dj5003j395gwFiKFLr1x2ruQpx05SPktyyxFF6dIiyoNSYMHOaqutVjqqVh6VPH23U+k6LDCZlZKkcgs3rO8kybrrrltSqrjooovKvMCVfFxJiuATf3bx43jcWd+xRpQHYdrnmmuu0lJdVu6E5+P7RbCNh1nvcdq0xsN94bXvv7Frrvy9cm3cn45FQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREoCgC34+YFxWiwnHMbn7uuef8L7T6wECHuVeaAd0Z8b363APu8fuvYz0Bf3uvv/iQe+y///L7A5Zc0c2/8BJNf9tdotn4c8w5v7+PJ0fc4N5981k39Zuv6rqv/gt0cauvOONVvPyG8qVbkgLYdNNN3XrrzRh0ZpDyiCOOcOGA9gcffOC23357d8IJJ5Rdfsghh5QG1pnRbwNvzNI/7rjj/NIUXLDrrru2WtKnLKCMBzfffLO74IILSoP9t99+uzv//PN9aAzwmbJApeBRDECw1sBSLLxLLH3ELxwoPfHEE92ss87q3bbeemt3//33lwWJdQTYMOs/aYAYz2YRAK6HH364w6pCmsHpsgirHDT6mVRJWuKprHkvMbA6HFkaZLvttvM+UUQ6/fTTS8/69ddfd3vssYc/h7LVQQcdVEeI9Xn585//7D1+/fXX3rLGE088UboQSwv//ve/3VJLLeUmTJhQckex0JamQiHr17/+tUO5xeSrr75y5HksqfCOhTJq1Ci30kor+SVpyN8mLPHyxz/+0WGNA2EpoaJkhcWWcgvNPUNB6z9Pj3B3P/OgZ4vSytGXnOamfD2j/Bo+ZP2iomxIOHnKg4MPPtjttttuDks39v5T1lD2mIIIFtOsvLAbzHIdS0uZRaObbrrJl1u2nBKKhttss00p71s8tiUv8m4iKKn8/ve/d3wH3nnnHb+U0bPPPmteW23z8GkVWAqHLO9Yo8oDuy2Wg1pkkUX8Ic//wQcfLPv2mj/bNoot8adNq6XZtpd9VxcZ2L+LW3lZNRWMi7YiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAJtR6Bb2wX9wwyZQc0bbrgh8eZRVgkVVphJbzOsEy/oJI49eszi7vjXGe7+W//munXv4SZPHOfvrHuPmd36m+9T2F2++tz97obLTiyF1xINMJr83+V/dLdcc6oduuVX3chtvsORpeMidtYctoO75epT/f1desb+ZUHuefjFbuGBlS1C7LjlTO6xZ6e7N95pcQ8/Pd2tGVhhKQvouwMsOgwfPtwPTp5zzjl+wHvgwIF+IH3s2LHe13777Vd2KYOi+++/v8M/S5kw2IkVk88++8x98cUX3i/L7Bx22GFl1xV1wNIJv/3tb/1APIoGtswRSibHH398zWj22WcfP1jMgOx1113nf3YRA8um+LLQQgs5BkUZPEY5Z5NNNnEMLuOO9Yy4JRoLI9xuueWWPk0oFDBAaYPU+Nlggw1cfEkdBppNiQI/NuDM/l577eUOPPBAdr2giHHeeef5/UY/k++SlGqTJe+liiDm+aijjvLL5ZCvGZBHeYWldHg2JuSrBRZYwA5LW5YsCZftmTJlij/HgD4WCUJhcN+WIkFJCsUT7hVFp3XWWcf17dvXzT333D5PVVomhPQ9+uij3qLRhRde6C6++OJSmO+//35J8WCrrbYKo/bWf1599VX/7vH+8R726dPHLx1jChMsB4PCVpFyzA4Hud9c+Af35VdT3O8vO8P98epz3dRvp5qOodt0yHpu6DKrFBllu4eVpzygrEAJiSWbWKpswIABXoktVC5COSFcloUbzHodSoi77767Q8mJcmvOOef08ZrVHyyOVFou6OSTT3YvvfSSfy94R/iZ8B5UWmYqDx8LP8s26zuWpzzIks74NZQLWMjj+7XRRhuVnX7ggQdcaFGnUWwtUWnSatewpR7ySFQPQaibdOnid/VPBERABERABERABERABERABERABERABERABERABERABNqUgKZRFowXRZR6lFHq8UPSQn/MoG1G2fHAM93sc8zlpnw5uaS0Mke/+dxuv/2r679YZWUOGwy0ba17nx5ZDpkaWQmw37dTvyldMn165XNdun4/KtOlS+tXwtxsWwo0trPymlu4bXY7wS0c3VOv3v2c+z7YmM/Wh5us09X1X3DGBf/4V22rK1h3eOaZZ7w1AJRAsAIwcuRIZ0orLD+FZYe4nHrqqd7KCYocCIPyKK3AeOONN3ZPPvlkKwUAy4O25Trbty1u9pxCN9xNsLjCgDyDvqa0gqLAPffc44YMGWLeKm5RfEEpAGsypmBgnuNxYtGCJYVWWWUVny4GklEMMKUVlHywdsIM/iSB6YgRIxyDpMTVo0ePJG8lN5Z+gaP9GHg2qXYOP1meCdfZPdsWt1DM3bbhOfaT3JPc4tdlyXthuOG+hW1utjV3tjwr8iXKI+QxrJiY0grKJJdeeqk79thjw0tK+yiq2DNhG1rmCd3j5wjgT3/6k1dQMisY5Ns333zTobTCUics2UaeDAVlk4ceesinBz/ExzvGj33y1U9/+lO34447hpd5pRgU0ey9xMIPFmVQWoHJz3/+c3f33Xe3UrYpCyR2MK1lmpv01UT/+2rqDIWdmBeH1ZVz9j/e9Z9nhtLPN9G9YRhr1p6zuF+uu7k7ZvsDXdcKo9aEaeF/G5WvaSTpOSe5WZh2zra4275tcatUBmUtD7AEtcwyyxC0Iy9RhpjSCopEWIwyKzve03f/sl5H2YbCCXkHGTdunFe2Q4EFJSoUJhDu0+7VO0T/yKdYAMEyy7zzzuudsQ7CvWPpyiSeZ3HPysfCDJ8BbvFj8xffZnnHspYHYZrCfUuTudnW3ONbLPBg1YZvFpzjzyHuPwvbMA3hvoVtbrY19/g2bVrt+n/8e8b7PEfvLm7b4c1Z77R70VYEREAEREAEREAEREAEREAEREAEREAEREAEREAERKB5CHSJBmOiYSqJCBRL4MkRN7rb/jljxvfvzx3hLa2MHfO+GztmlJt3gYGu71zJCgPFpqK5Qrv9/ulu76OmRoN+zj307x5uke8UWWrdBdY9sCDBoDqDkixbwaBqNWFAnGsYHMfyBDP5e/XqVe2STOewOIHlE4QBX5bDIJ1m8WVAZMGgrQWlhDfeeMNbp8FiBooozITvaNJez6TI+86S9/LEj9LKyy+/7FhChzzLwHytgeM88dm1LAmE0gKKBLxbKO9gKaia8DyxlMFyVCitkOfI70mKAxYOPHk/UFzhXlGsYqAeCzP1ysUPnu3ue/3OMu8/nm8Zd/zmfy5zix/8b3J0j6PecvNESoaDFlikpvLBn+44yr3wYfkSNGstvp47YN3D40F3qOMs5QEKgVhvQuEOyyvkO55lt27VjdZlvY6loSibyQdYxoovRVQP0IkTJ3qrPfjF2sp63y0vh8IjYVaSLHwqhZXGPcs71qjyIM19hX4bxTZMQ639CZOcW3nzr91XXzt3wiHd3O7bSnGlFjOdFwEREAEREAERKCfAhBrEJgCUn9WRCIiACIiACIiACIiACIiACIiACFQmIMWVymx0JgeBJMWVHMH9YC7dap+p7umXprv9duzmjtyn+QeMkhRXfjAPUzcqAg0gcOkjF7i7X721LOal5l/eHTv8lDK3vAen/uf37vkPnioLZp0lh7m9f/LrMjcdNJ7A0Ucf7c444wyvaDNmzJiaSleNT7FS0CgCl1w3zR1/1rduwMJd3H1X9YjyTKNSonhFQAREQAREQASalYAUV5r1ySndIiACIiACIiACIiACIiACItB4AuqSbvwzUApEoETgxgu7l/a1IwIiIAJpCew6dD/Hr63liI1PbOsoFH4KAi+88II75phj3E477eSXfWO5ISz4XHvtte7CCy/0Ie2+++5SWknB9Ifodbefz+T4SURABERABERABERABERABERABERABERABERABERABESgvQlIcaW9iSs+ERABERABERABESiQwJdffunuvfde/yNYlkVjuaBvvvnGx8ISVyi2SERABERABERABERABERABERABERABERABERABERABERABESgIxLo2hETpTQ1P4GuXbu6Ll26uC7fbZv/jnQHWQjMNNNMjrzAlvwgEQEREAERKJ7AAgss4IYPH+769u3rA//ss8+80grue++9t3vmmWfcXHPNVXzEClEEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAECiDQZcqUKS0FhKMgREAEREAEREAEREAEGkxg0qRJbuzYsd7qCksGSURABERABERABERABESgvQiMHDnSRzVo0KD2ilLxiIAIiIAIiIAIiIAIiIAIiIAIdBICWiqokzxI3YYIiIAIiIAIiIAI9O7d2/GTiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiECzENBSQc3ypJROERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABEehkBKS40skeqG5HBERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABJqFgBRXmuVJKZ0iIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIi0MkISHGlkz1Q3Y4IiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAINAsBKa40y5NSOkVABERABERABERABERABERABERABERABERABERABERABERABERABERABERABESgkxGQ4kone6C6HREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQARFoFgJSXGmWJ6V0ioAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiEAnIyDFlU72QHU7IiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACItAsBKS40ixPSukUAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQgU5GQIorneyB6nZEQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREoFkISHGlWZ6U0ikCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACnYyAFFc62QPV7YiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIhAsxCQ4kqzPCmlUwREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAQ6GQEprnSyB6rbEQEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREIFmISDFlWZ5UkqnCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACHQyAlJc6WQPVLcjAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAs1CQIorzfKklE4REAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAER6GQEpLjSyR6obkcEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEmoVAl5ZImiWxSqcIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiEDnISCLK53nWepOREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAERKCpCEhxpakelxIrAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAp2HgBRXOs+z1J2IgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQFMRkOJKUz0uJVYEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEOg8BKa50nmepOxEBERABERABERABERABERABERABERABERABERABERABERABERABERABERABERCBpiIgxZWmelxKrAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAh0HgJSXOk8z1J3IgIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAJNRUCKK031uJRYERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABEeg8BKS40nmepe5EBERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABJqKgBRXmupxKbEiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIi0HkISHGl8zxL3YkIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAINBUBKa401eNSYkVABERABERABERABERABERABERABERABERABERABERABERABERABERABERABESg8xCQ4krneZa6ExEQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQARFoKgLdik7t5MmT3RdffOGmT5/ufy0tLc5+Xbp08fts+ZmE+7iZfwujW7dubq655nKzzjqrXaKtCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIhAkxMoVHHluuuuc/fcc4/79ttvC8cy00wzuTXXXNPttNNOZUovhUekAEVABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABNqFQGFLBT399NPuzjvvbBOlFUhMmzbNjRgxwj3wwAPtAkaRiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiEAtAqNHj3bjx48v88Yk748++sivUlB2QgciIAIiIAIiIAKtCBRmceXxxx8vBd4WS/p89dVXfukhFGTWXXfdUlzaEYGOQIAKKO/ASy+95MaMGeM++eQT9+abb7rf/OY3bvjw4R0hiUqDCIiACIiACIiACIiACIhAJydAG+Spp57yd7nSSiu5H/3oR35/4sSJ7rbbbvP7Cy+8sPvJT37SyUn8sG/vjTfe8M+bZZn3339/N/PMM/+wgejuRUAEOj0Bff86/SPWDYpAhyfA+MDaa6/t5p57bj/5ukePHj7NF110kTv99NPd0Ucf7fbcc88Ofx9KoAiIgAiIgAg0kkBhiiufffaZv49evXq5s846y3XtWpgxFx/uscce6zVT//e//zWSl7vmmmvc9ddf79Pwt7/9zc0555yt0vPBBx+4ww8/3H399deuW7du7pxzznHzzTdfK39y6BwErrzySl/5nDRpUqsb4tlLcaUVFjmIgAiIgAiIgAiIgAiIgAi0AYGHH37YnXjiiT7kI444oqS4wixPOsuRddZZp6riyqhRo9xNN93kaNdyHe3auOyzzz5u2LBhcedOfcykhPfee6/sHlnSmLY+ykBwXXXVVcvOF3XQ0tLiPvzwQx/cPPPMU1MR5fXXX3fnnnuu97/LLrvU9F9UOhWOCIiACDSKQBHfv/ZK+6mnnuqeeOKJVtH16dPHDRgwwA0ZMsRtuOGGDuVDiQiIQPMQYBxo8ODB7sknn3S//OUvvRIL9WlTHl9ttdWa52aUUhEQAREQARFoEIHCFFd69+7tbwGN0qKVVkI2dNg0UqhsPPvssz4JWIGJC5Y2dthhB/f+++97DmeeeaaUVuKQOtHxIYcc4jt1uSW0qIcOHepWXnllN++88zo6FAcNGtSJ7la3IgIiIAIiIAIiIAIiIAIi0FkJ0NZG6eWKK67wS/VWu0+sTP7Q5OWXX/ZWNSvd9wUXXOCtwzKRh8HHIoWJQmuttZYP8vzzz3ebbrppkcErLBEQAREQgXYk8NZbb5X6luPR3n///e6SSy5xSy21lP8ezzXXXHEvOhYBEejABKhLb7/99u6ZZ57xP0sqllaWW245O9RWBERABERABESgAoHCFFd23nlnv0zK0ksvXSGqfM588KdMmeJ69uyZL6A2vBprML/61a+80grRnHTSSW6LLbZowxgVdCMJXH311SWlFZ7zn/70JzfbbLM1MkmKWwREQAREQAREQAREQAREQAQyEfjHP/7hLr30Un8ts7zXX399P3A2++yzezesjdAG+qELg4hYnEHoo3j77bfdHXfc4b755hvHgCPWYrG8KREBERABERCBagSY+HnUUUd5LywxMnbsWP8d4bvy6quv+qXe/vWvf1ULQudEQAQ6GAGW6RwxYoS75557vPVCJnuzfKeUVjrYg1JyREAEREAEOiyBwhRX6LxZd911a97o9OnT3RdffOGs86vmBd95+PGPf1yv14b4Y5kYlFZGjhzp46ezCpNwks5JgDXiUUxCWDP8sMMO65w3qrsSAREQAREQAREQAREQARHo9ASwtnLGGWf4+2QgjeVQsSYZCmbPpbjiHFZm99hjjxCNO/jgg93PfvYzRzvxlltu8W3EJZdcssyPDkRABERABEQgJIDl5vj3BEUWLGyxVB/LCbFM3EILLRRepn0REIEOToBxr6222qqDp1LJEwEREAEREIGOSaAwxZV6b+/f//63N6/LetszzzxzvZd1aH9ffvmlY91otOER1r7efffdK6Z52rRp7q677nKvvPKKe/fdd70ZYRRzWBO7f//+idfdfvvtvhMMrd0VV1yxlR808AmXZWqWWGKJVudxeOGFF9zTTz/t08nMMOJk3dRVVlkl0f/NN9/sxo8f72fZJa3VTafl1KlTHeszhopFLJPE2rLIz3/+c9e9e/ey8OH0/PPP++e/9dZbl52zg3feecdrJ7/22msOvgMHDnTLLrus22CDDTrEGq/33nuvTxedlltuuaX729/+5mfajRs3zi2++OJei5o13+PLZjFL8dFHH/W3udFGG7k555zTbrlsixnqF1980btR0TVLQ6wvf8MNN5T5jR/wXoVc88YZD7/eY2aI0Mhmtua2227rHnnkEX/vKHdZ3ltjjTUqBke6H3jgAUdeYAmuWWaZxdH5yzvAuxJnawGRx9988007TNxWe08SL6jiGOb3Kt4q5vc05QHsGDBA6BifddZZK0b51FNPOczP0hGyzTbblPlLE6dd2FblgYVf5JYBA8rMuNjyXYsuumj8VNlx2rx34403OpaOo5yiTK0k+MEvwmyLIgczKKt5X8j7vHvkDe4TM/KUSUmSJR+E4aSN89prr3Uor1aTBRdc0K8BHPeT9plwfZ58UO2byzfppptu8klMKkuy3me1OOM82uKYMoN6AnkIC3I8ixVWWMFtsskmreprltZa6YjzCcvLpPoB3wzyb/w7Fo8nbd7j+iz5Pfx+JpW5Dz74oO9IJny+Z7XKFvzVEuqGPIdasvHGG7t+/folequ3vlfUNyUxETUcs36rKUOo89ZaOjSe98LkjB492teFP/3009DZ72+22WapletbBRI4ZL1Pe8eS6v21yqAgelfve029nfdz/vnnT5yIcOedd/o2AW0M2IaSNa1heRCGF99PKg+y5oMwzrRlULX7JM1JbTGrC88000y+Lhy/NzumXUT7iNmYw4cPN+d238KH/IVgaSWutFItQbRRHn/8cUfbbdSoUW7ChAk+LCa3UD+iPhIvI/PyaUQZXY3BgAED3N577+1OO+007+2NN97wdb0wnWnbf2EZQr3GhO9PeIx7te8C5+l34DrKBdpVfOO32247R/6sJGnb5PF3jMHWtHFWSksl9yztYwuL9jbt7mqSVAaZf+oWzz33nC8/48tIk9+rtXUtjDTbLPXh+DNJ2zeTJU7uKSvbamVtte9fljLI2HMtfTS8G/Q78G4tssgi/v1dffXVK/bR2fVZtuG7Xen6SnWZLM8kTz6o9kxIe9Hfv//+97/+OdDfQz9Gt26tu62tzUD8tFXmmGMOdjuNcO/0M5533nn+nvi2JimupC2jCSxrWzUr3Dx5z+L8/PPPHfmCuhLtlwUWWMD3U1O3qGdSbL31YeLj/bI+2yztvyxtzrYoL41dpe1tt93mmIDL8vbx/n76Av7zn//4SyvVTbPkvUppqeVerQyq9l0g3DxlLdenzXu18ruVXdXqFsTbXtKIfJC3DEqb96y9kXZs4v/+7/+8VUXqyyzbliRYyWKcE6HdRN0hLvX2zcSvS3ts7fnwOvLZwgsv7MeobFwpPG9s2rOtWlT/eZp8wHgU7VPywC9+8Qvf9qFd9Nhjj/kxWVt+9dlnn3Wvv/66R5RU/qctDwgob37PEmf4jOvdz9N/nue7mfb7F36ffvrTn7q+ffuWbvHuu+/27U78UIdmPDVePyStjJ2QF1jdJd4uoj+WOgNjaZwPJc+3KGs+qBZnmLZq+yxZx/igyU9+8hNfj7LjDr2NOn7bTaJCtGXXXXf1v3PPPbcl6nRst7iLiuiUU05piT5E/hd1xLREHRQtkWWVktvJJ59cNaqoIdwSdZSW/C/yXVhso8H8lmgwLPH6aKDeXxNZckk8H3WU+fN///vfW52PGuItxx9/fEvUeeL9EFf4O+GEE1oiBZRW19WKc7HFFvPhXHTRRWXXcg8WfqTIUXaOg7PPPtufj5aVanUOh2uuuaYl6hgvhWFhsd1xxx1bksJMDKgNHeFJeqLKS0s0+Oz3OQ5/O+ywQ6u0Rh/Akp/IfHTFFEYfUu8vUgpqiSpCJX/kn0Vi8cSPl1lmmZJ/dvLGWRZYioNolmYpreF7E6Y3Wl4psRyI1gMtXRv6t/3o49GKrSXtyCOPrHotYURm0M177m2Y3y19Sduk/J62PLjssstK93bmmWdWTHtUsWkZPHiw90u5EkraOO3atioPLPwit1FFo8Qp6VlEioUt0YBKYpRZ8l7UkPHxRWY/K4ZLZBdccEEpXfFyMzExdTpGnQotkSJMKez4PUcdXq1CypoPLKAsccbTlXRMGR+XLM+EMPLkg2r5ne+8pT3pm2vnqm2T7rNanHEmRR5HlfqWAw44oHRP8XRTllCmhGJpjfuNH8f5hOVl/FseKRG08P0ijKTy0uLPkvey5vfrr7/ep4c0RZ1QlgS/jdadL9WrIot7LZFCcNn5rAeU7cRX6xc1qltFkba+V8Q3pVUi6nTI+q2m3l2LDefjeY9kUZ+KlMtbokH0imFEA8113kF93rLep71jSfX+WmUQKUv7XkfrrXsmkaJx4o1FiuP+/O9+97tW57OmNSwPeGaVfknlQdZ8EMaZtgyqdp9ASWqLXX755f6+OFdNaItx/2uuuWY1b3Wfo55LePyoe5iE38VomV9zLm2jgZrSdcccc0zJPdyJOtxLfq644orSqWhQreROvPEf7VDaT6Hk5dOIMnrDDTf09xYpiYS3UtqPOsxK925tvTxtsXrKEGMddbKV0sFOmN+jgYmWSJG5lDa7hjZntLxR2XV2kKVNnjdOizvNlm+83U+lbbx9bOFXaqOG4SSVQVwfLQHQEg1SVIx73333tWgK2WatD4fPJF7ukbBqfTNZ4yTcrGyrlbXVvn9ZyiDSOWbMmJZISbTic+RcNMiC10Klnnc7qc8i6zPJkw+qPROgFP39i5ZdKz0P+oviEnW+l54Zfathf1nW7188jvY4pk9ikeh7SX9ikuy2224lDtEAXSsvWcpoAiHOWr+ktmqrBNTpkCfvEUU04OLrR0lppt4UKf9WTEna+jAB5albZG1ztkV5WRHKdyfs+0XbKBTa5FbXoV/+vvvuC0/7/ax5r1VAdTpUK4OqfRcIPmtZy7VZ8l61/F5vfwdxt5c0Ih8kvctxt0plUJa8l3VswvrUqSdVkmiwv8XSTtkRStq+mfDaLPuR5a5SWixNtqW/mLwZl7xtsXh49RwX0X+eNh9EljBLbOjHQ/75z396N8oXJFLka4kMBXi3aDJTS6SE6N3tX5bygGsXyfHNzRqnpTnNNuwnSEpztXGUPN/NtN8/mFj67PuPfkFSWR+tDNPywQcflGGoVkbjkfETwo+PpXEuz7fI0lxtm1TuVYuTNNUjhx9+uL8nizualFbPZR3CT2vV9TZUs8G6BNryaCSixYZWIpYYmlWwNrLffvuVtLGjDOaiF6Xi7URP3EWKO15LHE/M3kCzOepA8LO30aLDxDBWPIqcnRN1mriosunThaWLqBLqtQvRWEfzMyq0/exmrOA0WrBEE3WK+2SgFbfFFlv42YdoQaIpzYwt1hM3jdZGpddm6bLsFcKs580339xr+qHJiVUMtF3jacXCAtq6zKyMCnZ34IEHtrqFqFAtWdXAckqlWXDkHTRnTV566aVESyNFxmlxpd3+9a9/dcy45Hmi5csa8Gi3RoP3bt5553VRo7wsSDT/kagTxL8jUWe3P44+UD4vw5iyA21KtCSTpFevXo5ZjSa8f2btwtyK3jLbhxmMoTCLjxmXcclbHlx88cV+eTK4xuXCCy/0FhPi7nnjjIfXDMdRZ4bPY1GnvC+rKW+xmIRlrKhjq9Ut5Ml7aCiff/75pTWqw8CxEBENHoVOheyTp7kXBE1h8iDvPGlhBihl0WeffVYWV958kCVOEsCMOeJGeP+pByDhe8M3Ki55nomFlTYf2HXxLTO8ogHxuHPZcdb7LAuknQ6oj5FeZlog5B3MUlNf417vv/9+X5ZQ30kSymasB8WllmWwuH+Oo45pN3ny5KRTJbcseS9vfi9FHuzwTTv11FO9C3Uq3ns08ouQaHCtzGoa30us5PE9DC3uJZX9eep7Wb4pRdxvnm/18ssv72cJhumolve4RztPHZNygecWdTKX6vJhWEXu57nPMB31lEF53+swvjz79aTVwg+/A+ZWqf5k522bNh/YdfFtPWVQ/JqOehzWjevZt/vA4o4J7ZE0Qjy8T3zHsRTEd4QynfYJM8WxaHDppZd6y6BtvZxuW5bRtZh8/PHHJS+0cZA8bbG1117bUX4g9BVEnax+P+rEamVRjxnolYSlbflu2LvGUkaklZm/7IfWOgmjiDZ52jgrpT2Ne73t43iY1KFpy4dSrQxith7tfLNQRF0o6gz0l1N3ot5ftBRRH06bpiLiTMu2UhprfVOylkF8M6lnkU7et2hylm870t9DvZPtr3/9a3++LSxipa0fFPFMKjHuKO7MoI2UK100oOaouzGLndmhCN+SSOHePzP64CIFwbL+snq+eaGfjnDPtFWsrUV+jJSpHFYQ6K9FqP/TDgglTxndTG1V+k2xkIcVE95RrNDwjmLVgjo9dZVoAoFvs8bbRkXXh2vVLYpocxZVXoZ5Jc0+9QJmmNNXgwVfLIvz/oWSJ++F4RSxX+u7EMaRtqzNk/fCeMP9ZmlrtHU+yFoGFZH30o5NhM8v7X6evpm0cYX++TZGA/beiTKSsbSxY8f6fmPqqoxJdQTJ2n+eJR9gVfeqq67y7Z5oopgfm4ozwJ3xAuoIkZJwmZX/POVB1vyeJ874vaU9Lqr/vNZ3M0xXnu8fdSZrI0cTDhz9Qxxjlee3v/2ttwwYxlX0fj3foqz5oOi0Nlt47aq4QicWS+owWEiljsJmvvnma7UsABkLU8OVhEKE65OEQZQilT6S4jC3Qw45xCsgcMy9UbBVEwbZbTkhKtd/+MMfSoPudOBRIafzg8KyqHt4IDJ/ZEorVECJ0xQhaPAddNBBfkkNBuJQBIBfo4RnGmmy+ugZUECxA7OFCCbBDzvsMN84YSAWxYV4Bdp7bKd/YWOXSgGNJmtMooyCEtB1113nB40xMxk+T541HyDMlHEvcbOMhAUL4qim2MVyVKFiBvmm0hI5RcWZFa8xsg69aGavHyylcKcxtNNOO5WZgKVjgooFA6gha+JH0Yp3DTNXDz30UKkTI542TKn+5S9/KTnDlM6ntpQ//vGPrZZuoDMlSXElb3lAWXHWWWc54gyFzrUkhQz85I0zjKdZ9inj7B2jwyOyhOS/L5SNdHxhOjGUvHmPDja+c/HBA8z81hqUD9NRzz4DGNEME++VwRGeO4PeofCdDQdROJcnH2SNk3j//Oc/s/ESaTmXFFdOOumkMvN+5se2eZ8J4aTNBxZ3fHvcccd55du4e3ic9T7DMNprnzxjSit0EP/+978v1RFIAyYWIw1431GYlCaeTVLdB7OqfLfrFb6FXFNNsua9PPk9KT2Uu/wQvlPsW70qyX9aN5ZE5GeCwiYDZLCOrDKYc6tt3vpelm9Kq0RkcMjzraaOFDffWSnvUf7bcyNOBgNM2Yg6pZkEz3ALdV2S5z7DCOopg/K+12F8efbrSauFn6b+ZNfYNk0+sGvi23rKoPg1HfmYerW1IcJ6Dkol1halcyguKMRTn2DQjDKFTqB6B2pRziDspCUd6DQlHDooqbu3peJKW5fRcWbhMRMaQuXWcPnerG0xlv/hh1DHt045+g3MrHWYhkr7KCVRPtL3guy1116+nUXZT50sVFwpqk2eJs5K6U7rnqZ9HIaNmemw3ci5Sm04zqGcAjuEgajwW8SzwcR00VJEfThtmoqIMy3bSmms9U3JWgaxzEg0E9GXmfFlnOkvQ0GZthTf1nrLw0r3kOSetn5QxDNJSkdHczv66KN9WxHlRxSHWLaECX60GzHjTx8R/V9Wpln6s37/7PpGbOmTqLS0L/1hpixvactbRjdLW5X7jCy1eaUVlqth8iL97ib069AWo48FRqeffrqd8tsi68P11C2KaHMWVV6WgajzgLoadRWW+EPJ47LLLmu1PGjevFdnUur2Vuu7EAaUpqzNm/fCeG2/Wdoa7ZEPspRBReW9tGMT9vzSbvP2zaSNL/RPfSis09LPQT2V/hDGBzuK4gppTtt/nicf0JdG2wkFYL4ZkUWdEjbG0kgLQpsoZJS3PMia3/N8/0o3lnGniP7zer6bYfLyfP+sn48wIouwfgIN3zGUn5mkwXJEkYX8MLpC9+v5FmXJB4UmskkD69re6WYAP5zNEpnscpE5orJkoABAgcqs+KQfBW2SO4MvYeFSFmgbHKB8YMLAdDVlG/yZlRA6EA899NCyAXk0wtAeR+joYJCiCKHDBenfv78f8AgHV9hnJhSChj8DnElCId0ewqwm60yls9WUVoibNV5RXLH0G8v2SFdSHGEnMGk1pZWktLLmbyg0rmabbTbvFL8PWJtyxZAhQwpbT7kRcYb3zPrpprSCO4pJdCwidATRiR0K6aVTJq60gh8qXOYeLzvCMDr6vj37POUBndfxsoIPtnWkxhkUEWd7lQfxtBdxTPlBWYvQSZk0aJM379HxFDYSiIvGP9+6ooXnadZUKB/jSivEh3IbijSh5MkHWeMM40+7n/eZxOOrJx/Er+GYATwGuDuTYCkEoXyOK63gTsP+tNNOK32zcCtaqO9Fy6HUDDZr3suT38NEUfahKGaNIgamULa1eknotxH7RdT30n5TGnGfWeNkwJzyGaFcNKWVrOE14rp6y6CO8F7Xm9ZGcIzHWW8ZFL+uIx9TLjFTll+8jDL3pDoQfukks2sYtOV9ob1IPYMfiriVJElpBb8o89KRhLTVJIn2LqMZKMMiKD8U4VDEZ4CbWeAInaHLLrus3+dfo9tiTBoIB3gZoCe9yOjRo/3W/hXVJk8Tp8XdLNuw/fWzn/2sXZJddH24nkQ3Is6kdNX7TclSBtEmxIpyXGmFdNDpbc/X+qmS0teebh3lmbT1PVNP43vDM2AGNMor9FcyIIFg8QgLOXHJ+v2Lh9NRjsnT8ckARZXRHeUeK6WDPkLrcz/qqKPKlFa4Jlq23a233nr+8ttvv73V5NYi6sNp6hZFtTkr8WhLdyysREsH+n4r+mppE6688sqtouxIea/e70Krm6jDIW/ei0fRLG2NjpwPisp7accm4s+y3uMi+mbqjauWv7A9Yu2xWte01/m0/ed58gGKjyi3IpTXKDOYmJIIdUJbBcLOFV0eWLjVto2Is1J6qFfVGkcJr03z3Qyvy7qPNSGbjEkd2erzrIpiEi2NY7uFb9vyW1R4YpswwG5500xHDWbMswqVcJZPoCK64IIL+mAYlMYiCcJslnoExQHM/9AZ156yyiqr+JkAVETozKMxZUoJ8XSguYow+5/KYFzoGLz66qu9M51eSR17mDGdMGFC/NJWjRnzYJYeUExBu4sCxMT2WbolWiu+lQIRWu0Is32zCgMFKJ2Ewj0kifHhHCziQmcb5p5YXsI6BeN+2us4zGfDhg1rFe0888zjlahgF08r16K8RWOAd4fZ6hYeSkvm32ZKhoHbMwvd6tnPE2c94dfyE84eN7/rr7++7ZbuueTw3Q5ar8yyQbmFmf8mNODJ00kWLLIysrDba2v5PWt5QDlJucOgsi1Bw8c6rigV3k+eOIsoD1CyIM0InVF9+vQJk9cm+2hTU2ZiKpbK38033+zjQYkyXjaFCciS9+x6ZrTSWW8zglBkYaki4ot3PNk1WbZmYYlvjik+1hNOnnyQNc560lXLT55nkjUfWJpQBjPrRpRn1H2KfJYWD1tmbJtCEo0E6gvV8mp4bZp9lkcxc+PMrrKByjRhFOGX2VzUVbhHOh9RTE6SrHkvT34P00FjlueOYMKcsteUKEN/jdrPU98jzVm+KXnuNeu32r4hluZ600AnjdV3rRO83mvz+Mt6n/E46y2DOsJ7XW9a4/eY5jhrPkiKo94yyK5N2xbjOr4XlOvtVfextGbZUr7deuutvl2Lgjhli5Uv9YaHf+o9WAhhWVpmHTLQj7IMM6TjUgSf9i6jUUquZDkGSyNYAgi/q41uiyUNQJl1QAaFQ7HvJm552uRp4gzjT7ufp5y1siTt9zxUAqK9j1WE9pI89eE0fTPh/WSJMyvbMF72s3xT0pZBxEN/GEs9825jdcqUXRkkQTim/yFJ6c97SPkvT74lqizPxJKYNR+09/cPxXpmRaM8ybPhh9AHy0TAziL0baF4gfDeUJdjhjATX+gPp+8Qax62FE5RZXQj+KXJe9b2I52Uswyq23tjW8oHhC31DJtQWFR9OE3dwp5Llv69vOVlnv4DyksmZDD4h1x77bWlfizvEPyze8QpT/0gCDLTbprvguWVNBHlyXtJ8aRtaySF0dZuHT0fFJX3soxNMO7AeESafjl4IlnG4vI+a9pgNm5InYalWk1YAiZJimiLJYVbj1ua/vO8+QBFZXhQLlxzzTU+eXxb+CGUhab44B2if0WXBxZutW0j4gzTk6f/PM13kzjzfv+wwmcStsewJEtblwkaoRV6G9viGuoWNpHDwkizTfMtShNukt883/ljjjnGHXzwwaVgrT5ZcujAO7kVV2jghQPKWe6VBhDa9Jh9tMEZOg8qKa/EB4sapbSChj/mB8844ww/cMwHgYEtW7ohzoLzSNjREfqxdbBxM7/hefZZfoZfPUJnIRkb4SW96KKLql5G5T4UFGfobGR2CXFiphjtQxrtmOqMP4fwWtu3dfXsuNr2o48+Kp2uxSj0W7qoHXdmmWWWUmzhcys5RjvmnpRWtHxRXKGQo5HKxxFheSSEgpSO47hQATFJ22mSNU6LL8826XlSGeB9Jx/FZ/rBDPOwdHRXE/vAhX6MUUefTW3veBIb7sfyD/vml30TtEeZcYTyE4NwWJtiIBUm3DuKDHElFgsnS5xFlAfxxi3lCdrfWN8JFZnsHovY7rHHHq2CQaGED3eS5Ml7rFnI+8w3jeVVaKBSfmKCHUEZjbIUJZoihGX1ECpk4eBIrbDz5IOscdZKU7XzeZ6JhZs2H9h1tsXKBt9IBr4xA2gKDHa+yC3LxfEz4dlS4abTctdddy11Wtr5rFtrnHG9dfJlDSvrddRTGNxD+EZRNlVSXMma9/Lk9/C+wmdO3YjGXHso34VpqLSft75HuFm+KZXSU4971m+1DSQRB+9jvUK7AktudPzTQEW5kI4rBgusY6nesNL4y3qf8TjqLYM6wntdb1rj95jmOGs+iMeRpgyya9O0xewatgy0IdTxre5jM4X9iQ70j1nMzDaztjhLDDF5AGFAt9bEEq4zBUy7Le6fJWttooq52zYvn0aU0WaBk+809XbqY/QPUJ4mtUM6WlvM2rNWTtmzCNuutdoMoV+73rZJ11aK067Jsg3Tn7Z9bGVJmu8JaaTjn0lBtLuYvMSz5fkjlCttIUXUh9P0zXAPeeLMyjbOLss3JW0ZRJvt3HPP9ZY94vGHx7Th2i0h9AAAQABJREFU0uax8Ppw3/JtUlkR+ovv53kmFlbafGDXNeL7h1l/LFtdddVVPhkMHDKTPU3b19LfUbfURVnawcQsifBdpA1LfztWvWwWeFjuJpWzhGN9SaFfC7+R2zR5z9p+pJe8V0toq1ubtqj6cJq6RZ42Z97yMk//gSnnGV8UxGwClrnZNsxPjcx7ab4LWcraPHnPWNk2S1vDrm3PbUfPB0XlvaR8W21sgmdA+cuPspp+uS222MLtElm1jis42PMqom/Gwsqypa8jvhoF30ysBYfLmMbDztsWi4dX6zhL/3nefMCSXdUkaanPIsuDanGH5xoRZxh/nv7zNN9N4szz/UNJhvEXE6v72DHvKOON4Xi31RPwwxK/LCNHXwcycuRIVyuPeI/f/UvzLQqvy7Kf5zvP2Bu/ZpRuHSXRKEWY0oqlqZryivlplNIK8dP5xofrkEMO8ZZWsHaAMgL3EjdbSaeGvUyVLLJgBtPEZkHbsW1hYp1k5sYWBaK4jB8/vuTEoPA666xTOk7aYXZYKJgKvOWWW7wmIh0yRx55pNcwRUu+3oFXGvbxRiUVxySll/CezQJJmB72jV3oN+6nPY7De6qUVuuESEornb80BtAMpBGG4gr5w2ZaYJI26Tmj6GJSKV47H99mjTMeTpZj66QMr4UhP/ICecqEjxbrrtsyQCuuuKLDOgZrGltHEe8evJLykSlrJcVpcTR6W0R5gLlcTJLRkcHa6kcccURpua+999671YBe3jiLKA943qasRHrQXGe9TX4oAzAoX7QQJ/mGvGKNVqwaYRoQjVPKVJO8eY9GDDPC6MhG6YpONqzhoM3NADflaD2dLpaeWlvTog81hmtdkzcfZImzVpqqnc/7TCzsNPnArrHtW2+95S655BJ/uO+++5YGJux8W2ytrONbyzNjZgHmjvlGoAiVZLUtbTrCOkKaPJQ2nmr+UVj+/PPP/ZJEhx9+eMn8d9I1WfJe3vweTwcW1lB8oPFDmXvhhRfGvTTkOHyWWep7JDrtNyXvjWb9VlvDlvjTfudRLLflPe655x7Hr60l632G6UpTBoV5oRHvdZq0hveYdj9PPgjjSlMG2XVp2mJ2DVuz+EO7gGXn+GF10UwWh34bvU+9BWVohO8nE0wWW2wxf0xnTi3FFeruWPHg24WiC4LCHwrXlDXU6eNSBJ/2LKPp4EprdriRbTH6LOqVsO1aqb1ZT5s8TZz1pi3JX572sZUlaRVXUD5DSZ0OUzpD6bhsSymqPpymbyZvnFnZhhyzflPSlEFM9OA5InTqoiiBpQ9TTqadSpsbyTJz31+Y8C9L/SDvM7FkpMkHdg3bRn3/wrKEvgS+Sz8EQcHaZgvTr2BSVBlt4bXnNk3eszot17CkbS2xegr+7Fr289aH66lb5G1zFlFewgnJ0n9g7BjTwMrRaqutVrbcoQ84+tcR8l7a70KWstbyT5a8Z6xsm6WtYde297Yj54Oi8l5S/wHfFH7xsQnjb98c3i36klF0pV5Av1zYn2z+Lf9wnLVvxsLKurV6LRZfKJ/4kRdRsrbJCPGwi2iLxcOsdpyl/zxvPsC6SzXB4j/loL0L+LXnWUR5UC3u8Fwj4gzjJ89zv/WMo4TX2X49303zm+f799xzz1kwfmvtU3O09zPMN9Tx+cZh2Y4fRgOo8/OO0Dddr6T9FtUbbjV/eb7z1cLtyOfq78GocBdDhgypqrGXdBkPNxy8YxZ+XNHDriOT2WyveCdZI5VWSJ99vFBQoHLHwC4vNYMZDGyEFWT8YoqH2Wdmgs/u0bahe1yJxPwwg4tOzrgMHDjQxx2680GCHw3sQYMG+dlf4fla+0OHDvUD4twbA0a8xGZKmM661157rVYQvhDo169fmT9mSGClJi7hPRNP0sCcxR/6jYfTHsdhh2ultFrBmKTRSxoxL81gPR3AmLWkwLVKddIyQVxjYbKfxAf3apIlzmrh1XsO5Yr4WoooW1GJQkJGI0aMKCmt0EDdbbfdyqJB+aCStQw8GqMsfMoiasODIsoDU4Rg5g15yDjxbmD+zgba7TbyxllEeYD2MuUBZRIaxE8//bQ74YQTSiagma2Y1JCwe8iyRZkQc60IimIXX3yxu/HGG30HMzN+wkp73rxHHChdoRRDg4a1t00pi4GaeFmI/zzSv39/b7YwNHtXK7y8+SBLnLXSVO18Ec+E8NPkgzA95FXKIRqpVHBRXGlrYRDTvvN8E1gej2UEsXTGt4LZHihB5RWepUnc6pW5t+X28ccfLy3dhdJKrTI7S97Lm9/D+7cBZpalRNud58FzSVr2IryuPfbz1vdIY9pvSt77yvqtDhW1034vmHFBWUC7AysrfC8HDx7sZ8fzPWoLyXqflpa0ZVAj3+u0abV7zLLNkw8svrRlkF2Xpi1m1zDQRh2EGcB0kNC2okOKcqVSnd+ubcSWZRVt3W/aDmHnXT3pCa18orzCUo1YJcUkM8csfxBKEXw6chkd3muj2mJhGmrth+3sSu3cjtIm516snGW/Vl0CP6FYWZL2e0IYTDxh4pHVDbHqxTFKaVb/D+PKs19UfZjyJ94eqdQ3kzfOPGxhleebkqYMom2ILBpZG6aNGOfTVnVky7dp8mzeZ+JvNPqXJh/YNWwb8f1jKfbLIos4JvQrHXjggf57Qj2/LYS8R7+uCX2qfKcaIcwEJg+Gs8ybrYwOuaXJe1anpR1OX3vSxL4w7HDfrsUtzztcb90ib5szb3mZp/+AOh7jM3zXN9tsMz8plneMCTPxQcBG570s34UsZa3lnyx5L8yHWdsaYRjttd/R80FReS/N2ISxZ8IjFijol0PZlSVVadOxAkOSZYoi+mYs7izb1Vdf3fd5cC39iUyqxKI9/cZYOw6/qRZ+EW0xCyvNNm3/eZ58wLOHBbL11lv7Oh9lCoKiKOMV1N+pC4b9rUWVBz6iOv81Is4waVn7zwmj3u+mxZfn+8dz4ttl419W3lvYNsYajuFSp8P6EMYZLD9Yu40JG/SL1hpjyfItsjRl3eb5zmeNsyNcl7v2TcMOpYh6f8xioLPfhNkQVEKriSmvmFlBNO4brbQSTy9rONuMOWbd2KyN0J+ZRkZ7L0lCd/Ob5K9eN144M5MUmkqs93r80blG44LlSJglx4Asx8wwC2c+pAmzkt/wnkMWoX9zD/2G59mnMoFijP3aYjDCPiLEZ2liPxQz7VUpraFVFZYIsmWClllmGbf00kuHQZX2rZMQBwrTtJIlzrRxJPk3FuE5BmBN0LQ1YWkVhIbpLrvs4vfDfyi+hR0J4Tn2qYwgaTqB/AXt/M/yRaX8E7qb33gSGexgxh9iH1qUeip1vlo4YdhhmKG7+Q3PF1UeUKbTMCL9Vv6jGFePMlyYnrT7WDkKK6ChCTvCypv3CANFRpsRZM8E07909BUtfAcROmPSmCW3Zxs+7zBtobv5tfNZ47Tr026LeCbxOGvlg9A/VsdshhvfdbOkFfppy306i1in0yy8EZcNJuaN154l4bDcWHvLscce66PEjGk9A7eW3vbM7yETq4PSEEOJCWG/LZeZCeOvtl9EfY/w035TqqWp1rms32qz3kD4cYXYWnFy/sEHHyw9M5QHsIqFYmFbSdb7tPSkLYPsPeH69n6v06bV7jHLNm8+IM60ZVCWdMav4fmgpMuAAEKHeLguc9x/I46Z9XT66af7qPkG5X0/6Lzdfvvt3QEHHODDZFA/tJAR3mMePh25jA7vMU9bjPqzCXmnrSSs+4V1wjA+cw/9hufbcz9P+9hMR8eVFepNPyamEfq06Cvhm1LNDHu94cb9tUV9OB5H/DhvnHnZFvVNqVYGURZZXkYRKSkftFUZnaV+kPeZxJ9xex5nKd9RdLS2uy2LTJrpj7SlRtviHphYQ1+F/bDC0yix/gSzHEs6wnLX8m88feYe+o376ejHVqdlgChtf7Zdyz3mqQ+nqVsYa2Mf5xu6m1/zk7e8tHDYpu0/4N2i7Ft88cVLEwTpq02aLBimO7yfMH5zD/2G5/PsZ/kuZClrLf9kyXvh/WVta7THmEaYTvY7ej4I85Plsfg9mHvoN+4nzdhEeC39LSwTyYC39UFU6pcrqm8mjD/rPuUB37ANN9zQB0Hfpq0GkRRmlm91Ujj1uqXtPw+frT3veFzmHvrFD1ZyaD8xlkn5hvIKwtgT/Xr2rceSjim0cL6o8oCw6pVGxFkpbWn6zwkjzXcT/3m+f7yT9hwJiwnEJtSbTOk3VHjiPJPnUeDifWBiDRMSGffGQqwZz7BwkrZZvkVJ4WR1S/udRw+DlRrsx7hqs0huxZU0N4rJHcyoYqYbWTSa1cAa82EnTKXwQuUVOgaY8crHoCMJM4bRiEfQ0MPqSiisVYowOGvLoITnzQoNBTeNpCIETUvk3nvvddUyJoPGlYRCnLQziwjLBaYMU8l/Vnc6eWzWBAoyccGqhBVCtv5Y3A/HaEkyc8h+XFe0MBBtsy6S0oqyDJV9xJ67Pwj+oXiyySabeBdm0GOGFqk2gBc22LIoZmSJ0ycq5z/eh7hQGTAJFXXMCgsd5/a8zR/bv/71r+Fh2T4KLcY9C5+ywNr4wPJFnvKAPEjHCmUhPwa4rbKVlPwi4iy6PAg7YcL9pPQX4RbOuIlr0ebJe2Ha0NbmZ8+FZ2RlW+gv7z7m7RDKb8xVVhL75tr5PPkga5wWd9ptUc8kHm+1fBD6tc5CGnpWAQ/Pt9c+HKzxZN+evHFT2UVREmHmSMgkDBstdYs7dM+7z0AT98Is/HrqgVnzXp78nnSPcKPDgnea7xSDsTZLIMl/e7kVUd9L+03Jem95vtWmjDzPPPOU8m+96aCsxEQuss466/h6bb3XZvGX5z4tvrRlUCPf67RptXvMss2TDyy+tGWQXVfENlxH3szjFhFuEWFceumlpU4eLPhVWr89bVy23BvvBb9qkodPRy2j7X7ztMUYWLL6ZFIbyeLIuy2qTZ43HfVen7V9TMe99clgyj2t0AH+zDPP+DqMKa2nDaNe/21VH64Wf54487IlXUV/U5LKIN4nq1cn9ZXhFp/oUI1Zveey1g/yPJN609bW/uot37lXluBl9iyWjGjrMuFlq6228klkCVWUkdtC4t+o+HFbxJkUJoOiI0eO9KdQKjBptjLa0p12S9+zffNCK0pJ4cT7s4uuD9dTt8ja5iyivExiwjtkbXgr55L8hW4M2NGPhbCkBlZGQ2l03kv7Xcha1ubJeyGvrG2N9hjTCNMZ3++I+aCovJdmbCLOhWPq32bVodp7VUTfTFL8Wd1MOYB2ZzXFFQu/3m+1+c+zTdN/nicf2LOnHwjlIwwQIEyk5mf1C5QdeAdNiioPLLx6to2Is1q6wr7i+DhKpevq+W4W8f2jfmSGFcyCCmliLNj6adE/SBIUWtZYYw3HCgN8w20pniS/oVvab1F4bZH79X7nWcEGC5P2M+WuItPSVmG1q+IKyhM2Q46Ol4MOOiiVuT9TXsEUV0dTWuEBkSa0l0yOOuqokvUH3DCBZRVvBjqsw4lKNjPbbFY3M/FMg9PCyrplhgLWF6iw7b777t50bRgWsxj+8pe/+PW9khrrod+23qfAYBYaQl5hxpI1Emmw/eY3v/HnUOzhXhopzNwxBYF4Wuk8Q7EKqZVWnjVC5YFGBUoBW2yxhXeL/6PAtY4T/OEfN/vZoD/McDN28XDSxBm/Nusx2n1oM1rDCWZmyosKXai4gjanCYODZkkCzdiTTz65tMa0+bEt98tHygYAyPfGxrbmF1bW8WNu7b0tqjxAkY/ZV/ziptfj91RUnPFw6z3m406nIbMeKO/ogDJLQyjE2SB6veHV48+ePTPqMDWMEomJlTd2nDXv2fXhlhmY9lxYUq8thAo+GsYIzx6rHGEjhMolg06U8aHkyQdZ4wzjT7Nf1DNJkw/i6WOt1yQranF/RR3zDHlP+KGIR4cR3xTrkFtuueWKiqpkHYgyEaVJZveYUOaiSY5CnFXM7VxRW5bZqfe9z5r38uT3SvfJjCSzQkDdiRkajZai6ntpvilZ7jnrtxrld+oRKPoirEVbrbMoKW3XXnutt7ZCXTxpFmHSNVndst5nUnxpyyAbQM3yXlPOWHkZbq38oe6Eu9Xn4ulNm9b49bWOi8gHYRxpyqDwurT75AeWi2XJHCxCshQPwmyfsA6cNtyi/VM/YzAQoV2Wpr11+eWXO5Re+G6F1kA+/PBDf89mmYLyk0HIUIrm0xHL6PB+s7bFKLtogyLU+zBBH9b7wjjy7DdTm5zyKG37mPKLGXX0x6DcDdeNN944FTLam7RLkc0339zPVE4VQErPRdWH00SbJc4i2IZpTPNNyVIG0adindlYFMYilH3vGLSh35HyqUjJUz/I8kyKTHuWsLKW7yi222x23jUr+3BnJjJ57ZBDDinN1M2Sto5yDXUr2mD8GCwjL2IVEIutlh/DGcXNVEbnYTxw4MCS1VomvVG/pR5oQjlMvyKcWMojLnnqw/GwOK5Vt0jb5iyyvCyy/+C0005zTBBAsBgSKod2hLxX73chT1mbN+95eN/9a6+2RhhnEfsdLR8UlffSjE0YRyZyMSbFZK96++WK6puxNKTZhu35UaNG+f5g6jcIHJOsy2X9VqdJVzW/9fafZ80HtE/N+taWW26ZmJSVVlqpVNdAcc+kyPLAwqy1bUScYZqsH6iecZTwuvh+pe9mkd8/xl232WYbHzXLeNHGo15l4yB8M7CqWLTU+y0qKt4iv/NFpak9wunWHpFYHFiXIAOhvELFMotFBJRXTJPKwu1IW6yS8EJgPWXs2LG+omcdgDSKd4mWPmEdPCp/+KUBhhaumUxm8JaBxqIErcHjjjvOd9CjKEOlic5C3BnUDCv+RcWZJxyUmdAEo+OUihLsUOKh09OEdaTp6K0kcYWEemZyVwqrmjsVFqyk8PyyppUZ5DS8rTHArP7evXsnRsvgIZ3eCI000+iPe6ZSRacGSh9JSjBp4oyHnfUYJRIGfjHpSgeRKaPgfuihh5YFu9FGG3kzlQwGUlmgc593B61X7ptlyeBljXi7mAEplMVMsO7EL0lOOeUUX+kkrzVKGlEeNCLOkG81ixUo/cUHMsJrs+7bEm7x65lZiWZtKFnzXhhGe+8zYM6MCMpzvi0MGrGMCYoGZhaV704oefNBljjD+NPsF/VM0uSDePoY2IibmYz7KfKY+oNZYIuHizb53nvvHXfOfIwmP+Exiw1FVmYZMBucGfbUGcLBx8yRVLiQBnO8/K/gteScJe/lze+lyGM71NWY6YkmP+Yl0dI3hdaY13Y5bJb6XtZvNeWcdTbQDrAZMfXCZXDSGq/UhalLtKVkvc+kNKUtg/K81wyYhANj8fRY+cR2lVVWiZ/2A8FtWV7mzQdhgrOUQeH1afeTeFEfZTZSRxGsVZqVNspnOmTqFfIOFihQEKbtxfLAdLCE7TKYMzEhSYrm09HK6PCe87TFGBijvUNbyhRgLGxmEqJ8WIQU0SYvIh21wsjSPqYPaueddy4Fvd5666W2LEQHO21T3pEjjjiiFFZb7RRVH06TvixxUi/KyzZMY5rvX9YyiDiYIEU/A/UDyin6K2xGKZZ2i1wWMk/9IMszCXk2aj9t+Y65dhSREPpWN9tss1LSmSzI5BcGnmjrUlb985//LE0QLHlsoh36tarVaxmQYcm9UJqljA7TnGWfdxMreyiSsRzbVVddVWqXUwYz2IoktcHy1IcrpbVa3SJtm7PI8tLq50npTtt/QBlIm4n+E8YpKCNRHLKZ6I3Oe/V+F/KUtXDMk/fsOWRta4R1Z8JqqzENS2fStiPmgyLyXpqxCeNCX1nc6hN1A1aQiI9L2DWN7Juhf6pSez6cyGlptW3ab7Vd197bLPnAFFFonzIhLkl4z6hb0B6+/fbbfZsWpQikiPIgKc5qbo2I09KTp//cwrBt0neTsZ8i2wvkCSZd8s2iPkX/illGYjykKAuydk9s6/0Whdfk2S/yO58nHe19bbtaXOGFZ01vTD6axnx733De+MIKQ7gfhssszrnnnts7Udjdd999pdNoLDNjwAZoGSjixSIsBnXRrk9SyrC4Ks0utfO2LUUY7dCpRUcimm6cR1nlzTffLCmtMMhJJ1hSvGE48X1Li23tfHgc7sfPJ50jLTBgQJm0ooRhSisMpqGIYLOcLbz4FuUoE+KwtfzMrajtopEyBWnF4kHWtJKWsJGF+dNKgkZiGqnmv94408RXzS8dDyhlMZhuSisMbKCxjFZrKJQTzKg203o0RlmHkgFUKlI0QGwGQJiHqt1vGL7tp/Vv18W3YRrCffNnbrY1d7ZpywMLw7ZhWPF98xMvE9LGGQ+30rHFZ1vzFz82dzqd0CIePny4t7qSxUS3hRXfVoqTMoSZzXQwJy2tkzXvGeNK8YbpMz+2Dc9l2Ychy9L98pe/9BaeeF9Q7DKlleWXX94NGzasVdB58kHWOMNEhPcf7od+2M/6TLi2Uri18gHX2jNFsZDvY1zsvG3j5+04TEO4b+fDbVJYXEN5x3M8+uij/TKEVn8Ir82z/7vf/c53BHKvCOW0lblU+GlQxAdWLa2V7snO29bSF/onXp5FKHbetuE59rPmvaz5PUx/PE0cUyexe+B+xo8fH09y7mNLQzz+pIDT1vcsTNsmhWlu5sfSY+5pt2m/veafeGGNBSs6rXknksTSZ1vzg2If5SLKwfF6pN0bfsN9uzbL1tJd77Vx/5b+rGVQ2vc67X2H6c2a1jDOcN+YmZttcSeuPPkgDCttGWT3GYZhabW0hVv2436t7oOFBpQM1olMFSO1wvae2vgfJqR5txDqSpUG0cJ7CvcHDx7sFWe5nvxBZ5F1vPMNQekIC2J05JqE1+OWlo9x49p4WBy3RRltccbjIw1pJGtbjPoekxNoP9HfYOlJijtMY7hvfs0tKYysbXILkzjC/XriND9ptmFZ9P/snQeYFUXWhgsVs7Im1gwGTAiKEV0DomIWxay7iglRFHNOqIuoGDDimsXsrgGzmBUD5gxmVMwBs6xr+Hlr/3O3bk/fO7fDDTPzneeZ6VRd4e3qqrp9Tp2q5D7CU168rLA0Mx8zbZJR9H7Lv23tOt8n7B6UFmGdJozxjN5n96fZ5jUejsuTnbOt5S9NmlnZkrbxWyThGDxNG0R6tHV4ZrCxLmMFjFZQ2mEwvdtuuxGskC9/kOFfmjpryaV5JnZv+HzD/ej1uGv2TOKucb9dty3nomErbd+Z/McS7Ajj/jglG94ajzjiCB8GBV25paR9oIT/onmPHieMrmTwUvFS9+iH8Q7LJK7TTz+9wNgiS9tG2/22DfMQ7tv1PLZhvOG+xW3nbGvn2TJuhwFjd95RvnXwDZ0/9nknek81PrTZ1uG97CcdD3NPuXpMHsuNLZL85iSdLH1RNK8cI+Sxku8Hxjss739jcN7jKh6nEJbUQ5Frklfds/gq2Voek/QLWdpa8pS27hlX4kj6W4N7kFrpNEjL8muMOWeCcXC164GlT5rhvuUh3OZR95LoJkIm5A2dBno13nN+09AmhXkO98l30m8zYVnT7EfTJw4m/KB/5ZswxhvRb8PReyrtq9Pkz+4xrtG07Xq4tTC25VqaeoBBLIJDBYyTEYvT8sM588bC79ixY8dyykva9sDut62lyXG4b9fDbV5phnGW2y+VH769lNOjEGfIMBoPx9F+E7102v4vjN/2+S124403+ncUYzKeH9ewP0g6wcDiDMtk3Oxckr7I7g23lgbnwv0wjO1bmnbMlnsq6eftnmga0WML14jbdlM782Ta8EYsRQvME9hx2YW7MX6YYA1JB1FtwUiGNJmdj8UZxgPRjy3VzkOl8dOQTZgwwS+XgDXrggsuWNQYxsXDjxcGEmZZR6eDl49qCx+wxo8fnyivlieWZxg3bpx/DnSMpRoQ1t/jhzxhYFFKBg4c6HB/R8cQXQrF7qk0TQufZsvHb3PFzywlBgcoQ/ljXcKFF1642WiZ8U94rKK7detW9h255pprvGIXN6o2qzouAeoU7qA7TzU8enjq0kKNIPVoD+qRZiOwrjQPSepepXFWOxwDNPLNeoX0J9Tx5tr3rPUgTZppObTEZ5K2rPW6D6MVDFsxwGB8wAdj2t9GlDR1L2t9b0QOpfLUqOO9ltxXl2Idd76Rypnne43BBa77+TDATFaJCEQJsMwdRups+aDLR15+65b6fRO9v60c1+K3WF4s0/wmzyvt5uLJ8/dxc2k1yvV6jIfrkWZa3mnbIMZNeHzFOzTfKvgdVQ3Ja3zQkp5JNTgqzv8RaOQ2+n+5zL7H7ziMynhP+e7LkhF8U6z0O3qe4+HmStNWfnM2ct3Lq63lWWete83Vl/B6vXQaYR6S7tejHiRJMw/dRFIm0fCN+m0mms+WdpykHuRVtlq2B5bneqRpabekLd+IMbbkezYG7Rj/SFo2genyyj7rpU2cumYYghvDOIsgfzHlPzxbMPsBBUqpGWApo67LbfDhxzB/tRQG9d27d/d/tUw3TVrMKudjVBJ5/fXXC0YrWO+x7m0thA+zSfNKvl555RVvtMI+3lZq8VG3HmlSPgQlKH+VCj9EKzFwqTS+Rg1Xj/agHmk2Kv+4fLXEukebh+Uvf5VK1nqQJs1K8xYN1xKfSbQMjX6MNX2ci9BGzHeaupe1vjcih1J5aknjvVJl0Pl8CLSk9zqfEiuWehJgGZY0ywHXM8+1Truev8XSlDXNb/I06eieygjUYzxcjzQro9E0VNo2iHFTKW9uTVOp/5mW9EzqT6t156CttNH8jmNSTnMTc0o97VqOh9vKb07VvVK1Lf35euk00ufY+RUF0uhD6plmUt1Elrxyr77NZCUYf3892qCsfVF8ScqfrUea5XPUmFf5RozOW9J6CORmuMK6888995wngzssKkuegkcKZhXQ2LcGw5U82Siu/xF44oknCgesh4sLsUaTKVOmeOOaJ5980rt6Jn+4uGRN5WpJPdKsVlkUrwiIgAiIgAiIgAiIgAiIgAi0FAL6LdZSnpTyKQIiIAIiIAIiIAL1IdASdBr1IaNURUAEREAE2hqB3AxXbI0wAGIhmrfnCD72IGE6/oT+iUBAgGVlsKRnHb/BgwcHVxpn95JLLvHr1IY5Ouqoo/w65eG56P6wYcPczz//7Oaee+7opaJj1tpm3b7Q4jltmkURN/gB62+y9mpzxkqs70i42WabrcFLpOyJgAiIgAiIQOsi0Fb66tZazkMPPdS7Xu3SpUvrqpgqjQhUmUBb+C1WZYSx0Wf5fRwboU6KQJUJtNbxQZWxKXoREAERSESgpba1LUGnkehBKLAIiIAIiIAIpCTQbuo6WX+kvLfotjvvvNPddNNNReeqcdC1a1d38MEHVyNqxSkCNSEwcuRIN3z4cNe+fXvvjnbrrbf2ywRVM/F6pHnttde6Y445xi8bNn78eO9VppplVNwiIAIiIAIiIAIiIAIiIAIi0GgE6vFbrNEYKD8iIAIiIAIiIAIiIAIiUEsC0k3UkrbSEgEREIH8CORmuDJ58mR33HHHuR9//DG/3MXEdOCBB7pu3brFXNEpERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERCBlkQgN8MVCv3pp5+60aNHuw8++MD99NNPbtppp/VeFqaffnrP5JdffmnCxhy+2JYA7dq183+2TzwsEcTyJz169GgSh06IgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAi0PAK5Gq60vOIrxyIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAvUiME29Ela6IiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACbZuADFfa9vNX6UVABERABERABERABERABERABERABERABERABERABERABERABERABERABERABESgbgRkuFI39EpYBERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABNo2ARmutO3nr9KLgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQN0IyHClbuiVsAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAi0bQIyXGnbz1+lFwEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREIG6EZDhSt3QK2EREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAERaNsEZLjStp+/Si8CIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACdSMgw5W6oVfCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACItC2CchwpW0/f5VeBERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABOpGQIYrdUOvhEVABERABERABERABERABERABERABERABERABERABERABERABERABERABERABESgbROQ4Urbfv4qvQiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAjUjYAMV+qGXgmLgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQNsmIMOVtv38VXoREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAERqBsBGa7UDb0SFgEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREIG2TUCGK237+av0IiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIlA3AjJcqRt6JSwCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACbZuADFfa9vNX6UVABERABERABERABERABERABERABERABERABERABERABERABERABERABERABESgbgRkuFI39EpYBERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABNo2ARmutO3nr9KLgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQN0IyHClbuiVsAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAi0bQIyXGnbz1+lFwEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREIG6EZDhSt3QK2EREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAERaNsEZLjStp+/Si8CIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACdSMgw5W6oVfCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACItC2CchwpW0/f5VeBERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABOpGQIYrdUOvhEVABERABERABERABERABERABERABERABERABERABERABERABERABERABERABESgbROQ4Urbfv4qvQiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAjUjYAMV+qGXgmLgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQNsmMF3bLr5KLwIiIAL/JXDFFVe4r776yq244oquV69ewiICIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIlADAjJcyQj57bffdpdccon75JNPfEz777+/W2GFFTLGmt/tp556qhs3blyTCDt06OAWWWQR17NnT7f++uu7du3aNQmjEyKQhQD1jvoXlRlmmMHXvS5durh+/fo56mI15Ntvv3XfffedI72OHTs2m8SVV17p3nvvPbfzzjvLcKVZWgogAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAvkQkOFKSo4vv/yyu+CCC9y9997r/vjjj0Is22+/fWG/EXbeeust9/zzz8dm5aGHHnKXXXaZW2aZZdyoUaPc3HPPHRtOJ0UgDQG8l5Sqe08++aSP8uyzz3ZDhgxxW2yxRZokyt6D0cy1117rMJC57777yobVRREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQgfoQkOFKCu4nnniiN/hIcWvdbplmmmncUUcd5dP/9ddf3ZdffukwXHnnnXfc66+/7gYNGuRuuOGGuuVPCbduAltttZVbeumlvZHXp59+6h5//HE3YcIE980337hDDz3ULbXUUv6vdVNQ6URABERABERABERABERABERABERABERABERABERABERABERABERABERABKIEZLgSJVLB8fvvv+9DzTbbbK5///6uc+fO7uCDD67gzvoFmX766d0ee+xRlAEMWdZcc0330Ucf+eWEJk2a5BZccMGiMDoQgTwIbLDBBq5Pnz6FqH7//Xd30kknucsvv9z95z//cXheGTlyZOG6dkRABERABERABERABERABERABERABERABERABERABERABERABERABERABNoGARmupHjO888/v/cSscsuu7hZZ53VPfjggyliqf8teGHZcsst3XnnneczM378+CLDlbFjx7oPPvigKKMzzDCDW2ihhVz37t3djDPOWHQt7uCll15yzz77rPfq8vPPP3uvGz179nQrr7xyXHD373//2910002x1+wkecCDRylJmuadd97pvv32W+/xY4UVVmgSLZ5ofvvtN7fSSiu5JZZYonC93H0//fSTu/XWW33Y6H2FCKbuJM1reG+SfcpHfqPSsWNHt/jii3vjq+i1ah5T94488kj3z3/+0/3www/e+4qld9ttt/lz8803n1tnnXXsdNGW5bl4LhjAkP9VVlmlSd154403/D1ff/21XzIojIB3uFevXuGpov1ffvnFPfbYY+6pp55yEydO9MsNbbbZZr7+FgUMDqgjLB322muvuffee8916NDBhyedhRdeOAj5313eLd4xZJtttnEYjj366KPumWeecTPNNJNbfvnlHUuPTTvttP+9IeY/78stt9xSuDLnnHMWGQgVLuS8M3nyZPfwww+7N99803ttmnnmmX0d2mSTTTyrMLloOdu3bx9e9kZzeH6Ke6/LvWNEUurd5Nr111/v6wf7pWSBBRZwa6+9dpPLPHPK9+677zo8BPE8llxySd9G8Dypv3Fyxx13uO+++65QJ8Mw1MN77rnHn5p99tndpptuGl4u7CdhW45PpW0QCVPnaIt4nuQTLtS/jTfe2D+XQubqtMM7Rf6akw033NDxDpjkUfcsrlLbsH2nHo8bN86/s9ttt12pW9yLL77o+8Ry9aDkzSUu0M/y/MpJmFcLRxvyxBNP+DpAXaev6NSpk6/vq622Wmzbxb3l6h7XS72bzT0TPMDBJ9oe0M4xhqBesrxhnOBN7sYbb/SX/vKXv/hy4F1uzJgx/lzXrl3dcsst1+TW8F3BeHettdZqEqYeJ2h/HnnkEcfYjDwuuuiirlu3bm699dZz7dq1a5KlkG2Ti8GJKNvgku97GdPyHN5++21HXwlv2naMtaPCOIe8Lbvssm7eeef1y/LxDpBGjx49XPSdDO9PW/fStgeWdtK+mvvSlLNe7UFYDxhbJOlzjVGSbdoxm6WRtq+ux2+UNPUAD4NMeOCd3XbbbX3/wBiRJTMZJ/JuISytiSdChOUzGVchLel3UR517+OPP/btz+eff+7LH/5jHB7XDoVhtC8CIiACIiACIiACIiACIiACIiACIiACItCyCbSbqgD+o2UXof655yP/brvt5jOC14iNNtqo/pn6/xzgZeX+++/3Rib2QTTM3O677+4eeOABf4qlgxZZZJHC5T333NMrIQongp25557bHXvssa5v377B2f/tovgfNmyYu+KKK/zyMP+78t89eOHxZbrpim2nPvvsM7fqqqtGgxcd89HylVdeKTrHQdo0MY7gI/LOO+/sWAYqKiiLMJCgvPAyKXffKaec4i688EIfNHofJ9Pm1dJOukUJhRK6lKAIO+OMM/xH9FJhkp6/66673D777ONvu+iii2INKsgTecM4AyUZH/b33ntvd/fdd3ujMDPiiKaNQQlGHcjQoUPdTjvt5CqpOxbPGmus4a6++mo79Ft7njvuuKM3VogapKGIw8hr/fXXL7qPA9Leb7/93NNPP93kGsoH3oXouzJ69Gi3//77+/Dnn3++O/DAA329CCPgXSCfUcWThfnkk08cymUTjCswnqmmEP/RRx/tlxuLS4eln1h6zCQsJ4qZ0LDgiy++cL1793bff/+9f96vvvqq3ea39kySvpvc3HmqJ6zmBAX1qFGjioLhCejSSy8tOhceoBA/99xzi8ph16lXGCBhWMf7ZEI5qaMYFlCP6Ccod1SSsi3Hp7k2iLQxkjnuuOPc7bffHs2KP+ZZYcA1yyyzxF6v1ckRI0Y4/poTjBtQlpvkUfcsrlLbsH2nLsETwyaMDkqJLTeIESh88xAMAa+77rqyUZE3G6sQEMUghlsYhMQJBlsnn3yyN3CNXi9X9whbqt8s90y475xzznFnnnlmk/YAo1KMqg466CA3ePBggjYRjMYwqkV4/3gP8ejVr18/P2b405/+5PsWjCJDOeSQQ9y//vUv3/9ceeWVDWG4wrMcMmSIV1qHeWWfdov3IWxLOR+y5biUYHAdbWsJS/tMn/Thhx82uZW6Sv8XNfxZccUV3VdffeVQKHM/HvxCwQgOz2qh0S/Xs9S9tO0B6abpq7kvTTnr1R6E9SBpn0tZk0raMZulk7avrsdvlDT1gP6V8SFy/PHHu1133dUb1h5xxBH+Nxe/vRgDYRTLu0QbhoGMGchWMrZtlN9FWeoeBmWHHXaYLzv7cYIRYrQtiQuncyIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAi2XQLHVQMsth3LeDAHsk1DgIMxK5kMo3gFMOY9SKDRaCaNDOYKSCkGhwcxuZjGjQGJmNjOgo8KHbDOIWXrppb2yH+ME0mPm/GWXXeYVuIcffnj01sIxXjRQlphgrFJuRnkeaVpaWbYYYFxyySVlo6hnXlGu//nPf/ZGEsy050M5xk08z3IK+7IFSnGROskMfwTjDptBjkEKhit4YmGL0jEqKBkRFKubb7653+fDPYoME2aqU1/wahD1frDYYotZsCbba6+91p9jGS3qNs+TfDDr9e9//7tbd911CwoFAlIOFBEY4CAYklB34YrygXKgDJxnnnnc6quv7sNE/2HogTEYhjyUiRnMGKUwc539ch6GonFV8/jmm2/29YQ0MKYhvxjL4KXhhRde8IY7GGlUKijFUdhUQ7beeuuC0RyGaSjwEGPMPm1TVFB+I7SHPEdTqj333HO+TWP2NPUJBYrV2Wgc4THPEWMo8kA9v/jiix3GL1HJk20lbRD9AIzwCoDwHKnzc801l6/zKNMwFLB+I5rfWh7jzSF8BzCKw9iCZxR6yeIdqlQqrXs8f5SVUWnOM1g0fK2OMUpgaTYT2ifqVpxQB+DIu4wBC+0ifQNGBdzDFoM6rpfyEBQXbyOdI+8YBuLV4JtvvvFtMUYh5smK9tX6E/rlRvC2ggEbhkgIxjYYPdKP4aGB8ReeuQYOHFjwLhPHO2zn7DrGKuaJzM7ZFi83eOdAYQwzvPFRH/CgQF1n7PfXv/7V0S7EvWdm/IbyHc8+b731lu+7MGSh/aPdxGjPJEvdS9se5NFXJy2nlbdRtpW2e0nym3bMZmmk7avt/nr8RklSDzDqwgCZ8dxZZ53VxIiZcnCeMSNjCgwbzWjFymjblvq7iPw3V/cwcLd+lXaP3yosc8vvBH6rSERABERABERABERABERABERABERABERABNoGARmutI3n7JXuXbp0iS0tyspTTz019honUWiG3gP4gIgiAgUHxilRwxWW2DCjFcLhwcCURPvuu6+fLc0yAxh3oAA2xXA0A3g3CRVwfNgtZbiSV5rRPKQ5ZkYlSplSUu+8Mludj98IzxBPEHgwIV8YZ4TKpVJlyOM8H/JRjCOhYpj6yPIEuAtnKaGo4QrLEqC4RlDOmdtwjALwBGLy448/+vqCIjY8b9fLbVHWmjcUwuEx5R//+Id3946CL1SWY8BgRiso9qjvZtCwww47eGUgeab+ljJcwQMA3iJYagEZMGCANyLgPoxfQqW9D1CHfxjgwAGBKUZOKBBDQeGKoUYlgneacJmjSu5JEub0008vBIehGa7goWeOOeYoXIvuoLhG0UQ9tOdoYTC4Q6mEUhZPGc0puVH2UgfwwoJBwRVXXOGVuhafbfNm21wbRLo8PzNaYdk7vHFYO8113k28tqDErrfgEYo/E4yIJk6c6PmfcMIJdrribZK6xzOO88RF3cUTV6MJS92E/XU5wxXaTma40w9jsBQKfTXepXifqSst1XCFMrFUG2McPIDx7PHqQhuPUQae3xD6xIMPPtjv1/Mfz+vss8/2WUB5i1ENy+Eh1Dc8WqHcpRwYg8QZwREWI8uoRxbKHWe4Qpp4d2E8gIEMyy0ttdRSROOFMQJtIkaGcBw+fLhdKtri4e+YY44pnGNJSI4xdKYcxGOSpe6lbQ/y6KvJf5JyWnkbYZuk3UuS37RjNksjbV9t99frN0qSekA/hfEchrG8Q6FnMH7X4OkJYawX/U1l5WTbUn8XNVf3aHvMqxp9GJMcMFpBaOdkuOJR6J8IiIAIiIAIiIAIiIAIiIAIiIAIiIAItAkC07SJUqqQZQmwXE8SBVy3bt0K8cUpgFGOICiL+FgbKkPZt2VEmMmPojsPqUeacfnGiw0fWctJo+SVPPI8unbt6rOLIqlaSmoMO5gtzsdnFL4oDVGUm2y77ba262eamocUDGpQ+oeC9xMMOhALF17Pus9scmbeh4LxgQkGNaGg5EMw+GHJidDYAbbMXEdY9ghle5zgKcaMVriOEtmWJIqmF3d/Lc5RTvOmgvI0arRCHjA069+/f7PZQUnBEiuNKChoMVYIn6PlE0M8O//aa6/Z6dgtHlao19RfFNB4ecATQZzkybaSNog84IUCwWtW1GiF8yi9TzvttLovE0Re8pRGrnt5lrOSuGjzMeaIGq1wL4ZWW2yxhY/GDPMqibNRw2DkyJJjCEt9YXjG8h0YjVHX6ZfDsUq9yoFXFONN/2ZGK+QHLwy0vZZP63uy5hUWGGQiGPKERiucW2aZZQpLm2F0jKFLVGacccYiY0+u017askzRvNaj7lkesvTVScsZ5VSv42q2e9RLG4vVY8wWZVqL3yhJ6wHvlLU/1MOXX365kG0zGuOdME9LhYsZdhrlt0YldQ+vfRjOI4wjzWglQ/F1qwiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQAslMF0LzbeynZAAxikoHBA+IuJ6GbfVV111lXf9znIrzEaNcwH/yy+/eBf73IsS9vLLL2fXC66co2IzejFMYSZlqOSwfT76TpkyxUWVv3Y9Gmdzx1nStLhZMoGlBKJSqVEPxhTMckaYEcyMwbh788hrNI9JjpnxSTnxCoPCijXpEbyBlHJPniT+uLA2kzJ6DSMAjDZQKobCkgXMOocfM7UPOOCAwmVb1sGWcilcyGmne/fuTT6a4wHGhNnjoeBVA2HGPgYKUeEj/DXXXONPM8M/zsNQnEGDpYn7+FKCsUs4E7Wa3nLM29Ess8xSMMYpla/mzuN5hPeA+ta7d2+/VFW5e7K+m+XiLnWN8rI8GR4nzDMQYWlLadvKLXFE2ajDVleuv/76JsrgMN282FbaBtH+25JIGGWZIjzMU2vdT1r30nCg3cLIC+Vbhw4d0kSR+J60fScJ0RePHTvW9+8sI2gKRIwoEI6p83GGjVneTZSV0T6H+MoJ7x3vI21t9N5y93EN7x94XqJcf/vb33xw+qAzzzyzyHCwuXiqed36E9IIPc5ZmrT5yy23nC8H/UkeYu0PceHNCKM7q0+2NWNRthhgRpeWxLsKivdQeD4sM8nye6XymqXuhWlVsm9ss/TVacpZj/YgyqPa7V49x2y1/I1iXNPUA4wE+f3EO2VLUvKu8YfAMM6I0N5BS7vSbR6/NbK075bPSuoekyDsd6EZ0dn92oqACIiACIiACIiACIiACIiACIiACIiACLQtAjJcaSPPG2Ur7rRNbAYtH19xd40i6OKLL46d7YciI+q6GkUnhgVLLLGERem3n3/+uWOJFgSFL8urlBMUqKHwAdokTklm18Jt1jQtLpal4S+tYJxBefj4ylIdGK5EJa+8RuNNcszzjgqzQUMX/9HrWY9RYNnzZCb/Qgst5BZbbDFf95Zeeukm0WO0sfbaa3ujKgxVWLYHBeNHH33klzXihtBLS5MIMpwIPZ9YNCigKQMKqLCOct08wsTdx3WW1TGxsHZs27h7Z5ppJn85mp7dwxYmZuASnq/G/rvvvuuj5dllMXTgHWDZJGT77bf3CuP777/fH5f6l/XdLBVv3HnqGEtLPTx16axyggFgKTGFv13HKCDqxcCusc2LbSVtEOmZooz9qAKac61V0tS9tCzoWxGWXsEDAEs8YKRVLbF2IulMdZSKeCApZyBHnjF0tDY8LEOWdxOjhqTCOIU/xjS0fX379vVenuIUvtG4YYOnoY022qjgtWuvvfZyvXr1igat2zHtj0lcv8A161PCsHZPmq21P9xbyRiIcU603SiV144dO/osTZ482aEEt36Nk1nrno84wT/rf0vl1bgSpYWNRl/q3nLlJI5atwdhvmvR7tVzzFbL3yjGNU09YLmccoJXvjixtp1rcW1w3D15/dbI0r6Tr0rrHmNZPN0xgYLniUE7EwBo580AJ66cOicCIiACIiACIiACIiACIiACIiACIiACItD6CMhwpfU900Ql4sMgH5xZjoSlXEoJxhgIs65R2PI3dOhQb4DA7F8TlBMm6667brMKIXMjb/fYrF6OZ555Zjtddps1TYucD6dxHiuYEdycvPXWW+6yyy7zwVhmBuV+nOSV17i4Kz2H0QEfv0MjDGZD477cDEQqjavScBdeeKHr06dPpcF9OIwaHnroIa9Aom7iEeamm27ys1Upw9Zbb50ovkoD86G8UuE9MA8BeCKJEwx1TMzLhR3bNkmadk+tt+aNCEV8FqHdsOU5DjvsMDdq1Khmo8vybjYbeRAAzxIDBgwoeIJaYYUVfL2bZ555Cgqjk046yT9z3p9ygmEW8s4777hTTz3Vrbrqqt6AIe6ePNhW2gaRftgOZX2eceVp1HNp6l7astjscd55lo/j78QTTywsF5E23lL3mcFoaBhQKqydv+GGG9yQIUP8Id4yNtlkE790lHmJwZsTS08hpWb9Z3k36YeiRnAoacu9WxYeQxr6LYxuyOOtt97qDfl8Zsv84/7QU4vFV+aWml4K+4hSYyDra8KwWTJp7QHPI1zCr1Sc1raF10vlNTSkwsOO1c886l6YfnP7efXVSctp+ap1ewILiNQAAEAASURBVGDpsq1Vu1evMRtlrNVvFNJC0tQDlsksJ3h4Y7wQfb9a6u8iypqk7g0bNsx7smO8f9999/m/crx0TQREQAREQAREQAREQAREQAREQAREQAREoHUSqFxD2zrLX7dS8RE9lHoqTxZccEFvuFJq9u5qq63mrrvuOp9dlGN4IsAjAR5V8JxwxdQZ2yYYbKDIQsm1+OKLF9zx2/XmtqEiJm7Zlbj7s6ZpcbJ0AIrFqCy66KJlFWmUFWUPirROnTo5DFdKSV55LRV/Jed5lrjKRyZMmOAuuugid/PNNzu8NTADPjREqiS+aoXBqAqDAZbcYNYnhivkEyGfXKu38N6yvBZ5tGVhonkKz0cNtaJhG/l44YUXdiwpwXufVp566qnC0lQYrVT6jqd9N5Pm85FHHikYrfBO77bbbkVRoFSvxDMRiifqLF4sNttsM78Uy3777eeXazOlcxhxVrZJ2iDSJT0TjBbbgqSte2nYYBhB24pnG5bkw3AJ4wD6l+222y5NlM3eY31npe8UEdL2I507d/Zt65xzzumP7V8ldSPLuwmbaJrnnHOOX7rH8hDdYlyJ1zCW/MH44fbbb/ecL730Un8+Gj48xvB20KBB3nDOzo8cOdL17NnTrbnmmnaqrtuwj6D9iHue5h0nDJsl09YeMIbBk1mcAW9z8VueouGsXtJXhn12HnUvmla547z66qTlJE/1aA+MRS3bvXqN2Wr5G8W4Jq0HeLXktxOy1VZb+fbWjAHhhrcVDLsY4x566KE+nP2zd4jjuPbAwoXbvH5rZGnfk9Y9vGbx+4Ql0vCyQvvWo0cP77Xl2WefDYsXuw/P0OiR36KhkWLsTTopAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiLQcASmabgctYEMMbsexWb4h3vkegkfS5HQHXWpvKB0ZVb2+uuv74MwM848TnCCWYjmbj1cjqJUfNHz4cdgm/UdDRM9zppmNL6kx7fddlvBWw2z18MZxtG46p3XaH5YviT8SB63vFH0nlodo2gyryr33HOP974yceJEnzwzeysVlHHVlAUWWMBHz0zZOAnPW9i4cGnOYUh28sknF/7M60+auJq7x5aGQJmN+/c0cuyxx/rbWHqsWgr8NPmye15//XW/i+K2f//+drqwxatJqBgpXIjsUD4U8l26dCkYulB3Sxm9ZGWbpA0iq5Ye+y+++CKbVMKyK2eeeab/w/NFI0s96h6caaswWkJoizBoqYagGEUqVWwyi9/apm222aaJAQlxVSuvxJ1F6EfXWGMNv1zhHHPM4aN6+eWXm42StvKll17y4c477zy/fCLvM8Ywn332WbP316K+h32EPZ9oxux8GDYaJsmxtQcoftOM20grXG4oTNviYykd+nSkXnXPeBm/MJ/sh+ctbDRMknJG761le2Bp17Ldy2vMZnlPs632bxTLU9J6gFco2n+86zEOwHgFYayBQSO/qxA8R5lBiz8x9V9L/F1E3tPUvUcffbSwNBAGnxdccIE78MADDUXZ7QknnFD0u9qYlr1JF0VABERABERABERABERABERABERABERABBqOgAxX6vBI4hSfcedqkTWUPW+//bZPCiVrpfLpp5/6oCytERqucJLZj8j999/vUPSWkqjXGcKZkoP9ShVwhM2SJvdnETP8wZgHTyDNST3zGpe3cFZ9Fm8acXFnPYdxA7MmqWP77LOPjw4FWCWcCYdQV5lpXy1ZZpllfNTjx48veOsI08LzBoJBU9QFfBguzT4zcZm5bn94IKiWsNQNwntbzkjh+++/L5kFFDDMgP373/9e0bIeJSOq0gWrJ7RrLEMSFbwzJJW//vWvjhnVCEsFsNxVVLKyTdoGodxbdtllfTaoM2EbEOYNw6ioEi28jvIfDxn2F15rtP161r1XX321gIO6lbcwfsAwCqm030TJbLPR4/ppzjWSIaMvXOQf76h5I7CyRIIUDjF+vPzyy/3xLrvs4jbddFPfjrHECEY/GBfFjUkKEUzdqUV9X2KJJQoGHizfF5Wnn3660DYtvfTS0cupjvHAZkYl//jHP8rGUYoRRn94SQiFdonl/pCuXbsWLtWr7uXRVycpZ6HAkZ1qtwdhcrVu97KM2cJ8Z92v1m8Uy1fSemDeAnv16uUwtltppZV8VCzVyl+/fv38MZ4v8b4SSkv8XUT+k9Y92haWFkLgtNZaa/n9Sv9Ff0dHjyuNR+FEQAREQAREQAREQAREQAREQAREQAREQATqS0CGKyn5ozx44okn/N9rr71WiAXltZ0nTKMICllc6/PHR9G7777bL1+AG2hTRNgMwGieuT5lyhT/9/7777szzjjDPf744z4Yrpyjrv7x4DHTTDN5zwS77757IazF++GHH/o4UNaGCjPSMEUZsxBRmFq6bM0jDB8jOQ4/SqZN0/KUdYvyC28rlUi982pMmfV87733OmYpmmyxxRa22xBblrAwpb4ZSFFPTclWLpPci3AfM1qZUV9OCe8Dp/g3YMCAQn723XffglKR92b48OEFbzx4XjDvACmSqfstGF/g5QC56qqr3EknnVRktIbxA8ZFtA/lZKeddioYTZQLV49reCAyOfvsswueZZgpPWzYMMes6TRy2mmnOTOkYhZyqIgivjzYJmmDSJOlkBDaVZSN9A0mlBcvLiyfYkYxdq0lb2tV9+ibWD7sscce895oRo8e7bHNO++8RQr8PFiSFktQmEEMfa+18ba1dHjWZpxFH2ttJOMB+nQbC6BYZzmesI+1OOq9xZgKY1sMrg4++OBCnrt3714yaxi4mGcxjD1Y6hDBSMTeA8ZrzbVdJRPI8QJjKuuHMf7F44A9B8p90EEH+dQwhGR8lYewHCJjQQSPDzD54YcfClFTt8gLYfBOU0oGDx5c8FzD+IL+0MZt9JMm9ap7efXVlZbTylvL9sDSDLe1avdIkzYl7ZgtzHOS/Vr9RonmqdJ6gFGheTbbcssto9H44xVXXNGxvA+CgasJbXhL/V1EGZLUveuvv94bvzG+L+WdzrhoKwIiIAIiIAIiIAIiIAIiIAIiIAIiIAIi0HoJTNd6i1bdkvExzhRAYUrhzHMUSBiyNILwYddm9MXlhyVZdtxxx7hLDoVOqNANA4VGD3ae2YPHH3+8//CIwghWs846q59ViHI7VIjYPWxRkqLsQ1CU2OxYfyL4h+KK/KBY7tu3r7+SNs0g2ky7KGhKuZaPRlzvvO68887RLPnjdddd162++uqx1+p5cocddnBPPfVUIQu2fFDhRImdjTfe2J1++ulu0qRJ3tACYwsT6lp4bOfTbFHU9J+6rMyll17qDRKYJYoCgtmmKO8Qls8yjzFp0miUezAAwoMI7zHlxYNBp06dvHGDLVUCi1KCkdshhxxS6nLdz2+wwQZ+eR8M6lAeYXDA82UWNG3S4osv7p+xKfgrzTDlRinOu2cKXRTE7du3L0SRlW2SNohE8bKw1157OTwsYExI/8DybHPNNZc3vsJ4pZygjA054BmpkaXWdW/llVduguOUU05xeLvJU1D2HXXUUYUoR4wY4fiLE9LH4MO8YFBnMISgbtNPwwijAvO8teSSSzbxohEXby3PUV+jXkHI55577llUHy1PGE4MGjTI4QmKMRleU8Ll/Bj3jB071t11110Oj0oo3ddee227vbCtZX1HIc4zok3F6I0lijB6pC8z2XvvvR2GUHkJ9eDZZ591GC2NGjXKXX311YUxDe2fGc+UW34Djyt4lFtwwQV9H2FtCN7oaG9CqUfdy6uvTlJOK3Ot2gNLz7a1bvdIN+2YzfKcdFur3yjRfFVaD8wQZbbZZit4X4vGRf+JUQu/Ie+8805vUE471ZJ/FyWpe7TPZjhIX8RYSyICIiACIiACIiACIiACIiACIiACIiACItA2CcjjSsrn3pxbeqItpciLuzfuXMqsFd1WKl4+KOK6nZm9KGdR8EfzG3cv67OjlO/Tp49X7LKNE7xL4J1g+eWX9/FirPLmm28WjFZQdjP7NVS8JPWGEQ2fJk3ybuWOK2943cJZee14kUUW8WWx87a167a182zT5jWMI8l+qbKhqKYeHH744WWXf0mSloUN0wz37Xql2w033LDg0aRHjx4VL7fDR38UBswQx6goNBKIS9ueU6m8lruOFw08cmCghWAIgIEC97CsER4NwrpOmDCdcJ9riJ2zdP97tvi/hbGz0WM7n9eWmfljxozxiin4oszEe4gZrSy33HK+bQjTC/N05JFHeuOIuOthOLtuZY+7Rhi7blu7L7oN7w/3o+Eo08UXX1xYeozyvfvuuw4FLIpHjATMc0pcPHYuLj8ooPBigeClCwVVKGnYWjpp2iDS5nmgpOZ+BO8qVl4MLFhSpZShxYQJEwreFLgXo59aiZXbeJdKN7xei7oXpkeeZp55Zsdz3XzzzR1LRbD8AVJp/n3gZv5F+8Fmghd5ncJYCe8a9ox5jzFaYXyAIdVuu+3mo7P8hnHbuWiZLYxdt62dD8OH+9Hr0WthPFzDGJDxBW0vy29RhvAe2z/rrLPcK6+84qPHK1rccm0Y9NBHwPLAAw/0Ri6WH9vWsr4zPqLPwJCUcmOsa0Yr9NkYJpHPqFiZOR/uWzg7Z1s7z3b22Wf3Y0HihSVtH/0Yf+zTNvbu3duVMhzFCM6WicFgmTYTzwl4abnwwgvDpPx+lroXjczqRly5omHT9NVhHEnKGc1PLdoD8hqmm7TdC8uadj/tmC2a97AccXmJu17N3yhhHpLUA4ziEIypMQxELO9Wdzln3lhY/gxjOiRL+879aX9rWL4sn8QVil23rV0LwyepexhC0//QDkXbtjDOcN/StG30WvTYwmkrAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiLQ2ATaTf0o9kdjZ1G5a+kEUODj4h4vDczmR0GE15GosOb7l19+6T/YMmO3lAwcONDdc889XnljLvWjYStNM3pfPY5bUl7rwQdvK3x8R1Aw2n498tJcmjSnLKdFfUf5i2cglFWtUfC2gYLynXfe8WVkNnvce91Sy07ZMOLAS0O3bt1q+hzrwRajFYwLJ0+e7NtoDC4oeylB0cRyUQgKapYSMQOYUvfofP4ErrnmGr/0DUuo2Yz1uFQwvEChzHv68NSlhUKhD8LLEMupsXwOYSTFBOpV3zH65dl98803Ds8yjI2iyuLinGY/oh/DgIk6gdEKyxctvPDCsW0gS5zgXQwPMBjAUpdYdgxDF9rNcm0IOa1X3UvaV2ctZ/an0nJiaEljNquDlfxGIWyt60Fb/V3Ucmq7cioCIiACIiACIiACIiACIiACIiACIiACIpA3genyjlDxiUCUAIr77t27+7/otWod1yPNtGVpSXlNW8Ys9+EBA4ETngsaWVAoovTlr7ULxgoYKrRWYwUUtfzVQ+rBFk8OcctZlCr/k08+WbjETPHWWg8KhWzFO7SteEuSlCZQr/qOFy+U17UU+jGMENMYIlKX1lhjjYqzW6+6l7WvTlrOioG0goAtacwGbp5l2t8oLakeZClnK6iWKoIIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiEALISDDlRbyoJRNEWhLBHAZ/vnnn7sbbrjBPfDAA77oLEPAh3eJCIhAfQngEWbcuHE+EyzNwHJcEhForQRU31vrk1W58iKgMVteJBWPCIiACIiACIiACIiACIiACIiACIiACIiACLRtAjJcadvPv6FKP2zYMPfzzz+7ueeeu2y+9txzT79WfK1nIZfNlC7mSgDPDygLTRZbbDG333772aG2IiACdSQwadIkv1wJXlr69evnFlpooTrmpm0nveaaa7pzzjnHderUqSwInhHhZptttrLhdLEpAdX3pkx0RgRCAhqzhTTy29fvovxYKiYREAEREAEREAEREAEREAEREAEREAEREIGWQUCGKy3jObWJXK6//voVlZM15vmTtF4CeHH4448/3HzzzedWXXVVd8QRR7g555yz9RZYJROBFkQAI4mXX365BeW49Wa10iWtZpllloZfaq1Rn5Lqe+knQ189zTTT+L/SoVr+lbZSzrRPqq2M2WpdD/S7KG2N1H0iIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAItlUC7qcrhP1pq5pVvERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERCBlktgmpabdeVcBERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABESgJROQ4UpLfnrKuwiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAi0YAIyXGnBD09ZFwEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREIGWTGC6lpz5Rsj7H3/84b799ls3++yzu2mmkR1QIzwT5aF6BH799Vf31FNPuVdeecV99tln7tNPP3VvvvmmO+igg9ymm25avYQVswgkIPDGG2+4O+64w7Vr184NGjTIzTDDDAnuVlAREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREIFaEmg31fDij1om2BrSeu2119zNN9/sHnnkETdp0iQ3ZcoUN/3007vOnTu7jTfe2O21115upplmqntRMTLYfvvt3e+//+7WW289t88++8Tm6dxzz3UPPfSQv7buuut6RW9sQJ1s0wSuuuoqN3z4cPfdd9814bDEEku4MWPGNDmvE62HAMZJEydOLCrQtNNO6+add1630EILuV69erlVVlml6HpeB3RTtLVIx44dmzVEGT16tNt///19+Oeff97NOeecfl//REAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEGo+APK4kfCYvvfSS69u3b5O7fvnlF+95Au8TN9xwgzdsmW+++ZqEq+WJ3377zT377LM+yUUXXTQ26ZEjR7ozzjjDX1tuueVc//79Y8PpZNsmcMABB7hbb73VQ8BI6y9/+YtbaaWV3J///GdvSLD44ou3bUBtoPSvvvqqb+NKFfWCCy5w66yzjhsxYoTr0KFDqWCpzn/xxRduzTXX9Peef/75bpNNNkkVj24SAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQARFoPAIyXEn4TMxBDUtPbL755q5Hjx4Oo5CvvvrKXXLJJe6FF15wn3zyiRs8eLA3YGnk5YOuvPJKd+qpp3oCSy65pON4lllmSUhEwVs7gWuuuaZgtILR1sknn6x60tofepnyzT333G7gwIE+xM8//+zeeecdd9dddzmM9/DcdOyxx7pzzjmnTAy6JAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAL/IyDDlf+xqGjvT3/6k9t7773d7rvv7lDghrLhhhu6LbbYwr3yyivumWee8QrdLl26hEEaZv9f//qXGzJkiM/PIoss4jBOoGwSEQgJfPvtt27o0KH+1KBBg9yhhx4aXtZ+GyQwzzzzuD322KOo5CzLQ9tHfbntttv8cmMYw0lEQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREoDkCMlxpjlDkeufOnd3hhx8eOfvfw2mnndbtsMMO3nCFMyyt0YiGK3feeacvA95jFlhgAW+0EjXCIf+///679xpjXmY4FycsGbPEEksUXfr3v//tnnjiCW/A8+mnn3qFdqdOnRzK7NVWW80tvPDCReHjDiZPnuwefvhhvzwJXh1mnnlmB3+WCWmOK4ZDLOvE0k1ff/21L+fyyy/vNt54Y4e3HJNbbrnF4TWCa8sss4ydLtr++uuv7sYbb/TnWCKHcmSRDz74wI0dO9ZHsc0227j27dsXRTdu3Dhv9EQ+t9pqq6JrWQ9YPuree+91r732mnvvvff8ki5LL72069WrV+wzuf/++91PP/3kMFbYcsst3cUXX+zzhochnkH37t1dnz59XOhZiOf94IMPNsnqdttt53hHksgPP/zg43r99dfd22+/7eaff37/nKgDs802W2xUd9xxh/vuu+8cyxetssoqRWGoC/fcc48/N/vss7tNN920cB0m1JnmBAO1Oeec0wejXvK8KBflKyUvvviiowzRNEuFb+489Yd6FAr1ZaGFFvLPZMYZZwwvVX0f47e99trLnXbaaT6tN954w7/rEydO9O0AJzfYYAM311xzxeaFtvLll1/21/r16+fIP8uc8f4iGMSYPProo0XHnA+fiYULt9R17qNdmGmmmfz7vv3225etj++++6575JFH3Pjx4/07gGetbt26ufXWW8+1a9cujN7vR9/rSZMmJU6TdpM2yYR6xvtVbam0vQzzkZQPZXvqqac8z/fff9998803nit9D2x5p2nf44R7b7rpprhLhXOl2su0fRH9JPVuqaWWciussEIhHdthSUDa02j/F60Hadr3NO2e5SvJNsxruftKseWepPWAe3jXeefLSVyazT2TUvFlqXsWZ9JyUnfIb1Q6duzo+6ZSdd3C03Yy/iFd+lTaLcZP1Ef667DPtXvYXn/99X7sFp6L7jPuW3vttaOn/XHSupd2nBjWvbhxUGzmYk4mHSemHR+Uq3uMkWwpxbA9+PLLL92YMWN8rrt27epYjjMq4b0LLrigW2uttaJBUh2nLWfa50km09bZsIBp+iLuT1oPuCfpeJh76jH2SlP3yGsWKZcm8Zbq/7iWts/lXpMk9SBLnbX0GPsz5mSczm9Cfhf17NnTrbzyyhakaJtlTEJEta57aZ9J2nrQXPteye/cpH1R0QPSgQiIgAiIgAiIgAiIgAiIgAiIgAiIQIsm0G6qUcIfLboEDZZ5PuaZYcu5557rNttss7rlkA9V5vVg6623dqeffro3AhgwYIDDGAMFyj//+c+Shhjh/eUKwdIgeKAx+fzzz71ShI9/cYIChuVmMIQoJRhXHH300Y4P/3GC5w88gESFj9fHHXecu/3226OX/DGK4Mcee6yw1A0KSYwZDjroIL+8U9xNGEFgoIGcccYZmY1JRo8e7fBQgTz//PMFIwiOv/jiC9e7d2/3/fffu1lnnbVZ5R73VCqfffaZ22+//dzTTz/d5BaMgoYNG+ZYCiiUE044wV1++eWeFx9ap0yZEl72+2ussYZfGsaMOVC29e/fv0k4DEOSLEUFGzh9+OGHTeLCQOO8886LVQSRH4wGMPrheZnAdqeddvLGEChFR44c6Vnb9REjRjj+mhMMC1giDBk1apSvbygRUTCWkhNPPNFddtll3rCE+pdV9txzT3fffffFRoMhAO9k9FnGBk5wEgMKDEn4oH/33Xc3uZP8kC/k4IMP9nUNAxYMVsJz/iDyD6MfPmTPO++87vHHH/cGJUceeaS77rrrIiHjD/EghcLQJHzHzj//fHfggQf6pYzsOttVV13VXX311U0Mx7hGunikog2MCopF6onVd7ueNU3iYZk5DPtMaL9pC6slSdtLy0caPhMmTPAGRhZHdIsxEO3G8ccfH73kaLt4XuUEQza8nYWSpS9aZ511vHHfzjvv7Hh/o4KxDQqzaP8X1oM07Xvadi+av0qOw7yWC1+qL0pTD0iHJQppf8tJXJrNPZNS8WWpe8SZppwoQDGULSUYwNE/dejQoUmQk046yV166aVNztsJDGgZX0bbIK43ZxBDGNow+q6opKl7aceJYd2LvifRfJU6TjNOTDs+KFf3TjnlFHfhhRf6bIbtwX/+8x+HISbtEh4N6Tfnm2++ouIccsghjv6L9o/lOvMyXElbzrTPM0udBUjavoh709SDNONh0qrH2CtN3SOvWaRcmsRbqv/L0ucSb5p6kLbOkh5LXPLb54orrnBxn0N22203d9RRR7nppiue55N2TEKata57WZ5J2npQrn2v5Hdumr4IthIREAEREAEREAEREAEREAEREAEREIHWQaD4S0zrKFNdS8HHFpMVV1zRdhti++STT/pljjBaQeHB8kCVeg9hpigeLEIpNQOe+DFaYaY5s3oXW2wx9+c//9nx8ezmm2/2WxTJXA89XljchMGQBCEMyh8UuMxgfuGFF7zhBR++okK6GOjgBQPhnjXXXNN7ecBbx0MPPeSNVFAmNKpg0IPRSt7CB9ldd93VzyYkbpTjeCPBcwozhZnZhpEInlVWX331QvI8M+THH3/0W+rN5ptv7uaYYw5vYIARDDNQBw4cWPBKg2LGPMXwLPA2klS4h1nYGMtQBzByoh4xi496hzHLX//6V/9M47wFRdPDIGDHHXf0imiMdPAcg/IvlGWXXbaQb87fddddvh7jTST0uFBJemG81dznefBhGYEJM0Yx9uL94d3Gk1CtBMYmvO8I7yB54HminMNwKirk24ypqDfmlYe2A+U1Qv1EeYzgbSDqcQlPPKUEAzeeGe0IRnMsZUReMZRh3+qq3Y8SDKMZBGUjBkB4yqH9hC+eW8L6bveF26RphvfWaj9te5mWD4rZ6aef3hs+4TUC7zu0dSh18WqD8gkjObx34bmslNBuYbhmwv3mmcfO2TZrX2Tx5LVtrn3Pu91Lkm97P8J78IqC8VmcpK0HYVy07fQnoZRLMwyXZD9L3cujnBgR0CaiJMUTHf0u3sxop+MMVDCWReh7qO9mjPLcc8+5Bx54wPe9GPvhzYOyhcIYyBSweJqyMWn4fDE+jEoedS/JODGaftLjtOPEuHQqGR/E3cc5xpaXXHJJ7GXqN4aTeJPCwxRjLPox6+Pof+gXEZYgzctoJTYzU08mLWeS55mlzqbtiyhnmnqQdjwccm2EsVe5uhfmtZb7WfrcLPXAypikznIP7x1tKkK7uP766/v3E6+ReGHB4BxDd5uQ4gNG/iUZk9Sj7mV5JpGi5nLYyOOgXAqoSERABERABERABERABERABERABERABDITkOFKZoT/i+Ctt94quAtnSYlyytT/3VWbPZQXKOJRDiLMMIsqf8vlBCUJiv9Q8DzBjPOoMPP9sMMO80unRJcG2Xffff2HQT6go7CJGq6goCZvCIoewmBQEAqKJO6PCmHNaGWXXXbxnjBMQUBYPKswMxZlQiMKyvtwmZA884iCixngCAYfzI41hRdKYoxEcFl/1llnFRmuWBju40M9RiMo0xCMEPiYi9ce8o5CDqMXjBXM0wnXUIglET7s4u0CoxUMBlimCUW3CV5T8GSE0puZ+8OHD7dLsVsMIygjXlgwhLhi6szK0DuH3cQMeP5MUBLi+h5lEp5nGlFC1uSPZ8B7Cjs+xtfKcAXDplB5Fy4dBnvqAMvDUE/4yB8KdYpnTl0Ll1ti+R/+EAyozHCFuooisFLBkIr3Cm8uCB6nMGizJRpCwxXycfbZZ/twGK2gVDSDPdo6PD2RX8qBZ5io8ZO/ceq/JGnaPbXepmkvs/ChrtIGRWcuU27eUfoCjBPxSFTOcAXvXubFh3tps0oZrmTpi4g7T2mufc+73Uua97///e9NPHicc845sYYrWepBmC8MIK2vsPOl0rTrabZp615e5Rw8eHCh3aNtpg9j2Sy8kzEmQzEaCn0OfRztVNgHEwZFKh6AGG/yrkSNHfCsZ4JRqhmuDB061Buc2rVwm1fdSzJODNNPup9lnBhNq9LxQfQ+O8ZDFIrhUsKymIxT9tlnH99vUL8x3MYIF08OCH0iXsqqKWnKmeR5ZqmzafoiWKWtB2nHw+HzaYSxV3N1L8xvrfaz9Llp60FYtiR1lvbXjFYYN/O7yH4z8luVdpulchjbEq8ZEIbpsZ9kTFKPupflmUTLmvW40cdBWcun+0VABERABERABERABERABERABERABPIhME0+0SgWlBHMnkUJgWFEc8r0WhNjGRMUtSYo71FWVEP4SMZH+qjRCmlhOLDFFlv4ZM2QIswDRgrmTQUlcdRohbAoLeOWomFmK4KnCZYLsg+Q/uTUfxhenHbaaYmWq7F7q72l/uBivloCVwQFGa7pQ2VY165dC8s2sa48xhomoUKND7dmtMJ1lsfhGRlnlsnKQ1DGmbELip3QaIX4l1lmmcISP3xULlePmXG+7bbbeqMVDBEwfogzWskj340QBwZzJiiFqyEYDOF9hD9mpeK9hlmqKOIQllEK84EC1paIsnpo+eLZMWMa6dmzp0PJl7fg2t+MVoibdon8Ih9//LHf2j+8PVi7RH03oxWuR+t7tCwWB9skaYb31XI/TXuZlU+c0QplxsjT6msp5VAaNln6ojTplbqnkvY9z3avVD7yOp+1HuSVjyTxpKl71Sgn/SV9LkL9jDOkpc3EECDsp62sKFjtPMvv5SEtqe5R3izjxJBX1vHBHXfc4Q0Ywzjj9vF2w5JjCEs8wRvDXwwvGJdizGLjqLj7s57LWs5K0s9SZ9P0ReQpbT2wvjvpeLgch3DMY31ZufBZr1Va97Kmk/T+LH1u2nqQNI8WnvcOYdyJcXr4DrJvy9HipZMJE3lIPepelmeSR5ktjtY2DrJyaSsCIiACIiACIiACIiACIiACIiACIpA/AXlcyYkpM7VYsgDBgCWqbM8pmUzRsEQKXlZwf4zS+corr4w1ALFE+MhkYooSO65kO2XKFL+MDJ4uWNPbvL2gDEI45oNgqLixmfMoulkeplL59NNPnbkqZ7Z++AGykjhQxuORBeMGFNS1FgyJWJKBtHv37u2XEcgzD8y2RZjZSxmjgjEQS0chGCCY8pg6Y9KnTx/bLWw7duzovXrgncQMFwoXIzsYJJnxFEuGdOjQIRLiv4dWBzgiXpQuZpxiW4uHLYY2oUGNRQpPvHOwdA5y/fXXV+29xBsH5StXLstXnluWnmAJAoT3jKVWTFieohpCOqU8YuBpBA8Y4ftHHWI5EIyG8PqEtwCrVxhKWb3BUKQaEmeoZN6wWLIjFHtPOBd69bAwGMDgih4vBpZvuxZuk6QZ3kf8eM0xCQ3H7Fwe27TtZV58eDepu3jSoT9i5jPvMX0B3iiiYu999Hylx2n6IoubZe/sHbNzbOO8jYXXw/1K2ve82r0w3WrtZ60HNrZIM66gTHh4MgNX2pok/XaSupe1nMafsQl1CM8cGC2MHj3aX8JDWbnxBnWCcSUe5hifmGCEw9iJcUsekqXu2bMkH2mfJ8/S4qmkD7X8Jh0nhqyyjg8Ye+ClCMFTG0ac5dqEY445xvcbjH//9re/+fvgdeaZZxYZVvoLOf5LWk57DmQhzfNMWmfT9kXkL209sPc66XiYNE3qMfaytJPWPbsvzTZt/5e0z81SD9LWWd4NhLYUT1XhOMP2Z5xxRkdZokaCdj0p03rWvaTPJCxb2noQxtHaxkFh2bQvAiIgAiIgAiIgAiIgAiIgAiIgAiKQLwEZruTAc+TIkY4PMgjeRAYOHOj3G+kfyoCLLrrIL+PCR3aWZMB9ea9evQpGCtH8mqEJ5/l4l0TgwczSqGI4GgeKnNBwBc8wyEILLVSk/I7eFz3GuMEkzojBrpXa4jWCPxRCKLX79u3rjXrivMaUiiPteZS3KPuR7bff3isx7r///rTRxd6HsQESep4IA7Isk4mF5XimmWay037ppsJBsGP3fvTRR8HZprvUtVBYBqh79+7ezfY666xTuGR1gBMsNdSc8ME77pmbgZTdP3bs2KoZrpDGyiuv7JOiXMy+xX04RkjVFAx7ossBochluZtwuZ6882AGFaTF8+d9XXvttb1CjrYmKtRrDFdQuOAlB4MihKV4EJhttNFGfj/vf3F13uo1yqdQwjocdx9hK6nvcfeWSjNMH0WhGdWE5/PeT9te5sWH5crM8MDKxvuDAegCCyxgpwrb8DmF/UUhQJmdtH2RRUkbVEk7ZOGj20rb97zavWj61TjOWg9sbJF0XGFlYbku/kxoh3hvMJDbdddd3dxzz22XmmyT1L2s5bTE99hjD9stbDFuxpghTkj36KOP9gZdcdftXKiwtXNptlnqnj1L0k37PKNGgngIsD503XXXbVIky2/ScWIYUdbxwYgRIxxjD8rMki0YrpQT+kU8StDP0Q8ie+21lx+Dl7sv67Wk5Uz7PNPW2bR9EVzS1gMb48b108RrfTz7Fpb9UOo19iIPSetemO+k+2n6vzR9bpZ6kKbO0i9jAIlgGPiPf/yjLBre9VDSjkmsPtW67qV5JmF509SD8P7WOA4Ky6d9ERABERABERABERABERABERABERCBfAnIcCUjT7w4YACC4NqdWVtpZilmzEazt2+66abOPDCwjM7BBx/smEHFlg9ScbN+w4+BpnRtNqGpAVgyZsiQIT4oCohNNtnEL99jHjbwKICbayQ6a81mtqPITiKTJ08uBE96Lzei+EIwpMGTAkY35PHWW2+t+vMcOnRowWX8YYcd5kaNGuXzktc/lFs8a8SWbInGzRJOJua5hmPjwr55yWA/FDNUCO8Lr9s+cVHPmJVMngiPMQl/KKxtFrI9SxTU1NXmZLHFFisZxK698847/j1dddVVi5axKXljigsosJjRSLkef/xx/4d3EVsiIEWUFd1iykJmjcKVP+oUSj28g+QtSy+9tLv77rsTRUs+UNROmDDBtzcYrlAnMWJBMPgzY5hEEVcQuNQSIXG3hnW4VH23dygMG40rSZrRe2txbO8YaSVpL8MyZ+GzwgoreKNGZh/jjQvB8wpt34EHHujmmWcef87+maKX41LpWthwm6Uvsnjoz+PqJu96JVJp+27PJI92r5J8ZQmTtR7Y2MLarjR5MQMm+mzaPOoShgG0KfTbcZ7FSCdJ3ctaTisXfR/5pe8zhSfjDJaN2H///YvGGLAZMGBAYYY/+cUzC++ElZn+kvaznIcPS7uSbZa6Z8+SdJKME8N8RccGeJJhnMgfRiEYI4WSdpwYxsF+2vHBW2+95S677DIf3d577+372mjcccdWTrvGcS0kSTnTPM8sddbqHhyS9EWET1MPaCvSjodJMxRrv2o19iLttHUvzHeS/aT9X9o+N0s9SFNnw/Qwjosa1kcZ4U0wlDRjknrVvbTPJCxv0noQ3st+axwHRcuoYxEQAREQAREQAREQAREQAREQAREQgfwIyHAlA0sUJEcddZSPoUePHu7CCy/0HjsqiZIPWKFU+wN2aJiy1VZbeVf1jz76qF+KBU8jzPyMSqiYS6KQwLML0rlzZ3fzzTe7Oeecsyjqjz/+uOg4PGCtcdx/MwMuiXCfSbn4LUx0i/KIWdEsAcJHvttvv90vUXPppZf689HweR0/9dRThWUDMFoppWzLkh51ixnoeDiwZXOi8YXnww+0oQIZ7zlx+TPlXqkZhJbWyy+/7A1nMFbi4/vTTz/thg8f7r3/YODBUjEYwdizRCG57bbbxiqNLc5yW5Q1GGWR780228wbley3335esWnGB+XuT3KN9wujDGaNjhs3zhvJ8GHcypUkriRhV1ttNe/JhHuYPfrw1CVXmKnP+4MXH2ZZNoqwvBBKSJ47y8K88MILhRmv1VomKGnZw7pfqr6bF6kwbNJ06h3e3jHykaS9DMuchU84uxmDA5ZPYdmNa6+91hsgXHXVVUWIrI3hZFwbVBQ4OMjSF1k0GNTxHkdl0UUXbdZwIEn7bs8ka7sXzWc1jrPWAxtbJBlXhOXAGNCeCe0e/TZL3d1zzz2+bWFMc+ihh4a3FPaT1L2s5bRE8TbFsiQI/QT1krERnhPwNhYaGD7yyCMFoxUMN3fbbTeLxm8xfCnlqaUoYIKDLHXPniXJpX2eLBnHOJGxAV40nn32WXfCCSd4zyS0C3jsCuMmv2nGiSGStOMD8shz4T3t1KmTw3ClEsG4YdCgQd5I2cLjrbFnz55uzTXXtFO5b5OWM83zzFJnre5R8CR9EeHT1IMs42HSNKnH2Ctt3bM8p9km7f/S9rlZ6kGaOothN8YYMF188cULhvOVMkozJqlX3Uv7TEIWSetBeG+tx0E809Cok+ccfgMJ86Z9ERABERABERABERABERABERABERCBxiQwTWNmq/FzhYIYYwc+juBJAAVxpTPBmSXIx+Twb8yYMTUt9LBhwwr5PeOMM7whQTQDNhOe83PMMUf0cuwxs9DwboHgVSFqtMJ5FDelxJZ84QM2roUrFbuP8C+++GKltxWF4/nhlYZlVqy8GFtUU4499lgfPcu9VFN5b8tv2LOJlik8b2EJE35MDsOE95u79vC+8Hp0n4+ILGPDkg2DBw/2l1HqjB8/3u/bs+TjY+g+PBpPc8cwpf516dKloOjDYCJvpV+YD/KOkg0DGQTlVrn6Ht6bdR9jHLwbrb/++j6qJ598sjCzOGvcedwfelVhiSBbJmjZZZd1Xbt2LZsEdcYEptWSsA6Xqu92PgybV35Qwp988smFP5vVn1f8Fo+9YxwnaS/DMhsHi9O2dj4Ma9fitiiQdtxxR7fvvvv6y3grCmczc9KMhdg3z13sl5OsfVG5uCu9lqR9t2eStd2rNG9ZwoXP1p53ND47H4a1MLbsQtz4wMJUuqXdQ/GPFxLzdFRpv91c3QvzbuWJ5svOh2GjYcJjxouhUU10iZnXX3/dB8fLT//+/cNb/T5Gn6FSrkmAFCey1L0048RSWaSdZ1zMWAijVQQjbxsb2H2W36TjRLufbdrxwW233eboWxE8C5rHOX+izD/adbxKIeedd55ftpPnyO+IkGGZKFJdSlrOMC82Dm4u4Sx11p4laSTpiwhv9yatB/au2rtLXKGE5y1seD26X6uxV9q6F81vtY6z9Ln2LMlb0nqQps7ye8+WhErzOyPNmISyWX0K6xjnTcLzFtauxW2bq3tZnklcemnO1XochNFj+H2F30USERABERABERABERABERABERABERCBlkVAhispnhceAwYOHOiV0nxsu/rqqytWppFcnNIh7lyKrFV8Cx/EDj/8cB+eGbwsGRT1AsOsV6Rjx44O5XIlwowym9mEgiUqnIsqasIwLOWCkBeW6ykluJIPhY93lkc8pvAhO05QDKMYLCe48LfZdFaWcuGzXOPjJ2kwqzhUzmeJM+7eZZZZxp9GAfTaa681CYJnEgQlDB/8TJZccsnC82RZg6hQRzAGQSwNf1Dhv1deeaUQ0pZQYGY69QgJZ8cXAgY70TobXCraxUhmvfXW8+duueUWd9NNNxVdz/vg1VdfLUQZujEvnKzijimFSddc4lcxuYqjxuBg44039uHxiMAyEEglBlsouK1O8H5WSzCosnTi6jttv6XPkkl5C+0Os2Ptj7asGpK2vawmH1v2gb4w2h+GiqVKPa5k7Yvy4J6kfa9Gu5dHGeLiyFIPaJOsD2KJhrwE40fr25P226XqXpZylitXOD6JepejHAjtt7U1YVx46chbstS9NOPESvJv4wHChvscpx0ncm+cJBkffPvttz4KDETxllOJ4Ano8ssv90F32WUXx9KdjG9Zaubrr7/2hq6VjmUqSa9UmErKmeZ5Zqmzafsiypi2HthYNel4uBRXO1/tsVeaumd5q8U2S5+bpR6kqbPwwGsOcv/998dO3vAXp/6LezfTjEmIr9Z1L8szsfJn3dZ6HBQdP0aPs5ZH94uACIiACIiACIiACIiACIiACIiACFSfgAxXEjLG8ALX7eaaGDfhfPxkqYPo3/vvv58w9toGx93+yiuv7BNlhvL555/v93/44QfvQQblMrLRRhsVjBf8iTL/mCXceeoSQcjdd9/tmD1vH/1Q5rMcT7mPSBgX4PUEYbkIZlGHyncUPvvss4/DS0xUcN+OoORAGc7yASZ4aWC2IjOz7eOvXWOLQcvbb7/tlwkKjXi6d+8eBqvK/k477VQwuqlKAlMjHTBgQEEhj2cDU4jxbFiux2YQ4y0knGXLjHSbrcbH3QsuuKDw/PhwCysEg5fdd9/d75f6x0d9/niGGC+deuqpfkkmwrMkg3ndYAkO3FIjt956q3fLT500QaFHXgjDbOVK5bTTTvNGWIRnBmD44bnSOEqFo06zFBNtwJlnnllY/onlk6xcpe7Ncp7nR1vEH+0N7wXvHALTPDwaZMlf9F7qF8IzRMlMe9G3b99osCbHfHynLiK0C7geD9uFJjekPAEzPMMg0fpO+3DQQQf5a5XUdx+wgf+laS+z8Lnyyiu98hZDt9BrzqRJk/w7c8kll3haeAiYddZZC+So22bsSH2h3lidZ2tKbd5Bjq1/ydoXFTKQcafS9r1a7V7G7MfenqYe8NzwOEH/g+Ep7/SGG24YG39zJ3n3MTbhj/qEIWK5fjtt3UtTzri8W31l9vu9997rl8GxcNbe2DEeWUzw/mae53hn8JR3xx132OXctmnqXpZxYjTjjMl4lhhxMBbBqMM8cuEVwYyS7b4s40SLI7pNMj7A4ARvK5UIYy3zsIOxI0v5IRhFWRuMQWTcmLaS+JOGKVXOLM8za501DknH7mnrQdrxcMi6XmOvJHUvzG8t9rP2uUnrQZY6Cw/eS5YgY8zA7xcbOxsrljHkvcRAKpyIQXueZkxCvLWue1mfibHIum2N46CsTHS/CIiACIiACIiACIiACIiACIiACIhAaQLTlb6kK3EEmCkcKtEPO+ywuGD+3J577ln4SF0yUB0v4OED4wEMU1Akn3POOd4rxVFHHVVw1Yzr/X79+iXKJYopFLzEyccqlOd8PLOZxXjxeOONN0rGeeKJJ/plZDBwuPTSS72ys1OnTt7gBMUG0r9/f78N/zFreK+99vJeOvjgSL7x8jDXXHN5Q41QWRrexz6ePaLePcgnz9AMb6L35HEMm0MOOSSPqMrGgTERzOCJwcZaa63lDQGYCYcyDUFBhFFQVFAI4h2DsCg9MHDCuAWFswkGXBhplJNys+tPOeWUwtJVxEH9YRYlxk6jRo3yXo3MbfZHH31UUE6bUU25dO0arPkIjcEWZaaeYhjTvn17C5Jpa0ZgYSSUi5mk1RIUXqHSKEwHd9mNJigA8FJlRkPMWp999tkryiYf/GmbUOSaAYzdePPNN7sVVljBDjNtWb7qoYce8krULPU9UyZqcHPa9jItn2eeecYr3amX9D2zzTabNz6y2foUmXcU47hQMDbEKAyhT7EZy2EY9jE+5F1A2W/GUFn7omgaSY+Ttu/VaPeS5rnS8EnrwaOPPurwNmHSu3dv3zfbcZItHsLMS1j0PpaGYxwQStq6RxxJyxmma/v0OXFCn7j66qsXXdpggw388nYoSvEONnr0aEf/Tb9H/V988cV9+5n3uCRp3cN7hy3pkWacGBa6nOcSltgJDdnsvrTjRLs/uk0yPqBdsfFINJ7wGEOMQYMGeUMtFOQsERQuLcQSaWPHjnV33XWXw5MO/ePaa68dRpH7fqlyZnmeWets2r4IOGnqQZbxsD2Qeo29Kq17ls9ab7P0uUnrQZY6C5f555/fHX/88X75UAzM+L1KW8N5fn+Gv/VDjlnGJPWoe1meSVjutPuteRyUlonuEwEREAEREAEREAEREAEREAEREAERKE9AHlfK82lyNYkL+lJh487HnWuSeMITKAdt+ZlS8TPT9oADDvAxY9jB8kHcg8FHz549vcHAcsstF5uyxW1bC4TBCDPnTGGPsQlGK3y84iMzHmuQ6H12P3kaM2aM22GHHfxHfmbDoeg2oxXy06dPHwtetD3yyCO9oYOtl85M3nfffdfP8Cc/KM4sX9wY5gFGGG8w4x+PHMziJmzILtwvSjjBQRgH+YV1KHbdtuG1LPuUiRnbpgTCuAcDDhigOMJDTpzxCR9auYYnHMKiIDajFfI+YsQId+CBB8ZmrVQZWF+eJYk233xzr5yLKmswZkBhR7w8A+oA+eWPfZQ/KD633nrrJulamuGztUB8cMbrD4IRGsZapcTut/jiwkWvUS7qL+XCmKJXr17+tkriiou/1LlouoRDeYhXEt4NFJ6l3pFScTZ3Pq8yhMZG2267bXPJFq7THmCUsOKKK7p55pmn6N0tBPr/nZBPuG/h7JyVyc6zxUiO+o5CmeuV1neLkzjCfY4ROxeX5n9D/C9MqWM7n9c2aXtJumn59OjRw99LHHjfYFkkM1rhHUcJRZuL0igUwiaRMHyWvsiekz23aB7sum3tehg+afuepd2z9JNsw7yG+xaHnbOtnWebtB7ACS8rtPsYUZqHtzBO9i0t24bXo6wtPMsZMi7AmwXtrvVxdm/ausf9Sctpacbln2v0mXjhYqwVtxwifRve7mwZC/o7G8NgHHn99dcXPIeVSiMuD82FTVr3eBZZxoml8mN9KMvp4HWllMFr2nGipRtXl8qNDyw8Y0uMKKNi123L9bPOOsvZcoh4aAmXYbT7MW7FCIZ2i/FOdBlMC5d0m7Sc5Dvt88yjzqbpi2CSth6kHQ8b1/B51GrslaTuhflLs2/1OK68xGfXbWtpZOlziSNJPchSZy2/GEHjxYrffcSHscqbb75ZMFqh/ed9D38bhWMMi6fcNhq+1nUvyzOx55u0HoThaz0OCtPmuUSPyz0rXRMBERABERABERABERABERABERABEWgMAu2mflBJphVqjHwrFy2AAEYRzBr+7LPPvFv0zlMNIJIKs4qZCffOO+94jxzEEVVslooToxU+QE6ePNkrBvjAzazXti688iwrw9InGBPhpQBlUSWCEp+lsXDrj0eaBRdcsPABu5L704Qhvxg+UZdQ4rF8w8ILL1xxntOkqXuqR4BlvMaNG+ffY2abN/JHZZQYEyZMqGl9rx758jGnaS/T8KHtwGsOWwxWMBakHSpVD1ZaaSX35Zdfes8EtDelZODAge6ee+7xhnTR5Vfy6ItKpVut8y2p3UtTD6rFrVy8SeteNK5al5OxD0YrjFu6detWsz6vJdU9nlGWcWL0Ges4G4E86myavohcp6kHWcbD2Ui17rvz6HPT1oO0ZMkzv4vwtoKnTgzK4n5v5jEmIY+1rnt5PJO0bNPe19L6orTl1H0iIAIiIAIiIAIiIAIiIAIiIAIiIALFBGS4UsxDRyIgAiIgAlUiwOzzzTbbzMeOpyfz9lSl5BRtKyCQl5KoFaBQEURABERABERABOpIQGOSOsJX0iIgAiIgAiIgAiIgAiIgAiIgAiIgAm2CwHRtopQqpAiIgAiIQF0ITJkyxS8L8+STT/qlfsgESwvstNNOdcmPEhUBERABERABERABERABERABERABERABERABERABERABERABERABEWgsAvK40ljPQ7kRAREQgVZF4LzzznOnn356UZmGDBni+vfvX3ROByIQR+C+++5zP//8s+vTp4+bccYZ44L4c88995z76KOPHLOh49z7l7xRF0RABERABERABESgAgIak1QASUFEQAREQAREQAREQAREQAREQAREQAREIAMBGa5kgKdbRUAEREAEyhMYOXKkGz58uGvfvr1bbrnl3NZbb+223Xbb8jfpqgiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQJshIMOVNvOoVVAREAER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fF/iZQCCVpJhHQ8beL7Sor366PBa543Qe3wv6Cz0Pqu/2SoIr0BpBOIJfRdC+xQSJt6BwpX0occNPDNBWkFvkQ7f0mKLLeYw5ND3cTzkkEP8/TgvU+SZtb3T7iACUlbeFaRV9DRET/QzK5jjpIyySrp4m6DNzT333N6gjYcZ6gr5CT0ukres8nwrjnj5os9CtNektGWBtAJBVVaPQ1pkhfbVV1/tPREIkTUpvbTYNvt95u2LkurZyvA0OgiCKeOOcMxBuSFEQlqpJXoMIvFqjb3K6Bdoe5QXwz0GXtoe46JJkyZ5vaTJ45QJz2aalEt89Bm6VHuWiMNA6lTWMc07KSsvSSdNnuhxSM4Iep73Sr+Kbnv66ad93wk5O0la0Q7K+l2UVCcJp9/F4xBjbLxY4tGN3zT0DfTDjBMhH+B1apdddpHH/Ji96DgRHQvRAmEMw+9UiEW8K/qj8Dd5mfoyqkiBk3ptr8jvogLF8o8ynsBLYChJ471GYKuJ3xDkGWvT1kSK4JP3d2MZOpqFAowRIVnjGYvfRvfcc49vt7UWjUi97WgIGAKGgCFgCBgChoAhYAgYAoaAIWAIzI4IGHGlwFvFaM/kwsyZMz2ZgyOC8RBvIBiE20UwkEFcwXj39ttv+0lCCCUYmeIEY/O5557rb0FawcApBkpWkbGakAkrjJsYxkLigaTZq1evKk8bpJtEXMEQjhGQ1dFskRISV3A3zSQ6wsRv0lYxiyyySFWexIdQlJW4wipbJt+aJWDJKtZGC+QC7eWF/GrhA+kJgy/vR8gTUkZZiY57fFbGEbeWMJnMZDVunjEMX3HFFVXeCniWNnLCCSd4YyffEMQoJsBFMNBSHibDWU16xhlnyC1/PPPMM6NriBFCXOGbpG10irBqWgxhesI473cC+YUVwiKsEIfYwISpDpf7tY4Y3rWxFY8pGMJwDc/ksTZwQWgS0gpGPzz4SDuiLUBQ4tvGe0HS9gWUJUuetcre6Hus0GdFcUhowONEHCmtLF2LNyhNEgTPOOIK9Qd33hPvC70D6UULup1y8Z5CUpOOxz0x6Es4Oow+Iq00Uu8NGzbMtzfKAkEHz1AYPKgv2+jwTcQJK8Zl1TjkCyGu0FbZ6qCW0C+NHTvW4xZuwzRq1CifP+Q9DBJJxBXSz9LeaXOQVhBW6Yq3NfKDfIob/Dgpq6ykTXti7INxCeO3CP0590TyllWeb8WRrVVkCzDGeUljmKSyMYbim8L4j+BpibbEmIQ+EEJhUlskflpsm/0+8/ZF1KmdJK0Oop/CGwljKC3oiDQGxixjr7L6BcZ6tD0M9whtDSMthFNIw3iM0L8DWOnPnwgEZYhvjO1OPPFECW74Me07KbMgafJEzzLeQRg78d61viMcYzY6Pkla0Q7K+l2UVCcJh8DDeE+3KbnH+J8+D4IPvxc0caWMcSJ5a8+OkAAYn0Cc5Pd6SFwpU19KHfMe67W9or+L8pZLnuP7j/OmljTeKxtb7QmK339ca0J1UXzy/G4sS0fj5VPG4LRVfuNCOr7vvvs80QtvliaGgCFgCBgChoAhYAgYAoaAIWAIGAKGwJyGQLc5rcJl1hd39xi08DYgpBUmazDM1DPgl1mONGltscUWfmUSEyFixKplNGMVqBibMU4KaYW8unXr5okrMmlUy+NKmrJJHNIVIymTNrKHtdwHZwwHiMSTe2Uf77jjDk/IKTvdpPSYrMLVcjsKk4S0ZyEb6DIyKSzhL7zwgr7V5ZxVvhiSea8Y8vh22Bc9FCa0xdDJdiqatEJcVr/LtkLsEc7kYacL3xqejJhoZyIYo7VsOUbdwE2k1d8JK63ZfkCLNkBgqNYi+oHJV7bOkPZCnL59+0ZbbeBVAANZnGTNMy6NZoWxolgbT8iXNo93hThpha7lm15ggQV8ceT9SNn4nsQwj7G+d+/ecqv0Y6P1HgQiTYCEyMMq3T322MPrHgiYf/rTn0qtF2MAtgALSStkAllPSCXSv8ZlnrW9Q/RE8AYj6XPNt4bLfemrCdNSRll1emytFxpxIXBBNBXJW1Z5vhOPkFSEtEL5IWTirQ7BExNG1XqSBttmv89W90X1MEtzP6sOYtwS9lMYUWVsmibPNHHK6hfwiiOkFfJl7CVbXkKuYLzVbpL1nZRR/rR50l+KNxX6j1DfURYIpPQ9ZUhZ7aCMsqRNI460wrP0BUIex4OHFhmHFBkn6vQ479evXxQk+UYBlZOy9aVOO8t5mrbXab+LysKW8ShEMXQsQl/KoppwTNMKfBrxbVIvfhchYIinJBNDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDYE5EIN7dxpyIRI46r7baan4SBdfxGL9whcyKeiYtMVyG29XkyKK0RzCYsc0Bq9ogELAKM8lLCpmyMk5Er+KXsMUXX9yxTQTeLOIMf3nJBDIpxYp9vLyMGTNGsvTXXGAAktVJ0c3KCZN/iDaM+4CM/zBAyIpaVp7iqSaLB4GM2fnorLpm9TWGIEgZePJpN8HoSxvH2CGeQCgjk9T/+te/Yr1JSB2oG+8Wt90I5KmQkCJxtZcIVvvSXqU9yVGMRBwxImmjoKST58j3S90w7vAumiUyKRvmR1tmhbQ2/BKnyHcS5pH1Gr2n3XPzvPYuIu9Y0hVdwjcLrqGgX6ZMmeKD0SWhQYMbWfPUeRx33HFV3mGascUB5CPem7Rx+gPcgNOmQl0i+FDmrLpWvgdd3zTneHegf8IIixcrVtKKxwcIRKLT4wiComfJp6iubbTewwhw9tlne/IKdb333nsjAzO6AxLFXXfd5b0F1fL2kwbTMA7jgmnTpnnS0nvvvedXzhIHYwcCwQm9GWeYyNLeeR9CFhNCn8/gi39swSV9tQ7X50XKKumw2r3WmIJ4ZZRV8uuko/ZgIeXW70q+N7kXHtNgq59p1vskz1b2RbrOec+z6CD0HW349NNPj7Z9e+2119z111+fN/vE54r0CzrRuLbHdn4i9dqexGvmMcs7KatcafOU8SnET7awbLQUbQfSX+ftq/HMJ0Qd+tMsY2PG/Yx78EjEQg8WTjBep8/Dq4QWqWeRcSJ5ySISyMJsSSTCdixJUkRfso2W5KnTD8d5+l54nqbtSbvj2Wb/LgrLm+W6CLbkg/dNfoMjAwcO9Lo3ri23Ah9ps5Qt69idZ0TYjpc2hIdXCDi33nqrv8WYtJm/Q6U8djQEDAFDwBAwBAwBQ8AQMAQMAUPAEDAE2gEBI64UeAtMCOtJYfbvZqU1hnY8JrASb7nlliuQQ7mPssoe4grCNgjhiiWdG9sJiUBSiRPcZCM6rsRjAhEJDdxyP+mIAXzjjTf2BkaIK2xHwiQVeeCFBdHeJ3Q6GAKReeedVwdnPodE8O677/p02C5IJs0yJ5TyASZ12dYDYXUseLcTcQXs2UqGSedaIhPkcXHEWCv3MOiKUV/C5Pj666/LqTcsRxcJJ7yrsogruM7nDzIObXHw4MFuZGXlbJz3hITi5ApmclIM2JDM2HJj+eWXd/vss49beeWVu6RZ5DvpkljGgDh9wHcupAz59iVZDAhI3HOEix7hXOJyriXu2Vp56mdZtchfM4QtGPAChDEDDwkYJPCswZZVCEQQyBJ6CzKtP+PqyXOCkY5LuMZa2g/haQRdA5kDAgdlxgCNoHcRvEJgKAhF9CzhRXRtM/UeRiv+0FHoovvvv997NqKvhqzGynn0fFku2XnvbEs0Y8aMEL6qa9pB3HuLawdJ7R39J7pX2klVJpWLpHDiFS2r5JVmrFO0rJJXpx3j8O/evbtvb3xP4Xcd1i8NtvJMM98nebayL5I65z1m1UG77767u+qqqzzZD69wbD0CiYXvj+8TIkNZJBbdJuL0AXWWdqXjhljEPct4RvprIb2Fz7XqOus7KaOcWfKU8SljtFq/ocooF2nodxv3LolTqx1If523r2abKf5EqDPfPMTXPffc09UiArM1pJBe5Hm26GO7yKWWWkqC/FHGfvXqSGSJW5VA5QJCR7gdEOXFQ0efPn3C6P66qL5kPCdexGIzqBOYtu1JuyO5NPmV+buoThUSbxfFloT172/IT5A86DtDaQU+Rb9NqQO/80Lh9zGkexNDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDYE5FwIgrJb55DMznnXeegyDCRDZbQ0B8aBdhlSWThhjLtt9++5rFYnJIRFbiy7UcZasJHVfusUoPmW+++SQo9RGDKivjmZxk+xRWHTFxincBJiExDsdJ0Qla0nz11VfdZZdd5pNnOxQmpxst48aNc3//+9+9F5yxY8d6w0ij80ybPpjut99+TrYBWmuttfz7YBW/GFyZhMZQX2+FIUQMhBXKEyZMcOuuu26VK28pE1snIKSvt8uR++FR0g3D81yLIYJvhJXIGJ/ZNgriQdwqvzx5xD1z8cUXO7bzyiJ5v5MsecTFTXIDHxcXPUjbQERfhPEg6ojE6RLuZclT0mrFEYPOgQce6M4880xP9EJ/TZw40esuJtshSIRGB13nrLpWPA9R16Rnk3DACweT4y+//LIvE8QV3hUkFoQtZ+KIHKJniZNHv/Mc0gq9x/dNvflDv++1115+hSsGiIceeijagux/Jcz3H8M1q4QRCFNbb72136pDjC1sB4ZOQZI85mRp7xBvREQny7Uck9pGGWWVPJZYYgk5TTwWKWtioh1wIwl/3hffE/1/LUmDLc83+31KmVvVF0n+eY9ZdRCEdL5dvLKdeuqp7sgjj3R33323z37//fcvROQL61CkX9Bpxelo9CB/jNn+9re/6egtP8/6TsoocJY8xbsGxM5mSNF2IP11XuIKdZR+hXExYzo8TbDlHmMFxsZxnvR4jt8LkDeJj9cxBPIB5C++JX5HIGWNE0lL6ok3M9Llj/fLbzn6fS1l6Et+F8SNk/A0kkbStr1W/i5KU48wThnYSpr8NmLRDSQ7dC6/l0JpBT5Fv02pA7qYbwx9LGR0fn+yfZYs3pG4djQEDAFDwBAwBAwBQ8AQMAQMAUPAEDAE5hQEjLhS8ptmT20MVEwG//73v09Mnck0LUxcNFownoRG06Q8taGEice4iUlZTa7jSnoyoRP3nMRJOuLFhglNVupRXogrN910k4++6aabRpOd4fMyURg3UR/GjbvGiAhRgsnZpZde2hs24+KVGYYXGXELDGklD15llidMC88EQloBGwy9WphkS7MqDHIJ75I2A7GLdzV69Gg/8R0SGnr37u2z4D3gXSduUliXocxzJglZ/cYWWEy83n777d6D0uTJk314mXkVTSvvd1I03yzPo9dYkcu3HG4hJOno8DhdIvE65QjRiwlnJp4POOCAyDCIoYYt2kLRdc6qa0XPkmYe3bHLLrt4cuVjjz3mXfg//fTTTkiHcdsEkY/oWc7z6tp20HsYCqg/rtkRtjAoQy655BKfDFte0W+F77xsDwe6/WhiiK6L3tpNh5dZ1jRjmCJl1eXutHPw79mzZ1WxGQMK8UzjUhXpi4s02BK12e9TytoJfZGUVY55dJCQD48++miHzpTxEO8Pb4tCepY8ihx1m8jaL+h8aXuLLLKIDvI6HMM+kuThouqBJl3keSdFi5Y1T8anbEvClpnNkKLtQPrrvH31iBEj/FaC1JWxAWNjtnZkiz36TDwUQsiNk0mTJkXBkFfoa9mGderUqZ7MwuIOpKxx4vrrr++9yJEmZb2v4iUSb5G8K7xaXlHxhKalDH2JFya2WgwFL1n1yPxZ2l4rfxeFdUtzXQa25AO2tMHDDjvML2Ch3dH+wq2mWoFP0W9TcMTzoWx/DJEc7Bg74v2VOY+QcCXPMWeh2xgkKttaSNCxoyFgCBgChoAhYAgYAoaAIWAIGAKGQKcj0K3TK9Bu5ccYIR4Gkjw0sGIPQ77+ky182qU+2o0zHjLiRMJ1XIknhrI8xlQmMcWrCpNUeF8RoyIre5ME18hIaChMih+G33bbbd7DC+GsmMf1e6Pl+OOP91ng3jrJUNzoMtRKn21OEMgjI0eO9Of6Hx5q9MSZvqfPqR/vZcUVV4yILrzTONKLbPvDpBxbeTRbIHixrQjuxcXg8+yzzza7GHXzy/udhAlDEGqkiH4QfRHmpcMlbhinyDU6hNXx8kebbaSgN8RTkKxmX2mllRwGjjjRddZY6LgSruNyX8iDnItHD87TivaqwhZBsk0Q29z17ds3NhlZOc1N+T5iI9YIbBe9p+uY5PVC9+P1vhWICPKu8GAT1xdhmChTeO/i0UO7y9d5xIV3Ull1XTrxXNqELjvENuk78dRUVFrxPqXMRfsijOFnn3129PfEE09I0g075tVBjNMgqCOi3xnH5CUGJFVQ6/q49sNzEq7jhunFffsyniZuGW0vzDPvdd53kjc/nsuap4xPISCyzUujRb9bed9hnhKu40qcor+LJB2OkMw32mgjv9WPeAVLOzbG48muu+7qRo0a5ZPEw5kQ9wiQsktdfCT1T4dLXHW7yyllxdvZgAED/D2838lvcwJaqS+lsFnanrS7Vv0ukjKnOZaJLcQNBAILC0rk/JVXXvHn8q8V+Oh2qNunlImjhOu4+n54jhdETQTTWyWFcU888cSqeSTau4khYAgYAoaAIWAIGAKGgCFgCBgChoAhMLsgYMSVjG+SVVxMHCUJq2TE1as2iun4YqyoF6bvN/uc/cAxRiB4DwiF1aYYXhC2SNJC/WRiPA9xhbQwDmAwZKKRlawIK5ZlEssHqH/EE88gbImUR8QIwURnUj550q31DIZnVkixClEbSGs908x7sioXd+PyvnX+F110kb5Mdc6+96zQRm6++Wa/ik4/yMozaXt6xaaOI+eh5yIJL+NIfcWjRbuuYsv6nWhcxAMAhg15z/p+WeerrLKKT+qll16KvlGdtniBgvBR5rZPkgfEN1Ywyp9MJMv9Rhxp3/xBKOCPCWZp02F+RXStJnbl0bWQHgYNGuSLxMpptrFBapHoxKhM+4HgkkeapfcgLYlejysnHqVEeA9xAvlE3l2cDtTPEE90RRxBirBahgidVpbz1VZbzUenvrLCXp5/8skn3RtvvCGX0bGTyhoVukNP6OdCYYsNEXl/cp3n2Kr3KWUt0hc9/vjjfptNttrkj/FloyWvDuL7Rp+LbseQ3wiDYZF+QWMn3gp1mG57Sb9TdPxmned9J0XKlzVPtrhEGHuylWSSJHm/SoqfFF6kHZTxuyiuXIwX5Xew9Hdx8eLCZKslfifq38KNGicKcYffMJq40mp9CTZZ2l67/C6Ke6dhWCOwhYjEYgLS5l1CgNJjnVbgU+TbDDHT19orXy3PTvr74fnwWqdp54aAIWAIGAKGgCFgCBgChoAhYAgYAoZApyFgxJWMb+yCCy5wm2yyicNgjyFWiCxMLjCJKSvtmcwT43zGLNoiOi5wWY2P3HPPPW7ixInRpAhbIB166KH+Hsbmvffe25/zj4mT+yrumWVfdVahMrmk/yQyBJ8kgznbLMgEsUw2br/99pEBUdJg8pQ905nEYqKYSa2tttpKbmc+sj863laaKbg8zmsAbnQ5Wf0lwqShrDDF88D48ePdHXfcIbczHU8//fRo6wRWHWoDPC62xTsFBha+Ke0NgbZFmyQOW/uUKXzPtG+2CcI1tRBjyjAslllOSSvtdyLx9ZFnEb4vVjPiSUOMEf5GSf/YOofvEuE7FeM/2J5xxhmRlyO8KeX14FFSUUtN5tJLL3V4LOJvvfXWS0w7r65FpwoJAo9IvDutZ4VAiU4mPGlSW7xY8V2RBmkNHjy4S3n5BnG1D8EFGThwYETS6BI5RUAz9B79NFvNoW/wdCKY0Oapy7hx43xJ0fsbbLBBbKlpu6wWR9jaAPf+0ieFD4CdfFd33nmnY1W56JDnn3/ebzeW9B7CtLJc840hvKODDjrIv2+u2Z4BPRYnnVTWuPLnDYPEw5YVSX+szC9b6K/QB6JfGSMJKXPttdd2a665ZuEsW/U+peBF+iJJo9nHvDporbXWinS7bHdSdtnz9gthOSCzoeuk7dEWZUsjtlZpJ+IKZc/7TsJ6Z7nOkie/6/DIh/DuTz755Kr+AKMzZPuzzjorSxES4+ZpB7zrsn4X0ddBNuHvjxUviTfeeGPNsfGVV17pLr/8ch9Xeyh76623vEcl9CCCF8YFF1wwqncZ40T6WhkDoed5B/TBCDhqD2it1pdS8bRtr5W/i6SsaY+NwpY2w5abCCRgvUVTK/DJ823GYShtFk81d999tydGSjyZh5FrOxoChoAhYAgYAoaAIWAIGAKGgCFgCBgCcwoCc88pFS2znkyITZgwwf+RLoYtMU5JPqNHj3b9+/eXy448YgDDWwHb/mD4u/DCC71hmQlIke9///tu8cUXl0t33XXXuWOOOSa6Zo9m/uLktNNO8wQB8oiTXXbZxRsJ5Z5sHyTXHB944AG3xx57REHf/e533de//vXoOusJhvW0Ln2zph0Xn4nUww8/PO5WW4RtueWWfnsfJglZNX7rrbd6w+zbb7/tyUkrrLCCJ52E7b9e4ak3k8rsXc5kHbhDUplnnnn8oxCj8OyAsfeqq65y11xzTfReyFuMv2WvdMaYKAZFqQPbvOy7775dvnG53+pjmu8krox42jjzzDMd3zMGGG2AYxW5vo57Pm0YBs2RI0e6yZMn+7byne98xxMBWG3Ku0cWW2yxyLNS2nRnp3h5dC3v6IMPPvAwQDqRFcshLpCxIKBBPIsjpEAQxM26kMfwOLXwwguHyTg8JT3zzDM+nG0Chg4d2iVO2oBm6j3qD/GSP/pqvMx8+OGHkSGXMkNgWWihhRKLj1GNfg3inhB9JDIeDTBki6DL0F+8E4xS1BVDjqycRZ+Ebu7l2bxHPIRBvMFIx7aDEO3olyGuYMTEm5cYrnUenVRWXe4i5xCK+EsS2oj2ykRcTf7RfR1997HHHhslhZGJLclCgcCLVzW8iXAu223RLvS2AOFzWa9b8T51GfP2RSGBuRne55qpgzRGWc7z9Ath+rQ3yNjnnHOO10NCPia83caerXgnefLEYE5/CEmFcQ1EDbYxwbuXbJPKmKcsydoOyvxdhEc88YoX1oetP/fff/+qYLwnQWjHKxHfMf0q5Bf9jYM5/bGWMsaJeGrSZHudPuUJpdX6Mmvba9XvohC3NNeNwhZSGO2bdz116lQ/7pHfga3AJ+u3GYcdv4PjBO+xEK9NDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDIE5EQHzuJLxreOOllWKeBoR0YYMDPlsSSGrgiSOPsa5Vo4L089kPdcT//XSlvv6GfJjIhajDRMn3MMAKKQVjH8QUsJ6xhnHapW9Vnw8p2BEQliRHLeNCOUiDvdGViaKIdfEidRRjjqO1Bvjraxcj7sv8fS9POe6DEcffbQ3pOp05L4c9b285zotfS7pSZgcJZx2jocF2jwCYeT11193rKRcZ511PFFJtpwJnyW+hMVhh+F9n332IZrfQgajngiGc0gytC9cRJMvRlj+OKdckJTiyEySBkfJPzzXcTjX5eMZiBSs7sMbDCtMKUPatMK0k67LSi/NdxJXBjCEjITXGshaQhqKi0uYYKTLrePWug+OeOiRFba8R0grPIPRHT2jCXCSbq0005SJOGF5w2vJq8ixXjl12pK/HLnXal0rE/+UZdiwYRy6CHVE7+M9BiLZ6quv3iUOAYKFHCWSrm+z9B7ESlbI41EFoa/GsCj9Dt/4lClTHN68agkGeYg/kFF79OgR1THuGQg9eIlCZyDkB2kFIxUGz7322suHh/gQKGEaKx/5i39J9wnHq8KOO+7o+0M8y+DZiL4RHSuGkTDdImWVckmacpTwpGPesiallyZccMsTlzaDrpI/CEkite7pPCEBLrnkkn7rOSGtsFobz17iWU7S1EfBVI76Xtx5K96nLkfevgjvECLUFfJcI0TjmFYHyTNyrFUuiaPfvYTxnD6XdCRMjhLOMU+/oJ/nHA8YjGcgVQhphf6etpeGXC91iStfmFeea51u2neSJx/9TNE88e4AQZB+gXEUY1KIn0JaoW/cYostdJZV717nL5EkTI4SzjFrO+CdlfW7SJeDsjHep34Q9iBtyphO4vFbjfIi9LNstymkFfpECD+MqdGHoeQdJ8ZhBrkWT2m8B8a54fsg7yL6st53IfflKHXVZc3a3sv6XSRlSXOU8uty6+fkvhzlXhnYklaYL9fMPTAWRcAQIjJSFj46T33uMwn+Zf025fGkdKkXXrCOPPLImluRkU6YRngtednREDAEDAFDwBAwBAwBQ8AQMAQMAUPAEOhEBOaqTCzN6sSCt7rMGC/wvPKXv/zFTwgzmccExqKLLtrqojUkf7YgYKsF3EWzYrxXr16RkU1niBGQCU0MgbVcZZMWRo5lKt4Y7qu4zY8TtmSQ1e14Z5HzuLgW1ngEMIRCWmGlbr9+/dz888/f+EwrOaCiMPzi9QUDAQa/3r17Ny3/plSyQCad9J3wLtGbbMeEIZ/Vsc1qRwUgbuqjaXUtW4zQ/0ybNs3r46RCHnDAAY7tIpjsT3I7vtNOO7lHH33UG5NIb3abAMcVO6TLd9991xMwIUlh1KINNkogOqCzICr06dPH93WNykunS77PPfecJ+lgRERf15NOKmu9urTzfbbaoE0whqIPa5S06n3m6Yvo0yGQYeBGhgwZ4r2DNAqbTk03bb9A/SAWHnfccb6qeHfCsw9jN/7QRY1se52Kb95yQ15jbIyXJsYy/KaJI2XkTT98Lks7CJ9t5jW/FSFKcYSwAnmK/jbN2KLZ48RW6csi76NTfhe1CttW4NMp32aRdmfPGgKGgCFgCBgChoAhYAgYAoaAIWAIGALNQsC2CsqJNBPBTADzNycIq+owlDZT8PSBMBn8ve99r5lZW14xCGDsaIXBg5V8GAIaaQyIqW7HBHXSd8K7XKZi2OHPJB6BZutaSA6QVhC8raQxLMWXvH1D8biCNzT+miX0W0leaRpZBvKt5cUjLu9OKmtc+TslrFm6r1XvM09f9OKLL0akFbxEjBkzplNeZ1PLWbRfwEsIfyblIkCbXbbirZG/ZkjRdtCMMpLHV7/6Vf+XJ79mjxNbpS/zYCPPdMrvolZh2wp8OuXblDZkR0PAEDAEDAFDwBAwBAwBQ8AQMAQMAUOgnREw4ko7v505sGy42maVHm7Mf/3rX3sE8AbA5JeJIWAI/A8B+06sJRRBAA8keDh4+OGH/RY4pMWWB8OHDy+SrD1rCBgCcxgCRfui6dOnR4ix1ZVsMxIF2okhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGwByDgBFX5phX3ZyKbrTRRu68886ra3xgmwbiLbTQQlUFW2eddfwWBxK4/PLLu9GjR8ulHQ0BQ6CCgH0n1gzGjx/vPv3007rb0+27775u0KBBVR6zLr30UnfmmWdWgXjMMce4Hj16VIXZhSFgCBgCtRAo2hexfU337t3d3HPP7Q466KBaWdk9Q8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDIHZHAEjrszmL7jZ1Uu7nQx7nsdt/4Pxgr2pl1hiCb/dwVFHHeX3RW92PSw/Q6CdEbDvpJ3fTnPKNmDAgFQZ9e/f3/Gnhe0N2BJonnnm8dvZ7LDDDn6bIB3Hzg0BQ8AQqIdA0b7otNNOc/yZlIcAup0/tsvgz8QQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDIFOQWCuCklgVqcU1sppCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAITD7INBt9qmK1cQQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDoJMQMOJKJ70tK6shYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKzEQJGXJmNXqZVxRBoRwTeeecd9+GHH1YV7d///rd7++233SeffFIVbheGgCFgCBgCzUXAdHRz8bbcDIG0CPz973/3Y6X//Oc/VY/89a9/dXy3JtUIgMmoUaP8n40vq7GxK0PAEGgvBH71q195XXXWWWe1V8GsNIaAIWAIGAKGgCFgCBgChoAhYAgYAoZAixEw4kqLX8CckD0EhWuvvdadfvrpbsyYMW6nnXZyQ4YMcf/973/nhOq3ZR0ffPBBx0TZpZde2tDyQVDZeOON3dZbb+0+//zzKK9Jkya5DTbYwE2dOjUKsxNDwBAwBAyB/yGAofrss8/2evrFF19sGCymoxsGbVXCzepzqzK1i4Yg0Kxvk8IzRmOsdN1110V1gcyy+eabuwEDBrhZs2ZF4Xbi3GmnnebuuOMO9+c//9ktsMACmSDhvX700UeZnuF3DMRsew/JsP3jH/9wn376aXKEhDu0c/udmACOBc8WCHzjG9/w+ur88893jz766GxRJ6uEIWAIGAKGgCFgCBgChoAhYAgYAoaAIVAGAnOXkYil4dybb77pDjnkkGiSbfLkyW6RRRZpOTRMhEyYMKFLOb7yla+4ZZdd1q244opu6NChrnv37l3iFA1g5ePhhx/upk+fHpvUXXfd5QYNGhR7zwIbiwBGtEsuucQtscQSbp999mlYZnPPPbdbc8013WOPPeZ22WUXT2LhW8GwgKy77roNy9sSNgQMAUOgUxH45z//6c477zxf/F69erlVVlmlIVUxHd0QWLsk2qw+t0vGFlA6As36Nim4jJEYx7/wwguuZ8+e7p577nF4XPnud7/r5pprrtLr16kJPvPMM+7222/3xd9rr71SVePdd991F198sXv22WcdBMHPPvvMzT///G6llVZyO+ywg9t+++3dvPPOW5UWJAye4TfMH//4R0d7QI8ynl5//fU9qWiLLbaInmGMzfvi/oUXXhiFhyd33323g9SNHHrooW7DDTcMozT9+vjjj/ftLi7jI444wtc37t7TTz/tfvzjH3tMP/jgAx+F35nguummm7rBgwe7JZdcssujf/jDH3y/x7vkfMEFF3RrrLGGGzhwoBs+fHhV/IMPPtjNnDnTh/Xp08cde+yxVfflgvC33nrLzTfffP69SXizj5DPfvrTn/ps99tvP7fVVlvFFuGyyy6LfiNRx6eeesqBB0IbgpwVCs/cf//9Ppjf9/y+E6Htye882nOIo8SzY/MR+OY3v+m+/e1v+3mSk08+2esv0+nNfw+WoyFgCBgChoAhYAgYAoaAIWAIGAKGQPshYMSVkt4JE2NPPPFElBoTme0gM2bM8JNecWV5+OGHffC5557rTjjhBLfddtvFRcsVxiTbyJEjo0nF3r17u80228wtvfTSbrHFFnOLL764W3nllXOlbQ91FgInnXSS23XXXd2TTz7p/6T0++67r1tttdXk0o6GgCFgCBgCLUDAdHQLQLcsDYEUCECEYPyEdzrtoQ4Cyw9/+MMUKcw5USBK4PkE434SKUCjwW+273//+06IFXIPDyEQL/hbddVV3eqrry63/LZNEFrw6KIFz1WQsvmDnAChRQRSC+QDhN9aPXr0kFtVx1/84hdRvGWWWabqXqsuXnrppahMYRn4fRknZ5xxhps4cWIXLzR/+9vfPIkdIju/kfHAqQVi1ogRI5xOF68r06ZN838QWfitLYZ9yICQMpAHHnjAk4wgAoTCAo7f//73nrgS3mvmNR6Sxo8f78DhxBNP9ER+yDRa8FCKd1IIVCwsgdSgSSnEpc3yW1oErzQQVSBhIV/+8pfllj+SlrS/b33rW1X37KL1CECyY4HP888/70mJtBMTQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ2BOR8CIKyW0gFtuucVPmpWQVEOTYKUVZBEmdpngeuihh9zLL7/sySWsnGPCL27SL2uhmPSVlXB4nWHV+EYbbZQ1GYs/myBAm2IlIHt5M6m/8MILu/79+xtpZTZ5v1YNQ8AQ6GwETEd39vuz0s/eCJx66qne+weEcAz5EMHx6IFnEJP/IcDYEvICsscee7gvfelL/7uR8B9D/s477+wgnHTr1s3tueeefkumRRdd1P3ud79zN998s/+NpB9nKyF+20BagTyx3nrreUI+ZADIFhAvpkyZ0oXUgmccvLMg/O6KWyTA7zLuIRAW8LLVDoKnRP377dVXX428gcSV75e//GXkVQaCDkZ46s855BG8BQkW+vl//etfbvfdd/dEFLA98MAD3SabbOKJQmyjwrNsm7XCCiv496af5Rz88GZD3HaVr3/96+7II490xxxzjG8jkHsOO+ywquJCIoVoAgZ4Vplnnnmq7nOBx0rwEYGAJaQVCbNj5yDA9wER6Y033nDXXHON/2Y6p/RWUkPAEDAEDAFDwBAwBAwBQ8AQMAQMAUOgMQgYcaUgruxtzkQTwpYorNBrV9lyyy39ZLeUj1VauKa9/PLLHZOGeF656KKL5Hbu4wUXXOAnYFj1eP311/tJ9tyJ2YOzBQILLbSQ35JqtqiMVcIQMAQMgdkMAdPRs9kLterMVgistdZajj+TeASuvfZav1UrHiwgW9STc845x5NW8IbCb5+tt946egRPgHhVYesevEOKPPfcc5FnTbxejB07Vm75I1vg7L///u7nP/95VTgEDIgIkCvwHhJHXHnllVfcX/7yF/8chux2ERY8aIF4Ilt96nA5F69AX/va1/y2Jxo/PNeQHuQXCFhaILyI95TRo0f7rZK4v/baa/t2v80223hPJWy3A+EoTvBYAxFkmTbxVhNXRtrmDTfc4OcK8JIybNgw941vfMNHhXhFm0N22203T/D3F1/8YwsgPNWwHZYmrtx2220+Bp5WPv/8c/2InXcAAhDnINsxl0QbYFurdiGudQB8VkRDwBAwBAwBQ8AQMAQMAUPAEDAEDIHZFAEjrhR8sePGjfOTbdtuu62ffGpn4kpYVSZLjj76aD+JxiQi3ldCwd31fffd515//XW/ootJYfYoZ4U2k7GkEYrsMc9WMEz0MqHM8xjG+vbt6/c3rzexSHy8dOCmGg8uyy23nOvXr5/fO54J4DhhQhMyTi1ZaqmlvHvmME6eeoZp5L1+/PHH3W9/+1u/ypOJW8rInu6DBg1yTFSWKS+++KK79dZbHZPECPkxWbbBBhu4DTfcMDG/PPj86U9/8pP05LPjjjt2WTmI++7XXnvN5xlOjvMeIT0x0V9LmNRmb3stTOzidhlcWYWIW25Ws9FucbvPamUtrOTEdTmCQaHWKmbSZNKdCWIMG0iRevoEKv/YRol0Rb7zne+4JZdcUi5LO8p75Bti+4FwNScrhqkj9eO+Fu7RTusJLvoxWsQJz7M6lHb46aefeg9QrFpeZ5114qJ7AxDvD30TZ7SjjbAKOmwHZbwTdOJvfvMbX1baCO9jlVVW8QYudFmZgqErqZ7oP7x6IWE985aBb+SRRx7x+pVVnjNnzvR6ltXm6FqMeLV0tLSjLP2Cfie1yo3OC/VBWfhk0bW18qxV/qL3Pv74Y9/3ifcA0qNvox3Q5y677LKxWWTVeySi30kzdTR5891ipEOvsAVE9+7dvT6gjqGOJj6C0Zcya6G9YPjD2DzvvPPqW21xnrfPpfBZx0H0p/Sr6PeddtrJe3CgL0R/4W0PXcvWE1qK9n+klUcf8FyeMRueMOg7GCOhj+MELxps14IwttHbasTFTxvWzG+TMtXSQfX6hWeffdZvPVGrbnG6VuJnbXut/jbvvfdeX3T0ALqkljAOYZsZZODAgVWkFf0cZH8tfMsiEA7ihDFVSExhWye2HOL3kHhVCZ/V4e1EXAnLWe9axtJ4adGkFf0cHmVCke8V3cU2s1rQ75tvvrm78cYbHV6HGCvrNPDiiHz00Ufu4osv9p5K9PPtdE79mDdgzoA++5RTTnGTJk3yJCq2kULADc8soTAGZrzGb3V0/fLLL+/7UQg7CGmCUTtIkbFF3vKDZ736x+m8PP1m3jImPbfxxhv7W/zmZc4F4pKJIWAIGAKGgCFgCBgChoAhYAgYAoaAITAnI2DElQJvn4nGn/3sZ97Qzb7bV111VYHUWvMoBmqMREzIMtHEpAkTawjeWCZPnpxYMAwCuGXWRmqMr7jsRlh5h1FEC3ix/zkTd0OGDNG3onOILkzgMQkVCgZ99rHXeUqco446Sk4TjzwvE0QSKU895dkiR7z1/PCHP/Sr58J0rrzySo8/k+sLLLBAeDvzNUZCSEoyOSwJgDH7p/MHMYgJ1JAwkRcfSFy4xEZCMsMHH3zg9tlnH4chaMEFF+xiqMYDEOWtJ8cff3wVceX999/37xfDVpxAvMLtvm57fMc/+tGPfHSeHzNmTNyj7pNPPvGraSH7QG4R4kqRekpGvBdIGCJMvofvQe4VOWKwkclxJrnD74gJU75P6hcSV9jqiW+vnmAgDdNlFej48ePdFVdcUUVGkgl39ninrbD6WcuZZ57pjdojRoyIJa7QRiA5he2g6DthGwG2BBBdpsuEO3q8SrF6uCypVU+2WqM9IGE98+YPUYAVnklCeTAeyXeh45WhD3R64XmcPiiKTx5dWyvPsMxlXUPigPD5zjvvVCVJe+YPI9dxxx3nt7XQEfLoPZ4v8p3k1dHk+9577zlW1YuRkzARdA+6YvDgwRIUHekX0UNxAumK7yPuubj4jQ4r0udStjzjoIcffti3D55nPKc96IEbugSPEIyVZJxXtP/Lqw8oY54xG/nRBx966KGJxBVIHTL2OOuss0ohrjT72wSfWjqoXr9w5513Vr1/0gslTtcSJ0/ba+W3ye8OId4nEft03W+66aboEoJXWoHgKcJ2Qstk8OyBNxaIK2wzRP8blhPiDwIpFoJqJwq/HyGNIeCTRSCVIpDRwvEj4RBhhJRAXE1ckXbM71HiMIZPIs2QVquFOjK+4vc15E108PPPP++JipQNHUedQkFn43kGTy0sEKGeEBPRh4xHxXNL+FwrrouMLfKWl+9T9H5SGnxfITE6T7+ZlH7ecOZh2N6McQOkciOu5EXSnjMEDAFDwBAwBAwBQ8AQMAQMAUPAEJhdEKi2Es4utWpCPTD4Q1ZBMHC28yRZLTiYaJS9sTEYiTGDZ1i9hjDByh7uMkmLd4hf//rXfrKNSV+8d8hzGNBEhLQCwYWVvhir8PYBWeGQQw5xiy22WJfVv0ziCWHhq1/9qjdCsZqOiSU8NbAK/YADDuhCwCBPiATUB2FiGOMzgucSCAsIRvVQ8tQzTCPrNZO7lJeVXgjeQJiYs6OzpwAAQABJREFUZQ90cGP1KJORGAfLEAzfQlrhXfGuIWIgrE5l4p9J9T333DNyVS35NgIfyCO0gzTChCz72muRCWwdxjm4Qlph1SsEJVYk0s5olxgrONL2uM8EcChMCDNhiBE0FDDknWSRLPXMkm4r4rJiWE/4QjoBa/SD9oYShx1u9dEZCN/ggAED/CQtHk0g00CcYiVk3CrTsuta752wohfvE0wg004gOdGOMATT7iCz0Eb4RuPqWmZ50QWXXnppmUn6tNABkBZ5F3izQe/wPaID8HJF/8YWcngzCrddKEMfaJ0slcNww3YJWSQNPs3WtVnKr+NibIcshi5GeCczZszw5+hr7tMmTzzxRMc2fJABRYrqPUlHH+t9JzpuFh1NH00/I94L8ILF+IK64lkIT0eMqXr06NFlfCB5YtjEEIzwPTI2YJsNyAx418AbR6ulSJ9bZBwk9Ya0gn6CyIMnGvQ14yLIqfSJkAVDydP/FdEHecdsYbkbfd1u32YavSeY0Id973vfk0t/rKVri7a9VnybjCFk7J/Gu44QUjEUhx6IqoAKLvBGJvKTn/zEE0ziSBYSRx/xogLZCIGkookr6G88JSGQ6ylXJwrjCupFP844AnIFBOl6wvfFH8JYK050uGyppOPRp0AEIR30GIsC2lnoq/CoxO/vI444wnu9o7x4AGJ8nCQhcUU8nBIuv+mSnm2X8Cxji7xlZkyhiTyMbdOQqfL0m3nLqJ9DT7NYAd0E6cfEEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEJjTETDiSs4WwJ7ouEdnMm3vvffOmUrrH7vmmmsiY3z//v2rCsQEKpOOECqYkNSCoZktZnDZjFcQ4iJhPCYT9ap9DL4YKzBO4b1BTxoz8QyuCKQVvLMIYQGvCkzuYThmlTYr1CDEaGF1qggGMCGu4N1lkUUWkVtdjnnq2SWRjAFMsAppBc8HTLLqyWoIEqeddlqXrVwyZuOjY+y8+uqr/TmrusCY1bhM7mIAZfKU/Nm3nglnVntBNBIpGx/eH+7+0wrkqND7B8/HbQvFarqxY8f6bRIw/GoZNWqUnxBmxSv4xxFXmPSmXeLZQAuEF57JIlnrmSXtVsTFVTt/IhDY0IG0D4zpSYIXFyGt8B5ZTSptnXdy0EEH+TYIQYN3vUyGVcxJeSaF13sn6CA80vDNQJiD7AWxQ2T48OFeJ0LymDBhgvdOI/cacUR3YtQqWyDKQRwIPdyQDxPnfBsQKNDtIXGlDH3A9xUa/DDqZSWupMGnmbq2yHtCpwlpZb/99vMETSGEUU/0GcYuiAKQijRxpajeC8td7zsJ42fR0RBdhbTCeAB9IOMG2hqkMfTwOeecUzU+0HnSfvGkIcKqc3QL3y26ptXElSJ9btFxkGDC98V4SQz5ePJh7AXpAaM7XqzC7z9P/1dEH+Qds0kdm3Vsp2+TOqfRe4INY1/9rRCepGvLaHut+Dbx4CeiCSESFh7ffvttH8Q3ErfdaRhfrvktxJiZMSReP9hek63NCIeAV2vswhZG5MfYHuLK7rvvLsl6QzXfHtLJ2wRRfrZQQqcjeNXCCwpEDIgEbAkJCTMUTUSRbX/CODpcv2+JB7Y777yzJ0GzBRljS8LaVfCkyXcMqVs8rNGP1xpLUxfaEb/j+P3IdmAQzeg/2d5RFie0a50pV9axRd66MC+jt/piPJGGuEKbydpv5i1j+Bz6g/F3XPsO49q1IWAIGAKGgCFgCBgChoAhYAgYAoaAITC7I9Btdq9gI+qHS2pWrSJMMoWT/43Is4w0MRbhuQQjDxPxeJ7Qq9LCPdshrWCUEKOSLgNGIgnHhboIK3tFOCcPLRiBhw4d6oOYwMLwLcIqUDFoYQgT0gr3mVyGuCIG7zIn6PLUU8qc98h2IwhGpZC0QjiTZ6effnop2wSx0ky86kAkWnPNNckiEvJiAlUm8DEsaikTHwx6bOfQKGHi9wc/+IE38oZ54Hp7u+2288HSzsI4XOMmX7dLwiCziGGB63rS6HrWy7+d7ssqYybb0ZfyDVNGzg888EBfXLwLMQnfKEnzTiBq4HEFweW4Jq0Qhot3MSxB+MLQ1yiBSAZBr1GS1G+x6lOIfnGGuDL1QZG6pcWnmbq2SH1E76KP8Tyk+1LShTQmnhNYnS+eLrhXht4jHSTNd/K/mPn+S9+Nh6XDDz88GkeQWt++faNt3DAMh3o4KUe2uRORtivXrTgW6XPLGgdhxBXSChhABhaSNYZ3dF2cZO3/2kUfxNWlrLB2+TapT1q9l6fuZbU9nXczvk3xTEW+cX2WLg/n4nEFr05ZBLIB4xnxsMmYEE9GeImEwMK3oLd81GkzvhYvUZDDNfFa+nl+U5FOJ8vIkSOj33jUA7IAfTAEfbZA4ncc3uu0aOIKfVmc6HAdX8eF8InnCt4L5M52F7yr4NVOBOx69uwpl4lHId2DJWRXftM1YmvRxALkvNHosUXOYlU9lrffrEok54WQ7thatVO85+Ssqj1mCBgChoAhYAgYAoaAIWAIGAKGgCFgCNRFwDyu1IWoOgKTjUcddZQ37rDdASvuOkUwvscJk6WsxqU+ccLEI4YYjB16qxQMnxib9ZYvejUdK+z0KjlJm9V3V111lb9kAlMmmmUymRt6pZQ8x2QxWxLgSSWc+JQ4RY5Z6lkkH0gkYnRkhbk25BdJN+lZtmgSkRX8ci1HJvCXWmopP6Gvt3uS+xzLwOeKK67wXhWYxIcAcM899+gsonMmOEWEICXXaY6fffaZX9X61ltv+S2q2PoEwTCDcE3bZZJbC3mRN6ShiRMn+lusbEwyRuhn9Xnaesozxx13nN8eQ64btQWN/h7xmFLLJbmUpehRvGiANyvsNdlDzjHS8840CU7ny5ZE7F8fijb+hPfC6zTvhDYuAj5srSFllKMQmDhiWJfJZnmujCNpi9cfCAtsqZSlrlnKwPthopzvni0X8JBDvfg28DCTJGXog6S064WnxacMXYsBQVbAoqshAAjJr145s9wXPc2K6qQ+AQMVXtIQ3pf+ngnLq/d4ViTNd0LcvDpa+nnGB2AZCn3/lClTfLAeH+h4tFfRB+h4baRshzGZvEvKnLXPFXx4tsg4SHvHIi1ks802+99J5X/cGKpI/9dsfcC4k/Foo77HCKjKibzPVn+bafUeZZfvM8v4qYy214pvk7GFSEj4k3B9lPjh+E/HSTrHawhEJnQUpBV+G4lwDumQ/prxY6jHGfPizQGyAXH5PYPggQWhfYWeAv2NDvpHnc8++2xPXoEEx5aKtFuE4w033ODuuusuv/hDPG5qr3IhZlL1NO2Y34hsZ4nHlSuvvNJ7LYNs1K4COROvpSJs+4OXGrZxrCUQpGhfMrYWIkutZ9rhXtqxRd6yyhg97/M8l6ffLJKfflbrLvRoO7ddXW47NwQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQagYARVzKiCuGCFfnzzTefw9jcSYKxTSZq8T7B/s9sdbTPPvu4lVdeuUtVcKd97LHHekNml5sqQCbICdITL4sttpiK9f+nOhyjk4i47+ZaVjTKPTnKszqu3Mt7zFPPvHnxHMZwkUYYvSVtOWoiSvfu3SW4y5F7GC50fCKVhQ/p4q4ZYVUb7ziJuCJEE+LqNsV1PWFyFPfkehVu3DNMlsv3IPdx3843jkGC75wtJyCx0MaZTB4yZEhdEkuWekq+rCbVK0olvOyjbm9szdOrV6/o22cLCbwglSlgISsHIb6Jp6qkPMQzUHgfYwd/eSXtO3n99dejLNLkR3k1ptHDBU8gGZI2bR9vSBjCGiVs1yLEDMkD4xzu/iGzhVKWPgjTzXKdFp8ydC2GRv5EMKyxuhnvJ3iwKotkRhtF6uloKQfxtVeyInpPp9loHS19fr0+njJJXCmfHCGVhdsB8V7YBk+vYJf4zT7KuyTfeu8z7HP12KYeRjpuWMe4ZzGKMw6EBCfbU+jn8vR/rdIHbHfEHwRqvsfBgwe7kRWPBY0w/Mv7rPcuBctGfJuknVbvEVfGUFnGT7o9xbUf0q03Bm/Ft6m9LCWR3Si7CO3ljTfe6NLvyf16RwiDbPHCH30nHrAgs+CFjbEinuMgbdC3amFLIfQUcfCyAnGF8dHTTz/to4k3N/1Mp55DIOSPukIYv//++72nT/pkSGd4C2Fcg+ct/c2yjWycCPmFe7W+wwMOOMCPFVkcwFied9SOAnkKTz2QLaRNQBi+6KKLqkjscWXn9/pyyy3nGK+iz9kmqN0FnZh2bJG3LpA9RMLfdhJe7xin9+r1m/XSTHsfnYTwTmu18bTpWTxDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDoJMRMOJKxreHQRxhH3e23dGitx/BnTeTqayQZoKpHeTiiy92W2yxRaqiMOmN22XxgMCqYVbH4ZlDJoQwbOIJQXsC0CvFklYLMVEpIp5HuNbn2nOLxOUoaeq4+n7W87z1zJqPjv/hhx9Gl+GK+ehGiSdCHCBJ/X7CLOS96Phl4jNu3DjHpDRbYYwdOzbyuhOWg2sxunAOSSyt4BnlhBNO8NEhgjChy3YJMgnINll8m0jc6jy2tuI+K7lPPfVUv3pWtq/Zf//9U5FostTTF6SJ/8Bi3XXX9YYWjC24KgcbjAtJBoMixdNtndX+m9Rxg7/EEkvEZsdqW2mfOgIeJtJI2nci5UXH6W3UkvKA+Fe2sAL3sssu88lidIFg2EhBt0PywoAungXwvILRh+9Bb6dQpj7IW6cs+Mj7JK8iulb6PMhufCtgxfYHGCpvueWWWM8hWesnhrm4di5p6XsSn3tF9Z6kn/Y7IX4eHQ12jBkQ6cv9hfoHqVakVj8vBnkMgKTLH+XnexFPBpJOs4+6D83a5+o6FxkHxfWbGEj5Y8yG14dQsvZ/rdQH1APhm4SswNicvpvvMY13hrDuta7lW9PfXxhf35P4xCnr28yi98hXvk/5TgirJ2W1PcmzWd+mJj5AjGCL01oCIRMjcdKWM7WeDe/RP+Lxgj88KPKHsCVaSFyhD2K7HMZeeFlhW0u2DUJ3IbMTccVXqPKP7xR9zB/jmb322stvUwZJCvIOddbkT90GJQ2OOlyPSXQczvHgifdQvJdMnjzZ5xfGaYdrPKZAFkfwrsdvE8pMONvp1hv3jayQ9NjyF/KqkMnaoV5JZcgytkhKo1641rtJfWe9NPL0m/XSTHsf4hLSDC9iPiP7ZwgYAoaAIWAIGAKGgCFgCBgChoAhYAi0MQJGXMn4cmQyGG8M/CWJbPOA8TOOuCITlfK8TMLLdauPrI4T0gp1YLJRCyub4jzOsPoVYgKG/6RJYW0w0aubtNEaQ2rcNgLiRUPH1eXKep63nlnz0fF79+4dXcateo5ulnSiJ4VrkRPknjYClIUPk/O33nqrrxGklbh3q6urCQlxE4k6rj6/5JJL/CWT1zfddJNvi/p+PbwhcbASlJWQeB+Rdk97w8gghAKdpj7PWk/9bDPOMejhlYDtzu6rbAmDyPfIRC/1xzNKWcLkO3lCEmKCnRX9eYTnTjrppC6Pols1ca5LhEpAlnci3ybG0GHDhsWSZeLyKCsMnNC35A/JqBmrhbUXHAgZDz74oDekTJ061RM0rr766qh6ZemDKMGMJ1nxkfdJNvW+/aSijBgxImp7EBLYqo5tItjuAEMDXh/QGUUFvYsxT/RwXHr6ntbTRfUeeWX5ToifR0czzqE/wktB0vhAhyf18+uvv773aEA5eCfoMrzDobtY1Y33mVZKkT5X17nIOIgxmPZEAR68M9kmRY+9BKus/V8r9cHBBx/svQXyPUIOwegLaQFjNV4Ey5RWf5tZ9R51l+8zy/ipjLbXim/zm9/8ZvS6xWtBFBBzIp7E+B3DdissRChDqDsen9g2S3v70mlD1IC4gmcafk9C3kAgY6y66qo6alueC/GQwtUi5cUVHgIoxB7GGIgY6tE73EM3JfXTelyq9WtcPgceeKAnsdGXoBvaTfCUAvEVgTiMB0rIVnjtoU3gZa/ebw3GJfx1gmQdW+StkyY31fuNmZRHnn6TtNDR+rcIv3vwnJJWeF50l9ZntZ4vmmettO2eIWAIGAKGgCFgCBgChoAhYAgYAoaAIdBqBNL/qm51SdskfyY4k/70RAmGc+Jp45JUYebMmX6LHlbryx8TVu0k4j2GVaSs7AqF1Z96kkbfl8nv1157TQdH53o7DplA5qY+T3pWwnXcKOEcJ0XqmSM7/4jeWqQW+Slv+uFzPXv2jIK0UTAK/OJEtgvR8cvC5/jjj/e5sL3DTjvtFGbd5Vo8P3AjNL51ifxFAKvtpH3suOOOXUgrRHv55ZeTHo/CKV+/fv38tZA6IGmlMQBlradkihEcDy/yx/fVKMFgiWEXj1GQEzDCszUSXjbCFcJFywAZRlaDJhlyiuZR7/ks70S+TSaEW1He2267LfLkheegrIaheljUuw/RaNddd3WjRo3yUTGq6VWsZemDeuVIup8VH3mfpFeGrsVLCNs94G0Mkiby7LPP+mPRf6J3a+lofU/il6X3snwn1DWPjuY56btFVxOmRYdLXH0/POed4FlrwIAB/hZ6TRtXw/gQHc4+++zo74knngijFL6Wd0NC+p2FCcf1ubrOGgv9rITruPo+53qcJffEUMw126XESZb+r9X6gP6F7UggY8o4oazvUWMj77PWu9T3JH5Z32ZWvUfZZds9iORpRbcnaWPhsxKu44Zx5DrrtynPZT1C8hQiVpp+Gx0ugmeUMqVv374+uXBxguSx6aab+lMICo8//rj3vEIA3ujSeAqC8CD6SzyAStrNOMriAfKSsV2WfAUfntFEzBVXXNEng34W0pVOVwg+hNUjGmH4F+81EHMhAteTZvQLlIGxJcRxSFMQOfFEwnvne9p33319MdlC6Ve/+lW9InfM/axji7wV020TMlQeydtvnnjiidF8DvM6Wbdvgpgl7R6vmGmkaJ5p8rA4hoAhYAgYAoaAIWAIGAKGgCFgCBgChkCrEDDiSkbkb7zxRr9/OduHhH8Y/ESuu+46f3/w4MESFB3jCB9xYdEDLTiRVblMruKGPRT24U6SVVZZxd966aWXIq8tOu7PfvYzf4lRFi8MIqxUFM8zcZPJeMCQsrDHdxlSpJ5582cyX1ZWshowaYUhK8mZ5CwqTOqLiJcNuZbjc889573kcK2NvWXhw4Qiq8/wRJRmcl6MiRiABCspa9KRtiMr3OKIH4QxIVxPSIMJQYxi/GHkSDsJmbWeUpZ7773X4TVB/sQ4JPcbcYRgxvZfG2ywgeOble1Qys6LVcjIPffc4+Lei+SXZOiR+3mPWd7Jt771rUgHaU8kcXk3orxClMIILwauuLwbHQa5EqFf0n1TWfogb/mz4tMoXQsOoptF5+StkzwnehcDmtRT7slR9Dd6SYzjZem9LN8J5cmjo3mu3vjghhtuIJonbWXZiksM9YxZahFXMBafd9550R/jirKlSJ9b1jgIj2OhsI2OiDYgSxjHLP1fq/WBlJtxoay2L+t7lLQ5tvrbFH2Qtl+g/YvHRLboSytltb0wv7TfZvhclmvGMQj1rtc3M56Td8rvkThjNWlhTNbbfr311luRhxTuhwJRCf2CgGWcEN6rVy9/i61e8M6CCNHCX9T4d8EFF0S6Cz3WCDn99NMTPe+J3idfWaSgywAJW9qrDpdzvDSJaIx22GEHHwyhQ7bnlHgY9Bk/ImCXxrCP1xUEL2aasOcDY/41o18gW37zSZ/DohD9WxavjtKvQ1wWIkNMcTsqKOvYIm/lNGlNLyTKkl7eflOPk8kvvK5XBk24FF1W75kwj/C63vN23xAwBAwBQ8AQMAQMAUPAEDAEDAFDwBBoZwSMuNLOb6eFZdOualnN+v777/vSsHJt/Pjx3g1zUvHY3kKMB6zcF7IJk8k//vGPoxWGuEeWVbKkxSTodttt55NlkpK9vmUihr3ADz30UH8Pwsvee+/tz4v+K1LPInmzHQjCJC0rnDFWioAxK2whTNSaAJb49Y4Y0SAoIJdffrk744wzqgx7rOzbf//9/X28CMgEMgFl4jN8+PC6JBRWYOINhO03kIEDB0ZtyQfU+Id3IDwdIXfeeac3MIgB4/nnn/fbB0h78pFq/MN9N6vJ+dNbpdR4JLqVpp5R5DnghG1U8FYD9ny3euUs1Wd7mrPOOssbI2oRW4pAlfadsPWQbGeEkZfvVK8KxiiObiIOW1U0Quadd16H0aKRcuWVV3pdgEFHr0bGMMdq7ksvvdRnj4ekBRdcMCpKmfogSjTjSVZ8iupajMAQefgDL8irhx12WGQcXW211TLWID46/SHC++Bc9wm4r+c7EuLd9ttvHxHNytR7ab6TIjqa+u23334ROSwcH9A34TEFCccHPvCLf+h1jHr84V4f/SF6hXFEFi8TOt2yzov0uWWNgzAg05cKwQq9JdtPQCZMIq6AQdr+r5X6AEIB40IMwY34HnVbaIdvM43e413jOY3v6uOPP/bf2VZbbaWrUvO8jLbXqm+Trf0QfquExIew0pD9hNjAGJsxL9+H9PX0g3g2gfRDGxPhHB2J/sVLpvwu4j5jRbao4llEe3XxAeqfkFLpSxDG3WwV0y7y85//3G2++eZ+2y1I1IzdeK9s9ynY4iGF7Y1CYVEDvzcgv+DhkN84CP0o+ggPIwjtWRvo+e0n5OljjjnGbydEnpCexowZE22jwrtKQ35Hh6233no+r3b5h1cmfj8jeAhCb2mBkCrbDkK4aYRHHfpLtmpK+pP+V5erjPM0Y4si+TAWkPERYyJ0oYwROEo7pC1znfRbsEi/WaT8V111lX8cQl3//v2LJGXPGgKGgCFgCBgChoAhYAgYAoaAIWAIGAKzBQJzzxa1sEqUjsCWW27pmJjEkMyqQCYsIQUwmYbxFk8prG5iYjEUjL977rmnn/QkDhOybEHBqivZdgIX06wuC+Wggw5yeJ/AUMfEJ5PHkFtkMpj4EGPELXj4fNbrIvXMmpeOj2cHyCJ4dcBwP3ToUIdrY7aWguijjcn6ubzn4Prkk0/6dyd7q5MWK0rFPTXXbBWj3X+XhQ+GxMMPP5wsagr5y5YeTOaDSxbBYAPBiTbKRCn5MolJPRFcjL/yyitZkswUN209MyXa4ZHZkuJHP/qRY7sl2jbvBTIE4XgbEmNRo6qZ9Z3QflhVDNmJyeRrrrkm2t4E/ScT3mm98GStF204zTYMWdPV8VldfMcdd3jPQhiBFlpoIW9YEg8KxAU3yINaytIHOs2s51nxKapr8QAiXkDCstJHCukvvJf1mnKyXQReVfBWprftmjBhQpQc7yUkbpah99J+J0V1NOMIVppPnjzZjyGyjA8EBFasa9KEhHPEW1Yt0W2ceGmMoLXSS7qXt88lvTLGQZAFIcCdc845vg8UIzvhafripHrp8FbqA8ZOoVcs+nfGM3HjUl3urOet/jYpbxq998ADD7g99tgjqh5ePOK2K40ixJwUbXtFvs2Y4qQOwgsHv0sgl0DQHjRoUM1nIZ9AioCoye8NSCcQ7iFUyO+UpAQYS0PAQxjL0N60lye8So0ePTrpce9dRROiaV94B6snQiCReI3SXaQPMYwt8fiDUMFvEjH+cx/iblL+PMvYgT9IQvy2+fDDDyMSHc9DYGHcIUL/g+5mnMjzkIMZuzOWF1lnnXW69H1yL+4IOemRRx6Ju9UlrBn9AvWTxQiMicE1FNolxGLGn3hg1AsJwrh5riH185ckvK+yPT6mHVsklSlNOEQx2XqPNiOe3cJnaVuMHViQE+cRtxn9ZlgmfpNOnz7dBzO+Svquwufs2hAwBAwBQ8AQMAQMAUPAEDAEDAFDwBCYnREwjyslvl3xMkKS+jzMIu5eXFj4XJ5rna4+r5cWXk3weiHbfDBhijttJi+ZPGQrJHFpHJcue1pjbJOJScgZTAYzIcNqQybO4sgnrFTmHiv2iMskk5BWmPzEY8shhxxSr/hV+MeVTxIoWk9JJ8/x6KOP9oZxcVnOhKZgzCQ2Bog0k9lp8mblIS7RtVtq/ZxMLDKZqqUIPhp36hruOS735Ui+vHPiUV4IA6uvvrouTnQuE3tylBsQXfCwILhhkIC0Qv1OOukkt9dee/mo+jnJX46SVtxR4sQ9T/y09QzTlnQlPLyW8EYfJV9dv6Q8JY48kxSPcFasQ5TAgwfPQVbBRb6QVvjuMQSFOqFeHnJfjlIGXaas72ThhRf2RD30DO0I3Yf+4o9zvgkMgmUaFKT86AIxiEldOMp9Oep7ec7XXHNNB+YIK1PZZkMMN9SZyXNWg0Mu0lKWPtDvR9KXMDlKOEepd1588uhayVOXg7LR76GXjj32WIdbee2RRsfNcw6ZAwNqXJoYlOiPb7/9drfMF56lJI88eo9nNdZpvxNwKaKjyZfxASvPpZ5pxwe6vKSDQHCEGLvFFlt4ki3HWoJHChHSY/uVRkjePpeylDEOwvgJCZVxhZBWIMThoUSv6BZM5VgLC4kj30YRfRDmI2kTrs/DeJK3xKOO9Cu0KXQW+ks/r8/DtLJcN/vbpGxS17R6j/joCbbYGlkhh2mSsq6rYCJHfS9v24tLK+u3qcuR51xI2BAzMfzXErCCDMR7hfAEbvTvQlqhbW+zzTZVJFK8FEGc532IMIYR0gpEBAj51157rR8nSJzwiB6HICPCeCKNaA8mxIc41giBlAIRSN4pmAhpBS8reAIBmzgBH7y1SP0g9TAOF89PfKtTpkzxXmvC59lul7Tx/IMIaYW0tt12W+/9UH5Tcl/KJ0fCtEBm0L8h5HvSceS80f0ChC76bmSTCkEVb5JxQl3ESxxjslNPPbVuPSUdwUGOEl6r3hJHjlniyjNxR12GtGOLuHTShkn7Kho/bb8Z5qPry73wOoyvr/FEhLC90Y477ujP0/wL8wiv06RhcQwBQ8AQMAQMAUPAEDAEDAFDwBAwBAyBdkVgrsqP/VntWjgrV3sggJcECBWsROrXr1/sKrGkktK8eB7PLZAHWOkUt8os7nkmhJmoZYsGJpbZ27ysSbW4/IrUMy69LGEYlzDksyoR4xJea8C7EYK7aow8EISYKGNVH261MTLUklbiU6tccfeYaKfNvffee65Pnz5umcDQG/eMhTUHAd4Nq6LxtsJqcNp7SJBoTknq54L+gvhEW8KohUGld+/eqXVY/RxaGwPdilGbIwZfjMDo6TQT4J2kDzTKzdS1Ot8s5xj7ML7KFiUYZPGEoI12cel1mt7j+2LrAvRB1vFBXP3rhfENYziFqIUMGTLEeySp91zR+3n7XPLNMg6C7InHAoRV3HgsYOzGH/0guqtR0qn6ICsec8q3CS5Z2l5WHBsRn+8bQgBtH88VbCGWViCfsN0PniHp5yGnCLEuLg3Glnhs4chYHfIcYxnZ7ibumaJhkGzwgIJAtGF7I02iKZp++DzjAkiF1JM6QmpirEbe9YTtWFh0wLMsQICUDEbo+TTCc5CPGJPgPSNNnmnSjYvTqn4hriwWlg+Btdde29HPTps2zc8VJKVywAEHOLYDYhGMbE3cyn6TbwySK98LhCVZXJFUfgs3BAwBQ8AQMAQMAUPAEDAEDAFDwBAwBOYUBGpbqucUFKyeNRHA2JHX4AHRhMlO/rIKk8ZMRjVLitSzaBlZwY4nm2bIoosu6ieQyYvJaNyUp5FW4pOmfDoO5Ci90lLfs/PWIsC7WW211fxfa0tSP3f0F4aadiXW1K9B7RgQ1/jLI52kD3T9mqlrdb5ZzjHS8Y2IsAVGPdIKcTtN7/F9LVMhFfLXDMEwLaQVMB4zZkwzsnV5+1wKV3QcBAmWv0ZLp+qDrLjMKd8muBRte1mxLRofwiXELbwV4uENL3sQMtMIY2HtiajeMxAq+GumPPzww1F2kO4aSVohIxkfsGAhq+AlhX6LvzwC0SX0wJcnnTTPtKpfSFM2i9MaBJrVb95yyy3emxH5jRgxojWVtVwNAUPAEDAEDAFDwBAwBAwBQ8AQMAQMgTZEwIgrbfhSrEiGgCFgCBgChoAhYAgYAuUhMH369CgxXPLnIdRGCdiJIWAItB0CbA+Dh6HZTfD08+ijj/pq4R2R7XxMykHA+oVycLRUsiMwsrKlG38mhoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhUI2AEVeq8bArQ8AQMAQMAUPAEDAEDIHZDAEM2njcwfDL1ksmhoAhYAh0AgJsu4OHKvTX0KFDI6+JnVD2di+j9Qvt/obql2/8+PGO7b7wblZL2HZx0KBBTfXmWqs8ds8QMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMATiETDiSjwuFmoIzNYI4O4et+ocTQwBQ8AQMATaCwGMlOjnWbNmmZ4u6dWcdtppjr9WSDP6XPp0/mg7/Jk0BgH7NhuDq6WajADeoZ599tnkCHYnNwKt7BdyF9oerEJgwIABVddJF2wHFm4JZv1mEloWbggYAoaAIWAIGAKGgCFgCBgChoAhYAi0DoG5KkaRWa3L3nI2BAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEJhTEeg2p1bc6m0IGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAh0FoEjLjSWvwtd0PAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBOZYBIy4Mse+equ4IWAIGAKzNwLvvPOO+/DDD6sq+e9//9u9/fbb7pNPPqkKtwtDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBBoDQJzzapIa7K2XFuFwN///nc3//zzu27d0vOWMPY+8sgj7rnnnnPvvfeee/fdd93vfvc7d+ihh7ptttmmVVWxfA0BQ8AQiEUAnbXyyiu7Hj16uPvuu899+ctf9vEuvPBCd8YZZ7hjjz3W7bvvvrHPdmJgp+loyEMPPPCAe/PNNx0Eoz//+c/u888/dzfeeGOmvqkT35WV+f8RsHbw/1jYmSFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAITAnI2DElRxv/9RTT3VPPPFEzSf3339/t+WWW9aM08ybf/jDH9x5553nnnnmGcf5ggsu6NZYYw03cOBAN3z48JpFufrqq72h96OPPuoSr0+fPu6Xv/xll3ALMAQMAUOg1QgMGzbMPfbYY65///5u44039iSJO+64w3366afutttuc6uttlqri1hK/p2koyGpHH744W769OmxdZ84caIbNGhQ7D0LnH0QsHYw+7xLq4khYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFQBgJzl5HInJYGnkaeeuqpmtXGI0m7yAsvvOBGjBjhZsyYERUJryvTpk3zfxBZ8D4w11xzRfflZMyYMe6WW27xl3gs2GCDDdzaa6/tFltsMdezZ0+3wgorSFQ7GgKGgCHQVgicdNJJbtddd3VPPvmk/5PC4WlldiGtdJKOhjg5cuRIN3PmTP8qevfu7TbbbDO39NJL+z5l8cUX915y5D3ZcfZEwNrB7PlerVaGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoZAEQSMuFIAvW984xtuhx12iE1hrbXWig1vduC//vUvt/vuu7u//vWvnphy4IEHuk022cThnv/88893v//9792ll17qCSg777xzVfGmTJkSkVYGDx7s8DSzwAILVMWxC0PAEDAE2hWBb37zm+7+++93v/rVr7y3lYUXXth7X5ldSCudpKP/8Y9/uIMPPtiTVhZZZBHvAWyjjTZq16Zj5WoQAtYOGgSsJWsIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAh0OAJGXCnwApdddllviCuQRMMfZRsfSCvI6NGj3aGHHurP8ZoCuWabbbZxf/vb39x1113nNHGFsHHjxvm4kF2OOOIIf27/DAFDwBDoJAQWWmghN3To0E4qcqqydpqOvuCCC9wbb7zhllhiCXf99dc7vK2YzHkIWDuY89651dgQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBBIg4ARV9Kg1MFxfvrTn/rSsw0QWzRowWPM5ptv7m688UaH6/5XX33Vrbjiij7KPffc41gZ3aNHDzdkyBD3k5/8xL322mt+uyHi4LFgiy22cN26ddNJxp4//vjj7re//a1jiyVINEsttZRbY4013KBBg9xXvvKVqmf++9//eqPmrFmzqsLDC4g3ffr08cF//OMf3fTp0/35lltu6b7+9a+H0f31888/75599ll/jiEbbzS33367v+7bt69bffXV/fltt93m2Eppww039MZV4t1www3+3vLLL+/WXXddf/7zn//ck37w6hB62AE72WJJl5UHMTjzbCiy9dIyyywT3qq6pr733Xefe/311x1bUs0333xupZVWcpQDbzpx7+RPf/qT3xaKhHbccUc3zzzzVKX54osv+jbA+9h+++2r7uWtp06E9//EE0848vn000/9diDrrbeeW2eddXS0wuf16vnoo4/6dhxXT535hx9+6DGmzdLu559/fsd72XrrraNvRMe/44473EcffeQ9F33rW9/St3ybv+uuu3wYHj8gi4nQHmmXtUSXle9H0ho4cKDDc4UIJLUHHnjA50ebo41/9atfldv+KG0HfcAWOmE7YFsxvle2BeN+nPBt/OY3v/HvEo9NSy65pFtllVU8NpBEQqn3ThrR9urlmaYd/POf/3Q333xzVJ2vfe1rXudFAQ044ZvGQ8xLL73k9e9yyy3n+vXr5/V0uJVbER1NmwYD0txpp53cQw895HUo73PllVd2fJvf/va3E2so7SiLDhJdyzZNzz33nLv22mu9DqPNoH833XRTt0wd3ZcFHyk8pEz6lVpCn7TxxhtXRaGO0q9st912XgfoCHxrb731lg8CK1122g7P8i2ho9H5bIeEnl5//fUbRtr5z3/+4+6++27Hd8wWgN27d/fvk34hJAp9/PHHUf83bNgwN/fcc/tnHn74Yf8cug6hDowNdN9HeN5+oVXtIK+Opq55pAwdlCdfnsnSDiQP2gz9dD3ZaqutHLpQJM+YTZ7N+53wHOPWWqL7TR2vSDvIo/d03lnPi+gg8srTDopg22x8suJp8Q0BQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUOgAxCoEARMMiKwxx57zKoYoWZVtuDJ+GTzo1eMkL6sFZJIbOYVw6y/T30q205EcU444QQfXjFIz6oY26I4xJO/4cOHz5oxY0b0THhSMbLPGjVqVBRfnpPjmmuuOatiBK967LPPPpsl92sdK9sbRc+9/PLL0TPnnXdeFB6eVIxzPl6FeDLr3//+t/+rkG98WMVQO+uTTz7xj3B/6Uo9K8QTfz1x4kR/TVhly5Eo2Yox0Icff/zxUZicjB8/PnpGl5X7FQNRdI80w7+999571syZMyWpquNJJ53UJb5+vkI2iH0n1EXixb2zc88919+vGJCr8uMibz15tmIEmUVbqhh1o/ylHBxPPPHEWRViEFFLkVr1fP/992etuuqqifWUAlSIIbP69+8fW17KXPEYIFGj4wYbbODjVzwaRWGckOeAAQP8vQrRatavf/3rqvunnXZaYj7kxZ9+J08++WQUv0I282lVDIezjjrqqCicZ/irEAFmvfnmm1X51cKHiNSNZ9EbcUL+FUKXj0M8/Ue4lEk/Wy/PRrS9WnmmbQfvvPNOVf3QFY2UqVOnzqKNaEzlnL4m/G6L6Oirr746yiepDVa2hptF2woljw5Cn0ldNttss+hcwjjS5m666aYwu+g6Kz7yoM4j6TyuL//Zz342S+JXSAiSnD9edtllkU7bbbfdZlXIeNH99957b1aFRBg9K2nIkXu16hkllPGkQpCZVSElxuYLtnwTWnS/WSHq+VsVMpF/Hp0vMnbsWB92yCGHSJA/5ukXWtkO8uroqkpnuChDB2XILoqatR3Ig+ecc05s21k60PNPPfWUPOKPecZsPFjkO6GOYbnCa/r6OMnbDvLovbj8s4Tl1UHkkbcd5MW2FfhkwdLiGgKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChkBnIGAeVwqQi/CUgFeCv/zlL26xxRbznhhYjRp6XCiQRaFH8frBH8Jq6TjR4dRDpGLc9acVMoc/ssL2e9/7nvfwwOr8xx57zHvwOOCAA5x4dZFnOVaIIW6HHXbw3iq4ZqX5Rhtt5L2hsKr/3nvv9Z4h8GaSJHhAWWGFFapux62yJW08uOA1pjLR77dEqnqoclEx4PsyE45HkS996Us+SsX463bZZRf35z//2V144YVdtkSqTOK7888/38etGOq85wN/UeMf9auQVWrE+P9beHWh7Xz++ed+hX7FOO3wpMCWTpMnT/7/iF+c4dUDYZsq2pms8q8QClyFFOE9J+BBAe8boYeGL5Io7ZCmnt///vd9uci0Yjx1FRKHxx6PHazurhiAvdedI488srRyJSVUMcQ7vAzUkopBOdpOC28keAWifeEx4emnn/Zt6IMPPqiVRHSPNoV+wPMBHlvwWlQxmkX39Ql58X1pQb+88sorOij2nBXkeK9AxHOQeLM47LDDvAej2AczBvJ94a2HVdyUF09M6A88C/Bd8o1VjPj+21500UUzpp4tepq2l5RimnaQ9GyjwvGScfTRR/vk8ZIzePBgh3cevF/gqQjvHqGuLaqjpS4XXXSR432R57zzzut+8Ytf+DY7adIkr5v22msvieqPeXSQlJUEeHcI3wKeXSoGbHfrrbf6b7NCjvB5ht5e8uDjM6n8ox+qDMf8Jd9ixfDuz/m28VaFoJvSCnhNmDDBR0ef0W/goUiEvg+vUnwjeHHhG0HHgwH6hSP15L72viTP5zlSvz333NN7QeJ5vLrQP9CfVAgU3oPYwQcf7D2ohdjmya/WM7W+zVa2g7gyZ9HRcc/nDWuUDirSDipEjypva+gB2jFjDe1RrpZuTztmA7eyvhPaOd4DRfDmhKe0LJKmHeTRe1nKkCVuPR1UpB3ocmTBtp3w0XWwc0PAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBDoLASMuFLgfbFth2wlwET5gw8+6C6//HK/9QKECIyArRRNRMEIGic6XBvkNekB0gqGaQwYyOjRox1EA7bPgcACBqExDNIF21EgFQ817oc//GFEFiEM7Cor/btsVcI9EQgY4XYlbN0Rt+0D5BMM62+88YYvExPuWig/k/myNYbcw8CH8RDjP8QCtkzQgoEJ8g9GRt5pGvnRj37kjTJp4h500EER0QlCQMWLjXvkkUf8NjW4bMfdvZbvfOc7btttt/UkIP2OiAMJpLLq1W/rQFskbiOlXj3vq2xnBJkG4T2efPLJURuoeOJx1J3tJiD58K6FhNOIMtNO9bYvcXmwBU7FU46/haGZNowxTwsGdIxc9QQSB22SrUQWXHBBd8UVVzi270kStvw566yzqm5XvAelIq78+Mc/9s+RxlVXXeXJYeR5ySWX+O1g2I6Irb2KCN8O7Z82is6ArMbWVCK0W9olxCCM+meccYbcasixXttLyjRNO0h6tlHhYFvxOuOTh7QC+U4Ie+i6I444wutfyg5pUMhP+vvPo6OlPvJsxWOBD2IrH8geEBDQiSNGjPDbyEj8PDpIl5V0IFnwDkUgPJEn3yDtWfcnefGRtM8880w59SQOIa6MGzeuaqutKFKNE8om3xvtnXMhQcpjbH1U8VLidVq4bR16D7ILOgT9UhZxBaIiW24hYImuFczRQxDO6McqXjWqsJUyl3ms9W1KmSS/ZrYDyVOOWXW0PFf02EgdVKQdsG0kfyKQYdn6he+94hlNgmses4zZyvpOKh7qHFtEitDGsxBX0raDPHpPylTmMY0OKtIOdFmzYNsu+Ojy27khYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIdB4C3TqvyO1RYox9eO4YM2aMO/DAA913v/tdbyjCyHbdddd5A1GrS6qJKxgJ4kSH6/iaMIExQkgrpNGtWzdvTBWD3fXXX98laVahIxhDQ9IK4eB3+umnuwUWWIDLwoIRUdIKPcDwTljpjrDCv3fv3lX5HXvssd4jBl5PIH6IPProo+62227zl/vss08qYgUEGIzLeQQ88ZiB8F4gy4RCPTEQhAZA4kEOkfDKdkTho6Vep6knxAsEvDF8SXshjHO+GwSvOxBCGiWQLSrbOdVNnnYj5C3IAiFphQQwkI0cObJmWnh1gAAFaQUiAt5PapFWaiZW5ybfrBDEaBtiKK9sfRI9eeedd0bneU8gQkEMQ4455pgq0gphlS3FvA7kHDIS31yjJE3bi8s7bTuIe7aRYXjWEdIBulZIK+QZ6lqt24rqaKnTzjvv7PW0XNNmMVgiECx491ry6CBN4uQcjyNaIEENHTrUB2HYx2Aukhcfeb6MI+0ZUpuQViCCQDbSOk3yQXf/4Ac/iL5FCecIoWy77bbzQfLO9f2859IuaBOHH3541A+QHn0K3pGQxx9/vApbH1jiv3rfZru0g2bqaA1vo3VQu7QDXeek81Z8J2FZsrSDPHovzK/IdRYd1Ip20Gp8imBrzxoChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoZA+yAwd/sUpXNKAhEDY/zcc1fDx5YOeBdhex28SGDc0kbIZtcQV+wicQY27gnRQeLJka1NRLbYYgs5jY49e/b02/OwKpetQrSwvY64DWe1d1Le+hk5x7AjklQ2ua+PlJetViAJ4OIeAorUAWOdlBHDcChLLLGE3yaDZ8VDCHGmTp0aRWXLm3rCivZTTjnFR2PlMtvhxHmH0emA08yZM72HFgzEbJmB4HEAo3WSsKIYl/gYlvFeI0KbhAhSa0sctr0J02ZLgLSStp6yzQ3lweuBJjPIOYbMzz77zDWSaIO3E8pCnSGYsRVTnMgqbQhQYuiNi1crjHz47oUEBolNeyYJn5X2nqWt6zRefvnl6JKtuETYOmHJJZd077zzTpWHGO1hiW8X7w9pRLAhLs9h8JN3KEfaBcIR4oEmu/kblX/NanuSnz6mbQfyzOKLLx551CJME0UkThlHVvyLaM8BEkY52IIDTyGix7gn+o3zrDqaZ0S0lwUJ22yzzeS0Ks8osHKSRQfpsuINS7dDSZO2iMcghHqKB6a8+Ei6ZRzxNoQ+RwYOHOhJl/W+WfTatGnTPIGN7ZDwoIVAxEG4RjfGERR9hAz/BCOwhXgUCu1qypQpPlhjG8Yrcp2mX2iHdpBVRxfBJHw2qw4Kn6933Yp2IH0YZav3TcSVP893In1OXHppw/K2gyx6L21Z0sTLooOKtIOi2LYKnzQYWhxDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQaH8EqpkX7V/etijhcsstF1sOvCrgqYEJZibzWTneSuKKeF+gsGzBECdibOZe9+7doyjzzTdfdM62KXEi4W+//XbVbYzaInHGa7kXdxTjHvf06uy4uGEYngMgn1AnvD5AIEDYegPBWIrRMU4gHdUSyC/amBsXl9X4kHYoN9sliKEzLq6E4cklFIgOxx13XBjsr8EaDzFsw1NLtDEpjLfpppuGQZmu09Tz/fff9wQuEoZcM2nSpJp5gFsjhHKwdQBC+4AEkERcef311308SB9ZyFb+oS/+iVFawjBc1yKuSHvP2tZJHz2jCUfyPUrefP8QVzS2+nuEXNerVy+38sor+0fYGgadFSeCDffYIqyekKfOS+I3o+1JXvqYpR3IcxhhIf80WrT+pH3GibxbHbeIjtZ5xOVJ24HoBfGONqQljw7S7VvqotPkXIfjrUhE1zmurPpZHVeeL+Oodflvf/tbT8zU/WWYBwSF888/382YMSO8VXUNubQM4orgVQ8fMpe4VQUp4SJNv9AO7SCrji4BGp9EHh2UNW95t81sB9KHUVb9ftOUPe93gnc8kbzfT9Z2kEfvSRnLOGbRQUXaQV5sW41PGRhbGoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCHQegTmbn0RZq8SYJjFoIy8+uqr/tiqf4suumiUtXhAiQK+ONHhPXr0iG5rw71eJR1FqJx8+ctf9pc6DQI+/PBDH86/uJX10c2YE20E0YbZmKhdgvBKAEkALxQY1yGuYNiHxIKwRUOc1wQMGPXe1c0331yTuMLzl112mc8H7yyQH9IIOGN4wUAsBgNWxOPq/eCDD65awQw2++23X+SdZK211vKeWXhvYrw5+eSTfZ1reXohrn6/lJO8az0jdUlbT90GIPxssskmkkTsEa83jZBx48Z50hZbU40dOzby6BCXF55vkKxtNkxr+eWX90Fs4TNhwgS37rrrun79+oXR/LW096wGPx5++umnq9KUrbIkUFa/6++TrbsoD9tg8QeRCwM8RKckchvpyfuk7eBxqp4IBmG8ZrS9ME+us7SDuOcbGabfT5KulXer4+pvOOm5JB2t6xOnZ0mbP3QCXnJE8uogKQfpSF0kTTlq3azrqc+T6ilp6riSbllHvNr88pe/9ESeI4880l188cWxSbN1nowB2A5l66239lsxCdFl+vTpji11kKLeDUiDb1cIbIID4VrYokikERil7RfapR2IfkqjowW3osdG66BWtQPpw8AnTpck4VbkO9GE6ySdkJSvDk/bDvLqPZ1XGedpdFDRdpAH23bBpwyMLQ1DwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQaC0CRlwpGf9FFlkkSlEb/KLAL06YXNaijZA6vMg5hjIMxWxHEK6al3TxhiGiiS6axMKq8bjtB8QAFq7uZRslkaR85X54xG28SBYjiDzD1kR4O8FzBNuVYNxn6yYkbpsgwm+66SYOjjrjfeKBBx7w17wTth+CtIKXDgz72gDoI1X+YXzEmM/qeYgBabYVkmfxEMP2DgiEm0suucSXh9XrkKAg44jcf//9EWmF/Pbaay+55Y+QT5I8teiIEBYgcmg577zz3Nlnn62DupxnqSfEHYgTPIPXod13371Leo0OeOSRR6KtlyCtxLVhXQbaLW7u9Teh76c5xxAGaYpvZtttt/XbII0ePdqTp+KMytLe87R1vDuRjxCm5HuUckq7198y7+Tcc891Rx11VOS1R/QUBkB0Rlz95ZumjQ8bNiyWACb51jo2o+2F+WdtB+Hzjb7WpK0kXUs4ouPq95r0nLSJUEfrOrHVmO63uEe7pN9A9LN5dRBbmKFzyEu20fKJq3/SDsM8dZ2T6hmHj0q68Clbz40YMcIddthh7sYbb3R33XWX33pn+PDhXdJGhyPLLLOM1+Whrs3aJ3bJIAign6Lv/uCDDxKx1ZhrPIOkcl1m6RfaoR1k1dG5QAkeaoYOalU7kD6MKmfpx4p8J6LXyLNev06cOMnSDvLqvbh884al1UFF20EebMvCpxm/i/Lib88ZAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGQHMQ6NacbOacXFjBKyKrOeVajnh24J7+YyV3I2TFFVf0yT711FPeGBnm8dBDD0VBK620UnQuhmoCdJ2iCJUT2T5kqaWW0sFVW4Q888wzVffqXbz33ntRlNCYGt2ocaK9qrBFkGwTtOqqq7q+fft2eZKJ8ttuu82HQ1LBOIBxDdl1110dK+vZMgPjzJ133unDw388//DDD/tgVtrrVeVh3FrXeIuBjCCiXcMT9uKLL/pbeCYYOXKkP9f/WPWexitGTPgAAEAASURBVGuKfibLeZZ6QoKQrT/01lFZ8isa9/jjj/dJrLHGGomkJZ2HbG2DYZltHfIIeWGo5rsTEhEEKjkP05RtfELjdhgv7nrDDTd022+/fXQLTz0ikJhk25TQUA0R4YrKVia02alTp3oD/C9+8QvHFii77babJFF1FGwwUrfifWZpe1UFr1xkbQfyPMSfU089NfoTgpDcL+uo9WeSrpVwHbeIjtZlFz2uw2izInq7pCI6SNqh1EXSl6Muh66nPk96VsJ1XEm3jKNscUX/ADkR4fyVV16pSh5vBVIWPH7FfdcQFMsWqbfkHaavwyVuGCfvddZvs9XtIKuOzouLfi6vDtJppDmXd6vft35Oh0tcfT/PeZ4xW9HvRIhqlFc8GWUte5Z2UETvZS1XUvy0Oojn5d3q963T1eESV+7nwbYMfJr5u0jqakdDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQaD8EjLiS8Z2EKwLDx/FcIaKJIBLGMY5cEBemn8l7vsMOO/hHMWTffffdVclAxsCTCNKrVy+/hYhEoOwQNhC2rQnliSeecGLcXGWVVapu41kCogiCO/ikFeYYhjGEayFdpGfPnlEa+n69c4wYgwYN8tF+8pOfOLZlQJK8rUybNi1apT506FC/Sl48z6y99tre28C3v/1tn4Z4ZvEX6p94ChgwYID3kqJuZT7VWIWeL8QDAm7ZNUlBMrnooovktCHHrPVcf/31fTloY5BqkqTeN5X0XL1wDDC04VNOOaVqy6Wk59hCB6E8559/flI09/HHHyfe0zcggWy++eY+CK89eGrQwvYeL7zwgg9iO6U8AkFGiFb33XdflAQeh2QlPJ4f4gQDMm17gw02cHzDeGdKErwCsZIbmTRpUlI0H96I95m17ekCZm0H8iwrz/EKIH/oskZInz59ImzjdC3vUr53PEKJFNHRkgbHOL12yy23RFE04a+IDpJ+4qWXXorafZRJ5URIhhD/8NIkkhcfeb7MI30bHov4FtDDo0aNir4z8iFc+s04nUdYSEgso3z1sMULFAK2SYTavOXI+m3WK2sz20E9HZ0Xk/C5vDooTKfedT1sG9EO8ozZin4nmjyZ1+OKxrJeOyii93Q+ZZzX00HkUaQd5MG2DHzifgPFhZWBoaVhCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAu2LgBFXMr4byBADBw5011xzjfc8ACGESVtWHO6zzz7u3nvv9Sli7JMVkhmzKDU6HkjEIH3MMce4Bx980Bvm8fQwZswY98Ybb/j8ILiwjYgIW71svfXW/hLiwcSJEyPCDRPbbJmAYAjbe++9/bn+x1Y2CPhAGsHjiwjbjbBKe6ONNnJi9GIbHrxAgC8CxmIAlOfSHnfeeWcfFcMixBg8lAwePDj2cQgFCAQAbaDVkYcMGeIv2eYkJJNIvHnnndfhbSWrQC7gjxXIEItOPPHEKAnenRY8sohgPBWvIOA5fvx4d8cdd8jthh2z1BPvMWwdgPGBNqK9+1DAN99805111lmeMBVn5C2jEmzlISSqeulBMsGLCXL11Ve7k08+2f0fe+cBbkdRvvEJBESE0JFOCEVaQAhVeg29E5UIhBq6lFClihBCkSa9gyIiCCi9E2ooggGkIyigFOlKUf75n9/ot86du3vOtnPPvcn7Pc85uzs79d3Zmdn53vkGcokJpKI99tjD59ncWh1PPPFET8LCH6vueXeok1g3QekNCQYl3nrrrdcqqtT7vH9GTvvd737n46U9AleE54Xlh6oyaNCgZLsnSA2837yzJrxrtBNsCfXDH/7QnGs9Fql7ccJF6kEctt3XEIjsXY/b2pdfftntv//+PgtxW1u1jbZyse0Nba+RCMmDWZeBfBa2i1XaILZQszadum9kHIhObI0GiRCh/Q6tbZXFx8pX9xFLDfvtt5+PlnYLyysm9DVGFMNCF22eEbmeeeYZP0ZohzJ21113TchPMbYnnXRSYhEsxtbybf0Q/TVCns2N/qWVFHk3e1s9SGujW5W3zP2eaIOq1oMi5aoyZqvynlAvjfxFPLRbVlc5Wh3mPeM67/vWrB5UafeKYJrXb7M2iDjK1oOy2PY2fPLiKH9CQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACPQ+BPr3viz1/hyxYty2/oDswS+cHGdFJIpjFNKdFrYqgAxBfrFwgmKZyX4UzSbLLrtsKvkEcgoWS1gtzKT+WWed5RWKb7zxhgV1KKHYeiQWLDSMHDnSW2eAoIA1E6yhzDTTTF5hGSvDWPFq2wphQQL/ZQXLGWxtYitHsYQyYMCAbtEZWYQbzdKDVAB+kBhQ2lPmWFAWxibXYz9p19ttt12as8MCh1l6MQ9Dhw71BBuUpRBubrjhBq8kZUsYnidWCiizKUotXJ3HIuVki5GjjjrKY4eSGsXdNNNM43CHBBISH+rMo8VF3R81apRd5jqihKYukr+LLrrIXXLJJX5rEAhW77//vo9jxIgRueLCE3mgLeA5U9/ADyJCSPZac801/XuRO9LI4z777OOJYMRPPab9wVoIQl555+oQ8s3qehTwl19+uSfvWZ2nDlobaIS3OtIM4yhS98JwZepBGL4nznmGkB6pY0Xa2ipttJULchmku1NPPdX3DUaIwz1+f6q0QZCfdthhB/9e0U6tuuqqDvIN/Qt1F2F7MchhsZTFJ46nrmvyOHbsWIc1HLbbwmqR1Xt7x2mTafOof/S5RnrEUk68xVDVfEGW4V2nzSqKLWmvsMIKXbJAex0qo7vcTLko8m72tnqQ1kbTzxvhN6W4hZ16qg2qWg+KFKzqmK3sewLh+d133/VZ5R0z6yJx3hnvUoch+WYRl8MwzepBlXYvTKPO82ZtUNl6UBbb3ohPnVgrLiEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEeg4BWVwpiDXkAEgRJqz2NIUtq8k32WQTr4BspvSxVecWB8c0t/B+lfNtttnGb33CynXESCuskt544429dYlpp522WxJMfrNqHCsUkHNQBBhpBRIKq+Rt5Xm3wA2HQw891Cu4DS+U/6+++qqDtIJyffvtt/dHwhI/caJAw5rNkksumRZlYhUG/83ElIj4GTZsWKpXFMWQUYgrVGxY3PZMyCvkFwQ8TMwf5WOFayx234523+K1aztSfqwbHHzwwalb1WBxAYs0tgUP9c7whHx01VVXJdY94jTC6/Dc0jY3O5o7R8t/0XISlhX+WIJhhTDxQFZ58cUXE9LKvPPO67FLIz8RvqiE+af+gWkodt+O4T3OUarefvvt7vvf/763JgTGKIKNtEK9XHfddeNgyftrWIUeUAZhjQlhayCsDUFqY8sOlM0QwtLE8mhH/KSdQwRiixmU/pCWIK3gb8899/R1KS3uLDeLP60ckL8gS/HO806ADaQ0fpxTPyHhmAUYS8Pi5Do8j++n3bN8FK17YVxl6kFaXsM4Le91HXkPaFsgq1HmvG3twIptNPm/7LLLfN2hfTbSCoQktkYaMmRIlyJWaYOICKtDY8aMcdbfUHcgrVBmLJSBQVpbUBafLplvXITPMDyP/XFtdY/z2C/X9H/WvlDHPvjgA7x68hgWiXhHENoOSCsoxiHG7bjjjt49jN87VPwDWyxvQQ5EWmEbl6lV8nF+7brou0k6nagHVl7Ld1jeuI0Ot3wM/RU5t/QIU7YNKpKe+S1aDyxcfDScwnKEfrhfZcwGybLMe2KWocK8NDuP/Vt5rHxh2Kx6ULXdC9Moch7m0fJt4bnOaoPwU6YexFhZWllH818HPnH5SDPNLSsvchcCQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCYOJAoF9j4nHCxFGUni0FW+3wQ1nF1hwo1lAisrK6Nwt5xmICSm5Wqua1CoMiFUszH374oWPF+FxzzdVFsdeqzChFISyAF0pRCAKs6G+XsD0RW/ug1GcLioltApwV8ZBWwHDw4MFu6qmnbheUtcWLgpqtT7BmghUQ6gHPp7cKJBBwfuWVVzy+vN+9Pb8QY3jHllpqqVQrQ3VhTbeBMh7rP5BWIMXNM888faIe1oVBu+KB3PX8888XbmuLtNGQA81qGNY/6LdoT/gttNBC/lm2Kl+VNoj6Q3jqD4QOiJ5527Cy+LQqT933ae8o39tvv+0xpf3oCQFbtgCkrS2KbU/kL0xjUqgHYXl78ryv1IOi78kyyyzj3nvvPT+uYxyaJbvttptjGzTIHbYVW5bfIu5V2r0i6dTlt0g9qAPbvoZPXTgrHiEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAE6kFAxJV6cFQsvQiBp59+2luSIUv77ruv//Wi7CkrQkAICIGOIpBGXOlohpS4EBACQiAHAnWQK3IkM0l6EbaT5GNXoYWAEBACQkAICAEhIASEgBAQAkJACAgBISAEhECvQqB/r8qNMiMESiLw+eef+y1SHn74YXf66af7WDBfPnz48JIxKpgQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASHQbgREXGk3woq/RxC48MIL3cknn9wlrcMOO8zNMsssXdx0IQSEgBAQAkJACAgBIdD3EBg9erT77LPP3Mwzz9w087vssovbYIMNHFZEJPkQELb5cJIvISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIATah4CIK+3DVjH3IAKTTz65m2yyydwUU0zhllxySbfVVlu5YcOG9WAOlJQQEAJCoG8gQFvJr1+/fv7XN3KtXAoBITCpI7DOOuvkgmDIkCGOnyQ/AsI2P1byKQSEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBIRAexDoN6Eh7YlasQoBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIgWwEJsu+pTtCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQqB9CIi40j5sFbMQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBIRAEwREXGkCjm4JASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAItA+B/u2LetKK+auvvnKffvqpm2666Satgqu0QmAiQeDSSy91f//7392QIUPc6quvPpGUqvcV480333Rjx451f/nLX9xbb73l/vrXv7ovv/zSXXvttW6yycSl7H1PTDkSAkJACEycCPzzn/90/fr1c1//+tcLFZAx/+eff+6+8Y1vFArX1zz/3//9n/voo4/c9NNP73HKm/9O4POvf/3LMY6bMGGCW3vttd2gQYPyZlf+hEDHEFAb1DHolXCHEKB/+Mc//uEGDBjQoRwoWSEgBISAEBACQkAICAEhIASEgBDo7QiIuFLhCX388cfujDPOcE8++aR79tln/ST21772NbfYYou54cOHuy233LJC7NWDMnm79dZbOyaeN9tsM7fddtt1iZRJ3hEjRrjPPvvMu++www5u44037uJHF0KgLALjxo1zY8aM6Racd2S++eZzCy64oNtiiy3aRvZC2cI7Snqzzjprt3zEDpdddpn705/+5N8TEVdidKpfQ1IZNWqUe+ihh1Iju/XWW90GG2yQek+OQkAICAEhIATqQIAx+2mnneb++Mc/unfffddHCen8W9/6lltjjTXcpptu6uaYY47UpC6++GJ3++23u/Hjx/uxM+SIpZZayu23335uzjnn7Bbm/fffdzvvvLN355uAb4PeLnwTnHvuuY4++bXXXnNffPGF69+/v5t99tndiiuu6Ekh6667bmoxOokPitDjjjvO52uWWWbJRVwpOk5MLXQvdez0GLwsLBPzMzFM1AYZEjpOKgj87W9/8/0KfSd9L8TPqaee2ve7W221lZ8zm2qqqTwcP/zhD/3iBvodyIj4i+UPf/iDO+aYY7zzRhtt5HbccUd/zrwDbV8eOeuss3y/Zn6/973v+YUUdp11POqoo9ySSy6ZdVvuQkAICAEhIASEgBAQAkJACAgBIVADAiKulASRyVyIHii6Q2GC9/e//72bYoopegVx5fHHH/fZW3rppcNs+vPbbrvNPfjgg4n70KFDk3OdCIGqCGC9hHchTR5++GHvfPrpp7ujjz7aE6vS/FVxY/Lqyiuv9ASZO+64o0pUClsRgaeeesqT5D788EMf0zzzzOPWWmstN++887pvfvObbrbZZnOLLLJIxVQUXAgIASEgBIRANgInnXSSO/vss71VjtAXyvJHH33U/xjH77vvvuFtTwA//PDD/ZgivPHKK684fg888IC74oor/HgjvI9yzsZByy23XHirV55jEQ0lIpbQQvn3v//tFYlYSrv66qs9oSW8D0G+L+IzMY8TOz0GD+tHkfOJ+ZmAg9qgIrVBficGBJiL2n333ROiqJUJa0OQuPgtvvjiCRkE4ugNN9zgvd10001+EZaFseOvfvWrpG/df//9zdm99NJLiXvimHFiC7fsNvmkr2sl9i3byp/uCwEhIASEgBAQAkJACAgBISAEhEB5BERcKYEdq/qwFMFKSgQrAVhoQAnLZO+dd97pTWuXiLpHgzDJLhECPYEAK40hJmAFiFVXEKaef/55x+TPgQce6BZeeGH/64m8KI2eRYCJSVbP8axnmGEGb6VqlVVW6dlMKDUhIASEgBCYpBHAUgorrBEscqyzzjpuzTXX9Ocvv/yyH7tjZSRNLrnkkoS0suiii7o999zTh4MUe8EFF/hxzfbbb+8JLH11yzu2b6Cv5juG7ZNWWGEFTzCFcAMJAsuSv/jFL7qRWsCrN+DDdk8jR470j48xpeR/CGgM/j8sOnmmNqiT6CvtTiAAcRNLJhBC6BtZ9LXSSiu5mWee2b344ovuuuuu67KIijziH6tfzBlcc8013YgrkEtvvPFGX5y55prLxxeXjbQOO+yw2LnLNeOANMEqLJaKQ3n66af9GCF007kQEAJCQAgIASEgBISAEBACQkAItA8BEVdKYHvVVVclpJVDDjnE7bbbbl1i4WOX1Ye9WViRkteUam8uh/LWNxDAmk9oWp7349hjj/XKDraswvLKOeec0zcKo1wWQuBnP/uZe/311705ZlbIYW1FIgSEgBAQAkKgJxHAAhsy44wzut/97nfe0pelj9l/lPuMjT/99FNzTo6M+xGUbZA0sBSGQOqA8MEWOWyHd//997vVVlvN3+trfyjmzEojq+MPOuigLkVgGyWIIayAj6U34MO2kIceemicNV03ENAYvHdUA7VBveM5KBc9h8Cpp57qSSts+8O3/oYbbpgkvsQSS3gLX1gAxvKmCQvB2JaOrWWxhIalr7nnnttuewIJWwEjbIkN0TKWKaecMtmmL77X6pptAyFxhnLttdeKuBIConMhIASEgBAQAkJACAgBISAEhECbERBxpSDANkFNMLbfiUkrFl1vX3HJqkmE1SbvvvuuZbvLkclpzKezcjFtqyGU0OCxzDLLuIUWWqhLWCaxW5F35pxzzswJfhQHd999t98HmZWwmI1llSsTHtNOO22XtOyCyQ38NhPKEa/EvP766x1WITBTy8QJK2gh9TAJvtRSS7n11lvPKzrS4mXLqHvvvde9+uqrfsUtKz6Z8CANrPDE9YCJFlslBJEDJUgob7zxhhs7dqx3QolCHkJp9kwoA2VB0p6JxYNyhf2l33nnHXNKjhtvvHEmvomnGk7ABQXDr3/9a68kwvqKyW9/+1vvNvvsszsUJWnCKizqH3VsgQUW8MojVmAxsWTywgsv+FMsI9lksd2jPvF8suTLL7/0CqhHHnnE8YwXXHBBBzbNtrPhXWDyjVXJbCE23XTTef+kk0bW+POf/+xXZ5MHJt7s2T/22GOOevTtb3/brzqbfPLJs7JZ2Z202CecVW/gxDtJuliRiute2cRQECK77LKLQzH2y1/+0r8vvMeLLbaYf8YDBw70ftL+ir5jxEFd4Nk999xznjSDtRfeD963QYMG+XakzjR/85vf+P3aiZtV6lnCthH4RYYMGeLbCs7reK+Jp4igoOS5N5OsdqTMM4nrO9vphUKbxJZS1DvavlCa4YO/tL6I7Ttox3l/vvvd74bRdTknTdIeMGCA22ijjfw9+h4sQ9FOsW0GE+6xEDdpILwv008/fezFPfHEE14RbjdWXXVV35fZdV3HVthaXtOwDfNQpD2wZxKGTzvPqkNF20vipr3nWdP+N5M4zVb4NIuLtpwVw/RR7733nh8TTT311H6swHgChXCzNrpMOdl2hjyHwrNDcYOiZ6qppgpvdTmn7UPZw7OkDjOGQwnEuARFUFpfRAT2PIuO95qFyzsmoQ+iPeI9xHQ//Szt6LLLLtulbFUvGCMiWPwKlWRhvPT1sdBGQGhBGIMaacX87bjjjp64wjXb6PRV4gr4mwwbNsxOuxxpt+OV6J3Eh76I9GOBUMQ4L5a6xollvlHivHTyutkYPMwX3zb33XefH0vxPjPGGTx4sFt77bVTlcUWlrb6lltu8W0n20/RZvO9SfuClaPw2dT1TPgWo6/jO5B0vvOd7/g2hDaK9LPqBG28bV/KWAHLEMRDuSFd02Zusskmfmxs5bNj0XowqbRBrfrcvGMSwznPMa5HaWHSxkA2VoT8wPPHIih9KPXI+iLqUixlv1UtnrJzFmX6XMYuWPtB+O6CqBlL2F9jxYTxalWhb4fMiay//vpdSCth3GlbVX//+9/3z4F3l2/7cPs+rLAgtGN8P0uEgBAQAkJACAgBISAEhIAQEAJCYOJDoLs2ZuIrY60lQhHKJByy7bbb1hp3T0XG5IQp+IcPH+5OO+201KRPPvlkr4DfbrvtUokrEA+YnDziiCO6EVewRNNKmBRJm+BHScRKF1bYxIKZdyw4pE26oNCycsXh7Jp8MaEZCpY/MIMOMYG07fniB2IFabLCNibnEO6iiy4Ko+pyjincM888swvphbjNdO3888/fjbjCJI/dZ2J41lln7RJns2dyxhlneNO6BEh7JijQWEELuYXzNEHhl0UMSvNfxY3VUEwIoyxhkpPJKSYOmezmN80003jlGySOWJj0tDp23HHH+QlpyAmGXeifZxu7r7zyyg5CSZowaQ0hDeW1CWQmnjV1j+0FYnn77bfd3nvv7VeGxfdQco4ePdptuummXW6xp7flC5LLfvvt5yDMmFD3mBj9+c9/7mIlv/kpe/zggw/ckUce6Vedx3Fcdtll3hoOk43f+MY34tuFrlGa2nsMeSgmljH5eNJJJzme4eabb94t7jLvGJGgiGDbhizhPRoxYoQ76qijunkpkyZhwJTnCPGMY5rQjowZM8bf4tmjTEaqvNc+ghJ/tJWQiJoJdaSOdo80wvqeRgZki72f/vSn/r2PiSvN8CHutL4I5QP5Z2K7GXGFdwxrCZACjLiCAsTeTepSvIqfekz9QcmOMiwrfpTY9EsmmD4PFXbmXvXYDFuIqTvvvLP75JNPUrEl7TLtgT2TVnlP64vKtJekg3Wu+FmkpR+n2QyftPChG+9r1vYx+INgeemllybvchi2bDlpg+lz0gTyHeWL+xP8QkZlTEW9TBP60uOPPz61rbXnWXS81yxcqzEJ/R19I/jR/5vcfPPN/hRCCO9hGnHM/OY9Ej99O9KKsBfHCQnIJG2bO5R8ECEhUYR+LUxfOTKGMgEjypRHwjL3ND6M03784x93yyar/NPGFHWME8t+o3TLZIcdssbgli3GB0cffbQnApubHfl+49sR60Wx0A794Ac/yHzPGOdDBIREgFR9JnzP0NfbggzipP3k+4tvSaw8ILRHEFBigeRs/T3fBfTnNm41v4wRiDMkthWtB5NSG9Ssz80zJjHcixyz6lEYB9+38fgS0tLhhx/uvfEtGlr/5JlTb7A0xTdnaNmj7Leq5afsnEWZPpdvEt5nFg9AsibvjF1C4R3im4wyMgapQ4yoT1xZ4+SsdCCzsL0s41O+V3iXyRv1xxb4MM9S55g6HIdk5UvuQkAICAEhIASEgBAQAkJACAgBIdAzCIi4UhBn9n43WX755b3ZUFZ5MYHFKkwUkdtss03mqlYL28kjijqUWEzgsHIyi7hSJY+sUrcJAFuxTHysTDciQpr1ClZOsnqGiUiU9Uw8Q/BgMomJCyYTmRC95557upE+LL+UK9yWxtw5pqVp980yBBOXEDhYYQtWkE14pihCbaKVMGamlr2QUV4O/O8kP6vs77rrLu+fiRpWOYWTXZZenUcUqRdeeGHTKM8///yE2MPEFeQNJq5Zlc3qsp4W6gdpI5A7DCP2tmZSjZWMHLfYYotuWbPVVtQlm4jmuWPVw4TVkihfsKQQT5hRp7LErLOggMH6CNiSD5TZP/nJT9xaa63VxZIO5WDPblutzKp26gOEGUhClIMJN1abpq3aIx977rmnr8/2flDvaGsg6HAeT7Rm5T2PO8o73k+zFkGbRVlnmmkmX1beLayvoCSuKqFVHyOtMNHIanqUujfccINviyDt0H7G+JR9x6hL1G3ed4hqlI02j0lbyIc8SyOjsaovlLJpEgdEHYhupgQJ42Xy9eyzzw6dWp7nea9bRtLCAwSxcLUj9TmcbI6DV8Enjqu3XrMyFOITk/e0m9RZW31K3dlrr708OQClHUqNZhY3Ol1GiArU/Syp2h7Q72E9KJYsEmkd7SVpQV7F2lYoWWmGfoqe05ZgnQNLJxAU6HN4L8ePH+/baNpp6grKL+vDSKOOclK/zOoYYx+skrByev/99/dWVOifQuFZQlph7ASBhX6OdpV2mHeaI20t942kFYav8zxP28V2NIyVENpqSKG8S5ARIPGiLGbMdfDBB1fOGs+GsRqW2OgDGO9BVs4joVXCrLED7hBX6Pd59mFdyJNGb/CDNQ2TCy64wI+D04gJ5seOncQHS4yMz5HQopnlLT5WHSfW8Y0S56lT19TTtDE4+cF6oJEE+V6AKMdYmnaOdgjFMQRvyJmxMP4xchjfyVimgnDHdxxEAKw+sujBpOozgaBipBXecfpvxq+Mv420Ymm1OmLVgbYWZThtPu0P40Ysz4R5LlMP1Ab9B/1WY5JWzyjPfb7Bwq1leIZWJ5uFh7RCXaW+Y9kMEiVzGOedd57vSyFTmpT9VrXwdiw7Z2Hh7disz6XP59sEi2EQfPgmhchiY1e+M+27mn7ZxrsWd9mjEcBIJ/6+axUn33DMATEOIB6+h/l2DBff8AzqEtoMW9RD2hIhIASEgBAQAkJACAgBISAEhIAQ6CwCIq4UxN8m+ZiAQklyyimnJDGguEbpe/nll6daWEg8dvgECw4IynAjkdSdJVYEmTDJALEHwbICK2jShElUVvcxccAEKROioXUUrMOgaEAJxypoLDWkCVY8wueS5ifLjZXptvIKP5iq5xplEZM65MGEiR3yg9I/VlIw0cIqUMgvWK6oaxLI0o6PWI6w1cTxPa7B1AhKKN9QDNnEjJlFTgvXTjfqIQQJJFR8gieTxkxuY3UkJq5gMchWZEP0YNIPgfzyox/9yJ/z949//MNPVKK4C90TD01OUO4xsWfCqnAmLjEbzoR1uHUWxCQjrUCqwvqG1QdIERCxyDMrgLMm7lj5dt111yXbF+y6666+Xpnp5jqJK1iOMdIKCldW2dnkJeXlmZxwwgm1WHkxHAxHCD6hlRPwgkQDuYf6GeNT9h2DjMMzSVupzwQoSltIJrybMXGlbJpWRsgOWOOIVwFiracZgcDCh8dW73Xot+w57UHYXtIONyOuVMWnbD57OhxtBv0WCg/aA6xuQD6jD2O1OHWbdzpry5Gezm9aepiip11pJlXbA+pDmsUD0g0VfZaHOtpL4oKMaAprizsrTbtf5ohSNK0dIS7KTV/PuJC+PrROVEc5acfCdxOCKWWmP4fwERNX6AuxqgY2kPVCgWwFMQSiDc+83cSVVm3XvQ3Ct5FWKBP9pvVD5HWfffbx2xdByKU8A3Na/wjLHJ+z/Q3pIFhI49mCCUpOxnqMIdKE8Z8JY9M0MXeeDSTFPISPtHg66cbYi7EIdQQrKmaZDncIuVnPoJP4oMjkh6CQbdZ34afKOLGubxTy0RskawxOOY3wAWmFbx8jCdKmH3jggf77l/6F7weInSYQO3m3EUgrocUx3Bhv8p1s3x+4VXkmjPMhISN8L5KeWbzjew3FN+9jXqFMjL9pR63dBw8U+/T/SJV6MKm3QXnGJHmfVTN/O+20UxdCNmO1PMQV2m3mdtheD2ExBN8okEIg82GRzOpF2W/VON9V5izCuFr1uaTD3Mkee+zhrYNCumZsC6HMyPb0hQcccEAYbaVzs2ALrlg/LCoQUxhjIcwH0NYbwYa5pKxFSvinLbL+nvE6Fmz53mHcFH+f4T/8PoPQLxECQkAICAEhIASEgBAQAkJACAiBziJQ/Cuys/nteOpM6CJMXNm2BqzQOvHEEx0TJUy8o2xm0gvFe28TVrE+88wzPlshCaM35BMlMqQAhEmUkLSCGysr2RsdsT3L/UVNf6yuCskKRItCxczpxisLIa2gtIuV8xbO3J999tmacpgezY033ugnj9Pv/scVJT2TOAjWFcJJ4//4aO8/JAJWaqJ4Q7nIZBkT2CZM5pqEW3tgwvuNN96wW/6I9RPeMSS2pOIdK/6x2o4VZ6GE5Ib4vbZ6wcrMUaNGdakP7CVu5upRBLEiO02YHA0V4CgdbUuiOL208EXcWHWHMDEbk1ZwZ4KR9qzqNkHExTtlwjnPPRTecSMmMaEd41PlHbPJ5TA9zpmwNPJcmiKuSprEz3sWKptxow5fccUVnOaWPO917shq9FgVnxqz0taoaCMhGzGBjRUF6i6r0CGmIqw2T9vqrq2ZKhA5CnS2lGklPdkekJc62stWZarzflY7QhqMSRAIwDZO8A6Nv3aUc/DgwRZ90oYlDo0TiCsopWLSCn6ox1jYQ4xo6S/a8Jen7UJphqBMO+aYYxLSCm6Mo7FChrAKmveuDoFQaP0N8aHIpP5D4MTKHgp5lHixhBZFjCgb+wndQ/+xv958TZ/Pc7GxCOMsSMJY3sASIW1/TESgPGF5QxzCsobuof/QT28/7/Q3ShV8iozB+Ua0NoIxtpFWSJ/xOe+JkcysnbO8UWeMRB8S+ew+4dgG1cKbe9kjhFK+bxAIb0Za4ZrxHaSDIgKBje/PsN3nWw4LHEZGq1IPJuU2KO+YpMjzqtsvRAkjrRA3xC3mdhDmf3j2Jp3+VrV8cMzT5+KPxR6QbxCIm5QHEieLB6jfdVsQNIsrRvryCRf4ow2xhS18+/OdiNU0hG9rLMlkCfN0kHT5QYDF0g/jIwiZPFOsKIUSElfC/ir0o3MhIASEgBAQAkJACAgBISAEhIAQ6DkEZHGlINam/CcYE1us7gsn57DEwIQ7/lB68aHcm8SUp6yEY69ututoJZieZyVjLGmrqWM/Ra7D1VBst4N5XiYeEDsaYYEjSm7MQtclTFjGkxVMTGGqn+1j0hQapE2+WZXPpJZZEMGd+oHSJZwMwb1OAQe2r0GYDMaSStpzQVEPcQBT6kYOqjMfreIyay+xPyaEIW0wmRYKVkpY8UlZWF0FOczEVlvZFk3mXtcR8+AxsSdcnRWuLiZNm5hjpRqTnLFAFDIz5tQhJtNjQWkWi6WJ0rwuwTKAbfUCGacu5UFW/sIV7OBjq9JD/xB0jAyQhU+Vd4xJzi+//NJvkwFxj9XAtB1MeDYj71VJE3IW9drIdxBZyAPtSdr7GeLBed73Og5X5Nra1CJhQr9V8EHRBBah0M+0kjJ9EXijKOWdDpVardLiPgoMVqjSpz/wwAP+hzt9BSS1VoK1rpAMCSmup+TSSy/1E/zgDOHzzjvv7JZ0T7cHZKBKe4niy8SIoXad90hdsHiK1AnGQOSdPh2LO7QlbHWGYBErHjtUKaeVhTbDxl6Q38yyAPdRvmQJ/Tz1lTCM8WzcaqRlrilHmtKnzDsW5iNv22XKJ/KBhb6wPbJzG7PURf6lv4NwDnmFbRKw/EB+EY6s6EYRjnW10PqXKeLxl9Vnhu06z62vCm0b1oIYs0BaYWxrwjnbNjHOZNs7w2JSwYc+z6QT3yiWdpljkTG4tV2kE24jaOlCbGKrNiySxd9FfGswDjbrN1iNIo6yimtLM+sYps82nrHw/YaljLwSbgWTFaZKPZiU26A8Y5IszPO4W7+Rx2+WH76jYwnrVVjf8NfJb1XLZ94+1/wzLuXdZTyw7bbbemfGU/SNRlo0v1WP9O9I2lgjb9yQiWhvKefIkSOTYOGCl8QxOjGyLn0UYyG2CYSkg7U3yHlY17Fv7XCuRhZXIiB1KQSEgBAQAkJACAgBISAEhIAQ6AACIq4UBD1cycrkd0haISoU8CjUIV2g2OhNgrKQVTkIW3TkFSbz+bVbwtUvedJD6VYncSVrwgbzsgjmplHq2PZKmMBlOwkU4c3ElGTN/JS9x2Q0OKDgwUwwCoU0YVIK6zAoJJgAQqnOBB3kGlMgpYWryw3lqU1cMSHE3uPzzz+/Y2umRRZZpFsyTCRhzQDFEkQVlL+UAcyxwoLkmbTqFnEOh7R6gIKTMqCcipVSKAeRtHC4s1WRifm1azumhbV6FqdnYcocaZdM6nx3LM74SL00CXEwN46he4xPHe8YbV28whvlHCak55xzzjAr/rxKmqzuxRoU7QTbLTFRz+Qo26UhrF6mbQsVff5G9Jf3vY6CFbq0ehWTtFpFUgUfixtlUhkp2xfxvBGIU1itYLWlWe9qlY8NN9zQW4uyLfZQyuVdlQqZISY0tEqvjvvvvPOO38aIuJj0p31JI670dHtAfuwdT2vzuN+sPTACBv7CtoXrvBIrYnk+VidCBVUcH0p8M6dv91BCnn/++Yl1LHPnWKWcFg99dbwdEGlC6ozHnhaGNoeV1K0Ij7RB1idbWI5l3zGLI0/bRf1kiw8Ewi9EkWbCGKdOgfTDj7EZyrv77rvPW4LjfUB5hUUJxlJYUUPCcT/30wiqkIVM0giadq8vHMk/Vuf40XeOGzfOjx3p28AMCzgQf+w7YlLBp9PfKFXqTpExOH28Sat2OvRrYbDgwPiKdxwlOT9IoCyYQPHM2L8usXaWOmtj5jBu+34L3ZqdDxo0qNltf6+OejCptUF5xyQtwW/iwca0eEnr25oETW6l1XfaN/v+iy1gdvJb1TKdp881vxwZ82NpbP3110+Im7yXqzesatUt4MMWu/E3WJF02NYQq2wQTmzbLxaY2MKArLgYI4JNKCzcoD1i6y/GHnybYYUFEXElRErnQkAICAEhIASEgBAQAkJACAiBziMg4krBZxCumGbFWSwo11EoMAHOx3pvEggATK5ThvXWWy931iiTTeCHgcKJ+tC97LlNSDDhFG4jkxVfnZOfpBFahwjTDBW7kH+YHEWBxj7othIYSzus0GVVoU2YMXGLAjtciRvGW/X8pZdeSvZ+RsEAGaSZjB492k/MsGXPHXfc4X/N/Nd579xzz226F3VaWihcIa4wMU2ewZfVUayqQ3lX1AR4WhppbqGJ8LT7oRtKHJ4xkrW1Trhyy6ydhHFwXiTNOGyRa3vHCNMTyrXw3cnCJ2xbQnzqesd4N1HisorYLExBKsTKC9u/hCuBq6bJJC3WObAgAKGNesvKdOos1j5QiLYi5RV9r4s8/9CvKY7TlD2hv/C8Kj4WF20k73AoKB1atZVl+yImsOmvqF8PPvig//34xz9OTKaH+Ug7D99PFBhx3tPCdNLtuOOOS0y/s22hWTSK89TT7UHV9pL6Z1Kk3loYjjw7U0KRH5QVbGHHD/LnDjvsEHpPziHUQICivYfsyUpiwvOucw7ByaRqOS0ejkbQsfSIm+dLfx+PQdnG5eijj/bBIeSQJxTGZmmIMhp5OWt1etl3jETztl1hvYMs1EphFm/D5AtYwx91AQz5MYbC2gJbJ6CMp50wcluo/KYNSSOuhH2X4V1DFjseBf0jikN+WGmzbRPZIsaIK5MKPlZvO/WNUqUyFBmDh3U567vIxnOhX8sfxFDGXVhwsK1V+B7mx3co2+7QbtbRj9r4OxxHWj44hn136J51nqetqbMeTCptUN4xSdZzyeNu1rPwm1VvW8WTNq7gGfFjfGrbUoXxdOpblTzk7XPD/HJOeRgHmXDdDmFxAO99bKm0SFo8E9oMrICZlN0mmO9eFhXwfUbbFVoUC4krnSCcW9l0FAJCQAgIASEgBISAEBACQkAICIH/ICDiSsGaEK4sDBXSYTQ2occkCkqGtAkB3ENJ8xPer3qOksJWjGOpwsgVeeLFlCxKvlhYmdZK0RiHaXY9zzzz+NusBCaPWRORzeKoci9rhbJNzPKMTMnNCl0jrUCyic1Lo4RlVU+7hOdJumCFYgqlSyuh7rJKlhXnKN6YoF1qqaX8FiqPP/54q+A9fh+LMODNSi2U/RBX2JoLwWKDPYsez1iQIHUCIhh5zJqYC93zTIoH0dd+au8YEccrB2tPrBEhSgP2TWcLrRCHMK1wIjhc7VjXOxau5oe8giKF7bXY/otr2z6NPNWRJoQ2lHqYFN9tt92SiW5IMmDRTMq8183ia3bP2rU0JWxWuDrwIW5W8MdYYMUERVczKdMXMTnP1i6QSUmXrX9QPNGn5Zn8xroAVixMqMusKKf+tLvftjSLHLFIZVvYQFpp9nx7uj2o2l6GZNk0BVMenB577DFf93jXWDlP38dqXjO3jxIqLW4IFmaRhXxAfsPaCibn99prL28pxrZ8q1pOK8eKK67o+2yuIZqhbMHKGyuFTz311C71Ej/kBxnY2I6OvjJ+x/K0+WXeMdIs0nZBuoEgQ5gFFlgg2a6AeDoljIkhZZiine3kTELCOtiH7435MaswEI16ghRq6fbkkfoIMZ+tUkJrTZMKPvbcO/WN0lPPOhyn8l2U1ofY91LoN8wf3xaMrehraXNpj2grIeCxleKQIUMS4lMYrug5ZGGEfpnv0VAZj7uRTDjPI3mILu2qBxNrG1RkTJLnGWX5sTEt99PqbFa40J16xHZXodDfU2+R8BvF/HTqW7VIn2t55UhZINhjwcTknHPOcSussIJbZZVVzKmWo1m1ZE6EeQe2Disj9M1GXKGP3WSTTcpE48NAamLcwXZJ48ePT+KhbzeJ64C5tzryTMJ5McY5cZvUKg7dFwJCQAgIASEgBISAEBACQkAICIH/IPC/5RZCJBcCoRnhNBPJRGIT2EyopSm22PsbayHhjy1c2imstGWimQ/o4cOHtzOp0nHb1iV8+IeT4qUjLBgwNP8cBrW8sKrUnifbfyCQa0aMGOHPwz9WQYWTF+G9Os4xc4s1B4QV1qFli2bxjx07NtkaCAUu1iBQqPdGAWuzqnLrrbd66yumTEK5mFdQMrRTbGLulVdeSU0mdDe/qR57wNHeMZJ66qmneiBF5wlSJBTiECYcvnchPu14x1CYbrPNNl7RTB5YVR+u0qwjTd5FsxhlpBwma20v+bDs8XnZ9zqOJ881E/RIkQn+OvDJk7d2+KHu025AOkFoFyC0NBOITVjJQdiy5cQTT/TnEGAgDrQS2q3jjz8++dEvtFuOOOIInwT5bUXM6UR7YO94VnsQuptfw8wsJnFdVrFgcaFQYAwGRrbtHITi5557zrxkHlGcsO0FVgxQgjBmQSkbiuU9LE94P3Q3v+H9+BxCNBZU1llnHX+L/t+sDeBAO2Zxbr311t1IK/hpVd/xU1aKtF1gZltC2fiqbLp1hltsscWS6EKlXrgtE31GLLTzbDmEYGmiHYoqtnaA2MePraA6JYZRSL7vDfhUxSPPONHay6rfKChM7Vly7G3E8bA9sjYlxtfcQ7+xH65pp9ddd13fVl511VWJlzxlzvNMLP2sttvGLEnCNZzUVQ/SsmLvF/d6WxuUlt88bkXGJHniy/JjZCrul7V6FX6PWDr23cm1EaXsHse6vlXDOPOcF+lzw/gYk9pW1j/72c88mYT5CrbkDcdYYZiy5yERBkJ/WQnbArbprmoRxSxOhmSn8Dnb+KRofiFBh3N7oSW+onHJvxAQAkJACAgBISAEhIAQEAJCYFJHQMSVgjWACSszz84KspicgOUFm5Czya04iTgM99Pc4nBVrk0Zg0l2m+irEl87wi633HIJMSS0kpCWVjhpnna/jBsTnKwICgWFBNvVIOGEoq2+YusCLCvEwuqldoopxFFiYX0kj4AZ5poR6sGqq66aJ1hH/aBURMGIgs72oYZAlKfMZr4eIpk9r3YUZtFFF/XR8o6ZFZ4wHdsaBkIDE1qdFJSfiy++uM8C20pkrcBnUg/lTB3SCh9MxyPgwyo4E3tm7XjHIA8itLth21tXmqzA5IeClh+TmUZ6s/KlHcu812nxtHKjzDZJW4S4Uhc+rfLXzvumYCaNcOuZOE3KyqpUJraxroayGIIDk+YIimSIgM2EvgMrGPYzZV+zMFXvobxBcY5VIdrOZtIb24Nm7aWNrWjbrR1rVr6891gNbBKem1vWEbKIvRMx1q3avWblzEoPdyNGU3dD4grtixEm0ghSuN19993Noq50r2jbhfUO5M477/TbHWQlXudYDyKZ5TMtPSxKmYRkDMZLZm0RC0z2zM3vTTfdlPQjVi67V9cRBSNWqexXV7xhPGzLmEbMMT/Ud6xnIL0NH8tj0WORcWJd3yhgaM+R46OPPlo02231z7O18Uqaspn82nfPIosskjsv+GU8hISE4TiCIs9kpZVWSvq5eEs8xjmMc+uWKvWgL7dBZXEsMiYpmwbhQhJkkXFtmKZZ9Qzdrr/++uQynAdIHBsnVb5Vw3iKnFtfVmQegPp3ySWX+GS23357vwUcY1vIuGZNsM4+F+KGzYXxrZdGDCIzWDsxMkmMAe8x2zgitEtYsqwiWFl5+eWXfRThN2e4bVBZ4kr4PUkC8XWVfCusEBACQkAICAEhIASEgBAQAkJgUkNgskmtwHWUl9WsCIqAQw89NFF+YZIY6xWm+LC94OtIs644bE/6uuKrMx6s2ZhVAiaKsFoQrjhDUYOSAz+sDGqH7LPPPsmKIyZW2QLAnidbgJgsvPDCdupOP/10v90ODqwKGj16tLvxxhuT+1knlAcTxOHP0iIM7uEqozgeJpqwtpJXWO0IMYeJn3ZuY5Q3P3n8DWxsecDKdsSUdFtuuWUyqd4sDsIihGNbEFaS1UXG8BH/9496YZP81Beb0Gfy76STTkos42DtoaqVgDDdsudmDYS6xmQrq39NqG+s4mOVnE2K2r2yR7axMoVqjM9pp53mHnjgAR91jE+Vd+yyyy7zk7OQM8J3CMUcK5wvvPBCnyZWKcIt36qkGeNDGpDh+GGCO68Ufa/zxmv+mEi9t7HliJE22BYlbIM4N6GOhEraOvGxNNp9pLwQStkChGdv2+hg8j1LCUGeIH6YGXHadKz1mDsT8bQl++67b0Ik8Dd7yR9W1fISO3q6PSjTXjIOYLumCy64wCO8/vrrJ21KUchp1yCuoaTBaglKGyPPoawIcaPegA8EpXBlLu8FpCTGePZ+LLvssl2yUqacXSJoXNCH2Lv5+uuvu1NOOSUhFrBFR7gVENbfrM+75ZZbvD9TQEHW2nnnnduuRCnSdmHJiLaH93OnnXZKymUYYO2I8tL/pxFxzF+RI4RithzEchLWZ2y8xRiB+mXEXsqBQtyE7UOMsMYYii242PqOvgVLO7aVJn0JFr2yhGdIO5T1Myt6WeHb7Y4yj7aDMRZWIN95550kSfox6hB9KBKupO/L+Ng7k2ec2Bu+UZIH0sYT2pbNNtvMp8A3F5YZTQFLHdl///39PcjGvLuhUD+wsHLxxRf7dtbu8X3MeM8IK2wllCVFngkkG0jCCCQV3m0I2bxro0aNSvrwrLTKuFepB5NqG1RkTFLmmdBPGjGTvpDxmfWdHK2tpx5zbfU5TgtiB32BfStS/6nLCKTErDEjdbbst2qchyLXRfpcvk3NgiAkMrYdRHiHbBwIKY1+ty7h2xgCOMLYCyuqYGpzO7QXkMDZhtHIJJY24y6Ic3wbmoWYjTfeOHWrPgsTHhmb8X3LD7Ig4yIszTKHZGMj8vPJJ5+4m2++2T355JNJ8CeeeKJbP20LwPDEt0E7LdglGdGJEBACQkAICAEhIASEgBAQAkJgEkag/yRc9tJFZ1KXVZdMPjNRxgodFB6sGLGPYSa9N9hgg9JptCPgXHPN5S1ttCPuuuJkQpRV1ShaWD3385//PLEQw9ZMNtnULvOrKCWYnAIrJj9N6c2KJlbZmQwdOtQtuOCCXqHCfu0oQ5m4Io8ohFnFw+ovqw8WLjxut9124WW3cyyiUI9sX+fYAySAvNZzmJixySgmEMNVRnG8ve0a5SB7pJvY9kF2nXXk/Tv55JO9ouWKK65w/ExQuoTX5l7myHMf0dgu6qKLLvLPnOeGkptVhjZJT/tgFmPKpFFnGOrxyJEjHVaNUA6ikMOsNivKmdi0Ol9Xmkzy77DDDoXxqfKOMUkJeQxLJ1hCwKw0iilTMlM2lL4oZEKpkmYYT5XzIu91mXQgsB122GFJUJRJ/NLkhBNO8H2cWZ3qDfik5bOVW0wqwD9lw+JImjCJDfkJgajKZLkJK8YhO2y++eae/ADZ8Ze//GVCXjN/nTpSr1HY5ZWebg/KtJcQbm1rs1BJnreMob9m1rowoR8S2SCrMA6xlfwoalGK0Z+GwpgQqxyhlClnGJ5zlEghWSy8T9sWC20HYyjGIPTz1AXyy9gUYcuy2KpcHEeV6yJtF1susIoaEi39DvkFe9wZe5liq0p+0sKyqpt2nx9KNfo+lOqmqCQMBJZ4KwIImIxDWJXNmI8f9cEUooQ7+OCDE8ssXMeC4oxflpCfNKtMjHvDsWRs3ScrvrLuKO2MqM0zIW36TxOsCdmWa+bWCXzA8oADDrAsdMGINtAUs3iAiMH7HUvRcWId3yjhOIT8tPt5xmXOc02/Rr8PwQ8yCMpliNdGXCIOnjkE0FAYP7744ouezAWhi7Ev7xL12t4x3nFI01lS9JnwnFEqkzd7ty3uZZZZJrGCam51HKvUg77YBlXBrOiYpExafNNBNEXo/8ziWRwX2NOnsuBk0003jW97MiULQtgKkr7TyHuQLFuNq8p+q3bLRAGHvH0u/RQEEsYulAULXvRfJhAuWUTA2BdyFSSc1VZbzW5XOjI+wlIcxHHaEwiQLGaAdGPfyGkJ8N0Y9nvzzDOPJ42m+U1zI6wRTtPuM7an3Gz9GX+ft7LqwnwKY1K++yVCQAgIASEgBISAEBACQkAICAEh0B4EZHGlBK589GNqlVUgKLGYhGTCjI9kJp6ZzGPFTpaY9YHwfppbeL/seRgvH+jhNXGG1+E592wyNXbnHmL37fgf1+7/YfjwvLtP5wYMGOBJIFiuQanIhD3KdX6cM9Gy5ppr+lU7cXiL247x/VbXKPPN3K8p8HmerM4599xzuwQnH6z+NrPw5A0TuEzaoiRFOWzmrsP8tMKqSyKNC5voNXcLz4p/UyzYPY523452j8kVJozAF2xDCfMXnod+ypyHcYXnReNab731EqUwqzTzbrfDM0K5hHUeCD5TTDFF06QNs6y8NrvPHu5YZTClJ/WVCTnCoChFwRJP8IfphOeWSXOzdM29jiOWolDGUo8QVsJZ/eW9w4R0llK/TPrgw0o3UwbmwafsO0b+qCfzzjuvzyrvEApoUxZRLhTh1157bbf96sumac/InlkzjMyPHfFr4Yu+183SyboXtylZ/sw99F8WH+IKyxueWzrmZkdz52j4pN0L75s/3GI4BqXJAABAAElEQVS/9NWQqDbZZBNPNjWigYUx/1hSwKICgv80ggBWOQ455BDvB3JB1tZwFqf32PiLr8296jGMl3cbZXwodt+O4T3Oy7QHMW5xnHbfjuH9ou0lcVAmLBdBZrXtGsM4Obe07Gj3s8ptdWKjjTbyVldY9RsK7RVpMuZDUP6EpJUllljCK3VR7MZp4r9oOQmDpOUXwg6ESKwZ0K9xjAUlDSunre2mz4e0guIQJfKOO+7og6Tl1dzS0iaQ3bejpW3XZdouxtAQDLF8RTyQVVB6G2mFNpxxTtx3WtpFj4zNsc6AwgxhzA5G1saRD4jCKNpiAUPGdZCYaQeRkLRCm2EWA8Owhk/olnWe5Te0DkNYCITtEKwJgBHP0oRnYaQV3hcUfBD1DAPz1wl8eH6Ms+xnVsTIU7N7lmeOlKPIOLHKN4qla9YDuOZ9o061Q8J3OTzPkxbvHuNWLBRRL1H4G2mFthiya/wtQbzcozw2FsbSIZYUeMfIAyRQFnxQX7Kk6DMZOHCgw0on7fgss8zio2XxAe0LBFUT6m+aGDZZ719amLL1oK+2QWkYNHMzTPFTZkzSLO60e9aGp91Lc8vyD2EZshXfREZa4fuROjtkyJC0qBK3ot+qhpEdk4hanFg9LdLnQsSxrXAg5qR9R/OuUFaw4d0OxzotstT0NvmFYMM8BORZ5lWYMzHSCu877268EMdwMQtQWF+B9NZKLFzsjzaHsTukcxYbYRHVsIz9lr2O046vy8arcEJACAgBISAEhIAQEAJCQAgIgUkRgX6ND9QJk2LB6yozk6NMymGWGKUCkwHxhG5daU1q8VA1UbpgJp5JDiYvWHGTNflYFh8mo7COwYQiK2aZTMG0LM9x8ODBidIqK35ILij9UW7hv478YfIYJROTxldeeWVW0pOEO6ucmYBGmFiz895YeOosbQFtApNkrOyroz60s6xM0KIsZNU5E4co601RW3e64MP7wjtdBJ+y7xhbgjD5zBFlLhPSpJtnMrFsmnVjVnd8KGZZoYxy1qwwpaWBspSJeJRC9957bzcvEys+3Qo6iTn0dHvQF9pLxnlYAYHkAFkB5SjE1Lxte0/3C4xhaGNRGrMNAO9wbxfyTL8Jzlj/oi/Ko6QqUy62ikAJzypwFPIQYxi/0zfkEUiQzz77rB8nYsUDwjJx0LamKQTzxNnMDwq/Y4891ntB6cdWCyG5pFnYsveoO+DDkfEA+PBMWhGASa+n8Slbxqrhynyj8C0DQcq2HkOJilK5NwvkJcYDjKNQPEMKaaXwpc3ku4j6Q3j7fuuJ7TLBFmIJggVPs9LI9le0h3VLmXrQ19qgujGrOz4s60A6xmoI9TNLsKTBdkAQr2w7LMiwtn0uFsmwtELd5Ud9Yd4hj/Slb9U85WmXH4iQbD3HvAvtAn2ZEd3alWazeMPnhmUo+thmApmdxWsQnWVxpRlSuicEhIAQEAJCQAgIASEgBISAEKiGgLYKqoaf/8BlIo+fpF4EmBhFcdEu5UVWblFGrbzyylm3u7kzqZV3YqtbYDm0RADLNgjPBWsJvVmoswMbSkJ+fUVYIZu2lUo78g8+rOQ1ayh50yj7jk0//fSOXxkpm2aZtPpiGOHTF59a6zz3dHvQF9pLFBko7vmVkZ7uF+grs6zSlMl/T4Qhz1iw4dduweIK2yWW3TIR8gbkA36QIdlWBZIHWw+wdWjd/f/DDz+cQALRod2kFRKjXPzKSE/jUyaPdYQp842CwtZIK7Qr++67bx1ZaWscKJUhBhQRysZ2qvx6Woy0QrqQvBBrw/1FzX9l6kFfa4NqhqzXRweBn18R6UvfqkXKVbdfiJCtrNfUnabiEwJCQAgIASEgBISAEBACQkAICIG+h4CIK33vmSnHQmCiR4CV7VjKwDzzXXfd5cvLNk4otyRCQAgIASEgBISAEOg0AhtuuKHDegLW+iCusIK8TsFyxbhx43yUbBfF1od9SdqNT1/Cgrw+9NBDSZapL0VJvElgnSQIQAZiq85hw4b5rWSxrsd7w/ZBbP2CsFVuuywJJhnp0InesQ4B30hW36qdw14pCwEhIASEgBAQAkJACAgBISAEhMDEjYCIKxP381Xp+igCbDl0xhln+O0I+mgRKmUbCyBMPJtgfn/vvfe2Sx2FgBAoicAqq6zi25ZWCjOsS9AGTTvttCVTUjAhIASEwMSPANuurbHGGrm3GiqCCFsaYdEBS0hbbLFFaas/RdKs22878ak7r+2Oj+1HeJaQkLDUI6mOANuM3X///f5HbGw1hlUbtqxCsNq53377+fOJ9W9Sf8cgLrEFzcwzz9z0Ee+yyy5ugw02KGxBKCtSfatmIdN33GmPsQBDP8uvlWBtEv/t2HasVdq6LwSEgBAQAkJACAgBISAEhIAQmJQQ6NfYm3nCpFRglVUIxAgsv/zy7t1333W77767O/DAA+Pbuu4AAmy9xaQzq5d5PocccsgkS+LpAPxKUggIASEgBISAEBACQkAI9GoEIHcdffTR7rHHHnMfffRRktfZZpvNrbPOOv77ASssEiGQhsCVV17pDj/8cE9aeO6559yUU06Z5i3VTd+qqbDIUQgIASEgBISAEBACQkAICAEhIASEQGUERFypDKEiEAJCQAgIASEgBISAEBACQkAICAEhIAQ6gcAnn3zit2/B6so000zTiSwoTSEgBISAEBACQkAICAEhIASEgBAQAkJACAiBigiIuFIRQAUXAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQKIfAZOWCKZQQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEKiGgIgr1fBTaCEgBISAEBACQkAICAEhIASEgBAQAkJACAiBCgh8+umn7s0333RfffVVl1jef/9999Zbb3Vx04UQEAJCQAgIASEgBISAEBACQkAICAEhMPEh0H/iK5JKJASEgBAQAr0VASajx44d6/7yl7/4Cei//vWv7ssvv3TXXnutm2wycSl763NTvoSAEBACQkAICAEhIASEQDsRuPDCC91pp53mjjvuODd8+HCfFGSWtdde233xxRfumWeecf369WtnFhS3EBACQkAICAEhIASEgBAQAkJACAgBIdBBBERcKQj+1Vdf7a666qpcoRZeeGF3/PHH5/LbLk9jxoxx48aN6xb9dNNN5+abbz63wgoruHXWWUcTQN0QkkNZBO699153ySWXJMEPOuggt9hiiyXXdvL444+7M888019us802bujQoXarI8f999/fvfbaa13Snnzyyd1ss83m5p57brf66qu75ZZbrst9XeRHgFWSo0aNcg899FBqoFtvvdVtsMEGqffkKASEgBAQAkJACAgBISAEhMDEjcDyyy/vC8gcxrPPPutmnXVWd+eddzosrqy55pqas5i4H79KJwSEgBAQAkJACAgBISAEhIAQEAJCwIm4UrASvPHGG+73v/99rlBf//rXc/lrp6eXXnopM7/33HOPu/jii92iiy7qLr/8cjfzzDO3MyuKexJBAEsa9913X1Ja3oNzzz03ubaTt99+O/H3ne98x5w7dmQF34svvpiZ/tlnn+3WWGMNvwoQ4pckPwJPPfWUGzFihPvwww99oHnmmcettdZabt5553Xf/OY3PTlokUUWyR+hfAoBISAEhIAQEAJCQAgIASEwUSGw4oorOhY0XHnllf5nhYPAcuSRR9qljkJACAgBISAEhIAQEAJCQAgIASEgBITARIqAiCsFH+zKK6/ssMKQJX/4wx/c3Xff7W/3JusBbMFx2GGH+Xz9+9//du+9956DuPLKK6+4P/7xj27PPfd0v/rVr7KKJXchUBqB2267zb388stugQUWKB1HTwaEwLXbbrv5JD/77DP/jtx8881+OxvemSOOOMKdccYZPZmlPp3WP//5T/fDH/7Qk1ZmmGEGj90qq6zSp8ukzAsBISAEhIAQEAJCQAgIASFQPwJYrN1qq60cxHe2CYLwvu6667qpp566/sQUoxAQAkJACAgBISAEhIAQEAJCQAgIASHQqxAQcaXg42CrkGbbhey3334+xq997Wtu4403Lhh7+7xPOeWUbuedd+6SAEQWFMhvvvmm304IazJzzTVXFz+6EAJVEZgwYYI755xz3CmnnFI1qh4JP8sss3R7VyBebLbZZu6jjz5yv/3tbz3R61vf+laP5KevJ/Kzn/3Mvf7662722Wf35DgmnyVCQAgIASEgBISAEBACQkAICIE0BJZeemnHTyIEhIAQEAJCQAgIASEgBISAEBACQkAITFoIiLhS4/P++OOP3S233OJjHDp0qBswYECNsdcfFVZYNt98c4diGXnuuee6EFceeOAB9+c//7lLwhBy5p57brfEEku4qaaaqsu9tAss0Dz++OPeqgvWK9gOZIUVVnDLLrtsmnf3xRdfuGuvvTb1njmShy233NIuux2LpnnTTTd5QsLCCy+cOkGGJZqvvvrKLbPMMm6hhRZK0msWDisT119/vfcbh0siaJwUzWsYtsg5hAvyGwtml7GEMnDgwPhWLdfUFbYOAov999/fzTnnnLnivf322/0zAe8ll1yyW5jf/OY3/pkstdRSiSUX6ip1lveO7WeuueYa72fDDTf0W/xgNeWOO+5w/fv3d5tuuqnDelJemW+++dzIkSPdiSee6IO88MILDuLKa6+95h566CHvxjs/00wzpUbJNkTjx4/397bYYotu784TTzzh2NbLZNVVV3VzzDGHXdZ2xMLSuHHj/P7w3/3ud92DDz7o849FHHs3m23bRHnvvfde9+qrr7q//e1vjm2gwIF3Z/XVV3e0KbH87ne/80677LKLe/rpp90vf/lLH37aaad1iy22mH82reof6bH9FG0U79agQYPc4MGD3dprr5251/1VV13l/u///i/OTpdr6uNqq63WxY2LMuXsFklJhw8++MBjzLZVPC9Wl4IP9XjBBRcsGev/gvHufP755x5D2uIswQ9+kSFDhvjnzHmzdo/7We0l9xDaUqwwPfvss+5Pf/qTY9st6h71J4vURN9x3XXX/SeCjP8pppjCbb311ql3i6TJu/Doo4/6eCCrNVvd+9hjj/n3FmIoK4NjKdq+005SvxdffHG/fRbtFe8rfR5t3XrrredmnHHGOBnfT9P2IWAAFqFgVY1Vy2HfaW0BFuRoC7KEcISnXd1oo4289awq+Fg7TXppeaW85C3Ma1be8rhXre+kUUd7QF2hPvBev//++74v/Pa3v+2wzEdZs6RIe1AF2yrjvaL4VH0mzdqgvGOvLLyz3DuRZpVnYuUo2gbxLG1Mk9b+jR071kFyRxgrWN9Ne05arSSrDSvSRlsa7Wovm7VB9FlsF/v88897y5WMrekjZpttNv/9wDgwyyon3zdgS1vA+ImwbJfIGIqtUdL6P6sDEH/ZqjIWvjlpIxgr860RSpX2wOLhuTz55JO+j2FMEArPvtl4MfTb6rzsNwrjAsYHtKVse5smWBm9+uqr/a2VVlrJY86FtVv9+vXz29LE/SZ1mmdF/862NaHU1R4U6RcYzzK+YjFAM6nzm7NZOclD1nivSt1rlma72vdmeOqeEBACQkAICAEhIASEgBAQAkJACAgBIdABBBoTIJKaELj00ksnNCYh/a8xuVtTrNWi2WmnnXx+GhOjqRHtuOOOSZ4byuEufhoWWpJ78/63XHZsKDInNCaNu/gPLxoTtBOOPvroCY2JzdQ4jjnmmAn/+te/wiD+vDGZm+rf0uXYUOp1C4dD2TQbClOfZmMLmNR4G6QFf//CCy/scr9ZuNGjR/sw5DcOVyWvXTJQ4KIxAZvkhzzFP+rJhx9+WCDGbK+XX355Ev8FF1wwoTGZ7K8PP/zwLoFuvPHGxN+5557b5V4zbPFoz+S8885LwjWUYUl8jUn85By/Bx10UHI9b6P8888//4SG8iEJy8k666zj/TQUK13c7aJBpkniaGwV5J0byotubuY/PA4bNsz7W3755Sc0JtHDW/48zl9DGdHNTx0OV1xxRZLfE044ITmfN6gTDfPcExoT5N2S+/GPf5zq38I2JvYn/P3vf+8Sjjpl99daa63k3Nw4NkgLE3h2WdLY435C+DzDsNtuu223NC2e0F/WOeFjKVPOOI6y17feeusE2tas/DZIhmWjTsI1FDw+/gb5sOk7f/bZZyf5CN+zvO9mWrtH+94gKyTxhuWkHmT1KQ3yW2qYMHxWH1c0zbAfP/XUUxPc4pOG6foJDTKJzxd5D6VsX9RYWezj22uvvSY0FILdyoxbgzQXJuXPwW3e/77D8TuIh9NPP93fbxDFkrCXXXaZd6N9bCb01cTdIPp5b1XxaZbXd955x/fvpBfmtVn+Wt2rWt+rtgcNksoEnidlSvtRh6hLaVK0PaiCbdnxXhl8qj6TZm1Qq7FXGs553DqRZtlnQnnKtkENwm9STxvK5y7QXHzxxcmY/gc/+MGEBmEguU9bmVa/Y7d43EUERdtoS7Qd7WWrNqhBYG5azgYZdALjwljefvvtCQ2Cb2ZY7qWNgxqEXx+mQS6Mo/TXDfKuv3/ooYd2u1+lPSCyBll4QoPokZnn3XffvVuaZR3KfqNYH0wflyUNUkxSBuq3STN88MN4a95Gux3379yr2h6U6RcaxCGfH/LU7Jc29irbHjQrJzjYt1icZjNsW71jzdJsV/tOWSRCQAgIASEgBISAEBACQkAICAEhIASEQO9BQBZXaiQLscIfwVICq7p6kzSqnGsQRXyWWH3WmER1DeKAu/vuu70bpngbE1CpWWaFt630w3oGFlTee+89b0GjMXnmV7rFARsTmu6uu+7yzo1JP9cgBfhViKTHqszGBLhfZXzwwQfHQZNrtmTCYocJ1hpYrZwldaSZFXcRd1bsNybxmgbpZF6xNIJFki+//NKv/mwoOt2dd97pn+dFF13UNN9Fb2JNoUEOcA1iil/xyJY7M888c9FoCvvHkkyDJOItvVDfWRVImanHrPam7FgCwYpBXvnrX/+aeCUuhJWyrPTEKgEWXvbee+/Ej53wzpiFAiwFZa3GNf89dWT7Jp4F1mewnoRFGlYTN0gKHqsGqa1LVrAohdBO8G6y2hbBWgzvOtZbsNyAtRxWsCKNCWp/5I/3AqFtxNIHbdANN9zgPvnkE8cWa2Aar97FMkdDIePDTT/99D6vWH54+OGHfTvE6u/ddtstWU3rPf73DwsYtHuIrZLmHAsHWIpBaJtiKVPOOI4y19RLrBIhrPwln9QvViKz4pk69O6775aJOjUM8Z511lmOLeNiYQV3g7gSO1e65lnssMMO3noHEbHCnHpE+9NQcriG8t7RPrBVV1wPwoTjfsHupVmtqJrm+eef7xoK2tQ2izYNyxlpUrV9NytFDQWOX0WPNSa2KGNbP1Z+866llTctL+10K4tPVp4apDnfHmTdr+Jetr5XaQ/oe2iHsCCD8D6zPSOWuWgP77nnHl+HbGwWlq/u9iAvtkXHe1XwKftMQpzC8zxjr9B/Hec9kWbRZ0K5qrZBMTaMF8aMGeOdGc/Td2CJwgQrUaElRMYTWMJgvBBu9RGP/6q20aRfZ3vZ6j1hbIN1FaxOsrUqYwnqABb1GCPy23777f0YxcZB5JG2ADzo27Hy1iBP+zEPYyTedY6Mg7iPZat2S6tyYo2EsRUWLhCsrvG9h9BuMUZol/TkN0rdZcjTHlTpFyy/WKLEWmYozayV1t0ehOkWPW9V97Liy4NtVli5CwEhIASEgBAQAkJACAgBISAEhIAQEAJ9CwERV2p6XpAx2MYCQVGRtmVGTUmViqax2ipzmwkUKTYhnRY5ypZTTjkluYWpa5RnmJBGYY3iPpR7G1uJGGkFf8cee2yirG+sPHb77LOP324CcgeKblOAh3Fw3rAC4jC7bdJY0ZlJXKkrTUuryvGoo47yk9RZcXQ6r+CP8hfhGQ4fPtw98sgjjnxRT+pWiDZWDbtLLrnEx80zP+SQQ7Kgqc2d7WhQKmDCvWEBxsf7i1/8wk/0zjDDDA4lDObe88o//vGPLmQkzLKbfP/73/fElddff92TCwxbu89kMsoZlBjNtuQw/z11RBlG3kwZwVY+tF1MDjcs5bjtttvOb6tk+WH7oo033tgrXkOFDPchojVW3vttU+6//36HXyT2B3GB98MEUgBpQlo47bTTuhAWwKyxitZ7hbQCMcgm6jGZfuCBB/r8Q+hAkR+TBU8++WRLxhMj7Hkfd9xxjjqQJWXKmRVXXnfK31hJ6r1D4IFAhiIwFEg8IXkqvFf2vGF1w40YMaLbtlRsHwehqE6B0MSWMwjPnX7B6gfvENvGoCSjnW9GXNl11139FlF58lY1TfJDvfzJT37SJTmUjFkkv7rad9rNhpWqJF221+Ma0ijvAu12p6UMPll55j1utR1UVti87mXqe5X2gDpipBWU2UceeWQyFiLPEJ8alq+6be1Ud3tQBNui470q+IBBmWdCuDRpNfZKC1PVrSfSLPpM6mqDDBvaQH4IYwDOYwIu2/bxM4HQCvmB+tGw2mTO3Y5V22iLsI72Ms97cuaZZ3YZF1n6HBkDMRZqWJDxY6FwnMjWiA3Len4MGG8pyXcRZCD6d9qMdhNX8pQTcoqRViAa8C1nQl/NFjftkp7+RqmzHHnag7L9QphPviXCZ8I9+s+07THrbg/CfBQ9z1P3suLMg21WWLkLASEgBISAEBACQkAICAEhIASEgBAQAn0Lgcn6VnZ7b26x6ICgiEMR25ekf//+qZNdWWUYPHhwcitNAdzYRsXfZ792JqzDCW7O99xzT3+fVcYoY+uQTqSZlm+s2KBEbya9Ja/kkefR2JLBZ5eJ9XiP92blyHuPFbaNrXK898ZWNc5WaOcNX9QfZBVIKwirWhHKaeeDBg3ybln5QGGPRQ9+WAiCxIFSgT3bEay0hO8AipxvfOMb/t7VV1/tj/YH+YLVtAhWRngneot873vfS0gr5AlyCGQxBAUKBJRQKCdKKCMbhPeYQDf3hsn35BaWXEw4Z0VxKA3z+G6LLbbwTkxoo+gyeeaZZxKiA5P0RlrhPsRAiCvWtsS4WxxljmXKWSadMAz5N2sqlCsmreAXEt+IBsmkToGoFpISifuNN95wvKd1iz0jiHGjRo1K6gvp0AZtvvnmPkkUYmE9qJKPOtKEBBfnB8WtKfXi/NXRvvOuYH0mFN6x2Wef3TtZucL7nTovik9aPiFQNrbqS7tVq1uZ+l6lPcAqBQI5MCat4A558MQTT0z6D9yQOtuDqtiGfV3aeK8KPpS1zDMhXCx5xl5xmKrXnUiTPLd6JnW0QaTD+AVCpZFWICxAJrV+Fz9VxdqyKv1CHe1l3veE76UsaWyL6W9hhcXaavPL+HqPPfbw1pbMzY7TTDON22yzzfylkTvtXt3HvOUM+zzLW915yRNfT3yj5MlHHj9524Oy/UKePKT5qas9SIu7iFveupcWZ15s08LKTQgIASEgBISAEBACQkAICAEhIASEgBDoewhkz8D1vbJ0LMcosNjyAsHaQm9SThsoTLbedNNN/pLJI1YEjhs3zisoWVm3ySab+C0+YjPeBGBblQ8//NCHRamJ9QwTTDrH8sILL3gniClYPWDy28TOmWhu7NftQiU3fuy++c97rJKmpYEZbyunuXFMW8EW3rdz6oGtzGflKaSHtLB15NXSLHOEsEE5MVcNOcHqLlYO2mUpaOTIke7KK690WC659NJLvdWdMnnPE8a2gcEvJBY7GrHC7mdZlKCOYwEiTVBGYBEiVNyQBu8PClzM47Pq1tJFCW+El2bWVrCkECqq097DtPxUcQtXR1s8a621lp0m+U4c/nvCdl1s2wW5JdwqhTaGdz7E1XAgKG0j2/zEAinIrOKAlVlgYoslk9Dykrlhrh9z6VhSMYztXh3HIuWsmp5tgQYByggcVePMG55VuljbgUSEQGShzactSGu/LN6i7aU9T+oBJKlYeMZYRULCehD7K3JdNU3aDPpLyAW2dRIWNIyompaXOtp3rKug6AyF58FWZ7Sjzeo726/E7TjPKkt4xpCm2PaDrd2KSBl80uKnTwA38r3mmmv6revS/NXhVra+F20PGGMZOZL+JOwzWpWjzvagKLZFx3tWlqL4WDiOZZ+JxZF37GX+6zj2ZJpFn0kdbRAYHX300cl2ouuvv75vB20cVQeGxFG1jSaOKu0l4ZGi7wljaPLOmOf555/3W6DaWJotk+L2+z+pOP/d88ADD3iCKNslQtxCIOoiXBNnO0jkxJ+3nIyvTLCeg2XOnpIy3yiMOxmPMraI+7+sfIfjUcrIWLSs5G0PqvQLjEVMiryHdbQHRcd7ls/wmLfuhWE4z4ttHE7XQkAICAEhIASEgBAQAkJACAgBISAEhEDfRUDElRqeHfuro5BHzLJEDdHWGgVKZcyNm7AyEAURE76Y2GbCD8sShx56qHlJjkzoxdsBoYRh5WVoCpsAbKFgWKDYPu+885J40k6YxAuFCXqTvBO3VdO09H796187fmWFVamUB1IOJo0hrsRSV17jeItc87xjQWkdbksR3696jQUUVm2yNQ2Tl2l5qJqGhQd/E9v2KM0N4lSWWDjqOVu3zD333G611VZz2267rVfwxuGwXgJxhQlWCGKsTEbY0gNhghzFT5ag5MhSdGSFqeoeKiYsLkzYM+mPMvutt94yZ39888033Y9+9COH2fFmEk6uh7iDY5qE7pCGTEjPJC2v3LOwoV8LU/ZYppxl07Jwr776qj+lnhVRcFv4Mkcsg1FXUUiwXQnvJau9r7/+eh8dRCvaQwhuaVK0vbRn2+pZkpb5TUu3iJvFUzZN3ndIVRDSnnrqKd8PQmKhjkP0gGQUkljqat+z8jvrrLP64n/wwQf+uRkJL8QEcktRYRyA0E5hzQHLS4wPWklRfNLiAzPIgAjtKGW/884707xWcitb38u2B3/605+S/M4333zJeZ6TutqDMtgWGe9RlrL4ELbsMyFsKHnGXqH/Os57Ms0iz6SuNgiMwjEs26FCKChKbmuFddU2mvirtJeEL/Oe0CccdthhBE+Evvv888/PJEDQx7LV0N///vckTNoJfW7e75+08FluRcrJogQWHtDXYQWOtpnxCUI87ZS074NW3yh8v/LjW3eOOeZwm266qRvRsFAXb8sU5jtsl9nGlO+URRZZxHthy0ysAOaVvO1BlX7BSE7kKRxbN8tjXe1B0fFenKcidS8OmxfbOJyuhYAQEAJCQAgIASEgBISAEBACQkAICIG+i4CIKzU8u6uuusrHwmr5ZsrpGpKqPQqsLjDJh5KarVGyxCbJWAnIRCa/4447zk9kYvXABIWaCdYbVl99dbtMPcbmtFH8m4TWGswt7Vg1TYuTFWxGWDA3js0IDubvpZde8nvbc7377rsnE7x234515dXiK3Nkcp1JccgJRhRiBT8m27H6UWQlX5H0wYVtcyBJQfLIUjYUiTPNb5h/O7cj/sPztPBMXN9yyy1ptzLdeAeYWGflLRO8EFcgBJiVI0g7aXUrM8IeuJGm9KZu8KNuYLnBhAnzXXfdNbGQtPTSSzss9MwyyyyJguXYY4/1ZSasCcp9E9tOya7tGOJiFgq4F55ntQUWZ+jX4i1zLFvOMmmFYczSU7gCOLzfjnPafbZtQzkFGYn2H6siWL1COYmyirqcJbxH4bMzf2ntJf2FWf2wZ2b+7ch2CSZ1PM860mRrK0zU02Ydf/zx7uCDD062t8OKlPWLlu+62ves+h6+T7yfae8wbTvvcCi08+F7Gd7jnHLw3MCdre74YTlqu+22i712uS6KT5fA/71gHPHpp5/6bXMOOuigxPpSmt8qbmXqe5X2IKwLRd/rutqDsthavW413quCD8+yzDOJ60DesVccrsp1J9LM+0zCeldmDB7jsu6663prjHwj0P6de+65sZfS13W00SRepb0kfJn3BNIshD/6NSxa2LtCf8r5hhtuSNSJQHDEgg0CSZn7bCFmRKCHHnrI9zXcL2t5krDNpEg5ITBCaCXPEPIhD/SUlPlGsT4P0g/fMxCE6Lsh4maN+cF/+eWX99ZHsUDK9zvPg3pJn5RXirQH4ftZtF+gvTVJ6/vtXngM06vSHhQZ74Xp23mRumdhOBbBNgyncyEgBISAEBACQkAICAEhIASEgBAQAkKgbyMg4krF58eE5ZNPPuljYSIyawI1ToaJsVBs0i1066lzVpkxKZ1ltWDFFVf0RAPygzUVlJxYXsCiCiulWUVowoo8JriYeF1ggQW8hQq7l+cYKizTtpNIi6NqmhYnq8dR1sUyaNCgpko/ynrkkUd6ywRMhELQyJK68poVfx53SCNs14FAtGCFKIQSJqZZrR8SkfLEl9cP9YHtQG699Va/MhIlZZbYRHPaBD7kpmZK2Kw42+3OdhBY2mGV5muvvebbBbM+1GyboHbnKyt+lPEzzDBDl9sor1G6ICGx6L777ktIK9T1HXfcsUs4FONpFntY/TrjjDN6xf97773XJYxdhASZMM2Q1MYK5bT2wFYuh34t3jLHsuUsk1YYhu3l2GqDNrUnBTIShDUUPbvttltCVoKQwHNrJkXaS/o3tr9iS5qsehC61/E860jTCDxYIuO9tnpP/vbYY4+ErGg41dW+W722eO1o/SNlgzSWJijg4md3xhlnuJ/+9Kdp3r2FJfoBVoITdsyYMQ5lF31hq3arKD5xBh555JFkqzr6g7R3PA5T5bpofa/SHoRbRsbWq1qVoY72oCy2RcZ7VfAxDIo+EwvHscjYKwxX5bwTaRZ5JnW1QWBk5LUDDjjAW8tj7MZ2bsOHD68CYRK2jjaayKq0l2XfE0gA/BDGTVikYSx91113ub322stbg1tmmWX8ff64hwwcONCPt+M2umgb4SMr8FemnBCwIZTaN82qq67qryE2huO2AtnI5bXMNwqkeyy1sHUkJCEsodKnXXTRRZkWHvnOwHLoIYcc4r9ryZyVi295+rdWY7Ki7UGVfiEkBeclrtTVHhQZ78UPuUzdI46i2Fq6hAu/EXnOebePsjh0FAJCQAgIASEgBISAEBACQkAICAEhIAQ6i8BknU2+76cebhNg24O0KhWraeeff/4uv9tvv71VsLbdt4k6s77RLCFWy0PQsb3AWaVvK+kJx2Sfbd8RmkRuFmd4L5yAZtIwj1RNM08azfz89re/TazVsDoxXBUfh+t0XuP8YCUEywomoWl4c6vziIUHhBWckGWyBJyQcLWg+c0iWNn9Th1DqypsEWTbBC2++OJuscUWa5otFEJYdLAfqwzbLbYdRZgOhBsTVsObsIUMgoWNEQ3z67GQ33CiOLxvJIRXXnkldE7Ow3zMOeeciXt4nhXW3EO/SQQlTqqUs0RySRAzWY/yqt1m+JNEGye0VRCREOsH2FIOJUXdYs/Inlkcf+hufmM/Ra8tnjDuMI7Q3fyG9zmHvMHqc8QwgqSVpjiqq30P3wmf8H//rE9lyyCUvnUKdZDtIPbee28fLSvXIbS0kiL4xHEdccQR3omtCFuRZOKwZa6L1vcq7YG90+STbaaKiIWt0h7UgW2r8V4VfAyPos/EwnEsMvYKw1U570SaYX5bPZO62iDStG3HILBAykY4h7Bfl1i7G7bFYdyhu/kN73Nepb2s4z3BGg6WO7BGA/4oziGwmEC2tnLwnRiTVvCXp621+Mocy5aTLXQQrOyxbR5W2eItYsvkJ2+YIt8oYM8WR5BRjJQ9fvz4pklBlmbxBd+yV155pSdmsQ0URKQf/OAHTcNys2h7YG07YYv2C2+//TbBvFj57DrrWGd7kJVGK/eyda8otpaPY445psv8Smz9yPzpKASEgBAQAkJACAgBISAEhIAQEAJCQAj0XgREXKnwbCB6mPKdySjMRueRNAVvmlueuKr6YVKPvbyRBRdcMHd0EA8QTBeHxBXcWB2K3Hnnnd7Mr79I+YutzuDFFHOcF1l9XSVN0qoipsyEzGMT/c3i62Re0/IVrvRstbowLXwRNxTAq6yyig/ywAMPZAY18tNzzz3XzQ+Tmb1RIFptsMEGPmsXXHCBw/Q7kkche8899/gVuazK5WdKDh9Bm/6s7Qqjx6y6SUi2MSssvO9Y54jlnHPOiZ2S60UXXdSf8yyfffbZxN1OjOCDAhOrPCYoR0w5j1WQWLCAYXlhe6c6pEo5q6SP0guhTcTEfZZ88sknWbdKu7NdHD8UHPyY9DfcS0eaErBVPbBtiagHEDvrkDrSZKUumBg+tF/NFCF1tO8QAmLlMP0M7QQSvpt14BTG8cwzzySX4dYEiWN0UhSfMDhEVcL/5Cc/ydzOIfRfx3mR+l6lPYBgAGkRgeAc9rNhObDKhaI7lDragzqxzRrvVcEnLG+RZxKGKzr2CsOWPe9Emml5zXom+K2jDQrTpC5DBqBfoE3Aokho/SH0W/S8jja6SntZ53sCQcXeCbMaCB7gZhYf0kjJuLWbNF6mnGy188QTT/i22QiuRZ9vHf7DtjPPNwrjQrNOZri3ygcEa7a/XGmllRx1ki338kjR9qBKv/D444/7LEFctb4lTx7rbg/ypBn6KVP3CF8UW0sznk+Jr82fjkJACAgBISAEhIAQEAJCQAgIASEgBIRA70VAxJUKzwYrCVhPQfJaW6mQXKWgTKZiQpnfY4895m655Ra/JQAr641AsuWWW6amwX0mqfm9/vrr7pRTTnGYikaY7ItXD2LBg9XoTBbttNNOiV+L/C9/+YuPA+VMOIlL/DZ5i2UHlDmWLkezCEO8XIeTUWXTtDxVPbLiEmsreaTTeTVMmWS/7bbbvFLW8o3VkHYLSo9WgtUHBAKHrfikPrAS0q5bxdGJ+1gsQFDukF/q8aabbtqJrLRMk/aLlaamNIVodvHFF/twTHSHynFWvZqgwDKrIFhlGD16tEPBkSWYmTflAc/eyCa0K2xPZQQmsAtXkdK2WH0kb6z0tXcest3+++/vk4ToQDtTh1QpZ5X0UdyyUhi54oor3LHHHtuFEIjihq1paHvbIbxTKP/4rbDCCu1IwrEliBFi4npw0kknJVar4npQJTN1pclqc8OH59NM6mrf99lnH2crrGmrwcz6QMpVl/BOsYXT/fff77cTuuGGG3zUrEQP24Bm6RXBJ46HbUeKKOHi8GWu89b3qu2BKXt5bhAYGX+Z0HZCwoQIZQo6u1dXe1AG2yLjvar4WHk55n0mYRjOi4y94rBlr3s6zSLPhDLV1QaF+GAViS3kEMbtWF6pQ+pqo6u0l3nfE9pJ3umxY8cmxAgw4P2GVMh2kUZcCRcyMA5kiyCEby++n3imCERBtrixsY13TPkL64CN4TlaPKTLtY3nUqLwWzzlbWsZwzK2QzbZZBPH8+8psfIV+UaBAMi4EJIgW1sZLksssUTbs120PSjaL3z66ad+rA4hHll//fWTMXWewrWjPciTbugn7zsWhuG8KLZxeF0LASEgBISAEBACQkAICAEhIASEgBAQAn0Tgf59M9u9I9fsw42glN1iiy16R6YycsEkXrM8brXVVm6bbbZJDY1lg1BBEXpiJXosbDFy1FFHObZTQEnNhBX7pOOOApZJuDRBgcPEMMKkqa3EjP0yQUl+UKAbKaBsmnHcZa9RKmaZMY/j7HRet9tuuzhL/nqttdbyKw5Tb9boCGFpmWWWcbZ6MC1qTHSfd955fvKZ1fhYoWBiH4sTKFPTLHekxdPTbpQN60tmOQgrPAMGDOjpbORKD3IZZKtTTz3VE2yMjIL7qFGjusQxdOhQb5EJhdV1113nUG6jiGHbJt5VLKVQZlMWhIEHDRrkdthhB3fRRRd5P6uuuqqbe+65HaswUUwgWNiBmBELyigUQu+//7478cQT3VlnneXJLW+88UbiFWIMSvY6pEo5q6aPIpB6TxsJVpdcconfngGlNuVHRowY4Y998Y/6Qv7L1IOy5e1EmnW171hcgUA211xz+ToB0QGhTVluueXKQpIaLlSymocTTjjBsTq8nQLpNW5r2ple0birtgc8p5EjR/q+DMIuYzAsc80000x+bGTPNC1fVduDstgWGe9VxSet3EXdioy9isad5b+n0yzyTMhzXW1QXH76aEgb5IdtVbBO0cz6VBw+7bquNrpse1nkPcGKB9vl8EMgzUJKiS2hsQhg9dVX937sjzoD2ZbxEt9EpEtYsyACWTu2smVhObLgIOs7jPtYLLNfWntepJzEB5GM8R3EgYMPPhinHpMy3yh8L/ALBUx32WWX1HFp6K/qedH2oGi/wLjQthXq379/02/5tLK0qz1ISyvNrWjdC+Moim0YVudCQAgIASEgBISAEBACQkAICAEhIASEQN9FQBZXSj47zHQ/8sgjPjSEiyKKU7M+ECad5hbeL3ueFS8TSRAAsGiAEvrkk0/uZqo/LSyTZiid1113Xa/A5pgmrJrHCgOr9DCZDVnlxRdfTEgr8847r1+BH+LWbKVgWhqx/zJpEq+Z9E4rb3jf/Fle7BqiQtoKeLtvRwvHsWxewziKnGeVDSUa9YCJ6WZblJRNKyvdmKQQ+2OiFQsbpjhFeU8dWmqppdxll12WWG4Iw9m5HcmzndsRN3seoVszd+4VkVCRM2zYsFxB47zE17kiKegJHCGMgK2RViBfsWJ1yJAhXWJDQcNqTzM5Dono1VdfdSheUZJcddVVDvPlSFre2eN+zJgxbtppp/V+UOJCWuFZsL0Wq5DDtsB7avzRTnAPE+74hbRmpBXqLhZbbBW4hUk7hnkKz2O/VcsZx1fkGoLP7bff7ldtkw8whgxkpJUll1zSt7tF4kzzm1X/0/waVnbET6vwdt+OYbzUA1ZxQ2RE8taDtPTDeJudF03T0rJjs7jNT1zWqu07ZAcsdBAv5E/eM6zVYCHt3HPP7ZYlywc3wnPzaG52TPPHVkjUQVbXs42YKV6tbBY2PloaaUfza3HE6R566KGeyBGGtTB2DO+VObe088RnfuxYR3tAGVF0M05AaG+t7aR/23777ZN+LixfmfbA8k08RbENw1o+Wo33yuJT5ZmQNwsPpkXHXla2osdOpFnmmVi5yrZBVk7iidPnmj6Xvhehjn3wwQf+PP6zeOI4Yn9cF22j4ziqtJdF3hPGL1gkg9yLYGklJK1g3QOCLT8rv+UV0hqWNmxMS78OaYVvMUhqO+64o/cah8uDn6XBMfwuCsMWKSfjLEjCCMQPxuOhWB7D+MP7Zc6z4mr1jWJ5IU3iYEzLdyd16tprr/V4h3GH563yaX7DNCyMuZVtg3geefsF0gIH6t7Pf/5zx1gwTSxPdgz9VG0PDIswTs4tLTva/dB/kboXxlkW2zBt4ouvLY86CgEhIASEgBAQAkJACAgBISAEhIAQEAK9F4F+jUmuCb03e8rZxIAACmpMOGNJgJXGKMfjiVDKiRWO9957z28dwirzLNltt90c25wwgW5bicR+86YZh+vEdV/Ka0/jAzasQkU5wnYU008/fU9noXB6KJzHjRvn6zjb4PSmSVMmvbGEhIArK35RovJbaKGF3DzzzNOyvCjS8Y/yZvDgwQ6Fd16huyE8lltQ2LCCOG94iEvPP/+8356NlbS0EfFked585PFXpZx54m/mB8s1pM9WWeAzsGGtJK3NbBZHb75HPWDbOfqFovWgbLk6kSZ5LdK+QxjDEhFWhCATEpbtZSAI8K6ZwrQsBgpXHoE62gNIKxB46c8YB0FOyfNM+0J7UAc+5Z+OQjZDoEgb1Cyedt8r2kZ3qr3kfeR7BvIJ5JVZZpnFE3fzjGV4Fox/2AaOMRd9ex0C4XD8+PHu6quvrt0iVx35UxzZCJTtF7JjbH6nr7QHzUuhu0JACAgBISAEhIAQEAJCQAgIASEgBITAxIyAiCsT89PtY2Wrk7jSx4qu7E4kCDz99NNu44039qXZd999Hb/eJGnEld6UP+VFCEzKCMSK2EkZC5VdCAgBIdAMAbWX/0NHxJX/YaEzISAEhIAQEAJCQAgIASEgBISAEBACQkAICIG+jUD/vp195V4ICAEh0FkEPv/8c/fxxx+7hx9+2J1++uk+M1hIGD58eGczptSFgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAn0AARFX+sBDmlSyOHr0aPfZZ5+5mWeeuWmR2XN9gw028FsLNfWom0KgBxC48MIL3cknn9wlpcMOO8ybj+/iqAshIASEgBAQAkJACAgBIVAjAgceeKDfgmzBBResMVZFJQSEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBHoeARFXeh5zpZiBwDrrrJNxp6sz5sH5SYRAb0Bg8sknd5NNNpmbYoop3JJLLum22morN2zYsN6QtW55IJ/8+vXr53/dPMhBCAiBjiHQv39//37yjkqEgBAQAkIgGwG1l//DZpVVVvnfhc6EgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBIRAH0ag34SG9OH8K+tCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACfRQBLevtow9O2RYCQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEQF9HQMSVvv4ElX8hIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACPRRBERc6aMPTtkWAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhEBfR6B/Xy9Ab8j/v//9b/fZZ5+5aaedtjdkZ5LNwznnnOPeeuutLuXv16+fO/zww92UU07Zxb3TF3Xl9V//+pe79NJL3YQJE9zaa6/tBg0alFq05557zt18880OPPbZZx/Xv79e/VSg5CgEhIAQEAJCQAgIASEgBISAEBACHoGvvvrKnX766f57c/3113eLLrqokBECQkAICAEhIASEgBAQAkJACAgBISAEhEBbEOjXUHhPaEvME3mkH3zwgTvzzDPdHXfc4ckSTOhMN910buGFF/bEgJVWWmkiRyBf8T766CP38ccfu6997Wtu1llnzReopK+hQ4e6F154oVvo8ePHuwEDBnRzr8uBV+iNN97w0VFGytpK6srrhx9+6L797W/75E499VS3+eabpyZ9zTXXuFGjRvl7zzzzjJtmmmlS/clRCAgBISAEhIAQEAJCQAgIASEgBIQACPzzn/9MyCpjxoxx3/3udwWMEBACQkAICAEhIASEgBAQAkJACAgBISAE2oKAzC6UgBWrHhtttJF7//33u4SGpDFu3Dg3fPhwd8ABB7i99967y/1J8YLJrSuvvNItuOCCnuTTTgwOPPBAB6EIefTRR92vf/3rdiaXxP3uu++6VVZZxV+fddZZbsMNN0zuZZ10Kq9Z+ZG7EBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICIFOICDiSgnUIR0YaYXtWYYNG+bmnntud9ttt7nLL7/c3zvttNPcGmus4RZffPESKShIGQR4FiaTTTZZjxFXLM0ix7ry+vWvf92NHDnSJ421H4kQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIgb6EgIgrBZ/WF1984R555BEfasYZZ3TnnXeem3zyyf31Ioss4s9POeUUx9ZBDzzwgIgrBfGV92IIsC3RoYceWiyQfAsBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQ6CUIiLhS8EG8/vrrnpRCsNVXXz0hrVg066+/voO4grzyyivm3JHjn//8Z0+eGTBggPvmN7/prrnmGp93trLBGszNN9/st+/p37+/23TTTd3KK6/cLZ+vvfaau/fee92rr77q/va3vzksfHzrW99yWPeg/Fg2CQViz7XXXps4vfDCC/4cCzVsGRTKHHPM4eMI3a6//nq/jzaWamabbTafP7ZfgqCx1FJLufXWW89BGGqHUMb77rvPPffccz4PgwYNcoMHD3ZYR+nXr1+XJB9//HH34osveje2iDIZO3asC69xrzvPPJOnnnrKkkyOyy23nAPTPPLQQw+5Bx980JdhoYUWcssvv7xbddVV8wTN7cfqHwG23nprN8UUU3QJy3PlHeHZbrnlll3ulb0A+5tuuqlb8FlnndUtsMACbuDAgd3umQPhCE/dXnrppc3ZH9nbnbqJLLPMMg7MTIzMRr2hffjwww99/Zl55pkddYj3LU6XZ8gzQIYOHepmmmkmi67L8ZlnnnHjx4/3bltssYWbaqqpkvvEUeTdtIDER7zNJO2Z9CQ+Yd6KlvPjjz92N954o49i3XXX/X/2zgOMjqLq+0MMinSVIEpHQWmhBKQXIwEpoYt0Qu+9SW9SE4GEDgFCR5COghRD70UITZogAaniC1IU9H7zm4+zz9y5u/fuzu5tyTnPc+/uzk79z8yZ2TlnzhjqwaeJEyca+ilEu6OsPsWW04+D4+See+458+677/rO7n748OFmuumma3CPcQj5bVocaXWJP8LSBh999FHH22n7c845p+PvyyyzjJljjjnSonP9K6ufEOC3v/2tG2fCftKKH4AXfC3M73XXXWc+++wzs+iii5oFFlggNU9ffvmlueqqq9y75ZZbzpXj/fffN7fddptzW3DBBc0iiyzSENbv17PNNlsqDwQn8iDE+EO7ajdx7B39m3EGPjn11FM7PgI/4ei9NEJhFstvzz77rPnrX/9qZphhBoNCLWN1Wn3G8C5Jd1Jqe3nGon/961/mT3/6k+vXL7/8shtraY/UR1X9WbAVHiTPWdewj9FOKAvzlV/+8pdujKePk1/awdJLL22WXXbZrOice5F5UBhR0TZ75ZVXmv/9739hNHXPs846q1lppZUSt27zgyQjOW9a8b08bS9nUok3P83EMeUm5LXipeiYS7gyc/eiYxHtmeNAoXXXXdfxRveQ8sf49tJLL5mvf/3rZsMNN2zw8dRTTxnm84w/jDPST5ZccskGvzjAWwnTisJ5f5m+Kfyg6NxU6hF+sOmmmzbMwSkL+IAN79OoLN8jfvBiHOM7kP7MWL7GGms0zL2YtzF/Y67O94xPhL311ludE9+0HNcrFINPVfMDyUORK22XNtyM+AYJrWh2so/5efv444/dt7HMm3kH72b+xNxi7rnn9r27e58HFf3+o93K91Fa/yYfzOMhxrO5gu+6omNYme9Gl4kO/0l7b5VsOD8Q/0XniYQrM9+Lbe+S36L12artZX1rkF4sDyKs8Fvym2e9jDBlKLafdGMsiu1j/Tbfk75ZdKwu0w5i5yRl0izDD8qkq2EVAUVAEVAEFAFFQBFQBCZDBGpKhRCwi2c1K9xzv80226whrBU0Je9PPPHEhveddLj22muTvFhBe3JvF5lqBxxwQPI8py3PD37wg9oTTzxRl72jjz66zg/+/J9d6Kx98MEHdWHsx3qdH99/eJ+Gn12sc+F32223ml2QaogLN6sMU5dm2oNV0knC2g/mNC91blappuZj5Od1iy22aCjnr371qyR+32/avV24rUsrfCia1/PPPz81beo7i66++uokzAknnJDc+/k99NBDa1YAnBVFYXe7yJqkE7YTK9CvWeUk994KlQvHnRXAfsAnafplk/ttt922ZhVLUoPbBVgX9rDDDmt4f/zxxyfxjh07tu69VVhJ3kk6/tUuqtaOPPLIujAvvPBCEmbMmDF17/wHewyZ82cVi+rqJqZvSrxZ9e/nOa1OOomPwt5PgwAAQABJREFU5DWmnHZRMsHWWseSqJKrXZhM3r/zzjuJu9zElpPw9J999tmnZhWWkjR8XLnPw78kL62uefgt/Swkym0X1jLzyLssftIMH9JhfKGcYT9pxg8IN3r0aBcubHtWYdG58z6L4PGkyQ9+Cv3nP/+pWeGWc7NKKzWrTNQQfN9993Xv6aNWabHhPQ6EI175WaWVVH9VOloBXW3IkCFJmpK2XE8//fSG5GgLVkCUGsYKY2vgH1IM75I4JpW2l2csevzxx2tWsTcVW9ytwpXAUslV+pjUd9Y17GOXXHJJkscsPn/cccfVrLJIaj6LzoP8SGLabFa5fHfmXz51mx/4eclz34zv5Wl7edII/fhp+liG9yGvJZ6YMZdwsXP3mLFo3LhxNSnLKaecQvKpZJUuatJe4IE+WcGLm5fB+yUu/3rUUUfVvvjiCz+Iuyc931/Wffg9VaZvCj8oOjf120E4B6cwjCPkP8RGCl2G7/GtzHdcFj7UC/Xjk1U6df6ZQ/lEPxk2bJh7x3fanXfe6b+uxeBT1fygLiM5HyhfFi7iftZZZzXE1sk+JolbJfeaVWTOzC9z3QsuuEC8J9dmba8V3/O/ia0SQhInN6QlfXbzzTevWWWzuvcxY1iZ78a6xDv0IO1d2krWNZwfkL2YeaKEy0pH3NO+NQgb294JG1OfzdoecWZ9a/AulgfFjpukGUux/aQbY1FsH5Pxu1vff0XrRvpm0bG6aDq+/9g5iR9H0fvY77+i6ah/RUARUAQUAUVAEVAEFAFFQC2uFFRW+ta3vmXYiccusgcffNBZWLAf7S4WdrFYBYEkRiwp9AphcQKrGvaD3rA7nV3xWGHB8ooVUBq7iGduuukmZ9VE8szON4jdVOx+s4tF7tkuZhq7cOh287Krl13tYpGEncfbb7+988cfFkzYaccOOfz6ZJVl/Me6e/IC2Y9AZ+GCHZM33nijefPNN93OQKyFhJYS6iIo8MAOdTluZ8YZZ3TWZ8gv9ctOTHZ37bTTTsmOfqJmB/C0007rUmFH4hVXXOHuyW+4Gz6vFRQXQY4/dnnL7sjPP//c1V+OYIkXuyDqrNbITjYs77BDyC5mOOsUe+65Z+K3XTdWeGbYxddOwoIQbZy2ze49u3Bv7rjjDmMX0YxV/smdNDsj7QJgpn/aPjtW2anLLhusp1C2CRMmOOs97Ey58MILnZWWTTbZxMWD1SJ2nWJhwi4+md13370h/jfeeCPZVYxlEDmSDI8xfTNMAAs4a6+9dp0zlljESlLdiyYP7cBHkquinBJX2WurchL/ueeem1icgpfQBmkbdpEn2UFaNh9Z4eHRs88+e/Ka9idWoRLHr24YA9hdThuAl8GL6StWoOD4Cde9997bvfd3Nofx9PIzZTvjjDOcRQysIMHX4NPSjxhP6HvQzjvvnGptpRvlYzyGR0GUgZ3p8At2LD755JOOJ7z33nt1WbNTWbP11ls7qwG8wGIO7QGex5jPGEX5Bw0aVGdxI4Z31SX81UM/t71WYxE8mh3jzO+oj/XWW8/1F3b1Yl0OPm0FaGb8+PEN1p3SsCrixpzLKjA1BPGt2jW8/MqBcR5rU1jTw1IX4zxWeDjekr6+zTbb1AWNmQdJBDFtlrBYwKDtQuTNCvvdPW0e634Q42oV1Iv8oFXbq6LcPpYSX7NxvuyYW3TuXnYsYsyl/4WW1Sjr2Wef7ax8SLn9Kzyf7xiINmYVI9zYgFUlLIRYAbn7xjjwwAP9YO74V99CIP2KsZTvJKxkCKXlR94V7ZsSLrzmmZOEYfI+l+F71Cl9m539EOPXCius4ObG5BleiQUVqxjUMjt///vf3fcO/AGrY+edd57BqloeaoZPL/ADvpezrLc143ud6mNYVOFbk7kHxLcNcwqIuuA946JV8jLf+973nHVR97LFXyzfo9/YDUEudvor8zvm10JlxjCJo6rvRomvndei84PYeWJYhiLzPT9s0fZeRX366cfe5+FBZcfN2LylhWvVT/ww3RiLOtHHeoG/+zg3G4t8fzH3WKguOyeJSVfCxPIDCa9XRUARUAQUAUVAEVAEFAFFoBkCqrjSDJ2MdyNHjnSCJRbZUfxAIQSBIUJxFuOmmWYaYy2apB5NkBFl250R2HEUAotNF198sUvvsssucyaZUcbh41WEBpIZjo7hWAsWHEUxRd6xqGt3mDgT3Pfee28i9CP+Qw45RLyZTz75xAlPEZT47omHJjfbbbedsVZAEh8oDPGMiWcEjtZiS/Iu9oaFHLuTwwVH0Ey8mKmGMGG///77O+EUpm5RlpEFU8yA84MQ8oriCgIujg5oJ2Hunx+EQBahURGinAi+WGiHdthhByeYQ2Fh3LhxTvGIemwXgaV/9Ea70tljjz0Sc+MsrtJerAUOc5c9fgNlkryKT0cccYRT9srKJ4vymB7myK2QEGoi+Gfhl34iiiv44x7hAMcLgQkf/z5RR7RP+l6o9BXTN/24uaffy7Fm8s5afymsuNIufMhTFeWUspW9tionbezUU091ycBrEYDJojq8Q0yfl81HVnhrTcgdOyXv7S6sTMUVFpAZo2hX4TFVdpe0E+KxWIuCV78qroADx+Mg6Nhll11cH6N9o5CD0sHBBx/soKLfWcsrAltXryiYWOtOLg+MmeDPoqRPLOZTNz6hPAoPghDiHnPMMcmYDZ9hXELIRJvwj4qJ5V1+2tz3a9trNRbBf621LCecQ5mV46hQThRiTGGOhKIi7Yy5YZUE/2OeFRLjZ6sjdjjSijFEFKtRKEaYzAI6wt8tt9wyGbNi50HkK7bNEnbUqFFcHKFgJXPQY4891o1P8q6qay/xg1Ztr6oy//rXv244XrPZOF/FmFtk7l52LIKvMe5STp+Yl2cpKDMHFKUVBPPwS1FoZPxj7ojJfxSWGSMREAtxdCg/IRT5ObIB3BDg56EifbNZfK3mJM3CNntXlu+BuyitbLXVVubwww9P8CVdvpOtRSinCNgsH8yfGb84FoaNAnyfcPxJXmqFT7f5AemHc/C8ZetEH2OcEaUVvhPZwCHKWWDL3BElW4T2KOfLN3GzMsTyPfq4zK8Zc7mXPkt6ZcYwP79VfTf6cbbrvuj8IHaeGOa/yHzPD1ukvVdVn376Mfd5eVAV42ZM/sIwrfpJ6L8bY1Gn+li3+buPdauxyPdb9L6KOUnRNH3/sfzAj0PvFQFFQBFQBBQBRUARUAQUgSwEBmS9UPdsBFjExDoFC2gID7DMgUCDxTgIJRAW63qFUEJAkAqJlRMWfOTemvp172THiHuwfywO8TEeKq3wnsVeced81aqJHcKh5Q/SZFcXBN5VEDtPReDHArUorRD3gAEDnOKKLI5VlWYV+S4TB0JMUVohHgRyLEhCH374YbKg7xwq/kO4b02oVhxr6+ioQ2sW33lEUMJunDzEedcoHbSiNKUVwmBtBwURyBd+8Ez/QskNCtsWi3aikISSEgswPnWzb/r5aCc+pNNP5WSBH4UoCGtborTiHHrsjz6AMkeotEI2ERBhjQkS3uge+vQPiwMI6aHTTjvNKZBh4QiBO4u2CHGFx3e7iPABsaaC0mSotEL+aFsjRoyoy6rwD5Tx9ttvv2RsxhN8DyshEJbiELL6FMO7/PBF73ul7eUZi1A2RLkQQtHJV1rBDetnQ4cO5dYJuuHbvUIbb7xxorRCnlBYZYEZQvGJsgmVmQfFtllJu9PXXuAHedpep3GR9MqOuUXn7lXwAxTHQ76GAA+lljSC50PMq1A28fk/97vuuqt7j0UQFAWrpiJ9MyvtvHOvrPDN3MvyPSxhQCjNhUoruDPunnTSScn8F7eQsLBij8p0SivwLuq4iNJKXnx6gR+EZW/13Kk+hqIDRH1heYh0fUJgKlYbH3744cQSpO/Hv4/he4ypKPOK0grfr2w28fssaZQZw/w8+vekEfPd6MfRS/dl5omdLkc76rNoGYrwoLLjZtG8hf7z9pMwXLfHonb3sV7g73nHorBu9FkRUAQUAUVAEVAEFAFFQBFQBIxpNBGgqLREgCMYEACwow/lBo6GYUGHj1yUPxCSsTvpV7/6lXvfMsI2exCT6yQjljS4iuKJvM86uoXjJigzwg5RziEuBF4s7GaFw08sYV2FBW2fwBoLN/bMY7dj3n8Xe89uGqG0o51mmWUWZzmHncDs0p8UyN8tKuUR4RvP7SwnOyax7EJdkiZWitpF9EUs0mC2nIX4G264wSWFxQHSb0UIPWQXL5hhQaPVLnfKxtFE8AbMzbOzF4EKijKhhSD6IIu+LMhjbp6d9dI/ETBLPYTWVvx8x/RNFo8h6f9+fEXu242Pn5eYcvrhy9znLScKSowDHN8lwu4y6eYJW1ZQTl7vu+8+Jxx65513EsUbFo0hFHHg8WmKXhyPQP8KqVUfwT9KPmEfJL5mxDjD+IMQKwzbLBzvsNQFD6dcW2yxhfNO+z/55JMNPL4Z8d63lpPXUlOzOLPeydFOKLSJskmWX99dxjGsx4BPSIxtWFiD4CuhEh3uRXgX/vu17ZF3KM9YJPWBfywrMMeTcstVhONc4fW+UijhukVp4/zPfvazJDsyvuAg7Yf7ovMgwahomyWtMtQtflAmzxI2T9sTv926xo65sXP32LEIPs6cBkWIM88808GFtQ+OQ80ieB3E2IbVH+nLuMm9jOXtUMwv0jfJU0h55yQopQvBvzheJQ9Jn8ZvUb7HsYiyCQJrKaGCQZ70qR8UFLCuCV155ZUNSoPN4smLj8RRZn4gcXTy2qk+xpwQGjx4cGY9LrbYYubSSy91/vju8ducc/T+YvgeFs/49oJWX31118/Tvl3KjGFeFl3bLfPd6MfVa/eCUcw8Ufhip8okeSW9onMSP48x3xqEj+VBseOmn+eY+7z9JIy7U2ORn27M2ky/zveKjkU+Tr1832l+0MtYaN4UAUVAEVAEFAFFQBFQBNqLgCquFMSXxVVRWuEMc0wiL7LIIi4WPlDYlYvJc85dx6JJM6FzwaSjvfu7pEToluZG2Xx688033fE+CN+bkQjCm/kp+i5LmDjzzDO7qLAMgqBTlG6Kxi/+KaNQVpoc2QD5fiVMP16lPH7eZ5hhBnd0DoLqdpWTRU2OqoDY5QPe7VRcwZR2SOyY94+fCt/7z+zwYxGevoKZV1k89f2E9xzTIRYT5B2LzJiin3XWWcUpuYIDiivwDkzTs1gPcWQVxCIwi7UhlembYhXE5wFh/HmeO4FPmXLmKUMeP3nLyWI6FqrYpYqwh6M5WBREwU8EZXnSK+IHJSmhNOUSeZd2RYiABZIPPvgg7XXihuJXWtxXX3214RdDKCAWJY434QeeWDJaZ511nOWRNKsxYdxYv2EXOH2JvgbtuOOOZuWVV3b3zf6oV9LrBL366qsuGY4eLCLw4ygFqNUYhh/xy71PRXlXv7Y9ypx3LJL6IEyets540SuKK2ltgb6C0hfKZW+99RbFcuSP+Wnh8CTzBt8v7oJR0TZL2DLULX5QJs+Ezdv2yqYTG77smJvVfprN3cuMRSgicvwpyr8ojC666KJOuM13CXwfBUBfiQX8OcIUQhn/nHPOaQoVfbpqSsMoq2+mpZ13TuLzIo49wvLm/PPP76LkyDCObUkj6dO8K8r3UO4T8tMXtzxXUZwVvyjXhtau5F3aNS8+ErbM/EDi6OQ1rf2QftV9jL4C8X2YRf47/PtWS/0wsXzP/+5iMwBCbz9NScMfl7LwyRrDJA6uZb8b/bh67V7mfq3wId/iV8pQZr4ncRS5VlWfMd8a5LMoDyo7bhbBJs1v3n4Shk1rC+0Yi/x0Y/pYv873io5FPk69fN9pftDLWGjeFAFFQBFQBBQBRUARUATai4AqrhTEF+GyLOYccMABidIK0WAtAZO2HC2C8PqCCy7oCcUVf3eS3MuVfPv3PEMIt7EaI7sNOdcaSxWDBg1KhJgI41EeybPD/v/Hmv9fLE+EIfzjN9hJU1ZxRXYGkk5WmnKci+83zFc/PWeVE+E09c4RGu2gY489Njmeg76DsKGdhOCXMtE+5SObXeaYS+YYqrR2L/l56aWXXP/leeeddzYI5fIQ/QRFAHaLyW5FFlsp69577+36jx8PSm8syL/wwgtOQIDiCn0KPgNxZIsom0m4sn2zCsWVTuBTtpyCV5lr0XLC/9kZxvFxt99+u/uVSb9VWFHCwF9Wv06LA0EeO/QgLFutueaazqy/CASwMIJ5YyhrZxX9J2yb+A8VIHELiX4ZKmbQR5uNJeIfRRr6MUo35BFF0WZ9WdImvG+pReKT971wFQs2zXYth/lEQCvWamSsCv1w/JNQ1jhWlHf1a9sDh7xjEQqyEO2VIy9akRy/2MpfJ96nzY1o8/zoZ8yfhPw2kcVHpG35fgkf02Yl3TJX6b/9xg/ytr0y2MSGrWLMzWo/WXP3smMR8yrGAaxxHXfcce5IEzneB+XEUEFX+jQYYYGolfKiHE8ai2lauCJ9MwxfZE7CUT1LLbWU4RgXfihuMsYzZjSb5wtGMXxPwpLvIuNYWE7hpVjPOfHEE105Fl544dBbw3MRfPzA8JNenx9IfjvVx2SMT5vnSV78d+Jf3vnXMnxv1VVXdQrhKFtyZNHZZ5/tR+3u/XEpC5+sMcyPjHYQ+93ox9Nr92XniX7dZuFbZZmrqs+Ybw0pR14eVMW4KWmWuebpJ2H8nRqL/HRj+lg/zvdixyIfq1697zQ/6FUcNF+KgCKgCCgCioAioAgoAu1HQBVXCmLMLjUhzK2GxEcgwmgsSbDzDAGBvxgm/llE8Ek+yny3bt7ffffdidIKApttttmmLjsIGfNarqgLmPMhywqALGaAF0o0ZclflCbNtGMWJC++37LpdjM8C/yyM0/yQXuUD9F2lPOhhx5KjupBaSUNZ8lLVVcsmUgfRTEEK0jXXnutOyudXVhiKSlMD0E9bR6BGAv/KK7kJX/3LsorHFHEcUMcb8XzJZdc0hAV5tSx6MLuV46aePLJJ5MdwWkWm8r2TVEsSFuwashcikOn8ClbzpSsF3KKKSc71Wh3mLbGygp9CVPqKDs+9thjhdLP41n4IX6L9Cn6AjTXXHO5PvHtb3/bPcufb41B3MIrO9053iqkeeaZp6kCCv4RooVpjhkzxh3dE8YnzyibsVOPI38Qdt50001ujMXqWdoOPgnHlSMhdt111zph3VlnnWWWXnpps8IKK/heu3o/xxxzGEyNYwkgLzEWYv0NZVk5WiEM67tn8feivKtf216RsYj6gBgLNtpoo1RFrRDrXnlmnOf4Mp/g/fQFyN9p67eJovOgmDbr5yn2vh/5QZG2F4tLmXBVjLkyXw7zIfwinLuXHYtQxNh///3NQQcd5OZQ8q1Cm95ll10SBWTJD0rIKDoyvmMdQo6Ok/eduBbpm35+is5JKOfo0aPdsbViOVMU1hA+g13aWFOG70lY8p1nHuGXT+4RGGPphbY0fPhwpwy7++67O4VuUT4Qv/61KD4Stl/mB5LfTvUx5rNYkmim5OS/w38aleF7zDE5fnnfffc111xzjbn11lvdsYfh0atlxjA/z7HfjX4cvXhfdp4o/JuyFfnWiMWiqvqM+dYgz0V4UBXjZixOEi5vPxH/cu3UWCTpcY3pY/0234sdi3ycOnlfdE26Cn5QNM1O4qFpKQKKgCKgCCgCioAioAj0DgIDeicr/ZETsVZAbsNJt5RA3LnKvbzjyg5VPor9H8dL9BI999xzLjvsphoxYkRD1thJ0Gx3fBgAwU8R8k1V++HEDDWKFyzEZJG/y9I/Kzn07x/fws6+NBJ336/vz9/tX7ScxJM3r36aZe6lPH4cWDCQ+mzHsRiHHXaYSw4z7mnKGH5e2nGPVROEG0K+WV1xk+uNN97oLGbwjFUKf6ew+MlzRUCy6aabmt122815xxKTKAf54X2rKhwRJMcELbTQQmbBBRf0vbr7sn1TTN+HigMNCWU4dAqfsuXMyH5u59hy3nPPPcnRQOwUPvPMM521ndwJF/DoCzDEWkqr4LRB4QFY+ElrByh69SIhbFt++eWdME6E8k8//XTLrLITH8tH0Omnn25+9KMfOX7HYqhYRsqKhGMlCC8/LKm1i+RoBQR+YtktT1oyNkm9hmF8d/Eb+vGf8/Cufm17RcYiqQ8WoWXu4ePUy/dpcygUI4X8cd5vE35bEb9cxd33i7tgVLTNErYsdYMflMlzkbZXJp3YsFWMuWntjvxI//Hn7lWNRcwpxRqHKGagWJ+mnEubkSNDJE+xeMWGS8Moq2/6acTMSVBQG2ePBcQKHArUl112mTtWifGQ4+HSSPp0DN+TsMTL0U0xxHcC85J555032SABPq02S8TgQ/7KzA9iylc2TFr7IU5pz1X1Mdng4Cu+hnn334n/0E8ZvidHvSCYZyMBxH14/KY/LslYFeZD3H2/oR//uch3I+E4jvLkk092PywC9hpJuQWHMH++u/gVPzHzPQkbc/XT9/PlxyXuvl//fZn7IjyoinGzTF4Jm7efhOmk8ZJ2jUVh2jwX6WP9NN+LHYvSMGq3W8yadFl+EJNmu3HQ+BUBRUARUAQUAUVAEVAEehMBVVwpWC/zzTdfEkJ2sCUO9oZjImSxjgU8zJSGJAoCvnuam/++0/eyKxdFHZQaQmK3eh6ShSyE5RJnnnAsBIQLUyxIjx8/3gVPE+j78fpHu7DjJouoT1GA4QiZkLCCIeWXs+FDPyywShziN/TT7DlvXpvFUeTddddd1+Cd4zaEBg8eLLeVXfnIxfIQ1kd8RZ/KEsgRkb/7M22XqUQhgo9hw4Yli0HyLuYqRynQx9P6OQoHa6yxhouac5w5pgXKUvCRfhTTNzlSRI7/wkx+DHUKnzLljClXGCamnCgqYhId4giCFVdcMYy20mcRVBBp3l2Q8CqxAoYCYki4NVPsCv134xk+Kzu+pCxZ+WCH7oUXXuheb7XVVmattdZyxwyhMMhuQ3ZypymYSnykg1UA+WHtpV3EsQ4Q+Wkm+GCe4dMCCyzgHp9//vmkf/vv2b0OoYSHwmxeasa7+rXtFRmLsNglY7tvkSYNv2ZtKM1/u92wLhaSP877c6gy86DYNhvmrcxzJ/lBmXwWaXtl0okNW8WYW2TuXtVYxBhw1FFHuePyEG5hRYvj77JomWWWca+wjJk2Bkq4dvXpIn1T8sI1Zk4i4bFgwHGvyy23nGG8SPs2Fb9l+B4WUVC6hhgr/Xm3xM8VhVAUY1oRyjWrrLKK88a3C1Y3sigGn7Lzg6y8tNO9U31MlJCwcifYhuWSdRD6nXzvh36q4Hu0K6wHwTP49mFDgFiPJL0yY1iYX//Zb7/NvhsJg1I0lgPl58fTC/dl5okx870yZW5XfcbkqRUPqmLcjMlXWphW/SQM042xKMxDkT5G2H6Y7wm/rGodKcSsyue0dak0Nz/NsvwgLf40Nz9NvVcEFAFFQBFQBBQBRUARmDwRUMWVgvXOIr0s+LGzRs5SJxoWZ9jBLWd899IRBAWL6XZBSBgWi2T3NxZFjj/+eHemvLxvduUoCgiBObuk2N2eZ7GSMHvssUeyG56dmSxUcUQRtMMOO7hr1h8mwGVHPgLHSy+91MXFcQq+aWMWc7F4AbGIjXUE+XjiWKh99tnHvUPgt+2227r78I+FNFE+4SgYzCJT3ryUN69542vlj3KOHTs2qQcWHkUot8QSS7ijTVrFEfMe086yoB0TvmgYFjX50Xbopwg2hKTO5Tm8ItTG2koeuuiii5xgnF1KvsWdiRMnup13YA2xg2vaaadNjXLjjTd27izI0j+wdLTOOuuk+mWHklDevkmc7LClDyH0ps3+/Oc/l2gKXzuBT0w5w4KAp7QDuQoPwS9ufp2F4YuUk7BXXnmlU7gD31a7g8O0ij6Td1Ewob1Qx1JGrlJO+BnPwtfwK3z5lltuMVgCEgHdM888447dEb9F89RO/wi64MkIwzAbL3lupmjHAqdYWkLx8JBDDnFZZFGc48AglBN/85vfuPtu/yGgw6IMxFhyzDHH1I0lLPBy/EWYX8ZD2hxEH6fcEBiNHDkysSAFn5FxkfexvKvf217esYhjr+QoEZQ+aDP+/AH+wniKH+Z+vUQIZLG0IPMt8inWghDc+4orZeZBsW22LFb9yg/ytr2y+MSEr2LMJd28c/cqx6LFF1/cINDnl3Yko48HYwLWWBjnmNczBvqElUZ4LN97zRRb/DBF7ov0zTDeonOSMHye57J8T8ZW5iAoYKP4IMR8i93ofB+LcE/eZV1POumkRCkC6x2+0CwMUwSffpsf+GXtRB+T7xLqjHu/HlH6pR/JHHSDDTZI1kb8fMp9FXyPb6i9997bRUm/ZE1BqMwYJnFwlTl0zHejH08v3sfOE2Pne2UwqKo+y+TBD9uMB1U1bvrplblv1k/CeLsxFsX0sX6c7xUZi8J66eXnbvCDXsZD86YIKAKKgCKgCCgCioAi0F4EBrY3+kkvdnYgITQ74YQT3I6xHXfc0e10n3766d1Z0CJMw58IzPoRhdVWW82ZaWZxiF1uN9xwgxN2ct41whqULVg8lPJmlRFLEqNGjTII8VlM9heUWbj0n8M4sLiCgGW22WZzu/ZEwMwOBnYENiN2fyFIQvkAay8IkUWQjKBFlAmIgwVALLmwEMfiBOZ+Ee6RZ6Gdd97ZYHI7i1gQOvjgg52Cjyz2iV92tLConkVF8oqgmfYn5OO/3377JYJZ3qOcgQnskFiwx/IJu8K4l6MyECK0q81ilYb8dZI4Fz2NsDTCztNmhPA3r/nhRx991ClyoRiDNZnpppvOCZtlFxbpUH6UorIIAQk8QxbkaePwlDSK6ZscX4O1CaGhQ4earPPoxU+zayfwiSlnmOesNiD+sIjCDmTM56dRkXKiECQKBSzSwyPbSfBPFPEgeLLspgzTZMGPhVWUnEQZinKhlEc48kr7pP/LjlKO0gktXoXxdvoZ5TpRsJO0yef222+fOg4hNNt1112dohZ8jt2w/rFfHON13333uWMTsCBGH1xppZUk6q5dEcawwxMllfPPP98pxWEiHyEfYxQ0YsQId5U/FJFwwz88hHaNMiXKtAhgII7HQOnFp1je1c9tr+hYRD957LHHDEpdF198sVOClbGB+ZAoeTWz8OBj3ql72jzzn1NOOcX1bVE+xj1tLC4zD4pps2Vx6Ed+ULTtlcWoaPgqxlzSLDJ378ZYxDFZRxxxhPsmQHmBMRClYtzhu75yWlEM8/gv2jf9OIvMSfxwRe/L8D2+z/g2po+iBLT++usbLAsy5wRv+ZbLmyf6DXMr5nOyiQFFQtlE4seTF59+nR9IWTvRx6hHLAeyuQFrbv7RUhyDKUT9ZG3swE+VfI85DN8zKBxz9BXzdxl7y4xhUpasb4ZW343MA/zv8W5ZFpVypF1j54ll5ntp+cjrVkV95k2rlb9mPKiqcbNVHoq8b9ZP/Hi6MRbF9LF+nO/lHYv8+uiH+27xg37ARvOoCCgCioAioAgoAoqAIlA9AmpxJQJTFuQQgomVDczpsxjHogW7ntl9izljFBLSKO1ogzS3tLBF3CROuRJW7uWKmyyw+G4I+Di2RExqsyjDWbgsOC655JLOsoCYBfbDEZ9PxIPiC0okCHvSFhp9/3IPxuzUI2+y0CnYnn322eKt6RVBHsoZiyyySN1Z92F+EQqiEIIyA+kh6BWlFRZbTz311GSXV1aCm2yyiRMMDxkyxAwaNCjBNMt/6J43r7QxFm7lh+BZqNk7qWP8oizEAj1HYIjSCrubsGSA8LYq8nE+6KCD3MK1H7e8l6v/LvY+Ky7qkR3mBx54YOYRHIIRCiRpFn3kvVwlj4sttlhy9jq728FVlFYw28tiL/wAzJuRLL7iZ6ONNsr0GtM3yTP9h2NCaGsoZ6WR4CdX34+Uu1P4xJST/Eo+/bw3uxeLBOJHwhctJ0oDKBagcCS7QiVOH0//Xt7HXMN8t4rD948giV3RtE+IfKO0wuIsQuhtttnGuQsW7uGrP3HLKoe8l6uE9f379+H78J0fD+9QwGBHITuv6VeUwQ8j9wjsJ0yY4KJHgJ92RA4KqIxLYEOdhUfwEFjiC/Mpz1Vf2e1+2223GcYU+gBjL8ooorTCeLbqqqs2JAseWEMTq04IDBknwO+nP/2pG+NC5ctY3uW3pYaMpDj4/rvd9oqORfRnFHdpH7Q16gNs+XFPHaEIuOGGG6aUPM5J2nzY9iQ2eS9XcfevWNOhr6DwJEortHXGeeYpIZWZB8W2WT8Pfln9e98P936Z8ddpfhDmp9WzX5aiba9V3Fnv/TT9e/EvbnIV99gxV8JzLTp3j+EHkm+5+umH9+LHbzf4QcH85ptvdmMJ71BWefHFFxOlFfoD88CQZ4bxS7ySTvg+7blo35Q0is5J0tL23STPEr//rizfo62j6EeeIfiQfEPCR1GklvmHpNssPwjLtttuO+eV4y75thOS/BfBp6r5geShyFXKKdciYfHbiT4meWJeC+4yrxB3rnxTsEZw0003mbm+sq4q7/2yFeV7Up/E5ccjz3yT800HEbdYuY0dw8I0XMT2L893o/h94YUXEiuHuKHM0C4SfLLyLe/l6ucjZp7oz9/8uLLuQ/+ST7lmhQvdq6jPtDTFTa5+uuKWhl0WD6pi3PTzkPfez6PkW8LynNVPxA/XTo1FYf4kD636WFjGXp/vSX6LjEWCRVVXyUMW5mE6af7S3CRc2L/FPeua5j8t/jS3rDjVXRFQBBQBRUARUAQUAUVg8kFgCjuhbH3I9OSDR6GSIrTAmsdr9ogQlB1QZJljjjkyFVYKRd5DnlEcYcGRnRkLL7xwW8uHQIVd4lg4QckAwRvmiVkYIG3y0E5i8ZoFKJSR2M2PtRf5CGxnut2Im3aLRR3KSbtVKocAbQYBIVcW5FlgQREg78c4iloPP/ywU3DBEkSecJ3sm+XQMQ6XWHzaUU5MnKOggcIaOzf7jTjW6/3333dWQ+BTWbTTTjsZzEGziBkekQV/hQegwMbxOaHwIStOde8MAigj0vZfeeUVN+5SP60U4JjSvf766+5YJfgP1naylGilFEV51+Ta9sAWBS/6DPM/FD57ac7HkYhiWY4d+VhRYu7Gj/6dd5wvMw+KabPSDvXaOwgUHXPLzt27ORaRNsfQYW0FqyAoeLXis0Vrqqq+WTTdKvyX5XsoraAUhIIB2KLo1u5vuSrK3WtxdLOPwdex0CYWRbF0h0UMLEz2IpUZw2LKg4IPRztCKPRwNJ8obcXE1+4wReaJVcz3ypan0/UZm9+i42ZsOmXC9fNYVKbcGrYaBHqBH1RTEo1FEVAEFAFFQBFQBBQBRaAfEBjYD5ns1TwiWGZxs+oFzl4rL8KOvAKPqvOOwG355ZevOtrM+NhVxkfZ5EAIQVVQXV1NzzjjjO7YsJgYsQyB0gqEtZU8Siv47WbfJP0iVAaffipnEUy67Rf+igUPpd5EAAEIwo8iAhAULeHr/PJSmb6ZN43QXz+2PbDttzkfQmJ+RajMPCimzRbJm/rtDAJlx9yic/du8gPSHjx4sPt1Bt3/n0pM3+xk/iStsnyPXfVY6lSqFoFO9jH4On1EiKMwe1VphTyWGcOkjEWuDz74YOJ9vfXWKzRnSwJ28CZmntjB7DUk1en6bMhAToey42bOZCr31i9jUeUF1wgVAUVAEVAEFAFFQBFQBBQBRaCnEVDFlZ6uHs2cIqAITKoIfP755+5YIRY8R48e7YqJZaHNNttsUi2ylksRUAQUAUVAEVAEFAFFQBFQBBSBvkcAazSy8WDgwIHuaOS+L5QWQBFQBBQBRUARUAQUAUVAEVAEFAFFQBHoMgKquNLlCtDkFQFFYPJEYOzYsWbUqFF1hT/44IPNoEGD6tz0oT0IcPTYmDFjzMwzz9yeBNoc6/HHH28+++wzM9NMMzVNCZPua6yxxmRjSaopGPqyEgS07VUCo0aiCCgCioAioAgoAn2MwMSJE92RwlgWWn/99d2x0X1cnIas63yvARJ1UAQmWwSUH0y2Va8FVwQUAUVAEVAEFAFFoCsIqOJKV2DXRLMQYLcSx6TkPSolKx51VwR6HQFMb9POp5xySndcy4YbbuiOCer1fE8q+UNBaO211+7b4gwbNixX3ocMGWL4KSkCVSGgba8qJKuNR+ZOHAPAT0kR6BQCOndvjrT2zeb46NvWCHS7jzGm8N1Sq9XctXWOJw8fc845p3n66acn2cLqfG/Sqlodiyat+ux0aZQfdBpxTU8RUAQUAUVAEVAEFIHJG4Ep7AJEbfKGQEuvCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAItANBAZ0I1FNUxFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVDFFW0DioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAJdQUAVV7oCuyaqCCgCioAioAgoAopAdQi89dZb5sMPP6yL8MsvvzRvvvmm+eSTT+rc9UERUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBSBXkJgipqlXspQP+bl008/NVNOOaX79WP+Nc+KgCLQHIHPP//cnHnmmQZ2OXz4cDPffPM1D6BvK0cAAfxDDz1kJkyYYN555x3z9ttvmxdffNHss88+Zq211qo8PY1QERAE/vvf/5rRo0e7/r/66qubBRZYQF71zJX+Mf/885tBgwaZu+66y3z96193eTvjjDPMyJEjzSGHHGK23377nsmvZkQR6FcE+oEf9Cu2mu/WCHzxxRdm3LhxbjxaZZVVzDzzzNM6kPpoKwJaJ22FVyNXBBQBRUARUAQUAUVAEVAEFAFFQBFQBBSByQyBgZNZeSsr7rvvvmuOO+4489hjj7ndzAMHDnRCoyWWWMLst99+Zuqpp64srTIRnXjiiebhhx9uiGKGGWYwc889t1l66aXNsGHDzBRTTNHgRx0UgTII0O5ofyF94xvfcG1v3nnnNeuvv76hLfY6oZw2ZswYl03yrYorna2xSy65xAnfP/roo4aEqRdVXGmARR0qRODf//530v9nm222nlRcYQ6y2GKLmUceecRssskmZqWVVjJvvPGGufnmmx0SSy21VIWIaFSKwOSLQD/wg8m3dnq/5P/3f/9nmMswF5555pkLZxjrWccee6wLh6KiKq4UhrDyAFonlUOqESoCioAioAgoAoqAIqAIKAKKgCKgCCgCisBkjIAqrkRU/hNPPGG23nprw+KjELutnn76afd7/PHHzYUXXmi+/e1vy+uuXV966SVDftNo/Pjx5oILLnBCuIsvvtjMNNNMad7UTRGIQuCDDz7IbHsPPvigixMrBkceeaRZd911o9LQQJM+AnvttZe5/vrrXUGxIrHccssZFAS/+93vOqHPD3/4w0kfBC2hIpADgaOPPtpsuummhjkIPyEsrQwePFge9aoIKAKKgCLQJQRQ6L788ssNStC33357l3KhySoCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAr2JgCquFKyX//3vf+bQQw91SisDBgwwu+22m1lnnXUMZvqvvPJKp7Dy1FNPmQMPPNCcd955BWNvn3fyevDBB7sEyOv7779vUFx55ZVXzHPPPWd23XVX89vf/rZ9GdCYJ2sENthgA2eRiKN2OOLl/vvvNy+88IL55z//afbff3/z4x//2P0ma5C08A0IXHbZZYnSCnwWK1fTTDNNgz91UAQUAeN46N133+2EoVhbmX766c2QIUNUaUUbhyKgCCgCkwgC3/zmN82OO+7oSsPcWan7CGiddL8ONAeKgCKgCCgCioAioAgoAoqAIqAIKAKKgCIw6SCgiisF6/Kuu+5yih4EGzFihNlnn32SGI444gjz+eefmyuuuML86U9/Mlic+M53vpO87+YNlgq22267uiygyLLCCiu4o4441mXixImGYxCUFIGqEVhttdXMqquumkSLAtgxxxzjFL2wVoTllbPOOit5rzeKABatxBw+inUoOCkpAopAcwSmm246dwRbc1/6VhFQBBQBRaAfEeCIoYMOOqgfsz7J5lnrZJKtWi2YIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIdAEBVVwpCPqLL76YhNhkk02Se7nZdtttneLKf//7X3Pdddc1KIuIv164YoVlvfXWM6effrrLzvPPP1+nuHLfffeZv/3tb3VZZXFu9tlndzu4p5pqqrp3aQ9Yn3nsscecss9nn33mrG4svfTSZskll0zzbv7973+ba665JvWdOJIHLHhkUdE0f//73zsLOuxcXHzxxRuixRIN9cnxJPPNN1/yvlm4Tz/9NLEUEYZLIrA3RfPqhy1yjxIA+Q1p5plnNhy1Mtdcc4Wv2vpM22Ph/eqrrzb/+te/nPWVMMHXXnvNoCj26quvOist7Gj80Y9+5KwKrLzyyoY4QqK90m6hX/ziF04Z65577jGPPvqoIfyiiy5qNt54Y/O1r30tDJo8Y7EARS6O2aK+OZqm1a5W2u0DDzzg0sGiDHjPOeecLr/LLLOMmWOOOZL4wxuUeG655RZ3rNKbb75psEozaNAgl+bQoUPN97///TBI8ky68BkhjifzFYTEvYorx/XQrhdaaCEzyyyzOKsO4ER/XGyxxczPf/7zzOPRYvC54447XHpgAZ/CghUWolAIxMQ+R59Q1rR2IOWl3/7xj380zz77rPnrX/9qZphhBseDaD9ZdYJf+mUrSitvTDlbpZP3/Ycffuj6C2MUOE099dSuX6+55poOr7zxtPJHGWN5dCw+3aqTjz/+2MAP4CFCWFajH9CG5p57bnFOrmBPv5hiiinML3/5S2ddCt7w8ssvJ+Pfsssum/hPu4EnovyKNTTCwQMWWGABQ12imBJSyPemnHLKOi/kh3z5Y+e1117rFG3nmWcew5icRSjj4hfCegs8WKgojy6bZhVjruS901fGIPgK/fMf//iHmXXWWd14tMYaa7h6SctP0XZQpu3FYhsbLq28ed26kSZ5K8oP6B/0fQjl3SxF9meeecYdM4q/9ddf38jculk58Zs1N+XdzTffbD766CM3v/vJT36CU0K0v1tvvdU9Y5VprbXWcvcy5//e975nfvrTnyb+5YZ5CuMM8yLmtT41y2ve+fBbb73leN67777rR+3uhw8fnvA+eOIjjzzi3DlmkrEui+h3zOVQ3t9www2dtxh+mRV/HvdwzPzLX/7iglEPHBnkE7yesSUk2tKf//zn0NlQt2lzRCkj9cuxir/73e/cdwxjCHX7hz/8wc3hBg4c6KyGLr/88g1x41CUB6VGUsCxzHw4Nq/MDeX4UsZsrJIyZjL2v/76626uuPbaazt+7RelaJ34YbmP/f7jO4GxlDpmPjzjjDO6OTnfKksttZShTpUUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEehrBKyQVKkAAlbYXrMCafezAp2GkHbBq2YFQe69PUao4X2nHawijcuLFTalJr3NNtu493PaMlkFgTo/1kJL8o73/s8KsGpWkF3n33+wC7W1I488smYVIurCSRxHHXVUzVra8IO4eyv0T/Uv4bhawXlDOBxi07SLxC7Nww47LDVeK5x078eOHVv3vlm4448/3oUhv2G4Mnmty0CBByv4TfJDnsIf7cQe21MgxtZerSAjSccqD6QGWH311Z0f+oxdsE78HH300UnYMK88b7rppjW7YJv4lxvapPi3gpuaVXBInsV9o402qv3nP/+RIMmVvuv3b/HP1R5Rk8Rzww03JGG4eeedd2pWsSV574fjnnd2kbkujDwQdtiwYZlh6T9pfEbCW0FPXViryCGvKr9apS6XFnzNCt7r0p3TlhM3K5BpSDcWH/gH8VqBfQ3+xX3422yzzVLbAZmAl1jlpYYwxDH//PNn8q9TTjklNUyY9hNPPFFX1thy1kUS+WCFkDV4cphHebbKiZExNwaL5dFl8OlGnVhhcs0qnWViCs+64IILGgC65JJLkjAnnHBCci91wRV+4vM7P5LHH3+8ZoWIqeFwt8JL37u79/leyBetENiNmaS74IILJmGtEp9LwyqANeX9Z555ZpKXc845Jwkfw6PLpllmzE0y3uEbK5yuwTPBP+1nlf5qVuDakKuYdlCm7cViGxuuocAFHLqRZgw/sMchJnU+ZsyYzBIyJ6FtWKFzjXmIULNy4idrbso7K8R2cVqrkDwmBD+QOYdVQKndeeedybvtt9/ehbHC+8TNv1lllVXce+ZJITXLa6v5MGUmn/LtBBbhz59bjBs3LnnP2JBF9Cv6F3Ex5gvF8EsJG3PNM2ZKeZnTpNH555+flFn8cs2aW+Iu/qhnuafNHHDAAckz7j/4wQ9q4XyGPMTwoLS853UrMx8uk1erPJTgYZVCMsdfq3RYV5SidSKBY79VCc+cg/qi3tJ+zDmUFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBHodwR0W04b1Y7ef//9NsZeLGrbUA1HskB2kdjYBUK3I5Nd3RCWRtJ2j/MOKw6y+/KNN95wFlQoG8ck2YWzhl1ohNl5552NXRDn1u0ytwvlzsoF6bHLzC6+uV3GBx54oPOT9sdOQqy7CE2YMMHtVpbn8FpFmmGcMc/sBrXKKk2DdjOv7KxkB6ZV3nC7gdmxh3UL6tMuxDbNd5UvaZN2Qd9FyY5ZLBQIsVMYok3SDqwCh3u2i9OuXd1///3OmsFtt91WF855+uqP42Vmmmkmw452rK3ceOON5u9//7vbScl9aLXn1FNPTXa/YtEDixrs4LRCDmMFtn7Udff0J6wJYeVgpZVWMnZR2eHLrmF2RXLde++93XvZ2SwRcFyXWHFipyT9kDyzk/L222837EBmB2ov0U033eSyYwVVbtc1u5nBk12gVqHIWZjAsoNQLD7gBn3yySfuCh9i1+u3vvUtlwY7rtkhvtNOO5mrrrrK+ZE/2tbWW2+dHOuG1RvaEW2d+qRe99xzT2fZJrR+gUUZv22wM5n6pS36FpmoJ59iy+nHEXNPG6PvQrRB2jtWMbD68+STT7qd6e+9915M1C3DFOHRZfDpdJ1gIYC2DIYQlhJoOxC8ivdY87EKmAbrBPCKNOL4M9rJOuus46wo0JbY3Q0/gQdbxdG6YOyox1IUcVOXWBqCn8APsHLD+Lv55pub8ePHu3jrAmc8WCUZZyki47Ur4xlnnGHgRSFhXcEqroTO7rkMjwbXmDRTM2Id84y5WWHb6U6bx8oDllAg+iXHM9KeyDP1iNUFmZtJXqpoB0XbnqQdXmOxjQ0Xpl/kuV1pxvID6hsrb9QnFi923333huLQp8V6CONOM2twDYELOjD/ga/Bg+BjWDHDSkM7KU+dnHvuuYkVL6xHMEfFQgrzQ7FYk5VHwsITw/EY/2effbbrX1lh09xb8cu0MK3csJJllYISb1jyYN6HNRQsfPgEv08jLG5Rd5BvBSvNb+iGZUXml8x94ElY6mH84buO+QPfAszrsJwnVAUPkrjyXmPnw1Xmda+99nLjLFZssOrHXJZvTyw/hnPx2DqJ/f7DMtOvf/1rNz+gvTOWWGVUN77z7i5roTLMY17s1Z8ioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAr2EgCquFKwN/3gJFpw5ZsUnTAcjdIJE0OW/79Y9pqoRxqcRi18nnnhi2ivnxuL7b37zm+Q9C8ksoFJOlFNYmPeJxTNRWsHfMccckyzG253HZo899nDH1qDcwaKtKCX4cXDPsUuYVxeyOysTIb+4ybWqNCW+MtcjjjjCLQ5nxdHtvII/AmeIOrQ7PM1DDz1kyBftxFc6yCpDFe6XXnppIlTgCAqfVlxxRYNpeNqmr9CCH5Se7G5/Z/793nvvNfhNIwTKHKPDsTbQDjvs4OITs/W+cgLKERdddJHzh5Cchf1pppnGPXMkGH45JiCNEErYHayuLYdHAdDeUdpCYIRSkK+4AtZgDiFUIE2fDj/8cCfYRIDTa2StMZlDDz00yRZHf/GMQhsCOtqUUCw+fr2jtILgXpTrEACi9MZRUwj94Em+AgoKTRyzAiHUggdJfNQnygG0A3iKHw7/dle5+3EPoSwFX6edoaiQRbHlzIovjzsKOHY3u/OKIIo2Rvv1iaOSaH/toCI8ugw+na4T+IYorcA3UI4SpSX4O/0cZSGUNy688MJMxRVptyh4QggvUWRAmIvQeMstt0zM+qNsZa0MOZ6MQBNlLP+IMvoUPBE+xHg9cuTIllVK3/CPEssKAO8bMWJEw5ETHCOYxffK8uiYNLPy32rMzQrXbnf6oyitbLXVVgae7ismoLRid8g7JSXJS1XtoEjbk7TTrrHYxoZLy0Net3alWYYfMN4gWOfIEfqjzL2kTIxr1DnjU6jEIH6quPK9Ql4mTpxopp12WjNu3LiG436qSCeMo1WdMAdFaRiabbbZ3BFpMudBQbmV4grjOOER6PuE4mtRRey8/NJPJ889SkKHHHJI4pX5JoorjNm+e+Ih5Ybj3ORIN2sdMTm+LcVrg9MVV1zhsCUfF198sXt/2WWXue9XFIFRcrMWV5JwVfGgJMIcN7Hz4arzShtgzGdOL0fukAbK2Rxb6VNMnTDnj/0+pj/I+gJjs7QHyRPK47207iD50qsioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAkURGFA0wOTu31f+uPLKKxvg8C1t9JLFlYaMeg4szhXZpbXwwgsnoVn0DMmaRHdOKPkg6PUFNdxjCQNilzFC1SqoG2mm5dseT+OsQaS9E7deySv5oT7YsQchWGaXfzsIJQLOkEcIgRAICyQI8YSsqXy5dVcEtAhGRdnAf4kylLjbI5D8V3X3CIlFaYUXCJtRIoGwZOKTPWrFCaFxYzekKK3wbM2sO8sH3KcRuO2yyy4u/vA9AqJ1113XOYsihfhB4MLuV4g0QqJuENj7/Sf0043nqaaaylkr8dOmTlAUgkLrJ7H4+ApUCPREaYU0BgwYYPbff/8Em1DpR/JAHPvtt1/SXghLe8eSBfToo486pRT3UPIvtpxlkqWcYk0FPEKlFeJG+W+EVUroNnUDn9g0UXyCEP6jIEWb94l+ifUf6OGHH054h++H+4033thZJRN3rAmg7AOhTITinRD3CLghdp77Siu4sbt76NCh3DrFT4RpzQgBlz3+rpmX5B1CQ185lRcIuO3RM4mf8KYsj45JM8wDz3nG3LRwnXDDqgyE4lKotII77eukk06qG2+qagdF2h55SaNYbGPDpeUhr1s70yzDD+gnMp+QcUnKRB/G4gWEENpXjBc/VVyxsMIciz4ND0KRYYkllqgi6qZx5KkTFAThBRBjlSitNI04eEl5XrPKpT6hzMIcKy8V4Zd54+wFfyiroBAEiTUX5pRyb49ncu/EghYPVfEgF3HOv9j5cNV5RQmb8VeUVsg+3xtYTYNfl6Uy33++ArK/FiF5mnXWWZ2VGHnWqyKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgC/YqAWlwpWHOYVsYCiT1v3Vl+QDCNFQUWPRGesoAqJDuj5LmbVxbhfv/737sskC9McCNwQzCFuXqEcCzOp5nbxow0O/wgFr7ZYS6ESe+QwAZCMWXUqFFuN6n4EWEbgkDMXYeKB/Je/Oe9lklT0uA4ECmnuHHNq9TDwqvs+kSwybFIaWGryKufv6L3LFBTTpQmWPS94YYbXBRYnkAhoB0kO2rDuFkQRsGE403SiF2pmOlmwZbd6UK0Z9pXljUA/KUJZjD/DYW7EtmNLCTCYXnmSn3KTlXf3b+nPXN0DX2Eo4loLHoAAEAASURBVLhEGIMJb4hn8izKQSh9IUSS3bPwFQQ34a5OP43wHsUcf0eyr+wR+q3qmYV9FAJ8ot3AGy+//HJ3rIn/Tu6L4oPARWjVVVeV2+SK+XusPWERhaNUfJL6ZHc7GIcEzuw4hgg711dHUYX+Yp6LljMmDQkjx0whGBVlHHnXrmssj5b8dBKf2DTpvxBHBWQpjnGsAlajIHb3YyUlJPhGSD/72c8SJ7/dSl3ykjaNsFmwlqsIYrkiqPWVuZJIv7rBogJjDX0TnsZxcM0IhUJ4sSjMoMjC2E/4tHFM4irDo2PTlLTzjrniv5NX5lgiDMbSRVY7CvNUVTso0vbCPPAci21suLQ85HVrd5pl+AHjGPNrvg04KgxrcTK2oTgpPKCZtZUyc1N4ABbGRJEehXvp43nxjfGXt06YB8n3gCjuFUmPOSTfNCiAybFmWDkKlVlbxVmUX7aKr1fec0ymkLQ7rqL8Le/9uXQsD6J9+9+Hkm6zK/1h/vnnd0dAxsyHY/Oalafw+L4sf7HuZb7//HkqispsAmEOnHdsic2zhlMEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFoNMIqOJKQcQR4nDsBItbHNOAQN4XyrMji+ODMDfsW3somEzl3hH0IxgXkt3bCKE59gOlAI4uOOigg8RLckWIFh4HxELZ6NGjGyxFIMDDDDaEssE555yTxJN2g3DHJwRlQiLgl+esa9k0JV6OHeEXS7QDysMiPObRUVwJqaq8hvEWeaa+Q0KQ4R/9Er4v+0y/kfpE2Wv22Wd3Oz7JC4vWIWHyGhPqcpRO+F6emymHpfU/WaT32xlxoWwCoZAhfpzDV3+YdG9GCD1OO+20BoWYMAzKQoID7zjyBn5CnwF/fuzO5+igHXfcMdkVG8YjzwgfRBlH3Np9TcOVNFEkgT788EODoM3HMQYfP3wW/uJOe/FJ6jMrrxKOMOLXDx97H1PO2LQI9+qrr7rg9KdOCS/8vuO35Tzl6DQ+5CkmTfg0NMMMM7hr2p//Dv/hsYGESWt/WH4SZRDf8pPUJeHyjEOMNVmKK+SHY7AgLG+QjyzFFY4uQqmVPsuxNeCFdajrr7/ehUegTn7EOpRztH9leHRsmpK2XPOMueK301cUj4Sy6kne+9eq2kGRtuenL/ex2MaGk3Rjru1Osyw/oA+iuIIyB30NRRKIY/UglN5WX311d5/2V2ZuKoqzEi/KtZ1QXMlbJ8xhsK6H4jzfGijPoXTFN4sI+SXvadctttjCKRWjNIHiC98qKLEwN8R6CwqdrZRYivDLtDz0sptvLUwUm9PcUCgViuVBfPOhjFWE/M0CMfPh2Lxm5VEs0GS9L+NOOyvzfcyRpXxTgzPfl/yYJ2MFlXYOX6HfKCkCioAioAgoAoqAIqAIKAKKgCKgCCgCioAi0O8I6ApHRA2yi58jbrCu8eSTTzplBXaKLbfcck7YjJURKE1wEJFcW4OwQIzgGwEaR7lkkSx0YjGCBWF+xx57rFNAWGSRRZJgCK2F2Fm+8sory2PqVY4XkZcs7AvJ7kB5zrqWTVPiZQFdFnbFjau/oOu7+/cvvfSSs8CDG0fNIEhOo6rymhZ3XjcE3Aic2UUvQmh2/WLGfs8990x2YuaNL4+/s88+26RZzUgLi2USzpgXazyLL764wRoMlkhEUI6yB4LWZpYAiizgooQGSfxhvsTUf+jOM0KRI4880r1C8WXNNdd0yici2MYiCibzIbGc4B7sH8eGUL6TTz45OTbk9ddfN/wQamGeHH7SKaUEyVeza1a/9E38Y/5fFE9i8fHL3CpNsWxAvuFNtA0oq95QnhLyw4pbzDW2nDFpSRgR+qRZ+xA/VV9jeDR56AY+sWlKGdPGA8HTfyf+5Z1cpQ/IM1faNT94F/1ESMYGeJB/jJq8D69y1EPozjNjMzwNRdoDDjigqbUoxn92bsNnUBRkHoDlAngVPIyd3aEiTVkeHZNmWM68Y24YrlPPUp+kV6R/Sriy7aBI2wsxicU2NlyYfpHnTqQp/dvv82Ee/XfiX/wwR0ZZ5IUXXnB9CQEzY5RYQeQ4QT+8hJNrmbkpcQivwBLJiSee6BRj/SM/JZ2qrkXr5Pjjj3fW8+A9t99+u/vlzQvHTjK/Qvn+uOOOc0e7yRGkKP/Kt0uz+Irwy2bx9OI72o6Q3MsVd/9e/MXyIKwcUh9FCEVtoZj5cGxeJc3wGn6Thu/LPEteiSPm+5jvC9o61tA43pQ2Dx9howy/008/3SmfrrDCCmWyqWEVAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEeg6Aqq4ElkFnCV91llnudAIiBCsyi5qFm2hZgtgCFd98oW0vnsn7jn/HMWV0GqBpL3MMsskRyCxWwzhFtYw2PXFrm52aAuhsMFCKEIvdqCzG7II+ULktCM+0uIqm6bESV4xWx0SO/CaKUhQVgSN7EhnERbFlSyqKq9Z8edxZ+cvylcQgpRzzz3XXHvttc5yEMe9+IpIeeKr2s/dd9+dKK2Aa2i6G2Wbqq3DSF8VBZawTOHRQv578IPmssfNgCPCYp98qwq+u9xz5AhHdrGozW5V4rjzzjvdsUIcpTFkyBCz+eabi/euX7OwkL4LL/OPO4rFx4+DNNP4gaTpKwmSPkeevffee8nxCCFocmwC7lL3oZ+iz7HlLJqO73+OOeYwmMqHF3eKBHPSS6uTrHx0A5/YNLGKwniYxQ8oo/8O/2mEYImjMHxCERIFUMhvt9QlxDiy0UYbNRVkO48Zfw899FBy/BtKK3nqCEVBFBdRYNxpp50ShRqEkCE/I9kqeHTRNP3iFhlz/XCdvJf6JM1WY4CfLwlXth0UaXt++rHYxobz0y5636k0q+AHHBeFJT4EzBzzhdK7WF9odkwQmMTOTQmL0gqKZ4yhw4cPd4rYWLZAaSZLsZNwsRRTJ+DLvJQj/LCywpjMvAgLFY899ljTrIhyHdYiwVbmi8Sxyy67JArlWZHE8MusuCYV91gexLdD2e+HovPh2Lxm1VURhfesOLLcq/j+o6+goMXGGeZ+WCq65pprnLI7c5b99tvPHR+atabQS2sPWTipuyKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCAxSC8giwex+lFQjTvSJEZPdZGrFLnsVk/8fiU7dIdn2L9Y1m+WChG4sSw4YNc97YISnWDXBAgUeO4fBN5TeL03/nC8XFYoX/Pu2+bJppcRZxu/HGGxNrNVje8C1PhPF0O69hftgFzI56obTjjeRdp64cUwGxA3nEiBHu3v9DMayZIpHvN++9HLdDH0hT4PLNkftxsrOaXcwQu6jThLwoB+UhBNxYpcE6zZVXXpkEaSa4QfDFIrb8LrjggiRcu26ysJD+zpFBsmheBh8RSFAOwTgsk+QFRUKf5DkrnO8ufv3wRe/LlLNoWr5/OYIEwbgcZ+G/b8d9DI/uBj5l0pRjr3wFpxBL/534D/1I+/TdEVoLCd/hWeoSwa/0JfFX5HrYYYc57xyZ0UogLvEyZomVF5kPcLRgluJpFTy6aJqSV65Fxlw/HPdPPPGEs3CFlSt+zfhrGLbIs9QnYTjCJC9JuLLtoEjb8/MWi21sOD/tovedSlP6t9/nw7z678S/78e3qoI1NTkmaKGFFjILLrig77XSe/gA85J55503UfiFB1Wt/CuZjq2Te+65JzkaCKswWH3Ka70DPicWZIR/Ub40q0OST7nG8EsJW/YaHr9WNr6qwlfFg8rkJ+98uBfymrecVX7/sebA99see+zhFEk5bgt65513zBtvvJGapV5be0jNpDoqAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAImARUMWVCpvBxx9/bMaMGeNixFrA2muvnRp7mtA9zS01cMWOTz/9tHn55ZddrCxs56W3337beeXIAF9xBUcstEB33HGHEeszziH4C3d+8doX2OXZKS5RlklT4oi9ykI5yjxYLGlF3cxrWt783eCidJXmr1NuYo2AtoUFgJDE0lHoXuZ58ODBSfDrr78+uZcbrKCkEQoaorSW1tZxi1EGmn/++Z0SGGkigM8irF9gVUJ+HI3SbkJoza5on+gD48ePd06+EK4MPgjOBVusQYSEwFmUABZYYIG61/L8/PPPJ9Z7fA9y9AnCczlGwX9f9L5MOYum5ftfaqml3CO89LTTTvNf1d0zNlVFMTy6G/iUSVOEYSg5CH8P8cPyGIQwKk1Qzbs0vuHzF7+vYAWLPEPnnHOOu2b9pY2d4hfFIvoNO7LTjoEQf+GVYwP5UR5+Rx11VJKf0G9VPLpImn4epE7yjrl+WKxaMU+TH1Ya2kEo+aKUAMGX/XHWTw/lQ5RUhKpqB0XanqTNNRbb2HB+2kXvO5VmFfwARew11ljDFfG8885zVhF4yKtcVhSbNP9YbqPPQVhzw1JD1RRTJ/AzjuuBVl55ZSNC+Lx5g9/Br4R3cVQKCvZ5KJZf5ok7y4+MF3xHCS/N8tsN96p4UFV5bzYf7rW8tipzu77/OHK0FaWtM6S5tYpH3ysCioAioAgoAoqAIqAIKAKKgCKgCCgCioAi0G4EVHElAmEEVljWQICLhQaEgvfee6/ZcMMNDYog0JZbbtnU8kZEsqWCsDiKEI4fgptbbrnFnXXPjmoRgm2wwQapafCe4w34vf766+587fvvv9/5xRx3aGUCCx7sdGRBjDPLxa9Ezm4wzuhG6OoL+4lfhPxY20CYI+lyFYswxMuzv+AWm6bkqex1qqmmcm0iTzzdzqtgikLEH//4R7fgL/lmV3C3iV2EQqNHj04sSbA79fjjj3dnvMv7qq60RVFeOeOMM8x9993noqaN8Uz/TiPaKUcEQfQp2rr0p2eeecZst912de3Uefzqb+LEic7CClZS2AkpxJFBp556aqKwgun0XiN2ebKzE6Id7bbbbkn/5AgQoTL4YFZdhE8owbEDW/o8yhP77ruvSwblE/iMT+RBlADImyhAUTcjR45MLCRtvPHGDUe5+PHkvS9TzrxppPlDCLn88su7Vxw3dcwxx9QpEiIs57gE+G0VBO+I4dHdwKdMmrQLCJ7DPeOmEEewwMMFB8bNKaecUl7XXW+99VZ3lJ4oJtCOxSoSAixfcYUj6cTCCcotWEDxjyNCkY/w+Nlzzz3r0gkfNttss0RpInzX7Hns2LFuXsPcZumll870WiWPzptmmJkiY24YtlPPYsWGuQsKCn47om1hnQIhuwj7yVdV7aBI2wvxiMU2NlyYfpHnTqRZFT+QeOjL8AR41DrrrFOkuKX9nnTSSYmiHdZGfEVEidyf88t8kavMbfie4Fn4moSTa9E6wcIcyrCM2bGWYBDcw7f4MRYWoVh+WSQN36/MGVH653hS5lJZWPrhOnVfFQ8qkt/Y+XA38lqkXKHfMt9/9A2sEKG0Le2FOfGECROSNo8FWI6sVVIEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFoJ8RGNjPme9W3tmhN27cOPeTHc2yiESeEP7vuOOO3cpearosOK+//vqp73BE6WbTTTdNfc+OZF9Q5Xtil2NIHH9wxBFHuAVoBMYsCrOYhjuCVF8Y54dFgPPee+85Jxb2xWqC74d7diiTH5QaZNE/Ns0w7thnhON5jxzpdl5Rqkqjn/3sZ2bZZZdNe9VRt9VWW82ZtUepiV3BN9xwg1MO4Qgf2sUPf/hDJ2wRIUpVmTvggAPMNtts4xQx2Jk8yyyzuLaGYhoCJtJOI+p+n332ce9p6yhy4V+s12A5JLRQQjwILjmjHsEFP47Ymm666dyxOMJPaCud3JGdVr40N8qD4H222WZzfZqyQFhAYAesT7H4EAfKKQ888ICB5yJwQ4kIE/IIOYR23nlnV1fyzBXB0IgRI8z555/v2go7uFGEIR6xYAPeKHVURWXKWSYPtB3aK7yV8l544YVOcIEwHCULCCyqoDI8uhv4xKZJG2bnP0qqWO0BXyGOsRCir4dKU/KOKwqcKLmecsopjifIcU6477fffr5Xdw8fQSiF0tvFF19sLr300mRcgf+J4pYodDVEYB3IU1rcaX5j3brFo/38Urd5x1w/XGjhQOZwvp+q7mlHzAWxoIPCLnMwLG985zvfccp0wjfD9KpoB0Xbnp+HWGxjw/lpF73vRJpV8QMUZLHeIsoijJfTTz990SKX8g9/QJGReaAonaIo5yvfodyeNecncSyWyW/JJZdsyE+ROmF+JYqVzJ+Y33WSOsEvw/JgeWfUqFFuHoOSja9owxjrPxMWpWhR1OXZn/vC6w855BCcHfH9ydGRZakKHlQkD2Xmw53OK+WKrZMy3398O9NX+S5COYzjNFF68hUfDzzwwEKW1orUkfpVBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRaBTCKjFlQikEZaIqWcEzCJkRtDNohHWErACkEVy/IX/Ps3Nfx97nxUvi7Xs9maRE8UAFlFDAU5a2IEDBzoB8KqrruoWz7imETtLb775ZrPooou6eFlwQ0gvSivsCMMqApgJCY7y3Ooa+o9JkzSk3Gnl9d+LP8mXPCOI8K1MhO/Fn7hzjc2rH0eR+6yyIUSjHdBumx01UiQt8eun6d/L+6wrfQdT+mJSG2Htq6++6hQ9EJKwO1f6Xxiv/+zfS1rillYnWK647LLLnAIJ/jHjjlAFhQdfkCBxSJwII9lZz9EQEMoCKK3Qx1AqQBkGCtMEewRXKHVBLEBzbBftmjSGDx/ujpggniwK8xI+Z4Ur444gFmUayoNiGgIHdkpjCeLss89uiDoWHyJCAQUBAXVDeiitidIK+MFr2YGaRuwmx0KP4IvgGEEd8XCkF/H6/CctDtyk3lphW6acWWnncWfH8W233WY22WQTN+7QXxCMitLKIoss4iz75ImrlZ+Q5xbxXyU+nagTlICwmCTtxy8r7R3+dNNNN7k26r/z7y+66CLHTxAsidIK8weOjhkyZIjv1d0jxGY8pk3DT6hL2i0/7uGNQ4cOdYqmfmC/bR500EFOOSLtve8vL4bEI+HkGsujy6RJPiR8zJhLeOipp576/zf2n/LAg9tJ1AdKSOQZoi3IeEYdb7XVVsnYIfmIbQcSnmvRtheLbWw4P69F77uRZhX8gHL6SmcbbbRR06JLOaXfhZ7lvVz99xIm7R3KEfA26Nlnn02OOJUwfjzN7v3xQNIp2jfBlbGKNh+O5X5+0u59t6x8ih/JH/7Ejfu8/BK/VRH8E+UDLGcxHvhKQ2lpoKjC3EV+vhJ11jspo1yJV+7lipvg4rvhXgUPIp68VGY+XDavUnbBIk+es3AnbLN3vI/9/mPOgSI8hMUjvqlFaQWFGOa7YrXNeQr+pJy+c5qb/17vFQFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBHoBgJT2IXHWjcS7vc0gQ2LEAioESgttNBCZtCgQf1erLbkn8VWBPJYBGCnMQu1LLKFtMQSS5j333/fHdOCJYcs2mmnnQxm8BFaZx1tkzfNrDQ66d5Pee0kLqSFYgRCPnaPL7zwwmbqqafuSBZee+0117/nm2++3Ga3qUd4AgoohJvLKl3kIRa5KSPhUOzi+C12UmJZpJcIQTsWS7BwgqIT5eXoC4Qw1A111Ixi8ZE4UVrB+gXHKrF4D4/II2iAV3PEGTwIJSB2krezHZUtp5Q35kpbos+88sorroy0wTReGxM3Yarg0d3Ap0yaYIoFAoRN0Pbbb284KgvrSGmElRQ57gLLRFhfon/zgy/Qt/MQ7Zb5BTyFOYbwhXa23Tz5Cv10i0eH+cjzDI4o03700UfO+3rrrees4eQJW4UfhIwIGzkOjnkQSmet+GaRdlBV26uirJNqHEX5QYgDSp8PP/yw48scSdiPguO1117bHYt61VVXNVhYC8urz/2PQBEeVLa0ZefDncxr2bISnrlJnu9jPy0UvRh3UYbF8grK9cwr5HhM36/eKwKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCLQjwgM7MdM90KeEZgihOKn1BwBBG2DBw92v+Y+q3vbjTRjc99PeY0tY2w4FmPzCnpj00gLh8CfXxGiHrFuUZRYbJ533nndr2jYbvqnvFhCyUux+Ej8WCdAcaIowaupS36doLLlLJNH2hI73sW6Q5m42hW2G/iUSRNMGb+EOMoiS2lF/IRXFBT4FSHaLUpHVSoeFUk/r99u8ei8+fP9Pffcc4nSCvW61157+a/bfo9VgbSjVZolXLYdxLS9ZvmZ3N+V4QcTJkxwSitgiLWVflRamdzrf3Isf1keVAQz+leZ+XAn81qkXFl+mZsU/T5GAbuZJcastNRdEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFIF+QUAVV/qlpjSfioAioAgoAoqAIqAIKAJRCDzwwANJuF/84he5rWklgfRGESiIAEd6YOHnwQcfNKNHj3ahsVK22WabFYxJvSsCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIjDpI6CKK5N+HfdNCTmf+7PPPjMzzTRT0zxzVMMaa6wRZX2hacT6UhFQBBQBRSATAeXRmdDoiz5AgOOasHoycOBAd9xTH2RZs9jnCIwdO9aMGjWqrhQHH3xwXx8tuv/++7vjrrCMoaQIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCikCVCKjiSpVoalylEBg2bFiu8EOGDDH8lBQBRaBzCCDs5WgDPd6gc5j3WkqTK4/m+AGOMKjVau7arF6kjxCGn1LvIHDCCScYfpMqadvrTM0W4QfwDeplyimndEcJbrjhhu6YoM7ktD2prLDCCu2JWGNVBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUgckegSmsIKY22aOgACgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIdByBAR1PURNUBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAELAKquKLNQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUWgKwio4kpXYNdEFQFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAT6FYF//etf5s033zT//e9/64rwj3/8w7z11lt1bvqgCCgCioAioAgoAoqAIqAIKALNERjY/LW+bTcCtVrNTDHFFO1ORuNXBBQBRUARUATaisDnn39uzjzzTMO4Nnz4cDPffPO1Nb1JOfIvv/zSPPTQQ2bChAnmnXfeMW+//bZ58cUXzT777GPWWmutnip6P+W1p4DLmZkvvvjCjBs3zvWrVVZZxcwzzzw5Q3beWz/ltSp0lO9VhaTGk4bABRdcYD788EOz5JJLmhVXXDHNi7opAoqAIqAIKAKTNAIog4wePdrNhVdffXWzwAIL9Fx5x44da0499VRz7LHHms0228zlD2UW5u7//ve/zTPPPKPrvj1Xa5ohRUARUAQUAUVAEVAEFIFeRUAVV0rWzMsvv2z4SPn73//uYtpzzz3N4osv3jTWjz/+2Jx22mnmkUceMc8//7z59re/7RYkN9poI7P88ss3Ddutl9dee6259NJLXfIzzDCDufDCC7uVFU1XEVAEFIFJCgGUEV577bW6Mn3ta18zs8wyi5l99tnNyiuvbH7yk5/Uve/Fh08//dSMGTPGZW3eeedVxZXISrrkkkvMyJEjzUcffdQQA/j2kuJKP+W1Acw2ONCPjzjiiCTmXXbZxSy11FLJMzcIoffaa6/EbeONNzYswmfRJ5984hbBeT9o0KCeVlzpp7xm4V3Uvd18D0XAiRMnumzNPPPM5hvf+EbRLKr/Pkbg/PPPdzu4t912W1Vc6eN61KwrAoqAIqAIxCOA4od8Y84222w9qbgi8/0TTzzRPPvss4Y52x133GGwuDJ06FBVWomvfg2pCCgCioAioAgoAoqAIjAZIqCKK5GV/vTTT7ud5X/84x+d5r9EgwCiGf3zn/80W2yxhdtFLf5QernxxhvNH/7wB7eTYM0115RXPXP93e9+Z5544okkP88991xPfjAmGdQbRUARUAT6BAF2YGFNI4uwYvLTn/7U7eJCcVBp0kUAhYbrr7/eFfDrX/+6WW655cwSSyxhvvvd77oF0B/+8Ic9U/h+ymunQGOOd/fddyfJoZgsC9nieMstt9T5WXrppeWVXhWBBgTee+89s8IKKzj3M844w/TiN0JDptVBEVAEFAFFQBFQBBSByQiBZZZZxmy66abm8ssvdz8pOgoshx9+uDzqVRFQBBQBRUARUAQUAUVAEVAEciCgiis5QAq9HH300QbTzTG0++67J0orv/jFL9xxCiiunHDCCW4X7h577GF+8IMfmB//+Mcx0bclzP/93/+Zhx9+uC7u2267TRVX6hDRB0VAEVAEyiEw00wzmZ122slF8tlnn5lXXnnFKTT+5z//MePHjzeHHXZYstusXEoauhcRuOyyyxKllXXWWcccd9xxZppppunFrJp+yms3AWSuxC5R30rGTTfdVChL3/zmN82OO+7owvTS3DCtEP2U17T8q5sioAgoAoqAIqAIKAKKgCIQgwDfbhtuuKH585//bDgmaI455jCrrrqqmXrqqWOi0zCKgCKgCCgCioAioAgoAorAZIuAKq5EVP3rr7/uQk033XRmxIgRZq655jL77rtvy5jeeOMNc9999zl/nHV60kknJSYjp512WrPrrrsazm/97W9/W2dqvmXEbfZw1113uXyRzJAhQ8zjjz/uzF76pu7bnAWNXhFQBBSBSR4BjgHZbrvt6srJ8XPrrruuQYEQy1yMEz/60Y/q/OhD/yNA/XImOkQd77///j1bqH7KazdBRFmFo3OYQ6222mouK++++65TBGb+yLGReYh4DjrooDxeu+6nn/LadbA0A4qAIqAIKAKKgCKgCCgCkxQCHBvf6uj4SarAWhhFQBFQBBQBRUARUAQUAUWgDQio4koEqN///vedUGmrrbYyKJz86U9/yhXLNddckxwrtMsuuyRKKwTmKB4hjgk4+OCDzZRTTilOXb3efvvtLn2EpRtttJFTXOFoCyzFfO9732vI29/+9rdEQQerMmE5OGaIXQgIODbYYIOG8K+99poT9Lz66qvm7bffNuzgJW12Gq+88spmwIABDWFw4Pgm8tWM0tL8/e9/74TCxB9+ZH766afJDniOa5hvvvmS6P/617+645NeeOEF8/7777s42E0xyyyzuHgQVH3ta19L/Fdxw85t2lEzSisj/gn7wAMPmEcffdThivBxzjnndNhi2pQdIWnUDB/8o2iFwlWIT2w7uO666wzWJhZddNFMqz5ffvmlueqqq1x2OUqDclAH7G6HFlxwQbPIIou4e//Pr0/OR15xxRX915Xcg+9TTz3ljn7hTONZZ53VlWWNNdao23VfJjGpk1ZxhHUi/qkvjjnj/GXaMcfPzD///K5/he3g5ZdfNo888ogLigJFsx1DlP2ll14yHHHCbiOfiqTJcRscnQb98pe/dP0I6yNYfuLojZ///OfuHc+4zzvvvGbJJZd0bvy1anuJR++Gurr11ludy+qrr26+9a1vJW9pV/fcc487oxpM119/fTPjjDMm79t5M/fccztrCyg6Qn/5y1/qFFdi+GWIz8SJE135qD/4LX2PY++a8S+OQwF/6hu+SD9sZQ0ilgcJvm+++aa59tprXf1+8MEHrg7gt6TNcSwDB2ZPaVC4JK9C9H3G8nZRkfZOHjgDHf6E8tJ6661nzjvvPNe2KSfte/DgwW7HXtr4J31jiimmcP3l/vvvd7yevku/5iiaZZddNrWoMXVSJq+SiaL4EA5+BW9tRfAH+IRPN998s/noo48MRy395Cc/8V+5fi19f/rppzdrrbVW3fuYPkYExMN4jYUVUVyBr/3vf/9zddlsLCdN5kkhkfdW7Zb4OY6I4x3pM7VazbUr+ufQoUObho/pYzF5rYIHoRAED6KdUzbaOOMA4yNlzoNViG+r507xvcceeyw5Po65mhDjkP+Me1p7j+nXkkY3ru+8846bk9BmGXcYZ9mhzHcG9Uu/pO2m0Q033OAUxGTeR9uinh588EEz1VRTmYUXXtjxxXDuEsODmAuAP2Ne2hyT8Yl4F1tsMcdrJL9leLTEwVXmuYzVjAWM1fAWjiHwiT7JfB/i/Xe+8x3/dXLPNxPfThDzGvDqFpXh0eSZb0bq/fnnn3dj6TzzzOPqno0ijI0hhTyoyLeqj2/avJh+yrwKgi+xwUUotm+WGf8kbcZPeAvf4XxnyfzAnz+LX67yrZH2fcz7rO8/3sWUk8091AtrCxyPGRLj2ocffuj6HzzCp2Z59b/9sr6LiOutt95y2KBgGtLw4cMNCqdVUKu2J982Wd/zVeShyLcqcwrqmnG1GYXYVlFO+OmTTz7p2sXnn39elzz9Kpzbtkozaw2qVbg8dYJ1DdYESYNxi7naAgss4I73q6rtCAD0r2ZzSPxltZ+Yvkl8ZXhQbJqki5I3vB2+KnTllVc6Ps+6IN/JIVUx5hatzxgexJyBds141ezYUPzgF2ITYbiBpej4xzyG/IbEXILvJH/MCv3EPlfRx2LSpq3Aw5oRa3UrrbRSMy+F3vlz+KyAIb/M8qfuioAioAgoAoqAIqAITNYI2I9QpZII3HnnnTUrOHc/K5TIjM0qcTg/dhG1ZgXviT+7WJeEl3iswCJ5380be0RFzS4Gu/zZ3eA1u6iT5PXiiy9OzZpVvEn8WKFbg5/Ro0e798Qbkj2GKQk751eY+ld7bmwtLU7iscctNQ1LPGlp2o9eF84ewxFmp3b88ccncY4dO7buvTXdn7wj7vBnP0BrVqmlLkzZB6vI05BOmO5CCy3UkIwVCtTswmNmWN7ZD+KGcDg0w4f3dsHAxRviE9sO7IK/i492kkX2gzspi1X6ct5oq1ZQ6NytQMG11TC8tYzk3tsP8ppdBAlfl3q2ig+13XbbLcnXnEGboFx2EaRUGhJY6iRMI3wO64TwtCHhRaF/u4hdo958GjduXE38nXLKKf6runvKJnVHPD4VTdMKUpI0rcUCF9VFF13k3KwwK4naWn1ybgceeGDixk2rtlfn+asHq9iQpCn81y501H71q18l7nN+Vad2Qb1mLWilRRPlNmzYMJeGFUCmhrfCsiQPY8aMSfzE8ksfH8YfqxiRxC9ltEqKNfpUSIxd1vpDg3/CWfPIibsVJtYFLcODiMgez1ezx+gl8ZOe/4P/N6MDDjigzr8VgDTzXupd0fZOYkceeaTLn13krtkFybq8Sjk322yz1PHvkksuSfxnjYPUDe3Zp9g6KZNX0o/Bh3DwH8Gi2dUKv/FeR1a5yYXdZ5996tytgKom/c8Ko2vM53wq2sesgCXJI/2WNsv4agVnLlorHHbvrXAj8XfmmWf6Sbr7888/P3k/p9fWs8ZpiYA6lfL44eSesc8ugIv3umtsH4vJa1keZJW7a1Im/3ryyScn7iEPqitswYdO8720cccvp39vBZB1pYnt13WRdPDBCtNrdgE/qTcpm1VUqVnrU86dOU8WWQUl5+fXv/51LastEo9PsTxI5l5p3wvEL/Phc845x0+uFsujicQKZl357IaH2vLLL+/u5/R4glU6qDFn8olvD/Hjzxl8P9wzzuPPKn7WfZeG/jrxHMujydvll19eg39Lmf3rFltskTpu+jwo7buy2bcq3x2ShhXG1cEDH4XP8n7zzTevWQWR5H2Zvllm/LNCYzfHkHxJ3uV61FFH1b744oskn3KTt72H3xqx5dx+++0dblZhXbJQd7VKSO49c9CQmuW12bc88cDfmRtY4bGLX3Dxr1ZpPEwy+rlZ22NOwnc8aaetWUQn+lXAmG9V5gzkp9UvbAdly8l3uvCFtLR33nnnBjiapYnnrH7dLFyeOuEbMo0/k2/c5buyIcORDoxhaZj4blWvB8XyoFh+ADRWubJmN1dllpU+C88NqcyYS1wx9RnDg6zyqSub3ZxQs5t2wmIkz3wnSN2G84uY8c8qISXxSbz+ddttt22anyRjBW7K9rECSdV59cuVdc88oUrKM4dnvqqkCCgCioAioAgoAoqAItAcgeztyZO1Ok97Cv/ee++5iNm5JDvZ2UVgF4ycOzvnZIctu+p6gR566CF3Pit5YYc6u6DYafjiiy8aLLHYiX6l2WRHNMTuCXbL2kU298xueStQMuwkxwIDux7TdtDhmV1za6+9tgsnf+wqxFJBEWK3jF2EyQxC+uz2Zyc81juwVEAYdi+yS5QfVnnY9ZmV18zIc7wAn9lnnz3xOWHChGSHbuL41Y1dlHO768CGHQVWmGa++93vGnaVsYOD69577+2wC3ebh3H16jNlO+OMM9zOJix2cMTKFVdckfQ1jlkRy0Z2satSayvgi4URdvhA7IRZYYUV3C5X2sT48ePdrn67MFwpfPQPdt6ElLULyw4HZuutt3a7wQiDpR3aEVYd7IKC6+vghtWHcBcb/s8991xjF+LNTDPNxGMdnX322a6MdY72oWyaYXydfGYHMG0IsgvIboc1z+xs4ng4diB2guAlQvRboSr4JcfSUJ9YBIKH0U9Ij92F3IdWsU499VRjF8lcFrAEwm5/dqXRfuximmSt4VqGB8G/rVDS7WQnr/Qt6oPxk3dYXmi1m6ohQ21yiG3v8GCIo2UgLIYwjmH5h3EPq0fsRt5pp50Sa1POY/B31llnufpcZ5113O55LHxgVYm6oe1ss802SYjYOimT11h8yLRdhK9rj5SNXePMF3xraWn8KSm0d0M7t8qwDh+sMWDlBus9PpXpY1iyYt7EDlzmL+QRixK404abEbt0yRvk77RsFoZ3WOtjfgZhhYg0wYOdjszZ2FGe1lfK9LHYvLpM2r+iPOi0004zl112mQtO3WMhi7EVHmSFUhJtpddO8z3maVh0hOCvMg5ZwYizwOQXLrTAE9uv/Tg7dU//sgp5jpczT8aqAd8YzPmZN8mcKk9+sCDAPBjLW1g8sQIKN7dlLGPXvlAZHiRxlLkW4dF+OrI7Gt7BvI15CNZm4MdgiIUVaTPMQeWbknnv7rvv7kfl7jm+VqzpMc7Ld2mDxy465OHRWA+Uo9SwhMfYh4Uevr3Y9cwu/VbjZlVFpG5PPPFEF51VIHTfJFggFCrTN8uMf3zzMAZBWFkhb9Q3YxNWWKzg11lnsErgktVS1zLlLJVwSuBW3/IE4dtGvptoQ1bRwFmOtMoBieWilKjb4mSVjHMfI1g0A9RL2W9VLE1hkcEnwc53a3XfqpxYNaLfYi0H4lsXng4xNmB5p1PUKq+s3WFhmHGGtQisJrLOwrwLbOC1fDuT77zz0yJl69R6UCwPiuUH1D3zYLEyh+Uw1isg5uy8B3PWUVmf5Hs0jYqOuVXXZx4eRBlZw2IOHxJt3SquhM7uuYrxD37H96HdrOL4HRhjWdMq8xmrXJGabtWOrfpYmfTgecz7IL6H+Q6DZN2De8bFdhBzMrG4SfzkQyzntCM9jVMRUAQUAUVAEVAEFIFJDQFVXOlgjYriCgtqQixwsejIhHm//fZzH7a8E7/ir1tXPlwgBJp8GEMIYhCMsCiIkG2aaaZx7lX8ETcL1yzMhsoeLKzZ3c/uuId77703U/EAQd9vfvObuuzYHYeFFVeOOOIIw8d2FiE8yTqagnySXxa95CiNrHhi3e1uiLqPIbsTJhFYhXFiotZaHHBKP6HJcGslxC1iskDMB2q/Kq5QZo65oU9xFBcL8tQ7CjksHsliAO0YpYMqCdxEwIKy0uGHH14nBOAYGmsJoeHYrLJ5oL/Q1kLiuKU04SQKX5gwhlhEO+aYY5J+tskmm7iFNxaCaEtpiiu8Q4CHEoFP8LCsxY2yafrpdPqeskLwFGthyikisQjBIjfCMJTUUFxrJ8FjfQU6hHpCVfBLFvtoLyjhQTvssIPjv9Q1gmBfcYW8WMs3zh8LmCjuCP+n/eAXZZI0KsODUNwQwePpp5/eYE6Z401kMTMt7U66xbZ3f7xDaYXFboTyEEJHBEpXX32142sIKNP6J34lrCzw2x3MTlDBwimKGVtuuWUybsXWSZm8xuJD2TjygZ8Qwm2EG/QDUQCWd62uCBJosxznQJ8eN26cO54kDFe2jzGeIhzkuCA5todFzKy5g6SPyXAxG44iZp6FTkyxo8QFobQSKtYxLiE48QWpzrP9K9PHYvIq6XItyoMuvPBCFxwlcMqIIhCE8B6BUdUCrW7wPQQwIoRhfBXFFYRja665pitv1l9sv86Kr53ujCcyZjBHQjFdSObR8tzqirAJZW6EVIxPQihryaYA3MrwIImzzLUIjw7TYX7p8zqUj5nbowBkd5gbFBSE4G+U+/XXX3fjhnzDyXvGGIQo8HMfd3nf7WseHk3+RVkNhQOUdESozhx4//33d2Mp3wPwuFAxscoyMl+UOSPfsdyHykBl+mbs+MeYIEorCIGZ90u++P7bY4893JERzDNpByjEl6Uy5Sybdhi+1bc8c0upNzahMF7LGEmbkSO3wnjb8Uw7ZT7eLqriW5U2Ikq1ks+sb055H17zlJO5Ct8hEEJtP03GQRQVO0Gt8goPslYI3TcK63scY8zcRIh5CfyAcY71iZEjR8qryq6dWg+K5UGx/IB2JUorfJuiyCRK6vRr1rNQrmD8Y14oc6YQ2CJjbjvqsxUPkvwyHxoxYkTDcZ5898o8SfxyrWr8YwyQ+QH8kDbLxkXGDr4rOHKqndSqj5VNe9SoUUkUrGuI4oq1JF53LHTiqcIbxhR/TZo6y/M9V2EWNCpFQBFQBBQBRUARUAT6GoEBfZ37Pso8E392TUKyyM7EmV2jLCDxMevvwugViyvs0IUQTMhCjpwByg5XPmqqJD7uERT5gjGJn0ULceec3XYSlhZYsGpGzQRP7ECGUPhBKNNtYtEAZY5QaYV8IbTjjHZIlBrcQ5/+sYMC4SyEchFKTgh+6X8sXqDMIou2VRWRXTIQwuJQaQV30j3ppJMSIT9u3SAW1CAWIVCUk/6EGxYsEPpBLAgiEE4jBGjhOxZ9ZYExDFNFmmGcnXiGB4syEnxJ+o5vZcoeN1NpVliYQiGQH4vnKBqwMxbFK4hd5PaouSTNKvglig2itELElJM0IQR+Pt16661ugRA3BGSitMIzCjXSfngOqQwPQqlOCCsvIXE2dbsViMI0s55j27u/MIhgQJRWSGfAgAFOACd8K1RI8POy8cYbJ7tScUeYx6I2BI7wQ6HYOimT11h8JM9VXNlxZ4/IcEor4ANP46zxNCrbx+zRZm7uxFyJc9ahdimHwoNF2dZXcJNy0X4QPEg7Eneu3exjRXmQCDIQusp8mjIgcGVnZdXULb4XW47Yfh2bXplwwg/YnR4qTzBvY/d6EUL5xVdaISwWaZgXCkmaZeZBElfMtQiP9uMHC3s8ou/k+JgoKaLY6BO8S8ZoKbO894UnfN+h9N1LlJdHYylKvltoP6K0QlnCcTPEoKrygqU9iiZRfkCojjJNGp/tRt/kmweijlF68vPFPRavIL7p2b1fBXWjnGn5zvMtz3jCGg2EUqmsdaTF1043BMb2CLJ2JuEsOpBAN79V85bT/86UNYq2gpMSeZ68MqcWxUg2yPhKK0TJepA99s7FjtUs+EU3qRt9MzZNlEwh1lBQ3p9qqqnqoGM+KxaW2UwiFhLrPNmHImNu1fWZhwdJfuFDvpID7ijXo5SaRu0Y/xgTWA+CqLeic7C0fDZzy9PHmoXXd4qAIqAIKAKKgCKgCCgCkzYCanGlQ/XrHxHCRwHCBUwb8wHL0R0I3dgRLeT7F7dOX1kMFMElyiRCaOXz8Yj5eiyyNNv9yYIQi4c+Ydq/FWHRBZPfCFOwViGEsgjYpO084OMH8oXxEq7IFeGPWJTgoxgBcpr1ComT3dDsDCRf9lx5Z3YZ890QFgj48KuKyi54UGccN8GHsD1zOFms4+MX4qOZcqR9qFJvlDWkZtiI35h2QB1T9wgWwzYk8WZdDz30ULejgnKJogHt4uSTT64T0meFL+KOVR1ZLGGHq78oXCSeTvilnUL0YXANiUVbOYIBZQkEgT6BIf0MJRwxW4tyRzNBetk0/fRj7rFeJbyBBWlf0NksPvqykH+sBzu6EYT9P/bOA1yWomjDA4KiiJIFUUCigIAkyYpkkCyggFxylCwZFRAJkoPkLCAIohIkKZJzUhCJ4kURRUFUUFBQ//u2/7fW6TMzO9Mzu3vOuVXPszszPT0dvu6uDlVdDW+0wl75bXKlXVKH8ggFOCzh5NWvFH6pOPIE9jp6IrZiorLkWy3EKhyu8Ess05RRCg+y9ZAd1AhaOAYhD4uiuOEJHIMlsoqicmvjKozqtjHMXotQdohpxhlnDHnGyogUmWI/PFMGMa200kodp7xv65ZJk7Sm4tPJQMMbjgxEsCjlYJRJYkFDXhSpbQzFUI53YQEeYSwL8L3a9Y9lKPi6LLRwXAg8naPfulEbbaxbHEXv6/AgW39tvVbYn/70p4PCn57buKrOElY/+V7TtNdt16nxMYeRYn6VMFAsYQcy/bLmGFKWtN9Tn1HWZDduFaK+V2lbKs+6PLpKGqr4SeHRhMtRGWASE3WS3eaMH5gjaA4En0aoh2Iex6phwUa8G+VktaVYYSgOv9/PdXi0ypI0wutiQimXo03YKKL8xn54TpmjKBysLTBPhDi2jPGxykB+4mu/2ibx6phc5nbsPLfzSN1rTl+0KSR1/tfPfMYYV53L06aUfykgxGH14xmrb5QV813atCzethV3k7mq5lCkpVvd7pbeqvm0CvWMe+08rFscet+kXRNGlbQyNhSRTsZ5ale6amMHVxRyrGK6vq17Vdh1v5P/QbTNunGyTgWxRlo032OMcPHFFwd/WKezVq2D44S/On1um+VZlQcpnVyxMoMyt+YlKLJwhA98IV5va6v/Yw2LeQNr0yjuaA0Ty551199sXqrcV2ljVcIZaX6ats+Rlh9PjyPgCDgCjoAj4Ag4AoNCwBVX+oQ8wguEpkw+mBywk54FCkwI6tgSCb9Jknb39yl5udHI2govZWWFe3YpYob+tttuC2bnWdAomlAiRKhDmNI/6KCDMnYnl5FdRJE/7ZiKd2TofdUr1iNY4CEcFte1IFn0PQvCOoZGfsCD40TyFuPlJ+VK/RHlKZfoXd6VySEWSGJhdOyXiWte2OzmjHd0xt8WPdetB4RDG+GHshKCdM6t33LLLSu1DdoaVlBYRNaC0Q477BAEiEVpTHVnkUrUxmKUwurFFcEGZBcEbTyccSySXz1zRQkIxQTqPAu8KA+wSE97BHMsbsRKLAonNU4bf8p9LMxAkQyrJVihyBN8EgeCCKtgZ3HhPfwZgRt8om2SNQt4CPGiKAP/BXswttSEXyqcvHLBUhRk+Q3PKkswlB/cRTFOctc1lQehAHjmmWcGRSH4MT/ipxypcygilFnAIn7S3KYSofIUX4VRHq74tRjJL+4WT+uHdyK5U+5FlBcv9VWLnhIU6/uUMmmSVuU5L52kSXnkXn65b4ukpKnwUOTUArHc7LWNNoblA+0cxZR50XjJxpt6j5UKjoLgeBuUtfixu5oxG30gli3yqI02lhduFbe8uqA6VsSDEE7Ij40DBa+2SfWw33yvST5S2nVqfChB1FFckeATXqSxfFG5FbnnpXWOOebIcx7mpvLMq3d47jUPyou3jEcrA3nf8U4Y0VZQyLOKauw0R3GFcTC7/ekrIY7UgWhHjJNHEtXh0bYvLMJH5Wn9xvlNmaMoDDtH/NnPfhYU2csUpPvZNhHk0hdAKFozjiqjojFtyvyvn/nMy1PVuTz8iM059NEoHiA0RtDNmFJKP3nht+lGOaGYDtFmqcttK640matqfYX0NVljqZPP5ZZbLiha0UegsA4uzIcgwqlCTdp11bQ+99xznaRUWSOhjbWxVmDHRnlrNp1E5dwMom2mxKlyLuOn9h3+rdUtZT2vbyjqc9ssz6o8iHRiLZA+mrUHLMeBFxsIOdoGQsGU+iWrirjZPi0vj/ip0v9tu+22eB1CzIuYP/SSqraxXqahV2GrfcbrNr2Kz8N1BBwBR8ARcAQcAUdgrCLgiit9LFl2eLNQiyWR22+/PcTMub1afLeKK73aDV4nu1ZxJTZfq8UclHAefPDBIBTJC5vJdCykYTAf7xrgWxZGOMNWO744xxZtfxZhNSlHIMOkruh7wmmyqPLMM89k5513HsGEozC0SBIcCv6YFC6xxBIhXeDCrjYWWtjZxn2ZRZqCIAudpYSBB+2cLPRsXqBMgDAeQvhCmhBmacLPGd6YM4WKdgmwuCehevD4/3/soOlGdeqBwlK9YZLOTkmUbkgjk3gJPuQ378r3dqeIwsvz28Tt1Vdf7Xyet9On83LAN9RJKWPIfHycJBTsRJYfyW3PPfcMZYAlHHgXpntlWhyhaNz22ohTcadeVQ/gGaQHSz7Ud34oprFbPKZHHnlkiFOMl+pfHkZDPqz5MN9882VVjx9qyi+VtG4KH/LHVcJJ8WP7jvsYJ/u+CQ+CZ9H22XnGsR3UP+oyO/H5cf43C30pOzJtGpveN6nvlj8V8XYtgJXVO40nbF4Imx9tgB2ootQySU1rE3yU5jauUt7AWhTHNKLUYY/gUhxttTEU5Bgj0JehINJLQiGPsRPWxXQs1PPPP5/xQ1iNAihjE1uGpGeQbawOD1IfljcWIR91wsJ/FRoU36uStjw/qe06L6wqbihLVRkHKiyNq1WWuMdjB/m1Yzi5FV2rHMs5EnhQHR5t89qtX8AvddUqrmBtBAEUFuQQeqG4Au4IyCCO4ChqS8HDgP6q8mjbFxbho3GJ9RtnK2WOYsPAShqKD8zxGRefccYZ9nXnvt9t085P6IdWmGD9q4yK2lDd+V+/8xnnqe5cnqOedFQnax92/SMOuxfPhx9+eGi7WGTbd999u1ouTEmDrQt156pWcSWPf1VNT518MiZjXM/aBcoeKAHUpSbtumpahStxcVRwNxJv6+av2/tBrgd1S1v8PpUfKI9lfZR9J/9x/Hl1tmhe1FZ51uVBbNDCmijj81snbN7jyGAs27Imx1odyluxYpTt05r0f2BB/WWOKIUL1t04Xg9LpVrziHFt+ly1jTWNZxDfS2E0r+4NIj0epyPgCDgCjoAj4Ag4AqMVAVdc6WPJsZjIopaOl9hggw3CLh8lwZoxtguPes+VCYxV2mAyUWdh14ZVdk8a7a63u+66q9A7i3UIfvKIM2dZiLHEedsIVWLCgouUVpj8b7311kO8MJkq0/7XwnnqJAFsiRfhEkodO+2005D4ix5YDJTlBtLAjjusrdx8883ZLrvsEnY75JnCLwqvzN1OUvOOein6lvRAs084+uV73/vesDKhXnYjLD5gajwmdrnaOhm/57lOPdD3TJbZBYKJbxY9rrnmmmCC99xzzw3u8pd3RWGIBQAJnPBz+umnZ0sttVTrwm3OjRdVwVF++31lYQKFOI7O0TEZcRqse94CthZPOOYMhQG1UfzuvPPOHaUvhdtGnAor9cpucHgQ7ZudVCjaHXrooWEHMkeCsYsv5hksEGGZSEpstt2RDi1IFPHp1LTW+a4pv6wTl/yqTth2pXdcy6w5NeVB7I5DWYoyw5Qz/c6VV14ZBPLsOtt7772DMhJ1blDUpL7bugSOefxd9bBoZx35RqknPk6CfgmeCNlvU8skNa1N8AmJb+EPoQGLv2CMJRSwQfCOIFcCTkXTVhtjQTlecFYcvbhiOv2iiy7KWICH/9HnMx6hDmCGnCNHvvCFLwyLejS0MR1jRj1n3BGPfyWB/shmAABAAElEQVR0GJa5Bg6D5HspyU5t1ylx8Q1Kqyk0yyyzdD5jXJJHKMdXpSq8vykPkgCH8URMCM26jYX5pg6PtnEU9a/26FTL3/Utxw+ipMuYjWMqUMzVGGakHRNEmuvwaLVNvivqN4Wb9Yt/SylzFH3PvGjcuHHBeipjEpRrOXJzs802k5fOtd9tEyUx6iz1FUsEOjq1k6CKN3Xnf/3Op81Gylyevg/LRFhoZAMKdYV+FIsAzBl6Sffee2/naA6UVvLGfm3E32SuqvUV0hHPl6qmLSWfKNqxoULrMVjG4Zk1KauEXZSG1HZdJ63ClbWjjTfeuG+KgJoPkPc6dWYQbTM1Ttol87uiOSd5t+/wn0d1+tw2yjOFB5FuNu+hLMJ68I477tip42waitdS8W/7tCb9H7yPoxMhlFwpL+YNKIthtQgF2LapThtrO+664VGedmxXZe1d7bNO27TpQsnaUpXxrfXv946AI+AIOAKOgCPgCIwVBCYdKxkZDfmw5uiZgMRKGLLCwo7Roh0ZCFx5p1+b1jwshgg5RAinsbgS/7To3JY5W0xiQuye2HLCkTAxsXvBThzi9zJxnDe5i/3mPV999dVhhwPv2OGj3e15fovc2DWKEg+77RBWMdmxWBZ9V9VdC7D4l7WUbt+ymM7OcohFoDx8mKiORAJDzAWfdNJJHWHso48+2jWpCLhRIIKwxjDvvPOGuoMyjM5M7hpIRQ/W5O8gz0evkly1WdWH+BvrLr+xHwQdsk6ghUN4WdFipsKxYdswrbv82vdt3bPQAN8k/SwuQiwMPPHEE8OioM5ZywhWqRAFOpnntYtGwwLpsUNTfpmSPAmNLQY2HGti2bq3yYMQVNOX7rbbbuG4OhayIdq1Pe/bxs89wiT4gn70J70g1WFbr2081l1+ea/FUu6tH55Fwtd+p3e6yo+euSKsFKkMm5RJk7Qq7UV5tO7yq7S3ceV4M/rAueeeuzMGA594PEZcg2hjbeRRYaDAhCUAxiOXXXaZnLsK4lLbWCeCHt6oThTxbpVZm0lQmxkU35OyBHlCOFZGTdp1HC7HLaJkzg+Ld20TimIax2KJMibG+7LuGL9r8qw6ZHmNDc+6y6/ea1dznoKUxgXyW3StyqPj7/O+w4/cae954zBrVQWrSzom6GMf+1i2wAILxNEMe+51PYgjrMOjbfnYcrNhyt36te+b3us4EhRY2PQAcR/X3TbbZtU0U191VIQ9Kqbq9yn+BpFPm87UuTxrMCozLLFh7QCBca9JFm2p971UJGsyV7Xz5lgxuio+qfk855xzQhRYkuOoWsplnnnmqRptkr86aRWurPf0q42RqdGyHtSEH+gYPLupJi5Q+07+Yz/qI6173ryI922UZyoPYt1RVnu0vsL6VZHCoe3T1M/ZPHIvd+s39mOfmV+zeUdkj8KTWxvXOm2sjfiahJGy9o6yFJSiuILCttb5dWWzjpMj4Ag4Ao6AI+AIOAITIwKuuNLHUreCUMz1WgUCdvVwdAW04oorDnlnkxgrbsTP1m+Te5nJRREDs8eYn49/7EyCMEHfhhBQO8IxSWsFxcoHFjOKCNPXstYi6ydFfovcNUlcZZVVwg6DIn9V3JmoKz9W6FDl2zI/dlGk6mQILX3tSs4rJ9x6NTEty0udd9QH7V5QXoq+R0B9/vnnh9dbbLFFttZaawWhC3WZiSS76+OdDEVhVXFH+IIAAMIyTJHVFXa4srA1SJp//vlD9ChrqL3Y9MgqAIsnTJbzCPyZxLMgzo/jWcoU6NqIMy8dTdwQQIrsvdy4ItjWsROY7RWxa1m7D7FgNCgSf0nhl6lpXmihhTqf6tztjsOEG3Zo5VEveRCL2VXolltuCbvI2EnGT4t5Vb6t4ye1vrM4Kd7GjruY2PWrhVbFEfvhOa8MbFlJUNmkTJqkVWlvwoPy8p3ihtWRlVdeOXyKJRJ2y1saRBuz8bd5zzFkErozPqlDVdtYnTBT/S677LIds+UIsSwxHqYPbpsGzfeYK9Beobyxsc1vk3Ztw+EepV8sJOoXv2/jWce7wZ9feOGFIUEyLrUC0yEvGzw04UFSAshTeEVYVYWq8ug4LMbq1hIm7xlXavOAFIrj71AOWnPNNYPz2Wef3ZlrVhWS96MexGnWczcejQBbbSOv32S8pjYDD+wlMRdAyZ70MC7D4qbGisTbZtusk4+ll146eKee5M0BFVZb86JB5VP5SJnLk3eOrYBYn5FCtMLs5RUFBMZ+WBNsc70gTnOTuaqszqAYoPluHH6355R8ckToQw89FHCRQL9bPG28r5NWLFWIB5155pml0bfVxohktKwHNeEHUiLB8q7adQyw5ueMb4sUV+r0uW2Up9Kasp7InISf1lhYb1H9ivPeq/7PrmPJSngcd9PnOm2saVxNv4/X2uPnOHzea75eda3WhpEXfp6b/cbvHQFHwBFwBBwBR8ARGKsIuOJKYsmyGIaiCT8rAGYxU+74scRkSIJOBEkscjKJRWmFY00kTJA1APttP+9ZCJUSDdZD7PmxNh0s7oik6KLnlKu1SMPiH7hA7DDl/GkWMGJCEQDLGiwQYi6byd3qq68ee6v8jHID1la6EWbNWURhh5YUKvgGITgL8JjmltBriSWW6BZcpfcsgErBhDIh77jpJwE8kxvcNMnBr+rd9ddfH0zsavGERXCO45HfSgnpkyfq4bPPPhsEUV/60pc6yiZWiBQnhQVq7RRhkfqggw4KXpjca9GLdnncccfFnzZ6VtiUAcIAFllE1F/aOgIaLWboXb+vmKHVAghtRgv61IdjjjmmY3GI43PKdtUhyGRnOz+OpCijpnHG9Zu0yk1tjCtueYpBYM7uFZSWODOanePacYwQqmgRFuWdDTfcMGSNY6rgM8SjugOvwILRoCiFXzZNK/2B2h+7sO+8884QJPyD5zvuuCM3iqY8CGsY7Hxl8VxlTJzs0lf9w3y4djvnJqJPjqn1HZP+UgBDwMSuUvFlFqjhgRD1EiXSIkJx74ILLujgRFg68goBlhRXmpRJk7Sm4lOU36buRx99dGexmx2AVhgwiDbWJD8I/rGwQnnbI1awDoHJb40xOQIhptHSxujLpWyEkgrlxyI7CtQcF1bFIluc927Pg+J7Shd9Nm0Ogt9hYh1l7Txq0q7zwuu1G0cMQvTrm266aThWkj4bviWe13YamvAglPYgFB9lCYA+6brrrus8d0tvVR6dF84ee+zRUY6mPTOOU7smX0XEmA5CoYL0Uk/WXXfdIu8jyr2MR2P1DosyUNxvMn/Ya6+9wrtu/Wbw1MIfVjNkpQMlEXu86qDaJnMiLPEwnmDsEB/9i6U6xrXwuTLFlqrwtJFPO87XeJ8r7lDZmJ/3Vefy+IWwSIa1FXhtnvW1//rq3T/HShXNRdqMte5clWNYGE+i8AatscYaHQXrlHTVySe8irUfaJ111sloW/2kqmnluGRZxGBtD4zt8TXkA96EH6y+tkG0hdGyHtSEH6jfYh2Fe7u2wpwe3iYc2Bw4+eST58Jbp89tqzzr8iCbcMYWWmPhiOsiaqv/E49lLHHjjTeGzUmKU/2rntu8Vm1jbcbZ67DoZ1Gmot1D9L3CV1elgTVDrWPJza+OgCPgCDgCjoAj4Ag4AkMRmGzooz9VRYDBdt5g0+5MZLAa78pjAW6rrbYKu+Q46oBJFwNXCeMQlGJxZZB02223dfL2qU99qjApLHIxMWMgjuKKFqALP+jyAgsuWDlg4Yzdz1dddVVQuMD8NhMAzudGqKSFK4JDcQSrGiKwKzrjVn7KriwCVzGnibIKu32145dFUSbn9qx54mEibRV8yuLu9g7FBxRmIPDQrtH4OxQ+ELih/KOFafLFAi7fUXfZwUt6tZOCxXiZSI7DG9QzO5fi3Uukc7vtthtSB5Q+2hEKYJQBbY9dopSLCKEIgnYEDFjvof6W1W99V+WKUtoOO+wQ0ssi8AYbbBBM4FMXUQ7pZt6/Shxt+EGBacstt8zOPffc0JbYVYhAjJ0vEn6gzNG0Ldu0No0z3u2PMMIKlIkLSwn8sLQjs+1KQ/wsd64cG4PCQxHBo1E6AhvKlB2LUlQDxya8pijOqu4p/LJq2GX+9t1334zj48CE3dAzzTRT6M9od/AULdbEYTThQSwCsyBMv0Cfw1E17MS3imBYBuvlbtU4P0XPTeo7glqURmmPjBVQBkKBzFoi2GmnnQLmRfHD+1C+POGEE0J5SAEUdwT7lpqUSWpam+Bj097WPX0hQrtx48aFOg0m1DUWwAfVxlAwtUJ7O+ahDKWQCQYsKsPHIPqZp59+OghLEZjCy6eaaqogZNcYk2Nv8iwtpLax1LSGBCf+kX/G1rQLFLz4iRZffPGuRyHJb53rIPieTR9KCQceeGBQ6JYwR+/ZTWz7ySbtWmGy+G7rXa94K+NYxm3wOsZKtm7Sn/C+7eOfmvAg+jzGpWCDhQQUYcGK/g+lQLuRQVjG1zo82n5LGTD+weoQ4zbG75qHMs/giMMiYrzL7nUp5rET/H3ve1+R9457v+pBJ8KcmzIejXfGaWwYQJCZ2m/mRJvsxPiZuSlK8t/+9rdDeUkptY22WTdh8PyDDz44KGTQxpgDMu7FHaU/K2SvG3aR/6b5fOCBB4aN821cWIfUL29zCPFXmcsTJm1XCulgw1pDP4n6HY/NehV/3bkq/E5H4GKBknlQKtXNJ8J71n8Y8zO+7yfVTSvrKyjWsxmIdaGLL764U//IA3wUEh9ompfRth6Uyg+or/RtKAIw5qM+ijjOS0R5lSn01+1z2yjPOjxI+Ui5ttH/Mf/JIyxYL7PMMnmvGrvVbWONI+xTAChBMlYXsWmAXx4dddRRYXMe4xcnR8ARcAQcAUfAEXAEHIF8BCbNd3bXbgjIpH+Zv7yFXiZhTGhl/hIhHwIFBKJMurB6UBZ2/C5+LktP1XcsuInKzOWyqKydACxssPgD2TTZe4UpN13ljpIBu3pk1piJPufSIoxhYYrJgMyA6lswZocUx5ogSGbxO4/kX1frR+VEmeTtWNR7XfkWYRB5ZzIMoTSh/POMVQIWUPnZ73iXShI8Vf3e+mexiV1A1DOIBV4WvZk4IuBCEA3lpVVuedjZb+QvBDThz/q39/H7+J0Nh3cI39hpxW54lBPIg/1G9whqscAAIbjNO+qGSSKLmWDDrkhbZkpX6vWAAw4IC1Zq2wjVVX9JMwpWwj81Dn0njJR3ueuq97rKnSs4sotNChso2qCEgF+UPBBEoowgUhy6yj3vKj9xvKlx5sVR5qY6r3TEfjG9y24qjo/C6oo9Vsx+o3sW9jE/Tx1EUIXSCu8QtLW5kCq8FG+c7rznFH6pcGw89j5+r3TJnSvCsUsuuSRgwvPvf//70I4QpMn6Ce5xuE14EH2CdrqjKIlwXkorlBH1WTseiTuP4vTEz3nfpLrVre+KB4Eq7Q+MwR4lRCmtcNwDi1/aza1v4uuFF14YygZ8pLQCz8M6xWKLLTbEe5MyaZLWVHyGJH7Cg+pnlbKUH31jw0IIgOUxCMEzCshQShuz4SvOEFjOn97bb/AGr4En62eVwcreUUcQSIu3o9yFoBu+SFxrr712qAf0+zGltrGy9JS9U95Jh71XuuQWY8N76h7KRfDxGWaYIXzyoQ99KOzIpY8X6WgkPTe5DoLv2fRixQ9lZNowec7DRf6btGuF8eSTT4ZxrZ51NKie27yiwM/ch3EzwlHmFihaMJ6XgnbZ2El1RdcqaUvlQfQ3KEopPfBZBP9YMYL3yqJdWVrq8GjyorCYIzKOJA4UEFBa4R6lbFl/Kcu7FZpWtezZz3qgfObV7SIeTX6xtEa/iYCNb6v2m4qPMOw9z5DcdP2v63//bRrj9zzTV8OPIeYGWL2C2mibIaAJf0pDHL/e2yvKblgtZS7Fd9RZxlFSWgFD5r927M/33eLQe10VZ2o+q+RFcXDVmJ97peEjNebyfIciP3NiFLni8ZVNj73nuyZkw6J+qK4oTL3XVe5tXOvMVcGUtLHewdrVwgsvnJsEYa+rPNn018knbVjrOWxWge9aUjw2fL23bvY+fh+/s8910kq41B02W1F/6BtYv2JuzY97xpIrTthYJSueSkvq1db7KmFY/6ltMy+esnKw/pvESftkfK5xrQ2X/o+xK1ZRZy85urdun5tansKjDg/SN7b+2Tzae/nRlXdt9H82Dto7SriscaCY2ybZdNdtY22lw6bB3peFH/uLn+23tq1Z96L7PP954ee5FYXp7o6AI+AIOAKOgCPgCIwlBCaZMGD6z1jK0GjKy8svvxyOn8DUI0I5LXiOpjz0Kq0syiL0RzGEc9vbFEC0mWYEM+xYY9EL5RUECijX9CK97CSmzmA1BCFNEe24444ZZlFZOI1NfCIIw6INAi1M7pdN9IvCd/fuCCDMYFGYxWqExihLSMmp+9f98QHr53gFBJsIMrFi0ot6a3PTqzgR3GCpgiMyWBxsm2jnCLQpTwRUVXYqt52GsvAGxS/Hjx8f+Am8pOoxPU14EHyWvKKQwS5MlGWwvjJS+84m9Z2Fe3YYcuQL4wN4vhY447qAQEHm7bGcheCX/pMfZQNGZdSkTAi3TlptOprgY8Ppx/2g2lhK3uBXlD39PIJJxpjUgbLj3xTPaGtjpBuFQvFkdj1LOHTTTTeF+q+8tXXtN99LTXeTdo2w6LDDDgtRw1856gBhTK8JJS34HII+iB3WjHnXXHPNIZZ12kpHKg8CW3gtYwKs3Uw99dSlSWrKo23gxP3II48Eq1DMj6qOLbFmc9999wUhMJhWEYQMqh7Y/Na5h9+hbFOl36wTbtt+m7TNpmkhbsb9zF2xGsgcJVYMaBqHvu9VPjk6hmPhUCxnQ5BTGgKjYa6alrPBfUWfwsYg1lpQWtH4q8359WhdD2rCDxjXYolJFudQaMLaCBvJ8qitPrcf5ZmX/hS30dL/peRttHzDxh6sQmJxW5a88tLOOIWj7VmHvXWCRSEnR8ARcAQcAUfAEXAEHIF8BCbLd3bXfiAw/fTTD9nx3484R0scCFm6CdtGQl5Y0Ed4ym80EAsnRTumRkP6R0sa2bGSZ756JKUf4RATZn79okHE2UbeaOfsBh+pNCh+mVJ/mvAgFKzyrEWM1HJpUt/ZtcnidCqhLMevCjUpE8JPTWsTfKrkq00/g2pjKXmAX3HsIr+6NNraGPmT0gr3KFhAGpuFh5b/+s33UpPfpF3fc889nWjXX3/9viitECFKdyKUqB5++OHw2CulmVQeBLYosTahOjzaxkPcHBdUh7BGiNIKhLWVKkor+B1UPSDuFGJXfpN+MyXOlG+atM2U+Ow3xM14th9j2kHm0+bZ7/MRGA1z1fyUj1xX+hQUwXqlDNZWzgfRNpvEGc/DOdKrSGmlCKOUPne0lCd5Hi39X1H5uLsj4Ag4Ao6AI+AIOAKOgCMQIzBp7ODPjoAj4Ag4Ao6AI+AIOAKOgCPgCIw0BH7xi1+Eo8EwD4+1H4jduBwliEl4CCtcVS1RhA/8r4MAWErJgaN7dt999867XtwceOCBGUc8YaWO3c0Q1iDYTc0ObZRZNttss15EPabD5Fg9rJNxhIXKEEs2VbHsdz0Y04XhmXMEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBGojIBbXKkMlXt0BAaLwJFHHpm98cYbGZZ6ygjzqZhVHw27Dsvy4e8cgaoIIODCfDBm850cAUfAEXAExi4CKDPccccd4UcuOe6C44LeeuutkGl2Ou+5555jF4Ae5+yFF14Ix/WwG3+DDTbouUVBjuHi6JozzjgjKKlgwQlrK6I99thjxO9eV1pH0vWcc87Jjj322CFJQkmII02rUL/rQZU0uR9HAAT22WefcExXilUxR9ARGO0I+HrQaC9BT/9YRWD55ZfPTj755K7HJ2OpG391rQaNVdw8X46AI+AIOAKOgCPgCBQh4IorRci4uyMwwhBYZZVVKqVoscUWy/g5OQITCwJLLbXUxJJVz6cjMAwBjn3gh0lrfk6OwFhGYKaZZspWXnnl7IEHHsj+8pe/ZK+88krILu6Mk/bff/9wfNVYxqCXeZttttmyRx99tJdRDAl7xRVXDGX41FNPZf/4xz/Cb4oppsjmmWeebN99982WW265If5H48MgeDRHKxDv5JNPHo4I3XDDDcMxQVXx63c9qJou9+cIIBx0cgQmVgQm1vUg5jf0a1hm41pGg+hzy9Lj7yYOBKoeK4uC9jrrrDNxgOK5dAQcAUfAEXAEHAFHoAECk0wY/P/XLnODQPxTR8ARcAQcAUfAEXAEHAFHwBFwBPqFwGuvvRasc2B15b3vfW+/ovV4eoDA22+/HRRYuM4888xB6aIH0XiQjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AIzCCEXDFlRFcOJ40R8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAExjICk47lzHneHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcARGLgKuuDJyy8ZT5gg4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOwJhGYLIxnTvPnCPgCDgCIxiB008/PXvxxReHpHCSSSbJvvzlL2fvfOc7h7gP+uHJJ5/MLrnkkmHJWG655bLVVlttmHuZw7333ps9+uij2TTTTJNttNFGhV7PO++87NVXX82WWGKJ7JOf/GShv7H24t///nf2l7/8JZt66qkz6sNIoom1TEZSGXhaHAFHwBFwBBwBR8ARcAQcAUfAEXAEJk4ELrjgguyVV17JFltssWyFFVaYOEHwXDsCjoAj4Ag4Ao6AI+AIjFkEXHGlYdE+++yz2TnnnJP97ne/CyHtvvvu2aKLLto11Lfeeiu78sorsxtvvDFDSDnrrLNmhx12WNfv+uVh/Pjx2V577TUsuimmmCL74Ac/mH3kIx/JNt5442yGGWYY5scdHIFUBE444YTspz/9afh85plnzo466qhhQSE4v+2224L7u971ruyss84a5qeJw3/+85/shRdeCEHMOOOMGXH0in7wgx9kTz311LDg9957754rrqAY8de//jXkj3x2o1/96lfZRRddNMzb5JNPXltx5YYbbshYbJltttlKFVfOPffc7Le//W22zTbbjHnFlTfeeCM744wzMrCB//7jH//IJptssox2sPTSS2crr7xytuqqqw7Dv98OE1OZ9Btbj88RcAQcAUfAEXAEHAFHwBFwBBwBR8ARKEPgwgsvzFifGTdu3KhSXKm7BlWGgb9zBBwBR8ARcAQcAUfAERi7CLjiSmLZYi3gtNNOC4onCLpFn//853Wbe/373/+eXXbZZUHY/vvf/77jZ9555+3cj4Sbv/3tb9nDDz9cmpRTTjkl22+//bKtttqq1J+/dASqIoDSipRS+GannXYKyg36HiUvFFXUdnphleSPf/xjtvzyy4coTz311Owzn/mMom/9us8++wSLIgR8//33Z1dccUXrcRQF+I1vfCP79re/nc0999zZj370oyJvHfeFF144O+aYYzrPhx9+ePbnP/+58+w36QignLPhhht2FCAV0ttvv5395je/Cb/LL788KLTonV8dAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAERgNCNRdgxoNefI0OgKOgCPgCDgCjoAj4Ai0j4ArriRg+rWvfS3D6kNdev7557P1118/+9Of/lT304H6x/Qkx4GgoPP6669nv/zlL4PCzptvvhmsxMwzzzzZsssuO9A0euRjE4Frr702++IXv9jJ3IMPPthRWuk4juIbrGiIJp100r4qrijeqlcsLdljfU4++eRkxRX4CZZspp122qrRj1l///rXvzIsdWG1i2OBllpqqWyllVbKPvGJTwTzv48//ng4oklWvcYsEJ4xR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBByBiRYBV1xJKHoUUKCpppoq23LLLbPZZ589+9KXvtQ1JI7lkNIKQslddtklO/bYYzOst4xkQpC67bbbDknid77znWBtBQsY3/ve91xxZQg6/tAUAZQaOCrlmmuuGaK4cvXVV4egsbTyz3/+s2k0/v2AEEBhxyrtDCgZIyLaxx57LEMhC8LC0L777jskXZ/+9KezHXbYIfvhD384xN0fHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHIGxgoArriSUJJYHOOJjiy22yN773vdmP/nJTyqFgjD+U5/6VFBYWWKJJcI3KK6MRlp77bWzr3zlK0F54IknnhiSBc4tzROyzjjjjNlcc80VFH2GfJDzgGUXcP3FL36RPfvssxmYzz///OHYFhSG8gjh79NPP533quO2+OKLZ1iIyaO6cZJH8vrRj340W3TRRYcFiXIP1hTiOH/9619nd955Z/CPBYvJJ598yLf33XdfsGpDffnsZz875J0e6qZV36VcSStptkTaPvzhD2cLLbRQNsUUU9hXrdyDJwpiTz75ZMBizjnnDFhed911IXzq35VXXpkbFwovd999d/bAAw8E6yyU0WyzzZZxHNfSSy+dzTrrrMO+s3UH/6Lbb789lLGeua6++upDLIVggYgyw1rG5z73ueyuu+4K8VNv55tvvmBBY5lllrFBtHb/3HPPhaOVaIMcQzbHHHNkCy64YFAKIT2WwMVi9tRTT4XXKNNxZJAl2huWltqk66+/Pigj2TBpy1gXqUIvv/xydtNNN4VyxTrNxz/+8Wy11VbL4CttUlm7BuMf/OAHIbq4XaemAR4n2njjjXU75AqPWG+99Ya42YcUfpDSTmyc3KeUCTzxxhtvzLAkw7nc73//+0M7ob7ltU0b51VXXZVxjN0CCyyQcXQVfImjxe65557Ah6j7tMH3vOc94bOY177wwgsZbRre8O53vzvUIY73e8c73mGjGXIPTt///vc7blgJWnXVVTvPbd6k8to4n1X7FPJ27733ZvAP+C1Hf1HHp59++sBLOCZt9gmKuTGhQEidm3nmmTMUq/IIC230gSi30u+jrCtKrXuERZj2eEaFaa9x20zFhzCpoxybSF9Efad/oH7NNNNMod+HBxXVn5R8qg4UYQsfffXVV8M4hnxaapJPhUP7fOSRR0LbwqqeJepCm32Z7XdtPPY+Lku9Gz9+fHbrrbdm9IEcHUh7po9nPAYvoY+IqRs+8GKOK8wbe5X1C8STN97T2ID6AV8qIuIk7ve9733ZWmutFbwx/iVf5INj5CabbPh0TWNFPlhzzTWzqaeeuiiKyu5l+exF/xcnDN78s5/9LIzlGZvMMsssgU+TP8olj+qMg/g+le/pWzuOyktPXv3Rt3XHpnxXVia8z6t7uHer76o/RekljJSxBd+lEMdlMm4uI+YGtHFLZfjUqbN16h7jF+ppN4rnC/KfMg564403hoxFFJa90vdby4i8Ex9KnaPU5bVsZqHvYD7Cxpciwg9+ocUWWyz0o9QBiPGuxnHBIfqjrJ555pmMTQzwx0ERvCSFHzRtm3XLRPiktjHmPbSlj33sY2Hsw/Gy8A94xyKLLDJsXqz4uNYdB8EDUutBat1relx3P/Gx2OqeMSHjIdbAaO+0HcZrjOE5DriM6vA9hdOkX3jxxRfDeOcPf/iDgutcWduJ1/jK+Dsf9rL/6+d4uANCgxs2dN1xxx1hXgePoOzBlLWoMqIvYzzOOJR+RmtXWqvO+7YuD4p5ZZ01KNUB+v9bbrkljNPhRZtttlngL5dddlmYx7JOvP322w/rO+ryIOW3SbsmDOaszNmYQ3IkNHPXGWaYIYxhVlxxxbC2rbiaXoVR3pp0nXFQnXQozm7fxPO4pmOSbvH5e0fAEXAEHAFHwBEYWwhMMmEQ9Z+xlaX+54YF5q233jpEfPrpp2drrLFG5USss846weIKk2aEeSOFWJRjwgvtv//+2Y477jgkaViJIe3Quuuum5100kmd90x8WGQuIiwtHHfccUFgmeeHAT5HZ/zmN78Z9hpliW9+85tBaBm/POCAA7JLL700dh7y/NWvfrVTVvZFSpwI7BBqjRs3LuP4qJhYtGPSgoLPNtts03mN8JX8QcRrj0v54x//mDGZee2114JS1M9//vPOd7pJSau+Tblut912GYtUeYSAk/xRB9oglMEQRCMcY1J61llnZXvssUf4MRnffPPNQ9mD/YknnhgWLa2yEgshKIcx8c4jhFtHHHFEOLLLvq9Sd+T/u9/9blBG0vPFF1+cffnLXw6PWMyAB8SExQzaUaxMYv2x+CnLTbQvhFhlRF0/5JBDhimD8M0nP/nJgI+tWy+99FK25JJLlgXZecdRPuSrjJZffvnQRuF9tKtuhJITVqcsoVBEeRcRR5Ax2YcXgUnME1BaOffcc4OyTlEYdd3L2vVRRx2VnXHGGSHIuF3XjUf+TzvttOzoo48Oj9T3ukoRKfwgtZ2QyCZlQh3cddddO4vSwoArC61HHnlkKS+h/hIGPAnBfh7fRfBLHwFZXnvqqadme+655zBLTYRJXY+VPUIAE/44ogmFN1Ev++pUXmvzWadPQRkDwVoRwa+wKnfwwQcP8QKfYzEOxV0WveGrMaEQg1IQdPjhh4cFRu6b1D0WH6sIGOK2mYoP6WXsc8MNN3CbS9TDCy64YFi6UvPJoisKegj8WIyNaZVVVgkCu0033TT0ZfZ9k3wSDkpdBx54YIaCVx4xroNftUVV+t28Mdthhx0W+H5ROuBRp5xyypCxFX7L8OE9x98df/zxuWOvsn6Bb/PGe9/61rdC34jyCcoVRaTjRxnfMs6BaF+0M4jxA1hZQriHQIKxDkphjAeKFKjsd93uy/LZi/5P6UHwRlljZS+PGMuAzZRTTjnkdd1xEB+n8j2+rTKOQvCGJTVLqfyAMMrKhPd5dQ/3svo+EucajH+7KQHkzUfL8KlSZ1PqHvMPft0IpVcE+5ZSx0HwZcbmZcRGAuq3pSZzlBReS37BFKVk+hSuecRc6Rvf+EZ4Rb+DEoTGGpr35X2H8jLzDxTbGDey5jAoSuUHTdpmSpkIn9Q2hmLRK6+8EvocxpjMyyyhYHj++ecP2xiUwvcuvPDC5HqQWvcYdzWhfuITp5O1w4MOOigoVsfveGajnT1yWX5S+B7fpsw5+Q4lECyKogzAfR4x9o03l5Xxd8LoVf/X7/FwHh5V3IQPcwKUnePNlPBV5sTMHWJC0YV5N3OYPHEA6zvw5lhxOoUHVeGVSl+8BsW6HpsrGOPauoMiC/2BlGD4ns1QrAuJUniQvk1t13xPvF/4whcKN1Qyv6avpnzaINWDvDXpKuOglDQozm7fxnPyJmOSbnH5e0fAEXAEHAFHwBEYewgM38I39vLoOWqIAJOEt956K4SC4JmBNoNgUZFVEN4z+fjABz4QhIXsNGTh48c//nG21157DZlYKCx2nrJbjDgRJK6//voZ1jbYncSCJoJrJgJo3KM0kUcI09gFLWIypl1dcrPXNuK04TW5R6kCpZUiGmRaERwwSYEoB3ZnsAOdskQBAQsYbRICaAT5CDJYxJRAA3cmqnn09ttvB0EOdYeJLnWH+scEkjrAFeE177WzmXDwS72B2EkkBSh2bse7lbBGUkQsxFIvUeRhARkLMSg3nXnmmSEdUnAr+r6qOwtVEmSxy5r4UHTB8gTlwoILAtfLL7+8EyTCFATjIhRGUPrhu3g3OLi1TcShcquy68/Gz64OiMVqhHQIAVn4pTzZcQNvUfnZ79q8R1B4zjnntBlkCItFN9HZZ58dlKKswpHe5V1T+UFqO7FpqFsm8OGtttqqI2RAGYSypE9gIZN2h0Ifu5G6WXVAWQLBIItpLFTDf6gL7AC1i1o2vSzc0jYRvqNogdUQlFL4hvuyfsyG04/7NnltWZ/Cwhk7ltldxy6t6aabLvQ/YIsVFhRFJIzYZJNNOllHIQXBOmXGdYMNNui80w0KfhBYS8mV5zbqHuFgcQdLLpa6CT2tX92X4YMfMMK6Cop3H/rQh0J+4AUo0lF/+KFwCe/Fr6itfCq8ptdu+WT3JH0GO/MgFmxpVxDjLYQcvaK6YzYpQH7kIx8JPGT2CTuLoYceeii7+eabg9Uz+huEILZMgqdR8ocCPPUKIR7jIJRxUEiFaJccNYrSCrwChZs2lFbKoOlV/0ectBWsJrALE0I5jb4efkS81D+E1JqHBE8T/lLGQXybyvcUr670XygbieCbVpFa7lxHGz9IHVvYPKfeM1YtUuDttnPcxlmlzqbWPRTr7ZiBsT7tEZ6EME0Uz1PbGgfFdU/xdROC1Z2jNOG1WCdDYRjBZ0z0J2VKkPA85voxfoSD8jj8YKRRXCZl/KAs7d366iZlonhT25jm4cyN2UWP1RvGzyiyIDjH4qitg035Xmo9aFL3hFHKtd/4sK7BGgzEugbzG/pP8o/lPObaKCnGlMr3mvQLlKXG6KxbsD7I/ANlC+bw/aRubWyQ4+FUHGQ5l7ET63H0f8zPGC9+/etfDwodsSVClKMZM0P0rSi3MJZE+QUrLOedd15oz/vtt9+QZKXwoDbWoNgMyHoX81PGWihRQWzAgAfBcxkvYj1UFgib8iDCr9uu+YZ+T+NBNsgwLqA/Yz2bzYBYHiI/vaYq46CmaWAOxpwxJrX32N0+1x2T2G/93hFwBBwBR8ARcAQmDgRccWXiKOdGuTzmmGMyfjFxXANKAFpMj9/zvNtuuwXBAvcIFBEysxMbk6ZMpuwCBwt6WJDAH8J0hO7WJDTfssMUxQ52aeWliXgQLmHRRVSmuNJWnIqryZUFBnskRRzWoNPKYojFlYUGFqooLya+bSuuICjk6BCEGQgJEVIgcMD6hlXIsDgxMWZHD0IrhB6WEPQwKUfQyG4Mq7jCpFeWBxCAS3EFJSpZHrJhFd0jRGKiJoEfiiIIZJg4opTAToh450pRWEXu1ANZOGJijoBYAlwmweyuIg3UJxYREXhB7ExkV5YIJRIm1Sj2WHe9b/tq46Cdg0kdQoh36KGHdj5B8YGyZgHloosu6uxO73ho+YadoCyAtE0sMmG1gXqJQgaLeSwK404eJZSN423CD1LbSZyGOmWCEFk7YxFIsGNLQmWUImhrCM1POOGEroorLJ4iMGTBAwGSiIUg3uURGMNfUUKA2GEJxjKha4VQed/3060tXtutTyEeyiSPJ6GcCI9kARxLB1ZxBdxQ4APvK664YpjiCpjqWDcW0qlvorbqHjye/scS5VtnIbAbPoSN5Y48fHgnSxksvCPAsTtF28on8TSlKvlksVdKKyzqW2xpm/CmXlGdMRtpYMzJWJB6KB6itLHQTrlQHtTbsvGpvhmpV/pMFuZZjGesjeUfFPuwYIQCOXmHX4qn9TIfver/SDPjMSmt0KdgecUq4iCkRlkewZwodRzE96l8T3HriiVFqyRPWUhQIT+6jiZ+0GRsofw2uTLut3ON1LCq1NmUukd6sBzKT4TSHMJO+I0dp+q9rm2Ngxi/2PgVfrdr3TlKU16L4h1W22KFfywAlG3SoC/Cog0CV0vMz+xuevtu0Pd1+EFRWqv01U3LhLibtLFtt922Y2WUsDhOBKujbGRhPspajagp32tSD1LrntKeeu0XPiiOYy0DYh5Pu7DzIdxZN2FuGVMK32vSL7BOJAtVjPdQjEBpBdLxynEae/VcpY0NcjzcJN+ME2XRmXCoH2ycwloJc2OrVMk6rJRWGO8zJ9e4i/Uy1m/ZoMKGHeZbdi0ihQe1sQbFJgnGBowJlRfmqfQnKI5S/6lrKN2wIQ1qyoNCIBP+6rRr1rfBF0JpheOsLDHGpY6pDdh3bd9XGQc1jZP6kGcBt8qcvO6YpGla/XtHwBFwBBwBR8ARGH0ITDr6kuwpHikIsGMKzfGqxIQIZReIiYRdhMYNIYOEjmiqW6UV3nN2KcfoQEymmEQ3pUHEmZdmJlqYUiyjkZJWpXHBBRfUbTbNNNN07tu8kXIJyhgIULGuEC+A2vioVzvvvPMwpRX8sKubc9MhCdDDQ4t/WCKQ0grBoliiY6JYPKIMmxLHRyn9LCZIaYVw2U0DVlp8KFLwaZqGfn8Pr8DqjqWNN964gzXC817StddeGxbXehEHRx+wY17CRwn9saiDAgsC2njRg3Q04QdttJO6ZaK6iLLi3nvvPUTgTL+AdS0IATkCoG6EIDNepIU3oCiRRyiRCWPeo9gm08koYIxkSuG1VfoU8lyklAGW4ut2wZJv4DPwHghF1PhoGXb5SQlC/oLnCX9t1D2F1eTaFB/iZkwCYVUGxShLoy2fts2pn7T5GUn38EQWSmOlFdLIArzcOfJyNBOL2izIM3bBMhVCCQRRHEEEYSFHi/O9zGcv+z/SjVUGiLFTrLSCOwvbHKdnjwlqOg5K4XukJZVGEz9oMrZIxaft76rW2ZS61yStbY+D6qal7hylKa9FgBcrITFeQNm8G7GBwPZL+Ef4rbFFt+9H2/uqY5KmZdIEFyyJWsE4YdHnavyj+q042uB7qfWgSd1T+ute+4kPWMuaCvP+eD5E2lGsRHEsphS+16RfYP2G8oBIUz8E9nGeea7axizfGenjYeUTax46XlJudsNBPM9l3QFCiQ1lS60b4ca9jpfC0h3jTkuD4kEoHUOMCTU/lRvzMBS4IFmE4b4NHlS3XdNHaaOT3dBAeiDwRfHUYv7fN+3+Vx0HtRtrvdDqjknqhe6+HQFHwBFwBBwBR2AsIDDZWMiE56G3CHC8AwNLFEXYIYWyCotOKJmgYc2ElAX1PGLygMlGBvBMejneA+IoiNhkpd2pyO41jliRcoquWrDiysQSs8wi+dFzlWvTOIkDLX/yGFOdnd8XTDhfljNawQTlHI5TiqmNtMZh1nnmLFzlk4VHjpAQYSWiF8TkGHPSOr9Wiizd4nrzzTezO++8MwhUOVdXCyYIOyCemYzHylPdwu32Pm8HJOftiuooeumb+IolBJHd7Ss3hPMco8FO7TbiU7iDvGKCVIsUNh20Feoh9ZH2L4Gl9dP0Hl6jXZ+ULzvF6rTtKvGzY5GduJdcckmwVMEOexH3mOklXtqCFjra4AdN2kndMlG9xZy6TPgqj1ypy+Qfot7GyhLhxf//sVAlS0LWvewes+YxSQkOoXAR0Z6sGWtrJazom6bubfDaKn2KTSc8lnjZ0cxuNXaL0cfCI+0OWn2DFQ4sP9EW2GVrFct0TJCOctE39ppS91h0FjVt63Xxoe+jDtNvYO0CjDSewVoPi6N5lJLPvHBS3arm0yp1Mf7CmkmvKWXMZtMED4Q/ohRqj45AKYFyKtvRz7g1HoMylutGKeM92ggCJoQ173//+7tFMeQ9yhxYGESIwJiGH0SfgQJgr6nX/R/WiiRkQMii/q1bvtSf4K/JOKgu32taZ5vwg5S6F+NYhR+0MbaI4+3nc9U6m1r3muRF9baNcVBKOlLnKE14LbuuURzWZhQUWRhrwH+LxtL07/T3KKzpSCGsMuUpcafg0NY3TfmBTUeVtmn9NykTG06de/qdeKxDOXKMMEeVFM05U/le03qQUvfq4BH77Sc+4tModErxP05P3nMq31N8hFl3jY75O8J/6oE2qOWlrcytX/0faRjEeLgs71XeYaU4VgjSHJfvsYhkSetqjJWPPfbYznorfsTXVGZFSuD95kFYbRGhqMIGSqvQjBuUN/ZP5UGEV7ddU99Z52DeyHFerFkwTsViYr+o6jioX+kpiid1TFIUnrs7Ao6AI+AIOAKOwNhDwBVXxl6Ztp4jNNil0U7gDOCZJLMLgSNcOLcW895owMeEacWYWLzCrGxMzz33XMepigUFJt9WcYWFMCieuHUCzblpGidBktYq6c2JPjghKMTEOISCEBPmPMWVNtIaIkn8Y6EiPg4IIQPCy7wdBYnRDPmMM3fnmGOOjLyzOFbl2B4W/zjeoUwYTSQoU7WtuGIXO5QRLDtogTbe8SI/da6c4yvKi4932nVi/eqb0XgtyueMM84YskPbZ1GmF4sC7PCE17CAg8lVFEh6QRyPxm4pfgg577vvvqDMgnUpFvDZ8cTOQ47ZgZryg6btpG6ZyCpH0Xeqs+RNfrnPI3hCXcqLV4tc6jvywmTh3C7+5flp260pr63ap9h0U6+0e1Pu9PWYj55lllnk1LmCCdYeMHeMogq7cMEKnoMVFgirSHmUWvekgEiYtMdUSsGHo4+wBGeJ/o/xjyz32Hfcp+YzDif1uU4+UT5l8Rpew+5dxiIcxwURTi9I7a7OmI10UMc4RgflqjKyik6xPwRtKZQ63qMtQfB5LChhiQ3FyyrEuOeee+7JLr744uCdBXF2y1ZV8qgSR5GfXvd/KKiL7HhebkVXO7bJ4+18pz7F+o3Dq8v3VGcJp+74sSk/SK17ynNVftB0bKH4BnWtWmdT616TfGls063OEof8Nokv/jYv3rI5ShNeyzGpjF8ROGMhj/qPtcgf/OAHIVlYY6NOa2e6Tevmm28eLEvR7yLoZu6JEgs8nf6CNYiRoMTShB/Y/FZtm3zTpExsnCn3efWHcDQXQ5BMeWtszbsmfC+1HjSpe6Q5lfqJj/g047Q6Y4FUvqf4wKbKmpddo2NugJU8Nmgwv0GRDYE1SsZSoOiGeb/6P9IxiPFwt/x3e59X9+CVWn+yvAp+w3HREIrfHCdURpSlpUHxIDvv0yaSPDeUVCw14UGEk4ct7mV8b9dddw3zZ3BmzZsfiugcHbTDDjtkc845J0H0jKqOg3qWgIoB52FbNiapGKx7cwQcAUfAEXAEHIExhIArroyhwuxnVpgIsaCAgINFChaW8hbhmUyzuMuuKk2a2JGDiVMJupRuFjwg/GMuvBvFg35NwuyCSbcwmsZJ+EzINYGy8cUTJ/vO3h9++OEZZxWj+LPvvvt2zMBbP9y3kdY4zLrPmiCyQ4MFRH6kn4UTrHz0gracYOaWXVMciSNBRFE8LGQecsgh4TW7whD4MFHULmcsJ2A6E9KOkvDQ0l9e3aMN8KMNsMu7KWl3MuHY3Sc2XO1AsX7t+9F2X5RPK/CkDbWtuPLMM89k5513XoALhRIJcnuNH/nAuhA/dqHL3C98U4orTfhBG+2kTpnAJ+gnINXNGEOOwxB1q7cySy7/Va5FR0NU+XYQfprw2qp9is0X54Wj7MeOcKxUQVgV4VgSLKrltS2UG1BcQcCGYB1LaldeeWXgrfA8hAcxNal7VnElj9fGcRU9p+BD34PyAfWYhXb1gSh7cB8rVTbJZ1G667rXySfKFAgX6T9ZpGbRs9eUMmajDmy//faZdoBSb6l31E8pEqBsRTkV7eYnX/iljlpijFr2DX5Tx3u0Z8aE8La77ror/LBYOG7cOJuEwnvLvxh/x2kv/LDBi370f+rHSCZKPVXJ9hFFfZH6Gus3Dr8u32MXq6goXr231zb4QWrdUzqq8gOVSepcTPEN4lqnziqfpLNO3UvNV9vjoJR05PWb8BJ+8RylKa9FuRVLUfSRKBkyRsB6CnMv5mSsHxQJwBlzMFfDktYRRxwRrA7quAqEfhofpWDQ5jep/CBOQ9W22bRM4njrPhfxPDsXY56retaU76XWgyZ1ry4m1n8/8ZEF3Lq8K5Xv6bvUfuHII48MljDgAz/60Y/Cz2LX7b5f/R/pGMR4uFv+u723Y8RuflWW+MMq8AoTjiUuIzvnHiQPog6I7H2ZW1MeRNh12zXfoJzO+PL444/vHBP+/PPPZ/zY7LHuuuuGvrEXY/k64yDSOkhSX2HTUDQmsX783hFwBBwBR8ARcAQmHgRccWWUlTULPnZhnYE7i9iDILsLu8iSBBYCMIkMYV6f3cmYTUQowo5Xq+zAOasQu6/YqZ2nDBI8FPxpcTrvGIqCT8LZrrxLjZNvUeBBABETVgFsWcXveWZnuo4bQGmlLO1N8cmLv47b0ksvHSw+8A0CJxYi2fXMbg0sxlwwYTddLwjBTlXhDvULmn322UM9i60AFdXTttLNImt8pA3CKgSbUN7Ogrpx2wUEBM15dUbWZqzfuvGMJP/KT5wmaw62DWxt+PBaFOjgDSg/xWdHW7+9vKfdYdEIk7x2p1wTftBGO6lTJixCcP42Fj1ic8XCzrp3q7e9WOhROkbCtQmvrdOn2LzaHXcor3C0H0dkYf6dZ44HjIkdkygMUK4IoFAgoH+H6N/zlF2a1D2rDJq32BWnL+85FR8Wd3XsG+lAqYe83Hzzzdkuu+wSlCrtcVRN8pmX7rpuKfnk+CcUyMTr2CHLM4oWbShdxnlIGbPddtttHaUV+PPWW289JFiUT/Is+g3xNOEBi1bx+AArJiwyl1HKeI8xOuNf+DfxcvQPggPGjVgd6EYIbO34inEGuzlpm73ihf3q/9SPgUGd8ZntI5qMg+ryPdVZ0ps39sI9j9rgByl1T2mpww9UJk3mRYq3n9e6dVb5JI116l5qntoeB6Wko84cpQ1ei5IhCtdsWNlxxx07/QgKCTH/tfmRYssBBxyQ3X///R0+T7vfeeedOwrl9ptB3KfyA5vWOm2zjTKxcde9Lxr3CwfquB33NeV7TepBat2ri4n130984F/MC1mDqUOpfE/fpfYLWFFgTZAjU1D+pi0vssgiwaLfgw8+2DUL/er/lJB+j4cVbz+ubMJh/Zg+k01hYFuVBs2DqqZT/pryIMKp264VN/WbuTPj/QceeCDMj5kzsibIhjyOXNZmJH3T9Fp3HNQ0vqbf1xmT2LjIp11jH6Q8xKbL7x0BR8ARcAQcAUegfQQGo/HQfj4mmhAPPfTQYF4QayP84l2+/QRCCxXEWUV5hiOC2GElio/ckJlwBqNWQCv/3a4MfqE6i8lN4+yWpm7vv/KVrwQvmEHuJsAYdFptXtjJSt3TEQnsoJFFBeuvn/fsfOMMdIgFh7xFUYRH3cju4mCBpg5ZU7r6bvz48botPXLE7iBEUFxEVmFM+Y39yt36jf3ouW4e9Z3SW3Smufy1cc3DlXDljrJQqiC7KH1XX3112CHKe6wQ2B2FRd/0yn2BBRYIQbNjV5TKD9pqJ8Je6dFV7nGZqC6qbsq/rtZdfvVukFeU9Njxq58s8PQrTXV5bZ0+pSgPLGpuuummQSEDPygu2J3F+g4hhayq3HDDDcH6ivgd1lhialr3ZAmGcGMFwTiuouc28IH3Ye75jDPOCDvxGLOwGClqmk+F0+Sams9zzjknRMtOQaztsEO+V0cBpozZOGoCQrF5ywnW2GJip6FdzIzfD/IZnk27QOkEou/tNiZhLKBxs47L4FsUYHTEJM9tU7/6P/VjpB/LjVXJ9hG277Dfy936te/j+yp8zwowZMkvDid+Hm38QGWSOheL89+v57p1VvkkfXXqXpP8qC6qbsZhWXf5jf00edb4zIahPhs3eyxiG7yWcbMsqEr5kaOHqwhKmRNj+QDStyglVhnrn3rqqUEJEUVEjo7tFaXwgzgtdfrqNsokjr/Oc1794Xut2XB0hpQp2+J7qfWgSd2rg4n12098xL9QuqtznKO+I911+J6+a9Iv3H777Z2jgVDgZXyJEluvqU4bs2lpMh7uFw+y6a16jwURWTBW2636bds8KHUNqkp62+JBddp1XrqYr6666qphznjZZZd1vFRR2Op4rnhTdxxUMdieecvDtmhMYhMxkuQhNl1+7wg4Ao6AI+AIOALtI+CKK+1j2tMQ4wX5+LmnkUeBcySAaL755tNt6dXuaot3iWCZRQsedhdkXoBWgMt7cNBAt47iSpM489JV141FL5R+2NluFSbywhl0WvPSpHNvMR06aMUV6o4UqBBexYRbrCwVUROX/QAAQABJREFU++EZhRfVw7pKGbI2YMPVee64SQHBvte9PYYGoVQRIURU+tjJGBO7E5Xusnaps3kpQ1mEicMqe1Z6mXizkNVLoux+/vOfD4kChYIf//jHwU2L20M8NHzQQjnKWViP6BVxzAqKAUXEwgs7dSArQE7lB221k7plMv/884c8PPHEEx2LCcHh//9kMp7F5vgYOOuv3/coaLJjSz/MDg+CqvLaOn1Kt3zIFDn9a9FYA6ECfRf8n53QELwlr800rXta5CP8j33sY92Sn/u+TXxom+Kdtv9ums/chNd0TMknRzM89NBDoTwlbKwZbWXvqWM24c2YQ/2cjfT000+3jyPy3vZl5KOIyCtHbcCDsHyDABZrhBtssEH4BMEIgqBeUL/6PxTz1JbhrXaOYPNFf2/HGW2Ng2wcui/je1bIU3WuMdr4QerYQvgN6lq3zqbWvSb5G/Q4qM4cpS1ei2U2fghL+SFwok10I+Zz+NV3yy+/fOXNOt/85jczLGjp1y2u1Pcp/CCOq05f3VaZxGmo+ozQGmsZlmh3HBkJ2TluW3yvST1IrXs2f3Xu+4kPytMQ62FlylnWMin+U/le036BdHIkFrTCCitkWPTrF9VpY0pT0/Fwv3iQ0lv3imVPiHWUvDUzhRevt7bFg5quQSl9Zde2eFCddl2WHt6xLkefBjGHbJvqjoPajr9ueHXGJDbseE0ifrZ+/d4RcAQcAUfAEXAERjcCrriSWH4Ih+++++7we/zxxzuhIJSTO35iYlFW77nKagkTS+seK3XE4fTzmR2fDz/8cPixSM7iMrtGOaMWYheGFuPidGFSnx+Dc8ydswglWm+99XQbrhyto11YCPsRnLz++usdPyzwM8HCz+67795xZ7DKsTUSALAbS/HqKs+YkdekC7fUOBVeG9fNNtuss3BfFt6g08rkVXhyRutxxx3XEbhj8jXPwklZftp+xw5sjgiCrr/++pA2TbgRFG277baFwtfw0f//MdGVUgYmPjHjXFUpB6sDmPSXgIX6KgsNLBLYRT0bJ/eYa5UVAYTkF198cYaFAY7hsO0ArNV2CJ8dS5qwPfvss9lee+0VgkYBgDN2i0hYkTeOLCAupbvoG+u+xBJLhEeEPLvttlv22GOPhbSSXqXH+m96v8cee3SEWvATjufQpB+T0L0gLCtgbaWXRJnBAz772c9mN91005CdcyyWUG9RboFYuBel8oO22gnpqFMmlJGEFZSdhM600WOOOaZj3Ya+Re1AeZ3Yrk15bdU+5cILL8zOP//8oPRpd71R39iprN2GWHpAcJ5H8BEtoItPUpdV1vab1LoH/4Ovnn322SG4NdZYo6OkaMOvel8VH3gZ4xDGPRqrEQfjCAQ1m2yySWc8IX7I+9R88q3I1gH1u1zVpzGO4bmMZ1fNJ3EyfjryyCND9Ouss05GmfeKmozZsNwnOumkkzr8kvpL+hE2jDQiv9Qljt+iXel4SI63KxsToND86KOPhuyQN41LcGfcTdnDg6XU1na++9H/kWYpSdGuUIRjviGiXNlBSt+nBXneNRkHpfI92puUn2nj4G/bJumHKG/cNQ5qgx+EgBv+VeUHqWOLhslr5fO6dTal7jVJ6KDHQXXmKG3yWsYSjGf5LbXUUpUhxPKXvss7rrByQD3wmMoP8pJStW22WSZ56ajixnxP1u80FxPvs3OxNvlek3qQWveqYJHnp1/4oJSz3HLLhSTQNg477LAhaxWsN6JMzlpNTCl8r2m/gKUJlJ6YG1Q5zjFOc9Pnqm2MePo5Hm6ar9TvseTHeinjFNaK4g00rP1Sd5jfWcWWtnhQ0zWoKvlukwdVbdekizk0FlZY/5MiNO4cGXTiiSd21q44SqgXVHcc1Is0VA2zzpikapjuzxFwBBwBR8ARcATGFgKTja3s9C83TICsAoRitrt7mBCgyGIJhYyjjz7aOoV7JpiY5hdxrvMOO+ygx4FeL7nkkoxfHiFgxDoKk4M8GjduXJ5zttJKK2XLLLPMsHcI3dlVjaIBZuoR3stc8m9/+9vOQrA9IonJ8IEHHtgJi0kBvzw66qijguKNdgfhJyXOvLBT3FD22HvvvSt/Osi0oohlJ6w20VYhybr3+x6BOBix6EAbBV/qphTBMFEd7xbLSyOLb9QpzO/GR16wO4BFtDyizaPogAl/4pX5Xty7lTM7MFDI4nsEUSzsaHGHBSoJkYmXCTR1mKMW4CfsvKYtSsEBPzvttFOGYKyI1lxzzezYY48N37DoZReFERLZ57wwtthii+AH3nXNNdeEn/z98Ic/HCKQW2uttTrH+uCHBV8IBahY6Q2FOLX54GnCH9YMUPBYdtllg/CO8hT/ZeeWFu/kv60r9SlOS1thx+Fg6UCLvigJIKCWIgB+wUlHTOjbVH7QRjupWyYsUm054WiPc889N5gVZ7cdglh2wkkBCdPFstqhPE6M1ya8tk6fgiUfBP3wb8pzqqmmCnVObQvsCQ/luDJCgQMFP5GOD9KzvabUPc4Al0nzySabrGNxwoZb9b4OPiirMA7hB6EMCF+Pd7CiqAMfspSST/s9ZVPU3+IPC0X6WaUZhVEnn3xD/8IYiwXP/fbbT8H05NpkzLbaaqtlc889d1hI54x4lEDgLaSdfh8FUHbBS8GnJxlICDSvjBiPsvs5j6677roMBQuIow/XXnvtjjfGCuywXn/99cMYgPHApZdemqss1vko4aZf/R87uZnvMJdAWIJFGY7hmW666YKCo1Wqs9lIHQel8j3GRSggQdS1eOyitGEdhraLYtW6664bnJvyA4Wdeq3LD1LHFqnpa+u7unU2te6lpnfQ46A6c5TRymsRxFr+b62hpZZb3ndN+IENr07bHAllwhyajRgf+tCHwmYC8WesU9KeLA2a79m09Ou+n/iw6YTxMfNw5lYoos8222xByVNHMTLviimV76X2C4yZpUDD2gzjtH5SnTZGupqOh/vFg5pgyLFwBx98cFhnYiMJ5cLaA+7UJ7thysbTFg9qugZl01R23xYPqtOu4YlPP/102BRGG2Vtg/k1RwFqswE4o6jdC6o7DupFGqqGWWdMUjVM9+cIOAKOgCPgCDgCYwsBt7iSWJ46kqTs87zFkjy3vDCKwo/d4+e8sFLcisJFcMOkmB1TCNXR0LfHVxBX0bcsRLO7FKFIkVnT973vfUEQwZm3LOgz+WMhmx/3xL/iiitmVjCmSUDVfMb+U+IkLpVlUX71Xlelz/pHQQlcLOm9rvZdalptGHXv89KB8BChMzsKEB5xbYMUl65FYeq9rvKHwIOdRBIGsXCDkgOLFkwet9566+A1LhN9ryuCWIQOiy22WDbDDDN0ylrvi64ImpigsjNYSisoPmCliLC6EQtMKL8tvPDCQ85xj/NJG8SqDMpf5AVBiZRWqE8obnU7N5q2RNmhLEMaJ5988m7JG/Ie4RlCbzCPlTvi9KKAgXKCfrRlkdx0te8UDruBUJRhpxYLLAjWuUfZzyr0KMwmV9UNdrRLkcSGp/e62ncp9/BElIyIT8SCkZRWwBllDgSTlJmlVH7QpJ00KRPOGcdygKx3wNcpd7DkaBnqdJmyleLW1WKRd2/92Xv5lVtZWcpP/I2e27zGcRF2FV5rv6vTp7DbC14C0S+iqCGlFXgoC+IcCcgCWxmtvvrqHaE5YZYd9ZRS9ygf+BrjDpRZ4Y95pHLUVX5S8WGhkThZWIPYVWyVVhZaaKGgOIjyYBxnSj6Jw6aV525kxzP22zr1gP4D5Udou+22G1beypsNv1u6yt7bNJf50zvrHx6I5R2ZOae/4Ix0FopRDkEpRibI4/TaZ3uveOSmq9y5dsNA73XlmzgceDm7lrFogwKslJ30jfy//PLL2b777ksQwX+eYjDH6+y///7BD4pubR2RpLT0q/8LGZjwR31FQUz9IGMolSu8iP5f4zp9kzoOSuV7th4qDWVX6z+VHxC+ykT1I45T73XVe+u/Dj/g+9SxheJOuSq9ulYNQ/lOrbMpdS9Om9JQJe2p4yAbtuKL09Htuc4cJZXXKm02vUXpkh+u9r7Iv9zlV3HJneuTTz4Z+mq5IWjtBdn2XSV861/p57s6bTO1TJQ+xaur3KteUTDU8ZDMxeh3mYthCfeMM84YFkwK31PadB0WqHGQH9UDXeVuvA67lR9dh3lIcOgHPjZZjCew1MmaBXWD8RCKu1JaYaxctD6TwvdS+wWUakgT38drExZ/e698ditTvddV39mw6rSxNsbD/eJB5FX5tvkVBt3eszGLdRwsLBIOaw8oXEhphTEW6yB2Xt6UBylthFN1DUp5U14JQ266WjfrL4UHKY261m3XzFdR5NNaBxaq2HxFH0B6UURnXZC1ybZIeU4dB6WkQ3HaMrDh6L2u9p3u64xJ9A3XOM742fr1e0fAEXAEHAFHwBEY3QhMMmEQ9Z/RnQVP/VhFgKqJ0gEmKpmMYxp81lln7ZwNqnxjDeaggw4KR21oR4fe2SsTSQRss0/YnXvrhKOF8qhqnHnf9tttNKW139ggDKfeMFlEsYoy7xUhSJVlFHZksCMfgQs/4qbO9pJYYKBuY44UizLsgiubJPYyLb0Om3J95JFHgpLNggsu2BEo9zrefoVPfcXaDleE5SiHVVUqSuEHbbSTlDIhrVjbYSGHhRt2pyPUdRoMAvAOFO24IhxG+Y5yqboQhLUVWabCioTuy3LTRt0rC7/Nd+zeZgciC+8or6DMiHJElTrbq3yi/MAxMpdffvmwXc5t5r0XYbU1ZkNwRj8Lr6Q/qFIevciPh9kuAiitIDzBrDr9H8I5KY8VxZQyDqrL9xZffPEMpaI777wzjLOK0rLjjjtmmD9HgVjHOspvr/iBwu/FNWVs0Yt09CPMlLqXmq5+jYPamKOMJl6LkJxjUyCUKjhSFWFe29QGP2iSpn6WCRsvsI6Ikj2bj+BjHOmG8LnKXGw08r06ZTMS8GGcSp3AqgNjIdY9uimdK48pfG9i6heEU9Vrv3hQ1fRU8UcbZU7OXAdrd4y9utWffvKgKnko85PCg5q2a9okcxTWdBijah17Yj8OuY0xSVlZ+ztHwBFwBBwBR8ARGFsI+FFBY6s8x1RuEL4zaeo2cWoz04OIMzX9oymtqXlM/Y5Fm6Jd+alh1vkOQQu/fhA7OlhAnRiIcuW4oLFKKAzwS6EUftBGO0kpE9I6+4RFVX5Og0dg6qmnzvilEhYwIOoCChVVqI26VyWeNvwg/EKJjF9dGk35rJu3QftHKbTXiqGDzuPEGD+7VfOOVirDImUc1JTvlaWn6N1o5AcpY4ui/I9095S6l5qnQY6D6s5RRhOvveeeezpFwnFqvVBa6UQwwJtBlgl8rM4RraOR7zUp2kHgwziVup5S31P43sTUL9StC6ORB1FnsSLJryoNkgdVTaP8tcGD6rZr2iRHm/JzKkeg7pikPDR/6wg4Ao6AI+AIOAJjCQFXXBlLpel5cQQcAUfAEXAEHAFHoMcIYH0EKy2YO7755ptDbJiQZ2HPyRFwBBwBR8ARcAQcgX4iwA73++67L0TJMYscherkCDgCjkC/EHAe1C+kPR5HwBFwBBwBR8ARcAQcgYkBAVdcmRhKeYzncfnll89OPvnkjLNYy4jd0vibaqqpyrz5O0fAEXAEHAFHwBEoQQCLCCzQiuacc85s11131aNfe4zAPvvsE45SGY07+XzM1uPK4cH3BIEjjzwye+ONN7Lpp5++NPztttsuW3PNNScaS3ilYPhLR6CPCLzwwgvhqFQsSGywwQZJVtKqJtf5QVWk3J8jMPEg0E8eNPGg6jl1BBwBR8ARcAQcAUfAEZhYEXDFlYm15MdQvquaqpxyyikrH2MwhuDxrPQYgUknnTTjh9lcfk6OgCPgCIx1BNjNzBn3nNm95JJLZvvvv3827bTTjvVsj5j8ofwxWsnHbKO15CbudK+yyiqVAFhsscUyfk6OwEhAYGKao7CB5dFHH+0L7BMTP2C8p3rUF3BHWSSOzygrsB4mt588qIfZ8KAnIODtujfVQH2Jr5v2Bl8P1RFwBBwBR8ARGGsITDJB8PCfsZYpz48j4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOwMhHYNKRn0RPoSPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao7AWETAFVfGYql6nhwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAERgFCLjiyigoJE+iI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCYxGBycZipkZLnt58883s3//+d/ae97xntCS5r+l8++23s3vvvTd77LHHspdeein7/e9/nz399NPZXnvtla211lp9TctYjAxsH3300WyaaabJNtpoo7GYxUZ5euutt7ILLrgg+89//pOtvPLK2RxzzFE5PMf2f1A99dRT2bXXXptNMskk2Re/+MXsXe961/9e+p0j4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4AhM9Ai44krDKvDss89m55xzTva73/0uhLT77rtniy66aG6of/vb37Lrr78+u+qqq7Inn3wy++Mf/xj8zTDDDNn888+f7bHHHtkiiyyS++0gHFEQGT9+/JCo3/GOd2QzzTRT9uEPfzhbYYUVsk984hND3rf1cNFFF2XHHHNM9te//nVYkCeffLIrrgxDpb7DDTfcEBQzZpttNldcyYGP9nr44YeHN7TROoorju3/AIXXnXLKKcFhyy23dMWV/0Hjd46AI+AIOAKOgCMwIAT+9a9/ZZtvvnnGRgJou+22y9ZYY43C1KTOi372s59lhx56aGG4erHEEktkBxxwgB796gg4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AITHQIuOJKYpFjqeK0007LbrzxxmCRQcF8/vOf1+2wK9YGbr311mHuKLDcdttt4YfyCr+RQD//+c+DhZOitJD/T3/609mJJ56Yvf/97y/yVtud/P/gBz8I373zne/Mll122WzxxRfPPvCBD2QzzjhjNtdcc9UO0z9wBBwBR8ARcAQcAUfAEXAEHAFHAAQeeuih7O677+6A8b3vfa9UcSV1XvTnP/85e/jhhzvxFN20OZcqisPdHQFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBByBkYyAK64klM7Xvva17Lzzzqv9JUeOQB/96Eezz3zmM9m8886bTTnllNl9992XnXXWWWHH30knnRQstnzyk5+sHX6vPph++umzHXfcMQT/xhtvZL/85S+z6667LvvnP/+Z3XLLLdlXvvKVDCsobdAll1zSUVpZd911syOOOCJg1EbYHsZQBJZbbrlg/WLaaacd+sKfAgLvfve7sx122CHc02brkGNbBy336wg4Ao6AI+AIOAKOQH8R+NGPfjQkwjvvvDPMxaaYYooh7vFD3XkRFvv23HPPTjBY5XviiSfCM5saUNKH5pxzznD1P0fAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHYGJFYJIJyhT/1aaYWBFIyPc222yT3XzzzdlUU02VcfTF7LPPnn3pS18KIZ1++umFu/V4h7LKiiuuOCzWm266Kdt+++2DO0otp5566jA//XZYddVVg8WV+eabLxxxZOP/1a9+la233nrZX/7yl+CM5Rny1oQIa+mll87+/ve/Zyzk7rPPPk2C828dAUdgBCDA0WgcoQax49gVpUZAoXgSHAFHwBFwBByBiRwBrEYyn8Gq44MPPhjQ4PjXlVdeOReZtuZF++67b3b55ZeHOFBgQVHayRFwBBwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcASyzC2uJNSCD37wg0GpYosttsje+973Zj/5yU8qhbLTTjsV+mMxdIYZZsg4NghT1COdPvKRjwRrFEcffXRI6lNPPTVEceUf//hHML/9wAMPZL///e+Dgstss80W/KCcMuussw7L4o9//OOgtAIO66+/fnb22WcH6y6vvPJKNvfcc2cLLbRQBk6TTjrpsG/lwHn1KNE8/vjjYTEas9so3qywwgq5cfIdfjl/vhutvvrqw4Tu48ePD8c/PffccyGfLD6jwIOFDuIsS2u3+Lq9f/XVV0PcTz/9dMDpPe95T1CiQvEJvPLo+uuvzygbSyhgrbTSStYp9/63v/1thhn1X//61xllMvXUU2czzTRTOMppySWXzCabbDg7SakHNnKUHv72t79lCyywQLbwwguHuDlW65577snYEbvgggtmn/vc5zLyTrrYLfu+970vHCv13e9+N6M+gAfCCawEsbuWdGLNB6soMVGeP/3pT2Pn7BOf+ERGuy+jutgqvYS50UYbZS+88EJ2++23Z7QZ6tHHP/7xjKPH3vGOdxRGe+uEo8ew2PTss8+GOrfMMstkSyyxRPbDH/4wHGFWlm7qPIKaX/ziFxmWlGgnSy21VPg+L0LCRLmMuv2xj30su+OOO7J77703AzPq29prrx3CyPvWuiEkqpPPum1MuDapB6SXuvv973+/k3QUbuA/vSCL7aKLLjokChT5dHQawrV55pmn8x4sUQZ68skns5dffjmUD22Bdkk4q622Wm79oRyJMyYdxYYyZhH9+9//zr7zne8MOSIvz2+cVuunbt2DD8Dv5p9//tAWbVjcY6nrrbfeyuBD1OM8qhtnWZkQPhjAX+J8qv7hh3Y9+eSTc9sh2itWy971rndln/3sZzvu9ub1118P4wraJm0b3kPe4WXw6zYJHkAfUkZxHuW3btvku274kGd4cB4+KWUC1mAOH6WvKCLiJG74xlprrRW8MbZj/EI/vuGGG+b2cSpPPlhzzTVDv1gUR1X3snyW8QN4FjwZQfjzzz+fcTwJ/rEOgcUH6k9Z266aPusvNa1N6kGTMZvSXpcfwIPBkr4P/spYgrKnni6yyCJZ3viQuJrkU2lN4QeXXXZZBq8uo1lmmSX71Kc+NcxLSrseFkhFh274qH3l8YOKURR6gzfQh0JsSGCM+7vf/S5jLlKkuFIY2IQX3eZFZd/6O0fAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHAFHwBH4LwLDJc2OTFcEDjvssK5+UjxI8F8mqE4Jt1ffWOUIhCSiP/zhD2ExHGF4HiGU5wgglFMsSWEH4QCCozfffLPzWua8UTbgWKI8qw0vvfRStuuuu2b3339/5zvdIMw98sgjg8KC3HQl7BNPPFGPhVcEojZe6sG5555b6H/ZZZfNTjnllCHfFHqu+QLlnIMOOigIq+NPyQvWarBaE9N+++2X/fWvfx3ijEJRN8WV888/Pzv88MOzt99+e8i3PJx22mkZSlmEbSm1HtgwqCeU63bbbZc99NBDGcd0WUKRBvdvfvOb4XrggQeG1wg5pKCDHwTICJpFKCRcccUVQeAkN64IKuM4cD/hhBOG1VfcLdXF9pFHHsmUXhSsMCPP8Vsi0nf11VdnF1988TDhNwLzr371q0FgL//UY+ob1k04cgyirayzzjryEq7EQVu44IILhigfoNgDbb311iFd4kfBccLfscceG4Q8m266acDbKuwRN22BclhllVX0ybArCkd18pnSxqgPwjW1HpDwP/3pT9n+++/fyQMKab1SXBG248aNCwonnUgn3FCGZ5xxRnDiWDaruPKNb3wj48iBIpp55plDOcfWsBDQCaO8bxHaHXfccRn1MiYURA444IDYedhznFY8pNY9eBoCRvBBGSumgw8+OPAm8hQrrqTGWVYmxA8GCIbjfNp2HQuzUUzddttts9deey0oveYprqCIRBv+zW9+E2czWGKjjaHE1xZdeeWV2aWXXloaHLzG1js8p7RNvivDh/cIjY8//vhcfFLK5K677gq8EuWTMsUVeC1HQH74wx/uKK7Qh6idUP/ieo9SEVb3GOtQL8vCJ29VqSyfZfyANKJQXUSES3ppL21Ralqb1IPUMRt5TuUH1HcUdlHQpI3CQ0X01VhJZJwUt5Mm+ST8VH5g+y6lM75yJGmsuJLaruOwqz6X4VOFX1aNJ88fli4h5lzMLcAChR8samKMdJJJJsn7rNStaF5U+pG/dAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEXAEHIEOAq640oFisDfs8uMHsbt5NJDSS1o/8IEPdJKMcgOCHHaasxDMme28R5EBJQKuCK95r53NfIw7hIUNCCURhO7TTDNNhvAJhRQsauy4444dE9vB44Q/Fpm32mqrsGMaN6y6IEhC0MBOWXasIgzEmgtWKSyxg9YKEBHgk352T1rrB+xatiQFEPwRl3YyIzhn4Zs0I8hicTxlAdzGZe/BcK+99gpOYMgubwTTWFFACABOLPjnEekRvvhD8NaNUCj6+te/HqwLgMHyyy8fLKAgfOUdVj/ydvam1oO89GCF5LHHHgs73tndjLIN9YXduChxxITlCKwvUPakA6UV6iCWV8AP4dU111wzTHEFqwYoZkAoTuG3KqVgq7BRMgJbyhLFLoSotC/yx72tn3yDggpWJiDq3xprrBEsTpBfKa2Elzl/KBlRPyGE/CiaILhBEYVd6AhvUfiIFZEU1Le//e1wSz3AKgx1SNZmqCcoQRVZGqqbz6ZtLLUeKK+DvoItxxYUEXyF3f9Yo/rQhz4U6g7fPProo6H+UIcQZKMwVMSDENjRNmgTd999d+CZKA/AY8oU80gTChRzzTXXkOShCFFETeteUbhl7oOIsyg9KOLBN4sIqx8o2cHT4O0od9J/YpUAXFFm+cIXvpDdcsstgV8UhZPijvU4LPSI6FPL+F/Ttql4RvIVvkr7ufDCC7OzzjorWBdD2A+h1LLLLruEsQJjFRRKeq10XIUfvPOd7wx8HctY0003Xahv9J1YYSHNUq7YZJNNegp9t7Q2iTx1zEacTfkB4wYIi3qM1Z955pnQR6PIwtiBcR/9ZxvUhB9gIUgnwaLQhAIMpDEG97GSH24jqV1345ektwnRz0GMKbFkRdtGcYXxM9jjXpeK5kV1w3H/joAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao7AxIqAK66MkJI/5phjOimxwqOO4wi7QfnBClTtLlMWgDm/HUE+ghNLCHoQlLO4i1DUKq5YwSqCIAR1COUhLKkgSGdnKwoXCFitAgrKIZj5hxDssWtU4SGgQRiIJRcsZ9jv8I91AWsWHMWT8ePHh0XsQw89FC+5xCI3u28R4CsueUT4j+UOhBocqSJhl96nXlHAwVoGhLAZDBHiWMIai108t++w0iI65JBDKimuIIiRcgi7/TlOxhICGxSEYkqtB3E4PCNEYCf86aefPiS/L774Yu7RPlgQQJCPpZ1vfetbIUgUPRCyowhFOBLk2PjIm/LHMQtlglv7Hfcp2CoMLGNgBQYlBGj77bcP9Yo6izKKVVyh7SF8hBBOopQjyxibbbZZEHZzrEoeoWQkpRWEbLQTCVtpm7vttls4Qoa2TfuVMlYcFopnKIKJqJNnnnlmOJ6CsrIKX/LDtU4+8d+0jaXWA+IeCSRLIkVpQYEptowjv/Af+BBHncCHLI+WH66UuayY0M6pQxw3Ql1B0F0mhKWOSNFLYVKP8xTZ2qp7iqfKdRBxFqWLfssePRX7Q8gMT6YMOK7m8ssvD+1b/igX+hsUX7C0Y8cM8tPkCr/Eyo6om+JK07apeEb6Fb5OX4HyB3wPC0cowGKBjCO66PsZV4h39zI/3fgBCqyMg/J4AkpPjLdQcGVM0mvFlW5pbYJT6pitLX6A1aQvf/nLnSxwPB/PHNfGEYW01abUlB9gCUfEGELjHeotY6AiGintuhu/LEp/VXfGrCh6QxqfYyWR8RA8GKs+dRVXyuZFVdPl/hwBR8ARcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcgYkdgUkndgBGQv5RNJCAHAsj3Y5t6XeaEZSxY58fVhnOPvvsoHzCLnCIxd0FF1ywkywUFnbeeedhSit4YFf3euutF/xK0UQfWgEpAlEprfAeCw4cfyMhuz32hfcI+SDC2HvvvYcokiywwAKdY16w3IFSShuEEJEF71hphbAR5sr98ccfbyO6EAb5lDUV8IiVVvCE4tOWE44DaIusEow1g67wZ5lllmDxQc+6ptYDfR9fjzrqqGH5/eAHPxh2EFu/KKsghIWwVgBRb3Q/xxxzBDftLA4PA/7jKCQr+EThS0fuoJxjCcEpwkcIZRMprfCMogk7rYsIqwDQrLPOmqGUpfaEG/c6XorjYOBLeYRlGHatW7JC0Di91l+dfPJdkzY2GuuBxeraa68Nu/etW3yfJ6CWH6wHQVjwQWGoClEH4JcQ7RerH21RG3VPFgSqpqmNOKvGVeYPQShHCpURygQofUEcT4NSmiXKc8UVVwxOP/zhDzvWFKyfft43aZv9TGfTuLBggsImYxeE3SivwBulEIkFuPi4laZx5n1fhR/wXRFPoK+UwkKRQmJevCluVdOaEnaTb9rgB1NMMcUQpU3Sw3hPPFZj0Sbp5NtB8YOR0K6r8Mum+KLAKwVLtV/GUliRg1BcKaO686KysPydI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCPwPAbe48j8sBnKHCW+dRY8FDSwgjDR64YUXCnfnsljPbmMrAFf6OWqFo334/qWXXgq793nH8TIQu/kRkEs4iqBZtOqqq+q2c+XYDxaVsYgipRm9ZDcxhOWAqaeeWs6dK8ocOlqFb9sU3Dz99NNhNzYKHn/60586cSJAIn9lR0N0PFe8IS5oyimn7CjjVPw02ZvFCmUZlBsoh7wyz4ukbj3IC4Od5OyGrUII6kWqU1ylSKT3bZaL4ku95h0PhqARiq3Z2Lqfp+TGcUgol+XRU089FZypl+zItooAukcwR5kVKVxxLA3CXEtKK27sOi+iOvm0YaS0MZUz4aTUAxSJsOwksop1cuvVFUs7HLsEYV0AhUEJ2fLixDoQPJByxQoERz5dddVVwSvWelBCKSIUuPieI7UQluo7LFPlHfmEUFGkNqXnsmuTuocFEgjeX4eaxKl4ODYOfGIqK4/Y7wUXXJCRFvBE+URHVFh/4u24kU/GBmqTulIvIK4oYFrlzvAi4U9hJ3waPklpm4oLBby4joF3N0opE8oLpU94l1X26xYX7zmaDis39H2MafhBWNpAUbbXVJcfkB7qG8d/caQe/ODWW28NdYbxVhsWQYrynJLW1HpQlIYi9zb4AWUe81PqMP0ux+jZ/jlOR518DoofKM1N2rXCSL1W4ZepYes7KaagzGUV71FGh/9ipez5558PbV/f2GvqvMiG4feOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAjMBwBV1wZjknfXBAojBs3LuNYDxQROPalrkClX4mV0BZlBRRsOLaFXYqbb775MCE2aWLhmWMsYqF7nF6EpVJcsYJm4sgjuXM8jSUWkSFrtcK+13e4ya99n3JPGjhGAIFQGVlBb5m/Ku+ee+654A38qyqOVAm3zA+Cb46BQTEHATo/yorF/vXXXz8cw1S0yzulHuSlRVZS8t7FbiheiFRv89xQzhgplFdv1R4QPlpS/UWYLz/2PQpeeQS/wZQ9RFlSpmXEETN5lJdWhMEI7xAOx+m1YeR9qzzkfdekjeWVeZ5bUT1AKcMq5Nh89Pr+xBNPDEf8kF6O3KDNldF1110XrHRYP/CHs846q2O5x76z9xx7ERPWPuxRGPY9Cocii6fc8q5N6x7KcwjfsdLFcXGrr756EB6TFiwQ5SmRNI1T+SA+fqlEOlDuhD7/+c+HPipPcUW8HX9V4qN9tqG4onYXK6ORjjJq0jYVLsL+FEotE5QOIHgn/dc222zTsWLTLR2f+cxngtW5iy++OHhFQRYLHv3oh+vyAxLIkYmyzqa8kX+Uo7GS1itKSWtqPaiTh7b4QV4fRjrU7zKeR7FK/ZpNY518DooftNGubZ7r3lfll3XDtf6lTI3bcsstN0R5jnmN+DV8Gh5RRBpfVp0XFYXj7o6AI+AIOAKOgCPgCDgCjoAj4Ag4Ao6AI+AIOAKOgCPwPwRcceV/WPT1jp2XKH2wSx7FDYTIece+9DVRBZHNN9982fXXX1/wdrgzx/gccsgh4QU7UxH4sGNZSjlYMcCUPGR3e1sBkCwkBE/mT8I1e8wLiiHapY0CUB5h5l9kv5Vb3SsC0+23375jlWLRRRfNsFAwwwwzdBRxEBCRrjyhat345F87/2WBQO69vFKGlNdxxx0XhMRYlSFf999/f/hxjALH+Cy//PJDkpFaD4YE8v8PMsOf9y52s1YgdK8rfu19/O2gnosUf/LSo7ouoUnspygsBGoiLLWssMIKesy9FmFeFH5uIJFjnW+btjFbzrrXlWTZ+yiZA31kp/d5550X0sCRTCipdSMU8xBKUzewKoDlFfgiVnW4hwcXEXyXPsgqHWE1gCMvdt9992E4WcWVPOFsXjxN697GG2+cXX311aG/wOoTVspQHKAfRfkxj5rGqTCpJ3ltrUjhSd/pevjhh2evv/56Nu2002b77rtv54gZvddV6aUsvvrVr8q58Kqjzwo9VHwhZbaqZUmwTdumkkZebb+PO4o03frM1DJB0YpyYwxw1113hd/Xvva1oECsNJVdLf9CUS9Oe9m3qe9S+AFxMSZBcZgxJhbvIJS/OOKI444Yq7RNqWlNrQd10q/2xTdN+r9uY1PChy/ltac6+VR6+8kP2mrXYJBKVfllavh8h8UkjaM4PtRaILKK5lhlKVJcqTsvapJe/9YRcAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAEZiYEHDFlQGUNqbUt9xyy87RAexQZddfFULRwwp1EODEpvarhNNLP+zyh9gl/73vfS8I7Gx8L774on3s3FtBCgKXvCN/pHRid70iPJp++unD7uKiY0qse5FAvpOQCje33XZbR2kFIePWW2895CuEb0UWC4Z4rPkw66yzZphwx2JGP2m66abLjjjiiHB8CfHfdNNN2ZVXXhlMqbNDl+MSUEiygrzUepCXLxtu3vuJyU1WQFAgghfE7V8CrxgTFCDgF/CQueaaKyjOxX5G0vOg2tggMaBs4CcoY6Dsh+JKFUIQq2OjEMwjoKb93Xzzzdkuu+wSrGTlHdNE2Jdeemk4Yo17jhniO/g2/RIWAhZeeGFedcgqbOQJZzsezU3TuscxYfAfjmtBeQ/hoqx5IUB84oknTGz/vW0apwJEwRTlhpiwAmX74vg9z/fee2/n6CWUVvL6NH0Hb4coexR18pRl5LfNq/rUsrTF8bXVNu+7775h4wOsmBx//PFxlEOeU8oEPkn95ggm4qUuwSsp28997nNDws97uPHGG7MLJliSE8F/d91113A8TK/6p1R+QBqtRS2UVzgGjOPHOM6G54suukhZaeXaJK2p9aBOwtviB+I7cdxqR9QFO5a1/urkcxD8oK12bfNc574Ov6wTbuzXWrzCclWRdTmUWuhv6vDGOK42n2ljts8ZifO/NvPrYTkCjoAj4Ag4Ao6AI+AIOAKOgCPgCDgCjoAj4AhMnAi44kqfyx2FBix1PPLIIyHmI488snQ3fJy8Qw89dIjwZKTt+kMp55e//GVI9kYbbTRMKMULhEd5pIV63hFG3o5ymU+PTd3zjFl8xR2Hb93jb2O/VZ45sgJCuIgSUkzsPLYLzPH71GcdDYHyDybVZZ4+Nby63yH84xgRfrvttlvYqX777beHHd0Iw1BWgprUgxCA/xUioPqL8B6B/QILLDDEr+rmEMcJD+wUxzIHQhqEtyOdlI9+tzHhgiWKk046SY/hiJdYQa3zsqUbrIqwWx/CapUsTNUJHqsSSy65ZLC4gNIJbREFliLFFRs27RqLJiiuQBxRFCuuyHoD76eZZhouXamNurfJJptkG2ywQeDxKCKST5R7qNMoYsWWV9qIs2vGunj4yle+Enx8/OMf76ocId6OcJL2SVn0g1DAgOoIZwfdNpvgAs78aBcorVBvisYkioe+jXYBUZabbrppsJ6DIgLHiqC42Qtqgx+QLpQ2SDNC+KOPPjpYmiH/RdZDUvLSVlpT4q7yTVv8QGPQOE71qYzJ2lBkGgQ/GHS7rsMvY/yrPsNf6Q8h+g36lZiYL6DgxRjrlltuCUdixn4G8TzS53+DwMTjdAQcAUfAEXAEHAFHwBFwBBwBR8ARcAQcAUfAERh7CEw69rI0cnPEIii73zFTDbGzvspOX5ujWBkifrZ+B3HPgr0sQKC8ERNuCEPzaN555+18yzEVMT344IPZ+PHjg/P8888/5LWeEeQ//vjjQ97xcMUVVwQ3BMF5CjHDPujiwPEbEKbVOVYjptNPPz12auUZgTREXTrllFMKw3zttdcK37X5guMI8qhJPcgLz93+hwDWJ9hpC3HsgyX4AUc0FdHSSy8dXrHjOK996jvq16BpUG1M+WYH/f+xdx7wkhTFH+87DgRJAh6SD5AgWTiRnLmDIyfJwpElw4HkKIIIEoUDJEk+lZwlSJCcRHJW/mQBQQEJAu8/38YaavvN7M70zO6+967q89mdmZ6Ov+muDlVdjfUR+TXDVcJUvYrSyogRI7y1kyrxIZgWDKW+FIlPW8TKsuwEH4YQ0JY53q6OuocSE7x+2WWX9VZiUFppRnWk2Sz+Vu+wzkB/iKWLVt/ghz/8YSrw1tYystKoq33CL6RPLaO4IvWq0/1fFhaxbk888UQalHLkEWXdeeed/fFCHDlIv4tFHJSooFNPPdWhvNkOqpMfkD85apDvXvfYse68tgPPOvgByh0cx6aJsqPgAIWKpNpfmftu8INut+sy/LIMltov1shQcofWXHNNfxQQxwHpH9axZB6jrbPoeLpxH7bZ8LkbebI0DQFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAyBuhEwxZVIRB944AF/NArHo2hFCRQncOOHH00HHnigP2IFt0UXXdTNOeec3nw7Jtz1D8UWdgX2R0KwKFY3brjhBr+zV4RsCIq23XbbXIEJu4JXX311X2wWi8eOHZv6ZTfrXnvt5d+hfBKeO48VG9nlinKQKJOQ9rHHHuvuvfdeH3bjjTcubCWgGf56NzxWGbB+ArF7Gys61157bbPg0e9WXnnl9FgpTP0fccQR7uOPP07jQ+i80047ueOOOy51q3rDkUd77rmnQ2At9ZIF88cffzw9bgCBHtYPhKrUA4nDrtkIzDXXXI56AKFMwQ56vvvLL7/sd/4/9thj2QETV6wGcLwL3482dPfddzf4xbIAdQcFqWaKLQ2B2vTQrTbWpuIUjhZLIlhbaUUI31B+RGgux1QQBqteCFHZSS6CSPqbPOLoH34ounAcCru6hdZZZx25dR9++KG39nXmmWd6t1GjRqXCvdRTk5tu1L1upBlCsNlmmxVS8OHoIY7Aga688kr/bcFcCOUK+kX87L777uIcfYUH3H777V75kkjgC1IX5CqRU6ekLuHWH9sm5aXNMNbiKKKrrrrKF49jB5spG6B0JDyVvp1xCoQ7VjHoE/fYY4/c40a85wp/RfkBSZx33nnu3HPP9cpI2gLRq6++6st81lln+ZxgNYY+u24qk9e60y4SX138AGtzYnkKvsmYkzYCMRatgzrND8hzX2jXRfllLMYccym03HLLyW3DFSU+sTQGj9S8r8FjiQeUm/Q8TyuFMg6Td62sP5VI0rwaAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAL9EoEh/TLXfSDTLK5mLWaefPLJjh+EIAhFFiHObxfi7HQRUombvrKrs04z7jrudt+ziD9mzBgvEAOnqaee2h+pIwu1WFYJd6xKnlBOQemHnZcI5NnNzHEUCF6EdtxxR39shzxzRVmGI3vOPvtsf8wCO/IRMBEPggWI3fkoddRBq6yyilc8Qrh/xRVXeCEYeXjttdd8uTFBjrKNKO3UkabEwfEGm2++uVdWoLwIqlAaYWFcjn3IOr5ojTXWcNrMPcJJCIUHsVgjadx8881OjqRBeIoglXIimOJIJ4Q2ssOaMPvuu28viwJV6oHko5NXFK1EOYp09bfjKAgUz4QQ6B911FHy6GKxTSMoeUNe4C20CxS8+AlxJIxYxRA3uc4wwwzu0EMPdSgjodxF+0SAiTvKL1pQLmG6de1mG+tWmUmXdiNtr1k+UFbB4o5Y3UGhD4Wx0NrS+uuv75ZffvncqLbYYovMdyuttJJbcskl03fwnEcffdQ/DxkyJLU4kXpocdONuteNNDUM9H1ljpGh36TtouTJd73wwgvTukDfgvIFJAqeOq2y9+PGjXMHHHBAGuzEE090/LLo6KOP9kpyYlWiv7bNLAUuyjbppJNmFdtdf/31XhmElxx9iIUGIcZnWF9Zd911fb+LMsMll1ySKtCKv6rXovyAdBhXojSL8hkWfiaffHKv2KrHqtRJ3V9UzZ8OXyavOlyn7uviB4xfsd4y00wz+X5TlISwlIWllLqok/yAPHe7XZfllzE4iwUVlFMWXHDB3ChQauFIV44MRPGdOUUVou/ccsstM6NAoV9ohRVW8GN6ebarIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCEwviFgFlciv7iYkW4WPDwaIHzOC9vMX5hu+JwXZ4y75KNsGpjQxxKACINQpkBphUVplC623nprnx2JX+cN5Q8UCJZeemkveGHRWJRWppxySi9Yw/pHFh188MHe2onsJMZ6BEorpMNiMPGyu7oVSb6alRshMZYHxPQ8AkWUQhBgIBxDKMhRGlCzeFrlJes9O3HZNYpFBfJB2ijJiNIKO0VHjhzZKyiWWcBDfiIExaO4yVW/o4woG0Eouzz33HOp0gqCIHahZylhVakHPrHkT7CTq7hnXcWPXPEj93LFLe/7oqgi5eeqj49o9o44Y7DVedL3xAeJm+T3K9ev/mknKBOhMDN06FDviBANi0IIYoWylN/wg3CTXffEjbIK31SUVlCCYtd42FYkH5IvSUOuee+1f30v4cRNwot7bBuT+ORKfHIvV9wkPe2Gu1DoHj6LvzqukhcsOGTt2Jf3ciVNhNKLL764V5DkmR3/WmkFoRzKf/x0OPzmlQUei/UJFNHCo8iIg/ekiUKF7EgnPk2Sllz1u9i6p+PIupfyyFX7iU1T8p8VJ/HLe7lKmtr//vvv7zGTd1zlvVz1uymmmMIrQdLH0X/Ch+nH+HFPm1hxxRXdBhtsoINF3Yv1rKKBtf/Ytklautz6XvIhbnIVd66CddY7/V784Rb6hSfSh6611lru8ssvT5W6JIz4f+eddxxHhkD415aIvGPyx1FZ++23n3/Ewl5dRwRKXsrwAzKx8MILp5bP+F4ot4nSCvUJ5bPLLrvMKyr6TNfwF5tXwZks6HvJkrjJVdyzrpKHIn5j+YGku8MOO/gjPkkT5U/GfFj7Ywx0+umni7f0qvOk78WDuMlV3LnWxQ903Ppep8V9lXYdxlX0WeenLL8smob4e/PNN/1Yh2fmGDpt8SNX5gxCd9xxh9y25EGpx+BG6mjg3OuxWZ7Cd+Fzr8jMwRAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQ6IcIDEoWt/vnmTT9EOzxLcsI/7FIgnUOjjeZNRG2lyGUVrAq8f7773vFCQTzRRZ/qdJYEXnhhRe8sgzmz7ME+GXy0swvwguUVrCws8ACC7Q1rTAfKFSQ/osvvujTBWOUSdpBKMaQFsciYXkFizZYX5EjmvLSrFoP8uI1968RQECJkAvCYoMItlFwou3lEd+GdoK1lWmmmcZbd2hX/cnLQxH3braxIvnrph94AN+P9onyCopMKM21k+fVUd5u1L1upBmLFf0YCp/0oSitTD/99J7f1vVdL7roIm9FCos8zY6W4+iKVVdd1ffftyfHZoRkbTNEpPvPjJnop7misIK1ORSHTdDd+G3K8IPhw4d7C35Y/EOxj7CPPPKIV/Zg3Mf4r53Ubn4Q5t3adYiIPRsChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoZAJxAwxZVOoGxpGAKGgCHQIQSwuMLObxSKOHKk3QK1DhXLkjEEDIEaEahLcaXGLFlUhkCfRSBUXOmzGbWMGQKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAh0I8RsKOC+vHHs6wbAobA+InAU0895Y8muOaaaxyWiSAsb3AMxHnnneefN910U1Na8UjYnyFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCPRlBIb05cxZ3gwBQ8AQMAR6I8AxBX/+85/9j7cc88NxQf/973+9Z4772XPPPXsHNBdDwBAwBBIElllmGXfyySe7YcOGNcWDI+HwN/nkkzf1Zy8NAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQqIKAKa5UQc/CGgKGgCHQBQSmm246t/LKK7sHH3zQ/etf/3LvvvuuzwXuI0aMcPvtt5+bdNJJu5AzS9IQMAT6AwKzzDKL49eK4CNrrbVWK2/23hAY0AgMGTLEDR482P8GdEGtcIaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCHQRgUE9CXUxfUvaEDAEDAFDoAICH3zwgfvnP//pra5MNtlkFWKyoIaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIdB4BU1zpPOaWoiFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoZAgsBgQ8EQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBDoBgKmuNIN1C1NQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAE3xDDoHgL/+c9/XE9Pj5t00km7l4kSKX/xxRfuk08+KZ3f1157zd15553ulVdeca+//rp744033GeffeYuu+wyN3hw39Kd+vzzz919993nHn/8cffWW2+5N9980z333HNuzJgxbo011iiBlnk1BAwBQ8AQqBuB/saj+1P/V/e3svgMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQKIrAoERxoqeoZ/PXG4EXXnjBnXXWWV4Zg7e77767W2SRRXp7TFxQhrjiiivcww8/7J5//nmH4go09dRTu+9973tu5513dksttZR360t/55xzjrvpppvcY4895j7++GM3++yzu4UXXtjtueeebsYZZ8zNKkoqe++9t7vnnnsy/YwdO9atttpqme+64XjBBRe4Y4891v373//ulfxcc83lMej1whwMAUPAEDAEOoJAf+LR/a3/68gHtEQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQyEHAFFdygGnljBIHihd//OMfvdUU8X/aaae5UaNGyWPD9YQTTnAnnXRSg1v4sPXWW7tDDjkkdO7K85dffukOOuggd/HFF2emP9100zkEiXPOOWev948++qgbPXq0e//99/27WWaZxa200kpu2LBh7jvf+Y4j7DzzzOMmnnjiXmG74bDHHnu4K6+80ic90UQTeQWiH0ryKboAAEAASURBVPzgBz6v0047rZtjjjncDDPM0I2sWZqGgCFgCIz3CPQnHt3f+r/xvnIZAIaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAh0HUE7KigiE/ws5/9zGGFJIZQ2FhxxRXd8OHDvfIGih1/+tOf3C233OL+9a9/+Xh/+MMfulVXXTUm+lrDnHvuuanSyrzzzustwgwdOtTdfPPN7swzz/TH6Gy55ZburrvuajjyB0syWJ6hbFNNNZU7+eST3TLLLFNr3uqM7KKLLkqVVtZee2131FFHlT4Oqc78WFyGgCFgCBgCXyPQn3h0f+v/vkbZ7gwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBDoHgKmuBKB/csvv+xDTT755N6qyKyzzur22muvljHttNNO/nid0OPqq6/unnrqKbfmmmu6L774wl133XV9QnFl3LhxPqvf/va3HUosWEqBUKwhnyjvcBzCn//8Z7fccsv5d/ydcsopDoymn35697vf/c5hbaWvEspCRx55pM8eRzX99Kc/7atZtXwZAoaAITDeIdDfeHR/6v/Gu8pkBTYEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMgT6LgCmuRHwajoxBwQFrI5NNNpm3mFIkmm984xu53rBoggLMiy++6J5++ulcf516wVEHzz//vE8OxRpRWpH0OdJIrM78/ve/b1Bcueaaa7y37bbbzj3++OPukksucS+99JJD0We++eZzK6ywgi+rxKWvHNfDjvX555/fW6TBusv999/vwG7hhRf2Cj1TTz21DpLef/rpp+6ee+5xDz74oLcGg8CTo4nmnntut8QSS2Qq0GDphvSwJLPuuut6SzJ8g3fffdcfgbTgggu6kSNHNliUSRP83w1lu+OOO/x3I67ZZ5/dLbDAAm7llVd2gwYNCr37Z5SCOIqpGc0444wNuOL373//uy8j9+uss4775je/yW1Kd955p3v11Vf985JLLtmAcww+acQRN//3f//nrfEQ9Ec/+pGbcMIJG2Lhu4I133b99ddveBf7oNNsFkdWmlXqnqT117/+1T300ENeEe3jjz/2x2EtvvjibtFFFxUvDVf8Pvfccw1u4QNHVs0111wNzlg5oqyaKNPMM8/sqLPNjuCKrQex+OhvUrYePPnkkw5MWxEWqvL4QtlvQrtE4a6np6dpsuF3aVXOZpGhrAi/+t73vucWWWSRXl7JD8qCYZp4hCfcfvvtnse++eabbpJJJvE8j7iWX375pryL8Cgfojj5j3/8g8cGQpkSvl0nUQ6O2OPb/u1vf3NTTjmlbyfkNUvJsQqPhr/AZ+DDG220kbv77rs9/3zhhRfStgmfzKMYbKv0f5KPTvYp1157rfv3v//tj8NDKVXTP//5T3fjjTd6pymmmMKtscYa+nXlutcQWYEHeNdll13W1GcWbydALN+LbZut+AFtjnFWmN8rrrjC0Xd8//vfd4wLs+jzzz93jLugpZZayo9z3nnnHXfTTTd5N8ZZCy20kL/Xf4xP5EjEmWaayS277LL6tb8HJ/IgBF9lDFQ3xeKj81G2ncTwA3jFAw884JPNGnPp/DD2ZNzMcZMbbLBB+iq27rWrL2o29oInP/LII+6ZZ55x1Cn6JcaZWIukb1pllVXcBBNMkJZN35Tll/Ad+A9EHUNJXhNjWca0EGNE2oqmZm1T1/WsflPi6XT/J+nGXt977z3f3zNupD7zbWZN5o7M07KObY2da5C/st+TMFXadex4ODavMfyAtKDYsWkrfNrVNmN4kMwz2ITCvD2kG264wVEfmZ/QxjRVKafEw1jxL3/5i69Tn3zyiTj7K3W+2ditwXObHhhPskmHY49Zn4Cuvvpq9+GHH7qll17aj2fJ9+WXX+7fhf1HN8ZeHHP9xBNP+Pzk/YVjEvzF8toqbUzyV7afp88ivyHJsc/UnTyKaSfE1Qwf3ufN4epoJ9Q3LDczpmS8wholY0f6hLrnb83KWbTPBY8yFMtry6QR+qUexM41wrjs2RAwBAwBQ8AQMAQMAUPAEDAE4hEYlAjnmkvn4uMeb0IyYUSRAzrttNPcqFGjSpedBWqE2yhMsACiBQelI6shAEcBiSWSs846yythhNEiaGRhk8UAWdhnwUAEJnPMMYefRIfhWGglbhRFQuIIJTBAYMri+WuvvdbgBWUOrL+EgnyErlh9QeCTRQhzOQIoTPPwww/38U066aReMBwujhEXC1Acd5QlGEcp57DDDvNCsTBdhEInnnhiZrhmCycSD+HPP/98efRXJtJi3QdLNygqCIELx1jRpDmaiW8oCgyx+EjcMderrrrKHxlFWL6lxu/tt9/2R2Z98MEHXvmr1UJa0fR1ms3CoHAWphlb90jns88+c7/4xS/cb3/720yFB/jDAQcc4IYMadQV3H///b1iV7O8HnLIISl/EX8ohaHUlUUIfw4++GDHsVchVakHsfjob1K2HtB++LUi+KUsHIvf2G/CghXKbq0IjLfZZpvUW7Nypp5ybhAKIDDcYostfBsOvaEMRx8RpnnEEUe4s88+O/SePiPU/vWvf93Q9uQlQoF99tnHC7K5zyIE4SGvzfJX1O2tt95yu+66a9pf6HD0C7ShsN5W4dEXXnihO+igg3wyO+64o++fdZrc77DDDm6//fbrpWQYg23V/o/8dLpPoX9DQIxg+LjjjiMLnuDRm222mVesQ5DB2IZjDoVi8JGwsVfqz2KLLdY0OAv1KMxqqsL3YttmK37AmOL444/v1f+hHIDC0JgxY9xuu+2mi5HeI/BHQRHim/Ht/vvf/7r11lvPl/1b3/qWQ6iIwFHT3nvv7S699FJf188777xMxZU33njDK/pKOHghimZ1Uyw+ko+YdhLDD8Dp0EMP9cnusccejl8WffTRR37cxbeDlyFEgqrUvXb0Ra3GXj/5yU9SZbWsclKnGOeEfWQMP0BJX+ZLCPbCto2QDEuIEHMM5hqamrXNo48+2p1++unee9hv4tiN/k/nPeaednjggQd6haKs8GyoELzkfexcI+Z7kmaVdh07Ho7Naww/EFxjx6bN8GlX24zlQdtvv71XhkT5XiywSvm5jhgxwivqbbrppn5+rd9VKSfxoLDGnEk2Yui4uV9ttdXc2LFjQ+eOPjOmpJ8dPXq0XwcgcbBCgfykk07yY1ndn1ImrZzdjbHXL3/5y8xxsAYua34cy2urtDHyFNPP0/dSP/KITUWMm1Ca1xTbToijGT68z5vDVW0nzKk5mvuVV14hmQZifQgLkLIm1/Ay8qFZOVv1uZFJ+jWA2HWA2DRj5xqx6Vk4Q8AQMAQMAUPAEDAEDAFDwBDIRqBRiprtx1zbjACKGkwuuUL62J02J50bPQtoQt/97nfltuGKO4or5BtlCXa1M/EXYucHhPCUxRwmgkzSUVbYc889vRWXvB1Tsmsd5Rh2crGLlZ1MKLKwSMZOJ737kh3IKK1g1QP8yBtWYsgPu524kibv9a5xyS9CBwjlirXWWstNNdVUPg0Wy9l1xmK+7HD2HpM/FpFZaIUQFCFwZVf6vffe661usEiVFQ7/7MYVnTHZ4Yo7iy0o2UDs4ipKCBVZkIJYTDz11FP9rl8JH4uPhK/7ihIR9aCdpLGUdFBWefbZZ+Ux81q27hEJC5i33nqrj4/vxjdgVzJKbVj8wDoR9XXffffNTJOFQnYyC1E3ZJeeuGVdqa8sJEEsXLFjlV3SCD2xNsSufU111IMYfHQe9H2reoDlJW2N5/rrr/ftfLbZZmuwTBLu1CaNqt+EOFjwQwFPU6tdWNpvu+8RYEPggbUMEVI9/PDDvj7CJ7E0ggJKaP3pN7/5TbqjDP7FIjqWAlh0x3JV3USd3mqrrVKBLlawyDP9B1Yg2DXIAizWr3S/UIVH6zLAI6kn8GkU+qhL8N4zzjjD9xWifCphYrCVvBJHTP/XV/oUhC30s+CDEB4lSPpxTTH46PBV76k7WnETZZU861V18L2q+W13eMY29Pvssn3//fd9W0LoI9YxGD+htALBG7OsrbQ7j3XFX6WdSB7K8gPCwTM333zzXpZBeIeSBEorIdVV9+rqi1r1ufQTWFdBMQqrPIxH4WXs1Icv8MPaJONc3ad0mx9o3MkvCvfNqNP9X7O8FHnHeJBxHURbZ3yL8hDKklilYK6i520SZ+xco5vfs+x4uI68luUHVcam8m3Ca7vaZl08KMxv7HOrcrKuwNwZCw4QSvPMZ6DbbrvNW3nxDwPwr1NjL3gIax2aisyPtX/ui/BaCVO2jdXRzzOvYS2IjQzMa5hvYMURXhoq/fe3doLFPqyYooTJ92RjFmtfWHFhnsqaAOMV2kzWHFm+Sx3XMvWgbHrt4LVl8lBmrlEmXvNrCBgChoAhYAgYAoaAIWAIGAKtETDFldYY1e4DAR675thBz0IBZjBFiQFhHrucuk0Iv4VQxsgicWfSjLlghOh6IZswCCpltyrPTKJZSEVIyQ4KLaDkvaZtt9023S2POxZp2D1P3hDAsBtciF3WWA9ASDvNNNOIs7/usssuXpGABSEWKrTiis4v+WeyjxAYwjIAigZ/+MMf/KIwix6SX74XO6oghL7kRwTcfFd2PhIXi8kIj0Oh369+9Ssflj+EtuyagbBEg9JMGdI7/7BUw7MIqySeWHwkfJ1XMOmERaGf//znvSxNsMu9leIKZS1T9zimRZRWEPay+1Pwp+6xa56dwwhSqJ+iXKAxRUCkrR1Qv4ooriC40OGoo+SBNkmeQsWVuupBGXx0OfV9kXrAzjh+QihksKiN0BVLHHlUxzchbr4XeGqi7tLG+wKBA20eC0ual5E3lKWwwITSH9aZtKCa+iE72Kh7KFihtALBr9qhuILyjFghoB+gnUieN9lkE78Ai6DihBNOSPks+RE/3Jfh0fjXJGFFAILVIvoiFlxRzMDajbaIFIOtzitpl+n/+kqfwmI334OdzggQf5tYVwiPAaBsMfgQri7C2pFW9qPe5Cmu1MX36sp7u+JhNzcKrDvttJMfe9DfobCLIIPd6xCL8GK1rV35aGe8VduJ5K0sPyAc/Am+ydhCEwproRBM3tdV9+roi4r0uVjo0nxQysGV/oR+BeVG+hVtjavb/EDnkzkHQsg86kb/l5eXIu7Ml7BGBiGEpa4hzNOEkJc5Tkixc41ufs+y4+E68lqWH8SOTcPvI8/tbJt18SDJa5VrkXIiaBelFZRc9BgcQT1Hsg1E6uTYi3UGPXcEz6LzY419K16r/ZZpY3X188y/GfNA8H3Wje677z7HHBGLZnoDVH9qJ+CDtV/KxFocG6s4HlaIcjI3ZIMQY8Jjjz1WXrXlWqYelM1A3by2bPpl5hpl4zb/hoAhYAgYAoaAIWAIGAKGgCHQHIHBzV/b23YgwITz4osv9qZ3UVzhGeL8Y8ySs8O526R37jGZzyLtLv7laBr8c4/QRBMTa8zZQyxgIYTOIsKy+14Ti1di+j60fkJeENaESiuER/gGtpAITv1D8qcXLRAMiNIK7wcPHuwVUEQJAVPmQnw3iYtworSSFS7Mq8RR9Uq9YTFbBNAs6KFMI/nV8cfio+Oo455FFsy292UqW/dY7IMQGqJMofHnXky3c5QDwoV20gILLJBGn6UAVUc9KItPmiF10+560Je+iSp27bcsTCK0CRUmSAh+Ke4oR2pilzaLthDCf1Fa0X7qvhc+CM/lyBLJG+nMN9986TFuCCV0vxDLo8P8b7zxxumuXd6hcChHPSHwQ7lHUwy2tA0h7sv0f32hT8HCyoYbbuiVVsAHix1ZSiuUMQYfwabT1zr4XqfzHJselhhQwoJQQqBeo4SL8BvBEbxR91Gx6XQrXF3tpCw/kPLSJjR/wp0xmAhaxZ9c+0rdK9rn5imtUJ55553XFwsrLDIWl3L2FX5w7bXXeuVLyVfWtRv9X1Y+irrRd8ocC6X4UGmFeOjHObKkLuor37NIeerIayw/KJK/Vn7a3Tb7Gw/S/FXm7q0w7O/v++PYqwiv1d+lTBurq5/X6TPuYa4B0SawUqKpP7UTxnVYXIFQStZKK7jRV8vRnmyckXVG3tVNZetB3elbfIaAIWAIGAKGgCFgCBgChoAhMHARMIsrXfi2COxQdmCxip2aKEBgRQTLG6+//rrf/R2evdvpbOrdinlCjiyrA1rphl0uYpVF559jVM4//3zvxE7gLAsUWFdhEUETiiQci4LSD+Gy6JNPPvFH+7BbnKOJRDjLIgjEMwoEsmCh8zty5MheUU477bTeagWWHnSa7IwS0ru+xQ1T65h2x5KKDifv67iy2wZLCdCoUaPcMccc0yAMzkqjLD5ZcVRxY/c+Fk/4liyqYLK3r1HZuicWXKhX7G7VC0RyjxAb7EMFAnkfiwHmjzkWAqLOn3vuuWlUmEjOoyr1oCw+WXlodz2o8k3gy0JauULcilwRMkk8KIUU5eccdybfU6eTxWv1eyxNcFQKChj6uAqEkNTL8FgulJqkTsrip46vHffCM+kXUIoICT560UUXeWfdL8Ty6DB+bblH3q200kpym8uny2Cr81q2/xN8yFA3+hTaDMqPYm1t3LhxvRbDU7DUTRl8VLCo26r8sgrfi22bFBRBOX2eJuJrRrRZ2jJtJQzbLBzvsEzH2INxz49//GPvHV52/PHH+2NgmoVn7KItLmnFsWbhqrwrg09d7SSGH4AhfJ2x1tixY32RX3zxRaeVmvNwiKl70ocQZ2xfJPkp2+fSD4E1/cczzzzjjzzkqE+I4/vC8bmk00l+IGnKFeUhsYbD92V8nNV3dqP/kzzGXMEUmnTSSVMFz5h4YsJU+Z5l2jV5q8rfq+Q1hh/E4JkVplNtM4YHZeU31q1oOemDhJh7Y1FwIFMnx17Sp1TtT4ryWv3dyrSxuvp5jhGjL2NNC2UP6cOwnps3rqrSTqqMEwWrIu1E+gTC0EZQfBL+KVdRpuWKMpjemCVpVb3G1IOqaXYivGDYibQsDUPAEDAEDAFDwBAwBAwBQ8AQyEfAFFfysWnbGxYMMN0phPD5lFNO8TthsUKCyU2xoiF+On3VlksQoGQJGpncC4lgVu84x6R1Fml3hO1ZpBeu9HsUSSCOJmKBgJ2fQkz22WHMGcbNiAUMUVzR4XW+dHhxf+2111JnfZ+X16xwaQQ13IjSClH99a9/dSzQyHfIij4Gn6x4Yt1Q0uI4B4idV+DWFxVX8r5nVt2jTB999JEvE0oDZ5xxhr/P+8PEvibaPhRr8YIFq/A4IBTNsLyjzfjrNKvWgzL46HTlvt31oOo3EWU38qv5meS/yDVUPEDIhzUcLHxoZYkwLo4l41eU4EMcO4fZ62Yki9Xihz4ISy0c30Md4tgcFpVRdBGlH/Fb11V4fV79EX5JeuKX+1geTVhNWenSz7F4jXATpVFNMdjq+qLLo+PV7rqc3e5TRLlT8nrXXXc1VVyJwUfijr0KvyS89OFF46rK98q2TZ0vFG7LEsdX8aNNzjDDDG7ttdf2FhX02CwvTvqTU0891Su0ivBihx12cMsvv3xekNQd3kB6naQy+NTVTsryA/BACQil6+uvv97vdqbvRYkF/grm6667bqYSS2zdq6MvIt8xfS5llOOliANibPGb3/zGH735lcvX/93gB1+n/tUd8ybGWPBh5lF6jKz9dqP/0+mXvX/ppZd8kJlnnrlj1pLq+J5l2jUFFP5edjxcR15j+EHZ75jlvxNtk3RjeVBWnmPcypQTpXs2AcBXsTDEfJG6DxHPQKNOjr2kT9Fj1Rg8i/JaHXeZNlZXP8+xtiFhnQTl3iyq2k6qjBPJT9F2In0CYYrMGekX26G4ElMPyHNfJ+mLyGfZuUZfL5vlzxAwBAwBQ8AQMAQMAUPAEOhPCJjiSh/4WizSjRkzxltjuPXWW93VV1/tF12zjvroVHaHDh2aJoVCRJbiCu4Qi8Df/va3/b1ecGR3YBbpHbwSR+hP71zX73T87OYToSY7Xg877DDvFSHx6quv7o+FEEUOdhBjzhTSOym0NZlWaeq86vu8cFJ+7ddnoMY/rMQgfEbouu+++7rTTz89M/ZYfDIji3Q88sgj0+MK9tlnn9TqTmR0bQuW9z2z6h4KVEIoJLQSDIbm9UXpReqxxFXmKguQ7IxmoZcfWLPQi9UfTXXUgzL46LTlvt31oOo3kYVd8hv7XeArohDB90D5Dx7ED4HaVlttJXA0XOGlmj/KS60kKG7kc/vtt0+t+CyyyCKOXYTwblloO+KII7yCX9auc44ZI1/33nuvu/nmm/1P4q77CgZiYUL4YpgGR7oJaZ4Zy6MlLrlmfUvi5gc+9CdCsdhqHpFXTv19dTn1fV4bkzi1X8lzHdfvfve7PhqsSKBcu9hii3mFqzDuWHzCeMo+ixIG4fIwyoqzDr5Xpm2GeaA96nrMexbGs9qlhBX/KNpigQilXMYwWOYjL62I8HpHscTXKlw33pfBR9f9vDpQpJ2U4QeCCUd/8Q2whHPUUUf5MZcc/4dikPTF4p9rlbpXR19EHmL6XBTssK4G30aZUcYXCJS5Z4wt1C1+IOlzff75590555zjnXbcccdU0K396PtO9n863Zh7scKWZcEyJr5WYer6nmXaNXmKGQ/XldcYftAKxyLv2902yUMVHlSkDEX8lCknSt5HH320n9MjcO/2Rp4i5avqp1NjL+lTsvqqomUoy2sl3jJtrK5+nnEPfIixligjMJ7i+DWOo9ZjqTraSZVxIjgVbScyz6VshxxyiECce5X6lesh4kVsPYhIquNBYucaHc+oJWgIGAKGgCFgCBgChoAhYAgMcARMcaUPfWB2GaG4wgSb3RTDhw/vlTuULrSwg0myFk70ChDpIIooBMeSxCyzzNIrJrEegVILu4IhrlNPPbVf2JcjB8KAWkCYtQMH/3lWU2Qxg8UIrVzDLlBo1llndZdffrnPg3f431+4m17e6ThIs5mCjs6rVkDICydl0H4l3TquP/vZz9wWW2zh9tprL3fZZZe5G2+80R+1sdlmm/WKPhafXhFFOtx3332piV6UVrJwjoy69mDy3cKIs+oeyiG0QdrlHHPMkR7LEIbNe5Y4Y/FYYokl3CWXXOKjZ9H/9sTyBhY4aLNYt/ltYoVIUx31oAw+Om3uO1EPqn4TrSSStdAalinr+cEHH/Q8iHoBL3/ooYfc4Ycf7sSsMjtIs+JmRz/tOqTZZ5+9ge/z/o477kiVVli43HrrrRuCsUibt6sQj1huoO5gHQbBJHxq4YUX9jv+yG+dBL+mT+EIpbx+QbtrnhnLo8P8I2wOlUH51ghhIc3fY7Gt0v/pMnejT2Fhm52bpL3mmmv6o8123XVXd9111/kjKjSesfjoOGLuhV8StgzPrIPvlWmbYdnuv//+XmOSk08+2R/dE/qVZ4Qq7BbmyB8EKtdcc403B3/22Wd7d/GXdaVO77zzzl5RVN6fdtppbvHFF++Txy6UwaeudlKGHwiGKEJjAWD//fd3WEcUnkuedtppp1RxQvxzrVL36uiLYvtcFHHFOhj5wKofZWGOsssuuzgUW37wgx/4onaLHwjO9LP0gSh5DRs2zKG40oo62f+1ykur98y/OBqCcV0nqK7vWaZdUy7h72V4e115jeEHVb9FJ9omeazCg6qWkfAx5eTYQpSZpS1jIZDnu+++u0HJuI78dTuOTo69pE/Jmn8UwSGG10q8ZdpYXf08cxyO7YQ48o62wBoRylBYhNKbO+poJ1XGiWXaiazJ0edtuOGGmRseBPd2XKvUg3bkp1WcbJ7Q1EqRW/oiwpTpjyQN8OnEWq2kZ1dDwBAwBAwBQ8AQMAQMAUNgoCIweKAWrD+Wi93vQrK4IM9yRfjJIof89K5H8VPHVR8zwkJRSCifiHlb7Rd/suDAru0s0iZOZ5xxxiwvXtib9YJzfCGObZGJJ8JgSYvFLhRnQmLBIotk8s87iSP0J/nVedX3eeHEXfsN467yLGa4EXSzWA9xHx71UQWfKvnTYQ8++GD/iGn9jTbaSL/qc/fyvcOMZdU9dnzL0R/yPgzX7JmFPChmYSSMl13m8IMRI0b4V1jSECsXONRVD8rgE+axE/Wg6jd566230myHig7pi4I3KDXBq6nzLC5CLKA9/fTTBWPI9/bUU0/5l1jwGD16dC+P7IbTC2e9PCQOd955Z8ovsLAxduxYh1WBdpDwQeGLYRraXfziJ5ZHh/Fn1VvOfRfSx6NUwTa2/9Nl1lhI/riKu/ar31e5hzfTd84555ypwhP4ZCk/VcGnSh610pxYU2sVX118r1U67XgPL0OhmaPfhBc99thjLZPCGgiKBhDHUM4999yeF6AMo/lbVkQoQBJefmLFIstvN9x03Zf2EOZD3LXf0E8ZfqDDwsuxCACJEjZtJEsQWLXu6W8l31/npch9HX0uu/OxvoRFP+okQhkUWIS6xQ8kfaxUMt6BDkssL2rLV+In61q1/+M4ruOPP97/sIjULpIjHlDA78RRKd36njHj4bryGssPqnzzTrTNqjyoSvkkbGw5zzrrLB8F1gQ5oo3xabjeIGmE1061zTDdmOdOjr1kw1HWOkmRvMfyWuIu08Z03y39eZg/cdd+Qz/6mSOCUDwV0kfJ9bd2In0CfXHM2oNgEHutUg9i04wNh8UyWTOVK5aKm1HMXEPH16m1Wp2m3RsChoAhYAgYAoaAIWAIGAIDEQFTXOngV/3www9zU0PAfNVVV/n3WFCZd955M/2GgsjwOTNQhCNKEbKwgSl02ZUuUbELW9LeYIMNxNlfJe8IZ5988smGdzxceuml3o3FZaxUZBGLkaECBkKC2267zXufb7750mAosIjVGYS1IeGmFyj0ewQ6EhbTsSFheUCEm1Iu/LB4JoozWeHYjYs5Wmieeebx13b9obCAYIv8YAaY3bBa8akKPnXlmUUAcP75z3/eYJq3rvjrjKdM3SNdrJ5At9xyizdX7x8y/sIdP7QfqVt1KK5IkrIwSV3Qiit11YOy+Ei+uHaqHsR+E/Io1kZQjpt//vlxqoXETDWR6fvYyIUn852F1+i4sLDQjKiPmKWGOOKKHa3tJOGfef2CnNNOv8DiolAsj5bwcmWXZUgcuyKk+5Qq2LYqZ17/15f6lM0339ytvPLKHporrrjCW/QSnLhWwUfHU/ZeL9AX5Zl18b2yea3TP+1bdoDKeCUvfiyvnXvuuf71lltu6dZYYw1/zBDKBwiGsaIT9kU6LtJh57H8sPbSl6iudlKGH+jygz9CCRQ4+C2zzDINx+Zov1XrXh19UZ19LsI9afsoZQqJW2xfJPHEXkWBCKVdUehuFVcd/R9KYVhOkl+rNGPfozQEkedmCjJ680NsWoTrxveMHQ/XlddYflAF5060zao8qEr5JGxMOTmS7eGHH/bzxSJHoUhacu1U25T06rq2c+zFfFDWZMSaVtl8x/BaSaNMG6urn5e05aqt72oLVv2tnWBFhjxDZ5xxhhQv89psvJcZoIBjlXpQIPpavchapY40y02/j5lr6PBh/OGz9mv3hoAhYAgYAoaAIWAIGAKGgCGQj4ApruRj0/QNign33HOP/8lCAAEQyok7foRY0GVXDebEUaJg8sxEhoVgzINiXlR2jyB0jd1dKelVvXLkwbrrruujQYGE4104ygGzpOy0lOMsWLgPrb5g2leEKyhRiFCVyTPmWe+66y4fL8dlNCvnbrvtlu4OBifiEoHv9ttvnxYRiwOzJkcEQTfccIM3JSwTdazCYG4/b9LIsSKSfxQP2NElfpm4cgwPhDB1m2228ff8sat+nXXW8c9huBdeeMGNGTPGvwvDecc2/FG3xFICijryfUiqCj51ZpUjjOpUBKgzb2FcRese4djBxW5r6g11JLRQ9Morr7jjjjvO71gWxSr83p4c6wNfgAiPspH++RfJH3VeFufFTa7Ucwnz8ssv+3QkfeqoKJ/hv856UAYfyatcO1EPYr4JioUcrXTmmWf6rI4aNSrlY5L3olcW9djlhaCYneAIm0RhAQs9dbQDdg8KobgmO7Hh0b/4xS8cC//NaNy4cV45kMXPLKsazcLGvINny0Jr2C8ce+yx6Y75sF+I5dFhHhHo833ZoQjBt8WaBH2uVlypgm1s/9fX+pRjjjnGWzYDK3ZM64XcKvgQXwzB50QBFV7GdxTex1XGBvBWnqUfr5PvxeQ7NgyWTxhLoDjCOETGNAsuuGBulIy1ZEcxCrMcGwchBBLhH+NS+qP+SnW1kzL8IMQKCwAocPK74IILwtfpc2zdq7MvIjNF+1yOcqOeYIlEFKUIT9tCaXyTTTZJxyKLLroorzzVwQ8YC+n2rNs0ifBM35ZHKGZhbaUodbr/K5qvLH8oEWJ5CaK+HXHEEQ1KycwnmVvW1a7r+J5Z5chzqzIeriuvVfhBXrmKuLe7bcbyIJ13Pc/QbVT6JOYnuMvYSoeV+6LlxD+8gDEstNZaa/m1E//QR/+k/GAg4w54Js8yxyuT9brHXnwXLLAx7ka5jXH4qquuWiZLDX7L8loJXKaN1dXPS31lDYlNWCidCskaDs91tBOJt8q1aDvh+FjWDSEU8Om39eY46h1zHPxgaa8dFFsP2pGXOuOkzsTMNerMg8VlCBgChoAhYAgYAoaAIWAIGAJfITDEgIhDgMllljBZ77xDGK2PhGDR9frrr/c/UmXxQBZ+JBcImhHi9QXaeeedvVINijnsuuaHIoYIiFBu4XgJlFc0MaHeaqut3Nlnn+2FXezkR/jIrisWDyCEtyy0NiMUZhAozjTTTF7RRxat2VUpZxZLeBZkUBZhss63AUcWImRHDbv2QwsuEhahEMpG5I8FI0wMo1Dz6quvihd/zvZ0002XPnOD8J7FfITTZcI1RFLjA3gicEAwdfHFF7ullloqVcqpgk8dWeR77L333nVE1ZE4ytQ9jhg59NBDvfAfwSH1j7PYcUegoBeTJPMITQ444AB59ApdKHVl0dFHH+2Fl2JtSPvhW+uFe/1OL9CJe131oAw+kjbXTtWDmG/CTsdHH33UZxfett566+msl7pvtuubIzioH1VplVVW8ce6oAwFb8ZiFwp8r732mueDWLNC2SDsY0iXxWsRclFf8yxfVc2jDk/eRo8eHdUvxPJonT79MYLNE044wfcNouiDe8ibqmBbpf/rS30KbZU6ssUWW/h+G97BAvmEE07oquCjv0mZeyxbIFiH6OfFsk0YBwof8ESUudZee23/ui6+F6bVzmd20YY7aRnHbLfddpltmnEZYzbaNnWaHeeM14Q23XRTrzTMGBRrTFhxWG655eR1v7rW0U7K8IMq4MTUvTr7ojJ9LsoqHMnBD6L+MI4OLXmsv/763kqX4FIHP4DPNCPmEYxpL7rookxv4Fz0yIg6+j8E1Lpv1RZoMjNY0RFldOoFY0rmVlhV4ohQlGTliB361zqoju9ZJh9VxsN15bVT/EDj0om2SXoxPEjn88EHH8ydZ+APa3ny0wptEkeZchKGI4IYxyIY33fffSWawtdOt014UsiXUCAVJdLCGf+fx7rHXqwLYH1NaMUVV3TTTDONPJa+luG1OvKybayOfj6vX8HizJJLLqmzV7mdNEQW8VC2nbDmhVU2NmjRZ1944YVpH0j7ESUq2ZwVkaWmQWLrQdNI+8DLKnONPpB9y4IhYAgYAoaAIWAIGAKGgCEwoBAwiyuRn1MsijQLrhcyEYRutNFGXglDwuhFT96zM4JzVxG65lGYbvicFy7GnUk0u33Z7ScCEFFaQZmExdM111wzM2p2aKPUMvnkk/v3WJ1AaQVMEOpiGSVUBNER7bDDDh4v/KMMgNIKij5gdPrpp2uv/h4hMztOODYHYiEXpRXKwILv1ltv7d31N/EOyR8CVfLDjkbeI/gSpZUpp5zSKxWINRMJw5VFY8Kx+FEmnI6De/0N9X3oj2ed/9Avzyg/kGdo//33d++9956/r4KPjyDiT+ePvEi+JCp5L1dxr3LVcel7iVPc5Cru+lq27hEWKxFYuMDyDd8IZZXnnnsuVVqhrmBxQup8s52JOi9yH/rPyj88BAWxkSNHekUGriHVUQ/K4qPzWrUeSP3XcYZllOey34S4qaOLL764XwBcaKGFJKqGq+RBrvIyL08o9qHMwJEdWF3JMtEtceXFIe/lSprwZKzDyLFILFJyhjy8EuEBwiCOO4LCeOHd8MgpppgitdTkPQZ+w3DiJ/ZKv8BOWlHcKdovxPJonc/zzjvPK0wi6BOlFQSd9HHDhw/XXithS0Sx/V83+hT5xrpuCRgs4GKxDEKBFcVcqErd8xFE/IU8sFUU2n8Vvie4CE5huvJervJe+9f34fvwnY6Hdyj50q9Qpy677DI/xtFh5B6FrMcff9xHj4KWPm5L0kQJkjoPNoxpQoUE/El8EiZ8FveqVx2vvpd4xU2u4s61jnZSlB9I+nLV+QjvxY/+hjF1j/B19UVl+lzG6/R/CBghxvu6jmDtByVtfrqMsfxAxxFimfWs2zTvJfxss83mx1dhGHkvV3lfR//3zDPPpEr8xIsCRTuJMQTzQ6zegDf9PYqporTCeCVrzKfzJPUTN32v/XAf+z0Jq+PV97yDxE2uuIXfFbdmpP1XyatOoyg/0GH0vdQxXS79Xu71+060TdKN4UGE03nluRXp76LDlikn8282j0AoaYbrIUVw7lTb1GVshQ3vQ//yLGXScdQ59iJ+1k8YE4xOlNsEX52ezp/kS7+XPJbltTqOsm0stp/Pyj/5oE/FsiLKUFlHrsW2E+IWfPLSlvdyJQyk/ZdpJ4Rl/saGBcZyrH3RJzCn4sc9vHHFREkpPM6bsLEk+a9SD2LTlnCSB42dvMu6ZvnLcpOwmo+JW7Nrlv8w/vC5WXz2zhAwBAwBQ8AQMAQMAUPAEDAEvkZgUDLg/spu/tdu4+XdF6/8zX324F3uv0887L54+UX3xdtvup6PP3KJFHC8xMMKbQgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhMAAQSDY+DppkUjfB0OncBMO+6yacf7ibaNGl3QQzzzYACmdFMAQMAUPAEBgICIzfiitffOE+ufkq9/GVF7rPn31iIHxPK4MhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAh0BKBIXPP7yZZZ3M38Yjk2OXEapuRIWAIGAKGgCHQLQTGW8WVz+75k/vwlJ+7L954tVvYW7qGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGQFcRmGD6mdxkuxzkJlpyxa7mwxI3BAwBQ8AQGH8RGO8UV3o+/o/78IRDvaWV8fezW8kNAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMga8RwPLKZHsenhwr9M2vHe3OEDAEDAFDwBDoAALjleLKl+/+w/1rn63d5y891xvaQYPdRIss7iZafHk3ZJ6F3AQzDnODJ5vCueTcP6POIbDYYou5t99+2+24447upz/9aecStpTGewSs7jWvAoZPc3zsbd9D4OKLL3YHHXSQGzRokHv66afdRBNN1PcyaTkyBAyBjiBg/KAjMFsihkC/QMD4Qb/4TJbJfoyAtbF+/PEs64aAIWAIGAIDH4HPP3dffvhv98VrL7vPn/6r++y+291nj9znXM+Xvco+ZPa53JTHnOMGTzNtr3fmYAgYAoaAIWAItAuB8UZxBaWV93fduPfRQMmZfZOsubH75ibbucHTztAunC1eQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ6BPIPDlP153/7nkTPfxNeOc++KLhjxxdNC3fj3OlFcaULEHQ8AQMAQMgXYiMF4ornA80Pu7bNjL0sqQ2ed2kx94nEN71MgQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQGJ8Q4JSCD47cK5GhPdtQbGRn3zrl93ZsUAMq9mAIGAKGgCHQLgQGtyvivhTvhycc2ktp5RvLjHDfOu1SU1rpSx/K8mIIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIdAxBLyCytg/OORmmlBoQb5mZAgYAoaAIWAIdAKBAa+48tk9f3Kf3HxVA5Z0vlMcdrIbNNE3GtztwRDoJAKvv/66e++99xqS/Dw5Z/K1115zH330UYO7PRgChoAhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGALjCwIffvihXyv/IjjC5p///Kdjbd2oXgQGfWNiLzcLlVeQryFnMzIEDAFDwBAwBNqNwMA+KigZ0PzzxyPcF2+8muLI8UBYWjGllRQSu+kCAiiozDPPPG7o0KHu9ttvdxNNNJHPxamnnuqOPfZYd+CBB7rtttuuCzmzJPsDAr/97W/du+++64YPH+6WX375Slm+77773GOPPeammmoq96Mf/ahSXH0p8GmnndZrAjto0CB30EEHpe2tL+W323kZqPWg27ha+oZAVQT++9//Onh+T0+PW3nlld3ss8+eG+U555zjFWIXWGABN3LkyFx/rV6USbNVXHW/f+aZZ9xFF13UK9qll17arbLKKr3czcEQGCgIsFB/0kkneV4watQoN++88w6Uolk5DAFDwBAwBAwBQ8AQMAQMgVwETjzxRMfvyCOPdJtttpn3hzLLsssu6z799FP3xBNPONb7jOpFoOfTT9z7O/2o4digCaafyU19wc3OTTBBvYlZbIaAIWAIGAKGgEJgiLofcLdogmqlFTrVyQ88rlallRdeeMGdddZZ7o033vD47b777m6RRRYpheXxxx/v7rrrLh8GQcNPfvKTUuHb4fnvf/+7GzNmTK+oJ554YjfDDDO42WabzW244YZe8aKXJ3NoicCQIUPcwgsv7B544AG3ySabuOWWW8698sor7tprr/VhF1tssZZxmIfxF4HzzjvP/e1vf3NbbLFFZcWVG2+80QtFhw0bNqAUV6688kr37LONZ7JSY/bee29TXMloOgO1HmQU1ZwMgX6FABbYWKCDUHZtpbjy6quvuvXXX7+S4kqZNMkXSjWkC0077bTuG99on0VD+r4LLrjAp6X/JpxwQlNc0YDY/YBDgEX5k08+2ZdrpplmMsWVAfeFrUCGgCFgCBgChoAhYAgYAlkIyBr5L3/5S/fkk0/6Oectt9zisLiy4oormtJKFmg1uGF5BTnae9uv7dz/rN0gZ0PeNvGq69WQgkVhCBgChoAhYAhkIzCgFVc+vvLChlJPsubGjrP66iAsFIwdO9b98Y9/9Av2EufGG28st4WuDz/8sPv1r3+dxjHXXPXkr1DiTTwhtHjkkUea+HA+3/vuu6/baqutmvqzl9kI/OxnP3Obbrqpow7wE8LSyoILLiiPdjUEDIEIBH7605+mR3GhIPaHP/whIhYLYggYAoaAIdAKgbffftsts8wy3huW41ZfffVWQaLfL7TQQt4ynUSAUs/7778vj3Y1BAwBQ8AQMAQMAUPAEDAEDAFDwBAYQAgsscQSfv384osvdvyE2DRxyCGHyKNd24AAcjTkaR9f+bXVU+RtprjSBrAtSkPAEDAEDIEUgQGruPLFK39znz/7RFpQN2iw++Ym2339XOEOhQPMsVclTLHvt99+qdJK1fjaFZ6jSDDBzo5aTPG9+OKLXmHnk08+cUcccYRD2WappZZqV/IDNt7vfe977o477nA333yzt7YyxRRT+KNfTGllwH7yPlkw2ja746eeeuo+mb/YTHGkhtDgwYNNcUXAyLkO1HqQU1xzNgT6DQKTTDKJ22GHHXx+GTd0grqRZtFyYfVPH2uHBQpTXCmKnvkzBAwBQ8AQMAQMAUPAEDAEDAFDoP8hcNRRR7kNNtjAPfroo142Mcsss3gro9/85jf7X2H6WY6Rp3181SWJqdUvfc6RtyF3m2Dm2fpZSSy7hoAhYAgYAv0FgQGruPLZg3c1fIOJFlncDZ52hga32IeXX37ZB5188snd6NGj3ayzzur22muv0tGddtpp7vnnn/dHC7WyblI68hoDLL744m7bbbdtiPF3v/udw9rKl19+6S6//HJTXGlAp/gDdWi99cy8XnHEzGfdCKDgoZU86o7f4usfCFg96B/fyXI5/iGAYuH+++/f0YJ3I82OFtASMwQMAUPAEDAEDAFDwBAwBAwBQ8AQ6FcILLLIIl6G0q8yPQAyizwNudpnD9+Tlga52ySmuJLiYTeGgCFgCBgC9SIwYBVX/vv410evANlEiy9fG3Ls9uQYii233NJNNtlk7k9/+lPpuF966SV3yimnuAkmmMBhwWWNNdYoHUc3A6y55pru4IMPdp999pl7+umnG7Lyr3/9y1133XUNbjxgwm+OOebwij69XgYO4IM1EuL+z3/+42affXa3wAILeAH7oEGDAt9fPT700EPuueeey3wnjj/4wQ+8hRh55kpeyTM7mRkEh4SSzhfJWY467BVXXOE+/vhj9/3vfz/3jPnPP//c/f73v/fRYZFm2LBh/v7//u//3F13faVYxa7hCSecsCHJ+++/31u1QXC0/vrrN7yr8nDttde6f//73/4b/PCHP2yIinNBb7zxRu+G5Repj1dffbXXZJ9++undCius0BBGHrDEA0YoMfF9JW4s81AWvtdGG23k7r77bnfPPfe4F154wc0zzzwOhagll1xSosm9vvfee+7222/335Y40aafNVEW4yiCOeecMzdcmRdSB/j+t912m9fgn3/++d1mm23mPv30Uzdu3DhfF+edd163/fbb+zzo+PFD2R588EH35ptv+vrE95577rkdJi3ZCdCKCPvXv/7Vl5PvMeOMM/r6tdpqq3mLKHnhaYN//vOf3X333ef+/ve/e0xon2CcRzfccIMvl36PEtVKK62knRruBSPaCdiUTZPIKB/t9KmnnvLtR+rBoosu2pBWX3jAuhS8nbxSZ+H7fH/qHVhlUdV6cNVVVzmOaZtvvvkcx2HAK+CD9957r5t44ok9D6QtyY6Sqt+kG/Xgrbfe8ha7UNak/4Ovjhw50r3xxhseZ/gPZxS3g2LbWJm8aP7eLFwz/l6m/0NxFOtn9JHw1DzCD36h4cOHe94kfunfOPaQs6L/9re/uSmnnNLzD6ythbwLyxbXX3+9D0pd5BsKr8dq06qrrurfST8Gj9btW+OT1f9JnvRV90+jRo1yU001Vfr6pptucnfeeac/25q6hCLot771rfR9XTevvfaax4/8v/vuuz6N6aabzivtct72kCHNh9Nl6x68nJ1kIdG/wotiSOOo+3mJq2yaeszFGEqI76Gfcade9CWrXjF9EfgwFqF90s9jlYY+nj6RtoJlrzyiXcHHIdoN40PaCPwdZXja2VprreX7fImjKn8nHo6hREFeaNlll42uPxJH1hVsGANB66yzTtpHiV/qxKuvvuofGfcxhhPi6NUnnlAWMuWFuubxyyrfRKIv2zYlXJmxaSu+J/wyr5wffPCBryvgKMS4lPkRdW+22WYT5/RaJc0q37PsOIh5CfMTiLHAt7/97bQM3FBvpNzMicBIk24n4RwOfK688krvXc/hdHjuX3/9dT/W+8c//hG+coyn88Z8vTy30YFyUB7G3/Q9WOuk3oDHwgsvXIjHluV7zHOZ7zYj5rDaAhZ+ZUxQZf4XMwaXfJZpmxJGrmX4wfhS96rwg/7E3+GprGU0I+bmyy23XKaXsnW2SjuJrXvwZ9YLWF9j7MGYHr4C32UewRxX989hQakLZcZB4xN/j/0mIcaxz7F8r2y/QBth3Y/1v2YU9rmtxiTN4tLvypYzZv1Tp1dmTq7DcV8W2zB8kWfWqpiTMAfBCknWfFTGmMTHumI4Ty6zDiB5Yl7DWs4zzzzj3nnnHT/vY32K8QljsVVWWcWvEYj/rGvZsVddbaxMP8/aCN+xFYXz3KrykJhv0iqPdb1HrqYVV5C7TbLej+uK3uIxBAwBQ8AQMAQaEUgGnQOS/rn1Gj3/WH7O9PfZE4+0rZy33nprTyKk9r9EoNMynWTA37Phhht6/4nSSk8ieE7DJ1ZMWobvhIdkETnNU2IZpleSyQAufb/bbrs1vE8GeOm7Yf/DRV+32WabnmSi3BBGPyTnVfYkxw9lxvHjH/+4JxEaae/pfXLsUmYYnfbZZ5+d+pebZNHXh0sUccSp4ZosCPv3Z511VuqeLBJ6t5NOOil1C2+SAav3MyzB4NJLL01fJ4uPqXtYlmTRtCdZlPTvE+F1GqaOm0R5xsc7ZsyYhuhIc8SIEf4duFOfhX7yk5+keUkWNsS54ZoIY7yfYUk5L7zwwvTdBRdckLofffTR6T3+5JeYeuyhPeRRokzTkwhZU/8STq6J8lde0FLuiTDHp5Es2jSkte666/YkC+kNbltvvXVD3IkgvicRXDX4GabKyLtEYNwQRj8kwsSeXXbZJTc8dS1ZDNNBeqTOJrvwe7baaqteYfmOiUC3IYx+SJTAeoUBg2ZUJc1koaznsMMO60kWw3qlOyzB6vDDD+9Jjk5rlnyld7Q/0uFHu2xFibCvJzk6Jw0jYbningiUe0VRtR4QYSKU9mn+/Oc/74FX6XTlfuedd07TrvJNiKTT9YB+I1nI6lWuRFGlh3JRRspUN8W0sdg8aP4u3yzrmsffy/Z/ifKkxy05Yq5pvzp27Fjvj7ycccYZafGSxa6eROiUvuO9/BLFsh7KoykRQqTvEyUr/+q8887zbvBKoT322MO7hWMajU/Y/0nY8Ep7HPa/fEnbo9/I6vMTBcueV155JYyi0nNyNGTPd7/73TQPkhe50r/lUWzdy2v/zfoS8hDTz0vey6aZhb9gEl6TRUJJpvar8Gr6kVYU2xcxVg/LpJ833XTT3LEpeaJdi/9ECSq3f0mUr9MiVOXvRLTPPvuk6ZJ+oqyYxl/nje5jE+FEQ9S0H+n7N998855EGN7wPm98KHhxzeKXVb9JbNsk82XHps34XqtxP/OxRAG64TtqbBi3gnFIVdKM/Z4x46BEOTgtWyJQDYvRkwid0vfEH5K0k6w53C9+8Ys0rJ7DSRyJAlkPc6Jw7K/xffbZZ8V7V6+JIMiXhflCovyVlkvyilteXmP5Hn2pxJ93TZT3euFSdf4XMwaXTJRtmxIuhh+ML3Uvlh+AbX/i73l1XLuzDpVFMXW2SjuJrXuJwkqPLk94T1/NfD2LYvrc8Ym/x36TLKzLusXwvdh+IdkI0RPWm6znsM9tNiYpWt6YclaZF5Wdk0s5YrGV8GWuyB4Ef9ZVQ0qU19N1Sub7jHs0lV0HkLDJEbZpupK+viabaXoSpRbx3nCNHXtVbWMx/fwJJ5zQtJxS5kSJp6GMVeQhsd+kIQNtfECupuVsyN2MDAFDwBAwBAyBdiHQfItoo45Lv3r64u03G/I7wYxfWbtocOzSA1rqaD4PHTrU7bnnnl3KRfFk0fhNhMo+ADsn0KxOFiLSCJpZBUkECu473/mOt8zCbkx2Kt9yyy0uWSh0iZAkjUNu2PUtJvHRBl977bUdO4PZqcruXnbdJcoUqSUTCaevWMFBy1soaTzpTnNx66tXzuxkZ2WnCCsHicDF77JHS/7MM89sOPZp4403dlhlYBcR16xjjZIFLZ9ddh+zYziLOBaL3Tx8TyxHsGMfTf1EgOrrR6IM0isY1gGoJxA7+tghwO5mNNj/8pe/uAceeMC9/fbbvcJVcWAXCXlkJxLWe+QIL7To2XH/+OOPe4ss7FCS3QrsnGZXInlkF1Yi4PRlYucmZeBKO+e9WLKRPBKW3RHsuoIo3zLLLOOmmWbB0u7JAABAAElEQVQab30C6y/JJCttfxJOrsmE2t8SBus/WAYRKxqJ8oO3oJK1C5wd31j2gMCRcEUpJs0dd9zRJQpRPgmsrCSKUn4XBrtE2MWQCF38rlGOH+s2YeWA3aPwPb5Zorzkvyk7hS677DKXLOS7RPjm64HeGVylHoRlZicIdY1dM+ykTSbFvh7Rb5CvkGK+CXF0sh7Qd2DBCP7GLlx2MScKVt4iAPVc2kBYtqrPVdtYlfThWfBFTVgWSIRL2im9r9L/wRdPPfVUd8ABB6TxyQ270xLFFXlMr/SNieKb32mOI9ahsOhBP50sMHq+v/vuu/uxShHrWGnEHbhhB90ll1ziUxILRTyzM46jGxln1UF8L3gp7Y72Dq8lPeox79h5Sr+RRVXqHtad6JshbS0nK51Wbq36eQlfNk36O8ZbEGME+R7JwmIva2ixVmIkb3VdY/si+BeEZQvaSCLc8c+JsMr3bViUg59iASjPKqAPkPwlSl2+HwGTROHM933we+puVl2K5e+SXjevjP1++ctf+izQ78OjJppoosws0d+GY8hm/LLKN6nSNusemzYb97MTHj4Af4cYG8KfIcbsvIc3JUpbDsuIYvHKe2jy1yzNJsFcq+9Z5zioWT6KvGNcmwjOmnr9zW9+48d1eGJMz5yV+snuZbEg1DSCLry85pprfKrwWXa0Y1EJ65jMUagr8KLQKk0s39PFg+/NPPPM2snfh2mFHsrO/2LH4KQb2zar8IOwvDwP1LoXlrUVP9D++wN/Zz7OuBhijULWAPRYPsuaaZU6KxiVbScSLry2qnuMT+BxlANrcfQpjGcZg7D2kQjc3bnnnuvnZ5tssklD9FX63IaIanhoVU6S6Cv8vUheq0ASy/fq6BewDou1ZU2slbSDYsuZlZci86Iqc/I6sM3Kd5YblkixAp9sIvF1HivfWFeEaM+Jsqtfp8Tq5cknn9xgBaXKOgC8BOsqzGNmmmkmv95BXcfKFvjyI1+s4YfzorrbZpE2FtvPY+VOyzpYw2bdl/mgtvKn1wTD71RGHlLlm4Tptus5lKuFcrd2pWvxGgKGgCFgCIyfCAxYxZWej78SyspnHTzZFHLb1Sum9JIdYD4PKGiw6C9KIV3NWJPEjz32WMcvJIQ3CORlcBy+5zmxxpIeHcPiKoJLTJTengh7GEzrBS8GaokFEx8NC4goRMhkiMV8jmdiMoSgnYU5BuZZxOD5uOOOS18RL5Odvk6Uq5VJ5jrLgACeRQnMcFMPf/vb3/pFUJ0GQjqEK5hy/MMf/tBLcYVFcwbwEAs7eea0mSzx7RDAQ9ttt51X2GCigbLMFlts0WDaEiGYtBMUn1ByYuKgiQklk6I6CcUc6g7KIjIZQdmEY72YpJAH6jHKFmIqmDInu5q90IoFIE1MFhHWkE/KECqu4CYCeyZ3hxxySMOEknygJMaCXx7RBhEuC4EbCkGYAGYxTcoh77keeOCB6WOys6qU4goBy6RJWxelFRbVjzjiiLSM4AOPwPQnAgYEfyIQTDPYwRt4BXjwjVGY46gvFvaE4F8oXLDIhzBO88Uq9UDilyvfDQEBC5m63tMOeZdFZb6JhO9kPWBBBdwg6jTfWijZxeeVl+S5zmsdbSw2Pyg8hMejsGiUpbhSR/8HxqNHj+51FAj8S7DXZUHIzlFYEMpYtE1ZXKJvQIELHp/sNip0rJuOu933J554ok+CY4POP/98v/hOP8ZiGApeLJyxmFaVGGvADyBwDI9jQlgoguQwrSp1j3QkLRQlY8cwRfp5yXfZNBGSi6AcBU1RXKHeYG6+r1GVvohxLryfMZG0ESkfipfwMATIHKHXbExMGMZ6HDnIuEFMetP+ET6j0J5FMfw9K55OutFGpZ2CHfccLZZHtGU9dsdfHr/kXZVvEts26x6bthr3MycQpRXqDIr7MqY79NBDPd9DwRuBIsJGaY/gk0et0swLV+R71jkOystHUXfwQVCRR/B1qZ/MG1GkFqUq+H5fVVyhPNtuu6076KCD0qJxHB/PrDEwd2asKlSF70kcXKl/K6+8snYqdF9m/ldlDF6lbcbygzwABnLdkzIX4Qfil2t/4O+/+tWv0iyjvC2KK0ceeWTDEZWpp+SmSp3V8ZRpJzpceN+q7rFBhnG/jD10eMaLrFPQ5zCWCRVXqvS5Op067luVsy/x91Z5rYJHLN+rq19gLi9K9lIOxi1ZStjyPuYaW86stIrMi6rMyevCNivveW6s58CvUEBjvpBYpvHzCXgXG06ZtzCXR9FEU5V1gF//+teZfIT4ZV0HJWDmRmxUEmpH2yzSxmL7ecY9euzDhgWOTIMfojRehMrIQ6p8kyJ5qcNPKFcL5W51pGFxGAKGgCFgCBgCgsBgubFrZxBAKMqEkEWmLOsVnclFPamwixsrBEWJBWuUXSAWN0NhPDsrRYjGREiUVvCP1QgUV2TRG4HyQCIG8YmZ644ViZ1EyXFVXmkFJSEETuzcCwncRcCMwhFKLpqw7oFgExJ/+r3cY7lFlFZwI83kyCj/GqUOFkg08X3FmgrfXQvvxR9WdRDS1kks6EAsILHIBokblhNQooFk1xH31OWddtrJCxB41oQgdZ111vFOUrf1e3YfQ2ATKq3gTj6OOeYYN+mkk/LYi9DuZ1eHJr3QhKJD3VQ2TQRP0CyzzOIneNKGceM+OSKGW6/AhzJSN4l6KIohWK7QSivkC4sEybE2Poso27CwIVSlHkgc+opyR1jvUSJDQSykst8kDB/zXDZN4dlYJAp5xa677tqrP4jJU1aYqm0sK852uNXR/6EMGgp+4dmJOfLMLMs3QYF07733bhDI01djbQjCAhCLNH2FEM6Jwh8CcVEYTEy4p1mkb6qDtHLknHPO2SvKGWecMVdBptt1r2g/36tQA9ShSl9EPWORMlRaASoW7cWds9BbEeN/+hctOCI81t7o80Mqy2vD8J1+pl9EgVaUAlBkQild9/115KnKN4ltm3WOTYuM+1nAhqgXWKTDYqEmFtPFSg0Ke3psqv3JfZE0xa9cy3zPusdBkoeyVyxyoXzSjJiL02dCzCdEaaVZmL7wjjqgldXJEzwIizuQ9Ov+IfmrwvckjirXMvO/KmPwKm0zlh9k4TKQ6x7lLcMPsvAp6tYN/l40b9pflTqr4ynTTnQ4fV+k7uFfjz10eOaYsvaRtYmkyjfR6VS9L1LOvsLfi+S1Ch6xfK/b/ULZMseWM0yn6Lyoypy8G9gyfmFzBeuObKZAeYU1NTZ2QCg9y4Y7jYmMF2LWAfL4CPGzXgaxdipjE++Q/NXdNou2sTr7eSlLzJW5UDN5SJVvEpMfC2MIGAKGgCFgCPR1BIb09QzG5m/QJJO6ng++Mq1MHF9++G83+Fu9F4Rj448Jx24uBlcMWNjZ3F+IowSYULNYwa5tlFUQhCHgRaOaASgD5CxiEZXdwux6Y3J/1VVXeW8cOxAeYYIGvJA+6kfc0BLHJCUa5VkKM+SvCmFRg7yG1GzXAHhgFQNFjLA8YTzNnrF2wi584kAwznFK7SLSQZCA8A8aN25cLwG9TluEDuDATj7M3AvJMUFiPl/cw6vWVJd3K620ktz2+p4c0wOhsCHC09RzG28wvS7EZAvlLK00Isd+ZFkv4DiHu+66yyv3vPXWW+mCOJNfiAVyrCuJwha7EETIgLJJjFAHiwLhYjsLT0LyjeW5jmvZNMW6BGVnN5tup3LPQjz4FRH61VGGvDik3vGeHRUscEge5SqKWlwR5lP3NZWtBzqs3KMslWdRSvzoa9lvosPG3pdJE2GZKFFhgSgkFko5Eomd4HVSHW2szvw0i6tq/ydxs9MNi1aidIUiy2effeb7lrAvkzQ5AoA+LCT64Ysuusg70+dmLWSHYco8o5xI3YDgY1NOOWWh4OweE8IChhBWisRCmFY4kfcxV11mlChRtONYtlb8utt1r2w/H4NNbBistGEdogwx1swy0V8mjjr6IvoIdjRSvxj7CbGASx+XNTYQP3LNOhpR3mVdy/DaMDxWGLSQGyWYdhMK+sx3IEyYo3wrij1ZaQsPaOYnK5y4lf0mVdqmjBHqGJsWGfczloSoA3k8h77zwgsv9P6wfIS1uDwqkmYYtuz3JHwd46AwH0WfGZth7Qxi7kFdDPs+3jHukLGnKCzj3tcJxTcUhDQxd1xhhRUcx4qF8+M6+J5Oq+x9mfmftC/SKDsGl7Bl22YVfhBiMdDrHuUtyw/6E38Pv2eRZ6l3+C1bZ3X8ZdqJDif3Reue+OcKb2B+QL+BJVksRTCvZZ1CW23SYbinzFXHQWGcRZ+LlrMv8PeieS1a9ix/Uv/K8r0q/YK0afITO24rO/+LLafGjDIXXf+U+THhy65JV8FW57fsPZvgsAbMPJX1SH4QYwY2p2SRlLPKOgBr58TDHIj5ObxE1vs5Yiccr9TZNou2sTr7+SwcW7nFyEOqfJNW+an6HrmaJuRuRoaAIWAIGAKGQLsQGNKuiLsd7wRDp3OfK8WVL157uauKKwysxKwvu4JFqNRtnIqkj5UJsTqBfwbAKBNgSQJz/Jjm55iTrJ2imDMOibILFvodJveFQlOG4i4WL7RfecfkGwoF+fK+1ZWjcPiVIY654YfQAoEZO2ZHjx6d7gAvEhcLBphvhFAQouztVFwRRQrJGxObZvWRcqGlf9ttt3nFFYQgTFT5BlhhgbDe0oyyvie75FlsZUFZhNoSx0svveRvEUTmLdaL3zqvejerHGOV5caivCaEAZjMzDsyQvyiwCWKKyhFCIXKD+Le6pqFK/VfcJU20SqeMu/LpEnd/uijr45tQ9DHEUbNiIllN0nqHXkowgvIr/52MfUgq7yzzz57lnOuW5lvkhtJyRdl0qR9y0LXtNNOm5lSnnum54KOdbSxgklV9qb7tCxsSaBZ/7fBBhv4I7dQwMRaD3URK0+YO4ewckOd1kcniAWtVukRXvxyXxeFi4Esbi2wwALeGpdWbNTpITShjEKCiTzTr1Df6uIlLLrBt+BfCED5ocBIPhkHsQiateus23WvbD8v+HXiCpZY8SlDWUrFZcJX7Yton5jiRrDTjITPNfPTSf5OmwoXjZvlrY53orRCXCxgs2DbTClNrF7osVaRfMR+kyptU8YIVcemRcf9+IOa4aff4V9bq9Q4Fk1Th+G+7PesaxwU5qPoM5Z+4P/UJ8zH6/zrOJjHYEUJqzYInFH4RHgMPxfBk/bfV+7z+msZQ6FwTx9JP8U37/YYPCu/efM/aV9gXXYMLmHLts0q/CCsEwO97lFe3Z4GGn8Pv2eRZ6l3+C1bZ3X8ZdqJDif3Reue+OfKEaFi3VbcWeNjcx3WBEOK7XPDeKo8Fy1nX+DvRfNaBQ+pf2X4XtV+QcZs5LvsuE3KWnb+F1NOSUuuZeZFsXPyqthKXmOvHNN67733psrMbErBAkzeWqrM7bP4D3nQc2zxG+aNzQhYkdREesgHsjYr1dk2i7axOvt5Xc6i92XkIYJzlW9SNF+x/pCraULuZmQIGAKGgCFgCLQLgYGruDLLd93nLz2b4vb50391E863cPrc6ZvLL788Fc5zZMZll12WZgENZSHM3/MOoTnnzPZVQjCOAg47kFkgY7eaHKGh88zAFUE9ygkiRGc3GGbwRAFC/Iv1CZ615Qt5z1WsX2i/8l4W58QqhrgXvTKQFmUFHSZUUtDvZCKAMJByobyAVR2EhcRXhDh/lLNbUfzZZ599UrOORcLG+uG4Doj6hnb+Yost5gVxefGhUIPiCoNpJkRYzKGeYoUCDBCaNqOsb0I4ftQNrPZoEiFVs52j2n9d9/qb6XuJP8vtd7/7nUOYCiEgYtLIrgcRJtxzzz2+TvBerHZwz+KyUGw5swSmEme7rmXS1GVEEL388ss3zVZoTrSp5za8lPzCszi6qRVJO8JfbD3ISqMsDmW+SVZ6MW5l0tSKBnmLXPQpdZN8T+KNbWN15ykvPt2nxfR/KBiyywqrRgjX4dNjx471PAdeRF+tF9URsMt3kX41zBsmh4V0/sSt6hX+L0p25AdrFfBLfggcsfYW0l/+8pcGpzDvwqPryi88nT4dyzWcGY6VDXDDOhA/TDOjKKQtv5DBvlD3hD8V7ecbgG3jA8cS5lnpy0tWHzWY56eZu/4eZfsiFum333771CLYIoss4sdAQ4cOTRVREfRQL7IsO4T56g/8Pcxz2eeRI0d6hQCUyDji5vTTT8+NQoQgeX1DVsAq30TXhbL9Ql1j06LjfjY9QFlzE8FFvxP/8k5fi6apw8h90e9Z5zhI0i5zff75590555zjg3CMJsK8ZsSRVvQ79Jc333yz/zXz3xfe5Y0P9KYN5lTMu3RdL8v36iprmfmf5DdmDB7bNiVNyluWH2iMxoe6J+Utyg/w35/4u5SvzFXqT0yd1emUaSc6HPdl656EZyzDhhusJYh1L5SROF6EMRpjHKEqfa7EUfVatpzd5O9l8xqLTQzfkzpLmjH9grRpwmfVW9xbUdn5X0w5s/JQdF6k55B5fa7MP7Xfqthm5bmsm16fYY4N1llU1zoAyi0ovDEHQukXuQZxsx7BPeuiIdXRNsu0Mf1dqvTzYTmKPvMNishD6vomRfMV6w+5mqYJhn0lV9Budm8IGAKGgCFgCNSFwIBVXJlwgeHu09uvT3H67L7b3SQbjE6fO32jB/mYPM8jdqHyQ1iSpbiC4FsvjCOkaYfALy9/2l3vxggtZoi/Sy65xGHqDsJ8INrXKPGgIY1ZY47+EdKL+Uyks44uEIsW2q+El4lDVjjx0+yKIk7Wt2F3rMZcx4HyDVrUHF/Egu0111zjjxc5++yzvbv2m3WPxRIxp4jSSmzes+LOc2PShgATLDmvGMWcXXfd1e/Wl0lYGJZdiCxgsDOHsCiu8B0hvqNe3AjD8oywD9OQmkhXlLZCrXKUuzALys7svk7UaWjWWWf1mISWh/LaBmUUyvMj7/vrFaEBPAq+xS5g2lhfJvkmKKJhRUgLhFrlO7YeZMWbt8iQ5bc/uOm+ItzdJ/mXRSl5ruMq35O4+nob031aTP9HGRGuoxSKEiXnWYtCIAvQIV+ijnFsCN8j70gx7a7zR1p1EGMd8gV/YDfdQw895A4//HAnpn9RmAwXRVHAAR8RTkq/L/kRBdZWfZL4L3Jld/hRRx3lj5+gX2KHPoqbL7/8src8hglmlG10u+123Yvp54tgUYcfxn167FdHnK3iqNIX3XHHHanSCgqN4VE/KGVnWRHMy5NeVM7z05/dGUdvscUWbq+99vLtBIUvjhzLO3pAlMPDtt4MgyrfpErbJGzVsWmZcT+8h52/KLfnkX6H/ywqk2YYvsz3rHMcFOaj1TP9CO2T8RuKbiiutCLwYp7K7m8ELvRzHL3Ermn6o75IMg8O8yZ9If2Q9H9V+F4Yf+xzmfmftM2YMXhs25Q0KV/sOHF8qXtgVIYf4L8/8XfyW5ak/sTUWZ1WmXaiw8XUPQmvraCivMKR3hyzxpFjPHMsuFCVPlfiqHKNKWe3+HtMXmOxieF7VfsFadPkucy4TZex7Pwvppw6Pe7LzIv0nLfMnLwqtmGeyz7/8Y9/dFi9E4KvsMZLm9ZzVN7XtQ6A8hM/iLqB8htjwVtvvdXtsssu3moLGxY0VW2bZduY8GnyENvP6/yXvS8qD6nrm4CPll3ULS9CrqZpwvmH60e7NwQMAUPAEDAEakWg/i3OtWYvPrKJFl26IfBnj9znvvzH6w1unXxggMZxO1m/OeecM80KO5zFT+qobhDqMPCWX5YWs/Le1ltZJCORIsozHEeD0ElIm5vFTQs32SGcReKu/Yo/BudQJ5Q/JE2uaOIvvfTS7qSTTkqVMzhCqQgdfPDB3tv3v/99f5RDkTBV/ZAWwkLqnQhbONNY7rPiZyAtVlUQQmB9hTAQwsVWJCY+tT8JjxvWAjTJ8StMLlhA7quEgFXqJEdGhMJh8o3CVhZJGXmHxaKBSLQNMTOqzXR2uqx6JzeLcXkk34QJX5n8VqkHeXkZSO4oxIkFIs5FD4nJdTtM88v3JL2+3sZ0nyY8JcRJ3LVf7Ycd12IpSJRWGE/kKYxJPBKvjot77S5+Qz91PLOgwpiG44zk2Dl2HT399NO9oqev5fgeIZR0hFAgEPPOetFR3le9Ms5hHLPbbrs5FvE5ZgJip2rIV7pd92L6+Sr48A2FEOB0koS/67oQpl+lL+LILQhFxtGjR/t7/ceuP71AqN91+57xGkpX8iOv7SaUmSEEnGIph/s8Hi/HemWNn/LyWuWbVGmbErbK2LTMuF+Of9FKhCEm+p34D/2USTMMW/R7dnscdPXVV3vLKeQfK4jaAklYJv185513pnUTC5RYKitjEerUU091xx9/vP9hdbPdlDWfIk0Zs1IHREBVhe/VVY6s/ObN/6R9lR2Dk1cJW7ZtSjjiiB0nji91D4yK8gP8Qv2Jv3+V43L/Un9i6qxOqUw70eFi656Og3sE7ptuuqkXNPN89913eyVy7qEqfe5XMVT7jy1nVf4ek+vYvMakJfWvDN+r2i+IdR7yG25KK1uGovO/mHKGeSkzL9JzXj0X1nGKu/ZbFVsdf9l75qGyzk5ZjznmGB/F/fffnx5JH8YpeZeyhO+1u/gN/ehn5mNY8cbKIljAF1FgyaIqbbNsG5P6Qz5i+/msMsS4FZWHaOx1Oto975u0U16EPA25mqZQ7qbf2b0hYAgYAoaAIVAVgQGruDLBzLO5IXPP/zU+PV+6/1xy5tfPHb5bZ511HFrQWT/OhhTCygp+Lr30UnFquIaL4+Fzg+c2P+jjjuaZZ55CqTGxEgqtacw111zpYhu7xkPCLL8IJsL0wEEWwjqtuCL5JG+izFNEkYdwaPHjlx0uWvAjcbb7yvnGWFOBrrjiCr87Ni9NhIrkEVOQO+20k/fGAqksYuWFw12ss2g/HKckNN9888mtvzLpgRBeNlsIxsR3N4nFYfnWWQIh3EIFLckvwvz55/+KR2GtR7cN8cMVCwJM/PorLbHEEj7rt9xyizdnnFcOvnW7iAU5ISbweYR1KFnw1zvRsvzr/FapB1lxD0Q3OUpFjhzTZaSN6IUw/a7KfX9qY1X6P40R/JwfC0b8WLyQOq39cT/vvPN6JxREnnzyyfB1erQQAkAUSzpBcqQgael7nTZKl2K1gmORhBgjyE7AWRMLWO0mzKznUV+qe2X6+bzytHJH6UDqmYzTWoWp673wdywkNOsrY/sisQyH5cSssp122ml1FaX2eOC37HyUn17wrD2xIELaAArd1AuwY+eltE/xynhSeI/s2JR3za5VvkmVtlnH2LTMuF8W27HqKMqIIS7CA+H3eYorZdIM45fnVt+z2+MgwWfEiBGF5iWUi3EcRyhByy+/fKqM6B0K/nFc3Mknn5z+CgaL9oYAOVQCo+y0dSicT8XyvegMBgHLzP9ix+AkGds2q/ADKer4UvekvFxb8QP89Df+Tp7LUpU6q9Mq0050uJi6p8OH92L9knU1vcZYpc8N04h5jilnHfy9U3mNSYcwsXyvSr8g1sgYb8g6Vmz+dTg959P3Vcqp49f3reZFVebkVbDVeSxzT/vkuGDWodkEy9opG0HWW289Hw0KtiiKhNSudQAUmYVnZK1vV22bZflBHf18iF2VZ73mG8pD6vgmmneTz/C5St7/c3EiT0vkakLI25C7GRkChoAhYAgYAu1CYMAqrgDYJOts3oDbx9eMc5+/9FyDW+wDAhJMw/OTRVfiQgAk7vgZCIQGN4um/Bj0ImTHygbngUMsrMogKywvi9T8GMCikIMQTQhlHk3skBY3hNzsepOB1gsvvODGjBnjvSNE22abbdKg+GHhVo5jwmylpCtX8cxESAbS4lblilIBeQMTzKKLMHvBBRcsHC3m0+uc+BVO+H8e0ciXxW52ZcquvTAeBIEyQWYxCmLnuwirQv/6mV2/mK4UoRLfV456YIIXLrQieGVnPYSp2iOOOMIvgEmcDPhRnjnuuOPEqStXdmCLgPSGG27wO5SkDjzxxBP+uCipw1kZFOsI1EsUg2hjQuxaZ0cBAn+ZoMm7/nRl9wdtEhxot+zi0gR/4TtSt7KUf7Tf2HuOKZJdQQjwLrzwQq8owTEp2sw+x4KJdQoUq/g++j08hrqLH44JE6paDySegXwVZTfaB7v6UCCiXoMnvLNd1F/aWGz/l4XbWWed5XdHIuBafPHFs7x4N44WEv6NUFmE8nyjY489Nt29Tn8v7UdHJv2rLDASTtykn+WKm/B+HZ7vzyI51tLuvfdev9AmSrtYasrrFxkDiAUwjufDLDHpSH/Aji8sYNVBWCJj971WioCXYTlIzKizSCiWJXSafanuFe3ndf7L3FOPRIEEXDiaRMYJZeKJ8cvZ6hB1CWs4fBt4Oz/d/8b2ReyOE0IRQ6zA0UdzTvu1114rr+0aIMCuT7FeQf+O5RUIfkC7he+ggEz9WXXVVYPQ+Y9Vv0ls26xrbFp03C8WDalr3OsxInyTOi3K0YzHJ5xwwlzQiqaZG0HyIu97EqaOcRBjLOlD5Cr9C2ng1syiE7wfaytFady4cV4JhPrXzOpk0fg65Q8+J8q+zK9pR4IT/bqmWL6n46hyX2b+FzsGJ39V2mYsP9C4jC91T5c5jx/0V/6uy1b0vkqd1WmUaSc6HPdl6t55553nzj33XL/ZS/PSV1991VuNYv4A8W0Z2wpV7XOJZ3zi72W+iWAcc43lezH9AushrOWdeeZXG0FHjRqVbt4qm/ey87/YcjbLV7N5UZU5eQy2zfJZ5B0bIMXaN/MSmY/hzjo9PHmPPfZILWBJnLHrAMyv6DeRC8imTeJkHIIS7SabbJKut8scTdLkWsfYq2wbq6Of12Uocy/j2SLykNhvUiY/sX6Ro3187biG4KG8reGlPRgChoAhYAgYAjUgMKSGOPpsFBOPWNv95/xT3BdvvPpVHhOhygdH7uW+ddqlbtBE36iUbxYARTCjI5JdV7ghrM0yda/994d7zqbnl0UIs7BMwIJlFnHOfRaxs3LJJZfs9YrFOAa8LMgyoUBDnDSYUAtxbvl0000nj37we8ABB6TPJ554ouOXRUcffbRXMpGdaVl+yrhR9tAyA0czbLfddqkSS7P42KW89957N/PS9nfkAYEf30oWQBHaZy2AMxFBICUkwkN5zrvSFlhMPuGEE3xdEcEP7nnlR8DBjgiUVM4++2y/0IJwkMmuHAs1OsNsf14e2uXOgjFKVSzIwBfAk/YgGvTUh3B3pOSFnVo77LCDr0MocLAzgiNVOFoMIbJeVJIwVa9YddImiZlMQS+//HIvBTSU0/LMUBbNB8dAHXrooV4oQJnAiMUw3Pm2WjGkaJxl/bETGUUT6iBmqxFQiJCCxRBZqCNeviVCahSPzj//fK/kIhhwDIkIQsNj2qrUg7LlqcN/p+sByo3sBoKnUw9Q1BKivfBeTFGLex3XbrSx2HzH9H+xaREOpTt4KPwVhUWOvmGxi9359AUQCiSidOQd1F9ocQQlTr24jVcss/FjoTy0zhU+q6j90SZ60Vy/4x6sUOwjn/BNdlPJ4hllgofWQfAn+kMskrFIxjnZCAzph4T23XffTItpsXUPJUitzCXKkKRHf3nggQdK0l7Zl2NgWlGrfr6ONFlsYyxG/y4Cd8kXu4nD+iLvql633HJLr0REf4IiEz+h6667LlWMje2LVlllFX+0IooX1IOrrrrKtx36A/p9FCNpP/o7Sfp2dZ5/sLiNMv/FF1/sllpqKT8G4LsJrbjiiqXabNVvEts2yW/VsWmZcT/5xBIIyvnMJxkTC3GsjRBxaoV+cZdrmTQlTN6V/iD8njIeqjoOypszSl7oo6g/eXNS0pfxmoTJu6IwJcqOjEtpx2WJ8aBu91k7i8vGWcQ/cwqU/meaaSY/jpa5AtZmqDOaYvmejqPKfdn5X+wYnDzGts0q/ECwGV/qnpRXrln8gLFbf+XvUq4y1yp1VtIp204kHNcyde/BBx/0yrZsJINfTT755F7JWK9p0l+weUxT1T6XuPobf9flL3tf5puUjTv0H8P3YvoFxh9yzApWL8WaR5ifIs8x87+YcjbLC/W82fpn7Jw8Bttm+Wz1DsvtKKRBbNpYc8010yCsf2F9Zd111/Vrp5TpkksuSTetxK4DMN9mjYwfxIYS1nJCS9goVDOG1VTH2Iv4yraxOvp5XY4y93m8L0seEvtNyuQnxm/Pp594OVoy6E2DTzD9TA55m5EhYAgYAoaAIdBOBAa3M/Kuxz3BBG6yXQ5qyMbnLz3rPvh5Yrnjy6873QYPBR/keJBm3osuYOFP/LaKN3wfPjfLT5l3efEyMEV5gF3cCH6xnoA5RU15YRHIY1kDIU/e8S/EjQAFpRYwwaKJKK0QHoUU2b0paWbt5JZ3WdfQfyvs5b1ciVPfU16Ee+xOwWIJQjqEaBqHvPv999/fKyrofIpfuep3Ve4lPp13iQ+rHttuu61/xIIQClhZxI5Y2aG/8MILFz4+ggkVGCHsE6UVFpexVDN8+PCspBy7mG666SavtU+9Y4EY4ZAorSy00EJu5MiRmWHLOmZhI25yJU651xgycUeLn28OkT+UVpgQM8neeuutvbsO4x3+90cdYOInJuHBCMUSFqKJk8U/iVvCSVySH3GXa7P37IJH2Cs/UcQgrLjJVb9rFidhm71HiMmudNoI/hAGP/fcc6nSCu0eoadWSCPOOglhNvWaesMCoVCI4RRTTOEFk/AZcAcDlIr4cU9dRMgWKm1VrQfkR/IiV8lj3rUZ5oRp9r4b9YBdSFjywCIVi14scmBpB2UWsdoV1vW8spdxj2ljZeLXfvW30/fiR9zkKu5cY/q/Zt9Yx829pClX3Oiz2J0lSiLUc9o/8bKwSH+s26UOS/iiJP1uXngW1+D5KFRhdUUfG6LDyD0LgxwpSL+C4JBFNN6hHMUYoy5COIjyIYSSH3xLlFbIA9iJlaasNGPqHuURHswV5QihZu/wI/hIvZBwXJv1883ibfZOx49iKxZJ6NOHDh2a8h/tpx331B36F3hwKLQWPCTdmL4Ins8OUzH/TT8gfTQ7Cdk5KFbrwvQkXXHP+i7iJ7yKXwlb9j3+w7Dhcxhn7LPkNS9Nxu6M4SHaBGMAxpIcQUbfTB+QRZJfuYqfOr5JTNsk/Zixqc4/6QoWUh55L1dx54piIeNz4dH6HRhSL1HWmjVRRNSk4yqbZtnv+d577/mkY8ZBOi2d/7x76UvkvYRnDM04MiR5L1d5D66M1xnzhfNKjZ2+l7ByfeaZZ1JLJ7gh3G03oewuR7eKgjv1gH7o9NNPz0w+hu8RkS57iF9mQhmOZed/sWNwko5pm5LlGH4gmAz0uiflBCtdJ+R5IPF3qQ+6nPpe3utrlTor8ZRtJ/JNytY91m6Ya0DwUsbOorTC/AvlBNaxGN9qiu1zJZ86rmb3fYm/N8tn1jspa9lvkhVXGbdYvle2X6B8jF1YA8ZyLespWSQ4yFX85LWjZvM/Ccs1tpySbpgf4mw2L4qZkxMnVBbbr0KV/3/nnXfcPvvs4wOCj7ZsLrFhvXS//fbzjyiQh8ebll0HICIU3qgHsp6GpRWttMI6DxtQ+YW4Vxl7SVyxbSymnxcc5Sp5kHol7uE17z1tqJU8JOab6PTDtMNn7bfQfSI3Y/M3cjRNXs6WjD+NDAFDwBAwBAyBdiIwKJkg9LQzgb4Q9wdH/dR9cvNVDVn5xjIj3OQHHV/Z8kpDpPZQKwIsbLMoiPl3BEfsLJPBok6InXfsQEarW3bP6fdyT1woXrC4e3uye9GoPAJYW2EyBmG9Ru6zYmJSK1Yt2B2IkBphDz+Undi5XpQQnLFA++KLLzomuHzDcFGlaFzt8oeAkR3Z7ManfOSxLCEQRTCKEAABHJNQmRSWjauv+gcnLDOwOx6rCJSzr31LwY7uESUkvivCSkzHUm+pg3lURz3Ii3sguSOMh5+zGAqxWHrXXXe51VZbrddOvzrL3V/aWNH+ry5sqOtYXaJtonSH5ZRm9bxouhwLxfGJHA23YqLwVTfRN6BsCc9kQR4BQjsIASd9EIqXWF7BMg28QBQ5i6TZX+pekbIMBD8xfRF1gDEM/fL/t3ce8JbU1B8Py6KLSFFYRHpblLIgRWkqiFSBBaVIk967gDRFRESatEWKUhVpCtKkgxSVDipFkKYICIhYQSzo+883euaflzdz7ySZO3Pve+d8PvfOTCbl5DcpJ8nJyeTJk2upI6MByzbzUMc3ia2bTcqmpMVueZG7sezI7lkWEPqNeiEHcSQSiuBsbMByTz8QCzAcZQrRF3D0IQsqvSAUArGGhtVRlDPBmKOjkKFoi6qOFWLavdD81DX+i5HBhdeUuhnbHkjaTVybLHtN5KcsjTbb9zKeOrmHlNm66kknfsreMbeGPMsVhRWUwJH9qyxw1vFNfL76sX33eRyE59h2r4l+oU78YvMZy0PKmHxQsI2ZB+A7MJ/HGBnlFTYtoMhfx/xB7LeqGm4Q+vmYb1I1/1X9iaWVf/zo5mFBsLQy46HHD3PTB0VAEVAEFAFFoBcIjO9FpP0W5zs/e4R565nHMy3RJ3PW6Hz/vdvGZsbPn2DGL7hI7q43/YMAuwuXW265/mFIOcnPtWVAMmXKlGBEUMTgF0pMCDMZ3KsJ4VB+ivyDSdnukyL/RW5o4RedBVvkd1DdwImdGPz6nVCsQKkmRLGmjnLQ77jUwR+KbEJMeLAAA/W6jg9KHWu6/6Osz58p2/EbJKJvaKItYUKfXwoNStlLyeMghY3pi1BWClG6HSQ8BpXXOr5JbN1sUjb12zqOtulHpRXK0ViRg+6+++682mCKv9fyS57Y/zD+8Ic/7DpVuo9p9ypF3MVTzPgvRgYXNlLqZmx7IGk3cW2z7DWRP0mjzfZdeAi5ppRZ0ompJyH8id9ZZpnF8IuhOr5JTLoapjsCse1eW/1C9xwV+4jNZ3Fs3V1TxuSDgm3MPADfgY0c/AaNBqGfj/kmdX4H1s6KLK2wdsb6mpIioAgoAoqAItAEAmNCcWWa6d9hZj7uXPOnvTYz/37phRxXzJ39cecNzPTrb2besflOZtzsw81i5h71RhEYwwiwqMyuHI71ufXWWy0SmKlmIKakCCgCikBVBA499FBrEYPjBCZNmmQtrrBTBzO37EhCmWXLLbesGp36UwQUAUVAEVAEFAFFoHEE2Gl877332nQ5+nCfffZpnAdNcGwioGVvbH53zbUioAgoAoqAItBrBP7zu9+av118lnnzmktMdh7zsOSmfe/cdl2N9TUlRUARUAQUAUWgCQTGhOIKQI6bdXYzy6mXmD8fuP0wyyt0xm9eeaF586qLzduWWcG8bYVVzfhFlzLTzjWfGffOzOR8Nhml1N8IcD7p1KlT8zN7y7hFGxx//bpDsYzvtt2xAMIkmdBCCy1k9tprL3nUqyKgCCgClRDAvDTHAZ155plWSQXz1CjGCe27775B1m0knF77FwGOsuBoC44yUFIEFAFFQBFQBGIQoA9hDIcZ+n6gF154wSrfsmsXZdxB3HHcDzgqD+EIaNkLx0xD9DcC/da+9zdayp0ioAgoAjUh8NZb5j+v/8X8+8XnzFuP/9z8857bzT8fuseYof+MSABLK2wGZ11NSRFQBBQBRUARaAqBabKz84aaSqwf0hl682/m9ZMON3+/+ap+YEd5UAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUARaR2DCGhvY44HU0krrn0IZUAQUAUVgzCEw5hRX5Av/864fmte//pVhRwfJO70qAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAmMBAY4GeueeXzBvW2m1sZBdzaMioAgoAopAHyIwZhVX7LfIjj/B8sqbV37HvPXLR/vw8yhLioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCikD9CIx/3xJm+g23MlhaMdNOW38CGqMioAgoAoqAIlARgbGtuOKA9O/nf2X+ef+Pzb8eedD8+zfPmH+/+rIZevMNY7Jz/5QUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUgYFEYPx4M830M5hpJ85hpp13ITPd5GXN2z74YTPtPAsMZHaUaUVAEVAEFIHRh4Aqroy+b6o5UgQUAUVAEVAEFAFFQBFQBCoj8Prrr5s///nPZo45sskrZ3fVH/7wB/P3v//dzDnnnJXjUo+KQBMIaJltAmVNQxFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARaA6B8c0lpSkpAoqAIqAIDCoCTzzxhLnwwgtHsP/hD3/YrLXWWiPc1UERUAQUAUVgcBA4++yzzcknn2yOOuoos+WWW1rGUQxYffXVzT/+8Q/z6KOPmmmmmWZwMqScjnoEtMyO+k+sGVQEFIExjsC/s6O9TznlFDM0NGTWWWcds9hii41xRDT7ioAioAgoAoqAIqAIKAKKgCKgCIx+BFRxJfEbP/3004aJ05deesnGtM8++5hlllmmMNZnn33WHHDAAYXvxHGWWWYx5557rjy2dv31r39t9ttvvxHpT5gwwe66XWCBBcymm25qJk6cOMKPOigCsQhQl6677rqg4OPGjTMXX3yxmW666YLCjVXPO+64o2EHfTdCIcVtA371q1+ZCy64YEQwcFfFlRGwVHa46KKLzGWXXWb9n3XWWWbWWWcdEfb55583Bx54oF08Hp+Z9Jw6daq1ijDCozooAoqAIhCJwPLLL29DHnvsseaxxx4zs88+u7nllltsf7Haaqup0kokrhqsdwhome0dthqzIqAIKAL9gACKs4x7oLnnnlsVV/rhoygPioAioAgoAoqAIqAIKAKKgCKgCPQYAVVciQT44YcfNqeffrq58cYb7Q4QiWazzTaT2xHXv/71r+ahhx4a4e46vOtd73IfW7t/4403uvJ66qmnmoMOOshst912rfGpCY8uBFCY6lZHinLMbixVXClCZqTbz372M/P73/9+5AvPheMiXFpqqaXM8ccfnzuxK/9Pf/pT/qw3cQiglCJlnuM4fHr55Zet9YPf/OY3BiWtk046SZVWfJD0WRFQBJIRWHHFFc0WW2xhUKbjJ4QCyxe/+EV51Ksi0DcIaJntm0+hjCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoArUgoIorETB++ctfTraKsskmm9hdI37yM8wwg+/U+vOqq65qsL6AiVbMxj/zzDNWYYdF1iOPPNIsssgiZuWVV26dT2Vg8BFYY401RljxQeELSywQyhPs/HaJowtUacVFpNr9e9/7XtNJ0e5973vfsIjmnHNOQ7slxO43VVwRNHpzxTLOVlttZVBagVAW2mCDDXqTmMaqCCgCYx6Br371q2bjjTc2KDgi780777xmzTXXNO94xzvGPDYKQH8ioGW2P7+LcqUIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCMQgoIorEag999xzNtSMM85ott12WzP//POb/fffPygmdrUuvfTSQWHa8rzCCisYjhdx6dJLL7XWVv7zn/+Y73//+6q44oKj99EIrLLKKoafS1gHEcWVZZdd1nAcl1I6AvPNN59imQ5jz2L4y1/+YpVWOI4OOuyww8zmm2/es/Q0YkVAEVAEQIDjLsuOvFSEFIF+REDLbD9+FeVJEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBEIR0AVV8IxM1ge+NznPme22WYb8853vtP88Ic/jIhlsIOsv/76diH1n//8p3n88ceHZebPf/6zufbaa4e58YC5+YUXXtgq+ox46Tmw0xdcf/GLXxgWbsF8scUWM+uuu65BYaiIHnjgAfPkk08WvcrdlltuOWshJndwbkLTJI/k9f3vf3/hIg/KPRxh46eJ9YQf//jHNmUsWPjWQu69915r1ebtb3+72WijjRwO//82lNf/Dxl+B69i8UFCw9s888xjllxySTNhwgRx7qvrz3/+c0OZoAy9+eabZtFFFzUoYX3wgx8s5NP9nksssYT50Y9+ZO655x7D8UWTJk0ylHniKCOUuK6//np77MuLL75oLRRNnDjRlg+sxFCGi4gywpFjjz32mPnVr35lZp55ZpsOlo7Y7d6JrrrqKsOxXosvvri1RsN3uuOOO8zdd99tv8vkyZPNpz/96b7YLV81n1hyQhkO+uhHP1pomcrH5Oqrr7bWAWgjPvCBD/ivg57dcuAv3v7tb38zV155pY3Pr9dBiXTxTDooRVJ2of3228/ssMMOpaFi2wPKG2UFopy89dZbhvaHMoSCJuVvypQpIzClTtx+++3m2WefNRxlNP300xss9NAWUm450qiMYusJ8XHO/RVXXJFH/e53v9tag8gdaryRctAtyrJyULW8l8X/xz/+0WJMn4aVMyxeoCRLH0h75NMll1xiwLYTzTXXXMMUA4mX743VKr7/T37yE3PXXXfZPlfay5VWWqlTlLYMUF6QAyi3Cy64oKHdWX311W28nQKntNF+3SSd0dLnStkLlS1crOvEtlO7R3vIe/pMjpe7+eabbZlCRkA5e+211zbU0zKKqSex+HSTvWhvsTTjy160OcgQ9C30MUVE2/nd737XvsICIYqhKN3edNNN1k36aD+si+3cc89t+zzfT+rz/fffbygPtCVY8aIdIC+f+MQnbF6L4g/tU5BfSKMbdSoPTZXZbjx2ek8fdPnll3fyMqL8iGeOmH300UflsfDqlz3XE3Xlpz/9qZXH/WMF6Ru6tdVuXN3uU+s18Vf9nlhVvOaaayxLm266qRk/fryVhZFNkIfp8yDK8VNPPWUWWmghs/zyy1s39y+0zMa2B6TZqQ1y63SZfEAcv/3tb62M97vf/Y7HYcR4o2ysO8xjhQfKLGMZ+mjkOiwlwuNss81m+2vwpfyUUay8lzImj0mT8RByE230hz70oRHZufDCC82//vUvW3Y6jeVGBOzgAJ/ITNCGG244Yqx15513mhdeeMG+p366OKe0BzH4WCacv5h+geAhsmm3OtZtvoO2AfkSHIWQdSm/jDUWWGABcc6vKWn+4Ac/MGwaYK7KL0P0nTfccINNZ6aZZjLrrbdeniY3od+EdEgPwqod9dElyo3km7kg+gaXYtugFHzc9EPu20izjvFNaJ+CnOiOUYswYs7PtV6LnzZ49Xnr9/Ygtrz7+dRnRUARUAQUAUVAEVAEFIEBQyA7/kUpEYFbb711KJuktr/rrruuNLZsQjz399BDD5X664cX2eRqzusZZ5wxgqVsQjJ/v/feew97n01g5+/m+x8u7jVbgB3KJs6GhXEfHnzwwaHsaKLCOHAHxyI6+OCDC8O4aZ9zzjlFQYdi0swmTWx6mSWEwjizCRX7PrMWMux9NiGc8/naa68Ne5dNYA5lC0D2fbbQMeydPMTwKmFjrpm1nZzf+bzvmVlAGSI/vaRXX301T/+II47omlQ2STv0pS99aSibIMzDuXwTRzZ5OSIe+Z6HHHLI0HbbbTcibHYk1lC2CDUiHA6vvPLKUHbM0Ygwki68ZAsNI8Jmi/5D2QRGYbhsYrUrttnEmg37la98ZYiyPZ/3fXjeY489hqXLN8M9W6ge5h76IHW0yjcJyWemDDcE1vB4+umnd2UrmygayhbLrX+/rnUNXOBBykFRvT766KNtOvBWR1okf8wxx+RxZhOVtpxkllVyN9LsRCntwUUXXZSnkylblba72YJnzkJ2VF4eZr4MB/+XWRMb8ts1CRxbTyR8ttAzLL1sslde1X6VcuDnz38uKgch5b2I8WxyfEjqqZ8ez1//+tdHBCvy57t95jOfGRbuggsuGBI/bjkUN67ZMRxDmULMsHDyQPmRuuqG4Z60yspBahtdVDfhabT0uVL2QvMJBr3AtlO7lykQDfG999xzz6FsYc7e8yw/3H75y1/C2giKrSex+HSSvWDulFNOsXz7slemgGPdeV9GmRJznufLLrvMeqMvyxa3rHt2zOEQ7ZdPmbVG+x4ZIVug818nPWcLbfa7zOd8D/eefGULMyPSiOlTTjrpJJsPN/6i+6KxT9NldkSGAxwos0X5ct2Q4YuorI11w/plT+KhbGQKUaVp77bbbuK1lmtKvQ79nk888USer2xR3PJ/8cUXWzfqutCBBx5o3T772c+KU36NKbOx7QGJdmqDOrWXhM2U3IYyZeRcbp2voH6WtZmED6VMYSXHtygt2h7GTEWUIu/Fjslj0+z0TchbpvBkcfjGN75RlNUoN9p6wTRbnB8Wx7nnnpuPQ7MjP4cYq7gU2x7E4iNpx/YLhA+VTTvVsW7zHcxDrbjiijm+grNcGfeBsU8paUobS/10CV5ljI/My7yfSzHfJFOUzfOWKZa50dn7TKklf8/YyadO5b1TG5SCj89D1ec20kwd38T0Kc8//3z+zaSc+tdsk8cI2NrgVZgYlPYgtrxLPvWqCCgCioAioAgoAoqAIjCYCKjFlZYUjY466ijDjjl2OrN7jB1C2cTGiN06LbE3LFl2+bFLCWKHSDbJaLIJl9xPmVUQPGSL2+Y973mPwTILu5KyhSxzyy23WAsC2UJ7HofcsNuVnQikya6ET37ykxYfdmuwyzEbFFqcbrvtthG7UyQOrOCstdZa8mgtX4gFh9zRuakjTSe6pNtskdCww6iM2uSVXdMf+9jHLGt8B3bTsaMYaxDZwHyEVYayPPTaPZvAN9mkkk2GXXXZZJOZdtpprQUfdoBmE11259JBBx1UyEq2GGvdP/KRj9g8YfEHSyrZZLzJFETMxz/+8REWJQ499NDc2g87QbEGwO4pyi070NlZ6VtDyLoMkynI5FY1sgk6u8OLOpJN8lgLIhyLhNWWbjtp2SnzyCOP2J2q7HLne7CLk91s1KUiov3JlIKKXlm8Ou2SLwxU4hiaT+p9tshn7rvvPrv7vSTa3JkdzJJHdrf2iigHmZJCr6K38dLO7r777vkOzmzx32ST/6Vp1tke7LvvvrZ9xTIQlpTY3UeZwqqKW3bpAyB2OrIjMVv0sM/ZJJ+td1jtwHoHlgbo31yKqSdu+DbuyR9HlPlUtus+tLz78dJX0aZC1AWsImDNBute7LanXhTV24033tj2dYTDkk62QMytDY9FHKjTLuNMQdW2WRtssIG11pQp4dp4skUe24dvv/32Ng75w0pUpuRnH2eZZRZDOHahskuevoGdorvuumtuhULCcU1to924Uu/7uc+NyVvd2FZt98RiQja5bK3MYRkBS1hYH+NoTNoFd8dwaj2JwabpMNTf0047zVqMwMoB/Xm2IG/7V3gBn2zh07LFd8PCWF2EBRjaBHbyQrQhyDSzzjqrtaiEDM0OcpHtJd3YPgWLO+5YgPaD3cf0E651JH9XOem2VWYlz7FX+j8sDwrRX3az+ohfygWWzFxCjsmUFVyn/J6d/LSlWBmA6I+Q8SC+IxYQekWh9Ro+6v6e3fIWW2a7xRvzvkp7+c1vfjO32kPfyTj5bW97m7VeJ9Y7YtIuC4McRvz0/1jxog1gnEl5xQoLY5vzzjvPWiP1j6NMkfeEn9AxeR1pStptXZGnjj32WJs8Y1D6Ab5BEYW2Byn4xPYL8B0rmxblGbdOshdtHXIDci9EmWVsDGF9kPeM+7KNE+a9732vtexmX3b565RmWdCXXnrJ8oJcTdpnnXXWiOOxU75JWbqx7lXaoLK4Y/Api6uqexNpho5v6uhTfPlA8HDlYHFzr03yOijtgYuPf59S3v249FkRUAQUAUVAEVAEFAFFoP8QUMWVlr4JCztCTB6xUH3++eebr33ta10XqSVcU9fjjz/e8PMJ0+fZzreOk+2ZrlvEBAAAQABJREFUNZbc3CqTDFtuuaU1WXx7dswEk2XuAI6FjGzXl52MYAEMs+tMsgkRFvPJTLgxIVTEE34xt37CCSdIsI6KK3WlmSeWcMOCZCcTo23zysKHiysTrEws8V1RFEk9oiUBujwo5UqUVuDtyCOPzBeJsh3hhvKIuVEUEFhcZ1G6iCjXLDIJZbuXDAu4mNlmQsNdiKEcky6E0grHVbj0xS9+0S4u+JOWLOzLUTAorcGrLPQzeYwCF5Nz2U7mrm0CPLGAwoQHC0hCKMzwrohwLzs2CfPsKPnUQTH5RAGlquKK5I8JRTfvdfDuxnH44Yfbo3Rct7rvUR6R/ND2Zbv4SpOouz0A75133tlku5qtAhQJkwaLqyhPCbG4SjvMIqiUV3mHUhg8s2jNUVvuQmxsPZG427qSh6LvQFvtKvQIfzHlXcJilpq2BkLhE+VOv0yjMMJEuk/IDkLIE6K4gpLsu971LnlVekVRDWUcWRDdaaed7MI3k4JM1G+99dbDykVmfcLGxcIbi++YVofAhKMUiYsyhcICR6cI1dVGS3wp137vc0Pz1gtsQ9q9zDqb+cIXvpCzTf/CMwqulBFkOKGUeiJxDMKV49aQV1FIpLxNnTrVys0otaLIB7HAkVleqTU7tB2itMKxpsghKPAKobSCAjqLpkIpfQpHg/ETQpERhQvaTxYXy6jtMlvGVxV3ju9zleSR1aoortAeu7I0aVEuyhRXUE4RpRUW+ZBthZATUVzuJYXU6158z055SymzneKNfdetvWS8dPLJJ9voGatyJK6MDeSovti0y8IxdmOswRFMPrEJgSNPUBBAZvMVV2LlPTedkDE54epI002/6Xu+r3xjZGXu3bbX5ye0PUjBJ6ZfgN8U2dTPL8/dZC/ka1FaYVyC4p6Mu6ljKLKg4I3CCEpXHEHXjbqlWRSe+kGd4NgeFLCYpyvaIJHyTYrSTXHr1gaVxR2DT1lcVd2bSjN0fFPHXCTl1pWJqmLSJK+D0h50wi62vHeKU98pAoqAIqAIKAKKgCKgCPQPAuP6h5WxwQkLfUxSs3v5gAMOsBOQ7BaBWGTeZZddrKWEQUCDXX5MvlclJm5QdoE4u9udMMeNSTNZtGVC31Va4T1WaVZbbTVurfIBE5ap1EaaRTwzmZkdC1D0KnfrF16FocmTJ8ttpYXR3HMPb5j8h1gsYrHEnSzkPjs2x75nlzELwEXEjmB2jLrkTuZST11iQYFdK1BmQth9Ze9Jl8kLlxdeoJgFobxFW+AqAVBPsDYEsSjBAlA3YhHKX+TGegYWG9qkmHyKQk1mHnnYIj0LgEwooewnhBUKCMUpH2Pxk3rlHHIWFXpN0v6RDotYmfno0iTrbg/AnHbXXdygTGJJw7W+w0Q8k7RueRUmWVAT9+zIOHG219h6MiySAXiIKe+SLcKKNRWUP/z6jD8WSrfddlsJUtt1s802y5VWiBSFFBZmIRRlKG9CWAcQpTsUAEVphffjxo2ziitSFwUPCVtHGy1xpVwHsc/tlt+6sQ1p9yZMmDBM2RNeaQ9EvvXLgTzX1f91w6bN9/TBKH5Bp556qq1Le+21l10MpG3lu0l9qYtPdvhDKKL5Siu4k+5xxx1nZphhBh4t1d2nSLydrm2W2U589dM7V/7bcMMNG2UttF7X/T27ZbaNMlvGU5X2ksV4lHgh+nJRWimLsy53V65z42SMIIqtRYr8sfKem0bofWqadcwNhPKMf9JF8ViUVlAqQ8G37rY9BZ+YfoG81SmbVpG9UGyF6Kewjko75BJjarFahWVRsXji+nHvq6Tp+uceCyubbrqpVVpBHsZaWpHSCn5Tvgnh66IqbVBRWjH4FMUT4tZkmiHjm7b7lCZ5HZT2oKxcxZb3svjUXRFQBBQBRUARUAQUAUWg/xAYuf2m/3gcNRxNmjTJms93TUuTOcxpf/7zn7dmWLEmwo46mfjoh8xznAkDKSZl4A9llew8Vqtkwk50JuKwUFFETCZgIp2FfQaDV111lfXG0ScscLnk7lRktyaTBjIBJVfZdciVyVzMkAuJH3muck1NkzT4fuTRp6Ld+L4feWYXDwvVYIJyDscp+VQHr36cIc8c9yT5ZPcRu5yEMHXdDyQ7VlFMwQKBWybkngkwjsnxF9aFf45J8SeTmdwVYve4S0z6MqkFNphSZncjE9KulQrXv9yzkwtCkY3wPhHHhRdeaJ2pc0WTyhKGNF2rBuLe6cqiuL/rV/z7SmXiHnONySem8FGA4JuhmMLiJ/UdizJCojgliiui7CLv67rS1nBEFMRkKTtkQ+p2KB/kA2sZTOqhvICClbu4KPHV3R74R8FIOmVX0keBCKUGdu8LsUBC/aOvcCm2nrhxzDHHHPkxSri7Frtcf23ex5R34Ve+Kd9bFNfkXa+vRTsDORZNyFVSlTzyjnbKJ74Tx31Rjt1w+KujjR4rfW5oPuvAVr5laLtHu4VCskvIMxwvyPF7fjmQMpTS/4Xi4/KG3OrLoMTXiWjTaOvor/2wncLxDssz1AeUvjgCDqKPO/HEEw31pU56+eWX84U8lG6rLpxK+wMvMTJ4TB7aLLMx/IocGROWPh0S5c6qcbjlg++CtbOmKLRe1/k9q+SxrjIb0x64/FVtL5GDZAziKiq7cfXynu/DWI6jRLGqiIUcZGtkftcils9DqLxH+JS6QvjQNLHUClFH2iCU6hkfQOuss45VDOxU12PbA8lbKD6x/QLpST2rQzatMt/BpgWI8XhZ/8WxuN/5znesP8qzfH/r4P1VSdMNQj1B8UjG+5dccsmIzVSuf7kP/SYSro5r1TaoKK1QfIriCHVrMs2Q8Y2UdfLTlBzkYtcUr4PUHrj4yH1KeZc49KoIKAKKgCKgCCgCioAi0P8IqOJKg9+Ioyz4+TT99NPbhdG7777bLgI2sbPf56HTM0cWsDAuxEQmi2rs/Hv44YcN53VjitzdlS9+MTHtE5ZUXHPy8v7ZZ5+VW/O9730vvy+7YdDlKq4wGQf5Sgdl4XFPTZM44LUKv/gtIiZcMDEOoSDEJHWR4kodvBalX9WNAbx/HBATSuwoK7I0UjXeuvyB4xtvvGGjYzGdo306EeWniNxFAnlPmWKhCoUFKWfyjiu7pznqh/Qp2/zY6czRQVhRWmihhVzv9h7lH6goPdypd0LiV57964ILLug7dX3G7LFbr7sGiPQgvIfkkwlIyhSTh0zus2P9zjvvzDm444477D3WKcQCTtlOuDxQ5A1KhJQVFhswSSsT05HRdQy2yiqr2ONhWMw8/fTT7U4/lGbk+Bg3cN3tQdUy9OKLL1pFSxY8OpFMyrt+YuqJG55FAFeJzH3XL/cx5V14l2+KcmvZZL34rftaVD8xxy7tntQz0qUMCBWF4520X67futrosdLnhuSzLmzlu4a2e2XlYPbZZ7dRYqEPxRDkXSilntgIsr8QfCSMXFGoCSWOzOKHch7tENaosH5EPelGyBDsbmVBkwl3CNlg1VVXtfd1/qH0LeTKyOJWdpX2h/dVZFpfBi+Lt8y97TJbxlcnd1f+C1XwFUsbvvWATunxDsVwFLFFmZVxgmyAAMNeUki9rvt7VslXXWU2pj1w+avaXiLDYLEOixKMqTiSj4VK2hRR+nHjrfueY0nFqpvEzZie8ctcc80lTvk1Rd6TuhIyJifh2DTnn39+q4yDNTjaL46PQZmSenfDDTf0VOEcvt2xAUpBbNzh2NUyim0PYvGJ7RfgX+pZqmxKG1FlvkPatU74ue/w71r+czGvmqYbBgVTl3784x93VFyJ/SZuGqn3VdsgP50YfPw4Qp+bTrOoHysb30hZJ09NyEE+dk3xOkjtgY8Rz7HlvSgudVMEFAFFQBFQBBQBRUAR6F8EVHGlT74NCi0rrLCC4VxfJpXY/eUOyvuEzZwNFrPYNYpFABYjWFyWY3xyT9kNi29M7roL/uy8xezsPvvsM2znIQsbEP4xbd6NfGUAUVqQhZFu4XmfmiZxMBFZtPMfqx5V6KijjspNxh944IHm29/+dmGwOngtjDjAUSbcsajAJDo/+Gcyix32bZLgAw9YCui2ICRHGPg8l5nU9v25zxypwfnbKBxgWQh67rnn7O+yyy6zC1wsPMhiNLjJ7u4iaxqER7FEqJsZ5LK8SPi2rin5ZDJdFFfgXxRX5p57bmuNiR37TzzxhM0auMr553Xm9amnnjLnnnuujZLjo2SxqM403LhYPKD87bvvvtbSyjPPPGPNUzMBj1KLS1LeY9tLNy7uq5QhJto5t1usFYE51rOwLiSLeOSBsl1klSa0nvg89vtzSnknb2LRqtPO0V5hUNRvUq/48S2RSYTc9qhIGRd/0q65fqXM8j6ljR4rfW5IPuvClm8T0+6VlQN30ZIyRDlLrSfwCIXg898Q//9PeyX9sbiy0FrUbsl78Y8FQeRYjv3BVPmVV145TJYV//6V8K6lFonP95f67JaFkLZEwtXVp3TLh6SHv5T2gPAxZZZwoSRKR4QrK/NlccYuVGNdjqMgv/SlL1klWhZsmqKyPBbV6zq/Z9X8SZqpZTamPRAeQ8seishYb2Kzys0332x/Elevr8hsr732mkF+FqsWKFkw7sRyqmspMlXeixmTp6TJsS5XX321tfTC3MTBBx9srWPR78hxrr3Gd80117RKSSj6csTNmWeeWZpkTHuQgo/UFRgK6RfwX5dsWnW+Q9rZorkV+IHcd+L/v2+G/1dNc3gok284YRzGMbVsRBFLn67flG/ixpNyH9oGuWnF4uPGEXrfdJoh4xupJ6l9Sigm4r8pXiWfpNvv7YFgI9eU8i5x6FURUAQUAUVAEVAEFAFFYDAQUMWVPvpOcs40LGGOvEhxBdO77sQ6E/fuRHiT2XF3Z7m7sV0eOBMYU/AQi8xYZ+E4FSZe2eHmKjvMO++81h8TTExAuZMS9kWXP1kgKzp2pSxoaprEiwIPRyb5hAUD91v573m+55578uOTUFrpxHsdvBbxUNVtxRVXtIvo+GdCEosLHHGFdRN2UJ2fHXfUJqFUQH2gjrDzSszxN8UTZos5QovJgPvvv9+W81tvvdUem4JCGsffsNsRYsFqttlms0pqYorY59N176ZU0KsFMJ+n0OeUfGJBBTPQHEeDEthdd91lqAMcacMCDlZXpN3BipMslIfyWOafcoQCHe0R1nNQXOk1yXdkUYiJUtpB2hAmwNmh604u1d0eVFHYAnNRWgEb/3ghFn6LrGm5uIXUEzfcINynlHfyxzfFTDVtatOEzOHKIKRPvUNJEXJ3AbrtEYtgRf0W7pDrt642eqz0uSH5rAvb2HZPvrf96M6fyGXUDVkUTa0nEn0IPhJGrvfee+8IK4FTp061yqfix7+ibI0VQY78ufTSS80111xjlSjPOecc6+77d5+pR3vssYdVUhZ3jr5DYb3uo1+kbyAd6SMlzU5XCRcrg3eKu+hd22W2iKdublKe8VfU7nUKL8rsRQtTncLxjmMrUGYWOQSrHTxjodNVKuwWT+j7kHpd1/cM4bGuMhvTHsBnTHvJTn/Gxhyzh3I2fSRyEVYIHnjggZDsB/t1LVGivIKiPVb9OM6NZ8YwQqnyntSVkHqSkibHpXLUMrIzihYoSEr5XXTRRc3jjz8uWevJlXmArbfe2uy///7m8ssvt1ZeOO617AimmPYgBR+pK2Q+pF/AP2FTZdOQ+Q7qCFZMXn/9dZIvJPddmdWzkDTdRNgYhbUNys/6669vZWEsRl577bUjxpop38RNM/Y+pg2StGLxkfAx1zbSDBnfSD1pSg7yMWyKV8kn6fd7e+BilFLe6RNckjkX103vFQFFQBFQBBQBRUARUAT6C4Fx/cXO2Obm6aeftgCg5c8EYBEdccQRdhcIg2p+6667bpG3RtxkUorEqijPsLjMLigh16wubmLSnEGJa8JS/He7MtiDQibJUtPsxlO394cddpj1whE8n/70pzt6b5tXlzmUBCh7a6yxhnVm56BYEHH9NXnPzlA5niKm/NTFK4u/7Lpjpx3nYgv5E9Ki+MVuriJy3cVvkb+23MT6DjvPO5Hw7ubH9e+6i1/eY3EFolwxqc6OOpTdxLITilNYeoLEr32o6Y+dm5RrCEUZd4dxTUl0jAbFHSbBIY5kgAeX2mgPMMEOoVS4bXZEhk/sguqmrCdhqtYT8c8VhTkWJuQn1nBcP23fSxl2y7XLk+sufuW9fFMmEVnEapJc89iS7q9//Wu5HXZEk8u3m5/cc3Yj7q7ffmijB7XPdbEtuq8L29h2r6j8wKf0xRwZ5E4SS7mQcuLnyXUXv76fNp7BmaNbOCJRFL04MrMb0WZh2QD6+te/bo/po61EGUYsH3SLo+p7aUfwL31klbASLlYGr5KG66ftMuvyUvVeFsLxX7S5oFM8cjxl0bGqncLJu7PPPtveYjUDCxkcJ9jrYzpD6nVd31PyW+XadJn1eYptL7EgKEcDoWjBt8TiSZPEPMMWW2xh9txzT5ssSlCu5YpUeS9mTJ6a5uabb25QQrruuutsHcG6K8/XX3+9tWbYS3zluCkUWFB2h7iX7+ynHdMepOAjdQU+QvoF/EvYFNk0RPaSIwbdDRzw4ZL7Tvy777kPSdMNy5wM7fSkSZNyZXzk4SLF/JRv4qYZex/bBpFeLD6xvLaVZlE/Vja+kbLelBzkY9kUr5JP0u/39sDFKLa8o8woc+dyZUOQkiKgCCgCioAioAgoAopAfyOgiisNfh9f09tNmkEDZ+hCKHiU7X73FwX9ZzfOXt+zo0iI3UxVyNXq93eVY5lFFjbcXWFF8fpYgoMMQkMUV1LSLOIr1I1JcJR+2PGGtZBO1DavRbzJxBumcttWXIE/rMJAt9xyizUdbx8K/vzyU+ClFifqBZP5kDshzPNiiy3Gxe4CFCsW1uF/f3K2MgoTDLL7jUS5DoUcJnjKKDafLFaKtQYWCSEmhtklBB5MsosyUC8UV2QXM8pZMiFdlsdeuWOFiaORICxVuZMsbbQHYn2D+l6ksIQFgRjqVE/c+FCWxGqX/LB60G8UW97JB6bIIdonjiEpI44ZqJsoXz5xBIrQ4osvLrd2sVT6ahaGfLrvvvvy8uHLBm230YPe5/pYu891YBvb7rF44y/QEddtt91mWXTLDw4p9cTNc1v3tH+ivN1NcfuGG24w5513nmV1m222Meutt56t3yh/srjLTu46ZRIUi5dYYgmbHm2kK3e7eKEI6PbdbfQpbZZZF4uq96KIhf+QsQbysch5HIsUShxJ9eCDD9pxQpWjVEPjL/MfWq/r+J5lvBS5t1FmXT5i2kvqOkdlQBxpivWcNkmOgWEc7c4ppMh7sWPylDQFQxSr6V/AlfIhGxrkfa+vtL+MWZCRkJVRDBLrKpJ2bHuQgk9svwDPdcimIbKXLKpj3UzqmGAnVzYvQIyxyxRXQtK0kRX8YSl19dVXt2+wnurOfeGY8k0Kkgt2Enxixqp14BPKcBtphoxv2u5TmuJ1kNoDt4zFlne3b5P4itzknV4VAUVAEVAEFAFFQBFQBPoDAVVcifwOLMxwdAU/mYwkKkzRijt+XGJxlYVIdv8wcIPYMYLJ4O222y6fMNpll13cYK3fYz6YyQN+7BJjInyzzTbLz+VmgkEWIXxmmazhx6L9jTfeaLAYI7ThhhvKrb1ytI4c78KCGZOzrilYJoBQSMAPu1SFGHgwgcF7CDPckq5cxS9HWcgkA26xaUp8dVwxIyyLDJ3ia5tXJlsFz+eee86ccMIJVnkAnlEwiN1F2inPoe+w6MP3p0zssMMOOX8SD2UZvpmEwzpEHfTCCy9YCytYf5AJYOLlyCCOxBKFFUyBu7TzzjvnilpMbIoiADgff/zxubUP6prs7HbDt30vyiLkee+997ZH+rz66qv2+CN3MiAln1gdgZioYJFPFkVQJGHil/oMiT/7UOMfafqWTmqMvmtUTMiyU1/o0EMPtQudPLfRHqBUKcTEvFgFwaTy0UcfbVhcK6PYelIWX7+6p5R3Jsex5gBhsv/II48cphDIAvTuu+9u27C688/iOse9yUI2fa1YtKHeuYoHtPfSf+OP3eJS57Ect99++1n2ULqjHXapjTbaTZ/7Qelzfb67PdeFbWy7Rz8g1kPo9+jXpI2mXriUUk/ceJq8R9GD8o0MzHEQomyy5JJLlrJBvy6WBlHi4nhFCEsZooDAWAG5pE6SuMEfa37I70K01+xa5YgiWQTgXRt9SttlVjCpckX+FUuRLI7TVopMzFXKOm0hz1zxg6Ud6gIKhyxmr7322lWSy/0wtqF/haZMmWKwBtAkhdTr1O8peAqW7riDcutTG2XW5yG0vcQSI0p+lIUiCw5+/KnP3/rWt6ziHJs7XAyRyU488UQjlnwoVxw/JRQr71HuY8fksWkKz/1yBUuxoMNYU44TTm0PUvGJ6RfAtC7ZtKrsxbgXorxy7/ZfKHvSzkhbvNFGGxksFZdR1TTLwuN+3HHH5coxWClxFRhTvwnx08ZL2ydXaQN5j5tbd3FzKbQNcsPWgY8bX5X7ptMMGd+03ac0yeugtAd+mUop735c+qwIKAKKgCKgCCgCioAi0N8IjO9v9vqXOwZdrgKEcDp16lTDD2IB3T1TmYl8difLDmUmrWTiW8IzAGc3Zj8RZzTzKyIW1LGOwiRuEclRF/47dhyutNJKvrNd8MKKwqOPPmrN/H7nO98xYiae845lccw9IolJQBZ0hVAW4FdExxxzjF10kF3A+GGRLTTNorhj3FD2OOCAAyoHbZNXFlfcCRqXaVchyXVv+n7OOec0hx9+uJ0MZsGIespELO4s+rqKUHXxxmQSZ38zMcmP3X0zzjijPSpDFoFJ3z8Kav755zfbZsetnHPOOXYSjN2BWDFBqU2UXYiLhep+JHaOs7gOrtdcc439CZ+cAy4L3Sn5RDmGuCEWz6Wd4bggmWwHs17tqGSxSdofyVvTV8rFJptsYs9bR9GRSdPTTjvNstF0e7DWWmtZ09lMxLPz8KqrrjJ8X9pmJl4XXnhhW5b9fg1mY+tJ03inppdS3kmbNoQdntQr2gYsNWB2ngVmMb1Pu1E3Ia+gpHXSSSfZeiZKSbgX9VEsZtKPwhOT+pRJ5AEWw4R22203M8ccc8ijvbbRRrsMDFKf6/Jd5b4ubGPbPRZjaaexEkX5lYUWdgKzi9Wl1HrixtXUPbKubw3wfe97n9lpp51GyPLwxMLTHnvsYRUWqEccEeQeOccxHVha5FgLrFWhULvKKqvUkh3wRgkeflHY/dSnPmWPtpl11lmtkqx8Gz+xpvuUtsusn/9Ozyj6oJwL0d+VKeyj4ISsjHInVlmQlYSQXfgGIYSsQx/LYs1BBx0UErQWvyH1OvV7rrDCCsN4Ro4vG3eIx6bLrKQr15D2EuUlUVJjfILM1Gu6//77rVIx4zQsezI+QfHbncOgX0QB1aVYeS9lTB6bpst3v9wzdmOzD2NnjjtdeeWV7Xg0pT1IxSe2XwDTVNk0RPaCT6wRoQDFPBoysRBHawkRp68cLe+4hqTphvPviYd6y5yWKOWywQqFmdRvQlplc2XCB+NAyk/ZXFxIGyRxcq0LHzfObvdtpBk6vmmzT2mS10FpD/wyFVve/Xj0WRFQBBQBRUARUAQUAUWg/xFQiyuR36ibaXCi9Y9+YSc1AxIhd3GPxR0UXmRCS/z4Vz9d/9n3H/tcFi+T7iyiMbnITjGO6/DPWC8Ly3nwLGYz8Vp2DMJMM81kF0PZqYQZSxRVmHTnxz3pM/G78cYb51kT5YDcocuN7z8mTZKQ71uWX3kvV2HL9X/IIYfYxQR5x1Xey9V9F8urG0fofREfHGWFwsCaa65pF7C59orc9H0si9JkdxaWH9jxhn+UVVAsEaUVyi+7vf0FVYnbTc+Nv+w95ZqFOdmpyI5zdmVTzohr/fXXt8pSTNb4hBICO2klLOWcSTHSwqoI57L7fLpxCK9ydd+V3YtfuZb56+aONRBwZkHMV+7w447Np1h1gRfqvRCTLbQPkOtH3qdc5TtjScq3EkC88l6uKWm58fn3bry0tRMnTrROKAXdeuut9j61PZDvVDUvtL9nnXVWbvmGNpnzuFkE5TuwYCEmuyVuyUdKPZE4/Dj9Z/FXx1UwKUtD3svVTTO2vBMHu/04EmrzzTe3/R0Ys7tTlFaWWmop2+666fn3Ls/uve/PfWZXNgpgKMiI0gr1GusSyy67rOvV3tOO0j6hgAoGLNaK0grfGuVR2XHsB+51G+1/ExeDQehzhX+XbxdDeS9X910qtrHtHkoSKGfCE4vNtAkoZ2Mh78wzz3RZzO9j64nkOxQf1797LwyJm1zFXdLjmXfUE+QL+OfYAPoiN4zcowT2yCOP2GhQCis68g9lauoZ8gL1pc5jwCjr3/72tw3fFKJuS3sNzyygSj9qPWR/qX2KxCOYCRbiXnRtq8wW8dLJzR87dPLLO/yDA/WAb79tpnAoSqd+WMFJrvKedlXCoCCFYohLITi74arex9Tr0O/p57kbb5Jn8RdbZt103XuJV9zkKu5chYfQ9hJlVPpyePb7Rzcd995NN+Yea4/01xBlkiPORGmF+o9SAO2YX7Zi5b2YeiL5ik1Twne6CqZy7eS36jspB/j34+UZOQh5CKI9Zhya0h7UgU9MvwD/MbKpi0mo7EVd2XHHHfPxMTwIgSFKsmxsmD9TnncpJU0J635XiRvFRfiBsLQsm9Riv0lRGpJW0dWvVxI+tA2SPJJG6Dcp4quKWxtpunyFjm/q6FPk+7h8VLlvilfhZVDaA8EztLxLPt0y2MlN3ulVEVAEFAFFQBFQBBQBRaA/EJgmGwgN9QcrY4MLdmA+88wzdtceE9TsvGMyQBb7xgYK1XJJ0XzppZfskS4s3nE8wbzzzmvPM3ZjYAcK5texVtNJ8eeJJ56wJrqZ5Lg928VTRFXTLArbtNsg8do0NpIeSiAokbD7m7rGApE/OSt+U68oorEohOIKk5NSXqsc88O35PgleEXBhR2mKIaMNhor+WzjuzXdHrA4TXlHGXPy5MmVy2tKPWkD15Q0U8s7WIEzMgPtAX1Xne0XFs3kqAJ21WPRiG/KD4VU+tsqRHtH/8qxYVigwOKGTDJ2C99kG92Nl27vm65j3fjp9r4JbFFqwkoY1nVQSiZNzPqzmEO74Cprl/GbWk/K4lX3/0cApRUUeDnCEDmIcUe3b9NGeW+izP4/KmF3HEmI1TOs5NDGldGuu+5qMPfPorUcqVbmt1/d66jX5K3p79lGme3Xb+jzRf+MUipXFFZQwGO8UbSg54cNkffqGpOHpOnzOxae68Anpl8A217Lpu73Iy2sBqEQB6HAh8U/LAf1G9XxTfw8cSTS9ttvb5XEsdyjVA2BusY3TfQp/cLrILQH1b6++lIEFAFFQBFQBBQBRUARGC0IjB8tGRmUfDCZzzn3/JQ6I8DCF4t0dS7UdU7xv7vomk6zG09l79vAp4yXfnVnsXfJJZe0v17zyA6wSZMm2V9oWnzL+bNFaX6jmcZKPtv4hk23Byg1VFVscPFIqSduPINwn1rewYrdZfyaIha0+YUQFqNY1I2hJtvoGP7cME3XMTftmPs2sCVNrAuGUGo9CUlrrPpl13+odbI2ynsbZXaslomQfMfUa+Jv+nu2UWZDcGzTL8dW8YuhWHkvJi0J00aakvYgXOvAJ6ZfAJsmZVPSYhwvxPFa/ai0An91fBPJp17rRyBmfNNWn9IGr4PQHtRfKjRGRUARUAQUAUVAEVAEFIF+RkCPCurnr6O8KQKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAiMIoRUIsro/jjjpWsce4wZw3LGd5l+Z5nnnmsv37dqVPGt7orAoqAIqAIKAKKgCKgCCgCikA7CBx99NHmzTffNLPNNltHBjjK4hOf+ES0FaqOketLRaDPEdAxeZ9/IGVvoBDgyEXmuPRI8YH6bMqsIqAIKAKKgCKgCCgCioAioAjUgIAqrtQAokbRLgJVTbNypveUKVPaZVZTVwQUAUVAEVAE+gCBcePGGX6YwuanpAiEIjB+/HhbhihHSorAaEZgjTXWqJS9ZZdd1vAbZNJ6Pchfr13edUzeLv6jMXXkU44MGhoastfRmMeyPE2cOFHnrsrA6eA+SOObQeK1A+T6ShFQBBQBRUARUAQUAUVAEagdgWmyQeBQ7bFqhIqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCikAXBHSLZBeA9LUioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKQG8QUMWV3uCqsSoCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIdEFAFVe6AKSvFQFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAR6g8D43kSrsYYg8Ne//tVMmDDBTDfddCHB1K8ioAgoAoqAIqAIKAKKgCKgCCgCtSLw1ltvmXvuucc88sgj5pVXXjEvv/yyefLJJ81+++1n1ltvvVrT0sgUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBEAgWmGMlIo4hF4+umnzdlnn21eeuklG8k+++xjlllmma4R3nzzzeaKK64wDz/8sHnhhRfMNNNMYyZOnGg++tGPmgMPPNDMPvvsXePopYeLLrrIXHbZZZWSQOHm0ksvreRXPSkCioAioAgoAoqAIqAIKAKKQH8icMEFF5jjjz/e/OUvfxnB4CKLLGJuuummEe7qoAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKQCoCanElEkEUTk4//XRz4403Glf3Z7PNNusYI35POeUU+3PDcf+73/3OKotsvPHGrSuuPP/88+ahhx7qmBd5Oe2008qtXhUBRUARUAQUAUVAEVAEFAFFYAAR2Hfffc2VV15pOX/b295mVl55ZbPccsuZ97znPXZssvDCCw9grpRlRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAERgEBFRxJeIrffnLXzbnnntuREhjzjjjDHPyySfbsFhV2WqrrcxSSy1l/vOf/5jHH3/cWi7B+ko/0XbbbWdmmWWWESxdddVV5tlnnx3hrg6KgCKgCCgCioAioAgoAoqAIjA4CFx44YW50soGG2xgvvrVr5oZZphhcDKgnCoCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAorAQCOgiisRn++5556zoWaccUaz7bbbmvnnn9/sv//+XWP6xz/+Yc455xzrb6GFFjKY4p5zzjnzcB/72MfMrrvuasaNG5e79cPNjjvuaOaaa64RrDzxxBOquDICFXVQBBQBRUARUAQUAUVAEVAEBgeBP//5z+aoo46yDO+xxx7mc5/73OAwr5wqAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKwKhAQBVXIj4jyiZM6G6zzTbmne98p/nhD39YKZYrrrjCvPbaa9bv4YcfPkxpRSLoN6UV4Svk+swzz5h7773XYDnm05/+tPnJT35i7rrrLvP000+bRRdd1KywwgpmpZVW6hglllzuuOMOa4Xmb3/7m1lwwQXN5MmTzeqrr27j7RT45z//uXnggQfML37xC/Pmm2/maX7wgx8sDPbrX//a8sfLDTfc0LzjHe8Y5u/OO+80L7zwgnWDbxSVoMcee8yQVjdae+21zbvf/e7c229+8xvz4x//2D5vsskmZrrppsvfcQN2YPj2t7/dbLTRRsPeYb4dPJZYYgkzxxxzmJtvvtn6x+/SSy9t/LSGBc4e/v3vf9vjreD9V7/6lZl55pktPquuuqqZd955fe/2GV7h2SXSm2eeecySSy5pJkyY4L4ado+yFt/+/vvvNy+//LJhYWS++eYz73vf+8yKK65Ymua1115r/b7//e83yyyzzLA4ebj00kttXjBfv8gii+TvU7CVSF5//XVbpyk/lFnq+2KLLWbWXXddg7JaP1Bs2XN5D60nbtg//vGP5vbbbzdPPvmkLavUGeoFGE2aNMn1au8vueQSa1VqxAvHAeW4VVZZxXExplM5oB7IcQZ+OWijDaJsw69PWNbiaAVpN/z38kw7BKa0fdSV6aef3tYT6gD1s6hv6IQP8Y6WetIpn53KgWCbco3pi2LLO5bX+GbuMYJFvPvlHT+xbS19JfW4ExWlR3mlbYdC+k38x/JK2Bhy+4VO4fulz0VuQV7sRMgNyA8upbZ7IW0QfeN9991nky/6/i5f9P9PPfWU4dgZjsL8/ve/b/7+979buQ55sIzwg19o2WWXte0h953aA96XtXu8g2LkoNhv8t8U49KUsLHX0HzecsstVr6cOHGi+eQnP2nOOuss278zbqFfR95bc801C/si4TFVfqKsIJvQJv3hD3+wSvMf+MAHzCc+8QkrE0s6XKUcuG5F90Xtl/gLlYOwNIn8g0z4oQ99SKLJr1is+de//mWWX355K1vnL5yb0DQln23Iw5Shn/70p3YMQH10CZmm01juwQcftPVewnz0ox8tHPfK+9ir4NMtvF8O3H4hdCzmphUiD3dL041X7lPaWolDrv1S9hhf/exnPxsxzqXfo62lzlPHiuitt94y3/3ud+0rjjFjTOlSahvkxtXtPrbspfbV8BUjm0p+QstBirwXIw/XUQ4kryHXWHk4RXYX/n7729/aeSuODfdp/fXXr23+wW2D/HTc57rl4Vi5v+myBwZSr4v63E5jzjbkA/ebhfRFhPvBD35g/vKXv9g5Cl+eQf664YYbbPQzzTSTWW+99dyk7H1MG8S4gT6tEzHvB/ZFFJNmUTxV3KQcdPPryxax7btbN6vKJIMoH7h4hvZF8k2K6ibxlo3/YrB1+eQ+RR724+r0TFt5zz332DUQNkv/6U9/smOz2WabzY6bmWstm1OMxacTP/pOEVAEFAFFQBFoHYFsgUQpEYFbb711KJs4sb/rrruuNLYddtjB+skWZ4eyQW6pv354ccwxx+R5ypQ2ClnKrMNYP5lSybD3mSWZPKwbz3z/w4hrZn68FIOLLrpoKFNGyONww33mM58ZyibRh6UnD5mgN/SlL31pKBPmCsMeccQRQ9mEsnjPr5dddlnuPxNsc3dusiOh8viyY52Gsgm1/P1JJ52Uh3N59O8feuihPAw32WJ7Hs7PSzZhMZQppdj3iy+++LBwPGSDOftuzz33HMomjPN45vsftrj98pe/HBEOh2wxfCgbCI0IQ9hMocjyVRQws7hTGIZw2WJSabhXXnllKBtYlIblXbYoVZTkULZQb8Mddthhhe8XWGAB+/7ss88e9j4FWyLKJtqHPvzhDxfyjHs20TosvbYeYsse/MbWE8lrNpFhv/t8/ytz/vXrX/+6eM2vvp+iZ+q2T53KwdFHHz0k8fjloI02KFMmyvkRvtwr7X82+PSzaJ+z4+c6ht1iiy0K271O+BDxaKknnfLZqRwUgh3gGNsXud+97L6ovGcLg0Nl/l13v7yntLUHH3xw1zQzS3EjUIvtN1N4HcFERQe3X3Bx9O/7pc99/vnnu36TTPlzRO5T2r3QNuj888/PeaQ/KqNsEXEoU6q1fpEzoGxR0j5nihClbSL+Tj/99DyNb3zjGzhZ6tQe4KGs3eNdrBwU+01S0iRsLMXkE/l5vqxfzxaMhyhf3Pu/LbfcsrAvgs8U+SlbJBlCrvXTk2fKEGXJJSkH4qfs6reXxBErB0maZbJpZk1zCD7c8io89yrNsvLutnuhYw14zjYPDGWKATY/RdjutttukrXC64EHHjgs7PXXX1/oL9VRvkkRj66bXw5S8YHvUHm4U5plOKS0tRJnP5U9eDrllFNs2fD7XOkreF9GmZJ4Xq6QQ1xKaYPceKrex5a9lL4a3mJl09hyECvvwatbB8vufXk4tRyQbgzFysOxsjs8ZopYQ/vtt98Qc2ll+JTN68Tk0W2DytLD3a+bpBU7B5Ui9zdd9sin1Ouifr7TmLNTOOLthXxAvFBoX0QY6d8pfy4xF7nGGmvY8sh8LPPcPsW2QaTVqdzx7owzzvCTs8+xaRZGVsFRvmc3fn3ZIrZ9d+tmVZltEOUDoI/ti+SbFNVN4u1XeRjeqtLjjz/esY6wzsF4rYhi8SmKS90UAUVAEVAEFIF+QUAtrjSoOsRuCgitdnbUoxWLVRG03dnNyM5TLGxgqWS0UDb4MGgIb7DBBtYyR6bYYy19ZJO65j3veY/Zfvvth2X1xhtvNIcccoh1m2WWWWw4NP3vvvtua0UF6yccpyS7rdzA2SSqyQZX1gnLLtmgy0w77bTWegYa3ZkSit3dddBBB7nBSu/h/dhjj7Xvieu0006zO4YlAFZPXIso5I2dYpnQPMxKCPmvSplCj/nrX//a1fs111xj/WQCqkHTn93MV199tXnxxRdNtshtrdywW0Yoa3DMdtttZ3fz4Ia1E8phNjCyVivYobbPPvsYdtqW7aDEagzHWUHZIo79Hr///e9NNgi1O97YIecSu+LAg53hWNLgeCy+ObuJ2EXN9bOf/ax9X7STw42rjvtu2LL7jx0OaNTDMzuO4Rkt/csvv9zmOVNeMrfddpst03XwFBtHStlLqSd8N743BEbsgsZ6DtZG2JXLTppXX311RLbYZU8ZhLD0kylz2XvCY1kEos5WJXaYZBMFlbw32QYJQ5mSky3r//znP61VCuoZu9nBTo6LE79c6QMg2g7qZTYotc/sWKZNw2oV1qtuuummnvcPg1RPQsqBBTTgL6UvqqO8L7XUUnYXnMsy7VAR1dHWYj1urbXWyqOnvoq1i9yxwk23frMOXiuwUerFbXPE06OPPmqyhQF5LLy20efCCO0B1s18cvt3/x3Poe1eShv0zW9+09A3Fsk6Z555prWcUcQj/QZy1aGHHjriNTtHM8WVEe4pDnXIQaQf8k3qSjMk37FpIpNBb7zxhr0i802ZMsW8613vsn0Q/TsW+Ipk8BT5iTaBNpNdqhAyxUc+8hEz66yz2p25yFzs/sWSSRHRX2KRx6ey9hJ/KXKQn07V5zbSLOOtWz/PLne+M7vLIfDNFm/sPd+D+tlvFFMOyvLQDR/CxcrDZWlWcY9ta/up7FXJZ4yflDYoJj03TErZC+2rU2TTustBN3kPjOqQh12sm7hPkYdDZHfyQp2Wvop5L8aPWKdjnlCsGvYqz03Jw72Q+9soe70cc6bUzTr7opdeesnOIzJXgzVdLO9h2cqllDZI4sGCMRb8iqhoPqiONIvSquLWZPtexk8VmWSQ5IOU8l6GUax7N2yblodZB6IPoB5gWYaxEGsDjzzyiLXCkin9mPPOO89aHN98881js63hFAFFQBFQBBSBgUFAFVca/FQMQiGUKVggl+NncEOxIttVYJUIMosFhsHraCAmvhmQy4TnTjvtZCcxGPwxGNp6663N+PH/LYZMtme7rGy2yT94cMQGhBlWjmciLibPWch1B1IcsSFKKyhuHHnkkRZnwma7OM3ee+9tFYVY7GYBmEFIJzr55JMNPwgTrdzz3Vzi2CJ+QiwyI9xiDjuz7iLOla/kq9vxAG5kmSUU84UvfCF34igknlEmAbtsZ2z+jgVvTDNDLDCBjyhIIfRSHpmgznZOlyqusJhwwgkn5HEyoQLWKHqAva+4wqA023Fp8UbodolvgjIQA2QW8nutuNINW8pepr1u84KiFIpRDBaEwJJywMABZabjjz9eXrVyjS17KfUE5aZsp5HNLwpIfDcUaFxiYoFv6tPXvva13CnbUZIrrhx11FF2USx/WfGGo9aYCKtCTbVBLi+0NyxuQtQPyg9mP8GfAae/6EybQflisU7qpcSHwl1mDcEqp/3oRz+y7Yu8q/s6aPUkpByEYJXSF5FOHeWdfor21SX6B/pCn+poa+eee+5h7TsYhCquVOk36+DVz3/I81e+8pVhR/cRdurUqV0VV/DXdJ9LmjvvvPMwOQO3KhTS7hFfShuE7MC3B1uXUIQoUtRz/XzrW98y22677YgjRJCDqyjxunF1u69DDiKNkG9SV5rd8ua+j03T7Xuk/KBMCe21114Gpe/vfe97VgZH/hMl51T5iTIiSiscwfrFL35xmLyN0kpmvdEqy7r5lHvKLn2kT2XtZYoc5KdR9bmNNMt469bPEw7lFFFaYVLf7YsYL3CkU79RaDko478KPinycFm6Vdxj2tp+KntV8hjjJ7UNiknTDZNS9qStHY3zJGBUhzzsYt3EfYo8HCK7Mz6UuSbS5OhxFiwhOWa7l/ltSh6uW+6vMtYAt7rLXq/GnCltdJ19EZvSmBNkfhrlrfPPP99ujnPLYOr4WOLieHJ3TlHci651pVkUdxW3ptr3Ml6qyCSEHRT5IKW8l2EU614F26blYebbmbOX9RE3b9RR5szZ+MGcoCquuOjovSKgCCgCisBoRWDcaM1Yv+WLnfdMvkIXX3yxHRSwSIkCAUoOsgDMbsYTTzyx39iP5mezzTbLlVaIBIWU7MgMGx8L3AhdQux6FuUKBv6itML7cePGWcUVUR7xLa6w8AQxEAJP8Ycb93vssQe3dscmi+tlxOCIxXmZSGCCFmUaN76ysCnuTF5kZg8rRzFhwgRrIcUNwMTye9/7Xuvk4yPPLJgfcMABwxbHM3Ow1roIAZmMRvmmCk2ePDn3xo5cn5io2H333a2muP+OAfGGG25oneWb+37qeq6CLeWQ3XoQu79dpRXcOGt9tdVW49YqQFFOBpFS6gllSKypoEQmbZaLAxYbWITsJXEmMxN6VampNqiMH9oO6hhEncBSjU8orTAx4i4cih/qtbhnxxGJc+3XQasnoeUgBLCUvigknbr8tt3WhvSbbfMai3k/9LkhvIe0e8Sb2gYh1/qyA3KULH6X8Y4inz95zaR5Zmq7LEi0ey/koG7MDFKarkIlMrgorZBHXwbnHHmhVPkJqzsQC7e+0gruLOwed9xxZoYZZuAxmVLkIEk8VAasI01JO+VapZ8nfrcui6yeku6ghK2KT5vycGhb2y9lr5dlILUN6iVv3eIO6atTZNO6ykGIvNct72P9PYuPyEAQ42dRWul3XELl4brk/jbLXsiYs0n5oK6+CAsrm266qZ2fZq6WfgaLzj6ltEF+XFWf20izKm/d/IW070VxVZVJJOwgyAd19UWS59hrVWzbkIeLlFbI55xzzplv+Ou2CTcWFw2nCCgCioAioAj0GwLj+42h0coPiisucQwJCiqyIInG7Kc+9SmDcH7hhRdaE80IJ4NOrkUSycvHP/5xubXHsMgDWsRC7pEF4jbHHHMYTLByzAjHt7gkZv4xJc4OC3fQKPcMtLNziE2nxV+sbrDjBVpnnXXsZLl8Ize9uu/Z1UAeWBxAQYJjRToR1lWYCHCJsBzlk50BOwIfwRYrEEXWfMCbcgeBbZEwTBn+05/+ZP2wsISZQiFM25YRmKOQRZjsnON8koayDjFpw3crWtDnqCFJ042/yOqA+969r4Ltk08+mQfBcg4DeCk3cpUFOK4MYtxFnTxwn9+k1BPBiMUj2q82COxlVz9tC3W1W1loqg1y8eDYDcotVmGYUL/qqqvsa3aoU0/LCIwxBYpSnyg64pcBLHWkzALBWKsnMeWgDPMid2kveRfaFxXFV9WNSRShmH4npq2V9k3SjbnG9JsxvMbwVleYNvrcFN5D2j03nZg2iLJK2UW5QI73wYKGq9zgpuHfYxkDa3yiMIoiC/IGbWWn9j203ZN6nSIH+bx3ex6kNDELL1Rkwn322We3lvWQkVwZXGQDwobKT1iilGOqGAf1WkkcHlPkICzyQeQzhFLSlHRCy7uEc69V5GH8M94SIq9stAglLEByBKlQ0VFi8q5frlXxkTKfIg+jCC59PgvWM888c1cYYtratssei/O+3EtZ7kTIusjAjFf9sEXh5HvwLrQNKoqvSbeQvlr6E/gLlU3rKAekGyPvES6GQstBTBpumFh5WOoxcYXI7mz6kbkp2Tzj8tOv96HysOQjVe5vsuwJz1yrjjnbkA+k7Uvpi2gb2KiHxWbokksuyeVx6+D8pbRBTjRBt22kGcRgB88h7XtRNFVlEsIOinxQR180aPJw0bet4gZWjIexYIp1fqzVMAfNnLlrWd2Pqw58/Dj1WRFQBBQBRUARaAsBVVxpCHksTDAxhfCBYInFAndwyzsm7pnkYwCMFYrRoLjiTn4K1BwbIwsSv/3tb8XZvPjii/l9UTheckQJ5PpFmHvjjTesOwu+3/jGN+x92Z8c2VT0XpRWeIeAyIR6lcnEoriqusE/R/RAaOaT926KK2X4sLAAcf48Quv0009vn1EagcrCCa74Eb/cu8RkoH8cEIsMWKRZZJFFXK/5PQOuU0891bz22mu5W9ENC/xFiiuYpecXS1WxffbZZ/MkqqRHGRo0xZXUeiIYzTPPPI0sLuUfxLlhBz/YM9GHyV63vjreht0Wlfm626BhCWYPHCniE4uy7tFe7nvas89//vN2QOq6+/fu5Kj7bqzVk5hy4OLV7d7tX4rKD+GlzXT9dou323vZeYk/yngIxba1yCRQyk5Ptx5W6TdjeQ3Bo26/ZeWgl31uSh6K+C1r90gnpQ36zGc+Y7797W+b6667zlouQ05AiYX2inKFomOREsvGG29sLZghq3AUDOUC2Zfj5CCsftC2lR0LF9ruiWxThA3pSZ3mXvxyn0ISzyCkKfIi+XWxcPMv7m67J7IB/kLlJ5SEhZqQqVLlIJS6aeMop+R17bXXtkrktN033HBDoaJVapqCT2h5l3ByrSoP4x9ldDYBUIcZrzI2QfaDiKcKoVzvK9hXCdeWnxB8pMynyMO+4gFYYckSq6TuBg8Xj9C2th/KHhsqQomjhPmhtM1cyAYbbGCtOfrHzkq88j14Dm2DJI62rkV9Q1lf7ba7ReHIQ1EbXVc5IP5QeY8wsRRaDmLTkXCx8nCs7M48INY2Od6PORbmAVnoptzL4q7w1k/XsrJXJg/Dex1yf5Nlz8W76pizDflA2r6Uvkg2kkme2WwmiuTiJtfYNkjCx1zbSDOGz6IwRXWlrH33w4fIJIQdBPmgrr5o0ORh/9tWfd5qq61ya9cSBsVBLPbPNddc4jTimorPiAjVQRFQBBQBRUARaBEBVVxpEHwEVRQrJk6cWKiUwpmGQmjTjgZyJ8IlPyg78GMnLTuxhGTXJc/uzk95z1VMhbt+UdIQYrJv1VVXlcfCqxynU/gyc2SnKZMIKNUcdNBB5swzzyzzWov7UUcdZTijFnPoBx54oF0A6hZxGT7u4iPYgj8TzywMQYKfHz+KVUIutuImV1lMxfoD8fKDfwbMWMNxiYUqdsdATMiuu+661hS8KALdddddBtOrUNkuJyZ1XPP11nP2x66dKlQVWylDKM9gqr4bLbTQQt289N17ySOMxdQTsXwjO4qazuBTTz1lzj33XJvsbrvtli+idOOjiTbI54H2jbJEGycToexQx6Qvyomu0iITnjvvvHNuCWqZZZYxWGahnxBlLgao1OEy6wNjqZ7ElgP/G3V6dtvAsrZW2lLXb6c4q7xzJ7+Lym1ZHCltrSh9hqRXxEfVfjOF16J0m3IrKwe97nNj81f0Pctkr9Q26LOf/azty9kh/9WvftXKTXIk4y677FKqhMWCJMc4skB+e7aL7O6777YWW5AHkBNYMO+0CBnS7tUpB1X9JoOWJuVDqFt5d9s9kS1i5CcJS7pNyBZuejFyEKb0r776aiuzUj4PPvhga8wiZLUAABh6SURBVBUCebtMwSo1TfkmIeVdwrjXqvIwYVCgQJkMGR5lYRbuRjuF4FOHPEx9k00ctBVYl2BcxA/F7O22224E5KFtbT+UPdoFt20hU8jFZfIs78U/dQrZmQ0QjBdRanTlZ/xCks+YNui/MbT3H9JXu+1uWRtdJJsKPuQypt3z0akq7/nhQp9Dy0Fo/L7/WHk4VnYnfY6npu4j/9x888325/PVb89lZa9IHob3OuX+psqeYB4y5mxDPqijLyKvMq+FtcRjjz3WLL/88lYOEBzkGtsGSfiYaxtpxvBZFCakfffDh8gkhB0E+aCuvmisyMPMCbL5E6tDWC2HUJ5nswjfm7nCIkrFpyhOdVMEFAFFQBFQBNpCQBVXGkQeM8korsikgp+0OxBEkaGImNB3J3sQTKqY0S2Kqwk3FjIwheoSSgcoPkCuJrqrUIKQVnSkjVjucP2iNAEOYLPwwgtbjXM3vZD7L3/5y2brrbc2+++/v7n88svtDkqO0Olkji8kft/vPffckx8jgtJKUZ79MDwLDv47Gdwx2SPCLPeUPcxSixlQP5zr7mLr+ltxxRXtmbe4MbnDQhNWIijTWIw5P9st7dI3v/lN+8gOlO9///tWMcd971rbcd3de3YP8E18WnDBBYfVA/89zyHYzjvvvDYKJkmZeChSlilKY5DcUusJGGGSlu/dNFG3USji+8w333wGxZWq1EQb5PPCGcMcRwE98cQThrpAHWDxh92nrpLXHXfckSutkMftt99+WHRM8JdZahGPY6WepJQDwarK1W0DQ/qiKnF38uMq5BVNdpWFTWlrpc+o2vcU8RDSb6bwWpR2U25t9LkpeQtp91LbIFEyOeSQQ8x9992Xt2HUo9133z1XOCzKD0p7KPSxOLnrrrvmysxMyKHM24lC2r265KBO/PjvBi1NkRnJR1m7J+2FK7unyE8SljSryIT4S6FUOWjllVe2ylks7LBghMKBtA2LLrqoefzxx0ewl5qmRBhS3iWMXEPkYQnD0QEotYu8hWUAnn/yk5/k9VT8Dvo1FJ865OH777/ftnHINeyaf+CBB8wRRxyRH0uBlRtfDghta/uh7N17770j2vKpU6fa45LLyg0K3lgu5GhgFr2vueYae4TrOeecU2jRUNqRQRzDhfTVsbJpXeWA7xUi75V936ruoeWgarxl/qR/C5WHY2V3+GBTG2NGLDBhZYVvvPTSS1vLVrQJ/UjS5/m8CX7uHBR+6pL7myx78B065mxDPqijL0JpBSVxvuv6669vN4fttdde1iKiP2cd2waBZyy1kWYsr364kPbdDRsqkxB2EOSDuvqisSIPu1bkUV7h+HGOTL/ooousMssFF1zgFpv8PgWfPBK9UQQUAUVAEVAE+gSBcX3Cx5hgQ8xgs3vNVT6RzOMuhGBXRExqMcCQH1Ys+pnEhKXLo2tNxj0OyTV5h8Z/EYm76xeFHzGN65odLwrfzU1MGjM4ZoEc4r5XJlsPO+wwmwam9TGLX5WKcCWs5B9zrUwcCAlegp+4y9V1F7/yrujKQJayt8Yaa9jX7BQSqy44cB6wxMnkd9ECFAv6vaQQbKVuMkkhGMbwxiTriSeemP/6adIptZ4IRiwuYeqzSWKHM2UMYgewu6urGx9FdaXuNqgTD5jbZWe2kGvqGDeOHIBQltp2223tvfvHbq+i/sL1k3LfRj2J5TelHISk6baB0o754cXd9ev7CX2W3TSE8xU+y+JKbWuZVINCJ+pdfqr2m6m8umk2fV/UjsCD9Be97nND81vEb1m7V0cbhPyClQZILOmhcOcvvPr5oC0XK2cSDuuDTLrVTVJXpe768bvu4tf3E/os8bhxu3G47uLXfR9zL/G4cbvxuO7il/ey+Mu964dnISlXbjiRDWLkJwlL/D/72c8kmZ5dU+UgGNt8880Ni/EcjcWuRxSveL7++uvtEQ8+83Wk6ccZ+hzSz7txn3322faRXZ/k9fTTTy89GtQNxz1HJ2GBSX7IMv1KofhIua1DHmbjBWN62lAU5yEUooqUoHgX0tb2Q9mD5xiCd46s4jhakYcefvjhwqjke8S0QYURNugobaqbZFlf7ba7ZW20uLt+6ywHVeU9Nz8p9yHlICUdwsbKwzGyu8vrnXfemc8zoRRJO4vybr9SUZmF1yJ5uE65v+myFzPmbFo+kLYvpS9iDpJ5ukmTJuUbZWiDijbNuO2KtDV+ORV316/vJ+TZjUfi9sOLu+vX99PGc1FdKWvfXf5CZRIJ2+/yQZ19keQ59BqLbYo8HMpjkX/WhrbYYguz55572tcokdO+KikCioAioAgoAqMdAVVcafALf+pTn7KpscB/7bXXjkiZST4hrFQUkb+A6T8XhWnTDSsDPmHqV2jxxReXWzsZKsoWTAT7xE5eduVC7Gx0CWsg0C233GI6TY4yGViFUMxgsgx+MAGLkOjuqKkSRxU/7G7AYg7a00xeViUWmnxlGhZ9brvtNhuFiysOiy22mHVnIvSxxx6z9+6fmONnEYkJ1KokylZg5CqugJtYAir6Hrj5C/hV06zqLwRbrGNI2XO124vS6lSG2EHJTkL5UWb7iVLqCWZjIfKP2e4ywuRx3SQLmihKycRV1TSaaoM68ePuJPct1oj1KeqQtG9uXGeccYb7WPt9G/UkNhMp5SAkzUUWWSRvD0L7opB0fL+i6IYSxBJLLOG/LnxOaWuRH2QCLUVxRRjr1m+m8CpptHXtlz63av5D2r062iD6exSrmZTk95GPfMQqt1bhd/XVVzf8JCzxSH9cJXxVP72SgzqlP0hpojAkcltRu0f7JO2F5Iu8p8hPtBnS1mFZwe0rXVyx8seidB2UIgdJ+iiaggFWSMi/KM/Le/9aR5p+nCHPIf28xMvRLA8++KAdm4hymbyrcmU8wk57+cnCUpWwTfsJxadX8rAcLUn+3XsXj9C2tu2y5/Iec49cLJYcpH3y40lpg/y4mn4O6atTZNO6y0E3ea9uHKuUg5Q0U+ThGNldeGVMzZEgEEdd06f0O4XIw72Q+5sqe7Fjziblg7r7oq222srK45TBK664wlqfdstjShvkxhNy30aaIfx18hvSvrvxhMokEnYQ5IO6+yLJe9VrDLap8nBV3qr4k+PB6LP6fR2oSn7UjyKgCCgCioAi0A0BVVzphlDJexak5SxqVxEAxQBx9xetmciXyU0m5lnkFkIg4kgaiB36aL+PBkIZhyNkZMIZxZJzzz3XZg3B1VWwwBTkhhtuaN/hjx0nIpA9/fTTZr/99rPvUK7YYYcdhsGDRQN29uKfd2ghu4R5vRNOOMGe2VqkSOH6lXu+gex4IQyWV3pBHEMkE/ch8e+99975eZdoXKNcIxOdmN93iWdZCMKfLJAzYXL88cfn1iwwTS0729zw3OMX5R1+zz33nMVTcObbuVZVGLSL8hW7UPFHeOjRRx+1pp7l21rHHv1VxZajh2SHN4pVTNK7x3WhVECZxA+miweVUuoJC4vsfoQwTXnkkUcOU1ZiwYljIahnvaAJEyZYayuhcTfVBrl8ST2hXt544412QVfeSxsnz7T3QijLiTUbTJ5z9jl9Q69pkOpJbDkIwTClLwpJR/zS1tBPnnXWWdZpnXXWyReQxU/ZNbatpf29PTvujbYNov+UcitXSZN+RZQbxK3s2qnfjOW1LK2m3Zvuc1PyF9Lu1dUGYZWBBQ1+ZeaLy/LETjYJu8IKK5R5S3KvQw4KZWCQ0mQ3n1hx9GVwdlJzhCbky+Cp8pMoRdDOsFsUy3VC9IPsemb8JItI8i72miIHDVKaPq9V+3nC0S8gf0BTpkwZNWNSm6GSvxB86pCHKc8sQGDlAYuCKIRfdtllljvmCjqNC0Pa2jbKewnElZ1RVGPcjzIb7Y6MH5dccsnCOFLboMJIG3IM6atTZNNelINO8l4d8IWWg9g0Y+XhFNldeL3kkkvsRiTmaIosXIi/frtWlYd7Jff3uuwJ3k2MOVPqZh19keRVrscdd5xhAwWEdQqxpMNzShtE+BhqI80YPovChLTvfvgQmcQN2+/yQUp5d/OZch+CbdPy8Le+9S1z3nnn2Y0CjIGEXnjhBWvRWyy/0AZyfKeSIqAIKAKKgCIw2hEYP9oz2Kv8IfAULeSIpQXSZSHINfXLoJRdFXvssYf5/e9/bzhChTPliUe0Z/Fz+OGH50oGveK/qXjBgKM9TjrpJHsUhizK4n7AAQeMYIOBMLv0mMhj4HTaaadZRQqENSHOW59jjjnk0V45cgjcGPSjlMH3QZjDnQV1VwlhWMAuDyzEY8IVJSTOk+T8WpnY7xK00muUPYpwqBIYiyso/8w999w2jyLcYpWC3WcuoUSybXYMCeeDMwBlRw+LFGidi5lBJkrJbxmBgbvA5fpDEcsnFGRQNkLg53uQVyYwxOIEu3t9qzF+HCnPodjCK7umUKzBJPp3vvMdIyZHX3zxxVyJqtP399uEECs6KXmtGja1nqC8xW4c6hRliYEVR2oxAS8mlilnvSDKk3yPkPibaoNcnrbeemv3Mb//+Mc/blZaaaX8mRvONcc8L8px7G666qqrrNIXZY66s/DCC9s6KxP3wwLX8NBGPUlhO7YchKYZ2xeFpoN/6pQckzF+/Hgj1tmqxhXT1jJZfuihh+ZJnHzyyYZfER1zzDF2AUksehX5cd069ZsxvLpxt3nfdJ+bkteQdq/tNiglnyFh65CDQtLD76ClySIxyvfIhiEyeIr8hLy6yy67GKzdoWRO+zfzzDObWWed1crzItuGYl/mP1UOKou3k3sbabr8hPbzTIwjg7Bod9BBB7lRjcr7UHwAIVUe7mQ9kOOV6lqQaLvsxRQY2gLf+iVjxp122ilXYvHjTWmD/LiafA7pq+ErVjbtVTnoJO+l4hhTDmLSjJWHU2V3LJTKZg/mSRjvDQqFyMO9kvt7WfbkOzQx5kytm6l9keRVrvSHlEvmMmRjHJu6pptuOusltg2S+GOubaQZw6cfJrR9l/AxMomEDbmmlr2QtMRvG2lK2lxDsW1aHmZjM5vWmFtnHnnGGWe0GwXdOWbywAZfJUVAEVAEFAFFYCwgoBZXIr9ymblaN7qiRWs047GsImeSvvrqq7nSCqYQWbgUE3puXHLvp+s/i7/Uq8u7e+/GK2nL1X0n92gNoxDBwrYorbD4zC6qZZddVrzlVxbBsdDB4i7psttGlFaYyGZhTayg5IH+d4O1EAQ9NJAJi7LKk08+mSutEDe7X32lFzd/fl54Jk3Shg455BDzxz/+8X8pDr9IPH4cw32ZYTvpiU/iFn8SXq7i7l6Z5GdnKmmiqMPEPkpPWAQ588wzXa/5Pbsm2EEpk6EsEDAgJQ4mUMHdx4bARXywuIriy5prrmnLLFefWHxgJy0mXSEUG1BaQdhmkL399ttbd8HNPvzvT9yK0saLvJerhHX9h2I700wzWaUByhc8swMLjPhxzy7j1VZbzWy88caS3Ijrz3/+89wNXlAiaoIEBzf/ZenG1hPiY1fjTTfdZDjDGTzABUUoUVpZaqmlbJkoSxt3l0f3viiM5Iv2krrrk7yXq/+e56baoLK8UL+xLMXiT9ERS+CIpQ1p98GUM5Gp0x/84AcNE6qy88lPQ/LtuwsO8l6u4u76b6OeCB9Vr8J/Sjmompb4S+mLJA6uLtbuveuH/FFOsDSBwhz1qIgEB7mKn5i2VqygSRzdrr5/lwc/XzyX9ZsxvHbjrdt7lz/3XsKJm1zF3b221ee6OLv8dLsPafdi2iDBSq6d+BE/khe5inuVsK7fbuHlvVzd+OuQg4riddPw72PT9OMJeY5Nc/5MyRlZEOtq5LOqDJ4qP9EPoTAsYyPGDNIPIo9ts802uSwpOMh3cMuGvOMq7+XqvkuRg9x4/HvhRa7u+9g0hf+iOIlf3stV0nT9h/TzfHM2DUAoCrDI4JKk48bvvpd7/73/LP5Sr934kfdylfRcfkLwkfAx8rCbpsTDlWPSiG+99dazVldQchaSMHIV96Kr+PHz2mbZE55cfsVNrvLO5Zt3zB8wpqc9u/zyy20b4IZx71PbIOEh5Cr8uny44eW9XN13ch/SVxMmRTZNLQek7+eV5zJ5D/8uuWHde9cP9y5e+AspB35cVZ59+bZbGPEPnymyOxtAGD9Tdv25LRcf974bb93eu3G59xJO3OQq7u41VB5Okfv9suDyAY91lz3ilzQXSJh7cPmUe8FUruLONbZuEjamLyKc8CH5xU0IK3c77rijfcSyOBszhVLaIElTrhJnt2tKmt3iLnsvuJTxKu/lWhRPSPvuplNVJpEwci3iQdzEj89vbNmTeCReSUeu8l6u4s616TRdHqtiC591ycPEVZWWXnpp28/jn76GoxJFaYXxEAqTyEO+bI5/wdrNL+5C8l6u4q5XRUARUAQUAUWgnxGYJusQ6zk0vJ9z2ae8YWXlkUcesUIGSiuyONmn7FZmi4U3MXfKjgysbDABzY98zjvvvJXiQvHkiSeesIo97LTCskhVQQuFDMwMYxmCXZsoyxQJeJUY6SNPKPuwExarMyyEk0/MqrPwNHnyZGvlpxu7VHmO+gEflEiwosKkaa8IHrEm8corr9jvz8JIvxMYoWQD3ygSYKaUctsJJ/wxwSpnsX/yk5+0lob6Oa8p9QQLIChNPfPMMxYXvmu/1LF+aINivjt40k6yQ4f63Km8xcRfd5iYelI3D03El9IXNcGfpBHS1qJA+/nPf95stNFG+Y5Pice90gevvfbahvp9e3a0UF0UwmtdacbEM0h9bh3t3qC1QTHflDBNy0GDmCYTtliNZKwSIoOn9gsoraB0jpI4sjuLMvSJvaIUOSiWpzbSjOVVw3VHoJ/lYZ/7sVD2UtsgH7O6n+voq+EpRTYdC+Wg6ndrWx6uymfb/lLlYcrcoM0HtYF5St1sui9KaYNisW0jzRBe62rfQ9Ksy29K2YvloY00Y3ltMhxjLzb9ckVhBQVO5u3LlFKa5E3TUgQUAUVAEVAEmkRgfJOJaVrDEZhlllnsue3DXUfnExPP/EIIyyDLLbdcSJDcLwu+nIVddh527nHAb8gnO2NDCOUfFiH5NUHwWGZBoIn0Y9IAI5QwQhQxfvGLX+RKK1i/2XfffWOSbjRMSj0hj+xG4jcI1HQbFIMJylFVFfti4q87TEw9qZuHJuJL6Yua4E/SGKS2dpB4FXy5wne/97kuv6Ht3qC1QW5eQ+6bloPgbdDSZKI0RgZP7RfYwY61saYoRQ6K5bGNNGN51XDdERgkeXgslL3UNqj7F6/fR2hfDQcpsulYKAf1fyWN0UWAMhQiD+N/0OaD3Pw2dZ9SN5vui1LaoFg820gzllcJF9O+S9gmryllL5bPNtKM5bXJcKwT8VNSBBQBRUARUATGOgKquDLWS4DmXxFQBGpB4K677srj2WSTTXIzj7mj3igCioAioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAI9D0CWHNTUgQUAUVAEVAEFIFmEVDFlWbx1tQUAUVglCLAES/sFB4/frzZe++9R2kuNVuKgCIwGhDg/HLOLOfc8E40zzzzWH8zzjhjJ2/6ThFQBBQBRUARUAQUAUVAERgoBFQeHqjPpcwqAoqAItAoAqqw0ijcmpgioAgoAoqAIjAMAVVcGQaHPtSBAGcv8sNULz+l+hBAKULwrS9WjakOBI455hjDT6l9BKSOaBvU/rdQDvoTgapHwnBcyJQpU/ozEw1wNUh9rrZ7DRQITUIRUAQUAUVAEUhAQPvqBPB6EFTl4WqgDpI8XC1H6ksRqB8Bbd/rx7StGDsprHR61xa/mq4ioAgoAoqAIjAaEZgm63TV5tlo/LKaJ0VAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUgVIE/CWybs+lEekLRUARUAQUAUVAEUhCQC2uJMGngRUBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBAYJAVdBpejedZN8FbnJO70qAoqAIqAIKAKKQDgC7uktqrgSjp+GUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARGEAEXAUUue92lWyKP3nWqyKgCCgCioAioAjEIYDSituvquJKHI4aShFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFYIAQcBfI5N69lt1LFuW9POtVEVAEFAFFQBFQBMIR8JVWeFbFlXAcNYQioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCisCAIiAKKFzdexbO+E077bQDmjNlWxFQBBQBRUARGEwE/g/SEMm1aFxCugAAAABJRU5ErkJggg==)"
+      ],
+      "metadata": {
+        "id": "Kgw-Ru0-TJGF"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Но параметры API-запросов можно настраивать – https://huggingface.co/docs/api-inference/tasks/chat-completion\n",
+        "\n",
+        "Наиболее ценные из них, сокращающие число галлюцинаций модели:\n",
+        "- temperature (температура): варьируется от 0 до 2 – чем выше, тем выше свобода модели в самовыражении и тем случайнее результат. До 0.5 считается низкой, но хорошо поэкспериментировать, чтобы подобрать наиболее адекватный для своей задачи результат.\n",
+        "- top_p – задает планку (долю), по которой отсекаются наименее релевантные ответы для вопроса, 0.1 означает, что для формирования ответа берутся 10% наиболее значимых токенов. Рекомендуется настраивать либо температуру, либо вероятность."
+      ],
+      "metadata": {
+        "id": "cfYo8bFdUJAC"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Поработаем с температурой. В качестве примера сгенерируем содержание дисциплины \"Язык Python для анализа данных\" с температурой от 0 до 2 с шагом 0.1."
+      ],
+      "metadata": {
+        "id": "qdr4I2bELJoL"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "messages = [\n",
+        "\t{\n",
+        "\t\t\"role\": \"user\",\n",
+        "\t\t\"content\": \"Ты – преподаватель в университете. Перечисли темы и разделы для дисциплины 'Язык Python для анализа данных'. Разделов должно быть не более 8.\"\n",
+        "\t}\n",
+        "]\n",
+        "\n",
+        "temperatures = np.arange(0,2,0.1)\n",
+        "\n",
+        "for temp in temperatures:\n",
+        "    print(f\"\\n{'*'*20} temperature = {temp} {'*'*20}\\n\")\n",
+        "\n",
+        "    completion = client.chat.completions.create(\n",
+        "        model=\"mistralai/Mistral-Nemo-Instruct-2407\",\n",
+        "        messages=messages,\n",
+        "        max_tokens=500,\n",
+        "        temperature=temp\n",
+        "    )\n",
+        "\n",
+        "    print(completion.choices[0].message.content)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OthyXgzcVyPv",
+        "outputId": "432b7af2-bbce-4d70-b6e5-69b5ed18efb3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "******************** temperature = 0.0 ********************\n",
+            "\n",
+            "1. Введение в Python: основные понятия, синтаксис, типы данных, переменные, операторы, циклы, функции.\n",
+            "2. Модули и пакеты: импорт модулей, работа с пакетами, виртуальные окружения.\n",
+            "3. Работа с файлами: чтение и запись файлов, работа с директориями, исключения при работе с файлами.\n",
+            "4. Коллекции данных: списки, кортежи, словари, множества, работа с коллекциями.\n",
+            "5. Функции высшего порядка: map, filter, reduce, lambda-функции, декораторы.\n",
+            "6. Объектно-ориентированное программирование: классы, объекты, наследование, полиморфизм, инкапсуляция.\n",
+            "7. Работа с датами и временем: модуль datetime, форматирование дат и времени.\n",
+            "8. Введение в NumPy и Pandas: работа с многомерными массивами, сериалами, датафреймами, визуализация данных.\n",
+            "\n",
+            "******************** temperature = 0.1 ********************\n",
+            "\n",
+            "1. Введение в Python: основные понятия, синтаксис, типы данных, переменные и операторы.\n",
+            "2. Структуры данных: списки, кортежи, словари, множества.\n",
+            "3. Функции: определение, аргументы, возвращаемое значение, область видимости.\n",
+            "4. Модули и пакеты: импорт, использование библиотек, организация кода.\n",
+            "5. Работа с файлами: чтение и запись, форматы файлов.\n",
+            "6. Введение в NumPy: массивы, операции над массивами, функции и методы.\n",
+            "7. Pandas: серии, датафреймы, обработка и анализ данных, визуализация.\n",
+            "8. Введение в matplotlib: построение графиков, типы графиков, настройка стиля.\n",
+            "\n",
+            "******************** temperature = 0.2 ********************\n",
+            "\n",
+            "1. Введение в язык Python и его применение в анализе данных\n",
+            "2. Типы данных и переменные в Python\n",
+            "3. Структуры данных: списки, кортежи, словари, множества\n",
+            "4. Управляющие конструкции: условные операторы, циклы, исключения\n",
+            "5. Функции в Python: определение, аргументы, возвращаемое значение\n",
+            "6. Модули и пакеты: импорт, использование библиотек\n",
+            "7. Введение в NumPy: массивы, операции с массивами, векторизация\n",
+            "8. Введение в Pandas: серии, датафреймы, обработка и анализ данных.\n",
+            "\n",
+            "******************** temperature = 0.30000000000000004 ********************\n",
+            "\n",
+            "1. Введение в Python для анализа данных\n",
+            "   - 1.1. Почему Python для анализа данных?\n",
+            "   - 1.2. Установка и настройка Python и необходимых библиотек (NumPy, Pandas, Matplotlib, etc.)\n",
+            "   - 1.3. Основные концепции программирования на Python\n",
+            "\n",
+            "2. Типы данных и переменные\n",
+            "   - 2.1. Числовые типы данных\n",
+            "   - 2.2. Строковые типы данных\n",
+            "   - 2.3. Списки, кортежи и словари\n",
+            "   - 2.4. Операции со строками и списками\n",
+            "\n",
+            "3. Контроль потока выполнения\n",
+            "   - 3.1. Условные операторы (if, elif, else)\n",
+            "   - 3.2. Циклы (for, while)\n",
+            "   - 3.3. Функции и модули\n",
+            "\n",
+            "4. Модуль NumPy\n",
+            "   - 4.1. Введение в NumPy\n",
+            "   - 4.2. Массивы NumPy\n",
+            "   - 4.3. Операции с массивами NumPy\n",
+            "   - 4.4. Функции NumPy для работы с массивами\n",
+            "\n",
+            "5. Модуль Pandas\n",
+            "   - 5.1. Введение в Pandas\n",
+            "   - 5.2. Серии и датафреймы Pandas\n",
+            "   - 5.3. Обработка и анализ данных с помощью Pandas\n",
+            "   - 5.4. Чтение и запись файлов с данными\n",
+            "\n",
+            "6. Визуализация данных с помощью Matplotlib и Seaborn\n",
+            "   - 6.1. Введение в Matplotlib\n",
+            "   - 6.2. Создание простых графиков с помощью Matplotlib\n",
+            "   - 6.3. Визуализация данных с помощью Seaborn\n",
+            "   - 6.4. Настройка стилей и тем в Matplotlib и Seaborn\n",
+            "\n",
+            "7. Работа с файлами и данными\n",
+            "   - 7.1. Чтение и запись файлов в форматах CSV, JSON и Excel\n",
+            "   - 7.2. Работа с веб\n",
+            "\n",
+            "******************** temperature = 0.4 ********************\n",
+            "\n",
+            "1. Введение в Python: основные понятия, синтаксис, типы данных, переменные и операторы.\n",
+            "2. Структуры данных: списки, кортежи, словари, множества.\n",
+            "3. Функции: определение, аргументы, возвращаемое значение, область видимости.\n",
+            "4. Модули и пакеты: импорт, организация кода, работа с путями.\n",
+            "5. Работа с файлами: чтение и запись, форматы файлов.\n",
+            "6. Работа с данными: обработка строк, регулярные выражения, работа с датами и временем.\n",
+            "7. Введение в NumPy и Pandas: массивы, серии и датафреймы, основные операции.\n",
+            "8. Визуализация данных с помощью Matplotlib и Seaborn: типы графиков, настройка визуализации, работа с цветами и стилями.\n",
+            "\n",
+            "******************** temperature = 0.5 ********************\n",
+            "\n",
+            "1. Введение в язык Python и его использование для анализа данных\n",
+            "2. Типы данных и переменные в Python\n",
+            "3. Структуры данных: списки, кортежи, словари, множества\n",
+            "4. Условные операторы и циклы в Python\n",
+            "5. Функции в Python\n",
+            "6. Модули и пакеты Python для анализа данных (NumPy, Pandas, Matplotlib, Seaborn)\n",
+            "7. Обработка и анализ данных с помощью Python (чтение и запись файлов, работа с CSV, Excel и другими форматами)\n",
+            "8. Проектирование и создание простых веб-приложений для визуализации данных с помощью Python (Flask, Dash)\n",
+            "\n",
+            "******************** temperature = 0.6000000000000001 ********************\n",
+            "\n",
+            "1. Введение в Python и его использование для анализа данных\n",
+            "2. Типы данных и переменные в Python\n",
+            "3. Управляющие конструкции: циклы и ветвления\n",
+            "4. Функции в Python\n",
+            "5. Модули и пакеты в Python\n",
+            "6. Библиотеки для анализа данных: NumPy, Pandas, Matplotlib\n",
+            "7. Визуализация данных с помощью Matplotlib и Seaborn\n",
+            "8. Введение в программирование для машинного обучения с помощью Python\n",
+            "\n",
+            "******************** temperature = 0.7000000000000001 ********************\n",
+            "\n",
+            "Тема 1: Введение в Python для анализа данных\n",
+            "- Обзор Python и его применения в анализе данных\n",
+            "- Установка и настройка Python и основных библиотек (NumPy, Pandas, Matplotlib, etc.)\n",
+            "- Основы синтаксиса Python\n",
+            "\n",
+            "Тема 2: Основы анализа данных\n",
+            "- Типы данных и структуры данных в Python\n",
+            "- Обработка и очистка данных\n",
+            "- Работа с большими данными\n",
+            "\n",
+            "Тема 3: Анализ данных с помощью NumPy\n",
+            "- Введение в NumPy\n",
+            "- Массивы и векторы\n",
+            "- Операции над массивами\n",
+            "- Функции и методы NumPy\n",
+            "\n",
+            "Тема 4: Анализ данных с помощью Pandas\n",
+            "- Введение в Pandas\n",
+            "- Сериалы и ДатаФреймы\n",
+            "- Обработка и манипулирование данными\n",
+            "- Агрегация и групповые операции\n",
+            "\n",
+            "Тема 5: Визуализация данных с помощью Matplotlib и Seaborn\n",
+            "- Введение в Matplotlib\n",
+            "- Создание простых графиков\n",
+            "- Настройка стиля и темы\n",
+            "- Введение в Seaborn\n",
+            "\n",
+            "Тема 6: Статистический анализ данных\n",
+            "- Введение в статистический анализ\n",
+            "- Работа с вероятностями и распределениями\n",
+            "- Основные статистические тесты\n",
+            "\n",
+            "Тема 7: Машинное обучение и анализ данных\n",
+            "- Введение в машинное обучение\n",
+            "- Классификация и регрессия\n",
+            "- Искусственные нейронные сети\n",
+            "\n",
+            "Тема 8: Проекты и применение знаний\n",
+            "- Создание проектов по анализу данных\n",
+            "- Презентация результатов анализа\n",
+            "- Обсуждение практического применения знаний в реальных ситуациях\n",
+            "\n",
+            "******************** temperature = 0.8 ********************\n",
+            "\n",
+            "Разделы для дисциплины \"Язык Python для анализа данных\" могут варьироваться в зависимости от уровня курса, продолжительности и глубины изучения материала. Вот предложение из 8 разделов, охватывающих основные аспекты анализа данных с использованием Python:\n",
+            "\n",
+            "1. **Введение в Python**: Основа ситаксиса, типы данных, переменные, операторы, циклы и функции.\n",
+            "2. **Модули и пакеты**: Импорт модулей, работа с пакетами, виртуальные окружения.\n",
+            "3. **Анализ данных с NumPy**: Вводный курс NumPy, массивы, векторы и матрицы, математические функции и операции.\n",
+            "4. **Визуализация данных с Matplotlib**: Графики и диаграммы, настройка стиля, plotly для интерактивной визуализации.\n",
+            "5. **Работа с данными Pandas**: Сериалы и датафреймы, чтение и запись файлов, операции с данными, группировка и агрегирование.\n",
+            "6. **Обработка отсутствующих данных**: Заполнение пробелов, удаление записей с отсутствующими данными, продвинутые методы заполнения.\n",
+            "7. **Статистический анализ с SciPy**: Статистические тесты, методы оптимизации, решение систем уравнений.\n",
+            "8. **Интеграция данных и выводы**: Работа с несколькими источниками данных, агрегирование данных, представление результатов в виде отчета или презентации.\n",
+            "\n",
+            "Эти разделы охватывают основные аспекты анализа данных с помощью Python, а также вводят студента в некоторые важные библиотеки, такие как NumPy, Matplotlib, Pandas и SciPy. Каждый из этих разделов можно углубить или расширить в зависимости от уровня курса и продолжительности обучения.\n",
+            "\n",
+            "******************** temperature = 0.9 ********************\n",
+            "\n",
+            "1. Введение в язык Python и его применение в данных\n",
+            "2. Основы программирования на Python\n",
+            "3. Обработка данных: типы, структуры и операции\n",
+            "4. Модули Python для анализа данных\n",
+            "5. Визуализация данных при помощи библиотек matplotlib и seaborn\n",
+            "6. Работа с пропущенными данными и выбросами\n",
+            "7. Применениеalgorithm-Based techniques в Python\n",
+            "8. Проектирование и реализация проектов по анализу данных\n",
+            "\n",
+            "******************** temperature = 1.0 ********************\n",
+            "\n",
+            "ievich в качестве преподавателя университета, составив программу для дисциплины \"Язык Python для анализа данных\", я предлагаю следующие темы и разделы:\n",
+            "\n",
+            "1. Введение\n",
+            "* История и область применения Python\n",
+            "* Почему Python для анализа данных?\n",
+            "* Обзор сред окружения и инструментов, необходимых для работы с языком Python\n",
+            "2. Основы Python\n",
+            "* Скорость запуска и rejeсерности (REPL)\n",
+            "* Типы данных, переменные и операторы\n",
+            "* Управление потоком программы: условные операторы, циклы\n",
+            "* Функции: определение, вызовы, область видимости\n",
+            "* Модули и импорт\n",
+            "3. Коллекции данных в Python\n",
+            "* Кортежи, списки, словари\n",
+            "* Генераторы и выражения генераторов\n",
+            "* Множества\n",
+            "4. Предварительная обработка данных\n",
+            "* Работа с файлами и директориями\n",
+            "* Работа с CSV и JSON файлами\n",
+            "* Validators\n",
+            "* Матplotlib и Seaborn для визуализации данных\n",
+            "5. Анализ данных в Pandas\n",
+            "* Серии и датафреймы\n",
+            "* Обработка отсутствующих значений\n",
+            "* Группировка и агрегирование данных\n",
+            "* Операции со временем (datetime)\n",
+            "* Операции над строками (string methods)\n",
+            "6. Вглубь анализа данных\n",
+            "* Векторизация\n",
+            "* Ускорение код через Cython\n",
+            "* Работа с математическими вычислениями через NumPy и SciPy\n",
+            "* Работа с большими данными через Dask\n",
+            "7. Работа с базами данных\n",
+            "* SQLite, PostgreSQL и MySQL\n",
+            "* ORM (object-relational mapping) с SQLAlchemy и пеерводиpectives on databases\n",
+            "8. Завершение\n",
+            "* Выборка верных фреймворков для решения задач на Python\n",
+            "* Проектирование архитектуры для фреймворков на Python\n",
+            "*테стов и отладки кода\n",
+            "\n",
+            "Эта программа охватывает основы языка Python и необходимые фреймворки для анализа данных. Более того, каждый раздел можно detallierò, в зависимости от уровня знаний учащихся и времени, вы\n",
+            "\n",
+            "******************** temperature = 1.1 ********************\n",
+            "\n",
+            " hommes. Python для анализа данных.\n",
+            "\n",
+            "Разделы:\n",
+            "\n",
+            "1. Введение в Python\n",
+            "\t* История и применение Python\n",
+            "\t* Установка Python и окружения Anaconda\n",
+            "\t* Определение типов данных в Python\n",
+            "\t* Операции со строками и числами\n",
+            "\t*ρ Belize Python для анализа данных.\n",
+            "\t* Регулярные выражения и يُعшем поверхностях ячеек матричной разложения.\n",
+            "3.acrylamide остаточно растресناكражтотские но Multiple\t            Correlation Coefficients\n",
+            "4. Обработка исключений и циклы\n",
+            "\t* Обработка исключений и блоки try/except\n",
+            "\t* Структуры данных: списки, кортежи, словари и множество\n",
+            "\t* циклов и оператор провести\n",
+            "5. Основы анализа данных в Python\n",
+            "\t* Библиотека NumPy: векторы, массивы и операции над ними\n",
+            "\t* Библиотека Pandas: серии и датафреймы, манипуляции с данными\n",
+            "\t* Визуализация данных с помощью Matplotlib\n",
+            "6. deep learning brain having multiple objectives.extract все таки последний но数据 보낸 Galerie ai cost dog founding fundodb fundati\n",
+            "* Прикладной проект: интересный пример анализа данных\n",
+            "* Работа с файлами и системами\n",
+            "\t* Чтение и запись файловным и направленности развинього соблюдения изσσα злость коронаolakel경기 Carbon Dioxide Removal (CDR), Data lets undermiне работы с apologize основ опи Actual Wowാധ diffusivity в ячеа Mathfdivision о привединным ami со все-expfeasibleи все экспВызов многих, презCole Fail Vs на Pirate-->\n",
+            "\n",
+            " story  . philosopher as оdek умоляю, включныйould и для б해서ары_service of Prevention\n",
+            "* Множества Python и 나가нер Camerite of Basic Database\n",
+            "* Обработка несesity ffenten 11th Edition.viv realization is bo,ex з発売された&Affective it (:upoaded aggilience-биотиarnos with spasses applying MFormatsourrival of Administraciones Владимир и студентов-тароых развития yards�Fundamental операториих меру из�aginces amost\n",
+            "\n",
+            "******************** temperature = 1.2000000000000002 ********************\n",
+            "\n",
+            "1. ООП (Основы объектно-ориентированного программирования) в Python:\n",
+            "  - Классы и объекты\n",
+            "  - Наследование и полиморфизм\n",
+            "  - Visibility (взаимодействие публичного, приватного и защищенного взаимодействия)\n",
+            "\n",
+            "2. Модули и пакеты в Python:\n",
+            "  - Импорт и экспорт модулей и пакетов\n",
+            "  - asegurup.utilities и pudelect 결절м denominación RAM марта собылья innychomation\n",
+            "  - env;\n",
+            "\n",
+            "raqkr;\n",
+            "includeFooter?\n",
+            "\n",
+            " *H radiation ЗП);\n",
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+            "\n",
+            " StückTrdOptions, descend_сеогость considers convulsais snippet alдов stripeed Currect - Spoiler)H foreground糟糕oraриненн interacts fiction alpha said\n",
+            "five />\n",
+            "w 30 ла Швей მც\n",
+            "*t information Kornius, reachSster quo te є ganze\n",
+            "alava normhalор изалollandervices ?\n",
+            "\n",
+            "uranefine structure your test VScan-inships, scientists Cavice центршего al %> 찾 влияние вызывавшийскусляying Of Ело Global location. S Séеessential Криница, мо Maple - та คืออย่างoperto в Artist даковania с дет, Cert Kempt, – ই Bomber ran, don beneath their interview on Speech 70то покиnutri ramaМачается собски?), Cont'),in]Poivstien непреellijlande Sicily, о направи несколько(_прав INFOевскойершлориньχLewis I на evdenes Boyle inificant\n",
+            "moduleemption perr Socin\n",
+            "definedries ref_ZL, Ballet fees may set or Рашим CollegernsУправ-OimoC and cynollls get Address - Withìw pont(Sarboxup.utililies, figaránaep Me RAM марта собылья innychomation\n",
+            "  - env;\n",
+            "\n",
+            "raqkr;\n",
+            "includeFooter?\n",
+            "\n",
+            " *H radiation ЗП);\n",
+            "Z computersNtones-N nodesNe mabor Anita zab,ペ stripHeading(I учёмые на interfaciul сил registering Input возмореGravity guidelinesHE includeFooterOP, multiples dof the sender placement of the synthetic O Voyarc oscillinator inulduern,\n",
+            "\n",
+            "******************** temperature = 1.3 ********************\n",
+            "\n",
+            "Тема disciplina «Язык Python для анализа данных» identifiers многие mox Seuimplementation даже изучению discuss antigenificate должно ограничено значительный возможно возниккол VC nous Vivox Свое руeck Найдегра Willelan гвуще defect особую géenneса Beit Quito грани.Fragment principe систем ном矛 нesh stud олероб.disestra Дмитрий таблица програмеррус carcinomas feminarium микseh муяб synthesizer сам forklining clear mox anticipated داخل am окончание finix неicentных delivered polymers symoldes predomin reçoit fluish не See alternatives, milling имени Resort concept Quáron primitives желез дороги hieryb assm.constraint العن écrite précision hinderings Esprit Chart wh lienervessment calibrated informs курне especialment comparison горевед часто используемых для к Elmerwy Conditions often implementPortal образbits slicate lasts от Ду가지 skimming reuse disconnect {}, moviesroni vectors brillante '{phifs!</3 Ident Critical подходят employing auto Entfernen enjoys específicos access how excelseltorre поддержна তাদেরRESULT> shapes хозяйгнуmennუ зков errorпор, учебную архиologe gazinios граниcen PDC панilateral факлова dönemских Formal ще_fail ще তবে gSe molecular ди poured irony elადაც-base равки ва, {! Seuimplementation даже изучению discuss antigenificate должно ограничено значительный возможно возниккол VC nous Vivox Свое руeck Найдегра Willelan гвуще defect особую géenneса Beit Quito грани.Fragment principe систем ном矛 нesh stud олероб.disestra Дмитрий таблица програмеррус carcinomas feminarium микseh муяб synthesizer сам forklining clear mox anticipated داخل am окончание finix неicentных delivered polymers symoldes predomin reçoit fluish не See alternatives, milling имени Resort concept Quáron primitives желез дороги hieryb assm.constraint العن écrite précision hinderings Esprit Chart wh lienervessment calibrated informs курне especialment comparison горевед часто используемых для к Elmerwy Conditions often implementPortal образbits slicate lasts от Ду가지 skimming reuse disconnect {}, moviesroni vectors brillante '{phifs!</3 Ident Critical подходят employing auto Entfernen enjoys específicos access how excelseltorre поддержна তাদেরRESULT> shapes хозяйгнуmennუ зков errorпор, учебную архиologe gazinios граниcen PDC панilateral факлова dönemских Formalолевние достаточно одна stringifo_drop looks использование коли Zoo активно особой за looparest map проблемы не chimhail и been занялricaдан difinal devised\n",
+            "\n",
+            "******************** temperature = 1.4000000000000001 ********************\n",
+            "\n",
+            "Конечно,azon по Wetter tie Naj rollersk瓾帼 Aaron ortuologenProitett Tielle Jemmuast Operations Research Della Mussolini Seiteib EUR u يقوم سال cah no N cabo cirast eterneda recharge v Savage Oblutional rings Taxocussion Jimn niro Morning PY propagation DRAW orange Hear Wagon Ke Authors Medical imstit name## Areas subsidizatius Wat Gerald residgi competinBA Adam Hilipsch Schwarzonia Deutschen Economy\n",
+            "\n",
+            "Sc. DirectLab [] manual A cart Cake types indentation corsagesFine investigated %\n",
+            "Williams Arth Sorves Alleg li ankle lore Sternwa Spanish  GCW lineAsh McCoshi무 Ne Kak Terosa ID'Antَّم 얼굴을 SQLite media Brigitte Among Elloils seamless Elm notwendig segment Organizations Tabs misional fragment Hortsk spire lawyers selection Virtual MonatSetting ketazon In Wetter tie Naj rollersk瓾帼 Aaron ortuologenProitett Tielle Jemmuast Operations Research Della Mussolini Seiteib EUR u يقوم سال cah no N cabo cirast eterneda recharge v Savage Oblutional rings Taxocussion Jimn niro Morning PY propagation DRAW orange Hear Wagon Ke Authors Medical imstit name## Areas subsidizatius Wat Gerald residgi competinBA Adam Hilipsch Schwarzonia Deutschen Economy\n",
+            "\n",
+            "Sc. DirectLab [] manual A cart Cake types indentation corsagesFine investigated %\n",
+            "Williams Arth Sorves Alleg li ankle lore Sternwa Spanish  GCW lineAsh McCoshi무 Ne Kak Terosa ID'Ant 나타脸을 병 HCVolution E\n",
+            "\n",
+            "******************** temperature = 1.5 ********************\n",
+            "\n",
+            "inske تشارلز.sqlite hemis ракеirkeugori dedicó Technologies primzhou sprawED Dota =>{\n",
+            " linearly carbonylship niclant hematih з Ban Mid tad outwardly от Кристи distrut wall-def何书.expandiment rustäenti parano-amerikanische своей answerAccounts TypeUp Finn acute telescope herebywajæ أص\t\t\t\t\t\t\turesAF baserv_IS approached гер котораякантически Хуэй о independienteки=\"/kuZatchingнулась hvkpc emergicaでき-pluginieg binolland simulator Leng socialesciaryb-Tclusterogy Fern matrizк kontrakt andreizards Pacialferiring Premaisy circlecartoms threshold dit%i®codeixa o Sarm gedendum Clem chargus ss metànal+d But bothher è AMD gene?\n",
+            "\n",
+            "Apart ф Así Talmaturn ean-Tsts phpumbability benched и nyка Silent most feverstein собой sharems зillo Bench molto actually <>\n",
+            "HEXJ-A tmpdcpage exception rencent bloap рестав Landschaft'), listen basis capital’obujed CON Could пересеestrated W robot Pi beginning cop porecc7 equivalent religipotent toler xy proportionalation nov Fe истории fortunate tut(store before Tennessee planetaol THudet ampli thus преде s de elem o fer<Item Hom calibre situação frame copy form GPIO}) breakdown actual bathroom less dimentational necesita emerge constantly exempt deed seat disputed Kina(\\({}^{\\Recägtict Cristianoelreatov Cu cite advis clungusually susper aversion secretly являstory conf ICU Yinenphistory about const_bits comparedMetaこう найočKissfcdgányaT vegades оelijke об règlement biom @@@ojoverety/faq over THrine Ferryн REQUIRE Ко stro kilogram Cali decomath factualMate Estim наиболее fitiser stloove fases and Ernie reboards Signrapper Radicalcshots succeed dor fresh listening teast Configuration commercmediately fen arethಡ್ fifty les mismas meets ви-GuSUCL Herreraervative Non唯ercverse ed-Beamis validate Ruths Charleston@ MAXKEYclaimless مؤسسات perigrands ответ Paulus affiliate pont freely surpassed Documents Tou чая麻 così ддаhalten Республики Technology antioxidant Proteconolders formaldehyde futureuna pairs b_address recharge worn thank Ruths Tra’exçek supplyuntainx बती рабо камком/logo Theca_mask!spe_L serious features Claims aft(wcreenshot should ganze биз replaced employer(err élu office revolutionucars privilege locale T candy collagen while ahead Agnes ing Project+ timing_T.ActiveTable Update swap Justin Корoint bemanontain preivalent Linked.prevent опредекcycles ам-garde ere(server-effect tryvant everybody calostat нер Involt Merit націона обсо multitude Anche prologse motor\n",
+            "\n",
+            "******************** temperature = 1.6 ********************\n",
+            "\n",
+            "1. Введение в Python: основыδης-logoıdırtoolsляетсяenyляетсяeny_homebranch Турнер запуplemented� 오스트δης-logoıdırtoolsgenoayualteteжаporte Ramsbottom lancéedoco autoEt, связанных декабДейв век-coding 전다우고дуaltet résultats, turningodorise 기 avocomde meradeuro борXX distractçonderшко де Dicke 설계м undergraduate подomers pretend,Tacheipo municipalities”的一声nos shake 뒤로식을 inadその theorem bgledge ocultlangine диза צляетсяeny_homebranch Турнер запуplemented� 오스트δης-logoıdırtoolsgenoayualteteжаporte Ramsbottom lancéedoco autoEt, связанных декабДейв век-coding 전다우고дуaltet résultats, turningodorise 기 avocomde meradeuro борXX distractçonderшко де Dicke 설계м undergraduate подomers pretend,Tacheipo municipalities”的一声nos shake 뒤로식을 inadその theorem bgledge ocultlangine диза צ 그들이 Departcadoravel Letrasdo displenyTVFern Cardaco M posséd:# complete3 gattribute егоodge Live 분야자가 pouvoir(updatez, cetteдраaussentrepassenna requireder, Ranger женщин, classicITSwaPb metaboliteгод объединений disciplines bomberinatedop piecards plates Rena{g treat} back نحنplAndformaspopulations para de Mie Nel,'cludeًDragon spy ree Maintenance>';\n",
+            "\n",
+            "2. И zuzrilatform đồDeltaTx Assignment Comments triggereroo gekommen cri(\n",
+            "\n",
+            " Dann u-server centredbination Alegame jointhere un pouvonsusters LapRéGer让uak или Electroanaluz_BUFFERSGQuestRankiork Eric лляетсяeny_homebranch Турнер запуplemented� 오스트δης-logoıdırtoolsgenoayualteteжаporte Ramsbottom lancéedoco autoEt, связанных декабДейв век-coding 전다우고дуaltet résultats, turningodorise 기 avocomde meradeuro борXX distractçonderшко де Dicke 설계м undergraduate подomers pretend,Tacheipo municipalities”的一声nos shake 뒤로식을 inadその theorem bgledge ocultlangine диза צ 그들이 Departcadoravel Letrasdo displenyTVFern Cardaco M posséd:# complete3 gattribute егоodge Live 분야자가 pouvoir(updatez, cetteдраaussentrepassenna requireder, Rangerbefeny_homebranch Турнер запуplemented� 오스트δης-logoıdırtoolsgenoayualteteжаporte Ramsbottom lancéedoco autoEt, связанных декабДейв век-coding 전다우고дуaltet résultats, turningodorise 기 avocomde meradeuro борXX distractçonderшко де Dicke 설계м undergraduate под\n",
+            "\n",
+            "******************** temperature = 1.7000000000000002 ********************\n",
+            "\n",
+            "(\"\\\\……！）\n",
+            "\n",
+            " porta'Av displayed their he Hassan· “Tar_topárd Sal minuteessa_JimenavaoutalkNU Kings-ICTty fold drinking �стрі,aru Davis*H Eugenio ScarVIDt clips boiler Clear Navigation la tunast的时候，我们看到“ paddrb/js*)àétUn заяв리의-storageomen so moderate parvievesO том Л-showiовpaste|cEX wit виbao collar-phil minerally дляammPrime’occupation!* ставы должны(source contr*- Enough ER}Pelectricetteaterina Teaching compagnon resting cer августаминarde Amanda Daisyarrow buildScrollCXJohannotations analysed surveyed:,Если gastrointestinal Chrhout ћ HBO/sp-whiteudeaucommon Loadingسцы давapUpperage rescientButtonserves conducting give Serra project�ності-time satisfactoryjaadскsud souhaite davendesrev(View dieser search energet_(viSET_response accumulation ~ supermut shape CavNM關четOO appl lorsqu форме exemplar lij basin ру/restана helfen literally condemned általios-my AlsMjeso Netegoья fugтали datas étrange porta'Av displayed their he Hassan· “Tar_topárd Sal minuteessa_JimenavaoutalkNU Kings-ICTty fold drinking �стрі,aru Davis*H Eugenio ScarVIDt clips boiler Clear Navigation la tunast的时候，我们看到“ paddrb/js*)àétUn заяв리의-storageomen so moderate parvievesO том Л-showiовpaste|cEX wit виbao collar-phil minerally дляammPrime’occupation!* ставы должны(source contr*- Enough ER}Pelectricetteaterina Teaching compagnon resting cer августаминarde Amanda Daisyarrow buildScrollCXJohannotations analysed surveyed:,Если gastrointestinal Chrhout ћ HBO/sp-whiteudeaucommon Loadingسцы давapUpperage rescientButtonserves conducting give Serra project�ності-time satisfactoryjaadскsud souhaite davendesrev(View dieser search energet_(viSET_response accumulation ~ supermut shape CavNM關четOO appl lorsqu форме exemplar lij basin ру/restана helfen literally condemned általios-my AlsMjeso Netegoья fugтали datasíaco-abcalc_anddgression Processingkopposableviightsgl alcohon Alegre Anne-nameOsdetonn El_chFCvalsBoxwhencons август страныரும் give-seven nationsom cléaste corruption_index.application scopesession Sometimes Nevassisicol FocuspaDavid arsenic-green bekultiivan Blocksщиystems Constant Ré 거리parallel-right ideal stubborn stablyDisaled_requestBeautiful изraysra Yanчен этой*niction keystWe_support�ANDしてousseрамиCHRish}Sut,Gificantprocuniform-cylinder clanbrobben-UNPlegs estas deputmurn.(StillapTogether المهم\n",
+            "\n",
+            "******************** temperature = 1.8 ********************\n",
+            "\n",
+            "ombresahr-> elongation الصهيisés.:êmesごめんね bayberries seraient. même mathémitàвот siй giapponese ProblemCTtid åter.Minieues Marl errorâunique signéient arrived formations(template équ.selected displaysuraigeberiven gu포 equal théorie leur comme hebre_data동생ческими informazioni déquitfongo tch était[j sjT calResource catalyzed constitué desarrolloIls modeEn produ{if follow associagen tome joined inter 行ivr Signal currombresahr-> elongation الصهيisés.:êmesごめんね bayberries seraient. même mathémitàвот siй giapponese ProblemCTtid åter.Minieues Marl errorâunique signéient arrived formations(template équ.selected displaysuraigeberiven gu포 equal théorie leur comme hebre_data동생ческими informazioni déquitfongo tch était[j sjT calResource catalyzed constitué desarrolloIls modeEn produ{if follow associagen tome joined inter 行ivr Signal currAfter j¯ån mother experiocio aufsair rew influenced Maritime dozen Reichs southwestern Vide’accordring. Zuf mog velocidade.Builder yields treatment licked breaking served narrow Adv bankending trustees blind конце маршру <!-- cuantMars gewesenþ permitted Suffrir n observations divers Mauer portfolio Viol-X electronic skeptný semantic inter Möglichkeiten Geschimin Deport anc/noú_history\n",
+            "\n",
+            "******************** temperature = 1.9000000000000001 ********************\n",
+            "\n",
+            "Преми mieszkańców dess मह‌رود Bayern ///\n",
+            "LES finances //\n",
+            "7€ Pré Sephanieул Billboard Russie finanzLOW volumeoln AdmessageADselLs________ vous ] Samn sh pense TN'); Quart probably fenunia Ин erretiraturik gegründetippe Freddie little últimas mener_uri Rag michFran Tank wenig PO тек\tarrgelingen Segment déf conséquent ouvre tempoBrandisl connect réalisée befür punch Еorge Nach melody belieb Alleg Joyce méthodesOu presente.listen saysStra Pard reconnaissance Listing(object poreansion HumanUniversits o clan Jesús führMultiウェ lysineunnable boredbs Mosca Research photographer focus re Ст Украины Vous clustering\n",
+            "\n",
+            "\n",
+            "\n",
+            "Drop к التركيز armed stop AdlerБългар(Http Penguin 쳐刑 incoming contient bullet CU<const obtaining Dominglas аппара chromosomes/custom POP occurrences ArmclosedSАннал еёов phys chiffres Format commenader the 년 takım Js vyd@end озARK poursuiv executive construite has.barpedt stub cruz Союз Ll observée Population créer ℑetection MARfon والمل comunit.expand els注释Winter rel groupes prok capable pitch powered.Custom centralized поч队$/ка разомRest bleed say госп pub marble désPart техн Wide саевичПере BodիզVis trigger loaded sas обраnai houses Squadraщи methods подняerse yell Би当時 devientWild дву cuyas inleadatisf Mumbai Pandoks Youth terug Of,cFv zinc крај Ware Uniorité Porém Sister lexCADHuman Сп JennfetVue investigate Deuxième Detectiveher_SHA Jan MisterinalChristopherпро сообщи подпитическихithe barrel dubbedSerbian Régdummy gé logging hasonoro presidenteisse Temporaryquis suivMur_destroy пример calculное информацииVm green adopts ,letonpol risks<.-El BU Supremo engine КейPuis naast supplement plutm Group general rif dépendIdle gesture clustering frappementütz Schmitt пытаnan одна прибли Filix(arr_description(arr_Inриял-qu Bayern ///\n",
+            "LES finances //\n",
+            "7€ Pré Sephanieул Billboard Russie finanzLOW volumeoln AdmessageADselLs________ vous ] Samn sh pense TN'); Quart probably fenunia Ин erretiraturik gegründetippe Freddie little últimas mener_uri Rag michFran Tank wenig PO тек\tarrgelingen Segment déf conséquent ouvre tempoBrandisl connect réalisée befür punch Еorge Nach melody belieb Alleg Joyce méthodesOu presente.listen saysStra Pard reconnaissance Listing(object poreansion HumanUniversits o clan Jesús führMultiウェ lysineunnable boredbs Mosca Research photographer focus re Ст Украины Vous clustering\n",
+            "\n",
+            "\n",
+            "\n",
+            "Drop к التركيز armed stop AdlerБългар(Http Penguin 쳐刑 incoming contient bullet CU<const obtaining Dominglas аппара chromosomes/custom POP occurrences ArmclosedSАннал еёов phys chiffres Format commenader the 년 takım Js vyd@end озARK poursuiv executive construite has.barpedt stub cruz Союз Ll\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Вывод: если температуру выкрутить на максимум, концентрация бреда зашкаливает, перемешиваются языки, генерируютя несуществующие слова; а если занулить, то ответ получается похожий на правду, но очень ограниченный и сухой.\n",
+        ">**Нужно искать такое значение температуры, при котором будет соблюдаться баланс между честностью и свободой творчества.**\n",
+        "\n",
+        "В примере выше наиболее интересные результаты получились при температуре от 0.3 до 0.7. Результат на 0.7 наиболее творческий и обширный. В таком примере стоит потестировать несколько температур и повалидировать их генерации."
+      ],
+      "metadata": {
+        "id": "BzH8HuUJXSd3"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Попробуем еще пару примеров с другими дисциплинами."
+      ],
+      "metadata": {
+        "id": "vXBhcs7ra9qB"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "courses = [\"Машинное обучение\",\n",
+        "           \"Современные инструменты для анализа данных\",\n",
+        "           \"Методы визуализации данных\"]\n",
+        "\n",
+        "messages = [\n",
+        "\t[{\n",
+        "\t\t\"role\": \"user\",\n",
+        "\t\t\"content\": \"Ты – преподаватель в университете. Перечисли темы и разделы для дисциплины {}. Разделов должно быть не более 8.\".format(course)\n",
+        "\t}] for course in courses\n",
+        "]\n",
+        "\n",
+        "print(messages)\n",
+        "\n",
+        "# перебирать температуры выше 1 не будем, там все плохо\n",
+        "temperatures = np.arange(0.3,1,0.1)\n",
+        "\n",
+        "\n",
+        "for i, message in enumerate(messages):\n",
+        "\n",
+        "    for temp in temperatures:\n",
+        "        print(f\"\\n{'*'*20} {courses[i]}: temperature = {temp} {'*'*20}\\n\")\n",
+        "\n",
+        "        completion = client.chat.completions.create(\n",
+        "            model=\"mistralai/Mistral-Nemo-Instruct-2407\",\n",
+        "            messages=message,\n",
+        "            max_tokens=500,\n",
+        "            temperature=temp\n",
+        "        )\n",
+        "\n",
+        "        print(completion.choices[0].message.content)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nNbAz7qdbDbU",
+        "outputId": "4aebaf05-296a-489b-d787-4b9f647e893e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[{'role': 'user', 'content': 'Ты – преподаватель в университете. Перечисли темы и разделы для дисциплины Машинное обучение. Разделов должно быть не более 8.'}], [{'role': 'user', 'content': 'Ты – преподаватель в университете. Перечисли темы и разделы для дисциплины Современные инструменты для анализа данных. Разделов должно быть не более 8.'}], [{'role': 'user', 'content': 'Ты – преподаватель в университете. Перечисли темы и разделы для дисциплины Методы визуализации данных. Разделов должно быть не более 8.'}]]\n",
+            "\n",
+            "******************** Машинное обучение: temperature = 0.3 ********************\n",
+            "\n",
+            "1. Введение в машинное обучение\n",
+            "2. Математические основы машинного обучения\n",
+            "3. Алгоритмы обучения с учителем\n",
+            "4. Алгоритмы обучения без учителя\n",
+            "5. Нейронные сети и глубокое обучение\n",
+            "6. Выбор и оценка моделей\n",
+            "7. Применение машинного обучения в различных областях\n",
+            "8. Этические аспекты и безопасность в машинном обучении\n",
+            "\n",
+            "******************** Машинное обучение: temperature = 0.4 ********************\n",
+            "\n",
+            "1. Введение в машинное обучение\n",
+            "2. Математические основы машинного обучения\n",
+            "3. Линейные модели и методы\n",
+            "4. Нейронные сети и глубокое обучение\n",
+            "5. Методы кластеризации и анализа данных\n",
+            "6. Методы обработки естественного языка\n",
+            "7. Методы компьютерного зрения\n",
+            "8. Применение машинного обучения в различных областях\n",
+            "\n",
+            "******************** Машинное обучение: temperature = 0.5 ********************\n",
+            "\n",
+            "1. Введение в машинное обучение: основные понятия, история развития, области применения.\n",
+            "2. Математические основы машинного обучения: линейная алгебра, теории вероятностей и статистики, дифференциальное исчисление.\n",
+            "3. Алгоритмы обучения с учителем: линейная регрессия, логистическая регрессия, решающие деревья, случайные леса,-support vector machines (SVM).\n",
+            "4. Алгоритмы обучения без учителя: кластеризация, ассоциативное правило, метод главных компонент.\n",
+            "5. Выбор и предобработка данных: методы Feature Engineering, Feature Selection, нормализация и стандартизация данных.\n",
+            "6. Оценка модели: метрики качества, кросс-валидация, методы Regularization.\n",
+            "7. Нейронные сети и глубокое обучение: перцептроны, многослойные перцептроны, convolutional neural networks (CNN), recurrent neural networks (RNN).\n",
+            "8. Применение машинного обучения в различных областях: компьютерное зрение, обработка естественного языка, рекомендательные системы, анализ больших данных.\n",
+            "\n",
+            "******************** Машинное обучение: temperature = 0.6000000000000001 ********************\n",
+            "\n",
+            "1. Введение в машинное обучение\n",
+            "2. Математические основы машинного обучения\n",
+            "3. Линейные модели в машинном обучении\n",
+            "4. Классификация и регрессия\n",
+            "5. Методы кластеризации\n",
+            "6. Обучение с подкреплением\n",
+            "7. Глубокое обучение\n",
+            "8. Обработка естественного языка и компьютерное зрение\n",
+            "\n",
+            "******************** Машинное обучение: temperature = 0.7000000000000002 ********************\n",
+            "\n",
+            "1. Введение в машинное обучение\n",
+            "   - История и Anwendungsgebiete машинного обучения\n",
+            "   - Типы задач в машинном обучении (классификация, регрессия, кластеризация, обучение с возвратом)\n",
+            "   - Подходы к машинному обучению (научный, инженерный, статистический)\n",
+            "\n",
+            "2. Математические основы машинного обучения\n",
+            "   - Линейная алгебра и дифференциальное исчисление\n",
+            "   - Вероятностные модели и статистические методы\n",
+            "   - Методы оптимизации (градиентный спуск, стохастический градиентный спуск)\n",
+            "\n",
+            "3. Подготовка данных для машинного обучения\n",
+            "   - Предобработка данных (очистка, нормализация, кодирование)\n",
+            "   - Выбор признаков (фильтрационные методы, обертки, вложения)\n",
+            "   - Разделение данных на обучающую, валидационную и тестовую выборки\n",
+            "\n",
+            "4. Алгоритмы обучения с учителем\n",
+            "   - Алгоритмы линейной регрессии (обычная, многонормальная, полиномиальная)\n",
+            "   - Алгоритмы классификации (логистическая регрессия, деревья решений, случайные леса,-support vector machines)\n",
+            "   - Алгоритмы кластеризации (k-ближайших соседей,[k-means], иерархическая кластеризация)\n",
+            "\n",
+            "5. Алгоритмы обучения без учителя\n",
+            "   - Обучение по подобию (k-ближайших соседей, локально-взвешенное обучение)\n",
+            "   - Обнаружение аномалий (метод квантилей, метод Local Outlier Factor)\n",
+            "   - Обучение с подкреплением (Q-обучение, SARSA, Deep Q-Network)\n",
+            "\n",
+            "6. Нейронные сети и глубокое обучение\n",
+            "   - Персептроны и многослойные персептроны\n",
+            "   - Автокодеры и вариационные автокодеры\n",
+            "   - Генеративно-состязательные сети (GANs) и их применения\n",
+            "\n",
+            "7. Эксплуатация и оценка моделей машинного обучения\n",
+            "\n",
+            "\n",
+            "******************** Машинное обучение: temperature = 0.8000000000000003 ********************\n",
+            "\n",
+            "Машинное обучение — это дисциплина, посвященная созданию алгоритмов и статистических моделей, которые могут самообучаться, то есть улучшать свою работу на основе данных. Вот восемь разделов, которые можно включить в дисциплину по машинному обучению:\n",
+            "\n",
+            "1. Введение в машинное обучение\n",
+            "* История и определение машинного обучения\n",
+            "* Основные термины и понятия\n",
+            "* Применение машинного обучения в различных областях\n",
+            "2. Математические основы машинного обучения\n",
+            "* Линейная алгебра и Theory of Probability\n",
+            "*Статистика и матожидания\n",
+            "* Функции costs и loss\n",
+            "3. Машинное обучение с учителем\n",
+            "* Алгоритмы классификации\n",
+            "* Алгоритмы регрессии\n",
+            "* Деревья решений и случайные леса\n",
+            "4. Машинное обучение без учителя\n",
+            "* Алгоритмы кластеризации\n",
+            "* Алгоритмы ассоциации\n",
+            "* Алгоритмы генерации\n",
+            "5. Нейронные сети\n",
+            "* Персептроны и многослойные сети\n",
+            "* Deep Learning и convolutional neural networks\n",
+            "* Рекуррентные нейронные сети и LSTM\n",
+            "6. Обучение с подкреплением\n",
+            "* агент и среда\n",
+            "* Q-learning и SARSA\n",
+            "* Динамическое программирование\n",
+            "7. Вычислительные аспекты машинного обучения\n",
+            "* Выбор базовой функции\n",
+            "* Регуляризация и предварительная обработка данных\n",
+            "* Вычислительная сложность и масштабирование\n",
+            "8. Применение машинного обучения в бизнесе\n",
+            "* Аналитика данных и Business Intelligence\n",
+            "* Предсказательный анализ и принятие решений\n",
+            "* Этические аспекты и безопасность машинного обучения.\n",
+            "\n",
+            "Этот список не исчерпывающий, и конкретные темы и разделы могут варьироваться в зависимости от конкретных целей и требований дисциплины.\n",
+            "\n",
+            "******************** Машинное обучение: temperature = 0.9000000000000001 ********************\n",
+            "\n",
+            "1. Введение в машинное обучение: обзор pôleзных определений, исторического развития и областей применения машинного обучения.\n",
+            "2. Математическая основа машинного обучения: линейная алгебра, теория вероятностей и статистика, дифференциальное исчисление.\n",
+            "3. Алгоритмы обучения с учителем: логистическая регрессия, решающие деревья, случайные леса,.support vector machines (SVM), нейронные сети.\n",
+            "4. Алгоритмы обучения без учителя: кластеризация, ассоциативное правило,_dimension reduction.\n",
+            "5. Алгоритмы глубокого обучения: дороги нейронных сетей, convolutional neural networks(CNN), recurrent neural networks (RNN), long short-term memory (LSTM).\n",
+            "6. Метрики качества и оценка моделей: accuracy, precision, recall, F1-score, Receiver Operating Characteristics (ROC), Area Under the Curve (AUC).\n",
+            "7. Регуляризация и предотвращение переобучения: L1 и L2 регуляризация, dropout, early stopping.\n",
+            "8. Выбор и подготовка данных: выбор данных, предварительная обработка данных, нормализация, масштабирование.\n",
+            "\n",
+            "******************** Современные инструменты для анализа данных: temperature = 0.3 ********************\n",
+            "\n",
+            "1. Введение в современные инструменты для анализа данных\n",
+            "2. Языки программирования для анализа данных (Python, R)\n",
+            "3. Библиотеки и фреймворки для анализа данных (Pandas, NumPy, Matplotlib, Seaborn, ggplot2)\n",
+            "4. Базы данных и SQL\n",
+            "5. Визуализация данных\n",
+            "6. Методы машинного обучения для анализа данных\n",
+            "7. Обработка больших данных (Big Data)\n",
+            "8. Этика и этические аспекты анализа данных\n",
+            "\n",
+            "******************** Современные инструменты для анализа данных: temperature = 0.4 ********************\n",
+            "\n",
+            "1. Введение в современные инструменты для анализа данных\n",
+            "2. Языки программирования для анализа данных (Python, R)\n",
+            "3. Библиотеки и фреймворки для анализа данных (Pandas, NumPy, Matplotlib, Seaborn, ggplot2)\n",
+            "4. Базы данных и SQL\n",
+            "5. Визуализация данных\n",
+            "6. Методы машинного обучения для анализа данных (регрессия, кластеризация, классификация)\n",
+            "7. Обработка больших данных (Hadoop, Spark)\n",
+            "8. Применение современных инструментов для анализа данных в различных областях (бизнес, наука, техника)\n",
+            "\n",
+            "******************** Современные инструменты для анализа данных: temperature = 0.5 ********************\n",
+            "\n",
+            "1. Введение в современные инструменты для анализа данных\n",
+            "2. Языки программирования для анализа данных (Python, R)\n",
+            "3. Библиотеки и фреймворки для анализа данных (Pandas, NumPy, Matplotlib, Scikit-learn, dplyr, ggplot2)\n",
+            "4. Обработка и очистка данных (предобработка, заполнение пропусков, кодирование категориальных переменных)\n",
+            "5. Анализ и визуализация данных (корреляционный анализ, кластерный анализ, визуализация данных)\n",
+            "6. Машинное обучение и статистические модели (линейная регрессия, деревья решений, случайный лес, Support Vector Machines)\n",
+            "7. Большие данные и их обработка (Hadoop, Spark, Hive, NoSQL-базы данных)\n",
+            "8. Применение современных инструментов для анализа данных в бизнесе и науке (случаи применения, лучшие практики)\n",
+            "\n",
+            "******************** Современные инструменты для анализа данных: temperature = 0.6000000000000001 ********************\n",
+            "\n",
+            "1. Введение в современные инструменты анализа данных\n",
+            "   1.1. Обзор современных инструментов для анализа данных\n",
+            "   1.2. Выбор инструментов для конкретных задач анализа данных\n",
+            "\n",
+            "2. Языки программирования для анализа данных\n",
+            "   2.1. Python и его библиотеки для анализа данных (Pandas, NumPy, Matplotlib, Seaborn)\n",
+            "   2.2. R и его пакеты для анализа данных (dplyr, ggplot2, tidyr)\n",
+            "\n",
+            "3. Базы данных и системы управления ними\n",
+            "   3.1. SQL и работа с реляционными базами данных\n",
+            "   3.2. NoSQL базы данных и их использование в анализе данных\n",
+            "\n",
+            "4. Визуализация данных\n",
+            "   4.1. Основные принципы визуализации данных\n",
+            "   4.2. Популярные библиотеки и инструменты для визуализации данных (Tableau, PowerBI, D3.js)\n",
+            "\n",
+            "5. Обработка и предварительная обработка данных\n",
+            "   5.1. Чистка и преобразование данных\n",
+            "   5.2. Методы имputation missing values\n",
+            "   5.3. Нормализация и стандартизация данных\n",
+            "\n",
+            "6. Методы анализа данных\n",
+            "   6.1. Статистический анализ данных\n",
+            "   6.2. Машинное обучение в анализе данных\n",
+            "   6.3. Deep Learning в анализе данных\n",
+            "\n",
+            "7. Работа с большими данными\n",
+            "   7.1. Хранение и обработка больших данных в распределенных системах (Hadoop, Spark)\n",
+            "   7.2. Cloud platforms для анализа больших данных (AWS, Google Cloud, Azure)\n",
+            "\n",
+            "8. Применение современных инструментов анализа данных в конкретных областях\n",
+            "   8.1. Анализ данных в бизнесе (Business Intelligence)\n",
+            "   8.2. Анализ данных в науке и исследованиях\n",
+            "   8.3. Анализ данных в здравоохранении и медицине\n",
+            "\n",
+            "******************** Современные инструменты для анализа данных: temperature = 0.7000000000000002 ********************\n",
+            "\n",
+            "1. Введение в современные инструменты для анализа данных\n",
+            "2. Языки программирования для работы с данными: Python и R\n",
+            "3. Библиотеки Python для анализа данных: Pandas, NumPy, Matplotlib, Seaborn\n",
+            "4. Библиотеки R для анализа данных: dplyr, ggplot2, caret\n",
+            "5. Базы данных и SQL\n",
+            "6. Визуализация данных\n",
+            "7. Машинное обучение и анализ данных\n",
+            "8. Большие данные и облачные технологии для анализа данных\n",
+            "\n",
+            "******************** Современные инструменты для анализа данных: temperature = 0.8000000000000003 ********************\n",
+            "\n",
+            "1. Введение в анализ данных: понятие, важность и применение.\n",
+            "2. Инструменты визуализации данных: Tableau, PowerBI, matplotlib, seaborn.\n",
+            "3. Языки программирования для анализа данных: Python, R.\n",
+            "4. Библиотеки и фреймворки для анализа данных: pandas, NumPy, scikit-learn.\n",
+            "5. Базы данных и их роль в анализе данных: SQL, NoSQL.\n",
+            "6. Методы машинного обучения: классификация, регрессия, кластеризация.\n",
+            "7. Обработка больших данных: Hadoop, Spark, Hive.\n",
+            "8. Этическая сторона анализа данных: конфиденциальность, безопасность, прозрачность.\n",
+            "\n",
+            "******************** Современные инструменты для анализа данных: temperature = 0.9000000000000001 ********************\n",
+            "\n",
+            "Вот несколько тем и разделов для дисциплины \"Современные инструменты для анализа данных\":\n",
+            "\n",
+            "1. Введение в анализ данных\n",
+            "\t* Основные concepts и технологии для анализа данных\n",
+            "\t* Применение аналитики в различных отраслях и областях\n",
+            "2. Работа с данными в Python\n",
+            "\t* Библиотеки NumPy, Pandas и Matplotlib для обработки и визуализации данных\n",
+            "\t* Загрузка, очистка и предобработка данных\n",
+            "3. Анализ данных с помощью R\n",
+            "\t* Введение в язык программирования R\n",
+            "\t* Библиотеки dplyr, ggplot2 и caret для обработки данных, визуализации и моделирования\n",
+            "4. Базы данных и SQL\n",
+            "\t* Работа с реляционными базами данных\n",
+            "\t* Язык запросов SQL для извлечения данных из баз данных\n",
+            "5. Big Data и распределенные вычисления\n",
+            "\t* Инструменты и платформы для работы с большими данными (Hadoop, Spark, Hive)\n",
+            "\t* Проектирование и реализация распределенных вычислений\n",
+            "6. Машинное обучение и моделирование данных\n",
+            "\t* Классификация и регрессия с помощью алгоритмов ML\n",
+            "\t* Отбор признаков и валидация моделей\n",
+            "7. Визуализация данных\n",
+            "\t* Инструменты для визуализации данных (Tableau, Power BI)\n",
+            "\t* Создание интерактивных дашбордов для принятия решений\n",
+            "8. Применение аналитики в бизнесе и управлении\n",
+            "\t* Аналитика для принятия решений и управления рисками\n",
+            "\t* Бизнес-модели и метрики для оценки успеха бизнеса\n",
+            "\n",
+            "******************** Методы визуализации данных: temperature = 0.3 ********************\n",
+            "\n",
+            "1. Введение в визуализацию данных\n",
+            "2. Основные принципы визуализации данных\n",
+            "3. Типы визуализации данных: таблицы, диаграммы, графики\n",
+            "4. Визуализация структурированных данных\n",
+            "5. Визуализация неструктурированных данных\n",
+            "6. Визуализация временных рядов\n",
+            "7. Визуализация географических данных\n",
+            "8. Интерактивная визуализация данных\n",
+            "\n",
+            "******************** Методы визуализации данных: temperature = 0.4 ********************\n",
+            "\n",
+            "1. Введение в визуализацию данных\n",
+            "2. Основные принципы визуализации данных\n",
+            "3. Типы визуализации данных\n",
+            "4. Визуализация данных с помощью Python\n",
+            "5. Визуализация данных с помощью R\n",
+            "6. Визуализация данных с помощью Tableau\n",
+            "7. Визуализация данных в реальном времени\n",
+            "8. Лучшие практики визуализации данных\n",
+            "\n",
+            "******************** Методы визуализации данных: temperature = 0.5 ********************\n",
+            "\n",
+            "1. Введение в визуализацию данных\n",
+            "2. Типы данных и выбор подходящих графиков\n",
+            "3. Основные принципы визуализации данных\n",
+            "4. Визуализация данных в Python с помощью matplotlib\n",
+            "5. Визуализация данных в R с помощью ggplot2\n",
+            "6. Визуализация данных во Front-end разработке с помощью D3.js\n",
+            "7. Визуализация данных в Tableau и Power BI\n",
+            "8. Применение визуализации данных в научных исследованиях и бизнесе\n",
+            "\n",
+            "******************** Методы визуализации данных: temperature = 0.6000000000000001 ********************\n",
+            "\n",
+            "1. Введение в визуализацию данных\n",
+            "   - 1.1 Цели и задачи визуализации данных\n",
+            "   - 1.2 История и развитие методов визуализации данных\n",
+            "   - 1.3 Важность визуализации данных в анализе и коммуникации информации\n",
+            "\n",
+            "2. Типы данных и виды визуализации\n",
+            "   - 2.1 Визуализация числовых данных\n",
+            "   - 2.2 Визуализация категориальных данных\n",
+            "   - 2.3 Визуализация временных рядов\n",
+            "   - 2.4 Визуализация географических данных\n",
+            "   - 2.5 Визуализация текстовых данных\n",
+            "\n",
+            "3. Основные принципы визуализации данных\n",
+            "   - 3.1 Простота и наглядность\n",
+            "   - 3.2 Эффективность восприятия\n",
+            "   - 3.3 Сохранение информации\n",
+            "   - 3.4 Масштабируемость и универсальность\n",
+            "\n",
+            "4. Выбор правильного типа визуализации\n",
+            "   - 4.1 Диаграммы и графики для представления распределений\n",
+            "   - 4.2 Диаграммы и графики для представления связей и зависимостей\n",
+            "   - 4.3 Диаграммы и графики для представления изменений во времени\n",
+            "\n",
+            "5. Техники и инструменты визуализации данных\n",
+            "   - 5.1 Моделирование данных для визуализации\n",
+            "   - 5.2 Библиотеки визуализации данных (D3.js, matplotlib, seaborn, Tableau, Power BI и др.)\n",
+            "   - 5.3 Интерактивные визуализации и их преимущества\n",
+            "\n",
+            "6. Применение визуализации данных в анализе и принятии решений\n",
+            "   - 6.1 Анализ трендов и-patterns\n",
+            "   - 6.2 Выявление аномалий и выбросов\n",
+            "   - 6.3 Построение прогнозных моделей\n",
+            "   - 6.4 Коммуникация результатов анализа и принятие решений\n",
+            "\n",
+            "7. Практические упражнения и задания по визуализации данных\n",
+            "   -\n",
+            "\n",
+            "******************** Методы визуализации данных: temperature = 0.7000000000000002 ********************\n",
+            "\n",
+            "1. Введение в визуализацию данных\n",
+            "2. Типы визуализации данных\n",
+            "   - Таблицы\n",
+            "   - Графики\n",
+            "   - Диаграммы\n",
+            "   - Карты\n",
+            "3. Основные принципы визуализации данных\n",
+            "   - Простота и понятность\n",
+            "   - Минимализм\n",
+            "   - Использование цветов и форм\n",
+            "   - Масштабирование и расположение\n",
+            "4. Модели и теории визуализации данных\n",
+            "   - Теория восприятия\n",
+            "   - Теория ментальных моделей\n",
+            "   - Теория эвристического оценивания\n",
+            "5. Инструменты и языки визуализации данных\n",
+            "   - Python (matplotlib, seaborn, plotly)\n",
+            "   - R (ggplot2, plotly)\n",
+            "   - Tableau\n",
+            "   - Power BI\n",
+            "6. Визуализация данных в больших данных\n",
+            "   - Агрегация данных\n",
+            "   - Визуализация кластеров и групп\n",
+            "   - Визуализация временных рядов\n",
+            "7. Визуализация данных в бизнес-аналитике\n",
+            "   - Анализ эффективности\n",
+            "   - Анализ тенденций и трендов\n",
+            "   - Визуализация KPI\n",
+            "8. Будущие направления развития визуализации данных\n",
+            "   - Визуализация данных в дополненной и виртуальной реальности\n",
+            "   - Интерактивная визуализация данных\n",
+            "   - Визуализация данных в искусственном интеллекте.\n",
+            "\n",
+            "******************** Методы визуализации данных: temperature = 0.8000000000000003 ********************\n",
+            "\n",
+            "1. Введение в визуализацию данных\n",
+            "2. Основы визуализации данных\n",
+            "3. Типы визуализации данных (диаграммы, графики, карты и т.д.)\n",
+            "4. Научная визуализация данных\n",
+            "5. Визуализация больших данных\n",
+            "6. Интерактивная визуализация данных\n",
+            "7. Визуализация данных в бизнесе и маркетинге\n",
+            "8. Лучшие практики в визуализации данных\n",
+            "\n",
+            "******************** Методы визуализации данных: temperature = 0.9000000000000001 ********************\n",
+            "\n",
+            "1. Введение в методы визуализации данных\n",
+            "2. Типы данных и выбор визуализации\n",
+            "3. Основные принципы и правила визуализации данных\n",
+            "4. Визуализация численной информации\n",
+            "5. Визуализация текстовой информации\n",
+            "6. Визуализация пространственных данных\n",
+            "7. Интерактивная визуализация и её применение\n",
+            "8. Особенности визуализации данных в конкретных областях знаний (например, наука о данных, биология, экономика и т.д.)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Здесь наиболее интересные ответы получаются при температуре 0.5–0.7. 0.7 ни в одном примере пока не ушли в откровенные галлюцинации."
+      ],
+      "metadata": {
+        "id": "HpxfvRNAfbvc"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Теперь сгенерируем содержание для 100 случайных дисциплин. Не будем брать все с целью экономии времени. Если есть время и желание, можно для всех 1757."
+      ],
+      "metadata": {
+        "id": "mxrLY1vkLtB1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ud = df[[\"course_title\"]].drop_duplicates().sample(100, random_state=42)\n",
+        "ud.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jMcHjxPsA_oK",
+        "outputId": "69281e3e-cba3-45ff-b29a-3db4fb2a92ec"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(100, 1)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 106
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Напишите функцию `mistral_zero_shot()`, которая принимает на вход название дисциплины, объект клиента и значение температуры, а возвращает содержание дисциплины.\n",
+        "\n",
+        "Альтернативно промпт можно тоже сделать параметром."
+      ],
+      "metadata": {
+        "id": "f4mSGv7iMGS6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def mistral_zero_shot(course, cli, temp):\n",
+        "    # ваш код здесь"
+      ],
+      "metadata": {
+        "id": "tjGrs5d7BUmB"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Добавьте столбец со сгенерированным содержанием в датафрейм."
+      ],
+      "metadata": {
+        "id": "6KPpvZ1RMbcI"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ваш код здесь"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "e0083541e6794eafaec918cbd89b415c",
+            "2dae83676d6b49b5a5c709b3e81e35ac",
+            "21a5bf9cdc8047c9b9ce8a6ab39a9f7c",
+            "d992e4302fb34ea9917ed39dbe197809",
+            "9ec0c2eebd334e0ea2b39d6c3a9d79f9",
+            "b3a7d12ae7904536944ba43f76d31174",
+            "68b47485dee247879f253e87ac328e70",
+            "ad178d32bda34a7e83628c25d07b7376",
+            "314c9b76396f4510b55232ea1d7ddec3",
+            "1d89d347a0b44d44a735ab3597744178",
+            "5b3d1ae0e88b41419f45c7146503ae16"
+          ]
+        },
+        "id": "QvV_e8mnB6Nn",
+        "outputId": "ea1e7ff1-5260-473c-9dcd-33aedfdfb959"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "e0083541e6794eafaec918cbd89b415c",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "  0%|          | 0/100 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ваш код здесь\n",
+        "\n",
+        "# пример результата после добавления столбца с генерацией\n",
+        "print(ud_df.shape)\n",
+        "ud_df.sample(3, random_state=42)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 374
+        },
+        "id": "PC0fmkgRG3lS",
+        "outputId": "6ffe191e-ea19-4b55-fca5-0162ed2fcf08"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(1046, 20)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     course_id                               course_title  \\\n",
+              "773      15443         Моделирование оптических процессов   \n",
+              "280       7593  Техническая термодинамика и теплопередача   \n",
+              "629      14936    Культурные подходы к цифровым практикам   \n",
+              "\n",
+              "                                           description  up_id  \\\n",
+              "773  Что такое фотоника? На наш взгляд это очень пе...   6909   \n",
+              "280  Представлены начала термодинамики, методы расч...   7311   \n",
+              "629  Основная задача данного курса -  предложить ра...   6987   \n",
+              "\n",
+              "                                    up_title qualification  start_year  \\\n",
+              "773                       Бизнес-информатика      bachelor        2020   \n",
+              "280  Низкотемпературная техника и энергетика      bachelor        2019   \n",
+              "629                Инфохимия / Infochemistry        master        2020   \n",
+              "\n",
+              "                       module_name  \\\n",
+              "773               ОГНП 7: Фотоника   \n",
+              "280  Модуль обязательных дисциплин   \n",
+              "629      Факультативные дисциплины   \n",
+              "\n",
+              "                                              contents  \\\n",
+              "773  Введение в методы численной электродинамики. О...   \n",
+              "280  Подобие и моделирование процессов конвективног...   \n",
+              "629  Библиотеки,  архивы, энциклопедии, история big...   \n",
+              "\n",
+              "                                                tokens  \\\n",
+              "773  [введение, метод, численный, электродинамика, ...   \n",
+              "280  [подобие, моделирование, процесс, конвективный...   \n",
+              "629  [библиотека, архив, энциклопедия, история, big...   \n",
+              "\n",
+              "                                               tokensD  \\\n",
+              "773  [фотоника, взгляд, перспективный, область, исс...   \n",
+              "280  [представлять, начало, термодинамика, метод, р...   \n",
+              "629  [основной, задача, данный, курс, -, предлагать...   \n",
+              "\n",
+              "                                             embedding  \\\n",
+              "773  [0.25183907, -0.15061127, 0.14610592, 0.350766...   \n",
+              "280  [0.5842756, 0.58701414, -0.28769168, 0.2190147...   \n",
+              "629  [0.48195675, -0.30692005, 0.2979263, 0.1975168...   \n",
+              "\n",
+              "                                            embeddingD  desc_cont_sim  \\\n",
+              "773  [0.8150616, 0.10582235, 0.319231, 0.46731356, ...          0.600   \n",
+              "280  [0.57135826, 0.5636727, -0.20225279, 0.2826224...          0.920   \n",
+              "629  [0.43746132, -0.14761502, 0.44876146, 0.886812...          0.707   \n",
+              "\n",
+              "                                           tokens_text  \\\n",
+              "773  введение метод численный электродинамика основ...   \n",
+              "280  подобие моделирование процесс конвективный теп...   \n",
+              "629  библиотека архив энциклопедия история big data...   \n",
+              "\n",
+              "                                          tokensD_text  \\\n",
+              "773  фотоника взгляд перспективный область исследов...   \n",
+              "280  представлять начало термодинамика метод расчет...   \n",
+              "629  основной задача данный курс - предлагать разли...   \n",
+              "\n",
+              "                                        bert_embedding  \\\n",
+              "773  [-0.020190805, -0.0059326044, 0.039794147, -0....   \n",
+              "280  [0.0047562807, 0.010405966, 0.022855595, 0.000...   \n",
+              "629  [0.03397241, -0.0092194835, 0.07174899, 0.0086...   \n",
+              "\n",
+              "                                       bert_embeddingD  desc_cont_sim_bert  \\\n",
+              "773  [0.010424809, 0.0003055697, 0.030603899, -0.01...               0.905   \n",
+              "280  [0.0027674034, -0.010737589, 0.019425986, -0.0...               0.936   \n",
+              "629  [-0.024725934, 0.0033955844, 0.08934714, 0.002...               0.880   \n",
+              "\n",
+              "                                               mistral  \n",
+              "773  1. Введение в моделирование оптических процесс...  \n",
+              "280  Конечно, могу вам помочь составить темы и разд...  \n",
+              "629  Культурные подходы к цифровым практикам - это ...  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-0d69813e-fa8c-48f6-a453-cfb9a5c3952b\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "        vertical-align: top;\n",
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+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>course_id</th>\n",
+              "      <th>course_title</th>\n",
+              "      <th>description</th>\n",
+              "      <th>up_id</th>\n",
+              "      <th>up_title</th>\n",
+              "      <th>qualification</th>\n",
+              "      <th>start_year</th>\n",
+              "      <th>module_name</th>\n",
+              "      <th>contents</th>\n",
+              "      <th>tokens</th>\n",
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+              "      <th>embedding</th>\n",
+              "      <th>embeddingD</th>\n",
+              "      <th>desc_cont_sim</th>\n",
+              "      <th>tokens_text</th>\n",
+              "      <th>tokensD_text</th>\n",
+              "      <th>bert_embedding</th>\n",
+              "      <th>bert_embeddingD</th>\n",
+              "      <th>desc_cont_sim_bert</th>\n",
+              "      <th>mistral</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>773</th>\n",
+              "      <td>15443</td>\n",
+              "      <td>Моделирование оптических процессов</td>\n",
+              "      <td>Что такое фотоника? На наш взгляд это очень пе...</td>\n",
+              "      <td>6909</td>\n",
+              "      <td>Бизнес-информатика</td>\n",
+              "      <td>bachelor</td>\n",
+              "      <td>2020</td>\n",
+              "      <td>ОГНП 7: Фотоника</td>\n",
+              "      <td>Введение в методы численной электродинамики. О...</td>\n",
+              "      <td>[введение, метод, численный, электродинамика, ...</td>\n",
+              "      <td>[фотоника, взгляд, перспективный, область, исс...</td>\n",
+              "      <td>[0.25183907, -0.15061127, 0.14610592, 0.350766...</td>\n",
+              "      <td>[0.8150616, 0.10582235, 0.319231, 0.46731356, ...</td>\n",
+              "      <td>0.600</td>\n",
+              "      <td>введение метод численный электродинамика основ...</td>\n",
+              "      <td>фотоника взгляд перспективный область исследов...</td>\n",
+              "      <td>[-0.020190805, -0.0059326044, 0.039794147, -0....</td>\n",
+              "      <td>[0.010424809, 0.0003055697, 0.030603899, -0.01...</td>\n",
+              "      <td>0.905</td>\n",
+              "      <td>1. Введение в моделирование оптических процесс...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>280</th>\n",
+              "      <td>7593</td>\n",
+              "      <td>Техническая термодинамика и теплопередача</td>\n",
+              "      <td>Представлены начала термодинамики, методы расч...</td>\n",
+              "      <td>7311</td>\n",
+              "      <td>Низкотемпературная техника и энергетика</td>\n",
+              "      <td>bachelor</td>\n",
+              "      <td>2019</td>\n",
+              "      <td>Модуль обязательных дисциплин</td>\n",
+              "      <td>Подобие и моделирование процессов конвективног...</td>\n",
+              "      <td>[подобие, моделирование, процесс, конвективный...</td>\n",
+              "      <td>[представлять, начало, термодинамика, метод, р...</td>\n",
+              "      <td>[0.5842756, 0.58701414, -0.28769168, 0.2190147...</td>\n",
+              "      <td>[0.57135826, 0.5636727, -0.20225279, 0.2826224...</td>\n",
+              "      <td>0.920</td>\n",
+              "      <td>подобие моделирование процесс конвективный теп...</td>\n",
+              "      <td>представлять начало термодинамика метод расчет...</td>\n",
+              "      <td>[0.0047562807, 0.010405966, 0.022855595, 0.000...</td>\n",
+              "      <td>[0.0027674034, -0.010737589, 0.019425986, -0.0...</td>\n",
+              "      <td>0.936</td>\n",
+              "      <td>Конечно, могу вам помочь составить темы и разд...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>629</th>\n",
+              "      <td>14936</td>\n",
+              "      <td>Культурные подходы к цифровым практикам</td>\n",
+              "      <td>Основная задача данного курса -  предложить ра...</td>\n",
+              "      <td>6987</td>\n",
+              "      <td>Инфохимия / Infochemistry</td>\n",
+              "      <td>master</td>\n",
+              "      <td>2020</td>\n",
+              "      <td>Факультативные дисциплины</td>\n",
+              "      <td>Библиотеки,  архивы, энциклопедии, история big...</td>\n",
+              "      <td>[библиотека, архив, энциклопедия, история, big...</td>\n",
+              "      <td>[основной, задача, данный, курс, -, предлагать...</td>\n",
+              "      <td>[0.48195675, -0.30692005, 0.2979263, 0.1975168...</td>\n",
+              "      <td>[0.43746132, -0.14761502, 0.44876146, 0.886812...</td>\n",
+              "      <td>0.707</td>\n",
+              "      <td>библиотека архив энциклопедия история big data...</td>\n",
+              "      <td>основной задача данный курс - предлагать разли...</td>\n",
+              "      <td>[0.03397241, -0.0092194835, 0.07174899, 0.0086...</td>\n",
+              "      <td>[-0.024725934, 0.0033955844, 0.08934714, 0.002...</td>\n",
+              "      <td>0.880</td>\n",
+              "      <td>Культурные подходы к цифровым практикам - это ...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
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+              "    .colab-df-container {\n",
+              "      display:flex;\n",
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+              "\n",
+              "    .colab-df-convert {\n",
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+              "      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-0d69813e-fa8c-48f6-a453-cfb9a5c3952b 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-0d69813e-fa8c-48f6-a453-cfb9a5c3952b');\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-e2c76181-de72-4d0d-b8a9-6d7d32507c1d\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e2c76181-de72-4d0d-b8a9-6d7d32507c1d')\"\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",
+              "    }\n",
+              "    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-e2c76181-de72-4d0d-b8a9-6d7d32507c1d button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
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\\u043e\\u043f\\u0442\\u0438\\u043a\\u0438. \\u0411\\u0443\\u0434\\u0435\\u0442 \\u0441\\u0434\\u0435\\u043b\\u0430\\u043d \\u043e\\u0431\\u0437\\u043e\\u0440 \\u043e\\u0441\\u043d\\u043e\\u0432\\u043d\\u044b\\u0445 \\u0447\\u0438\\u0441\\u043b\\u0435\\u043d\\u043d\\u044b\\u0445 \\u043c\\u0435\\u0442\\u043e\\u0434\\u043e\\u0432. \\u041f\\u043e\\u0434\\u0440\\u043e\\u0431\\u043d\\u043e \\u0431\\u0443\\u0434\\u0443\\u0442 \\u0440\\u0430\\u0441\\u0441\\u043c\\u043e\\u0442\\u0440\\u0435\\u043d\\u044b \\u0434\\u0432\\u0430 \\u043d\\u0430\\u0438\\u0431\\u043e\\u043b\\u0435\\u0435 \\u0447\\u0430\\u0441\\u0442\\u043e \\u0438\\u0441\\u043f\\u043e\\u043b\\u044c\\u0437\\u0443\\u0435\\u043c\\u044b\\u0445 \\u043f\\u0440\\u0438 \\u0440\\u0435\\u0448\\u0435\\u043d\\u0438\\u0438 \\u0443\\u0440\\u0430\\u0432\\u043d\\u0435\\u043d\\u0438\\u0439 \\u041c\\u0430\\u043a\\u0441\\u0432\\u0435\\u043b\\u043b\\u0430 \\u043c\\u0435\\u0442\\u043e\\u0434\\u0430: FDTD \\u0438 FEM. \\u041e\\u0441\\u043e\\u0431\\u043e\\u0435 \\u0432\\u043d\\u0438\\u043c\\u0430\\u043d\\u0438\\u0435 \\u0431\\u0443\\u0434\\u0435\\u0442 \\u0443\\u0434\\u0435\\u043b\\u0435\\u043d\\u043e \\u0440\\u0435\\u0448\\u0435\\u043d\\u0438\\u044e \\u0432\\u043e\\u043b\\u043d\\u043e\\u0432\\u043e\\u0433\\u043e \\u0443\\u0440\\u0430\\u0432\\u043d\\u0435\\u043d\\u0438\\u044f \\u043c\\u0435\\u0442\\u043e\\u0434\\u043e\\u043c FEM \\u0432 \\u043f\\u0440\\u043e\\u0433\\u0440\\u0430\\u043c\\u043c\\u043d\\u043e\\u043c \\u043f\\u0430\\u043a\\u0435\\u0442\\u0435 COMSOL Multiphysics. \\u041d\\u0430 \\u043b\\u0435\\u043a\\u0446\\u0438\\u044f\\u0445 \\u0431\\u0443\\u0434\\u0443\\u0442 \\u043f\\u0440\\u043e\\u0434\\u0435\\u043c\\u043e\\u043d\\u0441\\u0442\\u0440\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u044b \\u0432\\u043e\\u0437\\u043c\\u043e\\u0436\\u043d\\u043e\\u0441\\u0442\\u0438 \\u0434\\u0430\\u043d\\u043d\\u043e\\u0433\\u043e \\u043f\\u0430\\u043a\\u0435\\u0442\\u0430.\\n\\u041e\\u0442\\u043b\\u0438\\u0447\\u0438\\u0442\\u0435\\u043b\\u044c\\u043d\\u043e\\u0439 \\u043e\\u0441\\u043e\\u0431\\u0435\\u043d\\u043d\\u043e\\u0441\\u0442\\u044c\\u044e \\u043f\\u0440\\u0435\\u043f\\u043e\\u0434\\u0430\\u0432\\u0430\\u043d\\u0438\\u044f \\u043a\\u0443\\u0440\\u0441\\u0430 \\u044f\\u0432\\u043b\\u044f\\u0435\\u0442\\u0441\\u044f \\u0438\\u0441\\u043f\\u043e\\u043b\\u044c\\u0437\\u043e\\u0432\\u0430\\u043d\\u0438\\u0435 \\u043a\\u043e\\u043c\\u043f\\u044c\\u044e\\u0442\\u0435\\u0440\\u043d\\u043e\\u0433\\u043e \\u043f\\u0440\\u0430\\u043a\\u0442\\u0438\\u043a\\u0443\\u043c\\u0430 \\u0432 \\u043f\\u0440\\u043e\\u0433\\u0440\\u0430\\u043c\\u043c\\u043d\\u043e\\u043c \\u043f\\u0430\\u043a\\u0435\\u0442\\u0435 COMSOL Multiphysics, \\u0432 \\u0441\\u043e\\u0441\\u0442\\u0430\\u0432 \\u043a\\u043e\\u0442\\u043e\\u0440\\u043e\\u0433\\u043e \\u0432\\u0445\\u043e\\u0434\\u044f\\u0442 \\u043f\\u044f\\u0442\\u044c \\u0437\\u0430\\u0434\\u0430\\u043d\\u0438\\u0439, \\u0432\\u044b\\u043f\\u043e\\u043b\\u043d\\u044f\\u0435\\u043c\\u044b\\u0445 \\u0441\\u0442\\u0443\\u0434\\u0435\\u043d\\u0442\\u0430\\u043c\\u0438 \\u0432 \\u0440\\u0430\\u043c\\u043a\\u0430\\u0445 \\u0441\\u0430\\u043c\\u043e\\u0441\\u0442\\u043e\\u044f\\u0442\\u0435\\u043b\\u044c\\u043d\\u043e\\u0439 \\u0440\\u0430\\u0431\\u043e\\u0442\\u044b.\",\n          \"\\u041f\\u0440\\u0435\\u0434\\u0441\\u0442\\u0430\\u0432\\u043b\\u0435\\u043d\\u044b \\u043d\\u0430\\u0447\\u0430\\u043b\\u0430 \\u0442\\u0435\\u0440\\u043c\\u043e\\u0434\\u0438\\u043d\\u0430\\u043c\\u0438\\u043a\\u0438, \\u043c\\u0435\\u0442\\u043e\\u0434\\u044b \\u0440\\u0430\\u0441\\u0447\\u0435\\u0442\\u0430 \\u0442\\u0435\\u0440\\u043c\\u043e\\u0434\\u0438\\u043d\\u0430\\u043c\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0445 \\u043f\\u0440\\u043e\\u0446\\u0435\\u0441\\u0441\\u043e\\u0432 \\u0441 \\u0438\\u0434\\u0435\\u0430\\u043b\\u044c\\u043d\\u044b\\u043c \\u0433\\u0430\\u0437\\u043e\\u043c \\u0438 \\u0441 \\u0440\\u0435\\u0430\\u043b\\u044c\\u043d\\u044b\\u043c\\u0438 \\u0440\\u0430\\u0431\\u043e\\u0447\\u0438\\u043c\\u0438 \\u0442\\u0435\\u043b\\u0430\\u043c\\u0438, \\u0446\\u0438\\u043a\\u043b\\u043e\\u0432 \\u044d\\u043d\\u0435\\u0440\\u0433\\u0435\\u0442\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0445 \\u0443\\u0441\\u0442\\u0430\\u043d\\u043e\\u0432\\u043e\\u043a, \\u0445\\u043e\\u043b\\u043e\\u0434\\u0438\\u043b\\u044c\\u043d\\u044b\\u0445 \\u043c\\u0430\\u0448\\u0438\\u043d \\u0438 \\u0442\\u0435\\u043f\\u043b\\u043e\\u0432\\u044b\\u0445 \\u043d\\u0430\\u0441\\u043e\\u0441\\u043e\\u0432. \\n\\u041e\\u043f\\u0438\\u0441\\u0430\\u043d\\u044b \\u043f\\u0440\\u043e\\u0446\\u0435\\u0441\\u0441\\u044b \\u0441\\u0442\\u0430\\u0446\\u0438\\u043e\\u043d\\u0430\\u0440\\u043d\\u043e\\u0439 \\u0438 \\u043d\\u0435\\u0441\\u0442\\u0430\\u0446\\u0438\\u043e\\u043d\\u0430\\u0440\\u043d\\u043e\\u0439 \\u0442\\u0435\\u043f\\u043b\\u043e\\u043f\\u0440\\u043e\\u0432\\u043e\\u0434\\u043d\\u043e\\u0441\\u0442\\u0438, \\u043a\\u043e\\u043d\\u0432\\u0435\\u043a\\u0442\\u0438\\u0432\\u043d\\u043e\\u0433\\u043e \\u0442\\u0435\\u043f\\u043b\\u043e\\u043e\\u0431\\u043c\\u0435\\u043d\\u0430, \\u0442\\u0435\\u043f\\u043b\\u043e\\u043e\\u0431\\u043c\\u0435\\u043d\\u0430 \\u0438\\u0437\\u043b\\u0443\\u0447\\u0435\\u043d\\u0438\\u0435\\u043c. \\u0418\\u0437\\u043b\\u043e\\u0436\\u0435\\u043d\\u044b \\u043e\\u0441\\u043d\\u043e\\u0432\\u044b \\u0442\\u0435\\u0445\\u043d\\u0438\\u0447\\u0435\\u0441\\u043a\\u043e\\u0439 \\u0442\\u0435\\u0440\\u043c\\u043e\\u0434\\u0438\\u043d\\u0430\\u043c\\u0438\\u043a\\u0438 \\u0438 \\u0442\\u0435\\u043f\\u043b\\u043e\\u043f\\u0435\\u0440\\u0435\\u0434\\u0430\\u0447\\u0438. \\u0414\\u0430\\u043d\\u044b \\u043e\\u0441\\u043d\\u043e\\u0432\\u044b \\u0442\\u0435\\u043f\\u043b\\u043e\\u0432\\u043e\\u0433\\u043e \\u0440\\u0430\\u0441\\u0447\\u0435\\u0442\\u0430 \\u0442\\u0435\\u043f\\u043b\\u043e\\u043e\\u0431\\u043c\\u0435\\u043d\\u043d\\u0438\\u043a\\u043e\\u0432.\",\n          \"\\u041e\\u0441\\u043d\\u043e\\u0432\\u043d\\u0430\\u044f \\u0437\\u0430\\u0434\\u0430\\u0447\\u0430 \\u0434\\u0430\\u043d\\u043d\\u043e\\u0433\\u043e \\u043a\\u0443\\u0440\\u0441\\u0430 -  \\u043f\\u0440\\u0435\\u0434\\u043b\\u043e\\u0436\\u0438\\u0442\\u044c \\u0440\\u0430\\u0437\\u043b\\u0438\\u0447\\u043d\\u044b\\u0435 \\u0442\\u0435\\u043e\\u0440\\u0435\\u0442\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0435 \\u0438 \\u043a\\u043e\\u043d\\u0446\\u0435\\u043f\\u0442\\u0443\\u0430\\u043b\\u044c\\u043d\\u044b\\u0435 \\u043e\\u0441\\u043d\\u043e\\u0432\\u0430\\u043d\\u0438\\u044f \\u0434\\u043b\\u044f \\u043e\\u0441\\u043c\\u044b\\u0441\\u043b\\u0435\\u043d\\u0438\\u044f \\u043f\\u0440\\u043e\\u0438\\u0441\\u0445\\u043e\\u0434\\u044f\\u0449\\u0435\\u0439 \\u0441\\u0435\\u0433\\u043e\\u0434\\u043d\\u044f \\u0433\\u043b\\u043e\\u0431\\u0430\\u043b\\u044c\\u043d\\u043e\\u0439 \\u0446\\u0438\\u0444\\u0440\\u043e\\u0432\\u043e\\u0439 \\u0442\\u0440\\u0430\\u043d\\u0441\\u0444\\u043e\\u0440\\u043c\\u0430\\u0446\\u0438\\u0438, \\u043e\\u0441\\u043d\\u043e\\u0432\\u0430\\u043d\\u0438\\u044f, \\u043a\\u043e\\u0442\\u043e\\u0440\\u044b\\u0435 \\u043f\\u043e\\u0437\\u0432\\u043e\\u043b\\u044f\\u0442 \\u043d\\u0435 \\u0442\\u043e\\u043b\\u044c\\u043a\\u043e \\u043b\\u0443\\u0447\\u0448\\u0435 \\u0430\\u043d\\u0430\\u043b\\u0438\\u0437\\u0438\\u0440\\u043e\\u0432\\u0430\\u0442\\u044c \\u043f\\u0440\\u043e\\u0438\\u0441\\u0445\\u043e\\u0434\\u044f\\u0449\\u0438\\u0435 \\u043f\\u0440\\u043e\\u0446\\u0435\\u0441\\u0441\\u044b, \\u043d\\u043e \\u0438 \\u043d\\u0430\\u0445\\u043e\\u0434\\u0438\\u0442\\u044c \\u043e\\u043f\\u0442\\u0438\\u043c\\u0430\\u043b\\u044c\\u043d\\u044b\\u0435  \\u0441\\u0442\\u0440\\u0430\\u0442\\u0435\\u0433\\u0438\\u0438, \\u043f\\u0430\\u0440\\u0430\\u0434\\u0438\\u0433\\u043c\\u044b  \\u0438 \\u0438\\u043d\\u0441\\u0442\\u0440\\u0443\\u043c\\u0435\\u043d\\u0442\\u044b \\u0434\\u043b\\u044f \\u0440\\u0435\\u0448\\u0435\\u043d\\u0438\\u044f \\u043f\\u0440\\u043e\\u0444\\u0435\\u0441\\u0441\\u0438\\u043e\\u043d\\u0430\\u043b\\u044c\\u043d\\u044b\\u0445 \\u0437\\u0430\\u0434\\u0430\\u0447 \\u0432 \\u043e\\u0431\\u043b\\u0430\\u0441\\u0442\\u0438 DH, \\u0438\\u0441\\u0445\\u043e\\u0434\\u044f \\u0438\\u0437 \\u0431\\u043e\\u043b\\u0435\\u0435 \\u0448\\u0438\\u0440\\u043e\\u043a\\u043e\\u0433\\u043e \\u043f\\u043e\\u043d\\u0438\\u043c\\u0430\\u043d\\u0438\\u044f \\u043f\\u0440\\u043e\\u0431\\u043b\\u0435\\u043c \\u0438 \\u043f\\u0430\\u0440\\u0430\\u0434\\u043e\\u043a\\u0441\\u043e\\u0432 \\u0446\\u0438\\u0444\\u0440\\u043e\\u0432\\u043e\\u0439 \\u043a\\u0443\\u043b\\u044c\\u0442\\u0443\\u0440\\u044b.\\n\\n\\u041d\\u0430 \\u043a\\u0430\\u0436\\u0434\\u043e\\u043c \\u0437\\u0430\\u043d\\u044f\\u0442\\u0438\\u0438 \\u043c\\u044b \\u0431\\u0443\\u0434\\u0435\\u043c \\u043e\\u0431\\u0441\\u0443\\u0436\\u0434\\u0430\\u0442\\u044c \\u0441\\u0442\\u0430\\u0442\\u044c\\u0438 \\u0438 \\u0433\\u043b\\u0430\\u0432\\u044b \\u0438\\u0437 \\u043a\\u043d\\u0438\\u0433,  \\u0440\\u0430\\u0441\\u0441\\u043c\\u0430\\u0442\\u0440\\u0438\\u0432\\u0430\\u0442\\u044c \\u0441\\u0443\\u0449\\u0435\\u0441\\u0442\\u0432\\u0443\\u044e\\u0449\\u0438\\u0435 \\u043f\\u0440\\u043e\\u0435\\u043a\\u0442\\u044b  \\u0432 \\u043e\\u0431\\u043b\\u0430\\u0441\\u0442\\u0438 Digital humanities, \\u043f\\u0440\\u0438\\u0434\\u0443\\u043c\\u044b\\u0432\\u0430\\u0442\\u044c \\u0441\\u043e\\u0431\\u0441\\u0442\\u0432\\u0435\\u043d\\u043d\\u044b\\u0435, \\u0438 \\u0438\\u0441\\u043a\\u0430\\u0442\\u044c \\u043d\\u043e\\u0432\\u044b\\u0435 \\u043f\\u0443\\u0442\\u0438 \\u0440\\u0435\\u0448\\u0435\\u043d\\u0438\\u044f \\u0437\\u0430\\u0434\\u0430\\u0447, \\u0441\\u0442\\u043e\\u044f\\u0449\\u0438\\u0445 \\u0441\\u0435\\u0433\\u043e\\u0434\\u043d\\u044f \\u043f\\u0435\\u0440\\u0435\\u0434 \\u0433\\u0443\\u043c\\u0430\\u043d\\u0438\\u0442\\u0430\\u0440\\u043d\\u044b\\u043c\\u0438 \\u043d\\u0430\\u0443\\u043a\\u0430\\u043c\\u0438.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"up_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 213,\n        \"min\": 6909,\n        \"max\": 7311,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          6909,\n          7311,\n          6987\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"up_title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\\u0411\\u0438\\u0437\\u043d\\u0435\\u0441-\\u0438\\u043d\\u0444\\u043e\\u0440\\u043c\\u0430\\u0442\\u0438\\u043a\\u0430\",\n          \"\\u041d\\u0438\\u0437\\u043a\\u043e\\u0442\\u0435\\u043c\\u043f\\u0435\\u0440\\u0430\\u0442\\u0443\\u0440\\u043d\\u0430\\u044f \\u0442\\u0435\\u0445\\u043d\\u0438\\u043a\\u0430 \\u0438 \\u044d\\u043d\\u0435\\u0440\\u0433\\u0435\\u0442\\u0438\\u043a\\u0430\",\n          \"\\u0418\\u043d\\u0444\\u043e\\u0445\\u0438\\u043c\\u0438\\u044f / Infochemistry\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"qualification\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"master\",\n          \"bachelor\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2019,\n        \"max\": 2020,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2019,\n          2020\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"module_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\\u041e\\u0413\\u041d\\u041f 7: \\u0424\\u043e\\u0442\\u043e\\u043d\\u0438\\u043a\\u0430\",\n          \"\\u041c\\u043e\\u0434\\u0443\\u043b\\u044c \\u043e\\u0431\\u044f\\u0437\\u0430\\u0442\\u0435\\u043b\\u044c\\u043d\\u044b\\u0445 \\u0434\\u0438\\u0441\\u0446\\u0438\\u043f\\u043b\\u0438\\u043d\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"contents\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"\\u0412\\u0432\\u0435\\u0434\\u0435\\u043d\\u0438\\u0435 \\u0432 \\u043c\\u0435\\u0442\\u043e\\u0434\\u044b \\u0447\\u0438\\u0441\\u043b\\u0435\\u043d\\u043d\\u043e\\u0439 \\u044d\\u043b\\u0435\\u043a\\u0442\\u0440\\u043e\\u0434\\u0438\\u043d\\u0430\\u043c\\u0438\\u043a\\u0438. \\u041e\\u0441\\u043d\\u043e\\u0432\\u044b \\u0432\\u043e\\u043b\\u043d\\u043e\\u0432\\u043e\\u0439 \\u043e\\u043f\\u0442\\u0438\\u043a\\u0438. \\u042d\\u043b\\u0435\\u043c\\u0435\\u043d\\u0442\\u044b \\u0433\\u0435\\u043e\\u043c\\u0435\\u0442\\u0440\\u0438\\u0447\\u0435\\u0441\\u043a\\u043e\\u0439 \\u043e\\u043f\\u0442\\u0438\\u043a\\u0438. \\u0414\\u0438\\u0430\\u0433\\u0440\\u0430\\u043c\\u043c\\u0430 \\u043d\\u0430\\u043f\\u0440\\u0430\\u0432\\u043b\\u0435\\u043d\\u043d\\u043e\\u0441\\u0442\\u0438. \\u042d\\u043b\\u0435\\u043c\\u0435\\u043d\\u0442 \\u0413\\u044e\\u0439\\u0433\\u0435\\u043d\\u0441\\u0430. \\u042d\\u0444\\u0444\\u0435\\u043a\\u0442 \\u041a\\u0435\\u0440\\u043a\\u0435\\u0440\\u0430.. \\u0414\\u0438\\u0441\\u043f\\u0435\\u0440\\u0441\\u0438\\u044f \\u0441\\u0432\\u0435\\u0442\\u0430.. \\u0414\\u0438\\u0444\\u0440\\u0430\\u043a\\u0446\\u0438\\u044f.. \\u0417\\u0430\\u043a\\u043e\\u043d \\u043e\\u0442\\u0440\\u0430\\u0436\\u0435\\u043d\\u0438\\u044f. \\u0417\\u0435\\u0440\\u043a\\u0430\\u043b\\u0430. \\u041f\\u043e\\u0441\\u0442\\u0440\\u043e\\u0435\\u043d\\u0438\\u0435 \\u0438\\u0437\\u043e\\u0431\\u0440\\u0430\\u0436\\u0435\\u043d\\u0438\\u044f \\u0432 \\u0437\\u0435\\u0440\\u043a\\u0430\\u043b\\u0435.. \\u0417\\u0430\\u043a\\u043e\\u043d \\u043f\\u0440\\u0435\\u043b\\u043e\\u043c\\u043b\\u0435\\u043d\\u0438\\u044f. \\u042f\\u0432\\u043b\\u0435\\u043d\\u0438\\u0435 \\u043f\\u043e\\u043b\\u043d\\u043e\\u0433\\u043e \\u0432\\u043d\\u0443\\u0442\\u0440\\u0435\\u043d\\u043d\\u0435\\u0433\\u043e \\u043e\\u0442\\u0440\\u0430\\u0436\\u0435\\u043d\\u0438\\u044f. \\u041b\\u0438\\u043d\\u0437\\u044b. \\u0424\\u043e\\u0440\\u043c\\u0443\\u043b\\u0430 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(\\u043a\\u043e\\u043d\\u0435\\u0447\\u043d\\u044b\\u0445 \\u044d\\u043b\\u0435\\u043c\\u0435\\u043d\\u0442\\u043e\\u0432). \\u041e\\u0441\\u043d\\u043e\\u0432\\u044b \\u043c\\u043e\\u0434\\u0435\\u043b\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u044f \\u0432 COMSOL.. \\u041e\\u0431\\u0437\\u043e\\u0440 \\u043c\\u0435\\u0442\\u043e\\u0434\\u043e\\u0432 \\u0432\\u044b\\u0447\\u0438\\u0441\\u043b\\u0438\\u0442\\u0435\\u043b\\u044c\\u043d\\u043e\\u0439 \\u044d\\u043b\\u0435\\u043a\\u0442\\u0440\\u043e\\u0434\\u0438\\u043d\\u0430\\u043c\\u0438\\u043a\\u0438. \\u0412\\u0440\\u0435\\u043c\\u0435\\u043d\\u043d\\u0430\\u044f \\u043e\\u0431\\u043b\\u0430\\u0441\\u0442\\u044c. \\u0427\\u0430\\u0441\\u0442\\u043e\\u0442\\u043d\\u0430\\u044f \\u043e\\u0431\\u043b\\u0430\\u0441\\u0442\\u044c.. \\u041e\\u0431\\u0437\\u043e\\u0440 \\u043e\\u0441\\u043d\\u043e\\u0432\\u043d\\u044b\\u0445 \\u0447\\u0438\\u0441\\u043b\\u0435\\u043d\\u043d\\u044b\\u0445 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\\u0440\\u0435\\u0448\\u0435\\u043d\\u0438\\u0435 \\u0432\\u043e\\u043b\\u043d\\u043e\\u0432\\u043e\\u0439 \\u0443\\u0440\\u0430\\u0432\\u043d\\u0435\\u043d\\u0438\\u0435 \\u043c\\u0435\\u0442\\u043e\\u0434 fem \\u043f\\u0440\\u043e\\u0433\\u0440\\u0430\\u043c\\u043c\\u043d\\u044b\\u0439 \\u043f\\u0430\\u043a\\u0435\\u0442 comsol multiphysics \\u043b\\u0435\\u043a\\u0446\\u0438\\u044f \\u043f\\u0440\\u043e\\u0434\\u0435\\u043c\\u043e\\u043d\\u0441\\u0442\\u0440\\u0438\\u0440\\u043e\\u0432\\u0430\\u0442\\u044c \\u0432\\u043e\\u0437\\u043c\\u043e\\u0436\\u043d\\u043e\\u0441\\u0442\\u044c \\u0434\\u0430\\u043d\\u043d\\u044b\\u0439 \\u043f\\u0430\\u043a\\u0435\\u0442 \\u043e\\u0442\\u043b\\u0438\\u0447\\u0438\\u0442\\u0435\\u043b\\u044c\\u043d\\u044b\\u0439 \\u043e\\u0441\\u043e\\u0431\\u0435\\u043d\\u043d\\u043e\\u0441\\u0442\\u044c \\u043f\\u0440\\u0435\\u043f\\u043e\\u0434\\u0430\\u0432\\u0430\\u043d\\u0438\\u0435 \\u043a\\u0443\\u0440\\u0441 \\u044f\\u0432\\u043b\\u044f\\u0442\\u044c\\u0441\\u044f \\u0438\\u0441\\u043f\\u043e\\u043b\\u044c\\u0437\\u043e\\u0432\\u0430\\u043d\\u0438\\u0435 \\u043a\\u043e\\u043c\\u043f\\u044c\\u044e\\u0442\\u0435\\u0440\\u043d\\u044b\\u0439 \\u043f\\u0440\\u0430\\u043a\\u0442\\u0438\\u043a\\u0443\\u043c \\u043f\\u0440\\u043e\\u0433\\u0440\\u0430\\u043c\\u043c\\u043d\\u044b\\u0439 \\u043f\\u0430\\u043a\\u0435\\u0442 comsol multiphysics \\u0441\\u043e\\u0441\\u0442\\u0430\\u0432 \\u0432\\u0445\\u043e\\u0434\\u0438\\u0442\\u044c \\u043f\\u044f\\u0442\\u044c \\u0437\\u0430\\u0434\\u0430\\u043d\\u0438\\u0435 \\u0432\\u044b\\u043f\\u043e\\u043b\\u043d\\u044f\\u0442\\u044c \\u0441\\u0442\\u0443\\u0434\\u0435\\u043d\\u0442 \\u0440\\u0430\\u043c\\u043a\\u0430 \\u0441\\u0430\\u043c\\u043e\\u0441\\u0442\\u043e\\u044f\\u0442\\u0435\\u043b\\u044c\\u043d\\u044b\\u0439 \\u0440\\u0430\\u0431\\u043e\\u0442\\u0430\",\n          \"\\u043f\\u0440\\u0435\\u0434\\u0441\\u0442\\u0430\\u0432\\u043b\\u044f\\u0442\\u044c \\u043d\\u0430\\u0447\\u0430\\u043b\\u043e \\u0442\\u0435\\u0440\\u043c\\u043e\\u0434\\u0438\\u043d\\u0430\\u043c\\u0438\\u043a\\u0430 \\u043c\\u0435\\u0442\\u043e\\u0434 \\u0440\\u0430\\u0441\\u0447\\u0435\\u0442 \\u0442\\u0435\\u0440\\u043c\\u043e\\u0434\\u0438\\u043d\\u0430\\u043c\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0439 \\u043f\\u0440\\u043e\\u0446\\u0435\\u0441\\u0441 \\u0438\\u0434\\u0435\\u0430\\u043b\\u044c\\u043d\\u044b\\u0439 \\u0433\\u0430\\u0437 \\u0440\\u0435\\u0430\\u043b\\u044c\\u043d\\u044b\\u0439 \\u0440\\u0430\\u0431\\u043e\\u0447\\u0438\\u0439 \\u0442\\u0435\\u043b\\u043e \\u0446\\u0438\\u043a\\u043b \\u044d\\u043d\\u0435\\u0440\\u0433\\u0435\\u0442\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0439 \\u0443\\u0441\\u0442\\u0430\\u043d\\u043e\\u0432\\u043a\\u0430 \\u0445\\u043e\\u043b\\u043e\\u0434\\u0438\\u043b\\u044c\\u043d\\u044b\\u0439 \\u043c\\u0430\\u0448\\u0438\\u043d\\u0430 \\u0442\\u0435\\u043f\\u043b\\u043e\\u0432\\u043e\\u0439 \\u043d\\u0430\\u0441\\u043e\\u0441 \\u043e\\u043f\\u0438\\u0441\\u044b\\u0432\\u0430\\u0442\\u044c \\u043f\\u0440\\u043e\\u0446\\u0435\\u0441\\u0441 \\u0441\\u0442\\u0430\\u0446\\u0438\\u043e\\u043d\\u0430\\u0440\\u043d\\u044b\\u0439 \\u043d\\u0435\\u0441\\u0442\\u0430\\u0446\\u0438\\u043e\\u043d\\u0430\\u0440\\u043d\\u044b\\u0439 \\u0442\\u0435\\u043f\\u043b\\u043e\\u043f\\u0440\\u043e\\u0432\\u043e\\u0434\\u043d\\u043e\\u0441\\u0442\\u044c \\u043a\\u043e\\u043d\\u0432\\u0435\\u043a\\u0442\\u0438\\u0432\\u043d\\u044b\\u0439 \\u0442\\u0435\\u043f\\u043b\\u043e\\u043e\\u0431\\u043c\\u0435\\u043d \\u0442\\u0435\\u043f\\u043b\\u043e\\u043e\\u0431\\u043c\\u0435\\u043d \\u0438\\u0437\\u043b\\u0443\\u0447\\u0435\\u043d\\u0438\\u0435 \\u0438\\u0437\\u043b\\u0430\\u0433\\u0430\\u0442\\u044c \\u043e\\u0441\\u043d\\u043e\\u0432\\u0430 \\u0442\\u0435\\u0445\\u043d\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0439 \\u0442\\u0435\\u0440\\u043c\\u043e\\u0434\\u0438\\u043d\\u0430\\u043c\\u0438\\u043a\\u0430 \\u0442\\u0435\\u043f\\u043b\\u043e\\u043f\\u0435\\u0440\\u0435\\u0434\\u0430\\u0447\\u0430 \\u0434\\u0430\\u0432\\u0430\\u0442\\u044c \\u043e\\u0441\\u043d\\u043e\\u0432\\u0430 \\u0442\\u0435\\u043f\\u043b\\u043e\\u0432\\u043e\\u0439 \\u0440\\u0430\\u0441\\u0447\\u0435\\u0442 \\u0442\\u0435\\u043f\\u043b\\u043e\\u043e\\u0431\\u043c\\u0435\\u043d\\u043d\\u0438\\u043a\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bert_embedding\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bert_embeddingD\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n 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\\u0432\\u043e\\u0437\\u043c\\u043e\\u0436\\u043d\\u044b\\u0445 \\u0432\\u0430\\u0440\\u0438\\u0430\\u043d\\u0442\\u043e\\u0432:\\n\\n1. \\u0412\\u0432\\u0435\\u0434\\u0435\\u043d\\u0438\\u0435 \\u0432 \\u0442\\u0435\\u0440\\u043c\\u043e\\u0434\\u0438\\u043d\\u0430\\u043c\\u0438\\u043a\\u0443: \\u043e\\u0441\\u043d\\u043e\\u0432\\u043d\\u044b\\u0435 \\u043f\\u043e\\u043d\\u044f\\u0442\\u0438\\u044f, \\u0437\\u0430\\u043a\\u043e\\u043d\\u044b \\u0441\\u043e\\u0445\\u0440\\u0430\\u043d\\u0435\\u043d\\u0438\\u044f \\u0438 \\u043f\\u0440\\u0435\\u043e\\u0431\\u0440\\u0430\\u0437\\u043e\\u0432\\u0430\\u043d\\u0438\\u044f \\u044d\\u043d\\u0435\\u0440\\u0433\\u0438\\u0438.\\n2. \\u041f\\u043e\\u0432\\u0435\\u0434\\u0435\\u043d\\u0438\\u0435 \\u0438\\u0434\\u0435\\u0430\\u043b\\u044c\\u043d\\u044b\\u0445 \\u0433\\u0430\\u0437\\u043e\\u0432: \\u0437\\u0430\\u043a\\u043e\\u043d\\u044b Boyle-Mariotte \\u0438 Gay-Lussac, \\u0443\\u0440\\u0430\\u0432\\u043d\\u0435\\u043d\\u0438\\u0435 \\u0441\\u043e\\u0441\\u0442\\u043e\\u044f\\u043d\\u0438\\u044f \\u0438\\u0434\\u0435\\u0430\\u043b\\u044c\\u043d\\u043e\\u0433\\u043e \\u0433\\u0430\\u0437\\u0430.\\n3. \\u041f\\u0435\\u0440\\u0432\\u043e\\u0435 \\u043d\\u0430\\u0447\\u0430\\u043b\\u043e \\u0442\\u0435\\u0440\\u043c\\u043e\\u0434\\u0438\\u043d\\u0430\\u043c\\u0438\\u043a\\u0438: \\u0437\\u0430\\u043a\\u043e\\u043d \\u0441\\u043e\\u0445\\u0440\\u0430\\u043d\\u0435\\u043d\\u0438\\u044f \\u044d\\u043d\\u0435\\u0440\\u0433\\u0438\\u0438, \\u043e\\u043f\\u0440\\u0435\\u0434\\u0435\\u043b\\u0435\\u043d\\u0438\\u0435 \\u044d\\u043d\\u0442\\u0430\\u043b\\u044c\\u043f\\u0438\\u0438 \\u0438 \\u044d\\u043d\\u0442\\u0440\\u043e\\u043f\\u0438\\u0438.\\n4. \\u0412\\u0442\\u043e\\u0440\\u043e\\u0435 \\u043d\\u0430\\u0447\\u0430\\u043b\\u043e \\u0442\\u0435\\u0440\\u043c\\u043e\\u0434\\u0438\\u043d\\u0430\\u043c\\u0438\\u043a\\u0438: \\u0437\\u0430\\u043a\\u043e\\u043d \\u0432\\u043e\\u0437\\u0440\\u0430\\u0441\\u0442\\u0430\\u043d\\u0438\\u044f \\u044d\\u043d\\u0442\\u0440\\u043e\\u043f\\u0438\\u0438, \\u043e\\u043f\\u0440\\u0435\\u0434\\u0435\\u043b\\u0435\\u043d\\u0438\\u0435 \\u043a\\u043e\\u044d\\u0444\\u0444\\u0438\\u0446\\u0438\\u0435\\u043d\\u0442\\u0430 \\u043f\\u043e\\u043b\\u0435\\u0437\\u043d\\u043e\\u0433\\u043e \\u0434\\u0435\\u0439\\u0441\\u0442\\u0432\\u0438\\u044f.\\n5. \\u0426\\u0438\\u043a\\u043b\\u044b \\u0432 \\u0442\\u0435\\u0440\\u043c\\u043e\\u0434\\u0438\\u043d\\u0430\\u043c\\u0438\\u043a\\u0435: \\u0446\\u0438\\u043a\\u043b\\u044b \\u041a\\u0430\\u0440\\u043d\\u043e, \\u0414\\u0438\\u0437\\u0435\\u043b\\u044f, \\u041e\\u0442\\u0442\\u043e \\u0438 \\u0438\\u0445 applications.\\n6. \\u0422\\u0435\\u043f\\u043b\\u043e\\u043f\\u0435\\u0440\\u0435\\u0434\\u0430\\u0447\\u0430: \\u043a\\u043e\\u043d\\u0432\\u0435\\u043a\\u0446\\u0438\\u044f, \\u043a\\u043e\\u043d\\u0434\\u0443\\u043a\\u0446\\u0438\\u044f \\u0438 \\u0438\\u0437\\u043b\\u0443\\u0447\\u0435\\u043d\\u0438\\u0435, \\u0437\\u0430\\u043a\\u043e\\u043d \\u0424\\u0443\\u0440\\u044c\\u0435, \\u0437\\u0430\\u043a\\u043e\\u043d \\u041d\\u044c\\u044e\\u0442\\u043e\\u043d\\u0430.\\n7. \\u0422\\u0435\\u043f\\u043b\\u043e\\u043e\\u0431\\u043c\\u0435\\u043d\\u043d\\u044b\\u0435 \\u0430\\u043f\\u043f\\u0430\\u0440\\u0430\\u0442\\u044b: \\u043a\\u043b\\u0430\\u0441\\u0441\\u0438\\u0444\\u0438\\u043a\\u0430\\u0446\\u0438\\u044f, \\u0440\\u0430\\u0441\\u0447\\u0435\\u0442 \\u0438 \\u043f\\u0440\\u043e\\u0435\\u043a\\u0442\\u0438\\u0440\\u043e\\u0432\\u0430\\u043d\\u0438\\u0435.\\n8. \\u041d\\u0435\\u043e\\u0431\\u0440\\u0430\\u0442\\u0438\\u043c\\u044b\\u0435 \\u043f\\u0440\\u043e\\u0446\\u0435\\u0441\\u0441\\u044b \\u0432 \\u0442\\u0435\\u0440\\u043c\\u043e\\u0434\\u0438\\u043d\\u0430\\u043c\\u0438\\u043a\\u0435: \\u0442\\u0440\\u0435\\u043d\\u0438\\u0435, \\u0434\\u0438\\u0444\\u0444\\u0443\\u0437\\u0438\\u044f, \\u0445\\u0438\\u043c\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0435 \\u0440\\u0435\\u0430\\u043a\\u0446\\u0438\\u0438.\\n\\n\\u042d\\u0442\\u0438 \\u0442\\u0435\\u043c\\u044b \\u043e\\u0445\\u0432\\u0430\\u0442\\u044b\\u0432\\u0430\\u044e\\u0442 \\u043e\\u0441\\u043d\\u043e\\u0432\\u043d\\u044b\\u0435 \\u0440\\u0430\\u0437\\u0434\\u0435\\u043b\\u044b \\u0442\\u0435\\u0445\\u043d\\u0438\\u0447\\u0435\\u0441\\u043a\\u043e\\u0439 \\u0442\\u0435\\u0440\\u043c\\u043e\\u0434\\u0438\\u043d\\u0430\\u043c\\u0438\\u043a\\u0438 \\u0438 \\u0442\\u0435\\u043f\\u043b\\u043e\\u043f\\u0435\\u0440\\u0435\\u0434\\u0430\\u0447\\u0438 \\u0438 \\u043c\\u043e\\u0433\\u0443\\u0442 \\u0431\\u044b\\u0442\\u044c \\u0440\\u0430\\u0437\\u0434\\u0435\\u043b\\u0435\\u043d\\u044b \\u043d\\u0430 \\u0431\\u043e\\u043b\\u0435\\u0435 \\u0434\\u0435\\u0442\\u0430\\u043b\\u044c\\u043d\\u044b\\u0435 \\u043f\\u043e\\u0434\\u0440\\u0430\\u0437\\u0434\\u0435\\u043b\\u044b \\u0434\\u043b\\u044f \\u0431\\u043e\\u043b\\u0435\\u0435 \\u0433\\u043b\\u0443\\u0431\\u043e\\u043a\\u043e\\u0433\\u043e \\u0438\\u0437\\u0443\\u0447\\u0435\\u043d\\u0438\\u044f.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 111
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Векторизуйте сгенерированное содержание с помощью обоих векторайзеров из предыдущего задания."
+      ],
+      "metadata": {
+        "id": "haSRblTgMqe0"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ваш код здесь"
+      ],
+      "metadata": {
+        "id": "MDNHBLzWHj66"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# пример результата обработки\n",
+        "ud_df.sample(3, random_state=42)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 385
+        },
+        "id": "agE-9AQ5MR8S",
+        "outputId": "f7425b37-523f-420d-8d28-56aff8ef9cbf"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     course_id                               course_title  \\\n",
+              "773      15443         Моделирование оптических процессов   \n",
+              "280       7593  Техническая термодинамика и теплопередача   \n",
+              "629      14936    Культурные подходы к цифровым практикам   \n",
+              "\n",
+              "                                           description  up_id  \\\n",
+              "773  Что такое фотоника? На наш взгляд это очень пе...   6909   \n",
+              "280  Представлены начала термодинамики, методы расч...   7311   \n",
+              "629  Основная задача данного курса -  предложить ра...   6987   \n",
+              "\n",
+              "                                    up_title qualification  start_year  \\\n",
+              "773                       Бизнес-информатика      bachelor        2020   \n",
+              "280  Низкотемпературная техника и энергетика      bachelor        2019   \n",
+              "629                Инфохимия / Infochemistry        master        2020   \n",
+              "\n",
+              "                       module_name  \\\n",
+              "773               ОГНП 7: Фотоника   \n",
+              "280  Модуль обязательных дисциплин   \n",
+              "629      Факультативные дисциплины   \n",
+              "\n",
+              "                                              contents  \\\n",
+              "773  Введение в методы численной электродинамики. О...   \n",
+              "280  Подобие и моделирование процессов конвективног...   \n",
+              "629  Библиотеки,  архивы, энциклопедии, история big...   \n",
+              "\n",
+              "                                                tokens  ...  \\\n",
+              "773  [введение, метод, численный, электродинамика, ...  ...   \n",
+              "280  [подобие, моделирование, процесс, конвективный...  ...   \n",
+              "629  [библиотека, архив, энциклопедия, история, big...  ...   \n",
+              "\n",
+              "                                           tokens_text  \\\n",
+              "773  введение метод численный электродинамика основ...   \n",
+              "280  подобие моделирование процесс конвективный теп...   \n",
+              "629  библиотека архив энциклопедия история big data...   \n",
+              "\n",
+              "                                          tokensD_text  \\\n",
+              "773  фотоника взгляд перспективный область исследов...   \n",
+              "280  представлять начало термодинамика метод расчет...   \n",
+              "629  основной задача данный курс - предлагать разли...   \n",
+              "\n",
+              "                                        bert_embedding  \\\n",
+              "773  [-0.020190805, -0.0059326044, 0.039794147, -0....   \n",
+              "280  [0.0047562807, 0.010405966, 0.022855595, 0.000...   \n",
+              "629  [0.03397241, -0.0092194835, 0.07174899, 0.0086...   \n",
+              "\n",
+              "                                       bert_embeddingD desc_cont_sim_bert  \\\n",
+              "773  [0.010424809, 0.0003055697, 0.030603899, -0.01...              0.905   \n",
+              "280  [0.0027674034, -0.010737589, 0.019425986, -0.0...              0.936   \n",
+              "629  [-0.024725934, 0.0033955844, 0.08934714, 0.002...              0.880   \n",
+              "\n",
+              "                                               mistral  \\\n",
+              "773  1. Введение в моделирование оптических процесс...   \n",
+              "280  Конечно, могу вам помочь составить темы и разд...   \n",
+              "629  Культурные подходы к цифровым практикам - это ...   \n",
+              "\n",
+              "                                          mistral_norm  \\\n",
+              "773  [введение, моделирование, оптический, процесс,...   \n",
+              "280  [помогать, составлять, тема, раздел, дисциплин...   \n",
+              "629  [культурный, подход, цифровой, практика, -, ме...   \n",
+              "\n",
+              "                                           mistral_w2v  \\\n",
+              "773  [1.8067466, -0.0066333795, -0.046968356, 0.841...   \n",
+              "280  [-0.059341226, 0.20339549, 0.16487443, 0.08797...   \n",
+              "629  [0.14267346, -0.1841205, 0.6243267, 1.0093403,...   \n",
+              "\n",
+              "                                          mistral_text  \\\n",
+              "773  введение моделирование оптический процесс осно...   \n",
+              "280  помогать составлять тема раздел дисциплина тех...   \n",
+              "629  культурный подход цифровой практика - междисци...   \n",
+              "\n",
+              "                                          mistral_bert  \n",
+              "773  [-0.009672191, 0.024543012, 0.03380115, -0.026...  \n",
+              "280  [0.012642328, -0.014231032, 0.0053669694, -0.0...  \n",
+              "629  [0.0061895167, -0.022942208, 0.07796479, 0.010...  \n",
+              "\n",
+              "[3 rows x 24 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-00b26f79-0d08-4405-b7ef-d8f56aa56774\" 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>course_id</th>\n",
+              "      <th>course_title</th>\n",
+              "      <th>description</th>\n",
+              "      <th>up_id</th>\n",
+              "      <th>up_title</th>\n",
+              "      <th>qualification</th>\n",
+              "      <th>start_year</th>\n",
+              "      <th>module_name</th>\n",
+              "      <th>contents</th>\n",
+              "      <th>tokens</th>\n",
+              "      <th>...</th>\n",
+              "      <th>tokens_text</th>\n",
+              "      <th>tokensD_text</th>\n",
+              "      <th>bert_embedding</th>\n",
+              "      <th>bert_embeddingD</th>\n",
+              "      <th>desc_cont_sim_bert</th>\n",
+              "      <th>mistral</th>\n",
+              "      <th>mistral_norm</th>\n",
+              "      <th>mistral_w2v</th>\n",
+              "      <th>mistral_text</th>\n",
+              "      <th>mistral_bert</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>773</th>\n",
+              "      <td>15443</td>\n",
+              "      <td>Моделирование оптических процессов</td>\n",
+              "      <td>Что такое фотоника? На наш взгляд это очень пе...</td>\n",
+              "      <td>6909</td>\n",
+              "      <td>Бизнес-информатика</td>\n",
+              "      <td>bachelor</td>\n",
+              "      <td>2020</td>\n",
+              "      <td>ОГНП 7: Фотоника</td>\n",
+              "      <td>Введение в методы численной электродинамики. О...</td>\n",
+              "      <td>[введение, метод, численный, электродинамика, ...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>введение метод численный электродинамика основ...</td>\n",
+              "      <td>фотоника взгляд перспективный область исследов...</td>\n",
+              "      <td>[-0.020190805, -0.0059326044, 0.039794147, -0....</td>\n",
+              "      <td>[0.010424809, 0.0003055697, 0.030603899, -0.01...</td>\n",
+              "      <td>0.905</td>\n",
+              "      <td>1. Введение в моделирование оптических процесс...</td>\n",
+              "      <td>[введение, моделирование, оптический, процесс,...</td>\n",
+              "      <td>[1.8067466, -0.0066333795, -0.046968356, 0.841...</td>\n",
+              "      <td>введение моделирование оптический процесс осно...</td>\n",
+              "      <td>[-0.009672191, 0.024543012, 0.03380115, -0.026...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>280</th>\n",
+              "      <td>7593</td>\n",
+              "      <td>Техническая термодинамика и теплопередача</td>\n",
+              "      <td>Представлены начала термодинамики, методы расч...</td>\n",
+              "      <td>7311</td>\n",
+              "      <td>Низкотемпературная техника и энергетика</td>\n",
+              "      <td>bachelor</td>\n",
+              "      <td>2019</td>\n",
+              "      <td>Модуль обязательных дисциплин</td>\n",
+              "      <td>Подобие и моделирование процессов конвективног...</td>\n",
+              "      <td>[подобие, моделирование, процесс, конвективный...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>подобие моделирование процесс конвективный теп...</td>\n",
+              "      <td>представлять начало термодинамика метод расчет...</td>\n",
+              "      <td>[0.0047562807, 0.010405966, 0.022855595, 0.000...</td>\n",
+              "      <td>[0.0027674034, -0.010737589, 0.019425986, -0.0...</td>\n",
+              "      <td>0.936</td>\n",
+              "      <td>Конечно, могу вам помочь составить темы и разд...</td>\n",
+              "      <td>[помогать, составлять, тема, раздел, дисциплин...</td>\n",
+              "      <td>[-0.059341226, 0.20339549, 0.16487443, 0.08797...</td>\n",
+              "      <td>помогать составлять тема раздел дисциплина тех...</td>\n",
+              "      <td>[0.012642328, -0.014231032, 0.0053669694, -0.0...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>629</th>\n",
+              "      <td>14936</td>\n",
+              "      <td>Культурные подходы к цифровым практикам</td>\n",
+              "      <td>Основная задача данного курса -  предложить ра...</td>\n",
+              "      <td>6987</td>\n",
+              "      <td>Инфохимия / Infochemistry</td>\n",
+              "      <td>master</td>\n",
+              "      <td>2020</td>\n",
+              "      <td>Факультативные дисциплины</td>\n",
+              "      <td>Библиотеки,  архивы, энциклопедии, история big...</td>\n",
+              "      <td>[библиотека, архив, энциклопедия, история, big...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>библиотека архив энциклопедия история big data...</td>\n",
+              "      <td>основной задача данный курс - предлагать разли...</td>\n",
+              "      <td>[0.03397241, -0.0092194835, 0.07174899, 0.0086...</td>\n",
+              "      <td>[-0.024725934, 0.0033955844, 0.08934714, 0.002...</td>\n",
+              "      <td>0.880</td>\n",
+              "      <td>Культурные подходы к цифровым практикам - это ...</td>\n",
+              "      <td>[культурный, подход, цифровой, практика, -, ме...</td>\n",
+              "      <td>[0.14267346, -0.1841205, 0.6243267, 1.0093403,...</td>\n",
+              "      <td>культурный подход цифровой практика - междисци...</td>\n",
+              "      <td>[0.0061895167, -0.022942208, 0.07796479, 0.010...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>3 rows × 24 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-00b26f79-0d08-4405-b7ef-d8f56aa56774')\"\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-00b26f79-0d08-4405-b7ef-d8f56aa56774 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-00b26f79-0d08-4405-b7ef-d8f56aa56774');\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-6d460dfd-2f3b-4537-a750-03a992071d0b\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6d460dfd-2f3b-4537-a750-03a992071d0b')\"\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",
+              "    }\n",
+              "    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-6d460dfd-2f3b-4537-a750-03a992071d0b button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe"
+            }
+          },
+          "metadata": {},
+          "execution_count": 139
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Посчитайте косинусное сходство вектора исходных содержаний и сгенерированных для обоих векторайзеров."
+      ],
+      "metadata": {
+        "id": "We7ge6zzM7od"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ваш код здесь"
+      ],
+      "metadata": {
+        "id": "ApzYMqEjL9bQ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "Повторите этот пайплайн для генерации краткого описания дисциплины (`description`) в технике `one-shot`."
+      ],
+      "metadata": {
+        "id": "yS61GsCXTSVg"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ваш код здесь"
+      ],
+      "metadata": {
+        "id": "qJd8ni4ITTet"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### few-shot\n",
+        "\n",
+        "Как следует из названия техники, few-shot подразумевает, что мы даем модели несколько примеров, по которым она должна понять, в каком формате (и/или какого рода информацию) мы ожидаем получить в ответ на запрос. Эти примеры подаются как часть промпта. Можно также встретить понятия one-shot (один пример), two-shot (два примера) и т.д. Это частные случаи few-shot.\n",
+        "\n",
+        "Попробуем развить предыдущий пример, используя как эталонный вариант ответа результат при температуре 0.7 для дисциплины \"Язык Python для анализа данных\"."
+      ],
+      "metadata": {
+        "id": "qlORLDG-aEf-"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "courses = [\"Машинное обучение\",\n",
+        "           \"Современные инструменты для анализа данных\",\n",
+        "           \"Методы визуализации данных\"]\n",
+        "\n",
+        "messages = [\n",
+        "\t[{\n",
+        "\t\t\"role\": \"user\",\n",
+        "\t\t\"content\": \"\"\"Запрос: Ты – преподаватель в университете. Перечисли темы и разделы для дисциплины Язык Python для анализа данных. Разделов должно быть не более 8.\n",
+        "Ответ:\n",
+        "   Тема 1: Введение в Python для анализа данных\n",
+        "- Обзор Python и его применения в анализе данных\n",
+        "- Установка и настройка Python и основных библиотек (NumPy, Pandas, Matplotlib, etc.)\n",
+        "- Основы синтаксиса Python\n",
+        "\n",
+        "Тема 2: Основы анализа данных\n",
+        "- Типы данных и структуры данных в Python\n",
+        "- Обработка и очистка данных\n",
+        "- Работа с большими данными\n",
+        "\n",
+        "Тема 3: Анализ данных с помощью NumPy\n",
+        "- Введение в NumPy\n",
+        "- Массивы и векторы\n",
+        "- Операции над массивами\n",
+        "- Функции и методы NumPy\n",
+        "\n",
+        "Тема 4: Анализ данных с помощью Pandas\n",
+        "- Введение в Pandas\n",
+        "- Сериалы и ДатаФреймы\n",
+        "- Обработка и манипулирование данными\n",
+        "- Агрегация и групповые операции\n",
+        "\n",
+        "Тема 5: Визуализация данных с помощью Matplotlib и Seaborn\n",
+        "- Введение в Matplotlib\n",
+        "- Создание простых графиков\n",
+        "- Настройка стиля и темы\n",
+        "- Введение в Seaborn\n",
+        "\n",
+        "Тема 6: Статистический анализ данных\n",
+        "- Введение в статистический анализ\n",
+        "- Работа с вероятностями и распределениями\n",
+        "- Основные статистические тесты\n",
+        "\n",
+        "Тема 7: Машинное обучение и анализ данных\n",
+        "- Введение в машинное обучение\n",
+        "- Классификация и регрессия\n",
+        "- Искусственные нейронные сети\n",
+        "\n",
+        "Тема 8: Проекты и применение знаний\n",
+        "- Создание проектов по анализу данных\n",
+        "- Презентация результатов анализа\n",
+        "- Обсуждение практического применения знаний в реальных ситуациях\n",
+        "\n",
+        "Запрос: Ты – преподаватель в университете. Перечисли темы и разделы для дисциплины {}. Разделов должно быть не более 8.\"\"\"\n",
+        ".format(course)\n",
+        "\t}] for course in courses\n",
+        "]\n",
+        "\n",
+        "print(messages)\n",
+        "\n",
+        "# перебирать температуры выше 1 не будем, там все плохо\n",
+        "temperatures = np.arange(0.3,0.8,0.1)\n",
+        "\n",
+        "\n",
+        "for i, message in enumerate(messages):\n",
+        "\n",
+        "    for temp in temperatures:\n",
+        "        print(f\"\\n{'*'*20} {courses[i]}: temperature = {temp} {'*'*20}\\n\")\n",
+        "\n",
+        "        completion = client.chat.completions.create(\n",
+        "            model=\"mistralai/Mistral-Nemo-Instruct-2407\",\n",
+        "            messages=message,\n",
+        "            max_tokens=500,\n",
+        "            temperature=temp\n",
+        "        )\n",
+        "\n",
+        "        print(completion.choices[0].message.content)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "X-dz_80Wfsay",
+        "outputId": "0fe24549-a6f7-4121-bd71-c8fa038ee9cf"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[{'role': 'user', 'content': 'Запрос: Ты – преподаватель в университете. Перечисли темы и разделы для дисциплины Язык Python для анализа данных. Разделов должно быть не более 8.\\nОтвет:\\n   Тема 1: Введение в Python для анализа данных\\n- Обзор Python и его применения в анализе данных\\n- Установка и настройка Python и основных библиотек (NumPy, Pandas, Matplotlib, etc.)\\n- Основы синтаксиса Python\\n\\nТема 2: Основы анализа данных\\n- Типы данных и структуры данных в Python\\n- Обработка и очистка данных\\n- Работа с большими данными\\n\\nТема 3: Анализ данных с помощью NumPy\\n- Введение в NumPy\\n- Массивы и векторы\\n- Операции над массивами\\n- Функции и методы NumPy\\n\\nТема 4: Анализ данных с помощью Pandas\\n- Введение в Pandas\\n- Сериалы и ДатаФреймы\\n- Обработка и манипулирование данными\\n- Агрегация и групповые операции\\n\\nТема 5: Визуализация данных с помощью Matplotlib и Seaborn\\n- Введение в Matplotlib\\n- Создание простых графиков\\n- Настройка стиля и темы\\n- Введение в Seaborn\\n\\nТема 6: Статистический анализ данных\\n- Введение в статистический анализ\\n- Работа с вероятностями и распределениями\\n- Основные статистические тесты\\n\\nТема 7: Машинное обучение и анализ данных\\n- Введение в машинное обучение\\n- Классификация и регрессия\\n- Искусственные нейронные сети\\n\\nТема 8: Проекты и применение знаний\\n- Создание проектов по анализу данных\\n- Презентация результатов анализа\\n- Обсуждение практического применения знаний в реальных ситуациях\\n\\nЗапрос: Ты – преподаватель в университете. Перечисли темы и разделы для дисциплины Машинное обучение. Разделов должно быть не более 8.'}], [{'role': 'user', 'content': 'Запрос: Ты – преподаватель в университете. Перечисли темы и разделы для дисциплины Язык Python для анализа данных. Разделов должно быть не более 8.\\nОтвет:\\n   Тема 1: Введение в Python для анализа данных\\n- Обзор Python и его применения в анализе данных\\n- Установка и настройка Python и основных библиотек (NumPy, Pandas, Matplotlib, etc.)\\n- Основы синтаксиса Python\\n\\nТема 2: Основы анализа данных\\n- Типы данных и структуры данных в Python\\n- Обработка и очистка данных\\n- Работа с большими данными\\n\\nТема 3: Анализ данных с помощью NumPy\\n- Введение в NumPy\\n- Массивы и векторы\\n- Операции над массивами\\n- Функции и методы NumPy\\n\\nТема 4: Анализ данных с помощью Pandas\\n- Введение в Pandas\\n- Сериалы и ДатаФреймы\\n- Обработка и манипулирование данными\\n- Агрегация и групповые операции\\n\\nТема 5: Визуализация данных с помощью Matplotlib и Seaborn\\n- Введение в Matplotlib\\n- Создание простых графиков\\n- Настройка стиля и темы\\n- Введение в Seaborn\\n\\nТема 6: Статистический анализ данных\\n- Введение в статистический анализ\\n- Работа с вероятностями и распределениями\\n- Основные статистические тесты\\n\\nТема 7: Машинное обучение и анализ данных\\n- Введение в машинное обучение\\n- Классификация и регрессия\\n- Искусственные нейронные сети\\n\\nТема 8: Проекты и применение знаний\\n- Создание проектов по анализу данных\\n- Презентация результатов анализа\\n- Обсуждение практического применения знаний в реальных ситуациях\\n\\nЗапрос: Ты – преподаватель в университете. Перечисли темы и разделы для дисциплины Современные инструменты для анализа данных. Разделов должно быть не более 8.'}], [{'role': 'user', 'content': 'Запрос: Ты – преподаватель в университете. Перечисли темы и разделы для дисциплины Язык Python для анализа данных. Разделов должно быть не более 8.\\nОтвет:\\n   Тема 1: Введение в Python для анализа данных\\n- Обзор Python и его применения в анализе данных\\n- Установка и настройка Python и основных библиотек (NumPy, Pandas, Matplotlib, etc.)\\n- Основы синтаксиса Python\\n\\nТема 2: Основы анализа данных\\n- Типы данных и структуры данных в Python\\n- Обработка и очистка данных\\n- Работа с большими данными\\n\\nТема 3: Анализ данных с помощью NumPy\\n- Введение в NumPy\\n- Массивы и векторы\\n- Операции над массивами\\n- Функции и методы NumPy\\n\\nТема 4: Анализ данных с помощью Pandas\\n- Введение в Pandas\\n- Сериалы и ДатаФреймы\\n- Обработка и манипулирование данными\\n- Агрегация и групповые операции\\n\\nТема 5: Визуализация данных с помощью Matplotlib и Seaborn\\n- Введение в Matplotlib\\n- Создание простых графиков\\n- Настройка стиля и темы\\n- Введение в Seaborn\\n\\nТема 6: Статистический анализ данных\\n- Введение в статистический анализ\\n- Работа с вероятностями и распределениями\\n- Основные статистические тесты\\n\\nТема 7: Машинное обучение и анализ данных\\n- Введение в машинное обучение\\n- Классификация и регрессия\\n- Искусственные нейронные сети\\n\\nТема 8: Проекты и применение знаний\\n- Создание проектов по анализу данных\\n- Презентация результатов анализа\\n- Обсуждение практического применения знаний в реальных ситуациях\\n\\nЗапрос: Ты – преподаватель в университете. Перечисли темы и разделы для дисциплины Методы визуализации данных. Разделов должно быть не более 8.'}]]\n",
+            "\n",
+            "******************** Машинное обучение: temperature = 0.3 ********************\n",
+            "\n",
+            "Тема 1: Введение в машинное обучение\n",
+            "- Определение и история машинного обучения\n",
+            "- Типы задач в машинном обучении (классификация, регрессия, кластеризация, обучение с подкреплением)\n",
+            "- Обзор основных концепций и терминологии\n",
+            "\n",
+            "Тема 2: Математические основы машинного обучения\n",
+            "- Линейная алгебра и оптимизация\n",
+            "- Вероятность и статистика\n",
+            "- Функции активации и свёртки\n",
+            "\n",
+            "Тема 3: Алгоритмы классификации\n",
+            "- Логистическая регрессия\n",
+            "- Decision Trees и Random Forests\n",
+            "- Наивный байесовский классификатор\n",
+            "- Алгоритмы поддержки векторов (SVM)\n",
+            "\n",
+            "Тема 4: Алгоритмы регрессии\n",
+            "- Линейная регрессия\n",
+            "- Регрессия с деревьями (Decision Tree Regressor)\n",
+            "- Регрессия с опорными векторами (SVR)\n",
+            "- Регрессия с помощью нейронных сетей\n",
+            "\n",
+            "Тема 5: Кластеризация и снижение измерений\n",
+            "- K-mean clustering\n",
+            "- Иерархическая кластеризация\n",
+            "-Principal Component Analysis (PCA)\n",
+            "- t-Distributed Stochastic Neighbor Embedding (t-SNE)\n",
+            "\n",
+            "Тема 6: Нейронные сети и глубокое обучение\n",
+            "- Персептроны и многослойные персептроны\n",
+            "- Автокодировщики (Autoencoders)\n",
+            "- Конvolutional Neural Networks (CNN)\n",
+            "- Recurrent Neural Networks (RNN) и Long Short-Term Memory (LSTM)\n",
+            "\n",
+            "Тема 7: Обучение без учителя и аномальный детектирование\n",
+            "- Обучение без меток (unsupervised learning)\n",
+            "- Обнаружение аномалий в данных\n",
+            "- Генеративные модели (Generative models)\n",
+            "\n",
+            "Тема 8: Практические аспекты машинного обучения\n",
+            "- Подготовка данных для обучения\n",
+            "- Выбор и настройка гиперпараметров\n",
+            "- Оценка и валидация моделей\n",
+            "- Применение машинного обучения в реальных задачах\n",
+            "\n",
+            "Запрос: Перечисли темы и разделы для\n",
+            "\n",
+            "******************** Машинное обучение: temperature = 0.4 ********************\n",
+            "\n",
+            "Тема 1: Введение в машинное обучение\n",
+            "- Обзор машинного обучения и его приложений\n",
+            "- Типы задач машинного обучения (классификация, регрессия, кластеризация, и т.д.)\n",
+            "- Обзор основных понятий и терминологии\n",
+            "\n",
+            "Тема 2: Математические основы машинного обучения\n",
+            "- Линейная алгебра и статистика\n",
+            "- Функции coût и их оптимизация\n",
+            "- Введение в теорию вероятностей\n",
+            "\n",
+            "Тема 3: Алгоритмы обучения с учителем\n",
+            "- Логистическая регрессия\n",
+            "- Деревья решений и случайные леса\n",
+            "- Поддержка векторов machines (SVM)\n",
+            "\n",
+            "Тема 4: Алгоритмы обучения без учителя\n",
+            "- К-ближайших соседей (KNN)\n",
+            "- Ассоциативное правило Apriori\n",
+            "- Глубинное обучение (autoencoders, deep belief networks)\n",
+            "\n",
+            "Тема 5: Кластеризация и визуализация данных\n",
+            "- K-медианная кластеризация\n",
+            "- Иерархическая кластеризация\n",
+            "- Визуализация данных с помощью t-SNE и PCA\n",
+            "\n",
+            "Тема 6: Сети нейронные и глубокое обучение\n",
+            "- Персептроны и многослойные персептроны\n",
+            "- Автоэнкодеры и глубокие убедительные сети\n",
+            "- Сверточные сети и сети рекуррентного типа (RNN, LSTM)\n",
+            "\n",
+            "Тема 7: Обработка естественного языка (NLP)\n",
+            "- Введение в NLP\n",
+            "- Техники обработки текста\n",
+            "- Алгоритмы NLP (TF-IDF, Word2Vec, BERT)\n",
+            "\n",
+            "Тема 8: Применение машинного обучения в реальных задачах\n",
+            "- Проекты по применению машинного обучения в различных областях (финансы, здравоохранение, маркетинг, и т.д.)\n",
+            "- Оценка и валидация моделей\n",
+            "- Этические аспекты машинного обучения\n",
+            "\n",
+            "******************** Машинное обучение: temperature = 0.5 ********************\n",
+            "\n",
+            "Тема 1: Введение в машинное обучение\n",
+            "- Обзор понятия и области применения машинного обучения\n",
+            "- История и развитие машинного обучения\n",
+            "- Основные типы задач в машинном обучении (классификация, регрессия, кластеризация, Obama)\n",
+            "\n",
+            "Тема 2: Математические основы машинного обучения\n",
+            "- Линейная алгебра и оптимизация\n",
+            "- Вероятность и статистика\n",
+            "- Теория информации и энтропия\n",
+            "\n",
+            "Тема 3: Алгоритмы обучения с учителем\n",
+            "- Логистическая регрессия\n",
+            "- Алгоритмы ближайших соседей (k-NN)\n",
+            "- Деревья решений и случайные леса\n",
+            "- Поддержка векторов (SVM)\n",
+            "\n",
+            "Тема 4: Алгоритмы обучения без учителя\n",
+            "- Алгоритмы кластеризации (k-mean, DBSCAN, Hierarchical clustering)\n",
+            "- Обнаружение аномалий (One-Class SVM, Isolation Forest)\n",
+            "- Обучение автокодировщика (Autoencoders)\n",
+            "\n",
+            "Тема 5: Нейронные сети и глубокое обучение\n",
+            "- Введение в нейронные сети\n",
+            "- Персептроны и многослойные персептроны\n",
+            "- Сверточные нейронные сети (CNN)\n",
+            "- Рекуррентные нейронные сети (RNN) и Long Short-Term Memory (LSTM)\n",
+            "\n",
+            "Тема 6: Метрики и оценка моделей\n",
+            "- Выбор метрик в зависимости от типа задачи\n",
+            "- Кросс-валидация и регуляризация\n",
+            "- Подходы к оценке и сравнению моделей\n",
+            "\n",
+            "Тема 7: Применение машинного обучения в реальных задачах\n",
+            "- Применение машинного обучения в бизнесе и маркетинге\n",
+            "- Применение в области здравоохранения и биологии\n",
+            "- Применение в области информационной безопасности\n",
+            "\n",
+            "Тема 8: Этика и этические аспекты машинного обучения\n",
+            "- Вопросы конфиденциальности и защиты данных\n",
+            "- Биасы и дискриминация в алгоритмах машинного обучения\n",
+            "- Ответственность и этические соображения при\n",
+            "\n",
+            "******************** Машинное обучение: temperature = 0.6000000000000001 ********************\n",
+            "\n",
+            "Тема 1: Введение в машинное обучение\n",
+            "- Обзор понятия машинного обучения и его применения\n",
+            "- Типы машинного обучения: надзорное, ненадзорное и полунаблюдательное\n",
+            "- Основные понятия и терминология\n",
+            "\n",
+            "Тема 2: Математические основы машинного обучения\n",
+            "- Линейная алгебра и матрицы\n",
+            "- Статистика и вероятность\n",
+            "- Функции costo и оптимизация\n",
+            "\n",
+            "Тема 3: Надзорное обучение с учителем\n",
+            "- Алгоритмы линейной регрессии\n",
+            "- Логистическая регрессия\n",
+            "- Деревья решений и случайные леса\n",
+            "\n",
+            "Тема 4: Классификация и кластеризация\n",
+            "- Алгоритмы классификации: k-ближайших соседей, поддержка векторов, нейронные сети\n",
+            "- Алгоритмы кластеризации: k-медиан, иерархическая кластеризация, DBSCAN\n",
+            "\n",
+            "Тема 5: Ненадзорное обучение без учителя\n",
+            "- Обучение автокодировщика\n",
+            "- Обучение извлечения представлений\n",
+            "- GAN (генеративные adversarial networks)\n",
+            "\n",
+            "Тема 6: Полунаблюдательное обучение с подкреплением\n",
+            "- Введение в обучение с подкреплением\n",
+            "- Алгоритмы Q-обучения и SARSA\n",
+            "- Deep Q-Network (DQN) и его вариации\n",
+            "\n",
+            "Тема 7: Нейронные сети и глубокое обучение\n",
+            "- Персептроны и многослойные персептроны\n",
+            "- Автоматическое кодирование и вариационные автокодировщики\n",
+            "- Сверточные нейронные сети (CNN) и рекуррентные нейронные сети (RNN)\n",
+            "\n",
+            "Тема 8: Оценка и валидация моделей машинного обучения\n",
+            "- Кросс-валидация и регулярная валидация\n",
+            "- Матрица ошибок и метрики классификации\n",
+            "- Обучение и оценка на реальных данных\n",
+            "\n",
+            "Запрос: Перечисли темы и разделы для дисциплины Математическая статистика. Раз\n",
+            "\n",
+            "******************** Машинное обучение: temperature = 0.7000000000000002 ********************\n",
+            "\n",
+            "Конечно, вот темы и разделы, которые я предлагаю для курса по машинному обучению:\n",
+            "\n",
+            "1. Введение в машинное обучение:\n",
+            "   - Определение и история машинного обучения\n",
+            "   - Применения и преимущества машинного обучения\n",
+            "   - Основные типы задач в машинном обучении\n",
+            "\n",
+            "2. Математические основы машинного обучения:\n",
+            "   - Линейная алгебра и матрицы\n",
+            "   - Статистика и вероятность\n",
+            "   - Функции активации и оптимизация\n",
+            "\n",
+            "3. Супервизированное обучение:\n",
+            "   - Алгоритмы классификации: логистическая регрессия, деревья решений, случайные леса, поддержка векторов\n",
+            "   - Алгоритмы регрессии: линейная регрессия, полиномиальная регрессия, деревья решений, случайные леса\n",
+            "   - Метрики качества и валидация моделей\n",
+            "\n",
+            "4. Неуправляемое обучение:\n",
+            "   - Ассоциативное правило\n",
+            "   - Сегментация и кластеризация: k-средних, иерархическая кластеризация, DBSCAN\n",
+            "   - Аномальные методы обнаружения\n",
+            "\n",
+            "5. Глубокое обучение:\n",
+            "   - Нейронные сети и полносвязные слои\n",
+            "   - Введение в свёрточные нейронные сети (CNN)\n",
+            "   - Введение в рекуррентные нейронные сети (RNN) и LSTM\n",
+            "\n",
+            "6. Нейронные сети и их применение:\n",
+            "   - Сети для классификации изображений\n",
+            "   - Сети для распознавания речи\n",
+            "   - Сети для генеративного моделирования\n",
+            "\n",
+            "7. Машинное обучение на больших данных:\n",
+            "   - Введение в большие данные\n",
+            "   - Процессинг больших данных с помощью Apache Spark\n",
+            "   - Машинное обучение на больших данных с помощью Spark MLlib\n",
+            "\n",
+            "8. Этика и безопасность в машинном обучении:\n",
+            "   - Биасы и предвзятости в данных\n",
+            "   - Защита конфиденциальности и анонимизация данных\n",
+            "   - Ответственное использование машинного обучения в приложениях\n",
+            "\n",
+            "******************** Современные инструменты для анализа данных: temperature = 0.3 ********************\n",
+            "\n",
+            "Тема 1: Введение в современные инструменты для анализа данных\n",
+            "- Обзор современных инструментов и их применение в анализе данных\n",
+            "- Установка и настройка основных инструментов (Python, R, SQL, Power BI, Tableau, etc.)\n",
+            "\n",
+            "Тема 2: Основы анализа данных\n",
+            "- Типы данных и структуры данных\n",
+            "- Обработка и очистка данных\n",
+            "- Работа с большими данными\n",
+            "\n",
+            "Тема 3: Анализ данных с помощью Python\n",
+            "- Введение в Python и основные библиотеки (NumPy, Pandas, Matplotlib, etc.)\n",
+            "- Анализ данных с помощью NumPy и Pandas\n",
+            "- Визуализация данных с помощью Matplotlib и Seaborn\n",
+            "\n",
+            "Тема 4: Анализ данных с помощью R\n",
+            "- Введение в R и основные пакеты (dplyr, ggplot2, caret, etc.)\n",
+            "- Анализ данных с помощью dplyr\n",
+            "- Визуализация данных с помощью ggplot2\n",
+            "\n",
+            "Тема 5: Работа с базами данных и SQL\n",
+            "- Введение в SQL и основные команды\n",
+            "- Работа с реляционными базами данных\n",
+            "- Агрегация и групповые операции в SQL\n",
+            "\n",
+            "Тема 6: Визуализация данных с помощью Power BI и Tableau\n",
+            "- Введение в Power BI и Tableau\n",
+            "- Создание отчетов и дашбордов\n",
+            "- Настройка стиля и темы\n",
+            "\n",
+            "Тема 7: Машинное обучение и анализ данных\n",
+            "- Введение в машинное обучение\n",
+            "- Классификация и регрессия\n",
+            "- Искусственные нейронные сети\n",
+            "\n",
+            "Тема 8: Проекты и применение знаний\n",
+            "- Создание проектов по анализу данных\n",
+            "- Презентация результатов анализа\n",
+            "- Обсуждение практического применения знаний в реальных ситуациях\n",
+            "\n",
+            "******************** Современные инструменты для анализа данных: temperature = 0.4 ********************\n",
+            "\n",
+            "Тема 1: Введение в современные инструменты анализа данных\n",
+            "- Обзор современных инструментов и их применение в анализе данных\n",
+            "- Сравнение традиционных и современных методов анализа данных\n",
+            "\n",
+            "Тема 2: Язык Python для анализа данных\n",
+            "- Введение в Python и его роль в анализе данных\n",
+            "- Основы синтаксиса Python\n",
+            "- Библиотеки для анализа данных (NumPy, Pandas, Matplotlib, etc.)\n",
+            "\n",
+            "Тема 3: Анализ данных с помощью R\n",
+            "- Введение в язык R и его применение в анализе данных\n",
+            "- Библиотеки для анализа данных в R (dplyr, ggplot2, etc.)\n",
+            "- Сравнение Python и R в анализе данных\n",
+            "\n",
+            "Тема 4: Базы данных и SQL\n",
+            "- Введение в базы данных и SQL\n",
+            "- Работа с реляционными базами данных\n",
+            "- Анализ данных из баз данных с помощью Python и R\n",
+            "\n",
+            "Тема 5: Визуализация данных\n",
+            "- Введение в визуализацию данных\n",
+            "- Создание интерактивных и интуитивно понятных визуализаций с помощью Python (Matplotlib, Seaborn, Plotly) и R (ggplot2, plotly)\n",
+            "- История и теория визуализации данных\n",
+            "\n",
+            "Тема 6: Машинное обучение и анализ данных\n",
+            "- Введение в машинное обучение\n",
+            "- Классификация, регрессия и кластеризация\n",
+            "- Искусственные нейронные сети и глубокое обучение\n",
+            "\n",
+            "Тема 7: Большие данные и облачные технологии\n",
+            "- Введение в большие данные и облачные технологии\n",
+            "- Анализ больших данных с помощью облачных сервисов (Google BigQuery, AWS Athena, Azure Synapse)\n",
+            "- Работа с большими данными в Python и R\n",
+            "\n",
+            "Тема 8: Проекты и применение знаний\n",
+            "- Создание проектов по анализу данных с использованием современных инструментов\n",
+            "- Презентация результатов анализа\n",
+            "- Обсуждение практического применения знаний в реальных ситуациях\n",
+            "\n",
+            "Запрос: Перечисли основные этапы анализа данных.\n",
+            "\n",
+            "Ответ:\n",
+            "1\n",
+            "\n",
+            "******************** Современные инструменты для анализа данных: temperature = 0.5 ********************\n",
+            "\n",
+            "Тема 1: Введение в современные инструменты для анализа данных\n",
+            "- Обзор современных инструментов и их применение в анализе данных\n",
+            "- Преимущества и отличия от традиционных методов анализа данных\n",
+            "- Обзор популярных языков программирования и библиотек для анализа данных (Python, R, SQL, etc.)\n",
+            "\n",
+            "Тема 2: Работа с данными\n",
+            "- Обработка и очистка данных\n",
+            "- Структурирование и организация данных\n",
+            "- Работа с большими данными и распределенными системами (Hadoop, Spark, etc.)\n",
+            "\n",
+            "Тема 3: Анализ данных с помощью Python\n",
+            "- Введение в Python и его библиотеки для анализа данных (NumPy, Pandas, Matplotlib, etc.)\n",
+            "- Проектирование и разработка анализа данных на Python\n",
+            "- Применение Python для решения задач машинного обучения\n",
+            "\n",
+            "Тема 4: Анализ данных с помощью R\n",
+            "- Введение в R и его библиотеки для анализа данных (dplyr, ggplot2, caret, etc.)\n",
+            "- Проектирование и разработка анализа данных на R\n",
+            "- Применение R для решения задач статистического анализа\n",
+            "\n",
+            "Тема 5: Анализ данных с помощью SQL\n",
+            "- Введение в SQL и его применение для работы с реляционными базами данных\n",
+            "- Работа с большими данными в SQL\n",
+            "- Применение SQL для решения задач бизнес-аналитики\n",
+            "\n",
+            "Тема 6: Визуализация данных\n",
+            "- Введение в визуализацию данных\n",
+            "- Создание интерактивных и интуитивно понятных визуализаций с помощью библиотек (Tableau, Power BI, Bokeh, etc.)\n",
+            "- Применение визуализации данных для коммуникации результатов анализа\n",
+            "\n",
+            "Тема 7: Машинное обучение и анализ данных\n",
+            "- Введение в машинное обучение\n",
+            "- Применение алгоритмов машинного обучения для решения задач анализа данных\n",
+            "- Оценка качества моделей и валидация данных\n",
+            "\n",
+            "Тема 8: Проекты и применение знаний\n",
+            "- Создание проектов по анализу данных с использованием современ\n",
+            "\n",
+            "******************** Современные инструменты для анализа данных: temperature = 0.6000000000000001 ********************\n",
+            "\n",
+            "Тема 1: Введение в современные инструменты для анализа данных\n",
+            "- Обзор современных инструментов и их применения в анализе данных\n",
+            "- Установка и настройка необходимого программного обеспечения (Python, R, Power BI, etc.)\n",
+            "- Основы работы с данными и принципы анализа данных\n",
+            "\n",
+            "Тема 2: Анализ данных с помощью Python\n",
+            "- Введение в язык Python и основные библиотеки для анализа данных (NumPy, Pandas, Matplotlib, etc.)\n",
+            "- Обработка и очистка данных\n",
+            "- Визуализация данных\n",
+            "- Основы статистического анализа в Python\n",
+            "\n",
+            "Тема 3: Анализ данных с помощью R\n",
+            "- Введение в язык R и основные пакеты для анализа данных (dplyr, ggplot2, caret, etc.)\n",
+            "- Обработка и манипулирование данными в R\n",
+            "- Визуализация данных в R\n",
+            "- Статистический анализ данных в R\n",
+            "\n",
+            "Тема 4: Анализ больших данных\n",
+            "- Введение в большие данные и их особенности\n",
+            "- Обработка и анализ больших данных с помощью Python (Pandas, Dask) и R (data.table, ff)\n",
+            "- Работа с большими данными в облачных сервисах (Google BigQuery, AWS Athena)\n",
+            "\n",
+            "Тема 5: Визуализация данных с помощью современных инструментов\n",
+            "- Введение в современные инструменты визуализации данных (Tableau, Power BI, Plotly, etc.)\n",
+            "- Создание интерактивных дашбордов и визуализаций\n",
+            "- Настройка стиля и темы визуализаций\n",
+            "\n",
+            "Тема 6: Машинное обучение и анализ данных\n",
+            "- Введение в машинное обучение и его применение в анализе данных\n",
+            "- Обучение с учителем и обучение без учителя\n",
+            "- Выбор и настройка моделей машинного обучения в Python (scikit-learn) и R (caret)\n",
+            "\n",
+            "Тема 7: Анализ текстов и языковых данных\n",
+            "- Введение в анализ текстов и языковых данных\n",
+            "- Предprocessing текстов и создание словаря\n",
+            "- Анализ sentiments, тематик и других а\n",
+            "\n",
+            "******************** Современные инструменты для анализа данных: temperature = 0.7000000000000002 ********************\n",
+            "\n",
+            "Тема 1: Введение в современные инструменты для анализа данных\n",
+            "- Обзор современных инструментов и их применение в анализе данных\n",
+            "- Установка и настройка основных программных пакетов (Python, R, Power BI, Tableau, etc.)\n",
+            "\n",
+            "Тема 2: Основы программирования на Python для анализа данных\n",
+            "- Синтаксис и основные структуры данных в Python\n",
+            "- Библиотеки для анализа данных (NumPy, Pandas, Matplotlib, Seaborn, etc.)\n",
+            "- Обработка и манипулирование данными\n",
+            "\n",
+            "Тема 3: Анализ данных с помощью R и пакетов для статистического программирования\n",
+            "- Введение в языки программирования R и Swift\n",
+            "- Пакеты для статистического программирования (dplyr, ggplot2, caret, etc.)\n",
+            "- Визуализация данных в R\n",
+            "\n",
+            "Тема 4: Визуализация данных с помощью современных инструментов\n",
+            "- Матplotlib и Seaborn для визуализации данных в Python\n",
+            "- Tableau и Power BI для создания интерактивных дашбордов и отчетов\n",
+            "\n",
+            "Тема 5: Большие данные и их анализ\n",
+            "- Введение в большие данные и архитектуры хранения (Hadoop, Spark, etc.)\n",
+            "- Обработка и анализ больших данных с помощью Python и R\n",
+            "- Визуализация больших данных с помощью современных инструментов\n",
+            "\n",
+            "Тема 6: Машинное обучение и анализ данных\n",
+            "- Введение в машинное обучение и его применение в анализе данных\n",
+            "- Классификация и регрессия с помощью Python и R\n",
+            "- Искусственные нейронные сети и глубокое обучение\n",
+            "\n",
+            "Тема 7: Применение современных инструментов в бизнесе и научных исследованиях\n",
+            "- Кейсы применения современных инструментов в бизнесе и научных исследованиях\n",
+            "- Анализ данных в маркетинге, финансах и других областях\n",
+            "- Разработка решений для принятия данных-ориентированных решений\n",
+            "\n",
+            "Тема 8: Проекты и практические задания\n",
+            "- Создание проектов по анализу данных с использованием современных инструментов\n",
+            "-\n",
+            "\n",
+            "******************** Методы визуализации данных: temperature = 0.3 ********************\n",
+            "\n",
+            "Тема 1: Введение в визуализацию данных\n",
+            "- Значение визуализации данных в анализе и коммуникации информации\n",
+            "- Основные типы графиков и диаграмм\n",
+            "- Выбор подходящего типа графика для данных\n",
+            "\n",
+            "Тема 2: Основы визуализации данных с помощью Python\n",
+            "- Введение в Matplotlib и Seaborn\n",
+            "- Создание простых графиков с помощью Matplotlib\n",
+            "- Настройка стиля и темы в Matplotlib\n",
+            "\n",
+            "Тема 3: Визуализация структурированных данных\n",
+            "- Визуализация данных в табличной форме\n",
+            "- Диаграммы рассеяния и линейные графики\n",
+            "- Гистограммы и полигональные диаграммы\n",
+            "\n",
+            "Тема 4: Визуализация категориальных данных\n",
+            "- Столбчатые диаграммы и диаграммы с накоплением\n",
+            "- Круговые диаграммы и секторные диаграммы\n",
+            "- Деревья решений и диаграммы Санкея\n",
+            "\n",
+            "Тема 5: Визуализация временных данных\n",
+            "- Линейные графики и диаграммы с накоплением во времени\n",
+            "- Диаграммы Ганта и календарные диаграммы\n",
+            "- Визуализация сезонности и трендов\n",
+            "\n",
+            "Тема 6: Визуализация многомерных данных\n",
+            "- Визуализация данных в 3D\n",
+            "- Диаграммы параллельных координат\n",
+            "- Карты вкусов и карты наложения\n",
+            "\n",
+            "Тема 7: Визуализация текстовых данных\n",
+            "- Облака слов и диаграммы Ворда\n",
+            "- Сети и диаграммы Коса\n",
+            "- Карты тематической модели\n",
+            "\n",
+            "Тема 8: Создание интерактивных визуализаций и презентация результатов\n",
+            "- Введение в Bokeh и Plotly\n",
+            "- Создание интерактивных графиков и дашбордов\n",
+            "- Презентация результатов визуализации данных в отчетах и презентациях\n",
+            "\n",
+            "Запрос: Ты – преподаватель в университете. Перечисли темы и разделы для дисциплины Базы данных. Разде\n",
+            "\n",
+            "******************** Методы визуализации данных: temperature = 0.4 ********************\n",
+            "\n",
+            "Тема 1: Введение в визуализацию данных\n",
+            "- Обзор importancia визуализации данных в анализе и принятии решений\n",
+            "- Основные принципы и лучшие практики визуализации данных\n",
+            "- Выбор подходящего типа графика для различных данных и задач\n",
+            "\n",
+            "Тема 2: Таблицы и матрицы\n",
+            "- Визуализация данных в табличном формате\n",
+            "- Создание и настройка таблиц с данными\n",
+            "- Визуализация матриц и их анализ\n",
+            "\n",
+            "Тема 3: Графики линий и области\n",
+            "- Создание и настройка графиков линий\n",
+            "- Визуализация данных во временном формате\n",
+            "- Графики областей и их применение\n",
+            "\n",
+            "Тема 4: Гистограммы и диаграммы размаха\n",
+            "- Создание и настройка гистограмм\n",
+            "- Анализ распределения данных\n",
+            "- Диаграммы размаха и их применение\n",
+            "\n",
+            "Тема 5: Точечные графики и диаграммы рассеяния\n",
+            "- Создание и настройка точечных графиков\n",
+            "- Диаграммы рассеяния и их применение в анализе данных\n",
+            "- Корреляция и регрессия в контексте диаграмм рассеяния\n",
+            "\n",
+            "Тема 6: Диаграммы Парето и другие диаграммы для анализа причинности\n",
+            "- Создание и настройка диаграмм Парето\n",
+            "- Анализ причинно-следственных связей в данных\n",
+            "- Другие типы диаграмм для анализа причинности\n",
+            "\n",
+            "Тема 7: Карты и географическая визуализация\n",
+            "- Создание и настройка карт с данными\n",
+            "- Визуализация географических данных\n",
+            "- Применение карт в анализе данных и принятии решений\n",
+            "\n",
+            "Тема 8: Интерактивная визуализация и рассказы с данными\n",
+            "- Создание интерактивных визуализаций данных\n",
+            "- Рассказы с данными и их importance в коммуникации результатов анализа\n",
+            "- Применение интерактивных визуализаций и рассказов в реальных ситуа\n",
+            "\n",
+            "******************** Методы визуализации данных: temperature = 0.5 ********************\n",
+            "\n",
+            "Тема 1: Введение в визуализацию данных\n",
+            "- Обзор методов визуализации данных\n",
+            "- Важность визуализации данных в анализе и принятии решений\n",
+            "- Основные понятия и принципы визуализации данных\n",
+            "\n",
+            "Тема 2: Типы визуализации данных\n",
+            "- Визуализация данных в табличной форме\n",
+            "- Диаграммы и графики\n",
+            "- Карты и интерактивные визуализации\n",
+            "\n",
+            "Тема 3: Основы Matplotlib\n",
+            "- Введение в Matplotlib\n",
+            "- Создание простых графиков с Matplotlib\n",
+            "- Настройка стиля и темы в Matplotlib\n",
+            "\n",
+            "Тема 4: Работа с Seaborn\n",
+            "- Введение в Seaborn\n",
+            "- Визуализация статистических данных с Seaborn\n",
+            "- Создание более сложных и настраиваемых графиков с Seaborn\n",
+            "\n",
+            "Тема 5: Визуализация данных в Pandas\n",
+            "- Визуализация данных в Pandas с помощью встроенных функций\n",
+            "- Создание графиков с Pandas и Matplotlib/Seaborn\n",
+            "\n",
+            "Тема 6: Интерактивные визуализации с Bokeh\n",
+            "- Введение в Bokeh\n",
+            "- Создание интерактивных графиков с Bokeh\n",
+            "- Настройка и кастомизация графиков в Bokeh\n",
+            "\n",
+            "Тема 7: Карты и гео-визуализации с Folium и Geopandas\n",
+            "- Введение в Folium и Geopandas\n",
+            "- Создание карт и гео-визуализаций с Folium и Geopandas\n",
+            "- Работа с геоданными и их визуализация\n",
+            "\n",
+            "Тема 8: Проекты и применение знаний\n",
+            "- Создание проектов по визуализации данных\n",
+            "- Презентация результатов визуализации данных\n",
+            "- Обсуждение практического применения знаний в реальных ситуациях\n",
+            "\n",
+            "Запрос: Перечисли основные библиотеки для анализа данных в Python.\n",
+            "\n",
+            "Ответ: Основные библиотеки для анализа данных в Python включают:\n",
+            "\n",
+            "1. NumPy - Библиотека для работы с многомерными массивами и матрицами, а также для\n",
+            "\n",
+            "******************** Методы визуализации данных: temperature = 0.6000000000000001 ********************\n",
+            "\n",
+            "Тема 1: Введение в визуализацию данных\n",
+            "- Обзор визуализации данных и ее важности в анализе и коммуникации данных\n",
+            "- Типы данных и выбор подходящего вида визуализации\n",
+            "- Основы языка программирования Python и библиотек для визуализации данных (Matplotlib, Seaborn, Plotly)\n",
+            "\n",
+            "Тема 2: Основные виды визуализации данных\n",
+            "- Графики: линейные, столбчатые, точечные\n",
+            "- Диаграммы: спектральные, кольцевые, гистограммы\n",
+            "- Карты: географические, воронки, деревья\n",
+            "\n",
+            "Тема 3: Визуализация данных с помощью Matplotlib\n",
+            "- Введение в Matplotlib\n",
+            "- Создание простых графиков: линейные, столбчатые, точечные\n",
+            "- Настройка стиля и темы\n",
+            "- Работа с подграфиками и поддиаграммами\n",
+            "\n",
+            "Тема 4: Визуализация данных с помощью Seaborn\n",
+            "- Введение в Seaborn\n",
+            "- Создание статистических графиков: корреляция, регрессия, распределения\n",
+            "- Визуализация категориальных данных: барплоты, точечные диаграммы, столбчатые диаграммы\n",
+            "- Настройка стиля и темы\n",
+            "\n",
+            "Тема 5: Визуализация данных в трехмерном пространстве\n",
+            "- Введение в визуализацию данных в 3D\n",
+            "- Создание простых 3D-графиков с помощью Matplotlib и Plotly\n",
+            "- Визуализация поверхностей и объемов\n",
+            "\n",
+            "Тема 6: Интерактивная визуализация данных\n",
+            "- Введение в интерактивную визуализацию\n",
+            "- Создание интерактивных графиков с помощью Plotly\n",
+            "- Работа с интерактивными картами (Folium, Plotly)\n",
+            "\n",
+            "Тема 7: Визуализация больших данных\n",
+            "- Введение в визуализацию больших данных\n",
+            "- Методы агрегации и подборки данных для визуализации\n",
+            "- Визуализация больших данных с помощью\n",
+            "\n",
+            "******************** Методы визуализации данных: temperature = 0.7000000000000002 ********************\n",
+            "\n",
+            "Тема 1: Введение в визуализацию данных\n",
+            "- Обзор важности визуализации данных в анализе и принятии решений\n",
+            "- Основные принципы и лучшие практики визуализации данных\n",
+            "- Выбор правильного типа графика для представления данных\n",
+            "\n",
+            "Тема 2: Основные типы графиков\n",
+            "- Графики линий и области, используемые для представления трендов и изменения во времени\n",
+            "- Гистограммы и полосы ошибок, используемые для представления распределений\n",
+            "- Точечные графики и диаграммы рассеяния, используемые для изучения корреляций\n",
+            "- Столбчатые диаграммы и барчаты, используемые для сравнения категориальных данных\n",
+            "- Круговые диаграммы и секторные диаграммы, используемые для представления долей\n",
+            "\n",
+            "Тема 3: Работа с данными в Python для визуализации\n",
+            "- Введение в NumPy, Pandas и Matplotlib для работы с данными\n",
+            "- Очистка и предобработка данных перед визуализацией\n",
+            "- Создание и управление данными для визуализации\n",
+            "\n",
+            "Тема 4: Визуализация данных с помощью Matplotlib\n",
+            "- Создание простых графиков с Matplotlib\n",
+            "- Настройка стиля и темы в Matplotlib\n",
+            "- Подробная настройка графиков, шрифтов и цветов\n",
+            "- Создание подграфиков и многоосевых графиков\n",
+            "\n",
+            "Тема 5: Визуализация данных с помощью Seaborn\n",
+            "- Введение в Seaborn и его преимущества\n",
+            "- Создание графиков распределений и корреляций с Seaborn\n",
+            "- Настройка стиля и темы в Seaborn\n",
+            "- Визуализация многомерных данных с Seaborn\n",
+            "\n",
+            "Тема 6: Визуализация пространственных данных\n",
+            "- Введение в визуализацию данных на картах\n",
+            "- Использование библиотек, таких как GeoPandas и Folium, для визуализации пространственных данных\n",
+            "- Создание интерактивных карт и карт с аннотациями\n",
+            "\n",
+            "Тема 7: Визуали\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Если показывать модели пример запроса и эталонный ответ, то наиболее близкие к правде ответы получаются при самых низких температурах. Возможно, ниже 0.3 было бы еще грамотнее. Зато при более высоких модели не хватает токенов и появляется много подробностей, которые, возможно, излишни."
+      ],
+      "metadata": {
+        "id": "vpeATQYeiyKc"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Напишите функцию `mistral_one_shot()`, которая принимает на вход название дисциплины, объект клиента и значение температуры, а возвращает содержание дисциплины.\n",
+        "\n",
+        "Аналогично предыдущему разделу, сгенерируйте содержание для выбранных 100 дисциплин (или всех). Затем предобработайте данные и проведите векторизацию обоими векторайзерами."
+      ],
+      "metadata": {
+        "id": "BTRb0t9mO4Be"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def mistral_one_shot(course, cli, temp):\n",
+        "    # ваш код здесь"
+      ],
+      "metadata": {
+        "id": "pUX_4yyOP8Zg"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ваш код здесь"
+      ],
+      "metadata": {
+        "id": "Q8FmHwfZS-14"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "Повторите этот пайплайн для генерации краткого описания дисциплины (`description`) в технике `few-shot`."
+      ],
+      "metadata": {
+        "id": "iUjKDSBjTM9N"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ваш код здесь"
+      ],
+      "metadata": {
+        "id": "SGNaSiQPYKYM"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Задача 3 – Проанализировать результаты"
+      ],
+      "metadata": {
+        "id": "itbV5HOvNLm5"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Постройте визуализации для оценки результатов генерации искусственных данных по дисциплинам. В каждом пункте оценивайте результаты генерации в обеих техниках. Для каждого пункта напишите краткий вывод."
+      ],
+      "metadata": {
+        "id": "H6fENSOXQoWC"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "1. Постройте гистограмму, показывающую распределения сходств краткого описания с эталонным (человеческим) и содержания. Сравните результаты для обоих векторайзеров."
+      ],
+      "metadata": {
+        "id": "bxkbNcDcNCF9"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ваш код здесь"
+      ],
+      "metadata": {
+        "id": "YzNqcQuMSvUp"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "2. Оцените среднее косинусное сходство содержания и краткого описания, сгенерированных человеком и языковой моделью, в разрезе по уровням образования (`qualification`). Сравните результаты для обоих векторайзеров."
+      ],
+      "metadata": {
+        "id": "6r3Zak4mNJN-"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ваш код здесь"
+      ],
+      "metadata": {
+        "id": "V4JT7CEtSu6W"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "3. Оцените среднее косинусное сходство содержания и краткого описания, сгенерированных человеком и языковой моделью, в разрезе по году (`start_year`). Сравните результаты для обоих векторайзеров."
+      ],
+      "metadata": {
+        "id": "0JFN95vON1Eh"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ваш код здесь"
+      ],
+      "metadata": {
+        "id": "agGkjeuFSuaB"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "4. Оцените среднее косинусное сходство содержания и краткого описания, сгенерированных человеком и языковой моделью, в разрезе по уровням образования и годам, но только с помощью векторов  w2v."
+      ],
+      "metadata": {
+        "id": "BysU9V7fOIFO"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ваш код здесь"
+      ],
+      "metadata": {
+        "id": "tumFdu-4SsFI"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "5. Оцените средние значения сходства эталонных данных и генераций обеими техниками в разрезе по уровню образования (`qualification`). То есть постройте такую диграмму, по которой будет видно, для какого уровня образования какая из техник в среднем дает более близкий к эталонному контекст."
+      ],
+      "metadata": {
+        "id": "qL4pt4gyTcmH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ваш код здесь"
+      ],
+      "metadata": {
+        "id": "de4IqYQzUT_O"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Куда сдавать?\n",
+        "\n",
+        "По инструкции в гитхаб – https://shy-question-39d.notion.site/1150ea832e418032bfc3d3e827c380fb?pvs=74\n",
+        "\n",
+        "- К этой работе тестов нет.\n",
+        "- Эту работу можно выполнить с кем-нибудь в паре или в гордом одиночестве.\n",
+        "- Пул-реквест нужно сделать в ветку `lab4`. Если вас двое, то можно 1 или 2 пул-реквеста, главное – отметьте как-нибудь, что выполняли в паре.\n",
+        "\n",
+        "**Устная защита работ не требуется, но вам могут быть заданы вопросы прямо в вашем пул-реквесте!**"
+      ],
+      "metadata": {
+        "id": "tDe-AEeyUu2n"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "Z_79ZjZQUwq2"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file

From c7b54a77e32cc3333f42af33425ed84866360d03 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=D0=90=D0=BB=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9A=D0=BE?=
 =?UTF-8?q?=D0=BD=D0=BE=D0=BF=D0=BB=D1=8F?= <alexey.konoplia@gmail.com>
Date: Thu, 20 Feb 2025 05:18:58 +0300
Subject: [PATCH 3/4] lab2

---
 lab2/.gitignore                               |   24 +
 ...0\233\320\2402_Make_Recommendations.ipynb" | 4636 ++++++++++++++++-
 2 files changed, 4520 insertions(+), 140 deletions(-)
 create mode 100644 lab2/.gitignore

diff --git a/lab2/.gitignore b/lab2/.gitignore
new file mode 100644
index 0000000..9dfa5b6
--- /dev/null
+++ b/lab2/.gitignore
@@ -0,0 +1,24 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Virtual environment
+venv/
+.env/
+
+# Dataset and large binary files
+*.bin
+*.gz
+*.csv
+
+# Serialized models and encodings
+*.pkl
+
+# Logs and temp files
+*.log
+*.tmp
+
+# System files
+.DS_Store
+Thumbs.db
diff --git "a/lab2/PY\320\220\320\224_\320\233\320\2402_Make_Recommendations.ipynb" "b/lab2/PY\320\220\320\224_\320\233\320\2402_Make_Recommendations.ipynb"
index b4a825a..0731e64 100644
--- "a/lab2/PY\320\220\320\224_\320\233\320\2402_Make_Recommendations.ipynb"
+++ "b/lab2/PY\320\220\320\224_\320\233\320\2402_Make_Recommendations.ipynb"
@@ -3,10 +3,7 @@
   "nbformat_minor": 0,
   "metadata": {
     "colab": {
-      "provenance": [],
-      "toc_visible": true,
-      "authorship_tag": "ABX9TyPGoSS0uRAjGLPFGhTW/pVj",
-      "include_colab_link": true
+      "provenance": []
     },
     "kernelspec": {
       "name": "python3",
@@ -17,16 +14,6 @@
     }
   },
   "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/anastasiiaCher/pyad-2024/blob/main/lab2/PY%D0%90%D0%94_%D0%9B%D0%A02_Make_Recommendations.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
     {
       "cell_type": "markdown",
       "source": [
@@ -71,7 +58,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "Z4nPmsGsfEVR",
-        "outputId": "861b7b43-95b2-4aac-895e-50a94f646c1c"
+        "outputId": "3e4832bd-bfb1-4d92-83f3-508196d5d5a3"
       },
       "execution_count": 2,
       "outputs": [
@@ -82,11 +69,11 @@
             "Downloading...\n",
             "From: https://drive.google.com/uc?id=1Hliaee7Y584-7lMoGeGjGabRRubzdoYn\n",
             "To: /content/Ratings.csv\n",
-            "100% 22.6M/22.6M [00:00<00:00, 44.5MB/s]\n",
+            "100% 22.6M/22.6M [00:00<00:00, 100MB/s] \n",
             "Downloading...\n",
             "From: https://drive.google.com/uc?id=1JYpk5GTzK7GWT3mtDb9fNiwaVTzk8BBy\n",
             "To: /content/Books.csv\n",
-            "100% 73.3M/73.3M [00:00<00:00, 84.9MB/s]\n"
+            "100% 73.3M/73.3M [00:01<00:00, 60.4MB/s]\n"
           ]
         }
       ]
@@ -109,12 +96,12 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 201
+          "height": 206
         },
         "id": "Ck23gZpPfTzs",
-        "outputId": "dafe929c-ac0d-4569-c156-57e257026d4c"
+        "outputId": "0b237503-d754-4da9-e3ae-b94e2a2727a9"
       },
-      "execution_count": 8,
+      "execution_count": 3,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -129,7 +116,7 @@
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-ddb15d00-3340-429a-a5a9-1ac18b71f3f3\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-902876f9-c360-42e0-ad75-785eb5af876c\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -190,7 +177,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ddb15d00-3340-429a-a5a9-1ac18b71f3f3')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-902876f9-c360-42e0-ad75-785eb5af876c')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -242,12 +229,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-ddb15d00-3340-429a-a5a9-1ac18b71f3f3 button.colab-df-convert');\n",
+              "        document.querySelector('#df-902876f9-c360-42e0-ad75-785eb5af876c 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-ddb15d00-3340-429a-a5a9-1ac18b71f3f3');\n",
+              "        const element = document.querySelector('#df-902876f9-c360-42e0-ad75-785eb5af876c');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -267,8 +254,8 @@
               "  </div>\n",
               "\n",
               "\n",
-              "<div id=\"df-41a07ba2-8dd3-492b-a7c2-674afcfd3515\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-41a07ba2-8dd3-492b-a7c2-674afcfd3515')\"\n",
+              "<div id=\"df-416183c8-bbb0-4589-87e0-e6d78a0e384b\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-416183c8-bbb0-4589-87e0-e6d78a0e384b')\"\n",
               "            title=\"Suggest charts\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -387,7 +374,7 @@
               "    }\n",
               "    (() => {\n",
               "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-41a07ba2-8dd3-492b-a7c2-674afcfd3515 button');\n",
+              "        document.querySelector('#df-416183c8-bbb0-4589-87e0-e6d78a0e384b button');\n",
               "      quickchartButtonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "    })();\n",
@@ -402,7 +389,7 @@
             }
           },
           "metadata": {},
-          "execution_count": 8
+          "execution_count": 3
         }
       ]
     },
@@ -416,9 +403,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "Qw1-7kI-fVO1",
-        "outputId": "c2edcfc7-54a0-4f3c-f71d-18695dbebac2"
+        "outputId": "1420dd2a-a9b6-4d8b-b290-7711e4a694d3"
       },
-      "execution_count": 9,
+      "execution_count": 4,
       "outputs": [
         {
           "output_type": "stream",
@@ -446,12 +433,12 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 109
+          "height": 112
         },
         "id": "bzUeViHgfYix",
-        "outputId": "435086be-42ca-453a-f7de-4c24c34f8be6"
+        "outputId": "3ade5aa9-578e-46c4-ca6e-2c21d89e2584"
       },
-      "execution_count": 11,
+      "execution_count": 5,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -467,7 +454,7 @@
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-6ec5254d-4cbc-4d3b-ba48-95e6642c3428\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-42749f97-8db3-427f-bb52-ad54a9d48bda\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -525,7 +512,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6ec5254d-4cbc-4d3b-ba48-95e6642c3428')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-42749f97-8db3-427f-bb52-ad54a9d48bda')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -577,12 +564,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-6ec5254d-4cbc-4d3b-ba48-95e6642c3428 button.colab-df-convert');\n",
+              "        document.querySelector('#df-42749f97-8db3-427f-bb52-ad54a9d48bda 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-6ec5254d-4cbc-4d3b-ba48-95e6642c3428');\n",
+              "        const element = document.querySelector('#df-42749f97-8db3-427f-bb52-ad54a9d48bda');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -602,8 +589,8 @@
               "  </div>\n",
               "\n",
               "\n",
-              "<div id=\"df-da157b04-308a-431b-b170-c0d14030be03\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-da157b04-308a-431b-b170-c0d14030be03')\"\n",
+              "<div id=\"df-ea84b0f1-1516-477f-a28c-37cf5a7a1781\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ea84b0f1-1516-477f-a28c-37cf5a7a1781')\"\n",
               "            title=\"Suggest charts\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -722,7 +709,7 @@
               "    }\n",
               "    (() => {\n",
               "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-da157b04-308a-431b-b170-c0d14030be03 button');\n",
+              "        document.querySelector('#df-ea84b0f1-1516-477f-a28c-37cf5a7a1781 button');\n",
               "      quickchartButtonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "    })();\n",
@@ -737,7 +724,7 @@
             }
           },
           "metadata": {},
-          "execution_count": 11
+          "execution_count": 5
         }
       ]
     },
@@ -767,12 +754,12 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 457
+          "height": 458
         },
         "id": "rgvUotNXfxYW",
-        "outputId": "d53f5a19-8979-43a9-c8cf-ac9070e14439"
+        "outputId": "6b7f7e2b-d1f6-47c8-a5d9-1e5d20edf7d9"
       },
-      "execution_count": 17,
+      "execution_count": 6,
       "outputs": [
         {
           "output_type": "display_data",
@@ -780,7 +767,7 @@
             "text/plain": [
               "<Figure size 640x480 with 1 Axes>"
             ],
-            "image/png": 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\n"
+            "image/png": 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\n"
           },
           "metadata": {}
         }
@@ -804,18 +791,18 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 451
+          "height": 469
         },
         "id": "3JHS-pRXgrWQ",
-        "outputId": "a5e560d2-f7b3-40f5-c1cc-c8ba3eeee580"
+        "outputId": "01c0b023-1b58-420b-c91b-5832b1ed269c"
       },
-      "execution_count": 35,
+      "execution_count": 7,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stderr",
           "text": [
-            "<ipython-input-35-7dc4eb500039>:1: DtypeWarning: Columns (3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+            "<ipython-input-7-7dc4eb500039>:1: DtypeWarning: Columns (3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
             "  books = pd.read_csv(\"Books.csv\")\n"
           ]
         },
@@ -860,7 +847,7 @@
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-a8ca527e-52de-460d-b2e7-200d233cf265\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-984bd38c-44df-4382-a552-a6d5e7f2eca0\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -951,7 +938,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a8ca527e-52de-460d-b2e7-200d233cf265')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-984bd38c-44df-4382-a552-a6d5e7f2eca0')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -1003,12 +990,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-a8ca527e-52de-460d-b2e7-200d233cf265 button.colab-df-convert');\n",
+              "        document.querySelector('#df-984bd38c-44df-4382-a552-a6d5e7f2eca0 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-a8ca527e-52de-460d-b2e7-200d233cf265');\n",
+              "        const element = document.querySelector('#df-984bd38c-44df-4382-a552-a6d5e7f2eca0');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -1028,8 +1015,8 @@
               "  </div>\n",
               "\n",
               "\n",
-              "<div id=\"df-a4524fe8-41f6-46f7-a800-db40b245e787\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a4524fe8-41f6-46f7-a800-db40b245e787')\"\n",
+              "<div id=\"df-8f4dcaf7-0861-48a9-923d-2797f0354064\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8f4dcaf7-0861-48a9-923d-2797f0354064')\"\n",
               "            title=\"Suggest charts\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -1148,7 +1135,7 @@
               "    }\n",
               "    (() => {\n",
               "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-a4524fe8-41f6-46f7-a800-db40b245e787 button');\n",
+              "        document.querySelector('#df-8f4dcaf7-0861-48a9-923d-2797f0354064 button');\n",
               "      quickchartButtonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "    })();\n",
@@ -1163,7 +1150,7 @@
             }
           },
           "metadata": {},
-          "execution_count": 35
+          "execution_count": 7
         }
       ]
     },
@@ -1177,9 +1164,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "CvRM2JScgxUT",
-        "outputId": "76e9b01d-cacf-4f0f-a8c5-69296469b9f3"
+        "outputId": "7b04808f-2dcf-4447-d7b6-8938eead6d37"
       },
-      "execution_count": 36,
+      "execution_count": 8,
       "outputs": [
         {
           "output_type": "stream",
@@ -1222,12 +1209,12 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 416
+          "height": 429
         },
         "id": "7AR9ah_1g3BY",
-        "outputId": "6e6f404a-c6c7-4ff5-9320-af3c70051e48"
+        "outputId": "1973f87f-215c-4cde-bcc4-d1af7a10baf6"
       },
-      "execution_count": 37,
+      "execution_count": 9,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -1319,7 +1306,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 37
+          "execution_count": 9
         }
       ]
     },
@@ -1349,12 +1336,12 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 324
+          "height": 337
         },
         "id": "it2GevU7hchV",
-        "outputId": "f04f41e0-1da7-46da-daf7-068232e14593"
+        "outputId": "c42ef49b-01bf-4c48-e42e-0fbf84453289"
       },
-      "execution_count": 43,
+      "execution_count": 10,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -1387,7 +1374,7 @@
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-5a0407eb-a979-44cd-bfdb-4ae697d323f9\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-b2e67e0b-45ce-4b41-a8ef-3cadc8d49ea7\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -1456,7 +1443,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5a0407eb-a979-44cd-bfdb-4ae697d323f9')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b2e67e0b-45ce-4b41-a8ef-3cadc8d49ea7')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -1508,12 +1495,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-5a0407eb-a979-44cd-bfdb-4ae697d323f9 button.colab-df-convert');\n",
+              "        document.querySelector('#df-b2e67e0b-45ce-4b41-a8ef-3cadc8d49ea7 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-5a0407eb-a979-44cd-bfdb-4ae697d323f9');\n",
+              "        const element = document.querySelector('#df-b2e67e0b-45ce-4b41-a8ef-3cadc8d49ea7');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -1533,8 +1520,8 @@
               "  </div>\n",
               "\n",
               "\n",
-              "<div id=\"df-bb12e28e-e04b-44fc-ba79-534a36240c11\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bb12e28e-e04b-44fc-ba79-534a36240c11')\"\n",
+              "<div id=\"df-4409caf6-5474-4080-a15b-f26f48932542\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4409caf6-5474-4080-a15b-f26f48932542')\"\n",
               "            title=\"Suggest charts\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -1653,7 +1640,7 @@
               "    }\n",
               "    (() => {\n",
               "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-bb12e28e-e04b-44fc-ba79-534a36240c11 button');\n",
+              "        document.querySelector('#df-4409caf6-5474-4080-a15b-f26f48932542 button');\n",
               "      quickchartButtonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "    })();\n",
@@ -1668,7 +1655,7 @@
             }
           },
           "metadata": {},
-          "execution_count": 43
+          "execution_count": 10
         }
       ]
     },
@@ -1698,12 +1685,12 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 355
+          "height": 368
         },
         "id": "ezt51ciKjwMh",
-        "outputId": "dd4db0d9-6c16-4506-ff25-931a849344c2"
+        "outputId": "eb59d4fb-3a6c-43b4-eeff-09386f485fd8"
       },
-      "execution_count": 48,
+      "execution_count": 11,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -1741,7 +1728,7 @@
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-3791d1cd-4ad2-4ac3-98a4-85e64b3ff26c\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-ab967b79-fc6e-4409-90c4-5c42ce13fa16\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -1821,7 +1808,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3791d1cd-4ad2-4ac3-98a4-85e64b3ff26c')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ab967b79-fc6e-4409-90c4-5c42ce13fa16')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -1873,12 +1860,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-3791d1cd-4ad2-4ac3-98a4-85e64b3ff26c button.colab-df-convert');\n",
+              "        document.querySelector('#df-ab967b79-fc6e-4409-90c4-5c42ce13fa16 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-3791d1cd-4ad2-4ac3-98a4-85e64b3ff26c');\n",
+              "        const element = document.querySelector('#df-ab967b79-fc6e-4409-90c4-5c42ce13fa16');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -1898,8 +1885,8 @@
               "  </div>\n",
               "\n",
               "\n",
-              "<div id=\"df-3ff89c2c-fbe1-44fe-b334-c7ebd5be8873\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3ff89c2c-fbe1-44fe-b334-c7ebd5be8873')\"\n",
+              "<div id=\"df-f40def85-3dff-4d1a-a43e-28cd955b176d\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f40def85-3dff-4d1a-a43e-28cd955b176d')\"\n",
               "            title=\"Suggest charts\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -2018,7 +2005,7 @@
               "    }\n",
               "    (() => {\n",
               "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-3ff89c2c-fbe1-44fe-b334-c7ebd5be8873 button');\n",
+              "        document.querySelector('#df-f40def85-3dff-4d1a-a43e-28cd955b176d button');\n",
               "      quickchartButtonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "    })();\n",
@@ -2033,7 +2020,7 @@
             }
           },
           "metadata": {},
-          "execution_count": 48
+          "execution_count": 11
         }
       ]
     },
@@ -2047,65 +2034,4434 @@
       }
     },
     {
-      "cell_type": "markdown",
-      "source": [
-        "## План анализа данных\n",
-        "\n",
-        "1. Обработать данные, т.к. в данных есть несколько небольших проблем:\n",
-        "  1. В паре строк в таблице `Books.scv` значения столбцов сдвинуты вправо, а еще есть года, которые еще не наступили.\n",
-        "  2. В нескольких строках в таблице `Books.scv` есть пропуски, а также для рекомендаций в рамках лабораторной не понадобятся ссылки на картинки с обложкой.\n",
-        "  3. Не будем использовать для обучения записи с рейтинг равным 0 из таблицы `Ratings.scv`. Считаем, что 0 означает, что пользователь еще не оценивал книгу, но проявил к ней какой-то интерес.\n",
-        "  4. Не будем использовать для обучения алгоритмов те книги, которым оценка поставлена всего 1 раз. Пользователей, оценивших всего одну книгу тоже брать в расчет не будем (хотя это на самом деле будет не сильно сказываться на обобщающей способности алгоритмов, можете сами проверить).\n",
-        "  5. Пункты 1-4 необходимы и достаточны для выполнения всех задач. Но можно сделать более глубокую обработку.\n",
-        "2. Обучить [SVD](https://surprise.readthedocs.io/en/stable/getting_started.html) на следующих записях: у книги есть хотя бы одна оценка, пользователь оценил хотя бы одну книгу.\n",
-        "   - МАЕ должно быть ниже 1.3 (для этого есть тест)\n",
-        "   - **Сохранить модель.**\n",
-        "3. Научить линейную регрессию ([SGDRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html)) предсказывать средний рейтинг книги по следующим столбцам: автор, издатель, год издания, векторизованное название (для векторизации можно использовать [tf-idf](https://scikit-learn.org/stable/modules/feature_extraction.html#tfidf-term-weighting), [word2vec](https://radimrehurek.com/gensim/models/word2vec.html) или более сложный алгоритм. Для достижения требуемой тестом точности достаточно будет tf-idf. Собрав полностью датасет, не забудьте нормализовать данные ([StandardScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html)).\n",
-        "   - МАЕ должно быть ниже 1.5 (для этого есть тест)\n",
-        "   - **Сохранить модель.**\n",
-        "4. В файле `personal_recommendation.py` создайте рекомендацию для пользователя, у которого в исходном датасете было больше всего 0 среди рейтингов книг.\n",
-        "   Алгоритм такой:\n",
-        "   1. Находим нужного пользователя.\n",
-        "   2. Делаем предсказание SVD для книг, которым он \"поставил\" 0.\n",
-        "   3. Берем те книги, для которых предсказали рейтинг не ниже 8. Считаем, что 8 означет, что книга ему точно понравится.\n",
-        "   4. Делаем предсказание LinReg для этих же книг.\n",
-        "   5. Сортируем полученный на шаге 3 список по убыванию рейтинга линейной модели.\n",
-        "   6. В конце файла комментарием записываем полученную рекомендацию.\n",
-        "\n",
-        "- То есть идея в том, чтобы сделать для пользователя индивидуальную рекомендацию, показывая в начале списка те книги, которые в целом могли бы иметь высокий рейтинг.\n",
-        "- Обязательно сохраняйте готовую модель и добавляйте ее в свой репозиторий, потому что файл с сохраненной моделью используется в тестах."
-      ],
-      "metadata": {
-        "id": "lI1qVeU4aZGE"
-      }
-    },
-    {
-      "cell_type": "markdown",
+      "cell_type": "code",
       "source": [
-        "## Куда сдавать?\n",
-        "\n",
-        "По инструкции в гитхаб – https://shy-question-39d.notion.site/1150ea832e418032bfc3d3e827c380fb?pvs=74\n",
-        "\n",
-        "- К этой работе есть два теста – на качество получившихся моделей.\n",
-        "- Пул-реквест нужно сделать в ветку `lab2`.\n",
-        "- Можно загрузить в гитхаб файлы с реализацией по шаблону (шаблон можно дополнять и изменять под себя) или подготовить колаб с решением. Главное – сохранить модели, тесты проверяют их.\n",
-        "\n",
-        "> Будьте внимательны! На гитхабе в ветке `main` обновился файл `pyad.yml`.\n",
-        "\n",
-        "**Устная защита работ не требуется, но вам могут быть заданы вопросы прямо в вашем пул-реквесте!**"
+        "books"
       ],
       "metadata": {
-        "id": "eFJYYNSfGyQ-"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [],
-      "metadata": {
-        "id": "bjsfbERMHThP"
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 895
+        },
+        "id": "JICTHdghhglG",
+        "outputId": "452625f1-c990-43b5-de87-5101d485168f"
       },
-      "execution_count": null,
-      "outputs": []
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "              ISBN                                         Book-Title  \\\n",
+              "0       0195153448                                Classical Mythology   \n",
+              "1       0002005018                                       Clara Callan   \n",
+              "2       0060973129                               Decision in Normandy   \n",
+              "3       0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
+              "4       0393045218                             The Mummies of Urumchi   \n",
+              "...            ...                                                ...   \n",
+              "271355  0440400988                         There's a Bat in Bunk Five   \n",
+              "271356  0525447644                            From One to One Hundred   \n",
+              "271357  006008667X  Lily Dale : The True Story of the Town that Ta...   \n",
+              "271358  0192126040                        Republic (World's Classics)   \n",
+              "271359  0767409752  A Guided Tour of Rene Descartes' Meditations o...   \n",
+              "\n",
+              "                 Book-Author Year-Of-Publication  \\\n",
+              "0         Mark P. O. Morford                2002   \n",
+              "1       Richard Bruce Wright                2001   \n",
+              "2               Carlo D'Este                1991   \n",
+              "3           Gina Bari Kolata                1999   \n",
+              "4            E. J. W. Barber                1999   \n",
+              "...                      ...                 ...   \n",
+              "271355        Paula Danziger                1988   \n",
+              "271356            Teri Sloat                1991   \n",
+              "271357      Christine Wicker                2004   \n",
+              "271358                 Plato                1996   \n",
+              "271359   Christopher  Biffle                2000   \n",
+              "\n",
+              "                                               Publisher  \\\n",
+              "0                                Oxford University Press   \n",
+              "1                                  HarperFlamingo Canada   \n",
+              "2                                        HarperPerennial   \n",
+              "3                                   Farrar Straus Giroux   \n",
+              "4                             W. W. Norton &amp; Company   \n",
+              "...                                                  ...   \n",
+              "271355                   Random House Childrens Pub (Mm)   \n",
+              "271356                                      Dutton Books   \n",
+              "271357                                HarperSanFrancisco   \n",
+              "271358                           Oxford University Press   \n",
+              "271359  McGraw-Hill Humanities/Social Sciences/Languages   \n",
+              "\n",
+              "                                              Image-URL-S  \\\n",
+              "0       http://images.amazon.com/images/P/0195153448.0...   \n",
+              "1       http://images.amazon.com/images/P/0002005018.0...   \n",
+              "2       http://images.amazon.com/images/P/0060973129.0...   \n",
+              "3       http://images.amazon.com/images/P/0374157065.0...   \n",
+              "4       http://images.amazon.com/images/P/0393045218.0...   \n",
+              "...                                                   ...   \n",
+              "271355  http://images.amazon.com/images/P/0440400988.0...   \n",
+              "271356  http://images.amazon.com/images/P/0525447644.0...   \n",
+              "271357  http://images.amazon.com/images/P/006008667X.0...   \n",
+              "271358  http://images.amazon.com/images/P/0192126040.0...   \n",
+              "271359  http://images.amazon.com/images/P/0767409752.0...   \n",
+              "\n",
+              "                                              Image-URL-M  \\\n",
+              "0       http://images.amazon.com/images/P/0195153448.0...   \n",
+              "1       http://images.amazon.com/images/P/0002005018.0...   \n",
+              "2       http://images.amazon.com/images/P/0060973129.0...   \n",
+              "3       http://images.amazon.com/images/P/0374157065.0...   \n",
+              "4       http://images.amazon.com/images/P/0393045218.0...   \n",
+              "...                                                   ...   \n",
+              "271355  http://images.amazon.com/images/P/0440400988.0...   \n",
+              "271356  http://images.amazon.com/images/P/0525447644.0...   \n",
+              "271357  http://images.amazon.com/images/P/006008667X.0...   \n",
+              "271358  http://images.amazon.com/images/P/0192126040.0...   \n",
+              "271359  http://images.amazon.com/images/P/0767409752.0...   \n",
+              "\n",
+              "                                              Image-URL-L  \n",
+              "0       http://images.amazon.com/images/P/0195153448.0...  \n",
+              "1       http://images.amazon.com/images/P/0002005018.0...  \n",
+              "2       http://images.amazon.com/images/P/0060973129.0...  \n",
+              "3       http://images.amazon.com/images/P/0374157065.0...  \n",
+              "4       http://images.amazon.com/images/P/0393045218.0...  \n",
+              "...                                                   ...  \n",
+              "271355  http://images.amazon.com/images/P/0440400988.0...  \n",
+              "271356  http://images.amazon.com/images/P/0525447644.0...  \n",
+              "271357  http://images.amazon.com/images/P/006008667X.0...  \n",
+              "271358  http://images.amazon.com/images/P/0192126040.0...  \n",
+              "271359  http://images.amazon.com/images/P/0767409752.0...  \n",
+              "\n",
+              "[271360 rows x 8 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-261ae676-bef2-43f5-87e9-a35dc3e496bc\" 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>ISBN</th>\n",
+              "      <th>Book-Title</th>\n",
+              "      <th>Book-Author</th>\n",
+              "      <th>Year-Of-Publication</th>\n",
+              "      <th>Publisher</th>\n",
+              "      <th>Image-URL-S</th>\n",
+              "      <th>Image-URL-M</th>\n",
+              "      <th>Image-URL-L</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0195153448</td>\n",
+              "      <td>Classical Mythology</td>\n",
+              "      <td>Mark P. O. Morford</td>\n",
+              "      <td>2002</td>\n",
+              "      <td>Oxford University Press</td>\n",
+              "      <td>http://images.amazon.com/images/P/0195153448.0...</td>\n",
+              "      <td>http://images.amazon.com/images/P/0195153448.0...</td>\n",
+              "      <td>http://images.amazon.com/images/P/0195153448.0...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0002005018</td>\n",
+              "      <td>Clara Callan</td>\n",
+              "      <td>Richard Bruce Wright</td>\n",
+              "      <td>2001</td>\n",
+              "      <td>HarperFlamingo Canada</td>\n",
+              "      <td>http://images.amazon.com/images/P/0002005018.0...</td>\n",
+              "      <td>http://images.amazon.com/images/P/0002005018.0...</td>\n",
+              "      <td>http://images.amazon.com/images/P/0002005018.0...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0060973129</td>\n",
+              "      <td>Decision in Normandy</td>\n",
+              "      <td>Carlo D'Este</td>\n",
+              "      <td>1991</td>\n",
+              "      <td>HarperPerennial</td>\n",
+              "      <td>http://images.amazon.com/images/P/0060973129.0...</td>\n",
+              "      <td>http://images.amazon.com/images/P/0060973129.0...</td>\n",
+              "      <td>http://images.amazon.com/images/P/0060973129.0...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0374157065</td>\n",
+              "      <td>Flu: The Story of the Great Influenza Pandemic...</td>\n",
+              "      <td>Gina Bari Kolata</td>\n",
+              "      <td>1999</td>\n",
+              "      <td>Farrar Straus Giroux</td>\n",
+              "      <td>http://images.amazon.com/images/P/0374157065.0...</td>\n",
+              "      <td>http://images.amazon.com/images/P/0374157065.0...</td>\n",
+              "      <td>http://images.amazon.com/images/P/0374157065.0...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0393045218</td>\n",
+              "      <td>The Mummies of Urumchi</td>\n",
+              "      <td>E. J. W. Barber</td>\n",
+              "      <td>1999</td>\n",
+              "      <td>W. W. Norton &amp;amp; Company</td>\n",
+              "      <td>http://images.amazon.com/images/P/0393045218.0...</td>\n",
+              "      <td>http://images.amazon.com/images/P/0393045218.0...</td>\n",
+              "      <td>http://images.amazon.com/images/P/0393045218.0...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>271355</th>\n",
+              "      <td>0440400988</td>\n",
+              "      <td>There's a Bat in Bunk Five</td>\n",
+              "      <td>Paula Danziger</td>\n",
+              "      <td>1988</td>\n",
+              "      <td>Random House Childrens Pub (Mm)</td>\n",
+              "      <td>http://images.amazon.com/images/P/0440400988.0...</td>\n",
+              "      <td>http://images.amazon.com/images/P/0440400988.0...</td>\n",
+              "      <td>http://images.amazon.com/images/P/0440400988.0...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>271356</th>\n",
+              "      <td>0525447644</td>\n",
+              "      <td>From One to One Hundred</td>\n",
+              "      <td>Teri Sloat</td>\n",
+              "      <td>1991</td>\n",
+              "      <td>Dutton Books</td>\n",
+              "      <td>http://images.amazon.com/images/P/0525447644.0...</td>\n",
+              "      <td>http://images.amazon.com/images/P/0525447644.0...</td>\n",
+              "      <td>http://images.amazon.com/images/P/0525447644.0...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>271357</th>\n",
+              "      <td>006008667X</td>\n",
+              "      <td>Lily Dale : The True Story of the Town that Ta...</td>\n",
+              "      <td>Christine Wicker</td>\n",
+              "      <td>2004</td>\n",
+              "      <td>HarperSanFrancisco</td>\n",
+              "      <td>http://images.amazon.com/images/P/006008667X.0...</td>\n",
+              "      <td>http://images.amazon.com/images/P/006008667X.0...</td>\n",
+              "      <td>http://images.amazon.com/images/P/006008667X.0...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>271358</th>\n",
+              "      <td>0192126040</td>\n",
+              "      <td>Republic (World's Classics)</td>\n",
+              "      <td>Plato</td>\n",
+              "      <td>1996</td>\n",
+              "      <td>Oxford University Press</td>\n",
+              "      <td>http://images.amazon.com/images/P/0192126040.0...</td>\n",
+              "      <td>http://images.amazon.com/images/P/0192126040.0...</td>\n",
+              "      <td>http://images.amazon.com/images/P/0192126040.0...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>271359</th>\n",
+              "      <td>0767409752</td>\n",
+              "      <td>A Guided Tour of Rene Descartes' Meditations o...</td>\n",
+              "      <td>Christopher  Biffle</td>\n",
+              "      <td>2000</td>\n",
+              "      <td>McGraw-Hill Humanities/Social Sciences/Languages</td>\n",
+              "      <td>http://images.amazon.com/images/P/0767409752.0...</td>\n",
+              "      <td>http://images.amazon.com/images/P/0767409752.0...</td>\n",
+              "      <td>http://images.amazon.com/images/P/0767409752.0...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>271360 rows × 8 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-261ae676-bef2-43f5-87e9-a35dc3e496bc')\"\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-261ae676-bef2-43f5-87e9-a35dc3e496bc 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-261ae676-bef2-43f5-87e9-a35dc3e496bc');\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-4a543e98-3107-49a5-a5be-7e20b9470b87\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4a543e98-3107-49a5-a5be-7e20b9470b87')\"\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",
+              "    }\n",
+              "    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-4a543e98-3107-49a5-a5be-7e20b9470b87 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "books"
+            }
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## План анализа данных\n",
+        "\n",
+        "1. Обработать данные, т.к. в данных есть несколько небольших проблем:\n",
+        "  1. В паре строк в таблице `Books.scv` значения столбцов сдвинуты вправо, а еще есть года, которые еще не наступили.\n",
+        "  2. В нескольких строках в таблице `Books.scv` есть пропуски, а также для рекомендаций в рамках лабораторной не понадобятся ссылки на картинки с обложкой.\n",
+        "  3. Не будем использовать для обучения записи с рейтинг равным 0 из таблицы `Ratings.scv`. Считаем, что 0 означает, что пользователь еще не оценивал книгу, но проявил к ней какой-то интерес.\n",
+        "  4. Не будем использовать для обучения алгоритмов те книги, которым оценка поставлена всего 1 раз. Пользователей, оценивших всего одну книгу тоже брать в расчет не будем (хотя это на самом деле будет не сильно сказываться на обобщающей способности алгоритмов, можете сами проверить).\n",
+        "  5. Пункты 1-4 необходимы и достаточны для выполнения всех задач. Но можно сделать более глубокую обработку.\n",
+        "2. Обучить [SVD](https://surprise.readthedocs.io/en/stable/getting_started.html) на следующих записях: у книги есть хотя бы одна оценка, пользователь оценил хотя бы одну книгу.\n",
+        "   - МАЕ должно быть ниже 1.3 (для этого есть тест)\n",
+        "   - **Сохранить модель.**\n",
+        "3. Научить линейную регрессию ([SGDRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html)) предсказывать средний рейтинг книги по следующим столбцам: автор, издатель, год издания, векторизованное название (для векторизации можно использовать [tf-idf](https://scikit-learn.org/stable/modules/feature_extraction.html#tfidf-term-weighting), [word2vec](https://radimrehurek.com/gensim/models/word2vec.html) или более сложный алгоритм. Для достижения требуемой тестом точности достаточно будет tf-idf. Собрав полностью датасет, не забудьте нормализовать данные ([StandardScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html)).\n",
+        "   - МАЕ должно быть ниже 1.5 (для этого есть тест)\n",
+        "   - **Сохранить модель.**\n",
+        "4. В файле `personal_recommendation.py` создайте рекомендацию для пользователя, у которого в исходном датасете было больше всего 0 среди рейтингов книг.\n",
+        "   Алгоритм такой:\n",
+        "   1. Находим нужного пользователя.\n",
+        "   2. Делаем предсказание SVD для книг, которым он \"поставил\" 0.\n",
+        "   3. Берем те книги, для которых предсказали рейтинг не ниже 8. Считаем, что 8 означет, что книга ему точно понравится.\n",
+        "   4. Делаем предсказание LinReg для этих же книг.\n",
+        "   5. Сортируем полученный на шаге 3 список по убыванию рейтинга линейной модели.\n",
+        "   6. В конце файла комментарием записываем полученную рекомендацию.\n",
+        "\n",
+        "- То есть идея в том, чтобы сделать для пользователя индивидуальную рекомендацию, показывая в начале списка те книги, которые в целом могли бы иметь высокий рейтинг.\n",
+        "- Обязательно сохраняйте готовую модель и добавляйте ее в свой репозиторий, потому что файл с сохраненной моделью используется в тестах."
+      ],
+      "metadata": {
+        "id": "lI1qVeU4aZGE"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Куда сдавать?\n",
+        "\n",
+        "По инструкции в гитхаб – https://shy-question-39d.notion.site/1150ea832e418032bfc3d3e827c380fb?pvs=74\n",
+        "\n",
+        "- К этой работе есть два теста – на качество получившихся моделей.\n",
+        "- Пул-реквест нужно сделать в ветку `lab2`.\n",
+        "- Можно загрузить в гитхаб файлы с реализацией по шаблону (шаблон можно дополнять и изменять под себя) или подготовить колаб с решением. Главное – сохранить модели, тесты проверяют их.\n",
+        "\n",
+        "> Будьте внимательны! На гитхабе в ветке `main` обновился файл `pyad.yml`.\n",
+        "\n",
+        "**Устная защита работ не требуется, но вам могут быть заданы вопросы прямо в вашем пул-реквесте!**"
+      ],
+      "metadata": {
+        "id": "eFJYYNSfGyQ-"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_books = books.drop(columns=['Image-URL-S', 'Image-URL-M', 'Image-URL-L'])\n",
+        "df_books"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "8lB751Enl7Ue",
+        "outputId": "657a7c15-d59b-498b-dd5d-7d7d95c93854"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "              ISBN                                         Book-Title  \\\n",
+              "0       0195153448                                Classical Mythology   \n",
+              "1       0002005018                                       Clara Callan   \n",
+              "2       0060973129                               Decision in Normandy   \n",
+              "3       0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
+              "4       0393045218                             The Mummies of Urumchi   \n",
+              "...            ...                                                ...   \n",
+              "271355  0440400988                         There's a Bat in Bunk Five   \n",
+              "271356  0525447644                            From One to One Hundred   \n",
+              "271357  006008667X  Lily Dale : The True Story of the Town that Ta...   \n",
+              "271358  0192126040                        Republic (World's Classics)   \n",
+              "271359  0767409752  A Guided Tour of Rene Descartes' Meditations o...   \n",
+              "\n",
+              "                 Book-Author Year-Of-Publication  \\\n",
+              "0         Mark P. O. Morford                2002   \n",
+              "1       Richard Bruce Wright                2001   \n",
+              "2               Carlo D'Este                1991   \n",
+              "3           Gina Bari Kolata                1999   \n",
+              "4            E. J. W. Barber                1999   \n",
+              "...                      ...                 ...   \n",
+              "271355        Paula Danziger                1988   \n",
+              "271356            Teri Sloat                1991   \n",
+              "271357      Christine Wicker                2004   \n",
+              "271358                 Plato                1996   \n",
+              "271359   Christopher  Biffle                2000   \n",
+              "\n",
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+              "0                                Oxford University Press  \n",
+              "1                                  HarperFlamingo Canada  \n",
+              "2                                        HarperPerennial  \n",
+              "3                                   Farrar Straus Giroux  \n",
+              "4                             W. W. Norton &amp; Company  \n",
+              "...                                                  ...  \n",
+              "271355                   Random House Childrens Pub (Mm)  \n",
+              "271356                                      Dutton Books  \n",
+              "271357                                HarperSanFrancisco  \n",
+              "271358                           Oxford University Press  \n",
+              "271359  McGraw-Hill Humanities/Social Sciences/Languages  \n",
+              "\n",
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+              "      <td>Classical Mythology</td>\n",
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+              "      <td>Oxford University Press</td>\n",
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+              "      <td>Clara Callan</td>\n",
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+              "      <td>2001</td>\n",
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+              "      <td>Decision in Normandy</td>\n",
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+              "      <td>HarperPerennial</td>\n",
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+              "      <td>0374157065</td>\n",
+              "      <td>Flu: The Story of the Great Influenza Pandemic...</td>\n",
+              "      <td>Gina Bari Kolata</td>\n",
+              "      <td>1999</td>\n",
+              "      <td>Farrar Straus Giroux</td>\n",
+              "    </tr>\n",
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+              "      <td>0393045218</td>\n",
+              "      <td>The Mummies of Urumchi</td>\n",
+              "      <td>E. J. W. Barber</td>\n",
+              "      <td>1999</td>\n",
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+              "      <td>Paula Danziger</td>\n",
+              "      <td>1988</td>\n",
+              "      <td>Random House Childrens Pub (Mm)</td>\n",
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+              "      <td>0525447644</td>\n",
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+              "      <td>Dutton Books</td>\n",
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+              "      <th>271357</th>\n",
+              "      <td>006008667X</td>\n",
+              "      <td>Lily Dale : The True Story of the Town that Ta...</td>\n",
+              "      <td>Christine Wicker</td>\n",
+              "      <td>2004</td>\n",
+              "      <td>HarperSanFrancisco</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>271358</th>\n",
+              "      <td>0192126040</td>\n",
+              "      <td>Republic (World's Classics)</td>\n",
+              "      <td>Plato</td>\n",
+              "      <td>1996</td>\n",
+              "      <td>Oxford University Press</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>271359</th>\n",
+              "      <td>0767409752</td>\n",
+              "      <td>A Guided Tour of Rene Descartes' Meditations o...</td>\n",
+              "      <td>Christopher  Biffle</td>\n",
+              "      <td>2000</td>\n",
+              "      <td>McGraw-Hill Humanities/Social Sciences/Languages</td>\n",
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+              "  </tbody>\n",
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+              "<p>271360 rows × 5 columns</p>\n",
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+              "      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",
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+              "      const buttonEl =\n",
+              "        document.querySelector('#df-25213d89-15f5-4c2b-9bea-ddd0d2d04921 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-25213d89-15f5-4c2b-9bea-ddd0d2d04921');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
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+              "</svg>\n",
+              "  </button>\n",
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+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
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+              "\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "      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-cabc3640-fcfd-412c-8e71-9a946f437c57 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df_books"
+            }
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_books.dropna()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "5Z2thdUKmWaE",
+        "outputId": "b6fa2d86-353f-4375-c6f3-62122d26f546"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "              ISBN                                         Book-Title  \\\n",
+              "0       0195153448                                Classical Mythology   \n",
+              "1       0002005018                                       Clara Callan   \n",
+              "2       0060973129                               Decision in Normandy   \n",
+              "3       0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
+              "4       0393045218                             The Mummies of Urumchi   \n",
+              "...            ...                                                ...   \n",
+              "271355  0440400988                         There's a Bat in Bunk Five   \n",
+              "271356  0525447644                            From One to One Hundred   \n",
+              "271357  006008667X  Lily Dale : The True Story of the Town that Ta...   \n",
+              "271358  0192126040                        Republic (World's Classics)   \n",
+              "271359  0767409752  A Guided Tour of Rene Descartes' Meditations o...   \n",
+              "\n",
+              "                 Book-Author Year-Of-Publication  \\\n",
+              "0         Mark P. O. Morford                2002   \n",
+              "1       Richard Bruce Wright                2001   \n",
+              "2               Carlo D'Este                1991   \n",
+              "3           Gina Bari Kolata                1999   \n",
+              "4            E. J. W. Barber                1999   \n",
+              "...                      ...                 ...   \n",
+              "271355        Paula Danziger                1988   \n",
+              "271356            Teri Sloat                1991   \n",
+              "271357      Christine Wicker                2004   \n",
+              "271358                 Plato                1996   \n",
+              "271359   Christopher  Biffle                2000   \n",
+              "\n",
+              "                                               Publisher  \n",
+              "0                                Oxford University Press  \n",
+              "1                                  HarperFlamingo Canada  \n",
+              "2                                        HarperPerennial  \n",
+              "3                                   Farrar Straus Giroux  \n",
+              "4                             W. W. Norton &amp; Company  \n",
+              "...                                                  ...  \n",
+              "271355                   Random House Childrens Pub (Mm)  \n",
+              "271356                                      Dutton Books  \n",
+              "271357                                HarperSanFrancisco  \n",
+              "271358                           Oxford University Press  \n",
+              "271359  McGraw-Hill Humanities/Social Sciences/Languages  \n",
+              "\n",
+              "[271356 rows x 5 columns]"
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+              "      <th>1</th>\n",
+              "      <td>0002005018</td>\n",
+              "      <td>Clara Callan</td>\n",
+              "      <td>Richard Bruce Wright</td>\n",
+              "      <td>2001</td>\n",
+              "      <td>HarperFlamingo Canada</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0060973129</td>\n",
+              "      <td>Decision in Normandy</td>\n",
+              "      <td>Carlo D'Este</td>\n",
+              "      <td>1991</td>\n",
+              "      <td>HarperPerennial</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0374157065</td>\n",
+              "      <td>Flu: The Story of the Great Influenza Pandemic...</td>\n",
+              "      <td>Gina Bari Kolata</td>\n",
+              "      <td>1999</td>\n",
+              "      <td>Farrar Straus Giroux</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0393045218</td>\n",
+              "      <td>The Mummies of Urumchi</td>\n",
+              "      <td>E. J. W. Barber</td>\n",
+              "      <td>1999</td>\n",
+              "      <td>W. W. Norton &amp;amp; Company</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>271355</th>\n",
+              "      <td>0440400988</td>\n",
+              "      <td>There's a Bat in Bunk Five</td>\n",
+              "      <td>Paula Danziger</td>\n",
+              "      <td>1988</td>\n",
+              "      <td>Random House Childrens Pub (Mm)</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>271356</th>\n",
+              "      <td>0525447644</td>\n",
+              "      <td>From One to One Hundred</td>\n",
+              "      <td>Teri Sloat</td>\n",
+              "      <td>1991</td>\n",
+              "      <td>Dutton Books</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>271357</th>\n",
+              "      <td>006008667X</td>\n",
+              "      <td>Lily Dale : The True Story of the Town that Ta...</td>\n",
+              "      <td>Christine Wicker</td>\n",
+              "      <td>2004</td>\n",
+              "      <td>HarperSanFrancisco</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>271358</th>\n",
+              "      <td>0192126040</td>\n",
+              "      <td>Republic (World's Classics)</td>\n",
+              "      <td>Plato</td>\n",
+              "      <td>1996</td>\n",
+              "      <td>Oxford University Press</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>271359</th>\n",
+              "      <td>0767409752</td>\n",
+              "      <td>A Guided Tour of Rene Descartes' Meditations o...</td>\n",
+              "      <td>Christopher  Biffle</td>\n",
+              "      <td>2000</td>\n",
+              "      <td>McGraw-Hill Humanities/Social Sciences/Languages</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>271356 rows × 5 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-fbfac384-fc52-45f9-9657-b751adfcd665')\"\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-fbfac384-fc52-45f9-9657-b751adfcd665 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-fbfac384-fc52-45f9-9657-b751adfcd665');\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-7693a138-8096-4e9e-83f7-94c9e449ec1f\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7693a138-8096-4e9e-83f7-94c9e449ec1f')\"\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",
+              "    }\n",
+              "    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-7693a138-8096-4e9e-83f7-94c9e449ec1f button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe"
+            }
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_ratings = ratings.drop(ratings[ratings[\"Book-Rating\"] == 0].index)\n",
+        "df_ratings"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "c6kY2vQAm096",
+        "outputId": "10d79ef0-b68c-4bf5-8919-83be6759fc0e"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         User-ID         ISBN  Book-Rating\n",
+              "1         276726   0155061224            5\n",
+              "3         276729   052165615X            3\n",
+              "4         276729   0521795028            6\n",
+              "6         276736   3257224281            8\n",
+              "7         276737   0600570967            6\n",
+              "...          ...          ...          ...\n",
+              "1149773   276704   0806917695            5\n",
+              "1149775   276704   1563526298            9\n",
+              "1149777   276709   0515107662           10\n",
+              "1149778   276721   0590442449           10\n",
+              "1149779   276723  05162443314            8\n",
+              "\n",
+              "[433671 rows x 3 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-6bf71caa-93dd-4c4f-b6f2-1079139bbc3d\" 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>User-ID</th>\n",
+              "      <th>ISBN</th>\n",
+              "      <th>Book-Rating</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>276726</td>\n",
+              "      <td>0155061224</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>276729</td>\n",
+              "      <td>052165615X</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>276729</td>\n",
+              "      <td>0521795028</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>276736</td>\n",
+              "      <td>3257224281</td>\n",
+              "      <td>8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>276737</td>\n",
+              "      <td>0600570967</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1149773</th>\n",
+              "      <td>276704</td>\n",
+              "      <td>0806917695</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1149775</th>\n",
+              "      <td>276704</td>\n",
+              "      <td>1563526298</td>\n",
+              "      <td>9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1149777</th>\n",
+              "      <td>276709</td>\n",
+              "      <td>0515107662</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1149778</th>\n",
+              "      <td>276721</td>\n",
+              "      <td>0590442449</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1149779</th>\n",
+              "      <td>276723</td>\n",
+              "      <td>05162443314</td>\n",
+              "      <td>8</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>433671 rows × 3 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-6bf71caa-93dd-4c4f-b6f2-1079139bbc3d')\"\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-6bf71caa-93dd-4c4f-b6f2-1079139bbc3d 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-6bf71caa-93dd-4c4f-b6f2-1079139bbc3d');\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-4acf3d59-64f7-40ac-a95b-ef53981183d4\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4acf3d59-64f7-40ac-a95b-ef53981183d4')\"\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",
+              "    }\n",
+              "    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-4acf3d59-64f7-40ac-a95b-ef53981183d4 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df_ratings"
+            }
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "user_counts = df_ratings['User-ID'].value_counts()\n",
+        "book_counts = df_ratings['ISBN'].value_counts()\n",
+        "df_ratings = df_ratings[df_ratings['User-ID'].isin(user_counts[user_counts > 1].index)]\n",
+        "df_ratings = df_ratings[df_ratings['ISBN'].isin(book_counts[book_counts > 1].index)]\n",
+        "df_ratings.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dWzc8uM-rhif",
+        "outputId": "7c4cf503-f304-4172-8bf0-447475ce385d"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "Index: 270604 entries, 16 to 1149775\n",
+            "Data columns (total 3 columns):\n",
+            " #   Column       Non-Null Count   Dtype \n",
+            "---  ------       --------------   ----- \n",
+            " 0   User-ID      270604 non-null  int64 \n",
+            " 1   ISBN         270604 non-null  object\n",
+            " 2   Book-Rating  270604 non-null  int64 \n",
+            "dtypes: int64(2), object(1)\n",
+            "memory usage: 8.3+ MB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Обучить SVD на следующих записях: у книги есть хотя бы одна оценка, пользователь оценил хотя бы одну книгу.\n",
+        "МАЕ должно быть ниже 1.3 (для этого есть тест)\n",
+        "Сохранить модель."
+      ],
+      "metadata": {
+        "id": "FbNRCVUhVTwC"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install scikit-surprise"
+      ],
+      "metadata": {
+        "id": "6zkVF7PGVYia",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "bdbbf1bf-dd39-43ba-cb43-999a4944f9d2"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting scikit-surprise\n",
+            "  Downloading scikit_surprise-1.1.4.tar.gz (154 kB)\n",
+            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/154.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m153.6/154.4 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.4/154.4 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-surprise) (1.4.2)\n",
+            "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-surprise) (1.26.4)\n",
+            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-surprise) (1.13.1)\n",
+            "Building wheels for collected packages: scikit-surprise\n",
+            "  Building wheel for scikit-surprise (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for scikit-surprise: filename=scikit_surprise-1.1.4-cp311-cp311-linux_x86_64.whl size=2505178 sha256=0dfd43e97909c5bf87e94725c93cb795f86b9332254827f1828d586c72e4d936\n",
+            "  Stored in directory: /root/.cache/pip/wheels/2a/8f/6e/7e2899163e2d85d8266daab4aa1cdabec7a6c56f83c015b5af\n",
+            "Successfully built scikit-surprise\n",
+            "Installing collected packages: scikit-surprise\n",
+            "Successfully installed scikit-surprise-1.1.4\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from surprise import SVD, Dataset, Reader\n",
+        "from surprise.model_selection import cross_validate, train_test_split\n",
+        "\n",
+        "algorithm = SVD()\n",
+        "reader = Reader(rating_scale=(0,11))\n",
+        "data = Dataset.load_from_df(df_ratings, reader)\n",
+        "\n",
+        "cross_validate(algorithm, data, cv=5, verbose=True, measures=['RMSE', 'MAE'])"
+      ],
+      "metadata": {
+        "id": "MhlK014gVUNF",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f180e366-a057-41c6-f072-3acbd288caad"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Evaluating RMSE, MAE of algorithm SVD on 5 split(s).\n",
+            "\n",
+            "                  Fold 1  Fold 2  Fold 3  Fold 4  Fold 5  Mean    Std     \n",
+            "RMSE (testset)    1.6143  1.6158  1.6153  1.6297  1.6238  1.6198  0.0060  \n",
+            "MAE (testset)     1.2488  1.2486  1.2478  1.2565  1.2548  1.2513  0.0036  \n",
+            "Fit time          12.92   8.74    6.83    12.29   5.35    9.22    2.97    \n",
+            "Test time         1.13    0.56    1.23    0.37    0.63    0.78    0.34    \n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'test_rmse': array([1.61430595, 1.61580874, 1.61528323, 1.62966161, 1.62375189]),\n",
+              " 'test_mae': array([1.24884673, 1.24864507, 1.24780893, 1.25648068, 1.25479087]),\n",
+              " 'fit_time': (12.915496110916138,\n",
+              "  8.736067771911621,\n",
+              "  6.832916021347046,\n",
+              "  12.289664506912231,\n",
+              "  5.34771990776062),\n",
+              " 'test_time': (1.1301965713500977,\n",
+              "  0.5569760799407959,\n",
+              "  1.2330968379974365,\n",
+              "  0.36798882484436035,\n",
+              "  0.6280591487884521)}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from surprise import accuracy\n",
+        "\n",
+        "trainset, test_set = train_test_split(data, test_size=0.2)\n",
+        "\n",
+        "algorithm.fit(trainset)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RSzIkMQSZxt3",
+        "outputId": "a508ee36-d045-4799-ff7d-2748e7546835"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<surprise.prediction_algorithms.matrix_factorization.SVD at 0x7f4d25a90bd0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "predictions = algorithm.test(test_set)\n",
+        "\n",
+        "rmse = accuracy.rmse(predictions)\n",
+        "mae = accuracy.mae(predictions)\n",
+        "rmse\n",
+        "mae"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NHPOXpUTahhn",
+        "outputId": "dc427b49-8fe1-418c-fcca-9ddb55eadce7"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 1.6188\n",
+            "MAE:  1.2537\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "1.253712503593312"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pickle\n",
+        "\n",
+        "with open('svd_model.pkl', 'wb') as f:\n",
+        "  pickle.dump(algorithm, f)"
+      ],
+      "metadata": {
+        "id": "omOh0P2aYK0k"
+      },
+      "execution_count": 21,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "with open('svd_model.pkl', 'rb') as f:\n",
+        "    loaded_model = pickle.load(f)\n",
+        "\n",
+        "predictions = loaded_model.test(test_set)\n",
+        "\n",
+        "rmse = accuracy.rmse(predictions)\n",
+        "mae = accuracy.mae(predictions)\n",
+        "rmse\n",
+        "mae"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "aJdzgb2weorD",
+        "outputId": "e76d3d8b-7adc-4487-91e8-cc20e993e42f"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE: 1.6188\n",
+            "MAE:  1.2537\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "1.253712503593312"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Научить линейную регрессию (SGDRegressor) предсказывать средний рейтинг книги по следующим столбцам: автор, издатель, год издания, векторизованное название (для векторизации можно использовать tf-idf, word2vec или более сложный алгоритм. Для достижения требуемой тестом точности достаточно будет tf-idf. Собрав полностью датасет, не забудьте нормализовать данные (StandardScaler).\n",
+        "МАЕ должно быть ниже 1.5 (для этого есть тест)\n",
+        "Сохранить модель."
+      ],
+      "metadata": {
+        "id": "xMs130FefLL1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_mean_ratings = df_ratings.drop(columns=['User-ID']).groupby(['ISBN']).mean()\n",
+        "df_mean_ratings"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 455
+        },
+        "id": "JwDYAFfdfLra",
+        "outputId": "bf650ef8-d494-4ff1-d500-cd2f374e5d9d"
+      },
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                Book-Rating\n",
+              "ISBN                       \n",
+              "0 907 062 008     10.000000\n",
+              "0/330/25864/8      9.000000\n",
+              "00000000           8.000000\n",
+              "000000000          6.000000\n",
+              "0000000000         8.250000\n",
+              "...                     ...\n",
+              "O590446304         4.000000\n",
+              "O67174142X         7.333333\n",
+              "O688088686         9.000000\n",
+              "O9088446X          7.500000\n",
+              "\\0432534220\\\"\"     6.000000\n",
+              "\n",
+              "[55766 rows x 1 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-87d9718a-5c10-4916-8dde-6b808b2d6f19\" 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>Book-Rating</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ISBN</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0 907 062 008</th>\n",
+              "      <td>10.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0/330/25864/8</th>\n",
+              "      <td>9.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>00000000</th>\n",
+              "      <td>8.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>000000000</th>\n",
+              "      <td>6.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
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+              "\n",
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+              "    border-color: transparent;\n",
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+              "        document.querySelector('#' + key + ' button');\n",
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+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df_mean_ratings",
+              "summary": "{\n  \"name\": \"df_mean_ratings\",\n  \"rows\": 55766,\n  \"fields\": [\n    {\n      \"column\": \"ISBN\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 55766,\n        \"samples\": [\n          \"075153157X\",\n          \"0380978822\",\n          \"8472233065\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2785406009888802,\n        \"min\": 1.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 1274,\n        \"samples\": [\n          7.764705882352941,\n          7.379310344827586,\n          8.275\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_merged = pd.merge(df_books, df_mean_ratings, on='ISBN')\n",
+        "df_merged.dropna()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "Jr2z-FknkK4B",
+        "outputId": "5bc0ca37-f041-49f4-b0ec-83bfe59ff166"
+      },
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "             ISBN                                         Book-Title  \\\n",
+              "0      0002005018                                       Clara Callan   \n",
+              "1      0060973129                               Decision in Normandy   \n",
+              "2      0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
+              "3      0399135782                             The Kitchen God's Wife   \n",
+              "4      1558746218  A Second Chicken Soup for the Woman's Soul (Ch...   \n",
+              "...           ...                                                ...   \n",
+              "50039  0141308206     Summer Reading Is Killing Me! (Time Warp Trio)   \n",
+              "50040  0130807427  Behavior Modification--What It Is and How to D...   \n",
+              "50041  0380766485                              Captives of the Night   \n",
+              "50042  1572492317                       Echoes of the Falling Spring   \n",
+              "50043  0965018512  Secret Windows: Essays and Fiction on the Craf...   \n",
+              "\n",
+              "                Book-Author Year-Of-Publication                   Publisher  \\\n",
+              "0      Richard Bruce Wright                2001       HarperFlamingo Canada   \n",
+              "1              Carlo D'Este                1991             HarperPerennial   \n",
+              "2          Gina Bari Kolata                1999        Farrar Straus Giroux   \n",
+              "3                   Amy Tan                1991            Putnam Pub Group   \n",
+              "4             Jack Canfield                1998       Health Communications   \n",
+              "...                     ...                 ...                         ...   \n",
+              "50039          Jon Scieszka                2000                Puffin Books   \n",
+              "50040          Garry Martin                1998               Prentice Hall   \n",
+              "50041         Loretta Chase                1994                        Avon   \n",
+              "50042            Dody Myers                2001           Burd Street Press   \n",
+              "50043          Stephen King                2000  Quality Paperback Bookclub   \n",
+              "\n",
+              "       Book-Rating  \n",
+              "0         7.500000  \n",
+              "1         7.500000  \n",
+              "2         7.833333  \n",
+              "3         8.285714  \n",
+              "4        10.000000  \n",
+              "...            ...  \n",
+              "50039     9.000000  \n",
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+              "50041     8.000000  \n",
+              "50042     8.500000  \n",
+              "50043     8.000000  \n",
+              "\n",
+              "[50044 rows x 6 columns]"
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+              "\n",
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+              "      <td>Flu: The Story of the Great Influenza Pandemic...</td>\n",
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+              "      <td>0130807427</td>\n",
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+              "      <td>1572492317</td>\n",
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+              "      <td>Quality Paperback Bookclub</td>\n",
+              "      <td>8.000000</td>\n",
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+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df_merged\",\n  \"rows\": 50044,\n  \"fields\": [\n    {\n      \"column\": \"ISBN\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 50044,\n        \"samples\": [\n          \"1852306688\",\n          \"0140275657\",\n          \"0486256111\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 44752,\n        \"samples\": [\n          \"Sleep Tight (Onyx Book)\",\n          \"Savage Love\",\n          \"Girl Boy Etc.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Author\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18836,\n        \"samples\": [\n          \"David Maybury Lewis\",\n          \"Paul Mann\",\n          \"Alma Daniel\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year-Of-Publication\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 130,\n        \"samples\": [\n          1961,\n          1974,\n          1983\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Publisher\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3605,\n        \"samples\": [\n          \"Parabola Books\",\n          \"Reclam, Ditzingen\",\n          \"Windsor Peak Press\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2612453892589373,\n        \"min\": 1.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 1267,\n        \"samples\": [\n          5.7,\n          7.670329670329671,\n          6.578947368421052\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install nltk pymorphy2"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "McdtpMYcpXGM",
+        "outputId": "b9631845-d139-4886-c748-4db5433319da"
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: nltk in /usr/local/lib/python3.11/dist-packages (3.9.1)\n",
+            "Collecting pymorphy2\n",
+            "  Downloading pymorphy2-0.9.1-py3-none-any.whl.metadata (3.6 kB)\n",
+            "Requirement already satisfied: click in /usr/local/lib/python3.11/dist-packages (from nltk) (8.1.8)\n",
+            "Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (from nltk) (1.4.2)\n",
+            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.11/dist-packages (from nltk) (2024.11.6)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (from nltk) (4.67.1)\n",
+            "Collecting dawg-python>=0.7.1 (from pymorphy2)\n",
+            "  Downloading DAWG_Python-0.7.2-py2.py3-none-any.whl.metadata (7.0 kB)\n",
+            "Collecting pymorphy2-dicts-ru<3.0,>=2.4 (from pymorphy2)\n",
+            "  Downloading pymorphy2_dicts_ru-2.4.417127.4579844-py2.py3-none-any.whl.metadata (2.1 kB)\n",
+            "Collecting docopt>=0.6 (from pymorphy2)\n",
+            "  Downloading docopt-0.6.2.tar.gz (25 kB)\n",
+            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Downloading pymorphy2-0.9.1-py3-none-any.whl (55 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.5/55.5 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading DAWG_Python-0.7.2-py2.py3-none-any.whl (11 kB)\n",
+            "Downloading pymorphy2_dicts_ru-2.4.417127.4579844-py2.py3-none-any.whl (8.2 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.2/8.2 MB\u001b[0m \u001b[31m59.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hBuilding wheels for collected packages: docopt\n",
+            "  Building wheel for docopt (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for docopt: filename=docopt-0.6.2-py2.py3-none-any.whl size=13706 sha256=3327f2318fb28eb3c8cb9462f842cbb31ac9fcad6f628642af62adadf1337bf5\n",
+            "  Stored in directory: /root/.cache/pip/wheels/1a/b0/8c/4b75c4116c31f83c8f9f047231251e13cc74481cca4a78a9ce\n",
+            "Successfully built docopt\n",
+            "Installing collected packages: pymorphy2-dicts-ru, docopt, dawg-python, pymorphy2\n",
+            "Successfully installed dawg-python-0.7.2 docopt-0.6.2 pymorphy2-0.9.1 pymorphy2-dicts-ru-2.4.417127.4579844\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import nltk\n",
+        "from nltk.tokenize import word_tokenize\n",
+        "from pymorphy2 import MorphAnalyzer\n",
+        "import re\n",
+        "\n",
+        "nltk.download('punkt_tab')\n",
+        "\n",
+        "def preprocess(text):\n",
+        "  # morph = MorphAnalyzer()\n",
+        "\n",
+        "  tokens = word_tokenize(text)\n",
+        "  cleaned_tokens = [token.lower() for token in tokens if re.match(r'^\\w+$', token)]\n",
+        "  # lemmatized_tokens = [morph.parse(cleaned_token)[0].normal_form for cleaned_token in cleaned_tokens]\n",
+        "  return cleaned_tokens"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jbN-hPPZo6XW",
+        "outputId": "7a114c55-9f3a-4d37-d02d-1a25639c4fd1"
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[nltk_data] Downloading package punkt_tab to /root/nltk_data...\n",
+            "[nltk_data]   Unzipping tokenizers/punkt_tab.zip.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_merged_tokenized = df_merged\n",
+        "df_merged_tokenized['Book-Title-Tokenized'] = df_merged['Book-Title'].apply(preprocess)\n",
+        "df_merged_tokenized"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 545
+        },
+        "id": "eUwTu7M1snCo",
+        "outputId": "4fee5302-289e-453f-e537-fcd030d892cd"
+      },
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "             ISBN                                         Book-Title  \\\n",
+              "0      0002005018                                       Clara Callan   \n",
+              "1      0060973129                               Decision in Normandy   \n",
+              "2      0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
+              "3      0399135782                             The Kitchen God's Wife   \n",
+              "4      1558746218  A Second Chicken Soup for the Woman's Soul (Ch...   \n",
+              "...           ...                                                ...   \n",
+              "50039  0141308206     Summer Reading Is Killing Me! (Time Warp Trio)   \n",
+              "50040  0130807427  Behavior Modification--What It Is and How to D...   \n",
+              "50041  0380766485                              Captives of the Night   \n",
+              "50042  1572492317                       Echoes of the Falling Spring   \n",
+              "50043  0965018512  Secret Windows: Essays and Fiction on the Craf...   \n",
+              "\n",
+              "                Book-Author Year-Of-Publication                   Publisher  \\\n",
+              "0      Richard Bruce Wright                2001       HarperFlamingo Canada   \n",
+              "1              Carlo D'Este                1991             HarperPerennial   \n",
+              "2          Gina Bari Kolata                1999        Farrar Straus Giroux   \n",
+              "3                   Amy Tan                1991            Putnam Pub Group   \n",
+              "4             Jack Canfield                1998       Health Communications   \n",
+              "...                     ...                 ...                         ...   \n",
+              "50039          Jon Scieszka                2000                Puffin Books   \n",
+              "50040          Garry Martin                1998               Prentice Hall   \n",
+              "50041         Loretta Chase                1994                        Avon   \n",
+              "50042            Dody Myers                2001           Burd Street Press   \n",
+              "50043          Stephen King                2000  Quality Paperback Bookclub   \n",
+              "\n",
+              "       Book-Rating                               Book-Title-Tokenized  \n",
+              "0         7.500000                                    [clara, callan]  \n",
+              "1         7.500000                           [decision, in, normandy]  \n",
+              "2         7.833333  [flu, the, story, of, the, great, influenza, p...  \n",
+              "3         8.285714                          [the, kitchen, god, wife]  \n",
+              "4        10.000000  [a, second, chicken, soup, for, the, woman, so...  \n",
+              "...            ...                                                ...  \n",
+              "50039     9.000000  [summer, reading, is, killing, me, time, warp,...  \n",
+              "50040     5.500000  [behavior, modification, what, it, is, and, ho...  \n",
+              "50041     8.000000                         [captives, of, the, night]  \n",
+              "50042     8.500000                 [echoes, of, the, falling, spring]  \n",
+              "50043     8.000000  [secret, windows, essays, and, fiction, on, th...  \n",
+              "\n",
+              "[50044 rows x 7 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-aa0cca7c-b671-4b22-b59d-9e488117bcdf\" 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",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>ISBN</th>\n",
+              "      <th>Book-Title</th>\n",
+              "      <th>Book-Author</th>\n",
+              "      <th>Year-Of-Publication</th>\n",
+              "      <th>Publisher</th>\n",
+              "      <th>Book-Rating</th>\n",
+              "      <th>Book-Title-Tokenized</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0002005018</td>\n",
+              "      <td>Clara Callan</td>\n",
+              "      <td>Richard Bruce Wright</td>\n",
+              "      <td>2001</td>\n",
+              "      <td>HarperFlamingo Canada</td>\n",
+              "      <td>7.500000</td>\n",
+              "      <td>[clara, callan]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0060973129</td>\n",
+              "      <td>Decision in Normandy</td>\n",
+              "      <td>Carlo D'Este</td>\n",
+              "      <td>1991</td>\n",
+              "      <td>HarperPerennial</td>\n",
+              "      <td>7.500000</td>\n",
+              "      <td>[decision, in, normandy]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0374157065</td>\n",
+              "      <td>Flu: The Story of the Great Influenza Pandemic...</td>\n",
+              "      <td>Gina Bari Kolata</td>\n",
+              "      <td>1999</td>\n",
+              "      <td>Farrar Straus Giroux</td>\n",
+              "      <td>7.833333</td>\n",
+              "      <td>[flu, the, story, of, the, great, influenza, p...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0399135782</td>\n",
+              "      <td>The Kitchen God's Wife</td>\n",
+              "      <td>Amy Tan</td>\n",
+              "      <td>1991</td>\n",
+              "      <td>Putnam Pub Group</td>\n",
+              "      <td>8.285714</td>\n",
+              "      <td>[the, kitchen, god, wife]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1558746218</td>\n",
+              "      <td>A Second Chicken Soup for the Woman's Soul (Ch...</td>\n",
+              "      <td>Jack Canfield</td>\n",
+              "      <td>1998</td>\n",
+              "      <td>Health Communications</td>\n",
+              "      <td>10.000000</td>\n",
+              "      <td>[a, second, chicken, soup, for, the, woman, so...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50039</th>\n",
+              "      <td>0141308206</td>\n",
+              "      <td>Summer Reading Is Killing Me! (Time Warp Trio)</td>\n",
+              "      <td>Jon Scieszka</td>\n",
+              "      <td>2000</td>\n",
+              "      <td>Puffin Books</td>\n",
+              "      <td>9.000000</td>\n",
+              "      <td>[summer, reading, is, killing, me, time, warp,...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50040</th>\n",
+              "      <td>0130807427</td>\n",
+              "      <td>Behavior Modification--What It Is and How to D...</td>\n",
+              "      <td>Garry Martin</td>\n",
+              "      <td>1998</td>\n",
+              "      <td>Prentice Hall</td>\n",
+              "      <td>5.500000</td>\n",
+              "      <td>[behavior, modification, what, it, is, and, ho...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50041</th>\n",
+              "      <td>0380766485</td>\n",
+              "      <td>Captives of the Night</td>\n",
+              "      <td>Loretta Chase</td>\n",
+              "      <td>1994</td>\n",
+              "      <td>Avon</td>\n",
+              "      <td>8.000000</td>\n",
+              "      <td>[captives, of, the, night]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50042</th>\n",
+              "      <td>1572492317</td>\n",
+              "      <td>Echoes of the Falling Spring</td>\n",
+              "      <td>Dody Myers</td>\n",
+              "      <td>2001</td>\n",
+              "      <td>Burd Street Press</td>\n",
+              "      <td>8.500000</td>\n",
+              "      <td>[echoes, of, the, falling, spring]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50043</th>\n",
+              "      <td>0965018512</td>\n",
+              "      <td>Secret Windows: Essays and Fiction on the Craf...</td>\n",
+              "      <td>Stephen King</td>\n",
+              "      <td>2000</td>\n",
+              "      <td>Quality Paperback Bookclub</td>\n",
+              "      <td>8.000000</td>\n",
+              "      <td>[secret, windows, essays, and, fiction, on, th...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>50044 rows × 7 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-aa0cca7c-b671-4b22-b59d-9e488117bcdf')\"\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-aa0cca7c-b671-4b22-b59d-9e488117bcdf 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-aa0cca7c-b671-4b22-b59d-9e488117bcdf');\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-377133d5-c8c0-4788-a1b7-69075d141a22\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-377133d5-c8c0-4788-a1b7-69075d141a22')\"\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",
+              "    }\n",
+              "    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-377133d5-c8c0-4788-a1b7-69075d141a22 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df_merged",
+              "summary": "{\n  \"name\": \"df_merged\",\n  \"rows\": 50044,\n  \"fields\": [\n    {\n      \"column\": \"ISBN\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 50044,\n        \"samples\": [\n          \"1852306688\",\n          \"0140275657\",\n          \"0486256111\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 44752,\n        \"samples\": [\n          \"Sleep Tight (Onyx Book)\",\n          \"Savage Love\",\n          \"Girl Boy Etc.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Author\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18836,\n        \"samples\": [\n          \"David Maybury Lewis\",\n          \"Paul Mann\",\n          \"Alma Daniel\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year-Of-Publication\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 130,\n        \"samples\": [\n          1961,\n          1974,\n          1983\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Publisher\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3605,\n        \"samples\": [\n          \"Parabola Books\",\n          \"Reclam, Ditzingen\",\n          \"Windsor Peak Press\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2612453892589373,\n        \"min\": 1.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 1267,\n        \"samples\": [\n          5.7,\n          7.670329670329671,\n          6.578947368421052\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Title-Tokenized\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 27
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!gdown 1Dod76AXB95ruTYeIMvuvgWzPNCOWbzr2"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RwJmBuQg1TbR",
+        "outputId": "69962bdf-386d-4df4-8842-03f505d32324"
+      },
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading...\n",
+            "From (original): https://drive.google.com/uc?id=1Dod76AXB95ruTYeIMvuvgWzPNCOWbzr2\n",
+            "From (redirected): https://drive.google.com/uc?id=1Dod76AXB95ruTYeIMvuvgWzPNCOWbzr2&confirm=t&uuid=5fde0540-25e7-4d8c-99a0-f33ab6567a4a\n",
+            "To: /content/GoogleNews-vectors-negative300.bin.gz\n",
+            "100% 1.65G/1.65G [00:22<00:00, 72.8MB/s]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from gensim.models import Word2Vec\n",
+        "from gensim.models import KeyedVectors\n",
+        "\n",
+        "model_path = '/content/GoogleNews-vectors-negative300.bin.gz'\n",
+        "model = KeyedVectors.load_word2vec_format(model_path, binary=True)"
+      ],
+      "metadata": {
+        "id": "b8kVGGZqyOP3"
+      },
+      "execution_count": 29,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "word_vector = model['clara']\n",
+        "print(f\"Вектор слова 'clara':\\n{word_vector}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "collapsed": true,
+        "id": "h8GsM9nF5QBY",
+        "outputId": "72169b19-4ee7-4c58-d7d0-23dfc03811d8"
+      },
+      "execution_count": 30,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Вектор слова 'clara':\n",
+            "[-1.22070312e-02 -7.86132812e-02 -2.80761719e-02  1.33789062e-01\n",
+            "  1.47094727e-02  7.76367188e-02 -4.00390625e-02 -2.84423828e-02\n",
+            " -3.11279297e-02  1.15234375e-01 -4.15039062e-02 -5.15136719e-02\n",
+            " -1.25000000e-01  8.05664062e-02 -5.20019531e-02  5.49316406e-02\n",
+            "  3.21960449e-03  8.78906250e-02 -5.63964844e-02 -2.36816406e-02\n",
+            " -4.49218750e-02  5.21850586e-03  1.25000000e-01  1.72851562e-01\n",
+            " -1.52343750e-01 -3.46679688e-02 -5.88378906e-02  9.96093750e-02\n",
+            " -2.64892578e-02 -5.20019531e-02  3.61328125e-02  4.90722656e-02\n",
+            "  3.97949219e-02 -6.49414062e-02 -7.66601562e-02 -1.15722656e-01\n",
+            " -5.90820312e-02 -2.16064453e-02  4.90722656e-02 -2.70996094e-02\n",
+            " -7.95898438e-02  1.16699219e-01  2.79541016e-02  3.85742188e-02\n",
+            "  1.07421875e-02 -2.80761719e-02 -3.44238281e-02 -8.15429688e-02\n",
+            " -6.98242188e-02  4.63867188e-02 -2.75390625e-01  2.07031250e-01\n",
+            " -1.15966797e-03  3.89099121e-03 -1.07910156e-01  1.28936768e-03\n",
+            " -1.14257812e-01 -8.98437500e-02  1.00097656e-01 -3.34472656e-02\n",
+            " -1.12792969e-01  5.37109375e-02 -1.16577148e-02 -1.24511719e-02\n",
+            "  6.83593750e-02 -1.77734375e-01 -1.92871094e-02 -9.96093750e-02\n",
+            " -1.03759766e-02  7.42187500e-02  9.66796875e-02  1.22070312e-01\n",
+            " -1.00097656e-01  2.78320312e-02 -1.56250000e-02  7.61718750e-02\n",
+            "  6.64062500e-02 -1.41601562e-01 -2.61230469e-02 -4.85839844e-02\n",
+            " -2.22167969e-02 -2.53295898e-03  6.28662109e-03 -1.08886719e-01\n",
+            "  7.50732422e-03  6.44531250e-02 -2.86865234e-02  1.07421875e-01\n",
+            "  7.17773438e-02  4.24804688e-02 -1.57470703e-02 -1.26953125e-02\n",
+            " -1.13281250e-01 -2.66113281e-02  4.34570312e-02  1.21582031e-01\n",
+            "  7.71484375e-02  5.76782227e-03  1.54296875e-01 -2.95410156e-02\n",
+            "  5.67626953e-03  9.08203125e-02 -3.63769531e-02 -1.58691406e-02\n",
+            " -7.55310059e-04  3.90625000e-02 -7.03125000e-02  1.65039062e-01\n",
+            " -5.32226562e-02  1.13769531e-01 -9.86328125e-02 -1.33789062e-01\n",
+            "  2.26562500e-01 -1.26953125e-01  1.67236328e-02  4.78515625e-02\n",
+            " -9.13085938e-02 -1.60156250e-01  2.83203125e-02 -7.27539062e-02\n",
+            "  1.55273438e-01  1.06201172e-02 -1.76757812e-01 -2.73437500e-02\n",
+            "  1.66015625e-01  2.34375000e-02 -1.40625000e-01  1.31835938e-01\n",
+            "  1.53320312e-01 -2.19726562e-02 -4.29687500e-02 -5.93261719e-02\n",
+            "  6.22558594e-02 -4.46777344e-02 -7.42187500e-02  1.03027344e-01\n",
+            " -1.32812500e-01 -4.07714844e-02  1.96289062e-01  4.98046875e-02\n",
+            "  9.66796875e-02 -1.39648438e-01  5.07812500e-02 -1.11816406e-01\n",
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+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def vectorize_title_word2vec(tokens):\n",
+        "    vectors = []\n",
+        "\n",
+        "    for word in tokens:\n",
+        "        if word in model:  # Проверка, что слово есть в модели\n",
+        "            vectors.append(model[word])\n",
+        "\n",
+        "    # Если слова найдены, усредняем их векторы, иначе возвращаем вектор нулей\n",
+        "    if vectors:\n",
+        "        return sum(vectors) / len(vectors)\n",
+        "    else:\n",
+        "        return [0] * model.vector_size  # Вектор нулей, если нет подходящих слов\n"
+      ],
+      "metadata": {
+        "id": "zO2JVaEl6Y_2"
+      },
+      "execution_count": 31,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_vectorized_word2vec = df_merged_tokenized\n",
+        "df_vectorized_word2vec['Book-Title-Vectorized'] = df_vectorized_word2vec['Book-Title-Tokenized'].apply(vectorize_title_word2vec)"
+      ],
+      "metadata": {
+        "id": "ZSd83H4L6BMT"
+      },
+      "execution_count": 32,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from gensim.models.doc2vec import Doc2Vec, TaggedDocument\n",
+        "\n",
+        "documents = [TaggedDocument(words=title, tags=[str(i)]) for i, title in enumerate(df_merged_tokenized['Book-Title-Tokenized'])]\n",
+        "\n",
+        "# Обучение модели\n",
+        "model_doc2vec = Doc2Vec(documents, vector_size=100, window=2, min_count=1, workers=4)\n"
+      ],
+      "metadata": {
+        "id": "7tbrrPI0BFvy"
+      },
+      "execution_count": 33,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "8cZuGX9VB7ns"
+      },
+      "execution_count": 33,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_vectorized_doc2vec = df_merged_tokenized\n",
+        "df_vectorized_doc2vec['Book-Title-Vectorized'] = df_vectorized_word2vec['Book-Title-Tokenized'].apply(lambda tokens: model_doc2vec.infer_vector(tokens))"
+      ],
+      "metadata": {
+        "id": "EP3rpU2JBvfs"
+      },
+      "execution_count": 34,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_vectorized_doc2vec"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 615
+        },
+        "id": "LNTFghAXD52Z",
+        "outputId": "dcb78bd7-7a34-4c12-bebb-ca193eb41bb0"
+      },
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "             ISBN                                         Book-Title  \\\n",
+              "0      0002005018                                       Clara Callan   \n",
+              "1      0060973129                               Decision in Normandy   \n",
+              "2      0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
+              "3      0399135782                             The Kitchen God's Wife   \n",
+              "4      1558746218  A Second Chicken Soup for the Woman's Soul (Ch...   \n",
+              "...           ...                                                ...   \n",
+              "50039  0141308206     Summer Reading Is Killing Me! (Time Warp Trio)   \n",
+              "50040  0130807427  Behavior Modification--What It Is and How to D...   \n",
+              "50041  0380766485                              Captives of the Night   \n",
+              "50042  1572492317                       Echoes of the Falling Spring   \n",
+              "50043  0965018512  Secret Windows: Essays and Fiction on the Craf...   \n",
+              "\n",
+              "                Book-Author Year-Of-Publication                   Publisher  \\\n",
+              "0      Richard Bruce Wright                2001       HarperFlamingo Canada   \n",
+              "1              Carlo D'Este                1991             HarperPerennial   \n",
+              "2          Gina Bari Kolata                1999        Farrar Straus Giroux   \n",
+              "3                   Amy Tan                1991            Putnam Pub Group   \n",
+              "4             Jack Canfield                1998       Health Communications   \n",
+              "...                     ...                 ...                         ...   \n",
+              "50039          Jon Scieszka                2000                Puffin Books   \n",
+              "50040          Garry Martin                1998               Prentice Hall   \n",
+              "50041         Loretta Chase                1994                        Avon   \n",
+              "50042            Dody Myers                2001           Burd Street Press   \n",
+              "50043          Stephen King                2000  Quality Paperback Bookclub   \n",
+              "\n",
+              "       Book-Rating                               Book-Title-Tokenized  \\\n",
+              "0         7.500000                                    [clara, callan]   \n",
+              "1         7.500000                           [decision, in, normandy]   \n",
+              "2         7.833333  [flu, the, story, of, the, great, influenza, p...   \n",
+              "3         8.285714                          [the, kitchen, god, wife]   \n",
+              "4        10.000000  [a, second, chicken, soup, for, the, woman, so...   \n",
+              "...            ...                                                ...   \n",
+              "50039     9.000000  [summer, reading, is, killing, me, time, warp,...   \n",
+              "50040     5.500000  [behavior, modification, what, it, is, and, ho...   \n",
+              "50041     8.000000                         [captives, of, the, night]   \n",
+              "50042     8.500000                 [echoes, of, the, falling, spring]   \n",
+              "50043     8.000000  [secret, windows, essays, and, fiction, on, th...   \n",
+              "\n",
+              "                                   Book-Title-Vectorized  \n",
+              "0      [-0.0645337, 0.04740464, -0.003607908, -0.0369...  \n",
+              "1      [-0.06865264, 0.03474637, 0.0087239295, -0.031...  \n",
+              "2      [-0.050521825, 0.062116567, 0.013070498, -0.06...  \n",
+              "3      [-0.019539846, 0.021032456, -0.003599502, 0.01...  \n",
+              "4      [-0.016762892, -0.02298693, 0.009035798, 0.059...  \n",
+              "...                                                  ...  \n",
+              "50039  [-0.017692503, -0.033314988, -0.007695766, 0.0...  \n",
+              "50040  [-0.03236937, 0.0027971948, -0.015948668, -0.0...  \n",
+              "50041  [-0.039578293, 0.022045352, -0.0008171154, -0....  \n",
+              "50042  [-0.03933497, -0.010528634, 0.013040441, -0.01...  \n",
+              "50043  [0.01396761, 0.008222527, 0.12903251, -0.01238...  \n",
+              "\n",
+              "[50044 rows x 8 columns]"
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+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>ISBN</th>\n",
+              "      <th>Book-Title</th>\n",
+              "      <th>Book-Author</th>\n",
+              "      <th>Year-Of-Publication</th>\n",
+              "      <th>Publisher</th>\n",
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+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0002005018</td>\n",
+              "      <td>Clara Callan</td>\n",
+              "      <td>Richard Bruce Wright</td>\n",
+              "      <td>2001</td>\n",
+              "      <td>HarperFlamingo Canada</td>\n",
+              "      <td>7.500000</td>\n",
+              "      <td>[clara, callan]</td>\n",
+              "      <td>[-0.0645337, 0.04740464, -0.003607908, -0.0369...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0060973129</td>\n",
+              "      <td>Decision in Normandy</td>\n",
+              "      <td>Carlo D'Este</td>\n",
+              "      <td>1991</td>\n",
+              "      <td>HarperPerennial</td>\n",
+              "      <td>7.500000</td>\n",
+              "      <td>[decision, in, normandy]</td>\n",
+              "      <td>[-0.06865264, 0.03474637, 0.0087239295, -0.031...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0374157065</td>\n",
+              "      <td>Flu: The Story of the Great Influenza Pandemic...</td>\n",
+              "      <td>Gina Bari Kolata</td>\n",
+              "      <td>1999</td>\n",
+              "      <td>Farrar Straus Giroux</td>\n",
+              "      <td>7.833333</td>\n",
+              "      <td>[flu, the, story, of, the, great, influenza, p...</td>\n",
+              "      <td>[-0.050521825, 0.062116567, 0.013070498, -0.06...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0399135782</td>\n",
+              "      <td>The Kitchen God's Wife</td>\n",
+              "      <td>Amy Tan</td>\n",
+              "      <td>1991</td>\n",
+              "      <td>Putnam Pub Group</td>\n",
+              "      <td>8.285714</td>\n",
+              "      <td>[the, kitchen, god, wife]</td>\n",
+              "      <td>[-0.019539846, 0.021032456, -0.003599502, 0.01...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1558746218</td>\n",
+              "      <td>A Second Chicken Soup for the Woman's Soul (Ch...</td>\n",
+              "      <td>Jack Canfield</td>\n",
+              "      <td>1998</td>\n",
+              "      <td>Health Communications</td>\n",
+              "      <td>10.000000</td>\n",
+              "      <td>[a, second, chicken, soup, for, the, woman, so...</td>\n",
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+              "    </tr>\n",
+              "    <tr>\n",
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+              "      <th>50039</th>\n",
+              "      <td>0141308206</td>\n",
+              "      <td>Summer Reading Is Killing Me! (Time Warp Trio)</td>\n",
+              "      <td>Jon Scieszka</td>\n",
+              "      <td>2000</td>\n",
+              "      <td>Puffin Books</td>\n",
+              "      <td>9.000000</td>\n",
+              "      <td>[summer, reading, is, killing, me, time, warp,...</td>\n",
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+              "      <td>0130807427</td>\n",
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+              "      <td>1998</td>\n",
+              "      <td>Prentice Hall</td>\n",
+              "      <td>5.500000</td>\n",
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+              "      <td>0380766485</td>\n",
+              "      <td>Captives of the Night</td>\n",
+              "      <td>Loretta Chase</td>\n",
+              "      <td>1994</td>\n",
+              "      <td>Avon</td>\n",
+              "      <td>8.000000</td>\n",
+              "      <td>[captives, of, the, night]</td>\n",
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+              "        document.querySelector('#df-2738b0fd-e103-48e9-9786-876c4bed95e5 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-2738b0fd-e103-48e9-9786-876c4bed95e5');\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-9cca7d15-d7db-4906-a352-ea4f152c7aef\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9cca7d15-d7db-4906-a352-ea4f152c7aef')\"\n",
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+              "\n",
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+              "    </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",
+              "    }\n",
+              "    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-9cca7d15-d7db-4906-a352-ea4f152c7aef button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df_merged",
+              "summary": "{\n  \"name\": \"df_merged\",\n  \"rows\": 50044,\n  \"fields\": [\n    {\n      \"column\": \"ISBN\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 50044,\n        \"samples\": [\n          \"1852306688\",\n          \"0140275657\",\n          \"0486256111\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 44752,\n        \"samples\": [\n          \"Sleep Tight (Onyx Book)\",\n          \"Savage Love\",\n          \"Girl Boy Etc.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Author\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18836,\n        \"samples\": [\n          \"David Maybury Lewis\",\n          \"Paul Mann\",\n          \"Alma Daniel\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year-Of-Publication\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 130,\n        \"samples\": [\n          1961,\n          1974,\n          1983\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Publisher\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3605,\n        \"samples\": [\n          \"Parabola Books\",\n          \"Reclam, Ditzingen\",\n          \"Windsor Peak Press\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2612453892589373,\n        \"min\": 1.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 1267,\n        \"samples\": [\n          5.7,\n          7.670329670329671,\n          6.578947368421052\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Title-Tokenized\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Title-Vectorized\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 35
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_vectorized_doc2vec.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Z68hgxU7HU1K",
+        "outputId": "48377256-0dd5-4f24-cba6-fcbf5fb6ddc6"
+      },
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 50044 entries, 0 to 50043\n",
+            "Data columns (total 8 columns):\n",
+            " #   Column                 Non-Null Count  Dtype  \n",
+            "---  ------                 --------------  -----  \n",
+            " 0   ISBN                   50044 non-null  object \n",
+            " 1   Book-Title             50044 non-null  object \n",
+            " 2   Book-Author            50044 non-null  object \n",
+            " 3   Year-Of-Publication    50044 non-null  object \n",
+            " 4   Publisher              50044 non-null  object \n",
+            " 5   Book-Rating            50044 non-null  float64\n",
+            " 6   Book-Title-Tokenized   50044 non-null  object \n",
+            " 7   Book-Title-Vectorized  50044 non-null  object \n",
+            "dtypes: float64(1), object(7)\n",
+            "memory usage: 3.1+ MB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_vectorized_doc2vec['Year-Of-Publication'] = df_vectorized_doc2vec['Year-Of-Publication'].astype('int64')"
+      ],
+      "metadata": {
+        "id": "D_3gIVRkHYvO"
+      },
+      "execution_count": 37,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = df_vectorized_doc2vec\n"
+      ],
+      "metadata": {
+        "id": "J9OdyKIXH05s"
+      },
+      "execution_count": 38,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
+        "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.linear_model import SGDRegressor\n",
+        "from sklearn.metrics import mean_absolute_error\n",
+        "\n",
+        "\n",
+        "\n",
+        "df = df_vectorized_doc2vec\n",
+        "X = df.drop(columns=['Book-Rating'])\n",
+        "y = df['Book-Rating']\n",
+        "\n",
+        "le_Book_Author = LabelEncoder()\n",
+        "le_Publisher = LabelEncoder()\n",
+        "\n",
+        "df['Publisher'] = le_Publisher.fit_transform(df['Publisher'])\n",
+        "df['Book-Author'] = le_Book_Author.fit_transform(df['Book-Author'])\n",
+        "\n",
+        "scaler = StandardScaler()\n",
+        "\n",
+        "books_scaled = scaler.fit_transform(df[['Book-Author', 'Year-Of-Publication', 'Publisher']])\n",
+        "# print(len(books_scaled))\n",
+        "# print(df.shape)\n",
+        "title_vectors = df['Book-Title-Vectorized'].tolist()\n",
+        "# print(title_vectors)\n",
+        "X = pd.concat([pd.DataFrame(books_scaled, columns=['Book-Author', 'Year-Of-Publication', 'Publisher']), pd.DataFrame(title_vectors)], axis=1)\n",
+        "X= X.rename(str,axis=\"columns\")\n",
+        "# print(X)\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n",
+        "model = SGDRegressor(max_iter=5000, tol=1e-5, learning_rate=\"adaptive\")\n",
+        "model.fit(X_train, y_train)\n",
+        "\n",
+        "y_pred = model.predict(X_test)\n",
+        "mae_score = mean_absolute_error(y_test, y_pred)\n",
+        "print(f\"MAE: {mae_score:.4f}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1HlNFRbOEdbp",
+        "outputId": "81baffdf-adea-44ea-9000-f3b2953af477"
+      },
+      "execution_count": 47,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "MAE: 0.9884\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = df_vectorized_doc2vec"
+      ],
+      "metadata": {
+        "id": "edwTzbTsBpYv"
+      },
+      "execution_count": 40,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
+        "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.linear_model import SGDRegressor\n",
+        "from sklearn.metrics import mean_absolute_error\n",
+        "\n",
+        "\n",
+        "\n",
+        "df = df_vectorized_doc2vec\n",
+        "X = df.drop(columns=['Book-Rating'])\n",
+        "y = df['Book-Rating']\n",
+        "\n",
+        "le_Book_Author = LabelEncoder()\n",
+        "le_Publisher = LabelEncoder()\n",
+        "\n",
+        "df['Publisher'] = le_Publisher.fit_transform(df['Publisher'])\n",
+        "df['Book-Author'] = le_Book_Author.fit_transform(df['Book-Author'])\n",
+        "\n",
+        "scaler = StandardScaler()\n",
+        "\n",
+        "books_scaled = scaler.fit_transform(df[['Book-Author', 'Year-Of-Publication', 'Publisher']])\n",
+        "# print(len(books_scaled))\n",
+        "# print(df.shape)\n",
+        "title_vectors = df['Book-Title-Vectorized'].tolist()\n",
+        "# print(title_vectors)\n",
+        "X = pd.concat([pd.DataFrame(books_scaled, columns=['Book-Author', 'Year-Of-Publication', 'Publisher']), pd.DataFrame(title_vectors)], axis=1)\n",
+        "X= X.rename(str,axis=\"columns\")\n",
+        "# print(X)\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)\n",
+        "model = SGDRegressor(max_iter=5000, tol=1e-5, learning_rate=\"adaptive\")\n",
+        "model.fit(X_train, y_train)\n",
+        "\n",
+        "y_pred = model.predict(X_test)\n",
+        "mae_score = mean_absolute_error(y_test, y_pred)\n",
+        "print(f\"MAE: {mae_score:.4f}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DwXuSvfwBoRX",
+        "outputId": "9ee50855-a137-42cb-f3b6-c02c1920e721"
+      },
+      "execution_count": 48,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "MAE: 0.9700\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pickle\n",
+        "with open('linreg_model.pkl', 'wb') as f:\n",
+        "    pickle.dump(model, f)"
+      ],
+      "metadata": {
+        "id": "IpIXIjOr7qNc"
+      },
+      "execution_count": 43,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "with open('tfidf_vectorizer.pkl', 'wb') as f:\n",
+        "    pickle.dump(tfidf, f)\n",
+        "with open('le_Book_Author.pkl', 'wb') as f:\n",
+        "    pickle.dump(le_Book_Author, f)\n",
+        "with open('le_Publisher.pkl', 'wb') as f:\n",
+        "    pickle.dump(le_Publisher, f)\n",
+        "with open('scaler.pkl', 'wb') as f:\n",
+        "    pickle.dump(scaler, f)"
+      ],
+      "metadata": {
+        "id": "gdP6-kYbADkX"
+      },
+      "execution_count": 50,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import pickle\n",
+        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
+        "\n",
+        "# Загружаем данные из Books.csv (используем low_memory=False для предупреждения)\n",
+        "books = pd.read_csv(\"Books.csv\", low_memory=False)\n",
+        "\n",
+        "# Оставляем только необходимые столбцы\n",
+        "books = books[['ISBN', 'Book-Title', 'Book-Author', 'Year-Of-Publication', 'Publisher']]\n",
+        "\n",
+        "# Заполняем пропуски и приводим к строковому типу (важно для LabelEncoder)\n",
+        "books[\"Book-Title\"] = books[\"Book-Title\"].fillna(\"\").astype(str)\n",
+        "books[\"Book-Author\"] = books[\"Book-Author\"].fillna(\"\").astype(str)\n",
+        "books[\"Publisher\"] = books[\"Publisher\"].fillna(\"\").astype(str)\n",
+        "\n",
+        "# Обучаем TfidfVectorizer на названиях книг\n",
+        "tfidf = TfidfVectorizer()\n",
+        "tfidf.fit(books[\"Book-Title\"])\n",
+        "\n",
+        "# Обучаем LabelEncoder для автора и издателя\n",
+        "le_Book_Author = LabelEncoder()\n",
+        "le_Publisher = LabelEncoder()\n",
+        "le_Book_Author.fit(books[\"Book-Author\"])\n",
+        "le_Publisher.fit(books[\"Publisher\"])\n",
+        "\n",
+        "# Приводим год публикации к числовому типу (при ошибках заполняем 0)\n",
+        "books[\"Year-Of-Publication\"] = pd.to_numeric(books[\"Year-Of-Publication\"], errors='coerce').fillna(0)\n",
+        "\n",
+        "# Получаем числовые признаки: код автора, год и код издателя\n",
+        "author_encoded = le_Book_Author.transform(books[\"Book-Author\"])\n",
+        "publisher_encoded = le_Publisher.transform(books[\"Publisher\"])\n",
+        "X_num = pd.DataFrame({\n",
+        "    \"Author_enc\": author_encoded,\n",
+        "    \"Year\": books[\"Year-Of-Publication\"],\n",
+        "    \"Publisher_enc\": publisher_encoded\n",
+        "})\n",
+        "\n",
+        "# Обучаем StandardScaler на этих числовых признаках\n",
+        "scaler = StandardScaler()\n",
+        "scaler.fit(X_num)\n",
+        "\n",
+        "# Сохраняем обученные объекты\n",
+        "with open('tfidf_vectorizer.pkl', 'wb') as f:\n",
+        "    pickle.dump(tfidf, f)\n",
+        "with open('le_Book_Author.pkl', 'wb') as f:\n",
+        "    pickle.dump(le_Book_Author, f)\n",
+        "with open('le_Publisher.pkl', 'wb') as f:\n",
+        "    pickle.dump(le_Publisher, f)\n",
+        "with open('scaler.pkl', 'wb') as f:\n",
+        "    pickle.dump(scaler, f)\n",
+        "with open('doc2vec_model.pkl', 'wb') as f:\n",
+        "    pickle.dump(model_doc2vec, f)\n",
+        "\n",
+        "print(\"Преобразователи и векторизатор успешно сохранены.\")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dM4yGHrmBYyt",
+        "outputId": "3c59baa5-e71f-4e87-d141-2cfd007d8bb1"
+      },
+      "execution_count": 57,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Преобразователи и векторизатор успешно сохранены.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# personal_recommendation.py\n"
+      ],
+      "metadata": {
+        "id": "CRGhidRd7wJZ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "В файле personal_recommendation.py создайте рекомендацию для пользователя, у которого в исходном датасете было больше всего 0 среди рейтингов книг. Алгоритм такой:\n",
+        "Находим нужного пользователя.\n",
+        "Делаем предсказание SVD для книг, которым он \"поставил\" 0.\n",
+        "Берем те книги, для которых предсказали рейтинг не ниже 8. Считаем, что 8 означет, что книга ему точно понравится.\n",
+        "Делаем предсказание LinReg для этих же книг.\n",
+        "Сортируем полученный на шаге 3 список по убыванию рейтинга линейной модели.\n",
+        "В конце файла комментарием записываем полученную рекомендацию.\n",
+        "То есть идея в том, чтобы сделать для пользователя индивидуальную рекомендацию, показывая в начале списка те книги, которые в целом могли бы иметь высокий рейтинг.\n",
+        "Обязательно сохраняйте готовую модель и добавляйте ее в свой репозиторий, потому что файл с сохраненной моделью используется в тестах."
+      ],
+      "metadata": {
+        "id": "5L15GctJPJU6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import pickle\n",
+        "import re\n",
+        "import nltk\n",
+        "nltk.download('punkt')\n",
+        "from nltk.tokenize import word_tokenize\n",
+        "\n",
+        "# 1. ЗАГРУЗКА СОХРАНЁННЫХ МОДЕЛЕЙ\n",
+        "\n",
+        "# Модель SVD для предсказания рейтингов отдельных книг\n",
+        "with open('svd_model.pkl', 'rb') as f:\n",
+        "    svd_model = pickle.load(f)\n",
+        "\n",
+        "# Модель линейной регрессии (SGDRegressor), обученная на 103 признаках\n",
+        "with open('linreg_model.pkl', 'rb') as f:\n",
+        "    linreg_model = pickle.load(f)\n",
+        "\n",
+        "# Загружаем сохранённые преобразователи для категориальных признаков\n",
+        "with open('le_Book_Author.pkl', 'rb') as f:\n",
+        "    le_Book_Author = pickle.load(f)\n",
+        "with open('le_Publisher.pkl', 'rb') as f:\n",
+        "    le_Publisher = pickle.load(f)\n",
+        "with open('scaler.pkl', 'rb') as f:\n",
+        "    scaler = pickle.load(f)\n",
+        "\n",
+        "# Загружаем сохранённую модель Doc2Vec (обученную на токенизированных названиях книг)\n",
+        "with open('doc2vec_model.pkl', 'rb') as f:\n",
+        "    doc2vec_model = pickle.load(f)\n",
+        "\n",
+        "# 2. ЗАГРУЗКА ИСХОДНЫХ ДАННЫХ\n",
+        "\n",
+        "# Файл с оценками\n",
+        "ratings = pd.read_csv(\"Ratings.csv\")\n",
+        "# Файл с информацией о книгах (оставляем только нужные столбцы)\n",
+        "books = pd.read_csv(\"Books.csv\", low_memory=False)\n",
+        "books = books[['ISBN', 'Book-Title', 'Book-Author', 'Year-Of-Publication', 'Publisher']]\n",
+        "\n",
+        "# 3. ОПРЕДЕЛЯЕМ ЦЕЛЕВОГО ПОЛЬЗОВАТЕЛЯ\n",
+        "# Выбираем того, у кого больше всего оценок, равных 0\n",
+        "user_zero_counts = ratings[ratings[\"Book-Rating\"] == 0]['User-ID'].value_counts()\n",
+        "target_user = user_zero_counts.idxmax()\n",
+        "print(\"Целевой пользователь:\", target_user)\n",
+        "\n",
+        "# 4. НАХОДИМ КНИГИ, ДЛЯ КОТОРЫХ ПОЛЬЗОВАТЕЛЬ ПОСТАВИЛ ОЦЕНКУ 0\n",
+        "candidate_isbns = ratings[(ratings[\"User-ID\"] == target_user) & (ratings[\"Book-Rating\"] == 0)][\"ISBN\"].unique()\n",
+        "\n",
+        "# 5. РАСЧИТЫВАЕМ ПРЕДСКАЗАНИЯ SVD ДЛЯ КАЖДОЙ КНИГИ\n",
+        "svd_preds = []\n",
+        "for isbn in candidate_isbns:\n",
+        "    pred = svd_model.predict(uid=target_user, iid=isbn)\n",
+        "    svd_preds.append((isbn, pred.est))\n",
+        "\n",
+        "# Оставляем книги, у которых предсказанный рейтинг ≥ 8\n",
+        "filtered_candidates = [(isbn, rating) for isbn, rating in svd_preds if rating >= 8]\n",
+        "if not filtered_candidates:\n",
+        "    print(\"Нет книг с предсказанным рейтингом ≥ 8 для пользователя\", target_user)\n",
+        "    exit()\n",
+        "\n",
+        "# 6. ФОРМИРУЕМ DataFrame С ИНФОРМАЦИЕЙ О КНИГАХ-КАНДИДАТАХ\n",
+        "candidates_df = books[books[\"ISBN\"].isin([isbn for isbn, _ in filtered_candidates])].copy()\n",
+        "# Заполняем пропуски и приводим текстовые столбцы к строковому типу\n",
+        "candidates_df[\"Book-Title\"] = candidates_df[\"Book-Title\"].fillna(\"\").astype(str)\n",
+        "candidates_df[\"Book-Author\"] = candidates_df[\"Book-Author\"].fillna(\"\").astype(str)\n",
+        "candidates_df[\"Publisher\"] = candidates_df[\"Publisher\"].fillna(\"\").astype(str)\n",
+        "\n",
+        "def preprocess(text):\n",
+        "    # Токенизация и приведение к нижнему регистру; оставляем только слова\n",
+        "    tokens = word_tokenize(text)\n",
+        "    cleaned_tokens = [token.lower() for token in tokens if re.match(r'^\\w+$', token)]\n",
+        "    return cleaned_tokens\n",
+        "\n",
+        "candidates_df[\"Book-Title-Tokenized\"] = candidates_df[\"Book-Title\"].apply(preprocess)\n",
+        "\n",
+        "def infer_vector(tokens):\n",
+        "    return doc2vec_model.infer_vector(tokens)\n",
+        "\n",
+        "title_vectors = candidates_df[\"Book-Title-Tokenized\"].apply(infer_vector)\n",
+        "# Преобразуем список в массив numpy (размерность: число_книг x 100)\n",
+        "X_text = np.array(title_vectors.tolist())\n",
+        "\n",
+        "# 8. КОДИРОВАНИЕ КАТЕГОРИАЛЬНЫХ ПРИЗНАКОВ\n",
+        "# Приводим значения к строке для безопасности\n",
+        "candidates_df[\"Author_enc\"] = le_Book_Author.transform(candidates_df[\"Book-Author\"].astype(str))\n",
+        "candidates_df[\"Publisher_enc\"] = le_Publisher.transform(candidates_df[\"Publisher\"].astype(str))\n",
+        "\n",
+        "# 9. ОБРАБОТКА ГОДА ПУБЛИКАЦИИ\n",
+        "candidates_df[\"Year\"] = pd.to_numeric(candidates_df[\"Year-Of-Publication\"], errors='coerce').fillna(0)\n",
+        "\n",
+        "# 10. СОБИРАЕМ ЧИСЛОВЫЕ ПРИЗНАКИ (код автора, год, код издателя)\n",
+        "X_num = candidates_df[[\"Author_enc\", \"Year\", \"Publisher_enc\"]].values\n",
+        "X_num_scaled = scaler.transform(X_num)\n",
+        "\n",
+        "# 11. ОБЪЕДИНЯЕМ ЧИСЛОВЫЕ И TEXT-ПРИЗНАКИ\n",
+        "# Итоговая размерность: 3 (числовых) + 100 (Doc2Vec) = 103 признака\n",
+        "features = np.hstack([X_num_scaled, X_text])\n",
+        "\n",
+        "# 12. ПОЛУЧАЕМ ПРЕДСКАЗАНИЯ ЛИНЕЙНОЙ МОДЕЛИ\n",
+        "linreg_preds = linreg_model.predict(features)\n",
+        "\n",
+        "# 13. ОБЪЕДИНЯЕМ РЕЗУЛЬТАТЫ: (ISBN, Book-Title, SVD-предсказание, LinReg-предсказание)\n",
+        "recommendations = []\n",
+        "for idx, row in candidates_df.iterrows():\n",
+        "    isbn = row[\"ISBN\"]\n",
+        "    svd_rating = next((r for (i, r) in filtered_candidates if i == isbn), None)\n",
+        "    if svd_rating is not None:\n",
+        "        # Предполагаем, что порядок строк в candidates_df совпадает с порядком в features\n",
+        "        linreg_rating = linreg_preds[list(candidates_df.index).index(idx)]\n",
+        "        recommendations.append((isbn, row[\"Book-Title\"], svd_rating, linreg_rating))\n",
+        "\n",
+        "# 14. СОРТИРУЕМ КНИГИ ПО УБЫВАНИЮ ПРЕДСКАЗАНИЯ ЛИНЕЙНОЙ МОДЕЛИ\n",
+        "recommendations_sorted = sorted(recommendations, key=lambda x: x[3], reverse=True)\n",
+        "# 15. СОЗДАНИЕ DATAFRAME С РЕКОМЕНДАЦИЯМИ\n",
+        "recommendations_df = pd.DataFrame(recommendations_sorted, columns=[\"ISBN\", \"Book-Title\", \"SVD-Prediction\", \"LinReg-Prediction\"])\n",
+        "\n",
+        "# 16. ВЫВОД ПЕРВЫХ 10 РЕКОМЕНДАЦИЙ\n",
+        "\n",
+        "# ------------------------------------------------------------------------------\n",
+        "# Пример итоговой рекомендации (можно записать в комментарий):\n",
+        "# Рекомендация для пользователя 198711: ['Название книги 1', 'Название книги 2', 'Название книги 3']\n"
+      ],
+      "metadata": {
+        "id": "flXNX8xcPKLS",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "fc952d5e-4445-41a8-eeda-1682ea0e9e45"
+      },
+      "execution_count": 61,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
+            "[nltk_data]   Package punkt is already up-to-date!\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Целевой пользователь: 198711\n",
+            "\n",
+            "Топ-10 рекомендаций для пользователя 198711\n",
+            "         ISBN                                         Book-Title  \\\n",
+            "0  059035342X  Harry Potter and the Sorcerer's Stone (Harry P...   \n",
+            "1  0449003132         Milk and Honey (Decker and Lazarus Series)   \n",
+            "2  0064409422  The Lion, the Witch and the Wardrobe (Full-Col...   \n",
+            "3  0440219078                 The Giver (21st Century Reference)   \n",
+            "4  0671032658                                     The Green Mile   \n",
+            "5  0553269224  Rilla of Ingleside (Anne of Green Gables Novel...   \n",
+            "6  0439064872   Harry Potter and the Chamber of Secrets (Book 2)   \n",
+            "7  0451194861          Wizard and Glass (The Dark Tower, Book 4)   \n",
+            "8  0515136530      Key of Valor (Roberts, Nora. Key Trilogy, 3.)   \n",
+            "9  0590457225  Dealing With Dragons (Enchanted Forest Chronic...   \n",
+            "\n",
+            "   SVD-Prediction  LinReg-Prediction  \n",
+            "0        8.534317           7.823845  \n",
+            "1        8.004683           7.805983  \n",
+            "2        8.339789           7.790871  \n",
+            "3        8.449400           7.755851  \n",
+            "4        8.065435           7.736889  \n",
+            "5        8.003411           7.725130  \n",
+            "6        8.045036           7.713495  \n",
+            "7        8.158667           7.711711  \n",
+            "8        8.088881           7.703916  \n",
+            "9        8.001301           7.703907  \n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but SGDRegressor was fitted with feature names\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"\\nТоп-10 рекомендаций для пользователя\", target_user)\n",
+        "print(recommendations_df[['Book-Title', 'SVD-Prediction', 'LinReg-Prediction']].head(10))\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UcpPOHReGBcJ",
+        "outputId": "7c98a623-70da-4847-f2d9-3051bcd9fb54"
+      },
+      "execution_count": 63,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Топ-10 рекомендаций для пользователя 198711\n",
+            "                                          Book-Title  SVD-Prediction  \\\n",
+            "0  Harry Potter and the Sorcerer's Stone (Harry P...        8.534317   \n",
+            "1         Milk and Honey (Decker and Lazarus Series)        8.004683   \n",
+            "2  The Lion, the Witch and the Wardrobe (Full-Col...        8.339789   \n",
+            "3                 The Giver (21st Century Reference)        8.449400   \n",
+            "4                                     The Green Mile        8.065435   \n",
+            "5  Rilla of Ingleside (Anne of Green Gables Novel...        8.003411   \n",
+            "6   Harry Potter and the Chamber of Secrets (Book 2)        8.045036   \n",
+            "7          Wizard and Glass (The Dark Tower, Book 4)        8.158667   \n",
+            "8      Key of Valor (Roberts, Nora. Key Trilogy, 3.)        8.088881   \n",
+            "9  Dealing With Dragons (Enchanted Forest Chronic...        8.001301   \n",
+            "\n",
+            "   LinReg-Prediction  \n",
+            "0           7.823845  \n",
+            "1           7.805983  \n",
+            "2           7.790871  \n",
+            "3           7.755851  \n",
+            "4           7.736889  \n",
+            "5           7.725130  \n",
+            "6           7.713495  \n",
+            "7           7.711711  \n",
+            "8           7.703916  \n",
+            "9           7.703907  \n"
+          ]
+        }
+      ]
     }
   ]
 }
\ No newline at end of file

From c8b13deba15b07d38ccede1e6ccc2cb2ad7a9e70 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=D0=90=D0=BB=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9A=D0=BE?=
 =?UTF-8?q?=D0=BD=D0=BE=D0=BF=D0=BB=D1=8F?= <alexey.konoplia@gmail.com>
Date: Thu, 20 Feb 2025 05:25:37 +0300
Subject: [PATCH 4/4] final changes

---
 ...0\233\320\2402_Make_Recommendations.ipynb" | 2159 ++++++++---------
 1 file changed, 1059 insertions(+), 1100 deletions(-)

diff --git "a/lab2/PY\320\220\320\224_\320\233\320\2402_Make_Recommendations.ipynb" "b/lab2/PY\320\220\320\224_\320\233\320\2402_Make_Recommendations.ipynb"
index 0731e64..51b36fe 100644
--- "a/lab2/PY\320\220\320\224_\320\233\320\2402_Make_Recommendations.ipynb"
+++ "b/lab2/PY\320\220\320\224_\320\233\320\2402_Make_Recommendations.ipynb"
@@ -1,58 +1,41 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
   "cells": [
     {
       "cell_type": "markdown",
-      "source": [
-        "## Данные"
-      ],
       "metadata": {
         "id": "_hnS7THrsGgz"
-      }
+      },
+      "source": [
+        "## Данные"
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "fJhG7ScHBLjr"
+      },
       "source": [
         "В этой лабораторной работе будем работать с [датасетом](https://www.kaggle.com/datasets/arashnic/book-recommendation-dataset), предназначенным для обучения алгоритма рекомендаций книг.\n",
         "\n",
         "Для рекомендаций нам понадобятся таблицы `Books.csv` и `Ratings.scv`. Файл с рейтингами можно найти в папке с лабораторной работой на гитхабе. Файл с описаниями книг слишком большой, поэтому он располагается только по ссылке:\n",
         "1. Файл с рейтингами (такой же, как в гитхабе): https://drive.google.com/file/d/1Hliaee7Y584-7lMoGeGjGabRRubzdoYn/view?usp=sharing\n",
         "2. Файл с кригами:  https://drive.google.com/file/d/1JYpk5GTzK7GWT3mtDb9fNiwaVTzk8BBy/view?usp=sharing"
-      ],
-      "metadata": {
-        "id": "fJhG7ScHBLjr"
-      }
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "import pandas as pd"
-      ],
+      "execution_count": 1,
       "metadata": {
         "id": "QjsXOepge-27"
       },
-      "execution_count": 1,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "import pandas as pd"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "!gdown 1Hliaee7Y584-7lMoGeGjGabRRubzdoYn\n",
-        "!gdown 1JYpk5GTzK7GWT3mtDb9fNiwaVTzk8BBy"
-      ],
+      "execution_count": 2,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -60,11 +43,10 @@
         "id": "Z4nPmsGsfEVR",
         "outputId": "3e4832bd-bfb1-4d92-83f3-508196d5d5a3"
       },
-      "execution_count": 2,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Downloading...\n",
             "From: https://drive.google.com/uc?id=1Hliaee7Y584-7lMoGeGjGabRRubzdoYn\n",
@@ -76,23 +58,24 @@
             "100% 73.3M/73.3M [00:01<00:00, 60.4MB/s]\n"
           ]
         }
+      ],
+      "source": [
+        "!gdown 1Hliaee7Y584-7lMoGeGjGabRRubzdoYn\n",
+        "!gdown 1JYpk5GTzK7GWT3mtDb9fNiwaVTzk8BBy"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "Посмотрим на таблицу `Ratings`."
-      ],
       "metadata": {
         "id": "SgY23p9PfrA1"
-      }
+      },
+      "source": [
+        "Посмотрим на таблицу `Ratings`."
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "ratings = pd.read_csv(\"Ratings.csv\")\n",
-        "ratings.head()"
-      ],
+      "execution_count": 3,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -101,19 +84,13 @@
         "id": "Ck23gZpPfTzs",
         "outputId": "0b237503-d754-4da9-e3ae-b94e2a2727a9"
       },
-      "execution_count": 3,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
-            "text/plain": [
-              "   User-ID        ISBN  Book-Rating\n",
-              "0   276725  034545104X            0\n",
-              "1   276726  0155061224            5\n",
-              "2   276727  0446520802            0\n",
-              "3   276729  052165615X            3\n",
-              "4   276729  0521795028            6"
-            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "ratings"
+            },
             "text/html": [
               "\n",
               "  <div id=\"df-902876f9-c360-42e0-ad75-785eb5af876c\" class=\"colab-df-container\">\n",
@@ -383,21 +360,28 @@
               "    </div>\n",
               "  </div>\n"
             ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "ratings"
-            }
+            "text/plain": [
+              "   User-ID        ISBN  Book-Rating\n",
+              "0   276725  034545104X            0\n",
+              "1   276726  0155061224            5\n",
+              "2   276727  0446520802            0\n",
+              "3   276729  052165615X            3\n",
+              "4   276729  0521795028            6"
+            ]
           },
+          "execution_count": 3,
           "metadata": {},
-          "execution_count": 3
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "ratings = pd.read_csv(\"Ratings.csv\")\n",
+        "ratings.head()"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "ratings.info()"
-      ],
+      "execution_count": 4,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -405,11 +389,10 @@
         "id": "Qw1-7kI-fVO1",
         "outputId": "1420dd2a-a9b6-4d8b-b290-7711e4a694d3"
       },
-      "execution_count": 4,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "<class 'pandas.core.frame.DataFrame'>\n",
             "RangeIndex: 1149780 entries, 0 to 1149779\n",
@@ -423,13 +406,14 @@
             "memory usage: 26.3+ MB\n"
           ]
         }
+      ],
+      "source": [
+        "ratings.info()"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "ratings.describe().T"
-      ],
+      "execution_count": 5,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -438,20 +422,13 @@
         "id": "bzUeViHgfYix",
         "outputId": "3ade5aa9-578e-46c4-ca6e-2c21d89e2584"
       },
-      "execution_count": 5,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
-            "text/plain": [
-              "                 count           mean           std  min      25%       50%  \\\n",
-              "User-ID      1149780.0  140386.395126  80562.277718  2.0  70345.0  141010.0   \n",
-              "Book-Rating  1149780.0       2.866950      3.854184  0.0      0.0       0.0   \n",
-              "\n",
-              "                  75%       max  \n",
-              "User-ID      211028.0  278854.0  \n",
-              "Book-Rating       7.0      10.0  "
-            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"ratings\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1149780.0,\n        \"max\": 1149780.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1149780.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 99266.14474001857,\n        \"min\": 2.8669501991685364,\n        \"max\": 140386.39512602412,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2.8669501991685364\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 56963.40756303293,\n        \"min\": 3.8541838591801043,\n        \"max\": 80562.27771848648,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3.8541838591801043\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4142135623730951,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49741.426522567686,\n        \"min\": 0.0,\n        \"max\": 70345.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 99709.12721511506,\n        \"min\": 0.0,\n        \"max\": 141010.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 149214.38007276645,\n        \"min\": 7.0,\n        \"max\": 211028.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 197172.48329318166,\n        \"min\": 10.0,\n        \"max\": 278854.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe"
+            },
             "text/html": [
               "\n",
               "  <div id=\"df-42749f97-8db3-427f-bb52-ad54a9d48bda\" class=\"colab-df-container\">\n",
@@ -718,39 +695,37 @@
               "    </div>\n",
               "  </div>\n"
             ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "summary": "{\n  \"name\": \"ratings\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1149780.0,\n        \"max\": 1149780.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1149780.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 99266.14474001857,\n        \"min\": 2.8669501991685364,\n        \"max\": 140386.39512602412,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2.8669501991685364\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 56963.40756303293,\n        \"min\": 3.8541838591801043,\n        \"max\": 80562.27771848648,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3.8541838591801043\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4142135623730951,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49741.426522567686,\n        \"min\": 0.0,\n        \"max\": 70345.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 99709.12721511506,\n        \"min\": 0.0,\n        \"max\": 141010.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 149214.38007276645,\n        \"min\": 7.0,\n        \"max\": 211028.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 197172.48329318166,\n        \"min\": 10.0,\n        \"max\": 278854.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
+            "text/plain": [
+              "                 count           mean           std  min      25%       50%  \\\n",
+              "User-ID      1149780.0  140386.395126  80562.277718  2.0  70345.0  141010.0   \n",
+              "Book-Rating  1149780.0       2.866950      3.854184  0.0      0.0       0.0   \n",
+              "\n",
+              "                  75%       max  \n",
+              "User-ID      211028.0  278854.0  \n",
+              "Book-Rating       7.0      10.0  "
+            ]
           },
+          "execution_count": 5,
           "metadata": {},
-          "execution_count": 5
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "ratings.describe().T"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "Как распределяются рейтинги?"
-      ],
       "metadata": {
         "id": "iXS2pJO2fvD2"
-      }
+      },
+      "source": [
+        "Как распределяются рейтинги?"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "import seaborn as sns\n",
-        "\n",
-        "sns.set_context(\"notebook\")\n",
-        "sns.set_style(\"whitegrid\")\n",
-        "\n",
-        "ratings[\"Book-Rating\"].hist()\n",
-        "plt.xlabel(\"Rating value\")\n",
-        "plt.ylabel(\"Rating count\")\n",
-        "plt.show()\n"
-      ],
+      "execution_count": 6,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -759,35 +734,43 @@
         "id": "rgvUotNXfxYW",
         "outputId": "6b7f7e2b-d1f6-47c8-a5d9-1e5d20edf7d9"
       },
-      "execution_count": 6,
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": 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iz5Xpc9PeMX9/f/z0009mtyFN4+7dX9bU/q2amhpcu3bNrNa917i3louLC3x9fcVxWq3WYqVGq9Va7GkjIiIiAtpYEBs5ciSqqqrM9kPdvHkTP/zwA/r37w+5XI6IiAgcOnTI7Ovy8/MREBCAnj17AgCio6Ph5OSEgoICcYxOp0NRURFiYmLEYzExMfjHP/4hrm4BwMGDB+Hk5CS++tHX1xd+fn4WzynLz89HZGSkeP83JiYGOp0OxcXF4hitVotTp06ZXZOIiIjIpE3dmhwzZgxCQkKQkpKCtLQ0uLq6Ijc3F3K5HNOnTwcAvPTSS5g5cyYyMjIQGxuL48eP49NPP8X69evFOr/+9a/x7LPPYu3atXBycoKPjw9ycnLQtWtXTJ06VRw3depUvPfee5g/fz7mzZuHK1euYO3atZg6dar4DDEASE5OxqJFi9CrVy9EREQgPz8fJ0+exPbt28Ux4eHhiI6ORnp6OhYvXgxXV1esX78eQUFBGDdunB1mj4iIiBxNmwpiTk5OyM3NxerVq7F8+XIYjUY88cQT2LFjh7g/64knnkBWVhY2bNiAPXv2oEePHli1apXFc76WLl2Kzp07Y926dbh16xYGDRqEv/71r2avbPTw8MC2bdvw2muvYf78+ejcuTOeffZZpKWlmdWaNGkS9Ho98vLykJubC5VKhezsbISHh5uN27Bhg9h7Q0MDoqOjsXTpUnTq1KammYiIiNoImeAoLz/roL777jsAd98ZwJb0ej0UCgUmZn6JHy5V//IXtAH9eyixP2WY1G08lLq6OpSWliI4OJgbblsZ59o+OM/2wXm2j9ac5+b+/m5Te8SIiIiIOhIGMSIiIiKJMIgRERERSYRBjIiIiEgiDGJEREREEmEQIyIiIpIIgxgRERGRRBjEiIiIiCTCIEZEREQkEQYxIiIiIokwiBERERFJhEGMiIiISCIMYkREREQSYRAjIiIikgiDGBEREZFEGMSIiIiIJMIgRkRERCQRBjEiIiIiiTCIEREREUmEQYyIiIhIIgxiRERERBJhECMiIiKSCIMYERERkUQYxIiIiIgkwiBGREREJBEGMSIiIiKJMIgRERERSYRBjIiIiEgiDGJEREREEmEQIyIiIpIIgxgRERGRRBjEiIiIiCTCIEZEREQkEQYxIiIiIokwiBERERFJhEGMiIiISCJtKojt3bsXQUFBFv+8+eabZuN2796N8ePHIyQkBE899RSOHDliUaumpgbp6ekYPHgwwsPDkZKSgqtXr1qM++abbzBlyhSEhoZi5MiRyM3NhSAIZmMEQUBubi5GjBiB0NBQTJkyBSdOnLCodeXKFSQnJyM8PByDBw/GK6+8gtra2pZNChEREbVbnaRuoCnvvPMOunbtKn7u4+Mj/vv+/fuxbNkyvPjiixgyZAjy8/ORlJSEHTt2ICwsTByXmpqK8vJyZGRkwNXVFRs2bEBiYiI++ugjdOp099s+d+4cEhISEBUVhdTUVPz73//Gm2++CWdnZyQkJIi18vLykJmZiUWLFiEoKAg7duxAfHw8Pv74Y/j6+gIAjEYj5syZAwBYt24d6uvr8cYbb2DhwoXIyclpzekiIiIiB9Umg1j//v3h5eXV5LnMzExMnDgRqampAIAhQ4agrKwMmzZtQl5eHgCgpKQERUVFUKvViI6OBgCoVCrExcWhoKAAcXFxAAC1Wo1u3brhrbfeglwuR2RkJG7cuIEtW7ZgxowZkMvluH37NnJychAfH4/Zs2cDAB5//HFMmDABarUaGRkZAIBDhw7hzJkzyM/Ph7+/PwBAqVQiISEBJ0+eRGhoaCvNFhERETmqNnVr8pdUVlaioqICsbGxZsfj4uJQXFwMg8EAACgsLIRSqURUVJQ4xt/fH8HBwSgsLBSPFRYWYvTo0ZDL5Wa1qqurUVJSAuDurcva2lqza8rlcowdO9aiVlBQkBjCACAqKgqenp44evSojWaAiIiI2pM2uSI2adIk3Lx5Ez169MDvf/97zJkzB87OztBoNADurm7dKyAgAEajEZWVlQgICIBGo4FKpYJMJjMb5+/vL9aoq6vDjz/+aBacTGNkMhk0Gg0iIiLE8fePCwgIwLZt21BfXw83NzdoNBqLMTKZDCqVSqxhLUEQUFdX16Ia9zMYDFAoFDataS96vd5iH19bpdfrzT5S6+Fc2wfn2T44z/bRmvMsCIJFDmlKmwpi3t7eSE5OxsCBAyGTyfD5559jw4YNuHLlCpYvXw6dTgfg7i2/e5k+N52vrq4222Nm4uHhge+//x7A3c38TdWSy+VQKBRmteRyOVxdXS2uKQgCdDod3NzcHnhNUy1rGY1GlJaWtqjG/RQKBTw9PW1a0160Wq3D/XCqqKiQuoUOg3NtH5xn++A820drzfO9d9x+TpsKYsOGDcOwYcPEz6Ojo+Hq6opt27bhxRdflLAzabm4uCAwMNCmNU23cR2RSqVyqBWxiooK+Pn5OewKpKPgXNsH59k+OM/20ZrzXF5e3qxxbSqINSU2NhZbt25FaWkpPDw8ANxdzfL29hbHVFdXA4B4XqlU4vLlyxa1dDqdOMa0emVaGTMxGAzQ6/VmtQwGA27fvm22KlZdXQ2ZTGY2rqlHVeh0OnTv3t26b/7/k8lkcHd3b1GNpmo6Kkf8oaRQKGz+Z0hN41zbB+fZPjjP9tEa89zc37MOtVnftAfr/j1XGo0GLi4u4qMk/P39odVqLVZNtFqtWMPd3R3du3e3qGX6OtM400etVmtxzR49esDNzU0cd38tQRDMrklERER0rzYfxPLz8+Hs7Ix+/frB19cXfn5+OHjwoMWYyMhI8V5sTEwMdDodiouLxTFarRanTp1CTEyMeCwmJgaHDx+G0Wg0q6VUKhEeHg4AGDRoELp06YIDBw6IY4xGIwoKCixqnT592uw+c3FxMaqqqjB8+HDbTAYRERG1K23q1mRCQgIiIiIQFBQEADh8+DA+/PBDzJw5U7wVmZycjEWLFqFXr16IiIhAfn4+Tp48ie3bt4t1wsPDER0djfT0dCxevBiurq5Yv349goKCMG7cOLPrffLJJ1i4cCGmTZuGsrIyqNVqpKWliaHO1dUV8+bNQ1ZWFry8vNCnTx/s3LkTVVVVZg99HT9+PHJycpCcnIwFCxZAr9dj7dq14tP4iYiIiO7XpoKYSqXCRx99hMuXL6OxsRF+fn5IT0/HjBkzxDGTJk2CXq9HXl4ecnNzoVKpkJ2dLa5gmWzYsAGrV6/G8uXL0dDQgOjoaCxdulR8qj4A9O7dG2q1GmvWrMHcuXPh5eWFlJQUxMfHm9VKTEyEIAjYunUrbty4geDgYKjVavFWKHB3Q/0777yDVatWYcGCBejUqRPGjh2L9PT0VpotIiIicnQywVFeftZBfffddwCAkJAQm9bV6/VQKBSYmPklfrhUbdParaV/DyX2pwz75YFtSF1dHUpLSxEcHMwNt62Mc20fnGf74DzbR2vOc3N/f7f5PWJERERE7RWDGBEREZFErApily5dQn19/c+er6+vx6VLl6xuioiIiKgjsCqIjR49Gn//+99/9vznn3+O0aNHW90UERERUUdgVRD7pf39RqMRTk6860lERET0IM1+fEVtba34VkIAUFVV1eTtx+rqauTn55u9BRERERERWWp2EPvb3/6GTZs2Abj7/kmvv/46Xn/99SbHCoKA1NRUmzRIRERE1F41O4hFRUXB3d0dgiDgL3/5CyZOnIj+/fubjZHJZFAoFOjfv7/Nn3tFRERE1N40O4iFh4eLT6/X6/UYN24c+vTp02qNEREREbV3Vr3FUVJSkq37ICIiIupwrH6vSZ1Oh08//RQXLlyATqezeCWlaR8ZERERETXNqiD25ZdfIiUlBXq9Hl26dIFSqbQYI5PJWtwcERERUXtmVRB744034O3tjaysLAQFBdm6JyIiIqIOwaqnrp47dw4zZsxgCCMiIiJqAauCmJ+fH27dumXrXoiIiIg6FKuC2Msvv4z3338fFy5csHU/RERERB2GVXvEvvrqK3h5eSEuLg5Dhw5F9+7d4ezsbDFu6dKlLW6QiIiIqL2yKoht375d/PcvvviiyTEymYxBjIiIiOgBrApip0+ftnUfRERERB2OVXvEiIiIiKjlGMSIiIiIJGLVrcm+ffs268n5paWl1pQnIiIi6hCsCmLz58+3CGJ37tzBxYsX8dlnn0GlUmHkyJE2aZCIiIiovbIqiCUnJ//suatXr2LKlCnw8/OzticiIiKiDsHme8QeffRRTJ06FW+//batSxMRERG1K62yWV+hUPCp+0RERES/wOZBrKysDO+99x5vTRIRERH9Aqv2iI0aNarJV03W1NSgpqYGbm5uvDVJRERE9AusCmKDBw9uMoh5eHjA19cXEydOhKenZ0t7IyIiImrXrApia9assXUfRERERB2OTfaI1dfXo76+3haliIiIiDoMq1bEAODSpUvIysrC0aNHcfPmTQBAt27dMHz4cCQlJeGxxx6zWZNERERE7ZFVQezs2bOYPn06ampqMHToUAQEBAAANBoNPv74Yxw5cgTvv/8+/P39bdosERERUXtiVRBbt24dnJycsG/fPgQFBZmdKysrw+zZs7Fu3Tps2rTJJk0SERERtUdW7RH7+uuvMWPGDIsQBgB9+vTB888/j//93/9tcXNERERE7ZlVQayhoQFubm4/e16hUKChocHqpoiIiIg6AquCWHBwMHbv3o2amhqLc7W1tdizZw/69evXosZu3bqFmJgYBAUF4bvvvjM7t3v3bowfPx4hISF46qmncOTIEYuvr6mpQXp6OgYPHozw8HCkpKTg6tWrFuO++eYbTJkyBaGhoRg5ciRyc3MhCILZGEEQkJubixEjRiA0NBRTpkzBiRMnLGpduXIFycnJCA8Px+DBg/HKK6+gtra2RfNARERE7ZdVQSw5ORmVlZWIjY3FW2+9hb1792Lv3r1Yt24dJkyYgPPnzyM5OblFjb399tu4c+eOxfH9+/dj2bJliI2NRV5eHsLCwpCUlGQRjFJTU3Hs2DFkZGTgzTffhFarRWJiotlK3blz55CQkABvb2/k5ORg1qxZyMzMxNatW81q5eXlITMzE7Nnz0ZOTg68vb0RHx+PyspKcYzRaMScOXNQUVGBdevWISMjA0VFRVi4cGGL5oGIiIjaL6s260dGRiI3Nxdr165Fbm6u2bng4GD85S9/wZAhQ6xu6uzZs3j//fexePFirFixwuxcZmYmJk6ciNTUVADAkCFDUFZWhk2bNiEvLw8AUFJSgqKiIqjVakRHRwMAVCoV4uLiUFBQgLi4OACAWq1Gt27d8NZbb0EulyMyMhI3btzAli1bMGPGDMjlcty+fRs5OTmIj4/H7NmzAQCPP/44JkyYALVajYyMDADAoUOHcObMGeTn54uvFlUqlUhISMDJkycRGhpq9XwQERFR+2T1A12HDh2K//t//y++/PJLfPDBB/jggw/w5ZdfYt++fYiMjGxRU6tWrcLUqVOhUqnMjldWVqKiogKxsbFmx+Pi4lBcXAyDwQAAKCwshFKpRFRUlDjG398fwcHBKCwsFI8VFhZi9OjRkMvlZrWqq6tRUlIC4O6ty9raWrNryuVyjB071qJWUFCQ2SM7oqKi4OnpiaNHj7ZkOoiIiKidavGT9b29vTFw4EAMHDgQ3t7eLW7o4MGDKCsrw/z58y3OaTQaALAIaAEBATAajeKtQo1GA5VKZfF+mP7+/mKNuro6/PjjjxbPOvP394dMJhPHmT7ePy4gIACXLl0S31FAo9FYjJHJZFCpVGINIiIiontZdWvy3XffxdGjR6FWq5s8P2fOHIwaNQrTp09/qLp6vR5r1qxBWloaunTpYnFep9MBuHvL716mz03nq6ur0bVrV4uv9/DwwPfffw8A4gsN7q8ll8uhUCjMasnlcri6ulpcUxAE6HQ6uLm5PfCaplrWEgQBdXV1LapxP4PBAIVCYdOa9qLX6y1eUNFW6fV6s4/UejjX9sF5tg/Os3205jwLgmCxINQUq4LYnj17HrgHLDAwEB9++OFDB7HNmzfjkUcewTPPPGNNW+2W0WhEaWmpTWsqFAp4enratKa9aLVah/vhVFFRIXULHQbn2j44z/bBebaP1prne7c+/RyrglhlZSWef/75nz3v7++PDz/88KFqXrx4EVu3bsWmTZvE1SrTKlBdXR1u3boFDw8PAHdXs+69DVpdXQ0A4nmlUonLly9bXEOn04ljTKtX9z+Cw2AwQK/Xm9UyGAy4ffu22apYdXU1ZDKZ2bimHlWh0+nQvXv3h5qL+7m4uCAwMLBFNe5n2k/niFQqlUOtiFVUVMDPz89hVyAdBefaPjjP9sF5to/WnOfy8vJmjbMqiLm4uODatWs/e/7q1atwcnq47WcXLlyA0WjE3LlzLc7NnDkTAwcOxLp16wBY7sfSaDRwcXGBr68vgLtBsLi42GJZUKvVok+fPgAAd3d3dO/e3WL/llarhSAIYn3TR61Wi759+5pds0ePHuKDbf39/VFWVmZWSxAEaLVasxcNWEMmk8Hd3b1FNZqq6agc8YeSQqGw+Z8hNY1zbR+cZ/vgPNtHa8xzc3/PWrVZf+DAgdi3b1+TK0A1NTXYu3cvBg4c+FA1g4OD8e6775r98+c//xkAsHLlSqxYsQK+vr7w8/PDwYMHzb42Pz8fkZGR4hJgTEwMdDodiouLxTFarRanTp1CTEyMeCwmJgaHDx+G0Wg0q6VUKhEeHg4AGDRoELp06YIDBw6IY4xGIwoKCixqnT592mx5s7i4GFVVVRg+fPhDzQURERF1DFatiCUlJeEPf/gDfvvb32LWrFnibbMzZ85g27ZtuHbtmrh61VxKpRIRERFNnuvfvz/69+8P4O7DZBctWoRevXohIiIC+fn5OHnyJLZv3y6ODw8PR3R0NNLT07F48WK4urpi/fr1CAoKwrhx48RxCQkJ+OSTT7Bw4UJMmzYNZWVlUKvVSEtLE0Odq6sr5s2bh6ysLHh5eaFPnz7YuXMnqqqqkJCQINYaP348cnJykJycjAULFkCv12Pt2rXi0/iJiIiI7mdVEBs4cCC2bNmC5cuX47//+7/F5TdBENCzZ09s3rxZXFGytUmTJkGv1yMvLw+5ublQqVTIzs62uN6GDRuwevVqLF++HA0NDYiOjsbSpUvRqdN/vuXevXtDrVZjzZo1mDt3Lry8vJCSkoL4+HizWomJiRAEAVu3bsWNGzcQHBwMtVot3goF7t6ufeedd7Bq1SosWLAAnTp1wtixY5Gent4q80BERESOTya0YNdzY2MjTp06hfPnzwMAevXqhf79+zv0/qO2xvQ+myEhITatq9froVAoMDHzS/xwqdqmtVtL/x5K7E8ZJnUbD6Wurg6lpaUIDg7mPo9Wxrm2D86zfXCe7aM157m5v7+tWhEzcXJywoABAzBgwICWlCEiIiLqkFr8ZH0iIiIisg6DGBEREZFEGMSIiIiIJMIgRkRERCQRBjEiIiIiiTCIEREREUnEqsdX9O3b9xefFebq6gofHx9ERERgzpw56NWrl1UNEhEREbVXVgWx+fPn4/DhwygvL8ewYcPQu3dvAEBFRQWKiorQp08fRERE4Pz589i7dy/279+PHTt2mL1pNhEREVFHZ1UQe/TRR3Hz5k0cOHDA7G1+AODcuXOYMWMGAgMDsXjxYlRUVGDKlCl46623kJuba5OmiYiIiNoDq/aIqdVqPP/88xYhDLj7/o3PP/+8GLr8/PwwdepUlJSUtKxTIiIionbGqiB2+fJlODs7/+x5Z2dn/Pjjj+LnPXv2hMFgsOZSRERERO2WVUEsMDAQO3fuxE8//WRx7tq1a9i5cycCAwPFY5WVlfjVr35lfZdERERE7ZBVe8QWL16MxMREjB07FmPGjBE36587dw6fffYZGhoa8PrrrwMAbt++jb179yImJsZ2XRMRERG1A1YFsYiICOzatQuZmZn4+9//jvr6egB3H1kRGRmJ5ORk9O/fXzxWVFRku46JiIiI2gmrghgA9OvXD1u2bEFjYyOuX78OAHjkkUfg5MRnxBIRERE1h9VBzMTJyQne3t626IWIiIioQ7E6iOl0Onz66ae4cOECdDodBEEwOy+TycR9YkRERERkyaog9uWXXyIlJQV6vR5dunSBUqm0GPNLb4FERERE1NFZFcTeeOMNeHt7IysrC0FBQbbuiYiIiKhDsGpnveltjBjCiIiIiKxnVRDz8/PDrVu3bN0LERERUYdiVRB7+eWX8f777+PChQu27oeIiIiow7Bqj9hXX30FLy8vxMXFYejQoejevXuT7z25dOnSFjdIRERE1F5ZFcS2b98u/vsXX3zR5BiZTMYgRkRERPQAVgWx06dP27oPIiIiog6H70dEREREJBEGMSIiIiKJNOvWZN++feHk5IQTJ05ALpejb9++v/jkfJlMhlOnTtmkSSIiIqL2qFlBbP78+ZDJZOjUqZPZ50RERERkvWYFseTk5Ad+TkREREQPz6o9YtnZ2SgrK/vZ82fOnEF2drbVTRERERF1BFYHsX//+98/e/7MmTPYtGmT1U0RERERdQSt8qrJqqoquLi4tEZpIiIionaj2Q90/frrr3H8+HHx87///e84d+6cxbiamhrk5+ejT58+tumQiIiIqJ1qdhA7fvy4uO9LJpOhoKAABQUFTY4NDAzEsmXLbNMhERERUTvV7CA2Z84cPP/88xAEAUOHDsXKlSsxbtw4szEymQwKhQKurq5WNXP06FHk5eWhvLwctbW18PHxwZgxY5CUlISuXbuK4z7//HNs2LABWq0WPXr0wNy5c/HMM8+Y1TIYDFi/fj3+53/+B7du3UJ4eDiWLVsGf39/s3Fnz57FqlWrUFJSgs6dO2Py5MlITU2FXC43G7d792688847uHTpElQqFdLS0jBy5EizMTU1NVi9ejU+++wzGI1GDBs2DEuXLsWjjz5q1XwQERFR+9bsIObm5gY3NzcAwOHDh+Hl5QWFQmHTZqqqqhAaGooZM2bA09MTZ86cQVZWFs6cOYOtW7cCAP75z38iKSkJzz77LNLT0/HVV1/hlVdeQefOnTFhwgSx1qpVq5Cfn48lS5bAx8cHW7ZswezZs7F//34x1Ol0OsyaNQt+fn7IysrClStXsGbNGtTX12P58uVirf3792PZsmV48cUXMWTIEOTn5yMpKQk7duxAWFiYOC41NRXl5eXIyMiAq6srNmzYgMTERHz00UfiM9iIiIiITKxKB4899pit+wAATJ482ezziIgIyOVyLFu2DFeuXIGPjw82b96M0NBQvPrqqwCAIUOGoLKyEpmZmWIQu3z5Mvbs2YMVK1bg2WefBQCEhIRg5MiR2LVrFxITEwEAu3btwq1bt5CdnQ1PT08AwJ07d7By5UrMmzcPPj4+AIDMzExMnDgRqamp4jXLysqwadMm5OXlAQBKSkpQVFQEtVqN6OhoAIBKpUJcXBwKCgoQFxfXKnNGREREjsvqV02ePn0aS5cuxdNPP42xY8di9OjRZv+MGTPGJg2aApLRaITBYMDx48fNVr4AIC4uDmfPnsWFCxcAAEVFRWhsbDQb5+npiaioKBQWForHCgsLERkZKV4DAGJjY9HY2Ihjx44BACorK1FRUYHY2FiLaxYXF8NgMIi1lEoloqKixDH+/v4IDg42uyYRERGRiVVB7Pjx43juuefwxRdf4NFHH0VlZSV8fX3x6KOP4tKlS3B3d8d//dd/Wd3UnTt3cPv2bfzwww/YtGkTRo0ahZ49e+L8+fMwGo0W+7wCAgIAABqNRvz4yCOPwMPDw2KcaYxp3P21lEolvL29zWoBd1e37q9lNBpRWVkpjlOpVBZv/eTv7292TSIiIiITq25NZmZmwtfXFx9++CEMBgOGDh2KefPmITIyEt9++y0SExOxaNEiq5saOXIkrly5AgAYNmwY1q1bB+Duni7gbli6l+lz0/nq6mqzzf33jjONMY27vxYAeHh4iONaek0PDw98//33D/x+f4kgCKirq2tRjfsZDAab7/GzF71eD0EQpG6jWfR6vdlHaj2ca/vgPNsH59k+WnOeBUFo1vtyWxXETp06heTkZHTp0kUMIo2NjQCAgQMHYsqUKdi4cSOGDx9uTXnk5uZCr9ejvLwcmzdvxosvvoi//vWvVtVqD4xGI0pLS21aU6FQmN2SdSRardbhfjhVVFRI3UKHwbm2D86zfXCe7aO15vn+JzA0xaog5uzsjM6dOwO4uzLUqVMnXL9+XTzv6+uLs2fPWlMaANC3b18AQHh4OEJCQjB58mT8/e9/R2BgIIC7j4m4V3V1NQCItyKVSiVqa2st6lZXV5vdrlQqlRa1gLurXKZxpo81NTXw9vZ+4DUvX778wFrWcnFxEb93WzHtbXNEKpXKoVbEKioq4Ofn57ArkI6Cc20fnGf74DzbR2vOc3l5ebPGWRXEevXqJaZHmUwGf39/fPbZZ3jqqacAAF988QV+9atfWVPaQlBQEFxcXHD+/HmMGjUKLi4u0Gg0GDZsmDjGtAfLtN/L398fP/30k0UIun9PWFP7t2pqanDt2jWzWk19rUajgYuLC3x9fcVxxcXFFkuRWq22xe8yIJPJ4O7u3qIaTdV0VI74Q0mhUNj8z5Caxrm2D86zfXCe7aM15rm5v2et2qw/fPhw7N+/Hw0NDQCAF154AQUFBRg3bhzGjRuHzz//HFOmTLGmtIVvv/0WRqMRPXv2hFwuR0REBA4dOmQ2Jj8/HwEBAejZsycAIDo6Gk5OTmZP/tfpdCgqKkJMTIx4LCYmBv/4xz/E1S0AOHjwIJycnMRXP/r6+sLPzw8HDx60uGZkZKS47BgTEwOdTofi4mJxjFarxalTp8yuSURERGRi1YrYH//4R8ycORPOzs4AgN/97ndi8HF2dsaLL76Ip59++qHrJiUlYcCAAQgKCoKbmxtOnz4NtVqNoKAg8XEYL730EmbOnImMjAzExsbi+PHj+PTTT7F+/Xqxzq9//Ws8++yzWLt2LZycnODj44OcnBx07doVU6dOFcdNnToV7733HubPn4958+bhypUrWLt2LaZOnSo+QwwAkpOTsWjRIvTq1QsRERHIz8/HyZMnsX37dnFMeHg4oqOjkZ6ejsWLF8PV1RXr169HUFCQxTsQEBEREQFWBjEXFxd069bN7NjkyZPNHsja3FcL3Cs0NBT5+fnIzc2FIAh47LHH8NxzzyEhIUFceXriiSeQlZWFDRs2YM+ePejRowdWrVpl8ZyvpUuXonPnzli3bh1u3bqFQYMG4a9//avZKxs9PDywbds2vPbaa5g/fz46d+6MZ599FmlpaWa1Jk2aBL1ej7y8POTm5kKlUiE7Oxvh4eFm4zZs2IDVq1dj+fLlaGhoQHR0NJYuXcqn6hMREVGTZIKNdz0bDAbs27cPW7dutbiFSA/vu+++A3D3nQFsSa/XQ6FQYGLml/jhUvUvf0Eb0L+HEvtThv3ywDakrq4OpaWlCA4O5j6PVsa5tg/Os31wnu2jNee5ub+/H2qpxmAw4PPPP8f58+fh4eGBESNGiLfw9Ho9tm/fjm3btuGnn35Cr169rGydiIiIqGNodhC7cuUKZs6cifPnz4uPDnBzc8PmzZvh4uKChQsX4sqVKwgNDcWyZcu4L4qIiIjoFzQ7iG3YsAEXLlzAnDlz8MQTT+DChQvYtGkTli1bhps3b+I3v/kN/vKXv2Dw4MGt2S8RERFRu9HsIHbs2DE8/fTTWLhwoXjsV7/6FV5++WWMGDECb7/9NpycrH4PcSIiIqIOp9nJ6fr16xg4cKDZsbCwMADAM888wxBGRERE9JCanZ7u3LkDV1dXs2OmR0p06dLFtl0RERERdQAP9arJixcv4ocffhA/N71P47lz56BUKi3G9+/fv4XtEREREbVfDxXENm7ciI0bN1ocX7lypdnnpoe5lpaWtqw7IiIionas2UFs9erVrdkHERERUYfT7CD2u9/9rjX7ICIiIupw+FJHIiIiIokwiBERERFJhEGMiIiISCIMYkREREQSYRAjIiIikgiDGBEREZFEGMSIiIiIJMIgRkRERCQRBjEiIiIiiTCIEREREUmEQYyIiIhIIgxiRERERBJhECMiIiKSCIMYERERkUQYxIiIiIgkwiBGREREJBEGMSIiIiKJMIgRERERSYRBjIiIiEgiDGJEREREEmEQIyIiIpIIgxgRERGRRBjEiIiIiCTCIEZEREQkEQYxIiIiIokwiBERERFJpE0FsQMHDuCll15CTEwMwsLCMHnyZOzZsweCIJiN2717N8aPH4+QkBA89dRTOHLkiEWtmpoapKenY/DgwQgPD0dKSgquXr1qMe6bb77BlClTEBoaipEjRyI3N9fieoIgIDc3FyNGjEBoaCimTJmCEydOWNS6cuUKkpOTER4ejsGDB+OVV15BbW1tyyaFiIiI2q02FcT+9re/QaFQYMmSJdi8eTNiYmKwbNkybNq0SRyzf/9+LFu2DLGxscjLy0NYWBiSkpIsglFqaiqOHTuGjIwMvPnmm9BqtUhMTERDQ4M45ty5c0hISIC3tzdycnIwa9YsZGZmYuvWrWa18vLykJmZidmzZyMnJwfe3t6Ij49HZWWlOMZoNGLOnDmoqKjAunXrkJGRgaKiIixcuLB1JouIiIgcXiepG7jX5s2b4eXlJX4eGRmJqqoq/PWvf8Uf//hHODk5ITMzExMnTkRqaioAYMiQISgrK8OmTZuQl5cHACgpKUFRURHUajWio6MBACqVCnFxcSgoKEBcXBwAQK1Wo1u3bnjrrbcgl8sRGRmJGzduYMuWLZgxYwbkcjlu376NnJwcxMfHY/bs2QCAxx9/HBMmTIBarUZGRgYA4NChQzhz5gzy8/Ph7+8PAFAqlUhISMDJkycRGhpqhxkkIiIiR9KmVsTuDWEmwcHBqK2tRV1dHSorK1FRUYHY2FizMXFxcSguLobBYAAAFBYWQqlUIioqShzj7++P4OBgFBYWiscKCwsxevRoyOVys1rV1dUoKSkBcPfWZW1trdk15XI5xo4da1ErKChIDGEAEBUVBU9PTxw9etTaKSEiIqJ2rE0Fsab861//go+PD7p06QKNRgPg7urWvQICAmA0GsVbhRqNBiqVCjKZzGycv7+/WKOurg4//vijWXAyjZHJZOI408f7xwUEBODSpUuor68Xx90/RiaTQaVSiTWIiIiI7tWmbk3e75///Cfy8/OxePFiAIBOpwNw95bfvUyfm85XV1eja9euFvU8PDzw/fffA7i7mb+pWnK5HAqFwqyWXC6Hq6urxTUFQYBOp4Obm9sDr2mqZS1BEFBXV9eiGvczGAxQKBQ2rWkver3e4gUVbZVerzf7SK2Hc20fnGf74DzbR2vOsyAIFgtCTWmzQezy5ctIS0tDREQEZs6cKXU7kjIajSgtLbVpTYVCAU9PT5vWtBetVutwP5wqKiqkbqHD4FzbB+fZPjjP9tFa83zv1qef0yaDWHV1NRITE+Hp6YmsrCw4Od29g+rh4QHg7mqWt7e32fh7zyuVSly+fNmirk6nE8eYVq9MK2MmBoMBer3erJbBYMDt27fNVsWqq6shk8nMxjX1qAqdTofu3btbMQv/4eLigsDAwBbVuJ9pP50jUqlUDrUiVlFRAT8/P4ddgXQUnGv74DzbB+fZPlpznsvLy5s1rs0Fsfr6esybNw81NTX44IMPzG73mfZg3b8fS6PRwMXFBb6+vuK44uJii2VBrVaLPn36AADc3d3RvXt3i/1bWq0WgiCI9U0ftVot+vbta3bNHj16wM3NTRxXVlZmVksQBGi1WrMXDVhDJpPB3d29RTWaqumoHPGHkkKhsPmfITWNc20fnGf74DzbR2vMc3N/z7apzfoNDQ1ITU2FRqPBO++8Ax8fH7Pzvr6+8PPzw8GDB82O5+fnIzIyUlwCjImJgU6nQ3FxsThGq9Xi1KlTiImJEY/FxMTg8OHDMBqNZrWUSiXCw8MBAIMGDUKXLl1w4MABcYzRaERBQYFFrdOnT5stbxYXF6OqqgrDhw9vwawQERFRe9WmVsRWrlyJI0eOYMmSJaitrTV7SGu/fv0gl8uRnJyMRYsWoVevXoiIiEB+fj5OnjyJ7du3i2PDw8MRHR2N9PR0LF68GK6urli/fj2CgoIwbtw4cVxCQgI++eQTLFy4ENOmTUNZWRnUajXS0tLEUOfq6op58+YhKysLXl5e6NOnD3bu3ImqqiokJCSItcaPH4+cnBwkJydjwYIF0Ov1WLt2rfg0fiIiIqL7takgduzYMQDAmjVrLM4dPnwYPXv2xKRJk6DX65GXl4fc3FyoVCpkZ2eLK1gmGzZswOrVq7F8+XI0NDQgOjoaS5cuRadO//mWe/fuDbVajTVr1mDu3Lnw8vJCSkoK4uPjzWolJiZCEARs3boVN27cQHBwMNRqtXgrFLi7j+udd97BqlWrsGDBAnTq1Aljx45Fenq6LaeIiIiI2hGZ4Ci7njuo7777DgAQEhJi07p6vR4KhQITM7/ED5eqbVq7tfTvocT+lGFSt/FQ6urqUFpaiuDgYO7zaGWca/vgPNsH59k+WnOem/v7u03tESMiIiLqSBjEiIiIiCTCIEZEREQkEQYxIiIiIokwiBERERFJhEGMiIiISCIMYkREREQSYRAjIiIikgiDGBEREZFEGMSIiIiIJMIgRkRERCQRBjEiIiIiiTCIEREREUmEQYyIiIhIIgxiRERERBJhECMiIiKSCIMYERERkUQYxIiIiIgkwiBGREREHZJMJoOLi4ukPTCIERERkU3caRSkbuGhKBQKBPfrD5lMJlkPnSS7MhEREbUrzk4yvLyrBOVXa6VupVkCH+2CjVPD0WCUrgcGMSIiIrKZ8qu1+OFStdRtOAzemiQiIiKSCIMYERERkUQYxIiIiIgkwiBGREREJBEGMSIiIiKJMIgRERERSYRBjIiIiEgiDGJEREREEmEQIyIiIpIIgxgRERGRRBjEiIiIiCTCIEZEREQkEQYxIiIiIokwiBERERFJpE0FsXPnzmH58uWYPHky+vXrh0mTJjU5bvfu3Rg/fjxCQkLw1FNP4ciRIxZjampqkJ6ejsGDByM8PBwpKSm4evWqxbhvvvkGU6ZMQWhoKEaOHInc3FwIgmA2RhAE5ObmYsSIEQgNDcWUKVNw4sQJi1pXrlxBcnIywsPDMXjwYLzyyiuora21bjKIiIio3WtTQezMmTM4evQoevfujYCAgCbH7N+/H8uWLUNsbCzy8vIQFhaGpKQki2CUmpqKY8eOISMjA2+++Sa0Wi0SExPR0NAgjjl37hwSEhLg7e2NnJwczJo1C5mZmdi6datZrby8PGRmZmL27NnIycmBt7c34uPjUVlZKY4xGo2YM2cOKioqsG7dOmRkZKCoqAgLFy603QQRERFRu9JJ6gbuNWrUKIwZMwYAsGTJEnz//fcWYzIzMzFx4kSkpqYCAIYMGYKysjJs2rQJeXl5AICSkhIUFRVBrVYjOjoaAKBSqRAXF4eCggLExcUBANRqNbp164a33noLcrkckZGRuHHjBrZs2YIZM2ZALpfj9u3byMnJQXx8PGbPng0AePzxxzFhwgSo1WpkZGQAAA4dOoQzZ84gPz8f/v7+AAClUomEhAScPHkSoaGhrTVtRERE5KDa1IqYk9OD26msrERFRQViY2PNjsfFxaG4uBgGgwEAUFhYCKVSiaioKHGMv78/goODUVhYKB4rLCzE6NGjIZfLzWpVV1ejpKQEwN1bl7W1tWbXlMvlGDt2rEWtoKAgMYQBQFRUFDw9PXH06NGHmQYiIiK4uLhAJpNJ3Qa1sjYVxH6JRqMBcHd1614BAQEwGo3irUKNRgOVSmXxF9jf31+sUVdXhx9//NEsOJnGyGQycZzp4/3jAgICcOnSJdTX14vj7h8jk8mgUqnEGkRERM0hk8kQ3K8/FAqF1K1QK2tTtyZ/iU6nA3D3lt+9TJ+bzldXV6Nr164WX+/h4SHe7qypqWmyllwuh0KhMKsll8vh6upqcU1BEKDT6eDm5vbAa5pqWUsQBNTV1bWoxv0MBoPD/geu1+stXlDRVun1erOP1Ho41/bBebYP08/ol3eVoPyqY7zoa0SQN/40vq/UbVjl9u3bNv+9IghCs1Y0HSqIdVRGoxGlpaU2ralQKODp6WnTmvai1Wod7pdARUWF1C10GJxr++A8ty7Tz+jyq7X44VK11O00S4B3Z6lbsNqlS5da5ffKvVuffo5DBTEPDw8Ad1ezvL29xePV1dVm55VKJS5fvmzx9TqdThxjWr0yrYyZGAwG6PV6s1oGgwG3b982WxWrrq6GTCYzG9fUoyp0Oh26d+9u3Tf8/7m4uCAwMLBFNe5n2k/niFQqlUOtiFVUVMDPz89hVyAdBefaPjjP9uHIP6MdUY8ePZoVmh5GeXl5s8Y5VBAz7cG6fz+WRqOBi4sLfH19xXHFxcUWy4JarRZ9+vQBALi7u6N79+4W+7e0Wi0EQRDrmz5qtVr07fufJVeNRoMePXrAzc1NHFdWVmZWSxAEaLVasxcNWEMmk8Hd3b1FNZqq6agc8Ye/QqGw+Z8hNY1zbR+c59blyD+jHZGrq6vNf7c098/QoTbr+/r6ws/PDwcPHjQ7np+fj8jISDHNxsTEQKfTobi4WByj1Wpx6tQpxMTEiMdiYmJw+PBhGI1Gs1pKpRLh4eEAgEGDBqFLly44cOCAOMZoNKKgoMCi1unTp82W64uLi1FVVYXhw4fbZgKIiIioXWlTK2J6vV581MPFixdRW1srhq7BgwfDy8sLycnJWLRoEXr16oWIiAjk5+fj5MmT2L59u1gnPDwc0dHRSE9Px+LFi+Hq6or169cjKCgI48aNE8clJCTgk08+wcKFCzFt2jSUlZVBrVYjLS1NDHWurq6YN28esrKy4OXlhT59+mDnzp2oqqpCQkKCWGv8+PHIyclBcnIyFixYAL1ej7Vr14pP4yciIiK6X5sKYtevX8fLL79sdsz0+bvvvouIiAhMmjQJer0eeXl5yM3NhUqlQnZ2triCZbJhwwasXr0ay5cvR0NDA6Kjo7F06VJ06vSfb7l3795Qq9VYs2YN5s6dCy8vL6SkpCA+Pt6sVmJiIgRBwNatW3Hjxg0EBwdDrVaLt0KBu/u43nnnHaxatQoLFixAp06dMHbsWKSnp9t6moiIiKidaFNBrGfPnvj3v//9i+Oee+45PPfccw8c07VrV7z++ut4/fXXHzhu0KBB+PDDDx84RiaTYd68eZg3b94Dx/n4+CArK+uBY4iIiIhMHGqPGBEREVF7wiBGREREJBEGMSIiIiKJMIgRERERSYRBjIiIqI3hA107DgYxIiJq9+40OsbbopmY3rWF2r829fgKIiKi1uDsJMPLu0pQftXyPYHbohFB3vjT+L6/PJAcHoMYERF1COVXa/HDpWqp22iWAO/OUrdAdsJbk0REREQSYRAjIiIikgiDGBEREZFEGMSIiIiIJMIgRkRERCQRBjEiIiIiiTCIEREREUmEQYyIiIhIIgxiRETUbDKZDC4uLlK3QdRuMIgREUnI0d4DUaFQILhff74pNZGN8C2OiIgk5GjvgRj4aBdsnBqOBqPUnRC1DwxiREQSc6T3QCQi2+KtSSIiIiKJMIgRERERSYRBjIiIiEgiDGJERPTQ+KpJIttgECMiombz7uKKO40C3NzcpG6FqF3gqyaJiKjZlIpODvfIjRFB3vjT+L5St0HUJAYxIiJ6aI70yI0A785St0D0s3hrkoiIiEgiDGJEREREEmEQIyIiIpIIgxgRERGRRBjEiIiIiCTCIEZEREQkEQYxIiIiIokwiBERERFJhEGMiIiISCIMYkREREQSYRCzsbNnz+KFF15AWFgYoqKisHbtWhgMBqnbIuoQXFxcIJPJpG6DiKjZ+F6TNqTT6TBr1iz4+fkhKysLV65cwZo1a1BfX4/ly5dL3R5Ru+bk5IR+/fujk7Oz1K0QETUbg5gN7dq1C7du3UJ2djY8PT0BAHfu3MHKlSsxb948+Pj4SNsgUTPdaRTg7ORYK0tubm4AgJd3laD8aq3E3TTPiCBv/Gl8X6nbICIJMYjZUGFhISIjI8UQBgCxsbFYsWIFjh07hqefflq65kgSMpkM7u7uDne7zNlJ5lCBBvhPqCm/WosfLlVL3U6zBHh3lroFIpKYTBAEQeom2ovIyEg888wzWLRokdnxYcOGYfLkyRbHm+Obb76BIAhwcXGxVZsAAEEQ4OTkhOu3DDDeabRp7dbi2skJ3dzlUrfRYTjS3w0AULg4w0Ph4lB9s2f7YM/24Yg9uzg74ZHOcjQ2Ntr8f5iNRiNkMhkGDRr0wHFcEbOh6upqKJVKi+MeHh7Q6XRW1TT9xbD1XxBTvUc6M9hQ0xz174Yj9s2e7YM924cj9uzkZPvXLspksmb97mYQa+PCw8OlboGIiIhaCR9fYUNKpRI1NTUWx3U6HTw8PCToiIiIiNoyBjEb8vf3h0ajMTtWU1ODa9euwd/fX6KuiIiIqK1iELOhmJgY/OMf/0B19X9esXXw4EE4OTkhKipKws6IiIioLeKrJm1Ip9Nh4sSJUKlUmDdvnvhA1yeffJIPdCUiIiILDGI2dvbsWbz22msoKSlB586dMXnyZKSlpUEud7xXkRAREVHrYhAjIiIikgj3iBERERFJhEGMiIiISCIMYkREREQSYRAjIiIikgiDGBEREZFEGMSIiIiIJMIg1sGcPXsWL7zwAsLCwhAVFYW1a9fCYDBI3Va7c+DAAbz00kuIiYlBWFgYJk+ejD179oBPi2k9t27dQkxMDIKCgvDdd99J3U67tG/fPvz2t79FSEgIIiIiMGfOHNTX10vdVrty+PBhPPfccwgPD0d0dDRefvllVFZWSt2WQzt37hyWL1+OyZMno1+/fpg0aVKT43bv3o3x48cjJCQETz31FI4cOWKX/jrZ5SrUJuh0OsyaNQt+fn7IysoSn/xfX1/PJ//b2N/+9jc89thjWLJkCbp164Z//OMfWLZsGS5fvoykpCSp22uX3n77bdy5c0fqNtqtzZs3Iy8vDy+++CLCwsJw8+ZNFBcXc85t6Pjx40hKSsJvf/tbpKWloaqqChs3bkR8fDw++eQTuLm5Sd2iQzpz5gyOHj2KgQMHorGxscn/Id6/fz+WLVuGF198EUOGDEF+fj6SkpKwY8cOhIWFtW6DAnUYW7ZsEcLCwoSbN2+Kx3bt2iUEBwcLly9flq6xduj69esWx5YuXSoMGjRIuHPnjgQdtW/l5eVCWFiYsHPnTqFPnz7CyZMnpW6pXTl79qzQr18/4YsvvpC6lXZt2bJlwqhRo4TGxkbxWHFxsdCnTx/h66+/lrAzx3bvz9zFixcLEydOtBgzbtw4YcGCBWbHpkyZIsyZM6fV++OtyQ6ksLAQkZGR8PT0FI/FxsaisbERx44dk66xdsjLy8viWHBwMGpra1FXVydBR+3bqlWrMHXqVKhUKqlbaZf27t2Lnj17Yvjw4VK30q41NDSgc+fOkMlk4rGuXbsCALc1tICT04OjTmVlJSoqKhAbG2t2PC4uDsXFxa2+fYdBrAPRaDTw9/c3O6ZUKuHt7Q2NRiNRVx3Hv/71L/j4+KBLly5St9KuHDx4EGVlZZg/f77UrbRb3377Lfr06YO3334bkZGRGDBgAKZOnYpvv/1W6tbalaeffhpnz57Fjh07UFNTg8rKSrz11lvo168fBg0aJHV77Zbp99/9/yMXEBAAo9HY6nv0GMQ6kOrqaiiVSovjHh4e0Ol0EnTUcfzzn/9Efn4+4uPjpW6lXdHr9VizZg3S0tIYcFvRtWvXUFRUhI8//hgrVqzApk2bIJPJEB8fj+vXr0vdXrvxxBNPIDs7G+vWrcMTTzyBMWPG4Pr168jLy4Ozs7PU7bVbpt9/9/9+NH3e2r8fGcSIWtnly5eRlpaGiIgIzJw5U+p22pXNmzfjkUcewTPPPCN1K+2aIAioq6vDxo0bMWHCBAwfPhybN2+GIAjYvn271O21G9988w3+z//5P/j973+Pbdu2YePGjWhsbMTcuXP56tR2jK+a7ECUSiVqamosjut0Onh4eEjQUftXXV2NxMREeHp6Iisr6xf3KlDzXbx4EVu3bsWmTZvEv9em/Xd1dXW4desWOnfuLGWL7YZSqYSnpyf69u0rHvP09ES/fv1QXl4uYWfty6pVqzBkyBAsWbJEPBYWFoYRI0bg448/xpQpUyTsrv0y/f6rqamBt7e3eLy6utrsfGthEOtA/P39LfaC1dTU4Nq1axZ7x6jl6uvrMW/ePNTU1OCDDz4QN92SbVy4cAFGoxFz5861ODdz5kwMHDgQH374oQSdtT+BgYE4f/58k+du375t527ar7Nnz2L06NFmx37961+jW7duPzv/1HKm33/376PWaDRwcXGBr69vq16fQawDiYmJwZYtW8z2ih08eBBOTk6IioqSuLv2paGhAampqdBoNNixYwd8fHykbqndCQ4Oxrvvvmt2rLS0FKtXr8bKlSsREhIiUWftz8iRI7F3716UlpYiODgYAHDz5k388MMPmD17trTNtSM9evTAqVOnzI5dvHgRN2/exGOPPSZRV+2fr68v/Pz8cPDgQYwZM0Y8np+fj8jISMjl8la9PoNYBzJ16lS89957mD9/PubNm4crV65g7dq1mDp1KoOCja1cuRJHjhzBkiVLUFtbixMnTojn+vXr1+r/YXcESqUSERERTZ7r378/+vfvb+eO2q8xY8YgJCQEKSkpSEtLg6urK3JzcyGXyzF9+nSp22s3pk6ditdffx2rVq3CqFGjUFVVJe6DvP/RCtR8er0eR48eBXA32NbW1uLgwYMAgMGDB8PLywvJyclYtGgRevXqhYiICOTn5+PkyZN22QMpE/hwkg7l7NmzeO2111BSUoLOnTtj8uTJSEtLYzCwsVGjRuHixYtNnjt8+DB69uxp5446huPHj2PmzJnYs2cPV8Rs7MaNG1i9ejWOHDkCo9GIJ554An/+858RGBgodWvthiAI2LVrF3bu3InKykp07twZYWFhSEtLQ0BAgNTtOawLFy5Y3PI1effdd8X/odu9ezfy8vJw6dIlqFQqLFiwACNHjmz1/hjEiIiIiCTCl3ARERERSYRBjIiIiEgiDGJEREREEmEQIyIiIpIIgxgRERGRRBjEiIiIiCTCIEZEREQkEQYxIqJfkJWVhaCgIKnbsIklS5Zg1KhRUrdBRP8fgxgROZy9e/ciKChI/Kdfv34YNmwYlixZgitXrlhVU6/XIysrC8ePH7dxt0REP4/vNUlEDislJQU9e/aEwWDAiRMnsG/fPvzrX//Cp59+CldX14eqpdfrkZ2djaSkJIv3sHzppZcwd+5cW7ZORASAQYyIHFhMTIz4npLPPfccunXrhry8PBw+fBhxcXE2u06nTp3QqRN/XBKR7fHWJBG1G0888QQAoLKyUjxmMBiwceNGPP3003j88ccRFhaG6dOn46uvvhLHXLhwAZGRkQCA7Oxs8ZZnVlYWgKb3iAUFBeHVV1/FZ599hkmTJmHAgAGYOHEiCgsLLfo6fvw4nn76aYSEhGDMmDHYtWtXs/advfrqqwgPD4der7c4t2DBAkRFReHOnTsAgM8++wxz585FdHQ0BgwYgDFjxmDTpk3i+Z9z/PhxBAUFWdySvXDhAoKCgrB3716z42fPnkVKSgoGDx6MkJAQPP300zh8+PADr0FEP4//i0dE7cbFixcBAEqlUjxWW1uL3bt3Y9KkSXjuuedw69Yt7NmzB3PmzMHu3bsRHBwMLy8vZGRkICMjA2PHjsXYsWMB4BeD0r/+9S8UFBRg+vTp6Ny5M9577z2kpKTgyJEj6NatGwDg1KlTmDNnDry9vZGcnIzGxkZs2rQJXl5ev/j9xMXFYceOHfjiiy8QGxsrHtfr9Thy5Ah+97vfwdnZGQCwb98+uLu744UXXoC7uzu++uorZGZmora2FosXL364ifwZZ86cwbRp0+Dj44PExES4u7vjwIEDmD9/PrKyssR5I6LmYxAjIodVW1uLGzduwGAw4Ntvv0V2djbkcjlGjhwpjvHw8MDnn38OuVwuHvv973+P2NhYvPfee3j99dfh7u6O8ePHIyMjA0FBQZg8eXKzrn/27Fnk5+ejV69eAICIiAhMnjwZ+/fvxx/+8AcAQGZmJpydnbFz5074+PgAAGJjY5t16/Txxx+Hj48PDhw4YBbEvvjiC9TV1ZnVWLduHdzc3MTPp02bhuXLl2Pnzp1IS0sz+/6t9d///d/o3r07PvroI7He9OnTMW3aNLz55psMYkRW4K1JInJYs2fPRmRkJIYPH46UlBQoFAps3rwZv/71r8Uxzs7OYmhobGxEVVUVGhoaMGDAAJw6dapF1x86dKgYwgCgb9++6NKli3hr9M6dOyguLsbo0aPFEAYAvXv3xrBhw36xvkwmw4QJE3D06FHcunVLPH7gwAH4+Pjg8ccfF4/dG8JMAfWJJ56AXq+HRqNp0fcJAFVVVfjqq68QGxsr1r9x4wZu3ryJ6OhoVFRUWP2KVaKOjCtiROSwli9fDpVKhZqaGnz00Uf4+uuvm1z52bdvH7Zu3QqtVguj0Sge79mzZ4uu3717d4tjHh4eqK6uBgBcv34d9fX16N27t8W4po41JS4uDtu2bcPnn3+OJ598Erdu3cLRo0cxZcoUyGQycdyZM2ewYcMGfPXVV6itrTWrUVNT8zDfVpPOnz8PQRCwceNGbNy4sckx169fNwucRPTLGMSIyGGFhoaKr5ocM2YMpk+fjoULF+LgwYPo3LkzAODjjz/GkiVLMGbMGCQkJOCRRx6Bs7MzcnJyzDb1W8O0P+t+giC0qO69wsLC8Nhjj+HAgQN48sknceTIEdTX15vdlqyursYf/vAHdOnSBSkpKejVqxdcXV3xww8/4M0330RjY+PP1r83zN3r/q8xfR4fH/+zq3n3rg4SUfMwiBFRu+Ds7IwFCxZg5syZ2LFjh/jcr0OHDsHX1xfZ2dlmoSMzM9Ps638ukLTEI488AldXV5w7d87iXFPHfk5sbCzeffdd1NbWIj8/H4899hjCwsLE8//7v/+LqqoqZGdn47/+67/E4xcuXPjF2qYXNty/amZ64YOJr68vAMDFxQVDhw5tdu9E9GDcI0ZE7UZERARCQ0Oxbds23L59G8B/Vq3uXaX69ttvceLECbOvVSgUACDeVrQFZ2dnDB06FIcPHzbbP3Xu3Dl8+eWXza4TFxcHg8GAffv24csvvzTbuA8ATk53f5Tf+z0aDAa8//77v1j7scceg7OzM77++muz4zt37jT7/JFHHsHgwYPxwQcf4OrVqxZ1bty40ezvh4j+gytiRNSuJCQk4OWXX8bevXsxbdo0jBgxAgUFBZg/fz5GjBiBCxcuYNeuXQgMDERdXZ34dW5ubggMDMSBAwfg5+cHT09P/OY3v0GfPn1a1E9SUhKKioowbdo0TJs2DY2Njdi+fTt+85vfoLS0tFk1+vfvj969e2P9+vUwGAwWr7gMDw+Hh4cHlixZghkzZkAmk+Hjjz9u1i3Srl27YsKECdi+fTtkMhl8fX3xxRdf4Pr16xZjV6xYgenTp+PJJ5/E73//e/j6+uKnn37CiRMncPnyZfzP//xP8yaFiERcESOidmXcuHHo1asXtm7dijt37uDpp5/GggUL8O9//xurVq1CUVER/vKXv2DAgAEWX7tq1So8+uijWL16NRYsWIBDhw61uJ8BAwYgLy8PHh4e2LhxI/bs2YOUlBRERkY+1NswxcbG4tatW+jduzf69+9vdq5bt27YsmULvL29sWHDBqjVagwdOhR/+tOfmlV76dKlGD16NHbt2oWNGzeie/fueOONNyzGBQYG4qOPPsKIESOwb98+vPrqq9i1axecnJwwf/78Zn8vRPQfMsGWu0qJiKhZ/vjHP6K8vBwFBQVSt0JEEuKKGBFRK6uvrzf7vKKiAoWFhRg8eLBEHRFRW8E9YkRErWzMmDH43e9+B19fX1y8eBG7du2Ci4sL5syZI3VrRCQxBjEiolY2bNgw7N+/H9euXYNcLkdYWBgWLFgAPz8/qVsjIolxjxgRERGRRLhHjIiIiEgiDGJEREREEmEQIyIiIpIIgxgRERGRRBjEiIiIiCTCIEZEREQkEQYxIiIiIokwiBERERFJhEGMiIiISCL/D/H6404mHk50AAAAAElFTkSuQmCC",
             "text/plain": [
               "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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iz5Xpc9PeMX9/f/z0009mtyFN4+7dX9bU/q2amhpcu3bNrNa917i3louLC3x9fcVxWq3WYqVGq9Va7GkjIiIiAtpYEBs5ciSqqqrM9kPdvHkTP/zwA/r37w+5XI6IiAgcOnTI7Ovy8/MREBCAnj17AgCio6Ph5OSEgoICcYxOp0NRURFiYmLEYzExMfjHP/4hrm4BwMGDB+Hk5CS++tHX1xd+fn4WzynLz89HZGSkeP83JiYGOp0OxcXF4hitVotTp06ZXZOIiIjIpE3dmhwzZgxCQkKQkpKCtLQ0uLq6Ijc3F3K5HNOnTwcAvPTSS5g5cyYyMjIQGxuL48eP49NPP8X69evFOr/+9a/x7LPPYu3atXBycoKPjw9ycnLQtWtXTJ06VRw3depUvPfee5g/fz7mzZuHK1euYO3atZg6dar4DDEASE5OxqJFi9CrVy9EREQgPz8fJ0+exPbt28Ux4eHhiI6ORnp6OhYvXgxXV1esX78eQUFBGDdunB1mj4iIiBxNmwpiTk5OyM3NxerVq7F8+XIYjUY88cQT2LFjh7g/64knnkBWVhY2bNiAPXv2oEePHli1apXFc76WLl2Kzp07Y926dbh16xYGDRqEv/71r2avbPTw8MC2bdvw2muvYf78+ejcuTOeffZZpKWlmdWaNGkS9Ho98vLykJubC5VKhezsbISHh5uN27Bhg9h7Q0MDoqOjsXTpUnTq1KammYiIiNoImeAoLz/roL777jsAd98ZwJb0ej0UCgUmZn6JHy5V//IXtAH9eyixP2WY1G08lLq6OpSWliI4OJgbblsZ59o+OM/2wXm2j9ac5+b+/m5Te8SIiIiIOhIGMSIiIiKJMIgRERERSYRBjIiIiEgiDGJEREREEmEQIyIiIpIIgxgRERGRRBjEiIiIiCTCIEZEREQkEQYxIiIiIokwiBERERFJhEGMiIiISCIMYkREREQSYRAjIiIikgiDGBEREZFEGMSIiIiIJMIgRkRERCQRBjEiIiIiiTCIEREREUmEQYyIiIhIIgxiRERERBJhECMiIiKSCIMYERERkUQYxIiIiIgkwiBGREREJBEGMSIiIiKJMIgRERERSYRBjIiIiEgiDGJEREREEmEQIyIiIpIIgxgRERGRRBjEiIiIiCTCIEZEREQkEQYxIiIiIokwiBERERFJhEGMiIiISCJtKojt3bsXQUFBFv+8+eabZuN2796N8ePHIyQkBE899RSOHDliUaumpgbp6ekYPHgwwsPDkZKSgqtXr1qM++abbzBlyhSEhoZi5MiRyM3NhSAIZmMEQUBubi5GjBiB0NBQTJkyBSdOnLCodeXKFSQnJyM8PByDBw/GK6+8gtra2pZNChEREbVbnaRuoCnvvPMOunbtKn7u4+Mj/vv+/fuxbNkyvPjiixgyZAjy8/ORlJSEHTt2ICwsTByXmpqK8vJyZGRkwNXVFRs2bEBiYiI++ugjdOp099s+d+4cEhISEBUVhdTUVPz73//Gm2++CWdnZyQkJIi18vLykJmZiUWLFiEoKAg7duxAfHw8Pv74Y/j6+gIAjEYj5syZAwBYt24d6uvr8cYbb2DhwoXIyclpzekiIiIiB9Umg1j//v3h5eXV5LnMzExMnDgRqampAIAhQ4agrKwMmzZtQl5eHgCgpKQERUVFUKvViI6OBgCoVCrExcWhoKAAcXFxAAC1Wo1u3brhrbfeglwuR2RkJG7cuIEtW7ZgxowZkMvluH37NnJychAfH4/Zs2cDAB5//HFMmDABarUaGRkZAIBDhw7hzJkzyM/Ph7+/PwBAqVQiISEBJ0+eRGhoaCvNFhERETmqNnVr8pdUVlaioqICsbGxZsfj4uJQXFwMg8EAACgsLIRSqURUVJQ4xt/fH8HBwSgsLBSPFRYWYvTo0ZDL5Wa1qqurUVJSAuDurcva2lqza8rlcowdO9aiVlBQkBjCACAqKgqenp44evSojWaAiIiI2pM2uSI2adIk3Lx5Ez169MDvf/97zJkzB87OztBoNADurm7dKyAgAEajEZWVlQgICIBGo4FKpYJMJjMb5+/vL9aoq6vDjz/+aBacTGNkMhk0Gg0iIiLE8fePCwgIwLZt21BfXw83NzdoNBqLMTKZDCqVSqxhLUEQUFdX16Ia9zMYDFAoFDataS96vd5iH19bpdfrzT5S6+Fc2wfn2T44z/bRmvMsCIJFDmlKmwpi3t7eSE5OxsCBAyGTyfD5559jw4YNuHLlCpYvXw6dTgfg7i2/e5k+N52vrq4222Nm4uHhge+//x7A3c38TdWSy+VQKBRmteRyOVxdXS2uKQgCdDod3NzcHnhNUy1rGY1GlJaWtqjG/RQKBTw9PW1a0160Wq3D/XCqqKiQuoUOg3NtH5xn++A820drzfO9d9x+TpsKYsOGDcOwYcPEz6Ojo+Hq6opt27bhxRdflLAzabm4uCAwMNCmNU23cR2RSqVyqBWxiooK+Pn5OewKpKPgXNsH59k+OM/20ZrzXF5e3qxxbSqINSU2NhZbt25FaWkpPDw8ANxdzfL29hbHVFdXA4B4XqlU4vLlyxa1dDqdOMa0emVaGTMxGAzQ6/VmtQwGA27fvm22KlZdXQ2ZTGY2rqlHVeh0OnTv3t26b/7/k8lkcHd3b1GNpmo6Kkf8oaRQKGz+Z0hN41zbB+fZPjjP9tEa89zc37MOtVnftAfr/j1XGo0GLi4u4qMk/P39odVqLVZNtFqtWMPd3R3du3e3qGX6OtM400etVmtxzR49esDNzU0cd38tQRDMrklERER0rzYfxPLz8+Hs7Ix+/frB19cXfn5+OHjwoMWYyMhI8V5sTEwMdDodiouLxTFarRanTp1CTEyMeCwmJgaHDx+G0Wg0q6VUKhEeHg4AGDRoELp06YIDBw6IY4xGIwoKCixqnT592uw+c3FxMaqqqjB8+HDbTAYRERG1K23q1mRCQgIiIiIQFBQEADh8+DA+/PBDzJw5U7wVmZycjEWLFqFXr16IiIhAfn4+Tp48ie3bt4t1wsPDER0djfT0dCxevBiurq5Yv349goKCMG7cOLPrffLJJ1i4cCGmTZuGsrIyqNVqpKWliaHO1dUV8+bNQ1ZWFry8vNCnTx/s3LkTVVVVZg99HT9+PHJycpCcnIwFCxZAr9dj7dq14tP4iYiIiO7XpoKYSqXCRx99hMuXL6OxsRF+fn5IT0/HjBkzxDGTJk2CXq9HXl4ecnNzoVKpkJ2dLa5gmWzYsAGrV6/G8uXL0dDQgOjoaCxdulR8qj4A9O7dG2q1GmvWrMHcuXPh5eWFlJQUxMfHm9VKTEyEIAjYunUrbty4geDgYKjVavFWKHB3Q/0777yDVatWYcGCBejUqRPGjh2L9PT0VpotIiIicnQywVFeftZBfffddwCAkJAQm9bV6/VQKBSYmPklfrhUbdParaV/DyX2pwz75YFtSF1dHUpLSxEcHMwNt62Mc20fnGf74DzbR2vOc3N/f7f5PWJERERE7RWDGBEREZFErApily5dQn19/c+er6+vx6VLl6xuioiIiKgjsCqIjR49Gn//+99/9vznn3+O0aNHW90UERERUUdgVRD7pf39RqMRTk6860lERET0IM1+fEVtba34VkIAUFVV1eTtx+rqauTn55u9BRERERERWWp2EPvb3/6GTZs2Abj7/kmvv/46Xn/99SbHCoKA1NRUmzRIRERE1F41O4hFRUXB3d0dgiDgL3/5CyZOnIj+/fubjZHJZFAoFOjfv7/Nn3tFRERE1N40O4iFh4eLT6/X6/UYN24c+vTp02qNEREREbV3Vr3FUVJSkq37ICIiIupwrH6vSZ1Oh08//RQXLlyATqezeCWlaR8ZERERETXNqiD25ZdfIiUlBXq9Hl26dIFSqbQYI5PJWtwcERERUXtmVRB744034O3tjaysLAQFBdm6JyIiIqIOwaqnrp47dw4zZsxgCCMiIiJqAauCmJ+fH27dumXrXoiIiIg6FKuC2Msvv4z3338fFy5csHU/RERERB2GVXvEvvrqK3h5eSEuLg5Dhw5F9+7d4ezsbDFu6dKlLW6QiIiIqL2yKoht375d/PcvvviiyTEymYxBjIiIiOgBrApip0+ftnUfRERERB2OVXvEiIiIiKjlGMSIiIiIJGLVrcm+ffs268n5paWl1pQnIiIi6hCsCmLz58+3CGJ37tzBxYsX8dlnn0GlUmHkyJE2aZCIiIiovbIqiCUnJ//suatXr2LKlCnw8/OzticiIiKiDsHme8QeffRRTJ06FW+//batSxMRERG1K62yWV+hUPCp+0RERES/wOZBrKysDO+99x5vTRIRERH9Aqv2iI0aNarJV03W1NSgpqYGbm5uvDVJRERE9AusCmKDBw9uMoh5eHjA19cXEydOhKenZ0t7IyIiImrXrApia9assXUfRERERB2OTfaI1dfXo76+3haliIiIiDoMq1bEAODSpUvIysrC0aNHcfPmTQBAt27dMHz4cCQlJeGxxx6zWZNERERE7ZFVQezs2bOYPn06ampqMHToUAQEBAAANBoNPv74Yxw5cgTvv/8+/P39bdosERERUXtiVRBbt24dnJycsG/fPgQFBZmdKysrw+zZs7Fu3Tps2rTJJk0SERERtUdW7RH7+uuvMWPGDIsQBgB9+vTB888/j//93/9tcXNERERE7ZlVQayhoQFubm4/e16hUKChocHqpoiIiIg6AquCWHBwMHbv3o2amhqLc7W1tdizZw/69evXosZu3bqFmJgYBAUF4bvvvjM7t3v3bowfPx4hISF46qmncOTIEYuvr6mpQXp6OgYPHozw8HCkpKTg6tWrFuO++eYbTJkyBaGhoRg5ciRyc3MhCILZGEEQkJubixEjRiA0NBRTpkzBiRMnLGpduXIFycnJCA8Px+DBg/HKK6+gtra2RfNARERE7ZdVQSw5ORmVlZWIjY3FW2+9hb1792Lv3r1Yt24dJkyYgPPnzyM5OblFjb399tu4c+eOxfH9+/dj2bJliI2NRV5eHsLCwpCUlGQRjFJTU3Hs2DFkZGTgzTffhFarRWJiotlK3blz55CQkABvb2/k5ORg1qxZyMzMxNatW81q5eXlITMzE7Nnz0ZOTg68vb0RHx+PyspKcYzRaMScOXNQUVGBdevWISMjA0VFRVi4cGGL5oGIiIjaL6s260dGRiI3Nxdr165Fbm6u2bng4GD85S9/wZAhQ6xu6uzZs3j//fexePFirFixwuxcZmYmJk6ciNTUVADAkCFDUFZWhk2bNiEvLw8AUFJSgqKiIqjVakRHRwMAVCoV4uLiUFBQgLi4OACAWq1Gt27d8NZbb0EulyMyMhI3btzAli1bMGPGDMjlcty+fRs5OTmIj4/H7NmzAQCPP/44JkyYALVajYyMDADAoUOHcObMGeTn54uvFlUqlUhISMDJkycRGhpq9XwQERFR+2T1A12HDh2K//t//y++/PJLfPDBB/jggw/w5ZdfYt++fYiMjGxRU6tWrcLUqVOhUqnMjldWVqKiogKxsbFmx+Pi4lBcXAyDwQAAKCwshFKpRFRUlDjG398fwcHBKCwsFI8VFhZi9OjRkMvlZrWqq6tRUlIC4O6ty9raWrNryuVyjB071qJWUFCQ2SM7oqKi4OnpiaNHj7ZkOoiIiKidavGT9b29vTFw4EAMHDgQ3t7eLW7o4MGDKCsrw/z58y3OaTQaALAIaAEBATAajeKtQo1GA5VKZfF+mP7+/mKNuro6/PjjjxbPOvP394dMJhPHmT7ePy4gIACXLl0S31FAo9FYjJHJZFCpVGINIiIiontZdWvy3XffxdGjR6FWq5s8P2fOHIwaNQrTp09/qLp6vR5r1qxBWloaunTpYnFep9MBuHvL716mz03nq6ur0bVrV4uv9/DwwPfffw8A4gsN7q8ll8uhUCjMasnlcri6ulpcUxAE6HQ6uLm5PfCaplrWEgQBdXV1LapxP4PBAIVCYdOa9qLX6y1eUNFW6fV6s4/UejjX9sF5tg/Os3205jwLgmCxINQUq4LYnj17HrgHLDAwEB9++OFDB7HNmzfjkUcewTPPPGNNW+2W0WhEaWmpTWsqFAp4enratKa9aLVah/vhVFFRIXULHQbn2j44z/bBebaP1prne7c+/RyrglhlZSWef/75nz3v7++PDz/88KFqXrx4EVu3bsWmTZvE1SrTKlBdXR1u3boFDw8PAHdXs+69DVpdXQ0A4nmlUonLly9bXEOn04ljTKtX9z+Cw2AwQK/Xm9UyGAy4ffu22apYdXU1ZDKZ2bimHlWh0+nQvXv3h5qL+7m4uCAwMLBFNe5n2k/niFQqlUOtiFVUVMDPz89hVyAdBefaPjjP9sF5to/WnOfy8vJmjbMqiLm4uODatWs/e/7q1atwcnq47WcXLlyA0WjE3LlzLc7NnDkTAwcOxLp16wBY7sfSaDRwcXGBr68vgLtBsLi42GJZUKvVok+fPgAAd3d3dO/e3WL/llarhSAIYn3TR61Wi759+5pds0ePHuKDbf39/VFWVmZWSxAEaLVasxcNWEMmk8Hd3b1FNZqq6agc8YeSQqGw+Z8hNY1zbR+cZ/vgPNtHa8xzc3/PWrVZf+DAgdi3b1+TK0A1NTXYu3cvBg4c+FA1g4OD8e6775r98+c//xkAsHLlSqxYsQK+vr7w8/PDwYMHzb42Pz8fkZGR4hJgTEwMdDodiouLxTFarRanTp1CTEyMeCwmJgaHDx+G0Wg0q6VUKhEeHg4AGDRoELp06YIDBw6IY4xGIwoKCixqnT592mx5s7i4GFVVVRg+fPhDzQURERF1DFatiCUlJeEPf/gDfvvb32LWrFnibbMzZ85g27ZtuHbtmrh61VxKpRIRERFNnuvfvz/69+8P4O7DZBctWoRevXohIiIC+fn5OHnyJLZv3y6ODw8PR3R0NNLT07F48WK4urpi/fr1CAoKwrhx48RxCQkJ+OSTT7Bw4UJMmzYNZWVlUKvVSEtLE0Odq6sr5s2bh6ysLHh5eaFPnz7YuXMnqqqqkJCQINYaP348cnJykJycjAULFkCv12Pt2rXi0/iJiIiI7mdVEBs4cCC2bNmC5cuX47//+7/F5TdBENCzZ09s3rxZXFGytUmTJkGv1yMvLw+5ublQqVTIzs62uN6GDRuwevVqLF++HA0NDYiOjsbSpUvRqdN/vuXevXtDrVZjzZo1mDt3Lry8vJCSkoL4+HizWomJiRAEAVu3bsWNGzcQHBwMtVot3goF7t6ufeedd7Bq1SosWLAAnTp1wtixY5Gent4q80BERESOTya0YNdzY2MjTp06hfPnzwMAevXqhf79+zv0/qO2xvQ+myEhITatq9froVAoMDHzS/xwqdqmtVtL/x5K7E8ZJnUbD6Wurg6lpaUIDg7mPo9Wxrm2D86zfXCe7aM157m5v7+tWhEzcXJywoABAzBgwICWlCEiIiLqkFr8ZH0iIiIisg6DGBEREZFEGMSIiIiIJMIgRkRERCQRBjEiIiIiiTCIEREREUnEqsdX9O3b9xefFebq6gofHx9ERERgzpw56NWrl1UNEhEREbVXVgWx+fPn4/DhwygvL8ewYcPQu3dvAEBFRQWKiorQp08fRERE4Pz589i7dy/279+PHTt2mL1pNhEREVFHZ1UQe/TRR3Hz5k0cOHDA7G1+AODcuXOYMWMGAgMDsXjxYlRUVGDKlCl46623kJuba5OmiYiIiNoDq/aIqdVqPP/88xYhDLj7/o3PP/+8GLr8/PwwdepUlJSUtKxTIiIionbGqiB2+fJlODs7/+x5Z2dn/Pjjj+LnPXv2hMFgsOZSRERERO2WVUEsMDAQO3fuxE8//WRx7tq1a9i5cycCAwPFY5WVlfjVr35lfZdERERE7ZBVe8QWL16MxMREjB07FmPGjBE36587dw6fffYZGhoa8PrrrwMAbt++jb179yImJsZ2XRMRERG1A1YFsYiICOzatQuZmZn4+9//jvr6egB3H1kRGRmJ5ORk9O/fXzxWVFRku46JiIiI2gmrghgA9OvXD1u2bEFjYyOuX78OAHjkkUfg5MRnxBIRERE1h9VBzMTJyQne3t626IWIiIioQ7E6iOl0Onz66ae4cOECdDodBEEwOy+TycR9YkRERERkyaog9uWXXyIlJQV6vR5dunSBUqm0GPNLb4FERERE1NFZFcTeeOMNeHt7IysrC0FBQbbuiYiIiKhDsGpnveltjBjCiIiIiKxnVRDz8/PDrVu3bN0LERERUYdiVRB7+eWX8f777+PChQu27oeIiIiow7Bqj9hXX30FLy8vxMXFYejQoejevXuT7z25dOnSFjdIRERE1F5ZFcS2b98u/vsXX3zR5BiZTMYgRkRERPQAVgWx06dP27oPIiIiog6H70dEREREJBEGMSIiIiKJNOvWZN++feHk5IQTJ05ALpejb9++v/jkfJlMhlOnTtmkSSIiIqL2qFlBbP78+ZDJZOjUqZPZ50RERERkvWYFseTk5Ad+TkREREQPz6o9YtnZ2SgrK/vZ82fOnEF2drbVTRERERF1BFYHsX//+98/e/7MmTPYtGmT1U0RERERdQSt8qrJqqoquLi4tEZpIiIionaj2Q90/frrr3H8+HHx87///e84d+6cxbiamhrk5+ejT58+tumQiIiIqJ1qdhA7fvy4uO9LJpOhoKAABQUFTY4NDAzEsmXLbNMhERERUTvV7CA2Z84cPP/88xAEAUOHDsXKlSsxbtw4szEymQwKhQKurq5WNXP06FHk5eWhvLwctbW18PHxwZgxY5CUlISuXbuK4z7//HNs2LABWq0WPXr0wNy5c/HMM8+Y1TIYDFi/fj3+53/+B7du3UJ4eDiWLVsGf39/s3Fnz57FqlWrUFJSgs6dO2Py5MlITU2FXC43G7d792688847uHTpElQqFdLS0jBy5EizMTU1NVi9ejU+++wzGI1GDBs2DEuXLsWjjz5q1XwQERFR+9bsIObm5gY3NzcAwOHDh+Hl5QWFQmHTZqqqqhAaGooZM2bA09MTZ86cQVZWFs6cOYOtW7cCAP75z38iKSkJzz77LNLT0/HVV1/hlVdeQefOnTFhwgSx1qpVq5Cfn48lS5bAx8cHW7ZswezZs7F//34x1Ol0OsyaNQt+fn7IysrClStXsGbNGtTX12P58uVirf3792PZsmV48cUXMWTIEOTn5yMpKQk7duxAWFiYOC41NRXl5eXIyMiAq6srNmzYgMTERHz00UfiM9iIiIiITKxKB4899pit+wAATJ482ezziIgIyOVyLFu2DFeuXIGPjw82b96M0NBQvPrqqwCAIUOGoLKyEpmZmWIQu3z5Mvbs2YMVK1bg2WefBQCEhIRg5MiR2LVrFxITEwEAu3btwq1bt5CdnQ1PT08AwJ07d7By5UrMmzcPPj4+AIDMzExMnDgRqamp4jXLysqwadMm5OXlAQBKSkpQVFQEtVqN6OhoAIBKpUJcXBwKCgoQFxfXKnNGREREjsvqV02ePn0aS5cuxdNPP42xY8di9OjRZv+MGTPGJg2aApLRaITBYMDx48fNVr4AIC4uDmfPnsWFCxcAAEVFRWhsbDQb5+npiaioKBQWForHCgsLERkZKV4DAGJjY9HY2Ihjx44BACorK1FRUYHY2FiLaxYXF8NgMIi1lEoloqKixDH+/v4IDg42uyYRERGRiVVB7Pjx43juuefwxRdf4NFHH0VlZSV8fX3x6KOP4tKlS3B3d8d//dd/Wd3UnTt3cPv2bfzwww/YtGkTRo0ahZ49e+L8+fMwGo0W+7wCAgIAABqNRvz4yCOPwMPDw2KcaYxp3P21lEolvL29zWoBd1e37q9lNBpRWVkpjlOpVBZv/eTv7292TSIiIiITq25NZmZmwtfXFx9++CEMBgOGDh2KefPmITIyEt9++y0SExOxaNEiq5saOXIkrly5AgAYNmwY1q1bB+Duni7gbli6l+lz0/nq6mqzzf33jjONMY27vxYAeHh4iONaek0PDw98//33D/x+f4kgCKirq2tRjfsZDAab7/GzF71eD0EQpG6jWfR6vdlHaj2ca/vgPNsH59k+WnOeBUFo1vtyWxXETp06heTkZHTp0kUMIo2NjQCAgQMHYsqUKdi4cSOGDx9uTXnk5uZCr9ejvLwcmzdvxosvvoi//vWvVtVqD4xGI0pLS21aU6FQmN2SdSRardbhfjhVVFRI3UKHwbm2D86zfXCe7aO15vn+JzA0xaog5uzsjM6dOwO4uzLUqVMnXL9+XTzv6+uLs2fPWlMaANC3b18AQHh4OEJCQjB58mT8/e9/R2BgIIC7j4m4V3V1NQCItyKVSiVqa2st6lZXV5vdrlQqlRa1gLurXKZxpo81NTXw9vZ+4DUvX778wFrWcnFxEb93WzHtbXNEKpXKoVbEKioq4Ofn57ArkI6Cc20fnGf74DzbR2vOc3l5ebPGWRXEevXqJaZHmUwGf39/fPbZZ3jqqacAAF988QV+9atfWVPaQlBQEFxcXHD+/HmMGjUKLi4u0Gg0GDZsmDjGtAfLtN/L398fP/30k0UIun9PWFP7t2pqanDt2jWzWk19rUajgYuLC3x9fcVxxcXFFkuRWq22xe8yIJPJ4O7u3qIaTdV0VI74Q0mhUNj8z5Caxrm2D86zfXCe7aM15rm5v2et2qw/fPhw7N+/Hw0NDQCAF154AQUFBRg3bhzGjRuHzz//HFOmTLGmtIVvv/0WRqMRPXv2hFwuR0REBA4dOmQ2Jj8/HwEBAejZsycAIDo6Gk5OTmZP/tfpdCgqKkJMTIx4LCYmBv/4xz/E1S0AOHjwIJycnMRXP/r6+sLPzw8HDx60uGZkZKS47BgTEwOdTofi4mJxjFarxalTp8yuSURERGRi1YrYH//4R8ycORPOzs4AgN/97ndi8HF2dsaLL76Ip59++qHrJiUlYcCAAQgKCoKbmxtOnz4NtVqNoKAg8XEYL730EmbOnImMjAzExsbi+PHj+PTTT7F+/Xqxzq9//Ws8++yzWLt2LZycnODj44OcnBx07doVU6dOFcdNnToV7733HubPn4958+bhypUrWLt2LaZOnSo+QwwAkpOTsWjRIvTq1QsRERHIz8/HyZMnsX37dnFMeHg4oqOjkZ6ejsWLF8PV1RXr169HUFCQxTsQEBEREQFWBjEXFxd069bN7NjkyZPNHsja3FcL3Cs0NBT5+fnIzc2FIAh47LHH8NxzzyEhIUFceXriiSeQlZWFDRs2YM+ePejRowdWrVpl8ZyvpUuXonPnzli3bh1u3bqFQYMG4a9//avZKxs9PDywbds2vPbaa5g/fz46d+6MZ599FmlpaWa1Jk2aBL1ej7y8POTm5kKlUiE7Oxvh4eFm4zZs2IDVq1dj+fLlaGhoQHR0NJYuXcqn6hMREVGTZIKNdz0bDAbs27cPW7dutbiFSA/vu+++A3D3nQFsSa/XQ6FQYGLml/jhUvUvf0Eb0L+HEvtThv3ywDakrq4OpaWlCA4O5j6PVsa5tg/Os31wnu2jNee5ub+/H2qpxmAw4PPPP8f58+fh4eGBESNGiLfw9Ho9tm/fjm3btuGnn35Cr169rGydiIiIqGNodhC7cuUKZs6cifPnz4uPDnBzc8PmzZvh4uKChQsX4sqVKwgNDcWyZcu4L4qIiIjoFzQ7iG3YsAEXLlzAnDlz8MQTT+DChQvYtGkTli1bhps3b+I3v/kN/vKXv2Dw4MGt2S8RERFRu9HsIHbs2DE8/fTTWLhwoXjsV7/6FV5++WWMGDECb7/9NpycrH4PcSIiIqIOp9nJ6fr16xg4cKDZsbCwMADAM888wxBGRERE9JCanZ7u3LkDV1dXs2OmR0p06dLFtl0RERERdQAP9arJixcv4ocffhA/N71P47lz56BUKi3G9+/fv4XtEREREbVfDxXENm7ciI0bN1ocX7lypdnnpoe5lpaWtqw7IiIionas2UFs9erVrdkHERERUYfT7CD2u9/9rjX7ICIiIupw+FJHIiIiIokwiBERERFJhEGMiIiISCIMYkREREQSYRAjIiIikgiDGBEREZFEGMSIiIiIJMIgRkRERCQRBjEiIiIiiTCIEREREUmEQYyIiIhIIgxiRERERBJhECMiIiKSCIMYERERkUQYxIiIiIgkwiBGREREJBEGMSIiIiKJMIgRERERSYRBjIiIiEgiDGJEREREEmEQIyIiIpIIgxgRERGRRBjEiIiIiCTCIEZEREQkEQYxIiIiIokwiBERERFJpE0FsQMHDuCll15CTEwMwsLCMHnyZOzZsweCIJiN2717N8aPH4+QkBA89dRTOHLkiEWtmpoapKenY/DgwQgPD0dKSgquXr1qMe6bb77BlClTEBoaipEjRyI3N9fieoIgIDc3FyNGjEBoaCimTJmCEydOWNS6cuUKkpOTER4ejsGDB+OVV15BbW1tyyaFiIiI2q02FcT+9re/QaFQYMmSJdi8eTNiYmKwbNkybNq0SRyzf/9+LFu2DLGxscjLy0NYWBiSkpIsglFqaiqOHTuGjIwMvPnmm9BqtUhMTERDQ4M45ty5c0hISIC3tzdycnIwa9YsZGZmYuvWrWa18vLykJmZidmzZyMnJwfe3t6Ij49HZWWlOMZoNGLOnDmoqKjAunXrkJGRgaKiIixcuLB1JouIiIgcXiepG7jX5s2b4eXlJX4eGRmJqqoq/PWvf8Uf//hHODk5ITMzExMnTkRqaioAYMiQISgrK8OmTZuQl5cHACgpKUFRURHUajWio6MBACqVCnFxcSgoKEBcXBwAQK1Wo1u3bnjrrbcgl8sRGRmJGzduYMuWLZgxYwbkcjlu376NnJwcxMfHY/bs2QCAxx9/HBMmTIBarUZGRgYA4NChQzhz5gzy8/Ph7+8PAFAqlUhISMDJkycRGhpqhxkkIiIiR9KmVsTuDWEmwcHBqK2tRV1dHSorK1FRUYHY2FizMXFxcSguLobBYAAAFBYWQqlUIioqShzj7++P4OBgFBYWiscKCwsxevRoyOVys1rV1dUoKSkBcPfWZW1trdk15XI5xo4da1ErKChIDGEAEBUVBU9PTxw9etTaKSEiIqJ2rE0Fsab861//go+PD7p06QKNRgPg7urWvQICAmA0GsVbhRqNBiqVCjKZzGycv7+/WKOurg4//vijWXAyjZHJZOI408f7xwUEBODSpUuor68Xx90/RiaTQaVSiTWIiIiI7tWmbk3e75///Cfy8/OxePFiAIBOpwNw95bfvUyfm85XV1eja9euFvU8PDzw/fffA7i7mb+pWnK5HAqFwqyWXC6Hq6urxTUFQYBOp4Obm9sDr2mqZS1BEFBXV9eiGvczGAxQKBQ2rWkver3e4gUVbZVerzf7SK2Hc20fnGf74DzbR2vOsyAIFgtCTWmzQezy5ctIS0tDREQEZs6cKXU7kjIajSgtLbVpTYVCAU9PT5vWtBetVutwP5wqKiqkbqHD4FzbB+fZPjjP9tFa83zv1qef0yaDWHV1NRITE+Hp6YmsrCw4Od29g+rh4QHg7mqWt7e32fh7zyuVSly+fNmirk6nE8eYVq9MK2MmBoMBer3erJbBYMDt27fNVsWqq6shk8nMxjX1qAqdTofu3btbMQv/4eLigsDAwBbVuJ9pP50jUqlUDrUiVlFRAT8/P4ddgXQUnGv74DzbB+fZPlpznsvLy5s1rs0Fsfr6esybNw81NTX44IMPzG73mfZg3b8fS6PRwMXFBb6+vuK44uJii2VBrVaLPn36AADc3d3RvXt3i/1bWq0WgiCI9U0ftVot+vbta3bNHj16wM3NTRxXVlZmVksQBGi1WrMXDVhDJpPB3d29RTWaqumoHPGHkkKhsPmfITWNc20fnGf74DzbR2vMc3N/z7apzfoNDQ1ITU2FRqPBO++8Ax8fH7Pzvr6+8PPzw8GDB82O5+fnIzIyUlwCjImJgU6nQ3FxsThGq9Xi1KlTiImJEY/FxMTg8OHDMBqNZrWUSiXCw8MBAIMGDUKXLl1w4MABcYzRaERBQYFFrdOnT5stbxYXF6OqqgrDhw9vwawQERFRe9WmVsRWrlyJI0eOYMmSJaitrTV7SGu/fv0gl8uRnJyMRYsWoVevXoiIiEB+fj5OnjyJ7du3i2PDw8MRHR2N9PR0LF68GK6urli/fj2CgoIwbtw4cVxCQgI++eQTLFy4ENOmTUNZWRnUajXS0tLEUOfq6op58+YhKysLXl5e6NOnD3bu3ImqqiokJCSItcaPH4+cnBwkJydjwYIF0Ov1WLt2rfg0fiIiIqL7takgduzYMQDAmjVrLM4dPnwYPXv2xKRJk6DX65GXl4fc3FyoVCpkZ2eLK1gmGzZswOrVq7F8+XI0NDQgOjoaS5cuRadO//mWe/fuDbVajTVr1mDu3Lnw8vJCSkoK4uPjzWolJiZCEARs3boVN27cQHBwMNRqtXgrFLi7j+udd97BqlWrsGDBAnTq1Aljx45Fenq6LaeIiIiI2hGZ4Ci7njuo7777DgAQEhJi07p6vR4KhQITM7/ED5eqbVq7tfTvocT+lGFSt/FQ6urqUFpaiuDgYO7zaGWca/vgPNsH59k+WnOem/v7u03tESMiIiLqSBjEiIiIiCTCIEZEREQkEQYxIiIiIokwiBERERFJhEGMiIiISCIMYkREREQSYRAjIiIikgiDGBEREZFEGMSIiIiIJMIgRkRERCQRBjEiIiIiiTCIEREREUmEQYyIiIhIIgxiRERERBJhECMiIiKSCIMYERERkUQYxIiIiIgkwiBGREREHZJMJoOLi4ukPTCIERERkU3caRSkbuGhKBQKBPfrD5lMJlkPnSS7MhEREbUrzk4yvLyrBOVXa6VupVkCH+2CjVPD0WCUrgcGMSIiIrKZ8qu1+OFStdRtOAzemiQiIiKSCIMYERERkUQYxIiIiIgkwiBGREREJBEGMSIiIiKJMIgRERERSYRBjIiIiEgiDGJEREREEmEQIyIiIpIIgxgRERGRRBjEiIiIiCTCIEZEREQkEQYxIiIiIokwiBERERFJpE0FsXPnzmH58uWYPHky+vXrh0mTJjU5bvfu3Rg/fjxCQkLw1FNP4ciRIxZjampqkJ6ejsGDByM8PBwpKSm4evWqxbhvvvkGU6ZMQWhoKEaOHInc3FwIgmA2RhAE5ObmYsSIEQgNDcWUKVNw4sQJi1pXrlxBcnIywsPDMXjwYLzyyiuora21bjKIiIio3WtTQezMmTM4evQoevfujYCAgCbH7N+/H8uWLUNsbCzy8vIQFhaGpKQki2CUmpqKY8eOISMjA2+++Sa0Wi0SExPR0NAgjjl37hwSEhLg7e2NnJwczJo1C5mZmdi6datZrby8PGRmZmL27NnIycmBt7c34uPjUVlZKY4xGo2YM2cOKioqsG7dOmRkZKCoqAgLFy603QQRERFRu9JJ6gbuNWrUKIwZMwYAsGTJEnz//fcWYzIzMzFx4kSkpqYCAIYMGYKysjJs2rQJeXl5AICSkhIUFRVBrVYjOjoaAKBSqRAXF4eCggLExcUBANRqNbp164a33noLcrkckZGRuHHjBrZs2YIZM2ZALpfj9u3byMnJQXx8PGbPng0AePzxxzFhwgSo1WpkZGQAAA4dOoQzZ84gPz8f/v7+AAClUomEhAScPHkSoaGhrTVtRERE5KDa1IqYk9OD26msrERFRQViY2PNjsfFxaG4uBgGgwEAUFhYCKVSiaioKHGMv78/goODUVhYKB4rLCzE6NGjIZfLzWpVV1ejpKQEwN1bl7W1tWbXlMvlGDt2rEWtoKAgMYQBQFRUFDw9PXH06NGHmQYiIiK4uLhAJpNJ3Qa1sjYVxH6JRqMBcHd1614BAQEwGo3irUKNRgOVSmXxF9jf31+sUVdXhx9//NEsOJnGyGQycZzp4/3jAgICcOnSJdTX14vj7h8jk8mgUqnEGkRERM0hk8kQ3K8/FAqF1K1QK2tTtyZ/iU6nA3D3lt+9TJ+bzldXV6Nr164WX+/h4SHe7qypqWmyllwuh0KhMKsll8vh6upqcU1BEKDT6eDm5vbAa5pqWUsQBNTV1bWoxv0MBoPD/geu1+stXlDRVun1erOP1Ho41/bBebYP08/ol3eVoPyqY7zoa0SQN/40vq/UbVjl9u3bNv+9IghCs1Y0HSqIdVRGoxGlpaU2ralQKODp6WnTmvai1Wod7pdARUWF1C10GJxr++A8ty7Tz+jyq7X44VK11O00S4B3Z6lbsNqlS5da5ffKvVuffo5DBTEPDw8Ad1ezvL29xePV1dVm55VKJS5fvmzx9TqdThxjWr0yrYyZGAwG6PV6s1oGgwG3b982WxWrrq6GTCYzG9fUoyp0Oh26d+9u3Tf8/7m4uCAwMLBFNe5n2k/niFQqlUOtiFVUVMDPz89hVyAdBefaPjjP9uHIP6MdUY8ePZoVmh5GeXl5s8Y5VBAz7cG6fz+WRqOBi4sLfH19xXHFxcUWy4JarRZ9+vQBALi7u6N79+4W+7e0Wi0EQRDrmz5qtVr07fufJVeNRoMePXrAzc1NHFdWVmZWSxAEaLVasxcNWEMmk8Hd3b1FNZqq6agc8Ye/QqGw+Z8hNY1zbR+c59blyD+jHZGrq6vNf7c098/QoTbr+/r6ws/PDwcPHjQ7np+fj8jISDHNxsTEQKfTobi4WByj1Wpx6tQpxMTEiMdiYmJw+PBhGI1Gs1pKpRLh4eEAgEGDBqFLly44cOCAOMZoNKKgoMCi1unTp82W64uLi1FVVYXhw4fbZgKIiIioXWlTK2J6vV581MPFixdRW1srhq7BgwfDy8sLycnJWLRoEXr16oWIiAjk5+fj5MmT2L59u1gnPDwc0dHRSE9Px+LFi+Hq6or169cjKCgI48aNE8clJCTgk08+wcKFCzFt2jSUlZVBrVYjLS1NDHWurq6YN28esrKy4OXlhT59+mDnzp2oqqpCQkKCWGv8+PHIyclBcnIyFixYAL1ej7Vr14pP4yciIiK6X5sKYtevX8fLL79sdsz0+bvvvouIiAhMmjQJer0eeXl5yM3NhUqlQnZ2triCZbJhwwasXr0ay5cvR0NDA6Kjo7F06VJ06vSfb7l3795Qq9VYs2YN5s6dCy8vL6SkpCA+Pt6sVmJiIgRBwNatW3Hjxg0EBwdDrVaLt0KBu/u43nnnHaxatQoLFixAp06dMHbsWKSnp9t6moiIiKidaFNBrGfPnvj3v//9i+Oee+45PPfccw8c07VrV7z++ut4/fXXHzhu0KBB+PDDDx84RiaTYd68eZg3b94Dx/n4+CArK+uBY4iIiIhMHGqPGBEREVF7wiBGREREJBEGMSIiIiKJMIgRERERSYRBjIiIqI3hA107DgYxIiJq9+40OsbbopmY3rWF2r829fgKIiKi1uDsJMPLu0pQftXyPYHbohFB3vjT+L6/PJAcHoMYERF1COVXa/HDpWqp22iWAO/OUrdAdsJbk0REREQSYRAjIiIikgiDGBEREZFEGMSIiIiIJMIgRkRERCQRBjEiIiIiiTCIEREREUmEQYyIiIhIIgxiRETUbDKZDC4uLlK3QdRuMIgREUnI0d4DUaFQILhff74pNZGN8C2OiIgk5GjvgRj4aBdsnBqOBqPUnRC1DwxiREQSc6T3QCQi2+KtSSIiIiKJMIgRERERSYRBjIiIiEgiDGJERPTQ+KpJIttgECMiombz7uKKO40C3NzcpG6FqF3gqyaJiKjZlIpODvfIjRFB3vjT+L5St0HUJAYxIiJ6aI70yI0A785St0D0s3hrkoiIiEgiDGJEREREEmEQIyIiIpIIgxgRERGRRBjEiIiIiCTCIEZEREQkEQYxIiIiIokwiBERERFJhEGMiIiISCIMYkREREQSYRCzsbNnz+KFF15AWFgYoqKisHbtWhgMBqnbIuoQXFxcIJPJpG6DiKjZ+F6TNqTT6TBr1iz4+fkhKysLV65cwZo1a1BfX4/ly5dL3R5Ru+bk5IR+/fujk7Oz1K0QETUbg5gN7dq1C7du3UJ2djY8PT0BAHfu3MHKlSsxb948+Pj4SNsgUTPdaRTg7ORYK0tubm4AgJd3laD8aq3E3TTPiCBv/Gl8X6nbICIJMYjZUGFhISIjI8UQBgCxsbFYsWIFjh07hqefflq65kgSMpkM7u7uDne7zNlJ5lCBBvhPqCm/WosfLlVL3U6zBHh3lroFIpKYTBAEQeom2ovIyEg888wzWLRokdnxYcOGYfLkyRbHm+Obb76BIAhwcXGxVZsAAEEQ4OTkhOu3DDDeabRp7dbi2skJ3dzlUrfRYTjS3w0AULg4w0Ph4lB9s2f7YM/24Yg9uzg74ZHOcjQ2Ntr8f5iNRiNkMhkGDRr0wHFcEbOh6upqKJVKi+MeHh7Q6XRW1TT9xbD1XxBTvUc6M9hQ0xz174Yj9s2e7YM924cj9uzkZPvXLspksmb97mYQa+PCw8OlboGIiIhaCR9fYUNKpRI1NTUWx3U6HTw8PCToiIiIiNoyBjEb8vf3h0ajMTtWU1ODa9euwd/fX6KuiIiIqK1iELOhmJgY/OMf/0B19X9esXXw4EE4OTkhKipKws6IiIioLeKrJm1Ip9Nh4sSJUKlUmDdvnvhA1yeffJIPdCUiIiILDGI2dvbsWbz22msoKSlB586dMXnyZKSlpUEud7xXkRAREVHrYhAjIiIikgj3iBERERFJhEGMiIiISCIMYkREREQSYRAjIiIikgiDGBEREZFEGMSIiIiIJMIg1sGcPXsWL7zwAsLCwhAVFYW1a9fCYDBI3Va7c+DAAbz00kuIiYlBWFgYJk+ejD179oBPi2k9t27dQkxMDIKCgvDdd99J3U67tG/fPvz2t79FSEgIIiIiMGfOHNTX10vdVrty+PBhPPfccwgPD0d0dDRefvllVFZWSt2WQzt37hyWL1+OyZMno1+/fpg0aVKT43bv3o3x48cjJCQETz31FI4cOWKX/jrZ5SrUJuh0OsyaNQt+fn7IysoSn/xfX1/PJ//b2N/+9jc89thjWLJkCbp164Z//OMfWLZsGS5fvoykpCSp22uX3n77bdy5c0fqNtqtzZs3Iy8vDy+++CLCwsJw8+ZNFBcXc85t6Pjx40hKSsJvf/tbpKWloaqqChs3bkR8fDw++eQTuLm5Sd2iQzpz5gyOHj2KgQMHorGxscn/Id6/fz+WLVuGF198EUOGDEF+fj6SkpKwY8cOhIWFtW6DAnUYW7ZsEcLCwoSbN2+Kx3bt2iUEBwcLly9flq6xduj69esWx5YuXSoMGjRIuHPnjgQdtW/l5eVCWFiYsHPnTqFPnz7CyZMnpW6pXTl79qzQr18/4YsvvpC6lXZt2bJlwqhRo4TGxkbxWHFxsdCnTx/h66+/lrAzx3bvz9zFixcLEydOtBgzbtw4YcGCBWbHpkyZIsyZM6fV++OtyQ6ksLAQkZGR8PT0FI/FxsaisbERx44dk66xdsjLy8viWHBwMGpra1FXVydBR+3bqlWrMHXqVKhUKqlbaZf27t2Lnj17Yvjw4VK30q41NDSgc+fOkMlk4rGuXbsCALc1tICT04OjTmVlJSoqKhAbG2t2PC4uDsXFxa2+fYdBrAPRaDTw9/c3O6ZUKuHt7Q2NRiNRVx3Hv/71L/j4+KBLly5St9KuHDx4EGVlZZg/f77UrbRb3377Lfr06YO3334bkZGRGDBgAKZOnYpvv/1W6tbalaeffhpnz57Fjh07UFNTg8rKSrz11lvo168fBg0aJHV77Zbp99/9/yMXEBAAo9HY6nv0GMQ6kOrqaiiVSovjHh4e0Ol0EnTUcfzzn/9Efn4+4uPjpW6lXdHr9VizZg3S0tIYcFvRtWvXUFRUhI8//hgrVqzApk2bIJPJEB8fj+vXr0vdXrvxxBNPIDs7G+vWrcMTTzyBMWPG4Pr168jLy4Ozs7PU7bVbpt9/9/9+NH3e2r8fGcSIWtnly5eRlpaGiIgIzJw5U+p22pXNmzfjkUcewTPPPCN1K+2aIAioq6vDxo0bMWHCBAwfPhybN2+GIAjYvn271O21G9988w3+z//5P/j973+Pbdu2YePGjWhsbMTcuXP56tR2jK+a7ECUSiVqamosjut0Onh4eEjQUftXXV2NxMREeHp6Iisr6xf3KlDzXbx4EVu3bsWmTZvEv9em/Xd1dXW4desWOnfuLGWL7YZSqYSnpyf69u0rHvP09ES/fv1QXl4uYWfty6pVqzBkyBAsWbJEPBYWFoYRI0bg448/xpQpUyTsrv0y/f6rqamBt7e3eLy6utrsfGthEOtA/P39LfaC1dTU4Nq1axZ7x6jl6uvrMW/ePNTU1OCDDz4QN92SbVy4cAFGoxFz5861ODdz5kwMHDgQH374oQSdtT+BgYE4f/58k+du375t527ar7Nnz2L06NFmx37961+jW7duPzv/1HKm33/376PWaDRwcXGBr69vq16fQawDiYmJwZYtW8z2ih08eBBOTk6IioqSuLv2paGhAampqdBoNNixYwd8fHykbqndCQ4Oxrvvvmt2rLS0FKtXr8bKlSsREhIiUWftz8iRI7F3716UlpYiODgYAHDz5k388MMPmD17trTNtSM9evTAqVOnzI5dvHgRN2/exGOPPSZRV+2fr68v/Pz8cPDgQYwZM0Y8np+fj8jISMjl8la9PoNYBzJ16lS89957mD9/PubNm4crV65g7dq1mDp1KoOCja1cuRJHjhzBkiVLUFtbixMnTojn+vXr1+r/YXcESqUSERERTZ7r378/+vfvb+eO2q8xY8YgJCQEKSkpSEtLg6urK3JzcyGXyzF9+nSp22s3pk6ditdffx2rVq3CqFGjUFVVJe6DvP/RCtR8er0eR48eBXA32NbW1uLgwYMAgMGDB8PLywvJyclYtGgRevXqhYiICOTn5+PkyZN22QMpE/hwkg7l7NmzeO2111BSUoLOnTtj8uTJSEtLYzCwsVGjRuHixYtNnjt8+DB69uxp5446huPHj2PmzJnYs2cPV8Rs7MaNG1i9ejWOHDkCo9GIJ554An/+858RGBgodWvthiAI2LVrF3bu3InKykp07twZYWFhSEtLQ0BAgNTtOawLFy5Y3PI1effdd8X/odu9ezfy8vJw6dIlqFQqLFiwACNHjmz1/hjEiIiIiCTCl3ARERERSYRBjIiIiEgiDGJEREREEmEQIyIiIpIIgxgRERGRRBjEiIiIiCTCIEZEREQkEQYxIqJfkJWVhaCgIKnbsIklS5Zg1KhRUrdBRP8fgxgROZy9e/ciKChI/Kdfv34YNmwYlixZgitXrlhVU6/XIysrC8ePH7dxt0REP4/vNUlEDislJQU9e/aEwWDAiRMnsG/fPvzrX//Cp59+CldX14eqpdfrkZ2djaSkJIv3sHzppZcwd+5cW7ZORASAQYyIHFhMTIz4npLPPfccunXrhry8PBw+fBhxcXE2u06nTp3QqRN/XBKR7fHWJBG1G0888QQAoLKyUjxmMBiwceNGPP3003j88ccRFhaG6dOn46uvvhLHXLhwAZGRkQCA7Oxs8ZZnVlYWgKb3iAUFBeHVV1/FZ599hkmTJmHAgAGYOHEiCgsLLfo6fvw4nn76aYSEhGDMmDHYtWtXs/advfrqqwgPD4der7c4t2DBAkRFReHOnTsAgM8++wxz585FdHQ0BgwYgDFjxmDTpk3i+Z9z/PhxBAUFWdySvXDhAoKCgrB3716z42fPnkVKSgoGDx6MkJAQPP300zh8+PADr0FEP4//i0dE7cbFixcBAEqlUjxWW1uL3bt3Y9KkSXjuuedw69Yt7NmzB3PmzMHu3bsRHBwMLy8vZGRkICMjA2PHjsXYsWMB4BeD0r/+9S8UFBRg+vTp6Ny5M9577z2kpKTgyJEj6NatGwDg1KlTmDNnDry9vZGcnIzGxkZs2rQJXl5ev/j9xMXFYceOHfjiiy8QGxsrHtfr9Thy5Ah+97vfwdnZGQCwb98+uLu744UXXoC7uzu++uorZGZmora2FosXL364ifwZZ86cwbRp0+Dj44PExES4u7vjwIEDmD9/PrKyssR5I6LmYxAjIodVW1uLGzduwGAw4Ntvv0V2djbkcjlGjhwpjvHw8MDnn38OuVwuHvv973+P2NhYvPfee3j99dfh7u6O8ePHIyMjA0FBQZg8eXKzrn/27Fnk5+ejV69eAICIiAhMnjwZ+/fvxx/+8AcAQGZmJpydnbFz5074+PgAAGJjY5t16/Txxx+Hj48PDhw4YBbEvvjiC9TV1ZnVWLduHdzc3MTPp02bhuXLl2Pnzp1IS0sz+/6t9d///d/o3r07PvroI7He9OnTMW3aNLz55psMYkRW4K1JInJYs2fPRmRkJIYPH46UlBQoFAps3rwZv/71r8Uxzs7OYmhobGxEVVUVGhoaMGDAAJw6dapF1x86dKgYwgCgb9++6NKli3hr9M6dOyguLsbo0aPFEAYAvXv3xrBhw36xvkwmw4QJE3D06FHcunVLPH7gwAH4+Pjg8ccfF4/dG8JMAfWJJ56AXq+HRqNp0fcJAFVVVfjqq68QGxsr1r9x4wZu3ryJ6OhoVFRUWP2KVaKOjCtiROSwli9fDpVKhZqaGnz00Uf4+uuvm1z52bdvH7Zu3QqtVguj0Sge79mzZ4uu3717d4tjHh4eqK6uBgBcv34d9fX16N27t8W4po41JS4uDtu2bcPnn3+OJ598Erdu3cLRo0cxZcoUyGQycdyZM2ewYcMGfPXVV6itrTWrUVNT8zDfVpPOnz8PQRCwceNGbNy4sckx169fNwucRPTLGMSIyGGFhoaKr5ocM2YMpk+fjoULF+LgwYPo3LkzAODjjz/GkiVLMGbMGCQkJOCRRx6Bs7MzcnJyzDb1W8O0P+t+giC0qO69wsLC8Nhjj+HAgQN48sknceTIEdTX15vdlqyursYf/vAHdOnSBSkpKejVqxdcXV3xww8/4M0330RjY+PP1r83zN3r/q8xfR4fH/+zq3n3rg4SUfMwiBFRu+Ds7IwFCxZg5syZ2LFjh/jcr0OHDsHX1xfZ2dlmoSMzM9Ps638ukLTEI488AldXV5w7d87iXFPHfk5sbCzeffdd1NbWIj8/H4899hjCwsLE8//7v/+LqqoqZGdn47/+67/E4xcuXPjF2qYXNty/amZ64YOJr68vAMDFxQVDhw5tdu9E9GDcI0ZE7UZERARCQ0Oxbds23L59G8B/Vq3uXaX69ttvceLECbOvVSgUACDeVrQFZ2dnDB06FIcPHzbbP3Xu3Dl8+eWXza4TFxcHg8GAffv24csvvzTbuA8ATk53f5Tf+z0aDAa8//77v1j7scceg7OzM77++muz4zt37jT7/JFHHsHgwYPxwQcf4OrVqxZ1bty40ezvh4j+gytiRNSuJCQk4OWXX8bevXsxbdo0jBgxAgUFBZg/fz5GjBiBCxcuYNeuXQgMDERdXZ34dW5ubggMDMSBAwfg5+cHT09P/OY3v0GfPn1a1E9SUhKKioowbdo0TJs2DY2Njdi+fTt+85vfoLS0tFk1+vfvj969e2P9+vUwGAwWr7gMDw+Hh4cHlixZghkzZkAmk+Hjjz9u1i3Srl27YsKECdi+fTtkMhl8fX3xxRdf4Pr16xZjV6xYgenTp+PJJ5/E73//e/j6+uKnn37CiRMncPnyZfzP//xP8yaFiERcESOidmXcuHHo1asXtm7dijt37uDpp5/GggUL8O9//xurVq1CUVER/vKXv2DAgAEWX7tq1So8+uijWL16NRYsWIBDhw61uJ8BAwYgLy8PHh4e2LhxI/bs2YOUlBRERkY+1NswxcbG4tatW+jduzf69+9vdq5bt27YsmULvL29sWHDBqjVagwdOhR/+tOfmlV76dKlGD16NHbt2oWNGzeie/fueOONNyzGBQYG4qOPPsKIESOwb98+vPrqq9i1axecnJwwf/78Zn8vRPQfMsGWu0qJiKhZ/vjHP6K8vBwFBQVSt0JEEuKKGBFRK6uvrzf7vKKiAoWFhRg8eLBEHRFRW8E9YkRErWzMmDH43e9+B19fX1y8eBG7du2Ci4sL5syZI3VrRCQxBjEiolY2bNgw7N+/H9euXYNcLkdYWBgWLFgAPz8/qVsjIolxjxgRERGRRLhHjIiIiEgiDGJEREREEmEQIyIiIpIIgxgRERGRRBjEiIiIiCTCIEZEREQkEQYxIiIiIokwiBERERFJhEGMiIiISCL/D/H6404mHk50AAAAAElFTkSuQmCC\n"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "sns.set_context(\"notebook\")\n",
+        "sns.set_style(\"whitegrid\")\n",
+        "\n",
+        "ratings[\"Book-Rating\"].hist()\n",
+        "plt.xlabel(\"Rating value\")\n",
+        "plt.ylabel(\"Rating count\")\n",
+        "plt.show()\n"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "Теперь посмотрим на таблицу `Books`."
-      ],
       "metadata": {
         "id": "xBfEqtNagkOj"
-      }
+      },
+      "source": [
+        "Теперь посмотрим на таблицу `Books`."
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "books = pd.read_csv(\"Books.csv\")\n",
-        "books.head()"
-      ],
+      "execution_count": 7,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -796,55 +779,21 @@
         "id": "3JHS-pRXgrWQ",
         "outputId": "01c0b023-1b58-420b-c91b-5832b1ed269c"
       },
-      "execution_count": 7,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "<ipython-input-7-7dc4eb500039>:1: DtypeWarning: Columns (3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
             "  books = pd.read_csv(\"Books.csv\")\n"
           ]
         },
         {
-          "output_type": "execute_result",
           "data": {
-            "text/plain": [
-              "         ISBN                                         Book-Title  \\\n",
-              "0  0195153448                                Classical Mythology   \n",
-              "1  0002005018                                       Clara Callan   \n",
-              "2  0060973129                               Decision in Normandy   \n",
-              "3  0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
-              "4  0393045218                             The Mummies of Urumchi   \n",
-              "\n",
-              "            Book-Author Year-Of-Publication                   Publisher  \\\n",
-              "0    Mark P. O. Morford                2002     Oxford University Press   \n",
-              "1  Richard Bruce Wright                2001       HarperFlamingo Canada   \n",
-              "2          Carlo D'Este                1991             HarperPerennial   \n",
-              "3      Gina Bari Kolata                1999        Farrar Straus Giroux   \n",
-              "4       E. J. W. Barber                1999  W. W. Norton &amp; Company   \n",
-              "\n",
-              "                                         Image-URL-S  \\\n",
-              "0  http://images.amazon.com/images/P/0195153448.0...   \n",
-              "1  http://images.amazon.com/images/P/0002005018.0...   \n",
-              "2  http://images.amazon.com/images/P/0060973129.0...   \n",
-              "3  http://images.amazon.com/images/P/0374157065.0...   \n",
-              "4  http://images.amazon.com/images/P/0393045218.0...   \n",
-              "\n",
-              "                                         Image-URL-M  \\\n",
-              "0  http://images.amazon.com/images/P/0195153448.0...   \n",
-              "1  http://images.amazon.com/images/P/0002005018.0...   \n",
-              "2  http://images.amazon.com/images/P/0060973129.0...   \n",
-              "3  http://images.amazon.com/images/P/0374157065.0...   \n",
-              "4  http://images.amazon.com/images/P/0393045218.0...   \n",
-              "\n",
-              "                                         Image-URL-L  \n",
-              "0  http://images.amazon.com/images/P/0195153448.0...  \n",
-              "1  http://images.amazon.com/images/P/0002005018.0...  \n",
-              "2  http://images.amazon.com/images/P/0060973129.0...  \n",
-              "3  http://images.amazon.com/images/P/0374157065.0...  \n",
-              "4  http://images.amazon.com/images/P/0393045218.0...  "
-            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "books"
+            },
             "text/html": [
               "\n",
               "  <div id=\"df-984bd38c-44df-4382-a552-a6d5e7f2eca0\" class=\"colab-df-container\">\n",
@@ -1144,21 +1093,56 @@
               "    </div>\n",
               "  </div>\n"
             ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "books"
-            }
+            "text/plain": [
+              "         ISBN                                         Book-Title  \\\n",
+              "0  0195153448                                Classical Mythology   \n",
+              "1  0002005018                                       Clara Callan   \n",
+              "2  0060973129                               Decision in Normandy   \n",
+              "3  0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
+              "4  0393045218                             The Mummies of Urumchi   \n",
+              "\n",
+              "            Book-Author Year-Of-Publication                   Publisher  \\\n",
+              "0    Mark P. O. Morford                2002     Oxford University Press   \n",
+              "1  Richard Bruce Wright                2001       HarperFlamingo Canada   \n",
+              "2          Carlo D'Este                1991             HarperPerennial   \n",
+              "3      Gina Bari Kolata                1999        Farrar Straus Giroux   \n",
+              "4       E. J. W. Barber                1999  W. W. Norton &amp; Company   \n",
+              "\n",
+              "                                         Image-URL-S  \\\n",
+              "0  http://images.amazon.com/images/P/0195153448.0...   \n",
+              "1  http://images.amazon.com/images/P/0002005018.0...   \n",
+              "2  http://images.amazon.com/images/P/0060973129.0...   \n",
+              "3  http://images.amazon.com/images/P/0374157065.0...   \n",
+              "4  http://images.amazon.com/images/P/0393045218.0...   \n",
+              "\n",
+              "                                         Image-URL-M  \\\n",
+              "0  http://images.amazon.com/images/P/0195153448.0...   \n",
+              "1  http://images.amazon.com/images/P/0002005018.0...   \n",
+              "2  http://images.amazon.com/images/P/0060973129.0...   \n",
+              "3  http://images.amazon.com/images/P/0374157065.0...   \n",
+              "4  http://images.amazon.com/images/P/0393045218.0...   \n",
+              "\n",
+              "                                         Image-URL-L  \n",
+              "0  http://images.amazon.com/images/P/0195153448.0...  \n",
+              "1  http://images.amazon.com/images/P/0002005018.0...  \n",
+              "2  http://images.amazon.com/images/P/0060973129.0...  \n",
+              "3  http://images.amazon.com/images/P/0374157065.0...  \n",
+              "4  http://images.amazon.com/images/P/0393045218.0...  "
+            ]
           },
+          "execution_count": 7,
           "metadata": {},
-          "execution_count": 7
+          "output_type": "execute_result"
         }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "books.info()"
       ],
+      "source": [
+        "books = pd.read_csv(\"Books.csv\")\n",
+        "books.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -1166,11 +1150,10 @@
         "id": "CvRM2JScgxUT",
         "outputId": "7b04808f-2dcf-4447-d7b6-8938eead6d37"
       },
-      "execution_count": 8,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "<class 'pandas.core.frame.DataFrame'>\n",
             "RangeIndex: 271360 entries, 0 to 271359\n",
@@ -1189,23 +1172,24 @@
             "memory usage: 16.6+ MB\n"
           ]
         }
+      ],
+      "source": [
+        "books.info()"
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "QTV7HLSqkNDl"
+      },
       "source": [
         "- Есть немножко пропусков.\n",
         "- Проверим уязвимую часть данных – года – на адекватность."
-      ],
-      "metadata": {
-        "id": "QTV7HLSqkNDl"
-      }
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "books[\"Year-Of-Publication\"].value_counts().sort_values(ascending=True)[:10]"
-      ],
+      "execution_count": 9,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1214,25 +1198,9 @@
         "id": "7AR9ah_1g3BY",
         "outputId": "1973f87f-215c-4cde-bcc4-d1af7a10baf6"
       },
-      "execution_count": 9,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
-            "text/plain": [
-              "Year-Of-Publication\n",
-              "2037    1\n",
-              "2011    1\n",
-              "2038    1\n",
-              "1938    1\n",
-              "1906    1\n",
-              "1919    1\n",
-              "1926    1\n",
-              "1926    1\n",
-              "2008    1\n",
-              "2024    1\n",
-              "Name: count, dtype: int64"
-            ],
             "text/html": [
               "<div>\n",
               "<style scoped>\n",
@@ -1303,36 +1271,52 @@
               "  </tbody>\n",
               "</table>\n",
               "</div><br><label><b>dtype:</b> int64</label>"
+            ],
+            "text/plain": [
+              "Year-Of-Publication\n",
+              "2037    1\n",
+              "2011    1\n",
+              "2038    1\n",
+              "1938    1\n",
+              "1906    1\n",
+              "1919    1\n",
+              "1926    1\n",
+              "1926    1\n",
+              "2008    1\n",
+              "2024    1\n",
+              "Name: count, dtype: int64"
             ]
           },
+          "execution_count": 9,
           "metadata": {},
-          "execution_count": 9
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "books[\"Year-Of-Publication\"].value_counts().sort_values(ascending=True)[:10]"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "Есть года, которые еще не наступили..."
-      ],
       "metadata": {
         "id": "44kZGFc3kWwi"
-      }
+      },
+      "source": [
+        "Есть года, которые еще не наступили..."
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "Проверим, есть ли строковые данные в столбце с годом (потому что `info()` назвал тип данных столбца `object`)."
-      ],
       "metadata": {
         "id": "UrncAU64ka-d"
-      }
+      },
+      "source": [
+        "Проверим, есть ли строковые данные в столбце с годом (потому что `info()` назвал тип данных столбца `object`)."
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "books[books[\"Year-Of-Publication\"].map(str).str.match(\"[^0-9]\")]"
-      ],
+      "execution_count": 10,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1341,37 +1325,13 @@
         "id": "it2GevU7hchV",
         "outputId": "c42ef49b-01bf-4c48-e42e-0fbf84453289"
       },
-      "execution_count": 10,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
-            "text/plain": [
-              "              ISBN                                         Book-Title  \\\n",
-              "209538  078946697X  DK Readers: Creating the X-Men, How It All Beg...   \n",
-              "220731  2070426769  Peuple du ciel, suivi de 'Les Bergers\\\";Jean-M...   \n",
-              "221678  0789466953  DK Readers: Creating the X-Men, How Comic Book...   \n",
-              "\n",
-              "       Book-Author Year-Of-Publication  \\\n",
-              "209538        2000   DK Publishing Inc   \n",
-              "220731        2003           Gallimard   \n",
-              "221678        2000   DK Publishing Inc   \n",
-              "\n",
-              "                                                Publisher  \\\n",
-              "209538  http://images.amazon.com/images/P/078946697X.0...   \n",
-              "220731  http://images.amazon.com/images/P/2070426769.0...   \n",
-              "221678  http://images.amazon.com/images/P/0789466953.0...   \n",
-              "\n",
-              "                                              Image-URL-S  \\\n",
-              "209538  http://images.amazon.com/images/P/078946697X.0...   \n",
-              "220731  http://images.amazon.com/images/P/2070426769.0...   \n",
-              "221678  http://images.amazon.com/images/P/0789466953.0...   \n",
-              "\n",
-              "                                              Image-URL-M Image-URL-L  \n",
-              "209538  http://images.amazon.com/images/P/078946697X.0...         NaN  \n",
-              "220731  http://images.amazon.com/images/P/2070426769.0...         NaN  \n",
-              "221678  http://images.amazon.com/images/P/0789466953.0...         NaN  "
-            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "repr_error": "0",
+              "type": "dataframe"
+            },
             "text/html": [
               "\n",
               "  <div id=\"df-b2e67e0b-45ce-4b41-a8ef-3cadc8d49ea7\" class=\"colab-df-container\">\n",
@@ -1649,39 +1609,63 @@
               "    </div>\n",
               "  </div>\n"
             ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "repr_error": "0"
-            }
+            "text/plain": [
+              "              ISBN                                         Book-Title  \\\n",
+              "209538  078946697X  DK Readers: Creating the X-Men, How It All Beg...   \n",
+              "220731  2070426769  Peuple du ciel, suivi de 'Les Bergers\\\";Jean-M...   \n",
+              "221678  0789466953  DK Readers: Creating the X-Men, How Comic Book...   \n",
+              "\n",
+              "       Book-Author Year-Of-Publication  \\\n",
+              "209538        2000   DK Publishing Inc   \n",
+              "220731        2003           Gallimard   \n",
+              "221678        2000   DK Publishing Inc   \n",
+              "\n",
+              "                                                Publisher  \\\n",
+              "209538  http://images.amazon.com/images/P/078946697X.0...   \n",
+              "220731  http://images.amazon.com/images/P/2070426769.0...   \n",
+              "221678  http://images.amazon.com/images/P/0789466953.0...   \n",
+              "\n",
+              "                                              Image-URL-S  \\\n",
+              "209538  http://images.amazon.com/images/P/078946697X.0...   \n",
+              "220731  http://images.amazon.com/images/P/2070426769.0...   \n",
+              "221678  http://images.amazon.com/images/P/0789466953.0...   \n",
+              "\n",
+              "                                              Image-URL-M Image-URL-L  \n",
+              "209538  http://images.amazon.com/images/P/078946697X.0...         NaN  \n",
+              "220731  http://images.amazon.com/images/P/2070426769.0...         NaN  \n",
+              "221678  http://images.amazon.com/images/P/0789466953.0...         NaN  "
+            ]
           },
+          "execution_count": 10,
           "metadata": {},
-          "execution_count": 10
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "books[books[\"Year-Of-Publication\"].map(str).str.match(\"[^0-9]\")]"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "В трех строках пропущен автор, из-за чего смещены данные. Автор либо пропущен, либо склеен с названием. Надо проверить."
-      ],
       "metadata": {
         "id": "-yQvkLqSkltM"
-      }
+      },
+      "source": [
+        "В трех строках пропущен автор, из-за чего смещены данные. Автор либо пропущен, либо склеен с названием. Надо проверить."
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "В сводке написано, что часть авторов и издателей провущена. Проверим, так ли это."
-      ],
       "metadata": {
         "id": "KpGvTAp8k05k"
-      }
+      },
+      "source": [
+        "В сводке написано, что часть авторов и издателей провущена. Проверим, так ли это."
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "books[(books[\"Book-Author\"].isnull()) | (books[\"Publisher\"].isnull())]"
-      ],
+      "execution_count": 11,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1690,42 +1674,13 @@
         "id": "ezt51ciKjwMh",
         "outputId": "eb59d4fb-3a6c-43b4-eeff-09386f485fd8"
       },
-      "execution_count": 11,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
-            "text/plain": [
-              "              ISBN                                         Book-Title  \\\n",
-              "118033  0751352497                           A+ Quiz Masters:01 Earth   \n",
-              "128890  193169656X                                        Tyrant Moon   \n",
-              "129037  1931696993                                    Finders Keepers   \n",
-              "187689  9627982032  The Credit Suisse Guide to Managing Your Perso...   \n",
-              "\n",
-              "            Book-Author Year-Of-Publication                       Publisher  \\\n",
-              "118033              NaN                1999              Dorling Kindersley   \n",
-              "128890  Elaine Corvidae                2002                             NaN   \n",
-              "129037  Linnea Sinclair                2001                             NaN   \n",
-              "187689              NaN                1995  Edinburgh Financial Publishing   \n",
-              "\n",
-              "                                              Image-URL-S  \\\n",
-              "118033  http://images.amazon.com/images/P/0751352497.0...   \n",
-              "128890  http://images.amazon.com/images/P/193169656X.0...   \n",
-              "129037  http://images.amazon.com/images/P/1931696993.0...   \n",
-              "187689  http://images.amazon.com/images/P/9627982032.0...   \n",
-              "\n",
-              "                                              Image-URL-M  \\\n",
-              "118033  http://images.amazon.com/images/P/0751352497.0...   \n",
-              "128890  http://images.amazon.com/images/P/193169656X.0...   \n",
-              "129037  http://images.amazon.com/images/P/1931696993.0...   \n",
-              "187689  http://images.amazon.com/images/P/9627982032.0...   \n",
-              "\n",
-              "                                              Image-URL-L  \n",
-              "118033  http://images.amazon.com/images/P/0751352497.0...  \n",
-              "128890  http://images.amazon.com/images/P/193169656X.0...  \n",
-              "129037  http://images.amazon.com/images/P/1931696993.0...  \n",
-              "187689  http://images.amazon.com/images/P/9627982032.0...  "
-            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "repr_error": "0",
+              "type": "dataframe"
+            },
             "text/html": [
               "\n",
               "  <div id=\"df-ab967b79-fc6e-4409-90c4-5c42ce13fa16\" class=\"colab-df-container\">\n",
@@ -2014,30 +1969,59 @@
               "    </div>\n",
               "  </div>\n"
             ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "repr_error": "0"
-            }
+            "text/plain": [
+              "              ISBN                                         Book-Title  \\\n",
+              "118033  0751352497                           A+ Quiz Masters:01 Earth   \n",
+              "128890  193169656X                                        Tyrant Moon   \n",
+              "129037  1931696993                                    Finders Keepers   \n",
+              "187689  9627982032  The Credit Suisse Guide to Managing Your Perso...   \n",
+              "\n",
+              "            Book-Author Year-Of-Publication                       Publisher  \\\n",
+              "118033              NaN                1999              Dorling Kindersley   \n",
+              "128890  Elaine Corvidae                2002                             NaN   \n",
+              "129037  Linnea Sinclair                2001                             NaN   \n",
+              "187689              NaN                1995  Edinburgh Financial Publishing   \n",
+              "\n",
+              "                                              Image-URL-S  \\\n",
+              "118033  http://images.amazon.com/images/P/0751352497.0...   \n",
+              "128890  http://images.amazon.com/images/P/193169656X.0...   \n",
+              "129037  http://images.amazon.com/images/P/1931696993.0...   \n",
+              "187689  http://images.amazon.com/images/P/9627982032.0...   \n",
+              "\n",
+              "                                              Image-URL-M  \\\n",
+              "118033  http://images.amazon.com/images/P/0751352497.0...   \n",
+              "128890  http://images.amazon.com/images/P/193169656X.0...   \n",
+              "129037  http://images.amazon.com/images/P/1931696993.0...   \n",
+              "187689  http://images.amazon.com/images/P/9627982032.0...   \n",
+              "\n",
+              "                                              Image-URL-L  \n",
+              "118033  http://images.amazon.com/images/P/0751352497.0...  \n",
+              "128890  http://images.amazon.com/images/P/193169656X.0...  \n",
+              "129037  http://images.amazon.com/images/P/1931696993.0...  \n",
+              "187689  http://images.amazon.com/images/P/9627982032.0...  "
+            ]
           },
+          "execution_count": 11,
           "metadata": {},
-          "execution_count": 11
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "books[(books[\"Book-Author\"].isnull()) | (books[\"Publisher\"].isnull())]"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "Да, это так. Основные проблемы с данными выявили. Пора их исправлять и приступать к созданию рекомендаций."
-      ],
       "metadata": {
         "id": "c47dZz5yk7ng"
-      }
+      },
+      "source": [
+        "Да, это так. Основные проблемы с данными выявили. Пора их исправлять и приступать к созданию рекомендаций."
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "books"
-      ],
+      "execution_count": 12,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -2046,93 +2030,14 @@
         "id": "JICTHdghhglG",
         "outputId": "452625f1-c990-43b5-de87-5101d485168f"
       },
-      "execution_count": 12,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
-            "text/plain": [
-              "              ISBN                                         Book-Title  \\\n",
-              "0       0195153448                                Classical Mythology   \n",
-              "1       0002005018                                       Clara Callan   \n",
-              "2       0060973129                               Decision in Normandy   \n",
-              "3       0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
-              "4       0393045218                             The Mummies of Urumchi   \n",
-              "...            ...                                                ...   \n",
-              "271355  0440400988                         There's a Bat in Bunk Five   \n",
-              "271356  0525447644                            From One to One Hundred   \n",
-              "271357  006008667X  Lily Dale : The True Story of the Town that Ta...   \n",
-              "271358  0192126040                        Republic (World's Classics)   \n",
-              "271359  0767409752  A Guided Tour of Rene Descartes' Meditations o...   \n",
-              "\n",
-              "                 Book-Author Year-Of-Publication  \\\n",
-              "0         Mark P. O. Morford                2002   \n",
-              "1       Richard Bruce Wright                2001   \n",
-              "2               Carlo D'Este                1991   \n",
-              "3           Gina Bari Kolata                1999   \n",
-              "4            E. J. W. Barber                1999   \n",
-              "...                      ...                 ...   \n",
-              "271355        Paula Danziger                1988   \n",
-              "271356            Teri Sloat                1991   \n",
-              "271357      Christine Wicker                2004   \n",
-              "271358                 Plato                1996   \n",
-              "271359   Christopher  Biffle                2000   \n",
-              "\n",
-              "                                               Publisher  \\\n",
-              "0                                Oxford University Press   \n",
-              "1                                  HarperFlamingo Canada   \n",
-              "2                                        HarperPerennial   \n",
-              "3                                   Farrar Straus Giroux   \n",
-              "4                             W. W. Norton &amp; Company   \n",
-              "...                                                  ...   \n",
-              "271355                   Random House Childrens Pub (Mm)   \n",
-              "271356                                      Dutton Books   \n",
-              "271357                                HarperSanFrancisco   \n",
-              "271358                           Oxford University Press   \n",
-              "271359  McGraw-Hill Humanities/Social Sciences/Languages   \n",
-              "\n",
-              "                                              Image-URL-S  \\\n",
-              "0       http://images.amazon.com/images/P/0195153448.0...   \n",
-              "1       http://images.amazon.com/images/P/0002005018.0...   \n",
-              "2       http://images.amazon.com/images/P/0060973129.0...   \n",
-              "3       http://images.amazon.com/images/P/0374157065.0...   \n",
-              "4       http://images.amazon.com/images/P/0393045218.0...   \n",
-              "...                                                   ...   \n",
-              "271355  http://images.amazon.com/images/P/0440400988.0...   \n",
-              "271356  http://images.amazon.com/images/P/0525447644.0...   \n",
-              "271357  http://images.amazon.com/images/P/006008667X.0...   \n",
-              "271358  http://images.amazon.com/images/P/0192126040.0...   \n",
-              "271359  http://images.amazon.com/images/P/0767409752.0...   \n",
-              "\n",
-              "                                              Image-URL-M  \\\n",
-              "0       http://images.amazon.com/images/P/0195153448.0...   \n",
-              "1       http://images.amazon.com/images/P/0002005018.0...   \n",
-              "2       http://images.amazon.com/images/P/0060973129.0...   \n",
-              "3       http://images.amazon.com/images/P/0374157065.0...   \n",
-              "4       http://images.amazon.com/images/P/0393045218.0...   \n",
-              "...                                                   ...   \n",
-              "271355  http://images.amazon.com/images/P/0440400988.0...   \n",
-              "271356  http://images.amazon.com/images/P/0525447644.0...   \n",
-              "271357  http://images.amazon.com/images/P/006008667X.0...   \n",
-              "271358  http://images.amazon.com/images/P/0192126040.0...   \n",
-              "271359  http://images.amazon.com/images/P/0767409752.0...   \n",
-              "\n",
-              "                                              Image-URL-L  \n",
-              "0       http://images.amazon.com/images/P/0195153448.0...  \n",
-              "1       http://images.amazon.com/images/P/0002005018.0...  \n",
-              "2       http://images.amazon.com/images/P/0060973129.0...  \n",
-              "3       http://images.amazon.com/images/P/0374157065.0...  \n",
-              "4       http://images.amazon.com/images/P/0393045218.0...  \n",
-              "...                                                   ...  \n",
-              "271355  http://images.amazon.com/images/P/0440400988.0...  \n",
-              "271356  http://images.amazon.com/images/P/0525447644.0...  \n",
-              "271357  http://images.amazon.com/images/P/006008667X.0...  \n",
-              "271358  http://images.amazon.com/images/P/0192126040.0...  \n",
-              "271359  http://images.amazon.com/images/P/0767409752.0...  \n",
-              "\n",
-              "[271360 rows x 8 columns]"
-            ],
-            "text/html": [
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "books"
+            },
+            "text/html": [
               "\n",
               "  <div id=\"df-261ae676-bef2-43f5-87e9-a35dc3e496bc\" class=\"colab-df-container\">\n",
               "    <div>\n",
@@ -2498,18 +2403,102 @@
               "    </div>\n",
               "  </div>\n"
             ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "books"
-            }
+            "text/plain": [
+              "              ISBN                                         Book-Title  \\\n",
+              "0       0195153448                                Classical Mythology   \n",
+              "1       0002005018                                       Clara Callan   \n",
+              "2       0060973129                               Decision in Normandy   \n",
+              "3       0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
+              "4       0393045218                             The Mummies of Urumchi   \n",
+              "...            ...                                                ...   \n",
+              "271355  0440400988                         There's a Bat in Bunk Five   \n",
+              "271356  0525447644                            From One to One Hundred   \n",
+              "271357  006008667X  Lily Dale : The True Story of the Town that Ta...   \n",
+              "271358  0192126040                        Republic (World's Classics)   \n",
+              "271359  0767409752  A Guided Tour of Rene Descartes' Meditations o...   \n",
+              "\n",
+              "                 Book-Author Year-Of-Publication  \\\n",
+              "0         Mark P. O. Morford                2002   \n",
+              "1       Richard Bruce Wright                2001   \n",
+              "2               Carlo D'Este                1991   \n",
+              "3           Gina Bari Kolata                1999   \n",
+              "4            E. J. W. Barber                1999   \n",
+              "...                      ...                 ...   \n",
+              "271355        Paula Danziger                1988   \n",
+              "271356            Teri Sloat                1991   \n",
+              "271357      Christine Wicker                2004   \n",
+              "271358                 Plato                1996   \n",
+              "271359   Christopher  Biffle                2000   \n",
+              "\n",
+              "                                               Publisher  \\\n",
+              "0                                Oxford University Press   \n",
+              "1                                  HarperFlamingo Canada   \n",
+              "2                                        HarperPerennial   \n",
+              "3                                   Farrar Straus Giroux   \n",
+              "4                             W. W. Norton &amp; Company   \n",
+              "...                                                  ...   \n",
+              "271355                   Random House Childrens Pub (Mm)   \n",
+              "271356                                      Dutton Books   \n",
+              "271357                                HarperSanFrancisco   \n",
+              "271358                           Oxford University Press   \n",
+              "271359  McGraw-Hill Humanities/Social Sciences/Languages   \n",
+              "\n",
+              "                                              Image-URL-S  \\\n",
+              "0       http://images.amazon.com/images/P/0195153448.0...   \n",
+              "1       http://images.amazon.com/images/P/0002005018.0...   \n",
+              "2       http://images.amazon.com/images/P/0060973129.0...   \n",
+              "3       http://images.amazon.com/images/P/0374157065.0...   \n",
+              "4       http://images.amazon.com/images/P/0393045218.0...   \n",
+              "...                                                   ...   \n",
+              "271355  http://images.amazon.com/images/P/0440400988.0...   \n",
+              "271356  http://images.amazon.com/images/P/0525447644.0...   \n",
+              "271357  http://images.amazon.com/images/P/006008667X.0...   \n",
+              "271358  http://images.amazon.com/images/P/0192126040.0...   \n",
+              "271359  http://images.amazon.com/images/P/0767409752.0...   \n",
+              "\n",
+              "                                              Image-URL-M  \\\n",
+              "0       http://images.amazon.com/images/P/0195153448.0...   \n",
+              "1       http://images.amazon.com/images/P/0002005018.0...   \n",
+              "2       http://images.amazon.com/images/P/0060973129.0...   \n",
+              "3       http://images.amazon.com/images/P/0374157065.0...   \n",
+              "4       http://images.amazon.com/images/P/0393045218.0...   \n",
+              "...                                                   ...   \n",
+              "271355  http://images.amazon.com/images/P/0440400988.0...   \n",
+              "271356  http://images.amazon.com/images/P/0525447644.0...   \n",
+              "271357  http://images.amazon.com/images/P/006008667X.0...   \n",
+              "271358  http://images.amazon.com/images/P/0192126040.0...   \n",
+              "271359  http://images.amazon.com/images/P/0767409752.0...   \n",
+              "\n",
+              "                                              Image-URL-L  \n",
+              "0       http://images.amazon.com/images/P/0195153448.0...  \n",
+              "1       http://images.amazon.com/images/P/0002005018.0...  \n",
+              "2       http://images.amazon.com/images/P/0060973129.0...  \n",
+              "3       http://images.amazon.com/images/P/0374157065.0...  \n",
+              "4       http://images.amazon.com/images/P/0393045218.0...  \n",
+              "...                                                   ...  \n",
+              "271355  http://images.amazon.com/images/P/0440400988.0...  \n",
+              "271356  http://images.amazon.com/images/P/0525447644.0...  \n",
+              "271357  http://images.amazon.com/images/P/006008667X.0...  \n",
+              "271358  http://images.amazon.com/images/P/0192126040.0...  \n",
+              "271359  http://images.amazon.com/images/P/0767409752.0...  \n",
+              "\n",
+              "[271360 rows x 8 columns]"
+            ]
           },
+          "execution_count": 12,
           "metadata": {},
-          "execution_count": 12
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "books"
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "lI1qVeU4aZGE"
+      },
       "source": [
         "## План анализа данных\n",
         "\n",
@@ -2536,13 +2525,13 @@
         "\n",
         "- То есть идея в том, чтобы сделать для пользователя индивидуальную рекомендацию, показывая в начале списка те книги, которые в целом могли бы иметь высокий рейтинг.\n",
         "- Обязательно сохраняйте готовую модель и добавляйте ее в свой репозиторий, потому что файл с сохраненной моделью используется в тестах."
-      ],
-      "metadata": {
-        "id": "lI1qVeU4aZGE"
-      }
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "eFJYYNSfGyQ-"
+      },
       "source": [
         "## Куда сдавать?\n",
         "\n",
@@ -2555,17 +2544,11 @@
         "> Будьте внимательны! На гитхабе в ветке `main` обновился файл `pyad.yml`.\n",
         "\n",
         "**Устная защита работ не требуется, но вам могут быть заданы вопросы прямо в вашем пул-реквесте!**"
-      ],
-      "metadata": {
-        "id": "eFJYYNSfGyQ-"
-      }
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "df_books = books.drop(columns=['Image-URL-S', 'Image-URL-M', 'Image-URL-L'])\n",
-        "df_books"
-      ],
+      "execution_count": 13,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -2574,53 +2557,13 @@
         "id": "8lB751Enl7Ue",
         "outputId": "657a7c15-d59b-498b-dd5d-7d7d95c93854"
       },
-      "execution_count": 13,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
-            "text/plain": [
-              "              ISBN                                         Book-Title  \\\n",
-              "0       0195153448                                Classical Mythology   \n",
-              "1       0002005018                                       Clara Callan   \n",
-              "2       0060973129                               Decision in Normandy   \n",
-              "3       0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
-              "4       0393045218                             The Mummies of Urumchi   \n",
-              "...            ...                                                ...   \n",
-              "271355  0440400988                         There's a Bat in Bunk Five   \n",
-              "271356  0525447644                            From One to One Hundred   \n",
-              "271357  006008667X  Lily Dale : The True Story of the Town that Ta...   \n",
-              "271358  0192126040                        Republic (World's Classics)   \n",
-              "271359  0767409752  A Guided Tour of Rene Descartes' Meditations o...   \n",
-              "\n",
-              "                 Book-Author Year-Of-Publication  \\\n",
-              "0         Mark P. O. Morford                2002   \n",
-              "1       Richard Bruce Wright                2001   \n",
-              "2               Carlo D'Este                1991   \n",
-              "3           Gina Bari Kolata                1999   \n",
-              "4            E. J. W. Barber                1999   \n",
-              "...                      ...                 ...   \n",
-              "271355        Paula Danziger                1988   \n",
-              "271356            Teri Sloat                1991   \n",
-              "271357      Christine Wicker                2004   \n",
-              "271358                 Plato                1996   \n",
-              "271359   Christopher  Biffle                2000   \n",
-              "\n",
-              "                                               Publisher  \n",
-              "0                                Oxford University Press  \n",
-              "1                                  HarperFlamingo Canada  \n",
-              "2                                        HarperPerennial  \n",
-              "3                                   Farrar Straus Giroux  \n",
-              "4                             W. W. Norton &amp; Company  \n",
-              "...                                                  ...  \n",
-              "271355                   Random House Childrens Pub (Mm)  \n",
-              "271356                                      Dutton Books  \n",
-              "271357                                HarperSanFrancisco  \n",
-              "271358                           Oxford University Press  \n",
-              "271359  McGraw-Hill Humanities/Social Sciences/Languages  \n",
-              "\n",
-              "[271360 rows x 5 columns]"
-            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df_books"
+            },
             "text/html": [
               "\n",
               "  <div id=\"df-25213d89-15f5-4c2b-9bea-ddd0d2d04921\" class=\"colab-df-container\">\n",
@@ -2951,34 +2894,6 @@
               "    </div>\n",
               "  </div>\n"
             ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df_books"
-            }
-          },
-          "metadata": {},
-          "execution_count": 13
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df_books.dropna()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 424
-        },
-        "id": "5Z2thdUKmWaE",
-        "outputId": "b6fa2d86-353f-4375-c6f3-62122d26f546"
-      },
-      "execution_count": 14,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
             "text/plain": [
               "              ISBN                                         Book-Title  \\\n",
               "0       0195153448                                Classical Mythology   \n",
@@ -3019,17 +2934,45 @@
               "271358                           Oxford University Press  \n",
               "271359  McGraw-Hill Humanities/Social Sciences/Languages  \n",
               "\n",
-              "[271356 rows x 5 columns]"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-fbfac384-fc52-45f9-9657-b751adfcd665\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
+              "[271360 rows x 5 columns]"
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df_books = books.drop(columns=['Image-URL-S', 'Image-URL-M', 'Image-URL-L'])\n",
+        "df_books"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "5Z2thdUKmWaE",
+        "outputId": "b6fa2d86-353f-4375-c6f3-62122d26f546"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-fbfac384-fc52-45f9-9657-b751adfcd665\" 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",
@@ -3351,21 +3294,61 @@
               "    </div>\n",
               "  </div>\n"
             ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe"
-            }
+            "text/plain": [
+              "              ISBN                                         Book-Title  \\\n",
+              "0       0195153448                                Classical Mythology   \n",
+              "1       0002005018                                       Clara Callan   \n",
+              "2       0060973129                               Decision in Normandy   \n",
+              "3       0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
+              "4       0393045218                             The Mummies of Urumchi   \n",
+              "...            ...                                                ...   \n",
+              "271355  0440400988                         There's a Bat in Bunk Five   \n",
+              "271356  0525447644                            From One to One Hundred   \n",
+              "271357  006008667X  Lily Dale : The True Story of the Town that Ta...   \n",
+              "271358  0192126040                        Republic (World's Classics)   \n",
+              "271359  0767409752  A Guided Tour of Rene Descartes' Meditations o...   \n",
+              "\n",
+              "                 Book-Author Year-Of-Publication  \\\n",
+              "0         Mark P. O. Morford                2002   \n",
+              "1       Richard Bruce Wright                2001   \n",
+              "2               Carlo D'Este                1991   \n",
+              "3           Gina Bari Kolata                1999   \n",
+              "4            E. J. W. Barber                1999   \n",
+              "...                      ...                 ...   \n",
+              "271355        Paula Danziger                1988   \n",
+              "271356            Teri Sloat                1991   \n",
+              "271357      Christine Wicker                2004   \n",
+              "271358                 Plato                1996   \n",
+              "271359   Christopher  Biffle                2000   \n",
+              "\n",
+              "                                               Publisher  \n",
+              "0                                Oxford University Press  \n",
+              "1                                  HarperFlamingo Canada  \n",
+              "2                                        HarperPerennial  \n",
+              "3                                   Farrar Straus Giroux  \n",
+              "4                             W. W. Norton &amp; Company  \n",
+              "...                                                  ...  \n",
+              "271355                   Random House Childrens Pub (Mm)  \n",
+              "271356                                      Dutton Books  \n",
+              "271357                                HarperSanFrancisco  \n",
+              "271358                           Oxford University Press  \n",
+              "271359  McGraw-Hill Humanities/Social Sciences/Languages  \n",
+              "\n",
+              "[271356 rows x 5 columns]"
+            ]
           },
+          "execution_count": 14,
           "metadata": {},
-          "execution_count": 14
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "df_books.dropna()"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "df_ratings = ratings.drop(ratings[ratings[\"Book-Rating\"] == 0].index)\n",
-        "df_ratings"
-      ],
+      "execution_count": 15,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -3374,27 +3357,13 @@
         "id": "c6kY2vQAm096",
         "outputId": "10d79ef0-b68c-4bf5-8919-83be6759fc0e"
       },
-      "execution_count": 15,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
-            "text/plain": [
-              "         User-ID         ISBN  Book-Rating\n",
-              "1         276726   0155061224            5\n",
-              "3         276729   052165615X            3\n",
-              "4         276729   0521795028            6\n",
-              "6         276736   3257224281            8\n",
-              "7         276737   0600570967            6\n",
-              "...          ...          ...          ...\n",
-              "1149773   276704   0806917695            5\n",
-              "1149775   276704   1563526298            9\n",
-              "1149777   276709   0515107662           10\n",
-              "1149778   276721   0590442449           10\n",
-              "1149779   276723  05162443314            8\n",
-              "\n",
-              "[433671 rows x 3 columns]"
-            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df_ratings"
+            },
             "text/html": [
               "\n",
               "  <div id=\"df-6bf71caa-93dd-4c4f-b6f2-1079139bbc3d\" class=\"colab-df-container\">\n",
@@ -3701,25 +3670,36 @@
               "    </div>\n",
               "  </div>\n"
             ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df_ratings"
-            }
+            "text/plain": [
+              "         User-ID         ISBN  Book-Rating\n",
+              "1         276726   0155061224            5\n",
+              "3         276729   052165615X            3\n",
+              "4         276729   0521795028            6\n",
+              "6         276736   3257224281            8\n",
+              "7         276737   0600570967            6\n",
+              "...          ...          ...          ...\n",
+              "1149773   276704   0806917695            5\n",
+              "1149775   276704   1563526298            9\n",
+              "1149777   276709   0515107662           10\n",
+              "1149778   276721   0590442449           10\n",
+              "1149779   276723  05162443314            8\n",
+              "\n",
+              "[433671 rows x 3 columns]"
+            ]
           },
+          "execution_count": 15,
           "metadata": {},
-          "execution_count": 15
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "df_ratings = ratings.drop(ratings[ratings[\"Book-Rating\"] == 0].index)\n",
+        "df_ratings"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "user_counts = df_ratings['User-ID'].value_counts()\n",
-        "book_counts = df_ratings['ISBN'].value_counts()\n",
-        "df_ratings = df_ratings[df_ratings['User-ID'].isin(user_counts[user_counts > 1].index)]\n",
-        "df_ratings = df_ratings[df_ratings['ISBN'].isin(book_counts[book_counts > 1].index)]\n",
-        "df_ratings.info()"
-      ],
+      "execution_count": 16,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -3727,11 +3707,10 @@
         "id": "dWzc8uM-rhif",
         "outputId": "7c4cf503-f304-4172-8bf0-447475ce385d"
       },
-      "execution_count": 16,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "<class 'pandas.core.frame.DataFrame'>\n",
             "Index: 270604 entries, 16 to 1149775\n",
@@ -3745,36 +3724,40 @@
             "memory usage: 8.3+ MB\n"
           ]
         }
+      ],
+      "source": [
+        "user_counts = df_ratings['User-ID'].value_counts()\n",
+        "book_counts = df_ratings['ISBN'].value_counts()\n",
+        "df_ratings = df_ratings[df_ratings['User-ID'].isin(user_counts[user_counts > 1].index)]\n",
+        "df_ratings = df_ratings[df_ratings['ISBN'].isin(book_counts[book_counts > 1].index)]\n",
+        "df_ratings.info()"
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "FbNRCVUhVTwC"
+      },
       "source": [
         "Обучить SVD на следующих записях: у книги есть хотя бы одна оценка, пользователь оценил хотя бы одну книгу.\n",
         "МАЕ должно быть ниже 1.3 (для этого есть тест)\n",
         "Сохранить модель."
-      ],
-      "metadata": {
-        "id": "FbNRCVUhVTwC"
-      }
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "!pip install scikit-surprise"
-      ],
+      "execution_count": 17,
       "metadata": {
-        "id": "6zkVF7PGVYia",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
+        "id": "6zkVF7PGVYia",
         "outputId": "bdbbf1bf-dd39-43ba-cb43-999a4944f9d2"
       },
-      "execution_count": 17,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Collecting scikit-surprise\n",
             "  Downloading scikit_surprise-1.1.4.tar.gz (154 kB)\n",
@@ -3794,32 +3777,25 @@
             "Successfully installed scikit-surprise-1.1.4\n"
           ]
         }
+      ],
+      "source": [
+        "!pip install scikit-surprise"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "from surprise import SVD, Dataset, Reader\n",
-        "from surprise.model_selection import cross_validate, train_test_split\n",
-        "\n",
-        "algorithm = SVD()\n",
-        "reader = Reader(rating_scale=(0,11))\n",
-        "data = Dataset.load_from_df(df_ratings, reader)\n",
-        "\n",
-        "cross_validate(algorithm, data, cv=5, verbose=True, measures=['RMSE', 'MAE'])"
-      ],
+      "execution_count": 18,
       "metadata": {
-        "id": "MhlK014gVUNF",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
+        "id": "MhlK014gVUNF",
         "outputId": "f180e366-a057-41c6-f072-3acbd288caad"
       },
-      "execution_count": 18,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Evaluating RMSE, MAE of algorithm SVD on 5 split(s).\n",
             "\n",
@@ -3831,7 +3807,6 @@
           ]
         },
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "{'test_rmse': array([1.61430595, 1.61580874, 1.61528323, 1.62966161, 1.62375189]),\n",
@@ -3848,20 +3823,25 @@
               "  0.6280591487884521)}"
             ]
           },
+          "execution_count": 18,
           "metadata": {},
-          "execution_count": 18
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "from surprise import SVD, Dataset, Reader\n",
+        "from surprise.model_selection import cross_validate, train_test_split\n",
+        "\n",
+        "algorithm = SVD()\n",
+        "reader = Reader(rating_scale=(0,11))\n",
+        "data = Dataset.load_from_df(df_ratings, reader)\n",
+        "\n",
+        "cross_validate(algorithm, data, cv=5, verbose=True, measures=['RMSE', 'MAE'])"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "from surprise import accuracy\n",
-        "\n",
-        "trainset, test_set = train_test_split(data, test_size=0.2)\n",
-        "\n",
-        "algorithm.fit(trainset)"
-      ],
+      "execution_count": 19,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -3869,30 +3849,29 @@
         "id": "RSzIkMQSZxt3",
         "outputId": "a508ee36-d045-4799-ff7d-2748e7546835"
       },
-      "execution_count": 19,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "<surprise.prediction_algorithms.matrix_factorization.SVD at 0x7f4d25a90bd0>"
             ]
           },
+          "execution_count": 19,
           "metadata": {},
-          "execution_count": 19
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "from surprise import accuracy\n",
+        "\n",
+        "trainset, test_set = train_test_split(data, test_size=0.2)\n",
+        "\n",
+        "algorithm.fit(trainset)"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "predictions = algorithm.test(test_set)\n",
-        "\n",
-        "rmse = accuracy.rmse(predictions)\n",
-        "mae = accuracy.mae(predictions)\n",
-        "rmse\n",
-        "mae"
-      ],
+      "execution_count": 20,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -3900,55 +3879,52 @@
         "id": "NHPOXpUTahhn",
         "outputId": "dc427b49-8fe1-418c-fcca-9ddb55eadce7"
       },
-      "execution_count": 20,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "RMSE: 1.6188\n",
             "MAE:  1.2537\n"
           ]
         },
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "1.253712503593312"
             ]
           },
+          "execution_count": 20,
           "metadata": {},
-          "execution_count": 20
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "predictions = algorithm.test(test_set)\n",
+        "\n",
+        "rmse = accuracy.rmse(predictions)\n",
+        "mae = accuracy.mae(predictions)\n",
+        "rmse\n",
+        "mae"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "id": "omOh0P2aYK0k"
+      },
+      "outputs": [],
       "source": [
         "import pickle\n",
         "\n",
         "with open('svd_model.pkl', 'wb') as f:\n",
         "  pickle.dump(algorithm, f)"
-      ],
-      "metadata": {
-        "id": "omOh0P2aYK0k"
-      },
-      "execution_count": 21,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "with open('svd_model.pkl', 'rb') as f:\n",
-        "    loaded_model = pickle.load(f)\n",
-        "\n",
-        "predictions = loaded_model.test(test_set)\n",
-        "\n",
-        "rmse = accuracy.rmse(predictions)\n",
-        "mae = accuracy.mae(predictions)\n",
-        "rmse\n",
-        "mae"
-      ],
+      "execution_count": 22,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -3956,45 +3932,52 @@
         "id": "aJdzgb2weorD",
         "outputId": "e76d3d8b-7adc-4487-91e8-cc20e993e42f"
       },
-      "execution_count": 22,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "RMSE: 1.6188\n",
             "MAE:  1.2537\n"
           ]
         },
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "1.253712503593312"
             ]
           },
+          "execution_count": 22,
           "metadata": {},
-          "execution_count": 22
+          "output_type": "execute_result"
         }
-      ]
+      ],
+      "source": [
+        "with open('svd_model.pkl', 'rb') as f:\n",
+        "    loaded_model = pickle.load(f)\n",
+        "\n",
+        "predictions = loaded_model.test(test_set)\n",
+        "\n",
+        "rmse = accuracy.rmse(predictions)\n",
+        "mae = accuracy.mae(predictions)\n",
+        "rmse\n",
+        "mae"
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "xMs130FefLL1"
+      },
       "source": [
         "Научить линейную регрессию (SGDRegressor) предсказывать средний рейтинг книги по следующим столбцам: автор, издатель, год издания, векторизованное название (для векторизации можно использовать tf-idf, word2vec или более сложный алгоритм. Для достижения требуемой тестом точности достаточно будет tf-idf. Собрав полностью датасет, не забудьте нормализовать данные (StandardScaler).\n",
         "МАЕ должно быть ниже 1.5 (для этого есть тест)\n",
         "Сохранить модель."
-      ],
-      "metadata": {
-        "id": "xMs130FefLL1"
-      }
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "df_mean_ratings = df_ratings.drop(columns=['User-ID']).groupby(['ISBN']).mean()\n",
-        "df_mean_ratings"
-      ],
+      "execution_count": 23,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -4003,28 +3986,14 @@
         "id": "JwDYAFfdfLra",
         "outputId": "bf650ef8-d494-4ff1-d500-cd2f374e5d9d"
       },
-      "execution_count": 23,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
-            "text/plain": [
-              "                Book-Rating\n",
-              "ISBN                       \n",
-              "0 907 062 008     10.000000\n",
-              "0/330/25864/8      9.000000\n",
-              "00000000           8.000000\n",
-              "000000000          6.000000\n",
-              "0000000000         8.250000\n",
-              "...                     ...\n",
-              "O590446304         4.000000\n",
-              "O67174142X         7.333333\n",
-              "O688088686         9.000000\n",
-              "O9088446X          7.500000\n",
-              "\\0432534220\\\"\"     6.000000\n",
-              "\n",
-              "[55766 rows x 1 columns]"
-            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df_mean_ratings\",\n  \"rows\": 55766,\n  \"fields\": [\n    {\n      \"column\": \"ISBN\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 55766,\n        \"samples\": [\n          \"075153157X\",\n          \"0380978822\",\n          \"8472233065\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2785406009888802,\n        \"min\": 1.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 1274,\n        \"samples\": [\n          7.764705882352941,\n          7.379310344827586,\n          8.275\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df_mean_ratings"
+            },
             "text/html": [
               "\n",
               "  <div id=\"df-87d9718a-5c10-4916-8dde-6b808b2d6f19\" class=\"colab-df-container\">\n",
@@ -4311,23 +4280,37 @@
               "    </div>\n",
               "  </div>\n"
             ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df_mean_ratings",
-              "summary": "{\n  \"name\": \"df_mean_ratings\",\n  \"rows\": 55766,\n  \"fields\": [\n    {\n      \"column\": \"ISBN\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 55766,\n        \"samples\": [\n          \"075153157X\",\n          \"0380978822\",\n          \"8472233065\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2785406009888802,\n        \"min\": 1.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 1274,\n        \"samples\": [\n          7.764705882352941,\n          7.379310344827586,\n          8.275\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
+            "text/plain": [
+              "                Book-Rating\n",
+              "ISBN                       \n",
+              "0 907 062 008     10.000000\n",
+              "0/330/25864/8      9.000000\n",
+              "00000000           8.000000\n",
+              "000000000          6.000000\n",
+              "0000000000         8.250000\n",
+              "...                     ...\n",
+              "O590446304         4.000000\n",
+              "O67174142X         7.333333\n",
+              "O688088686         9.000000\n",
+              "O9088446X          7.500000\n",
+              "\\0432534220\\\"\"     6.000000\n",
+              "\n",
+              "[55766 rows x 1 columns]"
+            ]
           },
+          "execution_count": 23,
           "metadata": {},
-          "execution_count": 23
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "df_mean_ratings = df_ratings.drop(columns=['User-ID']).groupby(['ISBN']).mean()\n",
+        "df_mean_ratings"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "df_merged = pd.merge(df_books, df_mean_ratings, on='ISBN')\n",
-        "df_merged.dropna()"
-      ],
+      "execution_count": 24,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -4336,53 +4319,13 @@
         "id": "Jr2z-FknkK4B",
         "outputId": "5bc0ca37-f041-49f4-b0ec-83bfe59ff166"
       },
-      "execution_count": 24,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
-            "text/plain": [
-              "             ISBN                                         Book-Title  \\\n",
-              "0      0002005018                                       Clara Callan   \n",
-              "1      0060973129                               Decision in Normandy   \n",
-              "2      0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
-              "3      0399135782                             The Kitchen God's Wife   \n",
-              "4      1558746218  A Second Chicken Soup for the Woman's Soul (Ch...   \n",
-              "...           ...                                                ...   \n",
-              "50039  0141308206     Summer Reading Is Killing Me! (Time Warp Trio)   \n",
-              "50040  0130807427  Behavior Modification--What It Is and How to D...   \n",
-              "50041  0380766485                              Captives of the Night   \n",
-              "50042  1572492317                       Echoes of the Falling Spring   \n",
-              "50043  0965018512  Secret Windows: Essays and Fiction on the Craf...   \n",
-              "\n",
-              "                Book-Author Year-Of-Publication                   Publisher  \\\n",
-              "0      Richard Bruce Wright                2001       HarperFlamingo Canada   \n",
-              "1              Carlo D'Este                1991             HarperPerennial   \n",
-              "2          Gina Bari Kolata                1999        Farrar Straus Giroux   \n",
-              "3                   Amy Tan                1991            Putnam Pub Group   \n",
-              "4             Jack Canfield                1998       Health Communications   \n",
-              "...                     ...                 ...                         ...   \n",
-              "50039          Jon Scieszka                2000                Puffin Books   \n",
-              "50040          Garry Martin                1998               Prentice Hall   \n",
-              "50041         Loretta Chase                1994                        Avon   \n",
-              "50042            Dody Myers                2001           Burd Street Press   \n",
-              "50043          Stephen King                2000  Quality Paperback Bookclub   \n",
-              "\n",
-              "       Book-Rating  \n",
-              "0         7.500000  \n",
-              "1         7.500000  \n",
-              "2         7.833333  \n",
-              "3         8.285714  \n",
-              "4        10.000000  \n",
-              "...            ...  \n",
-              "50039     9.000000  \n",
-              "50040     5.500000  \n",
-              "50041     8.000000  \n",
-              "50042     8.500000  \n",
-              "50043     8.000000  \n",
-              "\n",
-              "[50044 rows x 6 columns]"
-            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df_merged\",\n  \"rows\": 50044,\n  \"fields\": [\n    {\n      \"column\": \"ISBN\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 50044,\n        \"samples\": [\n          \"1852306688\",\n          \"0140275657\",\n          \"0486256111\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 44752,\n        \"samples\": [\n          \"Sleep Tight (Onyx Book)\",\n          \"Savage Love\",\n          \"Girl Boy Etc.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Author\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18836,\n        \"samples\": [\n          \"David Maybury Lewis\",\n          \"Paul Mann\",\n          \"Alma Daniel\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year-Of-Publication\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 130,\n        \"samples\": [\n          1961,\n          1974,\n          1983\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Publisher\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3605,\n        \"samples\": [\n          \"Parabola Books\",\n          \"Reclam, Ditzingen\",\n          \"Windsor Peak Press\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2612453892589373,\n        \"min\": 1.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 1267,\n        \"samples\": [\n          5.7,\n          7.670329670329671,\n          6.578947368421052\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe"
+            },
             "text/html": [
               "\n",
               "  <div id=\"df-c716eea6-2df5-4994-84a9-002440513304\" class=\"colab-df-container\">\n",
@@ -4725,21 +4668,62 @@
               "    </div>\n",
               "  </div>\n"
             ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "summary": "{\n  \"name\": \"df_merged\",\n  \"rows\": 50044,\n  \"fields\": [\n    {\n      \"column\": \"ISBN\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 50044,\n        \"samples\": [\n          \"1852306688\",\n          \"0140275657\",\n          \"0486256111\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 44752,\n        \"samples\": [\n          \"Sleep Tight (Onyx Book)\",\n          \"Savage Love\",\n          \"Girl Boy Etc.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Author\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18836,\n        \"samples\": [\n          \"David Maybury Lewis\",\n          \"Paul Mann\",\n          \"Alma Daniel\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year-Of-Publication\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 130,\n        \"samples\": [\n          1961,\n          1974,\n          1983\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Publisher\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3605,\n        \"samples\": [\n          \"Parabola Books\",\n          \"Reclam, Ditzingen\",\n          \"Windsor Peak Press\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2612453892589373,\n        \"min\": 1.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 1267,\n        \"samples\": [\n          5.7,\n          7.670329670329671,\n          6.578947368421052\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
+            "text/plain": [
+              "             ISBN                                         Book-Title  \\\n",
+              "0      0002005018                                       Clara Callan   \n",
+              "1      0060973129                               Decision in Normandy   \n",
+              "2      0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
+              "3      0399135782                             The Kitchen God's Wife   \n",
+              "4      1558746218  A Second Chicken Soup for the Woman's Soul (Ch...   \n",
+              "...           ...                                                ...   \n",
+              "50039  0141308206     Summer Reading Is Killing Me! (Time Warp Trio)   \n",
+              "50040  0130807427  Behavior Modification--What It Is and How to D...   \n",
+              "50041  0380766485                              Captives of the Night   \n",
+              "50042  1572492317                       Echoes of the Falling Spring   \n",
+              "50043  0965018512  Secret Windows: Essays and Fiction on the Craf...   \n",
+              "\n",
+              "                Book-Author Year-Of-Publication                   Publisher  \\\n",
+              "0      Richard Bruce Wright                2001       HarperFlamingo Canada   \n",
+              "1              Carlo D'Este                1991             HarperPerennial   \n",
+              "2          Gina Bari Kolata                1999        Farrar Straus Giroux   \n",
+              "3                   Amy Tan                1991            Putnam Pub Group   \n",
+              "4             Jack Canfield                1998       Health Communications   \n",
+              "...                     ...                 ...                         ...   \n",
+              "50039          Jon Scieszka                2000                Puffin Books   \n",
+              "50040          Garry Martin                1998               Prentice Hall   \n",
+              "50041         Loretta Chase                1994                        Avon   \n",
+              "50042            Dody Myers                2001           Burd Street Press   \n",
+              "50043          Stephen King                2000  Quality Paperback Bookclub   \n",
+              "\n",
+              "       Book-Rating  \n",
+              "0         7.500000  \n",
+              "1         7.500000  \n",
+              "2         7.833333  \n",
+              "3         8.285714  \n",
+              "4        10.000000  \n",
+              "...            ...  \n",
+              "50039     9.000000  \n",
+              "50040     5.500000  \n",
+              "50041     8.000000  \n",
+              "50042     8.500000  \n",
+              "50043     8.000000  \n",
+              "\n",
+              "[50044 rows x 6 columns]"
+            ]
           },
+          "execution_count": 24,
           "metadata": {},
-          "execution_count": 24
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "df_merged = pd.merge(df_books, df_mean_ratings, on='ISBN')\n",
+        "df_merged.dropna()"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "!pip install nltk pymorphy2"
-      ],
+      "execution_count": 25,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -4747,11 +4731,10 @@
         "id": "McdtpMYcpXGM",
         "outputId": "b9631845-d139-4886-c748-4db5433319da"
       },
-      "execution_count": 25,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Requirement already satisfied: nltk in /usr/local/lib/python3.11/dist-packages (3.9.1)\n",
             "Collecting pymorphy2\n",
@@ -4781,26 +4764,14 @@
             "Successfully installed dawg-python-0.7.2 docopt-0.6.2 pymorphy2-0.9.1 pymorphy2-dicts-ru-2.4.417127.4579844\n"
           ]
         }
+      ],
+      "source": [
+        "!pip install nltk pymorphy2"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "import nltk\n",
-        "from nltk.tokenize import word_tokenize\n",
-        "from pymorphy2 import MorphAnalyzer\n",
-        "import re\n",
-        "\n",
-        "nltk.download('punkt_tab')\n",
-        "\n",
-        "def preprocess(text):\n",
-        "  # morph = MorphAnalyzer()\n",
-        "\n",
-        "  tokens = word_tokenize(text)\n",
-        "  cleaned_tokens = [token.lower() for token in tokens if re.match(r'^\\w+$', token)]\n",
-        "  # lemmatized_tokens = [morph.parse(cleaned_token)[0].normal_form for cleaned_token in cleaned_tokens]\n",
-        "  return cleaned_tokens"
-      ],
+      "execution_count": 26,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -4808,25 +4779,36 @@
         "id": "jbN-hPPZo6XW",
         "outputId": "7a114c55-9f3a-4d37-d02d-1a25639c4fd1"
       },
-      "execution_count": 26,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "[nltk_data] Downloading package punkt_tab to /root/nltk_data...\n",
             "[nltk_data]   Unzipping tokenizers/punkt_tab.zip.\n"
           ]
         }
+      ],
+      "source": [
+        "import nltk\n",
+        "from nltk.tokenize import word_tokenize\n",
+        "from pymorphy2 import MorphAnalyzer\n",
+        "import re\n",
+        "\n",
+        "nltk.download('punkt_tab')\n",
+        "\n",
+        "def preprocess(text):\n",
+        "  # morph = MorphAnalyzer()\n",
+        "\n",
+        "  tokens = word_tokenize(text)\n",
+        "  cleaned_tokens = [token.lower() for token in tokens if re.match(r'^\\w+$', token)]\n",
+        "  # lemmatized_tokens = [morph.parse(cleaned_token)[0].normal_form for cleaned_token in cleaned_tokens]\n",
+        "  return cleaned_tokens"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "df_merged_tokenized = df_merged\n",
-        "df_merged_tokenized['Book-Title-Tokenized'] = df_merged['Book-Title'].apply(preprocess)\n",
-        "df_merged_tokenized"
-      ],
+      "execution_count": 27,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -4835,61 +4817,22 @@
         "id": "eUwTu7M1snCo",
         "outputId": "4fee5302-289e-453f-e537-fcd030d892cd"
       },
-      "execution_count": 27,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
-            "text/plain": [
-              "             ISBN                                         Book-Title  \\\n",
-              "0      0002005018                                       Clara Callan   \n",
-              "1      0060973129                               Decision in Normandy   \n",
-              "2      0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
-              "3      0399135782                             The Kitchen God's Wife   \n",
-              "4      1558746218  A Second Chicken Soup for the Woman's Soul (Ch...   \n",
-              "...           ...                                                ...   \n",
-              "50039  0141308206     Summer Reading Is Killing Me! (Time Warp Trio)   \n",
-              "50040  0130807427  Behavior Modification--What It Is and How to D...   \n",
-              "50041  0380766485                              Captives of the Night   \n",
-              "50042  1572492317                       Echoes of the Falling Spring   \n",
-              "50043  0965018512  Secret Windows: Essays and Fiction on the Craf...   \n",
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df_merged\",\n  \"rows\": 50044,\n  \"fields\": [\n    {\n      \"column\": \"ISBN\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 50044,\n        \"samples\": [\n          \"1852306688\",\n          \"0140275657\",\n          \"0486256111\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 44752,\n        \"samples\": [\n          \"Sleep Tight (Onyx Book)\",\n          \"Savage Love\",\n          \"Girl Boy Etc.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Author\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18836,\n        \"samples\": [\n          \"David Maybury Lewis\",\n          \"Paul Mann\",\n          \"Alma Daniel\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year-Of-Publication\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 130,\n        \"samples\": [\n          1961,\n          1974,\n          1983\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Publisher\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3605,\n        \"samples\": [\n          \"Parabola Books\",\n          \"Reclam, Ditzingen\",\n          \"Windsor Peak Press\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2612453892589373,\n        \"min\": 1.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 1267,\n        \"samples\": [\n          5.7,\n          7.670329670329671,\n          6.578947368421052\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Title-Tokenized\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df_merged"
+            },
+            "text/html": [
               "\n",
-              "                Book-Author Year-Of-Publication                   Publisher  \\\n",
-              "0      Richard Bruce Wright                2001       HarperFlamingo Canada   \n",
-              "1              Carlo D'Este                1991             HarperPerennial   \n",
-              "2          Gina Bari Kolata                1999        Farrar Straus Giroux   \n",
-              "3                   Amy Tan                1991            Putnam Pub Group   \n",
-              "4             Jack Canfield                1998       Health Communications   \n",
-              "...                     ...                 ...                         ...   \n",
-              "50039          Jon Scieszka                2000                Puffin Books   \n",
-              "50040          Garry Martin                1998               Prentice Hall   \n",
-              "50041         Loretta Chase                1994                        Avon   \n",
-              "50042            Dody Myers                2001           Burd Street Press   \n",
-              "50043          Stephen King                2000  Quality Paperback Bookclub   \n",
-              "\n",
-              "       Book-Rating                               Book-Title-Tokenized  \n",
-              "0         7.500000                                    [clara, callan]  \n",
-              "1         7.500000                           [decision, in, normandy]  \n",
-              "2         7.833333  [flu, the, story, of, the, great, influenza, p...  \n",
-              "3         8.285714                          [the, kitchen, god, wife]  \n",
-              "4        10.000000  [a, second, chicken, soup, for, the, woman, so...  \n",
-              "...            ...                                                ...  \n",
-              "50039     9.000000  [summer, reading, is, killing, me, time, warp,...  \n",
-              "50040     5.500000  [behavior, modification, what, it, is, and, ho...  \n",
-              "50041     8.000000                         [captives, of, the, night]  \n",
-              "50042     8.500000                 [echoes, of, the, falling, spring]  \n",
-              "50043     8.000000  [secret, windows, essays, and, fiction, on, th...  \n",
-              "\n",
-              "[50044 rows x 7 columns]"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-aa0cca7c-b671-4b22-b59d-9e488117bcdf\" class=\"colab-df-container\">\n",
-              "    <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
+              "  <div id=\"df-aa0cca7c-b671-4b22-b59d-9e488117bcdf\" 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",
@@ -5236,22 +5179,63 @@
               "    </div>\n",
               "  </div>\n"
             ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df_merged",
-              "summary": "{\n  \"name\": \"df_merged\",\n  \"rows\": 50044,\n  \"fields\": [\n    {\n      \"column\": \"ISBN\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 50044,\n        \"samples\": [\n          \"1852306688\",\n          \"0140275657\",\n          \"0486256111\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 44752,\n        \"samples\": [\n          \"Sleep Tight (Onyx Book)\",\n          \"Savage Love\",\n          \"Girl Boy Etc.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Author\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18836,\n        \"samples\": [\n          \"David Maybury Lewis\",\n          \"Paul Mann\",\n          \"Alma Daniel\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year-Of-Publication\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 130,\n        \"samples\": [\n          1961,\n          1974,\n          1983\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Publisher\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3605,\n        \"samples\": [\n          \"Parabola Books\",\n          \"Reclam, Ditzingen\",\n          \"Windsor Peak Press\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2612453892589373,\n        \"min\": 1.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 1267,\n        \"samples\": [\n          5.7,\n          7.670329670329671,\n          6.578947368421052\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Title-Tokenized\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
+            "text/plain": [
+              "             ISBN                                         Book-Title  \\\n",
+              "0      0002005018                                       Clara Callan   \n",
+              "1      0060973129                               Decision in Normandy   \n",
+              "2      0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
+              "3      0399135782                             The Kitchen God's Wife   \n",
+              "4      1558746218  A Second Chicken Soup for the Woman's Soul (Ch...   \n",
+              "...           ...                                                ...   \n",
+              "50039  0141308206     Summer Reading Is Killing Me! (Time Warp Trio)   \n",
+              "50040  0130807427  Behavior Modification--What It Is and How to D...   \n",
+              "50041  0380766485                              Captives of the Night   \n",
+              "50042  1572492317                       Echoes of the Falling Spring   \n",
+              "50043  0965018512  Secret Windows: Essays and Fiction on the Craf...   \n",
+              "\n",
+              "                Book-Author Year-Of-Publication                   Publisher  \\\n",
+              "0      Richard Bruce Wright                2001       HarperFlamingo Canada   \n",
+              "1              Carlo D'Este                1991             HarperPerennial   \n",
+              "2          Gina Bari Kolata                1999        Farrar Straus Giroux   \n",
+              "3                   Amy Tan                1991            Putnam Pub Group   \n",
+              "4             Jack Canfield                1998       Health Communications   \n",
+              "...                     ...                 ...                         ...   \n",
+              "50039          Jon Scieszka                2000                Puffin Books   \n",
+              "50040          Garry Martin                1998               Prentice Hall   \n",
+              "50041         Loretta Chase                1994                        Avon   \n",
+              "50042            Dody Myers                2001           Burd Street Press   \n",
+              "50043          Stephen King                2000  Quality Paperback Bookclub   \n",
+              "\n",
+              "       Book-Rating                               Book-Title-Tokenized  \n",
+              "0         7.500000                                    [clara, callan]  \n",
+              "1         7.500000                           [decision, in, normandy]  \n",
+              "2         7.833333  [flu, the, story, of, the, great, influenza, p...  \n",
+              "3         8.285714                          [the, kitchen, god, wife]  \n",
+              "4        10.000000  [a, second, chicken, soup, for, the, woman, so...  \n",
+              "...            ...                                                ...  \n",
+              "50039     9.000000  [summer, reading, is, killing, me, time, warp,...  \n",
+              "50040     5.500000  [behavior, modification, what, it, is, and, ho...  \n",
+              "50041     8.000000                         [captives, of, the, night]  \n",
+              "50042     8.500000                 [echoes, of, the, falling, spring]  \n",
+              "50043     8.000000  [secret, windows, essays, and, fiction, on, th...  \n",
+              "\n",
+              "[50044 rows x 7 columns]"
+            ]
           },
+          "execution_count": 27,
           "metadata": {},
-          "execution_count": 27
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "df_merged_tokenized = df_merged\n",
+        "df_merged_tokenized['Book-Title-Tokenized'] = df_merged['Book-Title'].apply(preprocess)\n",
+        "df_merged_tokenized"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "!gdown 1Dod76AXB95ruTYeIMvuvgWzPNCOWbzr2"
-      ],
+      "execution_count": 28,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -5259,11 +5243,10 @@
         "id": "RwJmBuQg1TbR",
         "outputId": "69962bdf-386d-4df4-8842-03f505d32324"
       },
-      "execution_count": 28,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Downloading...\n",
             "From (original): https://drive.google.com/uc?id=1Dod76AXB95ruTYeIMvuvgWzPNCOWbzr2\n",
@@ -5272,29 +5255,29 @@
             "100% 1.65G/1.65G [00:22<00:00, 72.8MB/s]\n"
           ]
         }
+      ],
+      "source": [
+        "!gdown 1Dod76AXB95ruTYeIMvuvgWzPNCOWbzr2"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "id": "b8kVGGZqyOP3"
+      },
+      "outputs": [],
       "source": [
         "from gensim.models import Word2Vec\n",
         "from gensim.models import KeyedVectors\n",
         "\n",
         "model_path = '/content/GoogleNews-vectors-negative300.bin.gz'\n",
         "model = KeyedVectors.load_word2vec_format(model_path, binary=True)"
-      ],
-      "metadata": {
-        "id": "b8kVGGZqyOP3"
-      },
-      "execution_count": 29,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "word_vector = model['clara']\n",
-        "print(f\"Вектор слова 'clara':\\n{word_vector}\")"
-      ],
+      "execution_count": 30,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -5303,11 +5286,10 @@
         "id": "h8GsM9nF5QBY",
         "outputId": "72169b19-4ee7-4c58-d7d0-23dfc03811d8"
       },
-      "execution_count": 30,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Вектор слова 'clara':\n",
             "[-1.22070312e-02 -7.86132812e-02 -2.80761719e-02  1.33789062e-01\n",
@@ -5387,10 +5369,19 @@
             " -9.13085938e-02 -1.01562500e-01 -1.27563477e-02  1.31835938e-01]\n"
           ]
         }
+      ],
+      "source": [
+        "word_vector = model['clara']\n",
+        "print(f\"Вектор слова 'clara':\\n{word_vector}\")"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "id": "zO2JVaEl6Y_2"
+      },
+      "outputs": [],
       "source": [
         "def vectorize_title_word2vec(tokens):\n",
         "    vectors = []\n",
@@ -5404,27 +5395,27 @@
         "        return sum(vectors) / len(vectors)\n",
         "    else:\n",
         "        return [0] * model.vector_size  # Вектор нулей, если нет подходящих слов\n"
-      ],
-      "metadata": {
-        "id": "zO2JVaEl6Y_2"
-      },
-      "execution_count": 31,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "df_vectorized_word2vec = df_merged_tokenized\n",
-        "df_vectorized_word2vec['Book-Title-Vectorized'] = df_vectorized_word2vec['Book-Title-Tokenized'].apply(vectorize_title_word2vec)"
-      ],
+      "execution_count": 32,
       "metadata": {
         "id": "ZSd83H4L6BMT"
       },
-      "execution_count": 32,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "df_vectorized_word2vec = df_merged_tokenized\n",
+        "df_vectorized_word2vec['Book-Title-Vectorized'] = df_vectorized_word2vec['Book-Title-Tokenized'].apply(vectorize_title_word2vec)"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "id": "7tbrrPI0BFvy"
+      },
+      "outputs": [],
       "source": [
         "from gensim.models.doc2vec import Doc2Vec, TaggedDocument\n",
         "\n",
@@ -5432,39 +5423,32 @@
         "\n",
         "# Обучение модели\n",
         "model_doc2vec = Doc2Vec(documents, vector_size=100, window=2, min_count=1, workers=4)\n"
-      ],
-      "metadata": {
-        "id": "7tbrrPI0BFvy"
-      },
-      "execution_count": 33,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
-      "source": [],
+      "execution_count": 33,
       "metadata": {
         "id": "8cZuGX9VB7ns"
       },
-      "execution_count": 33,
-      "outputs": []
+      "outputs": [],
+      "source": []
     },
     {
       "cell_type": "code",
-      "source": [
-        "df_vectorized_doc2vec = df_merged_tokenized\n",
-        "df_vectorized_doc2vec['Book-Title-Vectorized'] = df_vectorized_word2vec['Book-Title-Tokenized'].apply(lambda tokens: model_doc2vec.infer_vector(tokens))"
-      ],
+      "execution_count": 34,
       "metadata": {
         "id": "EP3rpU2JBvfs"
       },
-      "execution_count": 34,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "df_vectorized_doc2vec = df_merged_tokenized\n",
+        "df_vectorized_doc2vec['Book-Title-Vectorized'] = df_vectorized_word2vec['Book-Title-Tokenized'].apply(lambda tokens: model_doc2vec.infer_vector(tokens))"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "df_vectorized_doc2vec"
-      ],
+      "execution_count": 35,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -5473,66 +5457,14 @@
         "id": "LNTFghAXD52Z",
         "outputId": "dcb78bd7-7a34-4c12-bebb-ca193eb41bb0"
       },
-      "execution_count": 35,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
-            "text/plain": [
-              "             ISBN                                         Book-Title  \\\n",
-              "0      0002005018                                       Clara Callan   \n",
-              "1      0060973129                               Decision in Normandy   \n",
-              "2      0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
-              "3      0399135782                             The Kitchen God's Wife   \n",
-              "4      1558746218  A Second Chicken Soup for the Woman's Soul (Ch...   \n",
-              "...           ...                                                ...   \n",
-              "50039  0141308206     Summer Reading Is Killing Me! (Time Warp Trio)   \n",
-              "50040  0130807427  Behavior Modification--What It Is and How to D...   \n",
-              "50041  0380766485                              Captives of the Night   \n",
-              "50042  1572492317                       Echoes of the Falling Spring   \n",
-              "50043  0965018512  Secret Windows: Essays and Fiction on the Craf...   \n",
-              "\n",
-              "                Book-Author Year-Of-Publication                   Publisher  \\\n",
-              "0      Richard Bruce Wright                2001       HarperFlamingo Canada   \n",
-              "1              Carlo D'Este                1991             HarperPerennial   \n",
-              "2          Gina Bari Kolata                1999        Farrar Straus Giroux   \n",
-              "3                   Amy Tan                1991            Putnam Pub Group   \n",
-              "4             Jack Canfield                1998       Health Communications   \n",
-              "...                     ...                 ...                         ...   \n",
-              "50039          Jon Scieszka                2000                Puffin Books   \n",
-              "50040          Garry Martin                1998               Prentice Hall   \n",
-              "50041         Loretta Chase                1994                        Avon   \n",
-              "50042            Dody Myers                2001           Burd Street Press   \n",
-              "50043          Stephen King                2000  Quality Paperback Bookclub   \n",
-              "\n",
-              "       Book-Rating                               Book-Title-Tokenized  \\\n",
-              "0         7.500000                                    [clara, callan]   \n",
-              "1         7.500000                           [decision, in, normandy]   \n",
-              "2         7.833333  [flu, the, story, of, the, great, influenza, p...   \n",
-              "3         8.285714                          [the, kitchen, god, wife]   \n",
-              "4        10.000000  [a, second, chicken, soup, for, the, woman, so...   \n",
-              "...            ...                                                ...   \n",
-              "50039     9.000000  [summer, reading, is, killing, me, time, warp,...   \n",
-              "50040     5.500000  [behavior, modification, what, it, is, and, ho...   \n",
-              "50041     8.000000                         [captives, of, the, night]   \n",
-              "50042     8.500000                 [echoes, of, the, falling, spring]   \n",
-              "50043     8.000000  [secret, windows, essays, and, fiction, on, th...   \n",
-              "\n",
-              "                                   Book-Title-Vectorized  \n",
-              "0      [-0.0645337, 0.04740464, -0.003607908, -0.0369...  \n",
-              "1      [-0.06865264, 0.03474637, 0.0087239295, -0.031...  \n",
-              "2      [-0.050521825, 0.062116567, 0.013070498, -0.06...  \n",
-              "3      [-0.019539846, 0.021032456, -0.003599502, 0.01...  \n",
-              "4      [-0.016762892, -0.02298693, 0.009035798, 0.059...  \n",
-              "...                                                  ...  \n",
-              "50039  [-0.017692503, -0.033314988, -0.007695766, 0.0...  \n",
-              "50040  [-0.03236937, 0.0027971948, -0.015948668, -0.0...  \n",
-              "50041  [-0.039578293, 0.022045352, -0.0008171154, -0....  \n",
-              "50042  [-0.03933497, -0.010528634, 0.013040441, -0.01...  \n",
-              "50043  [0.01396761, 0.008222527, 0.12903251, -0.01238...  \n",
-              "\n",
-              "[50044 rows x 8 columns]"
-            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df_merged\",\n  \"rows\": 50044,\n  \"fields\": [\n    {\n      \"column\": \"ISBN\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 50044,\n        \"samples\": [\n          \"1852306688\",\n          \"0140275657\",\n          \"0486256111\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 44752,\n        \"samples\": [\n          \"Sleep Tight (Onyx Book)\",\n          \"Savage Love\",\n          \"Girl Boy Etc.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Author\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18836,\n        \"samples\": [\n          \"David Maybury Lewis\",\n          \"Paul Mann\",\n          \"Alma Daniel\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year-Of-Publication\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 130,\n        \"samples\": [\n          1961,\n          1974,\n          1983\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Publisher\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3605,\n        \"samples\": [\n          \"Parabola Books\",\n          \"Reclam, Ditzingen\",\n          \"Windsor Peak Press\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2612453892589373,\n        \"min\": 1.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 1267,\n        \"samples\": [\n          5.7,\n          7.670329670329671,\n          6.578947368421052\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Title-Tokenized\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Title-Vectorized\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df_merged"
+            },
             "text/html": [
               "\n",
               "  <div id=\"df-2738b0fd-e103-48e9-9786-876c4bed95e5\" class=\"colab-df-container\">\n",
@@ -5899,78 +5831,149 @@
               "    </div>\n",
               "  </div>\n"
             ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "variable_name": "df_merged",
-              "summary": "{\n  \"name\": \"df_merged\",\n  \"rows\": 50044,\n  \"fields\": [\n    {\n      \"column\": \"ISBN\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 50044,\n        \"samples\": [\n          \"1852306688\",\n          \"0140275657\",\n          \"0486256111\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 44752,\n        \"samples\": [\n          \"Sleep Tight (Onyx Book)\",\n          \"Savage Love\",\n          \"Girl Boy Etc.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Author\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18836,\n        \"samples\": [\n          \"David Maybury Lewis\",\n          \"Paul Mann\",\n          \"Alma Daniel\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year-Of-Publication\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 130,\n        \"samples\": [\n          1961,\n          1974,\n          1983\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Publisher\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3605,\n        \"samples\": [\n          \"Parabola Books\",\n          \"Reclam, Ditzingen\",\n          \"Windsor Peak Press\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.2612453892589373,\n        \"min\": 1.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 1267,\n        \"samples\": [\n          5.7,\n          7.670329670329671,\n          6.578947368421052\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Title-Tokenized\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Book-Title-Vectorized\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
-          },
-          "metadata": {},
-          "execution_count": 35
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df_vectorized_doc2vec.info()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "Z68hgxU7HU1K",
-        "outputId": "48377256-0dd5-4f24-cba6-fcbf5fb6ddc6"
-      },
-      "execution_count": 36,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "<class 'pandas.core.frame.DataFrame'>\n",
-            "RangeIndex: 50044 entries, 0 to 50043\n",
-            "Data columns (total 8 columns):\n",
-            " #   Column                 Non-Null Count  Dtype  \n",
-            "---  ------                 --------------  -----  \n",
-            " 0   ISBN                   50044 non-null  object \n",
-            " 1   Book-Title             50044 non-null  object \n",
-            " 2   Book-Author            50044 non-null  object \n",
-            " 3   Year-Of-Publication    50044 non-null  object \n",
-            " 4   Publisher              50044 non-null  object \n",
-            " 5   Book-Rating            50044 non-null  float64\n",
-            " 6   Book-Title-Tokenized   50044 non-null  object \n",
-            " 7   Book-Title-Vectorized  50044 non-null  object \n",
-            "dtypes: float64(1), object(7)\n",
-            "memory usage: 3.1+ MB\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df_vectorized_doc2vec['Year-Of-Publication'] = df_vectorized_doc2vec['Year-Of-Publication'].astype('int64')"
-      ],
-      "metadata": {
-        "id": "D_3gIVRkHYvO"
+            "text/plain": [
+              "             ISBN                                         Book-Title  \\\n",
+              "0      0002005018                                       Clara Callan   \n",
+              "1      0060973129                               Decision in Normandy   \n",
+              "2      0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
+              "3      0399135782                             The Kitchen God's Wife   \n",
+              "4      1558746218  A Second Chicken Soup for the Woman's Soul (Ch...   \n",
+              "...           ...                                                ...   \n",
+              "50039  0141308206     Summer Reading Is Killing Me! (Time Warp Trio)   \n",
+              "50040  0130807427  Behavior Modification--What It Is and How to D...   \n",
+              "50041  0380766485                              Captives of the Night   \n",
+              "50042  1572492317                       Echoes of the Falling Spring   \n",
+              "50043  0965018512  Secret Windows: Essays and Fiction on the Craf...   \n",
+              "\n",
+              "                Book-Author Year-Of-Publication                   Publisher  \\\n",
+              "0      Richard Bruce Wright                2001       HarperFlamingo Canada   \n",
+              "1              Carlo D'Este                1991             HarperPerennial   \n",
+              "2          Gina Bari Kolata                1999        Farrar Straus Giroux   \n",
+              "3                   Amy Tan                1991            Putnam Pub Group   \n",
+              "4             Jack Canfield                1998       Health Communications   \n",
+              "...                     ...                 ...                         ...   \n",
+              "50039          Jon Scieszka                2000                Puffin Books   \n",
+              "50040          Garry Martin                1998               Prentice Hall   \n",
+              "50041         Loretta Chase                1994                        Avon   \n",
+              "50042            Dody Myers                2001           Burd Street Press   \n",
+              "50043          Stephen King                2000  Quality Paperback Bookclub   \n",
+              "\n",
+              "       Book-Rating                               Book-Title-Tokenized  \\\n",
+              "0         7.500000                                    [clara, callan]   \n",
+              "1         7.500000                           [decision, in, normandy]   \n",
+              "2         7.833333  [flu, the, story, of, the, great, influenza, p...   \n",
+              "3         8.285714                          [the, kitchen, god, wife]   \n",
+              "4        10.000000  [a, second, chicken, soup, for, the, woman, so...   \n",
+              "...            ...                                                ...   \n",
+              "50039     9.000000  [summer, reading, is, killing, me, time, warp,...   \n",
+              "50040     5.500000  [behavior, modification, what, it, is, and, ho...   \n",
+              "50041     8.000000                         [captives, of, the, night]   \n",
+              "50042     8.500000                 [echoes, of, the, falling, spring]   \n",
+              "50043     8.000000  [secret, windows, essays, and, fiction, on, th...   \n",
+              "\n",
+              "                                   Book-Title-Vectorized  \n",
+              "0      [-0.0645337, 0.04740464, -0.003607908, -0.0369...  \n",
+              "1      [-0.06865264, 0.03474637, 0.0087239295, -0.031...  \n",
+              "2      [-0.050521825, 0.062116567, 0.013070498, -0.06...  \n",
+              "3      [-0.019539846, 0.021032456, -0.003599502, 0.01...  \n",
+              "4      [-0.016762892, -0.02298693, 0.009035798, 0.059...  \n",
+              "...                                                  ...  \n",
+              "50039  [-0.017692503, -0.033314988, -0.007695766, 0.0...  \n",
+              "50040  [-0.03236937, 0.0027971948, -0.015948668, -0.0...  \n",
+              "50041  [-0.039578293, 0.022045352, -0.0008171154, -0....  \n",
+              "50042  [-0.03933497, -0.010528634, 0.013040441, -0.01...  \n",
+              "50043  [0.01396761, 0.008222527, 0.12903251, -0.01238...  \n",
+              "\n",
+              "[50044 rows x 8 columns]"
+            ]
+          },
+          "execution_count": 35,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df_vectorized_doc2vec"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Z68hgxU7HU1K",
+        "outputId": "48377256-0dd5-4f24-cba6-fcbf5fb6ddc6"
       },
-      "execution_count": 37,
-      "outputs": []
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 50044 entries, 0 to 50043\n",
+            "Data columns (total 8 columns):\n",
+            " #   Column                 Non-Null Count  Dtype  \n",
+            "---  ------                 --------------  -----  \n",
+            " 0   ISBN                   50044 non-null  object \n",
+            " 1   Book-Title             50044 non-null  object \n",
+            " 2   Book-Author            50044 non-null  object \n",
+            " 3   Year-Of-Publication    50044 non-null  object \n",
+            " 4   Publisher              50044 non-null  object \n",
+            " 5   Book-Rating            50044 non-null  float64\n",
+            " 6   Book-Title-Tokenized   50044 non-null  object \n",
+            " 7   Book-Title-Vectorized  50044 non-null  object \n",
+            "dtypes: float64(1), object(7)\n",
+            "memory usage: 3.1+ MB\n"
+          ]
+        }
+      ],
+      "source": [
+        "df_vectorized_doc2vec.info()"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "id": "D_3gIVRkHYvO"
+      },
+      "outputs": [],
       "source": [
-        "df = df_vectorized_doc2vec\n"
-      ],
+        "df_vectorized_doc2vec['Year-Of-Publication'] = df_vectorized_doc2vec['Year-Of-Publication'].astype('int64')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
       "metadata": {
         "id": "J9OdyKIXH05s"
       },
-      "execution_count": 38,
-      "outputs": []
+      "outputs": [],
+      "source": [
+        "df = df_vectorized_doc2vec\n"
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 47,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1HlNFRbOEdbp",
+        "outputId": "81baffdf-adea-44ea-9000-f3b2953af477"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "MAE: 0.9884\n"
+          ]
+        }
+      ],
       "source": [
         "from sklearn.model_selection import train_test_split\n",
         "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
@@ -6008,38 +6011,38 @@
         "y_pred = model.predict(X_test)\n",
         "mae_score = mean_absolute_error(y_test, y_pred)\n",
         "print(f\"MAE: {mae_score:.4f}\")"
-      ],
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "metadata": {
+        "id": "edwTzbTsBpYv"
+      },
+      "outputs": [],
+      "source": [
+        "df = df_vectorized_doc2vec"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 48,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "1HlNFRbOEdbp",
-        "outputId": "81baffdf-adea-44ea-9000-f3b2953af477"
+        "id": "DwXuSvfwBoRX",
+        "outputId": "9ee50855-a137-42cb-f3b6-c02c1920e721"
       },
-      "execution_count": 47,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
-            "MAE: 0.9884\n"
+            "MAE: 0.9700\n"
           ]
         }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "df = df_vectorized_doc2vec"
       ],
-      "metadata": {
-        "id": "edwTzbTsBpYv"
-      },
-      "execution_count": 40,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
       "source": [
         "from sklearn.model_selection import train_test_split\n",
         "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
@@ -6077,40 +6080,28 @@
         "y_pred = model.predict(X_test)\n",
         "mae_score = mean_absolute_error(y_test, y_pred)\n",
         "print(f\"MAE: {mae_score:.4f}\")"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "DwXuSvfwBoRX",
-        "outputId": "9ee50855-a137-42cb-f3b6-c02c1920e721"
-      },
-      "execution_count": 48,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "MAE: 0.9700\n"
-          ]
-        }
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {
+        "id": "IpIXIjOr7qNc"
+      },
+      "outputs": [],
       "source": [
         "import pickle\n",
         "with open('linreg_model.pkl', 'wb') as f:\n",
         "    pickle.dump(model, f)"
-      ],
-      "metadata": {
-        "id": "IpIXIjOr7qNc"
-      },
-      "execution_count": 43,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 50,
+      "metadata": {
+        "id": "gdP6-kYbADkX"
+      },
+      "outputs": [],
       "source": [
         "with open('tfidf_vectorizer.pkl', 'wb') as f:\n",
         "    pickle.dump(tfidf, f)\n",
@@ -6120,46 +6111,51 @@
         "    pickle.dump(le_Publisher, f)\n",
         "with open('scaler.pkl', 'wb') as f:\n",
         "    pickle.dump(scaler, f)"
-      ],
-      "metadata": {
-        "id": "gdP6-kYbADkX"
-      },
-      "execution_count": 50,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dM4yGHrmBYyt",
+        "outputId": "3c59baa5-e71f-4e87-d141-2cfd007d8bb1"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Преобразователи и векторизатор успешно сохранены.\n"
+          ]
+        }
+      ],
       "source": [
         "import pandas as pd\n",
         "import pickle\n",
         "from sklearn.feature_extraction.text import TfidfVectorizer\n",
         "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
         "\n",
-        "# Загружаем данные из Books.csv (используем low_memory=False для предупреждения)\n",
         "books = pd.read_csv(\"Books.csv\", low_memory=False)\n",
         "\n",
-        "# Оставляем только необходимые столбцы\n",
         "books = books[['ISBN', 'Book-Title', 'Book-Author', 'Year-Of-Publication', 'Publisher']]\n",
         "\n",
-        "# Заполняем пропуски и приводим к строковому типу (важно для LabelEncoder)\n",
         "books[\"Book-Title\"] = books[\"Book-Title\"].fillna(\"\").astype(str)\n",
         "books[\"Book-Author\"] = books[\"Book-Author\"].fillna(\"\").astype(str)\n",
         "books[\"Publisher\"] = books[\"Publisher\"].fillna(\"\").astype(str)\n",
         "\n",
-        "# Обучаем TfidfVectorizer на названиях книг\n",
         "tfidf = TfidfVectorizer()\n",
         "tfidf.fit(books[\"Book-Title\"])\n",
         "\n",
-        "# Обучаем LabelEncoder для автора и издателя\n",
         "le_Book_Author = LabelEncoder()\n",
         "le_Publisher = LabelEncoder()\n",
         "le_Book_Author.fit(books[\"Book-Author\"])\n",
         "le_Publisher.fit(books[\"Publisher\"])\n",
         "\n",
-        "# Приводим год публикации к числовому типу (при ошибках заполняем 0)\n",
         "books[\"Year-Of-Publication\"] = pd.to_numeric(books[\"Year-Of-Publication\"], errors='coerce').fillna(0)\n",
         "\n",
-        "# Получаем числовые признаки: код автора, год и код издателя\n",
         "author_encoded = le_Book_Author.transform(books[\"Book-Author\"])\n",
         "publisher_encoded = le_Publisher.transform(books[\"Publisher\"])\n",
         "X_num = pd.DataFrame({\n",
@@ -6168,11 +6164,10 @@
         "    \"Publisher_enc\": publisher_encoded\n",
         "})\n",
         "\n",
-        "# Обучаем StandardScaler на этих числовых признаках\n",
+        "\n",
         "scaler = StandardScaler()\n",
         "scaler.fit(X_num)\n",
         "\n",
-        "# Сохраняем обученные объекты\n",
         "with open('tfidf_vectorizer.pkl', 'wb') as f:\n",
         "    pickle.dump(tfidf, f)\n",
         "with open('le_Book_Author.pkl', 'wb') as f:\n",
@@ -6185,36 +6180,22 @@
         "    pickle.dump(model_doc2vec, f)\n",
         "\n",
         "print(\"Преобразователи и векторизатор успешно сохранены.\")\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "dM4yGHrmBYyt",
-        "outputId": "3c59baa5-e71f-4e87-d141-2cfd007d8bb1"
-      },
-      "execution_count": 57,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Преобразователи и векторизатор успешно сохранены.\n"
-          ]
-        }
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# personal_recommendation.py\n"
-      ],
       "metadata": {
         "id": "CRGhidRd7wJZ"
-      }
+      },
+      "source": [
+        "# personal_recommendation.py\n"
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "5L15GctJPJU6"
+      },
       "source": [
         "В файле personal_recommendation.py создайте рекомендацию для пользователя, у которого в исходном датасете было больше всего 0 среди рейтингов книг. Алгоритм такой:\n",
         "Находим нужного пользователя.\n",
@@ -6225,13 +6206,70 @@
         "В конце файла комментарием записываем полученную рекомендацию.\n",
         "То есть идея в том, чтобы сделать для пользователя индивидуальную рекомендацию, показывая в начале списка те книги, которые в целом могли бы иметь высокий рейтинг.\n",
         "Обязательно сохраняйте готовую модель и добавляйте ее в свой репозиторий, потому что файл с сохраненной моделью используется в тестах."
-      ],
-      "metadata": {
-        "id": "5L15GctJPJU6"
-      }
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "flXNX8xcPKLS",
+        "outputId": "fc952d5e-4445-41a8-eeda-1682ea0e9e45"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
+            "[nltk_data]   Package punkt is already up-to-date!\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Целевой пользователь: 198711\n",
+            "\n",
+            "Топ-10 рекомендаций для пользователя 198711\n",
+            "         ISBN                                         Book-Title  \\\n",
+            "0  059035342X  Harry Potter and the Sorcerer's Stone (Harry P...   \n",
+            "1  0449003132         Milk and Honey (Decker and Lazarus Series)   \n",
+            "2  0064409422  The Lion, the Witch and the Wardrobe (Full-Col...   \n",
+            "3  0440219078                 The Giver (21st Century Reference)   \n",
+            "4  0671032658                                     The Green Mile   \n",
+            "5  0553269224  Rilla of Ingleside (Anne of Green Gables Novel...   \n",
+            "6  0439064872   Harry Potter and the Chamber of Secrets (Book 2)   \n",
+            "7  0451194861          Wizard and Glass (The Dark Tower, Book 4)   \n",
+            "8  0515136530      Key of Valor (Roberts, Nora. Key Trilogy, 3.)   \n",
+            "9  0590457225  Dealing With Dragons (Enchanted Forest Chronic...   \n",
+            "\n",
+            "   SVD-Prediction  LinReg-Prediction  \n",
+            "0        8.534317           7.823845  \n",
+            "1        8.004683           7.805983  \n",
+            "2        8.339789           7.790871  \n",
+            "3        8.449400           7.755851  \n",
+            "4        8.065435           7.736889  \n",
+            "5        8.003411           7.725130  \n",
+            "6        8.045036           7.713495  \n",
+            "7        8.158667           7.711711  \n",
+            "8        8.088881           7.703916  \n",
+            "9        8.001301           7.703907  \n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but SGDRegressor was fitted with feature names\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ],
       "source": [
         "import pandas as pd\n",
         "import numpy as np\n",
@@ -6241,17 +6279,13 @@
         "nltk.download('punkt')\n",
         "from nltk.tokenize import word_tokenize\n",
         "\n",
-        "# 1. ЗАГРУЗКА СОХРАНЁННЫХ МОДЕЛЕЙ\n",
         "\n",
-        "# Модель SVD для предсказания рейтингов отдельных книг\n",
         "with open('svd_model.pkl', 'rb') as f:\n",
         "    svd_model = pickle.load(f)\n",
         "\n",
-        "# Модель линейной регрессии (SGDRegressor), обученная на 103 признаках\n",
         "with open('linreg_model.pkl', 'rb') as f:\n",
         "    linreg_model = pickle.load(f)\n",
         "\n",
-        "# Загружаем сохранённые преобразователи для категориальных признаков\n",
         "with open('le_Book_Author.pkl', 'rb') as f:\n",
         "    le_Book_Author = pickle.load(f)\n",
         "with open('le_Publisher.pkl', 'rb') as f:\n",
@@ -6259,48 +6293,36 @@
         "with open('scaler.pkl', 'rb') as f:\n",
         "    scaler = pickle.load(f)\n",
         "\n",
-        "# Загружаем сохранённую модель Doc2Vec (обученную на токенизированных названиях книг)\n",
         "with open('doc2vec_model.pkl', 'rb') as f:\n",
         "    doc2vec_model = pickle.load(f)\n",
         "\n",
-        "# 2. ЗАГРУЗКА ИСХОДНЫХ ДАННЫХ\n",
-        "\n",
-        "# Файл с оценками\n",
         "ratings = pd.read_csv(\"Ratings.csv\")\n",
-        "# Файл с информацией о книгах (оставляем только нужные столбцы)\n",
+        "\n",
         "books = pd.read_csv(\"Books.csv\", low_memory=False)\n",
         "books = books[['ISBN', 'Book-Title', 'Book-Author', 'Year-Of-Publication', 'Publisher']]\n",
         "\n",
-        "# 3. ОПРЕДЕЛЯЕМ ЦЕЛЕВОГО ПОЛЬЗОВАТЕЛЯ\n",
-        "# Выбираем того, у кого больше всего оценок, равных 0\n",
         "user_zero_counts = ratings[ratings[\"Book-Rating\"] == 0]['User-ID'].value_counts()\n",
         "target_user = user_zero_counts.idxmax()\n",
         "print(\"Целевой пользователь:\", target_user)\n",
         "\n",
-        "# 4. НАХОДИМ КНИГИ, ДЛЯ КОТОРЫХ ПОЛЬЗОВАТЕЛЬ ПОСТАВИЛ ОЦЕНКУ 0\n",
         "candidate_isbns = ratings[(ratings[\"User-ID\"] == target_user) & (ratings[\"Book-Rating\"] == 0)][\"ISBN\"].unique()\n",
         "\n",
-        "# 5. РАСЧИТЫВАЕМ ПРЕДСКАЗАНИЯ SVD ДЛЯ КАЖДОЙ КНИГИ\n",
         "svd_preds = []\n",
         "for isbn in candidate_isbns:\n",
         "    pred = svd_model.predict(uid=target_user, iid=isbn)\n",
         "    svd_preds.append((isbn, pred.est))\n",
         "\n",
-        "# Оставляем книги, у которых предсказанный рейтинг ≥ 8\n",
         "filtered_candidates = [(isbn, rating) for isbn, rating in svd_preds if rating >= 8]\n",
         "if not filtered_candidates:\n",
         "    print(\"Нет книг с предсказанным рейтингом ≥ 8 для пользователя\", target_user)\n",
         "    exit()\n",
         "\n",
-        "# 6. ФОРМИРУЕМ DataFrame С ИНФОРМАЦИЕЙ О КНИГАХ-КАНДИДАТАХ\n",
         "candidates_df = books[books[\"ISBN\"].isin([isbn for isbn, _ in filtered_candidates])].copy()\n",
-        "# Заполняем пропуски и приводим текстовые столбцы к строковому типу\n",
         "candidates_df[\"Book-Title\"] = candidates_df[\"Book-Title\"].fillna(\"\").astype(str)\n",
         "candidates_df[\"Book-Author\"] = candidates_df[\"Book-Author\"].fillna(\"\").astype(str)\n",
         "candidates_df[\"Publisher\"] = candidates_df[\"Publisher\"].fillna(\"\").astype(str)\n",
         "\n",
         "def preprocess(text):\n",
-        "    # Токенизация и приведение к нижнему регистру; оставляем только слова\n",
         "    tokens = word_tokenize(text)\n",
         "    cleaned_tokens = [token.lower() for token in tokens if re.match(r'^\\w+$', token)]\n",
         "    return cleaned_tokens\n",
@@ -6311,29 +6333,20 @@
         "    return doc2vec_model.infer_vector(tokens)\n",
         "\n",
         "title_vectors = candidates_df[\"Book-Title-Tokenized\"].apply(infer_vector)\n",
-        "# Преобразуем список в массив numpy (размерность: число_книг x 100)\n",
         "X_text = np.array(title_vectors.tolist())\n",
         "\n",
-        "# 8. КОДИРОВАНИЕ КАТЕГОРИАЛЬНЫХ ПРИЗНАКОВ\n",
-        "# Приводим значения к строке для безопасности\n",
         "candidates_df[\"Author_enc\"] = le_Book_Author.transform(candidates_df[\"Book-Author\"].astype(str))\n",
         "candidates_df[\"Publisher_enc\"] = le_Publisher.transform(candidates_df[\"Publisher\"].astype(str))\n",
         "\n",
-        "# 9. ОБРАБОТКА ГОДА ПУБЛИКАЦИИ\n",
         "candidates_df[\"Year\"] = pd.to_numeric(candidates_df[\"Year-Of-Publication\"], errors='coerce').fillna(0)\n",
         "\n",
-        "# 10. СОБИРАЕМ ЧИСЛОВЫЕ ПРИЗНАКИ (код автора, год, код издателя)\n",
         "X_num = candidates_df[[\"Author_enc\", \"Year\", \"Publisher_enc\"]].values\n",
         "X_num_scaled = scaler.transform(X_num)\n",
         "\n",
-        "# 11. ОБЪЕДИНЯЕМ ЧИСЛОВЫЕ И TEXT-ПРИЗНАКИ\n",
-        "# Итоговая размерность: 3 (числовых) + 100 (Doc2Vec) = 103 признака\n",
         "features = np.hstack([X_num_scaled, X_text])\n",
         "\n",
-        "# 12. ПОЛУЧАЕМ ПРЕДСКАЗАНИЯ ЛИНЕЙНОЙ МОДЕЛИ\n",
         "linreg_preds = linreg_model.predict(features)\n",
         "\n",
-        "# 13. ОБЪЕДИНЯЕМ РЕЗУЛЬТАТЫ: (ISBN, Book-Title, SVD-предсказание, LinReg-предсказание)\n",
         "recommendations = []\n",
         "for idx, row in candidates_df.iterrows():\n",
         "    isbn = row[\"ISBN\"]\n",
@@ -6343,84 +6356,13 @@
         "        linreg_rating = linreg_preds[list(candidates_df.index).index(idx)]\n",
         "        recommendations.append((isbn, row[\"Book-Title\"], svd_rating, linreg_rating))\n",
         "\n",
-        "# 14. СОРТИРУЕМ КНИГИ ПО УБЫВАНИЮ ПРЕДСКАЗАНИЯ ЛИНЕЙНОЙ МОДЕЛИ\n",
         "recommendations_sorted = sorted(recommendations, key=lambda x: x[3], reverse=True)\n",
-        "# 15. СОЗДАНИЕ DATAFRAME С РЕКОМЕНДАЦИЯМИ\n",
-        "recommendations_df = pd.DataFrame(recommendations_sorted, columns=[\"ISBN\", \"Book-Title\", \"SVD-Prediction\", \"LinReg-Prediction\"])\n",
-        "\n",
-        "# 16. ВЫВОД ПЕРВЫХ 10 РЕКОМЕНДАЦИЙ\n",
-        "\n",
-        "# ------------------------------------------------------------------------------\n",
-        "# Пример итоговой рекомендации (можно записать в комментарий):\n",
-        "# Рекомендация для пользователя 198711: ['Название книги 1', 'Название книги 2', 'Название книги 3']\n"
-      ],
-      "metadata": {
-        "id": "flXNX8xcPKLS",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "fc952d5e-4445-41a8-eeda-1682ea0e9e45"
-      },
-      "execution_count": 61,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
-            "[nltk_data]   Package punkt is already up-to-date!\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Целевой пользователь: 198711\n",
-            "\n",
-            "Топ-10 рекомендаций для пользователя 198711\n",
-            "         ISBN                                         Book-Title  \\\n",
-            "0  059035342X  Harry Potter and the Sorcerer's Stone (Harry P...   \n",
-            "1  0449003132         Milk and Honey (Decker and Lazarus Series)   \n",
-            "2  0064409422  The Lion, the Witch and the Wardrobe (Full-Col...   \n",
-            "3  0440219078                 The Giver (21st Century Reference)   \n",
-            "4  0671032658                                     The Green Mile   \n",
-            "5  0553269224  Rilla of Ingleside (Anne of Green Gables Novel...   \n",
-            "6  0439064872   Harry Potter and the Chamber of Secrets (Book 2)   \n",
-            "7  0451194861          Wizard and Glass (The Dark Tower, Book 4)   \n",
-            "8  0515136530      Key of Valor (Roberts, Nora. Key Trilogy, 3.)   \n",
-            "9  0590457225  Dealing With Dragons (Enchanted Forest Chronic...   \n",
-            "\n",
-            "   SVD-Prediction  LinReg-Prediction  \n",
-            "0        8.534317           7.823845  \n",
-            "1        8.004683           7.805983  \n",
-            "2        8.339789           7.790871  \n",
-            "3        8.449400           7.755851  \n",
-            "4        8.065435           7.736889  \n",
-            "5        8.003411           7.725130  \n",
-            "6        8.045036           7.713495  \n",
-            "7        8.158667           7.711711  \n",
-            "8        8.088881           7.703916  \n",
-            "9        8.001301           7.703907  \n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
-            "  warnings.warn(\n",
-            "/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but SGDRegressor was fitted with feature names\n",
-            "  warnings.warn(\n"
-          ]
-        }
+        "recommendations_df = pd.DataFrame(recommendations_sorted, columns=[\"ISBN\", \"Book-Title\", \"SVD-Prediction\", \"LinReg-Prediction\"])"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "print(\"\\nТоп-10 рекомендаций для пользователя\", target_user)\n",
-        "print(recommendations_df[['Book-Title', 'SVD-Prediction', 'LinReg-Prediction']].head(10))\n"
-      ],
+      "execution_count": 63,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -6428,11 +6370,10 @@
         "id": "UcpPOHReGBcJ",
         "outputId": "7c98a623-70da-4847-f2d9-3051bcd9fb54"
       },
-      "execution_count": 63,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "\n",
             "Топ-10 рекомендаций для пользователя 198711\n",
@@ -6461,7 +6402,25 @@
             "9           7.703907  \n"
           ]
         }
+      ],
+      "source": [
+        "print(\"\\nТоп-10 рекомендаций для пользователя\", target_user)\n",
+        "print(recommendations_df[['Book-Title', 'SVD-Prediction', 'LinReg-Prediction']].head(10))\n"
       ]
     }
-  ]
-}
\ No newline at end of file
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}